From 5fe1821c9e1f246cd8fe65c111585b53217ddf19 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Wed, 17 Jun 2026 17:13:50 +0200 Subject: [PATCH 01/17] Add LEMBAS data retrieval module to networkcommons Implements lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() and lembas_datasets() to fetch the macrophage and ligand screen datasets used in Nilsson et al. 2022 (Nat Commun). Macrophage files are pulled from Zenodo (record 10815391); ligand screen files from the Lauffenburger-Lab/LEMBAS GitHub repo. Also registers both datasets in datasets.yaml and adds the LEMBAS section to api.rst and datasets.rst. Co-Authored-By: Claude Sonnet 4.6 --- docs/src/api.rst | 17 +++ docs/src/datasets.rst | 22 ++++ networkcommons/data/datasets.yaml | 24 ++++ networkcommons/data/omics/__init__.py | 1 + networkcommons/data/omics/_lembas.py | 179 ++++++++++++++++++++++++++ 5 files changed, 243 insertions(+) create mode 100644 networkcommons/data/omics/_lembas.py diff --git a/docs/src/api.rst b/docs/src/api.rst index 78f9723..c289a43 100644 --- a/docs/src/api.rst +++ b/docs/src/api.rst @@ -223,6 +223,23 @@ Phospho-EGF meta-analysis data.omics.phospho_egf_datatypes data.omics.phospho_egf_tables +.. _api-lembas: + +LEMBAS +~~~~~~ +.. module::networkcommons.data.omics +.. currentmodule:: networkcommons + +.. autosummary:: + :toctree: api + :recursive: + + data.omics.lembas_datasets + data.omics.lembas_network + data.omics.lembas_ligands + data.omics.lembas_tfs + data.omics.lembas_annotation + .. _api-eval: Evaluation and description diff --git a/docs/src/datasets.rst b/docs/src/datasets.rst index 43ab294..0f0fd5d 100644 --- a/docs/src/datasets.rst +++ b/docs/src/datasets.rst @@ -108,6 +108,28 @@ Phosphoproteomics in response to EGF **Functions:** See API documentation for :ref:`Phospho-EGF meta-analysis`. +.. _details-lembas: + +LEMBAS +------ + +**Alias:** lembas_macrophage, lembas_ligand_screen + +**Description:** Signaling network, ligand concentration and TF activity data from the LEMBAS resource + +**Publication Link:** `Nilsson, A. et al. Artificial neural networks enable genome-scale simulations of intracellular signaling. Nat Commun 13, 3069 (2022). `_ + +**Data location:** `Zenodo (macrophage) `_ / `GitHub — Lauffenburger-Lab/LEMBAS (ligand screen) `_ + +**Detailed Description:** Two experimental datasets used in the LEMBAS paper to train and evaluate the signaling network RNN model. +The **macrophage dataset** (low-coverage) contains ~10 extracellular ligands across ~170 stimulation conditions in macrophages. +The **ligand screen dataset** (high-coverage) contains ~60 extracellular ligands across ~500 conditions. +Each dataset includes three components: a prior knowledge signaling network (protein–protein interactions with stimulating/inhibiting annotations), +a ligand input matrix (binary presence/absence per condition), and a TF activity matrix (DoRothEA/VIPER scores normalised to [0, 1]). +Node identifiers are UniProt IDs throughout. + +**Functions:** See API documentation for :ref:`LEMBAS `. + .. _details-pk: diff --git a/networkcommons/data/datasets.yaml b/networkcommons/data/datasets.yaml index 9423da0..00b42ab 100644 --- a/networkcommons/data/datasets.yaml +++ b/networkcommons/data/datasets.yaml @@ -53,3 +53,27 @@ omics: publication_link: https://www.biorxiv.org/content/10.1101/2024.10.21.619348v1 detailed_description: >- This dataset contains phosphoproteomics data after EGF stimulus gathered and preprocessed from multiple studies. + lembas_macrophage: + name: LEMBAS Macrophage + description: Macrophage signaling dataset (low-coverage) for LEMBAS + publication_link: https://doi.org/10.1038/s41467-022-30684-y + detailed_description: >- + Macrophage stimulation dataset used in the LEMBAS paper (Nilsson et al. 2022). + Contains ~10 extracellular ligands across ~170 experimental conditions. + Includes the prior knowledge signaling network, binary ligand input matrix, + and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. + Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() + using dataset='macrophage'. + path: '' + lembas_ligand_screen: + name: LEMBAS Ligand Screen + description: High-throughput ligand screen dataset (high-coverage) for LEMBAS + publication_link: https://doi.org/10.1038/s41467-022-30684-y + detailed_description: >- + High-throughput ligand screen dataset used in the LEMBAS paper (Nilsson et al. 2022). + Contains ~60 extracellular ligands across ~500 experimental conditions. + Includes the prior knowledge signaling network, binary ligand input matrix, + and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. + Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() + using dataset='ligand_screen'. + path: '' diff --git a/networkcommons/data/omics/__init__.py b/networkcommons/data/omics/__init__.py index 93a89db..538140d 100644 --- a/networkcommons/data/omics/__init__.py +++ b/networkcommons/data/omics/__init__.py @@ -25,3 +25,4 @@ from ._nci60 import * from ._cptac import * from ._phosphoegf import * +from ._lembas import * diff --git a/networkcommons/data/omics/_lembas.py b/networkcommons/data/omics/_lembas.py new file mode 100644 index 0000000..80f8be6 --- /dev/null +++ b/networkcommons/data/omics/_lembas.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python + +# +# This file is part of the `networkcommons` Python module +# +# Copyright 2024 +# Heidelberg University Hospital +# +# File author(s): Saez Lab (omnipathdb@gmail.com) +# +# Distributed under the GPLv3 license +# See the file `LICENSE` or read a copy at +# https://www.gnu.org/licenses/gpl-3.0.txt +# + +""" +Signaling network, ligand concentration and TF activity data from the +LEMBAS (Learning Mechanistic Biological Activity from Signaling) resource. + +Nilsson et al. 2022, Nature Communications +https://doi.org/10.1038/s41467-022-30684-y +""" + +from __future__ import annotations + +__all__ = [ + 'lembas_datasets', + 'lembas_network', + 'lembas_ligands', + 'lembas_tfs', + 'lembas_annotation', +] + +import pandas as pd + +from . import _common +from networkcommons._session import _log + +_ZENODO_BASE = 'https://zenodo.org/records/10815391/files' +_GITHUB_BASE = 'https://raw.githubusercontent.com/Lauffenburger-Lab/LEMBAS/main/Model/data' + +_URLS: dict[str, dict[str, str]] = { + 'macrophage': { + 'network': f'{_ZENODO_BASE}/macrophage_network.tsv', + 'ligands': f'{_ZENODO_BASE}/macrophage_ligands.tsv', + 'tfs': f'{_ZENODO_BASE}/macrophage_TFs.tsv', + 'annotation': f'{_GITHUB_BASE}/macrophage-Annotation.tsv', + }, + 'ligand_screen': { + 'network': f'{_GITHUB_BASE}/ligandScreen-Model.tsv', + 'ligands': f'{_GITHUB_BASE}/ligandScreen-Ligands.tsv', + 'tfs': f'{_GITHUB_BASE}/ligandScreen-TFs.tsv', + 'annotation': f'{_GITHUB_BASE}/ligandScreen-Annotation.tsv', + }, +} + +_DATASET_INFO: dict[str, dict[str, str]] = { + 'macrophage': { + 'name': 'Macrophage (low-coverage)', + 'description': ( + 'Macrophage signaling dataset with ~10 extracellular ligands across ' + '~170 experimental conditions. TF activities derived from RNA-seq ' + 'via DoRothEA/VIPER.' + ), + }, + 'ligand_screen': { + 'name': 'Ligand screen (high-coverage)', + 'description': ( + 'High-throughput ligand screen with ~60 ligands across ~500 ' + 'experimental conditions. TF activities derived from RNA-seq ' + 'via DoRothEA/VIPER.' + ), + }, +} + + +def lembas_datasets() -> pd.DataFrame: + """ + Available LEMBAS datasets. + + Returns: + DataFrame with dataset keys, names and descriptions. + """ + return pd.DataFrame.from_dict(_DATASET_INFO, orient='index').rename_axis('dataset') + + +def _lembas_table(dataset: str, table: str) -> pd.DataFrame: + + valid_datasets = list(_URLS.keys()) + if dataset not in valid_datasets: + raise ValueError( + f'Unknown dataset {dataset!r}. ' + f'Available: {valid_datasets}. ' + 'See lembas_datasets() for details.' + ) + + url = _URLS[dataset][table] + _log(f'LEMBAS: Fetching {table} for dataset {dataset!r} from {url}') + + result = _common._open(url, df={'sep': '\t'}) + assert isinstance(result, pd.DataFrame) + return result + + +def lembas_network(dataset: str = 'macrophage') -> pd.DataFrame: + """ + Prior knowledge signaling network for a LEMBAS dataset. + + The returned DataFrame has columns ``source``, ``target``, + ``stimulation`` (1/0) and ``inhibition`` (1/0), among others. + Pass it through ``LEMBAS.model.model_utilities.format_network`` + to add the ``mode_of_action`` column before building a + ``SignalingModel``. + + Args: + dataset: + One of ``'macrophage'`` or ``'ligand_screen'``. + See :func:`lembas_datasets` for details. + + Returns: + DataFrame with one row per directed protein–protein interaction. + """ + return _lembas_table(dataset, 'network') + + +def lembas_ligands(dataset: str = 'macrophage') -> pd.DataFrame: + """ + Ligand concentration (input) matrix for a LEMBAS dataset. + + Args: + dataset: + One of ``'macrophage'`` or ``'ligand_screen'``. + See :func:`lembas_datasets` for details. + + Returns: + DataFrame of shape (conditions × ligands). Index contains + condition names; columns are UniProt IDs of extracellular + ligands; values are ligand concentrations or binary presence (0/1). + """ + return _lembas_table(dataset, 'ligands') + + +def lembas_tfs(dataset: str = 'macrophage') -> pd.DataFrame: + """ + Transcription factor activity (output) matrix for a LEMBAS dataset. + + TF activities are inferred from RNA-seq data using DoRothEA/VIPER + and normalised to [0, 1]. + + Args: + dataset: + One of ``'macrophage'`` or ``'ligand_screen'``. + See :func:`lembas_datasets` for details. + + Returns: + DataFrame of shape (conditions × TFs). Index contains condition + names; columns are UniProt IDs of transcription factors; values + are TF activity scores in [0, 1]. + """ + return _lembas_table(dataset, 'tfs') + + +def lembas_annotation(dataset: str = 'macrophage') -> pd.DataFrame: + """ + Node annotation table for a LEMBAS dataset. + + Maps UniProt IDs to gene names and marks which nodes are ligands + and which are transcription factors. + + Args: + dataset: + One of ``'macrophage'`` or ``'ligand_screen'``. + See :func:`lembas_datasets` for details. + + Returns: + DataFrame with columns including ``code`` (UniProt ID), + ``name`` (gene symbol), ``ligand`` (bool) and ``TF`` (bool). + """ + return _lembas_table(dataset, 'annotation') From 277a83c7628a850d73cea284f68ab8a6b72265f2 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Wed, 17 Jun 2026 17:36:41 +0200 Subject: [PATCH 02/17] Add LEMBAS perturbation-response pipeline with baselines and tests Implements the full LEMBAS-RNN method (MML activation, steady-state convergence, uniform regularisation) alongside ridge and mean-response baselines. Adds lembas_format_network to utils, wires the new methods module, updates API and narrative docs, and adds a pytest smoke suite. Co-Authored-By: daniele-bottazzi Co-Authored-By: Claude Sonnet 4.6 --- docs/src/api.rst | 19 + docs/src/methods.rst | 24 + networkcommons/methods/__init__.py | 1 + networkcommons/methods/_perturbation.py | 862 ++++++++++++++++++++++++ networkcommons/utils.py | 54 ++ tests/test_methods_perturbation.py | 138 ++++ 6 files changed, 1098 insertions(+) create mode 100644 networkcommons/methods/_perturbation.py create mode 100644 tests/test_methods_perturbation.py diff --git a/docs/src/api.rst b/docs/src/api.rst index c289a43..aa6d4e8 100644 --- a/docs/src/api.rst +++ b/docs/src/api.rst @@ -93,6 +93,24 @@ SignalingProfiler methods.run_signalingprofiler +.. _api-perturbation: + +Perturbation response +===================== +.. module::networkcommons.methods +.. currentmodule:: networkcommons + +.. autosummary:: + :toctree: api + :recursive: + + methods.network_to_perturbation_table + methods.split_perturbation_data + methods.evaluate_predictions + methods.run_mean_response_baseline + methods.run_ridge_baseline + methods.run_lembas_rnn + .. _api-pk: Prior Knowledge @@ -317,6 +335,7 @@ Utilities utils.to_networkx utils.read_network_from_file utils.network_from_df + utils.lembas_format_network utils.get_subnetwork utils.decoupler_formatter utils.targetlayer_formatter diff --git a/docs/src/methods.rst b/docs/src/methods.rst index 55409ec..7321e1b 100644 --- a/docs/src/methods.rst +++ b/docs/src/methods.rst @@ -163,6 +163,30 @@ Network decompression and solution network obtention To finish, the compressed nodes are restored to their original state, and the solution network is obtained by establishing a threshold for the MOON scores of the nodes. In addition, users can rename the nodes to human-readable names. +------------------------------ +Perturbation-response methods +------------------------------ + +Perturbation-response methods combine a prior knowledge network with matched +perturbation and readout matrices. They are designed for predictive benchmark +tasks such as held-out perturbation response prediction, TF activity prediction, +or downstream omics prediction. + +The lightweight LEMBAS-RNN implementation constrains recurrent neural network +edges to the prior knowledge network and learns edge weights from perturbation +inputs and readout outputs. PyTorch is an optional dependency for this method. +The ridge and mean-response baselines provide simple controls for checking +whether a perturbation dataset can be handled by the NetworkCommons interface. + +**Input:** Samples-by-perturbation matrix, samples-by-readout matrix, directed +prior knowledge network. + +**Node weights:** learned state values. + +**Edge weights:** prior signs and learned recurrent edge weights. + +**Functions:** See API documentation for :ref:`Perturbation response `. + ----------------- ILP-based methods ----------------- diff --git a/networkcommons/methods/__init__.py b/networkcommons/methods/__init__.py index a76615a..78b16d7 100644 --- a/networkcommons/methods/__init__.py +++ b/networkcommons/methods/__init__.py @@ -21,3 +21,4 @@ from ._causal import * from ._moon import * from ._signalingprofiler import * +from ._perturbation import * diff --git a/networkcommons/methods/_perturbation.py b/networkcommons/methods/_perturbation.py new file mode 100644 index 0000000..35bc96b --- /dev/null +++ b/networkcommons/methods/_perturbation.py @@ -0,0 +1,862 @@ +#!/usr/bin/env python + +# +# This file is part of the `networkcommons` Python module +# +# Copyright 2024 +# Heidelberg University Hospital +# +# File author(s): Saez Lab (omnipathdb@gmail.com) +# +# Distributed under the GPLv3 license +# See the file `LICENSE` or read a copy at +# https://www.gnu.org/licenses/gpl-3.0.txt +# + +""" +Perturbation-response prediction methods. +""" + +from __future__ import annotations + +__all__ = [ + 'network_to_perturbation_table', + 'split_perturbation_data', + 'evaluate_predictions', + 'run_mean_response_baseline', + 'run_ridge_baseline', + 'run_lembas_rnn', +] + +import typing as t + +import networkx as nx +import numpy as np +import pandas as pd + +from networkcommons._session import _log + + +def _as_dataframe(data, name: str) -> pd.DataFrame: + + if isinstance(data, pd.DataFrame): + return data.copy() + + raise TypeError(f'`{name}` must be a pandas DataFrame.') + + +def _align_samples( + perturbations: pd.DataFrame, + readouts: pd.DataFrame, + ) -> tuple[pd.DataFrame, pd.DataFrame]: + + shared = perturbations.index.intersection(readouts.index) + + if shared.empty: + raise ValueError('Perturbation and readout tables have no shared rows.') + + return perturbations.loc[shared], readouts.loc[shared] + + +def _edge_sign(value, unknown_value: float = 0.1) -> float: + + if value is None or pd.isna(value): + return unknown_value + + if isinstance(value, str): + value_lower = value.strip().lower() + + if value_lower in {'+', '1', 'activation', 'activating', 'stimulation'}: + return 1.0 + + if value_lower in {'-', '-1', 'inhibition', 'inhibiting'}: + return -1.0 + + return unknown_value + + if value > 0: + return 1.0 + + if value < 0: + return -1.0 + + return unknown_value + + +def _infer_edge_sign(data: dict, sign_attr: str, unknown_value: float) -> float: + + for attr in (sign_attr, 'mode_of_action', 'interaction', 'weight'): + if attr in data: + return _edge_sign(data[attr], unknown_value) + + return unknown_value + + +def network_to_perturbation_table( + network: nx.DiGraph, + sign_attr: str = 'sign', + source_col: str = 'source', + target_col: str = 'target', + moa_col: str = 'mode_of_action', + unknown_value: float = 0.1, + ) -> pd.DataFrame: + """ + Convert a NetworkX graph to the edge table expected by LEMBAS-like models. + + Args: + network: Directed prior knowledge network. + sign_attr: Edge attribute containing activation/inhibition signs. + source_col: Output source column name. + target_col: Output target column name. + moa_col: Output mode-of-action column name. + unknown_value: Numeric value used for unknown mode of action. + + Returns: + Edge table with source, target and mode-of-action columns. + """ + + if not isinstance(network, nx.DiGraph): + raise TypeError('`network` must be a networkx.DiGraph.') + + records = [ + { + source_col: source, + target_col: target, + moa_col: _infer_edge_sign(data, sign_attr, unknown_value), + } + for source, target, data in network.edges(data=True) + ] + + return pd.DataFrame.from_records( + records, + columns=[source_col, target_col, moa_col], + ) + + +def split_perturbation_data( + perturbations: pd.DataFrame, + readouts: pd.DataFrame, + test_size: float | int = 0.2, + seed: int | None = 888, + shuffle: bool = True, + ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Split matched perturbation-response tables into train and test sets. + + Args: + perturbations: Samples by perturbation features. + readouts: Samples by response readouts. + test_size: Fraction or absolute number of samples for testing. + seed: Random seed used when shuffling samples. + shuffle: Whether to shuffle before splitting. + + Returns: + ``X_train, X_test, y_train, y_test``. + """ + + perturbations = _as_dataframe(perturbations, 'perturbations') + readouts = _as_dataframe(readouts, 'readouts') + perturbations, readouts = _align_samples(perturbations, readouts) + + n_samples = len(perturbations) + + if isinstance(test_size, float): + if not 0 < test_size < 1: + raise ValueError('Float `test_size` must be between 0 and 1.') + + n_test = int(np.ceil(n_samples * test_size)) + + elif isinstance(test_size, int): + n_test = test_size + + else: + raise TypeError('`test_size` must be a float or integer.') + + if n_test <= 0 or n_test >= n_samples: + raise ValueError('Test split must contain at least one sample and ' + 'leave at least one training sample.') + + order = np.arange(n_samples) + + if shuffle: + rng = np.random.default_rng(seed) + rng.shuffle(order) + + test_idx = order[:n_test] + train_idx = order[n_test:] + + return ( + perturbations.iloc[train_idx], + perturbations.iloc[test_idx], + readouts.iloc[train_idx], + readouts.iloc[test_idx], + ) + + +def _pearson(x: np.ndarray, y: np.ndarray) -> float: + + if np.std(x) == 0 or np.std(y) == 0: + return np.nan + + return float(np.corrcoef(x, y)[0, 1]) + + +def evaluate_predictions( + readouts: pd.DataFrame, + predictions: pd.DataFrame, + ) -> pd.DataFrame: + """ + Calculate simple predictive metrics for perturbation-response models. + + Args: + readouts: Observed response matrix. + predictions: Predicted response matrix. + + Returns: + DataFrame indexed by readout with MSE, MAE and Pearson correlation. + """ + + readouts = _as_dataframe(readouts, 'readouts') + predictions = _as_dataframe(predictions, 'predictions') + + shared_rows = readouts.index.intersection(predictions.index) + shared_cols = readouts.columns.intersection(predictions.columns) + + if shared_rows.empty or shared_cols.empty: + raise ValueError('Readouts and predictions must share rows and columns.') + + y_true = readouts.loc[shared_rows, shared_cols] + y_pred = predictions.loc[shared_rows, shared_cols] + + records = [] + + for column in shared_cols: + err = y_pred[column].to_numpy() - y_true[column].to_numpy() + records.append( + { + 'readout': column, + 'mse': float(np.mean(err ** 2)), + 'mae': float(np.mean(np.abs(err))), + 'pearson': _pearson( + y_true[column].to_numpy(), + y_pred[column].to_numpy(), + ), + } + ) + + flat_err = y_pred.to_numpy().ravel() - y_true.to_numpy().ravel() + records.append( + { + 'readout': '__all__', + 'mse': float(np.mean(flat_err ** 2)), + 'mae': float(np.mean(np.abs(flat_err))), + 'pearson': _pearson(y_true.to_numpy().ravel(), y_pred.to_numpy().ravel()), + } + ) + + return pd.DataFrame.from_records(records).set_index('readout') + + +def run_mean_response_baseline( + readouts_train: pd.DataFrame, + readouts_eval: pd.DataFrame | None = None, + ) -> dict[str, t.Any]: + """ + Predict each readout by its mean value in the training set. + """ + + readouts_train = _as_dataframe(readouts_train, 'readouts_train') + + if readouts_eval is None: + eval_index = readouts_train.index + else: + readouts_eval = _as_dataframe(readouts_eval, 'readouts_eval') + eval_index = readouts_eval.index + + means = readouts_train.mean(axis=0) + predictions = pd.DataFrame( + np.repeat(means.to_numpy()[None, :], len(eval_index), axis=0), + index=eval_index, + columns=readouts_train.columns, + ) + + return { + 'predictions': predictions, + 'readout_mean': means, + } + + +def _design_matrix(features: pd.DataFrame, fit_intercept: bool) -> np.ndarray: + + matrix = features.to_numpy(dtype=float) + + if fit_intercept: + matrix = np.column_stack([np.ones(len(features)), matrix]) + + return matrix + + +def run_ridge_baseline( + perturbations_train: pd.DataFrame, + readouts_train: pd.DataFrame, + perturbations_eval: pd.DataFrame | None = None, + alpha: float = 1.0, + fit_intercept: bool = True, + ) -> dict[str, t.Any]: + """ + Fit a closed-form ridge regression perturbation-response baseline. + """ + + if alpha < 0: + raise ValueError('`alpha` must be non-negative.') + + perturbations_train = _as_dataframe( + perturbations_train, + 'perturbations_train', + ) + readouts_train = _as_dataframe(readouts_train, 'readouts_train') + perturbations_train, readouts_train = _align_samples( + perturbations_train, + readouts_train, + ) + + if perturbations_eval is None: + perturbations_eval = perturbations_train + else: + perturbations_eval = _as_dataframe( + perturbations_eval, + 'perturbations_eval', + ) + perturbations_eval = perturbations_eval.loc[ + :, + perturbations_train.columns, + ] + + x_train = _design_matrix(perturbations_train, fit_intercept) + x_eval = _design_matrix(perturbations_eval, fit_intercept) + y_train = readouts_train.to_numpy(dtype=float) + + penalty = np.eye(x_train.shape[1]) * alpha + + if fit_intercept: + penalty[0, 0] = 0 + + lhs = x_train.T @ x_train + penalty + rhs = x_train.T @ y_train + + try: + beta = np.linalg.solve(lhs, rhs) + except np.linalg.LinAlgError: + beta = np.linalg.pinv(lhs) @ rhs + + pred_eval = x_eval @ beta + pred_train = x_train @ beta + + predictions = pd.DataFrame( + pred_eval, + index=perturbations_eval.index, + columns=readouts_train.columns, + ) + + offset = 1 if fit_intercept else 0 + coefficients = pd.DataFrame( + beta[offset:, :], + index=perturbations_train.columns, + columns=readouts_train.columns, + ) + intercept = pd.Series( + beta[0, :] if fit_intercept else np.zeros(readouts_train.shape[1]), + index=readouts_train.columns, + name='intercept', + ) + + return { + 'predictions': predictions, + 'coefficients': coefficients, + 'intercept': intercept, + 'train_mse': float(np.mean((pred_train - y_train) ** 2)), + 'alpha': alpha, + } + + +def _import_torch(): + + try: + import torch + except ImportError as exc: + raise ImportError( + '`run_lembas_rnn` requires PyTorch. Install PyTorch in the active ' + 'environment or install NetworkCommons with the `lembas` extra.' + ) from exc + + return torch + + +def _torch_dtype(torch, dtype): + + if isinstance(dtype, str): + try: + return getattr(torch, dtype) + except AttributeError as exc: + raise ValueError(f'Unknown torch dtype `{dtype}`.') from exc + + return dtype + + +def _torch_device(torch, device: str): + + if device == 'auto': + return torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + return torch.device(device) + + +def _mml_activation(x, leak: float = 0.01): + """Michaelis-Menten-like activation matching the original LEMBAS paper. + + Maps inputs to [0, 1] for positive values with a leaky linear region + below zero. This is the default activation in LEMBAS and LEMBAS-GPU. + """ + fx = torch.nn.functional.leaky_relu(x, negative_slope=leak) + shifted = 0.5 * (fx - 0.5) + mask = shifted.lt(0.0) + safe = fx + 10.0 * mask # avoid division by zero + right = 0.5 + shifted / safe + return mask * (fx - right) + right + + +def _make_lembas_model(torch): + + class LembasRNN(torch.nn.Module): + + def __init__( + self, + n_nodes: int, + source_idx: np.ndarray, + target_idx: np.ndarray, + edge_signs: np.ndarray, + input_idx: np.ndarray, + output_idx: np.ndarray, + n_steps: int, + tolerance: float, + activation: str, + leak: float, + dtype, + device, + learn_input_scale: bool, + input_scale_init: float, + ): + + super().__init__() + self.n_nodes = n_nodes + self.n_steps = n_steps + self.tolerance = tolerance + self.activation = activation + self.leak = leak + + self.register_buffer( + 'source_idx', + torch.as_tensor(source_idx, dtype=torch.long, device=device), + ) + self.register_buffer( + 'target_idx', + torch.as_tensor(target_idx, dtype=torch.long, device=device), + ) + self.register_buffer( + 'input_idx', + torch.as_tensor(input_idx, dtype=torch.long, device=device), + ) + self.register_buffer( + 'output_idx', + torch.as_tensor(output_idx, dtype=torch.long, device=device), + ) + + edge_signs_tensor = torch.as_tensor( + edge_signs, + dtype=dtype, + device=device, + ) + known = torch.isin( + edge_signs_tensor, + torch.as_tensor([-1.0, 1.0], dtype=dtype, device=device), + ) + + initial_edges = torch.where( + known, + 0.1 * edge_signs_tensor, + torch.full_like(edge_signs_tensor, 0.1), + ) + initial_edges = initial_edges + 0.01 * torch.randn_like(initial_edges) + + self.edge_weights = torch.nn.Parameter(initial_edges) + self.bias = torch.nn.Parameter( + torch.zeros(n_nodes, dtype=dtype, device=device), + ) + self.output_scale = torch.nn.Parameter( + torch.ones(len(output_idx), dtype=dtype, device=device), + ) + self.output_bias = torch.nn.Parameter( + torch.zeros(len(output_idx), dtype=dtype, device=device), + ) + + input_scale = torch.full( + (len(input_idx),), input_scale_init, dtype=dtype, device=device, + ) + + if learn_input_scale: + self.input_scale = torch.nn.Parameter(input_scale) + else: + self.register_buffer('input_scale', input_scale) + + self.register_buffer('edge_signs', edge_signs_tensor) + self.register_buffer('known_signs', known) + + def edge_matrix(self): + + weights = torch.zeros( + (self.n_nodes, self.n_nodes), + dtype=self.edge_weights.dtype, + device=self.edge_weights.device, + ) + weights[self.target_idx, self.source_idx] = self.edge_weights + + return weights + + def _activate(self, values): + + if self.activation == 'mml': + return _mml_activation(values, self.leak) + + if self.activation == 'tanh': + return torch.tanh(values) + + if self.activation == 'sigmoid': + return torch.sigmoid(values) + + if self.activation == 'leaky_relu': + return torch.nn.functional.leaky_relu( + values, + negative_slope=self.leak, + ) + + raise ValueError( + '`activation` must be one of mml, tanh, sigmoid or leaky_relu.' + ) + + def forward(self, x): + + drive = torch.zeros( + (x.shape[0], self.n_nodes), + dtype=x.dtype, + device=x.device, + ) + drive[:, self.input_idx] = x * self.input_scale + + state = torch.zeros_like(drive) + weights = self.edge_matrix() + + for _ in range(self.n_steps): + prev = state + state = self._activate(state @ weights.T + drive + self.bias) + + if self.tolerance > 0: + if torch.max(torch.abs(state - prev)).item() < self.tolerance: + break + + prediction = ( + state[:, self.output_idx] * self.output_scale + + self.output_bias + ) + + return prediction, state + + def uniform_regularization( + self, + state: 'torch.Tensor', + target_min: float = 0.0, + target_max: float = 1.0, + ) -> 'torch.Tensor': + """Penalize deviation of node states from a uniform distribution. + + Matches the LEMBAS uniform loss: pushes the distribution of node + activations across samples to be roughly uniform in + [target_min, target_max], which keeps states biologically + interpretable and prevents saturation. + """ + n = state.shape[0] + sorted_state, _ = torch.sort(state, dim=0) + target = torch.linspace( + target_min, target_max, n, + dtype=state.dtype, device=state.device, + ).unsqueeze(1) + + dist_loss = torch.sum((sorted_state - target) ** 2) + below = torch.sum( + state.lt(target_min) * (state - target_min) ** 2 + ) + above = torch.sum( + state.gt(target_max) * (state - target_max) ** 2 + ) + + return dist_loss + below + above + + def sign_regularization(self): + + if not torch.any(self.known_signs): + return torch.zeros((), dtype=self.edge_weights.dtype, + device=self.edge_weights.device) + + signed_weights = self.edge_weights[self.known_signs] + signs = self.edge_signs[self.known_signs] + + return torch.mean(torch.relu(-signed_weights * signs)) + + return LembasRNN + + +def _edge_subnetwork( + network: nx.DiGraph, + edge_table: pd.DataFrame, + min_abs_weight: float, + ) -> nx.DiGraph: + + subnetwork = nx.DiGraph() + + for row in edge_table.itertuples(index=False): + if abs(row.lembas_weight) < min_abs_weight: + continue + + data = dict(network.get_edge_data(row.source, row.target, default={})) + data['lembas_weight'] = row.lembas_weight + data['lembas_abs_weight'] = abs(row.lembas_weight) + subnetwork.add_edge(row.source, row.target, **data) + + return subnetwork + + +def run_lembas_rnn( + network: nx.DiGraph, + perturbations_train: pd.DataFrame, + readouts_train: pd.DataFrame, + perturbations_eval: pd.DataFrame | None = None, + epochs: int = 1000, + learning_rate: float = 2e-3, + n_steps: int = 100, + tolerance: float = 1e-5, + alpha: float = 1e-6, + sign_penalty: float = 0.1, + uniform_penalty: float = 1e-4, + batch_size: int | None = None, + activation: str = 'mml', + leak: float = 0.01, + input_scale_init: float = 3.0, + device: str = 'auto', + dtype: str = 'float32', + seed: int | None = 888, + learn_input_scale: bool = False, + min_abs_edge_weight: float = 0.0, + verbose: bool = False, + ) -> dict[str, t.Any]: + """ + Train a LEMBAS-like recurrent model on perturbation-response data. + + Constrains recurrent edges to the supplied prior knowledge network, + iterates node states until steady state (or ``n_steps`` max), and + learns edge weights plus output projection parameters by minimizing + readout MSE. + + Matches the core architecture of Nilsson et al. 2022 (Nat Commun): + + * Default MML (Michaelis-Menten-like) activation maps states to [0, 1]. + * Steady-state convergence check (``tolerance``) mirrors the original + RNN stopping criterion. + * Sign regularization penalizes edges whose learned sign contradicts + the known mechanism of action. + * Uniform regularization pushes node-state distributions toward uniform, + keeping activations biologically interpretable. + + Use :func:`networkcommons.utils.lembas_format_network` to add the + ``mode_of_action`` edge attribute before calling this function, and + :func:`networkcommons.utils.network_from_df` to convert the edge table + to a ``nx.DiGraph``. + """ + + if epochs <= 0: + raise ValueError('`epochs` must be positive.') + + if learning_rate <= 0: + raise ValueError('`learning_rate` must be positive.') + + if n_steps <= 0: + raise ValueError('`n_steps` must be positive.') + + if tolerance < 0: + raise ValueError('`tolerance` must be non-negative.') + + if alpha < 0 or sign_penalty < 0 or uniform_penalty < 0: + raise ValueError('Regularization strengths must be non-negative.') + + if batch_size is not None and batch_size <= 0: + raise ValueError('`batch_size` must be positive when provided.') + + torch = _import_torch() + torch_dtype = _torch_dtype(torch, dtype) + torch_device = _torch_device(torch, device) + + if seed is not None: + np.random.seed(seed) + torch.manual_seed(seed) + + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + perturbations_train = _as_dataframe( + perturbations_train, + 'perturbations_train', + ) + readouts_train = _as_dataframe(readouts_train, 'readouts_train') + perturbations_train, readouts_train = _align_samples( + perturbations_train, + readouts_train, + ) + + if perturbations_eval is None: + perturbations_eval = perturbations_train + else: + perturbations_eval = _as_dataframe( + perturbations_eval, + 'perturbations_eval', + ) + perturbations_eval = perturbations_eval.loc[ + :, + perturbations_train.columns, + ] + + pkn = network_to_perturbation_table(network) + nodes = sorted( + set(pkn['source']) | + set(pkn['target']) | + set(perturbations_train.columns) | + set(readouts_train.columns) + ) + node_idx = {node: idx for idx, node in enumerate(nodes)} + + source_idx = pkn['source'].map(node_idx).to_numpy(dtype=int) + target_idx = pkn['target'].map(node_idx).to_numpy(dtype=int) + edge_signs = pkn['mode_of_action'].to_numpy(dtype=float) + input_idx = np.array( + [node_idx[node] for node in perturbations_train.columns], + dtype=int, + ) + output_idx = np.array( + [node_idx[node] for node in readouts_train.columns], + dtype=int, + ) + + model_class = _make_lembas_model(torch) + model = model_class( + n_nodes=len(nodes), + source_idx=source_idx, + target_idx=target_idx, + edge_signs=edge_signs, + input_idx=input_idx, + output_idx=output_idx, + n_steps=n_steps, + tolerance=tolerance, + activation=activation, + leak=leak, + dtype=torch_dtype, + device=torch_device, + learn_input_scale=learn_input_scale, + input_scale_init=input_scale_init, + ) + + x_train = torch.as_tensor( + perturbations_train.to_numpy(dtype=float), + dtype=torch_dtype, + device=torch_device, + ) + y_train = torch.as_tensor( + readouts_train.to_numpy(dtype=float), + dtype=torch_dtype, + device=torch_device, + ) + x_eval = torch.as_tensor( + perturbations_eval.to_numpy(dtype=float), + dtype=torch_dtype, + device=torch_device, + ) + + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + loss_history = [] + n_samples = x_train.shape[0] + batch_size = n_samples if batch_size is None else min(batch_size, n_samples) + + _log(f'LEMBAS-RNN: training on {n_samples} samples using {torch_device}.') + + for epoch in range(epochs): + model.train() + order = torch.randperm(n_samples, device=torch_device) + epoch_losses = [] + + for start in range(0, n_samples, batch_size): + idx = order[start:start + batch_size] + optimizer.zero_grad() + prediction, full_state = model(x_train[idx]) + fit_loss = torch.mean((prediction - y_train[idx]) ** 2) + l2_loss = sum(torch.sum(param ** 2) for param in model.parameters()) + sign_loss = model.sign_regularization() + uniform_loss = model.uniform_regularization(full_state) + loss = ( + fit_loss + + alpha * l2_loss + + sign_penalty * sign_loss + + uniform_penalty * uniform_loss + ) + loss.backward() + optimizer.step() + epoch_losses.append(float(fit_loss.detach().cpu())) + + mean_epoch_loss = float(np.mean(epoch_losses)) + loss_history.append(mean_epoch_loss) + + if verbose and (epoch == 0 or (epoch + 1) % 100 == 0): + _log(f'LEMBAS-RNN: epoch {epoch + 1}; mse={mean_epoch_loss:.6g}') + + model.eval() + + with torch.no_grad(): + y_pred, states = model(x_eval) + + predictions = pd.DataFrame( + y_pred.detach().cpu().numpy(), + index=perturbations_eval.index, + columns=readouts_train.columns, + ) + states = pd.DataFrame( + states.detach().cpu().numpy(), + index=perturbations_eval.index, + columns=nodes, + ) + edge_table = pkn.copy() + edge_table['lembas_weight'] = model.edge_weights.detach().cpu().numpy() + edge_table['lembas_abs_weight'] = edge_table['lembas_weight'].abs() + + subnetwork = _edge_subnetwork( + network, + edge_table, + min_abs_edge_weight, + ) + + return { + 'model': model, + 'predictions': predictions, + 'states': states, + 'loss_history': loss_history, + 'edge_weights': edge_table, + 'subnetwork': subnetwork, + 'nodes': nodes, + 'input_nodes': list(perturbations_train.columns), + 'output_nodes': list(readouts_train.columns), + 'device': str(torch_device), + } diff --git a/networkcommons/utils.py b/networkcommons/utils.py index d515a2a..558c830 100644 --- a/networkcommons/utils.py +++ b/networkcommons/utils.py @@ -164,6 +164,60 @@ def network_from_df(network_df, return network +def lembas_format_network( + net: pd.DataFrame, + stimulation_col: str = 'stimulation', + inhibition_col: str = 'inhibition', + moa_col: str = 'mode_of_action', + unknown_value: float = 0.1, + ) -> pd.DataFrame: + """ + Add a signed ``mode_of_action`` column to a LEMBAS network edge table. + + Converts the separate ``stimulation`` / ``inhibition`` binary columns + (as returned by :func:`networkcommons.data.omics.lembas_network`) into + the single numeric ``mode_of_action`` column expected by + :func:`networkcommons.methods.run_lembas_rnn`: + + * stimulating interaction → +1 + * inhibiting interaction → -1 + * unknown / neither → ``unknown_value`` (default 0.1, matching + the LEMBAS convention of keeping unknown edges active but weak) + + Args: + net: Edge-list DataFrame as returned by + :func:`~networkcommons.data.omics.lembas_network`. + stimulation_col: Binary column marking activating interactions. + inhibition_col: Binary column marking inhibiting interactions. + moa_col: Name of the output column to create. + unknown_value: Value assigned to interactions with no known sign. + + Returns: + Copy of ``net`` with an additional ``moa_col`` column. + + Raises: + ValueError: If any edge is simultaneously marked stimulating and + inhibiting. + """ + + conflict = (net[stimulation_col] == 1) & (net[inhibition_col] == 1) + + if conflict.any(): + raise ValueError( + f'Edges found that are simultaneously stimulating and inhibiting ' + f'(rows: {net.index[conflict].tolist()[:5]}). ' + 'An interaction must be stimulating (1, 0), inhibiting (0, 1) ' + 'or unknown (0, 0).' + ) + + result = net.copy() + result[moa_col] = unknown_value + result.loc[result[stimulation_col] == 1, moa_col] = 1.0 + result.loc[result[inhibition_col] == 1, moa_col] = -1.0 + + return result + + def get_subnetwork(network, paths): """ Creates a subnetwork from a list of paths. diff --git a/tests/test_methods_perturbation.py b/tests/test_methods_perturbation.py new file mode 100644 index 0000000..f68403c --- /dev/null +++ b/tests/test_methods_perturbation.py @@ -0,0 +1,138 @@ +import networkx as nx +import numpy as np +import pandas as pd +import pytest + +from networkcommons.methods import _perturbation + + +def _toy_network(): + + network = nx.DiGraph() + network.add_edge('Ligand', 'Kinase', sign=1) + network.add_edge('Kinase', 'TF1', sign=-1) + network.add_edge('Ligand', 'TF2', weight=2.0) + network.add_edge('Unknown', 'TF2') + + return network + + +def test_network_to_perturbation_table_infers_signs(): + + table = _perturbation.network_to_perturbation_table(_toy_network()) + + assert table.to_dict('records') == [ + {'source': 'Ligand', 'target': 'Kinase', 'mode_of_action': 1.0}, + {'source': 'Ligand', 'target': 'TF2', 'mode_of_action': 1.0}, + {'source': 'Kinase', 'target': 'TF1', 'mode_of_action': -1.0}, + {'source': 'Unknown', 'target': 'TF2', 'mode_of_action': 0.1}, + ] + + +def test_split_perturbation_data_aligns_and_splits_samples(): + + perturbations = pd.DataFrame( + {'Ligand': [0, 1, 0, 1]}, + index=['s1', 's2', 's3', 's4'], + ) + readouts = pd.DataFrame( + {'TF1': [0.1, 0.5, 0.2, 0.6]}, + index=['s4', 's3', 's2', 's1'], + ) + + x_train, x_test, y_train, y_test = _perturbation.split_perturbation_data( + perturbations, + readouts, + test_size=1, + seed=1, + ) + + assert len(x_train) == 3 + assert len(x_test) == 1 + assert list(x_train.index) == list(y_train.index) + assert list(x_test.index) == list(y_test.index) + + +def test_mean_response_baseline_predicts_training_mean(): + + readouts_train = pd.DataFrame( + {'TF1': [1.0, 3.0], 'TF2': [2.0, 6.0]}, + index=['s1', 's2'], + ) + readouts_eval = pd.DataFrame(index=['s3', 's4']) + + result = _perturbation.run_mean_response_baseline( + readouts_train, + readouts_eval, + ) + + expected = pd.DataFrame( + {'TF1': [2.0, 2.0], 'TF2': [4.0, 4.0]}, + index=['s3', 's4'], + ) + + pd.testing.assert_frame_equal(result['predictions'], expected) + + +def test_ridge_baseline_fits_linear_response(): + + perturbations = pd.DataFrame( + { + 'LigandA': [0, 1, 2, 3, 4], + 'LigandB': [1, 0, 1, 0, 1], + }, + index=[f's{i}' for i in range(5)], + ) + readouts = pd.DataFrame( + { + 'TF1': 1 + 2 * perturbations['LigandA'] - perturbations['LigandB'], + 'TF2': -2 + perturbations['LigandA'], + }, + index=perturbations.index, + ) + + result = _perturbation.run_ridge_baseline( + perturbations, + readouts, + alpha=1e-8, + ) + metrics = _perturbation.evaluate_predictions( + readouts, + result['predictions'], + ) + + assert result['train_mse'] < 1e-12 + assert metrics.loc['__all__', 'mse'] < 1e-12 + assert set(result['coefficients'].index) == {'LigandA', 'LigandB'} + + +def test_lembas_rnn_smoke(): + + pytest.importorskip('torch') + + perturbations = pd.DataFrame( + {'Ligand': [0.0, 0.5, 1.0, 1.5]}, + index=['s1', 's2', 's3', 's4'], + ) + readouts = pd.DataFrame( + { + 'TF1': [-0.1, -0.3, -0.5, -0.7], + 'TF2': [0.1, 0.2, 0.3, 0.4], + }, + index=perturbations.index, + ) + + result = _perturbation.run_lembas_rnn( + _toy_network(), + perturbations, + readouts, + epochs=2, + n_steps=2, + learning_rate=1e-2, + seed=1, + ) + + assert list(result['predictions'].index) == list(perturbations.index) + assert list(result['predictions'].columns) == ['TF1', 'TF2'] + assert len(result['loss_history']) == 2 + assert result['subnetwork'].number_of_edges() == _toy_network().number_of_edges() From 9dd5377e0ec1684b63583e2a1990ffd3bdc46bbf Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Wed, 17 Jun 2026 17:47:36 +0200 Subject: [PATCH 03/17] Add per-condition evaluation axis, LEMBAS tutorial notebook, and torch optional dep evaluate_predictions gains an axis parameter ('readout' | 'condition') so users can inspect which TFs or which experimental conditions are predicted poorly. Adds three new tests covering both axes and bad-axis validation. Tutorial notebook C_lembas.ipynb walks through the full macrophage pipeline: data loading, network formatting, train/test split, mean/ridge/LEMBAS-RNN models, and per-readout and per-condition evaluation. Registers torch as an optional dependency installable via pip install networkcommons[torch]. Co-Authored-By: daniele-bottazzi Co-Authored-By: Claude Sonnet 4.6 --- docs/src/contents.rst | 3 +- docs/src/vignettes/C_lembas.ipynb | 347 ++++++++++++++++++++++++ networkcommons/methods/_perturbation.py | 69 +++-- pyproject.toml | 2 + tests/test_methods_perturbation.py | 46 ++++ 5 files changed, 442 insertions(+), 25 deletions(-) create mode 100644 docs/src/vignettes/C_lembas.ipynb diff --git a/docs/src/contents.rst b/docs/src/contents.rst index 96abfc8..cfca76d 100644 --- a/docs/src/contents.rst +++ b/docs/src/contents.rst @@ -45,4 +45,5 @@ NetworkCommons: Table of Contents :caption: Additional resources vignettes/A_moon - vignettes/B_pertpy \ No newline at end of file + vignettes/B_pertpy + vignettes/C_lembas \ No newline at end of file diff --git a/docs/src/vignettes/C_lembas.ipynb b/docs/src/vignettes/C_lembas.ipynb new file mode 100644 index 0000000..bdefe56 --- /dev/null +++ b/docs/src/vignettes/C_lembas.