diff --git a/.gitignore b/.gitignore index 2a62cae..6aee07b 100644 --- a/.gitignore +++ b/.gitignore @@ -183,14 +183,21 @@ causarray/___*.py # (data/replogle_subset.h5ad is ~2 GB). docs/source/tutorial/replogle/data/ docs/source/tutorial/replogle/results/** -# the two small JIC tables are committed so the notebook runs without a refit +# the JIC table is committed so the notebook runs without a refit !docs/source/tutorial/replogle/results/replogle-r.csv -!docs/source/tutorial/replogle/results/replogle-r-legacy.csv # Unrelated tutorial directory (not part of causarray package docs) docs/source/tutorial/scp/ -# Mouse-brain Perturb-seq analysis is currently local and is not part of the -# committed documentation (it is absent from docs/source/index.rst). -# Regenerate with pipeline.py + L45Glut-py.ipynb. -/docs/source/tutorial/mouse_brain/ +# Mouse-brain Perturb-seq: the tutorial notebook, the pipeline it imports and +# the README are committed (the notebook is an orphan page, not in the index); +# data, results, the archived investigation and its scripts stay local. +/docs/source/tutorial/mouse_brain/* +!/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb +!/docs/source/tutorial/mouse_brain/pipeline.py +!/docs/source/tutorial/mouse_brain/README.md + +# SEA-AD: only the excitatory-neuron pseudo-bulk is committed; other prepared +# data (e.g. subclass pseudo-bulks) stay local. +docs/source/tutorial/case_control/data/* +!docs/source/tutorial/case_control/data/sea_ad_mtg_exneu_pb.h5ad diff --git a/README.md b/README.md index 9cbf54a..82f0a4a 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,13 @@ conda create -n causarray python=3.12 -y conda activate causarray ``` +On an Apple Silicon Mac, make sure the environment is native: `python -c "import +platform; print(platform.machine())"` should print `arm64`. A conda started from +a terminal running under Rosetta creates Intel (`x86_64`) environments, in which +causarray runs several times slower; `causarray` warns about this at import. +Create a native one with `CONDA_SUBDIR=osx-arm64 conda create -n causarray +python=3.12 -y`, then `conda config --env --set subdir osx-arm64` inside it. + The module can be installed via PyPI: ```cmd pip install causarray @@ -38,7 +45,10 @@ conda env create -f environment-r.yaml ``` The R tutorial runs from `causarray-r` and connects to the Python package in -the `causarray` environment. +the `causarray` environment. The two must share an architecture: on an Apple +Silicon Mac, create both natively (`CONDA_SUBDIR=osx-arm64 conda env create -f +environment-r.yaml`), since reticulate cannot load an arm64 Python into an +Intel R. The documentation and tutorials using both `Python` and `R` are available at [causarray.readthedocs.io](https://causarray.readthedocs.io/en/latest/). diff --git a/causarray/DR_estimation.py b/causarray/DR_estimation.py index 4989618..0b0b75e 100755 --- a/causarray/DR_estimation.py +++ b/causarray/DR_estimation.py @@ -73,8 +73,8 @@ def estimate_propensity_scores( ``K > 1``, every returned score is predicted by a model that did not train on that cell. Logistic models return calibrated treatment probabilities (``class_weight=None``) by default, matching :func:`LFC`. Pass - ``class_weight='balanced'`` to reproduce pre-0.1.0 fits, whose scores are - centred near 0.5 regardless of prevalence. + ``class_weight='balanced'`` for scores centred near 0.5 regardless of + prevalence. .. versionchanged:: 0.1.0 Default ``class_weight`` changed from ``'balanced'`` to ``None``. @@ -100,7 +100,7 @@ def estimate_propensity_scores( class_weight : str, dict or None, optional Class weighting for logistic propensity estimation. ``None`` (default) gives calibrated probabilities and matches :func:`LFC`; - ``'balanced'`` reproduces the pre-0.1.0 behaviour. + ``'balanced'`` centres scores near 0.5. Returns ------- @@ -462,6 +462,186 @@ def evaluate(factor, name=name, j=j): return factors, report +def select_propensity_factors( + A, X_A, candidates=None, treatment_names=None, covariate_names=None, + trigger=None, target=None, on_infeasible='best', + K=1, ps_model='logistic', mask=None, random_state=0, verbose=False, + class_weight=None, **kwargs, +): + """Drop, per treatment, the covariates that concentrate its weights. + + Latent factors estimated from expression can track a perturbation's own + effect. When they do, the propensity model partly separates that arm from + the controls and the inverse-probability weights pile onto the treated + cells that look least affected. This starts from every covariate and, for + each treatment whose support fails ``trigger`` but not already ``target``, + removes the candidate most imbalanced between that treatment and the + controls (largest absolute standardized mean difference), refits that + treatment alone, and repeats until ``target`` holds or no candidate is + left. Other treatments keep every covariate. + + It is the discrete counterpart of :func:`tune_penalty_factor`: a penalty + shrinks one named coefficient, while this chooses which covariates stay. + Neither makes a contrast identified when a covariate is affected by + treatment; the rule only keeps the weights from resting on a few cells. + + Parameters + ---------- + A : array-like, shape (n,) or (n, a) + Binary treatment indicators; all-zero rows are the shared controls. + X_A : array-like, shape (n, d_A) + Propensity covariates, including the intercept column. + candidates : sequence or None + Covariates that may be dropped, by name or index. Defaults to every + non-constant column, so the intercept always stays. + treatment_names, covariate_names : sequence, optional + Labels for ``A`` columns and ``X_A`` columns. + trigger : mapping or None + Conditions selecting the treatments to adjust, as for + :func:`tune_penalty_factor`; a treatment is adjusted when **any** + holds. Defaults to ``{'ess_treated_fraction_lt': 0.5, + 'overlap_ratio_lt': 0.3, 'auc_gt': 0.9}``. + target : mapping or None + Conditions that stop the removal, combined with **and**. Defaults to + ``{'ess_treated_fraction_gt': 0.5, 'overlap_ratio_gt': 0.3}``. A + triggered treatment that already meets ``target`` keeps every + covariate. + on_infeasible : {'best', 'all', 'none'} + What to do when ``target`` is not met even after every candidate is + dropped. ``'best'`` (default) keeps the fit, among those tried + (including the one with every covariate), that meets the most + ``target`` conditions, breaking ties by the larger treated ESS + fraction. ``'all'`` drops every candidate. ``'none'`` keeps every + covariate. + K, ps_model, mask, random_state, verbose, class_weight, **kwargs + Passed to :func:`estimate_propensity_scores` for every fit. With + ``mask``, the imbalance ranking and the support metrics both use only + the eligible cells of each treatment. + + Returns + ------- + drop_by_treatment : dict + ``{treatment: [covariate, ...]}`` for the treatments that lose at + least one covariate, ready to hand to :func:`refit_propensity_scores`. + report : DataFrame + One row per triggered treatment with the covariates dropped in order, + whether ``target`` was met, the number of fits, and the support + metrics with every covariate (``_all``) and with the chosen covariates + (``_chosen``). + + Examples + -------- + >>> drops, report = select_propensity_factors( + ... A, X_A, treatment_names=names, covariate_names=cov) # doctest: +SKIP + >>> pi, _ = refit_propensity_scores( + ... A, X_A, pi_hat=pi, treatment_names=names, covariate_names=cov, + ... drop_by_treatment=drops) # doctest: +SKIP + + .. versionadded:: 0.1.1 + """ + trigger = ({'ess_treated_fraction_lt': 0.5, 'overlap_ratio_lt': 0.3, 'auc_gt': 0.9} + if trigger is None else dict(trigger)) + target = ({'ess_treated_fraction_gt': 0.5, 'overlap_ratio_gt': 0.3} + if target is None else dict(target)) + + A_arr = np.asarray(A, dtype=float) + if A_arr.ndim == 1: + A_arr = A_arr[:, None] + X_arr = np.asarray(X_A, dtype=float) + if treatment_names is None: + treatment_names = (list(A.columns) if hasattr(A, 'columns') + else list(range(A_arr.shape[1]))) + treatment_names = list(treatment_names) + if covariate_names is None: + covariate_names = (list(X_A.columns) if hasattr(X_A, 'columns') + else [f'covariate_{j + 1}' for j in range(X_arr.shape[1])]) + covariate_names = list(covariate_names) + if candidates is None: + candidates = [c for c, col in zip(covariate_names, X_arr.T) if np.ptp(col) > 0] + else: + resolved = [] + for c in candidates: + if c in covariate_names: + resolved.append(c) + elif isinstance(c, (int, np.integer)) and 0 <= c < len(covariate_names): + resolved.append(covariate_names[c]) + else: + raise ValueError(f'candidate {c!r} is not in covariate_names') + candidates = resolved + cand_idx = [covariate_names.index(c) for c in candidates] + + mask_arr = None + if mask is not None: + mask_arr = np.asarray(mask, dtype=bool) + if mask_arr.ndim == 1: + mask_arr = mask_arr[:, None] + if on_infeasible not in ('best', 'all', 'none'): + raise ValueError("on_infeasible must be 'best', 'all' or 'none'") + fit_kwargs = dict(K=K, ps_model=ps_model, mask=mask, clip=None, + random_state=random_state, verbose=False, + class_weight=class_weight, **kwargs) + pi = estimate_propensity_scores(A_arr, X_arr, **fit_kwargs) + ctrl = A_arr.sum(axis=1) == 0 + + def imbalance_order(j): + case = A_arr[:, j] == 1 + c_rows, t_rows = ctrl, case + if mask_arr is not None: + c_rows, t_rows = ctrl & mask_arr[:, j], case & mask_arr[:, j] + smd = [] + for k in cand_idx: + c_vals, t_vals = X_arr[c_rows, k], X_arr[t_rows, k] + sd = np.sqrt((np.var(c_vals, ddof=1) + np.var(t_vals, ddof=1)) / 2) + smd.append(abs(np.mean(t_vals) - np.mean(c_vals)) / sd if sd > 0 else 0.0) + return [candidates[i] for i in np.argsort(smd)[::-1]] + + def closeness(metrics): + n_met = sum(_meets(metrics, {key: bound}) for key, bound in target.items()) + ess = metrics['ess_treated_fraction'] + return n_met, ess if np.isfinite(ess) else -np.inf + + rows, drops = [], {} + for j, name in enumerate(treatment_names): + before = _arm_support_metrics(A_arr, pi, j, mask=mask_arr) + if not any(_meets(before, {key: bound}) for key, bound in trigger.items()): + continue + # Each fit tried: (covariates dropped, metrics, scores of arm j). + tried = [([], before, pi[:, j].copy())] + if not _meets(before, target): + for covariate in imbalance_order(j): + dropped = tried[-1][0] + [covariate] + pi_try, _ = refit_propensity_scores( + A_arr, X_arr, pi_hat=pi.copy(), treatment_names=treatment_names, + covariate_names=covariate_names, + drop_by_treatment={name: dropped}, **fit_kwargs) + tried.append((dropped, _arm_support_metrics(A_arr, pi_try, j, mask=mask_arr), + pi_try[:, j])) + if _meets(tried[-1][1], target): + break + if _meets(tried[-1][1], target) or on_infeasible == 'all': + dropped, chosen, pi_j = tried[-1] + elif on_infeasible == 'none': + dropped, chosen, pi_j = tried[0] + else: + dropped, chosen, pi_j = max(tried, key=lambda t: closeness(t[1])) + pi[:, j] = pi_j + if dropped: + drops[name] = list(dropped) + rows.append({'treatment': name, 'dropped': ','.join(dropped), + 'n_dropped': len(dropped), 'target_met': _meets(chosen, target), + 'n_fits': len(tried), + **{f'{k}_all': v for k, v in before.items()}, + **{f'{k}_chosen': v for k, v in chosen.items()}}) + + report = pd.DataFrame(rows) + if verbose and len(report): + print(f'[select_propensity_factors] {len(report)} treatments triggered, ' + f'{len(drops)} adjusted, ' + f'{int(report.target_met.sum())} met the target, ' + f'{report.n_fits.sum()} fits', flush=True) + return drops, report + + def refit_propensity_scores( A, X_A, drop_by_treatment=None, pi_hat=None, treatment_names=None, covariate_names=None, penalty_factors_by_treatment=None, K=1, @@ -761,9 +941,9 @@ def cross_fitting( ps_model : str, optional The propensity score model. The default is 'logistic'. ps_class_weight : str, dict or None, optional - Class weighting used by the propensity model. ``None`` (default since - 0.1.0) gives calibrated treatment probabilities; ``'balanced'`` - reproduces the pre-0.1.0 nuisance fit. + Class weighting used by the propensity model. ``None`` (default) + gives calibrated treatment probabilities; ``'balanced'`` centres + scores near 0.5. Y_hat : array, optional Estimated potential outcome of shape (n, p, a, 2). The default is None. diff --git a/causarray/DR_learner.py b/causarray/DR_learner.py index 19deaed..22dc350 100755 --- a/causarray/DR_learner.py +++ b/causarray/DR_learner.py @@ -120,8 +120,7 @@ def compute_causal_estimand( ps_class_weight : str, dict or None Class weighting for the propensity model. ``None`` (default) fits calibrated treatment probabilities, which is what the AIPW weights - ``A / pi`` require. ``'balanced'`` is the pre-0.1.0 default and is kept - as a legacy option; it centres scores near 0.5 regardless of prevalence + ``A / pi`` require. ``'balanced'`` is kept as a legacy option; it centres scores near 0.5 regardless of prevalence and turns the estimator into an outcome-model plug-in. .. versionchanged:: 0.1.0 @@ -152,8 +151,8 @@ def compute_causal_estimand( a fifth dictionary whose arrays are added as diagnostic columns. The frame also carries per-pair support columns ``n_treated``, ``n_control``, ``count_treated`` and ``count_control`` (cells and raw - summed counts in each arm), added in 0.1.0 so that arms with no - observed counts can be audited without recomputation. + summed counts in each arm), so that arms with no observed counts can be + audited without recomputation. """ reset_random_seeds(random_state) @@ -275,7 +274,7 @@ def compute_causal_estimand( res = [] _count_control_shared = None _small_arm_floored = {} - iters = range(A.shape[1]) if A.shape[1]==1 else tqdm(range(A.shape[1])) + iters = tqdm(range(A.shape[1])) if verbose and A.shape[1] > 1 else range(A.shape[1]) for j in iters: if mask is not None: i_cells = mask[:, j] @@ -381,7 +380,7 @@ def compute_causal_estimand( def LFC( Y, W, A, W_A=None, family='nb', offset=False, Y_hat=None, pi_hat=None, cross_est=False, K=None, mask=None, - usevar: Literal['pooled'] = 'pooled', + usevar: Literal['pooled', 'unequal'] = 'pooled', thres_min='auto', thres_diff=1e-2, eps_var=None, min_counts=5.0, fdx=False, fdx_alpha=0.05, fdx_c=0.1, verbose=False, backend: str = "auto", ps_clip='auto', @@ -423,9 +422,9 @@ def LFC( mask : array or None, shape (n, a) Boolean mask indicating eligible cells for each treatment. It limits propensity-model fitting and final estimand computation. - usevar : str - Variance estimator for the AIPW pseudo-outcomes. Only ``'pooled'`` - remains: the influence-function (sandwich) variance ``var(eta) / n`` + usevar : {'pooled', 'unequal'} + Variance estimator for the AIPW pseudo-outcomes. ``'pooled'`` + (default) is the influence-function (sandwich) variance ``var(eta) / n`` of the estimator, where ``eta`` are the per-cell influence values of the log-ratio and ``n`` counts every cell entering the estimand. With calibrated propensity scores this equals the efficient two-sample form @@ -435,20 +434,23 @@ def LFC( ``n/(n-d)`` and p-values use a t reference with ``n-d`` degrees of freedom (see Notes). - ``'unequal'`` (the 0.0.6-0.0.9 default) applied a two-sample Welch - formula ``s₀²/n₀ + s₁²/n₁`` by arm. That is not the variance of an - estimator that averages pseudo-outcomes over all cells: for equal arm - sizes it is exactly twice the correct standard error, and for a rare - treatment fitted with class-balanced propensity scores it is an order - of magnitude too large. It was removed in 0.1.0 after validation on - the Perturb-seq, SEA-AD and Adamson tutorials; the argument is - accepted as an alias of ``'pooled'`` with a ``FutureWarning`` for one - release. + ``'unequal'`` is the by-arm Welch variance ``s₀²/n₀ + s₁²/n₁`` of the + influence values, with Welch-Satterthwaite degrees of freedom. It is + not the variance of an estimator that averages pseudo-outcomes over + all rows: it roughly doubles the standard error for equal arms and + inflates it far more for rare treatments, so do not use it for + perturbation screens. For small, roughly balanced case-control designs + (for example donor-level pseudo-bulk), where ``'pooled'`` can be + anti-conservative, it is the more conservative choice; check either + on permuted labels before relying on it. Neither estimator models within-donor or within-subject correlation. Repeated cells from the same biological unit should still be pseudo-bulked or analysed with a cluster-aware method. + .. versionchanged:: 0.1.1 + ``'unequal'`` restored as a conservative option for small + case-control designs. .. versionchanged:: 0.1.0 ``'unequal'`` removed; ``'pooled'`` is the only estimator. .. versionchanged:: 0.0.6 @@ -473,8 +475,13 @@ def LFC( .. versionadded:: 0.1.0 thres_diff : float - Floor applied to each arm mean before the logarithm, and the minimum - absolute difference between arm means for a gene to be tested. + Floor applied to each arm mean before the logarithm. + + .. versionchanged:: 0.1.1 + No longer also excludes genes whose arm means differ by less than + ``thres_diff``. That filter selected on the effect estimate: the + pairs it removed had p-values near 1, so BH over the rest was + anti-conservative. eps_var : None Deprecated and ignored. A model-based variance floor (see Notes) replaces the additive constant. @@ -504,7 +511,7 @@ def LFC( ps_class_weight : str, dict or None Class weighting for the propensity model. ``None`` (default) gives calibrated probabilities, which the AIPW weights ``A / pi`` require. - ``'balanced'`` (pre-0.1.0 default) centres scores near 0.5 whatever + ``'balanced'`` centres scores near 0.5 whatever the prevalence, shrinking the AIPW correction term by roughly twice the prevalence and turning the estimator into an outcome-model plug-in whose uncertainty the influence function no longer reflects. @@ -536,7 +543,7 @@ def LFC( Notes ----- - **Model-based variance floor (0.1.0).** The empirical influence-function + **Model-based variance floor.** The empirical influence-function variance of an arm whose cells all have zero counts is zero, and the logarithm of its floored mean ``max(mean, thres_diff)`` is then reported with a spuriously tiny standard error. A mean estimated from ``n_k`` cells @@ -551,42 +558,29 @@ def LFC( significant. An arm with no observed counts is kept estimable at the floor ``thres_diff`` (its AIPW mean is exactly zero), so complete knockouts of expressed genes are reported rather than dropped as - non-estimable. The SCARF tutorial documents the failure this prevents. + non-estimable. - **Expression threshold (0.1.0).** ``thres_min='auto'`` requires about + **Expression threshold.** ``thres_min='auto'`` requires about ``min_counts`` (5) expected counts in the smaller arm, i.e. a larger-arm mean of at least ``5 / min(n_0, n_1)`` counts per cell. Below that level a handful of counts in the small arm yields large positively skewed - statistics under the null (SCARF and Adamson negative controls); above it - the variance floor suffices. + statistics under the null; above it the variance floor suffices. - **Small-sample correction (0.1.0).** With in-sample nuisance fits + **Small-sample correction.** With in-sample nuisance fits (``K=1``) the pooled variance is multiplied by ``n / (n - d)``, where ``d`` is the number of outcome-model parameters (``W.shape[1] + 1``), and p-values use a t reference with ``n - d`` degrees of freedom. Both are - no-ops for large ``n``; on 85-donor pseudo-bulk data and 100-cell - perturbation arms they bring the null t-statistics from SD ≈ 1.08-1.10 to - ≈ 1.0 (label-permutation and fake-perturbation nulls on the SEA-AD and - Perturb-seq tutorials). + no-ops for large ``n`` and bring the spread of null t-statistics closer + to one for designs with tens of units or arms of about a hundred cells. """ if eps_var is not None: warnings.warn( - 'eps_var is deprecated and ignored since 0.1.0; a model-based ' - 'variance floor replaces the additive constant.', - FutureWarning, stacklevel=2, - ) - if usevar == 'unequal': - warnings.warn( - "usevar='unequal' (the 0.0.6-0.0.9 Welch-by-arm formula) was removed in " - "0.1.0 after validation on the Perturb-seq, SEA-AD and Adamson tutorials: " - "it is not the variance of the AIPW estimator (2x the correct SE for equal " - "arms, far larger for rare treatments). The argument is accepted as an alias " - "of 'pooled' for one release and will then raise.", + 'eps_var is deprecated and ignored; a model-based variance floor ' + 'replaces the additive constant.', FutureWarning, stacklevel=2, ) - usevar = 'pooled' - elif usevar != 'pooled': - raise ValueError("usevar must be 'pooled'") + if usevar not in ('pooled', 'unequal'): + raise ValueError("usevar must be 'pooled' or 'unequal'") def estimand(etas, A, **kwargs): eta_0, eta_1 = etas[..., 0], etas[..., 1] @@ -595,7 +589,7 @@ def estimand(etas, A, **kwargs): finite_means = np.isfinite(mean_0) & np.isfinite(mean_1) # An arm whose observed counts are all zero has an AIPW mean of # exactly zero (or numerically negative) although the gene may be a - # genuine complete knockout. Since 0.1.0 such arms stay estimable at + # genuine complete knockout. Such arms stay estimable at # the floor ``thres_diff`` and inherit the model-based variance floor; # the observed-support threshold below removes the cases where the # other arm is too sparse to support the comparison. Nonpositive @@ -623,17 +617,26 @@ def estimand(etas, A, **kwargs): n_params = int(kwargs.get('_n_params', 1)) in_sample = bool(kwargs.get('_in_sample', True)) df_resid = max(n_cells - n_params, 2) - var_est = np.var(eta_est, axis=0, ddof=1) / n_cells - if in_sample: - # Residuals of an outcome model fitted on the same cells are - # deflated by ~(n - d)/n; rescale (HC1-style) so that small - # designs (donor-level pseudo-bulk, ~100-cell arms) are not - # anti-conservative. Cross-fitted nuisances (K > 1) need no - # rescaling. - var_est = var_est * (n_cells / df_resid) - # t reference with residual degrees of freedom; equals the normal - # reference for large n. - df_eff = np.full(var_est.shape, float(df_resid)) + if usevar == 'unequal': + # Welch variance s0^2/n0 + s1^2/n1 of the influence values by arm, + # with Welch-Satterthwaite degrees of freedom. + with np.errstate(invalid='ignore', divide='ignore'): + v0 = np.var(eta_est[A == 0], axis=0, ddof=1) / max(n_0, 1) + v1 = np.var(eta_est[A == 1], axis=0, ddof=1) / max(n_1, 1) + var_est = v0 + v1 + df_eff = (v0 + v1) ** 2 / (v0 ** 2 / max(n_0 - 1, 1) + v1 ** 2 / max(n_1 - 1, 1)) + else: + var_est = np.var(eta_est, axis=0, ddof=1) / n_cells + if in_sample: + # Residuals of an outcome model fitted on the same cells are + # deflated by ~(n - d)/n; rescale (HC1-style) so that small + # designs (donor-level pseudo-bulk, ~100-cell arms) are not + # anti-conservative. Cross-fitted nuisances (K > 1) need no + # rescaling. + var_est = var_est * (n_cells / df_resid) + # t reference with residual degrees of freedom; equals the normal + # reference for large n. + df_eff = np.full(var_est.shape, float(df_resid)) # Under the working Poisson model Y_i ~ Poisson(s_i * tau_k), # Var(Y_i / s_i) = tau_k / s_i. The unweighted arm mean thus has @@ -664,11 +667,10 @@ def estimand(etas, A, **kwargs): low_support = np.maximum(mean_0, mean_1) < thres_min_j if obs_mean_1 is not None and obs_mean_0 is not None: low_support |= np.maximum(np.asarray(obs_mean_0), np.asarray(obs_mean_1)) < thres_min_j - idx = ( - ~estimable | - low_support | - (np.abs(mean_1 - mean_0) < thres_diff) - ) + # No filter on |mean_1 - mean_0|: it would select on the effect + # estimate itself, dropping exactly the pairs with p-values near 1 and + # making BH over the remaining pairs anti-conservative. + idx = ~estimable | low_support tau_est[idx] = 0.; eta_est[:,idx] = 0.; var_est[idx] = np.inf if df_eff is not None: df_eff[idx] = np.nan @@ -1022,10 +1024,7 @@ def gcate_lfc_batch( lfc_kwargs : dict or None Extra keyword arguments forwarded to :func:`LFC` - (e.g. ``fdx``, ``thres_min``). Since 0.1.0 the influence-function - variance (``usevar='pooled'``) is the only estimator and applies to - balanced and unbalanced arms alike; ``usevar='unequal'`` is accepted - as a deprecated alias (see :func:`LFC`). + (e.g. ``fdx``, ``thres_min``). **kwargs Additional arguments forwarded to both :func:`fit_gcate_batch` and :func:`LFC`. When a key collides with ``gcate_kwargs`` / diff --git a/causarray/__about__.py b/causarray/__about__.py index 3dc1f76..485f44a 100644 --- a/causarray/__about__.py +++ b/causarray/__about__.py @@ -1 +1 @@ -__version__ = "0.1.0" +__version__ = "0.1.1" diff --git a/causarray/__init__.py b/causarray/__init__.py index cf3adc1..8047e54 100755 --- a/causarray/__init__.py +++ b/causarray/__init__.py @@ -6,13 +6,16 @@ os.environ['NUMBA_THREADING_LAYER_PRIORITY'] = 'omp workqueue tbb' os.environ.setdefault('KMP_WARNINGS', '0') +from causarray._platform import warn_if_emulated +warn_if_emulated() + __all__ = [ 'LFC', 'gcate_lfc_batch', 'LFC_batch', 'fit_glm', 'fit_glm_fast', 'fit_glm_ondisk', 'reset_random_seeds', 'fit_gcate', 'fit_gcate_batch', 'estimate_propensity_scores', 'summarize_propensity_scores', 'plot_propensity_scores', 'refit_propensity_scores', - 'tune_penalty_factor', + 'tune_penalty_factor', 'select_propensity_factors', 'summarize_treatment_associations', 'plot_treatment_associations', 'align_test_mask', ] @@ -20,7 +23,8 @@ from causarray.DR_learner import LFC, gcate_lfc_batch, LFC_batch # ATE, SATE, FC from causarray.DR_estimation import ( - estimate_propensity_scores, refit_propensity_scores, tune_penalty_factor) + estimate_propensity_scores, refit_propensity_scores, tune_penalty_factor, + select_propensity_factors) from causarray.diagnostics import ( summarize_propensity_scores, plot_propensity_scores, summarize_treatment_associations, plot_treatment_associations, diff --git a/causarray/_platform.py b/causarray/_platform.py new file mode 100644 index 0000000..9916fa9 --- /dev/null +++ b/causarray/_platform.py @@ -0,0 +1,42 @@ +"""Warn when causarray runs on an emulated CPU architecture. + +On Apple Silicon, a Python built for Intel (x86_64) runs under Rosetta 2. It +works, but NumPy, Numba and the GLM fits run translated and are several times +slower. pip installs causarray into whichever Python it is given, so the check +has to happen at run time. +""" +import platform +import sys +import warnings + +__all__ = ['running_under_rosetta', 'warn_if_emulated'] + + +def running_under_rosetta(): + """Return True if this process is an x86_64 build translated by Rosetta 2.""" + if sys.platform != 'darwin' or platform.machine() != 'x86_64': + return False + try: + import ctypes + libc = ctypes.CDLL(None) # the running process, which links libSystem + value = ctypes.c_int(0) + size = ctypes.c_size_t(ctypes.sizeof(value)) + if libc.sysctlbyname(b'sysctl.proc_translated', ctypes.byref(value), + ctypes.byref(size), None, ctypes.c_size_t(0)) != 0: + return False # the key is absent on Intel Macs + return value.value == 1 + except Exception: + return False + + +def warn_if_emulated(): + """Issue a ``RuntimeWarning`` once if causarray runs under Rosetta 2.""" + if running_under_rosetta(): + warnings.warn( + 'causarray is running in an x86_64 (Intel) Python translated by Rosetta 2 ' + 'on an Apple Silicon Mac; its numerical code will run several times slower. ' + 'Install a native arm64 Python, e.g. ' + '`CONDA_SUBDIR=osx-arm64 conda create -n causarray python=3.12`, ' + 'and check with `python -c "import platform; print(platform.machine())"` ' + '(expect arm64).', + RuntimeWarning, stacklevel=3) diff --git a/causarray/gcate.py b/causarray/gcate.py index 1b92caf..6500718 100755 --- a/causarray/gcate.py +++ b/causarray/gcate.py @@ -28,13 +28,20 @@ def _check_input(Y, X, family, disp_glm, disp_family, offset, c1, **kwargs): kwargs_glm = {} kwargs_glm['family'] = family + if offset is False: + offset = None if offset is not None: if type(offset)==bool and offset is True: size_factor = comp_size_factor(Y, **_filter_params(comp_size_factor, kwargs)) kwargs_glm['size_factor'] = size_factor offset = np.log(size_factor) else: - offset = np.asarray(offset) + offset = np.asarray(offset, dtype=float).ravel() + if offset.shape != (n,) or not np.all(np.isfinite(offset)): + raise ValueError('offset must be a finite array with one value per row of Y') + # The fit reads the offset from ``size_factor``; without this a + # supplied offset array was ignored everywhere but the dispersion. + kwargs_glm['size_factor'] = np.exp(offset) else: offset = None if kwargs_glm['family']=='nb': diff --git a/causarray/nb_glm_fast.py b/causarray/nb_glm_fast.py index 0c67310..a31ddc2 100644 --- a/causarray/nb_glm_fast.py +++ b/causarray/nb_glm_fast.py @@ -143,9 +143,9 @@ def estimate_disp_fast( X : (n, d) array Covariates, including the intercept. A : (n, a) array, optional - Treatment indicators. They stay in the dispersion model: dropping - them lowers the estimated size parameter by about 13% on the Replogle - screen and moves the downstream results (measured 2026-09-21). + Treatment indicators. They stay in the dispersion model: without + them, treatment effects are absorbed into the dispersion and the size + parameter is underestimated. offset : (n,) array, optional Log-scale offset. method : str @@ -265,11 +265,6 @@ def fit_glm_fast( and by its dense batch fitter otherwise. Parameters and returns are identical to :func:`causarray.gcate_glm.fit_glm`; ``family='gaussian'`` is delegated to it, as are ``shrinkage`` fits. - - Measured on the Perturb-seq tutorial (29 perturbations, 2,926 cells, - 3,221 genes, 2026-09-21): 10.7 s against 46.8 s for the statsmodels pool, - tau correlation 0.9987 against it, 7,460 of 7,468 / 7,482 discoveries - shared. """ np.random.seed(random_state) diff --git a/causarray/utils.py b/causarray/utils.py index bbc5a49..20057fb 100755 --- a/causarray/utils.py +++ b/causarray/utils.py @@ -162,21 +162,8 @@ def reset_state(self): -def _geo_mean(x): - non_zero_x = x[x != 0] - if len(non_zero_x) == 0: - return -np.inf - else: - return np.mean(np.log(non_zero_x)) - -def _normalize(counts, log_geo_means): - log_cnts = np.log(counts) - diff = log_cnts - log_geo_means - mask = np.isfinite(log_geo_means) & (counts > 0) - return np.median(diff[mask]) - - -def comp_size_factor(counts, method='geomeans', lib_size=1e4, **kwargs): +def comp_size_factor(counts, method='geomeans', lib_size=1e4, min_mean=2.0, + min_genes=100, **kwargs): ''' Compute the size factors of the rows of the count matrix. @@ -185,41 +172,92 @@ def comp_size_factor(counts, method='geomeans', lib_size=1e4, **kwargs): counts : array-like The input raw count matrix. method : str - The method to compute the size factors, 'geomeans' or 'scale'. + ``'geomeans'`` (default): median of each row's log-ratios to the + per-gene geometric means, over the row's nonzero counts in genes + with mean count at least ``min_mean``. ``'libsize'``: each row's + total count. ``'scale'``: each row's total count divided by + ``lib_size``, so that ``counts / size_factor`` has ``lib_size`` counts + per row. lib_size : float - The desired library size after normalization for 'scale'. - + The library size after normalization for ``'scale'``. + min_mean : float + For ``'geomeans'``, genes with a mean count below this are left out + of the ratios; if fewer than ``min_genes`` genes pass, the + ``min_genes`` genes with the highest mean are used. ``0`` uses every + gene. A row with no counts in these genes gets its total count, + rescaled by the median ratio of size factor to total count over the + other rows. + min_genes : int + Minimum number of genes used by ``'geomeans'``. + Returns ------- size_factor : array-like - The size factors of the rows. + The size factors of the rows, with geometric mean one for + ``'geomeans'`` and ``'libsize'``. A row without counts gets zero. + + Notes + ----- + The median of ratios resists composition changes: a perturbation or a + latent factor that moves a minority of genes does not move the size + factor. In sparse single-cell data, however, most nonzero counts are 1 or + 2, and their ratios capture only part of each cell's sequencing depth. + The remainder stays in highly expressed genes, which scale fully with + depth, and shifts all of them together whenever two groups of cells + differ in average depth by chance; their test statistics are then too + spread under the null. Restricting the ratios to genes with enough counts + keeps the median's robustness while tracking depth. + + .. versionchanged:: 0.1.1 + ``'geomeans'`` uses genes with mean count at least ``min_mean`` + (previously every gene); ``'libsize'`` added; ``'scale'`` returns + one value per row (previously one per column). ''' - if method=='geomeans': - # Vectorized geometric mean (per gene, ignoring zeros) - counts = np.asarray(counts, dtype=np.float64) - log_counts = np.where(counts > 0, np.log(counts), np.nan) - log_geo_means = np.nanmean(log_counts, axis=0) - # Genes with all-zero columns get -inf - all_zero = np.all(counts == 0, axis=0) - log_geo_means[all_zero] = -np.inf - - # Vectorized normalize (median of log-ratio per cell) - log_cnts = np.where(counts > 0, np.log(counts), np.nan) - diff = log_cnts - log_geo_means[None, :] - # Mask: finite geo_mean AND positive count - mask = np.isfinite(log_geo_means)[None, :] & (counts > 0) - diff_masked = np.where(mask, diff, np.nan) - log_size_factor = np.nanmedian(diff_masked, axis=1) - size_factor = np.exp(log_size_factor - np.mean(log_size_factor)) - elif method=='scale': - size_factor = 1./np.sum(counts, axis=0)*lib_size + counts = np.asarray(counts, dtype=np.float64) + if method == 'libsize': + totals = counts.sum(axis=1) + positive = totals > 0 + size_factor = np.zeros_like(totals) + if positive.any(): + log_totals = np.log(totals[positive]) + size_factor[positive] = np.exp(log_totals - np.mean(log_totals)) + return size_factor + if method == 'geomeans': + gene_mean = counts.mean(axis=0) + keep = gene_mean >= min_mean + if keep.sum() < min(min_genes, counts.shape[1]): + keep = np.zeros(counts.shape[1], dtype=bool) + keep[np.argsort(gene_mean)[::-1][:min_genes]] = True + keep &= gene_mean > 0 + + def _log_median_ratio(sub): + log_sub = np.where(sub > 0, np.log(np.where(sub > 0, sub, 1.0)), np.nan) + log_geo_means = np.nanmean(log_sub, axis=0) + with warnings.catch_warnings(): + warnings.simplefilter('ignore', RuntimeWarning) # all-NaN rows + return np.nanmedian(log_sub - log_geo_means[None, :], axis=1) + + log_size_factor = _log_median_ratio(counts[:, keep]) + # A row with no counts in the selected genes falls back to its total + # count, put on the scale of the other rows by their median offset + # between log size factor and log total. + log_totals = np.log(np.where(counts.sum(axis=1) > 0, counts.sum(axis=1), np.nan)) + missing = ~np.isfinite(log_size_factor) + found = ~missing + if missing.any() and found.any(): + shift = np.median(log_size_factor[found] - log_totals[found]) + log_size_factor[missing] = log_totals[missing] + shift + finite = np.isfinite(log_size_factor) + size_factor = np.zeros(counts.shape[0]) + size_factor[finite] = np.exp(log_size_factor[finite] - np.mean(log_size_factor[finite])) + elif method == 'scale': + size_factor = counts.sum(axis=1) / lib_size else: - raise ValueError("Method must be in {'geomeans' or 'scale'}.") + raise ValueError("Method must be in {'geomeans', 'libsize', 'scale'}.") return size_factor - def subsample_ctrl_cells(ctrl_idx, n_ctrl=2000, random_state=0): """Draw a fixed subsample of control cell indices. diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index b103766..99d6bf6 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -1,5 +1,59 @@ # Changelog +## [0.1.1] + +### Fixed + +- `LFC` no longer excludes genes whose arm means differ by less than + `thres_diff`, which is now only the floor before the logarithm. The filter + selected on the effect estimate: it dropped the pairs with p-values near 1 + (about 10% of expressed pairs on a Perturb-seq negative control), so BH over + the remaining pairs was anti-conservative. +- Size factors (`comp_size_factor`, used by `fit_gcate`, `LFC(offset=True)` + and `gcate_lfc_batch`) take the median of ratios over genes with mean count + of at least `min_mean=2` instead of every gene. In sparse single-cell data + the old factors captured about a third of the depth variation; the rest + shifted all highly expressed genes of an arm together, so their null + statistics were too spread. On a Replogle negative control (30 fake + perturbations cut from the controls) this cuts false discoveries from 114 + to 14, and the null SD for genes above 5 counts per cell from 1.32 to + 1.08. `min_mean=0` restores the old factors; `method='libsize'` uses total + counts. A cell with no counts in those genes gets its total count, rescaled + to the other cells' factors, instead of a factor near the median. +- `comp_size_factor(method='scale')` returns one factor per row (total count + over `lib_size`); it returned one value per column. +- `fit_gcate` now uses an `offset` array. The fit reads its offset from + `size_factor`, which was set only for `offset=True`, so a supplied array was + replaced by ones everywhere except the dispersion estimate. +- `LFC` shows its per-treatment progress bar only with `verbose=True`. + +### Added + +- `select_propensity_factors` chooses the propensity covariates per + treatment: for a treatment whose weights concentrate (treated ESS < 0.5, + overlap < 0.3 or AUC > 0.9) it drops the covariates most imbalanced between + that treatment and the controls until its support recovers, and returns a + `drop_by_treatment` mapping for `refit_propensity_scores`. A treatment that + already meets the target keeps every covariate; when the target cannot be + reached, `on_infeasible` keeps the closest fit (`'best'`, default), drops + every candidate (`'all'`) or none (`'none'`). +- `import causarray` warns when it runs in an Intel (x86_64) Python translated + by Rosetta 2 on an Apple Silicon Mac, where its numerical code runs several + times slower (`LFC` on a 5,000-cell subsample: 159 s against 32-57 s + natively). The README shows how to create a native environment. + +### Changed + +- **crispyx >= 0.1.6 is required.** With latent factors in the outcome model, + crispyx 0.1.5's structured GLM fitter was about 2.5 times slower than + 0.1.6's (same estimates): 159 s against 62 s for LFC on 5,000 cells, 3,000 + genes and 10 treatments. +- `LFC(usevar='unequal')` is the by-arm Welch variance again (0.1.0 made it an + alias of `'pooled'`). It is conservative for small case-control designs: on + 85 SEA-AD donors, permuted disease labels gave 0-1 false discoveries with + null SD 0.6-0.9, against SD 1.1-1.8 under `'pooled'`. `'pooled'` stays the + default; `'unequal'` over-inflates the standard errors of rare treatments. + ## [0.1.0] Inference fix for small perturbation arms, and the GLM engine moves to crispyx diff --git a/docs/source/main_function/gcate.rst b/docs/source/main_function/gcate.rst index a8ca3f0..ef33b2f 100644 --- a/docs/source/main_function/gcate.rst +++ b/docs/source/main_function/gcate.rst @@ -8,5 +8,17 @@ criterion, then call :func:`fit_gcate` (or :func:`fit_gcate_batch` for large-scale screens) to obtain the latent factor matrix ``U``. Append ``U`` to the observed covariate matrix before calling :func:`LFC`. +Case-control studies with few units +----------------------------------- + +The first stage of :func:`fit_gcate` projects the factors orthogonal to the +treatment indicators, and with few units they can stay nearly so: on 85 +SEA-AD donors, the R² of disease status on four factors was 0.0003. Such +factors cannot adjust for anything aligned with treatment, yet in +:func:`LFC` they shrink the standard errors. Check calibration with permuted +treatment labels before reading discovery counts, and consider +``usevar='unequal'`` in :func:`LFC`, which is more conservative in small +samples. + .. automodule:: causarray.gcate :members: diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index e6b6513..874ee9e 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -34,7 +34,7 @@ memory when loaded. Choosing the variance estimator ------------------------------- -Since 0.1.0 ``LFC`` uses ``usevar='pooled'``, the influence-function +``LFC`` uses ``usevar='pooled'``, the influence-function (sandwich) variance ``var(eta)/n`` of the AIPW estimator, where ``eta`` are the per-cell influence values of the log-ratio and ``n`` counts every cell that enters the estimand. With calibrated propensity scores this equals the @@ -61,23 +61,29 @@ Two further safeguards apply to every gene: significant while a genuine complete knockout (``tau`` of -5 or more) still is. The ``var_floored`` column marks affected pairs and ``std_raw`` reports the pre-floor standard error. -* **Expression threshold.** ``thres_min='auto'`` (default since 0.1.0) - requires about ``min_counts`` (5) expected counts in the smaller arm, i.e. a - larger-arm mean of at least ``5 / min(n0, n1)`` counts per cell: 0.05 for a - 100-cell arm, 0.007 for a 700-cell arm. A fixed float can be passed - instead. - -``usevar='unequal'`` (the 0.0.6-0.0.9 default) applied a two-sample Welch -formula ``s0²/n0 + s1²/n1`` by arm. That is not the variance of an estimator -that averages pseudo-outcomes over all cells: for equal arm sizes it is exactly -twice the correct standard error, and for a rare treatment fitted with -class-balanced propensity scores it is an order of magnitude too large, so -real effects were estimated but not called. It was removed in 0.1.0 after -re-validation on the Perturb-seq, SEA-AD and Adamson tutorials; the argument -is accepted as an alias of ``'pooled'`` with a ``FutureWarning`` for one -release. Neither estimator models within-donor correlation; repeated cells -from one biological unit should still be pseudo-bulked or analysed with a -cluster-aware method. +* **Size factors.** ``offset=True`` uses median-of-ratios size factors + computed over genes with mean count of at least 2 (``min_mean``). Ratios + of sparse genes, whose counts are mostly 1 or 2, miss much of each cell's + sequencing depth; the leftover depth then moves every highly expressed + gene of an arm together and inflates their null statistics. +* **Expression threshold.** ``thres_min='auto'`` (default) requires about + ``min_counts`` (5) expected counts in the smaller arm, i.e. a larger-arm + mean of at least ``5 / min(n0, n1)`` counts per cell: 0.05 for a 100-cell + arm, 0.007 for a 700-cell arm. A fixed float can be passed instead. The + threshold looks only at expression level, never at the difference between + arms: filtering on the estimated effect would drop the pairs with p-values + near 1 and make the BH correction over the rest anti-conservative. + +``usevar='pooled'`` (default) is the influence-function variance of the +estimator. ``usevar='unequal'`` uses the by-arm Welch variance +``s0²/n0 + s1²/n1`` with Welch-Satterthwaite degrees of freedom. It roughly +doubles the standard error for equal arms and inflates it far more for rare +treatments, so do not use it for perturbation screens. For small case-control +studies it is the more conservative choice: on 85 SEA-AD donors, permuted +disease labels gave null statistics with SD 1.1-1.8 under ``'pooled'`` and +0.6-0.9 with 0-1 false discoveries under ``'unequal'``. Neither formula models +within-donor correlation; repeated cells from one biological unit should still +be pseudo-bulked or analysed with a cluster-aware method. Propensity diagnostics ---------------------- @@ -88,21 +94,20 @@ overfitting diagnostics. :func:`summarize_propensity_scores` reports overlap, tail mass, and inverse-weight effective sample size, while :func:`plot_propensity_scores` compares treatment and control distributions. -Since 0.1.0 both the standalone estimator and ``LFC`` fit calibrated -logistic propensity scores by default (``class_weight=None``), which is what -the AIPW weights ``A/pi`` require. The former ``'balanced'`` default centred -the scores near 0.5 whatever the prevalence; for a treatment with 0.6% -prevalence that shrank the AIPW correction term by roughly twice the -prevalence and turned the estimator into an outcome-model plug-in whose -uncertainty the influence function no longer reflected. ``'balanced'`` remains -available to reproduce earlier analyses. Because in-sample logistic fits with +Both the standalone estimator and ``LFC`` fit calibrated logistic propensity +scores by default (``class_weight=None``), which is what the AIPW weights +``A/pi`` require. ``class_weight='balanced'`` centres the scores near 0.5 +whatever the prevalence; for a treatment with 0.6% prevalence that shrinks the +AIPW correction term by roughly twice the prevalence and turns the estimator +into an outcome-model plug-in whose uncertainty the influence function no +longer reflects. Because in-sample logistic fits with ~100 cases against thousands of controls overstate separation, use out-of-fold scores (``K=5``) when judging overlap. Propensity scores used by AIPW are clipped with a prevalence-aware bound by default (``ps_clip='auto'``: ``lower = min(0.01, prevalence/10)`` per -treatment, and symmetrically above). The fixed ``(0.01, 0.99)`` used before -0.1.0 clipped every calibrated score of a treatment with prevalence below 1%. +treatment, and symmetrically above). A fixed ``(0.01, 0.99)`` would clip every +calibrated score of a treatment with prevalence below 1%. The resolved bounds are returned as ``estimation['ps_clip_bounds']`` and the raw scores as ``estimation['pi_hat_raw']``. @@ -116,13 +121,12 @@ Small perturbation arms ----------------------- Screens with fewer than ~200 cells per perturbation and thousands of shared -controls are the regime in which the pre-0.1.0 defaults failed (SCARF -tutorial, "Investigation" section): 83% of discoveries were genes with zero -counts in the perturbed arm, and real effects had t-statistics halved by the -Welch formula. In this regime inspect the ``count_treated`` and -``var_floored`` columns, keep the default expression threshold, and expect a -``RuntimeWarning`` listing how many pairs the variance floor -bound. +controls are where chance all-zero arms and sparse genes matter most. In this +regime inspect the ``count_treated`` and ``var_floored`` columns, and expect a +``RuntimeWarning`` listing how many pairs the variance floor bound. A +negative control is cheap: label random subsets of control cells as fake +perturbations of the real arm sizes and run ``LFC`` on them. If its false +discoveries concentrate in sparse genes, raise ``min_counts`` (e.g. to 20). Treatment-specific covariate diagnostics ---------------------------------------- @@ -182,6 +186,55 @@ sensitivity analyses, distinguish pre-treatment covariates from possible post-treatment variables, and compare propensity overlap, effective sample sizes, and effect estimates before and after filtering. +Library size in the propensity model +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +``prep_causarray_data`` adds standardized log library size to ``X_A`` by +default. Whether it belongs there is a judgement the data cannot settle. +Library size can be a confounder: capture efficiency and cell quality affect +both which cells end up with a detected guide and their measured expression. +It can also be a consequence of treatment: a perturbation that changes a +cell's total RNA changes its library size, and adjusting for it then removes +part of the effect. Both readings fit the same data. + +A practical rule is to fit the propensity model with library size and check +support. If every arm stays well supported, keep it. If some arms separate +from the controls with it (AUC above 0.9 or overlap below 0.3), their weights +rest on a few cells whichever reading is right; leaving library size out +restores support, at the cost of not adjusting for any depth-related +confounding. :func:`tune_penalty_factor` sits between the two and shrinks the +coefficient only as far as support requires. Either way, the outcome model +still normalizes for depth through the size-factor offset, and the choice +should be reported. The Perturb-seq and Replogle tutorials leave library size +out because several arms separate with it. + +Choosing covariates per treatment +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When treatment is assigned at random, as with guides in a Perturb-seq screen, +the propensity model only has to absorb chance imbalance. Latent factors +estimated from expression can instead track a perturbation's own effect; the +model then partly separates that arm from the controls and the weights pile +onto the treated cells that look least affected. ``select_propensity_factors`` +starts from every covariate and, for each treatment failing a support check, +drops the covariate most imbalanced between that treatment and the controls +(largest absolute standardized mean difference), one at a time, until the +target holds:: + + drops, report = select_propensity_factors( + A, W_A, treatment_names=treatment_names, covariate_names=covariate_names, + ) + pi_selected, audit = refit_propensity_scores( + A, W_A, pi_hat=estimation['pi_hat_raw'], + treatment_names=treatment_names, covariate_names=covariate_names, + drop_by_treatment=drops, + ) + +By default a treatment is adjusted when its treated ESS fraction falls below +0.5, its overlap below 0.3 or its AUC above 0.9, and removal stops once ESS +exceeds 0.5 and overlap 0.3. The intercept always stays. Library size is +better decided before this step; see above. + Choosing the penalty factor ^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -205,8 +258,8 @@ keeps as much of its adjustment role as the data support:: Dropping the covariate is the infinite-penalty limit, so it bounds what any finite factor can achieve. The search evaluates that endpoint first: when the dropped fit already misses the target, the treatment is reported with -``feasible=False`` after a single extra fit rather than an exhausted search, -and no penalty is applied. Otherwise the factor is found by bisection on a log +``feasible=False`` rather than searched, and receives the largest factor in +``bracket`` (``on_infeasible='none'`` leaves it unpenalized instead). Otherwise the factor is found by bisection on a log scale, and ``tol`` trades fits against how tightly the smallest qualifying factor is resolved. @@ -271,7 +324,7 @@ an extreme discovery from an invalid estimate. .. automodule:: causarray.DR_estimation :members: estimate_propensity_scores, refit_propensity_scores, - tune_penalty_factor + tune_penalty_factor, select_propensity_factors .. automodule:: causarray.diagnostics :members: diff --git a/docs/source/tutorial/SCARF/SCARF-py.ipynb b/docs/source/tutorial/SCARF/SCARF-py.ipynb deleted file mode 100644 index 282e88b..0000000 --- a/docs/source/tutorial/SCARF/SCARF-py.ipynb +++ /dev/null @@ -1,3396 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ee348e0d", - "metadata": { - "papermill": { - "duration": 0.00543, - "end_time": "2026-09-10T19:15:49.722623+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:49.717193+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "# SCARF Mouse-Brain Perturb-seq: causarray vs. Wilcoxon\n", - "\n", - "**Dataset (tutorial subset)**\n", - "- SCARF (Simultaneous CRISPR And RNA-seq in the brain Full brain screen):\n", - " mouse whole-brain Perturb-seq, two source files covering 58 gene\n", - " perturbations across 10 predicted cell types.\n", - "- Tutorial subset: the **L4-5 IT CTX Glut** excitatory-neuron subtype\n", - " (the pptx's own pilot cell type), all 58 perturbations + shared controls\n", - " → **22,396 cells**, **19,070 genes**, **58 perturbations**.\n", - "\n", - "**Pipeline overview**\n", - "```\n", - "subset_adata_SCARF_10celltypes_10perturbations.h5ad --\\\n", - " > prep_scarf_data.py (combine)\n", - "subset_adata_SCARF_10celltypes_48perturbations.h5ad --/ |\n", - " v\n", - " scarf_combined.h5ad (150,992 cells, 58 perts)\n", - " |\n", - " v restrict to L4-5 IT CTX Glut\n", - " scarf_L45_subset.h5ad (22,396 cells)\n", - " |\n", - " +---------------------------------------------+--------------------------------+\n", - " v v\n", - " prep_causarray_data -> estimate_r -> fit_gcate -> LFC crispyx.normalize_total_log1p -> crispyx.wilcoxon_test\n", - " | |\n", - " +----------------------------> scatter: causarray tau vs. Wilcoxon effect <-----+\n", - "```\n", - "\n", - "This notebook reproduces the causarray-vs-Wilcoxon effect-size comparison\n", - "from `260902_FinalSummary_Causarray.pptx` (slide 1: *\"Wilcoxon produces\n", - "extreme negative logFC values... causarray gives more moderate negative\n", - "effects\"*). The pseudobulk DESeq2 panel on that slide, and the r=0-vs-r=5\n", - "propensity/DEG ablation and 3-rule extreme-DEG-filtering comparison on the\n", - "later slides, are exploratory side analyses and are out of scope here.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b9f6c9d0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:49.731445Z", - "iopub.status.busy": "2026-09-10T19:15:49.731273Z", - "iopub.status.idle": "2026-09-10T19:15:52.258267Z", - "shell.execute_reply": "2026-09-10T19:15:52.257640Z" - }, - "papermill": { - "duration": 2.532673, - "end_time": "2026-09-10T19:15:52.259331+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:49.726658+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/dujinhong/miniforge3/envs/causarray/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "sys.path.append('../../..')\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from scipy import stats\n", - "from statsmodels.stats.multitest import multipletests\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Plain anndata, not scanpy -- reading an h5ad has no scanpy-specific need.\n", - "import anndata as ad\n", - "import crispyx\n", - "\n", - "from causarray import (\n", - " prep_causarray_data, fit_gcate, LFC, estimate_propensity_scores,\n", - " summarize_propensity_scores, plot_propensity_scores,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "127d1c70", - "metadata": { - "papermill": { - "duration": 0.001898, - "end_time": "2026-09-10T19:15:52.263637+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:52.261739+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Combining the two source files\n", - "\n", - "`docs/source/tutorial/SCARF/prep_scarf_data.py` produces the files loaded\n", - "below. Two things are worth calling out explicitly, since they are not\n", - "obvious from the file names alone:\n", - "\n", - "1. **The two source files share the same 102,595 control cells.** Both\n", - " `subset_adata_SCARF_10celltypes_10perturbations.h5ad` (10 perturbations)\n", - " and `subset_adata_SCARF_10celltypes_48perturbations.h5ad` (48\n", - " perturbations) contain the *identical* `Non_target` control-cell\n", - " barcodes -- verified by set comparison. A naive concatenation of the two\n", - " files would silently double-count every control cell. The prep script\n", - " instead keeps the 10-perturbation file whole and adds only the\n", - " 48-perturbation file's perturbed cells (excluding its duplicate\n", - " controls), giving one shared control pool and 58 unique perturbations\n", - " (150,992 cells total, `scarf_combined.h5ad`).\n", - "2. **Merging two separate `.h5ad` files has no `crispyx` equivalent.**\n", - " `crispyx` (checked `data.py`/`qc.py`/`sample.py`) only offers\n", - " single-file streaming filter/subset/write utilities\n", - " (`write_filtered_subset`, `subsample`, `filter_*_by_cell_count`) -- there\n", - " is no multi-file concat primitive. That one step is done with plain\n", - " `anndata.experimental.concat_on_disk` (disk-to-disk, not scanpy);\n", - " every other data-manipulation step in this notebook and in\n", - " `prep_scarf_data.py` uses `crispyx`.\n", - "\n", - "The notebook itself fits causarray/Wilcoxon on `scarf_L45_subset.h5ad`, the\n", - "combined data restricted to `predicted_group == '005 L4-5 IT CTX Glut'` --\n", - "the pptx's own pilot cell type. Fitting all 10 cell types at once would be a\n", - "~5-10x heavier GCATE run than the adamson tutorial; the combined file is\n", - "kept as a durable intermediate artifact so any other cell type can be\n", - "substituted by changing one filter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "dac37523", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:52.268546Z", - "iopub.status.busy": "2026-09-10T19:15:52.268311Z", - "iopub.status.idle": "2026-09-10T19:15:52.598166Z", - "shell.execute_reply": "2026-09-10T19:15:52.597597Z" - }, - "papermill": { - "duration": 0.333255, - "end_time": "2026-09-10T19:15:52.598983+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:52.265728+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 22396 × 19070\n", - " obs: 'scDblFinder.class', 'scDblFinder.score', 'scDblFinder.weighted', 'scDblFinder.cxds_score', 'sample_label', 'sample_id', 'species', 'source', 'tissue', 'sex', 'diet', 'genotype', 'condition', 'project', 'experiment', 'batch', 'pooled_sample_id', 'scp_id', 'scp_name', 'num_guides', 'guide_call', 'gene_target', 'num_rna_umi', 'num_genes', 'pct_mt', 'passes_qc', 'guide_umi_top', 'guide_umi_second', 'guide_umis', 'log_ambient_mse', 'log_ambient_mse_norm', 'predicted_class', 'predicted_class_probability', 'predicted_subclass', 'predicted_subclass_probability', 'predicted_supertype', 'predicted_supertype_probability', 'predicted_cluster', 'predicted_cluster_probability', 'cell_type', 'neuron_type', 'neighborhood', 'region_level1', 'region_level2', 'predicted_group'\n", - " uns: 'ctrl_label', 'pert_col'\n", - " obsm: 'X_pca', 'X_umap'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "adata = ad.read_h5ad('scarf_L45_subset.h5ad')\n", - "adata\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4610f6b9", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:52.604151Z", - "iopub.status.busy": "2026-09-10T19:15:52.604007Z", - "iopub.status.idle": "2026-09-10T19:15:52.611545Z", - "shell.execute_reply": "2026-09-10T19:15:52.611055Z" - }, - "papermill": { - "duration": 0.010801, - "end_time": "2026-09-10T19:15:52.612153+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:52.601352+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "58 perturbations, 15,135 control cells, 7,261 perturbed cells\n", - "Cells per pert: min=68, median=122, max=227\n" - ] - } - ], - "source": [ - "ctrl = adata.uns['ctrl_label'] # 'Non_target'\n", - "pert_col = adata.uns['pert_col'] # 'gene_target'\n", - "\n", - "vc = adata.obs[pert_col].astype(str).value_counts()\n", - "print(f\"{len(vc) - 1} perturbations, {int(vc.get(ctrl, 0)):,} control cells, \"\n", - " f\"{int(adata.n_obs - vc.get(ctrl, 0)):,} perturbed cells\")\n", - "pert_counts = vc.drop(ctrl)\n", - "print(f\"Cells per pert: min={pert_counts.min():,}, median={pert_counts.median():.0f}, \"\n", - " f\"max={pert_counts.max():,}\")\n", - "\n", - "# Sanity check: the shared-control de-duplication in prep_scarf_data.py\n", - "# should leave exactly one copy of the 15,135 control cells for this cell\n", - "# type, not 30,270 (which is what a naive concat would have produced).\n", - "assert int(vc.get(ctrl, 0)) == 15135, 'control-cell count does not match the expected de-duplicated pool'\n" - ] - }, - { - "cell_type": "markdown", - "id": "d982bf2e", - "metadata": { - "papermill": { - "duration": 0.001952, - "end_time": "2026-09-10T19:15:52.616424+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:52.614472+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Sparse-gene prefilter\n", - "\n", - "Following the pptx's v0.0.9 analysis (\"Sparse-gene filtering before\n", - "fitting\"), genes expressed in very few cells are dropped before fitting\n", - "GCATE. This is a `crispyx` call operating directly on the on-disk file, not\n", - "a scanpy filter.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3af8d859", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:52.621690Z", - "iopub.status.busy": "2026-09-10T19:15:52.621558Z", - "iopub.status.idle": "2026-09-10T19:15:54.128129Z", - "shell.execute_reply": "2026-09-10T19:15:54.127474Z" - }, - "papermill": { - "duration": 1.51056, - "end_time": "2026-09-10T19:15:54.128976+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:52.618416+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[cx] pp.filter_genes: Done 14227/19070 genes kept (75%)\n", - "14,227/19,070 genes kept (>= 50 expressing cells)\n" - ] - } - ], - "source": [ - "gene_mask = crispyx.filter_genes_by_cell_count(\n", - " 'scarf_L45_subset.h5ad', min_cells=50, gene_name_column=None,\n", - ")\n", - "print(f'{gene_mask.sum():,}/{len(gene_mask):,} genes kept (>= 50 expressing cells)')\n", - "adata = adata[:, gene_mask].copy()\n" - ] - }, - { - "cell_type": "markdown", - "id": "247e8296", - "metadata": { - "papermill": { - "duration": 0.00198, - "end_time": "2026-09-10T19:15:54.133433+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:54.131453+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Preparing causarray inputs\n", - "\n", - "For running **causarray** we need the following inputs.\n", - "\n", - "- `Y`: cell-by-gene count matrix (DataFrame with gene-symbol column names).\n", - "- `A`: cell-by-perturbation binary matrix (DataFrame); control cells have\n", - " all zeros.\n", - "- `X`, `X_A`: observed covariate matrices (intercept added automatically by\n", - " `prep_causarray_data`).\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ca56a322", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:54.138327Z", - "iopub.status.busy": "2026-09-10T19:15:54.138194Z", - "iopub.status.idle": "2026-09-10T19:15:55.036060Z", - "shell.execute_reply": "2026-09-10T19:15:55.035435Z" - }, - "papermill": { - "duration": 0.90118, - "end_time": "2026-09-10T19:15:55.036664+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:54.135484+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cells: 22396, Genes: 14227, Perturbations: 58\n" - ] - } - ], - "source": [ - "import scipy.sparse as sp\n", - "\n", - "X_raw = adata.X.toarray() if sp.issparse(adata.X) else np.array(adata.X)\n", - "Y = pd.DataFrame(X_raw, columns=adata.var_names.tolist())\n", - "A = pd.get_dummies(adata.obs[pert_col].astype(str),\n", - " drop_first=False).drop(columns=[ctrl])\n", - "\n", - "Y, A, X, X_A = prep_causarray_data(Y, A)\n", - "print(f'Cells: {Y.shape[0]}, Genes: {Y.shape[1]}, Perturbations: {A.shape[1]}')\n" - ] - }, - { - "cell_type": "markdown", - "id": "56d2ff28", - "metadata": { - "papermill": { - "duration": 0.002068, - "end_time": "2026-09-10T19:15:55.041159+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.039091+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Number of nonlinear factors\n", - "\n", - "We estimate the number of nonlinear latent factors *r* by fitting GCATE for\n", - "several candidate values and selecting the one that minimises the\n", - "penalised-likelihood information criterion (JIC). The pptx's own v0.0.9\n", - "analysis (slide 4) settled on *r* = 3 as \"a balanced option between model\n", - "fit, propensity overlap, DEG stability, and Wilcoxon concordance\"; we run a\n", - "small grid around that choice and report whether JIC agrees.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9e99b197", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:55.046160Z", - "iopub.status.busy": "2026-09-10T19:15:55.046032Z", - "iopub.status.idle": "2026-09-10T19:15:55.313745Z", - "shell.execute_reply": "2026-09-10T19:15:55.313316Z" - }, - "papermill": { - "duration": 0.271424, - "end_time": "2026-09-10T19:15:55.314723+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.043299+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Selected r = 8 (minimum JIC)\n" - ] - }, - { - "data": { - "image/png": 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Xu9QOTnQaAdd8VbXoacCv4f2L4ft77fErZB+0M8tbdYLbVtS8H1n1Msx5CFb9Fy7+r9M+TjXJboFMEGLOZXN4e+rbXNjVviGPDIxUcEJERESaLpMePf9x+6LcBCfMaqJbFsHZN5mlVHYQ4g8xcO13cNlb9vYPWxouOHEy2hyjP4Rx5BDkpZ/4xsWkeZsVUofiobSk9vsVY1cf5+AWO+X7iVB4PKjqsekj+/23Jtmvt3934j+Duyd4+kPREU7Wyj3pxKfncdGAiMrgREVd4bsmdKe4tIzP1+8/4TifrN1fmepfEZwwRnYLYUz3tlb5g72pOZX7TWaGqVd8VufWNYITxoReodY2PbeQxsoEIZY/OIH7p/awXndo7W29VnBCREREpBGIXQKHYqHfFVXBCcMrwM5ALi2Gnz488Tgm68GY8EjNjOwu46DbRIhfAWm7q/Yf3gdlpdBxeO3FUj2m2luTgeFEClC04HJPZ4edzR8G/8Eq97QlbQuJOSeoZSwiIiLSGB2Kg7enwfLn7QI3Q66HmxdBu6PSv00Zp6jR0O9ye9sQZZ1OhennYJgU7aPtWWhvO490bJyiXNi3uo5xFpSPM6pq36Df1f2oCJj0PM9+bVZfnYgJgOQfduzYo6zem25tR/doW+s9E1gw1sRmODSOr6cbZ0XW7r0wpkftcaKC/fB0c2V9/CFyCqpqGRuLfkmxtiMasGTCqZZ1uHxIR+t54uF8CotLnT0lERERETHiltvnoev42ueja3kZLBNccGQcDz/oNLyOcSaWj1P+uyoWJJmSUvtXV2VRV6goiRs1xqmfkUo8tXBtfdsypN0Q1h9cz7z4eVzb91pnT0lERETEcTEz4ds/2KWMvIPggn9D34ub9hmMGgetI2HL5zDsVgjvb+83NxRLngFXdxj426rjc9PtrArfYPCr9gX6kOvs87PwCbjmazuroaIM1u4FdpAjpFovh4v+U/d8TOkn03xv1L3Q8eyq/WY+JkMjLLp2lsfX/2c/73fZSf/x49Jz7dMQXLuEU5CvB238PIlLs485lrzCYlKyC+jZLqDO3gtRIXaz6+rjtPbz5L6pPfjHD78w6dklTOoTatVV3pGczYrdafxmaCeuHXH8WsUFxSU1ggJnqknk8Zj+GSH+XqTlFLA9OYvBnVo3+BxEREREWgzTcLr8Mttishrq6jVnrqeNujKhfVrb1/J1ZVBXV5gLOckQ2qfuxVYVY1cfx7cNTPgzzPsz/Gco9DwXvPzh4Db7vsDcM5h7DidSgEKYFjnNClDMjp2tAIWIiIg0Debi/McHqtKgTcryZW+c0or9RsfNHS58CT64FN45F6Ivs1O/TR8N0zzPpHNXDyysfR2WPAVjZ8D4av0jzEqowdfAxvfhtdHQfUpVAz3Te2L6c6c3z7wMeHUkRAyC0L7gFwLZSbBrHhzJgC7jYfidJz1sdr6dvWAaY9fFlHpKrtZn4dTGsEs4ZZUfV+GWMV1pF+jNn2Zt4cPV+yr3D+ncmksGtcfD7fgJ6C8v2sOLC3ZVvi4tyKOhmeaQ0e0DWbwjlZiETAUoRERERM6k5/uAV7UFMUdfk1cPZBjH6iXnFVCzX15dHBmjrj50I38PAeF2c+31b1Xt7zgM+v8K3GqWN21oClAIEztP5B9r/0FMegwHsg/QIaCDzoqIiIg0Xkmb4YsbIN18EewCY+6HsQ/aX+w3Fya4cMMcWPwP2PoVlBRCaC87ONH/SsfHOf9FaBcN69+BNa/ZvSl6ToMJj9YMcpwKs9Lr7JvhwDrY+SPkZ9rp5u362HMcfG3jLaN1DC8t2MW/F+7iD6Yh9+D2BPl4sC0xi799v53fvLGa/1416Lg9He4Y35WbRkdVvs7KyqLDCzS46IigygCFiIiIiJxB92yDwGoBg7qyJ5xtyTOw5Gm7IfeA34BPK0jeAnP+BO+eD1e869TefE36Lm7N3nTmbz/IloRMtiZkkV1QzGWDO/DsldUajTgocsb3x3zvgWk9uWNc3Tdw5obl1SV7WBObzqHcIlr5etA7PJBbx3ZhRNcQmoIQnxCrH8WapDXMiZvDjf1udPaURERERGorK7OzBeY+Yn9hb1YBXfqG3U+iOeowBK6eeeLjzAqtulZpGa6udsr26aRtX/KK/TiaabI3/V/Ut4qsh4osiKOZ/hDHyoxwfAy79FJgtXFW7k7j2Xk7uXFUFHeOr7r2Nz0s3r72LMb8cxF/+277cQMUphl39YbcZYXOWY1W0Vw8JuGo1XMiIiIiUr/MNfHRzaePdVxd2Q3Vy6ceKzPiZMYwqo+zdwksesLObB5zX9V+08Piqs/gxQF2oEIBilPz2foDzNx4AB8PNyJaeZOdWvcNiKPat/Lh8iG1swfOrqOxnjFzwwEemLnZugGa0CuUsEBvDuUVsvlAJhvjDzWZAIUxNXKqAhQiIiLSeJk+C1/faa/UN3qcCxf9t2bPBWkWIst7T8Sm59Kvg/1Fe4XMvCIycgutkkvH4+vpbvVi2H8oj5LSslp9KGLT7NJLkSFVfS4WljfCPqdL7b9Twf5e9AwLYOO+w9bvN30wGjNT4snYeTDb6otRPWgiIiIiIk7Qplp/CFMi9egebnnpdsml4zHZ0P5hdh+40pLa2crpdfS5qGyEXceiLlOi1fSzOLDWvt9y0r1Vk86guHZEZytToWtbfzYdOMylL688rfE6tPbhnsk9HDp2y4FMHpy5mQEdgnjnuqFWw77qikuqmuM1BZM6TeLvq//O9ozt7MvaR6fAZlC/WURERJqH2KUw6xa7v4GbJ0x5AobeYortO3tmcgYM6xLMy4v3sGxnKhcOiKjx3tJdqfYxUW0cGufbTYmsj8uwntcYZ2ftcYrKr99NAKIuFfs93Y/fh6IxMAuvTGb34bwidibn1Ar0iIiIiEgDixwJy5+DPYug3+U139uz0N52HunYODEzYd9q+3mNcRaUjzOqap/JPDdy0+oeL698v7vzFuA0/qvr4+jfoRU92gXUWhHVEP41dwclZWU8d+XAWsEJw/0EDfQam9berRkWbkfpTJknEREREacrKYaFT8B7F9rBieDucPNCu1yRghPN1siuwXRq48vXmxLZmphZo7TTSwt34e7qUiPr2QQOdqfk1Aos/GZoR2v77NydFBZXLR5asTvNCnQMjWpDl7b+lfuHlGdNv7l8L1n5dgmoCl9sOEBceh792gdZTbobO6tRdkR5madq51BERESkQbwzHX6cUft5RdnWb34PT3WGx4Ps/nJ17WtuosZB60jY8nnNP19Btt0jwtUdBv62ar/JaEjdaW+rG3KdvTX3ScXVrn/3LobdC+wgR/VecxVZGav+a/eMq+7njyBjL4QPrGqw7QSN/+q6AWXlF/PJ2n2kl6dtD+8STFS1tO8KmUeKWLYrlb4RgVZa+Oq96Wzaf9gKlAzq1IohnU+8oquxlnlambiS2XGzubn/zc6ejoiIiLRkh/fBzJtg/xr79aDfwblP22nN0qyZhT5PXdaPa99ey5WvruLCgRFWUGD21mT2Zxzhvik9agQW3lsZx4sLdnH3xO41sqFNudVfn92RT9btZ/q/l1klWVNzCvhuc5I13t8vjq7xe6f3C+ejNfGs3pvB+GcWM6l3O2sh0vakLJbtSrMyJx69oA9NRd/2gSzfnWb16/uNsycjIiIiDcOU/YlfCTkHwb8ddB5RuwxQQ/vVB+BWbXH37vn2F+PXfW9/Ye8bXPe+5sbNHS58CT64FN45F6Ivs4MC27+Fw/Ew4ZGagQXTe2/JUzB2Rs1+c1FjYPA1sPF9eG00dJ8CuakQM8vuPTH9uZq/t+8lsOFdiFsG/x4MPc8tb5IdA3sXgZsXTHsKZ1KAohpz8zFj1pbK12Zh3sUD2/OPS/rh41n1f+atCZmUlkF4kA83vbeO+dvterUVRncP4b+/HUyg97Gb4plasNVXcmUftUrLGSZ2msjfVv2NnYd2sjdzL12Cujh7SiIiItISbf0Kvv29vcLHXGRf8IJ9AS8thgkufH7bCJ6ft9MKKJjySyZz+o+Te3LxoPYOj2Ou43uFBfDR2n28szIOP083JvUO5b4pPWsEOQyz2Oi9G4byzoo4vtucyDebEq3fG+LvxUUDI7hjXDerD0VTYbI9Ku5dREREpAXY9g3MfhCyEqv2BUbAtKed2gAZ36MWcmfEQkAYdBp2/H3NkQku3DAHFv/Dvucx5ZdCe9nBif5XOj7O+S9Cu2hY/w6sec1exNVzGkx4tGaQwzABqqtnwupXYOssuzyU+b1+odDvChh1L7Rz7iIcl7Iyk0PT9G3cd8jqQXHZ4A48e+WAk/75f/ywnfP6hRNlmvK5YKWTPzNnBz/tO8wFAyJ46TdVzUvMzcrvP/7Juolp7evJPy6J5pyuwRzMKuCpH39h/vaDTO8fzn+vGnzM32dutsxKrwqlBXnsf+FKMjMzCQx0oPP7GXL7/NtZnrCcOwbewe0DbnfaPERERKQFKsyDOQ/ZK3yM9mfB5W/Zq6iasKysLIKCgpx+nSct67OPS8tl3L8WW5kfW/8yFY8mVoJWRESkpcjPzyc2NpaoqCi8vb1PPTjx2TWmftJRb5SXxb/y/TMXpCjMhe/utTMBvPxhxF2wYzaE9YNzn7JLPFU8//J22PRR1c8GdYLIUbX33VO1gLzSh5eBX1u45FX79d4l8Pm1cN9uOzuhgT8bXeM3owyKQX+dy6E8x7MHPr55uBUMqG9/Oq93rVVbH93UmnNfXGo117trQjdr1ZZREdMpKS3j75dEM6VvmPU6wNuD//52EBP+tYQftiSRePgIEa186vx9d4zvyk2jo2r8pe7wAk43LXKaFaCYGzdXAQoRERFpOAe3whc3QOov9o3UqHtg/J9qpoOLiMNMH48AL3eyC4qtHh29wxUcExERaTLMd49FeY6XdfrxgTqCE9ZA9rW1yazoMs6xck8evifX723un+3yQb/+0C4rteCvkPSzHZQ4mglStImyFyTdvMiej7neP3pfXQLC7TKw1bMRTA+G/aZZdLWm0NLkOD1AceGACHIKShw+vm2AFw3FlHUy8/v3wt2sjztUGaAI8LZPm8mgMLVsq/Nyd7NKPJlat6be67ECFOY486hQVtg4br7HdxqPxyoPdh/eze5Du+nW+qi0IBEREZH6vvla/xbMeRiK8+2bmktft2+gROSUubq60CcikDWxGcQkZCpAISIi0pSY4MQ/IuppsDK77NNTHR07/E+Jjvd9K8iBnz6wsxq6TrD3XfwKPHeMkkHeQeDpDy5uENCuan9d+45mylXtW1312gRR3L0gN82xuUqj5fQAxV8uqtmcrrFp7edpbY8UVQVRuoTY9Wp9PNzqTJUO9LGDDfnVfqapCPQMZGTESBYfWMyc+DkKUIiIiMiZk5cB39wFv3xnvzYN3swNjV+IzrpIPYhuH1QZoLjiLAe/lBARERFx1KFYu59Bh6E1e04c3QehPpgMiuykqtdJmyH/MHRs5n0rWgCnBygau5/3H7a2HVpXZUJ0DvalfSsfEg4fISnziNUsu7pdB7PLf8aXpmhK5BQrQDE7djZ3DLgDl5NJ6xIRERFxRPxKmHkTZCWAqwdM/isMu80s+9b5E6kn0e3tsk4xiVk6pyIiIk2JKbNkMhkcva7+3+UnPu63X0DnEY79bkc1ZGtjk0FRmAP5WXbGxZw/Qb8rITC84eYgZ0SLugM8Ulhi1V81gYXqzIoi897Rvt+cZDXEbuPnyahuVSv5zBf2Vw3rZD1/ZvYOSkur/s+4em86i3emWgGNAR2CaIrGdxyPp6sncVlx7Dy009nTERERkeakpBgWPwXvTreDE226wk3z4Zw7FJwQqWf92tv3I9sSs6z+eSIiItJEmMXCpsySIw9TWsl8eV/RELv2YBDY3j7OkfFOZqFymy72YqMD66r2HTkE6Xs4IxkUhilXNf9RyE6G856p/98jDa5JZ1Csi8vgk7X7recZuQXWdn18Bn/8bJP1vGuoH3eM61YjG+I3b6xmWFQbPr31nMr976yIY+62ZEZ2DbF6RpRRxtaELNbGZeDl7sqzVwzAz6vmqTINrhf+ksKsnxLYlZLD0Kg2pGQXMDsmCU83V/55eX/c6yj/1BT4e/ozqv0oFu5fyJy4OfRs09PZUxIREZHmIPMAzLwZ9q20Xw+4yr6p8LLLZ4pI/YoK8bfK0ppytbFpOXQLtXvqiYiISDNimkpPexo+u6Y8SFF9UUJ5sGHaU441yD5Z5jp+8O9g3qN2aSe/UFj4N3A5A9+JWkEY05T7YUjbBTfMBm87W1SatiYdoIhLy2XmxgM19sWn51kPwwQiqgcojmVyn3Zk5RcRk5jJkp2p1uqidkFe/Oqsjtw8pgvdQmvfNJsG1x/eOIxXFu+2siw+WBWPr5eb1TT77ok9rIZ0Tdm0qGmVAYq7Bt2lMk8iIiJyerZ/B1/fadeJ9QyA85+D/lfqrIqcQW7ljbI3xB8iJiFLAQoREZHmqs+FcOX7MPtBO8Og+pf6Jjhh3j9TJv8NCnPh49/YpZdG/J9dhqm++QaDmxcc3g/X/1gVsJAmz6WsrCGLhcmxZGVlERQURGZmJoGBzg9u5BXlMfbTseSX5PPp+Z/SJ7iPs6ckIiIiTVHREZj7CKx7034dMQguf9tOB28hGtt1nrSsz/6xr2N4b1U8N46K4s/n65peRESkscnPzyc2NpaoqCi8vb1Pb7DSErsnRc5B8G9n95w4E5kTLcTxPpvGcJ3XXDTpDAo5c3w9fBndYTTz4udZWRQKUIiIiMhJS/kFvrgBUrbar0f8Hib8Gdw9dTJFGkjf8j4Upu+eiIiINHMmGBE12tmzEDkpTbNJgjSIqZFTra0JUCjRRkRERBxmEnTXvwOvj7ODE35t4epZMOVvCk6IOLFRdqkaZYuIiIhII6MAhRzTmA5j8HH3ISEnga3p5SsfRURERI7nyCH4/Fr47g9QfAS6ToDbV0K3iTpvIk5g+ul5uruSXVDMvgy7V5+IiIiISGOhAIUckwlOjO0wtjKLQkREROS49q2BV0fDtq/B1d1umPfbmeAfqhMn4iQebq70DguwnsckqsyTiIiIiDQuClDIcU2LnGZtVeZJREREjtuMb+kz8M65kLkfWkfBjXNh5O/BVZebIo2lD8UW9aEQERERkUZGTbLluEa2H4mvuy9JuUlsTtvMgLYDdMZERESkSlYizLoF4pbZr/tdCdOfBe9AnSWRRiI6wg5QbE3IcvZURERE5BjU/7Xx0WfSMLSkTY7L292b8Z3GW89nx87W2RIREZEqO36EV0bawQkPP7j4Vbj0dQUnRBppo2xT4kk32iIiIo2Lh4eHtc3LU6+oxqbiM6n4jOTMUAaFnNDUzlP5fu/3zI2fy/1n34+ri+JaIiIiLVpRPsx/DNa8ar8O6w+XvwMh3Zw9MxGpQ48wf9xdXTicV0TC4SN0aO2r8yQiItJIuLm50apVK1JSUqzXvr6+uLi4OHtaLZpZ0GGCE+YzMZ+N+YzkzFGAQhwq8+Tv4U9KXgo/p/zM4HaDddZERERaqtSd8MUNcHCL/Xr4nTDpMXD3cvbMROQYvNzd6NEugG1JWcQkZClAISIi0siEhYVZ24oghTQOJjhR8dnImaMAhZyQp5snEzpN4Js931jNshWgEBERaYHKyuCnD+HHB6AoD3xD4OJXoMcUZ89MRBwQ3T6wPECRybRo3WiLiIg0JiZjIjw8nNDQUIqKipw9HSkv66TMiYahAIU4ZGrkVCtAYco8PXD2A7i5KrVJRESkxcjPhO/ugZiZ9uuosXaviQB9ySnSVES3D+Kz9QesPhQiIiLSOJkvxPWluLQ0aiYgDjkn/BwCPANIO5LGxpSNOmsiIiItxYH18OpoOzjh4gYTH4PffaXghEgTDFAYJoNCjbJFREREpLFQgEIc4uHmwcROE63npsyTiIiINHOlpbDsOXh7KhyOh1ad4IY5MPpecNUlpEhT0zssEFcXSMspJCW7wNnTERERERGx6O5SHDYtcpq1nRc/j+LSYp05ERGR5io7GT68BBb8Bcy/+X0vhduWQ8eznT0zETlFPp5udAv1r8yiEBERERFpDBSgEIcNDR9KK69WZORnsP7gep05ERGR5mjXPHhlJOxdDB6+cOF/4PK3wdsuDyMiTVd0REWZpyxnT0VERERExKIAhTjMw1VlnkRERJqt4gKY/Sf43+WQlwbt+sEtS2Dw78DFxdmzE5F60Le8D8UWZVCIiIiISCOhAIWclKmRU63t/Pj5FJUW6eyJiIg0B2m74a3JsPq/9uuht8JN86FtD2fPTETqUb/yAMXWRJV4EhEREZHGQQEKOSlnh51NG+82HC44zLqkdTp7IiIiTd3PH8NrYyBpE/i0gd98Auf9Ezy8nT0zEalnfSICrW1SZj5pOWqULSIiIiLOpwCFnBR3V3cmdZpkPZ8dN1tnT0REpKnKz4JZt8BXt0FRLkSOhttXQM9znT0zETlD/L3c6RLiZz3fmqg+FCIiIiLifApQyEmbFjXN2i7Yt4CiEpV5EhERaXISNthZE5s/BRc3GP8IXPM1BEY4e2Yi0kB9KGLUh0JEREREGgEFKOSkDQ4dTIhPCFmFWaxKWqUzKCIi0lSUlsKKf8NbU+BQLAR1hOt/gLH3g6ubs2cnIg0gurzMkwIUIiIiItIYKEAhJ83N1Y3JnSdbz+fEzdEZFBERaQpyUuB/l8O8P0NpMfS+EG5bBp2GO3tmIuKERtkxapQtIiIiIo2AAhRySqZGTrW2i/YtorCkUGdRRESkMdu9AF4ZAXsWgLs3nP8CXPk++LR29sxEpIH1jbADFPszjpCZp3KtIiIiIuJcClDIKRkUOohQn1Cyi7JZmbhSZ1FERKQxKi6EuX+GDy+F3FQI7QO3LIazrgcXF2fPTkScIMjXg45tfKznW5VFISIiIiJOpgCFnNpfHBdXpkROsZ6rzJOIiEgjlLEX3p4KK/9tvz77Jrh5IYT2dvbMRMTJosuzKFTmSUREREScTQEKOf0yT/sXUVBSoDMpIiLSWGz+DF4dA4kbwbsV/OpDmP4seNirpkWkZYsu70OxJSHL2VMRERERkRZOAQo5Zf3b9ifML4zcolyWJyzXmRQREXG2ghz48naYdTMUZkOnEXDbcuh9gbNnJiKNMECxNSHT2VMRERERkRZOAQo5vTJPncvLPMXO0ZkUERFxpsSf4bUxsOkjcHGFcQ/Btd9Cq476XESkhr4RgdZ2b1ou2flqlC0iIiIizqMAhZyWaZHTrO3iA4s5UnxEZ1NERKShlZXBqpfhzUmQsQcC28O138G4GeDmrs9DRGoJ8fciPMjber49KVtnSEREREScRgEKOS3RIdG0929vBSeWHVimsykiItKQctPgoythzkNQWgS9zrdLOkWO1OcgIsfVt6JRtso8iYiIiIgTKUAhp8XFxYUpkeVlnuJU5klERKTB7F0Mr4yEXXPBzQvO+5fdDNu3jT4EETmh6PZ2mScFKERERETEmRSgkNM2NXKqtV16YCl5RXk6oyIiImdSSRHM/wu8fzHkJEPbXnDLIhh6s1k5oHMvIg7pV94oOyZRjbJFRERExHkUoJDT1qdNHzoGdCS/JN8KUoiIiMgZcigO3jkXlj9nmk/AkOvg5kXQrq9OuYiclOjyAMXulByOFJbo7ImIiIiIUyhAIfVS5qkii2J23GydURERkTMhZia8OhoOrAOvILjiXbjgRfD01fkWkZMWGuBlNcsuLYPtyVk6gyIiIiLiFO7O+bXS3EyLnMabW960GmXnFuXi5+Hn7CmJiIg0D4W58OOD8NMH9uuOw+CyN6FVJ2fPTOSM2rT/MM/P38nG+EMUlZTRo50/N4yK4qKB7R0eo7S0jA9Wx/Px2n3EpuXi5+XOOV2CuW9qT6JCal6vfr5+P/d/sfm4443oGsxHNw+nuSwyMn0oFu9IZWtCJoM7tXb2lERERESav4QNsOhJOLDWLt/bthcMvwP6X+H4GKWlsO5N2PAuZOwBTz+IHA0TH4XgrjWP/el/8PUdxx8vagxc+y3OogCF1IserXsQGRhJXFYci/Yv4vwu5+vMioiInK7kLfDFDZC203ydCGPug7EzwE2XcNK8rdqTzrVvr8XDzYULBkQQ4O3O7K3J3P3Jzxw4dIQ7x3dzaJyHv9rCx2v30z3Un+tGRJKaU8B3m5NYuiuVWbePoHu7gMpj+0QEcvfE7nWO82NMEjsP5jCmR1uak+iIICtAsSVBfShEREREzrjYZfDhpeDmCdGXglcgbP8WZt0Eh+Pt+z1HfPcH2PieHdwYegvkpkLMLNizCG6cC6G9qo4N62ffQ9Zl29eQuh26TsSZdHcr9Vrm6bXNrzEnbo4CFCIiIqejrAzWvg5zH4GSQggIh0tft1e2iDRzxSWlzJi12YrJfXrrOZW9Eu6e1INLX17B8/N2cl6/8FoZEEdbuSfNCk4MjWzDBzcNxcvdzdp/2eAOXP3WGh7+KobPbj2n8vi+EUHW42iFxaW8vyoOd1cX62ebE5NBYcQkqMSTiIiIyBlVUgzf3GUvPLv+BwgfYO8fNwPenAyLn4S+l9TOgDha7FI7ONFpBFzzFbh72fsH/Brevxi+v9cev0J4f/txtOJC+57T1R0GXoUzqQeF1JuKPhQrElaQVaibHBERkVOSlwGfXAU/PmAHJ3qcC7etUHBCWoyVe9KJT8/jogERlcEJw9/LnbsmdKe4tMwqx3Qin6y1j/njlB6VwQljZLcQxnRvy9rYDPam5pxwnDlbkzmUV8SEXqG0DSi/AWwmKs7vzoPZFBSrUbaIiIjIGRO7BA7FQr8rqoIThlcAjL0fSovhpw9PPM6G9+zthEeqghNGl3HQbSLEr4C03Sce55dv4UgG9JgG/qE4U5POoFizN5352w9aKclbE7LILii2VjU9e2W1D9lBkTO+P+Z7D0zryR3jaqeRxyRk8vLi3Ww+kElqdoHVZK53eCA3j45iWJdgWprurbvTNagrezL3sHj/Yi7seqGzpyQiItL0Un5n3QLZiXba75Qn7JRdFxdnz0ykwazem25tR9dRTskEFow1sRkOjePr6cZZkW1qj9OjLUt2plrjdGnrf9xxPisPhvx6aEeam/atfGjl68HhvCJ2JufQr0PtDBIRERERqQdxy+1t1/G13+s6wd6a4IIj45jev53q6ItmSjXtng/xyyHkBCVRN5b3OBx8Dc7WpAMUn60/wMyNB/DxcCOilTfZqcWnfYF++ZDaadtn13FTY1ZS3f7hBjzdXZnaN4zwIB+SMo9Y+03Q5JnL+3PFWc3vJsaRLIqXN73M7NjZClCIiIicTLrvkqdg6b9MfScI7g6Xv113Kq5IMxeXnmtto4Jrl3AK8vWgjZ8ncWn2MceSV1hMSnYBPdsF4OZaO8AXFeJr/64TjHPgUB4rdqcRFujN2B4nXllmshBMSagK2flFNPpG2RFBLN+dRkxipgIUIiIiIicrPws8q702WQ3VMxsqmGbWRl0lnHxag28wpJcfcyyFuZCTDKF9wLUqQ7hSxdgnGufwPjujIyACuk3C2Zp0gOLaEZ25dWwXurb1Z9OBw1z68srTGq9Dax/umdzDoWOfmbPDfH3AzNtH1KhVa7IqLvjPcv69cFeLDlCsSlxFZkEmQV5ahSUiInLCi8OZN8P+1fbrQVfDuf8Ez+PX1xdprrLz7UVHpjF2XUypp+TM/NMcw8PaZpUfdyyfrz9AaRlccVaHOgMdR3t50R5eXLCr8nVpQR6NXd/2gXaAQo2yRURERE7e833Aq9p1omlIPf6hugMZhmmMXRevAMhKPP7vcmQMo+AEpfd/+h+UlcKg39Yd6GhgTTpA0b9DK6f97n0ZebQL8K7VSM/UcQ0N8CI9p5CWqEurLlapp12HdrFw30Iu6X6Js6ckIiLSeG372m6Ulp9pX2Se/zz0u9zZsxIRE1woLeOLDQesCmtXOrjw6I7xXblpdFTl66ysLDq80LhPp8mgMBSgEBERETkF92yDwGoBg7qyJxqT0lL4+X92s26zOK4RaNIBivpmVlB9snYf6bmFVur48C7BRIXUvXqxRzt/tiVmsT0py+o7UWFrYqaVTj6xl3ObizjTtMhpVoBiTtwcBShERETqUpgHcx6CDe/ar9ufBZe9CW2qvtgUaakqsh4qsiCOllNQfMzMCMfHsEsvBR5nnGW700g4fISR3YLp2MYuCXUiphl39YbcZYV2pkZj1q+8Ufb25GyKSkrxcHN19pREREREmg7vQPvhyHHHy24oyD52ZsTJjGEcb5y9CyFzP0SNhdaRNAYKUFRjgg0zZm2pfG1WS108sD3/uKQfPp41010emd6HG99dx2WvrLR6UIQFeVup5rNjkq2eFeZnmlN92pMxpfMUXvrpJVYnreZQ/iFae7d29pREREQaj4Pb4IsbIHW7vWpl1B9g/MPg1vi/yBRpCJHlvSdi03Nr9UTIzCsiI7eQIZ2Pf33p6+luZTXvP5RHSWlZrfJMsWl26aXIYyxGMj5bZzfH/tXZnWjOOrXxJcDLneyCYnan5NRYfCUiIiIi9aRNtf4QEYNqvnfkEOSlQ8dhxx/DlAH2D4ND8VBaUrs8U/px+lw0wubYFbQ8ptwtY7rw1Z0j2fToFDY9NoWPbh7GwI6t+PKnBB6YubnWiTPZFZ/eeg5tA7ysY15ZvMfatvb14IohHQgN9D5hfdp+j8+tfJzz5EKai8igSHq16UVJWQkL9i1w9nREREQah7IyWPcmvDHeDk74t4PffQmTHldwQqSaYV2Cre2ynam1zsvSXfa+YVFtHBonr7CE9XEZtcfZefxxDuUWMm/bQVr5ejC1b7tm/fm4urrQJ8IOSqjMk4iIiMgZEjnS3u5ZVPu9PeXfC3ce6dg4Rbmwb3Ud45R/D9t5VN0/m5cBO36wm3L3voDGwukZFIP+OpdDeY5nD3x883DO6WrftNSnP53Xu8brEV1D+Oim1pz74lK+3ZTIXRO60aNdeaMRYNGOFH7/0U9M6B3KW9eeRYfWvhw4dISXF+3m/i8280tyNn8+v0+zqk97ss2yf8n4xSrzdHkP1dIWEZEWzlwIml4Tv3xnv+42GS5+BfzbOntmIo3OyK7B1qr+rzclct3IyMqeb6a000sLd+Hu6sLlQzpUHm8yKjLKS7SaR4XfDO1oXcc/O3cnH940DE93e23Wit1pVqBjaFQburT1r3MOs35KoLCklKsGdqpRsqm5Mn301sRmsDUxiyucPRkRERGR5ihqnF1SacvnMOxWCO9fVZZpyTPg6g4Df1t1fG66nVXhGwx+1b4LH3IdxMyEhU/ANV+De/n1797FsHuBHeQI6Vb3HDZ9AiWF0P+GRtUrw+kBigsHRJBTUOLw8SZjoaGYsk5mfv9euJv1cYcqAxSH8wq5++OfrJTw568caK06MrqF+vPslQOsdPR3VsRyzTmd6Vyeot4c6tOebIDixY0vsjZ5LelH0gn2qf+gkoiISJMQvxJm3gxZB8DVAyb/BYbdbpYtO3tmIo2Su5srT13Wj2vfXsuVr67iwoER+Hu5M3trMvszjnDflB41AgvvrYzjxQW7uHtid+6Z3KPGgqNfn92RT9btZ/q/lzGhVyipOQV8tznJGu/vF0efsLzTr4c61hy7qYtub2dQbEnIdPZURERERJonN3e48CX44FJ451yIvgy8AmD7t3A4HiY8UjOwsPZ1WPIUjJ0B4x+q2h81xi7PtPF9eG00dJ8CuakQM8vuPTH9uWPP4afGV96pUQQo/nLRsW8MGoPW5auwjhRVBVFMsMI01DYp4RXBiQouLi6c0yWYn/YdtlYgHStA0dx1DOhI3+C+bE3fapV5urLnlc6ekoiISMMyNUGX/su+qCwrhTZd4PK3a9cbFZFaTHDh89tG8Py8nVZAwTRvNouF/ji5JxcPau/wGTN94XqFBfDR2n28szIOP083JvUO5b4pPY+ZPfHz/sPsOJjNgI6t6BXWMvoxVDTK3paYVWfPDhERERGpBya4cMMcWPwP2PqVnc0Q2ssOTvQ/ie9Oz38R2kXD+ndgzWt2b4qe02DCo8fOnjiwAVK2Qfsh0K5vo/o4nR6gaOzMDYrRobVP5T5zg2SYVPK6VOz3dGvZKyNNFoUJUMyOm60AhYiItCyZB2DWLRC/wn494Co475/2ChkRcYjpB/feDUNPeJzJmqieOVGdWUx03cgo63Eyvzfuqekt6lOKCvHHx8PNWpQVm5ZDt1D9t0pERETkjOgwBK6eeeLjxj9UM3OiOpONb8pEmcfJ/N7HG2e2bIv6Bv1IYQm7U3JIOHykxn7TDM68d7TvNyfxzaZEq5btqG4hlfsHdWptrSr6ISaJ7UlZNX5m58Fs62dMjdvBnVvT0gMUxvrk9aQdSXP2dERERBrG9u/g1VF2cMLTHy59Ay55RcEJOXkJG+DDy+GpTvD3cHh9PGz+/OTGKC2FNa/DyyPgiXbwzy7w2bWQvsexn1/+AjweZD/2rzvx8Yfi4O8R9vHf/uHk5ipO41ajUXbN+xsRERERkTOpSWdQrIvL4JO1dn3YjNwCa7s+PoM/frbJet411I87xnWrkQ3xmzdWW6WZPr31nMr976yIY+62ZEZ2DSGilQ9llLE1IYu1cRl4ubvy7BUD8POqOlVhQd7cMa4rLy3czUX/WcHkvu2sDIvEw/nM2ZpMYXEpfzqvV40mfS1RhH8E/UP6szltM3Pj5nJV76ucPSUREZEzp+gIzH0E1r1pvzalnC57C4K76qzLyYtdBh9eCm6eEH2pXU/W1KeddZNdo3bMfY6N890fYON70LYXDL2lqj7tnkVw41w7pfxYUnfAon+Ahx8U5Z74d5WVwVd3Ov5nlEYlOiKQDfGHrMVbJ1NGS0RERESkxQYo4tJymbnxQI198el51sMwgYjqAYpjmdynHVn5RcQkZrJkZ6pVd7VdkBe/OqsjN4/pYjW/Ptofp/S0atJ+tDae5bvSyCkoJsDb3fqd142IZGLvdvX4J23aWRQmQDEnbo4CFCIi0nyl/AJf3AApW+3XI+6y63+6t+zFCnKKSorhm7tMdzO4/gcIH2DvHzcD3pwMi5+EvpecOPgVu9QOTnQaAdd8Be5e9v4Bv4b3L4bv77XHP1YPlS9vg7BoCO4Gmz898bxN/dv9q2HyX2HOn072Ty1O1re8D4UaZYuIiIhIQ2rSAYorzupoPRx1TtfgOuvJTosOsx4na3r/cOshxzYlcgrPrH+Gn1J+4mDuQdr5KXAjIiLNiFkxbr4A/nEGFB8Bv7ZwyavQbZKzZyZNWewSOBQLA6+uCk4YpofJ2PvtYNhPH8Kkx44/zob37K1pulcRnDC6jINuE2H3fEjbXXcjveXPw8EYuHUprPj3iedsSkYt+AuMvBvC+jv8R5XG2Si7tLTM6t8hIiIiInKmtageFNLwwvzCGBQ6yCqbNS9+nj4CERFpPo4chs+vg2/vtoMTXcbDbSsUnJDTF7fc3nYdX/u9rhPsbUUD9hONY8ozdRpexzgTy8cp/13VHdwGS56GMfdDaG/H+lx8fScEdYSxD574eGmUTNa46aOXXVDMvgw7I11ERERE5ExTgEIarFn27LjZOtsiItI87FsDr46GbV+Bq7td0ubqWRCgTEGpBxnlDazrKuHk0xp8g0/c5LowF3KSoXVncHWr/X7F2EePY8pLfXU7hPSEUfc4Nt/VL8P+NXDRf2pmakiT4uHmSu+wAOu5KX0rIiIiItIQFKCQM25y58m44MKm1E0k5STpjIuISNNl6vIvfQbeORcy90HrSLvRsClr46rLKjmB/Kyaj+KCYx9nmMbYdTGlngqyTvy7TjSGcfQ4y561SzuZYIObBydkSkQtfAKG3Q4dh574eGkSfShiEk7w90tEREREpJ406R4U0jSE+oYyuN1gNhzcwNz4uVzb91pnT0lEROTkZSXCrFsgbpn9ut8VMP058D7GF8AiR3u+D3hVq+s/dgaMf6jxnKfkLXYAzjR5jxjoWGknk20REGb3uZAmLzqiIkChDAoRERERaRgKUEiDmBY5zQpQzImbowCFiIg0PTtm21/EHsmwa/pP/xcM+A24qImsnIR7tkFgtYDWscohVQS9jpUlUZB97MyIkxnDqD7Ol7dDmygY52DQZM2rcGAdXPstePo69jPSJBplmxJPZWVluOi/cSIiIiJyhilAIQ1iUudJPLn2SbakbeFA9gE6BHTQmRcRkcbPlOCZ96j9RawR1h8ufwdCujl7ZtIUmaCBIxk3bar1h4gYVPO9I4cgLx06Djv+GJ5+4B8Gh+Lt0mRH96FIr6PPxcEt9vaJ0LrHfGuSvf3V/6D3+XbGBWXw3vl1H7/hHfvRczr85qPjz1cahR5h/ri7unA4r4iEw0fo0FqBJxERERE5sxSgkAYR4hPC2e3OZk3yGqvM0w3RN+jMi4hI45a2C764vvxLWGD4nTDpMTUBljMvciQsfw72LIJ+l9d8b89Ce9t5pGPjxMyEfavt5zXGWVA+zqiqfYN+V/c48Svtxt09z7MbdLfqVDV+XQ24cw7CrrkQ0sMOpIQPOPFcpVHwcnejR7sAtiVlWX0oFKAQERERkTNNAQppMFMip1gBitmxsxWgEBGRxqusDH7+H/xwPxTl2V/IXvwq9Jji7JlJSxE1zm7AvuVzGHYrhPevKsu05BlwdYeBv606Pjfdzqowf1f9gqv2D7nODlCYJtbXfA3unvb+vYth9wI7yFE9G8g0xq6LKf1kAhSj7oWOZ1ftH3S1/Tha7DI7QGHGv+CF0zwZ0tCi2wdaAYqtiZlMiw7TByAiIiIiZ5TrmR1epGaZJzcXN7ZnbGdf1j6dGhERaXzyM2HmTfD1nXZwImoM3LZCwQlpWG7ucOFLUFYK75wL3/we5jwMr4yE1O0wbkbNwMLa1+G/Z9vb6szf38HXwL6V8NpomPtn+PI2+N+Vdu8J0+Rd5CjR5X0otqhRtoiIiIg0AAUopMG08W7D0LCh1nPTLFtERKRRObAeXh0NMV+AixtMfAx+9xUEhjt7ZtISmeDCDXOg03DY+hWsewt828Clb8CY+x0f5/wX4dx/Ai6w5jXYOQd6ToObF0JorzP5J5AmHqCISbAbZYuIiIiInEkuZbrqbBSysrIICgoiMzOTwEAHmic2UTN3zuTxVY/Ts3VPvrjwC2dPR0REBEpLYeWLdhmc0mK7vv5lb9csZSNyGlrKdZ40j8/+SGEJfR+bTWkZrPnTRNoFejt7SiIiIiKNTlO8zmuslEEhDWpip4m4u7iz49AOYjNjdfZFRMS5spPhw0tg/uN2cKLvJXDrMgUnRKTF8vF0o1uof2UWhYiIiIjImaQAhTSoVt6tGBYxzHquMk8iIuJUu+bZNf1Nw2APX7vm/+XvgE8rfTAi0qJFR1SUecpy9lREREREpJlTgEIa3LTIadZWAQoREXGK4gK74fD/Loe8NGgXDbcstpsJu7joQxGRFq9vRR+KRGVQiIiIiMiZ5X6GxxepZXzH8bi7urP78G72HN5D11ZddZZERKRhpO+BL66HpE3266G3wuS/godqrIuIVOhXrVG2iIiIiMiZpAwKaXBBXkGMjBhpPVcWhYiINJhNn8BrY+zghE9r+PXHcN4/FZwQETlKnwi70WNSZj5pOQU6PyIiIiJyxihAIU4xNXKqtZ0dN5uysjJ9CiIicuYUZMOsW+DLW6EwBzqPgttXQq/zdNZFROrg7+VOlxA/6/nWRPWhEBEREZEzRwEKcVqZJ09XT2IzY9l1eJc+BREROTMSNsKro2Hzp+DiCuMfhmu/gcAInXEREUf6UKjMk4iIiIicQQpQiFP4e/ozsr1d5ml27Gx9CiIiUr9KS2HlS/DWFDgUC0Ed4fofYewD4Oqmsy0icgLR5WWetqpRtoiIiIicQQpQiNNMi5xmbefGz1WZJxERqT85KfDRFTD3ESgtgt4Xwm3LoNNwnWUREQdFl2dQbFEGhYiIiIicQQpQiNOM7TgWLzcv4rPi+SXjF30SIiJy+nYvgFdGwu754O4N578AV75vN8UWERGHRUfYAYr9GUfIzCvSmRMRERGRM0IBCnEaPw8/xnQYYz2fEzdHn4SIiJy64kKY9yh8eCnkpkBoH7hlMZx1Pbi46MyKiJykIF8POrbxsZ6rzJOIiIiInCkKULRQJaVlrNqTztc/J1hb89oZpkROsbaz42arzJOIiJyajL3wzjRY8aL9+qwb4eaFENpbZ1REpB6yKGLUh0JEREREzhD3MzWwNF6zY5L4y7fbSMrMr9wXHuTNYxf0YVp0eIPOZUz7Mfi4+5CQk8C29G30DenboL9fRESauM2fw3f3QGE2eLeCi/4DvS9w9qxERJpNH4ofY5KJSchy9lREREREpJlSBkULDE7c/uHGGsEJIzkz39pv3m9Ivh6+lWWeTBaFiIiIQwpy4Ks7YNZNdnCi0zlw23IFJ0RE6lHfiEBrG6NG2SIiIiJyhihA0YKYMk4mc6KuYk4V+8z7DV3uaWrk1Mo+FGVlzik1JSIiTUjSJnh9LPz8P3BxhbEz4NrvoFVHZ89MRKTZZVAYe9Nyyc5Xo2wRERERqX8KULQga2MzamVOVGdCA+Z9c1xDGt1+tFXmKSk3ic1pmxv0d4uISBNigtirXoY3J0H6bghsD9d+C+MfAjdVrRQRqW8h/l5WKVhje1K2TrCIiIiI1DsFKFqQlOz8ej2uvni7ezOu47jKLAoREZFactPgo1/BnIegpBB6TrdLOkWO0skSETmD+lY0ylaZJxERERE5AxSgaEFCA7zr9bj6NC1yWmWAorSstMF/v4iINGJ7l8ArI2HXHHDzgvP+Bb/+H/i2cfbMRESavej25X0oEjOdPRURERERaYYUoGhBhka1sVK0XY7xvtlv3jfHNbSR7Ufi7+FPSl4Km1I3NfjvFxGRRqikCOb/Bd6/CHKSIaQn3LwQht4MLsf610xEROpTtDIoREREROQMUoCiBXFzdeGxC/pYz4/1tY553xzX0LzcvBjfcbz1fHbs7Ab//SIi0sgcioN3zoXlz9ldkgZfC7cshrBoZ89MRKRF6dfBLvG0OyWHI4Ulzp6OiIiIiDQzClC0MNOiw3nl6sGElTe7qxDg5W7tN+87y9TIqdZ2Xvw8Skp18yMi0mLFzIJXR8OBdeAVBFe8Cxf+Gzx9nT0zEZEWJzTAy2qWXVoG25OznD0dEREREWlmFKBogUwQYvmDE/j45uFcNbSjtc+UdnJmcMIYETGCAI8AUo+ksjFlo1PnIiIiTlCYC9/cBV9cDwVZ0GEo3LYM+l6ij0NExElcXFwq+1BsVaNsEREREalnClC0UKaM0zldg3lwWm883FzYmZLDroPZTp2Th5sHEzpNqGyWLSIiLUjyFnh9HGx83y5EOPo+uP5HaN3Z2TMTEWnxqvpQKINCREREROqXAhQtXJCvB6O7t7Wef78lydnTYVrUNGurMk8iIi1EWRmseR3emAhpOyEgHK79Bib+GdzcnT07ERExjbLLMyi2KINCREREROqZAhTCef3s0k4/NIIAxbDwYQR5BZGRn8H6g+udPR0RETmT8jLgk6vgx/uhpAB6TIPbVkDUGJ13EZFGJLq9nUGx82A2BcXqFSciIiIi9UcBCmFyn3Z2maeDOdZNhzN5uHowqdMk67nKPImINGNxy+GVkbDjB3DzhGlPw28+Ab9gZ89MRESO0r6VD618PSguLWNnco7Oj4iIiIjUmyZdO2HN3nTmbz9opRpvTcgiu6CYywZ34NkrB5zSeKWlZXyx4QCfrd/PjoPZFJWUEh7kw5DOrXn8wr74e9U8XSnZ+Tw7ZycLd6SQeaTIunC/eGB7bh/XFU/3phP7CfLxYEz3tiz4JYXvNyfRY3KAU+czJXIKM3fNZH78fP407E+4uzbpv6YiIlJdSTEseRqWPmPqO0Fwd7j8bQjvr/MkItKYG2VHBLF8dxoxiZn062BnVIiIiIiInK6m8y16HT5bf4A3lsWyaX8moYFepzWWSVW++f31PDBzMzkFxVw+pAPXjoi00pkX70ghO7+oVnDikv+u5LMN+xncqRU3jIwi2M+T5+fvtMYxwY6mZHr/8EbTh2Jo2FBae7XmUMEh1iavdfZ0RESkvhzeD+9Oh6X/tIMTg66GW5coOCEi0gT0Le9DEaM+FCIiIiJSj5r00vRrR3Tm1rFd6NrWn00HDnPpyytPeax/zt5hZRA8OK2XlQFRXV3Bhqd+/IWEw0f428XR/G54Z2tfWVkZ932+mZkbD/DFxgNceVZHmopJfdrh6ebK7hS7zFOPds7LojAZE5M6T+LznZ9bZZ5GRIxw2lxERKSebPsavrkL8jPBMwAueAH6Xa7TKyLHtGn/YWvxz8b4QxSVlNGjnT83jIriooHtHT5r5jr+g9XxfLx2H7Fpufh5uXNOl2Dum9qTqBC/Y/7c7JhkPlwdb2ULHCksoW2AF4M6teahc3sR0cqnRX5qJoPCiEnMcvZURERERJquhA2w6Ek4sBZKiqBtLxh+B/S/wvExSkth3Zuw4V3I2AOefhA5GiY+CsE1v9euYfu39s8lbYKiI+AfCh3Ohsl/haAOOEuTzqDo36GV9UW6m6vLaY2TnJnPeyvjGBrZplZwwnB1dbEeFUyGxXebk+jUxperh3Wqkfr84LSemEM/WbuPpiTQ24MxPUKs5+bP5mzTIqdZW1Pmqai0ZvaKiIg0Ieai59s/wGfX2MGJ9kPgtmUKTojIca3ak84Vr65iXWwG5/UL5+rhncjIK+TuT37mv4t2O3z2Hv5qC499s5WS0jKuGxHJuJ5tmbf9IBf+Zzm76ui9ZhYcPTRrC7d9uIH9h/K4oH8E14+Msu4TTKDELFBqqfqVN8renpRllcIVERERkZMUuwzengb7VkGfi+CsGyAvHWbdBEv/5fg43/0Bfrwfykpg6C3QfQrs+BFeHw8pv9Q+vqwMvr0bPr0aDsVD9GUw7DboNAL2r7WrHThRk86gqC8/xiRZDd/O6xdmBR/mbzto3Xy09fdiTI+2hAV51zje3JwUFpcyqnuIFZSoLjTQm55hgfy8/zD5RSV4e7jRlMo8zd9u+lAkcs+k7rX+bA1pSLshBHsHk56fzurE1YzuMNppcxERkVN0cBt8cQOkbrdfj/wDTHgE3Dx0SkXkmIpLSpkxazO4wKe3nmOVXDXuntSDS19ewfPzdlpBi+NlQBgr96Tx8dr9VnDhg5uG4uVuX5ebnnVXv7WGh7+K4bNbz6nxM++ujLOyLa45pzOPXdC31kIoM7eWyizOCvByt/r+mazr3uF2yScRERERcbAfo6kqYC5yr/8Bwst7KI+bAW9OhsVPQt9Ljp8BYcQuhY3v2cGFa74C9/K2BwN+De9fDN/fa49f3ZrX7GyLs2+Gc58GV7fac3OiJp1BUV+2HMi0ttn5xUz412L+8OnPPDNnh9WPYsw/F/Hmsr01jo9Lz7W2UcF13xRFhfhiqkLtz8ijKZnUu53V3HtPaq7VJNyZ3FzdmNx5svXclHkSEZEmxKzOWPcWvDHeDk74t4PffQmT/6LghIic0Mo96cSn53HRgIjK4ITh7+XOXRO6WwuLPl9/4lVen6y1j/njlB6VwQljZLcQxnRvy9rYDPam5lTuN4uLXlywy/oi/tHz+9SZpe3u1nJvn0xGeZ8I9aEQEREROSWxS+BQLPS7oio4YXgFwNj7obQYfvrwxONseM/emsV/FcEJo8s46DYR4ldA2u6aVQ2WPAWtI2HaU7WDE4abc3MYWu4VdjVpuYXW9oUFu6yVQPPuGUPMX6by9nVn0drPgye+386iX1IqjzeBDCPAu+4Pz9w8GVnlxx2rKbdpvF394WwB3h6M7dHWev5DIyjzNDVyqrVduG8hhSX2ZyQiIo1cXgZ89jt71UZxPnSbBLetgK4TnD0zEWkiVu9Nt7ajy69LqzOBBWNNbIZD4/h6unFWZJva4/SoPc6yXWkczitiSp92lJSVMTsmiZcX77Z6UcSl2QuUWrqKgNFW9aEQEREROTlxy+1t1/G13+tafr9sgguOjOPhB52G1zHOxPJxyn+XsWcRHDkEvc63S0Jt+waWPWcvKkzf0yg+RaeXeBr017kcynP8y/mPbx7OOV2D63UOptasEeznyatXD8HH044kTejVjqcvc+G6d9bx5vK9jO8VWm+/8+VFe6wVWhVKCxpHtsX0fuHM23aQ77Ykcc/kHk4t8zQodBBtfdqSeiSVVYmrGNtxrNPmIiIiDohfCTNvhqwD4OoBkx63m325aj2EiDjueNnKQb4etPHzPGHAIK+wmJTsAnoeo1+dyXi2fle1cbYcOGxt3dxcOPfFZexNrXrPDHHjqCgent7nuL/XLEIypWArNIZFSPUpur0yKERERERqyM8Cz2qvTVZD9cyGCqaZtVFXCSef1uAbfOKAQWEu5CRDaJ+6MyEqxq4+TuJP9tYc/8pISK/6PhoXV/ueferfW3aA4sIBEeQUlDh8fNuAOj7g01SRCTGqW0hlcKL6Ki1T9mhzeRmo6sdXZFIczfSxMAKPkWFh3DG+KzeNjqp8nZWVRYcXcLqJvUOtP6+5IfslOduptWVNmacpkVP43/b/MTtutgIUIiKNVWmJ3dDLpI2WlUKbLnD52xAxyNkzE5EmyJFs5eTM/NMcw6NWxnNFVvWby2KJjgjk6ztH0i3U38oWeGjWZt5YFkunYD9+N7xzk1uEVN+Nss05MY3H6wr+iIiIiLQoz/cBr2rXRGNnwPiH6g5kGF7H+K7VKwCyEo//uxwZwygoP87ITbW3K/9jl5a6eSGE9ITkzXbj7FX/gTZRcPZNtNgAxV8uinb2FOgS4m9tA3086qy1am6CcqrdvESWr+aKLV/ddbTYtDxrlVXHNvbKrLqYOrjVa+GWFTaOhqGmzNO4Hm2Zu+0g329OcnrzO1PmyQQoFu1fREFJAV5u9R+gEhGR05CZALNurkpFHfAbOO+ZqgsjEZEmoiKr2sPNhdevOYt2gd7W66FRbXjl6iFMe2Gp1ZvueAGKxroIqb5Ehfjj4+HGkaISYtNy6Baq/9aLiIhIC3fPNgis9v1pXdkTzlRWnt3r5gm//ggCw+3XnUfAle/DKyPs4IUTAxSquQCMKC8ZtSuldmPo9JwCMnIL6dDap3LfoE6trCyD5bvSKm9kKqRk5bMjOYuBHVvh7VFHqk0TML2//Rf1hy1Jtf58DW1A2wG0821HblEuyxOq1U8TERHn++V7eHWkHZzw9IdLXodLXlVwQkROiyPZysfKjHB8jKJaGc9moY7Rv32ryuBEhR7tAqzm2aZ5d+aRY5dtMguQzDjVH82JW41G2dVW5omIiIi0VN6BNR/HClCY947ObqiuIPvYmREnM4ZRfZyKnzEVDiqCExVCe9vNs03z7iN2uVNnaFEBiiOFJexOySHh8JEa+4d1CbbSt1fsTmfZrtQaq6iembOjxpf2hrnROL9/OPsy8vhwzb4axz89ewelZfDroZ1oqib2bmeXeUrLZXtS7aBNQ3J1cbXKPBlz4uY4dS4iIlKuKB++vw8+ucputmUudG5dCgN+pVMkIqfteNnKmXlF1uKhyJDa/Smq8/V0JzTAi/2H8qxSRHVlPFu/q9o4XcqfB/rUHfyoyLYuKHK8PG1zZMpfGTEJVSVwRUREROQE2tTRH6LCkUOQl153f4rqPP3APwwOxdullo9WMXb1cYK721tvu1RnLRX7i49fQvVMatIBinVxGfzxs03W46XyWq/r46v2vbx4d43jf95/mEnPLeHeT3+utRLomcv7W+nK17+zjjv/t5EnvtvGxS+v5JN1+61mcLePq/kXZMa0XkQEefPo1zHc9sEGnp79C1e8uoqZGw8wpkdbLh/cgabKlLQa37Ot9fz7LSeofdYApkVOs7aL9y/mSHHN4JKIiDSw1B3wxgRY94b9esRdcMPcE19IiYg4yCweMpbtrFo4VGFp+WKiYVFtHBonr7CE9XEZtcfZWXucc8qzqs2CpqMVlZRaDbV9Pd2sJt0tWd/yPhQxiQpQiIiIiDgscqS93bOo9nt7FtrbziMdG6coF/atrmOcBeXjjKraFzXa3qbZi/BrKCmCjL3g4Qe+IThLkw5QmJsEExAwj0U77JsMk3ZdsW9J+T5HDOrUmq//byST+7Rj5Z403lsVx+G8Qu4Y15VPbznHWoVVXWigN1/dOZIrhnRgffwh3loWS1pOAfdM6sEb1wyxelc0ZdP7R1hb04fC2WWe+oX0I8IvwgpOqMyTiIiTmH8LNrwHr42FlK3g1xZ+OxOmPAHuLfvLOhGpXyO7BlvllL7elMjWal+Cm9JOLy3chburC5cPqVoMZDIqTFDBbKv7zdCO1vbZuTspLC6vvQus2J1mBTpMb4kube1edEbnYD9Gdw8hLj2PT9ZWZUkbryzeYzXUnto3DHe3Jn0LVX+NshOyKK0jO0VERERE6hA1zi6ntOVzSNpcsyzTkmfA1R0G/rZqf246pO60t9UNuc7eLnwCiqtd/+5dDLsX2EGOkG5V+9t0ga4T7ECEuaevbvnzkJ8Jvc8HN+e1qnYpc/a3z1LZQC8oKIjMzEwCqzdWcZLcgmIG/20eBcWlfP/7UfSNOEYaUAN5bv1zvLP1HaZ0nsKz45516lxERFocU4vy27th21f26y7j4ZLXIKCds2cm0iQ0tuu8psAsGLr27bV4urly4cAIK8N39tZk9mcc4b4pPfi/CeWp6sDz83by4oJd3D2xO/dM7lFjnBkzN1sZ0d1D/ZnQK5TUnAK+25yEl7srs24fQfd2NZs8x6fnctkrK0nLKbSO79rWj62JWazck077Vj58eecIQgNq9qdoaZ+9ySbp+9gcK+iz+L5xJyy3JSIiItIcndJ1XuxS+OBSu09F9GV2/8bt38LheJjwCIy5v+rYRU/Ckqdg7AwY/1DNcb65Cza+D217QfcpkJsKMbPA3RtunAuhvWoeb4ITb5Uf130qhHSH5M32fII6wU3znXp/37KX/8gx+VllnkIrsyicbWrkVGu79MBS8orsmsEiItIA9q+FV0fbwQmzomPyX+HqWQpOiMgZNaJrCJ/fNoKzIttYAYUPVsfT2teTF341sEZw4kT+cUk/Hr+gDy4u8M7KOBb9ksKk3qF8fefIWsGJiiyKb/5vlJWhsSUhk3dXxllZ29ec09nKtj6Z4ERz5eHmSu8w+9ypzJOIiIjISYgaAzfMgU7DYetXsO4t8G0Dl75RMzhxIue/COf+0+QewJrXYOcc6DkNbl5YOzhRkUVxy2I7QyPpZ/tn0vfC2TfbP+PkxYfKoGgkGuPqqm83JXLXxz/ROdjXWh3lYu7snMQk+pw36zwO5BzgmTHPMC3K7kshIiJniGm4ZdI9F/0DykrsVNTL3oYOQ3TKRZrBdZ40jOb62f/pyy18tGYft43tyoxz67gJFhEREWnmmut1njMog0KOyaS1e3u4Wn09TGq7M5ngSEUWxZy4OU6di4hIs5eVBO9fBAv/Zgcnoi+HW5cpOCEiIpbo8vKv1XuEiIiIiIicCgUo5LhlnkyQwjCp9c5WkTWxLGEZuaZbvYiI1L8ds+GVERC3DDz84OJX4LI3wVsrQkREpGajbFMGSy0NRUREROR0KEAhx3Vev3Br+8OWJKfffPRs3ZPOgZ0pKClg8f7FTp2LiEizU1wAPz4IH/8KjmRAWH+4dQkMvMqksTl7diIi0oj0CPPH3dWFw3lFJBw+4uzpiIiIiEgTpgCFOFTmaV9GHjEJKvMkItIspe2CNyfCmlft18PvgJvmQ4jjjWhFRKTl8HJ3o0d5k3Fn3yOIiIiISNOmAIUcl6+nOxN72Z3cv9uS6PSzVdGHYnnCcrILs509HRGRps1kxv30Ibw2BpK3gG8wXPUZTHsS3L2cPTsREWnEotvbpf/Uh0JERERETocCFHJC0/vbZZ6+3+z8Mk/dW3WnS1AXikqLVOZJROR05GfCzJvg6zuhKA+ixsBtK6CHHQgWERE5nujyPhQxCWqULSIiIiKnTgEKOaHxPUPx8XDjwKEjViM8Z3JxcanMopgdN9upcxERabIObIBXR0PMF+DiBhMfhd99BYF2QFpERORE+kZUNMrOcvoiJhERERFpuhSgkBPy8XRjQu/QyiwKZ6sIUKxMXElmgVZsiYg4rLQUlr8Ab0+Bw/EQ1AlumA2j/wiubjqRIiLisD7hgbi6QFpOASnZBTpzIiIiInJKFKAQh5zfz15V+10jKPPUtVVXurXqRnFpMQv3LXTqXEREmozsg/DhJTD/MSgthr6XwG3LoONQZ89MRESa6CKmbqH+1nOVeRIRERGRU6UAhThkXM9QfD3dSDh8hE0HMhtNFsWc+DnOnoqISOO3az68MgL2LgZ3H7jwJbj8HfBp5eyZiYhIExZdXuYpJiHL2VMRERERkSZKAQpxeIXUxN7trOc/bGk8ZZ7WJK7hcP5hZ09HRKRxKi6EOQ/D/y6DvDRoFw23LoHB15imPs6enYiINHF9KxplJzp/AZOIiIiINE0KUIjDpvcLq+xD4ewyT1FBUfRs3ZPismIW7Fvg1LmIiDRK6Xvgrcmw6j/266G3wE0LoG1PZ89MRESaieiIQGu7NUEBChERERE5NQpQyCmVefp5v/OzFqZFTbO2s+NmO3sqIiKNy6ZP4LUxkPQz+LSGX38M5z0DHt7OnpmIiDTDDIrEzHzSc9QoW0REREROngIU4jBvDzcmlZd5MlkUzja1s13maW3yWjLyM5w9HRER5yvIhlm3wpe3QmEOdB4Ft62AXuc5e2YiItIM+Xu50yXEz3oek6g+FCIiIiJy8hSgkJMyvX94ZR8KZ5d56hjYkT7BfSgtK2V+/HynzkVExOkSf7KzJjZ/Ai6uMP5huPYbCGrv7JmJiEhL6EOhMk8iIiIicgoUoJCTMrZHW/w83aw07p8aQZmnimbZc+LmOHsqIiLOUVoKK1+CNydDxl4I6gjX/whjHwBXN30qIiLSMH0o1ChbRERERE6BAhRy8mWe+jSiMk/lAYr1B9eTdiTN2dMREWlYOSnw0RUw9xEoLYLeF8Bty6DTcH0SIiLSIKIrMyhU4klERERETp4CFHLSpverKvNUWurcMk/t/dvTL6SfVeZpXvw8p85FRKRB7VkIr4yE3fPB3RvOfx6u/MBuii0iItJAoiPsAMW+jDwy84p03kVERETkpChAISdtTI+2VkO8pEZW5ml27GxnT0VE5MwrKYJ5j8IHl0BuCrTtDbcshrNuABcXfQIiItKggnw96NjGx3quMk8iIiIicrIUoJBTK/PUO7TRlXn6KeUnDuYedPZ0RETOnIxYeHsqrHjRfm2CErcsgtDeOusiIuL0LIoY9aEQERERkZOkAIWckun9IxpNmacwvzAGth1IGWUq8yQiTV9pCcQugy1f2Fvz2jCvXx0NCRvAO8gu52TKOnnYq1ZFpBky/3//8HJ4qhP8PRxeHw+bPz+5MUpLYc3r8PIIeKId/LMLfHYtpO9x7OeXvwCPB9mP/etqv7/hXfjoV/BCP3uOT3a0y88t/DvkZZzcXKXJUh8KERERETlV7qf8k9Kije4eQoCXO8lZ+Wzcd4izIts4PYvi59SfmRM3h6v7XO3UuYiInLJt38DsByErsWpfQDgEd4e4pfbrTufApW9Aq4460SLNmQlQfngpuHlC9KXgFQjbv4VZN8HheBhzn2PjfPcH2PgetO0FQ2+B3FSImQV7FsGNcyG017F/NnUHLPoHePhBUW7dx2z6FPIPQ6cRENAOigvhwDpY+k/Y9DHctMDeL81a34hAa6sMChERERE5WQpQyCmXeZrcpx2zfkrg+y1JTg9QTO48mX+u+6cVpEjOTbayKkREmlxw4rNrgKOy0rKT7AcuMPYBGPMAuOmfb5FmraQYvrnL/v/99T9A+AB7/7gZ8OZkWPwk9L0Egrsef5zYpXZwwgQPrvkK3L3s/QN+De9fDN/fa49fF5O99eVtEBYNwd1g86d1H/e7L8HDu/b+hU/A0mdg1Usw5YmT+uNL082giE3LJaeg2OpXJyIiIiLiCJV4klN2Xr/wRlPmqZ1fOwaFDrKemywKEZEmxXwRaDInjg5OVOcbDGMfVHBCpCWIXQKHYqHfFVXBCcMrAMbeD6XF8NOHJx5nw3v2dsIjVcEJo8s46DYR4ldA2u66f3b583AwBi76L7i4Hft31BWcMPpcXNU7R5q9EH8vwoO8KSuDbYlZzp6OiIiIiDQhClDIKRvdwy7zdDCrgA37Djn9TE6LmmZt58bNdfZUREROTvzKmmWd6pKXZh8nIs1f3HJ723V87fe6TrC3JrjgyDimPFOn4XWMM7F8nPLfVd3BbbDkaRhzP4T25pTsKl8wcqo/L01O34pG2QmZzp6KiIiIiDQhClDIKfNyd2NyX7um8PebTfkRnF7myQUXNqdtJiEnwdnTERFxXM7B+j1ORJq2jPIG1nWVcPJpbWdUnajJdWEu5CRD687gWkcGRMXYR49jykt9dTuE9IRR9zg+55/+B4uehDkPw7vnw4K/Qlh/OOdOx8eQJi26vfpQiIiIiMjJU4BCTsv5/RtPmacQnxDOCjvLeq4sChFpUsyXjY7wV6NZkSYtP6vmo7jg2McZpjF2XUypp4ITlNFxZAzj6HGWPVte2uk/4OaBw37+CJY8Bav+A3HL7EyPq2fZARVpEaLLMyi2JqjEk4iIiIg4TgEKOS2jurUlwNudlOwC1sc3gjJPkXaZp9lxs509FRERxxyKh4V/P8FBLhDYHjqP0FkVacqe7wNPdax6LHuORiV5i93YesRdEDHw5H72+u/h8Uy4fy9c9Zldtu61MZAcc6ZmK41Mvw52gGJXSjZHCkucPR0RERERaSIUoJDT4unuypQ+Ydbz7zefoH56A5jYaSKuLq5sS9/G/qz9zp6OiMjxbf0SXh0NCevA3bd8p8tRB5W/nvZU3WVaRKTpuGcbzNhf9Rh9b93HeZdnPRwrS6Ig+9iZESczhlF9nC9vhzZRMO4hTplfMPSYClfPhLx0+Pb3pz6WNCmhAV5Ws2yTVL09WVkUIiIiIuIYBSik/so8xSRT4uQyT8E+wQwNG2o9nxNf3pxRRKSxKcyDb+6Cz6+DgkzoMBTuXA1XfgCB9n9TKwVGwJXvQ58LnTVbEakvJmhQ/eHuVfdxbY7RH8I4csj+4r+u/hTVefqBf5idpVVax2r2irGrj3NwC6TthCdC4fGgqsemj+z335pkv97+3Yn/rEEdoG0PSNho/zdPmj0XF5fKPhRb1ShbRERERBzk7uiBIscyslsIgd7upGYXsC4ug+FdHKylfoZMjZzK6qTVzImbw039bnLqXEREajHlTr64AdJ22NkRZgW1Wa1sar2bZra9pkP8Srshtuk5Yco6KXNCpGWJHAnLn4M9i6Df5TXf27PQ3nYe6dg4MTNh32r7eY1xFpSPM6pq36Df1T2O+W+Sadzd8zy7Z06rTo79ObIPmm+t9d+wFtaHYvGOVGLUh0JEREREHKQAhdRLmaepfcP4fMMBq1m2swMUkzpN4onVT/BLxi/EZcYRGRTp1PmIiFjKymDdmzDnYSgpsFc2X/o6dBlb8wSZYETUaJ00kZYsahy0joQtn8OwWyG8f1VZpiXPgKs7DPxt1fG56XZWhQkemBJLFYZcZwcoFj4B13wN7p72/r2LYfcCO8gR0q3qeNMYuy6m9JMJUIy6FzqeXbU/L8MOpob2rv3fu8VPQW4KRI09dqaINDsVGRQxiZnOnoqIiIiINBEKUEi9OK9/eHmAIpnHLuiLm+vRNdQbTivvVgwPH86KxBVWFsWtA2512lxERCq/xPv6/2DH9/brHtPgopdrfpEoIlLBzR0ufAk+uBTeOReiLwOvANj+LRyOhwmP1AwsrH0dljwFY2fA+Gr9I6LGwOBrYOP78Npo6D4FclMhZpbde2L6aTbpzjxgj9t+CLTtBf6hdqAkfhWk77KzwKY/q8+1BYlubzfK3nkwm4LiErzc1TtJRERERI5PPSikXozsGkKQjwdpOQWsjc1w+lk1ZZ6M2XGznT0VEWnp4pbDKyPt4ISbp93s+jefKDghIsdnggs3zIFOw2HrV7DuLfBtA5e+AWPud/zsnf8inPtPu6Tcmtdg5xzoOQ1uXgihvU7vU2jV0c6qcHGDXXNh5Ut28MPDx57jHashpLs+6RakfSsfWvl6UFRSxs7kHGdPR0RERESaAJeyMpODLc6WlZVFUFAQmZmZBAbaqdFNzQNfbOKz9Qe4engnnri4n1PnklmQybjPxlFcWsxXF31F11YnaCQpIlLfSoph6T9h6TNQVgrB3eDytyF8gM61SAvTHK7z5NS0xM/+6jfXsHx3Gk9e2o/fDHWwX4mIiIhIE9MSr/POFGVQSL2Z3j/C2s6OSaak1LlxryCvIEZEjLCemzJPIiIN6vB+eO98WPK0HZwYeDXcskTBCRERafb6VvShSFAfChERERFp5j0o1uxNZ/72g2xJyGRrQhbZBcVcNrgDz155aqtTS0vL+GLDAT5bv58dB7MpKiklPMiHIZ1b8/iFffH3sk9XXmExc7YmM39bCtuSskg8fMRqFN07PJDfDuvERQPb0xKN6BpspXSn5RSyJjadEV1DnF7maemBpVaA4vYBt+Pi4ry+GCLSgmz7Br75P8jPBM8AuOAF6He5s2clIiLSIKIj7D4UMYlZOuMiIiIi0rwDFKac0MyNB/DxcCOilTfZqcWnPJZp4nbHhxtZ8EsKvcICuHxIByvokHg4n8U7UsjO71EZoDA9Fu75dBOtfT0Y0S2EadFhpOcUWJkDd3/yMxvjD/GXi6JpaTzcXJnaJ4xP1+/n+81JTg9QjO84Hg9XD/Zm7mXX4V30aN3DqfMRkWau6AjM+ROsf9t+bZrGXvYWtIly9sxEREQaTL/yRtnbk7KsBV/mHkFEREREpFkGKK4d0Zlbx3aha1t/Nh04zKUvrzzlsf45e4cVnHhwWi9uH9e1VmZFdaEB3rz464Gc1y+8xgX3/VN7cfF/V/DeqnguHdyBAR1b0dJM7x9uBShMsOYvF/bF3Yk3JAGeAYxsP5LF+xdbWRQKUIjIGXNwG3xxA6Rut1+P/ANMeATcPHTSRUSkRenUxpcAL3cru313So6VZS4iIiIicixNejlL/w6t6NEuADfX0yvdk5yZz3sr4xga2aZWcMJwdXWxHhX6RARaZZyOXg3UNsCLq4bZjeBMiaOW6JyuwVZmSXpuoZVp4mzTIqdZWxOgUD94Eal3ZWWw7i14Y7wdnPALhd99CZP/ouCEiIi0SOa+ydwvGepDISIiIiLNOkBRX36MSaK4tIzz+oWRU1DMVz8l8N9Fu/ls3X4reHEyPNzsQIaba8s8tVaZp75h1vPvtiQ5ezqM6zgOLzcv4rPi2XFoh7OnIyLNyZFD8Nk18P29UJwP3SbB7Suh6wRnz0xERMSposvLPG1VHwoRERERac4lnurLlgOZ1jY7v5gJ/1pMSnZB5Xuebq48MK0nN43ucsJxSkrLmLUxAdOLeVQ35/ZfcHaZp0/W2WWe/urkMk9+Hn6Mbj+a+fvmMzt2Nr3a9HLaXESkGYlfBTNvgqwD4OoBkx6H4XeYZaPOnpmIiIjTRbdXBoWIiIiIOEYBCiAtt9A6GS8s2GUFFv53U2/CW/mwNjadh2Zt4Ynvt1t9Lsb3Cj3uyXx27g5+Sc7myrM60DMs4IRNuQuLSytfZ+cX0Vyc08Uu85SRW8jqvRmM6u7cYM3UyKlWgMKUebp78N24mAiSiMipKC2Bpf+CJU9BWSm06WI3wm4/WOdTRKSebdp/mOfn72Rj/CGKSsro0c6fG0ZFWaVWHf7PdmkZH6yO5+O1+4hNy8XPy926Vr1vak+iQvxqHT/yqYUkHD5S51imlOs/Lul3Wn+mliI6ws6g2JaUZS3iOt2SvCIiIiLNRsIGWPQkHFgLJUXQtpe94LH/FY6PUVoK696EDe9Cxh7w9IPI0TDxUQiu3b6A5/tB5r66xxpyPVzwAi06QDHor3M5lOf4l/Mf3zzc6nNQnyp6EwT7efLq1UPw8XSzXk/o1Y6nL3PhunfW8ebyvccNUHy0Zh8vL95D34hAHrug7wl/58uL9vDigl2Vr0sL8mguTMbEtOhw60bw+y1JTg9QjOkwBm83bw7kHGBbxjb6Bp/48xERqSUzAWbdAvHL7df9fw3T/wVexw9IiziqpKSEoqLms2ChJfHw8MDNzb5+lPqxak8617691iqfesGACAK83Zm9NZm7P/mZA4eOcOf4bg6N8/BXW/h47X66h/pz3YhIUnMK+G5zEkt3pTLr9hF0b1f7v+Hmd90wMqrW/v4d7C/d5cS6tPXHx8ONvMISYtNy6BaqfytFREREiF0GH14Kbp4QfSl4BcL2b2HWTXA4Hsbc59hJ+u4PsPE9O7gx9BbITYWYWbBnEdw4F0LrqCDjFQTDb6+9P2KQ0z8YpwcoLhwQQU5BicPHm0bU9c3chBgme6IiOFFhTPe2eLq7srm8DFRdPlu/37r56RUWwIc3DrNWZp3IHeO7ctPoqhufrKwsOjg3WFWvpvezAxSzY5L420XOLfPk6+FrBSnmxs9lTuwcBShE5OT98j18fafdd8LTH6Y/CwN+rTMp9bZQIjk5mcOHD+uMNmGtWrUiLCxMmZr1oLiklBmzNoMLfHrrOZX9DO6e1INLX17B8/N2cl6/8DozIKpbuSfNCk4MjWzDBzcNxcvdvs6/bHAHrn5rDQ9/FcNnt55T6+cCvT24Z3KP+vijtFhu5Y2yN8QfIiYhSwEKERERkZJi+OYurIvc63+A8AH2ORk3A96cDIufhL6X1J0BUV3sUjs40WkEXPMVuJd/V26+o3j/YrtPphn/aN5BMP6hRvk5OD1A8ZeLop09BbqE+FvbQB+PWu+5urrg7+VOTn5xnT9rGmmbGyizKut/Nw2jtZ+nQ7/T3CBV3CQZZYW1f3dTNrxLG9r4eVplnlbtTWd097ZOL/NkBSji5nDPkHv05YGIOKYoH+b9Gda+br8OHwiXv33iCwaRk1ARnAgNDcXX11f/RjXBAFNeXh4pKSnW6/DwcGdPqclbuSed+PQ8rhjSoTI4YZhr8rsmdOeuj3/i8/X7eWDa8XuLfbJ2v7X945QeNa67R3YLsRYhLdmZyt7UHGu1v9S/6MoARSYXD3K8LJeIiIhIsxS7BA7FwsCrq4IThlcAjL0fvrgBfvoQJj12/HE2vGdvJzxSFZwwuoyDbhNh93xI2w0hjmUcNwZOD1A0BiO6BvOfRbvZlZJd6730nALrS/YudazQMsGJB2dtpltbfz66eTjB/vWf3dG0yzyFWaWvvt+c5PQAxegOo/Fx9yExN5EtaVvo37a/U+cjIk1A6g77AuFgjP36nP+DiY+Bu2OBaBFHyzpVBCeCg+u3hKU0HB8fH2trghTms1S5p9Ozem+6tR3do/b1owksGGtiMxwax9fTjbMi29Qep4cdoDDjHB2gKCwp5YsNBziYlW8tYBrSqbWVDSAnp295cCkm8diZ6CIiIiItRlx5ueiu42u/13WCvY1f4dg4Hn7QaXgd45QHKExp6qMDFCUF8PNHkJUIPq2g4zAIaxz91VpUgOJIYYnV9M6UcWrfyr6RNIZ1CaZbqD8rdqezbFdq5ZfpZkXcM3N2WM+n96+5Gu7TdfuYMWuL1TzbBCdCFJyo5fx+4VaAYs7WZP52cTQeTizzZIIT4zqM48e4H60sCgUoROSYTF+inz6AHx+EojzwDYFLXoXuk3XSpN5V9JwwmRPStFV8huYzVYDi9MSl51rbqODaC4SCfD2sLN24NPuYY8krLCYlu4Ce7QLqbNAcFWJ/XnWNk5pdwH2fb6qxb2yPtjz/q4HW7z6eguISCotLK19n57fcvjIVjbK3JmRZzcpNZrqIiIhIs5OfBdUvEU1WQ/XMhgqmmbVRV0UGn9bgGwzp5cccS2Eu5CRDaB9wraMHXsXYdY2TcxC+OqoHRbdJcMnr4BfcdAMU5uJ7/6E8OrfxdUqPgXVxGZWp2xm5BdZ2fXwGf/zMvqHoGurHHeOqokU/7z/Mb95YzbCoNlY92wrmpuWZy/tz1RtruP6ddUztG0Z4kDfr4g+xaf9hotsHcvu4qr88K3enWcEJ8x3W0Kg2fLg6vtbczCorM05LZs6NaTyebso87Um3Vqo509SoqZUBij+e9UdcXZwXMBGRRurIYbvZ1NYv7dddxsMlr0FAO2fPTJo5Fxd9cdfU6TOsP9nlpVUr+sQdzZR6Ss7MP80x7PKqWUeVcb3yrI4M69KGHu0CrD50uw5m8+KCXSzekcpN761j5u0jjvtZv7xoj3V8hdKCPFqq7u38rXOYXVDMvow8Ik/QM0RERESkSXq+D3hVuz4cO6PuXg8mkGGYxth18QqwsxuOx5ExjILy4yoMuhoiR0Lb3nZVCFMxYvFTsHsefPxru7G2E+9J3U81E+Gxb2KYuTHBer3oj+PoFOzL499sJTTQq0ZQ4EwyK55mbjxQY5+pV2sehglEODqXQZ1a8/X/jbSa7pmGejkFxUS08uGOcV25c3w3fD2rTpXJwjDBCcNkCNTFNN9r6QGKijJP/ysv8+TsAMWo9qPw8/DjYN5BNqduZmDoQKfOR0Qamf1rYeaNcHgfuLrDhD/DiN+bZkTOnpmIiDSQuyd1r3WP8Pa1Z/Or11exLu4Qi3akMKHXsYPWd4zvyk2joypfZ2Vl0eEFWiSTPd07LIBNBzKtMk8KUIiIiEizdM82CKwWMKgre8LZxj1Y83WHs+Cqz+Dd82DfKtg1F3pMbVoBiqdn/8L2pGw+uWU41769tkbDOfMFf0MFKK44q6P1cNQ5XYOJe2r6Md83K6VeuXpIvf/elsyUxjIBijnbknmixLllnrzcvBjfcTzf7f2O2XGzFaAQEVtpCax4ARb+HcpKoFVnuxG2+QdbRJzOZGGaDNhNj00hyMde9S7NW0XWQ0UWxNHMQqJjZUY4PoZdeinwBOMYpjTRFUM6WgGK9XGHjhugMM24qzfkLits2X9nTR8KK0CRkMX5/SOcPR0RERGR+ucdaD8cOa6u7IYKBdnHzow4mTGME41jmMWYA39rByj2rW56AYp52w7y0lWDGNypNdWTP7qH+lvpuyIVhkUFE+LvSVpOISv3pFv1e51pauRUK0AxN24uD5z9gMo8ibR0WUnw5S0Qu9R+HX05nP8ceNt1s0WakpLSMtbGZpCSnU9ogLdVarGu2vv1xZTUrMhkdXd1oZWvB73CArlwQASXD+lQb/Xmh3RuzdqHJzr0RbI0D5HlvSdi03Pp16Hmf48z84rIyC20/l4cj8l+Dg3wssrRmv9vHP3/hdg0+57F0VX9rct7TxwpKjmpP0tLV9mHQo2yRUREpKVrU60/RMSgmu8dOQR56Xbj6uPx9AP/MDgUby+2PLoPRfpx+lzUxfS9MIqO4EyntJw9PbeAEL/a6Sp5hSU1AhYi5mbw3Gi7wfj3m09QR60BjIgYQYBHAKlHUtl4cKOzpyMizrRzDrw60g5OePjCRS/DZW8qOCFN0uyYJEY9vdDKNLj7k5+trXlt9p9JZuGBCR4sf3AC714/1MpW/cu3W7nhvXUUl1Q1Cj4dpoa9Cbiox0PLMayLfaO0bGdqrfeW7kqtLOXqyDjm/mR9XEbtcXY6Po7x8/5D1rZDazW0Pxmml58Rk5BJWUWNXBEREZGWyPSAMPYsqv3enoX2tvNIx8YpyrWzHmqNs6B8nFGOzSlhvb1t1YkmF6Do36EVC385WPm6oofGJ+v2MegEq5mk5Tmvnx2gmLP1oNVY3Zk83TwZ32m8PZ+4OU6di4g4SXEBzH4IPrrSXqEQ1g9uXQqDfuvUplAip8oEIW7/cCNJRzUNNk2Ezf4zGaSoCB6EBXkT3T7I6tv1xjVnWQ2Fv9hgZ1dk5Rfx0KzNDPnbPKIfm8NvXl/NtkQ7JXlPag6RM75nd0pOjXHfXLaXkU8ttL7QNCWezDGZR+ySPIdyC7nr458Y/o8F9Przj0x9filf/2z3Ravwq9dWWb3RnvxhOwP+MpeznphvlSGtzoxn5nXWE/Po8ciPTHl+CQu2V13fbojP4MpXV9HzkR8558kF1nh5hXWXC5L6NbJrMJ3a+PL1psQaK+9NaaeXFu6yMnZMlk4Fk1Fh/g6ZbXW/GWqXZH127s4a16ArdqdZgQ6TZdSlrX/lftMQu+LvWXXr4jJ4c1ms9ffd9FcTx/UMC7A+r0N5RVYfPxEREZEWK2octI6ELZ9D0uaaZZmWPGP3wjQllyrkpkPqTntb3ZDr7O3CJ6C42vXv3sWwe4Ed5Aip1n4h5Rc4crj2fOJXwar/gpsX9L4AZzqlXPkHp/Xk2rfXsSslh+LSMt5eEcuugzls3HeIT285p/5nKU2aufkL8fciLaeAFXvSGN8z1KnzmRY5jW/2fMO8+HnMGDoDt6PToUSk+UrbDV9cD8nlFwPDbofJf2mcTaykxTJfyjtaRsaUrnnsm63UtS7Z7DMht8e/2Wb1CXOk3JOPh9tpZyqM6BZC7/BAZm9N5ldnd+SGd9ZZ5Z/euf5sArw9+GhNPL99czWL7htH17b+9GsfZAUY/jilZ+UYX/+cyEUDI+qcS0FxKf3aB3Lb2C4EeHlYi2bu/WyT9YW2aWhcYeaGA9w4Ooqv7hzJxvhD3PfFJs6KbM3o7m0pLS3junfWkltQzPO/GkjnNn7sSsmuLEv1S3IW17y1lnun9OTpy/uTkVvAo19vtR7/umLAaZ0fOTF3N1eeuqyf1evOBIkuHBiBv5e79Xdqf8YR7pvSo0Zg4b2Vcby4YBd3T+zOPZN7VO4f0TWEX5/dkU/W7Wf6v5cxoVcoqTkFfLc5yRrv7xdH1/i9Zv9rS/cwsmsIHVr7WAGJHQdzWLYrFVcXF+v49q189BGeBNOPw/T525aUZfWhUAaKiIiItFhu7nDhS/DBpfDOuRB9GXgFwPZv4XA8THikZmBh7euw5CkYOwPGP1S1P2oMDL4GNr4Pr42G7lMgNxViZtm9J6Y/V/P3bv0SVrwIXcbamRJunpCy3c7acHGF85+HVh2bXoBiSOc2fHH7Oby+dC+dg31ZtiuN6IhAZt0xwqo9LFKd+ULkvH5hvL8qnh82Jzk9QDE8fDiBnoGk56ez4eAGhoYPdep8RKQBmLISP38EP9xvp0KaOoumpFPPaTr90uiY4ESfR+sny88EKZKz8un3+FyHjt/216lW7f7T1bWtH78kZ1vZDzuSs1n/50mVjYMfnt6HudsO8sOWZK4a1skKRJhrhIoAxd7UHLYkZPLclXUHAky2xi1jqmqqXjcyiiU7U/lhS1KNAEWv8AD+MMn+sjoqxI/3V8WxYne6FaBYvjuNTfsPM//esZVfdHcKrird8/qSvVw4sD03joqq/PnHL+xrZWY8cXE03h5a3HCmmeDC57eNsDJfTOCgqKTU+qL7j5N7cvGg9g6P849L+tErLICP1u7jnZVx+Hm6Mal3KPdN6VkjyGGYEmW7U3PYmpDJmtgMCopLrEU2prmz+bswsGOrM/AnbRllnkyAwmTDKANFREREWjQTXLhhDiz+B2z9CkoKIbSXHZzof6Xj45z/IrSLhvXvwJrX7N4U5vuNCY/WDHJYv3M0pO2ApE0QtwKK88E/FKIvheF3QochONsp34GaQMRzVw6s39lIszW9X7j15cOcrcn8/ZJ+1oo0Z/Fw82BS50nM2jWL2XGzFaAQae7ys+C7eyDmC/t15Gi49A0ItMvPiUj9q8jeMIGG3MJiBv11Xs3/WxaVEJ+Raz03TbWf/PEXKxN3cKfWfPVzIn3CA+neLuCYWSOvLN5tfWltgi+mdI95HB1YOXrRTNsAb9JzCqzn5svS8CCfWl9QVzDzjk/Pq1E6ysQ5S8vgwKE8uoXWPTepXyYg8N4NJ15IYrImqmdOVGeyYkwQyzxOZHiXYOsh9cuUf/ts/QGrD4WIiIhIi2cCAlfPPPFpGP9QzcyJ6lxdYdit9uNEIkfZj0bslAIUi35JsS72TWPE6szqtdKyMqevkJfG56zINrQN8CI1u8Cq+zu+l3P/jkztPNUKUMyPn8+fhv0Jd1PnTUSanwMbYOYNcCgOXNxg/J9g1D2g0m7SiJkySyaTwRFrYzO47p11Jzzu3evPtkouOvK768OelBw6tvG1vtA3PSo+uWV4rWMCfTysbWigN+d0CeabnxOtAMW3mxK5auixm7S9sWwvby2P5dEL+tCzXSC+nm789bttFB7VlNvdrWZ5KFMtyszH8D7BQglzPWuyO64bEVnrvQiV+BE5KX0jgqxtTHnvGRERERGR6k5pGfvTs3+xavfWVTP56R9/OZUhpSWUeSpvKmhWPDqbKevUyqsVhwoOsTZ5rbOnIyL1rbQUlr8Ab0+xgxNBneCG2TDmPgUnpNEzfRdMNoAjD1OuKDzI28pWqHMssN43xzky3un2nzBW7k6zyjuZUi6mtIup+W+uAyJD/Go82vh5Vv6MKfP03eZENsQfIj49lwsGRBxz/HWxGUzu045LBnWgT0Sg1XsiLs3OxnBUr/BAkjKPWOWkjrXie+fB7FpzNg9nZoGKNEUmI8q0dzELlVKy8p09HRERERFpZE7pDis2LZduobVT4k2jQ5MOL1KX6f3tLxvmbku2SjE4k8mYMGWerPnEOVaXW0SaiOyD8OGlMP8xKC2GPhfDbcugo/rNSPNjvvh/7II+1vOjQwsVr837jjTIPhXm3/OU7HySM/Ot8i3/XbSbm99fz8ReoVw2uAOjuoUwuFMrbvlgg5Vpuz8jjw3xGfxrzg42HzhcOY4JZmTnF/PIVzFWHwDTZ+JYOgf7sXxXmjXO7pRs/vTlFuuLz5NhyviYjJLbP9xoNUA281q0I4XFO1Ks928b29UqOfXnr2Ksuvnm2nfetoM89nXMaZwtkZbJx9Ot8t7RlE8TERERETntAEWAt4d1I3c0E5wwafYidTmrc2tCA7ysLyCW7051+kmaFmk3x52/bz5FpUXOno6I1Ifd8+HVkbB3Ebj7wAX/hiveBR81NpXma1p0OK9cPbjWl/rmtdlv3j9TTNBh6N8XMOrphVz79lqrKfZjF/bljWvOsoIiJiPjneuHMiyqDQ98sYkJzy7mro9+svo4mObD1a8tJ/Vux/akLC4aePwGyL+f2I2+7YO45q21/Pr11VYJycl925303F+9egj9OwTx+49/YtJzS3jqh1+s0k5G7/BAPr3lHOLSc7ny1VVM//cynp27wypHJSInL7qizFOCyjyJiIiISE0uZaYu00l6aNZmftp3mNd+N8RaxWaY1PrbPtzAgA6tePry/ic7ZIuXlZVFUFAQmZmZBAbWbOrYnDz+zVbeXRnHpYPbO73JenFpMRM/n0hGfgavTHqFUe0bd8MYETmO4kJY+FdY+ZL9ul00XP42tO2p0yaNWn5+PrGxsURFReHtfXpffpvm0aYnhcloMH0fTIbAmcqckJP7LFvKdZ7Ups/eZvrG/O27bVZ5NhPAFBEREWnqdJ1Xf06pM/BD5/W2VslNfHZJ5Wo9k9p/dmQb/jS9dz1OT5qb6f3DrQDFvK0HKSguwcvdzallniZ3nsynOz5lduxsBShEmqr0PfDFDZD0s/166C0w+W/goZXO0rKYYIQpjyQi0thER9iBua0q8SQiIiIi9RGgCPT2YNbtI1i2K81Kxff2cKNXWADDuuimWI5vSKfWtAv04mBWAct2pjGpz8mXZKhPUyOnWgGKhfsWUlRShIebh1PnIyInadOn8P29UJgDPq3hov9Cr+k6jSIiIo2IKctmJGbmk55TQHC1Em8iIiIi0rKdUg8Kw9QUHtOjLbeO7cq1IyIVnBDH/sK5unBeP7sW9g9bkpx+1gaHDibEJ4TsomxWJa1y9nRExFEF2fDlbfDlLXZwovNIuG2FghMiIiKNkL+XO11C7NLAMYnqQyEiIiIip5lBYazYnWY90nMKKxsKVnjmigGnOqy0ANP7hfPOijjmbTtIflGJlYHjLG6ubkzpPIWPfvnIKvM0psMYp81FRByU+JNd0iljL7i4wriHYPQfwdV5/y0RERGRE2dR7E3LJSYhk7E92up0iYiIiMipZ1C8MH8nv3trDSv2pJORV0jmkaIaD5HjGdypNWGB3mQXFFtlwpzNlHkyFu1fREFJgbOnIyLHUloKK/8Db062gxOBHeC6H2DsAwpOiIiINJU+FImZzp6KiIiIiDT1DIr/rdnHv64YwKWDO9T/jKTFlHl6e0Us329OZLKT+1AMDB1IqG8oKXkprEhYwYROE5w6HxGpQ04qfHU77J5nv+51Plz4Evi20ekSERFpAqLL+1DEJKjEk4iIiIicZgZFUUkpQzq3PpUfFbFM72/3oZi/PcUq8+RMri6uVpknY07cHKfORUTqsGcRvDrSDk64e8P05+BXHyo4ISIi0oRER9gBin0ZeWTmKeteRERERE4jQPGrszvy9c+Jp/KjIpZBHVsRHuRNTkExS3emOv2sTIuaZm0X719MfnG+s6cjIkZJEcx7DD64BHIOQtvecPMiOPtGcHHRORIREWlCgnw96NjGx3quMk8iIiIiclolngqKSvl4zV6W706jd1gA7m414xx/Pr/PqQwrLbDM01vLY/l+SxJT+oY5dT79Q/oT7hdOUm4SyxOWM6nzJKfOR6TFy4iFmTdCwgb7VAy5Hqb+Azx9W/ypERERacpZFPszjhCTmMmIbiHOno6IiIiINNUMil+Ss+gTEYirC+w4mG2tgKl4bEtUTVE5yTJP2w46vcyTi4tLZbPs2XGznToXkRZvyxfw2hg7OOEdBFe+Dxe8oOCEiIhIE6c+FCIiIiJSLxkUn9xyzqn8mEitMk/tW/mQcPgIS3amMtXJWRQmQPHu1ndZemApeUV5+HpopbZIgyrMhR8egJ8/tF93HA6XvQmtOuqDEHFASWkJG1M2kpqXSlvftgwOHYybq9sZO3cPL3+Y7MJs/j3h3zWeV0g7ksbrm1+3/l1NyUuhjXcberXpxdV9rmZ4+HB9piItUN+IQGtrMihERERERE45QCFSX1kL50aH8aYp87Q5yekBir7BfWnv356EnASWJixlWqTdl0JEGkDSZvjiBkjfBS6uMOZ+GPMAuOmfKRFHzI+fz1Nrn+Jg3sHKfe182zFj6AynlC00/5Ze88M1BHgGcO+Qe+nRugfFpcWsSFzB31f/nW8v+bbB5yQijSeDIjYt1+pF5++lf+dFREREWrpTviLctP8wP2xJsla/F5WU1njvtd+dVR9zkxZS5skEKOZvt8s8eXucuZWejpZ5ejvmbebGzVWAQqQhlJXBmtdg3p+hpBACIuCyNyBylM6/yEkEJ+5dfC9llNXYb7IWzP7nxj3X4EGKJ1Y/AS7w0fSPamQkdmvdjUu6X9KgcxGRxiPE34vwIG+SMvOt0sBDo9o4e0oiIiIi0hR7UHyzKZHLX13JrpQc5m49SHFJmfV85Z50Arw96n+W0mwNLC/zlFdYwuIdKc6eTmVQwpSjyC3KdfZ0RJq33HT4+Ncw+0E7ONHzPLh9hYIT0uKVlZVZpQYdeWQXZPPk2idrBSeMsvL/mcwKc5wj45nffboyCzJZkbCC3/T6TZ3lEgM97RIvItIy9Y2wsyhiElTmSURERKTJKS2F+JWw7RvIrsrgb/AMipcX7ebP5/fhmnMi6fvobB67oC8d2/jwpy+30DbAu14mJi2DyVowWRSvL93L91uSmRZtN852FlMbu1NAJ/Zl7+PtLW/TtVXXBqnjLdLixC6FmTdDTjK4ecHUv8PZN5n/KDh7ZiJOd6T4CMM+GlZv45myTyM+GeHQsWuuWnPaPZj2Ze2zAiNRgVGnNY6INE/R7QOt7Gn1oRARERFpgp7pai8ydfeC/Ezo/ys495/g5d+wGRTx6XmM7xlqPfd0dyWvqNj6ovnGUVF8vHbfKU9GWqbp/eygxILtBzlSWOLUuZi/x6ZOtvH6ltd5cNmD3DDnBqbOnGqV0BCR01RSDAv+Bu9daAcnQnrAzQtg6M0KTog0E5XZHIo3ikgdosszKLYmZOn8iIiIiDQ1V74Hf0qAB/bCzYvg8D54Y8JpZVOcUgZFK18PcguLreftAr3ZkZxNr7BAMo8Uk+/kL5il6enfIYgOrX04cOiIVebp3PKAhTOYIMT8fbUDEc6s4y3SbByKh5k3wYG19uvB18C0p8DTz9kzE2lUfNx9rEwGR2w4uIE7FtxxwuNenvgyQ9oNceh3n67OgZ1xwYXYzNjTHktEmp9+HewAxa6UbGtxko+nspRFREREmoyoMVXPw/vDtd/afUXfngo3zIaAsIbJoDg7sg3Ld6VZz8/vH85fv93GjJmb+f3HPzGiW/CpDCktvcxTeVDiuy1JTptHSWmJVaf7eKtBn177tHWciJykrV/Cq6Pt4IRXEFz+Dlz4koITIsf4d9GUWXLkMSJiBO1821kBgTrHwoUw3zDrOEfGM7/7dAV5BTGi/Qg+/uVjq6/F0bIKtWpapCULDfCymmWXlsH2ZP33QERERKRJK8yFQddAcDf44JJTGuKUAhR/vagvFwyIsJ7fMa4bN4/pQlpOAdOiw/jnZQNOaSLSspk+FMbC7SlOK/O0MWWjVaf7WEyQIjkv2TpORBxUmAff/B4+vw4KMqHD2XDbMoi+VKdQpB6Y/kgzhs6wnh8dpKh4/eDQBxu8j9Ijwx6htKyUq76/innx84jPimfv4b38b/v/uPqHqxt0LiLSuJhAqOlDYWxVo2wRERGRpuWrO+D9i+E/Q+HJjvBUR3h5GOyeDxmxDVniybPyuaurC7eN7QrmIXKK+rUPshqt7884wqIdKZznhDJPqXmpDh331e6vCPUNtZpp18dKU5FmKzkGvrgB0nbYxehH3wvjHgI3D2fPTKRZMaUHTQlCkwVYPdBuMitMcOJMlSYsKyvD3bXuS8kOAR347PzPeH3z6/xr3b9IPZJKa+/W9Anuw5+H//mMzEdEmlYfisU7UolRHwoRERGRpiUnBVp1hI7DIDAcAiKqtn7BZzZAkZ1fRIC3R+Xz46k4TsRR5ot+E5R4bclevt+c5JQARVvftg4d982eb6xHB/8OjGo/ynqcHXa2VRpDRKxvLWHdmzDnYSgpAP8wuPQ16DJOp0fkDDFBiPEdx1tZfibgbv5NGxw6+IxmTmTkZ9AxoKP1/O+j/l7rfTOHh4c/jPmfiEh1FRkUMYmZOjEiIiIiTcnVX9T7kA4HKAb8ZS5rH55k1Qvt/5e5dVY6NlX6zf69T06v31lKi3B+vwgrQLHgl4PkFRbj63lKCT6nzHyRY1abmobYFT0njhbgEUDvNr3ZmLqRAzkH+GTHJ9bDw9XDaj5aEbDoEtRF2RXSMuVlwDd3wS/f2a+7T4WLXwa/EGfPTKTZM8EIEzA/0zILMtmUuol1yeu4oucVZ/z3iUjz0zfCbpS982A2BcUleLmrUbaIiIhIS+XwN8Af3TycVj52ZsRHNw1HlW3kTKyk6tTGl30ZeSz6JbWyL0VD1/G+d/G9Vt3u6kGKijrefx35V2uVam5RLmuT1rI8Ybn1SMxNZHXSauvxr/X/ItwvnJHtR1rBimFhw/D39G/QP4uIU8StgFk3Q1YCuHnC5L/CsNtMipQ+EJFm5NEVjxKTHsO1fa9lQscJzp6OiDRBHVr70MrXg8N5Rew6mEN0eztgISIiIiItj8MBiuFdqmpIndP11OpJiZyozJMJSryyeA/fb0ls8ADFydTx9vPwY3yn8dbD1OCOzYplRcIKK1ixPnk9Sf/P3p2AR1WebwO/k8m+TPY9kISwBBL2HQTZRBREBbTVWrWIWm2t1WrV1urfflZxQ23rVre6tFoFRBFFZJVFCJtA2BJCErLvmcm+zOS73vdkkplkkgwhycxk7t91vddklpyczGiYOfd5nqc6H2tT18rl4uSC8WHjMTNSCSyGBwxndQUNLLom4IcXgB+eB5r1QNBQYPm7QOQ4a+8ZEfWBV+e9yueViC59UHakH/acK8GJXA0DCiIiIiIH1qMeOrOe347rxkXhuvFRiA/hmeHUexaPVgKK7WeKUF3fBG/3/m3z1JM+3uIDlmjpJNYvR/0StU21su2FIbC4UHlBXhfrlSOvINQzVFZXiDU9cjrUbkoPXiK7pMkB1t0JXNinXB/3C+Cq5wF3/ttAREREnUuMUsuAIiWXcyiIiIiIHFmPjv7eNj0WXx3Lwz93nENipBrXj4/GNWMiEKr26P09JIci/nuKCfJCVmmNDCmuGRtpd328PV08MTt6tlzCBe0FGVTszdsr20IV1Rbhi3NfyKVyUmFMyBhZWSECCzHfwtnJuZd/G6I+cnoj8OVvgboKwM0XWPIyMIb96ImIiKh7ooJCSMnT8ukiIiIicmA9CihWzRoi1/niKmz4KQ//2Z+FZ785LVs/icqK5ROje39PyXHaPI2OwOs70/HNiXyrBRS9abB6MG5W34ybR96Mel09DhceVgKL3L04rzmPo0VH5frH0X8g0COwtRXUjMgZ8Pfwt/buE3XUWAt892fg0LvK9cgJwIp3gcAhfLaIiIjIIoa5E6fztWjU6eGq4kk6RERERI7okvrnDAnxwYNXDJfryIVyPP5FCh5ee6zfAooD50ux9XSh7Ft6MleLyvomLJ8QjZduHNuj7en1zVh7OAefHcrG2cJK+UY5ws8TE2MC8H9LE+HTRbsh8fuveGMf9M3AHxeNwL1zhl7Cb+bYxOyJ163c5qmvuKvcZfAgFiYDuVW5ra2gDuQfQFldGTae3yiXGMw9Onh0a3VFYlBip22miPpN0Wlg7Uqg6JRyfeb9wNzHARc3vghERERksZhAL/i6u8jPcOnFVUgIZ9tTIiIiIkd0yUd+f8quwJc/5eLr4/morGvEVaP7b7DxZ4dysO5IDjxdVYj090BlcVOPt1XfpMO9Hx/BtjNFSAj3xYqJ0XBzcUZeRR12ni1CZd3wTgOKukYdHvr8GDxcVahp0F3Cb0TCqAg1YoO8kFlaI1+PpQOgiqIzUT5RuHHEjXI16hplJcWevD0ysEgrT8PxkuNyvX7sdfi7+8uZFbOiZsnLYM9ga+8+OZLmZuDwv4HNjwFNtYB3KHD9m8DQ+dbeMyIiIrJDzs5OGBWpxoGMMpzI0TCgICIiInJQPQooDK2dvvopFznltbK10yOLErAoKbzLKoPedtuMGNx9+RA5qPtYTgWWvd4ypLUHnt98Vh4MF7/HPXPiO1RWdOWF786iWFuPey6Px0vfp/Z4H8iozdOYCLy2Ix2bjucN6IDCmKvKFVMipsj14MQHUVhdKOdWiLBif95+VNRX4NuMb+USRgWNku2gZkXPkpUWLs4Dp9KEbExtOfDV74DTXynX4+cr4YRPqLX3jIiIiOy8zZMIKE7macEpVkRERESOqUdHNOev2YUxUX745fRYXDM2AqG+1hmOPSa6d/rzF2jq8MG+TEyJDewQThjO7unMocwyvL83A3+7Xhwg7vxxdHEWj46UAcWOs8Woqm/q1+DLVoR5h2HZsGVyNembcLz4uAwrxDpddhqnSk/J9faJt+Hr5ovpEdNb20GFevHAMfWSC/uBdasATTbg7AoseBKY9hvxh5FPMREREV2SpCilrVNKrobPJBEREZGD6tFR320PXi7nTwwU36bko0nfjKtHh8uD4VtPFSK3ohYhPu6YPTwE4X7mA5jaBqW107QhQbhpymB8fii73/d9oBoZ4Yshwd44X1KNbacLce24KDgyUR0xIWyCXL+b8DuU1JZgX94+7MnZg335+6Cp12BL1ha5hOEBw2VYIda40HFwFQeWiS6GXgfsXgPsfBZo1ikDsJe/C0RN4PNIZMv/32btA6oKAZ8wIGYGwNlFRGTDkiKVQdmn8rXQ6Zuh4glfRERERA6nRwGFCCc0tY349kQ+sspqcPfsIfD3cpNnvgT7uHd6QN9WiZ6nQmVdE+a9uBNFlfWt97mpnOXQ61WzhnT4vuc2n5GP/eiOqT2aedHQpG+9LuZ3kGmbp6tHR+CfO85h0/F8hw8o2hPzJ5bGL5VLp9chpTSlddh2SkkKUstT5Xov5T14u3pjavhUXBZ9GS6LvAwRPv03J4bslDYPWH8XkLlbuT7mZ8DilwB3X2vvGRF15tRXwOZHlP9/DdSRwKLngFFL+bwRkU0Snys9W+b4ZZRUY2jowDkJjoiIiIj6MKA4na/FL945ALWHi5xBcdPkwTKg+O5kAXLLa7HmZ+NgT0qqG+TlK9vScNnQYPxn1UhE+HsiOaMUj60/gac3nZZzLuYmtLXN2X++FB/8mIknlozCoECvi/6Zr+9Ix6vb0lqv6+treum3GTjEHAoRUOxMLZYBjq8HqwDMUTmrMDZkrFz3jrsX5XXlsrpCBBZihkVZXRm2Z2+XS4j3i5dtoER1xcSwiXBTufXzK0s27cw3wJf3KnMnXL2BJWuAsT+39l4RUXfhxGe3imn2prdr85Xbb/yw70KKj5cD3iHKXBrh/C7g89uAh84BKsdrz0hEF0fVMij7cFa5PNmNAQURERGR4+nRJ8f/9/Up3DAxGo9dPRKJT2xuvX3OiBD87pOfLmpb4/+6BeU1llcPfHLnNDmUuzc1Nysf6IO83fDmLRPh6aaS1+clhOG55U64/f2DeGfP+daAoqahCX9cexwTBgfgtumxPfqZ986Nx6pZca3XtVotol/plV9nwEgI98WQEG+cLxZtnopw3XjHbvNkqQCPACweslgufbNezqsQraBEWHGs+BjSNelyfXjqQ3i6eGJK+JTWwGKQ7yCLfoao2jhSdATFNcUI8QrBhNAJMighO9ZYB3z/BJD8lnI9Yiyw4n0gqONcHiLqY+J9SWON5W2dvv1jx3BC2ZCoSVQqK4bMsazdk6uXKGO0fF99I4CKC23X42YDTQ1A9n4g9jLLt0NEDivJKKDg+30iIiIix+PS05ZIzy4b3eH2MLUHiqva2iNZYunYSFTV6yx+fIivO3qbr4fyNIjqCUM4YTB7WAjcXJxxvKUNlPDCd2dRqK3D+7+a3OUA7a64u6jkMmhuYHWAuTZPS0ZH4O/bz2HTiXx+YOkBZydnJAYlynX32LvlrIr9+ftb20EV1xZjV84uuYRYdWxrWDEpbBI8XDq2a9uatRWrk1ejsKaw9bYwrzA8OuVRLIhZ0JPdJGsrTgXWrgQKTyjXp/8WmP8k4MLqGiKrEOHEM5G9tLFmpe3TassCaPwpD3Dztnzzoo3Uhf1t10W44eIOVJdc/K4SkUNKjFLmUKTkcVA2ERERkSPqUUDh7uos5zW0J850F1UIF+Opa5NgbUOClV6nas+OIYEIIHzcXVBl9PueytOivkmP+S8pB3Xbe37zWbl+NTMWT16T2Id7PvBdPUYJKHadZZun3uDn7ocrY6+US1QOiTkVIqgQ1RVHC48iU5sp139O/wfuKndMCp8k51aIwCJGHYNtF7bhwZ0PorndmbpFNUXy9jVz1jCksLeztI9+BHz7iHJA1CtYadMy7Apr7xkR2QtRQVGZ33Y9/zhQVwEMuvj5XETk2IOyT+Zqodc39/gEMCIiIiJyoIDiilFh+Pu2NLz2iwmtJ8vlVtTKodGLksJhb2bEB8lZB2lFlR3uK62qR1l1A4YEt51NOC8hFLFBHc8uzCitRnJGGcZG+yEhXC1bQNGlGRHmi/gQb6QXV2Pr6UJcPz6aT2kvVqiMCBwh1x2j70BVQxUOFByQgYVYBdUFyhyL3L147uBziPSORHl9eYdwQhC3OcEJzyU/h7mD5rLdkz2o0wAbfw+cXK9cF+1frn8L8LW/v+FEA45osyQqGSyRtQ/4z4ruH/eLtUDMDMt+9sUQFRQNVUCdFnDzAb77EzD6RkAdcXHbIfNyDwM7ngVykgFdIxCSAEy7Fxhzg+XPmF4PHHwHOPxvoCxdqZCJnQXMf8KyNn57XgG2Pql8fcdWYNDktvvEPp39Bji7Gcg9BGhyACdnIGQEMPYmYNJKy1qLkUMbFuYjK9Yr65uQXV6DGDOfs4iIiIho4OpRQPGnq0fiV+8fxMT/9z3qmvT42Vs/ytZO4wcH4OErR8BW1TboZJAi2jhF+Xu23j51SJAcyLb3XCl2pxVj1rAQebs4w1y0czIMbDa4+3LzH+Y+P5QtA4ork8Jx75yhff77OMpB9MVjImUgtul4PgOKPuTj5oP5g+fLJf7bP6853xpWHC48jLzqrg+WiZCioKZAzqaYHG508IJsT/ZBYN1KpW+8swsw73Fgxv2iZMzae0ZEhjM/LG2zFD9PCQnEQGyzcyiclPvF4/riQLGooBBEG6mfPgYqC4Cf/7f3f44jytgNfLwMULkBScsAdzVweiOwfhVQkQXMfsiy7Xz9e+DIB0q4MeUuoLoYSFkPpO8A7tgChCZ0/r3FZ4EdzwCu3kBjdcf7yzKUQexuvsr8kRFXKWFV6mbgm4eAc9uAmz65uLkmRo5lV+Dlrak4klWORl0zhof5YOVlcbh2nOVzycQZ+R/tz8InyReQUVINb3cXTB8ShIeuHIE4oxOQOvPmrnSs/vaM/Hr9vTN4AlIfcFU5Y2S4L47laHAiV8OAgoiIiAY2Wz8JyV4CCl8PV6y9Zwb2pZfIYWb6ZqU097JhwehPBzPL8Glytvy6rFqZfXEoqwx/+OyY/Do+1NskKPgpuwI3vb0fU+MC8b+7p7fernJ2wgsrxuDmtw/I4OXKxHBE+HngYFa5/GCUFKXGPXM4KNZalog2T9vS8ENqCbR1jVB7cF5HfwRD8f7xct2WeBtqGmvwxk9v4N+n/t3t94p2T2SjxD9ge18Gtv8NaNYB/jHAiveA6EnW3jMi6ikROix6TjlILMIIk5Ci5aDwotV9dxa7CD+ELX8GStKAlZsBD3Xf/CxHomsCvrpPeQ1/9Q0QMVa5fc6jwDtXADufBRKv7/7DR8YPSjgxeAZw6wZlPogw9ufAh9cBmx5Utt/ZAPYvfg2EJwFBQ4Hj/+v4GHcfYPFLwNibATej6puGauDfi4HUb4FTG5R9vUg/ppfitveS4apywjVjI+XMuM0nC3D/pz8hp7wWv5lr2clAf95wAp8kZ2NYqA9unxErT6r6+ng+fkgrxvp7ZmBYmG+n33uuqBJrvk+Fl5sKNQ2Wz8yjns2hEAFFSq4WS8b01gweIiIiIhtjDychWcFFny4rzkL67GA2Vv77IP7vq5NYdzgXhzLL5NBocdZ1f8osqca6Izly7ThbLG/LKq1pvU3MLbCUqP748rczZfsqEbx88GMmKmoacO+cePzvrunwcutRlkO9YHiYr/xQ2aDTY+uptsHM1H+8XL0we9Bsix675tAauX4q+gn6Zn2f7xtZSJxd/dF1wLa/KuFE0nLg17sZThANBKOWAjd+2LGtkggPxO3i/r7iFQSo3IGKbOBX37YFFnRpMnYB5RnA6BvawgnB3Re4/GFA3wQc/bj77Rz+QLkUlXKGcMLQ1m/ofCBrL1Byzvz37nkZKEwBrn0NcOok4BKv9+RVpuGEIM7gmv5b5evMvbhYTTo9Hl1/XOYz4qSi1cvH4M+LR+Hb+2fLKoqXv0+V1RDdEe/pRTgxJTYQX//uMjx29UisuXEc3r99Mqrqm/DnDSmdfq9O3yxPehoZoZYnL1E/zaHgoGwiIiJylJOQlv4DuPJvwD17gZCRyklIpendb8f4JKS7fwAW/j9lnugvPgPqtcpJSJ0xPglp5BLYios66i4CiFUfHsKOs0UYGa7GiHC1vO1cURUeWntMntX09q39dybuDZMGyWWp6fFByFy9uMsD4W/cMrHf9ocsd/XoCLza0uZp2QTOobCGCaETEOYVJiskzM2hMCiqLcL7J9+XK8QzRM6kEG2jRNsnVxWrX6widQuw4ddATanSX/7qF4Bxv+hxyw0iskEihEhYrMykqCoEfMKUmRN93f9f/B35Cyvnel3mHuUyfm7H+0S7LkGEC5ZsR5wZNXiame3MB85tBbL2AMHtqhEKTwG7ngNmPwyEjuzRryBbCBpfXoR96aXypKMbJkYjKUo5cC34uLvgvnnDcN8nR2Vr1T8u6uLMMKC10voPC4fD3aXt/4WZQ4Mxe1gIdqUW43xxFYaE+Jht7XQ6v1IGG2/tOn/RvwNdHFGxLojqfPH5UlTzEhEREQ3Ik5DG3WL+JKS1K5WTkBa0tF7q6UlI4j2+OAmp/Xt845OQRLCx9++wFRf1ieHzwzlyxsJ/Vk3FjHjTdk77zpXgro8OY93hHCyfyAPI1LvEDBARUIhyfE1tI/w8eaC7v6mcVXh0yqN4cOeDciC2cUghrgvPznoWrs6u2HZhG37I+QHFtcX4LPUzuXxdfWUVhggrZkbOlFUZ1Mea6oGtTwH7X1Ouh48GVrwPBA/jU080EIkwIm6WtfeCuiKHiRtdFx8ojD9UGIg+soK5Fk6eAUrlSndnV4k2S1UFQOgo80GVYdvttyPO7NpwDxA8ArjsgZ6/noYKD0OgchH2ny+Vl7OGK3PhjIlgQTiQUWbRdkR7pkmxgR23M1wJKMR22gcUZwsq8erWNPx23lB5AhP1vRHhvnBxdkJ5TSPyNHUm8wKJiIiIBoSBcBKSLQQUG4/l4d658R3CCWHG0GA5p2HDT7kMKKjXiQ+HoqQ/tbBKtnliCGYdC2IWYM2cNVidvBqFNW3ttkRlxSNTHpH3CwtjF6JR14jkgmQZVuzI3oGS2hJsOr9JLneVO6ZHTpdhxZzoOfD38LfSbzSAibR87a+AguPK9an3AFc8Zf5AGBER9Y+XRwHuRmeGX/4oMPcx80GGIHrSmiPOshKDybtiyTYEUQZubPdLyllVq7YBPa18PPQ+cO57ZXD28IUX/e2ZpUr7prigjkOs/bxcEejtJlu9dqWmoQlFlfUYEeYr5821FxesnCjRfjuivdRDnx9DfKhPj2bQ1Tfp0NDU1uKysq7xorfhiESFi3i/fypfixM5GgYUREREZD8c6SQkWwgoRJnzo1d1Xkp9+fAQvL83szf2i6iDxaMjkVqYik0n8hlQWJEIIUTbpiNFR1BcU4wQrxDZ/klUWBgT7ZxmRs2U6/Fpj+N48XEZVoiVXZmNndk75VI5qTAxbCLmDZ4nA4twb/Z5viRiFtCxT4BNDynDjjwDgeveAEYsurTtEhHRpXvgFKA2CgxsLTQuOAH88AIw4z4gclzPtpH6HfDNw4DfYGDZ2z3aRGVdk7wUg7HNEa2eCjR1l7gNJXzRtjzO4LUdorWTFht+MxOuqose14fXd6TLql8DfX3NRW/Dkds8iYBCzKFYlMT3g0RERGQnHOUkJFsJKDS1DQjx6fyDVIivO7S1PEuI+sbiMeF4eWsqdos2TzWN8gw6sg4RRoiZEpZydnLGuNBxcj048UGkVaTJoGL7he04U3ZGVlqIJSozEoMSZVAh1hD/IX36eww44h+qTX8ATnymXI+dBSz7F4fWEhHZCg+1six5nLkPFgb1lZ1/KLmYbQjG2/niHiAwDphj5gOVJUQ5+f9+CfiEArd9Bfja10HmU3la/HNHGu6cPcRk9sXFENXmq2bFtV7XarWIfqUXd3IAE8/5Z4dy5BwKIiIiIrvhCCch2VJAodM3my2RNnB2ckKTvq2kmag3DQ31lWX6ZwsrseVUAQeS2ykx9HB4wHC57hl7D3Iqc2RQIQKLo0VHcbL0pFx/P/p3xKpjW8OKxOBEGXRQJ3IPKwOVyjMBJxUw909K2V5fD8glol4hhsKSfevV1zDQqDQ7crzpfbXlQE0pMGhq19tw8wZ8woHyLECv6/jvQamZEvPCE8rl06Hmt/mu0soRP/sPMHKJ6X1pIpz4hVKafttGJejoIUPVg6EKor2q+qZOKyMs34ZyUpXaaDt/+PwYBgd64fcLhl1SqyLjgdzNDTyhxlKJkUoolJLXSahGREREZIsG+klIthZQiI9doierm4v5g4TG/VaJ+mpY9tnvK/HNiXwGFANEtG80bk28Va7S2lLZ9kmEFfvz9yNTm4l3U96VK9QrFPMGzcP8mPmyJZQYxk2id4Qe+PEfwLa/AvompaXG8neAwd0cuCIim+Dqqvwtq6mpgacnh8LaM/EaGr+mlyR2JrBnDZC+Axi9wvS+9O3KZcxMy7aTsg64sF/52mQ721q2c1nbbeN/aX47WfuUnrkjrlYCCP/BHcOJT29Weufe/rX5vroXIbZl9kRGaTVGR5tWMogq2rLqBkyMCehyG15uLgj1dUd2eY3Zk6wySpTXKza4bc6FaO0kjHh8s9ltLnt9n7x865cTcWWifVWH2INREWqIl6m4sh5F2jqEqj2svUtEREREjn0Ski0GFMsnRHf7mGUWPIaop64eHYE134s2TyVs8zQABXkGYfnw5XJVNVRhT+4eGVb8kPMDimqK8OnZT+VSu6kxZ9AcObdiRuQMeLo46EG9ykJgw6/bDlaNug645lXAk0PHieyFSqWCv78/ioqK5HUvLy9ZaUb2VTkhwgnxGorXUrymlyxuDhAQC5z4HJh6NxAxpu2MqF0vAM4uwLhftD2+ulT5QCPCA++gttsn3q4EFNufBm79EnBpmd53fidwbpsScgQPbXv8tf80vz/irCsRUFz2IDBocifhhH+vhBPC1CFBeH1nOnanFmPp2EiT+35IK1YeExdo0XY2HsvDocwy+bXJdlI7budnkwaZ3U5yZhkySqqxYGQYgrzdEB3goO87+pinmwpDQ32QWliFlDwN5jGgICIiooHE3k5CstWA4sUbxvbdnhBZQHxoSQj3xZmCSnx3qgA3dvJBkuyfj5sPFsUtkqtB1yArKkQrqB3ZO1BWV4av0r+Sy0PlIQdxizZQs6Nnw8+9Zz2j7Y7o8/3Fr4HqYkAENFetBibcJnpoWXvPiOgihYcrZ2IbQgqyTyKcMLyWl0zlAiz9B/DRMuD9q4Ck5crAu9MbgYosYN7jpsFC8r+AXas7DuSLmw1MuBU48iHw1ixg2ELl342U9UrZ9+I1l7afxalKOKGrB2IvA06s7fgY8UFnvFGYYoGZ8UGy1dKXx/Jw+8zY1tY/orXTP7anwcXZCSsmtp0UJSoqxAr0dpPL4KYpg2RA8dKWVHy8amprFfjecyUy6JgSF4ghIT6tj39uRUsQ1M4fPjsmAwoxX2LC4K4rN+jSJEX6yYDiRI4W8xLC+HQSERHRwGFPJyHZckBBZAsWj46QAcWm4/kMKByEm8pNhg9i/UX/F/xU/FPrkO3cqlz5tVguTi6YFD5JhhWiukK0hRpwmhqA7X8F9v1DuR6aCKx4DwhNsPaeEVEPiYqJiIgIhIaGorFR6YtP9kW0deqVygljIlxY+R2w8xng5AZA16D8rRfhxJgbLd/OkleBsCTg0PvAgbeUsvARi4B5T5h+cOmJqkIlnBDEhyRzxNlbFxlQuKicsXr5aNz2XjJufPNHLB0XCR93F2w+WYDsslo8tHC4SbDwwb5MvLotDffPH4YHrhjeevuM+GD8fPIgfHowG4v/vhvzEkJRXFWPr4/ny+397bqkHv7i1FcSo/yw/miurKAgIiIiGlDs5SQkK2BAQXbn6jEReOn7VHn2W0VNA/y92s6Uo4FP5aySMyjEenjSwzhbfrY1oEgrT5OVFmL97cDfMCZ4jAwqRGAR6xcLuyd6Ca67A8g7qlyffCew8P8Brmw1QTQQiAPcvX6Qm+xb9ETglk4O/BsTH1iMP7QYc3ZWztASq6euf0NZ7cXNAv6vbw4ki3Dh81/PwMvfp8pAoVGnx/AwX/zhihG4bnyUxdt55vrRsvr2v8kX8P6+THi7qbBgZCgeWjjCJOQg25AUqQx0PJnLgIKIiIgGIHs4CckKnJpF41yyOq1WCz8/P2g0GqjVFkx+d3BXvbpbDjJ8fvkY3DiZbZ5IcUF7QVZViLDiWPExNKPtz1u8X7wSVsTMx6jAUfbX4/34Z8DXDwANVYCHP3Dta1YbXkRERBeH7/McF1/7iyPaeCU9+Z38+vDjCxDk494nrwsRERHRpeL7vN7DCgqyS4tHh8uA4usT+QwoqNVg9WDcnnS7XMU1xXJehQgsDhQcQLomHekn0vH2ibcR4R3RWlkxPnQ8XESfP1tVXwV88xBw7BPluugluOxfgF9b720iIiKigUC03hoS7I3zJdU4mafF7OEh1t4lIiIiIupjNnxUjqhzV4+OwItblDZP5dUNCDAaiEgkhHiF4MYRN8qlbdBid85uWVmxJ3cP8qvz8Z/T/5HL390fcwbNkWHF9MjpcFfZ0Jl6eT8Ba1cqQ4ucnJW+g7MfApzZAoaIiIgG7hwKEVCcyNUwoCAiIiJyAAwoyC6JnsGjItQ4la/FllMF+NnkwdbeJbJhajc1Fg9ZLFddUx1+zPtRhhW7cnahor4CG85tkMvTxROXRV0mwwoxkNvXzdc6Oyw67+1/Hfj+SUDfCKijgeVvAzEzrLM/RERERP04h2LjsTyc5KBsIiIiIofAgILs1uIxETKgEIMTGVCQpTxcPDB38Fy5mvRNOFJ4RIYV27O3o6C6AN9nfS+XaPs0NXyqbAUlVrBncP88yVXFwJf3AmlblOsJS4Cl/wC8Avvn5xMRERFZUVKUn7xMydXydSAiIiJyABySbSM4WOXiZZZUY86LO6FydsLBPy9AINs80SVobm7GqdJTMqwQ67zmfOt9TnDC2JCxsrJCrEHqrgez6/Q6HCk6IudgiFZTE0InQGVJW6b0HcAXdwNVhYCLB3DlM8CklYC9DfQmIiITfJ/nuPjaX7yKmgaM++v38utjTyyEn5drr78uRERERJeK7/N6DysoyG7FBnsjMVItB+h9d7IAN01hmyfqOScnJyQGJ8r1uwm/Q4YmQ6msuLAdJ0pO4Kfin+R66fBLGBYwrDWsGBEwQn6vwdasrVidvBqFNYWtt4V5heHRKY9iQcwC8z9c1wjs+Buw5xURlQAhCcCK94GwUXxJiYiIyKH4e7lhUKAnsstqcTJfgxnx/VTFSkRERERWwYCC7L7NkwgoNh3PZ0BBvSrOLw6rRq+Sq7C6ULaAEoHFoYJDSCtPk+vNY28iyidKtoASYUVpbSke2vUQmkXIYKSopggP7nwQa+as6RhSlGcCa+8Acg8p1yf+SqmccPPiK0pEREQOKSnSTwYUKbkMKIiIiIgGOgYUZNcWj47A85vP4sfzpSitqkeQj7u1d4kGoDDvMNyUcJNcmnqNHK69LWsb9uXtQ25VLj469ZFcohVU+3BCELeJ+55Lfg5zB81ta/d0Yi3w9QNAvRbw8FNmTYy6tv9/QSIiIiIbm0PxbUoB51AQEREROQAGFGTXYoK8MTrKDydyNfjuZCFunso2T9S3/Nz9sDR+qVy1TbXYl7tPVlZsvbBVXu+MCCkKagpwuPAwpgQlAt/+ETj6sXLnoGnA8rcBf/73S0RERCTauAopeRo+GUREREQDnLO1d4DoUl09OkJebjqRxyeT+pWniyfmx8zHM7OewV+m/cWi73nlmzuQu2aYDCdEXUX62OU4e+0a1PmE9vn+EhEREdlLBYWQUVKNqvoma+8OEREREfUhVlDQgGjz9NzmM/gxvRQlVfUIZpsnsoJw7/DWr52bmzGhrh4hOh2KVSoc8XCHHsDN2ir8oawcbgAKVSo8GhKEQ9qDwKafyxZQkT6RiFXHyvkX4jLWT/k6xDPEZBA3ERER0UAm3s9H+HkgX1OH0/laTI4NtPYuEREREVEfYUBBdm9wkBfGRPvheI5o81SAX0yNsfYukQOaEDoBYV5hGF2ciUdKyxCu07XeV6RyRpHKBUkNDfJ68aBJ2DL2GkTWFWOMNhMZmgxUNlTKeRZi7c3ba7Jtb1dvxKhjTIMLdZy8zcPFo99/VyIiIqK+lhjpJwOKEzkaBhREREREAxgDChowVRQioNh0PJ8BBVmFGHz9UugcjD75Qof7QnR6hOoaoHNWQXXlaoRMuRO/NKqIaG5uRlldGTK1mcjUKIGF/FqbiZzKHFQ3VuNU6Sm5jBlXXRhCC3Eprod6hbLqgoiIiOxWUpQaW08Xcg4FERER0QDHgIIGzByKZ789g/3nS1FcWY8QX3dr7xI5Gr0OYw//B80yODAlrovbVZ5BwOQ7gHbtmkT7piDPILkmhk00ua9R14jsymwZWmRoM5QAo+VS26DttOrCy8WrNayQlRctAcZg9WA5O4OIiIjIliVFKnMoTuZqrb0rRERERNSHGFDQgDAo0Atjo/1wLEeDzScL8MtpbPNE/SxrH6DN6xBOGMjbq4uUx8XNsnizripXDPEfIpcxUXVRXl+uVFtolGoLQ+WFqLqoaarptOoiwjuidb6FcfUFqy6IiIjI1gZlpxVVorZBB083lbV3iYiIiIj6AAMKGjAWj4mQAcU3x/MZUFD/qyrs3cd1Q1RdBHoEytVp1YWh4qIluBCXouoirzpPrn15+zpUXbTOumgJLcTXrLogIiKi/hamdpfDskuq6nGmQIvxgwP4IhARERENQAwoaEC1eXrmmzM4kFGKoso6hPpyeDD1Iw9/yx7nE9bXe9Jt1YVJxUVLyyhD1cXpstNytSeqLkwqLlq+FoPBRVhCRERE1JvE+wsxh2Ln2WKk5GoYUBARERENUAwoaMCIDvDC2EH+OJZdge9SCvDL6bHW3iVyFIUnge/+1M2DnAB1JBAzo592quuqiwlhEzpWXVRlm20ZpanXIL86X672VRdinoVxaGEY1C0qMTjrgoiIiC51DoUSUHAOBREREdFAxYCCBpQloyNkQPG1aPPEgIL6WnMzcOhd4Ls/A011ShVFXYXRWGyDlgqDRasBZ9vsnyyrLvyGyNVeeV3LrAttpknLKNFGqraptsuqC+Mh3YavWXVBRERElhAVFEJKnoZPGBEREdEAxYCCBpSrRofjb9+cRnJmGds8Ud+qKQO+ug8487VyfdhC4Lo3lCHYmx+RA7NbicoJEU6MWmqXr0qAR4BcnVVdmGsZZVx18WP+j51XXbRUXIjgglUXREREZCwxUhmUnVpYifomHdxdbPNEDyIiIiLqOQYUNODaPI0f7I+jFyqwOaUAt7KKgvpC5l5g/Z2ANhdwdgWu+Csw7R7RQ0kJIRIWK0GFGIgtZk6Itk42WjnRl1UXxhUXhoHdYtZFV1UX4d7hraGFoeKCVRdERESOKTrAE/5erqioaURaYRWSopTAgoiIiIgGDgYUNOAsHh0hAwrR5okBBfUqXRPwwwvAD88DzXogMB5Y8R4QOc70cSKMiJvl0E++oepifOh4k9sb9Y0ypDDXMqqivgIF1QVydVp10a5llKi68HL16uffjoiIiPptUHakH/acK5GDshlQEBEREQ08DChowLlqdASe3nQaB0WbJ20dQtUe1t4lGgg0OcC6O4ELLUOix94MXP0C4O5j7T2zK67Orq1VEV1WXWgzWltGWVJ10RpctLSOElUdoV6hcHZy7qffjIiIiPpCYpRaBhQncjX4OZ9iIiIiogGHAQUNOFH+npgw2B9HLlTg25QC3DYj1tq7RPbu9Ebgy98qA7DdfIEla4AxN1p7rxyu6sJk1kVLkFFeX95adbE/f3+HqgtRYdG+ZRSrLoiIiOyHqKAQUvK01t4VIiIiIuoDDChoQFo8JlIGFJuO5zOgoJ5rrAW++zNw6F3leuQEYMW7QGDHmQtknaqLirqK1tDCMOdCXM/WZsuqizNlZ+RqL8wrzKTiQm5fHYcw7zBWXRAREdkQQ1un0/laNOr0cFWxOpKIiIhoIGFAQQPS1aPD8f++PoWDWWUo0NQh3I9tnugiFZ0G1q4Eik4p12f8Dpj3F8DFjU+lDfH38Mc4j3EYFzquQ9VFbmVuh4oL8bWouiisKZSrs6qL1uDCqPqCsy6IiIj6X0ygF3zdXVBZ34T04iokhKv5MhARERENIAwoaECK8PPExJgAHM4qx7cp+fjVzI5nXhOZ1dwMHP43sPkxoKkW8A4Frn8TGDqfT5idVV3IYMEvFnMGzTG5T1OvUSouWoILw6UlVRfGoYW4FJUXvV11odPrcKToCIprihHiFYIJoROgEoPXiYiIHJCzsxNGRapxIKMMKblaBhREREREA4xdBxQHzpdi6+lCOTDtZK5WnlWzfEI0XrpxbI+2p9c3Y+3hHHx2KBtnCytlCbHhQPf/LU2Ej3vHp+tUnhZv7krHgYxSlFc3wt/LFSMj1Lj78iGYER/cC78l9dTi0REyoPjmBAMKslBtObDxfuDUl8r1+PlKOOETyqdwAPFz95MVF11VXRgGdRtaRpXVlbVWXRzIP2DyfR4qD2XWhZ/prIueVF1szdqK1cmr5c8xDkYenfIoFsQsuMTfnIiIyH7bPCkBhQYrJkZbe3eIiIiIqBfZdUDx2aEcrDuSA09XFSL9PVBZ3NTjbdU36XDvx0ew7UwREsJ95RtfNxdn5FXUYefZIlTWDe8QUKw7nIM/rjsOXw8XzEsIRbjaA+U1DTieo8GRrHIGFFZ29egI/FW0ecosZ5sn6t6FA8C6OwBNNuDsCix4Epj2G3HaHp89B6y6wCCYrbporbhoCS4uVF5Ana4OZ8vPytVeqFdoa1hhmHMhth/uHd6h6kKEEw/ufBDNaDa5vaimSN6+Zs4ahhREROSQkqKUtk4ioCAiIiKigcWuA4rbZsTISoX4EB8cy6nAstf39Xhbz28+K8OJRxYl4J458R0qK9o7kaPBI+uOY2y0H96/fQr8vFxN7m/S6Xu8L9Q7xNyJSTEBONRSRbHyMrZ5IjP0OmD3GmDns0CzDgiIA1a8B0RN4NNF3VZdNOmbkFuV2zrfwrhllKi6EOGCWJ1VXRgqLsTXLx56sUM4IYjbnOCE55Kfw9xBc9nuiYiIHE5SpDIo+1S+Fjp9M1TOTtbeJSIiIiLqJXYdUIyJ9u+V7Yghyh/sy8SU2MAO4YSh72l7L245C11zM9bcOK5DOCG4qHjWtS1YPCZCBhSbGFCQOdo8YP1dQOZu5froG4HFLwEeHL5IlnFxdpHhgliXD7q806oL4wCju6oLc0RIUVBTgB3ZOzBv8LxenXlBRERk64aE+Miq+ZoGHTJKqjE01Mfau0REREREvcSuA4reIoYoN+mbcfXocFTVN2HrqULkVtQixMcds4eHyDPxjWlqG7E7rRiJkWrEBntj//lSHMuukGfyjB/sj4kxgVb7XcjUVUlKmycxiyJfUytnihBJZ78FNtwL1JYBrt7AkjXA2J/zyaF+rbowVFwcLjwsv+7OAzsfkKFIiGeIHKAt5lOIr0UrKbHEbfJrz1D4uPHgDRERDQyqlkHZ4j39yTwNAwoiIiKiAYQBRUu7JqGyrgnzXtyJosr61ifITeWMPy4agVWzhrTedjJXA9H1SRzsXvXBQWw9XWTypM4aFozXfjEBao+OlRXGMy8amtraQFXWNfbuK0uSCJcmxwQiObMM35wowB1s80SNdcD3TwDJbynPRcRYYMX7QFDH6imiPq+6gFJ1cbDgIFZ+t9Ki7xcBR351vlxd8XLxMg0uPNu+lsGGV4gMN9xUbr3yexEREfWlpJaAQnx2u3ZcFJ9sIiIiogGCAQWAkuoG+WS8si0Nlw0Nxn9WjUSEvyeSM0rx2PoTeHrTaTnnYm5CqMnjt58pQoCXG/71y4mYHh+EQm09Vn97BltPF8rve+3mznvYv74jHa9uS2u9rq+v6evX2qHbPImAYtPxPAYUjq44FVi7Eig8oVyf/ltg/hOAi7u194wc3ITQCTI0EPMqzM2hEDMoxP0br9+IivqK1tkWhlVcW9z2dU0xKhsrUdNUo7SX6qYyI8A9oK3ywhBoeLaFGOJ6oEcg20oREZFVJUYpcyhS8jgom4iIiGggsXpAMf6vW1BeY3n1wCd3TpNhQG9qblYOBgV5u+HNWybC000lr89LCMNzy51w+/sH8c6e860BheHxYkDb365PwsLEcHnd18MVr/1iPOa9uEsOZc6rqEWkv/mWQvfOjceqWW1Dm7VaLaJf6dVfi1pclRSO/9t4EkcuVMjWXVGdvCY0gIn/Z49+BHz7CNBYA3gFA9e9AQxfaO09I5JUzio8OuVRPLjzQRlGGIcU4rrwyJRH4OHigXCXcIR7K//udKamsaZDaFFYU9jhtgZ9A8rry+VKLU/tdHsuTi4I8gwyCS0MQYZxsOHj6gMnJw4uJSKivhuUfTJXC72+2eycQCIiIiKyP1YPKJaOjURVvc7ix4f49v6Zzr4eytMgqicM4YTB7GEhcHNxxvGWNlDGjxe9UOe1hBYG7i4q2eLp04PZOJGr6TSgEI8Ty6C5ofN2UHRpQtUemBwbiOSMMnx7It+kXRc5gDoNsPH3wMn1yvUhc4Dr3wJ8uz7AS9TfFsQswJo5a7A6ebUMEwxEKCDCCXG/pbxcvRDjqrSR6owI28Ug76LadiFGTbHJbSW1JWhqbpL3Ge+XOZ4unq2hhcmMDG9lLoYh3HBXsWqJiIguzrAwH/m5rLK+CdnlNYgJ8uZTSERERDQAWD2geOraJGvvAoYEK4NE1Z4dQwJxZo6Puwuq6po6PN7TVQVXlXOH7zFsp67R8uCF+tYS0eYpowybGFA4luyDwLqVQMUFwNkFmPc4MON+8T+2tfeMyCwRQswdNBdHio7IcEAc0Bftn0SFRW8TlQ7+Hv5yDQ8Y3uXMi9LaUqXyolYJLdq3lxLBRWVDJWqbanGh8oJc3Q0QNwzzbj8jw7BEW6m++L2JiMg+ic9dI8N9cSxHg5RcLQMKIiIiogHC6gGFLZgRH4R/7jiHtKLKDveVVtWjrLoBQ4LbztCJCfKSbYJEu6B8Ta0clm0srVDZTnSAVz/sPVliUVI4nvzqJI5eqEBOeQ1fm4FOrwf2vgJsfxpo1gH+McDyd4FBk629Z0TdEgflJ4dPtqmh3mHeYXJ1RYQTJTUlHVpJtZ+TUa+rl5UbYqWVt81iak/lpJJtpYwrL1qXUbChdlOzrRQRkQPNoRABhahUF3PmiIiIiMj+OVRAUdugk6GCaONkPIdg6pAgDA31wd5zpdidVoxZw0Ja21+88N1Z+bXxG2Bx1unNUwfL+17YfBYv3jC2tQfq/vOl2JlajOgAT4yNVvqkkvWF+npgSmwgDsg2TwW4czbbPA1YlQXA+ruAjF3K9aTlwJKXAQ/+/0jUl0R7p0HqQXJ1Rvy7qm3QdjkXQ1yW1JVA16xrvR2lnf9cD5WHDCraD/ZuP+xbzO8gIqIBMoeCg7KJiIiIBgy7DigOZpbh0+Rs+XVZdb28PJRVhj98dkx+HR/qjXvnDG19/E/ZFbjp7f2YGheI/909vfV2MUvihRVjcPPbB/Cr9w/iysRwRPh54GBWOY5lVyApSo175sSb/Gwx4Hr7mSKsP5qLtKIqTIkLRFFlPTan5MNN5YznV4yBi5n2T2TdNk8ioPj6RD4DioEqdQuw4ddATSng6gVc/QIw7hciVbT2nhFRS8Av2juJNSxgWKfPiU6vQ2ldqWk7qfazMmqLZRVGna4O2ZXZcnVFVFqYG+5tPCtDVGyIihEiIrJN4nOZkJKrkaG3+HeFiIiIiOybXX8KzyypxrojOSa3ZZXWyCWIIMI4oOjK+MEB+PK3M/Hy96nYl16CqvomOeD63jnx+M3cofByM32qxIDrj++Yijd2nsNXx/Lw0Y9Z8HJXyaHZ988fjlGRyptnsh1XtrR5EqFTdlkNBgWyBdeA0VQPbH0K2P+acj1sNLDiPSCk8776RGTbba4MAUIiEjt9nGgXZVx5YTwTw/g2EWKIyg2xzlWc63R7zk7OCPIIaqvCMJqLYXybCFh4UIyIqP+NCPeFi7MTymsakaepM6mKJyIiIiL75NQsTj0hq9NqtfDz84NGo4FazXCjr9z0r/348Xwp/nR1Au6abVoVQ3aq5JwyCDtfqZzC1F8DC54CXNnOhYiUtlKVjZUdQ4xqpQrDUJFRUqu0lbKEm7NbW+VFJ+2lxBJtr4gEvs9zXHzte9/Vr+7GqXwt3vrlRFn5TkRERGQNfJ/Xe+y6goLoYl09JkIGFJuO5zOgsHciWz32CbDpIaCxGvAMBK57HRhxlbX3jIhsiKh0EO2dxIr3j++yrVR5fbnZwd7GMzLEYxr0DcitypWrK76uvh1Ci/Zhhmgr5ers2ge/OZH9E1WvL29NxZGscjTqmjE8zAcrL4vDteOiLN6GXt+Mj/Zn4ZPkC8goqYa3uwumDwnCQ1eOQFywt8ljNbWNspr6WI6otq2FtrYRAd6uGBLsg1unx2BRUjirp2ykzZMIKESbJwYUREREZHdyDwM7ngVykgFdIxCSAEy7Fxhzg+Xb0OuBg+8Ah/8NlKUDbt5A7Cxg/hNAULvPvbUVwI5ngLwjQHkWUFcBeAUBQUOBKXcCI5davTU6AwpyKIsSw/Hklyk4lqNhmyd7VqcFNv0BOPGZcl38EV72L0Adae09IyI7bisV7Bks16igUZ0+rkHXYFJ50VqZ0W5GRm1TrazcqNRU4rzmfKfbc4ITAj0CTUMMEV4YtZcSy9/dnwdGyaH8mF6K295LhqvKCdeMjYSvhws2nyzA/Z/+hJzyWtmC1RJ/3nACnyRnY1ioD26fEYviqnp8fTwfP6QVY/09MzAszLf1seXVDfjsUDbGD/bHwsQw+Hu6orSqAdvOFOKe/xzBTVMG4dllY/rwtyZLJEX54bNDOTKgICIiIrIrGbuBj5cBKjcgaRngrgZObwTWrwIqsoDZD1m2na9/Dxz5QAk3ptwFVBcDKeuB9B3AHVuA0IS2x4o5rUc/BqInAQmLAc8A5fGpm4HPbgUm3AYs/TusiS2ebATLgvrPzW/vx770Ujx2VQLuvpxtnuyOSJrX3gGUZwBOKmDuY8BlDwLOKmvvGRFRq6qGqo6DvWuKTWZkiNXU3GTRsyaqLAwVGMbtpQxzMQy3eblyvpIt4vu8i9Ok02P+ml3I19TJEEEckBbEjLhlr+/F+eJqfP/g5R0qINoTc+VufvsApsQG4qNVU+QMOWHvuRLc8u4BTI4NxGd3T299vE7fLNvCuaicTbYjfu71r+1FWlEVtjwwG8ONQg2+9v3vcFY5lr+xDyG+7jj45wVW2AMiIiKiHrzH1zUB/5wEaPOAVd8DEWOV2+srgXeuAErTgN8kd6yAaC/jB+CDa4DBM4BbNwAu7srt53cCH14HxMwAfvVN2+P1OqULiapdnYL8uQuA4jPAvfuB0JFWe1lZQUEOZ/GYCBlQbDqRz4DCnojytR//AWz7K6BvAvwGA8vfAQZPtfaeERF14OPmI9cQvyGdPjv6Zj3K68o7tJJq32KqrK4MjfpGi9pKebt6mwz4Nm4xZWgvJapEXFVsK0W2S7xPyyqtwQ0To1vDCcHH3QX3zRuG+z45is8PZeOPi4zODDPj0+RsefmHhcNbwwlh5tBgzB4Wgl2pxThfXIUhIT7ydpWzKG3vWN4ufu7s4SEyoMgsqb6ogIJ636gINcRLVVxZjyJtHULVnDtGREREdiBjl3Ky7bhb2sIJwd0XuPxhYO1KpdJhwZNdb+fwB8rlvMfbwglhyBxg6Hzg3FZlXmtwS8VxZyf0ip8bP18JKMrOM6Ag6k+iV+1fNqTgeI4GF0prMDiIZ5vavKoi4ItfA+nblOujrgWu+Tvg6W/tPSMi6jFnJ2c5g0KshMDOD7Q26hrlEG9ZeWEUZrRvL1XdWC1XhiZDrq4Y2kqJ0MK4lZTxbQEeAXIfifrb/vOl8nLW8JAO94lgQTiQUWbRdrzcVJgUG9hxO8OVgEJsxxBQdKauUSdDE9Ga17glFFmHp5sKQ0N9kFpYhZQ8DeYxoCAiIiJ7kLlHuYyf2/G++HnKZdZey7bj6g0MnmZmOy0BRdaetoCiM411SjWGOEFHtIqyIlZQkMMJ9nHH9Pgg7D2nVFHcM4dtnmya+MMqwgnRH8/FE7hqtdIfz8oDfIiI+ouodojwiZCrKyKcaA0uzLSXMoQZTfomWZUh1hmc6XR7Ls4upi2lOmkvJSpFiHpTZmm1vIwL6tjCyc/LFYHebrKSoSs1DU0oqqzHiDDflsoIU3HBygkq5rYjhmW/tydDtnsqqW7AzjNFyNPU4f75w7ptK1XfpENDk771emVdY5ePp55JivRTAopcLeYlhPFpJCIiIuvOSXUzui6qGowrGwzEMGvBXAsnzwBlcHVpy2M601ANVBUAoaPMV0YYtm1uO2JY9v43gGa9cowt7XtAmwNc/mj3baX6GAMKckiLR0fKgOIbBhS2q6kB2P5XYN8/lOuhicCK90wH/RARkUl7pzi/OLk6Iw64VtRXmLaTqjWqxmhZIrwQQUZ+db5cXfFy8TJtJ2XUXsoQZohww00MgiOyQGWdMptFDMY2R7RcKtDUXeI2lDZn2pbHGdPWNuLVbWmt18Wg7j9dnYA7Z3Xess3g9R3pJt+rr6/p9nvo4iVG+WH90Vyc4KBsIiIisraXRwHuRifEiAP+Yl6quSBDEIOxO2u5pM3r+mdZsg2hXmvmezXArtVt151dgSv+HzDjPlgbAwpySFcmhuEvX6bIDzVZpdWIMXOGHlmRSHrX3QHkHVWuT14FLHwacPXky0JEdAmcnJxk6yaxRgSO6PRxYuZFaW2paRVGu1kZ4vbKxkrUNNUgU5spV1cC3ANMZmKYazElWk/1ZVspnV6HI0VH5L6LfZkQOgGqznqyksMaFOiFzNWL5dDsvIpabDyehxe/S5XDmV+7eUKHIdrG7p0bj1Wz4kyGJ0a/0k877kCSIpUP5ScZUBAREZG1PXAKMB6Sba56whYExAD/p1GGZmtygJR1wPb/B2QfAG74oOMQ7X7EgIIcUpBo8zQkCHvOlcg2T/fO6aYvG/Wf458BXz8ANFQBHv7Ata8BI5fwFSAi6keuzq4I9w6Xqys1jTUd52KYaS/VoG9AeX25XKnlqZ1uz8XJRc7kMK68CPMO6xBk+Lj6yLDlYmzN2orVyavlPhmIn/PolEexIGbBRW2L+pah6sFQBdFeVX1Tp5URlm9Dab2k7mI7ojWUCCvE+0SVkxOe/fYMPjmYjV9Oi+n0e8QwbuOB3M0NHEjfF0a1BBSi9VZpVb18b09ERERkFR5qZVnyuM6qG4T6ys4rIy5mG0JX2xEnaImwYtaDytffPwEc+bdycrCVMKAgh7V4TIQSUBxnQGET6quAbx4Cjn2iXI+ZCSz7F+AXbe09IyKiTni5eiHGNQYx6pgu20pp6jVmW0kZhxmldaVoam6SAYJxiGCOp4tna2hhPCMj1FtpMWWo1HBXubeGEw/ufBDNaDbZjvjZ4vY1c9YwpLAhsS2VrRml1Rgd7Wdyn6amEWXVDZgYE9DlNrzcXBDq647s8hpZCdF+DkVGidJ6KbabmRIGs4aFyIBCDN7uKqCg/uHr4Yohwd44X1KNk3laOfSciIiIyKYFGs2HiBxvel9tOVBTCgya2vU23LwBn3CgPEuphGhfDW6YPWHpTAkxnFsEFGLwNgMKov53ZWI4Ht+QIj/UiAGJln5ApT6Q9xOwdqUyMEi09rj8EWD2w+YH/hARkV0RlQ7+Hv5yDQ8Y3unjxMwL0VZKVGSYDPYWAYZRlYa2QYvaplpcqLwgV1f83P1kcHFBe6FDOCGI25zghOeSn8PcQXPZ7slGTB0ShNd3pmN3ajGWjo00ue+HtGLlMXGBFm1n47E8HMosk1+bbCfV8u0IhZXKzAsXMwO3yXpzKERAkZKnYUBBREREti92JrBnDZC+Axi9wvS+9O1tJ+tash3RnunCfuVrk+1sa9nOZZbtU2WBculs3RoGVlCQwwr0dsOM+CDsTlPaPP1mLts89bvmZmD/68D3TwL6RkAdDSx/G4iZ0f/7QkREVuXi7CLbOYmVhKROHyfCiZKakg5zMdrPyqjX1cvKDbG6IkKKgpoCOZticvjkPvjN6GLNjA/C4EAvfHksD7fPjEVipF9ra6d/bE+TIcGKiW0VlqKiQizx3k4sg5umDJIBxUtbUvHxqqlwc1FmR+w9VyKDjilxgRgS4tP6+JN5GtnSSe1h2papoqYBL2w+K7+eM4Jn6tvSHArx+qZwDgURERHZg7g5QEAscOJzYOrdQMSYtrZMu15QQoJxv2h7fHWpUlXhFQR4G51sM/H2lvkRTwO3fgm4tLz/Pb8TOLdNCTmCjY5x5h9XWjp5mFYmo6YM2PaU8vXQK2BNDCjIoS0eHaEEFMcZUPS7qmLgy3uBtC3K9YQlwNJ/AF6WnclIRESOSbR3GqQeJFdXbaVEpYUILb4+/zXeTXm32+2Kx5JtEEOoVy8fjdveS8aNb/6IpeMi4ePugs0nC5BdVouHFg43CRY+2JeJV7el4f75w/DAFW1VOjPig/HzyYPw6cFsLP77bsxLCEVxVT2+Pp4vt/e360yDsLWHc/C/g9lyTllUgCc83VTILa/FjjNFqG7Q4aqkcFw7NqpfnwvqXFKU8iE7JbeTHsxEREREtkQMoRbHvT5aBrx/FZC0HHD3BU5vBCqygHmPmwYLyf8Cdq0GLn8UmPtY2+1xs4EJtwJHPgTemgUMWwhUFwMp65XZE4vXmP7cn/6rPDZuFuA3CHDzAiqyleNxYv7ryKXA6BtgTQwoCI7e5unPG1JwKl+LjJJqxLHNU/8Q5Wxf3A1UFQKiP/iiZ4BJd4g+IP20A0RENNDbSon2TmLNjJppUUAh5laQ7RDhwue/noGXv0+VgUKjTo/hYb74wxUjcN14y0OCZ64fjYRwX/w3+QLe35cJbzcVFowMxUMLR5iEHMLVoyPkUO2jF8qRnFGG2kYd/L1cMSk2EMsmRMl2Uxc7nJ36TmLLoOwLZTVyNomfFweSExERkY0T4cLK74CdzwAnNwC6BiA0QQknxtxo+XaWvAqEJQGH3gcOvKXMphixCJj3hGnIIYy6VhmqnXMQyNoHNNYAngHA4GnA2JuUoMTK73GdmsUpZmR1Wq0Wfn5+0Gg0UKstmPxOvebW95JlH+KHrxzBNk99TdcI7PgbsOcV2VQDIQnAiveAsMQ+/9FEROSYdHodrlx3pWz7ZG4OhZhBIYZsb16+uc9mUPB9nuPia9+3Zj2/XVbV/PfOqTLUIiIiIuovfJ/Xe5RGrEQObMnoCHkpzs6jPlSeCby3CNjzshJOTPwVcOcOhhNERNSnROjw6JRHW8MIY4brj0x5hAOyiexQUst8kpNs80RERERktxhQkMNbmBgmhy2eztcivbjK4Z+PPnFiLfDmLCD3kDKU54YPgGteUfreERER9bEFMQuwZs4ahHqFmtwuKifE7eJ+IrLfORQnOCibiIiIyG5xBgU5PH8vN8wcGoxdqcX45ng+7ps/zOGfk17TUA18+0fg6MfK9UHTgOVvA/6D+RwTEVG/EiHE3EFzcaToiByILWZOTAidwMoJogEwhyIlT2PtXSEiIiKiHmJAQQRg8ZgIGVBsOsGAotfkHwfWrgRK02QTDcx+GLj8EUDFPztERGS9dk+Twyfz6ScaYBUUGSXVqKpvgo8732cSERER2Ru2eCICcOWocLiqnHCmoBLnitjm6ZI0NwP73wTema+EE74RwG0bgXl/ZjhBRERERL0m2McdEX4e8u2naNdKRERERPaHp5gQAfDzcsVlQ4Ox42wxvjmRj9+xzVPPVJcCX/4GSP1WuT78KuDa1wDvIP53RkREdLFyDwM7ngVykgFdIxCSAEy7Fxhzg+Xb0OuBg+8Ah/8NlKUDbt5A7Cxg/hNAUHz337/nFWDrk8rXd2wFBk3uWDF58gsg/ycg/xhQUwrEXAb8atNF/rJEPZMY6Yd8TR1ScjWYHBvIp5GIiIjIzrCCgqjF1aMj5OWm4/l8Trqj1wEZu5Xh1+JSXv8BeHOmEk6o3ICrngdu+oThBBERUU+If1/fWwRc+BEYdS0waaVy8H/9KuCHFy3fzte/B759GGjWAVPuAoYtBM5+C/xrLlB0puvvLT4L7HgGcPXu/DFnNgF71gCZewCfMMv3i6iXJEUpcyg4KJuIiIjIPrGCgqjFwlHh+JPqBM4WijZPlRga6svnxpxTXwGbHwG0eW23ufkADS2tsYKHAyveA8JH8/kjIiLqCV0T8NV9ygynX30DRIxVbp/zKPDOFcDOZ4HE67uvgBAnDxz5ABg8A7h1A+Dirtw+9ufAh9cBmx5Utm+OOPngi18D4UlA0FDg+P/MPy7xOmDEVUBYIlBTBrw0nK859aukSGUOxclctngiIiIiskesoCAyavM0a1iI/HrT8QI+L52FE5/dahpOCIZwIu5y4K6dDCeIiIguRcYuoDwDGH1DWzghuPsClz8M6JuAox93v53DHyiX8x5vCyeEIXOAofOBrL1AyTnz37vnZaAwRWnV6KTq/GeEjgQixwEqV4t/PaK+GJSdVlSJ2gYdn1wiIiIiO8OAgsjIYkObpxPtDsCTcialqJxAc+fPRuk5wMWDzxYREdGlEO2ShPi5He+Ln6dcinDBku2I9kyDp5nZzvyW7bT8LGOFp4BdzwGzH1YCCCIbFqZ2l8Oy9c3AmQJWURARERHZGwYUREYWjAqDq8oJqYVVSC2s5HNjLGtfx8qJ9rS5yuOIiIioozqt6WqqN/8siWHWgrkWTp4BgFcQUNrymM40VANVBUBADOBspgLCsO322xHtpTbcAwSPAC57gK8i2TwnJ6fWORQpeQwoiIiIiOwNAwoiI36erpjd0ubprV3p+PKnXPyYXgqdOCXL0VUV9u7jiIiIHM3Lo4DVg9rW7jXmHyfCC8FdOejagWj1VN/NgVhLtiG0387ul1paO/2TbZvI7uZQpORorL0rRERERHSROCSbqJ1BgZ7yct2RXLmECD8PPHnNKCxKUlpAOZzmZiDnoGWP9Qnr670hIiKyTw+cAtRGgYHxXAhbUHAC+OEFYMZ9ylwJIjvRVkHBgIKIiIjI3rCCgsjI5pR8fLAvq8NzUqCpwz0fH5H3O5yaMuB/twAH3uzmgU6AOgqImdFPO0ZERGRnPNSmq7OAQtwndFYlUV/ZeWXExWxDMN7OF/cAgXHAnMe6+UWIbEtiSwWFaNFa38RB2URERET2hBUURC1EG6enNp4yOwJa3OYEyPuvGBUOlbO45gAy9wLr71RmSzi7AmNuBH76b8udxs9Uy/OxaLX5PtdERERkuUCj+RCR403vqy0HakqBQVO73oabN+ATDpRnAXpdx3+fS83MuSg8oVw+HWp+m+8uUC5/9h9g5BK+omQzogM84e/lioqaRqQVViEpSgksiIiIiMj2sYKCqEVyRhnyNXWdPh/icLy4XzxuwBMDMnc8C3ywRAknxIGSVVuB614HbvwQULdrdaWOVG4ftdRae0xERDRwxM5ULtN3dLwvfbtyGTPTsu00VgMX9pvZzraW7VzWdtv4X5pfhsBkxNXKdf/BPfiliPp4ULZhDkUu2zwRERER2RNWUBC1KKrsPJww9teNJ3HztBhcMTIM4X4eA+/50+QA6+4ELuxTro+9Gbj6BcDdR7kuQoiExUDWPmUgtpg5Ido6sXKCiIiod8TNAQJigROfA1PvBiLGtLVl2vUC4OwCjPtF2+OrS5WqCq8gwDuo7faJtwMp64DtTwO3fgm4uCm3n98JnNumhBzBQ9seLwZjmyNaP5WlA5c9CAyazFeZbFJilBp7zpVwDgURERGRnWFAQdQi1NeysOF0QSX+siFFrjHRfjKouCIxDCPCfOXZW3bt9Ebgy98CdRWAmy+wZI3S1qk9EUbEzbLGHhIREQ18Khdg6T+Aj5YB718FJC0H3H2Vf6crsoB5j5sGC8n/AnatBi5/FJhrND8ibjYw4VbgyIfAW7OAYQuB6mIgZb0ye2Lxmkvf1+JUYM/LytdNtcplSaoSaghegcCVf7v0n0PUDUMFxYncTuauEBEREZFNYkBB1GJKXCAi/DzkQGxzcyhE9BDs647bZ8Ri2+lCHM2uwPEcjVwvfZ+KQYGeWCDCilFhmBIbCBeVHXVQa6wFvvszcOhd5XrkBGDFu0DgEGvvGRERkWMS4cLK74CdzwAnNwC6BiA0QQknzJ080JklrwJhScCh94EDbymzKUYsAuY9YRpy9JSopjxmmE/Vorqo7Ta/wQwoqF8Y5k6czteiUaeHqz29FyciIiJyYE7Nzc3mjsVSP9NqtfDz84NGo4FarebzbyWbU/Jxz8dHOhsBjTdumYBFScr8heLKehlUfH+qUJaT1zfpWx/v5+mKeQmhMqyYPTwEPu42nAUWnQbWrgSKTinXZ/wOmPeXtjYQREREdEn4Ps9x8bXvP3p9M8Y+tQWV9U3Y/PtZSAjnZyoiIiLqO3yf13ts+KgpUf8T4YMIIZ7aeMpkYLaYNfHkNaNawwkhxNcdP58yWK6ahibsTiuRYcX2M0Uoq27AF0dz5XJTOWN6fJAMK8QKU9vI3AqRTR7+N7D5MaUlg3cIcP1bwND51t4zIiIiIqKL4uzshFGRahzIKENKrpYBBREREZGdYEBB1I4IIa4YFY7kjDI5OFvMphDtn1TOnc+X8HJzwZWJ4XLp9M04nFWOrS3VFRkl1diVWizX4xtSMDbaT2kFZc25FbXlwMb7gVNfKtfj5wPXvwn4hPb/vhARERER9VKbJyWg0GDFxGg+p0RERER2gAEFkRkijBBVDz39XhFoiPXYVQlIL67CllNKWPFTdgWO5WjkMsytuGJkuKysmBwb0D9zKy4cANbdAWiyAWcXYP6TwPTfitPO+v5nExERERH1kaQopa2TCCiIiIiIyD7YdUBx4HypPEv9RK4GJ3O1st/o8gnReOnGsT3uW7r2cA4+O5SNs4WVcrhahJ8nJsYE4P+WJnaYIyDe+L6+85wckizmEQT7uGNkhBp3zorD1CE9O7hNA4uojhga6ivXvXOGyoqM7aeLZFix+1wJsstq8d7eDLn6fG6FXgfsXgPsfBZo1gEBccog7KiJvftziIiIiIisIClSGZR9Kl8rq5q7qoAmIiIiIttg1wHFZ4dysO5IDjxdVYj090BlcVOPt1XfpMO9Hx/BtjNFSAj3lSXBbi7OyKuow86zRaisG25ywPi7kwW45+PD8jGirY8IMvI1tfJ2EZq8sGIMbpg0qJd+UxooRLso47kVP6Qa5lYUorym0WRuxYyhytwK0Q7qkudWaPOA9XcBmbuV66NvBBa/BHhweCARERERDQxDQnzkZ8OaBp1sszo01Mfau0REREREAzmguG1GDO6+fAjiQ3xwLKcCy17f1+NtPb/5rAwnHlmUgHvmxHeorGjvhe/OQty67p4ZSGw5U8dQVXHNP/fg79vTGFBQl8TcikVJ4XI16fQ4cqEC358qkIFFZmkNdp4tluvPXyhzK5Qh2+EYHuZzcXMrzn4LbLgXqC0DXL2VYGLcTXx1iIiIiGhAUbUMyhbz4E7maRhQEBEREdkBuw4oxkT798p2CjR1+GBfJqbEBnYIJwRnM6XBF8pqEObrYRJOGAazhfq6o7SqoVf2jRyDmD1hmFvxp6tH4lyR+bkVL25JxeBAr9bKii7nVjTWAd8/ASS/pVwPHwOseB8IHtqvvxsRERERUX9JagkoxIlj146L4hNPREREZOPsOqDoLd+m5KNJ34yrR4ejqr4JW08VIreiFiE+7nIWQLhfx/Y64iz2U3lanM7XyrkTBuJMnaLKesxPCO3n34IGClEdMSzMV67fzFXmVmxrmVux51yJDMfe3ZMhl7+XK+aNaJtb4W1oQ1acCqxdCRSeUK6LIdjznwBc3K36uxERERER9aXEKOUEMjGnkIiIiIhsHwMK8eY1R3nzWlnXhHkv7pQBg4GYBfDHRSOwatYQkyfu8cWjcMe/D2L5G/vkDAoRYohKjM0pBZgcG4hnrh/d7cyLhiZ96/XKusbefm1pAM2tuGnKYLmq65uwO61YVldsP1OEippGrD+aK5eYhzJzSCDuUv+IaWdWw6mpFvAKBq57Axi+0Nq/BhERERFRvw3KPpmrla16zVXDExEREZHtYEABoKRaacf0yrY0XDY0GP9ZNRIR/p5IzijFY+tP4OlNp+Wci7lGVRHThgThf3dPx2/+e0QONTaI9PPADROjEdrNUOPXd6Tj1W1prdf19TV98frSACMqJBYlRcgl5laI8nVRWfH96UKUlZZgWcYrmK7aLx97zHUcjoxZjZl+SRjW3HxxcyuIiIiIiOzQsDAfeeJOZX0TsstrEBPkbe1dIiIiIiJbDijG/3ULymssrx745M5pmB4f1Kv70NysDMEO8nbDm7dMhKebSl6flxCG55Y74fb3D+KdPedNAoodZ4vwu/8exbyRoXj3tkmIDvBCTnktXt9xDg+vPY4zBZX4y5JRnf7Me+fGY9WsuNbrWq0W0a/06q9FA5yYPTF1SJBcfx5bhabP7oNrZTZ0cMaLjTfizbolaN5ZBuz8ATFBXnJmhWgFNSmmi7kVRERERER2zFXljJHhvnJ+W0qulgEFERERkY2zekCxdGwkqup1Fj8+xLf3e+j7eihPg6ieMIQTBrOHhcgzcI63tIESKmoacP8nRxEb7I2XbxzXWjY8NNQHL904Fhml1Xh/bwZunR7T6RtidxeVXAbNDa69/nuRA9Drgb2vwGn703Bt1gH+g6Fa/h5+5Tcag+TcigLsTS9FVmm7uRUJobhiZLu5FUREREREA2QOhQwo8jRYPCbC2rtDRERERF2w+pHJp65NsvYuYEiwj7xUe3YMCUT44OPugqq6ptbbDmWWQ1vXhKlxgR16moo2OtOHBOHohQqczOMZO9SHKguA9XcBGbuU64nLgGteATz8IGp9bp46WC6zcyuO5Mol51bEB+GKUeFYMDK029ZkRERERET2MocihYOyiYiIiGye1QMKWzAjPgj/3HEOaUWVHe4rrapHWXUDhgS3VUI06pTh1uJ2cwy3iwHbRH0idQuw4ddATSng6gVc9Tww/haRkHU7t+KQYW7FqUJcKKvBjrPFcv3pC2DsIH8sHKW0ghoW6tPp3AqdvhnJGWUoqqyTQ7ynxAVCxQGERERERGQDkqLUrQGFaOfLWWxEREREtsuhAoraBh1yK2plG6cof8/W20UPf9Geae+5Unmm+axhIfJ28Wb2he/Oyq+NS4PHDw6QB2O/ScnHnbOHYGSE8gZYSC2sxFfH8uSZ6RNiAvr19yMH0FQPbH0K2P+acj1sNLDiPSBkuEXfLmZPiAHvYj2+eCTSiqpkUCGqK45lV7Qu8d+9mFsh2kCJsGKi0dyKzSn5eGrjKeRr6lq3G+HngSevGSVDECIiIiIiaxoe5gsXZyc56zBPU2fy2Y+IiIiIbItTs2FCtB06mFmGT5Oz5ddl1fXyLHBxUHVSTKC8LT7UG/fOGdr6+B/TS3HT2/tla6b/3T3dZFtHL5Tj5rcPyOqIKxPD5QHXg1nl8mCtOAPns7unw8utLc95actZ/GP7OVklcUViGKIDPJFXUYfvThagoUmPP12dgLtmx1v8u4gh2X5+ftBoNFCr2wIPolYl54B1K4H8Y8r1qb8GFjwFuPZOW6ZCbR22nlYqK/adK0VDS6WQEODlKofEh/i44V8/ZKD9Hw1DncUbt0xgSEFERNQO3+c5Lr721nP1q7txKl+Lt345UX6+IyIiIupNfJ/Xe+y6giKzpBrrjuSY3CaGAYsliCDCOKDoiqiK+PK3M/Hy96nYl16CqvomRPp74t458fjN3KEm4YTwh4UjkBCuxn+Ts7AnTXm8GLYtfubtM2Ixf2RYL/6m5NBEhnjsU2DTH4DGasAzELjudWDEVb36Y8LUHvjF1Bi5xH/Pu1OLZVix/WyRPPtMzKzodBdbQgpRWSHmWbDdExERERFZkzjJTAQUJ3M1DCiIiIiIbJhdV1AMJEzdyKw6rRJMnPhMuR47C1j2L0Ad2W9PmJhbcTCzHB/+mIlvUwq6ffwnd07D9Pigftk3IiIie8D3eY6Lr731iPeuT3x5EnNHhOD9X02x4p4QERHRQMT3eb2HU5yJbFXuYeCt2Uo44aQC5j0O3Pplv4YTgpg9IQKHRUmWlcZ/fTwP2rrGPt8vIiIiIqLOJEb6ycuUPC2fJCIiIiIbZtctnogGJL0e+PGfwLanAH0T4DcIWP4uMHiqVXcr1NeyWRf/OXABnx/KweUjQrBkTAQWjAyDtzv/1BARERFR/xkVoYazE1BcWY8ibR1C1b0zt42IiIiIehePGhLZkqoi4ItfA+nblOsjlwJL/w54Blh7zzAlLlAOjy/Q1HUYkm3g4+6CMLU70our5fwKsTxcnTE/IUyGFWLQtoerqp/3nIiIiIgcjaebCkNDfZBaWIWUPA3mMaAgIiIiskkMKIhsxbmtSjhRXQy4eAJXrQYm3AY4ifHT1icGXz95zSjc8/ERORDbOKQw7OGLN4yRQwjPFlbi62P5st1TZmkNNp3Il8vbTYUrRomwIhKzhgfD3YVhBRERERH1jaRIPyWgyNViXkIYn2YiIiIiG8SAgsjamhqA7X8F9v1DuR46CljxPhCaAFuzKCkCb9wyAU9tPIV8TV3r7eF+HjK8EPcLCeFquf6wcLj8QCiCiq+P5yO3ohYbfsqTy9fDRYYZ14yNxIz4ILiqOBKHiIiIiHpPYpQf1h/NRUquhk8rERERkY1iQEFkTWXngbV3AHlHlOuTVwELnwZcPW32dREhxBWjwpGcUYaiyjo5m0K0fxIVFu05OTlhdLSfXI9elYAjFypkWLHpeD6KKuux9nCOXAFernK714yNwNS4ILPbIiIiIsdyLLsCL29NxZGscjTqmjE8zAcrL4vDteOiLN6GXt+Mj/Zn4ZPkC8goqZZzsaYPCcJDV45AXLC3yWNFG0tR8bnzbBHSi6pQXFUPP083TIoJwN2XD8H4wdZvuUkXJylSLS8ZUBAREZHNyD0M7HgWyEkGdI1ASAIw7V5gzA0XN7/24DvA4X8DZemAmzcQOwuY/wQQFG/6WG0ecHIDkLYFKEkDqgqVVvJi1u3M3wPRk2BtTs3NzZ21k6d+pNVq4efnB41GA7VaeSNNA9zxz4CvHwQaKgEPf+DafwIjr4Ej0OmbcTCzTIYV354oQGl1Q+t9Ib7uuDpJqayYMDgAzgwriIjIzvF93sX7Mb0Ut72XDFeVk3xPICovN58sQHZZLR6+cgR+M3eoRdt5bP1xfJKcjWGhPpiXECpDB1HV6e7ijPX3zMCwMN/Wx67+9gze3JWOmCAvTI0LRJCPOzJLqrHlVCHER6a/3zRetqm8GHztrauyrhGj/2+L/Prw4wvka0pERETUG3r0Pi9jN/DxMkDlBiQtA9zVwOmNQEUWMO8vwOyHLNvOV78DjnyghBvDFirt4lPWAy4ewB1bTLuyfP8ksPcVICAOiJ0JeIcApenAmU1KA/fl7yr7YkUMKGwEP7w4kPoq4JuHgGOfKNcHzwCWvw34RcMRNen0+PF8qZxZIQ48aGobW+8TQ7kXjxaVFZEYE+0nKzKIiIjsDd/nXfx7g/lrdsl2kiJESIryk7dX1Tdh2et7cb64Gt8/eHmHCoj29qWX4Oa3D2BKbCA+WjWldfbV3nMluOXdA5gcG4jP7p7e+vjNKfkI9HaXlaHGRNXoL97ZL6svDvxp/kXN0OJrb33zXtyJ8yXV+HDlFMweHmLt3SEiIqIB4qLf5+magH9OUioaVn0PRIxVbq+vBN65AihNA36T3LECor2MH4APrlGOJ966AXBpOQHj/E7gw+uAmBnAr75pe/yprwDvYOV2Y1n7gA+WAu4+wB/Otm3HCtj0nag/5f0EvDVbCSecnIE5jwG3bXTYcEJwUTlj1rAQPLdiDA7+eQHeu30Slo2Pgo+7izww8c6eDFz72l5c/sJOPLf5DE7laeVZjERERDQw7UsvRVZpDa4dG9kaTgjivcF984ahSd+Mzw9ld7udT5OVx4iZWMahwsyhwZg9LEQGD+eLq1pvF+0m24cTgrht2pAgVNQ04mxBZS/8htTfcyiElDzOoSAiIiIrytgFlGcAo29oCycEd1/g8ocBfRNw9OPut3P4A+Vy3uOmocKQOcDQ+UDWXqDkXNvto5Z2DCcEcVvcLKC2HCg8CWviDAqi/iAOqO9/XSmr0jcC6ihg2dtKaRW1cnNxxryEMLnqGnXYlVqMjcfysO10ES6U1eCNnelyDQnxli0WrhkTYdKagYiIiOzf/vOl8nKWmbPdRbAgHMgos2g7Xm4qTIrtGDqIM+nF+wyxnSEhPt1uy1WlnNfFOVn2OYdCvJ88mau19q4QERGRI8vco1zGz+14X/w85VKEC5Zsx9UbGDzNzHbmA+e2All7gGALWqI6u7ZcWjciYEBB1NeqioEv71WG0QgJS4Cl/wC8On5YpjYeripcmRguV01DE7afKZIfLnecLZatHf6+LU2uhHBfLBkTIQOL2G5aPRAREZHtyyytlpdxQR3/XffzckWgt5ucDdEV8d6hqLIeI8J8zYYKccFeys/qZjtCbkUt9pwrkXOyEsK7Lt+vb9KhoUlvMgOBrMtQhXMilxUURERE1AfqtICb0XVR1WCuXZIYZi2Ya+HkGQB4BSmzIbrSUA1UFQChowBnM21HDdvubjtCRbbSFsonDAhLhDUxoCDqS+J/9PV3AVWFgModuPJvwORVAGcpXBQvNxcZQIglPuhvPV2IjcfysTutGGcKKuV6cUsqRkf5ybBi8ZgIRAcoBx6IiIjIvlTWNclLMRjbHNHqqUBTd4nbUM4W07Y8rjONOj0e+N9PMnR47KqEbisoXt+Rjle3pbVe19fXdPl46nuJkUqoJKpxNTWNMuQiIiIi6jUvjwLcjd4jXv4oMPcx80GGIAZjm+Puq8yn6Iol2xDqu6kc1TUCX9wN6OqBK/5qPuzoRwwoiPqC+B99xzPAnpdFfycgJAFY8Z7VE8mBwNfDFdePj5ZLfMj87mQBNh7Pk/2qxZlxYj377RlMGOwvAw0RVoSpPay920RERGRn9Ppm/HHtcTmr4qYpg7BsQvczw+6dG49Vs+JMhidGv9LHO0pd8vdyw6BAT2SX1eJkvgYz4oP5jBEREVHveeAUYDwk24rDpi2i1wNf/kZpJzXhNmDsz2FtDCiIelt5JrD2DiD3kHJ94u3Alc8Cbjyjv7eJM+BunDxIrtKqenybUiDbQCVnluHIhQq5/t+mU5gSG4glYyNxVVI4gn1s/B8KIiIiB2eoejBUQbRXVd/UaWWE5dtQWi+pO9lOc3MzHl1/HF8czcX146Pwt+tGW7TvYhi38UDu5gaerW8LkiL9lIAiV8uAgoiIiHqXh1pZljyuq+qG+srOKyMuZhtCZ9sRM3I33gcc/x8w5mfAEts4k4YBBVFvSlkHbPy98ofC3Q9Y+iqQeD2f434Q5OOOW6bFyFWorcM3J/JlWCFCCjEAU6z/++okZsQHyTZQYraFOKOOiIiIbEtsy+yJjNJqjI5W5gcYiOrJsuoGTIwJ6LY9ZKivO7LLa6DTN3dozZRRorReMje/SlROPLLuOD4/nIOlYyPx4g1j4dxNayey/TkU4kSWlDzOoSAiIiIrCTSaDxE53vS+2nKgphQYNLXrbbh5Az7hQHkWoNd1bM1U2sWcC1E58dV9wE8fA0krgOveAJydYQtsYy+I7J0YUvPlb4G1K5VwQvxBuWcPwwkrES2dfjUzDuvvnYm9j87Dn65OwJhoP3mAYndaCR5ZdwKT/7YVK/99EOuP5HCAJRERkQ2ZOiRIXu5OLe5w3w9pym1T4wIt2k5Ngw6HMss6bifV/HaMwwlxQsPLPxvX7dwJsp85FByUTURERFYTO1O5TN/R8b707cplzEzLttNYDVzYb2Y721q2c1nn4UTiMmDZv6w+d8IYAwqiS5V/HHjrcuDoRwCcgNkPA7d/A/gP5nNrA6L8PXHX7Hh89dvLsPOhOXj4yhFICPdFo64Z288U4cHPjmHi01tx90eHZMVFTUPXwzKJiIiob82MD8LgQC98eSwPJ43OeBetnf6xPQ0uzk5YMbFtHoSoqDhXVCUvjYm5EcJLW1LlkGuDvedKZNAxJS4QQ0J8TGdOtIQTi0dH4BWGEwOqgkLIKKmW/x0RERER9bu4OUBALHDic+VYonFbpl0vAM4uwLhftN1eXQoUpyqXxkQreWH700CT0fvf8zuBc9uUkCN4aLtw4rdKODHqOmDZ2zYVTghOzaLBKlmdGKDn5+cHjUYDtfFgFbJd4n+d5H8BWx4HdA2Ab4SSQMbNtvaekQXOFVVi47F8fH08D+nF1a23e7qqMH9kqBywPWdECDxcbeuPNhER2R++z7t4+9JLcNt7yXBTOWPpuEj4uLtg88kCOUfgoYXD8dt5w1of+/L3qXh1Wxrunz8MD1wx3GQ7j647jk8PZmNYqA/mJYSiuKoeXx/Ph7uLM9bfMwPDwnw7bMfbTSUrMc1VTixMDENipGnbKb729mH6s9uQr6nD57+ejsmx3VfgEBEREfX6e/yMH4CPlimDtJOWA+6+wOmNQEUWMO9x5aRngx3PArtWA5c/Csx9zHQ7ohriyIdASAIwbCFQXQykrAdcPIA7tgChCR234+YDTP21EoS0l7AYiBhjtRecMyiIekKkl2Lifeq3yvXhVwHXvgZ4Ky0JyPYNDfXFA1f44vcLhuF0fqUMKsQBiwtlNfJSLHEwZOGoMCwZG4HLhobAzYVFZ0RERP1hRnwwPv/1DBkaiH+TG3V6DA/zxR+uGIHrxkdZvJ1nrh8tKyf/m3wB7+/LlOHDgpGheGjhCJPqCSGnvFZeVjfo8M8d58xuLzrA86ICCrId4nUTAUVKroYBBREREVmHOKl55XfAzmeAkxuUE55DE5RwYsyNlm9nyatAWBJw6H3gwFvKbIoRi4B5T5hWTwgVF5TLhipg94vmtye6wFgxoGAFhY3gmXV2JGM3sP5OoDIfULkBC58GptwFOLE/sb0TBWXHczQyrNh0PB95mrrW+/w8XbEoMVyGFdOHBMFF1XlYIWZdJGeUoaiyDqG+HrKFBPtXExE5Lr7Pc1x87W3HK1tT8crWNCyfEI2Xbhxr7d0hIiIiO8f3eb2HFRRE5uh1QNY+oKoQ8AkDYmYoLZ1ESdQPIm1sBoKGASves2rCSL3LyckJYwf5y/XYVSNxNLtctoHadCIfxZX1+N+hbLmCvN1w1ehw2QZKtAgwDh82p+TjqY2n5Bl6BhF+HnjymlFYlBTBl4yIiIjICpJaKl9EBQURERER2Q5WUNgIpm425NRXwOZHAG1e220ipHBXA6VpyvXxtwBXPa+UUNGAZ6iI2Hg8D5tTCkyGcIb6umPxmAgZVhRp63Dvf46I+MqEIb5445YJDCmIiBwQ3+c5Lr72tqNAU4dpz26DOK/k5FOL4OnGOWNERETUc3yf13tYQUHUPpz47FalQsKYqKQQy8UTuPafwOgVfN4ciKiQmB4fJNdTSxPxY3opNh7Lw3cnC1BUWY/392bKJT7wtg8nhOaWkEJUVlwxKpztnoiIiIj6WZjaHcE+7iipqseZAi3GDw7ga0BERERkAzjxlci4rZOonDB7iLmFhxpIvJ7PmQNzVTlj9vAQvHDDWBx6/Aq8e9skXDcuEh4uztB38Z+OuEu0fRKVGERERETU/608k6LU8uuUPC2ffiIiIiIbwYCCyEDMnDBu62SOqKIQjyMC4ObijPkjw/DKz8fj6euTLHpOnv32FNZ8n4pvT+Qjo6Rato8iIiIiov6bQ3GScyiIiIiIbAZbPBEZhw+9+ThyKFH+XhY97niOVi4DD1dnjAjzRUK4GiPCfZEQoXwd6O3Wh3tLRERE5HgMFRQnGFAQERER2QwGFEQGrpYdYJYDs4namRIXiAg/DzmA0VxNhJhBIUKHe+bEI7WwEmcLKnG2sBJ1jXocy9HIZUwM306IUCNBhBZyqREf6g13Fw50JCIiIuqJxJYKCvFerL5Jx/dVRERERDaAAQWRINo2bfpDN8+FE6COBGJm8Dkjs4O0n7xmFO75+IgMI4xDCnFd+Nv1SViUFNF6u2jvlFVaLcOK0wWVOJOvlaFFVmmNHL5dVFmMH1KL2/5gOzthSIh3a7XFyJZqCxGMiL7KRERERNS56ABP+Hm6QlPbiLTCKiRFKYEFEREREVkPAwpybGIw9g8vALueA5r1SnWEbOHUySHmRasBZ57BTuaJ8OGNWybgqY2n5EBsg3A/DxleGIcThlBjSIiPXFeNbruvur5JBhVnW0ILEV6Ir8WH6dTCKrlwrG07ag8XGVSI9lCyTVRLgOHjzj/xRERERK3v6J2cMDrKD3vOlSAlV8OAgoiIiMgG8OgVOS5NDrD+LiBrr3J97M3A1c8D6TuAzY+YDswWlRMinBi11Gq7S/ZBhBBXjApHckYZiirrEOrrIds/iTDCUt7uLpgwOEAug+bmZhRo63AmvxJnRHBRoJVfpxdXQVvXhOTMMrmMDQr0lGHFyHARXCgBRmyQ90XtCxEREdFAkhilVgKKPNP2mkRERERkHQwoyDGd/hr48jdAXQXg5gMseRkYc6NynwghEhYrbZ9ENYWoqhBtnVg5QRYSAcD0+KBeP+Mvws9TrrkJoa23NzTpZUghAwtZcaGEF4XaemSX1cr1/am2we7uLs4YLody+5rMuAjycefrS0RERANeUsscipRcrbV3hYiIiIgYUJDDaawFtjwOHHxHuR45Hlj+LhAUb/o4EUbEzbLKLhJdDDcXZ4yMUMtlrLy6QQYWZ1uCC9EmKrWgErWNOpzI1chlLNjHvWWmRUu1Rbgvhob6wMOVLc2IiIho4DDMnTidr0WTTg8XlbO1d4mIiIjIobGCghxH0Rlg7Uqg6KRyfcZ9wLwnABc3a+8ZUa8L8HaTVRzGlRx6fTMulNV0qLbIKqtBSVU9dqeJVWI6IyPYu2UgtxJaiK+j/D05lJuIiIjsUkygl5zTVVXfhHPFVbIdJhERERFZDwMKGviam4HD/wY2PwY01QLeIcD1bwJDF1h7z4j6lbOzE2KDveUyHthd09AkB2+Lgdyt8y0KKlFR04i0oiq5vj6e3/p4X3cXZRi3rLhoCy58PVz5ihIREZHNvx9KjFTjQEaZbPPEgIKIiIjIuhhQ0MBWWw5svB849aVyPX4ecN2bgG+YtfeMyGZ4ublg3CB/uYyHchdV1sv2B0qrqEr5tZh3UVnfhENZ5XIZiw4QQ7lbQouWdlFiKDdbJxAREZGttXlSAgoNVkyMtvbuEBERETk0BhQ0cF04AKy7A9BkA84uwPwngOn3idOmrL1nRDZPDOUOU3vINWeE6VDujJJqWWVxOr9txkW+pg455bVybT1dZDIjY3iYD0aEiTkZSnghqi1CfDmUm4iIiKwjKUpp63Qyz3QmFxERERH1PwYUNPDodcCeNcCOZ4FmHRAQC6x4D4iaaO09I7J7InAQAYNY145ru11T09g226KgreqipkEn2yeIZSzYx601rEhomXHBodxERETUH5IilUHZJ/O00Omb5dwtIiIiIrIOBhQ0sGjzgPV3AZm7leujbwAWrwE8OPyOqC/5ebli6pAguYyHcmeX15gM5BahRUZpNUqqGrDnXIlcBuLYQFywNxLEQO4wMeNCmW8hWkeJig4iIiKi3jAkxAeerip5IoWoDBUnSRARERGRddh1QHHgfCm2ni7EiVwNTuZqZV/05ROi8dKNYy9qOy9/n4pXt6V1+ZgbJ0Xj+RWm2y2qrMNL36Vi+9kiaGobEeXvievGReGeOfHyLGPqZ2e/BTbcC9SWAa7ewOIXgbE3iV41fCmIrDSEMibIW64rE8Nbb69t0CGtSAktTreEFmK+RXlNI9KLq+XahLah3D6GodyGFaFUXqg5lJuIiIh6QFRMjIpU43BWuWzzxICCiIiIyHrsOqD47FAO1h3JkWe/RPp7oLK4qUfbmWZ0xm97/zuYjQJtHWYPD+kQTlz/2j7kaWqxcFQY4oJ9cCizDC9vTcWRC+V4//bJ8uAc9YPGOmDrk8CBN5Xr4WOAFe8DwUP59BPZIE83FcZE+8tlPJS7uLK+rUWUrLioxLmiKlTVN8kDCGIZE6GwCCxkeBGhxshwX1mBwaHcRERE1J2kloBCDMq+dlwUnzAiIiIiK7HrgOK2GTG4+/IhiA/xwbGcCix7fV+PtjM9Pkiu9sTBstd2nEOAlysWjmo7+1dY/e0Z5FbU4v9dl4RfTotpPcD20OfHZWiy9kgObpw0qIe/GVmsOBVYtxIoOKFcn/YbYMGTgAsH8BLZE9HCKVTtIZdxINyoMwzlFhUXbbMtxN9fw9p2xmgot8pZngWZIAdyK0O5xdchPu5sE0VEREStEqOUORTt52QRERERUf+y64DC+OzbvrD2cA6a9M24fny0ScsmcTbv18fzMTjQC7dMHWxygO2RRSPwxdEcfJp8gQFFX2puBo5+DHz7R6CxBvAKAq57Exi+sE9/LBH1L1eVM4aH+cq1dGxk6+2irZ4IKs4WaHG6JbwQ16sbdDiVr5XLWKC3W1tgISsufDEs1FdWcxAREZHjDspOydPIE80474qIiIjIOuw6oOhrnx/Klpc/n2JaCXEkqxwNTXpcNiy4wxtZcfbviHA1fsquQF2jDh6uPPjV6+o0wNcPACnrlOtxs4Hr/wWoI3r/ZxGRTfLzdMWUuEC5jIdyi4qK0y1hhai2EDMuMkuqUVbdgH3ppXIZiC58sUFiKLcSXIhWUSPD1XIoN1v0ERERDWzDwnzkSWiVdU24UFYjZ2YRERERUf9jQNGJ5IwynC+pxvjB/vLMXWOZpdXyMq6TN7FxwV7yAFl2WQ2Gtfteg/omnQw5DCrrGnv6GjqWnEPA2pVARRbgpALmPQ7MvB9wZhBE5OhEqDAo0EuuhUZDuUVYnFZY1TqQ2y1mOvEAADTuSURBVDDjorS6Qf6dF+ubEwWtj/d2U2F4S7XFyAhfjAhTvvbzcrXSb0ZERER9UaUp5lcdy9HINk8MKIiIiIisgwFFF8OxhZ9P7jhHQpxlI/h6mH/6fNyV27UtjzPn9R3peHVbWut1fX3NxbxujkevB/a+Auz4G6BvAvwHA8vfAwZNtvaeEZGNE5Vso6P95DKmDOVuG8gtvhZBhmgTdfRChVzGIvw8WtpDtbSJCldjSIi3PMBBRERE9jmHQgYUeRosHsNqbCIiIiKHDCjG/3ULymssrx745M5pZgda9yZRzfDNiXx5Fu2SMW09z3vTvXPjsWpWXOt1rVaL6Ff65EfZv8oC4Iu7gfM7leuJy4BrXgE8TA82EhFdjBBfd4T4hmDWsLah3E06vaySO52vDOMWoYX4WrSOytfUybXjbHHr411VThgaahjI3RZehPr2fCi3Tt8sq/iKKusQ6ush21ipRD8qInI8uYeBHc8COcmArhEISQCm3QuMueHiTvI4+A5w+N9AWTrg5g3EzgLmPwEExXf//XteAbY+qXx9x1bzJ4fUaYGdq4HTXwFVhYBPGDByKTDnUcBDfRG/MJGV5lDkavjUExERETlqQCGGnlbV6y7qgFJf++pYHmobdfjZpEHwbqmGMGaonDBUUrQnhmgL6k4qLAR3F5VcBs0NbB1iVtr3wBe/BmpKAFcv4KrngfG3iInkF/uyEhF1y0XlLAMHsa4Z23a7tq4RqXKmhTKY21B1If7ei5Z+YhkL8HKVMy1a20SFqzE8zAdebl3/s7s5JR9PbTwlgxDjyo0nrxmFRUk8s5PIoWTsBj5eBqjcgKRlgLsaOL0RWL9KaXU5+yHLtvP174EjHyjhxpS7gOpiIGU9kL4DuGMLEJrQ+fcWnwV2PAO4egONSovTDhqqgX9fDRScAIbMBZKWA4UpwP7XgMwfgJXfKaEIkQ1KilK3BhQclE1ERETkoAHFU9cmwdZ81tLe6WfthmMbiKGqQkbLLIr2Mkpq5PBV0QedeqipHtj6lPLhVghLAla8B4SM4FNKRP1O7eGKSbGBchmIAxmiskIJK7QtbaIqcb64SlYG7j9fJpeByFXFvx9ypkXLYG5RbTE40EvOzxDhxD0fH0Fzu59doKmTt79xywSGFESOQtcEfHWf+MsB/OobIKIlMRUVCe9cAex8Fki8vvsKiIwflHBi8Azg1g2AS8uJPmN/Dnx4HbDpQWX75uh1ykki4UlA0FDg+P/MP27vq0o4IWaCXfHXtttFsLHrOeX+uX/q0dNA1NfErEEXZyf573aepg5R/p580omIiIgcLaCwNeIgk+hDKs50nTA4wOxjxOBsNxdn7Ekr6XCmTZG2Tp5dO26Qv+x7Tj1Qcg5YtxLIP6Zcn3K38oHX1YNPJxHZDPG3PzrAS64Fo8JMhnKfK6pSAot8Lc4WVso2USVV9cgoqZZr88m2odxebioMC/VBamFVh3BCELeJf2VEZcUVo8LZ7onIEWTsAsozgHG3tIUTgrsvcPnDwNqVwNGPgQUtrZc6c/gD5XLe423hhDBkDjB0PnBuq/K+K3hox+/d87JSCXH3D8Dev5vffnMzcORDwM0HuPwR0/suexA48BZw5CNgzmOsfiWbJD6viZDiVL5WVlEwoCAiIiLqfw4VUNQ26OTZrp5uqk7ffBqGY984yXz1hODr4YolYyKw/kguPj5wAb+cFiNvF2HFc5vPQt8M/HzK4D76LQa4nz4BNv1BaSPgGQhc9zow4ipr7xUR0UUd7EiK8pPLmAgoxFwL0Q5KhBfi69TCStQ06GQw3hURUoi2T+/uOY+Fo8IR6e8pg3IiGqAy9yiX8XM73hc/T7nM2mvZdkR7psHTzGynJaDI2tMxoCg8pVQ/zH4YCB3Z+fZL04HKfGVb7ds4iRNLYmYCZzcBZectm3dBZKU2TyKgOJmrwZWJ4XwNiIiIiPqZXQcUBzPL8GmyEiiUVdfLy0NZZfjDZ8qZ9/Gh3rh3TtsHrp+yK3DT2/sxNS4Q/7t7eoftNTTpseFoLtxUzlg2IbrLn/3oogTsTy/FE1+mYG9aCeJCvHEwowyHssoxe3gIVnTz/dROfaUSTBjaB8RcBix/G1D3zZByIqL+FuzjjuCh7pg5NLjdUO4a/Gd/Ft7fl9ntNp755oxconAvXO2BQaKCI9BTVnEMCvCUrQXFEvdxsDaRDRLDpN2MrouqBuPKBgMxzFowd1DfMwDwClLCga6I2RBVBUDoKMDZTFWvYdvttyPaS224BwgeAVz2QNc/o6v9lLcPafsZDCjIRokTCj47lIOUPNN5UkRERETUP+w6oMgsqca6Izkmt2WV1sgliCDCOKDozpZTBbL/6OIxEQj0Nv702FGo2gMbfjMTL245i+1nirH9TBEi/T3wwILh+PWcIbKfOFko94jSqkC0MnBSKW0AZj1o/sM0EdGAG8rtg4WJ4RYFFFH+HiitbkBdo15WVIiVbObbRD9tUWUxKNBTCTFawgtDkBHi627SnpCI+snLowB3o//3Ln8UmPuY+SBDEIOxzRGtnrR5Xf8sS7Yh1Lc7KLv7JaW106ptgMr1En+G2vzPILIhiZFKxeOJ3K6rGYmIiIiob9h1QHHDpEFyWWp6fBAyVy/u9P4lYyLlspQIKZ5fYdQXmC6OXg/8+E9g21OAvgnwGwQsf8d8GwIiogFsSlwgIvw85EBsc3MoxOHMcD8P/PDHeRD5twgpsstqkF1eKy9zymuRU14jvxatDBt1zbhQViMXUNphe+4uzkahhRJiyOqLljDD38uVAQZRX3jgFKA2OphvrnrCmsSw6x9eAGbcB0SOs/beEPWLURFq+W9rcWW9nCcoPuMRERERUf+x64CC7FhVEfDFr4H0bcr1kUuBpX9X2hYQETkY0Y7pyWtG4Z6Pj8gwwjikMJxrLe43tG2S7aJ83DF+cMe/mTp9M4oq65Bd1hZeZLeEF+LrfE0t6pv0SC+ulsscH3cX8wFGSzspcT8R9YCHWlmWPK6rygPRGrOzqoWL2YZgvJ0v7gEC45RqVkt0+zO6qbAgsgFiPqGoZkwtrEJKngbzGFAQERER9SseYaD+d26bEk5UFwEuHsCi1cDE2yGbqhMROahFSRF445YJeGrjKdm6yUBUTohwQtxvCRFiRPh5yiUqM9pr1OmRX1FnEloYvhYVGeIM0qr6JjnIWyxzArxcTcKLaFl9oYQX4jYxKJyILkGg0XyIyPGm99WWAzWlwKCpXW9DDK32CQfKswC9rmPrTMPsCePZEIUnlMunQ81v890FyuXP/gOMXGK6n+aUnu/4M4hsUFKknxJQ5GoxLyHM2rtDRERE5FAYUFD/aWoAtv8/YN/fletiaOOK94DQkXwViIhaQoorRoUjOaNMVkGE+nrIkKE3B167qpwxOMhLLnPqGnWtoUWOSYChXFbUNMp5TeU1GhzPMd+vO9TXvV31hWEWhhci/D3kPhBRF2JnAnvWAOk7gNErTO9L365cxsy0bDsp64AL+5WvTbbTUsUac1nbbeN/aX47WfuUgdgjrlYGdPsPbgsefCOA7APKUG4Rihg01gFZe5X7A1uGZRPZqMQoP6w/mosUzqEgIiIi6ncMKKh/lJ0H1t4B5B1Rrk9eBSx8GnD15CtARGREhBFiZpK1iOoH0epCLHMq6xqV0KKl4kKZfdE2A6O6QYci0ce7sh6Hs8o7fL/IWkR1h6GFlCHAkAO8Az0R5usB514MZIjsUtwcICAWOPE5MPVuIGJMW1umXS8Azi7AuF+0Pb66VKmqEOGBt9HfD1GhKgKK7U8Dt34JuLgpt5/fqVS0ipAjeGjb46/9p/n9Ea2fREBx2YPAoMltt4vq1wm3ArueU9YVf227TwQsdRXK/rNKlmxcUqTShuxkHge6ExEREfU3BhTU945/Dnz9ANBQCXj4Kx9+R17DZ56IyA75erhiZIRYHXvKNzc3ywqLbOPQwqj6QgQbDU16OchbrAMZZR224aZyRqS/R0sFhlF40RJoBHm7cYA3DXwqF2DpP4CPlgHvXwUkLQfcfYHTG4GKLGDe46bBQvK/gF2rgcsfBeYazY+Im60ECEc+BN6aBQxbCFQXAynrlbkQi9dc+r7OvB84+w2w91Ug/7gyXLsgBTj3PRA+WrmfyMaNagkoxL9NZdUNCPRuCfOIiIiIqM8xoKC+U18FfPMwcOy/yvXB04FlbwP+g/isExENQE5OTgjwdpNrTLR/h/v1+maUVNWbBhhG4UVeRS0adHpkltbIZY6nq8qo+qJtkLcSZnjBz9O1H35Ton4gwoWV3wE7nwFObgB0DUBoghJOjLnR8u0seRUISwIOvQ8ceEtpwzRiETDvCdOQo6fE9m7fBOx8Djj1JZC5B/AJA6b9BpjziGnbJyIbDt+HBHvjfEm1bPM0e3iItXeJiIiIyGE4NYvTHcnqtFot/Pz8oNFooFZ3PCvV7uT9BKxdqbQDcHIGZv8RmP2wckYgERGRGU06PQq0dUbVF7VyDoYhwBD3dfeuRe3h0lp5ocy9aAkzWoIMLzf+O0T9b8C9zyOL8bW3H/d9chQbj+Xhj4tG4N45vRDeERER0YDG93m9h5/SqXeJI0f73wC+fwLQNwLqKKVqov1gRiIiovZvSlTOMlwQC+g4h6O+SYe8ChFgGA/vVr4WgUZJVQO0dU04la+VyxzRIiq6XfWFMgfDS7aWcndR8XUhInLQORQioDiZyzkURERERP2JAQX1nuoSYMM9QNoW5fqIxcq8Ca9APstERHTJRHgQF+wtlzk1DU2tYYVsHWVUfSG+FuFFaXWDXMeyKzp8v5jjK4Z0G1dfKGGGUpERrvaQIQoREQ08SVF+8jIlT2PtXSEiIqKLoNM3IzmjDEWVdQj19cCUuEConJ34HNoRBhTUO87vBNbfDVQVACp34Mq/AZNXKUd7iIiI+oFo3zQ8zFcuczS1jSYVF0qA0RZo1DbqZBspsQ5mlnf4fhdnJ0SIAd5yaLdx+ygl0Aj2cYcz3wgT9RoRJL68NRVHssrRqGvG8DAfrLwsDteOi7J4G2L2zUf7s/BJ8gVklFTD290F04cE4aErR5gNO784moPkjHI5h+BsQaWci/PCijG4YRJnqA10iS2DsrNKa+S/F5xpREREZPs2p+TjqY2nkK+pa70tws8DT14zCouSImCTcg8DO54FcpIBXSMQkgBMuxcYc4Pl29DrgYPvAIf/rbTXF3PfYmcB858AguI7Pv7Y/4AL+5SW/EWnlPl2174OjP8FbAEDCro04n+kHc8Ae14W/Z2A4BHADe8DYYl8ZomIyKaIg01+UX6tZ8kaEyO5RGWFodrCeJC3uC23XBngrVRm1AIo7bANdxdnRLW2jGoZ3G30dYCXqxwkTkTd+zG9FLe9lwxXlROuGRsJXw8XbD5ZgPs//Un+P/mbuZbNCPjzhhP4JDkbw0J9cPuMWBRX1ePr4/n4Ia0Y6++ZgWHtAs0Xv0tFbkUtAr3dEOLrLr8mx+Dv5SaDZ/Hf18k8DWbEB1t7l4iIiKibcOKej4+Io5EmCjR18vY3bplgeyFFxm7g42WAyg1IWga4q4HTG4H1q4CKLGD2Q5Zt5+vfA0c+UMKNKXcB1cVAynogfQdwxxYgNMH08dufBjQXAK8gwCdc+dqGMKCgnivPBNatAnIOKtcn3g5c+SzgJnqHExER2Q8RHIgKCLHGDfI3exZ2YWVdW4AhggoZXihf52tqUd+kx/niarnM8XZTtcy9MKq+MJqF4evh2g+/KZHta9Lp8ej644AT8L+7p7eGivcvGI5lr+/Fy9+n4urREZ22ezPYl14iw4kpsYH4aNWU1hkzyydE45Z3D+DPG1Lw2d3TTb7nueVjEBus/H/6+s5zeH7z2T78TcnWjI7yUwKKXC0DCiIiIisTJ5E16ZtlC6dGnV5eiutNumY5n/AvG052CCfk94nPd4CsrLhiVLjttHvSNQFf3afs3a++ASLGKrfPeRR45wpg57NA4vXmKyCMZfyghBODZwC3bgBc3JXbx/4c+PA6YNODyvaNLf27sl3/wcDuNcC2p2BLGFBQz6SsAzb+HqjXAu5+wNJXlf+JiIiIBiDRuinCz1OuybEdZyuJN8ziTJ321ReihZS4raiyHtUNOpwpqJTLHH8v13bVF4YZGMp1D1cO8CbHsC+9VLbZuWFitEnFk4+7C+6bNwz3fXIUnx/Kxh8XtTszrJ1Pk7Pl5R8WDm8NJ4SZQ4Mxe1gIdqUW43xxFYaE+LTed9kwnjXvyMR/b9+mFHAOBdEAxT71NFAO2uubgSa9Xh6oNxzAFyd4GA7ei/uUg/ot9+lN75PfY/x162ONH9e2TePtGz+2sXU75rdrfl+UbcnbxH2t3298u/JYsXr8PAGy7ZOYTTE9Pgg2IWMXUJ4BjLulLZwQ3H2Byx8G1q4Ejn4MLHiy6+0c/kC5nPd4WzghDJkDDJ0PnNsKlJwDgo0qjuPnwpYxoKCL01ANfPsIcPQj5fqgqcDyd5QEjoiIyEG5qpxb5lGYryKsa9TJVjGtcy9aZmEoYUYNymsaUSGXBidyzQ9oFe1mDGGFYe6Fofoi0t9T7gPRQLD/vNJCbdbwkA73iWBBOJBRZtF2vNxUmGQmVJw9XAkoxHaMAwpybIY5FGIGCRENLHbZp556dLZ9pwfojQ+kt7u97aC4vsOBdPMBQOePNfzstp9n+ljTSgDTqgDjA/vGwYI4iN9o9Ds5OjEbUHTOteS5EIOzbUbmns7Dgvh5ymXWXsu24+oNDJ5mZjstAUXWHtOAwsYxoCDLFZxQ0rySVKUcSfRFu/xRQMX/jIiIiLoiqh/iQ3zkMqeqvqm1XVR2u/BCfC3uL66sl+vIhYoO3y+qlkV1R3QnAUaY2sN2SpuJupFZqrRJiwvq2MLJz8tVzofILDHfSs2gpqFJVi6NCPM1+99+XLASJna3nYsl2g00NOlbr1fWNfbq9qlvJUYqFTvnS6rl311RtUNE9s8u+9T38tn2F3NQ3HAgvON9nR+gb99+p/WxZs7ab92+8RnzZoKEDtttOdvecHZ9WyCgfH0JJ9sPCOLtjovKWR68F+99xMlL4lJcd1GJy7b72l83PFbM/lK+x1k+pu37lcca3+4qv1e5bv5nGm+/m8ca3Se+VqkM22/3M1XO8vcU7XnFvLKb3t7f7fMS6uvR909+nRZwM7ouqhqMKxsMxDBrwVwLJ88AZT5EactjujpxvKoACB0FOJupsDdsu7vt2Bi+46LuNTcDyW8DWx4HdPWAbwSw7F9A3Gw+e0RERL1AHARLCFfL1fGf4WZoahtb516YCzDE/AtRoSGWuTPLxYeCKP+28EK5bJmFEeCFYB83DvAmm1FZ1yQvxWDszv5/EQeVLm0byswXbcvjesvrO9Lx6ra01uv6+ppe3T71LVGpFq72QIG2DqfztWZb+pHl2E6HbOW/Q1E50VWf+v/76iQmxQTK66YHxTsekDc+K9605U3HA/SdnYlv2tbGfPubDtu1oG1P+9CBZ9sr2h90bz1I3/6geCcH6Ls+6G50oF8eVO/42Lafaf4AfevBe6MwofV6J0GCyX3i8U5OsiWtI5kSFyiroMR7QnP/f4tnI9zPQz6uz708CnA3ev7FydxzHzMfZAhiMLY57r6ANq/rn2XJNgTRkt+OMKCgrtWUAV/+BjjbMlxl+CLg2tcBbxvp30ZERDTAiTOE/L3c5Bod3daP3zjAKK6qb5170TrIu6UiI6+iVn6QzSytkcscD1fn1rkXyvBu40HeXvKsdSLq3r1z47FqVlzrda1Wi+hX+MzZ2xwKEVCINk8MKHqO7XRsf+Cs4dJwoNtwEFxv/BijA+Hydp257zXdRsfbm6E3anNj/L0dv18EBEYtdVrOuNc1Gz9W3+l+tj2uLQAQbTa7CqPFgc0CbT0m/W0rHIk4yN129rpyML31rHijA+1dHRRvOxvf6AC6ydnvxgFAu+vmzq43OphvOIu+41n7LfvS7j6z+2J0tj0NPOI1Fi3aRBWUeIWNQwrDKy7u75cq8gdOAWqjwMBc9QR1iQEFdS5jN7D+LqAyD1C5AQufBqbcJf6681kjIiKyEeJDlyhdFmtiTECH+8UHdXGwTYYWRtUXOS0VGeK+ukY9zhVVyWWOOAvdNMAwrcLwZhsU6kWGqgdDFUR7ovVOZ5URlm9Dab2k7mY7F0sM4zYeyN3cwHDP3iRFqbH1dCFScu3rzENbYmvtdNoOjrccaNcrw2V7cqBdZ+4gevsD6C1nv7ceQDdqV9P6OKMWOF0f+Dc9WK83uz9dBQKmv4+jt8CxhOGseMOB9q7Orm9/ULzTg+5GB/a7bH9jfOC9m1Y5re1vjLdrdLa98e0dqgIc8Gx7GpjEvyXi35T282XC+3u+jIdaWZY8rqvqhvrKzisjLmYbQnfbsTEMKKgjXROw6znghxeUDDJoGLDiPSBiDJ8tIiIiOyM+kIr2TmJNG9KxAlL0yxdVFoaKCzkLo6UKQ4QZJVX18iCvaHciljlB3m7K/IsO1ReeiArwNDlgS9Sd2JbZExml1R2qhjQ1jSirbjAbxhnzcnNBqK+7/O9aHJRrf/ZcRolSTRQb3HHOBTm2pJY5FCfzOCjb0jPyRZtB0YpQW9uI8poGPLb+RKftdIQHPzuG7WeK5MHyLg+0d3LmfMcD/c1GB/rbBQ/NzbJjMXVN/IlsO6O+7cC44Tbj6+J+Z5Pr5h7XdkDd5HtVTnB2Mj2b3nB/6+1G7XBUYr+MDs533KZyX1f7czJXiz+uO97tfwL/WTUVM4cG8z8VIjsjQogrRoUjOaNMDsQWJ22Jtk42OX8v0Gg+ROR40/tqy4GaUmDQ1K634eYN+IQD5VmAXtdxDkVpF3MubBgDCjJVcQFYdyeQ3TJoZvwtwFXPK/8DEBER0YDj5uIsD9J2dqC2tkHXEloYtY9qqb4Q18VBqdLqBrmO5Zg/oBemdm8d2m2ovohuGeQteseKMwOJDKYOCcLrO9OxO7UYS8dGmjwxP6QVK4+xoJ+w2M7GY3k4lFkmvzbZTqrl2yHHa/EkpBVVydYwHq4DP2AVw921tU3Q1ikhg2iHIy5l6CBvM71P3F5pdF+DKCm4CDUNOnx2KAfWJg9gOxkd1FaZO1DubPq49gfUjc58b729w8FysQ10c+C/+wPtJt/fcha88YF/5YB+V/tp9PsYb3+An00v5nu9vDW12z715k7iICL7IP6WTY+3g/+HY2cCe9YA6TuA0StM70vfrlzGzLRsOynrgAv7la9NtrOtZTuXwZ4woHBUImXL2gdUFQI+YUDMDODM18BX9wF1GqUUaMnLHf+HISIiIofi6abCsDBfucwRB6gMoYXxDAxDKylxIKpQWy/Xoaxysx8oREihBBhtg7wNgUaIj3uvHTjhwFb7MDM+CIMDvfDlsTzcPjMWiS1ntIvWTv/YniYPqq2YGN36eFFRIVagt5tcBjdNGSQDipe2pOLjVVNlGCfsPVcigw5xdt2QEB8r/IZky0SgGuTtitLqRvzrh3RMjg2y3TMxW4iqAUOo0D5Q0HR6W9vjRZu/SyWeHrWnqzzYLQLr7iwZEyHDoI4H0NsO6Hd94L/jgXZLDugbP4596R2DTfWpJyLHFjcHCIgFTnwOTL27rVNNfSWw6wXA2QUY94u2x1eXKlUVXkGms4An3q4EFNufBm79EnBpef97fidwbpsScgQPhT1xahY1mWR1YoCen58fNBoN1MaDVfrCqa+AzY+YToZ39QIaWwZnRk0Clr8DBLYN+CMiIiK6WOJtpjhwLFpGySqMlsoLEWDkyttquz3zVhxUjvY3tI/qGGAEeLladJDJmgNb+/V93gCxL70Et72XDDeVM5aOi4SPuws2nyyQ/w09tHA4fjtvWOtjX/4+Fa9uS8P984fhgSuGm2zn0XXH8enBbAwL9cG8hFA5UP7r4/lwd3HG+ntmdAjePk2+gIOZSpB2tlAr5xBMiglATEvbqYWJYbgyMdzi34Ovvf0Rfyvu//Qn2baov/5WiHkBlfVdVC20CxSUdkpt91c36HplP8TsFrWHK/w8XaH2VL4WoYP521zg5+Xaet3bTSX/Fv+YXoqb3m6pxu/CJ3dOs4+zXWnA4OB2IuptPXqfl/ED8NEyZZB20nLA3Rc4vRGoyALmPQ7MfrjtsTueBXatBi5/FJj7mOl2xAnmRz4EQhKAYQuB6mIgZT3g4gHcsQUITTB9/OEPlIoLoegkkH8MGDQNCByi3JawGBi5BNbCCgpHI8KJz25td94A2sKJhCXADf8GVBzoR0RERJdGHKwK8nGXa9wgf7MH5Yoq61vaRbUEGKIao6USQ4QJYkbG+ZJqucwRB8UMoUW0yfwLpY2UOHhmawNbqXsz4oPx+a9nyPBBBAqNOj2Gh/niD1eMwHXjoyx+Cp+5fjQSwn3x3+QLeH9fpvzvZcHIUDy0cITZ6gkRTqw7Ytp6RlT+GKp/xH9fFxNQkH3p6d8KEcaKkMAwh6G7NkntKxhEdVBvnDYo/vsWYYEMEzzaBQotoYK63X1K8OAqQ8DeOINcVJuIQKe7djricUT9ya761BPRwBU3G1j5HbDzGeDkBkDXoIQJIpwYc6Pl21nyKhCWBBx6HzjwltKaf8QiYN4T5qsnRDhx7L+mt4n2/oYW//6DrRpQsILCRvTL2VWirdMrSaaVE+2po4Dfn+g4ZIWIiIion4mD0uIglwwsjKovDO2jRNuo7ogDcqLNlBiYii4Olu15ZF6fHaTgWfSOi6+9/RAt4C57brtJlVV7nq4qXD48WFY7tK9i6ORPzEXxcHVuDRT8zAQKbcFDx9tE9YOtzPMxBD3opJ0OQ2EiIhoI+D6v97CCwpGImRNdhROCNld5XNys/torIiIiIrNcVc5KNUSgFxDf8X4xwDa3QmkV1Vp50TILQ7SVEu2lxFnKXREHz8QBSXFGJduNEDku8Tegq3BCqG3UYfPJwk7vd1U5mYYI7UIGs22SWh4nAgZ3F9WAOVNdhBDt2+qF91NbPSIiIrIvDCgciRiI3ZuPIyIiIrIiD1cV4kN85DJHtE35+McsrN58ptttiXYPROS4LP0bcMPEaFw2LNhsmyQx24SDlxVsp0NERESWYkDhSHzCevdxRERERDZM9FQfa2b2hTmiFzUROS5L/wYsmxDNaisLibZ5rEwjIiKi7thGk0rqHzEzAHWkUffP9pyUGRTicUREREQDgGFgaxfvfuT9HNhK5Nj4t4KIiIjIOhhQOBIx+HrRcy1X2n9Mb7m+aDUHZBMREdGAOoNX9Dzv4t2PvL+vBmQTkX3g3woiIiIi62BA4WhGLQVu/BBQtxtMJiorxO3ifiIiIqIBxDCwVQxoNSaui9s5sJWI+LeCiIiIyDqcmpubm630s8mIVquFn58fNBoN1Gp13z83eh2QtU8ZiC1mToi2TqLCgoiIiGiA0umbkZxRJofhin7zoqVLf1RO9Pv7PLIZfO3tk7X+VhAREZH94Pu83sMh2Y5KhBFxs6y9F0RERET9hgNbiYh/K4iIiIhsC1s8ERERERERERERERFRv2NAQURERERERERERERE/Y4BBRERERERERERERER9TsGFERERERERERERERE1O8YUBARERERERERERERUb9jQEFERERERERERERERP3OBXbswPlSbD1diBO5GpzM1aKyvgnLJ0TjpRvHXtR2Xv4+Fa9uS+vyMTdOisbzK5Tt1jQ04buTBdh6qgin8rXIq6iFm4szRkao8Yupg3HtuKhL+r2IiIiIiIiIiIiIiAY6uw4oPjuUg3VHcuDpqkKkvwcqi5t6tJ1pQ4I6ve9/B7NRoK3D7OEhrbclZ5Thgf8dQ4CXK2YMDcaipHCUVtVjc0oB7v/0JxzJKsdT1yb1aF+IiIiIiIiIiIiIiByBXQcUt82Iwd2XD0F8iA+O5VRg2ev7erSd6fFBcrVXXFmP13ack0HEwlHhrbeH+nrg1Z+Pw9WjI+CqauuS9fCVCbjutb344McsLJsQjbGD/Hv4mxERERERERERERERDWx2PYNiTLQ/hof5QuXs1CfbX3s4B036Zlw/Plq2cDIYFamWbZyMwwkhxNcdN08dLL8+kFHaJ/tERERERERERERERDQQ2HVA0dc+P5QtL38+ZZDF3+OqUsISlTOfWiIiIiIiIiIiIiKiAdniqS+JORPnS6oxfrBSpWEJnb4Z64/kwskJuGxocJePrW/SoaFJ33q9sq7xkveZiIiIiIiIiIiIiMheMKDoYji28PPJlldPvLTlLM4UVOLGSdEYEd51qPH6jnS8ui2t9bq+vsbin0NEREREREREREREZO+sHlCM/+sWlNdYXj3wyZ3TzA607k2imuGbE/nwdlNhyZhIi77nvwcu4PWd6UiMVOPJaxK7ffy9c+OxalZc63WNRoNBrwBarfaS9p2IiIiIbIvh/V1zc7O1d4X6meE153t8IiIiooGF7/EHUEC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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from causarray import estimate_r, plot_r\n", - "# Uncomment to re-run (takes ~20-40 min on this subset):\n", - "# df_r = estimate_r(Y, X, A, [0, 1, 2, 3, 5, 8], backend='fast')\n", - "# df_r.to_csv('scarf-r.csv', index=False)\n", - "df_r = pd.read_csv('scarf-r.csv')\n", - "best_r = int(df_r.loc[df_r['JIC'].idxmin(), 'r'])\n", - "print(f'Selected r = {best_r} (minimum JIC)')\n", - "fig = plot_r(df_r)\n" - ] - }, - { - "cell_type": "markdown", - "id": "d43a9c10", - "metadata": { - "papermill": { - "duration": 0.002476, - "end_time": "2026-09-10T19:15:55.320262+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.317786+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Estimate unmeasured confounders\n", - "\n", - "We run **GCATE** with the fast NB-GLM backend to estimate the selected\n", - "number of latent factors (batch, sample-pool composition, technical\n", - "variation, etc.). The `backend='fast'` option uses a custom batch NB-GLM\n", - "solver that is substantially faster than the default statsmodels backend.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ce785b95", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:55.326332Z", - "iopub.status.busy": "2026-09-10T19:15:55.326205Z", - "iopub.status.idle": "2026-09-10T19:15:55.338294Z", - "shell.execute_reply": "2026-09-10T19:15:55.337810Z" - }, - "papermill": { - "duration": 0.016197, - "end_time": "2026-09-10T19:15:55.339050+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.322853+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading pre-computed GCATE results from scarf-gcate-results.pkl\n", - "\n", - "Step 1 -- epochs: 9, best NLL: 0.901568\n", - "Step 2 -- epochs: 9, best NLL: 0.910631\n" - ] - } - ], - "source": [ - "import pickle\n", - "\n", - "_pkl = 'scarf-gcate-results.pkl'\n", - "r = best_r\n", - "_fit_matches_r = False\n", - "if os.path.exists(_pkl):\n", - " with open(_pkl, 'rb') as _f:\n", - " _saved = pickle.load(_f)\n", - " _cached_r = _saved['res_2']['U'].shape[1]\n", - " if _cached_r == r:\n", - " print(f'Loading pre-computed GCATE results from {_pkl}')\n", - " res_1, res_2 = _saved['res_1'], _saved['res_2']\n", - " _fit_matches_r = True\n", - " else:\n", - " print(f'Ignoring cached GCATE fit with r={_cached_r}; selected r={r}.')\n", - "elif 'res_2' in vars() and res_2['U'].shape[1] == r:\n", - " _fit_matches_r = True\n", - " print('Saving in-memory GCATE results to pkl...')\n", - " with open(_pkl, 'wb') as _f:\n", - " pickle.dump({'res_1': res_1, 'res_2': res_2}, _f)\n", - " print(f'Saved GCATE results to {_pkl}')\n", - "if not _fit_matches_r:\n", - " res_1, res_2 = fit_gcate(Y, X, A, r, backend='fast', verbose=True,\n", - " kwargs_es_1=dict(rel_tol=2e-4, max_iters=10),\n", - " kwargs_es_2=dict(rel_tol=2e-4, max_iters=10),\n", - " )\n", - " with open(_pkl, 'wb') as _f:\n", - " pickle.dump({'res_1': res_1, 'res_2': res_2}, _f)\n", - " print(f'Saved GCATE results to {_pkl}')\n", - "U = res_2['U']\n", - "print(f'\\nStep 1 -- epochs: {res_1[\"n_iter\"]}, best NLL: {min(res_1[\"hist\"]):.6f}')\n", - "print(f'Step 2 -- epochs: {res_2[\"n_iter\"]}, best NLL: {min(res_2[\"hist\"]):.6f}')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "878c5ebb", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:55.345093Z", - "iopub.status.busy": "2026-09-10T19:15:55.344984Z", - "iopub.status.idle": "2026-09-10T19:15:55.499022Z", - "shell.execute_reply": "2026-09-10T19:15:55.498419Z" - }, - "papermill": { - "duration": 0.157802, - "end_time": "2026-09-10T19:15:55.499634+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.341832+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# GCATE convergence diagnostic: plot NLL history for both optimisation steps.\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", - "for ax, res, label in zip(axes, [res_1, res_2], ['Step 1 (P1 projection)', 'Step 2 (P2 + L1)']):\n", - " hist = res['hist']\n", - " ax.plot(hist, linewidth=1.5)\n", - " ax.set_title(f'GCATE {label}\\n{len(hist)-1} epochs, best NLL = {min(hist):.5f}')\n", - " ax.set_xlabel('Epoch')\n", - " ax.set_ylabel('Penalised NLL per gene')\n", - " ax.grid(True, alpha=0.3)\n", - " if len(hist) > 1:\n", - " rel_drop = (hist[0] - min(hist)) / abs(hist[0])\n", - " ax.text(0.65, 0.95, f'Rel. drop: {rel_drop:.4f}', transform=ax.transAxes,\n", - " va='top', fontsize=9)\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "007a75bd", - "metadata": { - "papermill": { - "duration": 0.002709, - "end_time": "2026-09-10T19:15:55.505387+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.502678+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Estimate log-fold change based on counterfactuals\n", - "\n", - "We apply the doubly-robust causal estimator to obtain per-gene,\n", - "per-perturbation log-fold changes together with Welch (unequal-variance)\n", - "standard errors and Benjamini-Hochberg adjusted p-values.\n", - "\n", - "**Why this runs in batches of perturbations.** A single `LFC(...)` call\n", - "across all 58 perturbations at once internally builds a\n", - "`(n_cells, n_genes, n_perturbations, 2)` counterfactual-prediction array --\n", - "here `22,396 x 14,227 x 58 x 2 ~= 3.7e10` elements, ~296GB at float64. That\n", - "is large enough to exhaust memory even on a well-resourced workstation (it\n", - "is what killed this step the first few times this notebook was run). The\n", - "adamson/replogle tutorials don't hit this because they only have 20/29\n", - "perturbations; causarray ships a batched entry point for exactly this\n", - "regime (`causarray.gcate_lfc_batch`), but it re-fits GCATE separately per\n", - "batch on a capped cell subsample, which would both discard the single\n", - "global GCATE fit above and change the design (per-batch vs. global latent\n", - "factors, capped vs. the full 15,135-cell control pool used here). Instead\n", - "we keep the one global `U` fit above and batch only the `LFC` call itself:\n", - "10 perturbations at a time, each batch using *all* control cells plus only\n", - "that batch's perturbed cells (`~16,400` cells x `~10` arms, ~37GB peak\n", - "instead of ~296GB), with per-batch results cached to disk so a repeat\n", - "interruption loses at most one batch's progress.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "446e7c58", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:55.511521Z", - "iopub.status.busy": "2026-09-10T19:15:55.511408Z", - "iopub.status.idle": "2026-09-10T19:15:56.364801Z", - "shell.execute_reply": "2026-09-10T19:15:56.364202Z" - }, - "papermill": { - "duration": 0.857383, - "end_time": "2026-09-10T19:15:56.365474+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:55.508091+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading cached scarf-lfc.csv\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "causarray LFC: 825,166 gene x perturbation rows\n" - ] - } - ], - "source": [ - "import gc\n", - "\n", - "BATCH_DIR = 'scarf_lfc_batches'\n", - "BATCH_SIZE = 10\n", - "os.makedirs(BATCH_DIR, exist_ok=True)\n", - "\n", - "if os.path.exists('scarf-lfc.csv'):\n", - " print('Loading cached scarf-lfc.csv')\n", - " df_res = pd.read_csv('scarf-lfc.csv')\n", - "else:\n", - " offsets_full = np.log(res_2['kwargs_glm']['size_factor'])\n", - " W_full, W_A_full = np.c_[X, U], np.c_[X_A, U]\n", - " A_np, Y_np = A.to_numpy(dtype=float), Y.to_numpy()\n", - " pert_names = list(A.columns)\n", - " is_ctrl_cell = A_np.sum(axis=1) == 0\n", - "\n", - " n_batches = int(np.ceil(len(pert_names) / BATCH_SIZE))\n", - " batches = np.array_split(np.arange(len(pert_names)), n_batches)\n", - " print(f'{n_batches} batches of ~{BATCH_SIZE} perturbations each')\n", - "\n", - " for b_i, cols in enumerate(batches):\n", - " out_path = os.path.join(BATCH_DIR, f'batch_{b_i:02d}.csv')\n", - " if os.path.exists(out_path):\n", - " print(f'[batch {b_i}] already cached, skipping')\n", - " continue\n", - " batch_pert_names = [pert_names[c] for c in cols]\n", - " pert_mask_b = A_np[:, cols].sum(axis=1) > 0\n", - " cell_mask_b = is_ctrl_cell | pert_mask_b\n", - " print(f'[batch {b_i}] perts={batch_pert_names} '\n", - " f'n_cells={int(cell_mask_b.sum()):,}')\n", - "\n", - " Y_b = pd.DataFrame(Y_np[cell_mask_b], columns=Y.columns)\n", - " A_b = pd.DataFrame(A_np[np.ix_(cell_mask_b, cols)], columns=batch_pert_names)\n", - " df_b, estimation_b = LFC(\n", - " Y_b, W_full[cell_mask_b], A_b, W_A_full[cell_mask_b],\n", - " offset=offsets_full[cell_mask_b], usevar='unequal', verbose=True,\n", - " )\n", - " df_b.to_csv(out_path, index=False)\n", - " del Y_b, A_b, df_b, estimation_b\n", - " gc.collect()\n", - "\n", - " df_res = pd.concat(\n", - " [pd.read_csv(os.path.join(BATCH_DIR, f'batch_{b_i:02d}.csv')) for b_i in range(n_batches)],\n", - " ignore_index=True,\n", - " )\n", - " df_res.to_csv('scarf-lfc.csv', index=False)\n", - "print(f'causarray LFC: {len(df_res):,} gene x perturbation rows')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "24fab557", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:56.372440Z", - "iopub.status.busy": "2026-09-10T19:15:56.372312Z", - "iopub.status.idle": "2026-09-10T19:15:56.555583Z", - "shell.execute_reply": "2026-09-10T19:15:56.555046Z" - }, - "papermill": { - "duration": 0.187511, - "end_time": "2026-09-10T19:15:56.556348+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:56.368837+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total significant gene-perturbation pairs (padj < 0.05): 1,920\n" - ] - } - ], - "source": [ - "significant = df_res[df_res['padj'] < 0.05]\n", - "\n", - "discovery_counts = (\n", - " significant['trt']\n", - " .value_counts()\n", - " .rename_axis('Perturbation')\n", - " .reset_index(name='Count')\n", - ")\n", - "\n", - "plt.figure(figsize=(14, 5))\n", - "sns.barplot(data=discovery_counts, x='Perturbation', y='Count')\n", - "plt.xticks(rotation=90)\n", - "plt.title('Significant DE genes (padj < 0.05) per perturbation -- causarray')\n", - "plt.xlabel('Perturbation')\n", - "plt.ylabel('Genes discovered')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "print(f'Total significant gene-perturbation pairs (padj < 0.05): {len(significant):,}')\n" - ] - }, - { - "cell_type": "markdown", - "id": "d84db985", - "metadata": { - "papermill": { - "duration": 0.003024, - "end_time": "2026-09-10T19:15:56.562972+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:56.559948+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Propensity score diagnostics\n", - "\n", - "Positivity requires treatment and control cells with comparable covariates.\n", - "We use five-fold out-of-fold scores for diagnosis, and follow the pptx's\n", - "own v0.0.9 finding that penalised propensity models (`C=0.1`) were an\n", - "acceptable specification at the chosen *r*.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d989be81", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:15:56.570489Z", - "iopub.status.busy": "2026-09-10T19:15:56.570378Z", - "iopub.status.idle": "2026-09-10T19:16:00.881452Z", - "shell.execute_reply": "2026-09-10T19:16:00.880885Z" - }, - "papermill": { - "duration": 4.315734, - "end_time": "2026-09-10T19:16:00.882221+00:00", - "exception": false, - "start_time": "2026-09-10T19:15:56.566487+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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treatmentn_controln_treatedprevalenceoverlap_ratioaucbrier_scoreoutside_overlap_fractionclipped_fractioness_controless_treatedess_control_fractioness_treated_fractionscore_q01score_medianscore_q99
51Tpr15135910.0059770.4183720.8651620.1498350.0000000.013334.88905380.2910020.8810630.8823190.1139010.3318030.758965
45Srsf1151351880.0122690.5046140.8031610.1930140.0004570.014316.611381162.2545870.9459270.8630560.1076460.4372580.675439
32Pomp15135680.0044730.6038340.7060320.2066000.0000000.013740.84573660.6076830.9078850.8912890.2253090.4192840.753403
21Hnrnpu151351180.0077360.6757000.6859370.2195430.0000660.014033.988514111.9173300.9272540.9484520.2712340.4390770.733338
35Prpf615135780.0051270.6761000.7107040.1986900.0000000.013961.45109672.9838780.9224610.9356910.2103630.4135680.737356
41Sin3a151351090.0071500.6814480.7128490.2085930.0000000.013962.089873102.6722340.9225030.9419470.2375580.4277060.739180
38Qrich1151351260.0082560.6924880.6869460.2218880.0000000.014191.426358121.7116010.9376560.9659650.2805780.4450090.727620
17Elp1151351170.0076710.7058910.6616000.2288450.0000000.014687.736328111.3172620.9704480.9514300.2220360.4846610.637953
13Dhx915135910.0059770.7142420.3404120.2494240.0000000.015060.57143590.6071570.9950820.9956830.4180500.4993650.571892
33Ppp2r1a151351750.0114300.7180880.6930470.2204630.0000000.014539.731236165.1756580.9606690.9438610.1693840.4769040.653129
\n", - "
" - ], - "text/plain": [ - " treatment n_control n_treated prevalence overlap_ratio auc \\\n", - "51 Tpr 15135 91 0.005977 0.418372 0.865162 \n", - "45 Srsf1 15135 188 0.012269 0.504614 0.803161 \n", - "32 Pomp 15135 68 0.004473 0.603834 0.706032 \n", - "21 Hnrnpu 15135 118 0.007736 0.675700 0.685937 \n", - "35 Prpf6 15135 78 0.005127 0.676100 0.710704 \n", - "41 Sin3a 15135 109 0.007150 0.681448 0.712849 \n", - "38 Qrich1 15135 126 0.008256 0.692488 0.686946 \n", - "17 Elp1 15135 117 0.007671 0.705891 0.661600 \n", - "13 Dhx9 15135 91 0.005977 0.714242 0.340412 \n", - "33 Ppp2r1a 15135 175 0.011430 0.718088 0.693047 \n", - "\n", - " brier_score outside_overlap_fraction clipped_fraction ess_control \\\n", - "51 0.149835 0.000000 0.0 13334.889053 \n", - "45 0.193014 0.000457 0.0 14316.611381 \n", - "32 0.206600 0.000000 0.0 13740.845736 \n", - "21 0.219543 0.000066 0.0 14033.988514 \n", - "35 0.198690 0.000000 0.0 13961.451096 \n", - "41 0.208593 0.000000 0.0 13962.089873 \n", - "38 0.221888 0.000000 0.0 14191.426358 \n", - "17 0.228845 0.000000 0.0 14687.736328 \n", - "13 0.249424 0.000000 0.0 15060.571435 \n", - "33 0.220463 0.000000 0.0 14539.731236 \n", - "\n", - " ess_treated ess_control_fraction ess_treated_fraction score_q01 \\\n", - "51 80.291002 0.881063 0.882319 0.113901 \n", - "45 162.254587 0.945927 0.863056 0.107646 \n", - "32 60.607683 0.907885 0.891289 0.225309 \n", - "21 111.917330 0.927254 0.948452 0.271234 \n", - "35 72.983878 0.922461 0.935691 0.210363 \n", - "41 102.672234 0.922503 0.941947 0.237558 \n", - "38 121.711601 0.937656 0.965965 0.280578 \n", - "17 111.317262 0.970448 0.951430 0.222036 \n", - "13 90.607157 0.995082 0.995683 0.418050 \n", - "33 165.175658 0.960669 0.943861 0.169384 \n", - "\n", - " score_median score_q99 \n", - "51 0.331803 0.758965 \n", - "45 0.437258 0.675439 \n", - "32 0.419284 0.753403 \n", - "21 0.439077 0.733338 \n", - "35 0.413568 0.737356 \n", - "41 0.427706 0.739180 \n", - "38 0.445009 0.727620 \n", - "17 0.484661 0.637953 \n", - "13 0.499365 0.571892 \n", - "33 0.476904 0.653129 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "W_A = np.c_[X_A, U]\n", - "# class_weight='balanced' was the default before causarray 0.0.10; it is kept\n", - "# here so that this diagnostic reproduces the collaborators' pptx analysis.\n", - "pi_oof = estimate_propensity_scores(A, W_A, K=5, C=0.1, class_weight='balanced', random_state=0)\n", - "ps_summary = summarize_propensity_scores(A, pi_oof)\n", - "display(ps_summary.sort_values('overlap_ratio').head(10))\n", - "\n", - "weakest = ps_summary.nsmallest(4, 'overlap_ratio')['treatment'].tolist()\n", - "fig, axes, _ = plot_propensity_scores(A, pi_oof, treatments=weakest)\n", - "fig.savefig('scarf-ps.pdf', bbox_inches='tight', dpi=300)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "id": "d79815d6", - "metadata": { - "papermill": { - "duration": 0.003471, - "end_time": "2026-09-10T19:16:00.889685+00:00", - "exception": false, - "start_time": "2026-09-10T19:16:00.886214+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Comparison with Wilcoxon rank-sum test (via `crispyx`)\n", - "\n", - "A standard Perturb-seq analysis pipeline applies a **Wilcoxon rank-sum\n", - "test** (Mann-Whitney U) to library-size-normalised, log-transformed\n", - "counts -- comparing each perturbation against the pooled control cells\n", - "gene by gene. This does *not* adjust for the estimated latent factors used\n", - "by causarray. We run `crispyx`'s Wilcoxon test on the same data (both the\n", - "normalisation and the test itself are `crispyx` calls, replacing\n", - "`scanpy.pp.normalize_total`/`scanpy.pp.log1p`/\n", - "`scanpy.tl.rank_genes_groups(method='wilcoxon')`) and compare both methods\n", - "at BH-adjusted *p* < 0.05.\n", - "\n", - "**Which output field is \"the Wilcoxon effect size\"?** `crispyx`'s\n", - "`DifferentialExpressionResult.effect_size` is a rank-biserial/AUC\n", - "statistic (bounded, roughly `[-0.5, 0.5]` here) -- a valid Wilcoxon effect\n", - "size, but *not* a log-fold-change, and not what the pptx plots. The\n", - "quantity comparable to causarray's `tau` is the separate `logfoldchanges`\n", - "layer that `wilcoxon_test` also writes to its output h5ad (visible via\n", - "`result.result.layers['logfoldchanges']`); we use that below.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c92bde09", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:16:00.897969Z", - "iopub.status.busy": "2026-09-10T19:16:00.897749Z", - "iopub.status.idle": "2026-09-10T19:16:01.386185Z", - "shell.execute_reply": "2026-09-10T19:16:01.385735Z" - }, - "papermill": { - "duration": 0.49362, - "end_time": "2026-09-10T19:16:01.387064+00:00", - "exception": false, - "start_time": "2026-09-10T19:16:00.893444+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wilcoxon groups: 58\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wilcoxon: 11,758 significant pairs across 58 perturbations\n", - "causarray: 1,920 significant pairs across 58 perturbations\n" - ] - } - ], - "source": [ - "# Library-size normalise + log1p (required by Wilcoxon). Reuse the file on\n", - "# subsequent notebook runs.\n", - "norm_path = 'scarf_L45_subset_norm.h5ad'\n", - "if not os.path.exists(norm_path):\n", - " crispyx.normalize_total_log1p('scarf_L45_subset.h5ad', output_path=norm_path)\n", - "\n", - "wc_result = crispyx.wilcoxon_test(\n", - " norm_path,\n", - " perturbation_column=pert_col,\n", - " control_label=ctrl,\n", - " verbose=False,\n", - " output_path='scarf_L45_subset_norm_cx_wilcoxon.h5ad',\n", - ")\n", - "print(f'Wilcoxon groups: {len(wc_result.groups)}')\n", - "\n", - "# Tidy gene x perturbation table of Wilcoxon logFC + padj, pulled from the\n", - "# 'logfoldchanges'/'pvalue_adj' layers on the result h5ad (see markdown\n", - "# above for why this differs from `.effect_size`).\n", - "wc_h5ad = ad.read_h5ad('scarf_L45_subset_norm_cx_wilcoxon.h5ad')\n", - "lfc_df = pd.DataFrame(wc_h5ad.layers['logfoldchanges'], index=wc_h5ad.obs_names, columns=wc_h5ad.var_names)\n", - "padj_df = pd.DataFrame(wc_h5ad.layers['pvalue_adj'], index=wc_h5ad.obs_names, columns=wc_h5ad.var_names)\n", - "df_wc_full = (\n", - " lfc_df.stack().rename('wilcox_logfc').reset_index()\n", - " .rename(columns={'level_0': 'trt', 'level_1': 'gene_names'})\n", - ")\n", - "df_padj_long = (\n", - " padj_df.stack().rename('wilcox_padj').reset_index()\n", - " .rename(columns={'level_0': 'trt', 'level_1': 'gene_names'})\n", - ")\n", - "df_wc_full = df_wc_full.merge(df_padj_long, on=['trt', 'gene_names'])\n", - "\n", - "comparison_q = 0.05\n", - "wc_sig = df_wc_full[df_wc_full['wilcox_padj'] < comparison_q]\n", - "ca_sig = df_res[df_res['padj'] < comparison_q]\n", - "print(f'Wilcoxon: {len(wc_sig):,} significant pairs across {wc_sig.trt.nunique()} perturbations')\n", - "print(f'causarray: {len(ca_sig):,} significant pairs across {ca_sig.trt.nunique()} perturbations')\n" - ] - }, - { - "cell_type": "markdown", - "id": "2492357f", - "metadata": { - "papermill": { - "duration": 0.003795, - "end_time": "2026-09-10T19:16:01.394806+00:00", - "exception": false, - "start_time": "2026-09-10T19:16:01.391011+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Scatter plot: causarray LFC (tau) vs. Wilcoxon logFC\n", - "\n", - "This is the core comparison from the pptx (slide 1): *\"Wilcoxon produces\n", - "extreme negative logFC values, likely driven by sparse single-cell\n", - "expression [while] causarray gives more moderate negative effects,\n", - "suggesting more stable perturbation-effect estimates.\"* We build the full\n", - "merged gene x perturbation table (not just the significant subset) so both\n", - "the scatter and the discovery-overlap numbers use the same denominator.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "28fefe64", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-10T19:16:01.402640Z", - "iopub.status.busy": "2026-09-10T19:16:01.402506Z", - "iopub.status.idle": "2026-09-10T19:16:06.976099Z", - "shell.execute_reply": "2026-09-10T19:16:06.975480Z" - }, - "papermill": { - "duration": 5.578368, - "end_time": "2026-09-10T19:16:06.976675+00:00", - "exception": false, - "start_time": "2026-09-10T19:16:01.398307+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "causarray sig: 1,919 Wilcoxon sig: 7,486 both: 254 Jaccard: 0.028\n", - "Pearson r (all pairs): 0.243 Pearson r (either sig): 0.706\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Wilcoxon logFC range: [-26.23, 6.05]\n", - "causarray tau range: [-5.83, 4.74]\n" - ] - } - ], - "source": [ - "df_cmp = pd.merge(\n", - " df_res[['gene_names', 'trt', 'tau', 'padj']].rename(columns={'padj': 'ca_padj'}),\n", - " df_wc_full, on=['gene_names', 'trt'], how='inner',\n", - ")\n", - "df_cmp['ca_sig'] = df_cmp['ca_padj'] < comparison_q\n", - "df_cmp['wilcox_sig'] = df_cmp['wilcox_padj'] < comparison_q\n", - "df_cmp['both_sig'] = df_cmp['ca_sig'] & df_cmp['wilcox_sig']\n", - "df_cmp['either_sig'] = df_cmp['ca_sig'] | df_cmp['wilcox_sig']\n", - "\n", - "n_ca, n_wc, n_both = df_cmp['ca_sig'].sum(), df_cmp['wilcox_sig'].sum(), df_cmp['both_sig'].sum()\n", - "n_either = df_cmp['either_sig'].sum()\n", - "jaccard = n_both / n_either if n_either > 0 else np.nan\n", - "corr_all = df_cmp[['tau', 'wilcox_logfc']].corr().iloc[0, 1]\n", - "mask_either = df_cmp['either_sig']\n", - "corr_sig = df_cmp.loc[mask_either, ['tau', 'wilcox_logfc']].corr().iloc[0, 1] if mask_either.sum() > 1 else np.nan\n", - "\n", - "print(f'causarray sig: {n_ca:,} Wilcoxon sig: {n_wc:,} both: {n_both:,} Jaccard: {jaccard:.3f}')\n", - "print(f'Pearson r (all pairs): {corr_all:.3f} Pearson r (either sig): {corr_sig:.3f}')\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.5, 6))\n", - "rng = np.random.default_rng(0)\n", - "sample_idx = rng.choice(len(df_cmp), size=min(200_000, len(df_cmp)), replace=False)\n", - "ds = df_cmp.iloc[sample_idx]\n", - "colors = np.where(ds['both_sig'], '#e74c3c',\n", - " np.where(ds['ca_sig'], '#3498db',\n", - " np.where(ds['wilcox_sig'], '#f39c12', '#cccccc')))\n", - "ax.scatter(ds['tau'], ds['wilcox_logfc'], c=colors, s=2, alpha=0.4, rasterized=True)\n", - "\n", - "# Independent per-axis limits (99.5th pct of |value| on that axis): forcing\n", - "# a single shared/symmetric range would let the Wilcoxon tail's much larger\n", - "# scale crush the causarray axis down to an unreadable sliver.\n", - "xlim = np.nanpercentile(np.abs(df_cmp['tau']), 99.5)\n", - "ylim = np.nanpercentile(np.abs(df_cmp['wilcox_logfc']), 99.5)\n", - "ax.set_xlim(-xlim, xlim)\n", - "ax.set_ylim(-ylim, ylim)\n", - "ax.axhline(0, color='grey', lw=0.5)\n", - "ax.axvline(0, color='grey', lw=0.5)\n", - "ax.set_xlabel('causarray LFC (tau)')\n", - "ax.set_ylabel('Wilcoxon logFC')\n", - "ax.set_title(f'SCARF L4-5 IT CTX Glut: causarray vs. Wilcoxon\\n'\n", - " f'(r={corr_all:.3f} all pairs, r={corr_sig:.3f} either sig; Jaccard={jaccard:.3f})\\n'\n", - " f'note the asymmetric axes -- Wilcoxon logFC has a far longer negative tail')\n", - "from matplotlib.lines import Line2D\n", - "legend_els = [Line2D([0], [0], marker='o', color='w', markerfacecolor=c, label=l, markersize=6)\n", - " for c, l in [('#e74c3c', 'Both sig'), ('#3498db', 'causarray only'),\n", - " ('#f39c12', 'Wilcoxon only'), ('#cccccc', 'Neither')]]\n", - "ax.legend(handles=legend_els, fontsize=8, loc='upper left')\n", - "plt.tight_layout()\n", - "fig.savefig('scarf-scatter-causarray-vs-wilcoxon.pdf', bbox_inches='tight', dpi=300)\n", - "plt.show()\n", - "\n", - "print(f\"\\nWilcoxon logFC range: [{df_cmp['wilcox_logfc'].min():.2f}, {df_cmp['wilcox_logfc'].max():.2f}]\")\n", - "print(f\"causarray tau range: [{df_cmp['tau'].min():.2f}, {df_cmp['tau'].max():.2f}]\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "f3f1af4e", - "metadata": { - "papermill": { - "duration": 0.003857, - "end_time": "2026-09-10T19:16:06.984896+00:00", - "exception": false, - "start_time": "2026-09-10T19:16:06.981039+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "**Interpretation.** At BH-adjusted *p* < 0.05, causarray identifies **1,919**\n", - "significant gene-perturbation pairs and Wilcoxon identifies **7,486** (out of\n", - "the 58-perturbation, 14,227-gene intersection tested by both methods); only\n", - "**254** are shared (Jaccard = 0.028). This overlap is much lower than the\n", - "adamson tutorial's (0.268) -- plausibly some combination of: this pilot has\n", - "3x as many perturbations with a similar median cells/pert (~122, matching\n", - "the pptx's own pilot scale) rather than adamson's larger per-perturbation\n", - "samples; the selected *r* = 8 latent factors (higher than the pptx's own\n", - "r = 3, and JIC was still decreasing at the top of our grid, so a larger *r*\n", - "might fit even better) remove more shared variance than a smaller *r* would;\n", - "and/or the SCARF screen's perturbations simply have weaker transcriptomic\n", - "effects in this cell type than Adamson's UPR screen. None of these are\n", - "distinguished here -- a grid extending past r=8 and a sensitivity check\n", - "against r=3 would be the natural follow-ups.\n", - "\n", - "The scatter above matches the pptx's qualitative claim well: Wilcoxon logFC\n", - "spans **[-26.23, 6.05]** -- a long, almost entirely one-sided negative\n", - "tail -- while causarray's `tau` stays within **[-5.83, 4.74]**, a much more\n", - "symmetric and moderate range. Pearson correlation among either-method\n", - "discoveries is 0.706 (only 0.243 across all tested pairs, since most pairs\n", - "are noise for both methods), so where the two methods agree on significance\n", - "they also agree reasonably well on direction and rough magnitude. This is\n", - "consistent with the pptx's observation that Wilcoxon's extreme values are\n", - "\"likely driven by sparse single-cell expression\" (division by near-zero\n", - "control means in the log-fold-change formula), while causarray's\n", - "counterfactual-mean estimator is comparatively insulated from that specific\n", - "pathology. As in the adamson/replogle tutorials, the two methods still\n", - "answer different statistical questions -- Wilcoxon tests marginal\n", - "differences in log-normalised expression, while causarray estimates\n", - "latent-factor-adjusted counterfactual mean effects -- so disagreement\n", - "between them reflects adjustment, estimator, and filtering differences, not\n", - "necessarily false positives in either direction.\n" - ] - }, - { - "cell_type": "markdown", - "id": "d4dbdc6a", - "metadata": {}, - "source": [ - "## Investigation: why the SCARF discoveries look wrong\n", - "\n", - "The collaborators' summary (`260902_FinalSummary_Causarray.pptx`) reports two\n", - "problems with the causarray results on this screen: a cluster of \"extreme\n", - "negative DEGs\" that no filtering rule could remove cleanly, and very few\n", - "discoveries overall relative to Wilcoxon, which got worse from v0.0.8 to\n", - "v0.0.9. The working hypothesis was that the latent factors over-adjust and\n", - "wash out the perturbation signal. The cells below test that hypothesis and two\n", - "alternatives directly on the L4-5 subset, using the cached GCATE fit (`U`,\n", - "r = 8) and the cached LFC table (`df_res`) from above.\n", - "\n", - "Every step that takes more than a minute reads from `scarf_investigation/`\n", - "when a cached result exists and only recomputes when it does not:\n", - "\n", - "```\n", - "scarf_investigation/\n", - " variants/ LFC re-runs for Ppp2r1a, Tpr, Prpf6 under alternative inference settings (~13 min per outcome fit)\n", - " null/ negative-control run with fake perturbations drawn from control cells (~13 min)\n", - " enrichr/ Enrichr enrichment tables (network call)\n", - " *.png diagnostic figures\n", - "```\n", - "\n", - "Findings in one paragraph: **83% of the current discoveries are genes with\n", - "zero counts in the perturbed arm**, which happens by chance for sparse genes\n", - "with only ~100 perturbed cells; the estimator turns them into strongly\n", - "\"significant\" negative effects. Separately, **the default variance formula\n", - "inflates standard errors about 2x for expressed genes**, so real effects are\n", - "estimated correctly but not called. **The latent factors are not the cause**:\n", - "they barely differ between perturbed and control cells, and effect estimates\n", - "with r = 8 and r = 0 agree. A calibrated propensity model with the pooled\n", - "influence-function variance plus a Poisson variance floor removes the\n", - "artifacts, recovers 70-85% of Wilcoxon's hits, and yields sharp pathway\n", - "enrichment." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "afb5ea3e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "825,166 gene x perturbation rows; 15,135 control cells\n" - ] - } - ], - "source": [ - "import pickle\n", - "from scipy import stats\n", - "from statsmodels.stats.multitest import multipletests\n", - "import scipy.sparse as sp\n", - "\n", - "INV_DIR = 'scarf_investigation'\n", - "for sub in ['variants', 'null', 'enrichr']:\n", - " os.makedirs(os.path.join(INV_DIR, sub), exist_ok=True)\n", - "\n", - "# Raw count support per (gene, perturbation): total counts and number of\n", - "# expressing cells in the perturbed arm, on the filtered gene set used above.\n", - "_Xs = sp.csc_matrix(adata.X)\n", - "_lab = adata.obs[pert_col].astype(str).to_numpy()\n", - "_is_ctrl = _lab == ctrl\n", - "n_ctrl_cells = int(_is_ctrl.sum())\n", - "\n", - "def arm_support(pert_names):\n", - " rows = []\n", - " for p in pert_names:\n", - " m = _lab == p\n", - " Xt = _Xs[m]\n", - " rows.append(pd.DataFrame({\n", - " 'gene_names': adata.var_names, 'trt': p, 'n_trt': int(m.sum()),\n", - " 'trt_sum': np.asarray(Xt.sum(axis=0)).ravel(),\n", - " 'trt_nz': np.asarray((Xt > 0).sum(axis=0)).ravel(),\n", - " }))\n", - " return pd.concat(rows, ignore_index=True)\n", - "\n", - "support_all = arm_support(list(A.columns))\n", - "ctrl_rate = np.asarray(_Xs[_is_ctrl].mean(axis=0)).ravel() # control counts per cell, per gene\n", - "\n", - "def add_poisson_floor(df, n0):\n", - " \"\"\"Post-hoc lower bound on the log-scale variance: 1/(n1*mu1) + 1/(n0*mu0).\n", - "\n", - " An arm with all-zero counts has an empirical influence-function variance\n", - " of zero; a Poisson mean estimated from n1 cells cannot be more precise than\n", - " this bound. The floor is applied as max(std**2, floor) and BH is recomputed\n", - " per perturbation.\n", - " \"\"\"\n", - " df = df.copy()\n", - " df['var_floor'] = (1 / (df['n_trt'] * df['mean_treated'].clip(lower=1e-2))\n", - " + 1 / (n0 * df['mean_control'].clip(lower=1e-2)))\n", - " df['std_floor'] = np.sqrt(np.maximum(df['std'] ** 2, df['var_floor']))\n", - " df['stat_floor'] = df['tau'] / df['std_floor']\n", - " df['padj_floor'] = np.nan\n", - " for trt, g in df.groupby('trt'):\n", - " ok = np.isfinite(g['stat_floor'])\n", - " p = 2 * stats.norm.sf(np.abs(g.loc[ok, 'stat_floor']))\n", - " df.loc[g.index[ok], 'padj_floor'] = multipletests(p, method='fdr_bh')[1]\n", - " return df\n", - "\n", - "print(f'{len(support_all):,} gene x perturbation rows; {n_ctrl_cells:,} control cells')" - ] - }, - { - "cell_type": "markdown", - "id": "c56690b5", - "metadata": {}, - "source": [ - "### Finding 1: the significant hits are zero-count perturbed arms\n", - "\n", - "For each significant pair, look at the raw counts in the perturbed arm. A gene\n", - "detected in ~2% of control cells has zero counts among ~100 perturbed cells\n", - "with probability about exp(-2) ≈ 0.13, so thousands of such zero arms are\n", - "expected under no effect at all. `LFC` floors the counterfactual treated mean\n", - "at `thres_diff = 0.01`, takes the log against a control mean of ~0.025, and\n", - "gets tau ≈ -0.9; the pseudo-outcomes of an all-zero arm have essentially zero\n", - "empirical variance, so the standard error is tiny and the pair is called." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "53b3af39", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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value
significant pairs (padj < 0.05)1920.0000
of which zero counts in perturbed arm1589.0000
fraction0.8280
median control mean of zero-arm hits (counts/cell)0.0247
median tau of zero-arm hits-0.8470
median std of zero-arm hits0.1350
zero perturbed arms among all tested pairs: observed54929.0000
zero perturbed arms among all tested pairs: expected under no effect53912.0000
non-artifact significant pairs (perturbed counts > 0)331.0000
fraction of those that are downregulated0.0300
\n", - "
" - ], - "text/plain": [ - " value\n", - "significant pairs (padj < 0.05) 1920.0000\n", - "of which zero counts in perturbed arm 1589.0000\n", - "fraction 0.8280\n", - "median control mean of zero-arm hits (counts/cell) 0.0247\n", - "median tau of zero-arm hits -0.8470\n", - "median std of zero-arm hits 0.1350\n", - "zero perturbed arms among all tested pairs: obs... 54929.0000\n", - "zero perturbed arms among all tested pairs: exp... 53912.0000\n", - "non-artifact significant pairs (perturbed count... 331.0000\n", - "fraction of those that are downregulated 0.0300" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = df_res.merge(support_all, on=['gene_names', 'trt'])\n", - "sig = inv[inv['padj'] < 0.05]\n", - "zero_arm = sig['trt_sum'] == 0\n", - "\n", - "# Expected vs observed all-zero perturbed arms among *all* tested pairs, under\n", - "# a Poisson null at the control rate (no perturbation effect anywhere).\n", - "n_per_pert = A.sum(axis=0)\n", - "exp_zero = sum(np.exp(-n_per_pert[p] * ctrl_rate).sum() for p in A.columns)\n", - "obs_zero = int((inv['trt_sum'] == 0).sum())\n", - "\n", - "summary1 = pd.Series({\n", - " 'significant pairs (padj < 0.05)': len(sig),\n", - " 'of which zero counts in perturbed arm': int(zero_arm.sum()),\n", - " 'fraction': round(zero_arm.mean(), 3),\n", - " 'median control mean of zero-arm hits (counts/cell)': round(sig.loc[zero_arm, 'mean_control'].median(), 4),\n", - " 'median tau of zero-arm hits': round(sig.loc[zero_arm, 'tau'].median(), 3),\n", - " 'median std of zero-arm hits': round(sig.loc[zero_arm, 'std'].median(), 3),\n", - " 'zero perturbed arms among all tested pairs: observed': obs_zero,\n", - " 'zero perturbed arms among all tested pairs: expected under no effect': int(round(exp_zero)),\n", - " 'non-artifact significant pairs (perturbed counts > 0)': int((~zero_arm).sum()),\n", - " 'fraction of those that are downregulated': round((sig.loc[~zero_arm, 'tau'] < 0).mean(), 3),\n", - "})\n", - "display(summary1.to_frame('value'))\n", - "\n", - "per_pert = (sig.assign(kind=np.where(zero_arm, 'zero counts in perturbed arm', 'perturbed counts > 0'))\n", - " .groupby(['trt', 'kind']).size().unstack(fill_value=0)\n", - " .sort_values('zero counts in perturbed arm', ascending=False))\n", - "ax = per_pert.plot.bar(stacked=True, figsize=(14, 4), width=0.8, color=['#2a78d6', '#eb6834'])\n", - "ax.set_ylabel('significant pairs'); ax.set_xlabel('perturbation')\n", - "ax.set_title('Current causarray discoveries per perturbation, split by raw support in the perturbed arm')\n", - "ax.legend(frameon=False); plt.tight_layout(); plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ec308594", - "metadata": {}, - "source": [ - "### Finding 2: effects are estimated, then lost at the testing step\n", - "\n", - "For the pairs that Wilcoxon calls and causarray does not, causarray's `tau`\n", - "tracks the Wilcoxon logFC (correlation 0.7), so the effect is not washed out;\n", - "the t-statistic is simply too small. Compare the reported standard error with\n", - "a model-based negative-binomial standard error for the same log ratio,\n", - "`sqrt((1/mu1 + 1/r)/n1 + (1/mu0 + 1/r)/n0)`, using the GCATE dispersion `r`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a66363cf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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value
expressed pairs (control mean > 0.1, perturbed counts > 2)540004.000
causarray std / model-based SE: 10th pct1.710
causarray std / model-based SE: median1.950
causarray std / model-based SE: 90th pct2.240
Wilcoxon-only expressed pairs4728.000
corr(tau, Wilcoxon logFC)0.927
median |stat| as reported1.970
median |tau / model-based SE|3.860
fraction with |tau / model-based SE| > 2.50.940
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" - ], - "text/plain": [ - " value\n", - "expressed pairs (control mean > 0.1, perturbed ... 540004.000\n", - "causarray std / model-based SE: 10th pct 1.710\n", - "causarray std / model-based SE: median 1.950\n", - "causarray std / model-based SE: 90th pct 2.240\n", - "Wilcoxon-only expressed pairs 4728.000\n", - " corr(tau, Wilcoxon logFC) 0.927\n", - " median |stat| as reported 1.970\n", - " median |tau / model-based SE| 3.860\n", - " fraction with |tau / model-based SE| > 2.5 0.940" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_pkl_disp = res_2['kwargs_glm']['disp_glm']\n", - "disp_map = dict(zip(Y.columns, _pkl_disp))\n", - "cmp = df_cmp.merge(support_all, on=['gene_names', 'trt']).merge(\n", - " df_res[['gene_names', 'trt', 'std', 'stat', 'mean_control', 'mean_treated']], on=['gene_names', 'trt'])\n", - "cmp['r_nb'] = cmp['gene_names'].map(disp_map)\n", - "cmp['se_model'] = np.sqrt((1 / cmp['mean_treated'].clip(lower=1e-2) + 1 / cmp['r_nb']) / cmp['n_trt']\n", - " + (1 / cmp['mean_control'].clip(lower=1e-2) + 1 / cmp['r_nb']) / n_ctrl_cells)\n", - "expressed = (cmp['mean_control'] > 0.1) & (cmp['trt_sum'] > 2) & np.isfinite(cmp['std'])\n", - "ratio = (cmp['std'] / cmp['se_model'])[expressed]\n", - "wonly = cmp[expressed & cmp['wilcox_sig'] & ~cmp['ca_sig'] & (cmp['wilcox_logfc'] > -10)]\n", - "\n", - "summary2 = pd.Series({\n", - " 'expressed pairs (control mean > 0.1, perturbed counts > 2)': int(expressed.sum()),\n", - " 'causarray std / model-based SE: 10th pct': round(ratio.quantile(0.10), 2),\n", - " 'causarray std / model-based SE: median': round(ratio.median(), 2),\n", - " 'causarray std / model-based SE: 90th pct': round(ratio.quantile(0.90), 2),\n", - " 'Wilcoxon-only expressed pairs': len(wonly),\n", - " ' corr(tau, Wilcoxon logFC)': round(np.corrcoef(wonly['tau'], wonly['wilcox_logfc'])[0, 1], 3),\n", - " ' median |stat| as reported': round(wonly['stat'].abs().median(), 2),\n", - " ' median |tau / model-based SE|': round((wonly['tau'] / wonly['se_model']).abs().median(), 2),\n", - " ' fraction with |tau / model-based SE| > 2.5': round(((wonly['tau'] / wonly['se_model']).abs() > 2.5).mean(), 3),\n", - "})\n", - "display(summary2.to_frame('value'))" - ] - }, - { - "cell_type": "markdown", - "id": "01a20ba2", - "metadata": {}, - "source": [ - "#### Which variance formula matches the estimator?\n", - "\n", - "`LFC` estimates each arm mean as the average of AIPW pseudo-outcomes over\n", - "*all* cells. The default propensity model uses `class_weight='balanced'`, so\n", - "for a treatment with 0.6% prevalence the scores sit near 0.5 rather than near\n", - "0.006. The default `usevar='unequal'` then computes a by-arm Welch variance\n", - "`s0²/n0 + s1²/n1`. A small oracle simulation (outcome means known, so only the\n", - "variance formula is tested) shows which combinations report the estimator's\n", - "actual sampling variability." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "19718fa2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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control meanpropensitytrue SD of tauWelch SE (usevar=\"unequal\")pooled IF SE (usevar=\"pooled\")NB Wald SE
00.5balanced (pi ~ 0.45)0.0220.3360.0230.153
10.5calibrated (pi = prevalence)0.14523.2640.1550.153
22.0balanced (pi ~ 0.45)0.0130.2020.0140.091
32.0calibrated (pi = prevalence)0.09513.8360.0920.091
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" - ], - "text/plain": [ - " control mean propensity true SD of tau \\\n", - "0 0.5 balanced (pi ~ 0.45) 0.022 \n", - "1 0.5 calibrated (pi = prevalence) 0.145 \n", - "2 2.0 balanced (pi ~ 0.45) 0.013 \n", - "3 2.0 calibrated (pi = prevalence) 0.095 \n", - "\n", - " Welch SE (usevar=\"unequal\") pooled IF SE (usevar=\"pooled\") NB Wald SE \n", - "0 0.336 0.023 0.153 \n", - "1 23.264 0.155 0.153 \n", - "2 0.202 0.014 0.091 \n", - "3 13.836 0.092 0.091 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "rng = np.random.default_rng(0)\n", - "n0_sim, n1_sim, r_sim = 15000, 100, 3.0\n", - "p_sim = n1_sim / (n0_sim + n1_sim)\n", - "\n", - "def one_draw(mu0, pi_val):\n", - " A_ = np.r_[np.zeros(n0_sim), np.ones(n1_sim)]\n", - " lam = mu0 * rng.gamma(r_sim, 1 / r_sim, size=n0_sim + n1_sim) # NB counts, no effect\n", - " Y_ = rng.poisson(lam)\n", - " m0 = m1 = np.full(Y_.shape, mu0, dtype=float) # oracle outcome model\n", - " pi = np.full(Y_.shape, pi_val)\n", - " e1 = A_ / pi * (Y_ - m1) + m1\n", - " e0 = (1 - A_) / (1 - pi) * (Y_ - m0) + m0\n", - " t1, t0 = e1.mean(), e0.mean()\n", - " eta = e1 / t1 - e0 / t0\n", - " welch = np.var(eta[A_ == 0], ddof=1) / n0_sim + np.var(eta[A_ == 1], ddof=1) / n1_sim\n", - " pooled = np.var(eta, ddof=1) / (n0_sim + n1_sim)\n", - " return np.log(t1 / t0), np.sqrt(welch), np.sqrt(pooled)\n", - "\n", - "rows = []\n", - "for mu0 in [0.5, 2.0]:\n", - " for pi_val, name in [(0.45, 'balanced (pi ~ 0.45)'), (p_sim, 'calibrated (pi = prevalence)')]:\n", - " draws = np.array([one_draw(mu0, pi_val) for _ in range(300)])\n", - " rows.append({'control mean': mu0, 'propensity': name,\n", - " 'true SD of tau': round(draws[:, 0].std(), 3),\n", - " 'Welch SE (usevar=\"unequal\")': round(draws[:, 1].mean(), 3),\n", - " 'pooled IF SE (usevar=\"pooled\")': round(draws[:, 2].mean(), 3),\n", - " 'NB Wald SE': round(np.sqrt((1 / mu0 + 1 / r_sim) / n1_sim), 3)})\n", - "display(pd.DataFrame(rows))" - ] - }, - { - "cell_type": "markdown", - "id": "23921674", - "metadata": {}, - "source": [ - "### Re-running LFC under alternative inference settings\n", - "\n", - "Three perturbations with many Wilcoxon hits (Ppp2r1a, Tpr, Prpf6) are\n", - "re-analysed with the cached latent factors, using all control cells plus those\n", - "perturbations' cells. The outcome model is fit once per design (about 13\n", - "minutes on the fast backend) and reused across propensity/variance settings.\n", - "Results are cached in `scarf_investigation/variants/`.\n", - "\n", - "| tag | latent factors | propensity `class_weight` | `usevar` |\n", - "|---|---|---|---|\n", - "| `r8_default_balanced_welch` | r = 8 | `'balanced'` (default) | `'unequal'` (default) |\n", - "| `r8_calibrated_pooled` | r = 8 | `None` | `'pooled'` |\n", - "| `r8_calibrated_welch` | r = 8 | `None` | `'unequal'` |\n", - "| `r8_balanced_pooled` | r = 8 | `'balanced'` | `'pooled'` |\n", - "| `r0_calibrated_pooled` | none | `None` | `'pooled'` |\n", - "| `r0_default_balanced_welch` | none | `'balanced'` | `'unequal'` |\n", - "\n", - "The r = 0 designs test the over-adjustment hypothesis directly." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9fe546e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15,479 cells (15,135 control), 14,227 genes, perturbed cells: {'Ppp2r1a': 175, 'Tpr': 91, 'Prpf6': 78}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "calibrated propensity, median per perturbation: {'Ppp2r1a': np.float64(0.0122), 'Tpr': np.float64(0.0049), 'Prpf6': np.float64(0.0048)} | prevalence: {'Ppp2r1a': 0.0113, 'Tpr': 0.0059, 'Prpf6': 0.005}\n", - "{'r8_default_balanced_welch': 42681, 'r8_calibrated_pooled': 42681, 'r8_calibrated_welch': 42681, 'r8_balanced_pooled': 42681, 'r0_calibrated_pooled': 42681, 'r0_default_balanced_welch': 42681}\n" - ] - } - ], - "source": [ - "import warnings\n", - "\n", - "VAR_DIR = os.path.join(INV_DIR, 'variants')\n", - "PERTS = ['Ppp2r1a', 'Tpr', 'Prpf6']\n", - "_cells_b = (A.sum(axis=1) == 0).to_numpy() | A[PERTS].sum(axis=1).to_numpy().astype(bool)\n", - "Y_b = Y.loc[_cells_b].reset_index(drop=True)\n", - "A_b = A.loc[_cells_b, PERTS].reset_index(drop=True).astype(float)\n", - "X_b, XA_b, U_b = X[_cells_b], X_A[_cells_b], U[_cells_b]\n", - "off_b = np.log(res_2['kwargs_glm']['size_factor'])[_cells_b]\n", - "n0_b = int((A_b.sum(axis=1) == 0).sum())\n", - "print(f'{len(Y_b):,} cells ({n0_b:,} control), {Y_b.shape[1]:,} genes, perturbed cells: {A_b.sum().astype(int).to_dict()}')\n", - "\n", - "support_b = support_all[support_all['trt'].isin(PERTS)]\n", - "LOOSE_CLIP = (1e-4, 1 - 1e-4) # calibrated scores for a 0.6% treatment sit below the default 0.01 lower clip\n", - "\n", - "def run_variant(tag, W, W_A, **kw):\n", - " path = os.path.join(VAR_DIR, f'{tag}.csv')\n", - " if os.path.exists(path):\n", - " return pd.read_csv(path), None\n", - " print(f'[{tag}] computing ...')\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter('ignore')\n", - " df, est = LFC(Y_b, W, A_b, W_A, offset=off_b, verbose=False, backend='fast', **kw)\n", - " df.to_csv(path, index=False)\n", - " return df, est\n", - "\n", - "variants = {}\n", - "W8, WA8 = np.c_[X_b, U_b], np.c_[XA_b, U_b]\n", - "variants['r8_default_balanced_welch'], est8 = run_variant('r8_default_balanced_welch', W8, WA8, usevar='unequal')\n", - "_r8_rest = ['r8_calibrated_pooled', 'r8_calibrated_welch', 'r8_balanced_pooled']\n", - "if any(not os.path.exists(os.path.join(VAR_DIR, f'{t}.csv')) for t in _r8_rest) and est8 is None:\n", - " # Outcome predictions are too large to cache (~10 GB); refit once to reuse them.\n", - " _, est8 = LFC(Y_b, W8, A_b, WA8, offset=off_b, verbose=False, backend='fast', usevar='unequal')\n", - "if est8 is not None:\n", - " pi_cal = estimate_propensity_scores(A_b.to_numpy(), WA8, K=1, class_weight=None)\n", - " np.save(os.path.join(VAR_DIR, 'pi_raw_calibrated_r8.npy'), pi_cal)\n", - " np.save(os.path.join(VAR_DIR, 'pi_raw_balanced_r8.npy'), est8['pi_hat_raw'])\n", - " Yhat8 = est8['Y_hat']\n", - " variants['r8_calibrated_pooled'], _ = run_variant('r8_calibrated_pooled', W8, WA8, Y_hat=Yhat8, pi_hat=pi_cal, usevar='pooled', ps_clip=LOOSE_CLIP)\n", - " variants['r8_calibrated_welch'], _ = run_variant('r8_calibrated_welch', W8, WA8, Y_hat=Yhat8, pi_hat=pi_cal, usevar='unequal', ps_clip=LOOSE_CLIP)\n", - " variants['r8_balanced_pooled'], _ = run_variant('r8_balanced_pooled', W8, WA8, Y_hat=Yhat8, pi_hat=est8['pi_hat_raw'], usevar='pooled')\n", - " del Yhat8, est8\n", - "else:\n", - " for t in _r8_rest:\n", - " variants[t], _ = run_variant(t, W8, WA8)\n", - "\n", - "variants['r0_calibrated_pooled'], est0 = run_variant('r0_calibrated_pooled', X_b, XA_b, usevar='pooled', ps_class_weight=None, ps_clip=LOOSE_CLIP)\n", - "if not os.path.exists(os.path.join(VAR_DIR, 'r0_default_balanced_welch.csv')) and est0 is None:\n", - " _, est0 = LFC(Y_b, X_b, A_b, XA_b, offset=off_b, verbose=False, backend='fast', usevar='pooled', ps_class_weight=None, ps_clip=LOOSE_CLIP)\n", - "variants['r0_default_balanced_welch'], _ = run_variant('r0_default_balanced_welch', X_b, XA_b,\n", - " **({'Y_hat': est0['Y_hat']} if est0 is not None else {}), usevar='unequal')\n", - "del est0\n", - "\n", - "pi_cal = np.load(os.path.join(VAR_DIR, 'pi_raw_calibrated_r8.npy'))\n", - "print('calibrated propensity, median per perturbation:', dict(zip(PERTS, np.median(pi_cal, axis=0).round(4))),\n", - " '| prevalence:', (A_b.sum() / len(A_b)).round(4).to_dict())\n", - "print({k: len(v) for k, v in variants.items()})" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9d80819a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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n_sigzero-arm hitsneg frac (non-artifact)Wilcoxon sigWilcoxon recoveredmedian std (expressed)corr(tau, Wilcoxon) expressed
varianttrt
r8_default_balanced_welchPpp2r1a102120.07132685 (6%)0.1880.926
Prpf61411020.0066636 (5%)0.2670.933
Tpr86450.0087239 (4%)0.2550.916
r8_calibrated_pooledPpp2r1a2561300.4313261160 (87%)0.0810.786
Prpf621392320.21666574 (86%)0.1070.799
Tpr38581720.25872807 (93%)0.0800.783
r8_calibrated_welchPpp2r1a99NaN13260 (0%)7.0640.786
Prpf6101101NaN6660 (0%)20.7540.799
Tpr6161NaN8720 (0%)13.2020.783
r8_balanced_pooledPpp2r1a9802270.5013261282 (97%)0.0200.926
Prpf6103201880.38666639 (96%)0.0210.933
Tpr98261070.32872830 (95%)0.0230.916
r0_calibrated_pooledPpp2r1a1632340.2613261066 (80%)0.0910.918
Prpf611182320.23666533 (80%)0.1310.949
Tpr3931590.50872183 (21%)0.1480.916
r0_default_balanced_welchPpp2r1a6490.04132652 (4%)0.1870.945
Prpf62131740.0066637 (6%)0.2600.957
Tpr1801100.0087267 (8%)0.2320.949
r8_calibrated_pooled_floorPpp2r1a192500.4013261109 (84%)0.0810.786
Prpf686800.10666470 (71%)0.1070.799
Tpr117100.09872637 (73%)0.0800.783
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" - ], - "text/plain": [ - " n_sig zero-arm hits neg frac (non-artifact) Wilcoxon sig Wilcoxon recovered median std (expressed) corr(tau, Wilcoxon) expressed\n", - "variant trt \n", - "r8_default_balanced_welch Ppp2r1a 102 12 0.07 1326 85 (6%) 0.188 0.926\n", - " Prpf6 141 102 0.00 666 36 (5%) 0.267 0.933\n", - " Tpr 86 45 0.00 872 39 (4%) 0.255 0.916\n", - "r8_calibrated_pooled Ppp2r1a 2561 30 0.43 1326 1160 (87%) 0.081 0.786\n", - " Prpf6 2139 232 0.21 666 574 (86%) 0.107 0.799\n", - " Tpr 3858 172 0.25 872 807 (93%) 0.080 0.783\n", - "r8_calibrated_welch Ppp2r1a 9 9 NaN 1326 0 (0%) 7.064 0.786\n", - " Prpf6 101 101 NaN 666 0 (0%) 20.754 0.799\n", - " Tpr 61 61 NaN 872 0 (0%) 13.202 0.783\n", - "r8_balanced_pooled Ppp2r1a 9802 27 0.50 1326 1282 (97%) 0.020 0.926\n", - " Prpf6 10320 188 0.38 666 639 (96%) 0.021 0.933\n", - " Tpr 9826 107 0.32 872 830 (95%) 0.023 0.916\n", - "r0_calibrated_pooled Ppp2r1a 1632 34 0.26 1326 1066 (80%) 0.091 0.918\n", - " Prpf6 1118 232 0.23 666 533 (80%) 0.131 0.949\n", - " Tpr 393 159 0.50 872 183 (21%) 0.148 0.916\n", - "r0_default_balanced_welch Ppp2r1a 64 9 0.04 1326 52 (4%) 0.187 0.945\n", - " Prpf6 213 174 0.00 666 37 (6%) 0.260 0.957\n", - " Tpr 180 110 0.00 872 67 (8%) 0.232 0.949\n", - "r8_calibrated_pooled_floor Ppp2r1a 1925 0 0.40 1326 1109 (84%) 0.081 0.786\n", - " Prpf6 868 0 0.10 666 470 (71%) 0.107 0.799\n", - " Tpr 1171 0 0.09 872 637 (73%) 0.080 0.783" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "_wc_cols = df_wc_full[['trt', 'gene_names', 'wilcox_logfc', 'wilcox_padj']]\n", - "\n", - "def annotate(df):\n", - " d = df.merge(support_b, on=['trt', 'gene_names']).merge(_wc_cols, on=['trt', 'gene_names'], how='left')\n", - " d['w_sig'] = (d['wilcox_padj'] < comparison_q) & (d['wilcox_logfc'] > -10) # exclude Wilcoxon's own -inf artifacts\n", - " return d\n", - "\n", - "variants_a = {k: annotate(v) for k, v in variants.items()}\n", - "variants_a['r8_calibrated_pooled_floor'] = add_poisson_floor(variants_a['r8_calibrated_pooled'], n0_b)\n", - "variants_a['r8_calibrated_pooled_floor']['padj'] = variants_a['r8_calibrated_pooled_floor']['padj_floor']\n", - "variants_a['r8_calibrated_pooled_floor']['stat'] = variants_a['r8_calibrated_pooled_floor']['stat_floor']\n", - "\n", - "rows = []\n", - "for tag, d in variants_a.items():\n", - " for trt, g in d.groupby('trt'):\n", - " s = g[g['padj'] < 0.05]\n", - " expressed = np.isfinite(g['std']) & (g['trt_sum'] > 2) & (g['mean_control'] > 0.1)\n", - " rows.append({'variant': tag, 'trt': trt, 'n_sig': len(s),\n", - " 'zero-arm hits': int((s['trt_sum'] == 0).sum()),\n", - " 'neg frac (non-artifact)': round((s.loc[s['trt_sum'] > 0, 'tau'] < 0).mean(), 2) if (s['trt_sum'] > 0).any() else np.nan,\n", - " 'Wilcoxon sig': int(g['w_sig'].sum()),\n", - " 'Wilcoxon recovered': int((g['w_sig'] & (g['padj'] < 0.05)).sum()),\n", - " 'median std (expressed)': round(g.loc[expressed, 'std'].median(), 3),\n", - " 'corr(tau, Wilcoxon) expressed': round(np.corrcoef(g.loc[expressed, 'tau'], g.loc[expressed, 'wilcox_logfc'])[0, 1], 3)})\n", - "variant_summary = pd.DataFrame(rows)\n", - "variant_summary['Wilcoxon recovered'] = (variant_summary['Wilcoxon recovered'].astype(str) + ' (' +\n", - " (100 * variant_summary['Wilcoxon recovered'] / variant_summary['Wilcoxon sig']).round(0).astype(int).astype(str) + '%)')\n", - "pd.set_option('display.width', 250)\n", - "display(variant_summary.set_index(['variant', 'trt']))" - ] - }, - { - "cell_type": "markdown", - "id": "d5387d72", - "metadata": {}, - "source": [ - "Reading the table: the default (`balanced` + Welch) finds ~100 hits per\n", - "perturbation, half of them zero-arm artifacts, and recovers 4-6% of\n", - "Wilcoxon's hits. Calibrated + Welch keeps only the artifacts (the Welch SE for\n", - "a calibrated score is enormous). Balanced + pooled calls ~70% of all genes.\n", - "Calibrated + pooled is the textbook AIPW sandwich variance; adding the Poisson\n", - "floor removes the zero-arm hits and leaves 870-1,900 discoveries per\n", - "perturbation that recover 70-85% of Wilcoxon's.\n", - "\n", - "**Over-adjustment check.** With the same corrected inference, compare the\n", - "effect estimates with (r = 8) and without (r = 0) latent factors on expressed\n", - "genes." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ccbdb5fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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expressed genescorr(tau r=8, tau r=0)slope r=8 on r=0median |tau| r=8median |tau| r=0sig r=8sig r=0
trt
Ppp2r1a89030.8450.9690.1230.11620501253
Prpf691580.8190.8650.1480.1551643811
Tpr91230.7570.8340.1400.1413129199
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" - ], - "text/plain": [ - " expressed genes corr(tau r=8, tau r=0) slope r=8 on r=0 median |tau| r=8 median |tau| r=0 sig r=8 sig r=0\n", - "trt \n", - "Ppp2r1a 8903 0.845 0.969 0.123 0.116 2050 1253\n", - "Prpf6 9158 0.819 0.865 0.148 0.155 1643 811\n", - "Tpr 9123 0.757 0.834 0.140 0.141 3129 199" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "largest |standardized difference| of any latent factor between a perturbation and control: 0.049\n" - ] - } - ], - "source": [ - "a, b = variants_a['r8_calibrated_pooled'], variants_a['r0_calibrated_pooled']\n", - "j = a.merge(b, on=['trt', 'gene_names'], suffixes=('_r8', '_r0'))\n", - "ok = (j['trt_sum_r8'] > 2) & (j['mean_control_r8'] > 0.1) & (j['tau_r8'] != 0) & (j['tau_r0'] != 0)\n", - "rows = []\n", - "for trt, g in j[ok].groupby('trt'):\n", - " rows.append({'trt': trt, 'expressed genes': len(g),\n", - " 'corr(tau r=8, tau r=0)': round(np.corrcoef(g['tau_r8'], g['tau_r0'])[0, 1], 3),\n", - " 'slope r=8 on r=0': round(np.polyfit(g['tau_r0'], g['tau_r8'], 1)[0], 3),\n", - " 'median |tau| r=8': round(g['tau_r8'].abs().median(), 3),\n", - " 'median |tau| r=0': round(g['tau_r0'].abs().median(), 3),\n", - " 'sig r=8': int((g['padj_r8'] < 0.05).sum()), 'sig r=0': int((g['padj_r0'] < 0.05).sum())})\n", - "display(pd.DataFrame(rows).set_index('trt'))\n", - "\n", - "# How strongly do the latent factors track perturbation status at all?\n", - "Uc = U[_is_ctrl]\n", - "std_diff = pd.DataFrame(\n", - " [(U[_lab == p].mean(0) - Uc.mean(0)) / np.sqrt((U[_lab == p].var(0) + Uc.var(0)) / 2) for p in A.columns],\n", - " index=A.columns, columns=[f'U{k+1}' for k in range(U.shape[1])])\n", - "print(f'largest |standardized difference| of any latent factor between a perturbation and control: {std_diff.abs().max().max():.3f}')" - ] - }, - { - "cell_type": "markdown", - "id": "6db225e4", - "metadata": {}, - "source": [ - "### Negative control: fake perturbations drawn from control cells\n", - "\n", - "Three \"perturbations\" of 175, 91 and 78 cells are sampled from the control\n", - "pool (no true effect anywhere), and the same variants are run. Any discovery\n", - "is a false positive, and the standard deviation of the t-statistics should be\n", - "close to 1. Cached in `scarf_investigation/null/`." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8789f418", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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variantdefault_balanced_welchcalibrated_pooledbalanced_pooled
tests31348.0032884.00031348.000
false discoveries34.00307.00025648.000
zero-arm34.00133.00070.000
stat SD (expressed)0.531.1506.480
frac |stat| > 1.96 (expressed)0.000.0810.815
+floor: false discoveriesNaN100.000NaN
+floor: zero-armNaN10.000NaN
+floor: stat SDNaN1.020NaN
+floor, control mean >= 0.05: false discoveriesNaN21.000NaN
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" - ], - "text/plain": [ - "variant default_balanced_welch calibrated_pooled balanced_pooled\n", - "tests 31348.00 32884.000 31348.000\n", - "false discoveries 34.00 307.000 25648.000\n", - "zero-arm 34.00 133.000 70.000\n", - "stat SD (expressed) 0.53 1.150 6.480\n", - "frac |stat| > 1.96 (expressed) 0.00 0.081 0.815\n", - "+floor: false discoveries NaN 100.000 NaN\n", - "+floor: zero-arm NaN 10.000 NaN\n", - "+floor: stat SD NaN 1.020 NaN\n", - "+floor, control mean >= 0.05: false discoveries NaN 21.000 NaN" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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testsstat SDskewfrac |stat| > 2.58 (expect 0.010)false discoveries
control mean (counts/cell)
[0.0, 0.05)9259.00.862.380.02679.0
[0.05, 0.2)5544.01.050.450.01512.0
[0.2, 1.0)15839.01.050.120.0146.0
[1.0, 5.0)10966.01.050.080.0153.0
[5.0, inf)1071.01.130.150.0210.0
\n", - "
" - ], - "text/plain": [ - " tests stat SD skew frac |stat| > 2.58 (expect 0.010) false discoveries\n", - "control mean (counts/cell) \n", - "[0.0, 0.05) 9259.0 0.86 2.38 0.026 79.0\n", - "[0.05, 0.2) 5544.0 1.05 0.45 0.015 12.0\n", - "[0.2, 1.0) 15839.0 1.05 0.12 0.014 6.0\n", - "[1.0, 5.0) 10966.0 1.05 0.08 0.015 3.0\n", - "[5.0, inf) 1071.0 1.13 0.15 0.021 0.0" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "NULL_DIR = os.path.join(INV_DIR, 'null')\n", - "Y_c = Y.loc[_is_ctrl].reset_index(drop=True)\n", - "X_c, XA_c, U_c = X[_is_ctrl], X_A[_is_ctrl], U[_is_ctrl]\n", - "off_c = np.log(res_2['kwargs_glm']['size_factor'])[_is_ctrl]\n", - "rng_null = np.random.default_rng(1)\n", - "perm = rng_null.permutation(len(Y_c))\n", - "fake_sizes = {'fake175': 175, 'fake91': 91, 'fake78': 78}\n", - "A_null = pd.DataFrame(0.0, index=range(len(Y_c)), columns=list(fake_sizes))\n", - "_start = 0\n", - "for k, v in fake_sizes.items():\n", - " A_null.loc[perm[_start:_start + v], k] = 1.0\n", - " _start += v\n", - "n0_null = int((A_null.sum(axis=1) == 0).sum())\n", - "\n", - "_Yc_np = Y_c.to_numpy()\n", - "support_null = pd.concat([pd.DataFrame({'gene_names': Y_c.columns, 'trt': k, 'n_trt': v,\n", - " 'trt_sum': _Yc_np[A_null[k].to_numpy() == 1].sum(axis=0)})\n", - " for k, v in fake_sizes.items()], ignore_index=True)\n", - "\n", - "def run_null(tag, **kw):\n", - " path = os.path.join(NULL_DIR, f'{tag}.csv')\n", - " if os.path.exists(path):\n", - " return pd.read_csv(path), None\n", - " print(f'[null {tag}] computing ...')\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter('ignore')\n", - " df, est = LFC(Y_c, np.c_[X_c, U_c], A_null, np.c_[XA_c, U_c], offset=off_c, verbose=False, backend='fast', **kw)\n", - " df.to_csv(path, index=False)\n", - " return df, est\n", - "\n", - "nulls = {}\n", - "nulls['default_balanced_welch'], estn = run_null('default_balanced_welch', usevar='unequal')\n", - "_rest = ['calibrated_pooled', 'balanced_pooled']\n", - "if any(not os.path.exists(os.path.join(NULL_DIR, f'{t}.csv')) for t in _rest) and estn is None:\n", - " _, estn = LFC(Y_c, np.c_[X_c, U_c], A_null, np.c_[XA_c, U_c], offset=off_c, verbose=False, backend='fast', usevar='unequal')\n", - "if estn is not None:\n", - " pi_null = estimate_propensity_scores(A_null.to_numpy(), np.c_[XA_c, U_c], K=1, class_weight=None)\n", - " nulls['calibrated_pooled'], _ = run_null('calibrated_pooled', Y_hat=estn['Y_hat'], pi_hat=pi_null, usevar='pooled', ps_clip=LOOSE_CLIP)\n", - " nulls['balanced_pooled'], _ = run_null('balanced_pooled', Y_hat=estn['Y_hat'], pi_hat=estn['pi_hat_raw'], usevar='pooled')\n", - " del estn\n", - "else:\n", - " for t in _rest:\n", - " nulls[t], _ = run_null(t)\n", - "\n", - "rows = []\n", - "for tag, d in nulls.items():\n", - " d = d.merge(support_null, on=['trt', 'gene_names'])\n", - " fin = np.isfinite(d['stat'])\n", - " expressed = fin & (d['trt_sum'] > 2) & (d['mean_control'] > 0.1)\n", - " s = d[d['padj'] < 0.05]\n", - " row = {'variant': tag, 'tests': int(fin.sum()), 'false discoveries': len(s), 'zero-arm': int((s['trt_sum'] == 0).sum()),\n", - " 'stat SD (expressed)': round(d.loc[expressed, 'stat'].std(), 2),\n", - " 'frac |stat| > 1.96 (expressed)': round((d.loc[expressed, 'stat'].abs() > 1.96).mean(), 3)}\n", - " if tag == 'calibrated_pooled':\n", - " f = add_poisson_floor(d, n0_null)\n", - " sf = f[f['padj_floor'] < 0.05]\n", - " finf = np.isfinite(f['stat_floor'])\n", - " row.update({'+floor: false discoveries': len(sf), '+floor: zero-arm': int((sf['trt_sum'] == 0).sum()),\n", - " '+floor: stat SD': round(f.loc[finf, 'stat_floor'].std(), 2),\n", - " '+floor, control mean >= 0.05: false discoveries': int((sf['mean_control'] >= 0.05).sum())})\n", - " null_floor = f\n", - " rows.append(row)\n", - "display(pd.DataFrame(rows).set_index('variant').T)\n", - "\n", - "# Where do the remaining false positives after the floor come from?\n", - "finf = np.isfinite(null_floor['stat_floor'])\n", - "strata = pd.cut(null_floor['mean_control'], [0, 0.05, 0.2, 1, 5, np.inf], right=False)\n", - "display(null_floor[finf].groupby(strata, observed=True).apply(lambda g: pd.Series({\n", - " 'tests': len(g), 'stat SD': round(g['stat_floor'].std(), 2), 'skew': round(stats.skew(g['stat_floor']), 2),\n", - " 'frac |stat| > 2.58 (expect 0.010)': round((g['stat_floor'].abs() > 2.58).mean(), 3),\n", - " 'false discoveries': int((g['padj_floor'] < 0.05).sum())})).rename_axis('control mean (counts/cell)'))" - ] - }, - { - "cell_type": "markdown", - "id": "8062aaee", - "metadata": {}, - "source": [ - "The default is *conservative* on expressed genes (t SD ≈ 0.5) yet still\n", - "produces false discoveries, all of them zero-arm artifacts. Balanced + pooled\n", - "calls three quarters of all genes under a pure null. Calibrated + pooled is\n", - "mildly anti-conservative; the Poisson floor brings the t-statistics to SD ≈ 1\n", - "and removes the zero-arm hits, and the remaining false positives are very\n", - "sparse genes (control mean < 0.05 counts/cell) with a few counts in the small\n", - "arm, which show up as large positively skewed statistics." - ] - }, - { - "cell_type": "markdown", - "id": "28ec637c", - "metadata": {}, - "source": [ - "### Biology: pathway enrichment of the DE sets\n", - "\n", - "Enrichr (GO Biological Process 2023, KEGG 2019 Mouse, Reactome 2022) on up-\n", - "and down-regulated sets for the three perturbations, comparing Wilcoxon, the\n", - "current causarray defaults, and the corrected inference (calibrated + pooled +\n", - "floor, zero-arm hits excluded). Tables are cached in\n", - "`scarf_investigation/enrichr/`; the cell needs network access only when a\n", - "cache is missing." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f62af5c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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n genestop enriched terms (padj < 0.05)
perturbationdirectionmethod
Ppp2r1aupWilcoxon817Collagen Chain Trimerization [1e-03]; Collagen Biosynthesis And Modifying Enzymes [8e-03]; Neuronal System [2e-02]
downWilcoxon509Neuronal System [3e-07]; Signaling By NTRKs [5e-07]; Signal Transduction [5e-06]
TprupWilcoxon722Herpes simplex virus 1 infection [1e-04]; Positive Regulation Of Transcription By RNA Polymerase II [2e-03]; Regulation Of Transcription By RNA Polymerase I...
downWilcoxon150Positive Regulation Of Neuron Projection Development [5e-04]; Regulation Of Neuron Projection Development [5e-04]; Neuronal System [6e-04]
Prpf6upWilcoxon532Metabolism Of RNA [3e-11]; mRNA Splicing, Via Spliceosome [5e-06]; Transcriptional Regulation By TP53 [1e-05]
downWilcoxon134Neuronal System [5e-10]; Transmission Across Chemical Synapses [2e-07]; Neurotransmitter Receptors And Postsynaptic Signal Transmission [5e-05]
Ppp2r1aupcausarray current defaults84(none)
downcausarray current defaults18(none)
Tprupcausarray current defaults41(none)
downcausarray current defaults45(none)
Prpf6upcausarray current defaults39JNK Cascade [8e-03]; Stress-Activated MAPK Cascade [8e-03]; Regulation Of Cell Growth [8e-03]
downcausarray current defaults102(none)
Ppp2r1aupcausarray corrected1163Neuronal System [4e-03]; Collagen Chain Trimerization [2e-02]; Potassium Channels [2e-02]
downcausarray corrected762Neuronal System [4e-07]; Axonogenesis [9e-06]; Nervous System Development [4e-05]
Tprupcausarray corrected1060Regulation Of Transcription By RNA Polymerase II [3e-07]; Negative Regulation Of DNA-templated Transcription [4e-07]; Positive Regulation Of DNA-templated T...
downcausarray corrected111PTK6 Regulates RHO GTPases, RAS GTPase And MAP Kinases [2e-02]; Signaling By Non-Receptor Tyrosine Kinases [2e-02]; Negative Regulation Of NMDA Receptor-Med...
Prpf6upcausarray corrected785Metabolism Of RNA [6e-21]; mRNA Splicing - Major Pathway [3e-14]; mRNA Splicing [3e-14]
downcausarray corrected83Neuronal System [2e-02]; Defective CHST14 Causes EDS, Musculocontractural Type [2e-02]; Defective CHST3 Causes SEDCJD [2e-02]
\n", - "
" - ], - "text/plain": [ - " n genes top enriched terms (padj < 0.05)\n", - "perturbation direction method \n", - "Ppp2r1a up Wilcoxon 817 Collagen Chain Trimerization [1e-03]; Collagen Biosynthesis And Modifying Enzymes [8e-03]; Neuronal System [2e-02]\n", - " down Wilcoxon 509 Neuronal System [3e-07]; Signaling By NTRKs [5e-07]; Signal Transduction [5e-06]\n", - "Tpr up Wilcoxon 722 Herpes simplex virus 1 infection [1e-04]; Positive Regulation Of Transcription By RNA Polymerase II [2e-03]; Regulation Of Transcription By RNA Polymerase I...\n", - " down Wilcoxon 150 Positive Regulation Of Neuron Projection Development [5e-04]; Regulation Of Neuron Projection Development [5e-04]; Neuronal System [6e-04]\n", - "Prpf6 up Wilcoxon 532 Metabolism Of RNA [3e-11]; mRNA Splicing, Via Spliceosome [5e-06]; Transcriptional Regulation By TP53 [1e-05]\n", - " down Wilcoxon 134 Neuronal System [5e-10]; Transmission Across Chemical Synapses [2e-07]; Neurotransmitter Receptors And Postsynaptic Signal Transmission [5e-05]\n", - "Ppp2r1a up causarray current defaults 84 (none)\n", - " down causarray current defaults 18 (none)\n", - "Tpr up causarray current defaults 41 (none)\n", - " down causarray current defaults 45 (none)\n", - "Prpf6 up causarray current defaults 39 JNK Cascade [8e-03]; Stress-Activated MAPK Cascade [8e-03]; Regulation Of Cell Growth [8e-03]\n", - " down causarray current defaults 102 (none)\n", - "Ppp2r1a up causarray corrected 1163 Neuronal System [4e-03]; Collagen Chain Trimerization [2e-02]; Potassium Channels [2e-02]\n", - " down causarray corrected 762 Neuronal System [4e-07]; Axonogenesis [9e-06]; Nervous System Development [4e-05]\n", - "Tpr up causarray corrected 1060 Regulation Of Transcription By RNA Polymerase II [3e-07]; Negative Regulation Of DNA-templated Transcription [4e-07]; Positive Regulation Of DNA-templated T...\n", - " down causarray corrected 111 PTK6 Regulates RHO GTPases, RAS GTPase And MAP Kinases [2e-02]; Signaling By Non-Receptor Tyrosine Kinases [2e-02]; Negative Regulation Of NMDA Receptor-Med...\n", - "Prpf6 up causarray corrected 785 Metabolism Of RNA [6e-21]; mRNA Splicing - Major Pathway [3e-14]; mRNA Splicing [3e-14]\n", - " down causarray corrected 83 Neuronal System [2e-02]; Defective CHST14 Causes EDS, Musculocontractural Type [2e-02]; Defective CHST3 Causes SEDCJD [2e-02]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import requests, time\n", - "\n", - "ENRICHR = 'https://maayanlab.cloud/Enrichr'\n", - "LIBS = ('GO_Biological_Process_2023', 'KEGG_2019_Mouse', 'Reactome_2022')\n", - "\n", - "def enrichr(genes, cache_name, top=8):\n", - " path = os.path.join(INV_DIR, 'enrichr', f'{cache_name}.csv')\n", - " if os.path.exists(path):\n", - " return pd.read_csv(path)\n", - " genes = [g for g in genes if isinstance(g, str)]\n", - " if len(genes) < 5:\n", - " out = pd.DataFrame(columns=['lib', 'term', 'padj', 'overlap', 'genes'])\n", - " else:\n", - " r = requests.post(f'{ENRICHR}/addList', files={'list': (None, '\\n'.join(genes)), 'description': (None, cache_name)}, timeout=60)\n", - " r.raise_for_status(); uid = r.json()['userListId']\n", - " rows = []\n", - " for lib in LIBS:\n", - " for _ in range(3):\n", - " try:\n", - " e = requests.get(f'{ENRICHR}/enrich', params={'userListId': uid, 'backgroundType': lib}, timeout=60)\n", - " e.raise_for_status(); break\n", - " except Exception:\n", - " time.sleep(2)\n", - " for row in e.json().get(lib, [])[:top]:\n", - " rows.append({'lib': lib, 'term': row[1], 'padj': row[6], 'overlap': len(row[5]), 'genes': ','.join(row[5][:6])})\n", - " out = pd.DataFrame(rows, columns=['lib', 'term', 'padj', 'overlap', 'genes'])\n", - " out.to_csv(path, index=False)\n", - " return out\n", - "\n", - "de_sets = {\n", - " 'Wilcoxon': variants_a['r8_default_balanced_welch'].assign(sig=lambda d: d['w_sig'], eff=lambda d: d['wilcox_logfc']),\n", - " 'causarray current defaults': variants_a['r8_default_balanced_welch'].assign(sig=lambda d: d['padj'] < 0.05, eff=lambda d: d['tau']),\n", - " 'causarray corrected': variants_a['r8_calibrated_pooled_floor'].assign(sig=lambda d: (d['padj'] < 0.05) & (d['trt_sum'] > 0), eff=lambda d: d['tau']),\n", - "}\n", - "enrich_rows = []\n", - "for method, d in de_sets.items():\n", - " for trt in PERTS:\n", - " for direction, sign in [('up', 1), ('down', -1)]:\n", - " genes = d.loc[(d['trt'] == trt) & d['sig'] & (np.sign(d['eff']) == sign), 'gene_names'].tolist()\n", - " res = enrichr(genes, f\"{method.replace(' ', '_')}_{trt}_{direction}\")\n", - " best = res[res['padj'] < 0.05].sort_values('padj').head(3)\n", - " enrich_rows.append({'perturbation': trt, 'direction': direction, 'method': method, 'n genes': len(genes),\n", - " 'top enriched terms (padj < 0.05)': '; '.join(f\"{t} [{p:.0e}]\" for t, p in zip(best['term'].str.replace(r' \\(GO:\\d+\\)| R-HSA-\\d+', '', regex=True), best['padj'])) or '(none)'})\n", - "enrich_summary = pd.DataFrame(enrich_rows).set_index(['perturbation', 'direction', 'method'])\n", - "with pd.option_context('display.max_colwidth', 160):\n", - " display(enrich_summary)" - ] - }, - { - "cell_type": "markdown", - "id": "54196528", - "metadata": {}, - "source": [ - "The corrected sets recover the expected biology, and more sharply than\n", - "Wilcoxon: Prpf6 (spliceosome component) knockdown upregulates spliceosome and\n", - "mRNA-splicing genes; Tpr (nuclear pore basket) upregulates RNA transport and\n", - "nucleoporins; Ppp2r1a (PP2A scaffold) downregulates protein phosphorylation,\n", - "axon guidance and vesicle transport genes. The current-default sets are mostly\n", - "chance zero-count genes and show little or no enrichment." - ] - }, - { - "cell_type": "markdown", - "id": "7653c18f", - "metadata": {}, - "source": [ - "### Diagnostic figures\n", - "\n", - "For each of the three perturbations: (left) the current defaults against\n", - "Wilcoxon, with causarray-only hits sitting at Wilcoxon log2FC ≈ -22 (zero\n", - "counts in the perturbed arm); (middle) the corrected inference; (right) effect\n", - "estimates with and without latent factors." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "cefa76de", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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ZFi1aREREKSkpBIDWrVtHycnJlJKSQkREHTt2pHHjxomui7+/P82aNUvtdVO0atUqMjU1Fe5nWFgYjR8/nrZv304tW7YUtgsODqbBgwcL+1SvXl24rrt27SIbGxtav3696BpevnyZiIju379PNjY21Lt3bzp//jzFxcXR2rVrKTY2lohepksLCwsaM2YMxcTE0L59+8jExER0PRSFhIRQmzZthGuhimJ6V2fq1KnUtm1brduxqqsq5r1ERPPnz6eIiAhKTEykvXv3koODA/3www/C+pJ5CJFymg0KCiIzMzPR+1Hx2VCVT2s7dnZ2Nn322WfUoEEDIb/Kzs6m4uJiCgwMpG7dutH58+cpPj6ePvvsM7K1tRXyzu3bt5ORkRGtXr2aYmNjaebMmWRubi46D0Xya+zp6UmHDh2iq1ev0vvvv08uLi6Un58v3ENV5ZDu3buTp6cnnThxgqKioigkJITc3NyU9mvSpAlFRkbShQsXqFmzZqL318GDB8nCwoLWr19PCQkJdOjQIXJxcaE5c+YI28jTyZYtW+jmzZs0adIkMjMzE857/Pjx5OfnR+fPn6fExEQ6fPgw7d27V5TOLl++TIWFhbRr1y4CQHFxcZScnEwvXryg7OxssrS0pB07dgjHfPLkCRkZGdF///2nIZWJ6ZImtF2zCxcukJ6eHs2bN4/i4uJo3bp1JJPJaN26daJ0Jc+DKipdKOYVip+1uXbtGgGgW7duCcuuX78uXHsioqNHj9LGjRspOjqaoqOjaeTIkeTg4EDp6enCPgDI3t6e1qxZQwkJCXTnzh2lsvP9+/dpwYIFdPnyZUpISKAlS5aQvr4+nTlzRnRvNOX7RUVF1KJFC2rQoAEdOnSIEhISaN++fXTgwAEi0l6e1RUA2rNnj2iZhYUFffbZZ5SVlUU1atSg3r1707Vr1+jo0aNUp04dGjp0qLBtyffQw4cPycDAgH7++WdKTEykq1ev0vLlyykjI4MKCgrI0tKSPv/8c7p16xZFR0fT+vXr6e7du0REtHv3bjI0NKTly5dTXFwc/fTTT6Svry9K79qeO0XqyvSK6dXf359atWpFFy5coDNnzlCjRo0oKChICEfXuCmmC1axKisv11RXKSwspOTkZLKwsKDFixdTcnIyZWZmKi3Lzs7WqX6gqdyga/14ypQp9P777xPRy/RuY2ND1apVo7///puIiLZs2UKOjo7C9rrUKWbPnk1mZmbCu+HEiRPk6OhIX3zxhdrr1qdPH6FuUHLZhx9+SERU6jpwVTnPSZMmUY0aNejAgQN048YNGjp0KFlbWwv3Q1sbgboyliqlrZurSyO61s3U1enz8vJo8eLFZGFhIcQ5IyNDKb7Z2dl05MgRAkDnzp2j5ORkKiwspKioKFq5ciVdvXqV4uPjaebMmWRsbCzkB0RE/fr1IycnJ9q9ezclJCTQkSNHaNu2bWrLTbrcC3XPfYMGDWjw4MEUExND8fHxtGPHDoqKilJ5D4iIfH196fvvvxct01Z2LCwspICAAOrZsycREf36669kaWkpyivK2l6h7X5qera0tTEMHDiQWrZsSSdOnKBbt27RggULSCqVCm0hZ86cIYlEQt999x3FxcXRL7/8QlZWVirfzXLp6elkY2NDgwcPphs3btCBAwfI3d1dVKYrj/YTXcqjutx7e3t74VrqihvRK5C2zL/kw1lQUEC1atUSKrXyl8Bff/0l2nf37t1Uv359tcdMT08nqVQqqriXpGsjOgClBObs7Cy8GOSGDBlCo0ePFi07efIk6enpUU5OjrBfyUy1uLiY7O3t6ddffxVdD20VpRkzZpCnpycVFxcLy6ZNmyY6H13i88knn4gKz6rIMyV5hlHaRnQi1Y12EydOpHbt2onOQZNPPvmE2rVrJ1qmS/pRZdy4cdSnTx+N8StJXuC4du2a6Li///67sM2NGzcIgPCFyIcffkiBgYEqw8vMzCRjY2OKjIwULR85cqRQwNLlHqsCgMaMGSNa1rx5cxo7dqwo7osXLxZtU6NGDfrmm29Ey5o2bSo0Rutyrb/66ivq1KmTKIx79+6JKu5BQUHUqlUrpeNMmzaNiIh++ukncnd3Fxo1FJUsCMrPV7FSvH37drK2tqbc3FwiIoqKiiKJRCI817qIj48nAMI9atq0Ke3YsYMePXpERkZGlJWVRXl5eSSTyWjNmjVEROTk5KRUCJ4/fz4FBAQQkfLzPWPGDKpTp47acx06dCg5OztTYWGhsKxv377Uv39/tfG+ceMGeXp6kp6eHnl7e9PHH38sNESUDFdfX59MTU1Ff/PmzRNt98svv5CLi4sOV4tVVVUx71Xlxx9/pMaNGwufdWlEV/V+9PT0FD6ryqfLcmyilw2OFhYWwjtFrm7duvTbb78REVFAQIDKd68ujejbtm0TlqWmppJMJhO+6FVVDpG/nyIiIoRlT58+JZlMJjRGy/cr2ZgZExNDAOjs2bNERNS6dWv69ttvRXHauHEjVa9eXfgMgL788kvhc2ZmJkkkEvrnn3+IiKhbt240fPhwleen+M5TVe4iIho7dqzoy/rFixeTq6urzuUDIu1pQpdrNnDgQOrYsaMo3LCwMPLy8hI+l8yDKipdqGtEl8lkonf2pUuX1Ibh4+MjeqfPmDGDmjZtqnb7wsJCMjc3p3379gnLANCnn34q2k7dPSypa9eu9NlnnwmfteX7//77L+np6QnlBEW6lGd1UbK8kJubS/PnzycAdODAAVq1ahVZW1tTZmamsP3ff/9Nenp69OjRIyISv4cuXrxIAFQ2IKemphIAoVOEopYtW1JoaKhoWd++falr166iuGp67lRRVaYvmV4PHTpE+vr6lJSUJKyXl13PnTtXqrgppgtWsSojL9elrkJEZGlpKfqiUdUybfUDbeUGXevHe/fuJUtLSyoqKqKoqCiys7OjyZMnU1hYGBERjR49WlSG1qVOMXv2bDIxMRF9wRgWFkbNmzdXG4/du3eTmZkZZWVlERFRWloaGRsbC43cpa0DV4XzzMzMJENDQ9q8ebOwPj8/n2rUqEE//vgjEZWtjUCdstbNFdOIrnUzTXV6dc+fIsV2JHW8vLxo6dKlREQUFxdHAOjw4cMqt1WV5+pyL9Q99+bm5qVqJLW0tKQNGzaIlulSdkxISCBzc3OaNm0amZiY0KZNm0Tbl7W9Qtv91PRsaWpjuHXrFkkkEnrw4IFoefv27WnGjBlE9LJtp3PnzqL1/fv315g2fv31V7K1tRWVVVavXi1Kq+XRfqJLeVSXe+/v7y/qSKMLHs6lEpUci9DAwABNmjRBTEyMaJsmTZqIPvfq1QuxsbFqw4yJiUFeXh7at2//SnEzMjKCj4+P0nLF+Fy8eBHr16+HmZmZ8BcSEoLi4mIkJiYK25UMS/7TvJLDpegiJiYGLVq0EA2hojieo67xUXT58mX06NEDzs7OMDc3F34OlZSUVKo4ajNs2DBERUWhfv36mDRpEg4dOqRx+5ycHKWfEslpSz8rV65EkyZNYGdnBzMzM6xevVrj+SQkJGDgwIFwdXWFhYWF8BMxxX1K3kv52GfyexkVFaU27UVHRyM3NxcdO3YU3Z8NGzYIP03T5R6ro7hdQECAxucpPT0dDx8+VJqwNTAwUGk/Tdf64sWLOHbsmOicPDw8AED0kzvF56l69erCdevbty9ycnLg6uqK0NBQ7NmzRzS0gC7kP2vcs2cPAGDt2rUIDg5W+smiJvXq1UOtWrUQHh6O9PR0XL58GUFBQXBwcECdOnUQERGBM2fOICcnB+3atcOTJ09w7949jBw5UnT+X3/9tdLwLHJRUVFo3bq1xvHdGjRoAH19feFzyWulipeXF65fv44zZ85g+PDhePz4Mbp164ZRo0aJtgsODkZUVJTob/z48aJtZDKZaBJV9vaprLz3zz//RKtWreDo6AgzMzN89dVXpc5jVL0fb968iaKiIrVxL+uxL168iMzMTNja2oqe78TERNE7W9W7Vxclt7OxsUH9+vVF90GxHBITEwMDAwNhzgbg5VAfivvJ76mch4cHrKysRO/sefPmic4pNDQUycnJome/5LFNTU1hbm4uvIfGjh2Lbdu2wc/PD1OnTkVkZKRO51xSaGgoDh06hAcPHgB4+VNd+QTIpaEpTehyzWJiYlTmg4rpSq6i04Wi7du3i97ZXl5earcdNGgQNm/eDODlkAxbt27FoEGDhPUpKSkYM2YM3N3dYWlpCUtLS2RmZio9C6qeoZKKiorwzTffwMfHR7gOhw4d0lheAsR5WVRUFGrVqgV3d3eVxyhreVaVDz/8EGZmZjAxMcHPP/+MhQsXokuXLoiJiYGvry9MTU2FbQMDA1FcXCwah1rO19cX7du3h7e3N/r27YvVq1cLw/XZ2Nhg2LBhCAkJQbdu3fDLL7+IxrdWl84U372anruyiImJgZOTE5ycnIRlXl5eoneCrnHTli7Y61URebkudRVdaasflFedvU2bNsjIyMDly5dx/PhxBAUFITg4GMePHwfwcoiLoKCgUofr4uICc3Nz4bO2svh7770HAwMDYQimXbt2wdzcHJ06dQJQ+jqwoso4z4SEBBQUFIjeD4aGhmjWrJlSWqsIutbNSypN3UxTnf5VZGVlYerUqcK71szMDLGxsUK8o6KioK+vX6r7VZp7ofjcT5kyBaNGjUKHDh3w/fffa32WVbW/6FJ2dHV1xcKFC/HDDz+gW7duovKHXGnbK3S5n5qeLU1tDJcuXQIRwd3dXRT28ePHX6k8FxcXBx8fH9E1bNasmdL1fNX2E13Ko7rc+7LU/Xli0SpGsfJUsmCrC20Trsgn5aIS42WqGpNVJpOprMgpxqe4uBgff/yxaGwludq1awv/V2wwk0gkwvhQuioZZ3V0jU9JWVlZ6NSpEzp16oRNmzbBzs4OSUlJCAkJQX5+PoCX103x+JrGslWnUaNGSExMxD///IMjR46gX79+6NChg9K44HLVqlUTjROrjfye7dixA5MnT8ZPP/2EgIAAmJubY8GCBTh79qzafbt16wYnJyesXr0aNWrUQHFxMRo2bChcA7mS91J+PPm91JT+5Nv8/fffqFmzpmidfHJHXe5xaejyPCluQ0Q6NWKUPPdu3brhhx9+UNqm5AQrmp4BJycnxMXF4fDhwzhy5AjGjRuHBQsW4Pjx4zpPJmJkZIQhQ4Zg3bp16N27N7Zs2SKMBVgabdu2xbFjx+Dj44N69erB3t4eABAUFIRjx45BKpXC2dkZLi4uwpiiq1evFjXSABA1gpeky6RQZXlf6OnpoWnTpmjatCkmT56MTZs2YciQIZg5c6ZQ6DQ1NYWbm5vGcJ49e8aTEb2DKjrvPXPmDAYMGIC5c+ciJCQElpaW2LZtm9axp8tCMe5lPXZxcTGqV6+uNDYiAKXxP8tLyfugWA5Rlz+oemereoeXfGfPnTsXvXv3VtqmZIFf03uoS5cuuHv3Lv7++28cOXIE7du3x/jx47Fw4UJtpyjw9/eHr68vNmzYgJCQEFy7dg379u3TeX9d6HLNVF0/TXnx604XTk5OWt/bcgMHDsT06dNx6dIl5OTk4N69exgwYICwftiwYXjy5AkWL14MZ2dnSKVSBAQEKJVztD3/P/30ExYtWoTFixfD29sbpqam+PTTTzWWlwBxGtL2zihLeVadRYsWoUOHDrCwsBDydEBzeUfVcn19fRw+fBiRkZE4dOgQli5dipkzZ+Ls2bOoU6cO1q1bh0mTJuHgwYPYvn07vvzySxw+fFgYq16X8lZ51Be0HUPVcl3iVtp8gb1+r3rPdKmrlCYsTfWDUo/Bq4alpSX8/PwQHh6OyMhItGvXDq1bt0ZUVBRu3ryJ+Ph4jfONqVPaZ9HIyAgffPABtmzZggEDBmDLli3o37+/MKljaevAVeE85XmhpvdDebURqKJr3bwkedx1qZtpqtO/irCwMPz7779YuHAh3NzcIJPJ8MEHHwjxLssEwbrcCznF537OnDkYOHAg/v77b/zzzz+YPXs2tm3bhl69eqk8VrVq1YQviOV0LTueOHEC+vr6uHPnDgoLC3Wa1FRT/HW5n5qeLU1tDMXFxdDX18fFixeV0oaZmRmAsrXN6FKuLI/2E13Ko7rc+7LU/bkneiU6c+aM8P/CwkJcvHhR+AamrOrVqweZTIajR4+qXC9PICV7h5Qc4L+0GjVqhBs3bsDNzU3pz8jISKcw5Nup6vFUkpeXl+iaAVD6XJb4xMbG4unTp/j+++/RunVreHh4KH0La2dnh4yMDNGETtqum5GRkcpzsrCwQP/+/bF69Wps374du3btwrNnz1SG4e/vj9jYWJUvME3p5+TJk2jZsiXGjRsHf39/uLm5afzWNTU1FTExMfjyyy/Rvn17eHp6KmUeuvDx8VGb9uSTwyUlJSndG3kPIV3usTqq9tP0PFlYWKBGjRo4deqUaHlkZCQ8PT3Vhq14reVpzsXFRem8SlN4l8lk6N69O5YsWYLw8HCcPn1a7RcohoaGKtPWqFGjcOTIEaxYsQIFBQUqM3ptgoODERkZicOHD4sKo0FBQQgPD0d4eDjatWsHAHBwcEDNmjVx+/ZtpXOXN1wr8vHxwcmTJ8utgKmOvLdiaSdhu379Ovz9/SsiSqyKqIy8NyIiAs7Ozpg5cyaaNGmCevXqiSbc05Wq91y9evXUfmml67FV5VeNGjXCo0ePYGBgoPR8V6tWDQDg6elZLu/s58+fIz4+XuN98PLyQmFhoejL4NTUVMTHx4ve2YWFhaJJg+Li4vDixQvROzsuLk5lOUHe0UAXdnZ2GDZsGDZt2oTFixeLJkwvSVMZZ9SoUVi3bh3Wrl2LDh06iHrL6kpTmtDlmnl5eanMB93d3VWmq4pOF6+iVq1aaNOmDTZv3ozNmzejQ4cOcHBwENafPHkSkyZNQteuXYWJVEtOYqWrkydPokePHhg8eDB8fX3h6uqqNFmrNj4+Prh//z7i4+NVri+P8rWco6Mj3NzcRA3owMt7HxUVJconIyIioKenp7aHvEQiQWBgIObOnYvLly/DyMhI+AUc8LLcOmPGDERGRqJhw4bYsmULgJdpQpfyVnnz8vJCUlKSaHLh6OhopKWlCceurLixV1MRebkudRVdaasfaCs36Fo/Bv6vA8yJEyfQtm1bWFlZwcvLC19//TXs7e01pmV1dYqyGDRoEA4ePIgbN27g2LFjSj1xS1MHVuV1n6f8fVvy/VBQUIALFy4Ix9KljUBdm4AmutTNVaWRstTNVClLnOVOnjyJYcOGoVevXvD29oajoyPu3LkjrPf29kZxcbHwKwJVx1Y8L13uhSbu7u6YPHkyDh06hN69ewuTwavi7++P6Oho0TJdyo7bt2/H7t27ER4ejnv37mH+/PlKYZe2vULX+6np2VLXxuDv74+ioiKkpKQohe3o6AigbG0zHh4euHr1KvLy8oRlihO5lkf7iS7lUUDzvc/NzUVCQkKp6/7ciF6Jli9fjj179iA2Nhbjx4/H8+fPMWLECI377NmzR+ODZmxsjGnTpmHq1KnCT8/OnDmDNWvWAIBQCJgzZw7i4+Px999/v1JPuGnTpuH06dMYP3688E3w3r17MXHiRJ3DsLe3F2b0fvz4MdLS0lRuN2bMGCQkJGDKlCmIi4vDli1blGa3Lkt8ateuDSMjIyxduhS3b9/G3r17lV56zZs3h4mJCb744gvcunVL5bEVubi44OrVq4iLi8PTp09RUFCARYsWYdu2bYiNjUV8fDx27twJR0dHtb23goODkZWVhRs3biit05R+3NzccOHCBfz777+Ij4/HV199JZq9WpG1tTVsbW2xatUq3Lp1C//99x+mTJmi8fxUmTFjBs6fP49x48bh6tWriI2Nxa+//oqnT5/C3Nwcn3/+OSZPnow//vgDCQkJuHz5MpYvX44//vgDgG73WJ2dO3di7dq1iI+Px+zZs3Hu3DlMmDBB4z5hYWH44YcfsH37dsTFxWH69OmIiorCJ598ItpO07UeP348nj17hg8//FCY3fnQoUMYMWKEzoWP9evXY82aNbh+/Tpu376NjRs3QiaTwdnZWeX2Li4uOHr0KB49eiQqUHl6eqJFixaYNm0aPvzwwzJ90y9Pc2vXrhX9zC4oKAgXLlzAmTNnhFnTgZff8H733Xf45ZdfEB8fj2vXrmHdunX4+eefVYY/YcIEpKenY8CAAbhw4QJu3ryJjRs3qvzpuK4++OADLFq0CGfPnsXdu3cRHh6O8ePHw93dXfS+zMvLw6NHj0R/ig0oJ0+eFH56yt5OlZX3JiUlYdu2bUhISMCSJUtEDU+6unfvnvB+3Lp1K5YuXar0vlKky7FdXFyQmJiIqKgoPH36FHl5eejQoQMCAgLQs2dP/Pvvv7hz5w4iIyPx5ZdfCoXhTz75BGvXrhW9e1XlV6rMmzcPR48exfXr1zFs2DBUq1YNPXv2VLt9vXr10KNHD4SGhuLUqVO4cuUKBg8ejJo1a6JHjx7CdoaGhpg4cSLOnj2LS5cuYfjw4WjRooXwU9JZs2Zhw4YNmDNnDm7cuIGYmBih16yuZs2ahf/973+4desWbty4gf3796utxDk7O0MikWD//v148uQJMjMzhXWDBg3CgwcPsHr1aq1pUB1NaUKXa/bZZ5/h6NGjmD9/PuLj4/HHH39g2bJl+Pzzz1Uer6LTxasaNGgQtm3bhp07d2Lw4MGidW5ubti4cSNiYmJw9uxZDBo0qEz5pJubm9AjOyYmBh9//DEePXpUqjCCgoLQpk0b9OnTB4cPHxZ6kR08eBBA+ZSvtRk0aBCMjY0xdOhQXL9+HceOHcPEiRMxZMgQ0ZcPcmfPnsW3336LCxcuICkpCbt378aTJ0/g6emJxMREzJgxA6dPn8bdu3dx6NAh0Zc1YWFhWL9+PVauXImbN2/i559/xu7du9Wms/LSoUMH+Pj4YNCgQbh06RLOnTuHjz76CEFBQcJP5isrbuzVVERerktdRVfa6gfayg261o+Bl43LBw8ehEQiETqRtG3bFps3b9Y6ZIa6OkVZyIeAHDRoEFxcXIRfoQDQWgf+6KOPMGPGDI3hv+7zNDU1xdixYxEWFoaDBw8iOjoaoaGhyM7OxsiRIwHo1kagqoyljS51c3VppLR1M1VcXFyQmZmJo0eP4unTp6Ua7sLNzQ27d+9GVFQUrly5goEDB4p6uLu4uGDo0KEYMWIE/vrrLyQmJiI8PBw7duwAoLrcpMu9UCUnJwcTJkxAeHg47t69i4iICJw/f15jw3tISIjSF6vayo7379/H2LFj8cMPP6BVq1ZYv349vvvuO6UG57K0V2i7n5qeLU1tDO7u7hg0aBA++ugj7N69G4mJiTh//jx++OEHHDhwAACEX5f9+OOPiI+Px7Jly4Ryijry+z169GjExMQIv0oA/q/XfXm0n2grj+py78+cOSP8KrFUSjWCOisVbROibNmyhZo3b05GRkbk6elJR48eFbZRN4mRLrOKFxUV0ddff03Ozs5kaGhItWvXFk2EcOrUKfL29iZjY2Nq3bo17dy5U2liUVXxVpygQ+7cuXPUsWNHMjMzI1NTU/Lx8RFN1qhqP19fX5o9e7bwefXq1eTk5ER6enoaJ/3ct28fubm5kVQqpdatW9PatWuVrpO2+KiahGjLli3k4uJCUqmUAgICaO/evUoTdezZs4fc3NzI2NiY3n//fVq1apXGSUNSUlKEeACgY8eO0apVq8jPz49MTU3JwsKC2rdvr3GSLCKiAQMG0PTp04XPuqSf3NxcGjZsGFlaWpKVlRWNHTuWpk+frnHSusOHD5OnpydJpVLy8fGh8PBw0SQsqiYvef78uXBucuHh4dSyZUuSSqVkZWVFISEhwv0pLi6mX375herXr0+GhoZkZ2dHISEhdPz4cWF/Xe6xIgC0fPly6tixI0mlUnJ2dqatW7cqXTPFiVeKiopo7ty5VLNmTTI0NCRfX1/RBFa6XGuil5O39erVi6ysrEgmk5GHhwd9+umnwgQfQUFB9Mknn4j26dGjBw0dOpSIXqat5s2bk4WFBZmamlKLFi3oyJEjwraKz9DevXvJzc2NDAwMyNnZWRTumjVrRBNmleTs7Cx67tRxdnYmAJScnCxaXrduXQJA9+7dEy3fvHkz+fn5kZGREVlbW1ObNm1o9+7dRKT62l+5coU6depEJiYmZG5uTq1bt6aEhAQiUj2pjrbJgFetWkXBwcFkZ2dHRkZGVLt2bRo2bJho8rOhQ4cSAKW/khNMRUZGkpWVFWVnZ2u9Rqzqqqp5b1hYGNna2pKZmRn179+fFi1aJIqnLhOLjhs3jsaMGUMWFhZkbW1N06dPF00kpC6f1nbs3Nxc6tOnD1lZWREAYXK09PR0mjhxItWoUYMMDQ3JycmJBg0aJJqk75tvvqFq1aqRmZkZDR06lKZOnarTxKL79u2jBg0akJGRETVt2lQ0iai6e/js2TMaMmQIWVpakkwmo5CQEIqPj1fab9euXeTq6kpGRkbUrl07pYkQDx48SC1btiSZTEYWFhbUrFkzWrVqlbC+ZN4nV3LSuPnz55OnpyfJZDKysbGhHj160O3bt4lI9Ttv3rx55OjoSBKJRHjvyw0ZMoRsbGyUJkaSh1Myf1WkS5rQds2IiP7880/y8vIS0u2CBQtE6xXTVUWkC3UTi2qbVE/R8+fPSSqVkomJiTA5vNylS5eoSZMmJJVKqV69erRz506dJp9TfC+kpqZSjx49yMzMjOzt7enLL7+kjz76SOl51ZTvy8MZPnw42drakrGxMTVs2JD2798vrNdWnp09e7ZSGUCRqvMp6erVqxQcHEzGxsZkY2NDoaGhoutW8j0UHR1NISEhZGdnR1KplNzd3YWJ4h49ekQ9e/ak6tWrk5GRETk7O9OsWbOoqKhICGvFihXk6upKhoaG5O7urjRxm7bnThVtE4sSEd29e5e6d+9OpqamZG5uTn379hUmTn2VuLGKVVl5uS51FV0mFiXSXj/QVm7QtX784sUL0tfXpw8++EBYJp/cctmyZaJtdalTqJoIc9GiRVrfN0QvyxsAaNasWaLl2urAQUFBSvljVTjPnJwcmjhxIlWrVo2kUikFBgYq1bG0tRGoK2MpKm3dnEh9Gilt3UxVnX7MmDFka2tLANTWH1VNLJqYmEjBwcEkk8nIycmJli1bppQn5uTk0OTJk4U8w83NjdauXSusV1Vu0nYvVD33eXl5NGDAAHJyciIjIyOqUaMGTZgwQeME3c+ePSOZTEaxsbGi5erKjsXFxdS+fXsKCQkRlb8mT55MdevWFfLUsrZXEGm+n5qeLW1tDPn5+TRr1ixycXEhQ0NDcnR0pF69etHVq1eFbdasWUO1atUimUxG3bp1o4ULF2qddDYiIoJ8fHzIyMiIGjduTFu2bCEAomv6qu0nRJrLo7rc+9GjR9PHH3+s8VxUkRCV8yDETKs7d+6gTp06uHz5Mvz8/Co7OqyKu3btGjp06IBbt26JJj5h/0cikWDPnj0aezGWxZv4rH7zzTfYtm2b0lAwOTk5sLGxwYEDB0Q9ydn/6du3L/z9/fHFF19UdlRYBXgTn+e3UXh4OIKDg/H8+fNyH0N7/fr1+PTTT/HixYtyDbcidezYEZ6enliyZIloeXh4OHr16oXbt2/D2tpa5b5t27aFn59fmea/YG++YcOGAYDOv9hj7G3AeTljrKJNnToVaWlp+O2338otzIpqr3gTbN68GcOHD0daWlqZfgFYEZ48eQIPDw9cuHChVEMdATyxKGNVnre3N3788UfcuXMH3t7elR0dVkVlZmYiJiYGS5cuVTkG2/Hjx9GuXTtuQFcjLy8Pvr6+mDx5cmVHhTH2Dnj27BkOHTqE//77D8uWLVNaf/DgQXzxxRdqG9AZO378OE6cOFHZ0WCMMcbeKjNnzsTy5ctRVFSkcd4hptqGDRvg6uqKmjVr4sqVK5g2bRr69etXZRrQASAxMRErVqwodQM6wI3ojL0Rhg4dWtlRYFXchAkTsHXrVvTs2VPlmJCdO3dG586dKyFmbwapVFqqMZEZY+xVNGrUCM+fP8cPP/yA+vXrK63//vvvKyFW7E2SmJhY2VFgjDHG3jqWlpb8y+RX8OjRI8yaNQuPHj1C9erV0bdvX3zzzTeVHS2RZs2aCfMllRYP58IYY4wxxhhjjDHGGGOMqaFX2RFgjDHGGGOMMcYYY4wxxqoqbkRnb622bdvi008/LfP+4eHhkEgkGicomzNnDk9q8w4YNmzYOzUJSEWl6zt37kAikSAqKqrcw2aMvd10eX+sX7++3CcrLau//voLbm5u0NfXF8oiqpax8qdL+a0yvc50qlgWdnFxeWMnon3XymKs/HDaYa+D4vv1TX7fVjSJRIK//vqrsqNR6V6lPFDa9FVRZaOIiAh4e3vD0NBQeM+qWvY24UZ0xl7B559/jqNHjwqfuZBW9ZTHPfnll1+wfv36colPReAvcxhjDOjfvz/i4+OFz5X5bvz444/xwQcf4N69e8Jkz6qWvYqq3ljMql4jyvnz5zF69OjKjgZj76zjx4+jcePGMDY2hqurK1auXKl1n/Pnz6N9+/awsrKCtbU1OnXqJPpCOS4uDsHBwXBwcBDC/fLLL1FQUFCBZ/JqSnsd1q9fD4lEovIvJSVF2I6IsHDhQri7u0MqlcLJyQnffvttRZ+OSuX5vq2KHZGqWv72ulV2x42qkp9PmTIFfn5+SExMFNpLVC17FVUt/fPEokyrgoICGBoaal32LjIzM4OZmVllR6NC5efnw8jIqLKjUaksLS0rOwoqERGKioqqbHivgzzOBgacnTGmzet+Xl53WUEmk0Emk72246mTmZmJlJQUhISEoEaNGmqXvc247FA12dnZVXYUGHtnJSYmomvXrggNDcWmTZsQERGBcePGwc7ODn369FG5T0ZGBkJCQtCjRw+sWLEChYWFmD17NkJCQnD//n0YGhrC0NAQH330ERo1agQrKytcuXIFoaGhKC4ufqUG5Ip6j5flOvTv3x+dO3cWLRs2bBhyc3Nhb28vLPvkk09w6NAhLFy4EN7e3khLS8PTp0/L/Rx0we9bVhHkz2VVSV8JCQkYM2YMatWqpXHZW4XYa1VUVETff/891a1bl4yMjMjJyYm+/vprYf3UqVOpXr16JJPJqE6dOvTll19Sfn6+sH7o0KHUo0cPUZiffPIJBQUFCZ937txJDRs2JGNjY7KxsaH27dtTZmYmERGdO3eOOnToQLa2tmRhYUFt2rShixcvisIDQL/++it1796dTExMaNasWTR79mzy9fWlNWvWUJ06dUgikVBxcTH9888/FBgYSJaWlmRjY0Pvvfce3bp1SwgrODiYxo8fLwr/6dOnZGRkREePHlV7ne7evUvdu3cnU1NTMjc3p759+9KjR4+E9fL4bNiwgZydncnCwoL69+9P6enpwjZBQUH0ySefEBHR3LlzqWHDhkrHadSoEX311Vcq43Ds2DECQEeOHKHGjRuTTCajgIAAio2NVYqH/P8ARH/Hjh2jvLw8Gj9+PDk6OpJUKiVnZ2f69ttv1Z77q3J2dlaKR8lH/f79+9SvXz+ysrIiGxsb6t69OyUmJgrr5Wns22+/perVq5OzszMREV29epWCg4OFdBUaGkoZGRka43L9+nXq2rUrmZubk5mZGbVq1UpIH0VFRTR37lyqWbMmGRkZka+vL/3zzz/CvvLr//z5c2HZ5cuXCYAQ33Xr1pGlpSUdPHiQPDw8yNTUlEJCQujhw4dEVH73RPG5CwoKookTJ1JYWBhZW1uTg4MDzZ49W+O1kIcxZ84csrOzI3Nzcxo9ejTl5eUJ2xQXF9MPP/xAderUIWNjY/Lx8aGdO3cqXZODBw9S48aNydDQkNauXat0juvWraPExEQCQJcvXxb2f/78uXAN1IX333//Cel65cqVVKtWLZLJZPTBBx+I7gUR0dq1a8nDw4OkUinVr1+fli9fLlp/9uxZ8vPzI6lUSo0bN6bdu3crxUnRxo0bqXHjxmRmZkYODg704Ycf0uPHjzVeg//++4+CgoJowoQJ9Mknn5CVlRXZ29vTb7/9RpmZmTRs2DAyMzMjV1dXOnDggMb7xFhZ6fJe0Ja3KVL1DpM/40Rle2f8999/lJubSxMnTiQ7OzuSSqUUGBhI586d03qO2soo6soKAGjlypX03nvvkUwmIw8PD4qMjKSbN29SUFAQmZiYUIsWLUTlB0Xyd9quXbuobdu2JJPJyMfHhyIjI4Vt5HmC/P/qrtvs2bPJycmJjIyMqHr16jRx4kSt515SXl4ehYWFUY0aNcjExISaNWum9F5VzHdULSMiioiIoNatW5OxsTHVqlWLJk6cKJTZiIhyc3MpLCyMatWqRUZGRuTm5ka///67cD1K/g0dOpSINJcDFRUWFtKIESPIxcWFjI2Nyd3dnRYvXizapqCggCZOnCiU9aZOnUofffSRUr44fvx4mjx5Mtna2lKbNm2IiOjGjRvUpUsXMjU1JXt7exo8eDA9efJE2E9bGlZF13yiZJ71559/kpeXFxkZGZGzszMtXLhQFKazszN98803NHz4cDIzMyMnJyf67bffRNtERESQr6+vkKft2bNHY54WFBSksiymrewipy2PVZSZmUlDhgwhU1NTcnR0pIULF4rKwvLzXLRokfD5p59+ooYNG5KJiQnVqlWLxo4dq1S2W7VqlVAW6NmzJ/3000/Ccya3YsUKcnV1JUNDQ3J3d6cNGzaI1gOg1atXU8+ePUkmk5Gbmxv973//E9brkg5V1YEYk9P03lNMO9ryQPk7ZP/+/eTj40NSqZSaNWtGV69eFR1T2/tb0dSpU8nDw0O07OOPP6YWLVqo3ef8+fMEgJKSkoRlV69eJQAa88zJkydTq1at1K5XRd17vLyV5TooSklJIUNDQ9G7Jjo6mgwMDER19bK4desWde/enezt7cnU1JSaNGlChw8fFm3z+PFjev/998nY2JhcXFxo06ZNSu/Xkp91qZc9e/aMBg4cSNWqVSNjY2Nyc3OjtWvXEhEp5SXyth952v7mm2/I3t6eLC0tac6cOVRQUECff/45WVtbU82aNWnNmjWi+OvaHrBgwQJydHQkGxsbGjdunFDmU5e/qRIfH0+tW7cmqVRKnp6edOjQIQJAe/bsEbbRpZ1BU55Y1rq9uvOT348hQ4aQlZUVyWQy6ty5M8XHxxOR6rKeuraAqKgoatu2LZmZmZG5uTk1atSIzp8/T0S6lQfUtc0opjdt+axi2Sg7O5u6du1KzZs3p9TUVJVx11RGU1UO1VT21lYe1NROqi79Hzt2jJo2bUomJiZkaWlJLVu2pDt37qi97+WFG9Ffs6lTp5K1tTWtX7+ebt26RSdPnqTVq1cL6+fPn08RERGUmJhIe/fuJQcHB/rhhx+E9doa0R8+fEgGBgb0888/U2JiIl29epWWL18uvISOHj1KGzdupOjoaIqOjqaRI0eSg4ODqPEZANnb29OaNWsoISGB7ty5Q7NnzxYe6kuXLtGVK1eouLiY/vzzT9q1axfFx8fT5cuXqVu3buTt7U1FRUVERLR582aytram3NxcIfxffvmFXFxcqLi4WOU1Ki4uJn9/f2rVqhVduHCBzpw5Q40aNRJ9UTB79mwyMzOj3r1707Vr1+jEiRPk6OhIX3zxhbBNyYrDvXv3SE9PT1RAunLlCkkkEkpISFAZD/mLpnnz5hQeHk43btyg1q1bU8uWLUXxkDeiZ2RkUL9+/ahz586UnJxMycnJlJeXRwsWLCAnJyc6ceIE3blzh06ePElbtmxReUwiok2bNpGpqanGv02bNqndPyUlRTj+/fv3qUWLFtS6dWsiIsrKyqJ69erRiBEj6OrVqxQdHU0DBw6k+vXrC425Q4cOJTMzMxoyZAhdv36drl27RllZWVSjRg3heh89epTq1KkjVNhVuX//PtnY2FDv3r3p/PnzFBcXR2vXrhUKNj///DNZWFjQ1q1bKTY2lqZOnUqGhoZKmZO2RnRDQ0Pq0KEDnT9/ni5evEienp40cODAcr0nqhrRLSwsaM6cORQfH09//PEHSSQSOnTokMYwzMzMqH///nT9+nXav38/2dnZidLsF198QR4eHnTw4EFKSEigdevWkVQqpfDwcNE18fHxoUOHDtGtW7fo/v379Nlnn1GDBg2Ec8zOzi5VI3rJ8J4+fSo87+3ataPLly/T8ePHyc3NTbiuRC8r1dWrV6ddu3bR7du3adeuXWRjY0Pr168nopeVeTs7O+F89+3bR66urlob0desWUMHDhyghIQEOn36NLVo0YK6dOkirFcX56CgIDI3N6f58+dTfHw8zZ8/n/T09KhLly60atUqio+Pp7Fjx5KtrS1lZWWpPT5jZaXtvaBL3qYoIyNDeK6Tk5Np4cKFZGJiQteuXSOisr0znj59SpMmTaIaNWrQgQMH6MaNGzR06FCytrZWW4iW01ZGUVdWAEA1a9ak7du3U1xcHPXs2ZNcXFyoXbt2dPDgQYqOjqYWLVpQ586d1R5b/k7z8PCg/fv3U1xcHH3wwQfk7OxMBQUFRCRuRM/Ozlb5bty5cydZWFjQgQMH6O7du3T27FlatWqV6BzkFRR1Bg4cSC1btqQTJ07QrVu3aMGCBSSVSik+Pp7y8vIoLi5OaPCX5zuqll29epXMzMxo0aJFFB8fTxEREeTv70/Dhg0TjtWvXz9ycnKi3bt3U0JCAh05coS2bdtGhYWFtGvXLgJAcXFxlJycTC9evNBaDlSUn59Ps2bNonPnztHt27dp06ZNZGJiQtu3bxe2+frrr8nGxoZ2795NMTExNGbMGLKwsFDKF83MzCgsLIxiY2MpJiaGHj58SNWqVaMZM2ZQTEwMXbp0iTp27EjBwcHCftrSsCq65hPy8sOFCxdIT0+P5s2bR3FxcbRu3TqSyWRCxY7oZWXUxsaGli9fTjdv3qTvvvuO9PT0KCYmhoiI0tPTycbGhgYPHkw3btygAwcOkLu7u8Y8LTU1lWrVqkXz5s0T0iCR9rILkfY8VpWxY8dSrVq16NChQ3T16lV6//33yczMTGMj+qJFi+i///6j27dv09GjR6l+/fo0duxYYf2pU6dIT0+PFixYQHFxcbR8+XKysbERNaLv3r2bDA0Nafny5RQXF0c//fQT6evr03///SdsA4Bq1apFW7ZsoZs3b9KkSZPIzMxMeOfokg65EZ2po+29p5h2tOWB8neIvMFP/jy5uLgIjWy6vL8VtW7dmiZNmiRatnv3bjIwMBA13pWUnp5O1apVo9mzZ1NeXh5lZ2fTJ598Qg0aNBDyPkU3b94kT09Pmjlzps7XkEj1e1yVV62rluU6KFq4cCFZWlpSdna2sOyHH34gd3d3WrhwIbm4uJCzszONHDlSVLaRlyXkdSFVoqKiaOXKlXT16lWKj4+nmTNnkrGxMd29e1fYpkuXLtSwYUOKjIykCxcuUMuWLUkmk71SI/r48ePJz8+Pzp8/T4mJiXT48GHau3cvEb3sCCnv3JecnCyc09ChQ8nc3JzGjx9PsbGxtGbNGgJAISEh9M033wh1IkNDQ+GLGF3bAywsLGjMmDEUExND+/btIxMTE6G8pC5/U1RUVEQNGzaktm3bCnVKf39/USO6Lu0M2vLEstTtNZ0fEVH37t3J09OTTpw4QVFRURQSEkJubm6Un59PeXl5tHjxYrKwsBDOX105q0GDBjR48GCKiYmh+Ph42rFjB0VFRRGRbuUBVW0ziumLSHs+W7Js9OLFC2rVqhV16NBB4xd/mspohYWFlJycTBYWFrR48WJKTk6mzMxMpWXZ2dk6lQc1tZOqSv8FBQVkaWlJn3/+Od26dYuio6Np/fr1oue0onAj+muUnp5OUqlU1GiuzY8//kiNGzcWPmtrRL948SIB0PkbmMLCQjI3N6d9+/YJywDQp59+Ktpu9uzZZGhoSCkpKRrDS0lJIQDCw52bm0s2NjaiQrCfnx/NmTNHbRiHDh0ifX190TfuN27cIABCI/js2bPJxMRE1PgfFhZGzZs3Fz4r9r7p0qWLqGLw6aefUtu2bdXGo2RPdLm///6bAFBOTo4QD3kjOpHq+zNx4kRq166d2i8NFKWnp9PNmzc1/pU8b00mTZpEzs7Own1bs2YN1a9fXxSXvLw8kslk9O+//wrn4ODgIOohvWrVKrK2tha9ZP/++2/S09NT24tyxowZVKdOHbWFoRo1atA333wjWta0aVMaN24cEeneiK7YE2P58uXk4OAgfC6Pe6KqEV2xd0fTpk1p2rRpGsOwsbERNeD++uuvZGZmRkVFRZSZmUnGxsaiXpVERCNHjqQPP/yQiP7vmvz111+ibRTTIZFuhTVN4enr69O9e/eEZf/88w/p6ekJhSQnJyelwsn8+fMpICCAiIh+++03leerrRFdkTzTlBdM1MVZ8Z4UFhaSqakpDRkyRFiWnJxMAOj06dM6H58xXWl7L+iSt2ly+vRpMjY2FvLTsr4zMjMzydDQkDZv3iwsy8/Ppxo1atCPP/5YqnNWLKOoKysAoC+//FJ0LgBEPaO2bt1KxsbGao8lf6f9/vvvwjL59ZNX9Es2osvjo/hu/Omnn8jd3V1t3rR06VJq166d2njcunWLJBIJPXjwQLS8ffv2NGPGDCJSfteqWzZkyBAaPXq0KJyTJ0+Snp4e5eTkCA3vij3g5FTlk6UtB6oybtw46tOnj/DZwcGBFixYIHwuLCyk2rVrK+WLfn5+onC++uor6tSpk2jZvXv3hIZ/XdKwLtTlE/LrMnDgQOrYsaNon7CwMPLy8hI+Ozs70+DBg4XPxcXFZG9vT7/++isRvcy/bG1thfIfEdHq1au15mmKlVwi3cou2vJYRRkZGWRkZETbtm0TlqWmppJMJtPYiK5ox44dZGtrK3zu378/vffee6JtBg0aJHrOWrZsSaGhoaJt+vbtS127dhU+K74DMjMzSSKRiH6BqEgxHXIjOlNH23uvZNrRJQ+Uv0NUPU/yPFjb+1uVevXqKdV9IiIiCIDSL1FKun79OtWtW5f09PRIT0+PPDw8VDYUBQQEkFQqJQA0evRooVObrlS9x1V51bpqWa9DSV5eXqJ6PdHL3uxSqZSaN29OJ06coGPHjpGfn5+ooe7+/ftUv359Onv2rE7HKXm8pUuXEhEJefOZM2eE9TExMQTglRrRu3XrRsOHD1d5fFX7E71M287OzqJ7Xb9+faEDHdH/1Ym2bt1KRLq3Bzg7O1NhYaGwTd++fal///4qz0+df//9V2WdsmQjui7tDNryxLLU7TWdX3x8PAGgiIgIYf3Tp09JJpPRjh07iEi5zKmOubm52i/AdW3LUGybIVLdiK4pn5W/12JjY8nX15d69+6tFGZJupbRLC0tRZ0SVC3TVh7U1k6qKv2npqYSAI2dLioKDyL7GsXExCAvLw/t27dXu82ff/6JxYsX49atW8jMzERhYSEsLCx0Poavry/at28Pb29vhISEoFOnTvjggw9gbW0NAEhJScGsWbPw33//4fHjxygqKkJ2djaSkpJE4TRp0kQpbGdnZ6WxlxISEvDVV1/hzJkzePr0KYqLiwEASUlJaNiwIaRSKQYPHoy1a9eiX79+iIqKwpUrV4TZmLt06YKTJ08K4d+4cQMxMTFwcnKCk5OTcBwvLy9YWVkhJiYGTZs2BfByMgtzc3Nhm+rVq4smFlEUGhqKESNG4Oeff4a+vj42b96Mn376Ses19fHxER1Dfh1r166tdV/g5XhtHTt2RP369dG5c2e8//776NSpk9rtzc3NRedVVqtWrcKaNWsQEREh3LeLFy/i1q1bSuHn5uYiISFB+Ozt7S0aAy8mJga+vr4wNTUVlgUGBqK4uBhxcXFwcHBQOn5UVBRat26tcjzc9PR0PHz4EIGBgaLlgYGBuHLlSqnO08TEBHXr1hU+a0sHQOnviSol04Wux/X19YWJiYnwOSAgAJmZmbh37x5SUlKQm5uLjh07ivbJz8+Hv7+/aJmq5/NVqAqvdu3aonHMAgIChPutr6+Pe/fuYeTIkQgNDRW2KSwsFMaPl6cZxfPV5vLly5gzZw6ioqLw7Nkz0TvFy8tLY5xL3hN9fX3Y2trC29tbWCZPp9ruE2Nlpem9oGvepkpSUhJ69uyJzz//HP369QMAREdHl+mdkZCQgIKCAtH719DQEM2aNUNMTAwAYMyYMdi0aZOwPjMzE4BuZRRVZQXFayN/FhWfz9zcXKSnp2ss96jLkz08PNTuU1Lfvn2xePFiuLq6onPnzujatSu6desmjBM/YcIETJgwQe3+ly5dAhHB3d1dtDwvLw+2trY6xUFOnidv3rxZWEZEKC4uRmJiIq5duwZ9fX0EBQXpHKa2cqAqK1euxO+//467d+8iJycH+fn5woSsaWlpePz4MZo1ayZsr6+vj8aNGwvvZznF9/LFixdx7NgxlXPHJCQkIC0tTec0XJKu+YRcTEwMevToIVoWGBiIxYsXo6ioCPr6+gDEaUsikcDR0VF4fuPi4uDj4wNjY2Nhm5LXpLQ0lV2ePHmiNY9VlJCQgPz8fFE+a2Njg/r162uMx7Fjx/Dtt98iOjoa6enpKCwsRG5uLrKysmBqaoq4uDj06tVLtE+zZs2wf/9+4XNMTIzS5GaBgYH45ZdfRMtKXl9TU1OYm5uL8mNN6ZAxTUrz3tMlD5RT9TzJt9H2/vb09FQZV4lEIvpMRCqXy+Xk5GDEiBEIDAzE1q1bUVRUhIULF6Jr1644f/68aB6Q7du3IyMjA1euXEFYWBgWLlyIqVOnqgxXHV3qGOVRVy3tdSjp9OnTiI6OxoYNG0TLi4uLkZeXhw0bNgh59Jo1a9C4cWPExcWhfv36qFmzJmJjYzWGn5WVhblz52L//v14+PAhCgsLkZOTI7SXxMTEwMDAQHStPDw8XnmSybFjx6JPnz64dOkSOnXqhJ49e6Jly5Za92vQoAH09PSEzw4ODmjYsKHwWV4nkr9vdW0PaNCggZA/Ai/zqWvXrpXqnGJiYlTWKRW30dTOoKenpzVPLEvdXtP5ye9x8+bNhfW2traid4CupkyZglGjRmHjxo3o0KED+vbtK8r/dWnLUGybUUdbPgsAHTp0QNOmTbFjxw7R+SsqTT1DG23lwRcvXmhtJ1VkY2ODYcOGISQkBB07dkSHDh3Qr18/oW5QkbgR/TXSNtnVmTNnMGDAAMydOxchISGwtLTEtm3bRA29enp6QiYjV3LmbX19fRw+fBiRkZE4dOgQli5dipkzZ+Ls2bOoU6cOhg0bhidPnmDx4sVwdnaGVCpFQEAA8vPzRWGWfIlpWtatWzc4OTlh9erVqFGjBoqLi9GwYUNReKNGjYKfnx/u37+PtWvXon379nB2dgYA/P7778jJyQEAobGViFRmoIrLFRtnJRKJUoVOMa5SqRR79uyBVCpFXl6e2slLSip5HPnxNR1HUaNGjZCYmIh//vkHR44cQb9+/dChQwf8+eefKrffvHkzPv74Y41h/vbbbxg0aJDa9eHh4Zg4cSK2bt0KX19fYXlxcTEaN24sKvDJlWz0ULzX6u4JoL6wo8vkbqoKUPJl8sJAyfSuapZ5VelA8RlRVNp7okpp058mJff9+++/UbNmTdF6qVQq+qzqWVSk6/XTNTz5fSkZ19WrV4sKFwCEzFjbPVAlKysLnTp1QqdOnbBp0ybY2dkhKSkJISEhOr2jVN2TV31+GSsNTe8FXfM2RVlZWejevTsCAgIwb948YXlZ3xnqKqol4zFv3jx8/vnnovW6lFEUj1WSqmexLM/nqz7TTk5OiIuLw+HDh3HkyBGMGzcOCxYswPHjx3WaBLW4uBj6+vq4ePGiUuWjtBONFxcX4+OPP8akSZOU1tWuXRu3bt0qVXiA9nKgoh07dmDy5Mn46aefEBAQAHNzcyxYsABnz54VbaeuwaMkxXtfXFyMbt264YcfflDatnr16rh+/ToA3dKwXGnyiZJx1SX+pX1+y5LPaTqWPDxd8lhFZYnL3bt30bVrV4wZMwbz58+HjY0NTp06hZEjRwrlBV3PW9P7RE7T9dU1HTKmSmnee7rkgZqUzHc0vb9VcXR0xKNHj0TLUlJSYGBgoPZL2C1btuDOnTs4ffq0ULbfsmULrK2t8b///Q8DBgwQtpV/Se/l5YWioiKMHj0an332mcaGMkW61Aleta5alutQ0u+//w4/Pz80btxYtLx69eowMDAQfckt/zIjKSlJ65eKcmFhYfj333+xcOFCuLm5QSaT4YMPPhDymNI0+MvpUi/r0qUL7t69i7///htHjhxB+/btMX78eCxcuFBj2NrqP/Jl8vetru0B5VHXLWt+oeqYmvLEstTtteX56s6nNPcdAObMmYOBAwfi77//xj///IPZs2dj27ZtwhfUurRl6PJcqgtL8Z6999572LVrF6Kjo0WdWRSVpp6hjbby4O3bt0sVnty6deswadIkHDx4ENu3b8eXX36Jw4cPo0WLFmUKT1fciP4a1atXDzKZDEePHsWoUaOU1kdERMDZ2RkzZ84Ult29e1e0jZ2dnVDpkIuKilKqVAYGBiIwMBCzZs2Cs7Mz9uzZgylTpuDkyZNYsWIFunbtCgC4d+9emWesTk1NRUxMDH777Te0bt0aAHDq1Cml7by9vdGkSROsXr0aW7ZswdKlS4V1ig8k8DLjT0pKwr1794TCQHR0NNLS0tR+q68LAwMDDB06FOvWrYNUKsWAAQNEvWTLg5GREYqKipSWW1hYoH///ujfvz8++OADdO7cGc+ePYONjY3Stt27d1fKIBSp6vktd+vWLfTp0wdffPEFevfuLVrXqFEjbN++Hfb29qX6hYOXlxf++OMPoWcS8DK96unpKfXGk/Px8cEff/yBgoICpRe6hYUFatSogVOnTqFNmzbC8sjISKFnlzwTT05OFnqSREVF6RxnufK4J+XlypUryMnJEb5gOHPmDMzMzFCrVi1YW1tDKpUiKSmpVL0OAdXnWPL6yb8tLs31S0pKwsOHD1GjRg0AEArv7u7ucHBwQM2aNXH79m21BWQvLy9s3LhR6Xw1iY2NxdOnT/H9998Lz/6FCxd0jjNjVVlZ8jYiwuDBg1FcXIyNGzeKCu5eXl5leme4ubnByMgIp06dwsCBAwG8rMhduHABn376KQDA3t4e9vb2ov10KaNUNere/zKZDN27d0f37t0xfvx4eHh44Nq1a2jUqJHWMP39/VFUVISUlBSh7FNWjRo1wo0bN+Dm5qZyvbe3N4qLi3H8+HF06NBBab28V5LiOWoqByo6efIkWrZsiXHjxgnLSvZEs7S0hIODA86dOyecb1FRES5fvqy1l3CjRo2wa9cuuLi4CD39SypLGi5LPuHl5aVUPo2MjIS7u7vOjUseHh7YvHkz8vLyhMqjLvmTujSoiS55rCI3NzcYGhrizJkzQgPe8+fPER8fr/baXrhwAYWFhfjpp5+EBp4dO3aItvHw8MC5c+eU9ivJ09MTp06dwkcffSQsi4yMLFWZXVs6ZEwbXd97uuSBcqqeJ/mvnrS9v1UJCAjAvn37RMsOHTqEJk2aqP0SNzs7G3p6eqL8X/5ZU4MmEaGgoOCVvuxT51XrqmW5DnKZmZnYsWMHvvvuO6V1gYGBKCwsREJCgtCzNz4+HgCEDny6OHnyJIYNGyY0cmZmZuLOnTvCek9PTxQWFuLChQtCvTUuLg4vXrxQG6au9TI7OzsMGzYMw4YNQ+vWrYVfFKjL78uirO0BinTJ3+RlX8U6peI2mtoZdM0Ty7Nu7+XlhcLCQpw9e1b4NUBqairi4+OFvK00+bu7uzvc3d0xefJkfPjhh1i3bp3Sr7xel++//x5mZmZo3749wsPDVf6CDyh7PUMVbeVBbe2kmtK/v78//P39MWPGDAQEBGDLli3ciP42MTY2xrRp0zB16lQYGRkhMDAQT548wY0bNzBy5Ei4ubkhKSkJ27ZtQ9OmTfH3339jz549ojDatWuHBQsWYMOGDQgICMCmTZtw/fp14WV89uxZHD16FJ06dYK9vT3Onj2LJ0+eCA+7m5sbNm7ciCZNmiA9PR1hYWE69RhWxdraGra2tli1ahWqV6+OpKQkTJ8+XeW2o0aNwoQJE2BiYqL1hdGhQwf4+Phg0KBBWLx4MQoLCzFu3DgEBQW98jAWo0aNEq5FRETEK4WliouLC/7991/ExcXB1tYWlpaWWLZsGapXrw4/Pz/o6elh586dcHR0VPuTr1f5iVxOTg66desGPz8/jB49WvQtv6OjIwYNGoQFCxagR48emDdvHmrVqoWkpCTs3r0bYWFhop9alTRo0CDMnj0bQ4cOxZw5c/DkyRNMnDgRQ4YMUVtImjBhApYuXYoBAwZgxowZsLS0xJkzZ9CsWTPUr18fYWFhmD17NurWrQs/Pz+sW7cOUVFRwrfibm5ucHJywpw5c/D111/j5s2bOg2/o6g87kl5yc/Px8iRI/Hll1/i7t27mD17NiZMmAA9PT2Ym5vj888/x+TJk1FcXIxWrVohPT0dkZGRMDMzw9ChQzWeY2JiIqKiolCrVi2Ym5tDJpOhRYsW+P777+Hi4oKnT5/iyy+/1DmuxsbGGDp0KBYuXIj09HRMmjQJ/fr1g6OjI4CX36pPmjQJFhYW6NKlC/Ly8nDhwgU8f/4cU6ZMwcCBAzFz5kzhfO/cuaO1J0Xt2rVhZGSEpUuXYsyYMbh+/Trmz5+vc5wZq8rKkrfNmTMHR44cwaFDh5CZmSkMq2JpaVnmd4apqSnGjh2LsLAw2NjYoHbt2vjxxx+RnZ2NkSNHqo2/LmWUqkbVu1H+c/jmzZvDxMQEGzduhEwm07mC7e7ujkGDBuGjjz7CTz/9BH9/fzx9+hT//fcfvL29hU4Kupg2bRpatGiB8ePHIzQ0FKampoiJicHhw4exdOlSuLi4YOjQoRgxYgSWLFkCX19f3L17FykpKejXrx+cnZ0hkUiwf/9+dO3aFTKZDDdu3NBYDlTk5uaGDRs24N9//0WdOnWwceNGnD9/XtR7c+LEifjuu+/g5uYGDw8PLF26FM+fP9faG2v8+PFYvXo1PvzwQ4SFhaFatWq4desWtm3bhtWrV5cpDZcln/jss8/QtGlTzJ8/H/3798fp06exbNkyrFixQoe79JI8Txs9ejSmT5+OpKQkIU/TdB1cXFxw4sQJDBgwAFKpFNWqVdPpeNryWEVmZmYYOXIkwsLCYGtrCwcHB8ycOVP0E39FdevWRWFhIZYuXYpu3bohIiICK1euFG0zceJEtGnTBj///DO6deuG//77D//884/onMPCwtCvXz80atQI7du3x759+7B7924cOXJEp3MFdEuHjKmjrf5bUmnywHnz5omep2rVqqFnz54AtL+/VRkzZgyWLVuGKVOmIDQ0FKdPn8aaNWuwdetWYZs9e/ZgxowZwpAjHTt2RFhYGMaPH4+JEyeiuLgY33//PQwMDBAcHAzgZc9wQ0NDeHt7QyqV4uLFi5gxYwb69++vssHqVb3qcC5luQ5y27dvR2FhocrG1A4dOqBRo0YYMWIEFi9ejOLiYowfPx4dO3YUOn09ePAA7du3x4YNG9QOyeXm5obdu3ejW7dukEgk+Oqrr0RfWMiHDAkNDcWqVatgYGCATz/9VGObii71slmzZqFx48Zo0KAB8vLysH//fiEN29vbQyaT4eDBg6hVqxaMjY3VDu+lTVnbAxTpkr916NAB9evXF8pM6enpos4Y8vhoa2fQlicuWrSoXOv29erVQ48ePRAaGorffvsN5ubmmD59OmrWrCkMD+fi4oLMzEwcPXpUGMJUsYNmTk4OwsLC8MEHH6BOnTq4f/8+zp8/r9NoCBVp4cKFKCoqQrt27RAeHq5ySMRXaZtQpK08qK2dVFX6f/bsGVatWoXu3bujRo0aiIuLQ3x8vOgL/QpTccOtM1WKioro66+/JmdnZzI0NKTatWvTt99+K6wPCwsjW1tbMjMzo/79+9OiRYuUJiyYNWsWOTg4kKWlJU2ePJkmTJggTCwaHR1NISEhZGdnR1KplNzd3YVJMIiILl26RE2aNCGpVEr16tWjnTt3qpyUQD7Rg5yqybmIiA4fPkyenp4klUrJx8eHwsPDVe6fkZFBJiYmwqSR2ty9e5e6d+9OpqamZG5uTn379hVNYKkqPosWLSJnZ2fhs+LEonKtW7cWTSalji4TWyrGIyUlhTp27EhmZmbCRCGrVq0iPz8/MjU1JQsLC2rfvj1dunRJl8tQavJJF1T9ySUnJ9NHH31E1apVI6lUSq6urhQaGkppaWlEpH7ipqtXr1JwcDAZGxuTjY0NhYaGqp2FWu7KlSvUqVMnMjExIXNzc2rdujUlJCQQ0ctnYe7cuVSzZk0yNDQkX19fpQmmTp06Rd7e3mRsbEytW7emnTt3Kk0sqvh87NmzR3S+5XFPVE0sqpi2evToIZpFXF0Ys2bNEp7xUaNGUW5urrBNcXEx/fLLL1S/fn0yNDQkOzs7CgkJoePHjxOR6jRJ9HIC3z59+pCVlRUBECbyiI6OphYtWpBMJiM/Pz86dOiQyolFFcOTp+sVK1ZQjRo1yNjYmHr37k3Pnj0Tbbd582by8/MjIyMjsra2pjZt2tDu3buF9adPnyZfX18yMjIiPz8/2rVrl9ZJ2LZs2UIuLi4klUopICCA9u7dK9pHXZxV3RNVE96oej8xVh50eS9oy9tUhanqfS5/xsv6zsjJyaGJEycK+UBgYKBOk5tqK6OoKysoPneqJghSF1dN+yhOyqWYJ6h6N+7Zs4eaN29OFhYWZGpqSi1atBBNID579mxRWUKV/Px8mjVrFrm4uJChoSE5OjpSr1696OrVqyrjpW4Z0ctJMeV5lKmpKfn4+IgmXMvJyaHJkydT9erVycjIiNzc3Gjt2rXC+nnz5pGjoyNJJBIaOnSo1nKgotzcXBo2bBhZWlqSlZUVjR07lqZPny66jwUFBTRhwgSysLAga2trmjZtGvXt25cGDBggbKOuzBUfH0+9evUiKysrkslk5OHhQZ9++qkw+Ze2NKxKWfKJP//8k7y8vISyd8mJUolU5xe+vr40e/Zs4XNERAT5+PiQkZERNW7cmLZs2SJM0qXO6dOnycfHR5jsj0i3sguR9jxWUUZGBg0ePJhMTEzIwcGBfvzxR6X7onieP//8M1WvXp1kMhmFhITQhg0blK7dqlWrqGbNmiSTyahnz5709ddfk6Ojo+jYK1asIFdXVzI0NCR3d3fasGGDaL2qvLfkxGO6pEOeWJSpo+29p5h2tOWB8nfIvn37qEGDBmRkZERNmzalqKgo0XG1vb9VCQ8PJ39/fzIyMiIXFxdh8mI5+USDJR06dIgCAwPJ0tKSrK2tqV27dnT69Glh/bZt26hRo0ZCPLy8vOjbb78VTXAqz0MV86CS1L3HK0JZrgPRy8lTBw4cqDbcBw8eUO/evcnMzIwcHBxo2LBhlJqaKqzX5TokJiZScHAwyWQycnJyomXLlildm+TkZHrvvfdIKpVS7dq1acOGDUrvV8XP2upl8+fPJ09PT5LJZGRjY0M9evSg27dvC/uvXr2anJycSE9PT2j7UfVe1KVOVJb2gE8++UQ4LpHq/E2VuLg4atWqFRkZGZG7uzsdPHhQKU/QpZ1BU574qnV7Vef37NkzGjJkCFlaWgp5ZHx8vGifMWPGkK2tLQEQlRfk8vLyaMCAAeTk5ERGRkZUo0YNmjBhgvBs6lIeUJf36dKGVzKfVVU2mjhxIlWvXp3i4uKUwifSrYymy8SiRNrLg9raSRXT/6NHj6hnz55C+djZ2ZlmzZpV6gmVy0JCVAG/8WFMwb179+Di4oLz58/r9HPpikJE8PDwwMcff6yyJw9jFWXYsGF48eKFMKkuY4yxqmfYsGEAgPXr11dqPKqy4uJieHp6ol+/fu/0r4U2b96M4cOHIy0trcy/6nwThYaGIjY2FidPnqzsqDBWIcLDwxEcHIznz59X+K9UX5fw8HD06tULt2/f1jjRNCs/1atXx/z581UOT8EYe3PxcC6sQhUUFCA5ORnTp09HixYtKrUBPSUlBRs3bsSDBw8wfPjwSosHY4wxxqqm48eP48SJE5UdjSrl7t27OHToEIKCgpCXl4dly5YhMTFRGE/4XbFhwwa4urqiZs2auHLlCqZNm4Z+/fq99Q3oCxcuRMeOHWFqaop//vkHf/zxR6mGwmGMVb6DBw/iiy++4Ab01yA7OxsRERF4/PgxGjRoUNnRYYyVM25EZxUqIiICwcHBcHd31zg78uvg4OCAatWqYdWqVVyAYIwxxpiSxMTEyo5ClaOnp4f169fj888/BxGhYcOGOHLkyCtN9v4mevToEWbNmoVHjx6hevXq6Nu3L7755pvKjlaFO3fuHH788UdkZGTA1dUVS5Ys4Z6VjL1hvv/++8qOwjtj1apVmD9/Pj799FMEBARUdnQYY+WMh3NhjDHGGGOMMcYYY4wxxtRQP2U7Y4wxxhhjjDHGGGOMMfaO40Z0xhhjjDHGGGOMMcYYY0wNbkRnjDHGGGOMMcYYY4wxxtTgRnTGGGOMMcYYY4wxxhhjTI13qhGdiJCeng6eS5Uxxhh7c3F+zhhjjL35OD9njDH2JnmnGtEzMjJgaWmJjIyMyo4KY4y9U/73v/9VdhTYW4Tzc8YYqxycn7PyxPk5Y4yxylRYWAgAePr0qU7bv1ON6IwxxipHXl5eZUeBMcYYY6+I83PGGGOMVbQXWYW4eCcbL7IKK+wYz549w44dO/D06VNUq1ZNp324EZ0xxhhjjDHGGGOMMcZYuStto3jCk3w8elGAhCf5FRKfBw8eYO/evfDw8ICtra3O+xlUSGwYY4wxxhhjjDHGGGOMvdPkjeIA0NhUe1N0XTsj0b/avMgqRMKTfNS1M4KVlvDT0tLw77//IjAwEPXr19cpfDluRGeMMcYYY4wxxhhjjDFW7krbKG5laqBTY7ucLo30RITc3FxYWlqiT58+sLS01Dl8OW5EZ4wxxhhjjDHGGGOMMVbuStsoXlqqGulL9k63kOkhMjIST548Qc+ePcvUgA5wIzpjjDHGGGOMMcYYY4yxCqA43Epphl/RhapGennv9OKiQjxPOIMXL16gS5cukEgkZT4ON6IzxhhjjDHGGGOMMcYYK3eKw62Udoz0spD3Ss+8dwW5ubno3r07jI2NXylMbkRnjDHGGGOMMcYYY4wxVu4Uh1sp7RjpZWFAufBzkqGoRmPo6enBwODVm8D1yiFejDHGGGOMMcYYY4wxxpiIlakBGruYCEO3lPz8IqsQF+9k40VWYbkd78mTJ9izZw9u374NIyOjcmlAB7gRvUqaM2cOQkNDhc/h4eGQSCSIiIgQlo0cORLz5s3DhQsXMGjQIADAnTt3UK1atdce34omkUiQmZlZ6v2WLFmC77//HsDLWXjDwsLQoEED+Pj4IDg4GLdu3QIAJCYmonHjxvDz84O3tzf69u2L58+fAwCysrLQvHlz+Pr6wtfXF507d8adO3eEMFu3bo3ExMTyOVHGGGNvFc7PxcojP//vv//QvHlzeHl5oWHDhpg5cyaICIDm/Fx+fB8fH/j5+cHPzw8nT54EwPk5Y4wxxhhjlUU+tEvCk/xyCS8pKQn79++Hr68v6tWrVy5hynEjehUUHByMY8eOCZ/Dw8PRvHlzpWXBwcFo0qQJNm/eXBnRrNJycnKwaNEiTJgwAQCwd+9enDhxAlFRUbh69Srat2+PL774AgBQo0YNnDp1ClFRUbh27Rpq1qyJ+fPnAwBkMhmOHDmCK1eu4MqVK+jcuTOmTJkC4GVlfPLkyZg7d27lnCRjjLEqjfPzV6eYn1tbW2Pr1q2Ijo7GhQsXcPz4cWzduhWA5vxcLjIyElFRUYiKikLr1q0BcH7OGGOMMcZYZalrZwRHK8NyGdqlsLAQp06dQps2beDj41MOsRPjRvQqqEWLFnj48CHu378P4GUFe9asWQgPDwcA3Lt3D8nJyWjevDnCw8PRpEkTleGcPn0arVu3hq+vL3x8fPC///0PAHDhwgUEBATAx8cHzZo1E3rEhYaGYuLEiQCAZ8+eoW7dujh16hQA4Mcff0SDBg3g7e2NQYMGIS0tDcDLXnYDBw5Et27d4OXlhXbt2uHZs2cq46PuuPIed7NmzULjxo3h5uaGAwcOKO2/c+dOhISECJ+Liorg7OyM6OhopW3//PNPtGrVCmZmZsKyvLw85ObmgoiQnp6OWrVqAQCkUilkMpkQZmZmJvT0Xj4aenp6MDc3BwBhP/k6AOjWrRsOHDiAjIwMlefMGGPs3cX5efnn5/7+/nB1dQUAGBsbw8/PD7dv3wagOT/XhvNzxhhjjDH2LqiI4VNeheJQL2VBRHj06BEMDAzQr18/1K1btxxjKD7QOyMtLY0AUFpaWrmFWVBYTI/T8qmgsLjcwiQiateuHW3YsIFyc3Opbt26RETk5uZGeXl59Mcff1CHDh2IiOjYsWPUuHFjIiJKTEwkW1tbIiJKTU0lBwcHioiIICKioqIiSk1Npby8PHJycqKDBw8SEdHJkyfJ0dGRMjMzKScnh/z8/GjHjh30/vvv03fffUdERAcOHCAPDw96/vw5ERGFhobSuHHjiIho9uzZ5OrqSqmpqURE1L9/f/r222+VzkfTcRMTEwkA/fXXX0RE9M8//5C7u7uwLwDKyMigwsJCcnZ2pvj4eCIi+vPPP6ldu3Yqr9+IESNoxYoVwueioiL69NNPycTEhBwcHKhRo0aUkZEhip+vry9ZWVlRUFCQUhpp37492dvbU4MGDejBgweidcHBwfTPP/+ojAdj7KXt27dXdhTYW4Tz83c3Py8pOTmZHBwc6OLFi6L4qcvPAVDjxo3Jx8eHJk+eTJmZmaLwOD9nTDvOz1l5qoj8nDHGmGYXErNo/+UXdCExq7KjUi6Kioro2LFjtHnzZsrPz6/QY3FP9FdQWERYeyIVK44+xdoTqSgsonILOzg4GOHh4Th9+jSaN28OAGjatCnOnj0r/PRbk9OnT8PLywstW7YE8LJHtY2NDeLi4mBkZCT0AGvVqhXs7e1x9epVGBsbY+fOnRg1ahQKCgowbdo0AMCRI0cwaNAgWFlZAQDGjh2LI0eOCMfq0qULbGxsAAABAQFISEhQio+m4wKAqakpevTooTEMfX19jBs3DitWrAAALFu2TPh5t6L79+/D0dFR+Hzp0iXExsbiwYMHePjwIdq3by/a18jICFFRUXj8+DHq16+PlStXisI7cuQIkpOT0b9/f3z99deidY6OjkIvQ8YYY28ezs9fehPyc7n09HR069YNU6dORaNGjYTlmvLzu3fv4sKFC4iMjMSTJ08QFhYmCpPzc8YYY4wx9rYrz+FTKlt+fj4OHjyIp0+fokePHjA0NKzQ43Ej+it4llWIhy8KAAAPXxTgeTn+FEI+jmp4eDiCgoIAAEFBQTh27BiOHTumtdKtDhFBIpEoLZcvi42NhampKVJSUpCfn692n5KfjY2Nhf/r6+ujsFD5Omg7rmIYRUVFKuMfGhqKHTt24Ny5c7h9+za6d++ucjsTExPk5OQIn9evX4/g4GBYWVlBT08PQ4cOFY1JK2dkZIThw4dj48aNSuv09PQQGhqqtC43N1f4+Thj77KCgnykpT1HQUH5TAjC2OvC+flLb0J+DgAZGRno3LkzunfvLsxTokhVfl67dm0ALxv6x40bJ0wsKsf5OWOqcf7OGGOMvT3KY/iUquL+/fsgInTv3h2mpqYVfjxuRH8FNqYGqGH18luOGlaGsC7HBNisWTOkpKRgy5YtaNu2LQCgbdu22Lx5M548eaJ23FS5li1bIiYmBpGRkQCA4uJiPHv2DB4eHsjLy8N///0H4OUEWykpKfD29kZSUhLGjRuHI0eOoFmzZvj0008BAB07dsS2bduEcUJXrVqFDh06lOp8NB23NKytrdGtWzf06dMHY8aMgb6+vsrtfHx8EBsbK3x2dXXF0aNHUVDwspFk3759aNiwIYCXM/dmZWUBeHmdduzYIUxA8PjxY9GYsNu2bVOanCAmJga+vr6lOg/G3kbZ2VnIy81FdnZWZUeFsVLh/Fx3lZ2fZ2ZmonPnzggJCcFXX30l2lZTfv78+XNkZ2cL67Zv3w5/f3/R/pyfM/Z/CosIKekFKCwizt8ZY4yxd9DrHDu9tMd68eIFUlNT4erqiq5du8LI6PX0qn/zv3aoRAb6EoxoY4vnWYWwNjWAgb5yz6yyMjQ0RGBgIK5evQp3d3cAQP369ZGeno5WrVpp/YmCtbU19uzZg88++wwZGRmQSCSYP38+unfvjl27dmHSpEnIysoSfvItlUoxYMAAzJ8/H15eXli8eDECAgKwfft29O/fH9euXUNAQAAkEgl8fHyEn2DrysjISOVxTU1N8eTJk1KFFRoaivXr12PUqFFqt/nggw8wYsQIzJs3DwAwfvx4xMTEwNvbG0ZGRqhevTp+++03AMD169cxffp0AC8r1o0aNcKSJUsAvPxWKzQ0FIWFhSAi1K1bF5s2bRKOc+fOHQAQGuQZe5eZmJiK/mXsTcH5ue4qOz//5ZdfcO7cOWRlZWHPnj0AgL59+2LmzJka8/PY2Fh8/PHHkEgkKCwsRKNGjfDLL78Ix+H8nLH/Ix/i6uGLAtSwMsSQAHMAnL8zxhhj75KEJ/l49P9/rdu4Anqtv8gqRMKTfNS1MyrVsR49eoR///0Xvr6+sLW1Vfkr2YoiIaLyG/iziktPT4elpSXS0tJgYWFR2dFhZfTjjz8iLi4Oa9as0bhd165dMXfuXDRt2rTC4jJ9+nTUq1cPI0eOrLBjMPY22LFjB/r161fZ0WBvCc7P3w6cnzNWNaWkF2DF0afC5/Htq8HO4uUXfpyfs/LE+TljjFVdJRu5K2Lol4t3svHoRYEwPrsux0pMTMSxY8fQokULeHl5lXuctOGe6OyN0qBBA0gkEhw8eFDrtkuWLBH9BLwi1KhRA8OHD6/QYzDG2NvmwYMHmDZtGv755x/k5OTA3d0da9asQePGjSs7auw14fycsapLPsSVvCd6eQ5xxRhjjLE3g5WpQYX0QJeTT2wqbzjXdiwiwu3bt9GuXTu4uLhUWLw04Z7ojDHGKhz3XGNyz58/h7+/P4KDgzF27FjY29sjISEBLi4uqFu3rk5hcH7OGGMVq7CIVA5xxfk5K0+cnzPG2Kup6N7iVQERITo6GvXr14eBQeWe49t5hRljjDFWJf3www9wcnLCunXrhGWV1ZOAMcaYagb6EmEIF8YYY4xVTRU9bnllKyoqQnh4OFJSUuDs7AwzM7NKjY9epR6dMcYYY++UvXv3okmTJujbty/s7e3h7++P1atXa9wnLy8P6enpoj/GGGOMMcYYe5dVM9VHWk4RqpnqV1oc7j7Jw5bTz3D3SV65hpuXl4cDBw4gPT0dPXv2rPQGdIAb0RljjDH2Gt2+fRu//vor6tWrh3///RdjxozBpEmTsGHDBrX7fPfdd7C0tBT+nJycXmOMGWOMMcYYY6zqeZpVBEuZPp5mFVVaHCJuZeF2Sh4ibmWVa7h5eXkwNzfH+++/D5lMVq5hl9Xb19efMcYYY1VWcXExmjRpgm+//RYA4O/vjxs3buDXX3/FRx99pHKfGTNmYMqUKcLn9PR0bkhnjDHGGGOsFN6F8bPfNSUn56wsgW6mon9f1bNnz5CcnIwGDRqgbdu25RJmeeGe6OyN1LZtW+zfv7/U+0VFReG9995TWj5ixAhIJBJkZmYCAF68eAE/Pz/hz93dHQYGBnj27BkA4IMPPkBkZOSrnQRjjL2DqlevDi8vL9EyT09PJCUlqd1HKpXCwsJC9MfeDuWVn0skEvj4+Aj59smTJ4V1nTp1Eta1bt0aUVFRSuH98ccfkEgkorh89tln2Lp1a6njxhhjjDFWFcnHz054kl/ZUWHlxMrUAI1dTCr1SxFLE33Ur24MS5NXH1LmwYMH2Lt3L/LyyndomPLCXz0xrYqLiwEAenr/951LYWFhpc+KWxZffPEFpk+fLlq2b98+SCQS0TIrKytRJXvhwoU4fvw4bGxshHCmTJmC8PDwio4yY4y9VQIDAxEXFydaFh8fD2dn50qK0bvjbc/PIyMjVY6VuGPHDlhZWQEA/vrrL4wYMQKXLl0S1t+/fx+//fYbWrRoIdpv2rRpaN26Nfr37y+6Zowxxhhjb6Kq0GuZvX3Ka3LTW7du4cSJEwgMDET9+vXLK3rlimsEVdTp06fRunVr+Pr6wsfHB//73/8AAGFhYWjatCn8/PwQFBSEmzdvAgDu3LmDatWqCftnZmYKDcM5OTno378/vLy84Ovri06dOgEAHj16hODgYDRu3BgNGjTApEmTQEQAgDlz5mDIkCHo3bs3/Pz8cPr0aVSrVg3z5s1D69atsXTpUhw9ehQBAQHw9/dHw4YNsW7dOgDA+fPn4enpKYQFAAEBAfjnn3+UzvPx48fo1asXvL290bBhQ6xatUpY5+Ligrlz56Jly5aoU6cOvv76a6X9Hz58CAcHB2RnZwvLPvzwQ/z6669K2yYlJeHGjRto06aNsCw1NRVz587Fzz//rPF+rFu3DiNHjhQ+N2rUCI8ePRKuP2OMMd1MnjwZZ86cwbfffotbt25hy5YtWLVqFcaPH1/ZUasQnJ+/nvxcE3kDOgCkpaUpNYiPHj0aixYtglQqFS23t7dHnTp1cPToUZ2OwxhjjDFWlVWFXsvszfYiqxAX72TjRVahsKyunREcrQxf+csZIkKnTp2EBnRVx6p09A5JS0sjAJSWllZuYRYXFlBhajIVFxaUW5ipqank4OBAERERRERUVFREqampRET05MkTYbutW7fSe++9R0REiYmJZGtrK6zLyMgg+e3dvXs3dezYURQ+EVFOTg5lZGQQEVFhYSG99957tHPnTiIimj17NtWsWZMeP34shA+ANm/eLITz7NkzKiwsFMJ0dnamhw8fEhFRy5Yt6fDhw0REdPHiRXJzc6Pi4mKlc+3Xrx9Nnz6diIgeP35MtWrVorNnzxIRkbOzM3366adERJSSkkIWFhZ0//59IiIKCgqiffv2ERHRwIEDafXq1URElJycTNWqVRPOq6QNGzZQv379RMv69+8vhANA5X6RkZHk4OBABQXiezx8+HD69ddflbZnjCnbvn17ZUeBVSH79u2jhg0bklQqJQ8PD1q1alWp9uf8/P/CJ+L8nOhlHt64cWPy8fGhyZMnU2Zmpmj9kCFDqFatWlSrVi26fv26sHzFihX0+eefK8VFbu7cuTRt2jSlODD2ruL8nJWnisjPGWOMlZ/nmQV0ITGLnme+rCNdSMyi/Zdf0IXErHIJv6ioiCIjIyklJUVpXXkfqzxwT/RXQEWFyNq9BJnbfkDWnqWgovL5duT06dPw8vJCy5YtAbz82bV8GJFDhw4hICAADRs2xLx581SO66nI19cXsbGxGDduHLZv3w5DQ0MAL3/WPW3aNPj6+sLf3x8XLlwQhff+++/D3t5e+GxsbIwPP/xQ+Jyamoq+ffuiYcOGaNeuHZ4+fYobN24AAD755BMsX74cALB06VKMGzdOacgUADhy5IjQ+9De3h69e/cW9fgaNGgQAMDOzg6urq5ITExUCqPksX777TcMHDhQ5c+579+/D0dHR+Hzzp07YWRkhPfff1/j9Vu7di0++ugjpZ+7Ozo64v79+xr3ZYwxpuz999/HtWvXkJubi5iYGISGhlZqfDg/f7PzcwC4e/cuLly4gMjISDx58gRhYWGi9Rs2bMC9e/fw9ddfC+sSExOxevVqzJs3T+kYcpzXM8YYY4yxd5XiOPrl1esceDms5JEjR3Dv3j0YGxsrrS/PY5UXbkR/BcVpT1H05B4AoCglCcVpTyv0eElJSZg0aRI2b96M69evY9u2bcjNzQUAGBgYoKioSNhWvhwAXF1dER0djc6dOyMiIgINGzbE8+fP8fPPPyM1NRVnz57F1atXMXDgQNF+ihVXU1NTUcV5zJgxCAoKwrVr1xAVFQV3d3dh/969eyMqKgqXL1/Gvn37MHz4cLXnpVgZL/m55IOkr6+PwkLlho1mzZrB2NgYx48fx+rVq9UOCWBiYoKcnBzh87Fjx/Dff//BxcUFLi4uAIAGDRrg2rVrwjZZWVnYvn07RowYoRRebm4uZDKZ2vNijDH2ZuD8/M3OzwGgdu3awrmNGzdONLFoSUOHDsWxY8eQmpqK06dP4+HDh/D09ISLiwvOnDmDkSNHYvXq1cL2nNczxhhjjLG3maZhUxQbsstrSKD8/Hz8/fffyM3NRffu3WFubq60TVUcfogb0V+BnmU16Nu/rLTp29eGnmU1LXvopmXLloiJiUFkZCSAlz3Mnj17hrS0NBgZGcHR0RFEhGXLlgn7ODo6orCwUJisbcOGDcK6+/fvQyKRoHv37li4cCGICPfu3cPz58/h6OgIY2NjPH78GDt37ixVPJ8/fw5nZ2dIJBKcOHECV65cEdYZGBjg448/Rvfu3dGnTx/ReKQldejQQRg39cmTJ9izZw/atWtXqngAL3uvDR48GA0aNIC7u7vKbXx8fBAbGyt8XrFiBe7fv487d+7gzp07AIAbN27A29tb2Gbnzp3w8fGBh4eHUngxMTHw9fUtdVwZY4xVLZyfv9n5+fPnz4Wx1IuLi7F9+3b4+/sDANLT0/Hw4UNh2z179sDW1hY2NjYYOHAgHj16JJQDWrRogTVr1oh+GcF5PWOMMcYYe5NpG1u8ZG9zxW0rqiHbwMAAderUQdeuXVX2Qq+quBH9FUj0DWDaayLMBkyDaa+JkOiXT6KytrbGnj17EBYWBh8fH/j7++PUqVPw9vZG37590aBBA7Rt21bodQW8TIBLlixBly5d0KZNG+Tl5Qnrrl27hpYtW8LHxweNGjXCkCFD4OPjg0mTJiEyMhJ+fn4YMWIEOnToUKp4fv/99wgLC0OLFi2wfv16NG/eXLR+5MiRePDgASZMmKA2jCVLluDq1avw8fFBcHAwZs6ciWbNmpUqHgDwwQcfIDMzU+OxWrVqhaSkJDx79kzncNesWSOaUFQuKysLN27cKFMDAWOMsaqF8/M3Oz+PjY1FixYt4OvrC29vb6SmpmLx4sUAXk4k2rNnT3h7e8PX1xfLly/H/v37VQ5Jo4iIcPToUfTo0aPU58HY26KgIB9pac9RUJBf2VFhjDHGmA4UG8LljeRR93JUNqaX7G2uOHyLtrBL68mTJzh48CCICD4+PkrDJld1EiKiyo7E65Keng5LS0ukpaXBwsKisqPz1tuxYwd+++030ZioFeXcuXMYPHgwYmNjoaen/ruhH3/8ERKJRGms1NJauXIlHjx4gPnz579SOIy9K3bs2IF+/fpVdjTYW4Lz89frbc7PNTl48CA2b96MjRs3VtgxGKvq0tKeIy83F1JjY1haWnN+zsoV5+eMsfL0IqsQCU/yUdfOqEoNAfK6XbyTjUcvCuBoZYjGLibCdcnMKUJmXrGwXBVt11Ax7NJISkrC0aNH0bhxY/j4+JTp3Crbu5uqWIXq3Lkz4uPjsWfPngo/1qhRo3Do0CH8/vvvGivcwMufia9Zs+aVj6mnp4fp06e/cjiMMcZYVfa25+eapKWl4YcffqjQYzBW1ZmYmIr+ZYwxxqoqeS9qAGj8Djeiy8cvF41jbmogNJBXM9XHxTvZKhvK5dvKKTaqK4atq9u3byM8PBxBQUGoW7fuq5xepeKe6Iwxxioc91xj5Ynzc8YYqxycn7PyxPk5Y6w8cU90zUrTI13uVXqeAy+HRwReDomckZGB6tWrlynuVQWnKsYYY4wxxhhjjDHG2BtLsRf1u0rdlwnynvqmxnrCGOjalOx5XtovKYqLi3HixAlYW1vD19cXZmZmZT+pKoInFmWMMcYYY4wxxhhjrJRedaJFxsqbuslB69oZwUyqJ/y/tL31tU06WlJ+fj4OHjyI1NRUuLm5leo4VRl/RcMYY4wxxhhjjDHGWCnxONysqlE3brmVqQHMZPpCQ7gu6bVk+tZ1PHQiwr79B5BfrIcOHbrA1LT0w8BUVfyEM8YYY4wxxhhjjDFWSmWdaJGxiqJpWBt16VXdUC0lt9dluJyioiLo6+ujmqs/MorNkfQCsLMq+7lUNdyIzhhj7K1WUJCP7OwsmJiYwtCQC7eMMcYYY4yx8sHjcLOKpq6BW768mqk+nmYV6TREi7r0qu4XFaVJ348ePcLRo0fRo0cP+NarKcT5bcJjoldREokEmZmZpdrnzp07WLVqlWiZi4sLrl+/rtP+2dnZ8Pf3R0ZGBgBg+PDh8PHxgZ+fH5o2bYqjR48K2968eRMdO3aEr68vGjRogO3btwvrli5diu+++65UcWeMsYqSnZ2FvNxcZGdnVXZU2DuoKuTnRIQ5c+bA3d0dDRs2RNu2bYVtZ86cCW9vb/j5+cHPz0+Un0+aNElY7ufnB2NjYyxZsgQAsG/fPnz88celOi/G2JuJigpR9OwRqIjH+2WMMcZeN3VjkcuXR9zK0nmscnXq2hnpPNmoKomJiThw4AD8/f1hZmb2svHdxaTU465XdW/X2bzj5JXu0aNHl2n/ZcuWoVevXjA3NwcALFq0CFZWVgCAqKgodOjQAU+ePIFEIsGwYcMQGhqKYcOG4fHjx2jatClatWqFmjVr4uOPP4aHhwfGjx8PCwuL8jo9xhgrExMTU9G/jFV15Z2fL1myBNeuXcP169dhZGSE5ORkYduwsDB88803AICHDx/Cw8MDnTp1grW1tdBgDrzsWVKnTh3069cPANCtWzfMnj0bCQkJqFu3bllPlTFWxVFRIbJ2L0HRk3vQt68NoGZlR4kxxhir0tT1HC8rdUOwyD83rGks9EQvi1eN76NHj3Ds2DG0a9cOLi4uZYrDm4J7oldhCxcuRGBgINzd3bF161Zh+cGDB9GoUSP4+PggKCgI0dHRAIAxY8YgOjoafn5+6N69u7D9rl270LJlS9SpUwdff/212uOtWrUKgwYNEj7LG9AB4MWLF5BIJMLnK1euoGvXrgAABwcH+Pr6Cr3XjIyM0KlTJ1FvNsYYU6WgIB9pac9RUFD2b821MTQ0gqWlNQ/lwipNZefnCxYswA8//AAjo5fPQPXq1YV1JfP6jIwMSCQSFBcXK4W5YcMGhISEwNHRUVjWr18/rF27thRXgjH2pilOe4qiJ/cAAEUpSQD3RmeMMcY0UtdzXFcvsgpx8U42XmS9zHNV9eou2fDtbCd9pV7fZY0vESEvLw8ODg7o3bv3W9+ADnAj+ivLLy7E3axnyC8u/wKlRCJBREQEDh48iIkTJ+LevXtISUnB4MGD8ccff+Dq1asYPXq00Cts5cqV8PLyQlRUFPbu3SuE8+LFC0RGRuLcuXNYsGABHjx4oHSse/fuIT09Xak32fTp01G3bl307t0bO3fuFBrSmzZtik2bNgEAEhISEBkZiTt37gj7tWzZUjT8C2OMqcJDrbCq4m3Nz9PT0/HkyRPs2bMHLVq0QIsWLZS+5F6yZAnq16+PRo0aYdWqVbC1tVUKd+3atRg5cqRoGef1jL399Cyr/f8e6Hj5rz7/kJkxxkpSbPBk7FWHRtGlUVuXbXRNm2WJb2p6Hv7cdwhH/zsOiUQi6pjzNuNG9FeQX1yIzy/twdjzWxF2eU+5V7xHjRoFAHB1dUWrVq1w8uRJnD17Fn5+fvD29gYADBo0CPfv3xf9NFuRvDeanZ0dXF1dkZiYqLTN/fv3RT3T5L7//nskJCRgx44dCAsLQ37+ywd0/fr1OH36NPz8/BAWFoYOHTrA0NBQ2M/R0RH3798v+8kzxt4JJiamkBob81ArrFK9zfl5QUEB8vPzkZOTgzNnzmDHjh2YMmWKaHz1SZMmIS4uDpGRkfj666+RmpoqCjMiIgLp6enCL9DkOK9n7O0n0TeAaa+JMBswDaa9JlZ2dBhjrMp51V7HVdWb9uVAVYrvq44Hrkujti7b6Jo2SxvfvLw8/PvvP8jMyICNaxOd9nlbcCP6K0jOScetzBQAwM2MFCTnpFfo8SQSCYhINKxKyXXqGBsbC//X19dHYaHyS8XExAQ5OTlqw+jQoQMyMjJw7do1AICzszN27tyJqKgo7N69G2lpafDy8hK2z83NhUwm0+m8GGPvLh5qhVUFb3N+bmtrCzMzMwwePBgAULt2bQQGBuLChQtK+/r6+qJmzZoIDw8XLV+zZg2GDh0KfX190XLO6xl7N0j0DaBv4wgJ90J/q504cQLdunVDjRo1IJFI8Ndff2nd5/jx42jcuDGMjY3h6uqKlStXVnxEGatiXrXXcVX1pn058KbFVxNdGrV12aai0uapU6dgbiJFg+ad4FHr3ZoHkRvRX0F1mQXqmdsDAOqZ26O6rHwTj3yc0Tt37uDUqVNo1aoVAgICEBUVhZiYGADAtm3bUKtWLTg6OsLCwgJpaWllOlb9+vXx+PFj5ObmAgAKCwtx8+ZNYf25c+eQkpICV1dXAMDjx49BRACAf//9F9HR0Rg4cKCwfUxMDHx9fcsUF8bY2+l1jH/OWFm8zfk5AHz44Yc4ePAgAOD58+c4d+4cfHx8AEA4PvByeLbLly+LvhTPzMzEn3/+iREjRigdi/N6xhh7e2RlZcHX1xfLli3TafvExER07doVrVu3xuXLl/HFF19g0qRJ2LVrVwXHlLGq5VV7HVdVb9qXA68a38rqyV6Rxy3vtJmRkQEiQmBgIN7rEoJmbhZvXbrX5t0623JmpGeABf69kJyTjuoyCxjple/llEqlCAwMxJMnT7B06VI4OTkBADZu3IhBgwahqKgIVlZW2LFjBwDAx8cH9evXR8OGDeHq6ioaR1UbY2NjdOjQAUePHsV7772HoqIiDBs2DGlpadDX14epqSn+/PNPWFtbAwD27duH77//HgYGBqhevToOHDgg6o128OBBfPvtt+V4NRhjbzr5+OcAYGn5ZhTG2Lvhbc7PAeDbb7/F8OHDsWLFCgDAjBkz0KhRIwAv5z65desWDA0NYWBggGXLlsHT01MIb/v27fD390e9evWUjnXw4EH06dOnbBeFMcZYldKlSxd06dJF5+1XrlyJ2rVrY/HixQAAT09PXLhwAQsXLuS8gbG3gJWpARq/QQ2krxpfeU92ABV63iUnBLUyNSjTcRXDeB0ePHiAw4cPo2PHjqhZs+ZrOWZVJCF5d+J3QHp6OiwtLZGWlgYLi3frJwe6OHv2LObPn4/9+/e/UjjR0dEYM2YMTpw4UU4xY4y96Xbs2IFevXoiOzsLJiamPHwLeyWcn2tWXvm5Jk+fPkX79u1x/vx5GBnx88zYu2LHjh3CJMjs7SWRSLBnzx707NlT7TZt2rSBv78/fvnlF2HZnj170K9fP2RnZ4vmy5LLy8tDXl6e8Dk9PR1OTk6cnzPGKt3rapi+eCcbj14UwNHKEI1dTJSOq+2zqjAq+lxu3bqFEydOIDAwEPXr13/l8N5kPJwLEzRv3hw9e/ZERkbGK4Vz7949Hg+PMaaExz9n7PUor/xck4SEBPz666/cgM4YY++oR48ewcHBQbTMwcEBhYWFePr0qcp9vvvuO1haWgp/8l9mMcZYZauoYXkUh2tRHHZG8biKY7urGutd29A1pR0fXtOQMtnZ2Th9+jQ6depUrg3oVWki2NJ4c36bwV6LUaNGvXIYISEh5RATxhhjjJVVeeTnmjRv3rxCw2eMMVb1KU6GLf+Ru7pJsmfMmIEpU6YIn+U90Rlj7G2lOFyLtmFn5A3j8n+rmeojLjkXDWsaC9voEkZmThEycovwIqtQaKBX10Nd1ZAyxcXFSE1NhZ2dHQYMGKDy10Wv4nUNn1PeuCc6Y4wxxhhjjDHGdObo6IhHjx6JlqWkpMDAwAC2trYq95FKpbCwsBD9McbY26xkr3Fdel8r9kx/mlUES5k+nmYVCdtoC8fK1ABmMn1k5RaLeqOr66Fe184IpsZ6yMx52eheWFiIw4cP48SJEyguLi73BnT5Md+kiWvl3pzmfsYYY4wxxhhjjFW6gIAA7Nu3T7Ts0KFDaNKkSYU0uDDGWFWmrpd3yV7jF+9k4/bjPMQl56Krj4XWoWNeZBUiM6cIBBIauDVNRiqPQzVTfWTmFMHUWE/USK3Yy71kHM2N9fHoRQHiHqQjOfoE9PT08N5770FPT08p/NKOta5qvzdt4lo57onOGGOMMcYYY4y9wzIzMxEVFYWoqCgAQGJiIqKiopCUlATg5VAsH330kbD9mDFjcPfuXUyZMgUxMTFYu3Yt1qxZg88//7wyos8YY5VKl3HI69oZoaCYYKAn0bidvKd51L0cZOYVIz2nGJl5/9erXF0vbnkcIm5lITOvGBJ6uUzeY13TuO/yMJGeBFNTU3Tt2hXGxsYqw9d1rPVX3a8qevOa/RljjDHGGGOMvXZEhLS05zAxMeWJwt8yFy5cQHBwsPBZPnb50KFDsX79eiQnJwsN6gBQp04dHDhwAJMnT8by5ctRo0YNLFmyBH369HntcWeMvdvK2kO6PKnr5V2SlakBuvpYCHFVR97obCbVg6OVIRrWNMbTrCLxZKRqGsIBCNtn5BbpPO54QfZz1K8mg6mzLwDVc1voco6qqNuvKty30nozYskYY4wxxhhjrFIRFSMvNxcAYGnJjehvk7Zt2woTg6qyfv16pWVBQUG4dOlSBcaKMca0K+sklZUxPIku25VsdJaH72z3fz3U1cW3ZNjy7bU12ANAUlISjh49isDAQLi7u79S3Euz35s4ueibEUvGGGOMMcYYY5VKItGD1NgYJiamlR0VxhhjDEDZe0iXtRG3oht/1TU6X0nKwbUHucjMKUKQp3mZwykpJiYGkZGnUbtBAOxrupY5zmVR1vtWmbgRnTHG2FuroCAf2dlZ/LNzxhhjrBxIJBJYWlpXdjQYY4wxQVl7SJf38CRlUZre8CQBQPTy33JQXFyM27dvo65/MLL0bJDwJP+19gh/EycXfbNiyxhjjJVCdnYW/+ycMcYYY4wxxphIeQ9PIvciqxBR93IgIcC3tkypcbxkw3lperX7Oclgbqyvc+O9ugb64uJixMbGwsPDA127dkVadpHaYV/exHHLKxJfAcYYY28t+c/N+WfnjDHGWPmgokIUpz2FnmU1SPS5OskYY+ztUR6NxglP8nH9Xg5yCwkPXhSgq4+FKKySDeel6dUub7zXNja6quPIG+jz8/Nx5MgR5OTkwNXVFcbGxhq/FHgTxy2vSHwFGGOMvbUMDY24BzpjjDH2CvKLC5Gck47qMgsAQNbuJSh6cg/69rVh2msiN6Qzxt4a3OuWlaXRWDHd1LUzQkauDAmP8mCoJ1EaJkVx4tC6//+4dQGd0p2ucVRsoM/OzsY///wDY2NjdOvWDUZG2uvJb+K45RWJ3wqMMcYYY4wxxpTkFxfi80t7cCszBfXM7dGyqBBFz+8BAIpSklCc9hT6No6VHEvGGCsfmhonuYH93VCWRmPFdGNlaoC2HuaoY2uEiFtZqGaqL9pesed3aRvudY2j4nGysrJgZ2eHwMBA6Ovra9hTfRjvOr4SjDHGGGOMMcaUJOek41ZmCgDgZkYKmklsoG9fG0UpSdC3rw09y2qVHEPGGCs/mhoneViLd0NZGo0V0438C5fMnCIY6EkQcSsLliYvG61VfRFTcv8XWYW4kpQDkrwcA93q/w/fojjGuqo4lvyip+SxcjOeIisrC3Xr1oWdnV3pLwoT8JPPGGOMMcYYY0xJdZkF6pnb42bG/2Pvz+Pkuus7//d11tq6uqpXtbZuSS1ZsiVLFrIBL3jBLIMhBif8nAn8mBuYhGEAB3AWMAkwMOSa3CyXMBkYIL8wkyEMyQWTQPCDxICNg23AyJYl27L2Vrd6r+6u7VSd/dw/SlXubnVLLamlVkuf5+Ohh7pOnar61jlHOl2f86n3t9aJro9FpO65TzLRhRCXpdMVUCXW4sp2um8izD5unuuvsm/QZn2bgR9GjUgXYM4LMdMf/9hLJR55oURLUiUd19iV0hsZ6ygKTQltQfnl9deaGu2n/4UnefWrX73g9yPmpy71AM7Vgw8+iKIofPjDH17qoQghhBBCCCHEZcdUdf505z186Ybf4E933oMCKJqO1tolBXQhxBUlm9LZtS4pBcdloj75Zt7yz/lx03+uF6jrxfDTiRQgikglNO7a3kxns07JDmhPaXRljdNeiFEiSBgKlhs1YmB6O0y2rU1w7er4aR/b22E2nr+3w0QtHKH/hSd57WtfyzXXXDNj3YW+n3PdjperZfmv/+mnn+YrX/kK27dvX+qhCCGEEEIIIcRly1R1elKtSz0MIYQQYsHONX5nrm5uOLtvIly3NkE6rjW6vJsSGiN5j5wVsGtdEpi/E3xHd4LBvIeuKuSsgJ4OGhnrZzK7I351i86ut7yFzs7OU9Zd6PuRGKOZll0nerlc5p3vfCdf/epXaWlpWerhCCGEEEIIIYQQQogrmHTsXlqmd2Wf6+Om/zzXNxHm2+fZlE5vh8mRcZe85Teepz2lnbGzPZvSuXljCj+MTpmQdCGCIOCnP/0ppVKJ7du3z1lAr7/OQr5Zca7b8XK17IroH/jAB3jzm9/M6173uqUeihBCCCGEEEIIIYS4wp1N3Ie4cOqFbeCc4nfOVFw+XcTLfPfVnzNnBYzkPfYMVBnO1+6fq1CeswIyCY2cFZzV2B3H4eGHH2Z8fBxdX5yucYkxmmlZbYVvfvObPPPMMzz99NMLWt9xHBzHadwuFosXamhCCCGEEEIIIYQQ4gokE49eGhYzfmSu55q+rL6v6x3mJTvAskNKdoASQSquzjge6j+XqwF94y4oL0e2TNfbYVKuBpTsgLzlL6iAXalU+P73v086nebf/bt/h2EY5/XexdyWTRF9YGCAD33oQ/zrv/4r8Xh8QY958MEH+fSnP32BRyaEEEIIIYQQQgghrlSz86jF0ljIxYz58sgX8lzTl9X3+e6+CiN5j6aYSlfWYDTvcTTncu3q+Iznr6+ft3wipTaJ6Oxx1scGYNkhz/VXaUpotKdqnenzjdk0Ta666iquvfZaVHXZhY4sG8vmX/ju3bsZGxtj165djWVBEPD444/zV3/1VziOg6bN/BrEAw88wP3339+4XSwWWbt27UUbsxBCCCGEEEJcDjzPJQwDPM/FMKTTUgghxKVnIRczFtqtPtdzzbWst8OkZAcQvfyz7QYcGnPY0Z04peg910Sh9eJ5uRpQdkJS8VpBfmTKZd+gTUJX6Moap4x5cHCQgwcPcvvtt7Njx47Tvm9x/pZNEf3OO+9k3759M5a9+93vZsuWLXz0ox89pYAOEIvFiMViF2uIQgghhBBCCHFZqlQsojCiUrHIZKSILoQQ4tJ0uk7zvOUzmvcYLnhsWx2fsS6woA712err7jtRJVLgurUJhqY8DFXhyLjbKHrXX2uurvJ6Yb9ePK/f95gdQOSyImucMsHnoUOHePzxf2PNlhsoVIJzyi1faFe+qFk2WyidTrNt27YZy1KpFG1tbacsF0IIIYQQQgixeJLJFIqqkEymlnooQgghxAzTi8Gn6zQ/Mu5yNOdCFJGzgsZkn437Rx0ODNvcvDFFX86lbAdECqgohESkY9qc3eVKBCgKSlQrqt+1vZnn+quNXHOAbz+dZ6oSkE1prMzM7Cqfnq8+fULR69YmSMe1U4rcL7zwAr/4xS/YsONWKnrHjGL92VjMDPkrgWwhIYQQQgghhBCnZRgmqqpJlIsQQohLzlwTfs6Vi96e0ogbCiubjTmzzg8M2xiqwhOHLSatgOG8B1HUWG9li0lTQjulu3xde215/XmyKZ2mhMZI3mtknE9VA6Ysn6tXxhpd5dO704Fa4d4Ja2OCGV3yu/sqbGg3aGkyWLFiBb/yK7+CnsjOWOdsyYS4Z2dZF9Efe+yxpR6CEEIIIYQQQgghhBBiiSy0GJyzAlZmatEo9c7u6cXqu7Y3N4rafTmX9W3GKZ3o01/jyLjL0VGHA2HEXdubZ3SLzx7TyFSMozmlsSw7bVLSA8M2mYQ2I85l+oUBgKGJKkf2/IRd115Fb29vY/n5dJDLhLhnR7aUEEIIIYQQQgghhBBXsOWcjz29GFwvTMOpBebZhe285fPw3iKGWitu71qXbDymp2PmHIv17TP7+Q4M2+iz8s9njylv+RTskEIl4MkjFZriGrddnW6MY9vq+Ck56b3TXsOxbV76+U9ImBpr1qw59w21AMv5OLjQZGsIIYQQQgghhBBCCHEFW0752Kcr9J6uK3125/WRcRddVfDC6Ixd7HNtn3r++exIldnjq79OEEFLUiVSZo4nb/kzstCn3+f7Pv/yz99nRVsbt99+O7p+YffNcjoOLjbZGkIIIYQQQgghhBBCXMGWUz726Qq9p4soOT7u8MRhi5s3psgkNcrVgJSpAAp7Bqpct/bUSUPr5upirxfKd61Lzlg2MuVyLOdSshPcvuXljvNXbUg2Os4X8n6CIEDXdW655RZWrVqFoihns5nOyXI6Di42KaILIYQQQgghhBBCCHEFuxTysRcaJXKuhd4nDlscHXMA2LwyTtkJKdghk1aA7ToMTXnctb0Z4JRxzNXFPrvwXV82XPRBUajYIbv7KjMK7T0dC3s//f39PPHEE/zar/0aq1evPqv3eT4uhePgUiVbRQghhBBCCCGEEEIIsaRO12E+u8DemBAUFpzdffPGVOPvTFIDoLvV4OfHKkSRgutHPLy3yOqsQdkJ5xxH3VyF7/aUxoFhm1etT+KGULKD2sSjw/YpE4/Ofj/TX+ell17iySef5Pbbb8c0pSP8UqEu9QCEEEIIIYQQQgghhFiO8pbP7r4Kectf6qEse70dJl1ZY84O83qBvT6555FxlyOjDg/vLTa2/Zn2RU9HjHfc2EpPR6xWuF5XK3avzBhsX5PA1BUMVSFSmDGO6c97fNzhG09NUqgE7FqXnFEYz1kBmYSGG75cXC/YQWPi0dO9n7qjR4/ys5/9jDe96U1s2LBhQdvtQh6Dcny/TDrRhRBCCCGEEEIIIYQ4BzIR4+I5XZTI7M7v3g6TA8M2xskC9a6TE3ie7b6Y/nw7uhPsGaiiRMyIcpn+vAeG7UYkTE9HbN7nOjLuYtkhmzpjNCW0Uy4MzH4/kyWXw6M27U1dbLrh35Fobl/Q+GePb7GPQTm+X3Zlv3shhBBCCCGEEEIIIc6RTMR4cUwvsNcnCL12dXxG1/dC9sXsGJXZhXslgn2DNgC3XZ1uPF+5GlCyA65dHafihmTiKnnLn9GJPv25ek8umy/ffXokTbfn8aMf/ZBAb+Jw5w4yiUTjwsBCXMhjUI7vl0kRXQghxJLzPJdKxSKZTGEYcnIWQgghhBBCLA8yEeOFN7vwPX2C0Hfc2HrK+oVKMO8EpWfqrC45AcNTLuvajRnLB/MeuqqQjmvs7Ely9GSUzOys87qFHBdHxl0Gx4q8+NS/0ZxK0LRhB46vkYyrZ1W0vpDHoBzfL5NMdCGEEEuuUrFwbJtKxVrqoQghhBBiDp7nEoYBnueeeWUhhBBiEc3OD795Y4oNnbHGRKGz13visNUocs/O8p6euz5X3reK0vg7b/k89lKJbz+dx/Mj/DCiPaVRsoM5s87PNj98Q7tB+fjP6Gxv4y1vfhPZpjgRkI5rC54sdT6SZb745FKCEEKIi25253kyWfvlp/63EEIIIS4tlYpFFEZUKhaZjHxrTAghxMUzO1KkpyPWyCOf3qVuqvD8iSqvuSpF34SH60fzdosPTLg89EwBTYFbNjdx+5ZadEsyrrIyaxBSe+zQlAuKQktCo6fD5NjEy1nnAEdGbJ49XuH116TJWcG8Xe71cbanNHJWQFfSZXVnlre9+Q04ocGeAYf2lDbjfZ4PyTJffLIVhRBCXHT1znOATMbEMEz5QC6EEEJcwpLJFIqqyAVvIYS4AsyOT1lqc0WK5C2fPQNVDo86NMdrxecDwzaWE/JvBy3+/ataeOKwNWPiUYDn+qvsG7QZmXI5Nu6iabAyY3Dd2gTZlM51axOk4xrlasBEKcByI25YlyCKIp4/YbOh/eVO9iPjLv/6QolyNSBpqty1vRl4uQg+fTvWXzduKGSDEZ499gt+7VfvoaWlhf19lZcL3uuSi7LNJMt88S39vwQhhBBXHOk8F0IIIZYXwzBRVe2Cz10SBT5hIYeaaUfR5OOqEEIshcXsYp5eSK4/95mK83MV8Wcv2zNQ5ZF9RRKmSrKzliHentIYmHBZkTHIWQF3bW+e8doAkQJEEZmURrutYfsRRTvgy4/muGt7M5NWQMkJUCIo2gEpUyEZr6Vh267DkXGHVKx2u7fD5KbeJEdzLtlErZA/vQg+fTvWXzddPUZ+8HluuuV2WlpaGs8z/e+FbhNxcclWF0IIcdFJ57moe/DBB/n4xz/Ohz70IT7/+c8v9XCEEEIsoSjwsR76AsH4AFpnN6l77pNCuhBCLIHz6WKeXeydXkgGGj/3Mn9Bfa4ifn1ZuRrQlNCo2CEtTTotCa3RAZ6zAl5zVYp/O2ixqycxZwf7+jaToSmPa1fH2Tdo4/kRTx0qM1722XO8wta1CQpWAESAAkRU7JCbNqUYmvIYmvLYN2jTlNDYtS7Jm3dm2X2yk3x6x/tc21H3ihx99gB3/8pb6OzsbKy30ElIz+bChsS5LD7ZikIIIRbV7LxzIebz9NNP85WvfIXt27cv9VCEEEJcAsJCjmB8AIBgrJ+wkENr7VriUQkhrhTS6fuyhRR15zO7eDtXQb4ehTJfkXe+xwCU7FrueCqucsfV6cb+2t1X4ciow8+OWKRMhX2DNtd2nxqN0pdzGcp7VJ2Q121N05dz2XdC5fBYgKEpVO2Q5qTKs31VDE2hu80kGVfJpnTu2t7MnoEqFTukZAfkLZ/sPO9x+nYMgoBCocAt167mVVv+PYZhnPV2PdsLGxLnsviu7P8VhBBCLLrZeedCzKVcLvPOd76Tr371q3z2s59d6uEIIYS4BKiZdrTOboKxfrTObtRM+1IPSQhxBZHO3cUxu3g7uyBf/7l31vrTZVP6y53qJ2/Xn2fOeBigPaXxw3GHtS0GbhBx88ZTo0Pzls+hMYeBnMuIrrBxRW1y0GxSp7vdJKYrrMwaRAokDBVNU+huN1Gi2mMB0nENJYLRgs/DUy9PWjrfMTM2ZfHDHz5CU9zg7l958zkV0Ofajou9vjgz2ZpCCCEWleSdi4X4wAc+wJvf/GZe97rXnbGI7jgOjuM0bheLxQs9PCGEEEtA0XRS99wnmehCiCUhnbuL43TF29nd/nOtV1+nXA0oOyEw86LG9GL6t5/OM1UJKK9P0pTQ2NARo1gN6O2KkUlqpzz3kXGX5rhGKq7i+RGjBY+iE6IAW1fFKbsR6YRGe5NGwlRZlTFIxVTKTsiRcReg0QXvhxGGqvBcf5WmhDbnNxjK5TI/ePj7YKTouvo157pJxSVCfisRQgixqCTvXJzJN7/5TZ555hmefvrpBa3/4IMP8ulPf/oCj0oIIcSlQNF0iXARQiwJ6dy98I6MuxwddTgwbDc6uOdap16o7soa9HaYc0btHBl3maoGTFk+kVK7+DEy5fLisIemKaTj2rwxMU0xlaM5l6IT0hzXmNB8IhRSJhi6wrY1CVITLkoE69pNclZAe0qjL+cSncxIb06oKBEcGnNojmuNrPbpY9y3bx/tHSuYatrGikzsAm9dcaHJ/w5CCCGEuGgGBgb40Ic+xL/+678Sj8cX9JgHHniA+++/v3G7WCyydu3aCzVEIYQQQgghxCKZXgDv7TA5MGyjq8opk3DW9XaYlOwAJXq56P3w3iKGqgDT4mA6TMrVJJFSmyz0uf4qv+yrUHEj+nMu7WmN7z/r0hTXWNdu0pdzKdsBqYTG1tVxknGVih2SjKu8akOyViA/+VxPHLYYmvKIm2pjAtHdfRXKTkixGjJpBRBFtDbpZOIaXhgRKS9Pmrq6lKO1tZVXvepVPHO8SlTwyVkBPR0XaaOLC0KK6EIIIYS4aHbv3s3Y2Bi7du1qLAuCgMcff5y/+qu/wnEcNG3mVy9jsRixmHRuCCGEEEIIsdzMyJpfl+Su7c0zMs1ny6Z00nGNkbzXiFDRVQUvjGY8JpvS2dGd4Ln+Ko+8WCJvBeiqQmtKZeuaOC8NOewfqtKS0rl6ZZyqH3F8vBYRuXlVnJXNOsdyLutPdprv6E40Jig1VIVsSmNTZ+yUmJ9tq+MzCu45K5iRz+5OHuefn3mSm2+7k6s3rWNjZwxFUSQm6DIgRXQhhBAXnee5VCoWyWQKw5BfJq4kd955J/v27Zux7N3vfjdbtmzhox/96CkFdCGEEEIIIcTyU+9Ab0/Vfr9vT2ns7qvQ22Gya13ytI81VXj+RJXuVoO1bS8XsesxKfXnLtkBzw/a2G5AzFCJGRob2k22rU4wUvBRFDiec9i6Js6GrMnxMZvRko+iROzph65mneGiD0qty70XGM17FOyA11+Tpqfj5Uae6XE/05fXu8ujKELLH+SFZ5+lZdMtVIzOGY/LW37j/c8VYyMufbLXhBBCXHSVioVj2wCSn36FSafTbNu2bcayVCpFW1vbKcuFEEIIIYQQy9PsDvTdfZWXb5+hiPzzYxWGpjx+fqzSKKLPfu7nB6ocz7lsWRmjMxtHiWDfiSrHJjy6Wkzefn2WoUmPYc0nDGBF1mBF1gRFoeJFpEwFQ1O4ZmWMH75QZvOqGOVqwNGci+2GPHHYIpPUTlvwnh5VkzRCjh8/zp1vuIvJIH1K5/mM7SFF9GVJ9poQQoiLLplMzfhbCCGEEEIIIcSla67JPfOWz3P9VSIFrlubmFFwnh2DMvvv2c/bntIa0SiZuEa+4nNgqMqThwyik+vWO7rL1YC+nIMfRBSdkN64hqlC3FRZ2aw3XuPqVXFsv0JzQqU9pXHD+lqGeltSY9+gzc0bUzxx2GJg0mX/cJW37cpy7eo4h8ac0+a21x0ZdxmaqJIbPMKrrrua1dtfR0trjPXzZL2f7v1Lh/qlT/aOEEKIi84wTOlAFw2PPfbYUg9BCCGEEEIIcRpzdVIfGXfZN2hDFJGOazMKztPjT2bfnl44rj/vgWGbTKIW/dKR0UmYKpYTMVTw2NmTnJE7XnZCblifYqjgMZp3+eaQTU+byfrOGF1ZA6hNRjo05aEoCqOlgJwVcNvV6cZ41rbVXvva1XGeOmQRKVCuhtz2qjQ7uhOnzW2vv4d8scLUwZ/QFNc5NLKOsVKIoihzFt5nb4/6NihXA8pOOGO7ikuT7B0hhBCXJT+ImLR8WlM6uqYs9XCEEEIIIYQQYtmaq5O6t8OkXA2IlFM7rE9nekF++oSdxyZcRqZcFEXhjmvSlKshr1yfZNIKeOJgmUiBqhNSsENetT5JvhowVgrIWwHd7SZdWaNRmNdVhZihsLJZZ0O7OSOTPZvSea6/yr5Bm2tXx/m9N3XyxGGLmzfWvim9kBzzF49P0Pfsj0imW1i7/RY6m01ULVjwdqhvg1RcbYz7fElX+4UlW1QIIcRlxw8i/ubxCYbyHquyBu+5tU0K6UIIIYQQQghxjmZ3UteXTe/uXqjpBfnpBeuhwx5DUy5xU+PGjSlMFb7200lSpoLtR5SqAem4yoqMyTd/NsVoycP1I65dm+D2zU0cn3TZ01fhlRuS9K6I0RRTOZpzAXjisIWh1j4TtlcCHnuphK4qlJxal/pd25tPKTzXC+3l6std7PVCtRmUaetaS6Z7B2OlEFULzjhh6nTtKY0DwzbbVsdnTFR6PiR3/cKSLSqEEOKyM2n5DJ385WEo7zFl+XQ0G0s8KiGEEGJ58jyXSsUiiqIzryyEEOKKNzvnfHreeTalzxntMpr3GJryiBkq166O057S+MtHxhkrerQkdXraTcYKHmNFH9eLcMOIE5MerSmD1S0mbghPHbIoOxGJmMrNG1P8n6cmGC8GVN2AnrYYXhjRntL45s+nmKoE+CGExyv8ZH+ZO65Oc9Om1IxO7kgBoohIeXmcw0MDuL5CT/ca3vbG3hnd32cjZwVkErXt0tOxONt9vtx1sTikiC6EEOKy05rSWZU1Gp3oLXIVXgghhDhnlYqFY9tEUbjUQxFCCHEJmS8+ZHbO+fS889kd0vV1hwsecUNhfbtJU0Lj2ITLmhaDsZLPK9Yn6GzS2d1XwXYjym7Izu4kq1pMbCfk2tVx1raZ3LgpxUtDNhNln//fL6bYP+Tg+iFXdcUo2gErm3WOTbh0ZQxGiz5dzRrHxh38AI6OOeSrAZ4fcWDY5q7tzVy3NkE6rjUiYo4cfIlC/7Osv/bmRqF6rg79hTifgvd82/1cxyIWRrasEEKIy46uKbzn1jamLJ8WyUQXQgghzksyWcuIVRR1iUdy5YmiCEWR32OEEJem+eJD6lEl166O44a1vPN6JzrUisB7BqooEaxrry3rbjX4xdEKR3MuFTciaSp4QcSWrhiWHRKlYeuqGAdHXda3m/zKzgzHJlyeH6gyUQm4tlvnlk1NPHXQ4vmhMo4bkYpptDfpNCc18pWAY2MON21MsW1tgtu3NNGXc9nUFaNcDVmR1ak4EQMFlw0dMY6Mu+xal2RXSieKIqrDz1M68SK3v/aNbNqw5ozb5kz55OdT8JbYlqUhW1oIIcQF5XkuYRjgeS6GcfG+VqZrikS4CCGEEIvAMEwyGVOKuReI4zh8/OMf55e//CVvectb+P3f/30++9nP8rnPfY4oinjrW9/K//gf/4Pm5ualHqoQQswwXzd1ParEDWnkhNcjS/KWz7efzvPSiM2KZoOmhMaudUl291WYqgaM5j0Spkpnc4z1HTFK1YDerhhKVCs8tyQDTkx5/PyIRckOsf0Iqxrwk/0lfnHM4vlBm9GiR9JUaY/p3HF1E5YTUrR8Rgo+ubJPV4tJJqnNyHM/Pu7wxGGLu7Y344Y0JiJtT2mMl32iMOSet91Na2vrgibwrBe6y9WApoS2qJN9SmzL0pAiuhBCiAuqUrGIwohKxSKTkZO8EEIIIcR0DzzwAH//93/Pb/zGb/C1r32N48eP873vfY8vf/nLqKrKJz/5Sf7oj/6IL3zhC0s9VCGEmGH6pKC7+yqNQvHsIu/0ovMTB8s8cbhMTFPYvDKOqcJf/yRHJq5BGOH6EbYTsu5krEv9OZ7rr9LRrLPvRJUwhD0DVVpTOnFdoS/n0pdzODLmMmn5tKcMVFVh66o4B0cdjo+7VLyQpKFyYMShPV1rtprexT298N/bYfLw3iJa5PPc0F461m1n1erttLbWLgicqRM8b/mUqwGpuEoEC+oaX0hhfvZ2FxeXbHEhhBAXjOe5+L4PitL4KrgQQgghlg/XrlAZHSC5Yu1SD+Wy9a1vfYv/9b/+F6973et4//vfz6ZNm3jooYd461vfCkB7ezu//du/LUV0IcRFtdCibt7yeXhvEV2tfVtpV0qnUAk4MGxjqjQmDX1xyObZlIbthMR1lRVZg7dfn+XhvUWeOVYhBLaujtMUVxkr+Xz5sRytKY3hVXHSMY2yExKEcNPGJvomXHQlYrTo0dNqMlUJGCsGlOwARQHHD8gkdfpyLmvbDMpOiOOH6CpctSJGV9aY0Wlen/wUah3oD+8t4lYr+CeepCWdYEXWnNH1faZO8CPjLmUnpCtrNPLU51u3vp3L1YCyEza2obj0yF4RQghxwVQqFoHvoyhc1CgXIYQQQpy/KPApf+vzBOMnKHeuBaN3qYd0Wcrlclx11VUAbNiwAU3T2LhxY+P+TZs2MT4+vlTDE+KKczYdwZezhXRb14u/hqrghRG9HSZ5y+ebP5/CckIGJly2rUkwXPCYqgRAxJZVcQAiBQYmXDw/YrLi09Nq0NVskDBVjo45HB5xmLR8fn7E4q7rMljVkKmqj4qC4wZUQijZIZ3NOvGYynjRo+rqWG5ITFcoVkNiekDMiLF1TZwpyyMV0+lI197LsQmXsYLPD8cdVmQMTF3hru3NHBl3Ubwy3vHH6V6zite99jY0TZvx3s/UCd7bYTIy5fLs8VqRvh5pc7rtnIqrjaK7uDRduf8bCCGEuOBkIjIhhBBi+QoLOdT8GJGqok6NQlvPUg/pstTd3c1TTz1Fd3c3Tz/9NIqi8Itf/IKtW7cC8POf/5zVq1cv8SiFuHLIpI01p+u2zls+3/plnrzls2VVnM5mnejktBlHxl1WZAxGC14jX3zb6jh9OZdIgevWJihWi+wfsvnaTycJwggNhWRMoyOjk4ypZOIaEyWPUlWhVI149IUSVS9ERUHVFFIxhUIlIGGqRAq8/fosVTcEHNa2GnS3mewdqFK0Q1wvYnXWYGXWoLcjRiqmMpL3aIqp+GFEV8ZgpOA1JhPt7TCZKiaIr9nMK2+8bkYBvX7hoN69frpJQwt2yEje44nDFj0dsTm38fTYl+vWJq7oizbLgewdIYQQF4xMRCaEEEIsX2qmHbVjDcFIH2rHGlDk4+OF8L73vY/f/M3f5K//+q/ZvXs3f/7nf87HP/5xXnrpJVRV5Utf+hK/+7u/u9TDFGJel1vn9sWatPFS326n67Y+Mu6StwKmKiHpmEZTQmMk782ILXnbKzJkT+alP9dfbRTQAXQFpioBve0mQ3mPuKmyrt1EiSAVV6k4AaqikoipJHSVIARVUcimNG7amOL5gSrjRZ8wCtnXXwVgOO/R0axzfW+K69YmSMZVXhqyGc17HBt3cYMQ1wvZsjKOoihECly7Os6+QZvXXJWib8LDz5/guYEcY/oW9OaNHM157GoyZrzvkbzHs8cr2G5IyU5w+5b0qRsIuHljasbf823HeuzLpXgMiJlkDwkhhFhSnudSqVgkkymJfBFCCCEuEVHgExZyBK99J8HUOFr7Svjho0s9rMvShz/8YTo6OvjZz37Gb/3Wb/Hrv/7rbNu2jU9+8pNUKhU+8pGP8Id/+IdLPUwh5nW5dW5frEkbl/N26+0wKa9PEimw42RhvL589vY7Mu6yb9DGdkP2DVSZtAI0BbqaddwgorfTZPJYhWf6qhwec4hCKNoBLw3boEDnSh0lgmLVpyOtkTRVDF0hHa/lpLthyN//bIpiJSARV3nl+gQP7y0yNOUxZQW4AQznXRQUnhuwmbQC1nfGWInB0JRHJqHRN+GhFY6w78V9rL/2Fozo5Xia2e8bgChi/7DDkRFn3g7yno7YvB3os5/vco5wudQvFp2N5T16IYQQy16lYuHYNgCZzOX7y4MQQgixXESBj/XQFwjGB9DaVqO8/v9FKp1Z6mFd1t75znfyzne+s3H79ttv5/HHH1/CEQmxcFdCIfBCWK7brd5ZXnIC0rFa1Mn0wvnsyJP2lMaGdpOfHixxYNhhshKwptVgx5oEhWrAs8crWE7I4apNTNdImgqWG+KFIXFNZWjSx9AgV/YZKQS8OOiyIqORbdK55ao4Y0WfuOEz4vloWsQPni/z6t4UcV2hK2vgeiExE4pWyNo2g5ihkYlrFKoB166O44YQK+ynf/gAd7zuTbS1d8zoqN/dV2kUgOvvM99hMlLwGS54fPvpPL92Q3ZGgXihheOLdcFmKS3ni0WzLe/RCyGEWPbquen1v4UQQgixtMJCjmB8AIBoYpA0Adq0b4vVu9TVTDuKJh8pLxdf/OIX+dM//VOGh4fZunUrn//853nNa14z57qPPfYYd9xxxynL9+/fz5YtWy70UMUl5kooBF5uZkesnE2HcL2zfHjKZWWLSVNCqxWWp0002pdzeW6gyo61CbatTbAia5CMaeTKHhUnIleE/cMKk1ZATFcoVQM0tdZh7gURqgLtKZ2qH5FNKkyUA+KGgutFuH6IqRm8/to0K7Mm7SmNFwZtdh+3KFZC1nealKoB6zpNFBRScZWhKY9iJWCs6LNlVa07/PiQi+UE/F83tGAZa7nhuqvJZGoXjOvH8+6+ypwF4GxKp7crxtFxh6lqwJFx95Tu+7MpHF9O3dqzLdeLRXO5vPaMEEKIZaeemy6EEEKIS4OaaUfr7CYY60fr7EbNtDfum9Gl3tlN6p77LkohXQr3F9bf//3f8+EPf5gvfvGL3HzzzXz5y1/mTW96Ey+++CLd3d3zPu7AgQM0Nzc3bnd0dFyM4QpxWVjKDt16IZwoIh3Xzur121MacUNh04oYQVi7nbd8Ht5bRFcVOjM6IwWPMIp4bqDK7VuaAKg4AS1JnTDy8QLIWyEJQ0VVFW7cmORYzqM1pWM5IVMVD0NX6MgYTFk+m7piDE55GE0qHWmdjV0xlAhG8x4lO2Dr6jgrsgbDeZenDlm0pHQ2rogRKaBEtezzrzw2ge2FrG4x2LgiRr5UJTn+LHsyO7l95zrg1GL26QrA9Xx3JTr1/rMtHF9O3dqzXU4X2S6PdyGEEOKS4QcRk5ZPa0pH12RCUSGEEGK5UTSd1D33zVm0nt6lHoz1ExZyaK1dF3Q8S1W4v5L8xV/8Bf/xP/5Hfuu3fguAz3/+8/zLv/wLX/rSl3jwwQfnfVxnZyfZbPYijVKIy8tSduj2dpiUqwGRcvav35dzsb2IhK7QnND44QslKl5IsRLQnNRoiql0ZnROTHpsWR0jZwUcHrU5NOpg6iorswYVO6QpprJxpYmKwpTlE9NVcmUfx4tQUDF1hVzRZ8faOBNWyPoOE9uDpphKV7POsZxLX84lpius74yRiWv84kiZ0ZJPS1JjXbvJD18oMVzwCCLIplTGiyErsjorkh7d1Z8RS6ZY29XOT/aXGgX3shMCtWL26QrA2ZQ+76SiZ1s4XsixcDl3qy8X6lIPQAghxOXDDyL+5vEJvvijHH/z+AR+EM243/NcCoUpPM9dlNcaK3qnvIYQQgghzp+i6WitXacUq+td6sApXeoXylyFe7F4XNdl9+7dvOENb5ix/A1veANPPvnkaR+7c+dOVq5cyZ133smjj8rEs0KcjWxKZ9e65JIURLMpnduuTnP7lvQpWd4/2V/isZdK5C1/zsdGChBFrMjqeGHEVDXg8IjDwKSLEsHRnMvhEYcVWZ2xos9w3mVff5UwUmht0ljZbFD1QqpeSKESMlbyGc57nJh0yVsBZdunbPuU7ID2Zp2Roo8fRBSrIZ4fcWLK43jOxXZDxosek+UAQ4Wj4w6WG6Kg0JLSyVkBU9WAE1MemgLr2mL8+xtbuX6txlOPfZ9kpoOt199B3lHZN2jz/EAt3qYpplKyg3nf/4WwkGOh3q1+ZPz8P0uLcyOXLoQQQiyaSctn6OTX0IbyHlOWT0ez0bh/sSYRrRfrh/Ieq7IG77m1TbrehRBCiIvgdF3qF8rp4mXE+cvlcgRBwIoVK2YsX7FiBSMjI3M+ZuXKlXzlK19h165dOI7D//7f/5s777yTxx57jFtvvXXOxziOg+M4jdvFYnHx3oQQ4rzVI1mG8h5EEYdHHTZ1xtjRXctMr3dCr28zScc1ejtMCpWAH75QImozUFDoyOiUqyHbuxOUqyHDBY+f7C8TMyCTVMkkNAYmXdwgYqTg4QQhvg8xQyEV14CQXCkiiqAprpC3fExVZbTk0ZrS6GmPYeowWQmw7JB8NUTXFIp2iB9EGKrK6laVV25I0pzQaElovHZLE8m4Sjqmce3qGC1NBq++6TWMeq2U3Yj1bRrXro438uGnF6svZgzJmTrNL6ds8eVKiuhCCCEWTWtKZ1XWaBS3W2ad/BdrEtEzFeuFEEIIceHUu9Qv5utd7ML9UnniiSe4/vrricViM36+GBRlZkNCFEWnLKvbvHkzmzdvbty+8cYbGRgY4M/+7M/mLaI/+OCDfPrTn168AQshFk29gO75ES0JjUiBvBWwb9CmKaHRC43Mc1bE6O0wa8XmKZeqF7KuLYYXRthuyEvDNt3tJndfl+Gf9xR4cbCK5UT4QcSRMRcNcPwIQ4NcMUBTQ3TV4DVbmnDckEdeKFKqRPTnPDQNNBXiportR2QSKkGksqLZYGDCZVVWx9BU+sdrhXnTUFiVNXBDODZRG1vvyfz0F146RN8zh3nXb7ydqzetY/SlEr88WuHwqMPbr882Cte9J7fJxS5WnykX/XLKFl+uJM5FCCHEotE1hffc2sYH7myfszu8NoloC4Zxfr+Q1Iv1wJzFeiGEEEJcXuaLl7ncvOlNb2JwcPCUny+k9vZ2NE07pet8bGzslO7003n1q1/NoUOH5r3/gQceoFAoNP4MDAyc85iFEGcvb/ns7qtwfNxhd19lRlzJkXEXQ1UwdIVfuyHL26/PcvXKGHFDwXEDvvCv4+wfrDJa9BjNe3zrl3meH6jyy+NV8pWAZ/srDE157DtR5ciYw+ERm2MTLiFQrISMl1zyVQ9ThSAMiWkKYQh+FGLZUHJCjuc8XrkhSUuTTqSAG4LtQRCBioKuKhwcddFUBV1XuPmqJq5ZnaDsBExVAlrTOrdvaWLb2gS9HSZKBCgKhBETAy+i5fbQvuE6VLVWClUimLJ88lYwIyJldrRKfbtN315zLTvb/TD7sb0dJl1ZQzrNL2GX928gQgghLjpdUy54V3i9WD9l+bTIBKZCCCGEuExEUTTnzxeSaZrs2rWLRx55hHvuuaex/JFHHuGtb33rgp/n2WefZeXKlfPeH4vFLlpXvRCL7XKY1LHe6Xxg2CaT0ICXO55nR4XsGagyXPTRVYVv/bJAqRqgqdDTEePoyTxyyw3RFKi4IVPlgNGCx/qOGL2dCp0ZnRcGqjx3vEqhGhCh0JrS0FWVZk1jeMojbqiERNhRhBdEjBVdHjtgkYnrJE0f3Y8wdYVkTKU5rqGq0JLUyCQ1lAjGSz5T5YCOtE6+EtDTatAU19ixthY/s67dZDDvoRaPUh49yDWvegPdKzvY3Veht8NkR3cC4JTJVWfv67k6xOfrGl/IcTLfY6XT/NIne0cIIcSy4QcRk5ZP68nCeUez0ZhgtFWK6UIIIcRlKQr8KyLKZSndf//9vOtd7+L666/nxhtv5Ctf+Qr9/f28733vA2pd5IODg/zt3/4tAJ///OdZt24dW7duxXVdvv71r/Ptb3+bb3/720v5NoS4YM4UtbEc1AvF21bHyVnBjMLx9ALu7r4Kzw9Usb2IIIxY22JwJIi4vidBR1onV/aZKAWk4yp7+qv4QQQodKZ1XtGTZEXWoGQH/PRAmeG8S0yHlK7SHFcJQoVJyycMIxI6aIZKcwwUVaU5oeF5IV0ZjYgYLUmNzaviVJ2QEOhMGxTtAMcLGSt6DE15mJpCU0yjOa7ywqBN2Y0a8TM/PVhA1zRibT3cs7WXdDrN7r4KR0YdDgzb3LW9mduuTp+ynWbv67myyOfLJ1/IcSLZ5svX8vyXL4QQ4ooz12SigEwwKoQQQlzGosDHeugLBOMDaJ3dpO65TwrpF8Cv//qvMzExwWc+8xmGh4fZtm0bDz/8MD09PQAMDw/T39/fWN91XX7v936PwcFBEokEW7du5fvf/z533XXXUr0FIS6oC134vBid7tML5T0dL8eKzH7N3g6T4bzJaN7nlRuS/OJohXVtJmU7pOy4TFgBmqpwcNhhcNLD9iN62gx6u2JEUUTJDmhLalSdkGxSZ6LssaYlhqEpDOU9Kk6AF0DRCcmoOmUnQFMiyk7AjjUJsgmNq7p0ChWfXDHg6lVxOpt1Do059OdcXD/E8SOqbsiUF0Ea3DBifbvJhnaTkh2w+0ge+9jjaM2r2XztDaRPToxargaMFj0cL+K5/io7uhOnbPfZ+3quDvH5usYXcpxIx/nyJXtNCCHEsjDXZKLRyZ+nL5MJRoUQQojl5XSd5mEhRzBey84OxvoJC7mLOqnpleT9738/73//++e873/+z/854/Yf/MEf8Ad/8AcXYVRCXBoudOFzKTrdT/eaIwWffCVgohLQ2xXj+YEqtYhxhWtWxvHCiLaUxoERB88PIYLejhgvDtkcGXUoOQHZhMqJKRc/gJLt05UxgAhFVWqB5BEoSogfhBScWnz53oEqHRmdnlaDoSkfP/Sw/ZCbN6YYzXuMFjxWZAyuWxfnxRM2zsnIF9OP6GoxWZE1ODEyxeShx2lubuW1t+9sFMePjLuUnZCEqaIQEilzb4PZ+/psLnBIgfzyJntWCCHEReV5LpWKRTKZOqsJRlvi0GtMccxJ09WaaEwmuiprNDrRZYJRIYQQ4tI1V7H8TJ3maqYdrbObYKwfrbMbNdO+VMMXQiwzyynHfCkiPuZ6zbzl8/DeIiN5j6obokSwoztBOq7RntLoy7lECrQlNR56pkAypuL6EeOWz988Pk5TTONozkVXoBDXUBWFkBDXhyOjLkNTHjFTwdBAVRUcHwxdQ3EDImDCCrC9ED+ISJlaI/d8uOhjGrWJTzMpjSCEFVmDhK7QkdYZLfmsbNZpTSg8f/hx1vd0c9trbkJRlFPe71xxNqfb7pdDlI9YHLL3hRBCXFSVioVj2wBkMgv7JTEKfJx/+m/8ytgAXnY12Zs/1IhtmT3B6OzcdCGEEEKcm8U8p85XLD9Tp7mi6aTuuU8y0YUQZ+1CFj8Xu0B/sTqYZ4+7l9p26j05hiPjLoaq0JU12NQZY127yXP91cbkm00JjX0DVb68t4DrR3S3GaxpNTk4VOG4FZIwA1Kmih9GdGV03BRU3IDVLQZPHKzghxAD2pp0qk5Um5g0DIki0HVQgDCCJlNFUxVWZQ1cP2Qk75EwavnnVTsks0LDdkNipkoqrtEWQeBVeXEItLWvYf3VHTMK6LO3cU/Hy8vPtN0X6wLHcrqoI+a24L1WrVZ55JFHuOOOO0inZwbvF4tFHnvsMd74xjfKbNtCCCFOK5lMzfh7IeofsBUFzMIgankCTn7Ark8wCnPnpkshfSY5nwshhFiI+eYigXOb6HO+YvlCOs0VTZcIFyHEWbuQ3d3LsTu53mVuqArlagDAoTEHQ1X42aEyMVPlmpUxRooeSgStKY0fvlDiuYEqMV2hbAekYxrHJ1yqXshEOSCT0vitW1v5y3/xsCZcWpMG29bGePGEw9ExB9sPiekajh9iaBGGDjvWJGhOaTzXX0UBfD9CUUAJYVWrQXuTjqmrFO2AliadrmadqhsSMzTKTogbhIwUPbavSdCa0tg3aNMZDtC3/xmintczWtZQD1tkklqjaA2cUsDOW37jAsF1axPzFrYX6wLHcjxmxEwL3mtf+cpX+O53v8vdd999yn3Nzc184QtfYGBggA984AOLOkAhhBCXF8MwF9yBXrfQr3LPlZsuGekzyflcCCGuTGcbpzbXORXOfaLP+c7l0mk+08c//nFaW1tP+VkIcfYuZHf3UsSvnK8j4y66quCFEZECz5+wsd2AIITxko/nR0yUfIrVgCkrYN9gFduJ8MKI1S06o3kfJauwa10CzwvZe6KKZYf883NFVE0hrquYukL/hMdoyafqhHghGGpAwQJFg6tXxfnYr3Tx3T0F9vZXKNoRcUPB8SMScZX2tMHO7gRTVR93POSGdUk60jq7j1vs669SdkIiIla3GDQlNCbKPhPH91IqH2PttbeydmUrPz9WIRNX2TNQG1/d7AL2kXGXfYM2RBHpuHbBC9vL8ZgRMy34CPm7v/s7PvGJT8x7/4c//GE+85nPyIduIYQQi26hH7BbUzor0ioDE1XWtiUkI30Ocj4XQogr09nGqbWm9DnnHTnXiT5Pdy6XTvOXPfDAA3P+LIS4tCzHCSTbUxoHwoibN6bIJDWUCEpOgBJBruxzcNihOa6iqkAEk1ZANQhoSWjccXUza1sN9g3arGszWJE1WF0OqLgBmgIqCooSMZT3iKKQMFKIItAU8EIwTz6n54X84beH6GjScAMIgogoimhNqrSmDa7rSdDZbBCPqSQMrVbwjyJeHLQZL/pEEWxeGacra9LbYfKjp/uh2M9L+qvo74/z+kzAzp4kI/laN31X1qA9pXFswqUpptLbYTZiVdpTGteujjeiauaLWzlTDMtCY1qW4zEjZlrw3jt06BA7duyY9/7t27dz6NChRRmUEEIIMdtCPmDrmsK9uwzGpnw6WwyJcpmDnM+FEOLKdLZxarqmnDLvCJzfRJ9SLBdCiKWRt3yeOGxhqAo5K6CnI8ZtV6fZ3VfhyKjDSMGnJa1zdNzFNFRu2pQiUuCpgxaJmEoqpjJRCTg6ZvPw3iKEMFp0iekqJdsmV/IpViJQIkwd4qZCS5PG0JRPXAU/hKQOg3kXx4MjGrSmDLwgImFoaOrJQnbFx3FDetprF3ub4xrDRZ/uNpOyHbJxRYytaxK8dUcKNajQ1rWaY+U7SFRVWlI6SjSz4zub0tndV8GyQ1Jxlef6qxwac2iOa7Citg3qdvdV5oxbmR3DMrtoLjEtV44F713f9xkfH6e7u3vO+8fHx/F9f9EGJoQQQpyN+uRn6USKVapyVpnrVxI5nwshxJXpXOLUps87UifxK0IIsfxMj3KZHifSntL4/pCNF4R0ZQw6MzpHR10e21/mtmua2NGd4OljFX74fIGxckCpGqAoCiN5j7ITEIQBmgq2C34EGpAyFdZ1xCg5Ab0dBmPFAEOvXYh1vADHByOClqTKNSvjjJV8SnbAC4M2rh8RNxU6hnVu3dyEH0ZcszLGRMnnzTsyNCc17tik8/iPHqa9vZ3117yaoakUb1wdZ9IKiE72UO1al2y8x/r7LVcD9g3a2G5I0lRPiVWZL25l9vLZRXOJablyLPg3nq1bt/LDH/6QXbt2zXn/I488wtatWxdtYEIIIa5MZ5vZCjKh6NmQ87kQQojzJR3lQghRs9Aoj6UyPbqEFbFGoXd3X4XeDpOcFaCqMDTh0Zkx2Loqwf4hm6FRFxTYvCqOqSs8P1SlbIdoSq2DPIqgWluFdJMKhFRdSBiQTupoqkLKVNE1he42k4FJDzcAwpAyEUEAo0Wf/3BzK48dsDg+HlFxfZriCq4fMV4KeKavSlNcZarss6EjRrEakKDMw//8OKlsJ9t2vpq+yYBMQmPSChjMe7h+xNCUx13bmwFefu/AupPd7dMnEp29/+bqJJ+9fHbRXGJarhwL3svvec97uP/++9m6dStvectbZtz3ve99j89+9rP8xV/8xaIPUAghxJXlbDNbQSYUPRtyPhdCCCGEEGJxXMpRHnnL5+G9RQxVodys05SoFZOPjLscGXXY01dhRVYnbqgAFCoBIRErmw00oGwHPHOswkTJZ6Tg4XkQMxTyVZuKA0EEpgamoZKMaYwXPYIIcmUPTQXXh5gOY6UIx41oimvoukpMr01mGtcVvrenRDapMlH2QYlY0xojpqu4QYShQdkOCYENK2KU7ICXfvkzSuoqqsmt9E0GjUJ2yQ4wVIWBgsuGjhh7BqocHnXIWz7ZlM7KjEG5GtCU0GZc8DiX/Vcvmuctv3Ex4lK8gCIW34L38nvf+14ef/xx7r77brZs2cLmzZtRFIX9+/dz8OBB7r33Xt773vdeyLEKIYS4ApxtZivMP/mZOJWcz4UQQixUFPiLGttyLt82u1J4nsd73/tePvGJT7Bhw4alHo4QYoHqRdz2lNYoqAKXRHf6noEqQ1Mu2ZROR0avRbBUa5EnpWrAgVGbH77ocs3qBBs6YvgRVJ2QREwlFtMoVQKOTzhMWQFEEAFhFBGEEEW11zA0IIyw/YhsUiNvBbgejJd8olAhiEKSpoYXhICCoUNns0E6oVJxIoamHCxXJ5PUKFVDxos+t21Jo+sKmbjKSNGntyNGOphg3apOStfcRP+ET0+HSXtKa2xngCNxl1duSJKzAsrVgLwVMFUJuXqlTlfWoGQHpxTMzyeK5VK+gCIujLPay1//+te5++67+cY3vsHBgweJoojNmzfz6U9/mnvvvfdCjVEIIYQ4remTn6XjGpOWT+u0SdDETHI+F0IIcSaOZ3P0H/8b7eNDJDq7Sd1z33k/57l82+xKYRgG3/nOd/jEJz6x1EMRQpyFelfy9EkpgUuiuKpEAAq2E1I5ObFmBFh2SG9XjD39VVwfhiZdpqoBTaZK2fZ5rr9KFIb0tMdJmSpVr1YYb46Dqmr4YUipGqFEtW70shPSnNDIJHVMXWG04KMp4BERRaAQ0pLSaUlpJE2VtpROJqWxp6/KSNEjwqcpplHVa8/11GGLNS0Ga9tMKk7IxIkDDI4+z+brX086mWWdqtEUUxsTpUItA72+rXs6al34MH90S935RLFIFvqV56yPlHvvvVc+YAshhLhgzuYDdn0y0daTv/j4YcT//LcJRoq+ZKOfgZzPhRBCzMcNfX7v6X/gYDRBb9bk02P9JAq5837ec/m22ZXknnvu4R//8R+5//77l3ooQoizNFdB9XTF1bPNUj+X7PUd3QkG8x7HxhyeOFTm5k1NWE7Ai0M2r9+a5tbNKV4cspkoB4zkHYKgVmgvWAFBBMqEh6FHmKpCZ4uKG0JPq8FoMSAMPSynlomuaxCEEe1NGl5wcnLPqJZ7XnXB9SOa4rChM87VXTH2jzg0xTR6Oky8IGSsFBBGUHVDql5ExfGx3YCCHXBd8gh2oZ+1176WUbuJQt5hU2eMSGHGRKmzt082pXPb1elTtkm5GrBnoNoorJ/Ntp3rNXo5+a0DkEiXK8CC9/AnP/lJPvaxj5FM1ma4nZqaoqWl5YINTAghxJVpoR+wp08m2tVcO50dy7n05Vw2d8UlG30ecj4XQghxJsPVIoedEqFhsh+XoUyWtqbseT+vYZjSgX4aGzdu5L/+1//Kk08+ya5du0ilZv4u9Du/8ztLNDIhxJnM7mg+U3fz2UaBnM3604u9d21v5lu/zHM85/LdZwoUbR8FBc+PyKZ0NEVhouQRRSGmrhI3VAICfB8sJySyQyou5CsBulqLe2lN6YQoJGMQBBFhpFDxIkaLHn5Q64C3PQgjCKjdLtsBSVNhrOhTcQJeKPrcsD7BRMnH1FX8CFqSOsMFj6SpEDNV1mcCzMokd7z5V0inm3l4b5HmuNbINT8Sf7mr/OG9RfR6V/o822fPQJVHXijRklRJx7XGegvdtnOtJ5EuV5YF7+E//uM/5oMf/GDjQ3dPTw979uyRvDYhhBCLaqEfsKdPJnp03MXxQ9Lx2mQ5rh+yoTMm2ehzkPO5EEKIM1mZaGZjup1HK5NEkc8XvTIPfveLwJqlHtpl7a//+q/JZrPs3r2b3bt3z7hPURQpogtxGWlPaRwYttm2Or6g9RcSHVIvnperAWUnBGoxJ2+/PstfPjLOYN4hVw7objHJpDSmLJ8Xh6oUKrUC+sqsSdUN0BWFUItAiYiiWrc3gBuCF8BkJUAhQlUUdEOh4oYYOqxpNShVQ0YKHmEEmgYZvfb/V9xQOTrmkoopTFoBjhvxsyMV4oZKW1qno0nj58eqXL0yRm+bSiyYwMysQeu4nVBLcWTc5drVcfYN2rSntBkXLXb3VTCmdaVP3x57BqooUa0rX4mgJamRTWkz1jvTvqhv1/aUdso+kEiXK8uCqwtRfdaAeW4LIYQQF4vnuWhBmRVpleFCyETZr00Uo/jcdlWKe25ooSMtmehzkfO5EEKIMzFVnd/ZdCvPH9+HXq1wTFEZzPVDpuu8n3uxJyu9nBw7dmyphyCEuEiOTbhMln2OTbj0dMROuX+u6JCFdren4ipdWYP2lMb3n80zXPTpaTV46hAoRFT8iJs3phiY8ugbc7D1CF2FwSmXjiadVa06UyWfhKkxXvZnvIbrR3hhhOuCadQmGfWCWtf5wWGH9madpKkRhLXCfGtKww9hZcagYAc0xw1sN2S06FPxAnatT7Gy2eDpYxU8P8TApzX/LJgpJqpdOD488mKp9vhqQCahcWzCJWcFjW0zvZA9PVLlyLjLL49aTFXCWjZ6dwKo5aRPl7Nqz5uzAno65t+uULsoMd35ZKqL5Uf2tBBCiGWnUrEIXIdf3WlyYNzgn571Wdmi4/lwzw1ZVmYlwkUIIYQ4H2u8kKsdl8OKygbLYlXTGvafZ9E7Cnysh75AMD6AdnKyUimkCyGuREoEKMrJyT9PdS4xIdM7qjNJjW8/nedHL5YoVgOuXhXnhvVJfnKgjO0EfP3JSYp2SACoqsKk5WM5EcVqQGezgRvARN6HWePzfFAVUFQIAgipreL6YPsB/bmQTELD1DVCFBKmAigoKqzviKGrsLrFpFgNMTQV2wlRNVjbatBqlFjn7qaQ6GJS20o+5+EEEUQGmzpjbFsdJ2cFlKsBR0cdDgzb3LW9ed5Cdm+HybMpHQio2CFHxl2gNrHqkXG38ZgzdZOf6f5zyasXy9OC966iKJRKJeLxOFEUoSgK5XKZYrE4Y73m5uZFHyTAgw8+yEMPPcRLL71EIpHgpptu4k/+5E/YvHnzBXk9IYQQlx7Pc6lULAzDxA8jvvlLl8deKjBa9FnRrHHnNc10pKWAfjpLfT4XQgixPMRbVvBZs4uh3AArm9bQcu/vwz9+95T1zqazPCzkCMYHAAjG+gkLObTW8+9uF0KI5WZHd6KR7T2Xc4kJqXdUH5twGTrs0ZdzmCx7OD4MTrqs74jR0aQzVPA4MGxj6gqWG9EcV/HDqDFJ6ETZp+JERBHoaq3LvF5LV1VImGA5oBsvF9WbTAUvUIiiiJITklE12po0EqZC2QlJxRQycZW2Zp2XhmzCCAxVYbjg4/gRPR0x1uhVrMoGjnndTOQ8bDfECyI6m3WaEho9HTF6OmpF63oG+vRi+HT1wvbrr0mTs4Lat5andelP365n6iY/0/2Si37lOKs4l6uuumrG7Z07d864rSgKQRAs7ghP+slPfsIHPvABbrjhBnzf5w//8A95wxvewIsvvnjKhCtCCCEuT5WKhWPbAARamr6JUYbzPl4Q4Xjwlp0ZiXA5g6U+nwshhFgeFE2n5Z7fIXOaAvnZdparmXa0zm6CsX60zm7UTPtpxyDRL0KI5eRsOpJPV5g9187memG4XA0oVgIOjzmsyJjoGpiaStkNScZVUlWVIATbiyCKsJwIXVOImwoJXcXxQmw3IlIgZoDv1IroCR1iJlRdMDWw3VonugIEUURSVbA9hYoTEEVgeyG7epIUKiEjhYCkqXH4sMXRMQdFUSjaAQlTRa8MMJ5r4RWv2IwXQFvRY19/lYlybdLRuYred21vbmyjucyOYMlbPs/1V4mmbafdfZVF6R6XXPQrx4KPlEcfffRCjuOMfvCDH8y4/bWvfY3Ozk52797NrbfeukSjEkIIcTElk7WLpmYsScWOaE1qVNyAMILSyV/WxOkt9flcCCHE8uCGPsN2kZXZdjR17o+Np+ssn6sArmg6qXvuW1BhXKJfhBDLzbl0JDeKuwpctzZBNqU3nqdcDRrd6gsp9NYL83nL57EDZVw/wg8C3v7KFp4fqPL0sQrZhIp18nOToYGianhhQFxTMHSVnjaD5wcd/LBWOC85Lz9/GIHjQRRBpNYiXQhrRXRTrxXhXT/C9aFQCfBMhSNjDvGYSkeTxmQ5oOqGtCQ13BCyCZXO4CirOUpfZSd9Ex7vuLGV3X0VbDfCzLm8Yn2CWzY1nfL+Z1+EmH3hYXZhO5uqdbOP5L1GrMvZ7KvTXdg431x0iYNZPha8d2677bYLOY6zVigUAGhtbV3ikQghhLhYDMMk1WTwN49PcGLK5eiYQ9xQAWhv0tC1838NP4iYtHxaUy9PTDrXsuXqUjufCyGEuPS4oc/vPfMdDpfH2JTu5E933oM5RyF9vs7y0xXAFU1fUISLRL8IIS6mxShkLrQjefprHRl32TdoY7sBQ1Med21vbjy+HkECNIrjCxljNqVzfU+C/pxDKqaRjmkcy7lYdkS+6p781mlEOq5R8QIcDwItIqlE7D1RpeqeEoUOQBABIcRNBZWIKKzFuzTFYFXWIGmqVFwPLYSYAm4QMVL0WdVicGTcJaar5CsBmYRK2oTN+gGag1Emmm+iycxw88ZUY/sdGLa5elWcprhWe88n39dc23D6hYf6tpqrsD3X/llo9/iFjGyROJjlY1nunSiKuP/++7nlllvYtm3bvOs5joPjvHzZbHbeqxBCiKVXzzlPJlMYxpl/iZm0fIbyHo4Xka+EdGV0ql7Eq3ubzjsP3Q8i/ubxCYbyHquyBu+5tQ3glGXLvZAuhBBCnM5wtcjh8hgAh0pjDFeL9KRObV6ar7N8MQrg9QJ9dayfXOdqNqSzLMK18mWrv7+f1atXo2lX8lYQ4sJZjELmmTqSj487PHHYIhNXQal9nujtMClXAw6NORj1jO91yUbRfM9AlXI1aDzWUJV5x3h83OGRF0usyhhsW5MgldCo2CEvDFUhjNDUkKShU7Z9NBVyxQAvqmWaq0DFDbFsmB7qqJ78OwSCEBJx0Ilww9rEoqoGMUPD0FWmKgGOF6KpCm1NOq4fEYQR40Wf7jYD24OeNhPLCckmFNriOjt3vImn+msTrRarQSNipR7XUq4Gc+6X2ftrIRcwZu+fs9nPFzKyReJglg/1zKu87Itf/CKve93ruPfee/nxj388475cLseGDRsWdXDz+eAHP8jevXv5P//n/5x2vQcffJBMJtP4s3bt2osyPiGEEAtXzzmvVKw57/c8l0JhCs+rfe2uNaWzKmsQMxS6MjpbVyd4w7Y0H3hdx3kXt+sFeoChvMeU5c+5bLm7VM7nQgghLk0rE81sSncCsCndycrE/JNN1zvLp0et1AvgwIKyz+d7XuOt/5nPXLeT+9sS/MHe7+GGy/8cfK7WrVvHNddcw0MPPbTUQ7mi5C2f3X0V8pfB73/i9Ho7zFOytxfbEyfzwI/mXArVgPaURjalc9vVad5+fZYNK2KN1693WlvVgH0nqjzyYgldVfDCaN4xPvJiiX97qcyP95foy9Ue++P9JX56wGLCCmltMk5+XlKwPfBPThgaRGDoCs1xFXValVChlole/zkCKjYUHXDcWrE9jMDzQ6IoQlchCCI0RSGmq6zMGly9Ks41q+PEDI3//No23rQ1xqsyh7lpY5Krrn0V5SjBSMHn0KjDw3uLjbiVbEpn17okO7oTp+yXvOVTrgak4uqMuJZd65IXLA7lQj7/hR67WDwL3kNf+MIXeOCBB3j3u99NoVDgrrvu4lOf+hQPPPAAAEEQcPz48Qs20Lr77ruP7373uzz++OOsWbPmtOs+8MAD3H///Y3bxWJRCulCCLGE5uo6r+ec1/+ebfpkopmMia4pvOfWNqYsn3Rco2QHtCxSzEq9QF/vOm85+YvMXMuWq0vlfC6EEOLSZao6f7rzHoarRVYmmueMcjmds8k+P50Rt8IRt4SinL4j/krw6KOPcuzYMb71rW/xq7/6q0s9nCuGxCxcOc4l1/psI2DqcSWGqjBa8unLufR0xOZ8/fqxN1z0QVFYlTFYMa2YPNekmKsyBp3NGisyOntPVHnqkMXApIOpKXgBBIFCBJTtiOBkAf1krDklO0LTQqZ/ojL1WsN8fZmm1rrRORnjokYQhRApCrYXMZz3UFUwNIWS7aNrBus7dQYnPda0mljlEiMvPEYikcWPoOzUXm9ls46dUFnbbtIUU2cUzOfaL0fGXcpOSFfWOKvCs2SPi/O14KPmy1/+Ml/96ld5xzveAcD73/9+3va2t1GtVvnMZz5zwQZYF0UR9913H9/5znd47LHHWL9+/RkfE4vFiMViF3xsQgghFmZ2QRxqOefJJCeL6zSK6/WCu6Io+IFP07SoF11T6GiutUVoio9Vzi84DuZ0phfopxfm51q2XC31+VwIIcTyYKr6eRWsF5p9fjr1jvhDpbEzdsRf7m677TZuu+02fvM3f3Oph3JFkZgFcTpne5GlpyNGJqnxrV/msd2Q6DQfK+rH3LbVcXJW0Cj85i2fh/cW54x1uWlTimRcZd9Alb3HK/RNOPh+hONFBCGUaqku+LMCz3WlVhQvVmrrQa24njAVgjAiAjQF4if/GVSd2mMirRYFQxQxXnTxAmpRLmmNvBVSrPoMTqh0ZgwUN8/xPb/AzHajd23nlk1N5KyAkh2wvjNGoRrQFNNoSmhnLHAv5N/lXAXz2ftLiuribC34KDl27Bg33XRT4/aNN97Ij3/8Y+688048z+PDH/7whRhfwwc+8AG+8Y1v8E//9E+k02lGRkYAyGQyJBKJC/raQgghzp3nuYRhgOe583adz1Vcr1QsKpZF1a6QiCepViuN55heLJ/92POdBHR6gf50y5arpT6fCyGEWP6iwD/vLvOFOF1HvBv659wpf6mpVqs88sgj3HHHHaTT6Rn3FYtFHnvsMd74xjdKg9gSOJfuZHHlqBdx21PanJ3hczky7pKJayRNlevWJuYt5E4/9no6annnD+8tkk1ouH7EQMHllRuSpzz/4VGHwyMOx8YdStWIcK4ZQqep3+0HtY50qBXQdQ00RSEZUwjCEA1YkTEoVgKqboii1jrqFSXCj2rFc0OPaE9pGIpKd5tOFEX0dJj05VyaVejuvJprtm1rvNeejpeL3dMvFpzJQv5dznWBY3bxXb5pIs7Wgo+S9vZ2BgYGWLduXWPZ1q1b+fGPf8xrX/taBgcHL8T4Gr70pS8BcPvtt89Y/rWvfU2uxgshxCWsUrGIwohKxSKTaWkUyaebq7ieTKawrDKmYRKd/PWuUqkyOGmjaTorWxNEgTcjFqY+MeiJKZdsQuM9t7YxlPfY0BEjbp7VNCCXraU+nwshhLj0ne6CdBT4WA99gWB8AK2zm9Q9913wQnpPqpUo8AkmR1Az7XgK/N4z3+Fwudah/qc771nWhfSvfOUrfPe73+Xuu+8+5b7m5ma+8IUv0N/fzwc/+MElGJ0QYj71Yu7uvsqMYmx9EtCbN6YacS110wu52ZTOT/aX2DdoM5r3GnEtcxXiH3mxxL7+Khu7YrQ16XRljNokpUltRqf1aN6j7AR44dwFdF2Z2YkeUbs9/X/6EHADmLJCEmate11VwPdrz6lEtft1NQJFIWWqxHWVlrSGZYd4YURag41dCcKpo7S6Dv1BD1t6Oti17uXCf307XTur2/5cTb8gMVe3+uzi+7l800S6169sC97jt9xyC9/+9rd5zWteM2P5Nddcw49+9CPuuOOORR/cdFF0hstnQgghLknJZApFVebNPIdahMvs4rphmHR0rGhkqPtBxP/++SiPH7AJw4AdazR++5YU6SbIZFoAGCt6nJhyOTDsULYDfrC3iKkrdGUN/upda6WQztKfz4UQQlza6hek63OBvOfWNjQCwkIOgLCQIxgfACAY6ycs5M47tuVMZhfuc294J4fLY8DlkZX+d3/3d3ziE5+Y9/4Pf/jDfOYzn5EiuhBL5EyF094Ok5IdUK4G5C2/MYEocEoRfXYhNzoZh1KfcLRcDdjRnTjl9VZlDF7SbRwvwlDhUM5hXXuMI+Nuo3D/1MEyB0dsRou1JqJC1cYLZo41mKe0FvHy5KHTKQp4HoQqaJpC0lQp2yGhDxUXWpLQ3W6ystlAU6FYDUnGa132Qe5FCtYhxo2d9LSavG5rmrzls2egihLBvoEqh8ZcXjxRpavFpFwNuO3q9ByjW5gZneXrkmfsLj+Xb5pI9/qVbcF7/GMf+xi7d++e876tW7fy6KOP8q1vfWvRBiaEEOLyYBgmqqqdknV+ugzz+jrT7y+5KsMljaofUajAU8ci4jGfD/+7l7sZWlM62YRGxQnRVIUJO6AlqTFa8OnLOWxZJfFfcj4XQghxOpOWz9DJAsFQ3mOqaJP44ZcIxgcIzc0oTVm0zm6CsX60zm7UTPucz7OYkS+zC/crXO+yyko/dOgQO3bsmPf+7du3c+jQoYs4IiHEdGcqnGZTOum4xkje48i425hAtP73dNML8lDr6t62NkHZDugbd4kU+OmhMo/sK5GKKfynOzro6YixttUgjGC86LJ/yKYpprK6NWo8zxOHLV4csjk67hKEEVXHmdFdrik0JhOdz+z7VBU8v9aJroQQRhHrOgzipsJI3scPI9JxjbaUTiKmEjMUetpjvG5rmuP7f87hiX4GEq/CJc3mzloe/JFxl18etRgt+Dh+hOUEtKdNiKLTZsQvxMWYw0DmSbiyLfi3me3bt7N9+/Z579+6dStbt25dlEEJIYS4fM2Vfz5bsVxmdLJKOlYiZtSyyFubsqxvNzk4pFEgpDmpUQ0Nyq5KIv7yV8/fe3s7igK5UsDuvgqaCisyOuvaJUcU5HwuhBDi9FpTOquyRqMTPePnqZ4sYBN4ROU8qXvuO22BfLEjX9RM+4zCfbxlBX/aOndW+nLk+z7j4+N0d3fPef/4+Di+71/kUQmxPF2IuI2FFE5nx7TUO9Dr42lPaeSsgKOjNnv6q1zXnaRYDejLOdheRGdGZ11bDKsa8JOXyrw4ZNOa0njisAXA1346SRSB5UY0xVQMXWFTZ6zxHq9dHefJQxabOk329tsUg1OL4nEd7LP4r8QPIaDWja4qMDLl0xzTeP22Zg4M2gxMeWiKgheEnJgM6F0Ro+qF9OVc2jpWEbrrSVg6sRAyca2xHSw3wnJC8hUfFIWiFbKzx+S6tadveJq9LU+XI3+hyDwJV7az3vN79+6dc7miKMTjcbq7u2XCEyGEEPOab3LROj+I+IfdHgMTHqtbTP79DbHGum/Z2cxV7Q6PvujhRCprWkzScY2hKZfvPlNgpOizKmvwn1/bQckOSBgqJ6Zc1rVLJvpscj4XQggxF11TeM+tbUxZPi0pHY0At30NwdARaNIbhfPTRbgsVuTL9MlDZxfuTVjWES7Tbd26lR/+8Ifs2rVrzvsfeeQRucAtxAJdiLiNhRRO51onb/k8vLeIoSocCCMyCY1nj1cYnPKJqKCrCk8esvDDiGxSY2pNgKIoTFk+SVPBNBRcL+R//XQS14vI2wE39yapuCGmodJ6ckLT9pTGz49VMFSYrAZoGkSzYlyi6MwF9Nl56QCGBtkEVL1aUX0w7/HoiyV62mO0pDQURaFUDUFRmMqXUK09HFWuZzDVih6L6DIiujI66ZhGb4fJnoEqKVMhm9KIIuifcEiaKsVq2CiIT4982dGdmJH5PpL3ODBsk0lowML3sWSZi8Vw1kfOddddh6LM/x0LwzD49V//db785S8Tj8fPa3BCCCEuP3Pln083mq8yMFFF1w3GLYVQS6OoOn/9WI5HXyrjeR7XdCq861UJNq1t5W+fmOTQSJVj4zZbViUYykPJDuhornWwb0nM7Gg43WRpVxI5nwshhJiPrimN82gU1OanOpsZqmZ3js8X+XI6buifOnnoBc5eXyrvec97uP/++9m6dStvectbZtz3ve99j89+9rP8xV/8xRKNTojlZanjNqZPKpqzAnRVwQsjbt6Y4tiES2uTjhvAzu4kPz9qEYYRChGaqpAyFSJq2ePr2k1MXeWFIRvXj6h4AVEQ8rMjFQwNLDfE9kKuWZXgwLDNSN7juRNVwjAiDEGlNkFo3Zn+D1epdZzXV9QA06idD3b0JHE8ODRqE0Uhrh8xXvRZ22YwnPeouAFZo8qK0jNUtVb6SybrYhGjBY8NHSZ9OY9rV9cmQFUiiJsat6xKcGy8lhvf1qTNiL45Mu7y/EAVFIWmhNYolNf36bZpE5EulGSZi8Vw1kfOd77zHT760Y/y+7//+7zyla8kiiKefvpp/vzP/5xPfepT+L7Pxz72Mf7oj/6IP/uzP7sQYxZCCHEZi6s2bfGA0ZLH+q5mWlI6k5bPsZMT7UxVIsqOSjYL7W213FZNCQjDiIrtsWV1nJZ5fjGq2g5ffXSMXEVlTWuM99zadsUW0uV8LoQQYiHCQo5wYhDFjMHJnPMzdZUrmn7GyJczGa4WL8jkodO72y+VGJj3vve9PP7449x9991s2bKFzZs3oygK+/fv5+DBg9x77728973vXephCrEsLHbcxtl0MOctn2/+fIqyUytf37wxxVMFj0iBYjVgaMrD8yMmyj6ZhIKpK3RmDJpiKus7a98APTTiULYD1rSaXLMmzosnbKII2lI6x22Xqhfgh7WmoaNjDqtbTK5dHWei7NPRpDOYd1HUmQV0VakVyWd3mU8XUisQavX1NfB9SJoKLU0Gvh/y4nBENqHTnKhth2I1YMoKMIMyW/XdOMl1uKktbFkVp1wNWd8Ro2AHM/LOd3QnaErUutKTcZWIWib89AlYaxO1JlCimRdDpu/bno4F7Lxplvriirg8nPX/LH/8x3/MX/7lX/LGN76xsWz79u2sWbOGT3ziE/ziF78glUrxu7/7u/KhWwghxILMmEg0DPi161TKrs7KdgNdU2hN6azO6Pz0QEDVC4kZOoVKhB9Ac0LF93VuvUrj117ZysqW5LyF8cFcicFJF03XGMqrTFl+o9PuSiPncyGEEAsxvasczVhwV/npIl8WUshemWhe9MlD5+xuv0QK6V//+te5++67+cY3vsHBgweJoojNmzfz6U9/mnvvvXephyfEFWt2B/PpiupHxl26MgYjBY9rV8d54rDFoVGHXNnn4LDD9euTHBx1cIOIv30qz/o2nWvWJPjVV2T4+bEKP36hxOFRGz8Exw9JxVWmKj7jRZ+OtEbcUHCBkaJDFEC+EmCoCm4I29ckGC/6jBU9it7MankYzSyqz8cPawX0IKpNKFqnKhEDky6WHWIoColYSEJXKVghphqRd+McCK+lo2kNt17dxFjBJ1KgM6Nz9coY+wZt1rfVitfTC+HXrU2QjmunFLazKZ3bt6RPGd/sbX82Fzgky1wshrM+gvbt20dPT88py3t6eti3bx9Q+4r48PDw+Y9OCCHEFaE+2ahlldE1nXRTEy26jmGYTExNUnBNfnGsjOOHaIpC8mSO3vf3FMhXAtrSBu97bccZc89Xt6dZ3VolV1FZlTXm7Vi/Esj5XAghxEL4YYj/2v+bhG+jPvbUeU0QCgsvZJuqzp/uXNzJQy9Ud/tiuffee6VgLsQlZnYH8+liQerrvPUVGY6MuxiqQiquUnYUEjGFQyM27WmNvnEXU1M5PuGxfa3B8UmXvBWQK3nYPkQhFKoBR0ZtJq0Ax4sIwojWJp2hgo/rAie7y586YrG6RSdSoOKGFKshwQLzt2ZnoEeApoI/LU+9WA3ZfbRK0Q6In4ybaU1qTFZC1ir9tJs5+pquxyPOK9YnuG5tgoenimTiGum4hhtCJlGbCHR29/hCCtvTC+Wzt/2FimiR/HQxn7OeZW3Lli187nOfw3XdxjLP8/jc5z7Hli1bABgcHGTFihWLN0ohhBCXHM9zKRSm8Dz3zCufgWGY+IFPU1OaWDxOIpEEYKJQ4s9/MMX/559HOJpzUBJlVC1gZ3eCX39VlpGij6ooFKohJTuY87mnjzMRj/HeO1fzG69u5T/c3HrFRrmAnM+FEEIsTKVi4Xo+dqxpUZ5vrkI2QBT4VHOD9BXHcEP/gsSu1LvbgUXrbl8Mn/zkJ6lUKo3bU1NTSzgaIcR02ZTeKODmLZ/eDpOurNEomB8fd/jGU5McH3dmrGuqULADdnYnuef6LD2tJv2THi8OugQhJz+HKOzrr/IPP5/i6aMWhUoAEegqKIqK5QSU7JCYrrC2NUbKVGlJqqTiCglDYaTgMVn2+eELZV4atHl+oHrGyUOn8yMwplUFUyZ0tegzlqkq5Cs+Pe0mHU0G29cmWJExuKHpMN3KUY5Hvbz2mjRv2ZkhHatN9nnX9mY2rIjRntJqn9GiiHI1IG/NP7i85bO7r3LKOvVCeb2oPX3bz769WKa/phDTnfVvI//9v/937r77btasWcP27dtRFIW9e/cSBAH//M//DMDRo0d5//vfv+iDFUIIcemod48Dc04UWo9oSSZTp9w317oKCuVyiY6OFVQqFvmixe8+VGFgKiRmRoytehI/XiDpZ3n/6+8lFlmsSKuMlsLTdpVPH2eqyeBvn5hkKO+xKmtc0ZnoS3U+f/DBB3nooYd46aWXSCQS3HTTTfzJn/wJmzdvXtTXEUIIsTjq5/GFnM/nEp3MUa9no88V0xIFPlMP/SV/6I1xNBFn05qrAYWjVo7eVDuf3Hgn2abmWuzbebgQ3e2L4Y//+I/54Ac/SDJZayLo6elhz549bNiwYYlHJoSAWd3n65KNWJfdfRWePV7heM5lYMLlP93RznP9VZ4+VmE07zJWCsgmNd6wLU3MVDE1CIKAdNqgI62hRAplJ6Ti1iIrQ2pxKjETmkwYKQQEITTHYcMKk58esCjaIbqqUvYCQmDS8nllb22S0qq7kNCWmeI6KB4E1PLPX39Nmu8+UyBfCYkUiBnQFNeouiGbukwcL6LFfgnsUX4wtZOVnS10NhscGnN4yXIYKXoEIY2JVS07ZLjoY0/Utt9tV58a0wLwXH+VfYM25WrAju5Eo2g+vWA+u3P9QkW0SH66mM9ZH2033XQTfX19fP3rX29ktb397W/nHe94B+l07R/Du971rkUfqBBCiEvLmT5UTy9eR1FEoTBFMpma8wNwMpnCssooJx+naAb/4/EqA1MhfggeZUgWSGqgxQscmRhlc1OKe3fFcJUmotN8ZXH6OCet2kSkAEN574rORF+q8/lPfvITPvCBD3DDDTfg+z5/+Id/yBve8AZefPFFUqlzK9AIIYS4cAzDnPNi+UJEgY/10BcIxgfQOrtJ3XMfpnZqITuYHGFocpCjLSZ4DvunhkDTiGk6Bwqj7B0Z47oulbaW849eMVX9kopwgdrvSae7LYRYWvViantKY3dfZUa0yMpmnYmST1fG4Mi4S6TAaNHj4KhDsRpStFWOjcdIJzQSpsb2niRVJyJpauRKPhU3IGlqKMBI3iOKwLKhfPJbtooCth/xxIEyg5MeAbXu8TCCppjKjrUJKnZIFIEdhMQ1mOcLunOy3FqECyG4AXzrl3lUoCmhko4p2G5EoeLhBhqO66HrGl58JSeKnYSaSUtC5dCYw2jeo+KGVJwQ9WQn+13ba9/2qTghx8arrEjrPLynwFDB4/XXpGdMJhopNCYgneuixcUk+eliPud0VDQ1NfG+971vsccihBBiGTnTh+rpxeswDMlPTeL7Pm1tHTO61A3DxDDMRgd6Mpni+GgRL1KIGwoVz8MwQlyvGVctstZsZ/vKVfhVC9/3+cfnphrd6HN1lk8fZ6sasSprNDrRr+RMdFia8/kPfvCDGbe/9rWv0dnZye7du7n11lsv6liEEEJcWGEhRzA+AEAw1k9YyKG1dp1SyPbTWcK2lax3cxxLxLm6ZRWKonC4lEOzW/jXvTEODnr89h214vKk5dOa0q/Yb5MJIS6O6dnYu9Yl2d1XaRR3eztMSnaAEtWKxfsGbdpTGqYKthvRFFNx/IiujEFHRufAkEM2pWK7EaoKVSfA8UPylYCJko/lRmiKAkQzJgFVgVI1ZKpc61TXlNofVa39GSv5xA2VKStAPTmBqHZyctCFCAElBNMAy4lQFVCA1rRCS8pgyPMwdQ0tqHJz+nnG9F4G3E6aUwotKZ07rmnGckLIwqbOGK0pjX2DNteujje2XckOaJnQeHGoSskO8fyIpKnOKKLPNcmodIKLS805VQ+OHDnC5z//efbv34+iKFxzzTX8zu/8Dr29vYs9PiGEEMvM9AJ5JtMy5zrFYoFyqdgoqsPMYvfq9jRrWqtU/QrPJH7GeFTAdJrpGbqZT//KVaRiCSzPYWjM4tCwR1MyzolJh+MjE3R31r7uPbtQD7Xswffc2saU5dMy7cO3H0RX5AfyS+F8XigUAGhtvbS6AoUQQpw/NdOO1tlNMNaP1tmNmmk/ZR039Pn9577HobYkveY1/NXWN9DTXPvd4PnxCb7zpIdmaoyWQsZLHt97tnjZxbIpikKpVCIejxNFEYqiUC6XKRaLM9Zrbr40MtyFuFSd7YSQZ1p/9sSVs6NF0nGNo6MOPztisb4jRs4KODBsk01pRMA1qxOsyBp0NRsMTHhMWSEREbmiT1taY327SSqmkiv5OL5P3FDQ1Yipam2ST12tdZ17Pi8X1iNqxfIQcqWAuOGxbU2ClqTK8fFaR/eZCuiGAt60dQyt9lqaViuopxMa7Wmd9iYNRYGgmufG1F4mo3aM5tW8stlkYMrj3bfUfn9/eG+Ru7Y305zQeOKw1YhyqW+769YmGJry8Hydsh0QM1Vu3jjzG6izu7/PpxNcJgYVF8pZH03/8i//wt133811113HzTffTBRFPPnkk3z5y1/me9/7Hq9//esvxDiFEEIsE3NlpauqSralFcMwKRSm8P2ZE8bM1Zkej8UJYgXKWgF8sM0iRUfl/zyZZ+9AyG+8qolHDhQ4Pumj5avc1GsQiyqMj1cbXe1zZbbrmjIjwsUPIv7m8YnL7gP5mVwK5/Moirj//vu55ZZb2LZt27zrOY6D4ziN27OLCkIIIS4Ns8/niqaTuue+GZnos9UnGlUUOOqVMY1YI6v86kyWn8bHyFVC1rTGiCIuy1i2KIq46qqrZtzeuXPnjNuKohAEZ5HRIMQVaHbR+3zXn52NPbvQ29thcmDYpitj4IcR7SmNkbjKirTOtjVx0jGNkIixokfVq00aujprcDzn0Z/zODLq8op1Ca5aEeepwxZjJR9TVym7Pm4Aflj7kzAg9GqF9JBaXIuiQBRBsRpw++Ymfn60gqZBEM7fia5QK5anYgp+EGF7oKi1wrtz8uNZZ1plVavJmhaDzozBqqpL8+hzHPXW4jZt4vWb0xSrIWtaTdwQ9vRVGMr7/OJohURM5eiYQ8UN2bgiRlNMbRSy79rePKOwXc+UvxCF7rM9DoRYqLM+mj72sY/xkY98hM997nOnLP/oRz8qRXQhhLjCzZWVrigKyWSK8fFRFBTMWIxsSyvJZArPcxvLoVbwHit6HBqvciLvEqaawSxiOmkcvUR/IU0m5XEiH5GzYH0ruL7PW3e2oUZFFJQZE5qebiI0P4g4OGJzYspFVZTL6gP5mVwK5/MPfvCD7N27l5/+9KenXe/BBx/k05/+9AUfjxBCiFMtdKLwKPCxho7hGrXJMesXsBVNR2vtmvdxsycabYslOW5NsjLRjOtU+L+u06iEJj1dbQCXZSzbo48+utRDEOKycKYJIWd3KM+3/uwYl/lkUzo3b0zN6L5GUYjHVMaLPmP49Occjuc8xoserU06m5U4pqZweMQhlVAYK/m8amOKQ6M2Q1MOVT/CnXW9zPZqueVhWOtQDyIgqhXFs0kVN4Rfe0Uzf13yKNs+hQqg1KJg6h3nCpCKQxhAxY0wdTCM2vME4cniuwohCmtbTdIJlbTuYus6L2mvZtwz2dlucsumJqA2EWjJDmhKqDTFFFZkda5dnahtl4SGZYd0ZY1GwXx2Af3hvUV0tfb5b7EL3TIxqLhQzvpI3b9/P//wD/9wyvL3vOc9fP7zn1+MMQkhhLgMVSoWtZS/iObmTCNipVCYIgwCXM8lk22hVLH5Hz/q51/dxyk3F1DtZpInXkmx99+wNpygGCR4deL/Zn17jLVtCY6NePS0aiR1B12NAzQ64Oof4OeKa6l3oJ+Y9GofxlsM1rSYl80H8jNZ6vP5fffdx3e/+10ef/xx1qxZc9p1H3jgAe6///7G7WKxyNq1ay/0EIUQQjDzG2bzqU8gGo70EaXb0X/tQ0SBf9oO9DpTfXmi0bZYko/v+R6Hy7WC+v9721tIAu3JVOP8PVcs23J32223LfUQhLgsnGlCyD0DVZ4fqFKyE9y+Jd1Yf3ZX9Nl0MuesgExCI2cFjaKtAhzNuahRhOvXJhqtuiFOwWNjl8lwwSMMoViJ0ICqE2E5IWUnwpnvCyfhqYsioOpGOG5Ad0ecGzc18c/P5gkiiBm1orh38sucqgKuC25Yi4jxAwiCWie6ptbWT5kqHWmDguXTbPdRPnGMx4uvxotU2lIKrUm9UQQfzHvoqkJXs8HGG+KNbdfTEZtRNIdTO8OPjLsYqoIXRhek0H2m40DiXsS5OuujpaOjgz179rBp06YZy/fs2UNnZ+eiDUwIIcTyNF+MSr2DzTDMkx1ttZ+TyRSWVSahJak6Dl/84SQ/6ZukuiZPFCr4sSKuWSTUbVQFQsPm+u0WmuJz7y6Dot2J67lAROD7xOJxDMNsFM6b4xp/+8Rko2vtXTemcZ0KZT/GUN5DVWFV1uQdr86ycUX8svlAfiZLdT6Pooj77ruP73znOzz22GOsX7/+jI+JxWLEYrEzrieEEGLxLeSbXfUJRKMwRJ0awR0fJNr9A4LxAbTOblL33HfGQnpPqpXj1iSHy2MAHCqNkfOr9GRmzpkxO5ZNCCHmMlehVIkARan9PW2dcjWg7NSq1PXs85IdUK4G7OuvsG/Q5uaNqVMKxNO72U21lg1+7era3AZxQ6G7xWD/sMOaVoPjORcviNh9rErMUAipxbHsPl5hqhKSK3v4IbXK+CwR4J+6GADfj/i3gxZ3bW/mmb4KVRdOfsGXMARdAT+qda/XI17CCJImVJ3aOpoKa1pMVrWYKFHIRu0lTHuEJ+0dOL6Caaisyuq8bmsaqHWhD015tCQ13rAtPWc8y/QidntK48CwzbbVtYan2dnyF5vEvYhzddZHy2//9m/z3ve+l6NHj3LTTTehKAo//elP+ZM/+RN+93d/90KMUQghxDJiGCaWVabJmLuroFqtEJzMRE8ma5OMapqOruvYYZzxso2muYR2AswqWjWLkl+J4iUgVqXTTHB1ooVKxcK1bb63JyBXUWmN+/zGKxMkk6lGl3n/pEN4MjPQ0FWG8h5DEyXSukfcjBpfCV/TalxRBXRYuvP5Bz7wAb7xjW/wT//0T6TTaUZGRgDIZDIkEokL9rpCCCHOzfRvds2nPoFoNHIcpX0FiXgcZ3wAgGCsn7CQO22kC9S+IRZVDNYazRyrTrI508XKRHPjvst9AvAvfvGLPPTQQ7S2tvK+972P1772tY37crkcr3zlKzl69OgSjlCI5WWuQumO7gRNCY32lMbuvgolO2Cs4FO0AzZ1xmZkn6fjGiN5j58dsfBOVp97OmKN5y1XA5oSWiPy5RtPTXJ0zGFgwkXTFEbzLk8dDCjZESszBpqqcGjEYdIKUKkVt90IbBdOTLo4flRrGKp9dXdBdBXiMZXXXJXiF0crjBZd/JMxL55f6zJPxRW8AFw/qhXpgdYUNCcM/HhIrhSg65CMqcR0haQzRCyc4Gjs1eSmVOIxSMc1etpjZJInt9vJdvlIgUIl4Ln+KofGHDJxbcb2rjs24TJZ9jk24dLTETtjp/hCnE83ucS9iHN11kftJz7xCdLpNH/+53/OAw88AMCqVav4L//lv/A7v/M7iz5AIYQQy4fnueTzkygoJzNUIZ+fJIpCKhWLimXhBz7JZIpkMkWlYlEu1SaJzLa00pI02N/6S45ljhNGYJQ6MfpuQA1NUvvuomNlgb940wZaUlkmrYCy55OrRPi+x3A+oOyqrDBqmer9kw5PHKxQdUOa4yqv7E3R3Wayqq3WiZ5MpnjPrcZl95XwhVqq8/mXvvQlAG6//fYZy7/2ta/xm7/5mxfsdYUQQlw4sycQBfA7uwnG+tE6uxvL5uLaFYrD/XzrUJr+KY91xvW862qbzV1rMVV9xgTgXc06d78iQ2ezcVmdt7/whS/wwAMP8O53v5tCocBdd93Fpz71qcb5OQgCjh8/vsSjFGJ5matQWi/e7u6rMJL3aIqp+GFEJq7RlNBmFGN7O0zK1YCujE5fzmVdm8HuvgrtKY3RvMf3ns3jBXDTVSm6mg0cPyQMYef6OI+8UGL/kEPJDjA0hbYmDT8AVT2ZP06tG7yu7NTCzv2w0US+IKoCLUmdyUqA64eUqrXlEaBrtT8JQ0VXQzQFqm5tDKqmsSJjcHjMIVJAQaE9EbCiSSHRtoGu1l5e2O8QMzxWNOtECnQ2vxxzk45prGoxMFSFJw5bTFoBthuSNNUZ27te6K7YISgKVjVYtMlEz6ebfDGK+OLKdNZHjaIofOQjH+EjH/kIpVIJgHQ6vegDE0IIsfxMzz2vTyRasSyCIGjEtuia1ljXMEya0rUuM0VReGHgIH5yCrUa4vkhYayAmsqhlFeghib2eCc/fEHhl31FRoo+HSmF1kRIztLoatVY1VY7H7WmdGKaStUN0TUFQ1d58440N2xoQtcUEvGXo0Gu1K+EL9X5PIoW2FojhBBiWZk9gWjy7v9McOIQ2ppNp0S51CcrTcRilL/1ecrDA8TpwFr3q3hOEx3JVrJNtd8PJi2fobxHGEU8+lKZvpzLhs4Y77m17ZRC+nLtWP/yl7/MV7/6Vd7xjncA8P73v5+3ve1tVKtVPvOZzyzx6IRYnk5XKJ1eYN/RnZiR3z398U0JjZIT0dak0zfhkUnUWrmHCh6HRl0A2pp0xksBh4Ztxks+Lw7baErt/y6iCFVRGZhwGS/XMsRjGnghqNrJTHJqMTPOHJnnMY15M9IVwFCgf9LhueNVLDecUZhvTqjomlorrjsRMU0hbkRomsKr1icoOZCJqyhErGny2OjtJiivJta8DV03uOkqnfGCT9UPcbyQohPSntIa221du8kThy2uXR1n0gqIFLhubWJGcbxe6E7FVW7cmKJcDRYtRmUpusklS12c116X4rkQQojppuemGoZJNlvLMdU0DcMw6ehYQaVi4fs+jm3j+z66rpNMphjo7yOYLLKGBENRCZeQKGZR2fRT9PwqksduJKZrDE7ZDBdDMkmNgUmHu65WWdGaoHdVO7qmND5A3/f6Dg6POkyUA7qyOjt7UsvqA/XFJOdzIYQQiykKfCr/9MV5M9GLxQLlUhEvsAmmRvhsV4onEh6u+k9sjG9gRdfbGXLLrNSaaZGcrZ0AAP1DSURBVE3prMoaHBmrzY5nnoxnm7L8GRfCp3esr8oacxbZL1XHjh3jpptuaty+8cYb+fGPf8ydd96J53l8+MMfXrrBCXEZml1g76VW8G2vBI0JQut55+VqrUC8vs0kZwW0pzTyZZ+YrtCUUHnd1ia8APrGbQYmXYIoRFVUskmNkh3g+FCqejg+KEpEPa1Fi2qFdF0Fb55C+byTjFJ7XKSCpqpMVXy6MgZxA2wPUiZcuzbBlBWyf6hKTK81OZm6QtxQUVSVWzcnOT5hUMlPsKLyS8r6aqLmzThWgO1FbGg36XNdMgmNMIRMXKMv59KU0ChUAh55sUTeClidNbjt6rk/S0zPQp9rwtHZzqZIvRTd5JKlLha013fu3ImiLOwXkGeeeea8BiSEEGL5mp2bWo9tURR1xv2VilWLeQEC38f3fcpWhWIl4j8172CrUuGL408TmRVQffzkBGayguenmbDADz2G8z5NMXh4f0BPe0jvqlM/QP/3/7CWE1Mu69pjxE11ibbKpUPO50IIIc6GG/oMV4usTDRjqgsvGNQnGoUzZKKnWznR1smLehlHVQmUkOFgjA8/8y1G7SLdiXb+v7t+lffc2sZ4yeOfdhcYKfqsyhq0zCpg1DvWgUaRvSWlL4vO9Pb2dgYGBli3bl1j2datW/nxj3/Ma1/7WgYHB5ducEJcAerF0QPDNpnEy7ne2ZTeKBDnLZ+cFXBswgVFobVJJ5NQ+dYvC7xmU5JJKyBuKDi+SmfaACUiCCJGigGEJwvnai3GJQghBJSg1o1+Tt/TjKAtpdHebHDtmgRJU+OmTU0cGbXZtDLOmhaD/skyLSmNsh1iGgrpmEpPm8nO7iQbVsToTGu89PRPGWI9Ba2X7SmDpoTKvv4qfeM2mqpieyE3rE+RjKtE0NhOectnqhISnea/1pwVkElo5KyAno4zF74v9SK1ZKmLBR2Vb3vb2y7wMIQQQlxJPM9F13QiwHEd7FKR771kMFxIc6Koc3AijbqhhTBeixkhXkHxdTQVWlMRrU1JilUfQ1eIxzRGSyHjJY8pK+DElIuqKAzlPapeyJZVMlllnZzPhRBCLJQb+vzeM9/hcHmMTelO/nTnPQt+bH2i0bky0d3QZ0pxyUQO8aZO/p/e9UyNHMIPA5RQxa0keY4ciqIylB/mvz1+nA+9ZgOaqvCbr2mjZAdzzmVS71ivX0iP6wFf/NcRchWVNa1zx79cKm655Ra+/e1v85rXvGbG8muuuYYf/ehH3HHHHUs0MiGWp7ON3WhPaTx7vFLLRo+pcxZJ9wxUeX6gyvp2k+5Wg6myT/+kS8UNeWnIxvZCEqbKdd3xWqNQGPJEzm1M5gm17nFNBcupFc7PJ+QwAkp2wI7uWgF9pFjLL+9oTmE7EYOTHhU7pOKExAyFysnc9SCELStjtOl5NrR3EEWvo6UcUq6GvG5rmicOW0xYIYWKR2va4Pp1KcpOSL4acPPGFDkrYNvqOMcmDJQIdqyd/7PW2RadeztMSnZAuRqQt/xLLjJFstTFgvb+pz71qQs9DiGEEJc5z3MpFgsAJBJJfN+vRbt4HuNFj/GyyvGiSV8uREHDHNuI33Ki8XgzU6CTJrrbklQ82LIyDtDIRv/7J0cZtxQGJn262w26W2OndKld6eR8LoQQYqGGq0UOl8cAOFQaY7haPONj6lnnyWRqxkSj9SgXN/T5vd3f5sDAC6y3He4xWzjS2sTapjaGizZ63y5ilTbya36JnyiQDlvIhyZf+vE4hWrIqqzBu25MY5Xzjei4Ol1TeM+tbY0O9L7hCQYnXTRdYyivnhL/cin52Mc+xu7du+e8b+vWrTz66KN861vfusijEuLSkLd8nuuvzpm5PZ+z7WjOnZwYM28FeGHEunaz8Zrr20z6ci77TlQpVgKGiz4AiZhKV8agZIcEYcjwlE/cgN7OGP0TDody3imThLoemAaNSJfzEUSgayr9kz6bVtayxk9MeSQMhY0r4hw4UmaiVAtd17VaFJbvh4wUPH72i6cxrT7uueceNnQmOJIrsSprkElqXLs6zuMvlQCFlc06HRmDoSkPQ1XIWQG71iUB6OmInTKm2RcvzrbonE3ppOMaI3mPI+OuFKzFJUeOSCGEEBdcFEWMj49iV6tomoau104/1UoV13PoyqbJpHwqwyGh4hGaVdRKC4qXJDKqqF6CDUYrN6yP8f7XdzW60ACmLJ/JfJ7/598sjuZCbF9lc1eM/3Bz6yXbcSaEEEJc6lYmmtmU7uRgcYxNsTRdZvKMj6lULBzbBiCTaUHNtM8opA9XixwqjBD6Lv8WU9kf5HEdSJtJdmTaKaqrsE0VdfD/z96dx0lWl4f+/5y1Tu3V1Xv3dPfsMMPMMDDIgAOIcgUhigE1xkQvCWqukBsI5JervK6aaHIDMcZwjUExiUmM9yYagxoNuYoIGAFlHZhhYGDWnpnel9rOqbOf3x/VVdPbLA2zz/f9esF0nzrLt6u76lvnOc95nkvJ5hwCKw6LZcYrAapSu8tsYLxMWvWmjjEzu1FVpEagvLslTXe+ypglz1v+5VSybt061q1bd8jHzzvvPM4777wTOCJBOHXsHHXZcsCGKCJtKEcVWH19GdBxdg45eH7EP/9iEkWRMFSJgUmPXSMOu0cdNBW2D9k0JxW8EK5alWLcCvH9kKeiKkEQ8tRuC9cLmagEBIBMrXQLQABUvdf1NMwhAXFdYkNfnI1LEnRkVV7YW2X/hMvOIQdFqgXswxCaUxo9eY1Jy2OV/DJypcBbr/4lXptQeX5vmS39VXYbDu252vvnomad3SMOmYTC+p4463vmb74627EoxyJKpginslP3k4QgCIJwxoiiEAkJTdcbddInJsaxqhWIIiyrxPUrZZ7aHWItfYbQKCFXsyS3XkOUKJDx8mS6ExBFuK6FHnlEYS0DrTWjkdIzNCUt7KFa93cniCjbAYYuz8iKm56xNp96U9JTvXaqIAiCIBxvuqzyuXXvYtd3/5KW0R14A+NA92G3md5gPAp8zAe+2Gguql37YRIjQyxLt/JSeRxCD01W0bU4v9O1kSVSkgdLKqPVOOMVl1I1xqgfsGPYJRNX6M5rtKdkbNcnpSuNYx1K3Ihx69WLGpnpp8O8/uKLL867XJIkDMOgt7eXWGxu9qcgnMmmN/c82sDq68mAvvLcNEuadf75F5Nk4wp+EGHoMmu7DV7aV8V2I0pWSNWLGC8HdOY0Hni2RMqQcH2w3ZCRkockSbh+SMUOUWRI6CDLULbfePb5dLXAvMSq7jhuCGu74/y/LWX2Tbi4ASQ0mZaMzqKcSndeI6bJGGGJuGtSbbucX+yTGS6btKcV1vbG6cpq6DI8tcvCUCRWdcVZt+hg5v/xuHgxH1EyRTiVib9MQRAE4bipB7AlSSKRTM4IZEuALCsEU910ilWPWMIhiBdBCgkSBSTVQym3EzNkckmV4WLA3sFx2jO1fdQz0OJGjDuu68H4ySiTVsCiJr2RcVbPivPDiEBJHzJAPrsp6alcO1UQBEEQTgS1XKBrdACoNQilqf2w609vMB5MDBGM7iMKI+z9r2H/059CeZJPtS2ieO1vce+2h9hhlzgn28HlPasoFwq8d2MGTdUoWSb3P1bF9sD2IvpaFP7LeSme2VHg6094dOZUPvRmlUwqddgL5NMz008H69evP2wDcE3TeP/738/999+PYRgncGSCcPJMb+55vI2ZAUtaY/hhRHdOo+KEuCFsWpnCDcpIEoyXfTJxmcGij4TE7lGPrCETRuB6EYocEEQSYVirP66qYB7jAHrdZMVnz6jDmm6Dx3eYmNUAP4TOjMak5dOT11jaFiP0bKJKiSE7w2v2BtL7IrrNKl05jdasxvs2pimYPvc/MsaEGZCIyVx5TpLzew/fW+qNlm8RhNON+OsWBEEQjptSqUilXCIMI7LZJqAW1B4dGaJqV6eai8qYYYxc3CarxIgUl0izkbw4eDFUZNIJGS+AzlRILi6hqOqcDDRDl/mdt7fNyThLJJL4YcQ/P+PSPzHCkhadj1zZMidAPmH6DEzdfjhQ8E7p2qmCIAiCcCLMbhBKqB71HV71bZ0DO4kSOaTyJLIso04MsaQwwV9s/ABDrkVnPEPoy3xta5WCWaUlUebGdTJLWySGyzJhEDJStPnWzwOGSz7LWmWGSiGDYxWcqklra/sR7zQ7XXznO9/h4x//OL//+7/PxRdfTBRFPP300/z5n/85f/AHf4Dv+3ziE5/gk5/8JJ///OdP9nAF4YxTb2wpRZBPKhwoePTmNRKGTHtG5eVBm5ShoCgSqZjM/kmPMIwYr/hU7JBQAnzQ1IgIkCRwvOMTQAeIItgz5jJm1pp+Pr/HwvEj/DBCVyWCSELyyiSGn8TSWnnTkg4Klk/RDrmgN8HS9hjLWnUKps+3nylQsQNMJ2BVl0Eqrhyx/vxCy7fUg+4tSYUxMzjqxq+CcKpY8F/ro48+ypVXXnkchiIIgiCcDQqFCUqlImEY4AcRD74Wp+DK5OMGoTqK5OtIgQqRDJpD6MRIKCG/cn5AW1LCdx0cWQKyc/Y9X8aZpuk4pPjpqwcw7ZDXhh1+aX2WnuaZJ9z5pEpXTmtkop/KtVOPBTGfC4IgCEciKeqMBqH86wOz6p7rhwyq17fVxgcpB+D9+B8YLY/SURjD/N6XUbuX0/ue2wkihS89PMIjL1cwNOjJ+IwUFG6+ooP3v1ln58AEf/7DIqPlENuXWNxisLRVJRt3kKhdnJ9dG/109b/+1//if//v/80111zTWLZu3ToWLVrEpz71KZ566imSySS/93u/J4LognAcTG9s+dqIg+1FPLq9ggRMWAGSJGH7EW0ZldeGbCp2gCZL2F6E5daC5REguRBTayVXEhpU3GM7Tplas9BMXGbc9NHlWqPPc7oMBiY9RsoeCV1mbYtJbOQXDMiLUJpWc9XKFC1Jhcd3mFy8NNFoDvrYy2W2D9Te169bl6U9px1VSZbZ5VumZ6YDM7LU698PFTy2D9pk4wrw+munC2eG2XcznOoWPMJ3vOMddHd385u/+ZvcdNNN9PT0HI9xCYIgCGeATCaLqqrIstxYlsvl8VyXarXKZMFhuBSgKD4vFwI2D6eQlyUJYyaymwQvRkTIpAU/fDnkgxfJ+J6HpmkLOmmWpFqmRqEaEJkRf/+fY/z+dR0Y+sFxqYrEzVc0n1a1U98IMZ8LgiAIR0NSVJR8R+P76XXPYWYz0UxKntFIVFJUYm09lCbH+IPFi9hT1lg84PFb/fvomRzEeNuvMqG3UajWygeUTId4zkMNAzynSktGZzIeIcsyiizRmpb5jcubOKczThR6jeA9nBl9TbZs2UJfX9+c5X19fWzZsgWolXwZHBw80UMThDPW9Ozo3eMuo0WPoh2SNRRs18NxQyarIT1NGuVqSC4pk40rJGMyZVumJakwYXpYDrV6ldHBc48ogop/7MesyJBPynghKJLElgM2a3sTvH11mgMTLt3NGqoskQmHMBatpattGcm4wrJWnZ2jLtl4LRO8r7W2v0iC9mytHMubVySPOpg5u3zL9Mx0YE6Wej24vqbbaGSiC2e3Y9GM9kSSj7zKTAMDA9x+++088MADLFmyhGuuuYZvfetbuO4xvrQmCIIgnPZqtVGbZtT3TCSS9C1eRiabJaG4NBkBr40GvDDiUl7xM3yjQKjaBHoFe/EzhFLt5HnSDChYIbIs43rugm7dbk1rXLwsTkqX0DWJX+yq8uWfjOIHM2+urGeyn64n3wsh5nNBEATh9ajP7fV5OJFIEjMM4rEY5gNfpPLPf4r5nb8kCnw8z6VYnGQ8sNnrlghlhf/Mpfm9c5bz6Z5ORiZH2Tu+g4RcYkmLzJuWxrl6lUpMr2VHWpZJkxFx+YoY5y2K87ZVac7pjKMqEpKs4UgpJFnDDyL+5tEx/vw/RvibR8fmzO+ni3PPPZd77rlnxlzseR733HMP5557LgAHDhygvf3wtekFQTiygunz7B6LzfuqDBU8Ht9hsnVflc39VQpmQNEOWLsozqoug86cRjgVHN875jJc9DB0mWVtMdJxBT+Sa1noUS37XFdr67rhsRuvPO3ftCERRHBOR4xFeY1Ny2sXE7MJhSvPTfPmtlGuWqFSSp7DiNxDR5POleemySVVlrXqdMzKNF/fE+etqzO896LcG8oGnr7v+Y6TS6psWFzLgN+wOHFaZB4Lx9d8fyensgUH0fP5PLfddhvPPfcczzzzDOeccw6//du/TWdnJ7fddhsvvPDC8RinIAiCcJqzLJOBgX2USgXGx0cpl8toqsrGbgfT8yis/X8E2WGIV0CrEskBYbwIMYtlrdDdpNKc1mon6kYczzt8sLd+PMsyURWJ/35VG5edkyIZq2WOTJohk+ZxSA05TYj5XBAEQThaUeATTAzN+1g9qK5YJYLRfUCtCWlYHGtkqTcrBiuz7RzwTExFZTQW49VcE7fs/il3vPx9tqYeQ5OrvDIU8ldPxviPnSkSyQyJRJJEIs5/e1s7/9+1bY2eJvVm4Pc9PMbXfjrOUNHlkVcqbDtg88grFUbL3rxjPdX91V/9FT/4wQ9YtGgR/+W//Bfe/va3s2jRIn7wgx/w5S9/GYBdu3Zx6623Hpfj33fffSxZsgTDMNiwYQP/+Z//edj1H3vsMTZs2IBhGCxdupSvfOUrx2VcgnA81DNgpQiShkwurrC4VWfTihQxTaJgBkRSrano2m6DEPCCkIod8tKATRDWao8rioTtBchSLYAOtUBbdAxzchSpVr5FkyERA12VOafDYFm7we1Xt9HXGqNg+vz7C0X2vPIsE3teZGlzxNpugzU98XkD2dMD2PVlAM/usSi8znOk6fue7ziCMNvp9nfyhka5fv16PvGJT5DP57nnnnv42te+xn333cell17KV77yFc4777xjNU5BEAThNFcoTGCZJpZlEgYhkgSJZJK47TKujROpzsGVJUCvIk02s7E1wUcvUWnJqLS1tjVKucxuLHqo40EtS87QZX736jbu/dEIjhexKH/m1z0/WmI+FwRBEA4lCnzMB75IMLqPUD+HKPCRlLnzp5TKIWeaCYqjqO2LkbMtJMJaSCmRSPLbyy7nxYkDjFLGljyGJJUd1jASEmN+lZhbperW+poMlWUqrkxrRmuUbosbB481txn47HTP0/OOsje/+c3s2bOHb3zjG7z66qtEUcR73/tefu3Xfo10Og3Ahz70oeNy7G9+85v87u/+Lvfddx+bNm3i/vvv59prr2Xbtm309vbOWX/37t1cd911fPSjH+Ub3/gGjz/+OLfeeiutra285z3vOS5jFIRjaXo9752jLqYd0pnT2bA4wYObizz+WoXRos8TtsnTu01sN+DlARvPD0nEFLwwpCOjM1r2MDQFxwsgqO3b9SF8gzfESIAsgSrXAuhIoCsynTmVRXkdSZL41Y1NjeDjc3srFHY+ieYXufbad9HTkWfJURxnek3q0620hiCcaK/rVeF5Ht/73vf42te+xkMPPcRFF13El770JT7wgQ8wMTHBxz/+cd73vvexbdu2Yz1eQRAE4RR2qOZiUKuFHgQBvlf7YKapGhXbYtv4GKGZBV8DfSqQHoHkGRgDa3nLRoWEbGFVYCQKSaczxOOJqeMw5zj1MaRS6cZx/SBipOTxb88VcYOIfErhv27KH1XZljOhxuqhiPlcEARBOJKwONbIMCfwCItjM2qkQy3Qbn3vvtpj2VYS19+CH4aY5SJxr0oUixE3XZbHmnA9hyiMsKNgqglfRAR0ZGQqhdqN0otb9MNe6J7dDHxFe4y3nptiz5jL4had1vTpG/xJpVJ87GMfO+HH/cIXvsCHP/xhPvKRjwBw77338sMf/pAvf/nL3H333XPW/8pXvkJvby/33nsvAKtWreKZZ57h85//vAiiC6eF6fW8l00tqwfWE4ZMZ06jaAds7reYNAP2jDlYboTjQRiFxHUNywnJxBTimkIUBUyateB5ENUajMJUVjoHv59NmvaYrkAuDqqmQATqVCPTKIpIxhRWdcdYuyhByQ5Y1hojm1AO7icIkVWNZeuupqcjf9TPw/TA+exGoYJwpltoY9MFf7r4nd/5Hf7pn/4JgA9+8IN87nOfY82aNY3Hk8kk99xzD4sXL17orgVBEITTzOyg+fTmYrObfiYSSTzPpVwqEXkOgQSf3fVTnixWic5zkX2DUIpAdQEJpAjNN+hrCvA8Fz8ICMMQ13GIJxKNeqnJlMZwoYoaVlDlWpA78H1ihkFXV0/jlu/XhqrsHrU5tytOsSpRtgMMXT5skLy+bf0k/eYrms+YQLqYzwVBEISjESQyBNk2mBgk0hSCRAZl1jqNQLskEZbGiSoFin6E9/0v45XGCHJtVC++nqpd5oBXIWQqyxIJBZUWbxEpPc+f/kobCj4J1SEKPVDmD+TM1wz8I1e2nBHNwXfu3Mm9997Lyy+/jCRJrF69mttuu41ly5YdeePXyXVdnn32WT7xiU/MWH711VfzxBNPzLvNk08+ydVXXz1j2TXXXMPf/u3f4k01gZ/NcRwc5+Cdh6VS6RiMXhDeuNkNMtf3xEkbCi1JhZeyGgNFj7efl+L//nySouXTlFBZ2xMnQCITk9k36dGeUXm+v4qm1M4hzGpICKgqJHUZJIlMXEaSJEqWT9kJUSWQJAmzGqGq0NOsc8HiBHtGHXRVxvVDbLdWMubiJQkWt+nsGXWJ67Ua7DtHXc6JbLZt28b61ReQSV++4AD49MD57OdBEM50C737YsGvjm3btvGXf/mXvOc970HX539xdnV18cgjjyx014IgCMJpZnbQvF5i5VClVhKJJKZZQUdn20g/r9kmSBBpNpEqgxRAqCK7CYgUzu0wkaV0rUFYBL7vYxhxYnqMmGGgxxL89SMj7B4qkY+HvPd8iMViUzVUa2Oo3/ItRx6BH1CxXFb3ZGhKqkcMks+9XdynNTP3pPB0JOZzQRAE4WiUTZPKJTei2GV4YTtVx0E3EjPWkbMtKG29BCP9KG29yNkW2LMdaXKYEIgGdzHyk3/g2a4EoSzX5n5qGZptYRdtQ5fySDCGpir85iUGgetgWdKcC/LT1ZuBH+r709EPf/hDrr/+etavX8+mTZuIoognnniC+++/n+9///u8/e1vPy7HHRsbIwiCOQ1L29vbGRqavxb+0NDQvOv7vs/Y2BidnZ1ztrn77rv5zGc+c+wGLgjHSS6psoxagO3NK5Lkkip7Rx3GrZCsoRCPSchIDJc9nt1TZWmLjqZKNKdVRks+phNguSGqCpVqhKFKJAyF9rTKys4YP321zL5xD88Lac/q5HtkNEVhUbPGO9Zk6B932dxfZbjksaRF5cLFSd68IskL/VWQJDozKh05jWa1zHe/+xB9fX1kEwobFmuNhqlHm1krAufC2Wyhd18s+JXy8MMPH3mnqspb3vKWhe5aEARBOI14novv+yiq2ghY15qLHZyALMukUJggisLG47lcnr17d5P0JDrlFGORheTHajXRQ5XaabWEYuUol5N8/xWDJkPml1Y6JOJxEskkTflmNE1npOSxb7wKwJglUfE1DENhsiqRzdVOpOu3fO+ybTb2SVx/YYKVvbVg+UjJO2yQfPbt4mdSDXUxnwuCIAhHTVHRW7qR5NfmvVAuKSrJG36HsDiGnG1BUlTSHT1Ucq1EOzcTeQ6aNQmd51Gf55n6vy2XeSX7U3y9wr/ZrbwvuJGcIR2x98mZ6BOf+AR33HEH99xzz5zlH//4x49bEL1OkmZm8EdRNGfZkdafb3ndXXfdxZ133tn4vlQq0dPT83qHKwgLdqjSDdOXQy14XrYDRoo+2wdtNi1P8s+/mGTCDEjEZJoTCq8N2+wZ83D9kMmSTyTBpOnhh6ApEo4X0ZxSKFg+FTskE5dZ1prhiR0WL/Tb2G6t5nnJcVilGCSNkM17qzy722RNd5xdYy6VaojrR1x2rkwuqXJ+b5xUvJYhv6v/AC+++Bjr169n/fr1jdedqGsuCEdvoReRXvcratu2bfT39+O67ozl119//evdpSAIgnAasSyzUTZldl3yukJhArNSxvN8hocHCYOAcrlE1aoghbB09EJ2jFqETgJn6ZOEiodabcIYWIPspCjFVGwfiq5BqBnosdqHw1KpSCaTJZ/U6GmOs3c0oisj09uV5xuPFxizHBblxxuZ5Tdf0cxIMUFMssmkUo1s8yMFyee7XfxMI+ZzQRAE4XAymSzq1AVzWVYOOedLitqole7aFuZ3voRUGiUMfIggUy4QBH6tRMtUHF0CzslmeJYB/ChkUK5iq1Wy2bYjjmt2ObYzoYfJyy+/zLe+9a05y2+++eZG7fHjoaWlBUVR5mSdj4yMzMk2r+vo6Jh3fVVVaW5unnebWCxGLBY7NoMWhCOYHTAvmD4PvlhCmyr/OD1wtnPUZeeww/ZBm64mDdMOMe2An++y6GnS+OdfTKIrEv1jDmEIkVSrjV6yA2w3ZKTk4/gHLxHqSu3f0VJQ7zVK1Q352asmlhtCBJoCUQSuB68M2ixqqjUplWWJgmWytDWGlpLoyetIU0XTi1bAk69WGCh6rMj6dJ/zJi64YM30H7txIaAlqfDsHouWpMKYGRx1ZrogCIe24FfQrl27uOGGG9iyZQuSJM252hwEweE2FwRBEM4Qs0u3zNdUNJVKUyhMEkUhoyPDeJ6DJMkEYchYFX6ovIDVNwGGCUhIvoHxygaUwACg6sGLBwKuWuaS1jXMShVTqmW010q7GPz6JUn2Dto0xSUmSg4Ttoosz8wsVxWJrnwCP4jXTrDlCFWRjipIfibcHj4fMZ8LgiAIR2P2XWZHwxreRzC6H0lSQJIo+iEfvHQjdqN8WASRRMrLs7h4PluVQYggpkgoSu0zRalUBGpB/NmB+9nl2P7rpjxff3zisD1MTocge2trK5s3b2bFihUzlm/evJm2tiNfWHi9dF1nw4YNPPTQQ9xwww2N5Q899BDvfve7593m0ksv5fvf//6MZT/60Y+46KKL5q2HLggnwvTA+c5Rl11TgfHr1mXYOeqiyhJeGM0o3VAwfYYmXZ7aZWLaIYvyGoWqz+a9Np0ZlUnTZ0V7jEdeqzBY8JCAmEqt3rksE0YRtn9wDBHgzPMxOgT2T3gYGuiqhKHAcKX2+dv1wHIDMnEZ04lI6DL5tILjhIREVOyAvaMO//zzCSb3v4xNnJH4Em44v2Xen33D4gTP7rEYKnhs3mNR9SMq1YC3rEofl+ddEM4WCw6i33777SxZsoQf//jHLF26lKeeeorx8XF+7/d+j89//vPHY4yCIAjCKWi+0i2zm4p6nocsywC4ro3v+2i6QS6b46mRfVjpQVC8qbb0EZFqEyUKUK5lsjUnIIxCVjebmBUZTdOIQtBUFdd1kAA/qNCSVIiI6M6n6cqV580s94OIv3l0jN1jLktadD5yZUsjkH4mBsmPRMzngiAIwvESb+nETzfhT45i9l7A/zNdRmf036gFsYvaJKNllzf1Lma/P8nqbCe9yTxWuUylXCIIQxzHprW1fUYgfXbPkteGbXaOOMRUmYGCx2jZQ5GlGVnqp0Oj8I9+9KP81m/9Frt27eLNb34zkiTxs5/9jD/90z/l937v947rse+8804+9KEPcdFFF3HppZfy1a9+lf7+fj72sY8BtVIsBw4c4Otf/zoAH/vYx/jSl77EnXfeyUc/+lGefPJJ/vZv/7bRtFwQTobppUyWtepsH7RRZYmdoy4tSYXtYcSm5ckZGdk7R11eHnTYOeJQsgJ2jdiMlAMkoGIHXNgX56fbKxyY8AmntvEDkCRACgnDOcM4pCiCIAJVihipHFyuqWDoMkEAkhQQhBGDkx4VJ8Iedth2wOa8Tp1EaSuLlGGctkt478amOT/H9DIu9QsFERF7Rl2iU+8tTxBOOwsOoj/55JP85Cc/obW1FVmWkWWZyy67jLvvvpvbbruN559//niMUxAEQTjFHSozXVNVJElurKfIMmMTJR7dY0Ab9fPo2r+egWzmpxZEeEFIRyqiOxMSM5Jks1lSqTRRFKFpOp7nkpr6t54Bf/MVeiOzPAo9ipVadvxoBR55pYLlhOwZc3n3hhydubMveF4n5nNBEAThWJl+N5oqyzg/uJ+wOEYQT+OuuwLjiUcPzvcHvyAkIp5w+fSaa3EMhc54Bl1WkRJJfN+nXC7h2FVKpSLNza2N7aaXY+vIqPznKxX2jNXKkl223OCbTwwzUZVZlI9x8xXNp02j8E996lOk02n+/M//nLvuuguoNfn+wz/8Q2677bbjeuz3v//9jI+P89nPfpbBwUHWrFnDgw8+SF9fHwCDg4P09/c31l+yZAkPPvggd9xxB3/1V39FV1cXX/ziF3nPe95zXMcpnH0OVcd8vsenNwnMJVWuW5fhZ69V2LzHIhWXmaj4bDlQ5aUDNjtHHZa26KxZFMdQJbwgZNIMcKeyyCVA1yJeG3EoWkFjmabUgt6eD74PC7l3M6SWde4GtYz1OlWGniad7UM2phMShJCLKzTFZUoSlG2fcPApVK2K03EFV65rp691Zmmk2Q0S67WeC6ZOZ8496saJgiAc2oKD6EEQkEqlgFrttIGBAc455xz6+vrYvn37MR+gIAiCcPLMV6LlUObLTJeQkGQZVVXJZHJUKiUkWeFfX9J4bUiGVnnqE6SEXG4l8drlyGF9HxI9GZtbLvbIZuLk8820t3fOGp827/iiaWOoZ8dL0uwGZRFnMzGfC4IgCAvheS5hGOB57pzPBNPnW8MuU923g8A2UQd3Y4wfoJxSgL45+2xRE/z31d1k4gn0WIKJik8+WbtQXg+aV8qlOdtNL8fmhxH3PzLOOZ0xHC/iTX0S33nWRVEVBgoyI8UqMcmmPS0zXA5P6UbhkiRxxx13cMcdd1AulwFIp09c+YVbb72VW2+9dd7H/v7v/37Osre85S0899xzx3lUwtnuSI0yp5dt2bR8bkPiJ14z2Tvq0pXTaG/SePJVk6GST6Ua8FJaxXIjBgou5WqIP5VVLku1/1wPSgQkYwoRAV4AYQBBWHt8AUnoQO3sIwAIp27EnVruhfDqcJVCNUSeWm57EYuaVS5dYfDoK2VcpYNCrpsrVubnDYgfqkHiQhsnCoJwaAt+Ja1Zs4YXX3yRpUuXsnHjRj73uc+h6zpf/epXWbp06fEYoyAIgnCSzFei5Wh4notj29hOFU3VCcOAfHMLfuAxVPAo2DaTa34M2lQzyxAkX0MOp2eFRSQUCQ+NlpZ28vnmOccYHR1Gmspoy2Z1qrbDXz8ywpgl05lVufF8qdEMLSlrvPXcVKOcS2v61MtAO5HEfC4IgiAshGWZRGGEZZlzPhNMvxutaFUIi6Ook0P4Uchdy5fys/b8jPWzQRNXqhu4bW0PTfEkVcfhG7+ozim3Um9qqmk6xeLkjIvm9XJsfhA1stKXtemc15viqd0eY5ZMV04jJtkErsOvbIgRKunTplH4iQyeC8KpYr6s89kZ1rPX0WV4cqfJivYYD20rY3sRQ5MuHU06O4ZtXtpfpWCFyAr0tehYXsi+cRddgXRC5gebC4xVfNKGjB+AH4SoioQsgeVGpGMylhsRBOBO1T73vZnjnh4QP5SYDFM9RRtUGfwQZBkmzRDXA12FpC6hyJCSq8gDT3PXO67kmf0Z1nYbuAuN3AuCcMwsOIj+yU9+EtM0AfjjP/5j3vnOd3L55ZfT3NzMN7/5zWM+QEEQBOHkmV2iZbZDZapblkm1akEEVduECAqFCSRkZK/AnvRDBLpE45ZuOSIwKkS6heTUTho1CRJxmc58DMOoNRqdfgJdKhWxq1U0XW+M78BYmQMTteyzfeMeE2WNruZErRFpEHH9hVkkCVrT2mlxAn08iflcEARBWIhEIokkSzM+E0z/HJDNNuHaFvaebUh6HFnV2WZo/KytaWptqTHtr8s38btLesjGkyiqih0aDBSKhGHInpEKI8UEXflE4y63YnFyxkX92U1CZzcJv/XqRTNKu1mWNPX54dS7gH7BBRc0mnoficj6Fs5082Wdz86knr3OlgM2qZhMqRqwpttg95jLYMkHSeLFvVWCCMIoYrIcYHshXhARhhFFL8KdKgPlBrUyLWlDomzXeibpCkhy7XzFDw+WeZnP0dzfGk5bTwI6szJlOySKoD2nYtkRBQKSMZkwgmRUpLnwIj0rlrNiUZaVPRLP7rFmNEudr7zNbEcqhyMIwtFb8CvommuuaXy9dOlStm3bxsTEBE1NTUc9+QuCIAinh9klWmabnaneqIOu6aTSGSzLRHYVgjCESML1XMrVMTzVR440QimifkateHEkNwGATMTipoAPXxKjpaUFTdOnss5pHAtAUZQZAfzuljTd+WojE72tSSORSGK7Ifc9PEqhGrCoSefmK2ZmtZ+NxHwuCIIgLISm6ciyAhy8qD39c0AmJVP6l79AGdwNjokduNy5/qJ6973GfrrVFGNBmRG3jBpFZLI58kmFvOEzUPBpS0FMsoFEY5vpF/UP1SR0eo3zGd8rh/8sc7L98i//8skegiCcMmZnnR/NOpuWJ7HckM6MyppFcTqadFqSCmNmwPm9cdwwYrjoY9oh2wZt3rQ4wXDRY6joU3FC4pqMJEVMWiHRVJa3EkR4MkRIaEqtkWj0BitBevVSMUBHTkGVJSJC0nGFtoyGnwC97NGUVJDtSS7QNtPat44rr3hTIxBeb45ab5Z6NGVajlQOB0SgXRCO1oJeHb7vYxgGmzdvZs2aNY3l+Xz+MFsJgiAIZ6rZmer1k2nf91FVlWw2x8DAPogiisUJZFkhm2yld1jhgB9ha7Wa6JIXJ75jE1KkACG3byyyqg2kME5zcyvF4iQSEhFR41j1W7ynZ8TFjdiM7DNVkajaDvf+v308vtMjadRO/k/VhmInipjPBUEQhIWoXySPomhG4Hz654CwOEY0doAwipA0g6daWpiMTQuETcXRR3yT1ak2WhUDb6omgutYvG+9QsnVacloxGOxGXefTb+oP1LyTosmoUfrD/7gD072EAThpJsexN2wOHFU6wE8u8diWavOirZYLSPdUDi/N95YpyWpMFz2ycZl9k14VOyQh14qE9MkkjEJz4vozGnsGXdxp0q06Ao4PqBAJEUUKhH2QrqHzkOTa0H4iNp1xYwhEyGRjRQycRVNkRgoeIRRbb1YKstw7EIuX3kOMK3uexixaXmSMTOgJak0fv7DBb6P5sLEkQLtIsguCDUL+utXVZW+vj6C4A2+gwiCIAinvflKudRPpn3frwXTA59UMg0SyLKKrMhEkYpdvJqw6RfInkIkhyReuxwlMKb2LKNKEnFDoa29a8Z+j6bBaT37zPNcihWToYLDpOljqBGWE9KUUE7ZhmInipjPBUEQhIWwLBPHrBD5HvFYDADVMDhQLdBsmZiBTyKRIWzqIBzaQ6l5EX/UKh0scTDtBqcMGje1rSMeixPTY2Qy2cZjLS21+X5oaADbdgj1LMsWtc8owZZPqo0a6B0ZFT+M8IPorC/TJgins6PJlp69HsDOqdImmXgtSl12Ah58sYQqSwwWXF7ab7Nn1KFUDenK1fY7VvYYmAwII1jRHiOMIkw7JJh6wwoCCCPwJWqNRF/HzyNzcDuJWs3zuCYRhhDTZS7oS3BOZ5wfbyuRjcuoikzGkEmUt5OLebzoruaA18SDL5boadZZ1qqzfdBGlSXGzIANixM8u8c6qufsaBqLHinQfrS/H0E408kL3eCTn/wkd911FxMTE8djPIIgCMJpop6JZllmY1ktU6yJTCaLoqooSv1DVu1E2nE8vrc9wY7hZiKrFSlSUCstKHZmxr5/tDuJrKUhihgfH8XzDn5Y9oOIkZJHqVLBMk1GR4fxPPeQ48slZBbldc7tivO21SlueVurONFGzOeCIAjC0fE8F8+xkR/+OlQmcX9wP0Yizl0v/hv/7cdf4vcfvg/r+1/BskzcK3+Nwqb388SajUzo07LDIxrFgNck2uhN5onpMWJTPU9KpSK+X+vYZ1kmtuPyL5sD/u6JKn/9yAjjkxONub5eA/2/vbUFgPsfGedrPx3HD95grQVBEE6aZa06HTltThC3YPo8u8eiYPoUTJ9KNSBpyI0s85cHqjyz0+T7z5doz2ikYwqaLOGHEcMFH9MJCQFVlqi4ET1NGgldxvNrgfIx02fPmDOj3rkb1QLgzlEG0CVqTUOnUxVQpFpWe1OyFkDXZEgaMrmkzMVLEwyVPApmwI5hl94mhXPkbSyJD3PumrUsbdVRJAnTDvj2MwUArluXYVl7bEbAe77nbKGOJsu8JalQrNay3wXhbLbgS0hf/OIX2bFjB11dXfT19ZFMzmw2J5qdCIIgnB0O13RU03RUVSXwfSpmmSgKcWyLyapMoariBBKxgfMAkJ3UVBmXg7aPafzJwxKfvnqSdCKG49ioSi3b7F83hwwUPNrTMu88N0SVJSzLnFPvVNN0TLNCLp3h1qtbmTR90oZCyQ5QFemsD6SL+VwQBEE4GpZlYo8cQBneS5RaQzDSz+DgTl6ZOAC+yy5dYXR0hHN9GznfQqEwzn+8+BCk5wZ2lpPn5txGHMtBk5VG/5RKuQTQKNMWag5F10SSYe+oxcikDmHQeFzTdBQZhkq1wPuZUNZFEM5mh8qWnp15PlLy8cKIJc06j+8wUWSJV4YdAj/iMUXi969tw3RCBooeq7tixGMy71ib5oFnC+wacfH8iISu0JQKcbwITZKxnFoEfXr2ONSC4EdzbS4CnGkbNsUBCcpVUGSQkEkaEq4XEYSwKBfj+X6bsbLHpBkQ1yXsA88SU0xWvukaOvNpxh2TRXmNJ3eYIMGDL5a4bl1mRqmbo8kwPxpHk2U+ZgZk47U6832tb/iQgnDaWvArTjQ+EQRBEODwTUc9z8X3fRRVJZ9vbWSfZWIh1SDEWvpzwngB2WoisfuSOdvrukzJlSiFCTrTBvF4As9zqfgxBgpFAIbLIUqihYTizAjk18vM+L6Pqqh4nksikaQpqc5oRPZfN+Up2QH5qdrpZxsxnwuCIAhHI5FIMh5LoGfbiPyIINtGSs+yJNHM7sokyx2XnvYl6M2djO/bzeQjf09/uzFrL7VG4jvCCf7uxQqrMjE+cJHfmKN932/8B9Db3kRrymS45JNLqeSzBoHvUimX8H2f5ubWGWVdunLaWV+qTRBOd/NlRNezrFuSCrvHXUrVgHRc4aFtZYYLHpYTsLbbYOuBKpoMj+8wGa/4vNBv8fQui0uXJfjOMwVeHrSpuhFjFZ+YCpIksbQ9hueHyIXa8WVqGeRuUMsu1xQI/NrXR3OfiwQkYtCc1hg3a/XNqx64foji1LLVWzIauiZx+cokz+6tkotDJhnjvJbzGTQ1OvNp9oy5TJi1mjLL22NMmAEDky6b91W58tz0MX1+C6bPcMFjsOixpnv2+/ZBR1NXXRDOBgtuLApw880309PTc1wGJAiCIJw+5quLDrXbsicnJgjDAEWRax10qGVjrOic4KfSIJEcEOlVwlgFxa7VQ9WkkKQOaUNhUbPBhSu7MfT6/ZFJkkFEV85qnDC3ZeOoyszmQ/UyLoqqEjOMRoB9wvQbjcj2T3h8+SejFKshXTmNm69oPqsC6WI+FwRBEI6WEoW0ehXKb/t1ePIZ/LfeiK7F+OM1v0QxcugOwWhqx/Ncqv92H19P+JS0+TLCI5AkCpHFuNWKp6Qbnx/qTcSnl4l73wUq//ALj2qg8b0XI969ZuZda/WyLtObiZ/uHn30Ua688sqTPQxBOK4OVT5kvozoerb1s3ssTDtkWUeMih3w9C6b4ZLP4hadmAYbFicZLvq8MmBTtHz2T3j4QcSOYRtdkSlUfSrVWmDclCAVk/H8CNsL8adKufgAUwF0WYJwKrs8AhRA16EtpTJY9PGC2r6iCPzwYKBdimpB88CXUKSIcCr6rkqgarWylE1JmQOTPp5VomXiSVqaL2F3sYmSHfC/Hxpl3SIDoojFbTqdOZ3hgseuMRfpDVasqj+/lWpAKq6wrFWvNSwdcyGKDptlfqyy3gXhdLfgxqKf//znuemmm47XeARBEITTwPRs72AqIDs7K91xqwRTWWVRdPAex8GKDIdIoji3xeWjF5pYUpJNF3ThVIuEvt7IVNM0/YgnzIdqQjo9Yy2XlJm0AmRJOitvARfzuSAIgnA0Qtem+PmPEBZGMLKtSEuuQ4kZZDJZNE2nHm/xPJex3a8w7lbY05Qk67hY6jynmhHYZoaupTF623Mz5unpGenxeAJPTuJSJQx89k84BEoTuSZtxt1n9WbiZ4p3vOMddHd385u/+ZvcdNNN4kK3cEY6VPmQlqTCk69W2DXqoMswYQZEEqzvibOsVa81Ct1X5cCkS8EKkKitc163QakakE3IPLmjQuBHxHQZLwjxg4iWJhXXV3C8AD+o5fYUrBDLtae3a2iIqCX+RNHMZWEAVTdo1E+PIoipEloUYU1VnAmA9qxOTA0YLLioEWiKRD6pkIjJTJoBP9pSon/fAOdrL/Kat4htu2P0tAa8tL9KGMHeCY93rs82LjIUTJ/2Y1D7vL592Q4az/+yVp1KtfY8v579H009dUE4kyy4sehVV13Fo48+ehyGIgiCIJwu6tnewIxs77pMJku+qQVdNxpZHHWBlUYtdiE7qca/dZt6bFKGRG82wK4UcGybQmFiRmZa/YT5UBln9eam00/M69vdfEUzv31VC7e+rZVFTbXHp98CXm9aejY0JxPzuSAIgnAkwf5XCQsjEIaEkyPge6iqOmeOtSwTSU/QXBhjXFUYTCbm7iySSFZ6WZZp4R2rdcrFCQYG9jXm9+n9VDzPZXFnM20pCPyAlkRIWzY+7/x+JhkYGOD222/ngQceYMmSJVxzzTV861vfwnXnNlAXhJNtetPPhThUQ8wxM2Cw5LNjyOHBF0tsOWCzdV+VnaMuuaRKqRry6rCL6UT05HUuWppgzVQA/bp1GVQZqm5IyQ3RVYlUTMEPJWw/pDOrYmgSnTkFVQY/AsurlVuZTz3DvC6kVualYEWoUi3zXAIkOSIIDgbWkrrMUMFlwvRRFFCUWhD9wsVx3vemJmQJ5NBmvfoCmytL2VxZTCRJNMUVPrAxx+ougxsvzLJhcaIRlM4l1Rnfv171/azviTee/1xS5S2r0lx5bvp17b9+QWTnqHiPEs4OC36VXHvttdx1111s3bqVDRs2zGlEdv311x+zwQmCIAinpkNle9dpmk4imcQ0KzOy0KsubB1OEA8uIdItJDcxraloRE+TRjodI9fURC6Xx/NcUtrBTPQ3anrG2uyMdj+IZtRMP9NLvIj5XBAEQTgSuXMpRBDZFsSToGpomk6xONn4DFAqFRjq303qR3/DmG9R0Gdnhk/NpW6C3sIFrFoRJ2uEWFUH36tFsDRNw7LMxmeKRCKJqkj8xuVN7B0cp6+z6Yyek+vy+Ty33XYbt912G5s3b+ZrX/sav/3bv80tt9zCr//6r/PhD3+Y888//2QPUxCAo2tIOZ/5SoMUTJ+yHbC+J07RDti4JNHIRF/WqlMwfRw/xPECNi5P8rZz07x0wObp3SbtGY2ndlnIsoShyiT1iMUtMfZNuPiBz1DRY0ypNfaMYpBLSExaEe5U7D9kblNRb1aj0HpAPRmTcbyIqh+hAH4ACUPC9SNkCdwgQpHA8SNimkRCkQkjKFVDSk7Im5eovDCY4kX5UsJsgvNiCivaY3TkNJozGh9/Z65xcaIlWWvkeayzvI+2NMvRZJm3JBW2D9qHracuCGeSBb8Sb7nlFgC+8IUvzHlMkiSCIHjjoxIEQRBOaYdrKlonSRKVSokwPDgvvDKmUvaVWuaGM7OmS0yJQJFZvGQZmqZRKtUaiCYSyWMSQJ9t9i3g02umnw0lXsR8LgiCIBxRtYLcdw5ULYgnUCQJz3Mbd6NlszojA/vRH/pbtN2bGU6n8OT5b3ZWnRQfvSDHBZ0WqpwgFk/g6z6pVJrR0eFaU75kimy2qbFNFHi0Z3Q8p0ox8A558X46P4iYMP3TvnH4+vXr+cQnPkE+n+eee+7ha1/7Gvfddx+XXnopX/nKVzjvvPNO9hCFs9yxbDa5c9TFtEOWtsfYsPjgnSx7R2tZ6YoMv9hhIssSYQAPbSuzpb+K64eMVgKaEjLbDthoqoQsSWwbqBIGEapcq1ZetiJCYKQUENdrNc8TGngBIEEUHAyizw6oF21I6OD6IEshtlfLSpcBWQbVgFxCpWyH5BIyKV3Bi6B5KvBsuSGaDJV9m+nyx7jy7ddRcUNkJBKGzJJmvREsB9i8r8rWfVUMXSZr1ILU163LLDiQ/kZLrRzNRZIxMyAbVw5bT10QziQLfiWFs+/LFwRBEIQpnucyUSgwXvLALeJ5B2/vDEJ4eUxnqu3OnG0zMVjWlsDzXDzPpViYbDTAbG4+/p/KptdMn17i5Uwl5nNBEAThcPwgYkLKEm9bQji2D6WtF0K1cWG7npGuuyZSeYIxVeG337Rh/p1F0Da8nnMu9NFVgwhoyjc39iEhERHNuWhe/973/UbgPpFg3qbm9TF/7afj7J90ycUVbr2qdVqD8tOD53l873vf42tf+xoPPfQQF110EV/60pf4wAc+wMTEBB//+Md53/vex7Zt2072UIWz3BtpNjk7wDs9IL931OGhbWU6MypDRZ+94y6TZkBrWkVTZNpzKi/srbJvwiUIoTWtMOAEqIpEBAyXfIKw1vhTkSTcqTroUVQ7H6kFwyXiMQk9gGK19plYplYL3dCg4hyslR5Gte+hFnSvl3JBqu3XD6Ezp6OUPEBi9SIDVZHxpspDBmFAt/0CoV3g8iuvprMtwYMvlvD8CK0qsb4nTl9rrPHcSBEgSXRmVIp2iCpL7Bx1F/xcv947Bermu0hyuN+bIJwNzuwIgSAIgnBClSoV/u6nk+ydCOhMB1yz5OBjBVum5MgYUoQdzQyit8YDVreDEs80TphLerH2yfQYqDdCPVwGW71m+uGalgqCIAjC2WB6ibPu5l/j1y538WJxood+0linXC5RLBQwHZ82t8r3O9qJDjVvm2lWJdJYXkirrtHa2o4fROwdHiOfUvADH12PUa1ajI4Ok8vlZ5R48X0fRa0F8Kf3ZZl9V9yE6bN/0mX7oIPlhEgS/M7b206bOf13fud3+Kd/+icAPvjBD/K5z32ONWvWNB5PJpPcc889LF68+CSNUBCOjc37qjyzy+L5pMLbV6dnlC158MUSW/qrbNck2tMqE6bPstYY7TmN69ZlAHh5oPYeoMgwbvposoyhS9heLUAeEGHoEkQSulYrr2K71Ho1RaCqtXIunhc1ss4TKiTjClU3JKZGyDJEIVSnlXxX5VpWuqrIxFSwXGhPq6hyhO2HtKY0Rko+ERK9zVqtlvv+V6naJqvedDWdbU389WNjbO23ySZleptjbN5X5cpzD96he35vnFRcaQSm60HruqPNMH+jAe5cUmVZ/fhT388OzL+RCymCcDp6XX/tpmny2GOP0d/fP6fJyW233XZMBiYIgiCcXjzPpX+4xJO7fWxfYqCocGmXSkavlQV5fF+MwYqCrILsRYRT2egSYPkS2Tg0p7VGsLu5uZVCYYJ4fJ7mZAt0uBPu6WaXeDnTiflcEARBmM/0EmcHShFDYYK05xFFYWNOrVYtKmYJypNU9QSPtGQOub/mAxfTvcigJa2gKCp+EPHXj4xwYMKlNQW/fF5AtWpRnirlBpBMpnBsG9OsoCoqMcOo9VyZ+lgwX6m3fFIlF1ewnJBETGbSDE+r8mzbtm3jL//yL3nPe96Drs//eaWrq4tHHnnkBI9MOFscKkD7ekuD1LebXd9bimByqiHp4ztMNFli+6DN2m6DibLPomYNKYL+CQ9FkkgnFNZ2G3z7mQJZQ6ZQCcjEFRw/JJdQmSgHFKwQ0w7xgwhdBUOt3YWyvD1OPqXw1E6T8UpQC56HoMoRYVQ7F1FlCCKwvdp+w7BWCma0EjTKuyiAoUmoqsSiJp3l7ToHJnyShsz+CZdgKqvddAN0WWZtZ8Qvr8/y3eBcCtXlaJrBzlGXVwZsxq2AuC5j6HIt83ya2YHp2UHqo80wPxYB7tnHEpnnwtluwa+o559/nuuuuw7LsjBNk3w+z9jYGIlEgra2NnHSLQiCcAY6VCa357mN2uUAlmkRRhFRBFEU4fku6BBEEpO2zOKsTxipYEDRkbF9iZgc0WSEXLXUR5UlHNuu3bbt2EhTx4A3VhN9eiNUoUbM54IgCMKh1Euc1cuitDWlCL0qkiQ35tIg8JFlGVdR2WmNsrV58Tx7kiCCjb0JfuuyJKHvMjkxzuCkzUhFRVEVCq5CxffJ6rUSMa7nkkqlGxfRZzcY1zSdZEqr1T2XoxlZ5qoicetVrUgSTJohi/KnV3m2hx9++IjrqKrKW97ylhMwGuFsdKgA7estDVLfbvug3QiUb1qeJJJg04pUoyb44ztMVFnigeeKTJR91vbGWd4eY9foBKPlgMtWJvnFbov/fKWCqkgsadXJp1RaUwoHCj7tOZm9Yw5VrxbwDiPw/IiYDqNlj/4xF9MNCcNamZYwAsLav7oMPhAEtVrnfhiwvCXGvkmn9rhay3j3/dpdOk1JibguMVoKQIpoy6q0Z1R2jjrYboQsS3TETfSB59i7+wKWd/UhRbUM86IVcG6XQUr3aM2oLGnWOL83Pud5O9xFixMZyJ59LJF5LpztFvzXf8cdd/Cud72LL3/5y+RyOX7+85+jaRof/OAHuf3224/HGAVBEIST7FCZ3JZlUimXAEilM/S2p9jYV2HYVGlP+uSMWmqFIkU0J0LGLZk3dbtsaLf50e44O8Y1ZBk2dHn0tCTJZLJYlonv+4esj/p6HE0j1LONmM8FQRCEQ1EVif+6Kc+XfzLKpBnyT7+ocPMVzUiS1MgGL5WKhEFAfGgXD7Q2z7MXqfHvtas02lpaGB8fxXVskqpHW0plwo7RnpbpbI5NNQAE3Y8RRVFj7q73SqmbXmqmK6dx8xXNMwLphi7zO29vO63Ls23btm3eu8Suv/76kzQi4WxxqADt6w3c1tdf0200AuWP7zDJxhU6clqjiWg2oUw11AwYKLhc0BdnfU+cH21RGSsHPN9f5YLeBNmEQkyVWN8XJ59Q+OvHxknqCl4QEUUScS1CkQBZwg1C3KpMwfKwnIggqtVJl6RaVrmq0MhKj2kQTL1VRBHsHHOIpgLonTkN2w0ZNwMSmoQsyfgBTJoeGUNm37jLm5enMN2QgUmXVnmCFd5mzMxKnhxuJZVw0VWJ83vjjJkBq7vihCF4QUTRDufN7D/cRYsTGcgWQXNBmGnBr4bNmzdz//33oygKiqLgOA5Lly7lc5/7HDfddBM33njj8RinIAiCcBIdKpM7kUg2mn9mMllUVeXDl6uUXIXQGmdakjrXLq9SdiUSasT3tifYMaETUyPakz5vXRqQy2VnnDAfqYa58MaI+VwQBEE4nJIdUKyGyDIMFLxG6QU4eHHddx0iTSfrOofcT7PXwpu6umZ8hojpMT761jwVVyZ0JrHNMql0pnExffq6sy/kTy81Ux/X7HItp2t5tl27dnHDDTewZcsWJEkiimrJCNJUrfkgCE7m8ISzwKGCpocLph4ua7q+XcH00WTYNlDl7eel8QIo2wFb+i22HKhlpwMcKPjoikQQ1ra9bEWKiYpPUpdpy6hcsjzZuOD27WeKRMCuUZu4LjNh+siSRD6lYrkRLiEyIaqm4HoBYVCriR7TIKZIqIpE2Q6pv6xShkTWUCjZISWr9t7XlJBZ0W4wMOmiqzJlOyRtKFhuQHtGo+yGaCH8dHsZPwBNhiXSy2y1V/La3nZaMxbL2g1Wd9ZKuegyPLPbpD2joqpy4+ee7Vhnm7/ecjyCIMy04FblmqY1JvH29nb6+/sByGazja8FQRCEM0stuN00J6CtaTrNza00N7ciyRoVP0YkK1QqZSqV8ox1FRlyRoTpyZiuREyNcAKJfCKipy2F6zpYlnnY4x1PfhAxUvLwg+jIK58BxHwuCIIgHE69pAtAV+5gWRTPc7Ftm1JhgvwvvkP8sX/gr845Z/6duPDZjsspVkJKlQqFwgQS4Ac+qiKRVF3KhQk8vxYUn2/+TySSRNQy3y3LPOS4DuV0mt9vv/12lixZwvDwMIlEgpdeeomf/vSnXHTRRTz66KMne3iCMK961vTOUfew62zurzJRCdgzXnu9b91v851ni+wacXh8h4kU1V7TaUMhFZfZO+qQjMm884IsF/QliCRY221QrgY88ZqJ64ccmPDQ1doFpyiKkKSIXEJFlWt1zs/pjrO+J8H6vgQJrRZAlyPoatJY3RUnptZqoQc+VL2Iqh+hyBKSDH4ImYRKXJNQZYkwjFjSotOS1Xjfm5o4r9tgaYtOa0bl3M4Yq/NlLlmZxmq/kl12B0EY0ZRQuagvztL2GMtadbYcsJmoBOyb8LigL0Ffa2ze5yuXVNmwOHHMAt5H8zsSBOHIFvyKvOCCC3jmmWdYuXIlb33rW/n0pz/N2NgY//iP/8jatWuPxxgFQRCEU1z91ur9ky79o1VyuktaUbh2eS14Pl0mFtKSDAGfVCziPed5hKGKXbUoFCZOSt3yI90afiYS87kgCIJwJO+6IANItKYPlkWxLBO7aqHbFeTxfu48fw3I8+dmLSuu5sUoxk/3lOnM2fzqhlrN87iSoFQqUixOEgQBmq6RyWTn3Yem6QSBj2PbFAoTdHUl+a+b8uwec1jSEjvsfH26ze9PPvkkP/nJT2htbUWWZWRZ5rLLLuPuu+/mtttu4/nnnz/ZQxSEOQ6XNT29seimFSl2jjqULJ/HXi6TS8i0ZGoXwjYtT5JNKBwoeGSTHqMlv1H2RYrg8dcqTFg+uiJTcQIMVaZ/0iUMA0qWTEKXiADHh+GSi+9H+KGEociMmz77Jhw0TaZihyRjtUx3LwyxvVrhqVCCrC6TjStYbggSaCrsn3SJqTJBFJGKKTSlVH7zsjxuCCnDA1liTZdOMPwC/WN7+aW1S3l6r8pwJSIXV2jPaaxZFG8EyzctT2K5IV1Z7YQ25xQNQQXh2FhwJvqf/Mmf0NnZCcAf/dEf0dzczC233MLIyAhf/epXj/kABUEQhFNf/dbqqhMwXPSxHJ9xS6bszj1RVeRaaZdfXmVxw7kW8ZhKMpFGUVVyufxJGD3z3hp+phPzuSAIgjCb57kUi5NUbYev/XSc+x8Z5/vPH6zNFkUhpVIRWVEI4xlGJBiKzZ9JCaAHacZMCUmWKbg6jhSno6ObRLJ2wVyRVSJCcrk8lmU2ap97nsv4+CjDw4OMj4+SSqVJJJPkcnmqtsNXHz7APz05wdcfn6BqOxSLkzPqptedbvN7EASkUikAWlpaGBgYAKCvr4/t27efzKEJwgwF0+fZPRYF05+RNT19ORzMgB4zA968IklzSuXp3RZjFZ+RcsDi5lgjIzs3FUzPJRSWtOis7TYoVgOe32vxi10mW/dX2T5k0z/uMVjw8H2wPQldk0jEFEDC8SJsDyIkWlK1oPyuEZdJM8R2Qwy1VtIlCCMGJz1UpfbzxFRoy6goUi2DvT2joKsSMVWiVPUJw4hUXCafVHh0e4Wndpr87LUK+B6j23/GgYFB1l7yDn683aG3WedXL8lzfl+cghnw0LZy4/noa43x3otyJAyZF/qrjeWzn9OF/g6O5FhntgvC2WrBr6CLLrqo8XVraysPPvjgMR2QIAiCcPrJJ1Xa0zI7K1Xy8RCFkIQWkVDnv3W6XtoFQFFkNF0jFUujaSenfmn91vB6ptqRbg0/E4j5XBAEQZitXn98tBzMCD4PjJVRgyK+X8sIDzyX5JP/Qqwwypix8pD7s32VeDICVaO7SaerOY3rWCQSSTzPo1icJJVMY9tVVKU292azeqNxueM4xGIxck15urp6ANixf4wDEy6KqjBQkBkYL5NWPfwwIlDS5Kc1Ez3d5vc1a9bw4osvsnTpUjZu3MjnPvc5dF3nq1/9KkuXLj3ZwxOEhkM1vpy9fFmrTtkOqFQDNu+rosoSq7oMJio+53QatGVq6xRMn837quwYdsgYChU35O9+NkFPk8auMZcwjDA0GT+IcPwQXY2I6wquD2EY0dmkAhF7x10cP6AtraJrEq4f4QchqgKKJJOOS6iyTBBGVN0AmVrQXJYkhko+LSkVP4A3LUuSTyj8YpeJF0DVjXB9n0krpD2jYHsRqiwR9yaJRy5t57yNh18L8IJaQPvXLs3z2MsBk6ZP0fK5/5ExrluXwQ1rteC37quCJJGKKyybet4q1YCKE855Thf6OxAE4fhZ8CutWq0SRRGJRK2L8t69e/nOd77D6tWrufrqq4/5AGe77777+LM/+zMGBwc577zzuPfee7n88suP+3EFQRCEQ1MViV/ZoLF/WMeuFPi/myMqjsSPdsW5dnn1sNsqqtY4ebYsk2z2xN9mqCoSN1/RzKTp0zTt5PtMdrLnc0EQBOHUUy+p1p1N0JUr0z9uE5c9KpMFNDmCCGRFhkIRbbSfR/OZQ5ZyIYJiMcs+FP7HNSqLWjVcx2o0CQWIGwkiInK5PJ7nzmhk7vs+um7jB8GMGundLWm681XGLJmunEZXcxqravKtZz2Gy2MzyracbvP7Jz/5SUyz1h/mj//4j3nnO9/J5ZdfTnNzM9/85jdP8uiEs9HshpTTy7MAtCQVnt1jNcqEVKoBSUOeUTZkYNJDk6VawLw9xtVr0mzeV2XrviplJ+CF/iqvjTgUzFqHz4Qu89K+KnvHHAYmPVKGTFyvvc9EQNkGGRlZkrA8CMKQgUmPFR0Gjg8VJ6BoRQwUHFSlVi/d8SCmhgShgh+EmE6ALNXeI3IJGUWpNSQdLXms7orTntYwYjJrFiV4do9JBOgKBFGErkick/c5MGHS27OI5mQfqYTKdeuUGU1Sz++NA/Do9gqmE/LgiyWWtMYoVwMWt+qkYwrLWvVGMDxpyKRiMmU7aGT4H64h6Bsp0SIajQrC67PgV8u73/1ubrzxRj72sY9RKBS4+OKL0XWdsbExvvCFL3DLLbccj3EC8M1vfpPf/d3f5b777mPTpk3cf//9XHvttWzbto3e3t7jdlxBEAThyDKpFHmzwlef03lpOCSmhkiSP29JlzrDSBCLxWhpaZsK6J74euh1qiLRmjk5mfAnw8mczwVBEIRTU62xp44fRFy7NsPXf1ZhvOjyva1w4zoJJIgbcaqKhjq2l/svveSQ+5LHu2iVs0zYCi46makyJcCM+T6RSE4FyQ8uqzcuLxYncWx7qlRLEs9zsaomv3xRHk3TaE1rqIpE2ZUZLo8BB8u21Of002l+v+aaaxpfL126lG3btjExMUFTU1OjGbggnCgF0+fBF0uocu1vb0NSnZn9vDjBs3usxvcAFSekI6c1ArM7R12KVsBrww6/eVmetb215A0pAiSJ4YLPHt+bKrUiYcRk1nYb7B5xkCWJlR06VTdkoiLjBREVOyQXl6g4IYosEVMiKn6EhMSV5ySJ6xLP764ybvpEEdhOhKxAGEHVhwwQ02SqXoQmQ1+LztreBH15jV2jLqYTIwgjjJhMxlDY7Tg0pzSiMCJlKHhBRF/KxBj5Befml+GFXeyZ8FkbV+lp1nFDyCaURma9BNx4YZYtB2zWdhs8tcti0grozGmk4rULEdOD4fWLCwBXnpue8XzXM9brge9cUn3dGehHymIXQXZBmN+Ca6I/99xzjczvb3/723R0dLB3716+/vWv88UvfvGYD3C6L3zhC3z4wx/mIx/5CKtWreLee++lp6eHL3/5y8f1uIIgCMLheZ6LZZmEWpayIxFTQxxfIqlHpPX5S7okk2k6Orvo6VlMJpMjm22akWkmHF8ncz4XBEEQTl31ZpxfeniUF/aHqJpG0TMI1QxRBOMT47hbHmV/PM7g1N1MsymDadZObEJXVbqadNYubkXT9KkgfdOcrw8lkUgSM4xG0L1UqfAPPyvylZ+M8v3nS4316mVbgNOibMt8fN9HVVW2bt06Y3k+nxcBdOGk2DnqoskSfhjNCPR25A42xWxJKhSrAS3JWlb19Ezq+vqlakAqJvPULqtRw/v83jhruw3acypLW3QuWppgTW+cjKHwwHNF9k94JHQZXZHobYmxYXEcSQKiCNuPCKIILwxJxhRSMYmyHfDYKybDxQBNldBVCUWBbFKmOSET1yTycYnWtMayNp3unEY2obCiI0ZfXqdoh1y+Mklvi86mFSnevjrNsvYYl65M0p5RSU4F0LPRKKnxJ0l0rCLeeR5ZQ2HvqMPTu0y+/UyBXcMOO0dddo66bN1XZcsBGzekUcqlI6tiaBIDRY+hgsfOUXdGvfL6xQVp6vRp+vNdD3zvHJ3b/2GhZv8e5/vdv55jLbSuuyCcbhb86cKyLNLpNAA/+tGPuPHGG5FlmUsuuYS9e/ce8wHWua7Ls88+yyc+8YkZy6+++mqeeOKJ43ZcQRAE4chKlQr7hstMmA79EyHDRYm2lMeFHfacdYMQXDnBuhWriBuHbkY2nR9ETJg+GUOhZAcz6p3OZrshrw7Z5FMKHVkdVZHwg4jRsocfRAQBlJ2AFe0GqiIxUvKQJBrZbLOPebhjnc5O1nwuCIIgnNrqzThjeGTsYXy3mRWLsmTjAVEUYpWLtOx7hUlFITpEKZeu6rn8zysDfMPg4lW9GPqCc7caF+gPZqqDHRqMWSVUVWP/hMPeoXF62zIAvGe9jBNlacvGT8t5W1VV+vr6CILgZA9FEICZGdKHykYeMwOycYUxM6CvNUYqrjSCrxumsqWvW5fhwRdLpOIyO4cdtg/aXLcuQyquMFLy8cKQN69IUrQCfrSlxJ5RlyAIcUN4cX+VtKFw9doMVTdi6/4qFSdEUyRaUhrtOYXtAw6lasDTu01WdsRIxGTScZ39kx5E0JbRaAsjJiwfTYHBgkfBCkgbCgcmfLYeKJCOyQxMeLRlVP7tuQIvDcR4x5oM6ZjCpSuS7B11eXXIIe+PkF96MUuWLm1kjuuazHDZB6lWiqYlqbBnzJ2/ZEtc4dIVKVqStedsdhD7/N54rU761PLp2ebLZv1e3ogjZbG/3lIxok67cKZb8F/18uXL+e53v8sNN9zAD3/4Q+644w4ARkZGyGQyx3yAdWNjYwRBQHt7+4zl7e3tDA0NzbuN4zg4jtP4vlQqzbvesTTfh70jrT8xMY5dtTDiCfL5ZgCGhwaZmBhHlmUSiUQjM6FatXAcB9/3iaJwxr56+5bQ2bnouPxcgiCcGo72PeZo1quvEwQBo6MjVC0Tx7GRJJlYrBbclmSZqmUedkxBCP/+Wpxn9unsNxVAAhT2lhX+189iXLu8ylr14Lr/sSPOuCXx8KtbuHZ5FeUI59X1bcZMmbGqQks8oCUZzrut68MXn8rSX5RRZXjLYpcPXhznwR0x/nO7w6QtYXu1BkJLmuGCJQl+vsNBVhTesjLBr75JJx6LUXUc/vlpmwOTLj3NcT761rbT8oT8cE7WfC4IgiCc2vJJle6MRO/P/56cM4CTaWPRsptQ5RQgoVkl4kOv8fOervl34EssjfVhpDKcu6QVRfIpFo/u/Gj655d6k1Og0S+lPRdncVuKgYJH3vBJyLX1AQLXIWlIqMr82fGng09+8pPcddddfOMb3yCfz5/s4QhnufkCrfUgaaUakIorjdros4Ouy1p19o46PL7DJBOXWbMoTiomc6BQq49eLxWyfdDG9SO+/UyBqhuyZ9ShVA1pSilEbshwycfxYPuAw/s3NmF5EeNlH8sNyCYU2tIadkvEC/1V3ACGSz4XL0uSjsmEURXT9pGIKNoBuiIzUPAo27UgfDYBwyUP0w4IQpX/0hXjJy+VeOmAw/4JjzCENYvidGQ1MvYexst5HOV8WrrSbFhce59Z3xNHiqBiByTjCut74rUmoU5IZ05vBNCn15AfM2tj72udm0x0uOD2GynfslCv91hvpE67IJwOFvyq+PSnP82v/dqvcccdd3DVVVdx6aWXArUstgsuuOCYD3C22beyRVF0yNvb7r77bj7zmc/MWf7AAw80Gqkda2EYEIURkiwhy8pRrV8LiNd+DlWt/Upc15sKkktM//GiaP6yCADbX92NYRhv9EcQBOEUdrTvMUezXn2dIAwIw/Cw7y+HE0QSbY7E25okwtzcx3U3wglcXtm+kyCSaHUkWiWgCttfDZGPEJuub9Mig2tI6HKEdIhtvUDiYkPmoqnPpLFqxJM/j9BcmcsSEMYhikCSQJZA3Q+X6LWdhHslHhyVUKSIIJSIqiFdQFCW+Jd/UVGONNAF+JVf+ZVjtq/X62TP54IgCMKpSVUkbloXMbplgs/2ZdmleizZ/QifXnVd7bzEd1ADl/FDnIM17byIFT0ay/s6SCSSjbrmwBGbh08PnE9vMjp9bPVGoSk9xHWsOfXVT2df/OIX2bFjB11dXfT19ZFMzvx5nnvuuZM0MuFMtNC61wXTbzQOjWBGbXSAvaMOD20rk4nJVKoBL+6vsmPIoadZJ5dU6MyobFqeZMyslX95ob9K1pAZLPkUzIB94y62G5GJy+QTCrstHwUw3QBNhS0HbH7zsjyPvVLh1WEHCTBUGQkJhYiqH6GrKlv3VZm0AlRZqtU316A9rTFuehi6jCpLKIrEhX1xntpdxdBlmhMKPU0ambhCMiZjxCQuX5lEVyUmdv6CgYFBVi6+glAzWN8TbzwnuaTK+b3xGc/j9Izxw9WQPxMztU9koF8QToYF/3W/973v5bLLLmNwcJDzzz+/sfyqq67ihhtuOKaDm66lpQVFUeZknY+MjMzJTq+76667uPPOOxvfl0olenp6uPHGG49blt2JyESvVqsEwdwaU92Lelm0qO+Y/0yCIJw6Tp9M9LqIa5dXaVG3cu45y6Zloss0J0IuWmgmun0wE32+befLRL96KhP9Z/NlovdNy0RfluC6Q2Siv+8MzEQ/WfO5IAiCcOrT8q2UFvWyJxxDUnV2+Sb7zQnCMEIaG8CTJB7r6py7oQ+Xd6S5ca1PpTRJJpWaNxh+KNPXrTc59TyXYnGy8ZhlmeQSSTQtNqMs3JEC9KeDX/7lXz7ZQxDOIvW63T/eWuJXNzbNmxk9e/1649B6gHhZq07B9Hmhv8qjr5QZLvqEwPL2GJPlWsxiqOgyXIS9ozKP7zBpSiokppp7GprEkhadSdNn0vLJpxTaMxrxmMxgwWfcD8jqMi8dsImpMlv3WbgBWE5ABKSMWt30ABlDCXH9CCeCyUqAG4T0teh053XyCYV9EzKmG+B4sGlFkn3jLglNJowgm1T59jNFVrYbBJHEyvYYu0csUhNP43sO11z3LgYq6rwXHA7X/HN2CZbpteSf3WMd9gKGaO4pCKee1/VK7OjooKOjY8ayiy+++JgM6FB0XWfDhg089NBDM07uH3roId797nfPu00sFmsEgk6U+oe9hazf3j73A+iinj4W9cwfEB8Y2IdZKeN5Ppqm4nk+mUyWdFrcfi8IZ7qjfY85mvWmr5PPt7yhcV28MWKo6PKXD+7mqV02xarEpb1VLuoOWZ4P2LWztp4iw7tXB8SyrSzv6TjqmugXb4yYNH3ShkLZDmg6TJ3yN10csmPEIZeQGzXRV66M+PXLPYKwVuu84gQsb6vVRB8t+0A0oyZ6IpHkv11VO+bhjnW6OxnzuSAIgnDqkxSVpe/+75zz9Ld4xZygV0vDeBECj/i+LeyPxynNcwesUsnyy0sTNDU1kcvVypEs5PxovnWnZ6cDR53Vfrrx/VrA8eabb6anp+ckj0Y4Gyxr1fnx1hIVO+DxHeYRg+iza6TXM46f3WOx5YCNKku05zRWdcQo2yGtKZU9Yw4TlQAksL2QkZLHpOnTkdPZuDTB4hadwZLPSNEnCCIiRaIpqRDTZfpadKpeSMEMOKdTZfeoy44Rm3I1QFNlMobM83uqjJketh9gqBKJmAwRxFQJJJm4pjBZCfD8iEXNGvvHQZMjRko+yztjIMGiJo3XRtxaJnpC4Tcvy/PgiyWqVZvRgsKy1VcQjydYFocX+qtEUq2MSz2wvaxVp2wHDBc8nt9rkTWmStzAnCB4/Xk7mox0UV9cEE49C34lvvWtbz1sd/Cf/OQnb2hAh3PnnXfyoQ99iIsuuohLL72Ur371q/T39/Oxj33suB3zVFLPGk2l0th2FUXRQIpo7+giiqLT/vZFQRBOX6oi4Xkez+/zGa3qRMDPDyi4ocuuQsjyaevKUkRLUsZ1rKMOoquKRGtGAzhiczJDr9VdnL19Z27+k+3OnHbEY56JTuZ8LgiCIJy6pt/Nds+bPsAvdu8iVSrQ9MS32Kn2kNu/mQOpFN7spqJRxP/sOJdVS7vo6Vl8zMYzXyb7mXjeo6oqn//857nppptO9lCE08gbyVbOJVV+dWMTj+8w2bT8yK+pemb1zlGXZVPfQy2IXKkGM4LLW/otHnyxxOIWnWQsIJdU2Lgkwdd/NoHphJSqPi/uq5IwZIYKHhGwutvAdCOK1YDQCli9yGCw6JGNKyiyhCJHKBJoigxhSMGK2Ke4DBc9CCWShjpVwgWQwZAlRiseciShKNCeSZGKyey3PEwnYN8YXLqi9nObbkjFDSkN2dhmgbg5wKtOL2F4HqN7PLpaXKBWUoYoIm0ojcB2LqmSNhS27rcpmj7jik93k8r9j4zRka2dS8wOgh9N7XBRX1wQTj0LDqKvX79+xvee57F582a2bt163Cf897///YyPj/PZz36WwcFB1qxZw4MPPkhf35ldwqT+Qdb3fVzHxnFdZFkhDAOIar+D5ubWkz1MQRDOEvOViilbNn/5H/sYKknUK6vbgcyr4xqq7LF0Wkxb03T0mHFGngCfTk7mfC4IgiCcuizLxLKqDBV9frw9YO+wQrtr8p7SGOS6sWWZ37vwAph1IfbisQoXnhM75vP77Oz0My0DfbqrrrqKRx99lN/4jd842UMRThMLyVaeL+De1xo7Ygb6kY6XS6q8ZVV6xnpbDtiYTsieMZcrz01zfm8tuP7OCwLcIGLr/irg8dzuKrIMBTPA9Wv1zV0/wg1CmhIKm1akeOmAjeeHjFUC4rrMBb0x+ic8KnZILq6gyxIHCh5XrEiypjfOc7urNCUUXh12aEkp1GLqtferprSKG4SYXgRSwEv7bdZ0Gxi6wt4xG8Udp3X0ebTcUq5YmaLkhHRm1EYgu36xYHZgu34h4bURB1WW+PYzRbSpO1nffWF2zvN4NLXDRX1xQTj1LPgV+Rd/8RfzLv/DP/xDKpXKGx7Qkdx6663ceuutx/04p5L6LYwRULWrOLaNH9QaAiaTp2/3eUEQTk/Tb6vOZnX8IOJ//8cAz+3ziZiZlTZhy8TUaEYDUD8IUFX1qPpGCMfPyZ7PBUEQhFOTHkvwD09U2FcI2D1i05122eNmMBPNSKHPtmyWon5wDleDgD96cQvpcDXW2jzxuDg/eb2uvfZa7rrrLrZu3cqGDRvmNBa9/vrrT9LIzhynap3p1zuuhWQrzxcAX+hxD3e8vaNOI6t90/JazfH2rEYqrpBLqhRMn5GSh+1HnNtpIMkSH7gkxwPPFhgsuIxXJLIJFdcPa9nmEmiqRFyTKFghIyWf5pTC7nGP7pxGok3m0mUJvv7EJO0Zla0DDr+0PstlK1I8/mqFeExGkaDqhUhI9LXEaMuoRFICXaYR6B4q+ixu0Ug4A7gHnmXRygvpXHzOvM/J7IsF05+/t6yqXSx48MUSK9tjFKsBv7qx6ZT6OxME4Y05Zq/mD37wg1x88cV8/vOfP1a7FKYkEkl836dcLhEGITHDILQsQikiiiCTmXtlUxAE4XiZflu157nsHS4xUrRxA4mQmVlpUgSrWh0oHlyWTqVFFvopTMzngiAIZ7eyKzNhq8S0gDCKqLgyXUkZs+cSpB27WV0s0uI4DBkGMvC2kVEmYteyo/ti3qVLVKuWmOdfp1tuuQWAL3zhC3MekySJIAhO9JDOOKdqnenXO66FZCvPFwBf6HHnO149kPzkqxVeG3aw3JCPvKWF//bWFnaOurQkFR57ucxrIw6vDNjIEmSTCkld5gcvlIhrEk1JFV2T6MvrVJyA4ZJPS0rFjyCuy5TsgAv6DAaKAVEI/RMeH3pznjEzoCun8sI+G8uN+NP/GOF/3dhJR5POBb0Ru0YdDE2mYAUU7YC3r0mTS6o8+kqZrpxG2Q7oyeuUqiGZhEb+gsu5cPVSXuivsnlfleaEwpYDNmu7Ddyw1hB0zAwaAfbZz18uqXLduswpeaFGEIQ37pi9op988kmMeZrLCG+cpumoqkoYBIRhSFO+mWQyTbEwSTqTRdP0ecsrCIIgHA/Tb6suFicxqGLIDjC3fnhMCTknH7B3Koje1d1DR0eXeJ86hYn5XBAE4eyWT6p05TR2DdskdTA0GVlWUKwCAIkw5F8ef4IXcllaHJcWJcPua9bRlArJpDInd/CnuTAMT/YQzninap3pEzGu6QHweuBbl6FYDVjTffCz30Kz0+uB5EiCVFyhM6PO2MfOUZctB2xsN2Bxs44RkymYAT9+qUwURSxu1VndbZA2FMpOSKsuE0YSL+23ySZlFEXigt4Ew2Wfy1oNntptIiPx8kCVnrzOys44S1sNvv98gaod8G+bi3RkVJ7vr7KyPUZCl3l5qFZmZeeoy4akihTV+iit6ozhj25lRU8vQXxxY7xP77aYtAJiioQsw75xlzWL4mwftMnGa41DNyTVGb+36T/zhsVv7I6cU/WOCUE42y341XjjjTfO+D6KIgYHB3nmmWf41Kc+dcwGJsykaTqyoqArSuMWySDwSadrH1Rnl1cQBEE4ETRNx3Wr/NIyi9GiwVNDMxt6Lsp66NNmGkVRpi74IQLpJ5mYzwVBEIT5qIrEzVc0s+2Azjf+c5Qg8BmrRFjZgxN6Igy5dGKSENjz/o+TM2RamptpbmkVWejCKe1UrTN9osdVD3wXqwGaLPH4DhOAMTOgbAeYdkjZDkgbyhEDufVA8ppugzEzoCWp8O1nChTMgPLSBOt74o1a4kuadcbMgB3DNh0Zlf2TLkuaddb1JvjxtjKuFzJeCZAkave3yhoXL0mwbcBmqODTP+aQjslUnJCXBmwKVkDKULhuXQY/iHjpgM1L+6pskyQGCi4HJjwWNWnEYzJBENGSVHh2j8XiFp2EDmOv/ZxqYZyll6wjk6nFWZYBT2gSrhewvD1OEELWUCCKyMRlUjG58TNP/709u8diqOBRqQak4kd+3urmC5ifqndMCMLZbsGvxmx2ZukQWZY555xz+OxnP8vVV199zAYmzOR5Lr7n4Tg244qC57noWgzPc4HkvF3rBUEQjifPcxkfH8U0bX60K8a2sblBcceTKbu1Ei+SJGGZJqpSm3rEBb+TS8zngiAIwqGoisTq7jTN8SEOTDjk4z5KVx/R2MSM9fa8+3+AkQJA12Nks00nY7hnFNM0eeyxx+jv78d13RmP3XbbbSdpVMKZoh6wbUnWsqnXdBs8vsNEnQqkZ+MKqZhMR06jUg0WFMjNJhT6WmM8+kqZ7QM2ECFFCXJJlfN742zeV+UHm4tU/YjVnQarug3acyqSLBFFEfmkwvZBH1mB8bJHPqmSNWSe2mlyoOABEi1plZ5mnQnTR5UlJkwfSZLYcsDmXRdkeWXYoVINkeWIXELFC0L2T3q0ZRQuOyfFmBmwc9jhFd9DH3oS1w+46u2/RCZzsNZ5LqmyblEcSZJY2hIjFVcaFxxUWaJU9Ti/Nz7n568H1sv2zOftSFnl8wXMT9U7JgThbLfgIPrf/d3fHY9xCEeQSCQJwpAgCCgVC6iqius6ZLJZxsdHgVptdJHZKQjCiWJZJp7nMTRpsb+oU/HlOesYWkRajxgCVFUnkUyiqKq44HcKONnz+X333cef/dmfMTg4yHnnnce9997L5ZdfflLHJAiCIBykKhI3rJPYurtKa9xFUXW8VDP733ozsYkByis3QqJ2QVZRVGy7iue54nzkDXj++ee57rrrsCwL0zTJ5/OMjY2RSCRoa2sTQXThdZldWqURsJ0qOZJNKI3A+vR639O3O+p91kulaBIFqxYYh1qg+PHtFZ7dUyVtyJzbGeNXNzbx+A6Ttd0Gj26vULR8Ll2R4NldFiARBPDygE3/hIcmQ1tOZ8PiOF4IrWkVTZVY3Kzxn6/W9jFmBnRkVHY7Aed2GazuNHh50GbfuEdbVkOKajXNt4cRmqJSUDpwUj28MgodzTN/vvN7441s8qIVsH3QbhyrI6s1ysJMV89Kn/28HSmrfL6A+al6x4QgnO3Eq/IUNr3OOUAsFiPwfWIxnSAIUWSFiYlxXMdB1TRUVRWZnYIgnDCJRBLHtokp42wZ1mFWU1GIuPn8CspUbD2MAiqVMrGYIU6wz3Lf/OY3+d3f/V3uu+8+Nm3axP3338+1117Ltm3b6O3tPdnDEwRBEAA/iHhoV5KXdldojitcu7wKkoTbuwa3d01jvWy2CVVVMYw4lmWK85E34I477uBd73oXX/7yl8nlcvz85z9H0zQ++MEPcvvtt5/s4QmnqelB3MNlONczyesO10S0XvJl+r6ml0pJGQpMZYhn4gqPvFTi+X4LL4go2RHbB2zSMYVNy5P8+KUyrw05SMCBcQ8vjFAVCIkoOxGSBH4k0ZlVGSr6tGc1hooe163LsOWATUdWY8sBm03Lk1zYlyAII1rTGsm4MpX5LZFLKlSckP6hMZJjm+lcdTljxrlsG7DZOeQ0yszULyBM/9l3jrpk4wrbBh0UGbwwOmyG+Ozn7UhZ5SJgLginjwW/UoMg4C/+4i/41re+Ne8tZhMTE4fYUjga0wPnlmVimSalUq0jn6oo6DEdWZaRJBk/8PG9iDAKUWRZZHYKgnDCOa7DjkkVJ5wdQIffOL9A67T+YmEQYJkVmptbT+AIhUM5mfP5F77wBT784Q/zkY98BIB7772XH/7wh3z5y1/m7rvvPm7HFQRBEI7e7lGHp/c4qF7EgK9QsOfO9QCdXd2Nc5dDnY/4QcSE6ZMxFEp20Pg3n1RRFWnOuiMlDy8ImayEtGQUOrI6qiLhBxFDRZd94w6DBZ8rzk0TRsy7n9PR5s2buf/++1EUBUVRcByHpUuX8rnPfY6bbrppTj8T4czyRppJHm7bIwVx60H2eg30lqTCSwdsBooeb1+dnhFYr69bL/lSP96GpMpjL5d5erdFLqk0Atyblid5fIfJlgM2MhKGJhGEETtGHGK6woGCx2DJo2j5GJpENiGTjMkkNJ2NK5LIwNO7LRblNCRZ4vKVSf7z1VrZmQeeK6JIEUEksaqzlonentNY3BLj1UGbrfstErpMb0uMt69Os6t/Pzue/ykXXHAB5y9LU7QCClMlWuqlbGButnj9eZMA24tY0Rab9/dzqN+BCJILwpljwa/kz3zmM/zN3/wNd955J5/61Kf4n//zf7Jnzx6++93v8ulPf/p4jPGsMr1BaCKRxDQr+J5LhARE2LaNpmpEUUgYRUiSTEzXSaUzIrNTEIQTanJinMmJcRKSA8ztQN88q1SgJEnoukEmc7AW9/QLh+I97MQ6WfO567o8++yzfOITn5ix/Oqrr+aJJ56YdxvHcXAcp/F9qVQ6buM71izLpFCYIJfLH/Fit2WZDA8dwPN8Ojq7yGRyJ2aQgiCccMdz/ptv3yMjQwwc2EcQ+Pi+f8R9VGz4+MNN2L6MhMGijM/j+wxWzrPuKy9vPey+ghD+Y0ecMVNmrKrQHA8Yryq0xANakiHXLq8S0zXCMCRC4cHXDJ7erzBUkQgjiaQW8bZlAb+xKc2/bzf4l2eKTJgRERBTR7h6TZKuDHz0rW3Ejdhhx3Kq0zQNSapdDGhvb6e/v59Vq1aRzWbp7+8/yaMTjrc30kzycNvO1/xy+nr1IHG9Bvr2QZvn9liMlmrvFR95y8HXVUtSYfugzZpuY0ZwHSCSYNIKgAg3hF+7NM/eUYfJis+KNp2YLrN/3GP/hIvrRbSnFRK6jKFIKIpEW0ajtyVGa0bD8SLaUirJuEJLSiVlKJzfG2fnqMvS1hi7Rh1UCQpWSF+zTltWbfwc2wdtJsyAnSMOHTmVt52XAWuI3S88xmWXXcbKlSsbz8t16zJzStnMlkuqtFgBz++1WNKszVsP/Ui/A0EQzgwLfmX/n//zf/jrv/5rfumXfonPfOYzfOADH2DZsmWsW7eOn//856JO2xvgeS6+76OoKpqmY1kmuVyeatUi8H3GxkcIgwAnDFEVlTD0MYwERjw+IyglCIJwPNVPzq2qheu6TNgyEDG7nIs3LTtdQkKPxWhuaUXT9MY+fN8nmDqZF7d/n1gnaz4fGxsjCALa29tnLG9vb2doaGjebe6++24+85nPzFn+wAMPkEjMvYBzKvF9jzCMkGUJVdUOuV4UhTiOSxSFSEgoqiIuLAnCGSwMA6IwQpIlZFk57vt2HJswDI96H1VP4r3ttXpsERBTIlQbHMnlle07FzSeIJJodSRaZHANCU2K8AwJXY6QqrD91RBZOrhukyPxlpREOHXdUZIgVol45DEIXYXrmkKi3NRjgFGQkEvwrw8o6OqxfS6Pxq/8yq8cs31dcMEFPPPMM6xcuZK3vvWtfPrTn2ZsbIx//Md/ZO3atcfsOMKp6Y00kzzabQ9Xf7ueSd2b19i2v0o2odCVrX12qT82NOkyYQbsGXPnBNHX98Sx7JCBotdoXPr4DpP+CQ/TCTi3wyAblxnRZLrzGrtGXTqyGpNmQCqm0J7ReO9FOV7or7LlgM1gyccec0GSaE+H3P+IyQW9BkU7oCOrUrJDMgmFtkyt3vnmfVWkCDYtT7Jtf5VxU6G3SeP8RQYxuZV3vOMddHV1HbbW+6E8vsNkqOCR0OVD3iUgmoEKwplvwUH0oaGhxgSeSqUoFmulRt75znfyqU996tiO7ixjWWat5rlh4HluIyO9ubmVYnGSZCJN1TaJG0mqtoVhpMjmmkRDUUEQTqh6qSnf94iiEF2WmB1Az+gB5zQHBxdIEvl8C/l8c2Mfjm2jqCoxwxDlqE6Ckz2f1zPt6qIomrOs7q677uLOO+9sfF8qlejp6eHGG28kk8nMu82p4mgz0QcG9rGvf0/j+1Wr14pMdEE4g53qmehFC/7Hw3m8UEIi4uJOh+5cyPJoK+ees2xB45mRiW5PZaLbBzPRLzrKTPTrpzLRH5yRiS41MtHfcwZkov/Jn/wJ5XIZgD/6oz/ipptu4pZbbmH58uUnvSG4cPy9kbIfuaTKMmrZ0Mumvq872qBx/fjP7rFY3mEwXPQ4r9sA4IX+Kk/vtpis+MRjMk/tNtm6v0pfq85lK1KN4xWqtXJNY2ZAXyus7TZ48IUiVTckCKCjSeMdazPsGXfJxBWGih6yAq4fsritVgalnulddgKqTsRw0ePH2yq4XshwyWdpq84rYy4xVeLNy5N0NOkMFlyefM2kKamSiit89MoWfvZamby5lZ2vDHHRRRc1Ei+mZ4wDjez7Q5VzgVpgfvq/h3v+BEE4cy34Fb5o0SIGBwfp7e1l+fLl/OhHP+LCCy/k6aefJhY7vT+0nGz1k+v6v75f+5DreS6JRBLf94nH41hVi5geI5PNidrCgiCccJqmY5rDVCplbDfk316bG8T89bUl4lOf02VFRZIkFOVgZu309ztxEfDkOFnzeUtLC4qizMk6HxkZmZOdXheLxU7bzxiJRPKoLhJZltn4OpttIh4/tTPsBUF4YzRNP253YM2377a2DtraOo56H6/uG6P9FwMMlRUi4ICp8OvrTPbvPbhOU1MzS5YuP6p5/OKNEZOmT9pQGnWXy3ZA0zy1zC/cEDFa9nH9gEkzpCV9sCb6Lb0RN1yc58CEy1DRZ9PKFFHEvPs5HV100UWNr1tbW3nwwQdP4miE082hyonMFzSevc70QPuyVp3tgzZLW2ONYHgkwaRZC6BrisRI0Wek5DNQ8OjM6WxIquwcddFkiaIdUKkGFEyfcSugOakyGnpERPQ163hhRFKX8cOItYvi7BpxyMZV0rFaEDs3FQivOCFeGIIs0ZaWMV2Z916UxQ1r6TtV/2ACRtWJcP2QuCaxrFUnqUc0l56hXC5z+aUXzfgZ61ny0y8qrOk25pRzmf6c9LXG5mTeC4Jw9llwEP2GG27g4YcfZuPGjdx+++184AMf4G//9m/p7+/njjvuOB5jPGvM/sCrqiqObTcySRyn9rXveSRTaeLxBMXipAhCCYJwQnieS6lUpFwuYdtVPM9lX0lhuDL31umxqgLUbhsPA58oCvHcWmZc/cJgNtt0gn8CYbqTNZ/rus6GDRt46KGHuOGGGxrLH3roId797ncft+OeyizLZHxstPF9NpvDskxR4kgQhJPCDyK27xnED2rlVSJgsKzyne0J3mQcXK9ULjI5MU5be+cR96kqEq2ZWlkIQ5dn/Dvfup05DdDoa5n72KJ8jEX5MzOYVa1WiaKokTG7d+9evvOd77B69Wquvvrqkzw64VR3qHIi8y2fvc6MAPziRKNW+LJWnb2jDjuHHNb1xunIaGgK/OCFEis7dM7vS6DL8H+fnGBtt8HS9hiVasBIyefBF0vk4gqtGZUwgvO6DXRVotVQsN0QQ5epOCFGTGZtT4zFLTrP7rFY1qrTklTYvMcibcgsadZYs8hgfU8tQ/2F/loGfDqmEEm1iwJFO6CvJcZ5PXGyCYV///d/J4oi3nLVdWwfk1gm1QLiu4YdtocR163LNLLn6xcT+mblJx5NjfM30gxWEITTz4Jf5ffcc0/j6/e+97309PTw+OOPs3z5cq6//vpjOrizXS3bs0JK0ymViti2TTBVW9XzXMbHR1GVWvBKnGgLgnA81APndZVyCbtaxfd9JEkmrYcoMlOtjw/qTQczdxRBoVggCAPiRu0DsHjfOrlO5nx+55138qEPfYiLLrqISy+9lK9+9av09/fzsY997Lge91Q1cGBf4+tYLEYieXTZ64IgCMeaH0T8zaNjPPUKTFohQayM5CYII4WKKxNOj11H4LjOIfclLNy73/1ubrzxRj72sY9RKBS4+OKL0XWdsbExvvCFL3DLLbec7CEKp7BDlROpL9876vD4DpNNy5NzAr6zA+3T66T/8y8mqTghq7oMzu+Nc/8jYwQhBFP9j77zbBHTDdgx5HDFqhRLWnReG3EYLnjomkQY1gLomYTCdesyFK2Aoh2ytttgywGbrKGQitdKwEzPmK/6EdWyz6UdKTYsTlAwa4H5gUkPQ5O4dEWKZa06O0fdRib54ryMJElcfPHF5PN5Xth/MBBez7BXZYmdo+4RS68cTY1z0UxUEM4ub/hVvnHjRjZu3HgsxiJMqdcxtG0bx64yPj6Koih4rkMUSciKhOu6tcZjiiJOtAVBOG4sy6RSLgEQjyeQFYVMNosRj1OYnKA54XBZr80v9utMVBVkIhY3BaxoOdi8TNM0kECWZXQtRgTifesUdCLn8/e///2Mj4/z2c9+lsHBQdasWcODDz5IX1/fCTn+qaZSqTS+bmvvFHdpCIJw0gxMWPxoyyS7Jl0mzvkZYaxMwkqi77iCjE6jAShAKpWmubn1uNZ3P9s899xz/MVf/AUA3/72t+no6OD555/nX//1X/n0pz8tgujCG/L4DpNdI7ULX7NLkxwqAL9zqvnnUNGr1Td/sUQ2rjBc8vGCkB9srn1/YNJHlTz2TrjceGGWFW0xXhmwmTR9lrTFGgH03FTZl2xcwQ2ZkfEOUKkGlO2AJc06a7sNIulgEHvzviq7RmwsN+LNK5KN7O/6uOPRCP/v+w/xzne+k7a2NgCWTWWXtyQVXuivkonLpGPKUTX/PFyN88OVhhEE4cy14CD63XffTXt7OzfffPOM5V/72tcYHR3l4x//+DEb3Nmq3nDPdR0836fesE+SZRRJJmYYxPQYiqqKpqKCIBxX9X4Mvu/juA6qoqIoCgVrgjAMgQhZAjeUMbSItmTAbReX0Kdml1jMIB6Po8gK3d092HaVXC6PpunipPskO9nz+a233sqtt956XI9xOiiVCjhOdeo7iVwuf1LHIwjC2WX2XFwoTFKyKoyt/nfQHSDCill0LH2Ac5uuhqlrfoqiks8343ku1apFMNWsVNxl9sZYlkU6nQbgRz/6ETfeeCOyLHPJJZewd+/eI2wtCIdWMH1ycYWOnMbabqNRNuVQJUimB4nX9MR594VZdo66qLKEqkqc3xPniR0VsnGZREzm3M4YL+yrogchu8ZclrXGqJ8nWE7YyH4vmD6VakDSkBvHrzdEbUkqHCh4qLJE2lB4y6o0BdNn874qUgSmHVD1IJ9U6czNHPvevXv5yU9+wnnrLmTHpEaL6zBmBo0g954xly0HbIgiLp3WCPX1ml3+5kwkStUIwlzzF6I7jPvvv59zzz13zvLzzjuPr3zlK8dkUGc7TdPxA59sNkcu24SqaWSnmog25Zvp6OiiKd+Mqoo3MkEQji9N02lubsUwDFRFwXEdxsZG8VwP3w+YrMrsnlRwfIkokrBcmQlHntpWQ1UVHMchIiKKIlRFxfNc4OAFw+kNFYUTR8znp4YD+/sbX8uy1Hh9CIIgnAjT52LPc1H9MsXk7kYAva4/F6LLBxtC53J5lKn+TQAxwxB3mR0Dy5cv57vf/S779u3jhz/8YaMO+sjICJnM3Ebuwtlr76jD/31ygr2jDgXT59k9FgXTn3fdehkU0wm5oC+BG8LOYYcHXyzNu019/V3DtUB0PUhctgPaMyor2mK8OuygSBLNKY0bL8zSkdN45/kZ3npemo6Myk9eLjNaCggjSOgyj+8wG0HZihOSNpRGYLYekH58h4kmS5SmNSbdOeryzC6LH20tIUkS63oMLCfEcYPGzzw2NsbDDz9Mz6pLGFH6ePK1Cg9tKzf2OVTwqNgBhiaxpEU/Jlnjy1p1OnLaGZ2BXv+9/P/t3XmYXGWZPv77rLV2VfWaTifpTshKTNgChIACYUvYRUVQZHAU5jvDgKPgoPwUCCoiDg44jIqgAjPCuCGKAjGICWJIEELClhDIvnQ6vVZV136W9/dHdRVV3dXd1Z2url7uz3X11V2nzvLW6arz1nnOc553Zxu/mxJlDDmI3tLSgqlT+w4eU1tbi0OHDo1IoyY7w0hBVVQIIeBwOmFbNpqb90PTNFRX10LTdAafiKjkDCOFUKgrOxCoABAOhWBZJiQpfWq9fr8D+8MaDBswLaDKbaPeky7l4vX6UFHhhyxLkKT0HTWKqsI0zew6edJdPuzPyysztkmiJwAFAP5AJT8PRDSqcvviWCyKPZEoDjW+jvyRTgBTkWD0jGkCAKZlQNM0OJxO+Hx++P2VvKtsBNx+++348pe/jJkzZ2Lp0qVYtmwZgHRW+vHHH1/m1tFYkinNsn5HtGCwMzewvmV/HM1d6cE3Z9emg8imLWCYomAgPZNxbtgiGyR+Y18cbx9If2c5ttGFU2e7ccIsN65cWomUDWiyhJaQiXMXVsCty+iKmnDrMk5ocqM+oGW3VeNJZ8NnBiPd25bE7FodHqcMv1NGnU/FnCkORJJ2Tha0gkqPCrdThmUDsgz8+Z0INrwfwes7g1CcfuhHnY1OqR6HgyYgSWjwa6gPaDhtjgf1AQ1CAhIpGx6XMiJZ1QGPiiUz3UWta7CLHGNVsRcKxuvrIxqOIR89MgOPzZo1K2/6+vXr0dDQMGINm4wyt1NKkgTTMuGUJMTjcQSD6bIJhw41QwiRzlQ3TSiqypNtIiqZzMU6APD7K5FIxGHb6dIuTqcboYSBlogCSwC6DPh0C1cv7s6WckmlUvBWVKBuSgOE2IpkMgGHwwnLNBGLReH3V/K27zJif15esVgUwa4OCCEgyTKqKqvRNPMoBqGIaNTklnIBgGAwhP/cvhUoeHohQXJ+ECBJJROIRLrR0DBjdBo7SXziE5/Ahz/8YRw6dAjHHntsdvrZZ5+Nyy67rIwto7HmtDnpD+riaU50xCx4HXJesDO33IgkAKcuY26dIxv0veAYH559MwytwCCbuQNqZuYXEgAhIKR0APnC4wPZ+f1uE39+O4xIwsL6HVE0VGo4usGFgEfBJ05Mz/e/L3diX3sKfqeMC48P4IkNndn67J9eVgVJALs7DCyepuC4Ga68MiKfODGQfTyrOt02GTa6dr+K5qABv+8cOFw+GLbAOR+qQHvUymt7Uy3w4jYLkCRI+dcH85SqfMl4HXx0oJrwucbr6yMajiG/w6+99lp88YtfhGEYOOusswAAL7zwAm655RbcfPPNI97AySQWiyIWjSCeiMPldCMUCiIa6QYgIMsStJ5bJqPRCCRIEBAA/OVuNhFNMJmTak3TkUgkEA6HoGk6AoEqdIdD0HUHuiNRGCbg020IAaiygEtHNoAOAPF4DBKqEYmEIYRApLsbDoeT2edjBPvz8nK7Peju7s6Wb0kkYmVuERFNJoaRQlvbYWTGCU0kEti29xC2azsKzu+XgAWOSuxFGLKswOv1QQLQ0dHGMZpGWH19Perr6/OmnXzyyWVqDY0luUHeploHmmod2LQnhmjCRn1Aywv85gbCQzELB4MGZtboees5bY4nG3DOVSh4etwMFyqchQfkDHhUXLm0Eut3RLNB/ZNmuXFsoyvbJpcuI2UKHAqnL8ZlLgKcNseDYNTE+61JJFJ2Nkifu/3cxwGPiql+Cc+tXgNDiuPs5SvgcuUH/Ztq++67Yxtd8LoGHlB0KMHgoQTcc/8XE9FEf31EuYYcRL/lllvQ2dmJ66+/HqlU+sTP6XTiK1/5Cm699dYRb+Bk4nI40L1/B0zFgahlw+3xAJCgKBqcmopqFRCaCqfLhfb2Vug9ZV2YyUlEIyk3A92yTCTiMbS0HITT6YKq6ojGYnj2fQfaozJaowoCThsSgGPrUwg4BWRZhhACuq7DtCwEAlWQJAneigqeaI8h7M/LS9P07H4HAMuy2KcT0aiJxaLZpBy324NwOISwHILQrT7zyrbADa65cMnpU8e6unoEKisR7OpEKpWCqqo8do2Q5cuXZ0vgFfKXv/xlFFtDY01ukHc20iVWIgkLngLB4dzA8862FPwuBe1RC021hQfFHCgoXEzAOBPUX/duN97eH8fMWh1v7ItDSOkAfKbMSyZo3h61cMExPgQ8KjbticHnVODWZRw3w1Vw/bk2btwISBLmn3QeXC73gBnTuW0fbADQoQSDhxJwLzaje7ya6K+PKNeQ3+mSJOGee+7Bbbfdhm3btsHlcmHu3LlwOBylaN+kkUrEEPnN/XC0HQA8VUgtvwoOhxO1dU6EO9ug/uVxpGJdcM2YC/Osz8DldGe/9BIRDST3du3+Ati582SOK5n5Y9EohG0jHAoilUoilADaozIiKSCYlNHkT9dI/0hjAoqcrqLqcDihKDIURUEiEYcsS/B4vAygjyHsz8srHA5C0zSkUulbmV0uL/t0Iho1uX09AKiKipBkABJ6l0OHzxI42pcu2yJLMmrrpkDTNJimmbeO3or5/kH5jjvuuLzHhmFgy5YtePvtt3HNNdeUp1FUdplAcI1HAZAO8u5sS+GtgwlACCyb680Gt3PnbY9aqPEo6E7kl3uZXasjErfQnbCydawzpV2AD4LC2YFA4xYiSRuRuAWvS8muO7O+3AC7JABIEva0pnAoZKCyZ10VTiUvaJ4bgC5UPqaQcDgMr9eLpUuX4q2DBg6HLSi9StH09sa+ON46mEAkbuGMoysG3cfFlnLJtLnGo2DTntiIl4AhorFp2J9yr9eLk046aSTbMmkZRgrtu9+F2rofsizDGeuCUxjwmnFI/hoE92yH1NUCW9Ngte6Dy0xA8nj5hZSIipJf27zvMeODW7qlnnkqIUkx7N+/BzU1dajw+REKdkFVNWiaCY8OtEQUhJIybAHoikCtx0bAma7t7NAdkGQZkiSju7sbnl4n6jS2sD8ffYaRwoH9+2DbFiRJgtPpgsvtYp9ORCXVO6id+U4QCnUhGo3AbQAQfQsGX1OxEC41fYFVAGhu3o+Ghhmori5QMyHHYN8/qK/77ruv4PRVq1YhEomMcmtorCiUOT4bQCRuQUj5gdzMvNsPJeB3KdnfueVeAh4VXpeSNxhp74FEc7frccrwOmS8eSCOpCFQ6U6vLyM3ID6zRsfBoAFNlpAwBRyahJ0tSVS4lOw8vTO++8tkzg1sx8NtWLNmDc4880w0NTVhzhQFkpzKa2+hQHhuLfei93ERwfBMm3tfECCiiY2f8jLLBK8M3Qvhq4Uj0gHLVwtl4+8Ram+GqJoKa8lFsCunQE12Q6lrhF49FQ6F/zoiKk7vbLPe0rd0I+/ulvb2VsSiUbSjFV5PBSAhXc5Fd+BXbyfQnbKgKzaa/CbOPiqOaRU2FBlQFQVC2JAhwzRNaLoOWZGhKCoDhEQ92ttaEY/H0oOEKwpkWRo0GEVEdKT6C2q73R5YtsDrO1KQqgRETjkRWQCN7kDPo/S3hXgsXeZtxoyZA/btg33/oOJ95jOfwcknn4x77723JOvv6urCF77wBTz99NMAgEsuuQQPPPAAAoFAv8t89rOfxWOPPZY3benSpelSGzSiCpUZCXjUbGZ1biA3M8+iaU60Ry0smubE7o50NnkwaiLgURGMmukAPAQicStdK32Ko082de52d7alkDRsHA6bcGhSn4FMM3+3Ry1osoRQIl0XHQBaw2ZegH6oA1Ye2rcbLe9txPEnLkVTU1PBdQSjJp59Mwy1Vzb9QLXcB9vHxWA9cKLJhZHYMsvUI7QkQF/xj+juaIGdSsG19n8g2SbQug9yIgz7xPMhV1bB0zQfEgPoRDQEudlmhfQu3wIANTV1aEcrAoEqhMMh2LaNVCqJtoiFrqgNXQVSpgyfU2QD6JqmwbIsWJYFVdPhcjohS3K2JjoRpbW2HYZppk92FUVDw7RGBpmIqOQGCmonJTf2pDw9ddJ7CIHTWzvQ6DUAB5DpyhVVKWpspsG+f1DxNmzYAKfTWbL1f/rTn8aBAwewevVqAMA//dM/4eqrr8Yf/vCHAZdbuXIlHnnkkexjXef/uxRyA8aFsq17l0PJzJsZYLM9amWzzpd4VOxsS6E1bGJXWxJH1TrgdSn91kbPrGs2gO6jPNjZkoTPpcDrUrLbX9Ir8L557wdB/XMXVkBISJd5GUB/r8s0knh7y+sIzD4VCDT1u/zOthS0Atn0xQbsh1vXm/XAiSYXftrLLPMl1h+oRCoRg9uuRjiZghrvhtTdCclfg8A7LwLBVihTZwFN88vcYiKaaAqd5Lpcbvj9lYhGIzANAxBAIpGAHY/icNiDlCmjwmHjorkJKLIEXdehKDIMwwAgQZYlqKoKRVYQDHbCtm00N+9HIFDFYCFNauFwEIl4LPu4trYOiqKUsUVENFn0F9SOxaKwrCDenPJ32DnXvGUh8LmDh6DNViBrOhxOB1RFxcyZc2AYKfbnJfCxj30s77EQAocOHcJrr72G2267rSTb3LZtG1avXo2NGzdi6dKlAICHH34Yy5Ytw/bt2zF/fv/nnw6HA/X19SVpFxVWqOxIoUBuMGpiy/44JIF0pjk+KPtS41Gw3RaY4tf6LeGSu/7MNs5cUIFqt4Jn3wxjwdTCY+gEPCrm1jmwuzWJYNRCe9RChVPJC+Jn2pcbNO+9XSEEUtFOLJ1bh9l1l2Nvlz1gtndujfKdbSnM7mkLEdFI4lGlzDJfZoVlwn7mx7Da9sPt8MKuqITsDUBRFEjBw4DugOg4CDvUDqWKX1SIaOTl1kqNxaKIdIeRTCaRMlJQFBWmaSAUt1HtMlGhA7oCJCwBpyaQSiWhaRpkWYZt25AlBbrmQDgcgm4YME0TsWgUQDpjnQON0WTVcqg5+7ckSRAQ/BwQUUkMNrCnYaQQDodgmSbe7T4EU0rkPS8kGa3T5sLjCcDtdKKysgqyIvcEzxlALwW/35/3WJZlzJ8/H9/4xjdw3nnnlWSbGzZsgN/vzwbQAeCUU06B3+/Hyy+/PGAQfd26dairq0MgEMAZZ5yBu+66C3V1dSVpJ6UVKh9SKIt7Z1sKb++PA5KUzTTPLftywTG+ggNp1njSddQXTSt858NbBxMwLIG3DiawuLFw9vqxjS4AyNZr392R6lP+ZWdbCrsOJ7H9UAIXHOPLe10d4QT+svZFWPEQLr/846jxO1Hj79uWXKxRTkSjgUeVMcIOtcNq2w8AkGMhoLIOSqwbSt0MSACs9gNQ6hoh+2vK21AimpA+GFwUME0TAOB0uZFMJmEaJmzbhm1b8DkEajw2OmIyqt02KvQP7s3UNB2mGYcsyzCMJKIxGbawYVkGZFmC2+NBIFA15IHGBgsCEI1XDocLRioFw0iBASkiGmmD9beZC+aWbWNGJI7KVAqtupat21Ij66hfdBYgK4jHY6jw+SFJct462EePrNzSKKOlpaWlYOC7rq4OLS0t/S53/vnn4/LLL0dTUxN2796N2267DWeddRY2bdoEh6NwlnIymUQymcw+DofDR/4CJplCWeeFssdn1+roTrggib6B996lWnK1Ry34XUr6tzs/m709amHxNCdiKRsBl5Ktsd57+73rtUcTdt7Appk2bD+UgCpL6Qz1mW4s8ahIJBL47TOrkTRMHH3SOUO+W481yomolBhEHyNkfw1QORXGwfehTZsL/8e/ABEJZoPmqY5D6IaKZLALPp+fX1KJaMRkBzg2DBipFJKpFFRFgawo8FZUIJVKAJIMWdKgKimcPyeO7pQEn0OCnHPbt6rpcAFIJhOQJBmWZQJCwOF0Q1EUeDxeaJoGTdMAFD/QWCYIYJomVFXliTqNW4cOHUBXVxcAQFVVaLoGTddZEoGISqJQDfRM9rlpmpCQvmDeGezEg5GdqDRSmBmJ4J927ESkqgHzXLWI16VPF20bsC2rzzaGemGcRs+qVatw5513DjjPq6++CgAFx64RQgw4ps0VV1yR/XvRokU48cQT0dTUhGeeeaZPWZqMu+++e9A2TTaFssiHqr+BR4+b4cLOtlTetMGys3sPJprJZj8YNOB3KagPaDi+yY2dh5N49s1wnyzyYtqWaUtuNnzGnj174PM6MWX+qZhb7+6zvpHYX0REw8WjzhhimgaELWAkEwjufQ9ujxcOP2DaNjpMIBZND+4HANXVtWVuLRFNFOkBjgHLMrPlWFK2BZfihu5wYGrDDHR3h9EdDkKSJCiyQMApeuZNryNd/1xG0rbgdLrgrfDBtiwkek6sbVtkT7KHGjDMzG+aJk/UaVxrPXwIQPpDI0kyPG4P6usbeFGIiEoukzFumiZCoSAikW4YwkbCqSCaiGKfSECSFIR1HXWGgYWxFITRhlQ8DMtTCUgCwVAwL/McGHiwUho6y7Jw33334Ve/+hX27duHVCqV93xnZ2fR67rhhhtw5ZVXDjjPzJkz8eabb+Lw4cN9nmtra8OUKVOK3t7UqVPR1NSE999/v995br31Vtx0003Zx+FwGDNmzCh6GxNRfzXIB9PfAKDFrHtvWxLrd0Rx2hwPmmrz7xoIeFTM7lm2xqNg0QxXXiZ6jUfBnvYUwgkLfqeSl0VeqF0DBe5zn+vo6ICqqpg/fz7mz5/f7wWcwfbXcPcnEVExeFQpE2GZsEPtkP01kBQ1Xes81Aah6xDbX4G59WWEZQX64o9ALLsYkpb+Yqr2ZHASEY0Ut9uDZCIBWVag6zocDidcLjcMIwVJkhCJdMOhO2C6PLBtgUQi3hNA/yAjTVEUxGJRyLICp8uFurp6dHS0IZVKZS/+KaoK0zSz9VeB4oLhmbEjep+4E403Hm9Fz4UlCbrugLfCxwA6EY243IB5pr8FgFg0AtOy0mXXbAv3tb6Gg1YU9ZILEUkg5HIiYKTggQKhqDB8tbBcPkiSDGHbSCWTsG0bwWBnti/WNB1uN3r6Z/CYdoTuvPNO/OQnP8FNN92E2267DV/72tewZ88e/O53v8Ptt98+pHXV1NSgpmbwUqDLli1DKBTC3//+d5x88skAgFdeeQWhUAinnnpq0dvr6OjA/v37MXXq1H7ncTgc/ZZ6mayGW34kEyyOxC14XUrBzOz+1r1+RxS7WtNldXoH0XPXDQBnLqjIC4xv2R/H2wfimFmrY2pA71Ob/dk3w1B7blUtNoh94MABPP/881i6dGmfcQF6G6xmO8u5EFEpyYPPQiNNWCaiv/0vRH5xD6JPPQBhmbDcPtiVU6DYApJtA0YSSMVhvLkO4nf/Bcdf/w9T66eiuroGPt8go2oQEQ2BpukwLROmkf6yXF1dmy25EgoFEY10o7s7DFXToOtatjahqqo9WSISDNOEZZmQZMBX4Uc8HoOqqPB4vfD7A5BlGaqqZk/mHU7nkIPh6WB6JU/QaVwyjBRi0RhkWYGqqfAHAuzPiagkckusOJzOnjFLTCRTKRiGAYfuQMKl4qAVRco2sScVgkMAmm0jpGn45tHzcHjx2ehYchEgK+m7zRQFsixDkiQEAlUFtxeLRcvxcieUxx9/HA8//DC+/OUvQ1VVfOpTn8JPfvIT3H777di4cWNJtnn00Udj5cqVuO6667Bx40Zs3LgR1113HS666KK8QUUXLFiAp556CgAQiUTw5S9/GRs2bMCePXuwbt06XHzxxaipqcFll11WknZOVAGPiiUz3UMuTTK7Vkd9QIOQgJagkVe2ZbB1nzbHk65R3lPXPBg1sWlPDMGombfuTCA6E1Tf2ZaCJABIEiocSp91v7EvjuYuA91xq+gg9pa33sXq1X/C8Scuw8KFC/u0pbfcmu2FDHd/Dtdg7SWiiYWZ6GWQO4io1boPdqgdccUBc/lnoEa7IP3ibsht+yAkGdCdELIKNdQKpxGHUlVf5tYT0XhS7IBfXm8FEok4vN6KnEFG09myhpFCKplCsKsTQHogUVmWAaQD6A6HBtOyIckSdD2dFWKaJgQEqqtr4XZ7IEkSNE1HNBqBt8JXMICeqdMKgGM/0ITT2dmRHicAQIXXxzIuRFQyvUustLQ0I5VMwLJsyLKCaCyCepcPEdtApxmHHxrmdIfR5lLgsCy0OBxodTlRKytwuz1wudwwTROmZUJRlOyF9v62R8PX0tKCxYsXAwC8Xi9CofT3oosuugi33XZbybb7+OOP4wtf+ALOO+88AMAll1yC//7v/86bZ/v27dn2KIqCt956C//zP/+DYDCIqVOnYvny5fjlL3+JioqKkrVzIhtqre9MKZTc5YpdZ1OtA8c3Wdm65g2VGqKJ9J2jmYFBMyVdZqNvdncm8703IQFOTcLsekdRr0EIge07dqFy7odhVaTL+gxWjiU9YKqFSNzKDmxaTiwfQzS58FNeBrK/BkpdI6zWfVDqGiH7a+AyUjDam2ELASVQC3j96RII/jooiUh2PiKioSh2wC8hBFxOd7pGqmFAApA0DCQNFVPrGtC8fw9M04AkSZBlJR0kF3Z2eafDCU3XUVtbh2g0AsuyoCoqgsHO7Mm2YaSgKioMIwWg74l2LBZFpDsMIJ3lzrrnNFEYRgqHW5qRSiWhaRqqa2oZQCeiEZd74dzvrwQAhEJdMA0DiWQCDt0JWZGga04cSIZQoehQBSBZJq470ILHarxocehohI6Atw6apkNRVNRNqU/fmRaNQNgiW7olFotC09Ll1jjo98iYPn06Dh06hMbGRsyZMwdr1qzBCSecgFdffbWkZVCqqqrw85//fMB5hBDZv10uF/70pz+VrD3jzUAB8N5B7v7mG0owtph66IOtc3atju2HEtBkCZJAXuZ5n2V7ap733m7vx7OqdTR3GZhVnX8s6D2fbdvYtOVtSIGjcMqHz0Z71OoTqO8vkz3gUVHhVLKZ8YXaNZpYPoZocmEQvQwkRYXnshuzNdEBIPWHByE174Ltr4MdmAK1ux1aXSPcl/wLRCSYrZ1ORDQUg2WHZU64NU2HQASZIXx0lwe/3NyNQ8EoplUZuGi+E2oiBsuyoSgyLEuCEIAkpbPPPR4PnC43Eol4NgifspNwKa7s7d2DtcXt9sDsKffS3zymJdAZNVHlUaEqhQccIhprwuEQEok4AMC2bZZxIaKSyL1w7nanjz3JZAJA+kK5y+WGgIBlWZjq9GGGVoFXks2QzCR+WeXGne/vQXPDHEgnnAdN0WCaJlRNhRACbrcHhmEAkgS325PdVjQagdpzjsKL30fusssuwwsvvIClS5fi3/7t3/CpT30KP/3pT7Fv3z586UtfKnfzqB8DBatznwMwYFA79zfQfwC+2ID7QAHegEfFBcf4+gSfM9us8Sh9lu293d6Pc0utNNV+8Bp+81oQwaiJ7qM8OG22Ey+88AJaO0IIzJsCSa7AkpnuvHblZsEXCor3fl2jmQ0+lIFTiWji4ae9TCRFhVJVD2GZSOx4A4mDOyArKuRQKzyf/DI0h/ODwDlLuBBRkXqXb8kMylloHk3TEQx2wrZsyIqMQKAqm03WGjZxKNgFy7bQGgHgrIQUDsKyDAhhQ1EUSBLgcDhRWVkJ3ZEu42JbJmRZhqIo8PsD2RNvAAXbEotFEQx2IhCogtvtQXV1bb+vzbQEfvbXDjQHDTQENHzu9GoG0mlcSCbTA/dalgmfL4C2tsPZ9zwR0UiRJAnhcAhuy0QykUAw1AVhW5BkJT1AuGUiHotBVVVouo5PeOfg/UgrnKkodlf40BqohWvhWbC0dJ+uqgog0gOSyooKvz+QXrZnMFEA8OZkotOR+853vpP9+xOf+ARmzJiB9evXY86cObjkkkvK2DIayEDB6tzyIzNrBg5qFxuAH0r2cyRuYcv+OI6b4eoTkB5sm7nB7ULbHex3ZqDRw0EDMUPASibwxz/+GaqqYuX5F2F/WCp40aA7YaEtZGL7oQQuOMY3aLuHkw0+3Ox1lm8hmtz4qS8jYZmIPnk/EvvehYh2w/ZXwzl9Lhx105l1TkTDUkz5ltzsMQlAIhGHLWzEolE0TJsBTdPhlCOodJpoCZvwSBYi7el6zkLYsG2pJwPdAZfLDUBCMplAIFAFVVWhO0xYpgkhRPZ28v4Eg52IRfMz1fvTGTXR3POltTlooCtqotanDbgMUbkZRgqhYBcAAbfbDd3hQDTSjUQijhkzZrL8ARGNmHRJthTCYROyJMM0LTid6b7atEyoigpV03rGNQGmqG7McgRwKBnFrFgcAW8dwp4P+m1JUmAYKbS3t0FRVVRU+LLPGYaBaDSCQKBq0L6ehm/p0qVYunRpuZtBgxgoGzm3/Eh71MKSme7sYJSDBXBn1+qIxC10J6xsiZTBSrjk2tmWwlsHE0ikbDR3GdmA9EAB5MGy13O3m/u40Dp3tqWgyhKmBDTMmeLALF8Ce0PVOO2006AoCuryxyjGG/vieOtgAkfV6DBsAVWWsiVbBjJYO/rbN8MJhrN8C9HkxkhtGVmdLUi9+RKkZBzQdLhX/jtcc4+D1FMzONodgsuIQ69pYFCdiIpSzOBe2QE+vRUQQkBWVLS3HYZlWdi1czsam46Cy+HAZYtstIYMOKUEjFT69m9JkrKDhDqcLrg9HpiWCQlAW2sL5J4MdFVVs20wjBRs2+qphY68TPlAIP3tOfN7IFUeFQ0BLZuJXsnsDxrjMoP0JpNJ2LYN2xZwudywbQu6piMWi7L8ARGNmECgCpZlpX9MA5AAf6ASqqqiOxxGNBaBr8KPeDyGw8374Uh0418qFqLDcxTqkimEnRVQY2GYbh8gK1BVJT1GEwRsy4SRSkEIG4aRQkvLQZjGwCXYaOjuvvtuTJkyBZ/73Ofypv/sZz9DW1sbvvKVr5SpZTRUucHc4ZYfCXhUeF35Afj+ttFf2ZNI3ML7rcm8gHRm+5G4lR0kNLP8cMuTFHpNmdcbkIIw4u3w++fA03giuhMCgQKHDSEBEAJup4xT53r6HTB1qO0oZLjBcJZvIZrc+OkfCyRAlhXo1fXZYHm0OwTjDw/CCh6GY9pseC67kYF0IhpUoZIpvWUG+MyUWjFNE5VV1Whva4NtC7S3t8Lvr4SmqajzCwihIpmIQ1U1CCHgdLrg8Xrh0B2Ix2NwutywLBPh7nD2hHrGjJnZ7cVi0exAZADyMuXdbk/RJ9+qIuFzp1ejK2qikjXRaRyIxaKIRaNIpZKQJAkutxtVVdWoqqrOXkwiIhopmT41nY0eAgD4fOmgeTDYAUmSEQYQDYUQeOV30Lrb4fHVAidcgFYHMO/1Z+Hubofhq0XXyZdC0zR4vBWwLQuarkNVVNi2QEtLM5Se85JiLoJT8X784x/jiSee6DP9Qx/6EK688koG0ceRQgNzZgwlgDtQrfTuhIVowk5vo0BgN+BRccbRFTi20ZUXkM787k5YAwach1LypFA7Ax4VNe0H8cILL+Ckk04aNMB93AwXKpzKkLLti2lHIQyGE9Fw8KhRBsIyYXW2QFgm1EUfht2yG3L9rOxzkqLCZcRhBQ9DUVRYrftgh9qhsDY6ER0hw0jBNE0oPZnisVgUqWQSlmWhqqoKiUQCXq8PXV0dsC0bWk/dVI/Hi2BXJ2RZhtPlgq47EIvHYFsWVFVFVVU1jFQK8UQcDt3RZ3uZgcgAwDRNmKYJw0gNuZSFqkgs4ULjRvozFoMQACCg647se54Z6EQ0WiKRbmiaA8lkEqlkCloqAq27Pf1kdxt+1LUFB+wE5vosfDUCaOE2aPFuyIFqGEYKLqcbbk+6DxfChpFKwelyoaFhOktSjbCWlhZMnTq1z/Ta2locOnSoDC0aPcOtUT0WFGr7UMqiDGSguuVeh4z6gDbkgHHmce+BS3sbSsmTQu3ctOVtbN70dyw99SNYfPTc9OCiPfXhg1GzJP9nBseJqJR4dBllmTroqTdfAgBoiz4Mz5W3IPnX3yD66+9BqWuE57Ibodc0wDFtNqzWfVDqGiH7a8rcciKaCGKxKCzThMPp7Bl41EA80ZYOmGsaauumIBqNwLZsmJYBW6SzWzRdh6KqsEwTyWQSpmEiZSShazpcLjc0TUfDtBnZAUtDoa5skN4yzey23W4PVFVFMpFgKQuaFNxuN1JGEqqi9owhQERUWrFYFJHucPaxoqhwezxwezzoDodhOipg+adACR1Gc6AWB5ACZBm73C60qmHUuaqg+GsgyzJ0zQGBdEm3zo52SJDgD1TC5/MzgF4CmYFEZ82alTd9/fr1aGhoKFOrRsd4HrCxUNsHC+YeyUWD3AB9oWUHWnfv50pR8iTjQKeB5JRl6EQ6GTC3PnxurfNMmyJxC5Fk/5n1RETlxiPTKLND7bCad0Gk4gAAa/+7EJEgrLYDgJGCdXhvNuvcc9mNsEPtkP01eaVcDCOVV1OYiKhYmfItmUzwTIaZaZlwuz2wLAuhUAgSBDzuCsRiEaRSSWi6DlmSYdg2UskEZFlO/yhKT61zT7aUTCjUlS3Zkluuovc0lrKgia6t7TCi0Qgcuo6amjpUVVWXu0lENIFlzhE0TYfT5UYqlYRpmrCt9MVs27IACbAg0H78+dA7DkCurMf06Hs4YEYw1TcN0glL0R2oQ0WFD6qqQdN1VFVVY//+PUgk4hAQqK6uLfMrnbiuvfZafPGLX4RhGDjrrLMAAC+88AJuueUW3HzzzWVuXWmN5wEbh9P24Vw0yA2A966PXuy6t+yP4+39cXQnXDhzQcWQtlNM4N+yLGzcuBELFy6Ev2EO9u+Pp2ud9yi0rzLt9TiLy6wnIioXBtFHmeyvgdIwG1b7QdjREMxgK2J//S1EVyvscDvkQB0kbwAAIClqwRIusVg0r6YwEVGxNE3PywTXNB0CEfj9AcTjcRxqPgDDSEHXHTAMA5ZlQVFkdHa2Q1VVKIoC2xaQJBkutwcVFb4+wfDMwKVOScpe8JNlGQ6nM3vxz+3OZKZjRC4G8uIijUVdnR1IJOKQZRUCI/NeJyLqTzgcQqQ7nA2gG4YBh+6AAJBMJGFaBiAA2zRRtemP0Lrb4fXV4l9OuAAdwkCt5obb4YSipL8nCN2GruvQtPSFwHa0ZmuhU2nccsst6OzsxPXXX49UKj0gu9PpxFe+8hXceuutZW5daY3nMhz9tX2goHONR8H2QwksmuYsOiu92MB7Zt2NVRrWvdsNSQDHNroQ8KiQBABJSv8ucjvF1mBPpVJ4/vnnkUgkcPzxx+O4GXq2xvlA+2qwzHoiorGCR6hRJikqPB//N2jHnoHI734AEWqHtfVlQHNAnnoUJN0BEQnCqKjqNyDELE4iGqrcIHNuNrppmlAVFZFIN8KhEAzDhBAClmX3ZJgDtt2TdS5J0FxOxKJR2MKGy+Xuk41mGCkEg52QICES6Ybac7ItSRL8/srsfCN9MZAXF2msicWiSCTSd53JMgffI6LRE4/HEItGYNs2nE4nVEWFaRqwLBOWZUGKdEGPdKQv7oXb4ExEMdVb2VPmTUcqlYQsKzBME15vOlM10+dLEgf1LiVJknDPPffgtttuw7Zt2+ByuTB37lw4HI7BF6ZRMZQyLANlfbdHLfhdCtqjFtqjAw/wmZEbeO/dnhpPel2za/Xsut86mEBnxETCEDgYNHDaHA+EBCye5sSxja5+t9M7W7yYGuzxeBzPPvssXC4XLr74Yui6DjeA2T3LzwYYICeicY9HsTIx3lgHKdQGEQkCFVWQJAmSJEOdMhOyvwaRSHe/AaFMyQQiomLlBpndbg+SyQQkALrDCYfTCa+mI5VKwbJNqKoORZbR3d0NIWy4XC7Ytg2X24tkIgbLsiDLMgAgFOqCJEkIhYJw6A4oqgoJgIBAIFAFw0gVvOA30hcDeXGRxhLDSGH//r3pQXUhobKymu9NIiqZzIVyl8sNVVWRSCTSd8EoCuKxKCLRCCAAy+q5UO7yIeWrhRZqheGrhfD4oaoqJElCykjBFoAiSXA5XRDpkZGz3yNEz1gpVFperxcnnXRSuZsx5pVjMNKhlGEZKOu7d6A6ErfQnRh4wM3cwHtTbX57th9KwO9S8ta5aJoTuztS2HE4CVWWsH5HFH6XgvqAhkCvgUVzt9k7W7xQpnjv4P3MKgVz587FokWLsucJxe6v8VwPn4gmFx6hysDqbIHVvAtS/UygeRfk6qnQps+Dc/kVkP01sEPtcLl9ABgQIqKh6a+sSW6QORaLwrZspIwk/IGq7HPTps1AV2cHYvEYFCX9JVzTddiWBcu24PP5EQZgWYDb486WhQl3hxCLxqBqKioqfNB1B1RVhaZp/R7DRvpiIC8u0ljS1dmBWCwCSZJ7SiCl7+xgORciKoX8u7EqoWlRpFJJ6LoDXZ3tMA0Ttm1BCAEhAFlREDr5o0CkE6azAulC6RYkSYYEwOV0wOX25pVsy9zFJgR4PKMxoxzB16HUPz+20QWvSyk4b26gOhg1cTBoQJUl7HSm+n0thbY9u1ZHJG4BQsDjlPsMGNpU68BxM1x9stUB4I19cbx1MIFI3MIZRxeuj96fzL7fumM/RNdOiBNPx4nHHFNUm4czz3hWjos9RFQa/ASPMmGZiP/lF+ma6LYNMf9k4IzL4WmaDwCI/va/YLXth1LXCN9lN+YNKEpENBDDSKGt7TAkpG+1zg0qZ4LMhpFKnwRDwOV0ZwcFzcxjWibisSgURYXL7UY02g1V0SCEjc6OdlT4/Kio8MHlciMej0FRVXjcXpimCVmSoSoKLMuEhPRJfWabtm2N2Ek365/TWGYYKbS3t8E0TDgcDjhdbjidruzngYhopPW+Gysej8G2LKiqCp8/gEQiAUDAsm3IkgRJkmDZFoQ7kLce27bSd6MpWjqY7nJn+9nMmCoQgsczGjPKEXwdSu32QvMWKr+ysy0FTZZg2KLPa+kdgM1dX+Y5AEBPqaVCpVPyg+ofrFtIAITIG/izkEIXK2bX6uhq2Yvk3g3Q6o9BrVcreh8MZ57xjJn2RBMHP8GjLNXejFTLHshTmqCYBsSHPwa3J33V1+psgXVwB6DpsFr3wQ61FxxYlIiokFgsmi2l0l8GeO6gY86egT4zDCMFCYCsqPBWVKC7uxuJeBK6nr4HNV1L1URN7RQYRgqWacLhdMLn88PtSQe0M4Hy3DIu4XAIlmUhHA71qaE+3NfJ+uc0VnX2ZKHbtgVN03DUUXOyF32IiEoh90J5KNTVU0oKSCUTiEVjAACn0wVZUQFhIRqNZsu0ZPUE4AwjCcsyEIvJEEjfpZY7pookSzye0ZgxWPB1LGYAFyq/UuNRsN0WOG2Op087BwrAZp7zONO1yiPx4mqrZxw3w9Vn4M9CCl2saNm/A/u3bcRRx3wYcW0K9rSnshcFCu3rsfi/GC0TPdOeaDKZXEevUZatT+hwQImFIftrEJM12C4fRCwI57Q5EK+vQaLjIIya6emSCe0HAQnQF58O2V9T7pdARONIbiZafxnapmkinohDAKiqqgYAtB4+hGQqCQkSQuEuaGp6WafDiUQ8CkBAdziQiJsQAKyek3MBIJFIwDRNuFzubOA8ve3SnWCz/jmNZfF4LF0uQZbhcrlYaoiISqb3nVnhcAhdnR2wbBuVgUokU0lEIiFYlg2ny4nGxlnoaD0Eu6sNUVmDkD6oWyxsG0JWAEgQAhACsC2rT6kYWVZ4FxiNG2MxAzgzOOjiaU6kbGQz0XvXOs/QZeDtA3E0VvXN9O5dqzw3UJ3RO3g9UGZ7f3LnE0JAkiTU19fjwgsvhO6pws62FLoTfQP4udsai/+L0TLRM+2JJhN+kksoFosiGY3A/P0PoHQehFx/FBQhYMdCUKvqoZ92KWK//l4687x5JwQAeepMwDDgXH4FS7kQ0ZAUE6xTVRWyLEPY6RNjAAiGumAaJiAJCFsgmYzD7fZkByBLpVLQdQe8FX64XW4oqgrLTGelG6n0LaTJZAJqzzGrdxt8Pj8URYHP5x+110lULg7dAafTAU3XMaV+WrmbQ0QTWKE7s5LJBGzbRlewC4osQ9N06LoMv68SsUgY+ronUNPRDK+nEm3HXwAbEoQQkGUZDocjW7bFtm14vBW8cE3jWrkzgINRE1v2xxFL2HA7ZRw3w5UdHDRlA0tmutPt69XeXG8dTCCatPHsm2HMqB54ANBCwdrewetCwey9bUms3xHFaXM8aKp19Pt6TNPEunXrMH36dCxYsCA7fUk/AfzcbZX7f0FENBIYpS0ht9sDu6MZ9va/wzISsFr3Q6mdDtXhAro7kXzhCdgdzYAAtEUfBmQZdvsBKNPnsowLEZVEbiBb03TE4zG4XR4k5Dh8vgAikTAURYWsyLBtka51Lqcz1dwuNxxOJzRNRzDYCa+3AoaR/mKcm4nem6bp2cw11jOnicwwUjAtE15vBXz+SgadiKikMoN9mqYJw0jB5/PDNE10h0OIx2NIpVJwud1QdR3vtO5Bk5DgbT8ATdXgiHTCacRgeqpg2xYgSdAdTtTU1GbHPTFNE+FwCD6fn302jUvlzgDesj+OZzYH0Rm1cHSDM690Sm4weaB2njbHg/0dKUzxa9jZ1v+go/3pvb1C21+/I4pdrUkA6DeIfrgzihf+/Dx0ReC005r6PF/oNfTOlGc2NhGNdzyKlZCm6aio8CMsSxAAJFmBUt0AO9IF2V8DK9QGpX4WYKTgOufTUCqnwA61Q/bXMAudiIZlsCC1punZuuShUBcs04SsyPBV+OFwOOD1TkMw2IlAoAqJeByyLEOSZTidTiiqCrfbg1gsClVRYRgG1J5phUq45LYlo7965gyu00QQDocQj6UDT9U1hQfYIiIaCZl+E0iXWQuHQ1BVFVVV1aio8GHXrvchyxKSlonv7nsFexJBNKpe3FFRA1eoDYavBqbLD9M0IEkyNFWDqsjZgDwAJOLpeuqqqha8A4x9N002Q63rLQkAkFDhUBDwqMMKJjfVOvD/ltf0yfIuVsCjYjbyBxztvf3T5njyfvdm2zbWrH4GQnVj7nGnw+VyFb1tBs6JaCLhEa3ElKp66ItPh9W8E0rDbLg/+q8QkSAkbwCxp38Eq3VfOvO8cgokRe03A51fUomoGEMZdDNTP1WChKSVhNPlQkdnBzojFiy7A7rugKqqUBQVlmVBVdNdhmmaUHr+HmhbuW3J6O+2cA4WSuNdpp9OpZKABLS3t8LlcrPPJqKSyPSbiqrC4XTCNE3EolFEoxE4HE5UeCvQ3W0jrMs4aEahKAoOigT2nnwBpsSTSGpO2Ha6trGiyD1l3CzE4zFIABRVhbfCB6D/Ui7su2my6V0KpXcJk94B9mMbXRBSOph+bKNrWANqZrZR41HyAuFH0u7emmod/Wagd4ST2NNpYfHxp8B0VmFOnXPIr4GIaKJgEH0UOJdfAUhSNlCOnkC557Ibi84855dUIipGbpA6E9TLlG0B8suuGEYKtmUhmUpCVVQcOnQIv30LaIsAUwMqLjnahNPpgS0sAOmgeywWhWWacDid2az0/k6uCwXM+6tnzpqrNN51dXaguzsMWZahqhr0ns8L+2wiKoXeg4kbRgptbYch9TwvKwp03YlKSWCefwp2xTowQ61ApepGXFNgWRZk2QYgQVU1qJoKCSpcPWOfABi0jAv7bppsepdCyQ1OA+gTqA54VJy5oOKItpnZxvZDCfhdSt76c/WXJR+MmojELXicctGZ7Jl1ecw2bHj5JVQffR6m1dbgxJ4a7kREkxWD6CUkLBPR3/4XrLb9UOoa4bnsxrznB8o8741fUomoGLlB6lCoC8lEAtFoJDsAaCwWhW1ZME0zWzvVZZpIppJoi8TR2i2gaRoOhSzELB1+pwWpp6vIrXmeOWkfKEA4lAFAOVgojXeRSDfisShUVUd1TR2cPReaiIhGSu87U/3+dPA8FOqCpulwONIZoj6fH6lkAvFEDA7diZvrT0ZrIoyKYBCWYcIQNiQJEALQNA26w4G6uvpsP59J3sm9EJjZthAi2x723TTZ9C5P0juoHolb6E5YCEbNYWWdF5JZ96JpTrRHrX4D4f1lm+9sSyGStFEf0Ipu0862FLZt247Ygdex+IRl0Ku9HBCUiAgMopeUHWqH1boPMFKwDu+FHWof9oCh/JJKREOVCeB5czLRTdNEomewsFgsms0yi8WisOxWzKgBWiPAtAoZNT4N1VX12WUz6+NAY0T5DCOFVCoJIQBJytQPrix3s4hoggmHQ4h0h9PjLvSMb5KZJisKHLojOwC4aVlQZBUpIwk1Dsx46UmgswWWvw6xj3wSpm3Dsm1ACFR4K6BpWrYWeqHknUxgXQh79F840TgQ8KjwuhS0BI1hDQA60Hoz62qq7X++QgOGAkCNR8H2QwksmlZ8GRZ36jBiBzfDM+s06NWNWDLMDPSh1pAnIhrreCQrIckbgB1qhx1shRyog+QNFLWcsEwOMEpERyz34lumfEs4HMrWOE0mEjBNE0D65Nihabjmw24khRMOKQGXw4Hu7jBSqWT2ZL2t7TAS8TgURel3oDGiySYWi0JVNTgcDlT4/PD5/OVuEhFNMnpPAF2SJDQ37+8Zx8SG2+WBM9kNJdQGWwKUUCvkWAi6rxamaUFWJOgOZ17pyNzgeW5pOACQJLkcL49oTOqd/d1fIHs09DeIZ3vUgt+loD1qDRiEB9IDiJqmiflzmuCtuBStSdcRvZbBarETEY03PJKVkIgEIfmrobh9gKZBRILZeuj9LlOgBAwD6UQ0EgrVM08kEggFuyArMhS3Bz6vt+dE2Y1QqAtdne0wDAOWZUHXddiWDU3X4XZ7WKqCCOkAk2maqPD5UVlVzTs0iKhkfD4/VFXN639zp2majv3796A7HAYkAV13AhLgrJsOq2Ya0LoPdmAKTIcXMA24PV5UVPjg8/lhGAai0Qi8PXen5Q4M/sG4TJWQJKlPu4gmq95B89xA9mCDjvaeZyiZ2oWW629dxQb2DcPACy+8AJfLhWNPPC0bQC/UrmLbPdIXFZjZTkTlxiNPCcn+GqhTZsJq3QelrhGyv2bQZexQO6y2/QAAq3XfEZWAISLKVaieuWm2QVVVaLqO2top2eBfJjCo6450fQoAEgBZkfPmI5rsYrEo4rEYUkYS9fXT+NkgopIpVN6x9zSH7kBUkaEqGhxOBxy6A4ZpInrKxyDFQlD8NXArKkzTyFuPYaSgKmqf8U8yeOGcqG8Qt3f2d+7zvQcd3XU4ie2HErjgGF82ADzcTO1Cy/W3rv4y1HPF43GsXr0aqqpi+fLlePvQwO0qtt3FbHsomNlOROXGI08JSYoKz2U3Dqk0i+yvgVLXOKTAOxHRQHIHIstkoLvd6RPvTAabpukIh0MA0lltmax1f6Ay+3zmxJpBQqIPuN0edHS0wTJNBIOdDDQRUcn0Hlg0t9RKpo+urKqGaZmQIEF3OAAAyVQSTk8FUroOh9MN3eFAMplAIpFApDucl91eaOBwlm4jSntjXxxvHUwgErdwxtEVfZ7f2ZbKBstPm5P+TGWysLcfSkCVpbx66cVkahfKvi603JFkfa9btw4VFRVYvnw5FEXB7FpzwHWVq2xNOcvlEBEBDKKXnKSoQ8okH07gnYhoILFYFLFoBNFoBA6HE1ZPHXS/X4em6XC7e2qdJxKQACSTCXi9FYj1DChaV1ffc3LN4CBRrlgsitbWFtiWDV13IBCoKneTiGgCy69bnu67JQACgG1Z6OhoQ339NNTWTkEsFoVpmrB67ipLpZLQND09CDIEAEDq+YsXx4mKIyQAQkBI/Qe3M8Hy3R0pVDgVAOmM7AuO8eWVd8lMHyyjunf2dX8lTQIeFbN75p/d83gw3d3d8Hq9OPPMM5GwVGzZn8yut9QZ5sGoiS3745AEcGyjq6j2jnRmOxHRUPEINIqKHTB0qIF3IqKBuN0eRKMRSEiXZcnURM+IxaLpUi2yjEQ8jngijlQqhUQ8jmQyAdM00NAwo2Ameu+sOKLJJBjsRDgUhG3bqK6pZRY6EZVUbrZ4uu9Oh8EDgSq0tBzM3hFTWzsFKdtCqxmF1wBcmgPJRALRaDckWYGqKBBCQFFVuJwuGEYKhpHKqX0+WO1k9v00MQy1xvZxM1yocCqo8Sh49s0wNDn93ToT2M0NlkfiVl7we7gB4N7Z1wOVNBlKuZO9e/fiL3/5Cy666CLU1tZi655YwZIzpbKzLYW398cBSYLXpTA4TkTjAo9Uo0BYJqzOFsT/8gvYHQch10yHa/kVUKrqmWlORCOu98mtpunZrLRCJ7yZk3LdYSIRj8E0DCSTSegOPV0zVUgIBjuh9hyvck+uc7PieLs3TTaBQBW6u7thWSYcuqPczSGiCS63zIrbjZ7f6X69vn4aOjraoCgqOoKduH37GuyKdWK2uwp3zj8PAGBZNqxUCkJ3wOV2w6E74HS5YZomXC53dn2DBcnZ99NEUWzQOTfYvmSmG5v2xKDKEgxb9CktkgmW9x5YdLh6B98HKmlSbLmTrVu3YuPGjTjzzDNRW1ubXaZQyZlSmV2rozvhgiSGX56FA40S0WjjkabEhGUi+tv/gnVwB6z2g5DqG2G9+VfYzTuhTJ8Lz2U3AgDLtxDRiCl0cps58TaMFEKhrj71VNMnzQacLjckWYHH7YHL7YbfXwkAcLnceYONZeRmxTEzjSaTTObmjBlNBT8bRESl1LtueaYfTiYSOBALYlesE4oiY08yiKBkwuN0wurqhG3bsG0LLpcb9fUN2e8MhpHK9vkdHW2IdIdhmiaqq2v7bLvQwKNE40Vu4LXYoHPvYHvucv0Fb/vLPB9K4LfQvANltA800Glm+XA4jE2bNuGCCy5AfX193rKFSs6USsCj4swFfevKDwUHGiWi0cYjTYnZoXZYbfsBTQckAPFY+remw2rdB6vrMBJrfwmrbT+UukZ4LruRgXQiOiIDndz2DrCHw6HsibKqqnA4XEhKHri8OlwOR68SLh+sLzdgnjnpDoW6mJlGk0b+Z6myzK0hoomo0MVpw0ghHA5l+22fz599LtPvT5MlzHZXYXeiC3M8NXAlTHR2tENRFEgSoOuO7DpzM9qL1TuATzSe5AVeZ7qLCr72DrYfSW3uoQR+jzRInLv8cU4JXV1dqKmpwZVXXglN0/rMP1I1x0crQ5wDjRLRaBsX0do9e/bgm9/8Jv7yl7+gpaUFDQ0N+MxnPoOvfe1r0PWxfcCU/TVQ6hphte6Dvvh0OE7/OJJ/fRJm637YgTqYqWQ6yA7Aat0HO9TOeuhEdEQGOrnNnCRrmo5QqAtmzyCjAKA73Hjs5QjaYwLTq2x8/LgkrFSyZ34DwWAnAoGqbC3W3gFzZqbRZGEYKZimCUVV+X4nopIp1NfGYlFEusNIJpNwONJlpNSeY1EmKJ5IJPCVGaehCwbqdS8i4TAAQNd1+AOVsC0LpmnCMFIFvzP4fP7sOokmmmIDr70DwSOV6dzf9ntvLxg10Z2w4HXIww4SZ5ZrDACrV6+Gbdu46KKLCgbQR9JoZYgPZzBVIqIjMS6OMu+++y5s28aPf/xjzJkzB2+//Tauu+46RKNR3HvvveVu3oAkRYXnshvzyrWoH/83hA7shKm5kXA6ofYE2ZW6Rsj+mnI3mYgmgXg8Bqsniy1QmQ6Md8UldCZUADb2tEYQTVXB75TgdnvQ1nYYsWgUQDpIXihgzsw0mixisSgs04TD6WTpIiIqmUJ9rdvtQSKRgG1bEABSyWRe6ZVYLIpEPAbbtmDDRjgcggIJiqJBVWVYlgXbtpGIxxCLOQv22+zPabwbKBM6NyA+0HylCgT3F5Dvvb2dbSlEEzbqA9qwg8MBj4oFSGL16tVwuVxYsWIFJEk6ovYXYzQzxFnShYhG07g4yqxcuRIrV67MPj7qqKOwfft2/OhHPxrzQXQgHUjPzS6XFBWehlmQem7PVHsF2YmISiWT1SYAWJYJb4Uve3JeJQtMqZCxuyWCKRUyHHICQDpTJRCoyvvdX411oslA03REoxF4+Z4nohIqFMzWNB1OpxOJeAwAYFrpO8qSyQSam/fD662Aw+PBt99/Hjsj7ZiueHDz1JMhy+kL6MlkAk6XB55AJTPNacIqNrA60HwjEQgeSlmT3tsbqUD0tm3bUF1djdNPPx2yLB/Ruoo1kpn7g2FJFyIaTeM2YhsKhVBVVVXuZgxb7y/FLOFCRKMhc8JsmiYkpMtSZGqdq4qETy7RcKjdAb8LgG0h2NWdzW4rpsY60WRgGClIAILBTmiaxgtIRFRSvWuju90emKaZ7csVVUV3dxjxWBSpVAohVWB3rBOKouCAFUXK40SlKSOZTMGyBCAsqKrKYxeNaUdSV7vYwOpA841EIDg3SJ8tO9LP6+m9vczjYNTEpj2xIe+HlpYW+P1+nHDCCZAkaUQz0Eer5nkxRjNgT0Q0Lo82O3fuxAMPPIDvfe97A86XTCaRTCazj8M99QCJiCaj3JNwAHl/Z/i8XqhyuoRLOBwadJ2sg06TkdvtQTQagQQJsViUF5CIqKR6X7DWNB3V1bXZAb1VVYWu64hGumFbFjw20KhVYJ/RjVmuKrgTFlw+L2zbhiQBkqyU+RURDe5IynQUW7Kl1AHY3CD9UALquQbaD/29tl27dmHdunVYvnw5Zs2aNYKvaPA2ERFNZGU94q1atQp33nnngPO8+uqrOPHEE7OPm5ubsXLlSlx++eW49tprB1z27rvvHnT9I01YJkuzENGYlH8SXjloHdRiBhZj3VSaTAwjlb24FAhUwTBSvIBERCWTufidyRjvfbzJvZBtmia83gq4XG4IAP/fzDPQBQNSsBux7jBsw0Bt7RQoqspBQ2lcGKkyHeUM+OYG6WtiFrYfSmDRNOeQ2jTQfii0njfffBObNm3COeecg8bGxuy8xWSPF5thzhIqRDRZlTXKe8MNN+DKK68ccJ6ZM2dm/25ubsby5cuxbNkyPPTQQ4Ou/9Zbb8VNN92UfRwOhzFjxoxht3cwwjIR/e1/wWrbD6WuEZ7LbmQgnYhGTe/bvXsrNms8dz1+f2VJ2ko0HsViUUS603e1qarKzwcRlVTvi9+5cgPssVgULpc7GxyPxaKQAPhVFYcjKcRSBmzbRjKVRHXOWChEY1mhLPHhlBEZKwHfPe0pdEYtvHMwAY9DhscpF9WmgbLle7820zSxd+9eXHjhhairq8ubt5jAfbHBfZZQIaLJqqxHvpqaGtTU1BQ178GDB7F8+XIsWbIEjzzySFGDYjgcDjgcjiNtZtHsUDustv0AAKt1H+xQO2udE9GoGan65KxzTlRYJtsz8zcRUSkNdPE7FosiFo0inmiDy+mG25MOtBtGCqZpQvTMF6ishG2bsAVgGAbi8RgMIwVN07N302T+HuhCPNFYMJys8mJLu4yEgdYvJABCoDlkYKpfQ31AO+I2ZF6baZrYtm0bFixYgIsuuqhg/fNiLiaMlQsORERj1bi4fNjc3IwzzzwTjY2NuPfee9HW1pZ9rr5+7ASpZX8NlLpGWK37oNQ1QvYXd4GAiGgkZAJ8yUQCHWYbfD5/9kTYMFJoazsMCekv1YWC44PdNk5EyGZ6MshERKU2UMk0TdMRT7RBkRUIiGyfHQ6HEAp2wTBNaKoKj7cC/kAVUskENE0DACQTCUSjEag9d8z6/XqfC+gMqtNoCkZNvN85+OCZRxrkHU4QvtjAezBq4tk3w1BlCd0JCxVOJW+Z42a4UOFUUONR0B61RixQnUgksGbNGti2jTlz5mQ/570Vkz3ODHMiooGNiyPkmjVrsGPHDuzYsQPTp0/Pe04I0c9S5eE843IAgFJVz1IuRDSqNE2Hqqq9yk2kv6Bnbu3OnGjHYlEEg50IBKryBhrt77ZxosnOMFJoaWmGkUrBDJiorq4td5OIaBIzjBRcTjcEBGprp+QFuk3ThGWZkJD+LlBR4UNHRxt03QGXyw3DSMGbk4kO9M16D4dDiHSHYZo83lHp7W5PIWKlB7wtZRmRGo+SrUterEKB90xgPTcgvrMtBcMU2B9KweuQEU3Yecvktr1phD5S3d3deO655+D3+3H22WdDVRl/ICIqpcFroowBn/3sZyGEKPgzVmTqoUd//T0kXvx1uZtDRJOU2+2Bt8IHb6+ap263B7rDCYcjfdIQDHYiFo2io6MNoVBX9tZu0zKZcUYls2fPHnz+85/HrFmz4HK5MHv2bNxxxx1IpVLlbtqgYrEoDMPIlnMhIiolw0hl++dC3G4P3B5PnwC6z+eHP1DZ832gAgAQ6Q4jGo2gq7Md8XgMfn9ldtyTzLLprPdKfgegsphVo6M+oBWdnR2Mmti0J4ZgdGh9cnvUgt+VDnwXa3Zt37ZlAuvrd0TREjSymeqaKmFWrQNupzyk1zNc3d3dmD59Os4999ySBNCHu5+JiCYqXqocIayHTkRjgabpBTPGMlnqyUQCsVgUgUAVAEACEOzqhGmaUFUVqqL2nLCzlAuNvHfffRe2bePHP/4x5syZg7fffhvXXXcdotEo7r333nI3b0C59dB9Pn+ZW0NEE13m7jBhmaiABdlfU/RdrpZlwqHrEABSySSisQhsyy5qTKkMn8+fLV9FVGoBj4rGqe6i5x9OWRZgeOVgCmW/Z5ZfNM2ZzUQPeFRccIyv6JrrR1Kf/cCBA7AsC01NTWhoaBjSskMx3P1MRDRR8Ug4Qoqphy4sE3aofUhfgomIRkrurdqapsPt9qCjoy2bBdz7Vm7WQ6WRtnLlSqxcuTL7+KijjsL27dvxox/9aMwH0fu7QEVEVAputwfCMiE//ygiHc1Q6hrhuezG7DlEf4OAp8u3SRAQCASqEAx2wtlzJ5rb7Sn6IuBA9diJyiW3jAow9NroI1Xzu7/SLENZ/3AD1O+99x7+9re/4fTTTy++wcPEgUaJiPIxkjtCJEWF57Ib+w2SZ8q9WG37+3wJJiIaDYVOiHMzzXo/398JOtFICoVCqKqqKncziIjGnu5O2G0HIckSrNZ9SHUcQsLhzfbZ0WgE3l4XudOl2ULQdQc0TUNt7RReEKcJIy/wPPODzPUjyeoutf7alhugLqb9Qghs3rwZb7zxBlasWIFp06aVvO2ZiwKZsi5jcf8SEY0mHgFHkKSo/ZZwYbkXIhoremeY9xcg752ZTjTSdu7ciQceeADf+973BpwvmUwimUxmH4fD4VI3jYiorGKxKOKyA7K3Co5IJ9T6JsRVJ1KJBEzTTI/TkEoh7ojlDRhuWza6utK/g8EuzJjRxMHCacLoLzN6tMuODCVo31/bcrPWN+2J5c1TaP2SJAEALrnkElRXV4/4axrISO/fsXzRg4hoIONiYNGJIFPuBUC/5V6IiEZDJsM8FosOOB8HGaNirVq1CpIkDfjz2muv5S3T3NyMlStX4vLLL8e111474Prvvvtu+P3+7M+MGTNK+XL6GGyAPyKikeZ2eyAUBfbZVwMX/ws8l90IT4UfiqqmA+zxGOLxWHashsyA4eHuIEzDhJGMwWg9iGBHa5lfCdHIGCjwWmjwz1LKBJV3tg3+vaCYtvWeJ3f9hmHgpZdeQiwWwwknnDDqAfRC7TtSQ9l/RERjCS/7jZLByr0QEY0WZpjTSLvhhhtw5ZVXDjjPzJkzs383Nzdj+fLlWLZsGR566KFB13/rrbfipptuyj4Oh8OjGkhnaSMiGm2apueVYpEUFZqCnkHAFciSDJfLDVVNn1NkBgyvctYg3NUB9S9/gBpqhfPAbIhP3sRzDxr3BsqGzs3qHo0s56HUCi+mTnrvsimZmu/TKiz88Y9/gqqqUBTlyBs+TCNVSz6DtdaJaLzit6kjMNSBQgcq90JENBIMI4VwOAQgXe+8UBY5BwujkVZTU4OamuLusDp48CCWL1+OJUuW4JFHHoEsD35TnMPhgMPhONJmDhsvPBFRORTqrzPHIX+gCoaRgqbpCIW6oGk6PJ50vXQ/TESMCOB2A12HWEaSJoRiA6+ZYHt3wkKFUylJMD3gUTG7Z1uzex4XY7AAf+6FgnnVFp555hnU1NRg+fLlZQ2ij7SRDsoTEY0WHrmGKTtQaOs+yP4aeD75Zci6s9zNIqJJLhaLItKdrhetqiqD5TSmNDc348wzz0RjYyPuvfdetLW1ZZ+rrx+7AR5eeCKisSL/eORBKNSFZCKBaDQCVVFhmiYUSYFcPQ2i4yDLSNK4lxt4zh1MtD+ZIHskbh1xHe+Bgt7DqRM+2DK5Fwocmo2FCxdi8eLF2XroxbaNiIhKg0fbYbJD7bBa98E6tBvm3q0AAO+nvspbJYmorNxuT7Y+KrNmaaxZs2YNduzYgR07dmD69Ol5zwkhytQqIqLxK9PXezUd8XgMsVgUqqLAedZV8Ha3Q5k+l+cnNK4NNVidWxolE2QuxbaHU5JksGUCHhU17Qfx9ub9+PCHP4xjjjlmWG0jIqLS4MCiwyT7ayD7ayBScUi6C1aoDXaovd/5hWXC6myBsMxRbCURTTaapqO6uhbV1bUcEJTGnM9+9rMQQhT8GWs4mCgRjQeZQcDdbk9PvXQVwjKh/OXniK3+GWJP/4jnHzSuDTaoZaaOeDCa/z4PeFQsmek+oizt/rY93Czwwdq0detWvPDCC2hoaBh224iIqHR4yXKYJEWF55NfBgBYoTaoU2b2e6tktvRL234odY3wXHYjM0KIiIjGMA4mSkTlFItFEQx2IhCoKvrOssx8zkQ3Eh3NAACrdV9RNdENI5UdxJQX4Wks6a/+eCaQ3Z2wEE3YAEY+I7u/2t2lyALfsmUL3njjDVxwwQVFlbhjXXEiotHHo+4RkHUnvJ/66qCDi9qhdlht+wEU/0WWiIiIyoeDiRJROQWDnYhFowCKOw7lDiwu+Wug1DXCat1XdE10XjiksaxQ0DozzeuQRyQjeyjZ5cMp5dIfIQQkScLUqVMxc+ZMBAKBI14njT7WqCeaHPjpPkKSog4aEJeH8UWWiIiIyiOTkalpek9mJpiZSUSjxjBSUBQVmqZBUVQYRmrAY5BhpNDWdhixaBS2nc7Irbjo/yF2eD/0KTNg2jZika4Bs8x54ZDGskJB69xpIxG0HCy7PBMkrfEoaI9aI7LdVCqFP//5z1i8eDFmzJjR7zYZmB37WKOeaHLgp3sUSIoKz2U3DpqxTkREROWXyciMRiNQe/psZmYS0WiJxaKQAMiKgkQ8hnBYRXV17aDzA4Cqpo9Z8WQSKacPUjIJIDlolnm6tjqPczQ2FSpdUmw5k2ID0YNll2eCpNsPJeB3KQCOLFgajUaxevVquFwuTJkyZcBtHum2qPRG8u4EIhq7OLDoKMlkrDOATkRENLa53R44nE4EAlVwOJ3MzCSiUZU5Bjl0R9Hzuz1eNEybgeqaWvh8/uw63G5P3t9Ehdx111049dRT4Xa7iy4nIoTAqlWr0NDQAJfLhTPPPBPvvPNOaRs6DJlA9M62gQcKH2zQz8xAnqfN8Rxx+ZhkMonf//73qK6uxsqVK6HrhdfFwUPHj5EYyJaIxj5+womIiIhy5GdkMuhERKMrcwwy3Ck4Yung90ADf/Z3zMrNLGeWOQ0klUrh8ssvx7Jly/DTn/60qGW++93v4j//8z/x6KOPYt68efjWt76Fc889F9u3b0dFRUWJW1y8kcoQzs18b+r/xpBBWZYFh8OB008/HdOmTYMkSf3Om9lmMGpi054Yy7oQEZUZM9GJiIiIiIjGmHRwvDI7PkMykUAsFi13s2gCuvPOO/GlL30JixcvLmp+IQTuv/9+fO1rX8PHPvYxLFq0CI899hhisRieeOKJEWtXJngcjJrDXkfvDOGRWOdw7dq1C08++SQsy8L06dP7BND7a1ux2fRERFRaDKITERHRpGcYKYRCXTAMnqAS0djDkiw0luzevRstLS0477zzstMcDgfOOOMMvPzyyyO2nVIEjwdaZykD7G+++SZefPFFnHLKKVAUZUhtY1kXIqKxgfcCERFNEAPd6k1EA8tkeQIse0BEYw8H/qSxpKWlBQD6DIg5ZcoU7N27t9/lkskkkslk9nE4HB5wO6UYrHGgdZZqIM93330XW7ZswYUXXoi6uroht63YQVSJiKi0mIlORDRO9c6c5a3eRMPHLE8iIppIVq1aBUmSBvx57bXXjmgbvcuRCCEGrPF99913w+/3Z39mzJgx4PpLMVjjQOsc6Yxv0zRhmiZmz56Nj370owMG0AdrWzmVswQOEdFYMraOzkREVLTembOZ4B+DgERDxyxPIiKaSG644QZceeWVA84zc+bMYa27vr4eQDojferUqdnpra2tfbLTc91666246aabso/D4fCggfTRNNSM72DUxM62VMEBPxOJBNasWYOGhgaceOKJ0DRtpJs7akqVoU9ENN7wCEhENE71DpozCEg0fCyHREREE0lNTQ1qampKsu5Zs2ahvr4ezz//PI4//ngAQCqVwosvvoh77rmn3+UcDgccDkdJ2jRSBgqM99ZfcLm7uxvPPfcc/H4/jjvuuFI2d1SUoqwOEdF4xHIuRETjVDpoXsmAH9EIKLYcEgcgJSKiiWbfvn3YsmUL9u3bB8uysGXLFmzZsgWRSCQ7z4IFC/DUU08BSJdx+eIXv4hvf/vbeOqpp/D222/js5/9LNxuNz796U+X62WMiKEMZlqo/IsQIpuBfu6550JVx3/e4lgtM0NENNp4FCQimqCYWUtUvGLLIXEAUiIimmhuv/12PPbYY9nHmezytWvX4swzzwQAbN++HaFQKDvPLbfcgng8juuvvx5dXV1YunQp1qxZg4qKilFt+0gbStZ17/IvkUgEXq8XK1euhNvtHrA+/GgZSmY9ERENjEdRIqIJisE+ouIVWw6JYw8Q0VjHi+g0VI8++igeffTRAecRQuQ9liQJq1atwqpVq454+5v3xnDsUWMj03moddEz3nvvPaxfvx6XX345vF5vCVo2PKxnTkQ0cngUJSKaoBjsIxp5HHuAiMaa3kFzXkSn8aY1lC6fUijIO9YzqbsiBtb//XW07d2K8847b0wF0AHWMyciGkljrxciIqIRwWAfERHRxNc7aM6L6DTe1Pm1foO8Yz2T+s33D6Jl73uYf+J5mDZtWrmb08dwM+sLGesXNIiISo1HPiIiIiIionGqd9CcF9FpvDm+yQ1fP0HZsZpJbRgGotEojp03Ha6KSzBv6sS/aDXWL2gQEZUaj3xERERERETjFIPmNJGNZCb1SInH41i9ejUqKipwzjnn4OQ5/nI3aVSM1QsaRESjZWz1RkREREREREREY1AoFMJzzz2H2tpanHnmmeVuzqgaixc0iIhGk1zuBhAREREREU1WhpFCKNQFw0iVuylEk1IwamLTnhiCUXPQeTdv3oyZM2firLPOgqIoo9A6IiIaK3gZkYhoHDKMFGKxKNxuDzSNt1QSERGNV70HBiWitNEayLKYWt/Nzc2oq6vD6aefDllmLiIR0WTEoz8R0TiUOeGOxaLlbgoREREdAbfbA4fTmR0YlGiy6S8DPBPc3tlW2rs0ZtfqqA9o/db63rp1K1avXo329vZRDaAPJUOeiIhKj5noRETjUOZEmyfcRERE4xsHBqXJbnd7Co1T+04v9UCWuZnuS2a6+zwvhMCrr76Kbdu24YILLkB9fX1J2tGfYjLkiYho9PBITEQ0DvGEm4iIiIgmglk1hb/Tlnogy8GC1PF4HM3Nzbj00ksRCARK1o7+lPoiAhERDQ2D6ERERERERGXCcU5ositlvfOB9BekTqVS2LdvH+bMmYNLL70UkiSVo3klv4hARERDwyMyERERERFRmXBgUaLyKBSkjkajeO655+DxeHDUUUdxEFEiIspiEJ2IiIiIiKhMOM4J0djQ1dWFZ599FtOmTcPpp5/OADoREeVhEJ2IiIiIiKhMOM4J0djQ0dGB+fPnY8mSJWUr4UJERGMXg+hERERERERENCnt2rULXq8Xc+bMKXdTiIhoDOP9SUREREREROOEYaQQCnXBMFLlbgrRuPfmm2/ixRdfRDKZLHdTiIhojGMmOhERERER0TjBgUiJjpwQAhs2bMCOHTtw4YUXoq6urtxNIiKiMY5BdCIiIiIionGCA5ESHTkhBCRJwkc/+lH4fL5yN4eIiMYBlnMhIiIiIiIaJ9IDkVZC05iFTjRUiUQCf/vb32BZFpYtW8YAOhERFY1BdCIiIiIiIiIqu2DUxKY9MQSj5oivu7u7G08//TSi0SgkSRrx9RMR0cTGci5ERERERERjhGGkEItF4XZ7mG1Ok87OthRaggYAYIln5MIVHR0dePbZZzFr1iyceuqpkGXmExIR0dAwiE5ERERERDRGcOBQmsxm1+p5v0eKw+HA8ccfjw996EPMQiciomFhEJ2IiIiIiGiM4MChNJkFPOqIZqC/99576O7uxpIlS7Bo0aIRWy8REU0+vIeJiIiIiIhojODAoUT5hlMnXQiB119/HevXr0d9fX0JW0dERJMFM9GJiIiIiIiIaEwaTp30DRs2YPfu3bjkkktQXV1dyuYREdEkwSA6EREREREREY1JQ6mTLoSAJEmYPn06jjnmGHi93lI3j4iIJgmWcxkmYZmwOlsgrOJvKSMiIqKxxTBSCIW6YBipcjeFiGhMMi2B1rAB0xLlbgpNUgGPiiUz3QgMkoUej8fxxz/+Ee3t7WhsbGQAnYiIRhQz0YdBWCaiv/0vWG37odQ1wnPZjZAU7koiIqLxJhaLIplIAAD8ftYfJiLKZVoCP/trB5qDBhoCGgLlbhBRP0KhEJ577jnU1taisrKy3M0hIqIJiJnow2CH2mG17QcAWK37YIfay9wiIiIiGg632wOH0wm321PuphARjTmdURPNPbWom4MGLJvZ6DT2dHd34/e//z1mzpyJs846C4qilLtJREQ0ATGIPgyyvwZKXSMAQKlrhOyvKXOLiIiIaDg0TYffXwlNYxY6EVFvVR4VDQENANAQ0KDIUplbRJTPtm14vV4sX74cp5xyCiSJ71EiIioN1iAZBklR4bnsRtihdsj+GpZyISIiIiKiCUdVJHzu9Gp0RU1UelT89slyt4joA1u3bsX777+PSy65BDNmzCh3c4iIaIJj9HeYJEWFUlVf7mYQERERERGVjKpIqPVp5W4GUZYQAq+++iq2bduGFStWMPuciIhGBYPoRERERERERDQubN68GTt37sSll16KQCBQ7uYQEdEkwSA6EREREREREY1phmFAVVUsWLAACxYsgNvtLneTiIhoEuHAokREREREREQ0ZkWjUfz+97/H9u3b4Xa7GUAnIqJRxyA6EREREREREY1JnZ2d+N3vfoeamhrMmzcPwaiJTXtiCEbNcjeNiIgmEZZzISIiIiIiIqIxxzRNPPvss1iwYAGWLFkCSZKwsy2BlqABAFjiYUiDiIhGB3scIiIiIiIiIhpTotEoPB4PPvrRj8Lr9Wanz67V834TERGNBpZzISIiIiIiIqIx480338STTz6JVCqVF0AHgIBHxZKZbgSYhU5ERKNo3AXRk8kkjjvuOEiShC1btpS7OUREREREREQ0AoQQePnll/HGG2/g/PPPh64z25yIiMaGcRdEv+WWW9DQ0FDuZhARERERERHRCNq1axf279+PSy+9FLW1teVuDhERUda4uv/pueeew5o1a/Dkk0/iueeeK3dziIiIiIiIiOgIJRIJmKaJo446CjNmzGAGOhERjTnjJhP98OHDuO666/C///u/cLvd5W4OERERERERER2h7u5uPP3003jrrbcgSRID6ERENCaNiyC6EAKf/exn8c///M848cQTi14umUwiHA7n/RARERERERHR2PDMM8+goaEBS5cuLXdTiIiI+lXWci6rVq3CnXfeOeA8r776Kl5++WWEw2HceuutQ1r/3XffXXD9DKYTEY2uWCzGY28vFRUVkCSp3M0Yl4QQANifExGNNvbnhbFPH55Mf97U1ITFixcjEomUuUVERDSZDdafSyLTc5VBe3s72tvbB5xn5syZuPLKK/GHP/wh74VYlgVFUXDVVVfhscceK7hsMplEMpnMPj548CAWLlw4Mo0nIiI6AqFQCD6fr9zNGJcOHDiAGTNmlLsZREREANinDxf7cyIiGksG68/LGkQv1r59+/IyHpqbm7FixQr85je/wdKlSzF9+vSi1mPbNpqbm0ckUyAcDmPGjBnYv3//pP7CxP3wAe6LNO6HNO6HD3BfpPXeD8xaGz7256XBfZHG/ZDG/fAB7os07oe0QvuBffrwjGR/PpbxszM03F9Dw/01NNxfQzeZ9tlg/VFZy7kUq7GxMe+x1+sFAMyePbvoADoAyLI8pPmL4fP5JvybqBjcDx/gvkjjfkjjfvgA90Ua98ORY39eWtwXadwPadwPH+C+SON+SON+OHKl6M/HMr5nhob7a2i4v4aG+2vouM/GycCiRERERERERERERETlMC4y0XubOXMmxkEVGiIiIiIiIiIiIiIa55iJPkwOhwN33HEHHA5HuZtSVtwPH+C+SON+SON++AD3RRr3w9jE/8sHuC/SuB/SuB8+wH2Rxv2Qxv1AQ8X3zNBwfw0N99fQcH8NHffZB8bFwKJEREREREREREREROXATHQiIiIiIiIiIiIion4wiE5ERERERERERERE1A8G0YmIiIiIiIiIiIiI+sEg+hDt2bMHn//85zFr1iy4XC7Mnj0bd9xxB1KpVN58kiT1+XnwwQfL1OqRV+x+2LdvHy6++GJ4PB7U1NTgC1/4Qp95xru77roLp556KtxuNwKBQMF5Jvr7IaOYfTEZ3hOFzJw5s8974Ktf/Wq5m1VyP/zhDzFr1iw4nU4sWbIEL730UrmbNOpWrVrV539fX19f7mZNeuzP09if52Ofnsb+vH+TtT8H2KezP6diFXMM7U0IgVWrVqGhoQEulwtnnnkm3nnnndI2dAzp6urC1VdfDb/fD7/fj6uvvhrBYHDAZT772c/2+Uyecsopo9PgUTbU4++LL76IJUuWwOl04qijjppw31MGM5T9tW7duoLf7d59991RbHH5/PWvf8XFF1+MhoYGSJKE3/3ud4MuM5nfX2q5GzDevPvuu7BtGz/+8Y8xZ84cvP3227juuusQjUZx77335s37yCOPYOXKldnHfr9/tJtbMsXsB8uycOGFF6K2thZ/+9vf0NHRgWuuuQZCCDzwwANlfgUjJ5VK4fLLL8eyZcvw05/+tN/5JvL7IWOwfTFZ3hP9+cY3voHrrrsu+9jr9ZaxNaX3y1/+El/84hfxwx/+EKeddhp+/OMf4/zzz8fWrVvR2NhY7uaNqg996EP485//nH2sKEoZW0MA+/MM9uf52KensT8f2GTrzwH26Rnsz6kYxfYlub773e/iP//zP/Hoo49i3rx5+Na3voVzzz0X27dvR0VFRYlbXH6f/vSnceDAAaxevRoA8E//9E+4+uqr8Yc//GHA5VauXIlHHnkk+1jX9ZK2sxyGevzdvXs3LrjgAlx33XX4+c9/jvXr1+P6669HbW0tPv7xj5fhFYyu4fZX27dvh8/nyz6ura0djeaWXTQaxbHHHot//Md/LOr9MdnfXxB0xL773e+KWbNm5U0DIJ566qnyNKhMeu+HZ599VsiyLA4ePJid9n//93/C4XCIUChUjiaW1COPPCL8fn/B5ybb+6G/fTHZ3hO5mpqaxH333VfuZoyqk08+WfzzP/9z3rQFCxaIr371q2VqUXnccccd4thjjy13M6gI7M/TJnt/LgT79Az2531Nxv5cCPbpQrA/p6EbqC/JZdu2qK+vF9/5zney0xKJhPD7/eLBBx8sYQvHhq1btwoAYuPGjdlpGzZsEADEu+++2+9y11xzjbj00ktHoYXlNdTj7y233CIWLFiQN+3//b//J0455ZSStXEsGer+Wrt2rQAgurq6RqF1Y1sx33En+/uL5VxGQCgUQlVVVZ/pN9xwA2pqanDSSSfhwQcfhG3bZWjd6Om9HzZs2IBFixahoaEhO23FihVIJpPYtGlTOZpYVpPt/VDIZH9P3HPPPaiursZxxx2Hu+66a0Lf9p5KpbBp0yacd955edPPO+88vPzyy2VqVfm8//77aGhowKxZs3DllVdi165d5W4SFcD+PI39+eAm23uit8n+nphM/TnAPj0X+3Mqhd27d6OlpSXvM+ZwOHDGGWdMis/Yhg0b4Pf7sXTp0uy0U045BX6/f9DXv27dOtTV1WHevHm47rrr0NraWurmjqrhHH83bNjQZ/4VK1bgtddeg2EYJWvrWHAk/dXxxx+PqVOn4uyzz8batWtL2cxxbTK/vwCWczliO3fuxAMPPIDvfe97edO/+c1v4uyzz4bL5cILL7yAm2++Ge3t7fj6179eppaWVqH90NLSgilTpuTNV1lZCV3X0dLSMtpNLKvJ9n7oz2R+T/zbv/0bTjjhBFRWVuLvf/87br31VuzevRs/+clPyt20kmhvb4dlWX3+31OmTJnw/+veli5div/5n//BvHnzcPjwYXzrW9/CqaeeinfeeQfV1dXlbh71YH+exv58cJPtPVHIZH5PTLb+HGCfnsH+nEol8zkq9Bnbu3dvOZo0qlpaWlBXV9dnel1d3YDHmPPPPx+XX345mpqasHv3btx2220466yzsGnTJjgcjlI2edQM5/hbqI+eMmUKTNNEe3s7pk6dWrL2lttw9tfUqVPx0EMPYcmSJUgmk/jf//1fnH322Vi3bh1OP/300Wj2uDKZ318ABxbNKjRQTO+f1157LW+Z5uZmrFy5EpdffjmuvfbavOe+/vWvY9myZTjuuONw88034xvf+Ab+4z/+YzRf0rCM9H6QJKnPNoQQBaePJcPZDwMZr+8HYOT3xXh9TxQylH3zpS99CWeccQaOOeYYXHvttXjwwQfx05/+FB0dHWV+FaXV+/86Xv/XR+L888/Hxz/+cSxevBjnnHMOnnnmGQDAY489VuaWTUzsz9PYn3+AfXoa+/P+sT8vzmTv09mfT24jfQwtZKJ9xoayz4bTp1xxxRW48MILsWjRIlx88cV47rnn8N5772U/mxPJUN8bheYvNH2iGsr+mj9/Pq677jqccMIJWLZsGX74wx/iwgsv7DNGEn1gMr+/mIne44YbbsCVV1454DwzZ87M/t3c3Izly5dj2bJleOihhwZd/ymnnIJwOIzDhw/3uWozlozkfqivr8crr7ySN62rqwuGYYzpfQAMfT8M1Xh5PwAjuy/G83uikCPZN5mR43fs2DEhs5dqamqgKEqfK/6tra3j8n89kjweDxYvXoz333+/3E2ZkNifp7E//wD79DT25/1jfz4w9umFsT+fXErZl9TX1wNIZ3jmZnGO989YsfvszTffxOHDh/s819bWNqTXP3XqVDQ1NU2oz+Rwjr/19fUF51dVdcL2Uxkj1V+dcsop+PnPfz7SzZsQJvP7C2AQPaumpgY1NTVFzXvw4EEsX74cS5YswSOPPAJZHjyhf/PmzXA6nQgEAkfY0tIayf2wbNky3HXXXTh06FD2y8CaNWvgcDiwZMmSEW/7SBrKfhiO8fJ+AEZ2X4zn90QhR7JvNm/eDAAT9nYnXdexZMkSPP/887jsssuy059//nlceumlZWxZ+SWTSWzbtg0f+chHyt2UCYn9eRr78w+wT09jf94/9ucDY59eGPvzyaWUfcmsWbNQX1+P559/HscffzyAdG3nF198Effcc09Jtjkait1ny5YtQygUwt///necfPLJAIBXXnkFoVAIp556atHb6+jowP79+yfU8Xg4x99ly5bhD3/4Q960NWvW4MQTT4SmaSVtb7mNVH+1efPmCfU+GkmT+f0FACjPeKbj18GDB8WcOXPEWWedJQ4cOCAOHTqU/cl4+umnxUMPPSTeeustsWPHDvHwww8Ln88nvvCFL5Sx5SOrmP1gmqZYtGiROPvss8Xrr78u/vznP4vp06eLG264oYwtH3l79+4VmzdvFnfeeafwer1i8+bNYvPmzaK7u1sIMTneDxmD7YvJ8p7o7eWXXxb/+Z//KTZv3ix27dolfvnLX4qGhgZxySWXlLtpJfWLX/xCaJomfvrTn4qtW7eKL37xi8Lj8Yg9e/aUu2mj6uabbxbr1q0Tu3btEhs3bhQXXXSRqKiomHT7Yaxhf57G/jwf+/Q09ueFTdb+XAj26UKwP6fiDXYMFUKI+fPni9/+9rfZx9/5zneE3+8Xv/3tb8Vbb70lPvWpT4mpU6eKcDhcjpcw6lauXCmOOeYYsWHDBrFhwwaxePFicdFFF+XNk7vPuru7xc033yxefvllsXv3brF27VqxbNkyMW3atAm3zwY7/n71q18VV199dXb+Xbt2CbfbLb70pS+JrVu3ip/+9KdC0zTxm9/8plwvYVQNdX/dd9994qmnnhLvvfeeePvtt8VXv/pVAUA8+eST5XoJo6q7uzt7jAKQ/Z6zd+9eIQTfX70xiD5EjzzyiABQ8CfjueeeE8cdd5zwer3C7XaLRYsWifvvv18YhlHGlo+sYvaDEOkvEBdeeKFwuVyiqqpK3HDDDSKRSJSp1aVxzTXXFNwPa9euFUJMjvdDxmD7QojJ8Z7obdOmTWLp0qXC7/cLp9Mp5s+fL+644w4RjUbL3bSS+8EPfiCampqEruvihBNOEC+++GK5mzTqrrjiCjF16lShaZpoaGgQH/vYx8Q777xT7mZNeuzP09if52Ofnsb+vLDJ3J8LwT6d/TkVq5hjKADxyCOPZB/bti3uuOMOUV9fLxwOhzj99NPFW2+9NfqNL5OOjg5x1VVXiYqKClFRUSGuuuoq0dXVlTdP7j6LxWLivPPOE7W1tULTNNHY2CiuueYasW/fvtFv/CgY6Ph7zTXXiDPOOCNv/nXr1onjjz9e6LouZs6cKX70ox+NcovLayj765577hGzZ88WTqdTVFZWig9/+MPimWeeKUOry2Pt2rUFj1fXXHONEILvr94kIXoqwBMRERERERERERERUZ7Bi38SEREREREREREREU1SDKITEREREREREREREfWDQXQiIiIiIiIiIiIion4wiE5ERERERERERERE1A8G0YmIiIiIiIiIiIiI+sEgOhERERERERERERFRPxhEJyIiIiIiIiIiIiLqB4PoRERERERERERERET9YBCdaAxYt24dJElCMBgEADz66KMIBAJlbdOR6v2aiIiIJgP26URERDSSVq1aheOOO67czSCa9BhEJxpBDz74ICoqKmCaZnZaJBKBpmn4yEc+kjfvSy+9BEmS8N577+HUU0/FoUOH4Pf7R7vJ40pnZyduvPFGzJ8/H263G42NjfjCF76AUCiUN58kSX1+PvzhD+fNs3btWlxwwQWorq6G2+3GwoULcfPNN+PgwYOj+ZKIiGiMYp9eWuzTiYiIyueNN97Apz71KcyYMQMulwtHH300vv/97+fNk7mI3vvn61//enYeIQQeeughLF26FF6vF4FAACeeeCLuv/9+xGKx0X5ZRXnyySexcOFCOBwOLFy4EE899VS5m0TjBIPoRCNo+fLliEQieO2117LTXnrpJdTX1+PVV1/N60TWrVuHhoYGzJs3D7quo76+HpIklaPZ40ZzczOam5tx77334q233sKjjz6K1atX4/Of/3yfeR955BEcOnQo+/P0009nn/vxj3+Mc845B/X19XjyySexdetWPPjggwiFQvje9743mi+JiIjGKPbppcU+nYiIxivDMIqaNpZt2rQJtbW1+PnPf4533nkHX/va13Drrbfiv//7v/vMu3379rx++Ktf/Wr2uauvvhpf/OIXcemll2Lt2rXYsmULbrvtNvz+97/HmjVrht2+VCo17GUHsmHDBlxxxRW4+uqr8cYbb+Dqq6/GJz/5Sbzyyisl2R5NMIKIRlRDQ4O4++67s49vueUW8a//+q9i4cKF4vnnn89OP+uss8RVV10lhBBi7dq1AoDo6uoSQgjxyCOPCL/fn7fe3//+92LJkiXC4XCI6upqcdlll2Wf6+zsFFdffbUIBALC5XKJlStXivfee08IIURra6uYMmWKuOuuu7Lzb9y4UWiaJv70pz8Nunxue1avXi0WLFggPB6PWLFihWhubu53P/R+TUII8Zvf/EYsXLhQ6LoumpqaxL333pu3THNzs7jggguE0+kUM2fOFI8//rhoamoS9913X7/b+dWvfiV0XReGYWSnARBPPfVUwfn3798vdF0XX/ziFws+n9teIiKa3Nini4KvSQj26URENDIsyxLf+c53xOzZs4Wu62LGjBniW9/6Vvb5W265RcydO1e4XC4xa9Ys8fWvf12kUqns89dcc4249NJL89b5b//2b+KMM87IPv71r38tFi1aJJxOp6iqqhJnn322iEQiQggh/v73v4tzzjlHVFdXC5/PJ04//XSxadOmvPUBED/60Y/EJZdcItxut7j99tvFHXfcIY499ljx05/+VMyaNUtIkiRs2xbPPfecOO2004Tf7xdVVVXiwgsvFDt27Miua/ny5eJf//Vf89bf3t4udF0XL7zwQsF9lNlW7j678847xbRp04Su6+LYY48Vzz33XN4y69evF8cee6xwOBxiyZIl4qmnnhIAxObNm/v9X1x//fVi+fLl2ceF+v9cv/zlLwUA8bvf/a7Pc7Zti2Aw2O+2emtqahLf/OY3xTXXXCN8Pp/4h3/4h6KXHYpPfvKTYuXKlXnTVqxYIa688sqSbI8mFmaiE42wM888E2vXrs0+Xrt2Lc4880ycccYZ2empVAobNmzA8uXLi1rnM888g4997GO48MILsXnzZrzwwgs48cQTs89/9rOfxWuvvYann34aGzZsgBACF1xwAQzDQG1tLX72s59h1apVeO211xCJRPCZz3wG119/Pc4777xBl8+IxWK499578b//+7/461//in379uHLX/5y0ftl06ZN+OQnP4krr7wSb731FlatWoXbbrsNjz76aHaef/iHf0BzczPWrVuHJ598Eg899BBaW1sHXG8oFILP54OqqkW149e//jVSqRRuueWWgs+P97q1REQ0ctinF8Y+nYiIRsqtt96Ke+65B7fddhu2bt2KJ554AlOmTMk+X1FRgUcffRRbt27F97//fTz88MO47777il7/oUOH8KlPfQqf+9znsG3bNqxbtw4f+9jHIIQAAHR3d+Oaa67BSy+9hI0bN2Lu3Lm44IIL0N3dnbeeO+64A5deeineeustfO5znwMA7NixA7/61a/w5JNPYsuWLQCAaDSKm266Ca+++ipeeOEFyLKMyy67DLZtAwCuvfZaPPHEE0gmk9l1P/7442hoaCj6u8T3v/99fO9738O9996LN998EytWrMAll1yC999/P/uaLr74YixevBivv/46vvnNb+IrX/nKoOsNhUKoqqoqqg2Zds+fPx+XXnppn+ckSRpyabv/+I//wKJFi7Bp0ybcdtttBef59re/Da/XO+DPSy+91O82NmzYkP3OlLFixQq8/PLLQ2orTVJlDeETTUAPPfSQ8Hg8wjAMEQ6Hhaqq4vDhw+IXv/iFOPXUU4UQQrz44osCgNi5c6cQYvCstWXLlmUz3Hp77733BACxfv367LT29nbhcrnEr371q+y066+/XsybN09cddVVYtGiRSIejxe9/COPPCIA5F1B/8EPfiCmTJnS737o/Zo+/elPi3PPPTdvnn//938XCxcuFEIIsW3bNgFAvPrqq9nn33//fQGg36y19vZ20djYKL72ta/lTQcgnE6n8Hg82Z9MFtu//Mu/CJ/P12+7iYiIMtini4KviX06ERGNhHA4LBwOh3j44YeLXua73/2uWLJkSfbxYJnomzZtEgDEnj17ilq/aZqioqJC/OEPf8hOA9Dnrqc77rhDaJomWltbB1xfa2urACDeeustIYQQiURCVFVViV/+8pfZeY477jixatWqftfROxO9oaEh7640IYQ46aSTxPXXXy+EEOJHP/qRqK6uzn4/EEKIhx9+eMBM9JdffllomibWrFmTnZbp/3P7YI/HI9rb24UQQhx99NHikksuGfD1F6upqUl89KMfHXS+jo4O8f777w/4E4vF+l1e0zTx+OOP5017/PHHha7rR/waaOIrLs2DiIq2fPlyRKNRvPrqq+jq6sK8efNQV1eHM844A1dffTWi0SjWrVuHxsZGHHXUUUWtc8uWLbjuuusKPrdt2zaoqoqlS5dmp1VXV2P+/PnYtm1bdtq9996LRYsW4Ve/+hVee+01OJ3OIS3vdrsxe/bs7OOpU6cOmlHWu529r1CfdtppuP/++2FZFrZv3w5VVXHCCSdkn58zZw4qKysLri8cDuPCCy/EwoULcccdd/R5/r777sM555yT114gPfAJ69QSEVEx2KcXxj6diIhGwrZt25BMJnH22Wf3O89vfvMb3H///dixYwcikQhM04TP5yt6G8ceeyzOPvtsLF68GCtWrMB5552HT3ziE9k+qbW1Fbfffjv+8pe/4PDhw7AsC7FYDPv27ctbT+5dYxlNTU2ora3Nm7Zz507cdttt2LhxI9rb27MZ6Pv27cOiRYvgcDjwmc98Bj/72c/wyU9+Elu2bMEbb7yB3/3ud0W9nnA4jObmZpx22ml500877TS88cYbANI1zI855pjs9wMAOPnkk/td5zvvvINLL70Ut99+O84999w+z7/00kuoqKjIPs7su5Huhwvt496qqqqGlC1fSO828/sEFYvlXIhG2Jw5czB9+nSsXbsWa9euxRlnnAEAqK+vx6xZs7B+/XqsXbsWZ511VtHrdLlc/T4nem5DKzQ9tyPYtWsXmpubYds29u7dO+TlNU3Le16SpH6XLWZ9vbc9UDt66+7uxsqVK+H1evHUU0/1aRuQ3t9z5szJ/ng8HgDAvHnzEAqFcOjQoaLbTkREkxP79P7byT6diIiO1EB9IgBs3LgRV155Jc4//3z88Y9/xObNm/G1r30tb9BJWZb79C+5JcwURcHzzz+P5557DgsXLsQDDzyA+fPnY/fu3QDSZdA2bdqE+++/Hy+//DK2bNmC6urqPgNbZvqewaZdfPHF6OjowMMPP4xXXnklO2Bl7vquvfZaPP/88zhw4AB+9rOf4eyzz0ZTU9OA+6K3gQLBg/XTubZu3YqzzjoL1113Hb7+9a8XnGfWrFl5/bAsp0OJ8+bNy7tIf6QK7c/ejrScS319PVpaWvKmtba25pUQIuoPg+hEJbB8+XKsW7cO69atw5lnnpmdfsYZZ+BPf/oTNm7cWHS9MwA45phj8MILLxR8buHChTBNM2806Y6ODrz33ns4+uijAaQ77KuuugpXXHEFvvWtb+Hzn/88Dh8+XPTyI2HhwoX429/+ljft5Zdfxrx586AoChYsWADTNLF58+bs8zt27EAwGMxbJhwO47zzzoOu63j66afzrq4X4xOf+AR0Xcd3v/vdgs/33h4REU1u7NMLt5N9OhERHam5c+fC5XL12y+uX78eTU1N+NrXvoYTTzwRc+fOzbt4DAC1tbV9LqZm6pNnSJKE0047DXfeeSc2b94MXdfx1FNPAUhnWX/hC1/ABRdcgA996ENwOBxob28f1uvp6OjAtm3b8PWvfx1nn302jj76aHR1dfWZb/HixTjxxBPx8MMP44knnsjWWC+Gz+dDQ0NDwX4409cvWLAAb775Zl7d9ddee63Put555x0sX74c11xzDe66666i25Dx6U9/Gu+99x5+//vf93lOCIFQKDTkdQ7mn//5n7Fly5YBfwbKaF+2bBmef/75vGlr1qzBqaeeOuJtpQlolMrGEE0qP/vZz4TL5RKqqoqWlpbs9J///OeioqJCABD79u3LTh+sfuratWuFLMvi9ttvF1u3bhVvvvmmuOeee7LPX3rppWLhwoXipZdeElu2bBErV64Uc+bMyY5a/uUvf1nMnDlThEIhYVmWOP3008WFF15Y9PK92yOEyI7u3Z/er2nTpk1ClmXxjW98Q2zfvl08+uijwuVyiUceeSS7zDnnnCNOOOEE8corr4jXX39dLF++XLhcLnH//fcLIdI185YuXSoWL14sduzYIQ4dOpT9MU0zux4A2XqphfzgBz8QkiSJz33uc2LdunViz5494m9/+5v4p3/6J3HTTTf1uxwREU0+7NPZpxMRUemsWrVKVFZWiscee0zs2LFDbNiwQfzkJz8RQgjxu9/9TqiqKv7v//5P7NixQ3z/+98XVVVVef3Y6tWrhSRJ4rHHHhPvvfeeuP3224XP58vWRN+4caO46667xKuvvir27t0rfvWrXwld18Wzzz4rhEjXIz/33HPF1q1bxcaNG8VHPvIR4XK58sbwKNQX9a5TLoQQlmWJ6upq8ZnPfEa8//774oUXXhAnnXRSweUfeughoeu6CAQCebXLC+m9rfvuu0/4fD7xi1/8Qrz77rviK1/5itA0Tbz33ntCCCFCoZCoqqoS//AP/yC2bt0qVq9eLRYsWCAAiC1btgghhHj77bdFbW2tuOqqq/L64Nwa7737/95s2xZXXHGFcLlc4tvf/rZ49dVXxZ49e8Qf/vAHcdZZZw3Yf/fW1NTU77gpI2n9+vVCURTxne98R2zbtk185zvfEaqqio0bN5Z82zT+MYhOVAK7d+8WAMSCBQvypu/fv18AELNnz86bPtgJtxBCPPnkk+K4444Tuq6Lmpoa8bGPfSz7XGdnp7j66quF3+8XLpdLrFixItuBrl27VqiqKl566aXs/Hv37hV+v1/88Ic/HHT5/toz1BNuIYT4zW9+IxYuXCg0TRONjY3iP/7jP/KWaW5uFueff75wOByiqalJPPHEE6Kurk48+OCDeess9LN79+7segY74RZCiOeff16sWLFCVFZWCqfTKRYsWCC+/OUvi+bm5gGXIyKiyYV9Ovt0IiIqHcuyxLe+9S3R1NSU7VO+/e1vZ5//93//d1FdXS28Xq+44oorxH333denH7v99tvFlClThN/vF1/60pfEDTfckA2ib926VaxYsULU1tYKh8Mh5s2bJx544IHssq+//ro48cQThcPhEHPnzhW//vWv+wR0iw2iC5Huk44++mjhcDjEMcccI9atW1dw+e7ubuF2u7ODgQ6k97YsyxJ33nmnmDZtmtA0TRx77LHiueeey1tm/fr14phjjhG6roslS5aIJ554QgAQ7777bnadhfrgpqam7DoGC6Jn2vKjH/1InHTSScLtdgufzyeWLFkivv/972cH+MysJ7d/7220guhCCPHrX/9azJ8/X2iaJhYsWCCefPLJUdkujX+SEEMogEhENIoOHDiAGTNm4M9//vOAg80QERHR2MY+nYiI6AP79+/HzJkz8eqrr+YNxF0qjz/+OP7xH/8RoVBo0Fr0I+3RRx/FXXfdha1btxYc+4RovFDL3QAiooy//OUviEQiWLx4MQ4dOoRbbrkFM2fOxOmnn17uphEREdEQsE8nIiLqyzAMHDp0CF/96ldxyimnlCyA/j//8z846qijMG3aNLzxxhv4yle+gk9+8pOjHkAHgNWrV+Pb3/42A+g07jGITkRjhmEY+P/+v/8Pu3btQkVFBU499VQ8/vjj7GyJiIjGGfbpREREfa1fvx7Lly/HvHnz8Jvf/KZk22lpacHtt9+OlpYWTJ06FZdffvmwBg8dCb/4xS/Ksl2ikcZyLkRERERERERERERE/ZDL3QAiIiIiIiIiIiIiorGKQXQiIiIiIiIiIiIion4wiE5ERERERERERERE1A8G0YmIiIiIiIiIiIiI+sEgOhERERERERERERFRPxhEJyIiIiIiIiIiIiLqB4PoRERERERERERERET9YBCdiIiIiIiIiIiIiKgfDKITEREREREREREREfXj/wf3jZ/0gZeVPwAAAABJRU5ErkJggg==", 