From dc4d3f8db5e780badf358f4931773a6071dae8b0 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Fri, 25 Sep 2026 22:55:22 +0800 Subject: [PATCH 01/16] Stop filtering LFC tests on the mean difference; release 0.1.1 LFC excluded every gene whose two arm means differed by less than thres_diff before the BH correction. That filter selects on the effect estimate: the pairs it removes are the ones with p-values near 1, so BH over the remaining pairs was anti-conservative. On a Perturb-seq negative control (fake perturbations drawn from controls) it removed about 10% of the expressed pairs; keeping them flattened the null p-value histogram and lowered the null SD of the statistics from 1.09 to 1.04. thres_diff is now only the floor before the logarithm. The expression threshold (thres_min / min_counts) is unchanged: it looks at expression level, not at the effect. Docstrings and the LFC guide drop version-history prose and references to an analysis that is not in the repository; the versionchanged directives keep the history. Co-Authored-By: Claude Opus 5.5 --- causarray/DR_estimation.py | 12 ++--- causarray/DR_learner.py | 72 ++++++++++++--------------- causarray/__about__.py | 2 +- docs/CHANGELOG.md | 10 ++++ docs/source/main_function/lfc.rst | 65 +++++++++++------------- tests/test_DR_learner.py | 4 +- tests/test_inference_comprehensive.py | 4 +- tests/test_propensity.py | 2 +- tests/test_small_arm_inference.py | 31 ++++++++++-- 9 files changed, 111 insertions(+), 91 deletions(-) diff --git a/causarray/DR_estimation.py b/causarray/DR_estimation.py index 4989618..1ca097c 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 ------- @@ -761,9 +761,9 @@ 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..f1e7ead 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) @@ -435,15 +434,11 @@ 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 accepted as a deprecated alias of ``'pooled'``. The + by-arm Welch formula ``s₀²/n₀ + s₁²/n₁`` it used to select is not the + variance of an estimator that averages pseudo-outcomes over all cells: + it doubles the standard error for equal arms and inflates it far more + for rare treatments. Neither estimator models within-donor or within-subject correlation. Repeated cells from the same biological unit should still be @@ -473,8 +468,13 @@ 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 +504,7 @@ 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 +536,7 @@ 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 @@ -551,16 +551,15 @@ 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 @@ -571,17 +570,16 @@ def LFC( """ 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.', + 'eps_var is deprecated and ignored; 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.", + "usevar='unequal' is deprecated and treated as 'pooled': the by-arm Welch " + "formula is not the variance of the AIPW estimator (2x the correct SE for " + "equal arms, far larger for rare treatments). It will raise in a future " + "release.", FutureWarning, stacklevel=2, ) usevar = 'pooled' @@ -595,7 +593,7 @@ 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 @@ -664,11 +662,10 @@ 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 +1019,7 @@ 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/docs/CHANGELOG.md b/docs/CHANGELOG.md index b103766..d6f00e5 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -1,5 +1,15 @@ # 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. + ## [0.1.0] Inference fix for small perturbation arms, and the GLM engine moves to crispyx diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index e6b6513..cbc767a 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 +61,20 @@ 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. +* **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='unequal'`` is a deprecated alias of ``'pooled'``. The by-arm Welch +formula ``s0²/n0 + s1²/n1`` it used to select is not the variance of an +estimator that averages pseudo-outcomes over all cells: it doubles the standard +error for equal arms and inflates it far more for rare treatments. 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 +85,20 @@ 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 +112,12 @@ 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 ---------------------------------------- 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_inference_comprehensive.py b/tests/test_inference_comprehensive.py index 3928c04..2b29889 100644 --- a/tests/test_inference_comprehensive.py +++ b/tests/test_inference_comprehensive.py @@ -446,13 +446,13 @@ 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) ---- + # ---- I13: 'unequal' is a deprecated alias of 'pooled' ---- 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 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"): + with pytest.warns(FutureWarning, match="deprecated and treated as 'pooled'"): df_alias, _ = LFC(Y, W, A[:, None], family='nb', offset=True, usevar='unequal', backend='fast') pd.testing.assert_frame_equal(df_alias, df_pooled) diff --git a/tests/test_propensity.py b/tests/test_propensity.py index fb8e186..dfff260 100644 --- a/tests/test_propensity.py +++ b/tests/test_propensity.py @@ -29,7 +29,7 @@ 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') diff --git a/tests/test_small_arm_inference.py b/tests/test_small_arm_inference.py index b11407a..59b7991 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; @@ -281,7 +278,7 @@ 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 +315,27 @@ 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() From 0f095675ff8015f1cd95b5431d0e9c99942802bf Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Fri, 25 Sep 2026 22:55:25 +0800 Subject: [PATCH 02/16] Replogle: fit scripts write the nuisances; remove a local folder 3_run_batch.py no longer asserts version 0.1.0 and writes the nuisance file that the propensity refit reads. The README lists the refit script and which results file the notebook reads. A local analysis folder that .gitignore already excluded was still tracked; it is removed from the repository. Co-Authored-By: Claude Opus 5.5 --- .gitignore | 4 +- docs/source/tutorial/SCARF/SCARF-py.ipynb | 3396 ----------------- docs/source/tutorial/SCARF/prep_scarf_data.py | 125 - docs/source/tutorial/SCARF/scarf-r.csv | 7 - docs/source/tutorial/replogle/3_run_batch.py | 7 +- docs/source/tutorial/replogle/README.md | 16 +- 6 files changed, 16 insertions(+), 3539 deletions(-) delete mode 100644 docs/source/tutorial/SCARF/SCARF-py.ipynb delete mode 100644 docs/source/tutorial/SCARF/prep_scarf_data.py delete mode 100644 docs/source/tutorial/SCARF/scarf-r.csv diff --git a/.gitignore b/.gitignore index 2a62cae..4139ff2 100644 --- a/.gitignore +++ b/.gitignore @@ -190,7 +190,5 @@ docs/source/tutorial/replogle/results/** # 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. +# Local analysis, not part of the documentation. /docs/source/tutorial/mouse_brain/ 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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- "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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x40bh+StcoHz58iX16dOHOnbsKLwvWVlZtHXrVjI3N6fXr18L4frpp5/IyclJ9IVBcdLT00lXV1co6EVGRpKJiQm9fv2ajI2Nhev57bffSE9PjzIyMujBgwdkZWVF06dPp+joaLp69Sq1a9eOgoKCRHFYOPPt06cPOTg40N69eyk+Pp6OHTtGO3bsIKL/PZdNmzal0NBQioyMpBYtWhSbDm/fvp2kUimdOnVK5TaaNsJHRUURAFEhhSnH+e7/qMt3f//9d9qzZw/FxcXRtWvXqEuXLuTp6Ul5eXlKw6esEV5dPKnKo9Wd++LFi8IXX8nJyULevWbNGrKzs6M9e/bQnTt3aM+ePWRhYUEbN24kooL0wtraWkh39+/fT7Vr19aoEd7Y2Ji+//57io2NFfIaeT4ij2/FMsjevXtJV1eXVq5cSbGxsbRo0SLS1tamEydOiPaztLSktWvXUmxsLH311Vekra1NUVFRRESUkZFBdevWpeHDh1NERARFRUVR//79ydXVVagMyStwo0aNoujoaNq/f78ozb906RJpa2vTtm3bKDExka5evUo//fSTEIbC6d3Tp0+pRo0aNHfuXCG9JiIKDg6mzp07i+KlR48eNHjwYJXxpkiTZ0JdnOXn55Ovry81b96cLl++TP/++y81aNBA1FCg2KhZUc9FWRrhiYgaNmxIX331VZFl06dPJ6KCRqGZM2fSxYsX6c6dO0JevnPnTmF7VemIYrlZXZqhLs8nKvhyRF9fn5YuXUqxsbF08eJFjcuymlKXRk+YMIHs7e3p0KFDFBkZSUOGDCFzc3PhHIrp0AcffEDt2rWjiIgIoXwnz3fHjh1LPj4+dOnSJUpISKB//vmH/vrrLyIqeO/s7e2FesDx48epVq1aQucUeViLe+8UqSrPKz6vixcvJhMTE9q+fTvFxMTQF198Qbq6ukJ5RtOwFZe/vM04H/8fdXWUp0+fUnBwMPn5+Ql5pbJlROrrBcWVGTStF0dERJBEIhHKGRMnTiQrKyvq3bs3ERXULYyMjIQvnTSpS8jj2M3NjQ4cOECxsbHUq1cvcnR0FDXOFbZ//36SyWSiuuz+/ftJX1+fUlNTS1z3rSrXefnyZdLS0qK5c+dSbGwsbdiwgWQymegZUtc2oKp8paikdXJVz4imdbLi6vIpKSnCu5KcnEwpKSlKw5ycnEz169enzz//nJKTk4X7OW/ePDp37hwlJCTQX3/9RdWqVaMff/xR2O/AgQOkra1NM2fOpKioKAoPD6dvv/2WiFSXmTS5F/KOngsWLKBbt27RrVu3aMGCBeTg4ECnT5+mxMREOnPmTJEOhoUtWbKE3NzcRMs0KTeuXLmSTE1NKTExkf777z+ysLAQlRlK206h7n4W926pa1s4cuQImZiY0MaNGyk+Pp6OHj1KTk5ONHv2bCIqSKM9PDyoVatWdO3aNTp16hT5+vqqbYQfOXIkOTo60rFjx+jGjRvUo0cPMjY2FpXnytpuQqS+LKrJvf/iiy+oVatWKq9FGW6EV6BYiLhw4QJZWlpSnz59iOh/iZu8oYeo4EWXyWRCAVzeq6Jwo2N0dDQBoAsXLig97/z588nCwkLUw3jIkCFUrVo10bdbhRVXiFC0a9cuoSAk5+rqSnv37lW5z8cff0wBAQEq12vaCD9x4kTRNrNmzSJdXV1RYnz8+HGhka6wOnXq0C+//CLsp9i7KyQkhJo2bSp81qSyFRsbq/L+yK9Hk/Bcu3ZNZa+Cwtzd3UWFw5I0whMpb/S7cuVKiRr45GFV7E0SGBhI9erVEzWuTp06lerVq6fyWPKCgDyT1KRRsnPnzvT555+Lztu8eXPRNo0bN6apU6cSUUFPai0tLYqNjVV6vEGDBtEnn3wiWnbmzBnS0tKizMxMje6xMkOGDCELCwvKyMgQlv38889kZGQkNDQFBgaSj4+PaL8vv/ySXF1dRfG4cuXKIvsVF9e3b98miURC//33n+jYbdq0ESr/8rSlcO/TlStXUrVq1YioIC0CQKGhoUqvT7FSqazS9Pr1a7KwsBA1KPj4+AiZqab8/f2Fe7Ry5Uqhoahjx45CZXjYsGHUokULIiL6+uuvqX379qJj3Lt3T/gCikj8fsvvseIvTeQK94SXO3jwIAGgzMxMpfvk5ubS0KFDCQDZ2tpS9+7dafny5aKeDPLjGhoaiv5cXFxEx0pNTS32XrD/4Xz3f9Tlu4rklRx5I4MmjfDq4klZHl2ac8s5ODgUKbDOmzeP/Pz8iIjol19+UZruatII37FjR9Gyvn37ir4kVlYG8ff3p+DgYNGy3r17ixqzAdCoUaNE2zRt2lRojFm3bl2RND8rK4tkMhn9/fffRFTwLDk6OooaPXr37i309NuzZw+ZmJio7LGuWJ5RVua6cOECaWtrC/nG48ePSVdXt0TpjibPhLo4O3r0KGlra4vKGJGRkULDJlHR/KeingtljfC6urqi9Hry5Mkq91+8eDHVrl1b+CzPayIjI1XuM2bMGNGvGFSlI+p+jaeYZqjL84mI7O3tacaMGUqPp0lZVhPFpdHyL90L997Lzs4me3t7mj9/PhEVTYc8PT1Vlim6dOlCw4YNU7puzZo1ZG5uLur5evDgQdLS0hJ+daHuvVNGWXle8Xm1t7cXGnjkGjduTGPGjClR2IrLX95mnI//j7o6ChHRZ599VqQ3s+IyTeoF6soMmtSL8/PzycrKin7//XciKijzf//992RjY0NERGFhYaSjoyPU+zSpS8jjWP7rVKL/5QmKvzSSy87OJisrK9GvrD7++GOhkbykdd+qcp39+/endu3aifYLCQkhd3d34XNJ2wZUKW2dXPEZ0bROVlxdXtV1KaPYfqTM/PnzqWHDhsJnPz8/GjBggMrtleW3mtwLR0dH6t69u2ib8ePHU+vWrTXujPbZZ59R69atRcs0KTcSFXxJ3aJFC2rTpg21a9dOtH1p2ynU3c/i3i11bQstWrQo0jN88+bNZGdnR0QFbTra2tp07949Yf3hw4eLfTbS0tJIV1dX9EvPFy9ekIGBgfCslke7CZH6sqgm917eWbEkeEx4JQ4cOAAjIyPo6+vDz88PLVu2xPLly0Xb+Pn5Cf+3sLCAq6uraCwgHR0dNGrUSPjs5uYGMzMzpeMFbd++HbNnz8bOnTuFMYXlPD09oaenV+JrOHnyJNq1a4fq1avD2NgYgwcPxtOnT0UT5MTExKBHjx4qjxEeHo42bdqU+NyKCseDnKOjI6ytrYXPV65cQXp6OiwtLWFkZCT8JSQkID4+XtjOyclJNEaqnZ0dUlJSShSe6OholfenpOFRlJGRgS+++ALu7u4wMzODkZERYmJikJSUVKIwquPt7Y02bdrA09MTvXv3xtq1a/H8+XOV22dmZgIA9PX1i6xr1qyZaNxBPz8/3Lp1C3l5eQCAa9euoVu3bnB0dISxsTFatWoFACqvKS8vD99++y28vLyE+Dt69GiR7b28vESfC9/L8PBw1KhRAy4uLkrPceXKFWzcuFF0bzp06ID8/HwkJCRodI9V8fb2Fo0H6ufnh/T0dNy7d09YpvhMR0dHw8/PTxSPAQEBSE9Px/3794VlxcX11atXQURwcXERXdepU6dEz5yBgQHq1KmjNN4sLCwwdOhQdOjQAV26dMFPP/2E5ORktddcmFQqxcCBA7F+/XoABffi+vXrGDp0aImOExQUhNDQUAAF477Ln5vAwEDR8tatWwMouKcnT54UXbubmxsAKH3nwsPDoa2tjcDAwGLDUfg5s7OzAwCVaYa2tjY2bNiA+/fvY/78+bC3t8e3336L+vXrF4nHM2fOIDw8XPhTnBhMJpMBAF69elVs+FgBzncLqMt34+Pj0b9/f9SuXRsmJiaoVasWANXpsTKaxJNiHl3acz9+/Bj37t3DiBEjRO/2N998I7zX0dHRStNdTShu5+fnV+R+K0uvFSfKCggIKLJfcce+cuUKbt++DWNjY+GaLCws8Pr1a1F6Vb9+fWhrawufC6fX7dq1g6OjI2rXro1BgwZh69atJU4vmjRpgvr162PTpk0AgM2bN6NmzZpo2bJliY6j7plQF2fR0dFwcHCAg4ODsF5eDlL2/lX0c6FowIABovR6+vTpKrft168f7t69i3///RcAsHXrVvj4+MDd3V3YZvXq1WjUqBGsra1hZGSEtWvXFnkPNElHNEkzisvzU1JS8ODBA5VpRmnLssqoSqPj4+ORk5Mjej50dXXRpEkTlWOkTpgwAd988w0CAgIwa9YsRERECOtGjx6NHTt2wMfHB1988QXCwsKEdfJnwtDQUFgWEBCA/Px8xMbGCsuKe+9KIy0tDQ8ePFD7DmgSttLmL28DzscLqKujaEqTekF51NUlEglatmyJ0NBQvHjxApGRkRg1ahTy8vIQHR2N0NBQNGjQAEZGRiU+dknK4Lq6uujduze2bt0KoKBO/eeff2LAgAEASl73rSrXqSr/LFzXriia1skVaVonK64uX1a///47mjdvDltbWxgZGeHrr78Whbs0z76m90Kx3Dh06FCEh4fD1dUVEyZMwNGjR4s9T2ZmZpE2F03LjevXr0dERASuXr2KjRs3FpkbojTtFOruZ3Hvlrq2hStXrmDu3LmiY8vn4Xn16hWio6NRs2ZN1KhRQxTm4ty5cwc5OTlo0qSJsMzU1BSurq7C5/JoN9GkLKrJvZfJZCUuv7+5WYneIkFBQfj555+hq6sLe3t76OrqarSf4kuibEIVxWU7d+7EiBEjsHv3brRt27bI9oULc5q6e/cuOnfujFGjRmHevHmwsLDA2bNnMWLEiBJNSiZvRFJFS0sLRCRapuz4yq5BcVl+fj7s7OyExrnCCjecKt4LiURSZMJEdeRhLm7CG03DoygkJAR///03Fi5cCGdnZ8hkMvTq1Us0KYZEItEo3oqjra2Nf/75B2FhYTh69CiWL1+OGTNm4MKFC0LDSGFWVlYAgOfPnxdpWClORkYG2rdvj/bt22PLli2wtrZGUlISOnTooHKij0WLFmHJkiVYunQpPD09YWhoiIkTJxbZvrh7qe7Zy8/Px6effooJEyYUWVezZk2hwlOekxoVPpbi80tERc6lyXNWWH5+PrS1tXHlyhVR5RGAqECoLN4KP08bNmzAhAkTcOTIEezcuRNfffUV/vnnHzRr1kyjcADAyJEj4ePjg/v372P9+vVo06YNHB0dNd4fKEhHv/32W/z33384deqUMDlXYGAgli9fjqSkJCQkJAiTsubn56NLly748ccfixxLXqAtTN0zIlc4vuT3Ql2aUb16dQwaNAiDBg3CN998AxcXF6xevRpz5swRtqlVq1axacGzZ88AoETv2/uM890C6p7rLl26wMHBAWvXroW9vT3y8/Ph4eFR4omX1MWTsjgozbnl79ratWvRtGlT0Tp5OqeYH5aV4rUpuxZl6bUmaXXhNKRhw4ZCY0Fhhd/54vI5Y2NjXL16FaGhoTh69ChmzpyJ2bNn49KlSxp9YSw3cuRIrFixAtOmTcOGDRswbNiwUuV96p6J4uJMVfypWv6mnwtTU1M4OztrtK2dnR2CgoKwbds2NGvWDNu3b8enn34qrN+1axcmTZqERYsWwc/PD8bGxliwYAEuXLggOo66dETTNKO4PF+TslJpyrLKqEqj5ZXxkrxTI0eORIcOHXDw4EEcPXoU33//PRYtWoTx48ejU6dOuHv3Lg4ePIhjx46hTZs2GDt2LBYuXFjsMQsvL4+6grpzAJq9A4r7lSZ/eVtwPl5AXR2lJMdRVy/QtCysTqtWrbBmzRqcOXMG3t7eMDMzQ8uWLXHq1ClRR5qSKmkZfMCAAQgMDERKSgr++ecf6Ovro1OnTgBKXvetKtdZXB2x8D5lbRtQRtM6uSJN62QVldb++++/6NevH+bMmYMOHTrA1NQUO3bswKJFi4RtSvPsa3IvgKLpR4MGDZCQkIDDhw/j2LFj6NOnD9q2bYvff/9d6XmsrKxw48YN0TJNy43Xr19HRkYGtLS08PDhQ9jb22t0bcXlM+rup7p3q7i2hfz8fMyZMwc9e/Yscmx9fX2l8auujKqqDaXwscqj3USTsqgm9/7Zs2clru9zT3glDA0N4ezsDEdHR5UFCHkPGaCgYTMuLk74RgkAcnNzcfnyZeFzbGwsXrx4Idpm+/btGDp0KLZt24YPPvig3MJ/+fJl5ObmYtGiRWjWrBlcXFzw4MGDEh/Hy8sLx48fV7ne2tpa9E1YWlpaib7hL6xBgwZ4+PAhdHR04OzsLPqTNyBrQk9PT+23yvXq1VN5f8oanjNnzmDo0KHo0aMHPD09YWtri8TERNE2ivF269atYr89k/fkULwuiUSCgIAAzJkzB9euXYOenh727dun9Bh16tSBiYkJoqKiiqwr/CzLP9etWxfa2tqIiYnBkydP8MMPP6BFixZwc3NT+w33mTNn0K1bNwwcOBDe3t6oXbs2bt26Vew+iry8vHD//n3ExcUpXd+gQQNERkYWuTfOzs7Q09PT6B6rcv36deGXA0BBfBgZGYm+wVXk7u6OsLAwUeYQFhYGY2NjVK9eXXSswgrHta+vL/Ly8pCSklLkmmxtbdWGuzBfX19Mnz4dYWFh8PDwwLZt25Rup+p98fT0RKNGjbB27Vps27YNw4cPL9H5AcDf3x9SqRSrVq1CZmYmGjZsCKDg2/nU1FT88ssv0NfXF74ckN9TJyenItevrDLl6emJ/Px8nDp1qsRhKwlzc3PY2dmJekFp4ubNm9DV1UX9+vUrKGTvFs53CxSX7z59+hTR0dH46quv0KZNG9SrV69EvcDkNImn0pxbWV5VrVo1VK9eHXfu3CnyXssrze7u7krTXU0oS1OLuw6goAxw9uxZ0bKwsDDUq1dP42M3aNAAt27dgo2NTZHrMjU11SjsQEGvz7Zt22L+/PmIiIhAYmIiTpw4oXRbVen1wIEDkZSUhGXLliEyMhJDhgzR+Pxy6p4JdXHm7u6OpKQkUU+sqKgopKamFolXoOKfi7IaMGAAdu7cifPnzyM+Ph79+vUT1p05cwb+/v4YM2YMfH194ezsXOJe5UD5pBnGxsZwcnJSmWaUV9kaUJ1Gy8tdhZ+PnJwcXL58Wem9l3NwcMCoUaOwd+9efP7551i7dq2wztraGkOHDsWWLVuwdOlSrFmzBkDBMxEeHi7Kj8+dOwctLS2Vv5wsDyYmJrC3t1f7DlRG2KoSzscLqKujaEqTeoG6urom9WKgoHE6MjISv//+u+iXq8eOHUNYWFixvzrV9Bya8Pf3h4ODA3bu3ImtW7eid+/eojgrSd1Xmcq4Tnd3d6Vph4uLi9DQp65tQFVbgDqa1MmVXVdJ62Sq6OrqlirOzp07B0dHR8yYMQONGjVC3bp1cffuXdE2pXn2NbkXqpiYmKBv375Yu3Ytdu7ciT179ggdrhT5+voiJiZG1C6gSbnx2bNnGDp0KGbMmIFhw4ZhwIABojIQULp2Ck3up7p3S1XbQoMGDRAbG6s0vdPS0hLKh4XT0vPnzxcb13Xq1IGuri4uXrwoLEtLSxM9u+XRbqJJWRRQf+9v3rwJX19fjc4px43wpTR37lwcP34cN2/exNChQ2FlZYXu3bsL63V1dTF+/HhcuHABV69exbBhw9CsWTPhZxXbt2/H4MGDhYz+4cOHePjwIVJTU9We+9mzZwgPDxcaVGNjYxEeHo6HDx8CKHhwc3NzsXz5cty5cwebN2/G6tWrixzHzc2t2Ixr+vTpuHTpEsaMGYOIiAjExMTg559/xpMnTwAArVu3xubNm3HmzBncvHkTQ4YMUZuAqdK2bVv4+fmhe/fu+Pvvv5GYmIiwsDB89dVXosKYOk5OTrhw4QISExPx5MkTpd/Gurq6omPHjggODsaFCxdw5coVjBw5UvSNamnD4+zsjL179wpDePTv379IGFq3bo0VK1bg6tWruHz5MkaNGlVsbxFHR0dIJBIcOHAAjx8/Rnp6Oi5cuIDvvvsOly9fRlJSEvbu3YvHjx+rrPBoaWmhbdu2RTIeALh37x4mT56M2NhYbN++HcuXL8dnn30GoKDHhp6envAs/fXXX5g3b57KsMrjQP5tanR0ND799FPh2dRUYGAgWrZsiY8++gj//POP8A3kkSNHAABTp07F+fPnMXbsWISHh+PWrVv466+/MH78eACa3WNVsrOzMWLECERFReHw4cOYNWsWxo0bBy0t1cnlmDFjcO/ePYwfPx4xMTH4888/MWvWLEyePFm0X3Fx7eLiggEDBmDw4MHYu3cvEhIScOnSJfz44484dOiQRvGWkJCA6dOn4/z587h79y6OHj2KuLg4lc+Fk5MTIiIiEBsbiydPnoh6XYwcORI//PAD8vLyiv3ZrSoymQxNmzbF8uXLERAQIKQNurq68PPzw/Lly4WGegAYO3Ysnj17ho8//hgXL17EnTt3cPToUQwfPlxpAc7JyQlDhgzB8OHD8ccffyAhIQGhoaHYtWtXicMq98svv2D06NE4evQo4uPjERkZialTpyIyMhJdunQRbZuSkiKk2/K/wvF35swZtGjRotx6KTHOd83NzWFpaYk1a9bg9u3bOHHiBCZPnlySKNQonpTR5Nw2NjaQyWQ4cuQIHj16JMTr7Nmz8f333+Onn35CXFwcbty4gQ0bNmDx4sUAgP79+0NLS0tIdw8dOoSFCxdqdC3nzp3D/PnzERcXh5UrV2L37t1CmqpKSEgINm7ciNWrV+PWrVtYvHgx9u7dK/xaR2737t1Yv3494uLiMGvWLFy8eBHjxo0DUNBIa2VlhW7duuHMmTNISEjAqVOn8Nlnn4mGICvOgQMHsGzZMoSHh+Pu3bvYtGkT8vPzRT+3LczJyQmnT5/Gf//9J5TDgIJ707NnT4SEhKB9+/bFVsRUUfdMqIuztm3bwsvLCwMGDMDVq1dx8eJFDB48GIGBgUqHJAQq9rkoq549eyItLQ2jR49GUFCQ6Mt0Z2dnXL58GX///Tfi4uLw9ddf49KlSyU+h6ZphjqzZ8/GokWLsGzZMty6dQtXr14VhgApr7J1cQwNDTF69GiEhITgyJEjiIqKQnBwMF69eoURI0Yo3WfixIn4+++/kZCQgKtXr+LEiRNCOWXmzJn4888/cfv2bURGRuLAgQPCugEDBkBfXx9DhgzBzZs3cfLkSYwfPx6DBg1CtWrVyuV6VAkJCcGPP/6InTt3IjY2FtOmTUN4eLiQ3lRm2N4m70M+rq6OoilN6gXq6uqa1IsBwMPDA5aWlti6davQON2qVSv88ccfyMzMRPPmzVWGs7i6RElJJBL0798fq1evxj///IOBAwcK69TVffft26f2S/jKuM7PP/8cx48fx7x58xAXF4fffvsNK1asEJU51LUNqCpfqaNJnVzZM1LSOpkq8i+JHz58WKJOI87OzkhKSsKOHTsQHx+PZcuWFXnnZs2ahe3bt2PWrFmIjo7GjRs3MH/+fNG5FctMmtwLZZYsWYIdO3YgJiYGcXFx2L17N2xtbVX+oiwoKAgZGRmIjIwUlmlSbhw1ahQcHBzw1VdfYfHixSCiImErTTuFuvtZ3Lulrm1h5syZ2LRpE2bPno3IyEhER0cLveWBgnKIq6srBg8ejOvXr+PMmTOYMWNGsfFtbGyMIUOGICQkBCdPnkRkZCSGDx8OLS0toXd8ebSbAOrLoprc+zNnzqB9+/YanxMAeGJWBcom3ShMPuHF/v37qX79+qSnp0eNGzem8PBwYZsNGzaQqakp7dmzh2rXrk16enrUunVr0WQHgYGBBKDI35AhQ9SGRT7JgOJf4QktFi9eTHZ2diSTyahDhw60adOmIhN1QM3s7kREoaGh5O/vT1KplMzMzKhDhw7CMVJTU6lPnz5kYmJCDg4OtHHjRqUTsypOuqA40YlcWloajR8/nuzt7UlXV5ccHBxowIABwkRfyvZbsmQJOTo6Cp9jY2OpWbNmJJPJip00NTk5mT744AOSSqVUs2ZN2rRpU5EJPNSFR9lETgkJCRQUFEQymYwcHBxoxYoVRSY8+e+//6h9+/ZkaGhIdevWpUOHDqmdfGXu3Llka2tLEomEhgwZQlFRUdShQweytrYmqVRKLi4uoslflTly5AhVr15dmLiDqOA5HDNmDI0aNYpMTEzI3Nycpk2bJpp8Ytu2beTk5ERSqZT8/Pzor7/+Knbiv6dPn1K3bt3IyMiIbGxs6KuvvqLBgweLnmVlk8B069ZN9Pw/ffqUhg0bRpaWlqSvr08eHh504MABYf3FixepXbt2ZGRkRIaGhuTl5SWaNEuTe6xI/s7NnDmTLC0tycjIiEaOHCma1EzVJEehoaHUuHFj0tPTI1tbW5o6dSrl5OSUKK6zs7Np5syZ5OTkRLq6umRra0s9evSgiIgIIvpf2lLYvn37SJ6UP3z4kLp37052dnakp6dHjo6ONHPmTOGeK75DKSkpQhwCoJMnTwrrXr58SQYGBsKkY4XNmjVL9N6pMmvWLAJAP/zwg2j5vHnzCADNmzdPtDwuLo569OhBZmZmJJPJyM3NjSZOnCjEkWLcZ2Zm0qRJk4TrdXZ2pvXr1xOR8smJ1E2mfPXqVRo4cCDVqlWLpFIpWVpaUsuWLemvv/4StpEfV9nf+fPnhe1cXFxo+/btauOIcb6rqLh8959//qF69eqRVColLy8vCg0NFeWzmkzMqi6eVOXR6s5NRLR27VpycHAgLS0t0QRzW7duJR8fH9LT0yNzc3Nq2bKlaGK78+fPk7e3N+np6ZGPjw/t2bNHo4lZ58yZQ3369CEDAwOqVq0aLV26VLSNsjIIEdGqVauodu3apKurSy4uLqKJ4OT7rVy5ktq1a0dSqZQcHR2LvM/Jyck0ePBgsrKyIqlUSrVr16bg4GBhImdlz1LhiffOnDlDgYGBZG5uTjKZjLy8vEQTYiumd+fPnycvLy+SSqVCmi93/PhxAkC7du0qcq2BgYGiZ1yRJs+EJnF29+5d6tq1KxkaGpKxsTH17t1bmJCSSPlzVRHPhbKJWdVNTKhM7969CYCQp8i9fv2ahg4dSqampmRmZkajR4+madOmqZ3Aj6joRHHq0gx1eb7c6tWrydXVlXR1dcnOzo7Gjx8vrFNXlpWnGYXzf0Xq0ujMzEwaP3688C4EBAQIE/ISFU2Hxo0bR3Xq1CGpVErW1tY0aNAgevLkCREVlA/q1atHMpmMLCwsqFu3bnTnzh3hWBERERQUFET6+vpkYWFBwcHBwkSKqsKqbBLMwjSZmDUvL4/mzJlD1atXJ11dXfL29qbDhw+LjlOasL0rOB8XU1dH0WRiViL19QKi4ssMmtaLiYg++ugj0tbWFvKw/Px8srCwoEaNGom206Quoawu+/z5c7VpDdH/JjZ1dHQU1ZHU1X3l91edyrjO33//ndzd3UlXV5dq1qxJCxYsEJ1LXdsAkeryVWGlqZOrekZKWicjKlqX/+uvv8jZ2Zl0dHSKrTcqm5g1JCREqIv37duXlixZUiQ/3LNnj1CGsLKyop49ewrrVJWZ1N0LZW0Fa9asIR8fHzI0NCQTExNq06YNXb16VeX1EBH169ePpk2bJlpWXLnxt99+I0NDQ4qLixO2v3z5Munp6dHBgweJqGztFMXdz+LeLXVtC0QFbUz+/v4kk8nIxMSEmjRpQmvWrBHWx8bGUvPmzUlPT49cXFzoyJEjKsvncmlpadS/f38yMDAgW1tbWrx4MTVp0kQUp2VtN5Erriyq7t6HhYWRmZkZvXr1SuW1KCMhKufBON9xoaGhCAoKwvPnz1V++7Vx40ZMnDhRo6EvGHtTiAjNmjXDxIkT8fHHH1d2cKqkoUOH4sWLF/jjjz/K/ditWrWCj48Pli5dWu7Hrgj37t2Dk5MTLl26hAYNGojWySdp3bhx45sP2Fvg4MGDCAkJQUREBHR0eOqVsuJ8lynj5OSEiRMnYuLEieV+bIlEgn379ol6aFZlW7duxWeffYYHDx4UGe7AyckJs2fPVjm5Nr8777fQ0FD06NEDd+7cgbm5eWUHh72jOB9njL1Pbty4gbZt2wqTsZaHimynqOoyMjJQvXp1LFq0SOWv7CpD79694evriy+//LJE+3HrAGPvCYlEgjVr1iAiIqKyg8KqsJycHCQnJ2PatGlo1qxZkQZ4ADh16hROnz5dCaF7O2RkZGDDhg3cAM8Yq1CvXr1CQkICvv/+e3z66adFGuBjYmJgbGyMwYMHV1IIWVV35MgRfPnll9wAzxhjjJUTT09PzJ8/H4mJifD09Kzs4Lx1rl27hpiYGDRp0gSpqamYO3cuAKBbt26VHLL/ycrKgre3NyZNmlTifbmFgLH3iLe3N7y9vSs7GKwKO3fuHIKCguDi4qJy1vfSTsD8vujTp09lB4Ex9h6YP38+vv32W7Rs2RLTp08vst7NzQ03btyohJCxt8UPP/xQ2UFgjDHG3jlDhgyp7CC81RYuXIjY2Fjo6emhYcOGOHPmTIknla9IUqlUGPu+pHg4GsYYY4wxxhhjjDHGGGOsgqieRpcxxhhjjDHGGGOMMcYYY2XCjfDsndaqVasyTdoWGhoKiURS7CRBs2fPho+PT6nPwd4OQ4cOfWsm6SsPFfVcJyYmQiKRIDw8vNyPzRh7d2mSdmzcuFHlpH9v2h9//AFnZ2doa2sL5RBly1j506TsVpne5HOqWA52cnJ6ayaIV/S+lcNY+eDnhr0Jimnr25zWVjSJRPJeTi6qqCxlgZI+XxVVLjp37hw8PT2hq6srpLPKljExboRnrIymTJmC48ePC5+5sFf1lMc9+emnn7Bx48ZyCU9F4C+DGGPvu759+yIuLk74XJnp4qeffopevXrh3r17mDdvnsplZVHVG5tZ1WuIuXTpEj755JPKDgZj76VTp06hYcOG0NfXR+3atbF69Wq1+xw/fhz+/v4wNjaGnZ0dpk6ditzcXGH97NmzIZFIivwZGhpW5KWUSWni4dKlS2jTpg3MzMxgbm6O9u3bi76Ul39Rr/h35MiRCryS4sNbXmltVezAVNXytjetsjt9VJW8fPLkyfDx8UFCQoLQTqJsWVlUxee/rLgRnmkkJydHo2XvIyMjI1haWlZ2MCpUdnZ2ZQeh0pmamlaZHpaFEZGoMF7VjvcmvI1hZqwyvOl35U2XE2QyGWxsbN7oOZVJT09HSkoKOnToAHt7exgbGytd9i7jckPVZG1tDQMDg8oOBmPvnYSEBHTu3BktWrTAtWvX8OWXX2LChAnYs2ePyn0iIiLQuXNndOzYEdeuXcOOHTvw119/Ydq0acI2U6ZMQXJysujP3d0dvXv3LlN4KyoNL008vHz5Eh06dEDNmjVx4cIFnD17FiYmJujQoUORcsaxY8dEcdG6desKuQ51OK1lFUH+XlaV5ys+Ph6tW7dGjRo1hHYSZcuYAmJvXF5eHv3www9Up04d0tPTIwcHB/rmm2+E9V988QXVrVuXZDIZ1apVi7766ivKzs4W1g8ZMoS6desmOuZnn31GgYGBwufdu3eTh4cH6evrk4WFBbVp04bS09OJiOjixYvUtm1bsrS0JBMTE2rZsiVduXJFdDwA9PPPP1PXrl3JwMCAZs6cSbNmzSJvb29at24d1apViyQSCeXn59Phw4cpICCATE1NycLCgj744AO6ffu2cKygoCAaO3as6PhPnjwhPT09On78uMp4unv3LnXt2pUMDQ3J2NiYevfuTQ8fPhTWy8OzadMmcnR0JBMTE+rbty+lpaUJ2wQGBtJnn31GRERz5swhDw+PIudp0KABff3110rDcPLkSQJAx44do4YNG5JMJiM/Pz+KiYkpEg75/wGI/k6ePElZWVk0duxYsrW1JalUSo6OjvTdd9+pvPaycnR0LBKOwq/7/fv3qU+fPmRmZkYWFhbUtWtXSkhIENbLn7HvvvuO7OzsyNHRkYiIIiIiKCgoSHiugoOD6eXLl8WG5ebNm9S5c2cyNjYmIyMjat68ufB85OXl0Zw5c6h69eqkp6dH3t7edPjwYWFfefw/f/5cWHbt2jUCIIR3w4YNZGpqSkeOHCE3NzcyNDSkDh060IMHD4io/O6J4nsXGBhI48ePp5CQEDI3N6dq1arRrFmzio0L+TFmz55N1tbWZGxsTJ988gllZWUJ2+Tn59OPP/5ItWrVIn19ffLy8qLdu3cXiZMjR45Qw4YNSVdXl9avX1/kGjds2EAJCQkEgK5duybs//z5cyEOVB3vxIkTwnO9evVqqlGjBslkMurVq5foXhARrV+/ntzc3EgqlZKrqyutXLlStP7ChQvk4+NDUqmUGjZsSHv37i0SJkWbN2+mhg0bkpGREVWrVo0+/vhjevToUbFxcOLECQoMDKRx48bRZ599RmZmZmRjY0O//PILpaen09ChQ8nIyIhq165Nhw4dKvY+MVYamqQJ6vI1RcrSL/n7TVS69OLEiRP0+vVrGj9+PFlbW5NUKqWAgAC6ePGi2mtUVz5RVU4AQKtXr6YPPviAZDIZubm5UVhYGN26dYsCAwPJwMCAmjVrJio7KJKnZ3v27KFWrVqRTCYjLy8vCgsLE7aR5wfy/6uKt1mzZpGDgwPp6emRnZ0djR8/Xu21F5aVlUUhISFkb29PBgYG1KRJkyJpqmKeo2wZEdG5c+eoRYsWpK+vTzVq1KDx48cL5TUiotevX1NISAjVqFGD9PT0yNnZmX799VchPgr/DRkyhIiKLwMqys3NpeHDh5OTkxPp6+uTi4sLLV26VLRNTk4OjR8/XijnffHFFzR48OAieeLYsWNp0qRJZGlpSS1btiQiosjISOrUqRMZGhqSjY0NDRw4kB4/fizsp+4ZVkbTPKJwfvX777+Tu7s76enpkaOjIy1cuFB0TEdHR/r2229p2LBhZGRkRA4ODvTLL7+Itjl37hx5e3sL+dm+ffuKzc8CAwOVlsPUlVvk1OWvitLT02nQoEFkaGhItra2tHDhQlE5WH6dS5YsET4vWrSIPDw8yMDAgGrUqEGjR48uUq5bs2aNUA7o3r07LVq0SHjP5FatWkW1a9cmXV1dcnFxoU2bNonWA6C1a9dS9+7dSSaTkbOzM/3555/Cek2eQ2X1H8aIik/zFJ8bdfmfPP04cOAAeXl5kVQqpSZNmlBERITonOrSbkVffPEFubm5iZZ9+umn1KxZM5X7TJ8+nRo1aiRatm/fPtLX1xfVdwsLDw8nAHT69GmVx1VGVRpe3koTD5cuXSIAlJSUJCyLiIggAEK5QVmdpzRu375NXbt2JRsbGzI0NKRGjRrRP//8I9rm0aNH9OGHH5K+vj45OTnRli1biqSthT9rUh979uwZ9e/fn6ysrEhfX5+cnZ1p/fr1RERF8hF5m4/82f7222/JxsaGTE1Nafbs2ZSTk0NTpkwhc3Nzql69Oq1bt04Ufk3bARYsWEC2trZkYWFBY8aMEcp7qvI2ZeLi4qhFixYklUqpXr16dPToUQJA+/btE7bRpH2huPywtHV6Vdcnvx+DBg0iMzMzkslk1LFjR4qLiyMi5eU8VW0A4eHh1KpVKzIyMiJjY2Nq0KABXbp0iYg0KwuoapNRfN7U5bGK5aJXr15R586dqWnTpvT06VOlYS+ufKasDFpcuVtdWbC49lFVz//JkyepcePGZGBgQKampuTv70+JiYkq73tVwo3wleCLL74gc3Nz2rhxI92+fZvOnDlDa9euFdbPmzePzp07RwkJCfTXX39RtWrV6McffxTWq2uEf/DgAeno6NDixYspISGBIiIiaOXKlUJidvz4cdq8eTNFRUVRVFQUjRgxgqpVqybKzAGQjY0NrVu3juLj4ykxMZFmzZolJA5Xr16l69evU35+Pv3++++0Z88eiouLo2vXrlGXLl3I09OT8vLyiIho69atZG5uTq9fvxaO/9NPP5GTkxPl5+crjaP8/Hzy9fWl5s2b0+XLl+nff/+lBg0aiL5omDVrFhkZGVHPnj3pxo0bdPr0abK1taUvv/xS2KZw5ePevXukpaUlKmhdv36dJBIJxcfHKw2HPMFq2rQphYaGUmRkJLVo0YL8/f1F4ZA3wr98+ZL69OlDHTt2pOTkZEpOTqasrCxasGABOTg40OnTpykxMZHOnDlD27ZtU3pOIqItW7aQoaFhsX9btmxRuX9KSopw/vv371OzZs2oRYsWRESUkZFBdevWpeHDh1NERARFRUVR//79ydXVVWgMHjJkCBkZGdGgQYPo5s2bdOPGDcrIyCB7e3shvo8fP061atUSKv3K3L9/nywsLKhnz5506dIlio2NpfXr1wtfYixevJhMTExo+/btFBMTQ1988QXp6uoWyeTUNcLr6upS27Zt6dKlS3TlyhWqV68e9e/fv1zvibJGeBMTE5o9ezbFxcXRb7/9RhKJhI4ePVrsMYyMjKhv37508+ZNOnDgAFlbW4ue2S+//JLc3NzoyJEjFB8fTxs2bCCpVEqhoaGiOPHy8qKjR4/S7du36f79+/T5559T/fr1hWt89epViRrhCx/vyZMnwvveunVrunbtGp06dYqcnZ2FeCUqqJjb2dnRnj176M6dO7Rnzx6ysLCgjRs3ElFBg4C1tbVwvfv376fatWurLSSvW7eODh06RPHx8XT+/Hlq1qwZderUSVivKsyBgYFkbGxM8+bNo7i4OJo3bx5paWlRp06daM2aNRQXF0ejR48mS0tLysjIUHl+xkpDXZqgSb6m6OXLl8I7nZycTAsXLiQDAwO6ceMGEZUuvXjy5AlNmDCB7O3t6dChQxQZGUlDhgwhc3NzlQVxOXXlE1XlBABUvXp12rlzJ8XGxlL37t3JycmJWrduTUeOHKGoqChq1qwZdezYUeW55emZm5sbHThwgGJjY6lXr17k6OhIOTk5RCRuhH/16pXSdHH37t1kYmJChw4dort379KFCxdozZo1omuQV3JU6d+/P/n7+9Pp06fp9u3btGDBApJKpRQXF0dZWVkUGxsrfGEgz3OULYuIiCAjIyNasmQJxcXF0blz58jX15eGDh0qnKtPnz7k4OBAe/fupfj4eDp27Bjt2LGDcnNzac+ePQSAYmNjKTk5mV68eKG2DKgoOzubZs6cSRcvXqQ7d+7Qli1byMDAgHbu3Cls880335CFhQXt3buXoqOjadSoUWRiYlIkTzQyMqKQkBCKiYmh6OhoevDgAVlZWdH06dMpOjqarl69Su3ataOgoCBhP3XPsDKa5hHyssPly5dJS0uL5s6dS7GxsbRhwwaSyWRC5ZCooEJrYWFBK1eupFu3btH3339PWlpaFB0dTUREaWlpZGFhQQMHDqTIyEg6dOgQubi4FJufPX36lGrUqEFz584VnkEi9eUWIvX5qzKjR4+mGjVq0NGjRykiIoI+/PBDMjIyKrYRfsmSJXTixAm6c+cOHT9+nFxdXWn06NHC+rNnz5KWlhYtWLCAYmNjaeXKlWRhYSFqhN+7dy/p6urSypUrKTY2lhYtWkTa2tp04sQJYRsAVKNGDdq2bRvdunWLJkyYQEZGRkKao8lzyI3wTBl1aZ7ic6Mu/5OnH/IGQ/m75OTkJDTSaZJ2K2rRogVNmDBBtGzv3r2ko6MjavwrbPLkydS8eXPRsiNHjojK8YrGjRtHLi4uxcaZMsrScGXKWkctTTykpaWRlZUVzZo1i7KysujVq1f02WefUf369YX8X15GcHBwIGtra/L39y/yha58G1VxR1TQaLp69WqKiIiguLg4mjFjBunr69Pdu3eFbTp16kQeHh4UFhZGly9fJn9/f5LJZGVqhB87diz5+PjQpUuXKCEhgf755x/666+/iKigA6W8U2BycrLwrA4ZMoSMjY1p7NixFBMTQ+vWrSMA1KFDB/r222+FupCurq7wBYam7QAmJiY0atQoio6Opv3795OBgYFQVlKVtynKy8sjDw8PatWqlVCX9PX1FTXCa9K+oC4/LE2dvrjrIyLq2rUr1atXj06fPk3h4eHUoUMHcnZ2puzsbMrKyqKlS5eSiYmJcP2qylj169engQMHUnR0NMXFxdGuXbsoPDyciDQrCyhrk1F8vojU57GFy0UvXryg5s2bU9u2bYv94rC48llubi4lJyeTiYkJLV26lJKTkyk9Pb3IslevXmlUFiyufVTZ85+Tk0OmpqY0ZcoUun37NkVFRdHGjRtF72lVxo3wb1haWhpJpVJRo7s68+fPp4YNGwqf1TXCX7lyhQBo/E1Qbm4uGRsb0/79+4VlAGjixImi7WbNmkW6urqUkpJS7PFSUlIIgJBIvH79miwsLEQFaR8fH5o9e7bKYxw9epS0tbVF33hHRkYSAKERfdasWWRgYCD68iAkJISaNm0qfFbsAdSpUydR5WLixInUqlUrleEo3BNe7uDBgwSAMjMzhXDIG+GJlN+f8ePHU+vWrVV+6aAoLS2Nbt26Veyfqh4QiiZMmECOjo7CfVu3bh25urqKwpKVlUUymYz+/vtv4RqqVasm6qG9Zs0aMjc3FyXWBw8eJC0tLZU9OadPn061atVSWaiyt7enb7/9VrSscePGNGbMGCLSvBG+cE8IIqKVK1dStWrVhM/lcU+UNcIrFowbN25MU6dOLfYYFhYWogbgn3/+mYyMjCgvL4/S09NJX19f1LOTiGjEiBH08ccfE9H/4uSPP/4QbaP4HBJpVugr7nja2tp07949Ydnhw4dJS0tLKGw5ODgUKeTMmzeP/Pz8iIjol19+UXq9Je2pIs985QUcVWFWvCe5ublkaGhIgwYNEpYlJycTADp//rzG52dME+rSBE3yteKcP3+e9PX1hby0tOlFeno66erq0tatW4Vl2dnZZG9vT/Pnzy/RNSuWT1SVEwDQV199JboWAKLeWdu3byd9fX2V55KnZ7/++quwTB5/8saCwo3w8vAopouLFi0iFxcXlfnS8uXLqXXr1irDcfv2bZJIJPTff/+Jlrdp04amT59OREXTWVXLBg0aRJ988onoOGfOnCEtLS3KzMwUGu4Ve+HJKcsjS1oGVGbMmDH00UcfCZ+rVatGCxYsED7n5uZSzZo1i+SJPj4+ouN8/fXX1L59e9Gye/fuCV8caPIMa0JVHiGPl/79+1O7du1E+4SEhJC7u7vw2dHRkQYOHCh8zs/PJxsbG/r555+JqCDvsrS0FMp+RERr165Vm58pVpSJNCu3qMtfFb18+ZL09PRox44dwrKnT5+STCYrthFe0a5du8jS0lL43LdvX/rggw9E2wwYMED0nvn7+1NwcLBom969e1Pnzp2Fz4ppQHp6OkkkEtGvHxUpPofcCM+UUZfmFX5uNMn/5OmHsndJnv+qS7uVqVu3bpE6z7lz5whAkV/ByP3999+kpaVF27Zto9zcXLp//z41b96cAChtZHz9+jWZm5uLvhzXlLI0XJmy1lFLEw9EBb+srlOnDmlpaZGWlha5ubmJGtweP35MixcvpgsXLtClS5fo66+/Ji0tLdq8ebOwzf3798nV1ZUuXLig9joLc3d3p+XLlxMRCfnyv//+K6yPjo4mAGVqhO/SpQsNGzZM6flV9fIfMmQIOTo6Ch0fiYhcXV2FjndE/6sLbd++nYg0bwdwdHSk3NxcYZvevXtT3759lV6fKn///bfSumThRnhN2hfU5YelqdMXd31xcXEEgM6dOyesf/LkCclkMtq1axcRFS1vqmJsbKzyy3NN2zAU22SIlDfCF5fHytO1mJgY8vb2pp49exY5ZmGals9MTU1FHRqULVNXFlTXPqrs+X/69CkBKLbDRlWmA/ZGRUdHIysrC23atFG5ze+//46lS5fi9u3bSE9PR25uLkxMTDQ+h7e3N9q0aQNPT0906NAB7du3R69evWBubg4ASElJwcyZM3HixAk8evQIeXl5ePXqFZKSkkTHadSoUZFjOzo6wtraWrQsPj4eX3/9Nf799188efIE+fn5AICkpCR4eHhAKpVi4MCBWL9+Pfr06YPw8HBcv35dmBW7U6dOOHPmjHD8yMhIREdHw8HBAQ4ODsJ53N3dYWZmhujoaDRu3BhAwaQghcdVtbOzQ0pKisq4CQ4OxvDhw7F48WJoa2tj69atWLRokdo49fLyEp1DHo81a9ZUuy9QMDFou3bt4Orqio4dO+LDDz9E+/btVW5vbGxcLuPFrlmzBuvWrcO5c+eE+3blyhXcvn27yPFfv36N+Ph44bOnpyf09PSEz9HR0fD29hZN9BMQEID8/HzExsaiWrVqRc4fHh6OFi1aQFdXt8i6tLQ0PHjwAAEBAaLlAQEBuH79eomu08DAAHXq1BE+q3sOgJLfE2UKPxeantfb21s0hpufnx/S09Nx7949pKSk4PXr12jXrp1on+zsbPj6+oqWKXs/y0LZ8WrWrIkaNWqIwiq/39ra2rh37x5GjBiB4OBgYZvc3FyYmpoC+N8zo3i96ly7dg2zZ89GeHg4nj17JkpT3N3diw1z4Xuira0NS0tLeHp6Csvkz6m6+8RYaRSXJmiarymTlJSE7t27Y8qUKejTpw8AICoqqlTpRXx8PHJyckRpr66uLpo0aYLo6GgAwKhRo7BlyxZhfXp6OgDNyifKygmKcSN/DxXfzdevXyMtLa3YMo+q/NjNzU3lPoX17t0bS5cuRe3atdGxY0d07twZXbp0gY5OQZF43LhxGDdunMr9r169CiKCi4uLaHlWVlaJ54eR58dbt24VlhER8vPzkZCQgBs3bkBbWxuBgYEaH1NdGVCZ1atX49dff8Xdu3eRmZmJ7OxsYULb1NRUPHr0CE2aNBG219bWRsOGDYW0WU4xTb5y5QpOnjwJIyOjIueMj49Hamqqxs9wYZrmEXLR0dHo1q2baFlAQACWLl2KvLw8aGtrAxA/WxKJBLa2tsL7GxsbCy8vL+jr6wvbFI6Tkiqu3PL48WO1+aui+Ph4ZGdni/JYCwsLuLq6FhuOkydP4rvvvkNUVBTS0tKQm5uL169fIyMjA4aGhoiNjUWPHj1E+zRp0gQHDhwQPkdHRxeZIC4gIAA//fSTaFnh+DU0NISxsbEoLy7uOWRMlZKkeZrkf3LK3iX5NurS7nr16ikNq0QiEX0mIqXL5dq3b48FCxZg1KhRGDRoEKRSKb7++mucPXtWSLcK27t3L16+fInBgwcrPZ46mtQtyqOOWtJ4yMzMxPDhwxEQEIDt27cjLy8PCxcuROfOnXHp0iXIZDJYWVlh0qRJwj6NGjXC8+fPMX/+fAwcOBAAUL16dcTExBQbtoyMDMyZMwcHDhzAgwcPkJubi8zMTKGdJDo6Gjo6OqK4cnNzK/PY16NHj8ZHH32Eq1evon379ujevTv8/f3V7le/fn1oaf1vmsdq1arBw8ND+CyvC8nTWk3bAerXry96xuzs7HDjxo0SXVN0dLTSuqTiNsW1L2hpaanND0tTpy/u+uT3uGnTpsJ6S0tLURqgqcmTJ2PkyJHYvHkz2rZti969e4vyfk3aMBTbZFRRl8cCQNu2bdG4cWPs2rVLaRoiV5I6hjrqyoIvXrxQ2z6qyMLCAkOHDkWHDh3Qrl07tG3bFn369BHqBVUdN8K/YTKZrNj1//77L/r164c5c+agQ4cOMDU1xY4dO0QNxVpaWkJmJVd4UhJtbW38888/CAsLw9GjR7F8+XLMmDEDFy5cQK1atTB06FA8fvwYS5cuhaOjI6RSKfz8/IpMwKJsVnVly7p06QIHBwesXbsW9vb2yM/Ph4eHh+h4I0eOhI+PD+7fv4/169ejTZs2cHR0BAD8+uuvyMzMBAChsZaIlGbEissVG3clEkmRSqFiWKVSKfbt2wepVIqsrCx89NFHKrdXdh75+Ys7j6IGDRogISEBhw8fxrFjx9CnTx+0bdsWv//+u9Ltt27dik8//bTYY/7yyy8YMGCAyvWhoaEYP348tm/fDm9vb2F5fn4+GjZsKCo4yhVuOFG816ruCaC60KTueVe2b+HzyAsVhZ93ZRP9KXsOFN8RRSW9J8qU9PkrTuF9Dx48iOrVq4vWS6VS0Wdl76IiTeNP0+PJ70vhsK5du1ZUSAEgZOrq7oEyGRkZaN++Pdq3b48tW7bA2toaSUlJ6NChg0ZplLJ7Utb3lzFNFZcmaJqvKcrIyEDXrl3h5+eHuXPnCstLm16oquwWDsfcuXMxZcoU0XpNyieK5ypM2XtYmnezrO+zg4MDYmNj8c8//+DYsWMYM2YMFixYgFOnTin9wlhRfn4+tLW1ceXKlSIVGGUVDHXH+vTTTzFhwoQi62rWrInbt2+X6HiA+jKgol27dmHSpElYtGgR/Pz8YGxsjAULFuDChQui7VQ1mhSmeO/z8/PRpUsX/Pjjj0W2tbOzw82bNwFo9gzLlSSPKBxWTcJf0ve3NHlcceeSH0+T/FVRacJy9+5ddO7cGaNGjcK8efNgYWGBs2fPYsSIEUJZQdPrLi49kSsufjV9DhlTVJI0T5P8rziF85zi0m5lbG1t8fDhQ9GylJQU6OjoFPsF7uTJkzFp0iQkJyfD3NwciYmJmD59utL0/Ndff8WHH34IW1tbtdeijCZ1gbLWUUsTD9u2bUNiYiLOnz8v1G22bdsGc3Nz/Pnnn+jXr5/S/Zo1a4Zff/1V3SWJhISE4O+//8bChQvh7OwMmUyGXr16CfmLui8MlNGkPtapUyfcvXsXBw8exLFjx9CmTRuMHTsWCxcuLPbY6uo98mXytFbTdoDyqOOWNq9Qds7i8sPS1OnV5feqrqck9x0AZs+ejf79++PgwYM4fPgwZs2ahR07dghfbmvShqHJe6nqWIr37IMPPsCePXsQFRUl6gijqCR1DHXUlQXv3LlTouPJbdiwARMmTMCRI0ewc+dOfPXVV/jnn3/QrFmzUh3vTeJG+Desbt26kMlkOH78OEaOHFlk/blz5+Do6IgZM2YIy+7evSvaxtraWqi4yIWHhxepmAYEBCAgIAAzZ86Eo6Mj9u3bh8mTJ+PMmTNYtWoVOnfuDAC4d+8enjx5Uqrrefr0KaKjo/HLL7+gRYsWAICzZ88W2c7T0xONGjXC2rVrsW3bNixfvlxYp/hiAwW9A5OSknDv3j2h12BUVBRSU1NV9i7QhI6ODoYMGYINGzZAKpWiX79+5T6ztJ6eHvLy8oosNzExQd++fdG3b1/06tULHTt2xLNnz2BhYVFk265duxbJaBQp63kud/v2bXz00Uf48ssv0bNnT9G6Bg0aYOfOnbCxsSnRLyzc3d3x22+/Cb2jgILnVUtLq0iPQDkvLy/89ttvyMnJKZIxmJiYwN7eHmfPnkXLli2F5WFhYULvMnlhQF7wBAqe9ZIqj3tSXq5fv47MzEzhC4p///0XRkZGqFGjBszNzSGVSpGUlFSino+A8mssHH/yb61LEn9JSUl48OAB7O3tAUAo/Lq4uKBatWqoXr067ty5o7Kg7e7ujs2bNxe53uLExMTgyZMn+OGHH4R3//LlyxqHmbGqqjT5GhFh4MCByM/Px+bNm0WFf3d391KlF87OztDT08PZs2fRv39/AAWVwcuXL2PixIkAABsbG9jY2Ij206R8UtWoSvtlMhm6du2Krl27YuzYsXBzc8ONGzfQoEEDtcf09fVFXl4eUlJShHJPaTVo0ACRkZFwdnZWut7T0xP5+fk4deoU2rZtW2S9vGeU4jUWVwZUdObMGfj7+2PMmDHCssK94UxNTVGtWjVcvHhRuN68vDxcu3ZNbS/lBg0aYM+ePXBychJ+aVBYaZ7h0uQR7u7uRcqmYWFhcHFxKbYnWGFubm7YunUrsrKyhAqoJnmTqmewOJrkr4qcnZ2hq6uLf//9V2gEfP78OeLi4lTG7eXLl5Gbm4tFixYJjUS7du0SbePm5oaLFy8W2a+wevXq4ezZs6Let2FhYSUqr6t7DhkrjqZpnib5n5yyd0n+iyt1abcyfn5+2L9/v2jZ0aNH0ahRI7VfAEskEqEsvn37djg4OBTJrxISEnDy5En89ddfGoepNMpaRy1NPLx69QpaWlqiMpD8c3ENw9euXStxz9gzZ85g6NChQiNpeno6EhMThfX16tVDbm4uLl++LNRXY2Nj8eLFC5XH1LQ+Zm1tjaFDh2Lo0KFo0aIFQkJCsHDhQpV5fWmUth1AkSZ5m7zcq1iXVNymuPYFTfPD8qzTu7u7Izc3FxcuXBB+jfD06VPExcUJ+VpJ8nYXFxe4uLhg0qRJ+Pjjj7Fhw4YivzB7U3744QcYGRmhTZs2CA0NVfrrQaD0dQxl1JUF1bWPFvf8+/r6wtfXF9OnT4efnx+2bdvGjfCsKH19fUydOhVffPEF9PT0EBAQgMePHyMyMhIjRoyAs7MzkpKSsGPHDjRu3BgHDx7Evn37RMdo3bo1FixYgE2bNsHPzw9btmzBzZs3hUT9woULOH78ONq3bw8bGxtcuHABjx8/FhINZ2dnbN68GY0aNUJaWhpCQkI06rGsjLm5OSwtLbFmzRrY2dkhKSkJ06ZNU7rtyJEjMW7cOBgYGKhNeNq2bQsvLy8MGDAAS5cuRW5uLsaMGYPAwMAyD8MxcuRIIS7OnTtXpmMp4+TkhL///huxsbGwtLSEqakpVqxYATs7O/j4+EBLSwu7d++Gra2typ+uleWnfpmZmejSpQt8fHzwySefiHob2NraYsCAAViwYAG6deuGuXPnokaNGkhKSsLevXsREhIi+slYYQMGDMCsWbMwZMgQzJ49G48fP8b48eMxaNAglYWtcePGYfny5ejXrx+mT58OU1NT/Pvvv2jSpAlcXV0REhKCWbNmoU6dOvDx8cGGDRsQHh4ufDvv7OwMBwcHzJ49G9988w1u3bql0fBBisrjnpSX7OxsjBgxAl999RXu3r2LWbNmYdy4cdDS0oKxsTGmTJmCSZMmIT8/H82bN0daWhrCwsJgZGSEIUOGFHuNCQkJCA8PR40aNWBsbAyZTIZmzZrhhx9+gJOTE548eYKvvvpK47Dq6+tjyJAhWLhwIdLS0jBhwgT06dNH6GEze/ZsTJgwASYmJujUqROysrJw+fJlPH/+HJMnT0b//v0xY8YM4XoTExPV9uioWbMm9PT0sHz5cowaNQo3b97EvHnzNA4zY1VVafK12bNn49ixYzh69CjS09OFYWFMTU1LnV4YGhpi9OjRCAkJgYWFBWrWrIn58+fj1atXGDFihMrwa1I+qWqUpYvyn7I3bdoUBgYG2Lx5M2QymfDrPHVcXFwwYMAADB48GIsWLYKvry+ePHmCEydOwNPTU+jgoImpU6eiWbNmGDt2LIKDg2FoaIjo6Gj8888/WL58OZycnDBkyBAMHz4cy5Ytg7e3N+7evYuUlBT06dMHjo6OkEgkOHDgADp37gyZTIbIyMhiy4CKnJ2dsWnTJvz999+oVasWNm/ejEuXLol6WY4fPx7ff/89nJ2d4ebmhuXLl+P58+dqe4SNHTsWa9euxccff4yQkBBYWVnh9u3b2LFjB9auXVuqZ7g0ecTnn3+Oxo0bY968eejbty/Onz+PFStWYNWqVRrcpQLy/OyTTz7BtGnTkJSUJORnxcWDk5MTTp8+jX79+kEqlcLKykqj86nLXxUZGRlhxIgRCAkJgaWlJapVq4YZM2aIhilQVKdOHeTm5mL58uXo0qULzp07h9WrV4u2GT9+PFq2bInFixejS5cuOHHiBA4fPiy65pCQEPTp0wcNGjRAmzZtsH//fuzduxfHjh3T6FoBzZ5DxpRRV+8trCT539y5c0XvkpWVFbp37w5AfdqtzKhRo7BixQpMnjwZwcHBOH/+PNatW4ft27cL2+zbtw/Tp08XDZmyYMECdOzYEVpaWti7dy9++OEHpUNJrF+/HnZ2dujUqVNpo1IjZR2OpjTx0K5dO4SEhGDs2LEYP3488vPz8cMPP0BHRwdBQUEAgN9++w26urrw9fWFlpYW9u/fj2XLlol63/73339o06YNNm3apHI4MWdnZ+zduxddunSBRCLB119/LWrolw95EhwcjDVr1kBHRwcTJ04sti1Fk/rYzJkz0bBhQ9SvXx9ZWVk4cOCA8Azb2NhAJpPhyJEjqFGjBvT19VUOTaZOadsBFGmSt7Vt2xaurq5CeSktLU3UkUMeHnXtC+rywyVLlpRrnb5u3bro1q0bgoOD8csvv8DY2BjTpk1D9erVhaHtnJyckJ6ejuPHjwtDryp27MzMzERISAh69eqFWrVq4f79+7h06ZJGozBUpIULFyIvLw+tW7dGaGio0uEcy9ImoUhdWVBd+6iy5//Zs2dYs2YNunbtCnt7e8TGxiIuLq7UQ3G9cRU54DxTLi8vj7755htydHQkXV1dqlmzJn333XfC+pCQELK0tCQjIyPq27cvLVmypMjEDzNnzqRq1aqRqakpTZo0icaNGydMzBoVFUUdOnQga2trkkql5OLiIkwmQkR09epVatSoEUmlUqpbty7t3r1b6eQO8gkz5JRNcEZE9M8//1C9evVIKpWSl5cXhYaGKt3/5cuXZGBgIEy6qc7du3epa9euZGhoSMbGxtS7d2/RBKDKwrNkyRJydHQUPitOzCrXokUL0YRcqmgyMahiOFJSUqhdu3ZkZGQkTLiyZs0a8vHxIUNDQzIxMaE2bdrQ1atXNYmGEpNPXqHsTy45OZkGDx5MVlZWJJVKqXbt2hQcHEypqalEpHryq4iICAoKCiJ9fX2ysLCg4OBglbOBy12/fp3at29PBgYGZGxsTC1atKD4+HgiKngX5syZQ9WrVyddXV3y9vYuMknX2bNnydPTk/T19alFixa0e/fuIhOzKr4f+/btE11vedwTZROzKj5b3bp1E83mruoYM2fOFN7xkSNH0uvXr4Vt8vPz6aeffiJXV1fS1dUla2tr6tChA506dYqIlD+TRAWTMX300UdkZmZGAIQJUaKioqhZs2Ykk8nIx8eHjh49qnRiVsXjyZ/rVatWkb29Penr61PPnj3p2bNnou22bt1KPj4+pKenR+bm5tSyZUvau3evsP78+fPk7e1Nenp65OPjQ3v27FE7kd22bdvIycmJpFIp+fn50V9//SXaR1WYld0TZRMHKUufGCsrTdIEdfmasmMqS8vl73dp04vMzEwaP368kAcEBARoNDmsuvKJqnKC4junbJIlVWEtbh/Fic0U8wNl6eK+ffuoadOmZGJiQoaGhtSsWTPR5OuzZs0SlSOUyc7OppkzZ5KTkxPp6uqSra0t9ejRgyIiIpSGS9UyooJJReX5k6GhIXl5eYkmrcvMzKRJkyaRnZ0d6enpkbOzM61fv15YP3fuXLK1tSWJREJDhgxRWwZU9Pr1axo6dCiZmpqSmZkZjR49mqZNmya6jzk5OTRu3DgyMTEhc3Nzmjp1KvXu3Zv69esnbKOqvBUXF0c9evQgMzMzkslk5ObmRhMnThQmUFP3DCtTmjzi999/J3d3d6HcXXiiWSLleYW3tzfNmjVL+Hzu3Dny8vIiPT09atiwIW3btk2Y6EyV8+fPk5eXF0mlUqFcokm5hUh9/qro5cuXNHDgQDIwMKBq1arR/Pnzi9wXxetcvHgx2dnZkUwmow4dOtCmTZuKxN2aNWuoevXqJJPJqHv37vTNN9+Qra2t6NyrVq2i2rVrk66uLrm4uNCmTZtE65Xlu4Unb9PkOeSJWZky6tI8xedGXf4nTz/2799P9evXJz09PWrcuDGFh4eLzqsu7VYmNDSUfH19SU9Pj5ycnISJn+XkEzUWFhQURKampqSvr09NmzalQ4cOFTluXl4e1ahRg7788kul55Xnn4r5T2Gq0vCKUJp4OHr0KAUEBJCpqSmZm5tT69at6fz588L6jRs3Ur169YT6ZsOGDUWTshJpFg8JCQkUFBREMpmMHBwcaMWKFUXiJjk5mT744AOSSqVUs2ZN2rRpU5G0VfGzuvrYvHnzqF69eiSTycjCwoK6detGd+7cEfZfu3YtOTg4kJaWltDmoyxN1KQuVJp2gM8++0w4L5HyvE2Z2NhYat68Oenp6ZGLiwsdOXKkSH6gSftCcflhWev0yq7v2bNnNGjQIDI1NRXyx7i4ONE+o0aNIktLSwIgKivIZWVlUb9+/cjBwYH09PTI3t6exo0bJ0zerElZQFW+p0nbXeE8Vlm5aPz48WRnZ0exsbFFjk+kWflMk4lZidSXBdW1jyo+/w8fPqTu3bsLZWNHR0eaOXOmaJLiqkxCVIYBDRkrgXv37sHJyQmXLl3S6CffFYWI4Obmhk8//VRpbyLGKsrQoUPx4sULYVJixhhjVcvQoUMBABs3bqzUcFRl+fn5qFevHvr06fNe/1Jp69atGDZsGFJTU0v9i9K3UXBwMGJiYnDmzJnKDgpj5S40NBRBQUF4/vx5hf869k0JDQ1Fjx49cOfOnWIn6Wblx87ODvPmzVM6vAZj7P3Gw9GwCpeTk4Pk5GRMmzYNzZo1q9QG+JSUFGzevBn//fcfhg0bVmnhYIwxxljVc+rUKZw+fbqyg1Gl3L17F0ePHkVgYCCysrKwYsUKJCQkCGMqvy82bdqE2rVro3r16rh+/TqmTp2KPn36vPMN8AsXLkS7du1gaGiIw4cP47fffivRUD6Mscp15MgRfPnll9wA/wa8evUK586dw6NHj1C/fv3KDg5jrAriRnhW4c6dO4egoCC4uLgUO0v1m1CtWjVYWVlhzZo1XBBhjDHGmEhCQkJlB6HK0dLSwsaNGzFlyhQQETw8PHDs2LESTbz5Lnj48CFmzpyJhw8fws7ODr1798a3335b2cGqcBcvXsT8+fPx8uVL1K5dG8uWLePenYy9RX744YfKDsJ7Y82aNZg3bx4mTpwIPz+/yg4OY6wK4uFoGGOMMcYYY4wxxhhjjLEKolXZAWCMMcYYY4wxxhhjjDHG3lXcCM8YY4wxxhhjjDHGGGOMVRBuhGeMMcYYY4wxxhhjjDHGKgg3wjPGGGOMMcYYY4wxxhhjFeS9aoQnIqSlpYHnomWMMcbeTpyXM8YYY28/zs8ZY4y9b96rRviXL1/C1NQUL1++rOygMMbYW+/PP/+s7CCw9xDn5YwxVr44P2eVgfNzxhhjZXEl8RUOhqfiSuKrMh3nyJEjOHLkSDmFqng6b+QsjDHG3jlZWVmVHQTGGGOMlRHn54wxxhh729Sx1hP9W1KvXr2CgYEBmjdvDgMDg/IMmkrvVU94xhhjjDHGGGOMMcbeFi8ycnEl8RVeZORWdlAYq1AledbNDHXQ0MkAZoYl719+//597Ny5E48fP4aRkRG0tN5M8zj3hGeMMcYYY4wxxhhjrAqKf5yNhy9yAAANS9HgyNjb4k0863FxcTh79ixatGgBa2vrCjmHKvz2MsYYY4wxxhhjjDFWBZV12A3G3hZlfdZfZOQi/nE26ljrKe0hn5qaivPnz6NDhw6oXr16mcJaGtwIzxhjjDHGGGOMMcZYFWRmqMM94Nl7oazPuqqe9Pn5+UhNTYW5uTn69esHqVRa5rCWBr/FjDHGGGOMMcYYY4wxxqqEFxm5uJ6UCZIAPg4yjcZ+V9aTPicnB8eOHUNOTg66dOlSaQ3wADfCM8YYY4wxxhhjjDHGGKsg6oaKURT/OBs3/nsNEMFYX1ujHvKKPelfvXqFI0eOQE9PDx07doREIinTNZQVN8IzxhhjjDHGGGOMMcYYqxAlnXS1jrUe0jPzQJLSjxF/48YNmJmZITAwENra2qU6RnniRnjGGGOMMcYYY4wxxhhjFaKkk66aGeogsJ5xqc6VkpICU1NTNG7cGBKJpNJ7wMtpVXYAWFGzZ89GcHCw8Dk0NBQSiQTnzp0Tlo0YMQJz587F5cuXMWDAAABAYmIirKys3nh4K5pEIkF6enqJ91u2bBl++OEHAAVx06pVK5iamqJRo0ai7W7cuIGWLVvCzc0Nnp6e+OSTT5CVlVXkeMOHDy8SlgkTJsDJyQkSiQQ3b94Ubd+rVy+EhYWVONyMMcbefpyXi5VHXn7ixAk0bdoU7u7u8PDwwIwZM0BEAID09HR06NABVlZWReIvKioKPj4+wp+TkxMsLCwAAESEFi1aICEhoYxXyBhjjDHGGFPFzFAHDZ0MNBqKpizu3LmDAwcOIDk5GVpaWlWmAR7gRvgqKSgoCCdPnhQ+h4aGomnTpkWWBQUFoVGjRti6dWtlBLNKy8zMxJIlSzBu3DgAgImJCb755hts27atyLb6+vpYsWIFYmJiEB4ejtTUVCxatEi0zf79+5W+uL169cLZs2fh6OhYZN2XX36JL7/8spyuiDHG2NuE8/KyU8zLzc3NsX37dkRFReHy5cs4deoUtm/fDgDQ1dXFF198gWPHjhU5jru7O8LDw4W/Dz/8UPjSQyKRYNKkSZgzZ86buzDGGGOMMcZYuYuIiMCpU6fQpk0bODk5VXZwiuBG+CqoWbNmePDgAe7fvw+goJI+c+ZMhIaGAgDu3buH5ORkNG3aFKGhoUV6dsudP38eLVq0gLe3N7y8vPDnn38CAC5fvgw/Pz94eXmhSZMmQq+84OBgjB8/HgDw7Nkz1KlTB2fPngUAzJ8/H/Xr14enpycGDBiA1NRUAAU9/fr3748uXbrA3d0drVu3xrNnz5SGR9V55b3+Zs6ciYYNG8LZ2RmHDh0qsv/u3bvRoUMH4XNeXh4cHR0RFRVVZNvff/8dzZs3h5GREQDAwsICzZs3h6GhYZFt69atCy8vLwCAtrY2GjdujDt37gjrnz59ijlz5mDx4sVF9m3ZsiVq1Kih9HobNGiAhw8f4tatW0rXM8YYe3dxXl7+ebmvry9q164NoOALdB8fHyG/lkqlaNOmDczMzJSGWy4rKwvbtm3DiBEjhGVdunTBoUOH8PLly2L3ZYwxxhhjjFVNOTk5SEhIwAcffKC0o2xVwI3wZZSbR0hJy0FuHpXbMaVSKfz8/HDy5ElkZWXh/v376Ny5M+7evYvs7GycPHkSAQEB0NNTPY7Ss2fP0KNHD/z444+4fv06wsPD0aJFC2RnZ6Nnz56YPXs2IiIisHjxYvTq1QsZGRlYvnw5zp49i927d2PIkCEIDg5G8+bNcfjwYWzYsAHnzp3DjRs3YGhoKOrhfeHCBfz222+IioqCjY0NfvnllyLhKe68QEFDd8OGDXHlyhWsWLECkyZNKnKMnj17IjY2VmjU/uOPP+Ds7Ax3d/ci24aGhsLf37/EcZ+RkYFff/0VXbp0EZaNHTsWs2fPhqmpaYmP5+/vj+PHj5d4P8YYY28O5+VvX17+8OFD/P777+jcuXPxN0LB3r17UatWLfj4+AjLdHV14eHhIRoqiDHGGGOMMVb15ebmIjY2Fjo6OujatStsbGwqO0gqcSN8GeTmEdaffopVx59g/emn5Vp5DwoKQmhoKM6fP4+mTZsCABo3bowLFy4IP18vzvnz5+Hu7i5UXrW0tGBhYYHY2Fjo6ekJvdCaN28OGxsbREREQF9fH7t378bIkSORk5ODqVOnAgCOHTuGAQMGCL3LRo8eLfq5d6dOnYSxVf38/BAfH18kPMWdFwAMDQ3RrVu3Yo+hra2NMWPGYNWqVQCAFStWCD9RV3T//n3Y2toWG0eKcnJy0LdvX7Rv314Iy+7du6Gnp4cPP/ywRMeSs7W1FXpBMsYYq3o4Ly/wNuXlaWlp6NKlC7744gs0aNCg2DhUtH79elEveDnOrxljjDHGGHu7vH79GgcPHkRMTAzy8vKq1PjvynAjfBk8y8jFgxc5AIAHL3LwPCO33I4tH0s2NDQUgYGBAIDAwECcPHkSJ0+eVFtxV4WIlD6U8mUxMTEwNDRESkoKsrOzVe5T+LO+vr7wf21tbeTmFo0HdedVPEZeXp7S8AcHB2PXrl24ePEi7ty5g65duyrdzsDAAJmZmUrXKZOTk4M+ffrAzs4OP/30k7D85MmTOHHiBJycnITxpOrXr48bN25odNzXr19DJpNpHA7G3iZEhNTU58jJya7soDBWapyXF3hb8vKXL1+iY8eO6Nq1KyZPnqx0P1Xu3r2LsLAw9O/fv8g6zq/Z+ygnJxupqc+FCY4ZY4wxxt4WaWlp+PPPPyGTyfDBBx9AR6diJ3wtD9wIXwYWhjqwN9MFANib6cK8HGf4bdKkCVJSUrBt2za0atUKANCqVSts3boVjx8/Vjl2rJy/vz+io6MRFhYGAMjPz8ezZ8/g5uaGrKwsnDhxAgAQFhaGlJQUeHp6IikpCWPGjMGxY8fQpEkTTJw4EQDQrl077NixQxgrdc2aNWjbtm2Jrqe485aEubk5unTpgo8++gijRo2Ctra20u28vLwQExOj0TFzc3PRr18/WFhYYM2aNaIGhlWrVuH+/ftITExEYmIiACAyMlLjcEdHR8Pb21ujbRmr6hSH7CDKR9br13j1KqOSQ8ZY6XFerrnKzsvT09PRsWNHdOjQAV9//XWJzgkAGzZsQI8ePZSOG8/5NXsfvXqVgazXr0GUX9lBYYwxxhgrkdTUVDg4OKBt27ZvRQM8ALwdoayidLQlGN7SEs8zcmFuqAMd7fL72YOuri4CAgIQEREBFxcXAICrqyvS0tLQvHlz6OrqFru/ubk59u3bh88//xwvX76ERCLBvHnz0LVrV+zZswcTJkxARkaG8LN1qVSKfv36Yd68eXB3d8fSpUvh5+eHnTt3om/fvrhx4wb8/PwgkUjg5eUl/IxcU3p6ekrPa2hoiMePH5foWMHBwdi4cSNGjhypcptevXph+PDhmDt3LoCCidjq1KmDrKwspKamokaNGhg0aBC+//577Ny5E3v37oWXlxd8fX0BAAEBAVi5cqXasIwdOxZ//vknHj58iLZt28LIyAi3b98GUDC+fGRkJFq3bl2i62OsKpIP2fHgRQ7szXQxvKUlJBItSPX1YWBQdMJjxt4WnJdrrrLz8p9++gkXL15ERkYG9u3bBwDo3bs3ZsyYAaBgQvTk5GQ8f/4cNWrUQFBQEDZv3gygoBf/xo0bsWHDhiLnkX/J7uHhUaJrYOxtJ8+/JRLul8UYY4yxt0NSUhK0tLTg4OAABwcHYfmLjFzEP85GHWs9mJVjx6ryJKH36PeHaWlpMDU1RWpqKkxMTCo7OKyU5s+fj9jYWKxbt67Y7Tp37ow5c+agcePGbyhkYqtXr8Z///2HefPmVcr5GStPKWk5WHX8ifB5bBsrnDyyD3369KnEULH3Eefl74aqlJdPmzYNdevWVTpWPGPvg127dnF+zt44zs8ZY4yVVHR0NM6fP49WrVqhdu3aonVXEl/h4Ysc2JrpoqGTQSWFsHhV86sBxlSoX78+JBIJjhw5onbbZcuWaTwkTUXQ0tLCtGnTKu38jJUn+ZAd8p7w5TlkB2Ps/VLV8nJ7e3sMGzasQs/BGGOMMcYYKx0iwuXLlxEVFYVOnTrBzs6uyDZ1rPVE/1ZF3BOeMcaYRnLzSDRkB/ecY5WB83LGGCtfnJ+zysD5OWOMMU0REa5cuYI6derA3Ny8soNTajwAIGOMMY3oaEtgbaJbrmNmM8YYY4wxxhhj7O3xIiMXVxJf4UVGboWeJzs7G6dPn0ZWVhYaNWr0VjfAAzwcDWOMMcYYY4wxxhhjjDENxD/OxsMXOQCAhhU0VG1GRgYOHz4MAwMDaGm9G33IuRGeMcYYY4wxxhhjjDHGKtmLjFzEP85GHWs9mFXRudgqevz1Fy9e4ODBg6hRowZatGjxzjTCvzVXMXv2bEgkEtGfra1tZQeLMcYYY4wxxhhjjDHGykzeyzz+cXZlB0UlM0MdNHQyqJAvCV5k5CI2heBSzxMtW7Z8ZxrggbeoER4A6tevj+TkZOHvxo0blR0kVklatWqFAwcOlHi/8PBwfPDBB8LnXr16wd7eHhKJBOnp6cLyFy9ewMfHR/hzcXGBjo4Onj17JuwXFhZW9gthjDHG3lPllZdv2bIFXl5e8PHxga+vLw4fPiysmzBhApycnCCRSHDz5k3RcYgIs2fPhouLCzw8PNCqVSth3eeff47t27eX/KIYY4wxxhgrgzrWerA1062wXuZVhbJx5W/fvo3QM2F48koLWhbOkEjerfnoqubvGlTQ0dHh3u+VID8/HwBE3z7l5uZCR+etenwAAF9++SWmTZsmfB41ahRWrVqFatWqibYzMzNDeHi48HnhwoU4deoULCwshONMnjwZoaGhbyLYjDHGWJm8q3n5s2fPMGbMGMTGxsLOzg5nz55Fz549kZKSAqDgS/MvvvgCzZs3L3KcZcuW4caNG7h58yb09PSQnJwsrJs6dSpatGiBvn37vlO9bxhjjDHGWNVmZqhTYeOsl4fCw+UAKPXQOYXHlW9goI3r16/j2rVr8GsehEe5Wnj4PBuhr/Pg4yCrssPylNRbVau4desW7O3tUatWLfTr1w937twpdvusrCykpaWJ/t4W58+fR4sWLeDt7Q0vLy/8+eefAICQkBA0btwYPj4+CAwMxK1btwAAiYmJsLKyEvZPT08XvjHKzMxE37594e7uDm9vb7Rv3x4A8PDhQwQFBaFhw4aoX78+JkyYACICUDD8z6BBg9CzZ0/4+Pjg/PnzsLKywty5c9GiRQssX74cx48fh5+fH3x9feHh4YENGzYAAC5duoR69eoJxwIAPz8/Uc80uUePHqFHjx7w9PSEh4cH1qxZI6xzcnLCnDlz4O/vj1q1auGbb74psv+DBw9QrVo1vHr1Slj28ccf4+effy6ybVJSEiIjI9GyZUthWdu2bWFjY6PudmDDhg0YMWKE8LlBgwZ4+PChEP+MMcaYIs7LKz4vz8/PBxEJv2Z78eIFatSoIWzfsmVL0efCFixYgB9//BF6egUVCDs7O2GdjY0NatWqhePHjyvdlzHGGGOMsfdR4eFyVA2do6yXuyIrQ22kZubBylAbFy9exI0bN9ClSxe41XWCkUwbCU9zcPNeZpUelqek3pqvEpo2bYpNmzbBxcUFjx49wjfffAN/f39ERkbC0tJS6T7ff/895syZU6Hhorxc5Kc+gZapFSTa5ROdz549Q48ePbB37174+/sjPz8fL168AFDQM2vBggUAgB07dmDSpElqf8p95MgRPH/+HFFRUcLxgYLe3vv374eRkRHy8vLQrVs37NmzB7169QIAnDx5ElevXoWNjQ0SExPx9OlTODs7Y+bMmQCA58+f4+zZs9DW1sazZ8/QoEEDdOzYEY0bN4aFhQWOHz+Otm3b4urVq3jy5Ak6duxYJGwTJkyAm5sb9u3bh5SUFDRs2BA+Pj5o0qQJgILKdFhYGB4/fgxnZ2cMGzYM1atXF/a3t7dH27ZtsW3bNowcORIPHz7EsWPHsHbt2iLnOnXqFJo1a1aSWwGgoBHl6dOn+PDDD0XL/f39cfz4cdStW7fEx2SMMVZ1cF7+9ublVlZWWL16NRo0aAALCwtkZmbi2LFjau9PWloaHj9+jH379mHPnj0AgEmTJqFv377CNvJ8vl27dmqPxxhjjDHG2PtA2aSsikPnFO7lrtir/0VGLq4nZeJWShaMpVp4kpGHmjVrwt3dHcbGxsLx0jPzQJKKm/y1Mrw1PeE7deqEjz76CJ6enmjbti0OHjwIAPjtt99U7jN9+nSkpqYKf/fu3SvXMFFeLjL2LkP6jh+RsW85KE/1Nzwlcf78ebi7u8Pf3x9AwU/H5cOgHD16FH5+fvDw8MDcuXNFQ6ao4u3tjZiYGIwZMwY7d+6Erq4ugILeY1OnToW3tzd8fX1x+fJl0fE+/PBDUS9xfX19fPzxx8Lnp0+fonfv3vDw8EDr1q3x5MkTREZGAgA+++wzrFy5EgCwfPlyjBkzRulYTseOHcPYsWMBFPQ669mzp6jX2YABAwAA1tbWqF27NhISEooco/C5fvnlF/Tv3x9GRkZFtrt//36phjNav349Bg8eXOQn+7a2trh//36Jj8cYq7qy83NxN+MZsvPLJz1nVR/n5W93Xp6WloZVq1bh8uXLuHv3LtatW4devXohN7f4+5iTk4Ps7GxkZmbi33//xa5duzB58mTRuPGczzPGGGOMMSZWeFJWVRO0FjeuffzjbNz47zVS017h5e2TMKFnsLOzExrg5ecIrGeMVm7G78xQNMBb1AivyNDQEJ6ensUOByKVSmFiYiL6K0/5qU+Q97igYT8vJQn5qU/K9fiKkpKSMGHCBGzduhU3b97Ejh078Pr1awAF4+Xn5eUJ28qXA0Dt2rURFRWFjh074ty5c/Dw8MDz58+xePFiPH36FBcuXEBERAT69+8v2k+x8mtoaCiqfI8aNQqBgYG4ceMGwsPD4eLiIuzfs2dPhIeH49q1a9i/fz+GDRum8roUK/SFP+vr6wv/19bWVlqpbtKkCfT19XHq1CmsXbtWaAhQZGBggMzMTJXhUCYjIwM7d+7E8OHDi6x7/fo1ZDJZiY7HGKu6svNzMeXqPoy+tB0h1/ZxQ/x7gvPytzsvP3r0KExNTeHq6goA6NKlC54/f66244WlpSWMjIwwcOBAAEDNmjUREBCAy5cvC9twPs8YY4wxxljJqWqcBwqGoZFSBsyenUV1KxPUcnh/5v58axvhs7KyEB0dLRq/803TMrWCtk1NAIC2TU1omVqp2UMz/v7+iI6ORlhYGICCXm7Pnj1Damoq9PT0YGtrCyLCihUrhH1sbW2Rm5uL2NhYAMCmTZuEdffv34dEIkHXrl2xcOFCEBHu3buH58+fw9bWFvr6+nj06BF2795donA+f/4cjo6OkEgkOH36NK5fvy6s09HRwaeffoquXbvio48+gpmZmdJjtG3bVhg7Vv6z8NatW5coHEBBD7qBAweifv36cHFxUbqNl5cXYmJiSnTc3bt3w8vLC25ubkXWRUdHw9vbu8RhZYxVTcmZabidXjCZ462XKUjOfHvmEWGlx3n5252X165dG1evXhUmYj1//jzy8/NFw92o8vHHH+PIkSMACuLh4sWL8PLyEtZzPs8YY4wxxt40TcZTf5slP3sJyb1TsLJ1QMcObaGjo/POX7PcW9MIP2XKFJw6dQoJCQm4cOECevXqhbS0NAwZMqTSwiTR1oFhj/Ew6jcVhj3Gl9s4subm5ti3bx9CQkLg5eUFX19fnD17Fp6enujduzfq16+PVq1aoWbNmsI+Ojo6WLZsGTp16oSWLVsiKytLWHfjxg34+/vDy8sLDRo0wKBBg+Dl5YUJEyYgLCwMPj4+GD58ONq2bVuicP7www8ICQlBs2bNsHHjRjRt2lS0fsSIEfjvv/8wbtw4lcdYtmwZIiIi4OXlhaCgIMyYMUMYQ7YkevXqhfT09GLP1bx5cyQlJQnj6AJA165dhQnbXF1d0apVK9E+69atE03IKpeRkYHIyMhSNTIwxqomO5kJ6hoXDNtR19gGdrLy/fUUq5o4L3+78/IGDRpg+vTpaNWqFby9vTF+/Hjs2rVLmGx17NixqFGjBu7fv4+2bdvC2dlZONZ3332Hw4cPw8PDAy1atMD06dPRoEEDAAAR4fjx4+jWrVuJr4MxxhhjjLHSUjXZ6bsgNzcXrtVN4ezTEoHN/YRfz77L11yYhIiosgOhiX79+uH06dN48uQJrK2t0axZM8ybNw/u7u4aHyMtLQ2mpqZITU0t96FpWFG7du3CL7/8IhoXtqJcvHgRAwcORExMDLS0VH+3NH/+fEgkEoSEhJTpfKtXr8Z///2HefPmlek4jL3Ndu3ahT59+lR2MMpVdn4ukjPTYCczgZ7WuzP23LuE8/I3613Oy4tz5MgRbN26FZs3b66wczBWVbyL+Tmr+jg/Z4wx5V5k5CL+cTbqWOtVufHQyxK2yMhIREdHo2fPnkXK+orHfZGRi/B7mZAQ4F1TVuXiobTemqvYsWNHZQeBlUDHjh0RFxeHffv2Vfi5Ro4ciaNHj+LXX38tttIOFPzUfd26dWU+p5aWFqZNm1bm4zDGqhY9LR04GlpUdjAYqxLe9by8OKmpqfjxxx8r9ByMMcYYY4wpMjPUQcMq2ugs77EOQOMwEhEuXryImJgYdOzYUWlZX/Ga4x9n4+a9TEAigZFMu8rGR0m9NT3hywN/284YY+WHe86xysB5OWOMlS/Oz1ll4PycMcbePqXpCR8eHo7IqGg4eQehvpOVRvuVtCd8Vf71QGFVN2SMMcYYY4wxxhhjjDHGivUmGqJL0ks/OzsbOjo6qFevHl7LauJppjbiH2drtL+ZoQ5auRkrXafsOkvTQ78yvDUTszLGGGPs7Zebm4uvvvoKtWrVgkwmQ+3atTF37lzk5+dXdtAYY4wxxhhj7K1UGZObvsjIxZXEV3iRkStanp6ejj///BNRUVGQSqVwq2ECWzNd1LHWK/M5lV1nHWu9Uh9f1TVUhKr79QBjjDHG3jk//vgjVq9ejd9++w3169fH5cuXMWzYMJiamuKzzz6r7OAxxhhjjDHG2FtH3gBdlobukvamj3+cjTuPshCb/BqdvUxgZqiDJ0+e4MiRI3B0dIS7uzuA8h3nXtl1luX4b7IXPTfCM8YYY+yNOX/+PLp164YPPvgAAODk5ITt27fj8uXLlRwyxhhjjDHGGHs7lUdDt7xBOj0zD0Yy7SKN8YqN9HWs9RCb/Bo6WhLEP86GU14eDhz6G65u9dCscQNIJJKyXlYR5T1xbXl8eaEpHo6GMcYYY29M8+bNcfz4ccTFxQEArl+/jrNnz6Jz586VHDLGGGOMMcYYe3/Jh3UhCZQObaM4FIyZoQ46e5mgTjUpbKSZSHyWB4t67aBnUw8SiUQ01MubHPalJMwMddDQyeCNTOjKjfBVlEQiQXp6eon2SUxMxJo1a0TLnJyccPPmTY32f/XqFXx9ffHy5UsAwOXLl+Hn5wdfX1/Uq1cP8+fPF7YdOnQoatSoAR8fH/j4+CAkJERYt3z5cnz//fclCjtjjLH3w9SpU/Hxxx/Dzc0Nurq68PX1xcSJE/Hxxx8r3T4rKwtpaWmiv7dFVcjLv/vuO7i6ukJLSwsHDhwQbbt+/Xp4enpCR0cHK1asEK3Lz8/H+PHjUadOHTg7O2PVqlXCuoyMDAwbNgyenp5wdXXFtGnTQEQACr5U4S9UGGOMMcYYe/NK29At3w8AGjoZwMdBpnSMdWVjr5saaEPyLAYnjh5ADRNCdWsjYX3hRvv4x9mIf5SF3y+/wKnol1WuMf5N4Eb4d4iyintJrFixAj169ICxccEMxMHBwZg+fTquXbuGc+fOYeHChYiKihK2nzZtGsLDwxEeHo4FCxYIyz/99FOsXbv2rWooYYwx9mbs3LkTW7ZswbZt23D16lX89ttvWLhwIX777Tel23///fcwNTUV/hwcHN5wiN+s8s7L27Rpg0OHDqFly5ZFtm3YsCF27dqF/v37F1m3ZcsWREVFIS4uDhcvXsT8+fMRExMDoKBhHwAiIiJw8+ZNXLt2Db///jsAwNvbGzo6OggNDS31NTDGGGOMMcZKrrSTsyrr4d7QyQAARI36ir3G8/PzcebMGURHR6NLly6oZmEoWl+40b6OtR5eZuYh9sFrXEp49UYnkK0quBG+Clu4cCECAgLg4uKC7du3C8uPHDmCBg0awMvLC4GBgULD+KhRoxAVFQUfHx907dpV2H7Pnj3w9/dHrVq18M0336g835o1azBgwADRshcvXgAo6PWmp6cHCwsLteHW09ND+/btsXPnzpJcLmOMqZSbR0hJy0FuHlV2UFgZhYSEYNq0aejXrx88PT0xaNAgTJo0SeUvqKZPn47U1FTh7969e284xGVT2Xl506ZNUadOHaXbent7o169etDSKloc3LlzJ0aNGgVtbW1YWFigT58+2LFjB4CC3u6dOnWCRCKBrq4u2rdvj82bNwv79u/fH2vXrtUwhhhjjDHGGGPlQVlP9bLsF34vE+dvpSP8XqbS/ZKSkpCSkoJu3bopbS8s3GhvZqiDOrZSVDPVgZmh9hsZg72q4YlZyyg7PxfJmWmwk5lAT6t8o1MikeDcuXO4c+cOmjRpgubNm0MqlWLgwIE4efIkPD09sXXrVvTp0wc3b97E6tWrMWXKlCKT27148QJhYWF4/PgxnJ2dMWzYMFSvXl20zb1795CWliaqqG/YsAHdunXDV199hcePH2PNmjWwtbUV1i9evBhr1qxBzZo18c0338DHx0dY5+/vj0OHDiE4OLhc44Qx9v7JzSOsP/0UD17kwN5MF8NbWkJHu/wneGFvxqtXr4o0+mprayM/P1/p9lKpFFKptELD9C7n5aWVlJQER0dH4bOTk5MQpsaNG2PXrl3o3r07srKysG/fPtGv3/z9/TFx4sQyh4ExxhhjjDGmOcVJSxUnUlW1XNVkpxICIJEU/FvIq1evkJ2dDScnJ9SoUQM6OuJjh9/LhIQA75oy0Xl9HGQw1i864ev7gnvCl0F2fi6mXN2H0Ze2I+TaPmTnl+94RiNHjgQA1K5dG82bN8eZM2dw4cIF+Pj4wNPTEwAwYMAA3L9/H8nJySqPI+8RZ21tjdq1ayMhIaHINvfv34ednZ1o2YIFC7BgwQIkJSUhMjISM2bMQGxsLADg22+/xe3btxEREYERI0agU6dOonFvbW1tcf/+/bJFAGOMAXiWkYsHL3IAAA9e5OD5ezh23LukS5cu+Pbbb3Hw4EEkJiZi3759WLx4MXr06FEp4XnX8/KykEj+92WXfMx3oGBcfwcHBzRp0gRdu3aFv78/dHV1hfW2trZ49OgRcnJyyi0sjDHGGGOMsZJRNTyNsuXKxpP3rimDn7MhvGvK/rfdixf4888/haEqCzfAy499814mbvz3ush5SzIJalWdyLUsuBG+DJIz03A7PQUAcOtlCpIzK3YMdIlEAiISVYoLr1NFX19f+L+2tjZyc4s+wAYGBsjM/N/PS548eYJ9+/ahT58+AAoaD5o2bYqwsDAAQPXq1YWejD169ICJiYnQQA8Ar1+/hkwmA2OMlZWFoQ7szQoa+OzNdGH+Hn5j/i5Zvnw5evXqhTFjxqBevXqYMmUKPv30U8ybN69SwvMu5+VlUbNmTSQmJgqf7969i5o1awphWbJkCcLDw3Hy5ElYWFjA3d1d2Pb169fQ1dUVNcwzxhhjjDHGlKuoBmdVw8woW67JePIPHz7En3/+CWdnZzRt2lTlOT0cZPCsrl+mIWdKO759VcaN8GVgJzNBXWMbAEBdYxvYyUzK9fjr168HUDBJ29mzZ9G8eXP4+fkhPDwc0dHRAIAdO3agRo0asLW1hYmJCVJTU0t1LldXVzx69AivX78GAJibm0NfXx+nTp0CUNAo/++//8LDwwMARL3c//33Xzx9+hTOzs7CsujoaHh7e5cqLIwxVpiOtgTDW1pibBsrHormHWBsbIylS5fi7t27yMzMRHx8PL755hvo6VXOmIDvcl5eFr1798Yvv/yCvLw8PHv2DDt37kTfvn0BAGlpaXj16hUAICEhAT///DM+//xzYd/o6Gh4eXmVOQyMMcYq3unTp9GlSxfY29tDIpHgjz/+ULvPqVOn0LBhQ+jr66N27dpYvXp1xQeUMfbOeBd7OJdVRTU4q+p5rmy5laE2UjPzYGWorTJcERERaNy4MRo3bqyyA5GZoQ5auRkjsJ5xmYacKe349lUZdycsAz0tHSzw7VFh48hKpVIEBATg8ePHWL58ORwcHAAAmzdvxoABA5CXlwczMzPs2rULAODl5QVXV1d4eHigdu3a+OuvvzQ+l76+Ptq2bYvjx4/jgw8+gLa2Nnbt2oXJkycjNzcXOTk5mDJlCho3bgwAGDp0KB49egRtbW3IZDLs3r0bpqamwvGOHDmC7777rhxjgzH2PtPRlsDahHvVsvL3LuflAPD9999j5cqVePz4MYYOHQp9fX1cu3YN1tbW2LJlC6ZNm4bnz5/jzz//xA8//ID9+/fD19cXgwYNwqVLl+Di4gKgYELdevXqAQDu3LmDPn36QEdHBzo6OliyZIloXpgjR47go48+Ko/oY4wxVsEyMjLg7e2NYcOGaZR2JyQkoHPnzggODsaWLVtw7tw5jBkzBtbW1pz2M8Y0Im/YBaB0HHJNqRrv/G0kb2jWtMG5Iq79SUYeTGXaeJKRB0drcbh0M+4jL68O2rVrV+yvd8uTqnHq32YSKjzI5zsuLS0NpqamSE1NhYlJ+fZ0exdcuHAB8+bNw4EDB8p0nKioKIwaNQqnT58up5AxxqqiXbt2CUNWMfamcF5evPLKy0srOzsbjRs3xvHjx2FlZVUpYWCMlQzn50xOIpFg37596N69u8ptpk6dir/++kv4NRcAjBo1CtevX8f58+c1Phfn54y9v8qrAflK4is8fJEDWzNdNHQyKMcQlsyb+jKg8HnkX2SU57Uru478/Hz8+++/iI+PR5cuXWBmZlYu53pfvVtfKbAyadq0Kbp3746XL1/C2Ni41Me5d+8e/ySRMcYYqwTllZeXVkJCAr7//ntugGeMsXfU+fPn0b59e9GyDh06YN26dcjJyVE5H0hWVhaysrKEz2lpFTsHC6s471LvY1Y5yquHc0l7j1eU0vTsL817VPg8FXHt8vvyIiMXoTEvQbm5yLx3CS/TnqNbt24wMTHh97+MOMaYyMiRI8t8jA4dOpRDSBhjjDFWGuWRl5eWq6srXF1dK+38jDHGKtbDhw9RrVo10bJq1aohNzcXT548gZ2dndL9vv/+e8yZM+dNBJFVsPIaSoSxsqoqw5WUpkG8NO9R4fOou3Z1jeXFrY9/nI2b9zKB3Fcwz8iBs2975GsbiML98nUejPW1uTG+hHhiVsYYY4wxxhhjjGlEcTxg+Qi3xY0TPH36dKSmpgp/9+7dq9AwsorzLk6WyFhZqJr8tDjK3iN1E9aW5DzqJnotbn01WRYcdJNRy9YMr62aIuk5hO3k4ZYQKmQi2Xcdf13BGGOMMcYYY4wxtWxtbfHw4UPRspSUFOjo6MDS0lLlflKpFFKptKKDx96AqtL7mLG3mbL3qDx/ZaKud76q9Y8fP8bxvw/D1MYJRtW0YZxNyMknYbvCQ9bIe9IzzXHKyRhjjDHGGGOMMbX8/Pywf/9+0bKjR4+iUaNGKseDZ4wxpl5phrVRNayMui/LlK1PSkrC8ePHUa22F8jUGSQB6lSTKh1yhr+MKx0ejoYxxhhjjDHGGHsPpaenIzw8HOHh4QAKJtgODw9HUlISgIJhZAYPHixsP2rUKNy9exeTJ09GdHQ01q9fj3Xr1mHKlCmVEXzGGHvrKA47I/8MoMTD2qgbdkbduQsve/I8HS1btoR/I28Y6mtBQuAx38sZN8IzxhhjjDHGGGPvocuXL8PX1xe+vr4AgMmTJ8PX1xczZ84EACQnJwsN8gBQq1YtHDp0CKGhofDx8cG8efOwbNkyfPTRR5USfsYYq2zqxnJXpNhwXpaG9JLO0aB4LiLCmX8v496DFJCpE+rUqQMzQx0Y62sjPSufx3wvZ/x1BmOMMcYYY4wx9h5q1aqVMLGqMhs3biyyLDAwEFevXq3AUDHGqhpVw56wko/lrjjsTEmHoRGdz8lA6TlV3S/5OawMtXHpzks8i7+E1EfJcPJxFJ2/NEPjMPX4zWGMMcYYY4wxxhhj77yq2JhcFcOkqDwnDX3XKDZYq7ufqsZTT32Vp9FzUMdaD+mZeXj5Og8vMnKVbqvqfsnPffF2KqIvh0KLsvFRzx4wMDDQKIysbDhGGWOMMcYYY4wxxtg7ryo2JlfFMCnintGqKTZYl/R+yrePTX4NU5m22v3MDHVgJNMWhpVRtm1x9+tFRi5eZQNG5jZo2dQHBgYytWHUxNvwZVJl41hhjDHGGGOMMcYYY++8qtiYXBXDpIh7RmuupPdTvp1HdX08ycjTaL/C51DW+K3qfj179gzHz1yCzKExnFy9YW1WPg3wwNvxZVJl41hhjDHGGGOMMcYYY++8qtiYXBXDxFQr7XAzqpgZ6qAOUKJe5IXPcSXxlUaN3w8ePMDRo0dR180DUnNpuX/p8zZ8mVTZ+C1njDHGGGOMMcYYY4wxNSqix3dZjqlJ43diYiJOnDgB30Z+yP8/9u48Pq76vvf/62yzajSj3ZJteV+wjRcMScAJO0lDEoiblNwmTdvQJJdCIEDSAmn2JoU0SX+EplBIs9xe2iRtgNssZHHCvoPB2GC8yZYla99m3872+2M8gyRLsmRLlmR9no+HH/acOXPOdxb5aD7nc97f8kVTEhkjJ5OOT14dIYQQQgghhBBCCCGEOI6p6Pg+mW2OVPwudusvrTaIBHUqKyt55zvfSZdVKZEx00hecSGEEEIIIYQQQgghhDiOsTq+T3Ry0ol2kR9vP009eToGchzY9Ryh8jDnnb2BsooATS0Zgj5VImOmiTrdAxBCCCGEEEIIIYQQYraJpiy2N6eJpqzpHoqYAYqxMk09+THXG+tzM9p9g5cfbz+LKhRiTc8Q6+sirtbR1JOnqSdPMucQ8mmlwr18fk8t6YQXQggx61i2S3/KojKoo2vKdA9HCCGEEEIIMQdNRT64mL2Gx8qM1rE+1udmpPuiKYuHd8Yx1MJ33+qgxt6OLOvm+44Zg+u6PP3Y71BcF2PxhRyOKZy7WiMc0IaM7XjjEJNPXmEhhBCzimW7/OCJPtqjJg0Rg6vPr5JCvBBCCCGEEOKUm4p8cDE1JhoVcyLRMsNjZUYrco/1uRnpvqaePLqqYDouy2o8NPXkCfs1elM2i2refKzjOKiqyjnnnIO3rIrfvp5iqV85up73mEK7fH5PrVkbR3P77bejKAo33njjdA9FCCHEcVi2S3fcxLLdk95Wf8qi/egvMu1RkwG5dE4IIYQQQggxDSJBnc2LAxPK/xbTY7xRMSe6/kiW1XiYFzGOKXKP9rkZXvgvxsVUBzWW1Xm5fH05AMmMfUy2e1dXFz/96U9JpVI0NDRQVV5Yf2mdd9Qiu3x+T61Z+Sq/+OKL3Hfffaxfv366hyKEEOI4JrtzvTKo0xAxSturkF8YhBBCCCGEEEKMYaJd35PRJT7RCVeHd84XbyczNmV+jVja5ukDKQxVYWmdt1Q8b25u5pFHHuGtb30rwWBwSDF/8+LACY9fTK5ZV7lIJpN85CMf4Xvf+x5f+9rXpns4QgghjmOkzvWacuOEt6drClefX8VAyqJCMuGFEEIIIYQQQhzHRAviE11/vAYXyIEhne/DC//FvxNZm86oyd6O7JBYGoCOjg4eeeQRLr74YhYvXlza5kgxONGUxastGVwFNi70Swf8KTbrXu3rrruO97znPVx66aVShBdCiFlgKjrXdU05qUK+EEIIIYQQQojT34lku0+lwQVyYMyJUYsnAorPYd18H70pm2U1HsIBjXw+T11dHVu3bqWioqL0uOLErRUBlX97vJewT6MmrKO4sKstC65LyKcNmfh1Jr1GMDPHdLJm1bP4yU9+wssvv8yLL744rvVzuRy5XK50Ox6PT9XQhBBCjEI614UQQgghhBBCjNdkFmCbevI0deXY25Hl8vXl01rQjaYsElmbMu/QPPfBXfGdUZNE1ibk04Ysrw5qpQJ8yKfwhz/8AYBLL710SAEeoDdlE/ZrPLkvRfuASc5yWLfAz7qFfpZWe2iPmVQHtdL6o3XOT6eZOKaTNWueRWtrK5/+9Kf53e9+h8/nG9djbr/9dr7yla9M8ciEEEIcj3SuCyGEEEIIIYQYj8kswC6r8bC3I4uhKjT15Ke0oHu8kwdNPXlSWYd5EaN0/+DxFIvuyYxdyoJvi5oYqsJexyXs17DMHN17nsKyLP7oj/5oxHEUt9NYafD8oXSpE37jQj9NPXlcCoX6RTVD1z+Z/PvJNhPHdLJmTRF++/btdHd3s3nz5tIy27Z54okn+O53v0sul0PTtCGPue2227j55ptLt+PxOAsXLjxlYxZCCCGEEEIIIYQQQozfZBZgI0Gdy9eXD8lhnyrHO3lwvOc1PH4mkbVLGfBblgfpTdkcee1JspbKJRf/EX6/f8ztAJzZOHRi1mXDxjJ8/ZliJo7pZM2aZ3PJJZewa9euIcs+9rGPsXr1am655ZZjCvAAXq8Xr9d7qoYohBBilsk7Fh2ZOPX+cjzqrDkkCiGEEFPOtS2cWC9quBpFk2OkEEKIU2eyC7CnqqA73iL7SIZP2AqwpMqD4oKrgO6kOWtRBDN3Dr1phcMDLtXhiY/xdCxuzxaz5lUPhUKsW7duyLJgMEhVVdUxy4UQQojjyTsWn335IQ4ku1kRquWbm7ZKIV4IIYSgUIBPPXgXdk8rWm0jwa3XSyFeCCGEOI6TKXCPNmFrmV+j+XArB55+hve+592sXlBdyojf3pwekhV/ukxgerpSp3sAQgghxFTIOxaHU/3kHWvE+zsycQ4kuwHYn+imIyOTdwshhBAATqwXu6cVALu7BSfWO80jEkIIIU69aMpie3OaaGrk75STaVmNh3kRg2U1niH/VuOHGdj/FOe89TzmzZtXKPQvDtCbKuTGP30gRWfUpKknP+VjFCdnVp8ieeyxx6Z7CEIIIWag8XS51/vLWRGqZX+isE69v3yaRiuEEELMLGq4Gq22Ebu7Ba22ETVcPd1DEkIIIU7I8SZLHcvJThA72r5HWj68i34Z8EbLAPt2vMi73/1HBCN1bG9Olx5TjK1ZN99X6oSfrOctpoa8C0IIIU47I3W5LwpWDlnHo+p8c9NWyYQXQgghhlE0neDW6yUTXgghxKx3MoX0k50gdrR9D16+7OjtwcVyx3HY2dRBwinnjPPez/z55WxvTg/Z1uCi/aKa8e9bTB95F4QQQpx2xtvl7lH1Y4rzQgghhCgU4rXKedM9DCGEEAI48c7u8RbSx9OdPlHD913cR3VQI5G1OdiV5Zc7YtSFdPZ2ZLl8fTkBw+EPf/gD6UyWqtWXkLYUDvfkSGRtyrzquE8IjPS8pTt+eskrLoQQ4rQjXe5CCCGEEEIIcfo40c7u8RbSp6JzvLjvYrZ8MmOTzDkAhHwa23bF6U/axNI2b1sWZM+RKG2vPY7P5+MdF72L3+/JoqsK7QMmYb/GvIgBMCSWBkYvrieyNq+2ZNjQ6CcS1KU7fprJKy6EEOK0JF3uQgghhBBCCHF6ONlomOnYfjRlsaM1Q1NnjnK/Rk1YL024CrBlRRntMZM19V6a+0wyPYcoK49QuewtPL4vy8vNaRZXeXjfpnAp932kQvpoy15rzYCiUObX2DwoR36qXkMxNinCCyGEmBMs26U/ZVEZ1NE1ZbqHI4QQQgghhBBinE42Gmai2z/Z6JZoyuJnL0XZ254lHNTwe1U2LvQP2dblG8MAPP5KM2Waj9C8NSQzNs8fzNI+kMe0XHxelUU13lLu+7Kjjx1cSB+puL6sxkMi60dx31w+1a+hGJu88kIIIU57lu3ygyf6aI+aNEQMrj6/SgrxQgghhBBCCCFGdLLRLU09eaIpGxeo8Gs0VBil+6Ipi1dbMiRyNsTb6Nr/PIvWnsvy2mp2tGbI5h0qAxpLa71ctiY0ZLsjFdJHW3bh6qGPFdNLivBCCCFOe/0pi/ajv0C1R00GUhY15cZxHiWEEEIIIYQQYjJNpMP8VE8kOnh/JxvdsqzGQ2JpAMUFV4FU1qGpJ8/mo9nsu9qyJDv2UJHdh7/xbdQ1LOKBF6OEfCpeQyFnwvI6L+GAdkwG/PCxyiSrs4O8S0IIIU57lUGdhohR6oSvkF9ShBBCCCGEEOKUm0iH+ameSHTI/hYHTnqfIZ9WKuIXC+YA1UENn+rgVfvoCZ/H/EAtD++M0x61KPMqLKn1oiguijv6azCe10YK9TOLvANCCCFOe7qmcPX5VQykLCokE14IIYQQQgghgFNfqJ1Ih/mpnkh0svYXTVk88GKUgbRNckmAC84IlQrllmWx+43XCQUW0xx+Gw1hA0NXuHx9OS8cTFMX0Tlzvr80EStAImuTzNhEU1bpPRrPWE/1SQwxNnkHhBBCzAm6pkgEzQzR1tbGLbfcwq9//WsymQwrV67k+9//Pps3b57uoQkhhBBCCDGnnOpC7UQmBz3VE4mOtL+JnqSIpiwe3hmnI2aSyTu4g/q/stksv3r4N6TzDtm6BuYNKsBHgjpnNgZK6xYnYoVCR/3BrhwP74yX1j3eaxNNWSSyNmVe9ZSdxBBjkyK8EEIIMU6mmSedThEIBDEM+UXmRAwMDLBlyxYuuugifv3rX1NbW0tTUxORSGS6hyaEEEIIIcScc6q7zafSVHT1H+8kxfB9vtqSoT1qEvFrvH1VGRsX+gGIx+P8+te/RvGGqVh5DiG/hzK/Nq6s92U1HvZ2ZNFVpZQrP55xp7IO8yKGRNHMEPIuCCGEEOOUTqfIZbMAhMOz/5fU6fCNb3yDhQsX8sMf/rC0bPHixdM3ICGEEEIIIeawU91tPpVOtKt/tOJ9NGWRzNgEfaN3k+9ozfBaa4ZE1s+Fq0O4Cvh0hXUL/Wxc6KepJ0912qa5bYB5DY2cuXEzO9typLJDu+QHP4eDXTn2dmRLXe8A8yMGrjL+kyWTcXJFMuUnlzrdAxBCCCFmi0AgiNfnIxAITvdQZq2f//znnH322fzJn/wJtbW1bNq0ie9973vTPSwhhBBCCCHELLesxsO8iDHhwnOxeN/Ukz9meTLnEPJpxxShoymL7c1pemImh3tzvN6aIZqy2LjQz7qFfhS3UKA/dOgwj2/fS5IIbs06dh3JcaArx+72LK+1Zo7Z57IaD/GMTXvU5NWWzHHHMZpIUGfz4sBJFc9He13EiZEivBBCiFkv71gcTvWTd6wp3Y9heAiHKySK5iQcPHiQe+65hxUrVvDb3/6Wa665hhtuuIF///d/H3H9XC5HPB4f8kcIIYQQQgghhjvRwvNoxfuxivrFAnUs6wDQ3JtnR2uGSFAn5NNI5hxi7QcYaHqGgKFQ5lVRXNjVlqUzapLKOyyp9pDL23zjl53sakmXnsOyeV58ulLqlJ/IyYXiyYFo6uS/G5/oSQ0xMrmWQAghxKyWdyw++/JDHEh2syJUyzc3bcWjyuFtpnIch7PPPpt/+Id/AGDTpk28/vrr3HPPPfz5n//5MevffvvtfOUrXznVwxRCCCGEEELMIFMZjTJaJM9YUT3LajwksjYNGGTyDjnTRXEL9y2tNmjbv4OBtn2s3HwJSaWilP8OsL87R7lPY16Fh9+/Fmd/V46HtsfoS9ukMjaK8maczfHGMdxkTrR7OkUVzQTSCS+EEGJW68jEOZDsBmB/opuOjHRKz2T19fWsWbNmyLIzzjiDlpaWEde/7bbbiMVipT+tra2nYphCCCGEEEKIGeRURaMcr5M8mrJ4bE+Cba/FeXxPkp6ExbIaLw0VBourPURTFgd7TeoqArz//e9nw8oFpW7ySFDngjNCvHVJgEM9OTwqXL6+nBV1XlY1eHnpYJpf7Iixuz07oeiZwaR7feaS0xlCCCFmtXp/OStCtexPFDrh6/3l0z0kMYYtW7awd+/eIcv27dvHokWLRlzf6/Xi9XpPxdCEEEIIIYQQM9RkTDQ6msFd9qN1khfXSWZsXjuS4bUjWRQKk7AuqvGQyjk8tTeK2vsqwYYzmV+7kkgkUNpOsbi/rMbDrrYspu2yqy3Lh8+t5MzGANGUxc9eirKg0kMkqA15nhO5CkC612cueVeEEELMah5V55ubttKRiVPvL5comhnupptu4rzzzuMf/uEfuOqqq3jhhRe47777uO+++6Z7aEIIIYQQQogZaiqLy4ML74OL/dGUxY7WDIoLrgKprIOLi8+jsqhK5/X2PAsiOge6cmRTKXy9z+H3B5hXGSg9fqTi/pnzfbT25Tlzvm/I8/vg2ZERi+2TGTEjpo+8c0IIIWY9j6qzKFg53cMQ43DOOefw0EMPcdttt/HVr36VJUuWcOedd/KRj3xkuocmhBBCCCGEmGOiKYtE1qbMqx7TZd/Uk+e11gwoCkurPcQyNhG/Rn3YYHdbFp+h8Ps3kswP5pmXfBZfdQPvvuwCVFXl6X1Jnj6QIuhR2bKqrJTvXizIr1vgJ+8MHctoJxqm8ioAcepIEV4IIYQQp9R73/te3vve9073MIQQQgghhBAzzGRMwDqRbTT15EllHeZFDCJBne3N6VLXeXVQw+dRaQgbBHwqLhDwqYR8Gh88O8yT+1KsrPPwy1f60T3LqZi3GlVVeXhnnFcOpzncm0NTVTYtCgwpsFenbfZ2ZFk3qBN+LBIxc3qQd1AIIcRpJe9YEk0jhBBCCCGEELPQRKNXRiq4T2Qbxe7y6qDG9uY01UGttLypJ0992KDu6ESnxf0U9/FHi2McaG7l/DM20RkrY0m1h+/8rhvbgbqQjqEp2LbLU/uTuK7LugV+elM2iaxN2K/Rm7JZVDP+5yVmN3kXhRBCnDbyjsVnX36IA8nCJK3f3LRVCvFCCCGEEEIIMUtMNHplpIL7RLZR7DLf3pymqSvHXsfl8vXlRII6y4BE1qYrapLI2lQFNH72UpRo0sSTPEAgeYC3X3AxKd3PlWeF+dlLUV4/ksWyXS5eE+J/X1jBj57qY29HDtN2iWUdwn6NMq/KvKOF/Yk8LzG7ybsohBDitNGRiXMg2Q3A/kQ3HZm4ZMULIYQQQgghxCSbqk7tiUavjFRwH7yN8Y5zWY2HvR1ZDFWhqSfP5qBOJKgT8mm8dDDNQNrGoyt0Rk3KU3tYaHQQWHEBKb2mtO2GsEHIr2LZkLVcelM25ywJ0pe08agKi6sMmvtM1s33sajGO+HnJWY3KcILIYQ4bdT7y1kRqmV/otAJX+8vn+4hCSGEEEIIIcRpZ6Z0ah+vaD/ecUaCOpevL2dHa4ZkxiaasgBIZmy8hkJFQGVhhQGuS785j9dpZI1VRufRLvmQTyMSUGms8rC42sOiGi/Lajwsq/EQzdjoqkJzn3ncGJrxPq+RSITNzCbviBBCiNOGR9X55qatJ5QJb9ku/SmLyqCOrilTOEohhBBCCCGEmN3G06k9mUXh8WxrpHVGGudo2yp2vndGTZp68gB0xy0ChsoZdQrR/Y9z1fqzeLq9gWjKZmm1h3kRg2TG5mBXjsf3JcnlHVAUrjwrUtr25evLaerJUx0sFOCnqrt9ppwYESOTd0QIIcSsNVLh3KPqx42gGf44y3b5wRN9tEdN5pXrXLk5TE3IkGK8EEIIIaaN67ooivwuIoSYmcbTqd3Uk+dgV469HdlSzvqJGk+BeaR1hkfT7GjNcKArR7lPO2Zb0ZRFRzRPV9Ri3Xwf4YDG3o4sATXNkVefYmFDHbV19ayw8rgKbFzoJxLUiaYsHt4ZJ+hRONCZJ5VzeGZ/iss3ho8Zw/E64E+GRNjMbOp0D0AIIYQ4EcXC+d1/6OUHT/Rh2e4JP64/ZdEeNXEceHRPkjt/2zOhbQohhBBCnIhcLsdnPvMZLrjgAr75zW8C8LWvfY2ysjLKysr48Ic/TDwen+ZRCiHEiVlW48F0XPSjOesnu62xJjONpiySGZugTx11naaePK+1ZoimbCzHLUTFpCy2N6dL3fHNPXkyR/PcAeYFHVIHHiVYtZCz3no+zf02yZxTiJ8ZVMBvqDBYWOGhNmygAO0x86Se74mIBHU2Lw5IFM0MJe+KEEKIWalYOAdoj5oMpCxqyo0TelxlUKchYtDUnQPAaygT2qYQQggxm7m2hRPrRQ1Xo2jyFfFUuu222/jpT3/Kn/7pn/LDH/6Qw4cP84tf/IJ7770XVVX54he/yOc//3nuuuuu6R6qEEJMWDFnvRj9cjxjRc6MJ/s9mXOYFzFGLEIXi/SLazyEvBobGv0APLwzjqEWrjpaVuMhkfWTytgkMzZP7Bmgpd+F2reRNSp4+kAKgObuPLguu1TY1ZYl7FNBUVjX6GdJjYeOuMVla0In9DzF6UveaSGEENPONPOk0ykCgSCGMb5L54qF8/aoSUPEoGLQLy95xxo1F36kx+mawtXnV9GTsPif7VE649Yx2xRCCCFOV6kH78LuaUWrbSS49XopxJ9CP/vZz/g//+f/cOmll3LttdeyYsUKHnzwQa688koAqqur+cQnPiFFeCHErDWRCUZPNNN8rC74YgRNU2eOcr/G0jovmxcHANjenEZXFcyjXfGRoM7GhX4e3hmnv20fmZ4DsOAS8FSQtVxePpQh4FVp7cvxWluGMp+KacHyeV7OW1F2TFG9WGwfnAUfCeqS3T5HyTsthBBi2qXTKXLZLADh8PiK8MXC+UDKKhXSoVCA/+zLD3Eg2c2KUC3f3LR1SCF+tMfpmkJ9xODjF1Yfc58QQghx2rIt7IHWwj+7W3BivWiV86Z5UHNHb28vK1euBGDp0qVomsby5ctL969YsYKenp7pGp4QQpxSJ5ppPlYXfDGCJmu5+L1Di/SD91d83IHuHGbnTrJ9zaSr3koqZQMKadOhpkwnnrXx6Cqm7WBakLNd6kJ6qbA/fN+dUZO9HVnC/jcz6Ic/z5PpjJeu+tlD3h0hhBDTLhAIDvl7NMUO9ypvgL5cmnp/+TFxMR2ZOAeS3QDsT3TTkYkfM1GrrimjxsyMdZ8QQghx2tF0tNpG7O4WtNpG1HD1uB4mETaTo7GxkWeffZbGxkZefPFFFEXhhRdeYO3atQA8//zzzJ8/f5pHKYQQp8ZEuuYHqw5qvHI4jUKhKD24GD04Yqbs6GSsgwvXw4vnTv8BSLajNV5Ic7dOdzyH47iYlktXmcb8iEFtWKcyoBPyqXQnberCI39/LBbZ1833lTrhR3qeJ9MZL131s4e8O0IIIaadYXiO2wFf7HDfl+gibmYpN/ysKj+2073eX86KUC37E4VO+Hp/+VQPXwghhJjVgluvn1BB3bUtibCZJNdccw1/+Zd/yb/927+xfft2vv3tb/O5z32OPXv2oKoq99xzD5/5zGeme5hCCDGjDO/+7k3ZZPMOB3vz1EWMIcXoSFDnwtUhtjen6YyapQlii4XrZcCO1gzJZJbelM3hvkred+4fYXh8tKVjLK4yeP5gChSXRMZhRyLLxkY/b1nixVWgzKfhKscW/4v7Lo5lUc3oz2ekKwDG2+F+olcPiFNPflMSQggxKxQ73E3HpieXxK95Rux096g639y0ddRMeCGEEEIMpWj6hCJonFgvdo9E2EyGG2+8kZqaGp577jk+/vGP86EPfYh169bxxS9+kXQ6zU033cTf/d3fTfcwhRBiRhne/V3sdlfc0YvRg4vVsbTN3o4sjZUGD++M09ETw9P9LAcydezPNYKi8d5NPs6o93GoJ8eFq0Mc6M5ysCuPqrhYtoOrQHfM4lBPjiU1Xpp8+RPqRB+t2D7eDvcTvXpAnHryLgkhhJgVih3ue+Nd1HjLMFRt1E53j6ofE0EjhBBCiMmhhqtPKMJGjOwjH/kIH/nIR0q3L7zwQp544olpHJEQYqY60fzv0y03fHj3d7HbPZqyeLUlg6vAkirPkMlQBxerm3ryhP0au9qyZJMD+LueIqnX0astIWdZvNqapj9ps6DKYF7YwNAVVjf46U3Y9KcsNi0KsHGhnwc6o5i2SyJjn3An+mjFdulwP/3Mmp+8e+65h3vuuYfm5mYA1q5dyxe/+EXe/e53T+/AhBBCnBKDO9wHZ8IDHE71S9e7EEIIcYoomj7hCBshhBAn70Tzv0+33PDRur+bevLsasuC69I+YA6ZDLV4IqI6qJHM2AR9KmsbPGz79W/JBpfSZi0i5HNJmw7dMYt42qGqTKeqQaMratFYqVPm02is8uC68PDOOGV+lYipsWye94RPboxWbJcO99OPOt0DGK8FCxZwxx138NJLL/HSSy9x8cUXc+WVV/L6669P99CEEEKcIsUO9zLdV+p0/+zLD/HXL/6Yv3nlIfKONc0jFEIIIeaGYoSNFOBPD3fffTdLlizB5/OxefNmnnzyyVHXfeyxx1AU5Zg/e/bsOYUjFmJuWlbjYV7EmHB39Ik+7mREUxbbm9NEUyf+HW0i24imLBJZm5BXoS9ls7jKGPKciycinj6Qojtu0dreg6KoLN38LoyaVXgMlQVVHrasCHHW4gCbFgW4dG0Zrx/J0tyb49mmNIurPTRWe+hKWDx/IEVPzOLcFWVsXOg/4ecYCepsXhw4La5QEGObNe/w+973viG3v/71r3PPPffw3HPPlWaOF0IIMbcUc+KBEfPhhRBCCCHE2H76059y4403cvfdd7Nlyxbuvfde3v3ud7N7924aGxtHfdzevXspL38zFrCmZoxZB4UQk+JEu6Ono6v6ZLrvi13ryYxNd9xib0eWy9eXj1moburJk8o6dMYsNAWa+0wuX+8vxPAA1UGNVw6nCXkUokdeh+gBntTfhdfrJ2ConL00QDLjcOnaEItqvERTFj97KUoqZ6PrKmvqvXQlbFbUeknlHDqjJktrvWxeHCidLDhd4n7E1JiVnwzbtvnv//5vUqkU55577qjr5XI5crlc6XY8Hj8VwxNCCHGKFHPi9ye6R82HF0IIIYQQo/unf/on/uqv/oqPf/zjANx555389re/5Z577uH2228f9XG1tbVEIpFTNEohxGwxOPYFhsasjDebvljAD/pUTMdFVxWaekae+HT4/ioCKk/uS3HmfN+QEwGJrM2hrgz15usErF5WvuVdhMMRHt4ZZ17YoCdm0RG3eO1IBoD7Hu1lX1eWgEfjT94a4cz5fp4+kKIyqOEq8M515Wxo9A8ZL5wecT9iasyqT8auXbs499xzyWazlJWV8dBDD7FmzZpR17/99tv5yle+cgpHKIQQ4lQanBMvmfBCCCGEEBOTz+fZvn07t95665Dl73znO3nmmWfGfOymTZvIZrOsWbOGz3/+81x00UWjrisNckLMHUMK0osDwNDO9mTOKdw3RrF6cE76xoX+UuF+JK+2ZNjVluXM+T4uOCPE9uY0S2q87GrLsmV5sLSdZ/ancNK9ONYAwdUXYRkh+tI2S2q8WI6Lq8BA0uKlwxk64hbNfXliaQe/R6MzZlHme3My17BfY17EKJ1IkElUxXjMmkx4gFWrVrFjxw6ee+45/vqv/5q/+Iu/YPfu3aOuf9tttxGLxUp/WltbT+FohRBCnArFnHgpwAshhBBiNnv66adLherB/55Kvb292LZNXV3dkOV1dXV0dnaO+Jj6+nruu+8+HnjgAR588EFWrVrFJZdcwhNPPDHqfm6//XbC4XDpz8KFCyf1eQghTq2xstqL+fPVQa20TrEw7yqMK5u+mJMODOmcH2m/rgK4hSJ6cf+W42KoCr0pm2U1Hp58vZdndrVyOB3h6cw5/HR7lp8+189LB1Mc7ssxkLSo9Kt0J0w8KpR7VRZXeXjH6iBvXRqg3KehuIWxnznfRyxjlzrvB49XomjEWGbVp8Pj8bB8+XIAzj77bF588UW+853vcO+99464vtfrxev1nsohCiGEEEIIIYQQE/bud7+bHTt2sHTp0iH/PhUURRly23XdY5YVrVq1ilWrVpVun3vuubS2tvKtb32L888/f8TH3Hbbbdx8882l2/F4XArxQsxiY8WvFPPntzenS+sM7hQfT6H6cE+Opw+kMFQ43GfySlDjg2dHjtlvNGWhuLBuoX/I5KgNFYV4mWf2J3n2tQR0PI2ZraE7uQzTcfHpKp1Y5C0XAK+u0h7LY9nwenuWDY0BzltZVjqZsG13gjKvyobGQld+2K/Rm7JZdHQqjPHG7Ii5bVZ/MlzXPSXdAUIIIWYHy3bpT1lUBnV0beQvjkIIIYQQM5HruiP+eypVV1ejadoxXe/d3d3HdMeP5W1vexv333//qPdLg5wYixQwZ5/xxK8ML7xPJCv9968n2HkkS9CrHD0h6A6JpCn+/WpLhhcPpYkEtVIRvjhB68GeHNHeLhbmX6bDXcgzsfmkczZ+L+iaypkL/Hh1BY+hEE3apHMa3Umb+RGDg905fF6VdfN9HOrLc6g7RzRlU3e0iz+ZsUlkbaIpi0hQH/GkxKn6XMvPz+wxa96dz33uc7z73e9m4cKFJBIJfvKTn/DYY4/xm9/8ZrqHJoQQYgrkHWtCWe+W7fKDJ/poj5o0RAyuPr9KCvFCCCGEEGPweDxs3ryZbdu2sXXr1tLybdu2ceWVV457O6+88gr19fVTMUQxB8iklrPPeIrqEy28D1YX0SnrUdiw0E9d2MBVji3mH+7J8dtdMdoGLFbM87CjNUPIp+FRIZax2dToY1dvG936Sl5PzCORyWO7oORhdb2HmnKdqjKdM+f7eGxvkmTOYcU8L5arEM3a+CyHpw+k0BXI2y4+XaE6qNHUkwcglXVKk8UOPjlQLIonsjap7PHz70+W/PzMHrPm3enq6uKjH/0oHR0dhMNh1q9fz29+8xsuu+yy6R6aEEKISZZ3LD778kMcSHazIlTLNzdtPW4hvj9l0X70l4/2qMlAyqKm3DgVwxVCCCFmHdPMk06nTlnHtZi5br75Zj760Y9y9tlnc+6553LffffR0tLCNddcAxSiZNra2vj3f/93AO68804WL17M2rVryefz3H///TzwwAM88MAD0/k0xCwmk1oKGNrR/fYVZZQdzWHf0FjocH9qf5KuqMVblgboS9s8vidJd8IGF2xXIZUpFL2jaQs31sIrPTWkyjaw/XCGvkQe1wVdhaU1XjY0+smZLu0DJv0JiwNdOZI5h5UBL7XlBof78jT35SnzahiawqJqL2ce7Yp/am+CdN7lvBXB0md28MmBYgxPmVcdV/79yZKfn9lj1hThv//970/3EIQQQpwiHZk4B5LdAOxPdNORibMoWDnmYyqDOg0Ro9QJXyFdAEIIIcSo0ukUuWwW13Wmeyhimn3oQx+ir6+Pr371q3R0dLBu3ToefvhhFi1aBEBHRwctLS2l9fP5PJ/97Gdpa2vD7/ezdu1afvWrX3H55ZdP11MQs9zJdEyL08eQju7FAUI+jc6oWeo8f3Z/imTOpTdpoakK/QmLSKBQqC9eAB3wQrT5VdJ9rYQb3s4rRxSiKRPLgaAP6sIeLl5TRiLnsKs1S0VAY/k8Lx4dyhQVB6gt1+mMm9iWS0t/nvNWBAl5NTY0+nm1JcORAQsViGecEeNfJpp/f7Lk52f2kHdJCCHEjFPvL2dFqJb9iUInfL2//LiP0TWFq8+vYiBlUTFGJrzkxgshhBAQCAQBUBR1Urfr2hZOrBc1XI2iydfN2eLaa6/l2muvHfG+H/3oR0Nu/+3f/i1/+7d/ewpGJYQ43QzPLx98e3hHd/Hv6qBGc2+e9Y1+khmHtywN8PyhNPGMRn/KpjKokck74Nrsf+VZ7GychrWXcCThQXNTBDwa4OIzoD5s0Npv0tqfJ5a2WVhhcMXGMLvaMrx8KEO5T8NVQHEh6FNpCBtkci7xjMmGRj8bGv0kcjZdUYsty4MjPkcpiovRyKdCCCHEjONRdb65aeuEMuGhUIgfK4JGcuOFEEKIAsPwEA57jk54d/Jc28Lu7yTzyE9w+trQahsJbr1eCvFCCDFNJmvCzsmc+HN4fvnw7vfBxetIUGcZ8PDOOKblYugKHzgnAsCejiwHe3L4PSpdMZcV87wc7Epi513esM8m2a/TFcuRMm00DQJel7ylEk3ZGDp0xyzqwgbnrgyyqMZLb8pmUbVNZ8ykJqRTGzaoDOlsWR7k6QMpTMvl4Z1xLl9fzns3Rk7qNRBz17jbHjKZDD//+c9JJBLH3BePx/n5z39OLpeb1MEJIYSYuzyqzqJg5bgL8OMxUm78XCLHciGEEFPBtS1SD95F6j/+AXPXk7iug93dghPrne6hCSHEnFUscBfjXKZiO9GUxWN7Ejz+RoLoOL5bVQc1Yhmb6qBGNGWRyNqUedVR88xfbcnQPmDS3Jcnf7QQvu21OA9uj4GrYNkuDWU5Btr2cKBX5ZC6kf6sxt72HD1xm6zpoKICKi4uzX05XjqUJpV3SOdtOmMWh3tyLKvxYOgKS2q8BHwqdeU6DRUG4YDG5evLMXQFXVVO+rUUc9u4i/D33Xcf3/nOdwiFQsfcV15ezl133cW//du/TerghBBCiMlUzI0H5mRuvBzLhRBibrNsl+64iWVP7mSsTqwXu6cVDA8uLm46iVq9ADVcPan7Od197nOfo7Ky8ph/CyHEiVhW46HMq5LI2uMqkI+1ndEmGG3qyfNaa4ZdbdnjFqijKYunD6QwVIXelE1TT55U1qHMrw3psI+mLLY3pznck2N/dw5wOWOel66YiWm57G7P4tUVLNdlY02G6ujTeN0MHs3FoysoKPSnLAIelVV1flbM83L+6jLCAQ0FcB0Xj6YQ9mvsasnw8x0xmnrybFkepDaso7jgKpDKOjT15IkEdS5fX86yOq9MfipOyrirD//xH//BF77whVHvv/HGG/nqV7/KddddNykDE0IIMXuYZp50OkUgEMQwZu4vJuPNjT9dybFcCCHmrqmMZFPD1Wi1jVhdzaj+EIru4XhbzjvWhGPnTne33XbbiP8WQogTEQnqlPnfnNz0RHPKx8o4X1bjIZH1o7gct0C9o7XQ1R4JakPWHfzvaMri4Z1xdFVhr+NiWS7tUYuakI6mFjrfz10WoLk3z4pgP0bXDnamFtOtLSKiOlSXqYT8KpbjEs3YXLAqSHlA58z5PhZUenh+f4rach2PoRJL2aBCLu/w7IEUZ873ETr6epV5VeZFDKqDGtub0yyr8bB5ceCEXr9TbTLjg8TkGve7sX//fjZs2DDq/evXr2f//v2TMighhBCzSzqdIpfNAhAOz9wi/FyflFWO5UIIcfobbWLUkSLZxppHZSIUTSe49Xqsw7tJ//oHoCjYvUdwYr1olfOOWT/vWHz25Yc4kCxMwP7NTVulEC+EEFNg+CSnJ2t4gTcS1Llw9bFX2Y5EccHnUVlR6y0Vh4cX919tyXCwO4fjwNbNYX78/ABt/Xl64yZ1EYMz6r3s68yTM6E/b7IvsYKmdA1Br00m75LIOJy/uoyW3jztA3la+k22VHnZ1ZalNmTwiYtrqA5q/OT5AZbVedF1hWzOIZu3cZWhr1ckqLO9OT0kw348r8l0G567L2aOccfRWJZFT0/PqPf39PRgWXMrW1cIIeYq08zjODamWbjkMBAI4vX5CARGniF+MuUdi8OpfvLOxI45xQ7Au//Qyw+e6Jv0S/FnAzmWCyHE6a2YzZ78yTdIPfTPuPab/6dPdSSbounoi9ag1S0CQKttHDWOpiMT50CyG4D9iW46MvFJHYsQQoiCSFBn8+LACReHi9EwxTibk8mZ39Do58z5PlyF0vaGb99VoCducrgvx+H+PA1hg1TeoW3ApCtm4uCiRt8gTD+HMtX0uPPwexRCXo1U3kFTCoXOs5cGaKjwUBHUKPOqbFkeLHW2P30gRV3YoDyg0RA2yFouXkNFOfr1sNjxvr05TXVQGzWKp2iysvcny1jxQWJ6jfuncO3atfz+979n8+bNI96/bds21q5dO2kDE0IIMXOl0ylcxyWdThEOezAMzynpgD+Zzrmp7ACcLeRYLoQQp7dSNjuUJkYtdqKfiki2Ykf8SJ34g9X7y1kRqmV/onA8r/eXT/pYhBBCnLzhXdVjddYfryN8pHicwdtfdnS9Mq+GacPhnjx1YQO/oWCaCiouXXufg2QvkfJzqKvzc6AnT1VQY19njuzRYv3zB1OctzxIxix0xrsKLKrxsqimUFg3VAV0uHx9OTtaM/gMBZ9HJZlzeLUlQ5lfI5mxSeacwvM+TgzNZF9tcLLGig8S02vcnfBXX301f//3f88vf/nLY+77xS9+wde+9jWuvvrqSR2cEEKImSkQCKKoCoFAENPME4sNlLriJ8No3e4jdc6Nd5K5uT4pK8ixXAghTnfFbHYYuRNd1xRqyo1RC/CubWH3dw7poJ8oRdPRKueNWoAH8Kg639y0lXvO+VOJohFCnFaGd3bPdtVBjVjGpjqoAWN31o/UET789RjepT14+ztaM7x0MI3fo+Dism13gl+9GkMBQj6Xt/h2kE7G+EP8LCy1jJUNfq7cFGHTogCKUiik67pCNO3wwsEM3TGTNzqypLMO0ZTFY3sSdA7kCXpVGioMYmkbxYV1C/28dUmAWMamK2by7P4kiZw97m7yk73aQMwd4/6EfPKTn+SJJ57giiuuYPXq1axatQpFUXjjjTfYt28fV111FZ/85CencqxCCCFmCMPwoKoahuEhFhuY1Dz4sbrdB3fOLS+rhayff3uul864ddxJ5ub6pKwgx3IhhDjdjbcTfSTFKBu7pxWttpHg1usn9PiJ8qg6i4KVU7b92cQ0TT75yU/yhS98gaVLl073cIQQJ+F0y+PuTdmE/RqH+vL0puwxc8+Hd4QXJ1k11ML3rs1HM+SLr0s0ZfH0gRR5y+XpAykMFQ505fAaCm0DFgNJi76ExcJKneqwDwLzOJSeR85xAJcz5/v47a44zx1METAUAl6d2pBG3lIwbRdw8agq2w+neHpfkoG0TdCrsLjai6sovNaSAaVQhM87EPZrdMRMUArxNrNlIlYxe4y7Ex7g/vvv5yc/+QkrV65k37597Nmzh1WrVvHjH/+YH//4x1M1RiGEEDPYZOfBj5UTW+yc++5Z/4sz4udz9+/7eXRPEsd1SxEzYzleB+BcIMdyIYQ4vY2nE30kI0XZiFPDMAweeuih6R6GEGISnG553MXno7gcN/d8eEd4U08eXVUwHXfE12NHa4b2AZPDfXlMy2V3R46akIZHVVhd76EuYvDWBpPNnlexTJNmayGOalAX1hlI29z5226ePpCiP2nTlbDwaCohv040Y+EzFDyGSnW5TnNPnkO9Jj0JC1BwAFwXV4Gs5dLUmStlv1+2JsS5y4NsaPRPzQsq5rQJn5a76qqruOqqq6ZiLEIIIWahyc6DH9ztvixYjZE1abK6WBiqwqPqeFSdMsrpivXi1QvnknOmy7Jaz5yMmDkRciwXQggxXDHKxu5uGXNSVTE1tm7dyv/7f/+Pm2++ebqHIoQ4CadbHnfx+QzOex+vwZ3xI3XPKy5kLYd83iGetVlc5aEzZhIOKORMhw1V/VQnt3PEXUxaUensypPJO8SzFqkc+AyoChqU+zUKzfaF2Jm6coO2qMnKOg+NlV5qwjpH+vKkTZcKv8YFq8rIO5QmaTVUhd6UXep8X1QzGa+cEMca9/8MX/ziF7n11lsJBAofyoGBASoqKqZsYEIIIeamYrd7RyaOkTX58hu/5XAuyupIfSmappjv3h41uWh1GVduDlMTGr3D3bJd+lMWlXM0hqZIjuVCCCFGczJRNuLkLV++nL//+7/nmWeeYfPmzQSDQ68wvOGGG6ZpZEIIcWInF0Z6TLGY71Hhyb1JnmlKUhnQqY8YrFvoZ0Wdhwe3x4j3HGGj9zVeN1fjlDXidV3SMZOumI1lgwM4Dvh0m7CqES7TUVWF81YE6YxZeHSF1n6Lc5YE+ZO3VrK9Oc2z+5OgKOSdwomBpp48Z873sastW8q8P1nHm5xWzG3j/kR8/etf51Of+lTpi/uiRYvYsWOHZNYJIYSYdMWc2Cari8O5KKqqsi/ezWs9fayvrp1Qvrtlu/zgiT7ao+Zxc+NPd3IsF0IIMZIhJ6sr543rMa5tScF+Ev3bv/0bkUiE7du3s3379iH3KYoiRXghxJQ72QLy4Z4cTx9IsWV5kEU13hHXaerJc7Arx7NNKfZ1ZImlHBTXYnGth82LA3zvsR72d2bIpIP0ptYScyupdk1cIJFxcF3QVHAdcF2Iph2SOQfTgfNWFPZ75VkRvrOth2zeJXZ0UtbOgTwZ08Gvq3hUSln1puMS9mv0puxJ6YA/3eYEEJNr3J8I13XHvC2EEEKcCNPMk06nCASCGMbQyxsXhqpYHalnX7wbUmH+87Ecz1X28P6zI9SWG9SUG+Qdi8OpOPX+8tIEroP1pyzaj/4iVMyNryk3Tslzm2nkWC6EEGIkEz1ZfaoncZ0LDh06NN1DEEIwtzuZx1NAHuv1efpAioPdOYARi/CHe3I8sz9JS28O1wGPpjC/wmBVvZeuqMXTe2LEm7ez3NB4ylrAgBPBsh1i2UIOfd6iEDujFP52XVAAQ4OQT2VexGBJlYdXWzKsrveyIGJw6ZoQTT15DvXmiaZs/BGV5w+liaYsIkGdy9aEShPOTobhk9MKMdjc+h9FCCHEjJNOp8hlswDHZMsXo2m2d3Tz9Z938moiwbMtPTT1ZVle6+X8jfBPex7hcLqPNZE6vnXW1mMK8YOjaxoihuTGzyC33347n/vc5/j0pz/NnXfeOd3DEUKIOcl23AmfrB5pEldtnB30ky3vWHRk3jwZP/y2EEJMxFzuZB5PAfnVlgy72rIkMzYXnBEqLY+mLMJHC+FblgeJpixebcmQzNq4CoS8Gvu7c+w+kqU3aVLu11hR78W0IW+7/ObVfrr3vkZAzdLpbCDi0+lNWagUCu6qCrYDeRs8GnhU0A2I+DXmV3iYX1kY86G+PK+1ZcF1OXdF2dGTATl8HpWNjX5qwgapjE00baO4EA5oo3btn8gJmdNtTgAxucb9yVAUhUQigc/nw3VdFEUhmUwSj8eHrFdeXj7pgxRCCHF6Gdz9HggUMk+LfxcVL40v88P3Dz/B7kgbdmUexfbwnF7Fi1GFf32yj5iVxGOXEUu3c2RljKXlVUO2M5HomtPdTDqWv/jii9x3332sX79+yvclhBBidJqqTPhk9UyZxDXvWHz25Yc4kOxmRaiWr294H5/b8YvS7eJcMkIIMV5zuZN5PAXkRM6mYyDP4uqhJ2ubevKgKGxaFGBRjZftzWl2tWXpiJrgutRXeFha7WGvV6UvqVDm1dAVBY8H9nRkWa+/gm0p/Dq5Acs1yFk2hgZer0J1mYHpuBzpM9GUQhZ8XURnSY2XUEDBtRUs28FQFdJZBxwXV6GU896bsqkPG8yLGGxeHCCasohlC+s39eRHfc5z+YSMmBoTiqNZuXLlkNubNm0acltRFGzbntwRCiGEOO0Uu98ty0LX9WOiaAbnuPvLUzS7vWiai6lnMFydqNqHrio4toIL2K6NkatCtwIj7k/XlDkbQTPYTDmWJ5NJPvKRj/C9732Pr33ta1O6LyGEEMc30ZPVM2US145MnAPJbgD2J7rZOdA+5HZHJs6iYOW0jE0IMTtJJ/PYQl6N+goPIe/QiUyHn7xYVuMhmbGpC+l0xk2WVns4b0WQZ/cnaBsw8XsVtqwI80ZHmiU1HvZ2rOBA1EvGUsG1URTQj8bMBH0aCi6mBbGMha4q2K5LIu9QUWbQnbRwXZeldS4BnwpKofmoN2UDOV45nKa+XC+NLRLUuXx9eanLfTRz+YSMmBrj/p/l0UcfncpxCCGEmEOKXe+WZY0YRdOfsjgykCdnupgxL0vra4imOlDtAH6/yhmV1ZQbBi929VCRXECwYz31vggPv5Lk4xf65nS3+1hmyrH8uuuu4z3veQ+XXnrpcYvwuVyOXC5Xuj28a18IIcTJO5GT1YqmT1sETVG9v5wVoVr2Jwqd7+srGobcrvfLVdpCCDGZNjT6KfNrpcL04MiWzYvfbIiKBHUuOCPE9uY06bzDriMZFlYavNGZx7IdWnpy/ObFQ2zy7MKNbCGlhsnZeSyn8Hi/Bn6PSsCjkszaBL0a5ywN4NEV2vrzNHXnSGZs/B4PjZUeVA0awoXj2JJqD2W+whgf3hmnM2oS8KhDImXGc7JFTsiIyTbuT9MFF1wwleMQQggxhxiGh3DYMySWZrByn0b7gElXzKIurPNPl2+lPTfAN3Zt47noQXbG0ixgEVvMiwnqPswGL0GfTmfcoidhoqkKlXM8emYkM+FY/pOf/ISXX36ZF198cVzr33777XzlK1+Z4lEJIYSYjYpzxwzOgB9+WwghxLFOJO988GMAtjenSWRtUlmHZMYuFecjQb2UCd8ZN3nmQJJU1mFXW5a6kM5A0qJa6eIsYy/PDSwh5PHgM2xUFbALk616dJgX1lEUUFAI+VTaBo7Gwyhg6IWJWSv8On+2pZJn9qd48VCKxdVe1i30s6zGQ1NPnjPn+wBKOfVNPXmqg1ppMtbjPfe5PFGvmHzyCRJCCHFKDS68F4vxw8WzNg0RD9VlOoam0NyVxTVStOR6sVwH14V2u4eVtottKlSHXHKOwrxynf/ZHqMzbtEQMbj6/CopxM8gra2tfPrTn+Z3v/sdPp9vXI+57bbbuPnmm0u34/E4CxcunKohCiGEmGU8qj4kcmb47dNBS0sL8+fPR9O0468shBDjcCJ554MnZS3za3RGTcq8hclYE1m7tL1lwH8808/zB9NYtovtuHTHTUI+DY+qc05DjgWZffymZzXN6SrqyIHr4BztgneBnAnNvXk8GtSFPbiuS9uASXfcQldhcbWHtQsDvG9TuFD0z9ik8g6vtma4cHVZ6fnNixh8+NzCMWF7c5rOqMnejixhvzbqcx9ceJdceDGZ1ImsfPfdd3PppZdy1VVX8cgjjwy5r7e3l6VLl07q4IQQQpx+innw6XRq1HUqgzoLKg08ukp7NM8PnujlNy/aLPNX41V1fJrOAk8t5XqEhVVePnVZHdddUs0VZ4XpjFsAtEdNBlJWaZuWXfjlz7LdKX+OM9l0Hsu3b99Od3c3mzdvRtd1dF3n8ccf56677kLX9RGz6L1eL+Xl5UP+CCGEEHPJ4sWLWbNmDQ8++OB0D0UIcZpYVuNhXsQYNe88mrLY3pwmOuj7lKsAbmHS0+LjF1d7SGRt0lmHoE+lOqjxH8/08/vdCXoSJhnTRlcVAl6NvO3gVUwOJgL8tGMTh9JVuEBv3KY34WIO+ipgu4VCfCoPPQmbWMZCUwvxaUGvxpmNfi5cVUY4UCimb1kepCqos2Ghv9TlPvz5FZdtWR6kzKuSyNpDnl9RsfBeLMSP9TpNhpFea3F6GvdpnLvuuovbbruNj33sY8RiMS6//HK+9KUvcdtttwFg2zaHDx+esoEKIYSY/Uwzj2VZaEcnYx2Nrin8+ZZKXjiU5J9/l6Yr5nJkAG5/6/vod1OEAxoN3gra+m2WVHvxeVRCFArtDRGD9qhJQ8Sg4mi3wuCJXueV61y5OUxNyJhzXfLTfSy/5JJL2LVr15BlH/vYx1i9ejW33HKLdPgJIYQQI3j00Uc5dOgQP/vZz/jjP/7j6R6OEOI0MDzvfHjsykgd4BsX+gn5NKqDGk09eTwq/OT5AUzbxWeoNFQYKC68fiRLLG3j90C5T2cgbeFRbTb490DWZufAWlKmv7RvywUN8Blg2+BQiKNRFfDqoKigaSqbF3l5rS1HXbmO4sK/P93Hkr0e3rEqxIZGP//7oupS3MxIETKDn3Nvyi4V2od3uA+ekPVU5MJLt/3cMe5399577+V73/seH/7whwG49tpref/7308mk+GrX/3qlA1QCCHE6SOdTmFbFl6fD8MYvZvAsl3+/el+DnTl6EmaGIEMXqWMnzyXxrQVGiIqMHLszPs2lQMKNaE3M+H7UxbtURPHgUf3JDnUm2dZrXfOxdVM97E8FAqxbt26IcuCwSBVVVXHLBdCCCFEwQUXXMAFF1zAX/7lX073UIQQ02wyMspH2sbwQvDgQnRRsSD92J4Er7Vm6EvZ2LaLpilUBDR0VcFVwMUlmbPJmSqZvIlr5bm44nU01+F3A2vJWy6Dr3/VAI8BtSENBwXTdDAdl3BQQ3EVFLVQjLdchc2LA9RFDA525+iKWQykbDJm4UrnC84IsTmol2Jnis9lJCM9v+HP81QZayzi9DLuT9WhQ4c477zzSrfPPfdcHnnkES655BJM0+TGG2+civEJIYQ4jRS73wd3wQ/PiIc3i+a67jCw8Blsf4zebIT4/nOp8HvJmh4UBfwelfaoSU+i8EvW8Dz4osqgTkPEoKk7B4DXUEpxNTXlxrjHb9ku/Slr1k76KsdyIYQQw7muSyw2MOQ4PCnbtS2cWC9quBpFk86+0WQyGbZt28ZFF11EKBQacl88Huexxx7jXe96F16vd5pGKISYSSaja3qkbQwvBA8uREdTFjtaMygubGj0o7iQNV1UxSVvuyyr9eA3VLriJgousUwh391WHHKmwrmhA+QcjecS68g6GrrqEvBAJl+YXBXAcaAvZR8t5LsEDI1EykbXVQxN4Ui/hU9TiWdUVs7zcuGqIH1JGxUIeNRCVM5Rg5/L8BMOg29vXhw4oddvsp3qor+YPuN+l6urq2ltbWXx4sWlZWvXruWRRx7h4osvpq2tbSrGJ4QQYhYZqaA+2EgTsRYz4oHSfZVBnXnlOr/e302mMoprOySVPpxcgkxW4ZwlAUI+lc64VZqM9VBvnubePKvm+Y4psOuawtXnV9GTsPif7dFSob5iAr/sDI60ma2Tvs7EY/ljjz12yvcphBDiTa7rHHMcPult2hapB+/C7mlFq20kuPV6KcSP4r777uPnP/85V1xxxTH3lZeXc9ddd9HS0sKnPvWpaRidEGIyTUYX+2R0TY/V5T7SOF9tyfCrHTGKM2tVBjX2d2ZJZB1qQjr7OnPkbZf+pIVpucTSNoYGASVNzvXzTHQZaVPDdhU0XHQPGFqhAO8UB+CAlQfHdVEV0BQIejUUFPK2g0dTaOk3qQsb7GjJsLTGS3VIx28ozAsbpXEDpRMGcOwJB4l+EdNp3J+4t7/97TzwwAO84x3vGLJ8zZo1/OEPf+Ciiy6a9MEJIYSYPUwzT09PF8Wy9PG+yBcL9sVi/eDueF1TuHJzmDd6Eryh58mSRnX9GLaH8qDGVW8NM7/Cy0DKwrRd7nusD69R2HPeclha6z2mwK5rCvURg49fWM1AyqJiUDf7eDrci935wAl10c8EciwXQggxnKKoeH2+MedqmSgn1ovd0wqA3d2CE+tFq5w3ads/nfzHf/wHX/jCF0a9/8Ybb+SrX/2qFOGFOA1MRgH4ZLqmoymLV1syuEoh3320EwHDx+kqgAKKW5icdVdblmTWIZ6x0TVw0bFsh2jKIppxCBgKDZ4BLqp4g9/0nEFztqJUwHcAMw+Z4Tt1C5E0tlPIg189z8O8sE57zMKjqeRth6qQhq4oNEQMyvwqFQGVSFBnWZ2XVNahqSdPMmOz7fUEfkOhLWqyZXnh2Db8xINEv4jpMO6f3FtvvZXt27ePeN/atWt59NFH+dnPfjZpAxNCCDG7pNMpFBRc3FG/yA/ulB/aAV9xzLo+3ebwQAynwsCjhNDQ8AQsgl6VX7+a4OMXeqkpN4ZMxnrR6jKu3BwZkgc/nK4pQ4rn4+1wL0baDJ/0dTaRY7kQQojhFEUZ8Th8MtRwNVptI3Z3C1ptI2q4elK3fzrZv38/GzZsGPX+9evXs3///lM4IiHEVJnuAnBTT55dbVlwXUI+bcy89GTGJpG1iaYsllR5qAvpdMZNuqMmi6sMqkIaadMmm3fosy10VSGWdohlbBqCnZxb2cSjfStozbxZgB+NAng9UBXUyZgOdeUGq+p9dMUtyrwqZy0OYDrQEDYIelWSOYcyr8ryOh/LajzE0jZPH0ixbr6PjmievGnjuiq6qtCbsofEzkj0i5hO4/7krV+/nvXr1496/9q1a1m7du2kDEoIIcTsMzjvfbRM2cGF95Hy4Yss2+Xubd109Rio3ghKMMaaijoWO9WEPB4641apm70/ZfHnWypJZO0h3e3jNd4O92KkzfAu+tlEjuVCCCFOBUXTCW69fkZmwh8vOu9UsyyLnp4eGhsbR7y/p6cHy7JO8aiEEFNhOgrAg6NlisV1Vzn+iYADXTkGMjaKC2V+jYG0TU/c5un9SbKWS0/cQlMU/B6NmvLCc+pO5NHsPBtDLfyicy3dVgTreBV4CtE0ugqa5qJaoODSFbM43JfHxWVBwsJvqAR8KhsW+mnqyVMd1OhNFaZ37U3ZhP2F2yGvxqIaH0uqDOZVeKTjXcwoE/7p37lz54jLFUXB5/PR2Ngok8YIIcQcNFLe+3DDC/UjrW+aeQ53xelNueQtDd/Bt6L6MyxeWksiDSGvS0PEQ8inTUpG+0Q63Id30c9WciwXQggx1RRNn/QImrxj0ZGJU+8vx6OeWCFrpLloptPatWv5/e9/z+bNm0e8f9u2bXKCXAgmJ099rommLB7eGUdXC9+RNi8OcMEZodJ925vTI05Y2tSTZyBtM5CySgX7jY1+cCFnOxzuM+lLmjRWeFk5z0tX3CKZManWY7Q6Zfyf1rMpTJk6Po4LeQv6kw5eQyGWdUjmHAwNNFUlkbFp6spTV64TWR1ic1Dn8TcShVicjM2GRj/w5omFMr8mnxMxI034E7lx40YUZfQih2EYfOhDH+Lee+/F5/Od1OCEEEKcXsYq1Bc70yzLIqiZVAbBq6sYropm62hoVAQUPvSWCKvq/fQkTJq6c3gNhfaoSU/CRFOVMXPdR3I6dLhPlBzLhRBCuLaFE+ud7mGMW96x+OzLD3Eg2c2KUC3f3LT1hArxY12JNx2uvvpqbr75ZtauXct73/veIff94he/4Gtf+xr/9E//NE2jE2LmkAk1J66pJ4+hKpiOe0xH+FgTli6r8XCwyqA/adITM4lVeYhnHPxeFTPjlCZA7U6a9OwxSWVyXFSxB69u8gobJ1SA1xTwGwqaqlAd0rBsF5+h4tEVVtb70FWFlj4TVYVYtjSNayGn3nVxlWOvMJDPh5ipJvzJfOihh7jlllv4m7/5G97ylrfgui4vvvgi3/72t/nSl76EZVnceuutfP7zn+db3/rWVIxZCCHEaWKkjHhN1/H4fET8CnVhG9dxKPNpHO7No6oKT+5NsazWx/9sj9HcmwfggpVB/md7jM64dUJd8adLh/t4ybFcCCHmnsGTkGvYJH72/2F2HsYJrsW1rVMeGTPRrvaOTJwDyW4A9ie66cjEWRSsnPB+x3Pl3qn0yU9+kieeeIIrrriC1atXs2rVKhRF4Y033mDfvn1cddVVfPKTn5zuYQox7aY7T302GvyaDe8KX1bjIZG1SWYKue/D1zUdSOVcdrRkiGcc9nRk6I7bHOnPkcg45G0AmzI9x3trXyftGDzYcSYuI38HUwAXMFSoK9c4Ei1EyWgKLKv1cEaDj7Tpksk5pPMOb19ZxluWBti2O0E8axFNOxgqPLYnwZIqD4oL6xb62bjQP0WvnhCTb8K/aX3961/nO9/5Du9617tKy9avX8+CBQv4whe+wAsvvEAwGOQzn/mMfHEXQggxpsGXhHu8AXoSNnWhMloHXHrSWdYu8LG7LUckqNMRtVhd56UzbrG/K8uh3jwr67yYtss7VpfxwEsxYOxcd1Egx3IhhJhbhk9C/pfrzEIB3nFwLRMn1jvp0TFjOZGu9np/OStCtexPFB5T7y8/RaOdevfffz9XXHEF//mf/8m+fftwXZdVq1bxla98hauuumq6hyfEjCATak7c8NdseKRPyKfRGTXZ0ZpBcQvd5bG0zY7WDKblYuhQEdQo86v4DJV0Pk/edjBtKPakL/b30ZMP8ofeFThjdMAXo+FdFwbSNn4dTAcW1RjYQNZyGEjZ9CZsNjb6WVrnZVGNl+V1eQ715Cn3KezrzJHIOhzoypHNO6xb6JfIGTGrTPjTumvXLhYtWnTM8kWLFrFr1y6gcJl7R0fHyY9ukNtvv50HH3yQPXv24Pf7Oe+88/jGN77BqlWrJnU/QgghTp3ipeAeb4D/+2yCI/0mR/o7KPO6xHMO3lAO2zHQVQPHdclZLksrDJ7ckyx1wV+0uowVdT4aIulx5bqL6TuWCyGEmB7DJyGP6RG88xZjdjaj6AZquPqUjudEuto9qs43N20dd/f8ZOTHn0pXXXWVFNyFEFOqqSdPU1eOVw6nWVHrZXF1ofs9mbHZ1ZYF16V9wKR9IM8bHTlwXSIBnd64RXfcxrRAVRQ01aXBEyNuediZqIdEPYzSAT+cooCqQN6BMq9CPOOQyjlEkxa1YYOGiEFdxCh15m9c6EdxIZGzUVEI+FTSWYeDvXmUcUz6KsRMMv6gpqNWr17NHXfcQT6fLy0zTZM77riD1atXA9DW1kZdXd3kjRJ4/PHHue6663juuefYtm0blmXxzne+k1QqNan7EUIIceoULgmvIJFXaY+aZEybvR053mjP8or/cZ73bmNf5Eme2B+nN2FRFVB5z4Yw3UmbVfWFXxyv3BzG51G5+vwqrruk+oQnaJ1LputYLoQQYnoUJyEHCiery32EPngjkY9+HiVUSTyZwDTzx9nKyck7FodT/eQdq9TVDozZ1T74MVAoxC8KVo6rAP/Zlx/ir1/8MX/zykOlx89EX/ziF0mn06XbAwMD0zgaIcTpbFmNB8txiaYKRffelM3mxQEWV3twXZec6aAr4DVUyjwqXk2ltT/PjsMZOmJ5cpZDznRZ7O/hfXWvUedNUCi+H/+7V5kfakMaVWUKKOA1Cg/TVLAcqAsbbFjo58xGP5etCZU63CNBnQ2NfuojHs5bEeTC1SHWzvdRGdRKJxGEmC0m3BLwL//yL1xxxRUsWLCA9evXoygKO3fuxLZtfvnLXwJw8OBBrr322kkd6G9+85sht3/4wx9SW1vL9u3bOf/88yd1X0IIIY7PdV1isQECgSCGMfZkq2OtA4XiwLxyndfbLAxdwfFmyBgxPI6C6YthaClyVpjnmzP8r/Mcyv0q0ZTDsloPNaFCUWF4rvvg7Fspyg81XcdyIYQQ02PkSch1tMp5uK5DLpvFtS1C2Kjh6knPhx8pfuZ4Xe0nMxHrZOXHnwpf//rX+dSnPkUgEAAKV6Xt2LGDpUuXTvPIhBATNTzuZaaJBHUuX19eip8pdpsf6itMypq3HLyGytJqD36PykDK4mBXjkN9ORQUHGxW+4+wOdzCr7rP4HBmfP+vagrUBHSW1nk53GsS8oFHV4hnbXy6QkOFwVlLgqyo9ZLMOfSmbBbVvPn4Ha0ZXmvNkMj6uXB1iN6UTdivHbOeEDPdhP9XOO+882hubub+++8v5dV98IMf5MMf/jChUAiAj370o5M+0OFisUL2b2XlzPxlSgghTnfFL+3AqBOcDc58H88kaH6PytJaH+GgF8VXTZQBtEwI1Qqgay6O4/BfL0RJZB0qghp/vqUS1zGJJYcW+odn30p3/FAz5VguhBDi1BltEnJFUfEYOuq2H5Hsa0erbSS49fpJLcSPVBQ/Xqb7yRTSZ1N+vOu6Y94WQsweTT2FYjYwY/PrI0GdC1eHhixT3ML3sLzlMpC2OPxGHkV1aes3OTJgksgUcuA1HDbWJniw40y686FR9nAs24VoxiaWtqkp19FUMG2XvOViGApnLQnywbMjAKWTGEXRlEVTZ46s6ZbiZ2SSXjFbndD/CmVlZVxzzTWTPZZxc12Xm2++mbe//e2sW7du1PVyuRy5XK50Ox6Pn4rhCSHEnKAoKl6fr5TrPpLifWOtA4Ws2s64RcCrMl838BkKZ+cvwNZilJdFeKnCpC9pYdkuLzSlOWO+j1jGIZG18bhvFvoDgULhP2l5OdJvkrMcjvTLRK0jme5juRBCiFPDtS2cWO+IHe6ubaE4NmV2jlRfOwB2dwtOrBc7Uj1pmerDi+JV3sBxu9xPppA+0fx4IYSYDLOpOBxNWaWO+MXVHtqiXt5oy/Dc/hSOq5C3HaJpG9MEFJuzwu1sjy3g4e4zTmh/6ZxLb9Kmqgy8ukosVbgC+j3ry7nojFCp+L55caA0vqaePImsTciv4feqbGj0AzJJr5i9TuhT29TUxJ133skbb7yBoiisWbOGG264gWXLlk32+Eb0qU99ip07d/LUU0+Nud7tt9/OV77ylVMyJiGEmGsURSEcrhhznULm++i/hBbjakLeAA0Rg/aoSUWZRn/SZn97nlhaRQv24FVCpHIOtutiZF0aqxxW1fuoCOq4zpuF/mLnva47tEcLnSiVZRp+Y8JToJz2pvtYLoQQYuq5tkXqwbuwe1qP6XAv3ufEDdJ/+DF5TxgjG0WftwgrFOFvTjAKZiTDi+Lj6XI/2UJ6MT9+plMUhUQigc/nw3VdFEUhmUwe00BWXj5zu/mFEAWzqTjc1JPntdZMYaZUoKHC4Pn9KdI5m4BXJ+BViKXBUE2uqHsdF4Wd8Xry7sSfnwK4QF/CxKtDEod4xmF+hYfacoNtuxNEUzbJJQEuOCNENGXx8M44hqpQW66zrM47YyN+hJiICVclfvvb37JmzRpeeOEF1q9fz7p163juuedYu3Yt27Ztm4oxDnH99dfz85//nEcffZQFCxaMue5tt91GLBYr/WltbZ3y8QkhhHiTaeaJxQZGneytWDTP59KliVWvvbiGiqBGLJulY8GTHKh9lH2RJ1E0h0zexbJdIgGVP99Sia4ppcldDcNDIBDE6/Nh4WdeRMdjqPSnHL73eC+WLZd3F033sVwIIcSp4cR6sXsK34GKHe4j3df/0uO0NrWwP12G5z3X0JlPH1MkP1mDJ1Ud78Ss452IdTZzXZeVK1dSUVFBZWUlyWSSTZs2UVFRQUVFBZFIhIqKsZsehBATE01ZbG9OE03N3Embp9qyGg/rFvo5c76Pg705fvJsP9GMhaIoKAp4VAWvm+GqhldJ2l4e7DxzXAX44QGgGjA/ouPRCrE0PkNlaa2HyqCGaTs8tT9JZzTPQNrGPfrgpp48uqpgOi4bGv1sXhyYtAK8vPdiOk34U3zrrbdy0003cccddxyz/JZbbuGyyy6btMEN5rou119/PQ899BCPPfYYS5YsOe5jvF4vXq93SsYjhBDi+I6XCV+MqdF9PtqyA1R7/aRSCTTFpHPBM8T9HeBoWAZ49RR+tZzacp2c7ZLI2vg8Q88lFzvvg7ZLZSCBaWUJetVCZ31XlhV1PsmGZ/qO5UIIIU4tNVyNVtuI3d2CVtuIGq4+5j63x8G2wfZ5INFPrKeP+gXzpjRTfTxd7nnHmhNxMo8++uh0D0GIOWc2ZLefrPFOEtsdN3l4R5zehEUiZ5O3QE87uC6ENIcDqWqeGVjEseX1Y2mAzwM5CyynsMxrwPmry3jmQBLTguW1HjyGRqLMwaer6CrYrsKW5QE2LizEzQyO9Zns7ve58N6LmWvCn7g33niD//qv/zpm+dVXX82dd945GWMa0XXXXcd//ud/8j//8z+EQiE6OzsBCIfD+P3+KduvEEKIEzc8E74YP1OcRNUwPGhBh+tf/BmHU1EaPRGur3sH+2P9OP4EOBooNoZZRpVeRmXQQ9Zy6RiwyJoOlu2OWFTXNYVrL6lBUaA/adMRM/nxcwMsqPDIJK1M37FcCCHEqaVoOsGt14+YCW85DtbFf4bym9/RvfBt6APtWJULiMyrwzgFmepjxcXkHeu4mfGniwsuuGC6hyDEnDObsttP1OBi8zIo5b9XBjV2tWUp96vsOJzhmQMpYmmTRKbQqQ6wyN+HXzN5PTmPZwbGnttrMBsKRXwNXLewPU0FB5fltV4G0jbRjEusL4dCYWcLfQaN1V7qIgaRoD7ukwcnai6892LmmvAnuqamhh07drBixYohy3fs2EFtbe2kDWy4e+65B4ALL7xwyPIf/vCH/OVf/uWU7VcIIcSJG54JP7gz3h9SaUkN8JUdv+TJ3kO4jkarlaeit5/9/p1k7TSaAp5UPVVHtlAT8bK01ovtwKGeHN/6dTdn1Pv4+IXVIxbVfR6V6y+rZX9Xlh8/N4CqKLRHZZJWmL5juRBCiFNP0XS0ynnHLE+nU+RNC1dRWfHnnyQfT1OzYD6Gx8A082TSKRoCZRjTUPweT2b8SOZK97wQ4uTMpux2GH9X+2CDi82D899TWZv+lE3ngEl7LE88Y5POQTG488xQB2+vPMi2npUnNFaHQqHR0MGrKJT7VeIph+XzfLT05slbhSaqeMbG79GIlL2Z+T44Cx6mplN9tr334vQy4U/eJz7xCT75yU9y8OBBzjvvPBRF4amnnuIb3/gGn/nMZ6ZijEAhjkYIIcTMNrzTffDyeDyGZVnouo7u8/HZlx/i9VgbR5JRcFUcxcbN+HmpzSSxdADDLMN2HdZm38aZZ1SSyrl0xy3CAZXuhEU679IZs7hyc5j6yMidDLqmsKLOx4IKD+1Rk4aIQYX80jVtx3IhhBAzRyAQxLUtsC2cfI5gVRjDUzhJfbw4ualWzIyfSBzObO6ev/vuu3nwwQeprKzkmmuu4eKLLy7d19vby1ve8hYOHjw4jSMUQkynE4lQGVxsXgZ0RD10RS3OWhnkP5+PcmQgTyrnkMkXCucA51Y0c2aog//XuY6OXHhCY9QAFCj3QplfI+zT0VTIWg7dcYt1C32savBR7lUp82kkszbxnMNla0IsqinESG9vTpey4KVTXZyOJvxbyRe+8AVCoRDf/va3ue222wBoaGjgy1/+MjfccMOkD1AIIcTMMbjIDuDa1pBL3Ef70p5Op0gmCpO6RSoq6czG2RNtx7VsFFwUQHFVbFsjl/KS6i8j743hzVeQGPDzSiKD7ToEPBo1QS+27RLL2AS9KuPJJ3z3+hCxjM3yWsmEBzmWCyGEAF1V0f/wf1FSBvqj9+P/wKdL9w2PkzvVPKrO1ze8j50D7ayvaBhXMf1Eu+en21133cVtt93Gxz72MWKxGJdffjlf+tKXSsdn27Y5fPjwNI9SiNltqiNOptqJRKgMfs6xtM3rR7KE/RrNfSZnL/LT3p8jbzt4HMgU6vvETB//3bGBATMw4TF6jEI3fd6GhgoPZzUGaB0w2duRxTCguTfP2UuCzIsYbF785vaLE6Uuq/FMaRa8EDPBhD/ViqJw0003cdNNN5FIJAAIhUKTPjAhhBAzz+AiO0Dqwbuwe1rRahvxvPd/Y1kWmq4f86XdMDyomobH4yUQCFKetFmoh2jK97HaU0m7ncaj+ugyk+S1DGrrOgIKqLkyMl7ImxYBj0Imb5NzHRoqDBJZh7cuC1ATGv1QZtku//ZYL4/uSeK6Lm9ZGuBTl9YeM6HrXCPHciGEEE6sF7unFbSlaLFutHQcfAFc20JN9FM+LEP+VMo7Fp/b8YsJdbWfSPf8THDvvffyve99jw9/+MMAXHvttbz//e8nk8nw1a9+dZpHJ8TpYbZPxlnsah9csC4WqUc7wVCMoPnlK1GSGZuBjI3P0DhnSYB9XTlAwaurWPkcF9fu5+n+xexOHhtdNh4KkDMLRfigF2zbpbkvRyLrsGmhj1jO5bK1IbwejeqgNuQ5DHlvFgeOeZ7F5yKFeXE6OKlPsHxhF0KIuWVIZ5xtYQ+0AmB3t5DuasX2leP1+YZE0UChg97r8Zbui5SV83dLLuBgrJsfdr9K0oqBYxF06knM24XpjaOmy/F3nElFWRivbpDNO/g9KvG0w8p6L1VBnb++uGbMzvb+lMWh3sKlltG0zR9eT6KpCtdfVisd8UfJsVwIIeYmNVyNVtsIfWCHa7ED5ai2NeQEe3Dr9dNSiD+RrnbPKZhMdiocOnSI8847r3T73HPP5ZFHHuGSSy7BNE1uvPHG6RucEKeJ02UyzpFOJox2gmFZjYdf7ojx+pEssbSF5cD5K4O8eChNLm8DLm4+zftqXyNheUnZJ/baaCrgFCZl9WpQEdTwGSrRtFOIldZU3rbMh9ejsXlxgO3N6SHjHem9KT6nRNamfcBEP5oRvwwpyIvZbVyf2k2bNqEo4ytWvPzyyyc1ICGEEDPXkIlWNR2tthG7u6XQCV+3ECWXG/HS9eGXtRuGh4UNC0l7VXp7XqTeGyJl2tRll/J64BWwXayKdtJlUQ7lKrhQuRDH0Qn5wKPr/Nm5lSyvO360TGVQZ0m1h/2dhdmGQn6VgZQzJydnlWO5EEKIomK8nP+9/xseeBDroveTyeXQ0vFCdzyFE+xOrHfESV2nwuBJVU+0q92j6rMigmaw6upqWltbWbx4cWnZ2rVreeSRR7j44otpa2ubvsEJcZo4XSbjHKlgXfz34A5zKBSr19R7aerK0hErpL6/cChNxnToTzjU+DO8t2YnB1OVPNq3/GhA6MRoCugqWIBHgfqIzttWBLnkjBAPvxqnL2kT9CjUho8ttg8e7+B4msHrJDM2xqCM+Nl+RYMQ4/rUvv/975/iYQghhJiNgluvH5IJ7/GNnB84pHg/SJ03xGJfBS9095AeiJA8VIW7KIwS7C/8GuiopI0YL7QM4GZCzA/DRWfo4yrAQ2Fi1o9fWM17Nob5yfP99MRtGiL6nJycVY7lQghx+hs+V8toUokY+Z4O3Jp6FN3AGywjEAiiqqEhJ9jVcPWUjXVw0R0oTaq6rKyaT6+6iK9veB99ufQxXe2DHzdbut3H8va3v50HHniAd7zjHUOWr1mzhj/84Q9cdNFF0zQyIcRkmaxM+pFOJhSXDe4wB+iMmrhuYf4PTVFIZB1sxyaWcQGX7rSXp/sX80aylvHMsTWcAnh18OqFInl5QMVrqOxsybByno9Lzyxn2644oKC4hZMC1Wmb3pR93IL64Pidwa/bsqP3z/YrGsTcNa6f/i996UtTPQ4hhBCziGnmcRwby3EwJtAhN3hi13Q6hZs3+WTtRex5rh2rPwCuRrjlbeBLEp+3C8cXR82Eyaf94LhkLLh8Y+HLenfcpDKo4zpmaZvDY3CgUIivjxiEvBq9ij1pr8FsI8dyIYQ4vbnjjJJxbQt124/QOg+jzluMojQSDleU7h9+gn0q5B2rVHRfEarlmuVv5/VYB5qi8mjXfnbHulgXqT8mC37448aTFT/T3XrrrWzfvn3E+9auXcujjz7Kz372s1M8KiHEZJpIB3ex8Fwd1EoF6/EU7pfVeEhmbBJZmyVVHhJZm6f3J+mKm2RMB8eFWMZldVkXywN9PNx9Bm8k6074ObmAV1fxexWWRQwCHo1DvTnylsuPnxvgb99dy2VnlqO44CrQ1JXj9z05ltR4S+Md/PdIhp90OF2uaBBzl3x6hRBCTFg6ncJ1XNLp1JAO98FF9mJBfHjhPZfNYlkWtmWRSKX4+asOGfPN7gufplNphJnXdy4ZLY2VDhC1NRQgk4dcLscPnuijPWrSEDH4wEYVO58DGLHbHgrZ8J1xC79HpTNuzck4GiGEEKe30kSrjB0l48R6cfva0Q0Dt68NKhqG3K9o+qRE0Iz0O0HR4Mz3vfFu/nH3NrqycWzXQVNUDFUbMQu++DjHdXkt2k5LaoDloZqTHut0Wr9+PevXrx/1/rVr17J27dpTOCIhxGSbSCZ9sWC/tyNL2K8B44teiQR1yvwanVGT3pRNOutwuCeP47hUlxn0p/JsKGtlc+QID3edgXMC3e+DVZUp1IZ0NFUhHNBZ3+jnbcsDPLQ9hldXeHJfiv99UXXphEL7gMm8sIF1NFpGCupiLpJPvBBCiHErfqE2DA+KqhyT/14sssObBfHBy4rr57JZevt6ONyb5Tfmq0SXJNCzYZb0nkdjpUoy7VJbrlNZVsaFSy2+9hsTC4Vyn4qt+GiPpgFoj5rk3DBB37FjGawyqNMQMUqF+7kYRyOEEOL0Vpxo9XhRMsPXw5maY+JIvxMUDc58XxCIcCQ9wIJAhJxtUecrJ2pmRsyCr/eXs6ysmke79gPwnb2P8u2z/njWd8MD7Ny5c8TliqLg8/lobGzE6/We4lEJISbDRArOxUL9uvm+Uif8eERTFsmMTTJns6PZpD9tYdkuLhAJaCzWWljm7eRnHevpzZed6FMBIGjA2oYAb1seJJ61WVbj5bwVhe9iPo/K3vYc71gZ5OGdcUzLxdAVzpzvY1dbli3LgzKpqpiz5JMvhBBi3AZ/oVZV7ZjOtuETsA5fVsyG77N6ME2b3xzO0W/EUVywfFEyapLeZCV15S7vOauSjQsNUvEBLt/g50gMllR7WN0QouGQWSqo14b96NrIWfQAlu3Sn7L48y2VJLI2FUF9XHnyQgghxGyiaPq4omQGr2cHynH+3/9gmvkRI91Oxki/ExR5VJ1vbtpKRyZOlTfA3736C/bGu8k5Fv35FAuDFXx9w/uOKa57VJ1Pr7qI3bEuDFWjKdlzTLf8bLVx48YxJ1A3DIMPfehD3Hvvvfh8vlM4MiHEqTS4YB8OHM1EP7p8LDtaM7x0KM2BziyWS2lC00w2z85+i3KjhpeceSTtEzuZpwKqCqoChqEQy1hsb05TXaazcJVBU0+eRNamzKvxnk1hAHTVojWWZ0mNl11thc7+3pTNotl9AZMQJ0yK8EIIIcZtrC/UMPIErMVlppknFhsoFOO9fjqzQQ52GXhqIuS9MSrcEB7d4Ug0R2dUpyseZcsynQ9t0rnqHC/92QALKz3Es/a4C+qW7Q6Jrrn6/CopwAshhDhtjTdKprheMjYwYrzcZCge//OOxeFUf6mrffCkqsXi+Tc3beW5nkN8+bVfoSoKHZkYfbk0ZfrQYnPesQBYE66jKdk7Yrf8bPXQQw9xyy238Dd/8ze85S1vwXVdXnzxRb797W/zpS99CcuyuPXWW/n85z/Pt771rekerhDiJIx3otaJZMkrLgykLCwXDvVkSWUcPGqeyypf5yUWcCB98pXvRVUGFUGNgbRD2K+RytlYjsvDO+OsW+CnzKsyL2IM6d5/69IAvSl7SMa9EHPVhIvwjz32GBdeeOEUDEUIIcRMN1KRfbxKefCOy388l+KNdpeOuEYw9TZCvhTasl0cqHkKuyxA6OB5mP1emiIKr7TrNPWZ9CSitEfzNFQYLKjwjKug3p+yaD/6i2t71JQs+KPkWC6EEAIKJ9VHipcbL9e2xuy8HzyR6rKyalzg4NHi+Tc3bQUodcP/pOVlujJJFAUuqF2G6VjkHavUDT90WzXctfkqGoMVp0UUDcDXv/51vvOd7/Cud72rtGz9+vUsWLCAL3zhC7zwwgsEg0E+85nPSBFeiFluvMX14VnyYxXvNzT6SeUcfvFKjFjGIeCmeV/163TkQhxKV53UeFWgukxhY6OfgbSDoti4wNtXleHY8JalAfIOx4yr+NyKne/SAS/mugn/xvJHf/RHzJ8/n4997GP8xV/8BQsXLpyKcQkhhJilBufGm2a+FENT/IKftLx0p9K0xRRsB2xXxc5BIh/FMpK45THii5/BanoHT+5x2dtuYjoK6+b76IxaVJXp4y6oSxb8yORYLoQQc1cxpq0yqGMYnhHj5Y7HtS3s/k4yj/wEp68NrbaR4NbrAYYU5QdPwLo71gmAx4V98W5aUv3cuedR3oh3ssAfYWdvH7odRFUdUpbJDdv/u1Ss96hDt9WU7MFQtdOmAA+wa9cuFi1adMzyRYsWsWvXLqAQWdPR0XGqhyaEmGTjnah1eJb8WMX7SFAnkbPZ3Z5BsSz+eMFOdifqeHpgMZzAJKzq0UepKvg9CivrfSyq9tJguzx/MEU4oOPYsHFxgIVVY3f0CyEKJvxT0t7ezv3338+PfvQjvvzlL3PJJZfwV3/1V7z//e/H45HLSoQQYq4rdrynUkn0o11xgUBheSAQxEABX5Ks7eDTFVJ5sOwAbjaA642Bo+F4U7ieFHa+HNtxiWcc9nRk0VQFQ1OOKahn8w4He3IsrfHi86hDxvO+TeWAQk1IsuCL5FguhBBz00gxbUXH62ofvF7qwbuw2w5g97ah1i/G7m7B7u8k+9h/Yfe0loryxQlY98a7WBSoQO84yGEry3I9QN7M81j3frK2SVOiF8XVUVGoc+o5korj0RX2J7o5koxRRjk1/lBpMtfTKYamaPXq1dxxxx3cd999pWOxaZrccccdrF69GoC2tjbq6uqmc5hCiEkwkYlaB6sOauztyLJuvq/UFV8d1GjuzXOwN8fPnh8gm82Rdgwe6DiTfnPiVzkpgKZA2A/V5R78hkp9hcFfbKlkYZWHpp48W1YE2dWWpdyvjjsuRwhROLk1IZWVldxwww28/PLLvPTSS6xatYrrrruO+vp6brjhBl599dWpGKcQQohZIhAI4vX5iEQq8fp8BALBUmE+mozz2Zd/xlPar2ipfoakncf0JHAVG9fRwFEBFy0bRskHSwepkE9lQ6OPs5f6+bPzKoZE0WTzDp/6v63c8tN2PvV/W8nmHeDNQsO9j/bxi1di0/NizFByLBdCiLlppJg2eLOwnvzJN0g99M+4tjXqNpxYL3ZPKxieQrXGNNFqGwEKywG7uwUn1otH1fn6hvdR7w/TnOjBzWa4YyDPlzsH0NJxHMfBdhwsHEKKBz8B3l22hTWRQqF5eVktv33J4u4/9HL/UzFuX/9+7jnnT0vd8aeTf/mXf+GXv/wlCxYs4NJLL+Wyyy5jwYIF/PKXv+See+4B4ODBg1x77bVTsv+7776bJUuW4PP52Lx5M08++eSY6z/++ONs3rwZn8/H0qVL+dd//dcpGZcQc1k0ZfHYngSPv5EgmrLoTdmE/YWi+8M74xzsyrFtd4LfvZ7g5y9HqaeFq+pfRlOcEyrAA2gqeD1guwp+Q+VtK4L86bmVpW73zYsDnNkY4MPnVvL2FWXHZMALIUZ3Ur+5bNy4kVtvvZXKykruuOMOfvCDH3D33Xdz7rnn8q//+q+sXbt2ssYphBBihivG0AQCQcLhiqNLixO5Fm71kmdvtJO+lEvWEyW75GkcbxJyQVxvEiUXRFFcvG1n4lU1Gqs8nLU4QFVApTft0BAxWFztpT9lUe7TiGdtumImnTEL23HpjFk09+ZY3eCXPPhxkmO5EELMHcWYts7+DEu8CSK+auDNwrrrOlhH9mH3d6LXLBhxG2q4Gq22Ebu7Bc+Z5+O76EOlyWCLy7XaRtRwYdudmTgv9h0m51h0+Q2uTZhotQvJ+oNYOJg4KC6k3ATVaHxqSyNB30o6MnF008/3Ho0C0BrNsrffZm1N1WlXgAc477zzaG5u5v7772ffvn24rssHP/hBPvzhDxMKhQD46Ec/OiX7/ulPf8qNN97I3XffzZYtW7j33nt597vfze7du2lsbDxm/UOHDnH55ZfziU98gvvvv5+nn36aa6+9lpqaGj7wgQ9MyRiFON2MZ3LWpp48Lx1Ml06YLq72sLcji4uLriqYjkvYp5LP26zS9zPP08nPu9ZguxPrt9VV8OqgoBDwgK6rXHxGGTVhDw1hY8wIHOmAF2L8TuinxTRN/ud//ocf/OAHbNu2jbPPPpvvfve7/Omf/in9/f3ccsst/Mmf/Am7d++e7PEKIYSYoQb6+4jGBgiVlRMIBktZ8PDmhK5+x2KBt5LOeC9q3k8+1A2qA/4EOCoKKnX5BlZWlZHIa6xf6McFPvDWSnQVQj6Nf3+6nyMDedoHTBoiHmpDGqblEMs4RAIa88IG3XGTcp8mefBjkGO5EELMPbqm8LEtYaL//SOMzjZyP28E5qOGq1GrF2DufAIUyD76U4If+PSIsTSKphPcev2I0TUjL38zCk7x+DAv+2Oua3+F1pd/Rs6x8CgqpmtT7biUOybRgTYq5q9iUbASy3ZpiBi0RrO85HmUl3bHWVlee1p2wgOUlZVxzTXXnPL9/tM//RN/9Vd/xcc//nEA7rzzTn77299yzz33cPvttx+z/r/+67/S2NjInXfeCcAZZ5zBSy+9xLe+9S0pwgsxTuOZnHVZjYdXghoAyazN0wdSGKpCyKtRH9FYVuNhR2uGBXo7ptLLf7VvIGb5JzQOnwG1ZToohS74SFDn0rXlnLnQz+bFAaIpix2tGZIZm2jKkux3IU7ChH96rr/+en784x8D8Gd/9mf84z/+I+vWrSvdHwwGueOOO1i8ePGkDVIIIcTMl8vnsEyLVDoJQCqVpKamcDn54Ilav33W+/nm75t5qi/F4bLfAQ7gouSD6IrKO/xn44QDeLMKilLIfy/muXfHTdqjJpmcS9uASWVQp3XA4cyFfizbJeBV+eGTfcQyha75P99SSSJrUxGUPPjB5FguhBBzx+CJWHVNQU324Ym1gVKIjaGiDkXT8V/0IZz2JjA82L1HcGK9pQ734RRNH/G+wcvzjkVHJs48f4iL6lawO9bJylAN1+79HV25JCouCgoOLn4UQo7DSj1AQ+2bk5PqmsLV51exq7ebl3bHURTYn+imIxNnUbByal6wadTU1MSdd97JG2+8gaIorFmzhhtuuIFly5ZN2T7z+Tzbt2/n1ltvHbL8ne98J88888yIj3n22Wd55zvfOWTZu971Lr7//e9jmiaGceyVh7lcjlwuV7odj8cnYfRCzBzj6WwfbDyTs0aCOh88O0JTT55E1iaVdzEdlw2NfiJBnWw2y5palbYFy9iRryLfZaIA7hj7VYEyX+Hfhq6yap6XmnKDiqCGX1e5YHUZeefNcUWCOiGfRmfUpKknL53vQpyECf/07N69m3/+53/mAx/4wKiTtzU0NPDoo4+e9OCEEELMHlVVNWiaRllZiGQygcKbk7RGYwMYuoGqqni8fv7X2krSMWixPbj60TxaI4ueaCCd9lAd9uCS5483B1m3MFQqoFcGdeaV6zzSliBrujT35rlwdRmqAp1xi3KfykDKQVXhSL/Jod4cK+p8UoAfRo7lQggxN4w0Eas2KE5Gq20ER8c086Q0L2r9Uty+tiFxMici71jc/PIDvBHrYk14HrdvvJLOTIJXBo7wu449gIsNaIBPN7igeimfWbCZxXVL8Rq+IdvSNYW1NVWsLD/+pKzFwn+9v3zUTvnxrDMdfvvb33LFFVewceNGtmzZguu6PPPMM9x777384he/4LLLLpuS/fb29mLb9jETvtbV1dHZ2TniYzo7O0dc37Isent7qa+vP+Yxt99+O1/5ylcmb+BCzDDj6WwfbLxRLsX1oimLJt+bRf5EIsGvf/1rli5dyns2bKS5J091MIUTcPHoKpm8je04mA7oqoKuKswLG1x3aQ3zKzw8fSDF4iqD5j6TLcuDLKrxjjqG8ZwwEEIc34R/6/jDH/5w/I3qOhdccMEJDUgIIcTsFAgUImgA/P5AKR8+Ho9hmVbhj+3wiz1ZOqImcSWJ4ui4TiGzULF81HavpZn8Cz0AAM/rSURBVHaJwe72DI7t8KtXBljdUFYqouuawpWbwxzqzbOyHkwLtp4dpiZkMJCy3oyr6Tdpj+b58XMDLKjwDJnIVcixXAgh5orR5kcZHBvDAw8Sj8dIJuIEL/oIFbpyTMzMRLWk+nmsa38hBz4b52Cil+/se4yW1MCQ9VzAdhwOJHtpsUwWOC4jlYE8qs43N20ds3iedyw++/JDHEgWCvUjRdaMZ53pcuutt3LTTTdxxx13HLP8lltumbIifJGiDP09yXXdY5Ydb/2Rlhfddttt3HzzzaXb8XichQsXnuhwhZhxJrtQPbyzfnDRvre3l1/+6te4wQaqG9fyQlOSh16KkTk6p7bfsFFcSB+9reDyx5tD3PzuOp7an+RXO3rxe1XK/Sqr6n2EA9qYY5HsdyEmxwn/FO3evZuWlhby+fyQ5VdcccVJD0oIIcTsVsyAhzc75GPJOHv6o7RFy1BcFSXnx6tVkDUyAGjJKtKpEA6wqMpLc2+OZw+a3PNID9dfVouuKZhmHo+bZHGVTkfMYn65Q8TnomtKadLVq8+vYn9Xlh8/N4CqKDIp6xjkWC6EEKe34kSsw+dHGTtOpmYS9jy0EPv1137L9mgrtmNjuQ7u0TVcXHRFJZbPcvu+R1h45GX+v7dcRcLMHVNs96h6KYJmpG72jkycA8luYPTImvGsM13eeOMN/uu//uuY5VdffXUpe30qVFdXo2naMV3v3d3dx3S7F82bN2/E9XVdp6qqasTHeL1evN7RO22FmAsmEllT7KxPZmxcBVIZG0VRaOnNkmp6hKZMPe0DjaR2JNj2erxUgAfImEO35QLPNqV4el+SX+6I0Z+yCfk0HAfOaCicQJMiuxBTb8I/ZQcPHmTr1q3s2rULRVGOOeNt2/bkjlAIIcSsFggE0X1evvzMo+xLdONUhFkzcA4NFX7ekTqXZ3YXMuTVXBmuotHWbzO/0iZnKQS8Ku1Rk85YHo+uotlJ7HyOP96g0R21qPAr5HNpDMMzJO92RZ2PhrDBod48S6o9MinrMHIsF0KIuaGYqT6QssacH6W8PIyu66Ur2opc2xpxAtbjaQxWlHLgFwWrOJIewFBUsq45pDwf0DysKKvljUQn/WaGlmyUP3rku1R6g6wqr5tQN3u9v5wVobEja8azznSpqalhx44drFixYsjyHTt2UFtbO2X79Xg8bN68mW3btrF169bS8m3btnHllVeO+Jhzzz2XX/ziF0OW/e53v+Pss88eMQ9eiLlgPHE0Y60TTVm82pIhkbMJeTUWV3voiOb56fP97O/K4TNUQkRpigfI5s/EcjU0xWJnKEVrrznS7oawLIeXDmeoCel4NYWc5RLyqZiOS3VQY3tzetx59kKIEzPhn65Pf/rTLFmyhN///vcsXbqUF154gb6+Pj7zmc/wrW99ayrGKIQQYgZyXZdYbIBAIFiadLUYQWMYQy/D7MjEacnHiec1siTosB3auxT6Ujp6thz3aE+cA8QzcNOZOQJeHzuOOBzuNfnSg53MrzSoK4Mr1ljomguuggt4vIEhebd/vqWS/pSF44w1JdHcJsdyIYSYO4pXi1m2S3fcLJ2wHswwPATLjMIJbbVwhZlrW6QevAu7pxWttpHg1utLhfhicd4OlJPJ5Qon3FW1VLD3aDrfPusDtCf7qchmue3QExyO9wCgoKACAd3DW6uW0J6J4lF1ElYOr2bQm0tRZvgm3M0+nsia8awzXT7xiU/wyU9+koMHD3LeeeehKApPPfUU3/jGN/jMZz4zpfu++eab+ehHP8rZZ5/Nueeey3333UdLSwvXXHMNUIiSaWtr49///d8BuOaaa/jud7/LzTffzCc+8QmeffZZvv/975cmfRdiLhpPHM3wdQZ3xjf15NnVlqVjIE99hYcyv0ZX1GJHS5p4xuVtFS1sKG+nqf8sLLdwVYntwo6WLDnn+OPrT7k8uy9B2K/RGbfQFAj5NT52ftWE8+yFECdmwj9dzz77LI888gg1NTWoqoqqqrz97W/n9ttv54YbbuCVV16ZinEKIYSYYVzXIZ1KkkolqampK03CChAsM+iKZvCpWcrLyqj3lzNPi7Av3YWSLqepJ4fP9pC3wQimMSwfOhq2qzCQg5/v8fLhLdXEH4thOfB6W4bKoE5rPs+hPo0XWnK0xWBptYdLVsfY35nD79U40m/yL3/opqXPpCNqsrrBR2fckjiaYabzWH777bfz4IMPsmfPHvx+P+eddx7f+MY3WLVq1ZTtUwgh5rqRJmgdXIgf6X4l1ovd0wqA3d2CE+tFq5w3pDhvh2uxLvoz7EwS9effxUrF8DQsJ/TBG3Edi8xvfoinrZn+Ki/pUABQcBQXFVhbXs/XNryXL+36Fa7rEjXTpKw8LtCViXPxvFWlTvXB8TNjdbMPjqwZzXjWmQ5f+MIXCIVCfPvb3+a2224DCpOkf/nLX+aGG26Y0n1/6EMfoq+vj69+9at0dHSwbt06Hn74YRYtWgRAR0cHLS0tpfWXLFnCww8/zE033cS//Mu/0NDQwF133cUHPvCBKR2nEDPZ8XLTR4qi2dGa4em9SSzH5Y83R6gt09jf4bCvI0PIq9AZM3Fdh0uqD9Doj/JAx3qS9tBYp/zxm+ABsFxoG7BpjxaueNVUiGcL/z6ZPPuJROwIMddN+CfEtm3KysqAQn5ce3s7q1atYtGiRezdu3fSByiEEGLmMc08rguWbaNreqkDHt7sTG/uTlIdcPmLt0NVRSXvYyMvHEiQadhFYukzZHIhQMH2xdEzYQLNbwMXdFNhZ7vLnykwL+TwdFOerOnS3Jsl5FP52asWu45YBL0aLX1peuMK+zpdvIbOWUu8PLUvTd6CdN5hUd5hRZ1P4miGmc5j+eOPP851113HOeecg2VZ/N3f/R3vfOc72b17N8Fg8PgbEEIIMWGjTdA6/H7HcWjuTtIdC1AfrkarbcTubkGrbUQNV+PaFtbh3Vjdh1EUFXWgCz01AP/5NazuFlzdIO+6ZAe6+Nu9v+cNswujUmef30Ph+rQ3r3zb3t/C1ifvo9ITZFFZFd9e+cfc9PIDqKg4tskNy9+BR9VHjJ+Zqd3sJ0NRFG666SZuuukmEokEAKFQ6JTt/9prr+Xaa68d8b4f/ehHxyy74IILePnll6d4VEKcPoZ3m0dTFq+1ZNjRmiHi13j+UJrm7hyvtWVwXdjfmSWRdQgrSao9KX7avoGUfey8CoYGKpCzjrnrGA6guuDzQFWZhqrA0/uSvGdThGVHx7gMJlRMly56IcZvwj8h69atY+fOnSxdupS3vvWt/OM//iMej4f77ruPpUuXTsUYhRBCzDDpdApct3D5ua5jGJ5SIX4gU5gMVVU1OmNZUnmdKiAcDKCRwvEVvljqoQFcF2xLx/TFsPQ0HjNEwKuAC+lUki0LszT3aqxqCJDIOHh1FRcX07GwXRfHVdnRamGhodkOmSz0JAodHfVhnb94eyWr6/2jZuDOVdN5LP/Nb34z5PYPf/hDamtr2b59O+f//+zdeZxkdX3v/9fZa6/qpXqb6emZ7hmGZZiFGRlWEYgoiBiQGDUSjHpzxSgIycPIjRAxucEYf+hNcjUuwdxETTQuxCiJGgyCbMLAwAzLMHvPTO9LbedUnf33R3X19DozPVvP9Hyfj4fSXV116lsFdb51Pudz3t83vvGEPrcgCMKZKhVRSEVkcmWfxXXT10qpLeBaO4FuSBUkJUb8xo+NR8wA1Q74gW7C/DCkG1BblqLjUyiNAiA5NlJuiD4p4LVCP9ujBr4UmfBMB+fjiKIy6pRJqVF6yjliqs6qdCuvdW9lRcWm4b/+mfDG2+mtzBw/cyp2sx8vJ7P4LgjCydGV1SmVfYoVn72DNj9/pcjLByoEfkDOgr5Rh76CR9kJ8f2AQLKJSxX2VdL8S3ktUxe8rlEUieakwu6hI6jCUy3Av2llkqaMytZ9FXYNOUC1mL6r32Zbb4VLl8cZMv0j6m4/li56QTjTzLkI/6lPfQrTNAH48z//c66//nouv/xyGhoa+M53vnPcBygIgiCcemKxOJIskUqlARgc7B//WlifyIwdyNu0pFV01WZnvp+OZp16NU7ZTuNH8kTtNEUvINQt5HIGnCiyHLAkLbO0ScLAQtE9FqWg6MM5rdWD+P2jNl2NMtmUQXNG49ldFoETgiRhOj5tGY1ixeei5XFRgJ/FqTSX5/N5AOrrZy6m2LaNbdvjvxcKhZMyLkEQhIXC80P+8YkRclZAXVzhdy+tR8HHHzlYXK8t4DqQj2FI1Sg5AElRUepbcF0Hs2c39HcjyRJyupHYtR9E7TiH0PeQEnWE5RIYUeSGZiI//BL9GfDH52BpUv1IQ6YtmsEKHEJCuhJZ2iMp/iKzgt3PPUVzAAr7CPJDNKQytEUz7LdyrEw1kdWT49n2wKSF2U8369atG18U/XBE17kgnHrmGsVyIOeiyhI9oy4508PxA2RZRleqTUwjRQ8/CDBCk+ubX2a3Vc/+SprZCvAALWkFN6h2wx8qGl6Rql3zSxp03tAZY6Dg0ZRS6Ww8WETf1ltBlSWe2GGSjirA4bvbDxXDI6JqBGGyOX8K3vKWt4z/3NnZySuvvMLIyAh1dXVH/AVCEARBOL1pmo4sK2iazvDwIJVKBU3Tqp3xisQtFyfp7veJRwI+s/O/eCXXS1hKU3Y3ktx/AVbiAMPp/RAtIleSRPZsQA4VdDkkpbsULIWHXpK44TyZ92yIYEsGHS0NAOzsGaJUCigHGouydcQ1mb0jLh31GrIsETNc6mIKt12VPS0PyE+GU2UuD8OQu+66i8suu4xVq1bNeJ/777+f++6776SNSRAEYSGYuFj6wSvUIF8OKBZM3P/4In5hCLV5KbBo/HFhNS1mGssycbQYal0zSn4ApbkDteOc8YVa1a61uGYOVJ0wN8TAwF4iq84GZXp0AoBLQFw16Io28GphgCDwMB/6v8gD3SzKDyOnG1GaO/CSGf7X5n9nvzXKoliG+1Zdzzd/lacn59KSqj53X8GbMef+dPCbv/mb8z0EQRCOwVyiWHYOOmiyRL7i05bWSEUjnNMawXJC+vMuLx8os3/UISPnuL71VV4stPF0bslhx+AG0JrW6M97lKfkw0uAKoOqgq5IxA2ZbKI6zlzZJxFVWLU4ClSL6detTrFz0KExrox3wh8LEVUjCJPN6VPgeR6RSITNmzdPOlierXtNEARBODMoskwsFkfTql/UHNuiLhKwxy3w6MB27MDHC4tElJVYyx8j1MogBxBAoFcI9TKarZE0AixXxpBCegshSqyRZFymeay4X3EC/m2zy9M7y4xYFeriFlefk+Bjb25EliTq4yrFik/dadoRdzKcSnP5Rz/6UV566SV+9atfzXqfu+++m7vuumv890KhQHt7+8kYniAIwmlr4mLptSvUenIui1ISxsNfxNn6OJIexQOobx5fmHXqei41tXVfou+8A8UqIKcbxwvw/kgf/mvPIAGh71Esm7yuhViTpuHp1f3ecp7tpQG8MGDUNtk7MkqnJEO6YbzLvnssikaWJHrLeXblCvTkqr2eu4ccJCCiyzPm3J8O/vRP/3S+hyAIwjGoFakb4wqb9ljTOr5rneCNcYVixacppRLTJXYNOXQ26sQjMgMlm+39Nq/321gOXFA/wq9GlvFyqeWIxpBNqFyzKsWI6TNccimWQ4Kwutc1FKhPKqxpj7JvxKNke0hydUFY1wtpTmsMmT7p2MGO9fVLYwB0ZI/f+yOiagShak5FeFVV6ejowPf9EzUeQRAE4TTgug5B4OO6DqlUGlVVxw/Qodopnyvm+Zs9T2F6ztiht0wQGT1YgAeQICSkTg9JGyHrWj12jWrkcz5NKYW4Ael0HZ4fsm/Y5huPj/DEdpO8FWK7AWUn5BevlrC9kKIdnLadcCfTqTKXf+xjH+NHP/oRjz32GIsXL571foZhYBgzd1IKgiCciSZ2uddOfk9Vm5NrV6h94I0NjJoeaXuI8mvDSEaU0C6jpLOgqOMLs6qqxpDlYoeRSdvTNJ10euy5IrFJ45CcCrsNnQCfiA3vv2AlgxGjGosQVuPiZmqvLzhlHClAkWSQJJSGVhjoQW1eOt5l32DEaI2m2WeO0h6voz0VJ5rqo1SIsKyxOjfUOuHFIuyCIBzKiYhGqUWxPPpaked2WbwQV3jzuUmGTJ/GuMLPXymSMz0ycZXWtEZLRqNo+/SOOoRhyEDe5RevFMiXQ85L9pGTG3l85MjXZ4rr0Favs7ErzuZuC9PWMVT479dKOF5197u6PUoqqhDVfdJRHccDVZbobDNY0WTQGFd4+KUCqlzdTx/PjvVDRdUIwpnoqDLh7777br75zW+KDnhBEIQzlGWZhEGIZZmk03UHD8zHuK7DsF+h1xpFliS8MEAJFKRCE5MOxEMZvZRlWTzO4pTPlV0hlZ0q7VoUKXDpHSwQNQy+/UyZn71cpD/v4nghrhcSAKpSLeL3F1xihnLadsKdbPM5l4dhyMc+9jF++MMf8uijj7Js2bKT+vyCIAinu4ld7lPn35pJRXOqme/ZlEboZ3GaOwCque7v/Dg89CPqIox3yy9tStCUrsYTHKrgbxbzmAP7uW9wM0+c0wVhwNKizUAkQkhYvZMkzxhvoyHTGE2Scy0a9ARr6haxcs0NqMXceJe9E3j8r83/zj4zR84tE5ZCrnv0b0moBstbWrh7w83ossqo6Ykr4ARBOKzjGY0ytaAvhTBqVhdGreWpb95j8dK+Mqbtc/lZCfJlnyX1Gi/uLbNlf5mBvMuQ6eN4IW+s38mK+BC9lRSV4MiPY9IxhfdsrGPI9FnZGmXPsEPJ8mlMqvTnPRQFtvU6NCQVFEnC9kOShkI2pXLzhgyZuMqmPRauF7Iv77CxMzan1y0IwtzM+VPz13/91+zYsYO2tjY6OjqIx+OT/i4WjBEEQVj4aguzTux+n/r3NidDoxpjZzhMBBUNAy1WwqrEIVoEKUTydIydF/NKqJKzQt5+nkZ7vcFAKaAhERKVKuzpGWb3kIrjBmMNdRKLG1QUCToadc5uiSDLkuiEm4P5nMv/4A/+gG9/+9v827/9G8lkkr6+PgDS6TTRaPSEPa8gCMJCMbHLfa4kRSV+48cI8kNIiQylH/4Nft6g/MP/w+/deAe5CpMK2hML/rEY4wV5VZaRf/4PDA11s70lhS0BksygoYw90aEL4hIh/ZUC9Vqc/7vht1mZbkKXVag/GL/QW65G0fihz4htknfKmL6DbJcYsEv0VHIsT2bFiXdBEI7IkUSjzFZknnr71IL+miXV77ChBLoMj79u0pRWMVQJQ1XwA0hHFbYcqLCtr0J/zqMv5yKHPlfWbaNes/hO71qKXmTGcc1Gk0NSUYV0TOGFvRZ9OYeC5dOW0UgaCkMlD8vxaZIUDE0mrkvkywFdWYNMXCVnehQrPm4QsixrMGT6M8bQ1F5/seJjVoLx1y0IwtzM+VMjFo8RBEEQJi7MOlWta87zPBQ/RJcUJCQyfgrVr6Ngp3AjJfAVQk8j1GzcSoTeIsSTCX6zwSFfhub6OI7rUrATtNf57BlyiOoyEU2iI6uzOKPzjvUZssnqVDZqeiQjCgMFF0mCbFITXXGzmM+5/Mtf/jIAb3rTmybd/o1vfIP3v//9J39AgiAIp5mpXe5zJSkqSn0L/kgfbt9eMM7C7dtDvDRMtn5yBvHEgv/EgnzCtwmHe2gLFVaYZfqjGgEhFVmq9sCP/590sBN+Qiy8KqsYQZxyWeXHz1mcc5UybZyt0RQrkk1sK/RTr8cYdqxq7jwhAWHtSQRBEI7IkUSjzNYtP/X2qQX9zFghfuegw9PbS/TkPBKGzNvWpZFCWNqoM2T6rFoU4dmdJo5fwfdBlQPsQOW7vWuw59ABXzNshjyxw+S9F9ezosngqe0mbgAdaY23rIqyeV+ZgYJLEEpcsTJOvhKQM31iERmAF7vLbD1QobNRpzmjzXqCovb6E4ZMyyHuJwjCoc15YVaAD3zgA2JRNEEQBGFGhUKeUrHAfrvAfqdAkxIjZ8PS3DmMyBp1ezcyvORp/NQgkm5jL9pKdNfFBChYUoK44tDWJBOLp7jzn/vpy43SEIf7bmzC9hRSEZWILk0rstfFVb7+6BD//VoJgCvPTvChNzWKQvwU8z2Xh6EomgiCIJwK5HQjWstSGAWtZSlyunHS353Ao9cp0ZpMockqsVh14dVIpQipeuRUA9JQD38SNPHJwOFZxadkGEwujo9V3sODv0oSOKFPOSii+VGee91n8A0erZnJBShdVvmrdTfSWy6Q1Az+6Pkf8MzwXsIw5MrmFSyJL6xo1EcffXTaCWpBEE6urqxOseJTKvvjWe612yf+s1bQz5ne+IKstUJ1ACSMatPQ2vZqYT4dq3arP/F6iVd7K8TlMmclB3gmt4T/GjrrqMebTcqcv6jaPb9mSZR32hme3W2ytNGgqyXCxq44//CrYfwA+vMey7I6y5sN1rYf7NyvOD49eZdLVsRnjZiZ+PpFDI0gHL05L8z6+c9/nltvvfVEjUcQBEE4zXmeh23btMfr6IzV83pphEglza6BBPnAQnKjRPevw1rxOIQKQaRAqFsETpI//cEAl6xI0d6gcvHygP68hx8E9OQC/vHxIXYOVQvqtQL7RP25Mq/3lDArPpIksXvIEfnwMxBzuSAIggDVjvjkzR9H+e53Sd78LiTl4KGhE3j80fM/ZEdpgBXJJv5q3Y1osoz6X/8Pa/92/FIeJdOIlmlicN2b6N7/FBWnxMFqu4QUhoS1WJpqCzuKpBBTVaRQwrEjEMp4SgUI8fyQEdOjfkIcji6rdIwV2//uwvfQbY4AEkviddX4mgXkrW99K4sWLeL3fu/3uPXWW0XTmyAcB3PNMM/EVZIRhb6cy85BZ7zQPts2JnbI1wrVqxZFGDJ9urI6m/eVeWRrgcGCx6IGje19Noo9ys2tL/NaqemYXpsCNKV1nODg2K9bm+aSFfHx8e4cdDi3LcquQZuKF/Jqb4W2On28CL+2PUrPqIsmS+Ovd7b35VSNnxE59cLpRJ7rA66++moeffTREzCUw3vsscd4+9vfTltbG5Ik8dBDD83LOARBEISDXNchnx/FdR2gWuQ1DIN4NM6frbmRs4sbSOxdR//iZzBXPE658xlCKUCx6pBCkMtpJCeKLEGhHDJUKLOrr0hCD2hKqRQrIRVP4uUeH9MOMCs+r/VWGCy6eH7IQKH6T5UymWiAoYbEDJlljbrIh5/FfM7lgiAIwvyZOG9CtRCPok4qwMPBPHaA7cUBessF/JE+nJcexz+wA2doP7vdMvkdL/CVX3+HQSs35ZnGCvATL0aT4NPnXsc/X/QhLkh1kTF0WtRGLmlvJGkofP3RIf6//xjg648OjY9vIl1WWZ5sYnkyu+AK8AA9PT3ccccd/OAHP2DZsmW85S1v4bvf/S6O48z30AThtFUrku8cPPLPUVdWnxS5UtvGi91lNu2xyI0twDrTfYsVn5cPVNjRX+FbT47wyNYCT243ebmnzI+fz+MVeri2cQvP5tp5fKSTGVeuPgK6DMuaNM5pi9AYnxznlYmrrF8aGx9PU0rl3RvrOLctgumEuF44/n5k4irXrU7R2WycthEzR/PvWBDmy5y/vVx77bXcfffdbN26lfXr109bzO2GG244boObyjRN1qxZw+/93u/xzne+84Q9jyAIgnBQLeM9FotPy4B3XYfBwf7xr4/ptE4qlUZVVVxJ5n3/9X32SyM4XRUC1YFQJtBN/OgoslVHbOclSHYCKVQIQojHQMMmpUJMtvjU2xv4/MMDpOIaOwdcXD8kZ4X05Dx++GxufEHWlpSK7/tYnszGzijvvbSRlrTIhJ/NfM7lgiAIwvzw/JAHHxumJ+fSltH4wBsbZp0na3ns24vVTvjWaAovnyMIAlxF4b4V57ArnaYlY9CjazBjRntYzTqQan+SeO0VjccHbUaLG+hMlVmajJE3A/7uvwf59a4yZSdgz5DDO9ZnxuNpZuqQX4jq6+u5/fbbuf3229m8eTMPPvggf/AHf8Btt93G7/zO7/DBD36QNWvWzPcwhTPY6dhxfCSLsU41tet7YoG9L+dSKvskosr4+2AfKPOZf+tjTXuUYsXn9T6b1/sqlJ2AIAjRFBnTDnE8GLEkfl45ix1W42xPf1gRBTqyOtetTdOa1qYtprp30OaJHSbpiIzlhLhByJolUZozGue0RvCCcNL7cSp3uR+Jo/l3LAjzZc6ftNtuuw2ABx54YNrfJEnC9/1jH9Usrr32Wq699toTtn1BEARhuokLsdUWgnNdhyDwKRTySEiEhMRi8fGCvabpPL1rF8NBDp+AQLWptr37EEoERJGiBZIa+K6KrEBaD/ijN7pEtYCW+iiqLJFSHboaJYYsn6vOSXLxihj/9EQOWYLdQy6KDBFdZvdQtfMhakSwPDBUaUEfqB+r+ZzLBUEQhPkxYnr0jMUm9OTcQ0a2Tcxjb42m0GWVXCSJv3w9vbkedmebkeJpesNe6qwSO6PR2Z84BMWJk3Qa2Zcz6DcdIprKvgMGw5pPXaJCRxa8YGqW/NxOHCwka9eu5ZOf/CT19fV89rOf5cEHH+RLX/oSF198MX/3d3/HeeedN99DFM5Asy1aeio7lgLzxJMO65fGxn/vzTk8u9vihbjCzRsyfO+5PLsGbKyKz4auOBJgeyFlJ0CWQZYDLm/Yy45Ckj3lY1vLQgHqkyoXdETZuCyGE0wvPj+xw2TXgE1LRkMKYbTs82J3mTVLqicJpAW2PNPpfhJBOLPMOY4mCIJZ/3eqHbTbtk2hUJj0P0EQBGFuYrE4RiRCLHawW9qyTMKxg2XdMFAUlUIhT6GQx65UGB4epFWGujCJgoLsGRCGY8fUIRgWUjmFVUrgAyoBF7a7NMU9lrZkSCYSpFJpUokEt16W5qNvbuJDb2pkZUuUnOXxak+FEdNlSYOO5/mkdI/F6eqU1pbRpsXQTL38/kx3Os3lgiAIwrFzXQfFL9KcrM6VzUkZ2S+OR8nNpJbHXot+0SMxzMvfRd01H6KzZQVIMssDmU/u2k8wS11cIqSxDA3dF6K8dgm7hhRyZsBIyUNVJVJRCcsJiSoeV6yIcO6iCFeenSCbrJ4cmOnEwULmui7f+973uO666+jo6OCnP/0pf/u3f0t/fz+7d++mvb2d3/qt35rvYQpnqKnRK6eS2gKpueO4j5gac1KLeUkaCqOmR3/O5eGXCrx1VYLFDTqLGjRSEYXGlEIyopCOySQNiTdmtrFEHyDnRY55TIoCTUkVZIktByozXpVw6fI4nU0Gbz43SVeLQUSVCKWDefclOxDRLYIwTxb06aL777+f++67b76HIQiCcFrTNH28A37ibSEh0WhsLBM+h2NXiERjJJMpdCCwB/jT5pV8bpNK36hBrvMJ/OQgBCqyGyXSswopVFAkSBgBl7WXSSXryNTVTYq+aag72DEyYnq01Wk0JFQMTeKGC1L802N9jJRc4obM/7yymWxycgzNmdpFJwiCIAg1lmXiOzY3rVEoORoJPcB3bCxr+nw4Wwyd6zpE4ylCTeNPnSX0JpaR3vKP/P7KTgJ55t6uMAQkj4aiRn+oUPagGhUf0BDXUBWZqO5jOz5+4HHLJY0sbTTG42fq4yptGW18Dl/Ia7187GMf45//+Z8BeN/73sfnPvc5Vq1aNf73eDzOZz/7WZYuXTpPIxTOdKdyx/HRdukfKmJntpiTpY06y5oMRk2PnlEHx1UhhJGSz+a9JqYdIBGiySEXRV9ECnz+pW8NVnD0Jy8iKuiqRCqq4Pghgc+si6l2ZA06sgYA6Vj1hMDU13IqnkgRhDPBUe1BTdPkl7/8Jd3d3dMWirn99tuPy8COh7vvvpu77rpr/PdCoSBWmRcEQThGruuQy40A4FQsom4ZTZZwkPBcl2KxgAQEQYjrK3RoEXoDCQIFQgUJUKw6ZDuOqkgYavV/EcMgmUqTTtdNe75aMaA+rrG4Tqcn59KSUhk1fUxXQdNCRsoyIE3LjZ3L5fdnktNlLhcEQRCOXe1qNs/zSKouiqyi6pOvcquZLYbO8zy0wEX+f39CUByhsWLy0bWr2ZZKzvq8bVYFQoO8rqOGEATV20NJoqspgmn7RDUFNwx4aqdLb3GEUdOnLaPTllF5x/o0v3tpPcWKT90Cz4R/5ZVX+Ju/+Rve+c53ouszF8ja2tr47//+75M8MkGoOpUz4Y+2uDyxeN819nvt9dVOOuRMj1++WiSUYG17lN3DDjnTYyDn4gTw3J4yZTsgJGRxnU5f3sUPAobMgJfdJl4rZfFC5dADmYUEGCp0NRksqtcYLHq8+bwkbzo7yZDpH/b1Tj1xciqfSBGEM8GcP30vvPAC1113HZZlYZom9fX1DA0NEYvFaGpqOqUO3A3DwDCM+R6GIAjCgmJZJpLvg+8j//QbVEZ7STcsInrl71C2bYqFAr7vYRgGKcNBUgIKnb/GT/dDIIMbwThQ7YK/bLmGpqo0x13OXlZPMplieHgQgFQqjabpk4oB8USG69emCEL48Qt5/vXZHL15r1qcT2n826YcfQVvUsf7mdRFd6ROp7lcEARBOHa1q9pqJ7ZVWcYd6SM0pjco1QrzsVic0PcI8kOYKJSLefjBAxgD+yjLMnesXc3zdbUT5+Orr04SYuAES6mTk9Q3KAyVQlw/JK7D3iEbSYJiJUQCKl5Ik+PTl6ueTP/v10rsHnLoajLOiKvYHnnkkcPeR1VVrrjiipMwGkGYbj4y4Y+08H+0xeWJxfuZXl/O9Hj4pQI9o2MNPaMumahCf96jv+jj+yGBH5CKySxr1Nkz6BIJiqzVX+Mh5zy22i1zHtNEugIxXaI+rtDZZHDx8gRdzcZYt/sxbVoQhHkw573UnXfeydvf/na+/OUvk8lkePrpp9E0jfe9733ccccdJ2KMgiAIwikkahj4v/w2khnH3f8EQX0bwf4dqKUcaFFUTcX3PWRVp0wcWy0R6BZSoBDKPkolhWwnkJC5+hydpWmfqOTj2GWGhx3sSoVgrFUulUrjeR6KqqIbMR58bJj9Iy66ChU3BAJypkvckCh7KvmyhyxJkzreVUXiA29sYNT0FnwX3ZESc7kgCMKZp1aAjxoGpe99EX9wP6WmdtC6Jt2vVrAPfQ/zB3+NP7gPuWERducb0Msmjqrxx+et5Nn6+mq2DDBTAR6gzjoHLX8BBV/BC6A+obKkXiLwfUIkYlGdVw5UaK+T2NYXsLPPAUkiDKvb0xSJnQM2g0WP1syZcRXbK6+8MuNVajfccMM8jUgQquYjyuREFv6nFvhre8KJr2/noIPrhVh2gCSH/GpbhTAAOwhpSCj05jyCEAYKLumIglwe4JLoFl4sLsY+yu73GhXQFIhoEq1plW19Ns1Jdc7v/9TXeSpf0SAIC92cP3GbN2/mK1/5CoqioCgKtm3T2dnJ5z73OW699VZuuummEzFOAEqlEjt27Bj/fffu3WzevJn6+nqWLFlywp5XEARBOEixCiiFQSQlQej7uKUcTmM7paKJpNiEYYDnh3x/k8PeEdjcn0HuSONLPkolSXTXxUihQnMS1i/R8coFrHKFMPQw9OrVS6pWPdC2LBPHcTF9HVuW2D/isq2vgmkH1McVMtEA1wuIqgF5K6AurpAvB9M63lVFEhE0E8znXC4IgiDMj9qVZWFuADk3QCjLyKP9hA1LyOdHJ2XAu66D2bMb+rsJwgB3/w5YegFhPMX2pjaeq2+YUICfmRpI3LXufH74os7e4QA/kFBVCV1VScclojrkbGiMQxgE1MWgqyWKpsJbV6V5aa/F4zssAP5tU44PvalxQZ9I37VrFzfeeCNbtmxBmnAiQhp7n8XC6cJ8O9Yok6Mp/p7Iwv/OQYdd/Tbbeitctzo14+vryups3mMRi8iMFj368h6Fik9cl9FkWNKo8eudJr4fsLd7H2/NvsovhpfzWqn5mMcXApIMuirzXHeZVFRh95Az58L51BMZ83FFgyAIVTOvoHMImqaNfxFobm6mu7sbgHQ6Pf7zifLcc8+xbt061q1bB8Bdd93FunXruPfee0/o8wqCIAgHyelGlKbqiU85nkLWDBRZQdd0PM/F8zxGTI++fMBgUcIJJCQpRJIk5FBDChVUQu59e4aGdBJV06mva6SuroFYPEEqlSadzpBKpdGNGP+62ecfn7H50fN5EkZIsewS1aAhKZOJa2iqzO6hkLaMym1XZfmDqxvPiMvWj8V8zuWCIAjC/IjF4hiRCLHmduTsYgDk7GJCZOxKBcsyxxZbH6VQyONoMbx0FrtSxoqkUH79E5Tdm/lqNkVZPtQcKyEFMu173swLBxpYWg8RNSSiwVUr4zSnVPKWR+j7/M4bdDZ0JogaKumYRhCG9Od8/nNLgZ68R1udwspWg76Cx6jpnZw3ap7ccccdLFu2jP7+fmKxGC+//DKPPfYYGzZs4NFHH53v4QnCMasVf3cOOoe/85hMXGX90ticC88502PTHovcIfYbXVkdNwhRxxY4ne35u1oMIqrE6o4oV52TZEXWIG7IRDQZ2wlpSauYdsj+SoqH+lcdcwE+okJch2xKpimlEdUV6mMKuiKxbknksK9rptfZktEmndCY+PvROpL3WBCEyeZ82mvdunU899xznHXWWVx55ZXce++9DA0N8U//9E+cf/75J2KM4970pjeNdwQIgiAIJ97ERVFr3XGSohK/8WNI//xtwnIduqKg2UWk0MEMwmp8TODz2nCMQUshiOTxozkIZIJIgVC3UL04RljEdSMYuoERiZBO15HPj2JXKqiqiqbpjJZdRioqsgz7R10u6oBiKWRrj8ueQdAUmZVtMUw74Pp1aSK6TESf8/nlM858zuWCIAjC/KjFzABYb74Vf6gXpbEV6ee/qBbnY3EKhTylYoFoNEaoqOTe8A6C4jBOIUf2x/+HHkWiL2IQDQLKsjxrN3wYKlTcKM/trXDPtVGu6jJRNYW6hjh//bNBPDekezTEDgyGTYtULEJPvoJRCchbHjsHbCwnIB2R2bgizuI6ffwKt5m+mywETz31FL/4xS/IZrPIsowsy1x22WXcf//93H777bzwwgvzPURBOCYnM87mcN3eta78S5fHpy1wmjM9Xuwujy/EurY9SjKi0BhX2DPkEEqwo8/Gsn1Kts+y8BUyaZnHRzrpqaSPadwqYGgSigQVF1rSCksbdVozGs1pjSEzwA3m1sV+pIuzzvVKBdFRLwhzN+dKxV/8xV/Q2toKwJ/92Z/R0NDAbbfdxsDAAF/96leP+wAFQRCE+VO7dN2yzEm3S4pKqGgEda2EQYCxqAsvkiQMA4LAp99UkABkm0r7CwR6mSBSQqokkZwYS9IBMdlGkiRsx6ZSqeC6zniXnqbpDI+OUDJNMoaH43o8t9viy49ZvD4AbRmJs5pkwhC27q/Qm/P48Qt5Kk5Ab86hZ9TB88VJ29mIuVwQBOHMFk+mMZrbiSfTSJJELBbHskw8r9rRqKjV9V0CSUZONpB9/VdIbpmI67InFqOsKIeMo1FLWWQ7juf6qLJEezZOc0rjoedG2LzP5ak9Plt64VevV2hJqdhegCRBY0rF9kLyVoDrg+XCW1cl+cAbG4Bq7nKhVJrxu8npzvd9EokEAI2NjfT09ADQ0dHBtm3b5nNognBcHG1X+9E4XLd3rYA8ZPrTxvRid5kfb87zkxfyvNhdHr/95QMVfra1QMHy0VTYP2xxVvA8KfK8kF90TOOVgKgKER3SERldlYGQgaKH44WUvYC4IXPp8vhx6WKfyVyvVDheHfWCcCaZ895vw4YN4z9ns1kefvjh4zogQRAE4dQRi8Un/XMiSZaRrv0gYXEUqWkR0vAQmqbh+z5tSZ+k4bJ70dN46T6QQgghlDwaoz4tSRnLVRjo78PzPFzDRVVVVFUlFoszksvx/x7PMWBCS1rl8q4oL+13kICSq7A8JlOyA3RVolgOiGgSB0Zd/u8jA/x6V/XL8pVnJxZ8fuzREnO5IAjCma3WFe+6DkHgV+NnbBvP96pd8L6HUSlSkSFFQLB7C17gc/cFF+Aoh19ssLFvNYGvoCkh6ahENtvE3v4CPfkyqgKKLBHRJPbnPO54SxaQ+LdNOfoKHpctj/PwliK2F6KrEh2NEQAefGyYnpxLc1LmXeuNGb+bnM5WrVrFSy+9RGdnJxs3buRzn/scuq7z1a9+lc7OzvkeniCcVg6XX3+orvxQAiRw3ICX9pfZPmCTjij0FVxKlYBiJeDAqMP6yFbCIOQHA2uwgqM/sSADmYRETJUJgNVLYjhewKu91efdNeiwc9CmLq5y3do0HdmjfqpDmu09ma1D/ljXCBCEM9GcPzHlcpkwDInFYgDs3buXH/7wh5x77rlcc801x32AgiAIwvyZeOn6VJIkoRkR7LCOfLGAruukUhkcd5hcxSUXlAl0CwIZVBcCjTBikQ9teosR6uIKYejjeS7RaBQAu1IBIGcFDJRCFEVlpCyzoiVKY9xiqBTSnFLRtYBK0ccqB0QNHcsOMDSJvryHZQcA7BlyGDW9SQuyen7IiOlRH1fP6OK8mMsFQRAE13UYHOwnGIuSK1csdM3Acypov/gm/tABoplmKqsuRyoXeL4uw6up5CG2KIEEWqASVRTicWhLSwwXHVqaoLk+wWAhj2UHBEGIoUosadDJJjVUReL9lzewe9CmvV4nGVN5va/CWS0RWjMaI6ZHz1jsQX8xIFCSaNrCWnD9U5/6FKZZ7e7/8z//c66//nouv/xyGhoa+M53vjPPoxOE4+doFmg9mc+ztj2KVQn41fYSO/tssimVWJNMZ6POjn6bvX0mfaWQoaCTETdKMPeAiUkCoGiFEPVpzRjkygF9OZcgCKlPqISE6KpMW/rE7vNmK6qL2BlBOH7m/Al6xzvewU033cSHP/xhcrkcF154IbquMzQ0xAMPPMBtt912IsYpCIIgnIJisTie5xECqqoiawY/frLI6wMaQwUFpZwhJCQMHCRPR7XSqF4Ex5MYsQJaMwaKopJIpohGYwwPDwLQ2pCkra7MgVGH5pRMUnf48+sj7C+qPL4r5JevltBVGK1IRLWAppTKR38jy788PUr3cPVL4tLGg/mxUC3A17ro2jLaGb14q5jLBUEQzhyzZahbllmNjiNEVVV0TcdxbRoU8HL9BEAw0E340Bf5s3NW8moyiXWoLngJNOAN0SY8NYnlSIxWZOpiCpZl0pf3SBg+ddGQoRJ4XgBj6315fsg/PjEyaY4uVnzqxk6a18dV2jLa+N/rFmAh6C1vecv4z52dnbzyyiuMjIxQV1c3vpi6ICwEcy3qHm0xfbbnyZkeD79UwPVCXthrsaLJYM2S6PhjurI6ASH7RxwqTkgsIrO0QeOVXptUOMqbk5v4D3s1+0pxgrm88BlIQAgEIWSiKmc1G0Q0iVLZJ27InLcowtvXpafl1p9MJzPLXxAWujl/e3n++ef5whe+AMD3vvc9WlpaeOGFF/j+97/PvffeKw7cBUEQzjCWZeI6DpFolAPDFgPFkDAIkUKVyJ4NBPERJCuDpLooTpRUREZRIaIbJJPJ8UVYc7kRXMch8H2MSITfWqewr88mGbEp5mySqTTt2ST5LSNE1JCSI1EXV1nRbBDRZFw/5ENvauQd69OARDZZneIGCi71cXVSF11Pzp3WJX8mEXO5IAjCwnG4q7xq67sAk65uq8W5KIqKZkTZ1TNCU8ogiBnoDS24vd34mkF34LIllWJE1wnlQ3R8hqD5Ua5KrOO1Oh1Vj6KrEqOWj66V+ckWh72DPiNlUGQIJYlt/TaDRQ8I2TlgY2gSPTmXYsWfNEerisQH3tjAqOmRjCgL7qo2z/OIRCJs3ryZVatWjd9eX18/j6MShBNjrkXdo+3Enul5agV4xwvpz7sossSWAxUS0eoJxtrz7BlwyFkBEiGuF/Lzl4v4hQO0Vl7kBbOLA6UY/hGPZGYJA/yxc5G6KhGEEum4QsJQOKsV3rAsziUr4mTi6gmLoDkSInZGEI6fOX+SLMsimaxehvizn/2Mm266CVmWueiii9i7d+9xH6AgCIJw6ioU8phmCYAgCFA9k/poSN6UCSWPyrJnCSIF5HKa6O6NJFSJZVmF1UsTLGmNk0ok0DSdfH4UCQlN14nF4sRi8eqirWULz3MJwwDf92hujNIYD2hLSzSlVeoTMQZK/nhXnKpItGaqX7Sndr7/7qX1C76L7kiJuVwQBGFhONRVXrUO+Fr3+9QM9VrknCRJ/MPjo+wbkmkwivye9Y8EhQGCeJp9q67kzgOP0G/oBIfsxq7+zSvV8YPtMepiHq0pi7ZkhKjs8MIukz19sKxBJhyuxtaUbJ/uYYfvPzOCrMjsGaouBnjl2YkZ52hVqZ58X4hXtamqSkdHB75/rGU9QTi1HU1X+5EU7Y90uzsHHTRZAhXevbGO3cMOUjh5211ZnV39Go1JBUOF3pxLxRzlysRLPDJyNq8VG47w1c5sbM1Vyg4YKqgKRDSZZERhIO8RJOHctijNGY285fPwSwUuXR6nI2sc0/MerZMVHyQIZ4I5f4KWL1/OQw89xI033shPf/pT7rzzTgAGBgZIpVLHfYCCIAjCqae2kJttVyAMiURjxGJxSqUC1y63+b5jEBZcgkgBgCCaJ9QtMkaUd66SOKvVImrUTyoMeJ6HjkEqlUbTdCzLJJVKEwK+75HJVLvBNE1HVX3qEhE+cEXjpMvVJ5ra+V6s+ONddDPd/0wi5nJBEISF4VBXeU3ugK/D88Pxq8MmzoGeH3JgxMH1PPLFHHZxP5IqIZWK7Oh9+YgL8FKhgYY9lxBoKknV481dPme3G/zgRZm9wxL9RYmWlMSGDoXVTR7feA4qjscjr5ZY0RJhZUsExwt4x/oMwIxjXchXtX3qU5/i7rvv5pvf/KbogBdOG3Mt0B5NV/uRdGLPtN0Xu8tsOVChb9QhEVEIJVjWoFNKqYQSpGMKy9B5YofJ0kadjqxB19i2YrrEBUvjhEHAzzYPcqAQ5UBuPSX/2ArhURUMrVqAl8YWgI0bMromoaoSF3bGubAzNh4/873ncmzpLmM5AR+6Yn6K8CITXhCOnzl/gu69917e+973cuedd3L11Vdz8cUXA9VOunXr1h33AQqCIAinnkIhj+/7uI6DpGn0eSZN5erfHA8e3xOF0EeyYwSGiVLOIDsx2rMezbEKgVfNfy+XLQBSqTQApWK1aN/QkEXTdEyzRCZTP969N1BwGSyFpOIRBkvhtMvVJ5qaH5vQA8xSjkwsfkYX4EHM5YIgCAvFobLSa3NnLBbH80O+9t8D7B2yaEhG+PCVdQRueew+AUnN5rU+CY069tDCuUPP41kFfpKNEDQequtzrAAfKHQNX0g0qmOoMivaDM7rUJEi9eS9Ir0lMB0fpawTNeDF/hBVDvB9kPBIRGRcL6QprZI05Fm73RdyNvxf//Vfs2PHDtra2ujo6CAen3zlwvPPPz9PIxOE2c21QHu88sWnFv9n2m7R9ukddQjDENsNGTU9Ll4Rp1AOUGWJzZR5ZodJyT6Y7P4PT4ygSrCuI8a5bRp7tzzJm+os/l/uPJzQYC5HELW894m/q4rE6iVRRoo++YrPFSvjFO0Q1w1pq9PYNWhzYWeM9UtjALSlNXZH7BO+KOuhiEx4QTh+5vyt5eabb+ayyy6jt7eXNWvWjN9+9dVXc+ONNx7XwQmCIAinNifw+cv9T7C7PMrSSIbfT5zHa0MqpQAqnb8miBaRKwkiezYQBT7whoD6TB1IIbpujBfdVXX6dOS6Dqqi4roOUD0QTeoB9RGPIUtmcb1xyIPvifmxdXEVs5SbMRP3TCTmckEQhIVh6lw38SRzLW4Gqiex9w5ZvN7rUtnv4nkO77+wWtSRgDevVOgrQSoR5ynzOpZXdrLfNdkXMaqBxYfqhA8kEsVFvKEhzg3nSsTiBq11EVRZIpRt0lEFx5eJRRRKlRBkHQeZC7skekYqFF2VglWmvyARYvDVR4fIWQGyPL3b/VCv93T3m7/5m/M9BGEBOtFRInMt0B6vfPGpxf+Ztps0FFrrdJY1aPTlPZCgP+eRiiq4QYgUQktaoy/vsrRB46/+Y4DeUYd4RKEpGaL0PkXO8nglWEc2EdJXDCYV1Q8lqoMigWkfLMSrMjSnVbqyBucukilYPtm0xnvPTbJ72OG/thQYsXz+6+UiH3xTtev9khVxmjPavBbARSa8IBw/R/VJamlpoaWlZdJtF1544XEZkCAIgnDqS6XSSJJEDo89lRyEIbvLowxoBRwvSWCU8NK9hLJPqJUJ9TLLkzFWntWJIh3szhsZAcex0bRqFryqqpM69yb+E8AyC1y/0sZVkixpTmKWcsRi8fFYm6lURRo/cJ9pe2cyMZcLgiAsDBPnupm4roPil6iPa1R8l7ihkDN9Rssquu4hyxLLl2Q5t7fEwN69eBEDK5rga9mljBrGoQvwHnTseTNr0zFuXuuQTqhIkkUhZ4IkIUsyv7OhAVVJMFzy6cu7RHSVlnSE1a0ergM/2erg2DBU8GlNq4yYAXHNp+yp4yfcpy4+u1AiaGo8zwPgAx/4AO3t7fM8GmEhOdFRIvNVoD2S4v+aJVESUYWurE7e8nlih8n5iyI4ATTGFfYMOXRkdd5xQZpvPTnCzv4KjheQiStIQy+Ts1V+mV+FE0LJDVDgiBZjTeiQjCr4QYDnhfhUz2UmoxItGY1sSsPzQ6IZmVRE4YkdJumoQnNGw/ZCLDcgZ1b3CSKLXRAWljl/kq+88kqkQ3wR+8UvfnFMAxIEQRBOD5Ik056s5+xMC9vy/bSEBg1KhJ8Pa4A37f6KBHsODJNNqZOK7pWyRbFYmLEbvqa2uJzneSiyRDoh49jWnDrbJ3YEnunEXC4IgnDmsCwT37G59ZIomqbRN2JSZzgoro1jJwnDkPpUincN/R2FwR0E6XpeXnsVe/p+RZ3jUNJmL3gvriS4sD7GO8+2iUZiRKIxgiBAlmXCIMB2bCTf5b3rdXKWQrYuSd9omc//rMRPXvRJR0I2dEbpGbLIJmRkyScTkeisC1jRpnLOkmoUzkJcjHUiVVX5/Oc/z6233jrfQxEWmIUaJXKo4v/E7v9arMvOQQdVlthyoMJ1q1PjefHnL4qQt3xe2mcRAoviFRoSBi+Vl3Og4NOdcwmC6rnIwxXgJUBTqidGExGZuK6STYYUKgGaDJoqc8nyBJoqEdNlmlIqiahCY1xhyPRZUq/xsF0gFVHYOVhdqFpksQvCwjLnT/LatWsn/e66Lps3b2br1q3iS4MgCMICUit8z9RpbllmdUFWReEvOy7hxZEBKFTQJJnGmEfoaVBOgmqjmI3IdgJZdpGcAoWCMr59f6zzy3FsKmP58Kqqkk7rkxaUA7ArFVRVJVNXP6mbXXS2z52YywVBEBa+2jyuafpYp3XABy+Ls2NvEcUrI0kGnu8RBCHDO1+G/j1ENQl/5ADn/XqApXUKj2UbZ92+HIKXDLmqxaS5oRVXTlDfmCb0XTRNZ3h4EADP93ArFcwyxBSbkbxEb86laMNwKWTlIol3X9KMIVXQjCh3fKufnOVTF/f43kcbsdxgwS7GOtHVV1/No48+yvvf//75HoqwgCzkKJGc6fFid5lQgrXt0fFu8Zm6/7uyOtt6K2iyxM5BZzwvPm5IfPvpEXpGPZZGR1lnbGVnaTXdxTp2DbjjhXf5CHJoQsD1QZFDmlM6K1p0DK3a7b5n2CGuSzQmFOJRBSmsdurXxtyRhU17LJZlDbwgnHTSZKGdQBGEM9mc98Zf+MIXZrz905/+NKVS6ZgHJAiCIJwaJhbBJ3aQu65TPZiXQP75P+D07WV5KsvelVfg+PDvu2TMtT8C2YdAwXh1HVIo8xtdLoYRRdd0dN3ArlRQxorqmqaPL9I6aSE5z8PzPDRNw/M9EsnUpKK76Gw/OmIuFwRBWPgmzuOqqmJXKtj2KHXRANvRiEZjxGJxwiCg8uh3Ufu7UcollDBEDzxuycf4z9aWmTcegoKGUm7gxQNNdBfSHBit0N5g8j+ubMIs5VAVBUVRSKTqePCXw/QVAtrqVa5coVAfdxgxXQw1ZNMuixf3VtBUmc6sS74cVCPvrIAt+y3e0JlYsIuxTnTttddy9913s3XrVtavXz9tYdYbbrhhnkYmCCfGsebV7xx02HKgAmFIMqJMKriXyj7Fik/O9Ma33VanIYXVKJontzuUKgGPvlJk16DNEq2XNcmd9Bnns7k3xYjpTOp8l2UIghkGMYUExHSFJQ0aecsnCHy6zjJY0azz+Osmpl1tq2/JVE8kbtpjzbi4bG3MC/UEiiCcqY7bJ/p973sfF154IZ///OeP1yYFQRCEeTRbhrplmfiehxQEhKM9hGGIN9CNsqTA62Y9g11PVgvwALJPkOonVuigvcEgmUzR0JBF07RpXfZTn0fTdFRVxTJN8vlRopHYpEVaheNPzOWCIAgLx0zzeGLCSW9N08jnc0hhACN9uPF69EoJ2Xera71Eo4SzRJc1HFhOsnwWaSnJSFuckXIZTQrYO2ixc/8AdZEA3YgQjcbYN1ikrxAgKwqPvWaybyTCuiUqLQmH1/pgsOijyCHpaEC+LJMwJEp2SCamcP7i2IJejHWi2267DYAHHnhg2t8kScL3jySNWhBOH8eaV18rtofSwW7xWmE/lGAw7/H9vhxdLQZSCHuHHLb1VXDdgJIT4Hjg+WBIHuuS+3nOXsvuoSQDRX/aAqzeDAV4GZipLm86Ps/sMpGAhKHQ2eSyczDglQMVZBmuOjdFV1Y/osVlBUFYWI7bJ/ypp54iEokcr80JgiAI82y2DPXawbykaihNS7B2v4Idr8cxUvz71oCwcUIefKCg5Ftpy0DnojoMXcd1HWKx+BF1scdicUyzhK7phIQieuYEE3O5IAjCwjF1Hq/9XJtLe3r2YVcqBEi4yUZkrxfPSKC7w7iSxFe7OmfecCAhD68hpuuEeoTWtIaCQ38e6gwXrzRIyTdoaGjEdR00v0S9EbA/X42+USSPgq1z4wVp/nVTmQMF6Mt5FG0YKga8fV2G9UsjrF0SJxGtRtgtxMVYpwqOpM1WEBaQY82rz8RVrjgnOem2WmE7YcjkKz7b+iqMWh4bOuP05V32DDqYdkBjUuWcVg3FHsJsa6TfuoLSsEfFc6YV4KHa4R5SLbwrEkQM0FWZohnghNXbZRlkCUo2hKHHkgadtjqN5ozKawcqFO0AKWQ8p75ryvsgCMLCN+ci/E033TTp9zAM6e3t5bnnnuOee+45bgMTBEEQTj21fFlJkvA8l/3nXUnQtoYiMsOWxuv9CdRYHR4g+yqR7ZcT8VUWp2G06JKMxw5ZSJ+aQ69pOtlsM4VC/uS9yDOAmMsFQRDObK7roCoq6tiiq0Pr34aUH6D+hf9ALw3zWiLB3mRyxsfqZj0ZXePsVpUlTQluuUgncGCo5BKRfHw3RJZlPM8jGo2RTqe59fIQ0zP40Qt5RsoKi+sN1nbV8ev9Q6TiPqvbJQpln5iuMGr5rGyNjhfgBUFYmGqd3znTmxTLciwmFvZNO2BbTwVDk1nWoGNVAmQZCqaH47hEh55BDV0GYxcDElFDJh1VqNge9tg5MZlq8V2VwA2rne9BCHEJWlIavu+iugGpmMziep2c5TNQrHa3E0o0pVVkqgu1GqpENlV9fccaxSMIwulpzp/2dDo96XdZllm5ciWf+cxnuOaaa47bwARBEIRTTy1ftmQW8X0fs1JGMmLguhzIS0ihRnT3RkLdQnJiSKFCTPFoiockdH98G7EY0xZ7dV2HwcF+JKqXmU/s3rPtChISlmWSTuuHXDRWODwxlwuCIJwZZpsvLcvE93081yUkJAyhfssvSOx7GUuWuf2CdTBjFI1EMlXm9jeO0GhkWL44pD6VpFAIaEpJSOiEJHEcm7JljT93fSZDs6bz0aYUo6ZHRPV58NEehoohST3kw7/RxL8+ay743PfDMU2TX/7yl3R3d+M4zqS/3X777fM0KkE4sY41lmY2sYhMR6POqvYoQ6ZPyQ5YVKcTkWzc7qcpywZb/AsYGKygyNCc0ggJcSZclKLJoKlQdg/epkigKTIVL2BRnYblBKSiChd2xgmAx7eVGC55OH7A83ssJCRMJ6ApqeIGJ/Y1C4Jwapvzp/0b3/jGiRiHIAiCcBqodbGrmoYsycRiMRRFY2h4mJ/vrMaYSKGCZNe650I66zze1CUTiRh4nofvVeNqpi72OjjYT+D7yIpCLBYfLxx4njd2CejBOJrZFo0VjoyYywVBEM4MtfnS8zxUVR2fRz3Pw3ZsKnaFMAzRKkW00igAz2cy5PTZ59Z2NUaTKhORKth2eXwNF8euEAKGESHwPcqVMoRQ8n1UVSWd1sdjZbZ159jZb6MpAaOOzP7eAW65uIWSIy/o3PdDeeGFF7juuuuwLAvTNKmvr2doaIhYLEZTU5MowgsL1kyxNEfaKT7xfgDfey5HX84lHVPoyhqsao+yrEHn6V0mP9tSQFdlOvR+XJLsDM7FDyXyZRsvCDHtgLITToqjCaVqAV6WIfCrXfGaDIoskYoorGiNUB9XyFsehi6zcVmMqC6jyRJb9pcxKwGGJvGWVWm2HKhw6fL4rK9ZEISFT5xyEwRBEA7JdZ3xOBhN0ygU8vi+jyzLRGMJRvI5dpYrlJzYDI+WULWQTAQM3UBR1UlFgBrLMpGQkBWFbLYZTdPJ50exKxUUVSUWT8y4iKvIiBcEQRCE2elGjMGiT1IO8MdOXgPjJ8QDv9qW6UWTOPWtuKMHuPe8c2ftgieQuDmxmqhmoGkq0Wh17tc0nXKljK4ZAMTiCdKZ+vFFYCfO154f8rPXfPaMhmiKzIVLoC4q4dgW2XTdiXgbTgt33nknb3/72/nyl79MJpPh6aefRtM03ve+93HHHXfM9/AE4YSZaUHSI+0U37yvzNZ9ZXpzOn15j027TfryLsmIgu2GvGFZjCd2mDyzw6Sc78dWDQptizCjzRzotRkp+Xg+eCG4ToA8JQXL8UEdW4E1poOhQjyiEtNlWjMq9XGF9R1Rfv5Kib2DNlIIN78hA1T3daOWz+rFUc5fEqO9oboYazrmHXYRVhFXIwgL05w/zb7v84UvfIHvfve7M14mNzIyctwGJwiCIMw/yzIpFQsA+IGPXakQhhCEAcO5Ef6/vl+zzyvR31KHuutipPDgt1eJkA9t1GioT6OoKqlUesb4mIlF9ZkK7VMfM9uiscKREXO5IAjCwuf5If/0VJGenEtzUuZd641JnfBB4BN6DgQ+geeSb1vJgf6tjM62QLcPneFiruhcTswwUMfmdaiesI9GYoSERKOx6oKsmoampbEsc3wTruuwqzfP49ss/EBGVSR+6+ImkhF31hPrnh8yYnrUL/AO+c2bN/OVr3wFRVFQFAXbtuns7ORzn/sct95667T1XARhIaoVnxvj1eOJw3WKSyEgSfTnPHKWj+NBKqKSiSvUxRRCCTRZoj7s5ezEFvap51EXVzArASU7wPXAH9uWrEAyKuMWg4O3AYYGnlftfs8mNdrrNdobdQxNZqDg8b3n8tQnFLpzLhUvZPO+MlIIo2WfupjCmiVRYG4RNCKuRhAWJnmuD7jvvvt44IEHeNe73kU+n+euu+7ipptuQpZlPv3pT5+AIQqCIAjzKRaLk0imSCRTNDY2kUimiEQihCHsL41wwCvh+WBHioS6Nemxi1M+ekRDURRUVZ01v71aVK+b9HdN04nF4liWies6Mz5OODpiLhcEQVj4RkyPnrEiTn8xIFCS44ue+56HVciTfPr7KFaBRT/5Ikse+TqjkjxrF/zF6hL+9rzLqE+naW5upaEhO+nEeSweJ5ttxnUd7EoFyzLH43Bqc/ngYD9lyyQIfSQJVEUiYkz/DlDj+SEPPjbMlx4Z4sHHhvH8cNp9FgpN05DG3vvm5ma6u7uB6joutZ8FYSGpLciaM73x23YOOuzqt3lih3lEXeBrlkS5eHmc3zgvyYbOGO/ckOFta1IsbzZoSatoUsjeHVtormylL74eUkuw3RBNgYaEyoT4dzS1mvceNQ7eZqhwbluEVFQhCEN0VUJRZbJJDdsNGTU92us0MhGF5rRGJqoghVC0fUZLHs0Zdfw1dGV1EoZMb87hl68WJ73uqbqyOi0ZTcTVCMICM+dTat/61rf42te+xtve9jbuu+8+3vOe99DV1cXq1at5+umnRVadIAjCAqNpOg0NWaDawRaG4Vg3fEiDEmGxmuAV20Qup5GmRNJ0ZlziqjIpz30mh1o4TmS/H39iLhcEQVj46uMqbRltfLHTZERhoOCS1AMKxTxhcQQ1PwjpNvTCIHlF4VNr18y6vasTS0kY+ozz+cQr1GJjXwUm3i8Wi1Mo5LErFTJRlSvPTrJn2GVROiSuOgwPV2Pvpl4xN/FEQk/OZdT0yKa0Y35vTkXr1q3jueee46yzzuLKK6/k3nvvZWhoiH/6p3/i/PPPn+/hCcJxN1O3d2Nc4b8GbZrTGpv3lUlGlEMW4zNxla6xba1tj5KJq/zy1SIv7itjuyG+H+BZRZ4vrSOu1JGRQ9IZhfZ6jVd7KkQ0qLggj3XMR3SZREShYLkUbbh4eYwljQaKJOGFoEqQMGT2DNnEIwotaQ3HD1lUp1HxQ+qi1c73F7vLtNbpJI3J+TYHci49o271eaLKrF3uh4urmSsRbyMIp4Y5f/r6+vrGvwQkEgny+eoXpuuvv5577rnn+I5OEARBOKVYlolllrAdB0kCTZL5n8lVPLBNwu6vmxRFoxBwTVcZw4iP57wfarszFdtF9vuJIeZyQRCEhU9VJD7wxgZGTY9kROEfnxihJ+eS0R2uXR7iRdN4qSYAnDDkDy5YR6goM2xJIukkWN/QgKbrh5zPYXpk3MSfZVkmkUjwP69uZG/fMDHZoVQYxZ0Qi1ZbO0bT9GknEuoWcPHoL/7iLygWiwD82Z/9Gbfeeiu33XYby5cvFwuqCwvSTIuT7h52UGQJLwiRQg4ZyVIrLJfKPiW72tPeBfx6t8newTIJcy+Xb7yAfxk9H9/x2DVgE9VdCnbAyhYdxw+RZYjoENcgCCWsikdHNoquStSHYNkhAzkPSQY1hKVZg4oTcP7iKE0pje0DNpos8WqfjSJBGKvuQ9csiZKIKpNe24vdZXpGXQxN4vxFkVm73E9EwVzE2wjCqWHOn77FixfT29vLkiVLWL58OT/72c+44IILePbZZzEM4/AbEARBEE5bkqLRPVjCoEwYVi8JHyyqbO/LIDH58vWUERI1VNra2g97wD5bsX3igfxs3fLC3Im5XBAE4cygKhLZlMZAwR3vKO8rBJRcmXQmw8j66/BfeZ3X4zFerpt9UdQ/WXQBLY2N41fGHY1UKj1eYFcViSVNKSzLrC7qWj4YZzfxpPzEEwl1CzwTfsOGDeM/Z7NZHn744XkcjSCceDN1e0shRHSZFU0Ga5ZEx/PhN+2xphWla4XleESmJaPRGFf45hMjPPnaMBuMl1AdhSe3l3jvRXV877k8thPSn3cx7Qo7ByrYToAsQdKQcT0wx35XJLiwK87WfRUGSy79BQ9NhfZ6g4LpgSQR02WuOCfJmiVRHn6pQEeDTn/eJR1R2DnosH5pjPVxdTxypyurE0oQ0SRWtUe54uzkrO/L0RbMD1W8n+mEhyAIJ9+ci/A33ngjjzzyCBs3buSOO+7gPe95D3//939Pd3c3d95554kYoyAIgnAK8PyQf3h8lD0DEmlVZulYLOvWQQWYflC8utlhaWv6iLrYa8V213XI50dnLLSLaJrjR8zlgiAIZw7PD/GDkJaUSk/OoSkBCS0AP6DuuR8jy218duVZs2bBt9mtrMo009LSdkwnwWea69PpauG/9l1h4gn3mtqJhIWuXK42OMTG8nz27t3LD3/4Q84991yuueaaeR6dcCo4EyJFJnaQ14r0m/ZY40XpWvRMV1afVFjOjN1vT+8Il0aeZ8RL8PPc2XRKPt25UVYtilC2fUbKLnnLJwRkuRot4wYSilztpI/qcM3qFBd1xinbI+wagFHLo71eZ0WLQd7yKLsQj1Y73jNxletWp9g56HDl2QmGTH9SobtWUC9WfKQQVrVHWdsePeR7cLQF80MV7493vI0gCEdnzp/Cz372s+M/33zzzbS3t/PEE0+wfPlybrjhhuM6OEEQBOHU0Z8r05Pz0FSNvAfBWBF+b26mS9dDLmjzaF+8ZNKth+tmn63Q7roOnuehjHXQCcdGzOWCIAhnhtrCpj05l5aUyrsvUHCKRfpGPRJWH435frx0M6/U18/4+PT+c7lAOxtJzWBZ5nE5CX6ok+pTo2zOJO94xzu46aab+PCHP0wul+PCCy9E13WGhoZ44IEHuO222+Z7iMI8OxMiRWYqFteK0Y1xhYdfKqDK1ROG65fGDhblx+63LGsw5LbxvNmOLMO+EYfmlMZDm/K4fkDZDnH8ara7oYPtBkgSRKIKzXr1hF97ncYTO0w6GnVCQrIVlea0yurFUUIJBnIuO/tsljXodGSNSWPumHKxUG3spbLPQMHDDcLDFuGPtmAuut0F4dR3zHvujRs3snHjxuMxFkEQBOEUFpErZGMB/cWArtYUSp+EH8DeoelFeI2AN3QmpxXMD9fNPlssjWWZ+J6HEYmIKJoTQMzlgiAIC9PkhU09RsyAR7bCkKlSrzVyq6LjydLMXfChxFlyG8tbMyxuTh23k+BivZeZPf/883zhC18A4Hvf+x4tLS288MILfP/73+fee+8VRXhhwRVZj7Szf2JHvCZLuEE4/h7sHHTY1W/z8rZdnFXv8jtXnM/OwWaUzTme7y6zPKuze8ghX/YIg+quLh2FZFQhFVHYP+rheNUYGi8IkSV4/HWTZVkDVHj/ZQ38epfFK70V6uMK5y+J8e2nRujNu9VCffbQMY61sedMb/wEws5B54ScRBHd7oJw6pPn+oD777+fBx98cNrtDz74IH/5l395XAYlCIIgnHpSiQTvXCtz8/k+b11uASF7R2X6nOlfPt+/poCh+dNuj8XiGJHIrAfe1Q64ummF9sM9Tpib+Z7Lv/SlL7Fs2TIikQjr16/n8ccfP+HPKQiCcCaqj6s0J2XKlQq7Byt8/0WPFwciyIrKYEXlwOINhLM9uKzz3rMT3HJJklQicdxOgs8215/pLMsimazmRP/sZz/jpptuQpZlLrroIvbu3TvPoxNOBZm4yvqlsQUTRVPr7N85eHBh5r2DNt9+aoS9g/a0+3dldTqbDS5dHmfnoEPO9GiMK+zZ+RpW9zMM2tVjkmLFZ2VrlIuXxwlD8MNqrJWuwbmtUS5enuA3zkuztFEnqkF9XEGVJSKqRESTuPysOF4QcunyOB1ZAzcI8f2Qh18qkDM9Ll0ep7OpOo4jVYut6Wo2FsxJFEEQ5m7ORfivfOUrnH322dNuP++88/i7v/u74zIoQRAE4dSjaTrJRIKU4VHMjxKGIU/u05ieBx8Si0go8gwd8oc48K5lxLquM6fHQfVy+4GCi+fPWkoQJpjPufw73/kOH//4x/mTP/kTXnjhBS6//HKuvfZauru7T+jzCoIgnIlUReKmdSpy6DGYr7B/xENRFLxQoiEWoEUVCtpMeesSN3pXsrYjRTAWJSecWMuXL+ehhx5i3759/PSnPx3PgR8YGCCVSs3z6ATh+OvK6rRktElF6Sd2mOwasHlih0nO9Hj0tSL/+swIX//lEPuGq8cILx+o8NQOkxe7y7z44iaS5mtsDS7gmd40X3pkgD/7YS8PbRpl75BLruxTtgMcFzRVpuKHZOLVYxRVlVlcbxDRJYqVgJIdsHF5HEOvFuVrY7h0eZy4IdOc1ti8r8yQ6XPd6tRhu+Cnmu0kSm3x1pzpHeM7KgjCqW7ORfi+vj5aW1un3Z7NZunt7T0ugxIEQRBOHROL46lUmng8gW1XI2WK9vRpRCHgrHofTZ/bF9NaVM1cD/RrebdfemSIBx8bFoX4IzCfc/kDDzzABz/4QT70oQ9xzjnn8MUvfpH29na+/OUvn9DnFQRBOFN5RHF9iOkypu1zfiu8bdkI1y4vMxyP40+MoglDWgey3B+9kg+tTaJIEiGhuBLtJLj33nv5oz/6I5YuXcrGjRu5+OKLgWpX/Lp16+Z5dIJw/E0tSudMj3REpiWjjXe7P7fL4geb8jy/2+Lhlwr05Vx68i4Vx2f7gE0qVUdP8hIOVJLsG3X52dYSBctne7/NYN4lpslEdRmkkOFSwEDO4dUDFUZMj5UtEc5fHCFhKHg+SJKEVQnoyup4QYg2Fh3TkTX4n1c2cn57FCmEvpzLi93laYXzWjF976A9p6L6TFcECIKwMM35Oqba4m3Lli2bdPsTTzxBW1vbcRuYIAiCcGqoFce9IMSREjhqHX6wDwBZmlqED/nkpTmy9Unq6xvm9DxHmxE7Oe/WZdT0yKZm6uoTauZrLncch02bNvHJT35y0u3XXHMNTz755Al7XkEQhDNZcybKspYkTmhRF9e5cmkRxQ5QZEinWtEZJeH5+EhknSY+c/YFNCSjtLS0oKoqqVR6/Eo0zw8ZMT3q4yqqMkOO/BGYaRsVJ2DXoE1bRqMn59KZNYjoc+4XO63dfPPNXHbZZfT29rJmzZrx26+++mpuvPHGeRyZIBy9Q+W+T/3bzkEHJIl1HbGxLnMb0/ZZUq8RM2SuW53CCaAtGfLLX/6K15UV9Gbq2Jt30GSfqCaxenGETXssIppMT86pxm1JEASgyWDZAX0FD9cPcb2QlW0RzlsUpeyGxDSZAMhbPm11GmbZp1T2yZnepGz3nYMOxYo/bZHcWjF9W2+FdFSZ9LdDWWhZ/4IgzG7ORfgPfehDfPzjH8d1Xa666ioAHnnkET7xiU/wh3/4h8d9gIIgCML8isWquYj/8qzDY9sP4Lou59RF6QokVAUgpBpJEyIBlUChvr6BMJxbR3o1cmbuXz7r4+r4QXtbRqNugeRknkjzNZcPDQ3h+z7Nzc2Tbm9ubqavr2/Gx9i2jW0fzAUtFAonbHwTuWPxC7FYfFIMUu12TdNxXWf8n7WTR4VCHmBS0UoQBOFQXNehUMjjed540Ruq+xPHtimXLTzPw3FsPM9HVRVAolIp4/uH77T0A9h/IMq2Xp2SLfP06wFrW6Jc0WGT1DW0MMmbBi6BaILfXqES02Qcx2bP7h3TtvMfO6IMWzINsYBrl5dR5lgn9wP42e7k+DZ+9+Ik2ZZFfPzbffSM2IxaIekoLG6I8H9/d8kZV4hvaWmhpaVl0m0XXnjhPI1GEI5drTAN0wvSE//WBZTKPvGIPF6M3nKgTKnis6TR4JZL6snEVXoGcjz8H/+JHxj0VAysXJmc5ZOIyFzUFee5vWXa63X2j7rIssRwyUNRJOrjCo4fElFlFKW6uCtAa0plebPBuo4YPXmXdEThiR0m6ahCb8Ej12uzfcDm5g0ZMnF1WjF+YuG89vOqRRGGTP+Ii+piQdXpjnTRXkE43cz5v+ZPfOITjIyM8JGPfATHqV4uE4lE+OM//mPuvvvu4z5AQRAEYX5pmo6vJOkeHaBU8XAclz5NoSsWjhXha51wEind5/JVLcTiiZN26bqqSHzgjQ2Mmh51x9CZdyaZ77lckib/OwrDcNptNffffz/33XfftNt/8IMfEIvFTsj4AILAJwxCJFlCnrC+Qe32kBAJafyfklwdv+9XFySWZQVJAkmSZ31tgiAIUN2v+L4/vi9UlOo+x/d9giCgek47nPPJ7Ro/lGixJX4jLeEH1dtkC17fHqJIoIUO56p5cPPs2Rkgz7LL8kOJrC2RlYAybHt99vseaiz1tkQ9QAl++XiIrGic4wacnQjxY6DIILkS3/uehq6eHvvPd73rXce8jSuvvPKQ88UvfvGLY34OQTjZDtXlXbutMa7w8EsFNFmis7kaZ7lpj8WeAQfHl4hp1ZNxP3qmh/0v/ZyK3kzQupoOM2R7fwVJgoQh83q/TcX26c076IpEfULlnFaDoVKA7flENZn+gkdXs04iolAXVVm1ODqe7V4r/DbGFfYMOeRKHq/0VujMGuwcdCYVyjNxlS6qJxK6xn6fWEzvyJ6od/TMcKiTN4JwOpvzf82SJPGXf/mX3HPPPbz66qtEo1FWrFiBYcwt+/dofelLX+Kv/uqv6O3t5bzzzuOLX/wil19++Ul5bkEQhDNVfVxlWaPOtgNFJNmnJeGjABcvrvDkPgM/kJDlkA+tK7Ci8/yTPj5VkUQEzRzM11ze2NiIoijTut4HBgamdcfX3H333dx1113jvxcKBdrb27nppptO6EJ1x9oJD+B7HkYkQjpdd8LGKQjC6e9kdML/++tR/ntXhKIro4Um6/wnuU55hoAWhpadz2vhKhpiARsmdLf7ARRsmZRRja6Z2gm/4Th0wl9/iE74j958ZnXCr127dtLvruuyefNmtm7dyq233jo/gxKEY3SoLu/a3zbtsVDland6V1YfL8A2p1VMJ+DCzhg7Bmye2RtQ8lZiK628pzMJQDQiU7ED0nGF3QMOg0UPTZGJaDJXnpPkjSsTfONXI5zVYvDS/jIRXWag4JNN6VS8oNr1HlOmFdGHTB9kiYa4Om3x2BpRKD5xRESPsFBJ4dG2VMyD73znO9xyyy186Utf4tJLL+UrX/kKX//613nllVdYsmTJYR9fKBRIp9Pk83mxwrwgCMIceX7Ipq3bGB4eIhMJ2b59J51dXfyfZ1IMWjLZWMAdGwtcfpk4MSrMbuPGjaxfv54vfelL47ede+65vOMd7+D+++8/7ONP9bl8apF+ahFfEAThZKrtk0YqGr//93vpM/M0dP0zexMRVOCNg0Nc7Z9Nw/JVJDMpYtEI7UuW4jgu/7rZZ6RSjXz7wBsbUBUJzw+P+cqzmbZRcQL2DNm0pDX68i5LG8+8TPjZfPrTn6ZUKvH5z39+vodyXJ3q87lw8kyNHpmYu25WAvzcHvL9uzE6Lqe/6NOSUokaMjISsYjM2vYoP99a4J+fzmE5PqmITEeDwRXnJtg9WF2ItTmj8eZzE3zvuTztdRqpqEIoQTqi0Nls0BivxtBcujxOOqbwYnc1CiceVVjbHp0xEkVEpgiCMFen1TebBx54gA9+8IN86EMf4pxzzuGLX/wi7e3tfPnLX57voQmCICx4YeDSnNZoiIXjnW+6CndsLPDRCwvcsbFAKhGZ30EKp7y77rqLr3/96zz44IO8+uqr3HnnnXR3d/PhD394vod2XAwO9rN/315yuRHS6TpRgBcEYd54fsje/gK5XJ4DB/Zhk2P0gh+zoy6Jq2mUNY3Hso24kkz7vmdRZEgkqt2lpq8zZFUn+9qi53DwyrNjiX6baRsRXebstmqh6+y2qCjAT/C+972PBx988IRtf3R0lFtuuYV0Ok06neaWW24hl8sd8jHvf//7kSRp0v8uuuiiEzZG4cTLmR6b9ljkzMNfXXOi5C2fTXssoBpRs72vQvHAywzsfB654Wy8UOKWS+rpbI7w4t4yP3ohj1WpZmy90lNBUyCqyzQmNcpewHDRx3YCKl5AXJNYVKdzwwVpLlga451vyPDmc5O4QThegH/1QJl/eWaUzfvK9Bc88pVgxgJ87b0CWL80NmsB/lR4TwVBOLWcNqfrHMdh06ZNfPKTn5x0+zXXXMOTTz45T6MSBEE4vXl+yIjpUT+lo83zQwaLLp4Hffky//TkMA0RD9v3iXkaFy+uXnrpeLBjWGHAlDFtid9YvoSeUQfXD8hbAWe1RGY8kJ7teY9mzMeyLeHk++3f/m2Gh4f5zGc+Q29vL6tWreLhhx+mo6Njvod2XBSLRRzHplgszvdQBEE4g3l+yNf+e4DdfUUimBStIt3Lfwzy5Dm5LElIgNWyFMOIYEQiRKMxkG1a0yr9xUAsej7PnnrqKSKRE9fk8N73vpf9+/fzn//5nwD8/u//Prfccgv//u//fsjHvfWtb+Ub3/jG+O+6Lk46n87mM1ql9tzbeiuko9U1MTbvsejb/TJh2M0lV1zLT16DFiMc7zz3ghBJCunJu+wcdOhorEYqLm3UeXK7hTV2dc2GpTFGywFIjC+42pLRyMRVdg46pKMKQ6bPpcvj7Bt2aElrSCF4QYjrhTz8UoHrVqcmFdqP9L0ScTWCIEx12uwJhoaG8H1/WmZsc3PztGzZGtu2sW17/PdCoXBCx3goM2XLWpbJ8PAgum6QTKYol6tnU1OptOicEwThsGoZsjB5v2FZJrncCJFIlGIhj2maVCoWYRgShowt8hYQIvOT143xXNZrx7Jd/QB+sj3K5l6dIVOm5EkcXHwVIMVD2wI+0gkPPJ1i+7BGNdcs5O9fGCCu9zNoKahySHvK546NBfQJs83UTNlrD5Mpq6ra+EJlmhHj+y96DFkyrWmV37pA58evqewfqtCa0fjdS1PUZzJiH3qK+8hHPsJHPvKR+R7Gcee6DooiI0nyCV00VhAE4XD6c2V29xUoV2xeHHCIx36Al52hkBsEKGEAsQy+7+E6DuWyhe95vGu9QaAkxaLnJ8lNN9006fcwDOnt7eW5557jnnvuOSHP+eqrr/Kf//mfPP3002zcuBGAr33ta1x88cVs27aNlStXzvpYwzBoaWk5IeMSTr4TkcE9W1zL1Ntrz7lqUYQh06ejTqZnVGGH3oZdv4whL86yrIc3lhmfiau8/7KGSfExAFeeneDnrxRR5OoCz+e2RQglaEurdGR1zl8UZcj0aYwrbNpj0RivPq4xrrB72GHVogiJiMKaJVHWLImOLxY7dVHWI32vGuMK23orrFokrhQWBKHqtCnC10xdMT4Mw1lXkb///vu57777pt3+gx/84KQfHAeBTxiESLKELFd39p7n4vsBkgSyLFOL51cUZfw+giAIswkCH9/3gcn7Dc9zCYIQCAnDkCAIZny8H0pkbYmsBJRh2+sBslS9vcmWeFNKwk/ATAuHSFQXDLs4+hIXLpp8uyxBkKS6b5Ng2/YATT64ldme90j4oUS9LVEPMAqP/wpUR2IpwAD89D8ldO302Ye+613vmu8hCMeJZZn09R0gDAJisfj4Qq2CIAjzQQ1K1Ed9Xi/avN71E1x95iLQpT09oLXhxzPg+ZTLFulMHUYkMtY8JBY9P1nS6fSk32VZZuXKlXzmM5/hmmuuOSHP+dRTT5FOp8cL8AAXXXQR6XSaJ5988pBF+EcffZSmpiYymQxXXHEF//t//2+amppmvf+p1CC30B1NVvmhFlA9WhM7wbvGfp+48CpUO8QnPnc2bvGTh39CmOokEm8hldCRQggJeWGPxYFRh7euSuEEjHeo7x202dZboTcqkzN94obE+qUJsmmNrfvKIEm0ZnQ6sgYdWdi0xzr4/EtjbNpjjd/v4uUH37PrVqfGx3w079WQ6Y932ndkj9e7KgjC6ey0KcI3NjaiKMq0rveBgYFp3fE1d999N3fdddf474VCgfb2dm666aaTvviL6IQXBOF4O96d8BuOuBMeomrARzqf46nC6kmd8PWRgLgeTu6EX33oTvgNx9AJ/9YpnfBvEZ3wwjwZHh6kbFmomk5jY5ZUKn34BwmCIBxntWMOAp/rzg7pox9Xcme9/wd6++g/O4VmRJBlBU3TiUZjuK5zEkctAJOiXU6Wvr6+GQvnTU1Ns15tDnDttdfyW7/1W3R0dLB7927uuecerrrqKjZt2oRhGDM+ZrYGOeH4O1ViUCZ2jE8qyM/QSZ4zPZ59rZ99Wx9FizfwWq6RqAHFss+WfWW2HKjQPeQQHZAIAliWNdjWW+HS5XH+5ZlRSnbA0gadDZ0xzLJBIqJQH1OI6DKtKXW8+70rq9OV1SmVfYoVn72DNn2jDiHQ2aBNGtOxnpg4EVcXCIJwejttivC6rrN+/Xp+/vOfc+ONN47f/vOf/5x3vOMdMz7GMIxZvwScbJqmk05P3vlO7JRzXQfXVScV6QVBEA5F03QaGqa3VUzct9TXNx5yGxveEDJqetMuNb9wY0jvqEWuUODAcJmHXgpIKy6WXaA1HrBxscf+vXDXRQV2DssMWDJnLc7wGxvOpmgHOJ5PsRKyvMmYMRP+wo0zP++ROOecyY+9fdnRb0sQjodCIUcuN0LgB9TVN8z4uRQEQTgZLMvErlRwAomP/8hhe8sOyMxyZ8dhuZygT1Fpam5FlmUSiSTDw4O4rovneWJ/dpr69Kc/fdiC97PPPgtMv9IcDn21OVTXd6lZtWoVGzZsoKOjg5/85CfTYnVqZmuQE46/U6X4O7GI3TV2W607v3Z7rWs/b9rsfOG/KOqLIXoupRGPUArIRBVe6akwWvJIRCSWZnUUCbqHbUDix5vzlCo+gwWP5U3V1xuPKpQqAQdyLq1pjZaMxu5hh637yvTmdFozOqEEZiXgiR0mIyUPSZJoqTvyKwfm+voFQRDgNCrCA9x1113ccsstbNiwgYsvvpivfvWrdHd38+EPf3i+h3bMLMvEMkuYZolstlkU4gVBOClURSKbmn6puapIxCSLUWuYx1+XSEWieBWLq5Z6GBpEx3ZRugrnNAecQ8g557aSiCokogpw6MvXZ3veoxnzsWxLEI6V6zr09OzDdVwkCVTltPpqJQjCAqNpOoVCnif3OOxqfoIw3T/rfT/18iuMXvJupFw1vq6trZ18fhTXcfA87ySOWgDwfZ8vfOELfPe736W7uxvHmXw1wsjIyBFv66Mf/Sjvfve7D3mfpUuX8tJLL9HfP/2/kcHBwVmvNp9Ja2srHR0dbN++fdb7nEoNcgvd1OLv0cTTnKwx9eYc9vQVWNacQl1yBZIfhSDE9kKyuszSJp1hyyckJG4oJCIKFS/Esn0kSaKsSwwWPbwA/nNLgfMWRbj8rCQtGY1ViyLsGao+x95Bh4oX0p/zkJBIGPL4fXYPO0hh9QTBqfBeCYKwcJ1We5Xf/u3fZnh4mM985jP09vayatUqHn74YTo6OuZ7aMfEdatfdD3fR1VULMuc1jUvCIJwstQuZbfKZf75+YDn9suUvTKub/Bkt0HKCFjf5rByQoNUc0sr0ahYiFI484yMDOM6Ln7gE48nUNTT6quVIAgLjGmX2Vsa5We7TbzUgbE0uRlWd/E8LlNSmI1LINeNqqgMDw/ieR6JRBJFVUWs1kl233338fWvf5277rqLe+65hz/5kz9hz549PPTQQ9x7771z2lZjYyONjYe+GhLg4osvJp/P8+tf/5oLL7wQgGeeeYZ8Ps8ll1xyxM83PDzMvn37aG1tndM4hZNjrvE0J6MQ/WJ3NWJGyW1Hy71O0Pg2VrQ3EEowmPfoL3gYmkTSULjpgjQ/2JRDlSXOaongBiGaDP1FH4IQqUHi5X1lIpqMH0qsWRIdH/eQ6bNlf5mKG9JWp3Hp8jhDpj/ptXVkD54cmpQXL4rwgiAcZ6fdXuUjH/kIH/nIR+Z7GMeVZZn4nkcsFkdVVbGYmyAI86p2KftAwWOkoqBIIWW3ehxf9iUqnsKmXp3lLQcfUyzkKSRT4rJ14Yziug653CiOY6OpKpFIRBStBEGYF67rkCsV+NPXf86ruX56JQ0OsUZ53PfZ84a30ZFIoSgKumFQKlYXyszU1ZNO152kkQs13/rWt/ja177G2972Nu677z7e85730NXVxerVq3n66ae5/fbbj/tznnPOObz1rW/lf/yP/8FXvvIVAH7/93+f66+/ftKirGeffTb3338/N954I6VSiU9/+tO8853vpLW1lT179vC//tf/orGxcVJsrHDqmGs8zdSi/eGK8hP/Xnv84Qr4ASHhwItI1n6UxZegqDolO6AlU726taNRp+IE/PvmPIYisbhBp1D2ubAzRkfWIGd6vNhdplTx6S14NMZV8mWfd2+sm/S8XVmdYiWKFMLSRn1aAf5Y36tDEV31giBMJfYEp4Ba0V3kwQuCcCrQNJ3RXI5fvB4waGloqseipEtvQcEPJfwQ+osK4YQi/Aw9doKw4BUKeVzXQZIUIpEYLS2LxDwuCMJJU7tyLRaLY1km+wpDbC8MkLNCStqho0tWVaB+6VKSqTSKomBZJpquYxgR0RA0T/r6+jj//PMBSCQS5PN5AK6//nruueeeE/a83/rWt7j99tu55pprALjhhhv427/920n32bZt2/h4FEVhy5Yt/OM//iO5XI7W1lauvPJKvvOd75BMJk/YOIWjN9ds8qmF6MN10k/8O3BEneT1fg8RZ4BC0+UYaopYRKZU8dm8x+KcNoP6hMqr+y1e2meRMBSGTJ8l9Rpb95fHC+mJqELJDljRZJCIKjTGq/dLx7zxoncmrvKms5PkTI+HXyqgytIhx3Y8c9xPlQVyBUE4dYg9wTyb+OVZHLgLgnAqcF2HkZJPf8HnrBYD21VZrOZ48KWDHb5uCH5Q/RKrqiqJRELE0QhnHM/zIAyJRCNkm5pF4UoQhBOqdtygaTqu62BXKpTLFp7nkUqlaXMytCgpDgSDoFdm3c5FvRLN8rv4xd4472uV8X0fu1IhFo+LK9rm0eLFi+nt7WXJkiUsX76cn/3sZ1xwwQU8++yzJzRLvb6+nm9+85uHvE8YHmy3iEaj/PSnPz1h4xHm39RC9OG6w2f6e+3nqd3glUqFIAhYsrSLvZV67BFY1qCxtj3Kwy8V2Dvs8HJPhYu74vhIRLXqJT0RFSw3pLfgsW/E5R8eG+aCZVE6G43x+JlDRcnsHHTQZAk3CGcd2/F2qiyQKwjCqUMU4edZLfYBEDnwgiCcEmKxOIuaXNrqTPryHppn8uAracaCZcdIeEH1J13XCfwA13UAUYQUzhyqqhKLJ9A0TcTQCIJwwtWOG0yzhKqo2I5NEPhYlkkqlSaqG3xy6SW8K/fDyVP2FB12Ei8VZ8iSGSo4SBLE4nEymfqT92KEaW688UYeeeQRNm7cyB133MF73vMe/v7v/57u7m7uvPPO+R6ecAY7XHf41L9P/LmW/V4q+6xbFPIf//EfLFu2DCt1Ds/sdqmLq7TUVesgqaiMKkusaDZwg5D3bKzjmSaLdEQmashYlYCBgseWA2WGSz7P7AhY3V5tAnr0tSKDeZd8JWDVosi0MU4siNcK7hM71bs4shiduTieXfWCICwMYo8wzzRNxzRLJEQXvCAIpwhN02nONvFbFxzgya19fO1ZhZmO5ms9UWEIQRCILmDhjGJZJpZlkognqKtvEFezCYJwwtXm2cRYJ3wimSKXG0FCwrJM1EiEz3Q/Rl4ujk3SU8LiwpDWSoVM3VswjThNCaiP+0iSTFtbO67rkM+Piit058lnP/vZ8Z9vvvlm2tvbeeKJJ1i+fDk33HDDPI5MEA7tUB3loQSEIeXSCP/2b4/R2dnJhg0beHybSV1CJapKFCs+L3aXkZC4YFmUQjng0uVjkb26zIWdcTqyBt9+aoRc2WdFk8GiTMjKNoOurM7OQYet+8r05lxaMxpDpk/HlIt6agXxnOmxaY9FV1afVJg/EdExIhNeEISpxJ5gnrmug6qoooNUEIRTiueHfPMZkx+9GKHgzNROF2IoISChGwbpTJ04YBcWvIlREH19B/A9D0VRxH/7giCcUBPjK9PpOlzXwXUdNE0jm20e/9veSp5NhQMzb8T3+cwrr7DyrGtIXpDGSMdI6T6e5yCPZSSLK3RPLRs3bmTjxo3zPQxBOKxDFbDXtkdJGDLdm59izZo1nH/++UiSxJolURJRhV39Nj/fUuCslgiaKpGOyKSj1Wz3bb0Vdg3YAHRkDc5fFGF7v82SBp1rzk+NF7a7gGIlSmfWIBaRDxn/MrEz/4pzkuPj7Rr7+/GMjhGZ8IIgTCX2BPPEskxyuRESiSSKquJ5HpZl4rqO6D4RBGHe9YxYbOsPqbgz/z1FBU0Bw9BJpdLU1zec3AEKwjyYGAWhawYOiPgGQRBOOMsyscwSplkaL7ofLJbXEYtV71OslPADf8ZtpH2fy8shI4vPRVEUIKRUKhIGPkFQ7Zivddprmi464ufB/fffT3NzMx/4wAcm3f7ggw8yODjIH//xH8/TyARhdjnTo1jxkYBixSdnVhdFrXWBS+YAxLJccfVbaUgdXNug1pn+wl6LUiVgz7DDua0R9gw5GLrMqkURGpfHsZyATFRh76DNlgMV4rrMQMln56AzXtiuLb56JGqd+eGUHqOp0TE502PzvjJSyHjm/FyJTHhBEKYSRfh5ksuNYJkmvu+jKAoSErZdQVWq/0pE94kgCPOlXLH5zhO9DOR9nHDm+6xsqQbCp1IZstlmcZAunBGmRkGIApUgCCdDLBbHNEvjsTO1fVEsFseyTPr6DiApCg/seASpFkEjMSE3Dpocl95FXcSNGP/2qsZQpUyDEXDz6oOVKE3TSaerBXjREX/yfeUrX+Hb3/72tNvPO+883v3ud4sivHDcHM+YlJ2DDmYlIF/2Ccd+Xx9X2TFgs/3lFyj2bad51ZuRpHoaUtPH0ZbWYAlsXBZjy4EKZS8kJGDI9Fm/NMa6Dp++nMsTO0xUWSITV1jRZBx1YXtte5RkRDns42sRN0gSiahyVJ3sIhNeEISpxB5hntQ65xRFJfA9QkIymfrxg3pBEISTzfNDRkyP0VyB/mKIFwSAPON9r1xqI+UkMnX1YwUBRDFSWPBqBaoqMVcLgnBiTIyfqc2tEmCaJeKJxKT75nIjVFybLYVhnjN78KbmwAOLymW63JDFxRKDlsev94bYvk1Uk3nbagNZnjzXTyzyCydPX18fra2t027PZrP09vbOw4iEhep4xqTUitmrFkUYMn26sjq+7zO84xnskV4uvfI6Bt0YpbLP3kF7/D4AD79UQJMl1nXEOH9JjPYGnSe3m/TkXRrjyqzbz0zpWJ/LCYUjLYx3ZXWKlShSKDrZBUE4fkQRfp7EYnFisfgMX7LFl11BEE4+zw958LFhenIu2bhEOq5R8WfJoiFEVSV8CUaGh0il0oDolhMWvpkKY4IgCMdboZCnVCzgeR4NDVksyySfz+G6Lvl8jjAMsUyzGo0Vi/GFnY+wvTJKJfQJoLpi+hglCPiL3fvpkKKQaYFkPbKsIAUQBD7xeAJJmpzLMPmEo3Cy1BZiXbZs2aTbn3jiCdra2uZpVMJCdDxjUiYWtWuLoQ4PD1Oxirzr5huJxWJs2mONd7Ono8r4Y1VZwg1CurL6eDE9FpFp5eDiqjNtv3bfxrjCEztMtLF1LY5n1/lcIm4EQRCOlCjCz7Pal1zLMhkc7CeTqRddJ4IgnHQjpkfPWEdMfykgbhiYtkm1926qkJQRMopEPB7HiETEfks4I0wtjAmCIByLIzmx57oOnuehqlr1n4oyHk8T+AE7hg6wp5JDDkIC3582bashOBf9FkUlgZxqIBWTeOPKCLv6KzQnIIJ1El6pcCQ+9KEP8fGPfxzXdbnqqqsAeOSRR/jEJz7BH/7hH87z6ISF5ETFpPQOFnh1Tz8XnLuMG264YfwEX2NcYVtvhfMXRXCCycX/Wgf7o68V2bqvTFNSxQurne81U7vda53823orkwr5giAIpzpRhD9JDvUl23Ud+voO4LkeIC79FATh5KuPq7RlNPaP2BiSw4FBn8APCaWAULeQnBhSWO1cWZ6yaYiF5CRIixOHwhmiNo/7QTDfQxEEYYGYvMBq9fjAdR0AEskUqVS6ut/xPIxIBE3T0I0IruviOg62Y5NGoU2O80J+YMYEOVWS8ZMNSHoaI2KQTiV5zxsM8mWDsDyCKkuE4SwLwAgn1Sc+8QlGRkb4yEc+guNU/zuIRCL88R//MXffffc8j04QDppaFM+ZHlt3D/D6pl9gpFvZmV3E+qWx8fsPmT7pqIITMOn2iScCrErA3iGHnpzLuiWx6mNi1ecpVnzMSjD+mMNF1AiCIJyqxJ7qJJnpS/bEv+ladaXwWla8IAjCyaQqEh94YwN7+4bxzCLf+rWDLUG58xmCSAG5nCa6eyNSKPPbq8soYwf65bIlivDCgue6DoOD/UhUCyK1CCZBEIS5mNqUM1P2+sSiO0ClUqFSttD06v1TqTSDg/1YloksSyTiSW5rfiNf6N7Jc8lf4ctUu+ElCRVYnWwlq0QJAg9ZjqEbEXzPI6F6VFSFYrFIIE4unhIkSeIv//Ivueeee3j11VeJRqOsWLECwzDme2jCGWhi5MueIYdQqi5qOrETHaAL+NGTO7C7n6J+0Uo6Vq6Z1JWeMz2KFZ+EIU+KndFl2HKgwqXL43RkDWIRGV2TiWqMd7bXnidhyLRktPHtTo2oyZkem/ZYx1yMP54L1gqCIMxE7FlOkpm+ZNe+iGuaTjKVEhmzgiDMK1WRWNKUoq+vxLKMQ6iXCSIFAIJovtoRb0fJRCESjU7LkBWEhcqyTCQkZEUhm20Wc7UgCEdlalPOTNnrE48ZLMukUrawHRuAeDyBpulkMvX4vo+uG8jRCL3FV+lPbsdRZCQCtCBksR/SklnMpzuvIqjYuI6L53t4nkckEiGh6Qz7gyDm8lNOIpHgDW94w3wPQzjDTYx8GTF9CEOSEWVSJ3qtUE55GL1lNVdfvmZa8XrnoINZCWjJaJNiZ4ZNH2Vs99ORNVjbHsUs+/QWPC5dHicTV+ka28ZMRfGJBfPjtdDs8VywVhAEYSZiz3KSTP2SXY2g6cF1XdLpjMiWFQRh3nl+yGhZIpJu4qu/LiN5EnI5TRDNI5fTSE6MxQmPTCQkHqsu5BaNxg6/YUE4zU0siokCvCAIR2tqU87UzvjpnfLVTvgg8Alh/D61q9ACTeVtv/o7hiolHOVgpEyz63Fnb462hktormvE9zxy+VFkSUZVVdLpOgA0TRvrqJ8hx0YQhDPSxA54qEa+1DrhJ3aiXxBTeO6FLeTkJjpWrGbNkuiM3eO1xzTGFTbtsTDLPkgS57ZGcIOQS5cfbFLMVwLSEWXGRVmnmtSNf5wWmj2eC9YKgiDMRBTh54llmbhOdaElQRCE+eb5IQ8+NszuAYuXuk0KLkgoRHdvnJQJf+mSEpGIjm1XIKxl14o4GmFhm6lbVRAEYa6m7kumdsbP1CkfiUSolKuLp7qug+s6FPI5bMdmszXAkGMREDBxRVZTlulS0wSZRqLRGK7rkPRTeL43vh1NO9iJL65sEwShZlI3+Fh+e0d2ciRSEAT89y9/xa7de0h0Xs5ZS1Mzdqpv3ldGCmHNkujBaJmIwsXL9Wnd7TsHnfFFVmsF+0PFwkwsmNe65ncOOnTBUUfJnKgFawVBEGrEHmYeuG61+J5IJFFUlWg0Rj4/KjrsBEGYNyOmx/4Rly37LPaMHOymk0IFyU6O/RayKutDKKPpOkiSyIMXBEEQhKNUm0M1TSefH0WSJDzfIzHheCAWi4837dTuXyjkMW2TH49uJyAkkGQkQkIkZAnSepK96zawWFIYGOgjFo2hGwY6Br7nYVnmYU8sTu3KFwRhYZqag364bnDP83jkkUfoH8qRXHEVpSBCqeyTM71pRfWt+8ogSSSiyrSi+VRTI24OFwsztWAuomQEQTgdiL3TSWZZJn19B9A1nWQqTTpdRz4/OuuirYIgCCdDfVwlE5cx7WoBPpT8SR3wAKsbK7SkQtSxk4eqqowdoCMO0AVBEARhjmqd6LVjAc/3UBV10lVmmqZPi61saMhywC7Q65ks1dNYvkOLpPOqX0KVJDqijTToCQAqZQsJSCRTACiqekQn0Kd25QuCsDDNtXgdhiHxeJy3vuEy9hUkSmWfkh2wc9CZ9PiurE6xEkUKq1E0tUI/wKY9Fo3xauzMTEX5Q50ImG3x1LlEyYgFWAVBmC9ij3OS5XIj+J6Hw+TuF9MsTep6EQRBOJlUReIjV2V5YXeJfrNMufNpgkgBuZwmunsjUqiwrs0hGjGIRKPEYnHCMKwWDTwPdeygXhTjBUEQBGFuascEibHM98MVyV3XoV6J0iAbDPgWS6N19DslFskJfCnkt9MriKo6kWiMuvoGVFXF87z/v707D4+yPPcH/n1nX5KZyR4iJEFWkdWgLFZWla1CtVWpluKxck6Ph1qtrdWfCljFFavWUxc8Cp5W60ZxOQoFaVBEsICACMi+SUI2kplkJpn1/v0RZpJJJhvJZJLJ93Ndc+m829zz8OZ9Zu73mftBTbULCYmWVvXVDevXE1F8api8biopb7fbsW/fPowdOxY/+MEPAADpyeE15IMlZILHGdmntk78juOu0DEBhCZ8tRrVoddpWAanNbXg62/TllIyHDVPRLHCK04n8no9UKs1MBhNSElJC30A9no9jUa9EBFFU8OfmbtcTpSdLYPZ4IPonPAb7YASgN9UAdG5oHYnYGSGH1qtAQkJibBYrFAUFfQGA3w+H0fLERERnafgJKwtlX/xej1wOOxw1Ljw4JFPccbrRKbGjN9dMBYvnPkaJ9x25GoTcYHeChWA5OQUZGT0AgCUlZW0OSb26UTxo6nR3/WT1xVOHypr/EjQq9AvTRfax6ZUYPNnn2LgwIGNjhvcP5hor6zxo6DcC42qdq6JvCZK3Ay9wBAaCV9/XfC/HTHivSmcgJWIYoVJ+E7kcjmhALBYrGGjSuqPhGf9RSLqDA6HHVWVDvh8PqSkpKGsrARHCxw4XqQFvHqI2gPR1kDxGgGvDiPTPRAVAChIS8uAVls7kZvVmhR23SIiIqK2a6r8S/0+1uVyori8FPcd24BvnEXQq9RQKQo8AB68cDKKvU6kqgxAIABLohVJySmh41gsVmg0Guj0JhQ7vEg2a6BRc0JWop4iOPq7qtofqtHesBTLkRIPnDUBmA0qHCnxoLLGj9LC71F+ZAt6DxyJwcOGQ1GUiAnyYEK7qtoP7bkJVoPLGo5SD/5/Tr1KW62t8d4Rk6dyAlYiihVeeTpRw8mXgon2+iPhvV4PR5QSUafTaHQoc6lQ5PQjYD4L+LRQ+TWAqJBoqEZ2sg5WQ+20bw1xtBzFE94MJ6LO0PBaE6n8i9frQUlJERTUJstFrcbDpzZhj6sYAQhqAj6kaczIsaUjIzUdfYCwm+IulxNarRfV1a7aY5st+MuWShRUeJFl0+LWCSlMxBP1EMGEeGWNv8lSLMFtiiq8+Pb7GlyYqkN6cgIMmrEIJF4QqvseTJBX1viRaKhL6OeZNU2OYG9rHXaOVieieMQkfCdqOPkSUJtoj/ShmyNKiSiagiPiTCYzfH7B+3vV2HoIqMjZUluKRu0DfFqoq22Y2huYNaAaKkWBTquFy+Vk0p3iFicjJKLO0PBaE+mGdvBXtAKByWTGgbLTKPY6oVdpUOX3wKDSIEFngMfjhsNhD/3a9uzZMlQ67DAYjFCpVfB6PACA8moFBeeSbwUVXpQ7fUizaDv1fRNRbERKkje1Tf5+B9Rl38KnS8WECcMa7dMvTYeqaj8OFblhNdTVda9/jIZ2narG9qMu7DSr8ZPRthYT8RytTkTxiFe1GGiYdG/4oZtf+omovVoazVv/ulPs8KLQ4cfpGhcCiQ4oogB+LYwnL4G6KhUXja+EWgUYDCaYzAm8SUhxjZOlE1FnaM3EpyaTGT6fL/S8b1Imcg02uP1eCAS9Tck45Xag0FUB9bkfqrndNah02OHz+aBSq5CZckG9kfCJyLLVjYRPYoKLqMdpKbnt9/tRc/IrGD0lGDVgeGiffqgtEdPv3PMEoxoWgzqs7ExzFAHKnbXXs+CIeiKinoZXvhhg6QYi6miRJlp1OZ1wOqtCNdybkmzWwGZUQ/GYoaq2ImC0Q+2yQV2VhgQA2ZYAtFod9AYDrFYbS3RQXONk6UTUGVrzfUCr1UGj0cBdU3PuV2hJeOSiGdhffBJ/Kf0WhQEX+lt6oV/qBdCq1OcmSq+uLW9jTkBKShpMJnMo0e/zC64ZZQGgIC1RAwl4Ya9yQqRxqTki6nkCgQA++eQT+Hw+3PiTa2E0GkPrGtZo75emQ2WNH0orLx8jsmuPJQpLzBBRz8UkfAyJ34eAvRQqayoUNf8piOj8NfxZu8lkhtNZBeXcuua+6GvUCm6dkILVO8phPDYGonNB8ZigiBq2BB+q/SpYAKgUoKqqEhaLrVPeE1EstGZ0KhFRZ2l4TbKYE5FjtOHe7CtwQnEhL6s/Kr1uJBstKC8pgdfrQ2JiIkzmBGi1daVmfH7Ba5+XhdWDD352EAnE5L0RUdchIlCpVBgyZAiys7PDrh9A4xrtNrMGiQY1zlR4QyPkm6v5bjNrMPGixKi/DyKiroyZ3xgRvw9V7z0Lf+ERqLP6I+HHv2YinojOW6QyV2lpGWETtDXF6/XgwPflUESgiBqKO/gBWVDlVQGKBhqNGoqigs2WHM23QRRz/LUaEXUF9X/hZrUmhS3X6PR47Fg+TnqrUHlwPSxaAwYkpOGu9NFQqVVwOp2h7YPXs7NOX6N68LZznw8URdViDPwVHFH8Ki0txeeff44ZM2agX79+jdY3Nalq/cR8w5HyRETUWORPXBR1/rNn4N2zCf6CI/B+8zn8Z8/EOiQi6sa02trR7y6X81wZjWAyManRF2ev14OyshKUlZWEvmCn6ZxIMvjrbVX721KrETDoNdBodEhKTuHoYIpLXq8Hdnt56G+HiCgW6l+LgqPUXS5n2DYmkxkOTQDf+6rgEz+KayrhDfhw0FGEMr8bKpUKBoMRgvBf9CSbNciy1Y5sDdaDD35OUBQlYjxNxUBE3V+F04cdx1347vAJfPTRR7jwwgthMBgibnukxIOjRW588o0DFc66eSpsZg3yck2wmTVINathr/Yj1axu8rXq70tE1BPxFmWMiN8HCfggIlAUAE18+CUiaq2GJWma266q0hF67vP54Kqy47qLPHh6cwL8UAAo0CqCkZk+JJvVMJlrv8h7vR6OhqO4UzuHQlWr5lAgIjpfLY0sr9+PN/yFm9frgcNhB1A7QetASwb2nS1AssYAVQDob0lDus6EJIsNItLoNYKl58qdPiSZNdCoW/7uwfJcRPHrSIkHx098j/JDmzBx4gQMGDAgtK7hyPd+aTocKKyBRqU0mlQ1uG1ljR9WoxqlTj9y0sKPwVHyRES1eAXsBA1rv4vfB/fn70FRaaBoFWgv/gHUSRmxDpOIurlIX5YjfeE3mczw+epGojirKuH2+KBR/DDqBFUeBSoAFgNw6QV+GPRGJCZa4Pf5WqwvT9Qd1c2hoPAcJ6Koaelmef1+vGFprPo30DUaDZ4adS32FxyHrsYLj0GLLLMN4vFCRMLK19SnUStIs2gjrouE5bmIup6mSsO0hYggN1mNgD8TI3JmoH9uVtj6hklzm1mDmcMtodeNtG2CXoUEvQqVNf5QjMFjNKwnH+864t+IiOITrwhRJn4fnH//E/zFJ6GypsJ8w28hVRXwl56GuldfwOuB8cqbWA+eiFqtqZF0kb4sl58tQ4W9HDZrEtIzeoW2S0lJCx2rsqoKa4+YcKLYC0Wktk6ZAug0KvRN10Or0cDjdsPn9yEh0dJZb5Oo07RlDgUiovPV0sjyppLeLpcTDocdKrU6tJ3iD6C30QoYAYvFGtqO1zCi+NbeUeWBQABffPEFAGDChAkAGn+2j5Q0t5k1EV+vX5oOVdV+yLkf1zhrAmHJ+mAiuieNgOfIfyJqCq8IURawl8JXdBz+00cgx/cCAMw3/Bbq9Gz4i09C3XsAR8ETUZu0tuwMALg9bvi8Hjgq7UhKTgkl7YM/a3fVeLD7tIKT5SrsKNKjxidQIEjSKRjUSw1Ra+Hx1MDj9cBoMJ6rmc0v+BR/OOKTiKLtfK8zZWUlqKp0QKvVwWKxwuv1wOv1wON2QyAArLyGEfUQ7RlV7vV68emnn8LpdGL69OlNbhdMmgdruTc1ojs44luU2uR7gl6FTJu2Rybe6+tpI/+JqPV65lWxEykJNsjZIkhlOaDVwVdRBKmqgPnaX4WVqCEiaq221GhNSUmD1+uBTqsPldnwej0oKSlCWWUlHvy0EmdKzXB71fD5AZUCiCjQaQPoZfbBatRBER1UajUSEi0cYUdxpaX6zEREnanhNckT8KGw2gG1RgNFUaBSqaDWaEJ9cW0ZreAIeIRqxlssVl7TiOJApLIm7Ulub9iwAYFAALNnz4ZOF36NiPRaXxyqwpZDTowbYMYPR9oaHa9+KZr6yfe2vJ941Np/o57SHkRUh3/pUSZVFVAlZ0JqnBCfBxpbRijxrk7OjHV4RNQNtWW0m8lkRmbmBaioOBv6Qu5yOeEL+PGH41vwbXIFFKMVCUfHQqmdJRoGreB3E4EMcw3Er4ItOYVf6CkuteVXJURE0VY7SbQTTmcVrCmpuG/P/+FwVTH6GpNxR8pI6HR6aDSaUH9cv4xWw5rxvKYRdX8dVdakpqYGBoMB48ePh9lshvpcaauWXquowocqt6CowtdoewCNSs60hGVawrE9iHqebvOXvnTpUnz88cfYtWsXdDodKioqYh1Sq6isqVBn5gKKEqoJz5HvRNSZvF4PNGpNqJSMyWTGqeoKlKuqoFEBboMdWrMT6QETEg0BpFt1sJoFGjXg8/vCvvATxZO2/KqEiCja6iaJBo6Vn8HhqmIAwOGqEjjSBIlqVdj1qv5NeZMJoUnXeU0jig/nU9ak4ejqM2fO4B//+AdmzpyJtLS0Nr3WlRcnwqhX4fL+ka8pbR2VHzx2qlndbJmblsTLCHKWrSHqebrNFcvj8eD666/HuHHj8Oqrr8Y6nFZT1BqWniGimAj+rD2YQA9+KddqdRiY3gcX2TLh930PizcBUwcp+PSgoLxGBZXKiySTDmZTMvQ6fWg/lu6g9jh+/Dgefvhh/POf/8SZM2eQlZWFn/3sZ7j//vsb/SS6s7CGMhF1JfUnibYYDBiQmI5DlcUYaMlAblI6bAmWJvvf+pOuE1F8sJk16IfaEdP9zj1vSf3R1UlFJ7Fx40aMHTu22QR88LXqJ9QrnD6UOv2YOdzSYYnu4GvsOO5q1wjw7jSCvLkbBj25bj5RT9Vt/uIfeughAMDKlStjG8h5YOkZIoqF8FIbSQDCE+lPjLwOG/ftw6sbBH+p0MIXANKMfqQYA3DUCPpdmAWXywmv11tbwsbng//cKDsmLqmtvvvuOwQCAbz88svo378/vv32WyxYsABOpxPLli2LdXhERF1C/ZuDT426FoXVDvQyWqBTNf21jTfJieJXWxPOwVHV6fpqfLr2M0ydOhU5OTlRf922aO8I8O40grw73TAgoujjVYCIKE5FKrVRW2+2Co7KSny4T4Ndx/U4ejYAX6B2vduvQpIZyL0gNZTEdzqroFFroNZooDcY+DN3Oi/Tp0/H9OnTQ88vvPBCHDhwAC+++CKT8EREEehUGuSYk+H1emCvLA9LstdPvHN+C6Lup7UlVVpKODc8jtWkxoBkHyyWFMydOxdGo/G8Yohmoru9I8C70wjy7nTDgIiir3tcuc6T2+2G2+0OPXc4HDGMhoioc2m1OphMOPclPfi8tt5shQs4UeyEVq3AHwACAqgADM9S4aZLVBC/Dz4FUGs0SEi0wOv1cIQddTi73Y7k5ORYh0FE1KVFSrLXX8b5LYi6n9aOkG4p4Vz/OCP0QH5+PlwuF2bPnt1sAr6lGLpTorsrYzsSUX2qWL74kiVLoChKs4/t27ef9/Efe+wxWK3W0KNPnz4dGD0RUdfh9Xpgt5fD5XKirKwEZWUloVFy7poauFxOAHX1ZrPSEpCTboZOp4NFr8CoERg0Ap1SA3eNC5UOO6oqa29cmkxmWK1JTMBThzpy5Aief/55/PKXv2x2O7fbDYfDEfYgIupJtFodfH4fFEWB3V4eujEe/HVabQkb9tPUPVU4fdhx3IUKpy/WoXSqfmk6ZNq0oUlKz/f9B4/T2xLAxx9/DJfLhWnTpkFRlEbbNmzr4L4cpU1E1Dliektu4cKFmDt3brPb5Obmnvfx77vvPvzmN78JPXc4HEzEE1Fcql86xuvxAAA0Gk3To+MCfvxklAZ2tw4v+arwr5MKEnUBuLwKKlw+6HU1UKnVnf02qBtasmRJaN6Wpmzbtg2jR48OPS8oKMD06dNx/fXX47bbbmt238cee6zF4xMRxYuG9d29Xg8qKs5CAVBVVQmNuvbrm9WaxNIzFBeOlXpQ5a/9zNmTRgy3Z5LShmVk8swabN26FUajEVOmTIFGE/k4rE9ORBRbMb3ypqamIjU1NWrH1+v10Ov1UTs+EVFnaM2Ea8Eke4JWh+pqF4DakXMN9/N6PSgpKYK7phoqlRq+ag++rwC8AQWVXjUyrCpkJmuQYDZDq9PCYrF2ynuk7qutN9QLCgowefJkjBs3DsuXL2/x+LyhTkQ9SXiZGaCkpAgBfwAqtQo2W3JoFDxRvOibqkOJu+eOxj6fmuH1k+m5NXYkJibi0ksvhaIoUKlqix1EqvcefI3g6PvKGj+K7T4cKKzBzOGWZmvTExFR+3Wbq+zJkydx9uxZnDx5En6/H7t27QIA9O/fHwkJCbENjogoiiLVgm2YmK/9KXrtuuCXc7u9PGINWQUKNOfqwx+vqEKFuxpWAyBQMO5CoE+fXhAR1oCnVmnLDfXTp09j8uTJyMvLw4oVK0JfFJvDG+pEFK98fsFZpw/JZg006trSEfV/wVbbZwMqtQppaRnn+uS6BHxrbtITdXU2swbZvUyxDiNmzqdmeDCZbvIU4YMP8pF98XiMHtofNnPdr1h3n6zGntM1qKr2Y+JFiWGvteO4C0eK3Kis9kMUwGpQ40iJh6PjiYiirNtcZRctWoTXX3899HzUqFEAaicemTRpUoyiIiKKvkglZSIl5iPt5/P54PP54PXWlqjx+XzQ6fVItaQDAAb0CqCXzY+SygAsOg8yDAFUVVUiK6t2pLHX64HDYQcAWCxWfsmn81ZQUIBJkyYhOzsby5YtQ0lJSWhdZmZmDCMjIup8Pr/gtc/LUFDhRZZNi1snpECjVhrcVMe5/0ZOsrfmswARxR+bWQPTyUPYsmUL+gwZB7e+V6MkuigARGr/20C/NB0OFNbAYlQj3aJBglHdY3+JQETUmbpNEn7lypVYuXJlrMNoEUekEFFHq/+FPKjJWu8N9tNoNHDX1MDhsMPtroECwGROgFarq528tboKj1xjxP5TDqToPNBpNLDZkgHUXs/OnCmAy1kJrc4AjUbDL/l03tatW4fDhw/j8OHD6N27d9g6EYlRVERE0dPc94KzTh8KzpWTKKjwotzpQ5pFG7ZNpP6/vtZ8FiCi+OP1erFv3z7MmDEDRktaqOxMfSP7GJFoqE2uNyxNYzNrMHO4pVG5mqZEKm1DRERt1/LvwKlNgiNSXC5nrEMhojhW+8U8CUBt2ZngSPeGTCYz9AYD/D4fampq4PZ4UFNTg+KiQlRWOuD1euF2VSI3SWDQqWBLSkJ1tQtlZSVwOOzweb0QKNBqtfyST+1yyy23QEQiPoiI4lFz3wuSzRpk2WqT7lk2LcxG4ITzLDwBX6uPH/wswIE/RD2D3+/H0aNHodVqMWXabBS4a+duyss1NUqO28ya0PJdp6qx5VAVdp2qjri+JcEa9EdKIn/fICKi1uFtzA7GESlE1Jla+1N0AaBA4PN5UVlZidIqH6wGwOuugVarhUqtRmpaBjQaDaoqHQCAhEQLLFYbAJaiISIiaqvmvhdo1ApunZCCcqcPZiNw7+73cbiqGAMS0/HUqGuhU/FrGlG8a8sIc4/Hg/Xr18PpqkGZpMLpVeCsCQBAi7XcFQGgKLX/PY84zmfyWCIiaoyf7jpYSz8bJSLqSA3rvjdMlAeT9BqNBhqtDp7qarz/LXC2Wg+b3oMZ/QGP141EvQUGgwFarQ4ulxM6nZ6JdyIionZo6XuBRq0gzaLFCedZHK4qBgAcqixGYbUDOebkzgqTiGIkOMIcqE2kN5UMdzqdWLNmDcxmM3JHXYniSkGCXkGmTduqxPiIbGOzdd8bxtHQ+UweS0REjfFKGmXi9yFgL4XKmgpFzeYmoo5Vv+67y+Vstna8z+fD6VIXylwKNFo1Kv0mwGBAukUDnd4Ak8kMl8sJvU4P/bmEPBEREUVXL6MFAxLTcaiydiR8L6Ml1iERUSdoOMK8qWR4aWkp0tPT8YMf/ACO6gDUbazPHkyiVzh92HHc1WjfSCPdWQeeiKjj8WoaReL3wfn3P8Ffcgrq9GyYr/0VE/FE1OGa+7l7/VF4Go0G6VYdkox+nK5w48IMPQblZsHjdoVGwAcT7yypRURE1Dl0Kg2eGnUtCqsd6GW0sBQNUQ/RcIR5w2R4QUEBVCoVcnJykJOTc24f1XmPSm8qyR9ppHtLo+OJiKjteDXtQA1HvQfspfCXnAIA+ItPImAvhTo5M8ZRElG80Wp1MJlqS8+YTGhyBLvFYgVUaqjVZ+EPuFFdIzhaWI5kQwAiVdCcu0kYnPCViIiIOodOpWEJGqIern4y/PDhw/j888+Rd9l4nK6xdMiI9LbUdmcdeCKijsckfAeJNOpdZU2FOj0b/uKTUKdnQ2VNjXWYRBSnmpug1ev1wOVyQqU1Yn+xFsVOBTq1gm0nBCUuFy5M0+KWK5Igfi9HwBMRERERxYiIYPfu3di5cyeuuuoqFPtT2jQivbkyMm2p7c468EREHY9X1Q7S1Kh387W/Yk14Ioq65krSuFxOVFa58MD/nUVpFeD2e9ArtQpQJyLBqEOpS0GJvQbZ6ZawUfTB5L3JZGZ9eCIiIiKiTlBdXY1rrrkGqampCJS4caCwBkMvMLRqX5aRISLqulSxDiBeBEe9Awgb9a6oNVAnZzIBT0RRVVv7PSlistxkMuN0lQbFDj/cvmoUZGzG4YzNcF34LygaINUUgF6qUVJSBK/XE9ovOLre5XJ25lshIiIiIupRfD4ftmzZAq/Xi3HjxiE1tTafcKzMg7NVPhwr87RwhFr90nTItGlZRoaIqAtiZriDKGoNR70TUVS0dUS6y+VERcVZ2GzJoX1G9E1DkvEsvq9xwm+0w6TTQFCB2UMUDE1JQ/nZYijn9g2Ws2ludD0REREREbVfdXU11q5dC41GAxEJW6cIAEWp/e85HVVypidqru26s3h9X0Txhn+dHSg46p2IqCM1V+89koqKs3A5nfD7/fB6PTCZzDDodHjmpgx8fTiA1b5MHK8uQ67ehj46NTxuF2y25NC2QbWj6zmKhoiIiIgoGhwOBz755BOkpqZi8uTJUKvVYetHZBuRYFSHjWxnyZnzF69tF6/viyje8K+TiKiLqz8iPdKo+IbLbLZkAIBarQlL3qcnJ2P6ZcmYEhiEU5VlsATUUAUktI3JZD53HLAGPBERERFRlGk0GgwaNAgjR46EoiiN1kca2R5MyLPkTNvFa9vF6/siijdMwhMRdXH1R6Tb7eWNRsU3HClvMplDCXuHww6fzwev1xNKrOtUGmSbkuByOaHV60Ij4IPH8fl80Gg0nJCViIiIiCgKjh8/juLiYlx22WUYNWpUm/ZlyZnzF69tF6/viyjecGLWDiR+H/xnz0D8vliHQkRxymQyQ28whJWNibQMqE3eazQa+H0+nD1bhlOnjqOoqDA0ct5dU4Pqalej4wDghKxERERERFGwd+9e/POf/0RaWlqsQyEiok7EJHwHEb8Pzr//CVVvPQHn6ueZiCeiqKgdFZ8UNkI90rKgYGLd43GjqtKBivIylJQUQavVNUq4B49jsVgjJvWJiIiIKL4sXboU48ePh8lkgs1ma9U+IoIlS5YgKysLRqMRkyZNwt69e6MbaJzYvn07tm/fjlmzZqFv376xDqdZFU4fdhx3ocLJ3AYRUUdgEr6DBOyl8JecAgD4i08iYC+NcURE1JN5vR7Y7eUAAKs1CSkpaUhItECjM6CsKoBqtzuUZFefKz1DRERERD2Lx+PB9ddfj//8z/9s9T5PPvkk/vjHP+K///u/sW3bNmRmZuKqq65CZWVlFCPt3kQEAJCZmYk5c+YgIyMjxhG1LDjZ55EST6xDISKKC0zCdxCVNRXq9GwAgDo9GypraowjIqKeLFhuJlhSxmQyo1dWDtYdTcTfdgre2eGFo6oKHrcbbndNs/sSERERUXx66KGHcNddd2HYsGGt2l5E8Oyzz+L+++/Hddddh6FDh+L111+Hy+XCm2++GeVouye3241PPvkERUVF6N27d6t/cRBr/dJ0yLRpOdknEVEH4cwN7SR+HwL2UqisqTBf+6vQ/ytqNi0RdZ5gnffgZKrBke31R7gXVVTjVFk1NBotiioDcIsBgBMBvx8lJUVIS8tocl8iIiIiomPHjuHMmTO4+uqrQ8v0ej0mTpyIL7/8Ev/xH/8RcT+32w232x167nA4oh5rV1BVVYU1a9YgMTERycnJsQ6nTTjZJxFRx+JI+HZoWAceANTJmUzAE1Gnazh6PVKdeIOqBikGP2qqXchIVCHdakRaWgZUajUC/gBKSorg9XqarTFPRERE3VewXJ3Xy/ISdH7OnDkDAI3KqWRkZITWRfLYY4/BarWGHn369IlqnNHQ1hrpLpcL77//Pnr16oWrr74aWq02yhESEVFXxiR8O7AOPBF1FcEJWJsbvW7U6zH7Yh9uHq3FDXlaaNQKtFrduUS8CgrAEjRERERxjCXneoYlS5ZAUZRmH9u3b2/XayiKEvZcRBotq+++++6D3W4PPU6dOtWu14+FttRIDwQCMBqNmDBhAi6//HKoVOGpl/ZMesoJU4mIuicO2W6HYB14f/FJ1oEnopiqHb0ePnK9YYkar9eDBJMJZgCWhISwfdPSMkLbEhERUXxiybmeYeHChZg7d26z2+Tm5p7XsTMzMwHUjojv1atXaHlxcXGzk43q9Xro9frzes2uIlgbvaUa6QcOHMC+ffswZ84cZGdnR9wmmNAH0OaSL+3Zl4iIYodX7HZQ1BrWgSeiLsvlcsLlrILDYYfJZIbRaILJnBBKytcXKYlPRERE8YX9fc+QmpqK1NToDBDr27cvMjMzsX79eowaNQoA4PF48Nlnn+GJJ56Iymt2hgqnD0dKPOiXpoOticR2SzXSRQRff/019uzZg6uuuqrR6Pf6Us1qHCiswdALDG2OtbU3A4iIqGthOZp2UtQa1oEnoi7JZDJDAHi9XlRVOuD1eljrnYiIiIhCTp48iV27duHkyZPw+/3YtWsXdu3ahaqqqtA2gwcPxurVqwHUlqG588478eijj2L16tX49ttvccstt8BkMuGmm26K1dtot7aUmmnK7t278d1332H27Nm44IILmt221OmHRqVg82FnxLIyzZWcsZk1yMs1NXmzgIiIuiZetYmI4lSwzEz52TK4PW4m34mIiIgozKJFi/D666+HngdHt+fn52PSpEkAasur2O320Db33HMPqqurcfvtt6O8vBxjxozBunXrkJiY2Kmxt0fDke/tGV3u9XqhVqsxaNAg9O/fHwn1yj42pV+aDgcKa6BVKThS4mk0wp4lZ4iI4g+v5kREcc7n90GjVsPr9QBgDVgiIiIiqrVy5UqsXLmy2W1EJOy5oihYsmQJlixZEr3AouxIiQdHi9w4UFiDmcMt530cl8uFtWvXYuDAgRg6dGir97OZNZg53BK6EdAQS84QEcUfJuHbSfw+1oQnoi7L5XIi4A/A43XDakuOdThERERERDEXHImuOTcSHUCbR56Xl5djzZo16NWrFy666KI2x9BcjfmW6s8TEVH3w6t6O4jfB+ff/wR/ySmo07NhvvZXTMQTUZdiMpnhdFbBqDZyJDwREREREZoeid7aked+vx9r1qzBgAEDMHr0aCiKEq1QiYgoTjBj3A4Beyn8JacAAP7ikwjYS6FOzoxxVEREdYJ14V0uJ0wmJuCJiIiIiIDGo81bO/K8qqoKCQkJmDNnDsxmfr4mIqLWUcU6gO5MZU2FOj0bAKBOz4bKmhrjiIiIGtNqdbBak0ITs3q9Htjt5edGxhMRERERUWt88803WLVqFdxud6cm4CucPuw47kKF09dpr0lERB2LI+HbQVFrYL72V6wJT0SdzucXnHX6kGzWQKNu289fHQ47qiod8Pl8SElJi1KERERERETxIRAIYOvWrThy5AhmzpwJvV7fqa9/pMTT5pr10VLh9IXK+NhYt56IqNV4xWwnRa1hCRoi6lQ+v+C1z8tQUOFFlk2LWyektDkRT0RERERErXPixAmcOHES/fKuhtaU1OmvH6xV39qa9dHUlW4IEBF1J7xiEhF1M2edPhSc++BbUOFFudOHNIu21ftbLFZoNBrWiCciIiIiakZNTQ08Hg9yc3NR4k9GSVVtErqzk88N69fHUle6IUBE1J2wJjwRUTeTbNYgy1abdM+yaZHUzAfySPXfG9aIJyIiIiKicA6HAx988AH27t0LRVEwsJcZmTZtj08+28wa5OWaWIqGiKiNeNUkIupmNGoFt05IQbnTh6RzNeG9Xg9cLidMJnNYct3lcsJdUwMAsFrrJmaNtC0RERERUU8WrHeepHJg08Z1sKbnYtDQPABdazR6d8M68kRE3WQk/PHjx/GLX/wCffv2hdFoRL9+/bB48WJ4PJ6Wd44S8fvgP3sG4ufs5ETU+TRqBWkWbagWfDDZ7nI5w7YzmczQGwxhpWea2paIiIiIqCcL1jvftn0H0nIuhjZzBI6V8Tt/ewXb9UhJ7HI4RESx1i1uQX733XcIBAJ4+eWX0b9/f3z77bdYsGABnE4nli1b1unxiN8H59//BH/JKajTs2G+9ldQ1N2iKYkoTgWT7A3rvGu1OphMODfyPfg88rZERERERD2Z0VOEDEsaxl11FVQqVWj0NrUP68gTEXWTJPz06dMxffr00PMLL7wQBw4cwIsvvhiTJHzAXgp/ySkAgL/4JAL2UqiTMzs9DiKioNo6740/1Hq9HpSUFEFB7Yh5q1XX5LZERERERD2RiGDbtm3Yv38/Zs+ejaTEBABg+ZkOwlI+RETdJAkfid1uR3JycrPbuN1uuN3u0HOHw9Ehr62ypkKdng1/8Umo07OhsqZ2yHGJiM5XczXhFQAC4ch3IiIiIqIG/H4/PvvsM5w5c6Y2AZ+UFOuQiIgoDnXLJPyRI0fw/PPP4+mnn252u8ceewwPPfRQh7++otbAfO2vELCXQmVNZSkaIoq5+hOwmhO0OOv0IdmsCSs9w0lYiYiIiIjCVVZWwuVy4Uc/+hFMJlOswyEiojgV04lZlyxZAkVRmn1s3749bJ+CggJMnz4d119/PW677bZmj3/ffffBbreHHqdOneqw2BW1BurkTCbgiahLCE7AqtOb8NrnZXhhQyle+7wMikoLqzWJCXgiIiIionqqqqpw5MgR2Gw2/PCHP2xVAr7C6cOO4y5UODlZKxERtU1MM8gLFy7E3Llzm90mNzc39P8FBQWYPHkyxo0bh+XLl7d4fL1eD71e394wiYi6vGCd92KHFwUVXgBAQYUX5U4f0izaGEdHRERERNR1nD17FmvWrEGfPn3Qr1+/Vu93pMSDM+c+a7PGORERtUVMe43U1FSkpraunvrp06cxefJk5OXlYcWKFVCpYjqIn4ioS0o2a5Bl06KgwossmxZJ/HJAXZDb7caYMWOwe/du7Ny5EyNHjox1SERERNRDnD59GuvXr8ewYcNwySWXtGnffmm6sP8SERG1VrfIzhQUFGDSpEnIzs7GsmXLUFJSElqXmZkZw8iIiLqG+hOz3johBeVOH5LMGmjUSqxDI2rknnvuQVZWFnbv3h3rUIiIiKiHKS8vx7hx4zBo0KA272szaxqNgK9w+nCkxIN+aTrYOACGiIia0C16iHXr1uHw4cM4fPgwevfuHbZORGIUFRFR11F/YlarNYklaKjLWrNmDdatW4dVq1ZhzZo1sQ6HiIiIegARwZ49e5CdnY2hQ4d26LFZooaIiFqjW9R0ueWWWyAiER9ERFQ3MavJZI51KERNKioqwoIFC/CXv/ylVZOfAbWlaxwOR9iDiIiIqLUCgQA2b96Mb775Bn6/v8OP3y9Nh0ybliVqiIioWd0iCU9ERM2rnZg1CVotP/xT1yQiuOWWW/DLX/4So0ePbvV+jz32GKxWa+jRp0+fKEZJRERE8cTn82HdunUoLCzEj370I6SkpHT4a9jMGuTlmliKhoiImsUkPBFRHPJ6PbDby+H1emIdCsW5JUuWQFGUZh/bt2/H888/D4fDgfvuu69Nx7/vvvtgt9tDj1OnTkXpnRAREVG8URQFSUlJmD17NhISEmIdDhER9WC8VUtEFGe8Xg9KSoqgoHZSVquVo+MpehYuXIi5c+c2u01ubi4eeeQRbN26FXq9Pmzd6NGjcfPNN+P111+PuK9er2+0DxEREVFz7HY7du3ahSuuuAJjxoyJdThERERMwhMRdXc+v+Cs04dkswYatQKXywkFgEBYI56iLjU1FampqS1u96c//QmPPPJI6HlBQQGmTZuGt99+m1+OiYiIqMMUFRVh7dq1GDx4MBRFiXU4REREAJiEJyLq1nx+wWufl6Ggwossmxa3TkgJJd5NJjNrxFOXkZ2dHfY8+JPwfv36oXfv3rEIiYiIiOJAhdOHIyUe9EvTwVFWgE8//RRjxozBxRdfHOvQiIiIQpiEJyLqxs46fSio8AIACiq8KHf6kGbRsQQNEREREfUIR0o8OHPu83A/mwVTpkxBbm5ubIMiIiJqgEl4IqJuLNmsQZZNGxoJn2TmZZ26h9zcXIhIrMMgIiKibu7CVC2+P7ADIkmw5Q6FzWaLdUhERESNMFtDRNQFeb0euFzOFkvKaNQKbp2QgnKnD0nnasITERFR/GntZwOinsTn82HH1s9QXV6KAWOHxzocIiKiJjEJT0TUBblcTrhragCgxdIyGrWCNIu2M8IiIiKiGGnLZwOinkBEsHbtWni9XsyZMwdGozHWIRERETWJSXgioi6o/uSqRERERPxsQPGu/gSrthZKLAYCAahUKgwfPhxZWVnQaJjaICKirk0V6wCIiKgxrVYHqzWJPzcnIiIiAPxsQPEvOMHqkRJPs9uVlpbivffeg9PpRHZ2NhPwRETULbC3IiIiIiIiIqKY6pemC/tvJKdOncKnn36KUaNGwWQydVZoRERE7cYkPBERERERERHFlM2sQV4zZWgKCwuxbt16ZA8Zg9wBg6AoSidGR1SrLWWTiIjqYzkaIiIiIiIiIuqSRAQ+nw8ZGRkYcOlV8Bh7t1iypieqcPqw47gLFU5frEOJa60tm0RE1BBv2xERERERERFRlxMIBLBp0yb4fD5MnToVw/v1Co1CpnDB5DCAZn9RQO3TmrJJRESR8MpMRERERERERF2Kx+PBhg0b4HQ6MWPGDAAtl6zpyZgc7hw8B4nofPHKQURERERERERdyueff45AIIDZs2dDp2NiuSVMDhMRdW28QhMRERERERFRl+ByuWA0GjF27FgYjUao1epYhxSXOMEoEVHn4sSsRERERERERBRzhYWFePfdd1FUVISEhAQm4KOIE4wSEXUu3u4kIiIiIiIiopg6evQoNm7ciPHjxyMzMzPW4cQ91pAnIupcTMITERERERERUcxUVVVh06ZNuPLKK5Gdnd2qfVhOpX06soY8/y2IiFrGqyMRERERERERdbpAIIDy8nIkJSVh7ty50Ov1rd43WE4FACckjTH+WxARtYxXRyIiIiIiIiLqdPn5+QCAH/3oR21KwAMsp9KV8N+CiKhlTMITERERERERUaerqanBtddeC0VR2rxvR5ZTofbhvwURUct61FVSRAAADocjxpEQEXV/LpeL19MOkJiYeF5fPHsq9uVERB2L/XnHYH/eNsH+fNy4cfB4PPB4PDGOiIiIerpo9+WKBHu/HuD7779Hnz59Yh0GERFRiN1uh8ViiXUY3Qb7ciIi6orYn7cN+3MiIupqot2X96gkfCAQQEFBQcQ7Gw6HA3369MGpU6d69IcntkMdtkUdtkUdtkUttkOd9rYFR861Dfvy1mFb1GI71GFb1GFb1GFb1OqIdmB/3jbN9efxhH9jbcP2ahu2V9uwvdqup7VZtPukHlWORqVSoXfv3s1uY7FYesSJ1RK2Qx22RR22RR22RS22Qx22RedgX942bItabIc6bIs6bIs6bItabIfO05r+PJ7w3GobtlfbsL3ahu3VdmyzjqGKdQBERERERERERERERPGKSXgiIiIiIiIiIiIioihhEv4cvV6PxYsXQ6/XxzqUmGI71GFb1GFb1GFb1GI71GFbdB38t6jDtqjFdqjDtqjDtqjDtqjFdqBo4bnVNmyvtmF7tQ3bq+3YZh2rR03MSkRERERERERERETUmTgSnoiIiIiIiIiIiIgoSpiEJyIiIiIiIiIiIiKKEibhiYiIiIiIiIiIiIiipEcn4Y8fP45f/OIX6Nu3L4xGI/r164fFixfD4/GEbacoSqPHSy+9FKOoo6O1bXHy5Elcc801MJvNSE1NxR133NFom3iwdOlSjB8/HiaTCTabLeI2PeG8AFrXFj3lvGgoNze30Tlw7733xjqsTvHCCy+gb9++MBgMyMvLw6ZNm2IdUqdbsmRJo3//zMzMWIfVI7E/r8P+vA778jrsy5vGvpx9Ofty6mitueY2JCJYsmQJsrKyYDQaMWnSJOzduze6gXYR5eXlmDdvHqxWK6xWK+bNm4eKiopm97nlllsa/e2OHTu2cwLuZG29Vn/22WfIy8uDwWDAhRdeGJefa5rTlvbauHFjxM+C3333XSdGHDuff/45rrnmGmRlZUFRFLz//vst7tPTz6/20sQ6gFj67rvvEAgE8PLLL6N///749ttvsWDBAjidTixbtixs2xUrVmD69Omh51artbPDjarWtIXf78esWbOQlpaGL774AmVlZZg/fz5EBM8//3yM30HH8ng8uP766zFu3Di8+uqrTW4X7+cF0HJb9KTzIpI//OEPWLBgQeh5QkJCDKPpHG+//TbuvPNOvPDCC7j88svx8ssvY8aMGdi3bx+ys7NjHV6nuvjii/Hpp5+GnqvV6hhG03OxP6/D/rwO+/I67Mubx76cfTn7cupIre1/6nvyySfxxz/+EStXrsTAgQPxyCOP4KqrrsKBAweQmJgY5Yhj66abbsL333+PtWvXAgD+/d//HfPmzcNHH33U7H7Tp0/HihUrQs91Ol1U44yFtl6rjx07hpkzZ2LBggX461//is2bN+P2229HWloafvzjH8fgHXSu8+3bDhw4AIvFEnqelpbWGeHGnNPpxIgRI/Bv//ZvrTo/evr51SGEwjz55JPSt2/fsGUAZPXq1bEJKIYatsUnn3wiKpVKTp8+HVr2t7/9TfR6vdjt9liEGHUrVqwQq9UacV1POy+aaoueeF4E5eTkyDPPPBPrMDrdZZddJr/85S/Dlg0ePFjuvffeGEUUG4sXL5YRI0bEOgxqAvvzOj29P2dfXod9eWPsy+uwLyfqWM31P/UFAgHJzMyUxx9/PLSspqZGrFarvPTSS1GMMPb27dsnAGTr1q2hZVu2bBEA8t133zW53/z582XOnDmdEGFstfVafc8998jgwYPDlv3Hf/yHjB07NmoxdiVtba/8/HwBIOXl5Z0QXdfWms/EPf386gg9uhxNJHa7HcnJyY2WL1y4EKmpqbj00kvx0ksvIRAIxCC6ztWwLbZs2YKhQ4ciKysrtGzatGlwu93YsWNHLEKMuZ54XjTU08+LJ554AikpKRg5ciSWLl0a9z/d93g82LFjB66++uqw5VdffTW+/PLLGEUVO4cOHUJWVhb69u2LuXPn4ujRo7EOic5hf16H/XnzeuI50VBPPyfYl9diX86+nGLj2LFjOHPmTNjfpF6vx8SJE+P+b3LLli2wWq0YM2ZMaNnYsWNhtVpbfO8bN25Eeno6Bg4ciAULFqC4uDja4Xaq87lWb9mypdH206ZNw/bt2+H1eqMWa1fQnr5t1KhR6NWrF6ZOnYr8/Pxohtmt9eTzq6P06HI0DR05cgTPP/88nn766bDlDz/8MKZOnQqj0YgNGzbg7rvvRmlpKR544IEYRRp9kdrizJkzyMjICNsuKSkJOp0OZ86c6ewQY64nnheR9OTz4te//jUuueQSJCUl4V//+hfuu+8+HDt2DP/zP/8T69CiprS0FH6/v9G/eUZGRtz/ezc0ZswY/O///i8GDhyIoqIiPPLIIxg/fjz27t2LlJSUWIfXo7E/r8P+vHk98ZyIpCefE+zL67AvZ19OsRH8u4v0N3nixIlYhNRpzpw5g/T09EbL09PTm70ezZgxA9dffz1ycnJw7NgxPPjgg5gyZQp27NgBvV4fzZA7zflcqyP15xkZGfD5fCgtLUWvXr2iFm+snU979erVC8uXL0deXh7cbjf+8pe/YOrUqdi4cSMmTJjQGWF3Kz35/OoocTkSPtIEOw0f27dvD9unoKAA06dPx/XXX4/bbrstbN0DDzyAcePGYeTIkbj77rvxhz/8AU899VRnvqXz1tFtoShKo9cQkYjLu5rzaYvm9LTzojnd+bxoqC1tc9ddd2HixIkYPnw4brvtNrz00kt49dVXUVZWFuN3EX0N/2276793e8yYMQM//vGPMWzYMFx55ZX4+OOPAQCvv/56jCOLH+zP67A/r8W+vA778qaxL28d9uXsy6n1OvqaG0k8/U22pb3Op/+58cYbMWvWLAwdOhTXXHMN1qxZg4MHD4b+huNJW8+LSNtHWh6v2tJegwYNwoIFC3DJJZdg3LhxeOGFFzBr1qxGc0pRnZ5+frVXXI6EX7hwIebOndvsNrm5uaH/LygowOTJkzFu3DgsX768xeOPHTsWDocDRUVFje4CdTUd2RaZmZn46quvwpaVl5fD6/V2+XYA2t4WbRXP50Vzuvt50VB72mbs2LEAgMOHD8ft6KnU1FSo1epGowmKi4u75b93RzKbzRg2bBgOHToU61DiBvvzOuzPa7Evr8O+vGnsy5vHvrxp7MupKdHsfzIzMwHUjjKtP5K0O/9Ntra9vvnmGxQVFTVaV1JS0qb33qtXL+Tk5MTV3+75XKszMzMjbq/RaOK2TwvqqL5t7Nix+Otf/9rR4cWFnnx+dZS4TMKnpqYiNTW1VduePn0akydPRl5eHlasWAGVquUfB+zcuRMGgwE2m62dkUZfR7bFuHHjsHTpUhQWFoY+HKxbtw56vR55eXkdHntHa0tbnI94PS9a0t3Pi4ba0zY7d+4EgLj+GZZOp0NeXh7Wr1+Pa6+9NrR8/fr1mDNnTgwjiz232439+/fjiiuuiHUocYP9eR3257XYl9dhX9409uXNY1/eNPbl1JRo9j99+/ZFZmYm1q9fj1GjRgGorW/92Wef4YknnojKa0Zba9tr3LhxsNvt+Ne//oXLLrsMAPDVV1/Bbrdj/PjxrX69srIynDp1Kq6u3edzrR43bhw++uijsGXr1q3D6NGjodVqoxpvrHVU37Zz5864Oo86Uk8+vzpMbOaD7RpOnz4t/fv3lylTpsj3338vhYWFoUfQhx9+KMuXL5c9e/bI4cOH5ZVXXhGLxSJ33HFHDCPveK1pC5/PJ0OHDpWpU6fK119/LZ9++qn07t1bFi5cGMPIo+PEiROyc+dOeeihhyQhIUF27twpO3fulMrKShHpOeeFSMtt0ZPOi/q+/PJL+eMf/yg7d+6Uo0ePyttvvy1ZWVkye/bsWIcWdW+99ZZotVp59dVXZd++fXLnnXeK2WyW48ePxzq0TnX33XfLxo0b5ejRo7J161b54Q9/KImJiT2uHboC9ud12J/XYV9eh315ZOzL2ZezL6doaOmaKyIyaNAg+fvf/x56/vjjj4vVapW///3vsmfPHvnpT38qvXr1EofDEYu30KmmT58uw4cPly1btsiWLVtk2LBh8sMf/jBsm/rtVVlZKXfffbd8+eWXcuzYMcnPz5dx48bJBRdcEHft1dK1+t5775V58+aFtj969KiYTCa56667ZN++ffLqq6+KVquV9957L1ZvoVO1tb2eeeYZWb16tRw8eFC+/fZbuffeewWArFq1KlZvoVNVVlaGrk8AQp+JTpw4ISI8v6KhRyfhV6xYIQAiPoLWrFkjI0eOlISEBDGZTDJ06FB59tlnxev1xjDyjteathCp/UAxa9YsMRqNkpycLAsXLpSampoYRR098+fPj9gW+fn5ItJzzguRlttCpOecF/Xt2LFDxowZI1arVQwGgwwaNEgWL14sTqcz1qF1ij//+c+Sk5MjOp1OLrnkEvnss89iHVKnu/HGG6VXr16i1WolKytLrrvuOtm7d2+sw+qR2J/XYX9eh315HfblkbEvZ1/OvpyioTXXXACyYsWK0PNAICCLFy+WzMxM0ev1MmHCBNmzZ0/nBx8DZWVlcvPNN0tiYqIkJibKzTffLOXl5WHb1G8vl8slV199taSlpYlWq5Xs7GyZP3++nDx5svOD7wTNXavnz58vEydODNt+48aNMmrUKNHpdJKbmysvvvhiJ0ccW21pryeeeEL69esnBoNBkpKS5Ac/+IF8/PHHMYg6NvLz8yNeq+bPny8iPL+iQRE5V0WfiIiIiIiIiIiIiIg6VMsFU4mIiIiIiIiIiIiI6LwwCU9EREREREREREREFCVMwhMRERERERERERERRQmT8EREREREREREREREUcIkPBERERERERERERFRlDAJT0REREREREREREQUJUzCExERERERERERERFFCZPwRERERERERERERERRwiQ8URexceNGKIqCiooKAMDKlSths9liGlN7NXxPRERE8Y79OREREXWUJUuWYOTIkbEOg4g6AJPwRB3spZdeQmJiInw+X2hZVVUVtFotrrjiirBtN23aBEVRcPDgQYwfPx6FhYWwWq2dHXK3cvbsWfzqV7/CoEGDYDKZkJ2djTvuuAN2uz1sO0VRGj1+8IMfhG2Tn5+PmTNnIiUlBSaTCUOGDMHdd9+N06dPd+ZbIiKiLoj9eXSxPyciIoqN3bt346c//Sn69OkDo9GIiy66CM8991zYNsEb8A0fDzzwQGgbEcHy5csxZswYJCQkwGazYfTo0Xj22Wfhcrk6+221yqpVqzBkyBDo9XoMGTIEq1evjnVI1IMwCU/UwSZPnoyqqips3749tGzTpk3IzMzEtm3bwjqjjRs3IisrCwMHDoROp0NmZiYURYlF2N1GQUEBCgoKsGzZMuzZswcrV67E2rVr8Ytf/KLRtitWrEBhYWHo8eGHH4bWvfzyy7jyyiuRmZmJVatWYd++fXjppZdgt9vx9NNPd+ZbIiKiLoj9eXSxPyciou7I6/W2allXtmPHDqSlpeGvf/0r9u7di/vvvx/33Xcf/vu//7vRtgcOHAjrg++9997Qunnz5uHOO+/EnDlzkJ+fj127duHBBx/EBx98gHXr1p13fB6P57z3bc6WLVtw4403Yt68edi9ezfmzZuHG264AV999VVUXo+oESGiDpeVlSWPPfZY6Pk999wj//Vf/yVDhgyR9evXh5ZPmTJFbr75ZhERyc/PFwBSXl4uIiIrVqwQq9UadtwPPvhA8vLyRK/XS0pKilx77bWhdWfPnpV58+aJzWYTo9Eo06dPl4MHD4qISHFxsWRkZMjSpUtD22/dulW0Wq384x//aHH/+vGsXbtWBg8eLGazWaZNmyYFBQVNtkPD9yQi8t5778mQIUNEp9NJTk6OLFu2LGyfgoICmTlzphgMBsnNzZU33nhDcnJy5Jlnnmnydd555x3R6XTi9XpDywDI6tWrI25/6tQp0el0cuedd0ZcXz9eIiLqudifS8T3JML+nIiI2s/v98vjjz8u/fr1E51OJ3369JFHHnkktP6ee+6RAQMGiNFolL59+8oDDzwgHo8ntH7+/PkyZ86csGP++te/lokTJ4aev/vuuzJ06FAxGAySnJwsU6dOlaqqKhER+de//iVXXnmlpKSkiMVikQkTJsiOHTvCjgdAXnzxRZk9e7aYTCZZtGiRLF68WEaMGCGvvvqq9O3bVxRFkUAgIGvWrJHLL79crFarJCcny6xZs+Tw4cOhY02ePFn+67/+K+z4paWlotPpZMOGDRHbKPha9dvsoYcekgsuuEB0Op2MGDFC1qxZE7bP5s2bZcSIEaLX6yUvL09Wr14tAGTnzp1N/lvcfvvtMnny5NDzSH1/fW+//bYAkPfff7/RukAgIBUVFU2+VkM5OTny8MMPy/z588ViscjPf/7zVu/bFjfccINMnz49bNm0adNk7ty5UXk9ooY4Ep4oCiZNmoT8/PzQ8/z8fEyaNAkTJ04MLfd4PNiyZQsmT57cqmN+/PHHuO666zBr1izs3LkTGzZswOjRo0Prb7nlFmzfvh0ffvghtmzZAhHBzJkz4fV6kZaWhtdeew1LlizB9u3bUVVVhZ/97Ge4/fbbcfXVV7e4f5DL5cKyZcvwl7/8BZ9//jlOnjyJ3/72t61ulx07duCGG27A3LlzsWfPHixZsgQPPvggVq5cGdrm5z//OQoKCrBx40asWrUKy5cvR3FxcbPHtdvtsFgs0Gg0rYrj3XffhcfjwT333BNxfXev3UtERB2D/Xlk7M+JiKgj3HfffXjiiSfw4IMPYt++fXjzzTeRkZERWp+YmIiVK1di3759eO655/DKK6/gmWeeafXxCwsL8dOf/hS33nor9u/fj40bN+K6666DiAAAKisrMX/+fGzatAlbt27FgAEDMHPmTFRWVoYdZ/HixZgzZw727NmDW2+9FQBw+PBhvPPOO1i1ahV27doFAHA6nfjNb36Dbdu2YcOGDVCpVLj22msRCAQAALfddhvefPNNuN3u0LHfeOMNZGVltfpzxHPPPYenn34ay5YtwzfffINp06Zh9uzZOHToUOg9XXPNNRg2bBi+/vprPPzww/j973/f4nHtdjuSk5NbFUMw7kGDBmHOnDmN1imK0uayfE899RSGDh2KHTt24MEHH4y4zaOPPoqEhIRmH5s2bWryNbZs2RL6vBQ0bdo0fPnll22Klei8xfQWAFGcWr58uZjNZvF6veJwOESj0UhRUZG89dZbMn78eBER+eyzzwSAHDlyRERaHjk3bty40Ci7hg4ePCgAZPPmzaFlpaWlYjQa5Z133gktu/3222XgwIFy8803y9ChQ6W6urrV+69YsUIAhN3J//Of/ywZGRlNtkPD93TTTTfJVVddFbbN7373OxkyZIiIiOzfv18AyLZt20LrDx06JACaHDlXWloq2dnZcv/994ctByAGg0HMZnPoERxJ95//+Z9isViajJuIiEiE/XkQ+3MiIupoDodD9Hq9vPLKK63e58knn5S8vLzQ85ZGwu/YsUMAyPHjx1t1fJ/PJ4mJifLRRx+FlgFo9IurxYsXi1arleLi4maPV1xcLABkz549IiJSU1MjycnJ8vbbb4e2GTlypCxZsqTJYzQcCZ+VlRX2izgRkUsvvVRuv/12ERF58cUXJSUlJfTZQETklVdeaXYk/JdffilarVbWrVsXWhbs++v3v2azWUpLS0VE5KKLLpLZs2c3+/5bKycnR370ox+1uF1ZWZkcOnSo2YfL5Wpyf61WK2+88UbYsjfeeEN0Ol273wNRa7RumAkRtcnkyZPhdDqxbds2lJeXY+DAgUhPT8fEiRMxb948OJ1ObNy4EdnZ2bjwwgtbdcxdu3ZhwYIFEdft378fGo0GY8aMCS1LSUnBoEGDsH///tCyZcuWYejQoXjnnXewfft2GAyGNu1vMpnQr1+/0PNevXq1OKqtYZwN75RffvnlePbZZ+H3+3HgwAFoNBpccsklofX9+/dHUlJSxOM5HA7MmjULQ4YMweLFixutf+aZZ3DllVeGxQvUTiDDWr1ERNQS9ueRsT8nIqL22r9/P9xuN6ZOndrkNu+99x6effZZHD58GFVVVfD5fLBYLK1+jREjRmDq1KkYNmwYpk2bhquvvho/+clPQv1RcXExFi1ahH/+858oKiqC3++Hy+XCyZMnw45T/xdrQTk5OUhLSwtbduTIETz44IPYunUrSktLQyPgT548iaFDh0Kv1+NnP/sZXnvtNdxwww3YtWsXdu/ejffff79V78fhcKCgoACXX3552PLLL78cu3fvBlBbw3348OGhzwYAcNlllzV5zL1792LOnDlYtGgRrrrqqkbrN23ahMTExNDzYNt1dB8cqY0bSk5ObtNo/UgaxszPEtSZWI6GKAr69++P3r17Iz8/H/n5+Zg4cSIAIDMzE3379sXmzZuRn5+PKVOmtPqYRqOxyXVy7ud0kZbX71COHj2KgoICBAIBnDhxos37a7XasPWKojS5b2uO1/C1m4ujocrKSkyfPh0JCQlYvXp1o9iA2vbu379/6GE2mwEAAwcOhN1uR2FhYatjJyKinof9edNxsj8nIqL2aK4/BICtW7di7ty5mDFjBv7v//4PO3fuxP333x82aadKpWrUt9Qvv6ZWq7F+/XqsWbMGQ4YMwfPPP49Bgwbh2LFjAGpLuO3YsQPPPvssvvzyS+zatQspKSmNJgYN9jstLbvmmmtQVlaGV155BV999VVows/6x7vtttuwfv16fP/993jttdcwdepU5OTkNNsWDTWXSG6pj65v3759mDJlChYsWIAHHngg4jZ9+/YN64NVqto04sCBA8Nu8LdXpPZsqL3laDIzM3HmzJmwZcXFxWElkIiiiUl4oiiZPHkyNm7ciI0bN2LSpEmh5RMnTsQ//vEPbN26tdV13wBg+PDh2LBhQ8R1Q4YMgc/nC5vVu6ysDAcPHsRFF10EoLbjv/nmm3HjjTfikUcewS9+8QsUFRW1ev+OMGTIEHzxxRdhy7788ksMHDgQarUagwcPhs/nw86dO0PrDx8+jIqKirB9HA4Hrr76auh0Onz44Ydhd/lb4yc/+Ql0Oh2efPLJiOsbvh4REfVc7M8jx8n+nIiI2mPAgAEwGo1N9ombN29GTk4O7r//fowePRoDBgwIu/EMAGlpaY1uxAbrswcpioLLL78cDz30EHbu3AmdTofVq1cDqB3lfccdd2DmzJm4+OKLodfrUVpael7vp6ysDPv378cDDzyAqVOn4qKLLkJ5eXmj7YYNG4bRo0fjlVdewZtvvhmqMd8aFosFWVlZEfvgYD8/ePBgfPPNN2F157dv397oWHv37sXkyZMxf/58LF26tNUxBN100004ePAgPvjgg0brRAR2u73Nx2zJL3/5S+zatavZR3Mj6seNG4f169eHLVu3bh3Gjx/f4bESRdRJZW+IepzXXntNjEajaDQaOXPmTGj5X//6V0lMTBQAcvLkydDylmrI5ufni0qlkkWLFsm+ffvkm2++kSeeeCK0fs6cOTJkyBDZtGmT7Nq1S6ZPny79+/cPzR7/29/+VnJzc8Vut4vf75cJEybIrFmzWr1/w3hEJDTLelMavqcdO3aISqWSP/zhD3LgwAFZuXKlGI1GWbFiRWifK6+8Ui655BL56quv5Ouvv5bJkyeL0WiUZ599VkRqaweOGTNGhg0bJocPH5bCwsLQw+fzhY4DIFQzNpI///nPoiiK3HrrrbJx40Y5fvy4fPHFF/Lv//7v8pvf/KbJ/YiIqGdhf87+nIiIomPJkiWSlJQkr7/+uhw+fFi2bNki//M//yMiIu+//75oNBr529/+JocPH5bnnntOkpOTw/qwtWvXiqIo8vrrr8vBgwdl0aJFYrFYQjXht27dKkuXLpVt27bJiRMn5J133hGdTieffPKJiNTWY7/qqqtk3759snXrVrniiivEaDSGzV8SqR9qWKddRMTv90tKSor87Gc/k0OHDsmGDRvk0ksvjbj/8uXLRafTic1mC6vdHknD13rmmWfEYrHIW2+9Jd999538/ve/F61WKwcPHhQREbvdLsnJyfLzn/9c9u3bJ2vXrpXBgwcLANm1a5eIiHz77beSlpYmN998c1j/W7/GfcO+v6FAICA33nijGI1GefTRR2Xbtm1y/Phx+eijj2TKlCnN9t0N5eTkNDlnTEfavHmzqNVqefzxx2X//v3y+OOPi0ajka1bt0b9tYlERJiEJ4qSY8eOCQAZPHhw2PJTp04JAOnXr1/Y8pa+tIuIrFq1SkaOHCk6nU5SU1PluuuuC607e/aszJs3T6xWqxiNRpk2bVqoI87PzxeNRiObNm0KbX/ixAmxWq3ywgsvtLh/U/G09Uu7iMh7770nQ4YMEa1WK9nZ2fLUU0+F7VNQUCAzZswQvV4vOTk58uabb0p6erq89NJLYceM9Dh27FjoOC19aRcRWb9+vUybNk2SkpLEYDDI4MGD5be//a0UFBQ0ux8REfUc7M/ZnxMRUXT4/X555JFHJCcnJ9SfPProo6H1v/vd7yQlJUUSEhLkxhtvlGeeeaZRH7Zo0SLJyMgQq9Uqd911lyxcuDCUhN+3b59MmzZN0tLSRK/Xy8CBA+X5558P7fv111/L6NGjRa/Xy4ABA+Tdd99tlBBubRJepLY/uuiii0Sv18vw4cNl48aNEfevrKwUk8kUmky1OQ1fy+/3y0MPPSQXXHCBaLVaGTFihKxZsyZsn82bN8vw4cNFp9NJXl6evPnmmwJAvvvuu9AxI/W/OTk5oWO0lIQPxvLiiy/KpZdeKiaTSSwWi+Tl5clzzz0XmiA1eJz6fXtDnZWEFxF59913ZdCgQaLVamXw4MGyatWqTnldIhERRaQNBSCJiDrZ999/jz59+uDTTz9tdtIeIiIi6rrYnxMREdU6deoUcnNzsW3btrBJzKPljTfewL/927/Bbre3WIu/o61cuRJLly7Fvn37Is77QtSTaGIdABFRff/85z9RVVWFYcOGobCwEPfccw9yc3MxYcKEWIdGRERErcT+nIiIKJzX60VhYSHuvfdejB07NmoJ+P/93//FhRdeiAsuuAC7d+/G73//e9xwww2dnoAHgLVr1+LRRx9lAp4ITMITURfj9Xrx//7f/8PRo0eRmJiI8ePH44033mCnTURE1I2wPyciIgq3efNmTJ48GQMHDsR7770Xtdc5c+YMFi1ahDNnzqBXr164/vrrz2vy1Y7w1ltvxeR1iboilqMhIiIiIiIiIiIiIooSVawDICIiIiIiIiIiIiKKV0zCExERERERERERERFFCZPwRERERERERERERERRwiQ8EREREREREREREVGUMAlPRERERERERERERBQlTMITEREREREREREREUUJk/BERERERERERERERFHCJDwRERERERERERERUZQwCU9EREREREREREREFCX/H3wo/GgIAlVEAAAAAElFTkSuQmCC", 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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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" - ] - }, - "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/replogle/3_run_batch.py b/docs/source/tutorial/replogle/3_run_batch.py index 6fc0756..08b6ad8 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`` +"""Stage 3b: 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 ``5_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/README.md b/docs/source/tutorial/replogle/README.md index 5645e58..b135230 100644 --- a/docs/source/tutorial/replogle/README.md +++ b/docs/source/tutorial/replogle/README.md @@ -7,7 +7,7 @@ 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 +Only the notebook, the five 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. @@ -21,7 +21,7 @@ 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 @@ -30,6 +30,11 @@ regenerated. results/replogle_propensity_batch12_tuning.csv results/replogle_propensity_batch12_selected_scores.npz + 5_refit_propensity.py results/replogle_results_r{r}.nuisances.h5 + -> results/replogle_results_penalized.h5 + results/replogle_propensity_penalties.csv + copy the .h5 to results/replogle_results.h5 for the notebook + replogle-py.ipynb reads the files marked "notebook input" below ## Files @@ -40,11 +45,13 @@ regenerated. | `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 | +| `5_refit_propensity.py` | 7 KB | re-estimates LFC with the tuned library-size penalty on flagged arms, reusing the saved outcome model | | `4_cache_propensity_batch.py` | 14 KB | propensity-score sensitivity sweep, two stages | | `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_penalized.h5` | +| `results/replogle_propensity_penalties.csv` | 30 KB | **notebook input** — per-arm penalty report from `5_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 | @@ -58,6 +65,7 @@ regenerated. ## Rebuilding from the tracked files alone -Run `1_`, then `3_` and `4_` (hours each), then execute the notebook, which +Run `1_`, then `3_`, `4_` and `5_` (the slow steps), 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. From 13265870d5844cb3766f82d0f3d4e2190824bc19 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Sat, 26 Sep 2026 12:46:01 +0800 Subject: [PATCH 03/16] Compute size factors from well-expressed genes comp_size_factor's median of ratios ran over every gene. In sparse single-cell data most nonzero counts are 1 or 2, so those ratios caught about a third of each cell's sequencing depth (SD of log size factors 0.12 against 0.35 for log total counts on Replogle controls). The leftover depth sits in highly expressed genes, which scale fully with depth, and when a group of cells differs from the controls in average depth by chance, all of them shift together. On a Replogle negative control (30 fake perturbations cut from the controls, full pipeline) each fake arm's shift tracked its leftover depth (correlation 0.98), the null SD for genes above 5 counts per cell was 1.32 and 114 pairs were called; Wilcoxon on the same genes called 2. Swapping in total-count offsets removed the shift, while cross-fitting and a Poisson outcome model changed nothing, which rules out the in-sample fit and dispersion. The median of ratios now uses genes with mean count >= min_mean (2), or the 100 highest-expressed genes if fewer pass. That keeps its resistance to composition changes, which total counts lack and which the GCATE deconfounding tests depend on, while tracking depth: 14 false discoveries on the same null, SD 1.08 for highly expressed genes, and no change on the mouse-brain negative control. min_mean=0 restores the old factors; method='libsize' uses total counts. Cells without counts now get a size factor of zero instead of NaN. Co-Authored-By: Claude Opus 5.5 --- causarray/utils.py | 76 +++++++++++++++++++++++++++++-- docs/CHANGELOG.md | 10 ++++ docs/source/main_function/lfc.rst | 5 ++ tests/test_small_arm_inference.py | 56 +++++++++++++++++++++++ 4 files changed, 142 insertions(+), 5 deletions(-) diff --git a/causarray/utils.py b/causarray/utils.py index bbc5a49..e3bab91 100755 --- a/causarray/utils.py +++ b/causarray/utils.py @@ -176,7 +176,8 @@ def _normalize(counts, log_geo_means): 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. @@ -185,15 +186,80 @@ 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'``: ``lib_size`` divided by the column sums. lib_size : float The desired 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. + 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. ''' + 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 all genes. + missing = ~np.isfinite(log_size_factor) + if missing.any(): + log_size_factor[missing] = _log_median_ratio(counts[:, gene_mean > 0])[missing] + 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 = 1./np.sum(counts, axis=0)*lib_size + else: + raise ValueError("Method must be in {'geomeans', 'libsize', 'scale'}.") + + return size_factor if method=='geomeans': # Vectorized geometric mean (per gene, ignoring zeros) counts = np.asarray(counts, dtype=np.float64) @@ -214,7 +280,7 @@ def comp_size_factor(counts, method='geomeans', lib_size=1e4, **kwargs): elif method=='scale': size_factor = 1./np.sum(counts, axis=0)*lib_size else: - raise ValueError("Method must be in {'geomeans' or 'scale'}.") + raise ValueError("Method must be in {'libsize', 'geomeans', 'scale'}.") return size_factor diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index d6f00e5..6164d5b 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -9,6 +9,16 @@ 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. ## [0.1.0] diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index cbc767a..802cbaf 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -61,6 +61,11 @@ 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. +* **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 diff --git a/tests/test_small_arm_inference.py b/tests/test_small_arm_inference.py index 59b7991..25e5165 100644 --- a/tests/test_small_arm_inference.py +++ b/tests/test_small_arm_inference.py @@ -12,6 +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 @@ -339,3 +341,57 @@ def test_equal_arm_means_are_tested_and_counted_in_bh(): 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_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 From 69f4bb6773381956d31c34c3ab79eb07a1ab3b99 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Sat, 26 Sep 2026 15:11:14 +0800 Subject: [PATCH 04/16] Add select_propensity_factors; use it for Replogle and Perturb-seq Guides are assigned at random, so 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, next to tune_penalty_factor and with the same interface, starts from every covariate. For each treatment whose treated ESS falls below 0.5, overlap below 0.3 or AUC above 0.9, it drops the covariate most imbalanced between that treatment and the controls, one at a time, until ESS and overlap recover; the intercept always stays. It returns a drop_by_treatment mapping for refit_propensity_scores and a per-arm report. The Replogle and Perturb-seq tutorials leave log library size out of the propensity model and choose the latent factors per arm with it: knocking down transcription machinery shrinks total RNA, so library size separated those arms from the controls almost completely. 4_refit_propensity.py applies the rule to every Replogle batch (29 of 200 arms adjusted, one or two factors dropped each); 4_cache_propensity_batch.py, which only served the penalty sweep, is removed and the refit script takes its number. Co-Authored-By: Claude Opus 5.5 --- causarray/DR_estimation.py | 148 ++++++ causarray/__init__.py | 5 +- docs/CHANGELOG.md | 8 + docs/source/main_function/lfc.rst | 34 +- docs/source/tutorial/replogle/3_run_batch.py | 4 +- .../replogle/4_cache_propensity_batch.py | 422 ------------------ .../tutorial/replogle/4_refit_propensity.py | 107 +++++ .../tutorial/replogle/5_refit_propensity.py | 140 ------ docs/source/tutorial/replogle/README.md | 43 +- tests/test_propensity.py | 44 ++ 10 files changed, 358 insertions(+), 597 deletions(-) delete mode 100644 docs/source/tutorial/replogle/4_cache_propensity_batch.py create mode 100644 docs/source/tutorial/replogle/4_refit_propensity.py delete mode 100644 docs/source/tutorial/replogle/5_refit_propensity.py diff --git a/causarray/DR_estimation.py b/causarray/DR_estimation.py index 1ca097c..faa1bb3 100755 --- a/causarray/DR_estimation.py +++ b/causarray/DR_estimation.py @@ -462,6 +462,154 @@ 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, + 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``, 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}``. + K, ps_model, mask, random_state, verbose, class_weight, **kwargs + Passed to :func:`estimate_propensity_scores` for every fit. + + Returns + ------- + drop_by_treatment : dict + ``{treatment: [covariate, ...]}`` for the adjusted treatments, ready + to hand to :func:`refit_propensity_scores`. + report : DataFrame + One row per adjusted treatment with the covariates dropped in order, + whether ``target`` was met, the number of fits, and the support + metrics with every covariate (``_all``) and after the removal + (``_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] + 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 + smd = [] + for k in cand_idx: + c_vals, t_vals = X_arr[ctrl, k], X_arr[case, 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]] + + 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 + dropped, chosen, n_fits = [], before, 1 + for covariate in imbalance_order(j): + dropped.append(covariate) + pi, _ = refit_propensity_scores( + A_arr, X_arr, pi_hat=pi, treatment_names=treatment_names, + covariate_names=covariate_names, + drop_by_treatment={name: list(dropped)}, **fit_kwargs) + n_fits += 1 + chosen = _arm_support_metrics(A_arr, pi, j, mask=mask_arr) + if _meets(chosen, target): + break + drops[name] = list(dropped) + rows.append({'treatment': name, 'dropped': ','.join(dropped), + 'n_dropped': len(dropped), 'target_met': _meets(chosen, target), + 'n_fits': n_fits, + **{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 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, diff --git a/causarray/__init__.py b/causarray/__init__.py index cf3adc1..6317e1f 100755 --- a/causarray/__init__.py +++ b/causarray/__init__.py @@ -12,7 +12,7 @@ '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 +20,8 @@ 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/docs/CHANGELOG.md b/docs/CHANGELOG.md index 6164d5b..504f5f9 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -20,6 +20,14 @@ 1.08. `min_mean=0` restores the old factors; `method='libsize'` uses total counts. +### 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`. + ## [0.1.0] Inference fix for small perturbation arms, and the GLM engine moves to crispyx diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index 802cbaf..20ca64f 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -182,6 +182,34 @@ 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. +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. Covariates that the +treatment itself changes, such as library size after knocking down +transcription machinery, are better left out of ``W_A`` from the start. + Choosing the penalty factor ^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -205,8 +233,8 @@ 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 +299,7 @@ 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/replogle/3_run_batch.py b/docs/source/tutorial/replogle/3_run_batch.py index 08b6ad8..5d5d417 100644 --- a/docs/source/tutorial/replogle/3_run_batch.py +++ b/docs/source/tutorial/replogle/3_run_batch.py @@ -1,8 +1,8 @@ -"""Stage 3b: Replogle tutorial batched GCATE + LFC on the raw-count subset. +"""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) and its nuisances, which ``5_refit_propensity.py`` reads.""" +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 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..8215099 --- /dev/null +++ b/docs/source/tutorial/replogle/4_refit_propensity.py @@ -0,0 +1,107 @@ +"""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: knocking down transcription +machinery shrinks a cell's total RNA, so library size mostly records the +perturbation's own effect. 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 b135230..4cc7b0c 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 five 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 two small JIC tables in +`results/` are tracked in git; the rest of `data/` +and `results/` is local and regenerated. ## Pipeline @@ -23,16 +23,9 @@ regenerated. -> results/replogle_results_r{r}.h5 (resumable) 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 - - 5_refit_propensity.py results/replogle_results_r{r}.nuisances.h5 - -> results/replogle_results_penalized.h5 - results/replogle_propensity_penalties.csv + 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 @@ -44,28 +37,22 @@ 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 | -| `5_refit_propensity.py` | 7 KB | re-estimates LFC with the tuned library-size penalty on flagged arms, reusing the saved outcome model | -| `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** — copy of `results/replogle_results_penalized.h5` | -| `results/replogle_propensity_penalties.csv` | 30 KB | **notebook input** — per-arm penalty report from `5_refit_propensity.py` | +| `results/replogle_results.h5` | 283 MB | **notebook input** — copy of `results/replogle_results_selected.h5` | +| `results/replogle_results_r10.nuisances.h5` | 50 GB | **notebook input** — outcome model and latent factors per batch; the notebook reads one batch's factors | +| `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_`, `4_` and `5_` (the slow steps), 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. +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/tests/test_propensity.py b/tests/test_propensity.py index dfff260..d4cf383 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, @@ -685,3 +686,46 @@ 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}, 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, + 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) From 57f9bbb0dad8df05797353071a1c4a77ea59d22d Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 14:56:55 +0800 Subject: [PATCH 05/16] Honour fit_gcate offset arrays fit_gcate took its offset from kwargs_glm['size_factor'], which was only set for offset=True. A supplied offset array was therefore replaced by ones everywhere except the dispersion estimate. The array is now used, and it is checked for one finite value per row. offset=False still means no offset. Co-Authored-By: Claude Opus 5.5 --- causarray/gcate.py | 9 ++++++++- docs/CHANGELOG.md | 3 +++ tests/test_gcate_offset.py | 37 +++++++++++++++++++++++++++++++++++++ 3 files changed, 48 insertions(+), 1 deletion(-) create mode 100644 tests/test_gcate_offset.py 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/docs/CHANGELOG.md b/docs/CHANGELOG.md index 504f5f9..1311789 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -19,6 +19,9 @@ 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. +- `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. ### Added 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) From 29993a6fdf19845915ba09b1c64b908a797f4385 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 14:56:55 +0800 Subject: [PATCH 06/16] Restore Welch usevar='unequal' usevar='unequal' is the by-arm Welch variance again, with Welch-Satterthwaite df. 'pooled' stays the default. In small case-control designs Welch is the more conservative choice: on 85 SEA-AD donors, permuted labels gave 0-1 false discoveries with it, against up to 3,654 under 'pooled'. The gcate docs note that with few units the factors can stay nearly orthogonal to treatment, while under 'pooled' they still shrink the standard errors. The SEA-AD tutorial README no longer lists results/sea_ad_lfc.csv as a notebook input. Co-Authored-By: Claude Opus 5.5 --- causarray/DR_learner.py | 71 ++++++++++++--------- docs/CHANGELOG.md | 8 +++ docs/source/main_function/gcate.rst | 12 ++++ docs/source/main_function/lfc.rst | 16 +++-- docs/source/tutorial/case_control/README.md | 3 +- tests/test_inference_comprehensive.py | 23 ++++--- 6 files changed, 84 insertions(+), 49 deletions(-) diff --git a/causarray/DR_learner.py b/causarray/DR_learner.py index f1e7ead..eda8876 100755 --- a/causarray/DR_learner.py +++ b/causarray/DR_learner.py @@ -380,7 +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', @@ -422,9 +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 @@ -434,16 +434,25 @@ def LFC( ``n/(n-d)`` and p-values use a t reference with ``n-d`` degrees of freedom (see Notes). - ``'unequal'`` is accepted as a deprecated alias of ``'pooled'``. The - by-arm Welch formula ``s₀²/n₀ + s₁²/n₁`` it used to select is not the - variance of an estimator that averages pseudo-outcomes over all cells: - it doubles the standard error for equal arms and inflates it far more - for rare treatments. + ``'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) it gives more conservative + results than ``'pooled'``: on 85 SEA-AD donors with GCATE factors, + where ``'pooled'`` was anti-conservative under permuted labels (null + SD 1.1-1.8), ``'unequal'`` gave 0-1 false discoveries per permutation + (null SD 0.6-0.9). 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 @@ -574,17 +583,8 @@ def LFC( 'replaces the additive constant.', FutureWarning, stacklevel=2, ) - if usevar == 'unequal': - warnings.warn( - "usevar='unequal' is deprecated and treated as 'pooled': the by-arm Welch " - "formula is not the variance of the AIPW estimator (2x the correct SE for " - "equal arms, far larger for rare treatments). It will raise in a future " - "release.", - 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] @@ -621,17 +621,26 @@ 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 diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 1311789..b8d2306 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -31,6 +31,14 @@ that treatment and the controls until its support recovers, and returns a `drop_by_treatment` mapping for `refit_propensity_scores`. +### Changed + +- `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 20ca64f..4741b7a 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -74,12 +74,16 @@ Two further safeguards apply to every gene: 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='unequal'`` is a deprecated alias of ``'pooled'``. The by-arm Welch -formula ``s0²/n0 + s1²/n1`` it used to select is not the variance of an -estimator that averages pseudo-outcomes over all cells: it doubles the standard -error for equal arms and inflates it far more for rare treatments. Neither -formula models within-donor correlation; repeated cells from one biological -unit should still be pseudo-bulked or analysed with a cluster-aware method. +``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 ---------------------- 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/tests/test_inference_comprehensive.py b/tests/test_inference_comprehensive.py index 2b29889..4b4aca0 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' ---- - 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 sqrt(2) 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="deprecated and treated as 'pooled'"): - 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 1.2 < ratio < 2.2, 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 ---- From 2a086fb1e2a618d48a27dc65fd8ca13c520daa55 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 15:08:12 +0800 Subject: [PATCH 07/16] Replogle: drop the legacy log-normalised rank table Only results/replogle-r.csv, the JIC table on raw counts, is kept. The older table computed on log-normalised values is removed, with its mention in 2_estimate_r.py and the README; .gitignore no longer exempts it. Co-Authored-By: Claude Opus 5.5 --- .gitignore | 3 +-- docs/source/tutorial/replogle/2_estimate_r.py | 5 ++--- docs/source/tutorial/replogle/README.md | 3 +-- .../tutorial/replogle/results/replogle-r-legacy.csv | 8 -------- 4 files changed, 4 insertions(+), 15 deletions(-) delete mode 100644 docs/source/tutorial/replogle/results/replogle-r-legacy.csv diff --git a/.gitignore b/.gitignore index 4139ff2..77587e0 100644 --- a/.gitignore +++ b/.gitignore @@ -183,9 +183,8 @@ 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/ diff --git a/docs/source/tutorial/replogle/2_estimate_r.py b/docs/source/tutorial/replogle/2_estimate_r.py index 5f6ef87..bd139ca 100644 --- a/docs/source/tutorial/replogle/2_estimate_r.py +++ b/docs/source/tutorial/replogle/2_estimate_r.py @@ -1,6 +1,5 @@ -"""Re-select the number of latent factors for the Replogle tutorial on the -raw-count subset (the cached results/replogle-r-legacy.csv was computed on log-normalised -values). 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/README.md b/docs/source/tutorial/replogle/README.md index 4cc7b0c..46c3398 100644 --- a/docs/source/tutorial/replogle/README.md +++ b/docs/source/tutorial/replogle/README.md @@ -7,7 +7,7 @@ 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 +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. @@ -46,7 +46,6 @@ and `results/` is local and regenerated. | `results/replogle_results_r10.nuisances.h5` | 50 GB | **notebook input** — outcome model and latent factors per batch; the notebook reads one batch's factors | | `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/run_batch.log` | 11 KB | run record for `3_run_batch.py`; the last line reports rows, discoveries, zero-count arms and runtime | 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 From 345e4ff99c069c03cc59b8e7d05ec3745598cbe3 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 15:12:58 +0800 Subject: [PATCH 08/16] Replogle README: the nuisance file is optional for the notebook The 50 GB nuisance file only feeds the propensity walk-through, which the notebook skips when the file is absent. Co-Authored-By: Claude Opus 5.5 --- docs/source/tutorial/replogle/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/tutorial/replogle/README.md b/docs/source/tutorial/replogle/README.md index 46c3398..629f084 100644 --- a/docs/source/tutorial/replogle/README.md +++ b/docs/source/tutorial/replogle/README.md @@ -43,7 +43,7 @@ and `results/` is local and regenerated. | `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** — copy of `results/replogle_results_selected.h5` | -| `results/replogle_results_r10.nuisances.h5` | 50 GB | **notebook input** — outcome model and latent factors per batch; the notebook reads one batch's factors | +| `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_subset_norm_cx_wilcoxon.h5ad` | 92 MB | notebook cache — Wilcoxon comparison results | From 42dce1cdb122369e0d77ee67741db305bb9c4a90 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 15:51:12 +0800 Subject: [PATCH 09/16] Perturb-seq: align the R tutorial with Python The R tutorial was last rendered with 0.0.10. It still kept library size in the propensity model and tuned a penalty on it. It now follows the Python notebook: library size is left out, factors are selected per arm with select_propensity_factors, then LFC runs with the selected scores. It is re-knit with 0.1.1 and gives 8,161 hits, as the Python notebook does. - The R discovery count no longer includes rows whose padj is NA. - The unreferenced perturbseq_files/ figure is removed. - LFC shows its per-treatment progress bar only with verbose=True, so the tutorial outputs no longer carry hundreds of progress lines. Co-Authored-By: Claude Opus 5.5 --- causarray/DR_learner.py | 2 +- docs/CHANGELOG.md | 1 + docs/source/tutorial/perturbseq/README.md | 3 +- .../tutorial/perturbseq/perturbseq-r.Rmd | 352 +++------ .../tutorial/perturbseq/perturbseq-r.md | 735 ++++++------------ .../propensity-overlap-1.png | Bin 83524 -> 70782 bytes .../propensity-overlap-after-1.png | Bin 0 -> 72460 bytes .../propensity-overlap-tuned-1.png | Bin 85937 -> 0 bytes .../satb2-c01-regularized-1.png | Bin 34337 -> 0 bytes .../treatment-associations-1.png | Bin 76262 -> 66177 bytes .../unnamed-chunk-5-1.png | Bin 0 -> 50818 bytes .../unnamed-chunk-6-1.png | Bin 52303 -> 0 bytes .../unnamed-chunk-6-1.png | Bin 53117 -> 0 bytes 13 files changed, 338 insertions(+), 755 deletions(-) create mode 100644 docs/source/tutorial/perturbseq/perturbseq-r_files/figure-markdown_github/propensity-overlap-after-1.png delete mode 100644 docs/source/tutorial/perturbseq/perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png delete mode 100644 docs/source/tutorial/perturbseq/perturbseq-r_files/figure-markdown_github/satb2-c01-regularized-1.png create mode 100644 docs/source/tutorial/perturbseq/perturbseq-r_files/figure-markdown_github/unnamed-chunk-5-1.png delete mode 100644 docs/source/tutorial/perturbseq/perturbseq-r_files/figure-markdown_github/unnamed-chunk-6-1.png delete mode 100644 docs/source/tutorial/perturbseq/perturbseq_files/figure-markdown_github/unnamed-chunk-6-1.png diff --git a/causarray/DR_learner.py b/causarray/DR_learner.py index eda8876..e1da5b3 100755 --- a/causarray/DR_learner.py +++ b/causarray/DR_learner.py @@ -274,7 +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] diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index b8d2306..9fecd76 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -22,6 +22,7 @@ - `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 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-r.Rmd b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd index e3e2003..b96b886 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd +++ b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd @@ -14,7 +14,7 @@ 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) @@ -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.** Several of these knockouts shift library size strongly, and with it in the model their arms separate from the controls (AUC above 0.9, see below). Measured library size mixes capture depth with total RNA content, which a knockout can change, so it is not a reason a cell received its guide. +* **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..c60d9fe 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.md +++ b/docs/source/tutorial/perturbseq/perturbseq-r.md @@ -1,6 +1,7 @@ -# Perturb-seq Tutorial (R) +Perturb-seq Tutorial (R) +======================== -This tutorial uses an excitatory-neuron subset from [Jin et al. (2020), +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 @@ -8,10 +9,11 @@ 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) @@ -21,14 +23,6 @@ library(Seurat) ## Loading required package: sp - ## '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' @@ -63,13 +57,14 @@ A <- data.frame( colnames(A) <- sub("^trt_", "", colnames(A)) ``` -## Prepare inputs +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. +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 +83,7 @@ causarray <- import("causarray") cat(causarray$`__version__`) ``` - ## 0.0.10 + ## 0.1.1 ``` r # (Y, A) should be either data.frame or matrix @@ -100,7 +95,8 @@ list2env(dat, .GlobalEnv) ## -## Estimate latent factors +Estimate latent factors +----------------------- This example uses a fixed illustrative rank, matching the Python fitting cell. Inspect the JIC curve and nearby ranks in the Python example @@ -112,61 +108,13 @@ 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 +124,190 @@ 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]]$hist)))) ``` - ## Step 2 -- epochs: 29, best NLL: 1.705020 + ## Step 2 -- epochs: 29, best NLL: 1.709480 + +Propensity-score diagnostics and factor selection +------------------------------------------------- + +Read this before the effects. An arm whose propensity model separates it +from the controls has inverse-probability weights concentrated on a few +cells, and its discovery count then reflects the weights rather than the +biology. + +Guides are assigned at random, so the propensity model only has to +absorb chance imbalance between an arm and the controls. Two +consequences for its covariates: + +- **Library size is left out.