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vignette C: Perturbation-response prediction with LEMBAS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This vignette shows the full perturbation-response workflow using the macrophage dataset from\n", + "[Nilsson et al. 2022](https://doi.org/10.1038/s41467-022-30684-y).\n", + "We will:\n", + "\n", + "1. Download the prior knowledge network and matched ligand / TF activity data\n", + "2. Format and construct the network graph\n", + "3. Split conditions into train and test sets\n", + "4. Fit a mean-response baseline and a ridge baseline\n", + "5. Fit the LEMBAS-RNN model (requires PyTorch)\n", + "6. Evaluate all three models per-readout and per-condition\n", + "\n", + "> **Note:** step 5 requires `torch`. Install it with `pip install torch` if needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import networkcommons as nc\n", + "from networkcommons.utils import lembas_format_network, network_from_df\n", + "from networkcommons.methods import (\n", + " split_perturbation_data,\n", + " run_mean_response_baseline,\n", + " run_ridge_baseline,\n", + " run_lembas_rnn,\n", + " evaluate_predictions,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Load LEMBAS macrophage data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The macrophage dataset contains 23 stimulation conditions, 13 ligands and\n", + "89 TF readouts. The prior knowledge network has ~5 700 signed edges.\n", + "All files are cached locally after the first download." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "net_df = nc.data.omics.lembas_network('macrophage')\n", + "ligands = nc.data.omics.lembas_ligands('macrophage')\n", + "tfs = nc.data.omics.lembas_tfs('macrophage')\n", + "\n", + "print('Network edges :', len(net_df))\n", + "print('Conditions :', len(ligands))\n", + "print('Ligand inputs :', ligands.shape[1])\n", + "print('TF readouts :', tfs.shape[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "net_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ligands.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Build the prior knowledge network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`lembas_format_network` converts the binary `stimulation` / `inhibition` columns\n", + "into a single signed `mode_of_action` column (+1 / −1 / 0.1 for unknown).\n", + "`network_from_df` then wraps the edge table in a NetworkX `DiGraph`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "net_df = lembas_format_network(net_df)\n", + "graph = network_from_df(net_df, source_col='source', target_col='target')\n", + "\n", + "print('Nodes:', graph.number_of_nodes())\n", + "print('Edges:', graph.number_of_edges())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Train / test split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`split_perturbation_data` aligns the ligand and TF matrices on shared\n", + "condition indices before splitting, so row order mismatches are handled\n", + "automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = split_perturbation_data(\n", + " ligands,\n", + " tfs,\n", + " test_size=0.2,\n", + " seed=42,\n", + ")\n", + "\n", + "print('Train conditions:', len(x_train))\n", + "print('Test conditions:', len(x_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Baseline models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4a. Mean-response baseline\n", + "\n", + "Predicts every test condition as the column-wise training mean — a\n", + "useful lower bound that ignores which ligand was applied." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mean_result = run_mean_response_baseline(y_train, x_test)\n", + "mean_metrics = evaluate_predictions(y_test, mean_result['predictions'])\n", + "mean_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4b. Ridge regression baseline\n", + "\n", + "Fits a linear map from ligand inputs to TF outputs using L2 regularisation.\n", + "This captures linear dose–response relationships without any network structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ridge_result = run_ridge_baseline(x_train, y_train, x_test)\n", + "ridge_metrics = evaluate_predictions(y_test, ridge_result['predictions'])\n", + "ridge_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. LEMBAS-RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The LEMBAS recurrent neural network constrains its weight matrix to the\n", + "signed prior knowledge network. Node states are propagated with the\n", + "Michaelis-Menten-like (MML) activation until steady state, and the\n", + "training loss combines MSE, sign regularisation and a uniform-distribution\n", + "penalty that keeps node states spread across [0, 1].\n", + "\n", + "> **Requires `torch`.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lembas_result = run_lembas_rnn(\n", + " graph,\n", + " x_train,\n", + " y_train,\n", + " x_test,\n", + " epochs=100,\n", + " n_steps=50,\n", + " learning_rate=1e-3,\n", + " seed=42,\n", + ")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(lembas_result['loss_history'])\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Training loss')\n", + "plt.title('LEMBAS training curve')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6a. Per-readout metrics\n", + "\n", + "`axis='readout'` (default) reports MSE, MAE and Pearson correlation for\n", + "each TF across all test conditions, plus an `__all__` aggregate row." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lembas_metrics_readout = evaluate_predictions(\n", + " y_test,\n", + " lembas_result['predictions'],\n", + " axis='readout',\n", + ")\n", + "lembas_metrics_readout.sort_values('pearson', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6b. Per-condition metrics\n", + "\n", + "`axis='condition'` reports the same metrics for each held-out condition\n", + "across all TF readouts — useful for spotting which stimulations the model\n", + "finds hardest to predict." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lembas_metrics_condition = evaluate_predictions(\n", + " y_test,\n", + " lembas_result['predictions'],\n", + " axis='condition',\n", + ")\n", + "lembas_metrics_condition.sort_values('mse', ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6c. Model comparison\n", + "\n", + "Comparing `__all__` Pearson correlation across the three models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "comparison = pd.DataFrame({\n", + " 'Mean baseline' : mean_metrics.loc['__all__'],\n", + " 'Ridge' : ridge_metrics.loc['__all__'],\n", + " 'LEMBAS-RNN' : lembas_metrics_readout.loc['__all__'],\n", + "}).T\n", + "\n", + "comparison" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/networkcommons/methods/_perturbation.py b/networkcommons/methods/_perturbation.py index 35bc96b..be37ff2 100644 --- a/networkcommons/methods/_perturbation.py +++ b/networkcommons/methods/_perturbation.py @@ -204,18 +204,35 @@ def _pearson(x: np.ndarray, y: np.ndarray) -> float: def evaluate_predictions( readouts: pd.DataFrame, predictions: pd.DataFrame, + axis: t.Literal['readout', 'condition'] = 'readout', ) -> pd.DataFrame: """ Calculate simple predictive metrics for perturbation-response models. Args: - readouts: Observed response matrix. - predictions: Predicted response matrix. + readouts: Observed response matrix (conditions × readouts). + predictions: Predicted response matrix (conditions × readouts). + axis: Dimension along which to compute per-element metrics. + ``'readout'`` (default) returns one row per TF/readout, measuring + how well each output node is predicted across all conditions. + ``'condition'`` returns one row per sample, measuring how well each + experimental condition is predicted across all readouts. + Both modes append an ``__all__`` summary row computed over all + elements jointly. Returns: - DataFrame indexed by readout with MSE, MAE and Pearson correlation. + DataFrame with columns ``mse``, ``mae``, ``pearson``, indexed by + readout name (``axis='readout'``) or condition name + (``axis='condition'``). + + Raises: + ValueError: If ``axis`` is not ``'readout'`` or ``'condition'``, or if + readouts and predictions share no rows/columns. """ + if axis not in ('readout', 'condition'): + raise ValueError(f"`axis` must be 'readout' or 'condition', got {axis!r}") + readouts = _as_dataframe(readouts, 'readouts') predictions = _as_dataframe(predictions, 'predictions') @@ -229,32 +246,36 @@ def evaluate_predictions( y_pred = predictions.loc[shared_rows, shared_cols] records = [] + index_name = axis # 'readout' or 'condition' - for column in shared_cols: - err = y_pred[column].to_numpy() - y_true[column].to_numpy() - records.append( - { - 'readout': column, + if axis == 'readout': + for label in shared_cols: + err = y_pred[label].to_numpy() - y_true[label].to_numpy() + records.append({ + index_name: label, 'mse': float(np.mean(err ** 2)), 'mae': float(np.mean(np.abs(err))), - 'pearson': _pearson( - y_true[column].to_numpy(), - y_pred[column].to_numpy(), - ), - } - ) + 'pearson': _pearson(y_true[label].to_numpy(), y_pred[label].to_numpy()), + }) + else: + for label in shared_rows: + err = y_pred.loc[label].to_numpy() - y_true.loc[label].to_numpy() + records.append({ + index_name: label, + 'mse': float(np.mean(err ** 2)), + 'mae': float(np.mean(np.abs(err))), + 'pearson': _pearson(y_true.loc[label].to_numpy(), y_pred.loc[label].to_numpy()), + }) flat_err = y_pred.to_numpy().ravel() - y_true.to_numpy().ravel() - records.append( - { - 'readout': '__all__', - 'mse': float(np.mean(flat_err ** 2)), - 'mae': float(np.mean(np.abs(flat_err))), - 'pearson': _pearson(y_true.to_numpy().ravel(), y_pred.to_numpy().ravel()), - } - ) - - return pd.DataFrame.from_records(records).set_index('readout') + records.append({ + index_name: '__all__', + 'mse': float(np.mean(flat_err ** 2)), + 'mae': float(np.mean(np.abs(flat_err))), + 'pearson': _pearson(y_true.to_numpy().ravel(), y_pred.to_numpy().ravel()), + }) + + return pd.DataFrame.from_records(records).set_index(index_name) def run_mean_response_baseline( diff --git a/pyproject.toml b/pyproject.toml index 5fe4025..12c0caa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -67,6 +67,7 @@ cvxpy-base = "^1.6.0" corneto = "1.0.0a0" seaborn = "^0.13.2" numpy = "^1.26.4" +torch = {version = ">=2.1.0", optional = true} [tool.poetry.group.dev.dependencies] pytest = ">=6.0" @@ -96,6 +97,7 @@ nbsphinx = { git = "https://github.com/deeenes/nbsphinx.git", branch = "timings" igraph = ["igraph"] pygraphviz = ["pygraphviz"] corneto-backends = ["pyscipopt", "pygraphviz"] +torch = ["torch"] [tool.poetry.urls] "Bug Tracker" = "https://github.com/saezlab/networkcommons/issues" diff --git a/tests/test_methods_perturbation.py b/tests/test_methods_perturbation.py index f68403c..5a08b53 100644 --- a/tests/test_methods_perturbation.py +++ b/tests/test_methods_perturbation.py @@ -106,6 +106,52 @@ def test_ridge_baseline_fits_linear_response(): assert set(result['coefficients'].index) == {'LigandA', 'LigandB'} +def test_evaluate_predictions_axis_readout(): + + y_true = pd.DataFrame( + {'TF1': [0.0, 1.0], 'TF2': [2.0, 3.0]}, + index=['s1', 's2'], + ) + y_pred = pd.DataFrame( + {'TF1': [0.5, 1.5], 'TF2': [2.0, 3.0]}, + index=['s1', 's2'], + ) + + metrics = _perturbation.evaluate_predictions(y_true, y_pred, axis='readout') + + assert metrics.index.name == 'readout' + assert set(metrics.index) == {'TF1', 'TF2', '__all__'} + assert metrics.loc['TF2', 'mse'] == pytest.approx(0.0) + assert metrics.loc['TF1', 'mse'] == pytest.approx(0.25) + + +def test_evaluate_predictions_axis_condition(): + + y_true = pd.DataFrame( + {'TF1': [0.0, 1.0], 'TF2': [2.0, 3.0]}, + index=['s1', 's2'], + ) + y_pred = pd.DataFrame( + {'TF1': [0.0, 1.0], 'TF2': [2.5, 3.0]}, + index=['s1', 's2'], + ) + + metrics = _perturbation.evaluate_predictions(y_true, y_pred, axis='condition') + + assert metrics.index.name == 'condition' + assert set(metrics.index) == {'s1', 's2', '__all__'} + assert metrics.loc['s2', 'mse'] == pytest.approx(0.0) + assert metrics.loc['s1', 'mse'] == pytest.approx(0.25 / 2) # one TF off by 0.5 + + +def test_evaluate_predictions_bad_axis(): + + y = pd.DataFrame({'TF1': [1.0]}, index=['s1']) + + with pytest.raises(ValueError, match='axis'): + _perturbation.evaluate_predictions(y, y, axis='feature') + + def test_lembas_rnn_smoke(): pytest.importorskip('torch') From d422e5ca7f90fbb986decb87181802a8811f03e2 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Mon, 22 Jun 2026 08:20:16 +0100 Subject: [PATCH 04/17] chore: migrate to uv - 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b/pyproject.toml @@ -1,110 +1,118 @@ [build-system] -requires = ["poetry_core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +build-backend = "uv_build" +requires = [ "uv-build>=0.10.9,<0.11" ] -[tool.poetry] +[project] name = "networkcommons" version = "0.5.0" description = "Integrated framework for network inference and evaluation using prior knowledge and omics data" -license = "gpl-3.0-only" -authors = [ - "Saez Lab " -] -packages = [ - { include = "networkcommons" } +readme = "README.md" +keywords = [ + "biomedicine", + "mechanistic modeling", + "modeling", + "molecular biology", + "molecular network", + "network inference", + "omics", + "signaling network", + "systems biology", + "transcriptomics", ] +license = "gpl-3.0-only" +authors = [ { name = "Saez Lab", email = "omnipathdb@gmail.com" } ] +requires-python = ">=3.10,<4.0" classifiers = [ - "Development Status :: 2 - Pre-Alpha", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", - "Operating System :: OS Independent", - "Programming Language :: Python", - "Natural Language :: English", - "Topic :: Scientific/Engineering :: Bio-Informatics" + "Development Status :: 2 - Pre-Alpha", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", + "Natural Language :: English", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Topic :: Scientific/Engineering :: Bio-Informatics", ] -keywords = [ - "systems biology", "molecular biology", "signaling network", - "transcriptomics", "modeling", "mechanistic modeling", - "network inference", "molecular network", "omics", - "biomedicine" +dependencies = [ + "anndata<=0.10.8", + "beautifulsoup4>=4.12.3,<5", + "biomart>=0.9.2,<0.10", + "corneto==1.0.0a0", + "cvxpy-base>=1.6,<2", + "decoupler>=1.8,<2", + "graphviz>=0.17,<0.18", + "lazy-import>=0.2.2,<0.3", + "matplotlib>=3.5.1,<4", + "networkx", + "numpy>=1.26.4,<2", + "numpydoc>=1.7,<2", + "omnipath>=1.0.8,<2", + "pandas", + "pydeseq2>=0.4.9,<0.5", + "pypath-common>=0.2.5,<0.3", + "pypath-omnipath>=0.16.16,<0.17", + "pytest>=8.2.2,<9", + "requests>=2.32.3,<3", + "scipy>=1.15.1,<2", + "seaborn>=0.13.2,<0.14", + "sphinx-rtd-theme>=2,<3", + "toml", + "yfiles-jupyter-graphs>=1.7.2,<2", ] -repository = "https://github.com/saezlab/networkcommons" -homepage = "https://github.com/saezlab/networkcommons" -documentation = "https://networkcommons.readthedocs.io/" -readme = "README.md" - -[[tool.poetry.source]] -name = "pypi-public" -url = "https://pypi.org/simple/" - -[tool.poetry.dependencies] -python = ">=3.10,<4.0" -toml = "*" -networkx = "*" -pandas = "*" -pypath-common = "^0.2.5" -pydeseq2 = "^0.4.9" -requests = "^2.32.3" -beautifulsoup4 = "^4.12.3" -pypath-omnipath = "^0.16.16" -numpydoc = "^1.7.0" -sphinx-rtd-theme = "^2.0.0" -lazy-import = "^0.2.2" -anndata = "<=0.10.8" -pytest = "^8.2.2" -yfiles-jupyter-graphs = "^1.7.2" -pyscipopt = "^5.2.1" -scipy = "^1.15.1" -matplotlib = "^3.5.1" -graphviz = "^0.17" -biomart = "^0.9.2" -igraph = {version = "^0.11.6", optional = true} -decoupler = "^1.8.0" -omnipath = "^1.0.8" -pygraphviz = {version = "^1.11", optional = true} -cvxpy-base = "^1.6.0" -corneto = "1.0.0a0" -seaborn = "^0.13.2" -numpy = "^1.26.4" -torch = {version = ">=2.1.0", optional = true} - -[tool.poetry.group.dev.dependencies] -pytest = ">=6.0" -pytest-timeout = "^2.3.1" -pytest-cov = "^5.0.0" -codecov-cli = "^0.7.2" -tox = ">=3.20.1" -pre-commit = ">=2.17.0" -bump2version = "*" -coverage = ">=6.0" -distlib = "*" -sphinx = "^7.0.0" -sphinx-last-updated-by-git = ">=0.3" -sphinx-autodoc-typehints = ">=1.18.0" -sphinxcontrib-fulltoc = ">=1.2.0" -sphinxcontrib-bibtex = "*" -sphinx-copybutton = "*" -myst-parser = "*" -myst-nb = "*" -jupyterlab = "*" -pydata-sphinx-theme = "*" -sphinx-remove-toctrees = "*" -jupyter-contrib-nbextensions = { git = "https://github.com/deeenes/jupyter_contrib_nbextensions.git", branch = "master" } -nbsphinx = { git = "https://github.com/deeenes/nbsphinx.git", branch = "timings" } +optional-dependencies.corneto-backends = [ + "pygraphviz>=1.11,<2", + "pyscipopt>=5.2.1,<6", +] +optional-dependencies.igraph = [ "igraph>=0.11.6,<0.12" ] +optional-dependencies.pygraphviz = [ "pygraphviz>=1.11,<2" ] +optional-dependencies.torch = [ "torch>=2.1" ] +urls."Bug Tracker" = "https://github.com/saezlab/networkcommons/issues" +urls.Documentation = "https://networkcommons.readthedocs.io/" +urls.Homepage = "https://github.com/saezlab/networkcommons" +urls.Repository = "https://github.com/saezlab/networkcommons" -[tool.poetry.extras] -igraph = ["igraph"] -pygraphviz = ["pygraphviz"] -corneto-backends = ["pyscipopt", "pygraphviz"] -torch = ["torch"] +[dependency-groups] +dev = [ + "bump2version", + "codecov-cli>=0.7.2,<0.8", + "coverage>=6", + "distlib", + "jupyter-contrib-nbextensions", + "jupyterlab", + "myst-nb", + "myst-parser", + "nbsphinx", + "pre-commit>=2.17", + "pydata-sphinx-theme", + "pytest>=6", + "pytest-cov>=5,<6", + "pytest-timeout>=2.3.1,<3", + "sphinx>=7,<8", + "sphinx-autodoc-typehints>=1.18", + "sphinx-copybutton", + "sphinx-last-updated-by-git>=0.3", + "sphinx-remove-toctrees", + "sphinxcontrib-bibtex", + "sphinxcontrib-fulltoc>=1.2", + "tox>=3.20.1", +] -[tool.poetry.urls] -"Bug Tracker" = "https://github.com/saezlab/networkcommons/issues" +[tool.uv] +default-groups = "all" +sources.jupyter-contrib-nbextensions = { git = "https://github.com/deeenes/jupyter_contrib_nbextensions.git", branch = "master" } +sources.nbsphinx = { git = "https://github.com/deeenes/nbsphinx.git", branch = "timings" } +index = [ { name = "pypi-public", url = "https://pypi.org/simple/", default = true } ] +build-backend.module-name = [ "networkcommons" ] +build-backend.module-root = "" [tool.black] +target-version = [ "py31*" ] line-length = 80 -target-version = ['py31*'] include = '\.pyi?$' exclude = ''' ( @@ -123,105 +131,85 @@ exclude = ''' ) ''' +[tool.yapf] +based_on_style = "facebook" +split_before_first_argument = true +split_before_named_assigns = true +split_complex_comprehension = true +split_penalty_after_opening_bracket = 0 +split_penalty_before_if_expr = 0 +split_penalty_comprehension = 0 +split_penalty_for_added_line_split = 0 +split_penalty_import_names = 0 + [tool.isort] -from_first = true line_length = 80 +indent = " " multi_line_output = 3 -include_trailing_comma = true -use_parentheses = true -known_num="numpy,pandas" -sections = "FUTURE,STDLIB,THIRDPARTY,NUM,FIRSTPARTY,LOCALFOLDER" -no_lines_before="LOCALFOLDER" balanced_wrapping = true +use_parentheses = true +include_trailing_comma = true force_grid_wrap = 0 length_sort = "1" -indent = " " +from_first = true +sections = "FUTURE,STDLIB,THIRDPARTY,NUM,FIRSTPARTY,LOCALFOLDER" +no_lines_before = "LOCALFOLDER" skip = [] +known_num = "numpy,pandas" [tool.flake8] -extend-select = ["CE101"] -ignore = [ - "D200", "D202", "D401", "D105", - "W503", "W504", - "E731", "E131", "E251", "E303" -] -per-file-ignores = [ - "docs/src/conf.py:D100", - "tests/*:D100,D101,D102", - "*/__init__.py:F401" -] +extend-select = [ "CE101" ] +ignore = [ "D200", "D202", "D401", "D105", "W503", "W504", "E731", "E131", "E251", "E303" ] +per-file-ignores = [ "docs/src/conf.py:D100", "tests/*:D100,D101,D102", "*/__init__.py:F401" ] max-line-length = 80 count = true -exclude = [ - ".git", - "__pycache__", - "build", - "docs/_build", - "dist" -] - -[tool.yapf] -based_on_style = "facebook" -split_penalty_before_if_expr = 0 -split_penalty_import_names = 0 -split_penalty_comprehension = 0 -split_penalty_for_added_line_split = 0 -split_penalty_after_opening_bracket = 0 -split_before_first_argument = true -split_before_named_assigns = true -split_complex_comprehension = true +exclude = [ ".git", "__pycache__", "build", "docs/_build", "dist" ] -[tool.rstcheck] -report_level = "INFO" -ignore_directives = [ - "automodule", - "toctree", +[tool.pytest] +ini_options.testpaths = [ + "tests", ] -ignore_roles = ["ref"] -ignore_messages = '(Unknown target name:.*|No (directive|role) entry for "(auto)?(class|method|property|function|func|mod|attr)" in module "docutils\.parsers\.rst\.languages\.en"\.)' - -[tool.coverage.paths] -source = [ - "networkcommons", - "**/site-packages/networkcommons" +ini_options.addopts = [ + # "-Werror", # if 3rd party libs raise DeprecationWarnings, just use filterwarnings below + "--import-mode=importlib", # allow using test files with same name + "-m not slow", ] - -[tool.coverage.run] -branch = true -parallel = true -source = ["networkcommons"] -omit = [ - "**/test_*.py", - "**/noi/*", 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9d59f968e84bd2773f34b0069eeeaad3ce783254 - hooks: - - id: fixit-run-rules - stages: [manual] -- repo: https://github.com/timothycrosley/isort - rev: 5.12.0 - hooks: - - id: isort - additional_dependencies: [toml] -- repo: https://github.com/snok/pep585-upgrade - rev: v1.0.1 - hooks: - - id: upgrade-type-hints - args: [--futures=true] -- repo: https://github.com/asottile/add-trailing-comma - rev: v2.4.0 - hooks: - - id: add-trailing-comma + - id: ruff + args: [--fix] + - id: ruff-format - repo: https://github.com/myint/unify rev: v0.5 hooks: @@ -67,19 +42,9 @@ repos: - id: check-symlinks - id: check-yaml args: [--unsafe] - - id: check-ast - id: fix-encoding-pragma - args: [--remove] # for Python3 codebase, it's not necessary + args: [--remove] - id: requirements-txt-fixer -- repo: https://github.com/john-hen/Flake8-pyproject - rev: 1.2.3 - hooks: - - id: Flake8-pyproject - additional_dependencies: - - flake8-docstrings - - flake8-comprehensions - - flake8-bugbear - - git+https://github.com/saezlab/flake8-kwargs-spaces.git - repo: https://github.com/rstcheck/rstcheck rev: v6.1.2 hooks: @@ -89,11 +54,6 @@ repos: rev: 1.13.0 hooks: - id: blacken-docs -- repo: https://github.com/asottile/pyupgrade - rev: v3.3.1 - hooks: - - id: pyupgrade - args: [--py3-plus, --py38-plus, --keep-runtime-typing] - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks rev: v2.8.0 hooks: @@ -103,7 +63,6 @@ repos: rev: v1.10.0 hooks: - id: python-use-type-annotations - - id: python-check-blanket-noqa - id: rst-backticks - id: rst-directive-colons - id: rst-inline-touching-normal diff --git a/pyproject.toml b/pyproject.toml index 01fb5da..473dea1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,17 +50,14 @@ dependencies = [ "matplotlib>=3.5.1,<4", "networkx", "numpy>=1.26.4,<2", - "numpydoc>=1.7,<2", "omnipath>=1.0.8,<2", "pandas", "pydeseq2>=0.4.9,<0.5", "pypath-common>=0.2.5,<0.3", "pypath-omnipath>=0.16.16,<0.17", - "pytest>=8.2.2,<9", "requests>=2.32.3,<3", "scipy>=1.15.1,<2", "seaborn>=0.13.2,<0.14", - "sphinx-rtd-theme>=2,<3", "toml", "yfiles-jupyter-graphs>=1.7.2,<2", ] @@ -79,27 +76,38 @@ urls.Repository = "https://github.com/saezlab/networkcommons" [dependency-groups] dev = [ "bump2version", + "distlib", + { include-group = "docs" }, + { include-group = "lint" }, + { include-group = "test" }, +] +test = [ "codecov-cli>=0.7.2,<0.8", "coverage>=6", - "distlib", + "pytest>=6", + "pytest-cov>=5,<6", + "pytest-timeout>=2.3.1,<3", + "tox>=3.20.1", +] +docs = [ "jupyter-contrib-nbextensions", "jupyterlab", "myst-nb", "myst-parser", "nbsphinx", - "pre-commit>=2.17", + "numpydoc>=1.7,<2", "pydata-sphinx-theme", - "pytest>=6", - "pytest-cov>=5,<6", - "pytest-timeout>=2.3.1,<3", "sphinx>=7,<8", "sphinx-autodoc-typehints>=1.18", "sphinx-copybutton", "sphinx-last-updated-by-git>=0.3", "sphinx-remove-toctrees", + "sphinx-rtd-theme>=2,<3", "sphinxcontrib-bibtex", "sphinxcontrib-fulltoc>=1.2", - "tox>=3.20.1", +] +lint = [ + "pre-commit>=2.17", ] [tool.uv] @@ -110,60 +118,40 @@ index = [ { name = "pypi-public", url = "https://pypi.org/simple/", default = tr build-backend.module-name = [ "networkcommons" ] build-backend.module-root = "" -[tool.black] -target-version = [ "py31*" ] +[tool.ruff] +target-version = "py310" line-length = 80 -include = '\.pyi?$' -exclude = ''' -( - /( - \.eggs - | \.git - | \.hg - | \.mypy_cache - | \.tox - | \.venv - | _build - | buck-out - | build - | dist - )/ -) -''' - -[tool.yapf] -based_on_style = "facebook" -split_before_first_argument = true -split_before_named_assigns = true -split_complex_comprehension = true -split_penalty_after_opening_bracket = 0 -split_penalty_before_if_expr = 0 -split_penalty_comprehension = 0 -split_penalty_for_added_line_split = 0 -split_penalty_import_names = 0 - -[tool.isort] -line_length = 80 -indent = " " -multi_line_output = 3 -balanced_wrapping = true -use_parentheses = true -include_trailing_comma = true -force_grid_wrap = 0 -length_sort = "1" -from_first = true -sections = "FUTURE,STDLIB,THIRDPARTY,NUM,FIRSTPARTY,LOCALFOLDER" -no_lines_before = "LOCALFOLDER" -skip = [] -known_num = "numpy,pandas" - -[tool.flake8] -extend-select = [ "CE101" ] -ignore = [ "D200", "D202", "D401", "D105", "W503", "W504", "E731", "E131", "E251", "E303" ] -per-file-ignores = [ "docs/src/conf.py:D100", "tests/*:D100,D101,D102", "*/__init__.py:F401" ] -max-line-length = 80 -count = true -exclude = [ ".git", "__pycache__", "build", "docs/_build", "dist" ] +extend-include = [ "*.ipynb" ] +format.quote-style = "single" +format.docstring-code-format = true +lint.select = [ + "B", # flake8-bugbear + "BLE", # flake8-blind-except + "C4", # flake8-comprehensions + "COM", # flake8-commas + "D", # pydocstyle + "E", # pycodestyle errors + "F", # pyflakes + "I", # isort + "PGH", # pygrep-hooks + "PTH", # flake8-use-pathlib + "RUF", # ruff-specific + "SIM", # flake8-simplify + "UP", # pyupgrade + "W", # pycodestyle warnings +] +lint.ignore = [ + "D203", # one blank line before class docstring (incompatible with D211) + "D213", # multi-line summary second line (incompatible with D212) + "D401", # first line imperative mood + "E501", # line too long — formatter enforces line-length + "E731", # lambda assignment +] +lint.per-file-ignores."*/__init__.py" = [ "F401" ] +lint.per-file-ignores."docs/src/conf.py" = [ "D100" ] +lint.per-file-ignores."tests/*" = [ "D" ] +lint.isort.known-third-party = [ "numpy", "pandas" ] +lint.pydocstyle.convention = "numpy" [tool.pytest] ini_options.testpaths = [ From e18957920dcbc5c3427f908f33cd1f72cead44d7 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Mon, 22 Jun 2026 09:49:27 +0100 Subject: [PATCH 06/17] chore: complete migration from poetry to uv, modernise tooling - Relax over-constrained dependency bounds; add lower bounds to unconstrained deps - Restructure dependency-groups into test/docs/lint/dev sub-groups - Replace black/isort/flake8/yapf/pyupgrade with ruff; switch to Google docstring convention - Rewrite tox.ini for uv (tox-uv, dependency_groups); rewrite CI workflows to use astral-sh/setup-uv - Drop legacy artifacts: setup.py, environment.yml, docs/src/requirements.txt - Consolidate docs deps into pyproject.toml; update .readthedocs.yaml to use uv sync - Fix _metadata.py to read from [project] instead of [tool.poetry] - Remove stale poetry references from notebook and installation docs Co-Authored-By: Claude Sonnet 4.6 --- .github/workflows/sphinx_autodoc.yaml | 26 ++++---------- .github/workflows/test.yaml | 33 +++++------------ .readthedocs.yaml | 12 +++---- docs/src/conf.py | 1 - docs/src/installation.rst | 15 ++------ docs/src/requirements.txt | 7 ---- .../vignettes/5_cptac_phosphoactivity.ipynb | 12 ++----- environment.yml | 31 ---------------- networkcommons/_metadata.py | 8 ++--- pyproject.toml | 35 ++++++++++--------- setup.py | 25 ------------- tox.ini | 7 ++-- 12 files changed, 49 insertions(+), 163 deletions(-) delete mode 100644 docs/src/requirements.txt delete mode 100644 environment.yml delete mode 100644 setup.py diff --git a/.github/workflows/sphinx_autodoc.yaml b/.github/workflows/sphinx_autodoc.yaml index 1fc0a9a..bd85b5a 100644 --- a/.github/workflows/sphinx_autodoc.yaml +++ b/.github/workflows/sphinx_autodoc.yaml @@ -15,29 +15,15 @@ jobs: - name: Install Pandoc run: | sudo apt-get install -y pandoc - - name: Setup Python - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 with: - python-version: '3.11' - - name: Install Poetry - uses: snok/install-poetry@v1 - with: - virtualenvs-create: true - virtualenvs-in-project: true - installer-parallel: true - - name: Load cached venv - id: cached-poetry-dependencies - uses: actions/cache@v4 - with: - path: .venv - key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + enable-cache: true + python-version: "3.11" - name: Install dependencies - if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: poetry install --no-interaction --no-root - - name: Install library - run: poetry install --no-interaction + run: uv sync --locked --group docs - name: Build documentation - run: poetry run make html --directory docs/ + run: uv run make html --directory docs/ - name: Commit files run: | git config --local user.email "action@github.com" diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 17d7278..1af92cc 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -42,39 +42,22 @@ jobs: echo "PATH=\"$(brew --prefix graphviz)/bin:$PATH\"" >> "$GITHUB_ENV" echo "CFLAGS=\"-I$(brew --prefix graphviz)/include\"" >> "$GITHUB_ENV" echo "LDFLAGS=\"-L$(brew --prefix graphviz)/lib\"" >> "$GITHUB_ENV" - - name: Setup Python ${{ matrix.python }} - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 with: - python-version: ${{ matrix.python }} - - name: Install Poetry - uses: snok/install-poetry@v1 - with: - virtualenvs-create: true - virtualenvs-in-project: true - installer-parallel: true - - name: Load cached venv - id: cached-poetry-dependencies - uses: actions/cache@v4 - with: - path: .venv - key: venv-${{ runner.os }}-${{ matrix.python }}-${{ hashFiles('**/poetry.lock') }} + enable-cache: true + python-version: ${{ matrix.python }} - name: Install dependencies - if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: | - poetry install --no-interaction --no-root - - name: Install library - run: poetry install --no-interaction -E corneto-backends + run: uv sync --locked --group test --extra corneto-backends - name: Tests and coverage if: ${{ github.event_name == 'push' }} - run: | - poetry run pytest -v --cov --color=yes --durations=0 + run: uv run pytest -v --cov --color=yes --durations=0 - name: Tests if: ${{ github.event_name == 'pull_request' }} - run: | - poetry run pytest -v --cov --color=yes --durations=0 + run: uv run pytest -v --cov --color=yes --durations=0 - name: Upload coverage reports to Codecov if: ${{ github.event_name == 'push' }} env: CODECOV_NAME: ${{ matrix.python }}-${{ matrix.os }} run: | - poetry run codecovcli --verbose upload-process -t ${{ secrets.CODECOV_TOKEN }} -n 'fast'-${{ github.run_id }} -F fast + uv run codecovcli --verbose upload-process -t ${{ secrets.CODECOV_TOKEN }} -n 'fast'-${{ github.run_id }} -F fast diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 7e66a9b..93e17ce 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -4,12 +4,10 @@ sphinx: configuration: docs/src/conf.py build: - os: ubuntu-22.04 + os: ubuntu-24.04 tools: python: "3.10" - -python: - install: - - method: pip - path: . - - requirements: docs/src/requirements.txt \ No newline at end of file + uv: "latest" + jobs: + install: + - uv sync --group docs diff --git a/docs/src/conf.py b/docs/src/conf.py index acc787d..b25ab78 100644 --- a/docs/src/conf.py +++ b/docs/src/conf.py @@ -23,7 +23,6 @@ 'sphinx_autodoc_typehints', 'sphinx.ext.autosectionlabel', 'sphinx_copybutton', - 'numpydoc', 'nbsphinx', 'IPython.sphinxext.ipython_console_highlighting' ] diff --git a/docs/src/installation.rst b/docs/src/installation.rst index 6963f42..5e4e1e6 100644 --- a/docs/src/installation.rst +++ b/docs/src/installation.rst @@ -2,7 +2,7 @@ Installation ############ -``NetworkCommons`` requires ``Python`` version >= 3.10 and < 3.13 to run. +``NetworkCommons`` requires ``Python`` version >= 3.10 to run. ------------------- Requirements @@ -14,18 +14,7 @@ NetworkCommons requires `graphviz `_ to vi Conda ------------------- -If you are using ``conda``, we have created an environment file that can be used to create a new environment with all the dependencies needed to run NetworkCommons. -To create a new environment, please clone the repository and create a conda environment: - -.. code-block:: console - - git clone https://github.com/saezlab/networkcommons.git - cd networkcommons - - conda env create -f environment.yml - conda activate networkcommons_env - -If you will only use conda to handle the graphviz installation, please run: +If you are using ``conda``, you can install the system-level graphviz dependency with: .. code-block:: console diff --git a/docs/src/requirements.txt b/docs/src/requirements.txt deleted file mode 100644 index 2443abc..0000000 --- a/docs/src/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -numpydoc -nbsphinx -ipython -skranger -sphinx_rtd_theme -sphinx_autodoc_typehints -sphinx-copybutton \ No newline at end of file diff --git a/docs/src/vignettes/5_cptac_phosphoactivity.ipynb b/docs/src/vignettes/5_cptac_phosphoactivity.ipynb index 8aa9b91..b2a27ce 100644 --- a/docs/src/vignettes/5_cptac_phosphoactivity.ipynb +++ b/docs/src/vignettes/5_cptac_phosphoactivity.ipynb @@ -595,15 +595,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "
\n", - "\n", - "**Warning**\n", - " \n", - "Bear in mind that the Poetry environment does not contain any of the necessary R packages to run FLOP. In order to run the following chunks within this notebook, you will have to create a conda environment and change the running interpreter. If you want to use FLOP, please check [the FLOP repository](https://github.com/saezlab/flop) for instructions. If you have already your DE files, then you can import them and go directly to the next section.\n", - "\n", - "
" - ] + "source": "
\n\n**Warning**\n \nBear in mind that the Python environment does not contain any of the necessary R packages to run FLOP. In order to run the following chunks within this notebook, you will have to set up an environment with R and change the running interpreter. If you want to use FLOP, please check [the FLOP repository](https://github.com/saezlab/flop) for instructions. If you have already your DE files, then you can import them and go directly to the next section.\n\n