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qRBcuXKA7d+5QWloaderUiby8vOjs2bMUGRlJQUFB5O7urrSepnMdEhJCFhYWtGnTJoqPj6djx46Rm5sbzZw5U1gGADk7O9OOHTvo7t27NG7cODIzMxOOe/To0eTn50fh4eGUkJBAx48fp3///ZeI5AuIUqmU9u7dSwAoNjaWEhMT6c2bN5SRkUGWlpZyDSkvXrwgIyOjQlX6+vbtS23atBE+16tXj/bs2UMjR44UCgdZWVkkkUjojz/+ENZp1KgRnT17lu7du0cLFy4ksVgsfCGh2Chy8OBB0tfXp+nTp1N0dDRFRkbSzz//LMx3dXUla2trWrVqFd29e5fmzp1Lenp6FBMTozLNOTk5ZGZmRhMmTFD6IqEgXRrSiYjq168vd+1Y6fkUY+6TJ09o4cKFdP36dYqPj6fly5eTvr4+Xbp0SdiGLg3p5cuXp3Xr1lFsbCz9+OOPpK+vT9HR0UREauOztn2/efOG/P39KTg4WIhTUqmUnj59ShUqVKCpU6dSTEwMXbt2jVq3bk3NmzcX0jRy5EhydnaWy3Nl10Ad2fVr0aIFXb9+nc6cOUPu7u5yMURV+SM9PZ0cHR2pW7dudPPmTTp58iRVqlRJqBwXXK93795069YtOnjwINnY2Aj5FhHRtGnTyNPTk0JCQig+Pp42btxIYrFY+BJOFksaNGhAoaGhdPv2bQoICJDL76tXr079+/enmJgYiouLo927d8s1usjyuoyMDPruu++oevXqwrnNyMigCxcukL6+vtwX0P/88w+ZmpqqLfuoou2e0OWcLVmyhCwsLOjPP/+kO3fu0Pfff0+GhoZC3q3YMFlS90VRG9JXrFhBFStWFCq0smlOTk6Um5tLRPlf8B8+fJji4+MpLCyMGjZsSO3atROWl117Hx8fOnbsGN27d49evnypVGY+efIkbd26laKjoyk6OpqGDRtGdnZ2lJKSIiyjLd4/efKErK2tqVu3bhQeHk6xsbG0YcMGoeFYWzlWF6oa0q9evUoA6MCBAxQREUF6eno0e/Zsio2NpY0bN5JEIhEaMIjk86E9e/aQhYUFHT58mB4+fEiXL18W8tfw8HDS19enHTt20IMHD+jatWv066+/CtsZN24cOTo60uHDh+n27ds0aNAgsrKyEs6HLs+dIlVlecX79cmTJ2RiYkKjRo2imJgY2r9/P1WoUEGu4UzXtCneF0zZxxi/tdVLMjIy6MSJEwSArly5QomJiZSWlqY0TSqVaq0LaCorFKYuXLt2beFLr8jISLKysiIjIyN6+/YtERF99dVX1Lt3byLSrf5AlF8GsbCwoJkzZ1JcXBxt3ryZRCIRHTt2TGUapFIp2dnZCfWAgtNkHV8KW9/9EI4zLy+PatWqRU2aNKGIiAi6dOkS1a5dW+4e0tYeoK5cpUph6uGa7hFd6mGa6u+pqanUq1cvatu2rbDtgp0QZN68eUOzZ88mZ2dnSkxMpKSkJCLK/1J27969FBcXR9evX6eOHTtSzZo1hfibmppKlStXpoCAADp37hzdvXuXdu3aRRcvXlRbVtLlWqjKU54+fUoGBga0ZMkSSkhIoKioKFq1apXa5yk5OZlEIpFcmZxIe3nxyZMnZGVlRcuWLSOi/C/26tatK7QfFKVtQtv11PRsaWpPSE9Pp6pVq9LQoUMpKiqKoqOjqW/fvlStWjXhes+fP58sLS3pr7/+Eso45ubmGhvSz58/T3p6erRw4UKKjY2lVatWkbW1tVw5rjjaSrSVQXW59tHR0QSAHjx4oPZ4FHFD+ntSzCwvX75M5cuXp169ehFR/gNsaGgoZCQygYGB5OXlJVeonzx5Mnl5eQmfFRthExMTCQCZmJjQkiVL6Pr16zR37lwSiURyPS2nTJmisYfp0aNHSU9Pj2JjY1XO17Uh3c7OTi4TlQUpWYYh4+LiotS7bs6cOeTv7y+3XsFge/v2bQIgNIzpWmny9/dXalRu0KCB3PFoSw8Rka+vr8Zv2omIFixYQHXq1BE+F7YhnUh1w13NmjUL1Ujn6+tLs2fPlpsm+/a2YKYfExNDAOjy5ctqt+Xt7U0rVqzQmL6Cdu/eLRSAC+634LfMq1atIjs7O+Gzo6Mj/fDDDyq3d/LkSbKwsFBq7KxSpYpQ8NLlGiuSBaudO3cK0169ekUSiYR27doll/aCvd/j4uIIAF24cEGY9vLlS5JIJEKDtC7nOiAggH755Re5NG3dupUcHByEzwDoxx9/FD6npaWRSCQSev917NiRhgwZovL4FCuHqirIRPkNGAUbBpYtW0aVK1eWy4e0Wbt2LZmamlJOTg6lpKSQgYEBPX/+nHbu3ClUas+cOUMAKD4+nu7du0cikYj+++8/ue20bNmSpk6dSkTKz7e/v7/Qm10VV1dXuV5GeXl5ZGtrS7///rvadf766y+ysrIiY2NjatSoEU2dOpVu3LihtF0jIyMyNTWV+1PsddG1a1caPHiwxvPESsanGHNVad++PX333Xdy6dfWkK4qX5RVsNTF5/fZNxHR//73P7kv1IjyexPJKqapqalkZGSkMs/V1pCur69Pjx8/FqYdOXKE9PT0hC+bVZU/1q5dS1ZWVnK9Sg4dOkR6enpC76RBgwaRtbU1paenC8v8/vvvZGZmRrm5uZSWlkbGxsbCr+Fkhg0bRn369CEi+Z6xBfcDgN69e0dERObm5mobNBXzOlXlLaL8WFzwi/ouXboUOs/Rdk/ocs4cHR3lvsQkyv/ydNSoUUSkHHtK6r5Q1ZAOQC6fLljOUCTrfX727Flhmr+/P02aNEntOleuXCEASr39/v77b7nl1F1DGalUSubm5sIvUIi0x/upU6dSpUqV5H71WJAu5VhtFMsJL1++pE6dOpG5uTk9f/6c+vbtS61bt5ZbZ9KkSeTt7S18LpgPLV68mDw8PFSmee/evWRhYSH3ZULBYzc0NKTt27cL07Kzs8nR0ZEWLFggl1ZNz50qimV5xft12rRpVK1aNbm4sGrVKrk8Qde0Kd4XTNnHGL91qZdcv35dqVexqmna6gLaygq61oW//fZb+uKLL4gov5zfo0cPql27ttAj1cPDQygv61p/CAwMpCZNmshNq1evntIvfQoaN24ctWjRQvh89OhRMjIyouTkZCIqfH33QzjOY8eOkb6+vtyvjmVtFrJfqrxPe4A671MPV7xHdK2Haau/qzouVRTbjFRJSkoiAHTz5k0iIlqzZg2Zm5sLDaKKVMVZXa6FqjxF9oWxrg2lsme54H50KS8S5V8fsVhMU6dOJRMTE7ln+33bJnS5npqeLU3tCevXr1eKibLOcUePHiUiIgcHB5o3b54wPycnh5ydnTXeG71791b6pWW/fv3k7tXiaCvRVgbV5dq/ffuWAO2/Yi+IX+RWBAcPHoSZmRmkUilycnLQuXNnrFixQpjv6uqq8j2CDRs2hEgkEj77+/tj8eLFyM3NVfnu3by8PABA586d8c033wAA/Pz8cPHiRaxevRqBgYEAgLlz52pMb2RkJJydnYv87uKaNWuqfC9p3bp1hf+/ePECjx8/xrBhw+TeOyyVSpXev+jj4yP8X/Y+uaSkJHh6euqcppiYGIwYMUJumr+/P06fPl3o9Cj666+/sGzZMty7dw9paWmQSqWwsLDQOW26GjduHEaOHIljx46hVatW6N69u9y5UfTu3TuV7/szMDCQuxaenp4oV64cYmJiUL9+faSnp2PWrFk4ePAgnj59CqlUinfv3mkchOf06dP45ZdfEB0djZSUFEilUmRmZiI9PR2mpqYAABMTE1SpUkVYx8HBAUlJSQDyr+fTp0/RsmVLldu/evUq0tLSUL58eaVjjI+PB6D9Gmvi7+8v/N/a2hrVqlVDTEyMMM3IyEjuXMfExMDAwAANGjQQppUvX15pPW3n+urVqwgPD8fPP/8sLJObm4vMzEy594oX3LepqSnMzc2Fczdy5Eh0794d165dQ5s2bdClS5dCv38uODgY9erVw3///QcnJyds3LhRGPxJV82bN0d6ejrCw8Px+vVreHh4wNbWFoGBgRgwYADS09MRGhqKihUronLlytizZw+ISCm/ycrKUrrOMpGRkUrvKVdU8FyJRCLY29sL50qV7t27o0OHDjh37hzCwsIQEhKCBQsW4I8//sDgwYOF5SZNmiT3GQCcnJzkPkskEmRkZGhMHys5n1rMzc3Nxbx587Br1y78999/yMrKQlZWlpCn6qpg/ib7HBkZKTetYD5VlH1fvXoVp0+fVvlu6Pj4eLx79w7Z2dkq81xtKlasCGdnZ7njyMvLQ2xsrPCOU8XyR0xMDHx9feXS3bhxY2E9Ozs7AICvr6+Q38q2nZaWhsePHyMpKQmZmZlo3bq1XHqys7NRq1YtuWnqyisVK1bEt99+i+HDh2Pr1q1o1aoVevbsKRcTdTF8+HCsXbsW33//PZKSknDo0KH3GixR0z2h7ZxJJBI8ffoUjRs3lttG48aN1Q5IWZL3hSJzc3Ncu3ZN+KzpnaE2NjZo3bo1tm/fjoCAACQkJCAsLEzuvczXr1/HzJkzERkZieTkZOH5f/ToEby9vYXlFJ8hRUlJSZg+fTpOnTqF58+fIzc3FxkZGUplK03xPjIyEgEBASrf5VqUcqwqsmctPT0dVatWxZ49e2Bra4uYmBh07txZbtnGjRtj2bJlKvPMnj17YtmyZahcuTLatm2L9u3bo2PHjjAwMEDr1q3h6uoqzGvbti26du0KExMTxMfHIycnR+4+MzQ0RP369eXKWYrnTPG5ex8xMTHw9/eXiwuNGzdGWloanjx5gjdv3uicNm33Bcv3scVvXeolutJWFyiu+nmzZs2wfv165OXl4cyZM2jZsiUqVqyIM2fOoHbt2oiLixOOvzAU66IF63eq9OvXD/7+/nj69CkcHR2xfft2tG/fHlZWVgAKX9/9EI4zJiYGLi4ucHFxEeZ7e3sLdb569eoVen+FoUs9XNG1a9d0qodpqr8XVXx8PP73v//h0qVLePnypVx8rVGjBiIjI1GrVi1YW1vrvE1dr4VinuLr64uWLVuiZs2aCAoKQps2bdCjRw/hvlT07t07AJBra4mOjtapvNizZ0/s378fc+fOxe+//67y2S5s24Qu11PTs6WpPeHq1au4d++e3NhgAJCZmYn4+Hi8ffsWiYmJcmmWtYHkt3OrFhsbi65du8pNq1+/Pg4ePCh8Lo62Em1l0DZt2mi99rJxjApTz+eG9CJo3rw5fv/9dxgaGsLR0VGp4FvYyrA6FSpUgIGBgVyBHgC8vLxw/vx5nbejbaArPT09pYchJydHaTl1x1VwuiyjXLdunVxjJAClgk/B8yYrLMnWLy6FSU9Bly5dwpdffolZs2YhKCgIlpaW2LlzJxYvXiwso+t502b48OEICgrCoUOHcOzYMcydOxeLFy/G2LFjVS5foUIFvH79WuU8VQ2ksmmTJk3C0aNHsWjRIri7u0MikaBHjx7Izs5Wua2HDx+iffv2GDFiBObMmQNra2ucP38ew4YNkztOxftfJBIJ50XbvZeXlwcHBweEhoYqzSupUcgLniOJRCL3WV1QICKlc6vpXOfl5WHWrFno1q2b0jIFA7Oqcye7Z9u1a4eHDx/i0KFDOHHiBFq2bInRo0dj0aJF2g5RUKtWLfj6+mLLli0ICgrCzZs3ceDAAZ3XBwB3d3c4Ozvj9OnTeP36tVBItbe3R6VKlXDhwgWcPn0aLVq0EI5dX18fV69eVXrG1A3QpstgfJrOlTrGxsZo3bo1WrdujenTp2P48OGYMWOGXMN5hQoV4O7urnE7ycnJhW4YY8XnU4u5ixcvxtKlS7Fs2TLUrFkTpqammDBhgtq8uDAU8yXFc/O++87Ly0PHjh1VDsbj4OCAu3fvFjntMrJjKHgsisehKk9WXF/bPmT5x6FDh5S+PBOLxXKfNZVXZs6cib59++LQoUM4cuQIZsyYgZ07dypVIjQZOHAgpkyZgrCwMISFhcHNzQ0BAQE6r6+JLL26njPFZTStV5r3hZ6enta8uqB+/fph/PjxWLFiBXbs2IHq1avD19cXQH4jcps2bdCmTRts27YNNjY2ePToEYKCgpSeBW35y+DBg/HixQssW7YMrq6uEIvF8Pf3V9qOphimKc9433KsOufOnYOFhQVsbGzkOoeous6aKsouLi6IjY3F8ePHceLECYwaNQoLFy7EmTNnhC89QkNDcezYMUyfPh0zZ85EeHi4sE1d7rPiridoOsaCZVdd0lZccedT97HF7+Ksl2irCxTXQNRNmzZFamoqrl27hnPnzmHOnDlwcXHBL7/8Aj8/P9ja2sLLy6vQ2y1subt+/fqoUqUKdu7ciZEjR2L//v3YuHGjML+w9d0P4TjVxb+C04urPUCRrvVwRbrWwzTV34uqY8eOcHFxwbp16+Do6Ii8vDzUqFFDiIvvc+/rci0A5TxFX18fx48fx8WLF3Hs2DGsWLECP/zwAy5fvoxKlSopba9ChQoAgNevXwsN8rqWFzMyMoTzXpjyj6a2CV2up6ZnS1N7Ql5eHurUqYPt27crpakog4vrUp4ojrYSbWVQXa59cnIygMIdLw/9WgSmpqZwd3eHq6troUYCvnTpktLnqlWrqi0IGxkZoV69eoiNjZWbHhcXJ4xIrwsfHx88efIEcXFxKufb2Njg2bNncje4Yq82XdnZ2cHJyQn379+Hu7u73J+qzEodIyMj5Obmal3Oy8tL5XktanouXLgAV1dX/PDDD6hbty6qVq2Khw8fyi1jY2ODxMREuWnazpu643JxccGIESOwb98+fPfdd1i3bp3abdSqVQvR0dFK06VSKSIiIoTPsbGxePPmjdDD/9y5cxg8eDC6du2KmjVrwt7eHg8ePFC7n4iICEilUixevBgNGzaEh4cHnj59qvH4FJmbm8PNzU1tz7ratWvj2bNnMDAwULo+skCm7RprUnC5169fIy4uTuMvHry9vSGVSnH58mVh2qtXrxAXFydXONN2rmvXro3Y2FilY3J3dy/UyNs2NjYYPHgwtm3bhmXLlmHt2rUql5P11FR1bw0fPhwbN27Ehg0b0KpVK7lv8nXVvHlzhIaGIjQ0FM2aNROmBwYG4ujRo7h06RKaN28OIP/+zM3NRVJSktKxy3qXKvLx8Xmv3peF5e3tjfT09EKvd+vWLaUeqqz0fGox99y5c+jcuTP69+8PX19fVK5c+b0aHFUdn7ZfdOmyb1Vxqnbt2rh9+zbc3NyUnmvZ9TE0NFSZ52rz6NEjudgSFhYGPT09jb30vL29ERkZKfc8X7hwQWm9GzduCL2LgPxzZGZmBmdnZ3h7e0MsFuPRo0dKx1TYfNLDwwPffPMNjh07hm7dusk1HhSkrgxQvnx5dOnSBRs3bsTGjRsxZMiQQu2/4PEpfpbdE9rOmYWFBRwdHZUanS5evKi2caIk74ui6tKlCzIzMxESEoIdO3agf//+wrw7d+7g5cuXmDdvHgICAuDp6fnePfHOnTuHcePGoX379qhevTrEYjFevnxZqG34+Pjg3LlzKhtHiqtcLVOpUiVUqVJF6ReW3t7eKq+9h4eH2jxTIpGgU6dOWL58OUJDQxEWFoabN28CyO+11qpVKyxYsABRUVF48OABTp06BXd3dxgZGcntKycnBxEREe/VCFYY3t7euHjxolx95+LFizA3N4eTk1OZpu1T9bHFb13qJYXZlqa6gLaygq51YUtLS/j5+WHlypUQiUTw9vZGQEAArl+/joMHD2rspa2p/vA++vbti+3bt+PAgQPQ09NDhw4d5OYXpr6rqCyO09vbG48ePcLjx4+FadHR0Xj79q2QJ+jSHqDrtSxIl3q4qu2+Tz1MlfdJM5Bfb46JicGPP/6Ili1bwsvLS6kDoI+Pj/BrMF33rcu1UEckEqFx48aYNWsWrl+/DiMjI+zfv1/lsrL4WLCtRdfy4nfffQc9PT0cOXIEy5cvx6lTp5S2X9i2CV2vp6ZnS117Qu3atXH37l3Y2toqbdvS0hKWlpZwcHCQS7NUKsXVq1c1nW54enriypUrctMKtpvI9l3UthJtZVBA+7W/desWDA0NUb16dZ32CXBDepl4/Pgxvv32W8TGxuLPP//EihUrMH78eI3rTJo0Cbt27cK6detw7949rFy5EgcOHMCoUaOEZaZOnYqBAweq3UZgYCCaNm2K7t274/jx40hISMCRI0cQEhICIP+nUi9evMCCBQsQHx+PVatW4ciRI+99nDNnzsTcuXPx66+/Ii4uDjdv3sTGjRuxZMkSnbfh5uaGtLQ0nDx5Ei9fvlT7c4vx48djw4YN2LBhA+Li4jBjxgzcvn27yOlxd3fHo0ePsHPnTsTHx2P58uVKGW6LFi0QERGBLVu24O7du5gxYwZu3bql9bjOnj2L//77T6hoTZgwAUePHkVCQgKuXbuGU6dOaQwIQUFBCAsLUwowhoaGGDt2LC5fvoxr165hyJAhaNiwIerXry8c0759+xAZGYkbN26gb9++GnsWVKlSBVKpFCtWrMD9+/exdetWrF69WuPxqTJz5kwsXrwYy5cvx927d3Ht2jXhZ52tWrWCv78/unTpgqNHj+LBgwe4ePEifvzxRyHD1eUaqzN79mycPHkSt27dwuDBg1GhQgV06dJF7fJVq1ZF586dERwcjPPnz+PGjRvo378/nJyc5H7yrO1cT58+HVu2bMHMmTNx+/ZtxMTEYNeuXfjxxx91Pm/Tp0/HP//8g3v37uH27ds4ePCg2vvC1dUVIpEIBw8exIsXL5CWlibM69evH/777z+sW7cOQ4cO1Xn/BTVv3hznz59HZGSkXEE1MDAQ69atQ2ZmptCQ7uHhgX79+mHgwIHYt28fEhISEB4ejvnz5+Pw4cMqtz9jxgz8+eefmDFjBmJiYnDz5k0sWLDgvdIK5BfiWrRogW3btiEqKgoJCQnYs2cPFixYoPTT9dTUVDx79kzuLyUlRZj/4MED/Pfff2jVqtV7p4eVjQ815rq7uws9JGJiYvD111/j2bNnhT6+PXv2yOWLV65cwZgxYzSuo8u+3dzccPnyZTx48ED4Se7o0aORnJyMPn364MqVK7h//z6OHTuGoUOHIjc3F2ZmZhg2bBgmTZokl+fqUhg2NjbGoEGDcOPGDaFRslevXhorfP369RPWu3XrFk6fPo2xY8diwIABwmtdgPyf3Q4bNgzR0dFCb/ExY8ZAT08P5ubmmDhxIr755hts3rwZ8fHxuH79OlatWoXNmzdrTTeQ/xPgMWPGIDQ0FA8fPsSFCxcQHh6uNq92c3NDQkICIiMj8fLlS2RlZQnzhg8fjs2bNyMmJgaDBg3Saf+KNN0TupyzSZMmYf78+di1axdiY2MxZcoUREZGqn1uSvK+KCpTU1N07twZ//vf/xATE4O+ffsK8ypWrAgjIyOhfPPvv/9izpw577Ufd3d3bN26FTExMbh8+TL69etX6N52Y8aMQUpKCr788ktERETg7t272Lp1q9AoWBzlam2+++47nDx5EnPmzEFcXBw2b96MlStXYuLEiSqX37RpE9avX49bt24JZUSJRAJXV1ccPHgQy5cvR2RkJB4+fIgtW7YgLy8P1apVg6mpKUaOHIlJkyYhJCQE0dHRCA4ORkZGBoYNG1Zsx6PKqFGj8PjxY4wdOxZ37tzBP//8gxkzZuDbb7+Fnp5emaaNySur+K1LvURX2uoC2soKutaFgfy6/LZt2xAYGAiRSAQrKyt4e3tj165dch1gFGmqP7yPfv364dq1a/j555/Ro0cPuZ6l2uq7np6eahs3y+o4W7VqBR8fH+G4rly5goEDByIwMFB4vZMu7QGqylXa6FIPV3WPvE89TBU3NzdERUUhNjYWL1++1LmXvZWVFcqXL4+1a9fi3r17OHXqFL799lu5Zfr06QN7e3t06dIFFy5cwP3797F3716EhYUJ+1YsK+lyLVS5fPkyfvnlF0RERODRo0fYt28fXrx4obacpqenh1atWsl9oapLefHQoUPYsGEDtm/fjtatW2PKlCkYNGiQ0pcIhW2b0OV6anq2NLUn9OvXDxUqVEDnzp1x7tw5JCQk4MyZMxg/fjyePHkCIL8dZt68edi/fz/u3LmDUaNG4c2bN2rTCwBjx47F4cOHsWTJEty9exdr1qzBkSNH5HqpF0dbibYyqC7X/ty5cwgICChcuU3nt6kzOdoGXlA3CFFgYCCNGjWKRowYQRYWFmRlZUVTpkyRe7m/4sAUMuvXryd3d3cyNjYmX19fpQFutI1ATpQ/mMGQIUOofPnyZGxsTDVq1KCDBw8K83///XdycXEhU1NTGjhwIP38889Kg40qHrfiQB4Fbd++nfz8/MjIyIisrKyoadOmtG/fPrXrvX79mgDIDfI3YsQIKl++PAHQOBDozz//TBUqVCAzMzMaNGgQff/990rXQFN6iFQPNjpp0iQqX748mZmZUe/evWnp0qVKA3pMnz6d7OzsyNLSkr755hsaM2aMxsFFwsLCyMfHh8RiMckewzFjxlCVKlVILBaTjY0NDRgwgF6+fKn2eKVSKTk5OVFISIgwTTbYyN69e6ly5cpkZGRELVq0kBtcISEhgZo3b04SiYRcXFxo5cqVWge0W7JkCTk4OJBEIqGgoCDasmWL3GAtqgY52b9/PylmMatXr6Zq1aqRoaEhOTg40NixY4V5KSkpNHbsWHJ0dCRDQ0NycXGhfv36yQ3yocs1Lkg2oMeBAweoevXqZGRkRPXq1ZMbvEPdID7Jyck0YMAAsrS0FI5bNiq2rueaKH806kaNGpFEIiELCwuqX78+rV27VpgPgPbv3y+3jqWlJW3cuJGI8gcS8/LyIolEQtbW1tS5c2e6f/8+Eal+hmbPnk329vYkEolo0KBBctsdMGAAWVtbKw2eJNuO4uCaimTLeXp6yk2XDehRpUoVuenZ2dk0ffp0cnNzI0NDQ7K3t6euXbtSVFSU3DksaO/evcIzWqFCBerWrZswT9XgO5oGCM7MzKQpU6ZQ7dq1ydLSkkxMTKhatWr0448/UkZGhtx2ASj9ff3118Iyv/zyCwUFBWk8P6zkfIox99WrV9S5c2cyMzMjW1tb+vHHH2ngwIFyx6nLYKOrVq2i1q1bk1gsJldXV/rzzz+F+erisy77jo2NpYYNG5JEIpEbPC0uLo66du1K5cqVI4lEQp6enjRhwgThnKamplL//v3JxMSE7OzsaMGCBVoH2JJdv99++40cHR3J2NiYunXrJgxOJjvfqu6BqKgoat68ORkbG5O1tTUFBwcLA0UWXG/69OlCLB8+fLhcPpiXl0e//vqrEJ9sbGwoKCiIzpw5Q0SqBygrOKhcVlYWffnll+Ti4kJGRkbk6OhIY8aMEQZEVMzrMjMzqXv37lSuXDkCIOT3srS4urpS+/btlY5VNgCVJtruCV3OWW5uLs2aNYucnJzI0NCQfH19hUGdiFTfVyVxX6gabFSXQfcUyQaobNq0qdK8HTt2kJubG4nFYvL396d///1Xp8HpFPOca9euUd26dUksFlPVqlVpz549Kp9XTfGeiOjGjRvUpk0bMjExIXNzcwoICKD4+HhhvrZybGBgoFLsL0jd8RT0119/kbe3NxkaGlLFihVp4cKFcvMLHtf+/fupQYMGZGFhQaamptSwYUNhcNBz585RYGAgWVlZkUQiIR8fH2EwNSKid+/e0dixY6lChQokFoupcePGwmBx6tKqajBHRdoGGyUiCg0NpXr16pGRkRHZ29vT5MmTKScnp0hpY6p9rPFbW71E18FGibTXBbTVz3WtCx84cIAA0MqVK+XOEQC6deuWME3X+oOqPLpz584a8xiZevXqEQA6deqU3HRt9V3FmPihHOfDhw+pU6dOZGpqSubm5tSzZ09hgG4Zbe0B6spVigpbDydSfY+8Tz1Msf6elJRErVu3JjMzM411RVWDjR4/fpy8vLxILBaTj48PhYaGKsXBBw8eUPfu3cnCwoJMTEyobt26dPnyZSJSX1bSdi1U5SnR0dEUFBRENjY2JBaLycPDg1asWKHyWGRCQkLIycmJcnNzhWmayotJSUlkZ2cnN3hmTk4O1a9fXxhcuShtE9qup6ZnS1N7AlH+QM0DBw4UYl7lypUpODiY3r59KxzH+PHjycLCgsqVK0fffvutUr1BlbVr15KTkxNJJBLq0qUL/fTTT2Rvb690novSVkKkuQyqy7X38PBQKitrI/r/iWOlpFmzZvDz88OyZcvKOinsE/Dbb7/hn3/+wdGjR8s6KR+k0NBQNG/eHK9fvy72d61v2rQJEyZM0Ppt7IekdevW8PLywvLly+Wmh4aGomvXrrh//77aQVc+Z1lZWahatSr+/PNPpQH42IeNY+7HYebMmfj777/f+3VymgwePBhv3rzB33//XezbLgkZGRlwdHTEhg0blN4ZOXPmTOH1WuqIRCLs379fY88m9ulyc3PDzJkzlQbPZuxjw/GbMVaWiAgNGzbEhAkT0KdPn2LZZkm2TXwMgoODcefOHZw7d66skyI4dOgQJk2ahKioKBgY6D6EKA82ythH7KuvvsLr16+RmpqqNNIyYzLJyck4duwYTp06hZUrVyrNDwkJwbRp07gRXY2HDx/ihx9+4EZ0xliJycvLw7Nnz7B48WJYWlqiU6dOSsscPXoUv/76axmkjn0M7ty5A3Nzc42vrGCMMcaYdiKRCGvXrkVUVFRZJ+WjtWjRIrRu3RqmpqY4cuQINm/ejN9++62skyUnPT0dGzduLFQjOsAN6Yx91AwMDPDDDz+UdTLYB6527dp4/fo15s+fj2rVqinNnzdvXhmk6uPh4eGhccBDxhgrqkePHqFSpUpwdnbGpk2bVBboZe8NZUwVT09PYZBPxhhjjBWNr68vfH19yzoZH60rV65gwYIFSE1NReXKlbF8+XIMHz68rJMlp1evXu+1Hr/ahTHGGGOMMcYYY4wxxhjTQK+sE8AYY4wxxhhjjDHGGGOMfci4IZ19spo1a4YJEya89/qhoaEQiUQaB5OcOXMm/Pz83nsf7OMwePDgz2rgtpK6rx88eACRSFQiAwoyxj5NuuQbmzZt+mAGbfr777/h7u4OfX19oQyiahorfrqU28pSad6nimVgNze3j3bQxs+tDMaKhu8XVhoU89SPOY8taSKR6KMZ8L0kFaUMUNj7q6TKQxcuXEDNmjVhaGgo5LOqpn0OuCGdsSKYOHEiTp48KXzmwtuHpziuya+//opNmzYVS3pKAn+hwxj7nPXu3RtxcXHC57LME7/++mv06NEDjx8/xpw5c9ROK4oPvcGYfXiNKuHh4fjqq6/KOhmMfVbOnDmDOnXqwNjYGJUrV8bq1au1rhMeHo6WLVuiXLlysLKyQps2beS+SJ45cyZEIpHSn6mpaQkeSdGUxHkIDQ1F586d4eDgAFNTU/j5+WH79u0leBTa01tceeyH2PHoQ4tppa2sO2x8KDH822+/hZ+fHxISEoS2EVXTiuJDvP9V4YZ0plVOTo5O0z5HZmZmKF++fFkno0RlZ2eXdRLKnKWl5QfT27EgIoJUKv1gt1caPsY0M1baSvs5Ke0ygkQiga2tbanuU5W0tDQkJSUhKCgIjo6OMDc3VzntU8Zlhg+TjY0NTExMyjoZjH02EhIS0L59ewQEBOD69euYNm0axo0bh71796pdJzU1FUFBQahYsSIuX76M8+fPw8LCAkFBQUJcnThxIhITE+X+vL290bNnzyKlt6Ty7pI6DxcvXoSPjw/27t2LqKgoDB06FAMHDsSBAwdK5Di04TyWlQTZc/mh3F/x8fFo0aIFnJ2dhbYRVdM+C8RKVW5uLs2bN4+qVKlCRkZG5OLiQj/99JMw//vvv6eqVauSRCKhSpUq0Y8//kjZ2dnC/EGDBlHnzp3ltjl+/HgKDAwUPu/Zs4dq1KhBxsbGZG1tTS1btqS0tDQiIrpy5Qq1atWKypcvTxYWFtS0aVO6evWq3PYA0O+//06dOnUiExMTmj59Os2YMYN8fX1p/fr1VKlSJRKJRJSXl0dHjhyhxo0bk6WlJVlbW1OHDh3o3r17wraaN29Oo0ePltv+y5cvycjIiE6ePKn2PD18+JA6depEpqamZG5uTj179qRnz54J82Xp2bJlC7m6upKFhQX17t2bUlJShGUCAwNp/PjxREQ0a9YsqlGjhtJ+ateuTf/73/9UpuH06dMEgE6cOEF16tQhiURC/v7+dOfOHaV0yP4PQO7v9OnTlJWVRaNHjyZ7e3sSi8Xk6upKv/zyi9pjLypXV1eldBR81J88eUK9evWicuXKkbW1NXXq1IkSEhKE+bJ77JdffiEHBwdydXUlIqKoqChq3ry5cF8FBwdTamqqxrTcunWL2rdvT+bm5mRmZkZNmjQR7o/c3FyaNWsWOTk5kZGREfn6+tKRI0eEdWXn//Xr18K069evEwAhvRs3biRLS0sKCQkhT09PMjU1paCgIHr69CkRFd81UXzuAgMDaezYsTRp0iSysrIiOzs7mjFjhsZzIdvGzJkzycbGhszNzemrr76irKwsYZm8vDyaP38+VapUiYyNjcnHx4f27NmjdE5CQkKoTp06ZGhoSBs2bFA6xo0bN1JCQgIBoOvXrwvrv379WjgH6rZ36tQp4b5evXo1OTs7k0QioR49eshdCyKiDRs2kKenJ4nFYqpWrRqtWrVKbv7ly5fJz8+PxGIx1alTh/bt26eUJkVbt26lOnXqkJmZGdnZ2VGfPn3o+fPnGs/BqVOnKDAwkMaMGUPjx4+ncuXKka2tLa1Zs4bS0tJo8ODBZGZmRpUrV6bDhw9rvE6MFZYu+YG2mKZIVd4le7aJ3i+vOHXqFGVmZtLYsWPJxsaGxGIxNW7cmK5cuaL1GLWVTdSVEQDQ6tWrqUOHDiSRSMjT05MuXrxId+/epcDAQDIxMaGGDRvKlRsUyfKyvXv3UrNmzUgikZCPjw9dvHhRWEYWC2T/V3feZsyYQS4uLmRkZEQODg40duxYrcdeUFZWFk2aNIkcHR3JxMSE6tevr5SfKsYbVdOIiC5cuEABAQFkbGxMzs7ONHbsWKGsRkSUmZlJkyZNImdnZzIyMiJ3d3f6448/hPNR8G/QoEFEpLn8p0gqldLQoUPJzc2NjI2NycPDg5YtWya3TE5ODo0dO1Yo433//fc0cOBApXg4evRo+uabb6h8+fLUtGlTIiK6ffs2tWvXjkxNTcnW1pb69+9PL168ENbTdg+romt8KBir/vrrL/L29iYjIyNydXWlRYsWyW3T1dWVfv75ZxoyZAiZmZmRi4sLrVmzRm6ZCxcukK+vrxDL9u/frzGWBQYGqiyDaSuzyGiLrYrS0tJowIABZGpqSvb29rRo0SK5MrDsOJcuXSp8Xrx4MdWoUYNMTEzI2dmZRo4cqVSmW7t2rVAG6NKlCy1evFh4zmR+++03qly5MhkaGpKHhwdt2bJFbj4AWrduHXXp0oUkEgm5u7vTP//8I8zX5T5UVfdhnzdNeZ3i/aIt7snyjYMHD5KPjw+JxWKqX78+RUVFye1TW56t6PvvvydPT0+5aV9//TU1bNhQ7Trh4eEEgB49eiRMi4qKIgBq42RkZCQBoLNnz6rdrirq8u7iVlrngYioffv2NGTIkEKl7969e9SpUyeytbUlU1NTqlu3Lh0/flxumefPn9MXX3xBxsbG5ObmRtu2bVPKUwt+1qUOlpycTH379qUKFSqQsbExubu704YNG4iIlOKHrJ1Hdm///PPPZGtrS5aWljRz5kzKycmhiRMnkpWVFTk5OdH69evl0q9r3X/hwoVkb29P1tbWNGrUKKGcpy6mqRIXF0cBAQEkFovJy8uLjh07RgBo//79wjK6tCloioPvW49Xd3yy6zFgwAAqV64cSSQSatu2LcXFxRGR6vKdunp/ZGQkNWvWjMzMzMjc3Jxq165N4eHhRKRbGUBdO4zi/aYttiqWhzIyMqh9+/bUoEEDevXqlcq0ayqXqSp7aipvaysDamoTVXf/nz59murVq0cmJiZkaWlJjRo1ogcPHqi97iWNG9JL2ffff09WVla0adMmunfvHp07d47WrVsnzJ8zZw5duHCBEhIS6N9//yU7OzuaP3++MF9bQ/rTp0/JwMCAlixZQgkJCRQVFUWrVq0SMqeTJ0/S1q1bKTo6mqKjo2nYsGFkZ2cn1wANgGxtbWn9+vUUHx9PDx48oBkzZggP+7Vr1+jGjRuUl5dHf/31F+3du5fi4uLo+vXr1LFjR6pZsybl5uYSEdH27dvJysqKMjMzhe3/+uuv5ObmRnl5eSrPUV5eHtWqVYuaNGlCERERdOnSJapdu7bclwUzZswgMzMz6tatG928eZPOnj1L9vb2NG3aNGGZgpWIx48fk56enlzB6caNGyQSiSg+Pl5lOmQZUIMGDSg0NJRu375NAQEB1KhRI7l0yBrSU1NTqVevXtS2bVtKTEykxMREysrKooULF5KLiwudPXuWHjx4QOfOnaMdO3ao3CcR0bZt28jU1FTj37Zt29Sun5SUJOz/yZMn1LBhQwoICCAiovT0dKpatSoNHTqUoqKiKDo6mvr27UvVqlUTGnQHDRpEZmZmNGDAALp16xbdvHmT0tPTydHRUTjfJ0+epEqVKgmVd1WePHlC1tbW1K1bNwoPD6fY2FjasGGD8EXEkiVLyMLCgv7880+6c+cOff/992RoaKgUtLQ1pBsaGlKrVq0oPDycrl69Sl5eXtS3b99ivSaqGtItLCxo5syZFBcXR5s3byaRSETHjh3TuA0zMzPq3bs33bp1iw4ePEg2NjZy9+y0adPI09OTQkJCKD4+njZu3EhisZhCQ0PlzomPjw8dO3aM7t27R0+ePKHvvvuOqlevLhxjRkZGoRrSC27v5cuXwvPeokULun79Op05c4bc3d2F80qUX8F2cHCgvXv30v3792nv3r1kbW1NmzZtIqL8ir2NjY1wvAcOHKDKlStrbUhfv349HT58mOLj4yksLIwaNmxI7dq1E+arS3NgYCCZm5vTnDlzKC4ujubMmUN6enrUrl07Wrt2LcXFxdHIkSOpfPnylJ6ernb/jBWWtvxAl5imKDU1VXieExMTadGiRWRiYkI3b94kovfLK16+fEnjxo0jR0dHOnz4MN2+fZsGDRpEVlZWagvVMtrKJurKCADIycmJdu3aRbGxsdSlSxdyc3OjFi1aUEhICEVHR1PDhg2pbdu2avcty8s8PT3p4MGDFBsbSz169CBXV1fKyckhIvmG9IyMDJV54p49e8jCwoIOHz5MDx8+pMuXL9PatWvljkFWYVGnb9++1KhRIzp79izdu3ePFi5cSGKxmOLi4igrK4tiY2OFRn9ZvFE1LSoqiszMzGjp0qUUFxdHFy5coFq1atHgwYOFffXq1YtcXFxo3759FB8fTydOnKCdO3eSVCqlvXv3EgCKjY2lxMREevPmjdbyn6Ls7GyaPn06Xblyhe7fv0/btm0jExMT2rVrl7DMTz/9RNbW1rRv3z6KiYmhESNGkIWFhVI8NDMzo0mTJtGdO3coJiaGnj59ShUqVKCpU6dSTEwMXbt2jVq3bk3NmzcX1tN2D6uia3yQlRsiIiJIT0+PZs+eTbGxsbRx40aSSCRCRY8ov3JqbW1Nq1atort379LcuXNJT0+PYmJiiIgoJSWFrK2tqX///nT79m06fPgweXh4aIxlr169ImdnZ5o9e7ZwDxJpL7MQaY+tqowcOZKcnZ3p2LFjFBUVRV988QWZmZlpbEhfunQpnTp1iu7fv08nT56katWq0ciRI4X558+fJz09PVq4cCHFxsbSqlWryNraWq4hfd++fWRoaEirVq2i2NhYWrx4Menr69OpU6eEZQCQs7Mz7dixg+7evUvjxo0jMzMzIc/R5T7khnRWkLa8TvF+0Rb3ZPmGrNFP9gy5ubkJDW265NmKAgICaNy4cXLT9u3bRwYGBnINeAWlpKRQhQoVaMaMGZSVlUUZGRk0fvx4ql69uhDvFI0ZM4Y8PDx0Pn8yqvJuVYpaLy2t80BE1LhxY/ruu++Ez7Lyg6zeo0pkZCStXr2aoqKiKC4ujn744QcyNjamhw8fCsu0a9eOatSoQRcvXqSIiAhq1KgRSSSSIjWkjx49mvz8/Cg8PJwSEhLo+PHj9O+//xJRfsdHWWe+xMRE4V4dNGgQmZub0+jRo+nOnTu0fv16AkBBQUH0888/C/UfQ0ND4UsIXev+FhYWNGLECIqJiaEDBw6QiYmJUEZSF9MU5ebmUo0aNahZs2ZC/bFWrVpyDem6tCloi4PvU4/XdHxERJ06dSIvLy86e/YsRUZGUlBQELm7u1N2djZlZWXRsmXLyMLCQjh+dWWr6tWrU//+/SkmJobi4uJo9+7dFBkZSUS6lQFUtcMo3l9E2mNrwfLQmzdvqEmTJtSqVSuNX/5pKpdJpVJKTEwkCwsLWrZsGSUmJlJaWprStIyMDJ3KgJraRFXd/zk5OWRpaUkTJ06ke/fuUXR0NG3atEnuOS1t3JBeilJSUkgsFss1nGuzYMECqlOnjvBZW0P61atXCYDO385IpVIyNzenAwcOCNMA0IQJE+SWmzFjBhkaGlJSUpLG7SUlJREA4aHPzMwka2truQKxn58fzZw5U+02jh07Rvr6+nLfQt++fZsACA3hM2bMIBMTE7kvACZNmkQNGjQQPiv2xmnXrp1cJWHChAnUrFkzteko2CNd5tChQwSA3r17J6RD1pBOpPr6jB07llq0aKH2iwNFKSkpdPfuXY1/BY9bk3HjxpGrq6tw3davX0/VqlWTS0tWVhZJJBI6evSocAx2dnZyPaXXrl1LVlZWcpnvoUOHSE9PT22vyqlTp1KlSpXUFpIcHR3p559/lptWr149GjVqFBHp3pCu2Dth1apVZGdnJ3wujmuiqiG9SZMmSmmfPHmyxm1YW1vLNeL+/vvvZGZmRrm5uZSWlkbGxsZyvSyJiIYNG0Z9+vQhov87J3///bfcMor3IZFuhThN29PX16fHjx8L044cOUJ6enpC4cnFxUWp0DJnzhzy9/cnIqI1a9aoPF5tDemKZMFUVmBRl2bFayKVSsnU1JQGDBggTEtMTCQAFBYWpvP+GdNGW36gS0zTJCwsjIyNjYU4+r55RVpaGhkaGtL27duFadnZ2eTo6EgLFiwo1DErlk3UlREA0I8//ih3LADkekv9+eefZGxsrHZfsrzsjz/+EKbJzp+s4l+wIV2WHsU8cfHixeTh4aE2Jq1YsYJatGihNh337t0jkUhE//33n9z0li1b0tSpU4lIOY9VN23AgAH01VdfyW3n3LlzpKenR+/evRMa3xV7xcmoio+FLf+pMmrUKOrevbvw2c7OjhYuXCh8lkqlVLFiRaV46OfnJ7ed//3vf9SmTRu5aY8fPxYa/3W5h3WhLj7Izkvfvn2pdevWcutMmjSJvL29hc+urq7Uv39/4XNeXh7Z2trS77//TkT5cat8+fJCuY+IaN26dVpjmWKll0i3Mou22KooNTWVjIyMaOfOncK0V69ekUQi0diQrmj37t1Uvnx54XPv3r2pQ4cOcsv069dP7jlr1KgRBQcHyy3Ts2dPat++vfBZMQ9IS0sjkUgk9wtERYr3ITeks4K05XUF7xdd4p4s31D1DMnirrY8W5WqVasq1XMuXLhAAJR+hVLQrVu3qEqVKqSnp0d6enrk6emptrEoMzOTrKys5L7Y1pWqvFuVotZLS+M8EOX/SsHIyIhu3bolTHvy5AlVq1aNLl++rPU4C/L29qYVK1YQEQnx+NKlS8L8mJgYAlCkhvSOHTuq7T2van2i/Hvb1dVV6LBIRFStWjWhwxzR/9V//vzzTyLSve7v6upKUqlUWKZnz57Uu3dvlcenztGjR1XWHws2pOvSpqAtDr5PPV7T8cXFxREAunDhgjD/5cuXJJFIaPfu3USkXM5Ux9zcXO0X37q2Wyi2wxCpbkjXFFtl+dqdO3fI19eXunXrprTNgnQtl1laWsp1RlA1TVsZUFubqKr7/9WrVwRAY2eL0mYAVmpiYmKQlZWFli1bql3mr7/+wrJly3Dv3j2kpaVBKpXCwsJC5334+vqiZcuWqFmzJoKCgtCmTRv06NEDVlZWAICkpCRMnz4dp06dwvPnz5Gbm4uMjAw8evRIbjt169ZV2rarqytsbGzkpsXHx+N///sfLl26hJcvXyIvLw8A8OjRI9SoUQNisRj9+/fHhg0b0KtXL0RGRuLGjRvCyM3t2rXDuXPnhO3fvn0bMTExcHFxgYuLi7Afb29vlCtXDjExMahXrx6A/EEvCr5r1MHBAUlJSWrPTXBwMIYOHYolS5ZAX18f27dvx+LFi7WeUx8fH7l9yM5jxYoVta4L5A922bp1a1SrVg1t27bFF198gTZt2qhd3tzcvFjeobp27VqsX78eFy5cEK7b1atXce/ePaXtZ2ZmIj4+Xvhcs2ZNGBkZCZ9jYmLg6+srN5BN48aNkZeXh9jYWNjZ2SntPzIyEgEBATA0NFSal5KSgqdPn6Jx48Zy0xs3bowbN24U6jhNTExQpUoV4bO2+wAo/DVRpeB9oet+fX195d5v5u/vj7S0NDx+/BhJSUnIzMxE69at5dbJzs5GrVq15Kapej6LQtX2KlasCGdnZ7m0yq63vr4+Hj9+jGHDhiE4OFhYRiqVwtLSEsD/3TOKx6vN9evXMXPmTERGRiI5OVkuT/H29taY5oLXRF9fH+XLl0fNmjWFabL7VNt1YqywNOUHusY0VR49eoQuXbpg4sSJ6NWrFwAgOjr6vfKK+Ph45OTkyOW7hoaGqF+/PmJiYgAAI0aMwLZt24T5aWlpAHQrm6gqIyieG9kzqPhcZmZmIiUlRWN5R10s9vT0VLtOQT179sSyZctQuXJltG3bFu3bt0fHjh1hYJBfFB4zZgzGjBmjdv1r166BiODh4SE3PSsrq9BjpchiccGB0YgIeXl5SEhIwM2bN6Gvr4/AwECdt6mt/KfK6tWr8ccff+Dhw4d49+4dsrOzhUFa3759i+fPn6N+/frC8vr6+qhTp46QL8so5sdXr17F6dOnYWZmprTP+Ph4vH37Vud7uCBd44NMTEwMOnfuLDetcePGWLZsGXJzc6Gvrw9A/t4SiUSwt7cXnt/Y2Fj4+PjA2NhYWKbgOSksTWWWFy9eaI2tiuLj45GdnS0XX62trVGtWjWN6Th9+jR++eUXREdHIyUlBVKpFJmZmUhPT4epqSliY2PRtWtXuXXq16+PgwcPCp9jYmKUBj9r3Lgxfv31V7lpBc+vqakpzM3N5eKwpvuQMUWFyet0iXsyqp4h2TLa8mwvLy+VaRWJRHKfiUjldJl3795h6NChaNy4Mf7880/k5uZi0aJFaN++PcLDwyGRSOSW37dvH1JTUzFw4ECV29NGl/pEcdRLS/o8hIaGYvDgwVi3bh2qV68uTHdycsKdO3c0pi09PR2zZs3CwYMH8fTpU0ilUrx7905oG4mJiYGBgYHcufL09Czyu6BHjhyJ7t2749q1a2jTpg26dOmCRo0aaV2vevXq0NP7v2EO7ezsUKNGDeGzrP4jy2N1rftXr15diIlAfmy6efNmoY4pJiZGZf1RcRlNbQp6enpa4+D71OM1HZ/sGjdo0ECYX758ebk8QFfffvsthg8fjq1bt6JVq1bo2bOnXMzXpd1CsR1GHW2xFQBatWqFevXqYffu3XLHr6gwdQtttJUB37x5o7VNVJG1tTUGDx6MoKAgtG7dGq1atUKvXr2E+kBZ4Ib0UqSY6Su6dOkSvvzyS8yaNQtBQUGwtLTEzp075Rp79fT0hOAjU3BQL319fRw/fhwXL17EsWPHsGLFCvzwww+4fPkyKlWqhMGDB+PFixdYtmwZXF1dIRaL4e/vrzTAiKqRv1VN69ixI1xcXLBu3To4OjoiLy8PNWrUkNve8OHD4efnhydPnmDDhg1o2bIlXF1dAQB//PEH3r17BwBCgysRqQysitMVG2hFIpFS5U4xrWKxGPv374dYLEZWVha6d++udnlV+5HtX9N+FNWuXRsJCQk4cuQITpw4gV69eqFVq1b466+/VC6/fft2fP311xq3uWbNGvTr10/t/NDQUIwdOxZ//vknfH19hel5eXmoU6eOylHNCzaAKF5rddcEUF8I0na/q1q34H5khYSC97uqAexU3QeKz4iiwl4TVQp7/2lScN1Dhw7ByclJbr5YLJb7rOpZVKTr+dN1e7LrUjCt69atkyt0ABCCtLZroEp6ejratGmDNm3aYNu2bbCxscGjR48QFBSkUx6l6poU9fllTBea8gNdY5qi9PR0dOrUCf7+/pg9e7Yw/X3zCnUV14LpmD17NiZOnCg3X5eyieK+ClL1DL7Pc1nUZ9nFxQWxsbE4fvw4Tpw4gVGjRmHhwoU4c+aMyi98FeXl5UFfXx9Xr15Vqoyoqixo29bXX3+NcePGKc2rWLEi7t27V6jtAdrLf4p2796Nb775BosXL4a/vz/Mzc2xcOFCXL58WW45dQ0gBSle+7y8PHTs2BHz589XWtbBwQG3bt0CoNs9LFOY+FAwrbqkv7DP7/vEN037km1Pl9iq6H3S8vDhQ7Rv3x4jRozAnDlzYG1tjfPnz2PYsGFCOUHX49aUn8hoOr+63oeMyRQmr9Ml7mlSMNZoyrNVsbe3x7Nnz+SmJSUlwcDAQO2Xrzt27MCDBw8QFhYmlON37NgBKysr/PPPP/jyyy/llv/jjz/wxRdfwN7eXuuxqKJL+b+o9dKSPg9nzpxBx44dsWTJkvf6QmHSpEk4evQoFi1aBHd3d0gkEvTo0UOIK9oa/VXRpQ7Wrl07PHz4EIcOHcKJEyfQsmVLjB49GosWLdK4bW11Hdk0WR6ra92/OOq17xsjVO1TUxx8n3q8tjiv7ngKc90BYObMmejbty8OHTqEI0eOYMaMGdi5c6fwxbQu7Ra6PJfqtqV4zTp06IC9e/ciOjparhOLosLULbTRVga8f/9+obYns3HjRowbNw4hISHYtWsXfvzxRxw/fhwNGzZ8r+0VFTekl6KqVatCIpHg5MmTGD58uNL8CxcuwNXVFT/88IMw7eHDh3LL2NjYCBUQmcjISKUKZuPGjdG4cWNMnz4drq6u2L9/P7799lucO3cOv/32G9q3bw8AePz4MV6+fPlex/Pq1SvExMRgzZo1CAgIAACcP39eabmaNWuibt26WLduHXbs2IEVK1YI8xQfVCC/p96jR4/w+PFjoQdfdHQ03r59q/Ybf10YGBhg0KBB2LhxI8RiMb788stiH/3YyMgIubm5StMtLCzQu3dv9O7dGz169EDbtm2RnJwMa2trpWU7deqkFDgUqeoBLnPv3j10794d06ZNQ7du3eTm1a5dG7t27YKtrW2hfung7e2NzZs3Cz2VgPz7VU9PT6l3noyPjw82b96MnJwcpYzewsICjo6OOH/+PJo2bSpMv3jxotDTSxbcExMThV4mkZGROqdZpjiuSXG5ceMG3r17J3zJcOnSJZiZmcHZ2RlWVlYQi8V49OhRoXohAqqPseD5k32TXJjz9+jRIzx9+hSOjo4AIBRmPTw8YGdnBycnJ9y/f19twdnb2xtbt25VOl5N7ty5g5cvX2LevHnCsx8REaFzmhn7EL1PTCMi9O/fH3l5edi6datcQd7b2/u98gp3d3cYGRnh/Pnz6Nu3L4D8il1ERAQmTJgAALC1tYWtra3cerqUTT406vJ9iUSCTp06oVOnThg9ejQ8PT1x8+ZN1K5dW+s2a9WqhdzcXCQlJQllnvdVu3Zt3L59G+7u7irn16xZE3l5eThz5gxatWqlNF/WU0nxGDWV/xSdO3cOjRo1wqhRo4RpBXunWVpaws7ODleuXBGONzc3F9evX9faW7h27drYu3cv3NzchB7/Bb3PPfw+8cHb21upXHrx4kV4eHho7JlVkKenJ7Zv346srCyhMqlLXFJ3D2qiS2xV5O7uDkNDQ1y6dElo0Hv9+jXi4uLUntuIiAhIpVIsXrxYaPDZvXu33DKenp64cuWK0noFeXl54fz583KNVxcvXixUWV3bfciYKrrmdbrEPRlVz5DsF0/a8mxV/P39ceDAAblpx44dQ926ddV+eZuRkQE9PT25mC/7rNhAlpCQgNOnT+Pff//VOU3vo6j10pI8D6Ghofjiiy8wf/58pV/H6OrcuXMYPHiw0NCZlpaGBw8eCPO9vLwglUoREREh1FFjY2Px5s0btdvUtQ5mY2ODwYMHY/DgwQgICMCkSZOwaNEitTH+fbxv3V+RLjFNVt5VrD8qLqOpTUHXOFic9Xhvb29IpVJcvnxZ+FXAq1evEBcXJ8SzwsR0Dw8PeHh44JtvvkGfPn2wceNGpV94lZZ58+bBzMwMLVu2RGhoqMpf7wHvX7dQRVsZUFubqKb7v1atWqhVqxamTp0Kf39/7Nixo8wa0vW0L8KKi7GxMSZPnozvv/8eW7ZsQXx8PC5duoT169cDyA/2jx49ws6dOxEfH4/ly5dj//79ctto0aIFIiIisGXLFty9exczZsyQa1i/fPkyfvnlF0RERODRo0fYt28fXrx4IWQC7u7u2Lp1K2JiYnD58mX069dPp57DqlhZWaF8+fJYu3Yt7t27h1OnTqmsrAH5vdLnzZuH3NxcrRlJq1at4OPjg379+uHatWu4cuUKBg4ciMDAwCK/0mL48OE4deoUjhw5gqFDhxZpW6q4ubkhKioKsbGxePnyJXJycrB06VLs3LkTd+7cQVxcHPbs2QN7e3u1PwkzNzeHu7u7xj91P7F79+4dOnbsCD8/P3z11Vd49uyZ8AcA/fr1Q4UKFdC5c2ecO3cOCQkJOHPmDMaPH48nT56oPa5+/frB2NgYgwYNwq1bt3D69GmMHTsWAwYMUFt4GjNmDFJSUvDll18iIiICd+/exdatWxEbGwsgvwfA/PnzsWvXLsTGxmLKlCmIjIzE+PHjAeTfqy4uLpg5cybi4uJw6NAhnV7Fo6g4rklxyc7OxrBhwxAdHS18Sz1mzBjo6enB3NwcEydOxDfffIPNmzcjPj4e169fx6pVq7B582atx5iQkIDIyEi8fPkSWVlZkEgkaNiwIebNm4fo6GicPXsWP/74o85plV3vGzdu4Ny5cxg3bhx69eol9HqZOXMm5s6di19//RVxcXG4efMmNm7ciCVLlgAA+vbtCz09PeF4Dx8+rLWXRcWKFWFkZIQVK1bg/v37+PfffzFnzhyd08zYh+h9YtrMmTNx4sQJrFmzBmlpaUI+/u7du/fOK0xNTTFy5EhMmjQJISEhiI6ORnBwMDIyMjBs2DC16+lSNvnQqMoTN23ahPXr1+PWrVu4f/8+tm7dColEIvxCThsPDw/069cPAwcOxL59+5CQkIDw8HDMnz8fhw8fLlT6Jk+ejLCwMIwePRqRkZG4e/cu/v33X4wdO1ZI/6BBgzB06FD8/fffSEhIQGhoqNDY6erqCpFIhIMHD+LFixdIS0vTWv5T5O7ujoiICBw9ehRxcXH43//+h/DwcLllxo4di7lz5+Kff/5BbGwsxo8fj9evX2vtoTV69GgkJyejT58+uHLlCu7fv49jx45h6NChyM3Nfa97+H3iw3fffYeTJ09izpw5iIuLw+bNm7Fy5UqlX11o0rdvX+Tl5eGrr75CTEyM0HMR0NxD0c3NDWfPnsV///1XqA4r2mKrIjMzMwwbNgyTJk3CyZMncevWLQwePFjup/+KqlSpAqlUKpzLrVu3YvXq1XLLjB07FocPH8aSJUtw9+5drFmzBkeOHJE75kmTJmHTpk1YvXo17t69iyVLlmDfvn2FOr+63IeMFVSYvK4wcW/27Nlyz1CFChXQpUsXANrzbFVGjBiBhw8f4ttvv0VMTAw2bNiA9evXyz0f+/fvl3s9WevWrfH69WuMHj0aMTExuH37NoYMGQIDAwM0b95cbvsbNmyAg4MD2rVr9z6nUWdFqZcCJXceQkND0aFDB4wbNw7du3cXyknJycnCdv777z+VXwoW5O7ujn379gmvn5Xl+TKy14cEBwfj8uXLuHr1KoYPH66x/USXOtj06dPxzz//4N69e7h9+zYOHjwo3MO2traQSCQICQnB8+fP8fbtW7X70uZ96/6KdIlprVq1QrVq1TBw4ECh/liwE4YsPdraFLTFweKux1etWhWdO3dGcHAwzp8/jxs3bqB///5wcnISXg/n5uaGtLQ0nDx5Ei9fvkRGRobSdt69e4cxY8YgNDQUDx8+xIULFxAeHl6kjqDFYdGiRejXrx9atGih9lVHRWmHUKStDKitTVTV/Z+QkICpU6ciLCwMDx8+xLFjx+S+6CgTJfkCdqYsNzeXfvrpJ3J1dSVDQ0OqWLEi/fLLL8L8SZMmUfny5cnMzIx69+5NS5cuVRrYYPr06WRnZ0eWlpb0zTff0JgxY4TBRqOjoykoKIhsbGxILBaTh4eHMFgGEdG1a9eobt26JBaLqWrVqrRnzx6VgxfIBoSQUTVwFxHR8ePHycvLi8RiMfn4+FBoaKjK9VNTU8nExEQYSFKbhw8fUqdOncjU1JTMzc2pZ8+ecoNaqkrP0qVLydXVVfisONioTEBAgNxAU+roMtilYjqSkpKodevWZGZmJgwosnbtWvLz8yNTU1OysLCgli1b0rVr13Q5DYUmG5xB1Z9MYmIiDRw4kCpUqEBisZgqV65MwcHB9PbtWyJSP6hTVFQUNW/enIyNjcna2pqCg4PVjlgtc+PGDWrTpg2ZmJiQubk5BQQEUHx8PBHlPwuzZs0iJycnMjQ0JF9fX6XBp86fP081a9YkY2NjCggIoD179igNNqr4fOzfv1/ueIvjmqgabFTx3urcubPciOPqtjF9+nThGR8+fDhlZmYKy+Tl5dGvv/5K1apVI0NDQ7KxsaGgoCA6c+YMEam+J4nyBxvq3r07lStXjgAIA35ER0dTw4YNSSKRkJ+fHx07dkzlYKOK25Pd17/99hs5OjqSsbExdevWjZKTk+WW2759O/n5+ZGRkRFZWVlR06ZNad++fcL8sLAw8vX1JSMjI/Lz86O9e/dqHaBtx44d5ObmRmKxmPz9/enff/+VW0ddmlVdE1UD46jKnxgrCl3yA20xTdU2VeXjsmf7ffOKd+/e0dixY4X8v3HjxjoNeKqtbKKujKD4vKkaQEhdWjWtozhol2IsUJUn7t+/nxo0aEAWFhZkampKDRs2lBtMfMaMGXJlCFWys7Np+vTp5ObmRoaGhmRvb09du3alqKgolelSN40of6BMWWwyNTUlHx8fuQHZ3r17R9988w05ODiQkZERubu704YNG4T5s2fPJnt7exKJRDRo0CCt5T9FmZmZNHjwYLK0tKRy5crRyJEjacqUKXLXMScnh8aMGUMWFhZkZWVFkydPpp49e9KXX34pLKOurBUXF0ddu3alcuXKkUQiIU9PT5owYYIwOJi2e1iV94kPf/31F3l7ewtl7oKDpxKpjhO+vr40Y8YM4fOFCxfIx8eHjIyMqE6dOrRjxw5hEC91wsLCyMfHh8RisVAm0aXMQqQ9tipKTU2l/v37k4mJCdnZ2dGCBQuUrovicS5ZsoQcHBxIIpFQUFAQbdmyRencrV27lpycnEgikVCXLl3op59+Int7e7l9//bbb1S5cmUyNDQkDw8P2rJli9x8VTG34MBkutyHPNgoK0hbXqd4v2iLe7J848CBA1S9enUyMjKievXqUWRkpNx+teXZqoSGhlKtWrXIyMiI3NzchEGMZWSDDxZ07Ngxaty4MVlaWpKVlRW1aNGCwsLC5JbJzc0lZ2dnmjZtmsr9yuKmYtwpSF3eXRJK4jwMGjRIZTlJ1iZCpNt5SEhIoObNm5NEIiEXFxdauXKl0rlJTEykDh06kFgspooVK9KWLVuU8lTFz9rqYHPmzCEvLy+SSCRkbW1NnTt3pvv37wvrr1u3jlxcXEhPT084JlV5oS71n/ep+48fP17uXKqKaarExsZSkyZNyMjIiDw8PCgkJEQpDujSpqApDha1Hq/q+JKTk2nAgAFkaWkpxMW4uDi5dUaMGEHly5cnAHJlBJmsrCz68ssvycXFhYyMjMjR0ZHGjBkjDEisSxlAXbzTpb2uYGxVVR4aO3YsOTg4UGxsrNL2iXQrl+ky2CiR9jKgtjZRxfv/2bNn1KVLF6FM7OrqStOnT5cbeLe0iYiK8KI/xnT0+PFjuLm5ITw8XKefUJcUIoKnpye+/vprtb3nGSsJgwcPxps3b4SBdhljjH04Bg8eDADYtGlTmabjQ5aXlwcvLy/06tXrs/610Pbt2zFkyBC8ffv2vX/V+TEKDg7GnTt3cO7cubJOCmPFJjQ0FM2bN8fr169L/JeppSU0NBRdu3bF/fv3NQ44zYqPg4MD5syZo/JVFYyxTw+/I52VqJycHCQmJmLKlClo2LBhmTaiJyUlYevWrfjvv/8wZMiQMksHY4wxxj4sZ86cwdmzZ8s6GR8U2c9nAwMDkZWVhZUrVyIhIUF41/DnYsuWLahcuTKcnJxw48YNTJ48Gb169frkG9EXLVqE1q1bw9TUFEeOHMHmzZvx22+/lXWyGGNahISEYNq0adyIXgoyMjJw4cIFPH/+HNWrVy/r5DDGSgk3pLMSdeHCBTRv3hweHh4aR1IuDXZ2dqhQoQLWrl3LBQvGGGOMCRISEso6CR8cPT09bNq0CRMnTgQRoUaNGjhx4kSZv++ztD179gzTp0/Hs2fP4ODggJ49e+Lnn38u62SVuCtXrmDBggVITU1F5cqVsXz5cu5tydhHYN68eWWdhM/G2rVrMWfOHEyYMAH+/v5lnRzGWCnhV7swxhhjjDHGGGOMMcYYYxqoH9KdMcYYY4wxxhhjjDHGGGPckM4YY4wxxhhjjDHGGGOMacIN6YwxxhhjjDHGGGOMMcaYBtyQzhhjjDHGGGOMMcYYY4xp8Fk1pBMRUlJSwOOrMsYYYx8fjuOMMcbYx4vjOGOMsY/dZ9WQnpqaCktLS6SmppZ1Uhhj7KP3zz//lHUS2GeG4zhjjBUfjuOstHEcZ5+Kqw8ycCjyLa4+yCjrpDDGdEREIKIixyCDYkoPY4yxz0xWVlZZJ4Exxhhj74njOGOMvZ8qNkZy/zLGPmy5ubk4e/YsKlSogJo1axZpW59Vj3TGGGOMMcYYY+xzdvbsWXTs2BGOjo4QiUT4+++/NS4fGhoKkUik9Hfnzp3SSTBjH5hypgao42aCcqbcN5WxD112djZCQkKQnJyMKlWqFHl7/NQzxhhjjDHGGGOfifT0dPj6+mLIkCHo3r27zuvFxsbCwsJC+GxjY1MSyWOMMcaKBRHh0KFDEIvF6NixI4yMiv4rEm5IZ4wxxhhjjDHGPhPt2rVDu3btCr2era0typUrV/wJYowxxopZbm4u9PX14e/vD1tbW+jpFc9LWfjVLowxxhhjjDHGGNOoVq1acHBwQMuWLXH69OmyTg5jjDGmUmJiInbu3In09HTY29sXWyM6wD3SGWOMMcYYY4wxpoaDgwPWrl2LOnXqICsrC1u3bkXLli0RGhqKpk2bql0vKytLblDblJSU0kguYyXqTboU8S+yUcXGiN+RztgH6P79+wgNDYW/vz9MTU2Lffv81DPGGGOMMcYYY0ylatWqoVq1asJnf39/PH78GIsWLdLYkD537lzMmjWrNJLIWKk1cMe/yMazNzkAgDrckM7YB+Xp06c4c+YMWrVqhYoVK5bIPvjVLowxxhhjjDHGGNNZw4YNcffuXY3LTJ06FW/fvhX+Hj9+XEqpY58jWQN3/IvsEt1PFRsj2JczRBWbog9ayBgrHkSErKwsODg4oGvXriXWiA5wj3TGGGOMMcYYY4wVwvXr1+Hg4KBxGbFYDLFYXEopYp87WcN2STdwlzM14J7o7JNRlF9yfCivOZJKpTh9+jREIhFatWpV4oNic4/0D9DMmTMRHBwsfA4NDYVIJMKFCxeEacOGDcPs2bMRERGBfv36AQAePHiAChUqlHp6S5pIJEJaWlqh11u+fDnmzZsnfL558yaaNWsGLy8vVKtWDfv27QMAJCQkoE6dOvDz80PNmjXRs2dPvH79Wljv8uXL8PPzg4eHB1q2bInExEQA+d94BQQEICEhoYhHyBhj7FPCcVxeccTxBw8eoFmzZrC0tETdunWVll24cCFq1KgBb29vdO3aFW/evBHmvX79Gv369UPVqlXh5eWFKVOmAOA4zhj7fKWlpSEyMhKRkZEA8utDkZGRePToEYD8nuQDBw4Ull+2bBn+/vtv3L17F7dv38bUqVOxd+9ejBkzpiySz5hK5UwNUMfNhN9bzlghFOWXHKX1KxBNMjMzcfjwYaSnp6NJkyalsk9uSP8ANW/eXG4U9NDQUDRo0EBpWvPmzVG3bl1s3769LJL5QXv37h2WLl0qFO4yMjLQpUsX/PTTT4iJicHt27cREBAAAHB0dMT58+cRGRmJmzdvwsnJCXPmzAGQX8nu168fli1bhri4OLRr1w7ffvstgPyGgW+++Ybf+8cYY0wOx/GiU4zjFhYW+Omnn7Bjxw6lZY8fP44tW7YgLCwM0dHR8PPzww8//CDMHzp0KGrVqoW7d+8iJiYG48ePB8BxnDH2+YqIiECtWrVQq1YtAMC3336LWrVqYfr06QCAxMREoVEdALKzszFx4kT4+PggICAA58+fx6FDh9CtW7cyST9jjLHiUZRXFX0Irzk6f/48jI2N8cUXX8DY2LhU9skN6R+ghg0b4unTp3jy5AmA/Mr29OnTERoaCgB4/PgxEhMT0aBBA4SGhqrsmQUAYWFhCAgIgK+vL3x8fPDPP/8AyC84+fv7w8fHB/Xr1xd6yAUHB2Ps2LEAgOTkZFSpUgXnz58HACxYsADVq1dHzZo10a9fP7x9+xZAfq+7vn37omPHjvD29kaLFi2QnJysMj3q9ivrgTd9+nTUqVMH7u7uOHz4sNL6e/bsQVBQkPA5NzcXrq6uiI6OVlr2r7/+QpMmTWBmZgYA2LFjB/z9/YVvqAwMDGBjYwMg/yeHEolE2GZaWhr09PSENIvFYjRr1gwA8PXXX+Pvv/9GTk7+4CIdO3bE4cOHkZqaqvKYGWOMfX44jhd/HLe2tkaTJk1gamqqtOyNGzcQEBAAc3NzAMAXX3yBrVu3AgDu3buHa9euCV+CA5B7FQHHccbY56hZs2YgIqW/TZs2AQA2bdokxCwA+P7773Hv3j28e/cOycnJOHfuHNq3b182iWeMMVZsivJLjrL8FUhKSgqICE2aNEGrVq1gYFB6aeCG9CKS5hKSUnIgzaVi26ZYLIa/vz9Onz6NrKwsPHnyBO3bt8fDhw+RnZ2N06dPo3HjxjAyUv+tT3JyMrp27Yr58+fjxo0biIyMREBAALKzs9GtWzfMnDkTUVFRWLJkCXr06IH09HSsWLEC58+fx549ezBo0CAEBwejSZMmOHLkCDZu3IgLFy7g5s2bMDU1xbRp04R9Xb58GZs3b0Z0dDRsbW2xZs0apfRo2i8AvHr1CnXq1MHVq1excuVKfPPNN0rb6NatG2JjY4VBbf7++2+4u7vD29tbadnQ0FA0atRI+BwdHS18S+Xn54eBAwfixYsXcunz8/NDhQoVcO/ePaE3xqNHj+Dq6iosZ25uDnNzc+H1LoaGhqhRo4bcz/UZY4x9PDiOfxxxXJO6devi+PHjeP78OYgI27ZtQ2pqKpKTkxEdHQ0XFxeMGDECtWvXRps2bXD9+nVhXY7jjDHGGGOMfTyePHmCvXv34tmzZzA2NhY6wpYWbkgvAmkuYcPZV/jt5EtsOPuqWCvhzZs3R2hoKMLCwtCgQQMAQL169XD58mXh5+CahIWFwdvbW6iE6unpwdraGrGxsTAyMhJ6hDVp0gS2traIioqCsbEx9uzZg+HDhyMnJweTJ08GAJw4cQL9+vUTXtg/cuRInDhxQthXu3btYG1tDQDw9/dHfHy8Uno07RcATE1N0blzZ43b0NfXx6hRo/Dbb78BAFauXKn2vXxPnjyBvb298DknJwdHjx7FmjVrcP36dbi4uGD06NHCfCMjI0RGRuL58+eoVq0aVq9eLcwTiURy2yaSv8729vZCr0PGGGMfD47j+T6GOK5Js2bN8N1336FDhw7w9/cXepwbGhoiJycHYWFh6NOnD65du4bvvvsOHTt2hFQqFdbnOM4YY4wxxtiHLy4uDseOHUOTJk20DnhdUrghvQiS06V4+ib/FR9P3+TgdbpUyxq6k71fNTQ0FIGBgQCAwMBAnD59GqdPn9ZaAVeHiJQahoH/ayy+c+cOTE1NkZSUhOzsbLXrFPxc8D1E+vr6cpVTXferuI3c3FyV6Q8ODsbu3btx5coV3L9/H506dVK5nImJCd69eyd8dnV1RfPmzeHk5ASRSIR+/frhypUrSusZGRlhyJAhwk/CK1asiAcPHgjzU1NTkZqaKvfAZmZmCq+GYYwBOTnZePv2NXJyym7QEcZ0wXE838cQx7UZMWIEIiIicOnSJTRt2hTOzs4wNzeHq6srnJychPMdFBSE7OxsuYZzjuPsQ8CxkzHGGGNMvbS0NFy+fBlBQUGoWrVqmaWDG9KLwNrUAI7lDAEAjuUMYVWM7wWqX78+kpKSsGPHDuH93M2aNcP27dvx4sULte9TlWnUqBFiYmJw8eJFAEBeXh6Sk5Ph6emJrKwsnDp1CgBw8eJFJCUloWbNmnj06BFGjRqFEydOoH79+pgwYQIAoHXr1ti5c6fw/tC1a9eiVatWhToeTfstDCsrK3Ts2BHdu3fHiBEjoK+vr3I5Hx8f3LlzR/jcq1cvhIeHIyUlBQAQEhICX19fAPmvb5H9ND0vLw+7d++Gj48PAKBOnTrIzMwU3hG4Zs0adOnSBYaGhsK2Y2JihG0xxoCMjHRkZWYiIyO9rJPCmEYcx3VX1nFcG9kr1zIyMjB9+nR8//33APLjuIWFhdBzPiIiAgDg5OQkrMtxnH0IOHYyxhhjjCnLy8vDixcvYGZmhj59+siV48tC6b8R/hNioC/C0Kbl8TpdCitTAxjoK/fUel+GhoZo3LgxoqKi4OHhAQCoVq0aUlJS0KRJE7mGXFWsrKywf/9+fPfdd0hNTYVIJMKcOXPQqVMn7N27F+PGjUN6errwM3CxWIwvv/wSc+bMgbe3N5YtWwZ/f3/s2rULvXv3xs2bN+Hv7w+RSAQfHx/hZ9m6MjIyUrlfU1NTuXeV6yI4OBibNm3C8OHD1S7To0cPDB06FLNnzwaQ37N86tSp8Pf3h4GBAZycnLB27VoAwK1btzBlyhQA+Q9o7dq1sXz5cgD5P6Xftm0bRowYgXfv3sHJyQnbtm0T9iPrrV6jRo1CHQNjnzITE1O5fxn7UHEc111Zx/GsrCxUqVIFWVlZePv2LZydnTFgwADMnTsXANCmTRvk5eUhOzsbAwYMEF4ZIxKJhH1lZmbC2NgYe/fuFc4/x3H2oeDYyRhjjDEmLycnBydPnsS7d+/QpUuXUh1UVB0RKb7w+ROWkpICS0tLvH37FhYWFmWdHPaeFixYgNjYWKxfv17jcu3bt8esWbNQr169EkvLlClTULVqVQwbNqzE9sHYh2r37t3o1atXWSeDfUY4jn8aOI4z9mHgOM5KG8dxxhhjusrIyMDRo0dhaGiI1q1bQywWl3WSAHCPdPaRqV69OkQiEUJCQrQuu3z58kL9LPx9ODo6YsiQISW6D8YYY+xTwXGcMcYYY4yxT9ubdCniX2Sjio0Ryr3n6zPv3r0LCwsLNGvWTO3rIMsCN6Szj8rt27d1Xtbd3R3u7u4lmBpg3LhxJbp9xhhj7FPCcZwxxhhjjLFPW/yLbDx7kwMAqFPIhvSkpCSYmpoKYxeKRMX3+s3iwIONMsYYY4wxxhhjjDHGGCuyKjZGsC9niCo2RoVa78GDBzh48CASExMhEok+uEZ0gHukM8YYY4wxxhhjjDHGGCsG5UwNCt0TPTo6GpcuXULz5s1RqVKlEkpZ0XFDOmOMMcYYY4wxxhhjjLFSl5ubi4SEBLRv3x729vZlnRyN+NUujDHGGGOMMcYYY4wxxkpNbm4ubt++DT09PXTo0OGDb0QHuCGdfaSaNWuGgwcPFnq9yMhIdOjQQfjco0cPODo6QiQSIS0tTW7Zu3fvolGjRvDw8ED9+vURHR0tt//KlSvDz88Pfn5+WLp0qTDvu+++w59//vkeR8UYY4x9Hoojjqenp6NBgwbw9fWFr68v2rZtiwcPHgjLhoSEoG7duvDx8UHDhg1x48YNYV5ERAT8/f1Rq1YteHl5YcGCBcI8juOMMcYYY4yVrOzsbBw5cgSxsbHIzs4u6+TojBvSmVZ5eXnIy8uTmyaVSssoNUUzbdo0TJ48Wfg8YsQIREZGqlz266+/xldffYW4uDh8//33GDZsmNz85cuXIzIyEpGRkfjmm2+E6ZMnT8bMmTOVzhljjH3OpFIpfvzxR1SqVAkSiQSVK1fG7NmzOa8sBZ9qHJdIJDhx4gRu3LiBGzduoG3btvj2228BAK9fv0b//v2xdetWREVFYf78+ejXr5+wneDgYEydOhXXr1/HhQsXsGjRIuELc47jjDHGGCsJb9KluPogA2/Si14OK85tMVba0tLS8O+//0JfXx8dO3aEWCwu6yTpjBvSP1BhYWEICAiAr68vfHx88M8//wAAJk2ahHr16sHPzw+BgYG4e/cugPyRbStUqCCsn5aWJoxu++7dO/Tu3Rve3t7w9fVFmzZtAADPnj1D8+bNUadOHVSvXh3jxo0DEQEAZs6ciQEDBqBbt27w8/NDWFgYKlSogNmzZyMgIAArVqzAyZMnhd5cNWrUwMaNGwEA4eHh8PLyErYFAP7+/jhy5IjScT5//hxdu3ZFzZo1UaNGDaxdu1aY5+bmhlmzZqFRo0aoVKkSfvrpJ6X1nz59Cjs7O2RkZAjT+vTpg99//11p2UePHuH27dto2rSpMK1Vq1awtbVVWjYpKQnXrl1D//79AQDdu3dHQkKCXE83dWxtbVGpUiWcPHlS67KMMfa5mD9/PlavXo2VK1ciJiYGCxYswMKFC7FixYqyTlqJ4Dhe8nFcT08P5ubmAAAiQkpKCvT08ou28fHxsLW1hZeXFwAgMDAQDx8+xLVr14TtvXnzBkB+z3YjIyNYW1sD4DjOGGOMsZIR/yIbz97kIP5F0XvfFue2GCttGRkZsLe3R1BQEAwNDcs6OYVDn5G3b98SAHr79m2xbTNPmkPSV4mUJ80ptm2+evWK7Ozs6MKFC0RElJubS69evSIiohcvXgjL/fnnn9ShQwciIkpISKDy5csL81JTU0l2efft20etW7eW2z4R0bt37yg1NZWIiKRSKXXo0IH27NlDREQzZswgJycnev78ubB9ALR9+3ZhO8nJySSVSoVturq60tOnT4mIqFGjRnT8+HEiIrp69Sq5u7tTXl6e0rH26tWLpkyZQkREz58/J2dnZ7p8+TIREbm6utKECROIiCgpKYksLCzoyZMnREQUGBhIBw4cICKivn370rp164iIKDExkSpUqCAcV0FbtmyhXr16qTznAOTWiYiIIC8vL7ll6tWrR2fOnBH27+npSTVq1KBevXpRfHy83LKzZs2iyZMnq9wXY5+KXbt2lXUS2EekQ4cONHToULlp3bp1o/79++u8DY7j/7d9Io7jMi1btiRbW1uqXr06/ffff0RE9ObNG6pQoQKFhYUJ5xAA7d27l4iIrl+/ThUrViQXFxcyNjamLVu2yG2T4zj7HHAcZ6WtJOI4Yx+T12k5FJGQTq/Til7uLM5tMVZa/vvvP7p3715ZJ6NIuEd6EVCuFOn7liNt53yk718Byi2en9SEhYXB29sbjRo1ApDf40rWS+rYsWPw9/dHjRo1MHv2bLWvJSnI19cXd+7cwahRo7Br1y7h2568vDxMnjwZvr6+qFWrFiIiIuS298UXX8j11jY2NkafPn2Ez69evULPnj1Ro0YNtGjRAi9fvsTt27cBAOPHj8eqVasAACtWrMCoUaOEnnUFnThxAqNHjwaQ3wOsW7ducj3AZD/DtrGxQeXKlZGQkKC0jYL7WrNmDfr27QszMzOl5Z48eVKogQsU00sFeuZt3boVMTExiIqKQkBAAL744gu5Ze3t7fHkyROd98UYY5+6Jk2a4OTJk4iLiwMA3LhxA+fPn0f79u3VrpOVlYWUlBS5v+LEcfzTiOMnTpxAYmIievfuLfR6t7S0xN69ezFlyhTUqVMHoaGh8Pb2Fs7dwoULsXDhQqGX+w8//IDY2FhhmxzHGWOMMVbcypkaoI6bCcqZGnwQ2+LXw7DSdO/ePYSEhHy0r5iU4Yb0Ish7+xK5Lx4DAHKTHiHv7csS3d+jR48wbtw4bN++Hbdu3cLOnTuRmZkJADAwMEBubq6wrGw6AFSuXBnR0dFo27YtLly4gBo1auD169dYsmQJXr16hcuXLyMqKgp9+/aVW0+xEmtqaipXiR4xYgQCAwNx8+ZNREZGwsPDQ1i/W7duiIyMxPXr13HgwAEMGTJE7XEpVswLfjY2Nhb+r6+vr/KBq1+/PoyNjXHmzBmsW7dOqNArMjExwbt379SmoyAXFxc8efJE2B8R4fHjx6hYsaIwX5bWMWPG4P79+3j16pWwfmZmJiQSiU77Yoyxz8HkyZPRp08feHp6wtDQELVq1cKECRPkGnYVzZ07F5aWlsKfLO8tLhzHP504rqenh+DgYGzdulWY1rRpU4SGhuLq1atYsGABnj59Ci8vL7x8+RL79+9Hr169AOSf3wYNGuDixYvCuhzHGWOMMfap49fDsNJy48YNnDt3Dq1bt0a1atXKOjlFwg3pRaBnWQH6tvkNq/q2FaFnWUHLGrpp1KgRYmJihApdXl4ekpOT8fbtWxgZGcHe3h5EhJUrVwrr2NvbQyqVCr2ptmzZIsx78uQJRCIROnXqhEWLFgmNwq9fv4a9vT2MjY3x/Plz7Nmzp1DpfP36NVxdXSESiXD27FncuHFDmGdgYICvv/4anTp1Qvfu3VGuXDmV22jVqpXwPtUXL15g//79aNGiRaHSAeT3Zuvfvz+qV68ODw8Plcv4+Pjgzp07Om3P1tYWtWrVwrZt2wAAe/fuhZubG9zc3CCVSvH8+XNh2b1798LOzg7ly5cXpsXExMDX17fQx8EYY5+qXbt2Ydu2bdixYweuXbuGzZs3Y9GiRdi8ebPadaZOnYq3b98Kf48fPy7WNHEc/7jj+PPnz5GcnCx83rlzJ3x8fITPiYmJwv/nzJmDFi1awN3dHVZWVkLDPQC8fPkSly5dQo0aNYTlOY4zxhhj7FNXxcYI9uUMUcXGqKyTwj4gJfFLBZFIhI4dOxZ7x6iyUPTfk3zGRPoGMO06FnlvX0LPsgJE+sVzOq2srLB//3589913SE1NhUgkwpw5c9CpUyf07NkT1atXR8WKFdG6dWthHQMDAyxfvhzt2rWDs7Mz2rVrJ8y7efMmpkyZAiJCXl4eBgwYAB8fH4wbNw49e/aEn58fnJyc0KpVq0Klc968eRg1ahTmzZsHb29vNGjQQG7+sGHDMG3aNIwZM0btNpYvX44RI0bAx8cHeXl5+OGHH1C/fv1CpQMAevTogZEjR2rcV5MmTfDo0SMkJycLP7Hv1KmTMPBYtWrVULVqVYSGhgLI/3n54MGD8csvv8DCwkJo7MnKykKHDh2QlZUFPT09VKhQAf/++6+wHyLCyZMnMW3atEIfB2OMfaomTZqEKVOm4MsvvwQA1KxZEw8fPsTcuXMxaNAgleuIxeISHcGd4/jHHcefPHmC4OBgSKVSEBGqVKkifAEOAP/73/9w/vx5SKVS+Pv7Y/369QDye8bv3r0b3377LaRSKXJycjBx4kTUq1cPAMdxxhhjjH0eypkaoE4xvGaGfVpkv1QAUKT7QyqV4uLFi6hVq5ZcZ5ePnYgKvvj5E5eSkgJLS0u8ffsWFhYWZZ2cT97u3buxZs0auXellpQrV66gf//+uHPnDvT01P/QYsGCBRCJRJg0aVKJpSUkJATbt2+X+3k5Y5+i3bt3C69GYEyb8uXL46effsLIkSOFaXPnzsXGjRuF96Zrw3G8dHEc5zjOPm0cx1lp4zjOPhdv0qWIf5GNKjZGxfI+dMZKU3Hcv5mZmTh69CgAICgoSO51jx87fqJZiWjbti3i4uKwf//+Et/X8OHDcezYMfzxxx8aK99A/k/HZT3SSsrbt28xf/78Et0HY4x9bDp27Iiff/4ZFStWRPXq1XH9+nUsWbIEQ4cOLeukMRU4jnMcZ4wxxtj7Ka4evYyVhaL+UiEtLQ2HDh2ClZUVWrRoAQODT+sZ4B7pjDHG3gv3ZGOFkZqaiv/973/Yv38/kpKS4OjoiD59+mD69OkwMtLtvYwcxxljrPhwHGeljeM4+1xwj3T2OZNKpYiOjkaNGjW0dpL5GPETzRhjjLESZ25ujmXLlmHZsmVlnRTGGGOMMcZKDL97nJWFsv4CJyYuAbdj49GyeeAn9U50RZ/eVwOMMcYYY4wxxhhjjDH2mZC9Uij+RXap7/vOnTu4cPYUyMQO91/mlPr+SxN/RcYYY4wxxhhjjDHGGGNlqCi9yqvYGMn9W1qioqJw7do1NG3RBql65Ut9/6WNG9IZY4wxxhhjjDHGGGOsDBVloNrSfqUQEUEkEsHR0RHOzs6wtrYutX2XJW5IZ4wxxhhjjDHGGGOMsTJUVr3KFWnrGZ+Tk4MTJ07A09MTlSpVKoMUlh1+R/oHSiQSIS0trVDrPHjwAGvXrpWb5ubmhlu3bum0fkZGBmrVqoXU1FQAwJAhQ+Dj4wM/Pz/Uq1cPJ0+elFv+t99+g5eXF2rUqAEfHx9kZmYCAFasWIG5c+cWKu2MMcbYp+RDiOMymzdvhkgkwsGDB4VpPXr0gJ+fn/Cnp6eHf//9FwCQl5eHsWPHokqVKnB3d8dvv/0mrHfgwAF8/fXXhTouxhhjjDHGmHblTA1Qx82kTAYLLUjT+9YzMjJw4MAB5OXlwcnJqQxSV7a4If0ToqoCXhgrV65E165dYW5uDgBYunQpoqKiEBkZiXXr1qF3794gIgDAP//8g+3bt+PSpUu4desWTpw4AUNDQwDA119/jXXr1iElJaXoB8UYY4x9Joo7jgPAkydPsGbNGjRs2FBu2b/++guRkZGIjIzEH3/8AWtrawQFBQEAtm3bhujoaMTFxeHKlStYsGAB7ty5AwDo2LEjwsPDER8f/97pZIwxxhhjjH24qtgYwb6coVLP+JycHPzzzz8oV64c2rZtCyMj3XvOv0mX4uqDDLxJlxZ3cksVN6R/wBYtWoTGjRvDw8MDf/75pzA9JCQEtWvXho+PDwIDAxEdHQ0AGDFiBKKjo+Hn54dOnToJy+/duxeNGjVCpUqV8NNPP6nd39q1a9GvXz/hc7ly5YT/v3nzBiKRSPi8cOFCzJo1C5aWlgAAW1tb6OvrAwCMjIzQpk0b7Nq1q2gngDHGGPuIlXUcB4CvvvoKS5cuhVgsVrvehg0b0L9/f2GZXbt2YcSIEdDX14e1tTV69eqFnTt3Csv36tULGzZsKNzJYIwxxhhjjH0UVPWMz83NhaGhIQIDA9G8eXOhDVAVVY3mmnq5f0y4Ib2IsvOkeJiejOy84v9GRSQS4cKFCwgJCcHYsWPx+PFjJCUloX///ti8eTOioqLw1VdfoVevXgCA1atXw9vbG5GRkcLPs4H8RvCLFy/iypUrWLhwIf777z+lfT1+/BgpKSmoUqWK3PQpU6agSpUq6NatG/bs2SM0pkdHRyMiIgKNGzdG3bp1sXz5crn1GjVqpPQqGMYYY+xD8ynH8d9//x3Vq1dHgwYN1KYxMzMTf/75J4YNGyZMe/ToEVxdXYXPbm5uePTokfCZYzxjjDHGGGOfj4SEBOzevRs5OTlwdHSU62iriqpGc3W93D823JBeBNl5Uky8th8jw//EpOv7i70SPnz4cABA5cqV0aRJE5w7dw6XL1+Gn58fatasCQDo168fnjx5gsTERLXbkfVOs7GxQeXKlZGQkKC0zJMnT+Dg4KA0fd68eYiPj8fu3bsxadIkZGfnPwRSqRTx8fE4e/Ysjh07hnXr1uHw4cPCevb29njy5Mn7HzxjjDFWwj7lOJ6QkIB169Zh9uzZGtO4d+9eVK1aVUiPTMHCsey1bjIc4xljjDHGGPs83Lp1C6dPn4a/v7/wSmdtVDWafyjvfy+qj7Yhfe7cuRCJRJgwYUKZpSHxXQrupSUBAO6mJiHxXcm+E1wkEoGIVH7zo+nbIGNjY+H/+vr6kEqVGwpMTEzw7t07tdto1aoVUlNTcfPmTQBAxYoV0adPH+Fn3+3atcOVK1eE5TMzMyGRSHQ6LsYYY6wsfMpxPCwsDE+fPoWXlxfc3Nxw6dIlDBs2DOvWrZNbb/369XK90YH8GP/gwQPh88OHD1GxYkXhM8d4xhhjjH1uPpX3OzNWGHfv3sW1a9fQoUMHuLm56bzep9JorspH2ZAeHh6OtWvXwsfHp0zT4SCxQFVzWwBAVXNbOEgsinX7svePPnjwAOfPn0eTJk3g7++PyMhIxMTEAAB27twJZ2dn2Nvbw8LCAm/fvn2vfVWrVg3Pnz9HZmYmgPwe53fv3hXmX7lyBUlJSahcuTIAoG/fvggJCQGQX6E+c+YMfH19heVjYmLkPjPGGGMfmk85jvft2xfPnj3DgwcP8ODBAzRs2BDr169HcHCwsE5CQgKuXLmCPn36yG2rZ8+eWLNmDXJzc5GcnIxdu3ahd+/ewnyO8Ywxxhj73Hwq73dmTBe5ubnIyclBpUqV0KVLF9jZ2Skt87l+ufTRfTWQlpaGfv36Yd26dRoH3CoNRnoGWFirKxLfpcBBYgEjveI9nWKxGI0bN8aLFy+wYsUKuLi4AAC2bt2Kfv36ITc3F+XKlcPu3bsBAD4+PqhWrRpq1KiBypUry71fVRtjY2O0atUKJ0+eRIcOHZCbm4vBgwfj7du30NfXh6mpKf766y9YWVkBAL755ht8/fXX8Pb2hkgkQs+ePdG1a1dheyEhIfjll1+K8WwwxhhjxetTjuO62LBhA7p37w4LC/kvEAYMGIDw8HB4eHgAACZNmgQvLy9hfkhICLp3765z2hhjjDHGiupNuhTxL7JRxcaoTHq5yl5R8bG/35kxbbKysnDs2DFYlKsAMxdfVLExUbmc7MslAKjzCfY8V0dEii++/MANGjQI1tbWWLp0KZo1awY/Pz8sW7ZM5bJZWVnIysoSPqekpMDFxQVv375VqjQy4PLly5gzZw4OHjxYpO1ER0djxIgROHv2bDGljDH2Idq9e7cwSCJjpSElJQWWlpYcx9UorjiuycuXL9GyZUuEh4fDyIgrkox9zDiOs9LGcZwVxdUHGXj2Jgf25QxRx011wx5jrGjS0tJw5MgRmJubw9q9EV6kkdpnrqy/3CorH9WR7ty5E9euXUN4eLhOy8+dOxezZs0q4VR9Oho0aIAuXbogNTUV5ubm772dx48fY/Xq1cWYMsYYY4xpU1xxXJP4+Hj8/vvv3IjOGGOMsVLFPcLL1ufaaPo5ISIcP34cVuVtUc6tDmzMDaFvkKv2mStnavBZ9USX+WiO+PHjxxg/fjyOHTsmN+iWJlOnTsW3334rfJb1SGfqDR8+vMjbCAoKKoaUMMYYY6ywiiOOa9KgQYMS3T5jjDHGmCqfa6Pdh+JzfY3H5yIlJQUWFhYICgpC9HPC87dSiPT0dPr1x+f2JctHM9jo1atXkZSUhDp16sDAwAAGBgY4c+YMli9fDgMDA+Tm5iqtIxaLYWFhIffHGGPs4yXNJSSl5ECa+1G9lYwxxhhjjDHGPlpVbIxgX86QfxHwCbp79y7++usvvH37FiYmJnC3FcPUWA9p73J1Gkj0cxuI96P5qqBly5a4efOm3LQhQ4bA09MTkydPhr6+fhmljDHGWGmQ5hI2nH2Fp29y4FjOEEObloeBvqisk8UYY4wxxhhjnzT+RcCnh4hw48YNXL9+HW3atIGlpSWA/GttbqwvNI5ru+6f22uXPpqnwNzcHDVq1JCbZmpqivLlyytNZ4wx9ulJTpfi6f//OeHTNzl4nS6FjYVhkbYpzSUkp0thbWrAjfKMMcYYY4wxxj4q7/tqlZcvX+L27dvo2LEjKlSoIDevMI3jn9uXLJ/PkTLGGPuoWZsawLGcodAj3aqIwZp7uDPGGGOMMcYY+5gV9v31UqkUKSkpsLGxQa9evWBoqNw5rSiN45/6O9M/6iMKDQ0t6yQwxhgrJQb6IgxtWh6v06WwKkIP8uw8KRLfpcAgR1LsPdwZY4wxxhhjjLHSUpje4+/evcPRo0dhYmKCNm3aqGxEL6pPfWDaT++IGGOMfbIM9EVFauzOzpNi4rX9uJeWBHczW3hZNsXzt3nF0sOdMcYYY4wxxhgrTbr2Hk9JScHhw4dRoUIFNGvWrMTS86m/M51bDRhjjH02Et+l4F5aEgDgXloSJtQ1gDksitTDnTHGGGOMMcYY+1Coer1KVFQUXF1d0bBhQ4hEJVf3/dTfmf7pHhljjDGmwEFigarmtribmoSq5rZwNrOEkR6HQsYYY4wxxhhjn4aCr1exf/McNjY2aNSoEfT09D75d5iXND5jjDHGPkmyd6E7SCyExnIjPQMsrNVVaTpjjDHGGGOMMfYpEF6r8uY+jl67grZt28LR0RHA+73DnBvf/8/nffSMMcY+SQXfhV7V3BYLa3WVa0x3NbUGAEhzCcnpUljzq10YY4wxxhhjjH0CLE30kffiNqKjo9GuXTs4ODgI86rYGCE1Mxdp73LxJl2qU8P4pz6AaGHolXUCGGOMseJW8F3od1OTkPguRWkZaS5hw9lX+O3kS2w4+wrSXCrtZDLGGGOMlbqzZ8+iY8eOcHR0hEgkwt9//611nTNnzqBOnTowNjZG5cqVsXr16pJPKGOfmTfpUlx9kIE36dKyTgr7yGVlZeHZs2fo1KmTXCO6rGe5iIC0rDzEv8jWaXtVbIxgX87wkx1AtDC4IZ0xxliJy8nJxtu3r5GTo1ugLirZu9ABoKq5LRwkFkrLJKdL8fT/f6v+9E0OXnOBlTHGGGOfgfT0dPj6+mLlypU6LZ+QkID27dsjICAA169fx7Rp0zBu3Djs3bu3hFPK2OdF1utX18ZNxhRlZ2fjzp07yMw1gGPNlhAZmcvNl91jJILODeP8Whd5fAYYY4yVqJycbLx48Rwi5L86xdKy5L/F1uVd6NamBnAsZ4inb3LgWM4QVlwoYIwxxthnoF27dmjXrp3Oy69evRoVK1bEsmXLAABeXl6IiIjAokWL0L179xJKJWOfH1mjJvf6/XwVpdE6PT0dISEhkEgkSDNyxvOUXADyr2IpeI/pun1+rYs8PgOMMcZKVEZGOkQACAQTE9NS22/Bd6GrYqAvwtCm5fE6XQorfkc6Y4wxxphKYWFhaNOmjdy0oKAgrF+/Hjk5Ofh/7N15nJ11fff/17Webc4y+0wmM5OVhCSEpAEVoiwu0CKlUlu9q10sVesPhSraW7Bqq7cWemt9ILUi1Kq11u1WqButorJUQIFASICQfZlk9uXsy7X+/jg5J7MmM2EmM8l8no+HJue6rnOu71nIdc7n+lzvr2EYk96vVCpRKpWqt9PpiVF7QogTEhFdCpWL3OkWrUdGRviv//ovlixZwmWXXUa64KGo1oSTMqfzGZMTPGNJtIsQQog5UYlzMQyTcKSGxsZmDGNhHXx1TaExZkgRXQghhBBiCr29vTQ3N49Z1tzcjOM4DA4OTnm/22+/nXg8Xv1fe3v7XA9VCCHOWsmcQ7bgEgmqMy5ap1IpzjvvPC6//HJUVS0XzJeFT9l1Pp1c/uk+1mIhhXQhhBBzIp/PUSoWsW2LeLx2wRXRhRBCCCHE9CjK2KYD3/cnXT7abbfdRiqVqv6vq6trTscohBBns/0DFtmSRzSoTbtofeDAAfYc6GKIJladvwlFUUjmHH68Pcm/PjzI4YHSSe8vufwzJ6cThBBCzIlKjMuZjHMRQgghhBCzq6Wlhd7e3jHL+vv70XWd+vr6Ke8XCAQIBAJzPTwhhDirTJWDPtMIlR07dvD000+jt72SUKIGKMfB7B+weGJvjmzJJxRQ6Wyc+t9hiW2ZOSmkCyGEmBOGYZ6RiUWFEEIIIcTcueSSS/jRj340ZtnPfvYzLrrooinz0YUQQkxudA76yuO3K0X16eSXj2RtHvnVE6T6DrFqy+vpLUaxPX9MUfyS1RH6kg5bV528qU1y+WdOXi0hhBCLnuP6DOcc6mTSUSGEEEKc47LZLPv27avePnjwINu3b6euro6Ojg5uu+02jh07xte//nUA3vOe9/CFL3yBW265hXe961088cQT/Ou//ivf+ta35uspCCHEvJqqq3w6Rhe8p5pc9GSPv3/AouCorL74atZ11hMZt10ionPtpsTLeHYv7/md6+TVEEIIsag5rs9XHh2iO2mzJGFww2X1UkwXQgghxDnr6aef5sorr6zevuWWWwD4sz/7M772ta/R09PDkSNHquuXL1/OAw88wAc+8AH++Z//mSVLlnDXXXfx5je/+YyPXYhzjRQsz05TFcCnY3QX+EogU3TJFlySOaf6GZjs8fuGczz2xG/YuOkiztvwW9PqYh//+Rp9u7KfqYr1p/v8znXyagghhFgULM+hp5CmNRTDVE8c/oZzDt3HvyR0J21Gcg6NMblMWQghhBDnpiuuuKI6Wehkvva1r01Ydvnll/PMM8/M4aiEWJykYHl2nkyYKlt8ps8lEdGJBrXqhJ9bjhe7swWXSFCtPn4mk+Gn//UAGBFGinDxyvC09jn+8zX6NjDlZ0+y06d2dnxChRBCiJfB8hw+9Mz97Mv2szraxGc2X18tptdFdJYkjGpHeu1Z8uVNCCGEEEIIsTBNt6AqBcuz82TCVJ3gp/NcVjaaZAsumaJb/dxkSx4tCYNERGd4eJgHHniAtrYO4st+i1VNwWnvc/zna7LP22SfvUREP5Hffvy2KJNXQgghxDmvp5BmX7YfgL2ZfnoKaTojdQDomsINl9UzknOolYx0IYQQQgghxMs03YKqTPZ4bp1MOJ3nkojo1IQ09veVeGAkXZ0gtPIYoVCIzZs3s6TzPA4M2hPuf7J9jv98jb99ss/e2XiC40yQV0IIIcQ5rzUUY3W0ib2Zckd6ayg2Zr2uKRLnIoQQQgghhJgV51JxeK6dSycTTve5rGw02d1TxFAVBnMuW5aF2bNnD8/1D1Gz9EJWLltz2oXt043Okc/w5M6NT6oQQghxEqaq85nN10+akS6EEEIIIYQQs+lcKg6LuZeI6FyzMcb+AYsVDQbPPPMMO3bsoPOCy6rF86kK26cqsJ9uAV4+w5NT53sAQgghFg7btkilRrBta76HMutMVaczUidFdCGEEEIIIYQQZ1Qy57DtUJ5kzpl0fSKis2VZmF07nmLXrl387u/+LpvXdlATUMkUXQC2LAtP6CpviGikCi4NEW3S/axsNGlJGNUC/KnGIU5OqglCCCGq8vkcpWIRgHhcLuESQgghhBBCCDG5040NWYxO1Rnu+z6KorBs2TI2btxITU0NyZzDsaSNoSrsD1onJgAd9XoP5lziIY3BnEtn48T9jO8sl+zzl0deMSGEEFXhcGTMn0IIIYQQQgghxGSkKDt9J8scLxQK/OxnP+NVr3oVS5YsqS7fP2Chqwq257Oy0Zz09R7/uKfKNpfs85dHPuVCCCGqDMOUTnQhhBDiHGHbFvl8jnA4gmHI8V0IIcTskqLsqY3u2t+yLDxhmeLkeOCBB2hqaqKhoWHMfUe/vomITkPeZXdPkQ1tweo24zvOT5VtLtnnL49kpAshhBDijDh27Bh//Md/TH19PeFwmE2bNrFt27b5HpYQQpyzKpFt+XxuvocihBDiHFTJ9ZZYl6lVusj3D5yYh2x7V4En9mZ5at8QP/jBD1i+fDmvfe1r0TRtTIb5+Nd3dIyLmB/ySRdCCCHEnBsZGWHr1q1ceeWV/Nd//RdNTU3s37+fRCIx30MTQohzlkS2CSGEOBediWz22drH6K7yymMOpBx6Roosb4jxute9jra2tur2lcJ7pugSDWpj9i9XAMw/KaQLIYQ4Kcf1Gc45xIIa6aJLXURH15T5HtYElufQU0jTGophqnJ4W2j+4R/+gfb2dr761a9Wly1btmz+BiSEEIuARLYJIYQ4W52skD0X2ezj97e9q8DzXQUyxRCb2kPVdZX9jx7XdIvulXEne/bSnj9IOPg7Y4rocKJIni24E55jIqKfmHD0+O2pxi/mhryyQgghpuS4Pl95dIijIxbdIzZLEiZL6wxuuKx+voc2huU5fOiZ+9mX7Wd1tInPbL5eiukLzA9/+EOuvvpq/vAP/5BHHnmEtrY2brzxRt71rnfN99CEEEIIIYQQC8zJiuVz0Zk9fn+KDygKij92HTBhXJN1kVeW9yQtDg1YZIohltUZ7Hh2O1ryEOdtvoILO8ZmpjdENA4NWvgKLG8wOTRokSm61ZiXk70uMvHrmSGvrBBCiCkN5xy6kzYl26cv5dBQo9OdtBnJOfM9tDF6Cmn2ZfsB2Jvpp6eQpjNSN8+jEqMdOHCAu+++m1tuuYWPfOQjPPnkk9x8880EAgH+9E//dNL7lEolSqVS9XY6nT5TwxVCCCGEEELMo5MVy2dzwszRRezR+7uwI0RNSKMhonFwyKImoI4Zy/i/Z4ou+3tL6JrC7p4iS2oNckWPvqRTLcg///wO3NRRgsuv4MLVbROK47t7igznXPB9okGNmpBGb9Jme1cBxadcYK+f/HWR2JczQwrpQgghplQX0VmSMDg6YtEc1zF1lSUJg9pZ+NJi2xb5fI5wOIJhnPpgX4mYqTu+78rfdU2hNRRjdbSJvZlyR3prKPayxydml+d5XHTRRfz93/89AJs3b+aFF17g7rvvnrKQfvvtt/OJT3ziTA5TCCGEEEIIsQDMZrH8ZMZ0ci8Lj9n/SuD7TyUZybtcvPzEpJ8NeZcHdqTZuipCPKzx3JECO48WSOddHB/WtQZRfGhJGGxoC9KXKrKqOYDdvI4H9aUEAuWomC3jss83tAWrHemjC+LZgsvOY0WKlkv3iM01G2MT4lvGv14S9TI35JUUQggxJV1TuOGyekZyDtGgRqboUjtLGen5fI5SsQhwyvzWSsRMd9KmJVY+dPWmHZYkyjEzpqbzmc3XS0b6Atba2sq6devGLDv//PP5/ve/P+V9brvtNm655Zbq7XQ6TXt7+5yNUQghhBBCCHH2Op3i8VSd3MmcwwM70vSkbQolD3/UT+DH9uU40F++cnZJrcFPnkvRn7JpjBlcubaGllqz+ni7upIc2fEQyqo1qHWreM359QzmXBoiGtsO5atjrRTBOxsDY8axJaKTPH5F+N7+EoaqjCnCT0WiXuaGvJJCCCFOStcUGmMGAEFTnbXHNQyTXC5LzUm60Std6I7r0338S8DBQQuAkKlWY2YaYwamqkucywK2detWdu/ePWbZnj176OzsnPI+gUCAQCAw5XohhBBCCCGEqJhu8TiZc6pxKRd2hMZ0oo9+LENVaI0ZrGwJsKk9VF23dVWEvOURD6rkix74kAjrnN8aZOt5NdUi/q92drN72y9INC5BSawY0/m+7VC+ersygWhDRGMw5044EZCI6FzYEcJXQPGnF98iUS9zQwrpQggh5oVtW+iajm1bQGTC+vFd6C0xnd60w/KG8heBSkf6bMTMiLn3gQ98gEsvvZS///u/5y1veQtPPvkk9957L/fee+98D00IIYQQQghxlkvmHLIFl0hQPWXxeP+AxfNdBVAUakJateg+uqN9dCF6fHd7Z2OAzZ0uvUmbcFDl2k1xfAU2tYeq27quy6HnHqKpfTWvfuUWFEVBUcuPPX6sozPS4yFtzKSlo3PUc0WPloQxrW77MxWNs9jIKyqEEGJehMORMX+OV5noFMpF87+8sh5dVaqF85GcM2sxM2LuXXzxxdx///3cdtttfPKTn2T58uXceeedvP3tb5/voQkhhBBCCCHOcvsHLLKl6RWay5ODhiZ0d4/PSz9ZIfpkhfZMJkM0GuV1v/277BlQeO5okU3tJzrftx3KjxnryuP329AWZDDnki24EzrrpcN8YZBCuhBCiHlhGOZJs9ErE512J22WJAziEYUBK0NcKWegV+JmxNnj2muv5dprr53vYQghhBBCCCHOMZMVmqfKTE9EdK5YGz3lY1TuP1nkyuiO78p29WGVF158noFDz/NHf/RHdGd1dh7Lge8TDWpTFsXHZqSPHffoMZ8qrkYmF5178soKIYRYkEZPdBoJwYe3389L6X7Ojzfz2d+6XiYUFUIIIYQQQggBTCw0VyYL1dXyFczji9DjC8/JnMNje7L0pB0aIhqJiD6tyBUod7L3jJTYse1ZnFQXwWWvoeBoNEQgaBzPWZ9BUXwypyqUy+SiZ4a8skIIIRasykSnB9JD/Lq3B9vxSea7OZpNsSJWP9/DE0IIIYQQQggxj6YqMO8fsLAdn66UxStXhCfc5/tPJRnJu2SXh7mwI8QDO9I8eyiP5fiETZXOxkC1+N1RZ/DkgTx526Pl+JXRo4vVDRGNF146ilbsx22/gr5ikOeOFKgJacSCGj3p8uSmozPUT2ayovipCuWVsTZENLYdyktn+hxR53sAQgghxKkEvDARpxaAiFNL0Js4q7oQQgghhBBCzLZkzmHboTzJnDPfQxGTqBSY9w9YY5Y3RDR6UzYtcYPBnDvhPiMFl5Gcg6+Ub1uOj6kprGoJsHVVeR6vRERny7IwR4YsnjqYo2uoxIGBEg0RrfpYxWKRYwNJ6prbWfvKa4jHYuzrK9KfdljZaOJ4Pr1Jmwd3ptneVZjWc1rZaNKSGNvFPtmy0SpjHcy5k74eYnbIqQkhhBALXnM0yFvjV7M3NcLqeC114QD9aZs6mWxUCCGEEEIIMYckMmNhm2oSzsGcy/LGAOmiS6boksw51Q7tlY0m2UIYX4FN7SEAth/KU1ujs3FpiHh4bFf3gUGLTMHD930uXl7DYM6lsxHS6TQ/+ckDaNEWapZuZiDj8eyRPKqikCq6JCI612yMVbvfFX/s2E+W4T7+szbdOBiZlHRuyb8AQgghFjxdU3jX5U2M5OqIBjW+/thwdRLSGy6rl2K6EEIIIYQQYk5IYXJhm6rAXHm/sgWXbNFje1dhTL755eePnWx0ZUuA57sK1Q713qRdzURviRksqTNoiemEAgrZgsuBI7089siD1DR2YDRvpDtVzlLvT9m4PqxrDVTH9+aLExMmDwV47kiBnceKZAvuhPHM9ushZoe8skIIIc4KuqZQG9HZ01vk6LCNqkJ30mYk59B4PKdOCCGEEEIIIWaTFCbPLqO7vLcsC1dvZwvuSa8sWF5v0j1iUx/WGM65RIIqig+9SZvGuM6a1iDJnMuhfgtUhWPJbZx3/gYK4ZVkLY+S5ZEtuvSlHWIhjRd7SqxrC40Zy3i+Avh++U8xwakmWJ0PC2MUQgghziq2beG6LkNDA8RicQxj7rszHNfnK48OcXTEOt6NbrK0zqD2DB1QHddnOOdInIwQQghxGizPoaeQpjUUw1TlZ6gQ4uy1EIt74oTx3eTjC+rju8JHF9rjIY2dx4rEQxotCYOGiMZj+3J01BnsO769UujFDzURW3EJ+0tgDdn0pWziIQ1NVWmvM/F8nyVxY0ws0MrjY2uIaAzmXFY2mmxqD1XHKCZaiLFKC2MUQgghzir5fA7Pc8lm0ui6Tjw+twd+x/VHdaIrLKk1eNurEqxqDp6RonaliC9xMkIIIcTMWZ7Dh565n33ZflZHm/jM5uulmC6EOGstxOKeKEvmHDJFl5qACse7yaH8PlWuLKhMHlspaGcLLtmSB77PwQGLou0RawuystHk8b05XjhaYCTroClg9b1Awj/M2ldcjaMbFGyHrpRFS9ygN2Vz8fIwjuuzsiVQzV6HcsxMJcYlpCu0JMpXVG9ZFpbP0EksxFglebeEEELMWDgcQVU1aqIxwuHInOyj0gEeO56JProTvSVuYLs+juufkYL2cM6h+/iXMImTEUIIIWamp5BmX7YfgL2ZfnoKaTojdfM8KiGEOD0LsbgnyvYPWOSKHi0Jg5WN5qQd6JUTIbt7yp3nPj7pgkcspNKTdsgWXBI1Os8dKfDQi2m6Uw4h3ccc2UHcHcTvvJxQTYKGiEY0qPGKFWEGcy5XrK2pdpqPvlKhUijPlFx6Riw2doSoCagTJkAVEy3EWKWFNRohhBBnBcMw0TSN+vrGl/U4vuvgpQZR4w0o2olD0ugO8FhQJZn3qp3ov78lzj892M9/7UjTFNP4uzctobXWmNOCel1EZ0nCqHakn6k4GSGEEOJc0BqKsTraxN5MuSO9NRSb7yEJIcRpW4jFvcXkZNE6o09yTPY+JXPlQnkkqLKhLchgzqV3xGI462CoBi1xnVJIpVjyeOpgnmzJx/N9nFKOkJtjB6/gPCfMgb4SNAeqkTGDOZd4WKOzMTDluKMBjdoanWzBw49Df8rhgZE012yMSTH9LCLvlBBCiHnhuw65++7CHehCa+ogcv1N1WL66A7wwZyN4iv4vkJ7vYmmQn/axfd9dvdY/ON/97GuLTSncSu6pnDDZfWM5BxqJSNdCCGEmBFT1fnM5uslI10IIcTLdrJondHF88kK7vsHLPrTDrbns7zeJFN02dVdpC/t4AMrGgP0Jm160jbFkseqeg/NSnLUbuLZ7CZ8FNT+EkM5l7CpkDze9T6dqJ8LO0IcS9oYqoLig+P5GKrC/gFLTsycReSdEkIIMS+81CDuQBcAbv8RvNQgWl0LcKID/MhwiWcOFtEUqI/qfPiNzeiaQnNc59iwjaErRIPaGYlb0TVF4lyEEEKI02SqusS5CCHEOWwuJmGd7DFXNpYL4NnC5NEooycPzZY84ESBuyGi8fOBEi1xg8f25RjOOnSN2CRzLpbtU7TL2w+kHBQnQ2tqG/HaZlqXdTKSc8gUXPpSDvmSz6/359h2qMCaJQFqTLU6HmBMpMzo8V+zMcZzRwr4CmxdFalGwYizhxTShRBCzAs13oDW1IHbfwStqQM13lBdV+kA/82+LNsPFVEUGMm5HB2xWLskxBf+pJ39/UUeeSlHf8ZhScIgGtToT9vUSce4EEIIIc5Svu+jKPI9Rghx9pnNSVgrxfBM0SVXHFsMT0R0okGN3qTN9q4C0aBWLVQfHijx7d+M0BI3qI9qpAseG9qC1ccdzLksbwzgeD5bV0U4OGSBD88eKXAsaXFwyOL8JQEC7jCd1jMMKEsZVNezJaTx5otqeaG7wPZDBRzXpz/joKkKJcdj/dIQecvngR1p4kGVXb0lth/SWNkSGDP+StH/+aNFFB8uPz865XOfzRMSYvbIOyKEEGJeKJpO5PqbsIZ6KOhBHM/D0E6s1zWFLcsjtCR0+lIOzXGdZQ3lzLmgqbJ+aZg1rSFGcg7R4xOSVjLM5zLmRQghhBDi5SiVSnzkIx/h6aef5tprr+Wv//qv+dSnPsUdd9yB7/v83u/9Hl/60peIxSRLXghx9pjNSVgrRfmagFqdOHSyfWUL7pji/WP7cmSLLr3AsgYTBYXBnAuUeGxfjgvagjTHdHwF4mGNKxqjLK83KTo+v96XxVRhd0+J1y0pQHQditqOriq81F1iSa3ButYg/SkH8Ll4eYRM0aM5oXNBW4jH9uXQVYWetMNI1gEfFB9aEgYNEY1th/KsbDTxFcD3y3+e5LlXnpNYWOQdEUIIMW8UTacYqMEqFnHTKXRdJxyOYBjlL0ZBU+ULf9LOocESyxoCBE0Vx/UZzjnVzvPGmEF/2q5mqp+JmBchhBBCiNN122238Z3vfIc/+qM/4qtf/SqHDx/mRz/6Effccw+qqvLxj3+cj370o9x1113zPVQhxDlspp3Pp9p+NidhHT9p6FT7Gj0mKMelVP6Mh7Xqugd2pDnQXwKgLWHw/NEiuYJLS61J74jFSM5hXVsIa2g/hBrIh5fxl1c2kMq7PPhihmLJQ1cVIiGNN26Oo/jlzHOA7V0FDg1a1agWU4WC5VGyfTIllws7QmOK45vaQ9Uu+lM9d7HwSCFdCCHEvAqHy192HMehVCwCEI+f+NIQNFXWLil/SXFcn688OjSh87ySqV5ZXjvqy9b4wrsQQgghxHz63ve+x7/927/x+te/nhtvvJHVq1dz33338Xu/93sANDQ08K53vUsK6UKIOTXTzufZ6pSeTgF/ukX58dt1NgbobAxU99EQKRfTL2gLkrc8EiGNTMmlaLk8fdhhneVzeLBE13CJtcY+lmrdqA3NvOGiWhIRnf0DFrqqMJR3WdZksqk9VB1zMudUC/QF2+cNG6JcsTbKtkN5FEVhJO9waMCiNWFNODFwsuc2myckpiLxMadPXi0hhBDzyjBM4nET27bI53OYgfCUWefDOYfupI3n++zvLzGQsWlNmNVM9ZGcQ+2o+01VeBdCCCGEmC+Dg4Ocd955AKxYsQJN01i1alV1/erVqxkYGJiv4QkhFomZdj7PVqf0TAryUxV8kzmnOmnn6OL26H0c6Cvx4+4CkYDG2iUBwqbKQMbBcX0SEZ3GqILt+QQMnzU8T42Twlp6GZec30JnY6D6XH/+fBrX9UkXvAlFdNvxcX2oDWvkix4Pv5QhX/RYXm+wvN4gEtKmVTw/0yQ+5vTJqyWEEGJaKoXu0dErM9nuZPe3PIceK0tjOMq//yo1ofBd6SqPBTVaYjoPvZQF4AfbUrzzigZ0TanGvIzWn7bZ318ioKsS+SKEEEKIBaGjo4MnnniCjo4OnnrqKRRF4cknn2T9+vUA/OY3v6GtrW2eRymEONfNtLg7W8XgmRTkpyr47h+w2HmsSNHy6B6xq7EqlaL1ykaTZw/n6U3ZWI5dvV9f0sbyfNprDS5eEeHCjhAjWYsHUzXkQheyZ1DlvLTFtkPQENHYeaxAtuAwkHH57QuiY/avqwro8I6tdQzmXDJFl+e7CqAoXLIqwpZl4Umf00LoBpf4mNMnhXQhhBDTks/nJo1eGc22LQYG+lBQJmw31f0tz+FDz9zPvmw/7cEGmpJb0dCqhe/aiD6mq/yNm2IcGrQwdZXetDNlcdxxfX74TIpDgxYAV66tGRP5IoQQQggxH97znvfwjne8gy9/+cts27aNf/zHf+QjH/kIL730Eqqqcvfdd/PBD35wvocphBBzYqqC/GQF5tEF39HrVzaa9CVtnjqYw3bKk4waqsKzh/OsbgpwYUeI1U0BdhwpYDku+D4oCumSR6bgYmgKdinHLx95mpXrLqJm6Sb2HszRPWLx9cdHuG5zgt2ezwtHCzx/tISmwq7uEuvaQtXIGJoD1bF2NpbHD+UJRk9WoF4I3eAzeQ/EWPKqCCGEmJZKlnnlz8nk8zkUwMefsN1U9+8ppNmX7QfgSGGQFbEihXSkmnVeiXOB8kSiuqayoikwaR76aMM5h960w5rWACXb5/e2xCXWRQghxKIk84UsLO9///tpbGzk17/+Ne985zt561vfyoYNG/j4xz9OPp/nAx/4AH/zN38z38MUQswxKVqeUIlKMdTyMapS5B1d8N12KH+iAL0sTHPCYN2SELbns3VVhJ+/kGF3b5HepM2xZLlLfXVLgCODFvUxnf6UQ2tMI5V3qDdyHHr2SfRoK1/8xQDJAqgqaCq01Ro4ns8FbUGGsg7DWYe85VMTUscWwcd1nCciOlesjXIqC7kbfL6K/GfTfwsLe3RCCCEWjEqW+cmMLpaPj2+Z6v6toRiro03szfRzXqyJmzZ2kitQzTofP5FoY1SfNA99vNH3W9lk0hiVSBchhBCLj8wXsjC9/e1v5+1vf3v19hVXXMGjjz46jyMSQpyu0y0CLoTO5JdjNouflagU2/NpiGhsO5Sf8Ljju9MzRZdIQCUcVImHNVa2BBjJO/QlHZ45lCcR0ti4NETJ9ukatOloMHlin4VuDREb2onZugatYS1uMkt/usTKpgAXdoZQfIiFVIZzLs0xg5GESzioUmOqZIouNQF1QhF8Jq/FQstLH22+ivxn038LC3t0QgghzirTKbbD2Lx00zD5zObr6SmkaQ3FMFWdsFHeJpUtbzNZ4bzSrT5Vd91UE5AKIYQQi8n4K7tme74Q33XwUoOo8QYUTX5eCiEWn9MtAi7kzuTpmM3iZ+U1aIhoPLYvh+347O4pcs3GWHVflUiX/QMWmaJLf8rh4ECJ5Y0B9gctltebdI/Y+MDRIZsXugvVq5cd12NPb5GgDr2WwW/c1RhqG1c3KXQ2mtiOz/JGk5Cp0pu0eWJvjktWR3A8n84GE0NXqAlqZIseLQlj0slNz5ZC8MnMV5H/bPpv4ex9d4UQQpy1xuelm6pOZ6TuJNvUjvnRP1l3HTChsD7ZBKRCCCHEYjL+yq7ZnC/Edx1y992FO9CF1tRB5PqbpJguhFh0TrcIuJA7k6djNoufldfikV0ZupM2+aLH2iVB9g+U57vqTdpkCy7Hkja6qtAc03E8n5Z4OYalUmC3HZ+RnEt7nUFf0uZAf4kaU2M459DsHaQpqNMVXMLhTJCY4vDLXRnWLw1x8cowQVMlFtTo9mxCAZW+pMMrVoR58kCe+qjGsgazOqHpXL4Wi9HZ9N/C2TFKIYQQ55Tp5K1Ptk0l49Vx/THddQMZhx89m5LL1oUQQohx5vIKLS81iDvQBYDbfwQvNYhW1zJrjy+EEGeDs6kIOJtm+3kncw47jhboS1pctCLCyuOTeVZkii6249OVsnjlijAXdpyY+LPyZ2/KRlOgN2XzwrEiecvjFStCrFZfIuL1csC5iGhII5l3sTyfxqhOS8zAPp6JbnnwyhXhalf8AzvSaJpCwfZoTZgTctGn81qcTfnf4tTkHRRCCHHGjI50icdrT7rt+JiY0V3oLTGdpqjO4SGL5Q0mML6wbqOpyowmVZOJ2IQQQpyr5uoKLTXegNbUgdt/BK2pAzXeMOv7EEIIsTg8d6TAkUELFIWWmMGWZeExRWiA7hGbWEjj278ZYXNHkGePFDE0eOFYkWWNJh11JrsLLgMZl3TBw8MjntpGzCjwLK9E18Lonk9AV7Bdn+1HCrTVGrTXB7A8qvtsSxjs7S+VO95dn5UtgdPuNj9XYl9EmbyDQgghzpjxkS6TqRTbzUCYjKVWC9tjM14d4uETxe7a8InL1ltiOj/YlqI37Uy7O10mYhNCCHGuGX3yevwE4LNF0XQi198kGelCCHEWWagd0r4CzXGDRKQco7LtUJ6epMWhAYtMMcQVa6NcszHGp37Qw94+i6cO5ElENAazDpblc2zE5qVIkUzJx/M9wiaYus6Q0saQXk8yDwndZ21bEENTeKGniFVwefZwkVhYZ0NbECgXvrMlj9aYTqroccXaGjobA6f9vCT25dyycP6LEUIIcc4bHdcy1Q/8fD5HPl/g3x7PMlwsF8j/dGsdjuvTEtPpTTskIirJvFueDCZdnrG9ctm67frc+/AQMPmkapN1ns/1RGxCCCHEmTadk9ezQdF0iXN5GR577DEuuugiAoHAmL8LIcRcme8O6akK+ZvaQ0SDWjXv/EBfiRd7iugK7Osrsak9RCKioygKuZLH0jqdoKmyaWmAl/osdEVhMOtQsDxMP8fr6nbzonsBGb2J/mGHku3h+9AUNVjXGqS93uTIkMWlq8MYqsJj+3JAOUJGAbrTDvGgxmDOpbPx9J/vYo3+OVfJOymEEOKMGR3XkkqNTPoDPxyOMJBx6c8WsT2PI0MWd/9ygFTBoyWm85dX1lMb1vn6Y8NjJk6rXLZetDxiIZVkzmNp3dhJ1abqPJ/LidiEEEKI+TCd+UhG811HOsvnwe/8zu+wfft2VqxYMebvQggxV2ajQ/p0utqTOYftXQX29ZWIBbXyGGDM41QKziuB3T1FOutN+lI2tuNzz0ODvOa8CD4+zTGNhqhBS9ygL23TXmtg6CoNUY2e3n4ujexkf6GJowWdUMnBcT2yloeiwBN7s7z39Y28cXMCgMMDJf7tV8PYrse+3hJrlwRxPB9dVTgwUOIVKybPRX+5r4c4O8m7K4QQYtbM5DLyqX7gG4bJ0qY6etNd9KUcaiMqCgE0TaE37aCrCkFTnXTiNMf1+fpjwyTzLrURjT/dWjcmomWqzvO5nIhNCCGEmA/j5xo5Gd91yN13F+5AF1pTB5Hrb5Ji+hni+/6kfxdCiJdrquLubHRIj+5qrxTDGyLl7u2pisn7Byye7yqQzLsMaQ6vXBGuPk6m6Fa70VN5l5+/kCEaVFnSaHLl2hq+/ZsRhnMOX3xoiJCuUBPSCBlwZMiiJ2lh2XDF+RGOpIe5rOY5tqU72ZFtA8UnHARDV9Edn4Ll059xeGBHGoCdx4okQhqqCi8dLdJQY7CkzuAN66I8ti/HisbAtDrS57vLX5w56nwPQAghxLmjchl5Pp/Dti2GhgYYGhrAtq0J25Z/4NdOWnBPF12W1BqsawuytN4gYCh4HmO6xSsd6JMVylVFIVXwyBTdMY9b6TwHJnSeT/Z4QgghxGLgpQZxB7oAcPuP4KUG53lE4kz44he/yPLlywkGg2zZsoX/+Z//mXLbhx9+GEVRJvzvpZdeOoMjFkLMRKW4u39g4m+x6UjmHLYdypPMORPWrWw0aUkY1RiW3qTNY/tyJ93fykaTDe0hWuMGy48XqFc2mkSCKr/aneGff97Pg89nePDFDL94McuPn0uxs6vAoUGLazbGMFSFQtGlO2lTsDx6Ug4vdRc4MmiTKrocGCgx7IZ5YGAd29NthAyFxhqduhqdUEClNqxTX6MRDWocG7H59q9HONBfojtlkwhphEwVU1dY3RSgszHANRtjrGie3iSjo18PcW47a06T3H777dx333289NJLhEIhLr30Uv7hH/6BNWvWzPfQhBBCHDe6yzyfz5HNlM/067p+0q4427YYSg7TV8qyqrGNukiQJXGDvf1Fjg3bNMUN2hLmhA7zikrueSyojZl01HZ9HNev3kc6z4UQQiwGM51oVI03oDV14PYfQWvqQI03nIFRivn0ne98h/e///188YtfZOvWrdxzzz38zu/8Di+++CIdHR1T3m/37t3EYrHq7cbGlxEcLISYU6cb4VLpZM8UXXJFDzh5l3Xl8Te0BavF8akec1N7iE3toTGd8tGgxsEBi54Rm0f2ZLl4WYii7eIUffb1llAUhQvaghi6gqYBDqiKQnfSZijjUnJ9XlV7iHWqxQ/TazlSSBDQoSVusrEzRDrnkLcUhnIuv7MxTrpYjnBJ1Gi01ZpsXRXh0KBF0fGpDWtc2BECZta5Lznoi8dZ8y4/8sgjvPe97+Xiiy/GcRz+5m/+hquuuooXX3yRSGR6uX9CCCHm1ujLyMNhcBzn+N9P/u90Mpvmb158gEPFJGtizdyx+Q/JFF1295ToSTocHLBpq7N5W76OVnPsxVTjc8//dGsdI3mHH2xLce/DQ2Oy0OFE57kQQghxrprpRKOKphO5/ibJSF9EPve5z/EXf/EXvPOd7wTgzjvv5Kc//Sl33303t99++5T3a2pqIpFInKFRCiGma7IYl9Mt7lY6zGsC6pRd1mOiTJaFq/sZH4FyeKDEY/tyxEIqhZLP7p4i12yMsWVZuDrubMHlsjVRnjqUZ2WjyZ6eEqoCBdtFUXwO9xc5NFCiPqLRFDfZsCTI0WGLZM7FcT1e37CXjkCKZ3JbGMq6qEDAUFjeZHJ0yMLzoWB7dDaY9KYcrtkYo75GZ+uqCJ2N5cmd42GNmpAmGefilM6aT8d///d/j7n91a9+laamJrZt28Zll102T6MSQggxFcMwqa8/dZeS4/oczJU4YmXQNI39hRE+9d8H2LlXJ1VwcT0f2wPHA/DH3G845+B6/pjc80zRRVPLeeqVZZUsdCGEEGIxGH2F2HS70xVNR6trOVNDFPPIsiy2bdvGrbfeOmb5VVddxeOPP37S+27evJlisci6dev46Ec/ypVXXjmXQxVCTNNsZnSP7mSfqqg83W73x/blONBfojVu4CuQzLls7ypwxdpoddzZkgf44MNIzqE/Y3N0xMLz4NnDRQwdakyVowGNGlPhxWMFFBSKrseVjftpMnPs9F/BQMagZNsoCpiawtFhm6Ltoalw6aoIBwZsTM1lOOfytkvqgLEnICrFfSFO5qwppI+XSqUAqKurm+eRCCGEOF2VbvKupI9h1uOFUrjZKNv3Kti2h6IoxEIa9TUaV54XwvSz2HYNimpUu9BbYjotMZ3etDMm97wS8TI+C10IIYQ4142+QiyVGplRd7o49w0ODuK6Ls3NzWOWNzc309vbO+l9Wltbuffee9myZQulUol///d/53Wvex0PP/zwlI1tpVKJUqlUvZ1Op2fvSQghxjjdGJfJjO9kn2rSUoBU3p2wbvT2F7QF6RqyeMWKMMM5l6cO5tnfW2JTe4hERK+O9/tPjbC7p4iiQFutSUDXyBVdciUXzQFdVQjoPnt6y4X3hpBHydc4YCzDrq3hd85P8NSBPCMFm2wRfN/Ddn1qgiqJsM5gxkVVoGh7+KPSPSsnILIFd9od6Sd7PcS5b9rveKFQ4MEHH+TKK68kGo2OWZdOp3n44Ye5+uqrCQQCsz7I8Xzf55ZbbuHVr341GzZsmHI7OXALIcTCVpkcVENji3UFr1mjcO9/5+kt+oDPVRtivOM1dZi6gulnca0S+bxCSampdqH3ph3+8soGdJVq7rnj+vzu5hig0BidmIVe6WavW0Q56QvpOC6EEOLMGd2dLsRoijL2O5Dv+xOWVaxZs2bM/GSXXHIJXV1dfPazn52ykH777bfziU98YvYGLISY0kyK3zM1Wbf7c0cKPHUwT87yWNcaHLNu/4DFgb4Su3uKtCUMNiwNMZxzyZRc8iWPhqheHtvxbRsi5QlALcfH1BWChsLFK0IcGSzRk3QpuR6O6zOSd8hbHrV6nqsTz/Pr1Ep6rXoSVvnfrcvX1bCnr4jjOgQMlWs3xQiaKjsOFxjJuxiawpol5Yz2ikohP1N0p93RP5vd/+Lso556k7J7772Xz3/+8xN+fAPEYjHuuusuvvzlL8/q4Kbyvve9jx07dvCtb33rpNvdfvvtxOPx6v/a29vPyPiEEGKxs22LVGqEfD5HKjWCbVtj1g2MDPLS4FGswiAJ08LzPNoTQc6vbUBTdBJhlaa4wV9cXseSuEJYyWEYJmnbwAyEqYvoLEmUo1paYjrgjymif+XRIe55aIgfPZuaMLbK+i/+YpCvPDqE4/oTtjkXLaTjuBBCiDOn3J1eO61JR8WZ95GPfKR6lfXov8+lhoYGNE2b0H3e398/oUv9ZF71qlexd+/eKdffdtttpFKp6v+6urpOe8xCiJmpFHv3D1in3vgUVjaa1az0ZM5h26E82aLLSN5FU3xszx/TCb+y0cT2fHRVIVNySRXKRfRDAxbhgIqpKzRENB7Ykeb5rgJfe2wYVYFEpNw93hTXUYC6qIGpg+fAYKb8OM1Git9rfo79hSb2pOtIF+DQoMV/PDHMI7uynNcaoKFGZ1NnmA3tYVY1BwkFVEqOx+rmAH9wUQKAbYfyJHNO+QTEsjCb2kNT5sGf7PUQi8+0C+n/8R//wfvf//4p17///e/n3/7t32ZjTCd100038cMf/pCHHnqIpUuXnnRbOXALIcT8qExylkwOUyoWyedz1XXJbJqPPP8Tbnz6O3zshR9z1eo8f/aqAH+6tQ5NVbj8vAjrl4a4an2U+jAc6+nh6b3D3P1gL1//TYl/fyIDwA2X1fOXVzYAcM9DQ9WieKXLHU7ko492qvXnqoVyHBdCCCHECbfddlt18s7Rf59LpmmyZcsWHnzwwTHLH3zwQS699NJpP86zzz5La2vrlOsDgQCxWGzM/4Q4V1UKzMkF8tviZMXemY61UmxORPRqgT4S0rhqfZRNnWEMVeF7Tyc5PFDi8ECJB3akuaAtyMrmAIWSxwtHCxwdshjKuXTWm1yzMcahQYuD/SWeOpgjV3TYP1AiFtKpj2rs7inx6O4sT+7LM5jxKLqgKKC6Nr/d+CK/HlnGk6lluCj4HpQsj2Te4fmjeY4O2fzWshCrW4I0RDQyRZdESOP8JSE2dITGPIfRJxlGP8dTvV4n21ac+6b9ru/du5cLL7xwyvUbN2486dnol8v3fW666Sbuv/9+Hn74YZYvX37K+wQCAblEXQgh5kHl8vEaw8S2rTGTnQ25RQ4WRlCAw1aatGpzYX2Urz46QNdQgba6IO+/upHGqMFQcpiP/7hEd8rFdhVetdKiO6lWJw/VVCZMKlrpVp8qH/1U689V830cF0IIMT8sz6GnkKY1FMNUF8cxT5zaLbfcwp/8yZ9w0UUXcckll3Dvvfdy5MgR3vOe9wDlov6xY8f4+te/DsCdd97JsmXLWL9+PZZl8Y1vfIPvf//7fP/735/PpyHEgrHQ4j7GR72MNpOxjo+IWdloki24+MCFHSH2D1h8+4lh+o//JgubKgf6SxRKHitbAjx5IM/hQZsD/RatCYNHdmeJBFSyRZeuYYt4WCNn+axoNBnOuQykbXqTLpY3dhwRtYhFkG91b6Hom5g6GC6gQE1IBQ9yRZ9IEHKWzxvWRRnMueSKHhs6QkSD2oQc+el2lC+091bMr2l/AhzHYWBggI6OjknXDwwM4Dhzd+btve99L9/85jf5wQ9+QDQarV6GFo/HCYVCp7i3EEKIM2n0JGdQLqpXJjurN4OcX9vK7lQvK8L1XNC+mkxJpWuogOu4dA0WyGRSeG6E/rTOYB5MQ6NgexQclZUJg2hQoz9tEwtqY4ri0aDGcM7hT7fWkSm61biX0XRN4YbL6hnJOZOuP1fN93FcCCHEmWd5Dh965n72ZftZHW3iM5uvl2K6AOCtb30rQ0NDfPKTn6Snp4cNGzbwwAMP0NnZCUBPTw9Hjhypbm9ZFh/60Ic4duwYoVCI9evX85Of/IRrrrlmvp6CENNypiaGnM3JPufaTMY6voiciOjUhDQO9JX4Xl+SJXGD5pgOCrTGdDYsDZG3PPKWx9MH8pQcH03zaY7rHBkuEdBVvv90kkhAoWD51AR9VjaYbDtSwLZ9BtMniugq4OFzed0BloeH+XbPFoquiXt8XSysoikQ0jVyloeieli2R3utwYMvZogHVVJFjze0RYmHtWou+0zz5M+m91bMvWn/K7J+/Xp+/vOfs2XLlknXP/jgg6xfv37WBjbe3XffDcAVV1wxZvlXv/pV3vGOd8zZfoUQQsyO0ZOd/eOWN4/pjlP9Ek01Cr1plaaowo+ezTBUyLG0PkJrwqQv5bCqSeV9r4sTD7h89dEB+jIeSxJGtWgeDWp8/bHhalH9hsvqpyyS65pCY8w4k09/3s33cVwIIcSZ11NIsy/bD8DeTD89hTSdkbnP4BZnhxtvvJEbb7xx0nVf+9rXxtz+3//7f/O///f/PgOjEmJ2nalu4pN1gC800xlrpbjcENEAJmSg7+4pksy5JHMuADVBjY56k87GAKuaLZ4+kCdgKPz2BTGyRZftXQXa6xS6hm183yOZc8lZcF5LgEzRw7I8ciUX/3gAta5CxPC4om43tXqOBwY2gKLiHh9D0YG4pnBei4mpahwaKgEqK5oC7DhaoL3WwPWhNqLx8xcy5G2Pku2TWRHmirVj54w61WfkbHpvxdyb9ifhhhtu4JZbbmH9+vVce+21Y9b96Ec/4lOf+hSf+9znZn2AFb6/OCaDE0KIs10lwiUcjkw5sZmp6tUf8o7rc2wwwx9s0sk5Oo7n8++/LmCaBr0ph/e9voHeoQzrmlwMMgwlVbqGbEwzQHfSJlN0aYwZ9KftCdnni61YfjLzfRwXQghx5rWGYqyONrE3U+5Ibw1JRrUQYnGRbuLTM6a4vCwMjC2utyUMYiGVvqTDS71FLNvnNwfzDOVdXugqgAIrGkyaEwaZPpdjw+Xfbc1xHVNTOTLkge/Rl3ZojGnoGoSDKkYOSoDiw8Z4HwnT4snCFtKOguOBoYDtQzQIdRGNtoRJPKITCyu4frmJytRVNF3lVcvCHOgvsauniO34KEDO8lheb57oUG805TMiZmTahfR3v/vdPProo1x33XWsXbuWNWvWoCgKu3btYs+ePbzlLW/h3e9+91yOVQghxFmgMtEoMCreZfLlhWKJf3mon/4sNNXAWy9SsC2HeEglY8GBgQKf/mEeRYFNS3X+v9fV0Ryyaa/Xqx3plYzzxZp9Pl1yHBdCiMXHVHU+s/l6yUhfgGzb5t3vfjcf+9jHWLFixXwPR4hzlnQTn57JisuV4vru44Xp3pTNa86LkMw7DOVcDFXhwZ1pQma5Mzxv+dz3dJJs3qU3ZZMrOTTHDBqjOvv7SwxmHEp2HlVTMDWFbMklb0GNXsTQVPYUlnDQamUkB44Lmg4BTaEjphMJqKhA3vZZEzMYyboEVAgZCqahsqLB5NLVEcJBlaLjEzAUhnMujuPx2L4ca1qDY04UyGdETNeMPinf+MY3uO666/jmN7/Jnj178H2fNWvW8IlPfIK3vOUtczVGIYQQC9j4DvTRES6jt3EcB03XCYcj5PM5kslhkgWVo8MWjqfSj8FQ1uNnLzqkLZXf7M+RKbi4PgR0hUzRIxzKc9MbmnjXlUzIOJ9J9rnj+gwfn5h0sWSkgxzHhRBiMRp9FZhYOAzD4P777+djH/vYfA9FCLFIzTQ//sREoz6/3ptjOO9SesEnHFCxHZ/dvUUc1yOV9njt+TU8+GKW/pSN6/soCuiaxnDW4WiyRG/SoeBAIeuhAooCrg8NZpY3NT/P87mlvFRoJ5UHD4iHVMIBhXioPDnpUMamJqizpiXAiuYAoYDCI7sypBSFNa1B8pbPAzvSXNAWZEmtwdZV5d+mj+3LsXVVhHh4YmSNENMx41Mub3nLW+THthBCiKrxneZjJxo9sY3rOASCQQzDZGCgj3wuR0A36ctAX8ahOephWT79WZ+8DXnLR9OgWALP9wk6PgMZi8O9Qyypj+KjTloQP1UQmOP6fOXRoWllqZ+L5DguhBBCLAzXX389//mf/8ktt9wy30MRQsyhMzXh6UydLBt8/LrKc/AVUFBIhDUGcy7RoMruniLDOYf6qI6mKtRGdO7blmIg7WC5PpGAQludDp5CwfYYHikX0Ss8AB86QiNc07SLZzPtbE+3oas+NWEFz1MIB8r7Hcw6WC7oqkJtRKOzMcCWZWGSOZPelEMy57IkbpAsuOiqws5jReKh8li3LAvT2Rio7nfl8edZmYBUiOmY9ifl4x//OLfeeivhcDkbaWRkhNra2jkbmBBCiLPDZB3op9omkajD9Xx6CjXk7REUFPIWmMEAbbUOhweKxILg++WJZmrDGranUmN4BChxzy/zHEurDGcd2moDLK0rTzo6nclGh3POosxSl+O4EEIIsbCsWrWK//N//g+PP/44W7ZsIRIZ+13q5ptvnqeRCSFm05ma8HSmTpYNPn5d5TnUBFRaEgY+PkFTPT5nlUfRAcuBy9aESRc9fvlimnzRIxbWsB0NXVM4vy3E/r4CudLEsej4XFJ7iP8ZXsWebBOKCigQ0jVKjk/RAst1qQmo1AU1ltWbvGFjlE3tIaBcCH/DuigPvpgBYOuqCIM5l4ZIuYg+2XNcqO+LWNim/Un59Kc/zfve977qD/DOzk62b98ueW5CCLHITdaBDhMjX0Zvo6g6/+9ZlwMDA/SnQVc9SqpKIBDi9WuLJFsyvH2jS8qv4ckjPr856GD4CtGgSdrWePxAiUzRI1PwqK/R6U7CwYHStArkizVLfaEdx2+//XY+8pGP8Fd/9Vfceeed8zIGIYQQYj59+ctfJpFIsG3bNrZt2zZmnaIoUkgX4hxxNk5mWcmWT+Ycth3K0xApR6E0RDQODlmoKGxoDxENahwbttEVn4GMzbOH8gzmXDRFIWCqvPb8KKauUnI9dhwu0JNyUFWOt6ED+CwPDXOsWMf3ujfhoaAq4LrgepBRHUIBDdfxaYrpKIrCeS0BVrcEefXqmjGd5IM5l2TO5WB/iUhA5fLzowB0Nk7+HM/G90XMv2lXD3zfP+ltIYQQi8/4Yvlok00uWtm+N1mia7CA4nm4rlbuMLA9PvGffXTUB4kbKm9aD0vrQqzuiDOQGyBgKAwVwNWiKEoOTVUwdAXfV1iSMFjeGBhTII8GNfrT9oQc9JlkqZ9LFtJx/KmnnuLee+9l48aN8zYGIYQQYr4dPHhwvocghDgDFuqEp1N1ZI+Ootk/YLG/r8T2gsvKlgDZgsvzRwugKFzQFiRTcHFcj760i+V4HB220VWIBjU2LwuxsslkT6/F4/tyHBwoki2e2L+Cz5X1+1geHuZ7vZvJ2CY+5ax0jv9pu9Beo1EX0Tk8aBE0FJJ5l2Te5bkjBS7sCFXHurLR5NnjBX9/Gj/xFur7IhY2+cQIIYQ4bScrllcK66MjXyrbJ8Ia7Q0hDg7a1NVAvuSiKAoDGZ8ltQopO0gRj1AoTDxgsLIpUC2Qr24OcOXaGg4NWnTUm1x/UZzGqIGuKfzp1joODpZorzVPGvOia8qiiHNZiLLZLG9/+9v5l3/5Fz71qU/N93CEEEIIIYRYlCqd2A0RjW2H8tUM9+1dBZ7vKtCTNIkGNNLFcuH66QM5EhGdZY0mig87jhZ4rivP4UGLbMHHp9xo7qiglFwe35vjyf05VEVhKOudaEIHdMXlmqaXiOpFvtu9iRImsbBC3vKxjuenmyosbwzQXmfi45MI69iej+P4FC0PXxl3MmBZmD+4KFEtrM+1hZp9L+bWtN9pRVHIZDIEg0F830dRFLLZLOl0esx2sVhs1gcphBDizDtZt7ltW3iee9JiOUA8PjaD2zBMcrksiWiMm69ppGc4z3/8qpsnD6koikKixsTUVRKmhe4UyGRMmsORCR3k77yiYcxtx/XpHrH44TMpetMOsaBKMu+hqosrB/1kFspx/L3vfS9vfOMbef3rX3/KQnqpVKJUOhGiOH6sQgghJneyY7gQQoiX51wpoFY6srcdynOgr8TuniJbV0XY31uiaPv0JR2GNJfuYZvOBpNDQxaNUYWaoMbzRwo89FKGY0MWllt+PF8BTYGADrYD2eOt5arijymiq0CDmUPB5/7eC3HQ0VVwHB9dAU8tz5O1sskgEdEIGgotcZOWeHkMEVPF0JRqPjqMjWfJFly2dxXY1B6a0/dHMtYXpxlFu5x33nljbm/evHnMbUVRcF13dkcohBBiXowuiIfDjPlBns/n8D0f27YmFMvHTyw6+se8bVv4isKBdD/nNbUTMy3evFGjM+5w3pIIK5Y20psGs9SDVXCwrHIRdbIOcv/4Y6fSOb67zebQkMPBgRKdDSaO51Ef0UkVvEWVg34yC+E4/u1vf5tnnnmGp556alrb33777XziE5+Ys/EIIcS5arIrxoQQQsyO0y2gLsQCfDLnkCm6pAsu0ZDGY/tyREPleJRoUOUXuzLkih57+kusbw3w1ME8A2mb/f0Wx0Ysig74HqCA54Pvg6ZRLa5DeXlFQi8QM4ocKdTyg771GKpC0ICSU85EDwegNqDgo2BoKvGwRk1Q47I1New8VuTSVWEODdlsXRWpvoaj34P9AxY7jxXB94kGtTktcEvG+uI07U/UQw89NJfjEEIIscCMLoiP/0EeDkdAUXAcB9u2xnS7jZ9YtHJfx3GwPZdPHnyIw8UkKw7W8bfnv5FP/JdFX8ajsSbHRSt80pZJYyTIdet06uvqq49TKcibgTD//kSG7qRNXdDhylUKXUMOpmYykHHJFIq01Rncek0LBdtbVDnoJzPfx/Guri7+6q/+ip/97GcEg8Fp3ee2227jlltuqd5Op9O0t7fP1RCFEOKcMf6kthBCiNlzugXUhdLBPLqg/9yRAs8fK9Ic1UkVXWIBleaYTjig8MS+PCqQLrpEgyq/OZAHoHvEojmuUxvWGbAdbOVErrkP5AswWWtOSyDN7za/wM50K0cKtYCC54OpK/g+BEOwrCFAIqSzt79IsuCyXFW4ZmOMnceK6KpCwNR42yXRKZ9TQ0TjgrYgvjL3BW7JWF+cpv2OX3755XM5DiGEEAvM6IJ4OMzxPyPVdYoCruOQz+eIx80pLyM3A2F6kyUCZOlz8uzNDqOrCnszAzy85xD92fK2/Vk4MOgTCXr0eSrBWCvh8Iku9EpBfiDj0n38C+hgXiUQNGmvNzg05NBQo9Ga0ImHdAq2t+jjXEab7+P4tm3b6O/vZ8uWLdVlruvy6KOP8oUvfIFSqYSmaWPuEwgECAQCZ3qoQghx1ht/UlsIIcTsOZ0CaqXzuyagzmsHczLn8L2nk+XxrIiAAvg+qaJL0fJI5l3SJY9iySNkKKxbGuK162I8e6Qc/3JgoISiwMF+C0WBaFBhMOeP2YczyX5XhIe4uvElHhtezo7MEgB05Xj3uuMTDaqEghrXbIqjotCdslEUn5a4wW8O5jkyUELTVV65Ijzp8xp9kuLy8ycW2oWYLXLqRAghxCkZhjkq3qVSSFcJBIPV4vpkl5E7rs/XfpXkYG+GupCP7fs4apxhbZjzw3HaVZeGsM9gXqUxZpIpOOzpK9GWMMgUSqhuhlhNzfH9l/fTFg+zJFHuSF9aF2DFknpWLIGepM3f3d/NwQGb5nj5Uj6xcLzuda9j586dY5b9+Z//OWvXruXDH/7whCK6EEIIsZgdOXKEtrY2OT4KcY7YP2CRK3q0JIx5jXXZ3lVgd3cRy/GpDZd4/foo0aCGqcJvDuZR8Ejmyv3krXGD5oROoeSRK7ocS1rY7tjYFrfgYyhg+1Ps8LgmM8PPBtawP98AlHPSdbUc54IBmZKHrivs7S3x6jU1/Plr6ulO2bTEdX6zL8dzXQUubA8xmHPpbJz4+DO9SmAhxuyIs8OMPi1f/OIXue+++6irq+M973kPr33ta6vrBgcHecUrXsGBAwdmfZBCCCHm3/hCuaIoY/LRR19GXulOzzoBuoYKAHQlXXwU7BqbnONysOjxsyGNm7aqfH1HCF1X2H6ogKn5HBiwufOnfTRGFN59hU9zff2Y7robLjPHTDYKEDAU2upMGqI+AUMhU3QJmuqZfIkWvPk8jkejUTZs2DBmWSQSob6+fsJyIYQQYrFbtmwZq1ev5vbbb+f3f//353s4QoiXab7ztCuF41zBpTluMJJ1GCm4HBq0uLAjxAM70sSCGk3NAfJFjxe6C+zqKfLEfpvDgza5oovljS2iAzjeyQqLPlviR9mVbebXyWVj1igKeIChlScmDeoqJccnlXPpS9kMph06G00uaAux7VCB5piBrk/dzT/TqwQWSsyOOPtMu8Jw11138dd//desXbuWQCDANddcw+23315d77ouhw8fnpNBCiGEmH/hcGRMB/p45UJ3LYZhkk6nSI4Mo3tZ2utDqJrO8sYAkZoMKSUFnkJGT3KgUOTD/63w+P4C/7M7T6bok7XAdn26hn2eOOjw77+xcNyxLQ6VyUd1TcFxffrTNrGgxtJak5CpsiRuYLv+hPstZnIcF0IIIc4eDz30ELfddhvf+9735nsoQohZkIjobFkWnlb3czLnsO1QnmRuspCU01MpHNcENa5cF+WS8yIEdQVfKa8zVAXH89nUHiJbdPmf3TlePFZkd49FtuSRt8F2ITRJcuZko1TxuLpxNxdEezCVsVsYarmAnggphAIqtSEVXYPWuEksovKf25I8sjvLE3tzDOZc3rG1jivXRXnH1rpZ6x5f2WjSkjBkolAxY9P+BN5zzz38y7/8C29729sAuPHGG3nTm95EoVDgk5/85JwNUAghxMJwsrzVqfLRdVXhXVc2sv9oP5qdZCSjc7AnwjPFfgrAM9pO1MIl+J6G70M0CJGAQn3EIGdDNGSQLsBIzpk079xxfb788CAHBy2WN5i84zX1jOQdfrAtxb0PD7EkYXDDZfUy2SgL8zj+8MMPz8t+hRBCiIXu8ssv5/LLL+cd73jHfA9FCDHKmYgEmc1u6dGTcALVcSdzJq2J8vNI5V2eLbosiRuk8i6P7s3hOB6pgktAV8gXfdxKJ7pazjZ3TtKvZCoOb2zeRUB1+G7PJvLuid+HAQ3qajRs18cFArqCqasEfFhSqxPSVSIBHV3xuWR1BFOFx/bl2LoqQmfj7M2dJBOFitM17U/NwYMHufTSS6u3L7nkEn75y1/yute9Dtu2ef/73z8X4xNCCHEWGB/7EovF0XWdcDiCp7jkGEEt5gkZJp2ZC3lR+xUqCn4whWfm8Qs16Cpc2Ap1IYe/uCzCD3eZjOQ8ltYZ1E7xJac/bfPQS1nyJY9Dgxa/tyWBpir0pstdD91Je8oi/GIjx3EhhBC+6+ClBlHjDfM9lEWtUCjw4IMPcuWVVxKNjp0UL51O8/DDD3P11VfLhNtCLEBnIhJkNmNgxox32diJOnuSFtsP5YkGVXZ3F3nmYJ5v/WaYZM6h5PjURzS60w6WV45hgXKmuTeNi36HrDBPjCzD9ssFfI1ynIuqlicXdT1QVJ+mmIHr+dgOjORcXrG8fPXzcN6ls85k57EiB/pLAGMK6ZJxLubLtD9tDQ0NdHV1sWzZsuqy9evX88tf/pLXvva1HDt2bC7GJ4QQYoGYquvcti0cx0E7XjiHE93rlufwoWfu56VkN21ahGtrNmAVIiRCtaTVFAk/RkAPodR4rKp1eeMal85aFVN1uekNTRNy0MdTJiz2qYsYLEkYdCdtliSmLsIvNnIcF0KIxc13HXL33YU70IXW1AG0zfeQFq17772XH/7wh1x33XUT1sViMe666y6OHDnC+973vnkYnRDiZCYrck9V1D1VsXeq9bPZLW2q8PzRAh11YxuL9g9YPLE3R7bkY2iwp7dIMu/hei6FEoQDKkXbw/fAH1U4t12Yqo5eb+RYF+3jf4aX8+jwyupyBTANwAffg5LtEwkomIZGa8JkVVOA57uLtMR0muMGu3pL7O8rcd8zKd6xtQ6AravGxotKxrmYL9P+tL361a/m+9//Pq95zWvGLF+3bh2/+MUvuPLKK2d9cEIIIRYG27YYGOijUrceHfGSz+dwjxfS8/kchmFj2xbhcIQeK8u+bD8+8OvMMfYWR/DDCdalNmPGU/zZugZ+4uoM52yaIuUvpOFwiIAZwPdsGmNTd2HYtoXpZ7lsdYgjIy7LG0wao+Xc9Bsuqz9lEX6xkeO4EEIsbl5qEKfvCK7r4Pcehvrm+R7SovUf//EffOxjH5ty/fvf/34++clPSiFdiAVosiL3VEXdUxV7z0Qx+MkDeQ4PWtz3TIr2+kqsi8PzXXm6hi0ipkquCEMZF8/zsD3QNLBsDxQojgtAn6qI3hZMcm3TLranl6AroCrl4nnJLk/O6LjlbnQfMHWFaEgjoKu0xnUuXhnhdzbGGMy5rGw06UvZ9CRtzm8J0NkYmDTSZb4nbxWL17T/S7311lvZtm3bpOvWr1/PQw89JBOhCCHEOSqfz6Gg4ONPmGy0crtULJLNpFE1jYAZwHEcwqrCykgDzw114fvQn1ZwvRTp8JM0xkt8YSDB+zf8FrZnENEcTENH07TqPqfKZK+sd60S/+viAJ4WHVM0r0xGKk6Q47gQQixuarwBr7YZr78LpaEZNOngmy979+7lwgsvnHL9xo0b2bt37xkckRDi5ZiqqHuqYu9sF4NH56EfGrTwFbBdj6MjNgFD4YEdabauivDYvhw/3Zmha9giHlQpuT6u52E74B6vlOsKFNyT769iVXiAqxr38PDQSvbkWzA0aIpp9KRdfL8cCxMOgedAyQFFhfqoTjSgYRoqDRGNg0MWyvF9X3VBjJUtwerrMvp5VYrtknEu5su0P3UbN25k48aNU65fv34969evn5VBCSGEWFgqxfLJYl0qcS+OU25XMM0Amq6TSo5g2RZ/3foqXjQ7+ejzzzFIFs0OkQ/k6c8o9DPMv/cWuemSKDXhEMVCHtMM4KsavckSZqCErimTRsqMHVO5aO64PsM5hzrpRJ9AjuNCCLG4KZpO5E3vJX/gecIrNsBP/vu0H2t01roiBfkZcxyHgYEBOjo6Jl0/MDBQ/V4lxGJ3NmRhT1XUPVWxd7aLwZUO9909RbqTNl2DFn1pG1OFF7sLxIIqTx/Ms7o5gK6VJ/rUNAXDL3eMuz44XrlrfJo1dAByrslP+s/nSKEOBdBNGCl4KJSL6PGgyrLGAL7vc6C/hKr4rG4KoKoKLTGDx/blGM46oCjUhDS2LAtP2tm/u6dIPFRuupIiupgvM/7k7dixY9LliqIQDAbp6OiQSVGEEOIcU8k8r7BtC89zSadTuMd/6IVCYUqlIqFQiGw2Q7FUxLEdPPL860Nxiv2XEjLzYAcoLnuKXDiNacV4vjfM96MmN72hDl3XcTyfbz1ZoCfp0FZn8/ZXRXCt8gQzo8cwfkyO6/OVR4eq2eg3XFaPrilSXB9HjuNCCLE4+a6D/cCXUQe6sHf/htPNSB+ftR65/iYpps/Q+vXr+fnPf86WLVsmXf/ggw/KyW0hjpMs7OmrdHBvaAvy8xcyvFhy0VWF7qyNocJThwosSRgMpB1ee36Uh3ZlcT2PgwMWtgvOqNwWb4p9VCj4vKbuAMeKcfbnG0YtB9cBVfPRFAgFoDGu8eYtMbYdKlCwXOIhHVVV+F+vrOXgkEW+6BELqUQD2qTd+aOfV6UjXYj5MuN/hTZt2oQycXa3KsMweOtb38o999xDMBh8WYMTQgixMOXzOfzj07Vruk6pWCSdTqFrGtlsBgUF0zDBh4FSiOF8AQUNtRQFIHTwVRDIE3TDBKM6/ck8I3mXiK7T3Zfh6HAJXTcYzKuU/CCRoDIhUmY0x/XZ1V1gX1+JoKHSnbSrGemTFdcXMzmOCyHE4uSlBnEHugBw+49A7ellpI9/HC81iFbXMmE76Vqf2g033MAtt9zC+vXrufbaa8es+9GPfsSnPvUpPve5z83T6IRYWBZbFvbL6cAf3eH+5os1YiGNR3ZnAJPBrEtrXCdf8jg0WKQxqtGXsulNOQSME5Eu06EpLr/duJs6I8/29ImTsiqAUi7I6345J13XFRpqDPb0WSytMxnMOKxsCfCWi2vpbAwwmHPJFT1aEyZbloVP+bw6G2f0kggx69SZ3uH+++9n9erV3HvvvWzfvp1nn32We++9lzVr1vDNb36Tf/3Xf+WXv/wlH/3oR+divEIIIRaAcDiCoirEYnF0XSebzZDPZSlZFpqmYwYCBIJBDEMn6ieJBUp4gQy+Ur5IUPE1lGKUoKrQHndoCPtgZ8g6AaJBn9ZY+ZK9hrBHbVgjHq8dE+sC5a74VGqEQrHElx8e5P/8oJenDuZ5qac843ttRGc459B9vIOlUlxf7OQ4LoQQi5Mab0BrKkeJaE0dp52RPv5x1HjDhG0qXevZb/8Dufv/Cd+d/eOv5Tkczg1jeWffsf3d7343b3rTm7juuutYt24d119/Pb//+7/P+eefz5ve9CZ+93d/l3e/+93zPUwhFoRERGfLsvCCjXWZbZUO/P0D1mk/RqUY3xTTuWh5hNa4wW8tC3HZmihFy+PQoM0T+3NkiscDXDwIT3N6KQ2P3295nrBm8/96LiTtnGi8URTKOece+B6oGjREDRpjOssbTPoyDo1xg7ZaszqB6MpGk5aEsWhOlIiz34z/Jfr0pz/N5z//ea6++urqso0bN7J06VI+9rGP8eSTTxKJRPjgBz/IZz/72VkdrBBCiIXBMExUVcMwTMJhSJspUBQ0TcNzXfJWCc91KRSKOJ6Ld96TFPIZvEwt4QOXovgaquKzKmHz+mUWa5dqfPfpIknLozka5I9frTOcKhKgQD6XJhSc2HqQz+fI53Ic7kuzv18lX/II6h4tMZXf2xJH1xTqIjpLEka1I712kXwBPxk5jgshxOKkaDqR62+qdonz/ftm5XEm6zafbtf66bI8hw89cz/7sv2sjjbxmc3XY6pn1zH+G9/4Btdddx3f/OY32bNnD77vs2bNGj7xiU/wlre8Zb6HJ4SYJ6fbgX94oMRj+3Isqzf4nz054iENXVdQfEhENI6N2PzyhTSpgkO64NE9AnU1Gr7vYDlQmNY5SR8XlefSrezP1+P65eYnFcpd7R54PtQEoSZoUBvR2LwsRF1YR1EULuoMkSp6XNAW5JFdGXwFNrWHpuxEF2IhmvG3jZ07d9LZ2TlheWdnJzt37gTKl4339PS8/NEJIYSYN6MnEh3fDT6aYZi0tCwhn89hGCbJ5DBWqUg2l8X3Nf5jb55njF5804O6HG53Gr2QIKhBY9RjSdShdzhL94iBGVA5POCQzIapi6gUC2MjSEaPyQyEOdyXJh6EtrjPwQEfz1NY0aDSGC23VOiawg2X1VdjXhZ7rAvIcVwIIRYzRdNnpaB9qsepdK27/Uem7Fp/OXoKafZl+wHYm+mnp5CmM1I3q/s4E97ylrdI0VwIMcZkE5COj3uZLP7lsX05dnUXefD5NLVhnZ3HiqyoN/Dw2XmkSG/axvfKk4k6LvSlXDQV7FOFoR/XZGZ4bcM+7u/dwJ5c05h1QRPqwhojOZeGmEF7vUlzrDz3VUhX0TUF2/PZel6URERn26E8O48VwfeJBrUps++TOYftXQUUHy7sCC2aqxLEwjbjT+HatWu54447uPfeezHNcmHFtm3uuOMO1q5dC8CxY8dobj69zD0hhBDzq1KsdhynOpHo6Ek9J1OZ+NO2LQKBIMVCAVXRGMq7HMsX8ROVb2g+vlEg4iTY0mbz5vUeAUPD8CGqF0kVfZoioNopLNWkJhojFotX95PP5ygVixQtm+88XaI/q9JUA39xRSNvfoVLvpCnozE2pmCuawqNsWleq7gIyHFcCCEWr9EnpOfSdLrWT5flOdiey8qaRvZmBlgaTlAfOLu6GT/+8Y9z6623Eg6Xxz0yMkJtbe08j0oIsVCNn3B1sglYL2gL8vi+HPGQSs5y0fF5Yl+OfMkla4Hvg6mBaUCpkugyzSJ6Z2iY32l6id+MdFDyJv577nrlyUlXtQRpiOp01JsEDIWS41N0PEKuSn1U47kjBS7sCLGy0SRbcPGVk3fe7x+weL6rAIpCTWjqgrsQZ9KMP4X//M//zHXXXcfSpUvZuHEjiqKwY8cOXNflxz/+MQAHDhzgxhtvnPXBCiGEmHuVYrWm6wSCwRn92M7nc7iOg2GaGLaFWbJ4STtwYgNfQ8vV4ikKqZLGL4+Y/NmlNWTSSa5da6MGozTGTfLZNAoKuq6P6YavjKU3WeLYsIWmawwXAxQdjfaGIHDqsTquz0DGxvehKWYsui51OY4LIcTiVTnGnwmz1f0+2uhIl+WROlpDUY7mR/ib5350VsW7fPrTn+Z973tftZDe2dnJ9u3bWbFixTyPTAgxn6aaaHR83EulEJ0puiRzDomIjuXBsnqTvpRNOKBweMCi6PiU3HIRXQFMHVzKf5/u3KIdoRHe2LSLnw+uHtOJrlAuzPsKJEIa5y8J0lEfIGd5FG2PoK4R1BR8RaE5oXNowDpREF8W5vLzo6fc98pGk0wxhOIvnslmxcI3428al156KYcOHeIb3/hGNcvtD/7gD3jb295GNFr+D+FP/uRPZn2gQgghzoxKsfpUkS4nu29NNIZtWexP91Iw0+CpgI9SCoHuUCr59KYVkkWfN17gEdF1HNuixrDA1wgGQ/j4OI5DOpulP1mkrSFKKBggHjcxAyXa6mwG8ypLEgY1pkcqNXLKMTuuz5cfHuShl7IAXLm2hnde0bCoiulyHBdCiMVr9DH+bDQ60uWldD+gENC0sy7exff9k94WQixOk3Waw8S4l0REpyakVSclbci7PL43C/g0xXXSeRcfiJgqQV3B9X2SOY90cfoFdPBR8ekpxriv9wJ6S7Exaw0N4iGFzoYAbbUm/+tVtVgemCr85mCeYsmj6HgETY1oQGND+8wL4omIzhVrTxTcpzrRIMSZdFqfvJqaGt7znvfM9liEEEIsAJWYlpkYfal4PF6LbVt4nkdcCUM+AYly4RrVQ7NNghpkbYW8Dd9/psDbf6v8I9KyS5imieO6OLaNbTt899khhgsaS+sL3HjVUnRNIRQMcONVS6vZ57lsstphd7KxD+ccDg5a5Evl6xgPDVqM5JxFF/0ix3EhhFicTucYP5npzqMy21pDMVZHm9ib6WddvAVQ2J8dYHW0idZQ7JT3F0KIhazcge2SLZzoND/ZtpU/v/d0kodfzFC0fc5rMdnVU6JguTieT7FUjnKZyek6FY8rG/bheCqPDK+ivzTx31fbBRSV5U0Bzl8SwvJgy7IwyZzDkwfyJAsuiZDGBW3BWcs3n+pEgxBn0ml98vbv38+dd97Jrl27UBSFdevWcfPNN7Ny5crZHp8QQoh5NN0fyqMvFY/HTdLpFJ7vofoKsd51DISGwVdQPB3VtGgKaORtlZqggquFKCk+oaCLphmomoYG2FaJ7uEc/RkTVfMZzKtjit6js8+n22FXF9FZ3mByaNACYFmDSe0i/BImx3EhhBAvx/jj/pliqjqf2Xw9PYV0tXBe+fvZEusCoCgKmUyGYDCI7/soikI2myWdTo/ZLhaTkwNCzIcz0fk82T4SEZ1o8ESn+cmKxaO71JfEDdJFl2zRY9thl5LtUXLAd8GZ4bgMxeWapl1ENIsf9K0HQFXK8TA+oCkQUMHXQFFAVxWaYzorG02SOYcHdqTpSdsULI9Xr6nhwvbQmAlDgdN+bcdH3AgxH2b8L8JPf/pTrrvuOjZt2sTWrVvxfZ/HH3+ce+65hx/96Ee84Q1vmItxCiGEmAdT/VAud5y72LaFYZhjCtm2bTEyMsTgyBBHSnmGmw6CXgJATbahWiFWLXEJBRQ8LURHQ4g1yxLkc+Ufj7FYnEIhj21btDeGaYlZDBU0GsIeNebYGXHGd8Kfiq4pvPOKBn5vSxxQaIzqiyrWBeQ4LoQQ4uWbz4gYU9XHRLicLXEuo/m+z3nnnTfm9ubNm8fcVhQF13XnY3hCLHpz0fmczDljCspT7eN0isXr24K015vs6y1Rsj2KNjjTnEh0vGuadqEqPt/r2Yjll8elUC6gqwqEgwoBTUFRFUxV4eiwzbGkXX1OtuPjenBhe4iBlM3nd2XQVYX48Tga4LRf2/ERN0LMhxl/Am+99VY+8IEPcMcdd0xY/uEPf1h+gAshxDlkqh/K+XwOz/MZGOijsbG5eqm4bVt0HTlE3+AAXxjZwQvFNG5dvty+4GsEetahobF/ROUDWy2iUYvO1nJUSyjYOObxQ8EQjuvyZ69JkMz7xEwXq5QnFAyM2W6mHXG6ptCaWLxdDHIcF0II8XLNVkTMbLE856zqTH/ooYfmewhCiJN4uZ3Pk3Wb7x+weL6rUJ1wc6p9TLdYnMw5/GpvlkP9FiMFh1zJo1Dy8JTTK6KbqoPl6TwyvIK0HcRDBSj///GOdFUrd6A7PqhuuSvdPL6s8nyfPZwnYiqkSx4H+0sM5xxa4gYXtAXHPFfpKhdnqxl/y9i1axff/e53Jyy/4YYbuPPOO2djTEIIIRaI8T+UKx3giqLgeR75XJ50IEV9fbkInk6nSKVG6CkV2JnNk3EVMH1AQfEVFF8lZPgoCiiKT8DP4do1YyYKtW2LUrFILpdD1TRsO0lrQxO2beM4TrULHs7+SdPmgxzHhRBicZmvPPMzxfIcPvTM/ezL9rM62sRnNl+/4Ivpl19++XwPQYhF7VTRLS+383l0t/nK47dNFYKmypK4MeMi8mTj3d5V4P6nk/SlHIZyDumCz+leZ9saSPG7zS9yX+8FDFo11eUKENAhFADbUQiZCrURjbzloykKibDG6pYgTXGdbKF8Bc3qpgA7j/m0xnSWxA26UzZvWBels/FEM9TJXluZUFQsdDP+VDY2NrJ9+3ZWr149Zvn27dtpamqatYEJIYRYGEb/AK90gDuug6KA55e/MOXzOfr7e8nnstiOx6N7E2SiNq6RB1+BUhg934BaitDW4LCuVaUhqmFoCpZloWtFSsUiJauE67p4noeqqlhWEU3VSKXTlAgT0cpjqRT3F1pH3NlAjuNCCLG4zFee+ZnSU0izL9sPwN5MPz2F9FkZ9yKEOHPmatLKShG4IVKOMFnZaFb3lSq4tMYNmhMGiYjOtkP5SceQzDk8d6SAr8Cm9vIknZONV/HB1FQKlke6UJ5KdCYTilasDA9yVeNuHhtePqaIDuVCuqGBqqgsqdVZWmdgO1ATVNmwNITt+bxhXZTBnFvNdb+wI0RNSMNUYeex4oQi+qnIhKJioZvxp/Jd73oX7373uzlw4ACXXnopiqLwq1/9in/4h3/ggx/84FyMUQghxDyqFs+d8lQ1mq5TE42hqhr19Y3EYnEGBvpIp0awbYe0ZfB80sIN6mD45W95uk3g0BZ0X+NtG4t01GuEQxFUVSMUCqPpOoODA5SKBXTdIBqLYZoBgm4Iy3b44Ys6fZkSDWGPd10ZHjM+x/UZzjnURRZf3vnpkOO4EEIsLuFwBN91CBYz+DVRFG3sT0DfdfBSg6jxhgnrzgatoRiro03szZQ70iuTkC50X/ziF7nvvvuoq6vjPe95D6997Wur6wYHB3nFK17BgQMH5nGEQpy7ZmvSyvHd02OKwMvKv1lWHt92Q1uQwZw7Yd8NEY2HX8qMyU7feaxI0fLoHrG5ZmNs0vFe2BHi+aMFuoZKaAq4p1FFTxh5rmrcw08H1nIgX19drnA8mRNwfDCBdW0BWmImh4csmuMGG9pD1ecIJXb3FNnQFqx283/ziWEO9JfnyZpJIf1U7410rIv5NuNP3cc+9jGi0Sj/+I//yG233QbAkiVL+Lu/+ztuvvnmWR+gEEKIuTOdy70NwySXy1IOaIFAMEg4HEHTNGKxOMPDQ5SKBVRVwzAUdNcknTFQ64N4Sqr8ILqFFx6m1m7B8X3AJZ/LE46EiUZj2LaFpqqoqkooFMQ0A1hWCV3TcbQoAzkbVVUYLqpkLRXfy5FMDlMTq+XbTxXpTtosSRjccFm9FNNPQY7jQgixuOiqivbzr5Pu7SLY0kHNm2+uFsx91yF33124A11oTR1Err/prCumm6rOZzZff1ZlpN91113cdttt/Pmf/zmpVIprrrmGv/3bv60el13X5fDhw/M8SiHOXbM1aeX47umGiFYtKI8XD2tjCsqJiM5K4IEdaQ72l8hbHrmSRziosqLBpDtlYxzPHt+yLDzpeIfzLqni6UxK7FOjWSTtMF8/uoWce2JcAQ3qalQGMx5BA2pCKktrA/ieQtHx6aw3uaA9xMpGs1rU7h2x6E7a/Hh7ivXtITa1h9i6qhy9Wflzuk713kjHuphvM/7UKYrCBz7wAT7wgQ+QyWQAiEajsz4wIYQQc286l3vbtoWu6Wi6jq7r1Txy3/c4fOgAhUIOVdXQdR3HcfA88CyNYNcm8vEHQfEABcUJEg36LI2Doeuomo6mati2RTgcobbuRBdENpPGdV20kEZbQ5QliUy1WF4b0env6yWfyzGY9ehOlsfdnbQZyTk0xoy5fdHOcnIcF0KIxcUeHuDQSwco2j7B5H7WjAxiNrQA4KUGcQe68H0P5+ge3OFe9Mal8zzimTNV/ayKc7nnnnv4l3/5F972trcBcOONN/KmN72JQqHAJz/5yXkenRBiul3P47unB3Mu8ZDGoUGr2n1+ssLv/gGrPHmn51Mb0dk/UEIBNrSH+IPViWq2+jefGGbrqki1EH94oMS/PDzIQ7syJAsza0XX8HhD4x6ieon/17OxWkRXAFOH5Q0GybxHXUQjEdHYsDREJKCyrNFkIO2woT3E5WvLvx0q8TQ9aYeRrEOv45ebr3yoCWlcszE2613js3U1gRCn62V9ouWHtxBCnN2mM1nn6G0qk4GmUiM4jkM+k0bNJdHrW7Adh3w+x3DapxgwUa0a9FQrXjCNmqtDK8YZthU8X8U0gwQCAYKhcPVxKxOW2rZV3XcsFscwTG64zGQk51B7PL4lkSj/WG6K1bLk2ImO9GhQoz9tS8zLNMlxXAghzn2DSpxjWgv1dg99ZistWpzG4+vUeANqw1LsHY+CAsWHvkPkzX911nWln20OHjzIpZdeWr19ySWX8Mtf/pLXve512LbN+9///vkbnBBi2l3Ple7pZM5h26F8NRs9U3RPTDZ6ksLvykaTbMHFJ8hAyiGZc0kVXFY0Bqod6/c8NMhwzmVfb4nLzq9hU3uIx/bleP5oYcZFdFN1uLbpRQzV5Ye96+H49KS6AiEDQqZCMu/heD4qsLIxwCtWhnn16prq6zL6eVT+vqEtyMHmAPliuaPehwmTrc5WFMtsXU0gxOma1qdv8+bNKMr0ChLPPPPMyxqQEEKIM2c6k3WO3sa2LQYG+lBQwIfap/4TIzOE3txB78arsTyXrxZeILs6h28U8R0dvRAndPAVKL5GyoEX+2CznkZVEyRqgxMiZcYX1VOpEcLhCI2xE9uFw5Fqgf+GyyKM5ByiQY2vPzYsMS+TkOO4EEIsTo7r88PncjwS/1/Ewkl+a8NSroidiBxQNJ3QlW/F694Phok7eBQvNYhW1zKPoz73NTQ00NXVxbJly6rL1q9fzy9/+Ute+9rXcuzYsfkbnBBixl3P47PRR086Wpk4FMqd5D/cnsKyPTrqTRrjBtmSy8O7MhwZsmmN6zQnTMJBlWTO4ftPJelJ2eztKRIwVHqSNvv6SqxrDZDOzzzS5ZWJIzi+xg971uP4WnW5ppWL6CXXx7FcEmGNV6yKkAjpRAPl7bZ3FVDG1e1HF7VHx9aM7uiXKBZxrpnWp/hNb3rTHA9DCCHEQlbJUncc5/jkMz6aAkZmCN/38QaOEvFtXrKK7Cnm8EKp8jV9BjhGCd8sQNEEfEKGimGowOSd8LZtkU6nqrfd45OcTlXw1zWFxphBf9qm+/iXNIl5GUuO40IIsTgN5xx60w6rl0Qo2WGuu3jiSWatrgVt6Wrc/iNoTR2o8YYzPk7LcyZknE+27Fzx6le/mu9///u85jWvGbN83bp1/OIXv+DKK6+cp5EJIWDmXc/jC++V+287lGfnsSL4PtFgOT/913tzDOUc2mpNzl8SIF/y6Us5OK6PpilctT7Khe0hnjtSYEdXnn19JTy/XJweyZV/F43kHDKWN+3x1Rp5Mk6Ax0c68XwV/3gnunL8f4YKKAqu5+MBdVGDC5aG2HGkQKbksn/A4vmuAigKNSHtlK/N6NevMtmqRLGIc8W0/mX427/927kehxBCiAXKti16e7uxbZuaSA3hSA2GYeKhYMcaUIZ7sKL1ZH2NQjaCZwUgMKpdQXPwlfIXvZgJ5y+rpz6ukUjUTTrBaT6fI5tJA1ATjVUnNz2VuojOkoQxJktdlMlxXAghFqfRx8aVTSaN0YknmBVNJ3L9TXipQdR4wxmPdbE8hw89cz/7sv2sjjbxmc3XA0xYdi4V02+99Va2bds26br169fz0EMP8b3vfe8Mj0oIMR2T5aePLhyPXr+y0eRAX5EXu4uYKlzQFuTnL6SJexorGk3wYTDrsLo5QCrvsbEjSK7k8eDzGX66M8XunhLRoEIsqDPs+bge9KVsagIq6jTH2x4c4Y3Nu/j5wGr25ctX/QZ1wAfXh3hYJaArBAyV2ohOQFfZ2B5kd3eJ2oiOikJP0gK/nJ9+soL4qV4bIc4F8mkWQghxUvl8DtuycBwHTdeJx2tJpUZAAfd1f4qbGiTlKlglm2f7TFyv/KWQSsOba6D4KgHF48oVDmuXtxIKBqbcXzgcwTnehV7JSJ8OXVO44bL6MVnqQgghxGI23WOjounzFufSU0izL9sPwN5MPz2F8sn00cuO5EYwVO2c6U7fuHEjGzdunHL9+vXrWb9+/RkckRBiuk4VVbJ/wGJ/X4ndPUW2ropwYMDCcX12HiuypjVIW8JgX7/F6uYAfRmHoYyD7/sUHZ9Hdjk8pmY5PFhipFDpGFdRFJdM0QUPwqbKS70FstaEXU+wJtLP6xr28tDQqmoRHSASUFHwiIYMVjSaNMUN8pbHtRfGsDzY31tC0xRc16c7ZZPMuQRNlZbak+ecS4yLWAzkky2EEOKkDMPEME1CoRBANeIFFAKhMCPFKKpVYiht8cyQg9+aQykk8ANZFDuInmtALUVY22hx1RoF33OAQDUupjLZ6Oj9VTLSZ6oS81LhuD7DOUcmHxVCCLFojT42jj72LhStoRiro03szZS7z1tDMYDqspU1Ddy5+5ccyA6etDv9bIyC2bFjx6TLFUUhGAzS0dFBIDB184EQo03WDSxm36ny01c2muzuKWI7Pl97bBjX9dF1la2rIsTDGrquUhNQ2d5VwHU9upM2tuNStEFVQFGoFsl9oOB4WFmPgl0urA9mbErOqcdpKC6vSBzhx33rOFKsHbNuOOfRWKNScjyGsg6JsEZbwuDFnhLxoMbKlgDRoEam6NKfKu9sdVPglPEsM82WF+JsJP+6CiGEOCnbtgiYARzXwSoVSaVGCAVDgE8qlcK2S9i2g+eDnQ+jFuIQSqGMLCXYvQG1VIPiKyyr9amLaCSTw4TDEfL5HKViEZg6//zlcFyfrzw6JJOPCiGEWNQqJ5WDaonB/m5MY/4Ks5MVu01V5zObr+dIbpjK5WyVZT2FNLbncPO2/wec6FjvjNRNeNyzMQpm06ZNJ50M3DAM3vrWt3LPPfcQDAan3E4IkG7g6Xq5JxzGx7hUJuFc1mAymHNZ2WhyzcYYD+xIoyk+lg+vXVtDZ2OAZM7hos5yc5KmwFP7CxQsF18px6xYLnjjJvTMj+o8VyhPDOqWph6fgk9HaITDhTq+cWxLNQ9dBUImFC1QNchYHglNI5X32NtfIm/7tMQNwqbKpvYQiYhefq2CJ16rZM5h26E8DRGt+lxHv4YS4yIWA/mECyGEOKlK11qNYZJMDqNpOrlcrtwigY9hBrBsl18fC+C4OsFDF+FFhlFzdaheuUCuKT6bmwvYJZ1gMIRtn4iKCYcjk3anT9WxPl3DOUcmHxVCCLGojT6pXKPmeeN55c7CyrHdHe49I7noludwJDfMnS89xIHc5J3ld+1+ZEIhvDNSh+U5k3asjzZZPMz4YvtCdP/99/PhD3+Yv/7rv+YVr3gFvu/z1FNP8Y//+I/87d/+LY7jcOutt/LRj36Uz372s/M9XLHASTfw9MzGCYdKMT5bcHn+aHkSzmNJm3hIKz/usjDXbIyxvcsgV3BR/BP3yVs+SxMGNSGVnV15FBViQY286pEp+Pgn2a8P5ErgTrFeV1x+u3E3CSPPse44jq9V10VDEAvpREwFF2iK6jgerGgwWdMaIhJQCQdPFNFhYmG88trt7imeeK5SOBeLzIw/8Q8//DBXXHHFHAxFCCHEQmQYJuEwpNMpdE3HtUpo2WE8zwUUwuEwRwey9GVUMo5PccVTeME0aiFO6OArUXwN11f46f4Q/1+zT7FYQNM0XMchEAxiGCap1MiE7vTJOtZnEtUik49OTo7jQgixeIw+qZy0TBxdp7OlAV1V8dLDZL/9D2hNHUSuv2nOiumVbvEXUj30FdMsDSfYne7j6aEjXFTfganqJy2Ej+5Onyq2Zap4mIXu05/+NJ///Oe5+uqrq8s2btzI0qVL+djHPsaTTz5JJBLhgx/8oBTSxSlJN/DJVQrZDZFyAfjlnHCoFJQjQZUN7aEJHelQfj+uWBtl26E8B/pKfO/pJK0xnVTRJRbSSBfLv6VcF3JFj6V1JvusEs4pYlucKaroQdXmuuYX8FD4fz0XjimiK4CuqTTHdCJBjfOXBMmXPFriBhvaQ2xZFp7W8648tw1twTHPVYjFZMb/yv72b/82bW1t/Pmf/zl/9md/Rnt7+1yMSwghxAKSz+fIZtLgOoR/9V2c/iMosQtxSgVsTSVq+qSKCr6ZxwuWJwnzQil8M49SiqLho2kaRVelpiaKbdtoul4tolc6zkdntlb+XvlzplEtMvno5OQ4LoQQi8fok8odDSFWtUexSnm0zBC45QK7238ELzU4Z5ONVorkhlou6liuQ9Fz+OTz/8WaWLn7/FSF8Ep3+lSmU2xfiHbu3ElnZ+eE5Z2dnezcuRMox7/09PSc6aEJcc4Z04l+ksLxdKJfxnf/7x+wiIc1OhsnRmdVMtOTOYei7XNBW5CakMaBvhK2Ww5eKdk+e3pLuFO1mo/SVKNQ8n2SubHL24IpMk6Anw2uwfXVMet8IKQrbGgPURfWaYnr5Cwfx/NnVAwffbKm8/SmtBLirKeeepOxuru7+au/+ivuu+8+li9fztVXX813v/tdLGsaUwYLIYRYsGzbIpUawbYn/ntuGGb5MsNcEoZ7cF0XXAc7OUAqlURVPJpqLLADKFYYX/FRC3EUq/wl9byExdJajdXLW8lmM1ilIrqul+Nbcrlqbvr4SUfj8drqssmiWk6lMsGaFNFPkOO4EEIsHpWTyu99XQM3XFaPVcpTKhYpGCHQynFnWlMHarxhzsZQKZKrClzZfB5/t/FaYkYIVTnRfV4phN998R+ddr55pdh+thTRAdauXcsdd9wx5hhs2zZ33HEHa9euBeDYsWM0NzfP1xCFOGesbDRpSRinLBxXCu77B6b+bpyI6GxZFiYR0dneVeCJvVm2dxWm3D4eVPGBYsnl0GCJx/dmOTRUojVhEDJVUMqd5tOoo9PeEGRVY1zIdWgAAOGsSURBVIjKr5tmM0OzmWF/voH/Glg7poiucDwb3QBDV1lWb/IXVzSw9bwaVjYHuGZjTCamFWKGZlxIr6ur4+abb+aZZ57h6aefZs2aNbz3ve+ltbWVm2++meeee24uximEEGKOVaJU8vnchHW2bZW/rEUSKA1tqKoKmo4diOC6LpYDuwYMisufwjNzqMUIwUMXoRy/pPBVyz3e+ls62fQInuvhuC6O4xwv0Jc7MSbb72iVrjqAJQmDGtObsvAvpibHcSGEWFxGn1QOhyMEgkEi0ThqrI6a//XhOY11ATB8uKPzUj6/+ff5qzVX8lt1S1kdbaTkuiyP1GN7LpbnnJWF8Jfrn//5n/nxj3/M0qVLef3rX88b3vAGli5dyo9//GPuvvtuAA4cOMCNN944J/v/4he/yPLlywkGg2zZsoX/+Z//Oen2jzzyCFu2bCEYDLJixQq+9KUvzcm4hJgLo4vfJzPdgnuF4kPR9nmhq8DDL2VIjmv22T9gsaunxP4+i/0DFt95MskPn0nyk2eSPHUgh+14GDp403wemaJDU1wjaMDy0BC/37qTxkC2MhoCWnlSUVMHXYVIQME4nvLSm3ZGPY7Lc0cKE8YrhDg5xff9k81lcErd3d3ce++93HHHHei6TrFY5JJLLuFLX/oS69evn61xzop0Ok08HieVShGLnR25eUIIcaZMNbmnbVuk0ymsUpGSZREyTbz0EA/+5jnWrFmB68G3ng/z826H/KpfVe8X3vdq1FIUgI+8VuGytSEUFFRNRdN0ioU8NdEYsVicfD6HYZgUCnkAYrH4pBOMOq5fjWrJZZOUikUCwSDxeO0cvzrnLjmOCyHE4vTd736Xt7zlLXO6D991GLnv8xwePsY9jXEO1zawKtqI67m8lOmj4NjEzXA14mUxFdErstks3/jGN9izZw++77N27Vre9ra3EY1G53S/3/nOd/iTP/kTvvjFL7J161buuecevvzlL/Piiy/S0dExYfuDBw+yYcMG3vWud/GXf/mXPPbYY9x4441861vf4s1vfvO09inHcXEuSuYcHtiRpjtpE9QVLlldU42OSeYcHt+b41d7Mrg+HB60KVge/RmLfAk8r1xAN1Swp1lJX1av01EfINu/j1fFD/CzgfPYly/nrBgqrG4xedfl9fzkuTRHhi08F2pCGkFD4c0X1XLNpjjbDuV5Ym8WFIVLVkWmnZEuhDiNjHQoX272gx/8gK985Ss8+OCDXHTRRXzhC1/gj/7ojxgeHubDH/4wf/iHf8iLL7442+MVQggxR8pRKmOL17ZtMTDQhwI4rotllfA9j7wLlTnlRwoqO3oMFMtELcTxQqkxsS7g05AI0NKypFqoT6dTE/abSo2QSo7gHJ9hp76+cUJx3/dsTD+H70UmZKiL6ZPjuBBCiJnwXQcvNYgab5hR93ph8BgfKfXwUq3JgF+i3bV5MdUL+CgoDFo5wnpgwgSjo81kovGzUU1NDe95z3vO+H4/97nP8Rd/8Re8853vBODOO+/kpz/9KXfffTe33377hO2/9KUv0dHRwZ133gnA+eefz9NPP81nP/vZaRfShTgXJSI612yM8dyRAr4ydhLT/QMWBwYt6moMXM9neUOAI8MWTSmNPX1FUnkf35t+EV0B1i8N4nk+QTPLf/ZuoKcUJ6hDS1ynPqpzyaoa6mMmr1xVg7U7h+W4bOoMs3FpiAs7QkB5jJlieZJUmTBUiJmZcSH9pptu4lvf+hYAf/zHf8z//b//lw0bNlTXRyIR7rjjDpYtWzZrgxRCCDE/8vkcCgo+PqYZoFQsYts2nuuhHE/mUxRwXAXF1wgdfGV5glErXI11WR512byydkyhPhaLo+v6hMlF02aq/ICj9l8qFgGIx81xt2snFP7FqclxXAghFh/fdbAGuykYISLRya/6Otl9c/fdhTvQhdbUMe0oGN91OPzItzmgeBi2BUaIvO9xQbwFVVHYne6n1gyjqyqro03UB8Iczg1XJwu1PIej2RQ/fdqhL+VNa6LxqVies2AnIt2/fz933nknu3btQlEU1q1bx80338zKlSvnbJ+WZbFt2zZuvfXWMcuvuuoqHn/88Unv88QTT3DVVVeNWXb11Vfzr//6r9i2jWEYE+5TKpUolUrV2+l0ehZGL8TCMH5S0svPn3gVycpGk2zBxVdgeb3JoUEL2/PJFT3WLQlzcLBEf9rBOp6uMllchAroGqgKtCVUVhuHuHjzRv7pFxvRXZulEZ+Ll0fY2B6mMa4TDWqsbDRpiGjs6y2hqjobl4bGjC8R0bli7dxe9SLEuWrG3yJefPFF/umf/ok3v/nNmObkX8CWLFnCQw899LIHJ4QQYn4ZhonjpgiYAWqiMVzXIZvNYNt2tSM9anoYug82KL6GUjrxpey3mgu8b6uP5rsTHnd8EdwwzDFd68Ap/xQzJ8dxIYRYXCqF8OKx/diROpzL/5D6leumfX8vNYg70AWA238ELzWIVtcyrfs1DPayKmGwV1OI/v/s3Xl8HfV56P/PrGdfJJ0jybJlecUGbDYDZksIISGFEggkzdIbLmma3BtoA4X015TbkoZ0IUlzk97c3KRJG9LbLTdpQ5KmpQlkIQsBwmYwGIwtL5KtfTnbzJl9fn8cnWNJlmzLWJYtfd+vFy905szyPQfmzMwzzzxPJIkqK8iSxB9vupY7nv4XbN+jI5bhTzZfx//Y9j12V4ZYn2rlz899K/9j2/d4uTiIW0rzOt5IXwHGDY98+vCA7ZE4gcfvP/vtxrpPpRIyP/jBD7jhhhs477zzuPzyywnDkF/+8pd8+ctf5nvf+x5vfvOb52W7IyMj+L5/WBPTtrY2BgYGZlxmYGBgxvk9z2NkZIRly5Ydtsz999/Pfffdd+IGLginkHpTUoAts9Rdnx5g336wytN7TKpuQEyTaEmoBEGIaQf4YYiuSrheiO2CF0JTDNIJnQ3LdLJ6SEvlWZKeR3ta5pbXtSCFNLLMJwf169u+45p8Y7ogCCfGnM8gfvSjHx19parKlVdeeVwDEgRBEE4drusQ+D7VqkkkGiWfb6NSLk9p8Fl2ZEzn8OwwlYDlGUjEjv3EbaYA+1zeF45OHMcFQRCWlkYgPAxQdj8DIz0Ya88BOo9peTmTQ2ldiT/Ug9K6EjmTA45e7kXO5Ii1ruS+oR62t7bz6VgCWZLYVR7hjqf/hSdG9wIwZJfZNtrL7soQALvKQ7ww3sfuyhCqLFFQxzG8ChuzOZqO0iRwJv3V0pR1z1ZCZiH84R/+IXfddRef/OQnD5v+0Y9+dN4C6XWSNPX8LQzDw6Ydbf6Zptfdc8893H333Y3XpVKJzs5j+/9OEE519eB0LqHwzD6z8Xpy4PqXuwz2DNnkMyp9Yw7/8UIJQqg6IZYT4Pi1GukyoCtQscLGa4BxC5zAxTAqvLnpJZxYlDfdcB07+n2e3mfSFFNYldMZMfwpQfS6bEKdNcgvCMLxOe49aseOHfT09OA4zpTpN9xww2selCAIgnBqiMcTeJ6H53nYloXnecTicQqFscY8ng9ucPgFVFwLGbd1tFQr6XTmuLY/vbTLZJPrp0uytqjrp84HcRwXBEFYGuqB8KDnFbANGPNwzAKc9a5Zl5keJE/c9OEpr4+l3Et9uVhxhMviKc549lvsssusiGfZXR4GwA9DDM/h7/f9irXJHN2VEdanWjmnqYP1qVZ2lYe4dFkHv7+ui9ZU9LiO8cti6ca61qdaWRY7dZpcvvzyy3zzm988bPr73//+Ri3y+ZDL5VAU5bDs86GhocOyzuva29tnnF9VVVpaWmZcJhKJEIlETsygBWGBTC/hUlcPUv/05TJP7TV5LqGQisq80FPlnJUx/AB+sbPCy30Wnh9QdUOCiXrowbQaLgFg+VNfAwQ+lKshgRKyz8zw4thq1u51SMQUBgsu3QM223qqnLU8yrZ9JmvbI5zXGTssoC4Iwokz571rz5493HTTTWzfvh1Jkg67C+37/pEWFwRBEE4jmqbT0pKnWBynMF4Lntu23fjth9qJoBMefmEbIrFhZZq1K1rndOHruk6jGWksVmtYOlMpl1KpSKVcwnJcvvuiTF/BfU31U5cKcRwXBEFYOmqNOkOabvhd9P0vUv6/fwKONXMh3gmzBcknl3M51nIvkqIiZ3J4D36ePxzuZUdLK+uvfCuf7nmMQauM4dmktCgDVok/3vRONFlt1DH/y/NvOiF1zU/kuk60fD7Ptm3bWL9+/ZTp27Zto7W1dd62q+s6W7Zs4ZFHHuGmm25qTH/kkUe48cYbZ1zm0ksv5Xvf+96UaQ8//DAXXnjhjPXRBWExKBgeD71QQpVr58lbEmojsG47Pj9/1SAdlXm536pllNsh4xWPfUM2sajCvmGLohHUgufHsf2OSJHl0SLbzZU8WVxDIiLxcp/FB96QY/egzc9fqVCd6FTaldN5sbdaq5EOjeanIrAuCCfWnPemO++8k9WrV/PDH/6QNWvW8Ktf/YrR0VE+8pGP8JnPfGY+xigIgiAsINd18DwPXY9QrZoUi8Up749ZMwWtQ87IhbznsmVzDmqbpkGlXGtGpaoqmUzTEecvmAF9hVrw98CYzf6BUVa2pufUSG0pEcdxQRCEpcHzQx742WjjRvNvXb4J/Zwr8fu6UTrWMtul4LEEyWcr9zKdE3gcHOgmMtzLn2Z1uimw+uXv8yfn3MjvbngDf/nyI/RVS2xIt7Ey0TwlyK3L6gkrwXIi13UiffCDH+S//bf/xp49e7jsssuQJIlf/OIXfOpTn+IjH/nIvG777rvv5pZbbuHCCy/k0ksv5Stf+Qo9PT186EMfAmplWQ4ePMjf//3fA/ChD32IL3zhC9x999188IMf5PHHH+erX/1qo4G5ICxG3cMOmizhBmGjXMu23iov9lbZfqBKwfDJJhSa4wr7Rxz6Cy5+GFC2ZaqDNo4P/nEG0dfFh3lz/lV+PraGVESmo0nHsH0iukw2ofLms1LsGbIxrIDLzkjQntYIpVrJme5hh+0HLQhDUlFFlHcRhBNoznvT448/zo9//GPy+TyyLCPLMldccQX3338/d9xxB88999x8jFMQBEE4yeqlUzzPw/c8QkJM0yAIvCnzjRgyMDVYHpVCOjIQVodxE7k5BbUnl5PxPA/XdWZcPp3OoKoqeiROR7ZMX8ElFw+Iy7Vxi1rqMxPHcUEQhKVhzPDoK7gEYUj3kM2IGdL+9jsbJVr41oMzLncsQfKZyr1MV2/yuas8RFt7loNeFVnV2etXqapwZmYZf33xb56SmeIny7333ksqleJ//s//yT333APUGn5//OMf54477pjXbb/rXe9idHSUT3ziE/T397Np0yYeeughurq6AOjv76enp6cx/+rVq3nooYe46667+D//5//Q0dHB5z//ed7+9rfP6zgFYSHVg+f1fz+zz8S0ApAkMjGZ/cMOqixh2QEHxh18v/a4j+0GWN6sqz2qc9MHuaxpP48Mn4kdaeWDlzWRTaj0lzzefFateemI4XP+yjhFy6c9rU1pOppLKGxeHm0E1qcrGJ7IWBeE4zTnvcX3fZLJJFCrrdbX18eGDRvo6upi586dJ3yAgiAIwsKo1ycPAd/3iEZjeJ47ZR4/gOGqiiKF+KEEhGTUgAtWONxwhkOlpJCIx6YEtSfXNp8pQD65nIxtWbMGxSc3Hn3/63XGDY+kHuDY5oylYIQacRwXBEFYvGqlXGo9Q5oTKu1plZ+8UgHgu88U+cAbcqgzlGCZ7FiC5PX5ZirnUtdfLbGzNIjtOexNpVild3DAMzkr086yeDNDJZfmRC1T3PPDxuulVJ5NkiTuuusu7rrrLsrlMgCpVOqkbf/222/n9ttvn/G9v/u7vzts2pVXXsmzzz47z6MShMPNVqd8vtXroNdLvJRMn54xh7guM1r28cKQnQMWnjfRNFSuNQq1X2OlxLIX5cH+zdhKiraIjBdCe5PO5WckG5+/HiAvWz4VK6B7uNb3aKBQu1678szZf0tExrogHL857y2bNm3ihRdeYM2aNWzdupVPf/rT6LrOV77yFdasWTMfYxQEQRAWQD0zvFwuEQYBjuM06mjXlWwZw5HZ2mHz6phKV9qjNRVy4waTRCxGJtt0WFD7SA1Ep2+//u+jBd9VRSKfrtXnjEVFU6sjWajj+P3338+DDz7IK6+8QiwW47LLLuNTn/oUGzZsmLdtCoIgLCXTS7m8//Ut3HBBhn0jDroqM1DyGDe8xvHySGYKkk8/Fk9vSDpdsxphvFpiwDWRkTA9j5QWIQjh//58jMFiQEdW479e3szfPzY2p14nk28YLJbA+8kMoAvC6aZ72GkEiI8l6HsiA+/1ILrrhbzYZzFcdClWPSKqjGkHBAH4IUi1nCLs46njAihSwNUtu3ihvIx9ZguJqITrhhwYc/j2s0WGKz6VarwRIJ8c5K9/1rrZstDr863N61Sq/qwZ64IgzG7Ovyh//Md/jGEYAPzZn/0Z119/Pa973etoaWnhG9/4xgkfoCAIgrAwNE1HVVXCwKdqWfiei+dNfUYxHQloiQeMmjJXdDlc3mmRjYYoMrQvW05b27LD1js5QH607dcD7fXsdDhy8F04uoU6jv/0pz/ld37nd7jooovwPI8/+qM/4pprrmHHjh0kEuIJAkEQhNeqXsoFoK/gMm54tKY11rRGGkHqhOJQ2L+HeFvnMa+3HjA3UHDc2nlAOinP2JB08jIH/u2LxMISmqbgSTJDVgXXUdgZDhGpjJMmQ1/BZe+I3Rj3gTGXXYMW69uiswbIZ7phcDoF088///zDEhNmI7K/BaFmeomVozmWwPuRgu0Fw2Nbb5XhostzPVX8ICSuy2zqiPKdfotx0ycTg0xMwbA8JECWIDhCI+cjicgub23bgUxI0Y0iy5CKyiR0iVEjQJFgvOIRzvDTUQ+o1832ead8J6viR8xYr38HC/EUgCCc6ua8N7zlLW9p/L1mzRp27NjB2NgYTU1Nx3xCIAiCIJwe6lnpjjuMVa3i+1OfU1RkuHZdlbIjkdJrAfS65uaWGdc5OUA+l3FM/rdw/BbqOP79739/yuuvfe1rtLa28swzz/D6179+3rYrCIKwVDQnVDqyWiPA3DSRrf3+17cwbnhko1D+5l/iDx+g0toJ2tqjrjP0vUbAXG5Zjv7mW4nHE1MaknqD+/D270DtOqsRTHdG+kj27WdlPsGwEoCkoRDB9yQ6I02sV5oaGemrcxE6shoHxlz6Cg5ff2KcFU36rAHymW4YHEuW/anibW9720IPQRBOO9ODxUdSMDzKlk8yIh8x8D492F4PHOcSCo/sKLO9p0pf0UWmFiDvyGqUrQDbC1FlCVmGUtXHDyEEgqD277mKyi7vWPY8BTfGw8MbcUMFmVog/dL1SQ6MuugKXLo+wXmdsVk/89Fqns/HzQhBWIrmtDd4nkc0GmXbtm1s2rSpMb25+dTrgC4IgiC8dpqmk05nKBSLlF0NPXSRp13TKjJko1NPG2crwfJaxnGk4PvRSr8INafScbxYLC7YtgVBEBajyUHzpkklT+rlz/yxAeTCEKEsI48PQkvXUdc5OWAejh4khY+i6YQTDUm9wX2ExVHM/3wApa2rkZleUnT+dFkT/aHDBaGGV7iOQiFJOmPzmbecQSYWb/Q2satFbrk0xb6xgK8/MY4sSUcMkM90w+B08id/8icLPQRBWNS6hx0MK6A9qx0xk3ptXq/VF6/6jSD6QMFlZ79FwfCp2AG5pIIuy4ybHiNlj0xcYV2bzmjFp2x5FKthI3h+fMnoIW6g8kyxk1cqrUhIyFLt+qpsBaxq0VnepKPKEsuys2eGH63m+fFkl8818C4IS8WczjpUVaWrq+uwjERBEARhcXJdh4HBAb61LeDAuEZLNMmbV5enZJ7PpGvV/NXaniloPrnuejyOCKrP4lQ5jodhyN13380VV1wxJaA/nW3b2LbdeF0qlU7G8ARBEE5bk3uGTCdncqjtXUhDPSitKyE4+qWgPBEw9yeWkTM5oFZDPX7Dbbg7Hsf6xXdAkvCHegiKIyjN7QwFLjuTWQg89ikqF8hxupIZIpqE7ckctMZZlkxTLZuN4/f6tiwrmvQjBsjrtdH/6+XNlC1/yg0DQRAEOHoAeP+wzWO7DTYvj9I37qLKUiObOxmRaYrL7Bq0ef2GBC0plWf2VTHdAMOu/aPJEpYbULaC4wye16yMjXNxtocH+zfzcqUNCYhpEIvIyBIsb9IpWgFvPivF3lGnEfCfLdt8pprn9QB62fIxrFrx9mPNLp/LUwCCsJQcV430e+65h3/8x38UWWSCIAiLmOs6DA8PMjhucWDcRpEVRsyQsiMdloE+WSqVIQxfy2nlkc3UrHRy6ZdjbWa6VJ0Kx/Hf/d3f5YUXXuAXv/jFEee7//77ue+++07SqARBEBavep3z+A23EVYKtYD4tx48pmWjV/4GAEpze6N0S+h7mN/9It7QfsLSaK3haG4F416IPzzEQ086jBcyFOMHURXYrjzFZi6iM9PM/bu+R7cxzPpUK3+x6XqgdvyuZ9QPlz1myu083WujC4JwchwtAPzYboM9Qza7Bm0Sukw2oZDQJfaOuqzJ6Tz2aoVXBix6RhQ2dkTx/YB8WkUte7zUZ1F1agF0x5t1E0d1ZnKQq1p286OR9QTUMpQ0BVIxhXxaxfFCIpqEJkuMGD6pqMJAwaV72Jnxs2UT6ow1z+tZ9smITHtWE9nlgnACzDmQ/vnPf57du3fT0dFBV1fXYQ3CREMUQRCExaFUqTBaCdAlm7aUzGDJIxtxSelHDpK3tS97zbXMj1SqZaZ66ZNLv8TjHPa+cMhCH8c//OEP82//9m/87Gc/Y8WKFUec95577uHuu+9uvC6VSnR2HnuDPEEQBOFQnXNvsIegqY3k2+9EUY5+GTi5Pnq9oWhdveSLJMmQaSF+7W9jZNsxSiVGjZDe8YBUaTXj8QM4QUC3tIcxeYxyvI3+SgFZkthVHmLEq9KVacZ1HYrFcfRInO89V54xWH6610YXhKVqvppWHq1Z6GzvbV4epXfUIReT6R1z2dgRIQT6xx3CMGTM9OgvuIxVfMrVAMsLyKVUTMunZAX4PgTA8d7GOys5wOub9/C9wbPotZoA0CQIw1rNdVkKWdceJRWVeb63ygVdMTpbjl5mZabPPDk7XzQMFYQTY8570kI3R/niF7/IX/7lX9Lf38/ZZ5/NX/3VX/G6171uQcckCIKw2Hh+yP97ymL/kEN7NslNmy0GRg2isn3Usi4tLfnXvP0jZZUfrV768TQzXUoW6jgehiEf/vCH+fa3v82jjz7K6tWrj7pMJBIhEomchNEJgiAsXvWgt+97BEO9mIO96F0bjnk5YErZFpha8kVtW4XadSaJIMAPIYjYPJ/9TwZio4SSRy3cFOKHIXvL46xNN9FvFVmXbEV1Y3h+2DjuD5f9WYPlp3ttdEFYquaraeWR1nuk95wANq2IsWfYxvFC9g47rM1HaEqoWG6ArsgkozK2GzJccqg4IYNFlzAEz+e4a6JLhERkGPGb+bfhFGNeAl2BZBT8AKpOLcs9CCUSEZmSGZBJqGw/aLF5Zfyo391Mn1mUZxGEE2/OzUYB3v/+9y9IRtg3vvENfu/3fo8vfvGLXH755Xz5y1/m2muvZceOHaxcufKkj0cQBGGxGjM8Do47SJLMwTGH4XyVhGofdblU6vBHCo/HTFnnwmu3kMfx3/md3+Gf//mf+e53v0sqlWJgYACATCZDLBY7qWMRBEFYSupB73BgP1KujXjbsf3+z1YfHWo10hM3fbgWbI+nGSuM43kepudw987/YG/QR6DWwkxSqBAnSuArhJUM670r+f2tUR59PuBvflKgI6txy6W184flmTgd2fKMwfLZmqmezh599FHe8IY3LPQwBGFezVfTyiOt91jeIwwZKHrYbkBAiOEEjJU9DMfH92sZ4hU3xHFBliEIjrehKKiSz3WtrzDiZXjFWkkkEmNNk4LrgeUFJHSZQtXDckLasyoSEityOposcfm6Y7seEs1BBeHkkMI5FrJNpVJs376dVatWzdOQZrd161YuuOACvvSlLzWmnXnmmbztbW/j/vvvP+rypVKJTCZDsVgknU7P51AFQRBOS/WSKnokzt/9osD+YZOUWuUtq8v4noMfQMmWSUcCdu3qZuOGtVOWP/OszaTT2YUZvHBMFuo4LkkzBzy+9rWv8b73ve+Y1iGO44IgCMcn9D3c8RGGgzhRzSOdTPLtb3+Hd77znVPmm15arV5bXc7kkBT1sPfr/VSMSpmiWeFTQ0/xtNmPS9iIOCmo/Nma/8Izu3wO9KuYdsjFa+KYjkRUqz3m9p5Ls6xqlnFsEz0Sp+LIiyZYfiTRaJTly5fzW7/1W9x6662LvnyZOI4L8NpKvZyoMjEFw+P5nioVyycRU5BC+NGOMi/3W8Q0iSCoPRVjuQFVJ0SWwPaOL5Aekx1ubH+JAIVHxs7CRyMZlbmgK8ZwxSelK+QzKgMlj7guoUkS7U0aa/MRLlufECVZBOEUc5QH9A939dVX8+ijj87DUI7McRyeeeYZrrnmminTr7nmGn75y1+e9PEIgiAsRqVKhYMjJiWjwlsvyHL1WTrJiMQ3t0f4X7+M8+WnEnzn5Rj/ufvwDOJsU8u8BtE9P2So5OL5M5/CTn//aPMvVQt1HA/DcMZ/jjWILgiCIBw/H4WvbpP4Xw8P8nc/G6dUqcw4X73EimkaQC3zvN5ktB40L5dKDA8P1uqaj49iD/biWCbjgcWQaxKRtFrt4IkYuCYprM1Cs5LGsEKiCjy/3+DVfpOX+0wOjjt8/Ylx/uYnQ5hmFcc2yae1RhB9MR/P+/r6uPPOO3nwwQdZvXo1b3nLW/jmN7+J4zgLPTThOBQMj2f2mRSM19CFcgmolyDpHp75//MjfY9HW/ZY1/N8T5Wn9pr0lzxWt+hULJ/BokNCl0noMiNlD8v10ZSw0Vj0eH6BJELevmw7FjF+MLoZJ1QJw5CELjNc8ZEliYodsLxZ48ozEnQ26yzLarSlNdqymgiiC8IpaM575bXXXss999zDiy++yJYtWw5rUnbDDTecsMFNNjIygu/7tLW1TZne1tbWeDx8Otu2se1DpQhKpdK8jO1ojtQ0b/r7AONjo9iOTUtLvjHNNA327e3GMMoABEHI5J/yfGs7LS25WbchCMLiZpoGo6PDRPQIyVQa13XQNJ1q1cT3PDzfo1IuU7VMAj/A81yCMKw9szjBD+A/d8cYMWSGTYVyVeKAoVC7Ej70u5KQAy5Y7rAmPnUMhfFRnnzi5/Py+fRogm+/EDJiSrSlFd5xrgKhRxiCqmr4Qci/vazQX/TpbInwoWs6+fqTlRmblS11C3UcFwRBEBbOmOHRO1olDOFgwcVwZr4M1DSdUqlICMTjzpTrCtM0kADHtYkpcYxykfA//5bIYA+x/ArCK96B2xNH9mG10k5nLkqvOc4qvZmfb5OoWDa5lEZTPOTguM+qZqh6EvGJWsRDgYQZ6OQmrn/qAfR/e7bIQMlblMfz5uZm7rjjDu644w62bdvGAw88wO/8zu9w22238V/+y3/ht3/7tzn33HMXepjCMZqvWuCLzdFKkBzpezzW8iUFw+Nfny5QMDzKaxK8YeOh8pP7h20efaXMYNFFU2X2DtpU7ABFljFtl539LhUrRJJAkafWRZ8LXfLwQ5XHymfiyCkkJcCxQpoTMtGIQj6l8nxPFccLCXeGXLAqQVyTSccU3CA8rhIt89XYVRCEQ+a8Z912220AfPaznz3sPUmS8H3/tY/qCKY/Gh6G4ayPi99///3cd999h01/8MEHicfjMywxP4LAJwxCJFlClpUjvg+1GrZhGCLLCpqmTUxzcV131m3s3LkHTdNm3YYgCIub57n4foAkgSzLSEiEzJwBPBs/lMjbEjkZ1sQkgigE2cPnk4C4GuK5Dq/s7J63zzR9bDlbIicBZfjVUyGyFAISEuCHkLAl1gEMSTz44FO4NuQBdwz+ZVBBkU+tC+/pj9OfLAt9HBcEQRBOvuaESmdLjL0DLsuzGgm9lqHpjw00yrZALcEn8H1Mo4Lve+TzbY1gej3BJ5NtxnUdlMo49shBJELMgX2875XvMBza6FKE9ea5/MHaDXxnxyiD/Sq/GnJZm4eunMa7L2nhpzuKHBy3WJlTeXa/zagR0p7VWJ5vRtNkPD/kgZ+N0j1ks2/EYcOySKP5aFNCZczwaF5kpV/OO+88/vAP/5Dm5mY++clP8sADD/DFL36RSy+9lL/+67/m7LPPXughCkchalQfm6M1wDzS91hftp5tPlvA+LFXKzy+q0IyonDRtP72P3ypTO+Yx3DJxXR80jGVZEShNa2yfyTAsEMCqEXPjzOIvjo+yjW5V/mXgS14aprmmEzRDFBkAImmuIwqSazORegrukRV6C+4nLsiypq2yHEHwsXNHEGYf3Pes4IgmI9xHFUul0NRlMOyz4eGhg7LUq+75557uPvuuxuvS6USnZ2d3HzzzSe1JttcM9J7e/dhVCq05PLk8221RyxtmwO9+3HdmR9hampuoa1tmchIF4Ql6oRnpFenZ6Qf0pFw+aPXl9i/9/Aa6fNlekb61UfJSL95Wkb6byyyDLbXYqGO44IgCMLJ5fkhw2WXMITWtMYHr2plqJgCu0B5bBi/OEL5nz+J2t5F4qYPIykq8Xii1jR0IvvcNA0ymdq1habpjb9dV2NgfBQp1YLW/Sw7UgnGXQNJUrGkKr+M/pg/37mTrtLVxDUZ13eomDZnZBQ2LovS1Szxcm+RqBpwcAxakgrJmE7Z8onqMmOGR1/BJaLWKpHabsjaVp1UVOGBn40uqifOXNflu9/9Lg888ACPPPIIF154IV/4whd4z3vew9jYGB/96Ef5jd/4DXbs2LHQQxWO4mgBYmGq2bKnj+V7PFrAuL/k1RJEgFW5qfGRtqyKKtdKtrieRMn0aE2p2F6IpkhMriJ1POklm1N9vK55Lz8rbKDo6pTGPUChOSkxZoToGhTN2prXtEW4cHWcZEymd9SlZAevKZtc3MwRhPl32vzK67rOli1beOSRR7jpppsa0x955BFuvPHGGZeJRCJEIpGTNcRZTT7pPJb3M5kmoJYVWCoVcWyLYqmArkfQdA1ZkolEokSiUWzbIvADksnUxHKCICxF8XiicTOuJtGYPhcXbw0ZNzwCt8TweJWxssmze31+stNk1FJY1+TztrMs9GlHj/b2DvKt7XPe3lxs3hw2MtFmumjesHHq++9/feSI8wuCIAjCYuX5IX/76Ag/eaVWC/2qjUluvTxNQrEp2SbuvpcIPRff95CGegiKIyjN7WiaTktLnnQ6MyXRp37DXtcjNDe3YJoGruMQrt2CPNzDekmlxfMZ0RV8QmypyjOVHs5IGGzfpaPKkE/KvPPCKABf++kwB8cc2jIKHU0aY1WFFc0aqajCUMklHVXoyGr0FVyu2pjkxi0Z8imtEWAHGhnq+bS2MF/yCfDhD3+Yr3/96wC8973v5dOf/jSbNm1qvJ9IJPjkJz950huEC8LJ8Fqyp48WMH7zWSlGyx7tGY0Rw6crX5teMDxSEYXrz88wWvb42SsVyrZPz5hL2QqwXR8ZCABFgrm2ZuiMjnNp036+P7qZYTdNAEghmFZIPCqTjEq0JDSiuoTj1Z6tPW9VnLV5nYdeKKHKEt3DznHfkBE3cwRh/h3XHmYYBj/96U/p6ek5rBHKHXfccUIGNpO7776bW265hQsvvJBLL72Ur3zlK/T09PChD31o3ra5ENLpDLZtIVEr81K1qkT0KKriAxK2XcWyTDzfQwIi0RjZbPMCj1oQhMVAVSTyaQ3TjFItF5D1Kleusri43aFgSTzWG+XhPTFa4kGtjMqEcrmEpuvzGkivj+1Y3z/a/EvZQh3HBUEQhJNjzPDYO+Jg2rWnkLpHqjx9sMiaiErkp19HHz6AFNuILMsorSuRM7kpy09P9CkUxigVCwCoqkoqkSD9+LdgcB+Ba5GIJPnHgsoPz7uYz/c/gztRDOH1G6JUxlVUWcMNPNBSjBkeQ4aEE8qMmDK3XhojlUjQnIzy94+NNbLN/+vlzZQtf8oN8eaE2giwd2Q1mk7zgNGOHTv43//7f/P2t78dXZ85INjR0cFPfvKTkzwyQTjxpmegv5bs6ckB44Lh8XxPlVCC8zpjZBMqXfkI//2qXGN7BcPjsVcrPL2/Cn7AywM2Z7RF2Lg8yoFRl70jDrocUvZqQXQAXa4F0p1jeJhTJiCqePRaWf754AXYYS2hU52os56Oy7Qka9cl69p1NrRFSUYV4lG58X1cd066Md7FQNRrFxarOf/f/Nxzz3HddddhmiaGYdDc3MzIyAjxeJzW1tZ5vQB/17vexejoKJ/4xCfo7+9n06ZNPPTQQ3R1dc3bNheCpumNki6e5xGLxgiBfL6NsbFRrCGTIAiwrSqqqpGa5+CVIAhLj+s6OLaN4zh4nocigyxJjFdrj1iPmjJropMWkCRxQ+80sZDHcUEQBOHkaE6orM7p7BtxCPDZnvgp2/cXWa3E+ER5lEg8jqSoJN/8QdSuMxs10meTzTZjGkbjtdm/D//lx1F8F0mNULn0HTita7giHuObff0MBUXa/CZaPYeuliiD5YC2lEZEstBVmYGiz2AxoCURkJAVEorKmKHSPWQTUWUOjDvsHbZZ3x6d8lRZ7YmzlkXzxNmPfvSjo86jqipXXnnlSRiNIMyv6Rnox5o9faSAbMHweOiFEn3jLlFNIhVVpqxz96DFfzxXpCun87OdZQqGz6jh4wUBg0WPczpjhIREVYmiH+BPCpo7Psjy0T+XLnlc3/YyVqjx0/GNWF6EIKjVVo9q0JpS8QIICWlNa8Q0GS+Ey9YnGt/LWhZfNrmo1y4sVnP+v/muu+7irW99K1/60pfIZrM88cQTaJrGe9/7Xu688875GOMUt99+O7fffvu8b+dkm15HvZ4FUpuuNqarqoqqqgShgj5RCz2ZTB1l7YIgCHMTjydIplKUK6VGTe10JKAlHjBqyrTEA+SJRx1lWSaZTDWaIwuntoU+jguCIAjzT1UkPvCGHNefn2bH2Aj/s6eEH4R0BxWG8stYMToAinZMQXQATdNoam4h9D10p8ILA/tYGYZE/ABJBTmTB13lUwd/xXBQIuEl2FA8n8HhMjeenUBL5JH9Mr5jM2gEdGR1cslanWKbCC2ROF9/rMjeYRvXC2lKqnz9iQIrmg+vg74YnzjbsWPHjE+J3XDDDQs0ImExWugM4ePNQO8edugetNnZb3HdOekpY+8edlBliaa4wtr2CLmE0mhCuq23yneeKeB4sH/ErpWDMj1yCYViFYIw4IUeE9sLqNiHl3HxYUpgva5e+gUgrdrc0P4SLhEKiXPI2D5F00eiloS0Jh/Bl0IUCVY061yxPklf0UWbKOECTAk2L/R/oxNJ1GsXFqs575nbtm3jy1/+MoqioCgKtm2zZs0aPv3pT3Prrbdy8803z8c4Fz3TNLAtC2DKY5TTH6tMpzN4nofj2Oh6hMD3qFTKxGJx0WRUEIQTon5jL/ADPNfF8WDXmEJvQeHsnE06GpKNhuzaVZtf13UiemRKQzLh1CWO44IgCEvHQ8+X2T8eMO4msLRx2pQszVe9k6QqIf/wZ0cNotfPCTzPw7ctwh//Pf+fVKI7orLynHP56L5exttW0d6xmv1mlW3VPgzZoaoaROIGKT2OWSnRlcsBSUxTYnkmzormcqM8S1d7S632ebEWTKrYAZLksaJJXxR10I9kz5493HTTTWzfvh1JkggnmsBLUu3Gge8fT6tDQZjZQmcIH2/G9dq8zs5+qxF8XguNYPP0YG29znjZ8ukesOls1qg6kEnI7B6yAai6oKsSY0aA54ccQ+WWKQJAlUEO4cqWbkzSrN18CX17DBzPIaLJLM9qZBIKl61LUnUCXh20ec/WJjavjE8Jlk/+jLDw/41OpMWWYS8IdXP+v1rTtMaBva2tjZ6eHs4880wymQw9PT0nfIBLRb00S/3fkzPUXdelUBgjm21G0zTKpRIVo0QmnSUkRNdEAEsQhBOnfmOvUBin6sBf/iLNvrIKSEDIteuq3HxmtTF/LJZAj0REianThDiOC4IgLA31xpyeK5M/eBldHS4pKUqoZlCa48e0jvo5QQi4YwMMj/ezpzUBnsvuVJZ7zr2AYTyUpx/lLP98bC1AlSVUGd56bpQIKkEQUCoVaWnJN65X3v96fUp5luaESjamUHVCmhIKIOF4AWtaI6d9HfQjufPOO1m9ejU//OEPWbNmDb/61a8YHR3lIx/5CJ/5zGcWenjCInO6ZAjXA825hMKI4bM2r3P5ugSP7TbIJZSpweZV8UYm90MvlHC9EDMIKFd9VEXijGUx1rdGeLmvSiauEFM1NiyLMFTx2NlnMVI+FEZXJAhCOJb+oi1qhVBP8ivjTHJpnYOvGuwasKhYENND/DAkqsqULB/TDji3M9aotT49wFy/MZAzfcqWTzIin/L/jQRhKZvzWcn555/P008/zRlnnMFVV13Fxz72MUZGRviHf/gHNm/ePB9jXBKmZ56XSkUq5RK2ZVExyji2g2VVyWSaMIwSruNSqZTI5VoJQQSwBEE4YTRNxzAqKKrOv+xITAqiA0i8MqxQXnvoEesgDLBtC8gsxHCFORLHcUEQhKWh3pjzwLjDskyEnJZiRbNGayZ2zOuoX2N4nocZSZCJZFhpW7wai9KmRBkIHIq2iumNEY4Wacl1oKYKbIg3sTrVguQHuJ572Hrr5Vk8P2So5NKcULn96jwhMFj0WNWicdNFTeRTp38d9CN5/PHH+fGPf0w+n0eWZWRZ5oorruD+++/njjvu4LnnnlvoIQqLyKmaITy9nEk9UL6z3yITUxrzqbLEIzvKdGQ0EtGpwebne6q8fLDKwYJLS1xlWZOG64ecvSLKUMnjV3tNXC9EkUN0TeaNG1MMFjyGyofKKU0v7TKbMxJDvCm3i8er57Nx9TL6Ch4Hxx0iqoyvB6RjCqvzOp0tEdbkdEwnxA3CWYPj0z9ve1Y77cu6CMJiNue98y/+4i8ol8sA/Omf/im33nort912G+vWreNrX/vaCR/gUmc7NroWwapaeK5HqThOLJZAkqrokRiyotLc3CLKugiCcMK4rkPg+/SNlilWdQ4F0WsyakBKDxmYeG1bFkEiSalURFUP9XQQTk3iOC4IgrA0TG7MqSkSLx2scm5nfE6B6XqyT6lUoFIxsNdtxba6kcwREsO9dMoRhiPNOLLLnuyviJlN3BS5it86I0kiGkdVa5eb6fThN9s9P+SBn402Srz818ubiakStldL21zsQXSolW5JJpMA5HI5+vr62LBhA11dXezcuXOBRycIJ8f0cib1gPOm5dFGRjrAzn6LguFjuSGXrktQNH0eeqHE5uVRdg3VaqCPln1sJ6Tqh6xq0ZGRKFU9KlWfsYpHEELVCdgzbGN5AfJEFvrR1Oqih2xJH+Tiph5+UjgTOZFl/bIopltltCLTktLw/ZCC6RGGEuesiBFKIEkB8ejsXUtn+7yCIJya5hxIv/DCCxt/5/N5HnrooRM6IKEmnc6gqiqaVms4quk6xcI4pmkCEInGUGQZ3/dEwEoQhBNK03TGx0dRAoeXhw9/9HtZ1kOZOBeUJAldjyArtWyRmXo9CKcWcRwXBEFYOlRFIhVV+N1/6GWw6NGaVviLm1I0p1NHXbZealKSJPp695L45b9QrgzT19WKGgbs1DXeuqvIr7ryBKkykuThKRJFK4oaa6G5Od24lqmVrGTKdUu99AxAX8Fl95DFT181MO2AgaLHTRdlWZZd3OcTmzZt4oUXXmDNmjVs3bqVT3/60+i6zle+8hXWrFmz0MMThJNiesmZyZnzXflDGeubl0cxnYBl6Vqw/aEXSuwZsukddViTj3D+yjhVL8BxQwZLHr2jDhs7Iqxtj9CyV6VnzCH0YLTkMVr2iKgS2ZiE7YXYHnizFEuvB9uTisOmdD8/rZyHG8mQVGUq1YDL1yWQJIlzJ7Lfdw3ZdLXohBIYVkCx6hMCv9xlUKj6XL4uQVc+0lj/9M9bt5gajwrCYjLnvbFarRKGIfF4Lbiyf/9+vv3tb3PWWWdxzTXXnPABLlWTS724robneWSyTRTGR7FtG02TkBWZZPLoJ8GCIAhz4boOruuxc1TBCqdnT4Rc2HHoEW1FUdE0jXy+DaDR20E4dYnjuCAIwtKyZ9hmsOgB0Dfu8NKeMc5e6RAEfi1hZ1pSzpQGo55HxSgTlsZQC4OsKA6yKqvxi5YmLFnmq2c141CGQCGUfSJWgs6kTD4Tbay3VKkwOFalrTmkpam5sZ166Zl6RnomOv3SdHFnowP88R//MYZhAPBnf/ZnXH/99bzuda+jpaWFb3zjGws8OkE4OY5WcqaesV6s+izLaI3SJ6taNJ7ea/Jrm5K0pHXevClFNqHymf8Y4JW+Kk0JlW37TYqGT6nqIwOKDI4PgVcLkC9rVtk75IIEcR2cGQLqEj5d8TJDTpZfWFuJRVRa4wr5tMbFa+J0tui0N+lTGoauzesUTZ/HdhtsXh7FCeC5/WYj835yIP1onxtO/8ajgrCYzHlvvPHGG7n55pv50Ic+RKFQ4OKLL0bXdUZGRvjsZz/LbbfdNh/jXFImNxrVNJ1SqUixMI6sKCiKiiw7qKpCMpFqdHYXBEE4XpN/c6BWB9ULZP7h+RjTL2LXpG1WZmu/O5IkkUwm6Vje2bhYFpnopz5xHBcEQVhaOpskmuMhQ+WAfFKiswkcxyYMQkzTOOzYXe/VpOs6IZBJpRnq9/GjSfSB3VQlsCWJQJJxZAVCUJwYKT/F7+e3cN4yl+L4CK5dxXFcvv60zVAFuvIqH7wqRFWkxrnHLZemKFgSYVgLrF+1Mcm+EYdVOZ18avEHjt7ylrc0/l6zZg07duxgbGyMpqamRmNwQVgsjjfDerbSJ/tGXZoTCj3jHi1pvbGNXUMOEhJ+CD97pcJw2a9d0UzsUhENAhl0FXpGXPyg1mBUBaIamHZtPkUGXXK5rnUHmiqRXbeGsarPY68ahJJENh7yg+0lmhIqbzq7luBY/3wAj+02UGUJJ6g1Rc0lFB7bbXD5usQxfRevtTmsyGgXhPkx573p2Wef5XOf+xwA//qv/0p7ezvPPfcc3/rWt/jYxz4mLsBPANM0ppRH8DyPatVEkmUIIQxDXNcRTUYFQTghJv/mADi2ze5hj7J9eFmXK1dbU8q65PJt4nfoNCOO44IgCEuLY5vcdmlAxYazutIkojqapiPJ0mHHcNd1KJdLWFa1dq2h64Tf/yrpg3uwg4C+ZJqBWIxQkggBWQnIV5ez0TyXwniCn5UkeosS795iY1sW/QWLg2NRItEog+WAccMjn9Ya5x5eEPK9bUEjK/19r2uhbPk0JRZ/fXTP84hGo2zbto1NmzY1pjc3Nx9hKUE4fc2UYT1TsHf6tHrGesHwGDF8ALb3mDy3z8T2AsqWxy93Vbh2c4a4LuG4AZIEEUVirOLj+6AqoMngheD5ENdlTCdACkGWwQ9q76U0GVUJ8AOJ9U0e52rPU/QSWE1buHxlnKf3mLQkVaKqhOOFPLnHJJ9WiUVkNiyLMlBw6S84vHSg1jg0HVcagfCufKSRif7MPvOo2eavtTmsyGgXhPkx573JNE1Sqdrdtocffpibb74ZWZa55JJL2L9//wkf4FJSz8yoZ3bWT2wlQJJlZFlClhWqVRPf94lEY5hm9LB6g4IgCHNR/63RNJ1yuUSpXObn+3W8GR6pbk1MfQpG07STMkbhxBHHcUEQhKXDdVy+9/ODvFqOkstorO6MEgl84pqGLCuHXUOYpkHg+8iSTCwWR66M4Q3sRxk9SLJaQpJkxlUVX5KAkMBTaRvfgKFXGbdjuL7OqBVyzZnQEofWdJT2jELZ1+jIaiT1gGJxvLHdihehr1AEanXSy5ZPPr00zi1UVaWrqwvf9xd6KIIw7wqGR9nySUZk1ub1RrC8bPkYVq2WSj3Y2z3s0D1os7Pf4rpz0o0Ae/eww56J6bsHbHpGHWQZbC9k3PD54UtFNnXGGTM8qm7IgYKDokBEhhVNGqWqz7gREARQJcANak1Ec3GJsWqIFEJUl4mGMn4Qsioxxrjdwq/MtbSMBQwVPTZ2ROho0ti8PMqTe03SMZkwrGWvDxZcElGZ7gGbilXbr999SRPZiZsAk28OvNZs82NxMrYhCEvR7K2DZ7Fu3Tq+853v0Nvbyw9+8INGPdWhoSHS6fQJH+BSUs/MqFbNKdMVVUWWZQglAj8AQlzXw/NcbMvCNI2FGbAgCItCrSdDE67rYFTK7B+q8uSBw+v25aIuq5tqJ7qyXDt8FIuFkzlU4QQQx3FBEISlIfQ9Cv/yvzj7qS9xVc//46l9Lv/nR2P8318UKVUqMy4TjydIp1I0qyHJeJySL1FVokhu7cm1A4kEZe1QLpYUhryU+znPNv+EvvX/QYCDIisT68mQy7Xwu7+2kg+/uZX3v74FxzaxLQvXdchkmmjLxujI1gLnHVmNpiWWNfnHf/zH3HPPPYyNjS30UARhXnUPOxhWQDKmkE2ojaB494BNIipPCfauzeuULJ++cZfne6pTprtBiOuFxCIS61ojXLw6TkpXiKoSZStEk2FZVqOzSUUOJ5qE6hIj5VoQ3ZvIPLdcCGqhFapeSEyViGhQsXwSwTCha/D4cCsvWOsJAomunEbJ8klFFK47J01ni866tghvOjvNmzanGS557BlxkEJoy6qsykV499amxk2A53uqPL7baHyebEJly6r4vJZcORnbEISlaM571Mc+9jF+8zd/k7vuuourr76aSy+9FKhltZ1//vknfIBLyeT6xKZhYBgV8vk20ukMpmngOg5IYNkqKqCqGpFoVJRVEAThNak/DSNJEp7n0V+wsIPDA+k3bjDQG0cNiTAMl0AbsMVHHMcFQRCWhqA4glY4SFSTSBl9pKMF4pFljJgedhidcRlVlon+9Ov4w71UUzlKZ70R+8LraXVM5NFe/te6NViK0phfr0ZxkhayBL5mkcyNckX7GrLRAN+vJQQ5tkk2nkBVDpWSiccTU+qkVxx5SZRzme7zn/88u3fvpqOjg66uLhKJqdd1zz777AKNTBBOrOnZ0WvzOjv7LVIxhVRUmTHYa7sBu4Zszl0Za5R4uXxdgr97bAxFgkvPSPDMviqyDJIs4QUhPWMul6xL8qMdJcp2QNUFxw+JqWGtHJUMvg8BIIWAXGswmoiA5cDaRD8Xp7r5efFMxkigKTKbVmisaNbJRGW2H6zdVEzGFIaLHm4Qcvm6iUoCIYQSSEictyo+paFoKAFhWPs3on65IJzO5rzHvuMd7+CKK66gv7+fc889tzH96quv5qabbjqhg1tqalmhOq7rMDw8SOD7DA8Pks02E9EjuE6taUZTtgXbttB1vdGQVBAE4Xg16pT6Hn4Ij+2PMr3JqC4HnNt+6NHjMAwACWsiq6z+OzS9WbJw6hHHcUEQhKVBzuRQ21bSSQ92ZjkXtK2gvwJtKY2IZBGGU8u1ua6D0bcXBnvwAx+7by/qqgLJbT9EcmyeSa3hpVQ7YDeWOaN6Fq/GduAqFklifPTyFazJxXGtKlXLRFYUfM/D8zxUVSUeT5DJNAFQLI43erTkJ6Z5fsiY4dG8RILqb3vb2xZ6CIJwUmQTKmuZaMY58fq6c9JTmnNCLcD8racK7B+xGa8GrGjW6R52GmVfRgwfRYID4y4xTWKs4lGs+miyRMUOSEUkfrazwu4BG6tWHhzPB1eGiA6aBAWzdqWTjUFUV3D8gLIVcEm2h7MTB+mLX0hnJseWjEpMlbH8WvLQQMljoOixukXj3JUxdvZbqLLEiOHzho2pxvinfyaA8zpjpKKH6qWL+uWCcPo6rj22vb2d9vb2KdMuvvjiEzKgpWxyACqfb2N4eBAJiUJhDMuyKJeLhEjomk40FsN1avNnMiJYJQjC8atnhyU1nT39FXaOHl6b9Oy8O1EzvXbRresRJAmi0diU36HpzZKFU5M4jguCICx+kqKSuOnDBMURMpkcv43CuOEh+2V8x564KV5TT+SRZJ1IUxtO/17cVA7f85ErowzaBbqMfvJ2krKuUz8lyMppWl+5liAxzkq1mRVbE6iyhENILBpDVVVUVcXzvMPODyZnpwNYTsAXfzRMoeqzoknn/a9vWdTBdM/zAHj/+99PZ2fnAo9GEObfTLXPpweRu4cdxk2f4YrPiiYVTZUaweeC4VGp+sQ0iVREwnRDdEViRbPOKwerFE2fh18s43q17POYDo4DHrVSLjK1si51LSmNi9ckeGqfQVRxadIs1JVv4OazlpFLKOwddRguugwWPVbldaQQbDdkz4hDMlrl8nUJRgx/StD8WBqE1j/H9JI2giCcHuYcSL/qqquQpNlPaH784x+/pgEtZVMDUE3k822MjY1iVMo4jg1IeK6DrulomkY8nhBlXQRBOG6u61Aq1Rp8xWJxylWL772iMW4H0+YMSUcDUvqhM09VVVEUlVQ6PeV3aPpFsXDqEcdxQRCEpUNSVJTm2o1TFcjGQkolH0VVkaRD7bJM00BCIlRUkm+/E6+3m5GxIk7g8VcrWthPiv3xOJ4kAyESEioqrqsRejppu42SK7N70Obs5TLxeKKRga5p+pSEobr607hQy0T/wiOD/PjlMslo7RJ13PAWdeNRVVX5zGc+w6233rrQQxGEIzqeMiQzLVMv56LJ0pQs88nz5xIKF62Oc+ayCImYwuoWnW291VoZFmCo5DFq+IDEWMWle8iZqJ0OTgCeFRDTJeKKTEdW5cVeu54HRChBJqYwVvEJgVLVp2BYvKntILvsLiryBcRSCSpVn8GCy2O7DRw3oCunsyyrszavU7QC+sZdth+0SMYUtqyKH9P3MTkDHaBiB7RntSN+n6L8iyCcmua8N5533nlTXruuy7Zt23jxxRfFScBrFI8n8CYefTRNg2rVpFwqYjs2iiwjyzKSLOP7Hi0teRGoEgThuNQvZj3Po1Iu4QcBY+Nj/N2TAY/v9iCc2odal+FNq6soE5NrQdhajfTpJVwmXxQLpyZxHBcEQVh66iVTFL+C73koqkoYBo3ybPXrCkmSGBweomT7BMCwU2GvErI3EsefVBs9DOGCaCsfOCfG154KKNgK+YTEmpyCoqqk05ljLvHmug77B0sMF6tEVahYHk1xZUk0Hr366qt59NFHed/73rfQQxGEWR1PGZLZlulo0pDCWlB9cvD8sd0GqixBW4Qrz0w15n9mn8mLvVWQJFa3aOwZtqnaAftHXcYNF1mC3nGX1pRKyfKRJag6Ib7is+OgjySDHEBMg7OWR8jENAzbZ/eQQ0RyyBefxY/odOTW0lcMebzbYLjsYdi1zPf2jM7qnE7ZqpW4vO6cdCOwf6Rs8ulB8Ok14qf/faK+d0EQ5t+c98bPfe5zM07/+Mc/TmWW7u/CsamfbFbKJUzTIPBrP9aaqqFHIqiqh+97KIpGtWriuo6oQywIwpzVn35RVJVoLE65VGTUkHi2x6NgyfiN+ughugytCZ/oREKYJCvomkYQBkgT6xKB89OLOI4LgiAsHZ4fMlx2+e4zRfoKDs1Rj/dcHAMgDEJM0yCdlAnGBnAljbHxcapVE9uxazfMx4fpjkUIZ3iSaTywWd2W4A/faNNfClnRJGNbBslE/LDrE9M0MA0Dw6iQz7dNyVL3PI+E4rIsoxCEIboq874r0ou6rEvdtddeyz333MOLL77Ili1bDms2esMNNyzQyAThkJmCwMezTPewg2EdysR+Zp/JnkGbHw7btGc0zCCgUvUpGF4jAzuXUIjqMsvStdcVy2f7AZNitZZ5HtMVErrC7kELt1YtiSCslXLRZUhGJVa26BSrAaYdsq5NYV08QnusSkv5WWylicjKi9jT7xAicenaBCXLx3Il2rMa77gw2yhJ89B4ievOSTfqoc+mYHg89EIJTa79hm2ZaJQ6ORh+LIHx4/neBUGYfyfsttZ73/teLr74Yj7zmc+cqFUuaRE9gjJRT9BxbFRFRdd1fN8lBBzbwvdq//lEEEsQhLmoZ51pmk6hMEY0GsMrmRRsmXCi6KkuhUgSZKMBF3Q4ZKO1ZyKTiSR6REdTa/VRxZMxi4c4jguCICwunh/ywM9G6R6y2TfisLpFot/yqTgyK1vTSLJELBLBePDz2H17cJMtOBe+tZa4IytUfY/vKQFhODmgfehv27cZl1zaUgmaMrXH1sKglghkmgajo8PoeoTm5hbi8QSGUUECSqVio266Y1t4vk88nuC3r2rmC48MMl7x+Iefj3D7NStmDKYvpoakt912GwCf/exnD3tPkiR83z9suiCcbMdS9/tYlpkeGK6XemnPaGiqxKqsTsUODmsuuiyjkYzI7Bqy2dlvUzADwhDiusLZHVFePFjFdEKCsFbFRZNBkiCfUrhkXYKiEXBg3MQPZPYMOfiBQ1Ly0NTl0LyRoXLIQNGjKamyqTNGpeqz/aDF+tZIo0nq0UrSTC6/0j3soMoSbhC+piD48XzvgiDMvxO2Vz7++ONEo9ETtbolK53O4HsetmOTicWwbQvXcTHcMul0tlbaxfVqJ5yJpAhiCYIwZ/XyK8XiOFbVolIp43k6iiQhyyEEgCShyJCKhLxlTa2si67rpNJpoNZsVFFqh5BicVw8HbMIiOO4IAjC4jJmePQVXCJaLdgchAormhU6WlJomo4sKyhmCX+4F0VRUUvDqNUSVTmC7dp8YeQZngwKTA6eN0jQrEVJOCFqSqOlJY+maY066AMDfRTGx9A0nWg0SibTRDbbTKEwhud5h8rLAKpSa0haqgYUzQBJkugtwHDZY1l2ao30+s2BvoJLR1Y77RuSBsH0vjSCcOo5UbW6pweGswmV685J83xPlVCCVS36lOadBcOjbNWelf3VXoMDoy6WFxBRJRRF4nVnJFjRrLNz0CaqBwRWrcmoJEFUq5WhHCn7VGyfXFIjE5fp0IapmBZhahUkW0hHFQ4WHFY261y0Jk4uoVCp+mxaEeXcztiUcXYPO+gyfPXREdqyKlesTx5WfqXeSLQ1o3JeZ2zG70vUPheE09uc99qbb755yuswDOnv7+fpp5/m3nvvPWEDW6o0Tcfza13th6wBJAkqRgVVUXAch2ymCduxRY10QRBes3g8wUG7F9e1sao2mWgSWQJZgoojESAxYCgMGDKr9dqFbblUQtNqh44wDCiVivhe7TlK8XTM6UEcxwVBEJaG5oRKR1ajr+By1cYkN27Jkk9NzeKWMzmkluV4A/uQcstxIykCo4T79IM81xHH1SYHsqUpf304twUllAh8v1FyMh6HsbFRjEoZWZGJxmLE4wlc16FQGKs1KVVrgfPpPVaC8ggtsYAn9wcoqsR3nynwgTfkpoy3fnMAoK/gLvqGpIJwKjjeWt2Ta6DXA+TTA8fZhEoypvBib5Undxu8e2tTY57ne6q8eNAipkocGHXZN+KQiMhIaJi2zzP7qixv0rl4TZxn91Upai7Fau2pWlkGkNg/6rAsq7JuZZTV+gGMgy/wjHsWTRpcuCrOiwctFFmiOaFyzeZasLzeCBRqNdrr496SUPnnx8d44YCF3g+lasDl62oxmXrwf/LyswXJRe1zQTi9zXmvzWQyU17LssyGDRv4xCc+wTXXXHPCBraUZbPNWNUq5UoFfA+1WoJUllgsTmvbsoUeniAIi0g0EsW2LJKaS0wNMF2ZpBpQqKoEoQQqRJWJVvdItYzlietZz/MYHx8lEomSTKUX7DMIcyOO44IgCEuDqki8//UtjBseTQkVBZ+gOEiYySFNPFXmBQHFrW/DK4wQbWknUTVJ/Pj/Mjy0C3v5ebOuW0Nm0K2wSs3jBz6WZRGP12qej44MY9sWWiSOLSXw/BDHNpEAz/fQiSApGrsPjNAcl0mlkrhurRTCmzYqDFV1YhGFgZJ3WKB88s2Bjqy2KBqSGobBT3/6U3p6enAcZ8p7d9xxxwKNShAOmVyK5Vizqet1wlVZYmcQoskS2/aZrG2PHJapvTav88MXS4wZPv/vyXH++1U5sgmVsu2zf9iiNaPR2axRMHyQQioEuD6MGT4/31nBdANKVZ+1bTH2Djt4XoAPpGMyoSRRtUNyzivY4/vZHl7AnmqC5UWPZFTh3Vub+H9PjtOe0RqZ8cmITC6hHFbnvGB4ZGMK61t1Qgk0WWLE8NmyKj7jd3Us36cgCKefOZ95fO1rX5uPcQgTAsdC6n0Fxw4IbIvsL75JxDFQm9vJ/Jc/XOjhCYKwiJimQSwWIwgCRo0i+bhPWvc5WFbRlJCQkFwsQJ84Umi6huPadHR0UiwWCMMQq1pFUzVc1wHEUzKnA3EcFwRBWDpURSKf1gh9D+PBz9fKuLSuJHHTh4FavfKyaRIoEdyqiT3YS9osEK0aeOosl4ohZJQoa/QmTLMMSHiuh+97ZLPNKKpKYMt8e3tI2avQ0exw80UtRLQI+CbVqsXXflFisBTQEve5/S1NxCI6nufRnnFZlrQZqnh05eOHBcqn3xw4ncu6ADz33HNcd911mKaJYRg0NzczMjJCPB6ntbVVBNKFU8LkkizP7DOPKZu6e9hBm6gTfvm6BI/tNhiv+rzYWwUgFVWmZHFfd06aB58t4voh23qrvGFjiqodsm/EoWD6nLEsSiYh8/LBKlFNoaNJxfVDylbAYMnFdkMOjjmkYwoHxnximoQsS8R1BdMJKHkxkm2vp9ovk435tKY1KpbPvhGHd29tYsTwKVs+Q0UPLwgJJQ6rc9497BACl56RZG1eb9xQmO27OpbvUxCE04/Ye08hgWNR+Mv3448NkYmniWbbiR14GSnwkYf2Ufn6p5He+z/Qo/Gjr0wQBGEWpmkcajLq+0SjUeLqCLmEQl9JRlUgnwiouhJnt7lkoyGSJBEGoGgKlUqZlpY8sqzQ3JIjEomKUlOCIAiCcApzRvqwDnYDEA7sJyiONN7zfQ/XcbBtG0+JkFR1vrOiY+YVBfD66hV89MIVRCUFq2rieS6VShlV1SgwRj7fymCxl+EKINn8bGdAX3GMFVm48ayQgUKFobJKSMhYVWG4aLF2eRJVVamUS1y7zsbwNFITDUynq98cWAzuuusu3vrWt/KlL32JbDbLE088gaZpvPe97+XOO+9c6OEJS8yxZJsfazb15Pnq6/r3bUUCwLQCDOtQf4CBgkt7VuMNG5I8tc/kxZ4qUgivDFiMGz5jFR/Hq/VI8AIJ2wtYk4oQAq4fEtElSmZAU0ImE1Npiss4Ply2WsMffJZ90fWU9E5USWVFk082HrB5RYxfdps0xRWSMYUtq+K1LPrxWha6FMLatsiU8U//TCIYLghL05z3fN/3+dznPsc3v/nNGR8/GxsbO2GDW2rsvS/hjQ0SBiFyaRRd0ZG0CHJlDAKVoHsbRt8+9DVnLfRQBUE4DbmuQ6lUZHxslDAMsKwqgR9gGBUUKeTadVUKlsRjvVHGTJlkJOSGM0wUGSRZJpPNoigK0WiMQmEMVVXo6Ohc6I8lzJE4jguCICw9VS2Gm8ohjQ8g51cgZ3IApNMZ+vsPEobgebUs06Jn88CarhnXI/ds4vqzlpFQNNKZLMOug+8HaJqC41rEYlEsq0ouqdES8+grgx94EDj0FWTGq9CcUMjFQ8ZsjY6sSkdLCqj1bvE8Dy3i8oNnXcYshxXdo6d9Q9Ej2bZtG1/+8pdRFAVFUbBtmzVr1vDpT3+aW2+99bC+JoIwn46ldnc2obJ2Yt61E69nm2/yOkYMn1CSkMKQeLR2k6xS9VmV0xksuDy332Tr6jgdWY09QzYPv1TGcXwSERnPh6rjY3khqgJBCAfGbBRFojmucculTfz0FYPBcq0ZaUtSpVSuwMHHSSVStMVTICkAmI5PQpfZOWAR1yWyiUOZ8ZObitaD5QXDO6xOuiAIS9vMt/mP4L777uOzn/0s73znOykWi9x9993cfPPNyLLMxz/+8XkY4uLnug7F4jhGuhU/nkFyLaQwQAlc5HQO9ChEE0iSRCwWW+jhCoJwmjJNg0q5BBIoqko224zjOMi1bjwoMrTEQ359fZW3nWly00YTXQVFUclkssTjCTo6OrGsKqZh4Pv+rNuq/67VSr4IpxJxHBcEQVhaPD/ECBPIv/7fUW+6k8TbfqeRka5pOplMljCs9UMJS0N8bFkKX58521R3snjIeJ7H2Ngwqqqh6zqpdJbW1mV4E+cG2WyGWy5N8r6tKheuCPE9l+aIS2smQnNTE7e+Lssdb2nj9mtWEItGGmNpacmjJvIUHB1Zljkw5rJr0MLzwxnHc7rTNA1Jqt0kaGtro6enB6j1M6n/LQgny9q8TntWO2q2efeww55Bm4deKFEwvFnnqwehC4ZHLqEQ1SRW53TO64yRiioMlTwe222wd9hme6/Fwy+WqNoBEU2iKa5w0ZoEN5yf5ZL1cULA82qlXADcQKJU9Rmvejy+p0osIjNU8rDckKHRIhv9JykEWZrXXca4CS8cqNI9aDNu+PQVXOK6zJrWKO+4MDvlZkA2obJlVbwxrX5zoXtYXNMIglAz59tp//RP/8Tf/M3f8Ou//uvcd999vOc972Ht2rWcc845PPHEE6KO23EwTQPbslD0KO7bfo/w37+EHIkhj/URaFHkpmWo6WaU5euItIrsT0EQjk890wsgFoszNDSA77v4fjBlPkWGbDSctFyclpY86XStSWU221ybT1Fm3Vb9dw0gkxGNdE4l4jguCIKwdHh+yAM/G6Wv4NKWUviN83LY//5lrNE+An0Doe+hKgq+Xzs/KIz20p2cpVxbCLkwzZm5AJBJJFJUTZNoNEYqlUZRFFzHwXRt4okU7e3L0LRh3p20KTsK2RhEdI2WljyuW2tMGgYqKFPPE+oNRQ+MufQVHL7+xDgrmvRFmZl+/vnn8/TTT3PGGWdw1VVX8bGPfYyRkRH+4R/+gc2bNy/08IQl5lgzrtfmdXb2W6iyRPewM+sykzPcAZZlNNqzGtmEij7q8Hi3QWeTxnDFgzDk1QGLtoxOW1qlKalyZkeU/WMOe4dtVud1XuipIlEr57I8o1GyJQpGQDoiUbZC1uR1LDsg15RktHIG+eVrQJYZKLnYTkBck1mdj5CNKWzujLEqV6tznjN9Rgx/xpI2x9Jo9VgbsJ4KTqexCsKpas57zsDAQOOgnkwmKRaLAFx//fXce++9J3Z0i1y9zILneaiqWgtSrTwDM78CdfdzSLZJiITUtgL92vfjtK7CCwK02WNXgiAIs6pnegGMjg5TKBQYLjkkVB9llueTJEnC931M02gE0uPxBPF4Akma/aGmes10UTv91COO44IgCEvHmOHRNxHI6h2tMnqgQG5gP6qmge8SFEdqWeSBTzh2gOr2H1DceuGM64pXmvkfW1WiWoCqqkT0WiZ54Pv4EzfqkcB1PWzLplAYQ0JCUSTWdeZw3do46kF00zAwjArZbDOu6xCPJ9A0vdFQdNegxdefGEeWJPoKLuOGt2hqo9f9xV/8BeVyGYA//dM/5dZbb+W2225j3bp1ojm4cMqaXgJltuDsTPXUcwmFZ/aZPLffJBGR6R5xaIrJ2G5AJq5QNH0Giw6rchGe3mtSdWo10vNplYtWx/nxKwaWG1C2PEpWLXt9uOKzoT1KUOqhRR3Ejl9MtnU9HRmN7mGb5U06Sk7i5gsyjBk+oQSrWnQe222gyhI7g5BMrBZkmX5T4FgarW7rrfJib5WyFeMNG1Pz86WfIMdSvkcQhCOb856zYsUK+vv7WblyJevWrePhhx/mggsu4KmnniISiczHGBetRpkFIJlKY5oGo329pPa/hGTVTqjkahGGofqrH+C/6VYkRRXZnYIgHJf6haskSYyMjPDdHSqDZYVczOfX1pkzBtMlScJ1HWzLwjSNY/79qT0qLn6rTkXiOC4IgrB01LO7+wounS0xWlbEUVpX4g7uJ0wojFoutlEh/atv8z+jBj+86IKZV+RLXB9expp8lEwm0ygHE43FqFTKhIBj2wRBQDQaR9VUFEXF921i0VijdEz92icWi1O1htG1SK3vilK7LK2fO6iKxPq2KCuadPoKLh1ZjaZFGPS58MJDNy3y+TwPPfTQAo5GEI7N9MD5bAHm6Rnua4GHXqg18+zIaMR1mZLp89OdFXRFgmpAiETFCfn5qxU2LosRBJCMyrRnNM5fGWPXkMNIxWOs6hPVZBRJ4rzOGObgK3S4ryB1bOWc1bVEnu0HLWw3ZGNHlOvOSU8Z62O7DTRZomj5ZKIy/UWXTcujR8zYnq3RqhQCUq1B6anuWJvFCoIwuzmfjdx000386Ec/YuvWrdx555285z3v4atf/So9PT3cdddd8zHGRateZsHzPEzTQPVdUt//MmppBKT6D7IMvg+vPoV2xc3E820LPWxBEE5T9XIrFaNMoRoyakoossSIGVB2pCnlXOo0TUdVVTRNQ9N0isXxRsaYcHoSx3FBEISlo57dPVSsEpEs0skkxlt+i+K+V3Ff3M3w3p1Y5QLB4C5eOXs9njzL02a+zEWrXfL5VtraljUmF4vjqIqKoqrYvkc0EkNWZCKRKL7nNZ5M8zyPqmlSMcooioptW+iajqzIUzLSZxr7uOHRlFAXXVkXgGq1ShiGxONxAPbv38+3v/1tzjrrLK655poFHp2wlMyl5MfkrOa1QNnySUbkw4Kz09fZPeygTgSv47pEOibz3F4D2w3wfFiZixJVZXpGoSkvU6x6LMuqZGO1Mi+Xn5GkKaHwr08XySVluoccVuV1rMEdVIe7ORC5hDNSrYQStMQVYqrEqlyEzctjjeao9TFuWh5lxPCpVH22H7QgDBkxaiVeZsvYnq30zbkrYyRjymkRnBYNUwXhtZvzHvTJT36y8fc73vEOOjs7eeyxx1i3bh033HDDCR3cYlcvs1AYGyYY2I/8439A2f8CISGEgKKBrNSC6kAqlUYVwStBEI5T/eZdNBojh82KZpWeEZOWeEBKPzyIrqq1x6cVRUFWFKpVE8e2KJWKxOOJRnaZcHoRx3FBEISlRVUkEoqNbdmYpoQejWMpEZRqmdRP/5FYJIFWHMUN1s6yBonldHB+RxuqquK6TuOG+uRSbq4bp1AYI5ttRtM0TNMgHk80buRbdhUJGcuqkkgkkRWFfL5tYl0JPD9kqOTSPClorirSoivnMtmNN97IzTffzIc+9CEKhQIXX3wxuq4zMjLCZz/7WW677baFHqKwRMyl5MfkrOZ6WZNNnTGgVv6kHjj/xa4Kj+8yuHR9givWJylbPgldolyFJ7oNXum3kAAvqMU7Xuq1aMtorG+PMFB0GSzZFAwfWbYJJUhGFQaLLooE44bPmcsiRDWFJ/c3cbBwAaGqM+oZDJd9/CCkPaNRqgbsHXUwJpqUblkVb3y+rjzsH7bZPWjTNq3J6pFK1kwngtOCsLS85r1969atbN269USMZUkKfQ/pBw+gPf9TqBQgDKnnWkgd68AsIGfyqF1noTS3L+RQBUFYBGzbIvB9JAJ+faPDgaEKKT08rKyLqmog1W74BWGAbdnoeoQQsC2LqmkSBMGM2xBOL+I4LgiCsLi5rtNoNl5/GlazDZhoMKqYRQqKzMFkcsblO3Zv4n+9aSPNqTT+xJO09RIsk0u5maaBqqiN7PL69Ilka1onysAkk6mJLOxDT7hNborakdUWZWPRmTz77LN87nOfA+Bf//VfaW9v57nnnuNb3/oWH/vYx0QgXThp5lLyY3LgeHJZk+nB+MGCx7jp88Qug4Gih+uFvHTQojmhMFzykaXa07EJuZbVbtgBFTtAVyUSUZnmhMqOgxaqDD96qYzthvgh9I07RBWP9e4LNC/fwDY5hRE4rG1SufrMNEXLR5MlBoou7RmN3YM261sjM362EcOnPXuoCWp97DB7TXRBEJa22TvFzeL+++/ngQceOGz6Aw88wKc+9akTMqilJCiOEA7uA88Bv/YjXc/xVKIxIudcSep995F8+51IivjxFgTh+JmmgYSEaZpYVZOqUSYbPTyIXhOiazqKKhONRAlCH1VVyefbiESjqKr4PTpdieO4IAjC0mKaBr7n4fkexUKBgwd7cPQ4KCphEBCWRrjzgvNBOjxwHR+Q+OTFnaS02jVKJBqdtZF4PJ6Y8f1asL2JdDpLR0cn6XSWTKYJTdNxXYdicZzBQrXRFLXeWHQpME2TVKrWnPDhhx/m5ptvRpZlLrnkEvbv3z9v2x0fH+eWW26ZqHef4ZZbbqFQKBxxmfe9731IkjTln0suuWTexiicXNmEypZV8aOWdZluVU6nOaGwKqezNq/TPpHZXTA8lmU12tIqUV2mYPjsGrSxXZ+hksel6+OsbYuwPKty8Zo456yMsbE9QjIqM1LxeLmvSu+YQzwiUXVDNFli75BNxfZwqhXODp9E1mKsX7MK24XlTTqbOuKsaYvw5rNSbOqM8e6tTWiqRDqqkIwpM362yWM+0nsFw+OZfSaFJfLbJAjC7OYcSP/yl7/Mxo0bD5t+9tln89d//dcnZFBLiZzJIeVXglFqTJMAYimi176fxNvvRM2vEEF0QRCOW/0i1TQNRkYGsawqhmni+/UTQWnin0NUVSUMIRqNk0xlaGnJE4vFMU2DlpY8Lbk88mx1VIVTmjiOC4IgLC31AHcymaJQHMe2LHxJxo+lGD/rSvZj82omM+OyK9UzQNFQFBVVVRsB8JnUA+Zz6aNSL/sSlS06srUSLou1sehM1q1bx3e+8x16e3v5wQ9+0KiLPjQ0RDqdnrft/uZv/ibbtm3j+9//Pt///vfZtm0bt9xyy1GX+7Vf+zX6+/sb/4jmqEvHbIHkEcMnE1MYMfwpwfjuYYcQeMPGFK87I8mFa+K848IMK5p1rj4rSVc+QkKXGSh79BVcLluf4K0XZLlsfRLHCxg3Anw/RFdlzlkZI5NQkCTY0VPh8vizHHRaOBg5j5cGXDqaNJLR2nVJ96DNiOGzZVWcrnyEy9clKFk+gwV3xiD4kW4gTP88AwWX7mFnXr5fQRBOH3M+QxkYGGDZsmWHTc/n8/T395+QQS0lkqKir78A9xffBmqZHhKAa1N98H+j/39/K4LogiAcF9d1ME1josGXwdDQAL4fIEngeQFFSyYdCYjoKpIErus2lrVtG1lWsC2TpqZmMpkmihMX3wCZTBPSDJlrwqlPHMcFQRCWJqtaRZYkvDCsZaKHIa5d4c/O3DhjNjqBxLn6GXS2Z2luaiadnjnYfjT185GZmpVPrrH+/tdri7qx6Ew+9rGP8Zu/+ZvcddddXH311Vx66aVALTv9/PPPn5dtvvzyy3z/+9/niSeeaJR2+5u/+RsuvfRSdu7cyYYNG2ZdNhKJ0N4uyo0uRbPVUF+b1ylbtaadBcNrBKQnl4rJTiqVcmZHDDcIaYkr9IzajJd9TCtg34jNhmVRVuUjvPHMFD9+pYIuS5y5PIofhKiyRKlSRVPj7PcvRM1kOGt5jI3LogBkojKmE1KcNpYRw8dyAvaMOLRlteMu0TLb5xQEYemZ895fb0q2evXqKdMfe+wxOjo6TtjAlgLXdTDKRcJXnqRe0EUCkFWIxgmKQ/gHdiGv2UzoewTFkVoGuwisC4JwDOpZXoqq4rgOmh4htC30aJKHtocMlgKaYx7XrquiqQqKojay1MMQZFkmDCUsyyIed6Zc7AqnL3EcFwRBWFrq5wNm1USSZSRJwbZtCEPcZ/+dVy7ZMuNy71TW898uzbKqa/WUMiwzBcSPZftQq5c+Oag+ucY6sKgbi87kHe94B1dccQX9/f2ce+65jelXX301N91007xs8/HHHyeTyUzpj3LJJZeQyWT45S9/ecRA+qOPPkprayvZbJYrr7ySP//zP6e1tXVeximcWmaroZ5NqKSiSiNbe0tCpWB4/GJXhcGChy7XgvC5hEKl6lOaqF/+9SfHGS57VL0A268l+QwUPWTg6rPTrGzR2TPi0J7WGCi52CN7WGPtYv0l15NP5Xnw2SLbD1gUrYA3n5Vi+8EqpapPe1alYgeNsdQC4DGk8FAD0ed7qoQSnNcZO+aA+EyfUxCEpWnOe/8HPvABfu/3fg/XdXnjG98IwI9+9CP+4A/+gI985CMnfICLmWkaOH37kLf/4tDEZBNSMgtGATnbirJiPaHvYTz4efzhXpTWlSRu+rAIpguCABx7lpemacAQbW3L6B2uMGpaSHLIqClTdiSyso+m64ShTBCAoshEojGCwGN8bBRVVWlpyU+52BVOT+I4LgiCsLTE4wk8zyPwfQI/IAhqN80lz0UNHDTfx5lWri0eSLxr7Vl0rljZOL+YHBCfy/nA5POR413HYtbe3n5YlvfFF188b9sbGBiYMfjd2trKwMDArMtde+21/MZv/AZdXV3s3buXe++9lze+8Y0888wzRCKRGZexbbt202ZCqVSacT7h1De5weh0uYTCzn6LTctr2eHdww6P7zKo2CEjFY9NK2Ls7LfIxBTWt0Y4WHDpG3fpHXNRZYnWtMq4FxLXZcZMn1U5ncd2G7SlNRw/QC+8jFzqZjhzERtlle0HLQbGXQpVj3RM5rHdBmMVDySJVKRWD70eNO8edhoB84Lh8dALJfrGXaKaRCqqTPlM9fknZ9FPNpeGrIIgLF5zjsb+wR/8AWNjY9x+++04Tq0+VDQa5aMf/Sj33HPPCR/gYhaLRHB/+S0wy4cmOlXCmz9BIhoh0nUmsh7FHxvAH+4FwB/qISiOoDSLR+oEQTjyRe3kLC/TNEgmUti2heqVyegBI4ZESzwgpdeeiPE9D12P4HkekkQjO12Xa+8fKWgvnD7EcVwQBGFpqR+zHdfB9w+VcZNCnxXVKlcOj/DDZe2EE+VdVM/jT5ouoimWxHUdYOoTaXN9Mm3y+Ug8znGtY7G66qqrjlgq78c//vExr+vjH/8499133xHneeqppwBm3GYYhkccy7ve9a7G35s2beLCCy+kq6uL//iP/+Dmm2+ecZn777//qGMS5t/0APHRAsYzLZtLKOwddZDCWoPREcNnbV6naPr8vyfHac9otXrpcY/+gkNclwkJOH9llBHDZ1WLxr5Rl03Lo5y7MsYPXyohyxASElVlzmhTieoKv3lJlsd2G7heCCqs1vvZVuzhafdCpEqKKyTYvDzKrkGbS9Yl6MrrrG7RefFAlb3DDmXb59yVtcD5M/tMBgoulapPMqZQtnxUWSKbUFjXFjksID5b+Zq6I91MEARh6Zjzr4AkSXzqU5/i3nvv5eWXXyYWi7F+/fpZ70ILs5PLY0g9Owgnna+EQNVx0VZuIKbX7ujKmRxK60r8oR6U1pXImdzCDFgQhFPOTBe1MwW869loftVEVSSu3+BQqAYktQBlIgktmKiXmkgk8H0fSZZJxBMkU2nS6YzIIlskxHFcEARhaVIVDVmW8X0fAh/ZtdHCkD9/6SV+a98+PnvGGQxFE6xs6uTM/Gpi8fiU84vpZViOx4lYx2Jy3nnnTXntui7btm3jxRdf5NZbb53Tun73d3+Xd7/73UecZ9WqVbzwwgsMDg4e9t7w8DBtbW3HvL1ly5bR1dXFrl27Zp3nnnvu4e677268LpVKdHZ2HvM2hLmZLUA+PUB8tIDxZN3DDt2DNj8ctlFkiagus3vQpuqFVKo+u4Zsdg3a9I45vGFjku5hh33DDqYT0JxQea7Hoj2j8e/PlzCsgL4xh4vWJNi8PMreIYdMXKYzp9Oe0bhodRwngL5xh0w05NpNaVT1DH7Sk6DYG7BlImj+2G6Ds5ZFaU2rJKMKmbhCe5PO3lGXfcMOyWiVVFQhl1AAGBh32H7QYk1OZ+1EAF1knAuCcLyO+3ZaMpnkoosuOpFjWZIkJMJw6ut4PDHlpFVSVBI3fVjUSBcE4TAzXZDOFPCuB9QD3yeRTKEoKrJUIggkwvqPkCShaiqyrKDrERRFId/ajqZpmKYxJSgvnP7EcVwQBGFpqGWVg6qpjYxj1SwhBT4AWhhyZqXC/3nueX71tt8nn8rTtaKLdDojnkCbZ5/73OdmnP7xj3+cSqUyp3XlcjlyuaMnXF166aUUi0V+9atfNUrIPPnkkxSLRS677LJj3t7o6Ci9vb0zNjCvi0Qi4kb9SXSkhqBH+veRrM3r7OyvBcPdIGR9a4SBkssre0zCMCQdVTBsn2WZCE92G7zSb9OZ0zhvZYwdfRbpqMLzvVWqjs9gyadi+0QjCiU7pCWpUHVDlmdVHD+kOaEwZvhIgUXY+yTPy530cAavDkvENJlsXOWx3QZF02eo5DJQUmmb6KuwNq8zMO7QX/IwrYDhosdzlk9HRmPPiIPtBsSjMltWxWf9rNmEytqJ73HtxOu6uWTxC4KwuMlHn0WYL0pzO9LKjTBRpxAAPUY233bYSaukqLX5RRBdEISjiMcTRKLRGbPUHdehWBinXC7i+z5hGKKqGpFolHQqjSwpeL6HZdlUrSqjo8OUSkVsy8J1HTKZJnFRLbwmX/ziF1m9ejXRaJQtW7bw85//fKGHJAiCsOh4fshQycXzw9rx33EZNUKqVi2o7sXThJOuK0Kg/80fpCPdRiqWpKUlL473C+i9730vDzzwwLys+8wzz+TXfu3X+OAHP8gTTzzBE088wQc/+EGuv/76KY1GN27cyLe//W0AKpUKv//7v8/jjz/Ovn37ePTRR3nrW99KLpebt6aowtytzeu0Z7UZG4JuWRVvBICnvz6SbELlunPSbOqM8eazUiRjCkldRlclbDfEDUI2LY+hqjLP91TpHrZ5Zm+VUjVgVS6C44ec0xnjrOUx3rgxyVs2pVmT0zmzPUJLUmFZVuPFAzbP91j8/S9GGR8fJzX0c/R4liH9DMZNn2xc5oyOKF15HU2WKFV9FFmiZ8ShZPmN4HZ7k86yjEY8KuMGIQMFl289XWD3gE02oXJeZ+yon7d+M6J72Dmm6YIgLD0iKruAvCCgGm9BA+rVXeT8CpSmY3+kThAEYXopl3qWuus6jI4O43kejmMT+AFVs4rjWFOW9zy39qh3EKBrOrZdRVE1CMF1HJghMC8Ix+Mb3/gGv/d7v8cXv/hFLr/8cr785S9z7bXXsmPHDlauXLnQwxMEQVgUPD/kgZ+N0ldw6chqvGNLhH980mD/sElTJMa166oosoIXS1NacwGR8UGs1pX4bWsBWNaxfIE/gfD4448TjUbnbf3/9E//xB133ME111wDwA033MAXvvCFKfPs3LmTYrEIgKIobN++nb//+7+nUCiwbNkyrrrqKr7xjW+QSqXmbZzC3DKhT1QN75m2WbZ8tu2rlYh0g5DL1yeJR2Va4goPPltEkULWLYtQ9UKSEZlx06cprnD2iihVO2S47CJNRD26h21Kps8Z7VEyCZX+gstz+0wGyyEDrz5FrmMNuZWbqTgBTXGNC9fEG0Hw7mGHi9fEeWRHmagms7410qj7Xqn6JKIy53XGOK8zxr8+XWC45KFrUmO+o5ktW1+UfREEoU4E0hdI6HuU972CMt5zaKIkk/zAn4usc0EQ5mS22uWmaVApl6hWqwBIsoyqKnieTBAEU9YRArIkU6mUUBSV9ERtdEA82i2cMJ/97Gf57d/+bT7wgQ8A8Fd/9Vf84Ac/4Etf+hL333//Ao9OEARhcRgzPPomyjscGHd4qtvl4LhL4PuMmjJlRyIbDUGSGLn8PSjVEn4sDbLC8uWdJ6wUh+eHjBke6ajCuOkRhtCcUClZ/mHT6n+3pjVURWosP1x2D5s+ed3NCXXK9NPN9CadYRjS39/P008/zb333jtv221ubuYf//EfjzhPOKn+aCwW4wc/+MG8jUeY3VzqmZ+o8iMz1VR/sbeK5YX4fsjqfIS2rMaWVXF++nKZStUHCS5Iaay+IIJR9UlGFUIJhooeT3QbDBZcqm7A7oxGRJEaN/re3BFlWVqlSSnTmmsil3wTF6xO0z3sYDhuYzt19e/gHRcqjc9aH3PFDmjPao3A+rq2CB0TGernHkM2Osx+M0I0GhUEoU78EiyA0PcwvvVXhM/9BKVcgGQLxBMk33cfenP7Qg9PEITTzEwNR+uvPc/D930c10FVZFxJQlU1PM8jmKiNKskysiRhWVXCMERR5Cm10SebnP0uCHPhOA7PPPMMf/iHfzhl+jXXXMMvf/nLGZexbRvbthuvS6XSvI5xupka9x5p3lKplrmnaRqVSplstpl4PNFYj6bVnhSp7z9jY6OMDA9iVqs0ZZvoWrVG3LQSBGFG9d8Yz/NQVZV0OkO5XOLggf1Ylt04pgP4AfjlGCOGzEhVYWjAZ9RSyMUglwhI6ZMaNMkKfqKp8fLgwV442Puaxxsi8x+vRhmtKoxZGnZQu+xMx1Xa0jKDRZ+i6SFJEpm4SqlaG/8V63Rue9MyNE3nbx8d4Sev1OqEX7UxyQfekENVpMMy7t//+pbTNpieyWSmvJZlmQ0bNvCJT3yikS0uLG3HkgldD6BXqj4Vu5YsczxB3/p66k06J2+7bMWQQmhOKGw/aDXm2TfisG/E5ryVMcIw5MUDFm0plaLlsnl5lN2DNsmIjJdSkFBoTavoWq3CsK7CviEHubSbSPEVLrvwbXSPa2zrrbK6Rad7wOLft5noMmxeGZ8yvhHDn3LDYPJYC4bHQy+U0GSp0ZBUEAThRBGB9Hk200W4PzaAt38HoVGEwAWrjL75MiIrNy7waAVBOB3N1HC0Pr2lJQ9ApVzC9VyCMMTzXILg0EV0WM9Ol0CRVZKpDPF4gmJx/LBM98nZ74IwFyMjI/i+T1vb1PJlbW1tDAwMzLjM/fffz3333XfY9AcffJB4fPZmUSdKEPiEQYgkS8jykS/CgsDH9w8FssIQZLl246q+npAQCQlJrgV8XNed9HSIxNPPPHfU7QiCsDTVf2PCMESSJBRFadwsn8k6oCsiYci1YgphAhJ6iBqEvPKqhCKFOI7DKzu752W8fiiRtyVygKNNCnL7oJcl8kEI0UPTJB2QQO2T+NaDCoos41V8LlZqv5HeXpl/GVZQZAk/CHENnzzgjsG/DNamn2zvfOc7X/M6vva1r52AkQiL2bFkQtczyBNRecYa6ceqvp6y5ZOaFnxORRXW5nW29VYZq3jsHXXIxBV+ubtCxQownJBETKFgmDzRbbA8q1G1A6puQNUJWN8eRQIsL6QppnBmR5SkLtO3+1nKhX10nvNGBq0o2w8aEIakogp7Rxx6Rx0eeqHE5pXxxvh29ltkYrXxbZlc933i72f2mahyrQRNKHHSM/oFQVjcxK/DPJscdEonZfyxAcwf/jP+2CC4DkgSyArRN76boDiCnMmJ0i6CIJxQ6XQGVVUZHRnGmxK4mywknc4CIc3NLcDMme6apmMYFZIia1Y4TpI0NdhRDwrN5J577uHuu+9uvC6VSnR2dnLzzTeTTqfndZwwvxnpruvyysvbcd3axV0sFuPMs84RGemCIMxoLhnpdX4A/7k7xqgp0xIPuGBNlYf3HHq9Tn+RjRvWzst4p2Sku9My0lNHzkh/+6SM9J/XM9LXJ/mNWTLSf+M0zkgXhBNhcjb2XALAkwPHUKuDnozIEHJYaZf6aykEJAkprAXes3GFflVmQ3uE8zpjPLnbIJ9UMO0A0w0omrWyL1IIq1p1BgseqiJRqgasjJvsKw6w5oJruGhDKwCVqk8oQS6hsCYfwfJCrjsnPeVzbloebWSkT/8823qrmFZAa0adUlf9WG4uzKWMzlyIAL0gLC5iL55n9QBULBLBePDz+Ad3448cRMotJ6wUQNUgGqf6yD9BtYTSupLETR8WwXRBEI7L9NIrky+6Y7EYpXIRyZMJw6nBdEVR8TyHTDrbqIk5U6a76zqoiorrio71wtzkcjkURTks+3xoaOiwLPW6SCRywmr1Ho/ZnvaYbd76EyDAxI2pmdZT2zf7DvY2guiqqtHevlwE0QVBmNX03xiA5uYczc25GeevP1X2e2dGeOr5nVhOwEtDKr3jMrIMI4bMmkklg+PxOJvP2XJCx3zhRSHjhkdqoh46SDTFlUa26+Rp9b/zqUM1zz/whhw3bskcNl1VJN7/+hbGDY+m07xGuu/7fO5zn+Ob3/wmPT09OM7U86uxsbEFGplwOjne+t2TA8cAhhU0MtonB5/r/84lavvvmpzeCHavb4+ST6l05WvNPN+9tYlHdpSpOgHpqEJMl1mW0UjHFFIRhWS7wot7i7h2mf7xJgaUyyiPqlw08TmuPDPVKM3ieiErshqjpk/B8KZ8zq58LUD9zD6zEaCu13JHkrh0XaIRtD7W72a+GorOV4BeEISFIfbieVa/ePbHBvCHe2HiIlnyfch3glEAJPzubcjLVuEP9RAUR1BErXRBEI5D/SkYz/OwbQvTMLBti1gsTibbxLJlKxgfH8GxbcJQwvddNE1HkkDXo1StKpls86zrn60euyAcja7rbNmyhUceeYSbbrqpMf2RRx7hxhtvXMCRnXyu6zA0dOiGgqqqNE08CSIIgnC8Jj/9YlkWjmMTUVQe3qXxk/0almohOypJVaYz4yNNCqRnm078b5CqSOTTGgDL9EOBqaguHzZt8t+Tl1+WnTmgNXndp7P77ruPv/3bv+Xuu+/m3nvv5Y/+6I/Yt28f3/nOd/jYxz620MMTTjEnOrN5euC4bPm1xqET07qHHXKmz4jhk0soPLbbwPFCBosuq/O1pqK2ExDRZFa31NbRlY9wfpfPnkEbNwi58ZxaH4DuYYey5VMsltEHf46iZeiuptg16DBU9tncGeO8zlhjPlWW6C06KIrEi71VUlHlsCD09AD15FruxxMMn6+GovMVoBcEYWGIQPpJImdyKK0r8Qf3QzSO71hITpXQdZAitcYcuC7KivXImZkzSwRBEI7EdR08z0NR1Vog3aoSBAGarhMEAbFYnHg8QSqVZnR0GF2PEIvFqFTKRKMxxkZHkCSoVs0p5Sgml7WYS4auIEx39913c8stt3DhhRdy6aWX8pWvfIWenh4+9KEPLfTQTqrh4cEpJZaaW3IiG10QhNesfjPdMCq4joPrefxw2ygP75Vw1v6EatRCMVtIH7yIdCSk3i1FURTa2zuAuZW0El67f/qnf+Jv/uZv+PVf/3Xuu+8+3vOe97B27VrOOeccnnjiCe64446FHqJwCnktmc3Tg/CTG3fWp6eiCgMFt9G4dPtBCykMGSi57Bt2uWBVDMcLac9oeEFIf8nnpT6LIAjZ3BmjKx+hYHiULZ/WtMq5K2ONbVWqPuNjI4zt+gWdy1eSX7eF3JDLuOGRjCiNUjEDBZdkRKY1o5KMyIRhrfb6TEHo6QHqbELlDRtTr/FbPvHmK0AvCMLCEHvzSSIpKvr1/53yjicJ/+5PwK02Tl5Dy0Bq7SLxno+i5paLsi6CIBwX0zTwPY9ItNa9S5YVUqk4VctE1yJUqybVqolpGkhI+L6HqmokEslaAF5RcD13yvqmNxsVhNfiXe96F6Ojo3ziE5+gv7+fTZs28dBDD9HV1bXQQztpSqUCfQd7G6/j8UQjgCUIgvBa1J8WS2o6xWKRBx4f599fcGla9x32NskggaebBCNnsiwVRZ5YLpevldcqFsdrDUw9DxDH/pNhYGCAzZs3A5BMJikWa702rr/+eu69996FHJpwCjqezOZ6wLxS9anYtZv4k+ueT27cmUso7Oy32LQ8Sn/BoX/cIZtQ2D1oU3VCesdc7nxzvlGfvHfUYVtPlZZELRAOtWB4vURMPWu+e9hh+0ELZWQPK7s28mtvuBBJkljX6tGaVgklOLfz0CMy9ex3TZZY0xZhy6qZG8yLALUgCAtB/OqcRFXbxrccZLc69Q1ZQUo3IWsREUQXBOG4zVR2pTA+imkaBLEAx3XpHSqR1Dzi8TjZTIZCYQxVUQkBVdOIJ5Kk07VHMEVjUWE+3H777dx+++0LPYwFc/DgAXz/UFPAtes2iKxPQRBOiMlPje0ZKvNozyjJNf9Bd7NyaCbV47ozD/Dm1uXs2lWblE5nGB4eREJCj0SIRKOihNtJsmLFCvr7+1m5ciXr1q3j4Ycf5oILLuCpp55a0B4hwqnpeALH9YB5Iio36p/DzI07u4cdMjGFEcMnFVFoSqpEFYkrN6R4rqfKuZ1RMnGFTFxpBOdff0aSklU7rykYHmvzOgPjDs/tN8klFLryEbxCL1EpSXLVeSQzOkXTbwTZkxPZ5pPrmT+zz0SVJdwgnHLTQDTtFAThVCB+fU6C0PfwxwbQ7SouPsgKBD4hIKk6pJvRVpwhSroIgvCazFR2pVwpE/g+fmDyb6+oDBZlcnGVt5/nUbWqZLPNKIqC53lIQCQabQT1pjYWFRfUgnAi2JbZ+FvXIyJYJQjCCecEHp/e+wP2rtxLEJEPe7+X/Sjy8sbrQmGMWDRGSEg6nRE3906im266iR/96Eds3bqVO++8k/e85z189atfpaenh7vuumuhhyecBo4WXJ4eOO8edlhLLSi/tv56Ytm1E8vkEgqVqk82rpCJKrhByOYVUcp2yPM9VQ4WXFRZojWj0ppRKVd9Bku1cWxZFadoBQwUXH6xq8Jo70ts3/Y8zetfjxXGqNhBbb5JWfEwtVTN5DHXP1O9AakmS4fNLwiCcDKJX595FvoelX/9K5ztPyM0SqDHCFUdyXORJAn5rEtI3fRh1PwKkY0uCMJxm9xgzHUd4vEEnuehKiq+JFENoowaAYoC445CyfKQwgqyrLBq1VpM06BQGJuSfS4aiwrCieW6DrZtN17HYjM/qiwIgnA86ucCIzi8XDxAEHUB6bD5rk+ub/ydSCTI5VoJw1DURV8An/zkJxt/v+Md76Czs5PHHnuMdevWccMNNyzgyITTxdHqpk/OYn9mn9mYdy00AtOVqk8yppBL1J5e2TvqYNgBy9Iq/SWPTFRmVV4nFVEIJdBkiaLl05aurTcdqwXbcwmFZ/aZbF4eJQwDksUXeH7XAba+/tdw1Ay5hNLIfodaoLxS9SlbPgXDawTNZ8q87x52ZsxSFwRBONlE5HaeBcUR/P5usExwHZBkJD0GioocTyEHHrIeFUF0QRBeE9M0MA0DwxhEnsgwr9M0nWwizrKsxZilktEsIjj4/qGLa5F9Lgjzb3Cgb8rrWCw2y5yCIAjHrh5Aty2LatXEURT6A/PwGHoQ8Omd+1ix9RLqBaay2RbS6exJHrEwm61bt7J169aFHoZwGplL3fTJ804OTFesWmPRmCrRntUgDOkveVSdgOGSx1DJ44bzM1x5ZqqWAR91KFs+FSsgJMQNQi5fl2DE8OketClXfVa1SAz2V2ne+CYcNdOoc96VPzSebEIlGTvU4PRIWeYzZamDKPciCMLJJ35p5pmcyaF0rMMfOQieSxiJI62/EM23CW0DtW2VKOkiCMKc1S+a69ninufh+bXguefWMk1UVQVJwv7/27vz+KjKe3/gnzNn9klmJsuQBUgCQURkU7CItrK4gFJFbVVaa/Vaube1ttdqr8qvKthq1RarrW3droK91VutVluvG0qDWgQFBERBJGwJhJB9JrOfc+b5/THMkEkm+0wmy+f9euWVzJmzPOfM5Dwz3/M93ycUgoi04OJT9PCEIjBGAhARDZJkgN3uwLFjRxEI+GG1WBOyzznYKFHqKEoY9fXH4o9lWQ/XqMIMtoiIhovoxXQvWltbEVBDeKnmcNL55tYewyxhRLPFHp/mdjejoLCImegZcv/996OgoADXX399wvRnnnkG9fX1uP322zPUMhqs2geOe1M3ve285QC8AQ1CAo65FRxtDmNaiQWFTgO8AQ3BRgUhReCYW0VrUMPnNQHYTDoICRiXF80kt5l1kAQgQYpnmm+rbMKxI/tR2zIBBflfwWi7qcsgf+y5WDZ7bL96up/dZeQTEaUazzRpJsl62C79IYzTzoGihBAyWCFvfRuRulrIecWwXvIDZqMTUa+1DXIDgKaqsFpt8UC4xWJFIOCHwWBERFMRCocgQYErOxtANny+VpjMFrR63PD6WqMDjBmNCV+kWdqFKHWO1R5FOByOP3a5RvF/i4j6TVHCxy+ma1Ch4cEjm7C1tQloWxpdCPz8009xflMrai5fHh2vKfYUAI/HDb1ez9IuGfDEE0/g+eef7zD91FNPxdKlSxlIpw5SFThumw3uDURQlGNEljF64ijLj54HhASU5hmwvSoAq0GHnUeCgBCoaVbgsMjxwUu3VweiJVo8bkhH3odZy0ZjSIXdbEGWRe5RpvjBhjC8oUh8v3q6n73JyCciSgVGcFOobYZo7EOo0FT4X/09wp9+AEQ06CecDnX/diAchGg8ioi7ATrXmMw2nIiGnGRB7tjffr8PgYAfmqoiOzsbAKALK4AkIMsy9AYDIpEIFCUMTdNgNpkhBOB05iZsI9ngpUTUe3V1tThypCr+2GQyMRudiFLC7/fFL6bv9zVjs/8YAjrl+LMCACAJgUleH1onfQ0wZwEA9AZTdLwmKRo4i12ct1rR4fsMpU9tbS2Kioo6THe5XDh69GgGWkSZ0JvyJKkMHMfWMWW0GQ0+Dd6AFg9eTy+xYF99GHlWGUoEKMszYHdNCAVOA6aOtsQz0J02PbLNMqoO12Lfpg9gLxiHgGESvuoyoeB4oL0rsYC5gIAnEMGU0eZe7WdvMvKJiFKBZ5wUSlYGQWuqhXpoNyKeRiASQWTPxycy0CUAUscBgIiIutM+yB372+1uRigYhKzXw2Q2Q1ZVKOEwDAYDIpqGUDiESESDTqeDxWJFVlY2TGYzvzATpdGRw1UJj3Ny8piNTkQpETuXGE1WrP/YhFAk0qE2ujj+faN10pzjUyRk2azQ6WSMGVsCi8UaD56zrNvAig0uOm7cuITpGzZsQHFxcYZaRQOtN1nmqQwct12Xw6pie3UAWSZdvIb6/mMh7K8PYbzLhN01IQRUgWyTjFKXCQ7r8eA/omVZPvEEYS+ajK/MnJYQZI9p8UXXL4lokD6237EBTr0BLV4iptTFADkRDV48M6VQ+wxRoakI/PMv0JpqAU0FZD0gG2A4eRZE8zHIxeWQcwoy2WQiGmbanocMBuPxwUOjVFWF3+eF3mBAXp4LihJmAJ1oAAgRif+t1xuYjU5EfZLs7leDwQirFdh/1I0de2WYnaPgzz4aDaZHE9Jh1CI4eu4yjLE6AACSTodgMAhZluMDjcaC5tboeIC82DdAbrjhBtx8881QFAULFiwAAKxbtw633XYbbr311gy3jgZKusqTHKoPYUOlD2dPsKHUZerwfNtM+H31YfiCERQ6DXDa9CgHsOdoEAUOA9xBDQElgpAiII5fqNtRFcDmg358vPUg9GYb3JILIb0LDT4tPrBo2220BjV8Vh0AJAlZlmjwPH7xoMya0Jb2beMgokQ0mPCMlELtM0Qj7gZEGo9AHl0OFYAupwCGslNhu/SHEN4W6Bz5rI9ORCnV9jwU+8JttzviQXWz2Rz/22AwHv9CDgbTidKkrq4WiqLEH48qKGSAioj6pLNscY/HjZbmJighFYWHz8TR0goErM3x5yeZ8lHgPFFKUoJAJBKJl3lr+xmAZd0G1m233YampibceOON8XE0zGYzbr/9dixfvjzDraOB0p/s664CzhsqfdhfFwKApIH0tpnwyQb9vGiaHfvqw/AGNNR5VCgRgRljo9nkEQiI+s+AYBW8rtlw5so4aZSpw6ChsW1kmXQocxlxrEVFvk2GwyonbLf9MeAgokQ0WPGMlEY6Rz7kUSXQ6qpgPm0BTOd8A5KshyTroctlNhoRpYaihOHxuKGqKvR6fTxw3vYLd9uapx6PG95WDwSiCWuqqiIvz5XRfSAajhQljMPVh+KPo5mjDKITUd+0vess1vdrqgp/wA+95oVJ1qHe9Ql0ujCcigqDECgPqfhx0SwYpBOjj8qyDKPJCEmS4Pf7GDjPIEmS8OCDD+Kuu+7C7t27YbFYcNJJJ8Fk6hj0JEpme3UAn1UH0Bq0YN6k6NhIseD61OP1xs+ekPyzR9tM+Fgge+tB//EBSDVkWeT4PLEyLPvqwxgXiSBcvRl5ohZ5U8+DI8eJGWMtcLZZHogGwNtuY199uE35FhMHESWiIYmB9DSSZD1sl/0IEXcDpCwn/H//I7T6asijSmC77EfMRieifollnKuqCm+rB6FQCCaTCXq9Hg6HMeELt8fjhsfdAo/HDaPRhEhEg6JEA+9ElB41Rw7Hyyvp9XoUFhXDbndkuFVENNS0LenicORAUcKorz8Gv8+HQNAPCKDBE4I1SwWsboRlBaqkx0m+IO52ywhMODGYuMlkhtVmQ3a2HUJEIHG8pkEhKysLZ5xxRqabQUOQJABIUvT3cbFs7kKnAd+ek5swf/sM9vbB7HKXEa1BDXuPhWA3R7PGZ5ZZE4LsQggUFbow7fQzUOPVJ2TDtw+At91GeZttdIc10olosOKZKc0kWQ85txBq/WGoR/YCBiO0uipE3A2QmZVORP0Qyyw3W6wwW6wQACwWazxTrX0d1VA4hHA4WhNV06I1mw1GYzywl2wZIuq7Fne0tIIkSRgztgwFBUUZbhERDUXtS7p4PG74vK3w+X2AiN5ZZtVryJFNMEWM8Bs8MEl6NDrycKDsNBTqosEwnU4HWdYBAvD5vBAC8Hpb43XSiWjomV5iScgcBxLLtKz/ohW+gIYss4zpJZZobfMDfmyzyfjmLGeHcjBOmx7ZZhkOswwlIhLWW2RTsG/7hxj7ta+hYNy0eGC9Nagh2yx3Gpxvu24Gx4loqONZbAAITUVg3fOI1FVD6HQwTJwFKcuZ6WYR0TCh1+uh1+shATAdr4HudjcnfOm22x3w+31QwmFomgZI0cC5w5gTD5p3VnuViPrGYrYgFAzAbDYjO9ue6eYQ0RDV9g4zAPFyLhACkqSDFgHe3G/CJss2qHIQuZoKp6JiXCSMvDxzfD0mswWybIBOp4PZZIEkAVlZ2RnZJyJKjdjAoPvqw8j3a2jwaSh3GTGzzIqtB/34rDqAo24VRU4DsiwyhAQ0+zUAAvvqw5hp06PFp2J7dQCSiAbm25d8AYCmpiZUrH0TY8aMgSvHGn8eALwBjfXMiWjE4FluAKj1hxHaVgGEAkBEQ6SxBv5/PMbyLkTUL3a7A3q9PqHmciyIHguOx54zGIwoLCyG3++DwWBEY2M9dDo5obRL+y/qRNR3Hk8LfH4vdDoddDr5eIkX/m8RUe+1HwBU1uths2ZB1RQEA0E0+yM4ElLgtXggRwScmoofH2vB1EAYLYVeaLYcmM0WmE1m6GQdTCYzBAQkSYIQoostE9FgFivT0hrU4AtGsO2QH8FwBLXNRhTmGJFvkzFlrAXj8qMZ6bHAtySA1pAGb0CLr+Oz6gAgSciyyPFSLjF1dXV44403MHXqVIw/eRq2VQUTss/blotpXzqmq8FQiYiGIp7J0iwSDsL3998DnkYA0Q+qkUiE5V2IqN/afrGO1WH2tnoQCPiRlW3vMIBobH5FCceD5W3rNbf/ok5EfVNXV4sD+ysBCOh0MpzOXKiqCkUJs2wSEfWbwWBAJBKBEg4jFArCbgJGm8zYo9oRNLhRpsqYGggD2S5olujdMJGIAKTjNdKtNoRDIQC8eE40lMVqoWeZdCh0GiAB2N8QxlGPCl9YYE9E4KJp9g4B7LmnZMfLssQGEYUABET07+NafCoq60Iocdrwta99DeXl5R0GEwUSS7a0fz7WxrbzpxqD9UQ0kHiWSROhqdCaauF/42moX3yCaBBdAnQSJE2DPKoEOkd+pptJRMNErCxLKBz9YqyqKhob6wFEg+Vtg3d+vw+aqsbLwBBR6ihKGIerDyJ28VySgGy7HaFgEH6/jxeriKhP2o5j4na3wOttjV9El3XA1yeEcHboFIT1Abh0hmgmusUOHK+PLsu66KDHhcUAop8FZFnPzwFEQ1j7gT1bgxqmjjajLN+IDZU+GHRSvHwLkBhwLncZ4Q1oaA1GM9OhkyAJgQafhlIXIITAh5u3o7nFA2nGmZhZHh0qNN8mY8/RIKaMNveoTe1/p8NABOuJiGJ4lkkDoanw/e130I5UQm04DGjRkzokCXLReGR99y7oXWNY1oWIUiaWUZaVbYeihKGqKtwtzVBVFQASstMNBiN8Pi+y+OWZKOU8Hjc07USphKysbJZNIqJ+azuOiV6WgXalWWQdkG+RAERrF2u2nPiyOp0MQILTmRsPnDscRkiSNJC7QEQpFssEP1Qfwl8+akaBw4CpYy0odZngsMrxoHlM24BzucuIIy0KDDoJFpMEi15CgdOAcpcRTa1hvL9hI1qOHULh5LnxEjBOmx4NPg0Oi4yDDeF4Pfa2WeDtBxRN9wCjLT4VrUENWSZdWoP1REQxukw3oCcOHjyI733vexg3bhwsFgvKy8uxYsUKhMPhTDctKa2pFtqRSkR0OohwCJANgN0FqewUZN1wPwyFZQyiE1FKRcuy5MBqtcHhyIHd7oBOlqGqChoa6uH3++LzKkoYelkfz2QjotTxtrYiEolewJJlGcWjx8b/P5n5SUR9ZbXa4neSqZoGs8UKAT2aA9HBRjsTG5A8IiI4fPgQPJ6WAWsz0XDS4lOx9aAfLT510LVjQ6UPvlAEx9xKPJjstOkxs8yaEOQudxlR6DQg3ybjjU89CKsCSkRAEkBAiZ5I9tWHsbbifTTV1WDiGYvgcrngDUWwrz6cELQWEuKlYbprXzqPw46qAHzBCLIsMsu6ENGAGBJnmi+++AKRSARPPPEEJkyYgM8++wzLli2Dz+fDqlWrMt28BEJTEVj3PNRjhyACXgizDZI9C7r8QhhyilgTnYgGhMFghNFoRFNjAJIUQmNjfYesWGbHEqVeY2ND/G+j0QRFUTLYGiIablpbPVDCYShKBG9VmnGsVSDPquHC8V6Ygh6o1hPlXIxGI3Q6GTqdDuFwCEIno6GhDna7E0C0dIPb3Qyr1cYLfUTd6En5kEP1IWyo9OHsCTaUuky9Wn9P63zH2uENaMiyRAcQPXtC9DP92RNsCcu2X6fTpkc5gDc+9UBRBYx6CRdNs2NHVXSg0drmMCRIcBZOwLhJM3Hy6Oz4uspdRuyrD8MXjKDweOZ6+4z3nh6nVIhtx2bWxdtDRDQQhkQgfdGiRVi0aFH88fjx47Fnzx489thjgy6QrjXVQvlsA0Q4CGgqpLxiyCYLZEs2Iu4G+P/xGGyX/YgZ6USUUm1rp8a+DGuaBp1OD0gCJuOJD/McVJQoPY4ePQxFCcUfm0y9+xJNRAREy7i0tDTB6cyNX/T2+33w+7xobW2Fqqpo8KvYHwjAAguafQKOza8hy98Axe5C48yvAzoZiqJAlgUcDicsVis0LYL8/FEAop8bNE2F3xe9Y42fC4i61pNa3xsqfdhfF/0c0NtA+r76MPYdC2HP0WDSAUIBJM0IB4CZZdak22sf1G7xqXjjUw/cfg2egIals3PgtOkxvcQCgwjgwI4KZOWciemnjknMZD++rthApLHAfLJA+UDURG+/HWaiE9FAGrJnHLfbjdzc3C7nCYVCCIVOfKH1eDzpbtYJOh2gN0CvN0DOLYDmrock6aDVVSHibmBmOhGlVKx2qqqq0Ov1sFpt8broRqMJObl5GW4h0fB35HB1/G+j0YR8VwHsdkcGW0REQ1FLS1M8wN32LjKfzwshImgN+PC0Zxs+z/HBErZjibcUNl8DIAEGTz3kgAeaLQdCCOh0EvQGAyQAOTn2eDZ6rOSbgOAdakQ90JNa320zw3ur3GXEnqPBLgcI3V4dwGfVAUwZa8GMsZakGeFttR8YdF99GAadBE9Aw3iXKT6wqOJvxr6tb2P8+PGYM20sdLrECsAJAfkya5f7ke6a6AO9HSKi9obkmWffvn149NFH8dBDD3U53/3334977rlngFoVJecWwjDtHKhH9kLnGAXz2ZdAXzIJgdefglZXBXlUCXSO/AFtExENX7FM9FgWuqqq8cHIYjXTiSj9/H4fNO1EPVCrLQt2u4PlEoio15zO3ITfQPRuMrPZgmO1NahXfDiitMJhAjRjCyYVj4UadMHgqYdid0Gz2I8vJcFmy4bRaIS3tRUGYzBeysVqtUGn08HlKuB5iihFSl2mXmeixzhtelw0zZ4QHG87iCgASAKAJEESJwLJsVrhyTKzYwODxgLm5S4jWoMarEYJWeZoWRiv14v/+7//w+mnn45p06bB7dewrz6YsL6eZJn3tDQNEdFQl9Ez3MqVK7sNdG/evBmzZs2KP66pqcGiRYtwxRVX4IYbbuhy2eXLl+OWW26JP/Z4PBg7dmz/Gt0NSdbDdukP4f3fB6B8sRm+/TtgmHYObJf+EMLbAp0jn2VdiChlYpnoQDRw3rbES0yysi9ElDqKEkbNkWroDQZoqgqTyQSb1Qq/38dyCUTUa7FAd4yihOHxuHGs9iiEAPJkM8bos3BY9aLMkAWX0YbGmV+PZqJb7JBk/fFsdB30ej1MJjOCwSD8Ph8kSACinxl0OpmfC4gGkfZZ1m0HEb309OgdbkICfMEI1n/RGs9K76wmeSzwnW+T48H2bLMcH5zTaojAaMzCJZdcgry86N2rsfW1BjVkHw+29yT7e6BqoxMRZVpGz3A33XQTli5d2uU8ZWVl8b9ramowf/58zJkzB08++WS36zeZTBmpTxpxN0DdswWitRlCp4N2pBLC28JyLkSUcgaDET6fF1nHvwjH6p8rSjiedRarq+rzeeOZZwyuE6WOx+OG3+9HRNNgtzuRm5sH+XiJJSKi/vL7ffC2eqCTdTAY9AAEfuCYgkYtiDzZDIOkAyRAs+XAZDJDbzDA7/MhIiJQVQ12uwOhUBARTWMpF6IB1N8s7ViJmKmjzfH1ZJtlfHY4CAiBbLPcoXxLW7EA+NaD/niQu9xlhBACwaOf47WtVbj88svjQfTY8wDgDWgdAuNd7U+yrHVmqRPRcJTRs1l+fj7y83tW5uTIkSOYP38+Zs6cidWrV3eo2zXYCJ0uWicdgC6viOVciCgtFCUMvayHooQBnPhi7PG44W31IBgMQq/XQ9U06GV9PEM2MZOdgXSi/vC4WxAI+KJjoRwv76LX63mRiohSwmAwQgCAEDAaTfD7AzBIOhTqO9YqNhqNKCwajeqqA1BVFYoaHS/K5SrgBXSiAdbfLO1YqZj2gXBvQIOQon/vqw/Hy7c4rMkD122D3HaLDr6qLaiqPoxx0+fD7dcS5m1bMqZ9DfauBkRNlrXel/1n8J2IBrshcWaqqanBvHnzUFJSglWrVqG+vj7+XGHh4Mvy1jnyYZg4C1rjEch5o5G19DaWcyGitGg7CFlMLNtcVRW0etyw2bJgtdrig5B2thwR9Z7f70NDQwMAQIgILBYrTGYz/7eIKGUCAT/C4RAikcjxC+ei03k1TUMwGMDYknGoOVIFi9l2/CJ6Di+cE/VCKgK6qaot3nY9Tpsec0/JPvEcgNagBm9Aw46qALyhCIDOA9effPIJ6uvrMWHWQjQFDQkDm7aVLDDe2YConenJ/rfHEjFENNgNiTPT2rVrUVlZicrKSowZMybhOSE6/yCZCUJT4f/7HxFpbYTeNRa2K38KnbHjbVZERF3paemVWCmXtvx+XzRLXaeDyWCEgOgw6GGy5Yio9w5XVyESiWahy7IeBqMRDkdOhltFRENNsn7/xIVxFQa9AUajCaqqIBxWEFHD0Ps9UK12QCcDAHQ6GTqdDvLxGul5eS4I8KI5UV+kIqCbqtriXa0nFnzfeTiAMpcRRU5jh8D1vvow9h5244sjelxw6mRMmzYNfkWHffXhhPrpXV0wiAX8z55gQ4NP69FyPdn/9voSfCciGkiDuz7Kcddddx2EEEl/BpuIuwFafTUkSYeIpxHC25LpJhHREBQrveL3++LTYnXPo9lo6HS64XjwPD9/FLLtDjidufD7fR2WI6L+UZQwvF53/LHTmYO8PFcGW0REQ1Wyfr+pqRG1R4/A522FzZaF7Gw7zCYLZEkgb8tryP/oZeRt/T8gokGnk2E2m+Fw5CAY8ENTVVhtWfGxUYiod8pdRhQ6DWkP6KZiO5IAIEnINsmYWWbtENh2mQJo3bsOouUQqlqiJaCcNj1mllnR4IvWQt9X3/X3hFjAv8Gn9Wq53oq1i2VdiGiw4tkpxXSOfMijSqDVVUEeVcLa6ETUJ8lKr0QHDfUlDBoam9623nmsbroQAg5HDtzuZtZDJ0oxRQmjtrYGer0BiqrCaDCwpAsR9Vmyfj8cDiEQDCIQCMDv90PTVCiKAsnTBENrtKSUwV0HU0M11IJxMJnNUDUV4VAIZouVd8cQ9UOybOp01O/uS9Z2+7ZML7EgyyInDcbX1taiYu3bOHniybAWn9Jhnq4GK22rfaY4M8eJaKRiID3FJFkP22U/QsTdAJ0jn7XRiahPkpVesVpt8Pm8iGgR1NcfiwfTrVYbVFWFqqrxW8Bl1kMnSiuPxw2PuxmqqsBqscJgiJZdICLqi/b9vqKEYTSaoJd1CIcVBAIBCKFBaCr0mgolOw8GTwPkgAe5O9+FWl2AyLnfhdFoRDgUgqapcLubObgoUQqlqn53VwH5ngbrE9pSZo23p+3y2WYJ//znP3HqtNMh5ZQj3yZHn8OJkjANPi0+WGlpFzfVtQ/49/UCABHRUDckSrsMNZKsh5xb2CGILjQVWlMthKZmqGVENJQZDEa4XAXQyTpIQPz2b4PBCL1eD01V0dhYD2+rJz499tvhyEn6RbqzcjFE1DVVVREKhSCEgMlkRlHxGOTm5mW6WUQ0TDQ1NaK5qRGSpIPBoIfFYoYsIVrSZfOrAIDmqechYnVAkmWYvE1wWY0wmczQGwzw+3zw+7wJpWKIqH9SVe4lFgRvWxalxadi60E/dlQFelQyJd8mwx2I1ipPtu6d+49BlmV885vfhJRTjtoWBRsqfR3WPVAlbIiIhgteQkwBoandZqALTYXvb7+DVl8NeVQJbJf9iNnqRNRrsWB6bECymNjfAkAw4O/x+tqXhSGi7ilKGIGAH3r98fEIXKNYQoGIUsrnbYXf74VO1sNqsSIrOxueGi/qgs0YJQGG1kZI9nxoOYXQt9QhklcAn2SA02JFKBRERItwoFGiFGufhd3XUi/JyqLEAuA2s67TwHbb7XWWST4+34DDX36C/Uf34ysTr4LJZEK5K5o/OWW0GQ0+LWHdzCwnIuodnjH7qacB8tggpACg1VUh4m6AnFs40M0lomEgWs4Fx4Pp0cexW8Kt1jD8/p7XaWbZF6LeO1Z7FE2N9ZBlGXa7E7Isd78QEVESihKOXxxve+eYTpah08nQSToYTSZEJAkPuHehuqwA4/wB3NqqR9iShcaZX4fsb4XscCEXOrS0NCErKxtebyuczlwAYIkXojTpa6mXZMHrtsH1zoLybbeXrLa5pmn45KP34W+sw6WXLoHJZOqwva7KtxARUfcYSO+nngbIOQgpEaVS20zyE0F1W9Ige1eS1WInoq61tDRBUaJl2qy2LF6IIqI+66w/HzWqEJGIBhGJQALQqAVRpXihWR3Ya87Gl+UzUKzTw2gyweTMg8s1CsFgABIAr7cVelkPRQlDUcK884woTVI54Gb74Pqh+hA2VPpw9gQbSl2mDtvbVx/ukJFeXV0Nj8eDSy+9FBaLpd9tIiKijhhI76dkAfJkmSUchJSIUqltJnnsS7iqqtDr9VBVFZoaDfIl+9LcWfYbEfWMrDdAkqK/zWYz/4+IqM+S9eexxw5HTrxvz8/KQpnJiX1aI8YYs5FvyIJO1qFsXDnsdieAaBA+lpEuhEhaAo6IUqe/ZVHalmoBkFAmZkOlD/vrQgAQD6S33V758XWUu4zwer0Ih8MoKytDSUkJdLrOh8LrazkaGlr4OhOlD/+jUsA89wpAkiDnFECS9fB7m5NmfsQGISUi6q+2meRWa3RaMBiEt9UDs8UKs7nz8i6si07Ud4oShk6ng8lkhtlsYXCKiPqlbX9uMCjw+bzIMhjh9/ugqWq8P/d43Lh9zFk40FKH0TYnhKIhKysbiqKgsbE+vj4JErzeVrhcBfGLfA6HMT64OC+iEw0ebUu1AEgoE3P2hOjni9jv9mJB9cbGRrz+1ls46aST8JWvfKXLIHr7bbI2+vDF15koffgf1Q/x+uh1VdA58mG78qeQZD1rDhPRgGibWR7NWot+kdbr9V0OfMhzFFHfeTxuSJBgMBpRVFTMgBQRpYyihKGX9QgcHzRc1uthMBhRX38MwWAQ4UAAo03ZsJks0FmjYzOoqhofZDwr2w4BAQnRi+ZtL5bzIjpR5rUv15KsNEzs71KXKZ6JnkyLT8W2Lw7h4KfvY/r06TjttNN61IZUlqOhwYuvM1H6MJDeDxF3A7S6KmhHD0A9tAsAkPWtO1hzmIgGRPsvxXa7A3q9vtsAOc9RRH0Tu3gViagwmy0QQmS6SUQ0TChKGMFgMB5ENxmNsNqyoChhRDQNEU2DxWKFqqlQlBDCPgUGgwHZ2XZkZdsBAHa7A4AjfpG9LV5EJ8q8WLmWZq+KnKxo1vnMsuitpS0+NT5fT8py7KsP42DlHugKpmP8ydMgSVKP2tDfcjQ0NPB1Jkof/mf1g86RD50jH+qhXZCMFmju+k4HGyUiSiW/34fm5iYAgNliid+u3VUmOhH1nd/vQ23tEciyHrasbFitNgakiChlPB43mpsboSphmC1WyLIMVVUhSRI8Hg/0ej2ysrOhaRoCfj+ECCESEdBUFTm5eTAYjF2OgcKL6ESZFyvT0uhVO9Q/76rMS1tCCOzduxelBSX4YuxsGGQdtlcHkG2WWQ+biGgA8CzbT+bzroGIRBBpbYS+oAw6R36mm0REI0BLSxMCfh9kWQ+vtxX64wMY80syUXocralGc3MTTCYTyidMYhCdiFJKVVWoigIhAAjAaDRBU1V4Wt0Ih0MIh0MwmkyQAAihQdbrIcsSAgE/TH4zHA4jy7cQDXKxci07q/x441MPpo42x7PP823Rck3JyrzERCIRVLz3Lxw6dBDnL8zD4ukO7KsPwxvQWA+biGiA8CzbR/H66PXVkPPHwHrh9ZBzCyHJPKRElH5OZy40TYPRaEJ2th2KEmZgjyiN/H5/NBP0+KC+/H8jolSSAOgNBugkHWy2LOj1emjHg+vRZyPweFpgNlkhywYYDCYYjLFBSmODj7N8C9FQEI4AU8ZYEI60GxTyeJkXoGNAXFVVrFu3DscaWpB3ynmoC1kw9nj5jralYIiIKL26HtKZOhVxN0CtOwQRDkKtr4Ik6xlEJ6IBY7XaMHZsGQoKiuIlJvx+HxQlnOmmEQ07fr8POp0OkqSDXpYRCocy3SQiGmZkvR4WswUOZw6sNhssFuvxbPQwIkKDEAJKWEEg6ENERCDLMmRZhl7Wo6WlCYoSPl6+JYeDIFO37rvvPpx11lmwWq1wOp09WkYIgZUrV6K4uBgWiwXz5s3D559/nt6GDhEtPhVbD/o71DmPTWv/fLnLiEKnAeUuY8LfXQkEAtDpdFh44dcxpsCZML/TpsfMMivLuhARDQAG0vvKkgXReBTa4b0QLY2QspyZbhERDQOKEobb3dzrgHjsdm6/35emlhGNXI2N9VBVJVqjOMuOvDxXpptERMNA2z7fbnfAmZOL7OMDhwYCfkS0CBQlDL2sh8Vihc1mg82aBYPeAJ/fC73eAAEBCWD/T70SDodxxRVX4Ac/+EGPl/nVr36F3/zmN/j973+PzZs3o7CwEOeffz5aW1vT2NKhIZZVvq8+nHTa9uoANu71Ynt1AEBi4LurIHiLT8WHu45h6/bPkJ2djfPPPx8FubZeB82TBfp783xvpHJdRESDES9Z9oHQVPhefAhaQw0AAeH3IOJphC5/dKabRkRDXG/rm8YGFmt/WzcRpYaihKGEwxBCwGgyIS/fxf8zIkqJxD4/B1YrUF9/DBIkGE0mWKxWABJ0OgmjCgrhdrdACYcRCAQQiUTg9XowdmxZfIBRop665557AABr1qzp0fxCCDzyyCP42c9+hssvvxwA8Oyzz6KgoADPP/88/uM//iNdTR0SYtnhyeqbl7uM2FEVACQJkkhcrsWnYnt1AJIAppdYEoLjLT4Vr206iOChDXCNHo/ThYAkSX1qX0L5mCQB+O6eT+W2iIiGOmak90HE3QCtqeb4AwGICKIjAxER9Y/VaoPJbO7xF+LYl3BFCfN2bhq0Dh48iO9973sYN24cLBYLysvLsWLFCoTDg78Ukd/vgyRJMFuscDicsNsdmW4SEQ0TVqsNsl4PVVWhKGE0NTXC425BKByC3e5AXp4LFqsF2XYHFEWBoihQVRXZdjusNhvy80dlehdohDhw4ABqa2txwQUXxKeZTCbMnTsXH374YafLhUIheDyehJ+Rom2m+fQSC+ZMsGF6iSVhnn31YXxWHcDOI8GEbHYA+Nf2Svj3vwfkTMS8r87pcxAdQLflY3paXiYV2yIiGup4ibAPdI586EdPRKThKCAiMEybCzm3MNPNIqJhIFrftOcfPNsPLBbLULdabQyq06DxxRdfIBKJ4IknnsCECRPw2WefYdmyZfD5fFi1alWmm9clg8EIvcEAmy0LObl5/L8iopTy+31Qjl9UDAT8UBQFZkv03BMd+0RBwO+Hw5kDh8MJALDbHfFzUWNjPbytHqiqyrJTlDa1tbUAgIKCgoTpBQUFOHToUKfL3X///fHs9+EsloXdGtSQbZZR7jImZJc7jw8K2l65y4jWoAWSQIfAc0QJQ3OdjkknT+h37fPOtt/T51O5LSKioY5nuD6QZD2yvvGf0OZfBUgS5JwCDjRKRBnRPvDe29IwRANh0aJFWLRoUfzx+PHjsWfPHjz22GODOpCuKGE0NtZDCYd5cYqIUi4WKA8GQ4g0N8JsssTPM7HBQyOaBkkCwuEQcnkxj7qwcuXKboPWmzdvxqxZs/q8jfZZ0aKbciPLly/HLbfcEn/s8XgwduzYPm9/sIoFwb0BLV7WpBzRAHv7oHpbTpse8yZlxx8LIbBt2zY484thLxyHUwuAsnwjth70J11Pi0/tdhvJ9HU5IiJiIL3PJFkPvWtMpptBRJSgfYY60WDldruRm5ub6WZ0SlHCqK8/hlAoiIgWyXRziGgYslptUFUVkUgjIloEAgK2rCwoigKPxw1VVaFpCoSQ4tPy8lwJd5/Z7Q7o9Xr2+4SbbroJS5cu7XKesrKyPq27sDB693VtbS2Kiori0+vq6jpkqbdlMplgMpn6tM10SnUgOZaFfag+hA2VPkwZbe51rfBIJIL3338fNTU1KJlWCJ8aQaHTgAaf1ul6+lqPfEdVtJyMN6Bh7inZ3S9ARERxDKQTEQ0jvS0NQ5QJ+/btw6OPPoqHHnqoy/lCoRBCoVD88UDWVvX7fdEAugAczhzWRieilIoFw6NlWgxoaKiDw+GEoijwtkbPdeFwCJGIgE6WIetODG3VfpBS9vsEAPn5+cjPz0/LuseNG4fCwkK88847OO200wAA4XAY7733Hh588MG0bDOd0jUgZoNPg0EnYUOlD2dPiF7c6kmtcFVVsXbtWgQCASxZsgQKTPFAf0yy9SQb5LQnFwmEBECI6G8iIuoVDjZKREREfbJy5UpIktTlz5YtWxKWqampwaJFi3DFFVfghhtu6HL9999/PxwOR/xnoG4HV5QwVFWNZofabDCbzTAYjFCUMNzuZijK4B8klYgGt1gw3O/3QQiBLFs2hBCw2x1w5uTGBxt1OHPgco2CwWiExWIFEL1ormoqy7xQn1VVVWH79u2oqqqCpmnYvn07tm/fDq/XG59n0qRJeOWVVwBES7rcfPPN+OUvf4lXXnkFn332Ga677jpYrVZ8+9vfztRu9FkqB8Rs8anYetCPFp+KcpcRSkRAr5PQ4NPig422nScZnU6HoqIiXHzxxbDZbAkDlbb9uz2nTY9ylxH76sPxdccuErQfvLStGWMtmHNSFmaMtXQ6DxERJceM9D4SmoqIuwE6Rz7roxNRyrW9bRsABxClQam3t5HX1NRg/vz5mDNnDp588slu15+p2qrNTY1oam6EyWiC1WpL+D/kGARElAptS7EpigKfz4ssgzHhzjKDwQir1Qa3uxl6WX/8Ip4NihJOeEzUW3fffTeeffbZ+ONYlnlFRQXmzZsHANizZw/cbnd8nttuuw2BQAA33ngjmpubMXv2bKxduxbZ2UOvNEgqB8RMyG4vs+KiaXbsqAqgNaihxafCadN3mgHf3NyMLVu2YP78+fHXoF/bPx5YB7rOhOeAoEREfcezZx8ITYXvb7+DVl8NeVQJbJf9iMF0IkqptgE7AAze0aDUm9vIjxw5gvnz52PmzJlYvXo1dLrub4rLVG3VUDiEcCgIVVVgdzjjF7A4BgERpUo0SB7t71VVjQfGFcXQ4eJ5+3NPZ+eithfh+3rhPRXroMFvzZo1WLNmTZfzCCESHkuShJUrV2LlypXpa1gapWuAzXybjD1Hg5gy2gwgGqTOssjxrPBYcNsb0BKC60ePHsXatWsxefJkyLLc5+23D5wnC5JzcFEiotThWbQPIu4GaPXVAACtrgoRdwPk3MIMt4qIhpPOMtWIhqKamhrMmzcPJSUlWLVqFerr6+PPxQYwG0zy8lwIhYJoXzyUYxAQUU91dmdZ279jF81lvR4CgMfjRjAYhIRozeQYu90BhyMn/rizc1Eq7prhnTc0XPW3LnpnwegGnwaHRUaDT0OpKzotWXC7bXDd1ViDd999F2eeeSYmT57cr/3qSXZ5umrCExGNRDyL9oHOkQ95VAm0uirIo0qgc6RnUBciGrnafkn2+33Qy3oEAn4oSrjLLDFmktFgtHbtWlRWVqKyshJjxoxJeK59xttgYLXaUFo6PiHwRUTUG53dWdb2b4PBGL1Qnm1HY2M9vK0eWKw25OTkQlVVeFs90CIRhEJBuFwF3fbrqbhrhnfe0HDV14E5Y/O0BjX4ghEAicHoZOtNFtyOPT8+3wADcnHeeeehpKQkBXvWvZ6UeyEiop5hIL0PJFkP22U/Yo10IhoQsS+zqqp2myXGTDIajK677jpcd911mW5Gr7S9mMULVETUW8kC0u3/jl0oV5QwZFlGJBKB0WiEw5ETH9TY7/dBOv67u349FXfN8M4bGq6SBbd3VAWw80gQ3oCGuackr/Uey+bOMumSDlDak4zwFp+KyroQvNXbcajFhhkzZkCVzNh60B8P4qez/EoqaqKzPAwRURTPgH0kyXqWcyGiAWWxWOMZ6Z1hJhlR6vECFRH1VvuLccmmW63Ri+SqqkKW9cjKyobJZI7Pl5fngt3u4N0xRCnSPhgsJABCtK/klqBtNndPAsjJAs57awP4Ytu/EAm24PRLFgPoWG5le3UAn1UH0Bq0YN6kwTeAK8vDEBFF8QxIRDTIJQbxcrqcl5lkRKnHC1RE1B+dXYwzGIzQ6/UIBYPQ6/Vw5uTCYDDC7W6O3wHDfp0oddoHg2eMtSDbLHdZ8qS32dzttyGEQM3n70GnhXH+hV+H3R4NkrcvtyIJAJIU/T0IsTwMEVEUA+lERIMcg3hEmcVAFhH1R1f9eNvnYkF03gFDlB7JBgFNdXZ1221EIhHodDqcMet0jBo1Cnr9iW213/b0EguyLF0H9TMpHceKiGgo0mW6AURE1LVoEC+HtZmJ0kxRwnC7mxPKMBAR9VdX/Xjb5xQlHC3zotfz4jlRGjhteswss6a1xndsG2qgBS+++CI8Hg+Ki4sTgujtsf44EdHQwUA6ERERjVhtg+ex8gsej5sBdSJKq2QX7vx+H8KhIEKhIBRF4XmIKENafCq2HvSjxaf2afnDhw/jtddew6RJk5Cd3X2981g5mH31/H8nIhrsGEgnIhoCmClLlB4ejxuNjQ2ora2BwWCEyRwd6C8UDMLv92W4dUQ0XMUu3LU9z1itNggAEiS0tDTxPESUIf0JbNfX12Pt2rX46le/ihkzZkCSuhjJFNGgvTegwWbWpaWsS38vChARUSLeN0RENAR0NlAZEfWfqiiAEFCUMByOnHh2OksrEFG6GAxGeDxuCABWazg+sKjTmYuWliZkZWVDCMHzEFEG9HRgzVhJFqMO+PRwAGeWmTC+KB9LlixBXl5ej7a1rz4MbyiCQqchLWVd2g9+SkRE/cMzKRHREMABR4nSw253xP+O/X9xcFEiSjdFCSOiaQgG/PD7zfFzjqKEoZf1EELA4cjJcCuJ0i8V9cFTXWO8q4E1224rFqT+rNoHc8tOrD8URvm3LoZsdmDrQX+P2tPToH1fpXv9REQjDQPpRERDAAN7RKnn9/vQ0tIEpzOXF6mIaEAZDEboZBlGowkGgxFudzOsVhsvnNOIk4qM6Z6uozcB9/bzxh63BjXUu1XsORrE2RNs0FQV49VtcEdacfZZF/R6n7oK2qdCutdPRDTS8IxKREREI46ihFFbewSaGq0ZyqAVEQ0kRQnDZDTBZDZDUcJtyrfl8MI5jSipyJju6Tp6E+BuP++++jD2HQuhNaBBSIDdLKPBp8F/+BOY9RFcsvQymEymlO0TERENTgykExENI21rOxsM/PBO1BmPx42IFoEk6eB05ma6OUQ0gihKGKqqQtbrEy7i8YIejUSpyJju6Tp6E+BuP2+5y4g9R4OwW2SMsuthlEIYn2+AuWA2jEYjZFnudXuIiGjo4dmdiGgY4aCkRD1nMBiQlW1n8IqIBpTf74OmqjCZzfGL3uyzidKvNwHu9vM6bXpcNM2OffVhOKUWbHjvXYy2zkNOSUm6mpsWqa4nT0Q00vDMSUQ0jLC2KlHP2O0O6NtlgxIRDQT21URDk9OmR179YVT8swJnnHEGSoZYEB1ITU16IqKRjGdOIqJhhIOSEvUM/1eIKFN4/iFKrWRZ1v3JvO5s2WAwiPfffx/z5s3DuHHjUroPA4X124mI+oeBdCIiIiIiojTh+CVE6bW9OoDPqgNoDVowb1I2gP5lXrdfVgiBpqYm5OXlYenSpTAah+7/Meu3ExH1jy7TDSAiIiIiIhquYuOX+P2+TDeFaFiSBABJiv4+rtxlRKHT0KfM67bLapqGiooKrFu3DpFIZEgH0YmIqP94KZKIKMOYqUZERDQ8KUoYqqpC5pgMRCnVtvzK9BILsixyQtC8P5nXsWXD4TDeeusdhEIhfP3rX4dOl5iH2OJTsaMqACEBM8ZaEsrAcFBPIqLhiWd0IqIMi2WqAWDNVCIiomHE7/dBU1WYzGYAgNvdzAvnRCmQUH6lzNrnoHlnAe8Wn4qNWz+DFgEuvvhiGAyGpG3YeSQICIFss5zQBg7qSUQ0PPGMTkSUYbEMNWaqERERDS9t+3heOCdKnVQNmpks4N3U1IQ9dRq07PEoHjMxaRC9xaeiNahhfL4RVrMO5S5jQlCeg3oSEQ1PDKQTEWWYwWDkF2oiIqJhpG3ZNocjBwBgteL4b144J+qvnpRu6Ul5lfYB75qaGqxduxZTps9CUc74TgPh++rD8AUjKHQaMLMs+s+99aA/JVnyREQ0ePHMTkQ0iLF+OhER0dCTLPucF86JBlZPyqu0Dcjv27cP7733Hs466yxMmjSpy3UnyzhnFjoR0fDHQDoR0SDG28CJiIiGjtgF8NjFb2afE3XU4lOxt8mf9oE4exPYjkQi2L17N8477zyUlJR0O3+yjPj+DHBKRERDA8/yRESDGOunExERDR2JF8Bz4tN5hxnRCQcawvBqMoD0DsTZk8B2JBLB3r17MXHiRCxevBiSJKWtPURENPQxkE5ENIjxNnAiIqKho7ML4LzDjOiEcflG1IcMnWaK96S2eSqoqoqKigo0NTWhpKQEFoslbdsiIqLhgYF0IiIiIiKiFOjsAjjvMCM6wWnTo6TI2unzPalt3p3ugvHBYBBvv/02AGDJkiUwm8192g4REY0sDKQTERERERGlUW/uMBOaioi7ATpHPiSZX9do+OmuRnoqBu3sLhjf2tqK7OxsnHPOOdDr+X9GREQ9wx6DiIiIiIhoEBCaCt/ffgetvhryqBLYLvsRg+k07HRXIz0Vg3Z2FoxvaGiAx+PB+PHjsWDBgn5tg4iIRh5dphtAREREREQ0VClKGG53MxQl3O91RdwN0OqrAQBaXRUi7oZ+r5NosBmXb0Shs/Ma6TEtPhVbD/rR4lO7nJaM06bHzDJrQsZ7dXU1XnvtNXi93v7tABERjVhMbyAiIqIRR1HC8Pt9sFptMBg48B8R9V0qBxLVOfIhjyqBVlcFeVQJdI78VDSRaFDprkZ6TLLyLH2tn75nzx5s2LAB55xzDiZMmDBgA5oSEdHwwh6jj1i7kIiIaOhKZeCLiEa29gOJqppAk09Frk0PvSz1al2SrIftsh/xewYRkpdn6Wv99GAwiEWLFqG4uBhAagY0JSKikYc9Rh+wdiEREdHQ1j7wRUTUV20HElU1gWfeb0RNi4JipwHXn5PXp2C6nFuYjqYSDSnJaqX3pn56JBLBxx9/jFNPPRXTp09PeC4VA5oOZsy4JyJKD9ZI7wPWLiQiIhraooGvHJZ1IaKUavKpqDme5VrToqC5TR3nVNZSJ6KuKYqCt99+G4cPH4ZOlxj2GAlB5ljG/b56nm+IiFKJgfQ+iNUuBMDahUREREREBADItelR7DQAAIqdBuS0CdLFSkr5/b5MNY9oRAgEAnjttdcQiURwySWXwGZLvPtsJASZy109G9CViIh6Z3hefk0z1i4kIiIiIqL29LKE68/JQ7NPRU67GuksKUU0MAwGA8rKyjB9+nTIstzh+eFe1gXoXQkcIiLqOWak91GsdiGD6EREREREFKOXJbjshg610VlSiii9amtrsX79esiyjNNPPz1pEB04HmQus8bLurT4VGw96EdLm1JMREREyTCQTkRERERERERD1oEDB/DGG2/A5XJBkrof4Ldt8HwklHohIqLUYDo1EREREREREQ2IFp8Kuz1169uzZw82bNiABQsWoKysrEfLxILnwMgo9UJERKnBQDoRERERERERDYgDDWGUFPV/PUIISJKEvLw8LF68GAUFBQAQzzIvdxnj5Vvaaxs8Zz1xIiLqqSFX2iUUCmHGjBmQJAnbt2/PdHOIiFJO1QTqPApUTWS6KUREREREKTUuv2+Z323LsWiahn/+85+orKxEfn5+PIgOnMg231EV6LT2efs66URERD0x5HqN2267DcXFxdixY0emm0JElHKqJvDM+42oaVFQ7DTg+nPyOgxWRkT9pyhh+P0+WK02DvxHREQ0gPoavI4FyFUljLovPoCqqhg9enSH+WLZ5q1BLV6+hRnnRESUCkMqI/3NN9/E2rVrsWrVqkw3hYgoLZp8KmqOf+CvaVHQnCSDhoj6z+/3IRQMwu/3ZbopRERE1APlLiNG2XXYt/Ud+BUdvrZgESwWS0KmOnAi23zGWAsKnQbWPiciopQZMpdljx07hmXLluHVV1+F1WrNdHOIiNIi16ZHsdMQz0jPYfYMUVpYrbaE30RERJQZPalpDgB2iw5njM9Ga9Np8Mt5ONQskO9IHDi0beY5a58TEVGqDYleRQiB6667Dt///vcxa9YsHDx4sEfLhUIhhEKh+GOPx5OmFhIRpYZelnD9OXlo9qnIselZ1oUoTQwGIxwOZqgRERFlWmeB8LaOHDmCf/3rX7j00ktx+ill8cA7gA6/iYiI0iWjpV1WrlwJSZK6/NmyZQseffRReDweLF++vFfrv//+++FwOOI/Y8eOTdOeEBGljl6W4LIbGEQnIiIiomGv3GXssgRLZWUl3n77bcyYMQMmk6nDQKGdDRzavuQLERFRf2U0I/2mm27C0qVLu5ynrKwM9957LzZt2gSTyZTw3KxZs3D11Vfj2WefTbrs8uXLccstt8QfezweBtOJiIiIiIiIBomuSrAcPHgQH3zwAc4///xef5fvSaY7ERFRb2S0N8nPz0d+fn638/3ud7/DvffeG39cU1ODhQsX4oUXXsDs2bM7Xc5kMnUIvhMREVHmhEIhzJ49Gzt27MC2bdswY8aMTDeJiIhoRLnvvvvw+uuvY/v27TAajWhpael2meuuu65DAtvs2bOxadOmtLQxEokgEolgzJgxuPTSS5GTk9PrdbDkCxERpdqQuCxbUlKS8DgrKwsAUF5ejjFjxmSiSURERNQHt912G4qLi7Fjx45MN4WIiGhECofDuOKKKzBnzhw8/fTTPV5u0aJFWL16dfyx0ZieALWqqli3bh1sNhu++tWv9imIDnCwUSIiSj32KkRERDQg3nzzTaxduxYvv/wy3nzzzUw3h4iIaES65557AABr1qzp1XImkwmFhYVpaNEJwWAQb731FnQ6HebOndundbT41PhgpO3rphMREfXHkOxVysrKIITIdDOIiIioh44dO4Zly5bh1VdfhdVq7dEyoVAIoVAo/tjj8aSreURERNSN9evXY9SoUXA6nZg7dy7uu+8+jBo1KqXbiGWiz58/H3p938IVrI1ORETpwl6FiIiI0koIgeuuuw7f//73MWvWLBw8eLBHy91///3xrDkiIiLKnAsvvBBXXHEFSktLceDAAdx1111YsGABtm7d2um4ZL25IO7z+WCz2TB37lzYbDZIktTntrI2OhERpYsu0w0gIiKioWnlypWQJKnLny1btuDRRx+Fx+PB8uXLe7X+5cuXw+12x3+qq6vTtCdERERDW0/75L666qqrsHjxYkyZMgUXX3wx3nzzTXz55Zd4/fXXO13m/vvvh8PhiP+MHTs26XxVVVV48cUX0dTUhKysrH4F0YHjtdHLrCzrQkREKceehYiIiPrkpptuwtKlS7ucp6ysDPfeey82bdrUIWNt1qxZuPrqq/Hss88mXdZkMnWa5UZEREQn9LRPTpWioiKUlpZi7969nc6zfPly3HLLLfHHHo+nQzB99+7d2LhxI+bNm4fc3NyUtY+IiCgdGEgnIiKiPsnPz0d+fn638/3ud7/DvffeG39cU1ODhQsX4oUXXsDs2bPT2UQiIqIRoad9cqo0NjaiuroaRUVFnc7T3QXx5uZmfPzxx7jwwgu7XA/RUMWBb4mGH/4nExERUVqVlJQkPM7KygIAlJeXY8yYMZloEhER0YhVVVWFpqYmVFVVQdM0bN++HQAwYcKEeB89adIk3H///bjsssvg9XqxcuVKfOMb30BRUREOHjyI//f//h/y8/Nx2WWX9Xr7kUgEzc3NyMnJwbe+9S0YjaxlTsMTB74lGn74n0xEREREREQ0Qtx9990JZdVOO+00AEBFRQXmzZsHANizZw/cbjcAQJZl7Ny5E3/605/Q0tKCoqIizJ8/Hy+88AKys7N7vf13330XZrMZixcvHjRBdGYOUzpw4Fui4Yc9BBEREQ2osrIyCCEy3QwiIqIRac2aNVizZk2X87Ttpy0WC95+++2UbV8IgQsuuKDfg4qmEjOHKR2cNj3fT0TDzIj6j459GPB4PBluCRHR0Of3+3k+7afs7OxB9SVysGM/TkSUOuzH+4/9eO/E+nFr8TTUNnrjmd8tPhUHGsIYl5+5bHCXSYVXDsNl0uDxqBlpAxERDZy+9uGSGEEpYYcPH+4wSjgREVGmuN1u2O32TDdjyGA/TkREgwn78d5hP05ERINFX/vwERVIj0QiqKmpSUnmgMfjwdixY1FdXT0iPzxx/0f2/gM8Btx/7n8q9p+ZbL3Dfjx1uP/c/5G8/wCPAfef/XgmdNePj/T3ZVs8FifwWJzAY5GIx+MEHosTenos+tqHj6jSLjqdDmPGjEnpOu12+4h+k3L/R/b+AzwG3H/u/0je/4HGfjz1uP/c/5G8/wCPAfd/ZO//QOtpP87X5QQeixN4LE7gsUjE43ECj8UJ6ToWupSvkYiIiIiIiIiIiIhoGGEgnYiIiIiIiIiIiIioCwyk95HJZMKKFStgMpky3ZSM4P6P7P0HeAy4/9z/kbz/w8FIfw25/9z/kbz/AI8B939k7/9gxdflBB6LE3gsTuCxSMTjcQKPxQnpPhYjarBRIiIiIiIiIiIiIqLeYkY6EREREREREREREVEXGEgnIiIiIiIiIiIiIuoCA+lERERERERERERERF1gIL2XDh48iO9973sYN24cLBYLysvLsWLFCoTD4YT5JEnq8PP4449nqNWp09P9r6qqwsUXXwybzYb8/Hz8+Mc/7jDPUHXffffhrLPOgtVqhdPpTDrPcH39gZ7t/3B+/ZMpKyvr8HrfcccdmW5W2vzxj3/EuHHjYDabMXPmTHzwwQeZbtKAWblyZYfXurCwMNPNol5gP85+nP04+/H22I+zH6fM6Mn5qL3rrruuw2t45plnprehA6Avx0IIgZUrV6K4uBgWiwXz5s3D559/nt6GDpDm5mZcc801cDgccDgcuOaaa9DS0tLlMsPlvdHbc/R7772HmTNnwmw2Y/z48cPm80pMb47H+vXrk36G++KLLwawxenx/vvv4+KLL0ZxcTEkScKrr77a7TLD9b3R22OR6veFvk9LjWBffPEFIpEInnjiCUyYMAGfffYZli1bBp/Ph1WrViXMu3r1aixatCj+2OFwDHRzU64n+69pGhYvXgyXy4V//etfaGxsxLXXXgshBB599NEM70H/hcNhXHHFFZgzZw6efvrpTucbjq8/0P3+D/fXvzM///nPsWzZsvjjrKysDLYmfV544QXcfPPN+OMf/4izzz4bTzzxBC688ELs2rULJSUlmW7egDj11FPx7rvvxh/LspzB1lBvsR9nP85+nP14MuzH2Y/TwOvp+bi9RYsWYfXq1fHHRqMxHc0bUH05Fr/61a/wm9/8BmvWrMHEiRNx77334vzzz8eePXuQnZ2d5han17e//W0cPnwYb731FgDg3//933HNNdfgtdde63K5of7e6O05+sCBA7jooouwbNky/PnPf8aGDRtw4403wuVy4Rvf+EYG9iC1+tpn7dmzB3a7Pf7Y5XINRHPTyufzYfr06fi3f/u3Hr22w/m90dtjEZOy94WgfvvVr34lxo0blzANgHjllVcy06AB1n7/33jjDaHT6cSRI0fi0/73f/9XmEwm4Xa7M9HEtFi9erVwOBxJnxsJr39n+z9SXv+2SktLxcMPP5zpZgyIr3zlK+L73/9+wrRJkyaJO+64I0MtGlgrVqwQ06dPz3QzKMXYj7Mfb28kvP7sx09gP85+nDKrq/Nxe9dee61YsmRJWtuTST09FpFIRBQWFooHHnggPi0YDAqHwyEef/zxNLYw/Xbt2iUAiE2bNsWnbdy4UQAQX3zxRafLDYf3Rm/P0bfddpuYNGlSwrT/+I//EGeeeWba2jiQens8KioqBADR3Nw8AK3LnJ58Th3u742YnhyLVL8vWNolBdxuN3JzcztMv+mmm5Cfn48zzjgDjz/+OCKRSAZal37t93/jxo2YMmUKiouL49MWLlyIUCiErVu3ZqKJGTFSXv/2Rurr/+CDDyIvLw8zZszAfffdNyxvgQ+Hw9i6dSsuuOCChOkXXHABPvzwwwy1auDt3bsXxcXFGDduHJYuXYr9+/dnuknUT+zH2Y8nM1Je//ZG6uvPfpz9OA0d69evx6hRozBx4kQsW7YMdXV1mW7SgDtw4ABqa2sT/p9NJhPmzp075P+fN27cCIfDgdmzZ8ennXnmmXA4HN3u21B+b/TlHL1x48YO8y9cuBBbtmyBoihpa+tA6E+fddppp6GoqAjnnnsuKioq0tnMQWs4vzf6KlXvC5Z26ad9+/bh0UcfxUMPPZQw/Re/+AXOPfdcWCwWrFu3DrfeeisaGhpw5513Zqil6ZFs/2tra1FQUJAwX05ODoxGI2prawe6iRkxUl7/ZEbi6/+f//mfOP3005GTk4OPP/4Yy5cvx4EDB/Df//3fmW5aSjU0NEDTtA6vb0FBwbB9bdubPXs2/vSnP2HixIk4duwY7r33Xpx11ln4/PPPkZeXl+nmUR+wH2c/nsxIef2TGYmvP/tx9uPsx4eOCy+8EFdccQVKS0tx4MAB3HXXXViwYAG2bt0Kk8mU6eYNmNj/bLL/50OHDmWiSSlTW1uLUaNGdZg+atSoLs9VQ/290ZdzdLI+u6CgAKqqoqGhAUVFRWlrb7r15XgUFRXhySefxMyZMxEKhfA///M/OPfcc7F+/Xqcc845A9HsQWM4vzd6K9XvC2akH5ds4Jn2P1u2bElYpqamBosWLcIVV1yBG264IeG5O++8E3PmzMGMGTNw66234uc//zl+/etfD+Qu9Uqq91+SpA7bEEIknT4Y9GX/uzISXv+uDLXXP5neHJOf/OQnmDt3LqZNm4YbbrgBjz/+OJ5++mk0NjZmeC/So/3rONRe2/648MIL8Y1vfANTp07Feeedh9dffx0A8Oyzz2a4ZcR+nP04+3H2422xH+8c+3H24+mU6vNRe1dddRUWL16MKVOm4OKLL8abb76JL7/8Mv5aDibpPhbA0Pp/7s3x6Es/NJTeG13p7WuabP5k04eq3hyPk08+GcuWLcPpp5+OOXPm4I9//CMWL17cYRykkWK4vzd6KtXvC2akH3fTTTdh6dKlXc5TVlYW/7umpgbz58/HnDlz8OSTT3a7/jPPPBMejwfHjh3rcFVoMEjl/hcWFuKjjz5KmNbc3AxFUQblvgO93//eGm6vf1eG4uufTH+OSWx0+MrKymGV3ZSfnw9ZljtkANTV1Q2p1zaVbDYbpk6dir1792a6KSMe+3H24+zH2Y+3xX68I/bjHbEfT710n4/bKyoqQmlp6aB8DdN5LAoLCwFEs07bZpYO5v/nnh6PTz/9FMeOHevwXH19fa/2bTC/N5Lpyzm6sLAw6fx6vX7I91+p6rPOPPNM/PnPf0518wa94fzeSIX+vC8YSD8uPz8f+fn5PZr3yJEjmD9/PmbOnInVq1dDp+s+sX/btm0wm81wOp39bGl6pHL/58yZg/vuuw9Hjx6Nd+pr166FyWTCzJkzU972VOjN/vfFcHr9uzMUX/9k+nNMtm3bBgDD7nYpo9GImTNn4p133sFll10Wn/7OO+9gyZIlGWxZ5oRCIezevRtf+9rXMt2UEY/9OPtx9uPsx9tiP94R+/GO2I+nXrrPx+01Njaiurp6UP6/pvNYjBs3DoWFhXjnnXdw2mmnAYjWlH7vvffw4IMPpmWb/dXT4zFnzhy43W58/PHH+MpXvgIA+Oijj+B2u3HWWWf1eHuD+b2RTF/O0XPmzMFrr72WMG3t2rWYNWsWDAZDWtubbqnqs7Zt2zZk3gOpNJzfG6nQr/dFSoYsHUGOHDkiJkyYIBYsWCAOHz4sjh49Gv+J+cc//iGefPJJsXPnTlFZWSmeeuopYbfbxY9//OMMtjw1erL/qqqKKVOmiHPPPVd88skn4t133xVjxowRN910UwZbnjqHDh0S27ZtE/fcc4/IysoS27ZtE9u2bROtra1CiOH9+gvR/f4P99e/vQ8//FD85je/Edu2bRP79+8XL7zwgiguLhaXXHJJppuWFn/5y1+EwWAQTz/9tNi1a5e4+eabhc1mEwcPHsx00wbErbfeKtavXy/2798vNm3aJL7+9a+L7OzsEbP/wwH7cfbj7MfZj7fFfpz9OPvxzOnufCSEECeffLL429/+JoQQorW1Vdx6663iww8/FAcOHBAVFRVizpw5YvTo0cLj8WRqN1Kit8dCCCEeeOAB4XA4xN/+9jexc+dO8a1vfUsUFRUN+WMhhBCLFi0S06ZNExs3bhQbN24UU6dOFV//+tcT5hmO743uztF33HGHuOaaa+Lz79+/X1itVvGTn/xE7Nq1Szz99NPCYDCIl156KVO7kFK9PR4PP/yweOWVV8SXX34pPvvsM3HHHXcIAOLll1/O1C6kTGtra/y8ACD+2eXQoUNCiJH13ujtsUj1+4KB9F5avXq1AJD0J+bNN98UM2bMEFlZWcJqtYopU6aIRx55RCiKksGWp0ZP9l+I6AeBxYsXC4vFInJzc8VNN90kgsFghlqdWtdee23S/a+oqBBCDO/XX4ju91+I4f36t7d161Yxe/Zs4XA4hNlsFieffLJYsWKF8Pl8mW5a2vzhD38QpaWlwmg0itNPP1289957mW7SgLnqqqtEUVGRMBgMori4WFx++eXi888/z3SzqBfYj7MfZz/Ofrwt9uPsx9mPZ05PzkcAxOrVq4UQQvj9fnHBBRcIl8slDAaDKCkpEddee62oqqrKzA6kUG+PhRBCRCIRsWLFClFYWChMJpM455xzxM6dOwe+8WnQ2Ngorr76apGdnS2ys7PF1VdfLZqbmxPmGa7vja7O0ddee62YO3duwvzr168Xp512mjAajaKsrEw89thjA9zi9OrN8XjwwQdFeXm5MJvNIicnR3z1q18Vr7/+egZanXoVFRVJzxHXXnutEGJkvTd6eyxS/b6QhDhebZ6IiIiIiIiIiIiIiDrovigoEREREREREREREdEIxkA6EREREREREREREVEXGEgnIiIiIiIiIiIiIuoCA+lERERERERERERERF1gIJ2IiIiIiIiIiIiIqAsMpBMRERERERERERERdYGBdCIiIiIiIiIiIiKiLjCQTkRERERERERERETUBQbSiQaJ9evXQ5IktLS0AADWrFkDp9OZ0Tb1V/t9IiIiGo7YhxMREVEqrFy5EjNmzMh0M4ioEwykE6XY448/juzsbKiqGp/m9XphMBjwta99LWHeDz74AJIk4csvv8RZZ52Fo0ePwuFwDHSTh5Smpib86Ec/wsknnwyr1YqSkhL8+Mc/htvtTphPkqQOP1/96lcT5qmoqMBFF12EvLw8WK1WTJ48GbfeeiuOHDkykLtERESDBPvw9GIfTkRENPB27NiBb33rWxg7diwsFgtOOeUU/Pa3v02YJ3YBvf3PnXfeGZ9HCIEnn3wSs2fPRlZWFpxOJ2bNmoVHHnkEfr9/oHerR15++WVMnjwZJpMJkydPxiuvvJLpJtEQx0A6UYrNnz8fXq8XW7ZsiU/74IMPUFhYiM2bNyd0MOvXr0dxcTEmTpwIo9GIwsJCSJKUiWYPGTU1NaipqcGqVauwc+dOrFmzBm+99Ra+973vdZh39erVOHr0aPznH//4R/y5J554Aueddx4KCwvx8ssvY9euXXj88cfhdrvx0EMPDeQuERHRIME+PL3YhxMR0VCjKEqPpg1mW7duhcvlwp///Gd8/vnn+NnPfobly5fj97//fYd59+zZk9D/3nHHHfHnrrnmGtx8881YsmQJKioqsH37dtx11134+9//jrVr1/a5feFwuM/LdmXjxo246qqrcM0112DHjh245pprcOWVV+Kjjz5Ky/ZohBBElHLFxcXi/vvvjz++7bbbxA9/+EMxefJk8c4778SnL1iwQFx99dVCCCEqKioEANHc3CyEEGL16tXC4XAkrPfvf/+7mDlzpjCZTCIvL09cdtll8eeamprENddcI5xOp7BYLGLRokXiyy+/FEIIUVdXJwoKCsR9990Xn3/Tpk3CYDCIt99+u9vl27bnrbfeEpMmTRI2m00sXLhQ1NTUdHoc2u+TEEK89NJLYvLkycJoNIrS0lKxatWqhGVqamrERRddJMxmsygrKxPPPfecKC0tFQ8//HCn23nxxReF0WgUiqLEpwEQr7zyStL5q6urhdFoFDfffHPS59u2l4iIRhb24SLpPgnBPpyIiPpH0zTxwAMPiPLycmE0GsXYsWPFvffeG3/+tttuEyeddJKwWCxi3Lhx4s477xThcDj+/LXXXiuWLFmSsM7//M//FHPnzo0//utf/yqmTJkizGazyM3NFeeee67wer1CCCE+/vhjcd5554m8vDxht9vFOeecI7Zu3ZqwPgDiscceE5dccomwWq3i7rvvFitWrBDTp08XTz/9tBg3bpyQJElEIhHx5ptvirPPPls4HA6Rm5srFi9eLCorK+Prmj9/vvjhD3+YsP6GhgZhNBrFunXrkh6j2LbaHrN77rlHjB49WhiNRjF9+nTx5ptvJiyzYcMGMX36dGEymcTMmTPFK6+8IgCIbdu2dfpa3HjjjWL+/Pnxx8n6/bZeeOEFAUC8+uqrHZ6LRCKipaWl0221V1paKn7xi1+Ia6+9VtjtdvHd7363x8v2xpVXXikWLVqUMG3hwoVi6dKladkejQzMSCdKg3nz5qGioiL+uKKiAvPmzcPcuXPj08PhMDZu3Ij58+f3aJ2vv/46Lr/8cixevBjbtm3DunXrMGvWrPjz1113HbZs2YJ//OMf2LhxI4QQuOiii6AoClwuF5555hmsXLkSW7ZsgdfrxXe+8x3ceOONuOCCC7pdPsbv92PVqlX4n//5H7z//vuoqqrCT3/60x4fl61bt+LKK6/E0qVLsXPnTqxcuRJ33XUX1qxZE5/nu9/9LmpqarB+/Xq8/PLLePLJJ1FXV9flet1uN+x2O/R6fY/a8de//hXhcBi33XZb0ueHel1bIiLqO/bhybEPJyKi/lq+fDkefPBB3HXXXdi1axeef/55FBQUxJ/Pzs7GmjVrsGvXLvz2t7/FU089hYcffrjH6z969Ci+9a1v4frrr8fu3buxfv16XH755RBCAABaW1tx7bXX4oMPPsCmTZtw0kkn4aKLLkJra2vCelasWIElS5Zg586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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.lines import Line2D\n", - "BLUE, ORANGE, AQUA, GREY = '#2a78d6', '#eb6834', '#1baf7a', '#c8c7c2'\n", - "\n", - "def _scatter(ax, x, y, ca_sig, w_sig, title, xl, yl):\n", - " both = ca_sig & w_sig\n", - " ax.scatter(x[~ca_sig & ~w_sig], y[~ca_sig & ~w_sig], s=3, c=GREY, alpha=.4, lw=0, rasterized=True)\n", - " ax.scatter(x[w_sig & ~ca_sig], y[w_sig & ~ca_sig], s=6, c=BLUE, alpha=.6, lw=0, label=f'Wilcoxon only ({int((w_sig & ~ca_sig).sum())})')\n", - " ax.scatter(x[ca_sig & ~w_sig], y[ca_sig & ~w_sig], s=6, c=ORANGE, alpha=.7, lw=0, label=f'causarray only ({int((ca_sig & ~w_sig).sum())})')\n", - " ax.scatter(x[both], y[both], s=6, c=AQUA, alpha=.8, lw=0, label=f'both ({int(both.sum())})')\n", - " ax.axhline(0, c='#999', lw=.6); ax.axvline(0, c='#999', lw=.6)\n", - " ax.set_title(title, fontsize=10); ax.set_xlabel(xl); ax.set_ylabel(yl)\n", - " ax.legend(fontsize=8, frameon=False, loc='upper left')\n", - " for sp_ in ('top', 'right'): ax.spines[sp_].set_visible(False)\n", - "\n", - "for trt in PERTS:\n", - " fig, axes = plt.subplots(1, 3, figsize=(15, 4.8))\n", - " g = variants_a['r8_default_balanced_welch']; g = g[g['trt'] == trt]\n", - " _scatter(axes[0], g['wilcox_logfc'].clip(-25, 6), g['log2fc'], g['padj'] < 0.05, g['w_sig'],\n", - " f'{trt}: current defaults (balanced propensity, Welch SE)\\ncausarray-only hits = zero counts in perturbed arm', 'Wilcoxon log2FC', 'causarray log2FC')\n", - " g = variants_a['r8_calibrated_pooled_floor']; g = g[g['trt'] == trt]\n", - " _scatter(axes[1], g['wilcox_logfc'].clip(-25, 6), g['log2fc'], g['padj'] < 0.05, g['w_sig'],\n", - " f'{trt}: calibrated propensity, pooled IF variance, Poisson floor\\nno zero-arm hits; effects agree along the diagonal', 'Wilcoxon log2FC', 'causarray log2FC')\n", - " gj = j[(j['trt'] == trt) & ok]\n", - " ax = axes[2]\n", - " ax.scatter(gj['log2fc_r0'], gj['log2fc_r8'], s=3, c=BLUE, alpha=.35, lw=0, rasterized=True)\n", - " lim = np.nanpercentile(np.abs(np.r_[gj['log2fc_r0'], gj['log2fc_r8']]), 99.5)\n", - " ax.plot([-lim, lim], [-lim, lim], c='#999', lw=.8, ls='--'); ax.set_xlim(-lim, lim); ax.set_ylim(-lim, lim)\n", - " sl = np.polyfit(gj['log2fc_r0'], gj['log2fc_r8'], 1)[0]; cc = np.corrcoef(gj['log2fc_r0'], gj['log2fc_r8'])[0, 1]\n", - " ax.set_title(f'{trt}: effect with vs. without latent factors (expressed genes)\\nslope {sl:.2f}, r = {cc:.2f}: adjustment does not shrink effects', fontsize=10)\n", - " ax.set_xlabel('causarray log2FC, r = 0'); ax.set_ylabel('causarray log2FC, r = 8')\n", - " for sp_ in ('top', 'right'): ax.spines[sp_].set_visible(False)\n", - " plt.tight_layout()\n", - " fig.savefig(os.path.join(INV_DIR, f'scarf_diagnosis_{trt}.png'), dpi=130)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "149b3d36", - "metadata": {}, - "source": [ - "### Summary and open issues for the package\n", - "\n", - "**What went wrong on SCARF**\n", - "\n", - "1. *Zero-count perturbed arms are called as strong negative effects.* With\n", - " 68-227 cells per perturbation, chance zero arms are common for sparse\n", - " genes. `LFC` floors the treated mean at `thres_diff`, takes the log, and the\n", - " all-zero arm has ~0 empirical influence-function variance. This produced 83%\n", - " of the discoveries and the entire \"extreme negative DEG\" cluster. The\n", - " collaborators' Rule 1 (both arms ≥ 5 detections) was removing exactly these.\n", - " Adamson never showed it because its perturbations have 700-1,900 cells.\n", - "2. *The default variance is not the variance of the estimator.*\n", - " `class_weight='balanced'` propensity scores with the by-arm Welch formula\n", - " inflate SEs ~2x for expressed genes (and are ~15x off in the oracle\n", - " simulation). Real effects are estimated correctly and then not called. This\n", - " is why r = 0 looked \"more concordant with Wilcoxon\" on slide 4: it is a\n", - " power artifact of the SE, not evidence of over-adjustment.\n", - "3. *Latent factors are not the problem.* No factor differs between a\n", - " perturbation and control by more than 0.05 SD; r = 8 vs r = 0 effect\n", - " estimates agree (slope 0.82-0.96) and r = 8 has more power.\n", - "\n", - "**Package-level issues surfaced here (for discussion)**\n", - "\n", - "- `LFC` default `ps_class_weight='balanced'` + `usevar='unequal'`: the Welch\n", - " formula `s0²/n0 + s1²/n1` assumes a two-sample difference of arm means, but\n", - " the estimand averages pseudo-outcomes over all cells. With calibrated scores\n", - " the pooled influence-function variance is the correct sandwich variance.\n", - "- With calibrated scores the default `ps_clip=(0.01, 0.99)` clips essentially\n", - " every score for a treatment with prevalence below 1%.\n", - "- No model-based variance floor: an all-zero arm gets ~0 variance. The floor\n", - " `1/(n1·mu1) + 1/(n0·mu0)` on the log scale (or treating a floored arm as\n", - " non-estimable) fixes it without dropping genuine complete knockouts.\n", - "- `thres_min=0.01` and `thres_diff=0.01` are far below the count level at\n", - " which single-cell inference is stable; `eps_var=1e-4` is on the relative\n", - " scale and never binds.\n", - "- Even with calibrated + pooled + floor, very sparse genes (control mean\n", - " < 0.05 counts/cell) remain positively skewed under the null with in-sample\n", - " (`K=1`) nuisance fits. Candidates: a minimum-expression rule, cross-fitting\n", - " (`K=2`), or a t reference with arm-size degrees of freedom.\n", - "- `backend='fast'` silently falls back to statsmodels when `crispyx` is not\n", - " importable at import time; a warning would avoid unnoticed slow, slightly\n", - " different fits.\n", - "- The propensity diagnostics (`summarize_propensity_scores`, AUC/overlap) are\n", - " computed on the balanced-weight scores, so \"poor overlap\" partly reflects\n", - " in-sample logistic overfitting with ~100 cases against thousands of controls." - ] - }, - { - "cell_type": "markdown", - "id": "c736ff1b", - "metadata": {}, - "source": [ - "### Full re-run with the causarray 0.0.10 defaults\n", - "\n", - "The investigation above led to the 0.0.10 release: calibrated propensity\n", - "scores, the pooled influence-function variance with a small-sample\n", - "correction, a model-based variance floor, an expression threshold on observed\n", - "counts in the smaller arm, and a prevalence-aware propensity clip. The cell\n", - "below loads the full 58-perturbation re-run of this subset with those defaults\n", - "(`scarf_investigation/run_lfc_0.0.10.py`, cached GCATE factors, ~5.5 h;\n", - "`postprocess_0.0.10.py` applies the last two rules to the cached batches) and\n", - "compares it with the 0.0.9 result loaded at the top of this notebook and with\n", - "Wilcoxon." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9825fb7c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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discoverieszero counts in perturbed armfraction downregulatedperturbations with >= 50 hitsWilcoxon hits recoveredJaccard with Wilcoxon
version
0.0.9191915880.8314252 / 7484 (3%)0.028
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" - ], - "text/plain": [ - " discoveries zero counts in perturbed arm fraction downregulated perturbations with >= 50 hits Wilcoxon hits recovered Jaccard with Wilcoxon\n", - "version \n", - "0.0.9 1919 1588 0.83 14 252 / 7484 (3%) 0.028\n", - "0.0.10 12346 0 0.31 39 5046 / 7484 (67%) 0.341" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lfc10_path = os.path.join(INV_DIR, 'scarf-lfc-0.0.10.csv')\n", - "if not os.path.exists(lfc10_path):\n", - " raise FileNotFoundError('run scarf_investigation/run_lfc_0.0.10.py and postprocess_0.0.10.py first')\n", - "df_res10 = pd.read_csv(lfc10_path)\n", - "sig10 = df_res10[df_res10['padj'] < 0.05]\n", - "cmp10 = df_res10[['trt', 'gene_names', 'tau', 'padj', 'count_treated']].merge(\n", - " df_res[['trt', 'gene_names', 'tau', 'padj']].rename(columns={'tau': 'tau_009', 'padj': 'padj_009'}), on=['trt', 'gene_names']).merge(\n", - " df_wc_full, on=['trt', 'gene_names'])\n", - "w_sig = (cmp10['wilcox_padj'] < comparison_q) & (cmp10['wilcox_logfc'] > -10)\n", - "rows = []\n", - "for label, col, tau_col in [('0.0.9', 'padj_009', 'tau_009'), ('0.0.10', 'padj', 'tau')]:\n", - " cs = cmp10[col] < comparison_q\n", - " s = cmp10[cs]\n", - " rows.append({'version': label, 'discoveries': int(cs.sum()),\n", - " 'zero counts in perturbed arm': int((s['count_treated'] == 0).sum()),\n", - " 'fraction downregulated': round((s[tau_col] < 0).mean(), 2),\n", - " 'perturbations with >= 50 hits': int((s['trt'].value_counts() >= 50).sum()),\n", - " 'Wilcoxon hits recovered': f\"{int((w_sig & cs).sum())} / {int(w_sig.sum())} ({100 * (w_sig & cs).sum() / w_sig.sum():.0f}%)\",\n", - " 'Jaccard with Wilcoxon': round((w_sig & cs).sum() / (w_sig | cs).sum(), 3)})\n", - "display(pd.DataFrame(rows).set_index('version'))\n", - "\n", - "fig, ax = plt.subplots(figsize=(14, 4))\n", - "counts = pd.DataFrame({'0.0.9': cmp10[cmp10['padj_009'] < comparison_q]['trt'].value_counts(),\n", - " '0.0.10': sig10['trt'].value_counts()}).fillna(0).astype(int)\n", - "counts = counts.sort_values('0.0.10', ascending=False)\n", - "counts.plot.bar(ax=ax, width=0.8, color=['#c8c7c2', '#2a78d6'])\n", - "ax.set_ylabel('significant genes (padj < 0.05)'); ax.set_xlabel('perturbation')\n", - "ax.set_title('SCARF L4-5 IT CTX Glut: discoveries per perturbation, causarray 0.0.9 vs 0.0.10 defaults')\n", - "ax.legend(frameon=False); plt.tight_layout(); plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "causarray", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.20" - }, - "papermill": { - "default_parameters": {}, - "duration": 18.600955, - "end_time": "2026-09-10T19:16:07.616037+00:00", - "environment_variables": {}, - "exception": null, - "input_path": "SCARF-py.ipynb", - "output_path": "SCARF-py.ipynb", - "parameters": {}, - "start_time": "2026-09-10T19:15:49.015082+00:00", - "version": "2.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/source/tutorial/SCARF/prep_scarf_data.py b/docs/source/tutorial/SCARF/prep_scarf_data.py deleted file mode 100644 index 656e8b7..0000000 --- a/docs/source/tutorial/SCARF/prep_scarf_data.py +++ /dev/null @@ -1,125 +0,0 @@ -""" -Prepare the SCARF tutorial dataset for causarray. - -Source: subset_adata_SCARF_10celltypes_10perturbations.h5ad - (110,968 cells x 19,070 genes; 10 perturbations + Non_target controls) - subset_adata_SCARF_10celltypes_48perturbations.h5ad - (142,619 cells x 19,070 genes; 48 perturbations + Non_target controls) - -Both files share the *same* 102,595 'Non_target' control cells (identical -barcodes) -- verified by set comparison. A naive concatenation of the two -files would therefore double-count every control cell. The correct combine -keeps one file whole and adds only the other file's perturbed cells: - - combined = file_10pert (whole, 110,968 cells) - + file_48pert[gene_target != 'Non_target'] (40,024 cells) - = 150,992 cells, 58 unique perturbations, one shared control pool - -`crispyx` (as of 0.1.2) has no multi-file concat primitive -- only -single-file streaming filter/subset/write utilities (`write_filtered_subset`, -`subsample`, `filter_*_by_cell_count`). Merging two source files is done with -plain `anndata.experimental.concat_on_disk` (disk-to-disk, not scanpy); this -is the one step in the SCARF pipeline that isn't crispyx-native. - -Output: - scarf_combined.h5ad -- 150,992 cells x 19,070 genes, 58 perturbations - (durable combined artifact; not fit in full -- - that would be a ~5-10x heavier GCATE run than - the adamson tutorial) - scarf_L45_subset.h5ad -- 22,396 cells x 19,070 genes, 58 perturbations, - restricted to predicted_group == '005 L4-5 IT - CTX Glut' (the pptx's own pilot cell type). - This is the file the tutorial notebook fits - causarray/Wilcoxon on. -""" -from __future__ import annotations - -from pathlib import Path - -import numpy as np -import anndata as ad -from anndata.experimental import concat_on_disk - -import crispyx - -HERE = Path(__file__).parent.resolve() -SRC_10PERT = HERE / "subset_adata_SCARF_10celltypes_10perturbations.h5ad" -SRC_48PERT = HERE / "subset_adata_SCARF_10celltypes_48perturbations.h5ad" - -TMP_48PERT_UNIQUE = HERE / "scarf_48pert_unique.h5ad" -COMBINED = HERE / "scarf_combined.h5ad" -L45_SUBSET = HERE / "scarf_L45_subset.h5ad" - -PERT_COL = "gene_target" -CTRL_LABEL = "Non_target" -TARGET_CELL_TYPE = "005 L4-5 IT CTX Glut" - - -def main() -> None: - # ── Step A (crispyx): drop file B's duplicate control cells ───────────── - if not TMP_48PERT_UNIQUE.exists(): - print(f"Loading obs metadata from {SRC_48PERT.name} ...") - obs_48 = crispyx.load_obs(SRC_48PERT) - cell_mask = (obs_48[PERT_COL].astype(str) != CTRL_LABEL).to_numpy() - n_vars = ad.read_h5ad(SRC_48PERT, backed="r").n_vars - gene_mask = np.ones(n_vars, dtype=bool) - print(f" Keeping {cell_mask.sum():,}/{len(cell_mask):,} perturbed cells " - f"(dropping {(~cell_mask).sum():,} duplicate '{CTRL_LABEL}' cells)") - crispyx.write_filtered_subset( - SRC_48PERT, cell_mask=cell_mask, gene_mask=gene_mask, - output_path=TMP_48PERT_UNIQUE, - ) - print(f" Saved -> {TMP_48PERT_UNIQUE}") - else: - print(f"{TMP_48PERT_UNIQUE.name} already exists, skipping Step A.") - - # ── Step B (anndata, documented crispyx-impossible step): disk concat ── - if not COMBINED.exists(): - print(f"Concatenating {SRC_10PERT.name} + {TMP_48PERT_UNIQUE.name} " - f"-> {COMBINED.name} (anndata.experimental.concat_on_disk) ...") - concat_on_disk( - [str(SRC_10PERT), str(TMP_48PERT_UNIQUE)], - str(COMBINED), - join="outer", - ) - print(f" Saved -> {COMBINED}") - else: - print(f"{COMBINED.name} already exists, skipping Step B.") - - # ── Step C (crispyx): restrict to the pptx pilot cell type ───────────── - if not L45_SUBSET.exists(): - print(f"Loading obs metadata from {COMBINED.name} ...") - obs_combined = crispyx.load_obs(COMBINED) - cell_mask = (obs_combined["predicted_group"].astype(str) == TARGET_CELL_TYPE).to_numpy() - n_vars = ad.read_h5ad(COMBINED, backed="r").n_vars - gene_mask = np.ones(n_vars, dtype=bool) - print(f" Keeping {cell_mask.sum():,}/{len(cell_mask):,} cells " - f"(predicted_group == '{TARGET_CELL_TYPE}')") - crispyx.write_filtered_subset( - COMBINED, cell_mask=cell_mask, gene_mask=gene_mask, - output_path=L45_SUBSET, - ) - # Tag control/perturbation convention, matching adamson/replogle tutorials. - adata = ad.read_h5ad(L45_SUBSET) - adata.uns["pert_col"] = PERT_COL - adata.uns["ctrl_label"] = CTRL_LABEL - adata.write_h5ad(L45_SUBSET) - print(f" Saved -> {L45_SUBSET}") - else: - print(f"{L45_SUBSET.name} already exists, skipping Step C.") - - # ── Summary ────────────────────────────────────────────────────────────── - final = ad.read_h5ad(L45_SUBSET, backed="r") - vc = final.obs[PERT_COL].astype(str).value_counts() - n_perts = len(vc) - 1 # exclude control - n_ctrl = int(vc.get(CTRL_LABEL, 0)) - n_pert_cells = int(final.n_obs - n_ctrl) - print(f"\nFinal pilot subset: {final.n_obs:,} cells x {final.n_vars:,} genes") - print(f" {n_perts} perturbations, {n_pert_cells:,} pert cells, {n_ctrl:,} control cells") - pert_counts = vc.drop(CTRL_LABEL) - print(f" Cells per pert: min={pert_counts.min():,}, " - f"median={pert_counts.median():.0f}, max={pert_counts.max():,}") - - -if __name__ == "__main__": - main() diff --git a/docs/source/tutorial/SCARF/scarf-r.csv b/docs/source/tutorial/SCARF/scarf-r.csv deleted file mode 100644 index 5008d25..0000000 --- a/docs/source/tutorial/SCARF/scarf-r.csv +++ /dev/null @@ -1,7 +0,0 @@ -r,deviance,nu,JIC -0,-1.5934989277416498,0.039657757359416526,-1.5538411703822332 -1,-1.674890067685708,0.04032992273838968,-1.6345601449473182 -2,-1.6832852476710503,0.04100208811736285,-1.6422831595536873 -3,-1.692585705150745,0.04167425349633601,-1.650911451654409 -5,-1.7023117761799766,0.04301858425428233,-1.6592931919256944 -8,-1.7127713421052229,0.04503508039120181,-1.667736261714021 diff --git a/docs/source/tutorial/case_control/README.md b/docs/source/tutorial/case_control/README.md index 2fd7fee..4e6d86b 100644 --- a/docs/source/tutorial/case_control/README.md +++ b/docs/source/tutorial/case_control/README.md @@ -8,8 +8,7 @@ inputs, `results/` holds everything generated. sea_ad_case_control.ipynb reads data/sea_ad_mtg_exneu_pb.h5ad, results/sea_ad_r.csv (JIC table), - results/sea_ad_gcate.pkl (latent-factor fit), - results/sea_ad_lfc.csv + results/sea_ad_gcate.pkl (latent-factor fit) Everything here is tracked in git, including the fit caches, so the published tutorial renders without a refit. Regenerate `results/sea_ad_gcate.pkl` by diff --git a/docs/source/tutorial/case_control/results/sea_ad_gcate.pkl b/docs/source/tutorial/case_control/results/sea_ad_gcate.pkl index c26ac95..ab1f75a 100644 Binary files a/docs/source/tutorial/case_control/results/sea_ad_gcate.pkl and b/docs/source/tutorial/case_control/results/sea_ad_gcate.pkl differ diff --git a/docs/source/tutorial/case_control/sea_ad_case_control.ipynb b/docs/source/tutorial/case_control/sea_ad_case_control.ipynb index 9d3bf25..99da8b8 100644 --- a/docs/source/tutorial/case_control/sea_ad_case_control.ipynb +++ b/docs/source/tutorial/case_control/sea_ad_case_control.ipynb @@ -20,10 +20,10 @@ "id": "b0a2c6be", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:57.481851Z", - "iopub.status.busy": "2026-09-23T13:50:57.481701Z", - "iopub.status.idle": "2026-09-23T13:50:59.502818Z", - "shell.execute_reply": "2026-09-23T13:50:59.502481Z" + "iopub.execute_input": "2026-09-29T06:52:25.783488Z", + "iopub.status.busy": "2026-09-29T06:52:25.783042Z", + "iopub.status.idle": "2026-09-29T06:52:28.499603Z", + "shell.execute_reply": "2026-09-29T06:52:28.499008Z" } }, "outputs": [ @@ -31,7 +31,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "causarray version: 0.0.10\n" + "causarray version: 0.1.1\n" ] } ], @@ -82,10 +82,10 @@ "id": "f15968e4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:59.504509Z", - "iopub.status.busy": "2026-09-23T13:50:59.504302Z", - "iopub.status.idle": "2026-09-23T13:50:59.600215Z", - "shell.execute_reply": "2026-09-23T13:50:59.599942Z" + "iopub.execute_input": "2026-09-29T06:52:28.501638Z", + "iopub.status.busy": "2026-09-29T06:52:28.501413Z", + "iopub.status.idle": "2026-09-29T06:52:28.592615Z", + "shell.execute_reply": "2026-09-29T06:52:28.592053Z" } }, "outputs": [ @@ -113,10 +113,10 @@ "id": "3d7da682", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:59.601485Z", - "iopub.status.busy": "2026-09-23T13:50:59.601402Z", - "iopub.status.idle": "2026-09-23T13:50:59.606760Z", - "shell.execute_reply": "2026-09-23T13:50:59.606505Z" + "iopub.execute_input": "2026-09-29T06:52:28.593950Z", + "iopub.status.busy": "2026-09-29T06:52:28.593832Z", + "iopub.status.idle": "2026-09-29T06:52:28.601645Z", + "shell.execute_reply": "2026-09-29T06:52:28.601127Z" } }, "outputs": [ @@ -292,16 +292,16 @@ "id": "0eaa5d00", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:59.608051Z", - "iopub.status.busy": "2026-09-23T13:50:59.607956Z", - "iopub.status.idle": "2026-09-23T13:50:59.770595Z", - "shell.execute_reply": "2026-09-23T13:50:59.770294Z" + "iopub.execute_input": "2026-09-29T06:52:28.602986Z", + "iopub.status.busy": "2026-09-29T06:52:28.602873Z", + "iopub.status.idle": "2026-09-29T06:52:28.845120Z", + "shell.execute_reply": "2026-09-29T06:52:28.844552Z" } }, "outputs": [ { "data": { - "image/png": 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" ] @@ -353,10 +353,10 @@ "id": "36d3951c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:59.772016Z", - "iopub.status.busy": "2026-09-23T13:50:59.771900Z", - "iopub.status.idle": "2026-09-23T13:50:59.781494Z", - "shell.execute_reply": "2026-09-23T13:50:59.781248Z" + "iopub.execute_input": "2026-09-29T06:52:28.846635Z", + "iopub.status.busy": "2026-09-29T06:52:28.846518Z", + "iopub.status.idle": "2026-09-29T06:52:28.861675Z", + "shell.execute_reply": "2026-09-29T06:52:28.861188Z" } }, "outputs": [ @@ -403,16 +403,16 @@ "id": "dc04bfbe", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:50:59.782821Z", - "iopub.status.busy": "2026-09-23T13:50:59.782740Z", - "iopub.status.idle": "2026-09-23T13:51:00.067605Z", - "shell.execute_reply": "2026-09-23T13:51:00.067313Z" + "iopub.execute_input": "2026-09-29T06:52:28.862972Z", + "iopub.status.busy": "2026-09-29T06:52:28.862873Z", + "iopub.status.idle": "2026-09-29T06:52:29.110823Z", + "shell.execute_reply": "2026-09-29T06:52:29.110280Z" } }, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -453,10 +453,10 @@ "id": "a8a7f979", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:51:00.069100Z", - "iopub.status.busy": "2026-09-23T13:51:00.068981Z", - "iopub.status.idle": "2026-09-23T13:51:16.891283Z", - "shell.execute_reply": "2026-09-23T13:51:16.890916Z" + "iopub.execute_input": "2026-09-29T06:52:29.112342Z", + "iopub.status.busy": "2026-09-29T06:52:29.112236Z", + "iopub.status.idle": "2026-09-29T06:52:29.115939Z", + "shell.execute_reply": "2026-09-29T06:52:29.115540Z" } }, "outputs": [ @@ -464,328 +464,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'d': 3, 'n': 85, 'p': 22911, 'r': 4}\n", - "'Estimating initial latent variables with GLMs...'\n", - "'Fitting nb GLM (dense, 3 columns)...'\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'Estimating initial coefficients with GLMs...'\n", - "'Fitting nb GLM (dense, 7 columns)...'\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'kwargs_es': {'max_iters': 50,\n", - " 'patience': 5,\n", - " 'rel_tol': 0.0002,\n", - " 'tolerance': 0.0,\n", - " 'warmup': 0},\n", - " 'kwargs_glm': {'disp_glm': array([4.15230991e+00, 1.66305561e+00, 2.20028897e+00, ...,\n", - " 1.01546418e+01, 1.00000000e+08, 1.09595567e+01]),\n", - " 'family': 'nb',\n", - " 'size_factor': array([0.93865546, 1.43671869, 1.765853 , ..., 1.19711934, 1.16245053,\n", - " 1.58667384])},\n", - " 'kwargs_ls': {'C': 1000.0,\n", - " 'alpha': 0.1,\n", - " 'beta': 0.5,\n", - " 'max_iters': 20,\n", - " 'recheck_interval': 10,\n", - " 'sparsity_boost': 2.0,\n", - " 'sparsity_threshold': 0.5,\n", - " 'tol': 0.0001,\n", - " 'tol_cell': 0.0001,\n", - " 'tol_gene': 0.0001,\n", - " 'warmup_iters': 0}}\n", - "'Fitting GCATE (step 1)...'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/50 [00:0043\n", " 42\n", " 0.494118\n", - " 0.608527\n", - " 0.288483\n", - " 0.285443\n", + " 0.587486\n", + " 0.287929\n", + " 0.285489\n", " 0.0\n", " 0.0\n", - " 41.972006\n", - " 40.897901\n", - " 0.976093\n", - " 0.97376\n", - " 0.312448\n", - " 0.483506\n", - " 0.634342\n", + " 41.968707\n", + " 40.892454\n", + " 0.976016\n", + " 0.97363\n", + " 0.311755\n", + " 0.483235\n", + " 0.634593\n", " \n", " \n", "\n", @@ -898,16 +579,16 @@ ], "text/plain": [ " treatment n_control n_treated prevalence overlap_ratio auc \\\n", - "0 trt 43 42 0.494118 0.608527 0.288483 \n", + "0 trt 43 42 0.494118 0.587486 0.287929 \n", "\n", " brier_score outside_overlap_fraction clipped_fraction ess_control \\\n", - "0 0.285443 0.0 0.0 41.972006 \n", + "0 0.285489 0.0 0.0 41.968707 \n", "\n", " ess_treated ess_control_fraction ess_treated_fraction score_q01 \\\n", - "0 40.897901 0.976093 0.97376 0.312448 \n", + "0 40.892454 0.976016 0.97363 0.311755 \n", "\n", " score_median score_q99 \n", - "0 0.483506 0.634342 " + "0 0.483235 0.634593 " ] }, "metadata": {}, @@ -915,7 +596,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -954,7 +635,7 @@ "\n", "`tau` is the estimated natural-log expression ratio between the coded disease groups after adjustment for sex and latent factors, on the size-factor-normalized scale.\n", "\n", - "`LFC` uses the empirical AIPW influence-function variance, an in-sample degrees-of-freedom adjustment, a t reference, and a size-factor-aware Poisson variance floor. These corrections do not establish calibration for this dataset. Donors remain the independent units; the estimator does not account for repeated rows from the same donor.\n" + "`LFC` uses a t reference and a size-factor-aware Poisson variance floor. Its default variance (`usevar='pooled'`) is the empirical AIPW influence-function variance over all donors. Here we pass `usevar='unequal'`, which estimates the variance separately in each disease group (Welch) with Welch-Satterthwaite degrees of freedom. **In such a small sample (85 donors) the Welch variance gives more conservative results:** its standard errors are about twice the pooled ones, and under shuffled disease labels it called 0-1 genes per shuffle, against up to 3,654 with the pooled variance. It is conservative rather than calibrated: the null statistics have SD 0.6-0.9 instead of 1. Donors remain the independent units; the estimator does not account for repeated rows from the same donor.\n" ] }, { @@ -963,10 +644,10 @@ "id": "eab9b697", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:51:16.969528Z", - "iopub.status.busy": "2026-09-23T13:51:16.969403Z", - "iopub.status.idle": "2026-09-23T13:51:18.654357Z", - "shell.execute_reply": "2026-09-23T13:51:18.654051Z" + "iopub.execute_input": "2026-09-29T06:52:29.225630Z", + "iopub.status.busy": "2026-09-29T06:52:29.225462Z", + "iopub.status.idle": "2026-09-29T06:52:30.966132Z", + "shell.execute_reply": "2026-09-29T06:52:30.964793Z" } }, "outputs": [ @@ -976,8 +657,8 @@ "text": [ "'Estimating LFC...'\n", "{'a': 1, 'd': 6, 'd_A': 6, 'estimands': 'LFC', 'n': 85, 'p': 22911}\n", - "{'offset': array([-0.06330679, 0.36236182, 0.56863386, ..., 0.17991812,\n", - " 0.1505303 , 0.4616399 ]),\n", + "{'offset': array([-0.06326921, 0.36319112, 0.56963029, ..., 0.18039264,\n", + " 0.15097163, 0.46259047], shape=(85,)),\n", " 'random_state': 0,\n", " 'verbose': True}\n", "'Fit propensity score models...'\n", @@ -1005,7 +686,8 @@ "# Concatenate observed covariates with latent factors\n", "W = np.c_[X_cov, U]\n", "\n", - "df_res, estimation = LFC(Y, W, A, W, offset=offsets, verbose=True)" + "# Welch (by-group) variance: more conservative with 85 donors\n", + "df_res, estimation = LFC(Y, W, A, W, offset=offsets, usevar='unequal', verbose=True)" ] }, { @@ -1014,10 +696,10 @@ "id": "1999b2b3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:51:18.655868Z", - "iopub.status.busy": "2026-09-23T13:51:18.655777Z", - "iopub.status.idle": "2026-09-23T13:51:18.663002Z", - "shell.execute_reply": "2026-09-23T13:51:18.662749Z" + "iopub.execute_input": "2026-09-29T06:52:30.969842Z", + "iopub.status.busy": "2026-09-29T06:52:30.969626Z", + "iopub.status.idle": "2026-09-29T06:52:30.983779Z", + "shell.execute_reply": "2026-09-29T06:52:30.981729Z" } }, "outputs": [ @@ -1025,7 +707,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "FDR-controlled (BH, q<0.1): 10926\n" + "FDR-controlled (BH, q<0.1): 2683\n" ] }, { @@ -1075,117 +757,117 @@ " \n", " 0\n", " LINC01409\n", - " 0.046102\n", - " 0.039243\n", - " 0.066510\n", - " 0.056616\n", - " 1.174768\n", + " 0.046236\n", + " 0.075176\n", + " 0.066705\n", + " 0.108456\n", + " 0.615036\n", " 0.0\n", - " 0.243661\n", - " 0.363640\n", - " 0.686257\n", - " 0.996942\n", + " 0.540288\n", + " 0.810821\n", + " 0.681938\n", + " 0.998768\n", " 42\n", " 43\n", " 13514.0\n", " 16076.0\n", - " 327.615284\n", - " 343.072410\n", + " 327.603623\n", + " 343.106352\n", " True\n", " False\n", - " 0.039243\n", + " 0.075176\n", " \n", " \n", " 1\n", " NOC2L\n", - " 0.080903\n", - " 0.027455\n", - " 0.116718\n", - " 0.039609\n", - " 2.946760\n", + " 0.081108\n", + " 0.052583\n", + " 0.117014\n", + " 0.075861\n", + " 1.542472\n", " 0.0\n", - " 0.004233\n", - " 0.013569\n", - " 0.305215\n", - " 0.966875\n", + " 0.127006\n", + " 0.413605\n", + " 0.299293\n", + " 0.964412\n", " 42\n", " 43\n", " 4573.0\n", " 5424.0\n", - " 108.234543\n", - " 117.355011\n", + " 108.227333\n", + " 117.371216\n", " True\n", " False\n", - " 0.027455\n", + " 0.052583\n", " \n", " \n", " 2\n", " ENSG00000272512\n", - " -0.312502\n", - " 0.208608\n", - " -0.450845\n", - " 0.300958\n", - " -1.498033\n", + " -0.313083\n", + " 0.401955\n", + " -0.451683\n", + " 0.579899\n", + " -0.778899\n", " 0.0\n", - " 0.138161\n", - " 0.233562\n", - " 0.590896\n", - " 0.996942\n", + " 0.438285\n", + " 0.743681\n", + " 0.590020\n", + " 0.998768\n", " 42\n", " 43\n", " 88.0\n", " 149.0\n", - " 3.125004\n", - " 2.286298\n", + " 3.126400\n", + " 2.285991\n", " True\n", " False\n", - " 0.208608\n", + " 0.401955\n", " \n", " \n", " 3\n", " HES4\n", - " -0.611584\n", - " 0.115167\n", - " -0.882329\n", - " 0.166151\n", - " -5.310397\n", + " -0.611460\n", + " 0.222365\n", + " -0.882151\n", + " 0.320805\n", + " -2.749801\n", " 0.0\n", - " 0.000001\n", - " 0.000010\n", - " 0.062748\n", - " 0.650249\n", + " 0.007359\n", + " 0.074392\n", + " 0.062711\n", + " 0.639385\n", " 42\n", " 43\n", " 1560.0\n", " 3614.0\n", - " 70.508173\n", - " 38.250057\n", + " 70.506533\n", + " 38.253879\n", " True\n", " False\n", - " 0.115167\n", + " 0.222365\n", " \n", " \n", " 4\n", " ISG15\n", - " -0.355548\n", - " 0.107695\n", - " -0.512947\n", - " 0.155371\n", - " -3.301429\n", + " -0.355888\n", + " 0.208072\n", + " -0.513438\n", + " 0.300185\n", + " -1.710405\n", " 0.0\n", - " 0.001453\n", - " 0.005557\n", - " 0.242567\n", - " 0.939516\n", + " 0.091292\n", + " 0.348647\n", + " 0.242770\n", + " 0.937560\n", " 42\n", " 43\n", " 456.0\n", " 806.0\n", - " 16.164352\n", - " 11.327808\n", + " 16.168754\n", + " 11.327036\n", " True\n", " False\n", - " 0.107695\n", + " 0.208072\n", " \n", " \n", "\n", @@ -1193,32 +875,32 @@ ], "text/plain": [ " gene_names tau std log2fc log2fc_se stat rej \\\n", - "0 LINC01409 0.046102 0.039243 0.066510 0.056616 1.174768 0.0 \n", - "1 NOC2L 0.080903 0.027455 0.116718 0.039609 2.946760 0.0 \n", - "2 ENSG00000272512 -0.312502 0.208608 -0.450845 0.300958 -1.498033 0.0 \n", - "3 HES4 -0.611584 0.115167 -0.882329 0.166151 -5.310397 0.0 \n", - "4 ISG15 -0.355548 0.107695 -0.512947 0.155371 -3.301429 0.0 \n", + "0 LINC01409 0.046236 0.075176 0.066705 0.108456 0.615036 0.0 \n", + "1 NOC2L 0.081108 0.052583 0.117014 0.075861 1.542472 0.0 \n", + "2 ENSG00000272512 -0.313083 0.401955 -0.451683 0.579899 -0.778899 0.0 \n", + "3 HES4 -0.611460 0.222365 -0.882151 0.320805 -2.749801 0.0 \n", + "4 ISG15 -0.355888 0.208072 -0.513438 0.300185 -1.710405 0.0 \n", "\n", " pvalue padj pvalue_emp_null_adj padj_emp_null_adj n_treated \\\n", - "0 0.243661 0.363640 0.686257 0.996942 42 \n", - "1 0.004233 0.013569 0.305215 0.966875 42 \n", - "2 0.138161 0.233562 0.590896 0.996942 42 \n", - "3 0.000001 0.000010 0.062748 0.650249 42 \n", - "4 0.001453 0.005557 0.242567 0.939516 42 \n", + "0 0.540288 0.810821 0.681938 0.998768 42 \n", + "1 0.127006 0.413605 0.299293 0.964412 42 \n", + "2 0.438285 0.743681 0.590020 0.998768 42 \n", + "3 0.007359 0.074392 0.062711 0.639385 42 \n", + "4 0.091292 0.348647 0.242770 0.937560 42 \n", "\n", " n_control count_treated count_control mean_control mean_treated \\\n", - "0 43 13514.0 16076.0 327.615284 343.072410 \n", - "1 43 4573.0 5424.0 108.234543 117.355011 \n", - "2 43 88.0 149.0 3.125004 2.286298 \n", - "3 43 1560.0 3614.0 70.508173 38.250057 \n", - "4 43 456.0 806.0 16.164352 11.327808 \n", + "0 43 13514.0 16076.0 327.603623 343.106352 \n", + "1 43 4573.0 5424.0 108.227333 117.371216 \n", + "2 43 88.0 149.0 3.126400 2.285991 \n", + "3 43 1560.0 3614.0 70.506533 38.253879 \n", + "4 43 456.0 806.0 16.168754 11.327036 \n", "\n", " estimable var_floored std_raw \n", - "0 True False 0.039243 \n", - "1 True False 0.027455 \n", - "2 True False 0.208608 \n", - "3 True False 0.115167 \n", - "4 True False 0.107695 " + "0 True False 0.075176 \n", + "1 True False 0.052583 \n", + "2 True False 0.401955 \n", + "3 True False 0.222365 \n", + "4 True False 0.208072 " ] }, "execution_count": 10, @@ -1251,16 +933,16 @@ "id": "5d8ed073", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:51:18.664367Z", - "iopub.status.busy": "2026-09-23T13:51:18.664292Z", - "iopub.status.idle": "2026-09-23T13:51:18.787596Z", - "shell.execute_reply": "2026-09-23T13:51:18.787314Z" + "iopub.execute_input": "2026-09-29T06:52:30.987119Z", + "iopub.status.busy": "2026-09-29T06:52:30.986912Z", + "iopub.status.idle": "2026-09-29T06:52:31.137760Z", + "shell.execute_reply": "2026-09-29T06:52:31.137208Z" } }, "outputs": [ { "data": { - "image/png": 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", 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stPb3p63n3U6kogz279KevzetedLwG4SYQvU8lD1Grg5+UxCoSBhHvgohyQAFAyEJADNu+CcOww3hKDAkrdWRMLMFAylcsi/c+qiiYjVmrGB/AF4DqwGCWWQk1cFQxWtUmUHSJZIFrUIiXAJta3RkvFYw82kHyYWYrYeXwg5CEEC4WWYzlliOa8A1MA3HOgLCusIB4w+iBCEVdsFgTwrOyspq17FRzWrNmjU624rZSzswTnDtkWSP6i7wctn7QRhPj/FUwONh95gg5APJoBAbqCQTzlAMZ2DCQMU92pZKVTDCMOsKgwpjxey4NUSqI2AmF4IBoV0IC+kI+F5IErZ7gjD7jbFb70WIMCQcw9CF9wgVy+BJgXFt9tXa7xiGf2tgxhtiARXLMElhNWAjdey2/8bwW4KxbN3WdIQ2420NeBOQhIyQHlQAgiCN5Z6J53k3wsOeqL1u3bqYjhPvvzdmO/x9xHnEA+Ib5xUhX7gnKBhIssAcBkISAEI6EL+Mf2aYNUVoBWb8DPhHiookCPlAGIYVzAqCSDO+JqwJ7m0rmNWDsYZ/+iYsxB4ChCofmFE2s56x0pHxWsGsvBkbwKwbQmgiGYeYpcY/aMzIW7eDQQADELOff/rTn4JjBOFyIexhPx19wAiMdJ4OOeQQrX6CGHk7MHAQb45xtLf0q/Fs/N///V8w5Mn6gGfJWhYV94B9/AiLgPcLPQpgxGA2374feKIwWw1BaK9gFAkYrAgPgRHU1o7JiHfHtvAAYbZ66623lniA3yFEw4svvqhiLhyIv8fvpzVwj0Pg2mftUW0HhiDEnAFi3cSuo/IQZppRBcgYs7H8jsNhjGIzG27KpqIKlHUb3IOmCo/5vYf7jeBaw4BFOJNdeGL8sfyugRGlqJqGSYlYw5FiIdbzDq+G1ctlzlO4qnUd/XsTK/D0Ic/LWkoXAghjjZegIiQe0MNASILAP1AYDUuWLAnb+RkJyfhHhxlglDXE7BZiXSEyYEAjETQcMDThNYChgVlg1CVH7C3emzr6mNXCPzcYKpiFRrw6Qi7g8cAsOAyBttLe8VpBmU4Yopj5xhiQqIl/nGeffXbEECacQ3yOGUtsi/HDaIZRgBAfE4dsYokRroMZaxiJ4UKgOhskKH/77bc6NszyY+Yc+QEwhjGjD+8AZhbtYRi4T8KFORlgEOKBcJ1+/fpFNFyQCA2BipK1CHsyHg075ljRQn9wnSEqMEOP+8wAQ9QYOwjVwAwuZpUhJiM1SGsN7B/XDfuJ1jOhPcBog5GMkqbwmGCmH+cQuTmIt4cQxn2FkBcsj8Tpp5+u5x+/BZw3JLgiURbiEF4C060a5xZhiFjfCMN//OMf6rFBuV2EosTyOw6HKf0KAxa/OdwHMEAhhnAt8Br3EsaEawSxgN8aPHjhfiMQmPi7gJ4ICKHCuNC0D8IWkxyxXkv8PUCIHe55lFtGLle8iPW8I5wNYhglYfG9cS0hFkwydmu05e9NrODvJe5nhIRin/hbDAGO3xDuA0KSBQoGQhIEwgkwu4h/1vaQEPMPFrPACF2CEYP1kACJf9ymwVIkYHhg3RdeeEHj0WF0w/gwtdgxO4uZWoQIoG46wl/wTxxVe2D0o+Y6QibMjFwsdGS8BhMe9eCDD+ozjCT804xW/x6zi5hJxNiNNwMzgQjDsIoUfD/ET2OMCAWB8YCkza4G4gBjgBiCdwljRkgajDXM7sP4gUFvBwYRHpHAOYY3APuCdyFS3DdmmWHo476AoY9Z7XBgRhlhZpFCpwDCXG6++WY1fq2NtHDf4QEwDsSQI68DBjlyddrqXQCYgUeYFQRDvMKRDLj/0XfAVCCCcIMRCeMNIgzVi2AY2xNY7cDoxvfG7wrnBNcZFW9wbTCLjOOYUCTce/g9GmDowgi3hia19jsOB64XDHx8Bxj4+H0goRrXCUn1RjScddZZ+owZeEwW4FpG+o3g7wGEDO5XfAYPKb4PDN225Ajh2sNbE0/vQqzn3Rj8jz76qN6H+P3B2IfnCn0bYh1TrH9vYgV/47AtrhGeMWmCe+66665rl7AmpLNwoLZqp+2dEEJiADOn+EeM2UzMtBJCuh8w6uHNgfC1lk8mhCQ/zGEghBBCSKeCECDkicBrQbFASOrBkCRCCCGEdAoIwULpXiQlozFZpMRyQkhyQ8FACCGEkE4B+QXIpUAFMOQzIemeEJJ6MIeBEEIIIYQQEhHmMBBCCCGEEEIiQsFACCGEEEIIiQhzGERk2LBh2qmxLTXnCSGEEEIISeWiBOilgqaarUHBIKLJWGhmQwghhBBCSE8pd+yLsR0bBYOItooHc+fO7dwrQwiJCqqooCMtIYQQQjoXNEyNFeYwEEKShlWrViV6CIQQQgixQcFACEkadtxxx0QPgRBCCCE2KBgIIUnD8ccfn+ghEEIIIcQGBQMhJGm4/PLLEz0EQgghhNigYCCEEEIIIYREhIKBEJI0nHTSSYkeAiGEEEJsUDAQQpKGysrKRA+BEEIIITYoGAghScPMmTMTPQRCCCGE2KBgIIQQQgghhESEgoEQkjTcc889iR4CIYQQQmxQMBBCkobbb7890UMghBBCiA0KBkJI0rB06dJED4EQQgghNtz2BYSQzqP+xx+leeVKcffrJ5nbbMNTbWPrrbfmOSGEEEKSDAoGQrqIsqeflvVPPiXe+npxZmZK0UknSskJJ/D8W5g0aRLPByGEdCE+n08cDgfPOc9dVBiSREgXeRYgFsTnE3evXvqM91hONnLhhRfydBBCSBfxwAMPyMMPP9yhfVxyySWy7777SrxZuHChDB48WBYsWBBc9q9//Uv22GMPGTJkiNx///1ywgknyPHHHy+J4PPPP5dTTz1Vegr0MBDSBSAMCZ4FiAWH0ynO/HxpXrtWlwtDkwghhCSAu+66S84444wO7QPbjx8/XuLN9ttvL9OnT1fRAJYsWSIPPfSQHHrooXLEEUfIwIEDZfTo0ZIoZsyYIT/99JP0FCgYCOmKH1q/fhqG5K2sVLGgz5mZupxs5LjjjuPpIISQFGKzzTbrlP3m5eXJsGHDgu8rKir0GYIBXgbStTAkiZAuAAnOyFkQh0M9C3guPvkkJj4TQghpE/vvv7/ce++9cuedd8qf/vQnDc/BDP+3334bsh7eI2RmxIgRsttuu2mOGGbpwbJly4Iz9w8++KDuMxIff/yxTubsuuuuMnToUDnllFNCjmUPSWpqatIS2Vi2884763Fff/11PR6Oa7Y5+eSTdfnBBx8sO+64oxx00EH6PlxIEr4vwo/A3/72t+DY7SFJOPZ9990nY8aM0fOCfT777LMtPAPwUOyyyy66DgTIrFmzWhz3008/1bFjvREjRshVV10ldXV1wePOnDlTVq9eretim+4OBQMhnQhyFKrnz9dnJDgPuH+q9L/xBn0unjiR597G888/z3NCCEnqBGEYpXhOJE8//bR89dVXcvXVV8tNN90ky5cvl7PPPls8Ho9+/sknn6iR39DQINddd50+Vq5cqcv+97//Se/evTXcBxx22GFy9913Ryx1jZCjLbbYQo12HKusrEyN9urq6rDbwLCeNm2aHgv7hafgiiuuaLHed999p/s87bTTZOrUqVJaWioXXXSR/Prrry3WPfLII4P7uPTSS4NjtwMhAgE0btw43ecBBxwg1157rTz33HP6Oba78sorZeTIkZoDcfPNN4vb7ZZ//OMfeg6tnHfeebLtttuqABk/fry88MILum+AsUAQFRcX6z4RPtXdYUgSIV1dFYk5C4QQknLU1tbK77//rs/Z2dmy6aab6nMiyMzMlEcffVTS0tKCY4MRu3jxYp2tv+OOO2STTTaRJ554Qg1iAG/EgQceqHkLMIJNuE/fvn1lp512CnsceBLq6+vl3HPPlT59+uiyLbfcUl5++WUVDLm5uS0EBmbeYYCbhOBRo0apV+LDDz8MWbeyslIN+a222krfb7755uoZ+Pe//62vrfTv31+2CfzvxLM1VMnw888/y5tvvqnHhggx33ndunXqpYAnAtfvxBNP1O9jGDRokAqSRYsW6TkzQEidf/75+nqfffZRL8L7778vf//739WrUFJSouc/3Fi6IxQMhHRBVSTkLOB9zogRDEOKwm233cb7kRCSdMCjAGNz/fr1kpOTo88AM9CJKEmKUBojFkC/QD4cQmYgHv773//K6aefHhQLoKCgQPbbbz81emMF4TgQRcccc4yMHTtWDWcYyPAEhANGNc4VQoGsHHLIIS0EA8ZjxIIRLgDjbw+fffaZPtuPDS+C4bLLLtNniB14MnBNEXIFGhsbQ7ZD+JWVfv36aQhSTyWpQpLgSnvqqaf0xsJNClcS3F9WtxdcYbhRkfCCeDq4ytgdliRrVSQkOJuqSHivVZFIRDpa3o8QQjqD5uZmNWQhFjC7j2e8x/JEgDFYcTr95pzX65Wqqio12nuhhLcNhP3g81jBzD5yAGA8v/TSSxqKBPvr+uuvb2Fgg/Lycn1GqI4VzMbH+h3aG+5lRFy4Yxn++OMP9XZA9MDj8Mgjj2iIWTiysrJC3jscjoSHoiWSpPIwwE0G95m5mL/88ovGoCFOD24rXCi4uHBDIk4NyhluNbiX4Iayu8YISRSsitQ+elKJOkJI6gB7AzPtxiitqamRoqKikBn8ZAE5AzBu16LAho01a9ZIYWFhm/a33Xbbaf8DTOp+8803GnL0zDPPqJiAvWbFhC1hctcarmWERGeSn58fPJb12JhUXrVqldqVCFWCZwbiB94hXD/kdLz22mudPr5UJ2k8DHCjQSycdNJJGh+GuDO8RvIMBAPcXO+88458//33mqiC2DJ4Ip588km9OUxCCyHJAKsitQ/EkhJCSLIBAxw5CxAJ8CrgGe+TsUMyjGXkMcBmsnpAkDMwb968kFAbM6sfCSQvI4wJydMul0sjO5BEjGPYk4QB9o313n333ZDlGEtnY77XnDlzQpbfc889migNsYcwpMMPP1zPjxF78+fPD3pn2oKzlXPX3UgaaYwbGWWuUF7LiolvgypGnNmAAQNkhx12CFGzuEnwIzBJLoQkA0hwRs4CwpDgcYCIINFBshohhCQjMJIxKw0jHMZmMooFwwUXXKCz///3f/+nJUARdoOQTxj+Z511Vsis/Ndff60Jv+GSdxF+hARqhH9PmDBB0tPTNaIDidD2XAGAZmpIIEZ1JJwneCdgwP/nP//pdCMb1wZjQrlZjA+iAJPNb7zxhoa3I1QJSc0IsUK+BL478irgLQGmZGqs5OfnqwhBkjbKx9rDsLobSSOPYPij7BcusJW5c+fqMzLSkQFvz5w3s5L4jJBkAyIhd+RIioUYOeecczr3ghBCSAeASEBISzKLBbDnnntq1AZCuS+88EItv4qk3RdffFENawPEAxKkJ0+eHDYnAetCaEBwYJYepVt/++03DRcfPnx42GOjbCnKkCInFeujStGZZ56pn3V2VSkUzoBIQglUfCd4D2699VadkAaIUMF5uPzyy1VU4bvju2ByGqKpLRx77LEaloX/Wx999JF0dxy+JM7gQCgSwpL22msveeCBB1Q5Qq1CPdpvEIQm2ZuWxIppLW7ECSHxqpRE70LbQCMfuMAJIYSkHhs2bNAZd/Q5sM6433LLLZr7gP4QJHloi/2bNCFJduBGmjJlirq34EoC0DaRVH2yq33Ss4jYg4FEBbGlhBBCUhNUFkIZ01deeUUnfOFR+PLLL7XRHGw6krokpWDAjYZkZzTnQMkrk9GPzP9wnQWxDJ8RkgzeBMAeDB2rckEIIST1yMjIUC8xql4i7Ad5AUgORzgTwpRI6pJ0ggHZ7IgnQ5UkJM2g1rEB+Qso6WUHjTfQeZCQZPAmZO+9l75GwzbTg6F57Vp/DwYmPkcFoYWoyEEIISQ1Qej4Qw89lOhhkO6a9AyQWAOxcNRRR+nNZhULACICyTZofW5A170vvvhCPyMk0R2d8Vwzb744XC7t7uzzevUZQsJ4HwghhBBCUomk8TBACMCjsMUWW2gcM2LerGy22WZachUhSiifil4N6BJ47733aqksdOwjJFEdne3ehLzR+0vNRwv0NcRC8cknsVJSDNx4442df9EIIYQQkpqCAXV6UbMX3Z1R69fODTfcIEcffbQ8+uij8s9//lMfSHTefffdNTaOOQwkmTo6F02YoA9WSWobaMDIXgyEEEJIcpE0ggFeg1gar6FfAzwRhCRTR2eEJYX1JjBnoU20tzQyIYQQQnqAYCAkVWFH5/iB7puEEEIISS4oGAiJA+pRoDehw1xzzTXxuByEEEII6a5VkgghPZszzjgj0UMghBBCiA16GAghhBBCUoRLLrlEZs6cGdVTi8qR9vVQKAadmAcNGqRVJ//v//5PG60ZUHXyvvvua7E/VKTs3bu3jB49Ws455xzt3twZlJWVyS233CL/+c9/pKGhQUaMGCGXXXaZjjdWzj33XElLS5Pbb789pvVXrFghxx13nLz00kv6HT0ej0yfPl1efPFFWbZsmZSWlur3PvvssyU3Nzfqvr766iu544475L///a+eo3322Ucuuugi3YdhyZIlctttt2lPsfT0dNlzzz11nV4oyx6gtrZWr8Xbb78t69ev174W6JJtbR+AfdTX18uVV14pXQUFAyEkafjLX/6S6CEQQkjSU1xcrEZlONBZOdx6Pp9Pqqur5bPPPlNhAMP8iSeeCBEN4Omnnxanc2MASkVFhSxYsEDXXbNmjfzrX/9q15hhjLtcroifnXrqqVJeXq5Cx+126xhPPPFEefPNN1s11r1er9x0003yzjvvyLhx42IaD84HBMnEiRNVLAB0qMb3POWUU2TYsGFauRP9wb766iut4mc9L/aCHRgrKneiMA/OE/Z15plnyowZM3QdLMM6KN6Dyp8w+HEucSwIFggIgEqBuEbnnXeeDBw4UF577TU5/fTTdVwQUWDy5Mny5z//WcXMXnvtJV0BBQMhJGnYZJNNEj0EQghJejCLDoO2Pevtt99+sttuu+ms9eOPP65GrRV8BoPdCgxTGPOzZs2S66+/vkVj3Whgph6z9t9995089dRTYdeBof/999/LK6+8IjvssIMug/E9ZswYNdSjVdHErD0McMzawxsSK++9954a+g888IC+r6urU6P8pJNO0l5fALP66PV1wQUXyMKFC9UjEA7M+G+11VbadNicu4KCAu0thD5j6CWG423YsEG9F0bUYZ1JkybJ559/rvv+6aefZO7cucFWAgCeCvQmwzk0ggHn/+STT9YWA2+88YZ6jzob5jAQQpKq2zshhJDO5YADDpCdd95Znn/++Zi3aWu/q48//lhnwmH0w6jdf//9I677wQcfyIABA4JiAWAmfujQoTJv3ryox0FID/p4YSYfxn2sPPjggzo2hGmByspKOeKIIzRcywqEgPEQhANhQ59++qn2ELMKrQMPPFDmz5+vYgEgzMp+HuEBMvsACL964YUXZOzYscF14NWA8GtsbAw5LjwpP//8swqMroCCgRBCCCEkBup//FGq58/X50QDI9n+QGhOrGDmetWqVbJ8+fKQ5diH2V9TU5OsW7dOZ8VfffVVNYKjeRcwSw+DF8YsZsARzoS4/n//+9/6PhIwfDfffPMWy2FA47NoIO8BXoht2lCpEPuEd+Gggw4KESjXXXed7LjjjiHrGoN88ODBET0cOGfIVbjwwgtl11131QdCi/D9DRAByFXAMSA+/vjjD7n11lt1Ga4FQHjYLrvsoucY+0SOBbwUS5cu1bwUKxgv1n399delK2BIEiEkabjqqqsSPQRCCAlL2dNPa5NOb329NulE00704UkEq1evDpmNNyB0B4ZqLJhkXAgCazjoTjvt1GJdGLXjx4/X5N9IYD8wiiEy/vrXv6pQiNWIr6qqUg+DHeQuIO8iGttuu620lU8++USfhwwZEnU95C7A873//vtHPA5CtcDll1+uIUz333+//P7775qfgOsBLw68BDDwkZCO8CYkNJuQpCeffFLy8/Nb7Bc5HMifAEcddVTYcChcK+Q4dAUUDISQpOGtt96K+g+JEEISATwKEAvi84m7Vy/xVlbq+5wRI/x9eLoYhN4gXt6OSd5tC/b4d3gTYOAihAYz93PmzNGY/iOPPLLV/Zh9Yfu2xNUjATnS+p0Rn4+8ClQyKioqirgOchaQ54HE45tuuiniehBIYPvttw+uB+MeIgDnDeFWI0eO1LAseCDgpTnmmGP0/CKH5G9/+5smmpvQJwPWw36QmwHxAG/QY489FrIOhB7yIiCqWksM7ygUDISQpAGJX4QQkmw0r1ypngWIBYfTKc78fGleu1aXJ6JpJ2Llw3kC2uqlAJj5tgLPhYnFR8I0qvVg9hwGtj2+3y5iELMPwxjJzc8++6wMHz5cqxAhadqeSG0Fcf3hPAlY1tbciVjAfqOVh0XyNTze8JA88sgjUlhYGHFdE6IFUWDFhBkhmRufwehH3giqKBkRtPfee6tXBt4IeCasGI8Gkr9xDlBCFf8jkddhMN8BHprOFgzMYSCEJA3RZnsIISRRuPv10zAkeBZ8Xq8+4z2WpyoIy0G1HrtgsIMwGvxtvvrqqzXsKBqoUoTqPhAN06ZNUyMWggMhPZEqJAHkLyCMxw6WbbnllhJv8H2Q5ByOe+65Ry699FLZY489dOa/tUTqzQJJzcbTYEAOCDCVm5ArgtwGq8cEnyFn4n//+1/w+yJ5274vk1cBL4MVeBfM9+lsKBgIIUnDnXfemeghEEJICxB2hJwFcTjUs4Dn4pNPSkg4UjyAJwDx+fZE2nBgdh2ViJDAG2tDNIBwGpQsnT17tiYXI7QpEoj9R/nRxYsXh3hAvvjii5CGZfECoTyoOmTyDwzIV0DeAHIGEPIVS/nYLbfcUveHkFqEVhnef/99fTYegS222EIWLVoUsg56MSD5GmFPAH0frrjiiuC2BvTMAPY8CggIVFpqSznZ9sKQJEK6IPYVbmvMRKXqP5euAlU0MCtFCCHJBhKckbOQSn/PMVMNI9WAWXU0BTM1/REuFAuHHXaYVj9CpaRjjz1WZ8pjBcYwGqSZGfdwINQJoT9IEkbcPwxgNJzD7L5V1KAiEQz9joZjmXAhhPigtCqAYEG4EAz7ww8/XHsf2D0JpaWlenzkFfTv318f8BhAUMGTctZZZ2l+wq+//qr7QiiWSaxGF2rkROBhchjgdUHFJPRxABBHEBgIh4I3B1Wi4AlCYjTOg93bgvF3hqAKBwUDIT2kqgYhhJCOoSIhBYSCATPo6A8AYNgidAUG/MUXX6yhQ6jvHwvYFiFJSHxG4zZ0Jo7U9TgS0XIYMI5HH31UG5HhgeMhdh+hQdYcBjQ0Q2iPfQa+rcAjAEMePR6MYIAHBKIGs/zmnFm5IdBMDQY+Poc4MEU64EGBNwXeCSxH9SOIgvPPPz+4PcQDPBjIVTjnnHPUe4FQI4QgbbfddsFzBM8GxBISnHEsXC8kS6NLtBV4YCCgsK+uwOGz+kZ6KLiIoKuaX5Ce41lYNnmKVtVAghxiXuHGHnD/1JSYmUoEzzzzTMwzXoQQQkh7QedlGOKoYtTWhGEIJogxdGlOFBAVEDkoq9reSlJtsX+Zw0BIJ1fVgFgwVTXwXqtqkLBEaoxDCCGExLvbNWb2oyVjR6qw9OSTT4bti9BVYAwoeYueG51RdjYcFAyEdFL3z+5YVaOzMU1qCCGEkM7GdIk2JWZjIScnRzs0dzSPoqP/K9GnYd999+2yYzKHgZBOylMwVTXwGapq4LNUrqpBCCGEdCeQH4CQpLbgcDiCOQeJAjkoXQ0FAyGd2P0zFatq9LQ/goQQQgiJDkOSCOnkPAWIhNyRIykWYqwNTgghhJDkgoKBkA7APIX4gnrThBBCCEkuKBgI6QDdrftnoomlqyYhhBBCuhbmMBDSQZinED9YJYkQQghJPuhhICQOME8hPiSyCQ4hhBBCwkPBQAhJGhobGxM9BEIIIYTYYEgSIZ1UbpWlVNtOVzahIYQQQkhsUDAQ0kWN3Ejr7L777jxNhBBCSJLBkCRCOrGRG57xHstJ69xxxx08TYQQQkiSQcFASBc3ciOEEEIISSUYkkRIPH9Q/fppGJK3stIvFvCcmanLO0JPyYk477zzEj0EQgghhNigh4GQJG/khpyIZZOnyIrLr9BnvO+ufPXVV4keAiGEEEJs0MNASBI3crPnRMBjgffYf3f0NMybN09OPvnkRA+DEEIIIRYoGAjpBNSYj4NBb3IiIBZMTgQ8F5oT0Q0Fg9vNP0mEEEJIssH/zoTEkXjnGnRWTkSy8uijjyZ6CIQQQgixwRwGQuJEZ+QadEZORDJz9tlnJ3oIhBBCCLFBDwMhcaAzcw3imROR7FRVVSV6CIQQQgixQcFASBzo7FyDeOVEJDsjRoxI9BAIIYQQYoMhSYTEAWuugc/r7fa5Bp3F6NGjEz0EQgghhNigYCAkDsQz1wDhTdXz5+tzT+Of//xnoodACCGEEBsMSSIkTsQj1wCJ0sh9QHgTPBQQIdgvIYQQQkiioIeBkDgCkZA7cmS7PQvWxGk8431P8jRMnjw50UMghBBCiA0KBkKSLHEaCdMmcRrvNXG6h/Dzzz8negiEEEIIsUHBQEiSwMRpkXfffTfRl4EQQgghNigYCGkn8U5O7mlN2gghhBCSGjDpmZB20FnJyT2pSVs4HnvssUQPgRBCCCE26GEgJMmSkzuSOJ3qXHzxxYkeAiGEEEJsUDAQ0kaYnNx5rFu3rhP3TgghhJD2wJAkQjrQ1VkrGcXQ1Rneh54aZtQWdtlll0QPgRBCCCE2KBgIaWdyMsKQkJwMsRAtOZnN2GLnsMMO4/1ICCGEJBkUDIR0YnKyNd/BmZ8n3g0VUv7oY7otPQ0tueaaa2TatGm8JwkhhJAkgjkMhHRicrLJd/A2N0vzH8vEU1GhXony6dN53gkhhBCSElAwENKJwPvgcLnEs26d+Hw+EadT+yvUzItf/4buxCmnnJLoIRBCCCHEBgUDIZ3thRg1UkOS8HCIiKu0VHwej3ofuqopXKqwZs2aRA+BEEIIITaYw0BIJ1M0YYJUz5svvuZmraokjY0iaWkRqyr15CTpN954Q4488shED4MQQgghFuhhIKQLvAzFp54izqws8VZVaUhSpKpKnd0UjhBCCCGkrdDDQEgSVVUySdIQCw6nUz0SSJLW8KUe0L/hgQceSPQQCCGEEGKDHgZCElBVKVKOgrUpnM/rDWkK1xPyGq6//vpED4EQQgghNuhhIKSLiZajEKkpXM3ChT0ir2HFihWJHgIhhBBCbFAwENKF2HMU4EHAe2sjN3v4Elg2eUrUbboL22+/faKHQAghhBAbDEkipAsxOQrITTA5CnhvL7FqDV+KdZvuwMSJExM9BEIIIYTYoGAgpAuJlqMQz21SlcsuuyzRQyCEEEKIDQoGQuJIa4nJJkcBpVWRo4DeDDl77x11n/ZtopVlJYQQQgiJN8xhICROxNpwzeQolE+fLjXz5kvV3LlSPW+edoRGk7dwQiDWsqypDkOSCCGEkOSDHgZC4kAsDdfs3ofajxaIw+0Wn8+nnoMNM16SP06bpMIjHNa8hu5KXV1doodACCGEEBv0MBDSTmD4mxn/1hqu2b0P2Xvvpa8hFrxlZcF9eqqqum0FpFh4+eWXZdy4cYkeBiGEEEIsUDAQ0g7sAiBrxHD1KnjKysRVUtKi4Zq9lCpCkfDeW14esl9fQ4N4q6t7TGdnQgghhCQ/DEkipI3YBYCnslIqX31NvLW10lxWJo2//x6SmByuLKrP45GM7bfT9YK4XLpPcbu7ZQWkWLjzzjsTPQRCCCGE2KBgIKSNWAWAr7FRvIi79/nEVVqq3gVndrb0vvgiKQ70FIhUFrXomGP8IUxZWSoSVCw4HFJwxOE9MhwJ3H333YkeAiGEEEJsUDAQ0kasAsDb2Cji8YjAc5CRLu6SEv8PKz291bKo+WPGSPGpp+g2ztxccRUVSckpf5M+55/fY6/Jb7/9lughEEIIIcQGcxgIaSNGAGgOQ1WVCgBnTo440tJb5C6YpOhIZVF7SrnUWNlyyy0TPQRCCCGE2KBgIKQdWA39mkWLpOrtd/yJym63hhTVLFwYvidDGEGgIqGHCwXDlClTEj0EQgghhKRKSNLq1atl9913lwULFoQsv+6662Tw4MEtHg8//HDCxkp6JqYvQp8LLpC8gw/SpGV0bq587XVZd9/UqD0ZSHjO78HhWIQQQkiykpQehpUrV8opp5wilZWVLT5bvHix/OlPf5IzzjgjZPmAAQO6cISEbARCAB4GeBK0/8Lq1Rqq5CouDtuTgRBCCCEklUgqweD1euW1116TW265RRta2cGyJUuWqFgYNmxYQsZIei7WnARrrkGLpm2FBeLZsCGYz2DNayDROeaYY3iKCCGEkCQjqQQDxMBVV10lEyZMkD333FMmTZoU8vnSpUulpqZGtt1224SNkfRM7I3agjkJtqpJ8CZIQ6M48/PE4XarZwGfmZ4MJDpulJclhBBCSFKRVDkM/fr1kzlz5sgll1wimZmZYcORwOzZs2XUqFGyww47yGGHHSbz589PwGhJT23UZs9JCFc2tddZZ8nARx6W/jfeIAPunxrsyUCi8+yzz/IUEUIIIUlGUk3nFRYWRv3cCIb169fLP//5T6mvr5ennnpKQ5Qeeugh2XfffbtopKQn0SLkKExOQrjyqExyJoQQQkh3IKkEQ2scddRRsuuuu6owcDr9zhEkQP/1r3/VDrEUDKQzsIccRcpJsJZHjRbCRCJz88038/QQQgghSUZShSS1xsCBAzUUyYgFkJaWJvvss4/88MMPCR0b6b5E6tQcKSehtRAmEpknnniCp4cQQghJMlLKw/Duu++Ky+WSAw44IGR5Q0ODFBUVJWxcpPvTlo7MsYQwkciFDwghhBCSXKSUYHjzzTfls88+kz322ENyc3N1Gaom/fvf/5YRI0YkenikmxNrR+ZYQ5hIS9hPhRBCCEk+Uiok6fTTT1eBcOqpp2o1pVmzZskJJ5wgdXV1cu655yZ6eIS0K4SJbAQV0gghhBCSXKSUh2HHHXeUp59+Wu666y657LLLxOPxyPDhw7XR26BBgxI9PELaFcJENnLWWWfJtGnTeEoIIYSQJCJpBQNCjMLFM++yyy40KAghhBBCCOnpgoGQVCYVyqqialOyeUDQiJEQQgghyQUFAyFxxl5WFUnPeI8QpWQxzJNV0LDaGSGEEJJ8pFTSMyGpgCmrigpJpqyqp7paqmbPTopeDMncJ4J9GAghhJDkg4KBkDhjLavq83qlafly8VZVyfpnpsuyyVN0dj/ZBA3ea58IQgghhBAbFAyExAhm4Kvnz291Jt5aVrUJxnlNjThzc/19GJJgNt8uaJKpT8T111+f6CEQQgghxAYFAyExAK8AvAMrLr9Cn1ffcUdU8YB8gAH3T5XiiRPElZcnaf37J81sfjL3iXjppZcSPQRCCCGE2GDSMyFtjPlvWrFCyh9/QjbMeElcubkRE4aNAV7x6mtJ1/U5WftEfP3114keAiGEEEJs0MNASBti/n1NjRpiBPHgzMtrNcSoM2bzYw2Nag2MIXfkyKQRC6B3796JHgIhhBBCbNDDQEhbYv6dThGvV8TlEmd6ujjS01UIaIhRBMM7nrP5yVoONV7ceOONiR4CIYQQQmzQw0BIK1i9BL6qKn12ZmerWIg1xMjM5oP2egeSuRxqvDjttNMSPQRCCCGE2KCHgZAYsHoJahYtkqq331HPAsRCrCFGHfUOmNAoiAWTQN2ad4MQQgghpKNQMBASIyoKAp6CgnHj2hRiZPcONJeVSdlDD0ta376SP2ZMm0OjkimBOp4cfPDBiR4CIYQQQjojJKmhoUFWrVola9asES/iuwlJYuKRNNzWhGFr4rRn/XrxlJeLp6xMVl1zbcyN3JK5HGq82HTTTRM9BEIIIYTEw8PQ1NQkb7/9tsybN08+++wzWbduXfAzh8OhlU723ntv2W+//WTkyJGSlpbWnsMQEncSlTRsvAPwLEAswNMgbrc4XC4dD8KdYjH8k7Ucarx48MEHZY899kj0MAghhBDSXsEAT8Jjjz0mzz77rIqE0tJSGTx4sBxwwAGSl5cnHo9HNmzYICtXrpTZs2fLyy+/LL169ZKTTjpJJkyYIFlZWW05HCFxxR4WhJCethjrHcF4BxCGJB6PigV3SYm4ioranIdgQqMIIYQQQpJKMPznP/+Ra6+9VpxOpxx55JEqEnbaaaeo2yxevFjeeOMNefLJJ1VkXH311epxICQRdEbSMERIrLP98A4gZwFhSPAsQCx0xzyEjnDFFVckegiEEEIIaa9ggFg455xzZNy4cSoaYmG77bbTxwUXXKDeBuzj/fffj/WQhMSVeCcNtye8CQnOTatW6XZtrbLUE3j33Xdlq622SvQwCCGEENIewYCchfT0dGkPLpdLjjnmGDnssMPatT0h8QwLioexHi68qfzRx9RzkD1sWNR9Gk9Dw5IlkjF4cMxVknoCyIkihBBCSIoKhvaKhXjvg5COEK+kYXt4k7e5WTxr18rau+4WV35+VG+D3TMBj0N36tbcEQoKChI9BEIIIYTEs6xqRUWF3HLLLXLQQQfJzjvvLB9//LF8+eWX8ve//11+++23juyakE6jrSVRWwtv8tTVigeVwhwOcZWURO3AHGu35niUfk1F7r777kQPgRBCCCHxEgxlZWVy1FFHyVNPPSUZGRnS2Nioy1El6Z133pHjjjtOfvnll/bunpCkxtoTwbOuTA1/V2mpuLKz/fkR9fX+ZOoo/Rh8TY3ig3eiujpkXXgglk2eIisuv0KfY+3T0B3429/+lughEEIIISReguHOO+/U0qovvviiPPHEE+JDXXkR7b3w3HPP6ft77723vbsnJOlBGNGA+6dKr/POVW+B0+0Wn9cbNZkay5Dn0Pj779L462/SvHy5eKqqpGL2bPUoVM6ZE5MHorvCxo+EEEJIN2ncBtC0beLEibLDDjvI+vXrQz7bZZddtO/CjBkz4jFGQpLa04CHz+OJKZm6ZuFC8dTUiK+21r8AFcfS0qTy1dekeu774nC7xVtbK+mbbhq30q+pxP7775/oIRBCCCEkXoKhsrJSBgwYEPFzNHVDjgMhPTmZ2tqnAUBUODMyxGMEg8OB1unqSXDm5YmvoUEfnrIyzYfoaX0adtxxx0QPgRBCCCHxEgyDBg3SBOdjjz027OcffPCBrkNId6ZF4zaLF8BeDSl7772C+QsQAhrE19zsX9ntFmd6ujhyc8UHD4TH0yP7NNxzzz0ybdq0RA+DEEIIIfEQDEh4vvXWW2X77bfXvAXgcDg0GfqBBx7QkCU0bCOkuxKpcRtERO2iRf6+DG53sE9Dzbz5mr8gjY3iLCpSL4J6GBCZlJ0tjvR0XQ8doHtffJEKiI6UfiWEEEIIiQcOn8lWbiPY7LLLLpOZM2eqUMD7zMxMaUBIhc8nBx54oNx1110xd4VOJKNHj9bnuXPnJnooJEWAKEAFIw0lCngMYPznHXyQVL39jngqK7X6katXL0krLdVkaHgM8kbvLzUfLdDP8MPL3mOEpA8cqNsY4QGPQvHEidIT+fbbbxmWRAghhCSZ/dtuDwNEwk033aTdm2fPni1Lly7VCiebbLKJDmDkyJHt3TUhSQ/CkDzV1eLIzBDf+vXiyM4ST/l6qZj5quYoaP5BTY32Z3DmZIs0NPq9EBMmqMCoeGWmiMcjDd9+J9lDh2q1pY42k+sOoJcL8xgIIYSQ5KLdgsEwYsQIfRDSk6hZtEi8lRUiGwIOOodDRQMCjJym+3NJiQoG9GlA92d4DgC8CRAPxjOBsCYkTKOZXE/no48+ktNOOy3RwyCEEEJIPATDZ599FtN6u+++e3sPQUjShiNVvv6G+LOWA/h84vP6xJGR4a9slJ8vzrQ0cfTqJSWnT5LsYcPUc4BeCwg9Ql5DTyyb2hoIaySEEEJINxEMJ5xwgoYltcbixYvbewhCkhLt1lxXpz0UkMSsaUBerwqE3FEjNUfBVDgqOe1UKR4/PrgtQo6w3IiKnlY2tTUefPDBRA+BEEIIIfESDNdcc02LZR6PR9auXSvvvfee5jNcddVV7d09IUmLGv1ZWf7EZZRFNZWOsrI0RwEPFRWohpSerh4Jk5eAZ1RTiqXJW0/kjDPOoGgghBBCuotgOO644yJ+NnnyZDn++ONlwYIFsscee7T3EIQkb3fnIUOk+r33NBRJSU+X3P1GBT8vW7gwbMnVaE3eiEh9fT1PAyGEEJJkdErN0/T0dDniiCO05Coh3Q14DOq/+cafp1BSgsB7bcBW9c67Wmp19R13qFiAmECuAp7xHtsZIBKQ5BxOLGA95DpY1+8p7L333okeAiGEEELiXSUpEtXV1VKJ2vSEdDPWT5+u4UQaioSH16vLHXl5+hqlVRGqlNavX5sTmyM1g+sp7LnnnokeAiGEEELiJRh+/fXXsMsbGxs10fnxxx/XLtCEdCd09n/e/I1iIZDwLC6XODPSxZGWLt6VKzUZOpbEZuzPhCYBq2fCWnK1p4Qt3X777TJt2rRED4MQQggh8RAMBx98cMQqSabr8wUXXNDe3ROSlMC493k84iotFW95uXZwBiinqmIB4iA3N9jxOVpis92bkL33Xiy5SgghhJDuIximTJkSVjA4nU7p1auXjBkzRgoLCzs6PkKSClMWFV4A58AB4t1QodWQXDk5IeKgeOJEKRg3LmJiMzwLdm9Czbz5MXsmuivnnHNOoodACCGEkHgJhrPPPru9mxKSsljLonorq8SZnS2lk8+U7OHDQ8SBNdQoXDiRll0N08Atb/T+/j4OyHdwu6XgiMN7TDgS+Pbbb2W33XZL9DAIIYQQ0h7BgNyE9lZMIqQ7EbEsauA5WuKyERINv/2m75vLysRdUhL0JqCHA8RDxSszNXG6atbbKip6SuLz+++/LyeeeGKih0EIIYSQ9giGIUOGxNTZ2QrW//7779u0DSGpgIqECCVRIyUu1wR6M3jWr1cxgU7RSJj21dSIq6hIQ5kAch8gHkxYUk9KfEZIIyGEEEJSVDAcdthhbRYMhPQ0woYarVwp6597Tqrf/7cWBPA2NATXdxUUaLWl3hdfJPljxmj/hXChSrWLFvWIRm+orkYIIYSQFBUMN998c+eOhJBulBRtEpebVqwQb3W1VLz+hvjq6sSRnx9Y0S3i8YgjJ0eXOwOhe/bt8exrapJ1U+/XZ2dWlhSfekq3DVE699xz5e677070MAghhBBioVP8/16vV6qqquTdd9/tjN0TkvRJ0fAaqLehulrLrLr79NZlPjQzRClWkxPU0BBSCSlk+7VrNY8BHgnPhg3iramR5nXrZN19U7ttF+iKiopED4EQQggh8aqShC7ON910k8yZM0dqa2s11CIcaOJGSE/AJDQj3wCPqtmzpfyZ6cGOz97SUvGsWeNv9gaam8VTUy15+x6s29UHBIM1qbr2yy+l7OFHtDGcyXnwVlVpiFJ3DE3afffdEz0EQgghhMRLMPzrX/+SmTNnynbbbSe5ubny2WefyZ///GdZt26dfP311+J2uxnGRLodkcqlhquMlHfggVLx6mvB8CIVCg6HvobA9lVXi6+2TipfmSmVb7wp7sLCYLiRSapuXL7cfwCfT3OIIgnz7gL+hhBCCCGkm4QkzZs3T0aNGqWiwcQcn3HGGTJ9+nR55pln9P2KFSviN1JCEszqO+6QpX87RVZcfIksmzxFRUK4ykh41vciIeFF8A44MjPFVVwsUleny1VEIESpoUHXWTc1NNwoe9gwcebn6XrIYdCGcfn5urw7csMNNyR6CIQQQgiJl2CAJ2HffffV18XFxdK3b1/56quv9P0uu+wiRx11lLz++uvt3T0hSScWyh9/Qjzl5eKprhZPVZWKAuNxUM9Cfn6wshHeYzm8BQPunyr9b7xB+l59lbiLi/2JzBAJYbwFaAaHcCMDPA2lU6aIu7RUnDk5+tzrrCndMhyJEEIIId0sJCk7OzukzOqgQYPkR8vM6ODBg+W1117r+AgJSTAQBWikpgZ+Wpo4kEdQU6P3vwlPslc2sicym54NTatWSfmjj/n3ZRcMgRwFc0yz74iN4roh8FISQgghpJt4GLbffnt57733gu+32GIL+fLLL4PvEY7EJkykOwBD3efx+BOP0WjNGPZud9CADwk9cji0CVs4wx7G/8BHHpbCo4/yhyYZ0R0IT3Lm5UnTypUa8rTi8iuCoU/YV+7Ikd1aLIDff/890UMghBBCSLwEw8SJE+Wjjz6SI444QkuoHnLIIfLDDz/IBRdcIPfdd588+eSTGppESKoDUeDKzdUeCApyCRwOKTji8KABbw09wnPxxIkR94dt+l17rQx64nEpOOZocZWUqFBAuFHRscdop2d7PkR3LaNq5+233070EAghhBASr5CkAw44QP75z39qZ1aEJw0bNkzDCR588EH9fJNNNpGLL764vbsnJGkwHgRNZHY6xeFySeGRR0j+IYdoZ2bjZbCGHsW63/7XXiv1EyYEw43wvOGVmS06PWN5W/ZNCCGEEBIvHL4412lEKBKaL2211VaSlpYmqcDo0aP1ee7cuYkeCklirHkFNQsXtiijGo/uyzgGwpBMNSTkQ8CbAa9Fdw9HAk1NTSnzd4MQQghJZdpi/7Y7JOmEE06Ql156Saqrq0OW9+/fX3sz8J8+6WpgbGPGv7PCd0weAQhXRjUex21LPkR35PLLL0/0EAghhBASr5Ck5cuXyxVXXCHXX3+99mP461//KiNHjtSGbYR0NeEap8Vjxj8cpoxqZ4UNYdxpfftKw5IlkjF4sOSPGSM9hTXohE0IIYSQpKLd1v37778vX3zxhbz11lvy7rvv6qOgoEAOPvhgGTdunAwdOjS+IyUkAvbGaQjjwXuUIu2MmfnWyqhGGmOsZVEhflB61VtXp4nWKMVqxE9b9pOK7LzzzokeAiGEEEJsdMgdsNtuu+kDYQQLFixQ8TBr1ix54YUXNDQJXodzzz23I4cgJOEz/tGSoHEciAWEDQFrEnR7vB8QBOj27K3yh/qh3wPeQ/wgb8IICUdamuQdMFqKJkzoVsIBDR8JIYQQklw447ITp1P22Wcfuemmm2TGjBkamoSQJVMxiZDOxDrjjw7Kscz4dxR7GVXUDvjjtEmy/MKL9BkiIZz3o7V8B3R5Rrdn5C5AFOAZ7ytnzZJ1902V5nXrxFtVJZ6yMtnwwoshx+qKPI7O5sorr0z0EAghhBBiwx2vykionw7vwvfffy8ul0vzGg499NB47J6Qds34d/bMuymjql6B+6aKt9riFbjP7xVot/fD4VARYhq7Nf7+uwoFe4fo5g0bguFXnVW5iRBCCCE9m3YLhtWrV8s777yjQuHrr79W4wbxx0iEHjt2rBQVFcV3pIREAYaxMdC7Or5fvQIw5tEJOtAFGu+xPHvYsDblO+j6eXl+8YFu0vDg5eVJ+qab+lewV0FuahJPdbUeqyvzODqL//u//0v0EAghhBASL8EADwJEwqBBg2Ty5Mmar7CpMWoISQBtbZzWHqImHft84jCegXbmO+B16VlTQpKeS047VbKHD5f1Tz+j3osWx4RQgWejC/M4Oov169cnegiEEEIIiZdgOO6441Qk7Lrrru3dBSEpRaTkZfUK5OdporKvqUnXhcGO5eG8HwgdQnO2SKFDZn14DQD2AyFROP54KX/s8aDnwX8gp3adbqsnI1l59dVX5bDDDkv0MAghhBASD8Fw9dVXh7xvaGjQ8CQkP5eWlrZ3t4SkZOnW0iktvQJ2z4HJd4gldChcPkKfCy7QzypemSm+xkZxZGRIzqiRkh0oYZyIPA5CCCGEdH/i1mWtqqpKLr30Unn88ccpGEhKEi3cqLXk5VibrcWSBB1NVEA0FIwbp+vXLFqk4qFq1tt+kXLG6VqxKZX7NNx3332JHgIhhBBCbMS1LbM1dpuQVKK1XgmtNWuzb2+ardlFSCxN34yo0DAn5CxkpGtpVSMqsJ96xPs//Yz46vFKxFNbK2vv+JfkvPiC5I4cKanKzTffLDfccEOih0EIIYSQzhIMhKQisYQJRSvdGmn75jVrpOrtd1qIkNZChyAefA0N0vRbuX+BwyHOvNwQUYG+DEYsaOlVn0/fY3kqehYMy5YtS/QQCCGEEGKDgoH0eGLtlRCpdKt9e0lPV7GwYcZL4srJaSFCIu3HeCMqZs/2l1U1HjsVBKGXyVNRsfGNxbMXsjwFGTx4cKKHQAghhJDOEgyZmZly+OGHS+/eveO1S0K6hFjChKKVbrVu70VfhLIyrWTkQ7fm9HRxhREh9v0EQ5qqq8VTWelfiE7PgYpIKNdqFTA5e+0lG55/oUVfhvTNNpNUhn0YCCGEkOTDGa8d5ebmyk033SRbbrllvHZJSJdgwo0wkw+jHs+xVBiCRwC9FAC29zU3i2fdOn3vLC7W/UA8IL8gmgipnDNHyh58yF/5KC9vYzdnPNzu4LN1WyRV5x5wgL9RXACIE+Q1QHykKpdcckmih0AIIYSQeHoYKioq5LHHHpN///vfGnvsdru1edvBBx8sJ5xwgqSnp3dk94QkXadoEzaECkUt8hNOnyRr77pbXKUl4srKlia3Wzxr16poQBWj3FGjWuwPxn3ZQw+Lp7xcRYGzIN/fMbq52S8U0NfB4ZCCIw5vMaaB994jZU8+KeumThWHO03cffqkbIdnQgghhHRDwbB8+XIZP368rF69WkXC8OHDpbm5WX7//Xe57bbb5LXXXpPp06dLHmZMCekGnaJN2JCnulq8VVXizM2VtP79g0Z674svEld+vkhDo/gyMsXpdoujVy/J3GUXqf/qK6maO1dqPvoomPwcTJaGlwAiwesVb0WlODIzxVdXJ47sbHGmp2tjtvxDDgnbGTpjs83EkZ6R8h2eDfibQgghhJBuIhggCuBhePDBB2WUbeb0nXfekQsvvFDuvPNOueqqq+IxTkISirUSkjMvz5+vUFMjvqbGoJEO4x5iAA3cmpYvV69C/qF/1T4JDrdbXMXFIR4Ae7J0M8KZmpt1fwXjj5ecoUNb7QzdlvyLVACTDoQQQgjpJjkMH3/8sZx00kktxAI46KCDZOLEiTJ79ux2Dwyei913310WLFgQsrysrEwuuugi2WOPPWTXXXeVM844Q5YuXdru4xASCxt7I+SrMAh6BBoaoxrp3oqK4HZaQSkjXZOaaxctCjH2XUVFKihcJSXS95qrpc/55wf7KVhLtuIZ7yFgIuVf5I09WMdr1kklXnzxxUQPgRBCCCHx8jB4vV4pKiqK+Hn//v2lrq6uXfteuXKlnHLKKVJpqsUE8Hg8cuqpp0p5ebkmRyJnAp1hTzzxRHnzzTc18ZqQzsA+kw/vgXoYqqrEkZurSdIAxjy8CWmbbKLrVs+bLw6XS1976uvFu369Gv3rpt4vuQeMlryDD9JcCGtPBmuX6FhKvlrzLzS3Ytbb2gE6XAM6QgghhJAuEwx//vOf5fnnn5ejjjqqhaHe2Ngor776qhx44IFtFiHIfbjlllvCdo1GqNP3338vr7zyiuywww66DF6IMWPGyHPPPSennXZae78OIVGxN25DrkLRccdKdiBsCJ8jxyCccZ87en+pfGuW+NBbIQCSnCtmvKTr5v91nOQMGxY22TrWkCPT/RniI1oDumTnX//6V6KHQAghhJB4CYb99ttPw4XGjh2r4UdbbLGFpKWlaXgQhASSoseNGycvvPBCyHbHHntsxH0uWbJEcx4mTJgge+65p0yaNCnk8w8++EAGDBgQFAugT58+MnToUJk3bx4FA0loJaVIxn3uPvtI1buztS9DSAUkiOTaWql8/Q0pGDcurFEfrcN0exvQJTNTp06VK6+8MtHDIIQQQkg8BMOUKVNanRW8+eabQ96j+VQ0wdCvXz+ZM2eO9O3bVxYuXNji859//lk233zzFssHDRok7777bhu/ASHxq6Rkyq3aQ4zyxx4sDUuWaI8F7dgcaMSm+HzaqA2hTeXTp0v/a6/tUMnX7pAAjd84IYQQQrqJYHjqqafiOxIRKSwsjPp5VVWVehjsICSq2hLuQUhXsvqOO6Ri5qv+Cke5uSoaEGKEfIKK116XZvRYQD+FcAQatCFkqXjChIhioLWSr231RiQrm6V4p2pCCCGkO9JuwYC+C10N8hrgpQhHpOWExBN4ElDhCGQPGyYVb7wh5Y8/4Tf8nU7xNjereHCXlsqGF2foLL8JP4qI0ym+2lrdbyzdpaN5GmL1RiQr5557bqKHQAghhJD2CgYkIk+ePLndjdhQ2Qg9Gy677DJpLzh2OE8ClrFBHOls0Lht3X1TtWkbcObkiM/j8QuCtDT1MCCx2VNTI2vvvkdFQFRQZtXt9ocpWUOVIggD0zguXD+GtnojkpW///3vMm3atEQPgxBCCCHtEQxr166V0aNHaydWJDNvueWWMW33ww8/aFWjl156Sfbff3/pCMhf+Oabb1osR3fpWMdDSFtm7K3roSEbcg40cTmQf6DgPYQDHsZj0JpXAWCdwDaOnJzgcdCozSoMEOKE0qzrn39BHOnpKVsBiRBCCCHdXDDcfvvt2qztxhtvlIceekg23XRTbdq27bbbysCBA3WGH2VRN2zYoH0UvvrqK1m0aJH88ssvsvXWW2vX55GBRlTt5U9/+pO8/vrrsnjxYtluu+2CDd6++OILOeusszq0b9LziHXGPliByPQVcTo1BM4HrwDC5NLTxdfQEFzXWViovRg8kfqQOBwqEOCBcGRm+t+7XLLu/gf8PRswnowMcWRlaf4DhIoSCHtCBSQ0eku1CkixcOSRR8ZV6BFCCCGki3MYUOoUDdLmz58vzzzzjDz77LPac8GeP4DZ1ezsbNlrr720K3NHhYLh4IMPlkceeUTLpyJ0ITMzU+69914pKSmR448/Pi7HID0DGJzWDsqtzdhrBaJAszaED6kHAYZ+ZqYKBuCrrxdHdrak9e4tzWVl/qpIeAQSmxWnU9x9eoursChYVan6/X+rwNAyqKtX61i8AS9G0Gth8HpVKECspFoFpFjIysqKq9AjhBBCSIKSniEA8IBYgCcBvRfgWXA6nVJaWqpdnnfeeWftyxBPsL9HH31U/vnPf+oDQgWN2y699FLmMJA20Z6eBTmjRvobsJnchPR0FQuQyxAK6TsPkeY/lgUrFGWNOUDqv/7G75mAcGhuVs8CxIKWPM3NlYytt5aqOe+Jq7hYx4H9CKoqwXuBHIdwQER4vSlXASkWMBFxwAEHxE3oEUIIISSBVZJAenq6VkvqjIpJI0aM0EZudtCo7e6774778UjPIpaeBaYiUu3CT6Xuq680wdmVkyOZe+8tGZtvptWQjGfAU1YmjT/+JCWTTpOMzTYLhspYQ2dMbgIEBUKPckeNUqPXOg4VI1bPRDicTik57VQpnjhRehrdoTkdIYQQ0iMEQ11dnfz444+yYsUKfQ3PAnoh9O7dW3ML4u1ZICTetNazQCsiTZ0q3oqNZVFdffqoQGj47jvJGTHcLyCKi8Wzfr00r1+vHoSyRx6VktMnSW4gDM9asQivMRO+dur9UvvJJ1L57rtSPW+euPv3k+YVK/1CIi1NjWAHchgyMsSD8KP6+o0Dh5GclaWipDsCz2E0ukNzOkIIIaRbC4Zff/1V7rrrLpk3b56GI9krwSBECGLhwAMPlHPOOUc7MBOSrETqWRCsiGQRC8CzZo04N99MvJWBsqqZmepZULEQCCHyNTXptpFCZNC3ofq994L79fp8KhSQxJw1bJh6Lhp+/U3q4dGoqxNXQYF43G71SCAJGsdxolJSNzWQEZKEvKfu3JyOEEII6baCAZWJJkyYIC6XSw499FDZYYcd1KOAxGNQX18va9askW+//Vbeeecd+eCDD+TJJ5/UKkqEJCvhehZAQHhQMtUeEuTziWddmbjy87VpGzwMZQ8+pJ4FE0aEHg2Y9YYXYeDdd4VsDiGyYcZLfnGBpGZsF9gv8hyqZ8+W6kABAUdOtuSPHavdn+1lVruzgfz999+3uk6qN6cjhBBCuq1guPXWW6VXr14yffp0TWyOxLHHHqvehYkTJ2opViQpE5JKwAhFaFDYDAKnM2iw45HWt6+svPIqf0fnQJlVUD1njvxx7nnSa8rkoEG7fvr0jZ2fjVgIEEykRqIzSrbW1knNvPkqGHqSgYyCCbGQys3pCCGEkFQjQhmWlqAa0oknnhhVLBggLE444QT5+uuvOzo+QrocGKOFRx8l4rSUC3Y4tLNzv+uvC0k2zh8zRvL/fGBo6VR4Cbxe9Rj8fsKJsvqOO6RyzhypfHd26wdH6BG6PyNcqa7On8wbGBPyIsKJBXguqufP1+dU58orr0z0EAghhBDSXg8DkprXrVsX6+rBMquEpCJ9LrhAnzWEqKlJS6CiMhEEgp2iCRNUDHgrKlqGGlVWSvljj4mkZ4iguVtrFZB8PvEFtkdyc2u5ChAjFa/M9Cdg5+amfE+CM888U6ZNm5boYRBCCCGkPYIBfRfwj3zHHXeU0aNHR10XSdFPPPGEJj8TksqioWDcuJhCgbJHjNAwpBaN1oyAgFgwzdisYgHeBLMN8oGwns8XFCjRjgmxUP74E/79Yd9eL3sSEEIIISRxguGCCy7QxOezzjpLk52R9Ny3b19NekZ1JCQ9r127Vr777jstt7rNNtvIhRdeGP8RE9KFmFh5E/ZjFw4ov6oVldCcDU3c0AW6qWmjEW/p8IwqRz50isYyIxTwQFni0aMlfdNBsv75F0TQPT09vUUVMnt/CPV+YB2UMfZ6xVtbq54GfJaqeQ7jxo1L9BAIIYQQ0l7BUFRUJM8995y8+uqr8tZbb8lnn30mVVX+8pKG/Px8FRKTJk2SI488UtyBWGxCUhmIAmuVIhP2A8NdezVUVQcTntXET08XaWr0G/OBqkcQBj5L5SUIguy99hRXZqZk7rST5Oy5p/x+4onB5GfPhg26b3t5VjMWT2WleLE/7AsiBcfHMVC56aGH9TkVQ5MwGUEIIYSQ5MLd1s7OxxxzjD5AbW2t1NTU6Exodna25jkQ0p2AKICBDkMf3YWRk4D3MOQrZ83y92qw5SQg98AHb4PbrU3XfF6PqJKwJEVDGNS8/28t0Vr31ddS+8WX/v4O8ERgu+ZmfW/1FljH4iopES+8FRAL8FJAMODYxcW6vRljqnkaHnvsMfnTn/6U6GEQQgghxEKHspIhElARCbOCFAukO4L8BfUsoPsyuiyju3B9vZZIXf/c834REDDWDY7MTL8n4rhjNRfBmV/gL5dqMMLB6xVHXp6+r124MOiR8Fk9ExHG4srOVtGA9eCt0CpOJcWS3rdvcIymwhIhhBBCSEdgGSNCooCcBdPRuWndOmlasUIN++p588WZkREqBPQX5RQHkpYzM8UdCK9xFReHFQAAS1WM4HWgk7MmSaOjc16eNoizjwVeDoQgOdPS1OuBvhA4liszS5fjcz1+CnaDvuaaaxI9BEIIIYTYoGAgPYr29Cxw9SqV5rVrxbN6tZZORf6Ap6pKZ/hVFFhzdZCA7HCoEQ9jH4a7JjFnZ7fcMbwJcDTAwEdJ1AnjxV1aqv0e8Nzr7LNCQorwGvkT2A7jwTMqKfU+7zwpPvWUkOWp2g0aOVKEEEIISdEcBnRwRjWktvL888+3eRtCujJ5Odr6qICkRrgJE4JHIZBcDK+DhgV5vdJcWakhS8aTgLAiY+AjeVmrIwUwYUi+ujrxVVWJIzdXDXw0hGutjGukrs/dpRs0GkQSQgghJEUFw+DBg+XFF19U0YCcBRdKRhLSDZKXI3VPxuemiZpiREOgLCpEA8QEBAi8CJqZ4HJpiVWzbzwgOhxFbhUR3g0btFoSwpQKxx8v2UOHirexUZzp6XpMU8bVPhYkPwN4LcKtAyItTyVi6SRPCCGEkCQVDNddd502bbv66qtl3333leuvv75zR0ZIHDEJwxALJnkZxr4mBocxsq0Jxh50cDaJzUZANDdLzqhRkrXD9rL2nnv9icqBvgvak8Hp1H00Ll8unupqceblijQ1h3goXL16Sc2iRVE7NcPLoaVbUUEJMYR5eVJ61pSULJkaC7fcckuih0AIIYSQjuQwoJzq6aefLi+99JLMnz+/LZsSklDsCcOtJQab9eE5aJG0jIpEeXlSh8pGcDwEujOrmAg8HC6XioE1/7pTw5E8q1aLt6zM36jN5dLKRmvvvkfKH3tcPOvXa4lU5EbAM2HyK/CsTeHQ5wFCA96L6mpd1pYcjFTilFNOSfQQCCGEENLRpOezzz5bNttsM7n55pvbuikhCSNcwnC0xGDr+lqxKD9fG7K5+vSR9C23kLT+/f2lS9esEV9jY6ig8PkkbeAAv+cg4G2wfubMzhZHetrGrs/4POCZgDfClENVLwe2B4F1zHosmUoIIYSQrqLNrZiRu/DCCy9ol2cvDCl7WUlCkpS2Jgab9ZE/AGFQMfNVbYrmSEvf6KHo3VscGRnia2oKdnvGc8OSH7VpmwoJVE6CZyEQzuTFb8fS9TkY5hQQJ8hpCHo5srJ0fd0+ADwkiS6ZCg9HZyRY//nPf47bvgghhBCSIMEA8vPz9UFIqhFLYrAxhmG4V7z5lr+pWiDvQFxOcTY0qFgwpVPdxcXqZfA5HeJZs1ZDh1wlxdK8fMXGvgpWAnkMLYD3oL5e1txyqzStWqWCJf+v46T8scf8naIBGrW1o1pZIqtNtYUtt9wyLvshhBBCSPyge4AQmzG8bPIUWfb382X5OedI9bvvamUjzPJrGdTqGkkbNEh6X3yRlkE1oUvISfDV+sOHXKj00+zZGKYEcYCHdmNG74ZeLRu+BdB+DT5fMJchZ9gwceUX+EuxIqna4dB8h/Lp0+PSY6Kj1aasY40H999/f1z2QwghhJAEexjApZdeGvVzzIKmp6dLSUmJDBkyREaOHNneQxHSJQRLqcJbgLAgM6tvCIQQ1X/5paxevjzoBbCGLpU99LBWTGpGgjMEA0KYAh2hUToVM/I+iAVTdcl+CIQqFRWpQDEhPxrytG7dxj4QXq/UzPMLAxMO1Jmz/h2pNkUIIYSQHiwYPv/8c1m3bp3U1tbqexOiVFlZGRQMWmoy8HrEiBHy8MMPq4ggJBkxxrAjKyuiQa/g3m5qCunjYB4IW4JoUI+C2y3ukhJxFRVJ08qVkrn99tLw44/iDfxmwoKwJDSBc7u1JCsEQ+6okbJhxkuBQzvEWVrq7wERMNLb2mMiXtWmIBZaqzbVVi677LK47IcQQgghSRCS9M9//lOam5tl4sSJ8tFHH8mnn36qj08++URLr2ZkZMjTTz+tn8EbAYHx4IMPxnHohMSXYClVlEmNlMyPHAKXS5yFBf4qSYGKRgbM6ve9+iotxeouKlLx0fj772pY1//3v7pO5i47+0WJvflhoCmct6FBH+vuf0DDo2CYQwi4CgvFPWBA0NNhkqOtPSPMrH+4sSWi2lRbmTt3blz2QwghhJAkEAy33XabjB49Wq644goNOzIUFhbK3//+dznooIPkjjvu0M9OPPFE7eHw1ltvxWvchMQdaz4CHuJ0tOi/oKFFJcUiDY0RZ9bzx4yRkjNOV6O/6bffxAePAoz8jAy/5+DHn8SVny+ukhJxFBb6xYnTqSIj98Ax4srJ0YfJEah6+x1NfkbFJM+aNeIpK1MvBZKjEYrU1h4THQWiaMD9U6X/jTfoM3I54sXCQG8LQgghhHSDkKQffvhBDj300Iif77zzzjJr1qzg+2233VYbvhGSKqVXMYOvs+j4oaAr84cfSvW8+eJDQrM7LerMOvaBBmsITdJeCkhWLi8X58AB2ugtd/T+UvvRAm0M58vPl+w9RkivyZP1uCsWfa7iwZojgOTnrCFDZNU114obgqKkRIXDuqn363PewQepsMC6poKTfWzxLIUaS7Wp9pCH5G5CCCGEdA/BAM/BF198IePHjw/7+ZdffilFRUXB98h3gPeBkGQnkjEMz0Eko9u+HK+RZ+Dq1Ut8y5Zp/rTO/m+o0MZtxRMmqJcBzd0gKhq+/U5qFi5UoWHPEUAIFPIZFIfDLxbWr5dmJEJ7vZozgXULjzlahUU4QdBVSdEd5d577030EAghhBASL8Fw2GGHyQMPPCClpaXyt7/9TXr37q3L169fL88++6y88cYbcuqppwbDDLAMic+EdDcxEc4YT+vbVz9DCVRnUZF6ARBe5EjzeyYAPAJYH8Y+Pi978CHdDttjf/AWaEM4h0PzGUyX56bVq1VIBBOznU6tqoTEaHghwnkWuiopuqPgb8ajjz6a8g3oCCGEkO5EuwXD5MmT5ZdffpFp06bJk08+KZmZmVoBCVWSUB0JHVvPPvtsaWhokJNPPlm9C2eddVZ8R09IgrA2d0Poka+5WQ1/X02NrL3rLnFmZqlxj27PyIdAwnLemAOkaPx4NUzRL8GUJ21as0Z7PUAArLr2Oik5fZLmBpgyrch78DY3iyfgUUA/iGCXaLc72F0a+0DIkin3Gksp1PrA58liMKOQQleRKl4XQgghJGUFg9vtlrvuuks+++wzmTNnjixdulT/2W+66aZy4IEHBr0J1dXVcvPNN2sfBoYkkVQj3Ay01dCE8Q4vAioeIUxI8XrFU1fvb8KGUCRUXcrJkfQttwzuwyQqNy5dqiLDgGRmM/ufvskmGtbkyMkW75o1G5Ous7L8xwwcK+hpQM8Hl6uF9yBSKdSaRYvUy5FMBvOoUaO65Dip5HUhhBBCUlYwGHbffXd9RCI3NzdqcjQhySoQkFNgn4GGQWkMTWd+njSVl/s3DMzyh/RqQBO2QJUleBvW3TdVQ46QCwGjFInK5Y89HnJ8VFRqRuO3wBjU0N9QofkPEAvowyCo4IR9m+MCp1PcpaXa88HeSM1UfzJhTthn3tiDpWrW20lnMO+yyy5dEmrEBnSEEEJIFwkGeBSee+459TAsX75cQ5L69eun5VaPPfZY9UIQkipYPQeYqfc21IsrJzfEoNblgfCe4Cx/oH9CEJRJReM2EFgOIYBXyy+8SJouON+f3Iwyq+jijBAjC/BIINTJiApt2hYQJI6CAvFWVKhHw92vr4oJzZMoLFSxEKmkqrX6Ez7DMxKuk61jM7yWCHPs7FCjzm5ARwghhHQn2m3R19fXyymnnKIN2eBFGDRokAqIr7/+Wj7++GN58803NbeBnZ1JKmAPUWlGXkFlVYvypsAYmp76+pDE42Bjt7w88cGoD3ugeu2f4Coo0HAjDVey4/XqsWD4ImRId5uVKeJwQqXrGF29e4m7oFB8efnaGA5jVGPf7ZaCIw4PO6tuTdhG7kJ3NJhjCTUy3odYStESQgghpAOC4f7771excPHFF8sJJ5wQ9CY0NjZqh+fbb79dHn74YSY6k5QgXIgKchK0DGpGwLDOzJTsYcPU0Eeisxc5C9bGbjDs0Sk6UNkoxOsQcrBmrYrkRMPDtDR/Z2kba/51p3ozXLm5ktavn78pW3OzFBx+mFTMfFXE65NmVEpqaBB3cbFkjRguNdojollDjfA9ouUjhAtTMtWbkJCdqCToCy64oEPbtxZqZPc+QDREKkVLCCGEkA4KBjRlO/zww+X//u//QpbDowDPw08//aSlVVkZiaQC9hAVNFRz5uVpGVT7DDQeMObX3nW3v1uzyynehkatYoT1YbTHghelVrOywn+Ihm+1tS08HNm77ioNP/8i1e+/7/duOJ2SNXy41C38NFiiNdZ8BHuYEnI2lk2eElMSdGeVI0URhZ122qnd20cLNQrnfYCHoWDcOIoFQgghJAqBOIq2s2rVKu3mHC15cSVm9QhJAcyMOzwDGnrkcEivs8+SgY88LP1vvEHLnGYPH66z7zA84WlwBYSFIy1dQ4t8EBkZGZI+aJA4cnJiO7Atf8GgogWiAp4FlEwNGL7IbWhYvFiFinvAAH1u/Okn8VRX6zZGXMDo11n1GL537siR+tpqTOMZ7/Fd7WCWHsJixeVX6DPex4v//Oc/cb+ORugZ70N7zhMhhBDSk+lQp+cfwxgThiVLloR0eiYk2bHPuAdnzm2hLPAu5IwaGRIDD5DArB4Hp1PSBw6Uhp9/VkHRKrbwJUderjjdbvHl52kPBquHw5meHhJyAzGB8WoydgfyEWKtGtTZ5UjjkfMU6Toy0ZkQQgjpYsFwwAEHyAsvvCBDhw6Vv/zlLyGfIeF5xowZWimJkFTv5Gw1khEC1LxunVTMeEmN6tzRo6XgwDE6849k5uayMn/lI+QWoMNzVZU2b9NtjSiAQLCIBFefPlpFydvQoIY+wprUw3HWWerVsBq+GEuLkJvc3A4n8MZqTHd2OVLkPXXWdYyUt8HcBUIIIaSTBMM555wjCxYskH/84x9aCnHzzTfX5ej+jBKrqJqETs+EpBr2+PxgKEtenr+BmumsXFEhla++Ku7iIulzwQVS8fobIbkFmUN2Et8fHvGh0Ro6ORvRkJ6uHgTkKEAYeOCh8PlUaPQ69xxt2Gb3cLRm9BZPnKix+O3NK4jVmO7sWfopU6bI1KlTpcu9SIQQQgiJv2AoKCiQF198UR555BGZO3euLFy4UHw+nwwYMEBOO+00feTl5bV394QkhHA1/LVnQsBI9tk6KyM5Gf0MsoYM0dwCNyofZWRo9aLmFStVCKhYCHRpxraofJQ7en+peOnlkIZv6MdQ9933Ujx+vIqWSNWKIhm9kbwjsRrHsRjTnT1LX2Ppet1ZhDtPhBBCCIlMhzqrQRCcf/75+iAk2WhrJR97fD5Kn5Y9+JB2Z4aRjFKqQbHgcgWfEY5U9e674q2u1mMFcwvWrpWM7beT2o8W6Kro0uwsLVWRkda7t79jM8KVDA6HVM+dK7+vXCn133/v331ubthqRXajN9x3ba2BWbhtYjGmO3OWfo899ojbvgghhBDSxYIB/RXaAxu3kUQQS7dfO9b4fPRgaEafheZmWXXtdVJy+iStmLR26v1SPWfOxnwEiIPaWqme/x8NMYKXLa1//2CoTtExx0jjjz9pqVVTrhW9FxDepAnM6MFgejkEvBG1C/wCA8uRCwGhEi2pOJJXxJ6cjP0gORoVnlBCta3npytm6UcGKjYRQgghJAUFw5AhQ3SGtC1g/e8DM6WEdBXtreRj4vPhWVCxAG8CPAlOZ3D7gXffJavvuEPDkOBZ8NXUaLM2Z0G+NnTzVlWp8NBE5LEHqyjI/+s4TUjGZzDY3f37SfnjT/hzGKzJ0HZ8Pg1Tam5okPXTp0u/a6+N+btq1SRLcrK3qUn7RKB3hBM5FfX16r3ojEpHHeGWW26RadOmJXQMhBBCCGmnYDjssMPaLBgISQRtKRFqDasx8fkIQ4JnAWLB3atUXIVFIdsjwRkJxqtvu11qP/rIb9Qvqw3mKDjy8iR98DbacRnCwnQU1kZh782V+q+/8Q/AhDe1hs8n1fP8/R9MpSQz7kjfFZi8C4Q+QQTBY+EqLRFPWbnfA5KX2ymVjgghhBDSQwXDzTffHPXz6upqufHGG+XUU0+VLbfcMh5jI6RdxFLJJ1LIEh7IWUAYEgQAxEKkSkCNpg+J8RAEBEDzH3/oA8nPGVtsodtXvv7GxnUhvGMVC/DUFeSLt65Oahct0lAihBbhPTwF8F6E+64IO2pauVI2zHhJfOvW6XFdvXuJr7ZOcy3wHknZvqZmrdhk7YacyApCqJJECCGEkG7S6dlOfX29vPrqq7IGZScJSSDRuv2GC+OxdzXOHzNGcxYQThRuewCjGiFIyEWISEOD9mFQQ76uzm/kFxa0aNTWAuPJw3N2tviqqtXIX/OvO2Xt7XdoHwhvTY0+b5gxQ70X9u8KYYHPUPoVeRCaH1Ht30aBN0RES7oiv8Js01kdnGMFDR87C1N5Klz3akIIIYR0UpUkO0j4JCQZiFbJJ5aQpdYqARkvhg/5ASivihCmMGhnaJ9PvQEwzL0bKsSB11VVLVd2OKTgyCMkc/vtpWbhp1K3aJEmXxuvBPIlzGttDof9VVZJWr9+MuD+qcGxgj9OmyTeqmp/DgaER3Ozf3sdvFvLv2IcCFWCOIJHAiKhszo4x8p7770nEydOjOs+VSBOn65hXSry2pHkTQghhPRk4uZhICTZgKGbO3Jk1OZjKH8aKeQo0vbmMxidjvR0fZgZe8UY6RDRgSpImTsPUQPfs2GD+BASFIbcA0ZL/xtu0D4MSK4unTJZjXrdXyD52r9Tn78fROAYDT/95N8+MFYVRHV1uswBMYPt8EBydl6edqB2FRVpxSZXfr6KhWBzuvz8oIjCexVRKQy8JBBPCM1S7wrOo82jRAghhJAu9DAQkgrEq/mYyXdoWLJEDfHKt9+R+m++CXZ6zhoxXEpPOkmrKa255VatSuQqLtZqRaiYpI3fAl45R1qaFB11VMj+jQcEs+Las8HqwWtqCr6vfPMtqfngw+CsufaCSEvzCwusFxAaruxsyRo2TOq/+qrF90Y3iM7s4Bwr8ayQZELPINRUXDkc4i0vF+fAAeqZYZI3IYQQ0sWCAU3cbrrpJtl6663jtUtC4oo1oTdSyFFbkn6tidMoY5o7aqTkoxpSVZVkDB6suRCg/NlnxVNZKa6SEjXapbRUcxCc2dniLCjwhwrBmLX0OjH7VmMXwgCeCggME/5kEQ/Ib4CoMP0akIsQ3M50pkYIk8cjdZ9/rmPNG72/FE2YENKwLRYR1dlJ0X//+9/lzjvvjMu+rF4T9SbBOQOP0oYKPfddLYYIIYQQ6emCISMjQw4//PB47Y6QrmnkZjF6ozV7sxvK1sRphLn4E5BfUq+AVi5KT9d1glWNqqtVJHgKCgSBRA6IhUAOAcKWcDx4IZpWrQppupa+2WbS+Mcf2hwOoU8+dIa2J03Dk1BbK8319dpYruG778SZkSGO3r1VjGBNvMcxjPFc89ECFQxWWsvbaE8zvLayHjkbccKEniH0yllU5C8t6/Op96U9HqVUINFVrgghhHRPGJJEuj2xNHKLtk64rsgZgwYFZq/zxIvKYIF8guayMhUIG55/QWexvQ314srJFRe6R69ZoyExAJ6F7H32lpr5/xFBA7WSkhZN13TfNTXi7t1LvOXrJWefvaVq7vt+0RAOn0+7RKNJmyCMyZRwRVgT8hJ6926170KkDs7tbYbXVoYOHRq3fVm9JvCyuEtLJXe/UVI0fny7PErJTlcIOkIIIT0TCgbS7YmlKlKkddD7QI2wxkZNQPai6/KTT0nviy/yx/xvqPAnIJvwnwAw9BEWBO8B8hY0pMh4BZBT4PNJzbz5athrzoGt6ZqvuUmafvs9aPRDPGhYkXU/drR0qr/Hgib3mvAlj0ccubkt8hPwnVBmNBZjOdZmeB3lL3/5S5u3qZwzR/NIrGFgsXhNupOB3VWCjhBCSM+EgoF0e2Jp5GZdRzLS/eVP09Kkec0aDTfS5GFT+QjhPenpamDCm2AXC4rXK75AToJ2VrZWRkIp1tpakbQ0rWKEY2BcGjqDccHgq2/YuE8Igdo6qf3wI3HC8Me+7MfD2CAQjGdBD+zxiw10mj5gtIYhmfwE98CB/uZ0zc26z9aM5VjOYTy47rrr2pT4/MfZ50j1++8HE80r9t9fBt57T6tek+5mYHeVoCOEENIzYVlVIj29kZt1HU9Ntc7so/xpc0WFlD/1tD8EyBjiMN7r63V2Hgb2wEceluy99w5/YK9XHIiht3Z2DngXdOa/rk5LrHrKy6Xp99/FU10teWMPlpoFC1Q8BJOcsU1Tk36u+7MSqP6DdSBnnEWF+hoiAMY8vBsooYp8BfRqKJ18prgHDZK6hQv1uNgnmsu1VmY0lnPY1cCzoGIBoIoUOs6//74ub43uVkY21lLBhBBCSHugh4H0CFpL6IWxjNwBh8utBjYM8+ZVq0KbEUI0uN2afAwPA8B++lz4D1n6/fcbm6yZbWBUT5wgmTvtJKuuuVbDmXzoj4D9GEzIETpGe71S+drrWlEpWN3IAsKRPPBMhAlJgkDQ0CcIjXyPODMy9VjWakcIwYFHBB4NE7bkQHWmmhp9bm02urVzGA8mTZoU87oIQ9JzhF4YaGwHcdXY6F8eCE2KlKPQVR6TVCsVTAghhISDgoH0GCIl9JpYdhjqCPdxlZaqeFDsoT/oklxcHGJYYr8lZ5wu6+6b6u+vAAGQlSWFE8ZL7/PP13XqvvlGKma+Kl7kNVgFA4DxnubWxGZ8pnkK4QjXTdoY/mlpemwYir3OOkuyhw/X/AuAxmzBngQmtArb4VjwXuA74nvFYCxHOofxYvny5TGvi5wF9b7gnEIsBL6biq9WchS6o4HdFYKOEEJIz4SCgfRorLHsrtISf+nTsjJx9O0b4ikIvm5slPyxB+tLa8KwMdasRrrpwVCz8FNtlqZJ0OGM/kCStCmP2mZ8Pik47FA1ek3ir91Yzt57L3+/iOwskYqKjdtiPE6nFBxxeFIYmG+99ZYcffTRMa2L74mcBQ1LCogseH/WP/2M5p1Uvf1O1ByF7mhgd7agI4QQ0jOhYCA9GnuyqLe0VDxr1+psPcKSNIQIs9iYuc7N1TwB5DYsmzwl7Mx1+iabqPGJUqzrpk7VjsI6gw/RgfyDCIJB6qNUP7JiPAK2ZRteelnHBk9B1bz5mqNgNZZRkQnj9ZmwKbNpfr4UHXtM0BOSaiDBuezJJ/VcO9xp4u7TR79vxSszw1agsodd0cAmhBBCWoeCgfRo7LHsTuQo9OolJadPUmN79Q03ahiPs7BApKFRPQQwvlHdyBjjyAuoXfS534uAEqYul3hQVhViwwAj3freTixiAdjFAmhuFi+8BoEqTpUzZ2pcf8bmm6uxjNdI4g5uG0iSRthUvxuub1GKNJE8+OCDbd4mY7PNxJGeEVIhCOcf16i75CgQQgghiYRVkkiPJlz1n5LTTpXi8ePVkC4+9RR/AzZ4ChwOyR01SkWBqa6DJmnYrnr2bA2D0SZpTU3aYVm7CiO2viswpV1N7kVjo4ZWNa1dK01Ll2qoE6o9oR9D2iabSNpmm2pfCZO8nSxcc801cakQ5MrNlYLDD/NfV3iRGhq0AlV3CDsihBBCuhp6GEiPJ1L+Qbg4d1Dz0Ud+49Qh4oFIsHRU9q5fL64+vUUQ+oNeDAhBsuZAdDZIqEYSNGbXGxr8fSUsx/ZVVIjH7RZXZmabm7d1BatWrWrzNpESmIsnTtTPEZ6E61A16231QsTanK07dYEmhBBCOgIFAyEQAQsXRq2oY417x2ean1ARMMZNLwQkLft80ryubKORbsKA0tMlbcAAafrtt9DSq/EGx0tLE3dJiaRtOkhqF3y88bPAGL3l5eIoLpa0QYP8IVfwmCRJp+Mdd9yxXYZ8uARmrIfEZ3w3E5ZkEp9BNDEQrsJSd0uQJoQQQmKFgoH0eGBYIg/BmqsQybC09mtQIxShR/aeCWj0ZgMz/r3/fp4a6OjyHNyuM2hqkuw9RkjNBx+GejcCJVjVE9LYKPVff62fo4wsPovU6bgrZ9qPP/74qJ+3VirVKuwidT9eP326dr0Ot49IXaAhEPUeSSJxRQghhHQVFAykW9Ee47Z8+vRg/oL2MkADNI+nhWGZsd120rB4cWi/BoQdofcCsHga7CABGsdATgQMTxUM7QlVimEbJDNrX4a6OnHk5WkYUpC0NBUUMKB9gfHC4+AcOEDzNOxVhKIZ6J3B5ZdfLtOmTQv7WThDPpLIidScDWKvet589QQhhwNhW/Z92IWGZKSLd2WVOIrc4u7du9XjEkIIId0NJj2TbgOMW5Q7XXH5FfqM960BIxRVj0wOAgxJk5cAwzJonCLW//33xdfYqP0asD7WC5ZddTrFkZPTqjEPY3vgIw9L3pgx4iwo0FAlU91I9wODPhqtCQx0PPb5pOLNt1TU+KqrRbKzN1ZGyshQIxpdodUYxvpIFN5Q0aKKkN1Ax3cve/AhqZwzRxKBMeRNwrmKgPp6v8iJMaE9d79Rel485eW6HZ7Rodu6jxZJ1Bv8givW4xJCCCHdDQoG0i2wG7cmxAbLowGjD2EmrpISfx+DQH5B5vbbh1RDcmRk+EOIMjLElZUdDOPRJGOE9RQUiKuwcKPxbyfDX/ZTxwnPxSefaGiSzvZnZGj1It0/uhWHI9J+7bjdKmJUKGB/GF99vQqEwqOP1jKqrqIiraLkxDM+D/SJsHc6throalSvX68G9qprr4tJjLWHk046qU3VkForlQqBNuD+qdL/xhv0OWfvvfU7BcOzkNNRX6+CMJLQgLdGu0c3NsZ8XEIIIaQ7wZAk0i2IFK9uD7GJZITCcHQOQFhOpeYbFB5ztKy55dZgOIsPnYQxI9/QoEYj+jVIcbEa/fgMYUqCbeExSHOLr9pfVlVxODR0afmFF/lDgWzdnFHuNITAzL8a88GBuv1N36J5GJBbkZGhuRj25aVTJmupWNC0apU/zAiiItAdGTT89JMKGiMazLlBeVaIhWDZVqez00JyKnEe21gNyToGeD9qFixQAZc/dqx+Zs1twP0QPEc4vxCDEAS28rL2JGqTFB/puIQQQkh3hoKBdAvCxavHMgtsNUI1fyErS41B9GCo++YbqZj5qnhXrtQuz1mj95eG7xcHjcbCI4+Q9S+8qKE6BhijmMXHOvU//CCVb7ypgsCsE1PGQrhkaBi4EBLhBIPxPiCfIpyHAh2P4XWxGMNpffuqp0Dg2QiEV22Y8ZKGYSHPAuuYc4MwJBUrLpe4e5WKq7AoJjHWHmbOnCmHHnpoxM/DVUMy/HLscdKARO4AZY8/Ib0v/EdIzgW2cRcX+68HvEYNDSqYwt0nVqGB16ySRAghpKdCwUC6BbHMPkfCGNANS5ZIxuDBKhYQcoOSnKjfj0TZvIMPkj4XXBBMqkYIC9aHt0GTn+FFQBItREd6us7mlz/7rFS89HJ8SqhCFCC/AZ4OO9b9hxEMEDH2GXTz3pmbI81/LAuKDnxfq/cgRFw4nSoWEh2SY6+GFE4sKI2NsvbOu0I8ISECsa6uTfdJuOMSQgghPQEKBtJtiDb7HAmTU6CVcwIlM+FZgFiAIZ7Wr58ayHhfMG6c7rPMhKcg6bm+XjwI7XG71Tvhys8PNaTj1W8BeRIQJDk54isvb5N3wj4mfOfG5ctVCCGhF6LHCAYHvDQmoTdw/iCgTBhTZ4fk3HPPPTGth++ARnvNa9ZIc3l5S7EQAPkcWM861vbcJ4QQQkhPhoKBdCuizQLbS67Ci4ASp6aCjklkRhgSZtohFuz5EJW//+4P0UGTtkDCs4qC5mYtlVp03LFBAxQdo1E5SROQO4rXKx7kPrSjczQSnk0Xa2uDOg1fMonRwOmU5lWrNPzK7j3oKiP79ttvl+uuuy7qOrhu6+6b2qKLdVtozVuQDF2ek2EMhBBCSMoKBpSNHDp0qNSglr2NDz/8UHpZ4rUJCddPACFGJuRIbP0IpKbGP/tuy4eoWbRIxQQqBek2ttl8xMJnDx0afA8jr9e558jae+7xJ0EDJA3jmO0hUgWlcOA4ASGA3hFoGOeEeEFyd3p6sEEdzgdCqTSp2+RCRKjI1BUhOUuXLo2pyV6sYgHiB8KtLXR174lkHQMhhBCS0oIBRgXEApo8bb/99iGfFaK0JSGtNPyqeGWmGtQoLaplOiFETT+C3NygoDAhOPljD5bKWW/7E4/Dhf6gn0FjY7A8p5kdxqx8zvTpsnbq/VL7yScqUFSktBWT8BxGqITFWmEpgFZFQp8JdHoONKiDlwFVgtz9+oo0e0TcrrAN3LqKrbfeOurnGBdCwWLyLAR6T8CrEusMfVubw3WGhyCeYyCEEEJ6rGBYvHixPo8dO1ZKEUZCSBtLrnrgRUCp0kA/ApQOhYEG4xnx+dnDh2tIEsAMNfax4cUZ/so64UKCUJ4TM/fp6S1mh7NGjJC6RYu0aZqjuFiafv89NqPfCtYPHKNFGVYL2tkZoUt2wWA9XqBZmwc9BrKz9Tx4q2vEXVKS8ITmSZMmRf284bffwoqhFrjd4u7TR3trtMXYbm953nh6COI1BkIIIUR6umCAUKBYIO0tueqyeBFgTLtLS7ULcNH48TojjU7RxthDrgKSaz1VVSIVFRtn+s2zw6HhPq68PPUwWGeHm1askMqZM/0GO4xYVBoqLRXPmjWRBxxOkDgckj1ihDT+8os0I3woQnlVJF77EF6FkB14JcIJE4vB7aupER+EU1WVvkZTNwgmgCpPRjB11cz2hRdeKNOmTYv4eSMEQyw0N0vzihXiQnhiY6NUzZ6ti833iDT7397yvPH0EMRjDIQQQoj0dMHwww8/SG5urpx55pmycOFCjcseNWqUXHrppdK7d+9ED4+kSMnV4okTtfKR1XA0xh4Mfy2T2tCgMfMAokC7M5sGatnZ/jKnyAsI7BMeBhUaeXni2bA+NNYeRuyaNRryJOlpIo1N4cVCuDwHp1OKxh+v4698b67UffJJqBgIhEqhnwKMfmdBgb+LMUROtPCdgDjB+igN2/vii7Qi0u8nnOjfFrvOz5PSKVOCM+SJTMZFQ7aYQfhVIKG9/JnpUvHqa/4OzhBDyINAWdWsLMn/6zjJGTYs+H3aW543Xh6CeIyBEEIIkZ4uGOBhKC8vl6OPPlpOOeUU+emnn+Tee++VE044QRs/ZcOQIySGKj8qEgJGnnn2rF+vQsGAkB2EAqVtsonmKTSVlYkPs74I7cnOloxttpaSk0/W8qMwpmEsamI0DHW7sY48iSjdjHX9cCE3Ho+suPgScWZnB8KchkvDt9/58xICDdUgcDzryrSrM7wiMDjxDM8BQpUgZjwbNgQ7HCvo7YBcDiRE19WpgarGdGC/ftFRrctw/qxVljojGfe4446L+jm6N5dNe1IkSlhWCAgzy8rS8LLmsjJZN/V+vYaa5I3LUVWl3w1N6+B1Mt+nIxWh4uEhYOlXQgghyURKCoY77rhDcnJyggnPw4YN02TJCRMmqGDAMyGxVPkJl2+A1wpCdZCkjDwHeBcCBqA7J0eaUUYVXoC6Oqn/8itZ+fOV2r+haekfaqB3qP9ChG1h0HvRcA2i5Zdf/SFVs+eomDEN1dBzwYQQwehFOdWyhx5W0YOx66z7hg3q5cAMuynTCgMa3x/ocoCcCXzW1KTLsK9kSMYtOPSvUvXOu35RE0M+A3IZIAJVxBnPDQQTrm9gezS3w/cK+T7t/E7x8hAkulEcy7oSQghJacGw++67t1gG0ZCfnx9MiCakPbHm1XPnbqxEBOMSRnNamuQdMFpqPlqgBiBKruo6KHNqZuErKqX8scc3hglFqqbUEdD7YcMG8QTKnla9NUvD8dTrAcGTlSUlp50aYpimb7KJhtwEKz5lZ0vWsKHS8P1if8UmCIVAJ2hN9h42TPeDnhIYO/avXycry/91OjkZ9/nnn5eDDjoo7GdWcacJ6+6NpWMjgnyOigrxmNAwk9ehj43bQhjF8/ukuocgXNJ2Kn8fQgghPUwwIBRp9uzZMnz4cNliiy2Cy71erzQ2NkpRUVFCx0dSB3usOTwGMC6DM/wQCxkZ4i4slKIJE/SBbdAlee1dd2spVl0HRruZuTYJy4Ek56BgaGOztagE9qP5FIHXmrjscEjT6tUR+05Y4/TN7DFyNSAWrEZg8amn+BujBXMY8lWIqJiwhNqYylKmlGxXirtmfM+mKE3sMjIkrX9/acJ3hMfEhGHBQ2M8SCgjCxwOfzhXmO/TkVn2RHsI4imk102dqqFbphs6+0IQQkjPIuUEg9vt1k6wRx55pFx//fXB5e+9957U19fLiBEjEjo+kjpYY80lI92fIGutgIQZ9tpayR9//EZjMWBsh8zCWzolB70TpoOySWKOpRRoW7EayhA7lX4vR91330vTzz/rLDwMPhjC6DuRNWSIrlo9f75+99yRI6Vyzhyp++9/JaOxMfgdMZMMLwoStN29e4dUSYKhCMOx4Zdf1MOC87Dmlls1UToeuQy33XZbq+IOHhVNGsf3RyhRuHwGj0fHhPAtn+lhgWtgvQ4BTw26cUOAmLAs831AT2yeZhfS+G14V1aJo8it9wP7QhBCSM8j5QQDwo5OOukkeeKJJ7RS0l577aVVk+6//36tlLTPPvskeogkRbDGmiNZOCgW0tP9XoOAQQwhsPa++yRj8GBNbMZ29ll4LWmam6seCZ2pttLezs7hCOQwhCWQZF338cc6HpRv9a1fL83r1+sYVlxyqT+BOy3NH1bldknzylXBUJ2K/feX7OG7tzCS7TPr6k3BGDAzj4pP9tj/DvDwww9rQ8ZI4q5p+fKNHgNH5K7Ues7NeQ8TFoZO1868fL1+RccdKxteelkkNzfYi8JUxjKiq7sbyVZPSouk7Q0Vug5esy8EIYT0TFJOMIB//OMf0qdPH5kxY4ZMnz5diouLNdH5rLPOSvTQSIphYs2R0IsKOlpFyMTuI0ypqUnKHn4kxKgeeO89IdsBhL4gTwCJuGiGpsaqyyU+JObGC4QdxRrWhORmdEQ2s+tOpyZNo0eDs7BQPIHQJQXiAZ6H997TbtQIzwlnJAdDVSzeFG95uTgHDohbd2hUPAsHjo+wqvLHnwh+H01Et1SzihkInYyAZ8kiOCAWjEEMYQJQGau7G8nh8hWsSdsQmCgTrN4aIyTYF4IQQnoUKSkYXC6XnHzyyfogpKPAGMUDoUVWr4EDTdswmw3jONAbAUnRCOMxngbrbHNzeblUz57jn4FHmEw8chYcDnFv0l+TqrUkq6lg1EpOBGb+VaxgLG63NpXzBPIzvHYPiInvN30b6urUSEY/B6uRbEJVTKlQHB0dozEDjWRqa9nQ9sb+Dxo0KOJnyMGomPGSSGamJik34/vEWl7VLqbQOC/gUUKPBofbFVoG1SR5d/PmaZGazA24f2pIkrMpp8u+EIQQ0jNJScFASGdgjd0H9YsXS81/PvB/aJq1IfzmxRkqGKwsv/wKqZz5atsSnCN1Yrbi80nzsuXBjtLBSj/Waj9htvHB8Mc6mZn+DtSYiY92LPNZoOpT87p1KgasRrIJVcFMM/o6oGKTft20tJCyoeFmrGON/Yf3MBI4vk984kWuCcbbHkGGJPX0dH8fBnzXgOdF0CEbjfUCZVCR5A0vU3c3kqM1mUOOi/GmmDK9rJJECCE9EwoGQmxGrq+5STB9DoM5XCO1hu+/15lZYzyuvuMOqZw5M7QiUjTwOWawjbcgFgJhQ6ZvAjoza4gRKiVFOY52nq6ujr6eBYQraaUoNHxrbJTiSaeFNLmD8R/0wuDcZGZK5i67SPbw4VFnrGON/T/nnHNk2rRpkVcwGqG17tXm2SKScM5y9txDahZ8rPkpEDoaeub1qrFccvokLUNr9Yp0dyO5LU3mUrXqEyGEkI4TKBZPSM/FauQ68/O0szGMbHdpqb8TsgVN8kU4DDpD//ijlD/7rJQ//Uzo7L3VmDX9GALJ1MHPLSVRY8b0f0AYELpNtyY4PB7NwfA1xVj2FLPtEEeB7wJvi+nDEOKFQRO4nBxxoBFcY6NUz5kjf5w2SUWXzlhDoGBfTY1+I7S+3h/W1EGQLwIvgDPPfw0igRwMZ0G+hpQZzwzGW3jM0VI6eXIw3EhDx8x3DVxnuzDAa8y0d0exYBWBek/Dc+NwtOpJCd73zz6rrwkhhHR/6GEgPR5rWIaWSjV4PJI2YIA0/fqrGp8uJMViucMhNYsW+ZuhoQJRpDh6JCnDyMdsdkaG+NoTbw8gNGC4o1Gc1YCPRXBAAJh+A9EIhDv54F2AgV1crDPNdu9A+fTp2jXZhGfpdioOmnTdrBHDxQPvAxKKA4nJCImKNfb/8MMPD7scYmTt3fdoF+3WvjW8Kc7CfuLM8GqSLpLQsazy9TekYNw4f4WrqVM1UVtBiBLyV+5/oEeVT21Pkzlch9AeHXlSOmVKjzpfhBDSE6GHgfR4QvoxoHuwAcY+GpsVFIgLIUCIe3c4JG/swSoWYDDrLHYkAjPYSB7O3G67dp1n9+abS1rv3mp449FW9LgBoz4q8K706uV/7XCIr7JSvRNW7wCSvStnvR2aPxAQDihTCs9Czbz5/nHi3METUl2t5yvWGXqUTQ47o42mYSYBPQY8K1b6K16lpWlFKCR8YwYdggfG7aZPPSV9rrhcSiadJm5c35wcFYymRGxPmzmPxZNirgOuqcn/gDcOy3ra+SKEkJ4GBQPpccC4QfMyY+RYwzIw64yQF/RU0FlUh0N6nX2WGpeo19/74oskZ+hQNaSDnaGjgfh4l8vf6KytIUgOh5ScMFEr1mxyy82Sf8hf2rZ9Wprk7DHCL2rMsaPlVxiDHIIBRndZmY4dggozyysuvsSfTG0H1ZWqa9Sjgpl6lCJNHzRI3JtsouU4cb5i5cknnwzvATLhVwgdMvkcrYAcDDTjMyVy1TM0z3/dtZfG+PGSveuu/u7Flh4D9hAq+/3SUwm5DkgehwcNt3hdXVxCzgghhCQvDEkiKUW4cp1tKeEZqYKPPSwDWEtKovuvzqy63ZIzcqS/mhL6HLSW4BzoRqzei7aSkaGz3qbXQ/pmm8VUgQkx/BpmU1Ul5dOe9HtGrB6BcASMPxj4+j0RyuTzSeauu+hyneGPFlLl9UrBEYdL1ay3gwm0jvp6caEZWgdLkaoHCJ21MS6EZbVWWcpgwqaA9p8oUHFg7aXQWtJvRyo+dTeC18F0OA+cWyzrbuVmCSGEhELBQFKGcMYbiNWgC1fBB4YwjP/sYcNaVIGBeQxjXcMw4FEIGEqVr74q6VtvJWLKe0bD6/U3UIOhi9lxPMeIu7hYuzMjbl/JyBBBuE8rzeA0dCcwExwiFloZp7cG+7UIEodD6r/8SsN4gjPLdgLdnguPOlLyx47V89qRUqQ33nhji2Xa72LIEE2ubheBa6T9IvJCBYy127d9zB2t+JQqxCq4w3U4h8hCCdrudD4IIYS0hIKBpASRjH2A0IhYDDp7zXnE6MOYX3vX3eLKzw8RG0aceBDLbwz0QFgLDNCm5Sv8s/GthSTBaMfMPLazlWdtjeYVK0IXwPhva2fjWMOgYFRrrsPGUqTOoiI9twjjMVWEwuLxyIaXXpbKN9/Sc4gQqvaWIn3uueda9GLAta//5pvY+lZEQ70msSf9hutRoB29Z8/Wz7uDkdxWD4q9w3lQaBNCCOnWMIeBpATWLsPBWPO6On1Eiz+34m30lxdtLisTT22txujDSHaVlqhhDQGCUpFI7lWvAvadlxfY2OtPYg6E6iDcR0N/YiFQ6z+ssRtjPH6X4POpcDKN4RBOhHOKMJ6sYcM2loU1noVA2VINO4Jga2yUdVPvl8pZs9rdt+Dbb7+NHjvfEZCb0dQkZQ8/rNe4taRfa7gSenI0Ll3qF6pPPiXLJk9RYzuVsOdi2EV4rAnfJv8DD4oFQgjpGdDDQFKCsLHmAYM9lqZTwZnU2lqd8feUl/sN5N69xJWVLU01tZogC28D6vNrWI+pLoTZ9TAz+6i+0yHa4XXobJDIHTSuvR5pXr1ahUAdZpStY4VggNgJlJtFqVUNvfJ6peyhh2X988+3q9xm3759o+cwdAQIPZRXfWuWVL79jlTsv78MvPeeiGE5eM47+CCpeGWmNCH8DIITifEBj1FHwpPaknfTWZ4EXD940LRcsK3LMxu0EUIIsULBQFKCcLHmiJ1G4mVrMfMhjdlyc8UDgw+GMfoH1NWLp67Wb+wiMTYvzx8KZBJmjWchksGPvIL29ldoa9WkLkCrICEsyeGU5hUrg+fBYzXWA+dJG7i53X6PDbw1xoNiKbfZVoP6mmuuiRg7v/aee8RX1UHREBgfqH7/ffU0NK1a5TemA0ntBYcfJn0uuECNbJTPRbWlYO4JvCzI9whUlGqPcd3VidThwvmQh4Drh++M3BxvSYk409IiCu72Hrc7d8kmhJCeBAUDSRkixZq31nQqGM6EnIM1a4Idkx3p6X4jEYIA3gaEZQRCjmIC+0hLa19DNlNdKQlFgyMrWz0saHiGhnPoKh0C+k/Au9PcLDmjRkr13Pc3igWXy39O0MPBlNtsg7F4xhlnyLRp0yJe+z/OOVeaf/utY1/Q4ilB+FT9f7/VZnMmqb388Sekubxc6hZ+GugxUbjRu4FtTTO+QMnZtpCIRGp7LgZED5KWXYWFes9DLOPh6NUrbgnMrC5FCCHdC+YwkJQiXKx5a02n7LHoavQ5HOLu11dFRP5fx6kxhdlyUyoyJlBaMlxfggCOvDzJ2mNE+A+TUCgEQ5Ewe47vhvCtCLkDWK4enQkTpO/VV2lzO5P7gJCuzii3ufbe+zouFmzUfPiRlscNdvgOJFajPCyavSFMx5mRHhSZKozgbXA4pPDII2I2rk3+AJKFW+TiRMm7CbePtvaDsN//psQvysymlZZK2qaD1PNWcvokKZ44sU37jjTO9uRGEEIISV7oYSA9Jpxp7d33hMwue8rKxZWXJ0XHHy/pW2yxsYttW0D/ggghSxnbbC0Zm28udZ8slJTB2rsgIBrCAQMUYggVg2DwZu60k79yTiDXo73lNv/yl/DN6RA6VD13rsSVtDT1lBjPgl0Q+ZxOaVq+XBvRoXs17g1nwOuCnhO9zz+/zbPt8Er4mptiyruJVxiTPZxPQ8mQ3N7QKL6MTH1GsjsqHsWDcNWlmBtBCCGpDQUD6REg5KM8J0c8Ho+Gy2gcenW1FB57jBpUaM4GNJE32x+S05oXwFlcLO7CQmlctixsUnT994ul/utvIu8gWb0MIEqDOBjMPpdTKme+GpoI7XRKOozTY45ud7nNTTbZpMUyzExXvftu3M+XG8m+2VmasB2ughWqQEFMoJQqhCXuFXStbkvTwHAhSGjwAS9MrL0q4hHGZA/nw/3ekX4Z0WitGR4hhJDUg4KB9AhgKKE8KLolI4kVlX8Qxw0D0BhkmHnFbDIMnCYYkBABJgwlDJh9d/TuHdmQheehI30DEglCcyJUcEIzOA8Sou2g9OiPP6ogM43P2pr0+vDDD8tee+3VYmYdOQXxFgzNFRtEIlW6QlnZkhINOSuaOEHyDjywxXeIZdY/3Gy7t6FBw3/SN9kkpnMTrxl7a2NCvG4t96e9RGuGRwghJDWhYCA9Avusp6O+XvsHQDisf+45nWV29ekdNMicCFNBnwGfzx+mFMZ4huGs3gWTwGwH5VhTVTC0t9yr1yvrpk6V2kWfS8PixR2qBGSEHBKStVJRvKmrj1hBSXMxGho0tj+cWIh11j/SbHtbPDCtzdi3txqRvbN5VxQoIIQQkpow6Zn0CMysJ4x7zHriOWP77WTlFVfKhudf8Iee/PqbNC5frmVCISYKjz5KQ1EQgmMXBY6CAq2ypK0arA3NLLggOEx36J6Ew6klS2HktzXp9aqrrgqdWQ+U/XTgHHZFkzsIPFSBQonR9PTgzLg94ThsI8Ewycvh7jvsE8SawBxpH1gOLweayK24/IqwzeTamygdD1orRkAIISR1oIeB9Biss57wLKy+4cZg7X2tfANvwoYNapCl77GH1uIvGDdO12/47TdZc+ttwZl3X0WFPiMJVj0J2I8JmUlL087IzX/8oUYkQqGSOl8hjiDu38yCo0dFW0No3nrrLTn77LP1tc6g49og+RpioSu8NQFxB5GIpm2oGhQu9Aj3Uaxx+uHyB2DcmyRolKZFtalohrXZhyaWi6iHojUvB0ubEkIIiRf0MJAehZn1hAGLLreK3QPg80ndwoWy/PIrguvrDG2YMJ1gh2MTygKam6Xpl18kfdttJW///SR9660kZWnjrD68CuiYDVCutHHVKmkKNMKD96a1me7PP/88+BrnHk3UVGwFBF2nEygNC3GCDs9lTz7pr55VWyvO/LygtwREmvUPN8MPcB8BY+Rr47d166Rixkvyx2mTWngH7EBooEfEuvsfUMFRPn16RC8HS5sSQgiJJ/QwkB4HDDMYgdpwzXRztuP1SuUrr6gHouCQv0j1nPfC7wzbhxESSNJtfu+9qNWGuiXwBphGdnV1/o7IwOGQtXf8S1xFRVHzGYqKikLew8OjIWNWD05nYcLHUEEL5WQ9HlnzrztFkD+h3aurtDIWlsMoby1OP9wMf8agQaFNBAPiBIni0SofhfMm1Mybrx6KcF6OtiZKsyszIYSQaNDDQJKaSDHY7Y3NtlZEcuTk+BdGMkR9PqmePVtWXHa534AMR7j8BDMjDqyeh56U/GwH4V719f4k8yj5DHfeeWfIexi4CA9yQkh0di6IEY84TMADoGIhcH+gqZ92RLZ0eI4Upx9phh/fP6SJIPIlYNAXFkRt4BYuZwKhbrmjRobNj4A3x4gJ06wtUshUa3kQhBBCCD0MJGmJFIMdKaY8loosGw2vPH/ZVBMbj+dwDdh8PvFFmt1GvH5GhngD+QxhideseGAmOqXxetX4h9ch0kz3ySefLNOmTQu+VwMXYgNlVTsRR26uf3wwsnG9nQ7tVB0iFANN7XJHjdK3EKyR7rdIM/xIpMb9Cg+XuTecpSXaPC1ar4JIlZKKJkzQR7j8CO03AocJqj1FKG0ajx4PhBBCuj8UDCQpiWTIpPXt22L5uvum+kOMPJ5WS3gGDa8NFf4ZXqdTDTr3gAHa1TfoGYAxZ8KJIvUkwIy5db1wxMPIR7Wlzigr2tVAfNXUaKJ4rE28YAB7OlksaM+FwkK9l/IO+rNUvTVLRY2KBfU4OMTdt6+GWvm7JOcFjXLr/WYN64lk4MPDgLCkPldcLjUffijV8+aLr9kj4k6L2qug1d4GgUpO9t8GGsSh50OkMq7sykwIISQWKBhIUhLJkGlYsiRkuaSna2y5hon06RPTDGnO3ntJ1XtzgyEoztJSNcjTeveWrBHDpfq9ubpPpbUchDAdnhV4LOIVzhMIXUl5tAatM6phfMABBwRfwwAue/ChLsldaIZY9Pmk6u13xGtEoxkzLkFNjZbazR97sFTOeruFkG1es8a/raXyEaosYRnuW70/+/fTylxWYTvQ4h1obUa/tZyJSL8ZNIiLtG92ZSaEEBILFAwkKYlkyGQMHtxiOUAMuNVIQvlJu2FlDWXCTHHmkCHBDtAohQpDFmU06086SdZPn66iQme322OwxkssAHgX4ilAEgBCfhC+1ffqqyR/zJiI6w0ePLhFH4ZOxxKKFiIUA70fcK8UB7o9Y0wbXpkZapSvXKkVlXBfWisfYZ38v47TexT3Uv3X3+g+0UHa5DRAAJjqSW1ttmZPVG6P8c+uzIQQQmKBgoEkJZEMGRibTatWBZdriEhOjoYYKQ2NGrtd9tDDITO5MMzs4RpIYEVoCOLKjVFl4tL7XXut5Oyzj6y4+JKN1ZQSOcufwmIBQCyUnnlGVLEApk6dGsxh0GvSFc3awoFrbXpANDWpUNUGbtASLpd6FGCUq5hzuzX0R71dtspHla+/sXF/AQ+Ld/16cQ4YoOIklt4UbcnviRq2FAF2ZSaEENIaFAwkaYlkyFiX1yxaJBtenCGeDRv04cjJFoc41FiD1wECQqsiIZk1QhIqZngjlcBEvL0ToU7V1cH+AqTtZGw7WL03bQHXO+dPf5Lqd99NzClHaBI8DOnpwW7eyKnwNtSLt7JKe3CgPGrhscdI1ay3N1Y+cjiClY8868p0O1dpiQoEVFrCQ2f/s7JizuWwAs+C5uzY7nH8Jtpr/Fs9F4QQQoidFK/5SFKZWEqjRipbaUIwECOO2PK0TTfVGv++pmaNN/dUVEjzH8vE29ysIgBYy1lawzVaK4GJWWRHRnqwTj+q2jjQ4ZnETN2XX8VUAvfiiy8Oed9ryuTEnmV4ChoaNAypcs4cvS9cObmSttmmmiiN+6PgkENUYMLbZTxRzpJif+WjrCx96Gv0cICgCCTCxzL7Hw40bNPGg7Z73JRkjfSbIYQQQtoLBQNJCPGo/W6tTe+CJwAhIiYJ2ekMqZuPKjHhOvOCqtmz1YNg75hrSmB6aqqlecXKjRWP6pADkaBQmRTFV1urhm5rzA90RQ4Boi1RBBLjq+bMkZVXXuX3KuB+y8rWJHufpYkbwtty9t5bhYSg8pHDISWnnSrFp56ir7U8L0hPV3GB+7OtQHShYZsJbwrXG4IQQgiJNwxJIl2OfUbfU1am1XBQMtUe4x6tA224JE/1ABQWii8Q/mHq5mNbDXGxhGuYmvWe6upguEha//4h3gc8EP7hKEI5zXwtCwojMWfkvlL5yswuPnOpTdU770r9hAlRZ74/+eQTOeOMM4Lvca2caWlBL1Hccbn8OTCB5PmIBO4lnckvKxN3SUnIfWJPqEdDtSLLd8W9veqaa8Wdk6NJz+3td2CS9HUf69e3uMcJIYSQzoAeBtLlWD0DmLFtXr9eqxGtuva6EE9Da14IkxhtvAb+Gvn54sRzcbE4c3NVkBRNGN9iDI2//x4ULWkQHjAaq6v9YR0B7wP2bww0d+/e6sVAd2jEjjf+9luXnKvuBBrcofpUNHJM9+0AOmvemQ2ePZ7IXbytQCx4PNp4Dh4Hu5fKKoBxH9Z8tCBkc82BCFRIsnqxInV2jhS2Z0QyRBR6h8CbEekeJ4QQkvqh2ckCPQyk62+6gNEDzwLEQrDTstMZnHUFsXSgtSd5rr33Pql+/33/Pp1OyR4xXNfDDxIJ0qZWPoChmL7ppmrApW2yiTStXClFgfKZ5hhWLwbq82PM2Hf9V19vLL2Z6ApKKQTKi1pn3sNVSbKyLjtbjqiokAGB9yiAup2InCYOeVn85/x4h1Pu9nkFpvc/xSHOQO+EuT6ffCs+OdexcV4k3LKwHb7DgZCi3FwtDWsqa+F74N4Kl1BvrYDUnpKnrVVCglcM+RHtzYUghBCSOMoi/I1PVuhhIF2O8Qxo/wMYa+ik26vUH+YRmHW1eiFam5E1SZ6gYfFincXV2deSEqn99FP547RJWh61/PEnpBmVlLKyNPEZyawQACYJGsnTEAvAKP7gWJubNVYcaGUaQ8A4JbGBhPRos+qTJk0Keb/h5ZelSETucjj1ca84BAV054bZ9n8i8nqE/Tb6fPK0zyuPBkRGe3H4fCoyrUnFVjFgT6iP5A2zerHCESkRH8vxD2XA/VOl/4036HNbq08RQghJLPVR/sYnK/QwkE4hWu4BgNGjcd3XXqeeAFdhUQtDK9YZWXOs2i+/FA8M/9ISTUr11NVK02/rNP/AkZcnUlGhiafNdXX+ikdIGkXSqqVmPfIawil+JJWuvetu3bfD4ZTGispgQiyJHV9jo1afikSj7bPqDz4MeY8ezEhrh4jYYNv2MBH1OuzuE9nEJuT+i+gjETlRHPJje0WD0ymOtLQWvRNibX5mvGFoKgiQiB+JjYI5T0WWZKRrKVdzbJZBJYSQ1KU58Dc+mmc62aBgIAlzs2GmNm/0/lI9b34LQwsiIHvvvbQiTDQjzByrubw82GBNeyagYlLAmNdkZcSpm7AhGJOBz0omnSYZm20WFCLIlQgXBgXjDuEf3opKcaBqj9kXhIcJSQo07CKRwbkzPQ3Cse+++4Zc2/offpD1InKez39e0Qmjt4jsIiK/2rbtJw45WkTuE5/caNMEQx0OGSoODUlqN2635jBAmDYuX673hDWpOedP+4iroEDyx46N6DmIJEjtIhsPX3OTNP2+NLitMy83eJ+2JsgJIYQkL+52hKkmGgoG0qlutki5ByFVZVwuyR29vxQHYtvtn0FUhIt7N8fCrDUSkYNCAKJhwwa/AZ+ermFHOktrQCgUquNkZATFAowvGIFWxW+2xYww8hs81VXiq63z70Nr7Zfo8fRYgGKhVTBDH+0P4u677x68tqhOheZpJiQJNPt88i/xycPik15htj9ERD4WkTeRQC1xprFRxWvZw4/oWzRtKz1ritR++llI3kzDz79Ir7PPamHQR6sOZrqXGyGRd/BB0sIR4kvNuFdCCCGhxOqZTiYoGEiXu9nCiYrajxaoYAj3GSrOQDBEOhZyEoIz/BADgVl/ba6GKjhVVRs3CtSvN70brInQECcQHjDkPLW1WkIV66+5/Q7xIYzJNjut1XXQ9yHWWWscG8ndsSbZdkMc2VlRP7/jjjtk2rRp/mtr+hZYcDscMtIn8kwEwYCE57N9IheLTw6STiAgCnAtcV+tu2+q//7CtYXnpLlZque+p7kzKiqzsiT/r+MkZ9gwvyCtrtbwuKa1a9RbBUG04pJL/Xk8BQXBe77ilZniwLJ+fcXX4G8c6KtvUPEaiyAnhBCS3JTYirYk+99wCgbS5W62qKICNlmMcX3mWEheVoMNYgEE4tc1RCmwzFFQoOVWPfAGoDymwyH5Yw+Wyllvhxhf2FdzBdJqA/h8/nCmcEBEtAUIC4zHVFbqgXhWr9Gwr9ZmxTUkB4Z5GP4rPtkyyjH6OxxytE/kKfHJn6QTsHiSVCzgfVqa3lM65iaPv6+D06mfw1NSMeMlzZdREYT7y3L9TbicJz3d3z0a5YYhViFeV64KCUnSY6ZY3CshhJDwpFI+GqskkbgSSzWYaFVlYqk4Yz+WA51z09L8C40xDs1gvA4w5GDAuVziQp+GggIpPPoocfftG1KJCYmlCG+KifZWR+rpJVghwBobI1aDOO+884LX1t2/v742OQx/93nlXJ9XYEKf1EpzBoQmbS1dgBEPTU3+sDjjPQok1Zvr7XO7NvZ7sN47WMftn7dBaBs8W3rPZ2SIz7oe9tHsUaEQ6++DEEIIiRf0MJAud7O1FrvXlrg+67FQfafmww+l8t3Z2iRMXKi+VKidnKW+XjyrV6ux5sjMkA0vvawhSFqqdfVqLZXq3RCY+TWGWjTD3oQ/kbaB/JLGRnGg+VmYWfGvvvpKdtkFKc0iTpdL+jgc8lIEcXC8ZXlIX4VAaNJNYbYb7XDI6Hh3gkMoEoSm8R6FEZT4vv6BObUTObo0m/Am1bYBbwrC4SBqc/cbpT0rfBkZwRwZeCKqP/ww5eJeCSGEpD4UDCQhbrZooqKtcX3WY+WPGSM5++yj5VohBtBJOmjYI9ehoUF8jU3i7JcnnrJyzVNA+AdCldDtWTs5I9SotQRm9l9oNzCA3X36hJ0Vnzdvnpx88sn6OiQ0LJmx5qSY0CQsM/cQRGp2jghyFkRUECBcTnNkPB7xIQE/L0+N/5LTJwXLrVb/e56/90cg7wb7Q9Uw5Pp0Vtyrz+eT5uZmcaMiFO9xQgghASgYSMKwGvp2Q6UjcX0QDXXffKON2oJGGwwuzAKb/Ia6en/lJIdDPAUF4vJ6NbG08NBDZcOMGVrzXsnKkrQBA6QpED4Dn4PuAfstKBCfxagNzimbcJTm5pBCN7HOa4fza5htI+2vtSCncNvHC50hj3G9IG53WEMX1x8gXMmzZk2njLejhHyPQGWu4DgRloTFWVnicLs1bEifEfqGHAS07qiqUs9X2pCdpPGn/+l94szNVU9B8fjxwV3njhopG2a8FDiMQ5ylpZoH0bRiheSNGhX3uNfa2lr5/fff9Tk7O1s23XRTfSaEEEIoGEjC6QxDBVVpYGwh/Mizrsy/0DLjC4Gis7sICcnJkWYY+JWVkj10qBSMGycbFnws68vLpG6LLcT35ZciP/20sdkb9tHc7G9ABkMuELqkYgKVbXr3VoO4admykDHZjX1fGwz/cMvbYkx3puHta8d6mB2vnDNHxZ2VRx99VJ9hFMPYTiReiMnMTHF4POK25LYEr1turt8bFfBgYTnuJ6fXK860NOl17jlBb4HxBtTX18vKb76RutxcyRo8WPo1NYl7/fqwngJUBkOPEngrUFmpGfkNDocsq6+XTQO/lXiB3wN+g+vXr5ecnBx9Bttuuy09DYQQQigYSOcRS3hDZxkqMMBcuZjR9YmUlPhDO7SsZ3ZwxhdJpT40EkPCdHW1eBFO0ru3ZGy9tZQ3N0tZWZmOqfL3pZJvrWwUMBC9Xq/Ul5aKs75e0vBoapLmzExJLyoUT0Wl+PC9LeEqQe+EzXgOJxy6K2t8PjldfLK/xyfX/PBDUDCsW7dOm7ZBLL799tsivXrJJV6PpPm8cr0lP+F9n0+eEJ+UBs4X5vMHichZ4pD5IvJu4GxCqvUNuFC3EJGzbTkOrdGE/Ja0NFm96SDpvfQPccFzgEpGuHecTnFDMLpd0pyXJ2kbNgTzEPQ5cN+mb7LJRhGwzTZ6r/+KRnRbbLHxXi8qkm333TfsvY5ti089RfMVmpAQjWMfcohUoCnh77/H1ZjH7xSCHePKRGPCgJDH8jRTUIAQQkiPhR4GklCvQWcZKtbEahj47tJSyR01Soom+EM+GpevkKXvviPy4UfiRZfotDRxHXao5Gy3nVRWVsqPP/4oGxBr73ZL/uabiWw7WPJ/WBIMM6oYOFCyKirEVVcnTZiFxk7hfXC5pB6z42npsnj3YZK9Zo0MRLdeiCe3W0UFwp+sqImLEpyIdUfJ13Z/643788J4xUy3JBcYW76IfCk+qe3dW41oGL0QCYWFhdIYmMlf7nJJdXa2NFdXyzKfTwZYDGO0djvHIgDu8HnlJfHJCQ6nHBQ4e5N8XrlSHNI7RoPaY/UUocJXepp4nS75cautZMmWW8qWS5ZIv+UrxAHR4HLJmj59pAieAa/XXw41kMiNbfHel56uXoRsJHdbRKP5PeDexrO513GfhRPXyOfJGDpUfvr4Y3H17iMZ22wtOfX1YX8jbck/sK+LZ4zHCPaamhopKioKhogRQgjp2fC/AYk7bfEatGaodCQJM1LyNIyt5R6PlB18sJRjeVWVZA4cKDv95S/6+bJly3Q8DQ0NelyMqeLww6Xkp/+J77dfpTw/X9ZsvrkM/OYb2fzb7yS9ulqNyMVDhohvh+0lt75BVjQ1ytqsLPFstpn8uM02kl1bK7XZ2dJ79WrZdvEPkt7QoKErMDDxrTxOpzRmZsrvAwdIbU6OZNXWSl12tgxY+oeUBLwjmMGG2MB2Zha8rLRUGtLTJaOxUZwej1Tm58vPg/3fE8fMrayU3Kpq3aY+M1PyKyt1DC7EzQcqQsFMdqFsp8XL0ZieLj9vuaUUlZdLL6wf4RxjJM4onzks4Vdm3xnolZCVJa+vWiUH/vCDisl33nlHxowZI7/++quu8/LLL0vplltqmNiL5eVyTlW1uMNUrcKSHcQhn8cYGIXzXJOdLVkNDWrge10uKS8qlLzqGsloaBCXx6PXUsXCtoNlAxKUfT75YvhwyVu/PngdKwsLJX/DBin2ePR69f7hB9n819/0nHozMuSXwYPl06+/luI//tDtcR8NQC5MU5MsXbpUnBCIXq9sttlmKpJ++uknvc/we8Eyq7iGiM10OPSehGcsnDHflrC+SOvi2XyO/eM9E58JIYQACgYSd9riNYBBEslQiUdugz152ogZhBvBk1BbWirpW20l6YWFsnbtWsnPz1eDDM+IN8eYXejfAM/BLjvLstISXd5YVydLttpKlhcXhxiRJbm5siYrS6qqqtQo9MCILyzUB6gpKdHZaWwDAx8GK559LrfUZGcF1zP8svXWapiaY4DSNehvIbKuV68W61vRzwK9DKzY92fGkllXL5n1dVKfmSXrem/cN9YvRU8Nn0h9VqZk1tcHBQ3GkFtVJQOWLhWHT6QmJ1s87jSpKCyQ6ry84HbYH9bzrlkrDb/9Krm77SYLFiyQYcOG6XnPyMiQ0tJSve4452+99Zbsv//+eo3+/e9/y4yjjpRBK1dK2Xff6/3w/ZZbyPriYqn3+eTtb7+VLXNy5LvCosBNJdL444+yaOedpY9P9DvBM2DGa4x9u/FvvSZYXl1crOVZcQ1x31ivozm/1chZcDplzS67yNLNNpNceJwKC1VoOFav1lAreE7w+Pnnn/X3oPkzgXsRYuGbb76R5cuXqwDAMghVlJY1xnq03wiA8MC+ca5yc3OjCvRoYh6/LTyzShIhhBA7FAwk7rQ1vCGcoRKrl6KtHgisi1Aj7K+8vDxooJrwEACjy4RpABUIjY1q2GJdPHBcGGowGquKivxVbAIGHdY1+zXgPR52AREL4QzVjtDW/bU2Xny2YuDAiJ9ZX1cXFYln2R+yySabqCiAEJszZ44cdNBBsnr1avnggw9k/vz5kp6eLgUFBSowISYwK+/eYgv5palJvvziC1myfLnI8uV6Hfr06SO9d91Vvndt9IM0//qrrO7XT2qQxxLjOWjxHWNIusY9ACAacB8E9xGolgRw3fEd4F3AeriH8f6XX36RTz75RO8ZLMf3gKCAeNh+++11mbmvIxnz2BZi4X//+5/uMy8vT38vkQR6a2Ie+2XOAiGEEDsUDCTutDYjGmkbq6ESi5eiPR4IGKjV1dXqAcAx8YxlEDUlJSW67969e8tvv/2mggKf4/j4DO9h8JtwEiNs8MAy64ywVSyYZTD0YDSaGeaeDM5Fv379ZOHChfLFF1/I+eefL08++aR+NmPGDBV1zz33nL43ITtbbIH0ZVGxseeee0qyYERDJHB/rlmzRorhsXA6NeQNXoQVK1bos7lv4I3AbwVi6YcffggKWXNf238jRlTDs4B7FF4zAKGFY4UT6MxVIIQQ0h4oGEin0NHwhlhyG9pTXQkGPzwIeIaxBrEAIwxGlhE5JjQJY4d3AcAowwwujEOsi22xD+NxMMYcHjD4KmxNx7Adxozj2cVET2XLLbfUZOdBgwbp+QaDBw+WDz/8UE488US95qCurk5ee+214DVORXC/QIjiHoZHAELUKjrNOrjPsAzeB+Q8RLuvjajG/QZwP8JLg+OYexkC1fr7i0XMs3kbIYQQOxQMpNPoSHhDa4ZNe6srwXhCPDmMNYSAYBsYqzvssIPO/sLAwrKsrCx9r7kL9fUqFgCOB7AN1tkcyc8DB8qnn34qq1at0rFgfXgxYMBZvw/eM4k0VDD85z//kaOPPjpoNCMkaeedd9bzbQQDzjMSgeFlQJ5DqoF7Dg94qXBv4B6AkW9EJN7jNR74rri3NNytslK3wb0EL4sJUQImtwbrL1myRNeHwDbLsF94KcJ536KJ+Xj1RKHoIISQ7gUFA0laohk27Q2tsAsRhG7gPYwx634RfmSMHoAZYRixJq7eJKjC8MM++vbtq8YaDDsTq25mkAE+w3sjGnpqWBK8O4ceeqi+RtjRJZdcIltvvbWe57PPPlsWLVokU6ZM0YRoK3vssUfwtQlNioY5RqKB8W7Ni8G9gfvIGP9GMGAZ7qUdd9xRRQXOjXng3sF9jvwEIwZwviBeTegR7i28N8n68FCYUCXcy3YvRTgxH6+eKOwYTQgh3Q8KBpKSXopoHojWZjejCRHrfvF65cqVatzBgDK5CQibMUmsCJfBDC9i1GGomZljbAPjDtuYJGiIDQgbk8cQTjikupjAbPibb76phqv5Lrh+u+++uyYugyFDhugzknvnzp2rBjHi+ffZZ5+gCHvllVf0XJnQLxjc22yzjT4wc468B3iKzOw8zjVExauvvqqeo9GjRwfHhM+xv3jlPpieCZHA98X1xz1iKizhOyIvBveLyUfA+PEeIUT4zuZ+gTCAUMC5xGtsi/wGnAvc69gG9zu+F8KW4OUygsJ4xyBwERZnQuMwnta8b/HoicKO0YQQ0j2hYCApSzjDP9bZzWjhUjC4EC5jDHvM0BoPBIw1zNzC2INBi+OY8CYYwFgPFW5g1OJzvDehTmZ/JkkWhiAMSjwb49I0Lktl0YDzN3bs2OB7hBIhuRlJzgYIBHgT0HvBzIIjrOsvf/mLrofvv9tuu+k6MI632mor+eyzz/Q8A1w76zG++uor+fLLL/U1DG1cJzO7j2OZ6xcPWstBwfU1XigTloZ7Bg98husNoxzPyN/A98V7POMegrAwYUgQXnjGMZFbg3sK+8U5gUiAsIBXAmIBx8Izwrc+/vhjTYLGe2yD8eB40YhHQjQ7RreE4VmEkO4ABQNJ6X+0VsM/HrObVsEBg9PkLMDg/OOPP4IzvDDUMGMNAxDGPmbAcXzkMWA8iD03idXYFsuMuLCC5TgOQkswGwzjMJXFQjgwgw5j3ioYvvvuO80bwfmdPXu2PPHEE5roDMPfzG4jgRc5C7169VLvBIxnezK5/RgAOSW4VvBGAFxPLOuqZHNzr2GsRiAYoWgt1Yvx4B7B/QRPgQktwjKcFzzDc2XyG3BeTAlW3GdmPewL2+Jewv0GjxfEArwMeODehAfDVPiKNu6ONm+ziw4IH1y31sRKd4XhWYSQ7gIFA+kW/2hhkMHwMt1y2xNSYRccEAUw5k3TLRg/ZmYYhhmMNqwLIx9GLZ5hMEFAmKRUvMa4sL49DMmUWsV7jNMk+Zr8h+4Avhu6N9uTlXHehg4dqjPqMCiRx3DwwQfLe++9p54Y4xlAAzNsCzGA8wJhZfplGGAIY1Ye1wBGOmbtITwgGHAdMLOPMDIIkK7A9Okw3gWrUMEyY9gDE6Jm7geTs4D7DPcODH+si3OEbXE/4hnnwJQAhhcBpVoR8obXyF8wXjHcX+b+sxrtkcR4R6ubWUUHvosROBAx7U2gTlUYnkUI6U5QMJCU/0drFRdYF4YUZkfbGlIRLpwCy2CUIazGGIH4HDHlMETxGkIB4gHGEAxTzOaaZGjsz9p7wSoasG98Zgw508/BWl0pFYGBOmvWLH1tytCOGDFCRZcVfG80HIMowHc/5JBD5MUXXww2yMPsOLogm3XhocBsOcB5sx4DHh1UV8L+IDCMJwgz9PAEdTXWfh0GU3XL5LSYyltYhu8DMYB7CuOGwW+M9v79++s5xDJzX2IdHAP3N4Qszh/EghHG2A73pqnEhHvTjKk1Md7R5m3mdwAvkqne1N4E6lSG4VmEkO4EBQNJ6X+0dnEBQ91UKoo1pAJGjSmdao/hhnfBhIUAGLEmtMiEmpj4cGyLmVyT+Iwx4L0xEM14rWMHMOKwHtZP9aTncDkM4YCBD5EFLwIM4ZEjR+pynIPnn39eDWJrzwqcI4g0cx3sOQx24GVAGBO8ChAk2DYRmDwVYIQrnnFfG4GI7wpjH88QB0i0x31mEqGNF8HkQCC8CiLBCAp4HyCkcF/ieCa0DecI96QRr8abZRfjWIYcEdPpOR6YfByMq70J1KkOm+QRQroTFAwkpf/R2sUFZmJhqGy33XbBajR2rOEYCPlAuIsJN0JojKleg2PCgMOsNWLMYbwZ8YDwFxihMOxMciuOD8MM4gNCxRispjmcNTQF4zIVfkwIS08C1welU3G+DjzwQC2ViqRnLENYEs4hkp4xUw1wflEBCQIjFnD+kWgNgxwGdSIEgzXB3VxvACMalY1MngHuDdw7MPRxz+K+wwMCAfc+hJVJjoaYMF4ThFxBDJiwI4Rl4V7FPoxHAgIM5xCJ0Kasq/X3AqEKzw+Og3HFK2yIxnJ8ckIIISRZoGAgKf2P1mqYwPhBTDwMMBg/MJLsxo81HAMeBcTXw2iD8YTXMPB33XVXnXE13YfN/k01GwgKzPLOnz9f47TNLC+MXwgOIwQwFizD/mGQYblJZjXJreb7GsEAcWFKYHaXPIZwQIiZpngmtAi9ExCyAgGH84HzbMC6MJARGobrAMMX4UsGCLq99tor+B73DM4hql0lCuNBMNfX5CDgfkLYFB4mHAnnwSQw4940PRisncbxHXHfmZ4gVkGM10bc4n40FZawHsSJ+R1Yfy8YE+5RgN9MLGFDRmwbz4m1kVxbO0r3BDqaE0IIIckCBUMSwLJ77f9HawwTnEMTI4/SmzC2IAywD4D9wHjDOjC+MDP7/fff64wtjBkYZia+HDO/eG16CVgNH3gW8B6GH8QB4u0RLoLtYeCasA4ThmRCQ0ztfmvDN8wKm3hxGI3GA2E6A5seBKnamM2O6b9gOPzww0PeT548WT788MNgdSM7O+20U/C1ua52xo8fH3yNnAgDPBixNHyLN8azZHJXTOUsYN7DE2XCd0zSPO4NPExOC+5T7AOhXLgv8Zk1vAefwfuCdeGpwP1k7iMsh8fBeDnM/Wy8G/i9mB4Q0cKGjNg2PSJMuJH5Hm3pKN2T6GhOCCGEJAMUDAmGZfc6/o8Whgk8AqbePIwfM1MLcQCDCUYODCm8h6EDAwevYcwgxANGmkmWhkFmNZwi9XuAUQajC8YTQkSsM6jYDt2fEU4Cow/7MHkPJtkZ+4JwwHhM3DnWM+tj256EuV7dCTMLb7p/47qaSlswvmHo417F7L4pGYv7D/cwRIHZB+4n3GcQF/BamZwEA+4prA/vC9Yzvx28t4sLcz9jTPBIGE9GtNA/k/uAMCeME9tgn8YDYprHxdJROlFwYoYQQtoPBUMCYdm9+GHCkGC0wEgxCcsIuTDGGIwc04gNhinWMyFDmPWFIYewD5O/YDWcwvV7gIFrDRExjcLM+ojTx7HwwMywETGYlcUDAgLjgCEJwWNKwxrvQ08D1+z111/X89YdMP03jNcAM/nmvjOfQyTi3sP1xv2Ae9jcT9YqWjDycX/hPQSFVZzivsHDCGWEOsFLhnvbVCmyiwBsi/sMYXvmXo4WNmRyhUyuBbxq1oR0fE/8fpI1sZkTM4QQ0jEoGBIIy+7Fj3Ax0yam22rkmLwAiAgYWDCuYKRhe4QY4ZqY0IpIIRT264b17LO4AJ/vvffeGgaFz7EuRAuObark4NgIc9p9993VmERnY1Mlp6eFcJjZ7u6C8SjgPsT9B0FkPE3IlcGzteoW1ofBj3sD9yXWNZWhTGUlJD+j4Z1pwGY1hE35VNN/AfcWRLNdXFg9ZbGGDZncB4hfgHGbPBFTEcz87trSGbor4MQMIYR0nOT6y97DYCWR+GI3fswyq5GDWVcYUjCqYOQYYwwzrYiRNyEk0Yz1WK+bNUHUGHEQLTAWMQOMY2GW13T5hXEIkYEZZIzVbIP17FWUukP5VTs//vijllfFrHt3AF4C3G+4N8z9ZjqCQxggXAj3BzwMJrcF1xr3C3IOIBogQHGP4hmfW+9NYwgjpA6CFIIT99cee+yh29vv5Uiz7LGEDVkFufEo2HMYkjWxmRMzhBDScSgYEggriXTd+bUaOZh1hbCAoWU6Q0MwYB0zc9vR64blKHMJ7wIMFogCCALEl5uuzjC2YGghbArGJZbB4DMx6FYBgnFBXMDQhAFpPCUmmbo7AO8Ckp732WcfSWVwreC9wrWEQY77DdcZ3w9iCCICvRVw/XHd8RmejZAwFaJwP2A58mNw7SE4sK7xZOEZ6+B+gWDAtkYc77vvvnpfY18mrKktzRBbE+T2KkmmW3WyhSIBTswQQkjHoWBIMKwkEj8izaCGM3JgJCGvoL0VXKJdNxhpEAsQJKaEqqlcA+POJLUaww/bmoRsGH2mMpJJwsZ7hC0htOXbb79V74TxjuA7WQVEqoPzk4pYuzrjeuK6IGcBOS4w5iFUIRiwHgQDhAOuN4QDQtBwLxiBgW3N/YntTDUteJ2wzBjp2AdE6LJly/QeMYn0eL948WLNBUGSNH4PEBym10hHGqlZvRHhwqLCdY5ONJyYIYSQjkPBkAQkUyWRVKW1OGW7kROP8x5pexiC8CzAiLPW0YdBB6MQxhvGh89MlSQTqoT1TCw6DEvMLuNzPMP4MyEkWNfMNmMW2lqWNZWBQZyq4N6CMY57wniD4D3CMlS8wvWB8MN1ReUu3KfAJDojjAhiEkLDdF6GWECo1h9//BHs42FCj3A/mGR7iA4zk45x4LhWkYBtTK4EiFYRqTvmB3BihhBCOgYFA+kWdGWccrTyjKYZlrXxGow+jAuGIYw4vDY18E2/B5P8CkMQn5sSsTvuuKMaO6ZijglVMQ3BIBqwf4wFy7A/PBAXn4o5DqlYIQnXAdcWOQe4dhBzmP3H9TS9EUzyM67zf//7XxWAptQvrhOuO64jBAASm3GfAIgBvIZxj/sBxjn2iXVwH0B8YFtT6QvrGw+FaQZnfg+4PzBOjC1e+QaplB/AiRlCCGk/FAykW9CWOOWO1GNvLfzC1MNHzgJmdc0M75577qnGnAkRGTx4sBp7iDnH+mZ2GcalyaUwBo7JZ8BxYDwaMWAavpmmYAAiBfvB9lgP71NJOCCH4aCDDpJUAecZHgEY7AhBwzXGvYfrZUKJTPM+XA8Y7Pgc2+DaYBsIDXhWTPIyOo6jQ7URiiakCeFH5v6A0DD5LLi/4LkwYW84DkQEjoljWX8PuO9iSeyPFeYHEEJIzyBlBcOCBQvkrrvuUnc9jK0jjjhCpkyZknQl/UhyxSl3JN46lvALqwEFI9BUysFrrGPPe4DhaM2vQO4DDD4YhZiFNrO05vvBIMQ6JmbdeDFM918Yp1gOYYFtjLhItY7RyY7piWCqEeFcY4bdJC2ba2r6HWAdGO1Yjmtumq8BGProGI5nLIPQNF3Kzf0EsYDtjTcDgsCUTsU1R0lgeB4gUPGZme2HR8r0FcH9E2tif6wwP4AQQnoGKWldf/XVVzJp0iTZb7/95Mwzz9QEv6lTp+o/y8svvzzRwyNJGqfc0XjrWMIv7AYUhIJVuNjDIqzvMftrqiWZyk3WceH7IUQJBiJyJBD6YmaxTaM341WBkYpjmxKu2C+2C5fngHVhRBoRkki23357SXZwrkxncHOdIRxMV3D8HTJhYXg2YWS4Tkhy3nXXXYNGOzwJuJYQDBALCFvCfWq9r3D/YJlJmjadyK0eAyMKcc1xfBPihopTuJfi5VEIB/MDCCGk+5OSguHee+/Vf7T33HOP/hOEcMCM32233SannnqqzuCRnkm0OOWOxlvHGn7RXgMK2yGGP9p2MBhhVON+xzoYt+kWjJnloUOHBo3/XXbZRQXDW2+9pTPf2D+esd0WW2wRzKNA+BREBarrGEMzUSR74jYEAK65qXSF84cwMxN2BOEGo33//fcPVsoCQ4YM0bwDiALrDD+uCRqxAZPwbr+vzH0BjxPyY8J5DEyi/JIlS4JhaHiG1wHbdnYCMvMDCCGke5NyggH/mBcuXChnnHFGyD/BsWPHys033ywffPCBHHXUUQkdI0lOOhpv3Zbwi/YaULFsh++AEBSMH4Y/jFjEvcOQNEYrxgbPAcp6jhgxQn755Rc1LDFLDcEEkQCDFsDwxfoQ2hAYSMpNVPM0hBjiOyUS0/MC58V4bgDOLSYq4CVA1SJ4GWCUw5DHtcD6OIcQbcgpwHrW/gSRjHYcD/s1oXLh7iu8Nnku4QQlXmMc8FaYvAYTppaMCcgk+elIrhchpPuRcoIB/6jxT9huVOAfNWaN8Q+TkM6Kt06W8AsYgPAomPAqGJMYG4xF+/fCePHbQKjK5ptvrsuwngl5gjcCM9/YBuU8ER6D/ZqysN2lx4MdkzdiP684lwgNwjPOmUkaxzITMoXlOEfG04DQIJxPvDYeBJO/EM/7KpqgxFggPOAtwr1hqm4xr4u0lWTvrUEI6XpSTjAgZAIg2c+OKUdJSGca/MkQfmEXPxDMphyn/XvZQ51AuNfYBgbvzjvvrB4LExcPwxpJtzBEzUw5Zq47o/rSqFGj4ro/fC8Y/hiv6aiM84Gx41xBDJmEZawDAxs5BzC+8Yxzaq611Uuw1157qdcGnph4GVTx6AtiRGB7BTEhqdJbgxDStaScYLB2UyWkPSSDwd/V4sf+nSO9NpWcYECb5GsYnTCMP/74Y63kAwMcs+jwPsCohpDA+niGodERj8SXX36pJWjbg/mOGBvyCPA98BrjQiiWSSCGcIAg2GabbYLN1cxMKkK1IBpaKz2K84IE9ER7mpLVA0ZSl1TqrUEI6TpSTjDgH70JCbADAwf/7AnpKXSG+AmXfA3jYeTIkSGz6sajYUJ78IzPENIE4YCEXyzHA7kRMNpbA4IkFnDcAw88UHsYYP/IF8BYzZghFMx5QQijaaIGL4LxCMAYwgMCyG5gx1J6NFmFZ7KOi6QG7K1BCOkWggFGCv6gwSCxsmrVKp0tRAwvIST+Rme0WXVjYMMAR6I1Zuqt/SXwbDXcf/rpJ/n6669bHBceCzumfCh+2wgpgvcAoVOxzp5DXOARThhE+q6E9FTYW4MQ0i0EA/7xDx8+XGbPnq29GIyhMmvWLDUE2hvOQAhpnViNa+t65jcKYx8PCACUEj3ssMOCMdOff/65ln9FKVhw/vnnx91bSGFASGwwtI0QkvKCAaCj84knniiTJ0+Wo48+Wksx3nfffTJ+/Hjp169foodHCGmjIT9s2DB9nHzyyTJt2jSeP0ISDAU2IcRK64G6SQgMi/vvv1/DkM4991x54YUX5PTTT5dLLrkk0UMjhBBCCCGkW5GSHgZTfjHeJRgJIYnlmGOO4SUghBBCkoyU9DAQQronbDJGCCGEJB8UDISQpOHZZ59N9BAIIYQQYoOCgRBCCCGEEBIRCgZCSNJw8803J3oIhBBCCLFBwUAISRqeeOKJRA+BEEIIITYoGAghScOSJUsSPQRCCCGE2KBgIIQkDQMGDEj0EAghhBBig4KBEJI0sPkiIYQQknxQMBBCkoazzjor0UMghBBCiA2Hz+fzSQ9np512Eo/HI/369Uv0UAjp0axdu1Z69eqV6GEQQggh3Z6VK1eKy+WS//73v62uSw+DiGRkZLDDLCFJAMUCIYQQ0jW43W61gWOBHgZCCCHk/9s7DyApijYMN4o5Z4wYEUSCmLOIWQyIiCImRAVECWLAgEowYAZERQRFQUQRE4qgImIAsxgwooAJswKCIOxfz1fV+/cOO3e7d7Pe3fI+VVt3Ozs7293T4Ys9QgghYpGHQQghhBBCCBGLFAYhhBBCCCFELFIYhBBCCCGEELFIYRBCCCGEEELEIoVBCCGEEEIIEYsUBiGEEEIIIUQsUhiEEEIIIYQQsUhhEEIIIYQQQsQihUEIIYQQQggRixQGIYQQQgghRCxSGIQQQgghhBCxSGEQQgghhBBCxCKFoYj4888/Xe/evV2TJk1cw4YN3THHHONGjBjhli5dWtFFK0pefvllt+OOO7p///23ootS5Xn99dfdSSedZP32wAMPdHfccYfatUDMmTPH7b777tbmIjmWLFnihg0b5po2bWr9+JBDDnHXX3+9mzdvnpo5YUaOHOmOOuooV79+fXf44Ye7Bx54wKVSKbVzgbj22mttrRPJQX9t1KiRtWv09fPPP7vKSPWKLoBIbrFq3769+/rrr13Hjh3dVlttZQJBr1693LfffusuueQSNXWCTJ061XXt2lVtmgDvv/++O/fcc13jxo2tD0+fPt3deeedJmhdccUVauME+eGHH9zZZ5/t/vrrL7Vrwtx+++1u6NCh1r677babmzFjhvVj+vfDDz/sVlhB9rkkuP/++90NN9zg2rZt6/baay9rX94zX5x//vmJ/Ib4P6+++qr1X5Ess2bNcvPnz7c1bqeddsr4bN1113WVESkMRSTAvvPOO27QoEFmoYX999/fOuSDDz7oLrzwQrfqqqtWdDGrPCxK9957rxs8eLBbc801K7o4RUH//v3ddttt5/r16+eqVatmisNqq63mbrrpJhMKNtlkk4ouYpUHL+OTTz7pbrzxRlliC8CCBQtMWTjjjDNcly5d0vPvBhts4C666CKbn/fee+9C/PRy14+Zf/Ged+vWzY7tt99+JnyxzklhSD5q4fLLL3c1atQwY4NIDgxjgKdsww03dFUBmTyKhOrVq7sTTjjB7bnnnhnHt99+e7do0SL3xx9/VFjZionHHnvMjRo1yvXo0cO1bt26ootT5aFvIkwdeuihpix4mETxmk2ePLlCy1csfPbZZ9Znjz/+eNe3b9+KLk7RgceG+ffII49cZv6Fn376qYJKVlwwRwwZMsR17tw54/gqq6xic4lIPhRp6623dscdd5yatgAKA4pCVVEWQB6GImGPPfawV5QXX3zR3FsbbbRRhZSr2Dj44INdy5YtzQKOZVyUj9mzZ7vFixe7bbfdNuM4XgU8Yl999ZWaOAE23XRTN2HCBLMUoqCJZKG/9uzZM+v8C4r/Tk5h8G1JDDiGsBdeeME98cQT7swzz0zoVwSMHTvW8vSefvppM5KJZPn0008tSoEwXOZk+vNBBx3kunfv7jbeeGNXGZHCUAXAckI4QRxrrLGGWWSjkIA3ZcoU64ArrrhigUu5fLQxuSEiOebOnWt/s4V30eaE1InyU1ljYosZYusJEcXIULt27YouTtGBkEUIGNStW1cKQ8IbI+BdQHbYfPPNk7y0CDwMv/32m2vRooXlPX3xxRdmhDzttNPcmDFj3Oqrr+4qG1IYqgALFy50V155ZeznDOiowkA8LfHK7NjhJ1WRbBuL8uN38ArDkYQoBmGWePott9zSdkoSyUOoDHkLxNYPGDDANW/e3EJGq1KIR2WFvAV2+kKYFYXhlltuMaOYT3hmo4QddtjBnXrqqaYw8LeyIYWhCrD22mtbDHIusMUnW6uyq0GzZs1cnz59JIwl3MYi2XaHbFtP4l1Ya6211NyiSvH4449bvkitWrUsQVfencJAeB0vaNCggW2v+uijj1qIhyg7w4cPd9OmTTOPe3TLcN6z25d2/Co/bG0dBaWBNdEnRFc2pDAUmZUcqxbboLVr1y69W4cQlRVCvEjYnzlzZsbxH3/80fozuycJUVVgpy+2UmWHJJ4lggVRJJtcPnHiRNu/Hu9N6G0grFE7+ZSfcePGWTuzW10UQr8wRLKNrSg7hCKNHz/e8k7D/D087oRHr7feepWyeaUwFAkkzLB1Ks9ewMMgV6KoCqy88so2aTJ58iwGb7l69tlnTZHQVpSiqkC+AsrCiSeeaAnQyhsrXLjMySef7K666qr0sXfffde8lHXq1CnQry4/kLsQzR0jYmH06NEW8lVZhdmqRPXq1W2OIIyOZ2V5SODHUBbd7bKyIIWhiNzgkyZNckcffbTbZptt3Ntvv53xeb169WzrOSEqG3jFTj/9dNehQwdTdD///HOLSW7VqpXt7iNEZeebb74xjwLWQiyw7733XsbnWMAVW19+CNdo06aNPQcH7w3GBh6Qd9ddd1ksOFvbivIR3bHOC7JejhDJ9GNyS8k1xTO2zz772K5JAwcOtJ2SeLZIZUQKQxG5Ef1WaLyiYMGtWbNmBZRMiJIhbpOJkifldurUyQSr8847Tw9hElUGtqwlvhvhNVuyory+yUGoLbkLWL0RuMgRwVDGsxlkFBNVhW7dutl2zOTdkDey/vrr29zRsWNHV1mpliKWRQghhBBCCCGyoCc9CyGEEEIIIWKRwiCEEEIIIYSIRQqDEEIIIYQQIhYpDEIIIYQQQohYpDAIIYQQQgghYpHCIIQQQgghhIhFCoMQQgghhBAiFikMQuTJZZdd5nbccUf37bffVpq243Eqt9xyi9trr71c/fr1Xd++fXP+bv/+/a0+X331VYnnvfLKK3YeTxVPqg3/+ecfV4zk2qaiavQX7iNPFf7555+X+ezLL7+0su28887u119/zfp9xgznhC+eTLz77ru7U045xT322GM2hkXh8feC+ayY+vCiRYvcwQcf7CZNmlSQ6wuhJz0LUQRMnDjRDRo0yO27777uyCOPtIVJVByHHnqo22qrrexJnqL8tGzZ0u29995upZVWqpDmvOaaa0yw32ijjZb57Mknn3Srr766+/vvv+3/Nm3alFiPXXfd1f7nydC//fabjd0rrrjCvf7666b0V6tWraB1EcXZh1deeWV34YUXumuvvdaNHTvWrbbaagX5HbH8IoVBiCLg888/t7+dO3c2D4OoWGrXrm0vkQy77LKLvSqCZ5991n300UduwIABy3yGV+CZZ55x++23n/viiy/c6NGjS1QYGjZs6I477riMY23btnV9+vRxDz74oNXxtNNOK0g9RPH34WOOOcb66X333ec6duxY0N8Syx8KSRKiCFi8eLH9XWONNSq6KEIUFUOHDnUHHHCAW2eddZb57K233nLff/+922233dxBBx1k4UkffPBBXtfHo3DJJZeYR+qee+5xS5YsSbD0YnlixRVXdEcffbQbMWKEhSgJkSRSGIQoID/88IPFru6zzz4W40y40L333ruMUDBv3jzXu3dvt//++7sGDRq4M844w3322WcW50w8fEkQt+qtn0cddVRGOBJWzwsuuMDir+vVq2fWzUcffbTUcv/+++/uyiuvtHJjFbvooovc/Pnzc673G2+84c4880wLv9hzzz1du3btrD5RPv30U7PIUmfO6969u/vzzz+XOadLly5mxa1bt66d16FDB6ubZ+rUqVbvyZMnm7WWc/G0nHTSSVaWKMOGDXNHHHGEnYNVbvz48VbeqHX3/fffd2effbZr1KiRWYdbt26d9Xql5TD4uOlPPvnE+gN18PeZY7nwxBNPuBNOOMHKzPc7derkZs2alf585MiR9huEz4QQ7sLxF1980d5TR8JriOGm7lyPfvPII48sUwf6HyEz9EvqT9gbIIzccccdFnpFv0ZYvuGGG6wfh7z55pvu1FNPtf5HfSk/9QhB4CaUgnvGtQ477DB32223ZQg82eK/cxlb+faLKNOmTbPXIYcckvVzQpCA3CF/DvkIZQknQdAjRyKuP9AetGM2D8aUKVOsnng4/Jih3xIC4/s4ik9Z8iR8P5g9e7Y7//zzbUwzJzAGORaycOFCd/vtt1tbcE/oN4TIMJ+EUFbuMfed85jD5s6da32TcUjf9H2da1EvQrgoC9dkPFK/6O/nMlfkCvMddeG+UA76//HHH5/1/j799NM2t3IeY+Lhhx+2cUe94vpwPu2az/rQpEkTy6XBMyZEkigkSYgCQVI0cassPAhoW2yxhXvttdfczTffbCEOCFyAgENYApbJFi1apIU7Fs+lS5eW+juXX365CWETJkxwF198cTrOmt/gGggjCLrrrbeeCcYoAjNmzHCXXnpprGBy+umn2zkIe1tuuaWFXVx11VU51XvcuHG2aG+99damKGD1QkCnLCz8XM+DcICwSh0QLhGsERy8AoTF9uSTT3Y1atRwZ511lltrrbWsXpzHgkmdV1jh/3aPq6++2q277rru3HPPtZjyIUOGuPPOO8+9/PLLbv3117dzbrrpJjd48GBbfCkTQgahXGuuuWaGssW94rvbbrtt2r3vY9QRdlA48oXrUH8EpZ9++snKR1kRykuKbaY9EAwaN27smjdvbrHvWBHpL6NGjXI1a9a0duL+Ivg3a9bMBAsSIBFwOA9BwuOFlGOPPdb6KMJFjx493B9//GF19tD/sH5TZ9oZwZj+yjlY131/5V4QUvP2229buehzKEucx+coN1jSaT/6HZ9z3/GMIfghaNLn6KMI+XfffbcpjlHlJ9+xlU+/yAb3hXIfeOCBWccJ7U3bU0fairFHW9Kf840hr1Wrlv2lP6LcR6HNDj/8cBtD3P+w3MSsr7LKKvY5n9GmtCV1pBzMJyh0CN3nnHOOyxcUDcYK5WKOYVwOHz7czZkzJ62k0B78Ln2gadOm1mc4j/5Ifgb9NPTSMCcg8DKvUGbGNvAd5g/mLPo6ig7CN+3KedzDH3/80cYwZUFRLstcURrMXczJrVq1ctttt5375ZdfrA6Uhf7GWAD6O0oRCgXl+e6770w5xdtbmsc3l3bNd31AaaEf0rdRcIRIjJQQIi8uvfTSVK1atVKzZ88u8byuXbvaeW+99VbG8Z49e9rxCRMm2PsxY8bY+6FDh6bPWbp0aap9+/Z2vF+/fqWWiXM498svv0wfa9myZapevXqpmTNnpo8tWbIk1a5dOzt3+vTpWb87fPhwez9q1Kj09xYtWpRq1aqVHR89enRsObj+vvvum2rcuHFq/vz56eNcu3bt2qnrrrsuow0HDBiQ8X3KXLdu3dQ///xj76+55hp7P2fOnIzzevfubd//6KOP7P2UKVPsfdOmTa2snscff9yOjxw50t7PmjUrVadOnVSHDh2sjT1Dhgyx81q3bp2uR5MmTVLNmjXLuB7lat68udXRlzGX+0Gb8b5t27YZ5/Xv39+OT548OfZalJm269WrV8bx7777LrXLLrukOnbsmD72/fffpxo1apQ69thjU7/++quVk3rMmzcvfQ515Dfvueee9LHFixenWrRokapfv37q999/z6hD3759M37X12X8+PEZx3nP8WHDhtn7QYMG2XvK4VmwYIHdo9tuu83ef/DBB3bOc889l3GtLl26pNq0aZN+7/vLwoUL8xpbufaLOGgr+nI2KDPXuPnmm9PH6K8cY0xna7OSxs5rr722zH2JQn05Z8SIEelj1GuPPfZIderUyd6PHTvWzpk2bVr6HPozdenevXsqX3w/6NGjR8bxyy67zI7PmDHD3tOWvB84cGDGeePGjbPjffr0SR/jfcOGDTP6Rtg3qYNn4sSJdoy+/Pfff6ePd+7c2Y7PnTs3r7nC34tJkybF1tn3y3BOhk8++cSO+7HIuGK8MSeE/ev555+388K+E+3DubZrWdYHxg7tJUSSKCRJiAKAVeill16yEALim0Pat29vf1944QX7i+WLXVawZHmwaoaW3nzBGvbee++ZO53YaA8WNixn/nezgVWa8mCl9mD9xttQGlj0CKsg5INreLDQYTHzv+3Bwh2CpQ2rsw9hwOpNeMLGG2+cPgdrtLfGYy0OIZwltNTXqVMn3R7APeHeYP0Md6OhbngYPISFYIUnHAKPB5ZNXoQG8BvUkbrmC2EzIdHyZYP7hCWRsvhy8Fp11VWtf9E+WI5h0003tdAHrNTcA85ji92opZN7Q1iDp3r16maxpG2joTp4YkKwqnM9QijC8vAeCzJWecDSCz179jTrKHWgzIRv4NEB7iv3gdh9+h2/D7feeqslbpZ3bOXaL+Ig5Cv0iGULR8Kq7/H/e+twWfKQStoliTbefPPNM8JNCLfCM+THkm93vC14a7gu4x4P0HXXXefKCh6hEN+GfitZ2pz7Gw2Zok0Y/9F7QshQNu8OHskwBGybbbaxv4QZhV4bPDvgt7rNd64oCaz0eErCORlvgA938+GZjBXmBLxjYf+iv+GZTKJdy7I+0GdplwULFuRYYyFKRyFJQhQABF4WqGyLxoYbbmiCFa5rL5RsttlmFnIQEv0ugqsXqDy43Vmko/hr+8U2hMU7PCdbuAflQYjM9r2S8NcMlRQP4QdRNthgg4z3hFVEhScWZMISEIJpKwR5v3BHXfJRAcQv4v48H/MfbRfaPhQM/XmEtkTDW8LYe2Kp8yFa32j5sjFz5kz7Gwr4URDYvaBE2AIhZMS1E9aRrYzU1bd1VACL9otomSkPAhPx8dnw30c5InTiueeesxchQQh9xNOT8+CFW0KeUBAINaFMPJsAgYtwimgZ8x1bufaLOBDEfahM9DiCOqEf1Ms/k4Vxw/mE19FOvk1zgWtmK2sI44H2I5eEkDbuOeFIlMErdtxv+gohP/QBlDvuFfeDFwJ5WYjru34s+nkj2z3jXqEw0N4+LCiunrRfOBf68kZ/3x/39zDfuaI0KAPhTNzLb775xl5eUfC5IH6eIPwyW52nT59e7nbNdX0I8X2WsaLtVUVSSGEQogCUllzIYuAXAITjXCZ14mLHjBmTcez666+35MB8ft9bo6MLUGnfzyVh0i/K2YSGbJQmvGAxJAmQeGyEHqzKJEkSk0zdo5QWo+wVkZLqHtaD30aAzcb222/v8qUse+z7du/Xr19W4RXC2HCEBGKhffthZYz2r6gyGAoo0c+ibUrbYOUmCTMb/t5zHZJGyZXASvrqq69afgvKDLHmxH0DFmmEYM5BCMcqzrkkjhIzHr1X+YytuDrkCt/LtmsRFn76ElbcMDckBGGTWPxc8cIllveSwJNAjsfzzz/vTjzxRPO2oFyFFm5yKPAY+XanXRHY8YqQGF4WSuu73Je4c5hz6A/hfYgb+9n6Zi6/n+9cURJ//fWXeR2//vpruxbPtyE/wydhh/XKZT4pidLqlev6EOL7bFmVQyGyIYVBiAKA9Qw3MonDUUhowxLmQwewQuL+ZpIPJ3hvWfaQ+BYN4YkTWknKg2y/74/5389mfcYySRJjuBCGO/LEgSUsW9nhxhtvNIGXRT1XECopJ0ndYcgQVr+y4C2+CAJhYiltj1XaPzsBgRhYqNmFJwQBhB16/ivLnS8LD4EjsTLEhw+F96lXr15mrSa5mDbnYWAkuodQ19DaC1hQoTSrOH3r3XffNYEsKtwhSHtrKxZnklMJG9phhx3svuMJIZSCRNhu3bqZwIU1mFAbQi540e9QjklmRdgNd5rJd2yVFzwW0V274KmnnrK/KE0IqCHUkURelHuS23MR2mgHFADudWkPXcTTh1JBaBjWabwt4byA54GEcwRdlDFeWMZpb5QL+m8hHuxIv2Bc4gWNej0Zb4TLFZIk5wq8MzzbBk9OmPAe3b3Ie1IZO9HnruQyX+ZCrutDCOMfRSTqvRCiPCiHQYgCwMTOjjYsVkz2IcRrg7dMEn6BkOOFEM8DDzywjHKA8Bq+wnjdqKCDcIkAFy5cCIl+a8yoIOahPFiliXkOv/fQQw+VWm8seoRpYF0N42dZULmej8vNFRY+BI1QAMCC7uPH892zni0PEZLZ2SSE8mJVDOtB27JjCaFgHoRZBHEEQW9dLDT+PtFvwrAKhBdi9sOnA2NRJkSFXWIQFNmthvvGjkbRdvVt6OuFkISnIi7UKCwPQirnh9DXsKjjQYA777zTdsFCaQiFfRRS7gEvdnLhnDC+HeXHx3FnE7bzGVvlBQUY5TCEdic/CGsz4V9YnMMXuSNsj4nygsKTC+y6RYgbYVm5eKHYwhOljX6L0Bo+EAyvDG3KdrAewpK8cSHOgl9eqDtbhhISFIJXifGf1D35L+YKHx4WDcO8//777a8f+4TYobyiAIfXR5H3D9MsL7muDyH0WeavQt1rsXyi3iREGWGRz7Ztno8X7tq1qy0ceAbCrR+J6yYR0AuChBP47fpY5FnYETTYirCsYSzA9UjGQ6jBve63VUXQQqCMexIxwghJm2w/ihULayTfy2UBJCyCcAjqjuBEuBSLK0Ircdb5JnIT644AyjURihA+eY6EX9DzeTYEIFwhTLGtJpZgHsiFpZr2D0M6+J8kSrYDpT2oC94RrJeEjmCtjVqWCwXWee4XghhhJvQdrLi0KUIKSc5eOGIbUgRyvw0s7UY4Cn8ROLxXBEGCrUax7tMvSUSmXmy9mS0nJoT+xLXwXvAdvAP0E5QrLOSEbgB9DyWCvscWqLQXgjYKDe8ZOwhDhNfgAfn444/NO4FnAuWSbUaj3h1PrmOrvDCW8bb5fAHwAijtEAdloq6Mo9BCzXM9vBLEvSPpmmRvFB+/xW0uoAiSzM69JeQrhHJxL9hggHJgdWfsEuKFgOuFYBQflA7GdxJPJWcLVNqGMDQ8HORS8BdPEePOJ6QXiiTnCq5FH6RtGfvg507mBn8tlBPmAhL7yRcigZm+wndzDcssjXzXB0LDPvzwQzOOCJEkUhiEKCPekhqFhQKFASGGPfBZQAlPYJHBvYyAhzDlQYDA6o+lmORQrLcIYbxnwSprfCwWUCxfJO2ygGFFZrGJy3sIy0OcM99DuEMI4AFICIhYQEuDRRPheuDAgVZ3BEPCV3j4W5xHJA6EWr5PKAVlISwHYRDBmR2gWDTzXRjZ7xzhlUUYIZMEaPIDCCMJ25rrYlG866677P6wEJNoiKCGEvFfQp/htxH62P0GqyZeEBQDH6ZEKBICKMqQF/oJSaC+CORhaBLKG/2AXXOwbCOc4xHIxQpMG6G80C4+oRmPFkIsDwn0YRB4CTiP69KOeGpQZugHKEDAveUcnjGBckFOAF4IhGe8OHHPpsh1bJUXBEcMAwj0fjcblCvaP7q7TQjjH+WLfoti6mE8+gfk4WFZe+21TVhnbNGncjUO0MYoU8TtR8MUGSN4f2hT+jiKJMdo83CXMrxOPCiRPpSEwsC9ou/RLxireBbwNqK00C+yPSk7SZKcK1CsGB/s1MW94T4xRmhXjr3zzjvpkE0UYuZ8+jHn4pViLqHts4Wz5Uu+6wNGAMaa31hAiKSoxt6qiV1NCJE3WMAQQKITP1tRYt0inpvkRlF+WGyZ8qKeIY4heGOdRiEoZhCg8KqgLInSYewh+CIICxGC0kDoZTZlCKUR5Rdvz3+5PjB/4QFEcSpPMrYQUZTDIEQFw4KCsBpNYvN7reMpEMnA8xUIlYjuk8/iSpiP2lpEIeyJ0J98829E8YP3CO8pHsqolZ9E7yTmk3zWB8I/URbwsklZEEmjkCQhKhgsUcRxn3POOWYxwv1NbDHx8jw8DVe4SIYGDRpYnDyhOCzAhMjwl1AfYruJwxYihDwLniEyePBgS3gXwkN+CAoDIUOEATFXk+zOfELYWPQhdoVeH0iCJz8pl4dsCpEvCkkSohJAMhsx//xlRwySBMkzIEG3rHvIi+ywoBNXj9WYmH8WdnZ4+S/irCsDCknKH5KG2fLVx8YL4WF3NQR6digj0Zo5hBwINkxIaivZXNYHwqNQbtmsIamkfyFCpDAIIYQQQgghYpHpUgghhBBCCBGLFAYhhBBCCCFELFIYhBBCCCGEELFIYRBCCCGEEELEIoVBCCGEEEIIEYsUBiGEEEIIIUQsUhiEEEIIIYQQsUhhEEIIIYQQQsQihUEIIYQQQgjh4vgfzuIZymZuYNkAAAAASUVORK5CYII=", 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" ] @@ -1305,16 +987,16 @@ "id": "bbdb1644", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:51:18.789014Z", - "iopub.status.busy": "2026-09-23T13:51:18.788895Z", - "iopub.status.idle": "2026-09-23T13:51:19.096243Z", - "shell.execute_reply": "2026-09-23T13:51:19.095955Z" + "iopub.execute_input": "2026-09-29T06:52:31.139255Z", + "iopub.status.busy": "2026-09-29T06:52:31.139155Z", + "iopub.status.idle": "2026-09-29T06:52:31.359600Z", + "shell.execute_reply": "2026-09-29T06:52:31.359098Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1393,7 +1075,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.10.20" } }, "nbformat": 4, diff --git a/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb b/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb new file mode 100644 index 0000000..fea0ed6 --- /dev/null +++ b/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb @@ -0,0 +1,2104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b44ab081", + "metadata": {}, + "source": [ + "# Mouse-brain Perturb-seq with causarray 0.1.1: L4-5 IT Glut\n", + "\n", + "This notebook runs causarray on one cell type of the mouse-brain CRISPR screen,\n", + "L4-5 IT CTX Glut (58 perturbations, 15,135 non-targeting control cells), from\n", + "counts to biological readouts. Every step is a function in `pipeline.py`, and\n", + "section 7 runs the same steps for every cell type.\n", + "\n", + "**What we learn from this cell type**\n", + "\n", + "* **56 of 58 perturbations change expression**, with 9,981 hits in total. How many hits a perturbation gets does not track how many cells it has (Spearman 0.02), so the counts reflect how strongly each perturbation acts.\n", + "* **Three perturbations dominate: Prpf6 (1,887 hits), Ppp2r1a (1,486) and Tpr (1,457).** Prpf6 and Tpr up-regulate genes of their own machinery (Prpf6, a spliceosome component: mRNA splicing; Tpr, a nuclear-pore protein: transcription and RNA metabolism), and all three down-regulate neuronal genes; Ppp2r1a's up-regulated genes have no clear theme.\n", + "* **The proteasome subunits Pomp, Psmc1 and Psmc5 give the most similar responses** (correlation of effects 0.35–0.49), as expected for three parts of one complex.\n", + "* **A few genes respond to many perturbations**: activity-regulated Bdnf and Adcyap1 go up in most of the 9–10 perturbations that hit them, while Atp1a1, Scube1 and Ahi1 go down, suggesting a shared response of these neurons to disruption.\n", + "* **The results can be trusted on this cell type.** On fake perturbations made from control cells, causarray makes 14 false hits over 30 fake arms (Wilcoxon: 99), and its statistics are close to N(0, 1) (SD 1.03).\n", + "\n", + "Words used below:\n", + "\n", + "* **arm**: one group of cells. Each test compares two arms, the controls and\n", + " the cells carrying one perturbation (Tpr, Prpf6, ...).\n", + "* **hit** (discovery): a gene–perturbation pair with BH-adjusted p < 0.05; BH is\n", + " applied within each perturbation.\n", + "* **tau**: causarray's log fold change (natural log) of a gene's expression,\n", + " perturbed versus control, after adjusting for the latent factors." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "37b9e942", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:24.418695Z", + "iopub.status.busy": "2026-09-30T04:23:24.418407Z", + "iopub.status.idle": "2026-09-30T04:23:26.495509Z", + "shell.execute_reply": "2026-09-30T04:23:26.495140Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "causarray 0.1.1\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy import stats\n", + "\n", + "import causarray\n", + "import pipeline as pl\n", + "from causarray import estimate_propensity_scores, summarize_propensity_scores, plot_propensity_scores\n", + "\n", + "print('causarray', causarray.__version__)\n", + "TAG = 'L45Glut' # results go to results/L45Glut/\n", + "Q = pl.Q # FDR level, 0.05\n", + "BLUE, ORANGE, PURPLE, GREY = '#2a78d6', '#eb6834', '#8e44ad', '#c8c7c2'" + ] + }, + { + "cell_type": "markdown", + "id": "603652ad", + "metadata": {}, + "source": [ + "## 1. The pipeline for one cell type\n", + "\n", + "Each step caches its output under `results/L45Glut/` and is skipped when the\n", + "cache exists, so the cells below only load results; with no cache the same\n", + "calls compute everything.\n", + "\n", + "| step | function | output |\n", + "|---|---|---|\n", + "| select the cell type | `pl.subset_cell_type(src, '005 L4-5 IT CTX Glut', 'L45Glut')` | `data/L45Glut.h5ad` |\n", + "| counts and designs | `pl.load_inputs(tag)` | genes seen in ≥ 50 cells |\n", + "| latent factors | `pl.fit_factors(tag, ...)` | `gcate.pkl`, rank r = 8 |\n", + "| effects | `pl.run_lfc(tag, ...)` | `lfc.csv` (all controls + 10 perturbations per batch) |\n", + "| calibration check | `pl.run_null(tag, ..., seed)` | `null/seed*.csv` |\n", + "| Wilcoxon reference | `pl.run_wilcoxon(tag)` | `wilcoxon.h5ad` (crispyx) |\n", + "\n", + "Three settings are fixed for all cell types:\n", + "\n", + "* **rank r = 8** latent factors, chosen by JIC on this cell type and reused to save time;\n", + "* **`min_counts = 20`**: a gene is tested only if the smaller arm is expected to\n", + " hold at least about 20 counts (the package default is 5; section 4 shows why);\n", + "* **propensity check**: before each `LFC` call, `pl._propensity` flags any arm\n", + " whose propensity weights concentrate and adjusts that arm only (section 2)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a3eaad75", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:26.496790Z", + "iopub.status.busy": "2026-09-30T04:23:26.496644Z", + "iopub.status.idle": "2026-09-30T04:23:29.533390Z", + "shell.execute_reply": "2026-09-30T04:23:29.532965Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "22,396 cells x 14,227 genes; 58 perturbations; 15,135 controls\n", + "cells per perturbation: min 68, median 122, max 227\n" + ] + } + ], + "source": [ + "Y, A, X, X_A = pl.load_inputs(TAG)\n", + "res_2 = pl.fit_factors(TAG, Y, X, A)\n", + "df = pl.run_lfc(TAG, Y, A, X, X_A, res_2)\n", + "\n", + "n_pert = A.sum(0)\n", + "print(f'{Y.shape[0]:,} cells x {Y.shape[1]:,} genes; {A.shape[1]} perturbations; '\n", + " f'{int((A.sum(1) == 0).sum()):,} controls')\n", + "print(f'cells per perturbation: min {n_pert.min():.0f}, median {n_pert.median():.0f}, max {n_pert.max():.0f}')" + ] + }, + { + "cell_type": "markdown", + "id": "55d9d883", + "metadata": {}, + "source": [ + "## 2. Check the propensity model before reading any effect\n", + "\n", + "causarray reweights cells by the propensity score, the estimated probability\n", + "that a cell carries a given perturbation, from its library size and latent\n", + "factors. If these covariates nearly separate an arm from the controls, a few\n", + "cells get huge weights and dominate that arm's estimates. Three numbers per\n", + "perturbation catch this:\n", + "\n", + "* **AUC**: how well the scores separate the two arms (0.5: not at all; above\n", + " about 0.9 is a warning);\n", + "* **overlap**: the area the two score histograms share (1: identical; below about\n", + " 0.3 is a warning);\n", + "* **treated ESS fraction**: how many perturbed cells effectively remain after\n", + " weighting (near 1 is good; below 0.5 means a few cells dominate).\n", + "\n", + "**Library size is a judgement call.** It could be a confounder (capture depth,\n", + "cell quality) or an effect of the perturbation (total RNA), and the data cannot\n", + "tell which. The rule in the `LFC` documentation is to keep it while every arm\n", + "stays supported. `pl._propensity` applies that rule automatically: for a flagged\n", + "arm it drops the covariate most imbalanced against the controls (library size or\n", + "a factor) until support recovers, and leaves the other arms untouched.\n", + "\n", + "The scores themselves are small, about 0.01, because controls outnumber the\n", + "perturbed cells about 100 to 1. That is expected; a rule like \"scores must lie\n", + "in [0.05, 0.95]\" is meant for balanced designs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "387b182f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:29.534512Z", + "iopub.status.busy": "2026-09-30T04:23:29.534445Z", + "iopub.status.idle": "2026-09-30T04:23:31.037424Z", + "shell.execute_reply": "2026-09-30T04:23:31.037043Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "median propensity score of perturbed cells: 0.009\n", + "all 58 perturbations: AUC <= 0.74, overlap >= 0.56, treated ESS fraction >= 0.36\n", + "perturbations flagged by the automatic check: 1 of 58\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " n_treated auc overlap_ratio ess_treated_fraction\n", + "treatment \n", + "Tpr 91 0.736 0.560 0.363\n", + "Pomp 68 0.672 0.679 0.771\n", + "Srsf1 188 0.679 0.699 0.874\n", + "Prpf6 78 0.674 0.701 0.748\n", + "Sin3a 109 0.667 0.729 0.753" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "W_A = np.c_[X_A, res_2['U']]\n", + "pi = estimate_propensity_scores(A, W_A, K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", + "PS_BINS = 400 # scores sit in [0, 0.1]; 40 default bins would lump them together\n", + "ps = summarize_propensity_scores(A, pi, clip_bounds=None, bins=PS_BINS).set_index('treatment')\n", + "_, flagged = pl._propensity(A, W_A)\n", + "\n", + "print(f\"median propensity score of perturbed cells: {np.median(pi[A.to_numpy() == 1]):.3f}\")\n", + "print(f\"all {len(ps)} perturbations: AUC <= {ps['auc'].max():.2f}, \"\n", + " f\"overlap >= {ps['overlap_ratio'].min():.2f}, treated ESS fraction >= {ps['ess_treated_fraction'].min():.2f}\")\n", + "print(f'perturbations flagged by the automatic check: {len(flagged)} of {A.shape[1]}')\n", + "display(ps.nsmallest(5, 'overlap_ratio')[['n_treated', 'auc', 'overlap_ratio', 'ess_treated_fraction']].round(3))\n", + "\n", + "show = ps.nsmallest(4, 'overlap_ratio').index.tolist()\n", + "fig, axes, _ = plot_propensity_scores(A, pi, treatments=show, clip_bounds=None, bins=PS_BINS,\n", + " overlap_bounds=(0, 1))\n", + "A_np, ctrl = A.to_numpy(dtype=float), A.to_numpy().sum(1) == 0\n", + "for ax, t in zip(np.asarray(axes).ravel(), show):\n", + " j = A.columns.get_loc(t)\n", + " ax.set_xlim(0, 1.05 * pi[ctrl | (A_np[:, j] == 1), j].max())\n", + "fig.suptitle('The four perturbations with the least overlap (x-axis zoomed)', y=1.02)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7e876811", + "metadata": {}, + "source": [ + "The fit above uses all 58 perturbations at once, as an overview. `LFC` itself\n", + "fits the propensity model inside each batch (all controls plus 10\n", + "perturbations), and the pipeline runs its check there. The arms it adjusted are\n", + "written next to the batch results:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0d13a683", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:31.038621Z", + "iopub.status.busy": "2026-09-30T04:23:31.038529Z", + "iopub.status.idle": "2026-09-30T04:23:31.043045Z", + "shell.execute_reply": "2026-09-30T04:23:31.042737Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arms adjusted inside their LFC batch: 1 of 58\n" + ] + }, + { + "data": { + "text/html": [ + "