** Several of these knockouts shift + library size strongly, and with it in the model their arms separate + from the controls (AUC above 0.9, see below). Measured library size + mixes capture depth with total RNA content, which a knockout can + change, so it is not a reason a cell received its guide. +- **Latent factors are included, then checked arm by arm.** A factor + can also track a perturbation’s own effect; when one does, + `select_propensity_factors` drops it for that arm only. It flags an + arm whose treated ESS falls below 0.5, overlap below 0.3 or AUC + above 0.9, and removes the factor most imbalanced between that arm + and the controls, one at a time, until ESS and overlap recover. + +``` r +factor_names <- paste0("U", seq_len(ncol(U))) +W_A <- cbind(X_A[, 1, drop = FALSE], U) # intercept + latent factors +propensity_names <- c("intercept", factor_names) +pert_names <- colnames(A) + +# In-sample scores match what LFC uses; the out-of-fold fit checks whether +# that fit generalises. +pi_before <- causarray$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 +345,18 @@ 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]]$savefig( - "perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png", - dpi = 120L, bbox_inches = "tight" -) -knitr::asis_output( - "![](perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png)" -) -``` - -![](perturbseq-r_files/figure-markdown_github/propensity-overlap-tuned-1.png) +Estimation and results +---------------------- ### Estimate log-fold changes @@ -505,193 +368,17 @@ variance floor; see the `LFC` documentation for details. ``` r offsets <- log(res_gate[[2]][['kwargs_glm']][['size_factor']]) # use the precomputed size factors -# pi_hat carries the tuned propensity from the section above, so the estimate +# pi_hat carries the selected propensity from the section above, so the estimate # and the diagnostics describe the same scores. res <- causarray$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 +401,51 @@ 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 = ","))) +``` + + ## Total significant gene-perturbation pairs (padj < 0.1): 8,161 + +``` r +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$Perturbation) + ## Perturbation Count + ## 1 Cul3 746 + ## 2 Satb2 561 + ## 3 Asxl3 514 + ## 4 Mbd5 504 + ## 5 Upf3b 492 + ## 6 Med13l 460 + ## 7 Stard9 459 + ## 8 Ctnnb1 336 + ## 9 Ash1l 330 + ## 10 Scn2a1 322 -# Plot the number of discoveries for each perturbation condition +``` r ggplot(discovery_counts, aes(x = Perturbation, y = Count)) + - geom_bar(stat = "identity", fill = "royalblue") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + geom_bar(stat = "identity", fill = "royalblue") + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ggtitle('Number of Discoveries (padj < 0.1) for Each Perturbation Condition') + xlab('Perturbation Condition') + ylab('Number of Discoveries') ``` -![](perturbseq-r_files/figure-markdown_github/unnamed-chunk-6-1.png) +![](perturbseq-r_files/figure-markdown_github/unnamed-chunk-5-1.png) + +### What the selection changed for Satb2 + +Satb2 is the only arm the selection adjusted: dropping U9 and U7 from +its propensity model raises its out-of-fold overlap from 0.34 to 0.42, +while the outcome model still uses every factor. Its effect estimates +barely move (correlation 0.91 with the all-factor fit), and it has 561 +discoveries instead of 348 because its weights no longer rest on a few +cells. 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wwdHq6F~$Y_t4H~7HG6=5 + - r-dplyr + - r-ggplot2 - pandoc From 2165e503d1059809bab81c1b0d5dffd2cf735329 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Wed, 30 Sep 2026 12:36:51 +0800 Subject: [PATCH 14/16] Add the mouse-brain Perturb-seq tutorial; ignore local SEA-AD data L45Glut-py.ipynb is a causarray 0.1.1 tutorial on one cell type of a mouse-brain CRISPR screen (L4-5 IT Glut, 58 perturbations). It covers the per-cell-type pipeline, the propensity and calibration checks, the biological readouts and a comparison with Wilcoxon, and ends with the loop for all cell types. It is rendered in the docs as an orphan page, not listed in the index. pipeline.py, which the notebook imports, and the folder README are committed with it. The data, the results, the archived investigation notebook and its scripts stay local. .gitignore now ignores prepared SEA-AD data other than the committed excitatory-neuron pseudo-bulk, such as the subclass pseudo-bulks. Co-Authored-By: Claude Opus 5.5 --- .gitignore | 14 +- .../tutorial/mouse_brain/L45Glut-py.ipynb | 2104 +++++++++++++++++ docs/source/tutorial/mouse_brain/README.md | 37 + docs/source/tutorial/mouse_brain/pipeline.py | 336 +++ 4 files changed, 2489 insertions(+), 2 deletions(-) create mode 100644 docs/source/tutorial/mouse_brain/L45Glut-py.ipynb create mode 100644 docs/source/tutorial/mouse_brain/README.md create mode 100644 docs/source/tutorial/mouse_brain/pipeline.py diff --git a/.gitignore b/.gitignore index 77587e0..6aee07b 100644 --- a/.gitignore +++ b/.gitignore @@ -189,5 +189,15 @@ docs/source/tutorial/replogle/results/** # Unrelated tutorial directory (not part of causarray package docs) docs/source/tutorial/scp/ -# Local analysis, not part of the documentation. -/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/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb b/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb new file mode 100644 index 0000000..c74ae03 --- /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).** Each up-regulates genes of its own machinery (Prpf6, a spliceosome component: mRNA splicing; Tpr, a nuclear-pore protein: transcription and RNA metabolism) and down-regulates neuronal and synaptic genes.\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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      " + ] + }, + "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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      " + ], + "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
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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
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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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      " + ], + "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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TKu+77z4z1UczBrrmRKfk+UozdvrFXn8J1kyEZj40O/Dtt9+abOOqVavMfhMnTjS/Ems5cT12/fVct13IF0vXr+26LkeD4bFjx5r1S/qY9Mu0/grtWo+k57Vpsn6x0uBDswSvvvqq+ZKrX4Q8tWjRwqwRWr16tdlXA19t76Brplzrq3Sql74eujbtueeeMxktvW+dHqXPpX5pzSlfjtHqx+trewW9vZZw1xLt+n7Uab5aov6mm24y7QKeeeYZr/31tdUv3fp+0v31+dE1TZq18fW58eV18PX5OB/N+GhmR2+XfkqmNsrW6/R11uPQoOGuu+4yJfJzypfHpEGDTunToEYDD33uXnrpJdPGJCfHnV6g3rNWtPHITPoxdd2dvlaaidLHop8x+pmjx6PTQfXzyPOzTRuy63RRfV71eL/55hvzHte1xPqYPI0bN878mKGzFzTjp4/ztddeM++7ESNGmCx2ds+7vuZ6f2+88YZ5rnUMPdb07yGr3s/+/owEEOTyuPgLgAugVfG00p6W49Yqf1otTivRaWW1l156yau8d3YN07WRsLZI8PTOO+8469ata6q/1a5d2/n666+bEuU6hrZ1ON+YruuHDx/urFq1qjm28uXLm2qaWhXUs0qntmPQKoFa/lzbL2glTV8bpruqbqav7umydOlS59VXX+0sXry4GV8f6x133OFVIn3atGnOVq1aOePi4pzFihUzz5+2F8isqbWWs2/Xrp15XvTxPPTQQ6Y5eHpaLr1Ro0amsp+Oq4975cqVmY6Z3t13322O1cXXY7Ty8eakvYK+RlqlT187rfZXtmxZ0x5DS9VnZt26deYYteKflm3XUvK+NpzO6etwvufDc8zMaAVEfe1cFRe1QqwnLaOv96/HoVVv169fn2XVzazuIyePScva6/1o03CtiKkVG7X9iR6b7u/LcWfWXiFQ79np06eb4/vyyy+dOZVV1c3MxtTPQv0Ma968uTkWPaYmTZo4p06datpUpK+wqW0P9DXUisb6nOhnXGbvL1f7AW3volVY9XPuoosuMuN6VjfOin4OatuISpUqmedPny99zvXvqXr16pa+n9O/Rr6OmZPPHwDBzaH/CXSwCQDBRpsQ6y/wmrkCrwOsoZlfzSBpgRlf1/4FYsy8phk9nRGRWeVkAMitiFzfEgAAwEf6u7JWCdY1hlYFZP4YM6/t2rXLrG91VfkEAKsQ6AEAAL/TQCyn1XUDMaY/6ZplbWGjPf60bcPatWtl+PDhpuhJ+vWAAHChKMYCAACQB1yVN7XwibY70EJYWs1Vp2xm1gsPAC4Ea/QAAAAAwGbI6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoZWHFihUyePBg6dKli5w8edKnJ3PRokUycOBAuf7662XKlCmSlJRk5WsFAAAAAD4h0MvETTfdJGPHjpWYmBj55ptvfArYZs2aJd26dZPatWtL9+7dZfr06SbgAwAAAIC85nA6nc48v9cgd+DAASlbtqwsXLhQunbtKocOHZKSJUtmuX9qaqpUrFhRhg4dKo8//rjZtmbNGmnSpIksXbpUOnTokIdHDwAAACC/I6OXCQ3yckKDOg0Oe/bs6d7WuHFjqVGjhgkWAQAAACAvRUg+kZiYaE6eoqOjzelC7dy50/y/UqVKXts1y+e6DgAAAACCOtD7qFVbCTUbu1wpEydO9No2fvx4mTBhwgWP7VrDV6BAAa/tsbGxFGQBAAAAEBqBniM8XELNuHHjZMyYMV7brMjmqaJFi5r/Hz16VAoXLuzefuTIEalfv74l9wEAAAAA/g30whwSaqyappkZXY/ncDhk1apVUqVKFbNNp4lu2rRJ+vXr55f7BAAAAADJ7xk9q40ePVrKlCkjDzzwgJQrV046depkeuddffXVEhUVJS+//LKkpKTIDTfcEOhDBQAAAJDP5CrQCwu3d7HOGTNmyNy5c+Xw4cPmcp8+fSQyMlIefvhhadv2v+sTf/75Z6latarXbbQVQ7Vq1aRUqVKyfft2eeedd6R8+fIBexxAsE2fTkhICPRhAAByQHsKx8fH85wB+Wfqpr0DvXbt2pmKmenVqlXLfX7q1KlexVe04ubatWvN9M3Tp09L06ZNJS4uLs+OGQh2GuRp1hsAEFozmADkq6mb9g706tSpY07ZadWqVYZtYWFhcvHFF/vxyAAAAADg/MjoAQAAAIDNkNEDAAAAAJvJZTEWqm4CAAAAQLBi6iYAAAAA2AxTNwEAAC6QXVvIHDx40LaVN2kdAbvLXaDnsHfVTQAAgJyghUzosWsAC7iQ0QMAAAAAm2GNHnAB7DpVxx/sPP3HSkwlAgAAAczoUXUTUEzVgdUIhgHkV3n942le/gDJj3gIofYKrNEDAACAdez84yk/4iFkAj2hGAsAAAAABC0apgMAAACAzeQuoxfmsPxAAMAlPxe5ya9Fa1i/AgCAtcjoAQg6dl6nEUjBHEDv3r07aANcgtD8+77Mjz/S8H4HJL+v0SOjF2jDhg2TH3/8McvrX3vtNbn00kvz9JgABDcC6Nyxw5f3YMb7Mrjk1/e7v39w8OcPAQTnyArtFUJUly5dpG7dul7bDh06JO+++640btxYKlasGLBjAwAACCWh/INDfg3OcX5M3QxRPXv2zHT7iBEjpE2bNlK6dOk8PybkL/789dPfU6D49RMAANgdxVhsply5clKhQgXZsGGDNG/ePNCHY/vAxB8BSagEIfz6CQBA4L+jXOh3kVD53oGcI6NnM8eOHZOtW7dKVFRUoA8l6IRKYMIUDP8H7RfyjyL/IOa/H3Xy8w86AOz/HYXvHfZFw/QQNWnSJFm1alWGD4lff/1VatWqJfXr1w/YsQH+xj+ICNb3h6/4YgUA8DcyeiEqLi5OSpYs6bWtQIEC0qNHDxkwYICEh4cH7NgAAABCKcOfk8w9GXmECqpuhqiRI0cG+hAAAADyXYY/VDLyVge5BLihh2IsAAAAgM1YHeSGSoCL/2HqZoj/Ac+bN0+2bdsmZ86c8bpu8ODBUrt27YAdGwAAAICQK8bisPxAkDNHjx6VFi1amPPHjx83LRX0///8849ppN67d2+eUgAAACCfIqMXol544QVp3LixzJ07V66++moZNWqUdO7cWV5++WV55ZVXpFmzZoE+RNi8LD1z+gEgdLBeC8h/crlGL8zyA0HOaEP0m266ScLCwiQiIsJ8OXc4HKZIy2uvvSZr166VJk2a8LQiR/LzonUAsDPWawH5T+6qboZRuj/QTp8+LYULFzbny5QpIzt27PCqinTy5MkAHh0yQ4lnAAAA5BWmbmZj+/btJqDSNW9RUVHZPpEbN27MEFxpn7uaNWuKv+mUzTFjxpjeen///bds2rSJhulBiGwZAAAA8grtFbJYe9SzZ08zPbJYsWKmouWsWbOkS5cuWT6Rt99+u+zcuVMqVqzo3tapUyd5/PHH/fLCjR07VurVq2fOa+GVdevWyeTJkyU2NlZmz54tJUqUkPyCTBmAYMHnEQAgWJDRyyJoS0lJkf3795vAaeLEiXLjjTeabFmpUqWyfDKHDh0qEyZMkLym6/Q0oHQFlYsXL5a9e/eaSpz5AZky5Cc5KZiT2wI6WaFZ7vnxeQQACPH2CvYtxqJfhBYsWCBz5swxQZ6699575emnn5aPPvpIhg8fnuVtdZrnqlWrpFy5clK2bFm/Hudzzz1nKm1mFszpdfplLr8EekB+4o9AwlcU1gEAwO7FWMLtW4xl9erVkpaW5u5RpzTga9CggQnisqOtDRYuXCi7du0yzcpnzpwpjRo1kry2b98+M+UUAIBAZZDzKtN8PmSiAeRXuay6GXoN0xMTE83JU3R0tDmlb0Suihcv7rVd17wdOXIky/Fvu+02E+QVKlRITp06JQMGDJAePXrI+vXrpWDBgpY9Dv3H8Oeff5bNmzeb8+mniv7777/mH2UNTAH4jrVVQHBlkK0KMnfv3m1ZIEnQCCCU5JuMXnx8vFlr52n8+PEZAqXIyEjz//RB4blz50wQl5VbbrnFfV7bHkydOlWqVasmK1asMEVZrNKyZUsTdGpAqudr1arltVZP1xB2797dPe0UgG9YWxXccvKl39cMEV/ag1sgg8ysMH0ZQCjJN4GefknQFgSe0mfzVJUqVdzTHz0Lr+jl5s2b+3x/2ttOaVEUK2mTdNWsWTNp2LChVKpUydLxASAYEYgDAJAnUzdDrxhLZtM0M9OkSROTMdOCLI0bNzbbtmzZIlu3bpWOHTu699PWCzqe9snTLyDaZ08zai5Lliwx/69fv75fHs/VV1/tl3EBAAAAhL58k9HzVUREhGlToNk/LWiilSt1imf79u1NY3KXIUOGSNWqVU11Tm27oJd1nZ5O19Sedo899phpyeBZ1AUAAAAAgjfQc4ReMZacuPPOO01WT5uka8sELapy3333eT1uLXaibRRcWbs33nhDXnnlFfnggw/M9mnTpkmfPn0C+CgAAAAA5Fe5zOjlrv1eKNFsnJ6yMmPGDK/Lul5u+vTpfj0m13TSypUry8mTJ6VAgQLu4jEAAPiCwjYAkD8wdTOEaMbw7rvvNoGeBqHaML1Lly6BPiwAQAihsA0A5A/5po+eHZQtW1Z+/PFHs17Q6XSaxu4pKSmZ7hseHm77KbYAAAAAMkdGL4TccccdJoM3adIkc3nRokVZ7vv111+T7QMAAADyqVwWYwm99gp20KpVK9m9e7ds3rxZRowYIb179zZN0zPTqFGjPD8+AAAAAMGBjF6IiYuLM8Hd/fffLxdffLFp8QAAAAAA+bJhut306tUr0IcAAAAAIEiF5bZheqid7Gj9+vXSs2dP07cvJiZG6tatK5MnTzYV1QAAAADkX0zdDFEbN26USy65RNq1ayfjx4+XIkWKyNatW+Xll1+W3377TebNmxfoQwQAAAAQILRXCOGeetpLb+bMmV7bBw8eLPXr15dt27ZJzZo1A3Z8AAAAAEIuo5erm8FCu3btkgEDBmTYXqlSJWnQoIGpzkmgBwAAAORPuWyvQCPuQKtSpYrpo6dZPU979uwxa/cqV64csGMDEDrGjRvn87regwcPyujRo8+7n64Zjo+Pt+DoAABAbrFGL0Tddddd0qJFC/n333+lW7duZo3eX3/9Ja+99pp07NiRbB4An2iQN2XKFEufLV+CQQAAEISBXlgEUzcDrV69evLrr7/Kww8/bIqxHDt2TKpVqyZ33nmnjB07NtCHBwAAACCAchexMXUzKOhavPnz5wf6MAAAAADYIqNn0750AAAAAJCP2yvkqs86AAAAACAPUIwFAAAAAGyGjB4AAAAA2AwZPQAAkC/kpG/khfSSzAz9JQHkNYqxhKi0tDR56qmnZO7cubJ//35z2dPs2bOlQ4cOATs+AADyQ99IX9FfEkBotFegGEvAvfrqq/Lcc8/J3XffLVWrVhVHupYXdevWDdixAQAAAAgsMnoh6rvvvpPJkyfL7bffHuhDAQAAABBkKMYSokqUKCGFChUK9GEAAAAACEIUYwlRffv2lYceekiuueYaKVKkSKAPBwAAAEDIB3oOGqYH2pdffilr1qyRKlWqSL169SQqKsrr+qefflpatmwZsOMDAAAAEGqBXkS49UeCHGnQoIEpxJKVYsWK8YwCAAAA+RTFWELUwIEDA30IAAAAAGzVXoGpmwAAAAAQtHK12C4sPDzkTna0fv166dmzp5QrV05iYmJM7zxtuaANYQEAAADkX7nM6Hk357ajRYsWyXvvvSenT5+Wdu3ayfDhwzMUPLHiNrm1ceNGueSSS8z9jB8/3lTe3Lp1q7z88svy22+/ybx58/xyvwAAAACCH+0VMjFr1iwZMmSIPProo1KhQgWJj4+XJUuWyBdffGHpbS7EK6+8IjfeeKPMnDnTa/vgwYOlfv36sm3bNqlZs6Zf7hsAAABAcGONXjqpqaly3333yf333y8PP/yw2dasWTNp0qSJLFu2TDp06GDJbS7Url27ZMCAARm2V6pUyVTk3L17N4EeAAAAkE/lriGeIwRPPtLedAcOHDBr31waN24sNWrUkIULF1p2mwul/fN0qmh6e/bsMWv3Kleu7Jf7BQAAABD88s0avcTERHPyFB0dbU6edu7c6c6MeapYsaL7uvRyc5sLddddd0mLFi3k33//lW7dupk1en/99Ze89tpr0rFjR7J5AADkgXHjxvlUBO3gwYMyevRon8bUAmu6BAQA8j7Qy0mKLEjoB+bEiRO9tmkRkwkTJnhtS0pKMv8vUKCA1/bY2Fj3denl5jYXql69evLrr7+aqaL6OI4dOybVqlWTO++8U8aOHeuX+wQAAN40yJsyZYqlT4uvASEAWB7oOSU0f3EbM2aM17b02TxVtGhR8/+jR49K4cKF3duPHDliipxkJje3sYKuxZs/f77fxgcAAACQn9bohSAN6uLi4rxOmQV6urbO4XDIqlWr3Nt0yuemTZvMdZnJzW0AAAAAIKgyemnOUMzp+Uabj3fq1MlMw7j66qtNHzztTZeSkiI33HCD17SKMmXKyAMPPODzbS7Uf/7zH1mxYoW89NJLMnv2bHM+K7pP69atLbtvAAAAAHYP9NLE1mbMmCFdu3Y1a95KlSol27dvl3feeUfKly/v3ufnn3+WqlWr5ug2F6pt27ZmvLJly7rPZ0X3AQAAAJA/kdHLhFbPXLt2rZmKefr0aWnatKmZ6ulp6tSpXsVXfLnNhdIG6S7aEF376Glz9vQWL15ssooAAAAA8qdcZvTsO3XTJSwsTC6++OIsr2/VqlWOb2Ol5557TkaNGpVpoKfX6dTSzK4DAAAAYH9k9Gxo3759UqxYsUAfBgAAAIBQCvRSU22+SC+IaaZO1wdu3rzZnE/fB1AbqGtPH229AAAAACB/yl2gZ/dqLEGsZcuWUqJECdOzT8/XqlXLa+qoFoLp3r27adYOAAAAIH9ijV6Iuemmm8z/mzVrJg0bNjRFYAAAAADAgoye/YuxBDvt1wcAAAAAmSGjF8J0Ld68efNMq4UzZ854XTd48GCpXbt2wI4NAAAAQOCwRi9E6Rq9Fi1amPPHjx83rRT0///884/UrVtXevfuHehDBAAAABAgYbm5UZrTGXInu3nhhRekcePG8tdff5mA7+mnn5Zdu3bJiy++6F7DBwAAACB/Cstte4VQO9nNhg0bTGEWrbQZERFhpnE6HA4ZOXKkhIeHy9q1awN9iAAAAABCq4+e/TJkoeb06dNSuHBhc75MmTKyY8cO93UxMTFy8uTJAB4dAAAAgNArxmLDqZChrHPnzjJmzBiJi4uTv//+WzZt2iT169cP9GEBAAAACBCKsYSosWPHSr169cx5Lbyybt06mTx5smmUPnv2bNNUHQAAAED+RHuFENWpUyf3eV2n9/jjj5sTAAAAANAwHQAAAABshoxeCPnPf/4jK1as8Gnfl156SVq3bu33YwIAAAAQfMjohZC2bdtK+fLlfdq3bNmyfj8eAAAAAMGJYiwh5MYbbwz0IQAAAAAIAUzdtIETJ05IgQIFJCoqKtCHAgAAACAIhOXmRqlpzpA72dE777wjlSpVkqJFi8rSpUvNttdff11mzJgR6EMDAAAAEGqBntPpDLmT3Sxbtsz00ps0aZJX0RXtqffYY49JYmJiQI8PAAAAQIgFeimpzpA72c2cOXPk4YcfloEDB0qRIkXc24sXLy6lSpWSVatWBfT4AAAAAAQOxVhC1IEDB+Sqq64y5x0Oh9d1qampkpSUFKAjAwAAABCiUzdD72Q39evXl++++y5DoLdy5UrZsmWLNGzYMIBHBwAAACDkMnopqWnWHwlyZPjw4dKsWTOzFm/Pnj2mGMvixYvl1VdflTvvvFOKFSvGMwoAAADkU2T0QlSFChXkxx9/lKNHj8quXbvk2WeflXnz5skjjzxizgMAAADIv1ijF6L++OMPqVKlisydOzfQhwIAAADADhk9bUsXaie7GT9+vPz222+BPgwAAAAAtsno2bBdQaipXr26bNq0Sbp27RroQwEAAAAQZJi6aaHt27fL6dOnpW7duhIVFZXtvhs3bpSTJ096bStZsqTUrFnTp/u66667pEuXLlKmTBm57LLLpHDhwl7XFypUSCIicvXyAgAAAAhxuYoE7DgV8kIcPHhQevbsKRs2bDDVLs+cOSOzZs0ygVhWbr/9dtm5c6dUrFjRva1Tp07y+OOP+3Sfo0aNMkVY+vfvn+n1X3/9dbb3DwAAAMC+mLppAQ3aUlJSZP/+/RIbGysTJ06UG2+8Uf7++28pVapUlrcbOnSoTJgwIVf3+dJLL8mJEyeyvL527dq5GhcAAABAvs3okdLzzOYtWLBA5syZY4I8de+998rTTz8tH330kel3lxWd5rlq1SopV66clC1bNkevgbZWuPTSSwnoAAAAAFi1Ro9Az2X16tWSlpYmLVq0cG/TgK9BgwYmiMvOyy+/LAsXLjRTMDUDN3PmTGnUqJFPr8Fnn31m1vSRuQMAAACQb9foJSYmmpOn6Ohoc0pv/fr1JtuWlbCwMGnZsqU5rw3LVfHixb32KVGihBw5ciTLMW677TYT5GnRlFOnTsmAAQOkR48e5r4LFix43sejgeX3338v3bt3P+++AAAAAPKXfFN1Mz4+3qydS9+LLrM1cs8995xpXZAVDQ41yFKRkZHm/+mDyHPnzpkgLiu33HKL+7xWzJw6dapUq1ZNVqxYYYqynE/r1q2lV69eZp1eZlU327RpI6VLlz7vOAAAAADsJ98UYxk3bpyMGTPGa1tm2TylUyh9VaVKFfP/ffv2eRVe0cvNmzf3eRxtk6D27t3rczGW5ORkmT17tjmlN3/+fLnyyit9vn8AAAAA9pFvpm5mNU3zQjVp0sRM09SCLI0bNzbbtmzZIlu3bpWOHTu699PWC3r/2icvISHB9NnTKaAuS5YsMf+vX7++T/c7b948yx8LAAAAAHugGMuFPoEREab3nWYLtYdehQoVzJTQ9u3bS+fOnd37DRkyRKpWrWqqc2rbBb2s6/R0uua6devkscceMy0ZPIu6AAAAAECeBXp0V/B25513mqyeNknXIi5aVOW+++4Th8Ph3kercGobBVfW7o033pBXXnlFPvjgA7N92rRp0qdPnxy/Ft999538+uuvpiiMBo3XXXedexooAAAAgPwpV4FeSijO3fQzzcbpKSszZszwutywYUOZPn16ru9Pi7/o/emUUW2xUKRIEXnrrbdMgPnmm2/KDTfckOuxAQAAAIQ2pm6GqNdee820YtC1fxdddJHZlpKSIi+88IIMHTpUunbtmm3VTwAAAAD29b9qIDngdDpD7mQ32t7h4Ycfdgd5rvWCY8eONWsBz9esHQAAAIB95W7qZqr1B4Kc0aBOq3dmRrfr9QAAAADyJzJ6IUqnZmqzd1dbBqXN00ePHi3Hjx+XZs2aBfT4AAAAAAQOxVhC1MCBA+XPP/80LRy0J19cXJwcPHhQKlWqJB999JFfegYCAAAACA20VwhR2mz9xRdflLvvvlt+++03OXbsmGmvoP37YmJiAn14AAAAAAKIjF6Iq1GjhjkBAAAAwAWt0UtNC72T3aSmpsr9998vJ0+e9Nr+2WefyRdffBGw4wIAAAAQslM37deuINRos/Vz586ZtXmeLr/8cmnUqJF06NBBChYsGLDjAwAAABByUzetPxDkzHfffSd9+vTJsL1o0aJSsWJFWbNmjbRp04anFQAAAMiHchXopZHRCzjN5G3ZsiXD9qSkJNm+fbvExsYG5LgAAAAAhGigl0rD9IDr1auX9O3bV+rXry89e/YUh8Mhhw8fllGjRpkgUKdvAgAAAMifchfopbFGL9CuueYa01qhd+/eEhkZKUWKFHH30VuwYIFpvwAAAAAgf8rl1E3rDwQ598QTT8jgwYPNer3Tp0+bPnpXXXUVzdIBAACAfC6XGT3rDwS5U716dXMCAAAAABeKsQAAAACAzdBeAQAAAABshjV6AAAAAGAzrNEDAAAAAJvJ5dRNym4CAAAAQLBi6iYAAAAA2AxTNwEAAADAZsjoAQAAAIDN0F4BAAAAAGwmdxm9NOsPBAAAAABgDYfT6cxxCc1ODyyRULP4yY6BPgQAAAAACN6MXjIZPeSBj1q1tXS8zu/NFKtFFS5s+Zhhkbn6s8xedKzlQ4alpVg+pjM11dLxHFFRYrmc/zZ2XsknTlg+ZmTBgpaPKZH+eD798A+KxS9RwuGD1v9JFi1m+ZhJYZGWjxkdbvmQIg6H9WMmJ1s7Xrg/HrgfhPnjOK3/jEtzWv+ahzmsPU5nYqJYzeGPz8ywMMuH9EfTtKhY6793IOcoxgIAAAAANkMxFgAAAACwmdz10fNHjjfErVixQmbMmCH79u2Tjz76SOLi4s57m0WLFsl7770np0+flnbt2snw4cMlyh9TzQAAAADkK2G5XaMXaid/uummm2Ts2LESExMj33zzjSQlJZ33NrNmzZJu3bpJ7dq1pXv37jJ9+nS5/vrr/XugAAAAgA2dOHFCnnjiCTl27Jh7m9PplC+//FKefvppeffddyW/oRiLBaZMmSJly5aVhQsXmoDtfFJTU+W+++6T+++/Xx5++GGzrVmzZtKkSRNZtmyZdOjQwYrDAgAAAELCH3/8IStXrpRz585JjRo15LLLLpNixXwvXKUB3iOPPCJ9+/Z1327cuHHy4Ycfyo033ujelpKSIt9++61s3rxZSpcuLV27ds3R/dg+o6dTN0Pt5E8a5OXEmjVr5MCBA9KzZ0/3tsaNG5s3tQaLAAAAQH6g34k1ydGlSxcT6OkyqHfeeUcuvvhis8TpQsyfP98Ee0899ZTcdtttsmrVKqlXr5688MILsmvXLnnzzTelevXqsnz5crGjXGX0klL9UBrZzxITE83JU3R0tDnltZ07d5r/V6pUyWt7xYoV3dcBAAAAdqaz3K655hoJDw+Xbdu2SZEiRdzXaQ2LjRs3eu2vl5cuXSphYWHSunVradq0aabjnjp1ygRze/fulcWLF8vBgwdNIKnj6+UqVaq497355pvlrrvuknXr1ond5CrQ2/bKFRJqJkyYIBMnTvTaNn78eLM9vf/85z+ydevWLMfSgimff/55ro/FtYavQIECXttjY2N9Wt8HAAAAhDr9Pv3nn3/KL7/84hXkqUKFCknLli3dl3X93dSpU01Ni8jISPM9XgsZZvZdXtfmJSQkmP8nJyeb8xpU1qpVK8O+bdq0MZk/O/JDZ+bgpGnbMWPGeG3LKpunxVV0QWdW9FeHC1G0aFHz/6NHj0phj4bbR44ckfr161/Q2AAAAEAozKT7/vvvzXfhSy65JNvxfv75ZzP9UrNuVatWdSdmGjZsKH369JGLLrrIa3+tfv/EE0+Yivi9e/eW/v37Zzn2xx9/LK1atRI7irBLEKeRel766quvMmxzTbvUAivps3Wezpw5Y/4/YsQIqVmzpnth6OrVq83tRo8eLfmdFrgBAABA6ImPj/dpJp0mOcqXL3/e8ebOnSslS5aUOXPmmCydnlTBggXNur70gZ6vHnvsMXN7bZNmR7YI9DTIC4bAQAupaOpXf0HQN6MnDd7KlCkjDzzwgLmsC0BPnjxpfp3QqaDPPfecyRR+8MEHPr3hAQAAgFCeSafVLvfv33/e8bRAi95e1+150qmb2qosN1588UWZPHmyzJs3z1S+tyNbBHqBpmlh/aXh8OHD5rKmkHXusGb22rZt6045u1LNrttoOddq1apJqVKlZPv27abCEEEeAAAAQpmvBQ/btWsnL730kmmtoFU2s6LflfW7tSZTrPDyyy+bVmeffPKJ+T5uVwR6FtA3qVbMTM9zwacuHvWczqkVN9euXWvKvOqvE1o1SOcTAwAAAPnBddddJw0aNJCRI0fKokWLTAEWF+2nt2nTJtNrWguwaEC4YMEC6d69u3sfLZ6ovfBc9S988corr8g999xjkjRa8dPOCPQsUKdOHXPKTmaLPLU0bHa/XgAAAAB2FRERIV9++aXccMMNZgqm9pguXry47Nixw1Ti1NlxGui1b99eHn30UenVq5cprqLtEbTVgs6I++6773y+v2+++cbUyLj88stNbQw9udx7770BabvmTwR6AAAAAAKicuXKJqj78ccfTWEUrdapWTudDadTNl20uIsuj9I+eJrt0wbo2htPg0WlWb2HHnrIBIqeNTIaNmzovqzXPfjgg+Z8+kKOrgIvduJw2uBR6YsYDMVYYK2PWv13faNVOr83U6wW5dEewyphkX74/SU61vIhw9JSLB/TmZpq6XiOqCixnB8+MpOzaeeSW5EFC1o+pkT64/lM88OY1g6XcPig9X+SRYtZPmZSWKTlY0ZfWDehzDkc1o+ZnGzteBfYRinPhPnjOK3/jEtzWv+ahzmsPU5nulYAVnD44zMzLMzyIf0RCETFWv+9Azln/bsFAAAAABBQBHoAAAAAYDMEegAAAABgMxHB2mQx/QLJ7Bw8aP36CQSe1Wvqvuk/SKzW46v5lo/pcISFxJIYfwzqsHpdjNXrdlS49R+bkX5Yr+UP/ljH4Ujzwxo9i99H0UV8L9sdSJHh/vjt1vrXxymOoF8LlZzqDInPYUeIlFkI98e/Qfl0PV1KmvWveYTF6x0RPIIy0NMgLyfFVbQYCwAAAADgv5i6CQAAAAA2Q6AHAAAAADZDoAcAAAAANhOUa/QAAAAAIDdOnz4t06ZNkw0bNkj16tVlwoQJ+fKJJNADAAAAkOfOnj0rgwcPNudfeuklKVWqlNf1w4YNk+PHj5uK/I0bN/Z53JEjR8qmTZtk+PDhUr58ebPtwIED8t5778nmzZuldOnS0rdvX2nUqJHYGVM3AQAAAOS5pKQk+fDDD+Xzzz83QZinH3/8UWbNmmWu379/f47G/eGHH+SOO+6QAQMGSMeOHeW7776TSy+9VP79919p3bq1nDlzRpo3b57hPu2GjB4AAACAgOndu7e8+eabXi3TZsyYYba/++67Xvs6nU755JNP5Ntvv5WwsDATwPXr108cDoccPXpU7rrrLtm3b5+8/vrr8s0335iMYZMmTWTjxo0SHR3tHkdv+8gjj0j//v29xp43b5577F69ekmHDh0kVJHRAwAAABAw119/vQnOVq5caS6fOnVK5s6d657W6UkDs4kTJ0q9evWkYcOG8tRTT5lpmKpAgQLSs2dPE9BdfPHF5ny1atXMVE3PIE9VqVLFBIaeQd5NN91kpovqdM+LLrpInnjiCfnss8/8/vj9hYweAAAAAMskJiaakycNtNIHWy4xMTEmK6dZvZYtW8oHH3xggrj69et77bdgwQJZtGiRbNu2TYoUKWK2adavUqVK8uuvv8oll1xigr5Ro0ZJq1at3AFgZsenGcNOnTq5t82fP99kCteuXWuCPKXZwSNHjkioskWgp28Oz1QvQt+UKVMCfQgAAADIhfj4eJN18zR+/Phsq18OGTJE2rdvb74DahCma+zSW7hwoURERJgiK5qB05OKjIw0AZoGeufjdDpl6NChcvjwYXnhhRfc2zWA1Nu7gjyl00FLliwpoSrCLm8mAAAAAIGnVTLHjBnjtS2rbJ5L06ZNpUaNGmbdnFbM7NOnT4asoE611GmV3bp189p+7bXXSrNmzXwK8u68806zdm/p0qVSoUIF93Va3bNEiRJiJ7YI9AAAAAAEh+ymaWZHs3ojRoyQQYMGSeHChTMEeq4pmhoEarYtp4YPH26mZ2qQ16BBA6/rKleubKaG2gnFWAAAAAAEnLZDmD17tpnmmdX1//zzjzz33HNe2zVAO3jwYLZjjxgxQj7++GNZsmSJWf+X3s033yxbt26Vt99+271t79698scff0ioIqMHAAAAIODi4uKyLKCiNEDTdgs6/VL/r5UzdZqnFm3R9X1ZmT17tkybNs1M75w8ebLXdW+99ZbExsaa5ulvvPGGCQhfeeUVKVq0qAkq58yZI6GKQA8AAABAnitYsKAJwrTPXVaBX/rrtQVC9+7dzRTOc+fOmSmYVatW9brda6+95nUbreSp42RGC7m46JRRbcnw888/m2KPWpxFjzFUOZyucjVBRCtoUnURJ7b9ZemT8E3/QZY/qT2+mm/5mA6H9TOqHdExlo8paanWj2n1x1Famlgu3A+/j4WFxix6f/xj4UhNsX7Q8HBLh3OmWyNiiVysLTkfZ2TO18OcT5hY/zfk9MdnnMXjJac6Q+El98uY/hDulwO1+t8LZ0h8tqf44TgjHNaPGVmwkOVjIudC49sFAAAAAMBnBHoAAAAAYDMEegAAAABgMxF53TwxISHhvPudrzwq8oeowoWDfj3dZ1f3tHzMrnPetXzMmJKlLR/TGWbtOigjydq1UGnJSWK18Bh//D7mh/UrIbLexC9jJidbO54f1hc5Iv63+N8qiSnWr5uNCLf+9QkPgXVlZxP88Nnhh+eyULj1f+fOyKiQWOHrtPhzM80P70s//Csp4WEh8AeE/BnoaZDnS5EVLcYCAAAAAMgdpm4CAAAAgM0Q6AEAAACAzRDoAQAAAIDNEOgBAAAACFr79u2TJk2ayD///BPoQwkpeVqMBQAAAAA87dq1S1599VVZuXKlnDt3TmrUqCHdu3eX3r17S1hYmCQlJcmaNWskMdHa6tx2R0YPAAAAQEB8/fXXUr9+fdm5c6eMGTNGnn/+eenatat89tln8uCDD5p9ypcvL6tWrZLKlSvzKuUAGT0AAAAAee7IkSPSt29f6d+/v8noubRq1Ur69etnsnvq8OHDcuutt8qCBQukUqVKZtvZs2dl6tSp8u2335qsn2b/7rjjDnH4ofdpqCLQAwAAAJDnZs+eLSdPnpTHHnss0+sLFChg/p9+6qZe7tixo5w+fVrGjx8vxYoVk7lz58r7779vgkb8F4EeAAAAAMtoQJZ+PV10dLQ5edLgrWzZslK6dOkcjf/BBx/I2rVr5e+//za3Vxr4aQAImwR648aNk4SEhEAfBvxgypQpPK8AAAAhKD4+XiZOnOi1TTNvEyZM8NqmgVmhQoVyPP7y5culZcuW7iDPJSoqKpdHbE8hHehpkEdAAAAAAARXMkYLq3hKn81TWlxFp2+mpqZKeHi4z+Pr2r3cBIj5DVU3AQAAAFhGg7q4uDivU2aBXo8ePSQ5OVnee++9HI1fp04dWbdunQkQkTUCPQAAAAB5rnnz5jJ06FC55557ZNGiRe7tTqfTVNN86623Mr2dVuDUSpyPPvqopKWlmW2//vqrLFmyJM+OPRSE9NRNAAAAAKHrtddek1q1asnAgQNN0Fa8eHHZs2ePtGvXzqz1y0yVKlVMqwUNEqdNm2YyhhUqVDDTQPE/BHoAAAAAAkJ74N13331y7733yu7du021Tg3kPKd6ZtYwvUOHDqbq5vbt2yUmJsbsA28EegAAAAACShuda4CXGa2m2aRJk0yvq169up+PLHSxRg8AAAAAbIZADwAAAABshkAPAAAAAGwmKNfo6YLK0aNHn3e/gwcP5snxIDDCIq19ezoc1v+u0XXOu5aP+XXfgZaPed3irywf0/HfasaWSklMtHS8iNhYsVxYaPw+5kxOsnxMRyY9kC58UIf1Y0ZY+9nhTEywdDwzZpL1vZ8iogpYPqbTafmQ4khNsX7QcGtf84IFosRqEWF+eK/74bn0x+uTGuZ7I2xfpaRa+49QZLj1n+3n/PB3HuaH91FEuPVjRlo+ImwT6GVVSjU9X4JBAAAAAMhvQuOnaQAAAACAzwj0AAAAAMBmCPQAAAAAwGYI9AAAAAAErY0bN5pijf/++2+e3M4uCPQAAAAA5LmBAweaQCyr06xZs8x+aWlpkpiYKM5sSgFv377d3GbXrl3ubWk+3G7x4sVZ3v/y5csllBHoAQAAAMhzb775phw/ftzrdOTIEWnTpo3ExcVJ+/btfR7Ll6AuMx07dsxwDIMGDZKiRYtKq1atJJQR6AEAAADIc5GRkRmyaI8++qj8+OOP8sknn0ilSpW89v/mm29M8FW8eHG55JJLZOXKlWb7iRMnpH79+uZ8nTp1zDjXXnvteW+nwsLCvO7f4XDIhx9+KLfeeqs5PqUBpOv6QoUKSYMGDeS5554zwWUwI9ADAAAAEHA6VfP555+XF198Udq1a5fheg0CNcBav369tGjRQrp06WIycEWKFJFVq1aZfdauXWu2ffLJJ+e9XWY+/fRTc93QoUPd26Kjo93Zvv3798tLL71kjlMzksGMQA8AAACAZTQDdvLkSa+TbsvO77//Lrfffrs5DRs2LNN9nnjiCbn00kulfPnyMnXqVClYsKC89dZb5rqoqCh3UKaZt8j/z8ad73bpafCmU0Zr1qzptd2V0StcuLB06NBBRo0aZTJ/wSxCQpg+2aNHjw70YcAPpkyZwvMKAAAQguLj42XixIle28aPHy8TJkzIdP8DBw5Iz549pXnz5vLyyy9nOa5Ou3SJiIiQiy++WDZs2HDe47nEx9vt3LlTli5dKu+//36G62bMmCHTp083+5w5c0ZSU1OlatWqEswiQv1NBAAAACB4jBs3TsaMGeO1TTNtmUlKSpJevXqZtXI63dIzE5eerp/zpLfxZZ2cw8fbaZavWLFicv3113tt//zzz83jmTlzprRt29YUinn11VflhRdekGDG1E0AAAAAltGgToMhz1NWgd7w4cPN+rr58+dL6dKlsx33zz//dJ/XQE1vV7duXXemzrU9J7fz3P7222+blg/pj1XbLFx22WUmIC1TpowUKFBA1qxZI8GOQA8AAABAnps2bZqZEqmnZs2anXf/Rx55xEy51KmTDz/8sBw+fFgGDx5sritXrpzJBupav5zczmXRokXyzz//eBVhcdH1elqpUxuw61pDLRqT2fTOYEOgBwAAACDPvfHGG+b/GnRl1rBcAzRPI0aMMNMqtcfd3LlzTRawVKlS5jrNwj311FNy1113mfPXerRXyO52nkVYtH+fq02DpyFDhkj37t2lZcuWphiLrtXLqmBMMHE4c9pV8AJo4RSKbMBXiUcPW/pkORzW/66ReCLz0rwX4uu+Ay0f87rFX1k+pvjh+Uw5c9rS8SJiY8VyYX74fSzd2gErOM9T3Sw3HFlMuwk6Fv+zlpaYIKEgLaqA5WP64xtCpKRaP2i4tSUHUtKsf+ARYdb/nUtqioSC1LBwy8dMSbX2NYoM98N3hGTr3+thfngfRYRbP2ZMwYI+7afr87JbY6fTMfWk4Yqrl53Sy+nX3XlKTk52397X2+mx6No91xTQzLjCJh1Di7GkpKRkOSU1GIR0MRYAAAAAocnVEuF8NLByBWuuy9nxLOgS4+PtfDkWz9uHh4ebUzBj6iYAAAAA2AyBHgAAAADYDIEeAAAAANgMa/QQvKJjg73ehcSUzL7fS7AUTpnX6WrLx+zywduWjxkVV8TS8Zwp1hcqSEv1w+L6bBZ+51Zayn8XolvJ4Tx/U9qcCov639oJqzgtLpgTFm39Mfqjyok/6gSJ+KFQkB++elh9lOH+KJzih78ffxTF8sdxJiVbP2aBKGsfuzPJ+gJWBSKybvwdLJ9vyhEiRX2Qc2T0AAAAAMBmCPQAAAAAwGYI9AAAAADAZgj0AAAAAAQlbUq+c+dO83/kDMVYAAAAAOS5tLQ02b17d6bXlSxZUgoVKiR79uyRatWqyV9//SU1a9bM8X0cOXJEzp49K5UqVZL8hkAPAAAAQJ47efKkCeJKlSolsbHe1dafeuop6dOnT67HXrhwoUybNk0WL15sAsbDhw9LfkOgBwAAACBgXn/9denZs6dP+yYnJ8vevXtNhi48PFwOHjwoxYsXl4h0rYrmzJkjQ4cOlfbt20t8fLzkR6zRAwAAABAStmzZYrKAEyZMkBIlSkitWrWkbNmyMnfuXK/93n77benRo4cJBvMrMnoAAAAALJOYmGhOnqKjo80pM5qV04IrnipUqCCRkVk3nf/iiy9k06ZNZtrniy++KP3795dWrVpJxYoVLXoUoS8kA71x48ZJQkJCoA8DfjRlyhSeXwAAgBCkUyUnTpzotW38+PEmC5eZhx9+WCZPnuy1bdGiRVK7du0s70PHKl26tDl/9913y/Tp02XmzJnyyCOPWPIY7CAkAz0N8ggEAAAAgOBMyowZM8ZrW1bZvJyu0XNp0KCB1+WGDRuaypwI8UAPAAAAQHDKbpqmVZKSkjIkgooVK+bX+ww1FGMBAAAAEFKWL1/uPq/rAVeuXCmNGzcO6DEFGzJ6AAAAAAIms2IsRYsWNaesPProo6bqZvXq1U3PPa2ueeutt3qNefbsWTl69KhpzO4a/3xFXuyEQA8AAABAngsLC5MqVaqYQizpi7GMHDlSxo4da/rj6T7pg7M33nhDZsyYYdbl1axZU5YuXSoFCxb0Wie4ZMkScz4uLs700/OlyIudEOgBAAAAyHMagKXP5KWn7RIy26dFixbSvXv3LG/35ptvSn7HGj0AAAAAsBkCPQAAAAAhISoqykzl1DV5yB5TNwEAAACEBF1fd77pnvgvMnoAAAAAYDNBl9HTCjna8DA7Wi4V9heWlmLtgA6HteOJiDPM+mkDjjTLh5QuH7xt+ZgL+/2vhLFVrv1inqXjJZ0+JVaL9KjoZZW0FIvf69pT6MRJy8eMKe6HRrTW/1mKw2ntH1HyKeufy8gCsZaPmXLmjOVjhkdFWT5mSmL2/8YHg7TkZMvHdKamWj5mdFHr/yYdfvi3skCUH0rZp6UF/eewX/5+Up2WjxkZRt7HroIu0NMgb8qUKdnuM3r06Dw7HgAAAAAINYTwAAAAAGAzBHoAAAAAYDMEegAAAABgM0G3Rg8AAACA/WltjieffFL69u0rdevW9bruo48+kqSkJOnfv3+eH9eyZctk9erVEhkZKd27dzd9+0IRGT0AAAAAAQn0Jk6cKJs3b85wnQZ67733Xp4eT1pamlx77bXSr18/2b17t6xcuVIaNmwoX331lYQiMnoAAAAAgt7evXvlyy+/lNOnT0uLFi2kXbt27tZrr7zyitx9993y/fffy9atW6VWrVrSs2dPd8uQlJQUeeKJJ8z5iIgIqVq1qlx99dVSvHhx9/hz5syRhQsXmtvr9WratGkyZMgQ2bVrl0T5oWWGP5HRAwAAABDUfv/9d6ldu7YsXbpUDhw4IOPHj5eRI0e6Az3NDLZt21befPNN2bNnjwwbNkz69OmT6Vjnzp2TWbNmyUUXXSR//fWXe/vGjRulYsWK7iBPaTCp97d8+XIJNWT0AAAAAFgmMTHRnDxFR0ebU2Y0k6Zr4jxp0FW5cmX3ZZ3G2bVrV7Ovy5o1a7xuo0HZq6++as4PHz5c6tevL99++61ceeWVJos3YcIEr/0HDhxosnzvvPOOuayBpAaJOm3Tdd8//fST+f+mTZukY8eOEkpCMtCLiYmhabrNTZkyJdCHAAAAgFyIj483GTZPmoFLH2jlhGbaPvvsM/nxxx/l0ksvNVMyGzdu7LXPoEGD3Ofr1KljMnxff/21CfRcmbwvvvhCdu7cKWfOnJGjR4+a6aAuujZv5syZ0rp1a7nxxhvN9RpMxsbGmumioSYiVN88AAAAAILPuHHjZMyYMV7bssrmKa26qevpPK1fv94ruNJpmseOHTNVOE+cOGGCt4ceekiaNGni3qds2bJeY5QrV0727dtnzuv/L7nkEilfvry0adNG4uLiJCwszIzpolk/nRqqweGGDRukXr168swzz5jbeK7ly1eBnr6YWjXnfHT+LAAAAAD7ym6a5oWMOWnSJHPasWOHSfx06NDBKyOna+k8WyHs379fLr74YnNeM3VlypSRX375xV2g5bHHHpO1a9d63Y9ep0Va9KQ08EtNTTVBYr4M9DTI82Wq3ejRo624OwAAAAD5iLY6aNasmcm6VatWTe644w5544035Pjx4+59NJhzBWRbtmwx0zw166fOnj1rgkVXkKdTN7UgS3qaSWzQoIE5n5ycbKagakCZfppoKAjJqZsAAAAA8g9tm3DLLbfIZZddJoULF5ZPP/1UevfubaZV6lo6pZUxtcF59erV5cMPP5QePXpIp06dzHU333yzTJ061fTJ09YLCxYsEKfTmeF+HnjgAbMmTytvav88nd6pLRdCEYEeAAAAgIAUWNQiLXXr1s1wnRZDSUpKcl++9957pVevXrJ48WKTjZsxY4ZcccUVXrfRgEzbMGjLhFdeecVr3Z+ut9NKnp9//rkpyqK3L1SokPzwww9eY+j1GgRqE3edJqpTOCMjIyUUEegBAAAACEigl1UlTg300tNMnU7ZzIoGZNddd12W11epUsXde89Fp4N60gyeZgL1FOpomA4AAAAANkOgBwAAACBklS5d2kwB1amY+B+mbgIAAAAI6UDvQpqx2xUZPQAAAACwGTJ6CFrO1FRLx3OEh4vlkhItHzIl0foxo+KKWD7mtV/Ms3zMz7tlvYA6N65d8IlYLS05xfIx/fHeDIvww5jhfvgnIy3N8iFTE85ZOl5EdIxYLfncWcvHTIuNs3zMsHDrfw8Oj7L++fz/tlhBLQQO0UhMsf5vMtJp/aM/eup/1RitULJIQbHauSRrv8eoVD98ZoZFW//ZHpo1Ku2HjB4AAAAA2EzAM3rjxo2ThIQE9+WDBw8G9HgAAAAAINQFPNDTIG/KlCnuy6NHjw7o8QAAAABAqGPqJgAAAICQdPLkSZk6daqcOHEi0IcSdAKe0QMAAACQv23btk1+/fVXOXXqlFSqVEmaNGkiFSpUOO/tjh49amYEduvWTYoU8b34XFpamqxYsUI2b95s2jN07NhRCha0vihPIJHRAwAAABAQR44ckR49ekjTpk1l7ty5snbtWnn99delXbt20rdv3/PePi4uTu6++24pWrSoz/e5bt06adSokakVosHlpEmTpEaNGrJy5UqxEzJ6AAAAAPKcZtW6d+8uZ86ckb///ttk1jyv++STT7wCwlmzZsnQoUPlp59+MhnALl26SKlSpaRq1aoSGRnptd9tt90mf/75p2zZskWqVatmMnYu4eHhJqisW7eue9sNN9wgd9xxh6xatcpcTk1NlZdeesmcj4iIMPfRoUOHkMr6kdEDAAAAkOe++OIL+fnnn2XatGleQZ4KCwszwZfL/v37zRTNK6+8Up566ikTwJ09e9Y9dfPQoUNe+3Xq1Ekee+wxM36fPn1M4OdSr149ryBPaRCnY7o4nU7ZuXOnOW3atEkef/xxqV+/vuzatUtCBRk9AAAAAJZJTEw0J0/R0dHm5GnZsmUmQ9a2bVufx7700kvlueeec1/WQCwzV111lUyYMMGcv+WWW6R9+/Ym8CtXrlym+8+fP1+aN2/uvqxZPC3y4unGG280Ad+MGTMkFARtoJe+vx7yF8+WGwAAAAgd8fHxMnHiRK9t48ePdwdeLocPH5aKFSt6bdPiKAsXLnRf7tWrlynO4qJBmy/69evnPt+iRQuToduxY0emgd6zzz4r33//vSxfvtxru07fXLp0qQkmdXqpjuGa2hkKgjbQS99fDwAAAEDw04TNmDFjvLalz+YprZJ54MABr20aUGlgdfr0aXnzzTelQYMGXoFe+ime2RVpcXGt30tKSpL09D4efPBBmTNnjrRs2dIrCNVMo8PhkFatWpnxjh07ZqaKhoqgDfQAAAAAhJ7MpmlmNQ1T1+etX7/eBHTq4osvNqc9e/aYIMyf3nrrLbnrrrvk/fffl+uvv97rOp2eqY9BC7po8Ral1Tm1CEyooBgLAAAAgDyn0zJr1aolI0aMyPOG5++8847ceeedpkKnZ9EXFz2eYsWKuYM8zQZ+8MEHEkrI6AEAAADIc1FRUfLVV1+ZbJpWwtRWC9oKQZumL1682Jz3pWl6Ti1ZskQGDx5sKm3u27fPq+iKBn+aydPCK1r0ZeDAgSYY/fTTT+X48ePuaaChgEAPAAAAQEDUrFlTVq9ebYIvbV6ua+DKly9vqltqKwVts6BKlixpGqOn72OXvmF6ZvuFhYWZba7CLwUKFJCRI0dmWrVT+/cpbeCuhVc0wDt37pwpLqM9+zQADRUEegAAAAACRgMx7Xunp6yULVs2Q7sDVbx4ca/tme0XFhbmta1NmzbmdD7aN09Pnlq3bi2hgjV6AAAAAGAzBHoAAAAAYDMEegAAAABgM6zRQ9ByREVZO2BysrXj6YLd5IyNNy9URGys5WM6U1IsHzPp9CnLx7x2wSeWjvd5915itY4zpls+ZlzVqpaPGe5D/6KccoZb/09G2tkzlo+ZfPacpeNFWv8nKRHRMZaP6QhzWj6mOFMtHzL13FnLx3REWFsFz+GH10dSrf8clv8vUmGl6P9WkreWw/r3ZpFC1r5GaX7484mJsv7JdJCjQQ6Q0QMAAAAAmyHQAwAAAACbIdADAAAAAJsh0AMAAAAAmyHQAwAAAACbCbpALyYmRkaPHi0HDx4M9KEAAAAAyAMTJ06UBg0ayPfff5/huk8//VQ6d+7sl/vds2ePjB8/Xpo2bSpjxozJdJ9Dhw7JiBEjpEWLFtKpUyeZM2eOhIKgC/Ti4+NlypQpUrp06UAfCgAAAAA/S0xMlBdffFHOnDkj06dnbGN09OhR2bJli+X3u337dmnbtq05X7hwYdm9e3eGfZKSkuSKK66QDRs2yFNPPSXXX3+93HLLLTJz5kwJdvTRAwAAABAw8+bNk6ioKBM8denSxQR2xYsXN9ctXbrUZNw0q6YZP6Wz/37++WdJS0uTt956y2xzOp0yYMAAKVKkiEybNk1mz55tTsOGDZMZM2bI/v37pVWrVvLYY4+ZoE5VqlRJ/v77bwkPD5c1a9ZkemwffvihbN682dy+ZMmSJujbsWOHOaZbb71VHA6HBCsCPQAAAAABo4HYoEGDpH379lKjRg2ZNWuW3H333eY6nS55xx13yGuvveaeMlm2bFmzr0631ODt9ttvNxnBL7/80h2wHTlyRL7++ms5ceKEPPLII2bbyJEjzWVXcBgZGXneY1u2bJm0bNnSBHku11xzjTzzzDOybds2qVWrlgQrAj0AAAAAlk7F1JOn6Ohoc0pv586d8t1338nrr79uLmvQ9uabb7oDPc2+lS9f3gRlroye0sDrpZdekrvuukuKFSsm999/v7z99ttSuXJl9z4Oh8Os7ytRooS5PG7cOLn33ntz9Fj++ecfKVeunNc212W9jkAvj+mLmJCQkNd3CwvpOk0AAACEHq25ocVVPOlUxwkTJmTYV4O6Dh06SPXq1c3lgQMHygMPPCArV640mbTs6Fo5zdrdeOONZtpm3759va6vXLmyO8hTGjDmtOBjSkqKmVbqyRWwJicnSzCzZUZPgzwCBQAAACAwSZf0FSwzy+bpGjvNwmn2zzNb55rOeb5AzzPYKlOmTIbrIiIuPNTRQFGngXpyXfaczhmMbBnoAQAAAAiMrKZpprdw4UI5deqULF++3Kuoybp168y6PE3cFCxY0BRL0WIrmWUDFy1aJG+88YbceeedcvXVV5vsoJUuvvhiee655yQ1NdUch1qxYoUUKFBA6tWrJ8Es6NorAAAAALA/zdppYZOGDRuajJ7rpFMxtbf2Rx995C6+cvjwYdN+wUULoYwaNcoUYRk6dKjJIOq0z2PHjll6jAMGDJCzZ8/Ks88+ay7r1M+pU6ea7RrsBTMCPQAAAAB5SgOmL774Qnr27JnhOs2cdevWzWTslLY00GBQ19xpIKiFW26++Wa56qqrTLVO9cQTT5g+3NpOISdatmxpxtQ2DosXLzbn27Vr576+YsWK8vHHH8vzzz9vpodqS4b69eubLF+wY+omAAAAgDwVGxsrf/75p9SuXTvT67V9wYEDB8x5nQb6yy+/mF522mMvLi5O2rRpI9WqVXPvr1U5v/32W9m7d6+ZZtmvXz/p3Lmz15itW7c200I9vfPOO2b/7Nb2adZRx9UKodqnr1SpUhIKCPQAAAAA5KlChQplKMCSvgiKZ8VMV1sDV2sDzaylp20W9KSKFy/ubrruouv90t/nRRdd5NPxavBXs2ZNCSVM3QQAAAAAmyHQAwAAAACbIdADAAAAAJsJ2jV6WlJ19OjRubptTjveI0hl0i/lgoRb/3YPj/HDbyVh1o+Zlm6RsRUiCxa0fMy05BRLx+s4Y7pYbcnQOy0fs+fXn1s+ZkpiguVjRsRYX0Y63B9jRkZaOl5mvZsulMOCJr4ZB3WExJjhBQtZPqZTLD7OtNSQ+Gx3Oqwf0yHO4H99zFvT2uN0nvtf2X6rhMXGWj5mmtP65zLM4ucSwSNoA734+Phc3za3ASIAAAAA2AFTNwEAAADAZgj0AAAAAMBmCPQAAAAAwGYI9AAAAACEtH///VdGjhwpV1xxhdx1112BPpygELTFWAAAAADY1+nTp6VLly6ZXvfAAw9It27dfB5rxIgRcurUKXnkkUekZMmSZtu6devkzTfflM2bN0vp0qWlX79+Wd6fHRHoAQAAAMhzKSkp8tNPP8nkyZOlXbt2XtfVqlUrR2P99ttvMnHiROnQoYO5/NVXX8lDDz0kt956q3Tt2lXWrl0r119/vTzxxBMyZswYyQ8I9AAAAAAEzEUXXSRt27bN8vrExESZPn26fPvttxIWFiaXXnqpjBo1SqKjo+XQoUNy3XXXyb59+0zA+MYbb5hArlOnTvLnn3+K4//7gXbu3FlOnDghzz33HIEeAAAAAARSamqqXHPNNZKWlmamZ0ZGRsqUKVPkm2++kSVLlkiRIkXkySeflB49ekifPn3kqquukho1akjhwoUzjKX7JiQkSH5BRg8AAACAZTQDpydPmn3TU2YefPBBefbZZ722zZo1S6pVqyYff/yxWWO3bds2iYmJMde1b99eypYtK8uXL5fLLrvMZAM1AKxdu3aWmcGTJ0/KtGnTTECYX9gy0NM3wejRowN9GLgA+ksNAAAAQk98fLxZL+dp/PjxMmHChEz3HzBgQIY1emXKlDH/X7ZsmZw9e9YUUXE6neakNMO3ceNGE+idT3Jystx4440mGNSpm/lFhF3fXAAAAADy3rhx4zKsg8sqm3e+NXpaSbNu3bqmiEp61atX9znI++uvv+S7776TYsWKSX5hy0APAAAAQGBkN00zp3S93ffffy+tWrWSiIiIHFf17NOnj6xZs8YEeZUqVZL8hIbpAAAAAILSoEGD5NixY6avnhZmcQVwWoVTK21mJSUlRfr27SurV682QV7lypUlvyHQAwAAABAwWoxFp256nrRNgmt65oIFC+STTz4xBViaNm0qpUqVkvXr12c7DXPWrFnmNpoF1EbpnmNro/b8gKmbAAAAAPKctkDQypmZ8czAdezYUbZv3y5bt26Vc+fOSZ06daRAgQJe+3/++eemSqdL165dsxw7/W3tikAPAAAAQJ4LDw/PtlG6J218rgFeVlq2bOl1uWzZsuaUnzF1EwAAAABshkAPAAAAAGyGQA8AAAAAbIY1eghaySdOWDpeZFF/NMh0SCgIy2HfGV+kpaRYPqYjPNzS8eKqVhWr9fz6c8vHnN/1WsvHvOqd/1Yrs1Li8eOWj1moQkXLx3Q6nZaOl2jxZ5GKKVnS8jET/1t13FJREdZ/xqVZ/PqocKsP0w/HKP9fFt5SkVHWj5lm/WN3OKwfMyLM2lxFUkKCWC0stqDlYzrF+ufSGSLfZZBzZPQAAAAAwGYI9AAAAADAZvJ06mZMTIyMHj3aa9vBgwfz8hAAAAAAwPbyNNCLj4/PsC194AcAAAAAuDBM3QQAAAAQtDZv3iwlS5ZkJmAOEegBAAAACJg//vhDBgwYIHXq1JHKlStLhw4d5Pnnn5ezZ8+a61NSUuTIkSOSlpZ2wfe1b98+mThxotSoUcPcn50R6AEAAAAIiFmzZkmbNm2kbNmy8tFHH8mKFStMIPbvv//K448/bvn9XXvttSZwvP76603waGf00QMAAACQ5/bu3Su33Xab3HPPPTJp0iT39ooVK8pll12WoS/qL7/8ItOnT5ctW7ZItWrV5JlnnpHmzZu7p3e2bdtWXnvtNbPP1q1bpVatWjJlyhRp1KiRe4zffvtNHA6HTJ06VeyOjB4AAACAPDd79mxJSkqS++67L9PrNSDz9NBDD8mDDz4oy5Ytk+rVq8t1111nbu85vfM///mP3HvvvfLtt99KlSpVpFOnTnLq1Kksx7QzAj0AAAAAlklMTJSTJ096nXRbeps2bZIKFSpIkSJFfBr3hRdekMsvv9xk85544gnZs2ePydx50u2dO3eW2rVry6uvvirh4eEyc+ZMyY9sM3Vz3LhxkpCQEOjDgEU0zQ4AAIDQoy3VdJ2dp/Hjx8uECRO8tqWmpkpkZKTP49arV899vlSpUub/6dfZtW7d2n0+KipKWrRoIevWrZP8yDaBngZ5BAcAAABA4BMwY8aM8doWHR2dYT+tfPn++++bbF9m16cXFpZxMmL6dXwREREZLif9//TO/IapmwAAAAAso0FbXFyc1ymzQK5Xr14mq6cFVKyyZs0aryBw7dq1tm+jkBUCPQAAAAB5TqdiugqsvPXWW3Lu3Dmz/dChQ/L222+bXno59eijj8quXbtMcZbJkyebyp6DBg2S/Mg2UzcBAAAAhBbtlXfRRRfJc889J8OGDTOZv4IFC0r37t1NEJhT/fv3l0svvdQEiyVLlpQPP/xQypUr577+5ptvlm+++cYs+9KG7LqP+vHHH6Vu3bpiJwR6AAAAAAKmX79+5pScnGxOsbGxXtdrIKiBW4kSJbzW3um29BU7NXunawTPnDljxknfTkF77GW2Zq9YsWJiNwR6AAAAAAJOK3BmVoVTWyS4Mm+eMtvmUrBgwUy363rB/II1egAAAABgM2T0AAAAAIS0zKZ35ncEegAAAABCWlbTO/MzAj0Ercgs5lYHlTTvJp1WcCZb39QzLSXZ8jETT5y0fMywiHBLxwv3oflqTqUkJlg+5lXvvGH5mItuuc3yMVs9/ojlYxYs+79KaFZJOmXte/PswX/FatHpigdYIcH6P3OJirD+byhdb2NrOCwe1GH9yhZnWorlY3qXmLBIJg2pg/CfSkmz+O88PCZGrJaUkmb5mOFh1r/q6WqVwEZYowcAAAAANkOgBwAAAAA2Q6AHAAAAADZDoAcAAAAANhMUxVi0e31CwoUVODh48KBlxwMAAADAv1JSUmThwoXuy0WKFJE6depI6dKl/XJ/Bw4ckM2bN5vxtR2DIweVaJxOp/z4449y7tw5ueqqqyQUBEWgp0HelClTLmiM0aNHW3Y8AAAAAPzr9OnT0r17d2nevLmUKVNGjh49Kn/88YeMHDlSnn32WcvuZ8eOHWbMVatWSe3atWXLli1StmxZ+fjjj6VGjRrnvf0LL7wgL730kgnyEhMT5fDhwxIKmLoJAAAAIGAeeugh+eKLL2TFihXy4YcfynPPPSfffPONnDp1ymzX4GrPnj2ydOlS2bVrl9dtfdnn0KFDctddd8nevXtl2bJlJvArWrSoDB48ONNxtm3bZu7/33//215HAzzNPN57772ZHn9aWpq5rZ50P80aagYw0IIiowcAAAAAPXv2lAIFCsjvv/8uFSpUMBm/3r17y8qVK83lP//8Ux599FF58MEHzZOlQd359mnZsqXXExsdHS29evXyCtxc49x4443y22+/Sd26dc0Ymml84IEHsn1hUlNT5dVXXzXnk5OTZd26dVKtWjX5/PPPpUSJEgF7UQn0AAAAAFhGs2J6Sh9c6cmXdXSaQfNcp6fZNs2yRUZGytdffy3dunWTa6+9Vho0aJCjfTzpejsN5tILCwuTv/76S8LDw8VXep+azXPRx3711VfLpEmT5Pnnn5dAIdDzY4EY5N6FrtkEAABAYMTHx8vEiRO9to0fP14mTJiQ6f6avYuIiJBjx47Jiy++KBUrVjQZOp1qqTTzpsGU6tq1qzRt2lRmz55tAikXX/Zx+eijj8wU0QULFkh6Y8eOzVGQ50mnhO7cuVPOnDkjNWvWNFNRA4lAz48FYgAAAID8mDAZM2aM17bssnm6Hm716tWm6qZO3Rw2bJgUK1bMHeilL5iiQZQGVJ582UctWrRIBg4caNYBXnPNNZKeTv3MKc1AXnfddSawa9y4scTFxZn1gidPnpRAItADAAAAYBlfp2l6FmPRAC8r6QOmEydOZAjsfNln8eLF5n4ef/zxLCv256Tlgsvrr79upo3u27dPChUqZLY9/fTT8sorr0ggUXUTAAAAQNDyXP+m0zt1fV3r1q1ztM+SJUukR48eZvpoVtUzc0uzd5pBdAV5SguxBBoZPQAAAABBa+rUqaaaZfXq1U0/O/1/3759fd7n999/N4VZLrvsMqlXr55XUNilSxezPjA7WoVTWy1s3LjR3Ifr9pdffrkULlzYrAnUJV+PPfaY1KpVS+bMmSNr1qwJaMVNRaAHAAAAIM9p8RRdJ6fNy7Oja/jmzp1riqdceeWVcs8992QomJLdPocOHZIOHTqY8642CC66XQM9XVenxxITE5Ph/rWKp7ZuUO3atXOP0ahRIxPoXXHFFeZ+NcBbv369CQC1R1+gs3oEegAAAADyXMGCBb2ya1kpV65cptUzfd2na9eu5pSdypUrZ3ks2k/vfDK7D50qGkis0QMAAAAAmyHQAwAAABB0sptOmZN98iumbgIAAAAIOtlNp8zJPvkVGT0AAAAAsBnbZPQ0XZtV48PcOHjwoGVjIZcioyx96pz+eCHCrP+txJGDBqM+j+lMs3zMmOLFLB8zLNzajySnxeOpiJgClo+ZePy45WO2evwRy8f85ZHHLR/zum8XWj5mZMGClo4XW6qUWM0RZe3nmyoS6ZdPOctFhOW8GfF5paZYOlxacpJYLTUh0fIxI6P9MFXND/9ehOWiAfX5JCUkWDpeeLT1f5PRsbGWjynih78f/3xDQhCwTaAXHx9v6XhWBo0AAAAAkJeYugkAAAAANkOgBwAAAAA2Q6AHAAAAADZjmzV6AAAAAELDL7/8Ij/++GO2+wwePFiKFy+e6/vYsmWLfPXVV3LRRRdJly5dJL8h0AMAAACQp86cOSMHDhxwX37zzTeldu3a0q5dO/e21NTUXI29f/9+6d+/v+zZs8fcz5VXXkmgBwAAAAD+1rFjR3NymT9/vnTo0EGefPJJ97YNGzbIZ599JgUKFJD27dtLhQoVvMZYsWKFrFq1ymzXjJ22W1NOp1MefPBBueKKK6RTp0759sVkjR4AAACAoPLCCy9ImzZtZPny5fLtt9+agG3hwv/2Xk1ISJCuXbtKz549ZeXKlfLuu+9K27ZtTfZOlS9f3gSRDj/0cAwlTN0EAAAAYJnExERz8hQdHW1Ovnr55Zfl+eeflyFDhpjLZ8+elW3btpnzjz/+uKxevVrWrVsnZcqUcWf/NJOH/yHQy4KmfmmaHjhTpkwJ4L0DAAAgt+Lj42XixIle28aPHy8TJkzweYzSpUvL4sWLpVu3biaYi42NlUaNGpnrPvroIxk6dKg7yFP169fnBUuHQC+bNygAAACAnBk3bpyMGTPGa1tOsnlq5syZcs8990i1atWkZs2aZpqmXo6LizNFXKpUqcLLch4EegAAAAAsk9NpmpnRCpyff/65mQKq6/Q0cNy6davMmTNHSpYsaSprInsUYwEAAAAQVHQNntKAUdsj3HDDDbJ582az7dprrzUZP1fxFbV3716zjg//Q0YPAAAAQFAZPny4FClSRFq0aCGnTp0yffZcS6t0/Z9m+Zo0aSK9evUy1+vln376yX37Z5991vx/9+7dcuLECXNZx7vtttskvyDQAwAAABBQWlzFVWxF/fjjj/LNN9+Y9gnlypWT77//3gR2qmjRovLrr7/KvHnzZO3ateZ2kyZNksKFC7tvf+D/m7Fr9s91OSkpSfITAj0AAAAAAfXAAw94XdYeeNoEXU+ZiYyMlBtvvNGcMvPs/2f08jPW6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzVCMBcHLmWbpcI40a8czwvzwW4nDYfmQYVExlo8p1h+miMWvUdrZ//XXsUp4TAHLxyxUoaLlYxYsW87yMa/7dqHlY867MvNF9hei44zplo4XW6qUWC452foxIyOtH1OcEgofHs6UFEvHC4v2w2emH6Q6rX99wv3yLrL+NY8uVdrS8VLT/PBe98PrY/V3IyPMH686ggEZPQAAAACwmYAHejExMXLw4MFAHwYAAAAA2EbAAz3tcF+6tLXpdwAAAADIzwIe6AEAAABAoJw+fVpmzJghJ0+etNWLQKAHAAAAIM8lJSWZAEtPiYmJGa7/+OOPzXV79uzxaTwN1HR/DdzSO3funBnvvffey3Dd4cOH5bbbbrPdcjKqbgIAAADIc2fPnjUBlsPhkMKFC0ufPn3c123fvl1uuukmSU1Nla+//loqVjx/hWoN1HS8K6+8UgoVKuTe/tBDD8nMmTMlLi7OBHX9+/eX/ICMHgAAAICAueKKK+TNN9/02qaXO3To4LVt1qxZsnXrVq9tTqfTBHEbN240GTv14YcfmszeN998Yy5Xr15d1q9fL8OGDZP8hEAPAAAAQMAMHDhQfvjhB9m9e7e5rFm8t99+W4YMGeK13+effy7jx4/32vb999+bLF5UVJSsWrXKbNP///LLL7Jp0yZzWccpXry45DcEegAAAAAso+vtdL2c5ymzNXguWoH/6quvNpk59dVXX0lkZKR07NjRa7+hQ4fK/Pnz5dixY+5tb731llx11VVSs2ZNmTx5stn25JNPmozeqFGj8vWrats1euPGjZOEhIRAHwZyacqUKTx3AAAAIUjbp02cONFrm2biJkyYkOVtNOs2YsQIefTRR820zcGDB5u1e546deokZcqUkQ8++ECGDx8up06dkk8++UTeffddvz2WUGbbQE+DPIIFAAAAIO8TLmPGjPHaFh0dne1tunTpYqpwvv/++6b4yosvvphhn7CwMBMAauZPA705c+ZIbGysdO/e3fLHYAdM3QQAAABgGQ3qtMKl5+l8gV54eLjccsstpmBK+/btpXLlypnup4He6tWrZe3atWbaplbQ1PV5yEcZPQAAAAChw9XLLrv2B9pmoXPnznL//febgiuvv/66+zpt0eDqmQcCPQAAAABBoFq1aqaIyvloUZbrr79emjdvLg0bNvQq6qJjPPzwwyYYrFKlivn/okWLTEVPDQy1KIzrPnr16iXFihUTu2LqJgAAAIA8p9M5tQhLpUqVMr0+JiYm0+t1PZ9O9UzffkGLtyxZssQEf3/88Ye7vcKWLVtMkKdN1LUpu57Xkyvzp5lAHatIkSJiJ0zdBAAAAJDnChQokG0GTwOzzK7X9gt625tuuinDdZrRS1/dc+TIkdkeR4kSJXzKJIYaAj0AAAAAQW/nzp3y5ZdfyjPPPCP/+c9/bJeBsxqBHgAAAICgd+TIEVmzZo2MHj3atFdA9gj0AAAAAAS9iy++2KvKJrJHMRYAAAAAsJmgzeiNGzdOEhIScn177cGBEOe0eLzwcIsHFJHkZOvHjLD+z9IZZv1vOg5nmuVjpiZY2/cm+az1fXTCIyMtH9PptPrNLpJ06qTlY0YWLGj5mB1nTLd8zCVD7wz6YyxUvpzlY4b54TPOmZJi+ZgOPzQ2dlj9uemw/jMzLNwPn+0Oh4QCR6r176O0MGufz/AwfzyX1o/pDI2XHEEiaAM9DfKmTJmS69vr3F0AAAAAyI+YugkAAAAANkOgBwAAAAA2Q6AHAAAAADZDoAcAAAAgz50+fVpatWolV1xxhZw5c8brugcffFAeeeQRy+9z1apVMmLECLnyyiulX79+8sUXX2TYJy0tTaZNmyadO3eWq666Sl544QVJ8UNxKn8j0AMAAACQ5zR4+vXXX+W7776T559/3uu6rVu3yl9//WXp/X355Zdy2223yUUXXWQq/Ldo0UJuvPFGefbZZzMUdXzsscdkwIABMmjQIHnyySdNcBhqgrbqJgAAAAD702DrmWeekWHDhkmpUqUyXJ+UlCSXXXaZOR8RESFVq1aVW2+91WTlcrJP+/bt5ffff3df7tixoxw7dswEmffcc4/ZtnfvXnn55Zflww8/lN69e5ttBQoUkOuvv17uv/9+qVatmoQKAj0AAAAAATNw4EDZuHGjPP744/Liiy9muD4yMlKmTp1qzicnJ5tgrWfPnvLxxx9L165dfd6nYCb9YAsVKmSCRJdly5aZ/rbXXHONe5veXoPHpUuXypAhQyRUEOgBAAAAsExiYqI5eYqOjjanzISFhUl8fLxcd911cvfdd0uNGjW8rnc4HGYtn0u7du3k6NGjJvPmCuJ82Se9EydOmLV4GhC67Nq1S4oVK2ayeC563CVKlDDXhRLbBnoxMTE0TQ9hU6ZMCfQhAAAAIBc0aJs4caLXtvHjx8uECROyvI1m0Nq0aSMPPfSQzJkzJ8P1v/zyi7zxxhuyc+dOU7jl0KFDEhUVleN9XDSLp1MzNWbwXKOn2cDMAlIN/PS6UBJh5zcYAAAAgLylhU7GjBnjtS2rbJ6np556Slq3bu1eL+cZwOn6unvvvdes54uLi5N58+bJRx99lKN90gd5GhBqIZiiRYu6rytevLjJBKZ35MgRk9ULJbYN9AAAAADkveymaWbnkksuMUVP7rvvPhNwuXz22WcmiNM1fC7pAzhf9lGalbvhhhvMmkAN8ipUqCCemjVrZqadrlu3Tho2bGi2bdmyRU6ePClNmzaVUEJ7BQAAAABBYfLkybJ8+XL58ccf3dt0zdy2bdtM3z1XL7w333zT63a+7JOSkmKyfRs2bDBBXsWKFTPcv04f1fYLGjBqPz0tzKKtFnTd4OWXXy6hhEAPAAAAQFCoXbu2qWz577//urfdcccdJpCrUqWK1K9f3zQx79Chg9ftfNnn3Xfflfnz55vzOnVTi7e4Tq4AUQvDaKXO1atXS7ly5cxJp4V+8sknpvJmKAmtowUAAABgC4ULF5aff/7ZZNA8Pf3006YHXsmSJc3lIkWKmMbqf//9t5w7d07q1q1rKmbu2bPHfRtf9unWrZu5v8x4VtnUQFGna27atMlk9PT4NAAMNQR6AAAAAPJceHi4V0sEFy2kkn67Blq1atVyX9bG6umbq59vn9KlS5uTL7RdQ7169SSUhV5oCgAAAADIFoEeAAAAANgMgR4AAAAA2ExQrNHTjvSjR4/22nbw4MGAHQ+CQ8Jha98D0UX+1wzTMg6H5UM6ExMsHzMsOsbyMZNPnbR8zAiLjzMyViyni7KtlnjihOVjnj34v2plVolNtxYiWMfsOGO6peMtGXqnBPsxqrhKlSwf05mWavmYaWf+W9nOSoknrP08ii4SJ1Y7e+iQ5WMWrpCxNPyFSj531vIxI+Ks//c3Yd9eS8cr4I9G2FFRlg/p9EOOxg9fZRAkgiLQi4+Pz7AtfeAHAAAAAPANUzcBAAAAwGYI9AAAAADAZgj0AAAAAMBmCPQAAAAA2Mbu3bulW7ducvz4ccnPCPQAAAAABMSpU6dkypQpcsstt8itt94qL7/8spw+/b/qvPv37zdBW04q8p88eVK+/PJLSUhIsKTa9jfffCM333yzOYWSoKi6CQAAACB/OXbsmLRo0UJKly4tQ4cONS3XfvvtN2nUqJGsW7dOChYsKGfOnDFB29mz1rf+8EXLli2laNGi5rRs2TIJJQR6AAAAAPLcxx9/LPv27ZO1a9dKbOx/m9/269dPHn30UYmOjjZB3u233262DxkyRAoUKCAXX3yxVK9eXRYvXixvvvmm2U99+OGHMm/ePLPN5cCBAyZDuHXrVqlVq5bcc889UqxYMff0zrvuukueeeYZef/99zPdR82fP18qVKggU6dODblAj6mbAAAAAPKcBnJhYWGSkpLitV0DrYiICBPE3XTTTWabTpscNmyYXHPNNXLdddfJihUr5IEHHjDXbdu2zWQEO3bsaLKALldddZVERkaaqZ8aGF5xxRWSmprqNb3z8ssvz3IfpUFeqCKjBwAAAMAyiYmJ5uRJgzZX9s2ld+/eMmnSJGnatKn06dNHWrduLZdddpkUKVLkv4FKRIR06NDBnNcArGrVqu7bvvfeeyZI0+DusccekyuvvFJuu+02r/FHjRolDz74oDnfvXt3qVy5ssyZM8drrZ0v+4QqWwR648aNs2SxJYKHLsoFAABA6ImPj5eJEyd6bRs/frxMmDDBa1ulSpVk48aN8uqrr8q3334rL774ovlOr9M0p02bZgK9rLRp00Yefvhh6dmzp5QpU0a+/vrrDPt069bNK0vYtm1b+fnnn72COF/2CVW2CPT0DUFgAAAAAARHEmbMmDFe29Jn81y0EIuuydNTUlKSydRpZq5evXpy9913Z3s/tWvXNtMs69evLyVKlMhwfVxcnNdlzRRqAZic7hOqWKMHAAAAwDIa1GkA5XnKKtDzFBUVJYMHD5YGDRrI6tWrzTaHw5Hpvv/8848ppjJ27Fj55ZdfZPr06Rn2+fvvv70u61o+z+mfvu7jb59++qnJYub0uvMh0AMAAACQ57Q/3dKlS722bdiwwQRbjRs3NpdLlSpl/v/vv/+690lLS5OBAwdKq1atTNVMnfKpAd/mzZu9xnrmmWckOTnZnNepnX/++af07ds3x/v4m2YytTBNZnR7+mI1+WrqJgAAAIDQomvr7r33Xunfv7+ZhqnLsTSTp4VZRowYYfbRbKBW2bz++utN8Kd997TNwvr1602vPc34aaN1DdJ0XZ1m91wKFSokNWvWlIoVK8rvv/9u1g02bNjQ6xjOt48Welm5cqXs2LHDVOp0rel75ZVXTOGWC6EtHrR6qK4J1PNaBMaTPh+vv/66dO3aNVfjE+gBAAAAyHNNmjQxLQ0OHjwomzZtMlM3NehyZfE8py9qALh//36zFu/48ePy/fffS9myZd37aEC0fPlyc12VKlVkwYIF0qlTJzP2X3/9ZXrvZTYlU/vsaUYvq300yGrWrFmG23n22sutP/74wwS0mtHTKqWu4NYzCNXAVqeo5gaBHgAAAICA0YIsejpfUKinrGgRFc8Kmt3+/7xW9tRTdrLbRwMtf9FMpZ6++uorWbJkiTz33HOWjs8aPQAAAAAIEJ022rx5c8vHJdADAAAAkK9U+f/pnVZMwbxQ27dvl7lz51o+LoEeAAAAgHylcOHCZnqnL20f/E2npK5atUrOnTtn6bhBu0YvJiZGRo8e7dO+usgS9hNdNPC/sJyPIyLS8jGdSamWjylOp+VDRhaItXzM5HNnLR0vIjpGrOaIsP5jM6ZkScvHjC5SxPIxHVFRlo8p/1/S2kqFypezdLyOMzL2ZrpQS4beafmYPZcusnzMsEjrX/O0BGv/zlWsR0EGS4RZ/zt4YT98Zvrls72Qd/NoS6RZ/+9adNGilo7n8MO/FyLWvz6pqdaP6Ye3O3IoMjLSVB297LLLZNCgQVKuXDmv/oG5ndoZtIFefHy8z/v6GhACAAAAQDDRlhA//vijOX/fffdluL53797y9ttv2yfQAwAAAAC769WrlzlZjWQtAAAAAASI9gkcMmRIjq87HwI9AAAAAAgQbZh+5syZTK/T7SkpKbkal6mbAAAAAPLc6dOn5ezZs5k2S//333+lUKFCUrBgQb/c98mTJyUhIeG8jdr9affu3bJixQr5+eefzfk5c+Z4Xa/H9/rrr0vXrl1zNT6BHgAAAIA89/fff0vLli1l9uzZcv3117u3v/baa3LvvffK6tWrpXr16pbe53fffSfTpk0zPfQ0kDx8+LAEyh9//CEjRowwGb3ExERz3pMeX4sWLeSuu+7K1fhM3QQAAACQ5xo3biyPPvqoDBs2TA4dOmS2bd26VcaOHSsvvviiO8jTqYsHDhyQtLQ0dzbOmU17kRMnTphsWGZeffVVU8Xy8ccfl0C77rrrTKCpmTwN8vS852nnzp3y8ccfS4kSJXI1PoEeAAAAgIB44IEHTECnwZ4GdP3795cuXbrIrbfe6t5n8+bNprfc5MmTzf/LlCkj5cuXN1k5T0uXLpW6detK2bJlJS4uzjRE1ymgnjSo6tOnj+ldFyyuvvpqee655ywfl0APAAAAQECEh4fLO++8I19//bV07NhR9u7da9alZebDDz+U3377zaztGzVqlAnY9u/fb67TjKBmyG644QaT8du3b58cPXrUK2DMbwj0AAAAAFhG15tpsOV50m1ZqVOnjowZM0Z++OEHk9kqXrx4pvtNnDhRKlasaIJDbSyu59966y1z3cyZM6Vo0aJmH83WlSxZUqZMmSILFy4000HzI1sUY4mJiZHRo0cH+jBgIf3DBAAAQOiJj483AZen8ePHy4QJEzLd/9SpU/LBBx+Y4iMasPXt2zfLNX0uDodDGjVq5A7itmzZYi6Hhf0vj9W0aVOzn+5Tu3ZtyW8i7PJmAgAAABB448aNMxk6T9HR0VnuP3LkSClQoICsXLnSVOHUgim6Zi+95ORkr8tarbJYsWLmfFRUlLnsKSUlxRRt0evyI6ZuAgAAALCMBnVaDMXzlFWg98knn5j2Cu+//75cdNFFZuqmtlbYsWNHhn1/+ukn93kN6nS9XsOGDc1lzeZpu4Jz58659/nhhx9Mhq9+/fr58tUl0AMAAACQ57Rgyu233y5PPPGENGnSxGzTy23btjVFVNK3UHjkkUfkyy+/lE2bNsnQoUNNu4VBgwaZ63R/ba5+yy23yPr162XZsmUyfPhwGTJkiFSoUME9xvHjx02rBp0uquPreT1p9s9uCPQAAAAABGSKZ+vWrU3fPE8zZsyQPXv2yLvvvuu1/aWXXjL99a655hoTnH377bdSuHBhc51O/dTgTjN43bt3N33pdK2f3ib9NFENKrVpuhZt0fN60ubtdmOLNXoAAAAAQou2VciMZuAyC7wuvfRS6dWrV5bjaT8+7ZOXnVmzZkl+QUYPAAAAAGyGQA8AAABA0NIplmXKlDH98+A7pm4CAAAACFraUF3X5CFnyOgBAAAAgM2Q0UPQSgqLtHS8yHDrf9dITEm1fMyIqAKWjxnmh590Us6csXzMtNg4S8dzhHmXZbZmUIflQyZa/zaSBO+espYoEumH5zPS2r9zFWbx1J64SpXEaj2XLrJ8zPlXXGX5mO1fft7yMdOXSw/G1zzMD+/L5LNnLR+zeO06lo+ZdPqU5WNGlygpwS4twfrX5+zBg5aPGVmwoPVjxhWxfEwEBzJ6AAAAAGAzBHoAAAAAYDMEegAAAABgMwR6AAAAAGwrKSlJUlJSJL8h0AMAAAAQEAkJCe6T1cWa3njjDdN/r3DhwjJp0qQ8KQ4VTAj0AAAAAOS548ePS4ECBSQuLk6KFi0qBQsWlGbNmsmCBQsueOxz587JiBEj5NVXX5XExEQZP3682Z6cnCwPP/ywVKhQQWJiYqR9+/ayfv16sSMCPQAAAAAB89FHH5mM3qFDh6RDhw7Sq1cvWbdunaSmpppply6u8+m3p8/MpaWlyfbt280+DRs29MoWDhgwQObMmSPz5883259++mn59NNPxY4I9AAAAAAEnGb0nnzySXN+0aJFMn36dKlfv77JxpUoUcJk/k6cOOHe/sgjj0ilSpUkMjJSrrjiCtm9e7e57Ycffmgyg6pBgwYmW7hp0yb54YcfzHVvv/22tGjRQhwOh7Rs2VIeffRRsSMCPQAAAACW0amSJ0+e9DrpNl9oNk6zb+Hh4ebytm3bZMeOHfLXX3+ZDFyRIkXc29esWSN//vmn7N27VyIiIqRPnz7muptuuslc59pPb1evXj0zJbRixYrStm3bfPFqR0g+Mm7cOPNCI/hNmTIl0IcAAACAXIiPj5eJEyd6bdOs3IQJEzLdX9fN6Xf0Y8eOmX00yOvSpYt8++23EhUVJdOmTTMFVTyFhYWZ9XelSpUyl/V8jRo15Ndff5VLLrkk0/vRgLFmzZoyevRoU6hFtWrVSqZOnWoyf3aTrwI9fQMRQAAAAAD+Ta6MGTPGa1t0dHSW+998880mcNNsnWbevvzyS6lbt64J9DQDlz7IU2XLlpXy5cu7L1evXl2KFSsmmzdvzjLQczqd8t1330njxo3lwIEDZg3fsGHDpGvXrmZqZ6FChcRO8lWgBwAAAMC/NKjLLrDLrBhLz549M71O199lJrPWCDrtUwPGrJQvX96MpwVYNFOo9Hy1atVk5cqVZp2fnbBGDwAAAEBI0Yzcrl273Je3bNliCrVokZastGvXzgSIWrXTc9podgFlKCPQAwAAABBSNGAbMmSIKbaydetWc/7yyy93V9vMzHXXXSe1a9eW4cOHy759+0wLhlGjRplpolqF024I9AAAAADkOW1voFM8XRU209NKmllNAdWATQu29OjRw6zJK1mypOmP5xIWFmZuq/fholm7b775Rk6dOmX662lgqK0XdJs2T7cb1ugBAAAAyHNafCW7ivhaKEVPmdEA7p577jGnrALBhEzG1uIuH3/8seQHZPQAAAAAwGYI9AAAAACEjOymdOJ/CPQAAAAAhAydzrlmzZpAH0bQI9ADAAAAAJuxXTGWcePGZbmo8+DBg3l+PMi96MwLMF2ANKsHlIhw638ryaT/pwX+V3HKKuH/32jUSmFWP5/O//XJsYxH9S6rREX4Y8xQmdJi/RvemZJi7Xhp1r+PwiKt//tp//Lzlo/53Ygxlo/Zdc67lo+ZfPaspeM5PXpsWSXCDxX9HNHWj5l27GhI/BsUFmHtV1hHFlUfL0RUocLWj1m0mOVjSpj1rw+Cg+0CPQ3ypkyZkul1o0ePzvPjAQAAAIC8xtRNAAAAALAZAj0AAAAAsBnbTd0EAAAAgKycPXtWdu7cKVFRUVKzZk2xKzJ6AAAAAAIScK1evTrTQopbt26VPXv2eG1LSUmRbdu2ycmTJ3N9n/PmzZNy5crJDTfcIJMmTbJ07GBDoAcAAAAgz61du1aaNm1qAqz0+vXrJ0888YQ5/++//5reecWLF5euXbtK2bJlpVu3bmZ7Tk2aNEkeeOAB2bBhg8ycOdPSsYMNgR4AAACAoLVp0yZp1qyZHDp0SP766y/5559/ZP/+/TJkyBD3Pk6n02QH9bR+/Xo5ffq01xjJycnmul27drn3PXz4sE9jhyoCPQAAAABBq3379nL77bdLdPR/e8SWKFFCBg0aJMuWLfMK5G699VZz6tOnj5QuXdpMzzxz5oy5/tixY+a648ePy2uvvWbOL1myxKexQxXFWAAAAABYJjEx0Zw8aSDlCqasmvZZpUoV92UtrKJZOpejR49K586d5amnnpLHHnvMBH56vU7N1Omb/fv393nsUJWvAr2YmBiapoeIrJreAwAAILjFx8fLxIkTvbaNHz9eJkyYYMn4S5culbfeesusscuswMvevXtNJq9Dhw5mXw30rBg71ETktzcdAAAAAP8ZN26cjBkzxmtbbrJ5Docjw7Y//vhDrr/+ehk1apQMGDDAq2qmFlV57733TFXNuLg4M10zIsL3cCersUMVa/QAAAAAWEaDOg20PE+ZBXq6Hs41zTK9I0eOuK93+fPPP6VTp05mDd2zzz7rdZ1m4RYuXGgKquzYsUPWrFljAra0tDSfjjm7sUMVgR4AAACAPFetWjUpXLiwrFixwmu7Vr7UHnqNGjVyb9P1dRqIaaYtsyU+2nevSZMmUqlSJfe2xYsX+3Qc5xs7VOWrqZsAAAAAgoNOq9ReeQ8//LCppdGyZUvTv27y5MkmaOvVq5fZT1sgXHnlldKiRQuTcfMsutKwYUMJDw+Xdu3aycsvv2wye7Vq1ZI5c+aYqprly5fP9hh8GTtUEegBAAAACIj//Oc/UqdOHXn33Xfl448/No3LNcC7++673UGWNjevWLGiHDhwwLRF8LR8+XKTFezRo4e89NJL8v7778u5c+ekVatWMn36dPnkk0+89m/QoIG5Dxdfxg5VDqd2DLxAo0ePDpo0ZzAdCy5M8hnvRpfBKMWZcZHwhbrwv8iMIsP9cJyJCdaPGRVj6XhhzlRLx/vvoNbPeHeK9a+PP/jnKK1/wzuTkqwdL83691FYTKzlYx7buN7yMb8b4V1MwQpd57xr+ZjJZ89aOp4z1frX3OnjOqGcKFKrjuVjnjuwz/IxC5TNPqOSG84k79L9F8rhh6xN0okTlo8ZVbSY5WNKmPWf7pGxBS0fEznHGj0AAAAAsBkCPQAAAACwGQI9AAAAALAZAj0AAAAAsBmqbiJ4ORxBX/DCDzVOxJGaYvmYTj/8qaf4oRhLuMXFWFLPWVugQYUXLGT5mGl+qMDjj6I+EX5YsO+PEi+OqChLx0vzQ2GotATr35sW1FbLk8IpX/cdaPmYnd5+3dLxUi0u6KOST4fG+yglISEkii45Iiz+dy3M+mIsEbEFQqJwSqo/isBZPyRygYweAAAAANgMgR4AAAAA2AyBHgAAAADYTFCs0Rs3bpwkWDQn/ODBg5aMAwAAAAChKigCPQ3ypkyZYslYo0ePtmQcAAAAAP6xePFi+fLLL7Pd58EHH5TSpUvzEoRyoAcAAAAg/yhSpIhUrVrVfXnSpEnSuHFj6datm3tbZCT1Oy8EgR4AAACAPNWyZUtzcnn55ZelefPmMmrUKK/9Dhw4IE8++aRZ6vX111/L1q1bpVatWjJgwACJ+P82G659NAM4b9482bZtmwwcOFAaNmyYr19VirEAAAAACEqHDx+WF154QVq3bi1Lliwxwd2ECROke/fuGfa59NJL5ddff5Vy5cpJbGys5Hdk9AAAAABYJjEx0Zw8RUdHm1Nu9ezZU55//nlzfsiQISar98UXX3hN9dTtDzzwwAUcub3YMtCzsoonAsOq4jwAAADIW/Hx8TJx4kSvbePHjzeZuNzq16+f+3yVKlWkXbt2JsPnGehde+21uR7fjmwZ6FlZxRMAAABAzpIuY8aM8dp2Idk8VapUKa/LWo3z33//9dpWvHjxC7oPu2GNHgAAAADLaFAXFxfndbrQQG/v3r1el/fs2SMVKlS4wCO1NwI9AAAAAEFt+vTp7vOrV6+WFStWeBVkQT6ZugkAAADAPrZs2SKXXXaZVKtWTT7//HPTPkEvI2sEegAAAAAC6uGHHzaVNLMyf/58E+z99ddfcsstt8gVV1zhvk7bKWh9Dp0iiv8h0AMAAAAQULfeemu214eFhUmHDh3MKb0SJUpkaLQO1ugBAAAAgO1QjAUAAABAUGJaZu4xdRMAAABAUGJaZu6R0QMAAAAAmyGjh+CVnGzpcI7IKAkJ4db/WTokNDgsPlBHRKS1A4qI0w/PZrg/XiCH0/oxU1MsH9KZYv2Yjghr/4YST5wUq8WWLWv5mGHh4ZaPmXz2rOVjdnr7dcvHXHzr7ZaO1/KR+8VqyadPWT5mqSbNLB8zpnjxoP/3XKUkJFg6XkTBgmK1tGQ/fL45Ei0fM8zqf3wN659P5BwZPQAAAACwGQI9AAAAALAZAj0AAAAAsBkCPQAAAACwGYqxAAAAAMhzZ86ckV69erkvFylSROrXry933XWXlCxZMiCvyK5du+SOO+7IsP3pp5+WRo0aSSgh0AMAAACQ55KTk+Wbb76RRx99VFq3bi1Hjx6VKVOmyBtvvCFr1qyR4v6oAnsep06dMsf0wQcfSLFixdzbK1asKKHGdoFeTEyM7N69O9CHAQAAAMAHTZs2lS5dupjzV1xxhZQrV04++eQTufXWW2XGjBmyfPlyiYiIMPv069fP7LdgwQL5/PPPpX///jJ37lzZu3evtG3bVu6++26z/zvvvGMyhtdee63Zx5MGlK+++qr8/vvvUqZMGRk2bJg0btzYa58OHTpIWT+0wslLtgv04uPjZfTo0YE+DAAAAAA5pMGVJm4OHTok9957r8muPfDAAxIVFSVff/217N+/X8aOHWumWL733nsmWBszZoycPn1a7r//fhMAalZu9OjRJqDTaZjR0dFyww03mPEPHDggrVq1ksqVK8uQIUMkISFBbr/9dvn000+lQoUK7uMYOXKkOJ1OqVOnjgkEK1WqFHKvpe0CPQAAAACBk5iYaE6eNNjS0/nMmzfPBF/Nmzc3AZyebrnlFnPdTTfdJCdOnPCa+vnFF1+4A7SNGzeaaZ86u6906dJm259//mkyfq5Ab+LEiRIbGytLly41WUI1aNAgSUtLc497ySWXSMeOHSUuLk7mzJkjF110kfzwww/SrFkzCSUEerk0btw48yaEf+j8bAAAAITmDDsNqDyNHz9eJkyYkOn+jz32mJlKeezYMVm9erXcc889ctVVV5nM27PPPmsyfBp4lS9f3hRscdGsnGcWTi9Xr17dHeS5ti1btkxclixZIn379nUHeUqzhS41atSQn376ScLDw81lnSp65ZVXmiyi5zihgEAvlzTIIxgBAAAAMiZENBPnKbtsXvfu3U0xFg3iatWq5a64OX36dJOhe//99+XOO+80mTUNCC+++OIMAZpyOByZbkvzyNbpFE/PIivpFShQIMO2q6++OkPgGgroowcAAADAMhrU6bRHz1N2gZ6rGIsGe55tFSIjI02rhYULF8rhw4fNernbbrvtgo6tWrVqZopnTuhav/QBZCgg0AMAAAAQdF588UU5e/asOa/TN7UgSmpq6gWNOWTIEFPE5ZdffnFvW7RokSnSorTap1bwdNm8ebPJInr2+wsVTN0EAAAAEHS08IquudMs3Llz50zFTe1vdyEGDx4s27ZtM+0TdCqoLseqWrWqfPzxx+b6woULmzV5msHTLOS6devMmj5dKxhqCPQAAAAA5LlChQqZlglZVbN85JFHzFq/tWvXmsCrQYMG7img2h+vSZMmXvv37t1b2rRp47Wtf//+0rVrV69tkydPNuNu2LDBFHjRdYEuWgRm/fr1smnTJtOmwXPNYKgh0AMAAACQ51xN0LNTsGBBs3YvPa2mqSdPmpnTk6eaNWuaU3oavF1++eWZ3qdW3NSgMtSxRg8AAAAAbIZADwAAAABshkAPAAAAAGyGNXoIXuHhlg6XnOoUq51NSLJ8zIIFrO/TEh7msHzMtORkCXaO6BjrB027sLLOmXJa/94Uh/W/46UlW/9+D/PHa2TxY48uEieWC7P+9QmLjLR8TOcFljHPTGqS9e+jlo/cb+l4Kx9/SqxWf0h/y8cUsf6zI+nECcvHDPdD/7GI2FhLx0tNSBCrhWfTNy63HH74OxeH9d8REBzI6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAAIDN2LLqZkxMjIwePdqv93Hw4EG/jg8AAADY2cmTJ6VevXruy0WKFJH69evLuHHjpGnTpgE9NjuwZaAXHx/v9/vwdyAJAAAA2FlaWprs3btXZsyYIZ07d5ajR4/K448/LpdffrmsXbtWqlatGuhDDGlM3QQAAAAQMCVKlJCKFStKo0aN5O2335YzZ87IggULzHWvv/66tGrVSmrUqCHXXHONrFy50uu2d9xxh9x9991y7733SsuWLU2GcNKkSXL69Gm55557zOXmzZvLO++8k+F2Y8eOlYceekhatGghDRo0kKeeekqc/uhtGyAEegAAAACCZglWRESEnDt3Tl599VUTrI0cOVI+//xzqVOnjrRv31527Njh3v/IkSPy0ksvSXR0tAnmNHB79NFHpWHDhhIXFyeffPKJ3HnnnTJ48GBZt26d1+2mTp0qBw4ckLfeeksmTJhgAr0pU6aIXdhy6iYAAACAwEhMTDQnTxqI6Sk7mk17+umnJSUlRTp06CDXXnutPPjgg3LzzTeb659//nlZvny52Wf69Onu22nG74knnjDnL7roInnxxRelYMGCJuBzbdPbLl261ASALppF1IxheHi42a7TSHUcXaLlcDgk1BHoBYAuME1ISAjEXYcMO/2aAgAAkJ9ovYyJEyd6bRs/frzJmmVm6NChMmLECDl+/LgUK1bMrNmrXLmyybZddtllXvtqRu+nn37y2qYFXDyVKlVKqlWrlmHb4cOHvbZpgKhBnku7du3MPvv375fy5ctLqCPQCwAN8ghkAAAAYNekxpgxY7y2ZZfN0ymTWoxFq24WLlzYbNu9e3emt9PL6bOFnsFadtuc6dbfRUVFZXo5/fihKiIYsle0KgAAAADswZdpmpkVY/Gkl2NjY2XNmjVy8cUXu7evWrVKateubclxrvNYs6f0vvS4K1WqJHYQEQzZK1oVAAAAAHAJCwuT4cOHmwqa2m5Bq27OnTtXFi5caNbpWWHNmjVmrd+wYcNkz549prXDbbfdZorB2IE9HgUAAAAAW9F1frpmTtfg6bRKrcipgVmbNm0sGb9bt27y8ccfm4Iv2rxdL2tgaRcEegAAAADynK7J++eff6RkyZKZXl+gQAHT+kCDuxMnTpj9NNPnSatmpt+mbRYi0mXlNKBLP520QoUKpoWDq0qotmOwEwI9AAAAAHlOWxikX5uXGQ3QSpcunel1xYsXz3TNX3paddOqNYWhgobpAAAAAGAzZPQAAAAA5CuvZzLl024I9AAAAADkK8UzmfJpNwR6uaRVf3LbFoK+gYHhcFg/Zni49b8ERYT54UCdadYPmZpq+ZiWP/LUFH/Ue7Z+TD88l8406x97akJoNJANC7f2n7azhw6J1QoXiLV8zOSzZy0fMyImxvIxk0+f9sOYpywdr/6Q/mK1DW++Z/mYtW+62fIxU87T+zgY/iZVisXvd4cf/u1NSThn+ZiRmTQBv2B+eH0QHHhlcyk+Pj7XTzp9AwEAAAD4k70npgIAAABAPkSgBwAAAAA2Q6AHAAAAADbDGj0AAAAAAXHao0BTbGxsULU8OHfunGnqrkUYQ1HwPJMAAAAA8o3jx49L4cKFpVSpUlK2bFkT6DVq1Ejmz58fsGNKTU0199+5c2cpVKiQ9O9vfRXevEKgBwAAACBgZs+ebTJ7Gvh16dJFbrjhBlm7dq37eqfTKWlp3q2ikpOT5axHm42UlJRMg7bsZHabHTt2yNtvvy2jRo2Sa665RkIZgR4AAACAgNMpkpMnTzbTJRcvXizbt2+Xjh07SoECBUyD8+uvv1727Nlj9n3ttddM9u/uu++WChUqSHR0tLRp00b+/vtv+c9//iNFixY1GcKePXvKqVPevTa//vpradasmbm/0qVLy+OPP+4OCmvWrGkyel27dg2qaaS5EdpHDwAAAMA2NMumGbyIiAgZPny4CcQOHz4sBw4ckFtvvVW++uor974a1Gmmb+vWrbJ//36TEWzSpIlERUXJvn37THZOM4MvvPCC+zY//PCDdO/e3UzJ1P03btxosoP//POP2A3FWAAAAABYJjEx0Zw8acZNT5lJSEgwUzePHTsmEydONEGeTuF86623pFOnTmatnLr22mu9blewYEF5/vnnJTIy0pzv3bu3TJs2TZ566ikJDw93Z/R++eUX9230Op2SOWbMGHNZx37sscds+eoT6AWApolHjx4diLsOGVOmTAn0IQAAACAX4uPjTcDmafz48TJhwoRM9x80aJAJzIoUKSL16tUzUyvr1Klj1snddddd8v3338uVV15pArTq1au7b6dTNjXIc4mLi5OKFSuasTy3aebORTN8I0aMkPyAQC9Ab34AAADAjsaNG+fOmLlklc1zFWPRzFtmAaBWv9Tpmt9++63cf//95nu0rstTupYvvcy2edJ1d+cr0mIXrNEDAAAAYBkN6jST5nnKLtDLTvny5WXo0KEyZ84ckxV88cUXL+jYmjdvLsuWLZP8gEAPAAAAQNC56qqr5NNPP5W9e/fKtm3bZOnSpXLRRRdd0JgPPvigKcjywAMPmKqeWozljjvuMIVdXM6cOWPWDGrmT096XreFGgI9AAAAAHlOp1lqERUtvpKZp59+Wj744ANp2bKlKcqi6++0QIvSypp6W0+6TQuwZLft4osvlu+++07++OMPueSSS+Smm26SFi1aSI0aNdz76Hlt4K6ZP23zoOfr168voYY1egAAAADynBZf0WxZVrRVwty5czO97vbbbzcnT9o/T0/pM3gPPvig17bWrVubAC4r2srBDsjoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAAIDNUIwFwSss3NLhHE6nWK1QuPVjSmqK9WM6rP9NJ7poMQl6YdY/bqcfnkuJjLJ8yOzbxeZOZHSM5WOm+uHv0nmeZrk5VbhCRbGcHx538dp1LB/T4YfXPC3hrOVjlmrSzOIRrX99at90s+Vjzruyq+VjdvngbcvHlMhIy4eM8MOYVgtPS7N+UIs/3/7LD99lEBTI6AEAAACAzRDoAQAAAIDNEOgBAAAAgM0Q6AEAAACAzRDoAQAAALCVP//8U1q0aCGxsbHSvn17yY8I9AAAAAAExE8//SSdO3eWMmXKSNmyZaVbt26ydOnSCx537NixJtA7cuSILFmyxGzbsmWLjB49WkqUKGHuy+4I9AAAAADkuc2bN0unTp2kWbNmsmrVKtm4caOMHDlS4uPj5cSJExc09l9//SWtW7eWAgUKSHj4f1t23X777VKxYkUZPny4pKT4oZ1VkCHQAwAAAJDnvvrqK4mOjjaBXfny5aV48eImu7d48WIpUqSI2WfDhg0SEREhb7/9tjRs2NAEbp9//rkJ1DRrV7lyZfft1q1bJ3v37jX77927V2699VZz/pVXXjFjff/99+Y2un9+QKAHAAAAIM8VLVpUTp06Jb/99luW+zidTklNTZVnnnlG3nnnHZPp6969u7z00ksyf/58Eyzu3LlT7r//fnnttdekQoUKkpCQYKaCzpw505y/8847JT+KCPQBAAAAALCPxMREc/KkmTs9ebr55ptl9uzZcskll0iTJk3MVMsrrrjCrNNLv68GejrF02X79u3mNg0aNDCX9XZ6UprFU2FhYe7z+VFIPvJx48aZ6Bz2NWXKlEAfAgAAAHJBp2JOnDjRa9v48eNlwoQJXts0mNNpmitXrpRvv/1WfvnlF+nfv7+ZjvnDDz+YrJyLK6DzDBKvuuoq6dKli1xzzTXmfJ06dXi9Qj3Q0yCPQAAAAAAIzqTMmDFjvLalz9B5atmypTmpv//+W5o2bWq+6z/55JPufaKiorxu06pVK7PvZ599JsuWLZMHH3xQ+vbtK2+88YbljydUsUYPAAAAgGU0qIuLi/M6ZRfoeapRo4ZUqVLFtEU4n1KlSsnQoUPl/fffN2v1ZsyYIXv27LHgEdgDgR4AAACAPDd9+nS57777ZM2aNWbG3vHjx+WFF14wbRa04Ep2tPjKe++9J4cOHZJz586ZqZ6FCxeWkiVL5tnxBzsCPQAAAAB5TtfZ6Tq8QYMGmZYHmsn74IMPTAB37bXXZnvbYcOGyaJFi6R+/fomuPvyyy/NKSYmJsvb9OjRwxRn0RYLmjHU83rSFg52FJJr9AAAAACENp3SqUGXnrKiRViSk5MzVM+sVq2avPvuu1nebu/evabqpqd58+ZJWlpahn3tWpnTno8KAAAAgC3kJhALDw/PsE0Dv/TBn53ln0cKAAAAAPkEgR4AAAAA2AyBHgAAAADYTFCs0dPqOKNHj/Z5/4MHD/r1eBAsnBLsnJHezTut4EhNsXxMcWZceHyhHA6H5WMmplh7nNEZp+dfMIc/3pdpfhjTH2sQ/PA+8sNLZLnkc2ctHzOyUJzlYyadPmX5mGnHjlo+ZkpCguVjxhQvbul4SSdOSCg87i4fvG35mAv73Wr5mD2XLrJ8TEm09vl0+KMYR3hESHwO679ssKegCPTi4+NztH9OgkIAAAAAyG+YugkAAAAANkOgBwAAAAA2E+HvNXaspwMAAACAEAz0sltjx3o6AAAAAJ7Wr18vq1evzvZJufbaayUuzvriVeezZ88e+fPPPyU2NlaaN28uRYsWlVAUFMVYAAAAAOQff//9tyxcuNB9+bPPPpMqVapIkyZN3Ns6duyYp4FeUlKSDBo0SH755Rdp1KiRHDhwQDZs2CDTpk2TAQMGSKgh0AMAAACQp3r06GFOLjVr1pRu3brJk08+6bXfiRMnZMGCBdK7d2/ZsWOHbNmyRapVqyaNGzfOdJ9t27bJ1q1bpVatWtKwYcMM97tr1y75/fffTbauXbt2UqhQIfd1qampcsMN/9feeYBJUWxtuBZYcjKCqJhAxIQogldQjAiKCooiRgQDgl4E08W0YABFBUQMJBEjYEJFEMGAImYRUJFgRgUMIDnu/M9b9++9vbMzs929PTthv/d5BmZnZmt7uqur6tQ55zvnmGeeeabgtTvuuMP06NHDnHfeeSY3N9dkEhJjEUIIIYQQQqQlv/zyi/WmnX322eaCCy4w48aNMy1btjQ33XRTkc8Q6olBNmbMGNOiRYtCn4GBAweaxo0bm1GjRplbbrnF7L333mbOnDkF71epUsV06NCh0O/st99+ZuvWrfaRacijJ4QQQgghhAiNzZs324ebSpUq2UdQ9t9/f/P666/b54R8nnbaaeb66683u+yyS8FnqlWrZubPn2/KlStn3n//fdO6dWtz7rnnmiOOOMLMmzfP3HbbbbaNtm3b2s9ffvnlplu3bjZfsEKF/5lFhG4uWbLE/Pzzz2bs2LHmwQcftB7ATCMjDb1EKp8iOxg6dGiqD0EIIYQQQgQUahwwYECh1/Ly8kz//v0Dn88rrrii4Dkhl/n5+TbPz23oYR9g5Dmfad68uXn++eetocf/hHI6Rh7069fPev8wAvmMA3l5b731ljX2sDvw/GUiGWnoJVL5FEIIIYQQQqQODKi+ffsWeq0k3jzYYYcdirS1adOmQp9BzMXN3nvvbb1ywP/k9kW/j2FI3p7b0Ovevbt9AN68s846y+b+7b777iaTUI6eEEIIIYQQIjQwxFDLdD9Kauh5YdWqVYV+/vvvv83OO+9sn/M/P7tZvXq19Qy6vYLRnH/++dag/Pzzz02mIUNPCCGEEEIIkfG89NJLBc9XrFhhZs+ebUM44dhjj7W5d9TIc5g4caKpVauWLaXg/A7Km24+/fRT+3/9+vVNppGRoZtCCCGEEEII4YZ8u7Vr15p9993XPPbYY7YEA2qdgJom4izHH3+86dWrl/njjz/MAw88YIYMGWKNPaCAO2qcp59+ug3TpJTD6NGjrWiLu75fpiCPnhBCCCGEECKlYIg1bdq0yOu1a9e2ZRUofeBAXh2v1alTp9BnEVCpV6+eWbBggbnkkkvMzJkzC8RZYMqUKbbkAmIr69atM9OmTTM9e/YseP+UU04xkyZNsgqcn3zyif2bfIZyDJlITiQSiSTzD6B+IwVFEYStG9aHeuK2J6Gnl8vJCb3NnO3bTEaQnx96k5tzwg0yqFTehE8SrrnJT0bnTMI+XiT8a54UQr5GW9f8Y8Imt3rN0NvcvOqv0NvMT0LdqG1R4glhUHnHHUNtb8s//2TE965Yo0bobb5xftfQ2+zw9puht2k2h3s+c1zS+qFRvkJmjMM54c8XuaVcioDyCChq/v7776Zu3bql+rfTGXn0hBBCCCGEECLLkKEnhBBCCCGEyFhihXcKibEIIYQQQgghMpg99tjDPP3006k+jLRDHj0hhBBCCCGEyDJUXkGkLfmRcAUVyidBQ8OY8EU0tpcLX0Fky9bwk7erVMwNvc3ckK+5yQn/+kRMEgR4knCcSdF3SYIQTVLOZ8iCRhVq1jahk1+4TlMYVNrpv0WBwyUZA2cSOmfIojHlK1Y0YVMuGcIcubkZIZwy+YQ2obfZ4Y3XQm1vcyR830elZPT1JAihRbaHL7pkSlmMRcRGHj0hhBBCCCGEyDJk6AkhhBBCCCFEliFDTwghhBBCCCGyDBl6QgghhBBCiLRk48aNZsqUKWbDhg2pPpSMQ4aeEEIIIYQQImWsWrXKfPjhh+ajjz4ya9euLfTeihUrzOmnn25+++033+2uXr3avPfee+azzz4zmzZtMmUNqW4KIYQQQgghUkJeXp65//77zUEHHWQqV65slixZYs455xwzZMgQU6FCBVO1alVz2mmnmWrVqnluc+vWrebaa681L7/8smnUqJFZvny5+euvv8yoUaNMhw4dTFlBhp4QQgghhBCi1Jk+fbq58847zezZs83RRx9tX9u8ebMZM2aM/R9Dr0aNGqZHjx6mVq1a9n08frNmzTInn3yyNd4WL15s9tprL7PPPvsUMvSOPPJIM3z4cFO+/H/LVt10003m4osvtt5DXsvPzzdTp0617/F39t57b2sU5iShlFCqkKEnhBBCCCGEKHXmzp1rdtxxxwIjDypVqmR69epVJHQTT1+DBg3MTz/9ZH/u0qWL+eSTT8zuu+9uPv74Y3PrrbfaB+AF7Nq1a6G/1bRpU7N+/Xobwol3cPv27eaxxx4rMAwXLFhgjcVXX33V7LTTTiYbkKEnhBBCCCGECA28cTzcYMDxcHP44Ydbr9xtt91mLr/8clO/fn3PfwNjDW9euXLlzEsvvWQ6d+5srrrqqkJG2vz5861h+PPPP9tQ0IEDBxaEgObm5lqRF/cxn3rqqebuu++2n80GSt3Q69evX5lMhhT+GDp0qE6ZEEIIIUQGMmjQIDNgwIAiuXj9+/cv9FqbNm3MiBEjrAF21113mXr16pkTTjjB9O7d2zRr1izh3yAHDyPPaWfbtm1m6dKlhQy9WbNmmWnTptnXCQFt2bJlkXZ++OEH8+OPP1pvHx7DOXPmmGyh1A09jDwt4oUQQgghhMhOcOz07du30GvR3jwHwjR5fPPNN1Z1c+zYseaoo46yRlosw8zBbdAh4uKUYnBzzTXX2AdgSLZt29Z89913pk6dOvazHTt2tIZdkyZNTM2aNc2yZcvMmjVrTLag8gpCCCGEEEKI0MCow3ByP+IZeg4HHnig6datm3n33XetZ++pp54K9YpceeWV1mtHPh+gwImnj7IN77//vnn99dfNBRdcYCKRiMkWZOgJIYQQQgghSp2VK1cWMawIx0T5snr16oHbRVkzEtUuYiuAEQl47wjVdP8dhFiyCYmxCCGEEEIIIUodvGj33XefFVKhtAEpXk8//bQNn7zssssCt0u5hsGDB5tOnTpZVc5FixaZBx980Nbnc3L/2rVrZ9PJ7rjjDtOwYUMzYcIEM2/evKxR3AR59IQQQgghhBClzqWXXmpeeeUV68Hj/3feececeOKJNl/vgAMOsJ+JLphOGCg/O3l5jheQ1xwjjfILI0aMsF67SZMm2RIN48ePt88dEH157bXXbM4ehdVbt25tjUz+fraQE0lyIGqfPn0Kia9E/yxEPDav3xDqySmXlPqX4d8+25NwR27Zmh96m1Uqhr9PlB8J9yKVywn/ZEZM+B0pJxL+9clPwj5expzP7dtCbS9SPvzgl5z87aG3af5ffS5ckjFwJmGQ27o11ObyQ+5DUC4J/cjk5mbEPTn5hDaht9nhjddCbW9LufDPZaUKSbh/toc/dkSS0GbF2juE3qbwjzx6QgghhBBCCJFlyNATQgghhBBCiCxDhp4QQgghhBBCZBky9IQQQgghhBAiy1B5BZG2JEP4IROS1rdtzwzhFJMf/nH+vXZLqO3Vqv4/Ra6wyElCv6yQBBGN/LVrQm9zy6ZNobdZaZddQ28zv1y4U9um3341YVOpdm2TCZSrkAQhmiS0uS3kvlmhalUTNts2hCswBhWSIMZiNm9Ke+EUmNz29FDba//SRBM228qXzwjhlHK5MgeyFXn0hBBCCCGEECLLkKEnhBBCCCGEEFmGDD0hhBBCCCGEyDJk6AkhhBBCCCFEliFDTwghhBBCCJFSxo0bZzp16mTmz58fuI1ff/3V3HPPPaZr167mxhtvNF988YXvNmbNmmW6d+9uunXrZjIdGXpCCCGEEEKIlJGfn2/y8vLMW2+9ZR555JFAbbz99tumdevW5p9//jHHH3+82b59uznqqKPM+PHjPbdx9NFHm1tvvdX8/fff5tVXXzWZjvRUhRBCCCGEEClj+vTp1kAbPXq0ufzyy82QIUNMVVeZk19++cX06dPHDBo0yEycONEsXrzYNGzY0Fx77bWmRo0a9jOHHHKI+eabb0zFihULtZ2Xl2cuueSSgp/fe+8988ILL5h169aZI4880lx22WUm9/9LlTzzzDNmn332McOGDTPvv/++yXTk0RNCCCGEEEKkjLFjx5oLLrjAhm7WqlXLPP/884Xexwh88cUXTatWrcz69evt//x84oknWm8g7LLLLkWMvD322MOsWrWq4OepU6eadu3amV133dUcd9xxZuHCheaiiy4qeB8jL5uQR08IIYQQQggRGps3b7YPN5UqVbKPaP744w8bJvnxxx+bcuXK2fy4MWPGFPLCOfTq1cvcfvvt9vlZZ51l9tprLzNp0iRz3nnnFfnspk2brIfwlFNOKWTodejQwYZnwsUXX2z+/PNPk62kvaHXr18/e6FE2WLo0KGpPgQhhBBCCBEAQiwHDBhQJISyf//+RT775JNPmiZNmpimTZvanzH07rzzTrNo0SLTqFGjQp8988wzC57vvPPO5phjjjEffPBBEUMPL1+3bt3M6tWrbRimw+GHH26uv/5689hjj5nTTjvN7LnnnradbCXtDT2MPC36hRBCCCGEyAxw1PTt27fQa7G8eU7YJoYZYZsO1apVs68PHjy40Gdr165d5GeEU9xEIhFz5ZVXWmGXd955x9SrV6/gPYy/KlWq2Fy8m266yb43cOBA07FjR5ONpL2hJ4QQQgghhMgc4oVpRoM37ocffrBevZycnILX8fA9/PDD5u677y4QSgE+S7imw/fff2/z9NxGXo8ePWwo6Ntvv20OPPDAIn+zS5cu9rFt2zbrTMIbuGLFiiJGZDYgQ08IIYQQQghR6uC1O/nkk80555xT6HXy+/DmTZkypZC37YEHHrBCLBUqVLAeu88++8yMHDmy4P2ePXuayZMnWyPvoIMOKvL3EHA59thjrXALbTRr1sxs2bLFGn3ZiAw9IYQQQgghRKmydu1aK6QyfPjwIu/hDWzbtq0VZYkOq2zcuLGpX7++9QbefPPNBbl9zz77rM2942fyAd3gMaRcAwZd8+bNbcgmZRk+/PBDK+7i5OlRbB3jcenSpfb4nHBSPH/k82UaMvSEEEIIIYQQpcrGjRvNE088Ydq0aRPzfcI2qYvnBhVNDLAlS5bYwupusRaKo0eXZXBwwj87d+5szjjjDPPFF1/YMg3U3tttt90KPoe4S4MGDUw0lHzIRGToCSGEEEIIIUoVatm5BVii2X///e0jGgql84hm3333tY/iqFKlimnZsmXM9+K9nqkk3dCrXLmyrWTvsHLlymT/SSGEEEIIIYQo01QojToabtxGnxBCCCGEEEIkgpw8wjJ32GEHnSgfKHRTCCGEEEIIkbbUrFkzYZinSHNDj8KKFEePRqGeZZfI5s2htpeTW9GETf7/Sr6ERm75cqG3GdkS7rmE/CRIEe9cq1qo7eVHTOhENq4Pvc0tMca+klK+cuXw26wU/j20PQkXqXy5cG/MKjvtZMImp1L41yd/04bQ28wpXz70Nk258NusUC3csWN7Eu7JnJD7ZbLIqRD+0nBzJPx5rf1LE0Ntb8pZnU3YdJw5LfQ2jUlCP9qenaUFRBoZehh5SJdGo1BPIYQQQgghhPBH+FssQgghhBBCCCFSigw9IYQQQgghhMgyZOgJIYQQQgghRJYhQ08IIYQQQgghsgwZekIIIYQQQohSZeHChebQQw81s2bNKvLeLbfcYs4880yzfft2T22tW7fOjBw50rRs2dIcc8wxSTjazESGnhBCCCGEEKJUady4sTnppJNM165dzdq1awtef/vtt83gwYPNDTfcYMp7LPFy9NFHm88++8wcfPDB1oAU/0WGnhBCCCGEEKLUGThwoKlatarp3bu3/XnNmjXm0ksvNdddd51p1aqVfa19+/Zm7NixhX5v9erVpkmTJmbOnDn2548//tiMHj3aGo/if8jQE0IIIYQQQpQ6lStXNk899ZR5+umnzauvvmoNvtq1a5s77rij4DNHHnmkGT58eKHfe+aZZ8yKFStM8+bN7c9VqlQp9WPPBNKmYLoQQgghhBAi89m8ebN9uKlUqZJ9RHP44Yeb22+/3Vx44YX2dz799FNTsWLFgve7detmDb/PP//cHHHEEfa1cePGmYsuushUqCBTJhEVMsHS79OnT6oPQ5QyQ4cO1TkXQgghhMhABg0aZAYMGFDotby8PNO/f/+Yn+/Xr5955JFHrLGHQIubPffc05xyyinm8ccft4beggULrNGHJ1BkuKFHRxFCCCGEEEJkBhhuffv2LfRaLG+eA6Ir5OrVrFkz5vuXXXaZ6d69u3nggQdsvt6//vUv5eNlg6EnhBBCCCGEyBzihWkG5fTTT7ftTZw40ebnyRHkDRl6QgghhBBCiLQlNzfXXHLJJTada8uWLaZz586pPqSMQKqbQgghhBBCiLSG8M1Vq1aZTp06mRo1ahR6r1evXraG3r333mtLL/D84IMPNkuXLjVlGXn0hBBCCCGEECll2rRpcXP0nBIK5PKRqxfNTTfdZK666qoir++5556mLCNDTwghhBBCCJFSGjZsmPD9YcOGWS/dMcccU+S9+vXrJ/HIMhcZekIIIYQQQoi0ZMaMGaZnz55m+fLlZurUqak+nIxChp4QQgghhBAiLWnRooV55ZVXrNeuevXqqT6cjEKGnhBCCCGEECItIW/vwAMPTPVhZCQy9ETakpNbMdwGy4UvMls+9BaN2bhle+htVqmQG3qb5StWTPvvXrli+FeoXNWqSWizWuhtbtmWH3qblZLw3U0kEn6bJifc5pLQ140J/3tvWLky9DYrVi+sbBcGFapWCb3N/K3bQm2vfIj1vxy2bdoYepvl88O/z0358JeGlZLQ37eVD3d87zhzmgmbl09qF3qb7SdPCr3N3CpJGNtFWqDyCkIIIYQQQgiRZcjQE0IIIYQQQogso9RDNytXrmyr2kezMgkhJ0IIIYQQQghRFil1Q2/QoEExX49l/AkhhBBCCCGE8I9CN4UQQgghhBAiy5ChJ4QQQgghhCh1Vq9ebXJycszkyZOLvNepUyfTtm1bXZUSIENPCCGEEEIIIbIMGXpCCCGEEEKItOarr76y3r9x48aZI444wlSvXt0ccsgh5p133inymTFjxpjGjRub3Nxc6y0cMWKE2X///c19991n/69Ro4Y5+eSTzU8//VTwu85n7r77btOgQQNTpUoVc+qpp5rffvut4DP9+/c3rVq1KnRcEyZMMDvvvLNJR2ToCSGEEEIIITKChx9+2Dz55JPm999/t8baeeedZzZt2lToMw8++KCZOHGi2bx5s+nQoYN9bcmSJdYQfO+99+zzLVu2mGuuuabQ7/H67NmzzbvvvmsWLVpkNm7caNvPVGToCSGEEEIIIUIDA2vNmjWFHrwWj44dO1pPnPvx4osvxvzskCFDzEEHHWS9cv369bMl2jDK3AwePNgceuihply5/5k6VatWNSNHjjR169a1j6uvvtq8//77hX6Pz48ePdrssccepn79+vY5n/nss89MJlLq5RW8wEWLtsxF2WLo0KGpPgQhhBBCCBGwnNqAAQMKvZaXl2dDH2Px8ssvF3je3GIs69atK/LZhg0bFjzfYYcd7P+rVq0q9BkMwWgw3qjn7f5dxGDc1KlTx37OgRDO2rVrm2+++cY0a9bMZBppaehh5GmhL4QQQgghROaB06Zv376FXqtUqVIobePtK46KFSsG+j0vn4kmPz/fpCtpaegJIYQQQgghMhOMurAMu9Jk+fLlZtmyZQVevaVLl1qvH8Iujhcw2ntIXl+6ohw9IYQQQgghRJknPz/fXH755dbY+/nnn+3zli1bmiOPPNKeGxQ3Fy5caF544QWzYcMGM2PGDDN8+PC0PW8y9IQQQgghhBBlnoYNG1rDrnXr1qZRo0bWK0n5BAfKOgwbNsz06dPH1KtXz9x///2md+/eaXveciKRSMSkAZwwJy/P/VyUXbbGSMAtES7lpXRm45btobdZpYL/mPNiKRd+mxu3hBvnXrlieRM2OSb8ITNiwj+XW7aFnzNQKRn9KBlTUE7I93okCfkXAfJAimPdz/+rBxUWFavXCL3NClWrhN5m/tZtobZXPgkhZ9s2bQy9zdwkXB9TLvxxk1EubLatXRtqexWqVTNh8/JJ7UJvs/3kSaG3mVulavht1qptMo0RI0bYx7fffmuyhcxY+QohhBBCCCGE8IwMPSGEEEIIIYTIMmToCSGEEEIIIco0V199dVaFbYIMPSGEEEIIIYTIMlRHT6QvIYunbMsPPxm8fBIEScoloc1IEoRotm0P/3xuD7noaE4S9rLyI0m4PiYz+iZnNHSSIHQS9iWKJKEfbU/C/ZObBDGJirV3yAghp5yczeG2l5trwia3fPmMEPVJivhQEgpKR7aHLVyWkxHCKVM6nBt6m6e/8kLobebWCr1JEQB59IQQQgghhBAiy5ChJ4QQQgghhBBZhgw9IYQQQgghhMgyZOgJIYQQQgghRJaRNmIslStXNn369LHPV65cmerDEUIIIYQQQiSR/Px8s3jx4oKfa9WqZerWrWtykiE05JPffvvNVK1a1dSuXdtkKmlj6A0aNKjguWPwCSGEEEIIIbKTNWvWmMaNG5vdd9/dVK9e3axevdps3rzZ9O/f3/Tu3TslhueDDz5oHnjgAWtsrlq1yjRs2NCMHDnSNG/e3GQaCt0UQgghhBBCpIwRI0bYYuXLly8399xzj7n22mvNBx98UPD+li1b7Pvbt2+3xtiyZcvM1q1bC7UR/Zmff/7ZGo3xIILwjz/+KPTa+vXrzV9//WXmzp1rfvnlF/v80EMPNWeccYaJRMIvi5NsZOgJIYQQQggh0oIrrrjCVKpUycyePbvgNcI78fzddtttpk6dOqZFixY2pPLJJ58s8pl+/fqZHXfc0TRr1szsuuuu5tlnny3U/kcffWSNt7322sscdNBB5rjjjrNGHdSoUcPcddddZpdddrE/cxxnnnmmWbFihVm7dq3JNGToCSGEEEIIIdICvGp46ypWrFjkvfnz55sff/zR/Prrr+bee+81PXv2LGKAvfPOO+aHH36wHjs+c+mll1rvHuAJPOWUU8yJJ55of4/PDBgwwCxcuLBQG3gWeW369OnmzjvvNFdeeaWpWbOmyTRk6AkhhBBCCCFCg5BJ8u/cj0RhlBhuhF3ibbvkkkusR65z585FPodRVq1aNfv8wgsvtEahW8wFbr/9drPDDjvY5z169DD77befeeKJJ+zP48aNs4Iv9913n6lQ4b9SJa1btzZt2rQp1MbDDz9sPXkdO3a0n7v++utNJpI2YiyJwAW7adOmVB+GKEWGDh2q8y2EEEIIkYEgsohR5iYvL8+KrMT7/EMPPWQ9bBhvr7/+uqlXr16RzyHa4oB4C0R79AjHdHPwwQebpUuX2ucYk4RtOkZePPDi8di4caMVhWnVqpVZtGiRNRIziYww9DDytPAXQgghhBAiM5w0ffv2LfQa+W6JxFg6dOhghVSuu+46c8EFF9gwTfLx/BLtHNqwYUOBh49j4GevVKlSxRqso0ePtjmDp512mskkFLophBBCCCGECA0MKnLa3I9Ehp5D+fLlzeDBg63QCsZiEGbNmlXwfOPGjebjjz82hx12mP356KOPNp9++qn5888/C/3Otm3b7P/RSp5OPT1Qjp4QQgghhBBCBAQRloEDB5rx48ebr7/+2vfvEyL6zDPPmA8//NCcf/75pnLlyqZr164FeX0NGjQw7dq1M1OnTjVz5syxnscJEybY91944QVz8cUX29DRL7/80kycONF6FzEQeWQaGRG6KYQQQgghhMgu8OA1atTIljVwc/bZZ5v27dubsWPHmiFDhlhvIJ9z59ZR0JzXqlatWuh3n3rqKfP444+bJUuWWKMOFU5CMAGj791337VqnOQL8jN/C4MQunTpYl8bM2aM+emnn0zdunVNr169zGWXXWaPNdOQoSeEEEIIIYQodTDwEEiJxSuvvFLwvGHDhkU+h+EV63ebNGliPXHxQFAFjyGPWKC0ySMbUI6eEEIIIYQQQmQZMvSEEEIIIYQQGU2s8M6yjs6EEEIIIYQQIqOJFd5Z1pFHTwghhBBCCCGyjLT06KF206dPn4KfV65cmdLjEakhEnJ7FXLCbjE5VCifE3qbOdv/Wx8mTHLLhb9PVK5SWg5JhSiXhH4UMUm45jmZcFdyQtNfxSwZ5zIJt4/JrVkr/EbLhf/ltyejG4V9kZJx0csnY3xLxrwW/nePbC9am6yklMsN+XwmY56sUlgNMgxOf+WF0Nt87cxOobd57kezQ29T+CctV1WDBg0q9LPb6BNCCCGEEEIIkRiFbgohhBBCCCFEliFDTwghhBBCCCGyDBl6QgghhBBCiLRk27ZtZvny5SY/Pz/Vh5JxpGWOnhBCCCGEECJ7Wb9+vVm7dm3Cz+yyyy62ZMIhhxxifv/9d1O3bt1SO741a9aYTZs2mV133bVEn0klMvSEEEIIIYQQpcro0aPNPffcU/Dzn3/+aZX3q1evXvDa7Nmlr9757rvvmocffti89tpr9lg4riCfSQcUuimEEEIIIYQoVa699lobkuk89t57b3P11VcXeq1BgwZFfm/dunUx23N+J57RtS0qBBRvXCRStETJY489Zjp16mTuvPPOuMfu5TPpgAw9IYQQQgghRFozfvx4s+eee9rwTUI6X3rppYL3Nm/ebA477DD7aNSokalRo4a55pprzJYtWwo+QwjobrvtZgYOHGj/r1OnjqlXr571yrmZMGGC6dy5s8nNzY17LF4+kw7I0BNCCCGEEEKEBoYXHjP3g9dKwvTp083cuXOtR48a2926dbN5flCpUqUCj95ff/1l5s2bZ9566y0zbNiwIu1MnDjRfPrpp7YdvIoYbOT/ZSMZkaNHvK6Kppcthg4dmupDEEIIIYQQARg0aJAZMGBAodfy8vJM//79S9TmzjvvbJ9fddVV5pZbbjGLFy82TZs2LfQ5BF6qVq1qDbjXX3/d3HjjjYXe57j22GMP+5z3xo4dax5//HHbXraREYYeF1YIIYQQQgiR/vTr18/07du30Gt43UrCXnvtVfCc0Ez4559/7P/k2t12223m0UcfNRs2bDA1a9a0apiOYeimSZMmBc9zcnLMoYceag3GbEShm0IIIYQQQojQwKjD2HI/SmroJeLpp582I0eONG+++abZuHGjWbFihbnjjjvM9u3bi3x269athX4mj69ixYomG5GhJ4QQQgghhMhYyMlr0aKFOeKIIwpee++992J+9oMPPihk5JGvR52+bCQjQjeFEEIIIYQQIhaHH364GTVqlJk6dapp2LChVcWcPHmyVemMhhBPlDv33Xdfmx5GuYVLL7204P3Vq1fbsE9y/QgJReAFCAOtUKGC58+kA+lzJEIIIYQQQogyCSUTnNw7N5QwoBRC+fLlC+XW8ZoTctmlSxfz3XffmRtuuMGGbh511FHm7rvvNi+++GKR9h566CEzfPhws2TJElunb+bMmYX+LmUZZsyYUfC3KdkAs2bNsqUbvH4mHciJxKoUKEQasGXDhlDby4n8t0BmuI3mhN7k9iTckeXzi8aol5hy4Ud+bzfhns/y4V+epBAJ+XtDcr56MqaL8I80Eya1/CRMveW3Fc47CYUk1IhKxhhXbuv/amWFQU5S8nVyyu49uaVksvqxyA/5mpevmLz8sTDZtmlT6G2+dman0Ns896PZJt346quvbIgmpRTw6JUFlKMnhBBCCCGEEFmGDD0hhBBCCCFEVpMbIwQ021GOnhBCCCGEECKradSoUYFoSllBHj0hhBBCCCGEyDYQYxEiU9m0aVMkLy/P/q82dS7Vj3T/aOzQmKn5IvlzpebesrfuSMYxiuQj1U2R0axZs8bUqlXL/PPPP6ZmzZpqU+dS/Uj3j8YOjZmaL5I8V2ruLXvrjmQco0g+Ct0UQgghhBBCiCxDhp4QQgghhBBCZBky9IQQQgghhBAiy5ChJzKaSpUqmby8PPu/2tS5VD/S/aOxQ2Om5ovkz5Wae8veuiMZxyiSj8RYhBBCCCGEECLLkEdPCCGEEEIIIbIMGXpCCCGEEEIIkWXI0BNCCCFESlm/fr1ZunSproIQQoRIhTAbEyJT+fXXX83atWvNAQccUPAaRUGXLFlimjVrFqjNxYsXmypVqpg999yz4LXff//drFq1yhx44IEm3dm4caNNui5Xzt9+0N9//23mzp1r/vzzT1O5cmXTuHFjs//++5t0Ij8/33zxxRfm22+/NevWrTPVqlUzjRo1stfa7/dNFVu2bDE5OTkmNzfXpCuRSMT2o6pVq3r6/LZt2zx9ju9dvnx538fy5Zdfmm+++cbe6xxTw4YNTfPmzX235fW7bN++PVThgqD3pJvNmzeb119/3axYscL861//MocddphJB+bPn29uuOEGM3v27KT9jU2bNtmiz7vuumuJ21qwYIF555137Ji+xx57mLZt25r99tvPJOOYucfD7KP0AdqrUCH4EpD7iTmONpLxvZNBMu5JxmGoWLGi72uwdevWuO8zd5bk+sAvv/xi1zGsN5IxrzEe8b2TMX6KEIkIkSFs2LAh8u9//zty0EEHRU455ZTIjBkzCr2/ePHiyJFHHum73a1bt0aOOOKIyLx58wq9vm3btkirVq0iH330ke82//jjj8iBBx4YWbFiRaHX//nnn8jBBx8cWbZsWSQIP/30U+S6666LtG/fPtK6detCjzfffDMSJpyTTz/91PPn+W4XXHBBpEKFChGGFvejWbNmkblz55boeJ577rnIxRdfHDnhhBMKfe9OnTr5amfmzJmRffbZp+DYypcvX/C8fv36kWnTpvk+tu+//z7SsWPHSOPGjSMXXnhh5Lvvviv0/qhRoyI33HBDJEx69eoVeeihhwL9Ln2le/fukZNOOqlIP9q8eXNox/jDDz9E9tprL0+fXbVqVZF+E+9Rq1YtX8fx/vvvRxo2bBjzmterVy/y8ssvR8Jm3LhxkUsuuSTUNlu2bGm/i58x86yzzor8/fff9uf169dHmjRpYr935cqVI+XKlQvUh0aPHm3H2ubNm0ceeOCByPbt2wu937RpU3tP+OGXX36J7L777pH8/PxISWEu+OCDDwp+5lhOPPHEguu+7777Bh4vOb4ePXpEcnJy7Dhy1FFHRfbcc0877vXv3z8SNsx1QcakRDBOP/XUU54/v3z5cjtW1K5d244769ats2OFcw8xJvNaUEprPGIO6dy5cyRM8vLy7MMvHEeiMY5j9cqmTZsiXbt2jey4446Rdu3aRdauXWv7qNMWY1/0nBQGXJ933nkn9HZFuGTG1rUQxphevXqZl156yZxyyil2R79NmzbmgQceKDg37NSxW+uXzz//3O6YHnrooYVeZ5fqwgsvNE8//bTvNtkxx1MQvXNcs2ZNc9JJJ5nJkyf7bvO3336zx0h40w8//GDbqlevnvn4449t2FMYu9Ql4aKLLrK72xMmTDCPPfaY3eV96623zNSpU61X87jjjjPfffddoLZvu+0207dvX7vD+emnn5qmTZtar8x7773ny1vIDucZZ5xh+85nn31m+wu7vLSFh4/3OnbsaL7//nvPbbIre/LJJ1uvcPv27a038/DDDzfvv/9+od3bDRs2mHRg/Pjx5uyzz7a72ngkHI/OrFmz7HVKlUezevXq9ni8PKZMmeK53ZUrV5rTTjvNtGzZ0t4r7HBzzfHkzps3z3Tp0sWce+655uuvvzbZxkMPPWQ96jvssIP9eeTIkdabzX1Af5w0aZLp16+f9cJ7hTG4Z8+epkmTJuaQQw4xt956qzn99NOt58mB+4rx2A94xbiP/v3vf5vly5ebkkBffvzxx+1zjoN7evXq1fY1vjPjxznnnGOjDvzy8ssvm4kTJ9qxh3Hiww8/ND///LN9/d5777U/ZxuMvZw/xmHGtbPOOsteb87ztGnTzLJly8ywYcOyajxKNnfddZftK+4H6wbuJeYR7gWvPPLII2bGjBn2XgbOJ2PkK6+8Yj3ke++9t7nuuuuS+G1EOqPQTZEREB7x3HPP2cXYvvvua1/DoOjatatdaP/nP/8J3DYLvnghFPxdP4ugZLY5btw4c+aZZ9qJEQOUUCH+/+qrr8zxxx9vF0peufjii20IWyIWLlzouT0WTEzSGKMs2KFWrVp2ockCqF27duaKK64wgwcPtotNvzz44IPmo48+ssbt9OnTzdChQ23o0FVXXWWNNK+89tpr1tDGEHXDMbP443gxDJgg+/Tp46nNd99914avzJkzx24ODBw40Fx//fX2OzNxt27d2vf3ve++++xiMhEsLm+//XbfbbMge+aZZ+yCYuzYseaee+6xYUL8Ta6h13AhjHra8BLW5AX+LpsBYfPGG2/YkFzuHzeE67Jxcv/999twagyYgw46yFObLKL69++f8DN//fWXr2t/2WWX2dDSRCxatMj4gU0Hxgz3z9dee23BWMGCkP5AGKLXY6XP8DsYe0BfZ2HKJgn3DeHqQeD+efbZZ22fGTFihN18cy/yMdTZOPLLJ598YsNUCTOsUaOGfQ0jj3GAOeTqq6/21R4bQlyrVq1aFXqdc8D55H1CYr2AUcv3TgTpA1wzrzAmsLmWCDYKmT/83ENskjRo0MDeo0cccYSdP9hEgIcfftj2g1tuucWkajx68803zc0335zwM9znRx55pOdjGzVqlH0kgjmPuc0vnEse0bAJSaglm7c77bST5+vDOHbeeefZ+5JN30cffdTek8D5xWjm3vIaYnrllVfaTfAwxyORGmToiYyAHd5ddtmlwMgDBrWdd97ZDmYs+tm1DQLeF4wadiZZnLsnBRYcvXv39t0mixKMTwxT9+IRbxwG6wsvvOC7Tbxhxx57rH3O7icTARx88MHWe8iCobiFt3uiZ7HHTl882PX3CsYWk5Jj5AFt4+VywCj3s2BxwPDi+zL5/fHHHwXfG68uXl76gVfwrO24444JP4P3w+2dKA7O01FHHVWQp8DChMUL7Zx66qnW2AvynWkv0YIRAzMIsfoRCysWCH4MRxbh5FU5C/5Y4Nl5++23TdhwzBhqqbrm/H0MObcRFU1xGynR/PTTT6Zu3boxF39u49oP++yzjzWOnHsEIyz6e/Iz46ef/u42cshrxmtwwgkn2PGHzZQgYDS8+OKLcd8v7hom6u/HHHNMgZHnwL0ZJMKAcQ3jMRZ4ixONqbHOJeOmYzDFwq/XkT5C/inzWjycMdQL9A02Lp25l1xmxjh3PjubZH7mi2SMR+SLYdBjbMcDY98PrAHYsGUjNR5sQIYJ55ZzzAZu/fr1Pf0O3lZn3ODaY9ThbXdg7bTbbrvZ+ZgxwQtsJNapU8fmMcejpJ53UTrI0BMZASGKDLos2tyJ1OzKsrvOAoOdzyCwyLvjjjtseBfhEkxgeNzYFSX8sFu3br7bJPyExRWTLe0yYLPzxyKInV8WRX5hN8757hhphJ0FXQwQqoYQSaJwGz+iCEwsHANhPSyqnF1E9yTBot8tTBPke2PY0weYhDinfr83EzahZieeeKI9B+7dTSZ0vDqE6vrxHNBHnnrqqSKv5+XlWWON600fYLL1CsfGbnai6+PXE5GoH9EfHS8zCzuM6OJgEYAhirETbyH0448/hm7osahhMc3/XsBTxWbNmDFjbHixe/wghJPxY/To0dbz7BWMBDy+d955ZxEDwuGJJ57wZYxzzVk0JrrmhBv7Ac8TG0F41zleFs+EquLJZGHIPcq91KJFC1/9nY0id6g7Bir9lfuKjTfu0SDjMPdKWLApxPnE8GHjJBrmkyDh7hgSgwYNsteJiAoMNRa8RCpwLjkHfq45YfyJrjnjtB9oE69YojY5bq8wFrDJhpHE3MiYyTzpHiMYhzEkUjke4QkjTJdx1wlVjgYPrp+0iU6dOlmP3pAhQ+KGkBbn2fcLBt4HH3zgq13uRfqJIxzHRkzt2rULfYbx0k9/px8xfiXqR8VFIIg0IeScPyGSBkIc48ePj/keCcFVq1aNNGrUKHD7b7zxRuS8886zyfVt2rSJ3HfffVbMICgk7ZPw3qFDB9vm6aefHhkzZkwR4YIgCfRff/11pGLFilbs4bTTTrNJ8n/++afntlavXh3ZbbfdEibQkyz+1VdfeW7zzjvvjOTm5loBG0RJOL7PP/+8QPAGIQk/4i7RIg0OJJ0fcMABVhQAIQ2/Iif333+/Pc4aNWpYYRyO65BDDonUrFnTCioMHDjQV3t8N5Ldf/zxx5jv33333TYhnuMNUwyHdp999tmIXypVqhTZuHGjfX7XXXfZc8uxcU4RlAlT3ODXX3+191KYINriV4zlkUcesd+7WrVqVsyJa37ooYfadhAkueWWW3wfx5VXXhl59NFH474/efJkX+1yL3JPcm/G46KLLop88cUXvoUudtllF9sHudZ8Z0ekgb/nR9wFJk2aFDnnnHNivof4FPcUbS9ZsiQShL/++suOk5y7e++9N/L2228HEotxC1twjT/++OOC9xmDEZNZuHCh77b/85//FBLzoW3nOeMH/cx5DB06NGFbCI3sscceRUS7ovvZ7Nmzfc07+++/vxWkSfQdXnvtNc9tjh071gqhxQNBnttvvz0ShDDHo969eyc854jaXH/99aGK4Tz88MP24ZeePXva7+p+7LzzzrYfnX/++b7a+vLLL+28G49Zs2ZFTj31VF9tsvZhfnWEnGLBXPzJJ5/4aleUPjn8k2pjUwgv4B0gT6Bz584x3yd0kR1cEsezEXamCVcjTw3YQSfvCK8RHgu/MunspJIH42W31CvklZEvgJeje/fuVrABGGbwmPmVoHa8LoS1ORLehJohekBeETuYXG+/7dLeq6++WqS8At4Ir6Etbtj5JCwunmfkySeftDusTs6E1+8dpIxAcbAzj6eVthHmIC8R7y0eHsKN8f54pSTXNRZci+Ly9Ni1x6Pk1aPnQNgSOWTu8goI+RANkChcMtFx8P1LKoGe7HsSEF4hmoBQW0Lj8HhwbyJs5Tenjj4zfPhwGzYdq7QH3h1yaLkvveYYOTz//PPWC0m73O9cJ/orXlk8MfG8p7HunegQVdp0PEd43og+uOCCC4xfCPP3GvKJJ8ydbhAL7h/u8TBFR8Juk77OWBkvhxWvD+O91+uTrPGI3+dYwyw5k6xxGLEZxjE3hKwyDxEK6xfGNc5jrO/OvMz6gLbTYTwSpYsMPZERBg4LB4Q8WKSgupgM0YaSwqKPSYbJlYExkeocn8lWNbGyBEpn9E/EbdiIoH96FfQQsQ0SFnYIAcQzoAgLJM/Vr6FX1mDxTRg1whmEfBI2TbhtSUBwg1p3tMNGExsbXmskFgeh3YTok5flKOwC9xXhweRPE5Io4oMABwb80UcfbcPqCBfffffddcr+3wgMOueySUSfD3NTR4jSQitNkfaweHbi6kn47tGjR6h5HOQrsPNK7oU77w0Q0vC664uXid0vlL+c5/EexamDucGQSFRYNQxmzpxpj5n8NbyEiAqUVTjXXtUi2dl3kvHJ9WKhlQxYtKE8inw2UtrRO8Fh4UekIXqRzgZM9MLKr2AMRgP5b+Q5YajEepAflW4wLnHtua/ZlCpOrc4LjueSax+kbAxeETzXbin3kkIfd8aGyy+/3OYdhwWeHQy9G2+8sdCCmnxMjL+g5xRDMdoDh9cxeqxPNUSr4NGO9tL4EeChDQS/ABVGjPFkgLcUoTG8fEHyMRNBrr1f0ZRoUG8lsgTwCmP44s0lPzXIXEoOL/mDbHL4UaP2es3IAWTsIM+zpPcUZT+ivyNKsOSkhg0COH7LqIjSR9sTIu1hR5LBD8ELwicYXOIJhRSnNhZNhw4d7MIFEQAGSHaomSSoEwQMmF4Xv+eff75VX0QkhIUKz+PhVU0LmFzwHKFaSVhTIhUsv5DwTjghkw1hbIQwsiii5g7hhkHEEVjgc60wHtkJ5WcHwiKjZe4TwSIC45NJG8EHVDsRNnGgXyAMEGYNNAxeFjIsjouD70MtRxRbqanFoixe32QRW1wYVzScOxbUnDN+lx16jArOw4ABAwrqJoUFoXZ4yggh8gObL4RAuqXL2T3H88bCk37rFUpmYNDFU9FlDGBjxg9On2YBSbgv4ktudUSMNML5ghiRGHZcB1Qhua+5p/hbbBAhyBIk5It6b7TpFhFBGIKQSa+iF/RN7kOOi2NigYqYUSw4n15ULWmTjQanjhrGVzzlPc6zn35E2yzKWThGnzO/apYOjN30S8K03RBiiMgWYjRu9UivMEdQooU+Q2ifG2rN4ZH2G8bH/RwtAoWHm5BIxDm8eKM4hxwb3ls2Menv8cYjwvj8CEQ54zH9knIKzmYYob94YBEP8ePxwvAgEoLjw2PL+WSz1SkNwfzJ83jCKvFADZcyD47qLcfKPI6iK6GghDH6EaMBxluETLgvMaA5Nq4LaSRBwlWB88eaAQEw5nS+JxsziCZh+AXZ0ObaszntqJg6MA8z5gWp35sIvOxc93SMsBIuUpAXKIRvHnvssQJBgUQPP2Is33zzTWTXXXeNrF27tiCJHSEOhDqef/55+9rLL78cOfPMM1N6xRBhOfrooyM77rij/Y7HHXdc5Jlnnols2rSpxG1fc801VvTjp59+Knhty5YtkTvuuMMKvAQRo7n66qsje+21V+TCCy+014y/gUADQgX33HOP7+OrU6dOpE+fPlbMJicnJzJgwIC4Qi1hgKiMVxEN+gwJ6QgBFdc3/YqxANeZBP2PPvqo0OsvvfSSFTHwK8zhRxjBK+vXr7cCH9u2bSvyHsedSCQgHmH0bbfgxd57720FkRDuOfDAA23f/uCDDwo+g4ADQg5+QcwD0SGuE33BYe7cuVboBFEQvyDiwnVAbAeRhe+++y4yZcqUSIsWLex38MqyZcus6Az3XXF9k3HOC5999pk9f24BkniPIGIsiM0giPXtt9/a/sTY/OKLL1rBkiB9nfPWvn37mO8h/NG/f3/fbb711luRypUrW1EPRGPGjRtX6OFHwMqBMeSJJ56I+d5hhx3muc2VK1fa+YE5rLjr4wh7BRnbn3zyyciiRYts/+cc1K1bN3Lrrbf6FiNhXkDIpXnz5la0CYEkzu/MmTMjTZo08S2c4oiqIVDmcOyxx9o+BCNGjLB/tyTMmzfPjhWMy4g7IYiG2IlfHnzwwciee+4ZWbBgQcFrjCH0Ie7/H374wXebJ510kj13sWAuRsgqTFq3bm2F8ER6I0NPZBTz58+3imIsRmM9WNR5Zfr06ZGTTz65yOsszli8oS4XxNBjoGWhF8+4fP311wOpbbL4ReWQwRyDB8OPCSfIwsKBhWC842Fx5bdtjpFFEIYjExgKh47KHQu42267zXNbTHrVq1cvdAxcD4wqxxDza+ihHodhm+iB0lgQFUaMhR49esTtm6hz+uW6666Le846depkF1leYbOguO9Ov/Jr6KH2ysIklqH33nvvWcPAL2wwYNz7fS8WU6dOtQtK5/i4Dhjd9C1HcTKoocf92K5du5jvsalBf/DL2WefbRV/o1mzZo01UJcuXeqrPe49NkkYz+L1Tb9KwJzDfffd16r/xmvTbfh6AcObcTeWmiX90q1mecIJJ3hqc8KECVY1MRaoRCZSkowHv9e3b99ImHAvx9sU4P7hPvID17NLly6Rxx9/PLRrDmzoxJrbUEZlM8UP9B9HVZjNDK7znDlzCt7nPYxKv8yYMaNgQwQF2ypVqlglV3jooYcCbbjFgmNm88Xpo1wnx6D0AgYixl4sMNbZ8PEL3zueQijX7vvvv/fc1hVXXFHsfMEYKkMv/VHopsgoUOIiHMxvaFmielDREGZGmAzhHfEUPuNBOBNhO4TaxAtDJfRw7ty5vtTEgBwDavPxIO+EMBJCw8jdop4ZoS/u2lZeiFePjHAkQmT32msvX+0RekIuAyFs5Fo4Ya+cT2p4ETpDCInX0B7yGd3iJoTaTp8+3eZxsVFFmJ8fnFqMiWpdxSuGXBz0Fc5ZGH3TfX3i5av4DWcjlImQu0ShRkHqIhHuyXEQ0nTTTTcVCk0iDLZly5a+2+Qa0b8JYYp1j9HvY70XC0JduT/cBe1HjBhhQ6UIPXJCxYKQqJ5f0HBD+