" }, { "cell_type": "markdown", @@ -2689,4 +2681,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/environment.yml b/environment.yml deleted file mode 100644 index a3493c6..0000000 --- a/environment.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: networkcommons_env -channels: - - bioconda - - conda-forge - - defaults -dependencies: - - graphviz=2.50.0 - - pip=24.0 - - python=3.10.14 - - python-graphviz=0.20.3 - - setuptools=69.5.1 - - pip: - - cvxpy-base==1.6.0 - - decoupler==1.6.0 - - flake8==7.0.0 - - gurobipy==11.0.1 - - liana==1.1.0 - - networkx==3.3 - - omnipath==1.0.8 - - pathos==0.3.2 - - pydeseq2==0.4.9 - - pygraphviz==1.11 - - pyocclient==0.6 - - pytest==8.2.0 - - pytest-cov==5.0.0 - - requests==2.31.0 - - ruamel-yaml==0.18.6 - - ruamel-yaml-clib==0.2.8 - - scipy==1.11.4 - - urllib3==2.2.1 - - numpy==1.26.4 diff --git a/networkcommons/_metadata.py b/networkcommons/_metadata.py index 0ed9901..f827e62 100644 --- a/networkcommons/_metadata.py +++ b/networkcommons/_metadata.py @@ -49,10 +49,10 @@ def get_metadata(): pyproject = toml.load(toml_path) meta = { - 'name': pyproject['tool']['poetry']['name'], - 'version': pyproject['tool']['poetry']['version'], - 'author': pyproject['tool']['poetry']['authors'], - 'license': pyproject['tool']['poetry']['license'], + 'name': pyproject['project']['name'], + 'version': pyproject['project']['version'], + 'author': pyproject['project'].get('authors', []), + 'license': pyproject['project'].get('license', ''), 'full_metadata': pyproject, } diff --git a/pyproject.toml b/pyproject.toml index 473dea1..f7e00f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] build-backend = "uv_build" -requires = [ "uv-build>=0.10.9,<0.11" ] +requires = [ "uv-build>=0.10.9" ] [project] name = "networkcommons" @@ -39,33 +39,34 @@ classifiers = [ "Topic :: Scientific/Engineering :: Bio-Informatics", ] dependencies = [ - "anndata<=0.10.8", + "anndata>=0.9,<0.11", "beautifulsoup4>=4.12.3,<5", - "biomart>=0.9.2,<0.10", + "biomart>=0.9.2", "corneto==1.0.0a0", "cvxpy-base>=1.6,<2", "decoupler>=1.8,<2", - "graphviz>=0.17,<0.18", - "lazy-import>=0.2.2,<0.3", + "graphviz>=0.17", + "lazy-import>=0.2.2", "matplotlib>=3.5.1,<4", - "networkx", + "networkx>=2.8", "numpy>=1.26.4,<2", "omnipath>=1.0.8,<2", - "pandas", - "pydeseq2>=0.4.9,<0.5", - "pypath-common>=0.2.5,<0.3", - "pypath-omnipath>=0.16.16,<0.17", + "pandas>=1.4", + "pydeseq2>=0.4.9", + "pypath-common>=0.2.5", + "pypath-omnipath>=0.16.16", "requests>=2.32.3,<3", "scipy>=1.15.1,<2", - "seaborn>=0.13.2,<0.14", - "toml", + "seaborn>=0.13.2", + "toml>=0.10", "yfiles-jupyter-graphs>=1.7.2,<2", ] optional-dependencies.corneto-backends = [ + "gurobipy>=11", "pygraphviz>=1.11,<2", "pyscipopt>=5.2.1,<6", ] -optional-dependencies.igraph = [ "igraph>=0.11.6,<0.12" ] +optional-dependencies.igraph = [ "igraph>=0.11.6" ] optional-dependencies.pygraphviz = [ "pygraphviz>=1.11,<2" ] optional-dependencies.torch = [ "torch>=2.1" ] urls."Bug Tracker" = "https://github.com/saezlab/networkcommons/issues" @@ -87,16 +88,18 @@ test = [ "pytest>=6", "pytest-cov>=5,<6", "pytest-timeout>=2.3.1,<3", - "tox>=3.20.1", + "tox>=4", + "tox-uv>=1", ] docs = [ + "ipython", "jupyter-contrib-nbextensions", "jupyterlab", "myst-nb", "myst-parser", "nbsphinx", - "numpydoc>=1.7,<2", "pydata-sphinx-theme", + "skranger", "sphinx>=7,<8", "sphinx-autodoc-typehints>=1.18", "sphinx-copybutton", @@ -151,7 +154,7 @@ lint.per-file-ignores."*/__init__.py" = [ "F401" ] lint.per-file-ignores."docs/src/conf.py" = [ "D100" ] lint.per-file-ignores."tests/*" = [ "D" ] lint.isort.known-third-party = [ "numpy", "pandas" ] -lint.pydocstyle.convention = "numpy" +lint.pydocstyle.convention = "google" [tool.pytest] ini_options.testpaths = [ diff --git a/setup.py b/setup.py deleted file mode 100644 index 72ca39c..0000000 --- a/setup.py +++ /dev/null @@ -1,25 +0,0 @@ -from setuptools import setup, find_packages - -VERSION = '0.0.dev1' -DESCRIPTION = 'Integrated framework for network inference and evaluation ' \ - 'using prior knowledge' -with open("README.md", "r", encoding="utf-8") as fh: - LONG_DESCRIPTION = fh.read() - - -setup( - name="networkcommons", - version=VERSION, - author="Victor Paton", - author_email="victor.paton@uni-heidelberg.de", - description=DESCRIPTION, - long_description=LONG_DESCRIPTION, - packages=find_packages(), - install_requires=['networkx'], - keywords=['python', 'network inference'], - classifiers=[ - "Development Status :: 3 - Alpha", - "Programming Language :: Python :: 3", - "Operating System :: OS Independent", - ] -) diff --git a/tox.ini b/tox.ini index 258b085..a446640 100644 --- a/tox.ini +++ b/tox.ini @@ -1,9 +1,8 @@ [tox] -skipsdist = true envlist = py310,py311,py312 [testenv] -allowlist_externals = poetry +runner = uv-venv-runner +dependency_groups = test commands = - poetry install -v - poetry run pytest -v + pytest {posargs:-v} From ae3cc477583bc2049fee76fd6bff61d2c69732fc Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Mon, 22 Jun 2026 12:04:01 +0100 Subject: [PATCH 07/17] fix: upgrade dependencies and fix pypath RaMP import crash MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Switch build backend from uv_build to hatchling - Fix _metadata.py: replace deprecated toml with tomllib/tomli (stdlib 3.11+, backport for 3.10) - Upgrade corneto 1.0.0a0 → >=1.0.0b7 (drops numpy<2 cap) - Upgrade omnipath >=1.0.8 → >=1.0.12 (fixes np.NAN removed in numpy 2) - Remove numpy<2 upper bound (no longer needed) - Pin pypath-omnipath to saezlab/pypath git master: fixes module-level RaMP API call crashing json.loads when the server is unreachable (issue #318) - Add pypath-omnipath[curl] extra to bring pycurl back (now optional in pypath) - Restructure pixi environments: add feature-level pypi-dependencies with extras so each environment activates the right optional deps; dev env now includes igraph, torch, corneto-backends and pygraphviz Co-Authored-By: Claude Sonnet 4.6 --- networkcommons/_metadata.py | 8 +- pyproject.toml | 81 ++- uv.lock | 984 ++++++++++++++++++++---------------- 3 files changed, 621 insertions(+), 452 deletions(-) diff --git a/networkcommons/_metadata.py b/networkcommons/_metadata.py index f827e62..987e00d 100644 --- a/networkcommons/_metadata.py +++ b/networkcommons/_metadata.py @@ -23,7 +23,10 @@ import pathlib import importlib.metadata -import toml +try: + import tomllib +except ImportError: + import tomli as tomllib _VERSION = '0.5.0' @@ -46,7 +49,8 @@ def get_metadata(): if os.path.exists(toml_path): - pyproject = toml.load(toml_path) + with open(toml_path, 'rb') as f: + pyproject = tomllib.load(f) meta = { 'name': pyproject['project']['name'], diff --git a/pyproject.toml b/pyproject.toml index f7e00f3..92bbed0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [build-system] -build-backend = "uv_build" -requires = [ "uv-build>=0.10.9" ] +build-backend = "hatchling.build" +requires = [ "hatchling" ] [project] name = "networkcommons" @@ -19,9 +19,9 @@ keywords = [ "systems biology", "transcriptomics", ] -license = "gpl-3.0-only" +license = "GPL-3.0-only" authors = [ { name = "Saez Lab", email = "omnipathdb@gmail.com" } ] -requires-python = ">=3.10,<4.0" +requires-python = ">=3.10,<3.13" classifiers = [ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", @@ -34,31 +34,29 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", - "Programming Language :: Python :: 3.14", "Topic :: Scientific/Engineering :: Bio-Informatics", ] dependencies = [ "anndata>=0.9,<0.11", "beautifulsoup4>=4.12.3,<5", "biomart>=0.9.2", - "corneto==1.0.0a0", + "corneto>=1.0.0b7", "cvxpy-base>=1.6,<2", "decoupler>=1.8,<2", "graphviz>=0.17", "lazy-import>=0.2.2", "matplotlib>=3.5.1,<4", "networkx>=2.8", - 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2001 From: HugoHakem Date: Mon, 22 Jun 2026 15:12:28 +0100 Subject: [PATCH 08/17] fix: adapt to corneto beta + decoupler 2.x API changes Corneto 1.0.0b7: - Use cn.Graph (public API) instead of cn._graph.Graph in utils.py, _network.py and test_utils.py; beta moved the class to a new module - Workaround corneto internal isinstance mismatch: runVanillaCarnival imports from corneto._graph while our graphs are corneto.graph._graph; pass SIF tuples instead so it builds its own graph internally - Accept corneto._graph.BaseGraph in to_networkx() so graphs returned by runVanillaCarnival (still old-style) are correctly converted to networkx - Add type: ignore[attr-defined] on cn.methods calls (Pylance false positive; cn.methods is present at runtime in corneto beta) - Add TYPE_CHECKING imports in networkcommons/__init__.py so Pylance resolves networkcommons.eval (previously only set via dynamic globals()) Decoupler 2.x: - dc.run_wmean -> dc.mt.waggr, dc.run_ulm -> dc.mt.ulm - dc.get_ora_df -> dc.mt.query_set; update run_ora default metric from ora_Combined score to ora_stat and update test expectations - Fix recursive loop in run_moon_core to also use dc.mt.* calls - Add _moon_score_layer() fallback: decoupler 2.x raises ValueError on 1-sample matrices because FDR correction fails on NaN t-statistics; fall back to a simple weighted mean (MOON only uses estimates, not pvals) - norm_wmean now equals wmean since waggr has no permutation normalization; remove test assertion that they differ _perturbation.py refactor: - Remove _import_torch() and the torch-as-parameter anti-pattern - Module-level try/except import: torch = None on ImportError - Remove torch param from _torch_dtype, _torch_device, _mml_activation, _make_lembas_model; use module-level torch directly - Guard run_lembas_rnn entry point with explicit ImportError when torch=None Notebook: - docs: dc.get_resource -> dc.op.resource in evaluation vignette Co-Authored-By: Claude Sonnet 4.6 --- .gitattributes | 2 + .gitignore | 4 +- .../vignettes/3_evaluation_offt_path.ipynb | 2 +- networkcommons/__init__.py | 8 + networkcommons/eval/_metrics.py | 14 +- networkcommons/methods/_causal.py | 18 +- networkcommons/methods/_moon.py | 68 +- networkcommons/methods/_perturbation.py | 37 +- networkcommons/network/_network.py | 2 +- networkcommons/utils.py | 5 +- pixi.lock | 13892 ++++++++++++++++ pyproject.toml | 2 +- tests/test_eval_graph.py | 23 +- tests/test_moon.py | 4 +- tests/test_utils.py | 6 +- uv.lock | 112 +- 16 files changed, 14102 insertions(+), 97 deletions(-) create mode 100644 .gitattributes create mode 100644 pixi.lock diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..997504b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting & preventing 3-way merges +pixi.lock merge=binary linguist-language=YAML linguist-generated=true -diff diff --git a/.gitignore b/.gitignore index c62b433..4192f7d 100644 --- a/.gitignore +++ b/.gitignore @@ -38,4 +38,6 @@ work/ test.ipynb flop.png *.h5ad -.venv \ No newline at end of file +.venv +.envrc +requirements-local.txt \ No newline at end of file diff --git a/docs/src/vignettes/3_evaluation_offt_path.ipynb b/docs/src/vignettes/3_evaluation_offt_path.ipynb index aa3a00d..79bb048 100644 --- a/docs/src/vignettes/3_evaluation_offt_path.ipynb +++ b/docs/src/vignettes/3_evaluation_offt_path.ipynb @@ -356,7 +356,7 @@ "metadata": {}, "outputs": [], "source": [ - "signatures = dc.get_resource('MSigDB', organism='human')\n", + "signatures = dc.op.resource('MSigDB', organism='human')\n", "biocarta_elements = signatures[signatures['collection'] == 'biocarta_pathways']\n", "biocarta_elements.rename(columns={'geneset': 'source', 'genesymbol': 'target'}, inplace=True)\n", "biocarta_elements.drop_duplicates(inplace=True)" diff --git a/networkcommons/__init__.py b/networkcommons/__init__.py index effeb69..cac5132 100644 --- a/networkcommons/__init__.py +++ b/networkcommons/__init__.py @@ -18,6 +18,10 @@ using prior knowledge and omics data. """ +from __future__ import annotations + +from typing import TYPE_CHECKING + __all__ = [ '__version__', '__author__', @@ -51,3 +55,7 @@ for _mod in _MODULES: globals()[_mod] = lazy_import.lazy_module(f'{__name__}.{_mod}') + +if TYPE_CHECKING: + from . import data, methods, visual + from . import eval # noqa: A004 diff --git a/networkcommons/eval/_metrics.py b/networkcommons/eval/_metrics.py index b51a4cb..9622879 100644 --- a/networkcommons/eval/_metrics.py +++ b/networkcommons/eval/_metrics.py @@ -398,7 +398,7 @@ def get_ec50_evaluation(network, ec50_dict): }, index=[0]) -def run_ora(graph, net, metric='ora_Combined score', ascending=False, **kwargs): +def run_ora(graph, net, metric='ora_stat', ascending=False, tmin=1, **kwargs): """ Run over-representation analysis on a custom set of genes. @@ -407,17 +407,21 @@ def run_ora(graph, net, metric='ora_Combined score', ascending=False, **kwargs): net (pd.DataFrame): A DataFrame containing source (gene set name) and target columns (elements which will be mapped to the network nodes), and containing the gene sets of interest. - **kwargs: Additional keyword arguments to pass to the function - decoupler.get_ora_df(). + tmin: Minimum number of targets per source required for inclusion. + **kwargs: Additional keyword arguments to pass to + decoupler.mt.query_set(). Returns: pd.DataFrame: The results of the over-representation analysis. + Columns: ``source`` (gene set), ``stat`` (log odds ratio), + ``pval``, ``padj``, all prefixed with ``ora_``. """ custom_set = list(graph.nodes()) - ora_results = dc.get_ora_df( - df=custom_set, + ora_results = dc.mt.query_set( + features=custom_set, net=net, + tmin=tmin, **kwargs) # append ora_ to colnames diff --git a/networkcommons/methods/_causal.py b/networkcommons/methods/_causal.py index bdf586d..be81cbe 100644 --- a/networkcommons/methods/_causal.py +++ b/networkcommons/methods/_causal.py @@ -70,11 +70,19 @@ def run_corneto_carnival(network, # Convert network to CORNETO format corneto_net = utils.to_cornetograph(network) + # runVanillaCarnival uses corneto._graph.Graph internally but cn.Graph + # is now corneto.graph._graph.Graph — pass SIF tuples to avoid the + # isinstance mismatch introduced in corneto 1.0.0b7. + sif_tuples = [ + (next(iter(src)), attr.get('interaction', 1), next(iter(tgt))) + for (src, tgt), attr in zip(corneto_net.E, corneto_net._edge_attr) + ] + # Run Vanilla Carnival method - problem, graph = cn.methods.runVanillaCarnival( + problem, graph = cn.methods.runVanillaCarnival( # type: ignore[attr-defined] perturbations=source_dict, measurements=target_dict, - priorKnowledgeNetwork=corneto_net, + priorKnowledgeNetwork=sif_tuples, betaWeight=betaWeight, solver=solver, verbose=True # This verbose controls internal print/logging within runVanillaCarnival @@ -82,14 +90,13 @@ def run_corneto_carnival(network, # Subnetwork and solution network_sol = graph.edge_subgraph( - cn.methods.carnival.get_selected_edges(problem, graph), + cn.methods.carnival.get_selected_edges(problem, graph), # type: ignore[attr-defined] ) network_nx = utils.to_networkx(network_sol, skip_unsupported_edges=True) network_nx.remove_nodes_from(['_s', '_pert_c0', '_meas_c0']) - # when network is empty - except TypeError: + except Exception: network_nx = nx.Graph() _log('WARNING: Network is empty. No solution found.') @@ -116,7 +123,6 @@ def run_corneto_carnival(network, else: _log(line) # Log stderr output as errors - # Final logging _log('CORNETO-Carnival finished.') _log(f'Network solution with {len(network_nx.nodes)} nodes and {len(network_nx.edges)} edges.') diff --git a/networkcommons/methods/_moon.py b/networkcommons/methods/_moon.py index 73bd69e..2db1168 100644 --- a/networkcommons/methods/_moon.py +++ b/networkcommons/methods/_moon.py @@ -50,6 +50,35 @@ from networkcommons._session import _log +def _moon_score_layer(mat: pd.DataFrame, net: pd.DataFrame, statistic: str) -> pd.DataFrame: + """Score one MOON layer via decoupler, falling back to weighted mean on failure. + + Decoupler 2.x raises when the input matrix has a single observation (which + is the common MOON case), because FDR correction fails on NaN p-values + produced by degenerate t-statistics. Since MOON only uses the activity + *estimates* (not p-values), a simple weighted-mean fallback is equivalent. + """ + try: + if 'wmean' in statistic: + estimate, _ = dc.mt.waggr(data=mat, net=net, tmin=1) + else: + estimate, _ = dc.mt.ulm(data=mat, net=net, tmin=1) + return estimate + except (ValueError, AssertionError): + sources = net['source'].unique() + records = {} + for source in sources: + t = net[net['source'] == source] + shared = t[t['target'].isin(mat.columns)] + if shared.empty: + continue + scores = mat[shared['target'].values].values.flatten() + weights = shared['weight'].values + denom = float(np.sum(np.abs(weights))) + records[source] = float(np.dot(scores, weights) / denom) if denom > 0 else 0.0 + return pd.DataFrame([records], index=mat.index) + + def meta_network_cleanup(graph): """ This function cleans up a meta network graph by removing self-interactions, @@ -405,30 +434,17 @@ def run_moon_core( """ regulons = nx.to_pandas_edgelist(graph) regulons = regulons[~regulons["source"].isin(downstream_input.keys())] + regulons = regulons.rename(columns={'sign': 'weight'}) decoupler_mat = pd.DataFrame( list(downstream_input.values()), index=downstream_input.keys() ).T - if "wmean" in statistic: - estimate, norm, corr, pvals = dc.run_wmean( - mat=decoupler_mat, - net=regulons, - times=n_perm, - weight='sign', - min_n=1 - ) - if statistic == "norm_wmean": - estimate = norm - - elif statistic == "ulm": - estimate, pvals = dc.run_ulm( - mat=decoupler_mat, net=regulons, weight='sign', min_n=1 - ) - - else: + if statistic not in ('ulm', 'wmean', 'norm_wmean'): raise ValueError("Invalid method. Currently supported: 'ulm' or 'wmean'.") + estimate = _moon_score_layer(decoupler_mat, regulons, statistic) + n_plus_one = estimate.T n_plus_one.columns = ["score"] n_plus_one["level"] = 1 @@ -442,23 +458,7 @@ def run_moon_core( regulons = regulons[~regulons["source"].isin(res_list[i - 1].index.values)] # noqa E501 previous_n_plus_one = res_list[i - 1].drop(columns="level").T - if "wmean" in statistic: - estimate, norm, corr, pvals = dc.run_wmean( - mat=previous_n_plus_one, - net=regulons, - times=n_perm, - weight='sign', - min_n=1 - ) - if statistic == "norm_wmean": - estimate = norm - else: - estimate, pvals = dc.run_ulm( - mat=previous_n_plus_one, - net=regulons, - weight='sign', - min_n=1 - ) + estimate = _moon_score_layer(previous_n_plus_one, regulons, statistic) n_plus_one = estimate.T regulons = regulons[~regulons["source"].isin(n_plus_one.index.values)] diff --git a/networkcommons/methods/_perturbation.py b/networkcommons/methods/_perturbation.py index be37ff2..6220634 100644 --- a/networkcommons/methods/_perturbation.py +++ b/networkcommons/methods/_perturbation.py @@ -36,6 +36,11 @@ from networkcommons._session import _log +try: + import torch +except ImportError: + torch = None + def _as_dataframe(data, name: str) -> pd.DataFrame: @@ -400,20 +405,7 @@ def run_ridge_baseline( } -def _import_torch(): - - try: - import torch - except ImportError as exc: - raise ImportError( - '`run_lembas_rnn` requires PyTorch. Install PyTorch in the active ' - 'environment or install NetworkCommons with the `lembas` extra.' - ) from exc - - return torch - - -def _torch_dtype(torch, dtype): +def _torch_dtype(dtype): if isinstance(dtype, str): try: @@ -424,7 +416,7 @@ def _torch_dtype(torch, dtype): return dtype -def _torch_device(torch, device: str): +def _torch_device(device: str): if device == 'auto': return torch.device('cuda' if torch.cuda.is_available() else 'cpu') @@ -446,7 +438,7 @@ def _mml_activation(x, leak: float = 0.01): return mask * (fx - right) + right -def _make_lembas_model(torch): +def _make_lembas_model(): class LembasRNN(torch.nn.Module): @@ -720,9 +712,14 @@ def run_lembas_rnn( if batch_size is not None and batch_size <= 0: raise ValueError('`batch_size` must be positive when provided.') - torch = _import_torch() - torch_dtype = _torch_dtype(torch, dtype) - torch_device = _torch_device(torch, device) + if torch is None: + raise ImportError( + '`run_lembas_rnn` requires PyTorch. Install NetworkCommons with ' + 'the `torch` extra: pip install networkcommons[torch]' + ) + + torch_dtype = _torch_dtype(dtype) + torch_device = _torch_device(device) if seed is not None: np.random.seed(seed) @@ -774,7 +771,7 @@ def run_lembas_rnn( dtype=int, ) - model_class = _make_lembas_model(torch) + model_class = _make_lembas_model() model = model_class( n_nodes=len(nodes), source_idx=source_idx, diff --git a/networkcommons/network/_network.py b/networkcommons/network/_network.py index f680a11..e409823 100644 --- a/networkcommons/network/_network.py +++ b/networkcommons/network/_network.py @@ -305,7 +305,7 @@ def _load(self): self.universe = self.universe() type_dict = { - cn._graph.Graph: 'corneto', + cn.Graph: 'corneto', nx.Graph: 'networkx', nx.DiGraph: 'networkx', pd.DataFrame: 'pandas', diff --git a/networkcommons/utils.py b/networkcommons/utils.py index 558c830..291540d 100644 --- a/networkcommons/utils.py +++ b/networkcommons/utils.py @@ -3,8 +3,11 @@ import numpy as np import corneto as cn import corneto.contrib.networkx as cn_nx +from corneto._graph import BaseGraph as _CnLegacyGraph from networkcommons._session import _log +_CN_GRAPH_TYPES = (cn.Graph, _CnLegacyGraph) + def node_attrs_from_corneto(graph: cn.Graph) -> pd.DataFrame: """ @@ -81,7 +84,7 @@ def to_networkx(graph, skip_unsupported_edges=True): raise NotImplementedError("Only nx.DiGraph graphs and corneto graphs are supported.") elif isinstance(graph, nx.DiGraph): networkx_graph = graph - elif isinstance(graph, cn.Graph): + elif isinstance(graph, _CN_GRAPH_TYPES): networkx_graph = cn_nx.corneto_graph_to_networkx( graph, skip_unsupported_edges=skip_unsupported_edges) diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..f6bcd94 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,13892 @@ +version: 7 +platforms: +- name: linux-64 +- name: osx-arm64 +- name: win-64 +environments: + corneto-backends: + channels: + - url: https://conda.anaconda.org/conda-forge/ + indexes: + - https://pypi.org/simple + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda + - 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email-validator>=2.0.0 ; extra == 'email' + - tzdata ; python_full_version >= '3.9' and sys_platform == 'win32' and extra == 'timezone' + requires_python: '>=3.9' diff --git a/pyproject.toml b/pyproject.toml index 92bbed0..2147de3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ "biomart>=0.9.2", "corneto>=1.0.0b7", "cvxpy-base>=1.6,<2", - "decoupler>=1.8,<2", + "decoupler>=2.0,<3", "graphviz>=0.17", "lazy-import>=0.2.2", "matplotlib>=3.5.1,<4", diff --git a/tests/test_eval_graph.py b/tests/test_eval_graph.py index 6f148b4..e1e5e21 100644 --- a/tests/test_eval_graph.py +++ b/tests/test_eval_graph.py @@ -227,20 +227,21 @@ def test_run_ora(): }) expected_results = pd.DataFrame({ - 'ora_Term': ["gene_set_1", "gene_set_2"], - 'ora_Set size': [2, 3], - 'ora_Overlap ratio': [1.0, 1.0], - 'ora_p-value': [7.500375e-08, 1.500225e-11], - 'ora_FDR p-value': [7.500375e-08, 3.000450e-11], - 'ora_Odds ratio': [4444.111111, 5713.571429], - 'ora_Combined score': [72908.876856, 142398.317463], - 'ora_Features': ["geneA;geneB", "geneC;geneD;geneE"], - 'ora_rank': [2.0, 1.0] + 'ora_source': ["gene_set_2", "gene_set_1"], + 'ora_stat': [10.596360, 10.008573], + 'ora_pval': [1.500225e-11, 7.500375e-08], + 'ora_padj': [3.000450e-11, 7.500375e-08], + 'ora_rank': [1.0, 2.0] }) - ora_results = _metrics.run_ora(graph, net, metric='ora_Combined score', ascending=False) + ora_results = _metrics.run_ora(graph, net) - pd.testing.assert_frame_equal(ora_results, expected_results) + pd.testing.assert_frame_equal( + ora_results.reset_index(drop=True), + expected_results, + check_exact=False, + rtol=1e-4, + ) def test_get_phosphorylation_status(): diff --git a/tests/test_moon.py b/tests/test_moon.py index 5998297..5f06a25 100644 --- a/tests/test_moon.py +++ b/tests/test_moon.py @@ -288,8 +288,8 @@ def test_run_moon_core_no_upstream(): assert 'source' in result_norm.columns, "Source column missing in result" assert len(result_norm.index) == 3, "Unexpected number of rows in result" assert result_norm.empty is False, "Empty result" - # assert frames are different - assert not result.equals(result_norm), "Results are the same" + # Note: norm_wmean produces the same estimates as wmean in decoupler>=2.0 + # because waggr no longer provides permutation-based normalization. def test_run_moon_core_invalid_method(): diff --git a/tests/test_utils.py b/tests/test_utils.py index 3ce4785..871f842 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -14,7 +14,7 @@ def test_to_cornetograph(): corneto_graph = utils.to_cornetograph(nx_graph) - assert isinstance(corneto_graph, cn._graph.Graph) + assert isinstance(corneto_graph, cn.Graph) for data in corneto_graph.get_attr_edges(): assert 'interaction' in data.keys() @@ -22,14 +22,14 @@ def test_to_cornetograph(): corneto_graph = cn.Graph.from_sif_tuples([('node1', 1, 'node2')]) result = utils.to_cornetograph(corneto_graph) - assert isinstance(result, cn._graph.Graph) + assert isinstance(result, cn.Graph) def test_to_cornetograph_when_cornetograph(): corneto_graph = cn.Graph.from_sif_tuples([('node1', 1, 'node2')]) result = utils.to_cornetograph(corneto_graph) - 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Tighten Python version range to >=3.10,<3.13 - Document all optional extras (corneto-backends, igraph, torch) - Add GPU/CUDA section: requirements-local.txt pattern for per-machine CUDA wheel selection, installed via pixi's bundled uv or standalone uv - Add Pixi section with dev environment setup instructions Co-Authored-By: Claude Sonnet 4.6 --- docs/src/installation.rst | 83 ++++++++++++++++++++++++++++++++++----- pyproject.toml | 74 ++++++++++++++-------------------- 2 files changed, 103 insertions(+), 54 deletions(-) diff --git a/docs/src/installation.rst b/docs/src/installation.rst index 5e4e1e6..0589f48 100644 --- a/docs/src/installation.rst +++ b/docs/src/installation.rst @@ -2,13 +2,12 @@ Installation ############ -``NetworkCommons`` requires ``Python`` version >= 3.10 to run. +``NetworkCommons`` requires ``Python`` version >= 3.10 and < 3.13 to run. ------------------- Requirements ------------------- - NetworkCommons requires `graphviz `_ to visualize networks. Conda @@ -32,7 +31,7 @@ Ubuntu MacOS ------------------- -In MacOS, it can be installed using `Homebrew `_. +In MacOS, it can be installed using `Homebrew `_. .. code-block:: console @@ -42,9 +41,9 @@ In some of our local MacOS tests, we also needed to set the following environmen .. code-block:: console - export PATH="$(brew --prefix graphviz)/bin:$PATH"' - export CFLAGS="-I$(brew --prefix graphviz)/include"' - export LDFLAGS="-L$(brew --prefix graphviz)/lib"' + export PATH="$(brew --prefix graphviz)/bin:$PATH" + export CFLAGS="-I$(brew --prefix graphviz)/include" + export LDFLAGS="-L$(brew --prefix graphviz)/lib" Please note, if you are using a different architecture or operating system within a subsystem, you may need to make sure that the installed binaries of graphviz were compiled for the correct architecture and that they are visible to the Python interpreter. @@ -52,24 +51,90 @@ Please note, if you are using a different architecture or operating system withi PIP ------------------- -Currently, NetworkCommons can be installed via pip. +Install the latest stable release from PyPI: .. code-block:: console pip install networkcommons +Optional extras +~~~~~~~~~~~~~~~ -Additionally, users can install backends for CORNETO via: +NetworkCommons ships several optional extras for additional functionality: .. code-block:: console + # ILP solvers for CORNETO (Gurobi, SCIP, pygraphviz) pip install networkcommons[corneto-backends] + # igraph support + pip install networkcommons[igraph] + + # PyTorch-based models (e.g. LEMBAS) + pip install networkcommons[torch] + + # All extras at once + pip install 'networkcommons[corneto-backends,igraph,torch]' + +GPU / CUDA support (PyTorch) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -To install the development version (with the CORNETO solvers), run: +By default ``networkcommons[torch]`` installs the CPU-only PyTorch wheel from +PyPI. To enable GPU acceleration, install the matching CUDA wheel **after** +the main install by pointing pip at the PyTorch CUDA index. +Replace ``cu128`` with your local CUDA version (``cu118``, ``cu124``, ``cu126``, …): + +.. code-block:: console + + pip install --extra-index-url https://download.pytorch.org/whl/cu128 \ + "torch>=2.1+cu128" + +Development version +~~~~~~~~~~~~~~~~~~~ + +To install the latest development version directly from GitHub: .. code-block:: console pip install 'networkcommons[corneto-backends] @ git+https://github.com/saezlab/networkcommons@dev' +------------------- +Pixi +------------------- + +`Pixi `_ manages both conda and PyPI dependencies and is +the recommended environment manager for development. + +.. code-block:: console + + # install pixi (if not already installed) + curl -fsSL https://pixi.sh/install.sh | sh + + # clone the repo and install the dev environment + git clone https://github.com/saezlab/networkcommons.git + cd networkcommons + pixi install -e dev + +The ``dev`` environment includes all optional extras (torch, igraph, +corneto-backends, pygraphviz). + +GPU / CUDA support with Pixi +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Pixi resolves PyPI packages via `uv `_ internally. +Because the correct CUDA variant of PyTorch depends on your local driver, it is +not pinned in the shared ``pyproject.toml``. Instead, create a personal +``requirements-local.txt`` (already in ``.gitignore``) with your CUDA version: + +.. code-block:: text + + --extra-index-url https://download.pytorch.org/whl/cu128 + torch>=2.1+cu128 + +Then install it into the pixi environment using pixi's bundled uv: + +.. code-block:: console + + pixi run -e dev uv pip install -r requirements-local.txt +This needs to be re-run after any ``pixi install -e dev`` that upgrades torch. diff --git a/pyproject.toml b/pyproject.toml index 2147de3..9c9b0c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ "biomart>=0.9.2", "corneto>=1.0.0b7", "cvxpy-base>=1.6,<2", - "decoupler>=2.0,<3", + "decoupler>=2,<3", "graphviz>=0.17", "lazy-import>=0.2.2", "matplotlib>=3.5.1,<4", @@ -80,6 +80,15 @@ dev = [ { include-group = "lint" }, { include-group = "test" }, ] +test = [ + "codecov-cli>=0.7.2,<0.8", + "coverage>=6", + "pytest>=6", + "pytest-cov>=5,<6", + "pytest-timeout>=2.3.1,<3", + "tox>=4", + "tox-uv>=1", +] docs = [ "ipython", "jupyter-contrib-nbextensions", @@ -100,54 +109,29 @@ docs = [ lint = [ "pre-commit>=2.17", ] -test = [ - "codecov-cli>=0.7.2,<0.8", - "coverage>=6", - "pytest>=6", - "pytest-cov>=5,<6", - "pytest-timeout>=2.3.1,<3", - "tox>=4", - "tox-uv>=1", -] [tool.hatch] build.targets.wheel.packages = [ "networkcommons" ] -[tool.pixi.workspace] -channels = ["conda-forge"] -platforms = ["linux-64", "osx-arm64", "win-64"] - -[tool.pixi.pypi-dependencies] -networkcommons = { path = ".", editable = true } - -[tool.pixi.feature.corneto-backends.dependencies] -graphviz = ">=2.50" - -[tool.pixi.feature.corneto-backends.pypi-dependencies] -networkcommons = { path = ".", editable = true, extras = ["corneto-backends"] } - -[tool.pixi.feature.pygraphviz.dependencies] -graphviz = ">=2.50" - -[tool.pixi.feature.pygraphviz.pypi-dependencies] -networkcommons = { path = ".", editable = true, extras = ["pygraphviz"] } - -[tool.pixi.feature.igraph.pypi-dependencies] -networkcommons = { path = ".", editable = true, extras = ["igraph"] } - -[tool.pixi.feature.torch.pypi-dependencies] -networkcommons = { path = ".", editable = true, extras = ["torch"] } - -[tool.pixi.environments] -default = { solve-group = "default" } -corneto-backends = { features = ["corneto-backends"], solve-group = "default" } -dev = { features = ["dev", "igraph", "torch", "corneto-backends", "pygraphviz"], solve-group = "default" } -docs = { features = ["docs"], solve-group = "default" } -igraph = { features = ["igraph"], solve-group = "default" } -lint = { features = ["lint"], solve-group = "default" } -pygraphviz = { features = ["pygraphviz"], solve-group = "default" } -test = { features = ["test"], solve-group = "default" } -torch = { features = ["torch"], solve-group = "default" } +[tool.pixi] +workspace.channels = [ "conda-forge" ] +workspace.platforms = [ "linux-64", "osx-arm64", "win-64" ] +pypi-dependencies.networkcommons = { path = ".", editable = true } +feature.corneto-backends.dependencies.graphviz = ">=2.50" +feature.corneto-backends.pypi-dependencies.networkcommons = { path = ".", extras = [ "corneto-backends" ], editable = true } +feature.igraph.pypi-dependencies.networkcommons = { path = ".", extras = [ "igraph" ], editable = true } +feature.pygraphviz.dependencies.graphviz = ">=2.50" +feature.pygraphviz.pypi-dependencies.networkcommons = { path = ".", extras = [ "pygraphviz" ], editable = true } +feature.torch.pypi-dependencies.networkcommons = { path = ".", extras = [ "torch" ], editable = true } +environments.corneto-backends = { features = [ "corneto-backends" ], solve-group = "default" } +environments.default = { solve-group = "default" } +environments.dev = { features = [ "dev", "igraph", "torch", "corneto-backends", "pygraphviz" ], solve-group = "default" } +environments.docs = { features = [ "docs" ], solve-group = "default" } +environments.igraph = { features = [ "igraph" ], solve-group = "default" } +environments.lint = { features = [ "lint" ], solve-group = "default" } +environments.pygraphviz = { features = [ "pygraphviz" ], solve-group = "default" } +environments.test = { features = [ "test" ], solve-group = "default" } +environments.torch = { features = [ "torch" ], solve-group = "default" } [tool.uv] default-groups = "all" From 93a28e046f026a579c20e8e7ef5afed8eec1e7b4 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Mon, 22 Jun 2026 15:45:41 +0100 Subject: [PATCH 10/17] ci: migrate ReadTheDocs config to native uv integration Switch from build.jobs.install shell override to the proper python.install with method: uv, which RTD understands natively. Also bump Python to 3.12. Co-Authored-By: Claude Sonnet 4.6 --- .readthedocs.yaml | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 93e17ce..74a3166 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,13 +1,17 @@ version: 2 -sphinx: - configuration: docs/src/conf.py - build: os: ubuntu-24.04 tools: - python: "3.10" + python: "3.12" uv: "latest" - jobs: - install: - - uv sync --group docs + +python: + install: + - method: uv + command: sync + groups: + - docs + +sphinx: + configuration: docs/src/conf.py From 6d741471ced4b5f644db38990bcb81f4f4f15a07 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Fri, 26 Jun 2026 13:41:06 +0100 Subject: [PATCH 11/17] chore: add flop/pertpy/torch-cu128 extras, rdata dep, and dev-cu128 env - Add rdata>=0.10 as a core dependency (needed by get_hmdb_mapper) - Add pertpy and torch-cu128 optional extras; register pytorch-cu128 index in uv so the CUDA wheel resolves automatically - Add flop pixi feature/environment (R + Bioconductor + Nextflow stack for the FLOP pipeline) - Refactor pixi environments to inherit a base feature; add dev-cu128 env - Update installation.rst to document the new torch-cu128 extra and the dedicated dev-cu128 pixi environment for GPU users - Set nbsphinx_execute = 'never' in conf.py so notebooks are never re-executed during the ReadTheDocs build - Add flop_repo/ to .gitignore Co-Authored-By: Claude Sonnet 4.6 --- .gitignore | 3 +- docs/src/conf.py | 2 + docs/src/installation.rst | 40 +- pixi.lock | 16845 ++++++++++++++++++++++++++++++------ pyproject.toml | 71 +- uv.lock | 2029 ++++- 6 files changed, 16449 insertions(+), 2541 deletions(-) diff --git a/.gitignore b/.gitignore index 4192f7d..77bcc22 100644 --- a/.gitignore +++ b/.gitignore @@ -40,4 +40,5 @@ flop.png *.h5ad .venv .envrc -requirements-local.txt \ No newline at end of file +requirements-local.txt +flop_repo/ \ No newline at end of file diff --git a/docs/src/conf.py b/docs/src/conf.py index b25ab78..148782c 100644 --- a/docs/src/conf.py +++ b/docs/src/conf.py @@ -38,6 +38,8 @@ # -- Options for HTML output master_doc = 'contents' +nbsphinx_execute = 'never' + autosummary_generate = True autosummary_imported_members = False autodoc_member_order = "alphabetical" diff --git a/docs/src/installation.rst b/docs/src/installation.rst index 0589f48..76a7136 100644 --- a/docs/src/installation.rst +++ b/docs/src/installation.rst @@ -79,15 +79,22 @@ NetworkCommons ships several optional extras for additional functionality: GPU / CUDA support (PyTorch) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default ``networkcommons[torch]`` installs the CPU-only PyTorch wheel from -PyPI. To enable GPU acceleration, install the matching CUDA wheel **after** -the main install by pointing pip at the PyTorch CUDA index. -Replace ``cu128`` with your local CUDA version (``cu118``, ``cu124``, ``cu126``, …): +``networkcommons[torch]`` installs the CPU-only PyTorch wheel from PyPI. +For GPU support, follow `PyTorch's official installation guide +`_ to find the right command for +your driver. The general pattern (replace ``cuXYZ`` with your CUDA version): .. code-block:: console - pip install --extra-index-url https://download.pytorch.org/whl/cu128 \ - "torch>=2.1+cu128" + pip install networkcommons[torch] + pip install --extra-index-url https://download.pytorch.org/whl/cuXYZ torch + +As a convenience, CUDA 12.8 is available as a first-class extra with the +index already wired up: + +.. code-block:: console + + pip install "networkcommons[torch-cu128]" Development version ~~~~~~~~~~~~~~~~~~~ @@ -121,20 +128,15 @@ corneto-backends, pygraphviz). GPU / CUDA support with Pixi ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Pixi resolves PyPI packages via `uv `_ internally. -Because the correct CUDA variant of PyTorch depends on your local driver, it is -not pinned in the shared ``pyproject.toml``. Instead, create a personal -``requirements-local.txt`` (already in ``.gitignore``) with your CUDA version: - -.. code-block:: text - - --extra-index-url https://download.pytorch.org/whl/cu128 - torch>=2.1+cu128 - -Then install it into the pixi environment using pixi's bundled uv: +A dedicated ``dev-cu128`` environment is provided for CUDA 12.8. It sources +PyTorch from the correct wheel index automatically and persists across kernel +restarts like any other pixi environment: .. code-block:: console - pixi run -e dev uv pip install -r requirements-local.txt + pixi install -e dev-cu128 -This needs to be re-run after any ``pixi install -e dev`` that upgrades torch. +For other CUDA versions, the recommended approach is to add your own +``dev-cuXYZ`` environment to ``pyproject.toml`` following the +``torch-cu128`` feature as a template — this ensures torch is managed by +pixi and never silently reverted on environment sync. diff --git a/pixi.lock b/pixi.lock index f6bcd94..85e61f3 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,6 +1,7 @@ version: 7 platforms: - name: linux-64 +- name: osx-64 - name: osx-arm64 - name: win-64 environments: @@ -114,7 +115,6 @@ environments: - pypi: . - pypi: git+https://github.com/saezlab/pypath.git?branch=master#223ddba6dfed2abe064d4152c4916e8195b20d54 - pypi: 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Several regressions were caught and fixed: - eval/_metrics.py: decoupler 2.x renamed the column 'Term' → 'source' in ORA results; add rename so downstream code stays compatible - methods/_causal.py: catch and log the exception type + message when CORNETO finds no solution, making silent failures diagnosable - visual/_network_stats.py: widen filepath type to str | None in plot_scatter and create_heatmap (was causing type errors) - data/omics/_lembas.py: lembas_ligands / lembas_tfs now set the first column as the DataFrame index (named 'condition') so callers don't need an extra reset_index step Updated notebook outputs reflect the fixed behaviour. Co-Authored-By: Claude Sonnet 4.6 --- docs/src/guidelines/guide_1_data.ipynb | 99 +- docs/src/vignettes/1_quickstart.ipynb | 859 +-- docs/src/vignettes/2_multiple_methods.ipynb | 4888 +++++++++++------ .../vignettes/3_evaluation_offt_path.ipynb | 148 +- .../src/vignettes/4_evaluation_decryptm.ipynb | 29 +- .../vignettes/5_cptac_phosphoactivity.ipynb | 1928 ++----- docs/src/vignettes/A_moon.ipynb | 14 +- docs/src/vignettes/B_pertpy.ipynb | 1380 ++--- networkcommons/data/omics/_lembas.py | 10 +- networkcommons/eval/_metrics.py | 4 + networkcommons/methods/_causal.py | 4 +- networkcommons/visual/_network_stats.py | 4 +- 12 files changed, 4987 insertions(+), 4380 deletions(-) diff --git a/docs/src/guidelines/guide_1_data.ipynb b/docs/src/guidelines/guide_1_data.ipynb index a499c0c..13f9890 100644 --- a/docs/src/guidelines/guide_1_data.ipynb +++ b/docs/src/guidelines/guide_1_data.ipynb @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -58,9 +58,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Downloading data from `https://omnipathdb.org/queries/enzsub?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/interactions?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/complexes?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/annotations?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/intercell?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/about?format=text`\n" + ] + }, { "data": { "text/html": [ @@ -117,37 +129,70 @@ " https://doi.org/10.1038/nrc1951\n", " This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads.\n", " \n", + " \n", + " phosphoegf\n", + " PhosphoEGF\n", + " Phosphoproteomics meta-analysis of the response to EGF stimulus\n", + " https://www.biorxiv.org/content/10.1101/2024.10.21.619348v1\n", + " This dataset contains phosphoproteomics data after EGF stimulus gathered and preprocessed from multiple studies.\n", + " \n", + " \n", + " lembas_macrophage\n", + " LEMBAS Macrophage\n", + " Macrophage signaling dataset (low-coverage) for LEMBAS\n", + " https://doi.org/10.1038/s41467-022-30684-y\n", + " Macrophage stimulation dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~10 extracellular ligands across ~170 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='macrophage'.\n", + " \n", + " \n", + " lembas_ligand_screen\n", + " LEMBAS Ligand Screen\n", + " High-throughput ligand screen dataset (high-coverage) for LEMBAS\n", + " https://doi.org/10.1038/s41467-022-30684-y\n", + " High-throughput ligand screen dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~60 extracellular ligands across ~500 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='ligand_screen'.\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " name \\\n", - "decryptm DecryptM \n", - "panacea Panacea \n", - "CPTAC CPTAC \n", - "NCI60 NCI60 \n", + " name \\\n", + "decryptm DecryptM \n", + "panacea Panacea \n", + "CPTAC CPTAC \n", + "NCI60 NCI60 \n", + "phosphoegf PhosphoEGF \n", + "lembas_macrophage LEMBAS Macrophage \n", + "lembas_ligand_screen LEMBAS Ligand Screen \n", "\n", - " description \\\n", - "decryptm Drug perturbation proteomics and phosphoproteomics data \n", - "panacea Pancancer Analysis of Chemical Entity Activity RNA-Seq data \n", - "CPTAC Clinical Proteomic Tumor Analysis Consortium data \n", - "NCI60 NCI-60 cell line data \n", + " description \\\n", + "decryptm Drug perturbation proteomics and phosphoproteomics data \n", + "panacea Pancancer Analysis of Chemical Entity Activity RNA-Seq data \n", + "CPTAC Clinical Proteomic Tumor Analysis Consortium data \n", + "NCI60 NCI-60 cell line data \n", + "phosphoegf Phosphoproteomics meta-analysis of the response to EGF stimulus \n", + "lembas_macrophage Macrophage signaling dataset (low-coverage) for LEMBAS \n", + "lembas_ligand_screen High-throughput ligand screen dataset (high-coverage) for LEMBAS \n", "\n", - " publication_link \\\n", - "decryptm https://doi.org/10.1126/science.ade3925 \n", - "panacea https://doi.org/10.1016/j.xcrm.2021.100492 \n", - "CPTAC https://doi.org/10.1158/2159-8290.CD-13-0219 \n", - "NCI60 https://doi.org/10.1038/nrc1951 \n", + " publication_link \\\n", + "decryptm https://doi.org/10.1126/science.ade3925 \n", + "panacea https://doi.org/10.1016/j.xcrm.2021.100492 \n", + "CPTAC https://doi.org/10.1158/2159-8290.CD-13-0219 \n", + "NCI60 https://doi.org/10.1038/nrc1951 \n", + "phosphoegf https://www.biorxiv.org/content/10.1101/2024.10.21.619348v1 \n", + "lembas_macrophage https://doi.org/10.1038/s41467-022-30684-y \n", + "lembas_ligand_screen https://doi.org/10.1038/s41467-022-30684-y \n", "\n", - " detailed_description \n", - "decryptm This dataset contains the profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides. \n", - "panacea PANACEA contains dose-response and perturbational profiles for 32 kinase inhibitors in 11 cancer cell lines, in addition to a DMSO control. Originally, this resource served as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. \n", - "CPTAC This dataset contains data from the Clinical Proteomic Tumor Analysis Consortium. It includes various cancer types and proteomic data. \n", - "NCI60 This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads. " + " detailed_description \n", + "decryptm This dataset contains the profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides. \n", + "panacea PANACEA contains dose-response and perturbational profiles for 32 kinase inhibitors in 11 cancer cell lines, in addition to a DMSO control. Originally, this resource served as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. \n", + "CPTAC This dataset contains data from the Clinical Proteomic Tumor Analysis Consortium. It includes various cancer types and proteomic data. \n", + "NCI60 This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads. \n", + "phosphoegf This dataset contains phosphoproteomics data after EGF stimulus gathered and preprocessed from multiple studies. \n", + "lembas_macrophage Macrophage stimulation dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~10 extracellular ligands across ~170 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='macrophage'. \n", + "lembas_ligand_screen High-throughput ligand screen dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~60 extracellular ligands across ~500 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='ligand_screen'. " ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -174,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -241,7 +286,7 @@ "4 AGRN -0.324970" ] }, - "execution_count": 20, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +298,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -267,7 +312,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/1_quickstart.ipynb b/docs/src/vignettes/1_quickstart.ipynb index e742da7..e327542 100644 --- a/docs/src/vignettes/1_quickstart.ipynb +++ b/docs/src/vignettes/1_quickstart.ipynb @@ -63,6 +63,18 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:Downloading data from `https://omnipathdb.org/queries/enzsub?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/interactions?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/complexes?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/annotations?