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droppedauc_allauc_choseness_treated_fraction_alless_treated_fraction_chosen
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Tprlog_library_size0.7360.5790.3630.998
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" + ], + "text/plain": [ + " dropped auc_all auc_chosen ess_treated_fraction_all \\\n", + "treatment \n", + "Tpr log_library_size 0.736 0.579 0.363 \n", + "\n", + " ess_treated_fraction_chosen \n", + "treatment \n", + "Tpr 0.998 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pathlib import Path\n", + "reports = sorted(Path(f'results/{TAG}/lfc_batches').glob('batch_*_propensity.csv'))\n", + "adjusted = pd.concat([pd.read_csv(f) for f in reports], ignore_index=True) if reports else pd.DataFrame()\n", + "print(f'arms adjusted inside their LFC batch: {len(adjusted)} of {A.shape[1]}')\n", + "if len(adjusted):\n", + " display(adjusted.set_index('treatment')[\n", + " ['dropped', 'auc_all', 'auc_chosen', 'ess_treated_fraction_all', 'ess_treated_fraction_chosen']].round(3))" + ] + }, + { + "cell_type": "markdown", + "id": "768e74e7", + "metadata": {}, + "source": [ + "**Conclusion.** The scores are small (median 0.009 for perturbed cells), as expected with about one perturbed cell per 100 controls, and no perturbation is separated from the controls: AUC is at most 0.74 and overlap at least 0.56. One perturbation fails the weighting check: **Tpr**, whose treated ESS fraction is 0.36, so its weights rest on about a third of its cells. For Tpr alone the check drops library size, the covariate most imbalanced between Tpr and the controls, and its ESS fraction returns to 1.0. Tpr cells have the largest libraries of all arms (18% above the controls, where the median arm is at 98%); whether that is Tpr knockdown raising total RNA or a technical difference, these data cannot say, which is why the choice is made only where support fails. The other 57 perturbations keep library size and every factor." + ] + }, + { + "cell_type": "markdown", + "id": "5b7bb2d4", + "metadata": {}, + "source": [ + "## 3. Estimate the effects\n", + "\n", + "`pl.run_lfc` calls `LFC` on all controls plus 10 perturbations at a time and\n", + "stacks the batches into one table, one row per gene and perturbation. The\n", + "columns used most are `tau` (log fold change), `padj` (BH-adjusted p-value),\n", + "`count_treated` (total counts of the gene in the perturbed arm) and\n", + "`mean_control` (mean counts per control cell)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "165d389c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:31.044096Z", + "iopub.status.busy": "2026-09-30T04:23:31.044028Z", + "iopub.status.idle": "2026-09-30T04:23:31.248866Z", + "shell.execute_reply": "2026-09-30T04:23:31.248485Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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L45Glut
gene x perturbation pairs tested565064
hits9981
perturbations with >= 1 hit56 / 58
hits that are down-regulated24%
hits with zero counts in the perturbed arm0
median |tau| of hits0.36
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" + ], + "text/plain": [ + " L45Glut\n", + "gene x perturbation pairs tested 565064\n", + "hits 9981\n", + "perturbations with >= 1 hit 56 / 58\n", + "hits that are down-regulated 24%\n", + "hits with zero counts in the perturbed arm 0\n", + "median |tau| of hits 0.36" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spearman correlation of hits with arm size: 0.02\n" + ] + } + ], + "source": [ + "tested = df[np.isfinite(df['stat'])].copy()\n", + "tested['hit'] = tested['padj'] < Q\n", + "hits = tested[tested['hit']]\n", + "\n", + "display(pd.Series({\n", + " 'gene x perturbation pairs tested': len(tested),\n", + " 'hits': len(hits),\n", + " 'perturbations with >= 1 hit': f\"{hits['trt'].nunique()} / {A.shape[1]}\",\n", + " 'hits that are down-regulated': f\"{(hits['tau'] < 0).mean():.0%}\",\n", + " 'hits with zero counts in the perturbed arm': int((hits['count_treated'] == 0).sum()),\n", + " 'median |tau| of hits': round(hits['tau'].abs().median(), 2),\n", + "}, name=TAG).to_frame())\n", + "\n", + "per_pert = pd.DataFrame({\n", + " 'down': hits[hits['tau'] < 0].groupby('trt').size(),\n", + " 'up': hits[hits['tau'] > 0].groupby('trt').size(),\n", + "}).reindex(A.columns).fillna(0).astype(int)\n", + "per_pert['hits'] = per_pert.sum(1)\n", + "per_pert['cells'] = n_pert.reindex(per_pert.index).astype(int)\n", + "per_pert = per_pert.sort_values('hits', ascending=False)\n", + "\n", + "fig, ax = plt.subplots(figsize=(15, 4))\n", + "x = np.arange(len(per_pert))\n", + "ax.bar(x, -per_pert['down'], color=BLUE, label='down')\n", + "ax.bar(x, per_pert['up'], color=ORANGE, label='up')\n", + "ax.axhline(0, color='0.3', lw=0.8)\n", + "ax.set_xticks(x, per_pert.index, rotation=90, fontsize=7)\n", + "ax.set_ylabel('hits (down below 0, up above)'); ax.legend(frameon=False)\n", + "ax.set_title('Hits per perturbation')\n", + "plt.tight_layout(); plt.show()\n", + "print(f\"Spearman correlation of hits with arm size: \"\n", + " f\"{stats.spearmanr(per_pert['hits'], per_pert['cells']).statistic:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3dcd523a", + "metadata": {}, + "source": [ + "**Conclusion.** causarray finds 9,981 hits in 56 of the 58 perturbations, none of them from an arm with zero counts of the gene. Three quarters are up-regulated. The number of hits does not follow the number of cells (Spearman 0.02; the arms have 68–227 cells), so a perturbation's hit count reflects how strongly it changes expression. Three perturbations stand apart, Prpf6, Ppp2r1a and Tpr with about 1,500–1,900 hits each, followed by Ap2s1 (439), Psmc1 (360) and Srsf1 (304)." + ] + }, + { + "cell_type": "markdown", + "id": "e6498251", + "metadata": {}, + "source": [ + "## 4. Check calibration on fake perturbations\n", + "\n", + "Before reading the effects biologically, check that the test is honest on this\n", + "cell type. Take control cells only and label random subsets of them as fake\n", + "perturbations, with sizes drawn from the real arms (68–227 cells); each random\n", + "draw (seed) gives 10. Nothing differs between the arms, so every hit is false,\n", + "the test statistics should look like N(0, 1), and the p-values should be flat.\n", + "Wilcoxon on the same fake labels and genes is shown for reference.\n", + "\n", + "### Why `min_counts = 20`\n", + "\n", + "With the package default (`min_counts = 5`) a few false hits appear in genes that\n", + "are nearly absent from these neurons, where a handful of stray counts in 100\n", + "cells gives a large fold change. The table applies each threshold to the same\n", + "fake perturbations (seeds 0–2) and to the real ones." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "402621ff", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:31.249952Z", + "iopub.status.busy": "2026-09-30T04:23:31.249879Z", + "iopub.status.idle": "2026-09-30T04:23:33.179903Z", + "shell.execute_reply": "2026-09-30T04:23:33.179457Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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fake: false hits (30 arms)fake: false hits in genes < 0.05 counts/control cellreal: hits
min_counts
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" + ], + "text/plain": [ + " fake: false hits (30 arms) \\\n", + "min_counts \n", + "5 54 \n", + "10 32 \n", + "20 21 \n", + "40 11 \n", + "\n", + " fake: false hits in genes < 0.05 counts/control cell real: hits \n", + "min_counts \n", + "5 30 10514 \n", + "10 12 10236 \n", + "20 2 9981 \n", + "40 1 9653 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from statsmodels.stats.multitest import multipletests\n", + "\n", + "def load_null(seeds, lfc_dir):\n", + " ca = pd.concat([pd.read_csv(f'{lfc_dir}/null/seed{s}.csv') for s in seeds], ignore_index=True)\n", + " wc = pd.concat([pl.wilcoxon_table(f'results/{TAG}/null/wilcoxon_seed{s}.h5ad') for s in seeds],\n", + " ignore_index=True)\n", + " ca = ca[np.isfinite(ca['stat'])]\n", + " return ca.merge(wc, on=['trt', 'gene_names'], how='left')\n", + "\n", + "def apply_min_counts(d, mc):\n", + " \"\"\"Keep pairs passing LFC's expression threshold at min_counts=mc, then redo BH per perturbation.\"\"\"\n", + " d = d[np.isfinite(d['stat'])].copy()\n", + " thr = mc / np.minimum(d['n_treated'], d['n_control'])\n", + " observed = np.maximum(d['count_treated'] / d['n_treated'], d['count_control'] / d['n_control'])\n", + " d = d[(np.maximum(d['mean_control'], d['mean_treated']) >= thr) & (observed >= thr)].copy()\n", + " d['padj'] = d.groupby('trt')['pvalue'].transform(lambda p: multipletests(p, method='fdr_bh')[1])\n", + " return d\n", + "\n", + "null5 = load_null([0, 1, 2], f'results/{TAG}/min_counts5')\n", + "real5 = pd.read_csv(f'results/{TAG}/min_counts5/lfc.csv')\n", + "rows = []\n", + "for mc in (5, 10, 20, 40):\n", + " n, r = apply_min_counts(null5, mc), apply_min_counts(real5, mc)\n", + " fh = n[n['padj'] < Q]\n", + " rows.append({'min_counts': mc,\n", + " 'fake: false hits (30 arms)': len(fh),\n", + " 'fake: false hits in genes < 0.05 counts/control cell': int((fh['mean_control'] < 0.05).sum()),\n", + " 'real: hits': int((r['padj'] < Q).sum())})\n", + "display(pd.DataFrame(rows).set_index('min_counts'))" + ] + }, + { + "cell_type": "markdown", + "id": "6ead01eb", + "metadata": {}, + "source": [ + "**Conclusion.** With the default, 30 of the 54 false hits sit in genes with fewer than 0.05 counts per control cell, mostly glial and immune genes that are nearly absent from these neurons. `min_counts = 20` removes those (2 left) and cuts the false hits overall by more than half (54 → 21), at the cost of 5% of the real hits (10,514 → 9,981). Raising it further gains little. **The pipeline uses `min_counts = 20` for all cell types.**\n", + "\n", + "### Calibration at `min_counts = 20`, on fake perturbations not used to choose it" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "baf9d271", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:33.180967Z", + "iopub.status.busy": "2026-09-30T04:23:33.180898Z", + "iopub.status.idle": "2026-09-30T04:23:33.841577Z", + "shell.execute_reply": "2026-09-30T04:23:33.841288Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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false hits (30 fake arms)fraction p < 0.05 (target 0.05)
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" + ], + "text/plain": [ + " false hits (30 fake arms) fraction p < 0.05 (target 0.05)\n", + "causarray 14 0.058\n", + "Wilcoxon 99 0.052" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "causarray z under the null: mean 0.02, SD 1.03 (target 0 and 1)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "null20 = load_null([3, 4, 5], f'results/{TAG}')\n", + "display(pd.DataFrame({\n", + " 'false hits (30 fake arms)': [int((null20['padj'] < Q).sum()), int((null20['wilcox_padj'] < Q).sum())],\n", + " 'fraction p < 0.05 (target 0.05)': [round((null20['pvalue'] < 0.05).mean(), 3),\n", + " round((null20['wilcox_pvalue'] < 0.05).mean(), 3)],\n", + "}, index=['causarray', 'Wilcoxon']))\n", + "print(f\"causarray z under the null: mean {null20['stat'].mean():.2f}, SD {null20['stat'].std():.2f} (target 0 and 1)\")\n", + "\n", + "z = np.sort(null20['stat'].to_numpy())\n", + "theo = stats.norm.ppf((np.arange(1, len(z) + 1) - 0.5) / len(z))\n", + "bins = np.linspace(0, 1, 21)\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 3.8))\n", + "axes[0].scatter(theo, z, s=3, color=BLUE, rasterized=True)\n", + "lim = max(abs(theo).max(), abs(z).max()) * 1.05\n", + "axes[0].plot([-lim, lim], [-lim, lim], color='0.5', lw=1, ls='--')\n", + "axes[0].set_xlabel('N(0, 1) quantile'); axes[0].set_ylabel('causarray z')\n", + "axes[0].set_title('QQ plot, fake perturbations')\n", + "axes[1].hist(null20['pvalue'].dropna(), bins=bins, color=BLUE, density=True)\n", + "axes[1].axhline(1, color='0.4', lw=1, ls='--')\n", + "axes[1].set_xlabel('p-value'); axes[1].set_title('causarray p-values (flat = calibrated)')\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2dea7cbc", + "metadata": {}, + "source": [ + "**Conclusion.** On fake perturbations that were not used to choose the threshold, causarray makes 14 false hits over 30 fake arms, about one every two arms, while Wilcoxon makes 99 on the same genes. For scale, a real perturbation here averages about 170 hits. The statistics are close to N(0, 1) (mean 0.02, SD 1.03) and 5.8% of p-values fall below 0.05. The QQ plot shows the only departure, a slightly heavy upper tail (the 99th percentile of z is 2.50 against 2.33, and all 14 false hits are up-regulated calls), which is what to watch in other cell types; the QC table in section 7 reports it." + ] + }, + { + "cell_type": "markdown", + "id": "e48b8f22", + "metadata": {}, + "source": [ + "## 5. What the screen shows\n", + "\n", + "### 5a. Which perturbations share a response\n", + "\n", + "Two perturbations that act through the same pathway should move the same genes\n", + "in the same direction. The heatmap correlates `tau` between perturbations over\n", + "the genes that are a hit for at least one of them (perturbations with at least\n", + "50 hits)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cb270752", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:33.842786Z", + "iopub.status.busy": "2026-09-30T04:23:33.842716Z", + "iopub.status.idle": "2026-09-30T04:23:34.088680Z", + "shell.execute_reply": "2026-09-30T04:23:34.088310Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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correlation of tau
trttrt
PompPsmc10.49
Psmc1Psmc50.41
PompPsmc50.35
Ppp2r1aTsc10.33
Psmc1Tpr0.28
Prpf6Psmc10.25
Ppp2r1aStxbp10.25
Prpf6Tpr0.24
\n", + "
" + ], + "text/plain": [ + " correlation of tau\n", + "trt trt \n", + "Pomp Psmc1 0.49\n", + "Psmc1 Psmc5 0.41\n", + "Pomp Psmc5 0.35\n", + "Ppp2r1a Tsc1 0.33\n", + "Psmc1 Tpr 0.28\n", + "Prpf6 Psmc1 0.25\n", + "Ppp2r1a Stxbp1 0.25\n", + "Prpf6 Tpr 0.24" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "strong = per_pert.index[per_pert['hits'] >= 50].tolist()\n", + "hit_genes = hits['gene_names'].unique()\n", + "T = (tested[tested['trt'].isin(strong) & tested['gene_names'].isin(hit_genes)]\n", + " .pivot(index='gene_names', columns='trt', values='tau').dropna())\n", + "C = T.corr()\n", + "g = sns.clustermap(C, cmap='vlag', vmin=-1, vmax=1, figsize=(9, 9),\n", + " cbar_kws={'label': 'correlation of tau'})\n", + "g.fig.suptitle(f'Shared responses: {len(strong)} perturbations, {len(T):,} genes', y=1.02)\n", + "plt.show()\n", + "\n", + "upper = C.where(np.triu(np.ones(C.shape, dtype=bool), k=1)).stack().sort_values(ascending=False)\n", + "print('most similar pairs:')\n", + "display(upper.head(8).round(2).to_frame('correlation of tau'))" + ] + }, + { + "cell_type": "markdown", + "id": "fbb51cd3", + "metadata": {}, + "source": [ + "**Conclusion.** The clearest module is the proteasome: its subunits Pomp, Psmc1 and Psmc5 give the three most similar responses (correlation 0.35–0.49), as expected for parts of one complex. In the clustered heatmap they form one block with the RNA-processing perturbations Prpf6, U2af2 (another splicing factor) and Tpr (pairwise correlations 0.24–0.28 with Psmc1), so disrupting protein turnover and RNA processing moves a common set of genes. Separately, Ppp2r1a (the PP2A scaffold) resembles Tsc1 (0.33) and Stxbp1 (0.25). Most other pairs are only weakly related, so each perturbation largely has its own profile.\n", + "\n", + "### 5b. Genes that many perturbations move\n", + "\n", + "A gene hit by many perturbations is part of a common response of these neurons,\n", + "rather than a target of one pathway." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f93f2a86", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:34.089841Z", + "iopub.status.busy": "2026-09-30T04:23:34.089774Z", + "iopub.status.idle": "2026-09-30T04:23:34.353358Z", + "shell.execute_reply": "2026-09-30T04:23:34.353002Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "genes hit by >= 10 perturbations: 12 (of 5,861 genes with any hit)\n" + ] + }, + { + "data": { + "text/html": [ + "
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perturbationsfraction_downmedian_tau
gene_names
Scube1140.93-0.27
Snhg11140.57-0.04
Ahi1120.92-0.23
Atp1a1121.00-0.15
Rsrp1120.50-0.01
R3hdm1110.73-0.14
Adgrb1110.73-0.11
Amy1110.180.34
Coro6100.60-0.25
Atp1b1100.300.12
Cx3cl1100.80-0.14
Bdnf100.100.54
Cck90.56-0.13
Vstm2l90.78-0.26
Adcyap190.220.65
Scn1b90.78-0.12
Col4a290.110.46
Atp6v0c90.89-0.15
Prkcg80.500.01
Ccdc13680.380.14
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" + ], + "text/plain": [ + " perturbations fraction_down median_tau\n", + "gene_names \n", + "Scube1 14 0.93 -0.27\n", + "Snhg11 14 0.57 -0.04\n", + "Ahi1 12 0.92 -0.23\n", + "Atp1a1 12 1.00 -0.15\n", + "Rsrp1 12 0.50 -0.01\n", + "R3hdm1 11 0.73 -0.14\n", + "Adgrb1 11 0.73 -0.11\n", + "Amy1 11 0.18 0.34\n", + "Coro6 10 0.60 -0.25\n", + "Atp1b1 10 0.30 0.12\n", + "Cx3cl1 10 0.80 -0.14\n", + "Bdnf 10 0.10 0.54\n", + "Cck 9 0.56 -0.13\n", + "Vstm2l 9 0.78 -0.26\n", + "Adcyap1 9 0.22 0.65\n", + "Scn1b 9 0.78 -0.12\n", + "Col4a2 9 0.11 0.46\n", + "Atp6v0c 9 0.89 -0.15\n", + "Prkcg 8 0.50 0.01\n", + "Ccdc136 8 0.38 0.14" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "by_gene = hits.groupby('gene_names').agg(\n", + " perturbations=('trt', 'nunique'),\n", + " fraction_down=('tau', lambda t: round((t < 0).mean(), 2)),\n", + " median_tau=('tau', lambda t: round(t.median(), 2)),\n", + ").sort_values('perturbations', ascending=False)\n", + "print(f\"genes hit by >= 10 perturbations: {(by_gene['perturbations'] >= 10).sum()} \"\n", + " f\"(of {len(by_gene):,} genes with any hit)\")\n", + "display(by_gene.head(20))" + ] + }, + { + "cell_type": "markdown", + "id": "ce0d3eb9", + "metadata": {}, + "source": [ + "**Conclusion.** Few genes respond to many perturbations: 12 of the 5,861 genes with any hit are hit by 10 or more. Among them, the activity-regulated neurotrophin Bdnf and the neuropeptide Adcyap1 (PACAP) go up in most of the perturbations that hit them, while the sodium pump subunit Atp1a1 and Scube1 and Ahi1 go down. This suggests a common response of these neurons to disruption, on top of each perturbation's specific effects; the correlations in 5a stay low because this shared part is small.\n", + "\n", + "### 5c. What the strongest perturbations change\n", + "\n", + "Enrichr (GO Biological Process, KEGG, Reactome) on the down- and up-regulated\n", + "hits of the four perturbations with the most hits, separately." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "07770d9b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:34.354367Z", + "iopub.status.busy": "2026-09-30T04:23:34.354303Z", + "iopub.status.idle": "2026-09-30T04:23:34.387417Z", + "shell.execute_reply": "2026-09-30T04:23:34.387051Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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genesenriched termstop terms
perturbationdirection
Prpf6down412164Neuronal System [4e-19]; Transmission Across Chemical Synapses [9e-11]; Chemical Synaptic Transmission [1e-09]
up1475707Metabolism Of RNA [2e-60]; Processing Of Capped Intron-Containing Pre-mR [4e-43]; mRNA Splicing - Major Pathway [5e-35]
Ppp2r1adown355137Neuronal System [5e-08]; Signaling By NTRKs [5e-06]; Transmission Across Chemical Synapses [2e-05]
up11314Neuronal System [2e-06]; Protein-protein Interactions At Synapses [2e-02]; Potassium Channels [2e-02]
Tprdown18442Neuronal System [3e-04]; Regulation Of Neuron Projection Development [6e-03]; Ca-dependent Events [1e-02]
up1273279Gene Expression (Transcription) [1e-12]; RNA Polymerase II Transcription [3e-10]; Metabolism Of RNA [6e-10]
Ap2s1down17383Nuclear Events (Kinase And Transcription Fact [5e-08]; Signaling By NTRK1 (TRKA) [1e-06]; Neuronal System [1e-06]
up2668Regulation Of Potassium Ion Transport [4e-02]; Positive Regulation Of Developmental Growth [4e-02]; Positive Regulation Of Axon Extension [4e-02]
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" + ], + "text/plain": [ + " genes enriched terms \\\n", + "perturbation direction \n", + "Prpf6 down 412 164 \n", + " up 1475 707 \n", + "Ppp2r1a down 355 137 \n", + " up 1131 4 \n", + "Tpr down 184 42 \n", + " up 1273 279 \n", + "Ap2s1 down 173 83 \n", + " up 266 8 \n", + "\n", + " top terms \n", + "perturbation direction \n", + "Prpf6 down Neuronal System [4e-19]; Transmission Across Chemical Synapses [9e-11]; Chemical Synaptic Transmission [1e-09] \n", + " up Metabolism Of RNA [2e-60]; Processing Of Capped Intron-Containing Pre-mR [4e-43]; mRNA Splicing - Major Pathway [5e-35] \n", + "Ppp2r1a down Neuronal System [5e-08]; Signaling By NTRKs [5e-06]; Transmission Across Chemical Synapses [2e-05] \n", + " up Neuronal System [2e-06]; Protein-protein Interactions At Synapses [2e-02]; Potassium Channels [2e-02] \n", + "Tpr down Neuronal System [3e-04]; Regulation Of Neuron Projection Development [6e-03]; Ca-dependent Events [1e-02] \n", + " up Gene Expression (Transcription) [1e-12]; RNA Polymerase II Transcription [3e-10]; Metabolism Of RNA [6e-10] \n", + "Ap2s1 down Nuclear Events (Kinase And Transcription Fact [5e-08]; Signaling By NTRK1 (TRKA) [1e-06]; Neuronal System [1e-06] \n", + " up Regulation Of Potassium Ion Transport [4e-02]; Positive Regulation Of Developmental Growth [4e-02]; Positive Regulation Of Axon Extension [4e-02] " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import re, time, requests\n", + "from pathlib import Path\n", + "\n", + "ENRICH_DIR = Path('results') / TAG / 'enrichr'\n", + "ENRICH_DIR.mkdir(parents=True, exist_ok=True)\n", + "LIBS = ('GO_Biological_Process_2023', 'KEGG_2019_Mouse', 'Reactome_2022')\n", + "\n", + "def enriched_terms(genes, cache_name):\n", + " \"\"\"Enrichr terms with adjusted p < 0.05 (cached per gene list; delete the cache if the list changes).\"\"\"\n", + " path = ENRICH_DIR / f'{cache_name}.csv'\n", + " if path.exists():\n", + " return pd.read_csv(path)\n", + " rows = []\n", + " genes = list(genes)\n", + " if len(genes) >= 5:\n", + " r = requests.post('https://maayanlab.cloud/Enrichr/addList',\n", + " files={'list': (None, '\\n'.join(genes)), 'description': (None, cache_name)}, timeout=60)\n", + " r.raise_for_status()\n", + " uid = r.json()['userListId']\n", + " for lib in LIBS:\n", + " for attempt in range(3):\n", + " try:\n", + " e = requests.get('https://maayanlab.cloud/Enrichr/enrich',\n", + " params={'userListId': uid, 'backgroundType': lib}, timeout=60)\n", + " e.raise_for_status()\n", + " break\n", + " except requests.RequestException:\n", + " time.sleep(2)\n", + " rows += [{'lib': lib, 'term': re.sub(r' \\(GO:\\d+\\)| R-HSA-\\d+', '', t[1]), 'padj': t[6]}\n", + " for t in e.json().get(lib, []) if t[6] < 0.05]\n", + " out = pd.DataFrame(rows, columns=['lib', 'term', 'padj']).sort_values('padj')\n", + " out.to_csv(path, index=False)\n", + " return out\n", + "\n", + "FOCUS = per_pert.index[:4].tolist()\n", + "rows = []\n", + "for trt in FOCUS:\n", + " h = hits[hits['trt'] == trt]\n", + " for direction, genes in (('down', h.loc[h['tau'] < 0, 'gene_names']),\n", + " ('up', h.loc[h['tau'] > 0, 'gene_names'])):\n", + " terms = enriched_terms(genes, f'{trt}_{direction}')\n", + " rows.append({'perturbation': trt, 'direction': direction, 'genes': len(genes),\n", + " 'enriched terms': len(terms),\n", + " 'top terms': '; '.join(f\"{t[:45]} [{p:.0e}]\" for t, p in\n", + " terms[['term', 'padj']].head(3).itertuples(index=False))\n", + " or '(none)'})\n", + "with pd.option_context('display.max_colwidth', 160):\n", + " display(pd.DataFrame(rows).set_index(['perturbation', 'direction']))" + ] + }, + { + "cell_type": "markdown", + "id": "f5c1b1dd", + "metadata": {}, + "source": [ + "**Conclusion.** Prpf6 and Tpr up-regulate their own machinery, and Prpf6, Tpr and Ppp2r1a down-regulate neuronal programs:\n", + "\n", + "* **Prpf6** (spliceosome): up-regulated genes enrich for RNA metabolism and mRNA splicing (p = 2e-60); down-regulated genes for the neuronal system and synaptic transmission (4e-19).\n", + "* **Tpr** (nuclear pore): up for transcription and RNA polymerase II (1e-12); down for the neuronal system and neuron projection development.\n", + "* **Ppp2r1a** (PP2A): down for the neuronal system and neurotrophin (NTRK) signalling; its many up-regulated genes enrich only weakly (4 terms).\n", + "* **Ap2s1** (AP-2 adaptor, which internalizes receptors): down for kinase and transcription-factor signalling and NTRK1 (TrkA) signalling, consistent with the role of endocytosis in neurotrophin receptor signalling.\n", + "\n", + "The up-regulation of splicing and transcription genes after knocking down those very processes looks like a compensatory response; the shared loss of synaptic genes fits the common response in 5b." + ] + }, + { + "cell_type": "markdown", + "id": "67b74e7f", + "metadata": {}, + "source": [ + "## 6. Wilcoxon as a reference\n", + "\n", + "Wilcoxon on log-normalized counts is the usual first look at such a screen. It\n", + "does not adjust for the latent factors, so the two lists are not expected to\n", + "match, but most Wilcoxon hits should be found again and shared hits should move\n", + "in the same direction. The comparison uses the genes causarray tests." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1ae2dde2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:34.388464Z", + "iopub.status.busy": "2026-09-30T04:23:34.388390Z", + "iopub.status.idle": "2026-09-30T04:23:37.307339Z", + "shell.execute_reply": "2026-09-30T04:23:37.306931Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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L45Glut
causarray hits9981
Wilcoxon hits5322
Wilcoxon hits also found by causarray82%
shared hits with the same direction100%
correlation of effect sizes (all tested pairs)0.93
\n", + "
" + ], + "text/plain": [ + " L45Glut\n", + "causarray hits 9981\n", + "Wilcoxon hits 5322\n", + "Wilcoxon hits also found by causarray 82%\n", + "shared hits with the same direction 100%\n", + "correlation of effect sizes (all tested pairs) 0.93" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_wc = pl.run_wilcoxon(TAG)\n", + "cmp = tested.merge(df_wc, on=['trt', 'gene_names'], how='inner')\n", + "cmp['wc'] = cmp['wilcox_padj'] < Q\n", + "both = cmp['hit'] & cmp['wc']\n", + "display(pd.Series({\n", + " 'causarray hits': int(cmp['hit'].sum()),\n", + " 'Wilcoxon hits': int(cmp['wc'].sum()),\n", + " 'Wilcoxon hits also found by causarray': f\"{both.sum() / cmp['wc'].sum():.0%}\",\n", + " 'shared hits with the same direction': f\"{(np.sign(cmp.loc[both, 'tau']) == np.sign(cmp.loc[both, 'wilcox_logfc'])).mean():.0%}\",\n", + " 'correlation of effect sizes (all tested pairs)': round(cmp[['tau', 'wilcox_logfc']].corr().iloc[0, 1], 2),\n", + "}, name=TAG).to_frame())\n", + "\n", + "fig, ax = plt.subplots(figsize=(5.5, 4.5))\n", + "c = cmp.iloc[np.argsort((cmp['hit'] | cmp['wc']).to_numpy(), kind='stable')]\n", + "colors = np.select([c['hit'] & c['wc'], c['hit'], c['wc']], [PURPLE, BLUE, ORANGE], GREY)\n", + "ax.scatter(c['wilcox_logfc'], c['tau'], c=colors, s=2, alpha=0.5, rasterized=True)\n", + "ax.axhline(0, color='0.6', lw=0.8); ax.axvline(0, color='0.6', lw=0.8)\n", + "ax.set_xlim(np.nanpercentile(c['wilcox_logfc'], [0.05, 99.95]))\n", + "ax.set_xlabel('Wilcoxon log2 fold change'); ax.set_ylabel('causarray tau (natural log)')\n", + "for col, lab in ((PURPLE, 'both'), (BLUE, 'causarray only'), (ORANGE, 'Wilcoxon only'), (GREY, 'neither')):\n", + " ax.scatter([], [], color=col, s=12, label=lab)\n", + "ax.legend(frameon=False, fontsize=8, loc='upper left')\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0ac354b4", + "metadata": {}, + "source": [ + "**Conclusion.** The two methods agree where Wilcoxon finds something: causarray recovers 82% of the Wilcoxon hits, every shared hit moves in the same direction, and effect sizes correlate at 0.93. causarray finds about twice as many hits (9,981 vs 5,322), and in the calibration check of section 4 causarray made fewer false hits than Wilcoxon on fake perturbations (14 against 99). That check does not show that every extra hit is real, so the checks below look at what the extra hits contain.\n", + "\n", + "### Which method's extra hits carry biology?\n", + "\n", + "Shared hits say nothing about which method is better, so the checks below look\n", + "at the hits only one method calls, for the same four perturbations as in 5c:\n", + "\n", + "* **Exclusive hits.** Enrich the causarray-only and the Wilcoxon-only genes\n", + " separately. Noise should not enrich for anything.\n", + "* **Lists of equal length.** Longer lists give more enriched terms whatever their\n", + " quality, so also take each method's top *k* genes, with the same *k* (the\n", + " smaller of the two hit counts), and count the terms.\n", + "* **Expression support.** Are each method's exclusive hits well-measured genes,\n", + " or sparse ones where a few counts decide the call?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5ad0540b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:37.308482Z", + "iopub.status.busy": "2026-09-30T04:23:37.308403Z", + "iopub.status.idle": "2026-09-30T04:23:37.378690Z", + "shell.execute_reply": "2026-09-30T04:23:37.378315Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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genesenriched termsGO BP termstop GO BP term
perturbationgene list
Prpf6causarray only1379458141mRNA Processing [4e-15]
Wilcoxon only3100(none)
causarray top 53953928096mRNA Splicing, Via Spliceosome [1e-15]
Wilcoxon top 53953910033mRNA Splicing, Via Spliceosome [8e-09]
Ppp2r1acausarray only58300(none)
Wilcoxon only17610(none)
causarray top 1079107910746Chemical Synaptic Transmission [8e-07]
Wilcoxon top 1079107914176Chemical Synaptic Transmission [9e-06]
Tprcausarray only77322567Peptidyl-Serine Phosphorylation [3e-04]
Wilcoxon only6300(none)
causarray top 74774710259Regulation Of Transcription By RNA Polymerase II [7e-08]
Wilcoxon top 7477475239mRNA Splicing, Via Spliceosome [7e-05]
Ap2s1causarray only25200(none)
Wilcoxon only2000(none)
causarray top 2072075513Neurotransmitter Secretion [4e-04]
Wilcoxon top 207207329Nervous System Development [3e-03]
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" + ], + "text/plain": [ + " genes enriched terms GO BP terms \\\n", + "perturbation gene list \n", + "Prpf6 causarray only 1379 458 141 \n", + " Wilcoxon only 31 0 0 \n", + " causarray top 539 539 280 96 \n", + " Wilcoxon top 539 539 100 33 \n", + "Ppp2r1a causarray only 583 0 0 \n", + " Wilcoxon only 176 1 0 \n", + " causarray top 1079 1079 107 46 \n", + " Wilcoxon top 1079 1079 141 76 \n", + "Tpr causarray only 773 225 67 \n", + " Wilcoxon only 63 0 0 \n", + " causarray top 747 747 102 59 \n", + " Wilcoxon top 747 747 52 39 \n", + "Ap2s1 causarray only 252 0 0 \n", + " Wilcoxon only 20 0 0 \n", + " causarray top 207 207 55 13 \n", + " Wilcoxon top 207 207 32 9 \n", + "\n", + " top GO BP term \n", + "perturbation gene list \n", + "Prpf6 causarray only mRNA Processing [4e-15] \n", + " Wilcoxon only (none) \n", + " causarray top 539 mRNA Splicing, Via Spliceosome [1e-15] \n", + " Wilcoxon top 539 mRNA Splicing, Via Spliceosome [8e-09] \n", + "Ppp2r1a causarray only (none) \n", + " Wilcoxon only (none) \n", + " causarray top 1079 Chemical Synaptic Transmission [8e-07] \n", + " Wilcoxon top 1079 Chemical Synaptic Transmission [9e-06] \n", + "Tpr causarray only Peptidyl-Serine Phosphorylation [3e-04] \n", + " Wilcoxon only (none) \n", + " causarray top 747 Regulation Of Transcription By RNA Polymerase II [7e-08] \n", + " Wilcoxon top 747 mRNA Splicing, Via Spliceosome [7e-05] \n", + "Ap2s1 causarray only (none) \n", + " Wilcoxon only (none) \n", + " causarray top 207 Neurotransmitter Secretion [4e-04] \n", + " Wilcoxon top 207 Nervous System Development [3e-03] " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " hits median counts in the perturbed arm \\\n", + "causarray only 5622 170.0 \n", + "Wilcoxon only 963 135.0 \n", + "\n", + " median counts per control cell \n", + "causarray only 1.17 \n", + "Wilcoxon only 1.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rows = []\n", + "for trt in FOCUS:\n", + " g = cmp[cmp['trt'] == trt]\n", + " k = int(min(g['hit'].sum(), g['wc'].sum()))\n", + " sets = {\n", + " 'causarray only': g.loc[g['hit'] & ~g['wc'], 'gene_names'],\n", + " 'Wilcoxon only': g.loc[g['wc'] & ~g['hit'], 'gene_names'],\n", + " f'causarray top {k}': g[g['hit']].sort_values('pvalue')['gene_names'].head(k),\n", + " f'Wilcoxon top {k}': g[g['wc']].sort_values('wilcox_pvalue')['gene_names'].head(k),\n", + " }\n", + " for name, genes in sets.items():\n", + " terms = enriched_terms(genes, f\"{trt}_{name.replace(' ', '_')}\")\n", + " go = terms[terms['lib'] == 'GO_Biological_Process_2023']\n", + " rows.append({'perturbation': trt, 'gene list': name, 'genes': len(genes),\n", + " 'enriched terms': len(terms), 'GO BP terms': len(go),\n", + " 'top GO BP term': f\"{go.iloc[0]['term'][:55]} [{go.iloc[0]['padj']:.0e}]\" if len(go) else '(none)'})\n", + "with pd.option_context('display.max_colwidth', 80):\n", + " display(pd.DataFrame(rows).set_index(['perturbation', 'gene list']))\n", + "\n", + "ca_only, wc_only = cmp['hit'] & ~cmp['wc'], cmp['wc'] & ~cmp['hit']\n", + "display(pd.DataFrame({\n", + " 'hits': [int(ca_only.sum()), int(wc_only.sum())],\n", + " 'median counts in the perturbed arm': [cmp.loc[ca_only, 'count_treated'].median(),\n", + " cmp.loc[wc_only, 'count_treated'].median()],\n", + " 'median counts per control cell': [round(cmp.loc[ca_only, 'mean_control'].median(), 2),\n", + " round(cmp.loc[wc_only, 'mean_control'].median(), 2)],\n", + "}, index=['causarray only', 'Wilcoxon only']))" + ] + }, + { + "cell_type": "markdown", + "id": "00bdd16e", + "metadata": {}, + "source": [ + "**Conclusion.** For two of the four perturbations, Prpf6 and Tpr, causarray's extra hits carry the biology and Wilcoxon's do not; for Ap2s1 neither method's do, and Ppp2r1a goes the other way:\n", + "\n", + "* **Prpf6**: its 1,379 causarray-only genes enrich for mRNA processing and splicing (458 terms); its 31 Wilcoxon-only genes enrich for nothing. On lists of equal length causarray gives 280 terms against 100, with the same top term (mRNA splicing), at a stronger p-value (1e-15 against 8e-09).\n", + "* **Tpr**: 225 terms for the causarray-only genes, none for the Wilcoxon-only genes; on equal lists 102 against 52, and causarray's top term (transcription by RNA polymerase II) matches Tpr's role at the nuclear pore, while Wilcoxon's is splicing.\n", + "* **Ap2s1**: neither method's exclusive hits enrich; on equal lists causarray gives 55 terms against 32, led by neurotransmitter secretion.\n", + "* **Ppp2r1a is the exception**: its 583 causarray-only genes (mostly up-regulated) enrich for nothing, and on equal lists Wilcoxon gives slightly more terms (141 against 107), with the same top term (chemical synaptic transmission). Ppp2r1a's extra up-regulated hits have no annotated theme, so treat them with care.\n", + "\n", + "Across all perturbations, causarray's exclusive hits are, if anything, better measured than Wilcoxon's (median 170 against 135 counts in the perturbed arm; 1.17 against 1.00 per control cell), so the extra hits are not sparse-gene artifacts. Together with the calibration check (14 false hits against 99), this suggests causarray's extra power is mostly real signal." + ] + }, + { + "cell_type": "markdown", + "id": "90aa5bf9", + "metadata": {}, + "source": [ + "## 7. Running every cell type\n", + "\n", + "The loop below is the whole analysis for all cell types. It is switched off\n", + "here (`RUN_ALL = False`). Every step skips work already on disk, so after a\n", + "crash the loop can simply be run again.\n", + "\n", + "What it costs, measured on this cell type (22k cells, 14k genes, 58 perturbations),\n", + "with crispyx 0.1.6 (0.1.5 is several times slower):\n", + "\n", + "* **latent factors**: one GCATE fit per cell type, 34 minutes on 32 cores;\n", + "* **causarray**: about 5 minutes per batch of 10 perturbations with all 15k controls, running 3 batches at a time on a 32-core, 512 GB machine. **Cap each job's threads when running several at once** (`export NUMBA_NUM_THREADS=10 OMP_NUM_THREADS=10 OPENBLAS_NUM_THREADS=10` for 3 jobs on 32 cores); otherwise the jobs compete for cores and run about ten times slower. A batch can use up to about 90 GB of memory;\n", + "* **Wilcoxon**: a few minutes for all perturbations;\n", + "* **calibration check**: 3 fake-perturbation batches (seeds 3–5), the same cost as 3 causarray batches.\n", + "\n", + "On an Apple Silicon Mac, use a native (arm64) Python: an Intel Python running under Rosetta is several times slower, and `import causarray` warns about it.\n", + "\n", + "For 1,900 perturbations a cell type has 190 batches. The batches are independent,\n", + "so run them as separate jobs, as many at a time as memory allows\n", + "(`python pipeline.py lfc --tag --batches `), then call `pl.run_lfc`\n", + "once more to combine them. Memory grows with (cells in the batch) × (genes) ×\n", + "(batch size), so use `pl.run_lfc(..., batch_size=5)` for large cell types." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "79d4d994", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-30T04:23:37.379787Z", + "iopub.status.busy": "2026-09-30T04:23:37.379725Z", + "iopub.status.idle": "2026-09-30T04:23:38.063719Z", + "shell.execute_reply": "2026-09-30T04:23:38.063283Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cell typeL45Glut
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" + ], + "text/plain": [ + "cell type L45Glut\n", + "perturbations 58.00\n", + "causarray hits 9981.00\n", + "Wilcoxon hits 5322.00\n", + "Wilcoxon hits recovered 0.82\n", + "fraction down 0.24\n", + "hits with zero perturbed counts 0.00\n", + "null: causarray false hits 14.00\n", + "null: Wilcoxon false hits 99.00\n", + "null: SD of z (target 1) 1.03" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "RUN_ALL = False\n", + "SRC = 'data/combined.h5ad' # the full screen, one h5ad with all cell types\n", + "CELL_TYPES = { # tag -> value of obs['predicted_group']\n", + " 'L45Glut': '005 L4-5 IT CTX Glut',\n", + " # 'L23Glut': '...',\n", + "}\n", + "\n", + "def run_cell_type(tag, cell_type):\n", + " pl.subset_cell_type(SRC, cell_type, tag)\n", + " Y, A, X, X_A = pl.load_inputs(tag)\n", + " res_2 = pl.fit_factors(tag, Y, X, A)\n", + " df = pl.run_lfc(tag, Y, A, X, X_A, res_2)\n", + " df_wc = pl.run_wilcoxon(tag)\n", + " nulls = [pl.run_null(tag, Y, A, X, X_A, res_2, seed) for seed in (3, 4, 5)]\n", + " return pl.summarize(tag, df, df_wc, nulls)\n", + "\n", + "if RUN_ALL:\n", + " qc = pd.DataFrame([run_cell_type(t, c) for t, c in CELL_TYPES.items()]).set_index('cell type')\n", + "else: # this cell type, from the cached results\n", + " nulls = [(pd.read_csv(f'results/{TAG}/null/seed{s}.csv'),\n", + " pl.wilcoxon_table(f'results/{TAG}/null/wilcoxon_seed{s}.h5ad')) for s in (3, 4, 5)]\n", + " qc = pd.DataFrame([pl.summarize(TAG, df, df_wc, nulls)]).set_index('cell type')\n", + "display(qc.T)" + ] + }, + { + "cell_type": "markdown", + "id": "083cd60b", + "metadata": {}, + "source": [ + "**How to read the QC table.** One row per cell type; look for cell types that\n", + "stand out. For reference, this cell type gives:\n", + "\n", + "* 9,981 hits, recovering 82% of the Wilcoxon hits, 24% down-regulated, no hits with zero counts in the perturbed arm;\n", + "* calibration check: 14 false hits against Wilcoxon's 99 over 30 fake perturbations, SD of z 1.03.\n", + "\n", + "A cell type that fails the null check (many false hits, or SD of z well above 1)\n", + "should be looked at before its hits are used: start with its propensity check\n", + "(section 2), the arms the pipeline adjusted (`lfc_batches/*_propensity.csv`),\n", + "and the size of its arms." + ] + }, + { + "cell_type": "markdown", + "id": "76d31f5e", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "1. **How to run it.** One GCATE fit per cell type, then `LFC` on all controls plus 10 perturbations at a time, with `min_counts = 20` and threads capped when batches run in parallel. `pipeline.py` does each step and resumes after a crash.\n", + "2. **What to check before reading effects.** The propensity check (section 2): here only Tpr needed an adjustment, dropping library size for Tpr alone. The calibration check (section 4): about one false hit every two fake perturbations, with statistics close to N(0, 1).\n", + "3. **What this cell type shows.** 56 of 58 perturbations change expression; Prpf6, Ppp2r1a and Tpr change it most. Prpf6 and Tpr raise genes of their own machinery (splicing, transcription), and all three lower neuronal genes. The proteasome subunits Pomp, Psmc1 and Psmc5 form the clearest shared module, and activity-regulated genes such as Bdnf respond to many perturbations.\n", + "4. **Compared with Wilcoxon.** causarray recovers 82% of the Wilcoxon hits with the same directions and finds about twice as many. Its extra hits for Prpf6 and Tpr carry the expected biology while Wilcoxon's extra hits enrich for nothing; Ppp2r1a's extra hits are the exception.\n", + "5. **Next.** Run the loop in section 7 for the other cell types and compare their QC rows, then ask whether the modules and shared genes found here recur in other neuron types." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "causarray", + "language": "python", + "name": "causarray" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.7" + }, + "nbsphinx": { + "orphan": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorial/mouse_brain/README.md b/docs/source/tutorial/mouse_brain/README.md new file mode 100644 index 0000000..5928187 --- /dev/null +++ b/docs/source/tutorial/mouse_brain/README.md @@ -0,0 +1,33 @@ +# Mouse-brain Perturb-seq (L4-5 IT Glut) + +A causarray 0.1.1 tutorial on one cell type of a mouse-brain CRISPR screen. +Only the notebook, `pipeline.py` and this file are tracked in git; the notebook +is rendered in the documentation but not listed in its index. `data/` holds +inputs and `results/` everything generated; both are local. + + L45Glut-py.ipynb the tutorial: pipeline, propensity and calibration + checks, biological readouts, Wilcoxon reference, and + the loop for all cell types + pipeline.py the per-cell-type pipeline the notebook calls; also a CLI: + python pipeline.py lfc --tag L45Glut --batches 3 + python pipeline.py null --tag L45Glut --seeds 3,4,5 + +The input `data/L45Glut.h5ad` is a subset of collaborator-provided pilot data +and is not distributed with the repository. + +Results for one cell type live in `results//`: + + gcate.pkl, lfc.csv, lfc_batches/, wilcoxon.h5ad, null/ (pipeline outputs) + lfc_batches/*_propensity.csv arms the propensity check adjusted (L45Glut: Tpr) + min_counts5/ the same run with the package default min_counts=5, + used in the notebook's section 4 + enrichr/ cached Enrichr queries + +Memory: one LFC batch (15k controls + 10 perturbations, 14k genes) peaks near +90 GB. Three batches at a time fit on a 512 GB machine; nine did not. + +Threads: when running several jobs at once, cap each so that together they do +not exceed the cores (3 jobs on 32 cores: `export NUMBA_NUM_THREADS=10 +OMP_NUM_THREADS=10 OPENBLAS_NUM_THREADS=10`). Uncapped, the jobs slow each other +down about tenfold. With the cap, a batch takes about 5 minutes and GCATE about +34 minutes (in a native arm64 Python). diff --git a/docs/source/tutorial/mouse_brain/pipeline.py b/docs/source/tutorial/mouse_brain/pipeline.py new file mode 100644 index 0000000..591f4d1 --- /dev/null +++ b/docs/source/tutorial/mouse_brain/pipeline.py @@ -0,0 +1,336 @@ +"""Per-cell-type causarray + Wilcoxon pipeline for the mouse-brain Perturb-seq screen. + +Every step caches its output under ``results//`` and is skipped when the +cache exists, so a crashed or interrupted run resumes where it stopped. The +notebook ``L45Glut-py.ipynb`` walks through these steps for one cell type; the +same calls run unchanged for every other cell type. + + results//gcate.pkl latent factors (one GCATE fit per cell type) + results//lfc_batches/*.csv causarray, ~10 perturbations per batch + results//lfc_batches/*_propensity.csv arms the propensity check adjusted (if any) + results//lfc.csv causarray, all perturbations + results//wilcoxon.h5ad crispyx Wilcoxon, all perturbations + results//null/seed*.csv causarray on fake perturbations of controls + results//null/wilcoxon_seed*.h5ad Wilcoxon on the same fake labels + +Command line (run from this folder):: + + python pipeline.py subset --src --cell-type "005 L4-5 IT CTX Glut" --tag L45Glut + python pipeline.py lfc --tag L45Glut [--batches 0,1,2] + python pipeline.py null --tag L45Glut [--seeds 0,1,2] + +When several jobs run at once, cap each one's threads so that together they +do not exceed the cores (numba and BLAS otherwise start one thread per core in +every job, and the jobs slow each other down about tenfold):: + + export NUMBA_NUM_THREADS=10 OMP_NUM_THREADS=10 OPENBLAS_NUM_THREADS=10 +""" +from __future__ import annotations + +import argparse +import gc +import os +import pickle +import warnings +from pathlib import Path + +import anndata as ad +import numpy as np +import pandas as pd +import scipy.sparse as sp + +import crispyx +from causarray import (LFC, fit_gcate, prep_causarray_data, estimate_propensity_scores, + refit_propensity_scores, select_propensity_factors) + +HERE = Path(__file__).parent.resolve() +DATA_DIR = HERE / 'data' +RESULTS_DIR = HERE / 'results' + +PERT_COL = 'gene_target' +CTRL_LABEL = 'Non_target' +CELL_TYPE_COL = 'predicted_group' +MIN_CELLS = 50 # keep genes detected in at least this many cells +RANK = 8 # latent factors; chosen by JIC on L4-5 IT Glut, fixed for all cell types +BATCH_SIZE = 10 # perturbations per LFC call; peak memory ~ n_cells * n_genes * BATCH_SIZE * 16 bytes +Q = 0.05 # FDR level for both methods +MIN_COUNTS = 20 # LFC tests a gene only with >= ~20 expected counts in the smaller arm + # (package default 5); chosen from the null check in the notebook + + +def paths(tag, variant=''): + """File layout for one cell type. + + ``variant`` puts the causarray outputs of a sensitivity run (e.g. another + ``min_counts``) in a subfolder; latent factors and Wilcoxon are shared. + """ + out = RESULTS_DIR / tag + ca = out / variant if variant else out + ca.mkdir(parents=True, exist_ok=True) + return { + 'counts': DATA_DIR / f'{tag}.h5ad', + 'norm': DATA_DIR / f'{tag}_norm.h5ad', + 'gcate': out / 'gcate.pkl', + 'batches': ca / 'lfc_batches', + 'lfc': ca / 'lfc.csv', + 'wilcoxon': out / 'wilcoxon.h5ad', + 'null': ca / 'null', + 'null_wilcoxon': out / 'null', + } + + +# ── Step 1: one cell type ──────────────────────────────────────────────────── +def subset_cell_type(src, cell_type, tag): + """Write the cells of one cell type from ``src`` to ``data/.h5ad``.""" + p = paths(tag) + if not p['counts'].exists(): + obs = crispyx.load_obs(src) + cell_mask = (obs[CELL_TYPE_COL].astype(str) == cell_type).to_numpy() + gene_mask = np.ones(ad.read_h5ad(src, backed='r').n_vars, dtype=bool) + crispyx.write_filtered_subset(src, cell_mask=cell_mask, gene_mask=gene_mask, + output_path=p['counts']) + return p['counts'] + + +def load_inputs(tag): + """Counts and designs for causarray, restricted to genes seen in >= MIN_CELLS cells.""" + adata = ad.read_h5ad(paths(tag)['counts']) + counts = adata.X if sp.issparse(adata.X) else sp.csr_matrix(adata.X) + keep = np.asarray((counts > 0).sum(0)).ravel() >= MIN_CELLS + adata = adata[:, keep].copy() + X_raw = adata.X.toarray() if sp.issparse(adata.X) else np.asarray(adata.X) + Y = pd.DataFrame(X_raw, columns=adata.var_names.tolist()) + A = pd.get_dummies(adata.obs[PERT_COL].astype(str)).drop(columns=[CTRL_LABEL]) + Y, A, X, X_A = prep_causarray_data(Y, A) + return Y, A, X, X_A + + +# ── Step 2: latent factors ─────────────────────────────────────────────────── +def fit_factors(tag, Y, X, A, r=RANK): + """One GCATE fit per cell type, shared by every perturbation batch.""" + path = paths(tag)['gcate'] + if path.exists(): + with open(path, 'rb') as f: + res_2 = pickle.load(f)['res_2'] + if res_2['U'].shape[1] == r: + return res_2 + res_1, res_2 = fit_gcate(Y, X, A, r, backend='fast', verbose=False, + kwargs_es_1=dict(rel_tol=2e-4, max_iters=10), + kwargs_es_2=dict(rel_tol=2e-4, max_iters=10)) + with open(path, 'wb') as f: + pickle.dump({'res_1': res_1, 'res_2': res_2}, f) + return res_2 + + +def _propensity(A, W_A): + """Propensity support check; returns (pi_hat or None, report). + + The design holds the intercept, log library size and the latent factors. + Library size could be a confounder (capture depth) or an effect of the + perturbation (total RNA); it stays in while every arm is supported. + ``select_propensity_factors`` flags an arm whose treated ESS falls below + 0.5, overlap below 0.3 or AUC above 0.9, and drops for that arm only the + covariate most imbalanced between it and the controls (library size + included), one at a time, until support recovers. With nothing flagged + LFC fits its own scores (None is returned). + """ + names = list(A.columns) + cov = ['intercept', 'log_library_size'] + [f'U{k + 1}' for k in range(W_A.shape[1] - 2)] + A_np = A.to_numpy(dtype=float) + drops, report = select_propensity_factors(A_np, W_A, treatment_names=names, + covariate_names=cov) + if not drops: + return None, report + pi = estimate_propensity_scores(A_np, W_A, K=1, random_state=0) + pi, _ = refit_propensity_scores(A_np, W_A, pi_hat=pi, treatment_names=names, + covariate_names=cov, drop_by_treatment=drops, + K=1, random_state=0) + return pi, report + + +def _lfc(Y, W, A, W_A, offset, report_path=None): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + pi, report = _propensity(A, W_A) + if report_path is not None and len(report): + report.to_csv(report_path, index=False) + df, _ = LFC(Y, W, A, W_A, offset=offset, pi_hat=pi, min_counts=MIN_COUNTS, + backend='fast', verbose=False) + return df + + +# ── Step 3: causarray LFC in perturbation batches ──────────────────────────── +def run_lfc(tag, Y, A, X, X_A, res_2, batches=None, batch_size=BATCH_SIZE, variant=''): + """Run LFC on all controls + ``batch_size`` perturbations at a time. + + ``batches`` restricts the run to some batch indices (to spread one cell + type over several processes); the combined table is written once every + batch is on disk. + """ + p = paths(tag, variant) + if p['lfc'].exists(): + return pd.read_csv(p['lfc']) + p['batches'].mkdir(exist_ok=True) + U = res_2['U'] + offset = np.log(res_2['kwargs_glm']['size_factor']) + W, W_A = np.c_[X, U], np.c_[X_A, U] + A_np, Y_np = A.to_numpy(dtype=float), Y.to_numpy() + names = list(A.columns) + is_ctrl = A_np.sum(1) == 0 + splits = np.array_split(np.arange(len(names)), int(np.ceil(len(names) / batch_size))) + todo = range(len(splits)) if batches is None else batches + for b in todo: + out = p['batches'] / f'batch_{b:03d}.csv' + if out.exists(): + continue + cols = splits[b] + cells = is_ctrl | (A_np[:, cols].sum(1) > 0) + df_b = _lfc(pd.DataFrame(Y_np[cells], columns=Y.columns), W[cells], + pd.DataFrame(A_np[np.ix_(cells, cols)], columns=[names[c] for c in cols]), + W_A[cells], offset[cells], + report_path=p['batches'] / f'batch_{b:03d}_propensity.csv') + df_b.to_csv(out, index=False) + del df_b + gc.collect() + files = [p['batches'] / f'batch_{b:03d}.csv' for b in range(len(splits))] # results only + if not all(f.exists() for f in files): + return None + df = pd.concat([pd.read_csv(f) for f in files], ignore_index=True) + df.to_csv(p['lfc'], index=False) + return df + + +# ── Step 4: Wilcoxon ──────────────────────────────────────────────────────── +def wilcoxon_table(h5ad): + """Long table (trt, gene_names, wilcox_logfc, wilcox_pvalue, wilcox_padj) from a crispyx result. + + The log-fold change is the ``logfoldchanges`` layer (log2). The result's + ``effect_size`` field is a rank statistic, not a fold change. + """ + res = ad.read_h5ad(h5ad) + out = [] + for layer, name in (('logfoldchanges', 'wilcox_logfc'), ('pvalue', 'wilcox_pvalue'), + ('pvalue_adj', 'wilcox_padj')): + m = pd.DataFrame(res.layers[layer], index=res.obs_names, columns=res.var_names) + out.append(m.stack().rename(name)) + df = pd.concat(out, axis=1).reset_index() + return df.rename(columns={df.columns[0]: 'trt', df.columns[1]: 'gene_names'}) + + +def run_wilcoxon(tag): + p = paths(tag) + if not p['wilcoxon'].exists(): + if not p['norm'].exists(): + crispyx.normalize_total_log1p(p['counts'], output_path=p['norm'], verbose=False) + crispyx.wilcoxon_test(p['norm'], perturbation_column=PERT_COL, control_label=CTRL_LABEL, + verbose=False, output_path=p['wilcoxon']) + return wilcoxon_table(p['wilcoxon']) + + +# ── Step 5: null calibration on controls ───────────────────────────────────── +def null_labels(A, seed, n_arms=10): + """Fake perturbation labels for control cells only. + + Arm sizes are drawn from the real perturbation sizes, so the fake arms are + as small as the real ones. Returns a label per control cell (the control + label for cells left in the control pool). + """ + A_np = A.to_numpy(dtype=float) + n_ctrl = int((A_np.sum(1) == 0).sum()) + rng = np.random.default_rng(seed) + sizes = rng.choice(A_np.sum(0).astype(int), size=n_arms, replace=False) + labels = np.full(n_ctrl, CTRL_LABEL, dtype=object) + perm = rng.permutation(n_ctrl) + start = 0 + for k, n in enumerate(sizes): + labels[perm[start:start + n]] = f'fake{seed}_{k:02d}' + start += n + return labels + + +def run_null(tag, Y, A, X, X_A, res_2, seed, variant=''): + """causarray and Wilcoxon on fake perturbations of control cells. + + Nothing was perturbed, so every discovery is a false one. + """ + p = paths(tag, variant) + p['null'].mkdir(exist_ok=True) + p['null_wilcoxon'].mkdir(exist_ok=True) + out_ca = p['null'] / f'seed{seed}.csv' + out_wc = p['null_wilcoxon'] / f'wilcoxon_seed{seed}.h5ad' + ctrl = (A.to_numpy().sum(1) == 0) + labels = null_labels(A, seed) + if not out_ca.exists(): + U = res_2['U'][ctrl] + offset = np.log(res_2['kwargs_glm']['size_factor'])[ctrl] + A_null = pd.get_dummies(pd.Series(labels)).drop(columns=[CTRL_LABEL]).astype(float) + df = _lfc(Y.loc[ctrl].reset_index(drop=True), np.c_[X[ctrl], U], A_null, + np.c_[X_A[ctrl], U], offset, + report_path=p['null'] / f'seed{seed}_propensity.csv') + df.to_csv(out_ca, index=False) + if not out_wc.exists(): + run_wilcoxon(tag) # makes sure the normalized file exists + norm = ad.read_h5ad(paths(tag)['norm']) + norm = norm[(norm.obs[PERT_COL].astype(str) == CTRL_LABEL).to_numpy()].copy() + norm.obs['null_label'] = pd.Categorical(labels) + tmp = p['null_wilcoxon'] / f'norm_seed{seed}.h5ad' + norm.write_h5ad(tmp) + crispyx.wilcoxon_test(tmp, perturbation_column='null_label', control_label=CTRL_LABEL, + verbose=False, output_path=out_wc) + tmp.unlink() + return pd.read_csv(out_ca), wilcoxon_table(out_wc) + + +# ── Step 6: one-line QC per cell type ──────────────────────────────────────── +def summarize(tag, df, df_wc, nulls=()): + """Numbers to scan across cell types; see the notebook for expected values.""" + m = df[np.isfinite(df['stat'])].merge(df_wc, on=['trt', 'gene_names'], how='left') + ca = m['padj'] < Q + wc = m['wilcox_padj'] < Q + row = { + 'cell type': tag, + 'perturbations': df['trt'].nunique(), + 'causarray hits': int(ca.sum()), + 'Wilcoxon hits': int(wc.sum()), + 'Wilcoxon hits recovered': round((ca & wc).sum() / max(wc.sum(), 1), 2), + 'fraction down': round((m.loc[ca, 'tau'] < 0).mean(), 2), + 'hits with zero perturbed counts': int((m.loc[ca, 'count_treated'] == 0).sum()), + } + if len(nulls): + # Both methods are scored on the genes causarray tests. + n = pd.concat([d[np.isfinite(d['stat'])].merge(w, on=['trt', 'gene_names'], how='left') + for d, w in nulls]) + row['null: causarray false hits'] = int((n['padj'] < Q).sum()) + row['null: Wilcoxon false hits'] = int((n['wilcox_padj'] < Q).sum()) + row['null: SD of z (target 1)'] = round(n['stat'].std(), 2) + return row + + +def main(): + global MIN_COUNTS + ap = argparse.ArgumentParser() + ap.add_argument('step', choices=['subset', 'lfc', 'null']) + ap.add_argument('--tag', required=True) + ap.add_argument('--src') + ap.add_argument('--cell-type') + ap.add_argument('--batches', help='comma-separated batch indices (default: all)') + ap.add_argument('--seeds', default='0,1,2') + ap.add_argument('--min-counts', type=float, default=MIN_COUNTS) + ap.add_argument('--variant', default='', help='subfolder for a sensitivity run') + args = ap.parse_args() + MIN_COUNTS = args.min_counts + if args.step == 'subset': + subset_cell_type(args.src, args.cell_type, args.tag) + return + Y, A, X, X_A = load_inputs(args.tag) + res_2 = fit_factors(args.tag, Y, X, A) + if args.step == 'lfc': + batches = None if args.batches is None else [int(b) for b in args.batches.split(',')] + run_lfc(args.tag, Y, A, X, X_A, res_2, batches=batches, variant=args.variant) + else: + for s in args.seeds.split(','): + run_null(args.tag, Y, A, X, X_A, res_2, int(s), variant=args.variant) + + +if __name__ == '__main__': + main() diff --git a/docs/source/tutorial/perturbseq/README.md b/docs/source/tutorial/perturbseq/README.md index ae2a4c2..f9c5725 100644 --- a/docs/source/tutorial/perturbseq/README.md +++ b/docs/source/tutorial/perturbseq/README.md @@ -8,9 +8,8 @@ There is no prep script — the subset is committed directly. results/perturbseq-r.csv JIC rank-selection table, read by both tutorials perturbseq-py.ipynb Python tutorial - perturbseq-r.Rmd R tutorial; knit to perturbseq-r.md + perturbseq-r.Rmd R tutorial (same workflow via reticulate); knit to perturbseq-r.md perturbseq-r_files/ figures produced by knitting the Rmd - perturbseq_files/ figures from an earlier render name Everything here is tracked in git. Both tutorials are published in `docs/source/index.rst`. diff --git a/docs/source/tutorial/perturbseq/perturbseq-py.ipynb b/docs/source/tutorial/perturbseq/perturbseq-py.ipynb index 34ad8c8..c5a240d 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-py.ipynb +++ b/docs/source/tutorial/perturbseq/perturbseq-py.ipynb @@ -27,10 +27,10 @@ "id": "3e62cc8f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:54:28.564639Z", - "iopub.status.busy": "2026-09-23T13:54:28.564153Z", - "iopub.status.idle": "2026-09-23T13:54:30.698628Z", - "shell.execute_reply": "2026-09-23T13:54:30.698276Z" + "iopub.execute_input": "2026-09-29T07:42:18.337595Z", + "iopub.status.busy": "2026-09-29T07:42:18.337081Z", + "iopub.status.idle": "2026-09-29T07:42:21.038192Z", + "shell.execute_reply": "2026-09-29T07:42:21.037628Z" }, "papermill": { "duration": 2.325332, @@ -60,6 +60,7 @@ " prep_causarray_data, fit_gcate, LFC, estimate_propensity_scores,\n", " summarize_propensity_scores, plot_propensity_scores, refit_propensity_scores,\n", " summarize_treatment_associations, plot_treatment_associations,\n", + " select_propensity_factors,\n", ")" ] }, @@ -88,10 +89,10 @@ "id": "d496d9cd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:54:30.700354Z", - "iopub.status.busy": "2026-09-23T13:54:30.700172Z", - "iopub.status.idle": "2026-09-23T13:54:30.724265Z", - "shell.execute_reply": "2026-09-23T13:54:30.723894Z" + "iopub.execute_input": "2026-09-29T07:42:21.040229Z", + "iopub.status.busy": "2026-09-29T07:42:21.039996Z", + "iopub.status.idle": "2026-09-29T07:42:21.077787Z", + "shell.execute_reply": "2026-09-29T07:42:21.077137Z" }, "papermill": { "duration": 0.019636, @@ -136,7 +137,7 @@ "source": [ "## Prepare inputs\n", "\n", - "Use `Y` for cell-by-gene counts, `A` for perturbation indicators, and `X`/`X_A` for outcome/propensity covariates. GFP control cells have all-zero treatment indicators. `prep_causarray_data` prepares the designs, including an intercept and log-library size in the propensity model.\n" + "Use `Y` for cell-by-gene counts, `A` for perturbation indicators, and `X`/`X_A` for outcome/propensity covariates. GFP control cells have all-zero treatment indicators. `prep_causarray_data` prepares the designs, including an intercept and log-library size in `X_A`; the propensity section explains why the model below leaves library size out.\n" ] }, { @@ -145,10 +146,10 @@ "id": "91b339f8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:54:30.725781Z", - "iopub.status.busy": "2026-09-23T13:54:30.725657Z", - "iopub.status.idle": "2026-09-23T13:54:30.751025Z", - "shell.execute_reply": "2026-09-23T13:54:30.750750Z" + "iopub.execute_input": "2026-09-29T07:42:21.079218Z", + "iopub.status.busy": "2026-09-29T07:42:21.079084Z", + "iopub.status.idle": "2026-09-29T07:42:21.127421Z", + "shell.execute_reply": "2026-09-29T07:42:21.126877Z" }, "papermill": { "duration": 0.021681, @@ -205,10 +206,10 @@ "id": "75896bf4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:54:30.752456Z", - "iopub.status.busy": "2026-09-23T13:54:30.752374Z", - "iopub.status.idle": "2026-09-23T13:54:31.048695Z", - "shell.execute_reply": "2026-09-23T13:54:31.048441Z" + "iopub.execute_input": "2026-09-29T07:42:21.129246Z", + "iopub.status.busy": "2026-09-29T07:42:21.129084Z", + "iopub.status.idle": "2026-09-29T07:42:21.449904Z", + "shell.execute_reply": "2026-09-29T07:42:21.449338Z" }, "papermill": { "duration": 0.288372, @@ -222,7 +223,7 @@ "outputs": [ { "data": { - "image/png": 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OnHgiolbiSGwWDl7NgqmxEV4cFnLL/QaEOKO9px0upOZj1ZEEvDT85kwIIiIiUh4DDkRaYmVqJS2+dr633U+UaMouzW60t0SDkk4l11GuKUd+eb60xObF3va4IhOifpZE/ZJO9beLck4ig0EEG17b+xqqUNXgOBnFGdL2T4Z8wqADERE1n7gIYN1coCAVENl8o94Hes8RNRI560RErYTow1fTu+HxXr7wcbx1bwZxc9W8wUF4efUZrDwcjznhQbAwZWY2ERGRrmHAgail/6NTmcDNyk1a4Hz7k28RaLgxEHFTcKLkOgrKC1CmLkNyYbK03Clrw8HMQTr2jcEG6X1RBSMY4cNjH2Ko71CWVyIiIu1SVwL75gMRH0v/6sClDTBxOeDRiTNNRNTKHI7JwrG4bJgZq/DC0FtnN9R4sJMn/rv1MpJzS7DuVDKe7OPXIuMkIiKipmPAgUhHiTt47M3tpSXYIfi2+5ZWltYGIW7KmKhX0kk0yhZ9KbLLsm97PBF0SCtOk3o79PLopeXfjIiIWq2cBODX2cC1Y/Lzbk8DYz4EzKyVHhkREbUwcYPV/6qzG0TgwNPe8o4/Y2qswsyBgfjX7xewdH8snujlC5WKmXFERM1qxUPyzUFj5jdcr+nHtull4MIGoDQXmLdffv3GbZ6d+SG1Igw4EBkACxML+Nj6SMudGkTnlOVg/dX1+PzU53c8rghWEBERaUXUemDjn4CyPMDcDnjkM6DjBE4uEVErFRF9HScTcmBuosLzQ25/g1V9Isjw+c4riL1ehB0X0zG6g0ezjpOISDEaNZBwCChMB2zcAf/+gErhUnKPfw8Ym9Y9v7oTOPMj8MwfgGMAYOXc+DZqVRhwIGpFRO8G0cuhi2uXJu0v+j8QERHdl/JiYNtbwMlv5ec+vYAJS+UvH0REhNbeu+Hpvv5ws7No8s9am5tgal9/fLM3Bov2xTDgQESG6cJGYOsbQH5K3TY7L+CBD4H2Y5Ubl5VTw+fZcYCtB+DX5/bbqFVRKT0AImp53d26w93KXerVcCseVh7SfkRERPcsPQpYPKQ62GAEDHwNmLGFwQYiolZuz+UMnE3KhaWpMeYNbnp2Q41nBgRIfR9OJebiRPzty8USEellsGHNtIbBBiE/Vd4uXm8u5UXAunnAv72Aj9sAh75s+LooqbTlTXn9t+eALX8F8pKA9+yBTzs1vq0xqyYAvz1b9zx2H/BhgNzvjfQeMxyIWmmmw5u938Rre1+Tgg6NNY9+o/cbbBhNRET3RtRyPb4U2PY2oC4DbDyA8YuAoCGcUSKiVq5+dsO0/v5wtTW/62O42VpgfHdvrD6ehEURsegZcMMdt0REunZuXFHc9DJKW16Xums2ciD5Jh6R+SDOq5tSXsnUSjQJbfpYt/8diN8PPLFKLuO0659A6hm5L8ONRB8Hp0D55qI5e+TxiHJLN25rjK0nkJtY9zwwHKgsB5KOAAEDmz5e0kkMOBC1UiP8R+CTIZ9g/rH5SC9Ob/CalYkV+nv1V2xsRESkx4qzgY0vAZd+l5+HjgIeXQBYuyg9MiIi0gE7LqTjfHI+rM2MMS/87rMbasweFCQFHHZeTEdMZiGCXW20Ok4iIq0RwYb/eGnpYFVy5sN836bt/rcUwMy6afuWFQKnvwceWwgED5O3ifP4T9o3vr+FPWBmAxgZA7buddsb23YjUR4q8UjdcxEUMTEHiq43bayk01hSiaiVBx22TdiG5aOX48NBH2LJyCXwsfFBcWUxfrnyi9LDIyIifRN/EFg4UA42qEyB0R8AT65hsIGIiCQaTV12gyiL5GRtds8zE+JmgxHt3KUbh5fuj+UMExHdr5w4QF0O+PRu2LPBJUT7cysyHApS656nngNKcwFf9n0wBMxwIGrlRHmlXh69ap/P7jQb7x1+DyujVuKJsCdgbnz3Kc5ERNTKiFqrER8BEf8FqjSAUzAwcTng1VXpkRERkQ7ZGpWGS2kFsDU3wZxBQfd9vHmDg6QMh19PJeO1kW3vqTwTEVGzE2WNRKZBUyQcAn6YeOf9nloL+Pdv2ns3lYjgthSR4VBeCJTmyxkR2/4GdJoM2Hm23Bio2TDgQEQNjA0eiwVnF0hlljZc3YDJbSdzhoiI6NbyrgG/zgESD8nPuzwJPPgRYM7SFtSQaBD76c4rOJWQgwp1Fdq422DmwECM6+rdpKk6HJOFn44lIiolDxkFZahQa+Blb4ke/o54dkjwLcup3O/7EpF2qDVV+LQ6u0H8N+hgde/ZDTV6+juim58DTifmYuWhePxldFstjJSISMtEuaCmljUSpYzExXjRILrRPg5G8utiv6b0cLgbTkFylvK144BDdcmmkhwgKwbwH6j9DAdBlIc6swooSAOe+BF6J/kksOcD4NoxQF0BuIYBfZ8HOk9q2s/H7Zf7XaSdAwrS5QwTe2/Aty8w8BXAJbR53reZsaQSETVgamyKGR1nSOvLzy9HhaaCM0RERI27uAlYMEAONpjZAuOXAI8tYLCBGg0WTFp4GMfjsvFgJ09M7euH7OJyvLz6DL7ec7VJM3bw6nWciM9GWw9bTOzhg+n9AhDoYo11p5Mx5vP9OBRzvVnel4i044/IVERnFMLOwkQKOGiDkZER5oXLmRLfH0lAUVmlVo5LRKQYEUR44MPqJzc2e65+/sB87QcbBHHDUPengR3/B8TuBdIvAOufB4ya4fKxCJoI298GLmwEpm8ELOygV0SwYPkDQOJhoP04oOdMoDgLWDcbiPi4accQ8yx6Wbi1A7o+CfSeAziHAGd/kr9nxUU0z/s2M2Y4ENFNxoeOx+Jzi5FcmIwtcVukrAciIqJaFSXAtreBE8vk517dgYnL5LuiiG5QqdbgzXXnpO/IP8/rh47e9tL2l0e0wfhvDkp3PItggAge3M6Lw0IavXtZBCKeWnoU87dcwsYXB2r9fYlIO9kNn+2UsxtEKSV7S1OtTevI9h4IcLZCfFYx1pxIwowB2glmEBEppv1YYPJ3wNY35AyA+hfpRbBBvN5cRv4LKC8Cfpoilzrq/6Jc9kjbrJwBUcI7NwmYsaUuAKFPJWU3viQHgWZsBjy7yNuHvAksHQns/QDo8BjgHHz744T/FRj+98YDEd+Nk4M/c/dq/32bGTMciOgmliaWeLr909L60sil0Ih63ERERELGRWDJsLpgQ/8/ATO3MdhAt3QoJgsJWcUY18Wr9qK/YGNugpeGhaJSU4VfTiTdcQYtTBu/k29AiIt08TL+elGzvC8R3b+NZ5MRm1kEBytTqVm0NhmrjDC7uh/E0v1xUrCRiEjviaDCK+eB6b8DE5bJj69ENm+woSbLYfxi4O1U4K/RwICXgRl/AGPmy6/XXxf6PQ+8GtnwGI1ta6zM1N8zgBePySWE9E3cPrnJdqdJdRf9BXNbYPBfAU0lcHrVnY9jatH49qAhgIUDkB3bPO/bzPQ2w6G4vBLbotKw80IGLqTmIyW3BGYmKrTztMNTffzuuiZrwJt/3PK11x9oi+eHNENHdiId9kTbJ6SSSnF5cdiZsBOjAkYpPSQiIlKSaCIn6otufQuoLAGs3YDHFgIhw/m50G0dic2SHge1cb3ptfBQedvRuOx7nsWTCTnIK6lArwDHFn1fImoaEQD4fGe0tD43PAi2FtrLbqghyqyJrKXk3BKpdBN7tBCRQRBlkwIHKT0Kakz8AfkxeOjNrwUPkx8TDt773CUdA0pzAb9+Lfu+rT3gcCwuG6/+fBaOVqboH+KCBzp6IKuwDFvPp0k1WUVTuH+M63hXx/R2sJROVG7UK8BJiyMn0g82ZjZ4qt1TWHh2IZZELsFI/5FSjVQiImqFRLO4TS8DFzbIz4OHy8EGGzelR0Z6ID5LzjwIdL65dJG9lSmcrM1uyk64HdGXQQQTytUa6ed2XcqQjvH3h9tr9X3LKtUor6y7U7qglH2tiO6F6LMiyh05W5tJvVeag8iAmt4/AJ/suILFEbEY28WL312IiOjeiBJSZvWem5jLS33ZMfJjY6WLLB3lklGi2XZTib4MIpigLpN/7so2+Rij/9O879tM9Dbg4GZrgc+f6CrVXTU1rqsM9dfRYXj064NYeTgB47v7oIuvQ5OP6eNoiVdHtmmmERPpn6fCnsLKqJW4lH0J+5P3I9wnXOkhERFRSxNNzH6dDeQlASoTYPi7QL8XARUrc1LTFJTKTVxtLRr/6iFKHKXllTZ5OkWw4fNd8t3Sgqjd/uWU7ujkY6/V9/1mT0yD99GUFTd5jEQkq1Br8EX1f0fPDg6GtXnzXYJ4uq8/FuyNQVRKvlRSTZRbIyIiumuftgfM691wO/hNYOhbDfep6WthfotG1+a2Dftv3IkINuyrV6pK9MabuBzw6ta879tM9Dbg0N7LTlpu5Gprjif7+OGjbZdxNC7rrgIORNSQg4UDHm/7OL6N+lZqIj3IexDvFCIiai00amD/J3LjsSo14BgoN4b27qH0yKiVEzcIiUWUWI1OL5QuZk5YeAgfTeys1TIqzw8NxuxBdc1n8/Pz4fOZ1g5P1CqsPXkN13JK4GJjjql9/Zv1vRytzTC5p4908+GiiFgGHIiI6N68egGwq3fN+cbshuYw9C15EQ27My8B+/4LLBsNjPsa6DwJ+sYgb00zNZajUMZ3eeddfmklVh9LxNd7ruKnY4mIu4vUbiJDNa39NJipzHA28yyOpx1XejhERNQSxF0x340D9rwvBxs6TQbmRTDYQPekJsOgJuPgRoVllbfMQrgdKzMT6eaiRU/3QLCrDd5aFymVWNXW+5qbGEu15usvRNR0oizZV7uvSuvPDwmGpVnjjd+1STSPVhkBEVcycTG1+i5QIiKiu2Fh13BpLOAgtgtlt/i3pqzg1lkIt2NmLX/nevwHwKWNXNa26Hrzv6+WGVzAQa2pwrpTyVKz84F3mUIpTkjeXBcpZUeILyzD/rcXr/58BiXl6mYbL5Guc7VyxWOhj0nriyMXKz0cIiJqbpc2Awv6A/H7AVNr4NGFwIQldSe3RHcpoLqHQlx1T4X68oorkF1UjgCXm/ssNJWJsQr9gpxRXK7GueS8FntfIrq9NSeuSU2c3e3kKgQtwdfJCmM6eUrrSyJiW+Q9iYhupaqqipNjqJ+JU3UPhcb6JZTkAMVZjfdZaCpjE7lheEURkHK65d5XSwwu4PC/7ZdxKa0Ak3r4oK2HbZN/bm54ENa/MABn/28Uzr47Cj/O6YOuvg747XQyXv/1XJPu3hCN5OovRIZiZseZMDEywdHUoziXeef/HoiISA9VlAKbXwdWT5FPVj27yFkNXacoPTLSc32CnKXH/Vcyb3otIlre1ifQ6b7eI71A7sVgIm5tbsH3JaLGlVao8XV1dsMLQ0Okps4tZV54kPS48WwKUnJL+BERUYszNZWzIouL2f9J19R8JjWf0T0LGCA/xuy5+bWY3fKjf/U+96ogVX5UGbfs+xpCD4du/9yOnOKmX5z/aU5f9AuWvzzc6Mejifhmbww6eNnh3Uc63NU4/vZguwbP+we74MfZjhjzeQQ2nU3BS8NC0Mb91gEMNpUjQ+Zl44WHgh7ChpgNWHJuCb4c/qXSQyIiIm3KvAKsnQmkR8rPRVPo4f/XMvVKyeANCHaGn5MVNpxNwTMDAtDBy762pNGXu6OlIMHEHj61+4vMA7E4WZtJS42jsVnoHeh0Uz8pUTple1SaVB6ph7/jPb8vEWmPKFGcll8KL3sLPN7Lt0WntrOPA/oGOeFIbDaWH4jDOw+3b9H3JyIyNjaGg4MDMjIypMmwsrJiP0wdyGwQwQbxmYjPRnxG9yVwCOAYAET+AvSZB3h2ritptO8jQGUCdH2qbv+iLDn7wMoZsK53XTv+IODfH1Kpnvqu7gIu/g6Y2wO+fe79fVtrwGFsFy8UljW9ZJFoCt2YNSeS8Pb6SIR52GLVrD6wNr//X03UmBTj+2L3VZyIz7ltwIFN5cjQzeo0CxtjNmLvtb24nH0ZbZ3aKj0kIiK6XyKl+PT3wJY3gIpiwMoFeHQB0GYU55a0RpQ8mj+hE6YvP4bJCw9jbFcv2JibYGtUGpKyS/CXUW0Q5GpTu//KQ/H4fFc0Xh4eKjWHrjH7uxNSAEJcTBQXMcUd1BfTCnAsLlvq4fbhhM5SX4d7fV8i0g5RkljcCCi8OCxU6ofS0uYNDpYCDiLw8dLwUNhbsgcLEbUsDw8P6bEm6EC6QQQbaj6b+yJKHo39Evh+PLBiDNBxAmBuC1zcBOQmAMPeAVxC6vY/thjYNx8Y/KbcHLrGT1MAKyfAuztg5w1UlgLpUUDCQUBlCoz9Qu7rcK/v21oDDv8Y1/G+j7HmeBLeXHcOoW42+GF2HzjWuxPqftUcq6Ti9kERcRJV/0SqqpwnNGRYAu0DMSpgFLbFb8PSyKX4aPBHSg+JiIjuR2kesOkVIGqd/DxwMDB+MWCrhRNwohuI7OFfnu2PT3dcwe/nUlGh1kg38/x5ZFs82s27SfP16og22HclEyfis5FVVA5xH5iXgyWe6OWLmQMDG705SBvvS0R354ejCcgsKIOPo6ViWURD2riirbstLqcXSJUQnhuifD1rImpdREamp6cn3NzcUFHBsuu6QJRRuu/MhvoCw4GZ24C9/wGi1gPqcsAtTL7o33ly044hgg9XdwKJR+Tm0CLTQQQeuk8D+j4PuLVrnvdtZkZVet7BRAQb3lh3DiGuNvhpbl+42Gg39f/l1aex4UwKFj3dA6M7NP0LeH5+Puzt7ZGXlwc7OzZZJMMgMhsmbpoIIxhh46MbEWAfoPSQiIjoXiQdB36dCeQmymm34uS0/8uAyuDaezULnue1Tvzcie6suLwSgz7cIwUF/zuhMya3cDml+taevIa//HIWbrbm2P/GUEUyLYiISH/wXE979Ppb5c/HE6VgQ7CrDX6cc+dgg0jtvJpRiOQbGkedT86TXrvRH+dSpUZTInV7YIiL1sdPpG9EGaXBPoNRhSosO79M6eEQEdHd0miA/Z8Ay0fLwQYHf2DGVmDgqww2EBHRfVt5KEEKNvg7W+Gx7spmEYnyyB52FsgoKJNuIiQiIqJWUlLpXh26eh1vrouUSg+L5nGrjiTctE97L7sGWQlnknIxZckR9Al0ws/z+tVuX3EwHtsvpGFAsIuUli0upkYl5+NYfDbMTVT436QuWukJQWQI5nSeg33X9uH3mN/xXJfnpIbSRESkBwrSgHVzgbh98nNR7/PhTwELuZEuERHRff0zU1qBRRFy74Y/DQuFqbGy9zeamagwY0AAPthyCUsiYjGxuw9UqhuachIREZHW6e1VdJGlUFMMStRkbMyE7j5NKoM0sr078ksrcD4lT6oLq9ZUwd3eHI/39MWc8CCEuLGZHFGNLq5d0MezD46mHsWK8yvwdt+3OTlERLruynZg/bNAcRZgagWM+S/QbapcI5SIiEgLRMP33OIKBLlaY1xX3bgpaUofP3y5+yqiMwqx90oGhoW5Kz0kIiIig6f3PRx0Fet+kSE7lnoMs7bPgpnKDFsnbIWrlavSQyIiosZUlgE7/wEc+Vp+7t4JmLgccG3D+boPPM9rnfi5E93mv4/SCgycvxv5pZX4/ImuGNdVd5qy/2fzRSyOiL2p0gEREVF9PNfTHr3u4UBEyujl0UvKdCjXlOO7C9/xYyAi0kXXrwLLRtYFG/o8C8zeyWADERFp3fIDcVKwIdTNBg931o3shhqirJKJyghH47KlMstERETUvBhwIKK7ZmRkhLmd50rrP1/+GbmlPHEnItIZInn1zI/AonAg9Sxg6QRMWQ2M+RAwtVB6dEREZGDyiiuwbH+ctP7KiDYw1rE+CZ72lrUZF4ure0wQERFR82HAgYjuySDvQQhzCkNJZQl+uPQDZ5GISBeU5suNodc/B1QUAQGDgOcOAm3HKD0yIiIyUEv2x6KgrBJhHrYY0/HOPRSVMDc8SHrcej4NCVlFSg+HiIjIoOlt02giUj7LYXan2fjLvr/gh4s/YHr76bAxY4N1IiLFJJ8E1s4CcuIAI2Ng6FvAwNcAlTE/FF36jPZ8AFw7BqgrANcwoO/zQOdJTfv5wkzg9HdAyhkg9QyQmyhvfy+v8f1LcoE9/wFSTgE5CYDISLRyBpxDgN5zgHZj2TiciO5LdlE5Vhysy25Q6Vh2Q422HrYY0tYVey9nYun+OPzr0Y5KD4mIiMhgMcOBiO7ZCL8RCLALQEF5gVRaiYiIFKDRAAc/B5aNkoMN9r7AjC1A+F8ZbNAlcfuB5Q8AiYeB9uOAnjOB4ixg3Wwg4uOmHSPzErDrn8DFTYCxGWBqdfv9xfFPr5L3C3sI6PciEDJCPs6aacCml7XyqxFR6yWaMReVq9HByw6jO7hDl9VkOfxyMkkKlBAREVHzYIYDEd0zY5WxlOXwzsF3pObRT7V7ChYmrA9ORNRiCjOA354FYnbJz8WF7Ec+Bywd+SHoEnUlsPElkR8IzNgMeHaRtw95E1g6Etj7AdDhMcA5+PbHcW0LPCN+vjNgbgt82RPIir71/o4BwJuJgPENp/xlBcDSEcCplUDf5wC3dlr4JYmotbleWIaVh+Kl9ddGtpEyoHVZvyBndPK2R2RyHr47HC9lZBAREZH2McOBiO7Lg0EPwtvGG9ml2fg1+lfOJhFRS7m6E1jQXw42mFjKgYZJKxls0EVx++Tsk06T6oINgggaDP4roKmUMxHuxMYNCBgg/1xTiHJaNwYbat43eLi8nh3b1N+CiKiBRftiUFKhRhcfewwLc9P52REBkZosh+8OJ6CkXK30kIiIiAwSAw5EdF9MVaaY0WGGtL7i/ApUiJrURETUfCrLge3vAKsmAEWZgFt7YO5eoMczrMevq+IPyI/BQ29+LXiY/JhwsOXGU1EKxEXIGReijwQR0V3KyC+VLtoLr+pBdkMN0dTax9FSKqm09tQ1pYdDRERkkBhwIKL79mjoo3C1dEV6cTo2xW7ijBIRNRdxN/ry0cChL+XnvWYDc3YDbrxorIjS/IZLZVnj+2XHyI+NlUwS5a9EI+es6n2ag9Q8+gNg97+BTa8AX/YA0iOBwW/cuYwTEVEjvtkbg7JKDbr7OWBwG1e9mSMTYxVmDwyU1pfuj4VaU6X0kIiIiAwOAw5EdN/Mjc0xvcN0aX1Z5DJUitIQRESkXefWAAvDgZRTgIUD8PgPwEP/A0wtOdNK+bQ9MN+3btn/SeP7iWCEYG7X+OuixFFZ9T7NoTQP2DcfiPgvcHIFUJgOjPyX3EOCiOgupeaV4MdjidL6ayPb6k12Q43JvXzhYGWKhKxibItKU3o4REREBodNo4lIKya1mYSlkUuRWJCIbfHb8FDQQ5xZIiJtKCsENv8FOPuT/NyvPzBhCWDvw/lV2qsXALt6QQQTc+gkR3/gvTxAowbyrgHnfwV2/wtIOir3/WiszwMR0S18sycG5ZUa9A50woAQZ72bJyszE0zr648vdl/FoohYqcySvgVNiIiIdBkzHIhIK6xMrTC13VRpXQQeNFUaziwR0f1KOQMsCpeDDUYqYMhbwDO/M9igKyzsGi63CjiI14RbZTGUFdw6+0GbRBNpEXwY9Bow7B3g0u/AqW+b/32JyGAk55Zg9fGa7Ab96d1wo2n9A2BuosLZpFwci8tWejhEREQGhQEHItKaKe2mwMbUBldzr2JP0h7OLBHRvaqqAg5/DSwdIdf/t/MGnvlDLoEjLhqTfnGq7pPQWJ+GkhygOKvleynUNKuuaWhNRNQEX+2+igp1FfoHO6NvkP5lN9RwsTHHhB5ypuDiiFilh0NERGRQGHAgIq2xM7PDE2FPSOtLzi1BlbhgRkREd6cwE/hxMrDtb4CmAgh7GHj2AODfnzOprwIGyI8xjQTjY3bLj/7V+7SUguq65SqWUyKipknKLsYvJ5Kk9VdHttH7aZszKAgiQWPXpQxEpxcoPRwiIiKDwYADEWnV0+2fhoWxBaKyonA45TBnl4jobsTuBRYOAKK3A8bmclPox1cBVk6cR30WOARwDAAifwFSzzUspbTvI/mif9en6rYXZQGZV+TH+yHeSzSMvlFxNrDrH/J6yMj7ew8iajW+2BWNSk0VBoW6oFeA/v+7FOhijVHt3aX1JfuZ5UBERKQtvKWJiLTKycIJE9tMxKqLq7A4cjH6e/OOXCKiO1JXAHv+DRz4TNRTAlzDgInLAfcOnDxDIJoyj/0S+H48sGIM0HECYG4LXNwE5CbI/RRcQur2P7YY2DcfGPwmMPSthsf67bm69cL0m7eNeh+wri5zcuZH4NR3QOAgwN4XMLMCcpPkgFZ5IdBuLNBpUrP+6kRkGOKuF2Hd6eTa3g2GYm54MLZFpWP96RT8ZVRbuNlZKD0kIiIivceAAxFp3fQO07H68mqcTD+JU+mn0N29O2eZiOhWcuKBtbOA5BPy8x4zgNH/kS8Ok+EIDAdmbgP2/geIWg+oywG3MDnY0Hly049z9sfbbxN9PmoCDu3HyY2qrx0HEg4BFcWApSPg1xfoMkUOfOhpw1ciallf7oqGWlOFYWFu6ObnaDDT38PfET39HXEiIQcrDsXjjQfClB4SERGR3jOqYpH1ZpGfnw97e3vk5eXBzs6ued6ESIe9d+g9/Br9KwZ4D8DCEQuVHg4RkW46/yuw6RX5orCFPfDIF0CHR5UeFd0Bz/NaJ37u1FpdzSjEqE/3QVMFbHpxIDr52MOQbI9Kw9zvT8LWwgSH3xoOG3Pel0lE1BrxXE972MOBiJrFrI6zoDJS4WDyQamfAxER1VNeBGx4AVg7Uw42+PaRG0Mz2EBERDrYu0EEG0a2dze4YIMwop07glytUVBaidXHEpUeDhERkd5jwIGImoWvnS/GBI6R1peeW8pZJiKq38h30WDg9CqRbAqE/xV4ZjPg4Mc5IiIinXIlvQCbzqVI66+MCIUhUqmMMHdQkLS+/EAcKtQapYdERESk1xhwIKJmM7vjbOlxZ+JOxOTGcKaJqHWrqgKOLgKWDgeyogFbT2D6JrmGv2gqTEREpGM+23lF+udrTEcPdPAyvOyGGo9284aLjTlS8krxe3WAhYiIiO4NAw5E1GxCHEMwwm+EtL40klkORNSKFWUBP00BtrwuNwtuMwZ49iAQOEjpkRERETXqQko+NkemSb3lXxnRxqBnycLUGDMGBEjri/bFgq0uiYiI7h0DDkTUrGZ3lrMcNsdtRlJ+EmebiFqfuP3AwgHAlS2AsRkw5r/AlJ8Aa2elR0ZERHTb7Abh4c5eaOtha/AzNbWPP6zMjHEprQD7o68rPRwiIiK9xYADETWrDs4dMMB7ADRVGiw7v4yzTUSth7oS2P0+sPIRoCAVcA4F5uwG+syDdLsoERGRjoq8loftF9KhMgJeHm6YvRtuZG9lisd7+UrriyNilR4OERGR3mLAgYia3dxOc6XHDTEbkFaUxhknIsOXmwh8+yAQ8ZFo3gB0exqYtw/w6KT0yIiIiJqc3TCuqzdC3GxazYzNGhgIY5URDly9jvPJeUoPh4iISC8x4EBEza67e3f0cO+BSk0lVkat5IwTkWG7sAFYOBBIOgqY2wETlwPjvgLMrJUeGRER0R2dTszBrksZ0oX3P7WS7IYaPo5WeKiTp7S+ZD+zHIiIiO4FAw5E1KJZDmuvrEVWSRZnnYgMT3kxsOllYM00oDQP8O4JPLsf6DhB6ZERERE12ac7o6XHx7p5I9Cl9QXL54YHSY+/n0vFtZxipYdDRESkdxhwIKIW0c+rn9TPoVRdilUXV3HWiciwpEcBS4YCJ78FYAQMfA2YuRVwDFB6ZERERE12Ij4bEVcyYSKyG4a1ruyGGh297TEwxAVqTRWWHYhTejhERER6hwEHImoRRkZGmNN5jrS++tJq5Jfnc+aJSP9VVQHHlwJLhgGZlwAbd2DaemDEu4CxqdKjIyIiuiufVvdumNTTB37OVq129mqyHH4+noS84gqlh0NERKRXGHAgohYz1HcoQhxCUFhRiJ8u/sSZJyL9VpwN/DwV+OPPQGUpEDoKeO4QEDRE6ZERERHdtSOxWTh4NQumxkZ4YWhIq57BQaEuaOdph+JyNVYdTVB6OERERHqFAQciark/OEYqzO40W1oXZZWKK1gTlYj0VMIhuTH0pd8BlSkw+gNgys+AtYvSIyMiIrprVVVV+GSHnN3weC9fqXlya8/OnhseKK2vOBiP0gq10kMiIiLSGww4EFGLGh0wGr62vsgty8UvV37h7BORflFXAnvnA98+BOQnA07BwOydQL/nARVPq4iISD8disnCsbhsmBmrWn12Q42HO3vBy94C1wvLsP50sqKfDxERkT7hN2MialEmKhPM6jhLWl8ZtRJl6jJ+AkSkH/KuASsfAfZ+AFRpgC5PAvP2AV5dlR4ZERGRVrIbnuzjB097S84mAFNjFWYOlLMcFu+PhUZTxXkhIiJqAgYciKjFjQ0eC3crd2SWZGJ99Hp+AkSk+y7+DiwYACQeAsxsgPFLgMcWAOa2So+MiIjovkREX8fJhByYm6jw/JBgzmY9T/T2g62FCWIzi7DrUgbnhoiIqAkYcCCiFmdqbIoZHWdI68vPL0eFpoKfAhHppooSuSn0z08BpbmAVzfg2f1A58lKj4yIiEir2Q1P9/WHm50FZ7UeG3MTPNXHX1pftC+Gc0NERNQEDDgYALWmCodjsrDhTLL0KJ4T6boJoRPgZOGElKIUbI7drPRwiIhulnEJWDIcOL5Uft7/T8DM7YBTEGeLiIgMwp7LGTiblAtLU2PMG8zshsbMGBAg9bY4kZAjZYIQERHR7THgoOe2nk/FwA93Y8qSI3h59RnpUTwX24l0mYWJBaa1nyatL41cCrVGrfSQiIhkVVXAiRXA4iFARhRg7QpM/RUY9S/AxIyzREREBpfdMK2/P1xtzZUekk5yt7PAo928pPXFEcxyICIiuhMGHPSYCCo8t+oUUvNKG2xPyyuVtjPoQLru8baPw9bMFvH58diZuFPp4RARASU5wC/Tgd9fASpLgOBhwHOHgJARnB0iIjIo2y+k43xyPqzNjDEvnNkNtzM3PKh2zmIzC1voEyIiItJPDDjoKVE26R+bLqCx4kk128TrLK9EuszGzAZPtXtKWl9ybol0lxURkWISjwILBwEXNgAqE2Dkv4CnfgVs3PihEBGRQdFoqvBpdXbDMwMC4GTNDL7bCXGzxfAwNykJcumBuBb6lIiIiPQTAw566lhc9k2ZDfWJy7bi9Xnfn8CPRxNxJikXJeUsWUO656mwp2BpYonLOZexP3m/0sMhotZIlHSL+AhYMQbISwIcA4FZ24EBfwJUPFUiIiLDszUqDZfSCmBrboI5g9ib6G6yHNaevIbrhWXN/AkRERHpLxOlB0D3JqPg1sGG+nZezJAWQWUEBLvaoL2XHdp72kmPHbzseTcLKcrBwkEqrfRt1LdYfG4xBnkPgpGRET8VImoZ+SnAurlAfHXAs9Nk4KH/ARZ2/ASIiMggqetlN8wcGAgHK2Y3NEXvQCd08XWQmmx/dyger41q28yfFBERkX5iwEFPudlaNGm/R7p4Ibe4HBdS8pFVVI7ojEJp2XAmpXYfDzuL6uBDXSDC19EKKhGhIGoBonn0jxd/xNnMsziedhy9PXtz3omo+V3eAqx/HijJBkyt5UBDlycABj2JiMiA/RGZKn0ntLMwkQIO1DTipqh54UF4/odT+O5IAp4dEgwrM15SISIiuhH/ddTjuys87S2kBtGNVb0XoQIPewt89nhXGKuMpNr4mQVliErJx4XUfCkAIR7jrhchLb9UWnZfkjMhBJFa2646+FCTERHqbgNzE+MW/T2pdXC1csVjoY/h58s/Y3HkYgYciKh5VZQCO98Fji6Un3t0BiauAFxCOPNERGTw2Q2f7ZSzG0QpJXtLU6WHpFdGd/CAv7MVErKK8cuJa5jeP0DpIREREekcBhz0lAgivPtIezy36pQUXKgfdKjJSxCvi/2kbUZGcLOzkJahYXXNLwvLKnFJBCBS8xGVLD9eTitAQVkljsVnS0sNE5URQtxspDJM9csy8SSVtGFmx5n49cqvOJp6FOcyz6Gza2dOLBFpX+YVYO1MID1Sft73BWDEu4CJOWebiIgM3oYzyYjNLIKDlanULJrujvh+PXtgIP6+IQpL9sfiqT5+MDFmvyciIqL6GHDQYw909MSCqd3xj00XGjSQFpkNItggXr8TG3MT9AxwkpYaFWoNYjIL5SyIlPzarIi8kgqpsZhYfj1VdwwfR0sp+FAbiPCyg5e9Bevw013xsvHCw8EPY/3V9Vhybgm+HP4lZ5CItKeqCji9CtjyOlBRDFi5AI8uANqM4iwTEVGrUKnW4PNd0dL6vPBg2Fowu+FeTOzhi093RuNaTgm2nE+TyhgTERFRHQYc9JwIKoxs74FjcdlSI2nR20GUW6rJbLgXpsYqhHnYScv47vI2UZIpJa8UUcl5DUoyiZOsmmX7hfTaY4g7ZqQMiHrNqYNcraVjE93KrI6zsDFmI/Ze24vL2ZfR1omN2IhIC0rzgN9fBc7/Kj8PHAyMXwzYenB6iVqQaLT66c4rOJWQgwp1Fdq420j148d19W7Szx+Pz8a282k4EpclnXsWl6ulG19GtnfH80NCGs26HTB/N5JzSxo93pN9/PCfxzrd9+9FpC/WnU6WSgE5W5thWj9/pYejtyzNjKX5+2xnNBZHxOLhzp682Y6IqLVKPgns+QC4dgxQVwCuYUDf54HOk5r28wmHgUu/A/H7gdxEoLwYcPADwh4EBr4GWDrc/DOfdgLyEhs/Xo8ZwCOfQWkMOBgAEVzoF+zcrO8hSjJ5O1hKy6gOdRdo8oor5ACEKMmUkicFIq5mFCK3uAKHYrKkpYaZiQpt3W3l5tTVJZlEnwhrc/7fkGQB9gEY5T8KW+O3YknkEnw8+GNODRHdn2sn5BJKuQmAkTEw7B1gwCuAigFwopZ0OCYL05cfg6mxkXQ3sK2FCbZGpeHl1Wek4MELQ+/cQ0WUEs0pLkdPf0eM7+Yj9Xc/EpuFRftisfV8Gn59rj9cbG4ujybea+aAmxvjdvax19rvR6TrRBb7F9XZDc8ODuZ3sPs0rV8AFu6LQWRyHg7HZqF/sIs2PiYiItIncfuBVeMBYzOg43jA3A64uAlYN1v+/hn+lzsfY800oDgL8OsHdJkiF8oXwYeDnwMXNgKzdgA2rjf/nLk90Pe5m7d7dYMu4JVeui/2VqZSsKN+wKOsUo3o9OqSTNWBiIupBVK/CHFCJpYa4otigLN1bSaElA3haSf1mqDWaXan2VLAYXv8dsR1jUOg/c0XCIiI7kijAQ5+Buz5N6CplO8SmbAc8O3FySNSoIzLm+vOSd+ffp7XDx295Qv9L49og/HfHMSnO67gwU6eCHSxvu1xZg0MxITu3g3OE0UW7t83nMeqI4n4fGc0/vVox5t+zs7CFK+ObNMMvxmR/lh78poU3BNBual9md1wv5yszTCphy++P5IgZTkw4EBE1MqoK4GNL8kBghmbAc8u8vYhbwJLRwJ7PwA6PAY4B9/+OP2elwMN9bPvRTngP/4MnFgG7JsPPPS/m3/Owh4Y+hZ0FQMOpHXmJsbSF8maL5OCRlOFpJziBj0hxHpafinirhdJyx+RqbX7ixPh+o2pRVaECEzcT6ko0g+ijNIQnyFSWaVlkcvw/sD3lR4SEembgjTgt3lA7F75eYfxclqpOCkjohYnMl5FGZdJPXwanB+KXmIvDQvFSz+dxi8nkvD6A2G3Pc5zQ4IbzcL907BQKeBwNK4us5aI0OCGsK92X5XWnx8SLJUEovs3e1AgfjiagL2XM3E5rQBtPWw5rURErUXcPiAnDug6tS7YIJjbAoP/KmfZix6CI969/XEGvnrzNnF39uDX5YBD/EHoIwYcqEWoVEbwd7aWljGd6ppZXy8sw8Xq4ENNICI2s1DaHnElU1pqWJoaI8yzuiSTp9ygWpRo4gmz4ZnTeY4UcPgj9g883/V5qaE0EVGTRO8AfnsWKL4OmFoBY/4LdJsqn7QRkSJE2SNhUJub08HDQ+VtR+Oy7/n4JtU9woxvUSqtXK2R7u5Ozy+FnaUpevg5SueRRK3FmuNJUi8TdztzqXcJaYf4bvtARw9sjkyTshz+N7neBSciIjJs8Qfkx+ChN78WPEx+TLiPYIGqujeZ6haX7tVlwJkfgfwUuc+Dbx/AQ3d6kzHgQIoSmQyDQl2lpUZJuRqX0uqyIMSjCEqUVKhxOjFXWmqIhIdgV5vabAjRnFqsixRX0l+dXTujj2cfHE09iuXnl+Odvu8oPSQi0nWVZcDOfwBHvpafu3cCJi4HXFlGhUhp8VlF0mOgs3Wj5TnFeVv8dXmfe7HmRJL0GB7aeA31zIIy/OWXsw22DW7jik8f73rbc0ZxV3h5pab2eUFpxT2PkUgppRVqfLVHzm54cWgILEyZ3aBNc8ODpYDDxrPJ+MvoNvC0t9Tq8YmISAGl+UD9U0QTc3mpLztGfmysZJKlI2DlDGRV73MvTn9/64CGUJgOrL+hh0PICOCxxYB18/b5bQoGHEjniIyFbn6O0lJDramSyi7Vb04tlqyickRnFErLhjMptft72Fk0aE4tAhG+TpZS2j3ph7md5koBh9+if8O8zvPgatVIkxwiIuH6VeDXmUBq9QXF3vOAkf8ETNkPiEgXFJRW1jZvboworZSWV3pPxxbnhaJ3g4uNGeYNvvkL3+SevugT5IQ27rYwM1EhOr0An++KlkqgzF55XGo0favzw2/2xEj71tCUFd/TGImU9NOxRKTnl8HL3gKTe/nyw9Cyrr4O6B3ohGNx2VhxMB5/e7Ad55iISN992h4wr3d+OPjNm/sliKCEIBpFN8bcVs4+uBep54B9HwLWrsCAV25+XWTwBwwAXNsBJmZA5mVg73zg6g7gpyeAWdsVz/BnwIH0gujdEOJmIy1ju3jVNgnMKChr0JxarMdnFUu9IcSy61JG7TFszU3Qrl5zahGICHW3kXpOkO7p5dELXVy74GzmWXx34Tv8ueeflR4SEemiMz/JDbUqigBLJ+DRb4C2Y5QeFRG1gKTsYsz69gTUVVX4Ykq3RrMVXh4R2uC5uKFl+fReeHzxYRyPz8GeyxkYFube6PGfHxos1WivkZ+fD5/PmuEXIWomInP8m73y3ZUvDgvl955m8uzgICng8OPRRLw4LERqVE9ERHrs1QuAXb1Awo3ZDc0pJx748XFAo5Yz9hvLVhjyRsPnPj2BJ9cA3z4IJB4GorcDbUZDSQw4kN4Sd6O521lIy9Awt9rthWWVtX0hpN4QqXm4klaIgrJKHIvPlpYapsYikGHboDm1CErYW/IkURc+37md5+KFXS/g58s/Y1bHWXCwcFB6WESkK8oK5EDDuZ/l5wGDgPGLATv2fCHSNTWZDTWZDjcS5263yn64lWs5xXhi8RFkF5VjwdTu6B/ceDmlW/UWm9TDVwo4nIjPuWXAQdyUUv/GlKpynh+SfhENjUVJMR9HS0zs4aP0cAzWkDZuCHWzkbLufzqa2Gi2FRER6RELO3m50z5CWXWmQ2PfV83vsmdYbiLw7SNyP8LJ3wOB4U3/WdHLrOtTcsAh8QgDDkTaJtLyewU4SUuNCrUGMZmFdc2pq7Mi8koqpOCEWH49VXcMcVJevyeEWEQaMksytaxB3oMQ5hSGS9mX8MOlH/BC1xdaeAREpJOSTwFrZwI5cYCRMTDkLWDQa4CKGWtEuiigundDXFYROvnYN3gtr7hCChr08K8rpdmUzIYpS44go6AUXz/ZHcPbNR4wuB3H6mwI0SOMyBAVlVViQXV2w5+GhUolxah5iCDmnPAgvL72nFRWacaAQM43EZGhc6oOLos+DV7dGr5WkgMUZ8mNnJsqJwFY+TBQkApMXgm0feDuxyT6RggVJVAaMxyoVTA1ViHMw05axndHbUmm5NySeiWZ5ECE2HYtR162X0ivPYaDlamcCSECEd7i0R7BrtYwMebJe3MRAZ7ZnWbjL/v+gh8u/oDp7afDxsym2d6PiHScRgMc/grY9Q9AUwnY+wITlgJ+fZUeGRHdRp8gZ6msy/4rmbWlMWtERGfK+wTW3Shyp2CDyGwQwYYvp3THqA4e9zT3Z5JypEcfRyt+dmSQvjucIPW783e2wmPdvZUejsEb19ULH2+7LJX13Xg2hRklRESGTvRQOPAJELMH6DSx4Wsxu+VH/wFNDzZ8Wx1smLQCCHvo3saUfEJ+dPCD0hhwoFZ9MVt8yRRL/S+r4k47UYapJhAhHq9mFCK3uAKHYrKkpYa4UyjMo64kk3gUJZmszfmflraM8BuBQPtAxOXFyaWVOs3S2rGJSI8UZgC/PQvE7JKftxsLjP0CsGz6XdFEpIwBwc7wc7LChrMpeGZAgJRBWlNK6cvd0TBRGTW4OCcyHsQiejLU78tQE2xIzy/FV092wwMdbx9sEA2i3ewsbiqVeTw+G0v3x0nncXc6BpE+KiitwKIIObvh5eGh0s1X1LxE+TWR2fDh1ktYEhGLCd29mR1PRGTIAocAjgFA5C9An3mAZ+e6Ukr7PgJUJnKJoxpFWXLWg8hCqN+XoTbYkAJMXAG0e+T275txCbD1ACxvKDmecBg4/DVgbH7nY7QAXhUluoG9lalUB7h+LeDSCrUUdJBLMuVJgYiLqQXSF+Vz1/KkpYZoBC9KB9QGIURvCE876Qsv3T1jlbGU5fD2gbel5tFPtnsSliaWnEqi1uTqLuC3eUBRJmBiATwwH+jxjPwHl4h0nsgGnT+hE6YvP4bJCw9jbFcvqQTm1qg0JGWX4C+j2iDItS6DceWheHy+K1q6UPrqyDa120WwQWSidvNzkM7DxHKj+vv/fi5Vuug6INhFKpcpAgyX0wuxPzoTKiMj/PvRjvB24DkFGR7x35C4WSrI1fqmrCJqPk/28cNXu6NxOb0Ae69kYmjbuj6DRERkYIxNgLFfAt+PB1aMATpOAMxtgYubgNwEYNg7gEtI3f7HFgP75gOD3wSGvlW3XQQb8hIBn15AepS83Kj+/lG/AQc/B4IGy5kMxmZAxkU5q8JIBTz8KeDgC6Ux4EDUBBamxujobS8tgPwfrkZThcTs4tosiJpARHp+GeKuF0nLH5GptcdwsTGvzYIQzanFughMGKt4wexOxgSOwTdnvkFyYTLWRa/DU+3qRYmJyHBVlgO7/wUc+kJ+7tYemLgccGun9MiI6C6JGzl+ebY/Pt1xRQoEiP5abdxt8eeRbfFot6aVexHBBuF0Yq60NKZ+wKFfsDOuZhYiKjkPR+OyUVapls7HHu7shVkDA9HV94Y7w4gMQH5pBRZHxErrImjH8q8tR2RTTenth6UH4rB4XywDDkREhk40dZ65Ddj7HyBqPaAuB9zC5GBD58lNO4YINgjXjstLY+oHHAIHAdcvA6lngfiDQGUpYOMGdBwP9H0B8OkBXWBUJQrZk9bl5+fD3t4eeXl5sLO7y67kpNeuF5ZJTajrN6eOzSyEppH/0ixNjRHmaSsHIDzlBtWiRJMIcFBDay6vwb+O/AvuVu7YMn4LTI0blkcgIgOTHQusnQWknJKf95oNjHofMOXdyKQ8nue1TvzcSR98tvMKPtsZjVA3G2x9JZw3N7WwlNwShP93Dyo1Vdj44gB09mFgk4hIX/BcT3uY4UCkZeLOuUGhrtJSo6RcjUtpDZtTi+clFeqb7tITCQ/BrjZyKaZ6gYj6NYxbo3Eh47Dw7EKkF6djY8xGTGgzQekhEZE2aNRAwiGgMB2wcQf8+wPnfwV+fw0oLwAsHIBxX+lEHUoiIiJdlltcjmX746T1V0a0YbBBAV4Olnikixd+O52MRRGx+PrJ7koMg4iISFEMOBC1AEszY3Tzc5SWGmpNFeKuF8oBiOqyTGLJKipHdEahtGw4k1K7v6e9RYPm1KLhoq+TZatpRmZubI7pHabj4xMfY9n5ZVIAwkQ04SEi/XVhI7D1DSC/7m8dTK2AimJ53a8/MH6xTtSgJCIi0nWiGXpBWaWUMT2GDdEVMzc8SAo4bIlMRWJWMfycrZQbDBERkQJ4tY5IIaJ3Q4ibrbSM6yrXLhYVzjIKyhr0hBDr8VnFSM0rlZZdlzJqj2FrboJ29ZpTi0CEqIcsmiI2lQh8HIvLRkZBKdxsLdA70Eln74aa1GYSlkYuRVJBErbFb8NDQQ8pPSQiup9gw5pp4i9fw+01wYYO44HxS+RmXERERHRb2UXlWHEwrraXiUpHz+dbA/H9LLyNKyKuZGLZgVj8Y1xHpYdERETUovgtnkiHiGwFdzsLaRka5la7vaC0ApfSChoEIq6kFUp3MB2Lz5aWGqbGciCjfnNqcdIrmpjdaOv5VPxj0wUpkFE/k+LdR9rjgY6e0DVWplaY2m4qvjrzlRR4EM2kVUZND64QkQ6VURKZDTcGG+pLOir+KLbkqIiIiPSWaBRdVK6Wzv9HtXdXejit3rzwICngsObENam8lWMrL49LREStCwMORHrA1sIUvQKcpKVGhVqDmMxCRCXXlWQSwYj80kqpabVYfq3utSr4OFo26AmRWVCKt387f9PlvrS8Ujy36hQWTO2uk0GHKe2m4Nuob3E19yr2JO3BcL/hSg+JiO6W6NlQv4xSY/KT5f0CB3F+iYiIbuN6YRlWHoqX1l8b2abVlFzVZf2DnaXvXqJ87vdHEvCn4aFKD4mIiKjFMOBApKdMjVUI87CTlpr2yaIkU3JuSXXwoS4QIbZdy5GXbVHptz2uCECIrygi82Fkew+dK69kZ2aHJ8KekDIclpxbgmG+w/ilikjfiAbR2tyPiIioFVu0LwYlFWp08bHHsHpZ0qQcEfQRvRxeXn1GCgaJdQtTY34kRETUKjDgQGRgJ7Y+jlbSMqqDR+323OLyBo2pj8dnIymn5LZBB1FmSfR26BfsDF3zdPunserCKkRlReFwymH09+6v9JCIqKnUFcDlrU3b14YlIYiIiG4nI78U3x1OqO3dwOwG3fFQJ0/8d+tl6eavX09dw1N9/JUeEhERUYtg8XOiVsDBygz9g10we1AQPnm8K/4yum2Tfk40ktZFThZOmNhmorS+OHKx0sMhoqbKiQdWjAHO/3KHHY0AO2/An8FEIiKi2/lmbwzKKjXo4e+IwW1cOVk6xMRYhVkDA6X1pfvjoNbcpncVERGRAWHAgagVcrO10Op+SpjeYTpMVaY4mX5SWohIx53/FVg4CLh2HDC3B/q9WF3A7caybdXPH5gPqFh6gIiI6FZS80rw47FEaZ29G3TT4718YW9pirjrRdhxIU3p4RAREbUIBhyIWqHegU7wtLe46TJfDbFdvC7201Ue1h4YFzJOWhe9HIhIR5UXARteBNbOBMryAd8+wHMHgNH/BiZ/B9jd0Jzezkve3n6sUiMmIiLSC9/siUF5pUY6ZxdNikn3WJub4Om+cimlRRGxUs89IiIiQ8eAA1ErJBpBv/tIe2m9saCDOA0Wr+taw+gbzew4E8ZGxjiYchBR16OUHg4R3SgtElg8BDj9vfzXJvyvwDObAQc/+XURVHjlPDD9d2DCMvnxlUgGG4iIiO5A9AVYfZzZDfpgev8AmJmocDoxFycScpQeDhERUbNjwIGolXqgoycWTO0OD/ubyyZ5O1hgdL2m07rK19YXYwLHSOtLIpnlQKQzxN17RxcBS4YB168Atp7A9I3AsHcAY5OG+4qySYGDgE4T5UeWUSIiIrqjr3ZfRYW6Ssps6BvE7AZd5mprjgndvaX1RftilR4OERFRs2PAgaiVBx0OvDEMP83pi8+f6Iql03rC0lSF5NxSRERfhz6Y3Wm29LgrcReu5lxVejhEVJwNrH4S2PI6oC4H2owBnj0IBIZzboiIiLQgMasYv5xIqu3dQLpv9qAgGBkBOy+m42pGodLDISIialYMOBC1cqJsUr9gZ4zr6o0R7d3xZJ/qGqP7YqAPgh2CMcJvhLS+9PxSpYdD1LrF7QcWDAAubwaMzYAx/wWm/ARY885LIiIibflydzQqNVUIb+OKngG623ON6gS72mBEO3dpfel+ZjkQEZFhY8CBiBqYOTAQJiojHIrJQuS1PL2Yndmd5SyHLXFbkJQv3+1FRC1IXQnsfh9Y+QhQkAI4hwKzdwF95kG6nY+IiIi0Iu56EdadTpbWXx0RylnVI/PCg6THdaeSkVFQqvRwiIiImg0DDkTUgLeDJcZ28ZLWF0XoR5ZDB+cOGOA9AJoqDZadX6b0cIhal9xE4NuHgIiP5Jbz3aYC8/YBnp2VHhkREZHB+XJXNNSaKgwLc0M3P0elh0N3QWSjdPdzQLlag5WH4jl3RERksBhwIKKbzB0s332zOTJVqhGrD+Z2mis9bojZgLSiNKWHQ9Q6XNgALBwIJB0BzO2ACcuAcV8DZtZKj4yIiMjgiNr/68/UZDewd4M+mhseLD1+fzgBhWWVSg+HiIioWTDgQEQ3CfOww5C2rtBUAUsP6EeN0e7u3dHDvQcqNZVYGbVS6eEQGbbyYmDTy8CaaUBpHuDdE5gXAXSaqPTIiIiIDNYXu6Kl8/OR7d3Rycde6eHQPRCfXaCLNfJLK/HzcZaCJSIiw8SAAxE1al713TdrTiQhq7BMr7Ic1l5Zi6ySLKWHQ2SY0i8AS4YBJ78FYAQMfBWYuRVwClR6ZERERAbrcloBNp1LkdaZ3aC/jFVGmDNIziZffiAOFWqN0kMiIiLSOgYciKhRfYOc0NnHHqUVGnx3OEEvZqmfVz+pn0OpuhSrLq5SejhEhqWqCji+FFgyFMi8CNi4A0//Box4DzA2VXp0REREBu3zXVekf4of7OSB9l52Sg+H7sP47t5wsTFDcm6JVMKWiIjI0DDgQESNMjIyqs1y+O5wPIrLK/VizHM7y1kOP136CXlleUoPicgwFGcDP08F/vgzUFkKhIwEnj0IBA9VemRE+iX5JLBqIjDfD/i3J7B4KHDul6b/fGEmsP9/wM9PA591At6zl5dbyU8BDn8DfPco8EkH4J8uwEeh8n/P105o5VciouZ3ISUfmyPTYGQEvDycvRv0nYWpMab3C5DWF+2LRZWIJBERERkQBhyI6JYe6OgBPycr5BRX4JcT1/Ripob4DkGIQwiKKoqkoAMR3aeEQ8DCQcCl3wGVKTD6P8CTawAbV04t0d2I2w8sfwBIPAy0Hwf0nAkUZwHrZgMRHzftGJmXgF3/BC5uAozNAFOr2+9/dBGw7S0gJx4IHgL0fxHw6wtc2gwsGwmcX8fPkEgPfLbzivT4cGcvtPWwVXo4pAVT+/rD0tQYF1LzcfAqS8ESEZFhYcCBiG5fYzRcrjEqmkdX6kGNUZWRCnM6zZHWRVml4opipYdEpJ80amDvh8C3DwH51wCnIGD2DqDfC4CKpw9Ed0VdCWx8Se57MmMzMPZLYPS/gecOAq7tgL0fAFkxdz6Oa1vgmc3AW0nASycBO+/b7+/dA5ixBXj5DDDua7kE2uPfA8/8DhgZA3+8BlTqR58motYq8loetl9Ih0rKbghVejikJY7WZni8l6+0viiiCX//iYiI9IjeXjEQ5V1+O30NL/xwCkM/3ou272xBp/e2YfKiw9hwJvmejqnRVGHN8SRMXHBIOlbY37dIx/7LL2dRWKb75WSImsOkHj5wtjZDUnYJtpxP04tJHh0wGn62flJJpV+u3EWpCiKS5V0DVj4C7P0PUKUBujwJzIsAvLpxhojuRdw+ICcO6DQJ8OxSt93cFhj8V0BTCZxuQu8hGzcgYID8c03Rfizg3//m7WJb4CCgJAdIj7qLX4SIWtqn1dkN47p6I8TNhh+AAZk1MFAKJO2Pvi6VzSIiIjIUehtwOBaXjVd/PotDMdelplkzBwZiTEcPXErNx8urz+DdDefv6nhllWrM+e4EXv/1nBRcmNjDB9P7B6Cjtz32Xs5AQWlFs/0uRDpfY7R/dY3RiBi9qDFqrDLGrE6zpPVvo75FmZp3bxI12aU/gIUDgYSDgJkNMH4J8NiCpl/gJKKbxR+QHxvrexI8TH4U/821JFEiTXo0adn3JaImO52Yg92XMqSs4z8xu8Hg+DpZ4cFOntL6kv2xSg+HiIhIa/T2G4abrQU+f6Kr9A+0qXFd3OSvo8Pw6NcHsfJwAsZ390EXX4cmHe+/Wy9j16UMvPFAGJ4bIjfKrZ/5QNSaPd3XHwv2xuB8cj4OxWRhQIgLdN0jQY9gwdkFSCtKw/ro9Xg87HGlh0Sk2ypKgO1/B44vkZ+LbIYJywDnhv8mElE9pfmAWb3nJubycqPs6nIZjf33ZOkIWDk3raSStuQmAbF7ARt3wL1Dy70vEd2VT3dGS4/ju3kj0MWas2eA5oUH4/dzqdh4NgV/Gd0W3g6WSg+JiIio9WY4iKwGkVZaP9gguNqa48k+ftL60bimNV9KyyvFykPx6B3gdFOwQVCpjKSFqLWqX2N04T79qDFqamyKZzo8I60vP78cFRpmKRHdUsYlYMnwumBD/5eAmdsZbCC6k0/bA/N965b9n9w6MCGY2zX+usggKmuhchrqCuC3eYDI/hv5T0Bl3DLvS0R35UR8NiKuZMJEZYSXhrF3g6Hq5GOP/sHOUGuqsPxAnNLDISIiat0Bh9sxNZaDA8ZNbGq55XwqKjVVeLCTh1ROaf3pZHy956rUz0EEI4hIrjEq0rlFjdGolDy9mJIJoRPgZOGElKIUbI7drPRwiHSPKJF28ltg8RAgIwqwdgWm/gqMeh8wqX/bNhE16tULwJtJdcug13R7ojQaYMMLcvmm7tOBLk8oPSIiukPvhkk9feDnbMV5MmBzw4Okx9XHEpFXwpukiIhI/xlcwEHcGbDuVDKMjICBTSz7EnlNvnhaUFqJYR/vxSs/n8FH2y5L/RzC/7sHS5tQT1H0gBB9HuovRIZWY/Sh6hqjiyP0o8aohYkFprWfJq0vjVwKtUat9JCIdEdJLvDLM8Cml4HKEiBoKPDsQSBkhNIjI9IfFnYNl8bKKdXsJ9wqi6Gs4NbZD9oMMG56CTj3M9D5ceDhz5r3/Yjonh2JzcLBq1nSjXQvDA3hTBq4wW1cEeZhi6JyNX44mqD0cIiIiO6bwQUc/rf9Mi6lFWBSDx+09Whag8vrReXS42e7otHO0w47Xg3H+X+MxvJnesLR2hTv/3ERey5l3PYY3+yJQaf3ttcu/T7YrZXfh0gX774RdUav5RRDHzze9nHYmtkiPj8eOxN3Kj0cIt2QeBRYOAi4sF5uGCvKqkxdB9i6Kz0yIsPkVF2ys7E+DSU5QHFW85YwkzIbXgROrwI6TgQeXSBqhjbf+xHRPauqqsInO+TsBlHS1MeR2Q2GzsjICHMGyd+zVhyMl25mJCIi0meKN43u9s/tyCluejbAT3P6ol+wc6Ov/Xg0Ed/sjUEHLzu8+0iHuzqpE5ytzbBwag9Ymsm1bIeFuePDCUZ4ZsVxLD0Qi6Fhbrc8xvNDgzF7UGDt8/z8fPjwxjEyMB297TEo1EUqq7TsQNxd/XemFBszGzzV7iksPLsQS84twSj/UdJJPVGrJLJ8DnwC7PkAqFIDjgHAxOWAdw+lR0Zk2AIGyP/txewBOk1s+FpM9U0q/gOaL9iw8SXgzCqgw3hg/GL2bSDSYYdisnAsLhtmJipmN7Qij3TxkqospOWXYsPpFEyu7p9HRESkjxQPOIzt4oXCsqZH8EVT6MasOZGEt9dHSqmIq2b1gbV50381Wwt5X1GCqSbYUCM81FU62TtXXXbpVsxNjKWlRlW5aZPfn0ifzAsPlgIOq48l4U/DQqWG0rruqbCn8F3Ud7iccxkR1yIw2Hew0kMiann5KcC6uUD8fvl5p0nAQ5/UlXohouYTOEQO8EX+AvSZB3h2riultO8jOdOo61N1+xdlyVkPVs6AdeM32jQ92PAicOYHoP2jwPglDDYQ6Ul2w5O9/eBpb6n0kKiFiGsOMwcG4D+bL2Hx/lhM7OEDlYo3SRERkX5SPODwj3Ed7/sYornzm+vOIdTNBj/M7nPXF0CDXGykRzvLm4ME4h95G3MTFJZW3vc4iQzBgBBnKYsoKiUfq44k4KXhodB1DhYOUmmlFVErsDhyMcJ9wpnlQK3L5a3A+ueAkmzA1Bp46GOgyxSRw6/0yIhaB2MTYOyXwPfjgRVjgI4TAHNb4OImIDcBGPYO4FKvTvuxxcC++cDgN4GhbzU81m/P1a0Xpt+8TTR9rwlS7PtQDjaY2QDOIUDERzePLeyhugAIESkqIvo6TibkwNxEheeHNGOZNdJJU3r74ctdV3E1oxB7LmdgeDuWuiQiIv2keMBBG8GGN9adQ4irDX6c0xfONrdo1ncb/YOd8dWeq4jOKLjptazCMmQXlSPIxVpLIybSb6Ickejl8PLqM/j2UDzmhAfBwrRhZpAumtZhGn64+APOZZ7DsbRj6OPZR+khETW/yjJgx/8BRxfKzz06AxNXNLywSUQtIzAcmLkN2PsfIGo9oC4H3MLkYEPnyU0/ztkfb79tyJt1AYfcRPmxvBDY/3Hjx3PwY8CBSMeyG57u6w83Owulh0QtzNbCFE/28cOiiFgs2hfLgAMREektvQ44/Hw8EW+ui0RwdbDB5Q7BhpJyNZJzS6SySd4OdempfYKcEeJmg4NXs7A/OhODQl1rT/pEHUXhoc6ezfzbEOmPhzp54r9bL0v/Pa09eQ1T+/pD17lYumB86Hisvrxa6uXAgAMZvOvRwNoZQFqk/Lzv88CI9wCTuw/ME5GW+PQApv565/1EVsONmQ013rt9mc8GHlsgL0Sk88Qd7WeTcmFpaox5g5nd0FrNGBCI5QfjcCw+G6cTc9DNz1HpIREREd01FfTUoavXpWCD6PfcO9BJKu3y6Y4rDZZtUWkNfuZMUi5GfLIPr/18psF2Y5URPprYWTq5m7HiOF744RTe//0CHv3mEFYfT0JHbzs8x5RWolomxirMqW6SvmR/LNQaufG6rpvZcSZMjExwNO0ozmaeVXo4RM1D/MN4ehWwKFwONoga8E+uAR74gMEGIiIiHc9umNbf/5Z9C8nwedhbYFxXb2l9cUSs0sMhIiJqXRkO4s5qcU1F+PFodbr4DSZ098HoDh5NOp64c2DDiwOkQMWhmOsoLKuEl4OlVDvzhaEhsDLT26kiahaTe/nis13RSMgqxvaoNIzppPtZQJ42nng4+GGsv7peynL4avhXSg+JSLtK84DfXwPOr60r4fLYYsBO9//7JCIiaq22X0jH+eR8WJsZY144sxtaO1G+VmSRb41KQ/z1IgSwvDMREekZoypxOwVpXX5+Puzt7ZGXlwc7OzvOMBkkcSfWF7ui0cXHHutfGKAXjZjj8+IxbsM4aKo0WPvIWrR1aqv0kIi049oJYO1MuQGtkTEw7G1gwCuASvd7rBDpG57ntU783Kk5aDRVePCL/biUVoAXh4bgL6N5bkrAjBXHsOdyJqb29cP7j3bilBARtQCe62mP3pZUIiLlTe/nD3MTFc5ey8PRuGzogwD7AIzyHyWtL4lcovRwiO6fRgMc+BRYPloONogGsKIx7aA/M9hARESk48Rd7CLYYGtugtnVJUuJ5lZnuvxy4hqyCss4IUREpFcYcCCie+ZsY47JPX2l9UX7YvRmJmd3mi09bo/fjri8OKWHQ3TvCtKAVY8BO98DNJVAh8eAefsB316cVSIiIh0n+qCJkr7CzIGBcLAyU3pIpCP6Bjmhs489yio1+O5wgtLDISIiuisMOBDRfRF3YqmMIKX8XkrL14vZFGWUhvgMQRWqsCxymdLDIbo30TuABQOA2L2AqRUw9ktg4grA0oEzSkREpAf+iExFdEYh7CxMpIADUQ1Rqlb0chC+OxyPknI1J4eIiPQGAw5EdF/8na0xpqPckHZxRKzezOacznOkxz9i/0BKYYrSwyFquspyYNvbwA8TgeLrgHtHYO5eoPs08e2UM0lERKQHKtUafLZTzm6YMygI9pamSg+JdMwDHTzg62SJnOIKrD2ZpPRwiIiImowBByK6bzV332w8k4KU3BK9mNHOrp3Rx7MPKqsqsfz8cqWHQ9Q0WTHAspHA4a/k573nAbN3Aa5sMElERKRPNp5NQWxmERysTDGD2Q3UCBNjFWYPlL9nLdkfJ5XgIiIi0gcMOBDRfevi64B+Qc6o1FRhxUH96Ykwr/M86fG36N+QWZyp9HCIbu/samBROJB6BrB0BJ74CXjwv4CpBWeOiIhIz7IbPt8VLa3PCw+GjbmJ0kMiHTWppw8crUyRmF2MrefTlB4OERFRkzDgQERaMW+wfPfNj0cTkVdSoRez2tO9J7q6dkW5phwro1YqPRyixpUVAOvmAr/NA8oLAf+BwHOHgLAHOWNERER6aN3pZCRkFcPZ2gzT+vkrPRzSYVZmJni6X4C0vjgiBlVVzHIgIiLdx4ADEWnF4DauCPOwRVG5Gj8cTdCbZmw1vRzWXFmD3NJcpYdE1FDyKTmr4dzPgJEKGPo2MH0jYOfFmSIiItJDFWoNvqjObnh2cDCsmd1AdzC9nz/MTVQ4ey0PR+OyOV9ERKTzGHAgIq1dvK/JclhxMB6lFWq9mNlB3oPQzqkdSipLsOriKqWHQyTTaIBDXwLLRgHZsYC9LzBjCzD4dUBlzFkiIiLSU2tPXsO1nBK42ppjal9mN9CdOduYY2IPH2l9cUQsp4yIiHQeAw5EpDUPd/aCl70FMgvKsP50st4ESmZ3mi2t/3jpRxSKkjVESirMAH6cBGx/B9BUAO3GAs/uB/z68nMhIiLSY2WVanxZnd3w/JBgWJrxJgJqmtmDgmBkBOy+lIHo9AJOGxER6TQGHIhIa0yNVZg5MLD27huNRj9qjI7wH4FA+0AUlBdg9eXVSg+HWrOru4AFA4CrOwETC+DhT4HJ38lNoomIiEivrTmehJS8UrjbmWNKbz+lh0N6JNDFGqPbe0jrzHIgIiJdx4ADEWnVE739YGdhgtjrRdhxMV0vZldlpKrNcvj+wvdSeSWiFlVZDuz4P2DVeKAoA3BrD8zdC/ScKdJw+GEQERHpOVFu9Ks9V6X1F4eGwMKU2Q10d+ZWl69dfyYZ6fmlnD4iItJZJkoPgIgMi425CZ7u54+v98RId9+M7iDfiaPrxgSOwTdnvkFyYTLWRa/DU+2eUnpI1FqIHg2/zgaST8rPe84CRv8bMLVUemRERFp1NikXn+68glMJOahQV6GNu42UGTmuq3eTfv54fDa2nU/DkbgsqQZ+cbkaPo6WGNneHc8PCYG9pWmzvC+RNvx0LBHp+WVS+dHJvXw5qXTXuvs5oleAI47H52D5wTi8NaYdZ5GISGnie/yeD4BrxwB1BeAaBvR9Hug8qWk/n3AYuPQ7EL8fyE0EyosBBz8g7EFg4GuApUPzvG8zY4YDEWnd9P4BMDNR4WRCDk7EZ+vFDJuqTDGz40xpfcX5FagQf7CJmtu5X4CF4fLJgoU9MPl74OFPGGwgIoNzOCYLkxYexvG4bDzYyRNT+/ohu7gcL68+g6+r7/q+k+dWncKKQ/GwNjPB+G4+eLqvPyxNjbFoXyzGfnUA1wvLmuV9ie5XSbka3+yNkdZfHBYKcxNmN9C9mRceLD3+eCQRBaX8vkJEpKi4/cDyB4DEw0D7cXKFguIsYN1sIOLjph1jzTTgyALAzBboMgXoNVu+HnDwc2DxEKAws3net5kxw4GItM7N1gITuvtId3It3BeLpQFOejHL40LGYeHZhUgvTsfGmI2Y0GaC0kMiQ1VWCGx5HTjzg/zcrx8wfgngwDseicjwVKo1eHPdOcAI+HleP3T0tpe2vzyiDcZ/cxCf7rgiBQNEjfLbmTUwEBO6e8PNzqJ2W1VVFf6+4TxWHUnE5zuj8a9HO2r9fYnu16ojCcgsKJMycib28OGE0j0bFuaGYFdrxGQWYfWxJMwJl8ssERFRC1NXAhtfgnSiOWMz4NlF3j7kTWDpSGDvB0CHxwBnOVB8S/2elwMNtvWqg1RVAX/8GTixDNg3H3jof9p/32bGDAciahZzBgVKped3XkzH1YwCvZhlc2NzTO8wXVpfdn4ZKjWVSg+JDFHqWWDxYDnYYKQCBr8JTP+dwQYiMliHYrKQkFWMcV28ai/615RhfGlYKCo1VfjlRNIdj/PckOAGwQbByMgIfxoWKq0fjctqlvcluh9FZZVYuE/ObvjT8FApC5joXqlURphbHWQQZZUq1BpOJhGREuL2ATlxQKdJdRf9BXNbYPBfAXE96fSqOx9n4KsNgw2CuJg2+HV5Pf5g87xvM+PZDhE1iyBXG4xuL//RFL0c9MWkNpPgYO6ApIIkbIvfpvRwyJCIuxQOfwMsHQFkXQVsvYDpm4ChbwHGTDgkIsN1JFYOBAxq43rTa+Gh8rajcfdegtHEWP5KY6xStej7EjXFd4cTkFVUDn9nK4zvxr4hdP8e7eYNV1tzpOaVYtPZFE4pEZES4g/Ij8FDb34teJj8mHBDsOBuqKp7k6lMWvZ9tYQBByJqNnMHy3ff/HY6Gen5pXox01amVni6/dPS+pJzS6Cp4l1DpAVF14EfHwe2vQWoy4G2DwHPHQQCBnJ6icjgxWcVSY+BzjeXLrK3MoWTtRnir8v73Is11VkK4aEuWn3fskq1VCO9/kJ0N8T/ZxZFyNkNLw8PrQ2OEd0P0QPkmf4BtTd2idJyRESkRaX5DZfKm/uEIVv+973R0kWWjoCVM5BVvc+9OP1944GF5n5fLeEZDxE1m+5+jugd4IQKdZWU8qsvngh7AjamNojJi8GexD1KD4f0Xew+YMEAIHobYGwOPPgx8MQPgJV+9DYhIrpfBaVyiUJbi8azuUSJo5p97lZUSp7Uu8HFxgzzBgdr9X2/2RODTu9tr136fbD7nsZIrdfKQ/HILa5AkKs1xnbxUno4ZECm9vGHlZkxLqUVICL6utLDISIyLJ+2B+b71i37P7l5HxGIEMztGj+GuS1QVr3P3Uo9B+z7ELB2BQa80nLvq0Ws4UBEzWre4CAci8/Gj0cS8eLQENhaVKeF6TA7MztMCZuCJZFLsDhyMYb5DZNqRBPdFXUFsOc/wIFPRT0lwKUtMHE54FHX0JSIiO5dUnYxZn17AuqqKnwxpZuUsaBNzw8NxuxBgbXP8/Pz4fOZVt+CDFh+aUVtWVFmN5C2iSytJ3r5STd1LY6IweBGSscREdE9evUCYFfvgr6JectNZU68XB1Bo5avH1g7Qx8xw4GImtXQtm4IdbNBQVklfjqWqDezPbX9VFiaWOJC1gUcSjmk9HBI3+QkACvGAAfEnRBVQPfpwNy9DDYQUatUk2Fwq2yCwrLKW2Yh3Mq1nGI8sfgIsovKseCp7ugf7KL19xVlS8SNEvUXoqZatj8O+aWV0nnww52Z3UDaN3NgAIxVRjh4NQvnk/M4xURE2mJh13BpLOAgtgu3yiYoK7h1FsKt5CYC3z4CFF8HJn8HBIa3zPs2AwYciKh5/8iojDA3XO7lsOxAHMor9aMngpOFEyaETpDWF59brPRwSJ+cXwcsHARcOw6Y2wOTvgXGfgGYWSk9MiIiRQRU91CIq+6pUF9ecYUUNAhwubnPwu0yG0SwIaOgFF892Q3D27m3yPsSNVVucTmWH5DLib46so10UZhI23wcrfBwZ09pfVF1Ng0REbUQp+pSno31SyjJAYqzGu+zcLubFr99CChIla8htH2gZd63mTDgQETNblxXb7jbmSM9vwwbziTrzYw/0+EZmKpMcSrjFE6mn1R6OKTryouAjS8Ba2cAZXmAT2/g2f1Ah8eUHhkRkaL6BMmp4PuvZN70WkS0vK1PoNNdBRvS80vx5ZTuGNXBo0Xel+huLN0fJ2X3hnnY4oHb/H+U6H7V3Ni1OTJV+vtIREQtJGCA/BjTSN/PmOq+X/7V+zQp2PAwkC+CDSuAsIda5n2bEQMORNTszExUmDVQroEsatlqNFV6Mevu1u4YFzJOWl9ybonSwyFdlnYeWDwEOPUdACNg0F+AGVsAR3+lR0ZEpLgBwc7wc7LChrMpUpPn+iWNvtwdDROVESb28KndLjIPrmYUSo+3DjZ0wwMdPbT6vkTaIP5/u+JgXXaDyPYlai4dvOwxKNQFak2VlE1OREQtJHAI4BgARP4iN3muX9Jo30eAygTo+lTd9qIsIPOK/NhYsKEgRe7Z0O4R7b6vQtg0mohaxJTefvhy11VEZxRiz+WMW5Y/0DUzO87Eb9G/4WDKQURdj0IHlw5KD4l0SVUVcHwpsO1tQF0G2HgA4xcDQYOVHhkRkc4wMVZh/oROmL78GCYvPIyxXb1gY26CrVFpSMouwV9GtUGQq03t/isPxePzXdFSo11xwbaGCDYk55agm58DLqYWSMuN6u9/t+9LpA3i5pqicjU6eNlhVHv9ON8l/SayHPZHX8fPx5PwyohQOFiZKT0kIiLDZ2wCjP0S+H683L+x4wTA3Ba4uAnITQCGvQO4hNTtf2wxsG8+MPhNYOhbddtFsCEvEfDpBaRHycuN6u9/t++rEAYciKhFiEaLT/b1w6J9sdKiLwEHX1tfjAkcg99jf8eSyCX4bOhnSg+JdEVxNrDhBeDyZvl5mweAcd8A1nIJDyIiqiOaOv/ybH98uuMKfj+Xigq1Bm3cbfHnkW3xaDfvJk2VCDYIpxNzpaUx9QMO2npfoqa6XlgmBcyE10a2gZERsxuo+Q0McUF7TztcSM3HqiMJeHFYKKediKgliKbOM7cBe/8DRK0H1OWAW5h80b/z5KYdQwQbBNEDUiyNqR9w0Nb7NjOjqipxeyZpW35+Puzt7ZGXlwc7O+W7gxPpAlECYeCHu1GhrsK65/uju58j9EFMbgwe3fCotP7b2N8Q4qh8tJgUFn8A+HWOnPZobAaM/BfQZx7ACwtErQLP81onfu50J+//fgFLD8Shi68D1j/fnwEHajHrTyfjlZ/PwMXGHAfeGAoLU2POPhHRXeK5nvawhwMRtRh3Ows8Vn034eJ9sXoz88EOwRjhN0JaX3p+qdLDISWpK4Hd/66rsegcAszeCfR9lsEGIiKiViwjvxTfH0mQ1pndQC3toc6e8LK3kLJsfjudzA+AiIgUxYADEbV4jVFh24U0xGYW6s3sz+48W3rcErcFifnVKW/UuuQmAd8+BET8VzRvALpNBebuAzy7KD0yIiIiUtg3e2NQVqlBD39HhIe6KD0camVMjVWYOTBQWl8SEQuNhoUsiIhIOQw4EFGLCnGzxYh27lKv3SX74/Rm9js4d8BA74HQVGmw/PxypYdDLe3CBmDhACDpCGBmC0xYBoz7GjBns1EiIqLWLjWvBD8ek29IYXYDKeWJ3n6wtTBB7PUi7LiYzg+CiIgUw4ADEbW4ZwfLWQ6/nrqGjIJSvfkE5naeKz1uiNmAtKI0pYdDLaGiBNj0CrBmGlCaB3j3AJ7dD3SayPknIiIiyTd7YlBeqUHvQCf0D3bmrJAibMxN8HRff2l9cYT+lK8lIiLDw4ADEbW4ngFO6O7nIH0xW3koXm8+gW5u3dDTvScqNZX4NupbpYdDzS39ArB4KHByhfx8wCvAzG2Ak5yuTkRERJScW4LVx5ndQLrhmf4BMDNW4WRCDk4mZCs9HCIiaqUYcCAiRcwbHCw9fn84AYVllXrzKczpPEd6/PXKr8gqyVJ6ONQcRL2v48uAJUOBzIuAjTvw9G/AyH8AxqaccyIiIqr11e5oVKirMCDEGX2DmN1AynKzs8Bj3byl9UX7mOVARETKYMCBiBQxsp07glyskV9aiZ+PJ+nNp9DPsx86OndEqboU31/4XunhkLYVZwNrngb+eA2oLAVCRgDPHgSCh3GuiYiIqIHErGL8cuKatP7qiDacHdIJc8LlbFzRxyE2s1Dp4RARUSvEgAMRKfPHR2WEueFyL4dl+2NRodboxSdhZGRUm+Ww+vJq5JXlKT0k0paEw8DCQcDFTYDKFBj1b+DJXwAbV84xERER3eTL3dGo1FQhvI2rVDKUSBeEuNliRDs3KWl3yf44pYdDREStEAMORKSYR7t5w9XWHCl5pfj9XIrefBJDfIcgxCEERRVF+OnST0oPh+6XRg3s/RD49kEg/xrgFATM3gH0f1FExji/REREdJO460VYdzpZWn91RChniHTK3HC5fO2vp64hs6BM6eEQEVErwyspRKQYC1NjzBgQUFtjtErchqMHVEYqzOkkZzmsurgKxRXFSg+J7lVeMrDyEWDvf4AqDdBlCjAvAvDqxjklIiKiW/pyVzTUmioMC3NDNz9HzhTplF4Bjujq64DySg2+Oxyv9HCIiKiVMVF6AETUuj3Vxx9f776KS2kF2HclE0PaukEfjA4Yja/PfI3EgkT8cuUXTO8wXekh0d269Aew4QWgJAcwswEe+gTo8jjnkbRGrVajoqKCM6qHTE1NYWxsrPQwiEhHXc0oxPozNdkN7N1AulkGdl54EJ774RS+O5yAZwcHw9qcl3+IiKhl8F8cIlKUvaUppvT2w9IDcVKWg74EHIxVxpjVaRbePfQuvo36Fk+EPQFzY3Olh0VNUVEK7Pg7cGyx/NyzKzBxOeAsp54T3S+RrZWWlobc3FxOph5zcHCAh4eHdNGGiKi+z3dFQ1MFjGrvjk4+9pwc0kmjOnggwNkK8VnFWHMiCTMGyM2kiYiImhsDDkSkuJkDA/HtoXgcjs3C2aRcdPF1gD54JOgRLDi7AGlFaVgfvR6Ph/HueJ2XeRn4ZQaQESU/7/8SMOz/ABMzpUdGBqQm2ODm5gYrKytesNbDgFFxcTEyMjKk556enkoPiYh0yOW0gtreY68wu4F0mLHKCLMHBeGd9eex7EAcnu7rDxNjVtUmIqLmx4ADESnOy8ESY7t6Yd2pZCyOiMXXT3WHPjA1NsWMDjPwwbEPsPz8coxvMx6mKlOlh0WNEf1BTn0HbHkDqCwBrF2BRxcCoSM4X6T1Mko1wQZnZ2fOrp6ytLSUHkXQQXyWLK9ERDU+33VFOq14sJMH2nvZcWJIp03s4YNPd1zBtZwSbD6fhrFdvJQeEhERtQIMbxORTpgbHiQ9bjmfioSsIuiL8aHj4WzhjJSiFPwR+4fSw6HGlOQCa2cAm/4kBxuChgLPHmSwgZpFTc8GkdlA+q3mM2QfDiKqcSElH5sj0yAqrb08nL0bSPdZmBpjWr8AaX1xRIyUxUdERKTTAYfySg1iMgtRqdZob0RE1CqFedhhaFtXqR7u0v1x0BcWJhaY1mGatL4schnUGrXSQ6L6ko4BCwcBUb8BKhNg5D+BqesAW3fOEzUr1v3Xf/wMiehGn+28Ij0+3NkLbT1sOUGkF57u5w8LUxXOJ+fjcEyW0sMhIqJW4J4CDiXlary+9iza/d9WjPo0Aim5pdL29zZG4Zu9V7U9RiJqJeYNlpv2iqZmWYVl0BePt30cdmZ2iM+Px47EHUoPhwQR+In4GFj+AJCXCDgGADO3AwNeBlRM7iMiIqK7E3ktD9svpEMlZTeEcvpIbzhZm2FyT19pfVFErNLDISKiVuCerrp8uPUSLqYWYPXcvjA3qTvEgBAX/H42VZvjI6JWpE+gk9QwuqxSg5WHE6AvrE2t8VS7p6T1peeWMlVZafmpwHfjgN3/AqrUQMeJwLz9gE8PpUdGRIB0d2XAm38gr0QuP0VEpA8+rc5ueLSrN0LcbJQeDtFdmT0wSAqW7buSiUtp+Zw9IiLSvYDDjgvp+Me4DugV4ASjettD3WyQmF2svdERUasrXzGvupfDd4fjUVxeCX0hAg5WJla4nHMZEdcilB5O63V5K7CgPxC/HzC1BsZ9A0xYCliwqSPpF7WmSrowv+FMsvQonje3P685KwUCxBLyt83o+f4OTF16FGuOJ0Gjxffv4e+IY28Ph52FidaOSUTUnE4n5mD3pQwYq4zwErMbSA/5OVthTEdPaX0xsxyIiKiZ3dM3vayiMrhYm9+0vbhc3SAAQUR0t0Z38IC/sxUSsoqli1zPDAjUi0m0N7eXSiutiFqBxZGLEe4TzvrfLamyDNjxLnB0gfzcozMwcTngwpIHpH+2nk/FPzZdQGqeXLJS8LS3wLuPtMcD1RcLmsvgNq74aFJnaDTA9cIy6U7If2yKwubzqVg6rSdMjO+/JJmZiQputhZaGS8RUUv4dGe09Di+mzcCXaw56aSX5oYH4Y/IVGw8k4K/jGoLLwdLpYdEREQG6p6+NXb2ccDuS+m1z42qowyrjyeim7+j1gZHRK2PuHNsziA5y2HpgTi9akovmkebqcxwLvMcjqUdU3o4rcf1aGDp8LpgQ5/ngNk7GWwgvQ02PLfqVINgg5CWVyptF683p5pggIe9BTp62+OFoSFYMq0n9l7OxNqT16R98ksr8Na6c+jxrx3o+O42TFl8BBdS5PIMMZmFUobE1YzCBsdduj8WA+bvlkrO3VhSKaeoHC/9dBp9/7MLYX/fgtGfRkiZHfU9vuiw1Cvsg80X0eUf29Hz/Z34dIdc3qSGOJ4Yl8jMaPPOFoz6dB92Xaw7Xz2ZkI3JCw+j7Ttb0O+DXdLx9CmTjoiUcSI+GxFXMmEishuG8UYG0l+idG3fICdUaqqw4mCc0sMhIiIDdk8BhzceaIuPt1/B279FSv9YLT8YJ6Xciy+ifx3VVvujJKJWZWIPHzhbm+FaTgk2n0+DvnCxdMH40PHS+pJzS5QejuGrqgJO/wAsGgykRQJWzsCUn4Ex8wGTm7PwiJQgLrCLi9pNWQpKK/Duxig0VryoZtt7Gy9I+zXleOK9taF/iAvaedpha1SadMyZK44js6AMK2b0wqaXBqKjtx2eWnoEucXlCHa1QSdv+5sCBhvOpGBcV69GM79E355O3nZY9kxPbH9lMKb09sVra85KJUzq+/XkNViaGWP9CwPw1pgwfLE7GvujM6XXRMmnZ1Ycw8mEHHz6eFfsfHUw3nggDCpRsBqQ6lVPW3YMozt6YOsr4fjqyW44Hp+N/9sQpZU5IiLD790wqaePVJaGSJ/NCw+WHn86liTdQEBERKQzJZV6+Dth7XP9pNp/ovTJ/ujr6Ohlh3XP90eYB+tkE9H9sTA1xjP9A/C/HVewaF8MHunsqTfliWZ2nIm1V9biaNpRnM08iy6uXZQekmEqzQd+fxU4v1Z+HhgOPLYYsGvecjNEd6ukQo32/7dNKxMnwgdp+aXo9N72Ju1/4Z+jYWWmnT4Jwa7WuJRWIGUnXE4rwIm/j4C5ibH02tsPtcf2C+nYHJmGJ/v4SYGF7w4n4M/VN6HEZhYiMjkPn0xu/O+hyKaYW30BRBCl9EQpp82RqejmV5c5G+Zpi1dGtJHWRUkT0evn4NUsDAp1xYGr13E2KRc7XxuMIFe5mWv9C4OL98VibFdvzBoYWPvz743tIGVOvP9oR+nfHSKiGx2JzZL+zpgaG+FFZjeQARjS1hVt3G1wJb0QPx5NxLOD6/79JSIi0pZ7/hYqAgufTO6qtYEQEdX3dD9/fLM3BlEp+dIXvYGhLnoxQZ42nng4+GGsv7peynL4avhXSg/J8Fw7CaydAeQmAEbGwNC/AQNfBVS8YEjUXESwQ4R9ReCgqLwS3f65o8HrpRVqJGQXSetju3jhgy2XcCoxB939HLH+TArae9oh1N220WOLhtgL9l7F7+dSpYBKeaVGWm4Mltx4U4urrQWyCsuk9Qup+fC0t6wNNtxIjFv0BqqfeSESQEQv7Gs5xQhxa3xsRNR6iYyuT6pLtz3Ryw/erHdPBkDcxCXK1/517TmprNLMAYFSOUUiIiLFAw57LmVIKeqisWB94m40TVUVhrZ109b4iKiVcrAywxO9fbHiYDwWRcToTcBBmNVxFjbGbMS+a/twKfsSwpzClB6SYRBdbA99Aez+F6CpBOz9gInLAN/eSo+M6JYsTY2lTIOmOBaXjWdWHL/jft/O6IXegU5Nem9tickohK+TlXSBXvR4WD2370372FmaSo9udhboF+QsNaUUAYdNZ1PwZG+/Wx57yf5YLDsQh/97pD3autvByswY//z9Aspv6OFjYtww000kvonxCBZ3uFgizk9F9oXInrsRm2YSUWMOxWRJf5fFxdjnh/IucDIc47p64+Ptl5GeXyYF4if19FV6SEREZGDuKZT94dZLUq3cxu4C+XDLJW2Mi4hIKn0hmkiLsm3nk/P0ZkYC7AMw2l++wMheDlpSkA6sGg/sfFcONnR4DHh2P4MNpBd3Eoo79ZuyiNJAnvYWUiZBo8cSWVT2FtJ+TTmetkrRHbp6XSqn9EBHD6lfQ2ZhmfS3OcDFusHiZG1W+zOirNLv51KkngoJWUV4pIvXLY9/PC4bI9u747FuPmjvZQc/JyvEX5ezJZoqzNMOqXklUvmmxogG2FfSC24as1h4ZycR3S67QQRMRQYVkaEQ/+7NGBBYG/TXVs8nIiKi+wo4xF0vQojbzSnrolGgSFcnItIGH0crPNxZrskvesbok9mdZ0uPOxJ2IDZPv8auc6J3Agv6A7F7ABNLYOyXwMQVgKWD0iMj0ipxEf/dR9pL6zeGCmqei9fFfs1FlDLKKChFWl6pFOj9es9VzPnuBIaHuWFCdx8MDHFBdz8HzP3+pJTZmpRdjJMJ2fh422Wcu5ZbexwRnCgorcQ768+jX7Cz1KfhVvydrXEg+rp0nKsZBfjbb5FSU+q70TfIWcr6eG7VKamRtBjXnssZ2Hs5Q3pd1KgWJZ7+vv48olLypHPZHRfS8e6G8/cxW0RkqCKkv0k5MBfZDUOY3UCGR2T92ZibSL0c9l7OVHo4RERkYO4p4GBrYSp9kbuRCDaINHgiIm2ZGx4kPf4Rmdro3x1d1caxDYb4DkEVqrAscpnSw9FPleXAtreBHyYAxdcB947AvH1A92lyLRUiA/RAR08smNr9pgv04rnYLl5vTiKI0PvfuzDww92YvvyY1CT63bEdsGRaTynQIbImVszojT6BTnh97VkM+99evPTjaakPgouNeYNzxRHt3HExNV8q3XA7fxoegg7e9pi27BieWHwErrbmGNnB/a7HvnBqD3T2sceffjqNEZ/sw/zNl6RSSkI7Tzv8PLcf4rOKMHnhYTz0xX78b/tlqfwTEdGtshue7uvPvxNkkOwsTDGlt1xKaeG+GKWHQ0REBsao6h7y595adw6nE3Ox6Oke0l1pgkh9f3bVSXTxccCHEzujtcvPz4e9vT3y8vJgZ9ewySER3Z2nlx2VyiqJ2tvvje2gN9MXmRmJJzc/CWMjY/wx/g9429z+ohvVkxUDrJ0JpJ6Rn/eeC4z8F2DKi4Ok20pLSxEXF4fAwEBYWNz7/19FI2VRO1xkG4ieCeLu/ebMbKC7+yx5ntc68XNvHXZdTMeslSekPjj73xjaIJhKZEhScksQ/t89qNRUYcMLA9DFl9nDRNS68VxP4QyHtx5sB0szYwz/3z7pDjixiDvJHK3M8LeH2mlxeEREcikM4efjScgpKtebKenk2gl9PftCXaXGivMrlB6O/ji7GlgULgcbLB2BJ34EHvyIwQZqVURwQZQiEtkB4pHBBiKils1umN4/gMEGMmheDpYY29VLL8vXEhGRAQYcRPrduuf6Y/kzvaQ00zmDgvDD7D74aW5f2Fuaan+URNSq9Q92lhqVllSo8f2RBOiTuZ3nSo+/Rf+GzGLWR72tsgJg3Tzgt3lAeSHgPxB49iAQ9lDLfFhERETUqm2/kI6olHxYmxnXlvUkMmQ1/z/fcj4ViezHSURESgYcBFHDN7yNK+YNDpbu/ugT5KytMRER3fT3Zl64nOXw7aF4lFao9WaGerr3RFfXrijXlGNl1Eqlh6O7Uk7LWQ3nVgNGKmDo28D0jYA9y1ARERFR89NoqvBpdXbDjAGBcLI247STwQvzsMPgNq7QVAFLDzDLgYiItMPkXn/w4NXr0pJVWF7bkK/GR5O6aGNsRES1xnT0gI+jJa7llOCXk9ek7Cp9CZbM6TwHL+x6AWuurMHsTrPhYMH6qLU0GuDI18DOfwCaCsDOB5iwFPDvp+THRkTUPJJPAns+AK4dA9QVgGsY0Pd5oPOkpv18YSZw+jsg5Yxcdi43Ud7+Xt6tf+bsz0DiIflnMi4A6nJg3DdAt6e08zsRGYitUWm4lFYAW3MTzB4UqPRwiFrMvPAg7LuSiTUnkvDKiDYMthERkTIZDp/tvCI1cT0Yk4Xs4nLklVQ0WIiItM3EWCWVbxOWRMRKDVX1xSDvQWjn1A4llSVYdXGV0sPRHYUZwI+TgO3vyMGGdo8Azx1gsIGIDFPcfmD5A0DiYaD9OKDnTKA4C1g3G4j4uGnHyLwE7PoncHETYGwGmFrd+Wd2vw+c/BbISwJsPO771yAyROp62Q0zBwbCwYrZDdR69KsuX1taocH3h/WrfC0RERlQhsMPRxPx8aQuGN/dR/sjIiK6hUk9faSAZ2J2MbaeT8NDnT31JstBZDb8ed+f8eOlH/FMh2dgY2aDVi1mt9yvoSgDMLEAHvgA6DFDTJbSIyMi0j51JbDxJfEvAjBjM+BZnQ085E1g6Uhg7wdAh8cAZ7l84C25tgWeET/fGTC3Bb7sCWRF3/5nxn4hH9fBD9j/CbDrH9r7vYgMxO/nUhCdUQg7CxPMYnYDtTLiu8rc8GD86afT+O5wPOYNDoKFqbHSwyIiotaW4VCh1qCHv6P2R0NEdBtWZiaY1i9AWl8cEYOqG8q56bIR/iMQaB+IgvICrL68Gq2WKCGy4/+A7x+Tgw2u7YA5e+Q7fRlsICJDFbcPyIkDOk2qCzYIImgw+K+AphI43YQMOBs3IGCA/HNNFTxUDjYQUaMq1Rp8vjO6toGunYUpZ4panQery9dmFZVj7clrSg+HiIhaY8Dh8V6+2HAmRfujISK6g2n9/GFhqsLZa3k4EputN/OlMlJJWQ7C9xe+l8ortTrZccDy0cDBz+XnIsgwdw/g3l7pkRERNa/4A3UX/28UPEx+TDjIT4FIARvPpiD2ehEcrEzxzAD2bqDWW7521kD5//9L9utX+VoiIjKQkkplFRr8dDQWB65eRzsPW+kfp/r+/jAvHhFR83C2Mcfknr747nACFkXESDVH9cWYwDH45sw3SC5Mxq9XfsXU9lPRakSuBTa9ApQXABb2wNgv5RrmRET6rDQfqF/q3cRcXm6UHSM/NlYyydIRsHIGsqr3IaKWzW7YJWc3zAsPho35PX09JjII4sZS8d9DQlYxtkelYUwn/ShfS0REBpLhcCktH+297KAyAi6nFyAqJa92uZCSr/1REhHVM3tgkPT3Z+/lTFxM1Z+/OaYqU8zsOFNaXxG1AuXqchi8skJg/QvAr7PkYINfP+DZgww2EJFh+LQ9MN+3bhE9Em4VmBDM7Rp/XZRIKtOff8+IDMW608nSxVVnazMpi5aotZevfbqv/N/BoohYvSpfS0REuuWebuFYPbef9kdCRNREfs5W0h03f5xLxZKIWHzyeFe9mbtHQx7ForOLkFGcgY0xGzGxzUQYrNSzwNpZckNTIxUQ/lcg/HXAmHcPEt2JWqPGqYxTyCzOhKuVK7q7dYexqnkbOL594G2pz8wXw75osF7jesl1LD63GBHXIqS/YU4WTghzCpOytfp69m2dH+qrFwC7ekGExrIbiEgnlVdq8EV1dsOzg4NhzewGIqlfngg2nEnKxfH4HPQOdOKsEBFRy2Q4EBEpbV54UG3d3eRc/emHYGZshukdpkvryyKXoVI0CtV3GjUQt18umyQe1ZXAkQXA0hFysMHWC5i+CRj6NwYbiJpgZ8JOjP51NGZum4k39r8hPYrnYrtSRCm4xzc9jmOpx/Baj9ewbuw6LByxEL08euHfR/6NVsvCruFyq4CDeE24VRZDWcGtsx+IqFmIxrjXckrgamuOqdV3dRO1duK/hwndfaT1xREs9UdERPfmnm8zPZuUi82RqdKFvgq1psFri57uea+HJSJqks4+Dugf7IxDMVlYfiBOr3rHiKyGpZFLca3wGrbGb8XDQQ9Db13YCGx9A8hPqdsmLrhVlsnrbR8Cxn0FWPHuKKKmEEGF1/a+hio0LGMgMgrE9k+GfIIR/iNafDLfP/I+YAT8+NCPsDK1qt0e4hiCx0Ifa/Hx6B2n6t4Nok+DV7eGr5XkAMVZgG8fRYZG1BqVVarx1W45u+H5IcGwNGveDDIifTJnUCBWH0/EzosZuJpRgBA3W6WHRERErSHDQdxRPHHhIURnFGJ7VDoq1VXSurjwZ2thqv1REhE1Yt5g+QLO6mOJyCuu0Js5EhfrahpGLz23FJqqhkFbvQo2rJnWMNgg1AQbuk8HnviBwQZq1UT94+KK4iYtBWUF+ODYBzcFG6TjVP9v/rH50n5NOZ62ai/nleXhYPJBTAmb0iDYUMPOjHfm31HAAPkxZs/Nr8Xslh/9q/choma35ngSUvJK4W5njim9/TjjRPUEudpgZDt3aX1JRBznhojIkGk0QMIh+fpOQbqyGQ7f7Lkq3U0s6vt1+L+tePeRDvB1ssTffouEq62F1gZHRHQ74aEuCPOwxaW0Aqw6moAXhobozYQ9EfYEVpxfgZi8GOxJ3IPh/sOhd2WURGZDIxdGa13dCYhgihHvGqTWq6SyBH1+1N6d6+nF6ei/un+T9j365NFGAwR3KzE/UQp2BNoF3vexWq3AIYBjABD5C9BnHuDZua6U0r6PAJUJ0PWpuv2LsuSsBytnwNpZsWETGaLSCjW+2nNVWn9xaAgsTHmeQnSjeYODsP1COn47nYw/j2oDNzte5yEiMkgfBQPqcrlSRWke0PlxYMx/AXObls9wSMgqxtC2btK6mYkKxRWVMDIywqyBgfjpWOJ9DYiIqKnE3x3R5E9YcTBe+gKpL8QdweJuYWFx5GKt3YncYkQE/MbMhhvlJ8v7EZFeq824MFJ6JHrM2AQY+6UchF0xBtj4J2Db28CCAUDmRWDIm4BLvaD5scXA173kxxv99lzdUph+8zYRrKjv5Mq61y6sl7ed+q5u28Xfm/M3J9I54vtqen4ZvOwtMLmXr9LDIdJJPfyd0MPfEeVqDVYcild6OERE1FwmrwT+lgy8HgvM2QPkJgJLht13tsM9ZTg4WJmiqFxudOpuZ4HLaQUI87BDXkklSsv154IfEem/hzp74qNtl6V+MuIOHH1KixdllVZdXIULWRdwKOUQBnjrUTmNmotc2tqPyEBZmlhKmQZNcTL9JJ7f9fwd9/tm+Dfo4d6jSe+tDf52/jCCEeLyWFbhvgSGAzO3AXv/A0Stl+8kcgsDhr0DdJ7c9OOc/fH220Twon5WROKRm38m6Yi8CA5+QDs97iVEdBdKytX4eo/cCPel4aEwN2F2A9GtzA0PwrzvT2LVETmT3Mb8nluAEhGRLn9HqSGysKdvAnb8HVg+Gpi5FbD1uKfD3tO/GL0CnHAg+roUZHi4syf+uekCDsdkYX/0dfQPYdo3EbUcU2MVZg4MxL9+v4AlEbGY3NMXxir9uA3XycIJE0InSEGHxecW60/AobwYOPdz0/a1keu/ErXmTKymljXq79Uf7lbuUoPoxvo4iIv+4nWxn7Gq5S6S2Zvbo793f/x06Sc8GfbkTb9Pfnk++zg0lU8PYOqvd95v6Fvy0pj38nBXHlsgL0T0/+zdBXwcZfoH8F827mnctU3d3VIXpEINK7TUsUMOjiJ3cPeHo8BBcajQ0lIKFWpYqSd1d2+8kUYb983/887Es2m2TTZrvy+fZWZ3JzNvZreb2XnmeR7pxGlabpFUDnhKT1/uEaI7EH0cgl1tEZWWJ/XMmzM4mPuLiMjQFecB3Z8EUq4APzwEPHO45Uoq/WdCR4zr6i3NPzO0NeaGBUsHbmM7eeLDyV3vaSBERPfqkd5+cLQ2lw6Gd17SryvqZ3acCXOFOU6lnMKJ5BPQeckXgKVDges7GlnQBHDwAQLUqzVPRJCCCAv7LKz4F1Q7cFp5/7U+r7VosKHSW33fkhrcP/b7Y9gZuxOx2bGIuh2FHy//iOl/TOfLR0Q6L6+oFN+GV2Q3DG8jXbRCRA1TKEykcz3CigPRKClTcncRERmaLc8AqycCX/YB3vcDFvkBX/eVe3JmRLd0SSWLWn+EpBrqFXXUiYhamq2lGZ7oFyA1AFwSEYkxHT2kq4r1gYetBya0noCN1zZi2fll6OXZCzpJ9Jg4vlyuOV5WBNh5Aj1nAuEfVC5QY+GKfT92EaCFE6NE+mxkwEh8MvQTLDq2SGoQXUlkNohgg3heU0QvGTPRvFgFX3tfrH9wvZSN9b/j/0NqQSpaWbVCB5cO+Ge/f2psTEREzWX14Vik5xUjwMUGk7r7cMcSqeGh7j74eMc1JGYV4vdzSZjIfztERIYlNwVw8gP8+gIOXoC9d/W0ZplWTQUccgpLYG9lXjV/J5XLERG1lBkDArF0fxROx93GidhMqfSbvpjVaRY2X98s9XG4kHYBnVw7QafkZwBbnwOu/i7fbzMGmPg1YOsKeHQEtr9Wu4G0g7ccbOgwXmtDJtJnIqgwzG+YlPmUmp8KNxs39HDvofHMhozCDPjZyw1U3xv0Xr3nxTje7PcmxH9E9+Js/G0s3nUNp2IzUVJWjlAPO6ks4oRu6p38FRnV647H40JCFs4nZOFmZoH0eMyiBxr8mYGL9kh9nlR5rK8//vtQZ76YRkB8fxUXpQgvjGgDM2Y3EKnFytwUMwcE4H87rmFJRBQmdPPWmwu7iIhaRMJJYO/7wM1jQFkJ4NYO6PcM0GWqej+fmwqcXg0kngGSzshNmxsro7q4M5BVsVxdPZ8Cxn2q/vinb4QmqB1w6PrvHTj25ki42lmiy7931En0r76+VTwe9X7DB/1ERJrgZm8p1eJdezQOS8Ij9SrgIE7w3R90P36N+hXLzi3DZ8M/g86IOQBsmgdkJwCmFsCo/wB9F4jC9PLzIqjQ7gEg9pDcIFr0bBBllJjZQNQkIrjQ27N3i+zFrKIsnE09i+PJxzG1rZoHxkR3SfR7m7HiGMxNTaTSrPZWZth+MRkv/HxGChyIhqSNuX4rFx/9dVX6ExTkYgtrc1MUlJQ1+nNiW7MGBtV7vIuvI19HI7HqUAxu55cg2M1W7QAXEcmm9wvA1/sicTkpGwdupGFwGzfuGiIiIXo/sGaSfK6k0yTA0gG4/CuwaQ5wOxYIe6Xx/ZR6Bdj9H/mMuksIIPrlleQ3/nOWjkC/p+s/7t1dJ14btQMOa+f2g5O1nLmwdk6/qnNNRES6Yu7gYPx0LA67Lqfg+q0ctPGwh76Y03kOfov6DXvi9+B65nW0adVGuwMqKwUiPgQiPgLKlYBLa2DKCsBLRZ8eEVwIGqyNURJRM/jXwX/hQvoFzOg4A8P9hnOfUrMrLVNi4aZz0veodfP7o5OPfKL/hZGhmPT1QSzeeQ33d/ZCkKvtHdfT2t0O6+b1Q0cfR9hZmmH4x/sQlZrX6PYdrMzx0qjQZvt9SL9kFZRgaUSUNP/iyFCYKvhFluhuS2pP6+WH7w/FSP+WGHAgIqo4Z7LteTlQ8NQf1edKhi4Elo8C9r0PdHxIDiLciVtbYKb4+S6ApT3wRS8g/Xrju9jKERj2us6+FGp3yuoX7FKVeto/xEW639CNiEgbxImKsR09pfnKL5b6ItgpuKo2+/Lzy7U7mNvxwKoH5f4MItjQbTowL1x1sIGI9J7Iqto9dTf+1uNvLJNAGnEoMh2x6fmY0NW7KtggiKCBaN5bqizHhhPxamUz9g12kX6OSF2i2W12YalUwuuBzl7ccUT3YPagIClYt/96Gi4m3qHMBxGRsYgOBzKjgc5Ta58rEUGDIa8CylLg9JrG12PnDgQOlH/OgKgdcKhp8Id78PGOq4hMzW3+ERERNcG8sGBpuuVMApKzCvVqX87tPFeabo/ZjrjsBurxadqlbcC3g4C4w4CFPTD5O2DiV4ClnXbGQ0REeu9IVLo0HRxavwxHWEVpjqPRGRrbfnGZEhtP3sRXe2/ghyOxuJSYrbFtkW65nV8sBRwEZjcQ3Ts/ZxspE00fL+wiItJY+WkhZFj950IqssZjD2pu55cVAWfWAhH/A44vB5LPQ5fc0+VBM/oHYtvZRHy59wY6ejvgoe6+GNfFC+4OVs0/QiKiu9DdvxX6BDnjWHQGVh6Mxuv3t9eb/dfepT0G+QzCgYQDWHFhBd4Z8E7LbbykAPjrDeDECvm+dw9gyneAsxzAISIiulcx6XLZI9F3oS5HG3M421ogJq3x0kj3KjWnCK9sOFvrsSGhblj8cDdp2w0pKi1DcamyVuNh0i/L90cjp6gU7Tztq7JgiejezA8Lxq9nE/HbuSS8OqYtfFvZcFcSkWEqzAZqHiKaWcq3mjIi5amqkknWrQAbFyC9YhlNED00t9Tp4dB6JPDQUsDWRT8DDnMGB0u3qNRcbDmTiB+PxOL9Py5LpZYmdvPB5J6+0LT84lL8dTEZuy6l4FJSNhJvF8DCTIH2Xg54vK//XTUDE3VjP9t95/pY03r54sMpLCdCpA8WDAmWAg4/Ho3Ds8NbS7Wb9cW8LvOkgMPWyK1Y0HUBPG1b4MtxymVgw1NA6mX5/sAXgWFvAmYNn4QhIiJSV05haVXzZlVEiSRNZSWKuuN9g50R6mEvfVcQPZ7Ecf++q6mYs+o4fnl6QIOlxL7eG1nrO4KySI0GfqQzMvKKpYtPBNHDQ8HeDURNIkriDWztgoM30rHiQAz+Na4D9ygRGabFHQDLGseHQxbW75cgghKCaBStiqU9kJ2omfF1ny6XYXJrL5+3Sb0K7FsE3NgJ/PQIMHsHtN18uUkFUIPd7PDyqFDpdiouE29tvoBXN55tkYCDOJn40rqzaGVjjgGtXTG2kyfSc4uw/UIyXvj5DE7FZuLfEzqpta479Z1YdzweydmFCFORAk5EumloqLtUp/farVz8dDQO84c00qRHh3R3745eHr1w4tYJfH/xeyzss1BzGysvB06uBLa/DpQWArbuwKQl1el/REREeu6FkW3qZUKumNEbDy89jOMxmdh7NQXD23mo/NlnhoVgzuCgqvvZ2dnw/VTjQ6ZmIsq+5BWXoZOPA0Z3UP0aE9HdmRcWIgUcfj4ehxdGtJGy1IiIDM5LlwCHGoGEutkN2jb0tdr3fXsBj60Hvr9fLo99fQcQOgba1OSOa2fib2PrmQQprU6kGd/XQo243O2t8Nkj3aQ6guYVzayFV8e0w8SvDmLV4VhM6uGLrn5Oja5LZGaIm6r0a1HrVQQ1RndgCi6RvhBXsImDYVE+YcXBaMwcGAhLM1Poi7ld5uLEzhP45dovUl8HF2sNpMMVZALb/gZc3ladejfxW8COwVUiImpelZkNlZkOdeUWlTaY/aCp44SpPf2kgMOJmMwGAw7i2KHm8UN5MU+s6QvxPW7VoRhpXlwc11AWCxHdnbA2rlKJsivJOVhzNBbPDmvNXUhEhsfKQb41toxQ1EBvsKKchrMfNEGhALo9Lgcc4o5oPeBwT02jRSmlT3Zew9CP9mLKN4dwIyUXr41thxNvjcJXj/VAS+jg7SCVTaoZbBDc7C3xWF9/af5otNyg7l6J5nKlynKpR4VIwSYi/TG+qzc8HaxwK7sIW89oKI1NQ/p79Ucnl04oLCvED5d+aP4NxB4GvhkkBxsU5sDo94DHNjDYQEREGhFY0bshuqKXQ01Z+SVS6ZtA1/r9HTSpVUXvhoKSshbdLrWMJeGR0msrLj4b1tadu52omYjg3bwwucfb94dipF43RERGybmikoaqPg0FmUB+uur+Dpok+kZU9ujUsns6iz7ik3CEX03BE/0Dcej14fhhdl9M6ekr1V/VBeam8hUspiK60wQbTsRL00f6+DXLuIio5Ygg4exBQVUp9UpluV4dyIssB+Hnqz8jqyireVasLAPCP5TT7LJvyg2hRW2/Ac/J0XAiIiIN6FtRvnT/tdR6z0Vclx/rG+Tcovv+THymNGXTU8OTkl2IH47ESvPMbiBqfuO6esPL0UrKJNpyOoG7mIiMk+ihIETurf9c5B55GlCxTEtJOCFPneQL8bXpns4w7X55CLY+N0g6mSdKG+mSMmU5Np1KkHpjDGrt2qQeEVFpeeju7yQ1mSMi/SOChfaWZlIW1p4rKdAnQ/2Gok2rNsgrycPaK2ubvsKsBGDVeGDve0C5EujyCDA/AvBpmaw0IrqHAGH0fuD8Rnkq7hPpqYEhLvB3tsHWs4m4mJhVq5TSF3uuw0xhIl28VElkPIi/3WLaFKJBdFZBSb3Hj8dkYPn+aOniBNEHjgzL1/siUVSqRM+AVlL5FyJqXqLKxKyB+nlhFxFRswkaCrQKBM5vAJLO1S6lFP4RoDCTSxxVyksHUq/J06ZIuQIU3FZdyeLwV4CpJdB+HLTN7F6bRYuD9z/PJyE2Ix/zw4LhZGOBCwlZcLWzhKej9oIQH++4KtUTnNbLF2097z1QIJpFC4/0Vi+7QaQSFpcqq+6LfhZEpF32VuZ4vF8Avg2PxJKISIzUo4aBChOF1L/hHxH/wI+Xf8SMDjNgY25zbyu78juw9Vk5rc/CDnjgY6DrI809ZCJqLpe2AdtfA7JrlINz8AbGfgB0GM/9THrHzFSBRZM7Y8aKY5j27WGM7+YtZUZvv5iM+IwCvDI6VPp+UUnU3v9s93WpIelLo0Jrrevv689WzadmF9V77M0H2sO5olyS6DEn/v4PDHGFbytrKcBw9VYu9l9PhcLEBO9N7AQfJ+sW2APUUpKyCrD2WJw0z+wGIs1e2PX57uuITM3D7ispGKVH37OIiJqFqRkw/gvgh0nAyvuATpMBS3vg8q/A7Vhg+FuAa40+N8eWAuGLgCELgWGv117X5qer53Nv1X9s9LuAbUW5pIubgYOfAcFD5EwGUwsg5bKcVWGiAB5cDDj56WfA4XJSNh5ffhQOVma4mVmAR3v7SwGHvy4mIyGzAJ883E3tdXX/zw5k5qt/cv6nuf1UNngW1h6Nk65o6ejtgLfHdcS9EsGCP84nwdbCFA928VbrZ77eGyl9MaqkLMq/5+0TUfN5amAgVhyIlhpDnozNlK520xejA0bjK4evEJsdi/VX12Nmp5l3t4KSQmDnP+U/bIJXN2DKipavI0hEdxdsWP+kaE9b+/HsJPnxaas1F3RYMxmwdQMe+la+HxUObJgBvHJDPqAmaoIBIa7YsGAAFu+8JgUCSsqUUhbx30e1xcTuPmqv55dTN+/42Isj21QFHMR3hhupubiYkIWj0RnSBULi4ihxfC8ytbv5OfE1NTDiO5m4CKxPkDMGNPCdkYia58Kux/r5Y0l4FJZGRDLgQETGKSgMmPUXsO+/wMUtQFkx4N5ODjZ0mab+es6uvfNjQxdWBxyCBgNpV4Gks0DMQaC0ELBzBzpNAvo9C/j2hC64p2+P//fbJUzt6YvX72+Pjv/aXvX40LZu+NtPZ+66sWtukfplAkRTaFXWn4jHm1vOo52nPdbM7gvbJvST2HY2UWoy9nAvP7XX88ywEMwZLKcVCtnZ2fD99J6HQETNxMPBCg9198G6E/HSwfCSJ3rpzb41VZhidqfZ+Nehf2HVpVV4tP2jsBTpcepIvQpsnAXcuiDf7/8cMOJtwEw+CUNELaS8HChR8yIEUTbpz3/UDzbIKxIdXuTMh+ChgMK08fWJrChRY1Jd9l7AbfnK4KoD6NJiIP4IEDhI/fUQNUCc4F81q0+j+0dkNdTNbKgUs+gBtfdvv2AX6UbG4WZmPn4+zuwGopYiyipVXth1Ki4TPfz158IuIqJmI07wT/+l8eWGvV4/s6HSO3fRt1N8L9OD72b3dFb+/M0svD+ps8oTe6m5cmqzuv49oROaav3xeCzcdA5t3O3w45y+aFVxVVNT1ic8fBfNoi3NTKVbpfJi8yaNgYiaz9ywYCngsOPSLUSm5iKkRtkGXfdg8IP4+uzXSM5Lxubrm/FIu0caP7l5+gfgz9fkk5w2rvLVym1GtdSQiagm8e/wv+plSzauXC6ztEjN45M3EgELW/VXL8o2xR2pvi+CFWaWQF7a3Q+ViKiFfbX3BkrKyjGwNQNNRC1BnP+Z2M0HG07exNLwKHz7hG5cVUtERNDPptGW5grkFJbWezwqNQ8uTTzZfy/Bgdc2nZNOIK6d2w8udmpe/duAK8nZOHszC6EedozQExmI1u52UpqvOBe/fH8U9Im5qTme6viUNL/ywkqUKO9Qgk40Dtr4FLDtefkkp7gK+ulDDDYQkfoZDjlJ1fdF87PC24BfX+5BItJpcen52HBCLq310kjV2TFE1PzmhQVL078uJSM6LY+7mIiI7j3DQZy4Ew2Cvnq8R9UFcAm3C/DB9isY28kTLWXd8Tgs3HS+KtggarLeSUFxmTROawvTBhvEVTaLntZL+w02iKj5LBgSjJ2XbuGXkwlSmQZ3e+01t79bk9pMwtJzS5GYl4jfo37HxNYT6y8Ufwz4ZbZcDkVhBgz/JzDgb4DinuLKRNRcRFkjkWmgjthDwI9TGl/u8Y1AwAD1tn03RIZDcS5QmC03mP/rDaDzNMDB6+7WQ0TUwr7Ycx2lynKEhbqhV6Az9z9RC2njYY/h7dyx50qKdGHXew/Vr4RBRETG557ORL1xf3tk5BWj5//tRGGpEg8vOYyhH+2V+h28OqYtWsKhG2lSsEFcsSyagq05Eis1oat5E02sazoTfxsjPwnHy+tU95kQDca2nE6AhakCk3r4tsjvQUQto2eAM3oFtEJxmRLfH4zRq91uZWaFJzuKJrLAd+e/Q5mo815JzO//GFgxVg42OAXITYsGvchgA5EuEFdliLJG6txChssn/UWvBtUrAxx85OXUWd/d9G+ozHAQRNmmXf8CcpKB+z9q8i4gItIkcVX1ptMJ0vzLDfT+ICLNZzlsPHkTaXdZYpuIiAzTPWU42FuZY+PTA3AoMg0XErKgLAc6eTtiUBtXtBSRqSCCDcLaozUaHNYwuYcvxnRUP+Nix6VkZOaX4IEuXnBu4dJQRNQyB8MnfjiJH47E4plhrWHXhObyLe3htg9LwYaY7BjsjNuJsYFjgewkYPM8IDpCXqjTFODBTwArR20Pl4juhWgEPfYDYL0IMJrUaR5dETwYu0i9htH3Qgp2iAOiN4G068Cs7YCVg2a2RUTUTETmfZmyHCPauUuNyYmoZfUNckZXX0epNPXqw7EM/BER0d0HHJTKcilyvf1iMm5m5sMEJvBztoabnSXKy8thcrdX092jqb38pNvd6B/igphFDzT4/INdvKUbERmmke09EOxmK/Wb+flYHOYMlq/G0Qe25rZ4vP3j+ObsN1h+bjnGFAEmW58B8tPlsin3/w/o9tjdX9FMRLqlw3hg2mpg+2typkHNYIAINojnNcXGBTC1BG7HA0/9WR2AICLSUTdScrD1jJzdIEpmElHLE+eA5oWF4Nm1p/DD4RiplK2Nhf5c2EVERM3vrv4KiIDCnNUnsPdqCtp7OqCtp4P02I2UXLyy8awUhFj2ZC8NDJOIqOkUChPMDwvGa7+cx3cHojFjQCDMTfWnx4EIOKw9vxITbhyByamd8oOenYEpKwHXNtoeHhE1FxFUaPeA3NMh9xZg5yH3bNBUZkMlEbD8Z4pmt0FE1Iw+231DyrYf3cEDnXyY4UmkLaKXp7+zDeIy5Abu4nsWEREZr7sKOGw4eRPHojPw45y+GBDiWq+nwrwfTuKXkzcxuSf7HxCRbprY3Qf/23ENSVmF+PVsol71a3HMScUvqdnwyM6R7pf3XQCTUf8BzCy1PTQiam4iuBA0mPuViKgBV5Nz8Ns5ORPsxZHMbiDSJlOFCeYODsI/t17E8gNReLyvP8z06MIuIiJqXnf1F0CcnHtmWEi9YIMwoLUrnh4agi0VKa1ERLrI0swUswYGSfNLwqOkLC2dJ8Z4+kdgSRg8spORqTDFsx5uONrtIQYbiIiIyCh9tvuadIh0f2dPdPBmvxkibZvS00/qhRmfUSBVvyAiIuN1VwGHy0k5GBLq1uDz4jmxDBGRLnusr7/UMPrqrRzsu5YKnVaYDfwyBxD9GkrygMDB+GHIfETYWGPZuWXaHh0RERFRi7uUmI0/zidLleCY3UCkG6wtTPFEvwBpfmmEnlzYRURE2g84ZBUUS82hG+Jmb4nsgpLmGBcRkcY4WptLQQdhSXik7u7pmyeBJYOBCxsBE1Ng+D+BJ7dias+/wczEDMeSj+FMyhltj5KIiIioRS3edU2ajuvijVAPe+59Ih3xZP8AWJopcO5mFo5EZWh7OEREpA8BhzJluVSbr8GVmZigVKlsjnEREWnUUwMDYaYwkQ6Ez8Tf1q29LT5HD3wKrBgNZMYAjv7ArO1A2CtSXXcvOy+MCxknLbrsPLMciHQRr+rTf3wNiXTT+ZtZ2HnpFsTX0r+NaKPt4RBRDS52lpjaS+6RtzRChy/sIiIi3WkaLRLiXtlwFhZmquMUxaUMNhCRfvBytMaEbj745dRN6WD468d7Qifk3AI2zwei9sr3O0wExn0GWDvVWmx259nYGrkVETcjcCXjCto5t9POeImoFnNzc2man58Pa2tr7h09Jl7Dmq8pEelWdsPEbj5o7W6n7eEQUR1zBgXjx6Nx2Hs1Fddu5TALiYjICN1VwGFyDzlSfSeT1FiGiEgXzAsLlgIO2y8kIyYtD4Guttod0I1dwOYFQF4qYGYN3PcB0ONJSAWK6whwCMCYgDH4M+ZPqZfDx0M/1sqQiag2U1NTODk5ISUlRbpvY2MDExX/hkm3MxtEsEG8huK1FK8pEemG03GZ2HMlRcq6f57ZDUQ6SXynGtvRE39eSJZ6OfxvaldtD4mIiHQ54MA/FERkSNp62mN4O3fpi+vyA1F4d2Jn7QyktBjY8x/g0BfyfY9OwJQVgFvbO/7YnC5zpIDDztidiMqKQrBjcMuMl4juyNPTU5pWBh1IP4lgQ+VrSUS6YfGu69J0UncfBGn7QhEiuuOFXSLgsPVMAl4Z3RaejlbcW0RERuSuAg5ERIZmfliwFHDYcOImXhwZClc7y5YdQHoksHEWkFTR/LnPPGDU/wHmjR+Uh7YKxVC/odgXvw/fnf8O7w16T/PjJaJGiYwGLy8vuLu7o6SkhHtMD4kySsxsINItJ2IyEHEtVerBxd4NRLqtu38r9AlyxrHoDKw8GI3X72+v7SEREVELYsCBiIyaOBDu5uckNY5efSgGL4++c1ZBszq7Dvj9ZaA4F7BuBUz4Cmj3wF2tYl7neVLA4feo3/FMt2fgY+ejseES0d0RJ6x50pqIqHl8slPu3TC1lx/8nG24W4n04MIuEXBYezQOzw1vDXsr9kQiIjIWqrs/ExEZ0ZXIC4bIpYhWHY5FXlGp5jdalCP3atg8Tw42BAwEFhy862CD0NmtM/p59UNZeRlWXlipkeESERERadPhyHQcikyHuamJdOKSiHTfsLbuUmP3nKJS/HQsTtvDISKiFsSAAxEZvVEdPBHoYoOsghKsPxGv2f2ReBpYEgac/QkwUQDD3gRm/Ao43ntmwrwu86Tp5uubkZLPmvFERERkWI3cF++Ssxse6e0PHydrbQ+JiNSgUJhg3mD5wq4VB2JQXKrkfiMiMhIMOBCR0TNVmGBumHwwvHx/NErKNHAwrFQCh74Elo8CMqIAB19g5h/AkH8ACtMmrbqXRy90c+uGYmUxVl1c1WxDJiIiItI2kdkgyrJYmCnwzLAQbQ+HiO7ChO7ecLe3RHJ2IX49m8h9R0RkJBhwICICMLmHL1ztLJBwuwB/nE9q3n2SmwqsnQrseBNQlgDtHgQW7AcC+jdbWajKLIcN1zYgszCzWdZLREREpO3shsreDY/18YeXI7MbiPSJpZkpZg4MlOaX7Y+S/k0TEZHhY8CBiAiAlbkpZg6QD4aXhDfjwXDkXuDbgcCNXYCZFfDAJ8DDawAb52bd74N8BqG9c3sUlBZgzeU1zbpuIiIiIm2IuJ6Gk7GZsBTZDUOZ3UCkjx7vGwBbC1NcSc5B+LVUbQ+HiIhaAAMOREQVpvcLgI2FKS4lZePAjbSm7ZeyEmDn28APDwG5twC39sDcvUDv2SIlodn3uchymNtlrjT/0+WfkFOc0+zbICIiImrR7IYdV6X5J/sHwN3BijufSA85WpvjkT7+0vzSiChtD4eIiFoAAw5ERBWcbCykZoSVWQ73LCMaWDEGOPip+LoM9HwKmLsH8Oig0X09wn8Egh2DkVOSg3VX12l0W0RERESatOdKCs7ezIK1uSnmD2F2A5E+mzUoSOqbJ3qynL+Zpe3hEBGRhjHgQERUw+zB8sGwyHC4kHAPB8PnNwJLwoCEk4CVIzBtNTDuU8DCRuP7WWGiwJzOc6T5Hy79IJVXIiIiItLn3g0zBgTC1c5S20MioibwcbLG+K7e0vySiEjuSyIiA8eAAxFRnYPhcV28pPkld5PyW5wHbHkW+GU2UJQN+PUDFhwEOkxo0f17X9B98LHzQUZhBn659kuLbpuIiIioOey4dAsXE7Oluu/zwoK5U4kMwNzB8r/lP84nIT4jX9vDISIiDWLAgYiojnlhctr+7+cS1TsYTjoHLBkCnFkDmCiAIa8BM38HnPxafN+aKcwwq9MsaX7lxZUoLitu8TEQERER3SulshyLK7IbnhoYBGdbC+5MIgPQwdsBg9u4QlkOfHcgWtvDISIiDWLAgYhIxcFwWKibdDC8fP8dshzKy4Ej3wLLRwDp1wF7b2DGr8CwNwBTM63t14mtJ8Ld2h0p+SnYFrlNa+MgIiIiulvbLybjSnIO7C3Nqq6IJiLDML/iwq51x+ORmccLo4iIDBUDDkREKiyoSN9fdyIeGaoOhvPSgZ8eAba/Bogsgrb3A08fBAIHaX1/WphaYEbHGdL8d+e/Q6myVNtDIiIiImpUWY3sBtFXy9HGnHuNyIAMbO2CDl4OKCgpw5ojsdoeDhERaQgDDkREKvQPcUFnH0cUlijxw+E6B8PREcA3A4Br2wFTS+D+/wGPrAVsnHVmX04JnYJWlq1wM/cmtsds1/ZwiIiIiBr127lEXE/JhYOVGWYNCuIeIzIwJiYmmD9EvrBr1eEYFJaUaXtIRESkAQw4EBGpcTBcUFwGlJUAu/8PWDUeyE0GXEOBubuBPnPFD+jUfrQxt8H0DtOl+eXnlkNZrtT2kIiIiIgaVFqmxGe7rkvzolG0gxWzG4gM0f2dveDjZI203GJsOpWg7eEQEZEGMOBARNSAsR094e9sI5VU+vPAEWDl/cD+/4nmDUCPJ4F5+wDPzjq7/x5t9yjsze0RmRWJPXF7tD0cIiIiogZtO5uIqLQ8ONmYY+ZAZjcQGSpzU0VVBtOy/VFSKTUiIjIsDDgQETXAzFSBOYODcL/iCEZFTAFuHgMsHYEpK4HxXwAWtjq97+wt7PFIu0ek+aXnlqJcNLkmIiIi0sXsht3Xq5rK2lmaaXtIRKRBj/T2g6O1OaLT8rDz0i3uayIiA8OAAxFRQ4rz8ditj/G1xeewRz4ynbsCC/YDnSbpzT57osMTsDazxuWMyziYeFDbwyEiIiKqR5RViU3Ph4utBWYMCOAeIjJwtpZmmN7PX5pfGhGp7eEQEVEzY8CBiEiV5AvA0qEwO7Ma5TDBl6UT8BT+g3In+cBYX7SyaiU1kBaWnVum7eEQERER1VJcqsTne+TshqeHhsDGgtkNRMZgxoBAWJgqcCruNk7EZGh7OERE1IwYcCAiqkmUHTq2DFg2HEi7Cth5ImfaRnxp8ijOJObhcFS63u2vmR1nwlxhjlMpp3Ai+YS2h0NERERUZePJm7iZWQA3e0s83pfZDUTGwt3eCpN6+EjzSyKitD0cIiJqRgw4EBFVys8Afn4c+OMVoKwIaDMGePogHDqMxMO9/KRFloTr38Gwu407JraeKM0vO88sByIiItINRaVl+LIiu+GZoSGwtjDV9pCIqAXNGRwsTXddvoXI1FzueyIiA8GAAxGREHMQ+HYQcPV3wNQCGLsIeGwdYOtadTCsMAHCr6XiclK23u2zWZ1mwdTEFIcSD+FC2gVtD4eIiIgI64/HIzGrEB4Olni0j36VrSSipmvtboeR7T2kJPPl+/Xvwi4iIlKNAQciMm5lpcDe94FVDwLZCYBLa2D2TqDf04CJSdVifs42eKCLtzS/VA9Tfn3tfXF/0P3SPHs5EBERkbYVlpThy703pPnnhrWGlTmzG4iM0fwhcpbDL6cSkJJTqO3hEBFRM2DAgYiM1+14YNU4IHwRUK4Euj0OzAsHvLupXHx+mHwwvO1sIm5m5kPfzOk8ByYwwZ74PbieKZcvICIiItKGtUfjcCu7CD5O1pjWWy5dSUTGp1dAK3T3d5IayK86FKPt4RARUTNgwIGIjNPlX+USSnGHAAt7YNJyYOLXgKVdgz/SyccRA1u7oExZjhUH9O9gONgpGCMDRkrzy88v1/ZwiIiIyEgVFJfh632R0vxzw1vD0ozZDUTGysTEpOrCrjVH4pBXVKrtIRERURMx4EBExqWkAPjtZWDddKDwNuDdA1gQAXSZqtaPzw8LkaY/H49DVn4J9M3cznOl6faY7YjLjtP2cIiIiMgIrTkSi7TcIvg5W2NKT19tD4eItGxUB08Eudoiq6AE647Ha3s4RETURAw4EJHxSLkMLBsOnPhOvj/wBWDWX4CzfEWNOga3cUV7LwfkF5dhzdFY6Jv2Lu0xyGcQlOVKfHehYj8QERERtRBx9fK34XJ2w/PD28DclF9JiYydqcIEcwYHSfPfHYhGaZlS20MiIqIm4NEdERm+8nLgxApg6VAg5RJg6w5M3wSM+g9gZnHXKb8LKhqbrTwYLTU81Dfzu8yXptsityE5L1nbwyEiIiIjsvpwLNLzihHoYoNJ3X20PRwi0hGTe/jCxdYCCbcL8Pv5JG0Ph4iImoABByIybAWZwPongd9eAkoLgZARwNMHgdYj7nmV93f2khocpuUWY9OpBOibbu7d0NuzN0qVpVh5YaW2h0NERERGIqewBEsi5OyGF0a2gRmzG4iogpW5KWYMCJTml0ZEoVxcNEZERHqJAQciMlyxh4FvBgGXtwEKc2D0u8DjGwE79yatVqT+V6b8LtsfJTWR1tdeDr9c/wVpBWnaHg4RkXFIOAmsmQIs8gfe8wKWDgPObVD/53NTgf0fA+ueAD7tDLzjKN80vV2iZvL9wRjczi9BsJstxndldgMR1fZEvwBYm5viYmI2DkWmc/cQEekpBhyIyPAoy4DwD4Hv7weyb8o9GmbvAAY8Dyia52NvWi8/OFqbIzotDzsv6V9Zon5e/dDZtTOKyorww6UftD0cIqJ7UqYsw/Hk4/gj6g9pKu7rrOj9wIqxQNxhoMMEoNcsID8d2DQHiPifeutIvQLs/g9w+VfA1AIwt2mZ7RI1A9EMVlyoIbw4MlSq2U5EVFMrWwtM6yU3kl8SIX9eEBGR/mHAgYgMS1YCsGo8sPc9oFwJdHkYmB8B+PRo1s3YWprhyf4B0vw34fqX8it6UVRmOay7ug5ZRVnaHhIR0V3ZFbsLY34Zg1l/zcJr+1+TpuK+eFznlJUC254Xn77AU38A478Axrwnl/hzaw/sex9Il8vM3JFbW2DmH8Dr8cDzJwEHn5bZLlEzWHEgGtmFpQj1sMMDnb24T4lIpTmDgyHikRHXUnE5KZt7iYhIDzHgQESG48ofwLcDgdgDgLkt8NASYNJSwNJeI5sTNUYtzBQ4G38bx6IzoG+G+A1Bm1ZtkFeSh7VX1mp7OEREahNBhZf3vYxb+bdqPZ6SnyI9rnNBh+hwIDMa6DwV8Opa/bj4+zTkVUBZCpxe0/h6REnAwIHq/11rru0SNdHt/GIp4CAwu4GI7sTP2Qb3VQQlRS8HIiLSP2baHgARUZOVFAI7/wkcWyrfFydVpqwEXEI0unNd7SwxtacvfjwaJx0M9w12gT5RmCikLId/RPwDP17+ETM6zICNOuU5iIi0SJRNWnRsEcpRP7NMPGYCE3xw7AMM8xsGU4UpdELMAXkaMqz+cyHD5WnsQcPZbgNEgH7xrms4FZuJkrJy6Ur3WYOCMKGberX803KLsO54PC4kZOF8QhZuZhZIj8csekCj26WmW74/GjlFpWjnaY+xHT25S4nojuaHBeP3c0n49WwiXh3TFt5O1txjRKSbRK+0ve8DN48BZSWAWzug3zNAl6nq92g7vRpIPAMknQFux8mPv5Ol2e1qGDMciEi/pV4Flo+oDjb0fw6YvUvjwYZKcwcHw8QE2H0lBddu5UDfjA4YjQCHAKmk0vqr67U9HCIilUTZOvE5dSPzBlZfXF0vs6HWsihHcn4yTqWc0vzeLMyufSstUr1cRkXZIlV/m6xbATYumiltpK3tqnA4Mh1Tvz2M49EZuL+zF6b380dGfjFe+PkMvtp7Q611XL+Vi4/+uortF5NhYaqQGou2xHapaTLyirHyoJzd8PKoUCjYu4GIGtHF1wn9g11Qqiyvyo4iItI57NHWIGY4EJF+Ej0TTv8A/PkaUJIP2LgCD30LtBnVosMIdLXFfZ088cf5ZCnL4X9Ta5Ss0APi6t/ZnWbjX4f+hVWXVuHR9o/C0tRS28MiIiNSUlaClIIUqRxSzZsIKqTmp1bdLywrvKv1ip/VuMUdAMsajW+HLASGvV5/ORGMECwdVK9HlDjKTmz+8Wlru3WUlimxcNM5qZXEuvn90cnHUXr8hZGhmPT1QSzeeU0KBgS52t5xPa3d7bBuXj909HGEnaUZhn+8D1GpeRrfLjXNkohI5BWXoZOPA0Z18ODuJCK1zBsSjMNR6fjpWByeH9EGjtbm3HNEpDvq9kqrLF86dCGwfJTcK63jQ41fDFvZo82ri3xs/kUvIP265rerYQw4EJH+KcwCfn0RuLhJvh88FHhoKWCvnS+x88NCpIDD1jMJ+PvoUHg56lfK74PBD+Lrs18jOS8Zm69vxiPtHtH2kIjIQLISbhfdrhc8EPNimlog388oVL8HjoOFA+zM7ZCY1/hJcjcbN2jcS5cAhxon880YsFXlUGQ6YtPzpTKElSf9BRE0eH54Gzz/02lsOBGPf4xtd8fd7WZvKd1aert071JzirD6UGxVdoOJSAslIlLD0FA3tPWwx9VbOVh7NA5PD9XuyTMiIpW90rpNV90rbeMsuVfayLcb79Embi29XQ1jwIGI9Ev8ceCXWXJdO4UZMPwtYMALgEJ7FeK6+jmhb5AzjkZnYOXBGLxxf3voE3NTczzV8Sm8f+x9rLiwApNDJ8NcwSuIiKhhhaWFUgChZvCgcr7mrURZot7nkMIc7jbuVTc3azd42HjUfszGDdZm1lIPhzG/jJHWr6qPg+jhIH62h3sPzb+EVg7yTZ3lhKKKjIO6inIazkJoCm1tt44jUenSdHBo/SBQWBv5MfE31FC2S9WWhEeioKRMOlYa1vYuvkwTkdETAcq5YcF4ZcNZqSzbrEGBsDTTkd5MRETs0XZHDDgQkX5QKoGDi4E97wHlZYBTADBlBeDbC7pgwZAQ6aSFuPrm2WGt9S7ld1KbSVh6bimS8pLwW+RveKjNQ9oeEhFpgbJcKWUcNFTaqDKokF3cwAlsFZytnKUAQmXgQAQDRPCgcl5MnSyd1L7yWZSCW9hnIV7e97IUXKgZdBD3hdf6vKY7DaMF54qrMkW/BO/utZ8ryATy0wG/voaz3Tpi0uWyR0Eu9UsXOdqYw9nWAjFpeQazXZKlZBfihyPMbiCieze+qzf+99dVJGcXYuuZREzr5cfdSUS6gT3a7ogBByLSfdlJwOb5cuqY0GkK8OAngFV1eQRtG9pWv1N+rcysMKPjDHxy8hN8d+E7jA8Zr1sn64ioyfJL8quCB/WyESp6KKTlp6G0vFSt9VmZWlVlHtQMHtTNVLAwtWj2V29kwEh8MvQTLDq2qFYDaTEGEWwQz+uUwIHAgU+AyL1A5ym1n4vcI08DBhrOduvIKZTfU/ZWqr96iBJHyVmFOrfdotIyFJcqa6xPvYwdkn29LxJFpUr0DGiFsDau3C1EdNcszBR4amAg3v/zCpZFRGFKD182nicizRN90Gp+hRFlU+uWTjXyHm2NYcCBiHTbtb+ALU/LV2Ga2wD3fwR0exzQsRrA4srceWHB+Lsep/xOazsNy88vR2x2LHbG7sTYoLHaHhIRqaFUWYr0gvR6wYO62Qm5Jblq7U+RJeBi7VIdOLB2V5mdIPopaLMeuwgqDPMbhlMpp6TfUYxLlFHSyWBp0FCgVSBwfgPQd77cFK6ypFH4R3KJQPG3rVJeuvx3z8YFsHVpue1SLV/vjcRnu6ub9imL8rmH1JSUVYC1x+Kk+b+zdwMRNcGjff3xxZ4buJ6Si71XUzCiPZvPE5GGLe4AWNb4njNkITDsde72u8CAAxHpptIiYNc7wJGv5fuenYEpKwHXNtBV40TK746rSMoqxNbTiZjWW79Sfm3NbTG9/XSpgfSy88swJnAMmzsSabnpsggS1Gq0XCc7QdxPK0yTSiGp+++8bn8EcaK+5n0RbNCXPi4iuNDbszd0nqkZMP4L4IdJwMr7gE6T5auPLv8K3I6V+xG5tq5e/thSIHyR6i83m5+uns+9Vf+x0e9WBynudrsaUplhUJlxUFduUWmDWQja3O4zw0IwZ3BQ1f3s7Gz4ftrswzRIX+29IWWHiB5X/UOaEDQjIqPnYGWOx/r6Y2lEFJZERDHgQESa99IlwKFGBkHd7AbByHu0NYYBByLSPWk3gI1PAcnn5Pt9nwZG/Vv1h7yOpfzOHhSEd3+/jCURkZjSU/9Sfh9r/xi+v/g9rmVeQ/jNcAz1G6rtIREZpJKyEqnRct0myzWzE8StoLRArfWZmpjC1dq1VgaCqn4JIuBAWhIUBsz6C9j3X+DiFqCsGHBvJ5/07zJN/fWcXXvnx4YurJ0V0VzbbYLAih4K0el56OxbuxxiVn4JMvKKpbI7urZdkalYM1uxvFg/AnHadjMzH+uOx0vzLzG7gYiagSirJLLIj0Vn4Ez8bXTzc+J+JSLNESf1K0/sN8TIe7Q1hgEHItId5eXAmbXAH68CJXmAtTMw8Rugrf6U9nmkj79UfiEyNQ+7r6RgVAf9Svl1tHTEw+0exsoLK7Hs3DIM8R3CLAeiu8xKyCrKkksZVQQUamYnVN4XjZnVZW9hXzsjoWaGgq1c8kg0ZtbJUkJUm29PYPovje8VkdXQUNr2O1ma266G9A12ker577+WKjUArSnieqq8TJCzwWzX2InshpKycgxs7YJ+wcxuIKKm83K0xviuPvjl1E0sjYjE14/35G4lIu0y8h5tjWHAgYh0g2h88/vLcp1pIXAwMGkp4FD7BIGuEw0op/cLwDf7IrEkPFLvAg7Ckx2exNrLa3Eu7RyOJh9FP69+2h4SkU4oKiuqn5FQ5yaCDGI5dYiyRZUBhIYyEsS8tZm1xn83Ik0aGOICf2cbbD2biJkDA9HR27GqpNEXe67DTGEiZQVWEpkH4uZsayHdWmq71HRx6fnYcOKmNP/SyFDuUiJqNqJfngg4bL+QjNj0PARUZLEREWkFe7TdEQMORKR9N08Cv8wCMmMAE1Ng2BvAoJcAPb1a96kBgfhufzROxGbiREwGegXq19WToizLpDaT8NOVn6QsBwYcSJPKlGVab/or+h9kFmbWbrSsIjvhdtFttdfZyrJVddPlBm5Olk5QmCg0+rsR6QIzUwUWTe6MGSuOYdq3hzG+m7cUoN9+MRnxGQV4ZXQogt3sqpZfdShGyhZ8YUQbqSRPTX9ff7ZqPjW7qN5jbz7QvipIcbfbpaYTgZxSZTnCQt307viHiHRbW097DG3rhn1XU7F8fzT+b2InbQ+JiIyZkfdoawwDDkSkPUolcOhzYM//AcpSwNEfmLwc8Nd+vbmmcHewwqQePvj5eLzU2Ewfv3A/1fEpbLi6AceSj+FMyhl0c++m7SGRAdoVuwuLji2STupXElf1L+yzECMDRjbLNvJL8usFD1RlJZSKzyA1WJpa1g4cWFdMbeWMhMqMBQvTe78qm8gQDQhxxYYFA7B45zX8di4JJWVKhHrY4++j2mJidx+11yOubr3TYy+ObFMrK6K5tkuNi07Lw6bTCdL8y3UCRUREzZXlIAIOG07GSwHppmTBERE1mRH3aGuMSbkoNkzNLjs7G46OjsjKyoJDzc7mRCTLuQVsWVBdY67DRGDcZ4C1YTQAi0zNxchPwqX5nS8NQWt3/buC8l8H/4XNNzYjzDcMX434StvDIQMMNry872WUo/ZhiAnkRuufDP3kjkEHkRmRXpheL3ggZSdU9EoQt5ySHLXGI7brYu1Suz+CipuDhQP7mhCP84wUj+/v7KV1Z7D5dAJGtHPHdzN7t9CrQkTGRJy+Gv/lQZxPyJICzC+ydBsRNSMe6zUfZjgQUcu7sQvYvADISwVEbfL7FgE9ZgAm8olGQxDiZodR7T2w49ItLN8fhUWTu0DfzO48G1sjtyLiZgQup19Ge5f22h4SGQgRLBCZDXWDDULlY+8dfU/qXZBWkCZlINzKq5GdUJCC9IJ0lJWXqbU9GzObev0Rat4XUxFsED0ViIjo7t1IycHWM3J2Q90yWEREzcXExETKcnj+p9NYfTgW88NCYG2hn2V4iYgMGQMORNRySouBPf8BDn0h33fvCExZIad+GaD5Q0KkgMOmUwlSaQFRakmfBDgEYEzAGPwZ8yeWnV8mXXFO1FQlZSXYEbOjVhklVUSgYcGuBXdcxtTEVAoU3CkjQZQ8srPQvwwjIiJ98tnuG1CWA6M7eKCTj9ycm4hIE+7r5AnfVta4mVmAjSfj8UT/QO5oIiIdw4ADEbWM9Ejgl9lA4mn5fu+5cuMbc/06CX83ega0Qu/AVjgek4mVh2Lw2lj9C6zM6TJHCjiI8jdRt6MQ7BSs7SGRjisuK0ZyXjISchOQmJuIxLxEeZqbKD0mMhRUZTaoIgIJwY7BKjMSxH1nK+cWbzBNRES1XU3OwW/nEqV5ZjcQkaaZmSowd3Aw3t52EcsPROOxvgEwVRhOpjwRkSFgwIGINO/sOuD3l4HiXMDKCZjwFdD+QaPY8/PCQnA85gTWHInFM0NDYG+lXyVbQluFYpjfMOyN34vvLnyH9wa9p+0hkQ4EFJLykmoFESqDCmJe9E9oLKAgSheVKEsa3db7g99Hb0/WASci0mWf7b4G0RXwgc5eaO/F3nVEpHlTe/li8a5riE3Px18Xk3F/Zy/udiIiHcKAAxFpTlEO8MerwNmf5PsBA4FJSwFHX6PZ66JxYoibLSJT8/DzsXjMDdO/DIF5XeZJAYffo37H012fhq+98bx+xhxQqMpQqAwqVMyLfgqNBRRE7wVvW29428k3HzsfeNl5wcfWR7rvaOGIsZvGNpjtIBo4iyyGHu49NPibEhFRU11MzMIf55OlNlwvjGzDHUpELcLGwgxP9gvA53tuYElElFRmSfR3ICIi3cCAAxFphiidtHE2kBEJmCiAIQuBsFcAIyt/olCYSM3M/vHLOXx3IBozBgTCwkwBfdLJtRP6e/XH4aTDWHlhJf7Z/5/aHhI1QVFZEZJy5QyFhLwElQGFxoiAgggiiOCBl61X1XzltJVlq0a/9C3ssxAv73tZCi7UDDqI+8JrfV5juSQiIh336a7r0nRcF2+EethrezhEZESeHBAoBRvOxt/GsegM9A120faQiIioAgMORNS8lErgyNfArncAUTLFwReYvAwIGGC0e3pCd2/8b8dVJGcX4teziZjcU/8yBOZ2mSsFHDbf2Iz5XedL9fNJNxWWFlaVPKqVpVARXBDNmO8moCAyFeoGFJwsnZp8FdnIgJFSI/JFxxbVaiAtMhtEsEE8T0REuuv8zSzsvHQLonT630Ywu4GIWparnaX0vWrt0TgsjYhiwIGISIcw4EBEzSc3FdjyNHBjp3y/3YPA+C8AG2ej3suWZqaYNSgIi/68giURkZjUw0fvUn57efRCd/fuOJ1yGqsursKrvV/V9pCMOqBQtxFzzebM6gQUbMxsagUQqoILYt7WB46Wji3yHhVBBdEj5FTKKan3g2gILcoosRE0EZHuE/XThYndfNDa3U7bwyEiIySaR/90LA67r6Tg+q0ctGGmFRGRTmDAgYiaR+ReYPN8IPcWYGYFjPkv0GsWpKK+hMf6+uPLPTdw7VYu9l1NxbB2+pUhIE4+z+08F8/sfgYbrm3AnM5z0MqqlbaHZZAKSgukkkd1AwmVwYX0wnS1Ago+9j5VPRPqBhccLBx0JuglggtsDE1EpF9Ox2Viz5UUmCpMmN1ARFoT5GqL0R088NfFW1i2PwofTunKV4OISAcw4EBETVNWAux5Fzj4GSDqsLu1B6asADw6cM/W4GBljsf7+kt1Rr8Nj9S7gIMwyGcQ2ju3x+WMy1hzeQ2e7/68toekl/JL8quaMkuBhYpSR5UBhYzCjEbXYWtuWzs7oU6DZl0KKBARkeFZXNG7YXIPHwS62mp7OERkxOaFhUgBh82nE/D30W3h4WCl7SERERk9BhyI6N5lxsiNoRNOyPd7PiVnNljYcK+q8NTAIKw4GI2j0RnSlYHd/VvpX5ZDl7lSo9+fLv+EmR1nwt6CDSJVBRRUZSZUPqZOQMHO3K5WmaOafRTEjQEFIiLSlhMxGYi4lgozhQmeH87eDUSkXT0DWqFXQCuciM3EyoMxWHhfO74kRERaxoADEd2b8xuB314CirIBK0e5V0OHCdybd+DpaIUJ3Xyw8eRNqbHZN9N76t3+GuE/AsGOwYjKisK6q+uk0krGHFCoDCTUbM6cWZTZ6DpEoKYyK6FW/4SKeRFQICIi0kWf7JR7N0zt5Qc/Z15kQkTaN39ICE6sPoEfj8biueGtYWfJU11ERNrET2EiujvFecAf/wDOrJHv+/UDJi8DnPy5J9UwLyxYCjhsv5iM6LQ8qe6oPlGYKKQgwxsH3sDqi6vxWLvHYGNuWCcb8kryVGYmVM7fLrqtVkChZqmjmuWPvOy8GFAgIiK9dDgyHYci02FuaiKd1CMi0gUj2rkjxM0Wkal5+PlYHOYMDtb2kIiIjBoDDkSkvqRzwMZZQLqo22sChL0KDHkNMOVHibpCPeylA+LdV1KwfH8U3nuos969A+8Lug9fnflKOgH/y/Vf8ESHJ6BvAQVVmQmVJZDUCSiIDIR6mQkVwQUGFIiIyBCVl5dj8S45u+GR3v7wcbLW9pCIiCQKhQnmDg7Gwk3nseJANGYMCIS5qYJ7h4hIS3iWkIgaV14OHF0C7PwnUFYM2HsDk5YCQYO59+4x5VcEHDacvIkXR4bCzd5Sr/ajmcIMszvPxn8O/wffX/geD7d9GBamFtAVucW59TITpObM4rG8RGQVZTW6DkdLx3p9E2o2ZmbvCiIiMjYis+FYdAYszBR4dhizG4hIt0zs7oP/7biGxKxC/HYuEQ9199X2kIiIjBYDDkR0Z3npwNZngGvb5ftt7wfGfwnYunDP3aPega3Q3d8Jp+NuY/XhGPx9dFu925cTQibg27PfIiU/BZtvbJb6OqTmp8LNxg093HvAVGGqsW3nFOfUK3dUswRSdnF2o+twsnSqF0SoCi7YesPOwk5j4yciItLH7IbK3g2P9/WX+lIREekSK3NTPDUwEB/9dRVLwqMwsZsPTExMtD0sIiKjxIADETUsOgLYNA/ISQJMLYHR7wJ95gI8cGsSceA7PywEC9acxOrDsVgwJAS2etbYTGQ0zOw4Ex8e/xDvH30fZeVlVc952HhgYZ+FGBkw8p7WLQIGVWWOVPRREAGHxrSybCWVNlLVR0HcbM31q3cGERGRNoVfS8XJ2ExYmSvw9NAQvhhEpJOm9w3AV3tv4EpyDvZfT0NYqJu2h0REZJT06wwXETU/ZRkQewjIvQXYeQABA+QSSvveB/Z/LK5pA1xDgSkrAE/96zegq0Z18JAaRovG0euOx2PWoCDoG2crZ2laM9ggiKyHl/e9jE+GfqIy6FAZUKjZP6HmfE6JegGFupkJNYMLhtbImoiISKu9GyqyG57oFwB3e2Y3EJFucrQxx8O9/bDyYAyWRkQx4EBEpCUMOBAZs0vbgO2vAdmJ1Y+JoIOlPZB+Q77f40lg7CLAgleENyfTisZmb2w+j+8OROOJ/gF61disTFmGxScXq3yuXASpAPz78L9xM+cmkvOTawUUckty1QpmqMpMYECBiIioZe25koKzN7NgbW4q9aEiItJlswcFSVnkB26k4UJCFjr5OGp7SERERocBByJjDjasf1LOYKhJZDqIm5k1MPEroNNkbY3Q4E3q4YNPdl5Fwu0C/H4uSWp0pi9OpZzCrfxbd1zmdtFtfHxSZMmoDiioCiSIx7xsvZihQEREpGO9G2YMCISrnaW2h0REdEe+rWzwYBcvbD2TKGU5fP5od+4xIqIWxoADkbGWURKZDXWDDTVZOQAdJrbkqIy0sVmQ3NgsIgoTunnrTWMz0SBaHV1cu6CnZ0/42FaXPRK9FaxFQIuIiIh02o5Lt3AxMRu2FqaYFxas7eEQEalFfF6JgMPv55Pwj7FtpSAEERG1HP2p30FEzUf0bKhZRkkVkeUgliONNzazsTDF5aRsqbGZvnCzUa8B24s9X8TLPV/Gw+0exmDfwQh2CmawgYiISA8oldW9G0SvKWdbC20PiYhILR29HTGotSvKlOVS+VoiImpZDDgQGSMRTGjO5ahJjc0e7eMvzS+JiNSbPdnDvQc8bDxgAtUZGeJxTxtPaTkiIiLSP39eSMaV5BzYW5phziBmNxCRfqnMylp3PB5Z+SXaHg4RkVFhwIHIGJmrmVIqGkiTxomrBs0UJjh4Ix3nb2bpxR43VZhiYZ+F0nzdoEPl/df6vCYtR0RERPpFXBX86S45u2H24CDpAgkiIn0yuI0r2ns5IL+4DGuOxmp7OERERoUBByJjE3MQ+P3vjSxkAjj4AAEDWmhQxs3HyRrjunrrXZbDyICR+GToJ3C3ca/1uMh8EI+L54mIiEj//HYuEddTcuFgZSZdGEFEpG9Eb7x5YfLn18qDMSgsKdP2kIiIjIbeNo3OLy7FXxeTsetSCi4lZSPxdgEszBRSBPvxvv6Y0M3nrtZXWqbEplMJ+Ol4HGLT81FUUgYvJ2uMaOcuXdXjbm+lsd+FqEWUlQIRHwERHwLlSjl7QSqZZFKneXTF1epjFwG8Or1FU343n07AH+eTEJeeD38X/WhsJoIKw/yG4VTKKamRtOjtIMooMbOBiIhIP4nvRZ/tul51fOJgxewGItJPD3bxxkfbryIxqxBbTifgkYpStkREpFl6m+FwLDoDL607i0ORaejg7SBdeXNfJ09cScrGCz+fwdtbL9zV+p5bexr/+OUcMvOKMa6LFx7r6w8na3MsiYjCg58fQEpOocZ+FyKNy7oJrBoHhC+Sgw3dHgeePwVM+wFw8Kq9rIM3MG010GE8X5gWJIKlQ0LdoCwHlh+I0qt9L4ILvT174/7g+6Upgw1ERET6a9vZRESl5aGVjTlmDmR2AxHpL3NTRVWW1tL9UVCKL1tERKRxepvhIDIOPnukG+7v7CX9Ean06ph2mPjVQaw6HItJPXzR1c+p0XWdib+N7ReTpWU3Luhfa33//vWilH7309F4vDCyjcZ+HyKNufwrsPU5oPA2YGEPPLgY6DJVfk4EFdo9AMQekrMdRNaDKKPEzAatmD8kGOHXUrH+RDxeHBkKZ1sL7QyEiIiIjFKJyG7YLWc3zB8SAjtLvf26SEQkEVkN4nMtKjUPuy7fwuiOntwzREQaprcZDiKrQZRNqhkcENzsLaXsBOFodLpa64rLyJemg1u71lvf8HZybfL0vKJmGjlRCykpAH57GVg3XQ42ePcAFkRUBxsqieBC0GCg8xR5ymCD1vQPdkEXX0cUliix+nCM9gZCRERERmnzqQSpvKyLrQWe7B+g7eEQETWZCJxO7yd/ni2N0K9MciIifaW3AYc7MTeVa9CbKtT79UI97KTpgRtpUs3SmvZeSZWmA0Jcmn2cRBqTchlYNhw48Z18f+ALwKy/AOdg7nQdb2w2PyxEml91KAYFxWxsRkRERC2juFSJz/fI2Q1PDw2BjQWzG4jIMDw1IBAWpgqciM3EydhMbQ+HiMjgGdxRZJmyXGr+bGICDGrtqtbPtPN0wIz+AVIZptGLIxAW6iY1oBallsTtb8NbY2ynOnXu6ygqLZMO0ivlFJY0+Xchumvl5cDJ74HtrwOlBYCtO/DQt0DrEdyZemJsJ0/4O9tImVcbTsbjyf6B2h4SERERGYGNJ2/iZmaBlDH+eF9mNxCR4XB3sMLE7t5Yf+ImlkZEYskTvbQ9JCIig2ZwAYePd1zFleQcTOvli7ae9mr/3L8ndIKfsw0+2H4F3x+qLmUyrK0b7ut852CD8PXeyKp6p4KySC7TRNRiCjKBbX8DLm+T74eMkIMNdnJZMNIPpgoTzA0Lxj+3XMCy/VF4rI8/zOqUeiMiIiJqTuLiqS8rshueGRoCawtT7mAiMijzwoKlgMOOS7cQlZqLYDe50gURERlgwKH7f3YgM1/9bICf5vZD/wbKG609Goev90Wio7cD3h7XUe11lpeX480tF7D1dIL0c6M7esDa3BSn4m7j39suYtLXh7BmTl/0DGjV4DqeGRaCOYODqu5nZ2fD91O1h0DUNHFHgF/mAFnxgMIcGPk20O9ZQM2yYqRbpvb0xeKd1xCfUYA/LyRjXFdvbQ+JiIiIDNj64/FIzCqEp4MVHu0j98MjIjIkrd3tMaKdO3ZfScHyA9H470OdtT0kIiKDpfWAw/iu3sgtUr9OuUjxVWX9iXi8ueU82nnaY83svrC1VP9X23DiphSseHtch6pmQsKQUDcsfbInRn4SIWU+rJ/fv8F1WJqZSrdK5cXmam+f6J4py4D9nwD73gfKy4BWQcCUFYBPD+5UPWZlbooZ/QOxeNc1LImIxINdvKT+DkRERETNrbCkDF/uvSHNPzu8tXQcQkRkqFkOIuAgSsi9PCoUrnaqzy8REZGeBxxEKaPmuCJn4aZzaONuhx/n9EUrW4u7+vk9V1KkqarMCREFd7e3xIWErCaPk6hZZScCm+YBMfvl+10eBh74GLBUv5QY6a4n+wfg2/BIXEjIxqHIdAxUsycNERER0d0QF17dyi6Cj5O1VJaWiMhQ9QlyRlc/J5yNv43Vh2Lw8ui22h4SEZFB0vt6KyLY8Nqmcwhxs8Pauf3gcg8R6pIyudlzRm6xyibUWQUlUhNpIp1x5Q/gmwFysMHcFnhoCTBpKYMNBkQETh/u7SfNL4mI0vZwiIiIyAAVFJdJJWmF54a3rpWxTURkaETW+PywYGl+9ZFY5BeXantIREQGSa/Poq87Hlcr2NBYOpw4oL6RkouE2wW1Hu8ZKPdm+GrfDalhWk1f7LmOolIl+ger7htB1KJKCoE//gH8/KjcJNqrK7BgP9D1Eb4QBmj2oCCpiXTEtVRcSszW9nCIiIjIwKw5Eou03CL4OVtjSk9mNxCR4RvT0RMBLja4nV8iXcBKREQGWFLpXh26kYaFm86jvFxOixMHy3V18HaQ/phUOhN/G48uO4K+Qc5YV6Mfw5P9A7H5VAIO3kjHiI/Dpd4NonbpqbhMnI67DScbc7w6hql2pGWp14CNs4Bb5+X7/Z8DRvwLMGPdSUPl52yDBzp7YdvZRCyNiMSnj3TX9pCIiIjIQOQVlUrlG4Xnh7eBualeX4tGRKQWcUHXnMHB+OeWC1LzaNHH04yff0REzUpvAw4iS0EEGyrrjqoyuYdvrYBDQ+wszbDpmQFYEh6FHZeSpQZCyvJyeDhY4dE+/nh2WAh8W9k0969ApB7xRj/9A/Dna0BJPmDjCjz0LdBmFPegkTQ2EwGHX88l4ZUxbflZRERERM1i9eFYpOcVI9DFBpO6+3CvEpHRmNrTF4t3XsPNzAL8eSEZ47p6a3tIREQGxaS8vPK0PTWn7OxsODo6IisrCw4ODty5dG8Ks4BfXwQubpLvBw+V+zXYNx5II8MxfflRHLiRhqcGBuLtcR21PRwiIqPH4zzjZEive05hCQZ/uFcqKbL44a54qDvLKRGRcfl01zV8uus6Ovs4YttzA6X+DkRk3AzpWE/bmDdLpKvijwPfDpKDDQozYOQ7wPTNDDYYoflD5MZmPx+LR2Ze/eb2RERERHfj+4MxUrAh2M0W47syu4GIjI8orW1lrsD5hCwcjkrX9nCIiAwKAw5EukapBPZ/DKwYA9yOA5wCgFl/AYNeAhT8J2uMBrV2RQcvBxSUlKnsV0NERESkrqyCEizbHyXNvzgyVKpnTkRkbJxtLTC1p580vzRC/kwkIqLmwbOXRLokOwn4YSKw+z9AeRnQaTKwYD/g20vbIyMtEum9lVkOqw7HoLCkjK8HERER3ZMVB6KRXViKUA87PNDZi3uRiIzWnMFBEDHXfVdTcSU5W9vDISIyGAw4EOmKazuAbwcC0eGAuQ0w4Stg8neAlaO2R0Y6QJwQ8HGyRlpuMX45dVPbwyEiIiI9dDu/WAo4CMxuICJjF+Bii7Gd5P6IzHIgImo+DDgQaVtpEbD9DWDtVCA/HfDoDMwLB7pPF5e2a3t0pCPMTBWYOzhIml8WEYUyZbm2h0RERER6Zvn+aOQUlaK9lwPGdpRPshERGbP5YSHSdNuZRCRlFWh7OEREBoEBByJtSrsBLB8JHPlKvt93ATBnF+AWyteF6pnW2w9ONuaISc/HjovJ3ENERESktoy8Yqw8KGc3vDSyDRTs3UBEhK5+Tugb5IxSZTlWHozhHiEiagYMOBBpQ3k5cGYtsCQMSD4HWDsDj/4M3PcBYG7F14RUsrEww5P9AqT5b8MjUS7eR0RERERqWBIRibziMnTyccCoDh7cZ0REFSr75a09GofswhLuFyKiJmLAgailFWYDm+YBW54GSvKAwMHA0weBtvfxtaBGPTkgEJZmCpy9mYWj0RncY0RERNSo1JwirD4UK82/PCoUJizbSURUZWioO9q42yG3qBQ/HY3jniEiaiIGHIhaUsJJYMlg4Px6wMQUGP4W8ORWwMGbrwOpxdXOElN7+UrzS8IjudeIiIioUeKYoaCkDN38nDCsrTv3GBFRDaLE3NwwOctBlFUqLlVy/xARNQEDDkQtQakEDn4GfDcayIwBHP2Bp/4Ewl4FFKZ8DeiuzBkUDFF2ee/VVFxNzuHeIyIiogalZBfihyPMbiAiupMJ3bzhbm+J5OxCbDubyJ1FRNQEZk35YSJSQ84tYMsCIHKPfL/DBGDc54C1E3cf3ZNAV1vc18kLv59PwtKIKHw8rSv3JBER6byz8bexeNc1nIrNRElZOUI97DBrUBAmdPNRex1KZbl08vynY3GITsuDraUZ+ge74JUxbRHkaltv+YGL9iDhdoHKdT3W1x//fagzDN3X+yJRVKpEr4BWGNzGVdvDISLSSZZmpnhqYBA+2H4FSyMiMbmHD8vPEZF6lUz2vg/cPAaUlQBu7YB+zwBdpt7dRcrHlwMnvwcyIgELW7n8+oh/AS4h9Zdf3BnIaqD8W8+ngHGfav2VY8CBSJNu7AI2LwDyUgEza+C+RUCPGQDr5lITzQsLlgIOW88k4JUxofBytOY+JSIinXU4Mh0zVhyDuakJxnX1hr2VGbZfTMYLP5/BzcwCPDustVrreXPLefx0LF6qtT1zQCBSc4vw27kkRFxPxaanB6CNh329nxHbmjUwqN7jXXwdYeiSsgqkJqgCezcQEd2ZCER/uec6rt3Kxb6rqRjWjiXoiOgOovcDayYBphZAp0mApQNw+Vdg0xzgdiwQ9op6u++3F4FTq+RgRZ958jnEC5uAyL3A7B2Ae7v6P2PpCPR7uv7j3t114iVjwIFIE0qLgT3/AQ59Id937whMWaH6Q4LoHnT1c5Ku6DwclY4VB6Lx5gMduB+JiEgnlZYpsXDTOcAEWDe/Pzr5yCf6XxgZiklfH8Tinddwf2cvlRkKNR2KTJOCDX0CnfHDnD7S1ajC5B6+mP7dUby55QLWz+9f7+ccrMzx0qhQGKOv9t5AcZkSfYOc0T/ERdvDISLSaY7W5ni0jz+WH4jGkohIBhyIqGFlpcC25yEd4D71B+BVUXli6EJg+Shg3/tAx4dUZyjUFB0hBxv8BwBPbgHMLOXHuz4CrJ4I/P6yvP66rByBYa/r7CvEHg5EzS09ElgxujrY0HsOMHc3gw3U7OYNkRubiSsXswpKuIeJiEgnHYpMR2x6PiZ09a4KNgh2lmZ4fngblCrLseFEfKPr+fmYvMzfR4dWBRuEga1dEdbGDceiMxCVmquh30L/3MzMx7rj8j4TARcTZtgSETVKlPozU5jgSFQGzt28zT1GRKpFhwOZ0UDnqdXBBsHSHhjyKqAsBU6vaXzvnVwlT4e/VR1sEIKHAq1HALEHgbQbevcqMOBA1JzOrQeWhAGJpwErJ+DhH4EHPgbMWe6Gmt/QUDe09bBHXnEZfjwqN4MkIiLSNUei0qXp4FC3es+JQIFwNDpDrfXYWJiiV6Bz/fWENrwecYX/xpM3pav9Rf+HS4nZMAbi9xW9Mga2dkG/YGY3EBGpw9vJGuO7ekvzSyKiuNOISLWYA/I0ZFj950KGy1MRLFBnPea2gH8/FesZUbGeim3VVFYEnFkLRPxP7v+QfF6nXimWVCJqDkU5wB+vAmd/ku8HDAQmLQUcfbl/SWPElYrzhwTj5fVnsfJgDGYPCqp1xScREZEuiEnPk6ZBLvVLJjnamMPZ1gIxafIyDckvLkVKTpEUaDdVmNR7PsjVRt6WivWk5hThlQ1naz02JNQNix/uJm27IUWlZSguVVbdzynUn2zCuPR8bDhxs6p3AxERqW9uWDA2nU7An+eTpM9Tfxf5bwwRGYnCbKDmIaLIPKiZfSCI5s6CqpJJ1q0AGxe5AsqdFOcBucmAewdAoeJcTuW6Va0n9xawpU4Ph9YjgYeWArbav9CEAQeiphLZDBtnyx82JgpgyGtA2KuqPyyImplovPm/v64iMasQW04n4OHe/tzHRESkU3IKS6uaN6siSislZxU2cR3m0jS7YrlK03r5oW+wM0I97GFhpsD1Wzn4bPd1qRnonFXH8cvTAxosNfT13khp2UrKonzoi8/3XJdKVYnASs+A+hkhRETUsPZeDlLmXMS1VHx3IAr/ntCJu4vImCzuAFjWOD4csrB+vwQRlBBEo2hVLO2B7MQ7b0eddQhFdbJzu08HAgcCbu0BMwsg9SqwbxFwYyfw0yNyo2ktl9JkwIHoXpWXA0e+Bna+DShLAAdfYPIyIGAA9ym1GHNThVRn9N3fL0spv1N7+kGh4spPIiIiY/TCyDa17nf3b4UVM3rj4aWHcTwmE3uvpmB4Ow+VP/vMsBDMGRxUdT87Oxu+n0LnRaflYdMpObvBWJtlExE11fywYCngsP7ETbw4MhSt7pARR0QG5qVLgEONIEDd7AZtG/pa7fu+vYDH1gPf3w/EHQau7wBCx0Cb2MOB6F7kpgJrpwF/vSEHG9o9CCzYz2ADacUjffzhYGWGqNQ87Lp8i68CEZGuSjgJrJkCLPIH3vMClg4Dzm24u3UolcDRpcDXA4B3PYAPg4H1MxpO2a5c/tvBwLuewH99gZX3A1f+QEupzEqozFKoK7eotMHMBfXXIZc7En8PGyMC8yJAL5yIyWxwOVGm0N7KvNZNH3y++zqU5cCIdu7o5uek7eEQEemlASEu6OjtgIKSMqn/DxEZESuH2jdVAQfxuKrsg5ql1y0byFy4m3UIja1HUCiAbo/L83FHoG0MOBDdrci9wLcD5YihqaXcFPrhNYAN09VJO0Qpiif6B0jzbGxGRKSjovcDK8bKVx11mAD0mgXkpwOb5sjN3tT124vAn68C5WVAn3lAm9HA1T/l4EXKlfrZmBtmyMuLLyw9ngA6TQLSrgE/PyoHIlpAYEXvhuiKXg41ZeWXICOvGIGu9fs71GRjYQZ3e0vEZ+ajTJxNryM6TS531Nh6KlVeqSpOJBmSGyk52HomQZpndgMR0b0T5fbmhQVL86sOxaDQwP5eEFETOd+hv0JBpnycr6q/Q00WtoCdJ5AZCyhVfMZUrrux9VQSfSOEkgJoGwMOROoqKwF2vQP88JDcnMWtHTBvL9B7jtZroxHNGBAIC1MFTsZm4kRMBncIEZEuKSsFtj0vTl8AT/0BjP8CGPMe8PRBufbqvvcbbyonREcAp1YB/gOA+RHA6P8DHvoWeHy9fGXU7y/XXv7SVuDyNsCvH/DMYeD+j4DxnwPPHAUc/YEdb8lfcDSsb7D85Wf/tdR6z0Vclx/rG+Ss1nryi8tU/p0TZS/UXY9wJl7ObPBtZViNQD/bfUPKbhjdwQOdfBy1PRwiIr32QGcv+DhZIz2vGBtPyqXqiIgkoodC5UXJdUXukacBFcs0tp6SPNVZCZG7K9YzSL2dnnBCnjppv7cnAw5E6siMka9KPLBYXC4I9HwKmLsX8OjI/Uc6wd3eCpN7+kjz34ZHaXs4RERUU3Q4kBkNdJ4KeHWt3QhuyKuAshQ4vabxfXZylTwd/lbt1O7goUDrEUDsQSDtRvXjV36Xp4P/DphbVz9u6wL0fwYoKwLO/Kjx12pgiAv8nW2w9WwiLiZm1Sql9MWe6zBTmGBKT9+qx0XGw42UXGla06N95DJIH++4huJSZdXjB2+kSYGLPkHOCHazq3pcNIjOKpBLLdV0PCYDy/dHS02kx3byhKG4mpyD387JzQmZ3UBE1HRmpoqqXj7L90epzLAjIiMVNBRoFQic3wAknat+XGQVh38EKMyqSxwJeelA6jV5WlPPmfJ0z7tAaY1j36h9wI3dctDCtXX14yKjueB2/fHEHgYOfyVXYmk/DtrGptFEjTm/EfjtJfnKQStHYNznQMeJ3G+kc+YMDsbPx+OlPg6ipEJrd3ttD4mIiISYA/J+CBlWf3+EDJenIligznrMbQH/firWMwK4sQuIPVD9pSQvRZ62ksvu1eIUUJ01MewNjZ+wWTS5M2asOIZp3x7G+G7eUjnA7ReTEZ9RgFdGh9YKFIjSFZ/tvo4XRrSpdeJ8QIgrHuntJ/2te+Dz/Rjezh2puUX47VyStL73JnaqtV3x+JKISAwMcYVvK2spwHD1Vi72X0+FwsREWl5cuWooPt11TaqiJa7Ibe+lRq1fIiJq1LRefvh013XEpOdj56VkjO3kxb1GRICpmZy1/MMkYOV9QKfJ8sVEl38FbsfKFwjVDBQcWwqELwKGLASGvV79eFAY0ONJ4NRqYMlguVxqXipwYZPcu+GBT2rv7YubgYOfAcFD5EwGUwsg5bKcVWGiAB5cDDjJF+loEwMORA0pzgP+/Ef1FYeiHMHkZTqRmkSkSoibnVRC4a+Lt7AsIhofTOnCHUVEpEmF2YDcCkAmsg5UNZXLuEP9VetWcr3VxkoqieOS3GTAvQOgMK3/fOW6a67HxlWeirJJbm1rLy++CEnL18iI0CARLNiwYAAW77wmBQJKypQI9bDH30e1xcTucoaeOv77UGe087TH2mNxWHkoBrYWphjZ3h2vjG5bK2gh9A9xwY3UXFxMyMLR6AwUlZbB1c4SD3bxxuxBQQbVUFlkjvx5IVmq8vnCyDbaHg4RkcGwFf3y+gXgy703pH55Yzp6Sv0diIikYMGsv4B9/wUubgHKigH3dnKwocs09XfQg58BHp2AEyuBo0vk3g5txwLD/1U7aCEEDQbSrgJJZ4GYg0BpIWDnLvdp6/cs4NtTJ14YBhyIVBHpUBtnAenX5XrLYa8CQ16TI5hEOmz+kBAp4LD5dAJeHh0KDwcrbQ+JiMhwLe4AWNY46VD3iqWagQlBXKWkirgaKlsuhdMgddYhiIzMSm1GARc2yiUhxRci84q/CfkZwJGvK9ZbXeJI08QJ/lWz+jS6nMhqaKgkkEJhgpkDg6RbY/oFu0g3YyCuvhXGdfGWAjlERNS8/fKW7o/C6bjbOBGbid6B6vULIiIjIE7wT/+l8eWGva76e4KgUAB958u3xgQOkm86jmdPiWoSeegimrjzn3Jk0t4LmLRMjiAS6YEe/q3QJ9AZx2IysPJgDBbe107bQyIiMlwvXQIcagQAVGU3aFOnKXKmZsx+4Jv+QOuRQFmJ3NtBXAklmKjIliC9cv5mFnZeugUFsxuIiDTCzd4Sk3v44Kdj8VgSHsWAAxFRI9g0mqiSaNzy06PA9tfkYEPofcCCgww2kN6ZPyRYmv54JBY5hfWbZRIRUTOxcqh9ayjgIJ6rm31Qk2gu11Dmwt2sQ6i5HpGZKa64Gvq6XNP15PdyXdl29wPTVsvL2FaUXSK9tXjXNWkqSlOJ8opERKSZfnmikpLcLy+Xu5iI6A4YcCCqbJj47UDg2p9yw5X7PgQe/QmwNY40fDIsw9q6o7W7HXKKSvHTsThtD4eIiJxV9FeoVJAJ5Ker7u9Qk6jlaucp92NQltV/vnLdddcjgiBDFwLPnwT+mQr8IxIY91l1CSfvbnx99NjpuEzsuZICU4UJ/jacvRuIiDRFBHRHtveQ5pfvj+KOJiK6AwYcyLiVlQJ73gVWjQdykgDXUGDuHrluGhtBkZ4S9a3nhclZDt8diEZxqVLbQyIiMm6BA+Vp5N76z0XukacBA9VbT0keEHdExXp2V6xHzZqu59fL006T1VuedNInO+XsBlHqI9DVVtvDISIyaPMrvmNtOpWAlOxCbQ+HiEhnMeBAxut2HPD9/UDER6J5A9D9CWDePsCzs7ZHRtRkE7p5w8PBEreyi7D1TAL3KBGRNgUNBVoFAuc3AEnnapdBCv8IUJgB3R6vXeYx9Zo8rannTHkqLpYoLa5+PGofcGO3HLRwba262XRNF7fIvR28ewDtxzfP70gt7nhMBvZfT4OZwgTPM7uBiEjjegU6o4e/E4rLlPj+UAz3OBFRA9g0moyT+KK97W9AUZZc63jcp7zCjwyKpZkpZg0Mwvt/XsGy/VGY3MNXynwgIiItEL0Uxn8B/DAJWHmffMxhaS/3U7gdCwx/q3ag4NhSIHwRMGQhMOz16seDwoAeTwKnVgNLBgNtRgN5qcCFTfLxzAOf1N/28hGAgw/g1lYur5RwSm4iLQIg01YBCjaN1leLK7Ibpvbyg5+zjbaHQ0RkFOYPCcH8H05izZFYPDOsNewseVqNiKguZjiQcSnOB359AdgwQw42+PYGFuxnsIEM0qN9/WFvaYZrt3Kx71qKtodDRGTcRLBg1l+Afz/5wofj3wE2zsCkZUDYq+qv58HP5F5TMAGOLgGu/QW0HSuXhHRvV3/5jpOA3BTg9I/y8mJebG/+fsDJv1l/RWo5hyPTcSgyHRamCjw3vE5WCxERacyo9h4IdrVFdmEp1h2P554mIlLBpLy8vFzVE9Q02dnZcHR0RFZWFhwcHLg7dcGti8DGWUDqFflL+qCXgGFvAKbm2h4Zkca8/+dlLAmPQp8gZ6yf3597moioGfA4zzjpyusuvr49vPQIjkVn4Mn+AfjPhE5aGwsRkTFaezQOb2w+Dx8na+x7dSjMTXktL5Eh0JVjPUPAT0UyfCKmdmwZsHSYHGyw8wCe3AKMfJvBBjJ4oqySuamJdFLiVFymtodDRERETSQyG8TfdQszBZ4ZyuwGIqKWNqmHD1ztLJBwuwB/nE/iC0BEVAcDDmTY8jOAddOBP14ByorkWsdPHwKCh2p7ZEQtwsPBChO7+UjzS8OjuNeJiIj0mMhu+HjHVWn+8b7+8HS00vaQiIiMjpW5KWb0D5TmRTY5C4cQEdXGgAMZrpiDwLeDgCu/AQpzYMz7wGPrAVtXbY+MqEXNCwuWpn9dSkZUai73PhERkZ4Kv5aKU3G3YWWuwNNDQ7Q9HCIiozW9XwCszU1xKSkbB2+ka3s4REQ6hQEHMjxlpcDe94FVDwLZCYBzCDBnF9D/GcDERNujI2pxbTzsMbK9u1RdbNn+aL4CREREekhcQbt45zVp/ol+AXC3Z3YDEZG2tLK1wMO9/aT5JRGRfCGIiGpgwIEMS9ZNYNU4IHwRUK4Euj4GzI8AvLtpe2REWjV/iHwV5C+nbiI1p4ivBhERkZ7ZcyUFZ29mwcbCtOrvOhERac/sQUFQmAD7r6fhYmIWXwoiogoMOJDhuPwr8M1AIO4QYGEPTFoGPPQNYGmn7ZERaV2vgFbo4e+E4lIlVh2K0fZwiIiI6C6zGz6pyG6YMSAQrnaW3H9ERFrm52yDB7p4S/PLItgvj4ioEgMOpP9KCoDfXpabQxfeBrx7AAsigC7TtD0yIp1hYmJSdTXk6sMxyCsq1faQiIiISE1/XbyFi4nZsLUwxbzBcm8mIiLSvvkV/fJ+PZeEhNsF2h4OEZFOYMCB9FvKFWDZCODEd/L9AX8DZv0FOPOLGFFdo9p7INjVFtmFpfj5eDx3EBERkQ4rU5bjcGQ6tpxOwHu/X5IemzUoSKobTkREuqGTjyMGhLhIn9krDrBfHhGRwIAD6SfR/fbESmDpUCDlImDrBkzfBIz+P8CMX8KIVFEoTDC34gqc7/ZHoaRMyR1FRESkg7ZfSMKgD/bg0WVH8OK6M4jPLIAJgEAXW20PjYiI6phX8R3r52NxyCoo4f4hIqPHgAPpn4JMYMMM4LcXgdICIGQE8PQhoPUIbY+MSOc91N1HqvucmFWI384lans4REREpCLY8PSaU0jKKqz1eDmAVzaclZ4nIiLdMSTUDe087ZFXXIYfj8ZqezhERFrHgAPpl7ijwLeDgUtbAYUZMOr/gMc3Anbu2h4ZkV6wMjfFUwMDpfkl4VFSE0oiIiLSDaIkx79/vSQFFxoinhfLERGR7vTLm1vRX2flwRgUlZZpe0hERFrFgAPpB2UZEP4RsPI+ICseaBUEzN4BDPybqBOj7dER6ZXpfQOkppNXknMQcT1N28MhIiKiCseiM+plNtQkwgziebEcERHpjnFdveHpYIXUnCJsPc1MciIybjxTS7ovOxFYPQHY+y5QXgZ0ngbMjwB8emp7ZER6ydHGHI/28Zfml4RHans4REREVCElp+Fgw70sR0RELcPCTIFZgyoyySMioWQmGhEZMQYcSLdd/RP4ZiAQsx8wtwUmfgtMXgZYOWh7ZER6bdagIJgpTHAoMh3nbt7W9nCIiIgIgLu9VbMuR0RELUdc1GVvaYbI1DzsuZLCXU9ERosBB9JNJYXAH/8AfnoEKMgAPLvIWQ3dHtX2yIgMgreTNcZ385bml0REaXs4REREBKBPkDO8HK1g0sDeEI+L58VyRESkW+ytzPFYPzmTfCm/YxGREWPAgXRP6jVg+Ujg2BL5fv/ngDm7ANfW2h4ZkUGZFyY3NvvzfBJi0/O0PRwiIiKjZ6owwdvjOkj7oW7QofK+eF4sR0REumfWwCCYm5rgWEwGTsdlans4RERawYAD6Y7ycuDUD8DSIcCt84CNK/DYBmDMe4CZpbZHR2Rw2nk6YGhbN4jyosv3R2t7OERERARgbCcvfDO9Bzwda5dNEvfF4+J5IiLSTR4OVpjQzUeaZ5YDERkrM20PgEhSmAX8+iJwcZN8P2gIMGkpYO/JHUSkQfPDQrDvairWn4jHiyPbwMWOwT0iIiJtE0GFUR08cSw6Q2oQLXo2iDJKzGwgItKPTPKNJ29i+8VkxKTlIdDVVttDIiJqUcxwIO27eQL4drAcbFCYASPfAZ7YwmADUQvoF+yMrr6OKCpVYvXhWO5zIiIiHSGCC/1DXKQrZcWUwQYiIv0Q6mGPYW3dpCIOyw+wXx4RGR8GHEh7lEpg/yfAijHA7VjAyR94ajsw6CVAwbcmUUswMTHB/CEh0vzqwzHILy7ljiciIiIiImqCeWHyd6wNJ24iPbeI+5KIjArP6pJ25CQDP0wEdv8bUJYCHScBCw4Afr35ihC1sDEdPRHgYoPM/BLpgJiIiIiIiIialknehZnkRGSkGHCglnd9J/DNQCA6HDC3AcZ/CUxZAVg58tUg0gJRomHu4GBpftn+KJSWKfk6EBERERERNSGTXPRyqMwkLygu474kIqPBgAO1nNIiYPsbwI9TgPw0wKMzMC8c6PGE+GvMV4JIi6b09IWLrQVuZhbgjwvJfC2IiIiIiIiaYGxHT/g7V2SSn4znviQio8GAA7WMtBvAd6OAI1/J9/suAObsAtxC+QoQ6QArc1PMGBAozS8Jj0S56HBGRERERERE98TMVIE5g4Ok+eX7o1Gm5HcsIjIODDiQZomTlmd+ApaEAUlnAWtn4NGfgfs+AMytuPeJdMgT/QJgbW6Ki4nZOHgjXdvDISIiIiIi0mtTe/qhlY054jLysZ2Z5ERkJBhwIM0pzAY2zQO2LABK8oDAwcDTB4G293GvE+mgVrYWeLi3nzS/JCJS28MhIiIybsoyIHo/cH6jPBX3iYwF3/9kIKwtTPFEfzmTfGnEXWSS898AGTO+//WembYHQAYq4SSwcTaQGQ2YmALDXgcGvQwoTLU9MiK6g9mDgvDDkVjsv56Gi4lZ6OjNZu5EREQt7tI2YPtrQHZi9WMO3sDYD4AO4/mCkGHj+58MzIz+AVLZ2rM3s3A0OgP9gl3u/AP8N0DGjO9/g8AMB2peSiVw8HPgu9FysMHRD3jqTyDsVQYbiPSAn7MNHuziJc0vjYjS9nCIiIiM84v2+idrBxuE7CT5cfE8kaHi+58MkIudJab09FXvOxb/DZAx4/vfYDDDgZpPbgqweQEQuVu+3348MP5zwLoV9zKRHpkXFoytZxLx27kkvDK6rRSEICIiohYqISAyG6Cq5IZ4zER+PngoL+Yhw3z///kPvv/JIM3t54nNx67h8JU4XL/pjzbudvUX4r8BMmZqvf8XAu0e4DGQHmDAgZrHjd3A5vlAXipgZg3ctwjoMQMwMeEeJtIzoozS4DauUlml7w5E453xHbU9JCIiIuMQe6h+ZkMt5fLzi+SeS0TGhe9/0l+ii8Mly4o7y+91Lfw3QMZMvP8T5GOloMHaHgw1giWVqGlKi4Ed/wTWTJKDDe4dgHn7gJ4zGWwg0mPzw0Kk6brj8cjMK9b2cIiIiIxD7i1tj4CIiIhId/FYSS8ww4HuXUaU3Bg68ZR8v/ccYPS7gLk19yqRnhvY2gUdvR1wMTFbaiL9txFttD0kIiIiw2fnod5yj28EAgZoejRELUtctfrjlMaX4/uf9NgTy4/hRFwGZg8KksrX1sJ/A2TM1H3/q3usRFrFgAPdm3Prgd9eBopzACsnYMJXQPsHuTeJDISJiQnmDwnB3346jVWHYqS+DlbmptoeFhERkWETQQQHb7lBtMoaxiby8yHDWb+YDI94X/P9TwZuxtAO2L/6BFYdT8X8kZ1hb2Ve/ST/DZAxU/f9zwsu9AJLKtHdKcoFNj8NbJorBxv8BwBPH2SwgcgA3d/JE76trJGeV4yNJ29qezhERESGT2EKjP2g4k7dXmgV98cuYrCBDBPf/2QEhrdzR4ibLXKKSvHzsfjaT/LfABkzvv8NCgMOpL7EM8CSMODsWsBEAQx9HZjxK+Doy71IZIDMTBWYOzhYml+2PwplSlVXGRAREVGz6jAemLYacPCq/bi4qk88Lp4nMlR8/5OBUyhMpOxxYcXBaJSUKWsvwH8DZMz4/jcYJuXl5TyDpAHZ2dlwdHREVlYWHBwcoNfEW+TIN8DOfwHKEsDBB5i8nGlMREYgv7gUAxftQWZ+Cb5+vAfu71zn5AcRkREyqOM80t3XXVkm1zMWzRFFvWJRQkBc/UdkDPj+JwNWVFqGQR/sRWpOET6Z1hWTeqi4iJP/BsiYaen9z2P85sMeDnRneWnAlqeB6zvk++0eBMZ/Adg4c88RGQEbCzM80T8Qn+++jiXhkbivk6fU34GIiIg0THyxDhrM3UzGie9/MmCWZqaYOSAQH/11FUsjovBQd5/637H4b4CMGd//eo8llahhUfuAbwbIwQZTS+CBj4GH1zDYQGRkZvQPgKWZAmdvZuFIVIa2h0NERERERKTXpvcNgI2FKa4k5yDiepq2h0NE1KwYcKD6ykqAXf8GVk+U05fc2gHz9gK95wC8spnI6LjYWWJaLz9pfklEpLaHQ0REREREpNccbczxSG9/aV5kkhMRGRIGHKi2zBhgxVjgwCeieQPQcyYwdy/g0ZF7isiIzRkcBIUJsO9qKq4kZ2t7OERERERERHpt9uAgmCpMcCgyHRcSsrQ9HCKiZsOAA1W78Avw7WAg4QRg5QhMXQWM+wywsOFeIjJyAS62uK+iYbSoM0pERERERET3zsfJGuO6yN+xlvA7FhEZEDaNJqA4D/jzNeD0D/Le8OsLTF4OOMnpfUREwvywYPx+LgnbziTildFt4e1kzR1DRERqOxt/G4t3XcOp2EyUlJUj1MMOswYFYUI3H7XXoVSW44cjsfjpWByi0/Jga2mG/sEueGVMWwS52mpsu0RERJowLywEW84k4o/zSfjHmLbwc+YFn0R6JeEksPd94OYxuUS9KEvf7xmgy1T116FUAseXAye/BzIiAQtbIHAwMOJfgEuI5rarQcxwMHZJ54AlQyqCDSZA2KvAzD8YbCCierr4OmFAiAtKleVYcSCae4iItK5MWY7DkenYeiZBmor7pJvE6zP128M4Hp2B+zt7YXo/f2TkF+OFn8/gq7031F7Pm1vO4+1tF6XXeuaAQAxt64adl29h/JcHcP1Wjsa2S0REpAkdvB0wuI2r9HftO37HItIv0fvlsvRxh4EOE4Bes4D8dGDTHCDif+qv57cXgT9fBcrLgD7zgDajgat/AkuHASlXNLddDTIpLy/nNzMNyM7OhqOjI7KysuDg4ACdI172Y0uBHTC/yIMAACTtSURBVG8BZcWAvRcwaSkQFKbtkRGRDtt3NQUzVx6HrYUpDi0cITU7IyLShu0XkvDvXy8hKauw6jEvRyu8Pa4DxnaSyxMY7XGejiktU2LEJ+HSa7Xp6QHo5OMoPZ5bVIpJXx9EVGoedr48pMEMhUqHItPw2LKj6BPojB/m9IGlman0+MEbaZj+3VH0DnTG+vn9m327lfi6ExGRJuy/noonvjsGa3NTHH59OJxsLLijibTgro71ykqBL3sB2YnAnJ2AV1f58aIcYPkoIP068OyxhjMUKkVHAKvGAf4DgCe3AGaW8uNR+4DVE4GAAcBTfzT/djWMGQ7GKC8d+OlR4M9/yMGG0PuABQcZbCCiRg0JdUM7T3vkFZdhzdFY7jFqMl6hTvcabHh6zalawQYhOatQelw8T7pDNMOMTc/HhK7eVSf9BTtLMzw/vI2UObfhRHyj6/n5mLzM30eHVgUbhIGtXRHWxg3HojMQlZrb7NslIiLSpEGtXdHBywEFJWVYc4TfsYj0QnQ4kBkNdJ5afdJfsLQHhrwKKEuB02saX8/JVfJ0+FvVwQYheCjQegQQexBIu9H829UwBhyMjUi7+XYgcO1PwNQCuO9D4NGfAFsXbY+MiPSAiYkJ5g8JluZXHoxBYUmZtodEekycFB70wR48uuyIVN5ETMV9niymxoJUIrNBVYpu5WPieZZX0h1HotKl6eBQt3rPiUCBcDQ6Q6312FiYolegc/31hNZfT3Ntl4iISNPfseaFyd+xvj/E71hEenFRWswBeRoyrP5zIcPlqQgWqLMec1vAv5+K9YyoWM+B5t+uhrFptLEQKTfhiypqeZUDLm2AqSsBz87aHhkR6ZkHu3jjo+1XkZhViM2nE/BoHzaYp3u/Qr3uoWPlFerfTO+h8bI4hkhUyhTH4+KgXLqVy1NlxXzltLRMLFfxnDRF1by46rv68eqfqVpnzZ+pXGfd7dVYvnKd8nI1fqZiWm97VT+LOj8r/0x6TlG9zIZa+0C0qMoqlK527x/CCyp0QUx6njQNcqlfukiU5nO2tUBMmrxMQ/KLS5GSU4S2HvYwVZjUez7IVW6yWXM9zbFdIiKilvBAFy98uP2K9B1r06kEPNaX37HIOGmzbOpdEc2dBVWli6xbATYuQHrFMg0pzgNykwH3DoCiOnu3SuW6a66nObbbAhhwMAa344Bf5gDxR+X73afLmQ2i6zkR0V0yN1Vg9uBg/N9vl7AsIgoP9/KDQsXJH6J7vUJdvJve2XYJ/YJdYAITlCqVFSen65zgVnkSvPpkuPg5VT9TeZK77knxypPhZWVie6h1or3eSfeqdconxavGU1b7xH7dk+/yclBzPHLwoOr3qBM0UP27831XKSWn4aAEtaycwlJpam+l+quHKHEkgo1NW4fcUyi7Yrnm2G5RaRmKS5U11ldyxzESERE15TvWrEFBePf3y1i+PwqP9OZ3LDI+OnNRWmE2ULOViih1VLPcUeUygmUDvR4s7eU+C41tp7F1CEXZzbvdFsCAg6G7tBXY9jxQmCW/GR9cDHSeou1REZGeEwfAn+++jqi0POy8fAtjOnpqe0jUDMSJ7KJSpXSSrbBEnor7onSW9HjJ3T1X+/kyFFZMs/JLkJR95yvUk7ML0e0/O/m6NjMRGxRXhytMTKSpqYmJFDCU5ivuS88rUP1c5bIVt9o/C5gpFBXL1V535c+a1ZiXt1W97prP1Vp3xXz18xXrVphIV6Qv2x/d6O/qbm/F9w81ydd7I/HZ7utV95VF+dyjRESkMY/08ed3LDK6758lSqWUfS2+T/5r68U7XpQmLlob1cFTZbZrs1rcAbCssY0hC4Fhr2t2mwZGbwMOIq36r4vJ2HUpBZeSspF4uwAWZgq093LA4339MaGbz12tT5wQWRoRhW1nExGXkQ8rMwW6+7fC88Nbq6wTq1OUZUDsISD3FmDnIXcwLy0C/nodOPm9vIxPL2DKd0CrQG2PlogMgK2lGZ7oF4Av997AN/tuwMHKTCp1IU7w9Qly1vwBgIEfdBWX1T6RXyhO4Ks6yV/xePV9Fc9VnOSv/Vz189XLlaFEXHqvo0xM6p8Arzx5bqpQ1DqJXfNEulm9E+B1TrjXWb76hHvNn61zIr1iWu9EesWydbdb9yR91WM1f6bOdmv+Lqp+ttbPNPS71AgKVP6sqBGs70Qmx2/nkqQrnVS9Y8Vv6OkofxaRbqjMMKjMOKgrt6i0wSwE9dchZx+Iv0fNtd1nhoVgzuCgqvvZ2dnw/fSOwyQiIrpnIvNuer8AfL0vEkvCI+FgZS5lbPI7Fqk6Hi4pU0pZ0qVlSul7nMiKLimtPoGv6vnKx6uWF1Nxv2I56fmK5cT94oqpWE9J3edr/vydtle13cp1VG/vbrKzW7Rs6kuXAIcaGQR1sxsEK4f62Qc1FeU0nIVwN+sQaq6nObbbAvQ24CDeYC+tO4tWNuYY0NoVYzt5Ij23CNsvJEuNJ0/FZuLfEzqptS5xAubx5UdxMjYT7TztMb1vALILS6R1Pbz0CL55vAdG6+rVu5e2Adtfq50uY+sOmJoD2Qny1+5BLwLD3pQfIyJqJjMGBOLb8Eicic/Co8uO6nZ9xXuog1/zqv26J/KrruKXpvfwXL1AQnVQQAQbdIE46W1ppoCVuak0taw7rfmcmSkszRWwqpjWe67WehSITMnFO79eanQMq2b1xoAQ16oT6ESCCJyIzxiRVi3eFTW/p1S+S8TzDHzqjsCKHgrR6Xno7OtY6zmR8ZSRV4yeAa3uuA4bCzO421siPjNf+pJd9/WNTpOzDwJdbZttu/LnV3U93fJiHksTEZFmzRwQKF0MeyruNh5ddsSgvmPpwnc8cQyh8uR5mXzRV82T8jWXqz65XnnCXsXJ/ornxby0LpUn8+v+TPVJe3l79U/wS+srrdyePOZy3b1OzDDKpoqT+pUn9hviXKO/gnf32s8VZAL56YBf3zuvQ5S6t/MEMmPli8nr9nFIV9GvoTm22wL0NuAgIryfPdIN93f2kmrdVXp1TDtM/OogVh2OxaQevujq59ToulYfjpGCDQ909sLnj3av+gIjshse/PwAXt90XgpqiGizzgUb1j9Z56s2gLwUeWrlCExdpbpzORFRE52MzZAOeqCh+opVJ/3rXI1f94S+yhI/DTxXN1ugofI/NWt2a5P4e2RV4yR/9Un86pP8qk72i8eszFWf7K/7XO2flZ+zMFXArMbf1uYmgghLIqIavUJ9UGs3njQmlcRni/iMqdtQTrxv+GVc9/QNdpGu1tx/LRXju3rXei7ieqq8jBoZKWI9v55NxImYDGm+1nqu1V9Pc22XiIiopZyKy9Tod6x7/V5W82R33RPp1SfNG3q+7sn12lfH1w0AiKv0G3q+6vF7vJrfkJmbikxp8T3OpOL7nHxfelzcV5hI50/F4+YVy4n7NX9Oel5kXNd4XEyrfk7F8/Ljlc9Vb8+8Yrna26tcV/3nxfmF6d8d05+yqYEDgQOfAJF765euj9wjTwMGqreeC78AcUfk+Vrr2V2xnkHNv10N07Ez6Orr4O0g3epys7fEY3398dFfV3E0Ol2tgMOOi7ek6Ysj29Q6sRHgYoupvfyw4mA0/jifhGm9/KAzRORLZDaoPFVTwdwGCApryVERkZE1/VWl8lPp1Q3ncD4hSzp532j9/5rBgRqP6QLxZ6HyhH3Nq/arTtpXnuS/wxX9NU/o1w0cSPMNPKfJk/7axCvUqTmIL9uihqvIemW5Ad02MMQF/s422Ho2ETMHBqKjt2NVSaMv9lyXvrhO6elbtbzIPBA3Z1sL6Vbp0T5+UsDh4x3XsGZOX6mcqnDwRpoUQBBltILd7O55u0RERLr+HesfG89JZcBFTKLuyfWqq+obLJVTJ0BQ72R97Sv7q0/yG+6JelFttPJkeK0T8tIJ9bon1xs6EV958vxOJ9pr/1zdAIB43MKs/on/ms9Xncivs16pHKsBlE7tH+IqZfLoTdnUoKFy2frzG4C+8wGvLtUljcI/AhRmQLfHq5fPS5ezD2xcANsaF870nCkHHPa8Czy5FTCrOPaN2gfc2C0HD1xb3/t2tURvAw53Iv7xCaKmszrScoukqZ+zTb3n/JytpenhyHTdCjiIng2NdR3PSZKXCxrcUqMiIiMhTvDVvKpYlZyiUny1tyIFsInEsZNVnZP8d7qiv7r8zx2u9q9YrmYGQd0SQlYGfNJf23iFOjUH8eVK4zVcqcnE5+iiyZ0xY8UxTPv2MMZ385Yyh7dfTEZ8RgFeGR1aK1Cw6lCM1Kz5hRFt8NKo0FrZUY/09sPPx+PxwOf7MbydO1Jzi6SeHmJ9703s1KTtEhER6fp3rOzCUvz3jyvQhYuyap5cr3uVfGMn1+908rzu+qqu1m9we5XrvPPV/JXz5mbVz7MEp+7Qu4vSTM2A8V8AP0wCVt4HdJoMWNoDl38FbscCw9+qHSg4thQIX1S/AbW4ULzHk8Cp1cCSwUCb0UBeKnBhk9yL4YFPmrZdLTEzxIjwplMJ0smpQa1d1foZceVUTHo+4jPy0cbDvtZz4suIEJWWB50iGkQ353JERBqomxjWxhXtvRwaPKGv8mp/Fc+ZGcAVG1Qfr1AnMh4iWLBhwQAs3nlNChCIqyZDPezx91FtMbG7j9rr+e9DnaWea2uPxWHloRjYWphiZHt3vDK6rcrgQXNtl4iISFe+Y/Xwd5J6Fsknz+ucwG/k6vo7XT1ft2yOqgABT9STJundRWkiWDDrL2Dff4GLW4CyYsC9nXzSv8s09dfz4GeARyfgxErg6BK5t0PbscDwf6kOHjTXdjXIpFwUYzMgH26/ItVqndbLFx9O6arWz3y++zo+2XkND3bxwmePVPdwEAGI+z/bL12lG+xqiz2vDG1wHaIUSM2a39nZ2fD1cEVWVhYcanY2by7R+4FVDza+3IzfmOFARM1OZH3VbGLWkJ/m9uPVx0RkcMRxnqOjo+aO80gn8XUnIiJN4ncsouqLybVRNpXHegaU4dD9PzuQmV+i9vJ3Onm19micFGzo6O2At8d1VHudswYFSfVgxVVPkal5GBDiguyCEmy/kAyfVta4kpwDRSNv7K/3Rkqp35WURfnQqIABgIM3kJ3UQB8HE/l5sRwRUTMTf/D1qr4iERERERGRDuN3LCIZy6bqP60HHMZ39UZuUZnay4um0KqsPxGPN7ecl1Ks18zuC1tL9X81Uct149MDpEyHvy4mY/XhGLjYWuKxfv4Y0c4D05YcrtWwTpVnhoVgzuCg2hkOn0JzFKbA2A+A9U9WnNpTUd1s7CJ5OSIiY6+vSEREREREpMP4HYuIDIVBlFRafzweCzedQ2t3OykDwsVOdVDiXmw4EY9XN57DnEFBeOvBDrqXhnNpG7D9tdoNpB185GBDh/Ga2y4REYDtF5Lq1Vf00tX6ikREzYTp1saJrzsREbUEfsci0g4e6xlQhkNzBBteE8EGNzusbeZgg7D1jHwif1xXb+gkEVRo9wAQe0huEG3nIZdRYmYDEbUANv0lIiIiIiLidywiIoMIOKw7HoeFm84jpCLY4NpIsKGguAwJtwtgbWEKHyfrWs/lFJbA3sq81mPL90fhwI00jOnoga5+TtBZIrgQNFjboyAiI8X6ikRERERERPyORUSk1wGHQzfSpGCDKAglGuusORJbb5kO3g4Y09Gz6v6Z+Nt4dNkR9A1yxrr5/Wst2++/u6Vm1IEutjAxAY5EZeB8Qha6+Driw8ldW+R3IiIiIiIiIiIiIiLSV3obcBCZCpXdJ9YejVO5zOQevrUCDncysbsPDkel4+CNdCngEORqizfub4cZAwJhacbGy0RERETURAkngb3vAzePAWUlgFs7oN8zQJep6q9DqQSOLwdOfg9kRAIWtkDgYGDEvwCXkPrLiwPmy78Cx5YCadeAwmzA0QcIHAQMfBFwDuLLSkREREREzcYgmkbrIjYaISIiIjJM93ScF70fWDMJMLUAOk0CLB3kQMDtWGD4P4GwV9Rbz7a/AadWycGKNqOBvFTgwibAzAqYvQNwb1d7+b/eBA5/Cdh5Au3uByztgeQLQOQewMJO/hmPDne/E4wQj++JiIiIDBeP9ZqP3mY4EBERERHphbJSYNvz4lof4Kk/AK+Kcp1DFwLLRwH73gc6PqQ6Q6Gm6Ag52OA/AHhyC2BW0b+s6yPA6onA7y/L66+Ucws48jXg5A8sOAhY1QiOHP4a+Ot14PBXwMSvNPFbExERERGREVJoewBERERERAYtOhzIjAY6T60ONggi22DIq4CyFDi9pvH1nFwlT4e/VR1sEIKHAq1HALEHgbQb1Y/fjgPKlYBfv9rBBiF0jDwVGRJERERERETNhAEHIiIiIiJNijkgT0OG1X8uZLg8FcECddZjbgv491OxnhEV66nYliAyJkQJp/gjQFFO7eWv75CnQWHq/hZERERERESNYkklIiIiIqJ7IRowW9Q8srasnXlQSTR3FlSVTLJuBdi4AOkVyzSkOA/ITQbcOwAK0/rPV6675npsnOX+EDv/CXzZB2h7H2BpB9y6BETtA3rOBPrOV+93JSIiIiIiUgMDDkRERERE92JxB8DSpPr+kIXAsNdVByYE0ShaFVFaKTvxzttSZx1CUcVylQb+DbD3An57ETjxXfXjfn2BLg8DpuZ33i4REREREdFdYMCBiIiIiOhevHQJcKgRAFCV3aBt4R8B4R/IDaq7PgpYOwHJ54G/3gC+fxCY+j3QYby2R0lERERERAaCPRyIiIiIiO6FaMRc89ZQwKGyYXPd7INKor9CQ5kLd7MOoeZ6osKBve8CfeYBYa8Ajj6ARUUPiMfWA+bWcuCBiIiIiIiomTDgQERERESkSc4q+itUKsgE8tNV93eoSQQK7DyBzFhAWVb/+cp111xPVWPowfWXt3WV+0FkxQN56XfxyxARERERETWMAQciIiIiIk0KHChPI/fWfy5yjzwNGKjeekrygLgjKtazu2I9g6ofKyuWp3lpqteXX/G4Wc3O10RERERERPeOAQciIiIiIk0KGgq0CgTObwCSztUugyR6LCjMgG6PVz8uMg5Sr9XPPOg5U57ueRcorQgmCFH7gBu75aCFa+vajaGFw18BhVm113VmLZARBXh1q244TURERERE1ERsGq0h5eXl0jQ7u4E6u0RERESklyqP7yqP9xplagaM/wL4YRKw8j6g02T5JP/lX4HbscDwt2oHCo4tBcIXAUMWAsNer348KAzo8SRwajWwZDDQZjSQlwpc2CT3bnjgk9rb7fgQcPJ7IGY/8HkPoO19FU2jLwBRewFTS2DsombZJ8aAx/dEREREhuuuj/GpQQw4aEhOjty4z8/PT1ObICIiIiItH+85Ojqqt7AIFsz6C9j3X+DiFrnckXs7OdjQZZr6G33wM8CjE3BiJXB0idzboe1YYPi/agctBIUpMP0X4Mg3wMVNwIVf5O3augOdpwKDXgY8OtzdL23EeHxPREREZPju6hifVDIpZ9hGI5RKJRITE2Fvbw8TExO0RBROBDfi4+Ph4OCg8e2RYeH7h/geIn4Gkb7Sxt8wcfgsvoh4e3tDoWCFUmPR0sf3Ao/RyJjx/U/Gjv8GyJjxGF+/McNBQ8SXT19fX7Q08UWbAQfi+4e0hZ9BxPcPGcvnD696Mj7aOr4X+PeVjBnf/2Ts+G+AjBmP8fUTL8kiIiIiIiIiIiIiIqImY8CBiIiIiIiIiIiIiIiajAEHA2FpaYm3335bmhLx/UP8DCJ9wr9hxPcPET8fiXh8QMRjZCJ+RzQMbBpNRERERERERERERERNxgwHIiIiIiIiIiIiIiJqMgYciIiIiIiIiIiIiIioyRhwICIiIiIiIiIiIiKiJmPAgYiIiIiIiIiIiIiImowBBwPx9ddfIygoCFZWVujZsyf279+v7SGRHnjnnXdgYmJS6+bp6antYZGOioiIwLhx4+Dt7S29V7Zs2VLr+fLycuk9JZ63trbG0KFDcfHiRa2Nl/Tr/TNz5sx6n0f9+vXT2nhJt7z//vvo3bs37O3t4e7ujokTJ+Lq1au1luFnEBkSHqORMeExJhkzHiOTMeMxvuFiwMEArFu3Di+++CLefPNNnD59GoMHD8Z9992HuLg4bQ+N9EDHjh2RlJRUdTt//ry2h0Q6Ki8vD127dsWXX36p8vkPP/wQn3zyifT88ePHpeDVqFGjkJOT0+JjJf17/whjx46t9Xn0xx9/tOgYSXeFh4fj2WefxZEjR7Bz506UlpZi9OjR0vuqEj+DyNDwGI2MBY8xyZjxGJmMGY/xDZdJubgcjPRa37590aNHD3zzzTdVj7Vv3166+k9EC4nudPWcuMr4zJkz3El0V8TV55s3b5Y+ZwTxp0RcuS6Cn6+99pr0WFFRETw8PPDBBx9g/vz53MPU4PunMsPh9u3b9TIfiFRJTU2VMh3El5SwsDB+BpHB4TEaGSseY5Ix4zEyGTse4xsOZjjoueLiYpw8eVK6yq8mcf/QoUNaGxfpj+vXr0snikVJrkceeQRRUVHaHhLpoejoaCQnJ9f6LLK0tMSQIUP4WURq27dvn3QSOTQ0FHPnzkVKSgr3HqmUlZUlTZ2dnfkZRAaLx2hEPMYkEniMTMaCx/iGgwEHPZeWloaysjLpKuKaxH1x8o+oseyY1atX46+//sKyZcuk98yAAQOQnp7OHUd3pfLzhp9FdK9EKcAff/wRe/bswccffyyV5Ro+fLiUKUNUk8ioevnllzFo0CB06tSJn0FkkHiMRiTjMSYZOx4jk7HgMb5hMdP2AKj5Uu/q/kOt+xiRqoOXSp07d0b//v0REhKCVatWSSdziPhZRC3l4YcfrpoXJ5F79eqFgIAA/P7775g0aRJfCKry3HPP4dy5czhw4EC9vcLjITIUPEYjqo2f72SseIxMxoLH+IaFGQ56ztXVFaampvWyGUQZirpXGhM1xtbWVgo8iBR+orshGkQL/Cyi5uLl5SUFHPh5RDU9//zz2LZtG/bu3QtfX19+BpHR4DEaGSseYxLVxmNkMkQ8xjc8DDjoOQsLC/Ts2RM7d+6s9bi4L0rjEN0NUbrk8uXL0kEM0d0QPUDEF8Kan0Wix4xo6MrPIroXorRbfHw8P4+oKnNTXPW0adMmqeyW+MzhZxAZEx6jkbHiMSZRbTxGJkPCY3zDxZJKBkCUvnniiSek8hOiJM7SpUsRFxeHBQsWaHtopONeeeUVjBs3Dv7+/lJWzLvvvovs7GzMmDFD20MjHZSbm4sbN27UahR95swZqWmreA+9+OKL+O9//4s2bdpINzFvY2ODxx57TKvjJt1//4jbO++8g8mTJ0sBhpiYGLzxxhtSFt9DDz2k1XGTbnj22Wexdu1abN26Ffb29lXZVI6OjrC2tpZKbfAziAwJj9HImPAYk4wZj5HJmPEY34CVk0H46quvygMCAsotLCzKe/ToUR4eHq7tIZEeePjhh8u9vLzKzc3Ny729vcsnTZpUfvHiRW0Pi3TU3r17y8Wfjbq3GTNmSM8rlcryt99+u9zT07Pc0tKyPCwsrPz8+fPaHjbpwfsnPz+/fPTo0eVubm7S55G/v7/0eFxcnLaHTTpC1XtH3FauXFm1DD+DyJDwGI2MCY8xyZjxGJmMGY/xDZeJ+J+2gx5ERERERERERERERKTf2MOBiIiIiIiIiIiIiIiajAEHIiIiIiIiIiIiIiJqMgYciIiIiIiIiIiIiIioyRhwICIiIiIiIiIiIiKiJmPAgYiIiIiIiIiIiIiImowBByIiIiIiIiIiIiIiajIGHIiIiIiIiIiIiIiIqMkYcCAiIiIiIiIiIiIioiZjwIGISE8NHToUL774InRFeXk55s2bB2dnZ5iYmODMmTNNWo6IiIiIyNjwGJ+IiPQdAw5ERPdo5syZ0gnzRYsW1Xp8y5Yt0uPGZvv27fj+++/x22+/ISkpCZ06dWrScvr8xYyIiIiI9BOP8WvjMT4REd0tBhyIiJrAysoKH3zwATIzMw1mPxYXF9/Tz0VGRsLLywsDBgyAp6cnzMzMmrScvvzeqpSWljbbuoiIiIioZfEYvxqP8avxGJ+ISD0MOBARNcHIkSOlk+bvv/9+g8sEBgbi008/rfVYt27d8M4779S6Qv/555+XrtJv1aoVPDw8sHTpUuTl5eGpp56Cvb09QkJC8Oeff9Y76H3uuefg5OQEFxcXvPXWW1LJokpi/sP/b+9uQ2v8wwCOX//ZbJrnWBFbREwYkiYnipAaorwYjtWWpxcmwkx5zkOnNOaFmPJQJNFeyAtphZhnkvBC2ULUxIuRZ7v/XVfdp3POzjnOOfcK2/dTp3buc9+/h/vU+p3fdf9+VyAggwcPli5dukhBQYGcO3curAytW8tYu3at9OnTR6ZPnx61H9++fZPy8nLJycmxH2E+n0/u3r0bfBJM2//y5Utb3aF9jibaefrUlJbl9qGoqMh+2IRqaWmxwM6QIUMkMzNTcnNzZdeuXcEyr169KgcOHLAy9dXY2Bi3vfH6rfdn1KhRdr+0Pfod6/cQi9aldep1kydPtvbV1tbGPB8AAAB/N8b4jPEZ4wNA6gg4AIAHnTp1kt27d8vBgwfl9evXnu7liRMnbOL7zp07Nim/cuVKWbBgga0EePDggcycOVP8fr98/vw57BpdIXD79m2prq6WqqoqOXr0aPBzDUAcO3ZMDh06JE+ePJE1a9bI4sWLbYI+sm4t58aNG3L48OGo7duwYYOcP3/eztX26OS/tunDhw822b9jxw4ZMGCAbZMUOrEfKtp5Opmvk/76d11dnaSlpcm8efMsyOCqrKy0gMPmzZvl6dOncvr0aQvKuGVOnDhRli5damXqa+DAgXHbG6vfem1xcbGUlpbKs2fP5MqVKzJ//vywIE4kNweF2z69zzNmzEjgGwcAAMDfiDE+Y3zG+ADggQMASElJSYkzd+5c+7uwsNApLS21v2tra3V2OnheXl6eU1VVFXZtQUGBs3Xr1uD7KVOmOD6fL/j+58+fTnZ2tuP3+4PH3r59a+XevHkzeE1+fr7T0tISPKeiosKOqU+fPjlZWVlOfX19WN1lZWVOcXFxWN1jxoyJ21ctKyMjwzl16lTw2Pfv353+/fs7gUDA3msfta+/87vzmpqarJ+PHz+2983NzU5mZqZTU1MT8xrtw+rVq5Nqb7R+379/3+pubGx0ErVt2zb7rhoaGhK+BgAAAH8nxviM8RVjfABIHSscAKAN6NPt+rS8Pn2fqtGjR4c9VaXb+ejWPi73if6mpqbgscLCwrAE1fqk//Pnz+XXr1/Wlq9fv9pWQV27dg2+Tp482WrLovHjx8dtm57/48cPmTRpUvBYRkaGTJgwwVYCeKFlL1y40LZ96t69uwwaNMiO67ZLSsvX7ZGmTZuWVJmJtDey37rllNaj911Xl9TU1Pw2P4c+/TRnzpyY20gBAADg38QYP3WM8QGg4yLgAABtQPfu1+16Nm3a1PofbVpaqy15dDI8kk6Ih9JAQugxN7AQutVQPO55Fy9etElx96WBiMg8DtnZ2XHLctsfGtxwj0ceS9bs2bPl/fv3NrmvW0PpKzSJs+ZSSFai7Y3stwZ6Ll++bLkyRowYYVtlDRs2TBoaGmLW9ejRI8sHAQAAgPaFMX7qGOMDQMdFwAEA2sjevXvlwoULUl9fH3a8b9++lhvA1dzcHHcCOxm3bt1q9X7o0KE2ca4T5prAWFcKaP6C0JfmOEiGXtO5c2e5fv16WNDk3r17kp+fn3L7NdCgKw4014SuLNCyIlcUaH806KD5HWLRtumqjrZorwYkdGXE9u3b5eHDh1ZOrCTQ+l1qQrmxY8cm0WsAAAD8KxjjJ48xPgB0bOl/ugEA0F7oNjyLFi2yp+JDTZ06VY4fP25P+fTq1csSC2tAoC28evXKEi4vX77cEiNr3fv27bPPunXrJuvWrbNE0brawefz2QS5BkR0a6WSkpKE69GVAJrEev369dK7d2/Jzc2VQCBgCazLyspSbr/eD9066siRI9KvXz8LjmzcuDHsnKysLKmoqLAk0Dr5r8GAd+/eWXJmt27dzkhXRujkv/ZN25hKe7UMDWxo0uecnBx7r3XFClLo6gZdwRK69RUAAADaD8b4yWOMDwAdGwEHAGhDO3fulLNnz4Ydq6yslBcvXkhRUZH06NHDzmmrFQ5LliyRL1++WG4CDWKsWrVKli1bFtYenTjfs2ePtaFnz54ybty4qFs/JfJ0lwYu/H6/fPz40fIfXLp0yX5QpEon68+cOSPl5eUycuRI276ourq61RZFGqRJT0+XLVu2yJs3byw4sWLFiuDnGljRAIqu6tD7ofc3lfZqDolr167J/v37LTiTl5dnAZxZs2bFDDgMHz7cgiIAAABonxjjJ4cxPgB0bP9p5ug/3QgAAAAAAAAAAPBvI4cDAAAAAAAAAADwjIADAAA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" ] @@ -266,10 +267,10 @@ "id": "060c9dd4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:54:31.050136Z", - "iopub.status.busy": "2026-09-23T13:54:31.049999Z", - "iopub.status.idle": "2026-09-23T13:55:17.733415Z", - "shell.execute_reply": "2026-09-23T13:55:17.733073Z" + "iopub.execute_input": "2026-09-29T07:42:21.451428Z", + "iopub.status.busy": "2026-09-29T07:42:21.451318Z", + "iopub.status.idle": "2026-09-29T07:42:57.928254Z", + "shell.execute_reply": "2026-09-29T07:42:57.926290Z" }, "papermill": { "duration": 52.24844, @@ -285,1870 +286,65 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'d': 30, 'n': 2926, 'p': 3221, 'r': 10}\n", - "'Estimating initial latent variables with GLMs...'\n", - "'Fitting nb GLM (structured, 1 covariates + 29 treatments)...'\n" + "'Fitting GCATE (step 1)...'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "'Estimating initial coefficients with GLMs...'\n", - "'Fitting nb GLM (structured, 11 covariates + 29 treatments)...'\n" + "'Fitting GCATE (step 2)...'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'kwargs_es': {'max_iters': 30,\n", - " 'patience': 5,\n", - " 'rel_tol': 0.0002,\n", - " 'tolerance': 0.0,\n", - " 'warmup': 0},\n", - " 'kwargs_glm': {'disp_glm': array([ 1.31485844, 2.49333886, 0.66275436, ..., 12.58894812,\n", - " 17.78187864, 10.8189429 ]),\n", - " 'family': 'nb',\n", - " 'size_factor': array([0.53193358, 0.87362742, 1.2235467 , ..., 0.5593801 , 0.73025856,\n", - " 0.77857223])},\n", - " 'kwargs_ls': {'C': 1000.0,\n", - " 'alpha': 0.1,\n", - " 'beta': 0.5,\n", - " 'max_iters': 20,\n", - " 'recheck_interval': 10,\n", - " 'sparsity_boost': 2.0,\n", - " 'sparsity_threshold': 0.5,\n", - " 'tol': 0.0001,\n", - " 'tol_cell': 0.0001,\n", - " 'tol_gene': 0.0001,\n", - " 'warmup_iters': 0}}\n", - "'Fitting GCATE (step 1)...'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/30 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
in sampleout of fold
aucoverlap_ratioess_treated_fractionaucoverlap_ratioess_treated_fraction
treatment
Satb20.9700.1340.7320.9270.2500.537
Med13l0.9090.2210.4490.8710.3110.365
Mbd50.9270.2310.6850.8960.2740.486
Asxl30.9330.2400.9380.9030.3310.906
Upf3b0.9230.2500.8030.8940.3100.712
Scn2a10.9180.2690.7460.8780.3080.544
Syngap10.8750.2860.7410.7900.4820.541
Setd50.8970.2870.5830.8510.3950.328
\n", - "" - ], - "text/plain": [ - " in sample out of fold \\\n", - " auc overlap_ratio ess_treated_fraction auc \n", - "treatment \n", - "Satb2 0.970 0.134 0.732 0.927 \n", - "Med13l 0.909 0.221 0.449 0.871 \n", - "Mbd5 0.927 0.231 0.685 0.896 \n", - "Asxl3 0.933 0.240 0.938 0.903 \n", - "Upf3b 0.923 0.250 0.803 0.894 \n", - "Scn2a1 0.918 0.269 0.746 0.878 \n", - "Syngap1 0.875 0.286 0.741 0.790 \n", - "Setd5 0.897 0.287 0.583 0.851 \n", - "\n", - " \n", - " overlap_ratio ess_treated_fraction \n", - "treatment \n", - "Satb2 0.250 0.537 \n", - "Med13l 0.311 0.365 \n", - "Mbd5 0.274 0.486 \n", - "Asxl3 0.331 0.906 \n", - "Upf3b 0.310 0.712 \n", - "Scn2a1 0.308 0.544 \n", - "Syngap1 0.482 0.541 \n", - "Setd5 0.395 0.328 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from causarray import refit_propensity_scores, tune_penalty_factor\n", - "\n", - "W_A = np.c_[X_A, U]\n", - "propensity_names = ['intercept', 'log_library_size'] + [f'U{k + 1}' for k in range(U.shape[1])]\n", - "A_np = A.to_numpy(dtype=float)\n", - "pert_names = list(A.columns)\n", - "\n", - "# In-sample scores match what LFC uses; the out-of-fold fit below checks\n", - "# whether that fit generalises.\n", - "pi_before = estimate_propensity_scores(\n", - " A_np, W_A, K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", - "pi_oof = estimate_propensity_scores(\n", - " A_np, W_A, K=5, C=1.0, class_weight=None, clip=None, random_state=0)\n", - "support_before = summarize_propensity_scores(\n", - " A_np, pi_before, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", - "oof_before = summarize_propensity_scores(\n", - " A_np, pi_oof, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", - "display(pd.concat({\n", - " 'in sample': support_before[['auc', 'overlap_ratio', 'ess_treated_fraction']],\n", - " 'out of fold': oof_before[['auc', 'overlap_ratio', 'ess_treated_fraction']],\n", - "}, axis=1).sort_values(('in sample', 'overlap_ratio')).head(8).round(3))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e8a79c4e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-23T13:55:18.097396Z", - "iopub.status.busy": "2026-09-23T13:55:18.097246Z", - "iopub.status.idle": "2026-09-23T13:55:18.242139Z", - "shell.execute_reply": "2026-09-23T13:55:18.241843Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "weakest overlap: ['Satb2', 'Med13l', 'Mbd5', 'Asxl3']\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ps_focus = support_before.nsmallest(4, 'overlap_ratio').index.tolist()\n", - "print('weakest overlap:', ps_focus)\n", - "\n", - "def overlap_panels(pi, title):\n", - " ctrl_mask = A_np.sum(axis=1) == 0\n", - " fig, axes = plt.subplots(1, len(ps_focus), figsize=(3.6 * len(ps_focus), 3.4), sharey=True)\n", - " for ax, pert in zip(np.atleast_1d(axes), ps_focus):\n", - " j = pert_names.index(pert)\n", - " treated = A_np[:, j] == 1\n", - " bins = np.linspace(0, max(pi[ctrl_mask | treated, j].max(), 1e-3), 30)\n", - " ax.hist(pi[ctrl_mask, j], bins=bins, density=True, histtype='step',\n", - " color='#4c78a8', label='control')\n", - " ax.hist(pi[treated, j], bins=bins, density=True, histtype='step',\n", - " color='#e45756', label='treated')\n", - " ax.set_title(pert, fontsize=10)\n", - " ax.set_xlabel('propensity score')\n", - " for side in ('top', 'right'):\n", - " ax.spines[side].set_visible(False)\n", - " np.atleast_1d(axes)[0].set_ylabel('density')\n", - " np.atleast_1d(axes)[0].legend(frameon=False, fontsize=8)\n", - " fig.suptitle(title, y=1.02)\n", - " fig.tight_layout()\n", - " plt.show()\n", - "\n", - "overlap_panels(pi_before, 'Before: library size unpenalized')" - ] - }, - { - "cell_type": "markdown", - "id": "87689643", - "metadata": {}, - "source": [ - "### Tuning the library-size penalty\n", - "\n", - "`prep_causarray_data` appends standardized log library size to `X_A`. For a few perturbations it differs enough between the perturbed and control cells that the propensity model can tell them apart almost perfectly. Measured library size mixes capture depth with total RNA content, and these data do not identify which of the two drives a given arm's shift, so how much to adjust for it is a modelling choice rather than a settled one.\n", - "\n", - "`tune_penalty_factor` penalizes **that coefficient alone**, and only for arms failing a pre-specified support check. Dropping the covariate is the infinite-penalty limit, so it bounds what any finite factor can achieve: the search evaluates that endpoint first, then returns the smallest factor meeting the target. The report shows both, so the penalty can be read against the limit rather than on its own.\n", - "\n", - "Choose the target to match the failure. These arms lose histogram overlap while keeping most of their treated sample, so overlap is the target; a screen whose arms instead lose effective sample would target that." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7dd65707", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-23T13:55:18.243589Z", - "iopub.status.busy": "2026-09-23T13:55:18.243466Z", - "iopub.status.idle": "2026-09-23T13:55:18.447905Z", - "shell.execute_reply": "2026-09-23T13:55:18.447601Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[tune_penalty_factor] 6 treatments triggered, 6 feasible, 48 fits\n" - ] - }, - { - "data": { - "text/html": [ - "
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treatmentpenalty_factorfeasiblen_fitsoverlap_ratio_unpenalizedoverlap_ratio_chosenoverlap_ratio_droppedauc_unpenalizedauc_chosen
3Satb227.384True80.1340.3380.3190.9700.875
1Mbd517.783True80.2310.3340.7370.9270.896
0Asxl310.000True80.2400.3140.7220.9330.911
2Med13l8.660True80.2210.3080.5740.9090.888
4Scn2a17.499True80.2690.3120.7710.9180.904
5Upf3b5.623True80.2500.3010.6770.9230.909
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" - ], - "text/plain": [ - " treatment penalty_factor feasible n_fits overlap_ratio_unpenalized \\\n", - "3 Satb2 27.384 True 8 0.134 \n", - "1 Mbd5 17.783 True 8 0.231 \n", - "0 Asxl3 10.000 True 8 0.240 \n", - "2 Med13l 8.660 True 8 0.221 \n", - "4 Scn2a1 7.499 True 8 0.269 \n", - "5 Upf3b 5.623 True 8 0.250 \n", - "\n", - " overlap_ratio_chosen overlap_ratio_dropped auc_unpenalized auc_chosen \n", - "3 0.338 0.319 0.970 0.875 \n", - "1 0.334 0.737 0.927 0.896 \n", - "0 0.314 0.722 0.933 0.911 \n", - "2 0.308 0.574 0.909 0.888 \n", - "4 0.312 0.771 0.918 0.904 \n", - "5 0.301 0.677 0.923 0.909 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "penalty_factors, tuning_report = tune_penalty_factor(\n", - " A_np, W_A, 'log_library_size',\n", - " treatment_names=pert_names, covariate_names=propensity_names,\n", - " trigger={'auc_gt': 0.9, 'ess_treated_fraction_lt': 0.5},\n", - " target={'overlap_ratio_gt': 0.3},\n", - " K=1, C=1.0, class_weight=None, random_state=0, verbose=True,\n", - ")\n", - "display(tuning_report[['treatment', 'penalty_factor', 'feasible', 'n_fits',\n", - " 'overlap_ratio_unpenalized', 'overlap_ratio_chosen',\n", - " 'overlap_ratio_dropped', 'auc_unpenalized', 'auc_chosen']]\n", - " .sort_values('penalty_factor', ascending=False).round(3))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9b2abbf3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-23T13:55:18.449369Z", - "iopub.status.busy": "2026-09-23T13:55:18.449248Z", - "iopub.status.idle": "2026-09-23T13:55:18.725697Z", - "shell.execute_reply": "2026-09-23T13:55:18.725385Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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penaltyoverlap beforeoverlap afterAUC beforeAUC aftertreated ESS after
Satb227.3840.1340.3380.9700.8750.681
Med13l8.6600.2210.3080.9090.8880.663
Mbd517.7830.2310.3340.9270.8960.929
Asxl310.0000.2400.3140.9330.9110.958
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" - ], - "text/plain": [ - " penalty overlap before overlap after AUC before AUC after \\\n", - "Satb2 27.384 0.134 0.338 0.970 0.875 \n", - "Med13l 8.660 0.221 0.308 0.909 0.888 \n", - "Mbd5 17.783 0.231 0.334 0.927 0.896 \n", - "Asxl3 10.000 0.240 0.314 0.933 0.911 \n", - "\n", - " treated ESS after \n", - "Satb2 0.681 \n", - "Med13l 0.663 \n", - "Mbd5 0.929 \n", - "Asxl3 0.958 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pi_after, refit_audit = refit_propensity_scores(\n", - " A_np, W_A, pi_hat=pi_before.copy(),\n", - " treatment_names=pert_names, covariate_names=propensity_names,\n", - " penalty_factors_by_treatment=penalty_factors,\n", - " K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", - "\n", - "support_after = summarize_propensity_scores(\n", - " A_np, pi_after, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", - "display(pd.DataFrame({\n", - " 'penalty': pd.Series({k: v['log_library_size'] for k, v in penalty_factors.items()}),\n", - " 'overlap before': support_before['overlap_ratio'],\n", - " 'overlap after': support_after['overlap_ratio'],\n", - " 'AUC before': support_before['auc'],\n", - " 'AUC after': support_after['auc'],\n", - " 'treated ESS after': support_after['ess_treated_fraction'],\n", - "}).fillna({'penalty': 1.0}).loc[ps_focus].round(3))\n", - "\n", - "overlap_panels(pi_after, 'After: library size penalized on flagged arms only')" - ] - }, - { - "cell_type": "markdown", - "id": "perturbseq-association-utilities", - "metadata": { - "papermill": { - "duration": 0.006639, - "end_time": "2026-07-22T11:47:52.486590+00:00", - "exception": false, - "start_time": "2026-07-22T11:47:52.479951+00:00", - "status": "completed" - }, - "tags": [] - }, + } + ], "source": [ - "### Treatment-association diagnostics\n", - "\n", - "The summary compares each perturbation with shared controls using Spearman correlations and standardized mean differences. P-values are BH-adjusted across treatment–covariate pairs by default. Association alone does not identify a confounder or justify removing a covariate.\n", - "\n", - "Library size and latent factors are derived from the measured transcriptome and may reflect perturbation responses as well as technical variation. Their causal role requires scientific justification. Factor labels refer to this fit; they need not retain the same meaning after refitting.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "perturbseq-association-utility-code", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-23T13:55:18.727154Z", - "iopub.status.busy": "2026-09-23T13:55:18.727020Z", - "iopub.status.idle": "2026-09-23T13:55:18.938080Z", - "shell.execute_reply": "2026-09-23T13:55:18.937733Z" - }, - "papermill": { - "duration": 0.167328, - "end_time": "2026-07-22T11:47:52.660451+00:00", - "exception": false, - "start_time": "2026-07-22T11:47:52.493123+00:00", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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treatmentcovariatecovariate_typen_controln_treatedspearman_rhopvaluepadjstandardized_mean_differenceconstantn_tests_in_family
217Satb2log_library_sizeobserved10651-0.6206014.352985e-181.388602e-15-1.670432False319
133Mbd5log_library_sizeobserved106119-0.5251682.377952e-173.792833e-15-1.196548False319
25Asxl3log_library_sizeobserved106130-0.5011712.061826e-162.192409e-14-1.147867False319
325Upf3blog_library_sizeobserved106100-0.5244645.917208e-164.718973e-14-1.193805False319
229Scn2a1log_library_sizeobserved10693-0.4791698.091610e-135.162447e-11-1.070860False319
145Med13llog_library_sizeobserved10675-0.4911242.226940e-121.183990e-10-1.072918False319
241Setd2log_library_sizeobserved10676-0.4504231.771080e-108.071064e-09-0.971646False319
253Setd5log_library_sizeobserved10671-0.4383441.046791e-093.710293e-08-0.952658False319
13Ash1llog_library_sizeobserved106122-0.3905311.003845e-093.710293e-08-0.806900False319
109Fbxo11log_library_sizeobserved106111-0.3943441.730357e-095.519838e-08-0.844210False319
\n", - "
" - ], - "text/plain": [ - " treatment covariate covariate_type n_control n_treated \\\n", - "217 Satb2 log_library_size observed 106 51 \n", - "133 Mbd5 log_library_size observed 106 119 \n", - "25 Asxl3 log_library_size observed 106 130 \n", - "325 Upf3b log_library_size observed 106 100 \n", - "229 Scn2a1 log_library_size observed 106 93 \n", - "145 Med13l log_library_size observed 106 75 \n", - "241 Setd2 log_library_size observed 106 76 \n", - "253 Setd5 log_library_size observed 106 71 \n", - "13 Ash1l log_library_size observed 106 122 \n", - "109 Fbxo11 log_library_size observed 106 111 \n", - "\n", - " spearman_rho pvalue padj standardized_mean_difference \\\n", - "217 -0.620601 4.352985e-18 1.388602e-15 -1.670432 \n", - "133 -0.525168 2.377952e-17 3.792833e-15 -1.196548 \n", - "25 -0.501171 2.061826e-16 2.192409e-14 -1.147867 \n", - "325 -0.524464 5.917208e-16 4.718973e-14 -1.193805 \n", - "229 -0.479169 8.091610e-13 5.162447e-11 -1.070860 \n", - "145 -0.491124 2.226940e-12 1.183990e-10 -1.072918 \n", - "241 -0.450423 1.771080e-10 8.071064e-09 -0.971646 \n", - "253 -0.438344 1.046791e-09 3.710293e-08 -0.952658 \n", - "13 -0.390531 1.003845e-09 3.710293e-08 -0.806900 \n", - "109 -0.394344 1.730357e-09 5.519838e-08 -0.844210 \n", - "\n", - " constant n_tests_in_family \n", - "217 False 319 \n", - "133 False 319 \n", - "25 False 319 \n", - "325 False 319 \n", - "229 False 319 \n", - "145 False 319 \n", - "241 False 319 \n", - "253 False 319 \n", - "13 False 319 \n", - "109 False 319 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "r = 10 # Illustrative rank; inspect the JIC curve and nearby ranks.\n", + "res_1, res_2 = fit_gcate(Y, X, A, r, verbose=False,\n", + " kwargs_es_1=dict(rel_tol=2e-4, max_iters=30),\n", + " kwargs_es_2=dict(rel_tol=2e-4, max_iters=30),\n", + ")\n", + "U = res_2['U']\n", + "print(f\"\\nStep 1 -- epochs: {res_1['n_iter']}, best NLL: {min(res_1['hist']):.6f}\")\n", + "print(f\"Step 2 -- epochs: {res_2['n_iter']}, best NLL: {min(res_2['hist']):.6f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "dcb4ca6f", + "metadata": {}, + "source": [ + "## Propensity-score diagnostics and factor selection\n", + "\n", + "Read this before the effects. An arm whose propensity model separates it from the controls has inverse-probability weights concentrated on a few cells, and its discovery count then reflects the weights rather than the biology.\n", + "\n", + "Guides are assigned at random, so the propensity model only has to absorb chance imbalance between an arm and the controls. Two consequences for its covariates:\n", + "\n", + "* **Library size is left out, as a judgement call.** It could be a confounder, since capture depth and cell quality affect both guide detection and measured expression, or a consequence of the knockout, which can change a cell's total RNA; these data cannot tell which. Most arms here have smaller libraries than the controls (median 64% of the control level). With library size in the model, several arms separate from the controls (AUC above 0.9, last column of the table below), so their weights would rest on a few cells. Leaving it out keeps every arm supported, at the cost of not adjusting for depth-related confounding; the outcome model still normalizes for depth through the size factors. The `LFC` documentation discusses this choice.\n", + "* **Latent factors are included, then checked arm by arm.** A factor can also track a perturbation's own effect; when one does, `select_propensity_factors` drops it for that arm only. It flags an arm whose treated ESS falls below 0.5, overlap below 0.3 or AUC above 0.9, and removes the factor most imbalanced between that arm and the controls, one at a time, until ESS and overlap recover." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4d95d16c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-29T07:42:57.932530Z", + "iopub.status.busy": "2026-09-29T07:42:57.932308Z", + "iopub.status.idle": "2026-09-29T07:42:58.683277Z", + "shell.execute_reply": "2026-09-29T07:42:58.680760Z" + } + }, + "outputs": [ { "data": { "text/html": [ @@ -2162,218 +358,152 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead th {\n", + " .dataframe thead tr th {\n", + " text-align: left;\n", + " }\n", + "\n", + " .dataframe thead tr:last-of-type th {\n", " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
treatmentcovariatecovariate_typen_controln_treatedspearman_rhopvaluepadjstandardized_mean_differenceconstantn_tests_in_familyin sampleout of foldwith library size
226Satb2U9latent10651-0.2847920.0003000.003831-0.647524False319aucoverlap_ratioess_treated_fractionaucoverlap_ratioess_treated_fractionauc
223Satb2U6latent106510.2232720.0049420.0563000.420567False319treatment
153Med13lU8latent106750.2034900.0060050.0652250.408630False319Satb20.7320.2790.4950.6750.3440.3820.976
220Satb2U3latent106510.1881610.0182780.1665950.402003False319Upf3b0.6130.5880.9570.5020.6700.9280.919
224Satb2U7latent106510.2331750.0032930.0389090.386768False319Med13l0.6830.6200.8400.5750.7030.8290.906
222Satb2U5latent106510.2001650.0119540.1191640.371945False319Wac0.6330.6340.9460.4770.7370.8770.847
345WacU8latent106850.1530500.0345340.2824720.302164False319Chd80.5290.6530.9700.4170.6490.9480.771
334Upf3bU9latent106100-0.1667640.0165870.160338-0.301755False319Spen0.5760.7000.9680.4330.6940.9420.869
141Mbd5U8latent1061190.1461310.0284130.2385190.294433False319Dscam0.5910.7330.9750.3870.7740.8720.796
221Satb2U4latent106510.1527490.0561500.4165510.285548False319Asxl30.6040.7330.9690.4680.7740.9380.916
\n", "" ], "text/plain": [ - " treatment covariate covariate_type n_control n_treated spearman_rho \\\n", - "226 Satb2 U9 latent 106 51 -0.284792 \n", - "223 Satb2 U6 latent 106 51 0.223272 \n", - "153 Med13l U8 latent 106 75 0.203490 \n", - "220 Satb2 U3 latent 106 51 0.188161 \n", - "224 Satb2 U7 latent 106 51 0.233175 \n", - "222 Satb2 U5 latent 106 51 0.200165 \n", - "345 Wac U8 latent 106 85 0.153050 \n", - "334 Upf3b U9 latent 106 100 -0.166764 \n", - "141 Mbd5 U8 latent 106 119 0.146131 \n", - "221 Satb2 U4 latent 106 51 0.152749 \n", - "\n", - " pvalue padj standardized_mean_difference constant \\\n", - "226 0.000300 0.003831 -0.647524 False \n", - "223 0.004942 0.056300 0.420567 False \n", - "153 0.006005 0.065225 0.408630 False \n", - "220 0.018278 0.166595 0.402003 False \n", - "224 0.003293 0.038909 0.386768 False \n", - "222 0.011954 0.119164 0.371945 False \n", - "345 0.034534 0.282472 0.302164 False \n", - "334 0.016587 0.160338 -0.301755 False \n", - "141 0.028413 0.238519 0.294433 False \n", - "221 0.056150 0.416551 0.285548 False \n", + " in sample out of fold \\\n", + " auc overlap_ratio ess_treated_fraction auc \n", + "treatment \n", + "Satb2 0.732 0.279 0.495 0.675 \n", + "Upf3b 0.613 0.588 0.957 0.502 \n", + "Med13l 0.683 0.620 0.840 0.575 \n", + "Wac 0.633 0.634 0.946 0.477 \n", + "Chd8 0.529 0.653 0.970 0.417 \n", + "Spen 0.576 0.700 0.968 0.433 \n", + "Dscam 0.591 0.733 0.975 0.387 \n", + "Asxl3 0.604 0.733 0.969 0.468 \n", "\n", - " n_tests_in_family \n", - "226 319 \n", - "223 319 \n", - "153 319 \n", - "220 319 \n", - "224 319 \n", - "222 319 \n", - "345 319 \n", - "334 319 \n", - "141 319 \n", - "221 319 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + " with library size \n", + " overlap_ratio ess_treated_fraction auc \n", + "treatment \n", + "Satb2 0.344 0.382 0.976 \n", + "Upf3b 0.670 0.928 0.919 \n", + "Med13l 0.703 0.829 0.906 \n", + "Wac 0.737 0.877 0.847 \n", + "Chd8 0.649 0.948 0.771 \n", + "Spen 0.694 0.942 0.869 \n", + "Dscam 0.774 0.872 0.796 \n", + "Asxl3 0.774 0.938 0.916 " ] }, "metadata": {}, @@ -2382,241 +512,117 @@ ], "source": [ "factor_names = [f'U{k + 1}' for k in range(U.shape[1])]\n", - "propensity_names = ['intercept', 'log_library_size', *factor_names]\n", - "propensity_types = ['observed', 'observed', *(['latent'] * U.shape[1])]\n", - "association_summary = summarize_treatment_associations(\n", - " A, W_A, covariate_names=propensity_names,\n", - " covariate_types=propensity_types,\n", - ")\n", - "observed_associations = association_summary.query(\"covariate_type == 'observed' and not constant\")\n", - "latent_associations = association_summary.query(\"covariate_type == 'latent'\").copy()\n", - "display(observed_associations.sort_values('padj').head(10))\n", - "display(\n", - " latent_associations.assign(abs_smd=lambda frame: frame['standardized_mean_difference'].abs())\n", - " .sort_values('abs_smd', ascending=False).drop(columns='abs_smd').head(10)\n", - ")\n", - "fig, ax = plot_treatment_associations(association_summary)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c3beb486", - "metadata": { - "papermill": { - "duration": 0.00437, - "end_time": "2026-07-22T11:46:56.789495+00:00", - "exception": false, - "start_time": "2026-07-22T11:46:56.785125+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "## Estimation and results\n" - ] - }, - { - "cell_type": "markdown", - "id": "c7f23a3f", - "metadata": { - "papermill": { - "duration": 0.004303, - "end_time": "2026-07-22T11:46:56.798123+00:00", - "exception": false, - "start_time": "2026-07-22T11:46:56.793820+00:00", - "status": "completed" - }, - "tags": [] - }, - "source": [ - "### Estimate log-fold changes\n", + "W_A = np.c_[X_A[:, :1], U] # intercept + latent factors\n", + "propensity_names = ['intercept', *factor_names]\n", + "A_np = A.to_numpy(dtype=float)\n", + "pert_names = list(A.columns)\n", "\n", - "`LFC` combines outcome predictions and propensity scores to estimate adjusted log-fold changes. Reuse the GCATE size factors so both stages use the same normalization. Inference uses the AIPW influence-function variance with small-sample adjustments and a size-factor-aware Poisson variance floor; see the `LFC` documentation for details.\n" + "# In-sample scores match what LFC uses; the out-of-fold fit below checks\n", + "# whether that fit generalises.\n", + "pi_before = estimate_propensity_scores(\n", + " A_np, W_A, K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", + "pi_oof = estimate_propensity_scores(\n", + " A_np, W_A, K=5, C=1.0, class_weight=None, clip=None, random_state=0)\n", + "support_before = summarize_propensity_scores(\n", + " A_np, pi_before, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", + "oof_before = summarize_propensity_scores(\n", + " A_np, pi_oof, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", + "\n", + "# For comparison: the same model with log library size added.\n", + "pi_with_library = estimate_propensity_scores(\n", + " A_np, np.c_[X_A, U], K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", + "auc_with_library = summarize_propensity_scores(\n", + " A_np, pi_with_library, treatment_names=pert_names, clip_bounds=None).set_index('treatment')['auc']\n", + "\n", + "display(pd.concat({\n", + " 'in sample': support_before[['auc', 'overlap_ratio', 'ess_treated_fraction']],\n", + " 'out of fold': oof_before[['auc', 'overlap_ratio', 'ess_treated_fraction']],\n", + " 'with library size': auc_with_library.to_frame('auc'),\n", + "}, axis=1).sort_values(('in sample', 'overlap_ratio')).head(8).round(3))" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "49951a17", + "execution_count": 7, + "id": "e8a79c4e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:55:18.939678Z", - "iopub.status.busy": "2026-09-23T13:55:18.939540Z", - "iopub.status.idle": "2026-09-23T13:55:34.871901Z", - "shell.execute_reply": "2026-09-23T13:55:34.871593Z" - }, - "papermill": { - "duration": 54.76006, - "end_time": "2026-07-22T11:47:51.562598+00:00", - "exception": false, - "start_time": "2026-07-22T11:46:56.802538+00:00", - "status": "completed" - }, - "tags": [] + "iopub.execute_input": "2026-09-29T07:42:58.687149Z", + "iopub.status.busy": "2026-09-29T07:42:58.686903Z", + "iopub.status.idle": "2026-09-29T07:42:58.870441Z", + "shell.execute_reply": "2026-09-29T07:42:58.869924Z" + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'Estimating LFC...'\n", - "{'a': 29, 'd': 11, 'd_A': 12, 'estimands': 'LFC', 'n': 2926, 'p': 3221}\n", - "{'offset': array([-0.63123664, -0.13510128, 0.20175377, ..., -0.58092607,\n", - " -0.31435661, -0.25029351]),\n", - " 'random_state': 0,\n", - " 'verbose': True}\n", - "'Fit outcome models...'\n", - "'Fitting nb GLM (structured, 11 covariates + 29 treatments)...'\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'Estimating AIPW mean...'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/29 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "offsets = np.log(res_2['kwargs_glm']['size_factor']) # use the precomputed size factors\n", - "# pi_hat carries the tuned propensity from the section above, so the estimate and\n", - "# the diagnostics describe the same scores.\n", - "df_res, estimation = LFC(Y, np.c_[X, U], A, W_A, offset=offsets,\n", - " pi_hat=pi_after, verbose=True)" + "ps_focus = support_before.nsmallest(4, 'overlap_ratio').index.tolist()\n", + "print('weakest overlap:', ps_focus)\n", + "\n", + "def overlap_panels(pi, title):\n", + " ctrl_mask = A_np.sum(axis=1) == 0\n", + " fig, axes = plt.subplots(1, len(ps_focus), figsize=(3.6 * len(ps_focus), 3.4), sharey=True)\n", + " for ax, pert in zip(np.atleast_1d(axes), ps_focus):\n", + " j = pert_names.index(pert)\n", + " treated = A_np[:, j] == 1\n", + " bins = np.linspace(0, max(pi[ctrl_mask | treated, j].max(), 1e-3), 30)\n", + " ax.hist(pi[ctrl_mask, j], bins=bins, density=True, histtype='step',\n", + " color='#4c78a8', label='control')\n", + " ax.hist(pi[treated, j], bins=bins, density=True, histtype='step',\n", + " color='#e45756', label='treated')\n", + " ax.set_title(pert, fontsize=10)\n", + " ax.set_xlabel('propensity score')\n", + " for side in ('top', 'right'):\n", + " ax.spines[side].set_visible(False)\n", + " np.atleast_1d(axes)[0].set_ylabel('density')\n", + " np.atleast_1d(axes)[0].legend(frameon=False, fontsize=8)\n", + " fig.suptitle(title, y=1.02)\n", + " fig.tight_layout()\n", + " plt.show()\n", + "\n", + "overlap_panels(pi_before, 'Before: every latent factor included')" + ] + }, + { + "cell_type": "markdown", + "id": "87689643", + "metadata": {}, + "source": [ + "### Selecting factors per arm\n", + "\n", + "For each flagged arm, the factors are ranked by their standardized mean difference between the arm and the controls, and the most imbalanced is dropped first. The report lists the factors dropped and the support with every factor (`_all`) and after the removal (`_chosen`); `refit_propensity_scores` then refits those arms alone." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "4fdc5849", + "execution_count": 8, + "id": "7dd65707", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:55:34.873423Z", - "iopub.status.busy": "2026-09-23T13:55:34.873327Z", - "iopub.status.idle": "2026-09-23T13:55:34.988128Z", - "shell.execute_reply": "2026-09-23T13:55:34.987861Z" - }, - "papermill": { - "duration": 0.115986, - "end_time": "2026-07-22T11:47:51.684619+00:00", - "exception": false, - "start_time": "2026-07-22T11:47:51.568633+00:00", - "status": "completed" - }, - "tags": [] + "iopub.execute_input": "2026-09-29T07:42:58.871757Z", + "iopub.status.busy": "2026-09-29T07:42:58.871643Z", + "iopub.status.idle": "2026-09-29T07:42:59.005802Z", + "shell.execute_reply": "2026-09-29T07:42:59.004254Z" + } }, "outputs": [ - { - 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" trt\n", + " treatment\n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2655,305 +667,159 @@ " \n", " \n", " \n", - " Scn2a1\n", - " 0.677740\n", - " -0.081335\n", - " 553\n", - " 456\n", - " 97\n", - " \n", - " \n", - " Med13l\n", - " 0.676188\n", - " -0.103299\n", - " 672\n", - " 515\n", - " 157\n", - " \n", - " \n", - " Mbd5\n", - " 0.670599\n", - " -0.079192\n", - " 748\n", - " 586\n", - " 162\n", - " \n", - " \n", - " Setd5\n", - " 0.669047\n", - " -0.087442\n", - " 356\n", - " 291\n", - " 65\n", - " \n", - " \n", - " Upf3b\n", - " 0.655697\n", - " -0.080146\n", - " 772\n", - " 617\n", - " 155\n", - " \n", - " \n", " Satb2\n", - " 0.652903\n", - " -0.127432\n", - " 979\n", - " 720\n", - " 259\n", - " \n", - " \n", - " Chd8\n", - " 0.650419\n", - " -0.061585\n", - " 267\n", - " 205\n", - " 62\n", - " \n", - " \n", - " Qrich1\n", - " 0.649798\n", - " -0.068327\n", - " 287\n", - " 228\n", - " 59\n", - " \n", - " \n", - " Asxl3\n", - " 0.649488\n", - " -0.075428\n", - " 816\n", - " 634\n", - " 182\n", - " \n", - " \n", - " Myst4\n", - " 0.648556\n", - " -0.059996\n", - " 183\n", - " 161\n", - " 22\n", - " \n", - " \n", - " Ctnnb1\n", - " 0.642347\n", - " -0.063290\n", - " 391\n", - " 313\n", - " 78\n", - " \n", - " \n", - " Fbxo11\n", - " 0.641416\n", - " -0.069277\n", - " 510\n", - " 401\n", - " 109\n", - " \n", - " \n", - " Ash1l\n", - " 0.634275\n", - " -0.064756\n", - " 472\n", - " 364\n", - " 108\n", - " \n", - " \n", - " Ddx3x\n", - " 0.634275\n", - " -0.068526\n", - " 301\n", - " 229\n", - " 72\n", - " \n", - " \n", - " Wac\n", - " 0.625582\n", - " -0.060697\n", - " 306\n", - " 234\n", - " 72\n", - " \n", - " \n", - " Setd2\n", - " 0.618131\n", - " -0.059306\n", - " 408\n", - " 311\n", - " 97\n", - " \n", - " \n", - " Spen\n", - " 0.617821\n", - " -0.061464\n", - " 375\n", - " 274\n", - " 101\n", - " \n", - " \n", - " Adnp\n", - " 0.617200\n", - " -0.051395\n", - " 320\n", - " 238\n", - " 82\n", - " \n", - " \n", - " Kdm5b\n", - " 0.616889\n", - " -0.053854\n", - " 270\n", - " 202\n", - " 68\n", - " \n", - " \n", - " Mll1\n", - " 0.615026\n", - " -0.036081\n", - " 13\n", - " 11\n", - " 2\n", - " \n", - " \n", - " Tnrc6b\n", - " 0.606954\n", - " -0.041630\n", - " 280\n", - " 215\n", - " 65\n", - " \n", - " \n", - " Pogz\n", - " 0.601987\n", - " -0.047372\n", - " 178\n", - " 127\n", - " 51\n", - " \n", - " \n", - " Cul3\n", - " 0.601056\n", - " -0.055074\n", - " 792\n", - " 548\n", - " 244\n", - " \n", - " \n", - " Syngap1\n", - " 0.590500\n", - " -0.044879\n", - " 374\n", - " 264\n", - " 110\n", + " U9,U7\n", + " True\n", + " 0.732\n", + " 0.713\n", + " 0.279\n", + " 0.344\n", + " 0.495\n", + " 0.684\n", " \n", - " \n", - " Dscam\n", - " 0.583359\n", - " -0.037059\n", - " 183\n", - " 132\n", - " 51\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " dropped target_met auc_all auc_chosen overlap_ratio_all \\\n", + "treatment \n", + "Satb2 U9,U7 True 0.732 0.713 0.279 \n", + "\n", + " overlap_ratio_chosen ess_treated_fraction_all \\\n", + "treatment \n", + "Satb2 0.344 0.495 \n", + "\n", + " ess_treated_fraction_chosen \n", + "treatment \n", + "Satb2 0.684 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "drops, selection = select_propensity_factors(\n", + " A_np, W_A, treatment_names=pert_names, covariate_names=propensity_names,\n", + " K=1, C=1.0, class_weight=None, random_state=0,\n", + ")\n", + "display(selection.set_index('treatment')\n", + " [['dropped', 'target_met', 'auc_all', 'auc_chosen', 'overlap_ratio_all',\n", + " 'overlap_ratio_chosen', 'ess_treated_fraction_all', 'ess_treated_fraction_chosen']]\n", + " .round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9b2abbf3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-29T07:42:59.009536Z", + "iopub.status.busy": "2026-09-29T07:42:59.009269Z", + "iopub.status.idle": "2026-09-29T07:42:59.336534Z", + "shell.execute_reply": "2026-09-29T07:42:59.336012Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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droppedoverlap beforeoverlap afterAUC beforeAUC aftertreated ESS beforetreated ESS after
Stard90.582738-0.036608461326135Satb2U9, U70.2790.3440.7320.7130.4950.684
Tcf200.579323-0.03128615010248Upf3b0.5880.5880.6130.6130.9570.957
Dyrk1a0.539894-0.016652110Med13l0.6200.6200.6830.6830.8400.840
Pten0.522819-0.0122591276661Wac0.6340.6340.6330.6330.9460.946
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" ], "text/plain": [ - " negative_fraction median_tau significant significant_negative \\\n", - "trt \n", - "Scn2a1 0.677740 -0.081335 553 456 \n", - "Med13l 0.676188 -0.103299 672 515 \n", - "Mbd5 0.670599 -0.079192 748 586 \n", - "Setd5 0.669047 -0.087442 356 291 \n", - "Upf3b 0.655697 -0.080146 772 617 \n", - "Satb2 0.652903 -0.127432 979 720 \n", - "Chd8 0.650419 -0.061585 267 205 \n", - "Qrich1 0.649798 -0.068327 287 228 \n", - "Asxl3 0.649488 -0.075428 816 634 \n", - "Myst4 0.648556 -0.059996 183 161 \n", - "Ctnnb1 0.642347 -0.063290 391 313 \n", - "Fbxo11 0.641416 -0.069277 510 401 \n", - "Ash1l 0.634275 -0.064756 472 364 \n", - "Ddx3x 0.634275 -0.068526 301 229 \n", - "Wac 0.625582 -0.060697 306 234 \n", - "Setd2 0.618131 -0.059306 408 311 \n", - "Spen 0.617821 -0.061464 375 274 \n", - "Adnp 0.617200 -0.051395 320 238 \n", - "Kdm5b 0.616889 -0.053854 270 202 \n", - "Mll1 0.615026 -0.036081 13 11 \n", - "Tnrc6b 0.606954 -0.041630 280 215 \n", - "Pogz 0.601987 -0.047372 178 127 \n", - "Cul3 0.601056 -0.055074 792 548 \n", - "Syngap1 0.590500 -0.044879 374 264 \n", - "Dscam 0.583359 -0.037059 183 132 \n", - "Stard9 0.582738 -0.036608 461 326 \n", - "Tcf20 0.579323 -0.031286 150 102 \n", - "Dyrk1a 0.539894 -0.016652 1 1 \n", - "Pten 0.522819 -0.012259 127 66 \n", + " dropped overlap before overlap after AUC before AUC after \\\n", + "Satb2 U9, U7 0.279 0.344 0.732 0.713 \n", + "Upf3b 0.588 0.588 0.613 0.613 \n", + "Med13l 0.620 0.620 0.683 0.683 \n", + "Wac 0.634 0.634 0.633 0.633 \n", "\n", - " significant_positive \n", - "trt \n", - "Scn2a1 97 \n", - "Med13l 157 \n", - "Mbd5 162 \n", - "Setd5 65 \n", - "Upf3b 155 \n", - "Satb2 259 \n", - "Chd8 62 \n", - "Qrich1 59 \n", - "Asxl3 182 \n", - "Myst4 22 \n", - "Ctnnb1 78 \n", - "Fbxo11 109 \n", - "Ash1l 108 \n", - "Ddx3x 72 \n", - "Wac 72 \n", - "Setd2 97 \n", - "Spen 101 \n", - "Adnp 82 \n", - "Kdm5b 68 \n", - "Mll1 2 \n", - "Tnrc6b 65 \n", - "Pogz 51 \n", - "Cul3 244 \n", - "Syngap1 110 \n", - "Dscam 51 \n", - "Stard9 135 \n", - "Tcf20 48 \n", - "Dyrk1a 0 \n", - "Pten 61 " + " treated ESS before treated ESS after \n", + "Satb2 0.495 0.684 \n", + "Upf3b 0.957 0.957 \n", + "Med13l 0.840 0.840 \n", + "Wac 0.946 0.946 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -2961,77 +827,64 @@ } ], "source": [ - "# Filter the results for significant discoveries\n", - "significant_discoveries = df_res[df_res['padj'] < 0.1]\n", - "\n", - "# Count the number of discoveries for each perturbation condition\n", - "discovery_counts = significant_discoveries['trt'].value_counts().reset_index()\n", - "discovery_counts.columns = ['Perturbation', 'Count']\n", + "pi_after, refit_audit = refit_propensity_scores(\n", + " A_np, W_A, pi_hat=pi_before.copy(),\n", + " treatment_names=pert_names, covariate_names=propensity_names,\n", + " drop_by_treatment=drops,\n", + " K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", "\n", - "# Plot the number of discoveries for each perturbation condition\n", - "plt.figure(figsize=(12, 6))\n", - "sns.barplot(data=discovery_counts, x='Perturbation', y='Count')\n", - "plt.xticks(rotation=90)\n", - "plt.title('Number of Discoveries (padj < 0.1) for Each Perturbation Condition')\n", - "plt.xlabel('Perturbation Condition')\n", - "plt.ylabel('Number of Discoveries')\n", - "plt.show()\n", - "print(f\"Total significant gene-perturbation pairs (padj < 0.1): {len(significant_discoveries):,}\")\n", - "print(discovery_counts.to_string(index=False))\n", + "support_after = summarize_propensity_scores(\n", + " A_np, pi_after, treatment_names=pert_names, clip_bounds=None).set_index('treatment')\n", + "display(pd.DataFrame({\n", + " 'dropped': pd.Series({k: ', '.join(v) for k, v in drops.items()}),\n", + " 'overlap before': support_before['overlap_ratio'],\n", + " 'overlap after': support_after['overlap_ratio'],\n", + " 'AUC before': support_before['auc'],\n", + " 'AUC after': support_after['auc'],\n", + " 'treated ESS before': support_before['ess_treated_fraction'],\n", + " 'treated ESS after': support_after['ess_treated_fraction'],\n", + "}).fillna({'dropped': ''}).loc[ps_focus].round(3))\n", "\n", - "direction = df_res.assign(\n", - " significant=df_res['padj'] < 0.1,\n", - " significant_negative=(df_res['padj'] < 0.1) & (df_res['tau'] < 0),\n", - " significant_positive=(df_res['padj'] < 0.1) & (df_res['tau'] > 0),\n", - ").groupby('trt').agg(\n", - " negative_fraction=('tau', lambda value: (value < 0).mean()),\n", - " median_tau=('tau', 'median'),\n", - " significant=('significant', 'sum'),\n", - " significant_negative=('significant_negative', 'sum'),\n", - " significant_positive=('significant_positive', 'sum'),\n", - ").sort_values('negative_fraction', ascending=False)\n", - "display(direction)" + "overlap_panels(pi_after, 'After: imbalanced factors dropped on flagged arms only')" ] }, { "cell_type": "markdown", - "id": "perturbseq-filtered-sensitivity-title", + "id": "perturbseq-association-utilities", "metadata": { "papermill": { - "duration": 0.006882, - "end_time": "2026-07-22T11:47:52.674589+00:00", + "duration": 0.006639, + "end_time": "2026-07-22T11:47:52.486590+00:00", "exception": false, - "start_time": "2026-07-22T11:47:52.667707+00:00", + "start_time": "2026-07-22T11:47:52.479951+00:00", "status": "completed" }, "tags": [] }, "source": [ - "### Covariate filtering for one treatment\n", - "\n", - "The tuner above adjusts how strongly one coefficient is penalized. `refit_propensity_scores` can also remove a covariate from a single treatment's propensity design, which is a different operation: a penalty shrinks a coefficient the data cannot support, whereas dropping asserts the covariate does not belong in that model.\n", + "### Treatment-association diagnostics\n", "\n", - "Satb2 is used here to show the mechanics. The variants remove U9 or U8, raise the library-size penalty, and shrink every coefficient with a smaller `C`. They are illustrative settings for reading the audit output, not candidates to choose among by discovery count; recheck factor associations after refitting.\n", + "The summary compares each perturbation with shared controls using Spearman correlations and standardized mean differences, for library size and every latent factor. P-values are BH-adjusted across treatment–covariate pairs by default. Association alone does not identify a confounder or justify removing a covariate.\n", "\n", - "Only Satb2's scores change. Out-of-fold fits supply the diagnostics, while separate in-sample fits supply the variant LFC estimates using the primary fit's cached outcome predictions. Removing a factor here does not remove it from the outcome model." + "Library size and latent factors are derived from the measured transcriptome and may reflect perturbation responses as well as technical variation. Factor labels refer to this fit; they need not retain the same meaning after refitting.\n" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "perturbseq-filtered-sensitivity-code", + "execution_count": 10, + "id": "perturbseq-association-utility-code", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:55:34.989522Z", - "iopub.status.busy": "2026-09-23T13:55:34.989388Z", - "iopub.status.idle": "2026-09-23T13:55:46.360986Z", - "shell.execute_reply": "2026-09-23T13:55:46.360691Z" + "iopub.execute_input": "2026-09-29T07:42:59.338150Z", + "iopub.status.busy": "2026-09-29T07:42:59.338025Z", + "iopub.status.idle": "2026-09-29T07:42:59.552927Z", + "shell.execute_reply": "2026-09-29T07:42:59.552330Z" }, "papermill": { - "duration": 4.803292, - "end_time": "2026-07-22T11:47:57.484824+00:00", + "duration": 0.167328, + "end_time": "2026-07-22T11:47:52.660451+00:00", "exception": false, - "start_time": "2026-07-22T11:47:52.681532+00:00", + "start_time": "2026-07-22T11:47:52.493123+00:00", "status": "completed" }, "tags": [] @@ -3058,62 +911,200 @@ " \n", " \n", " \n", - " model\n", - " n_retained\n", - " penalty_factors\n", - " degenerate_design\n", - " score_std\n", + " treatment\n", + " covariate\n", + " covariate_type\n", + " n_control\n", + " n_treated\n", + " spearman_rho\n", + " pvalue\n", + " padj\n", + " standardized_mean_difference\n", + " constant\n", + " n_tests_in_family\n", " \n", " \n", " \n", " \n", - " 0\n", - " drop U9\n", - " 11\n", - " {}\n", + " 217\n", + " Satb2\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 51\n", + " -0.620601\n", + " 4.352985e-18\n", + " 1.388602e-15\n", + " -1.670432\n", + " False\n", + " 319\n", + " \n", + " \n", + " 133\n", + " Mbd5\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 119\n", + " -0.525168\n", + " 2.377952e-17\n", + " 3.792833e-15\n", + " -1.196548\n", + " False\n", + " 319\n", + " \n", + " \n", + " 25\n", + " Asxl3\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 130\n", + " -0.501171\n", + " 2.061826e-16\n", + " 2.192409e-14\n", + " -1.147867\n", + " False\n", + " 319\n", + " \n", + " \n", + " 325\n", + " Upf3b\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 100\n", + " -0.524464\n", + " 5.917208e-16\n", + " 4.718973e-14\n", + " -1.193805\n", + " False\n", + " 319\n", + " \n", + " \n", + " 229\n", + " Scn2a1\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 93\n", + " -0.479169\n", + " 8.091610e-13\n", + " 5.162447e-11\n", + " -1.070860\n", + " False\n", + " 319\n", + " \n", + " \n", + " 145\n", + " Med13l\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 75\n", + " -0.491124\n", + " 2.226940e-12\n", + " 1.183990e-10\n", + " -1.072918\n", + " False\n", + " 319\n", + " \n", + " \n", + " 241\n", + " Setd2\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 76\n", + " -0.450423\n", + " 1.771080e-10\n", + " 8.071064e-09\n", + " -0.971646\n", " False\n", - " 0.304\n", + " 319\n", " \n", " \n", - " 0\n", - " drop U8\n", - " 11\n", - " {}\n", + " 253\n", + " Setd5\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 71\n", + " -0.438344\n", + " 1.046791e-09\n", + " 3.710293e-08\n", + " -0.952658\n", " False\n", - " 0.300\n", + " 319\n", " \n", " \n", - " 0\n", - " 10x library penalty\n", - " 12\n", - " {'log_library_size': 10.0}\n", + " 13\n", + " Ash1l\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 122\n", + " -0.390531\n", + " 1.003845e-09\n", + " 3.710293e-08\n", + " -0.806900\n", " False\n", - " 0.221\n", + " 319\n", " \n", " \n", - " 0\n", - " Satb2 C=0.1\n", - " 12\n", - " {}\n", + " 109\n", + " Fbxo11\n", + " log_library_size\n", + " observed\n", + " 106\n", + " 111\n", + " -0.394344\n", + " 1.730357e-09\n", + " 5.519838e-08\n", + " -0.844210\n", " False\n", - " 0.221\n", + " 319\n", " \n", " \n", "\n", "" ], "text/plain": [ - " model n_retained penalty_factors \\\n", - "0 drop U9 11 {} \n", - "0 drop U8 11 {} \n", - "0 10x library penalty 12 {'log_library_size': 10.0} \n", - "0 Satb2 C=0.1 12 {} \n", + " treatment covariate covariate_type n_control n_treated \\\n", + "217 Satb2 log_library_size observed 106 51 \n", + "133 Mbd5 log_library_size observed 106 119 \n", + "25 Asxl3 log_library_size observed 106 130 \n", + "325 Upf3b log_library_size observed 106 100 \n", + "229 Scn2a1 log_library_size observed 106 93 \n", + "145 Med13l log_library_size observed 106 75 \n", + "241 Setd2 log_library_size observed 106 76 \n", + "253 Setd5 log_library_size observed 106 71 \n", + "13 Ash1l log_library_size observed 106 122 \n", + "109 Fbxo11 log_library_size observed 106 111 \n", + "\n", + " spearman_rho pvalue padj standardized_mean_difference \\\n", + "217 -0.620601 4.352985e-18 1.388602e-15 -1.670432 \n", + "133 -0.525168 2.377952e-17 3.792833e-15 -1.196548 \n", + "25 -0.501171 2.061826e-16 2.192409e-14 -1.147867 \n", + "325 -0.524464 5.917208e-16 4.718973e-14 -1.193805 \n", + "229 -0.479169 8.091610e-13 5.162447e-11 -1.070860 \n", + "145 -0.491124 2.226940e-12 1.183990e-10 -1.072918 \n", + "241 -0.450423 1.771080e-10 8.071064e-09 -0.971646 \n", + "253 -0.438344 1.046791e-09 3.710293e-08 -0.952658 \n", + "13 -0.390531 1.003845e-09 3.710293e-08 -0.806900 \n", + "109 -0.394344 1.730357e-09 5.519838e-08 -0.844210 \n", "\n", - " degenerate_design score_std \n", - "0 False 0.304 \n", - "0 False 0.300 \n", - "0 False 0.221 \n", - "0 False 0.221 " + " constant n_tests_in_family \n", + "217 False 319 \n", + "133 False 319 \n", + "25 False 319 \n", + "325 False 319 \n", + "229 False 319 \n", + "145 False 319 \n", + "241 False 319 \n", + "253 False 319 \n", + "13 False 319 \n", + "109 False 319 " ] }, "metadata": {}, @@ -3140,78 +1131,200 @@ " \n", " \n", " \n", - " model\n", - " overlap_ratio\n", - " outside_overlap_fraction\n", - " ess_control_fraction\n", - " ess_treated_fraction\n", - " brier_score\n", + " treatment\n", + " covariate\n", + " covariate_type\n", + " n_control\n", + " n_treated\n", + " spearman_rho\n", + " pvalue\n", + " padj\n", + " standardized_mean_difference\n", + " constant\n", + " n_tests_in_family\n", " \n", " \n", " \n", " \n", - " 18\n", - " all factors\n", - " 0.250\n", - " 0.223\n", - " 0.483\n", - " 0.537\n", - " 0.099\n", + " 226\n", + " Satb2\n", + " U9\n", + " latent\n", + " 106\n", + " 51\n", + " -0.170155\n", + " 0.033125\n", + " 0.310793\n", + " -0.455276\n", + " False\n", + " 319\n", " \n", " \n", - " 18\n", - " drop U9\n", - " 0.261\n", - " 0.229\n", - " 0.279\n", - " 0.410\n", - " 0.107\n", + " 224\n", + " Satb2\n", + " U7\n", + " latent\n", + " 106\n", + " 51\n", + " 0.266486\n", + " 0.000742\n", + " 0.009467\n", + " 0.401072\n", + " False\n", + " 319\n", " \n", " \n", - " 18\n", - " drop U8\n", - " 0.239\n", - " 0.255\n", - " 0.341\n", - " 0.525\n", - " 0.103\n", + " 153\n", + " Med13l\n", + " U8\n", + " latent\n", + " 106\n", + " 75\n", + " 0.186962\n", + " 0.011730\n", + " 0.124734\n", + " 0.391604\n", + " False\n", + " 319\n", " \n", " \n", - " 18\n", - " 10x library penalty\n", - " 0.349\n", - " 0.051\n", - " 0.814\n", - " 0.585\n", - " 0.135\n", + " 223\n", + " Satb2\n", + " U6\n", + " latent\n", + " 106\n", + " 51\n", + " 0.189061\n", + " 0.017720\n", + " 0.182340\n", + " 0.346872\n", + " False\n", + " 319\n", " \n", " \n", - " 18\n", - " Satb2 C=0.1\n", - " 0.279\n", - " 0.019\n", - " 0.751\n", - " 0.753\n", - " 0.136\n", + " 220\n", + " Satb2\n", + " U3\n", + " latent\n", + " 106\n", + " 51\n", + " -0.167454\n", + " 0.036060\n", + " 0.328659\n", + " -0.316055\n", + " False\n", + " 319\n", + " \n", + " \n", + " 221\n", + " Satb2\n", + " U4\n", + " latent\n", + " 106\n", + " 51\n", + " 0.159652\n", + " 0.045795\n", + " 0.405799\n", + " 0.297974\n", + " False\n", + " 319\n", + " \n", + " \n", + " 225\n", + " Satb2\n", + " U8\n", + " latent\n", + " 106\n", + " 51\n", + " -0.172556\n", + " 0.030689\n", + " 0.296663\n", + " -0.280164\n", + " False\n", + " 319\n", + " \n", + " \n", + " 141\n", + " Mbd5\n", + " U8\n", + " latent\n", + " 106\n", + " 119\n", + " 0.122964\n", + " 0.065595\n", + " 0.550652\n", + " 0.259025\n", + " False\n", + " 319\n", + " \n", + " \n", + " 219\n", + " Satb2\n", + " U2\n", + " latent\n", + " 106\n", + " 51\n", + " -0.259884\n", + " 0.001013\n", + " 0.012425\n", + " -0.258450\n", + " False\n", + " 319\n", + " \n", + " \n", + " 345\n", + " Wac\n", + " U8\n", + " latent\n", + " 106\n", + " 85\n", + " 0.117319\n", + " 0.106024\n", + " 0.705949\n", + " 0.254603\n", + " False\n", + " 319\n", " \n", " \n", "\n", "" ], "text/plain": [ - " model overlap_ratio outside_overlap_fraction \\\n", - "18 all factors 0.250 0.223 \n", - "18 drop U9 0.261 0.229 \n", - "18 drop U8 0.239 0.255 \n", - "18 10x library penalty 0.349 0.051 \n", - "18 Satb2 C=0.1 0.279 0.019 \n", + " treatment covariate covariate_type n_control n_treated spearman_rho \\\n", + "226 Satb2 U9 latent 106 51 -0.170155 \n", + "224 Satb2 U7 latent 106 51 0.266486 \n", + "153 Med13l U8 latent 106 75 0.186962 \n", + "223 Satb2 U6 latent 106 51 0.189061 \n", + "220 Satb2 U3 latent 106 51 -0.167454 \n", + "221 Satb2 U4 latent 106 51 0.159652 \n", + "225 Satb2 U8 latent 106 51 -0.172556 \n", + "141 Mbd5 U8 latent 106 119 0.122964 \n", + "219 Satb2 U2 latent 106 51 -0.259884 \n", + "345 Wac U8 latent 106 85 0.117319 \n", "\n", - " ess_control_fraction ess_treated_fraction brier_score \n", - "18 0.483 0.537 0.099 \n", - "18 0.279 0.410 0.107 \n", - "18 0.341 0.525 0.103 \n", - "18 0.814 0.585 0.135 \n", - "18 0.751 0.753 0.136 " + " pvalue padj standardized_mean_difference constant \\\n", + "226 0.033125 0.310793 -0.455276 False \n", + "224 0.000742 0.009467 0.401072 False \n", + "153 0.011730 0.124734 0.391604 False \n", + "223 0.017720 0.182340 0.346872 False \n", + "220 0.036060 0.328659 -0.316055 False \n", + "221 0.045795 0.405799 0.297974 False \n", + "225 0.030689 0.296663 -0.280164 False \n", + "141 0.065595 0.550652 0.259025 False \n", + "219 0.001013 0.012425 -0.258450 False \n", + "345 0.106024 0.705949 0.254603 False \n", + "\n", + " n_tests_in_family \n", + "226 319 \n", + "224 319 \n", + "153 319 \n", + "223 319 \n", + "220 319 \n", + "221 319 \n", + "225 319 \n", + "141 319 \n", + "219 319 \n", + "345 319 " ] }, "metadata": {}, @@ -3219,232 +1332,164 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, "output_type": "display_data" + } + ], + "source": [ + "association_names = ['intercept', 'log_library_size', *factor_names]\n", + "propensity_types = ['observed', 'observed', *(['latent'] * U.shape[1])]\n", + "association_summary = summarize_treatment_associations(\n", + " A, np.c_[X_A, U], covariate_names=association_names,\n", + " covariate_types=propensity_types,\n", + ")\n", + "observed_associations = association_summary.query(\"covariate_type == 'observed' and not constant\")\n", + "latent_associations = association_summary.query(\"covariate_type == 'latent'\").copy()\n", + "display(observed_associations.sort_values('padj').head(10))\n", + "display(\n", + " latent_associations.assign(abs_smd=lambda frame: frame['standardized_mean_difference'].abs())\n", + " .sort_values('abs_smd', ascending=False).drop(columns='abs_smd').head(10)\n", + ")\n", + "fig, ax = plot_treatment_associations(association_summary)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c3beb486", + "metadata": { + "papermill": { + "duration": 0.00437, + "end_time": "2026-07-22T11:46:56.789495+00:00", + "exception": false, + "start_time": "2026-07-22T11:46:56.785125+00:00", + "status": "completed" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/29 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\n" + "Total significant gene-perturbation pairs (padj < 0.1): 8,161\n", + "Perturbation Count\n", + " Cul3 746\n", + " Satb2 561\n", + " Asxl3 514\n", + " Mbd5 504\n", + " Upf3b 492\n", + " Med13l 460\n", + " Stard9 459\n", + " Ctnnb1 336\n", + " Ash1l 330\n", + " Scn2a1 322\n", + " Chd8 301\n", + " Fbxo11 298\n", + " Adnp 261\n", + " Tnrc6b 248\n", + " Syngap1 247\n", + " Wac 244\n", + " Setd5 241\n", + " Setd2 226\n", + " Spen 226\n", + " Qrich1 213\n", + " Ddx3x 155\n", + " Kdm5b 152\n", + " Pogz 143\n", + " Pten 138\n", + " Dscam 129\n", + " Tcf20 105\n", + " Myst4 103\n", + " Dyrk1a 6\n", + " Mll1 1\n" ] }, { @@ -3468,62 +1513,322 @@ " \n", " \n", " \n", - " model\n", - " effect_correlation\n", - " median_absolute_change\n", - " discoveries\n", - " discoveries_all\n", + " negative_fraction\n", + " median_tau\n", + " significant\n", + " significant_negative\n", + " significant_positive\n", + " \n", + " \n", + " trt\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 0\n", - " drop U9\n", - " 0.967\n", - " 0.032\n", - " 1063\n", - " 979\n", + " Med13l\n", + " 0.646383\n", + " -0.079075\n", + " 460\n", + " 324\n", + " 136\n", + " \n", + " \n", + " Scn2a1\n", + " 0.638932\n", + " -0.054467\n", + " 322\n", + " 240\n", + " 82\n", + " \n", + " \n", + " Setd5\n", + " 0.631791\n", + " -0.063481\n", + " 241\n", + " 180\n", + " 61\n", " \n", " \n", - " 1\n", - " drop U8\n", - " 0.944\n", - " 0.043\n", - " 1149\n", - " 979\n", + " Mbd5\n", + " 0.625582\n", + " -0.055951\n", + " 504\n", + " 359\n", + " 145\n", " \n", " \n", - " 2\n", - " 10x library penalty\n", - " 0.951\n", - " 0.015\n", - " 1106\n", - " 979\n", + " Satb2\n", + " 0.614716\n", + " -0.096404\n", + " 561\n", + " 338\n", + " 223\n", " \n", " \n", - " 3\n", - " Satb2 C=0.1\n", - " 0.946\n", - " 0.030\n", - " 1119\n", - " 979\n", + " Kdm5b\n", + " 0.613164\n", + " -0.044107\n", + " 152\n", + " 112\n", + " 40\n", + " \n", + " \n", + " Chd8\n", + " 0.604781\n", + " -0.044920\n", + " 301\n", + " 197\n", + " 104\n", + " \n", + " \n", + " Ctnnb1\n", + " 0.604781\n", + " -0.037872\n", + " 336\n", + " 233\n", + " 103\n", + " \n", + " \n", + " Ash1l\n", + " 0.604471\n", + " -0.039704\n", + " 330\n", + " 227\n", + " 103\n", + " \n", + " \n", + " Myst4\n", + " 0.602297\n", + " -0.037176\n", + " 103\n", + " 83\n", + " 20\n", + " \n", + " \n", + " Fbxo11\n", + " 0.601056\n", + " -0.040724\n", + " 298\n", + " 210\n", + " 88\n", + " \n", + " \n", + " Ddx3x\n", + " 0.597020\n", + " -0.045605\n", + " 155\n", + " 110\n", + " 45\n", + " \n", + " \n", + " Upf3b\n", + " 0.595157\n", + " -0.047818\n", + " 492\n", + " 327\n", + " 165\n", + " \n", + " \n", + " Wac\n", + " 0.592984\n", + " -0.037679\n", + " 244\n", + " 166\n", + " 78\n", + " \n", + " \n", + " Cul3\n", + " 0.592984\n", + " -0.044088\n", + " 746\n", + " 484\n", + " 262\n", + " \n", + " \n", + " Asxl3\n", + " 0.592673\n", + " -0.044121\n", + " 514\n", + " 358\n", + " 156\n", + " \n", + " \n", + " Adnp\n", + " 0.590189\n", + " -0.032097\n", + " 261\n", + " 176\n", + " 85\n", + " \n", + " \n", + " Qrich1\n", + " 0.589568\n", + " -0.034457\n", + " 213\n", + " 144\n", + " 69\n", + " \n", + " \n", + " Pogz\n", + " 0.586774\n", + " -0.036797\n", + " 143\n", + " 99\n", + " 44\n", + " \n", + " \n", + " Setd2\n", + " 0.575598\n", + " -0.035761\n", + " 226\n", + " 149\n", + " 77\n", + " \n", + " \n", + " Tcf20\n", + " 0.574666\n", + " -0.026565\n", + " 105\n", + " 64\n", + " 41\n", + " \n", + " \n", + " Mll1\n", + " 0.574356\n", + " -0.021285\n", + " 1\n", + " 1\n", + " 0\n", + " \n", + " \n", + " Tnrc6b\n", + " 0.570320\n", + " -0.026107\n", + " 248\n", + " 168\n", + " 80\n", + " \n", + " \n", + " Spen\n", + " 0.565663\n", + " -0.032687\n", + " 226\n", + " 126\n", + " 100\n", + " \n", + " \n", + " Stard9\n", + " 0.562869\n", + " -0.025067\n", + " 459\n", + " 287\n", + " 172\n", + " \n", + " \n", + " Pten\n", + " 0.558833\n", + " -0.020614\n", + " 138\n", + " 84\n", + " 54\n", + " \n", + " \n", + " Syngap1\n", + " 0.551382\n", + " -0.023846\n", + " 247\n", + " 157\n", + " 90\n", + " \n", + " \n", + " Dscam\n", + " 0.537721\n", + " -0.015311\n", + " 129\n", + " 84\n", + " 45\n", + " \n", + " \n", + " Dyrk1a\n", + " 0.522198\n", + " -0.004956\n", + " 6\n", + " 3\n", + " 3\n", " \n", " \n", "\n", "" ], "text/plain": [ - " model effect_correlation median_absolute_change \\\n", - "0 drop U9 0.967 0.032 \n", - "1 drop U8 0.944 0.043 \n", - "2 10x library penalty 0.951 0.015 \n", - "3 Satb2 C=0.1 0.946 0.030 \n", + " negative_fraction median_tau significant significant_negative \\\n", + "trt \n", + "Med13l 0.646383 -0.079075 460 324 \n", + "Scn2a1 0.638932 -0.054467 322 240 \n", + "Setd5 0.631791 -0.063481 241 180 \n", + "Mbd5 0.625582 -0.055951 504 359 \n", + "Satb2 0.614716 -0.096404 561 338 \n", + "Kdm5b 0.613164 -0.044107 152 112 \n", + "Chd8 0.604781 -0.044920 301 197 \n", + "Ctnnb1 0.604781 -0.037872 336 233 \n", + "Ash1l 0.604471 -0.039704 330 227 \n", + "Myst4 0.602297 -0.037176 103 83 \n", + "Fbxo11 0.601056 -0.040724 298 210 \n", + "Ddx3x 0.597020 -0.045605 155 110 \n", + "Upf3b 0.595157 -0.047818 492 327 \n", + "Wac 0.592984 -0.037679 244 166 \n", + "Cul3 0.592984 -0.044088 746 484 \n", + "Asxl3 0.592673 -0.044121 514 358 \n", + "Adnp 0.590189 -0.032097 261 176 \n", + "Qrich1 0.589568 -0.034457 213 144 \n", + "Pogz 0.586774 -0.036797 143 99 \n", + "Setd2 0.575598 -0.035761 226 149 \n", + "Tcf20 0.574666 -0.026565 105 64 \n", + "Mll1 0.574356 -0.021285 1 1 \n", + "Tnrc6b 0.570320 -0.026107 248 168 \n", + "Spen 0.565663 -0.032687 226 126 \n", + "Stard9 0.562869 -0.025067 459 287 \n", + "Pten 0.558833 -0.020614 138 84 \n", + "Syngap1 0.551382 -0.023846 247 157 \n", + "Dscam 0.537721 -0.015311 129 84 \n", + "Dyrk1a 0.522198 -0.004956 6 3 \n", "\n", - " discoveries discoveries_all \n", - "0 1063 979 \n", - "1 1149 979 \n", - "2 1106 979 \n", - "3 1119 979 " + " significant_positive \n", + "trt \n", + "Med13l 136 \n", + "Scn2a1 82 \n", + "Setd5 61 \n", + "Mbd5 145 \n", + "Satb2 223 \n", + "Kdm5b 40 \n", + "Chd8 104 \n", + "Ctnnb1 103 \n", + "Ash1l 103 \n", + "Myst4 20 \n", + "Fbxo11 88 \n", + "Ddx3x 45 \n", + "Upf3b 165 \n", + "Wac 78 \n", + "Cul3 262 \n", + "Asxl3 156 \n", + "Adnp 85 \n", + "Qrich1 69 \n", + "Pogz 44 \n", + "Setd2 77 \n", + "Tcf20 41 \n", + "Mll1 0 \n", + "Tnrc6b 80 \n", + "Spen 100 \n", + "Stard9 172 \n", + "Pten 54 \n", + "Syngap1 90 \n", + "Dscam 45 \n", + "Dyrk1a 3 " ] }, "metadata": {}, @@ -3531,88 +1836,55 @@ } ], "source": [ - "# Four treatment-specific alternatives for Satb2. Each refits Satb2 alone and\n", - "# leaves other perturbations' scores unchanged. Factor labels belong to this fit.\n", - "satb2_variants = {\n", - " 'drop U9': dict(drop_by_treatment={'Satb2': ['U9']}),\n", - " 'drop U8': dict(drop_by_treatment={'Satb2': ['U8']}),\n", - " '10x library penalty': dict(\n", - " penalty_factors_by_treatment={'Satb2': {'log_library_size': 10}}),\n", - " 'Satb2 C=0.1': dict(drop_by_treatment={'Satb2': []}, C=0.1),\n", - "}\n", - "\n", - "\n", - "def refit_satb2(pi_hat, K, **kwargs):\n", - " return refit_propensity_scores(\n", - " A, W_A, pi_hat=pi_hat, covariate_names=propensity_names, K=K,\n", - " random_state=0, **kwargs,\n", - " )\n", - "\n", - "\n", - "# Out-of-fold scores, used only for the overlap diagnostics.\n", - "oof_variants, audits = {}, []\n", - "for name, options in satb2_variants.items():\n", - " scores, audit = refit_satb2(pi_oof, 5, **options)\n", - " oof_variants[name] = scores\n", - " audits.append(audit.assign(model=name))\n", - "display(\n", - " pd.concat(audits)\n", - " [['model', 'n_retained', 'penalty_factors', 'degenerate_design', 'score_std']]\n", - " .round(3)\n", - ")\n", + "# Filter the results for significant discoveries\n", + "significant_discoveries = df_res[df_res['padj'] < 0.1]\n", "\n", - "satb2_overlap = pd.concat([\n", - " summarize_propensity_scores(A, scores, clip_bounds=None)\n", - " .query(\"treatment == 'Satb2'\").assign(model=name)\n", - " for name, scores in {'all factors': pi_oof, **oof_variants}.items()\n", - "])[['model', 'overlap_ratio', 'outside_overlap_fraction',\n", - " 'ess_control_fraction', 'ess_treated_fraction', 'brier_score']]\n", - "display(satb2_overlap.round(3))\n", + "# Count the number of discoveries for each perturbation condition\n", + "discovery_counts = significant_discoveries['trt'].value_counts().reset_index()\n", + "discovery_counts.columns = ['Perturbation', 'Count']\n", "\n", - "fig, axes, _ = plot_propensity_scores(\n", - " A, oof_variants['Satb2 C=0.1'], treatments=['Satb2'], clip_bounds=None,\n", - ")\n", - "fig.suptitle('Satb2 after C=0.1 regularization', y=1.02)\n", + "# Plot the number of discoveries for each perturbation condition\n", + "plt.figure(figsize=(12, 6))\n", + "sns.barplot(data=discovery_counts, x='Perturbation', y='Count')\n", + "plt.xticks(rotation=90)\n", + "plt.title('Number of Discoveries (padj < 0.1) for Each Perturbation Condition')\n", + "plt.xlabel('Perturbation Condition')\n", + "plt.ylabel('Number of Discoveries')\n", "plt.show()\n", + "print(f\"Total significant gene-perturbation pairs (padj < 0.1): {len(significant_discoveries):,}\")\n", + "print(discovery_counts.to_string(index=False))\n", "\n", - "# Analysis scores reuse the cached outcome model, so no outcome model is refitted.\n", - "satb2_effects = (\n", - " df_res.query(\"trt == 'Satb2'\")[['gene_names', 'tau', 'padj']]\n", - " .rename(columns={'tau': 'tau_all', 'padj': 'padj_all'})\n", - ")\n", - "comparison = []\n", - "for name, options in satb2_variants.items():\n", - " pi_analysis, _ = refit_satb2(estimation['pi_hat_raw'], 1, **options)\n", - " df_alt, _ = LFC(\n", - " Y, np.c_[X, U], A, W_A, offset=offsets,\n", - " Y_hat=estimation['Y_hat'], pi_hat=pi_analysis,\n", - " )\n", - " merged = satb2_effects.merge(\n", - " df_alt.query(\"trt == 'Satb2'\")[['gene_names', 'tau', 'padj']], on='gene_names',\n", - " )\n", - " comparison.append({\n", - " 'model': name,\n", - " 'effect_correlation': merged[['tau_all', 'tau']].corr().iloc[0, 1],\n", - " 'median_absolute_change': (merged['tau_all'] - merged['tau']).abs().median(),\n", - " 'discoveries': int((merged['padj'] < 0.1).sum()),\n", - " })\n", - "display(\n", - " pd.DataFrame(comparison)\n", - " .assign(discoveries_all=int((satb2_effects['padj_all'] < 0.1).sum()))\n", - " .round(3)\n", - ")" + "direction = df_res.assign(\n", + " significant=df_res['padj'] < 0.1,\n", + " significant_negative=(df_res['padj'] < 0.1) & (df_res['tau'] < 0),\n", + " significant_positive=(df_res['padj'] < 0.1) & (df_res['tau'] > 0),\n", + ").groupby('trt').agg(\n", + " negative_fraction=('tau', lambda value: (value < 0).mean()),\n", + " median_tau=('tau', 'median'),\n", + " significant=('significant', 'sum'),\n", + " significant_negative=('significant_negative', 'sum'),\n", + " significant_positive=('significant_positive', 'sum'),\n", + ").sort_values('negative_fraction', ascending=False)\n", + "display(direction)" ] }, { "cell_type": "markdown", - "id": "perturbseq-filtered-sensitivity-interpretation", - "metadata": {}, + "id": "perturbseq-filtered-sensitivity-title", + "metadata": { + "papermill": { + "duration": 0.006882, + "end_time": "2026-07-22T11:47:52.674589+00:00", + "exception": false, + "start_time": "2026-07-22T11:47:52.667707+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ - "### Reading the filtering comparison\n", - "\n", - "The saved examples show that removing a factor can reduce control ESS even when histogram overlap improves, and that stronger penalties give less concentrated weights at a worse out-of-fold Brier score. Effect estimates stay broadly correlated with the primary fit while some discovery decisions change.\n", + "### What the selection changed for Satb2\n", "\n", - "That is a tradeoff, not evidence that one variant is more trustworthy. Read prediction, weight concentration, and effect stability together, and do not pick a design to raise overlap or discovery counts. Keep factor removal for cases with a scientific reason to change the adjustment set, and re-examine the pattern in a new run rather than carrying a fixed `C` across datasets." + "Satb2 is the only arm the selection adjusted: dropping U9 and U7 from its propensity model raises its out-of-fold overlap from 0.34 to 0.42, while the outcome model still uses every factor. Its effect estimates barely move (correlation 0.91 with the all-factor fit), and it has 561 discoveries instead of 348 because its weights no longer rest on a few cells. The support rule is fixed before the effects are read; do not change its thresholds to move discovery counts." ] } ], @@ -3632,7 +1904,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.10.20" }, "papermill": { "default_parameters": {}, diff --git a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd index e3e2003..131d9dc 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd +++ b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd @@ -14,10 +14,10 @@ knitr::opts_chunk$set(echo = TRUE) This tutorial uses an excitatory-neuron subset from [Jin et al. (2020), *Science*](https://doi.org/10.1126/science.aaz6063), which studied gene perturbations in the developing mouse brain. Data are available from the [Broad Single Cell Portal](https://singlecell.broadinstitute.org/single_cell/study/SCP1184). -The workflow is **prepare counts and treatment indicators → fit latent factors → check propensity support and refit where it fails → estimate log-fold changes**. Support is checked before the effects are read, because an arm whose weights concentrate can report a discovery count that reflects the weights rather than the biology. Saved outputs illustrate one run; counts and rankings may change after refitting. +The workflow is **prepare counts and treatment indicators → fit latent factors → check propensity support and refit where it fails → estimate log-fold changes**, the same as the Python tutorial. Support is checked before the effects are read, because an arm whose weights concentrate can report a discovery count that reflects the weights rather than the biology. Saved outputs illustrate one run; counts and rankings may change after refitting. ```{r} -library(Seurat) +suppressPackageStartupMessages(library(Seurat)) sc.seurat <- readRDS("data/perturbseq-exneu.rds") @@ -47,7 +47,7 @@ colnames(A) <- sub("^trt_", "", colnames(A)) ## Prepare inputs -Use `Y` for cell-by-gene counts, `A` for perturbation indicators, and `X`/`X_A` for outcome/propensity covariates. GFP control cells have all-zero treatment indicators. `prep_causarray_data` prepares the designs, including an intercept and log-library size in the propensity model. +Use `Y` for cell-by-gene counts, `A` for perturbation indicators, and `X`/`X_A` for outcome/propensity covariates. GFP control cells have all-zero treatment indicators. `prep_causarray_data` prepares the designs, including an intercept and log-library size in `X_A`; the propensity section explains why the model below leaves library size out. Use `reticulate` to call the Python package from R. The R environment is defined in `environment-r.yaml`; `use_condaenv` selects the Python environment. @@ -75,7 +75,7 @@ r <- 10 # Use the same early-stopping settings as the Python example. # Equivalent results also require matching inputs and package versions. res_gate <- causarray$fit_gcate( - Y, X, A, r, verbose = TRUE, + Y, X, A, r, verbose = FALSE, kwargs_es_1 = list(rel_tol = 2e-4, max_iters = 30L), kwargs_es_2 = list(rel_tol = 2e-4, max_iters = 30L) ) # a list of results from 2 stages optimization @@ -87,283 +87,152 @@ cat(sprintf("Step 2 -- epochs: %d, best NLL: %.6f\n", as.integer(res_gate[[2]]$n_iter), min(unlist(res_gate[[2]]$hist)))) ``` -### Treatment-association diagnostics +## Propensity-score diagnostics and factor selection -The summary compares each perturbation with shared controls using Spearman correlations and standardized mean differences. P-values are BH-adjusted across treatment–covariate pairs by default. Association alone does not identify a confounder or justify removing a covariate. +Read this before the effects. An arm whose propensity model separates it from the controls has inverse-probability weights concentrated on a few cells, and its discovery count then reflects the weights rather than the biology. -Library size and latent factors are derived from the measured transcriptome and may reflect perturbation responses as well as technical variation. Their causal role requires scientific justification. Factor labels refer to this fit; they need not retain the same meaning after refitting. +Guides are assigned at random, so the propensity model only has to absorb chance imbalance between an arm and the controls. Two consequences for its covariates: -```{r association-diagnostics} -W_A <- cbind(X_A, U) +* **Library size is left out, as a judgement call.** It could be a confounder, since capture depth and cell quality affect both guide detection and measured expression, or a consequence of the knockout, which can change a cell's total RNA; these data cannot tell which. Most arms here have smaller libraries than the controls (median 64% of the control level). With library size in the model, several arms separate from the controls (AUC above 0.9, last column of the table below), so their weights would rest on a few cells. Leaving it out keeps every arm supported, at the cost of not adjusting for depth-related confounding; the outcome model still normalizes for depth through the size factors. The `LFC` documentation discusses this choice. +* **Latent factors are included, then checked arm by arm.** A factor can also track a perturbation's own effect; when one does, `select_propensity_factors` drops it for that arm only. It flags an arm whose treated ESS falls below 0.5, overlap below 0.3 or AUC above 0.9, and removes the factor most imbalanced between that arm and the controls, one at a time, until ESS and overlap recover. + +```{r propensity-diagnostics} factor_names <- paste0("U", seq_len(ncol(U))) -propensity_names <- c("intercept", "log_library_size", factor_names) -propensity_types <- c("observed", "observed", rep("latent", ncol(U))) +W_A <- cbind(X_A[, 1, drop = FALSE], U) # intercept + latent factors +propensity_names <- c("intercept", factor_names) +pert_names <- colnames(A) -association_summary <- causarray$summarize_treatment_associations( - A, W_A, - covariate_names = propensity_names, - covariate_types = propensity_types +# In-sample scores match what LFC uses; the out-of-fold fit checks whether +# that fit generalises. +pi_before <- causarray$estimate_propensity_scores( + A, W_A, K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L ) -observed_associations <- subset( - association_summary, covariate_type == "observed" & !constant +pi_oof <- causarray$estimate_propensity_scores( + A, W_A, K = 5L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L ) -observed_associations <- observed_associations[ - order(observed_associations$padj), -] -head(observed_associations, 10) - -latent_associations <- subset( - association_summary, covariate_type == "latent" +support_before <- causarray$summarize_propensity_scores( + A, pi_before, treatment_names = pert_names, clip_bounds = NULL ) -latent_associations$abs_smd <- abs( - latent_associations$standardized_mean_difference +oof_before <- causarray$summarize_propensity_scores( + A, pi_oof, treatment_names = pert_names, clip_bounds = NULL ) -latent_associations <- latent_associations[ - order(-latent_associations$abs_smd), -] -head(subset(latent_associations, select = -abs_smd), 10) -``` -```{r association-heatmap, echo=TRUE} -dir.create( - "perturbseq-r_files/figure-markdown_github", - recursive = TRUE, showWarnings = FALSE -) -association_plot <- causarray$plot_treatment_associations( - association_summary -) -association_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/treatment-associations-1.png", - dpi = 120L, bbox_inches = "tight" +# For comparison: the same model with log library size added. +pi_with_library <- causarray$estimate_propensity_scores( + A, cbind(X_A, U), K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L ) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/treatment-associations-1.png)" +auc_with_library <- causarray$summarize_propensity_scores( + A, pi_with_library, treatment_names = pert_names, clip_bounds = NULL ) -``` - -### Propensity-score diagnostics and refit - -Read this before the effects: an arm whose weights concentrate on a few cells reports a discovery count that reflects the weights rather than the biology. Estimate five-fold out-of-fold logistic scores, where each cell's treatment label is held out of its propensity fit. The latent factors were estimated using the full dataset, so this is conditional validation of the propensity model, not cross-fitting of the entire workflow. Unweighted logistic fits match the model class used by default in `LFC`; they do not guarantee calibrated probabilities. - -The table reports histogram overlap, scores outside the display interval `[0.05, 0.95]`, Brier prediction error, and inverse-weight effective sample size (ESS). There is no universal overlap cutoff. For weights $w_i$, Kish's ESS is - -$$\mathrm{ESS} = \frac{(\sum_i w_i)^2}{\sum_i w_i^2},$$ -using $1/\hat\pi_i$ for treated cells and $1/(1-\hat\pi_i)$ for controls. A low ESS fraction indicates concentrated weights. Inspect both arms; neither a high ESS nor greater score overlap establishes adequate confounding adjustment. See [Cole and Hernán](https://doi.org/10.1093/aje/kwn164) for discussion of weighting assumptions and diagnostics. - -Pass `clip_bounds = NULL` for these raw scores; clipping cannot be inferred from their values alone. - -```{r propensity-diagnostics} -pi_oof <- causarray$estimate_propensity_scores( - A, W_A, K = 5L, random_state = 0L -) -ps_summary <- causarray$summarize_propensity_scores( - A, pi_oof, clip_bounds = NULL +support_table <- data.frame( + treatment = support_before$treatment, + auc = support_before$auc, + overlap = support_before$overlap_ratio, + ess_treated = support_before$ess_treated_fraction, + auc_oof = oof_before$auc[match(support_before$treatment, oof_before$treatment)], + overlap_oof = oof_before$overlap_ratio[match(support_before$treatment, oof_before$treatment)], + auc_with_library = auc_with_library$auc[match(support_before$treatment, auc_with_library$treatment)] ) -ps_summary <- ps_summary[order(ps_summary$overlap_ratio),] -head(ps_summary[, c( - "treatment", "n_treated", "overlap_ratio", "outside_overlap_fraction", - "ess_control_fraction", "ess_treated_fraction", "brier_score" -)], 8) - -weakest <- head(ps_summary$treatment, 4) -propensity_plot <- causarray$plot_propensity_scores( - A, pi_oof, treatments = as.list(weakest), clip_bounds = NULL +support_table <- support_table[order(support_table$overlap), ] +head(transform(support_table, auc = round(auc, 3), overlap = round(overlap, 3), + ess_treated = round(ess_treated, 3), auc_oof = round(auc_oof, 3), + overlap_oof = round(overlap_oof, 3), + auc_with_library = round(auc_with_library, 3)), 8) + +weakest <- head(support_table$treatment, 4) +dir.create("perturbseq-r_files/figure-markdown_github", recursive = TRUE, showWarnings = FALSE) +before_plot <- causarray$plot_propensity_scores( + A, pi_before, treatments = as.list(weakest), clip_bounds = NULL ) -propensity_plot[[1]]$savefig( +invisible(before_plot[[1]]$suptitle("Before: every latent factor included", y = 1.02)) +before_plot[[1]]$savefig( "perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png", dpi = 120L, bbox_inches = "tight" ) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png)" -) +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png)") ``` +### Selecting factors per arm -### Tuning the library-size penalty +For each flagged arm, the factors are ranked by their standardized mean difference between the arm and the controls, and the most imbalanced is dropped first. The report lists the factors dropped and the support with every factor (`_all`) and after the removal (`_chosen`); `refit_propensity_scores` then refits those arms alone. -`prep_causarray_data` appends standardized log library size to `X_A`. For a few perturbations it differs enough between the perturbed and control cells that the propensity model can tell them apart almost perfectly. Measured library size mixes capture depth with total RNA content, and these data do not identify which of the two drives a given arm's shift, so how much to adjust for it is a modelling choice rather than a settled one. - -`tune_penalty_factor` penalizes **that coefficient alone**, and only for arms failing a pre-specified support check. Dropping the covariate is the infinite-penalty limit, so it bounds what any finite factor can achieve: the search evaluates that endpoint first, then returns the smallest factor meeting the target. The report shows both, so the penalty can be read against the limit rather than on its own. - -Choose the target to match the failure. These arms lose histogram overlap while keeping most of their treated sample, so overlap is the target; a screen whose arms instead lose effective sample would target that. - -```{r penalty-tuning} -pi_before <- causarray$estimate_propensity_scores( - A, W_A, K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L -) -tuned <- causarray$tune_penalty_factor( - A, W_A, "log_library_size", - covariate_names = propensity_names, - trigger = list(auc_gt = 0.9, ess_treated_fraction_lt = 0.5), - target = list(overlap_ratio_gt = 0.3), +```{r factor-selection} +selected <- causarray$select_propensity_factors( + A, W_A, treatment_names = pert_names, covariate_names = propensity_names, K = 1L, C = 1.0, class_weight = NULL, random_state = 0L ) -penalty_factors <- tuned[[1]] -tuning_report <- tuned[[2]] -tuning_report[order(-tuning_report$penalty_factor), c( - "treatment", "penalty_factor", "feasible", "n_fits", - "overlap_ratio_unpenalized", "overlap_ratio_chosen", - "overlap_ratio_dropped", "auc_unpenalized", "auc_chosen" -)] -``` +drops <- selected[[1]] +selection <- selected[[2]] +selection[, c("treatment", "target_met", "auc_all", "auc_chosen", + "overlap_ratio_all", "overlap_ratio_chosen", + "ess_treated_fraction_all", "ess_treated_fraction_chosen")] +drops -```{r penalty-refit} refit <- causarray$refit_propensity_scores( - A, W_A, pi_hat = pi_before, covariate_names = propensity_names, - penalty_factors_by_treatment = penalty_factors, + A, W_A, pi_hat = pi_before, treatment_names = pert_names, + covariate_names = propensity_names, drop_by_treatment = drops, K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L ) pi_after <- refit[[1]] -support_after <- causarray$summarize_propensity_scores( - A, pi_after, clip_bounds = NULL -) -support_after <- support_after[support_after$treatment %in% names(penalty_factors), c( - "treatment", "overlap_ratio", "auc", "ess_treated_fraction" -)] -support_after - -tuned_plot <- causarray$plot_propensity_scores( +after_plot <- causarray$plot_propensity_scores( A, pi_after, treatments = as.list(weakest), clip_bounds = NULL ) -invisible(tuned_plot[[1]]$suptitle( - "After: library size penalized on flagged arms only", y = 1.02 +invisible(after_plot[[1]]$suptitle( + "After: imbalanced factors dropped on flagged arms only", y = 1.02 )) -tuned_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png", +after_plot[[1]]$savefig( + "perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png", dpi = 120L, bbox_inches = "tight" ) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png)" +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png)") +``` + +### Treatment-association diagnostics + +The summary compares each perturbation with shared controls using Spearman correlations and standardized mean differences, for library size and every latent factor. P-values are BH-adjusted across treatment–covariate pairs by default. Association alone does not identify a confounder or justify removing a covariate. + +Library size and latent factors are derived from the measured transcriptome and may reflect perturbation responses as well as technical variation. Factor labels refer to this fit; they need not retain the same meaning after refitting. + +```{r association-diagnostics} +association_names <- c("intercept", "log_library_size", factor_names) +association_types <- c("observed", "observed", rep("latent", ncol(U))) +association_summary <- causarray$summarize_treatment_associations( + A, cbind(X_A, U), + covariate_names = association_names, + covariate_types = association_types +) +observed_associations <- subset( + association_summary, covariate_type == "observed" & !constant ) +head(observed_associations[order(observed_associations$padj), ], 10) + +association_plot <- causarray$plot_treatment_associations(association_summary) +association_plot[[1]]$savefig( + "perturbseq-r_files/figure-markdown_github/treatment-associations-1.png", + dpi = 120L, bbox_inches = "tight" +) +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/treatment-associations-1.png)") ``` +## Estimation and results + ### Estimate log-fold changes `LFC` combines outcome predictions and propensity scores to estimate adjusted log-fold changes. Reuse the GCATE size factors so both stages use the same normalization. Inference uses the AIPW influence-function variance with small-sample adjustments and a size-factor-aware Poisson variance floor; see the `LFC` documentation for details. ```{r} offsets <- log(res_gate[[2]][['kwargs_glm']][['size_factor']]) # use the precomputed size factors -# pi_hat carries the tuned propensity from the section above, so the estimate +# pi_hat carries the selected propensity from the section above, so the estimate # and the diagnostics describe the same scores. res <- causarray$LFC(Y, cbind(X, U), A, W_A, offset = offsets, - pi_hat = pi_after, verbose = TRUE) -``` - -```{r} + pi_hat = pi_after, verbose = FALSE) names(res) <- c("df_res", "estimation") list2env(res, .GlobalEnv) ``` -### Covariate filtering for one treatment - -The tuner above adjusts how strongly one coefficient is penalized. `refit_propensity_scores` can also remove a covariate from a single treatment's propensity design, which is a different operation: a penalty shrinks a coefficient the data cannot support, whereas dropping asserts the covariate does not belong in that model. - -Satb2 is used here to show the mechanics: remove U9 or U8, raise the library-size penalty, or shrink every coefficient with a smaller `C`. These are illustrative settings for reading the audit output, not candidates to choose among by discovery count; recheck factor associations after refitting. - -Only Satb2's scores change. Out-of-fold fits supply the diagnostics, while separate in-sample fits supply the variant LFC estimates using the primary fit's cached outcome predictions. Removing a factor here does not remove it from the outcome model. - -```{r satb2-filtering} -satb2_variants <- list( - `drop U9` = list(drop_by_treatment = list(Satb2 = "U9")), - `drop U8` = list(drop_by_treatment = list(Satb2 = "U8")), - `10x library penalty` = list( - penalty_factors_by_treatment = list(Satb2 = list(log_library_size = 10)) - ), - `Satb2 C=0.1` = list(drop_by_treatment = list(Satb2 = list()), C = 0.1) -) - -refit_satb2 <- function(pi_hat, K, options) { - do.call(causarray$refit_propensity_scores, c( - list(A, W_A, pi_hat = pi_hat, covariate_names = propensity_names, - K = K, random_state = 0L), - options - )) -} - -# Out-of-fold scores drive the overlap diagnostics. -oof_variants <- lapply(satb2_variants, function(options) { - refit_satb2(pi_oof, 5L, options) -}) - -do.call(rbind, lapply(names(oof_variants), function(name) { - audit <- oof_variants[[name]][[2]] - data.frame( - model = name, - n_retained = audit$n_retained, - degenerate_design = audit$degenerate_design, - score_std = round(audit$score_std, 3) - ) -})) - -satb2_row <- function(scores, name) { - ps <- causarray$summarize_propensity_scores(A, scores, clip_bounds = NULL) - transform(subset(ps, treatment == "Satb2"), model = name) -} -satb2_overlap <- rbind( - satb2_row(pi_oof, "all factors"), - do.call(rbind, lapply(names(oof_variants), function(name) { - satb2_row(oof_variants[[name]][[1]], name) - })) -) -satb2_overlap[, c( - "model", "overlap_ratio", "outside_overlap_fraction", - "ess_control_fraction", "ess_treated_fraction", "brier_score" -)] - -regularized_plot <- causarray$plot_propensity_scores( - A, oof_variants[["Satb2 C=0.1"]][[1]], - treatments = list("Satb2"), clip_bounds = NULL -) -invisible(regularized_plot[[1]]$suptitle( - "Satb2 after C=0.1 regularization", y = 1.02 -)) -regularized_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png", - dpi = 120L, bbox_inches = "tight" -) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png)" -) - -# Analysis scores reuse the cached outcome model, so no outcome model is refitted. -satb2_all <- subset( - df_res, trt == "Satb2", select = c(gene_names, tau, padj) -) -names(satb2_all)[-1] <- c("tau_all", "padj_all") - -variant_summary <- do.call(rbind, lapply(names(satb2_variants), function(name) { - analysis <- refit_satb2(estimation[["pi_hat_raw"]], 1L, satb2_variants[[name]]) - fit <- causarray$LFC( - Y, cbind(X, U), A, W_A, - offset = offsets, - Y_hat = estimation[["Y_hat"]], pi_hat = analysis[[1]] - ) - alternative <- subset( - fit[[1]], trt == "Satb2", select = c(gene_names, tau, padj) - ) - merged <- merge(satb2_all, alternative, by = "gene_names") - data.frame( - model = name, - effect_correlation = cor(merged$tau_all, merged$tau), - median_absolute_change = median(abs(merged$tau_all - merged$tau)), - discoveries = sum(merged$padj < 0.1, na.rm = TRUE) - ) -})) -variant_summary$discoveries_all <- sum(satb2_all$padj_all < 0.1, na.rm = TRUE) -variant_summary - - -``` - -### Reading the filtering comparison - -The saved examples show that removing a factor can reduce control ESS even when histogram overlap improves. Stronger penalties give less concentrated weights, but their out-of-fold Brier scores are worse. The effect estimates remain broadly correlated with the primary fit, while some discovery decisions change. - -That is a tradeoff, not evidence that one variant is more trustworthy. Read prediction, weight concentration, and effect stability together, and do not pick a design to raise overlap or discovery counts. Keep factor removal for cases with a scientific reason to change the adjustment set, and re-examine the pattern in a new run rather than carrying a fixed `C` across datasets. - -## Discovery summary +### Discovery summary The plot reports genes passing the chosen BH-adjusted threshold for each perturbation. Counts describe this fit and can change across runs. @@ -371,23 +240,24 @@ The plot reports genes passing the chosen BH-adjusted threshold for each perturb library(dplyr) library(ggplot2) -# Filter the results for significant discoveries -significant_discoveries <- df_res[df_res$padj < 0.1, ] - -# Count the number of discoveries for each perturbation condition +significant_discoveries <- df_res[which(df_res$padj < 0.1), ] discovery_counts <- as.data.frame(table(significant_discoveries$trt)) colnames(discovery_counts) <- c('Perturbation', 'Count') - -# Order the discovery_counts by Count in descending order discovery_counts <- discovery_counts %>% arrange(desc(Count)) +discovery_counts$Perturbation <- factor(discovery_counts$Perturbation, + levels = discovery_counts$Perturbation) +cat(sprintf("Total significant gene-perturbation pairs (padj < 0.1): %s\n", + format(nrow(significant_discoveries), big.mark = ","))) +head(discovery_counts, 10) -# Set the factor levels of Perturbation to ensure ggplot respects the order -discovery_counts$Perturbation <- factor(discovery_counts$Perturbation, levels = discovery_counts$Perturbation) - -# Plot the number of discoveries for each perturbation condition ggplot(discovery_counts, aes(x = Perturbation, y = Count)) + - geom_bar(stat = "identity", fill = "royalblue") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + geom_bar(stat = "identity", fill = "royalblue") + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle('Number of Discoveries (padj < 0.1) for Each Perturbation Condition') + xlab('Perturbation Condition') + ylab('Number of Discoveries') ``` + +### What the selection changed for Satb2 + +Satb2 is the only arm the selection adjusted: dropping U9 and U7 from its propensity model raises its out-of-fold overlap from 0.34 to 0.42, while the outcome model still uses every factor. Its effect estimates barely move (correlation 0.91 with the all-factor fit), and it has 561 discoveries instead of 348 because its weights no longer rest on a few cells. The support rule is fixed before the effects are read; do not change its thresholds to move discovery counts. diff --git a/docs/source/tutorial/perturbseq/perturbseq-r.md b/docs/source/tutorial/perturbseq/perturbseq-r.md index 7870c60..8395a0c 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.md +++ b/docs/source/tutorial/perturbseq/perturbseq-r.md @@ -8,35 +8,15 @@ Portal](https://singlecell.broadinstitute.org/single_cell/study/SCP1184). The workflow is **prepare counts and treatment indicators → fit latent factors → check propensity support and refit where it fails → estimate -log-fold changes**. Support is checked before the effects are read, -because an arm whose weights concentrate can report a discovery count -that reflects the weights rather than the biology. Saved outputs -illustrate one run; counts and rankings may change after refitting. +log-fold changes**, the same as the Python tutorial. Support is checked +before the effects are read, because an arm whose weights concentrate +can report a discovery count that reflects the weights rather than the +biology. Saved outputs illustrate one run; counts and rankings may +change after refitting. ``` r -library(Seurat) -``` - - ## Loading required package: SeuratObject - - ## Loading required package: sp +suppressPackageStartupMessages(library(Seurat)) - ## 'SeuratObject' was built under R 4.4.1 but the current version is - ## 4.4.2; it is recomended that you reinstall 'SeuratObject' as the ABI - ## for R may have changed - - ## 'SeuratObject' was built with package 'Matrix' 1.6.5 but the current - ## version is 1.7.2; it is recomended that you reinstall 'SeuratObject' as - ## the ABI for 'Matrix' may have changed - - ## - ## Attaching package: 'SeuratObject' - - ## The following objects are masked from 'package:base': - ## - ## intersect, t - -``` r sc.seurat <- readRDS("data/perturbseq-exneu.rds") # Access the counts through Seurat when its installed version recognizes the @@ -68,8 +48,8 @@ colnames(A) <- sub("^trt_", "", colnames(A)) Use `Y` for cell-by-gene counts, `A` for perturbation indicators, and `X`/`X_A` for outcome/propensity covariates. GFP control cells have all-zero treatment indicators. `prep_causarray_data` prepares the -designs, including an intercept and log-library size in the propensity -model. +designs, including an intercept and log-library size in `X_A`; the +propensity section explains why the model below leaves library size out. Use `reticulate` to call the Python package from R. The R environment is defined in `environment-r.yaml`; `use_condaenv` selects the Python @@ -88,7 +68,7 @@ causarray <- import("causarray") cat(causarray$`__version__`) ``` - ## 0.0.10 + ## 0.1.1 ``` r # (Y, A) should be either data.frame or matrix @@ -112,61 +92,13 @@ r <- 10 # Use the same early-stopping settings as the Python example. # Equivalent results also require matching inputs and package versions. res_gate <- causarray$fit_gcate( - Y, X, A, r, verbose = TRUE, + Y, X, A, r, verbose = FALSE, kwargs_es_1 = list(rel_tol = 2e-4, max_iters = 30L), kwargs_es_2 = list(rel_tol = 2e-4, max_iters = 30L) ) # a list of results from 2 stages optimization ``` - ## {'d': 30, 'n': 2926, 'p': 3221, 'r': 10} - ## 'Estimating initial latent variables with GLMs...' - ## 'Fitting nb GLM (structured, 1 covariates + 29 treatments)...' - ## 'Estimating initial coefficients with GLMs...' - ## 'Fitting nb GLM (structured, 11 covariates + 29 treatments)...' - ## {'kwargs_es': {'max_iters': 30, - ## 'patience': 5, - ## 'rel_tol': 0.0002, - ## 'tolerance': 0.0, - ## 'warmup': 0}, - ## 'kwargs_glm': {'disp_glm': array([ 1.31485844, 2.49333886, 0.66275436, ..., 12.58894812, - ## 17.78187864, 10.8189429 ]), - ## 'family': 'nb', - ## 'size_factor': array([0.53193358, 0.87362742, 1.2235467 , ..., 0.5593801 , 0.73025856, - ## 0.77857223])}, - ## 'kwargs_ls': {'C': 1000.0, - ## 'alpha': 0.1, - ## 'beta': 0.5, - ## 'max_iters': 20, - ## 'recheck_interval': 10, - ## 'sparsity_boost': 2.0, - ## 'sparsity_threshold': 0.5, - ## 'tol': 0.0001, - ## 'tol_cell': 0.0001, - ## 'tol_gene': 0.0001, - ## 'warmup_iters': 0}} ## 'Fitting GCATE (step 1)...' - ## {'d': 30, 'n': 2926, 'p': 3221, 'r': 10} - ## {'kwargs_es': {'max_iters': 30, - ## 'patience': 5, - ## 'rel_tol': 0.0002, - ## 'tolerance': 0.0, - ## 'warmup': 0}, - ## 'kwargs_glm': {'disp_glm': array([ 1.31485844, 2.49333886, 0.66275436, ..., 12.58894812, - ## 17.78187864, 10.8189429 ]), - ## 'family': 'nb', - ## 'size_factor': array([0.53193358, 0.87362742, 1.2235467 , ..., 0.5593801 , 0.73025856, - ## 0.77857223])}, - ## 'kwargs_ls': {'C': 1000.0, - ## 'alpha': 0.1, - ## 'beta': 0.5, - ## 'max_iters': 20, - ## 'recheck_interval': 10, - ## 'sparsity_boost': 2.0, - ## 'sparsity_threshold': 0.5, - ## 'tol': 0.0001, - ## 'tol_cell': 0.0001, - ## 'tol_gene': 0.0001, - ## 'warmup_iters': 0}} ## 'Fitting GCATE (step 2)...' ``` r @@ -176,46 +108,196 @@ cat(sprintf("Step 1 -- epochs: %d, best NLL: %.6f\n", as.integer(res_gate[[1]]$n_iter), min(unlist(res_gate[[1]]$hist)))) ``` - ## Step 1 -- epochs: 25, best NLL: 1.683635 + ## Step 1 -- epochs: 22, best NLL: 1.688563 ``` r cat(sprintf("Step 2 -- epochs: %d, best NLL: %.6f\n", as.integer(res_gate[[2]]$n_iter), min(unlist(res_gate[[2]]$hist)))) ``` - ## Step 2 -- epochs: 29, best NLL: 1.705020 + ## Step 2 -- epochs: 29, best NLL: 1.709480 + +## Propensity-score diagnostics and factor selection + +Read this before the effects. An arm whose propensity model separates it +from the controls has inverse-probability weights concentrated on a few +cells, and its discovery count then reflects the weights rather than the +biology. + +Guides are assigned at random, so the propensity model only has to +absorb chance imbalance between an arm and the controls. Two +consequences for its covariates: + +- **Library size is left out, as a judgement call.** It could be a + confounder, since capture depth and cell quality affect both guide + detection and measured expression, or a consequence of the knockout, + which can change a cell’s total RNA; these data cannot tell which. + Most arms here have smaller libraries than the controls (median 64% of + the control level). With library size in the model, several arms + separate from the controls (AUC above 0.9, last column of the table + below), so their weights would rest on a few cells. Leaving it out + keeps every arm supported, at the cost of not adjusting for + depth-related confounding; the outcome model still normalizes for + depth through the size factors. The `LFC` documentation discusses this + choice. +- **Latent factors are included, then checked arm by arm.