miscEKuG+G/hGE6CrRe4N7jOiAmEVbf5BxOmjTJhhOH1WZxBdODFE8nxI6Qd/IT6QMOhFiPHTvWcx9yQ/grIaZhwj2C2mR0KCDngzHVr6Iy15yQOvp49PUJKkrC7/G9Yyk3OrVn/eAu8k0/Yqx3F/nmOxOa77WOngPpF+QOMg8zT1JTzukvhMgyfwaFMQ3lXsamN9980wodkVdH6CJhk9TpRBWb+r6pGDvoR4zD1DV11wsl5Lt69eq+2lTB9Cwi1ZamEF53kL08YnkVEsGOnLu+khtCKNhV9uPRI2ykuHo17LSy0xykfl407Mqyg8eOMDv2fiG0rn79+nb3kDpenD+8jmeddZbd0fOL24tHe+zQO7uphBviVfLDDjvsEDPchN1/6uBdeumlvjx61Lpj5znRbraf0M1YsIuMp4gQH8CzEbR2IqFY7GqzE43njLYIybv55psjzZo18+XB5npSTy7s0E3Ha0a4GCFY7Jh369bNnmfCF3/++efQauXRP1999VVfIdp42PGCxyIvL8/uSlMXLohHj+M55phjrIeR78n1oe4U3hnCsrhWfuE4CDeM9gDjqcA7kajOXnFw31BHsl+/fpGRI0dGli9fHkk1nDM//Tho3TvOKeeWyAKu+Yknnuh7vnDuSepmEmIY9L6OhrGSUEXCAQlfxdPI8eHJ5N4Pyo033hj5/fff7fMPP/zQhljy3YPUfQPGdvpPNIxHfkO08VpxTA54xkkbcKBvBg3LxzPInMi5dMYf7kXq8vn1NANhyFyTnXbayc5JhLA647sbPL3MH17nIr4f6wzGO46L1xg/Ga+C9C3OGfMFYw9zI20x59L/X3jhBV9t4Q2mdnEiFLqZGcjQE2kPeQDF5RsEydFzck5YQMUDA4h8BK8wKRcXDkQoHvkhYRh6JYVJgMVErHApjDR3uJRT/NyroQcUiyfvggmHHAHyjvxAPkW8RQmLBIqc+10MkL+RqFgwC+Dhw4dHgkAoH8fEOXVChMg3oiB7kEXltdde67k4c6Li3UDIFYvJRBx++OGBF920f9lll9k+xSIOY8KvIcGCp7h7nHM7ZMgQz23yffbbbz8b5hsL8lFpN4ihx9jgzoFzXx+eu68P58YLLEjp02zAYDAznpC/RHuDBg2KBAHj/YwzzrDnjjGSDS7ykwmDJpw5KGySUdCb42PR5354NXLZFCGEPJkQUsfCn9A2NuIYp91GhR9YkJN/zDXmurIh4X4ENczIR+T6kk7wr3/9yxbNxpgMCht/GLUOnGNCickFZIwKYuS7N3QYm2mPMYUNCL/hpVyTRIZEcUXaSxPywunTzMOJNsLYSHHn/iYCIzR6vHCec47dY4efTdw//vjDrjGOP/54OxZj8AUpaq6C6dmD6uiJtAflMa/hdKhuNm/evNjPoRZHwVzUtFasWGHDu1CJKyko0iFlPGbMmLifQaEMtTOviolvv/22VVELU23TAcUzr/XIqMlVXLgpoS0ofDlFx5ELR8lw0aJFNqzkmmuu8aXMRugOSnSExMSC+mAokvpRn0xGoWvgexNmitIhIcGEXBIiCOeee659oJzoB9REvZZSOPjgg4sNzSEk0h3Sk0yChIgRMkxJBkJ+Ua2MVomjMDt9i/vBD9xDhMS5VUHdEApKSJ5fldlly5Z5DnflmKlx5gXKKRCSTckKnhPahkokYWhBoAYpIWYo8jlhoVwfxql///vfVjGWsFA/oKqLoiXXCeVAFBO5B1DIRcmX8DYvIZaMQYR0x1OHTDdQbURxETVkzmW0Qijh8ygYpxrGWsYj1BsZ41FrRYmTED5Clrnu/O8XVHQJBUTFlfubv8E86kdJGhiDFyxYEDfdACVcxku/yqDJgHmNsSdMSN/gfvECSuJ+788wUMH0LCHVlqYQqQCPDgIFYStrzp4924p5xNv1IxyLsB92RkV2MXHiROs1cNTd3En/eDKDKMglA0KOgoQvFRcCzHd2qwji6WLn3wkd8yvqk8jTLvzRuXNn66mOBV4uQs79QiikEwqMx9RR2Xz22WetB9srpeHRCxPUJVFvTXfwUDuRI3iE3KGVCNTEE+3IZH777TfrxWzcuLENx44WE8OLiJfcK4xpfkMeUw1jO/cz3mH6Kh5chJxE2UViLCIjYacJbwfCBSeccIJ9LVb9pXjgFaDQNYnV7EySSB5vRxmxCYQCvEBhVnYg2d3Gu+cWM0EAgF1UoPZbUPBucqwU/WUXlV11RF78erS8ENTrhReCXX7ql1Gbr2nTpuaUU04xJYXrFF08mmNEACYInAvqgEUXmK1du7Z9+AEvlOMtixYP4FrhbQ4Kni6OM7pmF33Tbx0nRCmoNRWmaMwVV1xhxQgAjxyeowkTJthajNQjdDybXkHUpm3btqZNmzbWk4WHKOwd9TAhIoBi4vzP/U8dK4QaSgJeSDzWjkePuoJ+a4olU/iBe9up10W/d4SXKEp99dVXW++RV28MfRsxqUTgkaLeWpDziBgNnqjo4t7UMcPb6QfG3K+++sqECd5V6nG+8MILdp5w1x6Fp59+2netMsZbvK1cF+5FvG7OvYUwk5fIl3hz78svv2wFTxyPHvdnSSMkEHphfI+em5hDvYix8HvMhYzleOa57njwn3/++YLaqxy717q4Tp+jPyOSg1iOXwE1L3BMRBVFFx7n3mHu9AP3HN8dbyFeZeYc6ghS15PxmfE0KIzrRBgwjjjjkVdRJJFiUm1pCuEX8sAoseDkmwD5FsgbI3nslZtuusnmKhSXE+TX20dOFvk15Lgh687uKXlqxNojThEridsLfMdzzz3Xfm+8guTZ0B7lDNzelOJAFtlrzqMjf+0HvJWUfiBp/cgjj7Q5e+Qc4DkgfyAIyHfvs88+MY8xaMI+159rEqvNIGIs5JIipoBYA54Ox6PH68jiv/nmm77bJCEfEZV4tdCC5AMdcMABVnAnLBAHQaTALQDheDzIgSXvLwjIdpPDQx4Z55V8RcqrlIR//vnHiiUgNkQeoTunLGhuFfmCzr3NPcm9yT1KHleQvEzOJ8dGbiaeCbyinF/yv/zIt7vB28Z9Qo4R54B+hdT65ZdfbnNWg3h43V48cvTGjx9fkGvGNeM+8OrRoy/zfRM9uEZ+YT5gfOR74tGMrnnnNZ/KzYoVK+x3JyokaJ5fNMjsk8NMXT9EMKKPM4ioD/Cd8bwiluKI+xB5MHjw4MB5uAh9IOjStGnTgvw8+vzixYsDz+e0E28O8ioORZ1F+rhT+5U+zflkHn7llVcKcqjxbvuB+Zrj43vHq1EXBKIW8D7Gq0nJsfqFfs486+4v/B3mM/pXkPxrvIGUM3GPR4zHrJ2CCMCJ0keGnsg4mGCYuJh03OIrTGoIA/iFwYowznj1hoJM5iymqM2FkUjIDO0zuaLIFxRESaht5zYUmcwQhGAy81qgmMU3i2gvD78hH0wkTAJMLO5JhYmHCSjI9QHCYVE6YzLne7ofQQrLUkORhTOTKYZYdJuOUqhfUF1D2Y7JkELHiNFwbTAsgsDime9OeDEbCNHHGUSB8emnn7ZKkYQ1haEayMKexZ6zIGNR5IQCslHAxkJJoA+OGjXK9h8WQKiNYkj7/e7cx2y4IHTBwoVNCARSWABxjv3Wt0xWwXSMZI7T3a9ZpN977712gR0kDIswNq/CD6ia+jX0CA9jY+eqq66y15tzmw6hm4SRFqeCHFTFM5bgDg/Gfb8wPmDghQm1TEsy38Sbe9l8cfdB/gbHj0poEBiLqB1J+Hf0+MbD6yYExpyTjuGGDRzuUYTVghh6wFyGuBQbbgjQIHzlfgTZEKVQfIMGDey4Q/H56O/NuOoX7r14Aj6Mc35rcALfm81a9yY64xHqqxSML4kKsCgdZOiJjIJJBW8Rgz8LX7ehhwIci2u/sKgP08ORLLp3724nh1ggx5wOuQRMUEwosWA3lEV6kGtOKYUw88rwwoRVODcaykiwsHYU/lgIBPHsON4xr3LdXsHAdRb5LFijF6rsAHth/fr1BbmmqLshj04f5fo7bbAYCCs3EUNt4MCBBYtsjEuMfzZVvIDxiYcd49ZdIJ3+hdEya9astCiYzoI5nueOhSEbNX5hIYoHysvDqxojCz/3JhiGChEHKCX6yctMpqGHFz3e9QkKc0Wi8+cnqsQBr31J1E9jgUEWploz4y/3XqyFPWUBiFoIAvN5kDzeaNi44v6OBRtDjG0oWAcx9BxFYIxSvPXR3mZUOf1CrmBxSsl+wWCONXYwHnN9vI7tbohIwgscLzoEA12kN8rRExkFKo4UViVmP1YeVBBFQeLMeRC7jwoYeQfkL/z/Rkig4rLJIMw8G/L8yDt08rvI2+Dcub8r+YsoZaJG6BWUBcmRiKXuGDQXyCn6S6HsKlWqmDDgOFBHTQbkLvAI6zhRRAyT5557rkg+iBvuLy+sXLnSFqynKDFKhLfffrvN/aPfcO35G++9954ZPXp0iY6XfCIUHFEzJUexZ8+eNm8PNdK7777b5lmRH+clpwzVSvq4O6eM/kUuE+quTs5ZKnPf4rXJ+WT8C9ImuZyo9oaZY0SejptLL73UPtIJ8tpQ9CTHjULZYSjtMm56zdn2CsqYF198sVV+jqdC6RdyCb0q9nqB+ZbxnX4d3Y+C9nXg9/j9unXrluj4yBVk7Jo3b14RdVvGKe57/kf92C8oOzPmMBfOnz/fKhyXFOd7h0nv3r1tP2J8o+/zP4rXqFI7r4c1HpFHSl5l0OsuSpFUW5pC+N1VJP+N0DtyAhyPHqEkhGThAQm680tdKXbrnJw8ckzYQQ8SQpEMKPxK6CY7v06BVcJz2EGnZo6fMDxCPNzhJtE/x3vNC9dcc40NoWGHlRAPigFPmTLFFnL1W2vJAa8WO9Tkx4QBYYaEIbFTGWSX0w312fAuO+DliM6F+Prrr20RX7/gbSL3FIW8sPKBwoLQQq5pMsAD+thjj9mcN+5JctYIj4y+VtwT7vzARLi9eIQnusMLqQl2++23p0XBdMKTuc/ZmWfs4W/Qv/BEFFfAOB6E1hH+m44QBYD3M1k4dRIJu4uuecdYVVKVR+qLlnRcYoxk/nI81dHHGSQ3jL7H/IWX0Wuem5dcQu5JvDiMcdyPjOmEGjs5mkG84kSkBAnBj4bjIuUgHkS9EM7rFaIWiPyg7xCyG7TGaLzrQ84bkQRBIz6iIR/aa11c5iQvkCrCePT888/bdRbHSqgpdR55iPRHhp7IOIjFJ1eFkALCPig8ipGGWEcQsQ8mWX4fA4rBzC2+wgIuSM5FWFDs1D04J8qz8TOBJcvQY/JgQol1jExA7u/iVaSDCdEtmhIdakjYnV+QnnYETmLl2ZDE7xVyQNxhoNE/x3stHoiOuI8lUT5Q2KE/6WLosZFBriOLq0R5JSw0vYqouA09BBu45xEiueSSS+z98+GHH3pqh5xY9zVIVDDda5F0N+QPel2seRUToU/5KTJfHBiyGMrkohJOHr3JxCYKYi+p5ssvv7Tnj3GcjQLGd/cjSBgsvPvuuzZvyS2ewbnA6AsCMvjkiRLqTM549HEGKW7O3OH0neixl4ef+cK519zjsLuPRo/LGKdBQsmj+7efUPJkbRIwvwQR7fFTMD1WCH0QoROMRq8h2l5zfU8++eS441F0fm+Q8HeRfBS6KTIOpJwpdk75AkKZkDRH3h35aEKU/EKoh1OQmLAzN4cddpiZNm2aSRWUYzjrrLM8fZYSBqmG0J4XX3zR02dr1qzp6XNcU2TH4xEknJNCvInCb5JRnN4rl112mTnxxBM9fdZPCBEbe/fdd58N3aRoMv09OtSO8KmffvrJc4gP4ZvFFRmvU6eOGTt2rPEDfYP7urhQO44TKX8vXHLJJQVlNOgzhJUOHTrUhk9RCuSoo47y1M7xxx+fsD+68VvYHe68804rM+8FryGEFM9Gbp/wttatWwcK4XLgXBE+y/mkT91666027JX73inZQUmIROHBpQVlEJDWHzJkSGhtEqpM6Y+LLrrI9mvuGfoq8vuUzVmyZIlv2XmOc9CgQTa8rrTmDr/zBWGRXvu91zJHYYaSR/Pbb7+Z1157zY5nhFwStu23pAT3TN++fX2XOfBCnz59EoaRUiTdL35Dz70wYMCAgrJQxcH4ItIPGXoiI8EAGzVqVChtFVf/rCSLojC+Jw8YN26cHUhjTVa89+uvv/qqp5cMyPmLtfAnnj9oriOLx+KMCb9g2Ddo0CDu+2wgpAqucTImTBaiGBFdu3a19SfZHMHYYbHqXBtyMbzWWHQozhjzs+ijnyxevLjYz9GmX2M8ugZdo0aNiq3dFgtqNjp1G4vr10H6EbU4w4Y8NXI9WbgyxkXXJKTWGDXIvEB/GTZsmDX24Prrr7f3JxtwbJSFlUcbVp5adO28koKxg/Honn+o9UY+8zHHHGM3Bsn5TPVxuueOMKCfhz0OQ7t27UJvk9px9EtqoXJtGO/4+ZxzzrH5ml6Nx6C1Br3WjqTWHY+w2bBhg62pFz2Wsz7wazhTd1VkNjL0RMbAzhwFO/HGxCo6ysTbq1cvT8VV3VDYmAKj7MS6f5cdwQcffND079/fpAPsmjNIx5p8Zs6caQ1SPxMTC2pnkY4Yi/tn57UgYDzQFp4pJhpEENjtZwFLoV2vXleEN7geLOqjC4W7cT7jBzw5sbxB9KNbbrnFLlbvuOMOk0pYNOOx22+//Yq8x32AOA0LS6+wQH/mmWfsghz47h06dLBehPHjx/s+h7DrrruayZMnm7DAE+TFS4UYRDwRlNKEIsQU4r7tttsKnT8MQLynCMB43ZCiUDYFmhNBe4wBGAZ+uPHGGxN6iygs7RW8V3hHHA444AAze/Zsc8IJJ1hjES9KuoCXlgXvyJEj7fcPwwjFaOA7xxqH2Dzw6o11g3cU4xAhFq5FMgTA3njjDfPxxx/b+wuDx+886YZ7L/r+oz2MKi/gwWNuYL5xnsfDr4AOAjR44bjmnFfne+I1PfXUU+3rXqMAkgmeTAqYh2noMYYgTkVx81ggNheGkIzIMEohPFSIUCCXJpFcNIID1MoJAnkm1IE69thjbQI7MuHkASL7HWYCdklAGjrW9//zzz9tMvyMGTNCL5oeRIyFnAYnsZ5cAIrYUnKAosoUKfcqcOIUQ3eex3sEKZhOrSFyQyh/4AgpkF9IDTjyzj755BPPbZEjhlAGtdN4kFfm/tl5zU85B6cos1P8N5acOUJEThFkL9C/o+sDklTP+evSpYtNsifPwqtwQzJy9CiVQD4I9yClFCiZQh+MfnBO06FgOjlg5BIipOHI6iOQRHvUk0Q0yiscz1133RVTmAGRF+poUrfKr6gPfYW6luQLIjPvp8/EgtqQscZZ8sjIW0NYA/EGr3U9kwnFxp0cylh5akHKXyAeQv9k3I2+H8j7JH/PL4hXOblPHG/0cSIW5hXyJcmfdIubML/RNkWu+Z96h0FgXERAJNY4zHF6xcn1Y6xx5/2VpGC6A6JDiJPFYtiwYZGuXbtG0gHq6iKkFibkZVK3l1y5WDUJg65lKClCKZu2bdsWGjN5BB2LRekhj57ICNjxwyuUKNSqc+fOZsKECebMM8/03T47gIS5IONOHg+7lXl5eXbnLzrMqbS58MILrScG+J/ckGgI9fPj3cETQXhHcbBD7QfCR/G+OZLLhDERIki5AfJWkOD3wi677GLeeecd63VznscjSGgt3gdyM9lFP+SQQ+zOL6GNeLjoY35l6CkvwCP6NTd4m72CV4Q8vXgeCHbO8aaxQ0/ImBfwDLLb7c4fYjeZ3V/yzvB4+A3bDBty8/CQcR8SfsXuuyPb79eLFQ33NfcI+TacV0K2kWHHc4qXLLpcgBf4feTWu3fvbscP5Nvx4OFZ5/XiPHRubrjhBvs9uWcIL3PuoZ9//tnmU1IShZBJP6FU7PA74Xvkkj3xxBP2ekf3TT/gMWAcih5nycV8++23bb9dtmyZSQc4FqII4hGkTxFRQt/kfiKEk/uQ7/v666/b0FhyIP1CniPjZDz8hGDSn+k7Tv/BY0bI9k033WTuueceGw1ALhdeaD/9E+jneByffPJJGxbpxo8X8s0337Reb+ZW53k8/M6/ySh5kgzI88Sjx/xFP42ecwhN96s58PXXX5sHHnigkMe9pOAFZDxjDqfUDfMNYcb0M/pRmCVbRJIoRaNSiMDg1aGsQiJQPAtSkDvdoUwB3joKKeMZ4rnzQHabHbWS7tKHBYVv8RxxPHgh2PmePXu2fQ/Za3YA/UJZAa9S0EHk0evUqWN3jvE4BinKjtKr23sX7+FHah/PJzu+xe3ePvHEE57bRHUR9cFYoGxJOQA/O+hIj/vxWPkFzxYeYVRwUePj+qBAGJRkFEx3ewopHsz5O+ywwwIrBeLJbd++vfW8cG0pQs5zlDjxzPkF9Vh+l2vllG6oXLlyiaTsnfMXr9wH98PNN99cxOOVTvgpRROvb44aNSpyzjnnWA/KeeedZ1U9g4wfiQjS3nXXXWfLILhLDuDNdCtS4z32EwHiHAsqi/GiDNLp2jJXEkHBPcNxo+KLdxdPc5D7KBkQ9ZHIk4nn2C9EIlHKKEzwenM/A/O3U76C/1GKTbeyP6IoMvRERvDrr78WWy+LhRoS116hRtXChQuLfQSphZUMCAljUZruEEbK4hz5aCZWJxSNRaefsgVuYyxIeGZxUJsOI4+FGqU1CLUj5IdNhVTTr1+/SF5eXsLPsMhE6t8rLPaHDx8e933k8NlISJdNg+gQ00MOOcSXbHs0hD46oXqEMLpLH1BDL0gdPaAsw7777muNSGqJsSFF6RDGjqAMGDCgYMHH86AGBHWuosO9Mfz8LvIzGe5tZ3GK8UnYOgYLIXx+DT5k9jEi2Bgg7Hv+/PmhHCNjJBsPq1evtj+zgULpoNq1a/sug0BYpvs+x+jbf//9C32mY8eONpzeL/TzIKUevMIm08iRI20Ic9DSF5Ql4PrGK3niDolljkoVbAaxBon3CDIOMxYx9ixYsCC0TQfKK7BJBmy2uQ1J5no2j0R6o9BNkREQYoLYAWUV4oUoIgrhJ8QFARPCEYqDEKUwBSeC4oQZEcKF+AGKoLyGAEKq1TbdECqFJDOKg4R3cN0QOSFkyAlB9QPS2GvXrrWhplWrVg3lGJ966ikbhkTIFOIrHCPqbyhSIoiACqHXEgfJgJBAwqwIH44lmsD5IEyO8CuvcO6Q2Y/HPvvsYyXe0wlHFRSlQ84JoYtBoQ86Yb6oZqJU68C9RD/zC2MIAg89evQwgwcPtgqxjBeEcCKPzt8gpNwPfFfCiBEv4pgJXyVcE+l+vxCeSimNaGXckqjKcjwIksTqXyjZEhbtlFlINYRUch8R0uaIMBG2y/1NuP6rr75qw7W9qiRyDRgfCLn74IMPzEknnWQFcqLPsV8I9yXE0gmDQ87+8ssvt2GGhHwjoORV4h+xFb4ffZBwykmTJhUZywiPDlJChvDiK6+80oaulnTOIXSea4DIGBC+yfnlvHI+mUMIYScc1g+EFlICxAupnDcZi8JW9KavICxHOgIiNtECW1988YU58MADSzRuct0IWaa/xhoHRPohQ09kBCx2WUyRr0b+jnviYiAaMWKEnXw++ugjz206gyDx7Cz646lRRcuypwpyjPj+TOIsqMi1In+Hejz333+/rxywZEKuEyqO0XkWU6ZMCdQe+VMsCDDCWEyTn1YSxThgAYQR4a6dxmYCynQorX744YcpNfQwFq677jqbN0fOBXlA7kUaiy0WdEzoQSCPkkUuC2AWWKgIsumR6nxUR+2W/sN9jgonOamfffaZ55pxXmCBjioscvEYeOSs0h/8wtjDZoZbIp7FOu2RZzdnzhzPhh5qjdzDzkYJi2oWU6j+YkzyHkaLXyOKxTK13xwwTKJfY7MolrprLDC8KdUQDSq9bMbQDu2Heb2CQg4rBrcjKU/+I5sZGGrffvutvc+9GnpcF4zvGTNmFNxDtI2x77WNeND3zj77bPucPFrqU9599912juJ8kxvlVRmVMYPvyHXgfuY6P/roowXvo4jMPe+3hAt5WdSqdfKzaNs9DtMv/SjhMpYzjjlwHrke3DOMy2zCDRw40LehV69ePfvIBDiXbNoy5kXXEiTXk803P7CRwZgZZl3P6PxcxgoU0BctWmT7p1/DUZQ+MvRExsDAz44UizR2OpnIWFgw4JBkjbHnpwgsAxaTypgxY6yngwELg49yAF6LeZcmo0ePtsf75Zdf2l1lIPyaBSWLVibERLXhMhUEXli4MCli5EbvVLLLiPHjB4xkFlPxJNIpXZBKWDTh2cEQQYSIY0LsAg8F/Z0FAAujIPD7tMt5RYwFo5dSAIg1YPw5fSsVsEjBS833Q5iEe9RZpLMwL0kdvbAKpkdvAsWr+8f4hCHoFYonY+xh1DqbTlwbFvwYkizg8X4gXuFVRAMvEP2HR3SJBPdrnEuvhh4RBfGgf7JBgYiV46lJJfSTv/76yz5fsWKFWbhwYYFQBWOnnw0jzhmiRQ6MQ8cdd5zdbAvzONlsYpHvjHF+j5ONBqInEJXC8OrSpUuhDSH6K0aW382y4gqm+y3PgueOcccBAxpxD+c+ZAOTOT0sGO8RkWGMSXXpHEcoCU894zCCY9FiNo888ohvQy8ZBdM5Z060A5ufROUwDzMnsQmRyjrDwhs5xG96/KwQKYfuShglD3Yq2VVkEkOVriT1YQhloq4NRh/1dqgzxA66n/pSyQZjDmMU71Y0hLzg8XR2hUsbdiS9TJ6EyvgJNwSMrkRGDYt2v963u+66y7bL/9GgUIdBkA6LAWqAOQY+BgkLAr4r3k2voVzREFqHgUJfd7zVhIKyOMfDh6GRKliYevGgp0sdPUfFkv+jIWyTUHM8k17A8CJEL15BY64RG1KE9HlVWk0FhIuyMGRB7bc4c9gQkstiGSMZrxh9ywnrw3jGQ+XVY0TBbZRL+T/Ra0FDydloYXODfkOEBt4TDH82XvDCpnJBzSZb2H+fKAXCAJ1NC4wU0jLY2HA8poxxjNNBIzjwXE6fPt2OdXiZ2Szr16+f6dmzp0k1pAww1mI0JXtjGW8hRhl93WutQ5E9yKMnMgoGfELMvOTW+YGcABZQPJDJJlyMhWQ65OZlgmw0YVtuA4HJmcVJdHhQkMU53i08UKUBBv/nn39uvcbpAIbdzTffHGqbeMWR/ncbVORtEbLKAt2vByFMOA5yTIrDj/cAz4GXIt54esLqZ9wPhA26Q26Lo7hNGs4NBmV0iFe6weKcvsUmRapD6OjPXHtyHjH4CAUExibKA/gNC+T8E2rnwMYIBpD7NYw1vyV+CMnHKMULyoYLXjjAg0aebiqNPMZyzlXYUQ6E9hLijAeYTVvSLghTdiA8mGsWZCwi/BWDmYdTYoRxIJ02bvEwEtYdlpHHRhAbS2wOYDA7Bh1RGmwicI7Z7AgK159jji6FwUZHuuTkitjI0BPi/3csMerY9SKki2RjdnvTCULPyBHBoDvrrLOsV4NwInK4MFT9hK2GDWFfbkMPryghsGF7h1jkMom7w6f8GLh48NweRWf32A27vuR7ZivOhkF0DTEnbDJVRp5jwPkRVPICoXWJPMIskAhnYyHj1dBjwc+C3iE6JxVYwAXJ+ysOvyFypQ0GCyIN3EepHtPJTWMswitGPhiLUme8IoLDD0SMRIeI+81ziwVGJ15fDGM8uqQPOPdgotp6mQ45qOQ2knpAzheGn7sGIZ5Xahb6ncPx3iFUxcYNOa14BsnNTCcjDxCXom+GAesAjDvOI+BJx8DDW8hYxcY1Id9BcvSYK6g3yzmMFQBIjVtCmEX6otBNUaYhdATjjrhzFgGEgBLmgyclHeB42PV0IKTQ2VEjpt95zsDOYtPZCU41jqFX0omMBTqqcajSOTlA7smKBRG5W+SaeYFdTRZrXG/OJSGQ7rZYnLN4C0vdMx0h54LNAfJfKITL98aL1rt3bzuhJ1LmzCb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      " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "most similar pairs:\n" + ] + }, + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      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
      \n", + "
      " + ], + "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]
      \n", + "
      " + ], + "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.** Each strong perturbation up-regulates its own machinery and down-regulates 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 the calibration check in section 4 shows the extra hits are not false positives: on fake perturbations causarray makes 14 false hits against Wilcoxon's 99.\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]
      \n", + "
      " + ], + "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": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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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 three of the four perturbations, causarray's extra hits carry the biology and Wilcoxon's do not:\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, each raising genes of its own machinery (splicing, transcription) and lowering neuronal and synaptic 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..c1dbe0b --- /dev/null +++ b/docs/source/tutorial/mouse_brain/README.md @@ -0,0 +1,37 @@ +# 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 + +Local only (not tracked): + + 1_prep_data.py builds data/L45Glut.h5ad from the two pilot source files + (they share control cells; see its docstring) + L45Glut-investigation.ipynb archived: how the pre-0.1.0 failures were diagnosed + legacy/ archived scripts behind the investigation notebook + +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() From 861c228a56074b6f4f740f4987c46f523dcfbbde Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Wed, 30 Sep 2026 13:32:31 +0800 Subject: [PATCH 15/16] Fix review findings in size factors and propensity selection - comp_size_factor: a cell with no counts in the well-expressed genes fell back to ratios against all-gene geometric means, which sit near one for sparse genes, so an almost-empty cell got a size factor near the median (0.91 for a cell with 0.6% of the median library). It now gets its total count, rescaled by the other cells' median ratio of size factor to total. method='scale' returns one factor per row instead of one per column. The unreachable old implementation and two unused helpers are removed. - select_propensity_factors: a triggered treatment that already meets the target keeps every covariate; on_infeasible ('best', 'all', 'none') decides what an arm that never reaches the target keeps, instead of always dropping every candidate; with mask, covariates are ranked on the eligible cells that the support metrics score. The tutorial selections are unchanged: every adjusted arm failed the target before and met it after. - _platform: look up sysctlbyname in the running process instead of a hard-coded library path. - Docstrings drop numbers tied to particular analyses (SEA-AD permutations, Replogle dispersion, Perturb-seq timings, the Rosetta benchmark). - The Welch test states the 2x ratio for equal arms and widens its bound. - lfc.rst points up to the library-size section; the mouse-brain README no longer lists local-only files; the L45Glut notebook's text now matches its output for Ppp2r1a and Ap2s1 and no longer says the null check shows the extra hits are real. Co-Authored-By: Claude Opus 5.5 --- causarray/DR_estimation.py | 86 +++++++++++++------ causarray/DR_learner.py | 14 ++- causarray/_platform.py | 7 +- causarray/nb_glm_fast.py | 11 +-- causarray/utils.py | 64 ++++---------- docs/CHANGELOG.md | 10 ++- docs/source/main_function/lfc.rst | 2 +- .../tutorial/mouse_brain/L45Glut-py.ipynb | 10 +-- docs/source/tutorial/mouse_brain/README.md | 8 +- tests/test_inference_comprehensive.py | 4 +- tests/test_propensity.py | 52 ++++++++++- tests/test_small_arm_inference.py | 20 +++++ 12 files changed, 176 insertions(+), 112 deletions(-) diff --git a/causarray/DR_estimation.py b/causarray/DR_estimation.py index faa1bb3..0b0b75e 100755 --- a/causarray/DR_estimation.py +++ b/causarray/DR_estimation.py @@ -464,7 +464,7 @@ def evaluate(factor, name=name, j=j): def select_propensity_factors( A, X_A, candidates=None, treatment_names=None, covariate_names=None, - trigger=None, target=None, + trigger=None, target=None, on_infeasible='best', K=1, ps_model='logistic', mask=None, random_state=0, verbose=False, class_weight=None, **kwargs, ): @@ -474,11 +474,11 @@ def select_propensity_factors( 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``, 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. + 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. @@ -503,19 +503,30 @@ def select_propensity_factors( '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}``. + ``{'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. + 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 adjusted treatments, ready - to hand to :func:`refit_propensity_scores`. + ``{treatment: [covariate, ...]}`` for the treatments that lose at + least one covariate, ready to hand to :func:`refit_propensity_scores`. report : DataFrame - One row per adjusted treatment with the covariates dropped in order, + 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 after the removal + metrics with every covariate (``_all``) and with the chosen covariates (``_chosen``). Examples @@ -564,6 +575,8 @@ def select_propensity_factors( 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) @@ -572,39 +585,58 @@ def select_propensity_factors( 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[ctrl, k], X_arr[case, k] + 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 - dropped, chosen, n_fits = [], before, 1 - for covariate in imbalance_order(j): - dropped.append(covariate) - pi, _ = refit_propensity_scores( - A_arr, X_arr, pi_hat=pi, treatment_names=treatment_names, - covariate_names=covariate_names, - drop_by_treatment={name: list(dropped)}, **fit_kwargs) - n_fits += 1 - chosen = _arm_support_metrics(A_arr, pi, j, mask=mask_arr) - if _meets(chosen, target): - break - drops[name] = list(dropped) + # 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': n_fits, + '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 adjusted, ' + 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 diff --git a/causarray/DR_learner.py b/causarray/DR_learner.py index e1da5b3..22dc350 100755 --- a/causarray/DR_learner.py +++ b/causarray/DR_learner.py @@ -440,11 +440,9 @@ def LFC( 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) it gives more conservative - results than ``'pooled'``: on 85 SEA-AD donors with GCATE factors, - where ``'pooled'`` was anti-conservative under permuted labels (null - SD 1.1-1.8), ``'unequal'`` gave 0-1 false discoveries per permutation - (null SD 0.6-0.9). + (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 @@ -572,10 +570,8 @@ def LFC( (``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( diff --git a/causarray/_platform.py b/causarray/_platform.py index 2008630..9916fa9 100644 --- a/causarray/_platform.py +++ b/causarray/_platform.py @@ -2,9 +2,8 @@ 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: on a 5,000-cell subsample of a Perturb-seq screen, ``LFC`` took 159 s -under Rosetta against 32-57 s natively. pip installs causarray into whichever -Python it is given, so the check has to happen at run time. +slower. pip installs causarray into whichever Python it is given, so the check +has to happen at run time. """ import platform import sys @@ -19,7 +18,7 @@ def running_under_rosetta(): return False try: import ctypes - libc = ctypes.CDLL('/usr/lib/libSystem.dylib') + 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), 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 e3bab91..20057fb 100755 --- a/causarray/utils.py +++ b/causarray/utils.py @@ -162,20 +162,6 @@ 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, min_mean=2.0, min_genes=100, **kwargs): ''' @@ -189,14 +175,18 @@ def comp_size_factor(counts, method='geomeans', lib_size=1e4, min_mean=2.0, ``'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'``: ``lib_size`` divided by the column sums. + 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. + 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'``. @@ -220,7 +210,8 @@ def comp_size_factor(counts, method='geomeans', lib_size=1e4, min_mean=2.0, .. versionchanged:: 0.1.1 ``'geomeans'`` uses genes with mean count at least ``min_mean`` - (previously every gene); ``'libsize'`` added. + (previously every gene); ``'libsize'`` added; ``'scale'`` returns + one value per row (previously one per column). ''' counts = np.asarray(counts, dtype=np.float64) if method == 'libsize': @@ -247,43 +238,24 @@ def _log_median_ratio(sub): 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 all genes. + # 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) - if missing.any(): - log_size_factor[missing] = _log_median_ratio(counts[:, gene_mean > 0])[missing] + 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 = 1./np.sum(counts, axis=0)*lib_size + size_factor = counts.sum(axis=1) / lib_size else: raise ValueError("Method must be in {'geomeans', 'libsize', 'scale'}.") return size_factor - 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 - else: - raise ValueError("Method must be in {'libsize', 'geomeans', 'scale'}.") - - return size_factor - def subsample_ctrl_cells(ctrl_idx, n_ctrl=2000, random_state=0): diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 4c9b034..99d6bf6 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -18,7 +18,10 @@ 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. + 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. @@ -30,7 +33,10 @@ 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`. + `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 diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index e9d38e7..874ee9e 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -233,7 +233,7 @@ target holds:: 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 below. +better decided before this step; see above. Choosing the penalty factor ^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb b/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb index c74ae03..fea0ed6 100644 --- a/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb +++ b/docs/source/tutorial/mouse_brain/L45Glut-py.ipynb @@ -15,7 +15,7 @@ "**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).** Each up-regulates genes of its own machinery (Prpf6, a spliceosome component: mRNA splicing; Tpr, a nuclear-pore protein: transcription and RNA metabolism) and down-regulates neuronal and synaptic genes.\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", @@ -1426,7 +1426,7 @@ "id": "f5c1b1dd", "metadata": {}, "source": [ - "**Conclusion.** Each strong perturbation up-regulates its own machinery and down-regulates neuronal programs:\n", + "**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", @@ -1565,7 +1565,7 @@ "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 the calibration check in section 4 shows the extra hits are not false positives: on fake perturbations causarray makes 14 false hits against Wilcoxon's 99.\n", + "**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", @@ -1886,7 +1886,7 @@ "id": "00bdd16e", "metadata": {}, "source": [ - "**Conclusion.** For three of the four perturbations, causarray's extra hits carry the biology and Wilcoxon's do not:\n", + "**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", @@ -2071,7 +2071,7 @@ "\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, each raising genes of its own machinery (splicing, transcription) and lowering neuronal and synaptic 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", + "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." ] diff --git a/docs/source/tutorial/mouse_brain/README.md b/docs/source/tutorial/mouse_brain/README.md index c1dbe0b..5928187 100644 --- a/docs/source/tutorial/mouse_brain/README.md +++ b/docs/source/tutorial/mouse_brain/README.md @@ -12,12 +12,8 @@ inputs and `results/` everything generated; both are local. python pipeline.py lfc --tag L45Glut --batches 3 python pipeline.py null --tag L45Glut --seeds 3,4,5 -Local only (not tracked): - - 1_prep_data.py builds data/L45Glut.h5ad from the two pilot source files - (they share control cells; see its docstring) - L45Glut-investigation.ipynb archived: how the pre-0.1.0 failures were diagnosed - legacy/ archived scripts behind the investigation notebook +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//`: diff --git a/tests/test_inference_comprehensive.py b/tests/test_inference_comprehensive.py index 4b4aca0..19ea94b 100644 --- a/tests/test_inference_comprehensive.py +++ b/tests/test_inference_comprehensive.py @@ -448,7 +448,7 @@ def test_type1_imbalanced_welch(self, 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 sqrt(2) larger for equal arms.""" + """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') @@ -457,7 +457,7 @@ def test_unequal_is_welch_and_more_conservative(self, null_nb_balanced): 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 1.2 < ratio < 2.2, ratio + 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') diff --git a/tests/test_propensity.py b/tests/test_propensity.py index d4cf383..0506a97 100644 --- a/tests/test_propensity.py +++ b/tests/test_propensity.py @@ -710,7 +710,7 @@ def test_select_propensity_factors_leaves_other_arms_and_the_intercept(): 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}, random_state=0) # unreachable + 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, @@ -723,9 +723,57 @@ 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, - random_state=0) + 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 25e5165..b9283af 100644 --- a/tests/test_small_arm_inference.py +++ b/tests/test_small_arm_inference.py @@ -370,6 +370,26 @@ def test_default_size_factors_track_depth_and_resist_composition(): 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 From c8ce044faf6897a29825a62e9bdf7bbe991cee1d Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Wed, 30 Sep 2026 13:33:50 +0800 Subject: [PATCH 16/16] Re-run the SEA-AD, Perturb-seq and Replogle tutorials with 0.1.1 The three notebooks are re-executed with the 0.1.1 changes: no mean- difference filter before BH, size factors from well-expressed genes, and propensity factors chosen per arm. - SEA-AD uses usevar='unequal' and says it is the more conservative choice in a sample this small (2,683 genes). The GCATE cache is refitted. - Perturb-seq leaves library size out of the propensity model and selects factors per arm; only Satb2 is adjusted (U9 and U7 dropped, treated ESS 0.50 -> 0.68), and its section shows what the selection changed in one paragraph instead of hand-picked filtering variants. 8,161 hits. - Replogle reads the per-arm refit (145,340 discoveries; SUPT5H 1,591 hits, its down-regulated genes enriched for translation and ribosome biogenesis). Section 5 walks through the batch with SUPT5H and SUPT6H and skips the walk-through when the local nuisance file is absent. The fallback to the legacy rank table is removed, and a heading that skipped from level 2 to level 4 is fixed. - All three describe library size in the propensity model as a judgement call and point to the LFC docs. 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xW5ImL&S`7>SB7^3#PE*)Ch@NfZxzC0w__5oCO+%O&R4*Xq?qi>JP}>@{{!2lUmO4c From c0f97620e0fbb2ee31abf2b80d626cbd9eca4cff Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 20:00:09 +0800 Subject: [PATCH 10/16] Explain library size in the propensity model as a judgement call Library size can confound (capture depth, cell quality) or reflect a treatment effect (total RNA), and the data cannot tell which. The LFC docs now say this in one place, with a rule of thumb: keep library size when every arm stays supported with it, and leave it out when arms separate. tune_penalty_factor is the middle ground. The Perturb-seq, Replogle and SEA-AD tutorials use the same wording and point to the docs, instead of calling library size a pure treatment effect. Only markdown changed; the results are unchanged. Co-Authored-By: Claude Opus 5.5 --- docs/source/main_function/lfc.rst | 27 ++++++++++++++++--- .../tutorial/perturbseq/perturbseq-r.Rmd | 2 +- .../tutorial/perturbseq/perturbseq-r.md | 17 ++++++++---- .../tutorial/replogle/4_refit_propensity.py | 7 ++--- 4 files changed, 41 insertions(+), 12 deletions(-) diff --git a/docs/source/main_function/lfc.rst b/docs/source/main_function/lfc.rst index 4741b7a..e9d38e7 100644 --- a/docs/source/main_function/lfc.rst +++ b/docs/source/main_function/lfc.rst @@ -186,6 +186,28 @@ 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 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -210,9 +232,8 @@ target holds:: 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. Covariates that the -treatment itself changes, such as library size after knocking down -transcription machinery, are better left out of ``W_A`` from the start. +exceeds 0.5 and overlap 0.3. The intercept always stays. Library size is +better decided before this step; see below. Choosing the penalty factor ^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd index b96b886..1bd0c42 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd +++ b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd @@ -93,7 +93,7 @@ Read this before the effects. An arm whose propensity model separates it from th 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.** Several of these knockouts shift library size strongly, and with it in the model their arms separate from the controls (AUC above 0.9, see below). Measured library size mixes capture depth with total RNA content, which a knockout can change, so it is not a reason a cell received its guide. +* **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} diff --git a/docs/source/tutorial/perturbseq/perturbseq-r.md b/docs/source/tutorial/perturbseq/perturbseq-r.md index c60d9fe..4c5f007 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.md +++ b/docs/source/tutorial/perturbseq/perturbseq-r.md @@ -145,11 +145,18 @@ 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.** Several of these knockouts shift - library size strongly, and with it in the model their arms separate - from the controls (AUC above 0.9, see below). Measured library size - mixes capture depth with total RNA content, which a knockout can - change, so it is not a reason a cell received its guide. +- **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 diff --git a/docs/source/tutorial/replogle/4_refit_propensity.py b/docs/source/tutorial/replogle/4_refit_propensity.py index 8215099..a52a410 100644 --- a/docs/source/tutorial/replogle/4_refit_propensity.py +++ b/docs/source/tutorial/replogle/4_refit_propensity.py @@ -1,9 +1,10 @@ """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: knocking down transcription -machinery shrinks a cell's total RNA, so library size mostly records the -perturbation's own effect. The model starts from the intercept and all latent +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 From 247db369a9e796f8af090155a818d80832466147 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Tue, 29 Sep 2026 23:16:46 +0800 Subject: [PATCH 11/16] Require crispyx >= 0.1.6 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, for the same estimates: 159 s against 62 s for LFC on 5,000 cells, 3,000 genes and 10 treatments (mouse-brain subsample). The full test suite passes with 0.1.6. Co-Authored-By: Claude Opus 5.5 --- docs/CHANGELOG.md | 4 ++++ environment.yaml | 2 +- pyproject.toml | 2 +- setup.cfg | 2 +- 4 files changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 9fecd76..38b9410 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -34,6 +34,10 @@ ### 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 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 From 9cddc1a96e78ece1bae6d3d197440e6ee38ed5f5 Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Wed, 30 Sep 2026 10:14:08 +0800 Subject: [PATCH 12/16] Warn when causarray runs under Rosetta on Apple Silicon An Intel (x86_64) Python on an Apple Silicon Mac runs translated by Rosetta 2, which makes causarray's numerical code several times slower. On a 5,000-cell subsample LFC took 159 s translated against 32-57 s natively, and the test suite 270 s against 64 s. pip installs into whatever Python it is given, so packaging cannot prevent this. Instead, `import causarray` now issues one RuntimeWarning that explains how to create a native environment, and the README says how to check for one. Co-Authored-By: Claude Opus 5.5 --- README.md | 7 +++++++ causarray/__init__.py | 3 +++ causarray/_platform.py | 43 ++++++++++++++++++++++++++++++++++++++++++ docs/CHANGELOG.md | 4 ++++ tests/test_platform.py | 34 +++++++++++++++++++++++++++++++++ 5 files changed, 91 insertions(+) create mode 100644 causarray/_platform.py create mode 100644 tests/test_platform.py diff --git a/README.md b/README.md index 9cbf54a..e19bb58 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 diff --git a/causarray/__init__.py b/causarray/__init__.py index 6317e1f..8047e54 100755 --- a/causarray/__init__.py +++ b/causarray/__init__.py @@ -6,6 +6,9 @@ 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', diff --git a/causarray/_platform.py b/causarray/_platform.py new file mode 100644 index 0000000..2008630 --- /dev/null +++ b/causarray/_platform.py @@ -0,0 +1,43 @@ +"""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: on a 5,000-cell subsample of a Perturb-seq screen, ``LFC`` took 159 s +under Rosetta against 32-57 s natively. 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('/usr/lib/libSystem.dylib') + 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/docs/CHANGELOG.md b/docs/CHANGELOG.md index 38b9410..4c9b034 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -31,6 +31,10 @@ 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`. +- `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 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) From 755121579c61e7c0bb2922a6b0dead23e4569cee Mon Sep 17 00:00:00 2001 From: jaydu1 <413075930@qq.com> Date: Wed, 30 Sep 2026 10:29:12 +0800 Subject: [PATCH 13/16] Rebuild the R environment natively; re-knit the Perturb-seq R tutorial The causarray and causarray-r environments must share an architecture, since reticulate cannot load an arm64 Python into an Intel R. The README now says to create both natively on Apple Silicon. environment-r.yaml lists dplyr and ggplot2, which the R tutorial loads directly. The R tutorial is re-knit with native R 4.4 and Python. Results are unchanged (8,161 hits; U9 and U7 dropped for Satb2). Seurat now loads quietly, and the newer pandoc changes heading style only. Co-Authored-By: Claude Opus 5.5 --- README.md | 5 +- .../tutorial/perturbseq/perturbseq-r.Rmd | 2 +- .../tutorial/perturbseq/perturbseq-r.md | 68 +++++++----------- .../propensity-overlap-1.png | Bin 70782 -> 69425 bytes .../propensity-overlap-after-1.png | Bin 72460 -> 72867 bytes .../treatment-associations-1.png | Bin 66177 -> 67242 bytes environment-r.yaml | 2 + 7 files changed, 32 insertions(+), 45 deletions(-) diff --git a/README.md b/README.md index e19bb58..82f0a4a 100644 --- a/README.md +++ b/README.md @@ -45,7 +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/docs/source/tutorial/perturbseq/perturbseq-r.Rmd b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd index 1bd0c42..131d9dc 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.Rmd +++ b/docs/source/tutorial/perturbseq/perturbseq-r.Rmd @@ -17,7 +17,7 @@ This tutorial uses an excitatory-neuron subset from [Jin et al. (2020), *Science 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") diff --git a/docs/source/tutorial/perturbseq/perturbseq-r.md b/docs/source/tutorial/perturbseq/perturbseq-r.md index 4c5f007..8395a0c 100644 --- a/docs/source/tutorial/perturbseq/perturbseq-r.md +++ b/docs/source/tutorial/perturbseq/perturbseq-r.md @@ -1,7 +1,6 @@ -Perturb-seq Tutorial (R) -======================== +# Perturb-seq Tutorial (R) -This tutorial uses an excitatory-neuron subset from [Jin et al. (2020), +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 @@ -16,21 +15,8 @@ biology. Saved outputs illustrate one run; counts and rankings may change after refitting. ``` r -library(Seurat) -``` - - ## Loading required package: SeuratObject - - ## Loading required package: sp - - ## - ## Attaching package: 'SeuratObject' +suppressPackageStartupMessages(library(Seurat)) - ## 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 @@ -57,8 +43,7 @@ A <- data.frame( colnames(A) <- sub("^trt_", "", colnames(A)) ``` -Prepare inputs --------------- +## 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 @@ -95,8 +80,7 @@ list2env(dat, .GlobalEnv) ## -Estimate latent factors ------------------------ +## Estimate latent factors This example uses a fixed illustrative rank, matching the Python fitting cell. Inspect the JIC curve and nearby ranks in the Python example @@ -133,8 +117,7 @@ cat(sprintf("Step 2 -- epochs: %d, best NLL: %.6f\n", ## Step 2 -- epochs: 29, best NLL: 1.709480 -Propensity-score diagnostics and factor selection -------------------------------------------------- +## 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 @@ -145,24 +128,24 @@ 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. +- **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. 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