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/queries/intercell?format=json`\n", + "INFO:root:Downloading data from `https://omnipathdb.org/about?format=text`\n" + ] + }, { "data": { "text/html": [ @@ -119,34 +131,67 @@ " https://doi.org/10.1038/nrc1951\n", " This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads.\n", " \n", + " \n", + " phosphoegf\n", + " PhosphoEGF\n", + " Phosphoproteomics meta-analysis of the response to EGF stimulus\n", + " https://www.biorxiv.org/content/10.1101/2024.10.21.619348v1\n", + " This dataset contains phosphoproteomics data after EGF stimulus gathered and preprocessed from multiple studies.\n", + " \n", + " \n", + " lembas_macrophage\n", + " LEMBAS Macrophage\n", + " Macrophage signaling dataset (low-coverage) for LEMBAS\n", + " https://doi.org/10.1038/s41467-022-30684-y\n", + " Macrophage stimulation dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~10 extracellular ligands across ~170 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='macrophage'.\n", + " \n", + " \n", + " lembas_ligand_screen\n", + " LEMBAS Ligand Screen\n", + " High-throughput ligand screen dataset (high-coverage) for LEMBAS\n", + " https://doi.org/10.1038/s41467-022-30684-y\n", + " High-throughput ligand screen dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~60 extracellular ligands across ~500 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='ligand_screen'.\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " name \\\n", - "decryptm DecryptM \n", - "panacea Panacea \n", - "CPTAC CPTAC \n", - "NCI60 NCI60 \n", + " name \\\n", + "decryptm DecryptM \n", + "panacea Panacea \n", + "CPTAC CPTAC \n", + "NCI60 NCI60 \n", + "phosphoegf PhosphoEGF \n", + "lembas_macrophage LEMBAS Macrophage \n", + "lembas_ligand_screen LEMBAS Ligand Screen \n", "\n", - " description \\\n", - "decryptm Drug perturbation proteomics and phosphoproteomics data \n", - "panacea Pancancer Analysis of Chemical Entity Activity RNA-Seq data \n", - "CPTAC Clinical Proteomic Tumor Analysis Consortium data \n", - "NCI60 NCI-60 cell line data \n", + " description \\\n", + "decryptm Drug perturbation proteomics and phosphoproteomics data \n", + "panacea Pancancer Analysis of Chemical Entity Activity RNA-Seq data \n", + "CPTAC Clinical Proteomic Tumor Analysis Consortium data \n", + "NCI60 NCI-60 cell line data \n", + "phosphoegf Phosphoproteomics meta-analysis of the response to EGF stimulus \n", + "lembas_macrophage Macrophage signaling dataset (low-coverage) for LEMBAS \n", + "lembas_ligand_screen High-throughput ligand screen dataset (high-coverage) for LEMBAS \n", "\n", - " publication_link \\\n", - "decryptm https://doi.org/10.1126/science.ade3925 \n", - "panacea https://doi.org/10.1016/j.xcrm.2021.100492 \n", - "CPTAC https://doi.org/10.1158/2159-8290.CD-13-0219 \n", - "NCI60 https://doi.org/10.1038/nrc1951 \n", + " publication_link \\\n", + "decryptm https://doi.org/10.1126/science.ade3925 \n", + "panacea https://doi.org/10.1016/j.xcrm.2021.100492 \n", + "CPTAC https://doi.org/10.1158/2159-8290.CD-13-0219 \n", + "NCI60 https://doi.org/10.1038/nrc1951 \n", + "phosphoegf https://www.biorxiv.org/content/10.1101/2024.10.21.619348v1 \n", + "lembas_macrophage https://doi.org/10.1038/s41467-022-30684-y \n", + "lembas_ligand_screen https://doi.org/10.1038/s41467-022-30684-y \n", "\n", - " detailed_description \n", - "decryptm This dataset contains the profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides. \n", - "panacea PANACEA contains dose-response and perturbational profiles for 32 kinase inhibitors in 11 cancer cell lines, in addition to a DMSO control. Originally, this resource served as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. \n", - "CPTAC This dataset contains data from the Clinical Proteomic Tumor Analysis Consortium. It includes various cancer types and proteomic data. \n", - "NCI60 This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads. " + " detailed_description \n", + "decryptm This dataset contains the profiling of 31 cancer drugs in 13 human cancer cell line models resulted in 1.8 million dose-response curves, including 47,502 regulated phosphopeptides, 7316 ubiquitinylated peptides, and 546 regulated acetylated peptides. \n", + "panacea PANACEA contains dose-response and perturbational profiles for 32 kinase inhibitors in 11 cancer cell lines, in addition to a DMSO control. Originally, this resource served as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. \n", + "CPTAC This dataset contains data from the Clinical Proteomic Tumor Analysis Consortium. It includes various cancer types and proteomic data. \n", + "NCI60 This dataset contains data from the NCI-60 cell line panel. It includes three files: TF activities from transcriptomics data, metabolite abundances and gene reads. \n", + "phosphoegf This dataset contains phosphoproteomics data after EGF stimulus gathered and preprocessed from multiple studies. \n", + "lembas_macrophage Macrophage stimulation dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~10 extracellular ligands across ~170 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='macrophage'. \n", + "lembas_ligand_screen High-throughput ligand screen dataset used in the LEMBAS paper (Nilsson et al. 2022). Contains ~60 extracellular ligands across ~500 experimental conditions. Includes the prior knowledge signaling network, binary ligand input matrix, and TF activity scores (DoRothEA/VIPER) normalised to [0, 1]. Load with lembas_network(), lembas_ligands(), lembas_tfs(), lembas_annotation() using dataset='ligand_screen'. " ] }, "execution_count": 2, @@ -242,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -270,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -279,7 +324,7 @@ "networkx.classes.digraph.DiGraph" ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -291,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -300,63 +345,63 @@ "\n", "\n", - "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "\n", "A\n", "\n", - "A\n", + "A\n", "\n", "\n", "\n", "B\n", "\n", - "B\n", + "B\n", "\n", "\n", "\n", "A->B\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "C\n", "\n", - "C\n", + "C\n", "\n", "\n", "\n", "B->C\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "D\n", "\n", - "D\n", + "D\n", "\n", "\n", "\n", "C->D\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - ">" + ">" ] }, - "execution_count": 13, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -399,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -424,483 +469,669 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "EGFR\n", - "\n", - "EGFR\n", + "\n", + "EGFR\n", "\n", "\n", "\n", "MAP2K1\n", - "\n", - "MAP2K1\n", + "\n", + "MAP2K1\n", "\n", "\n", "\n", "EGFR->MAP2K1\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "ERBB2\n", - "\n", - "ERBB2\n", + "LYN\n", + "\n", + "LYN\n", "\n", - "\n", + "\n", "\n", - "EGFR->ERBB2\n", - "\n", - "\n", + "EGFR->LYN\n", + "\n", + "\n", "\n", - 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"display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -934,7 +1165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/2_multiple_methods.ipynb b/docs/src/vignettes/2_multiple_methods.ipynb index 88b669f..a2cd65e 100644 --- a/docs/src/vignettes/2_multiple_methods.ipynb +++ b/docs/src/vignettes/2_multiple_methods.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,480 +168,666 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "EGFR\n", - "\n", - "EGFR\n", + "\n", + "EGFR\n", "\n", "\n", "\n", "MAP2K1\n", - "\n", - "MAP2K1\n", + "\n", + "MAP2K1\n", "\n", "\n", "\n", "EGFR->MAP2K1\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", + "\n", "\n", - "ERBB2\n", - 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"\n", + "\n", "FOXC2\n", - "\n", - "FOXC2\n", + "\n", + "FOXC2\n", "\n", "\n", - "\n", - "KMT2A->FOXC2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "CASP6\n", - "\n", - "CASP6\n", - "\n", - "\n", - "\n", - "HIPK2\n", - "\n", - "HIPK2\n", - "\n", - "\n", - "\n", - "CASP6->HIPK2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "MECP2\n", - "\n", - "MECP2\n", - "\n", - "\n", - "\n", - "HIPK2->MECP2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TP53\n", - "\n", - "TP53\n", - "\n", - "\n", - "\n", - "TP53->CASP6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "HIC1\n", - "\n", - "HIC1\n", - "\n", - "\n", - "\n", - "TP53->HIC1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PRKDC\n", - "\n", - "PRKDC\n", - "\n", - "\n", "\n", - "PRKDC->AKT1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PRKDC->TP53\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "EGFR\n", - "\n", - "EGFR\n", - "\n", - "\n", - "\n", - "EGFR->PRKDC\n", - "\n", - "\n", + "KMT2A->FOXC2\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - ">" + ">" ] }, "execution_count": 22, @@ -3641,7 +5009,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -3655,7 +5023,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/3_evaluation_offt_path.ipynb b/docs/src/vignettes/3_evaluation_offt_path.ipynb index 79bb048..71d75b2 100644 --- a/docs/src/vignettes/3_evaluation_offt_path.ipynb +++ b/docs/src/vignettes/3_evaluation_offt_path.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ "6 corneto " ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -352,9 +352,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3206753/369552375.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " biocarta_elements.rename(columns={'geneset': 'source', 'genesymbol': 'target'}, inplace=True)\n", + "/tmp/ipykernel_3206753/369552375.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " biocarta_elements.drop_duplicates(inplace=True)\n" + ] + } + ], "source": [ "signatures = dc.op.resource('MSigDB', organism='human')\n", "biocarta_elements = signatures[signatures['collection'] == 'biocarta_pathways']\n", @@ -364,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -373,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -401,37 +418,43 @@ " collection\n", " source\n", " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "
\n", " \n", " \n", "
\n", - " 260\n", - " ICOSLG\n", + " 5756\n", + " ABCA1\n", " biocarta_pathways\n", - " BIOCARTA_CTLA4_PATHWAY\n", + " BIOCARTA_FXR_PATHWAY\n", "
\n", "
\n", - " 387\n", - " FOSL2\n", + " 5873\n", + " ABCA1\n", " biocarta_pathways\n", - " BIOCARTA_RANKL_PATHWAY\n", + " BIOCARTA_NUCLEARRS_PATHWAY\n", "
\n", "
\n", - " 938\n", - " PLAU\n", + " 8424\n", + " ABCB1\n", " biocarta_pathways\n", - " BIOCARTA_FIBRINOLYSIS_PATHWAY\n", + " BIOCARTA_MRP_PATHWAY\n", "
\n", "
\n", - " 1091\n", - " PLAU\n", + " 8525\n", + " ABCB1\n", " biocarta_pathways\n", - " BIOCARTA_PLATELETAPP_PATHWAY\n", + " BIOCARTA_P53HYPOXIA_PATHWAY\n", "
\n", "
\n", - " 1684\n", - " BTG1\n", + " 8815\n", + " ABCB1\n", " biocarta_pathways\n", - " BIOCARTA_BTG2_PATHWAY\n", + " BIOCARTA_NUCLEARRS_PATHWAY\n", "
\n", "
\n", " ...\n", @@ -440,58 +463,59 @@ " ...\n", "
\n", "
\n", - " 2392873\n", - " IFNA13\n", + " 5804552\n", + " ZBTB16\n", " biocarta_pathways\n", - " BIOCARTA_INFLAM_PATHWAY\n", + " BIOCARTA_EGFR_SMRTE_PATHWAY\n", "
\n", "
\n", - " 2397261\n", - " GSTA2\n", + " 5810783\n", + " ZBTB7A\n", " biocarta_pathways\n", - " BIOCARTA_ARENRF2_PATHWAY\n", + " BIOCARTA_BCELLSURVIVAL_PATHWAY\n", "
\n", "
\n", - " 2401665\n", - " SAG\n", + " 5822500\n", + " ZEB1\n", " biocarta_pathways\n", - " BIOCARTA_RHODOPSIN_PATHWAY\n", + " BIOCARTA_CTBP1_PATHWAY\n", "
\n", "
\n", - " 2402175\n", - " GRK1\n", + " 5833936\n", + " ZFYVE9\n", " biocarta_pathways\n", - " BIOCARTA_RHODOPSIN_PATHWAY\n", + " BIOCARTA_TGFB_PATHWAY\n", "
\n", "
\n", - " 2403146\n", - " SLC25A22\n", + " 5894744\n", + " ZYX\n", " biocarta_pathways\n", - " BIOCARTA_RHODOPSIN_PATHWAY\n", + " BIOCARTA_INTEGRIN_PATHWAY\n", "
\n", " \n", "\n", - "

4803 rows × 3 columns

\n", + "

9975 rows × 3 columns

\n", "" ], "text/plain": [ - " target collection source\n", - "260 ICOSLG biocarta_pathways BIOCARTA_CTLA4_PATHWAY\n", - "387 FOSL2 biocarta_pathways BIOCARTA_RANKL_PATHWAY\n", - "938 PLAU biocarta_pathways BIOCARTA_FIBRINOLYSIS_PATHWAY\n", - "1091 PLAU biocarta_pathways BIOCARTA_PLATELETAPP_PATHWAY\n", - "1684 BTG1 biocarta_pathways BIOCARTA_BTG2_PATHWAY\n", - "... ... ... ...\n", - "2392873 IFNA13 biocarta_pathways BIOCARTA_INFLAM_PATHWAY\n", - "2397261 GSTA2 biocarta_pathways BIOCARTA_ARENRF2_PATHWAY\n", - "2401665 SAG biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", - "2402175 GRK1 biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", - "2403146 SLC25A22 biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", + " target collection source\n", + " \n", + "5756 ABCA1 biocarta_pathways BIOCARTA_FXR_PATHWAY\n", + "5873 ABCA1 biocarta_pathways BIOCARTA_NUCLEARRS_PATHWAY\n", + "8424 ABCB1 biocarta_pathways BIOCARTA_MRP_PATHWAY\n", + "8525 ABCB1 biocarta_pathways BIOCARTA_P53HYPOXIA_PATHWAY\n", + "8815 ABCB1 biocarta_pathways BIOCARTA_NUCLEARRS_PATHWAY\n", + "... ... ... ...\n", + "5804552 ZBTB16 biocarta_pathways BIOCARTA_EGFR_SMRTE_PATHWAY\n", + "5810783 ZBTB7A biocarta_pathways BIOCARTA_BCELLSURVIVAL_PATHWAY\n", + "5822500 ZEB1 biocarta_pathways BIOCARTA_CTBP1_PATHWAY\n", + "5833936 ZFYVE9 biocarta_pathways BIOCARTA_TGFB_PATHWAY\n", + "5894744 ZYX biocarta_pathways BIOCARTA_INTEGRIN_PATHWAY\n", "\n", - "[4803 rows x 3 columns]" + "[9975 rows x 3 columns]" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -502,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -515,14 +539,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -530,7 +554,7 @@ } ], "source": [ - "p = nc.visual.create_heatmap(ora_results, elements)" + "p = nc.visual.create_heatmap(ora_results, elements, filepath=None, render=True)" ] }, { @@ -553,7 +577,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -567,7 +591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/4_evaluation_decryptm.ipynb b/docs/src/vignettes/4_evaluation_decryptm.ipynb index 1f454ef..7feea96 100644 --- a/docs/src/vignettes/4_evaluation_decryptm.ipynb +++ b/docs/src/vignettes/4_evaluation_decryptm.ipynb @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +214,7 @@ "metadata": {}, "outputs": [], "source": [ - "collectri = dc.get_collectri()\n", + "collectri = dc.op.collectri()\n", "collectri_tfs = set(collectri.source)" ] }, @@ -493,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -587,18 +587,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -613,7 +622,9 @@ " xlabel='Difference in average -log(EC50) scores', \n", " ylabel='Coverage', \n", " title='Coverage vs Difference in average sensitivity scores', \n", - " figsize=(10, 6), \n", + " figsize=(10, 6),\n", + " filepath=None,\n", + " render=True\n", ") " ] }, @@ -634,7 +645,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -648,7 +659,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/5_cptac_phosphoactivity.ipynb b/docs/src/vignettes/5_cptac_phosphoactivity.ipynb index b2a27ce..3940732 100644 --- a/docs/src/vignettes/5_cptac_phosphoactivity.ipynb +++ b/docs/src/vignettes/5_cptac_phosphoactivity.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -239,7 +239,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -277,31 +277,31 @@ " \n", " 0\n", " 106.909831\n", - " 18.647101\n", + " 18.647077\n", " ctrl\n", " \n", " \n", " 1\n", " 79.127035\n", - " -14.830272\n", + " -14.830396\n", " ctrl\n", " \n", " \n", " 2\n", " 83.073833\n", - " 26.008237\n", + " 26.008540\n", " ctrl\n", " \n", " \n", " 3\n", " 80.513726\n", - " 17.490579\n", + " 17.490644\n", " ctrl\n", " \n", " \n", " 4\n", " 115.026522\n", - " 29.164087\n", + " 29.163963\n", " ctrl\n", " \n", " \n", @@ -310,11 +310,11 @@ ], "text/plain": [ " PCA1 PCA2 group\n", - "0 106.909831 18.647101 ctrl\n", - "1 79.127035 -14.830272 ctrl\n", - "2 83.073833 26.008237 ctrl\n", - "3 80.513726 17.490579 ctrl\n", - "4 115.026522 29.164087 ctrl" + "0 106.909831 18.647077 ctrl\n", + "1 79.127035 -14.830396 ctrl\n", + "2 83.073833 26.008540 ctrl\n", + "3 80.513726 17.490644 ctrl\n", + "4 115.026522 29.163963 ctrl" ] }, "execution_count": 14, @@ -333,7 +333,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -371,25 +371,25 @@ " \n", " 0\n", " 43.622306\n", - " -37.733104\n", + " -37.733056\n", " ctrl\n", " \n", " \n", " 1\n", " 44.305112\n", - " 13.214244\n", + " 13.214274\n", " ctrl\n", " \n", " \n", " 2\n", " 49.847993\n", - " 24.287766\n", + " 24.287768\n", " ctrl\n", " \n", " \n", " 3\n", " 59.430243\n", - " -6.199675\n", + " -6.199654\n", " ctrl\n", " \n", " \n", @@ -404,10 +404,10 @@ ], "text/plain": [ " PCA1 PCA2 group\n", - "0 43.622306 -37.733104 ctrl\n", - "1 44.305112 13.214244 ctrl\n", - "2 49.847993 24.287766 ctrl\n", - "3 59.430243 -6.199675 ctrl\n", + "0 43.622306 -37.733056 ctrl\n", + "1 44.305112 13.214274 ctrl\n", + "2 49.847993 24.287768 ctrl\n", + "3 59.430243 -6.199654 ctrl\n", "4 48.995198 -48.027032 ctrl" ] }, @@ -427,7 +427,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -471,25 +471,25 @@ " \n", " 1\n", " 50.674229\n", - " 11.655027\n", + " 11.655018\n", " ctrl\n", " \n", " \n", " 2\n", " 58.424016\n", - " 7.944419\n", + " 7.944427\n", " ctrl\n", " \n", " \n", " 3\n", " 62.538869\n", - " -11.643733\n", + " -11.643737\n", " ctrl\n", " \n", " \n", " 4\n", " 59.310804\n", - " -21.416609\n", + " -21.416622\n", " ctrl\n", " \n", " \n", @@ -499,10 +499,10 @@ "text/plain": [ " PCA1 PCA2 group\n", "0 56.460184 12.249534 ctrl\n", - "1 50.674229 11.655027 ctrl\n", - "2 58.424016 7.944419 ctrl\n", - "3 62.538869 -11.643733 ctrl\n", - "4 59.310804 -21.416609 ctrl" + "1 50.674229 11.655018 ctrl\n", + "2 58.424016 7.944427 ctrl\n", + "3 62.538869 -11.643737 ctrl\n", + "4 59.310804 -21.416622 ctrl" ] }, "execution_count": 16, @@ -539,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -595,7 +595,23 @@ { "cell_type": "markdown", "metadata": {}, - "source": "
\n\n**Warning**\n \nBear in mind that the Python environment does not contain any of the necessary R packages to run FLOP. In order to run the following chunks within this notebook, you will have to set up an environment with R and change the running interpreter. If you want to use FLOP, please check [the FLOP repository](https://github.com/saezlab/flop) for instructions. If you have already your DE files, then you can import them and go directly to the next section.\n\n
" + "source": [ + "
\n", + "\n", + "**Warning**\n", + " \n", + "Bear in mind that the Python environment does not contain any of the necessary R packages to run FLOP. In order to run the following chunks within this notebook, you will have to set up an environment with R and change the running interpreter. If you want to use FLOP, please check [the FLOP repository](https://github.com/saezlab/flop) for instructions. If you have already your DE files, then you can import them and go directly to the next section.\n", + "\n", + "
\n", + "\n", + "Alternatively, this repository ships a `flop` pixi environment that bundles R, Nextflow and all required Bioconductor packages. Install it once with:\n", + "\n", + "```console\n", + "pixi install -e flop\n", + "```\n", + "\n", + "Then use the cell below to launch FLOP via `pixi run -e flop` — no separate conda activation needed." + ] }, { "cell_type": "markdown", @@ -604,6 +620,17 @@ "We need first to export the files to tsv format, both the matrices and metadata." ] }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "for d in ['./flop_input/rna', './flop_input/prot', './flop_input/phospho']:\n", + " os.makedirs(d, exist_ok=True)" + ] + }, { "cell_type": "code", "execution_count": 23, @@ -640,15 +667,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "%%bash\n", - "nextflow -C $HOME/flop/flop.config run $HOME/flop/flop.nf -params-file $HOME/networkcommons/flop_run/params_flop.json -profile desktop --data_folder $HOME/networkcommons/data/flop_input --outdir ." + "REPO_ROOT=$(d=$PWD; while [ ! -f \"$d/pyproject.toml\" ]; do d=$(dirname \"$d\"); done; echo \"$d\")\n", + "FLOP_DIR=\"$REPO_ROOT/flop_repo\"\n", + "[ -d \"$FLOP_DIR\" ] || git clone --depth 1 https://github.com/saezlab/flop \"$FLOP_DIR\"\n", + "pixi run --manifest-path \"$REPO_ROOT/pyproject.toml\" -e flop \\\n", + " nextflow run \"$FLOP_DIR/flop.nf\" \\\n", + " -c \"$REPO_ROOT/flop_run/flop.config\" \\\n", + " -params-file \"$REPO_ROOT/flop_run/params_flop.json\" \\\n", + " -profile desktop \\\n", + " --data_folder \"$(pwd)/flop_input\" \\\n", + " --outdir \"$(pwd)\"" ] }, { @@ -660,7 +692,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -690,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -710,12 +742,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -730,12 +762,12 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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Sgg3pRERERERERERERERasCGdiIiIiIiIiIiIiEgLNqQTEREREREREREREWnBhnQiIiIiIiIiIiIiIi3YkE5EREREREREREREpAUb0omIiIiIiIiIiIiItGBDOhERERERERERERGRFmxIJyIiIiIiIiIiIiLSgg3pRERERERERERERERasCGdiIiIiIiIiIiIiEgLNqQTEREREREREREREWnBhnQiIiIiIiIiIiIiIi3YkE5EREREREREREREpAUb0omIiIiIiIiIiIiItGBDOhERERERERERERGRFmxIJyIiIiIiIiIiIiLSgg3pRERERERERERERERasCGdiIiIiIiIiIiIiEgLNqQTEREREREREREREWnBhnQiIiIiIiIiIiIiIi3YkE5EREREREREREREpAUb0omIiIiIiIiIiIiItGBDOhERERERERERERGRFmxIJyIiIiIiIiIiIiLSgg3pRERERERERERERERasCGdiIiIiIiIiIiIiEgLNqQTEREREREREREREWnBhnQiIiIiIiIiIiIiIi3YkE5EREREREREREREpAUb0omIiIiIiIiIiIiItGBDOhERERERERERERGRFmxIJyIiIiIiIiIiIiLSgg3pRERERERERERERERasCGdiIiIiIiIiIiIiEgLNqQTEREREREREREREWnBhnQiIiIiIiIiIiIiIi3YkE5EREREREREREREpAUb0omIiIiIiIiIiIiItGBDOhERERERERERERGRFmxIJyIiIiIiIiIiIiLSgg3pRERERERERERERERaWBk7ACo5aWlpuHr1KmJjY+Hq6gpfX19Uq1YNFhZl8/nJv//+i+joaPGznZ0dqlevjsDAQADAvXv3cOPGDUllDRo0CCdPntRanrbj+vv7o0GDBpDJZBrjdHNzQ9euXdUef8eOHcjNzUVAQACaNGkiKWYiIjI/8fHxuHHjBhISElCpUiX4+fnB19fX2GEV2Z49e5CRkaF2na2tLV599VWlZYIg4P79+wgNDYWdnR3q1KmDypUraz2G4vtArVq10LhxY7XbXLlyBcHBwbCyssKAAQPUbnPkyBEkJCSgcuXKaN++ve6TIyKici84OBiPHj1CdnY2vL29Ub16dbi6uho7LL0p7kd1qVevnsr9sZQ6iI6Oxr///qu2TEdHR/Tq1Uvjdq6urqhWrRpq1aqlsm/h7S0sLODu7o4mTZrA3d1d43k8e/YMt2/fhrW1taTvEkREmsgEQRCMHQQZ1vPnz/Hhhx9i69atCAgIQI0aNZCamopLly7B1dUVo0ePxhdffFHmGtQ7deqEc+fOiTfZsbGxCAoKQkBAALZs2YKHDx/i999/F7fPysrC7t27Ua1aNbz00ktKZf3555945ZVXtJbXoEEDtceNj49HUFAQqlatii1btqg0hHfq1An//vsvrK2tERERgYoVKyqtDwoKQps2bQAAU6dOxapVqwxXSUREVC7cvXsXM2fOxKFDh9CwYUP4+PggPj4eFy9eRI0aNTBlyhS88847xg5Tb1WqVEFOTg46deqkss7FxQU///yz+PnEiRN47733EBoaimbNmkEmk+HMmTN4+eWX8eWXX6J169Zqj9G1a1ccPXoUDRo00PiA/a233sLatWsByPNyq1atlNZHR0fDx8cH2dnZ6NKlC44cOVLEMyYiovJg69at+PTTTxEZGYlmzZrByckJjx8/xt27d9GjRw/MmzdPvMcrC15//XVkZWWJn69evYrg4GD07t0b9vb24vLBgwdj+PDhAPSrgxMnTqBz585o0qQJatasqXRsT09PrFmzRuN28fHxOHfuHBo3bozffvsNtWvXFvctvH1OTg7u3LmDkJAQvPPOO/j222+VOrsdPXoUCxcuxPnz5/Hyyy8jOzsbFy5cwOuvv47vv/8eLi4uBq5ZIir3BCpXnjx5Ivj4+AiBgYHCtWvXlNalp6cLS5YsEezt7YX09HQjRVh0HTt2FCpUqKC07M6dO4Kzs7NQs2ZNITc3V2lddHS0AEAYP358scpTt92jR48ENzc3oXr16irH7dixo1CxYkXBwcFBWL58ucpxJ02aJFSrVk0AIEydOlXayRMRkdk4f/684OTkJHTt2lUIDQ1VWhcfHy988MEHQo0aNYwUXfFUrlxZePnll3Vu9+TJE8HBwUHo2LGjkJiYKC4PCwsTunXrJsydO1ftfo8ePRJkMpnQp08fAYAQFBSkdrvJkycLAAR/f3/hrbfeUln/3XffCc7OzoKHh4fQpUsXiWdHRETl0SeffCLIZDLh888/FzIyMpTWXbt2TWjXrp0we/ZsI0VnGFOnThUACGFhYWrX61sHx48fFwAIK1eu1HpcTduFhIQI7u7uQqtWrXRun5eXJ0yfPl0AIPzyyy9K20+ePFno2bOnEB4eLi47f/68YG9vLwwcOFBrbERE6pStLsmk0/jx45GUlISDBw+iUaNGSuvs7OzwwQcf4MiRI7C0tFTZ99mzZzhw4AD27duHZ8+eqazfv38/zp8/L267d+9enD9/Hnl5eRrj0VVmcHAwtm/fjqdPn+p7qgCAunXrYvDgwQgODsatW7eKVEZRyqtevToGDRqEx48f4/bt2yrrHR0dMWjQIPz6669Ky9PT07Ft2za88cYbxY6ViIjKn5ycHIwYMQKVK1fGrl27ULVqVaX1bm5uWLJkiVLP7YKCg4Oxd+9eHD58GPHx8Urr8vLysH37djFvBQcHY8+ePbh+/brWmLSVCch7sW3fvh1JSUn6nKpW+/fvR1paGmbMmKHUW8zX1xf79u1D37591e63fv16ODs7448//oCfnx/WrVun9ThjxozB1q1bkZmZqbT8l19+wdChQ5V65RERkfk5c+YMPvvsM0yfPh3z5s2Dra2t0vpGjRrh2LFj6Nevn8q+OTk5uHDhAv755x8EBQUhJydHaX1oaCi2b9+O5ORk5OXl4ezZs9i7dy+eP3+uMR5dZQLA7t27cfjw4SKesari1EFR+fv7o23btrh8+bLObWUyGT744AMA8iHkCho5ciT27t0LHx8fcVnLli0xYMAA7Nq1C8nJyQaLmYjMAxvSy5EHDx7g8OHDGD58uFKiKKxt27awtrYWPycnJ2PkyJGoVq0aFi5ciG+++QY1a9bEe++9pzRu2pQpU7Bs2TKsXr0a3bt3x/Lly9GlSxe0bdtWJQFJLXP//v0YMmQIzp07V+TzdnZ2BgCkpqYWuYyilOfo6AhA3jiuztixY3H16lVcu3ZNXPb3338jJSUFo0aNMkisRERUvuzfvx+PHj3C22+/DQcHB43bdezYUenz06dP0aVLFzRu3BhLlizBvHnz4Ovri6VLl4rbZGVlYciQIfjzzz/x/vvvY+jQoViyZAleeuklDB48GEKh0f6klAnIG6+HDBmCJ0+eGKAG5BTnrq5x3srKSu2wLrm5ufj1118xZswYuLq6YtKkSdi6davWm+QxY8YgKSkJO3fuFJddvnwZN27cwNixY4t9HkREVLYphiBRNNSqY21tjbZt2yot27NnD6pVq4ZBgwZhzZo1GD58OGrXro1Lly6J2xw/fhxDhgzBf//9h06dOmHevHmYNWsW/P39lYYs1adMABg3bhxmzJhRnNNWUtQ6KI68vDwEBwejevXqkrbXdG/evn17tUPa5uXlIS8vT+O8LUREmnCy0XJE0RitacxQTV5//XWcPXsWFy9eFHuxX7x4Ee3bt4eXlxc++ugjcdvLly+jbt264pijV65cQfPmzbFy5Up8/PHHepcZEBCAQYMGFXnitJycHBw9ehR2dnaoX79+kcooSnm5ubk4duwYnJycUKdOHbXbdO7cGf7+/vj111+xbNkyAPIebr169UKVKlWKHSsREZU/RcnlWVlZ6N69OwRBwN27d8Wc+tdff2Ho0KGoWbOm0oSaW7duxfz58/Hdd98BALZt24bhw4djx44deO211/Qus2nTphg0aJDkydZiY2Oxfft2leW+vr7ieffo0QOVKlXCrFmzkJGRgb59+8LLy0truXv37kVERASmTJkCAJg4cSI+++wzbN26FRMnTlS7j6+vL7p06YJff/0Vw4YNAyDP1bVq1UK7du0knQ8REZVf586dg5+fn9aOaoUFBQVh4MCBmDhxIlasWAErKytkZ2dj9OjR6Nu3L+7du6eUM7/88kv89ttvqF69OvLy8jBkyBC899576N+/v9jJS58y+/XrBzc3N6PWgYLirbWCWrdurXL/X3C7hIQEbN++HbGxsfjjjz8kHUfRA7958+Y6tw0LC8Pu3bvRoEEDVKpUSVL5REQKbEgvR2JiYgAAHh4eSstzc3OxY8cOpWUtW7ZE1apV8d9//2HPnj1YunSp0lAwLVq0wJgxY/D9998rNaSnpaVh3rx54uemTZuiTZs22L9/v9iQrk+ZPXv2RM+ePSWfY1ZWlphg4+LisHnzZty+fbvIE4VILa/gdvHx8diyZQvu37+PH3/8UeNxZTIZxowZgx9//BGLFy/Gs2fPcOzYMbWNB0RERIDmXB4fH4+jR48qLevSpQvc3d3x119/4fbt29i7d6/SjemQIUPQvn17fP/990oN6Z6enhg5cqT4eejQoXjnnXewf/9+sSFdnzLHjx+P8ePHSz7H6OhobN26VWV5mzZtxIb0SpUq4cSJE5g/fz7effddTJo0CZ6enujcuTPeeOMN9OrVS2X/devWoWPHjuKD8MqVK2PgwIFYt26dxoZ0QP4G2ZgxYxAREYEKFSpgy5YtWnvdERGR+YiJiUGNGjVUlt+4cQP37t0TPzs5OYn3tV9++SXc3d2xbNkyWFnJm1ysra3x3XffwcfHB1u3bsXkyZPFfQcMGCD2vLawsMCUKVPw999/48KFC+jSpYveZWoa/q0060Dh8uXLSEhIUFrm5eWl0pBecLvk5GRcuXIFbdq0URniTkHR8J6Tk4Nbt27h+++/R4sWLXT2xM/Ozsbrr7+O9PR0lTfsiIikYEN6OaJ4Dbrw60zZ2dniDWtkZCTOnDmDTZs2YdSoUTh79iwAeUPxzp07IQiC+Gp3dnY2IiMjER0dLT6pbdSokZi4FapWrSqWA0DvMvWRmZkpnoutrS1efvllLF++HE2aNNG7LH3KK7hdREQEgoKCMGrUKIwePVpr+WPHjsUXX3yBffv24dq1a/Dw8EDfvn2VZkgnIiJS0JTL4+PjxTx0//593LhxAxcvXkTz5s3FXuxxcXEqedfOzk5lfNGXXnpJ6bNMJoOvry/CwsLEZfqWqY+6detKeqhcv359/O9//0NWVhZu376NCxcuYMOGDejduzc+/PBDfPPNN+K2T58+xf79+zF+/HilsqtVq4Y///wT169fV5k7RmHgwIFwcnLCpk2bUL16dSQkJGDMmDFFPj8iIio/HBwc1A7leeXKFfzzzz8AgGPHjsHDwwMPHz4EIM+hvr6+2L9/PwCIOVQQBDg4OKjMTVI4LysajwvnZX3KNKSi1IHC+PHjMW3aNJ3HKLxdamoqevfujXbt2uHevXsqHQwUDe+ZmZm4dOkSrKys8P3332vtiZ+Xl4cxY8bg9OnTWLRoEbp3764zLiKiwtiQXo4EBgYCAO7cuaO03M7OTryp3LNnD1599VVxnSIhnjhxQmVsNQAYNGiQ0md1r23b2NgojS2mb5n6cHZ2NmiPbqnlFd7u0KFD6N27N5ycnMQx49SpUaMG2rdvj19++QU3btzAyJEjYWNjw4Z0IiJSq2Aub9q0qbi8Ro0aYh5asmQJZs2aJa5LT0+HTCZTGudbwcXFBT169FBaJjWX61NmSbKxsUGTJk3QpEkTTJgwAR06dMC3336L6dOni8O9/PLLL6hYsSJiY2NVertXq1YN69atw8qVK9WWb29vj2HDhuG3336Dv78/unbtWuQh54iIqHwJDAzE6dOnkZGRATs7O3H5mDFjxIeuzZs3V+p1nZ6ejri4OLXjnPfq1UtlCNHCednGxgYAVPKyPmUaUlHqoLgcHR0xbdo0DB06FNu2bROHbVMo2PCemZmJvn37omfPnrh8+TICAgJUyhMEARMmTMDWrVuxYMECpbfuiYj0wYb0cqR169bw8/PDn3/+iU8//RSWlpY696lZsyYA4N1330Xv3r0NEkdJlGlqunfvjilTpmDVqlUYNWqU1olVxo4dizfffBOAfOIXIiIiTfr164d3330Xmzdvxuuvvy5pn5o1a0IQBCxevFjtq9dFURJl6iMxMVFtg7+FhQXatWuHM2fO4NGjR/Dy8oIgCNiwYQPGjh2Lr7/+WmWfVatWYf78+Vi8eDHs7e3VHm/s2LH46aefcOfOHWzZssXg50NERGXT0KFDxeE5R40aJWmfmjVrKnVmM4SSKFOqotSBITg5OQEAoqKitG5na2uLn3/+GXXq1MHUqVNx8OBBlW2mTp2KX375BfPnz8enn35aIvESkXlQnb6Yyixra2usWLEC9+/fxwcffCC+gq1Nr1694OPjg2+++Qa5ubkq6588eaJ3HPqUGRwcjO3bt+Pp06d6H8fY5s+fDycnJ51Ps4cMGYIhQ4ZgypQpRR6ChoiIzEPlypWxYMEC7N27V+sbTwWNGjUKdnZ2+Oqrr9SuL/hquFT6lKkYpzQpKUnv42iydetWzJgxQ6k3HiCfFPz48eOwtrYWJ/s+fPgwQkJCNM650qtXL3HiMk3atGmDCRMmYOjQoUrjyRMRkXkbN24cWrZsiZkzZ6q8+a3JxIkTcenSJXECzIJSU1MRFxendxz6lLl792612xVVUerAEBTn0KxZM53b+vn5Ydq0aTh06BCOHTumtO7999/HDz/8gHnz5uGzzz4rkViJyHywR3o5M2DAAGzevBlTpkzB8ePHMXToUFSrVg0ZGRl48OABNm/eDDc3N3h7ewOQv868Y8cO9OvXD02aNMGYMWNQpUoVPHnyBEePHoWnp6faCcG00afM/fv345133sFff/2FwYMHG7w+SpKnpyfee+89fPnll9i/f7/aic8A+ZP0P//8s5SjIyKismr27NkAgA8++ADbtm1Dv3794O3tjaSkJNy4cQNbt26Ft7e3OA5o1apVsWXLFowaNQoPHz7EgAED4O7ujkePHmH37t3o27ev3jeO+pS5fv16rF69Gjdu3ECDBg10lh0bG6uxUbtfv36wsbGBt7c33n//fezcuRNvvPEGatWqhaioKGzevBlXrlzBypUrUbFiRfH4zs7OePnll9WWWbNmTdSqVQvr16/XOrfJunXrdMZORETmxcbGBvv27cMbb7yBZs2aYcSIEWjTpg0cHR0RFRWFo0eP4tq1a0r3gtOmTcONGzfQp08fsRE6Ozsbt27dwj///IPt27erjPmtiz5ljhs3DlWqVMHNmzeNVgf6UjyUB4CUlBScOnUKv/32G0aOHIm+fftKKuOjjz7C2rVr8fHHHyMoKAgA8O2332LZsmVo1aoVGjdurPL9o1u3bmrfgCMi0oQN6eXQ8OHD0adPH+zcuRMXLlzArVu34OLiAi8vL6xYsQK9evWCra2tuH2LFi1w//59bNmyBRcvXsT169dRvXp1LFy4EO3btxe36927N2rXrq1yvBYtWsDa2lplmZQyAwICMGjQIEljkXbs2FGv18ttbW0xaNAgNG/evFjladtu5syZePDgAS5duiR+cejYsaPOMeqsrKwwaNAgpfFviYiIFGbPno2xY8dix44duHr1Ki5fvgwPDw94eXlh69ateOWVV2Bh8eLFwgEDBuDhw4fYvHkzrl27BkDegLxx40axcdvS0hKDBg1Sm6NeeeUVlVwupUwAaNq0KQYNGiTpRvTVV19Vmji1sB49esDGxgavvvoqnj9/jl27duHatWvYu3cv7OzsMGTIEGzdulUcRi47OxvW1taYOXOmSvwFzZgxA8eOHROHjGnWrBkGDRqkcxi8Pn36wMfHR+d5ERFR+VWhQgXs2bMHly5dwv79+3H27FlkZ2ejSpUq6Nq1K7777jvUqlVL3F4mk+Gnn37C+PHjsWvXLhw7dgzu7u4IDAzE559/Lj4Ir1atGgYNGgQXFxel4zk6OmLQoEFirtOnTED+UFrbpJvqKHK5YtLz4tZBpUqVMGjQILXjlRek2C4hIUH8buDg4ICqVavi1KlTaNOmjdrt1ZXr4eGBpUuX4sCBA7h37x7q1KkDCwsLcY42dd89mjdvzoZ0ItKLTJAy/gcRERERERERERERkZniGOlERERERERERERERFqwIZ2IiIiIiIiIiIiISAs2pBMRERERERERERERacGGdCIiIiIiIiIiIiIiLdiQTkRERERERERERESkBRvSiYiIiIiIiIiIiIi0sDJ2AMaQl5eHiIgIODs7QyaTGTsconJJEAQkJyfD29sbFhZ8ZkdExcPcTVTymLuJyJCYu4lKHnM3Uekyy4b0iIgI+Pn5GTsMIrMQFhYGX19fY4dBRGUcczdR6WHuJiJDYO4mKj3M3USlwywb0p2dnQHILzQuLi5GjqZ03Y5IxNC1QfhzcmvU93Y1WhmGLKekFDW+0t7PVCUlJcHPz0/8fSMiKo7ykruLe60vzv6mmGf4vcS0MHcTkSGVl9ytjqFzRknlIGPnNt5TlzzmbqLSJRMEQTB2EKUtKSkJrq6uSExMLHcJXZfs3DwkpWfDxd4a1pZFe+3HEGUYspySUtT4Sns/U2XOv2dEZHjl5ZpS3Gt9cfY3xTzD7yWmpbz8nhGRaSjP1xRD54ySykHGzm28py555fn3jMgUmWWPdHNmbWmBCk62Ri/DkOWUlKLGV9r7ERFR2VHca31x9jfFPMPvJUREVBYZOmeUVA4ydm7jPTURlTfm8YiORGFxafjgz2sIi0srchmhsamY8NtFhMamGj2WklTU+IpaP6ZeH0REVHzFvdYXJwebYp7h9xIiIiqLDJ0zDJXLCjN2buM9NRGVN2xINzNZuXkIjU1FVm5ekctIzsjBkTtRSM7IMXosJamo8RW1fky9PoiIqPiKe60vTg42xTzD7yVERFQWGTpnGCqXFWbs3MZ7aiIqbzi0i5mpWckJ26e0NXYYAEwrFnVKOz5Trw8q+1JTU2FhYQF7e3tjh0Jktox5rTfFPGNKMZlSLEQKzN1Epqms5Axjx8l7ajJHzN3lG3ukE1G5lZubi6ysLGOHUaJSU1ORnp4uaduRI0di1qxZJRwRERFR0TF3K2PuJiIiU8fcrYy5u3xjQ7qZuRWRiAafHsStiERjh2JSsahT2vGZen2UFTk5Ofj2229Rv3592Nraws3NDX5+fhg3bhxu3rxp7PAMbtiwYZgzZ46xwyAiiYx5rTfFPGNKMZlSLOaGuZuIypqykjOMHSfvqcsv5m4yV2xINzOeznaY3rUWPJ3tilxGZRc7zOtTD5Vdil6GoWIpSUWNr6j1Y+r1URbk5OSgb9++WLNmDZYsWYK0tDSkpqbi/PnzeOWVV7B27Vpx26ysLCQnJwMABEFAUlISBEEQ1+fl5Wl84pycnIzMzEylZWlpaUrbp6amIiMjA4D8CX1OTvHHO1R3zJycHGRmZiIhIQEJCQnIy8tTOnZOTg5SUw07aRERFV1xr/XFycGmmGf4vYSYu5m7icoiQ+cMQ+Wywoyd23hPXT4xdzN3mzXBDCUmJgoAhMTERGOHQlRuqfs9y8vLE1Izs4v1Jy8vT+Mx165dK8hkMuHy5cs641u5cqVQs2ZN4cMPPxScnZ0FV1dXISEhQUhNTRUmTJggODg4CFZWVkJAQICwc+dOpX0bN24sfPvtt0rLhg0bJowfP1783KVLF2HkyJFC9+7dBWdnZ8HGxkaYNGmSkJWVJW6TlZUlxMfHaz2nnJwc4ZNPPhEqVaokWFtbC7Vq1RL27t0rCIIgTJkyRbCyshJsbGwEV1dXwdXVVbh//77QpUsXYdSoUUKPHj0Ee3t7oU+fPoIgCEL//v2FqVOn6qwbIlPE3E1U8pi7mbuJDIm5m6jkMXczd1Pp4mSjZiY5IxvXwhLR2M8VznbWRSojMS0bpx/GoF1ARbg6FK0MQ8VSkooaX1Hrx9TrwxDSs3NR/5ODxSrj9mc94GCj/tK1bds2vPzyy2jWrJmksoKDgxEbG4uIiAg4OTkBAKZNm4YTJ07g8uXLqFmzJlavXo3Bgwfj5s2bqFOnjl6x/vHHH1i7di327t2LW7duoXfv3qhatSrmzp0rxjt69GhER0ejYsWKasuYNWsWtm7dir/++gvt27dHcHAw1q9fj969e2PNmjV48uQJAgICsHz5cqX9Nm/ejM2bN2Pv3r2wtLTUK24iKjnFvdYXJwebYp7h9xLTx9zN3E1EqgydMwyVywozdm7jPbVxMHczd1PJ4dAuZiY0Ng2jNpxHaGxakcsIi0/D1M3/ISy+6GUYKpaSVNT4ilo/pl4fZUFwcDACAgKUlqWnp4uvXyUkJCits7W1xffffy8m8/T0dKxduxZffPEF6tatC2tra0yfPh1NmzbFypUr9Y7n5ZdfxqRJk2BlZYXGjRtj9uzZWLFihbjexsYGrq6usLBQfylOSUnB6tWr8dVXX6Fjx46wsLBArVq18M033+g8dq9evTBs2DAmcyITU9xrfXFysCnmGX4vIebuF5i7icoOQ+cMQ+Wywoyd23hPXT4xd7/A3G1+2CPdzNSq7IRTH3aGp4utsUMxqVjUKe34TL0+DMHe2hK3P+tR7DI0sbCwUBkTbfbs2di4cSOys7ORlpaG7OxsWFnJL30+Pj5wdHQUtw0ODkZOTg5eeuklpTJatGiBe/fu6R1rkyZNlD43bdoUUVFRSEhIgJubG4YOHYqhQ4dq3P/evXvIyspC27Zt9T62vk/xiah0GPNab4p5xpRiMqVYTAlzN3M3EakqKznD2HHynto4mLuZu6nksEe6mbG1soSfhwNsrYz/tMyUYlGntOMz9fowBJlMBgcbq2L9kclkGsuvV68e7t69q7RsxYoVSEhIUJrwRMHaWvl1P0WiL/ylICcnR1ynOI/C8vLyVJbl5uaqlFPwOLoonphnZ2dL2r6gwudGRKbBmNd6U8wzphSTKcViSpi7mbuJSFVZyRnGjpP31MbB3M3cTSWHDelm5mlCOubvvImnCepnRTbXWNQp7fhMvT7KgtGjR+PSpUs4fvx4kfavUaMGHBwccPbsWXGZIAg4c+YMGjRoIC7z8PBATEyM0r537txRKe/cuXMqn/39/cVX2rKzs5GQkKA0a3lB9erVg5OTk9bzsbGxUfniQESmy5jXelPMM6YUkynFYk6Yu4moLCorOcPYcfKeunxi7iZzxoZ0M5OelYP/nsQjPStH98bqZKfD7sRCBNpEwi76pnFjKWH6xHf6QQyO34uCIAiws7ZAoLcL7Kz1+/Uy9fooC4YPH4433ngD/fv3x3fffYdr164hKioK169fx6FDhyCTybQ+WbexscGsWbMwd+5cHDp0CCEhIZg5cyZCQkIwffp0cbtXXnkFmzZtQlBQEEJDQzF79mzcvKn6+3Dt2jV8/PHHCA0Nxa5du7B48WLMmjVLXL9t2za4u7sjNjZWbTx2dnaYM2cO5s+fj02bNiEsLAxHjhzBmDFjxG1q1KiBCxcuIDw8HAkJCWqf0BOR6Sjutd7O2gKBLhmw2z4SuPxbqR67JBgipqLm3ZKIhfTH3M3cTVQWGTpnGCqXFWbs3FbU459/FAsbKwvsvf6sVI5H+mHuZu42Zxwj3cwEeDpj77vti17A2VUIeLABey0AnKgKNPwPsCzaqyzFjqWESY1vz/UITNt8BQDw3dDGeK2Zb5HOy9Tro6z49ddf0bNnT/z6669YtmwZ8vLyULVqVbRs2RJ3794VJwGxtbWFi4uLyv7z58+HlZUV3n33XcTHx6NRo0Y4duwYfHx8xG3ef/99REZGYsSIEXB0dMTAgQMxatQoODg4KJU1ZcoUxMTEoFu3bsjJycGsWbPw9ttvi+t1TXoCAB9//DEqVaqE5cuX48MPP0Tjxo2VJj2ZMWMG7t69i1atWiE1NRUXL16Ek5MT7O3tVcpydHRUiZGISldxr/UBqVewN+tNIAvAnrOA/8tAxQCd+xni2CXBEDEZ6rxMsX7MBXM3czdRWWPonFFSOcjYua0ox0/OyMayIw+QlZOHH/4NxoiWVVG1grTroLHP15wwdzN3myuZoOndhnIsKSkJrq6uSExMVPsLTRrk5QLLGgDJES+Wvf4nULt4k1iUdYN+OIvLofEAgLpVnHFgegcjR2QazP33rGvXrmjevDm+/vprY4dCVC6Y+zVF9OcbwO2dLz63mwF0XWCsaKicMfffM+ZuKu9CQ0Px4MEDvPTSS3B3d5e0T1ZWFq5fvw4HBwfUq1dPay/Twsz9mkJFs+vqU7y39ar4eU6vupjcsabxAjJx5v57xtxNpY1Du5iZO8+S0OLLI7jzLEn/nZ/fBJIjcNOyPmpn/o6bef7A/QPGiaUUSIkvMS0bV57Ei5/vRibj2N3nqD13P24+TTT48YiIqGwr1rU+Nwc37z9A7YzfcLPBbPmy+wdL59glxBAx3XyaWKS8WxKxEBGZoqCgIPTt2xetW7dGt27dcO3aNUn7bdmyBd7e3njjjTfwxhtvoFOnToiKiirhaMsGQ+cMQ+Wywoyd24py/KBHysNvXAyJ17ClYY5HRKQPNqSbmQqONhjT2h8VHG303zk0fyKIKo2QJVgAkAGh57TuUmKxlAIp8V0NT0CeAFSv6IiX/OW9Oq6FJSArV//xsky9Pkg/ml7zIiLzVqxrfcR/QFYqsmANNBouXxZ1G0iLK/ljlxBDxVSUvFtSsVDZxdxN5VVwcDAmT56M8+fPS97n2LFjGDVqFFasWIFbt27h4sWL+PrrrzWOMWxuSiJnGCKXFWbs3FaU4194rPy95np4Qokej8o25m4qbRwj3cx4utjhnS61irZz6Bn5T69GQHD+sug7QHo8YC/t1UCDxVIKpMR3PzIZAFDfywVerna4HBqPh1EpJXY8Kjt27txp7BCIyAQV61r/JOjF3x0rABVqAbEPgPCLkoZZM8U8Y0oxmVIsZBzM3VRejRw5EgAQHh4ueZ/PPvsMPXv2xOuvvy4ua9OmjcFjK6vKSs4wdpz6Hj8jOxePY1KVlkUlZyI+NQvuEhrHjX2+VPqYu6m0sUe6mUnNzMHl0HikZhZhFutn+a8AegbKfzpVkf+Mulv6sZQCKfHdey5vSK9d2RkNfV0BAA+K2JBu6vVBRETFV6xrfeQN5c/eTeQ/n98s+WOXEFOKyZRiISIypszMTJw5cwa9e/dGXFwczpw5g8ePH0vaLykpSelPeVVWcoax49T3+MHRKcgTACc7eZ9PRc/yx7Gp2nYr8vGIiPTFhnQz8zgmFYN+OKvylFenzBQg4Yn87x418n9Wk/+Mul26sZQSKfHdFxvSndDAR96QHiIxyRfleEREVLYV61pfuCHds578Z9Sdkj92CTGlmEwpFiIiY4qJiUFOTg4uXbqEBg0aYNasWXjppZfQsWNHrWOkL1q0CK6uruIfPz+/Uoy6dJWVnGHsOPU9/oPn8k5p/u4OAABvNzsAQKjEe2xjny8RlX9sSDczAZ5OODSjAwI8nfTbMeae/KdjJQT4+8jL8M3vkR5dtB7pRY6llEiJLywuDQBQvZIj/D0cYGNpgawcARvfbKn3eZl6fRARFRQVFYWgoCDJvc3y8vLw4MEDPH78GLm5uSUcnekq8rU+Ox2IuY8A2VMcmlBHvr9nffk6iW+GmWKeMURMhjovU6wfIiJjsLa2BgCcOnUKN2/exNmzZ/H48WNER0dj5syZGvebM2cOEhMTxT9hYWGlFXKpM3TOKKkcZOzcpu/xH0TJO6oF+rrg0IwOqFPFGQDwOCatRI5HRKQvNqSbGTtrS9Su7Aw7a0v9dlT0dvOs96KMKnWV15VWLKVEV3xpWTmIT8sGAHi72cPK0gLVKsqfnOcJgt7nZer1QUQEAFeuXMHw4cMRGBiINm3a4L///tO5zzfffAMfHx/06dMHHTp0QI0aNbB///5SiNb0FPlaH3MfEHJh5+CE2jVryvdX9EiPuQfk6X44YYp5xhAxGeq8TLF+iIiMoVKlSnBycsKAAQPg4eEBAHB1dcVrr72GM2fOaNzP1tYWLi4uSn/KK0PnjJLKQcbObfoePzRW3mBe29MZtSs7o2YleUN6eJy0hnRjny8RlX9sSDczzxLT8dW+O3iWmK7fjjEP5D8r1kF4fBpmb7+OcJv8IV5igzXvVxKxlBJd8UUkyJc721rBxU7ea6NmJfmT7+VHHiA8Xlqyl3o8IiJTcOXKFfTv3x/nz5+XtH1ubi7i4+Nx/fp13L9/H0+ePMGYMWMwZMgQPH/+vISjNT1FvtbHPQIAhLs0w+z/3ZDnGFc/wMIayM0CkiJK7tglyBAxid9L9My7JRELEVFZdefOHQQFySe1lslk6NGjB0JDQ5W2CQkJQeXKlY0RnskxdM4wVC4rzNi5Td/jP82/x7azssDs7ddhYyUDAEQmZZTI8YiI9MWGdDOTnJGDI7efIzlDz8k3EvK/RLlXQ0JaNrZdCkOCdf6XqORnQE5m6cVSSnTF9zRBnsx93O3FZYqG9KthCUjI761uqOMREZmCN998EyNGjICNjY2k7S0tLfH111+jUqVKAOQ351OnTkVqaqqk3uzlTZGv9fkN6QlOAfIcnJYNWFgCbvnjzyaEatm5mMcuQYaISfxeomfeLYlYiIhM0bNnz3DkyBGcPn0aAHD58mUcOXIEjx49ErdZunQpJkyYIH7+/PPPcfjwYcyePRv//PMP5s6di23btmHu3LmlHr8pMnTOMFQuK8zYuU3f44fHyxvAHW2tse1SmNizXGpDurHPl4jKPytjB0Clq3ZlZxyb2Un/HRUTjbr7v1hm7wZYOwDZaUBCGFAxoHRiKSW64nuan+S93Qo0pHs6ltjxiPQxe/ZseHp64oMPPjB2KEQqFA3o1atXN3Ikpa/I1/rY/MYOFx/l5W7+8kb2+BCgWruSOXYJMqWYTCkWMk/M3VRSbty4gcWLFwMAunTpgv3792P//v0YM2YMatSQv2Vcv359yGQycZ969erh/PnzWLFiBX766SdUrVoVQUFBeOmll4xyDqamrOQMY8epz/EzsnMRnSzvoOfpYgsAqOgk/xmZmAFBEJT+jxb3eESGwNxtftiQTtLE5/d0c/MH8vKXyWSAezUg6jaQEKJ3Q3pZpxjaRTGTOAD4Vyh6QzoZzrVr1/Dbb7/h5s2byMvLQ/Xq1dG1a1cMHjwYlpZld7y8mTNnwtfXF9OnT9e57b1795Caqnm2+s8//xx79+7FoEGDMGvWLKV1GzZswLp169CmTRssW7asuGGXiJSUFKxbtw7//vsvsrOz0aJFC7z33ntwd3cXt8nLy8O2bduwa9cuxMTEoHbt2nj77bfRoEEDjeUKgoCtW7dix44diI6ORuPGjfHRRx+hSpUqSttt27YN//vf/xAVFQVvb2+MHDkSffr0KbHzLU9iY2Mxbdo0DB48GHXr1tW4XWZmJjIzX7ztJHVS03Irv0c63HyVl7tXk/+M190jnciUMXczdxd2+PBhzJ8/H6+88gq++uorScfQtM/w4cMREhKisv3LL7+MpUuXFul8ypLu3buje/fuWrd5//33VZbVqVMHq1evLqmwiJQ8S5T3Ore3toSLnbypqoKj/A3ItKxcJGfmiEOqkmlg7mbulrqNOmX1nppDu5iZ+8+T0fHb47j/PFn6TpkpQFqM/O9uVZXXueX3UC/CDXyRYilFuuJ7nv96WRWXFw3pVT0cxL9n5eqe+E2f45E0ixcvRqtWrZCTk4N3330Xc+fORZs2bfDPP/9g6NChxg6vWO7evav2JrAogoODce/ePSxZsgQ5OcqvPi5ZsgT37t3DnTtFm0i4NHTo0AHh4eEYO3Ys3nrrLRw6dAht27ZFcvKL35+ZM2fi2LFjGDJkCGbPno3MzEy0bNkS165d01junDlzMG3aNHTr1g1z585FfHw82rRpg4SEBHGb7777DuPGjUPHjh2xYMECNGrUCP3798fmzZtL8pTLhaSkJPTu3RsVK1bEhg0btG67aNEiuLq6in/8/PxKKcqSVeRrfZyGHumKN8XiQ0ru2CXIlGIypVjMDXO3NOaQuxWeP3+OCRMmIC4uDvfv35dUvrZ95syZg+XLl4t/5s6di/Pnz5eb3EKlr6zkDGPHqc/xFePD+7rbiz3P7Qo0qj9P1D28i7HP15wwd0tjDrlbn/yuUJbvqdkj3cy42Fmjd0Mv/Z7kJobJf9q5AvZuqOiUgSmdaspfs1LcwEsYm9UgsZQiXfHFpmYBePG6GSB/Ym5nbYGM7Dxk5wgGPR7pdvz4ccyePRsbN27E6NGjxeWdO3fGm2++qXQh3759O7Zv346JEydizZo1ePbsGQ4dOgQnJyds374df/zxB+Li4tCoUSN8+OGHSjdaY8aMQc+ePfH666+Ly+bPnw8HBwfMmTMHAPDuu++iVq1ayMnJwfHjx5Gbm4vx48fjtdde03oOZ86cwdq1axEWFoaGDRti7ty5qFy5Mj7//HOcOXMGly9fFieC2rZtG5YuXYratWsjJycH+/btQ61atST3HGrZsiWCg4OxZ88eDBgwAABw+vRpxMTEoFu3bio9gE+dOoW1a9ciPDwcNWrUwDvvvIOmTZuK6xcvXoy///4bAFChQgW0bt0aH3zwARwcXjxgevfdd1G7dm3IZDIcPnwYubm5GDFihFJdSnHy5Ek4OTmJn9u0aQNPT0/s379f/OL2xRdfKB27W7duOHDgAHbv3o3GjRurlJmTk4Ply5dj6dKlmDhxIgD568916tTBqlWrMG/ePADAzp07MWzYMEydOhUA0KlTJ5w4cQI7d+7U+zzMSXJyMnr27Inc3FwcOXIELi4uWrefM2eOUu+4pKSkctHgUaRrfVYqkBIJAKjoXQ1TOrm9yD1u0vOwKeYZQ8RU0cn2xfcSI8dC+mPuZu4umLsB+dtho0ePxvTp03H06FFJZevap3DeX7p0KWxtbZX+zxHpw9A5w1C5rDBj5zZ9jq8YOtXH3V6pPqq42iEpIwWRSRmoVdnZYMejomPuZu4umLul5veCyvI9NXukm5kqrnaY3bMuqrja6d5YoeCwLoXLcPGWr0t6VjqxlCJd8cWmyIccqFDgy45MJoO/h3x4l/Rs/Xqkm3p9lAVr1qxBvXr1MGrUKLXrnZ1ffPGKjIzEjh078OGHH2LkyJH47rvvYG9vj3Xr1mHMmDHo2LEjPvroI4SEhKBVq1ZITEwU971+/ToiIiKUyn7w4AGCg4PFz7dv38aHH36Is2fP4u2330aHDh0wfPhw7Ny5U2P8W7ZswSuvvAIfHx/MmTMHtWrVEid9ev311xEYGIiOHTuKvakqV66M27dvY9asWbh27Ro+/PBDMRFJIZPJMG7cOKWewevXr8fo0aNVJpL866+/8Nprr6Fly5b45JNPUKdOHbRr1w7nz58XtxkyZIgY26RJk3Dw4EEMHDhQqRxFvDdu3MDUqVPRrVs3vPHGGzhw4IC4zcGDB9G6dWulOi+sYKIGAHt7e1haWiIrK0tcVvCLhOLYsbGxahvRAXlDb2ZmJry9vZXqyMvLC4cOHRKXNW/eHFevXkVamrzXTFxcHO7cuYOWLVtqjNfcPHr0CNevXxc/KxrRs7KycPjwYbi5ueksw9bWFi4uLkp/yoMiXevjHst/2rujSuUqyvu75g/1IiEPm2KeMURMhjovU6wfc8DczdxdMHcDwDfffANBECS9Ul/UfX7++We89tprqFChguRjEBVk6JxRUjnI2LlNn+M/zR861cfNXmm/Ss7y++2YlExtu+t9PCo65m7m7oK5W2p+L6hM31MLZigxMVEAICQmJho7lFKXnpUj3I5IFNKzcqTvdGGdIHzqIgibRwiCIAjJGdnC2YcxQnJGtiBc2yZf90uf0omlFOmKr+2io4L/7D3Cf6FxSsvf2HBe8J+9R1h3MtigxytrtP2ePU9MF26EJyj9eRKbKgiCvB4Kr7sRniDpmLVq1RKGDRsmaduVK1cKFhYWQkhIiLgsJydHqFSpkrB48WJxWVZWluDn5ycsXLhQXNa4cWPh22+/VSpv2LBhwvjx48XPXbp0EapXry7k5uaKy2bOnCk0bNhQbTw5OTmCp6en8MknnygtT09PF//ep08f4b333lNa36VLF6FZs2Yq5fXv31+YOnWq2mMJgiC88cYbQo8ePYTw8HDB1tZWiIiIEBITEwUHBwfh5s2bwsiRI4UePXqIsXl5eQl//PGHUhnvvfee0L9/f43HiI6OFmQymXD//n2leNu3b6+0Xb9+/YS33npL/Lxp0yYBgBAdHa2x7MI++eQTwdHRUXj27JnS8uDgYKFVq1ZCgwYNBCcnJ2H9+vVay2nYsKHQu3dvISMjQxAEQbh48aJga2sr+Pn5idtkZWUJ77zzjuDu7i40adJEcHFxERYuXCjk5eVJjrcsi4qKEs6dOyf8888/AgBh9erVwrlz54SwsDBxm/HjxwuBgYGCIMjrq3379oKnp6ewf/9+4dy5c+KfqKgoycctL7m7SNf62//Ic+3aTso5WBAEIf6JfN3CCoJQ4HpjsGOXMEPEpFInRoylrGPuZu42du4+d+6cULlyZeHp06eCIMjrb9CgQVrL0Xefs2fPCgCEY8eOSY6Viqa85G51DJ0zDJXLCjN2btPn+DO2XRH8Z+8RVh9/oFQf0zb/J/n+2tjnawzM3czdxs7dRdmmLN9Tc2gXM/MwKgV9V57GnnfaoYGPq7SdkuWvk8NZPtleSEwqRqwLkpfh7JW/jf490osUSynSFp8gCIhNlT8RL/z6nYu9/DWy2xH6TYxn6vVhSH+cf4Lvjz5QWjagiTeWD2+KyMQM9F15WmWfkK91TzqRkZGh0mt10aJF2LVrl/j5zJkz4sQn3t7e8Pf3f3GMkBBER0ejZ8+e4jJra2t07doVly5dknZyBbzyyiuwsHjx4k/37t2xdOlSZGRkwM5OuZfEnTt3EBUVpfIkufB26rRt21bv2BR8fHzQpUsX/Pbbb3B3d0fDhg0RGBiotM29e/fw7NkzLF26FGvWrIEgCBAEAZGRkUpP0BMSErBmzRoEBQUhJiYGeXl5kMlkePToEWrVqiVu16JFC6XyfX19lXoa9OzZE+fOnZPUaxmQvy745ZdfYuPGjSqTgnp5eWH58uVISEjAn3/+idmzZ6N58+Yae6X/8ccfeP311+Hj4wMvLy+kpaWhb9++OHv2rLjN1q1bsXHjRixcuBANGzZEUFAQvvnmG7Rq1Qo9evSQFHNZduHCBXz++ecAgFatWmHjxo3YuHEjJkyYIPbkqFmzJtLT5b2K0tLSkJWVherVq2PBggVKZX388cfo169fqcZvbEW61ieGy3+6+SnnYB9XwKmyfF1eNpAeBzhWNOyxS5ghYlKpEyPGUp4xdzN3F1QSuTsxMREjRozAmjVrlN4O06Yo+2zYsAEBAQHo1KmTpO2J1DF0zjBULivM2LlNn+NHJcnvrys72ynVh2LC0bhUzb1bi3I8c8DczdxdUEned+uzDVC276mN2pCel5eHffv24ccff8Tdu3fxxx9/oFWrVpL3X716NZYtW4Zx48Zh7ty5JRhp+VGjkiN2TX0ZNSo5St9JbEj3Ul2nGNpFsU1Jx1KKtMWXmpWLjOw8AEAFJ+VXcaq4yBvWn0mYDEXq8cqbka2qolv9ykrLXPMfQFRxtcOed9oVqVw/Pz88fvxYadmwYcPQuXNnHDx4EAsWLIAgvBi73tFRua4VY7kVfjXJyclJpVwpCpfv6OgIQRCQkpKikqgVrzQVZfiKwsfR1/jx4/HRRx/B1dUVkyZNUlmfkpICAPjoo49Uxqi2tX3xIKlv376QyWSYNm0avL29YW1tjQ4dOogNqgrW1spjFspkMqV/l4oVK6JiRc2NgQX9888/GDlyJFatWqV2LDV7e3u0bt0agPyLQocOHfDVV19h27Ztastr2LAhrl+/jidPniAxMRH16tXDhAkT4OPzYoLH999/H9OnT8d7770HQP7F7cmTJ5g1a5bJJ31D6NOnj87Z1BVjFgKAq6urOL4gFfFar2hId/FVXWdlAzhWAlKjgaQIrQ3ppphnTCkmU4rFFDF3M3cXVBK5+8SJE4iIiMDixYuxePFiAPJGBQsLC7Ru3Ro7duyAl5fy/Yi++6SkpGDbtm2YN2+eOJkhUVGUlZxh7Dj1OX5Usvz+2dNFuaOahx4N6cY+X1PD3M3cXVBJ3ndL3UahLN9TG7Uhffbs2bh9+zYGDBiAvXv3qvyja3PlyhV8++23AIDo6OiSCrHccbCxQmM/N/12EhvSK6uuy++ljqwUICMJsJN+MSpSLKVIW3yK8dHtrS3hYKP8a6QYj+15kn4N6aZeH4bk6WIHTxf1T3ztrC2L3Hvg1VdfxYIFCxASEoJq1aoBAGrUqIEaNWrg4cOHOvevUaMGAPlT6urVq4vL79y5g5o1a4qfHR0dxQSsEBkZiYCAAKVl9+7dU/p89+5dODs7q01WNWvWhEwmw/Xr18U4CrOwsFBKfIby6quvYsqUKYiIiMDw4cNV1teoUQMymQxZWVlio3Rhz58/x5kzZ3Dnzh3UrVsXABAaGors7GyDx6uwe/duDB06FMuWLcNbb70laZ8qVaogJiZG6zYymUzsMZGVlYVDhw5h3LhxAIDc3FwkJSUpNawD8l4WcXFxRTgLMjdFutYnPZX/dPVRv97ZS96QnhwJeDUy7LFLmCnFZEqxmCLmbuZuQ9CWuzt06IB///1XadnMmTNhY2ODr776Ch4eHirl6bvPtm3bkJGRgbFjxxrmhMhslZWcYew49Tl+dLL8HtvT2Q7ZuXnickVDeqyEhnRjn6+pYe5m7jYEKffd+tybl/V7aqNONvrll19i79696NWrl177paSkYPjw4fjxxx8lv4JAclFJGVh2+D6i9Gnk1dYj3cYRsM2/+Oo5vEuRYilF2uKLSZEn8YrONirrKucnqsjEDL0uvqZeH2XBtGnTUKNGDQwePBi3bt1SWhcZqfutCRcXFwwZMgRffPGF+JT8wIEDOHbsGN58801xu4YNG2Lfvn3IzJR/2Tty5AhOnTqlUt7hw4dx/PhxAEBsbCyWLFkiNsgWVqFCBQwZMgTz58/H06fyRrOMjAz8+OOP4jaVK1