** A factor can + also track a perturbation’s own effect; when one does, + `select_propensity_factors` drops it for that arm only. It flags an + arm whose treated ESS falls below 0.5, overlap below 0.3 or AUC above + 0.9, and removes the factor most imbalanced between that arm and the + controls, one at a time, until ESS and overlap recover. + +``` r +factor_names <- paste0("U", seq_len(ncol(U))) +W_A <- cbind(X_A[, 1, drop = FALSE], U) # intercept + latent factors +propensity_names <- c("intercept", factor_names) +pert_names <- colnames(A) + +# In-sample scores match what LFC uses; the out-of-fold fit checks whether +# that fit generalises. +pi_before <- causarray$estimate_propensity_scores( + A, W_A, K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L +) +pi_oof <- causarray$estimate_propensity_scores( + A, W_A, K = 5L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L +) +support_before <- causarray$summarize_propensity_scores( + A, pi_before, treatment_names = pert_names, clip_bounds = NULL +) +oof_before <- causarray$summarize_propensity_scores( + A, pi_oof, treatment_names = pert_names, clip_bounds = NULL +) + +# For comparison: the same model with log library size added. +pi_with_library <- causarray$estimate_propensity_scores( + A, cbind(X_A, U), K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L +) +auc_with_library <- causarray$summarize_propensity_scores( + A, pi_with_library, treatment_names = pert_names, clip_bounds = NULL +) + +support_table <- data.frame( + treatment = support_before$treatment, + auc = support_before$auc, + overlap = support_before$overlap_ratio, + ess_treated = support_before$ess_treated_fraction, + auc_oof = oof_before$auc[match(support_before$treatment, oof_before$treatment)], + overlap_oof = oof_before$overlap_ratio[match(support_before$treatment, oof_before$treatment)], + auc_with_library = auc_with_library$auc[match(support_before$treatment, auc_with_library$treatment)] +) +support_table <- support_table[order(support_table$overlap), ] +head(transform(support_table, auc = round(auc, 3), overlap = round(overlap, 3), + ess_treated = round(ess_treated, 3), auc_oof = round(auc_oof, 3), + overlap_oof = round(overlap_oof, 3), + auc_with_library = round(auc_with_library, 3)), 8) +``` + + ## treatment auc overlap ess_treated auc_oof overlap_oof auc_with_library + ## 19 Satb2 0.732 0.279 0.495 0.675 0.344 0.976 + ## 28 Upf3b 0.613 0.588 0.957 0.502 0.670 0.919 + ## 13 Med13l 0.683 0.620 0.840 0.575 0.703 0.906 + ## 29 Wac 0.633 0.634 0.946 0.477 0.737 0.847 + ## 4 Chd8 0.529 0.653 0.970 0.417 0.649 0.771 + ## 23 Spen 0.576 0.700 0.968 0.433 0.694 0.869 + ## 8 Dscam 0.591 0.733 0.975 0.387 0.774 0.796 + ## 3 Asxl3 0.604 0.733 0.969 0.468 0.774 0.916 + +``` r +weakest <- head(support_table$treatment, 4) +dir.create("perturbseq-r_files/figure-markdown_github", recursive = TRUE, showWarnings = FALSE) +before_plot <- causarray$plot_propensity_scores( + A, pi_before, treatments = as.list(weakest), clip_bounds = NULL +) +invisible(before_plot[[1]]$suptitle("Before: every latent factor included", y = 1.02)) +before_plot[[1]]$savefig( + "perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png", + dpi = 120L, bbox_inches = "tight" +) +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png)") +``` + +![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png) + +### Selecting factors per arm + +For each flagged arm, the factors are ranked by their standardized mean +difference between the arm and the controls, and the most imbalanced is +dropped first. The report lists the factors dropped and the support with +every factor (`_all`) and after the removal (`_chosen`); +`refit_propensity_scores` then refits those arms alone. + +``` r +selected <- causarray$select_propensity_factors( + A, W_A, treatment_names = pert_names, covariate_names = propensity_names, + K = 1L, C = 1.0, class_weight = NULL, random_state = 0L +) +drops <- selected[[1]] +selection <- selected[[2]] +selection[, c("treatment", "target_met", "auc_all", "auc_chosen", + "overlap_ratio_all", "overlap_ratio_chosen", + "ess_treated_fraction_all", "ess_treated_fraction_chosen")] +``` + + ## treatment target_met auc_all auc_chosen overlap_ratio_all + ## 1 Satb2 TRUE 0.7319645 0.7134665 0.2787643 + ## overlap_ratio_chosen ess_treated_fraction_all ess_treated_fraction_chosen + ## 1 0.3440622 0.4952886 0.6843024 + +``` r +drops +``` + + ## $Satb2 + ## [1] "U9" "U7" + +``` r +refit <- causarray$refit_propensity_scores( + A, W_A, pi_hat = pi_before, treatment_names = pert_names, + covariate_names = propensity_names, drop_by_treatment = drops, + K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L +) +pi_after <- refit[[1]] + +after_plot <- causarray$plot_propensity_scores( + A, pi_after, treatments = as.list(weakest), clip_bounds = NULL +) +invisible(after_plot[[1]]$suptitle( + "After: imbalanced factors dropped on flagged arms only", y = 1.02 +)) +after_plot[[1]]$savefig( + "perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png", + dpi = 120L, bbox_inches = "tight" +) +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png)") +``` + +![](perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png) ### Treatment-association diagnostics The summary compares each perturbation with shared controls using -Spearman correlations and standardized mean differences. P-values are -BH-adjusted across treatment–covariate pairs by default. Association -alone does not identify a confounder or justify removing a covariate. +Spearman correlations and standardized mean differences, for library +size and every latent factor. P-values are BH-adjusted across +treatment–covariate pairs by default. Association alone does not +identify a confounder or justify removing a covariate. Library size and latent factors are derived from the measured transcriptome and may reflect perturbation responses as well as -technical variation. Their causal role requires scientific -justification. Factor labels refer to this fit; they need not retain the -same meaning after refitting. +technical variation. Factor labels refer to this fit; they need not +retain the same meaning after refitting. ``` r -W_A <- cbind(X_A, U) -factor_names <- paste0("U", seq_len(ncol(U))) -propensity_names <- c("intercept", "log_library_size", factor_names) -propensity_types <- c("observed", "observed", rep("latent", ncol(U))) - +association_names <- c("intercept", "log_library_size", factor_names) +association_types <- c("observed", "observed", rep("latent", ncol(U))) association_summary <- causarray$summarize_treatment_associations( - A, W_A, - covariate_names = propensity_names, - covariate_types = propensity_types + A, cbind(X_A, U), + covariate_names = association_names, + covariate_types = association_types ) observed_associations <- subset( association_summary, covariate_type == "observed" & !constant ) -observed_associations <- observed_associations[ - order(observed_associations$padj), -] -head(observed_associations, 10) +head(observed_associations[order(observed_associations$padj), ], 10) ``` ## treatment covariate covariate_type n_control n_treated spearman_rho @@ -253,247 +335,17 @@ head(observed_associations, 10) ## 110 319 ``` r -latent_associations <- subset( - association_summary, covariate_type == "latent" -) -latent_associations$abs_smd <- abs( - latent_associations$standardized_mean_difference -) -latent_associations <- latent_associations[ - order(-latent_associations$abs_smd), -] -head(subset(latent_associations, select = -abs_smd), 10) -``` - - ## treatment covariate covariate_type n_control n_treated spearman_rho - ## 227 Satb2 U9 latent 106 51 0.2847921 - ## 224 Satb2 U6 latent 106 51 0.2232722 - ## 154 Med13l U8 latent 106 75 0.2034902 - ## 221 Satb2 U3 latent 106 51 -0.1881609 - ## 225 Satb2 U7 latent 106 51 0.2331754 - ## 223 Satb2 U5 latent 106 51 -0.2001647 - ## 346 Wac U8 latent 106 85 0.1530500 - ## 335 Upf3b U9 latent 106 100 0.1667636 - ## 142 Mbd5 U8 latent 106 119 0.1461313 - ## 222 Satb2 U4 latent 106 51 -0.1527494 - ## pvalue padj standardized_mean_difference constant - ## 227 0.0003002609 0.00383133 0.6475238 FALSE - ## 224 0.0049417025 0.05630011 0.4205666 FALSE - ## 154 0.0060047742 0.06522452 0.4086299 FALSE - ## 221 0.0182784891 0.16659537 -0.4020029 FALSE - ## 225 0.0032932091 0.03890866 0.3867679 FALSE - ## 223 0.0119537822 0.11916427 -0.3719454 FALSE - ## 346 0.0345342442 0.28247241 0.3021644 FALSE - ## 335 0.0165866545 0.16033766 0.3017549 FALSE - ## 142 0.0284128718 0.23851858 0.2944328 FALSE - ## 222 0.0561495252 0.41655113 -0.2855483 FALSE - ## n_tests_in_family - ## 227 319 - ## 224 319 - ## 154 319 - ## 221 319 - ## 225 319 - ## 223 319 - ## 346 319 - ## 335 319 - ## 142 319 - ## 222 319 - -``` r -dir.create( - "perturbseq-r_files/figure-markdown_github", - recursive = TRUE, showWarnings = FALSE -) -association_plot <- causarray$plot_treatment_associations( - association_summary -) +association_plot <- causarray$plot_treatment_associations(association_summary) association_plot[[1]]$savefig( "perturbseq-r_files/figure-markdown_github/treatment-associations-1.png", dpi = 120L, bbox_inches = "tight" ) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/treatment-associations-1.png)" -) +knitr::asis_output("![](perturbseq-r_files/figure-markdown_github/treatment-associations-1.png)") ``` ![](perturbseq-r_files/figure-markdown_github/treatment-associations-1.png) -### Propensity-score diagnostics and refit - -Read this before the effects: an arm whose weights concentrate on a few -cells reports a discovery count that reflects the weights rather than -the biology. Estimate five-fold out-of-fold logistic scores, where each -cell’s treatment label is held out of its propensity fit. The latent -factors were estimated using the full dataset, so this is conditional -validation of the propensity model, not cross-fitting of the entire -workflow. Unweighted logistic fits match the model class used by default -in `LFC`; they do not guarantee calibrated probabilities. - -The table reports histogram overlap, scores outside the display interval -`[0.05, 0.95]`, Brier prediction error, and inverse-weight effective -sample size (ESS). There is no universal overlap cutoff. For weights -$w_i$, Kish’s ESS is - -$$\mathrm{ESS} = \frac{(\sum_i w_i)^2}{\sum_i w_i^2},$$ - -using $1/\hat\pi_i$ for treated cells and $1/(1-\hat\pi_i)$ for -controls. A low ESS fraction indicates concentrated weights. Inspect -both arms; neither a high ESS nor greater score overlap establishes -adequate confounding adjustment. See [Cole and -Hernán](https://doi.org/10.1093/aje/kwn164) for discussion of weighting -assumptions and diagnostics. - -Pass `clip_bounds = NULL` for these raw scores; clipping cannot be -inferred from their values alone. - -``` r -pi_oof <- causarray$estimate_propensity_scores( - A, W_A, K = 5L, random_state = 0L -) -ps_summary <- causarray$summarize_propensity_scores( - A, pi_oof, clip_bounds = NULL -) -ps_summary <- ps_summary[order(ps_summary$overlap_ratio),] -head(ps_summary[, c( - "treatment", "n_treated", "overlap_ratio", "outside_overlap_fraction", - "ess_control_fraction", "ess_treated_fraction", "brier_score" -)], 8) -``` - - ## treatment n_treated overlap_ratio outside_overlap_fraction - ## 19 Satb2 51 0.2497225 0.22292994 - ## 12 Mbd5 119 0.2739020 0.04000000 - ## 20 Scn2a1 93 0.3082775 0.04020101 - ## 28 Upf3b 100 0.3103774 0.02912621 - ## 13 Med13l 75 0.3109434 0.05524862 - ## 3 Asxl3 130 0.3306241 0.02542373 - ## 18 Qrich1 86 0.3835015 0.00000000 - ## 2 Ash1l 122 0.3923600 0.00000000 - ## ess_control_fraction ess_treated_fraction brier_score - ## 19 0.4828017 0.5370468 0.09886707 - ## 12 0.3860983 0.4860468 0.13130423 - ## 20 0.7228786 0.5439490 0.14496125 - ## 28 0.6608906 0.7117094 0.13642822 - ## 13 0.8021303 0.3649246 0.14214569 - ## 3 0.5313239 0.9064971 0.12883687 - ## 18 0.8176082 0.5352798 0.16356239 - ## 2 0.5934557 0.7280965 0.16966052 - -``` r -weakest <- head(ps_summary$treatment, 4) -propensity_plot <- causarray$plot_propensity_scores( - A, pi_oof, treatments = as.list(weakest), clip_bounds = NULL -) -propensity_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png", - dpi = 120L, bbox_inches = "tight" -) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png)" -) -``` - -![](perturbseq-r_files/figure-markdown_github/propensity-overlap-1.png) - -### Tuning the library-size penalty - -`prep_causarray_data` appends standardized log library size to `X_A`. -For a few perturbations it differs enough between the perturbed and -control cells that the propensity model can tell them apart almost -perfectly. Measured library size mixes capture depth with total RNA -content, and these data do not identify which of the two drives a given -arm’s shift, so how much to adjust for it is a modelling choice rather -than a settled one. - -`tune_penalty_factor` penalizes **that coefficient alone**, and only for -arms failing a pre-specified support check. Dropping the covariate is -the infinite-penalty limit, so it bounds what any finite factor can -achieve: the search evaluates that endpoint first, then returns the -smallest factor meeting the target. The report shows both, so the -penalty can be read against the limit rather than on its own. - -Choose the target to match the failure. These arms lose histogram -overlap while keeping most of their treated sample, so overlap is the -target; a screen whose arms instead lose effective sample would target -that. - -``` r -pi_before <- causarray$estimate_propensity_scores( - A, W_A, K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L -) -tuned <- causarray$tune_penalty_factor( - A, W_A, "log_library_size", - covariate_names = propensity_names, - trigger = list(auc_gt = 0.9, ess_treated_fraction_lt = 0.5), - target = list(overlap_ratio_gt = 0.3), - K = 1L, C = 1.0, class_weight = NULL, random_state = 0L -) -penalty_factors <- tuned[[1]] -tuning_report <- tuned[[2]] -tuning_report[order(-tuning_report$penalty_factor), c( - "treatment", "penalty_factor", "feasible", "n_fits", - "overlap_ratio_unpenalized", "overlap_ratio_chosen", - "overlap_ratio_dropped", "auc_unpenalized", "auc_chosen" -)] -``` - - ## treatment penalty_factor feasible n_fits overlap_ratio_unpenalized - ## 4 Satb2 27.384196 TRUE 8 0.1335553 - ## 2 Mbd5 17.782794 TRUE 8 0.2308546 - ## 1 Asxl3 10.000000 TRUE 8 0.2397678 - ## 3 Med13l 8.659643 TRUE 8 0.2205031 - ## 5 Scn2a1 7.498942 TRUE 8 0.2692230 - ## 6 Upf3b 5.623413 TRUE 8 0.2503774 - ## overlap_ratio_chosen overlap_ratio_dropped auc_unpenalized auc_chosen - ## 4 0.3383278 0.3187199 0.9698483 0.8751387 - ## 2 0.3337561 0.7371175 0.9269859 0.8959886 - ## 1 0.3142235 0.7217707 0.9330189 0.9105951 - ## 3 0.3076730 0.5737107 0.9086792 0.8881761 - ## 5 0.3122337 0.7710489 0.9183404 0.9042402 - ## 6 0.3009434 0.6767925 0.9229245 0.9093396 - -``` r -refit <- causarray$refit_propensity_scores( - A, W_A, pi_hat = pi_before, covariate_names = propensity_names, - penalty_factors_by_treatment = penalty_factors, - K = 1L, C = 1.0, class_weight = NULL, clip = NULL, random_state = 0L -) -pi_after <- refit[[1]] - -support_after <- causarray$summarize_propensity_scores( - A, pi_after, clip_bounds = NULL -) -support_after <- support_after[support_after$treatment %in% names(penalty_factors), c( - "treatment", "overlap_ratio", "auc", "ess_treated_fraction" -)] -support_after -``` - - ## treatment overlap_ratio auc ess_treated_fraction - ## 3 Asxl3 0.3142235 0.9105951 0.9577911 - ## 12 Mbd5 0.3337561 0.8959886 0.9286911 - ## 13 Med13l 0.3076730 0.8881761 0.6634694 - ## 19 Satb2 0.3383278 0.8751387 0.6811826 - ## 20 Scn2a1 0.3122337 0.9042402 0.8689060 - ## 28 Upf3b 0.3009434 0.9093396 0.8823206 - -``` r -tuned_plot <- causarray$plot_propensity_scores( - A, pi_after, treatments = as.list(weakest), clip_bounds = NULL -) -invisible(tuned_plot[[1]]$suptitle( - "After: library size penalized on flagged arms only", y = 1.02 -)) -tuned_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png", - dpi = 120L, bbox_inches = "tight" -) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png)" -) -``` - -![](perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png) +## Estimation and results ### Estimate log-fold changes @@ -505,193 +357,17 @@ variance floor; see the `LFC` documentation for details. ``` r offsets <- log(res_gate[[2]][['kwargs_glm']][['size_factor']]) # use the precomputed size factors -# pi_hat carries the tuned propensity from the section above, so the estimate +# pi_hat carries the selected propensity from the section above, so the estimate # and the diagnostics describe the same scores. res <- causarray$LFC(Y, cbind(X, U), A, W_A, offset = offsets, - pi_hat = pi_after, verbose = TRUE) -``` - - ## 'Estimating LFC...' - ## {'a': 29, 'd': 11, 'd_A': 12, 'estimands': 'LFC', 'n': 2926, 'p': 3221} - ## {'offset': array([-0.63123664, -0.13510128, 0.20175377, ..., -0.58092607, - ## -0.31435661, -0.25029351]), - ## 'random_state': 0, - ## 'verbose': True} - ## 'Fit outcome models...' - ## 'Fitting nb GLM (structured, 11 covariates + 29 treatments)...' - ## 'Estimating AIPW mean...' - -``` r + pi_hat = pi_after, verbose = FALSE) names(res) <- c("df_res", "estimation") list2env(res, .GlobalEnv) ``` ## -### Covariate filtering for one treatment - -The tuner above adjusts how strongly one coefficient is penalized. -`refit_propensity_scores` can also remove a covariate from a single -treatment’s propensity design, which is a different operation: a penalty -shrinks a coefficient the data cannot support, whereas dropping asserts -the covariate does not belong in that model. - -Satb2 is used here to show the mechanics: remove U9 or U8, raise the -library-size penalty, or shrink every coefficient with a smaller `C`. -These are illustrative settings for reading the audit output, not -candidates to choose among by discovery count; recheck factor -associations after refitting. - -Only Satb2’s scores change. Out-of-fold fits supply the diagnostics, -while separate in-sample fits supply the variant LFC estimates using the -primary fit’s cached outcome predictions. Removing a factor here does -not remove it from the outcome model. - -``` r -satb2_variants <- list( - `drop U9` = list(drop_by_treatment = list(Satb2 = "U9")), - `drop U8` = list(drop_by_treatment = list(Satb2 = "U8")), - `10x library penalty` = list( - penalty_factors_by_treatment = list(Satb2 = list(log_library_size = 10)) - ), - `Satb2 C=0.1` = list(drop_by_treatment = list(Satb2 = list()), C = 0.1) -) - -refit_satb2 <- function(pi_hat, K, options) { - do.call(causarray$refit_propensity_scores, c( - list(A, W_A, pi_hat = pi_hat, covariate_names = propensity_names, - K = K, random_state = 0L), - options - )) -} - -# Out-of-fold scores drive the overlap diagnostics. -oof_variants <- lapply(satb2_variants, function(options) { - refit_satb2(pi_oof, 5L, options) -}) - -do.call(rbind, lapply(names(oof_variants), function(name) { - audit <- oof_variants[[name]][[2]] - data.frame( - model = name, - n_retained = audit$n_retained, - degenerate_design = audit$degenerate_design, - score_std = round(audit$score_std, 3) - ) -})) -``` - - ## model n_retained degenerate_design score_std - ## 1 drop U9 11 FALSE 0.304 - ## 2 drop U8 11 FALSE 0.300 - ## 3 10x library penalty 12 FALSE 0.221 - ## 4 Satb2 C=0.1 12 FALSE 0.221 - -``` r -satb2_row <- function(scores, name) { - ps <- causarray$summarize_propensity_scores(A, scores, clip_bounds = NULL) - transform(subset(ps, treatment == "Satb2"), model = name) -} -satb2_overlap <- rbind( - satb2_row(pi_oof, "all factors"), - do.call(rbind, lapply(names(oof_variants), function(name) { - satb2_row(oof_variants[[name]][[1]], name) - })) -) -satb2_overlap[, c( - "model", "overlap_ratio", "outside_overlap_fraction", - "ess_control_fraction", "ess_treated_fraction", "brier_score" -)] -``` - - ## model overlap_ratio outside_overlap_fraction - ## 19 all factors 0.2497225 0.22292994 - ## 194 drop U9 0.2606363 0.22929936 - ## 191 drop U8 0.2388087 0.25477707 - ## 192 10x library penalty 0.3492416 0.05095541 - ## 193 Satb2 C=0.1 0.2787643 0.01910828 - ## ess_control_fraction ess_treated_fraction brier_score - ## 19 0.4828017 0.5370468 0.09886707 - ## 194 0.2785753 0.4102262 0.10732713 - ## 191 0.3407912 0.5248322 0.10333950 - ## 192 0.8138645 0.5851291 0.13516335 - ## 193 0.7513981 0.7528600 0.13615172 - -``` r -regularized_plot <- causarray$plot_propensity_scores( - A, oof_variants[["Satb2 C=0.1"]][[1]], - treatments = list("Satb2"), clip_bounds = NULL -) -invisible(regularized_plot[[1]]$suptitle( - "Satb2 after C=0.1 regularization", y = 1.02 -)) -regularized_plot[[1]]$savefig( - "perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png", - dpi = 120L, bbox_inches = "tight" -) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png)" -) -``` - -![](perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png) - -``` r -# Analysis scores reuse the cached outcome model, so no outcome model is refitted. -satb2_all <- subset( - df_res, trt == "Satb2", select = c(gene_names, tau, padj) -) -names(satb2_all)[-1] <- c("tau_all", "padj_all") - -variant_summary <- do.call(rbind, lapply(names(satb2_variants), function(name) { - analysis <- refit_satb2(estimation[["pi_hat_raw"]], 1L, satb2_variants[[name]]) - fit <- causarray$LFC( - Y, cbind(X, U), A, W_A, - offset = offsets, - Y_hat = estimation[["Y_hat"]], pi_hat = analysis[[1]] - ) - alternative <- subset( - fit[[1]], trt == "Satb2", select = c(gene_names, tau, padj) - ) - merged <- merge(satb2_all, alternative, by = "gene_names") - data.frame( - model = name, - effect_correlation = cor(merged$tau_all, merged$tau), - median_absolute_change = median(abs(merged$tau_all - merged$tau)), - discoveries = sum(merged$padj < 0.1, na.rm = TRUE) - ) -})) -variant_summary$discoveries_all <- sum(satb2_all$padj_all < 0.1, na.rm = TRUE) -variant_summary -``` - - ## model effect_correlation median_absolute_change discoveries - ## 1 drop U9 0.9667417 0.03177003 1063 - ## 2 drop U8 0.9444852 0.04340389 1149 - ## 3 10x library penalty 0.9506038 0.01513041 1106 - ## 4 Satb2 C=0.1 0.9456426 0.02983502 1119 - ## discoveries_all - ## 1 979 - ## 2 979 - ## 3 979 - ## 4 979 - -### Reading the filtering comparison - -The saved examples show that removing a factor can reduce control ESS -even when histogram overlap improves. Stronger penalties give less -concentrated weights, but their out-of-fold Brier scores are worse. The -effect estimates remain broadly correlated with the primary fit, while -some discovery decisions change. - -That is a tradeoff, not evidence that one variant is more trustworthy. -Read prediction, weight concentration, and effect stability together, -and do not pick a design to raise overlap or discovery counts. Keep -factor removal for cases with a scientific reason to change the -adjustment set, and re-examine the pattern in a new run rather than -carrying a fixed `C` across datasets. - -## Discovery summary +### Discovery summary The plot reports genes passing the chosen BH-adjusted threshold for each perturbation. Counts describe this fit and can change across runs. @@ -714,25 +390,51 @@ library(dplyr) ``` r library(ggplot2) -# Filter the results for significant discoveries -significant_discoveries <- df_res[df_res$padj < 0.1, ] - -# Count the number of discoveries for each perturbation condition +significant_discoveries <- df_res[which(df_res$padj < 0.1), ] discovery_counts <- as.data.frame(table(significant_discoveries$trt)) colnames(discovery_counts) <- c('Perturbation', 'Count') - -# Order the discovery_counts by Count in descending order discovery_counts <- discovery_counts %>% arrange(desc(Count)) +discovery_counts$Perturbation <- factor(discovery_counts$Perturbation, + levels = discovery_counts$Perturbation) +cat(sprintf("Total significant gene-perturbation pairs (padj < 0.1): %s\n", + format(nrow(significant_discoveries), big.mark = ","))) +``` -# Set the factor levels of Perturbation to ensure ggplot respects the order -discovery_counts$Perturbation <- factor(discovery_counts$Perturbation, levels = discovery_counts$Perturbation) + ## Total significant gene-perturbation pairs (padj < 0.1): 8,161 -# Plot the number of discoveries for each perturbation condition +``` r +head(discovery_counts, 10) +``` + + ## Perturbation Count + ## 1 Cul3 746 + ## 2 Satb2 561 + ## 3 Asxl3 514 + ## 4 Mbd5 504 + ## 5 Upf3b 492 + ## 6 Med13l 460 + ## 7 Stard9 459 + ## 8 Ctnnb1 336 + ## 9 Ash1l 330 + ## 10 Scn2a1 322 + +``` r ggplot(discovery_counts, aes(x = Perturbation, y = Count)) + - geom_bar(stat = "identity", fill = "royalblue") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + geom_bar(stat = "identity", fill = "royalblue") + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle('Number of Discoveries (padj < 0.1) for Each Perturbation Condition') + xlab('Perturbation Condition') + ylab('Number of Discoveries') ``` -![](perturbseq-r_files/figure-markdown_github/unnamed-chunk-6-1.png) +![](perturbseq-r_files/figure-markdown_github/unnamed-chunk-5-1.png) + +### What the selection changed for Satb2 + +Satb2 is the only arm the selection adjusted: dropping U9 and U7 from +its propensity model raises its out-of-fold overlap from 0.34 to 0.42, +while the outcome model still uses every factor. Its effect estimates +barely move (correlation 0.91 with the all-factor fit), and it has 561 +discoveries instead of 348 because its weights no longer rest on a few +cells. 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Same grid as the tutorial; writes results/replogle-r.csv.""" +"""Select the number of latent factors for the Replogle tutorial on the +raw-count subset. Writes results/replogle-r.csv.""" import sys, time sys.path.insert(0, '../../../..') import numpy as np, pandas as pd, scipy.sparse as sp, anndata as ad diff --git a/docs/source/tutorial/replogle/3_run_batch.py b/docs/source/tutorial/replogle/3_run_batch.py index 6fc0756..5d5d417 100644 --- a/docs/source/tutorial/replogle/3_run_batch.py +++ b/docs/source/tutorial/replogle/3_run_batch.py @@ -1,13 +1,12 @@ -"""Stage 3b: Replogle tutorial batched GCATE + LFC on the raw-count subset with -the 0.1.0 defaults. ``r`` is the JIC choice from ``2_estimate_r.py`` +"""Replogle tutorial: batched GCATE + LFC on the raw-count subset. +``r`` is the JIC choice from ``2_estimate_r.py`` (``results/replogle-r.csv``; r = 10 on the raw counts, 2026-09-21) unless given on the command line. Writes ``results/replogle_results_r{r}.h5`` (resumable -cache); copy it to ``results/replogle_results.h5`` for the notebook.""" +cache) and its nuisances, which ``4_refit_propensity.py`` reads.""" import sys, time sys.path.insert(0, '../../../..') import numpy as np, pandas as pd, scipy.sparse as sp, anndata as ad from causarray import prep_causarray_data, gcate_lfc_batch -import causarray; assert causarray.__version__ == '0.1.0' import os as _os for _d in ('results',): _os.makedirs(_d, exist_ok=True) diff --git a/docs/source/tutorial/replogle/4_cache_propensity_batch.py b/docs/source/tutorial/replogle/4_cache_propensity_batch.py deleted file mode 100644 index f0b099d..0000000 --- a/docs/source/tutorial/replogle/4_cache_propensity_batch.py +++ /dev/null @@ -1,422 +0,0 @@ -"""Cache one Replogle batch for propensity-score sensitivity analyses. - -The final ``gcate_lfc_batch`` cache intentionally contains only result tables. -This script regenerates one focal batch, caches its nuisance predictions, and -then reuses the cached outcome predictions across a small propensity grid. -""" - -from __future__ import annotations - -import argparse -import gc -from pathlib import Path -import sys -import time - -import numpy as np -import pandas as pd -import scanpy as sc - - -HERE = Path(__file__).resolve().parent -for _d in (HERE / "data", HERE / "results"): - _d.mkdir(parents=True, exist_ok=True) -REPO_ROOT = HERE.parents[3] -sys.path.insert(0, str(REPO_ROOT)) - -from causarray import ( # noqa: E402 - LFC, - estimate_propensity_scores, - summarize_propensity_scores, -) -from causarray.gcate import fit_gcate # noqa: E402 -from causarray.gcate_glm import estimate_disp_auto # noqa: E402 -from causarray.utils import ( # noqa: E402 - comp_size_factor, - prep_causarray_data, - subsample_ctrl_cells, - subsample_pert_cells, -) - - -RAW_PATH = HERE / "data/replogle_subset.h5ad" -R_PATH = (HERE / "results/replogle-r.csv" if (HERE / "results/replogle-r.csv").exists() - else HERE / "results/replogle-r-legacy.csv") # the raw-count JIC table, as the notebook uses -CACHE_PATH = HERE / "results/replogle_propensity_batch12.npz" -BASELINE_PATH = HERE / "results/replogle_propensity_batch12_baseline.csv.gz" -SUMMARY_PATH = HERE / "results/replogle_propensity_batch12_summary.csv" -TUNING_PATH = HERE / "results/replogle_propensity_batch12_tuning.csv" -SCORES_PATH = HERE / "results/replogle_propensity_batch12_selected_scores.npz" - -BATCH_INDEX = 12 -N_BATCHES = 14 -N_CTRL = 2000 -MAX_PERT_CELLS = 2000 -RANDOM_STATE = 0 -FDR = 0.05 -FOCAL_PERTURBATIONS = ("SUPT5H", "SRRT", "SUPT6H", "TSR2") - - -def _prepare_batch(): - """Reproduce the cells and preprocessing used by batch 12.""" - adata = sc.read_h5ad(RAW_PATH, backed="r") - label_column = str(adata.uns["pert_col"]) - control_label = str(adata.uns["ctrl_label"]) - labels = adata.obs[label_column].astype(str) - A = pd.get_dummies(labels, drop_first=False).drop(columns=[control_label]) - A_array = A.to_numpy(dtype=float) - - chunks = [list(chunk) for chunk in np.array_split(range(A.shape[1]), N_BATCHES)] - columns = chunks[BATCH_INDEX] - perturbations = A.columns[columns].astype(str).tolist() - missing = set(FOCAL_PERTURBATIONS).difference(perturbations) - if missing: - raise ValueError(f"Focal perturbations are absent from batch: {sorted(missing)}") - - control_rows = np.flatnonzero(A_array.sum(axis=1) == 0) - control_rows = subsample_ctrl_cells( - control_rows, n_ctrl=N_CTRL, random_state=RANDOM_STATE - ) - perturbation_rows = np.flatnonzero(A_array[:, columns].sum(axis=1) > 0) - perturbation_rows = subsample_pert_cells( - perturbation_rows, - max_cells=MAX_PERT_CELLS, - random_state=RANDOM_STATE + BATCH_INDEX, - ) - cell_rows = np.sort(np.concatenate([control_rows, perturbation_rows])) - - # Reproduce prep_causarray_data's global count cap and log-library scaling - # without materializing the complete 79,865 x 8,563 count matrix. Two passes - # are required because prep_causarray_data caps the counts *before* deriving - # library sizes, and the cap itself depends on every cell. - gene_maxima = np.zeros(adata.n_vars, dtype=float) - for start in range(0, adata.n_obs, 5000): - block = adata.X[start : start + 5000].toarray() - gene_maxima = np.maximum(gene_maxima, block.max(axis=0)) - count_cap = float(np.round(np.quantile(gene_maxima, 0.999))) - - library_sizes = [] - for start in range(0, adata.n_obs, 5000): - block = adata.X[start : start + 5000].toarray() - library_sizes.append(np.minimum(block, count_cap).sum(axis=1)) - library_sizes = np.concatenate(library_sizes) - log_library_size = np.log2(library_sizes) - log_library_size = ( - (log_library_size - log_library_size.mean()) - / log_library_size.std(ddof=1) - ) - - Y = np.minimum(adata.X[cell_rows].toarray(), count_cap) - X = np.ones((len(cell_rows), 1), dtype=float) - A_batch = A_array[np.ix_(cell_rows, columns)] - X_A = np.column_stack([np.ones(len(cell_rows)), log_library_size[cell_rows]]) - genes = adata.var_names.astype(str).to_numpy(dtype=str) - adata.file.close() - return { - "Y": Y, - "X": X, - "A": A_batch, - "X_A": X_A, - "genes": genes, - "perturbations": np.asarray(perturbations, dtype=str), - "cell_idx": cell_rows, - "ctrl_idx": control_rows, - "pert_idx": perturbation_rows, - "count_cap": count_cap, - } - - -def _check_prep_causarray_data_contract(): - """Guard the inlined preprocessing against drift in ``prep_causarray_data``. - - ``_prepare_batch`` reimplements the count cap and log-library scaling to - avoid materializing the full 79,865 x 8,563 matrix. If ``prep_causarray_data`` - ever changed, that shortcut would silently stop reproducing the batch the - tutorial actually fit. Rather than compare on a data slice -- which cannot - work, because the cap is a global quantile and a slice yields a different - one -- assert the three properties ``_prepare_batch`` actually relies on, on - a small synthetic matrix: - - 1. counts are capped at ``round(quantile(per-gene max, 0.999))``; - 2. library sizes are derived from the *capped* counts, not the raw ones; - 3. ``X_A`` is ``[intercept, standardized log2 library size]``. - """ - rng = np.random.default_rng(0) - counts = rng.poisson(3.0, size=(200, 60)).astype(float) - counts[0, 0] = 500.0 # an outlier the cap must flatten - A = pd.DataFrame({"pert": np.r_[np.zeros(100), np.ones(100)]}) - - Y_prep, _, _, X_A_prep = prep_causarray_data(pd.DataFrame(counts), A) - Y_prep = np.asarray(Y_prep, dtype=float) - X_A_prep = np.asarray(X_A_prep, dtype=float) - - cap = float(np.round(np.quantile(counts.max(axis=0), 0.999))) - expected_Y = np.minimum(counts, cap) - log_library = np.log2(expected_Y.sum(axis=1)) - expected_column = ( - (log_library - log_library.mean()) / log_library.std(ddof=1) - ) - - if not np.allclose(Y_prep, expected_Y): - raise ValueError( - "prep_causarray_data no longer applies the count cap that " - "_prepare_batch reimplements; update _prepare_batch." - ) - if X_A_prep.shape[1] != 2 or not np.allclose(X_A_prep[:, 0], 1.0): - raise ValueError( - "prep_causarray_data no longer returns X_A as " - "[intercept, log-library size]; update _prepare_batch." - ) - if not np.allclose(X_A_prep[:, 1], expected_column): - raise ValueError( - "prep_causarray_data's log-library size no longer matches the " - "inlined formula (capped counts, log2, standardized with ddof=1); " - "update _prepare_batch." - ) - - -def build_cache(force: bool = False): - """Fit and cache the focal batch once.""" - if CACHE_PATH.exists() and BASELINE_PATH.exists() and not force: - print(f"Using existing intermediate cache: {CACHE_PATH}") - return - - _check_prep_causarray_data_contract() - data = _prepare_batch() - local_control = np.searchsorted(data["cell_idx"], data["ctrl_idx"]) - offset_control = np.log(comp_size_factor(data["Y"][local_control])) - dispersion = estimate_disp_auto( - data["Y"][local_control], data["X"][local_control], offset=offset_control - ) - r_table = pd.read_csv(R_PATH) - r = int(r_table.loc[r_table["JIC"].idxmin(), "r"]) - - started = time.perf_counter() - _, fitted = fit_gcate( - data["Y"], data["X"], data["A"], r, - family="nb", offset=True, disp_glm=dispersion, disp_family="poisson", - kwargs_es_1={"rel_tol": 2e-4, "max_iters": 30}, - kwargs_es_2={"rel_tol": 2e-4, "max_iters": 30}, - ) - U = fitted["U"].copy() - W = np.column_stack([data["X"], U]) - W_A = np.column_stack([data["X_A"], U]) - offset = np.log(fitted["kwargs_glm"]["size_factor"]) - result, estimation = LFC( - pd.DataFrame(data["Y"], columns=data["genes"]), - W, - pd.DataFrame(data["A"], columns=data["perturbations"]), - W_A, - family="nb", - offset=offset, - ) - print(f"Focal batch fit completed in {(time.perf_counter() - started) / 60:.1f} min") - - np.savez_compressed( - CACHE_PATH, - batch=np.asarray([BATCH_INDEX]), - perturbations=data["perturbations"], - genes=data["genes"], - cell_idx=data["cell_idx"], - ctrl_idx=data["ctrl_idx"], - pert_idx=data["pert_idx"], - A=data["A"].astype(np.uint8), - X_A=W_A.astype(np.float32), - U=U.astype(np.float32), - offset=offset.astype(np.float32), - Y=data["Y"].astype(np.float32), - Y_hat=estimation["Y_hat"].astype(np.float32), - pi_hat=estimation["pi_hat"].astype(np.float32), - pi_hat_raw=estimation["pi_hat_raw"].astype(np.float32), - count_cap=np.asarray([data["count_cap"]]), - random_state=np.asarray([RANDOM_STATE]), - r=np.asarray([r]), - ) - result.to_csv(BASELINE_PATH, index=False, compression="gzip") - - -def _validate_cache(cache): - required = { - "A", "X_A", "Y", "Y_hat", "offset", "genes", "perturbations", - "pi_hat_raw", - } - missing = required.difference(cache.files) - if missing: - raise ValueError(f"Intermediate cache is missing: {sorted(missing)}") - A = cache["A"] - perturbations = cache["perturbations"].astype(str) - genes = cache["genes"].astype(str) - if A.shape[1] != len(perturbations) or cache["Y"].shape[1] != len(genes): - raise ValueError("Intermediate cache labels and dimensions do not match") - if set(FOCAL_PERTURBATIONS).difference(perturbations): - raise ValueError("Intermediate cache does not contain every focal perturbation") - - -def auto_clip_bounds(A): - """Per-arm bounds matching ``LFC(ps_clip='auto')``. - - ``lower_j = min(0.01, prevalence_j / 10)`` and the mirrored upper bound, - with prevalence taken over the cells eligible for treatment ``j``. The - diagnostic has to use the same bounds as the estimator, otherwise its ESS - and overlap describe weights that LFC never forms. - """ - A = np.asarray(A, dtype=float) - ctrl = A.sum(axis=1) == 0 - lower = np.empty(A.shape[1]) - upper = np.empty(A.shape[1]) - for j in range(A.shape[1]): - eligible = ctrl | (A[:, j] == 1) - prevalence = float(A[eligible, j].mean()) - lower[j] = min(0.01, prevalence / 10.0) - upper[j] = 1.0 - min(0.01, (1.0 - prevalence) / 10.0) - return lower, upper - - -def run_sensitivities(force: bool = False): - """Reuse cached outcomes across the prespecified propensity grid.""" - build_cache(force=force) - # This cache is generated locally by ``build_cache`` and may contain - # object-string arrays when produced by older NumPy/pandas combinations. - cache = np.load(CACHE_PATH, allow_pickle=True) - _validate_cache(cache) - A = cache["A"].astype(float) - W_A = cache["X_A"].astype(float) - W = np.column_stack([W_A[:, 0], W_A[:, 2:]]) - perturbations = cache["perturbations"].astype(str) - genes = cache["genes"].astype(str) - Y = cache["Y"].astype(float) - Y_hat = cache["Y_hat"].astype(float) - offset = cache["offset"].astype(float) - baseline = pd.read_csv(BASELINE_PATH) - - settings = [ - ("fitted calibrated C=1", cache["pi_hat_raw"].astype(float), "auto", "fitted"), - ] - for label, class_weight, C in ( - # The displayed sweep varies regularization on the unweighted model that - # LFC actually fits; the balanced row is kept only as a historical contrast. - ("OOF calibrated C=1", None, 1.0), - ("OOF calibrated C=0.1", None, 0.1), - ("OOF calibrated C=0.01", None, 0.01), - ("OOF balanced C=1", "balanced", 1.0), - ): - scores = estimate_propensity_scores( - A, W_A, K=5, random_state=RANDOM_STATE, - class_weight=class_weight, C=C, clip=None, - ) - settings.append((label, scores, "auto", "oof")) - - # Column 1 of X_A is the standardized log library size that - # ``prep_causarray_data`` appends. These rows drop it from the propensity - # design only, as the endpoint against which a penalty on that coefficient - # is judged; the outcome model keeps its size-factor offset either way. - W_A_no_lib = np.delete(W_A, 1, axis=1) - for label, C in ( - ("OOF no-libsize C=1", 1.0), - ("OOF no-libsize C=0.1", 0.1), - ("OOF no-libsize C=0.01", 0.01), - ): - scores = estimate_propensity_scores( - A, W_A_no_lib, K=5, random_state=RANDOM_STATE, - class_weight=None, C=C, clip=None, - ) - settings.append((label, scores, "auto", "oof")) - settings.append( - ("OOF calibrated C=1, clip 0.05", - next(s[1] for s in settings if s[0] == "OOF calibrated C=1"), - (0.05, 0.95), "oof") - ) - - score_tables = [] - tuning_rows = [] - selected_scores = {"A": A.astype(np.uint8), "perturbations": perturbations} - baseline_indexed = baseline.set_index(["trt", "gene_names"]) - auto_lower, auto_upper = auto_clip_bounds(A) - for label, raw_scores, clip, score_type in settings: - if clip == "auto": - lower, upper = auto_lower, auto_upper - else: - lower = np.full(A.shape[1], clip[0]) - upper = np.full(A.shape[1], clip[1]) - clipped_scores = np.clip(raw_scores, lower[None, :], upper[None, :]) - # clip_bounds=None because the bounds vary by arm; count them per arm below. - summary = summarize_propensity_scores( - A, clipped_scores, treatment_names=perturbations, clip_bounds=None - ) - summary["clipped_fraction"] = [ - float(np.isclose(clipped_scores[:, j], lower[j]).mean() - + np.isclose(clipped_scores[:, j], upper[j]).mean()) - for j in range(A.shape[1]) - ] - summary.insert(0, "model", label) - summary.insert(1, "score_type", score_type) - summary.insert(2, "clip", str(clip)) - score_tables.append(summary) - for perturbation in FOCAL_PERTURBATIONS: - j = list(perturbations).index(perturbation) - selected_scores[f"{label}__{perturbation}"] = clipped_scores[:, j].astype( - np.float32 - ) - - result, _ = LFC( - pd.DataFrame(Y, columns=genes), - W, - pd.DataFrame(A, columns=perturbations), - W_A, - family="nb", - offset=offset, - Y_hat=Y_hat, - pi_hat=raw_scores, - ps_clip=clip if clip == "auto" else tuple(clip), - ) - indexed = result.set_index(["trt", "gene_names"]) - for perturbation in perturbations: - current = indexed.loc[perturbation] - reference = baseline_indexed.loc[perturbation] - aligned = current.join( - reference[["tau", "std", "padj"]], rsuffix="_baseline" - ) - finite = np.isfinite(aligned["tau"]) & np.isfinite( - aligned["tau_baseline"] - ) - standard_error_ratio = current["std"] / reference["std"].replace(0, np.nan) - tuning_rows.append({ - "model": label, - "score_type": score_type, - "clip": str(clip), - "treatment": perturbation, - "discoveries": int((current["padj"] < FDR).sum()), - "baseline_discoveries": int((reference["padj"] < FDR).sum()), - "discovery_change": int( - (current["padj"] < FDR).sum() - (reference["padj"] < FDR).sum() - ), - "median_std": float(np.nanmedian(current["std"])), - "median_std_ratio": float(np.nanmedian(standard_error_ratio)), - "tau_pearson_vs_baseline": float(np.corrcoef( - aligned.loc[finite, "tau"], aligned.loc[finite, "tau_baseline"] - )[0, 1]), - "median_abs_tau_change": float(np.nanmedian( - np.abs(aligned["tau"] - aligned["tau_baseline"]) - )), - }) - del result - gc.collect() - - pd.concat(score_tables, ignore_index=True).to_csv(SUMMARY_PATH, index=False) - pd.DataFrame(tuning_rows).to_csv(TUNING_PATH, index=False) - np.savez_compressed(SCORES_PATH, **selected_scores) - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--force", action="store_true", - help="refit the focal batch even when the intermediate cache exists", - ) - args = parser.parse_args() - run_sensitivities(force=args.force) - print(f"Wrote {SUMMARY_PATH.name}, {TUNING_PATH.name}, and {SCORES_PATH.name}") - - -if __name__ == "__main__": - main() diff --git a/docs/source/tutorial/replogle/4_refit_propensity.py b/docs/source/tutorial/replogle/4_refit_propensity.py new file mode 100644 index 0000000..a52a410 --- /dev/null +++ b/docs/source/tutorial/replogle/4_refit_propensity.py @@ -0,0 +1,108 @@ +"""Re-estimate LFC with a propensity model chosen per arm. + +Guides are assigned at random, so the propensity model only needs to absorb +chance imbalance. Log library size is left out. It could be a confounder +(capture depth) or an effect of the knockdown (total RNA); the data cannot tell +which, and with it in the model several arms separate from the controls. The +model starts from the intercept and all latent +factors; ``select_propensity_factors`` then drops, for each arm whose +weights concentrate (treated ESS < 0.5, overlap < 0.3 or AUC > 0.9), the +factors most imbalanced between that arm and the controls until its support +recovers. + +The outcome model does not depend on the propensity design, so this reuses +the saved ``Y_hat`` from ``3_run_batch.py`` and refits nothing else. + + python 4_refit_propensity.py +""" +import os, sys, time +sys.path.insert(0, '../../../..') +import h5py +import numpy as np, pandas as pd, scipy.sparse as sp, anndata as ad +from causarray import (prep_causarray_data, LFC, estimate_propensity_scores, + refit_propensity_scores, select_propensity_factors, + summarize_propensity_scores) +import causarray + +R = 10 +NUISANCE = f'results/replogle_results_r{R}.nuisances.h5' +OUT = 'results/replogle_results_selected.h5' +REPORT = 'results/replogle_propensity_selection.csv' +t0 = time.perf_counter() +log = lambda *a: print(f'[{(time.perf_counter()-t0)/60:6.1f} min]', *a, flush=True) + +if not os.path.exists(NUISANCE): + raise SystemExit(f'{NUISANCE} not found; run 3_run_batch.py first') +for path in (OUT, REPORT): + if os.path.exists(path): + os.remove(path) + +adata = ad.read_h5ad('data/replogle_subset.h5ad') +pert_col, ctrl = adata.uns['pert_col'], adata.uns['ctrl_label'] +Y = pd.DataFrame(adata.X.toarray() if sp.issparse(adata.X) else np.asarray(adata.X), + columns=adata.var_names.tolist()) +A = pd.get_dummies(adata.obs[pert_col].astype(str), drop_first=False).drop(columns=[ctrl]) +Y, A, X, X_A = prep_causarray_data(Y, A) +del adata +col_names = list(A.columns) +log(f'{Y.shape[0]:,} cells x {Y.shape[1]:,} genes, {A.shape[1]} perturbations') + +with h5py.File(NUISANCE, 'r') as handle: + batch_keys = sorted(handle.keys()) + +reports = [] +for batch_i, key in enumerate(batch_keys): + with h5py.File(NUISANCE, 'r') as handle: + g = handle[key] + Y_hat = g['Y_hat'][:]; cell_idx = g['cell_idx'][:] + offset = g['offset'][:]; U = g['U'][:] + names = [n.decode() if isinstance(n, bytes) else n for n in g['pert_names'][:]] + cols = [col_names.index(n) for n in names] + Y_b = Y.iloc[cell_idx].reset_index(drop=True) + A_b = A.iloc[cell_idx, cols].reset_index(drop=True) + W_b = np.c_[X[cell_idx], U] + W_A_b = np.c_[X_A[cell_idx, :1], U] # intercept + latent factors; no library size + covariates = ['intercept'] + [f'U{k + 1}' for k in range(U.shape[1])] + A_np = A_b.to_numpy(dtype=float) + fit = dict(K=1, C=1.0, class_weight=None, clip=None, random_state=0) + pi_all = estimate_propensity_scores(A_np, W_A_b, **fit) + drops, _ = select_propensity_factors(A_np, W_A_b, treatment_names=names, + covariate_names=covariates) + pi, _ = refit_propensity_scores(A_np, W_A_b, pi_hat=pi_all.copy(), treatment_names=names, + covariate_names=covariates, drop_by_treatment=drops, **fit) + before, after = (summarize_propensity_scores(A_np, p, treatment_names=names, clip_bounds=None) + .set_index('treatment') for p in (pi_all, pi)) + report = pd.DataFrame({ + 'batch': key, 'flagged': [n in drops for n in names], + 'dropped': [','.join(drops.get(n, [])) for n in names], + 'auc_before': before.loc[names, 'auc'].to_numpy(), + 'auc_after': after.loc[names, 'auc'].to_numpy(), + 'overlap_before': before.loc[names, 'overlap_ratio'].to_numpy(), + 'overlap_after': after.loc[names, 'overlap_ratio'].to_numpy(), + 'ess_treated_before': before.loc[names, 'ess_treated_fraction'].to_numpy(), + 'ess_treated_after': after.loc[names, 'ess_treated_fraction'].to_numpy(), + }, index=pd.Index(names, name='treatment')) + reports.append(report) + + df_b, _ = LFC(Y_b, W_b, A_b, W_A_b, family='nb', offset=offset, + Y_hat=Y_hat, pi_hat=pi, verbose=False) + df_b['batch'] = batch_i + with pd.HDFStore(OUT, mode='a') as store: + if '/meta' not in store.keys(): + store.put('/meta', pd.DataFrame({ + 'causarray_version': [causarray.__version__], 'family': ['nb'], + 'a_total': [int(A.shape[1])], + 'columns': [','.join(df_b.columns.astype(str))]}), format='fixed') + store.put(f'batch_{batch_i:04d}', df_b, format='fixed') + log(f'{key}: {int(report.flagged.sum())}/{len(names)} arms flagged, ' + f'{int((df_b.padj < 0.05).sum()):,} discoveries') + del Y_hat, Y_b, A_b, df_b + +rep = pd.concat(reports) +rep.to_csv(REPORT) +with pd.HDFStore(OUT, mode='r') as store: + total = pd.concat([store[k] for k in store.keys() if k.startswith('/batch_')]) +sig = total[total.padj < 0.05] +log(f'DONE: {len(total):,} rows, {len(sig):,} discoveries, ' + f'zero-arm={int((sig.count_treated == 0).sum())}, neg frac={(sig.tau < 0).mean():.2f}') +log(f'{int(rep.flagged.sum())}/{len(rep)} arms flagged -> {OUT}, {REPORT}') diff --git a/docs/source/tutorial/replogle/5_refit_propensity.py b/docs/source/tutorial/replogle/5_refit_propensity.py deleted file mode 100644 index 9eb9009..0000000 --- a/docs/source/tutorial/replogle/5_refit_propensity.py +++ /dev/null @@ -1,140 +0,0 @@ -"""Stage 3c: re-estimate LFC with a targeted penalty on log library size. - -``prep_causarray_data`` appends standardized log library size to ``X_A``. For -some perturbations it differs so strongly between the perturbed and control -cells that the propensity model separates them almost perfectly, leaving the -weights on a handful of cells. Measured library size mixes capture depth with -total RNA content and these data do not identify which drives a given arm's -shift, so rather than dropping the covariate everywhere this keeps it and -penalizes *only that coefficient*, and only for arms that fail a pre-specified -support check: - - trigger treated ESS < 0.5 or AUC > 0.9 - factor ``tune_penalty_factor``: the smallest factor bringing the arm's - histogram overlap above 0.3, found by bisection between the - unpenalized fit and the dropped-covariate limit. An arm whose - dropped fit cannot reach the target is marked infeasible and given - the largest factor, the closest support the covariate allows. - -``penalty_factors_by_treatment`` leaves every other coefficient, and every -unflagged arm, exactly as fitted. The outcome model does not depend on the -propensity design, so this reuses the cached ``Y_hat`` from ``3_run_batch.py`` -(``save_nuisances=True``) and refits nothing. - - python 5_refit_propensity.py -""" -import os, sys, time -sys.path.insert(0, '../../../..') -import h5py -import numpy as np, pandas as pd, scipy.sparse as sp, anndata as ad -from causarray import (prep_causarray_data, estimate_propensity_scores, - refit_propensity_scores, summarize_propensity_scores, - tune_penalty_factor, LFC) -import causarray - -R = 10 -TRIGGER = {'auc_gt': 0.9, 'ess_treated_fraction_lt': 0.5} -TARGET = {'overlap_ratio_gt': 0.3} # histogram overlap, the quantity being repaired -FOCUS = ('SUPT5H', 'SUPT6H', 'SRRT', 'TSR2') # arms whose before/after scores are saved -NUISANCE = f'results/replogle_results_r{R}.nuisances.h5' -OUT = 'results/replogle_results_penalized.h5' -REPORT = 'results/replogle_propensity_penalties.csv' -SCORES = 'results/replogle_penalized_scores.npz' -t0 = time.perf_counter() -log = lambda *a: print(f'[{(time.perf_counter()-t0)/60:6.1f} min]', *a, flush=True) - -if not os.path.exists(NUISANCE): - raise SystemExit(f'{NUISANCE} not found; run 3_run_batch.py with save_nuisances=True') -for path in (OUT, REPORT, SCORES): - if os.path.exists(path): - os.remove(path) - -adata = ad.read_h5ad('data/replogle_subset.h5ad') -pert_col, ctrl = adata.uns['pert_col'], adata.uns['ctrl_label'] -Y = pd.DataFrame(adata.X.toarray() if sp.issparse(adata.X) else np.asarray(adata.X), - columns=adata.var_names.tolist()) -A = pd.get_dummies(adata.obs[pert_col].astype(str), drop_first=False).drop(columns=[ctrl]) -Y, A, X, X_A = prep_causarray_data(Y, A) -del adata -col_names = list(A.columns) -log(f'{Y.shape[0]:,} cells x {Y.shape[1]:,} genes, {A.shape[1]} perturbations') - -with h5py.File(NUISANCE, 'r') as handle: - batch_keys = sorted(handle.keys()) - -records = [] -focus_scores = {} -for batch_i, key in enumerate(batch_keys): - with h5py.File(NUISANCE, 'r') as handle: - g = handle[key] - Y_hat = g['Y_hat'][:]; cell_idx = g['cell_idx'][:] - offset = g['offset'][:]; U = g['U'][:] - names = [n.decode() if isinstance(n, bytes) else n for n in g['pert_names'][:]] - cols = [col_names.index(n) for n in names] - Y_b = Y.iloc[cell_idx].reset_index(drop=True) - A_b = A.iloc[cell_idx, cols].reset_index(drop=True) - W_b = np.c_[X[cell_idx], U] - W_A_b = np.c_[X_A[cell_idx], U] # library size stays in the design - cov = ['intercept', 'log_library_size'] + [f'U{k+1}' for k in range(U.shape[1])] - - A_np = A_b.to_numpy(dtype=float) - pi = estimate_propensity_scores(A_np, W_A_b, K=1, C=1.0, class_weight=None, - clip=None, random_state=0) - before = summarize_propensity_scores(A_np, pi, treatment_names=names, - clip_bounds=None).set_index('treatment') - pi_base_saved = pi.copy() - chosen, tuning = tune_penalty_factor( - A_np, W_A_b, 'log_library_size', treatment_names=names, covariate_names=cov, - trigger=TRIGGER, target=TARGET, K=1, C=1.0, class_weight=None, random_state=0) - if chosen: - pi, _ = refit_propensity_scores( - A_np, W_A_b, pi_hat=pi.copy(), treatment_names=names, covariate_names=cov, - penalty_factors_by_treatment=chosen, - K=1, C=1.0, class_weight=None, clip=None, random_state=0) - after = summarize_propensity_scores(A_np, pi, treatment_names=names, - clip_bounds=None).set_index('treatment') - for name in (set(names) & set(FOCUS)): - j = names.index(name) - focus_scores[f'{name}__before'] = pi_base_saved[:, j].astype(np.float32) - focus_scores[f'{name}__after'] = pi[:, j].astype(np.float32) - focus_scores[f'{name}__treated'] = A_np[:, j].astype(np.uint8) - focus_scores[f'{name}__control'] = (A_np.sum(axis=1) == 0).astype(np.uint8) - tuned = tuning.set_index('treatment') if len(tuning) else None - for name in names: - rec = {'batch': key, 'treatment': name, - 'penalty_factor': chosen.get(name, {}).get('log_library_size', 1.0), - 'triggered': tuned is not None and name in tuned.index, - 'feasible': bool(tuned.loc[name, 'feasible']) if tuned is not None and name in tuned.index else True, - 'auc_before': before.loc[name, 'auc'], 'auc_after': after.loc[name, 'auc'], - 'overlap_before': before.loc[name, 'overlap_ratio'], - 'overlap_after': after.loc[name, 'overlap_ratio'], - 'ess_treated_before': before.loc[name, 'ess_treated_fraction'], - 'ess_treated_after': after.loc[name, 'ess_treated_fraction']} - if tuned is not None and name in tuned.index: - rec['auc_dropped'] = tuned.loc[name, 'auc_dropped'] - rec['n_fits'] = int(tuned.loc[name, 'n_fits']) - records.append(rec) - - df_b, _ = LFC(Y_b, W_b, A_b, W_A_b, family='nb', offset=offset, - Y_hat=Y_hat, pi_hat=pi, verbose=False) - df_b['batch'] = batch_i - with pd.HDFStore(OUT, mode='a') as store: - if '/meta' not in store.keys(): - store.put('/meta', pd.DataFrame({ - 'causarray_version': [causarray.__version__], 'family': ['nb'], - 'a_total': [int(A.shape[1])], - 'columns': [','.join(df_b.columns.astype(str))]}), format='fixed') - store.put(f'batch_{batch_i:04d}', df_b, format='fixed') - log(f'{key}: {len(chosen)}/{len(names)} penalized, ' - f'{int((df_b.padj < 0.05).sum()):,} discoveries') - del Y_hat, Y_b, A_b, df_b - -pd.DataFrame(records).to_csv(REPORT, index=False) -np.savez_compressed(SCORES, **focus_scores) -with pd.HDFStore(OUT, mode='r') as store: - total = pd.concat([store[k] for k in store.keys() if k.startswith('/batch_')]) -sig = total[total.padj < 0.05] -rep = pd.DataFrame(records) -log(f'DONE: {len(total):,} rows, {len(sig):,} discoveries, ' - f'zero-arm={int((sig.count_treated == 0).sum())}, neg frac={(sig.tau < 0).mean():.2f}') -log(f'{int((rep.penalty_factor > 1).sum())}/{len(rep)} arms penalized -> {OUT}, {REPORT}') diff --git a/docs/source/tutorial/replogle/README.md b/docs/source/tutorial/replogle/README.md index 5645e58..629f084 100644 --- a/docs/source/tutorial/replogle/README.md +++ b/docs/source/tutorial/replogle/README.md @@ -7,9 +7,9 @@ Layout convention, shared by the other tutorial folders: - `data/` — the raw download and the prepared inputs. - `results/` — everything generated: fits, caches, tables, logs. -Only the notebook, the four scripts, this file and the two small JIC tables in -`results/` are tracked in git; the rest of `data/` and `results/` is local and -regenerated. +Only the notebook, the four scripts, this file and the JIC table in +`results/` are tracked in git; the rest of `data/` +and `results/` is local and regenerated. ## Pipeline @@ -21,14 +21,12 @@ regenerated. 3_run_batch.py data/replogle_subset.h5ad, results/replogle-r.csv -> results/replogle_results_r{r}.h5 (resumable) - copy to results/replogle_results.h5 for the notebook + results/replogle_results_r{r}.nuisances.h5 - 4_cache_propensity_batch.py data/replogle_subset.h5ad, results/replogle-r.csv - stage 1 -> results/replogle_propensity_batch12.npz - results/replogle_propensity_batch12_baseline.csv.gz - stage 2 -> results/replogle_propensity_batch12_summary.csv - results/replogle_propensity_batch12_tuning.csv - results/replogle_propensity_batch12_selected_scores.npz + 4_refit_propensity.py results/replogle_results_r{r}.nuisances.h5 + -> results/replogle_results_selected.h5 + results/replogle_propensity_selection.csv + copy the .h5 to results/replogle_results.h5 for the notebook replogle-py.ipynb reads the files marked "notebook input" below @@ -39,25 +37,21 @@ regenerated. | `replogle-py.ipynb` | 1.1 MB | the tutorial | | `1_prep_tutorial_data.py` | 8 KB | downloads the screen and builds the subset (200 largest perturbations + 2,000 non-targeting controls, raw counts) | | `2_estimate_r.py` | 1 KB | JIC rank-selection table on a control-heavy subsample | -| `3_run_batch.py` | 2 KB | batch GCATE + LFC fit; resumable | -| `4_cache_propensity_batch.py` | 14 KB | propensity-score sensitivity sweep, two stages | +| `3_run_batch.py` | 2 KB | batch GCATE + LFC fit, saving the outcome model; resumable | +| `4_refit_propensity.py` | 4 KB | re-estimates LFC with latent factors chosen per arm by `select_propensity_factors`, reusing the saved outcome model | | `data/ReplogleWeissman2022_K562_essential.h5ad` | 1.4 GB | raw Zenodo download | | `data/replogle_subset.h5ad` | 2.2 GB | **notebook input** — raw-count subset; source for every other artifact | | `data/replogle_subset_norm.h5ad` | 2.2 GB | notebook cache — log1p-normalized counts; rebuilt from the subset in one `crispyx` call if deleted | -| `results/replogle_results.h5` | 283 MB | **notebook input** — batch fit results | +| `results/replogle_results.h5` | 283 MB | **notebook input** — copy of `results/replogle_results_selected.h5` | +| `results/replogle_results_r10.nuisances.h5` | 50 GB | optional notebook input — outcome model and latent factors per batch; the notebook reads one batch's factors for the propensity walk-through and skips it if the file is absent | +| `results/replogle_propensity_selection.csv` | 30 KB | **notebook input** — per-arm propensity report from `4_refit_propensity.py` | | `results/replogle-r.csv` | 0.6 KB | **notebook input** — JIC table on raw counts (tracked) | -| `results/replogle-r-legacy.csv` | 0.4 KB | **notebook input** — JIC table from the log-normalized era (tracked); the notebook falls back to it only if the current table is absent, and its preprocessing differs | | `results/replogle_subset_norm_cx_wilcoxon.h5ad` | 92 MB | notebook cache — Wilcoxon comparison results | | `results/replogle_supt5h_go_results.csv` | 2.6 MB | notebook cache — SUPT5H GO enrichment; regenerate when the input gene lists change | -| `results/replogle_propensity_batch12_summary.csv` | 23 KB | **notebook input** — propensity sweep summary | -| `results/replogle_propensity_batch12_tuning.csv` | 11 KB | **notebook input** — propensity tuning grid | -| `results/replogle_propensity_batch12_selected_scores.npz` | 281 KB | **notebook input** — the selected propensity scores | -| `results/replogle_propensity_batch12.npz` | 1.9 GB | stage-1 intermediate of `4_cache_propensity_batch.py`; **not read by the notebook**. Keep it only to re-run the stage-2 sweep without redoing stage 1 | -| `results/replogle_propensity_batch12_baseline.csv.gz` | 12.6 MB | stage-1 intermediate, as above; not read by the notebook | -| `results/run_batch.log` | 11 KB | run record for `results/replogle_results.h5` (progress bars stripped); the last line reports rows, discoveries, zero-count arms and runtime | +| `results/run_batch.log` | 11 KB | run record for `3_run_batch.py`; the last line reports rows, discoveries, zero-count arms and runtime | ## Rebuilding from the tracked files alone -Run `1_`, then `3_` and `4_` (hours each), then execute the notebook, which -rebuilds its own normalization, Wilcoxon and GO caches. `2_estimate_r.py` only -needs re-running if the rank choice is revisited. +Run `1_`, `3_` and `4_`, copy the refit results into place, then execute the +notebook, which rebuilds its own normalization, Wilcoxon and GO caches. +`2_estimate_r.py` only needs re-running if the rank choice is revisited. diff --git a/docs/source/tutorial/replogle/replogle-py.ipynb b/docs/source/tutorial/replogle/replogle-py.ipynb index 6a3a3e7..b5cd958 100644 --- a/docs/source/tutorial/replogle/replogle-py.ipynb +++ b/docs/source/tutorial/replogle/replogle-py.ipynb @@ -20,10 +20,10 @@ "id": "bb100002", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:32.654269Z", - "iopub.status.busy": "2026-09-23T13:19:32.654048Z", - "iopub.status.idle": "2026-09-23T13:19:34.721232Z", - "shell.execute_reply": "2026-09-23T13:19:34.720865Z" + "iopub.execute_input": "2026-09-26T07:01:21.852070Z", + "iopub.status.busy": "2026-09-26T07:01:21.851602Z", + "iopub.status.idle": "2026-09-26T07:01:24.411314Z", + "shell.execute_reply": "2026-09-26T07:01:24.410652Z" } }, "outputs": [ @@ -31,7 +31,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "causarray version: 0.0.10\n" + "causarray version: 0.1.1\n" ] } ], @@ -73,10 +73,10 @@ "id": "bb100004", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:34.722930Z", - "iopub.status.busy": "2026-09-23T13:19:34.722725Z", - "iopub.status.idle": "2026-09-23T13:19:35.111856Z", - "shell.execute_reply": "2026-09-23T13:19:35.111478Z" + "iopub.execute_input": "2026-09-26T07:01:24.413262Z", + "iopub.status.busy": "2026-09-26T07:01:24.413037Z", + "iopub.status.idle": "2026-09-26T07:01:25.064303Z", + "shell.execute_reply": "2026-09-26T07:01:25.063852Z" } }, "outputs": [ @@ -116,7 +116,7 @@ "source": [ "## 2. Prepare causarray inputs\n", "\n", - "`prep_causarray_data` caps extreme counts, adds an intercept, and includes standardized log-library size in the propensity design `X_A`. The outcome matrix contains counts, not log-normalized expression.\n" + "`prep_causarray_data` caps extreme counts, adds an intercept, and includes standardized log-library size in the propensity design `X_A`. The batch fit uses that design; section 5 explains why the final propensity model leaves library size out. The outcome matrix contains counts, not log-normalized expression.\n" ] }, { @@ -125,10 +125,10 @@ "id": "bb100006", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:35.113251Z", - "iopub.status.busy": "2026-09-23T13:19:35.113164Z", - "iopub.status.idle": "2026-09-23T13:19:36.624358Z", - "shell.execute_reply": "2026-09-23T13:19:36.624017Z" + "iopub.execute_input": "2026-09-26T07:01:25.065923Z", + "iopub.status.busy": "2026-09-26T07:01:25.065806Z", + "iopub.status.idle": "2026-09-26T07:01:27.331700Z", + "shell.execute_reply": "2026-09-26T07:01:27.331137Z" } }, "outputs": [ @@ -166,7 +166,7 @@ "\n", "The JIC table from `2_estimate_r.py` evaluates candidate ranks on a control-heavy subsample of the raw-count data. Select the rank with the smallest JIC and inspect nearby ranks when the curve is flat. Subsampling reduces computation; it need not select the same rank as a full-data fit.\n", "\n", - "The legacy fallback table uses different preprocessing. Treat it as a historical illustration and recompute the raw-count table before using its rank for a new analysis. A flat JIC curve does not establish that confounding is absent.\n" + "A flat JIC curve does not establish that confounding is absent.\n" ] }, { @@ -175,16 +175,16 @@ "id": "bb100008", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:36.625872Z", - "iopub.status.busy": "2026-09-23T13:19:36.625743Z", - "iopub.status.idle": "2026-09-23T13:19:36.868966Z", - "shell.execute_reply": "2026-09-23T13:19:36.868692Z" + "iopub.execute_input": "2026-09-26T07:01:27.333144Z", + "iopub.status.busy": "2026-09-26T07:01:27.333035Z", + "iopub.status.idle": "2026-09-26T07:01:27.583722Z", + "shell.execute_reply": "2026-09-26T07:01:27.583078Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -210,12 +210,8 @@ } ], "source": [ - "# JIC table on the raw counts (2_estimate_r.py); falls back to the pre-0.1.0\n", - "# table computed on the log-normalised copy only if the new one is missing.\n", - "r_path = Path('results/replogle-r.csv') if Path('results/replogle-r.csv').exists() else Path('results/replogle-r-legacy.csv')\n", - "if r_path.name == 'results/replogle-r-legacy.csv':\n", - " print('Note: JIC table from the pre-0.1.0 (log-normalised) run; run 2_estimate_r.py to recompute it.')\n", - "df_r = pd.read_csv(r_path)\n", + "# JIC table on the raw counts (2_estimate_r.py)\n", + "df_r = pd.read_csv('results/replogle-r.csv')\n", "fig = plot_r(df_r)\n", "plt.tight_layout()\n", "plt.show()\n", @@ -243,10 +239,10 @@ "id": "bb100010", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:36.870362Z", - "iopub.status.busy": "2026-09-23T13:19:36.870272Z", - "iopub.status.idle": "2026-09-23T13:19:43.388245Z", - "shell.execute_reply": "2026-09-23T13:19:43.387880Z" + "iopub.execute_input": "2026-09-26T07:01:27.585185Z", + "iopub.status.busy": "2026-09-26T07:01:27.585086Z", + "iopub.status.idle": "2026-09-26T07:01:34.272601Z", + "shell.execute_reply": "2026-09-26T07:01:34.272047Z" } }, "outputs": [ @@ -271,7 +267,7 @@ "output_type": "stream", "text": [ "\r", - "GCATE batches: 100%|██████████| 14/14 [00:00<00:00, 134371.30batch/s]" + "GCATE batches: 100%|██████████| 14/14 [00:00<00:00, 26973.02batch/s]" ] }, { @@ -338,120 +334,120 @@ " \n", " 0\n", " LINC01409\n", - " 0.000000\n", - " inf\n", - " 0.000000\n", - " inf\n", - " NaN\n", + " 0.323101\n", + " 0.281189\n", + " 0.466136\n", + " 0.405669\n", + " 1.149054\n", " 0.0\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.250666\n", + " 0.535747\n", + " 0.743786\n", " ...\n", " 2000\n", " 12.0\n", " 248.0\n", - " 0.119990\n", - " 0.115295\n", + " 0.114105\n", + " 0.157625\n", " True\n", - " False\n", - " inf\n", + " True\n", + " 0.278146\n", " AATF\n", " 0\n", " \n", " \n", " 1\n", " LINC01128\n", - " -0.095624\n", - " 0.376348\n", - " -0.137957\n", - " 0.542956\n", - " -0.254084\n", + " -0.095688\n", + " 0.247474\n", + " -0.138048\n", + " 0.357030\n", + " -0.386657\n", " 0.0\n", - " 0.799456\n", - " 0.867872\n", - " 0.910281\n", + " 0.699050\n", + " 0.865696\n", + " 0.450997\n", " ...\n", " 2000\n", " 20.0\n", " 546.0\n", - " 0.265424\n", - " 0.241219\n", + " 0.256268\n", + " 0.232883\n", " True\n", " False\n", - " 0.376348\n", + " 0.247474\n", " AATF\n", " 0\n", " \n", " \n", " 2\n", " NOC2L\n", - " 0.059683\n", - " 0.106689\n", - " 0.086105\n", - " 0.153919\n", - " 0.559414\n", + " 0.096539\n", + " 0.081480\n", + " 0.139277\n", + " 0.117551\n", + " 1.184821\n", " 0.0\n", - " 0.575939\n", - " 0.779285\n", - " 0.662723\n", + " 0.236223\n", + " 0.518468\n", + " 0.724830\n", " ...\n", " 2000\n", " 219.0\n", " 4278.0\n", - " 2.066363\n", - " 2.193445\n", + " 1.964351\n", + " 2.163443\n", " True\n", " False\n", - " 0.106689\n", + " 0.081480\n", " AATF\n", " 0\n", " \n", " \n", " 3\n", " KLHL17\n", - " -0.243954\n", - " 0.339267\n", - " -0.351951\n", - " 0.489458\n", - " -0.719063\n", + " 0.090186\n", + " 0.286134\n", + " 0.130111\n", + " 0.412804\n", + " 0.315187\n", " 0.0\n", - " 0.472183\n", - " 0.735848\n", - " 0.669826\n", + " 0.752651\n", + " 0.896647\n", + " 0.794920\n", " ...\n", " 2000\n", " 16.0\n", " 300.0\n", - " 0.145126\n", - " 0.113710\n", + " 0.137459\n", + " 0.150432\n", " True\n", - " False\n", - " 0.339267\n", + " True\n", + " 0.271956\n", " AATF\n", " 0\n", " \n", " \n", " 4\n", " HES4\n", - " 0.338578\n", - " 0.247739\n", - " 0.488465\n", - " 0.357412\n", - " 1.366673\n", + " 0.214933\n", + " 0.206852\n", + " 0.310083\n", + " 0.298425\n", + " 1.039068\n", " 0.0\n", - " 0.171875\n", - " 0.501027\n", - " 0.326743\n", + " 0.298894\n", + " 0.588576\n", + " 0.803012\n", " ...\n", " 2000\n", " 30.0\n", " 499.0\n", - " 0.243046\n", - " 0.340982\n", + " 0.233575\n", + " 0.289582\n", " True\n", - " False\n", - " 0.247739\n", + " True\n", + " 0.198972\n", " AATF\n", " 0\n", " \n", @@ -462,32 +458,32 @@ ], "text/plain": [ " gene_names tau std log2fc log2fc_se stat rej \\\n", - "0 LINC01409 0.000000 inf 0.000000 inf NaN 0.0 \n", - "1 LINC01128 -0.095624 0.376348 -0.137957 0.542956 -0.254084 0.0 \n", - "2 NOC2L 0.059683 0.106689 0.086105 0.153919 0.559414 0.0 \n", - "3 KLHL17 -0.243954 0.339267 -0.351951 0.489458 -0.719063 0.0 \n", - "4 HES4 0.338578 0.247739 0.488465 0.357412 1.366673 0.0 \n", + "0 LINC01409 0.323101 0.281189 0.466136 0.405669 1.149054 0.0 \n", + "1 LINC01128 -0.095688 0.247474 -0.138048 0.357030 -0.386657 0.0 \n", + "2 NOC2L 0.096539 0.081480 0.139277 0.117551 1.184821 0.0 \n", + "3 KLHL17 0.090186 0.286134 0.130111 0.412804 0.315187 0.0 \n", + "4 HES4 0.214933 0.206852 0.310083 0.298425 1.039068 0.0 \n", "\n", " pvalue padj pvalue_emp_null_adj ... n_control count_treated \\\n", - "0 NaN NaN NaN ... 2000 12.0 \n", - "1 0.799456 0.867872 0.910281 ... 2000 20.0 \n", - "2 0.575939 0.779285 0.662723 ... 2000 219.0 \n", - "3 0.472183 0.735848 0.669826 ... 2000 16.0 \n", - "4 0.171875 0.501027 0.326743 ... 2000 30.0 \n", + "0 0.250666 0.535747 0.743786 ... 2000 12.0 \n", + "1 0.699050 0.865696 0.450997 ... 2000 20.0 \n", + "2 0.236223 0.518468 0.724830 ... 2000 219.0 \n", + "3 0.752651 0.896647 0.794920 ... 2000 16.0 \n", + "4 0.298894 0.588576 0.803012 ... 2000 30.0 \n", "\n", " count_control mean_control mean_treated estimable var_floored \\\n", - "0 248.0 0.119990 0.115295 True False \n", - "1 546.0 0.265424 0.241219 True False \n", - "2 4278.0 2.066363 2.193445 True False \n", - "3 300.0 0.145126 0.113710 True False \n", - "4 499.0 0.243046 0.340982 True False \n", + "0 248.0 0.114105 0.157625 True True \n", + "1 546.0 0.256268 0.232883 True False \n", + "2 4278.0 1.964351 2.163443 True False \n", + "3 300.0 0.137459 0.150432 True True \n", + "4 499.0 0.233575 0.289582 True True \n", "\n", " std_raw trt batch \n", - "0 inf AATF 0 \n", - "1 0.376348 AATF 0 \n", - "2 0.106689 AATF 0 \n", - "3 0.339267 AATF 0 \n", - "4 0.247739 AATF 0 \n", + "0 0.278146 AATF 0 \n", + "1 0.247474 AATF 0 \n", + "2 0.081480 AATF 0 \n", + "3 0.271956 AATF 0 \n", + "4 0.198972 AATF 0 \n", "\n", "[5 rows x 22 columns]" ] @@ -528,11 +524,18 @@ "id": "replogle-propensity-title", "metadata": {}, "source": [ - "## 5. Propensity-score diagnostics and refit\n", + "## 5. Propensity model: diagnostics and factor selection\n", + "\n", + "Read this before the effects. Where the propensity model separates an arm from the controls, the inverse-probability weights rest on a few cells, and the arm's discoveries then reflect those cells rather than the perturbation.\n", + "\n", + "Guides are assigned at random, so the propensity model only has to absorb chance imbalance between an arm and the controls. Two consequences for its covariates:\n", "\n", - "Read this before the effects. An arm whose propensity model separates it from the controls has inverse-probability weights concentrated on a few cells, and its discovery count then reflects the weights rather than the biology.\n", + "* **Library size is left out, as a judgement call.** It could be a confounder, since capture depth and cell quality affect both guide detection and measured expression, or a consequence of the knockdown: knocking down transcription machinery shrinks a cell's total RNA, and SUPT5H cells have about a third of the control library size. These data cannot tell which. With library size in the model such arms separate from the controls almost completely, so their weights would rest on a few cells. Leaving it out keeps them supported, at the cost of not adjusting for depth-related confounding; the outcome model still normalizes for depth through the size factors. The `LFC` documentation discusses this choice.\n", + "* **Latent factors are included, then checked arm by arm.** Factors estimated from expression can also track a perturbation's own effect. When one does, the model separates that arm, and the weights pile onto the perturbed cells that look least perturbed.\n", "\n", - "The focal batch prepared by `4_cache_propensity_batch.py` carries its treatment matrix and propensity design, so the diagnosis and the repair can both be run here. `5_refit_propensity.py` applies the same procedure to all 14 batches to produce the results this notebook reads." + "The check flags an arm when its treated effective sample size (ESS) falls below half the arm, its histogram overlap with the controls below 0.3, or its AUC above 0.9. For a flagged arm, the factor most imbalanced between the arm and the controls (largest absolute standardized mean difference) is dropped from that arm's model, and the step repeats until ESS ≥ 0.5 and overlap ≥ 0.3. Other arms keep every factor.\n", + "\n", + "`4_refit_propensity.py` applies this to all 14 batches and produces the results this notebook reads (section 4). The cells below walk through the batch containing SUPT5H. They read that batch's latent factors from `results/replogle_results_r10.nuisances.h5`, a large local file written by `3_run_batch.py`; if it is absent, they show the per-arm report from `4_refit_propensity.py` instead." ] }, { @@ -541,13 +544,20 @@ "id": "5792aafe", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:43.389854Z", - "iopub.status.busy": "2026-09-23T13:19:43.389716Z", - "iopub.status.idle": "2026-09-23T13:19:43.457483Z", - "shell.execute_reply": "2026-09-23T13:19:43.457181Z" + "iopub.execute_input": "2026-09-26T07:01:34.274246Z", + "iopub.status.busy": "2026-09-26T07:01:34.274114Z", + "iopub.status.idle": "2026-09-26T07:01:34.363011Z", + "shell.execute_reply": "2026-09-26T07:01:34.360985Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "batch_0012: 14 perturbations, 4,000 cells\n" + ] + }, { "data": { "text/html": [ @@ -573,7 +583,6 @@ " auc\n", " overlap_ratio\n", " ess_treated_fraction\n", - " ess_control_fraction\n", " \n", " \n", " treatment\n", @@ -581,100 +590,80 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " SUPT5H\n", - " 0.070\n", - " 1.000\n", - " 0.000\n", - " 0.940\n", - " 0.998\n", - " \n", - " \n", " SUPT6H\n", " 0.046\n", - " 1.000\n", - " 0.001\n", - " 0.955\n", - " 0.999\n", + " 0.689\n", + " 0.331\n", + " 0.250\n", " \n", " \n", - " SRRT\n", - " 0.055\n", - " 0.992\n", - " 0.037\n", - " 0.584\n", - " 0.821\n", + " SUPT5H\n", + " 0.070\n", + " 0.714\n", + " 0.385\n", + " 0.358\n", " \n", " \n", " TIMM23B\n", " 0.078\n", - " 0.975\n", - " 0.082\n", - " 0.115\n", - " 0.411\n", - " \n", - " \n", - " SSRP1\n", - " 0.093\n", - " 0.960\n", - " 0.130\n", - " 0.113\n", - " 0.329\n", + " 0.595\n", + " 0.624\n", + " 0.830\n", " \n", " \n", " TSR2\n", " 0.083\n", - " 0.778\n", - " 0.439\n", - " 0.161\n", - " 0.996\n", + " 0.614\n", + " 0.622\n", + " 0.851\n", " \n", " \n", - " TRRAP\n", - " 0.063\n", - " 0.800\n", - " 0.520\n", - " 0.559\n", - " 0.995\n", + " SSRP1\n", + " 0.093\n", + " 0.586\n", + " 0.536\n", + " 0.853\n", + " \n", + " \n", + " SON\n", + " 0.075\n", + " 0.455\n", + " 0.632\n", + " 0.888\n", " \n", " \n", " SNRPG\n", " 0.068\n", - " 0.517\n", - " 0.635\n", - " 0.926\n", - " 1.000\n", + " 0.512\n", + " 0.574\n", + " 0.914\n", + " \n", + " \n", + " TRRAP\n", + " 0.063\n", + " 0.571\n", + " 0.797\n", + " 0.936\n", " \n", " \n", "\n", "" ], "text/plain": [ - " prevalence auc overlap_ratio ess_treated_fraction \\\n", - "treatment \n", - "SUPT5H 0.070 1.000 0.000 0.940 \n", - "SUPT6H 0.046 1.000 0.001 0.955 \n", - "SRRT 0.055 0.992 0.037 0.584 \n", - "TIMM23B 0.078 0.975 0.082 0.115 \n", - "SSRP1 0.093 0.960 0.130 0.113 \n", - "TSR2 0.083 0.778 0.439 0.161 \n", - "TRRAP 0.063 0.800 0.520 0.559 \n", - "SNRPG 0.068 0.517 0.635 0.926 \n", - "\n", - " ess_control_fraction \n", - "treatment \n", - "SUPT5H 0.998 \n", - "SUPT6H 0.999 \n", - "SRRT 0.821 \n", - "TIMM23B 0.411 \n", - "SSRP1 0.329 \n", - "TSR2 0.996 \n", - "TRRAP 0.995 \n", - "SNRPG 1.000 " + " prevalence auc overlap_ratio ess_treated_fraction\n", + "treatment \n", + "SUPT6H 0.046 0.689 0.331 0.250\n", + "SUPT5H 0.070 0.714 0.385 0.358\n", + "TIMM23B 0.078 0.595 0.624 0.830\n", + "TSR2 0.083 0.614 0.622 0.851\n", + "SSRP1 0.093 0.586 0.536 0.853\n", + "SON 0.075 0.455 0.632 0.888\n", + "SNRPG 0.068 0.512 0.574 0.914\n", + "TRRAP 0.063 0.571 0.797 0.936" ] }, "metadata": {}, @@ -682,28 +671,43 @@ } ], "source": [ - "from causarray import (estimate_propensity_scores, refit_propensity_scores,\n", - " summarize_propensity_scores, tune_penalty_factor)\n", - "\n", - "ps_cache_path = Path('results/replogle_propensity_batch12.npz')\n", - "ps_scores_path = Path('results/replogle_propensity_batch12_selected_scores.npz')\n", - "\n", - "# The focal batch stores its own treatment matrix and propensity design:\n", - "# intercept, standardized log library size, then the batch's latent factors.\n", - "_focal = np.load(ps_cache_path, allow_pickle=True)\n", - "A_focal = _focal['A'].astype(float)\n", - "W_A_focal = _focal['X_A'].astype(float)\n", - "focal_perts = _focal['perturbations'].astype(str).tolist()\n", - "focal_covariates = (['intercept', 'log_library_size']\n", - " + [f'U{k + 1}' for k in range(W_A_focal.shape[1] - 2)])\n", - "\n", - "pi_before = estimate_propensity_scores(\n", - " A_focal, W_A_focal, K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", - "support_before = summarize_propensity_scores(\n", - " A_focal, pi_before, treatment_names=focal_perts, clip_bounds=None).set_index('treatment')\n", - "display(support_before[['prevalence', 'auc', 'overlap_ratio',\n", - " 'ess_treated_fraction', 'ess_control_fraction']]\n", - " .sort_values('overlap_ratio').head(8).round(3))" + "import h5py\n", + "from causarray import (estimate_propensity_scores, summarize_propensity_scores,\n", + " summarize_treatment_associations, select_propensity_factors,\n", + " refit_propensity_scores)\n", + "\n", + "# The batch fit saved each batch's latent factors and cells in a large local\n", + "# file (tens of GB, not tracked). Without it, skip the walk-through and show\n", + "# the per-arm report that 4_refit_propensity.py wrote for all batches.\n", + "NUISANCE_PATH = Path(f'results/replogle_results_r{R}.nuisances.h5')\n", + "HAS_FACTORS = NUISANCE_PATH.exists()\n", + "if HAS_FACTORS:\n", + " # Read the batch that contains SUPT5H (its outcome predictions are not needed).\n", + " with h5py.File(NUISANCE_PATH, 'r') as handle:\n", + " for key in sorted(handle.keys()):\n", + " names = [n.decode() if isinstance(n, bytes) else n for n in handle[key]['pert_names'][:]]\n", + " if 'SUPT5H' in names:\n", + " U_focal = handle[key]['U'][:]\n", + " cells_focal = handle[key]['cell_idx'][:]\n", + " focal_perts = names\n", + " break\n", + " A_focal = A.iloc[cells_focal][focal_perts].to_numpy(dtype=float)\n", + " W_A_focal = np.c_[X_A[cells_focal, :1], U_focal] # intercept + latent factors\n", + " focal_covariates = ['intercept'] + [f'U{k + 1}' for k in range(U_focal.shape[1])]\n", + " print(f'{key}: {len(focal_perts)} perturbations, {len(cells_focal):,} cells')\n", + "\n", + " pi_before = estimate_propensity_scores(\n", + " A_focal, W_A_focal, K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", + " support_before = summarize_propensity_scores(\n", + " A_focal, pi_before, treatment_names=focal_perts, clip_bounds=None).set_index('treatment')\n", + " display(support_before[['prevalence', 'auc', 'overlap_ratio', 'ess_treated_fraction']]\n", + " .sort_values('ess_treated_fraction').head(8).round(3))\n", + "else:\n", + " print(f'{NUISANCE_PATH} not found; skipping the propensity walk-through.')\n", + " report_path = Path('results/replogle_propensity_selection.csv')\n", + " if report_path.exists():\n", + " report = pd.read_csv(report_path, index_col='treatment')\n", + " display(report[report['flagged']].round(3))" ] }, { @@ -712,10 +716,10 @@ "id": "1b5c7e69", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:43.459106Z", - "iopub.status.busy": "2026-09-23T13:19:43.458859Z", - "iopub.status.idle": "2026-09-23T13:19:43.654067Z", - "shell.execute_reply": "2026-09-23T13:19:43.653764Z" + "iopub.execute_input": "2026-09-26T07:01:34.367223Z", + "iopub.status.busy": "2026-09-26T07:01:34.366977Z", + "iopub.status.idle": "2026-09-26T07:01:34.568202Z", + "shell.execute_reply": "2026-09-26T07:01:34.567739Z" } }, "outputs": [ @@ -723,12 +727,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "weakest overlap: ['SUPT5H', 'SUPT6H', 'SRRT', 'TIMM23B']\n" + "lowest treated ESS: ['SUPT6H', 'SUPT5H', 'TIMM23B', 'TSR2']\n" ] }, { "data": { - "image/png": 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" ] @@ -738,33 +742,34 @@ } ], "source": [ - "# The four arms with the weakest overlap, before any repair.\n", - "ps_focus = support_before.nsmallest(4, 'overlap_ratio').index.tolist()\n", - "print('weakest overlap:', ps_focus)\n", - "\n", - "def overlap_panels(pi, title):\n", - " ctrl_mask = A_focal.sum(axis=1) == 0\n", - " fig, axes = plt.subplots(1, len(ps_focus), figsize=(3.6 * len(ps_focus), 3.4), sharey=True)\n", - " for ax, pert in zip(np.atleast_1d(axes), ps_focus):\n", - " j = focal_perts.index(pert)\n", - " treated = A_focal[:, j] == 1\n", - " hi = max(pi[ctrl_mask | treated, j].max(), 1e-3)\n", - " bins = np.linspace(0, hi, 40)\n", - " ax.hist(pi[ctrl_mask, j], bins=bins, density=True, histtype='step',\n", - " color='#4c78a8', label='control')\n", - " ax.hist(pi[treated, j], bins=bins, density=True, histtype='step',\n", - " color='#e45756', label='treated')\n", - " ax.set_title(pert, fontsize=10)\n", - " ax.set_xlabel('propensity score')\n", - " for side in ('top', 'right'):\n", - " ax.spines[side].set_visible(False)\n", - " np.atleast_1d(axes)[0].set_ylabel('density')\n", - " np.atleast_1d(axes)[0].legend(frameon=False, fontsize=8)\n", - " fig.suptitle(title, y=1.02)\n", - " fig.tight_layout()\n", - " plt.show()\n", - "\n", - "overlap_panels(pi_before, 'Before: library size unpenalized')" + "if HAS_FACTORS:\n", + " # The four arms whose weights are most concentrated, with every factor included.\n", + " ps_focus = support_before.nsmallest(4, 'ess_treated_fraction').index.tolist()\n", + " print('lowest treated ESS:', ps_focus)\n", + "\n", + " def overlap_panels(pi, title):\n", + " ctrl_mask = A_focal.sum(axis=1) == 0\n", + " fig, axes = plt.subplots(1, len(ps_focus), figsize=(3.6 * len(ps_focus), 3.4), sharey=True)\n", + " for ax, pert in zip(np.atleast_1d(axes), ps_focus):\n", + " j = focal_perts.index(pert)\n", + " treated = A_focal[:, j] == 1\n", + " hi = max(pi[ctrl_mask | treated, j].max(), 1e-3)\n", + " bins = np.linspace(0, hi, 40)\n", + " ax.hist(pi[ctrl_mask, j], bins=bins, density=True, histtype='step',\n", + " color='#4c78a8', label='control')\n", + " ax.hist(pi[treated, j], bins=bins, density=True, histtype='step',\n", + " color='#e45756', label='treated')\n", + " ax.set_title(pert, fontsize=10)\n", + " ax.set_xlabel('propensity score')\n", + " for side in ('top', 'right'):\n", + " ax.spines[side].set_visible(False)\n", + " np.atleast_1d(axes)[0].set_ylabel('density')\n", + " np.atleast_1d(axes)[0].legend(frameon=False, fontsize=8)\n", + " fig.suptitle(title, y=1.02)\n", + " fig.tight_layout()\n", + " plt.show()\n", + "\n", + " overlap_panels(pi_before, 'Before: every latent factor included')" ] }, { @@ -772,13 +777,9 @@ "id": "d5a86bea", "metadata": {}, "source": [ - "#### Tuning the library-size penalty\n", - "\n", - "`prep_causarray_data` appends standardized log library size to `X_A`. For a few arms it differs so strongly between the perturbed and control cells that the propensity model can tell them apart almost perfectly, and the weights then rest on a handful of cells. Measured library size mixes capture depth with total RNA content, and these data do not identify which of the two drives a given arm's shift, so how much to adjust for it is a modelling choice rather than a settled one.\n", + "### Which factors separate the flagged arms\n", "\n", - "`tune_penalty_factor` penalizes **that coefficient alone**, and only for arms failing a pre-specified support check. Dropping the covariate is the infinite-penalty limit, so it bounds what any finite factor can achieve: the search evaluates that endpoint first and reports an arm as infeasible rather than exhausting a grid. Among factors meeting the target it returns the smallest, so the covariate keeps as much of its adjustment role as the data support.\n", - "\n", - "Target a monotone metric. `overlap_ratio` and `auc` move monotonically with the penalty; `ess_treated_fraction` does not, because a completely separated arm has near-uniform weights and a deceptively high ESS that *falls* as the penalty restores genuine overlap. It is used to trigger and to report, not as the target." + "The standardized mean difference (SMD) of each factor between an arm and the controls shows what the propensity model is picking up. With random guides a factor should differ little between the arm and the controls. Here no single factor differs much (absolute SMD below 0.3), but together they let the propensity model pick out part of the arm, and that is enough to concentrate its weights. The table lists, for each flagged arm, its most imbalanced factors; the selection drops them in that order." ] }, { @@ -787,20 +788,13 @@ "id": "61f71a6f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:43.655538Z", - "iopub.status.busy": "2026-09-23T13:19:43.655416Z", - "iopub.status.idle": "2026-09-23T13:19:43.887578Z", - "shell.execute_reply": "2026-09-23T13:19:43.887281Z" + "iopub.execute_input": "2026-09-26T07:01:34.569603Z", + "iopub.status.busy": "2026-09-26T07:01:34.569483Z", + "iopub.status.idle": "2026-09-26T07:01:34.727635Z", + "shell.execute_reply": "2026-09-26T07:01:34.726022Z" } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[tune_penalty_factor] 6 treatments triggered, 6 feasible, 42 fits\n" - ] - }, { "data": { "text/html": [ @@ -823,142 +817,64 @@ " \n", " \n", " treatment\n", - " penalty_factor\n", - " feasible\n", - " n_fits\n", - " overlap_ratio_unpenalized\n", - " overlap_ratio_chosen\n", - " overlap_ratio_dropped\n", - " auc_unpenalized\n", - " auc_chosen\n", + " covariate\n", + " standardized_mean_difference\n", " \n", " \n", " \n", " \n", - " 2\n", + " 69\n", " SUPT5H\n", - " 1154.782\n", - " True\n", - " 8\n", - " 0.000\n", - " 0.306\n", - " 0.340\n", - " 1.000\n", - " 0.779\n", + " U10\n", + " -0.26\n", " \n", " \n", - " 3\n", - " SUPT6H\n", - " 177.828\n", - " True\n", - " 8\n", - " 0.001\n", - " 0.324\n", - " 0.483\n", - " 1.000\n", - " 0.860\n", + " 68\n", + " SUPT5H\n", + " U9\n", + " 0.15\n", " \n", " \n", - " 0\n", - " SRRT\n", - " 48.697\n", - " True\n", - " 8\n", - " 0.037\n", - " 0.324\n", - " 0.742\n", - " 0.992\n", - " 0.900\n", + " 64\n", + " SUPT5H\n", + " U5\n", + " -0.15\n", " \n", " \n", - " 1\n", - " SSRP1\n", - " 36.517\n", - " True\n", - " 8\n", - " 0.130\n", - " 0.331\n", - " 0.619\n", - " 0.960\n", - " 0.874\n", + " 79\n", + " SUPT6H\n", + " U10\n", + " -0.26\n", " \n", " \n", - " 4\n", - " TIMM23B\n", - " 31.623\n", - " True\n", - " 8\n", - " 0.082\n", - " 0.340\n", - " 0.671\n", - " 0.975\n", - " 0.887\n", + " 78\n", + " SUPT6H\n", + " U9\n", + " 0.19\n", " \n", " \n", - " 5\n", - " TSR2\n", - " 1.000\n", - " True\n", - " 2\n", - " 0.439\n", - " 0.439\n", - " 0.722\n", - " 0.778\n", - " 0.778\n", + " 74\n", + " SUPT6H\n", + " U5\n", + " -0.14\n", " \n", " \n", "\n", "" ], "text/plain": [ - " treatment penalty_factor feasible n_fits overlap_ratio_unpenalized \\\n", - "2 SUPT5H 1154.782 True 8 0.000 \n", - "3 SUPT6H 177.828 True 8 0.001 \n", - "0 SRRT 48.697 True 8 0.037 \n", - "1 SSRP1 36.517 True 8 0.130 \n", - "4 TIMM23B 31.623 True 8 0.082 \n", - "5 TSR2 1.000 True 2 0.439 \n", - "\n", - " overlap_ratio_chosen overlap_ratio_dropped auc_unpenalized auc_chosen \n", - "2 0.306 0.340 1.000 0.779 \n", - "3 0.324 0.483 1.000 0.860 \n", - "0 0.324 0.742 0.992 0.900 \n", - "1 0.331 0.619 0.960 0.874 \n", - "4 0.340 0.671 0.975 0.887 \n", - "5 0.439 0.722 0.778 0.778 " + " treatment covariate standardized_mean_difference\n", + "69 SUPT5H U10 -0.26\n", + "68 SUPT5H U9 0.15\n", + "64 SUPT5H U5 -0.15\n", + "79 SUPT6H U10 -0.26\n", + "78 SUPT6H U9 0.19\n", + "74 SUPT6H U5 -0.14" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "penalty_factors, tuning_report = tune_penalty_factor(\n", - " A_focal, W_A_focal, 'log_library_size',\n", - " treatment_names=focal_perts, covariate_names=focal_covariates,\n", - " trigger={'auc_gt': 0.9, 'ess_treated_fraction_lt': 0.5},\n", - " target={'overlap_ratio_gt': 0.3},\n", - " K=1, C=1.0, class_weight=None, random_state=0, verbose=True,\n", - ")\n", - "display(tuning_report[['treatment', 'penalty_factor', 'feasible', 'n_fits',\n", - " 'overlap_ratio_unpenalized', 'overlap_ratio_chosen',\n", - " 'overlap_ratio_dropped', 'auc_unpenalized', 'auc_chosen']]\n", - " .sort_values('penalty_factor', ascending=False).round(3))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "043fcd49", - "metadata": { - "execution": { - "iopub.execute_input": "2026-09-23T13:19:43.888957Z", - "iopub.status.busy": "2026-09-23T13:19:43.888861Z", - "iopub.status.idle": "2026-09-23T13:19:44.060351Z", - "shell.execute_reply": "2026-09-23T13:19:44.060048Z" - } - }, - "outputs": [ + }, { "data": { "text/html": [ @@ -980,80 +896,111 @@ " \n", " \n", " \n", - " penalty\n", - " overlap before\n", - " overlap after\n", - " AUC before\n", - " AUC after\n", - " treated ESS before\n", - " treated ESS after\n", + " dropped\n", + " auc_all\n", + " auc_chosen\n", + " overlap_ratio_all\n", + " overlap_ratio_chosen\n", + " ess_treated_fraction_all\n", + " ess_treated_fraction_chosen\n", + " \n", + " \n", + " treatment\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " SUPT5H\n", - " 1154.782\n", - " 0.000\n", - " 0.306\n", - " 1.000\n", - " 0.779\n", - " 0.940\n", - " 0.248\n", + " U10\n", + " 0.714\n", + " 0.639\n", + " 0.385\n", + " 0.550\n", + " 0.358\n", + " 0.63\n", " \n", " \n", " SUPT6H\n", - " 177.828\n", - " 0.001\n", - " 0.324\n", - " 1.000\n", - " 0.860\n", - " 0.955\n", - " 0.268\n", - " \n", - " \n", - " SRRT\n", - " 48.697\n", - " 0.037\n", - " 0.324\n", - " 0.992\n", - " 0.900\n", - " 0.584\n", - " 0.850\n", - " \n", - " \n", - " TIMM23B\n", - " 31.623\n", - " 0.082\n", - " 0.340\n", - " 0.975\n", - " 0.887\n", - " 0.115\n", - " 0.699\n", + " U10,U9\n", + " 0.689\n", + " 0.668\n", + " 0.331\n", + " 0.536\n", + " 0.250\n", + " 0.62\n", " \n", " \n", "\n", "" ], "text/plain": [ - " penalty overlap before overlap after AUC before AUC after \\\n", - "SUPT5H 1154.782 0.000 0.306 1.000 0.779 \n", - "SUPT6H 177.828 0.001 0.324 1.000 0.860 \n", - "SRRT 48.697 0.037 0.324 0.992 0.900 \n", - "TIMM23B 31.623 0.082 0.340 0.975 0.887 \n", + " dropped auc_all auc_chosen overlap_ratio_all \\\n", + "treatment \n", + "SUPT5H U10 0.714 0.639 0.385 \n", + "SUPT6H U10,U9 0.689 0.668 0.331 \n", "\n", - " treated ESS before treated ESS after \n", - "SUPT5H 0.940 0.248 \n", - "SUPT6H 0.955 0.268 \n", - "SRRT 0.584 0.850 \n", - "TIMM23B 0.115 0.699 " + " overlap_ratio_chosen ess_treated_fraction_all \\\n", + "treatment \n", + "SUPT5H 0.550 0.358 \n", + "SUPT6H 0.536 0.250 \n", + "\n", + " ess_treated_fraction_chosen \n", + "treatment \n", + "SUPT5H 0.63 \n", + "SUPT6H 0.62 " ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "if HAS_FACTORS:\n", + " smd = summarize_treatment_associations(\n", + " A_focal, W_A_focal[:, 1:], treatment_names=focal_perts, covariate_names=focal_covariates[1:])\n", + " smd['abs_smd'] = smd['standardized_mean_difference'].abs()\n", + " flag = ((support_before['ess_treated_fraction'] < 0.5) | (support_before['overlap_ratio'] < 0.3)\n", + " | (support_before['auc'] > 0.9))\n", + " flagged = support_before.index[flag].tolist()\n", + " display(smd[smd['treatment'].isin(flagged)]\n", + " .sort_values(['treatment', 'abs_smd'], ascending=[True, False])\n", + " .groupby('treatment').head(3)\n", + " [['treatment', 'covariate', 'standardized_mean_difference']].round(2))\n", + "\n", + " drops, selection = select_propensity_factors(\n", + " A_focal, W_A_focal, treatment_names=focal_perts, covariate_names=focal_covariates)\n", + " pi_after, _ = refit_propensity_scores(\n", + " A_focal, W_A_focal, pi_hat=pi_before.copy(), treatment_names=focal_perts,\n", + " covariate_names=focal_covariates, drop_by_treatment=drops,\n", + " K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", + " display(selection.set_index('treatment')\n", + " [['dropped', 'auc_all', 'auc_chosen', 'overlap_ratio_all', 'overlap_ratio_chosen',\n", + " 'ess_treated_fraction_all', 'ess_treated_fraction_chosen']].round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "043fcd49", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-26T07:01:34.731697Z", + "iopub.status.busy": "2026-09-26T07:01:34.731489Z", + "iopub.status.idle": "2026-09-26T07:01:34.920498Z", + "shell.execute_reply": "2026-09-26T07:01:34.919685Z" + } + }, + "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1063,26 +1010,8 @@ } ], "source": [ - "pi_after, refit_audit = refit_propensity_scores(\n", - " A_focal, W_A_focal, pi_hat=pi_before.copy(),\n", - " treatment_names=focal_perts, covariate_names=focal_covariates,\n", - " penalty_factors_by_treatment=penalty_factors,\n", - " K=1, C=1.0, class_weight=None, clip=None, random_state=0)\n", - "\n", - "support_after = summarize_propensity_scores(\n", - " A_focal, pi_after, treatment_names=focal_perts, clip_bounds=None).set_index('treatment')\n", - "comparison = pd.DataFrame({\n", - " 'penalty': pd.Series({k: v['log_library_size'] for k, v in penalty_factors.items()}),\n", - " 'overlap before': support_before['overlap_ratio'],\n", - " 'overlap after': support_after['overlap_ratio'],\n", - " 'AUC before': support_before['auc'],\n", - " 'AUC after': support_after['auc'],\n", - " 'treated ESS before': support_before['ess_treated_fraction'],\n", - " 'treated ESS after': support_after['ess_treated_fraction'],\n", - "}).fillna({'penalty': 1.0}).loc[ps_focus].round(3)\n", - "display(comparison)\n", - "\n", - "overlap_panels(pi_after, 'After: library size penalized on flagged arms only')" + "if HAS_FACTORS:\n", + " overlap_panels(pi_after, 'After: imbalanced factors dropped on flagged arms only')" ] }, { @@ -1092,7 +1021,7 @@ "source": [ "## 6. Results\n", "\n", - "### 5.1 Discovery summary\n" + "### 6.1 Discovery summary\n" ] }, { @@ -1101,10 +1030,10 @@ "id": "bb100012", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:44.061789Z", - "iopub.status.busy": "2026-09-23T13:19:44.061660Z", - "iopub.status.idle": "2026-09-23T13:19:44.090684Z", - "shell.execute_reply": "2026-09-23T13:19:44.090401Z" + "iopub.execute_input": "2026-09-26T07:01:34.921729Z", + "iopub.status.busy": "2026-09-26T07:01:34.921630Z", + "iopub.status.idle": "2026-09-26T07:01:34.951436Z", + "shell.execute_reply": "2026-09-26T07:01:34.950892Z" } }, "outputs": [ @@ -1112,28 +1041,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "Significant (padj < 0.05): 172,030 gene x pert pairs\n", - " Perturbations with >= 1 hit: 195\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Unique genes affected : 8,522\n", + "Significant (padj < 0.05): 145,340 gene x pert pairs\n", + " Perturbations with >= 1 hit: 194\n", + " Unique genes affected : 8,388\n", "\n", "Top-10 perts by discovery count:\n", "trt\n", - "SUPT5H 4584\n", - "MED12 4088\n", - "INTS2 3701\n", - "MED19 3358\n", - "HSPA9 3290\n", - "PHB2 3233\n", - "SUPT6H 3216\n", - "CSE1L 3206\n", - "NCBP2 2886\n", - "HSPA5 2849\n" + "CSE1L 3078\n", + "MED12 3068\n", + "TSR2 2940\n", + "LSM6 2524\n", + "NCBP2 2396\n", + "DHX15 2302\n", + "HSPA9 2169\n", + "INTS2 2138\n", + "TRRAP 2137\n", + "MED30 1983\n" ] } ], @@ -1154,7 +1077,7 @@ "id": "bb100013", "metadata": {}, "source": [ - "### 6.1 Volcano plot (all perturbations combined)\n" + "### 6.2 Volcano plot (all perturbations combined)\n" ] }, { @@ -1163,16 +1086,16 @@ "id": "bb100014", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:44.092119Z", - "iopub.status.busy": "2026-09-23T13:19:44.092030Z", - "iopub.status.idle": "2026-09-23T13:19:56.162270Z", - "shell.execute_reply": "2026-09-23T13:19:56.161988Z" + "iopub.execute_input": "2026-09-26T07:01:34.952671Z", + "iopub.status.busy": "2026-09-26T07:01:34.952564Z", + "iopub.status.idle": "2026-09-26T07:01:50.814075Z", + "shell.execute_reply": "2026-09-26T07:01:50.813413Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1205,7 +1128,7 @@ "id": "bb100015", "metadata": {}, "source": [ - "### 6.2 Discovery count per perturbation\n", + "### 6.3 Discovery count per perturbation\n", "\n", "Each bar counts genes passing the BH-adjusted threshold for one perturbation. Differences can reflect biological response, sample size, expression support, or model fit; the counts alone do not distinguish these explanations.\n" ] @@ -1216,16 +1139,16 @@ "id": "bb100016", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:56.164067Z", - "iopub.status.busy": "2026-09-23T13:19:56.163931Z", - "iopub.status.idle": "2026-09-23T13:19:56.250750Z", - "shell.execute_reply": "2026-09-23T13:19:56.250448Z" + "iopub.execute_input": "2026-09-26T07:01:50.816321Z", + "iopub.status.busy": "2026-09-26T07:01:50.816210Z", + "iopub.status.idle": "2026-09-26T07:01:50.923534Z", + "shell.execute_reply": "2026-09-26T07:01:50.923048Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1252,7 +1175,7 @@ "id": "replogle-wilcoxon-title", "metadata": {}, "source": [ - "### 6.3 Comparison with a marginal Wilcoxon test\n", + "### 6.4 Comparison with a marginal Wilcoxon test\n", "\n", "As a reference point, each perturbation is also compared with the pooled non-targeting\n", "controls using a Wilcoxon rank-sum test on library-size-normalized, log1p counts\n", @@ -1271,10 +1194,10 @@ "id": "replogle-wilcoxon-counts", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:56.252245Z", - "iopub.status.busy": "2026-09-23T13:19:56.252112Z", - "iopub.status.idle": "2026-09-23T13:19:56.496205Z", - "shell.execute_reply": "2026-09-23T13:19:56.495913Z" + "iopub.execute_input": "2026-09-26T07:01:50.924998Z", + "iopub.status.busy": "2026-09-26T07:01:50.924884Z", + "iopub.status.idle": "2026-09-26T07:01:51.172070Z", + "shell.execute_reply": "2026-09-26T07:01:51.171497Z" } }, "outputs": [ @@ -1282,9 +1205,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "causarray: 172,030 significant pairs\n", + "causarray: 145,340 significant pairs\n", "Wilcoxon : 425,125 significant pairs\n", - "Median discoveries per perturbation: causarray=488, Wilcoxon=2220\n" + "Median discoveries per perturbation: causarray=602, Wilcoxon=2220\n" ] }, { @@ -1320,52 +1243,52 @@ " \n", " \n", " SUPT5H\n", - " 4584\n", + " 1591\n", " 6842\n", " \n", " \n", " SUPT6H\n", - " 3216\n", + " 1231\n", " 6476\n", " \n", " \n", " MED12\n", - " 4088\n", + " 3068\n", " 5883\n", " \n", " \n", " MED30\n", - " 2527\n", + " 1983\n", " 5458\n", " \n", " \n", " INTS2\n", - " 3701\n", + " 2138\n", " 5373\n", " \n", " \n", " HSPA9\n", - " 3290\n", + " 2169\n", " 5326\n", " \n", " \n", " SFPQ\n", - " 2295\n", + " 504\n", " 5188\n", " \n", " \n", " DDX41\n", - " 356\n", + " 518\n", " 5049\n", " \n", " \n", " MED19\n", - " 3358\n", + " 1920\n", " 4946\n", " \n", " \n", " TSR2\n", - " 366\n", + " 2940\n", " 4941\n", " \n", " \n", @@ -1375,16 +1298,16 @@ "text/plain": [ " causarray Wilcoxon\n", "Perturbation \n", - "SUPT5H 4584 6842\n", - "SUPT6H 3216 6476\n", - "MED12 4088 5883\n", - "MED30 2527 5458\n", - "INTS2 3701 5373\n", - "HSPA9 3290 5326\n", - "SFPQ 2295 5188\n", - "DDX41 356 5049\n", - "MED19 3358 4946\n", - "TSR2 366 4941" + "SUPT5H 1591 6842\n", + "SUPT6H 1231 6476\n", + "MED12 3068 5883\n", + "MED30 1983 5458\n", + "INTS2 2138 5373\n", + "HSPA9 2169 5326\n", + "SFPQ 504 5188\n", + "DDX41 518 5049\n", + "MED19 1920 4946\n", + "TSR2 2940 4941" ] }, "metadata": {}, @@ -1451,10 +1374,10 @@ "id": "replogle-extreme-lfc-align", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:56.497656Z", - "iopub.status.busy": "2026-09-23T13:19:56.497517Z", - "iopub.status.idle": "2026-09-23T13:19:56.957158Z", - "shell.execute_reply": "2026-09-23T13:19:56.956861Z" + "iopub.execute_input": "2026-09-26T07:01:51.173482Z", + "iopub.status.busy": "2026-09-26T07:01:51.173367Z", + "iopub.status.idle": "2026-09-26T07:01:51.634784Z", + "shell.execute_reply": "2026-09-26T07:01:51.634295Z" } }, "outputs": [ @@ -1507,7 +1430,7 @@ "source": [ "#### Gene Ontology enrichment for SUPT5H\n", "\n", - "SUPT5H is the screen's canonical case: knocking down the SPT5 elongation factor should hit translation and ribosome biogenesis, so its gene list has a known expectation to be read against. It also carries the heaviest penalty of any arm, and the cell prints that alongside the result rather than leaving it implicit. Both methods are tested on the same gene background." + "SUPT5H is the screen's canonical case: knocking down the SPT5 elongation factor should hit translation and ribosome biogenesis, so its gene list has a known expectation to be read against. Its propensity weights were among the most concentrated before factor selection, so the cell prints that alongside the result rather than leaving it implicit. Both methods are tested on the same gene background." ] }, { @@ -1516,10 +1439,10 @@ "id": "replogle-go-analysis", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:56.958531Z", - "iopub.status.busy": "2026-09-23T13:19:56.958442Z", - "iopub.status.idle": "2026-09-23T13:19:57.055001Z", - "shell.execute_reply": "2026-09-23T13:19:57.054687Z" + "iopub.execute_input": "2026-09-26T07:01:51.636349Z", + "iopub.status.busy": "2026-09-26T07:01:51.636228Z", + "iopub.status.idle": "2026-09-26T07:01:51.724498Z", + "shell.execute_reply": "2026-09-26T07:01:51.723942Z" } }, "outputs": [ @@ -1527,8 +1450,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "GO focus: SUPT5H -- penalty 1155x on log library size, overlap 0.00 -> 0.31, treated ESS 0.25\n", - "(92 arms needed no penalty at all and keep >= 80% of their treated sample)\n" + "GO focus: SUPT5H -- factors dropped: U10, treated ESS 0.36 -> 0.63, overlap 0.38 -> 0.55\n", + "(171 of 200 arms kept every factor)\n" ] }, { @@ -1569,12 +1492,12 @@ " \n", " \n", " causarray down\n", - " 4405\n", - " 114\n", + " 642\n", + " 71\n", " \n", " \n", " causarray up\n", - " 179\n", + " 949\n", " 0\n", " \n", " \n", @@ -1585,8 +1508,8 @@ " DE_genes significant_GO_BP_terms\n", "Wilcoxon down 5646 22\n", "Wilcoxon up 1196 1\n", - "causarray down 4405 114\n", - "causarray up 179 0" + "causarray down 642 71\n", + "causarray up 949 0" ] }, "metadata": {}, @@ -1622,7 +1545,7 @@ " \n", " \n", " \n", - " 23\n", + " 10\n", " Wilcoxon down\n", " ribosome biogenesis\n", " 8.496590e-13\n", @@ -1630,7 +1553,7 @@ " 5430\n", " \n", " \n", - " 28\n", + " 13\n", " Wilcoxon down\n", " ribonucleoprotein complex biogenesis\n", " 9.065321e-12\n", @@ -1638,7 +1561,7 @@ " 5430\n", " \n", " \n", - " 38\n", + " 17\n", " Wilcoxon down\n", " rRNA metabolic process\n", " 5.047599e-10\n", @@ -1646,7 +1569,7 @@ " 5430\n", " \n", " \n", - " 45\n", + " 19\n", " Wilcoxon down\n", " rRNA processing\n", " 3.027228e-09\n", @@ -1654,7 +1577,7 @@ " 5430\n", " \n", " \n", - " 60\n", + " 25\n", " Wilcoxon down\n", " translation\n", " 2.632631e-07\n", @@ -1662,7 +1585,7 @@ " 5430\n", " \n", " \n", - " 134\n", + " 91\n", " Wilcoxon up\n", " proton transmembrane transport\n", " 3.728893e-02\n", @@ -1672,73 +1595,73 @@ " \n", " 0\n", " causarray down\n", - " ribonucleoprotein complex biogenesis\n", - " 7.093004e-35\n", - " 344\n", - " 4255\n", + " translation\n", + " 1.859947e-46\n", + " 150\n", + " 621\n", " \n", " \n", " 1\n", " causarray down\n", - " translation\n", - " 1.245411e-30\n", - " 410\n", - " 4255\n", + " protein biosynthetic process\n", + " 1.859947e-46\n", + " 150\n", + " 621\n", " \n", " \n", " 2\n", " causarray down\n", - " protein biosynthetic process\n", - " 1.245411e-30\n", - " 410\n", - " 4255\n", + " cytoplasmic translation\n", + " 1.099410e-43\n", + " 85\n", + " 621\n", " \n", " \n", - " 4\n", + " 3\n", " causarray down\n", - " ribosome biogenesis\n", - " 1.717395e-27\n", - " 240\n", - " 4255\n", + " ribonucleoprotein complex biogenesis\n", + " 2.308388e-30\n", + " 112\n", + " 621\n", " \n", " \n", - " 5\n", + " 4\n", " causarray down\n", - " RNA processing\n", - " 3.755719e-27\n", - " 592\n", - " 4255\n", + " ribosome biogenesis\n", + " 1.501356e-23\n", + " 82\n", + " 621\n", " \n", " \n", "\n", "" ], "text/plain": [ - " query name p_value \\\n", - "23 Wilcoxon down ribosome biogenesis 8.496590e-13 \n", - "28 Wilcoxon down ribonucleoprotein complex biogenesis 9.065321e-12 \n", - "38 Wilcoxon down rRNA metabolic process 5.047599e-10 \n", - "45 Wilcoxon down rRNA processing 3.027228e-09 \n", - "60 Wilcoxon down translation 2.632631e-07 \n", - "134 Wilcoxon up proton transmembrane transport 3.728893e-02 \n", - "0 causarray down ribonucleoprotein complex biogenesis 7.093004e-35 \n", - "1 causarray down translation 1.245411e-30 \n", - "2 causarray down protein biosynthetic process 1.245411e-30 \n", - "4 causarray down ribosome biogenesis 1.717395e-27 \n", - "5 causarray down RNA processing 3.755719e-27 \n", + " query name p_value \\\n", + "10 Wilcoxon down ribosome biogenesis 8.496590e-13 \n", + "13 Wilcoxon down ribonucleoprotein complex biogenesis 9.065321e-12 \n", + "17 Wilcoxon down rRNA metabolic process 5.047599e-10 \n", + "19 Wilcoxon down rRNA processing 3.027228e-09 \n", + "25 Wilcoxon down translation 2.632631e-07 \n", + "91 Wilcoxon up proton transmembrane transport 3.728893e-02 \n", + "0 causarray down translation 1.859947e-46 \n", + "1 causarray down protein biosynthetic process 1.859947e-46 \n", + "2 causarray down cytoplasmic translation 1.099410e-43 \n", + "3 causarray down ribonucleoprotein complex biogenesis 2.308388e-30 \n", + "4 causarray down ribosome biogenesis 1.501356e-23 \n", "\n", - " intersection_size query_size \n", - "23 249 5430 \n", - "28 351 5430 \n", - "38 201 5430 \n", - "45 178 5430 \n", - "60 421 5430 \n", - "134 37 1124 \n", - "0 344 4255 \n", - "1 410 4255 \n", - "2 410 4255 \n", - "4 240 4255 \n", - "5 592 4255 " + " intersection_size query_size \n", + "10 249 5430 \n", + "13 351 5430 \n", + "17 201 5430 \n", + "19 178 5430 \n", + "25 421 5430 \n", + "91 37 1124 \n", + "0 150 621 \n", + "1 150 621 \n", + "2 85 621 \n", + "3 112 621 \n", + "4 82 621 " ] }, "metadata": {}, @@ -1746,7 +1669,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1757,17 +1680,15 @@ ], "source": [ "# SUPT5H is the screen's canonical case: knocking down the SPT5 elongation\n", - "# factor should hit translation and ribosome biogenesis. Its support is the\n", - "# weakest of the large arms, so the audit below is reported alongside it.\n", - "ps_audit = pd.read_csv('results/replogle_propensity_penalties.csv').set_index('treatment')\n", + "# factor should hit translation and ribosome biogenesis. Its propensity\n", + "# support is reported alongside the result.\n", + "ps_audit = pd.read_csv('results/replogle_propensity_selection.csv').set_index('treatment')\n", "go_focus = 'SUPT5H'\n", "_row = ps_audit.loc[go_focus]\n", - "print(f\"GO focus: {go_focus} -- penalty {_row['penalty_factor']:.0f}x on log library size, \"\n", - " f\"overlap {_row['overlap_before']:.2f} -> {_row['overlap_after']:.2f}, \"\n", - " f\"treated ESS {_row['ess_treated_after']:.2f}\")\n", - "_best = ps_audit[(ps_audit['penalty_factor'] == 1.0)\n", - " & (ps_audit['ess_treated_after'] >= 0.8)].index\n", - "print(f'({len(_best)} arms needed no penalty at all and keep >= 80% of their treated sample)')\n", + "print(f\"GO focus: {go_focus} -- factors dropped: {_row['dropped'] if _row['flagged'] else 'none'}, \"\n", + " f\"treated ESS {_row['ess_treated_before']:.2f} -> {_row['ess_treated_after']:.2f}, \"\n", + " f\"overlap {_row['overlap_before']:.2f} -> {_row['overlap_after']:.2f}\")\n", + "print(f\"({int((~ps_audit['flagged']).sum())} of {len(ps_audit)} arms kept every factor)\")\n", "\n", "go_result_path = Path(f'results/replogle_{go_focus.lower()}_go_results.csv')\n", "focus_i = wc_groups.get_loc(go_focus)\n", @@ -1848,16 +1769,16 @@ "id": "replogle-extreme-lfc-scatter", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:57.056445Z", - "iopub.status.busy": "2026-09-23T13:19:57.056327Z", - "iopub.status.idle": "2026-09-23T13:19:57.711872Z", - "shell.execute_reply": "2026-09-23T13:19:57.711551Z" + "iopub.execute_input": "2026-09-26T07:01:51.725841Z", + "iopub.status.busy": "2026-09-26T07:01:51.725724Z", + "iopub.status.idle": "2026-09-26T07:01:52.419609Z", + "shell.execute_reply": "2026-09-26T07:01:52.419162Z" } }, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1958,10 +1879,10 @@ "id": "replogle-extreme-tests-table", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:57.713938Z", - "iopub.status.busy": "2026-09-23T13:19:57.713806Z", - "iopub.status.idle": "2026-09-23T13:19:57.743722Z", - "shell.execute_reply": "2026-09-23T13:19:57.743437Z" + "iopub.execute_input": "2026-09-26T07:01:52.422282Z", + "iopub.status.busy": "2026-09-26T07:01:52.422173Z", + "iopub.status.idle": "2026-09-26T07:01:52.447605Z", + "shell.execute_reply": "2026-09-26T07:01:52.447149Z" } }, "outputs": [ @@ -2009,8 +1930,8 @@ " 153/2000\n", " -25.379\n", " 0.0\n", - " -3.014\n", - " 0.078\n", + " -2.887\n", + " 0.199\n", " True\n", " Wilcoxon only\n", " \n", @@ -2023,8 +1944,8 @@ " 237/2000\n", " -5.511\n", " 0.0\n", - " -3.250\n", - " 0.258\n", + " -2.816\n", + " 0.361\n", " True\n", " Wilcoxon only\n", " \n", @@ -2037,10 +1958,10 @@ " 227/2000\n", " -5.470\n", " 0.0\n", - " -3.574\n", - " 0.033\n", + " -3.459\n", + " 0.109\n", " True\n", - " Both\n", + " Wilcoxon only\n", " \n", " \n", " 1\n", @@ -2051,10 +1972,10 @@ " 246/2000\n", " -5.193\n", " 0.0\n", - " -3.712\n", - " 0.026\n", + " -3.585\n", + " 0.094\n", " True\n", - " Both\n", + " Wilcoxon only\n", " \n", " \n", " 4\n", @@ -2065,8 +1986,8 @@ " 517/2000\n", " -5.048\n", " 0.0\n", - " -4.697\n", - " 0.001\n", + " -4.757\n", + " 0.020\n", " True\n", " Both\n", " \n", @@ -2083,17 +2004,17 @@ "4 INTS2 INTS2 True 3/354 517/2000 \n", "\n", " Wilcoxon_log2FC Wilcoxon_padj causarray_log2FC causarray_padj \\\n", - "2 -25.379 0.0 -3.014 0.078 \n", - "3 -5.511 0.0 -3.250 0.258 \n", - "0 -5.470 0.0 -3.574 0.033 \n", - "1 -5.193 0.0 -3.712 0.026 \n", - "4 -5.048 0.0 -4.697 0.001 \n", + "2 -25.379 0.0 -2.887 0.199 \n", + "3 -5.511 0.0 -2.816 0.361 \n", + "0 -5.470 0.0 -3.459 0.109 \n", + "1 -5.193 0.0 -3.585 0.094 \n", + "4 -5.048 0.0 -4.757 0.020 \n", "\n", " causarray_estimable decision \n", "2 True Wilcoxon only \n", "3 True Wilcoxon only \n", - "0 True Both \n", - "1 True Both \n", + "0 True Wilcoxon only \n", + "1 True Wilcoxon only \n", "4 True Both " ] }, @@ -2155,10 +2076,10 @@ "id": "replogle-support-rules", "metadata": { "execution": { - "iopub.execute_input": "2026-09-23T13:19:57.745116Z", - "iopub.status.busy": "2026-09-23T13:19:57.745028Z", - "iopub.status.idle": "2026-09-23T13:19:58.154004Z", - "shell.execute_reply": "2026-09-23T13:19:58.153702Z" + "iopub.execute_input": "2026-09-26T07:01:52.448940Z", + "iopub.status.busy": "2026-09-26T07:01:52.448822Z", + "iopub.status.idle": "2026-09-26T07:01:52.910030Z", + "shell.execute_reply": "2026-09-26T07:01:52.909428Z" } }, "outputs": [ @@ -2208,7 +2129,7 @@ " 0.001\n", " 4\n", " 12\n", - " 4\n", + " 0\n", " \n", " \n", " 2\n", @@ -2217,7 +2138,7 @@ " 0.002\n", " 5\n", " 37\n", - " 13\n", + " 2\n", " \n", " \n", " 3\n", @@ -2226,7 +2147,7 @@ " 0.023\n", " 5\n", " 331\n", - " 88\n", + " 19\n", " \n", " \n", " 4\n", @@ -2235,7 +2156,7 @@ " 0.023\n", " 5\n", " 331\n", - " 88\n", + " 19\n", " \n", " \n", " 5\n", @@ -2269,10 +2190,10 @@ "\n", " causarray_discoveries_removed \n", "0 0 \n", - "1 4 \n", - "2 13 \n", - "3 88 \n", - "4 88 \n", + "1 0 \n", + "2 2 \n", + "3 19 \n", + "4 19 \n", "5 0 " ] }, @@ -2356,7 +2277,7 @@ "\n", "Batch fitting makes a large perturbation screen manageable. The saved comparisons show that adjusted and marginal analyses can yield different gene lists while sharing enrichment themes. Expression-support checks help interpret extreme effects, and propensity diagnostics reveal weight concentration that discovery counts alone miss.\n", "\n", - "Report the selected model, the support rules, and the per-arm penalties together. Recheck these qualitative patterns whenever the analysis or caches are refreshed.\n" + "Report the selected model, the support rules, and the per-arm factor choices together. Recheck these qualitative patterns whenever the analysis or caches are refreshed.\n" ] } ], @@ -2376,7 +2297,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.10.20" } }, "nbformat": 4, diff --git a/docs/source/tutorial/replogle/results/replogle-r-legacy.csv b/docs/source/tutorial/replogle/results/replogle-r-legacy.csv deleted file mode 100644 index 0696f1e..0000000 --- a/docs/source/tutorial/replogle/results/replogle-r-legacy.csv +++ /dev/null @@ -1,8 +0,0 @@ -r,deviance,nu,JIC -0,-13.524044646812612,0.030448301618735667,-13.493596345193877 -5,-13.52535019088139,0.03769789724224416,-13.487652293639146 -10,-13.526764814697712,0.04494749286575265,-13.48181732183196 -15,-13.528363797285195,0.05219708848926114,-13.476166708795933 -20,-13.530391837715486,0.05944668411276965,-13.470945153602717 -25,-13.534007620669998,0.06669627973627813,-13.467311340933719 -30,-13.599772641274633,0.07394587535978663,-13.525826765914847 diff --git a/environment-r.yaml b/environment-r.yaml index 3fcf894..5719958 100644 --- a/environment-r.yaml +++ b/environment-r.yaml @@ -7,4 +7,6 @@ dependencies: - r-rmarkdown - r-knitr - r-seurat>=5 + - r-dplyr + - r-ggplot2 - pandoc diff --git a/environment.yaml b/environment.yaml index 30e0491..81f8338 100644 --- a/environment.yaml +++ b/environment.yaml @@ -41,7 +41,7 @@ dependencies: # optional fast GLM backend; also provides the marginal Wilcoxon # comparison used by the Replogle and Adamson tutorials - - crispyx>=0.1.5 + - crispyx>=0.1.6 # for development - build diff --git a/pyproject.toml b/pyproject.toml index 8ca662a..39e0c38 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,7 @@ dependencies = [ "joblib", "matplotlib", "sklearn_ensemble_cv", - "crispyx>=0.1.5", + "crispyx>=0.1.6", ] authors = [ {name = "Jin-Hong Du", email = "jinhongd@hku.hk"}, diff --git a/setup.cfg b/setup.cfg index 5c0e074..3e93a17 100644 --- a/setup.cfg +++ b/setup.cfg @@ -27,4 +27,4 @@ install_requires = joblib matplotlib sklearn_ensemble_cv - crispyx>=0.1.5 + crispyx>=0.1.6 diff --git a/tests/test_DR_learner.py b/tests/test_DR_learner.py index 7b3f022..92ce6d1 100644 --- a/tests/test_DR_learner.py +++ b/tests/test_DR_learner.py @@ -55,8 +55,8 @@ def test_float32_aipw_mean_uses_float64_accumulation(self): def test_nonpositive_arm_mean_with_observed_counts_is_nonestimable(self): # Control arm has observed counts but a negative AIPW mean (the # pseudo-outcome correction overshoots): that stays non-estimable. - # (Since 0.1.0 an arm with *no* observed counts is instead kept - # estimable at the floor; see test_small_arm_inference.) + # (An arm with *no* observed counts is instead kept estimable at the + # floor; see test_small_arm_inference.) # With calibrated scores the AIPW arm mean equals the observed arm # mean, so a negative value needs miscalibrated scores: pi = 0.5 for a # 1-in-4 treatment gives mean_control = (2*sum(Y_ctrl) - 2*mu)/4 = -0.5. diff --git a/tests/test_gcate_offset.py b/tests/test_gcate_offset.py new file mode 100644 index 0000000..851bfc5 --- /dev/null +++ b/tests/test_gcate_offset.py @@ -0,0 +1,37 @@ +"""fit_gcate with an explicit offset array. + +The fit reads its offset from ``kwargs_glm['size_factor']``. That entry used to +be set only for ``offset=True``, so a supplied offset array was silently +replaced by ones everywhere except the dispersion estimate. +""" + +import numpy as np +import pytest + +from causarray import comp_size_factor, fit_gcate + + +@pytest.fixture(scope='module') +def depth_varying_counts(): + rng = np.random.default_rng(0) + n, p = 120, 300 + sf = np.exp(rng.normal(0, 0.5, n)) + Y = rng.poisson(sf[:, None] * np.exp(rng.normal(1, 0.5, p))[None, :]).astype(float) + X = np.ones((n, 1)) + A = (rng.random((n, 1)) < 0.5).astype(float) + return Y, X, A + + +def test_offset_array_is_used_like_offset_true(depth_varying_counts): + Y, X, A = depth_varying_counts + _, res_true = fit_gcate(Y, X, A, 2, offset=True, verbose=False) + _, res_array = fit_gcate(Y, X, A, 2, offset=np.log(comp_size_factor(Y)), verbose=False) + np.testing.assert_allclose(res_array['kwargs_glm']['size_factor'], + res_true['kwargs_glm']['size_factor']) + np.testing.assert_allclose(res_array['U'], res_true['U'], atol=1e-6) + + +def test_offset_array_must_match_rows(depth_varying_counts): + Y, X, A = depth_varying_counts + with pytest.raises(ValueError, match='one value per row'): + fit_gcate(Y, X, A, 2, offset=np.zeros(len(Y) - 1), verbose=False) diff --git a/tests/test_inference_comprehensive.py b/tests/test_inference_comprehensive.py index 3928c04..19ea94b 100644 --- a/tests/test_inference_comprehensive.py +++ b/tests/test_inference_comprehensive.py @@ -430,7 +430,7 @@ class TestLFCIntegration: # ---- I11: type I error, balanced, unequal (Welch) ---- def test_type1_balanced_welch(self, null_nb_balanced): - """I11 — FDR ≤ 10% under balanced null (deprecated alias usevar='unequal').""" + """I11 — FDR ≤ 10% under balanced null (usevar='unequal', Welch).""" Y, W, A, _, _ = null_nb_balanced df, _ = LFC(Y, W, A[:, None], family='nb', offset=True, usevar='unequal', backend='fast') @@ -446,17 +446,20 @@ def test_type1_imbalanced_welch(self, null_nb_imbalanced): fdr = (df['padj'] < 0.05).mean() assert fdr <= 0.10, f"Imbalanced type I error too high: FDR={fdr:.3f}" - # ---- I13: 'unequal' is a deprecated alias of 'pooled' (0.1.0) ---- - def test_unequal_is_alias_of_pooled(self, null_nb_imbalanced): - """I13 — usevar='unequal' warns and returns exactly the pooled result.""" - Y, W, A, _, _ = null_nb_imbalanced + # ---- I13: 'unequal' is the Welch variance, more conservative than 'pooled' ---- + def test_unequal_is_welch_and_more_conservative(self, null_nb_balanced): + """I13 — same estimates, Welch standard errors about 2x larger for equal arms.""" + Y, W, A, _, _ = null_nb_balanced df_pooled, _ = LFC(Y, W, A[:, None], family='nb', offset=True, usevar='pooled', backend='fast') - with pytest.warns(FutureWarning, match="removed in 0.1.0"): - df_alias, _ = LFC(Y, W, A[:, None], family='nb', offset=True, - usevar='unequal', backend='fast') - pd.testing.assert_frame_equal(df_alias, df_pooled) - with pytest.raises(ValueError, match="usevar must be 'pooled'"): + df_welch, _ = LFC(Y, W, A[:, None], family='nb', offset=True, + usevar='unequal', backend='fast') + np.testing.assert_allclose(df_welch['tau'], df_pooled['tau']) + ok = np.isfinite(df_pooled['std']) & ~df_pooled['var_floored'] & ~df_welch['var_floored'] + ratio = (df_welch.loc[ok, 'std'] / df_pooled.loc[ok, 'std']).median() + assert 1.5 < ratio < 2.5, ratio + assert (df_welch['padj'] < 0.1).sum() <= (df_pooled['padj'] < 0.1).sum() + with pytest.raises(ValueError, match="usevar must be 'pooled' or 'unequal'"): LFC(Y, W, A[:, None], family='nb', offset=True, usevar='welch', backend='fast') # ---- I14: power under balanced NB signal ---- diff --git a/tests/test_platform.py b/tests/test_platform.py new file mode 100644 index 0000000..a9c681e --- /dev/null +++ b/tests/test_platform.py @@ -0,0 +1,34 @@ +"""The import-time warning for Python translated by Rosetta 2.""" +import platform +import sys +import warnings + +import pytest + +from causarray import _platform + + +def test_no_warning_off_macos_or_on_native_arm(monkeypatch): + monkeypatch.setattr(sys, 'platform', 'linux') + assert not _platform.running_under_rosetta() + monkeypatch.setattr(sys, 'platform', 'darwin') + monkeypatch.setattr(platform, 'machine', lambda: 'arm64') + assert not _platform.running_under_rosetta() + with warnings.catch_warnings(): + warnings.simplefilter('error') + _platform.warn_if_emulated() + + +def test_warns_when_translated(monkeypatch): + monkeypatch.setattr(_platform, 'running_under_rosetta', lambda: True) + with pytest.warns(RuntimeWarning, match='Rosetta 2'): + _platform.warn_if_emulated() + + +@pytest.mark.skipif(sys.platform != 'darwin', reason='macOS only') +def test_detection_matches_this_process(): + """On a Mac the check agrees with the interpreter's own architecture.""" + if platform.machine() == 'arm64': + assert not _platform.running_under_rosetta() + else: + assert isinstance(_platform.running_under_rosetta(), bool) diff --git a/tests/test_propensity.py b/tests/test_propensity.py index fb8e186..0506a97 100644 --- a/tests/test_propensity.py +++ b/tests/test_propensity.py @@ -15,6 +15,7 @@ LFC, estimate_propensity_scores, tune_penalty_factor, + select_propensity_factors, plot_propensity_scores, plot_treatment_associations, refit_propensity_scores, @@ -29,7 +30,7 @@ def test_intercept_only_scores_respect_class_weight(): A[60:80, 0] = 1 A[80:100, 1] = 1 - # Since 0.1.0 the default is calibrated; 'balanced' is the legacy option. + # The default is calibrated; 'balanced' is the legacy option. calibrated = estimate_propensity_scores(A, np.ones((100, 1))) balanced = estimate_propensity_scores( A, np.ones((100, 1)), class_weight='balanced') @@ -685,3 +686,94 @@ def test_tune_penalty_factor_scores_only_masked_cells(): got = report.set_index('treatment').loc['benign_arm', 'auc_unpenalized'] assert got == pytest.approx(expected) + + +# --------------------------------------------------------------------------- +# select_propensity_factors +# --------------------------------------------------------------------------- + +def test_select_propensity_factors_drops_the_imbalanced_covariate_first(): + A, X_A, names, cov, _ = _separating_design() + drops, report = select_propensity_factors( + A, X_A, treatment_names=names, covariate_names=cov, random_state=0) + assert drops == {'sep_arm': ['driver']} + row = report.set_index('treatment').loc['sep_arm'] + assert row['target_met'] + assert row['n_fits'] == 2 + assert row['auc_all'] > 0.9 + assert row['overlap_ratio_chosen'] > 0.3 + assert 'benign_arm' not in report['treatment'].tolist() + + +def test_select_propensity_factors_leaves_other_arms_and_the_intercept(): + A, X_A, names, cov, _ = _separating_design() + base = estimate_propensity_scores(A, X_A, K=1, clip=None, random_state=0) + drops, _ = select_propensity_factors( + A, X_A, treatment_names=names, covariate_names=cov, + target={'auc_lt': 0.0}, on_infeasible='all', random_state=0) # unreachable + assert drops['sep_arm'] == ['driver', 'noise'] # intercept kept + updated, _ = refit_propensity_scores( + A, X_A, pi_hat=base.copy(), treatment_names=names, covariate_names=cov, + drop_by_treatment=drops, K=1, clip=None, random_state=0) + j = names.index('benign_arm') + np.testing.assert_array_equal(base[:, j], updated[:, j]) + + +def test_select_propensity_factors_respects_candidates(): + A, X_A, names, cov, _ = _separating_design() + drops, report = select_propensity_factors( + A, X_A, candidates=['noise'], treatment_names=names, covariate_names=cov, + on_infeasible='all', random_state=0) + assert drops == {'sep_arm': ['noise']} + assert not report.set_index('treatment').loc['sep_arm', 'target_met'] + with pytest.raises(ValueError, match='candidate'): + select_propensity_factors(A, X_A, candidates=['missing'], + treatment_names=names, covariate_names=cov) + + +def test_select_propensity_factors_keeps_an_arm_that_already_meets_target(): + A, X_A, names, cov, _ = _separating_design() + drops, report = select_propensity_factors( + A, X_A, treatment_names=names, covariate_names=cov, + trigger={'auc_gt': 0.0}, random_state=0) # triggers every arm + assert 'benign_arm' not in drops + row = report.set_index('treatment').loc['benign_arm'] + assert row['target_met'] and row['n_dropped'] == 0 and row['n_fits'] == 1 + + +def test_select_propensity_factors_on_infeasible(): + A, X_A, names, cov, _ = _separating_design() + # Only the driver may go, and no fit reaches AUC < 0: the target is infeasible. + common = dict(candidates=['driver'], treatment_names=names, covariate_names=cov, + target={'overlap_ratio_gt': 0.3, 'auc_lt': 0.0}, random_state=0) + drops, report = select_propensity_factors(A, X_A, on_infeasible='none', **common) + assert 'sep_arm' not in drops + row = report.set_index('treatment').loc['sep_arm'] + assert row['n_dropped'] == 0 and row['n_fits'] == 2 and not row['target_met'] + # 'best' keeps the fit meeting more conditions: without the driver, overlap recovers + drops, report = select_propensity_factors(A, X_A, **common) + assert drops == {'sep_arm': ['driver']} + row = report.set_index('treatment').loc['sep_arm'] + assert row['overlap_ratio_chosen'] > 0.3 and not row['target_met'] + with pytest.raises(ValueError, match='on_infeasible'): + select_propensity_factors(A, X_A, on_infeasible='bad', **common) + + +def test_select_propensity_factors_ranks_imbalance_on_masked_cells(): + rng = np.random.default_rng(1) + n = 400 + A = np.zeros((n, 1)) + A[:40, 0] = 1 + ctrl_out = np.arange(n) >= 200 # controls outside the mask + shifted = rng.normal(0, 1, n) + shifted[ctrl_out] += 10.0 # imbalanced only through excluded controls + driver = rng.normal(0, 1, n) + driver[:40] += 0.8 + X_A = np.column_stack([np.ones(n), shifted, driver]) + cov = ['intercept', 'shifted', 'driver'] + common = dict(treatment_names=['arm'], covariate_names=cov, trigger={'auc_gt': 0.0}, + target={'auc_lt': 0.0}, on_infeasible='all', random_state=0) + drops, _ = select_propensity_factors(A, X_A, **common) + assert drops['arm'][0] == 'shifted' + drops, _ = select_propensity_factors(A, X_A, mask=~ctrl_out[:, None], **common) + assert drops['arm'][0] == 'driver' diff --git a/tests/test_small_arm_inference.py b/tests/test_small_arm_inference.py index b11407a..b9283af 100644 --- a/tests/test_small_arm_inference.py +++ b/tests/test_small_arm_inference.py @@ -1,8 +1,5 @@ """ -Stage 0 tests for the 0.1.0 inference fix. - -They pin the behaviour that the SCARF investigation showed was wrong before -0.1.0: +Inference for small perturbation arms that share a large control pool: (a) the reported standard error matches the estimator's true sampling SD under an oracle outcome model, for rare and common treatments; @@ -15,6 +12,8 @@ propensity weighting; (f) the prevalence-aware clip does not clip calibrated scores of a rare treatment wholesale, and the support columns report raw counts. +(g) size factors follow sequencing depth, so highly expressed genes do not + shift together when random arms differ in average depth. """ import warnings @@ -281,7 +280,7 @@ def test_auto_expression_threshold_scales_with_smaller_arm(): with warnings.catch_warnings(): warnings.simplefilter('ignore') df, _ = LFC(Y, np.ones((n, 1)), A, family='poisson', offset=False, backend='fast', - thres_diff=0) # isolate thres_min from the mean-difference filter + thres_diff=0) sparse = df.iloc[10:] tested = np.isfinite(sparse['std']).mean() assert (tested > 0.5) == expect_tested, f'n1={n1}: tested fraction {tested:.2f}' @@ -318,3 +317,101 @@ def test_threshold_uses_observed_support_not_model_means(): assert df.loc[2, 'estimable'] and df.loc[2, 'var_floored'], 'complete knockout must stay estimable at the floor' assert df.loc[2, 'padj'] < 0.05 and df.loc[2, 'tau'] < -3 assert df.loc[3, 'padj'] < 0.05 and df.loc[3, 'tau'] < -3 + + +def test_equal_arm_means_are_tested_and_counted_in_bh(): + """Genes whose arm means barely differ stay in the BH family. + + Excluding them (the former ``|mean_1 - mean_0| < thres_diff`` filter) + selects on the effect estimate: it drops exactly the p-values near 1, so + BH over the remaining genes becomes anti-conservative. + """ + rng = np.random.default_rng(11) + n0, n1, p = 2000, 200, 40 + n = n0 + n1 + A = np.r_[np.zeros(n0), np.ones(n1)] + Y = _nb_counts(rng, np.full((n, p), 2.0), 4.0) + Y[:, 0] = 2.0 # identical arm means + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + df, _ = LFC(Y, np.ones((n, 1)), A, family='poisson', offset=False, + backend='fast', ps_clip=None) + assert np.isfinite(df['std']).all() + assert df.loc[0, 'tau'] == pytest.approx(0.0, abs=1e-12) + assert df.loc[0, 'pvalue'] == pytest.approx(1.0, abs=1e-6) + # every gene is part of the BH family + assert df['padj'].notna().all() + + +# --------------------------------------------------------------------------- +# (g) size factors follow sequencing depth +# --------------------------------------------------------------------------- + +def test_default_size_factors_track_depth_and_resist_composition(): + """The default median of ratios uses well-expressed genes: it follows + sequencing depth in sparse data and ignores a shift in a minority of genes.""" + from causarray import comp_size_factor + rng = np.random.default_rng(12) + n = 400 + depth = np.exp(rng.normal(0, 0.35, n)) + mu = np.r_[np.full(150, 5.0), np.full(850, 0.2)] + Y = rng.poisson(depth[:, None] * mu[None, :]).astype(float) + log_depth = np.log(depth) - np.log(depth).mean() + assert np.corrcoef(np.log(comp_size_factor(Y)), log_depth)[0, 1] > 0.95 + assert np.corrcoef(np.log(comp_size_factor(Y, min_mean=0)), log_depth)[0, 1] < 0.9 + # a 4x increase in 20% of the expressed genes in half the cells + Y2 = Y.copy() + Y2[:n // 2, :30] = rng.poisson(4 * depth[:n // 2, None] * 5.0) + shift = np.log(comp_size_factor(Y2))[:n // 2].mean() - np.log(comp_size_factor(Y2))[n // 2:].mean() + libsize_shift = (np.log(comp_size_factor(Y2, method='libsize'))[:n // 2].mean() + - np.log(comp_size_factor(Y2, method='libsize'))[n // 2:].mean()) + assert abs(shift) < abs(libsize_shift) + Y2[3] = 0.0 + assert comp_size_factor(Y2)[3] == 0.0 + + +def test_size_factor_of_a_cell_without_well_expressed_counts(): + """A cell with counts only in sparse genes gets a size factor in line + with its total count, not one near the median cell.""" + from causarray import comp_size_factor + rng = np.random.default_rng(12) + n = 400 + depth = np.exp(rng.normal(0, 0.35, n)) + mu = np.r_[np.full(150, 5.0), np.full(850, 0.2)] + Y = rng.poisson(depth[:, None] * mu[None, :]).astype(float) + Y[0] = 0.0 + Y[0, 150:155] = 1.0 # 5 counts, all in sparse genes + sf = comp_size_factor(Y) + rel_total = Y[0].sum() / np.median(Y.sum(axis=1)) + assert sf[0] == pytest.approx(rel_total * np.median(sf), rel=0.25) + assert sf[0] < 0.05 + scale = comp_size_factor(Y, method='scale', lib_size=1e3) + assert scale.shape == (n,) + np.testing.assert_allclose(Y.sum(axis=1) / scale, 1e3) + + +def test_highly_expressed_genes_do_not_shift_with_arm_depth(): + """Random arms differ in average depth by chance. With median-of-ratios + size factors over every gene, most of that depth stays in highly expressed + genes and moves all of them together, one shared shift per arm; the + default, which uses well-expressed genes only, removes it.""" + from causarray import comp_size_factor + rng = np.random.default_rng(0) + n0, n1, a, n_high = 1500, 130, 12, 300 + n = n0 + n1 * a + depth = np.exp(rng.normal(0, 0.35, n)) + mu = np.r_[np.full(n_high, 5.0), np.full(700, 0.3)] + Y = rng.poisson(depth[:, None] * mu[None, :] * rng.gamma(10, 0.1, (n, mu.size))).astype(float) + A = np.zeros((n, a)) + for k in range(a): + A[n0 + k * n1:n0 + (k + 1) * n1, k] = 1 + shift = {} + for label, min_mean in (('default', 2.0), ('all genes', 0.0)): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + df, _ = LFC(Y, np.ones((n, 1)), A, np.ones((n, 1)), family='nb', backend='fast', + offset=np.log(comp_size_factor(Y, min_mean=min_mean))) + z = df['stat'].to_numpy().reshape(a, -1)[:, :n_high] + shift[label] = np.nanstd(np.nanmean(z, axis=1)) + assert shift['default'] < 0.2, shift + assert shift['all genes'] > 2 * shift['default'], shift