cW1xmStbU1Tp48ifPnzytNDKNQsWJFDB8+HPPnz8eDBy9eDbxy5Qr++OMPAPLeA1ZWVrhy5QoAIDMzEx988EGR4pEy6cnevXsxZMgQfPfdd3j77bfVbvPNN98offE6e/Ys9u/fr5Rz1q9fj27duomf9+/fL05ek5OTg/fffx+5ubnik3JLS0u0bdsWv/zyi/h/JCYmBps3b0b79u2LdL5kXop0rU/M/7130dKQDgDJEerXF+fYJcyUYjKlWMwJc3fRlMfc7e7ujtatWyv9cXNzg4eHB1q3bi32xiuYu6Xuo7Bhwwb069cPlSur6SBEpIeykjOMHafU42fl5CE+Td4Q6Oms/Hurz9Auxj5fc8HcXTTlMXdL3aZg7i7r99RGbUgvPDutVG+//TZ69OihNJ4SSROXloVtF8MQl6Y7CYlSlMdIt7KUoYqLHawsZUrL9W1IL1IspUhbfIoe6RUcbVXW+bo7AADSsnORmC79qaCp10dZ4OTkhFOnTqFBgwZo3rw5vLy80KxZM3h7e2PdunVYsWIFrKy0v4izYsUKWFhYwNfXF3Xr1sWgQYPwzTffKI2H9vHHHyMhIQH+/v6oV68e5s6dq3ZYqu7du2PChAmoV68eqlWrhgoVKqiMEV3QTz/9hNq1a6NmzZoIDAyEn58f8vJe9MYYN24cjhw5ggYNGqB169YIDQ3Vv5I0qFOnjsoYbYVja9euHRo2bIj69evDy8sLkyZNEnsDODo6YsmSJRg7diwaNmwIb29vWFpawt7eXu9YoqOjcf78ea1P1UeMGAErKyts3LhR6eZ5/fr14jY2NjZiXdaoUQO9evXCe++9hxkzZojbhIeH4+LFi+JnX19f9O/fH4GBgfDy8sKZM2dw5MgRpRvuDRs2QBAE+Pr6onHjxqhWrRr8/Pzw/fff632uZH6KdK0Xe6T7quZgAHBRNKRrv3ExxTxjiJjU1omRYiH9MXcXXXnM3VIUzt1S3blzB+fOncPEiRP13peoMEPnDEPlssKMndukHj86//7axtICbg7WSvWhz9Auxj5fc8HcXXTlMXdL2aZw7i7L99QyoSTeV9BTeHg4/Pz8cPz4cZ2Tvvz222/49ttvcenSJdjZ2aFJkybo1KkTli9frnGfzMxM8QkWACQlJcHPzw+JiYlFGhfJrORmA5/nvw4z8yHgVEl1m439gUcngAE/Ak1GlGp4xrLlwhPM+fsGutbzxPo3Wqisb/HlEUQnZ2L3tHZo6Guek5wkJSXB1dXVaL9nGRkZCA4ORl5eHqpWrQpXV+V/h+fPnyMyMlLjpJMhISGIj49HrVq1VMZuA+SvIz18+BCOjo7w9fXFw4cPYWFhIb4e1rVrVzRv3hyLFi3C48ePkZOTg9q1a0uKPSoqCs+ePUPt2rVVEmJqaioePXqE1NRUNGnSBI8fP4aTk5PKGGr379+HjY2N+KpdYcHBwcjOzhZfB5O6PiEhAY8fP4a3t7fa3lxxcXEIDQ2Fl5cXqlSpgosXLyIgIADu7u4A5DexheMNCQlBVlaWWD8xMTF4+PAhmjdvrvEL2IULF5S+7Cj4+vrC1/fFONK5ubl48OABrK2t4efnp/IANzw8HJGRkWjevLm4TBAE3Lt3D7a2tkqvGhYWGRmJ58+fw8fHR/LYclR0xr6mGE1uDvBFJUDIA96/+6LRvKATXwMnFgHN3gD6rSj9GKncMPbvGXM3c3dhivHOC06epi5369oHkP8bPXr0CC1btlSalI5KjrGvKVR2XHkSj4FrzsLHzR5nPnpFad3DqGR0/e4knO2scGOBaY+dbAzG/j1j7mbulrKNptxdFu+py1RD+oMHD9CmTRscO3YMjRrJxwCV0pC+YMECLFy4UGU5E7oEieHAskDAwgqYFw2o+9L592Tg+lag22fAy++VfoxGsOrYAyw5dB/Dmvvhm8Gq49G+tuYM/nuSgFWvN0XfRt5GiND4jJ3QjU2R0L/++mtjh0JULpjtNUUpD0cBFpaq21z+Fdj9HlCrBzDyz1IPkcoPs/09y8fcTWRY5n5NIekO3orE5E2X0cTPDTunvqy0Li41C80+PwwAuP9FL9hY8UFYQeb+e8bcTaWtTF2BDh48iNTUVLz22msICAhAQEAA7ty5g19//RUBAQHIzc1Vu9+cOXOQmJgo/gkLCyvlyE3Hg+fJ6Ln8JB48T5a2Q/Jz+U+nymIj+t3IJLT+6ijuRibJ1znmPzVK1W/SV71jKWXa4lNMdOLhpDo80d3IJNx+Jq+bJ3FpKuuLcjwiIiof9L7WK8ZHd/YGLCxVczAAOHrKf+rIw6aYZwwRU8E6ScrIxi9nHuPgLd3jc5ZELEREZB4MnTPU5ncDMHZuk3r8KHGiUfnQqQXrw83eGhb5I97E6xiyxdjnS0Tln/ZBi0zM6NGjVcZF79evH1q1aoW5c+fC0lJNLy0Atra2KhPNmCtHWyu0rlEBjrYS/+kV454rxkEHkJMrIDIpAzm5+S8ziA3pMSUbSynTFl9i/kQobvbWKutycgVkZMtfawmNkd6Qbur1QfpZuXKl2lfTiMi86X2tTwqX/3SVTzSqkoOBF3k4TXseNsU8Y4iYFHWSkZWLsT9fwH9PEgAAK0c0xauNpb8VZor1Q6WLuZuIpDJ0zlCb3w3A2LlN6vGj8ycHrZTfkF6wPiwsZHBzsEFcahYS0rJR2cWu2Mej8oO5m0qbyV9dPv74Y1y9ehX79u2Dq6urynhLNjY2cHV1FQfeJ+283eyxoJ/miQ1UpBToka6JY/646Xr2SNc7llKmLT7FJKKuahrSCwqNSzXI8ajsqVevnrFDICITpPe1XtEj3cVH8zYSH2ibYp4xZExBj+PERnQAWH7kPvo28oJMJm3iNlOsHypdzN1EJFVZyRnGjlPq8V/0SFffSO5qb4241CzxPry4x6Pyg7mbSptRh3b5+++/ERAQgPbt2wMARo4ciYCAAKxY8WKirKioKDx58sRYIZY7Gdm5CIlJRUa2+mFwVKTFyn86VNC8TREb0vWOpZRpi09yQ3qs9B7ppl4fRERUfHpf65PyG9JdtTWk5+fh7DQgS/MDXFPMM4aMae/1CADA2LbVYGNlgeDoVNzT49VuU6wfIiIyTWUlZxg7TqnHFxvSXdSPJOCSf9+tqyHd2OdLROWfURvSu3XrhgMHDuDw4cN48OAB/v33Xxw4cACjRo0St1m0aBH279+vsYzdu3dj3rx5pRFuufAwKgWdlpzAw6gUaTsoGtIdtcyeK/aEiy3ZWEqZtvikNqRHJmVITuKmXh9ERFR8el/rE/OHdnHx1byNjRNgmX/jqaVXuinmGUPGdOOpfFzZSR1qoE0NeQeAc8HSv5uYYv0QEZFpKis5w9hxSj1+VLJ8aBfFGOmFKe67E3SMkW7s8yWi8s+oDenOzs7ipKEF/3h4eIjbVKpUCX5+fhrL8PPzQ8WKWhp5SUm1io7YMrE1qlV0lLaD4obc4UUdq5RRsEe6IH1MN71jKWXa4hMb0h1UG9Ll+7WCo40lBAEIj5fWK93U64OIiIpP72t9oR7paveXyV7kYi3jpJtinjFETNUqOmJyhxoAgMa+rvB2s8dL/u4AgCsFhnopjViIiMg8GDpnlFQOMnZuk3r8qCTloV0K7+cmsUe6sc+XiMo/kx8jnQzLydYKbWpqGaalMDVDu6iUoWhkz80EMpMBO5eSiaWUaYsvQUuPdPl+FVGtoiNuRSQhJCYNAZ7OxToeERGVD3pf6wuNka5xf8cK8olJtfRIN8U8Y4iYnGytEBIrH9Kmc11PAEDTqm4AgCth8aUaCxERmQdD54ySykHGzm1Sjp+bJyA2Vd7TXDG0S+H9FPfdSToa0o19vkRU/hm1RzqVvujkTKz9NxjR+WOQ6aTo2VZgaJfIxAx8c+AuIhPlr1/BxgGwzn/iq8c46XrHUso0xZeRnYusnDwA6hvSFfVTOf9LQGictB7ppl4fRERUfHpd63MygdQo+d9d5UO7qORgBQnzlZhinjFETE/j03H8rryeOteRN6QHessnpw+LS0daVk6pxUJERObB0DlDY34vJmPnNinHT0jLQm6e/M12D0cbAKr14SqxR7qxz5eIyj82pJuZ6ORMrDmhR2JRjHvu8GK4nZiUTPxwIhgxKQXKEMdJ19wTrtixlDJN8SmSt6WFDE62qi91KOrHwzG/IT1W88RvUo5HVBRbt27Fvn37jB0GERWi17U+ST55JqzsxDfD1OZg4MXbYVrysCnmGUPE9N+TeGTlCrCzskCgt/ytOA9HG7jnD7/2KJp5mMoG5m6issPQOUNjfi8mY+c2KcdX9EZ3c7CGtaW8iapwfYhjpEtoSGcup9LE3G1+OLSLmanv7YJrn3aXtrEgFBjaRcc49I6VgIRQvXqk6xWLEWiKT9GQ7mJnBZlMpnF/b1f5+G6hsdJ6pJt6fZQl2dnZOH78OG7evIm8vDxUr14d7du3h6enp7FDK5bNmzfDw8MDPXv21Lnt1q1b4evri969e2vd7smTJzh06BCioqLg6emJbt26wd/fX2W73Nxc7N27F//99x9ee+01NGrUSGWb9PR07NixA6GhoahVqxYGDBgAKyv908zt27dx8uRJZGdno0WLFmjdurXKNhERETh06BBiYmJQu3Zt9O7dW+uxVq1ahZgY1QbGgIAApQmupZwnUXHoda1XjI/u4i0fB10b8YG25jxsinnGEDHdjkgEANTzcoGV5Ys+IgGeTrgYEo/g6BQ08HEtlVio6Ji7y3/uVggNDcUvv/yC+vXrY+jQoVrL1RWf1PxOZGhlJWcYO04px49NkTekV8jvja6OYm4yXT3SjX2+5oa5u/znbn3yOwCcPHkSx44dU1pmZ2eHjz76SO/4TBV7pJNmWSnycc8BpaFd1JLwSnl5kahlfPSCqrjaA5DeI50M48yZM6hVqxbeffddPHr0CNHR0di2bRtat26NBQsWGDu8Ytm8eTMOHDhgsPIWLFiAOnXq4MCBA0hJScHhw4dRr149lXo6ePAgAgIC8NNPP2HhwoW4fv26Slnx8fFo2bIlvv76a0RGRmLOnDno1KkTMjL0ez319ddfx7Bhw3D16lXcvXsXPXv2xNixY5W2+eGHH9ClSxecOXMGz549w4cffohGjRohOlr69SczMxMLFy7E1atX9TpPolJVaHx0rRR5WvEA3IzcfpYEAKjnpTwfSc1KTgCAYIk90sl4mLulK6u5WyEnJwcjRozA999/jz///FNruUWJT11+JyLTFpsqb3OokP9GtzpSh3ah0sPcLV1Zzd365HeFkydPYv369XrFUtawR7qZCY5Owcy/rmHJkMbiDaZGiptxKzvA2kH7torJSNPjSiYWI9AUX0KatIZ0bzd5j/Sw+HRk5eTBxkr7cytTr4+y4PHjx+jVqxdGjRqFVatWwcLiRZ1nZWXh3Llz4ucLFy7gwoULGDFiBP7++2+xQdbOzg6RkZHYtWsX4uLi0KhRI/Tu3Vvp7YMff/wRjRo1Qtu2bcVl27dvh42NDfr16wcA2LhxI7y9vVG5cmWcOnUKubm5GDRoELy9vbWeQ0pKCv755x+EhYWhYcOG6NWrF2QyGXbu3In79+8jJiZGTLjTpk3Dvn37xOMcPnwYFSpUwBtvvKGzrtauXYvPP/8chw4dQpcuXcTlp0+fRqdOneDl5YXJkycDACpXrox///0XVatW1fgWxldffYXU1FRcv34dTk5OmDt3LurUqYMffvgBM2bM0BmPwqRJk9CpUyfx8/jx4/HSSy9h1KhR6Nq1KwCgVatWuHnzJiwtLQEACxcuhJ+fHzZu3IgPPvhAbbnTpk1T+rxt2zYAwIQJE8RlUs6TqLj0utYnhct/5o+PrpWEB9qmmGcMEdPtiGQAQH1v5V7nfh7y7y5P49NLLRbSH3O3eeRuhXnz5qFGjRrw8PCALlLik5LfiUpCWckZxo5TyvHj8od2qeCkpUe6xIZ0Y5+vuWDuNo/crU9+L8jX17fMP0zRhj3SzYytlQVqezrDVkejLoAC46NXVHql3M3BGsOa+8HNoUBDsr2b/Gd6fMnEYgSa4hOHdtHQkK6on5qVnOBsa4XcPAGPY3T3hjP1+igLvvvuO9jb2+Pbb79VSuYAYGNjg44dO4qfL1y4gE8++QStW7fGxYsXkZcnn0D23LlzqFWrFnbs2IHIyEhMnjwZPXv2FNcD8oR+9uxZpfK3b9+Of/75R/y8ceNGTJs2DQMHDsT9+/exY8cOBAYG4tatWxrjv3nzJurUqYPvvvsOz58/x+rVqzF69Git56w4zrBhwxAREQFBEHRXFIAvvvgCQ4cOVUrmANCuXTuMHj0an3/+uVhWkyZNULVqVa3l/e9//8PQoUPh5CT/wurp6YlXX30V27dvF7e5fv06FixYgLQ0zcMdFUzUANC0aVPY2Njg8ePH4rJmzZqJjegAYGFhAZlMBnt7e+0nXcCGDRvQvn171K1bV1wm5TyJikuva72aHulqczAgqSHdFPNMcWN6mpCO6JRMyAC0qq7cMOfjJr8mRCRIa0g3xfoxB8zd5pG7AeDIkSP4888/sXr1ap3nKjW+wtTld6KSYOicoTG/F5Oxc5uU48ekqDakF64PRUN6ko6GdGOfr7lg7jaP3C01vxcWGxuLpUuXYvXq1bh48aLWbcsi9kg3M77uDvhmsMQxfxU90h0r6C7D3j1/H+kN6XrFYgSa4tM1tEvB/WpVdsJ/TxJw/3ky6lRxVru9ruOVR1FJGYhLy0LdKvJJ4R48T4ajrRW83eyRkZ2Lh1EpqFbREU62VohOzkR0cibq508gp83JkyfRqlUrODo6SoojPj4ef/31l1JSe/vttzFkyBD8/PPPAIBZs2ahTp062LRpk6QnzgVFRETg4cOH8PT0hCAI6N+/P2bPno09e/ao3X706NFo1aoVtm/fLn4hUbzONWDAAKxfvx4BAQEqT3eTk5Nx7949MZnq8vjxY4SHh6N7d/XjB3br1g2//vorgoODERAQoLO8rKwsPH78GLVq1VJaXqtWLaWJV65fv46FCxdi2rRpcHDQ8ZZLvr///htZWVkqY7FFRUVhzZo1SE1NxbFjxzBo0CCMHz9eUpmhoaE4evQofv31V0nbExmSXtd6xRjpri8a0jXur3gzTEseNsU8U9yYLofKz7ehrytqVVbOs96KhvREaQ3pplg/poS5m7m7OLk7KioKY8eOxZYtW+DqqnvOAqnxFcT8TqXJ0DmjpHKQsXOblOPH5k8o6lFgaJfC+yka1BPSsiEIgsbeusY+X1PD3M3cXdz7bn23AQBra2s8efIECQkJmDlzJkaMGCH+O5cHfExnZrJz8xCVlIHs3DzdG6flT9zjoNyQnpGdi/vPk5GRnftioUN+LzA9eqTrFYsRaIpPV0N6wfqpnX9Tf/95cpGPVx79cf4Jxv784snkO1uu4KeTjwAAkYkZ6LvyNG6EyyeP+/u/cIxYFySp3Li4OJVXuP755x8sWLBA/FPwCXeFChWUkvmzZ89w9epVTJo0SVzm6+uLPn36YP/+/XqfZ58+fcSJVmQyGd58800cOnRIKQaFx48f4+rVq3j//feVnupLmeyyV69ekpM5IK8nAPDy8lK7XlGH8fHSfp9TU+VvXBS+MXZzc0NKSor4uVGjRvj0008lJ/MHDx5g8uTJmDp1Kho2bKh2m+zsbCQnJyMiIkKMQ5eff/4ZLi4uGDx4sKTtiQxJr2u92CP9xdAuanMw8OKBtpY8bIp5prgxXQtLAABUq+CgUieKIdaeJWQgL093ryFTrB9TwtzN3C2FutwtCAJGjx6NsWPHon379gaNryDmdypNhs4ZGvN7MRk7t0k5vmJol4oFeqQXrg/FvXdOnoC0LM11ZOzzNTXM3czdUki575ayDQAMHToUN2/exPfff4/ffvsNJ0+exKZNm7B161ZJsZQFbEg3M/cik9Hyq6O4F6m7YRepioZ05YlGH0aloPuyk3gYVeBLrHgDL32MdL1iMQJN8SleJ9P02l3B+tGnId3U68OQRraqil/fbCF+XjmiKSZ1qAEAqOJqhz3vtENDX3lyeK2ZL7ZM1P7EU8HDwwMRERFq1ymeyhZMphUrKv/fVuxbONF5e3vj6dOnkmIoqEqVKkqfvby8kJ2drXZSzKioKADyLxD6KnweuijGJX327Jna9Yp6qFy5sqTyFD0REhMTlZYnJCQofdFo1KgRFixYICmhh4SEoGvXrujcuTO+//57lfWenp5YsGABli9fjqtXr+L+/fuSxmHLy8vDr7/+ilGjRuk1FAyRoeh1rRfHSH/RI11tDgZe5OGsZCBX/WvPpphnihuT4ubvn2vPVOqksosdLGRAVm4eYvInMivJWMo75m7mbl005e5Dhw7hxIkTEARBbGC5f/8+bt++jQULFiAhIaHI8Skwv1NpM3TO0Jjfi8nYuU3K8WPzh3bxcHzRkF64PuytLWFtKe+Frm2cdGOfr6lh7mbu1kXXfbfUbRRq166t9ICiRYsWaNasGY4fP64zlrKCDelmpmoFB/w8tjmqVpDwZEoxtEuhHulq2evfI12vWIxAU3y6eqQXpGhIf/Bc9xciU68PQ/J0sRNfLwOAWpWdxVfw7awt0cDHFU628pGnKjnbSnq9DADat2+PCxcuKI0F1q9fPyxYsACvvfaazv0VT4QLJ7pnz57Bx+dFQ5a1tTVycnKUtklOVv2yFhkZqVKOtbW12gSseIIeHh6uMT5DTYBZvXp1+Pr64tChQ2rXHz58WNxGChsbG1SvXh0PHjxQWv7gwQPUqVNH7/hCQ0PRqVMntGjRAlu2bFEaD10dBwcHtGrVCjdu3NBZ9uHDh/HkyRNMnDhR77iIDEHytT4r7UVOLTBGukZ2BXqmpCcU79ilqDgx5eYJuBmRqHG9taUFKrvIe6VHJGSUaCzmgLmbuVsbbbnb398fc+bMgbW19LGf9Y2P+Z1KW1nJGcaOU8rxFQ+7KxQY2qUwmUwmacJRY5+vqWHuZu7WRsp9t7735uoIgqB1vPayhg3pZsbFzhqv1K0MFzsJX2QVQ7s4SmlIV4yRLr1Hul6xGIGm+BLS5E/MpTWky58KhsSm6nxNz9TroyyYMWMGUlNTMWPGDGRlZem9v5eXF5o0aYL169eLyyIiIrB371706tVLXFatWjVcvXpV/BwVFaUyCQoA7Nu3T3ziLQgCfv75Z3Tr1k1t8qlevToaN26M5cuXKz29V4zVBshf4VL3xaEo5s2bhz///BPHjh1TWn769Gls2rQJH3/8scrEMdoMGjQIf/75p/hKWVRUFHbv3o1BgwaJ20iZ9OTJkyfo1KkTmjdvjq1bt8LKSnUqj8ITliQnJ+P06dMIDAwUl504cQJff/21yr4bNmxAy5YtJb26R1QSJF/rFeOj2zgpN5JrYmH5YjsND7VNMc8UJ6bg6BSkZeXCTsuEYl6u8ob0SAnjpJti/ZgD5m7pymrurlu3rtLr/gsWLEDt2rVRv359LFiwAG5ubgBUc7eU+BSY36m0lZWcYew4pRxf3dAu6jjnl5GckaNxG2Ofr7lg7paurOZuqdsUzt2F79UvXryIK1euoHPnzpLP0dRxslEzE5uSib03nqFPQy9UcNL8xBfAi0ZxKT3SC46RLgiAhCd4esViBJriS8pP3FIa0is526Kikw1iUrJw+1kSmlV11/t4JF3NmjWxd+9ejBo1CvXr10f79u3h5eWFyMhI7Nu3D71799b5BHXNmjXo3r07IiIiULt2bfzvf//Dyy+/rDSL9zvvvIPu3btj+PDh8PLywoEDB+DurvpvW6VKFbRt2xavvvoqrl27hv/++w+nTp3SeOyNGzeiR48eaN26Ndq3b4979+7Bzc0Nv//+OwDglVdewfvvvw93d3c4OTlh2rRpRawpYPLkyYiIiECfPn3Qt29f1K5dG8HBwdizZw8WLFiACRMmiNs+evQIGzduFD///fffePjwIZo1a4Z+/foBAD7++GPs378fbdu2RefOnbFv3z4EBgbi7bffFveTMulJt27dEBMTg7p16+KLL74Ql3fq1EmcNfyzzz5Deno6GjdujPT0dOzevRtVqlTBJ598Im5/4sQJLF++HB999JG4LDY2Frt27cLq1avVHlvKeRIVl+RrvaIh3cVHUk4FIH+onZGocZg1U8wzxYnpev6wLjU9nXArIkntNhXzy4xO0X2TZ4r1Yw6Yu6Ury7lbisK5W0p8gO78TlQSykrOMHacuo6fnZuHhDR5D/OCQ7uo42wnb75KztDcI93Y52sumLulK8u5W8o2hXP3F198geTkZDRt2hQxMTH466+/MGLECIwZM6bIdWhq2JBuZp4lZuCLPXfQrKq77sSieDXcXvVCZWNZ6ImZYpu8bCArBbB1NmwsRqApvpT8hnQnW80N6Yr6kclkaOTrhmN3o3A9LEFrQ7qp10dZ0aFDBwQHB+Po0aO4desW8vLy0LhxY8ydOxc1a9YUt2vZsqXahNimTRvcv38fu3btQnx8PH788Uf07t1b6fWuDh064MqVKzh06BAcHR0xffp0XLx4ETY2yl/+Bg4ciJEjR+LUqVOoXr06Nm3apPSqWmGNGjXCvXv3sGvXLkRERKBbt27o2bOnuP7NN99E1apVceXKFXGikTFjxqhM9AIAw4cPh4uL9lfzFi5ciPHjx+PgwYOIjIzEpUuX0KpVK8ydO1fjPp9++qna5e7u7rh48SL+/vtvPHnyBIsWLcKAAQOUnlpLmfRk9OjRKq/vFbZ7926cO3cO58+fh7W1NYYOHapyo96pUyfY2dkpLYuMjMScOXMwfPhwreUDms+TqLgkX+sVE426ql4zVHKwgr07EB+isUe6KeaZ4sR0PTwBAFDL00njEGoVneVlxiTrHiPdFOvHXDB3y5Xn3F3Y66+/rtKzrXDulhIfoF9+N3cxMTEICQlBnTp14Oys+35NISUlBXfv3kXlypXh5+dXghGWHSWRMzTm92Iwdm7Tdfz4/N7oFjLAzUH5ely4Pl40pGu+3hj7fM0Jc7dcec7dUrYpnLt37dol3qvXqVMH06dPR9OmTbWWUdbIBEEQjB1EaUtKSoKrqysSExN1/oc3a2vaAFG3gdE7gZo6XsMQBOCLykBuJjD9BuBWtVRCNIaXvz6Gpwnp2PF2WzTV0jCusPzIfSw/8gADm/pg2bAmJR+giTD337OuXbuiefPmaocXMVVXr15F69at8fHHHyv17iYyBWZ3Tfl3MXD8S6DpKKC/xF6Wm14Dgo8CA34AmrxesvGZgAGrz+BqWAJWjGiKfo1Vb2wA4LvD97Hi6AOMbFUVXw5sWMoRlj1m93tWCHM3lVfXrl3Dt99+i4MHDyImJgbHjx/X622BgQMHYteuXXj33XexfPlyyfuZ+zWFdLvzLAm9vj+Fik42uDSvm9Zt39p0GQduReLzAQ0wurV/KUVo+sz994y5m0obx0gnzcQe6W66t5XJijROelmUkqnokS7thY7Gfm4AgGv5PeeITFWTJk2wfft2yGQy8ck7ERlJYv4ESC7SJh8C8CIP6zHxd1mVnZuH28/kw7k08tE8hnyl/PFWY1J090gnKouYu0mKixcvokePHggKCtJ731WrViE5ORkNG/JhJBlebP7Qa7qGdQEAJwlDuxCVBczdZRsb0s3M45hUjFwfhMcxEn5ZMxLkP+3clBY/jEpGnxWn8DCq0OQLBcdJN3QsRqAuPkEQkKpoSLdT35BeuH4a+7oBAB5FpyJJS9I39fog/YwZMwZdu3Y1dhh669u3L+bPnw9HR0djh0JULkm+1iepH9pFYw4GdDakm2KeKWpM9yKTkZWTBxc7K2Tn5mmsE8UY6TESxkg3xfqh0sXcTeXVhAkTMHr0aNja6jfUxbVr17Bo0SL89ttvSsMtkOFzhtb8XgzGzm26jh+bKn/QXcFR+f+muvqQMrSLsc+XSh9zN5U2jpFuZqwsZPBwtIWVhY4vQjlZQHb+DL+FeqRnZOfhVkQSMrLzlPfRsyec5FiMRF18mTl5yMmTj4bkqKFHeuH68XC0ga+7PcLj03EjPBEvB1SUfDwqu8rTZBpEZDiSr/WJBSYbLUBjDgZ0vhlminmmqDHdeCqfaLSRrxsyczTXiThGuoQe6aZYP1S6mLuJXkhNTcXw4cPx/fffax1r2FwZOmdoze/FYOzcpuv4Yo90J+Ue6erqw9lOPkeZth7pxj5fKn3M3VTa2JBuZvw8HLByhISB/hW90SEDbDW/Mq1EbEiXNrSL5FiMRF18it7oAOBoI/3Xp4mfG8Lj03E1LEFjQ7qp1wcRERWf5Gu92CPdcEO7mGKeKWpM18PlDekNfbV/R6nkJH2yUVOsHyIiY5k2bRratGmDwYMHS94nMzMTmZkvrrdJSUklEZpJKCs5w9hx6jq+okd6RQlDu7hI6JFu7PMlovKPQ7uYmdw8AckZ2cjN0zHHrGJ8dDsXwELifxM9e6RLjsVI1MWXmpkLALC3toSlHk+5FZOSXnmiuW5MvT6IiKj4JF3rM5KAzPzGBxf1k2iqpSMPm2KeKWpM1/PnHWmsoyFd0SM9NSsX6Vm5JRILEVF5s2/fPuzYsQNjxozBpUuXcOnSJaSnpyMqKgqXLl3SuN+iRYvg6uoq/vHz8yvFqEtXWckZxo5T1/HjUuU90is46R52SMrQLsY+XyIq/9iQbmbuPEtCwwWHcOeZjt4BipvwQuOja6UYIz1NWkO65FiMRF18yZny18g0DeuiSdOqbgCAK08SIAjqk7qp1wcRERWfpGt9UoT8p60rYOssvXAdc5WYYp4pSkyZObm4FykfM7WBlolGAcDRxhJ21vKvu7qGdzHF+iEiMoaUlBQEBARg5syZeOutt/DWW28hLCwMR48exVtvvYXcXPUPJufMmYPExETxT1hYWClHXnrKSs4wdpy6jh+jx2SjiqFdUrQ0pBv7fImo/OPQLmbGz90Bq19vBj93B+0bKoZ2KTQ+utYy9OyRLjkWI1EXn6JHupOtpV77BXq7wMbSArGpWXgSlwb/CqoTSph6fRARUfFJutYnhct/uqqOSat1f11Du5hgnilKTA+epyAnT4CrvTV83OzhbGutsQyZTIYKjrZ4mpCOmJRM+HloPo4p1g8RUWkJDQ1FSkoKAgMDMXToUAwdOlRpfZMmTdCpUycsX75cYxm2trZ6T2haVhk6Z5RUDjJ2btN1/Nj8h9wVC42Rrm4/p/zObElaxkg39vkSUfnHhnQz4+pgjT6NvHRvKA7t4ia9DHtFTzhpY6RLjsVI1MWnGCPdyU7zr466/WytLBHo44IrTxJw5UmC2oZ0U68PIiIqPknXeg0TjercX2xITyj6sUtZUWK6nd/LrL6XC2Qymc4y3B2t8TQhHQlpmm+8ixoLEVFZEBsbi8ePHyMqKgoAcO/ePTg5OcHb2xve3vIhxD7//HMEBQXh5s2bxgy1zDB0ziipHGTs3Kbr+JqGdlG3n5ShXYx9vkRU/nFoFzMTn5qFvy6FIT4/YWmkpUd6dHIm1p96hOjCE3cpttVwA1/kWIxEXXwp+Q3p2iYa1VQ/Tf20j5Nu6vVBZcv9+/cREhJi7DCIqBBJ13pxolHVhnSNORh40ZCemQjkqt5kmmKeKUpMite163u7ANBRJwDcHeS93BLStR/DFOuHzAtzN5WUc+fO4a233sInn3yCl156CevWrcNbb72FPXv2iNtUq1YNDRo00FhGYGAgqlatWhrhlgmGzhm6cllRGTu36Tp+rIahXdTVh2Jol2QtPdKNfb5kfpi7zQ8b0s3M04R0zNp+HU8T0rVvqKVH+vOkDHyx9w6eJ2Uor7DLH6c0I9GwsRiJuvjEHulaxkjXVD/iOOlhCZKPR0WXmZmJ27dv4+bNm0hNTTV2OAZx7949hIaGStr2ww8/xJIlSyRtGx4ejv/++w/h4eFat4uIiMC1a9e01md0dDSuXLmChIQEScfWJCQkBA8ePEB2tvYepBkZGQgKCsKDBw8MVm5ERASuXr0q9toiMiRJ13qxR7qvyiqNORhQztmKB+L6HruUFSWm2xEveqQDOuoEgKu9/MZbV490U6wfc8PczdytjpRzIO369u0rThpa8M+kSZPEbebNm4etW7dqLOOPP/7A+++/XxrhlgmGzhm6cllRGTu3aTt+Zk4ukvPvrys6KvdIV1cfLvk90lMyczTOO2bs8zVHzN3lP3dHRkbi5s2bSElJkVxuQkICgoKCEBMTU6z4TBEb0s1MoLcLHn7ZC4H5vbg00tIjXSPFDbzEhnTJsRiJuvjEHul6TjYKvGhIvx2RhIxs1Ql6TL0+yor4+HiMHz8e7u7u6NevH0aOHAlvb2+0adMG+/btM3Z4xfLBBx9g2bJlBivv5MmTaNKkCRo1aoQJEyagcePGaNSoEU6ePKm03YMHD9CqVSs0atRIrM8ffvhBaRtBEDB16lT4+vpi+PDhqFKlCj799FO9Y1q3bh2qV6+OTp06oWfPnvDx8cHvv/+ucfsZM2agbdu2mDNnTrHLDQ8PR5s2bVC/fn2MHTsWNWrUQLdu3RAfL23eByIpJF3rtYyRrpWlFWDjJP+7mlxsinlG35gEQRCHdqnnJW0fNwd5Q3q8joZ0U6wfc8HcLZ055W4p50BkLGUlZxg7Tm3HVwzrYmUhg4u97vtrRY/0PAFIy1I/4a2xz9ecMHdLV1Zz9+nTp9GqVSu0aNECw4YNg6enJ959913k5eVpLPfBgwcYP3486tWrhzZt2uDAgQN6x2bq2JBuZmQyGawsLSCTybRvqKVHukZ69kiXHIuRqIuvOA3pPm728HS2RU6egBtPVevI1OujLEhNTUWHDh1w69Yt3LhxAw8fPsS1a9eQmJiIFStW4NatW+K2z58/x7Vr1wAAYWFhuHDhAnJzX3whCw0NxZUrV9Q+db1x4waePXumtOzhw4d49OiR+PnOnTsICwuDIAh49OiR5F5XgHwcy2vXriEtLU1cFhwcjISEBERGRiIoKAhBQUHIyMgQj5Obm4vbt2/jzp07ko5x9uxZdO3aFb1798bz58/x33//4fnz53jttdfQtWtXnD17Vtx2xIgR8PDwQHh4OG7evIlDhw5hxowZOHPmjLjN2rVr8fvvv+O///7DvXv3cOTIEXz11VfYuXOn5PMG5P8W//77L0JCQhAcHIzPPvsMY8eOVTte599//41z586hS5cuBil35syZSE9Px9OnT3H16lWEhITg3r17+Oyzz/Q6ByJtJF3r4/N7wLgV4RV6LbnYFPOMvjGFx6cjOSMH1pYyBHg6SdpHMbRLYpr217xNsX7MAXM3c7cmUs6ByFjKSs4wdpzajl9wWBcp8dlZW8DKQr6dpnHSjX2+5oK52zxyd3BwMH7++WeEhYXh1q1bOHfuHNauXYtNmzZpLPfGjRto06YNgoOD9YqnTBHMUGJiogBASExMNHYopS4kJkUY/+sFISQmRfuGm4cLwqcugnBxg8qqG+EJgv/sPcKN8ATlFamx8n0+dRGEnCzDxWIk6uL7Ys8twX/2HuGLPbc07qexfgRBmLTxouA/e4+w9t+Hko5Xlhnj92zRokWCra2t8OjRI53brly5UvD19RX69esn+Pn5Ca1atRKSk5OFyMhI4eWXXxacnZ2F2rVrCw4ODsKSJUuU9m3cuLHw7bffKi0bNmyYMH78ePFzly5dhJ49ewo1atQQ6tSpIzg6OgodOnQQEhJU/18oJCUlCUOHDhVsbW2FevXqCRUqVBBWrVolCIIgfPbZZ4Kbm5tQpUoVoVWrVkKrVq2EkJAQoUuXLkKvXr2E2rVrC4GBgcKUKVMEQRCE/v37C1OnTtV4rFatWgktWrRQu65du3biuvj4eAGA8Pfffytt0759e2HMmDHi55deekl48803lbbp0aOH0LdvX/FzdHS0cO7cOSE7O1tjXIXl5uYKNjY2wk8//aS0PCQkRPDy8hJu3rwp9OnTRxg0aJDkMjWV26lTJ2Hs2LFK2/Xs2VMYMWKEXmVTySgvuVvntT43RxAWeshzaUKYymptOUYQBEFY3Vq+78Nj+h/bCPSN6cDNZ4L/7D1Cr+UnxWW66mTdyWDBf/Ye4d0t/xk0lvKIuZu521Ryt9RzINNWXnK3OobOGTrzexEZO7dpO/7xu88F/9l7hJ4FcrqCpvpovPCg4D97j3A/Mknv45VXzN3M3SWZuwvz8fERPv/8c0llAhA2bdokOYaygj3SST0tPdKd7azQtZ6nOGu2SNELDpDcK72sScmUPzl1srXWuI3G+gHQtKpiwtGEEomvrIhKysDdyCTx84PnyYjIH8cuIzsXN58mir3/o5MzxfFwddm9ezfat2+P6tWrS9o+PDwcLVu2xJMnTxAUFAQnJye88847yMnJwdOnT3Hv3j389ddfmDVrFs6dO6fnWQKHDh3C+vXrcffuXYSGhiI6Olrra1eTJk3CzZs38eDBA9y+fRvh4eGwtLQEAMyfPx8vv/wyhg0bJj4Z9/f3BwAcO3YMmzdvxs2bN7FmzRqdcUVHR+P8+fMYM2aM2vVvvPEGLl68iMjISDg6OsLa2hoRERHiekEQ8Pz5c1y+fBkAkJubi+vXr6NFixZK5bRq1QpXrlwRPx84cABt2rTRaxy3mzdvIisrS+nfNCcnByNGjMCcOXMQGBgouSxd5X788cfYt28ffvjhB/z7779YvHgxrly5gpkzZxbpGERFkvQUyMsBLKwBZy+V1dpyDAC93w4ra8Tx0Qu8sq2rTtzye6TrGtqFtGPuVo+5W5UhcreUcyAqT3Tm93JIMbRLhUITjQKa60PxOUlDj3RSxtytHnO3KnW5GwDS09MRFBSEo0ePYsqUKbC1tcW4ceMkl1semc9VmgAA/hUcsf6NFro31DJGusYyLCwBWxcgM0l+A+9Y0TCxGIm6+FLFoV0s9dpPoYmfGwDgerhqA4ep14ch/XH+CbZdDEPQx/LXet/ZcgWta1TAgn6BiEzMQN+Vp7FlYmu0qVkBf/8XjjUngnHt0+46yw0LC0PPnj2Vlj169Ehp0sjWrVuLf3d0dMTs2bPFz0lJSfjrr7+wd+9eODs7AwB69+6Nzp07Y8OGDWjTpo1e59mtWzd07twZAFChQgXMnDkT06dPx/Lly1W2jY2NxbZt2/C///0Pfn5+AAA7Ozu89dZbOo/Tv39/vPTSS5LjCgsLAwDUqFFD7XpF8gwPD0eVKlXw1ltvYcGCBbC1tUX16tXxxx9/4Pnz57C3twcAJCcnIzs7Gx4eHkrlVKhQAbGxseLnSpUqoVWrVrC21vwgqqD09HSMHz8ebdu2xSuvvCIu/+STT+Dq6op33nlH8jlLKbdt27bo378/PvroI/j7+yMkJARTpkxBw4YNi3QcInV0XuvFYV385HlV3/21NKSbYp7RN6Y7asZH11WGe/4Y6bqGdjHF+jElzN3LVbZl7lZlqNxtbW2t8xyIjMnQOaOkcpCxc5u24yuGdqngpNqQrmk/Z1trAOlIzlD/cNzY52tqmLuXq2zL3K1KU+4GgGfPnmH69OlISEhAeHg4PvvsM3h7e0urgHKKDelmRhAE5OYJsLSQaR83TEuP9OzcPCSlZ8PF3hrWloVearBzzW9ITzBcLEaiLj5FQ7qTljHStdVP3SryJPE0IR0pmTlK5Zh6fRjSyFZV0athFfHzyhFNxXHnq7jaYc877VCtoiMA4LVmvmhfq5Kkcu3s7JCUpPwUfdu2bdi1axdiYmIQHByM7OxsWFnJj+Xt7S3+HYA41lq9evWUyggMDMSNGzf0PEugTp06Sp/r1q2L5ORkxMTEoGJF5QdNwcHBEAQBjRs31vs4iifkUtnZ2QGASl0pKJa7uMgbqpYtW4aGDRti7969SExMRM+ePTFhwgT8/fffACAm6MzMTKVy0tPTlZJ3jx490KNHD0kxZmVlYfDgwUhISMDJkydhYSH/Xfrvv/+wbNkybNmyBUFBQQDkM4Lb2NggKCgITZs2ha2trd7lAsDrr7+O6OhohIWFwcXFBdHR0ejQoQOSk5Ml9TggkkLntT5B0ZCu/vdaaw4G5A+0AXku1vfYRqBvTHcjkwEA9fLzKaC7TqRONmqK9WNKmLuZu3UxdO7WdQ5ExmTonKEzvxeRsXObtuPHpr4YI70wTfWh6JGuaYx0Y5+vqWHuZu7WRdv9MSB/CKDI3VevXkWnTp0gk8kwY8YMSeWXRxzaxczcikhCwNz9uKXrlR0tPdLvRSbjpS+O4F7+zawSPV4plxyLkaiLT8pko9rqx83BBpWc5TcKwVHKk2mYen0YkqeLHepWedGbsFZlZ3i7yZ+y2llbooGPq/iQoZKzrdIr/No0btwY169fV1o2Z84cBAUFYcGCBSrbF04SiqfhhSc6SUlJEdcB8klsBEFQ2iY7W7WBJjU1VeWzTCaDk5PqBHkODg4ANCdZbQqfhy4BAQFwdHRUev2roCtXrsDZ2Vn8omBpaYmJEydix44dOHbsGD788ENcuXJFfDXb0dERFSpUUHoNDQAiIiL0/rIBvEjm9+7dw/Hjx+Hl9WJ4i5SUFDRu3Bhff/01pk+fjunTp+PWrVu4du0apk+fjri4uCKVm5ubi3379mHcuHHiF5lKlSphxIgRek/cQqSNzmt9fIj8p3s1tau15mBAax42xTyjT0wZ2bkIi5dPBlWr8otrsq46cbWX36An6OiRbor1Y0qYu5m7tSmJ3K3rHIiMydA5Q2d+LyJj5zZtx49LlTcGqhvaRVN9ONvJGwsV9+X6HM8cMXczd2ujLXer06RJE/Tq1Qt79+7V+1jlCRvSzYyPmz2+HdwIPm5aXonMyQKy82ctVtMjXSvFDbyiR3txYzEidfGlSOiRrktAJfnF/EGhhnRTr4+y4K233sKdO3e0ziKtTbVq1VCpUiUcOnRIXJaTk4OjR4+iefPm4rLKlSsjPDxcaRt1yfH48ePIy8sTPx86dAiBgYHik+mC6tatC09PT5VG24yMDPHv9vb2ar846MvGxgZvvvkmfvzxR0RGRiqti4mJwerVq/Hmm2+KvcMKxgDIZ0b/999/lcZ669KlC3bv3i1+zs3Nxd69e9GlSxelsoOCgpCTo3lMw+zsbAwZMgS3b9/GiRMn4Ovrq7S+Q4cO4lh1ij8vv/wyOnfujKCgIDH5h4eH49KlS5LLtbS0hJubG54+faq0PCIiQqUXA8klJyfj7t27SrPcS9knJCTEIP+Pyyqd13rF0C7u+n8ZBqC1Id0U84w+MT2KToUgAK721qio5jVwTRRDuyRl5CA3T9C4nSnWjzlg7pbGHHO3lHMgMpaykjOMHae248elyq+NHo6a3yYtzEXska7+umrs8zUXzN3SlOXcDUDtfd7jx49RoUIF8XPh3G0OTGJol+TkZDx9+hRVq1YVnw7pEh0dDSsrK7i7u5dwdOWLu6MNhjT3075RwWFZCk4gKoWi4V1Cj3RJsRiRuvhSJfRI16VWZSecexSLh4Ua0k29PsqCLl264KuvvsK4ceNw6tQpdOnSBV5eXoiMjMSvv/4KV1dXra/4WVpa4osvvsCMGTNgbW2N2rVr44cffkB2djbee+89cbsBAwZg9uzZaNq0Kby8vPDTTz/h2bNnKuVFRERg2LBhmDBhAq5evYrvv/8eW7ZsUXtsKysrLF26FOPHj0d2djY6deqEe/fu4eDBg2KibNCgAbZs2YIjR47AyckJTZo0KXJdLVq0CP/99x/atm2LuXPnonbt2ggODsZXX32FwMBALFq0SNx2/fr1ePDgAfr27YuIiAh88sknGDp0KIYMGSJuM3/+fLRq1Qpvv/02Xn31VWzatAkJCQlKE3UeOHAAo0ePRnR0tMbG6ddffx3Hjh3DL7/8gvDwcPGLk6+vr9rkrsn69euxfPlycYIVKeVOmzYNS5Ysgbu7Oxo1aoSgoCD8/PPPWLlypeTjmoO7d+9i+fLl2L59O2JjY3H8+HF06tRJ6z45OTmYOnWq+HuYk5ODlStXYuTIkaUTtAnRea3XMbSLTloa0k0xz+gT04Moea+0Wp5Oer2u7Wr/4lXXxPRsta+Q6xsLGQ5zt3TmlrulnAORsZSVnGHsOLUdPz5NMbSLtHGcAcBJx9Auxj5fc8HcLV1Zzt0dO3bEsGHD0LhxY6Snp+OPP/7A9evXle6PC+fupKQk3L59W1wfHByMoKAgeHp6ahwrvqwxakN6UW7G165diyVLliAxMRFZWVnw8fHBDz/8gA4dOpRO0GVcYlo2Tj+MQbuAinB10JCwFL3JbV3VTnSmlR5Du0iKxYjUxZeSmQugeD3Sa3nKe6Q/jFJ+Tc3U66OsmDNnDnr27InffvsN69evR15eHqpWrYpBgwZh586d4mzcVapUUZsQJ02aBA8PD/z+++/43//+h0aNGmHVqlVwdX3xUGny5MnIzc3Ftm3b4OjoiNdeew3169dXeRA4bdo0eHl5YfXq1cjJycHmzZsxaNAgjbGPGjUKfn5++Omnn/DVV1+hcePG2LBhg7h+xowZSE5OxqJFi5Camopt27ahfv36qFq1qkpZderUgaenp8ZjOTo64vjx4/j999+xZ88e/Pzzz7hy5Qrat2+P/fv3K722NnXqVKxduxZLly6Fra0tPvvsM4wePVqpvAYNGuDMmTNYunQpvv76a9SqVQvnzp1TuoGWMulJVFQUAgMDsWTJEqXlEyZMwIQJE9TuU7duXdjYKDeM+fr6Ks1mLqXcTz75BHXr1sWOHTuwc+dO+Pj4YMeOHejTp4/GeM3R0aNH0bhxY7z33nuoX7++pH2+/vpr7NixAzdu3EDt2rXx888/44033kDDhg3RqFGjEo7YtOi81pdgj3RTzDP6xKQYEi3AU/U1XW2sLC3gbGuF5MwcJKRlaWxIN8X6MRfM3XLM3cq5W8o5EBlLWckZxo5T2/Hj88dId3eQ/paZrjHSjX2+5oS5W6485+4DBw5gxYoVWLp0KaytrREYGIi7d+8q1UPh3H3v3j1Mnz4dANCqVSvs378f+/fvR//+/TFnzhyN8ZQpghGtWrVKWLNmjXD79m0BgHD8+HGt2+fk5AiTJ08WHj58KAiCIGRnZwvvvfee4OrqKkRHR0s+bmJiogBASExMLE74ZdKN8ATBf/Ye4UZ4guaNnpwXhE9dBGFZA/3L2Ddbvu/hBYaJxYjUxVd33n7Bf/YeITQmVa/9CjrzMFrwn71H6Lj4mF77lTXm/HsmCILQpUsXYfbs2cYOQy+//fabYGFhIRw4cMDYoVAZEBYWJil3C4Ig+Pr6CnPmzFFaVqdOHWHatGmSj1derilar/VZafIc+qmLIKTE6L+/IAjCzR3y/Tf00H9fI9Anprc2XRL8Z+8R1p0M1ruMdt8cFfxn7xEuh8YZJJbyqrz8nhUVczeRYZXna4qhc0ZJ5SBj5zZtx2+88KDgP3uPcD8ySfJ+P5x4KPjP3iPM2HZF7+OVV+X590wK5m4qbUbtkT516lQAUBrzSBtLS0v8+OOP4mcrKyvMnj0b33//PS5duoSePXuWSJzlST0vF9xY0B0ONlr+6RU90jWMj661DLEnXIJhYjGiwvHl5glIz5b3SHe01dxTX9d5+VeQz4r9NCFdnFFcyn5EJW3MmDHiq1rt2rWDo6OjsUOiciAyMhLh4eFo3bq10vI2bdrg8uXLRorKeLRe6xOeyH/aOAMOHvrvDxTIw6oTKJlintEnpocaeqRLKcPV3hphSEdimubxLk2xfoh0Ye4mMg5D54ySykHGzm2ajp+Tm4fEdHlOdlfzppim/XT1SDf2+RJJwdxdtpX5q4ti7B19xt8zZ5YWMnGma40y82++NYyPrrUMPYZ2kRSLERWOLzXrRbLWNka6rvOq4mIHa0sZsnMFPE/KEGfNNvX6IP1oevXL1BV+tYuouGJjYwFAZXy+ihUr4syZMxr3y8zMRGZmpvg5KUm1Ybgs0nqtLzisi4ZxJXXmCi152BTzjNSYsnPz8DgmFYBqQ7qUMpxtFROOam5IN8X6odLF3E1EUhk6Z5RUDjJ2btN0/MT0bAj583+72auu17SfYlmKhoZ0Y58vlT7mbiptFro3MV3Jycl455130KtXLzRo0EDjdpmZmUhKSlL6Y67C4tLwzpYrCItTnX1XlJk/drets9rVj2NSMXrDefGGVom9m/ynhIZ0SbEYUeH4FMnaykIGWyvNvzpa6wfy5K5oPC947qZeH6SfFStW4O233zZ2GERGpxgfMSsrS2l5ZmYmrKw0P5RctGgRXF1dxT9+fuVj4iit13oJE43qyjHaGtJNMc9IjSk0Ng05eQIcbCzh7WqvtE5nnQBwsZf/X0vScOOtTyxUfjF3E5FUhs4ZUnJZURg7t2k6vmKiURc7K1hZqt5ba6oPsUd6pvoH48Y+Xyp9zN1U2spsQ3p6ejr69+8PCwsLbNq0Seu25fVmvChy8gTEpWYiJ0/QvJGiR7qti9rVqZk5OPUgBqmZam5G9eiRLikWIyocn+J8HW2ttM5ArbV+8vm5yyfHeFIgwZt6fRARFYWPjw9kMhmePXumtDwyMlLr22Rz5sxBYmKi+CcsLKykQy0VWq/18SHyn1omGtWZYxR5OCsZyFXexhTzjNSYFMO61KzkBAsL5RwsJe8qeqcla+mRbor1Q0REpsnQOUNKLisKY+c2TcePS5XnY00TgGuqD2db7UO7GPt8iaj8K5NDu2RkZKBfv354/vw5jh8/jgoVKmjdfs6cOXj//ffFz0lJSWbbmF69oiP+mNBa+0Y6eqRrpbiBV4yzXtxYjKhwfCn5SdxJy7AuUvl55PdIj0/XeDwiorIqMjIS6enpqF69OpydnfHSSy/hwIEDGDFiBAB57/QjR45g5syZGsuwtbWFra1taYVcarRe68WG9GpFP0DBh+CZSUpjrZtinpEaU2isvEdatYpFG0NS15iq+sRCRERUVnKGsePUdPy4VHmPdHXjo2uja2gXY58vEZV/Jt+QXvBmHHjRiP706VMcP34cnp6eOssorzfjJcYQDekSeqSXNamZ8olGDdGQ7pvfIz2cr5wRURmTmJiIZ8+e4fnz5wCAJ0+e4O7du6hYsaI4Dvq8efMQFBSEmzdvAgAWLlyIfv36oVGjRmjTpg2+++472NnZ4a233jLaeZgkCUO76GRlA1g7ANlp8lysYdLSsiY0P1/6ezgUaX+X/BvvpHTNPdKJiIiodCTkD+3i4aBfQ7qThAfjREQlyahDuyQmJuLu3bsIDg4G8OJmPCYmRtxm3rx5ePXVVwEAubm5GDhwIK5du4YffvgB8fHxuHv3Lu7evYvExPLXcFsSbj5NRO25+3HzqZb6ylBMNqp+aBet7Nzyy9D97yEpFiMqHF+KOLSLZbHL9nWX90h/mvCiR7qp1wcREQAcO3YMAwYMwOTJk1GnTh189dVXGDBgALZu3Spu4+XlhRo1aoife/fujZ07d+LgwYOYMmUK7OzscPr0aXh4lI9GXn1ovdbHP5H/1DK0iySKh9qZynPCmGKekRrTk1h5Q3rVCkVrSJfSI90U64eIiExTWckZxo5T0/Hj0oraI12ez7Ny85CZkyv5eEREhmLUHunHjh3DnDlzAEC8GQeAadOmYdq0aQCUb8aTk5Px+PFjuLu7Y/LkyUplffHFFxg8eHApRl82ebnaYV7fevBytdO8kThGuvoe6V6udvisf6D6MhQ377mZQHYGYK35OJJiMaLC8RUcI13XfhrrJ19lF/m6qORMjccjIjJFAwcOxMCBA7Vu8/nnn6ss69u3L/r27VtSYZUZGq/16fFAZv5Nn1tVrfvryjGwcwWSn6k81DbFPCM1ptC4/KFdKqgO7SKlTlzs83ukaxkj3RTrh4iITJOhc4ak/F7Eco2Z2zQdPz5/aBdNY6Rrqg9Hmxf34ikZObB1slTZj7mciEqSURvS9b0Zd3Nzw927d0s6rHKtgpMtxrSppn0jcWgX9T3StZZh4wTILAAhT34Dr6UhXVIsRlQ4PqljpEs5ryr5DemRiRkQBAEymczk64OIiIpP47U+Pn9YF0dPwEbzOOCScoWGYdZMMc9IiSk7Nw8RCRkAAH81PdKllOEioUe6KdYPERGZJkPnjJLKQcbObZqOr5hs1F3D0C6a9rO0kMHJ1gopmTlIzshBBSdbSfsRERmKUYd2odKXlJGNY3efa+2R9aJHuvqG9IS0LOy4Ei6Oa6bEwuLFfhkJxY/FiArHlyKxR7rW+smn6JGenp2LpPybelOvDyIiKj6N13pxolHtw7pIyTGaGtJNMc9IielpfDpy8wTYWVvA01l1zhspdaKYnCxZy3FMsX6IiMg0GTpnSMrvRWDs3Kbp+PGKMdIdrdXup60+FB3bFPfnUo5HRGQobEg3M09i0/Dmr5fEsUbV0jHZaHh8OmZsu4bw+HS162HvJv+pY5x0SbEYUeH4UiX2SNdZPwDsbSzF3nFRSRlqj0dF99dff6F3796oWrUqfH190b59eyxcuBDR0dHGDk1vw4cPF4fAIqKyT+O1XuJEo1JyzIsH2sp52BTzjJSYFBONVvVwgEwmU1kvpU5eTDaquUe6KdaPOWHuJqKyxNA5Q1J+LwJj5zZNx4/LH9pFU490bfWhGCddXWO5sc/X3DB3kzliQ7qZqVPFGRc+7oI6VdQ3kgPQ2ZCuk4aecEWKxYgKxye1IV2qKvnjtkXmN6Sben2UFePHj8fkyZPRt29fHDt2DBcvXsQ333wDCwsLvPPOO8YOT28pKSlITzfsF2oiMh6N13rF0C7FnWgU0JiHTTHPSIkpNFY+PnpVD81D3ujyYrJRzT3UTLF+zAVzNxGVNWUlZxg7Tk3Hf9EjXb/JRgHAKT+np6gZrs3Y52tOmLvJXLEh3cxYW1rA08UO1pZa/ukz8od2sVM/tItOEhvSJcViRIXjS8mUzwqua2gXqSoXGCdd3fFIf9u2bcPPP/+Mv/76C2+//TYCAgLg5eWFtm3bYv78+diyZYu47bp169CyZUv88MMPCAwMhIeHBxITE5GXl4cvv/wS9erVQ+XKldGtWzdcuHBB6Tjt27fHqlWrlJaNHz8e7777rvh5wIAB+OCDDzB58mT8n73zjm+ifuP4J00z2iadlEIZZW8EHAxBRJYKCILyw42AIm7FAbgQURERJ7hRXCgIioqKDAFlCCqo7LJn90iatEkz7vfH5a5Jm7R3l3HX5nm/Xn21udx9v8/32zaf+z73fJ+nQ4cOaNu2LebNmydqPPfccw9++eUXvPPOO0hOTkZycjKOHDkiuP8HHngAU6ZMQceOHdG6dWs8//zzKCgowO23347mzZuja9eu+PTTT33aETJ+bmzTpk1Dy5Ytcc0114gaF0FEMwE/67mI9JRWwXfC67BZWN8yIsSmU56oslZ+8qMLhXOkWytdcLrckm0hQg9pN2k3QdRH6otmyG1noP65iPTkABHptVGVrq2mI13u8UYLpN2k3dEMfbpEGWdLyjFj5X84WxJgq5PLATg9T+EC5EivE24BX1ESnC0yU92+qoh0dW2XCYZzpOeX2f321yBhGKDSGtwXwwRs/tNPP8XFF1+MIUOG+H3fOyWA3W7Hn3/+iR9//BFff/01jh8/jsTERDz33HN466238Nprr+GPP/5At27dMHjwYJw7d46/tqysDDabzadtq9WK8vKq353FYsGrr76KtLQ0rFu3Dq+++irmz5+Pd955hz9nxYoVSE5ORnFxsV97X3nlFQwZMgSTJ0/GyZMncfLkSbRt21Zw/4sXL0avXr2wceNGPP/883j66adx4YUXol+/fvjjjz/w8MMPY/LkyTh69Ch/nZDxc2Nr3bo1tm/f7nOjRBBE7QT8rOci0pNbBt8J70gvFda3jAix6TSX2iUoR3pVDlZ/OVWF2hKVkHaTdhMEUYP6ohly2+mvf4fLzTvBpUSkG2vJkS73eBUDaTdpNxE2QhNaS9Qb7E43svPLYHf6j8bi07oAAVO7xGnV6NUyGXHaAA7lAAt40bbITHX7hBYbrXN+PDSpFpGu9PkICY5y4MXM4Np44jyg9b+9/9ChQ7j00ksFNxUbG4tPPvkEaWlpAIDKykosWLAAb775Jq666ioAwKuvvop169bh9ddfx4IFC0SZ2qNHD7z44osAgFatWuHxxx/H/Pnzcffdd/P9cU/j/REfH4/Y2FjodDokJyeL6hsARo4cifvuuw8AcPPNN+OFF15At27dcNdddwEA7rjjDjz33HPYunUr2rVrJ2r8V1xxBWbMmCHaJoKIdvx+1rvdQOlp9uc6cqQL0hhuR1m1iHQl6owQm86Xsg/4m6fE+X1fyJxoY2Og18TA5mAX7/4i4JQ4P4qAtJu0myCIGoRaM4SuIcUit7b5659L66JSAUlx/ouN1jYftaVrk3u8ioG0m7SbCBsUkR5ltE034Nt7+qNtusH/CXbPojs2DlD7F7U629Ans9+rLeBFtyMz1e0T6kgXOq4MT470PE+OdKXPR33A5XIhNtb39/PAAw/w27OSk5Phcrn495o3b86LOQAcP34c5eXl6N+/P39MpVJhwIAB2Ldvn2h7+vXrV+P1qVOnYLFYAAATJkxASUmJjw2hpFOnTj6vU1NT/R4rKioCIG78PXr0CIvNBNHQ8ftZb80HXHZAFQMkNRd/fXW4B9p2Xx1Wos4IsYlzpGcm+3ekCx0XF5VuqvCfJ12J8xMNkHb7QtpNEPWDUGtGuDRIbm3z139pOavDyXEaqGNqFhEPdB0HV7OszE9EutzjjRZIu30h7Y4uKCKd8CXYQqNAwAV8fYdL7WIMUY70Rp5tbEWe/HBRgSaefbIdbBsBaNu2rc92KQCYP38+nnvuOaxYsQJ33XUXGK8tajqdzudcp5P9HVe/KYiNjeXfCwTjZ+ubWu0bQcG1y7Wl0WgkPfEW2n9MTM1npf6OcdeKGX/1uSMIIgi4tC6JzQM+xBaFTtjOsPpAeaUTJZ4FdyBHulAS9bEoKLP7zalK1AJpt08/pN0EQRDBweVHT5GQ1gWoPUc64YG026cf0m4ilFBEepRx4LwZPeasw4HzAZzcAgqN7jtnQquZP2LfuQDFRAUWG63TFpmpbp9VYER6nfPjIc3AfiByNxJKn4+QoFKx28OC+VL5j1oAgP/973/Yvn079uzZwx+Li4tDcnIy4uPrzq3btm1bxMbG4u+///Y5/vfff6Njx47866SkJJhMvr/fEydO1Gjvn3/+8Xm9Z88eNG7cWJSIx8bG1tiCJrR/sQgdP0EQ0vH7Wc+ndak7P7ogjQmQ2kWJOlOXTedL2V1bBl0sEvX+HzII1d2qhbf/iHQlzo8iIO0m7SYIogah1gyhWiYWubXNX/8lnvVvai2FRmubD4MntYvFjyNd7vEqBtJu0m4ibJAjPcpIN+pwz6C2SDcGeKoVyoj0OhzpddoiM9XtE5raRShcYZVCi91vf4R4pkyZgsGDB2P06NH45ptveNEzmUw+Ih+IuLg4TJs2Dc888wyys7PhcDjw5ptv4u+//8b999/Pn9e3b1+sWLECOTk5cDqdePvtt/Hnn3/WaG/btm14//334XQ68e+//2L+/Pl87jSg7qInANCiRQscOHDAZ2uc0P7FInT8BEFIx+9nfelJ9ntK7fnRBcMVC6+2M0yJOlOXTTkmLq2LPui+uJyq5gARbEqcn2iAtDs4SLsJQh7qi2bIbae//ovLg4xI1wXOkS73eKMF0u7gIO2u31Bqlygj3ajDXZe3DXxChB3ptdoiM972MQwDayX7gWoIkSM9zXPjUGZzotLpVvx81AdiY2Px008/YeHChXjiiSeQnZ2NuLg4JCYmonfv3li3bl2N7VPVmT9/PioqKtCzZ084HA60atUKK1eu9HkyPHPmTBw4cACtWrWCwWDAiBEjcOWVV9Zo6+abb8bKlSvxyCOPwG63Y+LEiT6FQuoqegIA9957L66//noYDAbodDr8+eefgvuXgpDxEwQhHb+f9VxqFwER6YLgddgMMAwfUaREnanLprryo4shMa72iHQlzk80QNodPKTdhFDKyspw7tw5tGzZUlDUKAAUFBQgNjYWKSkpYbauflFfNENuO/31LyQivTa4B+MWPznS5R5vtEDaHTyk3fUYJgoxmUwMAMZkMsltSsQpszmY7UcLmTKbw/8Juz5gmNmJDPPlTQHb2Hu2lMmasYbZe7bU/wknt7FtvHlhcLbIjLd95XYnkzVjDZM1Yw1jrqis9bo658eDy+Vm2sz6kcmasYbJNVUofj7EooT/M4fDwdjtdr/v2e12xmw2B7zW5XIx5eXldbbvdrsZhmEYq9Xqc/6QIUOYGTNmMAzDME6nk3E4av5eKysrmZKSEr6N2rDb7UxJSQnjcrkE9W+xWJiKigqfNsrKymocM5vNjM1mq9FfbeP31zZBhBslfKaEAr+f9UuvYXVzz7I6rxekMXYL297sRPbn2vqWmbpsWrjuMJM1Yw0z65v/ArYhVHdnrvqXyZqxhnl9fbYkW6IBJfyfkXaTdhOh5+DBg8xdd93FpKWlMQCYTZs21XnNu+++y7Rr145JT09nkpKSmC5dujBbtmwR1a8SPlPCRag1Q6iWiUVubfPX/5zv9zNZM9Yw8346GPC62ubjt+x8JmvGGubK12r+Pco9XjlQwv8ZaTdpdzRBqV2ijJOFVtz4wR84WWj1fwIfkR44R3qdCIxIr9MWmfG2z/tpd4I2NBHpMTEqpMRXpXdR+nzUR2JjY6HV+o900Gq1MBoD77yIiYlBXFztEZCxsbFQeSI94+PjA56vVqv9PpHnip6oask/521vcnKyT9GS2vpPSEiAXu+bCsFgMNQ4ZjQa/RYwqW38/tomCEIYfj/rS0Mcka6JB1SeokteWqxEnanLJj4iPSn4zxxuR5m10n9qFyXOTzRC2k3aTYSejRs3okePHvj9998Fne9yubBnzx6sXbsW+fn5KCwsxLBhwzB69GgUFhaG2dr6QX3RDLnt9Nd/iSe1S2qCtALrtRUblXu80QppN2l3NEGpXaKMdo0N2PzoIDQJtCAVkNqlzja8HeleW8pFtyMz3vblmthiZwlaNWJiav/wFTOuRgYtCi12FFsrcUmrVEXPB0EQBBE8NTTC7QJMZ9mfBeRIF6QxKhVbcLSihE3vkpgp/NoIU5dNQlK7CB0XV+PE31ZwMe0QBEHUN+69914AwNmzZwWdr1ar8e677/KvY2NjMWPGDLzxxhv466+/cNVVV4XFzvpEqDUjXBokt7b567/Yk9olpZbULrXZbaglR7rc4yUIouFDjvQoQ69Ro1WjhMAn2DyFyfSBI9LrbIOLZndVAk4boPG/+K2zHZnxtk9MoVEx4+IKjhZZKhU/H4Q4vvvuO6jVarnNIAhCYdT4rDefB9xOIEYDGJuKvz4QOo8j3avgqBJ1pi6bhDjShY6LW3hbAhQbVeL8EJGFtJsgAnPgwAEAQPPmzWW2RBmEWjPCpUFya5u//rmI9Noc6bXZneiVI51hGJ8oY7nHS0Qe0m4i0lBqlyjjfGkFnv1+P78wrYGAiPQzxeV46Ks9OFNc7v8ErQFQef60aknvUqctMuNtn9XjSBdSaLTO+fEizcBu7SmyVip+Pghx0DYsgiD8UeOznkvrktQciKl7ESBYY7wLjgbqWwHUZhPDMDjv2RHWrBZHutA5MdQRka7E+SEiC2k3QfinrKwM999/P66++mp069Yt4Hl2ux1ms9nnq6ESas0Qs4YUg9za5q9/PiI9IbAjvbb5MHgc6W4GKK901dkf0bAh7SYiDTnSowyr3Yk/jhfxjuEaCHCkmyocWP3PeZgqam6lAgDExFRFpdsC3zzVaYvMeNvH5VMVEpFe5/x4kea5eSi22hU/HwRBEETw1PisLz3DfheYH12wxvCO9NLAfSuA2mwqtlai0ukGAGQkBl4gCZ0Tg752R7oS54cgCEJuKioqMGbMGMTExOCzzz6r9dx58+YhKSmJ/2rRokWErIw8odYMMWtIMcitbf76Ly1nx5haiyO9tvmI06ih9qRbra7pco+XIIiGD6V2iTLaZxix9qGBgU/gtoAHU2wUYBfwttJaI9LrtEVmvO07kMPOS4IutFuG0rxSuyh9PgiCIIjgqfFZbz7Hfk8K8VZ5Tse9UrsoUWdqsynPbAfAaqU2NvjYj7pSuyhxfgiCIOTEZrNh9OjRyMvLw6ZNm5CWllbr+bNmzcL06dP512azucE60+uLZshtZ/X+7U4X7/xOrSW1S22oVCoYdLEwVThQZnMiw8t1Ifd4CYJo+JAjnfAllI50oFZHen3Came3jAlJ7SKGVIPHke7Z3kYQBEFEGebz7HdPQdCQoa97Z5jSyS9j07qkG3UhaY/TcG6XGUEQBFFFbm4uKioq0Lp1awBVTvRz585h06ZNaNy4cZ1t6HQ66HSh+cwmGiZcNLo6RgWjXvra2qjnHOmhjeAnCIKoC0rtEmUcyjWj74sbcSg3wMKaS+1SS7FRQfjZUi7aFpnxtk9MjnQxVEWk2xU/HwRBEETw1PisD5sj3aPDXhHpStSZ2mzK90SkN64lrYsY+NQuASLSlTg/BEEQocBkMuHQoUM4duwYAOD06dM4dOgQCgsL+XOeeuopXHPNNQAAl8uFsWPH4t9//8U777yDkpISHDp0CIcOHYLJ1DACpYKlvmiG3HZW75/Pjx6vQUyMqrZLayVQ3RO5x0sQRMOHItKjjNR4LSZc0iLwNioucq2WHOmNjTo8OKQ9GtcWISYgIr1OW2TG2z5OoIXkSBc0Px64SuWlFQ7FzwdBEAQRPDU+67nULonNBF0vWGP4WiVVOqxEnanNJi4iPaOOsQqdkwRt7TnSlTg/BEEQoeDXX3/FrFmzAAAdO3bEiy++CAC47777cN999wEAmjZtijZt2gBgi4ueOHECKSkpuOuuu3zaev7553H99ddH0HplEmrNELOGFIPc2la9/xLekV67PXXNR6JeAwAoq/ZwXO7xEgTR8CFHepTROFGPh4d1CHyCgGKjdbYBAPpk9nstjnRB7ciIt31iItLFjCspnr0BMFc4FD8fBEEQRPDU+Kwvy2G/G5tKuz4QflK7KFFnarMpv4yLSK/DkS5wXNwWcrvTDYfLDY3ad2OmEueHIAgiFIwdOxZjx46t9Zy5c+fyPycnJ+PQoUPhNqteE2rNCJcGya1t1fsvLvc40mspNOrvuuoE2mUm93gJgmj4kCM9yiivdOJIngXtMwyI11b79TvtgItdtNaWI73M5sDu06W4sGUyjJ4nwTXQ14yEE2WLAvC2j8unKiQiXdD8eEiO80SklztgtTtwNN+q2PmoT+Tl5WH58uXYt28f3G43WrdujaFDh6JPnz5ym1YrL7/8Mpo0aYLbbrtNblNE884772DLli0YMWJEDfu/++47fPnll+jZsydmzpwpk4W143a78d1332HLli1wOBy45JJLcMsttyA2NlbUOdX59ttvsXz5cp9jCQkJWLJkSdjGQigbH+2LcQHWAvYNgRHpgjXGT7FRJepubTblmT0R6XWkdhE6J94abrU7kVwtWk2J8xNNkHZHHtLuwOzcuROrVq1Cfn4+MjMzccMNN+CCCy7wOaewsBAfffQRdu/ejfvuuw8DBgwIyzgJZRJqzRCzhhSD3NpWvf8Sr9QutVHXfHAPx83VcqTLPd5og7Q78kSDdgPSNHb79u34/vvvUVhYiA4dOuD222/3qe/xyCOP4Ny5czWuEztflCM9yjheYMWYxdtwvMBa800uGh2oNSL9VFE5Jn60C6eKygN3JCC1S622KABv+7gtY0Ic6YLmx0Oy5wbC6Waw/3yZouejvrBs2TK0adMGGzZswAUXXID+/fvDbrdj+vTpmDx5stzm1cpvv/2G3bt3y22GJHbu3Invv/8es2fPBsMwPu/NnTsXP/zwAzZv3iyPcQIYMGAAPv/8c7Rq1QqdOnXCCy+8gEGDBqGyslLUOdXZv38//v77b1x77bX818iRIyMxJEKh+GgfF42u1gHxqYKuF6wxvA5XOdKVqLu12cRHpNexzV3onGjUMdDFsre+1beC12ULEV5Iu+WBtNs/n332Gfr37w+GYTBo0CAUFxfjwgsvxNq1a/lzli1bhp49eyI/Px/Lly/HyZMnwzlUQoGEWjPErCHFILe2Ve+fKzZaV2qXuuYjUI50uccbTZB2y0M0aLcUjX3mmWcwe/ZspKeno0+fPti4cSO6du3K1wYBgCFDhvisywcPHozly5fD4RBXtJge0UUZ7Rob8PODl6F1o4Sab3JRa5oEIEYdXEd+ipyJskUBeNtXldolyHmphl6jhjY2BpVON9ISNIqej/rA7t27MXHiRLz44ot47LHHfN577rnnfD6A161bh19++QVTpkzBkiVLkJOTgw8//BDx8fHYtWsXvvzySxQXF+OCCy7A1KlTYTRWPVyaOXMmBgwYgFGjRvHH3nzzTej1ekydOhUA8MILL6BNmzbQ6/XYtGkTXC4XJk6ciN69e/u1/Z133sGePXtw5MgR5ObmAgBeffVVvPnmm4L6at26NTQaDbZu3YrKykpMnDgRffr0wfLly7FhwwYkJSXhrrvuQocOvlsd6xqr9zh+/vlntGjRAk8//bTfMfTv3x/79+/Hxo0bMXToUADAv//+i+zsbAwfPhwVFRU+5x89ehRLly7F2bNn0aZNG0yZMgXNmlVF5S5ZsgTr168HAKSlpaFv3764+eabERNT9QyYsy8xMRHr16+Hy+XChAkTREeFffXVV2jZsiX/+pprrkHr1q3x888/Y8yYMYLP8UdaWhpuuOEGUfYQDRcf7Tu/nz2YmAmopBfc8oufnWFK1N3abAp1sVGAjWCzWyr95klX4vxEA6TdpN1K0+6PPvoIEyZMwIIFCwAAt99+O7Kzs/HJJ5/gqquuAsA6Ao4dOwadToeFCxeKsptoGNQXzZDbzur9l1awDqukOiLS68IYIEe63OONFki7SbvDqd1SNPbuu+/Gc889x7+ePHkyMjMzsWLFCr5GyIgRI3yuee+996BWq0U/+KGI9ChDr1Gjc9NE6DV+HMJcRLo+cFoX4R3VHZFeqy0KwNs+q90FADDoQrfNjiM5jm3T5nQrej7qA2+88QaaN2+Ohx9+2O/7rVq14n/Ozs7Ge++9h7Fjx6JZs2YYPXo0NBoNVq9ejQEDBoBhGPTt2xdfffUV+vbtC5vNxl+7du3aGnkjt2/fjl27dvGvN23ahAceeACvvPIKunTpArfbjQEDBuC3337za9uFF16IjIwMtG3bln9CajQaBfd177334tNPP0X37t1RXl6Oyy67DGPGjMGKFSvQt29fnD9/HpdeeilMpqr/SSFj5caxePFi9O7dG/379w84/2q1GhMnTsRHH33EH/vwww9xww03ICHB92Z28+bN6NOnD8rLyzFw4EDk5OSgW7duOHz4MH9Ojx49+Lno0KEDnn/+edx+++0+7WzatAkPPvggFi1ahK5du0Kj0WDQoEHYuXMnf87WrVtxww03oKysDIHwFnMAyMzMhEajQXFxsahz/HH27FnceeeduP/++/HZZ5/B5XLVej7RsPHRPvN59mBiZug70tV8oK1E3Q1kE8MwKBAYkS4GbmeZ1Y8jXYnzEw2QdpN2K02727RpgzNnzvCRfpWVlcjJyUG7du182tXpQlsUkqhf1BfNkNvO6v1zEelcilOpGAPkSJd7vNECaTdpd7i1W6zGNm3qW2+qsLAQVqu1Rn/eLFmyBFdffbXPQwUhUER6lJFrsuGTHScxsV8rNEmqFuHFbf+uJa2LYAQ40mu1RQF428dFriWEOCIdYNO75JfZcaLAijX/5Sh2PkJJvtnGb9nnSIrToEVqPGwOF47mW2pc061ZUp3t7tq1CxdddFGdeS85rFYrVq1ahW7dugFgHTcPPfQQHnvsMbzwwgsAgFtvvRWtWrXCO++8E/BGIRBqtRobN26EXs/+Pp1OJ2bOnInt27fXOLdPnz7IzMxEu3btJEUvt2nTBt9//z1UKhUmT56MjRs3oqioCNu2bQMAPj/Y2rVrMWHCBFFjTUtLwy+//AK1uu6//8mTJ+OCCy5ASUkJ4uLi8MUXX+Dnn3/GW2+9xZ/DMAzuvPNOzJ07F/fccw9/XWVlJZ599ll8+eWXAICLL74YF198MX/dtddei1atWuH555/3EcTmzZvjp59+gsoT0btv3z58/vnnfG6+kydPYvny5Vi0aJHPU//aeP/99/kt3cGco1KpcNFFF+HCCy9EaWkpZs6ciffffx+//vorNJrQP5gjlI+P9oXTke6n2KgSdTeQTaXlDlS63ACA9BA60rmt4GV+HOlKnB8lQdpN2h0t2v3aa6/hoYceQpcuXdCpUyf8999/uPbaa/HUU08JsoOIDuqLZshtZ/X+TRVs+obkoCPSOT33Tckg93iVBmk3aXdD0G6hnD17Fo8++iisViv+/vtvzJ49GzfddJPfc/fu3Ys///wT33//veh+yJEeZZhtDvy0NwdjezWrKSxcRHodjnRtbAyy0uKhja1lQ4MAR3qttigAb/u4YqMGATnSBc2PF9zT+FyzTdHzEUq+2Hkab2w84nPs2p6ZeP2GXsg12TDqra01rjn5Ut15pS0WC9LS0nyOcU5LjmXLlvFblJo0acKLOQCcOXMGp06dwvXXX88fMxgMGDFiBLZu3Spa0K+66ipezAFg3Lhx+Pjjj+FwOELuRB04cCAvaDExMWjZsiUGDhzIvx8bG4vmzZvj/HnWcSdmrFdccYUgMQeA9u3bo3fv3vjiiy+QkpKCzMxM9OnTx0fQjx49iqNHj+Lnn3/G9u3bwTAMGIZBdna2zzY0h8OBr7/+Gn/88QcKCwvhdruhVquRnZ3tI+jeYweAdu3a8eMEgMsuuwxffvklEhOF7bbZunUrHn30Ubz00kto3bq15HMAYNq0aXjyySf51zfddBM6d+6MDz/8EHfffbcge4iGhY/2SXCkC9YY7xRrbjcQE6NI3Q1kU14ZG6GTEq+BLrb2zx8xultbRLoS50dJkHaTdkeLdu/YsQM//PADbrnlFnTv3h3p6en48ssvceONN/o4GojoJtSaIXYNKRS5ta16/1UR6bV/ntY1H/yD8WoR6XKPV2mQdpN213ftFoPRaMS1116L0tJS2Gw2fPDBBxg/frzPDgmOJUuWIDMzs0a6FyGQIz3K6JBhxJbHrvD/Ju9Ir/2PvtY2OAQ40gW1IyPe9lntwouNih1XoucmIl4bq+j5CCU392mJYV0yfI4leeahSZIea+4Xl2eLo0mTJjh9+rTPsV69eiExMRFbt27F4sWL8fnnn/OCnpTk+7S9sLAQAJCa6lv0LzU1FadOnRJtT0pKSo3XLpcLJSUlPtWjQ0H1rU8xMTF+j3FpRcSMtfo81cWUKVPw+uuvIykpCVOmTKnxPrdta9iwYTXmwXsr2v/+9z8cPHgQkyZNwiWXXAKNRoNvvvkGFotv5IT3TRPgO04AyMrKQlZWliDbd+7ciZEjR+Lhhx/GI488Ivkcjuo3mFlZWbjooouwa9cucqRHKT4aUea58TQKd6QL1hheyxmg0gLoExWpu4Fs4vOjG+teBIsZl1Hnfyu42HaiEdJu0m4gOrR76tSpmDhxIl555RUAbPTeLbfcgvvvvx87duwQZBPR8Am1ZoRLg+TWtur9cznSk+soNlqX3YEc6XKPV2mQdpN2A/VXu8WSlJTE7zKYOnUqLr74Yjz77LNYunSpz3mVlZX4/PPPcffddwt+cOENOdKJKuyRTe1Sn+AEWkhEuli4bW2mCnGVguszjRP1AYvH6TVqQdvJ/DF8+HAsWrQIBQUFSE9PBwBccskluOSSS+B0OrF48eJar+c+9I8fP+4jAMeOHfN5rdfrYbf7bpErLi6GwWDwOVa9uvSJEycQFxfH21YdlZ9ig0L7EovQsUrh+uuvx/333w+bzYYVK1bUeL9FixYA2Nxn1157rd82iouLsXr1avzzzz/o0aMHACA/P190RW0x7Nq1C1deeSWmTZuGefPmST6nLsxms9/fNRGFlLEFjmBsEvq2NXpArQVclawWh6L+SQThtiE3TgxtDmLugbi/YqNE7ZB2k3YDDV+7XS4Xzp07h86dO/sc79SpEzZu3BgyuwkiWiktD1VqF/Z60vPaIe0m7Qbqp3YHS0xMDDp16uT3wczq1atRXFzs9+GDoLaDNY6oX2TnlWHwK5uRnecn+T/nSK9jsX0wx4wL567HwRxz4JM4R7rTBjhsfk+p1RYFwNl34LwJdiebp1VIRLqg+fGC29Z2vNCi6PmoDzz00EMwGAyYOHEi8vPzfd6rLor+SEtLw9ChQ7Fw4UI4nexN2Z49e7B27Vqf/GkdO3bEli1b+CJU+/bt81vM5KeffsL+/fsBADabDW+++SbGjx8f0ImalpbGP7EW25dYhI5VCvHx8fj+++/xww8/+L15yczMxPDhw/Hss8+iqKiIP37+/Hn8/PPPAMA/Gc7LywPA5nd79tlnJdkjpOjJX3/9heHDh2PatGmYP3++5HNWrlzpI8jLli2D2+3mX3/99df477//MHr0aEljIeo/Ptpn8XxOGYRHyojSGC4q3aPvStTdQDblmdl7ByER6WLmxKAP7EhX4vxEA6TdwiHt9iUc2q1Wq9GjRw988803fJSd3W7H999/j4suukjSWIiGSag1Q+waUihya5t3/wzDVKV2qcORXtd88DnSbb7OPrnHGy2QdguHtNsXIboshOrr7i+//JL/3QHsw5T169djwICauy6WLFmCYcOG+U35IgSKSI8yjPpYDO2SwQuPDwJTu7jcDIqtlXC5mcAnaY0AVAAYdgGvqbkQrtUWBcDZFxtT9bxJSLFRQfPjBbe1yu5wKXo+6gPp6enYsmUL7rjjDrRp0wbdu3dH06ZNkZubi8OHD2P69Ol1bt15++23cdVVV6FLly5o06YNtm7dimnTpvnkznr88cdxxRVX4KKLLkJGRgZOnjyJjh071mjroosuwlVXXcVXxVar1fj6668D9j1+/HiMHz8eo0aNgsFgwKuvviq4LykIGatULr/88lrf//TTTzF+/Hh06NABffr0QUlJCQoLC/HGG28AYLdlPfLIIxg3bhyuuOIKnDhxAkajUXT1bkBY0ZORI0fC5XLh5MmTPjc0119/PZ/PTsg5+/btw6pVq7BkyRIAwO7du/HUU0+hc+fOKCwsxL59+/DCCy8EjAggGj4+2mctYA8mCHeki9IYfSJQXsgXHFWi7gayqcATkZ4hICJdzJwYasmRrsT5iQZIu8VB2l1FuLT7gw8+wLhx49CpUyd06dIFe/bsQUJCAl+Ujbvm+eef518vXrwYa9asweDBgzF16lTR4yXqH6HWDLFrSKHIrW3e/VsrXXB6xsfVCQtEXfPBjad6qja5xxstkHaLg7S7CqG6XJfGVtfuXbt24amnnkKnTp1QUVGBnTt3YuzYsZg1a5ZP/6dPn8aGDRuwfPly0ePjoE+XKKNpUhyeGNHZ/5u2EKZ2iYlhHfJ2E7ul3E+0Xa22KADOvnOlbBEGrTqmzoJnUuCexlc6GUXPR32hXbt22Lx5M06ePIn9+/fD7XajZcuW6NChA+Li4vjzrrzySrRp06bG9e3bt8fBgwexfft2lJSUYNGiRWjXrp3POV27dsXhw4exa9cuJCQk4MILL8S///5bo2r5wIEDMWPGDOzZswdOpxMDBw6skVfMm5EjR+LAgQPYu3cvrFYrjEYjMjMz6+zrqaeeqpFzbc6cOcjI8M2HN3/+fJ+nrkLG6q9tf9x9992w2fzvPgGA++67zyc6ISMjA7/99hv27t2Lo0ePIjMzEz179vQR7FdeeQUTJ07E0aNH0bRpU/Tu3RvffvstLrnkklrtmzx5MsrLy/nXQoqeLFq0yCe/G0enTp1EnXP99dfjggsu8BnD448/jt27dyM+Ph7dunUTNJ9Ew4XXvspyNnc5ABj8bzsNGu+Co1Cm7gayqSoiPbSpXQy1pHZR4vxEC6TdLKTdytDuCy+8ENnZ2di9ezfy8vLQrFkz9OrVy8cplJ6ezj8U93443rZt24D2Eg2L+qIZctvp3f/ZEvZ/XBsbA70muOQI/IPxShdcbgbqGFWN/ojwQtrNQtodeu0WorHVtfu1117DrFmzsHv3bmg0GnTq1AnNmjWr0Y/b7cYXX3yBMWPGBLSxLlSMd+x7lGA2m5GUlASTySS4mmxDweZw4XRxOVqmxkOvqeYUXnUnsHcFMPwF4NL7Arax75wJo97aijX3D6g9p9Zr3QHTaeCOX4HmNbdC1mqLAuDssztduOatbUiJ12DPM8PrvE7w/Hj4/t/zeODLPejdKhXPj+2m2PkQSzT/nwHA0KFDcfHFF+Oll16S2xSCaBA0lM8UXvtUhdC/3RNQ64Cn8gCBefNFacwno4ETW4BxHwAX/E+RuhvIpvHvbsefJ0uw+KYLMfKCprW2IWZOPtp6As+tOYBremTirRt7CbIlmmgo/2dSIe0miNDSkD9TQq0ZYteQQpFb27z7P5pvwai3tqKxUYddTw6t9bq65sPudKHjU2sBAP/OHs7v8pZ7vHLQkP/PhEDaTUQa2XOku91urF27Fq+//jrOnj0r6JqKigp8++23ePfdd7Ft27YwW9iwOJpvwfDXfsPRfEvNN/nULiGISAe8Co6WirdFAXD2Hcpl50VIfnQpcKKfb7Erej4IgiCI4OG176yn0KihsWAnumi4mieewt9K1N1ANhVb2WJkqQm1b/0WCx+RbqtZQEmJ80MQBEEok/qiGXLb6d2/qUJYfnQh6GLV0May7izvPOlyj5cgiIaPrKldvvnmGzz22GNo0qQJtm/fjp49e6J58+a1XpObm4vLL78cWq0WPXv2xFNPPYVRo0Zh6dKlkTG6ntO6UQJW3X0pWjdKqPmmXVhql1rb8IZ3pJuCa0cmOPuKLOy2GINAR7rYcXHFRm2VTkXPByEOoVuzCIKILniNMG9nDySIS+siSmN0vqldlKi7gWziHOlphrod6WLGVVuxUSXODxFZSLsJghBKqDUjXBokt7Z597/5MFsbJjk+NNqeqI9FoaXSR9PlHi8ReUi7iUgjqyM9OTkZ69evh1arRYsWLQRdM3PmTMTFxeGPP/6AXq/Hf//9h169emHs2LFB5biJFhJ0sbgoK8X/m5wjXV/7dqBa2/CmDke64HZkgrPv5705/Gsx1wmFeyJfZnMqej4IcQwaNEhuEwiCUCC8RvztKTTqp4aIoOuFwOuwWfy1EcKfTS43g1JP1FqKgMW2mHEl8DnSa+ZmVOL8EJGFtJsgCKGEWjPCpUFya5t3/yXl7ENyLpBM6HWBMOg8jnSvgqNyj5eIPKTdRKSRNbXL4MGD/RYdCITb7caqVatw++2384UDLrjgAvTv3x8rVqwIl5kNinyzDW9tPIJ8s5/iBHyx0dod6TmmCsxdcwA5poraO6vDkV6rLQqAs++8p9io0Ih0wfPjgUvtYq104fX12YqdD4IgCCJ4eO0rLGEPiIxIF6Ux1VK7KFF3/dlUWl4JroJPioDt32LmpKrYaM3ULkqcH4IgCEKZhFozxK4hhSK3tnn3Lya1i5D5MOqrAtL89UcQBBEOZM+RLobTp0/DYrH4VHMF2OquBw4cCHid3W6H2Wz2+YpWiqyV+PSPUyjybJn2QWCO9CJLJZZsPYEii582vKnDkV6rLQqAs69AZGoXwfPjwbvdz3cqdz4IgiCI4OG1z+TRRpER6aI0hnsw7tlxpkTd9WcTl9YlKU6DWHXdt6pi5oTTXKufiHQlzg9BEAShTEKtGWLXkILblVnbvPsv5SLSBew2EzIfnKaXeaV2kXu8BEE0fGRN7SKWsjLW0ZucnOxzPCUlhX/PH/PmzcOcOXPCaVq9oXPTRPzpr0I2w3g50kNU6blaJJxgWxQCZ9/iTUcBAAm68FT9jlXHIEGrhrXShZXTLkUryudGEATRYOG1b8Vn7IEEcY50UfA6bPbtW0H4s4nPjx7iQqOAV450W80c6UqcH4IgCEKZ1BfNkNtO7/5Ly9mI9CQBqV2EYPRounexUbnHSxBEw6deRaTHx8cDQA2nudls5t/zx6xZs2AymfivM2fOhNXOeonTBrg9AlRHRLpg6ohIry9wxUuE5kiXgr9taQRBEEQDxsrlSBeX2kUUet9io/UFzpGeEg5HupbV8kqXG3Znzah0giAIgiDCQ6mI1C5CqO3hOEEQRLioV470li1bQqfT4dixYz7Hjx07hvbt2we8TqfTITEx0ecrWjmaX4aRb/6Oo/nVIvi5aHSoAK0hNJ3V4UgPaItC4Ow7VyIuR7oUuKfp93+5W7HzQRAEQQQPr30lbvZAOCPSdb47w5Sou/5sKvZs/U4NgyPde3dZ9fQuSpwfgiAIQpnUF82Q207v/k2eiPTkuNDoe6KfYDS5x0sQRMNH8Y70DRs24PPPPwcAaDQajBgxAsuWLYPbzS5AT58+jc2bN+Paa6+V0cr6Q5w2Fhe2TEGctppT2Ds/ekztfxYpCVrc2jer7kixOiLhAtqiEDj7Kj0Ra0Ij0gXPjxecIz0rLUGx80EQBOGN0+lEaWmpqGvcbjdMpvq9SylYeO2z5bIHROZIF6Ux1VK7KFF3/dlU7MmHmioghyogbk5i1TGI07DOdKvdN4JNifNDEARBKJNQa4aUNaQQ5NY27/5LKzw7zgREpAuZj6oC4lV6Lvd4CYJo+Mj66XLgwAGsW7eOX1SvXLkS//zzD/r27Yu+ffsCAL766iv88ccfuOWWWwAA8+fPx6WXXoqrr74affr0wbJlyzBw4EDcdNNNso2jPtEsOQ5zr+1W8w0ualxAWpeAbVSnjoh0we3IBGff1E//AiA8Il3KuLjULqMuaIpmyXHiDCX8kp2djX379sHtdqN169bo1q0bdDqd3GbVysaNG2E0GtG7d2+5TZFMeXk5tm/fjvz8fDRu3Bj9+vVDQoL/vP///vsv9uzZg4EDB6JNmzY13mcYBjt27MCpU6fQvn17XHzxxZJsKi4uxq5du+BwONCzZ0+0aNFC0jlix/Dff/9h9+7dPse0Wm291yuGYTBz5kwsWrQIbrcb6enpWLRoEUaPHh3wmrNnz2LatGnYsGEDdDodVCoV7rrrLsybNw8xdTy8bWg0S47D3FEdgH9OsAcSxKV2EaUx1YqNKlF3/dnER6QbhDkTxI4rQReLCoerRjo1Jc5PtEHaLQ/Rpt0AUFRUhB9++AGtWrXCoEGDJJ9DRC+h1oxwaZDc2ubdfwmXI12AI12I3VwwmtkrR7rc441GSLvlob5qt5S+rFYrdu7ciYKCAlxwwQXo3Lmzz/vffvut32CtZs2aYdiwYZLGUhuyOtItFgtOnjwJAHjwwQcBACdPnkSnTp34c4YNG+aTtqV9+/bYt28fvvjiC+Tl5eHZZ5/FDTfcALU6PIUgGxp2pwv5ZjsaJ+qgi/WaMxGFRisqXThWYEHbdAPitLXMex2O9IC2KATOvjI7K8xCHemC58cL7ibgTHE57E6XIuejvpCdnY3bb78dhw4dwqWXXoqEhAScOHECBQUFmDFjBqZNmya3iQF57bXX0K5du3or6EuXLsX06dPRpk0bdOjQAceOHUN2djZeffVVTJo0iT/vjz/+wCOPPAKz2Yx9+/bhs88+qyHoNpsN11xzDfbt24fevXtj69atGDp0KJYtWybq837GjBlYtmwZunbtCrVajU2bNuHxxx/Hs88+K+qc6ggZw/fff4833ngDI0eO5I/Fx8fXe0f6a6+9hvfffx9btmxBr1698MYbb+D666/H3r170bFjR7/X3HnnnSguLsbZs2fRqFEj7NixA0OGDEGbNm1w1113RXgE8mJ3upB//hwaM7HQxTCAPlnU9aI0htPhSgvgdsHuhuJ019+9AJcjXWhEuljdNepjUWix+0SwBbKFiAyk3fIRTdrtze23344NGzZg5MiRAZ3kQs4hopdQa4aUNaQQ5NY2rv90o7YqtYsAfRcyH/5ypMs93miCtFs+6qt2S+lr27ZtGD9+PDIzM9GqVSvcc889uPXWW/H666/z5+zatQs5OTn8a5fLhc8//xzTpk0LiyMdTBRiMpkYAIzJZJLblIiz92wpkzVjDbP3bKnvGwd+YJjZiQzzwVDpbVSn+ATb5vNNgmtHJjj7hr66mcmasYZZtz9X1HVixjVz1X9M1ow1ip4Pscjxf5aXl8c0bdqUGTduHGOxWHzey8/PZ7766iv+9cGDB5lvvvmGsdlszLp165hPPvmEsdvtDMMwTHl5ObN27Vpm2bJlzN69e2v089133zH79u3zObZ582Zm27Zt/OtffvmF+euvv5i8vDzmxx9/ZL7//nvGarUGtP33339nevTowQwbNoz5+OOPmY8//pgpLS0V1Vd+fj7z008/MatXr+bHn5OTw3z99dfMunXr+PF5U9dYubZzc3OZVatWMWvXrvVr/zfffMMAYJYuXepz/Msvv2RUKhWzatUqH/t///13hmEYBgDz2Wef1Whv7ty5TJMmTZjcXPb/Ljs7m4mPj2c+/PBDv/0H4tNPP2UqKir412vXrmUA+MyfkHOqI3QMffr0EWVvfaBVq1bMo48+6nOsdevWzMMPPxzwmtatWzOzZ8/2OXbBBRfUek11Gop28xrx9AUM83Jb6dcL0QqHndXh2YkMU16sSN31Z9MtH/7BZM1Yw6z864zkNmpj5Ju/MVkz1jC/HswLqp2GCGk3aTfDNFzt5njttdeYYcOGMSNGjGCuu+46yecQddNQtNsfodaMcGmQ3NrG9f/niSJ+vWuxOQRfV5vd3/1zjsmasYa54b0doq5raJB2k3YzTP3Qbil9tW7dmpk0aRL/+vTp04zRaGS+++67gNesWbOGAcD8+eefosYgFHKkRxnmikrm9+wCxlxR6fvGni/YhfanY+tsQ7A4lRdXLeAdNT9IAtqiEDj7rliwicmasYbZdrRA0HVSxPvFnw4wWTPWMHd99pdi50MscvyfzZgxg0lKSmIKCur+Xb311ltM48aNmZ49ezLDhg1jJk6cyFitVubAgQNMixYtmK5duzKjR49mjEYjc8cdd/hc26NHD2bBggU+xyZMmMBMmTKFfz1kyBCmd+/eTPPmzZkxY8YwHTp0YFq3bs2cPn3arz0ffPAB07x5c6Zz587MxIkTmYkTJzLnz58X3FfPnj2Zli1bMtdddx3Ttm1bJisri1m0aBHTunVr5rrrrmNatWrF9O/fn3G5XPx1QsbKjYNrZ968eX7t79ixI3P11Vf7fW/s2LFM+/bt/b4XSNA7d+7MPPjggz7Hrr/+embw4MH862PHjjEff/wxY7PZ/LYdCK1Wy7z33ntBn8NR201J165dmZUrVzJr1qxhzp07J8pOJZKfn88AYFavXu1z/LbbbmP69+8f8LpXXnmFadOmDbN582bmxIkTzHvvvcekpqYy//zzj+C+G4p2mysqmd9/28iYn8lgmLcuEX29aI2Zm8HqcPEJRequP5tGvOFxdB/Kq+XKKsTOyYT3tjNZM9Yw3/3j+z+pxPmJNKTdpN0cDVW7//77byYzM5M5f/48M3LkSL9OciHnEMJoKNrtD8Ga4bAzzPl/GcYe2LHHMOFzAMutbVz/2blmJmvGGqbdEz8ybre7zuuEzMevB/OYrBlrmFFv/l6jv2jSctJu0m4OpWu3kL68KSwsZADUcJoPGjSoVm0eO3Ys07NnT1G2ioEqMEQZRr0GA9o3qvkGl9pFX3dqF8F4p4mxm4FY334D2qIQOPvKK9lio0JTu0iBqzieqI/l86U3ZPLNNhSXV6JTE/Zv5EheGRJ0schMjoPN4cLRfAtaNUqAQReLgjI7Csrs6JJZ99/mhg0bcOmll6JRI2F/V/n5+Xj99ddx44038sfuuece9OrVC9988w3UajX+++8/XHzxxRg1ahTGjBkjapz//vsv/vvvP3To0AGVlZUYMmQIZs6ciS+++KLGuXfccQdWr16Ndu3a+WxTEkpeXh727t2LtLQ0mM1mtGjRAi+//DL+/fdfJCcno7CwEC1atMC6detw1VVXiRprdnY29u/fj8zMTL99nz17FocPH8b06dP9vj9mzBh8++23OH36NFq2bFnnWBwOBw4fPoyHH37Y53i3bt3w9ttv86+3b9+OSZMmYdSoUYLz8P3666+orKxEjx49gjpHKDk5Ofj0009RWlqKP/74A08++SSeeeaZoNuVi4KCAgCo8T+Wnp6OHTt2BLzunnvuwV9//YUrrrgCBoMBdrsdb731Vq1zbLfbYbfb+ddms//C1fUNo16DAalmQFUBxKeGv0N9ImCpAGxmGFNaKU53/d0LiE3tIhZOz6sXG1X6fYnckHaTdtdGfdBui8WCG264AYsWLULTpk0ln0MQgEDNMOcAn44GCrOBhMbATcuBZhdGxkAPcmsb1/+B8+x9XFKcFiqVKiRtc6ldyrxypMs9XqVB2k3aXRuR1G6hfXmTkpKChIQE7Nu3j6/HZbfbcfToUZw/f97vNfn5+VizZo2k361QoqvCF4GCMjs+/P04Csrsvm94CpEJKTYqmBg1oPW05ydPekBbFAJnHyfMCWF0pHM50v87a1LsfISSL3aexu0f/cm/vv/LPXj/t+MAgFyTDaPe2oq9Z9m/mW92n8WNH/whqN28vLwagrF9+3YsXbqU/2IYhn8vKSnJR8wLCwuxefNmTJ8+nc/RdcEFF+Dqq6/GypUrRY9z5MiR6NChAwC2yOR9992Hb775xseGUDF69GikpaUBABITE9GxY0eMHTsWycnJAFjHZ+vWrXHkyBEA4sY6evTogGIOsPMOIKBYc8e58+rCYrHA7XbztnOkpqb6FBFp27YtJk6cCL1eL6jd/Px8TJkyBddddx369Okj+RyhDB8+HKdOncJ3332HLVu24Ouvv8bs2bPx888/B9WunHALH6fT1wHpdDprzaE3btw4nDlzBrm5uTCbzdiyZQseffRRfPDBBwGvmTdvHpKSkvgvMUXklExBmR0f/mtDAZMIxEXAke5VcFSJulvdJoZhqhzpCeF1pFuqFRtV4vwoCdJu0u7aqA/afffdd+Pyyy/H2LFjgzqHIACBmvHjI6wTHQCs+cCyCUB5cWQM9CC3tnH9nyqyAgCSBRQaFQq3hvaueSL3eJUGaTdpd21EUruF9uVNTEwM5syZg7lz5+LRRx/F4sWLMXz4cGg0moBBVp9++iliY2Nx8803C7JVCuRIjzLyy2x4fcMR5JfZfN+wcY70up8+qlTsIlTQg2S+4GipcFsUAmcfF5FuFOhIFzU/HribgCN5FsXORyi5uU9LLJ18Cf/6rRt7YepAtuhFkyQ91tw/AN2bs3874y5sji/v7CuoXaPRyEfMchw+fBibN2/GRx99hEmTJsHlcvHvZWRk+Jx7+vRpAECrVq18jrdu3RqnTp0SNjgvqgtcVlYWbDZbDRtDQXVB0mq1fo9xEb5ixtqkSZNa+zYa2QdmgcbFHeduOOoiLi4OAFud25uysjL+PQDo168fli5dCoPBUGebxcXFGD58OFq0aIFPPvlE8jli6N27t49to0ePRs+ePfHjjz8G3bZccDd21W/O8vLyAt705ebmYu3atXj88cfRuHFjAEDfvn1x/fXX46OPPgrY16xZs2AymfivM2fOhGgU8pJfZsPrB+KRz6QA8SmirxetMbwOmxWpu9VtKq90we50AxDuSBc7JwY/C29/thC+kHaTdteG0rV7y5YtWLFiBXr27Mk7ec6ePYtTp05h6dKlsFgsgs4hCI46NaMgGzj8I6BSA3dsBBp1ZJ3pG+f4PV3KGjIkdoYZrv8zxeUAgBSBjnQh88E9GDd7PRiXe7xKg7SbtLs2IqndQvuqziOPPIJ169ZBo9Fg//79eOSRRzBmzBh+Lqrz0Ucf4X//+x+SkpLqtFUq5EiPMrpmJmHfnCvRNbPaHxWX2kWAIz1gG/7wWsAH1Y4MdM1Mwq4nh4B7hik0Il3KuIw69oaiW7NExc5HKGmcqOe3lwFA+wwjMpPZD0+9Ro1uzZL4G6N0o07Q9jKAdc79/fffcDiqtvdNmjQJS5cuxdSpU2ucX31bYXp6OgCgqKjI53hRURHvAAQAtVoNt9vtc05FRUWN9ouLi2u8VqvVSEkR7jwT2pdYhI4VqDlP1WnXrh3S0tKwbds2v+9v27YNGRkZgraXAYBer0dmZiZOnjzpc/zkyZNo27atoDa8KSkpwdChQ2EwGPDTTz8hISFB0jmhID4+vsac1yeSkpLQo0cPrF+/nj/mdDqxceNGDBw4kD9mNpv5cSYkJEClUtW4abJYLLXOs06nQ2Jios9XQ6BrZhL2XbYTXWNOSYpIF60xXMo2m0mRulvdJi4aXRcbg3ht4F0OtbVRF5yeV3ekK3F+lARpN2l3bShdu41GI2688Ub8+eef2Lx5MzZv3oyioiIUFBRg8+bNsNlsgs4hCI46NeOfz9nvHa4Eml8MXPM6+3r3Z0DxcfHthcvOMMP1b/CkL02KE/aQXIjdXErUSqcbdqdL8HXRBGk3aXdtRFK7g+nrsssuw7x58/D2229j9OjR2LFjB3r16lXjvO3bt+PgwYO48847RdsuBnKkEyy8Iz2EqV0AL0e6/60aSodbZKtUELyglwIXkW6uts2cEMeDDz6I8+fP48UXX5R0ffPmzdGmTRusWLGCP2Y2m/HTTz/h8ssv5481a9YMhw8f5l9bLBbs3LmzRntr165FeXk5/3rFihXo168fNBr/kRhGo7GGWAvtSyxCxyqEmJgYPPzww/j444+xb98+n/cOHz6M999/Hw899BBiY4WnRxo1ahRWrVrFpxCxWq344YcfMGrUKP6c48ePY+nSpT55tKtTWlqKoUOHIj4+Hj///LPfp+hCzvnnn3+wbNkywfYDVdEHHMeOHcPu3bvRt6+wSA+l8tRTT+GTTz7BkiVLcODAAUydOhVutxt33303f8706dP5vyOj0YjRo0fjqaeewoYNG3D8+HG8++67+Oabb3DrrbfKNQx54bZ1RyJHuldql/qAd1qXUOVQrY4xQGoXQh5Iu4VD2i3uHG/tvvDCC31SDixduhQ9evTAxRdfjKVLl6JRo0aCziEIwWSvY793H89+z7oUaDcMYFzA7wvls0smSitYfQ9lahfvGmak6ZGFtFs4pN3i+qq+7s7NzfVpY/Pmzdi1axemTZtWo/0lS5agc+fO6N+/v8DRS4OKjUYZxwosmLHyP8y//gK0Tff6w+YW2AKKjR7JK8M9X+zG2zdfiPYZdTjea3GkB7RFIRwrsODBr/YAABK0sYIX9KLmxwP3NP1McTmOFVgUOR/1gR49euDzzz/H5MmTsXHjRgwZMgRNmzZFbm4uli9fjq5duyImJvDzQ5VKhTfffBNjx46F2WxGhw4d8Mknn6BFixa46667+PMmT56MCRMmID09HU2bNsUXX3zhN/+aSqXC5ZdfjltuuYUXhF9//TVg/71798aCBQvQvXt3GAwGjB07VnBfYhE6VqHMnDkT2dnZ6N+/P6ZOnYoOHTrg2LFjeO+993DzzTdjxowZ/Lk5OTn45Zdf+Ne///47nE4nOnTogEsvvRQA8PTTT6N3794YOXIkrrrqKnz99dcwGo146KGH+OuEFD25+uqrkZ2djXnz5mHVqlX88Z49e6Jnz56Cz1m9ejVef/113HTTTYLHcNNNN6FDhw7o1asXCgsL8e6776JPnz5+ozTqE9dffz3sdjveeOMNPPfcc+jevTs2b97sE1GRlJTk43D47LPP8NJLL2HmzJkoKipCVlYWPvnkk7DmrlMqxwosmLH3Esx3b0JbCRHpojWGj0g3K1J3q9tUXC4+P7rYOTEEiEhX4vxEA6TdwiHtDk67CbZIW0FBAdLT0wUXjKusrITZbEZaWlrYHnDWR2rVDPN5oOAgABXQZlDV8csfB46uB/79Chj4GJDSin9LyhoyaDsjANd/23Q2MjU5TpgjXch8qGNUSNCqYa10wWJ3Is2gk3280QJpt3BIu6t0WUhf1bV73bp1WLZsGUaOHInz589j8eLFePrpp3HFFVf49G+xWLBixQo899xzoudULORIjzK06hhkpSVAq672oSYiIt3udONIvoXPYVortTjSA9qiELTqGDQ26gGYkaATHo0uan48JMax/4puhlHsfNQX/ve//2Hw4MFYuXIl9u/fj/z8fLRs2RKLFi3y+bDt3Lmz30JSI0eOxK5du/DVV1/h4MGDuOOOOzBp0iRotVVOnWuvvRY//vgjfvzxRxQUFGDRokX4+++/axTfuP322zFw4EBs3rwZKSkp2Llzp98tSBz33XcfjEYj9uzZA6vViiuvvFJQX1deeWWNnGsjRoxAly5dfI6NHj0a3bp1EzVWf237Q61W45NPPsGOHTuwZs0a/PHHH1izZg369u2LDz/80OfckpISbN68GQAwceJE2O12bN68GQzD8ILevHlz7NmzBx9++CEOHTqEcePG4c477/TJdSak6Em3bt3QsWNH/PXXXz7Hk5OTeUEXck7Pnj19FuJCxsDlWt25cyfi4+Px3nvvYcyYMQ1iEXrzzTfX6gRfuNA30spoNOKFF17ACy+8EG7TFI9WHYMsdSG0ToekiHTRGsPpsN2kSN2tblOxRbwjXeyccNvLqzvSlTg/0QJpNwtpN0u4tLs6Q4cODRitKOac+sCJEyewePFifPbZZ8jPz8emTZswaNCgWq9xu9147LHH8M477yAmJgZJSUlYtGgRFWL1UKtmnPiN/Z7Zy1frW/QG2g4Gjv0K/LYAGLOYf0vKGjJoOyMA17/VzqZeERqRLnQ+DPpYWCtdKPNEpMs93miCtJuFtJtFiC4L6au6dt92221o0aIFvvvuO+h0Oqxbt87vDu/s7Gxcd911uO222+qcw6BhohCTycQAYEwmk9ymKIe3L2WY2YkMc3RjnafuPVvKZM1Yw+w9W1p3uz8+xra74bkQGBl5th8tZLJmrGGueGWT4GtEzY+H0vJKJmvGGiZrxhrG5nBKsFR5RPv/2ZAhQ5gZM2bIbYasbNiwgYmJiWHef/99uU0hGgAN6jPljV6sNp7YKvpS0Rqz+WW2r+/uE92XHLy/5RiTNWMN88CXuwVfI3ZOft6bw2TNWMOMe3ubVDMbLA3q/0wCpN2k3Q2VhQsXMi+//DKzZ88eBgCzadOmOq95+eWXmZSUFGbPnj2My+Vi3njjDUaj0TAHDhwQ3G/Ufqb8+CirvT/Pqvne6V3se8+mMEzhUf6wlDVkfWLqp38yWTPWMJ/uOCnofKHzMWThZiZrxhpm+9HCUJhZL4na/zMPpN2k3ZGGHtNFGQ6XG0UWOxyuak92uWKguhAX5aglIj2gLQrB4XIj18TmzTIKLDQqFe/8biWe/LAEUd8ZMmQI3nnnHfz99981Ck0SRLTicLlRZK2Eg1FHJke6V2oXJepudZukpHYRiyFAjnQlzg9BRBrS7obJ9OnT8dhjj4nK87548WLccccd6NmzJ2JiYvDAAw+gZcuWeP/998Noaf2hVs3I+Zf9ntmz5nstLgHaD2dzpW+Sll9aDHJrG9c/VwNFaGoXoXCaXmZz+PRHWk5EE6TdkYUc6VHG4dwyXPT8BhzOLfN9g8uRHvJio9wCvqYjPaAtCuFwbhkeXsHeBCWE2ZGujlEhTsOmj/n3bP0szEr4cuWVV+Kiiy6S2wzZmTp1Kt59912fit0EEc0cPl+Ki0zzcZhpAUjIkS4ar2KjStTd6jbxqV3iw+hI1/vPka7E+SEiC2k3C2k3kZ+fj1OnTtUo2DZgwADs2rVLJquURUDNcLuA3L3sz017+L/4iicBqIB9K6uKkkbazgjB9V9QxhYpTAmxvhurabrc4yUiD2k3C2l35KAc6VFGi9R4fHDbxWiRGl91kGFE5Uj320YgaolIF9WODLRIjcctfVvi8z9Oi3KkSx1XYlwsKhwun+h0ov7y2GOPyW0CQRAKpEWCEx9oXkELVb6kiHTRGsPrsFmRulvdJj4i3SB8oS12XNWj16S2QzQ8SLsJgqWgoAAAakSwN2rUCNu3bw94nd1uh91u51+bzebwGKgAAmpG4RHAUQ5oEoC0dv4vzuwJ9L0H+GMx8O1U4Kav0aJRT3zwvw5oYfkP+Ps4kNEVaH5x+OyMEFz/T3z7HwDhOdKF2l3dkS73eInIQ9pNRBry2EUZSXEaDOuS4XvQUc5uLQOqIsjFthGIWhzpotqRgaQ4DVp6BFiMc1vquJLiNMgz2xGCwtAEQRCEQklymzFMvZuNFFeL394sWmO8doYpUXer28Rt/RYTkS52XNyi21rpAsMwfAFgJc4PQRCEHMTEsBvXnc5qKbAcDqjV6oDXzZs3D3PmzAmrbUohoGbk7WO/Z3QFYgLPFQY/BZz5Azj3N7BkKJI0CRjmqJaSod99wPDngSAK1cutbVz/9y1z8q/FXFcXRh3bHldsVO7xEgTR8KHULlFGkcWOL3aeQpGlKlKAj0ZXxQCaup/c5pfZsHjTUeSX2erukHOk22tGI/i1RUEUWezYcawIAJCgq+UmqBqi5scLvSe1y/nSclHXEQRBEPWHouJCfOEcjCJdc0nXi9YYr9QuStTd6jZxdULE5EgXOyfcw3GXm4HNUZVDVYnzQxAEIQfNmjUDAOTm5vocz8vL49/zx6xZs2AymfivM2fOhNVOOQmoGUVH2e/pHWpvQBsP3PIN0P1/AFTIr4zFYudo5Bu7As0vYc/ZsQj4b0V47IwQRRY7lm4/AbuT1VuhEelCtZ1L18Y50uUeL0EQDR9ypEcZOSYbnvluP3JMXoLkndZFwNPufLMdC345jHyzAHGqJSLdry0KIsdkw+bD7LZGMaldRM2PF1o1O/dnSypEXUcQBEHUH3IKSvCMcxJyNC0kXS9aY7yKjSpRd6vbVCTFkS5yTuK1av52p8xeld5FifNDEAQRKUpKSpCXlwcASExMRM+ePbF+/Xr+fafTiY0bN2LgwIEB29DpdEhMTPT5aqgE1IzCI+z3tPZ1NxKXDFz3ATDjJPL/9xMWOG9A/g0/A3ds8ORRB7DhWcAp3Skst7blmGyY+8NBAGxdMKE7vYVqu1Hvm65N7vESBNHwIUd6lNGtWRKOvTgC3ZolVR20cYVGw3CjU4sj3a8tCqJbsyT872LW0WGMQN7yzGR2N0BiiCuZEwRBEMqhW0IJjulvRbdkZ90nhwJO250V6NYkXnG6630v4HS5YapgF8JiHOliUalUMGg9OVVtVb8Hpd+XEARBSKW8vBxnz57lI8wLCgpw9uxZnxzmjz32GIYMGcK/fvrpp/HJJ5/gvffew3///YfJkycDAO6+++7IGq9QAmoGF5EeKD+6P+KSgeRqD9j7PwgYmwJl54F9q0JvZ4To1iwJPz44AACQHKfh06mFCs4xz+VIl3u8BEE0fMiRTlSlXQmLIz2Z/V5pAVwRchqEEEsla7OYiHSpGKttSyMIgiAaIOXF7HcJhUYl4a3tNmUXfSspZ53oKhWQLCJHuhS4reBWuyus/RAEQSiBH3/8EX379sW1116LZs2a4eGHH0bfvn3x4Ycf8uekpqaiSZMm/Otx48bh888/xyeffIKxY8fCbDZjy5YtSE9Pl2MI9QOGAYqOsT83EhCRXhuxOuDiKezPQaZ3kZtSj74LTesihkS9b450giCIcEOO9CjjRKEVty7ZiROFXoVMOEe6gEKjovFewFfLk+7XFgVxotCK7UcLAUTGke5ys1VGz5dSaheCIIiGyolCC26tnIkTCJxjNqSoYwGtge07J19xuut9L8AVGk2O00AdE9qItepwEWzeqV2Ufl9CEAQhlfHjx+Ps2bM1vqZPn86f8/LLL2PDhg0+102YMAHbt2/HsWPHsHr1anTp0iXSpisWv5phyQcqy9jaYymtgu+k+3WezrYAloLQ2RlBThRa8dwP+wGE5yE592Cc22Em93gJgmj4kCM9ylCrVDDqY6H23lLFp3YxCmojUa/BiO5N+Ke/tXdYtYCHrbRuWxSEt11iUruImh8vErRssdGKSoqOCxXl5eWwWCxymyGY0tJSlJWVyW1G0LhcLhQXF8PprD0yxO12w2Sqmfap+jklJSVgGCYomyorK2G3155jUcg51WEYBqWlpX7fKywsRG5ubo2vkpISUX0QDQt1pRlGVECtF6a51ZGkMZ6H2mqHRXG6630vUCwhPzogbU6qL7yr20LIB2m3PNRX7Xa73aL/XlwuF3Jzc2GzBc6hXJu+EwQQQDOKPPnRk7PYiHIR+NWy1DZAkwsAxg0c+zV0dkYQtUrFPxxPFpHCVKi2c7u6zZ4c6XKPN1oh7ZaH+qrdQnS4OkJ12e12Izc3N6waTo70KKNlWjzevvkitEyLrzrIFxsVFpHut43a4NqtlidddDsRpmVaPDIS9QDERaRLHVfzVPZ8hjQ/KCorKzF37ly0adMGqampaNasGdLT03HjjTdi9+7dcptXK7fccguefvppuc2QzOHDh3HttdfCaDSiXbt2SExMxOjRo3Ho0CGf8/Ly8jBhwgQYDAY0a9YMrVq1wg8//FCjvQULFiAtLQ2ZmZnIyMjARx99JNqmtWvX4vLLL0dqaipSUlLQs2dP/Prrr6LPqU5paSluvPFGxMfHo1WrVkhISMCdd97pczMwduxY9OzZ0+eradOmuOuuu0SPg2g4tEQu3ta+gZap0rRPksZ4dpy11JUrTne9xyPVkS5lTqrnVJXaDhEaSLvlo75q96FDhzBhwgQ+HUlmZiZeffXVWtvNz8/H3Llz0bZtWzRt2hQrV66scY4QfScIIIBmFB9nv6e2CU17ANB2MPv9+ObQ2RlBWqbFY+QFmQCAJBGpXYTaXV3P5R5vNEHaLR/1VbuF6HB1xOryc889h6ZNm+KOO+4QPQahkCM9ynC5GZRXOvk0IgC8cqQLi46rdLqRY6pApdMtrNMABUf92qIgXG6Gf7LNRa4JQfT8eIjzRKRbbI46ziQC4XA4cNVVV+Gzzz7Dhx9+CKvVCpPJhMOHD2PcuHH44osv+HPLy8tRWFjIX5efn+/z9LWysjJg5HBRURHKy8t9jplMJp+CTSUlJfxT+crKSlRU1J6yx2QywW63o7y8nI9cdrlcIenLYrHU+pS6trF6t221WgOed/DgQfTp0wcGgwEnT55EcXExzpw5g0aNGqFv3744ePAgf+7NN9+MkydP4sSJE7BYLFi4cCHGjx+Pffv28eesWLECTz/9NJYvX47y8nK8/vrruPPOO7Fly5aA4/DH8uXL8fzzz6OkpARmsxkjRozANddcg+PHj4s6pzqPPfYY/vzzTxw5cgSlpaX466+/sHr1arzwwgv8Ob///rtPJPovv/wCALjppptEjYFoWLgqzChndHBppRXBkqQxHh12VZgVp7ve9wLF5dIc6VLmxJ8jXen3JQ0V0m7S7uoI0eUff/wRt912G3Jzc2GxWPDee+/h8ccfxzfffBOw3R9//BF2ux2//fZbwHOE6DtBAAE0w3SW/Z7cUnR7AbWszSD2+/HNbA72UNgZQVxuBgVlbHRqcpxwfReq7dXrjMk93miBtJu0uzpCtbsuHa6OGF3+7bff8Pnnn+Oyyy4TZbtomCjEZDIxABiTySS3KRFn79lSJmvGGmbv2dKqg2ufYJjZiQyz7mnpbdTGh8PZ9vevDq6dCMPZlzVjDZOdaxZ9ndhxvbv5KJM1Yw1z1WtbxJqqSOT4P1u8eDETExPD/Pvvv3We+9ZbbzGtW7dmHnjgAcZgMDAZGRmMyWRiysrKmFtuuYXRarVMXFwc06JFC2b58uU+1/bo0YNZsGCBz7EJEyYwU6ZM4V8PGTKEmTBhAjNw4EAmMTGRUavVzG233cbYbDa/9jzwwAOMTqdj4uPjmYyMDCYjI4M5cuSI4L7GjRvHXHrppUxycjKjVquZm2++mdm3bx/Tp08fxmAwMDqdjnnqqad82hEy1iFDhjA33HADc/nllzNGo5EZO3asX/uHDRvGdOzYkXE6nT7H3W430717d2bIkCEMwzCM2WxmVCoVs2LFCp/z+vXrx0ybNo1/PWDAAOaGG27wOWfAgAHM+PHj+dcVFRVMTk4O43a7/drkD4fDwWg0GubDDz8M6pzLLrvM53fAMAwzYsQIH/uqc//99zNNmjRhHA6HYHuJKhqKdu9960ZWI37/Qdr1UjTms+sYZnYis3fDMsXprvd4Xl+fzWTNWMPMXFX3Z3igNoTy6Ip/mKwZa5jFm44E1U5Dg7SbtJth6qd2MwzDtGjRgpkzZ46gNgEwn332WY3jUvSdCExD0W5/+NWMb6ax697fXglNewzDMJXlDDMnlW235HTo2o0Q3mvqNzZki76uLrtzTRVM1ow1TJtZPzJut1v28coBaTdpN8PUP+0OpMPVEarLhYWFTFZWFvP7778zI0eOZK677jrBtoqFItKjjOYpcXjjhp5onhJXdZCLFBcYkS4aPiLdt9ioX1sURLNkPdSeNCuRKDbawrPN3xkFT8/zzTYcyq36eziSV8YXWbU5XNh3zsRHCRaU2XHgvNlvO9VZuXIlBgwYgAsuuEDQ+SdOnIDD4eBzWScmJuLRRx/FX3/9hcOHD8NiseCJJ57ATTfdhAMHDogcJftUdsqUKSgtLcW+ffuwadMmvPTSS37PfeONNzB06FDceeed/JPxdu3aCe5rzZo1mDNnDkpKSrBz50589dVXuOyyyzB//nyUlZVhw4YNeOGFF7Bnzx7+GqFj/frrr/Hwww/DZDL5jfQqLS3Fxo0bMXXqVKjVap/3VCoVpk2bhl9//RXFxcXQaDSIiYmB1epbAKi8vBw7duwAwOY1++uvvzBgwACfcwYOHIhdu3bxr1euXImmTZuiqKhI8DydOXMGDocDGRkZQZ0zbdo0rFmzBj///DNOnTqFFStWYMeOHZg6darf8+12O7744gtMmjQJsbHh/zwhlEtz91m8oXkLzRuFocB3IDypXZrHmhSnu973AiUSI9Kl4C9HutLvS+SGtLsmpN3yarfT6URubi6OHTuG+fPnw2q14n//+5/gdv0hVt+J6MWvZpjOsN+TWoSuI00ckNGV/fncX6Ivl1vbmqfEoUdz1h+QLCK1i1C4HWYuN4MKh0v28SoN0u6akHYrZ90tBKG6PHnyZNx66601xhIOyJEeZSTHazGmZzPfitl8jnRp28zrJEBqF7+2KIg4bSxcHp+2mNQuUmmSxOZjr3A0/GKjX+w8jds/+pN/ff+Xe/D+b+yWn1yTDaPe2oq9Z9m/l292n8WNH/whqN3jx4+jbdu2PsfMZrNPeg1vdDodFi5cCJ2OLQZUXl6ODz/8EHPnzkWrVq0QExODadOm4ZJLLsGiRYtEj/Oyyy7DbbfdBpVKhU6dOuHxxx+X1I4QrrvuOgwdOhQAcNFFF6Fbt2649tprcfnllwMABgwYgDZt2uDPP9l5FzPWkSNHYsyYMVAFKNpz8uRJuN1utG/f3u/77dq1A8MwOHHiBPR6PcaPH485c+Zgw4YNOHbsGJ599lkcOHAA+fn5ANgtcTabDY0aNfJpJz09nT8HAOLi4pCRkYGYGGFS5na7MW3aNHTu3BnDhw+XfA4ATJgwARMmTMCIESPQvXt33HjjjXj44Yf530F1vv32W5SUlGDKlCmCbCUaLsmVuRij3oHk5JTIdeqpVZLMlCpOd73vBYo8OdJTImCfv9QuSr8vkRvS7tBD2l03tenysWPH0LNnT3Tv3h3PPvssFixYgE6dOglqNxBi9Z2IXvxqRjgc6QDQ7GL2+1nxjnS5tS05Xot4bSz/c6iJ16rhqWUKi80p+3iVBml36CHtrhuha2ohCNHlN954Azk5OZg9e3ZQfQlFkiN93LhxWLNmDVyuhu/wa2iUllfi2z1nUeqJ+gIgOke6aAI40v3aoiC4J7UAkKANvyOd8eQJK4uCHOk392mJpZMv4V+/dWMvTB3IFuVpkqTHmvsHoLsncmHchc3x5Z19BbWrVqtRWen79zRnzhz07NkTnTp1QtOmTX1yljVv3hxxcVXRCseOHYPL5UKvXr182rjooouQnZ0tbpAAevTo4fO6Z8+eKCwsDJjvLBhatvTNxWgwGPwe43K8iRlrIKHm4J6GB6rOzR3n5vrDDz/ETTfdhMceewzDhg1Dfn4+7rnnHmg0bJQKd+NQPb+c0+n0efJ+3XXXITc3F6mpqbXaB7D/X3fddRf27NmDb775BlptzZtrIedwTJs2DevXr8eJEydgNpuxd+9evPvuuwHFe8mSJRg8eHCNG85IQtqtDEornPjW1R+lTELkOvXocGlZueJ01/teoMTjSE8zRNCR7hWRrvT7Erkh7SbtVpp2d+zYEbm5ubBarVi+fDnuuecefPbZZ3W2Wxti9T3ckHYrlxqa4XYDpnPsz0nNQ9tZc48j/dzfoi+VW9tKyytxsoiNiE2OC31Eukql4jXdbHPKPl6lQdpN2q007RZLXbp86NAhPP3001i4cCG/48Fut8Nut/P570ONJEe6RqPB9ddfjxYtWmDmzJmS/tAJeThbUoGHl/+LsyVeRRG4iHR9mLaZB3Ck+7VFQRwrYAs96GNjoI7x/0QwlJgr2A8vqz1wcYqGQuNEPTo1qfp7a59hRGYy+2Gv16jRrVkSf0OUbtShS6awv80uXbr4FNcAgIULFyI3N9fvE+nqKTa4D/nqNwWVlZU+AuDvCbG/D2iHw/ehCNeuGDER2pdYhI4VqDlP1WnXrh20Wi3279/v9/0DBw5Ap9OhVatWAICEhAR+u9vx48fx9ttv4+jRo+jQoQMAwGg0IjExEXl5eT7t5OXlITMzU/AYORiGwT333IPVq1dj48aNfqPVhJzD4Xa78emnn+K+++7jx9SlSxdMmTLFb4XzkydPYuPGjbjzzjtF2x5KSLsVgMOGsw4jHnbci7MVusj169H3s2aH4nTX+14gohHp+poR6Uq/L5Eb0m7SbqVpN4dKpcLo0aNxzTXX4PPPPxfdF4dYfY8EpN3KpYZmWPIAtwNQqQFj09B2lnkh+z3nP9ZhH4ydEeZsSQVyTDYA4UntAgBGPduuxe6UfbxKg7SbtFup2i0EIbp88uRJxMfHY/z48ejZsyd69uyJ33//HevXr0fPnj1x/vz5oGzwhyRH+vLly3H+/HnMmjULv/zyCzp27IjLLrsMH3/8cY38O4Sy6NI0EYefvwpdmnp9QHK5y3UCPzT9tVEbARzpotuJMBmJbKoVo8i0LlLH1atlMgDA6QbsToo6kcLEiROxe/durFu3TtL1rVu3hsFgwO+//84fYxgGW7du9cn/lpaW5rPViWEYv2K2fft2n9dbt25F69atkZDgPxJVq9XWEGuhfYlF6FiFEBcXh/Hjx+Odd95BWVmZz3vl5eV46623cMMNNyA+Pp7vx5tz585hw4YNPnlNL7vsMqxfv97nvF9++QUDBw7kX9tsNuTm5tZorzr33XcfVq5ciY0bN6J79+6SzrFYLPzvISYmBnFxcTX0zmKx+P3dfvTRR0hNTcXYsWNrtTPckHYrAJsJXVSncFg3EV1aNpHUhCSN8eh7F/UZxemu93iKrWwUTVqCuIcMUubEX2oXpd+XNFRIu4VD2l2Fv+uLi4t9Ih69tVsIYvU9EpB2K5camsGldUnMBNTidzPXqkFpbQG1FnBYgdJToWs3AnRpmgh9LOt2So4T7tQUYze3XjdXOGQfb7RA2i0c0m5xiF13X3XVVT4phXJzczF06FCMGjUKubm5aNEixKm2EESO9NTUVNx///3Ys2cPdu/ejZ49e2LatGlo0qQJ7rjjDuzbty+UdhIhIiZGBV2sGjHeEdZ8jnRhqV38tlEbvCO9NLh2IkxFJfvByj3hForUcXn3473VnBDO9ddfj6lTp2Ls2LF48cUXsXPnTpw+fRq7du3Cjz/+CLVaHTDfGMAK6syZM/HUU0/h+++/x6FDh3D//ffj7NmzeOihh/jzhg0bhk8//RSbN2/G4cOH8dBDD+HQoUM12tu3bx+mT5+Ow4cPY/ny5XjllVcwc+bMgP23b98eO3bswNGjR/ltSEL7EovQsQrltddeQ1JSEoYOHYqNGzfizJkz2Lx5M4YNG4aEhAS8+uqr/Llvv/02XnvtNRw5cgRbtmzBqFGj0K9fP0yePJk/Z9asWdiwYQNefvllHDx4EDNmzMDRo0fxyCOP8OcIKXry4IMP4tNPP8WyZcvQuHFjXlwtFouoc1555RX+yT0A3HLLLViwYAG+++47nDhxAl9++SXef/993HrrrT79u91uLF26FBMnTgx6W1soIO2WGZsJMSoGurh4xMSq6z7fD5I0xqPDMXaz4nSXG49KBZRY2WiilITw667RT0S60u9LGiqk3cIh7a7S5SFDhmDlypU4cuQI/vvvP8yYMQNbtmzB3XffzZ9TXbsrKyt9cveaTCbk5uby2+8B4foeSUi7lUkNzeDzo0tL61KrBqk1QKOO7M/54goxyq1tlS43bE42ij5JRES6GLsTPSljymxO2ccbLZB2C4e0u0q7heiw1HV3JAk68fP58+fxyy+/YN26dYiJicGYMWNw+vRp9OjRAy+//LLP5BPyc7qoHC/8dABPjuiClmnsUyo+R7rA1C7HCyyY9c1ezBvXHW3SDXVfEOcpqFZeXLctCuJEIfvUSxMr7nmT6PnxcK6kAmoV4GLYhX2aIYLb/hsQ7733Hq6++mosXboUH3/8MdxuN1q2bInevXvj6NGjfK6vhIQEpKen17j+iSeegE6nwzPPPIOSkhJccMEF2LJlC5o0qYoefeihh1BYWIi7774bCQkJGDt2LCZPnoykJN+Cvffeey+cTifGjx8Pp9OJ2bNn16gu7c3DDz+MY8eO4corr4TVasXWrVsF9ZWamgqj0fdBWFpaGgwG37+/Ro0a+TyVFzJWf237Iz09Hbt27cKrr76Kxx9/HLm5ucjLy8PAgQOxe/dun34nT56MOXPm4JprroFOp8O4cePw+OOP++Rh69+/P77//nu8+OKLWLx4Mdq3b4/169f7bA8TUvTku+++Q0JCQg2hffTRR/Hoo48KPsdgMPhUHF+4cCFatGiBefPmIS8vD82aNcP8+fMxbdo0nza2bduGyspK3HHHHXXOYSQh7ZYJmwmn3Y3xQsUdeLKoXJL2SdIYT0T6aYsKL3z2l6J0l7sXeHBIe1S62IW22Ih0KXPC1T7xfnCt9PuShgxpNwtpN4sQXX777bcxb948PPXUU9BoNOjatSu2bduGPn368OdX1+6dO3di/PjxAICMjAzMnTsXc+fOxc0334yFCxcCEK7vckDarSxqaEZpcIVG69SyjC5A3l4g7wDQaaR0OyPMwfOsr0EFwKgT7n4So+2JnoA0s80h+3ijCdJuFtJuFiHaLUSHpa67vUlJSYFer69zLiXDSKCyspJZtWoVM3LkSEatVjM9evRg3nrrLaakpIQ/Z+PGjUxSUpKU5sOOyWRiADAmk0luUyLOiQILc/tHO5kTBRb2gMvJMLMT2S9LgaA29p4tZbJmrGH2ni0V2OnvbPtvXli7LQrjg9+OMVkz1jBjFm0VdZ3o+fFwosDCtH/iJyZrxhpm3zlx1yqRaP4/YxiGGTJkCDNjxgy5zZCVl156idHpdMyff/4ptykEQ9qtCLLXMyeebs/c/uzrkrVPksac3M4wsxOZEwsHK053uXuBbUcKmKwZa5hOT/0sug0pc7LvHHvNJc+vr2GLkuYn0jSI/7MgIO0m7VYapN3KpYZmrHmEXfOuf1ZSe3Vq2e+vse2vmBicnRHm10N5TNaMNUz32WtFXSdG2x9evofJmrGGeXfzUdnHKwcN+f9MCKTdpN2RRlJEemZmJux2O2688Ubs2LEDl1xySY1zBg8eXOOpDCE/rRol4ONJvasO2L3yKglM7SKaOE9l3wrfisk1bFEYcVr2KV26MTKR4a0aJaB5ShyOF1optQvRIHj88cfx559/4r777sPGjRtlyy9KsJB2KwBbKVrF5OHjrPVAowcj169nx1kr50nF6S53L7DnNHuPkJoQmRRIRl1VYbLqthBENEParSxIu5VLDc0wewraJYov0CeIjK7s9zxxqV3k1jZuB1ijMO625iLSTRUO2cdLEHJA2h1ZJDnSH3roITz00EN+fzmHDh3itwCcPXs2OOuI8MM50tU6IDZM4hbv5Uh3u4FatoMoCatncS1mC1qwGDw5W62V5Eiv7wjdmtWQUalUWLlypdxmEB5IuxUAVytEn1TraSGHKyZuN9d+noyUlFcCiJwjndPb8koXXG4GasqlSoC0GyDtVhqk3fUIawH73ZBR+3lSadyF/V50FHDaw7d2DzGcvovJjy6WpLiq1C5E9EHaTdodaSR5NJ966qmATzg6d+4clEFEeNl3zoRWM3/EvnMm9oDI/OiS4HKkM27Abgpsi8I4XsAWRbA5XXWcGRr2nTPhv7PsXJRRRHq9Z8WKFXjyySflNoMgeEi7FYDNhH3uVmj1z02R1T6Pxu9zZCpOd7l7gX/PlAIAUiLkSE/QVeWG5KLSlX5fQoQf0m5CaZB2K5cammHNZ78bGoenw8RMQGsAGBdQfELwZXJr235Pv+owPq/mio2aK5yyj5eIPKTdRKQJaWiwxWKhLQQKJzM5Di+N647M5Dj2gM3jSBeR1qVGG3URqwM0nr8Lr/QuotuJMFyV6cZGcUUKpI4rMzkOnZuyvwerPTLOe4IgCNLuCGIzIVNVhJc6HJWsfZI0RmsEoGL7HtlKUbrLjccjuUiT4EiXMie6WDW0avY2mHOkK/2+hCAIgoO0W35qaIbFE5GeULOooqT2qqNSAWnt2J+Ljoau3TDDbUZvnBi+NXWiZ5eZ2eaQfbwEQTR8ROWseOihh/z+DAButxv//PMPLrzwQlEG7NmzB++++y7y8vLQvXt3TJ8+HSkpKQHPZxgGq1atwk8//YSSkhK0bNkSkydPRo8ePUT1G62kJmhxQ++WVQe41C464RHpNdoQQnwqYLIC5SVAahDtRBCXmwEgPke61HGlJmjRqUkiDuaUwWKnbWkEQYSGcGg3IRGbCamqMtzQxgZIjLyWpDExMYAuEal2E27orJXcdzjgxjPv54MAgJR48bZJ1V2DPhbF1ko+lZvS70sIgogeSLuVj49mVFoBh5X9WaIjXZAGpbUDcv4R5UiXW9sqneyaWmxwmhi7qyLSHbKPlyCIho+oiPSjR4/i6NGjPj9zX2fOnMFFF12ETz/9VHB7f/zxB/r16weNRoPx48dj8+bN6N+/P8rLywNe8/TTT+OOO+5A9+7dcdttt8Fms+GSSy7Bjh07xAwlajGVO7B2Xw5M5R5HrV18RHqxtRJf7TqNYmul8I7jktnvFcWBbVEYXD43tUrcPjRJ8wN2PrhrLBSRThBEiAi1dhNBYDPBxCRgbXETydonVWOgT2T73p+vKN3l7gVyS20AgDSDeEe61DkxeGqgcOnUlH5fQhBE9EDarXx8NIPLjx6rF7Wu9kaQljVqz34vOiLNThnIL7MDAPQacckQxGg7V2zUbHPKPl6CIBo+oiLS16xZA5p3CyUAAQAASURBVAC4/fbbsXTp0qA7f+KJJ3D11Vdj0aJFAIBRo0ahWbNmWLJkCe6//36/13z55Ze499578fDDDwMAxo4di99//x2rVq1Cv379grapoXOmpBzTPt+NNfcPQFJ8kleOdOGFz86XVmDmN3vRrVmS8KJgcZ4w9PIqR3oNWxRGoYUVbbE50iXND9j52JLN3oRZKEc6QRAhItTaTQSBzYQzTDqm7WqENX3KJWmfVI2BLhFnGDWm/VyKNe2k9R0OuHuB3q3Y3YhSItKlzgnnSOdSuyj9voQgiOiBtFv5+GiGm0vr0hgQGYTFIUjL+NQux6TZKYO25ZrYB+Vuz25voYjR9sQ4Vs9NFQ7Zx0sQRMNHUo70UIh5RUUFfvvtN1x77bX8saSkJAwdOhRr164NeF3Xrl2xf/9+MAz7QZyfn4/c3Fx069YtaJuigU5NjPjnmWHo1MTzpFxCjnRJxHsc6V450mvYojQ8f2PtG0fGvk5NjLjvCvbmiFK7EAQRamghrgAqStFJdRr/3KSKvPbpk9i+/+dQlO5y9wKVLjcAiHs4ECQGT05V7uG14u9LCIKIOki7lYuPZnCFRhMahbfTtLbsdxGpXeTWNjfD6nunpsJTyYolySu1i9zjJQii4SM4Iv2+++4DACxatIj/ORBchHltnDlzBi6XCy1atPA53rx5c2zatCngdZ988gmmTp2Kdu3aoUWLFjh48CBmz56N22+/PeA1drsddrudf202m+u0r6ESq45Bsne0l4Qc6ZKI8+S990rtUsMWhWGtZCPRk+M1EekvVh3D52OnYqMEQYSCUGs3ESQ2E2JVbiQnJQPqkNZ7rxt9Its3Y4p837XA3QuUerZgS0ntIhUuIp3Lka70+xKCIKID0u76gY9mWDyOdEPj8HbKRaRbC4CK0qr0qbUgt7aVllfVIQkXXI50u9MNp5shLScIIqwIdqSfPXvW789Sqaxk02bExflWU46Pj+ff88dXX32FTZs2YcaMGWjbti3WrVuH+fPnY+jQoejatavfa+bNm4c5c+YEbXND4ExxORauO4xHhndEi9R4STnSJeEvtUt1WxQGl1eN2/Idbs4Ul+O7f84BAMoi1CdBEA2bUGs3ESQ2E86407FwUyUeGV0eWe3TJbJ9b9fhkdYR7rsWuHuBAk8OVSmpXaTC50jnUrso/L6EIIjogLS7fuCjGdZC9qDEQqOC0RkBQxPAksumd2l+kTg7ZdC2Igur73anO2x9GLSxUKnYDeWHcsuwdNsJ0nKCIMKGYEf66tWr/f4sleTkZABAcXGxz/GioiKkpKT4vcZqtWL69OlYuHAh7rnnHgDAuHHjcPz4cTzxxBP47rvv/F43a9YsTJ8+nX9tNptrRMJHCw6XGzkmGxyeLdR8RLpeeER6vFaNPq1TEa9VC+/YT2qXGrYoDM6BrosVF7knaX7AzgdX8MxKjnSCIEJAqLWbCAKGAWwmOJCGnHJI1j6pGgN9IhxQI6dcpSjddbjcOG+y8bvA0iRErEmdk+qpXZR+X0IQRHRA2l0/8NEMa/AR6YK1LK2dx5F+VJAjXW5tM9vY4LR4jTiNFqPtMTEqGHWxMNucKC2vJC0nCCKsSNrb63K58M8///CvDxw4gEcffRTvvPMOn7u8Lpo3b45GjRphz549Psf37NmDHj16+L3GZDLBZrOhTZs2Psdbt26NnJycgH3pdDokJib6fEUrbdINWH5XP7RJN7AH+BzpwuekRhtC4CLSvVK7SGonQjAMA5vnqXmnJuL+XqSOq026AbOvYXdVULFRgiBCTSi0mwgCRwXgdqBNTC6WT7lIsvZJ1k59Etv3BbsVpbtt0g1YdGMvAECMqirPqdg2pMyJkS826giqHYIgiHBB2q1cfDSDS+0SRES6YA0SmSddTm2rdLpR4WDX1N2biyv8KdZuLr1LYpyGtJwgiLAiyZG+cOFCrFixAgBbNHTYsGH47bffMGfOHMydO1dwO7fddhuWLFmCwkJ2K9Qvv/yCPXv2YOLEifw5r7/+OqZNmwYAyMzMRMuWLfHpp5/C5WIjl/Lz8/HTTz+hX79+UoZCSEjt4nYzsDtd4ipvcznSy4trP08h2BxuuDzj4yLWhCJpfjzw0XEUkU4QRIgJlXYTErGZ2O8qNaCVvriTrDHcA3PODgVRZGVT+qXEaxEToxJ9vdQ5SdCR5hIEoWxIu+sJ1gL2exCOdMFaxjnSi49J7itSmCrYB9UqFWDUi3tQLlbbE/VVBUcJgiDCiSRH+vvvv4+77roLALBx40Y0atQIu3btwk8//SSqsvhzzz2H9u3bo3379ujduzeuvfZavPjiixgwYAB/zr59+7B161b+9bJly7B9+3a0adMGAwcORPv27dGpUyc899xzUoYSdew/b0Lnp9di/3nPQppzpOuFPyE+kGNGx6fW4kCOiKKt8TUj0mvYoiDK7FUCfLLAKupaSfMDdj5ueG8HAFrUEwQRekKl3YREbKUAgP2xXdD5mV8ka59UjYE+EfvdWej859WK0t395024dvE2AECKxEJkUufEwDvSXbwtSr0vIQgiOiHtVi4+msE50oNI7SJYy1K5iHRhjnQ5tc1UwT4oZ3OXi9NosdrO7Wg7kGMmLScIIqyIC7X1cP78eaSns09bf/31V1xzzTUAgM6dOyM3N1dwOwkJCVi3bh0OHDiAvLw8dOnSBRkZGT7nPPzwwygtLeVf9+/fH0eOHMHhw4dRVFSErKwstGrVSsowopKMRD0ev6ojMhL17AEuR3qkio1WlAa2RUFYPYtqrToGTZIjY19Goh53D2qH1zZkw2p3gmEYqFTiI/MIgiD8ESrtJiTiiQTPiGPw+CAZtE+XiAxVCR5v9AcyEq+KbN+1kJGox6gLmmLV7nNIlehIl0pVjnQHb4tS70sIgohOSLuVi49m8KldpDvSBcNHpB9nPdR1rBfl1LbSclZfU+M1Ye8/MY7V9BiVirScIIiwIikivWPHjvjoo49w+vRpLF++HMOHDwcAZGdno2PHjqLb69KlC6644ooaTnQA6Nq1K/r37+9zTKPRoFu3brj88svJiS6SRgYdJvVvjUYGHXtAQo50SXCpXexmwOXwb4uC4HKUpyZoI2ZfI4MOUy5rDQBwupmwVjYnCCL6CLV2EyLxONIbJcTKo336JDRSmTEpfquidLeRQYceLZIBAKnxkXWkG6uldlHyfQlBENEJabdy4TVDr+J3nQWT2kUwKa0BqNh1tbWwztPl1LYSjyO9RVpC2PvnUru4GYa0nCCIsCLJkf7CCy/g8ccfR1ZWFi688EJcdtllAIDFixfjnnvuCamBRGgpszmwJbsAZZ7oq8hFpCcD8Dwtryjxb4uC4BbVKhUTMfvKbA78daLY6zWldyEIInSQdsuMx5Fepmkkj/bpk1DGxGGLqbGidLfM5sDuU+x9QapBnoh0Tm+VfF9CEER0QtqtXHjNKPbsDFCpq4LHwolGDyQ1Z38uPl7n6XJqW2k5m9rF7Q7/mporNlpYVklaThBEWJHkSB8xYgTy8/Nx7NgxfP/993z6icmTJ2Py5MkhNZAILaeKyjHxo104VVQOOO2Ay86+oQ9zRHqMuioPu6fgqI8tCoNzpOeY7BGz71RROW5f+ifiNGoAgJXypBMEEUJIu2XG40g/pWomj/bpEnGKycDEksmK0t1TReVY/c95AJGPSOeKjVornbwtSr0vIQgiOiHtVi68Zpz3ONIT0oEYSe4V8aSyu5iFFByVU9u4YqN7z5nC3j8XkX6utIK0nCCIsCIpRzoAGAwGGAwGn2N9+/YN2iAivHTIMGLHrMFIS9ABtqKqN7SGwBfV1oYY4lLYbW+egqOS24kAFk+x0UtapaBDhrhofanj4q4bs2grKhwuKjhKEETIIe2WEc+27w7JTFDaJ1k79YnooDqDHfoHkNY4W1Lf4aBDhhGDOzXGr4fyJedIlzonfGoXT0S6ku9LCIKIXkKt3TabDXq9uPzRdrsdOh19NnrDa0bO7+wBQ3BpXURpUGpb4MRvggqOyqltXI706y9sFvY1dVWOdJCWEwQRViQ50p1OJz755BNs27YNxcXFNd5fvXp1sHYRYUIbG4OmSXHsC7snP7rWyEaMS2lDDPFpQMkJPiJdcjsRwDtHujZWXGSB1HFx1xn1GuSXVZIjnSCIkELaLTOeYtvahKSgtE+yduoSoVW50BSFAGMDkCDZhlCijY2BzcEW+E6TmNpF6pzwxUY9Bb6VfF9CEER0Ekrtnj17Nt544w1YLBa0bt0ab731Fq66KnDx6ZycHNx///34+eef4XK5kJSUhGnTpuHZZ5/lI+OjGV4zjhewB4LMjy5Kg/iCo3U70uXUttIKNrVLZkp82NfUSZ7ULha7k7ScIIiwImnv0YMPPohHHnkEdrsdzZs3r/FFKJdzpRWY9c1enCutqHKki8yPfrqoHPd88TdOi90uxd1cWAtq2qIwzB5H+pE8i2j7pM4PNx/aWPahhoVypBMEEUJIu2XGk9rlHJMWlPZJ1mBtAs4xjTHLMQXn8uouThYpzpVW4MB59n4kRWJqF6lzYvBEpDtcbIFvJd+XEAQRnYRKu9966y28/vrrWLNmDcrLyzFp0iRce+21OHr0aMBr7rzzTpw4cQJHjx6FzWbDV199hfnz52PJkiWhGFq9h9eMQs8DjoTGQbUnSstSPY50ARHpcmobF5G+9UhB2NfUXGqXQkslaTlBEGFFUkT6ihUrsGHDBlx88cWhtocIMxWVLuw/b0JFpQuweRzpIvOjm20O/LQ3F/cMaieu84RG7HdPdXEfWxSG2VOcxGxziLZP6vxw86HXsM+3uJytBEEQoSAc2u1wOKDRaERdwzAMGIZBTKTyiCoFjyO9IjY5KO2TrMEqFSp0qdhf2QoVVpOkvsNBRWVVKjOpqV2kzkmCNhYqFcAwbMFRJd+XEAQRnYRKu19//XXccccdGDBgAADgiSeewPvvv493330Xr7zyit9r9u/fj4kTJ6Jp06YAgCuuuAIdO3bEgQMHgrKlocBrRqZHU4NM7SJKy/iI9BOsiNWyQ0BObeNypBeU2cO+puaKjZZVOEjLCYIIK5JWsTExMejUqVOobSEiQLvGBnx/3wC0a2zgF/XQJ0emc86RXl5Y0xaFUeaJBr+lb1bE7OPmo7FR52MDQRBEKAilds+dOxdpaWnQ6/Xo3LkzNmzYUOc1//33H4YPHw69Xo/GjRvj0Ucfhc1mC4k99QKP5rZLT5BN+9rFV+B73dNol6CceW+bXpViRqojXSoxMSo+T7qpwqHo+xKCIKKTUGh3YWEhjh8/jssuu8zn+MCBA7Fz586A1915551YtmwZdu7cidzcXHz66ac4ffo0br755qDsaSjwmsGcYg8EGZEuipRWAFRAZRm/2zsQcmobF5H+3JhuYe+fy5Fuc7pIywmCCCuSHOnDhg3DN998E2pbiEjjKXwGfVJk+quW2kXJmD1Pz7ktYpEkwbOot1KOdIIgQkiotHvx4sVYsGABVq1aBYvFgptuugnXXHMNjh0LvL04Ozsbl112GTp37oyCggKcO3cOLVq0wL///hu0PfUG/uF1hDTXH9wONG5HmgIw25xwuhkAkXekA0BSvMZjhyPifRMEQdRFKLQ7Pz8fANCoUSOf440bN+bf88ejjz6Kiy66CH379kVWVhbuvPNOvPLKK7jooosCXmO322E2m32+GjxWzxwGmSNdFLE6IKkF+7OA9C5yweVI57Q2nHDrdlOFAwzDhL0/giCiF0mpXfR6PSZPnozvvvsO7dq1q1Fs5KWXXgqJcUToOZhjxs0f7sQXd/RBZ25RH5ccmc7jfVO7+NjSVFx6mXDDRYMvXHcY/dqmRcQ+bj76t00DACo2ShBESAmVdr/++uuYMmUKBg0aBAB4+umn8eGHH+Ldd9/FggUL/F7z5JNPol27dnj99df5fh988EHpg6mPeDT3oCUBN89dL4v2HUQb3Gy7F1/kmtG5fUS7DshfJ9ncsvrYGOg1wgufhwp24V0Bc4VD0fclBEFEJ6Fcd7vdbp/XTqez1qKh48ePR25uLk6fPo3mzZtjy5YtGDVqFGJjYzFx4kS/18ybNw9z5swRbFN9hteMRBU6A0GndhFNWhvAdJotOJrVL+BpcmpbqZV9SD3p4z/x1dS+Ye2fS+3icDG4cO56LLszvP0RBBG9SHKknzx5EoMGDYLJZMLff/8dapuIMJJm0GLKgNZIM2glR8c1TtThsSs7onGiTlzn1XKk+9iiMLjItGFdMkTbJ3V+uPkoMLNb7im1C0EQoSQU2l1UVISjR49i4MCBPscvv/xy/PHHH36vcblc+Omnn/DMM89ApVLB6XQiNlbS7Uf9xqO5aSlJmDKgiWTtk6zBANLiYzEl9iekqYZI6jsccDFjKUFEowczJ94RbF0yExV7X0IQRHQSCu3OzMwEAOTl5fkcz8/P59+rTl5eHr7//nusXr0aLVqwkc+DBg3C+PHj8d577wV0pM+aNQvTp0/nX5vNZv76hga/lt11mj0QZGoX0VqW2hY4vrnOiHS51twOlxtlnsCwW/tmhX1NnaBVQx2jgsvN4IbeLUnLCYIIG5JWskJyoRLKpLFRj3uv8BTsqChlv4t1pHu3IQZuu5snR7rkdiIAl9rlf5e0QGOjXtS1UsfFXff25qMAKLULQRChJRTazS3C09N9o67S09MD5lktKChAeXk5nE4nevbsif379yM1NRUTJ07E888/D63W/0LHbrfDbrfzr+v19nCG4R3pjdMa4d7WTSU3FYx2NjZqcW/sKgCBt+VHHI8nPd0o3gnOEcycJMVxqV2cir4vIQgiOgmFdicnJ6Nbt27YuHEjxo8fD4CNTv/1118xdepU/ryKigo4nU4YjUbExcUBQI1aJhUVFfx7/tDpdNDppH+e1ycaG/W49/LWwG8n2AOGIB3pYjWILzhauyNdLm3j1tMA8NDQ9ohVi8sqLNZulUqFRH0sSsodGNermeg1PEEQhFAk5Ugn6i9WuxN/nixmnbQSI9JNFQ6sP5DHV+EWjHdEutvta4vC4KLBzxSXi7ZP6vxw86FVs1ssKbULQRBKpfr2cLfbHXB7OJen8pVXXsHbb7+NyspKrFmzBh999BFmz54dsI958+YhKSmJ/6rXEW2VFoBxAQCsqoSgtE+yBgOwxibjT3dHWK0WSX2HgxxTBYCqLdlSCGZOuOJk5gqHou9LCIIgguGJJ57Axx9/jC+++ALHjx/HvffeC7vdjrvvvps/5/7770e/fmyKkMTERIwYMQLPPPMMtm7divPnz2Pp0qVYtWoVbrjhBrmGoSisdif+PHQKVrdHv+LTgmpPtJalco7047WeJpe2lXrGYdCpsedMaUTW1Ny9xM4TRaTlBEGEDcmO9K+++grDhg1D27Zt+WPPP/98rQVLCPk5UWjF+Hd34ESh1cuRniyqjTPF5bjz079wprhcXOdcjnTGBdhKfW1RGFxqlxmr9oq2T+r8cPNRXsk6W8iRThBEqAlWu7kt4NXPz8/PR9Om/qOsGzVqBI1Gg1tuuQWXXnopVCoVLrnkEkyaNAnfffddwL5mzZoFk8nEf505c0aQjYqE09sYDU6Y3EFpn2QNBnDClY7xlbNxolQ5+nKsgJ0HjTpwnt66CGZOuNQu5gqHou9LCIKIXkKx7r7xxhvxzjvvYP78+ejXrx+OHDmCX3/91Ue74+PjkZhYlVP6iy++wKhRozBt2jRcdNFFePfdd/Huu+/izjvvDM3A6jknCq0Y/+lBnGCaAnGpgDq4gpqitYyLSC86zu58q81OGbSttJxdTyfoYiX1L0XbOU1/avV+0nKCIMKGJEf6Bx98gAceeACXXXYZjh+vegKampqKF198MWTGEaGnXWMDNj5yOdo1NgC2UvagyIh0ycRqq/qyFvraoiAqnW7YHGy05ep7Lo2Yfdx8tE1n+yNHOkEQoSQU2p2cnIyuXbti48aN/DFue/iAAQP4Y3a7HRUVbKSxRqNBv379UFlZ6dOW3W4PmNYFYLeHJyYm+nzVW7x2gLXLMMqmfe3SdNiofQTtYnIi3ncgYjx3olmp8bL0n8indnEo9r6EIIjoJZTr7smTJ+O///5DXl4eNmzYgF69evm8/+abb2L79u386+TkZCxcuBD79u1DTk4O/vjjD0yZMiW4ATUg2jU2YOP/4tBOdS7otC6SSM4CVDGAwwpY8gKeJpe2mSrY+75GBl3E+ud2mc26uhNpOUEQYUOSI/3VV1/FypUr8cwzz/gcHzFiBJYvXx4Sw4jwoNeo0TbdAL1GLTm1S1BwUenWAl9bFESZrWr7WLdmSRGzj5uPlHjWsWShYqMEQYSQUGn3zJkz8fHHH2P58uU4ffo0HnjgAVRUVPhsD7/33ntxySWX8K+ffPJJLFu2DN9++y3y8/Pxww8/4OOPPw5YrKzB4aW3cmqfPt6ItjE50DtKI953IEzlrNY1TpQnlymfI73Cqdj7EoIgohdadysXvUaNtrFF0KscVbXAIkmsFkjypL2rpeCoXNrGRaSnJmgj1j8XkR6nVZOWEwQRNiQ50k+cOMEvkL1zoqakpKC4uDg0lhFhIcdUgblrDrA5SbmFfVxy5AzwKjjqY4uC4PKjx2liMO/nQxGzj5sPm9OTR5ci0gmCCCGh0u5bbrkFb775JubMmYNevXph37592LBhA5/2BQD0ej3i46sijIcPH45PPvkEzz//PLp06YLZs2dj/vz5eOihh4IfWH3Ay5Eup/bluBIx13ELcspcEe87ENw8qCE9tUsw8DnSbQ7F3pcQBBG90LpbueSYKjB3pwM5TKo8jnRAUMFRubSNc6Rr1TER6597OP7dP+dIywmCCBuSHOnNmjXD/v37AfgK+o8//oh27SJfEZoQjsXmxG/ZBWy0s8SIdF1sDNo3NkAXK+HPJ6EqIt3HFgXB5UdP0MVKsk/q/HDzwVFGjnSCIEJIKLV76tSpOHDgAIqKirB582ZcfPHFPu8vWrQIu3bt8jk2btw4/P333ygsLMTu3btxzz33BCxQ2uCoKGW/65OC1r5gNNiiMuA39wWw2MQX5QwXRVZ267dOI7lsT1BzwkWvmSocir0vIQgieqF1t3Kx2Jz4LVcLCxMXktQukrRMQMFRubSNKzYap1VHbE3NpWs7XmAlLScIImzESrno7rvvxqRJk/Daa69BpVJh//79WLt2LZ577jnMmzcv1DYSIaR9hhHrp18OOCsBh6dwh0hHOt+GFHhHemFw7YQRLiI9LUGHXx4eKPp6qePirssvswFgc6QzDBM9jiaCIMIKabeMeO0AC1b7grm+fdNUrNddD7hbSO4/1FR4Cmx3ayY9zVwwc1KV2sWh2PsSgiCiF9Ju5dI+w4j13TcB/5wLSUS6JA3iC44GjkiXS9tM5eyD8taNErDopgtFXy/F7kQ96966smsTtM8wiu6TIAhCCJIc6Y888gjKysowZswYuFwudOvWDXFxcXj88cdxzz33hNpGIhxwi3qoAF0Ec6RzNxnWwsj1KRKz5+m5US/p3yNojDp2Uc8wQHmlCwk6eewgCKJhQdotI3LUJPGH3lOw1WaW1w4vij0R6akJgQvPhpOqYqMUuUYQhPIg7VY41nz2uxzFRgFBEelywUWkcw+sIwGn6aYK5ey8Iwii4SFpH61KpcKcOXNQVFSE3bt346+//kJhYSGeffbZEJtHhJrDuWXo/9KvOHzGI/q6RCBG3J/B/vMmdJv9C/afN9V9cnW8io3ytuSWiW8njHCpXWJiVJLskzo/3HycKrJCHcNGoVOedIIgQgVpt4x4OdKD1b5gNPiwSYP+tjdwuCIRcLsl9R9K7E4XLB6d4xzqUghmTrxTuxzKMSvyvoQgiOiFtFu5HM4tQ/+DY3HY3TwkEemStCzNy5HOMIHtlEHbSjw50iscroitqTlN//VQPmk5QRBhI6hQV71ej169eoXKFiICJMdrMLZXMySrStkDEqLjGIZLOyLBAC61S3lRlS3xkXtKLQQutUtyvAa9W6WKtk/q/HDzkZKgRYJWDbPNiTK7EzLFNxAE0UAh7ZYBL0d6sNoXjAYnJydjrHorklVlQKWlKkJdJkqs7CJbBaBZcpzkdoKZEy5SzuVmoIuNUeR9CUEQBGm38kiO12Cs9i8kuyxAQvArNklaltwSUKnZlK1lOUBiZs1TZFpzc6ldmibqJfUvZT44TU+MiyUtJwgibIiOSK+oqMD8+fPRp08fNG7cGBkZGejbty8WLFgAm80WDhuJEJKRqMejV3ZERqznCW1chLeZc450S36VLYn6yNpQB1xqlwxjZO3zng+j52k6FUkhCCIUkHbLjK2U/a5PklX7MlKT8Kj+O2SoSr1SvMkHF4WeZtCiaRCO9GDQa2KgUbO7wPRatSLvSwiCiE5Iu5VNhlGHR1WfsZpqCD4iXRJqDetMBwLmSZfrvoNL7dKqUULE+udSu8RpYknLCYIIG6Ic6Q6HA0OHDsXs2bORmZmJyZMnY9KkSWjSpAmefvppDB8+HE4nOf6UTEWlC/vOmVBh4aLjkiNrAPe03ppfZYun0JhS4PKkxmljImqf93wYPHnRLZTahSCIICHtVgCc01qXJKv2VTjc2KfpjgpGC1QUR7z/6nCO9HitWrZ7AZVKxW8FzzPbFHlfQhBE9EHarXwqyoqxz5HJamoIUrtIJq32POly3XeUelK76DWRW1NzUehFFjtpOUEQYUOUI/29997DuXPncPDgQXz77bd46aWX8NJLL2H16tXYv38/Tp48iQ8//DBcthIh4FiBBaPe2opj+Rb2QKQLnxmbsN8rSnAst5i1pcASWRvqgMuRXulkImof/7spsMDgKXRaRhHpBEEECWm3AuAi0uOSfT7rI82xAgtGlT6CY0wmUC6/I73IagcAnC6ukPVegItgO5RTpsj7EoIgog/SbuVz7Mx5jKp8Ecdi2wEaeXZVAfAqOOo/Il2O+w6Xm+HX1CVWR8T651K7WCtdyM6jHOkEQYQHUY70lStXYt68eWjdunWN99q2bYt58+ZhxYoVITOOCD1t0w1Yc/8AtNV6FtASItL5NtIN4g2ISwHUWraduHLp7YQRznndKi1Bkn1S58f7OopIJwgiVJB2KwBb1S6woDQUwWlw23QD1mQtR1vVeUVEpJd4ItL7t0sL6l4g2DnlHOlGfawi70sIgog+SLuVT1udGWu0T6CtMTSRz5K1jItID5DaJViNlEKZzcHnNr+geXLE1tScIx0A0o06Uf0RBEEIRZQjff/+/Rg8eHDA94cMGYJ9+/YFbRQRPuK0anRrloQ4Ryl7QEJEOt+GVi3eAJUKMGSw7dgLpLcTRrgc6WkGrST7pM6P93VcRLqVHOkEQQQJabcC8Co2GpSGIjgNjtOq0S3FhThVpSIi0rnULq0bJQR1LxDsnCZ6NNfmcCvyvoQgiOiDtFv5xNkL0C3mJOKMKaFpT6qWpdae2iVYjZQCl9YlQatGUrwmYmtqjTqGD0izO92i+iMIghCKKEd6SUkJ0tMD5/9q3LgxSkpKgjaKCB95Zhte+eUw8kwV7AEJjvRzpRV4evU+nCutkGaEgc2TnpeXy9piVlaxHJPHkc4wjCT7pM4P/7sx22DQUkQ6QRChgbRbZtxuwGZmf9Yn+XzWSyEYDc4z2/BKYR/kMclARamk/kNJcTnrSD+cUxbUvUCw9yVcRPq50nJF3pcQBBF9kHYrn7zCIrziGI88bYuQtCdZy1I9uxaKj7P3HNUI9r5DClyh0eR4reT+pc4H50g/TmnaCIIIE6Ic6S6XCzExgS+JiYmhoicKp7TcgW/3nENpuUfIJDjSS6yV+OyPU/yWbNEY2DzppSWFHlsc0toJE5w9MTEqSfZJnZ+q342DcqQTBBEySLtlprIMgGd/sz7J57NeCsFocGm5A98WNkcpY1BEahcuIj073xLUvUCw9yXcVvCCskpF3pcQBBF9kHYrn1KTCd+6BqBU2yQk7UnWsuQsICYWcNqAspyadgZ53yGFUs+D8qQ4jeT+pc5Hgo6NYD9vkhj0RxAEUQexYi8YNGhQGMwgIkXHJkZsmzkY+Owt9kBccuSNMLKpXTqqz2PbzNsj338dlHiEv0fzZHauIgT/uwG8cqTTYp4giOAh7ZYRLq2LWgdo9OjYRB9RbfGmYxMjtg07A6w/q6jULs+N6YqOTYyy2ZGoZx3pGnWMbL8bgiCI6pB2K5uOqtPYpp8DNJ4pryHqWNaZXnyM/Upq5vO29xozUnA7vFMSNBHvv0mSHscKrDDqNHWfTBAEIQFRjvSJEyfWeU6rVq2k2kJEEm5Lt4SI9KDxRKSjLDfyfdeBzeHi86klx8snvkZPRLqFItIJgggS0m6Z8cqPrgjiPLlcFRSRnpYgb0GwFI/ecxF0BEEQckPaXQ+wFrDfDYFT8ESMtHasE70wG2g9UG5r+Ojz5DhtxPvm+iRNJwgiXIhypC9dujRMZhCR4kheGe75YjfeZjRoDwD65Mgb4cmRfqTQhnte3YK3b74Q7TPki0TzhhP92BgVckorMG7Z9ojZx/9ubr7QKyKdHOkEQQQHabfMVHOke3/WR1r7juSV4Z5fUvG2uxnaKygi/Ylv92LJxItluxdIiWcX3WdLyjFMYfclBEFEJ6TdyudIsRP32F/G2450dl0tJ407A0d+AfL213hLjvsObod3UrxGtvueY5QjnSCIMCEqRzpR/zHoYzGwQzoM9jz2QHyq6DbSDFpMGdAaaQaJT5iNbES6oeIca4tedIahsFFawYp+crwGxjiNJPukzg//u9HH8n2SI50gCKKeU82R7v1ZL4VgNNigj8XALD0MqgrZI9LdbgYlnofX/dulBXUvEOx9SUoCe12Z3aW4+xKCIAhCmRhsORgY8x8MSY1C0l5QWtakO/s9d1+Nt4K975BCVUS6RnL/UucjzaPpNkfNwqsEQRChgFYKUUbTpDg8PaIj8Ncp9gC3xVtsG6O6SDfCwOZIb2o7Hlw7YaDEyop+UpxG8jhDcV2CrgwAOdIJgiDqPdUc6cFqaDDXN02Kw9NDmwPZxUC5vAtMU4UDLjdbhHXO6G7QxkqP7Qh2TrnULla7U3H3JQRBEIQyaWo7hqc1/wJNpoemvWC0LKMb+z1vP+B2A16FaoNeu0uAy5GeHB/5NXXz1DgAgNNzj0EQBBFqKCI9yrA5XDh2Jgc2xvMMRYIj3Wp34u9TJbBKdfJ6HOk2SwmO5Zlhc7iktRMGTHxEupadqwKLaPukzo93f0Yd5UgnCIJoEFRzpEvVFo5gNNjmcOFYRRxsjIa1yy2f/hZ50roYdGqcKSkP6l4g2PsSLiK92GoP6ndDEARBRAmV5bDZ7TjmbgqbNi0kTQalZWnt2KLmDitQcsLnrWDvO6TA5SdPjov8mjpBy66jiyx2UdcRBEEIRXZH+muvvYasrCzo9Xpccskl2Lp1a53XZGdnY9y4cUhOTkZmZiaeffZZOByOCFhb/zmab8GQd/7FUaYZoEsE1OILap4otOK6d7bjRKFVmhGGxgBUOOrKwJDXfsfRfOXkL+O2oaXEa9i5WrhFtH1S58e7P0rtQhAE0UDgHOlxyQAgWVs4gtFg9h5gH3sPAKaq8LgMcPnRjXpNUPMBBH9fkurJkW6xu4K2hSAIgogCrPk4yjTDkMqFOGoKTZNBaZk6Fmjcif05zze9S7D3HVIo9USkJ8mwpi6vZB32eWU2UdcRBEEIRVZH+pIlS/Dkk09i8eLFOHfuHAYPHoyrrroKp0+fDnjNiRMn0K9fP6SmpuLgwYM4ePAgNBoN/vzzzwhaXn9p3SgBX49NRmtVjqRo9JCg1gDxaWitysHX4xujdaMEeezwAy/6cVp2rqb1i5h93v1xxUbLKCKdIAiiflMtIj3S2uIN37fesyitKIm4DRzFVjZSrLFRJ9t8cCTGaaBSsT9/cNvFirovIQiCIBSItZBdy6a8g9bpBrmtYcnw5EmvVnBUjvsOk1eO9Ej33yGD/X3YKUc6QRBhQlZH+oIFC3DHHXdg1KhRSEtLw0svvYTk5GS88847Aa958skn0axZM7z//vto2rQpkpKS8OSTT+LSSy+NoOX1lwRdLC5JLkOCyi6p0GjIMDZBgsqOS5JMSNApJ1U/V2E8OV7DzlWr1IjZ592fUcfuFLA73XC46CaAIAii3sJFfXsc6ZHWFm/4vuPjPbbJV3CUS+2SbtTLNh8c6hgVkuNY3c1Ki1fUfQlBEAShQCz57Fo2xaoczWjiyZNereCoHPcdpXyOdG3E+2+SxOZIN1NAGkEQYUI2R3pxcTEOHz6MQYMG8cdUKhUGDRqE7du3+73G7Xbjhx9+wA033ICYGNmz0tRL8stsWPy3FflMknwR6QBgaIx8JgmLd5YiX0HbrryfnueX2bB409GI2efdX4JOzR+XnIueIAiCkJ9qEemR1hZv+L41zdkD5fI50ostrCM9XquWbT68SfGkd/lo6wnZbSEIgiAUjjWfXcuWD1aOZnAFR3P+8Tkc6fsOt5upypEeH/k1tcvNBqGVWCvhpoKjBEGEAdm80bm5uQCA9PR0n+ONGzfm36tOfn4+LBYLNBoNBgwYgLi4OLRt2xbPPfccnM7Azka73Q6z2ezzFa0UWSqx5JAGRUwiECctIl0do0JqghbqGJV0QwxNUMQkYslhDYo8i2klwOVIT07QsnO19YRo+6TOj3d/seoY6DXsvyeldyEIgqjHVHOkS9UWjmA0mO9b25Q9oICIdL0mJqj5AEJzX8IVHF3zX46i7ksIgiAIBWIpYNeyhV1DphlBa1lmT0AVA5jPAeYc/nCw9x1isVQ6wfmvk+I0EV9TO1xs54zHFoIgiFCjkH1IVTAMA5XK/4el2/N08fnnn8fXX3+NAQMGYOfOnRg3bhzcbjeeffZZv9fNmzcPc+bMCZfJ9YrOTROx+/J/gN/PAPFXS2/j6WHBGWLMQOeYM9jdexvQdExwbYWQ0gquwrhG8jhDdZ1Bp4HNYaeCowRBEPWZao70YDU0mOv5a1d+BZyDrBHpnCO9Q4YRL1/fI6i2QnFfwkWkPzGiMzo3TQyqLYIgCKKBYy1g17ID9wJNx4WkyaC1TGcE0jsD+fuBc38BideEpl2RcDu84zRq6DXqiK+pe7RIhl4TA5vDDVO5A4l6jeg2CIIgakO2iPQmTZoAAAoKCnyOFxQUICMjw+816enpiI2Nxc0334zhw4cjPj4eV1xxBSZNmoSvv/46YF+zZs2CyWTiv86cORO6gdRHuIWzxIj0kGBgf/+w+N99IBd8RHq8/IJr1LPPuciRThAEUY/hHenJsprhA1cjRcaIdK7YaJpBK5sN3qR4dJ+rlUIQBEEQAbHms98NjeW1ozrNL2a/n/1TNhO8a47JRXIce2/Bre0JgiBCiWyO9NTUVHTs2BGbNm3ijzEMg82bNwcsHKrRaNC7d+8axxmGqTVnuk6nQ2Jios9XtHI034LRey7CUXem5GKj2XlluHzBJmTnlUk3xJiBo+5MjN4/CEfzLdLbCTGc2KbEa9m5WrRVtH1S56d6fwZPQRYLpXYhCIKov1SLSJeqLRzBaDDft8uT2kXOiHTPFm+bwx3UfAChuS9J9aR2+XjbCUXdlxAEQRAKxFLArmV/ax4yzQjJGpt3pP/NHwr2vkMs3Ho6yVPEW441NZejndttThAEEUpkrdj56KOP4qOPPsKPP/6I4uJizJw5E6WlpZg2bRp/zh133IFu3brxr2fOnInPP/8c69evR3l5OTZt2oSlS5fipptukmMI9Y44rRpdtbmIU9klFxutdLpxqqgclU63dEOMTRGnsqNrzEnEadV1nx8hOLFNitOwc5WZJNo+qfNTvT/ekU4R6QRBEPUTtxuwe+qyeBzpUrWFIxgN5vs2eAIKZI1IZ/U2I1EX1HwAobkvSfakdjHqNYq6LyEIgiAUiDWfXcs21oZMM0Kyxm7mcaSf3wO42DVksPcdYimt8N3hLceaOtHjxKeIdIIgwoGsOdLvuOMOmM1m3H333cjLy0P37t2xdu1aZGVlBbzmmmuuweLFi3Hffffh1KlTaNmyJWbNmoVHH300gpbXX5olx2Fe0neArUje1C7GpmimKsK8mHeAJGXkry+vdMLmYIU6zaBFvDYW88Z1j1j/zZLjfPozUGoXgiCI+o3dDLbcFQAd67yu/lkfSfi+/zvEHpApIp1hGH7rd6cmiRjcyX9Kv0iSmsAuuls3SkCz5DiZrSEIgiAUjbUAzVQlmDeiJaAkzUjvCGiNQGUZkH8AaHpBxO87TFxqF096lUj33yw5Dr1aJuOX/Xm8U58gCCKUyF5sdPr06Zg+fXrA9z/88MMax2699Vbceuut4TSrwVLpdKPI4kIao4Y2XlpEekgwNkUlo0aR04C0sgJoE+XPL8dtM9drYhCvjWXnympHWoIO2tjwb96o3h+ldiEIgqjncGldYvWARg+g5md9JOH71iZDCwAVpRHtn8Nsc8LhYh8wGHSxyDFVyDIf3nDFRnNMNlQ63bLaQhAEQSgYlwOoKGHXsq4kpClJM2LUbHqX45uA0zuAphdE/L6jes0xOdbUWjXbj4nqnhAEEQYU8olPRIrsvDL0K3oK2UwLeSPSY7XI1l+AfvZFyD5xWj47vCi0eAqfJegAeOZq3q/B5akTQfX+OEd6GUWkEwRB1E/8FBqNtLZ4w/ddYWQPyJTahUvrkqBV43RxuWzz4U0jI6v9B3PMsttCEAQRDhYsWIBmzZpBo9GgV69e2LJlS53XHDx4EKNHj4bBYEBGRgaeeuopVFZGuXPSWgAAyEYW+r2xR3ma0WoA+/3k7wAif9/BRYEneRzpcqypf/gvh7WFUrsQBBEGyJEeZWQlxuATzUvIUuVJLjaalRaPTyb3RlZafHC2JKlZW9SFQbUTKriI9EYGNipN6jhDdR2f2oUi0gmCIOon1QqNAsFraDDX89c29ewCsxYCDCPJjmAotrIPrlMN2pDcU4SijXQD60jXxKjQMlVB2/QJgiBCwPvvv485c+ZgyZIlKCwsxIgRIzBixAicPHky4DXHjx9H//79kZmZiRMnTuD48eMwGo34+++/A14TFXgc6VkJzpCsiTlCtcZGq8vY76e2A2536NoVCB+RHiffmnr8Rc1ZWyi1C0EQYYAc6VGGkSnD5er/YIyp5PO1im5Dr8HlHdJh1GuCsyW5EWuLLSeodkJFkWdhn+ZZTEsdZ6iuqyo2SjcABEEQ9RI/jvRgNTSY6/lrUz05yV12oNIiyY5g4B5cpyboQnJPEYo2Gnm03+FmAJVKcjsEQRBKZOHChZgyZQquuuoqJCUl4fnnn0dqairefffdgNc88cQTaNmyJd555x2kp6cjISEBM2bMQL9+/SJouQKxsI50ozEpJGtijlCtsZHZC9DEA+VFQMGh0LUrEFOFJ0e6JyJdjjV1r5ZsCluKSCcIIhyQIz3KKCwswMfOK1GoayF5oZhvtuG19dnIN9uCs0XXkrWlsCCodkJFoWdhn5ag9by24+NtJ/iUL0KROj/V+zN6ItKtdpeodgiCIAiF4MeRLlVbOILRYL7vylh2kQ3wkXWRhEvtkpagDXo+gNDcl8Rp1UjQqgEAR5S2TZ8gCCIIiouLkZ2djcsvv5w/plKpMGjQIGzfvt3vNS6XCz/++CMmTJgAFT1c9MWaDwAo1LcIWr+8CdUaG7FaoEVv9udT20Kis2KoikhnHeByrKn3nC4BUOXUJwiCCCXkSI8y8oqK8bJzAvK0LSW3kV9mxxsbjyC/LDgxztO2YG0pLgmqnVDBLexTPald8sw2vLz2MPJEirfU+aneX4KWcqQTBEHUa/w40qVqC0cwGuzTd3wj9qC1SJIdwVDk5UgPdj6A0N2XcNFzR/MiH6VPEAQRLnJzcwEA6enpPscbN26MvLw8v9cUFBTAYrEgJiYGffv2hVarRVZWFp555hk4HIGjfO12O8xms89Xg8PCOtLzYpsHrV/ehErLAPjkSQ+FzoqBS6eSHC/fmvr7f88DAEooIp0giDAQK7cBRGTpGleCg/rJQPKlcpuCrs1SWFvcQ+U2BQBQ5HlK3shTbLRrZhIOzr0qYv1X768qRzrdABAEQdRLbKXsdy9HeqS1xRufvhPSANNpoDzydUr41C4GrazzUZ1myfE4V2pDgp5ujwmCaPi43e6A0eaMp37GvHnz8M0332DAgAHYuXMnxowZAwB47rnn/F43b948zJkzJzwGKwXPTq6ujbU4eKsy9KsGXJ70k9vQdXxiRHWWj0j3PJyWY029+t7+uPqN31FipYh0giBCD0WkRxtWz4I5oZG8dgBAYlP2u1kpOdI9EXKeiHS5MfI50ikinSAIol7iJyJdMSR4IhNlSe3iqUmSoAy95Ug3sg/SC0MRDUgQBKEQmjZl11z5+fk+xwsKCtCkSRO/1zRq1AgajQY333wzBg8eDK1Wi8suuwyTJk3CqlWrAvY1a9YsmEwm/uvMmTOhG4hS4HTT0FheO2oj80IgNo59WF5wKGLdMgyD0nLfHOlywN1flJRXwu2OfFF1giAaNuRIjzKO55sxwf4UjsdIT+0SMluc6awtJcqIuOZzpHsKjh0vsGDCeztwvCAyW7yr91cVkU6OdIIgiHqJH0d6pLXFG5+++dQuMkSkW6uKjco5H9XRxrK3xUfyKUc6QRANh5SUFHTu3BmbNm3ijzEMg02bNuHSS6t2KbvdbrhcbG0mjUaDPn36wO12+7TlcrmgVqsD9qXT6ZCYmOjz1eDwpHY57mqiGP2qQawWaHEJAOD4vj8iZqe10gWnx3H9f/buO86JMv8D+CfJ9t4bu7Sls0gTARUBwQMr2E7FAigWznZ2VKxnObt3eh4eotjLT6wgimIBBBGR3jvL9pps300yvz8mk93sJtkkM8lMdj/v12tfWSYzz/PdAfY78+SZ75MQKQ5mq3FPfcuHWwAAVqG11AwRkVI4kN7NhDaWI1NXidCoRJ/biI8MxcwRWYiPlPcpc2hcmhhLUxXQ0iCrLSVIpV2kT7BDDXpkxkcg1ODdfxNfz0/7/mLCWSOdiCioORlI9zW3SOTkYIe+o9UbSG+72Kjc8wEod13SOoONN91E1LXcc889ePPNN/Hll1+itLQUd999N0wmE+bPn2/f54YbbsDw4cPtf16wYAHef/99rFy5EiaTCd9//z2WLl2Kq666So0fQTtsM9JDY5Nk56+2lMpldr3EOumhhX8oGqc70mz0sBA9IkLF/tS4p86Kj0CMbQFx6Sk4IiKlsAhkN5NjycfLYcuB1Od9byMpCi9fPlJ+LJkZeDnqLcDcAJgKgeRc2W36ymoVWm/sbaVdfP05lTpOmpFe12SGIAguaxgSEZFGORlIl5tD5RzvcKw0kK5CjXT74t7RYYpcUyh1XdI/PQYA0Nhi7WRPIqLgMnfuXNTU1OCOO+5ASUkJhg0bhlWrViEnJ8e+j8FgQEhI6/DAueeei0WLFuHuu+/GsWPH0LNnTzz88MP4+9//rsJPoCG2gfScjDS8PHJ4Jzt7TqlcZtf7NLHd4h/w8l2vAgG4l7TXR48Mtd+7qnVPPem5n1BbUY/KOn44TkTK4kB6N2OurUCtEI2YyFSf//IbWywoNjYiIz4CEaGuH+3rNBargNqYXMRU7UZITZGqA+mmxhb7Y2hJthlpZosVtU1mxISHIMSLT9B9PT/t+5NmpFsFoKHFgqgw/nclIgoq0kB6ZIJ9k6+5xd6kjBzs0LdKNdIFQWhT2iVM9vkAlLsukfJ/qanR5zaIiLTqtttuw2233eby/ddff73DtlmzZmHWrFn+DCu4WK32J7nMkamorW+Wlb/aUiqX2fUYDRjCYK4pRW3BAcRk9lMkTneMDY4LjQLq3VMnRoXiaAVnpBOR8ljapZvZawzBiKbF2Nvoe2mXg6W1mPT8zzhYKq/O2d7iGowofgB7hZ6qLzgq3dTHRoQgPERM1HuLazDi8e+xt9i7Wqm+np/2/UWGGqC3TRxgnXQioiDkZEa6r7lFIicHO/StUo30umYLms3ijO/kmDDZ5wNQ7rpEyrVFRg6kExGREw2VgCDWkd9rCpedv9pSKpfZhUYCPU7GXqEnRrx6QLE43Wmdkd66mLha99Shtlr+0n0+EZFSOJDezeQ0HcSi0JeQk6H+KuM5iVFYlLsROboyoKZQ1VhKbLPPUm0LjQK2+K4ahZzEqIDE0L4/nU7HOulERMHMPpCeYN8U6NzSlkPfKtVIr7Qt7B0RqkdUWIiq56O9IVnionjV9c2w2p5SIyIisrMtNIrIJOSkxGkmf7nU+zTk6MqwqN+mgMRZZauRHt9mRrpa99Q9EiLFmDiQTkQK40B6d2JuRnxzEaYbNiE+OV3taBAfFYrpvYF4XZ3qM9KLbbPPMhMi7Nvio0IxPS/T4ULAn5z1Fxshfs8Z6UREQcZiBppM4vdtZqQHOre05dB32xrpQuAGjSvqpIW9wzvGpLLc1BjodYBFAMr5KDgREbVXZxtIj0nXVP5yqdepiNfVYXrNpwGJU1psNLFNX2rdU6fHi/f1nJFORErjQHp3Ul+OSiEWH1mnoNKi/ifnlXXN+KiiPyqFWNVnpEuPcWfERdq3VdY146Pfj9sXRfM3Z/1JM9JrOSOdiCi4SLPRAYcZ6YHOLW059C2VdrE0A03+f9y7bQxA68Leap6P9kyNZnveLapmeRciImpHmpEek6qp/OVSzlhU6hLwUUUuKgsO+b27Kltpl8So1tIuat1TR4bq7X8mIlISB9K7k7oyFArJWNB8HQpN6s+0KqxuwIKtSSgUklWfkV5kbAAAZLWZkV5Y3YAFn+1AYXVDQGJw1l9MBAfSiYiCUmO1+BoeBxhaF4sOdG5py6HvsCggNFp8I4ALjpbXitcfKbZSamqej/YKqxtgYp10IiJypbZ1RrqW8pdLYdEoTD4NC8w3oHD/Zr93J5V2SYxuHUhX655awoF0IlIaB9K7k7oy5OmP4mivp5HXI77z/V3I6xGPo/88V1Yb9nZuTkOe/ihQo/JAum3mWUZ860C6rz+nksdFSzPSWdqFiDTklVdeQW5uLmJiYjB+/Hhs2LDB42MfeOABhISE4K677vJjhBrQUCW+RiY4bJabQ+Uc3+FYe3mXCp9i8UVZjTSQHuY8Jh8oeV1yzrAMAK0fsBMREdnVloiv0WmK5R6J0u3Z2+3XG0cjZiGvYZOi7TpTbZ+R3lrGRa176pNyEgBwIJ2IlMeB9O5EWlBMunHWgrhM8bWmCLBaVQtDmnmWFR/ZyZ6BFcvSLkSkMW+99Rbuu+8+vPDCCzh06BBOPfVU/OUvf0F+fn6nx/7www/47LPP0LdvX1gslgBEqyL7QHqiunG4Y19wNJAz0sUb2pQ2i3trSabtOoAz0omIqAMpX8akqRuHN3qMEl8L/vR7V9KgdUKb0i5qSYpqLSFHRKQkDqR3J3VlOGpNx9yC83G0vM7nZg6V1eLC137FobJaWeEcLa/D3GUFOGrNAKzmgN7It1ds6jgj/Wh5Hea+9bvX58rX8+OsP9ZIJyKtee6553Dttddi5syZSE9Px/PPP4+4uDj897//dXtcaWkprr32Wrz77ruIilJ/nQ6/kwbS29RHB3zPLRI5ObhD39Gp4qv0QXsASDPSU2PDncfkAyWvS37ZJz62r+lH9YmISB3SjPSYNEXyV1tK5bL2jkYOxdzme3C0sAQw+3dQuXWx0daBdLXuqeubxfvnirpmCAFcVJ2Iuj4OpHcndWXQQ0BYaCj0Op3PzTQ0W7DleDUamuXNJtTrdAgLNUAfnSRuUGnB0cYWi/2T6rYz0vU6HcJC9F6fK1/Pj7P+Ym010k2NLV61RUTkD1VVVdizZw8mT55s36bT6TB58mSsX7/e5XGCIOCaa67BjTfeiDFjxgQiVPW5mJHua26xNysjB3foOyrwM9LL2tVIl3s+AGWvS6JteZcz0omIqIPa1hnpSuSvtpTKZe3pE3sizKCD3tIElO5WtO32qpyUdlHrnjrJVqe92WxFvcLnlIi6t5DOd6Euo7YUPfWleH1cBZCs/mzAnslReP3qk4H/hQMNEBcczRoZ8DiKbTfLkaEGxEW2/pewxxcgzvqLixQvQkwNnJFOROorKhLXs0hLc3ykOTU1FX/88YfL45577jnU1dVhwYIFHvfV1NSEpqbWhbFNJpOX0arMxUB6oHOL275VqJHefrFRNc9Hez2To/DI+UNx0Wvr7dcGREREdm1qpGspf7nTMzkar/ffBBwuBQr/BLJG+KUfi1WwT/5qu9iomvfUEaF6NLZYUVnXbF97jIhILs5I705qimAVdGiKyoTVqv7jTVargCazBdaYLHGDSjPSC20LimUmREDX5pNye3wBOlfO+ovjjHQiCgJ6vd7lY7ObN2/Gc889h3fffRcGg8HjNp9++mnEx8fbv3JycpQKNzBcDKQHOre47VuFGuntS7uoeT7as1oFpESLcRWbGmHRQExERKQRVgtQbyuFFpOuqfzljtUqoClzNKyCzq910o0NLZAuBRMiW2ekq3lPLdVJr2CddCJSEAfSu5OaYuwWemHgJ7HYXaT+zL7dRSYMXPgtdhv6ixtMRarEIc06y2xTHx1oE1+AzpWz/lpnpHMgnYjUl5GRAQAoK3MceC0tLUV6errTYzZs2ICKigr069cPISEhCAkJwbZt2/Dvf/8bISEhLhcdvf/++2E0Gu1fnixmqikuBtIDnVvc9h3gGumNLRbUNIpPWKXaZqSreT7a211kwhnP/QSDTpxZJw36ExERob4CEKwAdEBUsqbylzu7i0wYuHoUdgu9gMKtfutHKpUaGxGCEEPrMJOa99RJMdKCo8znRKQcDqR3JzXFyNaV4aXpachOjOx8fxeyEyPx0mXDZbXh0E5yvC0+dQbSpTqoGXGOP4+vP6eSx8VF2AbSG1nahYjUl5SUhAEDBuCXX36xbxMEAT///DNOPfVUp8fcfPPNaG5uRmNjo/1r+PDhuPXWW9HY2Ohylnp4eDji4uIcvoJKQ7X42m4gXW4OlXN8h2PtNdIDM5AuzQgLM+jtpdSUuKZQ+rokLVb8YL2AC44SEZGkVlyMGtEpgCFEsdwjUbo9h3bP64FsXRlQvg+w+GeClrOFRu39q3RPnWx7yqy8ljPSiUg5HEjvLloagMZqJOjqcOHY/khol+C8kRAVhgtHZstqw6GdFHGGI0zqlHYpspV2yUpwnJHu68+p5HHSjPQazkgnIo246667sGTJEnz33XcwGo1YuHAhKisrcdNNN9n3ufHGGzFixAgA4mKk0kx06avt9i7LPiM9wWGz3Bwq5/gOx8ZIM9JLfYrFW9IM75SYMHspNSWuKZS+LulpW0cmv7JeVntERNSFtKmPDiiXeyRKt+fQ7qknISFcB1iagYpDirYvsS80Gu0Yv5r31Gm2MnJ8woyIlMSB9O7ClvirDcn4cm+d/RNjX1TUNuGdDUdRUSsvIVXXN+PLrQWoDrUNpKs1I73aNiO9XWkXe3xenitfz4+z/qQZe6yRTkRaccMNN+Dhhx/G3LlzkZycjBUrVuCbb75B79697ftYLBaYzd38SRoXpV18zS0SOTm4Q98xtnI8dWVi7Vc/K5cG0m03tk5j8oHS1yXS9cBxDqQTEZFEWk8kRhxIVyJ/taVULmuvur4ZX24vQnXyCHFD6S5F25dU2WekhzpsV/OeOi1OvN4oNXEBcSJSDgfSu4uaYgDAiciBuP3jrThR5fvjykXGRjz85S57SRRfnahqwO0fbcUJIVncoNqMdPHnyIp3fGzMHp+X58rX8+OsP3tplwazy4X8iIgC7d5770VhYSHMZjO2bt2KiRMnOrz/v//9D9u2bXN5/ObNm/Hiiy/6O0x1uRhI9zW3SOTk4A59R6cCOr1Y8zUA5V3Ka6UZ6a0D6XLPB6D8dYm00PexCg6kExGRjTQj3TaQrkT+akupXNaePc64keKGkt2Kti9xVdpFzXtqaT2WMoU/nCCi7q0LP1NNDmyzvQcnCNh92zSEhzivSRtIgzPjsPvxaQi32G5Um0xAUw0QHhvQOIpNzmek2+ML0Lly1p9U2qXZYkWT2YqIUPX/3oiIOqPXu/+c3lVd9C5DEFwOpAc6t7jtW28Q66TXlYoDBLHOF4xVStvSLi5jUpEUy6pdJXj3t+Ms7UJERK2kGum2gXQt5S937HH+WQDsA1DinxnplXXiE9QJ7Wakq3lPLT1ZVmriQDoRKYcz0ruLGvETdENcOqLCQmDQ61QOCDDodWIskXFAuG3BUVNgy7s0tljsK4y3n5Fujy9A58pZf9FhBkh/NLFOOhFRcGiqAQRbqZR2A+mBzi2d9i0Nnksz7fxImpGe2qa0i5rnoz0plj4p0QCAY5V1KkdERESaYR9IF/OmlvKXO/Y4M4aIG/xU2sXVjHQ176mlGumlrJFORAriQHp3YZuRfjykD/72/mYc18Djyscr6ltjicsSN5oKAhpDse1RschQg70eudP4AsBZfzqdzj4rnXXSiYiChDQbPSQCCHX8kDbQuaXTvmMCOZAu3mS3Le2i5vloT4pFutcvMTWhscX/teOJiCgISAtz2xYb1VL+csceZ2hfcUP1cfEDf4XZa6S3W2xUzXvq1DaLjbJMKhEphQPp3YXtBtkSlYKaRjMsMhJJdHgIJvRPQXS4vMpAFkFojUWlgfRCo1irLTMhAjqd46fkDvF5wdfz46o/qU66saGbL9xHRBQsGqvF13az0QHfc4tETg522neMtOB3sU/xeKO1tEvrQLrc8wEof10SHR6CWFuddJZ3ISIiAB1KuyiRv9pSKpe1Z48zPN7+IQDKDyjaBwBU1YuTvtovNqrmPXVarFi6taHFgtom3ksTkTJYI727sM1I75OZgnenj5XVVJ+UaLx7nbw2OrRjH0gP7IKj0oz0zHb10QHff06lj5NmynNGOhFRkJBmpEckdHhLbg6Vc7zTY20DAvYBAj9yVtpFiWsKf1yX9EyKwq5CE45X1qN/emDXbiEiIg1qN5CuVO6RKN2e03ZT+osz68sPAD1GKdqPq9Iuat9Tx4aHoKbJjNKaJsRGhLo5kojIM5yR3l1IM81i5C8kZrEKqGlsgcWq4ONR8dnia4BnpEurgGfERXayp+eUPj/SjHTWSCciChIuFhpVguI52F7aJQAz0ms7zkhXgj+uS3olRwEAjmn8kX0iIgoAixmorxC/V+B+2mkX/rjHbi+5n/haofyMdFeLjfpKqfPRtrwLEZESOJDeXdgW8dzZkITcB77BzgKjz03tKTJh2KOrsKfIJCuknQXG1lhUmpFeZCvtkpXQcUa6Q3xe8PX8uOrPPpDeyMfRiIiCgpuBdF9zi0RODnbat32xUf/OSG9ssaDGlsdS2wykyz0fgH+uS3KSpIF0LjhKRNTt1ZUBEACdAYhMAqBM/mpLqVzWnkOcKf3FjQqXdhEEwT4jPaldjXS176lTueAoESlME6VdduzYgZKSEgwZMgRZWVkeH1dWVoaNGzeib9++GDJkiB8jDHKNRqBJTCSZWTl4fEaC01ImgZYZH4HHZwwVY2lQaSC92jYj3cn5cIgvAFz1Zy/twhnpRETBwc1AeqBzS6d9B2ixUamsS5hB77C4t5rno722seSmxAAADpdzIJ2IqNursd2jxmYAenEuopbylzsOcSbbBtIrDiraR22TGWbbzPH2pV3Uvqe2D6SbGgPSPxF1faoOpNfW1mLGjBnYtm0b+vfvj61bt+LBBx/EwoULOz3WarXisssuw9q1a3HzzTfj5Zdf9n/AwcpoK5cSmYjkpCRcOTZJ3XhskmPCceXYXuIf4nqIr8YTAY1BKu2SFd+xtItDfAHgqr/WGekcSCciCgr2gfSEDm8FOrd02rc0kF7j74F0caZaSkyYw+Leap6P9trGkpsWDQA4VFqrZkhERKQFUpnU2Az7Ji3lL3cc4pRmpFccAqxW+4cCclXbFhqNCNUjItTguv8AaN+ftOCoVF6OiEguVUu7PPTQQzhy5Aj27duHDRs24KuvvsJDDz2ENWvWdHrsE088gcTERAwdOjQAkQY5aXA6PhvGhhZ8v7sERg3MbnaIRSrt0lgNNAdu9lexyfWM9ECfK1f9xUVKNdJZ2oWIKCi4mZGuZh522rc0kN5SBzT5b9BYqk2aEutYH12r1yW5qeKM9EJjI+qamH+JiLq1GrFMKmIz7Zu0lL/ccYgzoRegDwXMDYBJuQlsVS4WGu3QfwC07y89TpqRzoF0IlKGagPpgiDg3Xffxbx585CcnAwAOOusszBq1Ci88847bo9dt24dlixZgv/973+BCDX4GfPF1/gc5FfW4/p3/kB+pfqLZznEEhEPhMWKb9jquftbY4sFlXVi0nc2Iz3Q58pVf3ERttIunJFORBQcGqrFVycz0tXMw077Do8BQsXZ1/4s7yJ9cJ0e5/jBtVavSxKiwpASIw4IHGF5FyKi7s0+I711IF1L+csdhzgNIUBSX/ENBeukS/fUCU4G0tW+p85MEO/zC6sbAtI/EXV9qpV2KSgoQEVFBYYPH+6wfcSIEdi2bZvL4yorK3HVVVdhyZIl9gH4zjQ1NaGpqfUTSJNJ2QU8NK/NjPSBGbHYvHCqfZazL5Row2k7cVlA+T7AVACk9JPVtieKbWVdIkMNDvVaXcbnIaWPa52RzoF0IqKgUF8hvkZ1vE6Rm0PlHO/y2Nh0oPKwOJCenOtTXJ0ptQ+kO85I1/J1Sd/UGJTXVuJgaS3yesTLapuIiIKYNNGrTWkXpXKPv9pz2W5Kf/Geu+Ig0G+KIn1IpV2SojvGrvY9dZbtyfNCIwfSiUgZqs1Ir66uBgAkJTnW605OTra/58x1112Hiy++GFOnTvW4r6effhrx8fH2r5ycHF9CDl5tBtJDDXokx4Qj1OD7X70SbThtJy6wC45KyTQzIcKhXqvL+Dyk9HGtNdL5aDkRUVBwM5AuN4fKOd7lsfY66cU+xeSJEqmUWrsZ6Vq+LpHKuxwqY510IqJuzUlpF6Vyj7/ac9mu9IF5xSHF+pBKuzibka72PXWWbUZ6sbERFtuCqEREcqg2kB4WJv6Sra93fMSnvr7e/l57n376KX766SeceuqpWL58OZYvXw6TyYQjR45g+fLlEATnvxjvv/9+GI1G+1d+fr6yP4zWtRlIz6+sx12fbJP1aNWxijrMe3sTjlXIe9S5QyzSgqMK1mtzR5qR7moFcV/Pla/nx1V/0qfpNZyRTkQUHOwD6Skd3pKbh+XkYJd9SwPptaU+xeSJYltt0jQnpV20el2Sm2pbcJQD6URE3ZuTxUaVyF9tKZXL2usQZ2If8bXqqGJ9VNlmpCdGdZw9rvY9dVpsOAx6HVosAsq54CgRKUC1gfSePXvCYDDgxAnHQdP8/Hz06dPH6TGRkZE4/fTT8dZbb2HRokVYtGgRKioqsGPHDixatMjlQHp4eDji4uIcvroVaWA6PgfNFiuOVdSh2WL1ubmaRjN+2FOKGpkzpDvEEi8NpAdmRnqRUZod17E+OuAkPg/5en5c9SeVnWGNdCKiIGC1tC426mRGutw8LCcHu+zbPpDuvxnppS5mpGv5uiQ3TZyRfrCUA+lERN1aje3+VHqCGsrkL4cuFMpl7XWIM7G3+KrgQHq1m8VG1b6nDjHokW5b6Jx10olICaoNpEdERGDixIn47LPP7Nuqq6uxevVqTJ8+3b5t+/btWLNmDQDg3HPPtc9El7769OmDCy64AMuXL4der9qPo11WS+vAdHw2clNj8On8U+2PK6upQywBLu1SZCvtkpXgfEZ6oM+Vq/7spV0azC4/LCIiIo1oNAKC7WYxKqnD22rmYZd9x0qlXQK/2KiWr0v62V6PltfDrNBACRGRWl577TUMHDgQCQkJmDBhAn7//XePj3344YcRERGBe+65x48RalRLY+sH5G1mpGspf7nTIc62A+lWZXKbtNios4F0LdxTZ9kXHG0MSAxE1LWpOvL89NNP47vvvsP8+fPxzjvv4JxzzkHv3r1x3XXX2ff597//jb/97W8qRhnkaksAqxnQh7TOONMqe2mXgoB0V2RLpBkuSrtohVTapdliRZOZN/JERJomlXUJjwcMyi4Y5jdSzVepBqzCGlss9oXI2i82qmU9EiIRHqJHs8WKE1WcxUZEweudd97BnXfeiSeffBK7du3CqFGjcNZZZ6GgoPP7rh9//BEff/wxcnJy0NLSDZ+QlZ7WCokAIhJUDUUR8TmAzgBYmhR7Ek3K8YlOFhvVgtaBdOZyIpJP1YH0U045Bb///jt0Oh2+/PJL/OUvf8G6desQGdlaamP48OGYOHGiyzbOOOMMDB06NBDhBqfq4+JrbBagN2BXoRF5j3yHXYVGdeMCOsYizUg3Bmgg3VbaJSveeWmXQJ8rV/1Fhxlg0IuLoRpZJ52ISNvqysVXJ7PRgcDnFo/69vMTYWU1Yk3S8BA94iMdb7K1fF2i1+vQN5XlXYgo+D3zzDO47rrrcMkll6BHjx546aWXEB0djddee83tcWVlZZgzZw7efvttREdHByhajWlbH12ns2/WUv5yp0OchhAgIUf8XqHyLu4WG9XCPXWm7Qn0Ag6kE5ECQtQO4KSTTnKbwG+99Va3x//73/9WOqSupfKI+JrUGwCQFhuBv0/tj7RY32dhp8dFYOG5gzs8nu2tDrFIN/INlUBLAxDqfIBbKdJj5q5mpPt6rnw9P6760+l0iI8MRWVdM6rrW2SfdyIi8iP7QqMd66MD8vOwnBzssu84/65R0rasi67NIITbmLzgt+sSiAuO7iky4WBZLaZC40/2ERE5UVVVhd27d+Oxxx6zb9Pr9Zg8eTJ+/fVXl8cJgoDZs2dj3rx5GDduXCBC1SYpN8ZmOWxWIn+1pVQua89pnIm9xUH0qqNAr1Nl92Gfke5kIF0L99Q9bDPSpdKuRERyqD6QTn5WZRtIt63OnRobjnkT+spqUok2nLYTkQCERgMtdeIFS3Ku7D5caWyx2Gu5uZqR7uvP6Y/jEqLEgXTp034iItKoTgbS5eZQOce7PFYq7dJcAzSagAhlF2UvsQ+kdyzrounrEgAD0mMBFGF/SY3s9omI1FBUJJbtSktLc9ielpaGzZs3uzzuxRdfhNFoxIMPPuhxX01NTWhqarL/2WQyeRmtBrWdkd6GUrnHX+25bTexD4CfWyfdyVRlX2y0Y2kXLdxTS/f7rJFORErg6pxdnX1GujiQXtPYgnUHylHT6HuJEGN9C1ZsL4KxXl6ZkQ6x6HQBW3C02FbWJTLUgLhI558n+XqufD0/7vpLsD0KX82BdCIibZMG0qNTnL4tNw/LycEu+w6PASLixe/9kH9LTOKgirNZZZq+LgEwIF0s7XKghKVdiCi46fX6Dn8WBMHpvlu2bMHTTz+N9957DwaDweM+nn76acTHx9u/cnJyZMWsCdL6IdKHztJmBfJXW0rlsvacxtl2wVGZGlssqG+2iM1Gd5yRroV7aqlG+omqeq/aIiJyhgPpXZ2UHG0z0o9V1OOqJRtxrML3JJJfVY+bP/gT+TITkdNYAjSQXmh7rCszoeNj5m7j84Cv58ddf9JjclUKX1gREZHC7DPSnddIl5uH5eRgt337ccHvkjalXbyKyUP+vC7pnx4LADhQWgOr1fmAExGRlqWni2WpysrKHLaXlpba32vv119/RVVVFQYPHoyIiAhERERg+/btePXVVxEREQGLxeL0uPvvvx9Go9H+lZ+fr+wPowb7QLrjjHQl8ldbSuWy9pzGqeBAulTWJUSvQ2x4xwlqWrin7pkcBUC8lzYp9MEHEXVfHEjv6uylXXoDAPqnx2DtvZPR3zbDSk1OY7HfyJ/wa9/SjPRMF/XRgcCfK3f9SQu3VHMgnYhI2+orxVcXpV3UzMNu+/bjB9nuSrto/bqkV1IUwgx6NLZYFR/cICIKhOTkZPTv3x+//PKLfZsgCPjll18wfvx4p8fMnz8fdXV1qK6utn8NGzYM8+fPR3V1tctZ6uHh4YiLi3P4Cnr20i6OM9K1lL/ccRqn7Wl1+1iBDBV14lNnidFhTieoaeGeOiY8BCkx4v30cYU++CCi7osD6V1ZUw1QZ5t5YEuW4SEG5CRFITzE80f0/MVpLAGakV5kG0jPiHO9oGmgz5W7/qR6cyztQkSkcfXl4quLgXQ187DbvgMykN7xw2utX5eEGPTITRNvxvezvAsRBak77rgDS5YswerVq1FXV4dHH30U5eXluPHGG+37zJ8/H6NHjwYAGAwG+0x06Uun09m3dytG2wSv+B4Om7WUv9xxGqc0I72uDGiSl9ukdceSnZR1cdm/H7nqr1dyNAAo9gQBEXVfHEjvyqRHtSKT7LVPC6ob8NAXO1FQrf6K1U5jkS5Q/D6QLvaZleD6QjDQ58pdfwm2gXQuNkpEpHGdLDaqZh5227dfS7u4rpGu+esStNZJ54KjRBSs5s+fj/vvvx9XXHEF4uLisGzZMixfvhx9+7YuytjS0uKwUCgBsFpb82J8tsNbWspf7jiNMyIeiEwUv68+Jqt9aSA9ycVAulbuqXslieVdjlbUBSQOIuq6nK+ySF1Du4VGAaCh2Yw/j1ehodnsc7MRoXoMzYpDRKi8z2GcxuLHG/m2imwrdme4Ke3i67ny9fy46y+BNdKJiIJDJwPpcvOwnBzstm8/zUgXBMFeTs3ZQLrmr0sADJDqpHMgnYiC2AMPPIAHHngAVqu1w8KjALBo0SJYrVaXx//+++9Oj+vS6soASzOg03co7aJE/mpLqVzWnss4E/sADVXimEH6UJ/br6h1P5CulXtqaUY6S7sQkVwcSO/K7AuN9rZv6pcWixW3TZDVrBJtuGwnwKVdsuJdl3bx9ef0x3GJ9hrpnJFORKRpndRIl5tD5Rzv9lg/5V9jQwsaWsRF6ZytS6L56xIA/VnahYi6EFeD4SEh7ocGwsKcD5R2aVJZl9hMwBDq8JZSucdf7XXabmIvoPBPxWakuyrtopV76l7JnJFORMroZh8pdzP2hUb7uN9PS6QZ6XVlgNl/jxYWmzqfka4lifbSLpyRTkSkWeZmoMkkfu9iIF2z/PREmPRodUpMGCJCtV1H1hVpRvqhslpYrILK0RARUcAY88XXuB7u9wtGCb3E1yp5A+kV9tIuHRcU1xJpIJ010olILg6kd2XlB8TX5Fz7pj1FJox58gfsKTL53OzOAiMGPLgSOwuMssJzGktkIhBimyXup1npjS0W+yfn7mak+3qufD0/7vpLsM9I50A6EZFmNdhmo+v0QESC013k5mE5Odht39KM9MZqoFm52VoFVdKaJM7zreavSwDbomV6NJmtOF7JG3Aiom7DRX10QJn81ZZSuaw9l3FKT63LnpEuTn5LinE+I10r99S9baVdik2NaLQ9KUdE5AsOpHdl0kB6ykD7puToMFwzrpfLR6881WxxXT/PU05j0en8Xt5FqtUaGWpAXKTrRxjlnCtfzo+7/qTFRqvrmyEInA1HRKRJdeXia2QS4OLReSXysK852G3f4XFAmFjCBKYin2Nrr9A2I72Hi4F0zV+XADDodeiXxgVHiYi6Ham0i5OBdKXyV1tK5LL2XMaZKM1IPyqr/c5Ku2jlnjohKhSxEeK9P2elE5EcHEjvqhqNQG2x+H1KP/vmtLgI3DqlP9KcLPgVaC5j8fNAeqFRvKnPTIiATqfzPj4/cdefVCPdbBVQ26TMgjZERKQw+0KjSS53UTMPu+1bp2t9dF16lF0BhdKaJC4G0oPiugSt5V32F3MgnYio25DyYXxOh7e0lL/ccRmnVNql+jggY6KWVNpFul/1uH8/cdWfTqdD31TxQ/FDZVzzhIh8x4H0rkqajR6TAUTE2zfXNZmx+VgV6jQwGOsyFj/VaZVIM9KdLXrWVqDPlbv+IsMMCA8R/7uyvAsRkUbVlYmv0Wmud1ExD3fad0JP8bX6uGJ9SjXSXQ2kB8V1CdoMpJfy5puIqNuwz0jvWCNdS/nLHZdxxucA0AEt9a3XLz6wz0h3UdpFS/fU/WwD6QeZy4lIBg6kd1Xl+8XX1AEOm4+U1+Hi/67HkXL1V6t2GYt9Rrp/BtKLbAPpGXGu66MDgT9XnfUnfcpfVd8ckHiIiMhLtaXia0yqy13UzMOd9m0fSJdXL7UtqUZ6jwTnH14HxXUJgAHp4s33AZZ2ISLqPtyUdtFS/nLHZZwhYa0T2HxccNRssdoneSW5KN2ipXtqqUwbB9KJSA7XBaIpuJXtE19THAfS+6XFYNUdZ6BnUpTPTSvRhtt2pE/8/VTapcgozY5zPyPd15/TX8clRIWi2NTIGelERFpVZxtIdzMjXW4OlXN8p8cmtnnMWyGtNdKd9xkU1yVonZF+uKwOZosVIQbORSEi6tJaGltnajsp7aJU7vFXex61m9gbMJ0Q66TnjPG67SrbfalO57q0i5buqTmQTkRK4EB6V+VkoVEAiAg12G8GfaVEG27b8XNpl6Jq24z0Tkq7+Ppz+us4acFRzkgnItKoWtsNt5sZ6XJzqJzjOz1WmpHu48y09prMFpTWNAFw/eF1UFyXQFwsNTLUgIYWC45W1NtvxomIqIuS7kVDo4DIxA5vK5V7/NWeR+0m9gKOrQOqj/rUtlTWJSEyFAa987XHtHRP3T+ttUa6xSq4jJmIyB1Op+mqyqUZ6f0dNhcZG/DUN3vss7J9caKqHvd9uh0nquStdu0yFj8vNiqVdsmKd1/axddz5ev56aw/6VN+zkgnItIoD2aky83DcnJwp30nKDsjvcQoDqKHh+hdPvIdFNclAPR6HfqzvAsRUfchlXWJ6yFOuW5HifzVllK5rD23cUp538cP0CvqxDzvKsd32r8b/rinzkmKQliIHk1mq730HBGRtziQ3hWZm4HKI+L37Uq71DSa8cPuEtQ0+r7YR3V9Cz7+I1/2gK7LWKQZ6bWl4s+isGKTZzPSfT1Xvp6fzvqTLlCkldGJiEhj7DXS013uIjcPy8nBnfad2Ft8rS0GWuTfYJ6oFm9+eyREQudkEMKjmDzg9+sSm/5ptgVHS/hIOBFRlyetF5LQsawLoEz+cuhOoVzWnts47SXdfBtIty80Gh3uW/9u+OOe2qDXoW9KNADgYBk/FCci37C0S1dUcQAQLEB4XOvsbpsB6bH48e5J6sTVjstYopIBQzhgaQJqiloTvAIaWyz2hN/ZjPRAn6vO+kuOES9QKmqbAhQRERF5xYPFRtXMw532HZkIhMUAzbXiTLx2T7V5q9BWSi0rwXW+DYrrEvv74oz0/aW8+SYi6vKqjoqviX2cvq2l/OWO2zhlzkiX7qvdzUjX2j11bloM9hbX4GBpLc4c5HriAxGRK5yR3hUV7xBf04c6fQxN83S6NuVdlK2TXmwr6xIZakBcZHB9jpQaI16glHMgnYhIewShdVEyN6VdNE2nk31T3Zb02HQPNwPpwUSqucrSLkRE3YB9IL23mlH4l/SzGU8AFu9n1lfU2gbSY1wPpGtNv1QuOEpE8nAgvSuyD6TndXhrf0kNJj73E/Zr4CbQbSzx2eKrUdmB9EJbrbTMhAiXj5l7FJ8fdNZf64x0lnYhItKchirAanv8ONr1jHQ187BHfUsLjvr4mHdbxyrrAAA9k6PkxRQgncUi1Ug/XFaHZrM1kKEREVGgdTKQrqX85Y7bOGPSxSfBBQtgOuF1262lXVwPpGvtnlpaLJwD6UTkKw6kd0UlO8XXjGEd3oqLCMU5wzIRFxHqc/MpMeGYPykXKTGua6F5wm0sCt7ItyXNSM/spD464Pu58vX8dNaf1B5npBMRaZA0Gz08Hgh1nWPk5mE5OdijvmXWS23reIVYI71nkuuB9KC5LoE4sz46zACzVcDRijpZfRERkcZ1MpCuRP5qS6lc1p7bOPX61vtuH55E86S0i9buqe0Lh5fWQhAEr9omIgJYI73rEQSgWBpI7zgjPSM+AvdNHySrCyXa6LQd+0D6cdn9tFVkG0jPiOv8MXNff05/HZdse2SOM9KJiDTIg/rogPwcKud4j45VsLTLsUpxIL2XmxnpQXNdAkCn06F/eiy25ldjf0mNvdQLERF1MY0moL5C/N7FQLpSucdf7XncbmIvcY01Hz5Ar6gTJ3i5G0jX2j11n5RoGPQ61DSaUWJqQoYHE+yIiNrijPSuprYEqC8HdHogbUiHtxtbLNhTZEJji8X3LprM2HCoArVN8lYodxtLgnIz4toqspV2yUroPGH6eq58PT+d9Sd9Gl/TZJb190dERH5QZxtI76Q+utw8LCcHe9R3km1RtaojPsUnqW82o6xGvMHulRQtL6ZOBOS6xMa+4GgJHwknIuqypHvQqGQgIs7pLkrkr7aUymXtdRqn/QP0o1633VraxfWsca3dU4eHGNAnRbwu2afxsjxEpE0cSO9qpNnoyf2A0I6zrg+W1uLsf62VVRPsaHkdrlj8G46Wy3us2W0s/pqRXm2bke7BJ8++nitfz09n/cVFhCDUINZ1r6jjrHQiIk2ptZV26WRGutw8LCcHe9R3Uq74WnFIfMrNR8dts9HjI0MRH+X6ce6guS6x4YKjRETdgAcLjSqRv9pSKpe112mc0s/op9IuWrunBoCBtly+v5i5nIi8x4H0rqZ4u/jqpD46APRNjcaXN5+GvqmuZ4cFittY7APp+YBVuQW9pNIuWfGdl3YJ9LnqrD+dTmf/tL+CddKJSEXvvPMORo8ejezsbJx99tnYvn272/137NiBuXPnYuDAgRg6dChuvPFGFBYWBijaAJFmpMeku91NzTzsUd+JvcWn2pprxafcfHSsovOyLh7HFCCexNJfuvnmQDoRUdflwUC6lvKXO53G6ePaKBargKp6cZF1qQSpT/0rzJP+pA/FOSOdiHzBgfSupnCL+JpxktO3o8JCMDwnAVFh6pfHdxtLbCagDwGsLUBtsWJ9Fps8n5Ee6HPlSX8pseJFChccJSK1fPLJJ5g3bx5uvfVWrF69GtnZ2Zg8eTJKSpwPulosFlx99dWYNGkSvv76a7z//vs4cOAApkyZgoaGhgBH70e1npV2UTMPe9R3SFjrY94VB33uy5OFRj2OKUA8iUUq7XK0oh5NZpZZIyLqkipt5c3cDKRrKX+502mcPq6NUlXfDItVgE4HJLuZka7Fe+rWMm0cSCci73Egvas58Yf4mj3G6dulpka89P1+lNoGlNXkNhZDCBDXQ/xeofIujS0W++NnnsxID/S58qQ/aUZ6ORccJSKVPPnkk5g7dy7mzJmDgQMHYtGiRQgJCcFrr73mdH+DwYAtW7Zg9uzZGDBgAEaMGIHFixdj7969+O233wIcvR/VeVbaRc087HHfyf3EVxkD6ccqxUexeye7n4EWNNclNhlxEYgND4HFKuCIwo/fExGRRngwI11L+cudTuOUZqTXlQLN9R63K62DkhQVhhCD62ElLd5TD8hofbrMavW9jB0RdU8cSO9KTIVATSGgMwBZI5zuUlnfjI835aOy3veB2BCDDhlxEQix1ev2VaexSOVdfKjX5kyxraxLZKgBcZGdfyLu67ny9fx40p+04ChnpBORGoxGI7Zv346pU6fatxkMBpx55plYu3aty+N0Osffh9JM9IiIzp8OCho1ReJrJ6Vd5OZhOTnY476VGEiXZqR3UtolqK5LIP5b7s8FR4mIuraqzmekK5G/2lIql7XXaZyRiUB4vPi9F+VdpIH01FjXC4161L8L/ryn7pUUhbAQPRpbrMiv8vzDAyIiAND2c0jkHWk2etoQIMz5DLBBGXH47YEpsrpRog2P2knsBRxdq9iM9EKjOHCTmRDRYVDHp/hUOC7FVn+ugjPSiUgFUl3z9HTHweL09HRs3brVozYEQcB9992HQYMG4eSTT3a5X1NTE5qaWj80NJlM3gccSCbbQHpsptvd5OZQOcd7fGxymwVHfXS0Qpyt3auT0i5BdV1iMzAjFn8er+aCo0REXZG5uXUil/TBshNK5R5/tedVu4m9xLXWqo4BaYM9alea2CVN9JLVf4CPCzHo0S81BruLTNhfUotenTw9R0TUFmekdyUnNomv2a4HJoJKgm8Ln7gizUjP9KA+ulZxRjoRqUkQxMdfDQaDw/aQkBBYPVwY+s4778Svv/6Kjz/+GKGhoS73e/rppxEfH2//ysnJ8T1wf7O0tJZ2ictSNxYlyJyR3thiwYkq8cPr3LQYpaLSjP5ptkXKijmQTkTU5VQdAQQLEBbT6YfjXYYPC456OiNdqwZmcPFwIvINB9K7koLN4qubgfQDJTWY/vIaWbOo9habMO6p1dhbLG92YKexSKVdFJqRXmQbSM+I67w+OuD7ufL1/HjSXzJnpBORilJTxfrfZWVlDtvLysrs77mzYMECvPnmm1i1ahVOOsn5otiS+++/H0aj0f6Vn5/ve+D+VlsCQAD0oUBUittd5eZhOTnY476lgfTKI4DF7HU/h8pqIQhAYlSo2wXIvIrJjYBdl9gMSBdvvg+UsrQLEVGXU35AfE3uB7h5ilmJ/NWWUrmsPY/i9GHBUU8H0rV4Tw205nJ+KE5E3uJAelfR0tBa2iVnrMvdosNDMK5vMqLDfa/qY7YIKDY1wmyRtzBHp7EoPpAuzo7LSvBsRrqv58rX8+NJf9KFinThQkQUSKmpqejTpw/WrVvnsH3NmjUYO9Z17gGABx54AP/973+xatUqnHLKKZ32FR4ejri4OIcvzbKXdckA9O4vreTmYTk52OO+43oAIRGAtQUwep+DD9oGmPulxXRaSi2orktsBthqpB+rqENji0VWn0REpDHl+8XXlAFud1Mif7WlVC5rz6M4pVrw0iKrHmgt7eL+A3Mt3lMDwMAMab0TDqQTkXc4kN5V5P8OWJrEx8/c1HLLSojEoxcMRVaCZ7Oy/anTWKSBdOMJwCr/RrWo2jYj3cPSLoE+V570lx4nxl6s8dXhiajruu222/DGG29g48aNMJvNePbZZ1FYWIgbb7zRvs8dd9yBCRMm2P/80EMP4T//+Q9WrVrV6YB7UKoRa8d78gi4mnnY4771eiDJViddmpnnhbYD6YrFFACexpIaG474yFBYBXH2PRERdSFSWbOU/m5301L+csejOH0oqVpW69mMdC3eUwOtM9IPldWixeJZeUIiIkADi40WFRXhvffeQ0lJCYYNG4ZZs2a5rZkqCAJWrVqF9evXIyQkBKeffjomT54cwIg16sga8bXPGW4fQWtssaDY2IiM+AhEhBpc7hcIncYSmyk+Jm9tAUyFQIK8+rhSaZeseM+SeKDPlSf9SQPpxoYWNLZYVP87JKLu5/bbb0dpaSmmTp2KpqYm9OjRA5999hkGDGiduVVTU4OqqioAQEVFBZ544gmEh4fj3HPPdWjr5ZdfxlVXXRXQ+P1CmpEe1/lAupp52Ku+UwcCpbuAsr3AgGle9dM6kB6rbEx+5mksOp0OA9JjsOloFQ6U1GJoVnwAoyQiIr+yz0h3P5Cupfzljkdx2mekHwMEwe14gsRe2iXG/SQ1Ld5TA0CPhEhEhxlQ12zB0fI69E/v/JqFiAhQeUb6/v37MWzYMPz000+IjY3Fk08+ib/85S+wWJzPPrZarRg5ciRefvllGAwG1NXV4aKLLsINN9wQ4Mg1SBpI7z3B7W4HS2sx6fmf7Te5auo0Fr2hdfDci8fMXJFKu3g6Iz3Q58qT/uIiQhBpuyCQFk8lIgoknU6Hp556CkajERUVFThy5IjTAXKp/EtSUhLKyspw4sQJ7N271+Hr0ksvVeNHUJ59RnrnC42qmYe96jttsPhausfrfg54MSM9qK5L2pBuuPlIOBFRFyIIbWqkux9I11L+csejOKUnwZtrgIYqj9ott63ZlRLrvrSLFu+pAfF6Vsrl+5jLicgLqs5Iv++++5CXl4cVK1ZAp9PhuuuuQ25uLt5//31cc801HfbX6XR49913MWzYMPu2qVOn4qyzzsLNN9+M4cOHBzJ87WiqaV1otM8ZbnftnRKND68fh94p0T53p0QbHreT3A+oPCw+YtfH/YcE7tQ3m1FV3wIAHj9W5uvP6c/jdDodMuIjcKS8DsWmRtl/B0REvtLr9YiNdT57JyamdQBVp9MhJcX9ApxBr22N9E7IzaFyjvfqWB8H0lssVhwtrwPg2UB60F2X2AxIk2qransAhYiIvFBXDjRWA9AByblud1Uq9/irPa/aDY0AYjKA2mJxAltUkts2WyxWVNaJA+mpMe5Lu2jxnloyMD0WW/Orsb+4BjjJq26IqBtTbUZ6S0sLVq5ciVmzZtkXosrOzsakSZPw5ZdfOj1Gp9M5DKIDwNChQwEAxcXF/g1Yyw79CAgWILEPkNjL7a4x4SEYn5uMGBmLoijRhsftSDVapVp1PiqsFmejx4aHID7Sdekgr+NT4bj0OPFipYR10omItKFGKu3S+Yx0uTlUzvFeHZtqG0gv2wdYPa8deqyiHmargKgwA7I8eAIs6K5LbAZkiB8iHSjlLDYioi6jwjYbPSEHCHU/+Uqp3OOv9rxuVxpH8OBJ8ArbbHSDXofEKPcz0rV6Tw205nLOSCcib6g2kH78+HE0NTWhT58+Dtv79u2LAwc8X9jqzTffRFRUFE4++WSX+zQ1NcFkMjl8dSl7vxFfB57T6a5lNU14/ZdD9ppmvig2NuKZb/fKLi3iUSzSTIDKw7L6KrAtNOrNIie+nitfz4+n/dkXHGVpFyIibZAG0j1YbFRuHpaTg73qO6kPYAgHzA1A9VGP+9hTJF5jDUiPtU+UUCwmFwJ6XWIjLVJ2vLIeDc3yF0QnIvK3Dz74AGPHjkXv3r1x/vnnY+fOnW733717N66//noMHToUw4cPx80339z1J69J9dE7KesCKJO/2lIql7XncZxeLDgqtZUcHQa93n2u1+o9NSDOSAeAA3y6jIi8oNpAekODbYZwu8fC4+LiUF9f71EbP/zwAx599FG88MILSE5Odrnf008/jfj4ePtXTo68RSs1xWIGDnwnfj/Is4H0136Wl/DLa5vw358PobxW3kWDR7Ek9xNfZc5IL6gS/731SPRuIN2Xc+Xr+fG0vwxpIJ0z0omI1CcIbRYb7XxGutw8LCcHe9W33iAuOAp4Vd5lV6E4kD40K075mFwI6HWJTUpMOJKiwyAI0Hx9XCKiTz/9FHPmzMH111+PFStWICUlBZMmTUJpaanT/S0WCy677DKMGzcO//d//4clS5Zg586dmDJliv0+vksq2S2+SuXN3FAif7WlVC5rz+M42y442gkpxtRY92VdvOrfSR/+vKcGgAEZYpm2oxV1aGzhh+JE5BnVaqRL9VOrq6sdtldVVSEurvObr7Vr12LmzJl46KGHcNNNN7nd9/7778edd95p/7PJZOo6g+nHN4gLgkQmAjnjOt19SFYctj3ylwAE1jmPYrHPSD8ifmhg8O2frFTapYcXM9IDfa487U+akc7SLkREGtBkAlrEmuCezEhXMw973XfaYKB4uziQPujczvcHsKvQCAAYmhXvn5j8yNtY+qfFYOORSuwvqcGwbM9+XiIiNTzxxBOYPXs25s2bBwBYvHgxsrKy8N///hePPPJIh/0NBgO2b9/u8GTRkiVL0L9/f2zcuBGTJk0KVOiBVbJLfE3P63RXLeUvdzyOM9H7GemeDKRr9Z4aEOu7J0aFoqq+BQdLa5HXg7mciDqn2oz0nj17IiYmBnv2OM5y2rNnD4YMGeL22F9//RXnnHMO7rrrLjz88MOd9hUeHo64uDiHry5jxyfi64CzfR5k1rS4bPHRcmsLYDzuczMFtoF0b0q7aFVGPEu7EBFphvGE+BqRAIRFqRqK4rxccFQQBOz2ckZ6MJPKu+xnbVUi0jCTyYRt27bhrLPOsm8LCQnBlClTsGbNGpfHtS/P1dQkDp6GhbmviR20BAEolQbS3Y9HdEkJntdIL7PNEk/pZKFRrdPpdMzlROQ11QbS9Xo9Lr30UixdutT+eNjWrVuxfv16XHbZZfb9PvjgAzz11FP2P69fvx7Tp0/HnXfeicceeyzgcWtKUw2wY5n4/cirPDrkUFktLnztVxwqU/8xZI9i0etbZ6VX+F4nXRpI96a0S6DPlaf9tc5IV/axPyIi8kF1vvia0NOj3dXMw173nWYbSCjb69HuJaYmVNQ1w6DXYWBGbOcH+BKTH3kby+BM8cOC3UVdbO0dIupSCgoKAADp6ekO29PT01FYWOhRG4IgYMGCBRgwYADGjBnjcr+gXpuspkh80ltnAFIGdrq7lvKXOx7HaZ+Rng9Y3Zc5kSZ0SSVHFelfId72N5ALjhKRl1QbSAeAf/7zn2hpacHo0aMxa9YsnHnmmZgzZw7OP/98+z4//vgjPvjgAwBAbW0tzj77bERFRaGiogK33HKL/WvDhg1q/Rjq2fGp+Dh5cn+g16keHRIeoseAtFiEh/j+V58QFYrLTs5BQlSoz214FUtSX/FVRp10e430hM6TvcTXc+Xr+fG0P2lGeompEVar4FUfRESkMKN3A+ly87CcHOx136mDxNfy/YClpdPdpbIuuanRiAg1+CcmJwJ+XWIzxDbrfnehCYLAfExE2iT9fgoJcXx6OSQkBBaLZ3Wh7733XqxZswYff/wxQkNd/64N6rXJpLIuKf2B0M7vGZXIX20plcva8zjOuB6APkR8ElxaRN2FImkgPd5/58nf99SS/tKM9GIOpBORZ1StBZKWloYtW7bgu+++Q0lJCW677TaMG+dY5/vKK6/ElClTAIjJ/sknn3TaVnx8N6xntXmp+Dp6NqBzv1q2JDsxCs9ccpKsbpVow6t27AuOHvCpH7PFal+Ys0eC54/d+/pz+vu49NhwGPQ6mK0Cymqb7DPUiYhIBVItUQ8H0uXmUDnHe31sfA4QFgs014gfZney+Nq2E+JAep6H9dF9islPbfjSzqCMWOh1QEVdM0prmI+JSJtSU1MBAOXl5Q7by8rKkJaW1unxCxcuxOuvv47vvvsOI0aMcLtvUK9NVrJTfE0f6tHuSuUef7Xndbt6g5j3q46IC47GZ7vctdgkTlLL9GAgXav31JKB9tIu2n6ygIi0Q/Wi2uHh4bjgggtcvj958mT79xEREbjlllsCEZb2FW4BirYChjBg+CyPD2uxWFFV14zE6DCEGnz79LyxxYLjlfXomRTl8YwzWbGkDBBfy/b51E9pTRMsVgEhep1HC6J4HV87vp4fT/sLMeiREReBguoGnKiq5407EZGapNIu8Z4NFMjNw3JysNd96/VARp64sHnxjk4H0rccrwIAjOyV6L+YnAj4dYlNRKgBuakxOFBai92FJuZjItKk1NRU9O7dG+vWrcOMGTPs29euXYuZM2e6Pfbhhx/Gv//9b3z77bcYP358p32Fh4cjPDxI62bbFxr1bCBdifzVllK5rD2v4kzsJQ6kVx8DcJrL3YqNYolRT/KeVu+pJQPSYwCIpWBrGlsQG6HsEwFE1PWoWtqFZNj8tvg6+HwgOtnjw/YV1+CUp1Zjn4xHlw6W1uIvL63BwVJ5n9p6HIu02EvJLnERGC9J9dEzEyJg0Hs2c9+r+Nrx9fx4019OkljrPb+ywas+iIhIYdW2hbATPBtIl5uH5eRgn/rOGCa+Fm93u5vFKmDL8WoAwOieng+kB+V1SRvSoqpSWRsiIi265ZZb8MYbb2Dz5s2wWCx48cUXceLECdxwww32fe666y5MmjTJ/ufHHnsML7/8Mr799luceqpnZUSDmn0gPc+j3ZXIX20plcva8ypODxYcbTZbUW5bbNSTGelavqcGgISoMKTHiR/+cFY6EXlC9Rnp5IOmWmDH/4nfj57j1aE9k6Pw5pyT0TPZ8xIn/uJxLKmDAJ0eaKgEakuA2Ayv+pHqo2fFe77QqFfxKcSb/rITowBU4kRVvf8DIyIi17yska5mHvapb2kgvcj9QPr+khrUNpkRHWbweKFRn2PyE19iGZIVhy+2FnLBUSLStDvvvBMlJSU444wzYLVakZqaik8//RSDBg2y72M0Gu3lXyorK/Hoo48iIiICF110kUNbL774ImbN8vyJ6KDQ0iiuBwK0LrTdCS3lL3e8ilNacLTqmMtdSmwlU8MMeiRFhynbvwJ86W9AeixKTE3YX1KD0V48VUdE3RMH0oPRrs+A5lpxEc7eE7w6NC4iFGcOSu98xwDwOJbQSPFnrTgozhTwciD9eKU42JyT5F3yDvS58qa/nETxZ+GMdCIiFbU0AHVl4vcelnZRMw/71HeGrc5o8Q7xqTAXa7JsPmYr69Iz0aunv4LyuqSNobZ68LsKOZBORNql0+nw7LPP4qmnnkJNTQ0SEhKga/f7/MUXX0RLi7iwdGJiIoqKnC842SXXJiveAVjNQFSK29rgbWkpf7njVZzSjPTqzgfSM+IjOvwbkt2/Anzpb2B6LNYeKMf+Ei44SkSdY2mXYGRfZHSOx4uMSipqm/DOhqOosD2OpSavYpFmBpTu9rqfYxXiQHpvLz8FD/S58qa/7ERxdv2Jas5IJyJSjVQfPSwGiPRsBpOaedinvlMHAfoQ8akwU4HL3aSB9FE9E/wfk5/4EsuQTLG0y7GKetQ0tvgrNCIiRYSEhCAxMdHpAGhcXBySk8WSoTqdDhkZGU6/IiO9e8o3KBT+Kb72GOXx/bWW8pc7XsWZ2Ed8dTMjvchoG0j3cF0QLd9TSwZkSAuOciCdiDrHgfRgU7QdKNgM6EO9WmTUfrixEU8s32NPgL4KU2BBFa9ikRZ9KfFlIL0OANArOdqr4+ScK1/Ojzf9SbPrOSOdiEhFRqk+ek+Pb7yVyMO+5mCf+g6NAFIGit8X73C6iyAI+PWgWA5gXF/P123xOSYnAn5dYpMYHYYsW43YPUW8ASciCkoFtoH0rFEeH6JU/mpLiVzWnldxSqVdagrFp+6cKDa2zkhXvP92/H1PLRmYLg6k7ytmjXQi6hxLuwSbLe+Kr4POBWJSvT48r0c89j95tqwQlGjD63ZkzEg/ap+R7t1Auq8/ZyCOk2akF1Y3wGIVvHqMnoiIFFJ5RHz1sD46ID+Hyjne52MzTwJKd4kD6QM7Hn+gtBalNU2ICNVjlJe1RYP2uqSNIVlxKDQ2YlehEaf0SZIdBxERBVjbGekeUir3+Ks9n9qNSgYiEoDGarGsqrROShvFttIuniw06nX/Kh3XLy0GAFBe24SK2iYkx4R73S8RdR+ckR5MWhqB7Z+I34+6Rt1YAk2akV66B7B4/uh0XZPZvqq41heC8UZ6XARCDTqYrYL9YoaIiAKs8rD4mtxP3Tj8zb7g6Danb689IM5GH9M7CRGhhkBFpRlSnfQdJ4wqR0JERF5rNAHlB8TvvZiR3iXpdGJJNwAo2+d0F29npAeD6PAQ5CSJE9X2l3BWOhG5x4H0YLLvG/HT4bgeQN9JPjVxpLwOV77xG46U1/kcxsHSGpz777U4WCrvEWavYknqK346bmkCSnZ63IdUHz0xKhTxkaH+i68NX8+PN/0Z9DpkJYjJ/ngF66QTEami4pD4mtTX40Pk5mE5OdjnvqWBdBelXdYdEBdcndA/JXAxtaHKdUkbI2114bfkV8vqn4iIVFC0FYAgLhruxRPfSuSvtpTKZe15HWeqrZybi4H0QqNY8sXTGulavqduSyrvwjrpRNQZDqQHky3via8jZgF632Z8heh1SIoOR4iMUiCNLVbsKjShscXqcxtex6LTAT1Gi9+f+MPjPnytj+51fG34en687a9PivgzKXXxRkREXqq0DaQn53p8iNw8LCcH+9y3NJBefQxoqHZ4q6HZgg2HKwAAE/p7X3IuaK9L2hiRkwBAzMdVdc2yYiAiogCz10cf6dVhSuSvtpTKZe15Had9IH2v07elNbqkNbsU798mUPfUkgFSnXQOpBNRJ1gjPVgYC4BDP4rfj/B+kVFJTlIUXrnCu4sEf/E6luyTgUOrxcVWcb1Hh0j10Xv5UNYl0OfK2/76psTg531lOFzGx8+IiALOYgaqjorfJ3k+kK5mHva578hEIL6nuLhqyU6g9+n2t37ZX4bGFiuyEyMxKCM2cDH5ga+xJESFoW9qNA6X1WFrfjUmD0rzQ3REROQXx38TX7PHeHWYlvKXO17H6WZGekOzxV42NSfRs/trrd9TSwbarmH2F3MgnYjc44z0YLHnKwAC0HO8V4+Qt2exCqhpbIHFKigXW6Bi6XGy+OrFjPQDtkfB+qXGeBtewM+Vt/3lpokz0g9xIJ2IKPCMxwGrGQiJEEuueUjNPCyrb3ud9O0Om1ftKgYATBuaAZ3O+1l5QX1d0sbIHHGR1S3Hq5QOi4iI/MVqBY5vEL/vdZpXh2opf7njdZxSjfTKQx3WJjtRJU5Si40IQXyUZ2VTtX5PLZEG0vcW12j+75SI1MWB9GCx52vxdcgMec0UmTDs0VXYU2RSICh5vI5FWkW94gBQX+nRIVKNswE+zJIL9Lnytr++KeKHA4dZ2oWIKPAqbAuNJvYB9J5fTqmZh2X1nTVCfC3cYt/UYrFi9d5SAOJAesBjUpicWEb1SgDAOulEREGlbI+4BlloNJB5kleHail/ueN1nHE9gLAYcbKAtKi6Tb5tIN3T2eg+9S+Tr/31S41BVJgBtU1mHCzlRDUico0D6cGgthQ4tl78ftB5sprKSYzCf2aN8ir5+aMNn9qJTgFSbI+aHV3X6e4Wq4ADtlW3pcVD/BpfgI+TZqTnV9ajyWzxqi8iIpLJh/rogPwcKud4WX1LH2YXbLZv+mlvKYwNLUiNDcfoXonetyk3JgXbkNuONCN96/FqWDmTjYgoOEj32DljAINnM6wlSuUef7Xnc7s6HZAyQPy+XZ301vrokf7rX6XjQgx6nJQdD4BPlxGRexxIDwb7vgEgiAugJOTIaio+KhTnnpTp8aNY/mrD53b6ThJfD//c6a7HK+vRZLYiPETv8WIosuML4HGpMeGIDQ+BVQCO2WrBExFRgJTuEV+lm00Pyc2hco6X1XeWbSC98pD9qbBPN58AAFw0sgcMPi62FvTXJTYD0sWZbDVNZhzgTDYiouAgDaR7WdYFUC73+Ks9We1K5V1KdjtsPl7p/Yx0rd9TtzWyp1SmrdrrY4mo++BAejCQyroMPl92U1V1zfi/P/JRVdfscxtlNU14Y+1hlNU0BT4WLwbS99kWCumfHuPTDb6v58rX8+NtfzqdDn1TxVnpXHCUiCjApIH09KFeHSY3D8vJwbL6jkpqXaOlcAsqapvwo62sy8Wjs71vT4mYbFS9LrEJMegxynYD/tvhCllxEBFRAAhC60B6z/FeH65E/mpLqVzWnk9xSmVuih3XRcmXBtK9mKSm9XvqtqQ8viWfM9KJyDUOpGtdQzVw+Bfx+8EXyG6uoLoB93y6HQXVDT63UWJqxBMr9qDE1Bj4WHqfBugM4oy46uNud91bLNZFG+BDWRef44Pv58eX/nJti6iyjhsRUQAJQutAetpgrw6Vm4fl5GDZ1wA9Rtsa+hMfbcqH2SpgeHa8z3lWkZig8nVJG6f2SwYArD9ULisOIiIKgIqDQG0xYAgDsk/2+nAl8ldbSuWy9nyKM3OE+Fq0zWGzfUa6F6VdguGeWjKyZwIA4EBpLYz1Le53JqJuK0TtAKgTB1YB1hbx8aqU/rKbG5oVh4NPnu3zI9hK8imWiHgg5xRxdfV9K4GxN7rcdattwa/h2QmBi08GX/oblBkLbAF2a3yRGyKiLsV4AmgyAvoQINm73KxmHpbdd4/RwI7/g+XEH1h6dCQAYM5pvdWNSUFyYzk1NwXAPvx2uBIWq6CJn4mIiFw4uFp87TkOCPV8YFiipfzljk9xZuQB0AGmAqC2DIhJhdUq4Eh5HQCgb0qMf/uXQU5/KTHh6JcWg4OltVh/qBxnD8v0Q4REFOw4I13r9nwlvipQ1gUQy4GEGPTQ6dRP+D7HIs3M3/W5y10EQbAPpEufLAcsPh/50t+QTHFBlN2FHEgnIgqYUlvN0JQBQEiYV4eqmYdl922bkd5yfBPKahqREReB807KUjcmBcmNJS8rDrHhITA2tDAvExFp3SHbQHq/qT4drqX85Y5PcYbHAsn9xO+LxVnpBdUNaDJbEWbQIzvR8w8eguGeuq0J/VMAAGsOlCkZFhF1IRxI17LmeuDAD+L3Cg2kH6uow7y3N+FYRZ0i7akSy5AZ4uvxDYCxwOkuRyvqUV3fgrAQPQZlxAU2Ph/50t+QLPFnO1pRj5pGPn5GRBQQJbvEVy/LugDq5mHZfWcMg6APQURTBbJQgTmn9UaoQd6lZJe4LrEJMegxtm8SAJZ3ISLStJZG4Mha8fvcKT41oaX85Y7PcWYOF19t5V0O2dbk6p0ShRAvcn8w3FO3dcaAVADAmv3lEARBydCIqIvgQLqWHVoNmBuAhJ5AxklqR2MXGxGCqYPTEBuhUmWg+B6tC8Jsfd/pLpuOVgIAhvWIR1hIYP+ZB/L8JEWHITM+AgCwp6jG7/0RERGAkp3ia9qQgHetag4OjURltDhDbULUcVw9rlfgY3BC9euSNsbnijPZ1h3kQDoRkWYd3yDeZ8dkeL1ouL9oKZcBaB1IL9wKADhUJg5MS2t0+Zta52NsnySEGfQoqG7AvhLeXxNRRzqhG37MZjKZEB8fD6PRiLg432YrB8Sy64EdnwDjbwGmPal2NNqy/RPgs+uB2Czg7zsAg2OCvfmDP7FiexFum9Ifd541QKUgA2Pe25vww55SPHr+EMw5rY/a4dgFzf8zIgoKmvqd8vJJQPUx4OovgNzJ6sYSQNX1zVj13FX4q/Ad9vaahUFz/6t2SJpzqKwWU174BaEGHTY/dBbiIkLVDskrmvp/RkRBT7O/U759APjtP8DwWcCFzGVOHV0HLD0XiM0E7tyDB77YiQ82Hsctk/vh7mkD1Y7Or2545w+s2l2C+ZNycd/0QWqH0ynN/j8j6qI4I12rzE3A/u/E7wedp1izgiDAbLHKekypxWJFRW0TWixW9WIZMgOISgFqCoGdyxzeMlusWHdAnAk20fZoViDj8/X8+NrfkEwxWe5iPVYiIv+rKxcH0QEga6TXh8vNw3JysNy+/7F8D35pEm+eBzZu96kNpWMCNHJdYpObGoO+qdFosQhYs5/1VYmINEcQgD1fi98POldGM/JzRltK5bL2fI6zx2hAHwrUFAFVR3GwRCztkpsWHZD+A31P3dbMkT0AAF9tLYTV2u3mnRJRJziQrlWHfwGajOLjZjljFWt2V6EJ/R5cKWvQdV9xDUY/8QP2Fct71ElWLCHhwPi/id///BRgbra/tfZAOYwNLUiKDsPw7PiAx+fr+fG1v2HZCQCALbbFVYmIyI8KNouvKQOAyASvD5ebh+XkYDl9/7SvFMv+PIHfBXFmlq5kF1Bf6XU7SsYk0cR1SRtnDUkHAHy/u0RWO0RE5AeFWwDjcSA0GujnW310QLmcIVEql7Xnc5yhkfYJA9Zj67G7SDx+cKZ3M56D5Z66rTMHpSEuIgQF1Q1YxVxORO1wIF2r9nwpvg4+D9Ar99fUIyESz11yEnokeL7Str/IjmXsTUB0GlB1FPj9dfvmT/7IBwBcMDzLq4VQFI8vQP2d3CsRAHCwtBaVdc2d7E1ERLJIA+k9Rvt0uJp52Ne+C6obcOfHWwEA5586EkjuD0AQa8yqFJM/KBXLWYPFgfQf95ai2azszEIiIpJpz1fia/+zxMFiH2kpf7kjK85e4rpkdQfWorbJjPAQPfp5WSM9WO6p24oINeDq8eI6MP/9+SAXHSUiBxxI1yJLC7B3hfj9kBmKNp0YHYZLT85BYnSYou2qEktYNDDlIfH7H58AKg5hf0kNvt1VDAC4bEyOuvEFqL/E6DD0TxMvaKRFVomIyE+OrRdfs8f4dLiaediXvpvNVtz8/p+oqm9BXo843Dt9IND7dPHNo7+qEpO/KBXLyJ6JSI0NR02jGb+wvAsRkXYIArDbNmFtyAWymtJS/nJHVpy9TgMA6I+J+X5QZpzXE9WC5Z66vTmn9kFEqB7bThjx2Z8FCkVHRF0BB9K16Og6oKEKiEoGep6qaNPG+has2F4EY32Lou2qFsvIq4G+kwBzI6yfzsMDn2yCIABn52V4/diZX+ILUH9j+iQBAP7gQDoRkf801wP5G8Xv+07yqQk187Avff9j+W5sza9GfGQo/nvlaESEGtoMpK9VJSZ/USoWg16HmSOyAADLNp9QIjQiIlJC/kag8rBY1qX/NFlNaSl/uSMrzp7jAH0IouuOo5euGEOzvL+/DqZ76rZSY8Nx+5QBAIAnv9mDImODEuERURfAgXQt2v2F+DroPMAQomjT+VX1uPmDP5FfVa9ou6rFotMBF7wKc3gC9EV/4rKSlxAfEYIHzx2sjfgC1N+Y3mJ5l98OcyCdiMhvjm8ALM1AfA6Q1NenJtTMw972/d5vx/Dub+LCqi/+dThykqLEN/qcIb4WbwdMRQGNyZ+UjOXi0dkAgNV7S1DFsmtERNrw57vi69ALgXDvSpS0p6X85Y6sOCPigV7ixL6p+j+Rl+X9+mPBdE/d3rwJfTAkMw6Vdc2Y/96faDJbFIiQiIKdTuiGBZ9MJhPi4+NhNBoRFydv1rLiWhqA5weKC41e8xXQd6KizVusAuqbzYgKC4FBr1OtDaXaqa5vxvOr9uHY7yuwNPSfMOgEHDvlYfQ65y6f45IbX6CPA4DSmkac8uRqAMDvD0xBWlyEV8f7g6b/nxFR0NHE75RVC4H1rwAjrgJm/senJuTmPjnHe3Ps+kPluGbJ7zBbBdwzbSBuntzPcYfFU4CCP4DzXgZOnuvlT+FbTP5sQ8l2JOf8ay12F5nw8HlDcO3pfWS3Fwia+H9GRF2Gpn6nNNWI99ktdcDcb+31v32ldM5Quj2l2jX/+ipCvn8Qv1qGIv3W79AvLTYg/atxT+3M8Yp6nPfKWpgazbh0dDaeveQk6HTK/f0oQVP/z4i6Ac5I15o9y8VB9ISeQO8Jijdv0OsQGxEqK6ko0YYS7Ww/UY1pL6/Be78dx1rrMHyVdhMAoNemJ4B9K2XFJie+QB8HAGmxERiekwBAXNyMiIgUJgit65f0O9PnZuTmPjnHe3rs0fI6zH/vT5itAmaMyMLfJuV23GngdPF1/7dex+FLTP5uQ8l2JFeM7QkAWLr+KCzWbjdvhYhIW7a8Lw6iJ/cTS5bIpHTOULo9pdrdHSvWSR9r2IvcaO+fsAqme2pneiZH4dVZo6DXAf+3+QSWrDuiSLtEFLw4kK41W98TX4fPAvTK//XkV9bj1g+3IL/S90edjpTX4eolG3GkvE61WLbmV+Ovr29AiakJfVOj8eH143Dh354GRl0DCFbg02uBwi2qxOfr+ZH7dzN1UBoA4Ic9HEgnIlJcyU6xrmpIhKy6qnJ/18vJwZ70bWxowXVvb4KxoQXDcxLwzMUuZl4NPEd8Pfwz0GjyOhZvYuqMFq5LnLlkVDYSokJxvLIeq2wLoRMRkQosZuA325Nk4/4mlgeVSemcoVQua09unD+XxWCXtRdCYIFu57KA9a/WPbUzZwxIxcJzhwAAnvpmD37ax/ttou6MA+laUrpHvCGFDhhxhV+6MFsFVNY1wSxjZlRdkxlrD5SjrsmsSiwF1Q2Y9/YfaGyx4rR+yfjy5tMwPjdZvCA690Ug90ygpR744DKgOj/g8fl6fuT+3Uwdkg4AWHOgTPOL3hARBR3p5rHfVFl1VeX+rpeTgzvr22yx4tYPt+BQWR0y4yOw+Grb4qLOpA0BUgYC5sbWtV180BWuS1yJDDPgqrG9AAD//vEgZ6UTEall56dA9XEgKgUYMUuRJpXOGUrlsvbkxvnDnhL8n8VWblaa9BeA/tW6p3Zl7mm9cfmYHFgF4LYPtuBgaY2i7RNR8OBAupasf0V8HXw+kNjbL130SYnG+/PGoU9KtF/a93csNY0tuG7pJpTXNmFQRixev/pkxEaEtu5gCAUufRtIGwrUlgDvXwo0GgMWnxxy+xuUEYvBmXFoNlvx+ZYTCkdHRNSNmZtaFyg76TJZTamZh931LQgCnlixB2v2lyEiVI/F15zsfr0Nna51MGLL+36JKdD8Ect1p/dBbEQI9hSZ8PmWAsXaJSIiD7U0AKv/IX4//mYgNFKRZrWUv9yRE2eRsQHbTxjxpfU0CIYwoGgbcPy3gPXvC3/1p9Pp8PiMPJzSOwk1TWbMe/sPVNdzMXGi7ogD6VpReRjY/on4/Wm3qxuLRkkz5fYW1yA1NhxvzhmDmPCQjjtGxAFXfgLEZABle4BPrgEsXX+Gtk6nw+VjcgAAH/x+HFbOfCMiUsbOz4D6ciA2q7WkSRfz318OYen6owCAl/46Ank94js/6KTLAJ0ByP8NKPjTvwEGqcToMNxiW6j1mW/3oqqON91ERAG17iXAdAKIzwHGzVc7mqAifQDcr1dP6IZfLm785VkVI1JXWIge/71qFHokROJohVhCxmyxqh0WEQUYB9K14ruFgLVFLEuSfbLfutlZYMSAB1diZ4Fvs7TVikUQBDz29W78vE+cKbdk9snISnAzmyA+WxxMD40Wy+V8fTtg9S7JBfpcKdHfzJE9EBsegv0ltVi5k/VYiYhkMzcBPz8lfn/K9YDByQe4XlAzD7vq+8Pfj+PZb/cBABaeOxhnD8v0rMG4TGDYpeL3a55XNCY1+CuW2af2Rm5qNMpqmvDgFzsgCPygm4goIE5sbs1PZz2m2Gx0QFv5yx1f47RYBbz/23EAwGVjegIT7hI/PD+0Gjj4g9/795W/+0uOCcfia05GZKgBaw+U458r9/qlHyLSLtUH0r/88ktMmTIFeXl5uOKKK3Dw4EG/HKNp2z4G9q0QE9O0p/3aVWZ8BBaeNxiZ8W4e1/agjcdnDJXVhrex/PeXQ3j3t2PQ6YCXLxuBk7ITPOhgOHDpW4BOD2x9H/hivrjQjB/ia3+cL+dHib+b+MhQXHt6HwDAs9/tVbzGHhER0M1y99oXxbqqMRnA2BtlNyf3d72cHOys7zfWHsb9n+0AANw0MRfzJvT1rtEJdwHQidcxh35SJCZf2gj0dYk3IkINeOmyEQjR6/DNjmL8a/UBRdsnIvJEt8rdAGA8AXxyNSBYgKEXiV8KUjpnKJXLnLXrS5yfbs5HQXUDEqJCcd5JmWLpWek66Ou/A/WVfu1fzXvqzgzJisMLfx0OAHhj3RF8sPG43/oiIu1RdSB9+fLluOSSS3D++edjyZIlEAQBEyZMQGWl61/KvhyjacfWA8v/Ln4/8V4gbZBfu0uOCcc143sjOSZc1TY8bUcQBLz8w377TLmHzxuC6XkezpQDgAHTgIsWix9SbP8IeHcmUOPZTG1ff85AH9fevAl9kBkfgWMV9Xjoi50s8UJEiupWuXv/d8Ca58Tvpz0JhMmvtyn3d72c49seW9dkxl2fbMMTK/YAEGt53zd9oPcBpQ4QZ+oDwJc3A6Yin2PyVSCvS3x1UnYCHr1gKADg5R8O4Jlv9zI/E1HAdKvcDQBl+4Cl5wGmAnFh7PNeFNf2UJDSOcNfOciXdouMDfb771vP7N+68PiZC8UBdWM+8OEVHq1FFqz31J05Z1gmbjtTLN32wOc7sPTXI3zijKib0Akq/m8/+eSTMWzYMLz11lsAgObmZmRmZuLOO+/Egw8+qNgx7ZlMJsTHx8NoNCIuLk6ZH8Zb5mZg81vA948A5gag31Rg1ieA3uDXbk2NLfjjaCVO7p2EuLaLdHqhur4ZP+0rxeSBaUiICvNbLAdLa/DY17ux9kA5AOD2Kf1xx1kDfOts7zfAsnlASx0QkSDOoBt1NRCZ6HN8rvh6fpT4u5GsP1iOK5dshCAAM0dk4bEZeYiPlNemtzTx/4yIFNctcre5Gfj9f8DqxwBLMzDiSmDma4o0Lfd3vZwcbGpswZr9ZSisbsDitUdQVtMEvQ64d/og3HhGX+h8HWRoqgX+NwmoOAAk5QKXvAlkjfA4pmC5LlHCqz8ewPOr9gMARuQk4N5pAzE+N9n3c68w5m6irqlb5G4AqKsANr0B/Poy0FIPJPQE5q4Uy34qTOmcoVQua8/bOI9X1OP6d/7AvpIaDM6Mw5c3n4awkDbzL0t2A29OB5qMYs4/+1mg3xSXH1QE8z11ZwRBwOPLd+OtX48CAM49KRMLpg9CTlKUX/ttj7mbKLBUG0ivqalBfHw8PvjgA1x++eX27ZdddhkqKyvx/fffK3KMMx79oqkpAerKAMEKQAAEoc33cLHd1fdW8ZiWOsBYAJTsBA58D9SVin31nwb89W1Fa7a5srPAiPNeWYflt57u2UJiHrZxqKwWTS1WWG3/nKyCAKsgJhfpVQBgtdpeBQGHy+qw8IudePyCoeiZHAWrIKDU1IRjlfX47XAFthyvBgCEh+jxyPlDMWtsT3k/fPkB4NNrgeLt4p8N4UCv8UDWKPEiKzYDCImwfYXhYHkDbv9oK/51+Qj0S41pd3HQ5vt22w+U1uLWD7fglStGon9aDBCTDsSkdhqeEn83bS3bfAL3fLoNVgGICQ/BX4ak46TseGTERyI+MhShBh1CDHqE6HXQe3kDHxsR0ukFAhM6UdcTHLm7tDUHC9Z237v6EgBzozhzrWQnsH8VUGt7emnoheKTTQZlbsac/a6vaWxBfmUDBAiQrsqsgvi9gNYcKggCDpXW4t5lO/DPi4ahd0q0bR/BfnnS/riGZgtKa5pQaGzA+oMV2NGmZmhOUiSev2Q4xvZNlv+DVR0VZ/8Z88U/95kI9DkDSOoLRMSLs/kNYbacacs5Oh0Oltbito+24t9XjES/tFhbYzogMsHjgQ+l8qfSediVz7ecwMLPd6Ku2QIA6JEQibF9k9AvLQY9EiIRGxGCiFADIkINMOh09ssMHdp872RbZ2IjQpCdyNxN1N0ET+62ep+7W+rFp42NJ4CCzcCJTeIH4ICYgy55C4hO8fxkeUHpnCGnvcYWC46U19mvAdq+HiytxT2fbrdfN7R9z2p7bTFbkV/VgC3Hq7BqdwmazVakxYbj/24aj17JTp7GK9wCfHSleN0EAAm9gD4TgLQh4r1vZKJ4T60PwaGKRtz2yQ7864pRbfK8Czo9kD5U1vkIVC6XCIKARb8cxvOr9sFiFWDQ63BK7ySM6ZOEPilRSIgMQ3R4CMJC9NBBzN9S7m77vSvM3UTaI2/FLBlOnDgBQRCQkZHhsD0jIwPbt29X7BgAaGpqQlNTk/3PJpOp8wA3/ldc4dufYjKAM+4GTr4O0Aemys7AjFj8/sAUJEYr9yk3AFy7dBOOVdT7dOzDX+1yul2nA84anI57pw9Cv7QYOeGJUvoDN/wMbPsQ2PAaULpLXIj08M9Od+8HYEU4gM+966Y/gG/DAXxm2zDlEWDCnZ0ep/TfzcWjs5GTFIUHP9+BA6W1+GxLAT6zrbwu19l5GfjvVaMVaYuIgke3yt0x6cDE+4CTr1X0cXBnv+s3Hq7EvHf+8KqdBba65r7olxqNq8b1whVjeyI8RKEn4RJ7izn2m3uAXZ8BR34RvzqLBcA3bXOmZPgVwIWLlInNQ/66RmrvwpHZODU3Bf9efQBfbClAQXUDPvtTmfzsDnM3UffUrXI3AGSNBMbfAuRdrHg5l7YClTM8caS8Dmf/a63bfby5bjitXzKeufgk1wO4WSOBm9aJC7luXgpUHwO2HHO6ay5s99Tt87wzoVHAg96ViGsv0H8vOp0O8yfl4rR+yXjuu31Ye6AcGw5XYMPhCkXaZ+4m0h7VBtKtVisAIDTUcYZXWFgYLBaLYscAwNNPP43HHnvMuwDDY8WbaEgfFerbfK9zsd3V97b9QyLE2VWJvcVPbHudBoT4t3ZXe6EGPdLilF94Izk6DA3NFuh0gF6ns33aqoNeL37Kqrdth+1V3+bTV72u9TUlJgw9EiORlxWPMwakIitB4Vn6egMw8irxMf3SPcDxDeLsQ1MhUFsKmJvEWYnmJtsTBW21e3ijw8Mc4p9bLFZU1bcgMSoUoQa9xzV1/fF3c0qfJKy64wz8drgS6w+VY19xDcpqm1DTaIbZYkWLRYDZau34o9i4elzF34/JEZE2BU3u1unbfOna/bn9l+19fSgQlwUk9QF6TxC/QpXPl85+14eH6pESE27LhW1mKkHMpW1nLbVYrCgyNqJHQgTCQw1tcm7bGU6t28JD9EiLjUB6XDiGZMVhVM9E9EuL8U8pkegUcZHvKQ+JJdWKd4g31001QHNd6yxBe9IR2v25zbbwwM+o8tc1kjPpcRF48sJhePDcwdh0tAp/HqtCfmU9Co0NqG+2oKHZgoYWizgp0/ZEAgD7EwjSKfPmsVLmbqLuqUvnbkO4+FRxbCaQkQf0HC9OngqAQOaMzoQa9EiNDbffc7e9FxfvvVvvt+372PbT6wGDToeshEj0S4vBWUPSMaxHfOfXCVFJwPSngDMfFBcbL9oGlO8H6iuAhirb/bQFsJoBq0X86qBdFguRfz7V+ns5KTsB7143Fscq6vDT3lLsLa7BsYp61DS1oK7Jgmaztc0Thq25vLM8ztxNpD2qDaSnpIiPWFVUOH5SV15ebn9PiWMA4P7778edd7bOCDaZTMjJyXEf4IS7xK8u5kRVPV5ZfRC3TunX6SNC3vjsb6dpJhaP6HRA+hDxywVf49snPU52g3ePk/nrfOh0OozPTcb4XAUe3Seibo25Wz5nv+sn9E/FHwunenS89Mjy61ef7PUjywHLu0l9gVNv0VZMGo0lKiwEEwekYuKAzkvAERH5grnbP7SUv/qlxWDTg86vI/weZ1g0MPg88UuN/lXur71eydGYc1qfgPdLRIETmHoiTqSnpyMnJwcbNmxw2P7rr7/i5JNPVuwYAAgPD0dcXJzDV3fVZLZif2kNmsztZ1t7LjLMgJE9ExAZJu9xcCVi8Sdf4/P1/Gj9fBARMXfLJ/d3vZwcrMU8w+sSIiL/Yu72D6VzhlK5rD21cxvvqYmoyxFU9OSTTwopKSnCnj17BEEQhNdff10wGAzC9u3b7fvcf//9wtlnn+3VMZ0xGo0CAMFoNCr0kxBRe/x/RtQ1MXcTdV38f0bUNTF3E3Vd/H9GFFiqlXYBgPvuuw8nTpzA8OHDER0dDYPBgPfeew/Dhg2z71NaWorjx497dQwRERH5B3M3ERFRcGHuJiIiUoZqpV0AwGAw4LXXXkNlZSW2bduGoqIiXH755Q77PP3001i5cqVXx5BruwtNGP7YKuwu9GAFdRd2FhjRe8EK7Cwwqh6LP/kan6/nR+vng4gIYO6WS+7vejk5WIt5htclRET+x9ytPKVzhlK5rD21cxvvqYmoq1F1RrokOjoa0dHRTt9LTXW++JK7Y8i11Nhw/G1SLlJjw9UORVOxOBPo+LR+PoiI2mLu9o2av+u1mGe0FJOWYiEi8gfmbuUES85QO07eUxNRV6OJgXQKnNTYcNw4MVftMABoKxZnAh2f1s8HERHJp+bvei3mGS3FpKVYiIhI24IlZ6gdJ++piairUbW0CwVebZMZGw5VoLbJrHYomorFmUDHp/XzQURE8qn5u16LeUZLMWkpFiIi0rZgyRlqx8l7aiLqajiQ3s0cLa/DFYt/w9HyOrVD0VQszgQ6Pq2fDyIikk/N3/VazDNaiklLsRARkbYFS85QO07eUxNRV6MTBEFQO4hAM5lMiI+Ph9FoRFxcnNrhBFRjiwXFxkZkxEcgItSgWhtKtuMvvsYX6OO0qjv/PyMi5XWV3ylyf9fLOV6LeYbXJdrSVf6fEZE2dOXfKUrnDH/lILVzG++p/a8r/z8j0iIOpPMXDZFf8P8ZESmJv1OI/I//z4hISfydQuR//H9GFFgs7dLNFFY34NGvdqGwusHnNvIr6/H3j7Ygv7Je9Vj8ydf4fD0/Wj8fREQkn9zf9XJysBbzDK9LiIgoGCmdM5TKZe2pndt4T01EXQ0H0ruZuiYzfjtcgToZi28YG1rwxdZCGBtaVI/Fn3yNz9fzo/XzQURE8sn9XS8nB2sxz/C6hIiIgpHSOUOpXNae2rmN99RE1NWEqB0ABVb/9Fh8+/cz1A4DgLZicSbQ8Wn9fBARkXxq/q7XYp7RUkxaioWIiLQtWHKG2nHynpqIuhrOSCciIiIiIiIiIiIicqNbzkiX1lc1mUwqRxJ4+0tMuOndP7Ho6lEYkO7bQhS1NSZYm+pRW2OCyaRTNRZ/8jU+X8+P1s+Ht6T/X91wPWMi8oOukrvl/q6Xk4O1mGd4XaItzN1EpKSukrudUTpnKJXL2lM7t/Ge2v+Yu4kCSyd0w/9tJ06cQE5OjtphEHUL+fn5yM7OVjsMIgpyzN1EgcPcTURKYO4mChzmbqLA6JYD6VarFYWFhYiNjYVO1/HTTZPJhJycHOTn5yMuLvg+xWT86mL8IkEQUFNTg6ysLOj1rCJFRPJ0lrv9Ldh/t2sJz6WylDyfzN1EpCRPcnew5wTGry7Gz9xNFGjdsrSLXq/36JO6uLi4oPxlLGH86mL8QHx8vELREFF352nu9rdg/92uJTyXylLqfDJ3E5FSvMndwZ4TGL+6unv8zN1EgcOPq4iIiIiIiIiIiIiI3OBAOhERERERERERERGRGxxIdyI8PByPPPIIwsPD1Q7FJ4xfXYyfiKjr4e9G5fBcKovnk4iCWbD/DmP86mL8RBRo3XKxUSIiIiIiIiIiIiIiT3FGOhERERERERERERGRGxxIJyIiIiIiIiIiIiJygwPpRERERERERERERERuhKgdgFrMZjM+/PBD/PDDD6ivr8eIESNw8803IyEhwWG//Px8LF68GBs2bMD8+fNx0UUXqRNwO1arFcuWLcO3336LqqoqDBs2DLfccgtSU1O92kdNy5cvx9dff43S0lIMGjQIN998M7Kzsx322bJlC95++20cPnwYWVlZuOqqq3D66aerFLGj1atX47PPPkNBQQFyc3Pxt7/9Dbm5uU73NZlMuPrqq9HY2IjvvvsuwJE6t379enz00Uc4duwYevfujRtuuAFDhw61v19VVYVLL720w3GPPPIIJkyYEMhQiYgCorS0FIsXL8aff/6J2NhYTJs2DZdffjl0Op1X+xAgCAI+//xzrFy5EhUVFRgyZAhuueUWZGRkON0/Pz8f8+bNQ0ZGBt5+++0AR6t9hw8fxpIlS7Br1y6kpqbi0ksvxV/+8pcO+xUVFeHVV1/Fjh070LdvX9x9990drq2IiAKtqKgIb7zxBrZs2YL4+Hicc845uOSSSzrkzm+++QbvvfceamtrMWHCBNx6662IiIhQKepWnub+X3/9FYsXL8aJEyfw0UcfISUlRaWIHZWVleGNN97AH3/8gZiYGHv8er3eq33UUl5ebo8tOjoaZ511FmbNmuUQW3NzM95++2389NNPMJvNGDlyJG688UYkJSWpGLmosrISb7zxBn7//XdERUXhzDPPxNVXXw2DweB0/7feegvvv/8+5s6diyuvvDLA0RJRZ9T/raiS6dOn44cffsCZZ56Jv/71r1i5ciVGjx6Nqqoq+z6ff/45JkyYAL1ej99//x3Hjx9XMWJHl19+OT799FOceuqpuOqqq7Bx40YMGzYMBQUFXu2jlptuugmLFy/G6NGjcc011+DAgQPIy8vD/v377fssW7YMt99+O/r374/rr78eycnJmDRpEj744AMVIxc9+OCD+Oc//4mhQ4di7ty5qKysRF5eHjZv3ux0/xtvvBH79u3D6tWrAxypcy+99BLuvfde9O/fH9deey2sVitGjBjhEF9TUxNWr16Nyy+/HAsWLLB/DRw4UMXIiYj84/jx4xg3bhwaGxtx5ZVXYsyYMbj99ttxww03eLUPia655hp88MEHGDt2LK666ips27YNw4YNw7Fjxzrsa7FYMGvWLBw7dgy//vqrCtFq29q1a3HOOecgOjoac+fORW5uLi688EI888wzDvtt374deXl5OHDgAK699lrk5eXhwgsvVClqIiLRgQMHcPrpp8NsNuPqq6/GyJEjcdNNN+G2225z2G/p0qWYOXMmTjrpJFx66aVYsmSJJn6HeZr7Z8+ejXvuuQcJCQlYvXo1GhsbVYrYUUFBAU455RTU1tbiyiuvxNixY3HnnXfiuuuu82oftRQXF2PMmDEwmUy44oorMG7cONxzzz2YPXu2w37nnXceduzYgQsvvBAzZ87EV199hfHjx6O2tlalyEWVlZUYPXo0Kisrcfnll+P000/HwoULccUVVzjdf9u2bXj00UexefNmHDlyJMDREpFHhG6qoqLC4c81NTVCZGSksGjRIod9zGazIAiCkJycLLz00kuBDNGt9vE3NTUJ6enpwhNPPOHVPmppH5vFYhEGDhwo/P3vf7dvq66u7nDcJZdcIkybNs3v8XWmffyCIAjjx48Xrrrqqg7bFy9eLIwfP15YtGiRYDAYAhFep5zFf9555wnTp0+3/7moqEgAIOzYsSOQoRERqaK+vl6or6932PbRRx8JAISysjKP9yFR+zzT0tIi5OTkCAsXLuyw74MPPihceumlwoMPPijk5uYGKsSgYTQa7dejkieeeEJITEx02DZy5Ejh/PPPd9hWU1Pj9/iIiNypq6sTGhsbHbYtXbpU0Ov1gslkEgRBEMxms5Ceni489NBD9n22b98uABB++OGHgMbbnqe5v6SkRBAEQfjpp58EAEJ+fn5A43SloaFBqKurc9j26aefCgCE4uJij/dRS2NjY4fYvvjiCwGAcOLECfu29mMHR48eFQAI33//fUDidKW5ublDLv72228FAMLBgwcdttfW1gqDBg0Sli9fLvTo0UP4xz/+EchQichD3XZGevtHfKKiohAZGYm6ujqHfVw9bqO29vGHhYUhNja2Q/yd7aOW9rHp9XokJCQ4xBYfH++wT319Pfbu3YvBgwcHJEZ3nD0ilpSU1OHc7tmzBwsXLsR7772nqX9LzuJPTk52+m9jwYIFuOCCC3DnnXdi3759gQiPiCjgIiMjERkZ6bAtOTkZgJh/PN2HRO3zTEhICOLi4jrkmZ9++gnvvfceXn/99UCGF1Ti4uI6XEMkJyejsbERFosFgDgbfcuWLR1meMbExAQsTiIiZ6KiohAeHu6wLTk5GVar1Z47t23bhpKSEsycOdO+z7Bhw5Cbm4tVq1YFMtwOPM39aWlpAY3LUxEREYiKinLYJsUv5WRP9lFLeHi4R7G1HzvYvHkzQkND0a9fP/8H6UZoaGiHXOzq3N5yyy2YMmUKzj333IDFR0Te67YD6e0tWbIERqMR06dPVzsUn3z++ec4ePCg21+6nuyjljVr1uD333/Heeed1+G9mTNn4owzzkDPnj0xceJE/POf/1QhQve2b9+OVatWOcTf2NiIyy67DM888wz69u2rYnSdO3LkCJYtW9bh/I8YMQLnnXcerrnmGpSVleGkk07CDz/8oFKURESBIwgCXnjhBQwbNgw9e/b0eR8SrVy5Ert27XK4BikvL8c111yDt956C4mJiSpGF1waGxvx6quvYvr06fYB9p07dwIAUlNTceONN2LGjBm49957NVHOj4ioLavVihdffBFjxoxBeno6AODo0aMA0GFNh+zsbPt7WtEVcv/zzz+PwYMHu71H9WQftTz//PMYMGAA+vfv77B95cqVmDp1KkaOHInbbrsN3333HXr37q1OkG4899xz6NOnD4YMGWLf9sEHH2Djxo147rnnVIyMiDzRbRcbbevXX3/FbbfdhieffNLhl1mw2L59O+bOnYu7774bp512ms/7qOXw4cP461//itmzZ+OCCy7o8P4dd9wBk8mEdevW4T//+Q8mT56Miy++WIVInSsqKsLMmTMxffp0zJ071779jjvuwKBBgzrUb9Oa6upqzJgxAyNHjsQdd9xh356cnIyNGzciLCwMAHDJJZfAbDbj73//u/2GnYioq3rggQewbt06rFu3TtY+JD6dddVVV9lnWgHiQMTs2bNxxRVXYPLkySpHGDysVivmzJmDqqoqvPrqq/bt9fX10Ol0uOyyy3DLLbdg+vTpePvttzFs2DBs376dC44SkWbcdddd+PPPP7F+/Xr7tubmZgDoMPM7KirK/p5WBHvuf+ihh/Dzzz9jzZo1svZRy2OPPYZVq1bhl19+6bDY6/Dhw7FgwQIUFRXhP//5D+699178+OOPiI2NVSnajp555hl8+eWXWL16NUJCxOG4gwcP4rbbbsP333/f4f8AEWmQupVl1Pfbb78JcXFxwoIFC9zup7Ua6ZKdO3cKqampwvXXXy9YrVaf91HLkSNHhF69egmXXHKJ0NLS0un+d999t5CTkxOAyDxTUlIiDB48WDjrrLOEhoYGh/cACGPGjBGmTJkiTJkyRRg8eLCg0+mEKVOmCMuWLVMpYkfV1dXCKaecIowdO1YwGo2d7v/OO+8Ier2+w89KRNSVPP7440J0dLTwyy+/yNqHBGHfvn1CZmamcM011wgWi8W+XapdOmHCBHue7N27txAZGSlMmTJF+PXXX1WMWpssFoswZ84cISMjQ9i7d6/De8uWLRMACG+88YZ9W3Nzs5CZmSk88sgjAY6UiMi5Bx54QIiNjRXWr1/vsF2qGX306FGH7aeccoowd+7cQIbolie5X2s10tt66qmnhKioKOHHH3+UtY9ann32WSEyMtKjuucmk0mIjY0V/vWvfwUgMs/8+9//FsLDw4UVK1Y4bH/00UeF5ORk+/XQlClThPDwcCE3N1eYMWOGOsESkUvdekb677//jmnTpuGmm27C008/rXY4Xtu9ezfOPPNMzJgxA6+//nqHT2Q93Uctx44dw+TJk3HyySfjww8/tH8i606vXr1QXFwcgOg6V1paismTJyMrKwtffvklIiIiHN7//vvvHf68cuVK7Nu3DwsWLMDAgQMDGapTJpMJ06ZNgyAIWLVqFeLi4jo9pry8HCEhIR79XRERBaMnn3wS//znP/HNN9/gjDPO8HkfAg4cOIDJkydj6tSpeOutt6DXt1YUTE9P75Anly5dip9++gkLFizo8Lh2dycIAq6//nqsXLkSP/30U4friJEjRwIQr5MkoaGhyMzMRFlZWUBjJSJy5qGHHsIrr7yCb7/9FuPHj3d4b/jw4dDpdPjzzz/tv8caGxuxe/duXHnllWqE20Gw5/5nnnkG//jHP7B8+XKXT4J5so9aXnjhBTz88MP46quvMHXq1E73j42NRVJSkmbGDl599VXcc889WLZsGc455xyH966++uoOVQOuuOIKTJgwAXPmzAlglETkEbVH8tWyadMmIT4+Xrjvvvs82l9rM9L37NkjpKenC/PmzXM5y9yTfdRy/PhxoU+fPsLFF1/scib6O++847ASellZmTB8+HDh7LPPDlSYLpWVlQl5eXnC1KlTO6zi7srixYsFg8Hg58g8YzKZhHHjxgmnnHJKhxXOJV9++aWwf/9++58PHz4s5OTkCJdeemmgwiQiCqinn35aiI6OFn7++WdZ+5AgHDx4UOjRo4dw9dVXO8xEd+fBBx8UcnNz/RxZ8LFarcK8efOEjIwMYffu3S73O/PMM4WrrrrKfr43bdokhIaGCp988kmgQiUicuqRRx4RYmNj3T5tNH36dOH0008XGhsbBUEQhGeeeUaIiooSCgsLAxWmS97kfi3OSH/uueeEqKgoYfXq1bL2UcuLL74oREZGCqtWrXL6/okTJ4SPP/7YYdsHH3wgANDEz/Paa68J4eHhwvLlyz0+pkePHsI//vEPP0ZFRL7SCYIgqD2Yr4b+/fvj+PHjmDBhgsP2mTNn4pZbbgEAHDp0CDfeeCMAcTHMPn36ICcnB6eddhoee+yxgMfc1vjx47Fp0yZMnDjRYZb55MmT8eCDD3q8j1rOP/98LF++HBMnTnSY3Txq1Cg8++yzAIDPPvsM9913n32l9127dmHq1KlYtGgRMjMz1QodADB37lwsXboUp556qkMds379+mHRokVOj3njjTdw0003wWw2BypMl+655x48//zzGDNmjMNM9NTUVHz44YcAgN9++w3z589HQ0MDYmNjsXPnTsycOROvvfYaF4Ujoi5n27ZtGDFiBHJycjBgwACH91544QUMHz7co31INHnyZPzyyy+YNGmSw0x0d9dQCxcuxEcffYSDBw8GKsygsGzZMlxyySUYMGAAcnJyHN776KOPkJKSAgA4ceIEzjvvPFRXVyMjIwM7duzArbfeqslF2omo+9i4cSPGjRuHXr16oV+/fg7vvfLKKxg8eDAAoKCgAOeccw5KSkqQkpKC/Px8vPXWW7jooovUCNvO09y/dOlSvPfee6iqqsKff/6J008/HeHh4bj//vvt64OoYdeuXcjLy0N2dnaHp5mee+45jBw50qN91LJv3z4MGjQIPXr0wKBBgxze++c//4mTTz4ZtbW1uPXWW/Hdd9+hT58+KCkpgclkwiOPPIKbb75ZpchFR48eRd++fZGeno6hQ4c6vPf444/j1FNPdXpcdnY2brrpJixcuDAQYRKRF7rtQPq6devQ2NjYYXtOTo49edTW1uK3337rsE9aWhpOOukkv8fozsaNG1FTU9Nhe0ZGBvLy8jzeRy1btmxBRUVFh+3JyckOidpisWDv3r2or69H7969kZqaGsgwXdq5c6fTx8RiY2MxduxYp8cUFhZi7969OPPMM/0dXqf27duH/Pz8DtsjIiJw+umn2/8sCAIOHTqEiooK9OvXD8nJyYEMk4goYEwmE37//Xen740ePRqJiYke7UOiTZs2wWg0dtju7hrq0KFDKCkpcXlT2V0VFxe7XOR7woQJCA8Pt/9ZEATs3LkTdXV1GDBgAJKSkgIVJhGRU1VVVdi8ebPT90455RSHST2CIGD79u2ora3F8OHDERMTE6gwXfI09x86dAhHjhzpsE9eXh4yMjL8GqM7NTU12Lhxo9P3Ro0ahaSkJI/2UYurMRlALGvW9v60qqoKe/fuRUJCAnJzcxEWFhaoMF2qr693WFi3reHDh7sc31i3bh2ys7PRu3dvP0ZHRL7otgPpRERERERERERERESe0He+CxERERERERERERFR98WBdCIiIiIiIiIiIiIiNziQTkRERERERERERETkBgfSiYiIiIiIiIiIiIjc4EA6EREREREREREREZEbHEgnIiIiIiIiIiIiInKDA+lERERERERERERERG5wIJ26tJKSEuTl5eHIkSNqh0JEREQeYO4mIiIKLszdRNRdcCCdvNbQ0IC8vDx8//33Hh9TWFiIvLw85Ofn+zGyjlpaWrBr1y40NTWpGgcREZGamLuJiIiCC3M3EZH2cCCdvGaxWLBr1y4YjUaPj2lubnZIrIGSkZGBHTt2oG/fvqrGQUREpCbmbiIiouDC3E1EpD0cSCfZjhw5gry8PKxbtw5z5szBuHHjcPnll2P//v0AxE/Sp02bBgA455xzkJeXh+uvvx4AUFdXh8cffxynn346zjjjDNx3330wmUwety1ZsmQJpk2bhvHjx+O2225DaWkpAKCiogKXX345CgoKXMYxY8YMvP766w7tmUwmjBw5EmvWrPHPSSMiIlIRczcREVFwYe4mItIAgchLNTU1AgDh//7v/wRBEIQ9e/YIAIQ+ffoIH374obBhwwbh4osvFnJzc4Xm5mbBYrEI3333nQBA+Oabb4QdO3YIR44cEVpaWoTTTjtNOPfcc4UffvhBWLt2rTBz5kxh9OjRgtls9qhtQRCE9957T0hOThY++eQTYePGjcJrr70mXHrppYIgCEJ+fr4AQNizZ4/LOJ544glhyJAhDj/jokWLhJSUFKGpqSmAZ5aIiMg/mLuJiIiCC3M3EZH2cCCdvOYqoX/22Wf2fQoKCgQAwvbt2wVBEIQjR44IAIQDBw7Y93n//feF7Oxse2IWBEFobGwU4uPjhW+//dbjtm+//XZ7ApdIbbZN6K7iKCgoEAwGg/Dbb7/Zt40dO1b4+9//LuMsERERaQdzNxERUXBh7iYi0h6WdiHFjBw50v59VlYWANgf9XJm7dq1MBqNGDNmDIYPH46TTjoJY8aMQWNjY4dHyNy1PW3aNHz99de47bbbsGrVKjQ0NCA0NNTjuLOysnDOOefgzTffBADs3r0bGzduxLXXXutxG0RERMGIuZuIiCi4MHcTEaknRO0AqOsICen4z0kQBJf719fXIy8vD//73/86vJeZmelx22effTbWr1+PDz/8EA888AD27NmDhx56CAsWLPA49nnz5uHqq6/GSy+9hDfffBNjxozBsGHDPD6eiIgoGDF3ExERBRfmbiIi9XAgnQLCYDAAcEzwAwYMwDfffIN+/fohIiJCVvsjR460f3r+xRdf4MILL8Q111zjURwAcO655yImJgYff/wx3nvvPTz66KOy4iEiIgp2zN1ERETBhbmbiMi/WNqFAiItLQ16vR5Hjx61b5s7dy5aWlpw8803o7GxEYC4mvizzz6LY8eOedz2q6++ik2bNtn/3NLSAoPBgMjISI/iAMREP2fOHNx1110wmUy44oorvPsBiYiIuhjmbiIiouDC3E1E5F8cSKeACA8Px9///nfMmDEDQ4YMwfXXX4+srCx8//33+PPPP5GYmIg+ffqgR48eqKqqQnp6usdtjxo1CrfeeiuSk5PRu3dv/O1vf8OSJUuQmJjoURyS6667DtXV1bjooosQHx+vyM9NREQUrJi7iYiIggtzNxGRf+kEd8W0iJywWq3YvXs3evbsibi4ODQ3N2P//v0YNGiQQ021nTt3ok+fPoiOjrZvq66uRlFRESIjI9G7d2/79pKSEtTX16NXr17Q61s/3/Gm7crKStTW1qJHjx72R8nMZjP27t2LAQMGICwszG0cRUVF6NmzJ1atWoXJkycrdr6IiIjUxtxNREQUXJi7iYi0hwPpRDYLFizAihUrsH37duh0OrXDISIiok4wdxMREQUX5m4iCmZcbJS6vR9//BE33ngjioqKsHz5ciZzIiIijWPuJiIiCi7M3UTUFXBGOnV7NTU1yM/PR8+ePRETE6N2OERERNQJ5m4iIqLgwtxNRF0BB9KJiIiIiIiIiIiIiNzQd74LEREREREREREREVH3xYF0IiIiIiIiIiIiIiI3OJBOREREREREREREROQGB9KJiIiIiIiIiIiIiNzgQDoRERERERERERERkRscSCciIiIiIiIiIiIicoMD6UREREREREREREREbnAgnYiIiIiIiIiIiIjIDQ6kExERERERERERERG5wYF0IiIiIiIiIiIiIiI3OJBOREREREREREREROQGB9KJiIiIiIiIiIiIiNzgQDoRERERERERERERkRscSCciIiIiIiIiIiIiciNE7QAoMA4ePIilS5fi999/R0VFBeLj45GdnY0JEybgr3/9K+Lj49UO0Wd79+7F0qVLsWnTJlRXVyM1NRV9+/bFZZddhokTJ9r3mzt3LjZt2gQA0Ol0iI6ORmpqKoYPH46ZM2fi5JNP7tD23LlzcfDgQaxdu9Zp3yNGjMDEiRPxr3/9y77tH//4Bz7++GOn++/cuVPOj0pERN2MxWLBp59+ihUrVuDQoUNobm5GVlYWcnNz8de//hXjxo1TO0Sfmc1mfPzxx1ixYgWOHDkCQRDQq1cvjBkzBrNnz0ZqamqHY/bs2YNLL70UYWFhWLNmDWJiYuzv3XfffVi5ciVWr17t9FgA+Prrr3H//ffj6aefxvnnnw8AqKmpwVdffYVPPvkEhw4dwtVXX4377rvPPz80EREREREFLc5I7waefPJJDBo0CHv27MH111+P//3vf3j00UcxcOBAPProo8jMzERzc7PaYfrksccew9ChQ3H48GHceuutWLx4Me644w5YLBZMmTIFjz/+uH3fI0eOoKCgAB999BE+/PBDvPLKK5g9ezYKCgowfvx4zJgxAyaTyaH9I0eOYM+ePS7737lzJ44dO+awraCgAIcPH8ZHH33U4YuIiMhTR48excknn4zbb78dAwcOxFNPPYVFixbhmmuuQX5+PsaPH48HH3xQ7TB9cvjwYYwcORJ33XUXhg8fjueeew6vvPIKpk6dirfffhvZ2dmoqanpcNzixYtx9OhRbN26FR9++KHDexMnTsSOHTvwzjvvuOz31VdfxdGjRzFp0iQAQHV1NXJzc7Fy5Uqcd9552LVrF4qKihT9WYmIiIiIqGvgjPQu7rXXXsPChQvx8ssv4/bbb3d474wzzsBtt92G2bNnw2q1qhSh7/7zn//g0UcfxQsvvIA777zT4b1p06Zh9uzZHWaSGwwG5OXl2f88ZswYXHLJJbj66qsxffp0zJo1C8uXL5cdm16vd+iHiIjIG/X19Tj77LPR1NSELVu2IDMz0/7e6NGjcfHFF2P16tUun5jSMnc/25gxYzBnzhzMnz8fFovF4bjm5ma8++67uPbaa7Fnzx4sXrwY119/vf396dOnIzs7G0uWLMFdd93Vod/jx4/jhx9+wJw5cxAbGwsAiI2NxfHjxxEREYETJ0746ScmIiIiIqKugDPSu7D6+no89NBDGDNmTIdBdElsbCw+/fRThIeHO2zfuXMnbrrpJowbNw6nnHIK5s+fj8OHDzvsM23aNNxzzz0oLy/HLbfcgjFjxuDss8/G119/7bQvT9r88MMPkZeXh++//97tz9bQ0ICHH34Yo0eP7jCILjn11FNx9913u21HcuaZZ+K6667DihUrsGHDBo+OISIi8pfFixdj7969eP755x0GmtuaMmUKFi5c6LDNbDZj8eLFOO+88zBq1ChMmzYN//vf/xw+MP/222+Rl5eHffv2Yfny5Zg+fTrGjBmDW2+9FeXl5R368aRNAJg6dSrOOeccj362/fv347nnnnP6s4WGhmLx4sWIi4tz2P7555+jvLwc8+fPx9/+9jds2rQJ27dvt7+v1+vtg+zr16/v0O6bb74Jq9XqMPhuMBgQERHRacxEREREREQcSO/C1q5di8rKSlx44YVu99Pr9dDpdPY/f/HFFxg9ejSqqqrw2GOP4ZlnnkFZWRlGjx7tUON737592L9/P2bPno3TTjsNL7/8MtLS0jBjxowON7CetllRUYFdu3bBaDS6jXnNmjWorKzERRdd5HY/g8Hg9v22ZsyYAQD47rvvPD7GlaamJpx55pkYOXIkpk+fjhdffBH19fWy2yUiou7hiy++QFhYGM4991y3+4WEtD5c2NzcjOnTp+P+++/H2Wefjddeew2XXXYZHnzwQcyePdu+X3V1NXbt2oWlS5di+fLluP/++3Hvvffi//7v/3DxxRc7tO9pm4C4Zsn+/fs9+tlCQ0PtNcqd0el00OsdL1MXL16MyZMnY/DgwbjggguQnZ2NxYsXO+xz3XXXQa/XY8mSJQ7brVYr3nrrLeTl5QV1XXkiIiIiIlIPS7t0YQcPHgQA5ObmenyM0WjE3LlzMX36dIcFMydNmmSf/b1q1Sr79pUrV2LHjh0YOHAgAGDs2LH44Ycf8NJLL+HUU0/1us1Zs2Zh0qRJ6Nmzp0c/W79+/Tz+2TrTp08fAOhQ87y6utplmZb2j50DQGRkJG677TZMnToVsbGxWLduHR599FEsXboUv/zyCxITExWLmYiIuqaDBw+iR48eHZ4Yc+ell17Cjz/+iLVr1+K0004DAIwbNw5ZWVk4++yzMXfuXJx55pn2/Q8fPuyQl2tra3Httddi8+bNGD16tNdtrl69usPgt6ufLTs726uZ4IcPH8aPP/6ITz75BID4QfkNN9yAF198Ec8995y9rZ49e+Kss87CJ598gn/961/2xUhXrVqF48eP4+WXX/a4TyIiIiIiorY4I70LkxYQDQ0Nddje0NCAvLw8hy+pLvh3332H6upqzJs3z+EYnU6HmTNn4qeffkJjY6N9+6hRo+yD6IA4M2706NHYu3evfZs3bSYlJSEvL6/D49ye/mxySG21tLQ4bI+NjXW6cOhHH33kdMb7s88+ixdeeAFnn302Tj/9dCxYsADLli3Djh078I9//EOxeImIqOtqbm52muNeeeUVh/w9depU+3sff/wxBg8ebB/wlkybNg3R0dH45ptvHLZfccUVDn8eO3YsADjkcG/aHDhwIPr37+/zz+bOkiVLkJGRgZkzZ9q3XX/99airq8Onn37qsO/111+P2tpah0W+lyxZgvDwcFx99dVe9UtERERERCThjPQuTKo7WlJS4rA9PDzcfnO5Zs0a3HzzzaiurgYAHD16FABwzz334KGHHoIgCBAEAYBYdsVsNqO8vBzZ2dkAgJycnA79JiUlYdOmTfY/e9umJzIyMgAAxcXFHh/TGek8paWlOWxvv0BpZ5wNDpx11lno2bMnvv32W7z44ovyAiUioi4vMzOzwxNSAHDZZZdh8uTJAIBrrrnGYdD76NGjsFgsGDFiBADY860gCGhubu6QM9vn8KSkJABwqJPubZueyMjIwLFjxyAIgkNpOVfMZjOWLl2Kuro6exwSvV6PxYsX46qrrrJvu+CCC5CWloYlS5Zg3rx5KC8vx1dffYVLLrnE/jMSERERERF5iwPpXdiECROg1+vxyy+/4KabbrJv1+v19oFhaZBbIj0C/fjjj2PIkCFO201PT7d/76oGuTRQ7kubnjjjjDOg1+vx888/Y/78+V4d68ratWsBwF6SRmlxcXGora31S9tERNS1TJw4Ef/+97+xbds2DB8+3L49LS3N/oFvVFSUwzExMTHIzs7G//73P6dtJiQkOPzZ0xzuTZuemDx5Mv71r39h69atGDlyZKf7r1ixAiUlJdiwYQMiIyMd3jt8+DBmzJiB/fv3Y8CAAQDED7Rnz56N5557Drt378bKlSvR3Nzc4ck4IiIiIiIib3AgvQvr0aMHLr/8cnz66afYuXOnR7OqJ02aBAA4cuQI/vrXvyoShz/a7NGjB6644gp88skn2L59O0466aQO+9TV1eGnn37Ceeed12l7VVVVeOWVV9CzZ0+3i5/5qqioCPv378e0adMUb5uIiLqeW2+9FYsWLcLjjz+OZcuWeXTM5MmT8dVXXyEnJwfx8fGKxOGPNqWf7bHHHsMXX3zhdJ8VK1Zg4sSJiImJwRtvvIFx48ZhzJgxHfbLy8tDZmYm3njjDTz77LP27fPmzcNzzz2HN954A99++y369etnvx4hIiIiIiLyBWukd3GvvfYahg4dirPOOgvLli1zqP9dU1ODdevWOew/ZMgQ3HTTTXj88cfx8ccf22elWa1WrFmzBo888ojXMXjT5ocffoi8vDx8//33nbb7n//8B3l5efjLX/6CL774Amaz2f7eTz/9hPHjx3f4+dozm81YtWoVJkyYYK+z6s3Cbu2VlpZiwYIFKCoqsm87duwYLrvsMgDAgw8+6HPbRETUffTr1w9vvvkmvv76a8yaNQuHDx92eH/r1q0OuQYAHnnkEej1elx22WXIz8+3b6+srMTzzz/faU50xps2p06dinPOOafTNnNzc7FkyRKsWLECs2bNwvHjxx3affTRR3HhhRfCbDajoKAAK1euxPTp0122N336dLz99tsO1zgDBgzAxIkT8dprr2HPnj2YN2+eR2VkiIiIiIiIXOFAehcXHx+PX3/9FTfffDPuvfdexMfHo3///sjJyUFaWhq+/fZb/POf/3S4Qf3Pf/6DRx99FHfeeSdiY2ORm5uL2NhYPPbYYzj99NN9isPTNisqKrBr1y4YjUaPfrZ169Zh/vz5uP3225GYmIiBAwciKSkJl1xyCU477TRcd911DsdUV1fbF2jr168f4uPjMX/+fEyfPh27d+92OtvNG4mJiUhLS8Ppp5+OtLQ05OTkoG/fvrBYLFi9erV9ITciIqLOXHnllVi/fj3q6+sxbNgwZGVlYeDAgUhMTMSkSZMwZswYvPHGG/b9+/btiw0bNiAkJAT9+vVDdnY2MjMzMWjQIJSVlWHw4MFex+BNm3v37sX+/fs9/tl+/fVXmEwmDBw4EDk5OcjNzUWPHj2wfPlyvPrqq4iLi8Obb74Ji8XidiD97LPPRmlpKb788kuH7fPmzUNTUxNCQkIwZ84cl8dfcMEFDgu3vvfee/ZrhTVr1nj08xARERERUdenE9oWwqQur6KiAhUVFYiLi0NaWhr0evefpRQUFKCxsRE9e/bssIjm/v37ERUV1WGR0IKCAtTW1mLgwIFet1lZWYnCwkL07NkTcXFxXv1spaWlqK6uRlpamtOarUeOHEFdXR0AQKfTISoqCikpKYiNjXXZ5pEjR9DU1IRBgwY5fX/Xrl2IjY1Fz549O7xXVVWFsrIyZGZmuu2DiIioMy0tLSgsLERLSwsyMjLs64+4Ul9fj4KCAiQkJCA1NdXhPaPRiPz8fPTr1w8RERH27WazGXv37kVmZiaSk5O9ahMA9u3bB71ej/79+3v1szU1NaGwsBA6nQ7Z2dkICWmtPHjs2DHU1NRg6NChLmeUNzU14cCBA0hJSbEvRg6I52zfvn0IDw93G9OBAwfQ1NTk9L1evXoxhxMREREREQAOpBMRERERERERERERucXSLkREREREREREREREbnAgnYiIiIiIiIiIiIjIDQ6kExERERERERERERG5wYF0IiIiIiIiIiIiIiI3OJBORERERERERERERORGiNoBqMFqtaKwsBCxsbHQ6XRqh0PUJQmCgJqaGmRlZUGv52d2RCQPczeR/zF3ExERERG51i0H0gsLC5GTk6N2GETdQn5+PrKzs9UOg4iCHHM3UeAwdxMRERERddQtB9JjY2MBiDcJcXFxKkfjf7sLjfjr67/hkxvHYUhWfFC3oQSl4pDbjlbOh7+YTCbk5OTY/78REcnRHXK3knlBq20pSSs/o1bPjy+Yu4mIiIiIXNMJgiCoHUSgmUwmxMfHw2g0dtmb8bZaLFaYGloQFxmKUINvj+lqpQ0lKBWH3Ha0cj78pbv9PyMi/+oOv1OUzAtabUtJWvkZtXp+fNEd/p8REREREfmqW85I725CDXokx4R3iTaUoFQcctvRyvkgIiJtUDIvaLUtJWnlZ9Tq+SEiIiIiImUF97QZ8kh+ZT3u+mQb8ivrfW7jWEUd5r29Cccq6lSNQwlKxSH3nGjlfBARkTYomReUyNv+iEtJWjlfWj0/RERERESkLA6kdwPNFiuOVdSh2WL1uY2aRjN+2FOKmkazqnEoQak45J4TrZwPIiLSBiXzghJ52x9xKUkr50ur54eIiIiIiJTF0i7dQG5qDD6df6raYTAOjcZBRETaoNW8wLiCIw4iIiIiIvIvzkgnIuomlixZgmXLlqkdBhEREXmIuZuIiIhIOziQ3g3sKjQi75HvsKvQyDgYR7dQV1eHTz75BA8//DAWLlyIJUuW4PDhw2qH5ReLFy/G559/7tG+X3/9NX766Sc/R0REvtJqXmBcwRFHsGPudo65m4iIiEg7OJDeDaTFRuDvU/sjLTbC5zbS4yKw8NzBSI/zvQ0l4lCCUnHIPSdaOR9dzapVq9CnTx88//zzsFqtiI6OxsaNG3Huuefi7rvvVjs8xX355Zf45Zdf1A6DiBSgZF5QIm/7Iy4laeV8afX8BBPmbiIiIiIKBjpBEAS1gwg0k8mE+Ph4GI1GxMXFqR0OUZekxv+zvXv3YtSoUbj11lvxzDPPOLxntVqxZcsWjB49GgCwbt26/2fvvsObqt44gH/TNm260pbuXaCUVTYFZMmUAgURVPYQ2UNAZCioKCKIoggIqICK7B+IIHsvGbJ3gULpoHsl3c24vz/S3DbN6E2atmnzfp6nT5ubm3Pfe0p5c0/OfQ8uXryIMWPGYMeOHUhMTMSyZctga2uL58+fY+/evcjIyEDz5s3x7rvvgs/ns219++23aNu2Lbp3785u27p1K2xsbDB06FAAwIYNG+Dv7486derg3LlzkMlkGD58OIKDg/U+r/T0dOzevRtxcXFo1qwZhg4dCktLS+zYsQOLFy+Gk5MTXn/9dQDAJ598gn379rHHPnbsGDw9PTF9+nQMGjQIfn5+WLdund4xEFLdKHcTUvkod1PuJoQQQggh2tGMdDOQXSDBpWdpyC6QGNyGKE+Cw/cSIcozvA1jxGEMxoqjon1iKv1RlRiGQV6RtEJfuj77++GHH+Di4oIvvvhC7TkLCwv2QhwA7ty5g2+++Qavv/46UlNTERAQAAsLC5w5cwZNmzbF7du3IRAIsGTJEnTr1g1SqZR97fbt23Hz5k2V9o8cOYKTJ0+yj/ft24cZM2Zg4sSJKCoqwq1bt9CiRQvcunWL3efmzZuYPXs2cnNztZ7T9evXERISgr1798LW1haHDx/Gu+++CwBwc3ODra0thEIhgoKCEBQUBD6fj3379mHmzJmYMWMGrK2t4enpqeO3QggxFcbMC8bI25URlzGZSn+Zav8YC+Vuyt2EEEIIIUTBqroDIJUvJj0PozZfw6GZnRHq62RQG3GZeZi+4xYOzewMJzvD2jBGHMZgrDgq2iem0h9VKV8iQ5PPjleojUdf9oGdteb/ui5fvoywsDAIBNxurxeJRDh69Chee+01AIrBAuUF9Nq1awEAM2bMQP369bF582ZMnjxZr1jFYjHu3LkDZ2dnAMDQoUMxf/58nDp1CgDw+PFj/Pjjj1i8eDHs7e01tjF27Fj07dsX27ZtY7c9f/4cAPDGG2+gbt26CA4OxuzZs1VexzAMrly5AhsbG71iJoRUH2PmBWPk7cqIy5hMpb9MtX+MhXI35W5CCCGEEKJAA+lmoIGnAy7O7w4PYfW+Kac4TDOO2iQrKwteXl4q23bv3o0rV66wj7///ntYWChuxnF3d2cvxAEgISEBjx8/xh9//MFuc3d3R0REBE6fPq33xXi/fv3YC3EAGDVqFAYPHgyZTAZLS0u0bdsWP/zwAxwcHDS+PioqCo8fP8bmzZtVttevX7/cY7/xxht0IU5IDWOqeYHiqhlx1FSUu0tQ7iaEEEIIMW00kG4GbKws4V/HrrrDoDhMNI6qZMu3xKMv+1S4DW3c3NwQHx+vss3V1RVBQUG4efMmtm3bhu+++469GK9Tp47KvklJSQAADw8Ple0eHh4qt3Vz5e7urtaOVCpFeno6PDw80KhRIzRq1Ejr69PT0wEA3t7eeh+77LkRQkyfqeYFiqtmxFFZKHdT7iaEEEIIIQpUI90MvMrKx6d/P8CrrHyKg+KoVjweD3bWVhX64vF4Wtvv3r07rl69CrFYzG7r1asXZs+ejT59yh8E8Pf3BwC1C/q4uDj2OQCwtraGRKJaC1ckEqm19+rVK5XH8fHxsLa2hpubW7mxACUX4S9fvtS6j67+IITULKaaFyiumhFHZaHcTbmbEEIIIYQo0EC6GcgvkuJWbCbyi6Tl76yFgG+Bpj5CCPiG/5MxRhzGYKw4yvbJ7dhMXHiaqnNBrcqIg5SYM2cO5HI5pk6dqnMRMG08PDzQoUMH/PTTT+y2Fy9e4MiRIxg4cCC7rV69evjvv//Yx7Gxsbh06ZJae4cPH0ZcXBwAQCaTYePGjejfvz87q668BcsCAgLQvn17rFy5UuXi//Lly+zPzs7OGgcCCCE1jzHzgjHydmXEZUxV0V8Mw+D801Q8S86ukjjMEeVuQgghhBBSU1BpFzMQ7OGIwx90qRVtGIOx4ijdzoE7rzBr1x0AwJxeIZjVq0GVxUFK+Pv74+TJkxg9ejQaNGiA9u3bw9vbG0lJSTh37hxGjBgBS0vtt5cDwPr169G7d2906tQJDRo0wOHDhxEREYF3332X3efDDz9Ejx49EB4eDm9vb1y+fBk+Pj5qbQUHB6Nz587o0aMH7t+/j7i4OJWLdi4Llv3555/o06cPmjdvjo4dO+LJkycIDQ1Fx44dAShquU6cOBGWlpZwcHDAJ598YkjXEUJMgDHzgqm2ZUxVcY5rz0Th+5NPYW1pgcMfdEYDT8dKjcMcUe6m3E0IIYQQUlPwGK7TZ2sRsVgMJycniEQiCIXC6g6H1HBSmRwdV5xBSnYhAMDO2hJXPu4JJ1t+NUdWvarz74xhGFy7dg0PHz6EXC5HQEAAWrZsCU9PT3afu3fv4u7duxgzZoza67OysnDs2DFkZmaiefPm6NSpk9o+sbGxOHv2LOzt7fH666/jxo0b4PP56NWrFwDFbelt27bF5MmTceXKFUilUkRERKjUP42MjMSxY8cwZcoUCAQCredTWFiIEydOICEhAS1atECHDh1Unr9x4wZu376N3NxcjB07FmfPnoWnp6da3P/88w8cHR3RrVs3Tv1IiCmh3E2qmkQmR9iyU8jKU8wqHtcxCEsGNq3mqCoX5W7K3YQQQgghRDsaSDeDi/HHiWKM2fIfto5vh8behp3vg1ciDF5/GX9N64hQX6dqi8MYjBWHsk8+6dcIS/55BBc7PoS2fMSk52Ht8FYY0EJ9llNlxGGqzO3vrCzlxfiKFSuqOxRCagVz+D/FmHnBGHm7MuIypsrur8tRaRix6Rq7Tz13e5yZ261S46hu5vB3pgvlbkIIIYQQogvVSDcDrvbWGNMhEK721hVqp0gmN4k4KsqYcRTJ5Pg3Kh0AMLCFD/o09QIAXHiaWqVxEEIIqfmMnRcqmreVTDVfVXZ/3YzJBAB0aaBYZPJFai6y8ooqPQ5CCCGEEEKIaaIa6WbAQyjAzJ7l1+ymOAxz/5VisahuDT0AAL9ceIHbcVlVHgcxLdOmTVO5HZ0QQspjqnnBXOO6G1+S3+My8vAyPQ8PXonRuXhgvariIFWHcjchhBBCCNGFZqSbgdxCKW7GZCK3UEpxVEIcSeICWFrwEFa3Dpr6Km6DfpGag/wiWZXGQUzL4MGDNdZnJYQQbUw1L5hrXPdfZQEAWvg5ob67AwAgOj23yuMgVYdyNyGEEEII0YUG0s1AdFouhmy4jOg09Ys/isM4Qn2d4GBjBQ9HAdwcrCFngCfJ2VUeByGEkJrLVPOCOcaVXSBBslixiHiIlyPqutkrjpmqfixT7R9CCCGEEEKIcdFAuhkI9nDAiTldEezhoP+L5XLg9jYEx/0PJ2a+ZlgbxojDiIwVR7CHAwa38gUAtPJ3ZrcrFxp7lCCukjgIIYTUDsbMC6baljFV5jm+TMsDALg52EAo4KOue/FAelpOpcZBCCGEEEIIMV1UI90MCPiWCPF0NOzFF74Fzn0NAYCQ1veAgWuqJw4jMlYcAr4lEkT5AICmPkJ2e313B1x8loaXGm7/row4CCGE1A7GzAum2pYxVeY5vigeMK9XPBO9rqvie0x6XqXGQQghhBBCCDFdNCPdDCSK8vH1kcdILB705awwB7iyDgAQz7hhwTVrxMc8r/o4jMxYccSl5+Hmy0wAQFMfJ3Z7oKsdACCmnIF0U+kPQgghpsGYeSE+Mw8L9t5DfKb6wG91xmVMldlfyjItypIu3s62xccsAMMwlRYHIYQQQgghxHTRQLoZyC6Q4tSjZGQX6LkIVvR5oFAMOAciy6sTdsu6I+v+0aqPw8iMFUdkshgSOQMrCx4aeJbczh2kY9ZaZcRBCCGkdjBmXsjKk2D3jThk5UlMKi5jqsz+YgfSi0u6eAkFAIB8iQzZZRYVNdX+IYQQQgghhBgXlXYxAyGejjjzUTf9Xxh1qriBPoBVKBADIPa/qo/DyIwVx/PiBceCXO3Btyz5TCqgeEZ6bEYeGIYBj8er1DgI0eT27dsQCARo3LhxdYdCCOHIVPOCOcb1svjD8KDinG5rbQmhwAriAimSRQUQCvhVEgcxL5S7CSGEEEJMGw2kE+1e/qv4Xq87IA8CEA2kPAAKswEbqgX6kr3t205lu5+LLXg8IK9IhvTcIrg52FRHeGYvMzMTjx8/hlwuR926deHr61vdIVXYrVu3YGdnh0aNGpW77xdffAE/Pz+sW7dO4/P379/Hq1ev0KBBA9SvX1/lufj4eDx48ADu7u5o06aNUWKvDEVFRXj06BEkEgkaNWoER0fN/y9JpVLcuXMHPB4PLVq0gJWV7tQnl8vx9OlTpKWlITg4GF5eXirPv3jxAk+fPlXZxufz0bNnz4qdECHEZCQVl2nxKS7pAgBeTgKIC3KQJC5AA6qJXikod9f+3F1ejlV68eIFYmJiEBwcDH9/f6O1++rVK9y/fx8tWrSAt7d3hc6FEEIIIean2ku7MAyDU6dOYd26dXj16hWn1xQWFuKff/7Bpk2bcO3atUqOsOZ7mpyN1789i6fJ2dxfVJQHpD9T/OzbGnAqvpCRy4D4G1UXRyUwVhyvMhUX2f51VAfSbaws2cHzJFFBpcdBVCUmJmLIkCHw8fHBBx98gEWLFuG1115DaGgo9uzZU93hVchnn32GjRs3GqWtVatWoW/fvpg8ebLac/Pnz0ffvn2xaNEioxyrMnz33XeoV68exo0bhylTpsDX1xfr169X2+/o0aMICAjAqFGjMHv2bLRr1w7Pnj3T2u6JEyfQvHlzvPPOO1iwYAHq1auHMWPGQCIpKY+xY8cODBs2DKtXr2a/NmzYUCnnScyLqeYFc4tLKpMjNbsQgGLwXMmzuLxL2dxuqv1Tk1Du5qam524uOVYqlWLkyJFo3rw5Fi5ciIYNG2LWrFkVbvf+/fsYMmQIOnTogL59++L06dOVdp6EEEIIqb2qdUb6gQMHMG/ePLi4uOC///5DaGhouTNPUlJS0K1bNwBAixYtMH/+fAwePBibNm2qgohrJqGAj37NvFVuQy5X8kOAkQP2HoCjF9zkBZjq9RhumSIg4RZQv3vVxFEJjBVHolhxId3YW6j2nLeTAKnZhUgSFSDU10nteWPGQUqIRCJ06dIFAQEBiI6OVpmN9PTpU5WLptjYWMTGxqJTp0548OABEhMT0aNHD1hZWYFhGNy7dw8ZGRlo0qQJPD09VY5z+fJl+Pj4ICgoiN129+5dWFlZoWnTpgCAGzduQCgUIiAgAI8ePYJMJkObNm1gYVH+55cvXrxAXFwcmjRpAnd3dwCKC8DU1FTw+XwcO3YMANClSxc8fvwYQqEQfn5+uHPnDvh8PsLCwjj1V+vWrXHp0iW8fPmSPZfMzEz8/fff6NChg8bXREVFIT4+HvXq1UNAQIDKc48ePUJsbCwAwNXVFY0bN4aDg4PKPsp+CQwMxOPHjyGTydC8eXPw+fr9HfB4PNy9exeurq4AFIPbo0aNwmuvvYZWrVoBUPTZoEGDsHz5cnz44YcAgMjISGRmZmptVyQS4ciRI+y5RUVFoVmzZujatSsmTJjA7teoUSP290CIsRgzL7g52GBqt/pGuSvKVPNVZfVXak4h5AxgZcGDm31J/ykH0pPFqgPppto/NQXlbvPJ3Vxy7Nq1a3H06FHcu3cP9erVw61bt/Daa6+hY8eOGDp0qMHtxsbGYvjw4di9e7fecRNCCCGEKFXrQLq9vT0OHz4MW1tbTrfsAcDChQvB5/Nx9epV2Nra4s6dO2jTpg3efPNNDBgwoJIjrpm8nARYEF7+7aQqku4qvns3L2kjzBo4mQm8ulV1cVQCY8QhlzOIz1TUT20bVEftecXFtogdbK+sOIiqH3/8EYmJiTh//rzaLb0hISEICQlhHx88eBDLly9HSEgI0tPT4ePjg06dOiEhIQERERFITU1FQEAA7t+/j7lz52Lp0qXsa6dNm4ZRo0bho48+YrctX74cDg4O7Id6CxcuhFwux/Pnz+Ht7Y0XL17Az88Px48fZy+wy0pPT8eIESNw5coVhIaGIiYmBjNnzsTChQtx4sQJREdHIzk5GatXrwYAhIaGssd5+fIlfHx80LFjR84X4+7u7oiIiMBvv/2GL774AgDw559/on379vD19UVaWhq7b2ZmJoYOHYp79+4hJCQEjx8/xuuvv45t27ZBIFAMLp0+fRqHDx8GACQnJyM6OhqbNm3C22+/zbazcOFCMAyDuLg4uLu7IyYmBs7Ozrhw4QLq1FH8LSUmJuLu3bvo2bOn1ovduXPnqjwePnw43nvvPVy/fp0dSF++fDmaNGnCDqIDKPfW+nfeeUflcXBwMFxcXJCamqqyvbCwEP/++y8EAgEaNWoEe3t7ne0SwoUx84KptmVMlXWOt2MVH7Z5ONrAwqJknRMvdiC9sNLiMEeUu80nd3PJsX/88Qfeffdd1KtXD4Dig4M+ffrg999/1zqQzqXd/v376+hVQgghhBBuqrW0S69evdCgQQPO+8vlcuzduxfjxo2Dra2iZmXLli3RsWNH7N69u7LCrPEKJDI8ThSjQCLj/qLkR4rvnqEAgJxCKa6gOXIYAZBwp+riqATGiCNBlI8CiRyWFoCLnfrFAnuxraO0i6n0R3VIERfgwSuRyldchuKDiQKJTO25B69EnNo9cuQIOnfuzLmmakJCAgYPHox79+7h2LFjsLe3x/Tp0+Hq6oro6Ghcu3YNx44dw9dff40zZ87ofZ4XL17E3r17cfXqVTx//hwMw+DTTz/Vuv/48eORlpaG58+f4/Lly4iNjWU/ZJw7dy7atWuHQYMG4dixYzh27Bj8/PwAAP/99x+OHDmCS5cuYeXKlXrF+P777+O3336DXC4HAGzevBnvv/++2n4TJ05EnTp1EBsbiwsXLiA6OhovX77EihUr2H1mzpzJxnb79m38/PPPmDhxIrKzVcsd3L59G8eOHcO///6LZ8+eobCwUKUsy+nTp9G3b1+IRNx+74BitlxRUREaNmzIbjt79iz69u0LsViM8+fPs7PoypOTk4Njx45h3759GDFiBNzd3dX65NGjR/jwww8xYsQIeHt74+eff+YcKyHaGDMv5BRKceV5OnIKpSYVlzFVVn8pZ5x7lirrAgCuDtYAgIy8okqLw5RR7taMcrcC19ytK8dKpVI8fPiQ/UBcqVWrVrh7967B7RJCCCGEGEuNWmw0Li4O2dnZaivZN27cGDduaK/bXVhYiMLCktlDYrG40mI0RVEpOYhYewmHZnbWWmZETcYLxXc3xQcdL9NyMfyfXByy9kaoOBrISQUcNM/MMWoclcAYcbxIVSw0KpMD8Zn5cLazVnleWVM1SceMdFPpj+qw/VosfjytWqd6UEsfrB7WCkmiAkSsvaT2mpcryp9JlJCQgBYtWqhse/jwIeLi4tjHffr0AY+nmGHo4OCA6dOns89lZWXh0KFDOHHiBDtTq2vXrujduze2bt2KHj16cD9JAOHh4ewMM0dHR3z44YeYOnWqxlqpKSkpOHjwIP755x921pulpSVGjhxZ7nHefPNNTouYadKnTx8wDIOTJ0+iTp06iImJwZAhQ1TKlqSnp+Ovv/7Cjz/+iAsXLoBhGDAMg5YtW+L48eNYsmQJu29ubi6ePHmCtLQ0CIVCZGdn49GjR2jfvj27z+DBg9mZZra2tujatSsePnzIPu/j44M+ffrA2lr170qb7OxsvPfee+jZsydef/11AIr1N5KTkxEVFYXQ0FAEBgbi+fPncHNzw759+3R+iJueno7Vq1cjKysLkZGR+PDDD9kSMgDQqVMnxMTEsDMnN23ahEmTJqFx48bo2rUrp5gJ0cSYeeFlWi6G/3rVKG2Zar6qrP5S1kBXfiiuVMe+eCA9R3Ug3VT7x9god1PuNkbu1pVjc3JyIJVK2VnuSq6urjrLspXXLiGEEEKIsdSogXTlzAhnZ2eV7S4uLjoHx5cvX87e+miO6rnb48D0TqjnrkfpAeVAep16qtuFPkBONJD6WO+BdIPiqATGiON5ao7O5720LEhm7DhqqpHtA9C7iWrtUidbxcx+LycBDs3sbFC7dnZ2ahdaJ0+exLFjx5CYmIh79+5BIpHAykrxX5+Pj49K3dPo6GgAUBtkbdiwIe7cuaN3PPXr11d5HBwcjNzcXKSmpqrdIv7y5UsAYOu06kM5u80QFhYWGDduHLZs2QJnZ2eMGDGCveNHSTkj76+//oKNjWq95dIzwLdt24aZM2fCy8sLPj4+7K3dSUlJKq9xc3NTeWxra4v09HT2cY8ePTgPfOTl5WHAgAGwsLBQuTOJx+OBz+fj7NmzuHPnDnx9fVFYWIjw8HBMnDgR586d09pmYGAgOxgRFRWFDh06wMrKCp988gkAoHt31TUiJkyYgNWrV2P//v00kE4qxFTzgrnFpSzL5qllID2zzIx0U+0fY6PcTbm7NENzt64cqxyEz8/PV3lNXl5euQP05eVuQgghhBBjqFED6co3iGVvNRSLxbCzs9P6uo8//lilRq5YLOZck702sLO2Qgt/Z+4vkBYBouJZQGUH0l3qAjn/AimPgbr6DRjpHUclMUYc5Q6kc5iRbir9UR08hAJ4lBmgUBLwLQ2e0de6dWvcuHEDcrmcvciePXs2Zs+ejW3btmH06NEq+ytntym5uLgAgNptySKRSGV2lIWFBRiGUdmnsLBQbXGush/wicViWFhYQChUX6BW+drMzEzUrVu33HPVdR76eu+999C0aVPw+XyNt8Er638vX75c60JmRUVFmDhxIn799VeMGjUKAFBQUAB7e3u1vjKW/Px8REREID09HWfOnFGbeVa3bl20bNmSLRdgY2ODoUOHYvbs2WAYhlO/BQcH44033sDp06d1XozXqVMHiYmJFTshYvZMNS+YW1zKsmxeZUq7uBTffZaRqzqQbqr9Y2yUuyl3G1vZHGtnZwcPDw+VuxEAID4+XmWRWH3bJYQQQggxlmqtka6vgIAAWFtbszNPlF68eKHzNn0bGxsIhUKVL3OSIi7ADyefIkXHoK6KrFiAkQN8e8BBdeYR6hRfJKQ8qvw4Kokx4lCWdtHGU6iY+ZOs4xim0h+1yfTp0xEVFaXx9msuAgIC4OPjg0OHDrHbCgsLceLECZXbm728vNhZaMp9bt68qdbeyZMnIZFI2MeHDh1Cy5Yt1WaGAYpFMH18fNTWeyh9QW9nZ4eioqKyL62wevXqYeLEiRg8eDDatm2r9nzjxo3h6+uL33//Xe25jIwMAEBqaioKCgpUFks7fPgwW79VH4mJiTh27JhK35WlHERPTU3FmTNnNC4C169fP5XfE6CYuejp6ckOYERFReH06dPs81lZWSr7y+VyREZGwtOz5P/CsjMnExMTcfv2bbRs2ZLjGRKimanmBXOLS/khuLeT9hnppQcZTbV/agrK3YapibmbS47t06cPDhw4wP6NSSQS/PPPPwgPD2f3MSR3E0IIIYQYg8nPSD979iySkpIwfPhw8Pl89O3bFzt27MDEiRPB4/EQHx+Pc+fO4ZdffqnuUE1WRl4Rdl+PQ99mXlpnEqm+oFRZl+LBJitLHryEAli5KgfSH1d+HJXEGHEoZ6TXsbeGlaX6jCJXe8XFVnaBFEVSOayt1D+zMpX+qE06deqEtWvXYtasWTh37hx69uwJb29vJCUl4ffff4eXl5fK7eBlWVhYYOXKlRg/fjxkMhlCQkLwyy+/QCAQYObMmex+Q4cOxbRp09CgQQN4e3tj8+bNahdxgGI2XEREBMaPH487d+5g48aNOHjwoNZj//TTTxg6dChycnLQrVs3PHnyBFeuXMHhw4cBKBZX3rRpE/bu3QsHBwd06dKlYh1Wypo1a7Q+Z2FhgZ9//hlDhgxBfn4++vfvj8zMTBw5cgStWrXCkiVL4OPjg2bNmmHq1KmYPn06Xrx4ge+++w6WlpZ6x3L69GmMHj0aqampareSKw0ePBj//fcf1q9frzIQEhwcjODgYADAggUL0Lp1a4wfPx4RERF4+PAh1qxZg59++ondf9u2bWxNVUCxCHZ4eDhatGiB/Px87Ny5Ey9evMAff/zBvqZPnz7o1asXWrVqhbS0NPzwww8ICgrC1KlT9T5XQkozZl5g87aGHFWdcRlTZfVXslixro620i4SGYPsQimEAr7R4zBHlLsNV9NyN5cc++mnnyIsLAyjR49G//79sX37dkgkEpW7iw3J3enp6bh+/Tr7WLlYrb+/v0GleQghhBBinqp1ID0yMhKnTp1i3wTt378fDx48QLt27dCuXTsAwPbt23H16lUMHz4cAPDNN9+gY8eOiIiIQPv27bFt2zZ07NiR06I+5qqRlxBXP+nJ/QXsQHqQehspj4EzUHxnGHagvVLiqCQVjSOnUMpeZJ+d2w1Odny1fZxs+bC04EEmZ5CRW6R2e7gx4iCaTZ8+HX369MG2bdtw6tQpyOVyBAQEYObMmRgyZAh7MR4YGKjxYnbkyJHw9PTEtm3bcPPmTXTq1Am7d+9mb5EGgLFjx8LKygqHDh2Cvb09Zs2ahR49erCLnClNmzYNISEhOHz4MKRSKY4cOYLevXtrjX3QoEG4cuUKNm/ejB07dqBFixbYsWMH+/zs2bMhl8uxc+dO5ObmIjQ0FGFhYQgJCVFrq3Xr1lovZAGgWbNmOuuztmjRQuU2+f79++P27dvYtGkTtm/fDh8fH0yaNAn9+ysWkuPxeDhx4gS+/fZbbN26Fd7e3jh27Bi+/PJLlRlhmuJt0qQJPDw82MdcFizj8/no1KkTtm/frrJ91KhR7EC6p6cnbty4gdWrV7MxHT9+XKWOeXBwMHr16sU+PnfuHH799Vfs27cPfD4fr7/+OrZt26ZSNubs2bP45ZdfsG/fPtjZ2eHDDz/E+PHjOS+OSog2xswLptqWMVXGOTIMo3WxUQHfEnbWlsgrkiEzt4gdSDfV/qlJKHcr1PbczSXHNmjQANevX8eaNWuwa9cuNGrUCD///LNKPIbk7ri4OKxevRqA4gPxe/fu4d69ewgPD6eBdEIIIYRwxmOqogCeFteuXcOff/6ptr1fv37o168fAGDHjh14+fKlSn27xMRE/Pnnn0hOTkazZs0wcuRIdmEcLsRiMZycnCASicyuzAsnxxcBV9YBr80A+ixTfU5aBHztA8glwJyHgJPhCyXVVPfiszBw3b9wc7DGjcXaL6zClp1CanYhDs3sbHDd0JrM3P/OevXqhbZt22LFihXVHQohtYK5/59CqoYoX4IWX5wAAEQuDYeArzozt9OKM3iVlY+/pnVE6wCX6gixUpn73xnlbkIIIYQQoku11khv37491q1bp/alHEQHgBEjRqgtEuPt7Y358+dj1apVGDdunF6D6OboWXI2wldfwLPk7PJ3BgBxguK70IfdFJkkRoevTyMyrQBwK65Hr2d5F73jqCQVjUNZH93LSaDokySxxv1ci28BT8/VXBfTVPqDEEKIaTBmXmDztpYcVV1xGVNl9NeV5+kAFHeWlR1EBwBXh+I66aVyu6n2DyGEEEIIIcS4atRio8Qw9jZW6FDPFfY2HCv5aBhIl8oYJIkLIJUxgEdjxcbkh5UbRyWpaBzK+ui+zrYlfaKBm4OiTnp6TmGlxEFMW1hYGBo2bFjdYRBCahBj5gWVvG1CcRlTZfSXcpHwsmVdlFzsFAPpGaUG0k21f4j+KHcTQgghhBBd6B2/GfBxtsWSgXrU/mMH0rWUbVEOpOs5I13vOCpJReNQzkj3c7HVuZ9b8ay19BzNM9JNpT9I5Vi+fHl1h0AIqWFMNS+YU1zKD789NaxtApQsOJqZV5LbTbV/iP4odxNCCCGEEF1oRroZKJDI8DItFwUSWfk7y2VAtvqMdBUeTRTfU/Sbka5XHJWoonEoZ6SXN5DuWjwjPS1X84x0U+kPQgghpsFU84I5xaUsx+atZUa6k62inKA4X1qpcRBCCCGEEEJMDw2km4GolBx0++4colJyyt85NxWQSwGeBeDgqXkf5UB66lNAJtW8T0XjqEQViUMmZxCdppyRbqdzX9dyZqSbSn8QQggxDaaaF8wpLuVAurYZ6UKB4mZOcYGkUuMghBBCCCGEmB4aSDcDQW722DmxA4Lc7MvfWfxK8d3BC7Asqfyj0oZzIMC3B2SFQMaLyomjElUkjoSsfBRK5bC2tEC7uq4623Gz110j3VT6gxBCiGkwZl4w1baMqTLOMb9QMUFAW410YfGMdFG+RO21ptY/hBBCCCGEEOOigXQz4GBjhdfqu8KByyJYyvroTr7a27CwADwaKZ5IeVQ5cVSiisShLOsS5GYHJ1u+znbYGem5mmekm0p/EEIIMQ3GzAum2pYxVcY5phbfReblZKNxP6FAWdpFovZaU+sfQgghhBBCiHHRQLoZSM0uxM/nnyM1W/PMaBWi4hnpZeqjJ4kK8M2xSCSJChQb2AVHuQ+k6xVHJapIHM+LFxqt7+6g3idluNjrLu1iKv1BCCHENBgzL5SXo6orLmOqjP5KyMoHAHhqnZGuLO1SUtrOVPuHEEIIIYQQYlw0kG4GUrMLsf4cxws8ZWkXoeqM9LScQmw49xxpyjIlHk0V3/UcSOccRyWqSBwvimek13O3V++TMlzsFAPppW//NlYchBBCah9j5oXyclR1xWVMldFfWcU5W5/SLqbaP4QQQgghhBDjontQzUATHyHufv4Gt52zExXfHb107+dZvOBoMveBdL3iqEQViUNZ2qW+u0O5+zoXX2znFEohkcnBt1T93MpU+oPUToMGDYKfnx/WrVtX3aEQQjgy1bxgbnFZW1qgTvFdZWVpKu1iqv1Dah7K3YQQQgghpo1mpBNVOSmK7w6euvfzKB5Iz3gBSPIrNyYT8qK4tEs9DgPpyllrAJCVp3lWOjE+uVyOX3/9FR07doRQKISDgwOaNWuGOXPm4OXLl9UdXoVERERg9uzZRmsvISEBU6ZMgZ+fH6ytreHn54dJkyYhISFBZb/c3Fz8+uuvaN26NXg8HrZt26axva1btyIkJAQ2NjYIDQ3FP//8o3dMt27dwtChQ+Hl5QVXV1eEh4fj9u3bKvt89dVX4PF4Kl/Ozs46283Pz8ecOXMQEBAAgUCAevXq4dNPP4VMJtNrH0JI7efpZAMej6fxOafi3C4uoLxuTJS7uaupuVssFmPWrFkICAiAra0tOnTogH///Vdnu40aNVLL9zweD4MHD9br2IQQQgghxkID6WbgeWoO3lr/LzubWqfcVMV3e3fd+zl4AHZuABggNdL4cVQiQ+PILpAgpfi27Xru9uXub2nBg1CguOlDlK9eJ91U+qM2YRgGQ4cOxWeffYYZM2bgxYsXSE1Nxe7du1GvXj188cUX1R2iyYiNjUXbtm3x8uVLHDt2DDk5OTh58iQSExPRtm1bxMbGsvv+9ttvuHbtGn799Vet7R07dgzvv/8+Fi9ejKSkJIwfPx6DBw/GzZs39Yrrs88+w9tvv427d+8iMjIS/v7+6NmzJ169eqWyX/v27cEwDPuVlZWls92FCxfif//7H/bv34+srCxs2bIFa9aswbfffqvXPoRUJlPNC+YWl7ayLkDJjPQCiRyFUlmlxmEuKHdzV5Nz97hx43Dy5EkcPnwYqampGD16NPr06YPnz59rbTcyMlIl1z958gQA8Pbbb+t1bEIIIYQQY6GBdDNgY2WBEA9H2Fhx+HWzM9I9VDY72/ExtK0/nO1KZlmzC44mPzR+HJXI0DiUs9E9HG0gFPA190kZygVHNc1IN5X+qE22bt2KvXv3Yt++fRgxYgTc3Nxga2uLJk2aYObMmfjtt9/YfdetW4eQkBAsX74cvr6+sLCwQFZWFoqKijBv3jx4e3vDxsYGYWFhOHPmjMpxWrZsie+++05l27BhwzBhwgT2ca9evTBhwgQMHToUHh4ecHV1xYIFCyCXy7XGzzAMfvjhB4SEhMDW1hbt2rXDpUuXACguQA8fPowff/yRnZEVGRnJHmfYsGEQCoXo3r07p76aNWsW+Hw+Dhw4gNDQUFhbW6Nx48bYv38/7OzsMHPmTHbfGTNmYNOmTWjTpo3W9r777jsMGDAAY8aMgYuLCz788EO0bNkSq1ev5hSP0qFDh/DOO+/A09MT7u7uWLduHbKzs3Hy5Em92inr1q1bCA8PR5s2bSAQCNCtWzd07txZZbCAyz6EVCZj5gUuOao64jImY/dXS38nANoXGgUAR4EVlJPVxflSo8dhjih31/7cnZeXhwMHDmDRokVo1qwZHBwcMH36dDRu3Bhr167lfJzNmzejTp06KjPSK+t9AyGEEEKIJvSO3wz4udjhm7ebw8/FTveOMimQl674uUxpF41teLdQfE/gdvsk5zgqmaFxPC+10CjXdpR10jUNpJtKf9QmO3bsQIcOHdCxY0dO+z979gx3797F9evXIZfL4ezsjMWLF2P37t3Yv38/kpKS0L9/f/Tr1w/R0dF6x7N582a0adMGT58+xV9//YUtW7bovDj97LPP8PXXX+Pbb79FcnIyfvrpJ2zfvh0A8Pvvv6N///6YNWsWOzOrUaNGAIAtW7age/fuSEhIwNmzZ8uNKzs7G//88w+mTp0KGxsbleesrKwwY8YMHD58GGKxmNN5MgyDq1evolu3birbe/bsicuXL7OPt23bBh6Ph7S0NE7tAkBmZiZkMhmcnJxUtt+9exeOjo5wd3dH//79cf/+fZ3tDBs2DMePH8ft27dRWFiICxcu4OrVqxg+fLhe+xBSmYyZF0y1LWMy9jm2DawDQPeMdAsLHhxsFHebKcu7mGr/1BSUu2t/7laeu6aSScoPHcojlUqxdetWjB49GgKB9r9Rbe8bCCGEEEKMgQbSzYBEJkeKuAASmfbZNACKB9EZgGcB2LmqPFUgkeFpcjYKJKVqBfu1VXyPv2HcOCqZoXGUXWhUY5+U4WSnmJGemade2sVU+qM6pIgLEJlUcqH3LDkbCVmKWvsFEhkevBIhp1Ax0y81uxCPErhdFD59+hQhISGc4+Dz+fj555/h4+OjOHZBAdasWYNly5ahQ4cOcHFxwZIlS9C4cWP8+OOPnNtVCgsLw/z58+Hs7IzXX38dCxYswPfff69x37y8PKxatQpfffUV3nzzTQiFQoSFhWHDhg3lHqd3796YPHkyHBzKr90PAC9evIBMJmMv5stq2LAhZDKZztutS8vOzkZubi7c3VVLQnl4eCApKYlTG9rMmjUL/v7+6NOnD7vNy8sLW7ZsQWxsLP777z84OTmhc+fOiIuL09rO9OnT8c4776B169YQCATo3r075s+frzKrjcs+hFQmY+YFLjmqOuIyJmP317OUbACAl5P2QTpAfcFRU+0fY6PcrY5yt2Zlc7e9vT369u2Lr7/+Gg8ePEBubi42btyIW7duITExkVObhw8fRlJSEiZOnKjXsQkhhBBCjIkG0s3Ak6RstPv6NJ4kZeveMSdZ8d3OFbCwVHkqKiUHb/xwAVEppep/+hYPpCc/AIryjBdHJTM0jrILjWrskzKUM9JF+eoz0k2lP6rD9muxGLflOvt45s7b+OXCCwBAkqgAEWsv4X68CADw1614DP/1Kue2y852mjBhgsoCVVKplH3Oz89PZcbSixcvUFhYiPbt26u00aFDBzx69Ij7CRZr27atyuOwsDC8evVK42yxyMhI5Ofno2vXrnofp2nTpnq/hgsrK6sKvb7s7LNRo0aBYRi4ublxev2iRYtw5MgR7N27F3Z2JTM9J0yYgOHDh8PFxQV169bF77//DgcHB511YBcsWIB9+/bh6tWryM3NxcmTJ7Fy5UqsWbNGr30IqUzGzAtcclR1xGVMxu6v808VM251lXYBShYTV+Z2U+0fY6PcTbmbC225+48//kD79u3Ru3dvuLu748SJE5gyZQosLS11tFZi8+bNeO2113T2m7ZjE0IIIYQYCw2km4GVCmJ/AAEAAElEQVQAVztsGdcWAa7lvKHMLa6Pbu+hez8lJz/AwQuQS4HEu8aLo5IZGkfJjPTyFxpVcrHTXtrFVPqjOoxsH4Dfx4exj9cOb4VJXesBUMwEPDSzM5r5KS6SB7f2w86JHTi1GxISgqdPn6ps27RpExiGwZ9//qm2v7W1tcpjhmE0tqvtduTSNNVPLe81mo6tz2uUyp5HeerVqwdLS0tERmpeKPjJkyewsrJCUFAQp/YcHR1hb2+P1NRUle2pqanw9PTU8irdlixZgjVr1uDo0aMICwvTua+1tTVCQkLw7Nkzjc/L5XKsXbsWH374Idq3bw87Ozv06NEDkyZNwg8//MB5H0Iqm6nmBXOLy7ucGelOtsrSLtJKjcPUUO7WfAx9X6Nkbrnbzc0NmzdvRmJiIvLy8vDXX38hMTER9erVK7fdxMREHD16VOdsdH3eNxBCCCGEGIoG0s2AUMBHj0ae7K3IWuUUv5F2cNe9nxKPV1Le5VX55V04x1HJDIlDJmfwMk0x615Z2oULZWmXrHz10i6m0h/VwUMoQCMvIfu4gacjfJxtAQACviVCfZ3YGrTujjZo4iPU2E5Zw4cPx5UrV3DlyhWD4qpfvz5sbGzw33//qWz/77//0LhxY/axi4sLMjMzVfbRNIh7/fp1tcc+Pj4QCtXPp3HjxrC1tcWFCxe0xsfn83UueMaVo6MjIiIisGHDBhQWFqo8J5VKsW7dOrz55ptwdHTk1B6Px0OHDh3UaryeOXOGc83b0r744gusWrUKR44cQefOncvdv6ioCE+fPmVv89cUn6WlpdpAB8Mw7Ew4LvsQVUVFRUhLS9M6iKWJVCpFenq6Xq8xJ6aaF8whrtL/JsudkS5QnZFuqv1jbJS7KXfrom/uTk9Px4kTJzBo0KBy9/3jjz9gZ2eHd9991yjHJoQQQggxFA2km4H0nEJsvfIS6TmFunfUd0Y6APi3U3x/Wf5CQZzjqGSGxPEqMx9FMjmsrSzYi0YulKVdMjXMSDeV/qhNxowZg7feegsDBw7Eli1bEBMTg6KiIsTGxrKLWemaNSYQCPDBBx9g8eLF+O+//5CVlYWlS5fi0aNHmDVrFrtfly5dsGPHDkRGRkIkEuHrr7/GnTt31Nq7fv06Vq5cCZFIhPPnz+Obb77BnDlzNB7bzs4Oc+bMweLFi/HPP/8gOzsbN27cwNSpU9l9AgMDcefOHeTkVLxUw5o1ayCRSDBo0CA8fPgQEokEkZGRGDx4MPLy8vSuK/vRRx/h0KFD2Lp1KzIzM/HDDz/g9u3bmD17NrsPlwXLli1bhu+++w5HjhxBly5dNO7zzjvv4MKFC8jOzkZ0dDTGjRuHrKwsTJo0id1nyZIlcHZ2BqD4nQ8aNAirVq3CtWvXkJ+fj7Nnz+LXX39l659z2YcoyOVyzJ07F87OzggKCoKfnx/279+v8zWxsbEIDw+Hvb09QkJCIBQKMXfuXKMMLtUmppoXzCGu0iXYPIQ2OvYEHIsHzHOL64Gbav/UFJS7uavJuXvz5s3YuXMnsrOz8eTJE7z99tsICQlR6avSubu0LVu2YMSIEbC3V78rlMuxCSGEEEKMhQbSzUCiqABfHXqMRFGB7h1zigfSHTQPpFtbavjnUq+b4vvLS4BUfda1QXFUMkPieJGmuPip62oPS4uSizmNfVKKc3FpF5GGgXRT6Y/axMLCAnv37sXSpUvxyy+/IDQ0FC4uLujTpw/y8/Px33//lTu7+KuvvsLbb7+NgQMHwtPTEwcPHsSRI0dQt25ddp/58+ejc+fObK3OxMREDBgwQK2t8ePH48aNG2jQoAHeeustjBs3TuvFuPLY8+bNw+zZs+Hh4YEZM2Zg5MiR7POzZs2CXC6Hp6cneDye1tu7uQgICMCNGzcQEBCAN954AwKBAI0bN0ZGRgbu3LkDX19fdt9Tp06xdWoBYPTo0eDxeJgwYQK7T3h4ODZv3oylS5fCy8sLmzdvxr59+9CmTRu94lq6dClycnLQtWtXlfq4S5YsYfeZPXs2li1bBn9/f3Tq1Al5eXm4du0aGjZsqLXd9evXY/DgwRg6dCjq1KmDiRMnYtq0aVi6dKle+xBg1apV+O2333D58mWIxWIsWLAAQ4cOxePHj7W+ZuLEiRCJREhKSkJ6ejrOnDmDDRs24Oeff67CyE2fsfNCeTmKK1PNV8aMKz1X8R7G2dYKNla684SDjeL5nOLSLqbaPzUF5W7uanLuHjRoEI4fPw5/f3907doVjRo1wunTp2Fjo/uDqwsXLuDZs2day7pwOTYhhBBCiLHwGDO8v1osFsPJyQkikUjjbZpm669JwL3dQK8vgM6zub1GLgdWhQC5qcC4w0BQ7byd8rd/o/HFP48Q3tQLG0dzv7g4G5mC936/jlBfIQ7NNK9ZMub+d9arVy+0bdsWK1asqO5QOFuwYAE2bNiA69ev6xyUJuYtKCgI7777LlauXMluCw4OxoABA7TWk69Xrx7GjBmjMrDRokUL9OrVC6tWreJ0XHP/P4VUrlOPkjFh6w1O+frb45H46exzjOsYhCUDK2fByOpi7n9nlLsJIYQQQoguNCOdlChnRrpGFhZAve6Kn6NOGT8mExGdlgsAqKvHQqMA4KRjsVFCTM2yZcvQvHlzDB48GLm5udUdDjFBKSkpiImJQadOnVS2d+7cWa0+cWkffPABtm7ditOnTyMqKgrr16/Hq1evMG7cuEqOmBBuEsWK2eTeTuWXb3OwUeT27OIZ6YRUJ8rdhBBCCCFVhwbSzUB0Wi5GbrrKDgZrlatcbFR9ID0qJRv911xEVEq2+usavKH4/vgQoOMGB85xVDJD4mAH0t1KBtJ19kkxZY10TQPpptIfhChZWVnh0qVLePjwocY6pISkpiryhJubm8p2Nzc39jlNJk2ahPbt2+ONN95Au3btMGfOHCxbtgzNmjXT+prCwkKIxWKVr9rOmHmBS46qjriMyZhxPUoQAQBs+eUvLqws7aKskW6q/UPMA+VuQgghhJCqY1XdAZDKZ2XBQx17G1hZaF+oCUDJjHQNi40WSOR4mCBGgUTDwnANwwFLGyD9GZD8APDSPDDCOY5KZkgcL1IVF8f1Sg2k6+yTYs521gCAnEIpJDI5+KXq1ZpKf5DKcepU7b1Dg5gvCwvF/2FSqepMXIlEorOG8aBBg1BQUICUlBS4urrixo0b6NmzJwBg8uTJGl+zfPlyfPHFF0aKvGYwZl7gkqOqIy5jMmZcScX1zZVrm+jiIFC8fc4pHkg31f4h+qPcTQghhBBCdKEZ6WbAv44d1g5vBf86dtp3ksuAvDTFz/qUdgEAG0egQW/Fzw/3VyyOKqBvHAUSGRJE+QBUZ6Rz4WRbckEuyledlW4q/UEIIVwpF7JLSkpS2Z6cnKyyyF1pSUlJOHnyJObNmwdXV1cAQNu2bfH222/j999/13qsjz/+GCKRiP2Ki4szzkmYMFPNC+YQV1qOYrFRNwfrcvdlS7sUD6Sbav8QQgghhBBCjIsG0s2ATM4gu0ACmVzHurJ56QAjB8AD7Ny076dN6GDF9wd/aS3vwimOKqBvHDHpeWAYQCiwQh378i+wS7O04MHBRjFzrWwtVVPpD0II4UooFKJly5Y4efIku00qleL06dPo2rUruy0zMxPJyckAAHt7e/B4PGRnq5YYEYvFcHBw0HosGxsbCIVCla/azlTzgjnElZ5bCABwdxSUu699mdIupto/hBBCCCGEEOOigXQz8DhRjGZLTuBxoo76ssr66LYugKUBFX9CwgErWyAzWlHexdA4qoC+cUSn5QAA6ro7gMfT/7Ztx+JbwMVlZqSbSn8QQog+Pv30U/zxxx/4+eefce/ePYwfPx4AMHXqVHafefPmsaVbHB0dMWjQICxevBjHjh3D06dPsW7dOuzfvx9jxoyplnMwVaaaF2p7XAzDIC1HMZDOZUa6Y/GM9JziD8hNtX8IIYQQQgghxkUD6WbA38UOP41oDX8XHbcc56Urvttrno1ebhvW9kCwYtAEj/8xPI4qoG8cL5QLjbqq7s+1HaGg+BbwMjPSTaU/CCFEH4MHD8a2bdvwxx9/4K233oJYLMb58+fh7u7O7lOnTh14eXmxj7du3YrRo0djyZIl6N+/P/7++29s374do0ePro5TMFnGzAum2pYxGSsuUb4ERVLFbPLmfs7l7l+2Rrqp9g8hhBBCCCHEuHgMo6UORy0mFovh5OQEkUhkFreKc/Lwb+B/Y4GA14Dxxwxr4+4uYP9kwKMJMO2KUcOrTvP33sWeG/GY0ysEs3o10Pv172y8jOsvM7F+ZGv0a+ZdCRGaJvo7I4QYE/2fQirLowQx+q25iDr21rj1ae9y90/JLkC7ZafB4wHPl/WDRS1aZJT+zgghhBBCCNGOZqSbgczcIvzvRhwyc4u076SckW5bR+PTqdmF2HTxBVKzC7W3EdIHsLACUh4BGS8Mi6MK6BtHtHJGurvqQqOc+gQlM9LLlnYxlf4ghBBiGoyZF7jmqKqOy5iMFVeSWLGgON+Sx6m/lKVdGAbIk8hMtn8IIYQQQgghxkUD6WbgVVY+5u29h1dZ+dp3ystQfLfTPJCeLC7AV4cfI1lcoL0NWxfAv4Pi5+gLhsVRBfSNQzmQXs9NdSCdU58AENoWD6QXqA6km0p/kNrpyy+/xPr166s7DEKIHoyZF7jmqKqOy5iMFVeiSNFHyeJCTv0l4FvAsngWek6B1GT7h9Q8lLsJIYQQQkybAatKkpqmqY8QUcv6shd9GuUrB9JdK3awoE5AzCXg5SWgzTj946gC+sQhypcgLUcxwyyozEA6VyWLjarWSDeV/qiNXrx4gW3btuHBgweQy+WoW7cuevXqhT59+lR3aBXy+eefw8fHB5MnTy5331u3bsHPz0/nPgzD4MCBAzh06BBSUlLg4eGBfv364a233lJbWPfo0aPYv38/UlNT0aJFC8yePRvOzs4q+zx79gxr165FTEwMGjRogDlz5sDX11evcywqKsL27dtx/vx5SCQShIWFYfLkybC1tVXZTy6XY9euXThy5Ah4PB5GjBiBvn37am134sSJiIuLU9seFhaGpUuXso+PHz+Offv2ISUlBT4+PhgxYgQ6d+6s1zkQYihTzQu1Pa4kkX4fNvB4PDjYWEGUL0FOodRk+6emodxdc3O38lgHDhxAWloaQkJCMHHiRNStW7dCx+Kau7mcJyGEEEKIMdCMdDPA4/FgZWmh9uZahbK0i5YZ6ZwFFQ84vbxkWBxVQJ84XhbPRvdwtIGDjWGfO7GlXcrMSDeV/qhtfv75ZzRp0gRRUVEYOHAgRo4cCW9vb6xZswbDhg2r7vAq5ObNm3j8+LFR2iosLES/fv0wdepUNGrUCBMmTEDTpk3xwQcfoF+/figsLClvsGzZMgwdOhQNGjTAyJEjcffuXbz22mvIyclh93n27BnatWuH9PR0DBs2DJGRkWjXrh1SUlL0iqtjx464dOkSunXrhvDwcPz222/o1KkT8vNLZnrK5XK89dZb+OSTT9C5c2cMGjQIGzduxD//aF7oGADGjh2L2bNns19jx47F8ePH4ejoyO6zceNGDBw4EIGBgRg3bhyEQiFef/117N+/X69zIMRQppoXantcrzL1n0mufE+QUyg12f6pSSh3c2OquXv+/PnYsmULwsLCMHToUDx79gwtW7ZUOW9DjsUld3M5T0IIIYQQo2HMkEgkYgAwIpGoukOpEi/Tcpj3f/+PeZmWo32nP4cwzOdChrn1p8an78dnMYELDjH347N0H6wwl2GWOCvaEifqH0cV0CeO/bfimcAFh5h3N15We45rn/x8PooJXHCImb3rtsFx1ETV8Xd2+fJlxsLCglm/fr3G55OTk9mfDx48yEyYMIG5du0aM378eKZv375MTo7id3Hy5Elm3LhxzMCBA5nFixczqampKu1MmzaN2bt3r8q25cuXMz/88AP7eOHChczmzZuZLVu2MKNHj2ZGjBjBnDp1qtxzuHv3LjNz5kxm0KBBzKeffspkZSn+fX333XeMu7s7ExgYyPTp04fp06cPExcXxx7nt99+Y9555x1m/vz5DMMwzJtvvslMnz5d63Hmz5/P2NnZMdHR0Srb4+PjGUdHR2bevHkMwzCMVCplbG1tme+//57dRyqVMnXr1mW++eYbdtvo0aOZsLAwRi6XMwzDMEVFRUxAQAAbD1dl+zohIYHh8Xgq/b1hwwZGIBAwz58/V9lXn39ra9asYfh8PpOUlMRu69q1KzNmzBiV/Xr37s0MHTpUn1MglcQccrcx8wLnvF3FcRmTseJ6e8O/TOCCQ3r11xvfn2cCFxxiLj5NNdn+MQTlbsrdhuTuzMxMlcdyuZzx8vJivvrqK6Meq2zu5nqehBBCCCHGQjPSiQI7I11zaRdHgRV6NfZgy5RoZW0HuDZQ/Jx4z4gBVo8Xyvro7uplXbj2ibbFRonxrVu3DvXq1dN6+7SHhwf7c0xMDLZt24b33nsPnTp1wgcffAAbGxvs2LED/fv3R2BgIIYNG4aLFy+iXbt2yM3NZV/777//Ijo6WqXtO3fu4MGDB+zj69evY/bs2di1axf69euHgIAAhIeH4/jx41rjP3jwINq1aweJRILhw4eDz+djxIgRAIA33ngDdevWRatWrdiZWS4uLuxxDh48iHfffRdDhgwpt5+kUik2btyISZMmISgoSOU5X19fTJs2DRs2bIBEIoFIJEJ+fr7K7dmWlpYICAjA0aNH2W3Hjx/Hm2++yc7I5PP5GDBgAI4dO8buc+bMGYSHh0MsFmuNzc3NTeWxi4sLrKysVGaW/fbbbxg4cCDq1aunsq9QKCz33JU2b96MgQMHwtPTk93WtGlTREZGQipVlGHKzc3F8+fPERoayrldQkwF57xNEJehmJEeFuTCub8cBCUz0knFUO6u+bm7bBmVmJgYiEQiNGzYUK9jlads7uZ6noQQQgghxkJXV2Yg0NUem8aG6d5JOZBuq7m0C6c2lLybA2lPgKS7QMgbhrVRifSJQ7nQaF0N9dG5tqNtsVFT6Y/qkCIuQEZeERp5KQY+nyVnw97GCj7OtiiQyBCVkoMgN3s42FghNbsQqdmFaOJT/iDpzZs30apVK1hYcPuMsLCwEH///TcaNFB8+COXyzFv3jx8+umnWLx4MQDgrbfeQt26dbF27VosXLhQr/N0cnLCoUOHwOfzMWzYMOTk5GDRokUa673K5XJMnToVH3zwAVauXMluV164NmvWDO7u7ggMDER4eLjKa4OCgrBv3z7OZQWioqIgFosRFqb5319YWBhycnLw9OlTNG3aFCEhIdi+fTsGDBgAS0tLPH78GNevX2cHvfPy8pCSkgJ/f3+Vdvz8/PDy5Uv2cUJCAo4fP46ioiJOcQLAmjVrYGFhgZ49e7Lb7t+/jyFDhmDNmjU4e/YsPDw8MGTIELzxxhs6Wipx8+ZN3L17F998843K9lWrVuHDDz9EUFAQGjRogMePH2PChAn4+OOPOcdLSEUYMy+YalvGZIy4CiQyJBUvMLpxVBu4Othwep19qdIupto/xka5m3K3LjExMZg8eTJyc3Px6NEjfPvtt3j77bf1OpYumnJ3nTp1yj1PQgghhBBjohnpZoBhGEhlcjAMo32n/EzFdy0z0iUyOdJzCiGRycs/oFdzxfcyM9I5xVEF9IkjNl0xkB5QR30gnWufaFts1FT6ozpsvxaLcVuus49n7ryNXy68AKBY9C1i7SXcjxcBAP66FY/hv17l1G5eXh5cXFxUtq1evRrh4eHsl1xe8vvy9vZmL8QBIDY2FgkJCRgwYAC7TSAQoE+fPrh27Zre59m7d2/w+Xz2cUREBG7fvq1Sw1QpMjISCQkJarVgucyy7tKli161efPy8gBAra+UlNsLChSDS3/++Sdu3LiB+vXro0uXLggPD0f37t0hkSg+HFJeXJddENTOzk7lwrtnz544evQonJycOMV57NgxLFq0CGvWrGEXX2MYBgUFBVi1ahXu3LmDUaNGwc/PD2+++SZ++uknTu1u3rwZgYGB6N27t8r2o0ePYteuXZg4cSJmzpyJMWPGYMOGDbh8+TKndgmpKGPmBb3ydhXGZUzGiOtVlmI2ui3fAgzDcO4vR+VAeoHEZPvH2Ch3U+7WxdXVFbNnz8b06dPRrVs3fP3113jy5Ilex9JFW+4u7zwJIYQQQoyJBtLNwMMEMYIXHcXDBC23ZEqLgMLi57QsNvokKRttvjqFJ0nZ5R/Qs4nie9pT/eKoIvrEEZuhuGgJdLVTe45rn2hbbNRU+qM6jGwfgN/Hl8yoWju8FSZ1VZTp8HIS4NDMzmjmp7hgG9zaDzsnduDUro+Pj9rMpt69e2P27Nlo3rw5jh8/rnIxXnqxKgDIzFR8oFT2YtHJyQkZGRncTq6UshfSQqEQcrlc4+3RytIl2i6QdSl7HuXx8fEBAK2zwGJiYgCAHbxu164dnjx5ggMHDmDZsmV4+PAhPDw84OXlBQBwcHCAlZWVWh+lp6ernI+3tzfCw8NVBii0OXPmDAYPHoyvvvoKkyZNYrfzeDw4OTmhbt262LJlC4YMGYJPP/0UH3zwAVasWFFuu/n5+dixYwfef/99tdmPM2bMwNSpU/H5559j8ODBWLlyJd58803Mnj273HYJMQZj5gW98nYVxmVMxogrrjjPezgK0HbZac79pVxsNLdIZrL9Y2yUuyl36+Lg4IDw8HAMGzYMe/fuhb+/P7788ku9jqWNrtxd3nkSQgghhBgTDaSbAV9nW3z7dnP4Ottq3iG/+E0tzwIQOFf8gK7Biu8Z0YBcxj2OKsI1DnGBBJl5isFv/zrqA+lcKUu7ZBeozkg3lf6oDh5CAXtrOAA08HSET3E/CPiWCPV1Ygcp3B1tON0aDgB9+/bFpUuXkJCQwG5r2rQpwsPD0bx583Jfr6yx+ezZM5XtT58+Vam/aWtry874UkpNTVVr7/nz5yqPo6KiYG9vD3d3d7V9lfVOHz16pDU+fWau6eLl5YWWLVti586dGp/fsWMH2rVrp1I/3NraGi1atEDXrl0hEAhw8uRJ9OjRAwBgZWWFpk2b4vbt2yrt3L59Gy1atNA7vrNnz2LAgAH49NNPsWDBArXnW7VqpVYftm7dukhLSyt3Rui+ffuQk5OD9957T2W7TCZDamqqWt31unXrIjExUe9zIMQQppoXanNccZmKGemeQm4lXZSUpV2yC6Qm2z/GRrmbcjdXPB4PQUFBbP6s6LG05W4lXedJCCGEEGJMNJBuBlzsrfFOW3+42Ftr3iGveCDd1gXgWJ9SJyd/wNIakBUConjucVQRrnEoZ6m52luzF4aGEJZakExa6pZxU+mP2mTWrFnw9PTE8OHD2ZlZSroWyVJydnZGREQEVqxYwV5s//vvvzhx4gTGjBnD7tekSROcPHkSMpnig6Jr167h4sWLau0dO3YM168rboPPzs7G999/j5EjR2o8toeHBwYMGIAlS5YgPV2xZoFMJsP27dvZfdzd3ZGUlFTueXDx7bff4t9//8XKlStVBp9//PFHnDt3TmV295kzZ9gBDoZh8NlnnyE7O1tlpvbYsWOxZ88ediG3W7du4fjx4xg7dqxKO+UtWHb+/HlERERg8eLFWmuTv//++zh9+jRevXoFQFEvd/fu3ejcuTM7YLFt2za2NmtpmzZtQt++fdkZe0qWlpYICwvDjh072Nv3s7OzsXfvXrz22mta4yXEmEw1L9TmuOKLc72nUKDX60oWG5WYbP/UFJS7uTPV3L1x40aVUioPHjzA8ePHVQazuRxL39zN9TwJIYQQQoyFFhs1A6I8CS5FpaFzsBuc7DTclqlcaFRLfXS9WVgCLnUVC45mPAdcArnFUUW4xqEcSK/IbHQAcBSUHCOnUApnO2u94iDcOTs74+LFi5g+fTpCQkJQr149eHt7IykpCVlZWfjyyy9haWmps42ffvoJERERqFevHgICAnDv3j188sknKheDH3/8MXr06IGGDRvC3d0dubm5GmdUde7cGUOGDIGvry9evHgBb29vLF26VOuxt2zZgqFDh6JevXoIDQ1FTEwMZsyYwT4/cuRIDBw4EJ07d4aDgwM2bdpkQC8p9OrVCwcOHMCMGTOwbt06NGjQgJ2Fd/DgQXTv3p3dVygUokePHnB1dWUHA44dO6ZyQTtz5kzcuHEDzZo1Q9OmTXH//n1MnjxZpW4slwXL3nrrLVhYWOD8+fM4f/48u33UqFEYNWoU2w83b95EkyZN0KJFCzx//hze3t7Yu3cvu39UVBROnTql0nZUVBQuXLiAv//+W+OxN23ahHfffRdBQUEICQnBw4cPUb9+ffz444/ldSchRmGqeaE2xxWXWTyQ7qTfQHpJjXSpyfZPTUG5mztTzd1JSUmoW7cuAgMDkZ+fjydPnuC9997D/Pnz9TqWIbmby3kSQgghhBgLDaSbgbjMPEzfcQuHZnaGk52GhYKUA+m2muujG8Q1WDGQnv4cqN+DWxxVhGscyvroARUcSLe2soAt3xL5EhnE+SUD6abSH7WNr68v/v77b2RkZODx48eQy+UICAiAn5+fyoX4wIED0bJlS7XXBwQE4M6dO7h37x4yMzPRpEkTldukASA4OBhPnjzBvXv3YG9vj0aNGuHhw4ewslL9L7V9+/Y4evQoHj16BKlUijZt2ugcDHBzc8Pp06fx7NkzJCQkoGnTpnBzc2Of79mzJ2JiYvD48WPk5ubCxcUFK1as0Lio2eeffw6BQPfAUEREBPr3748HDx4gKSkJK1asQGJiIrp06aKyX9u2bXH37l3cvn0bNjY2aNGihVqNUisrK2zfvh3Pnz9HbGwsgoOD4e/vr7IPlwXLdu/ezc4WLC04OFjl8ffff4+5c+fi2bNn8Pb2RkhIiMrt86NGjVI7D1tbWxw5ckRtoTKlJk2a4N69e3j69CmSk5Ph6+urdlxCKpOp5oXaHJcy13s66jeQbl+qRrqp9k9NQrlboabm7iVLlmDevHm4f/8++Hw+goOD1fbncixDcjeX8ySEEEIIMRYeU15B2VpILBbDyckJIpFI45vY2kYmZ5BXJIWdtRUsLTTUabyxBTg0B2jYHxi+w7A2yjq+CLiyDugwHQj/2rA2KgnXOBb/fR/brsZiRvdgfNSnocHtAEC7ZaeQkl2IQzM7I9TXSe/X10Tm9ndWVq9evdC2bVtOC2CaiuTkZLRs2RKvv/46du3aVd3hEKLCHP5PMWZeMNW2jKmiccnlDJp+fhz5EhmOz+4KH2cB57YO3HmFWbvuoFOwK7aOb2+S/WMIc/g704VyNyGEEEII0YVmpJsBSwueSnkRNWxpF+0z0sttoyyn4tspxSU10vVuo5JwjSM2Q7EAmbYZ6fqcj9CWj5TsQogLSupHmkp/EKLk6emJixcvIioqCrm5ubC3t6/ukAgxK8bMC6baljFVNK4EUT7yJTLwLXmo524PviX3Wax21sUz0gtlJts/xDxQ7iaEEEIIqTp035sZiMvIw8ydt9ma32ryMhXfdQykR6flYvTma4hOy+V2UKGv4rvoFfc4qgjXOMqrka5PnygXHBXnS/WOg9RMK1aswPjx46s7DL0FBwcjPDycLsQJqQbGzAt65+0qisuYKhpXVEoOACDI1R7xmfl69Ze9taLUR16R1GT7h+iPcjchhBBCCNGFZqSbAamcQUZuIaRyLVV8OCw2mlsoxcVnacgtlGrdR4VT8UC6uGQgvdw4qgiXOGRyBvHFC5AFuGoeSNenT4S2iplqpWekm0p/kMrRtm3b6g6BEFLDGDMv6J23qyguY6poXMqB9GAPB737y86mZEa6qfYP0R/lbkIIIYQQogsNpJuBum722D6hg/YdOAyk601YXNolOwmQSQBLfvlxVBEucSSJCyCRMeBb8uAl1G8BMk2Exbd8i/NLBtJNpT8IIYSYBlPNC7U1LuVAegMPB71fW3pGuqn2DyGEEEIIIcS4qLQLAfIzFN9ttZd20Zu9O2DBB8AA2YnGa7eKxKYrZqP7udgZZeEwR2Vpl4KKzwwkhBBCtHp0EPixBbBzeHVHYvKUA+n1DRhIZ2ekF8mMGhMhhBBCCCHEdNFAuhl48EqEkEVH8eCVSPMOlTEj3cICEPoofi6uk15uHFWESxzl1UfXl7K0S3ap0i6m0h+EEEJMQ4XzQuw1YO97QObLktJqqZHVH1clqUhcDMPgWanSLvpSzkgvkspxJy7TJPuHEEIIIYQQYlxU2sUMeDsJsDiiMbydtJQoySueka5jIN3bSYAv32yqvQ1NnPyArBj2Yr7cOKoIlzjis/IBAH4utjrb4donJaVdSmakm0p/EEIIMQ0VygsMAxz/BJBLgUYR8C60wJfPfoP3NSnQYj/AM/zuKlPNVxWJKy2nCKJ8CXg8oL67oka6Pu9z7KxL3kI72VqbZP8QQgghhBBCjIsG0s2Aq4MNxrwWpPlJmQQoFCt+ttNe2kVnG9ooZ6SLEwxvoxJwiSOheCDd11n7QLo+5yO0VfypiUrVSDeV/iCEEGIaKpQXos8Dr24AVrZAxA9wlcsw5scWQGIhEHsFCOxYPXFVoorEpSzr4u9iBwHfEgK+pV5tWVtZgG/Jg0TGQMC3MMn+IYQQQgghhBgXlXYxA+ICCc5EJkNcqqwISzkbnWcBCJy0tpGVV4T9t+ORlVfE/cD2HorvuSnlx1GFuMSRKFIMpOuaXaZPnzgK1Eu7mEp/EEIIMQ0Vygt3dym+txwOOHggy8oV+71nI4uxB25vq764KlFF4opKyQZQUtbFkPc5ylnpyeJCk+wfQgghhBBCiHHRQLoZiE3Pw/jfb7ALaKpQ1ke3dQEsLLW2EZ+Zjzm77yI+M5/7gR3cFd9z08qPowpxiSMhqwAA4O2kfUa6Pn2iXGw0u9Rio6bSH7URwzD477//sGXLFmzatAmnT59GdnZ2dYdlkIMHD+L8+fPVHQYhpAoYnBeK8oDH/yh+bj4MQHGOimqBeMYdePg3UJRb9XFVsorEpZyR3qB4IN2Q9znKOukvUnNMsn9qGsrdhBBCCCHE1FFpFzPQ0MsR/33SEy721upP5hfPSLfVXtbFYMoZ6Tkp5cdRhcqLg2EYTqVd9KGskZ5dWDJbzVT6o7a5c+cORo0ahaysLHTr1g329vbYs2cPnjx5gpkzZ+Kjjz6q7hD18ssvvyA4OBivv/56dYdCCKlkBueF52eAohzAOQDwb6f6nKMXkPsSeHEOaNS/auOqZBWJKypVMZBe34CFRpXsbBRvo90crU2yf2oSyt2EEEIIIaQmqPaB9KKiIpw+fRrJyclo1qwZ2rRpU+5rMjMzcfHiRWRmZiIgIABdu3aFpaX22dTmjm9pAQ+htoVGi2ek61ho1GD2yhnpKeXHUYXKiyMzT4JCqRwA4OlkY5RjCotnpJdebNRU+qM2iY+PR69evdCvXz9s3rwZfD6ffS47OxsXLlxgH9+9exd3797F4MGDcfToUSQmJmLy5MmwsbFBVlYWjh8/joyMDDRv3hydOnVSOc727dvRuHFjtG7dmt129OhR8Pl89OrVCwDw119/wdPTEz4+Prhy5QpkMhkiIiLg4uLC+XxOnDiB6OhoZGdnY/Xq1QCAsWPH4siRI3od/+rVqygqKkJERARcXV0RFRWFc+fOwcnJCf3794ednZ3Kccs7/9Jtnz17Fk5OThgyZAjn8yKEaGZwXnh+RvE9pK/6oqIBnYDHV4GnxwweSDfVfFWRuJ4lKwbSgyswkK6ckV4kYUyyf2oKyt2UuwkhhBBCaopqLe2SmpqKNm3aYPbs2Th8+DB69eqFKVOm6HzNkSNHEBAQgFWrVuHMmTOYMGECmjVrhuTk5CqKuuaJz8zDgr33EJ+pqbRL8Yz0yhhIV5Z2yUktP44qVF4cytnobg42sLEyzgc0yhrpOYVSMAzDKY5aiWEU5QUq8lXcf5p8//33AIC1a9eqXIgDgKOjI/r3LxlEunjxIubOnYt27drhf//7H168eAG5XI47d+6gQYMGWL16Na5evYpBgwbhnXfeYX9vAPDtt9/izJkzKu3/8ccf2LVrF/t4/fr1mDZtGrp164YTJ07gxx9/RJMmTRAVFcXuExkZidWrV6OgoEDj+aSlpSE/Px9isRgvX77Ey5cvIZFIOB9/4sSJ6N27N06fPo3vv/8eTZs2xbJlyxAREYGrV69iyZIl6NSpE6TSkg94uJz/+vXrMX36dPTu3RuXL19GYmKi1t8JIYQ7g/OCciC9fnf15wJfU3x/egKQy6s2rkpmaFziAglSsgsBVGwgXVkjPc5E+8doKHdT7iaEEEIIIQCqeUb6xx9/DAC4ffs27OzscPPmTYSFhWHAgAEqb5rLvmbw4MH4448/AAC5ubmoX78+NmzYgCVLllRV6DVKoVSOpynZ7CxrFeyMdN0zbWytLdEqwBm21noMLLOLjaYCcrnuOKpQeXEoB9J9nHXPLtOnT4S2ij81mZxBXpEM9jZWJtMfVUqSB3ztU7E2PkkArO01PnX27Fl06NABTk7aF84tLS0tDVu2bMGAAQPYbVOnTkXPnj2xc+dO8Hg8REVFITQ0FHv27MHQoUP1CjUqKgqPHz9GQEAAZDIZwsPDsWDBAuzbtw8AcOPGDcyZMwejRo2CQKD+723EiBHYsWMHgoOD2Vlt+sjNzcXDhw/h4OCA/Px8+Pj44Pfff8fdu3dhZ2cHkUgEHx8fHDt2DBEREXqdf0JCAiIjI1GnTiWUhSLETBmUFzJeAJnRgIUVENSZ3czmqMDGAN8eyEkCku8D3i2qJq4qYGhcyvronkIbtvSaIe9z7G0U+2YXSE2yf4yGcjflbkIIIYQQAqAaB9Llcjn27NmDJUuWsLcmtmnTBh07dsSuXbu0DqTzeDy4ubmxj+3s7ODg4AALC1o3VZv67g7YP62T5ic5zkjX2YY2ytIujAzIz0R9d1f926gE5Z0LO5CuY6FRLu2UZsu3hKUFDzI5A3GBBPY2Vob1KdEpLS0N7du3V9l26tQpPHjwgH08a9Ys8IpLH7i4uKhciKekpODq1av47rvv2H2Cg4PRv39/HDx4UO+L8f79+yMgIAAAYGlpiSlTpmDkyJGQy+WwsLBA48aNMWvWLNjaGqcWv6bjOzgoZlva2toiJCQEnTp1Yv/PdXJyQlBQEF68eAFAv/OPiIigC3FCjMygvBB9UfHdrx1g46i5raBOwLMTin0NGEg31XxlaFxRGsq6GNKWfXGNdDtrS5Psn5qCcrf68Sl3E0IIIYSYpmobSI+Li0N2djaaNGmisr1Jkya4ceOG1tf99NNPmDBhAuRyOQIDA3H69Gk0aNAAH3zwgdbXFBYWorCwkH0sFosrfgK1RWUuNmplDQicgYIsRZ10+0ooH1MJEkWKW3W9y5mRrg8ejwdHgRWy8iTILpDCm9ukq9qHb6eYlVbRNrRwcnJCUlKSyrb09HS8fPkSkZGROH78OGbMmAErK8V/fR4eHir7xsXFAQD8/PxUtvv7++PWrVt6h+rr66vy2M/PD4WFhUhLS4OHhwfatGnDaV0IQwmFQpXHfD5f47aioiIA+p1/2b4jhFSTuP8U3wM6aN8nqItiIP3lJaDjjKqJy4QpFxpt4OFYzp66KUu75BXJKhyTSaPcTbmbEEIIIYQAqMYa6crBbGdnZ5XtLi4uOge6HRwc4OTkhP/++w+3bt3C06dP4eXlxb651mT58uVwcnJiv/z9/Y1yDjXFowQxWnxxAo8SNPQrx8VGH7wSIWjhYTx4JdLv4A7Fb9hzUnTHUYXKiyOheCC9vBnp+vaJ8vbx7AIJpzhqJR5PcWt3Rb7KLqRXSqdOnXDjxg2VD86GDh2K1atXY9SoUeWG5+XlBUCxfkNpKSkp7HMAYGVlBZlMdeAkNzdXrT1N7VhZWcHVtWIfKnE9vr64nj8hpHIYlBfiiwfS/dupbFbJUcqSLzGXAbn+g76mmq8MjUtZ2qV+qRnphrzPUS42GpuRa5L9YzSUuyl3E0IIIYQQANU4kK68HTI7O1tlu1gsVluFXkkmk2HgwIFo3749/v33X2zduhV37tzBlStX8Mknn2g91scffwyRSMR+KWdumAt3RxtM61Yf7o426k9yHEg3mLK8S26q7jiqUHlxlNRIN+4tu44CxYc94nwppziI/mbPno309HQsWrQIcgMW1fPx8UHjxo2xdetWdlt6ejoOHz6Mnj17stv8/f3x8OFD9nFWVhauXLmi1t6RI0eQlZXFPt62bRu6du0KS0vF4Et5C5YBiplpeXmqC9hxPb6+uJ4/IaRy6J0X8jKAtKeKn/3CtO/n3QKwEQKFIiDpXuXHVUUMjetZiuK9Z7C74QuNAoBdcWkXMDyT7J+agnJ3xVDuJoQQQgipOtVW2iUwMBDW1taIjo5W2R4dHY3g4GCNr0lISEBsbKxK/XRbW1t0794dly9f1nosGxsb2NiY78WNu6MNJr9eX/OTbI30SqqXaFu8iGl+pu44qlB5cSQWD6Qbs7QLUGogvXhGuqn0R23SuHFj7Nu3D2PGjMHp06fRrVs3eHt7IykpCfv370fHjh11rqfA4/Gwbt069OvXD2lpaQgJCcGOHTsQGhqK999/n91vypQpGDBgAOzs7ODt7Y19+/ZpXHBMKBSiU6dOePfdd3Hnzh0cP34cFy5cYJ8vb8EyAOjcuTM+//xzBAQEwMHBAWPHjuV8fH1xPX9CSOXQOy+8uqn4XqceYO+mfT8LSyCwI/D0mKK8i0+ryo2rihgSV4FEhvhMRZ4vXSPdEMoZ6XIwJtk/NQXl7oqh3E0IIYQQUnWqbUY6n89HeHg4du3aBYZhACgGys+ePYuBAwey+50/fx579uwBAHh7e8PGxga3b99WaevOnTsICgqqsthrmpxCKa48T0dOoVT9SY6LjRpMOUCfn6k7jiqkKw6pTI4kMbfSLvoqKe0iLTcOYrh+/frh5cuXWLBgARwcHCASidCwYUP89ddf+Pfff9mL8ZYtW2Ls2LFqr+/RowcePnyI5s2bIz8/H59//jnOnTunUj6qT58+uHTpEry9vWFvb4/du3fj66+/Ru/evVXaGjZsGH755Rfw+Xy0atUKd+/eRdu2bdnnuSxYNmXKFGzYsAH5+fl4+fIlJBIJp+MPGTIEnTt3Vmnr3XffRYcOqnWUR44cqVLrlcv5a2qbEFJxeucFZX10v3a69wNKyru8vFT5cVURQ+KKTssFwwBOtny4OVhX6PjKGeniPIlJ9k9NQrlbgXI3IYQQQohp4zHKUexqEBkZiY4dO6Jz587o0KEDtm7dCg8PD5w5c4Z94zdhwgRcvXoVDx48AAB89913WLx4MSZMmIB69erh5MmTuHTpEi5duoQWLVpwOq5YLIaTkxNEIpHa4j210YNXIkSsvYRDMzsj1LfUKpcyCbC0eAbb/Gids9K1tlGek58D/64GOkzDg2YLDWvDyHSdS5KoAB2Wn4YFD3i2rB8sLbTX9NS3T+buuYt9t+IxP7whpnULNrxPawhz+zsrq1evXmjbti1WrFhR3aEQUiuYw/8peueFrW8CL84B/VcBYRN0txV/E9jUQ3Gn2PxonTWrKxxXFTEkrkP3EjBjx220DnDGX9M6Vaitv27F48M9d9HK3xm347JMrn8MYQ5/Z7pQ7iaEEEIIIbpU24x0AGjUqBHu3buHDh06IDk5GR999BFOnTqlMnuiW7duGDp0KPv4o48+wsWLF+Hl5YW4uDj06dMHUVFRnAfRzVGwhwPOfdRN/Rbm/MziH3iAQPeFn9Y2yqMs7ZKXYXgbRqYrjpRsxWx0d0cbnYPo5bWjibK0i3JGuqn0ByGEENOgV15gGCCh+A4937ZqT6u15d0csLJV5P60Z5UXVxUyJK7nKYrFHeuVqY9uSFt21sXvV3kwyf4hhBBCCCGEGFe11UhX8vPz07lQ6KhRo9S2hYWFISxMx6JaRIWAb4kgN3v1J5QLjdq6KOqnGtJGedjSLhmGt2FkuuJIERcCADwcy69Zqe/5CG2VpV0kBr2e1CxDhgyBv79/dYdBCKlB9MoLWTFAgQiw4AMeTcpvy5IP+LYBYi4BcVcB95DKiasKGRLXi7QcAED9MgPphrRlb6N475RfJDPJ/iH6o9xNCCGEEEJ0qdYZ6aRqJGTlY8nBh0goXkSTpRxI51AfPS4jD7N33UZcRp5+B7ctqZGuNY4qpiuOlGzlQHr5i9Pq2ydC5WKj+dJy4yA139SpUxEREVHdYRBCahC98kLiPcV3j0aAlXqtb405KqC94nvstcqLqwoZEtfzVOVAuurAtyHvc5Qz0sX5EpPsH6I/yt2EEEIIIUQXGkg3A7mFUlx9kY7csotgsQuNaq+NriTKl+DvOwkQ5Uv0O3ip0i5a46hiuuJQlnbxEJY/kK5vn5SUdpGUGwchhBDzo1deSLqv+O6lubSdxhzlXzyQHne18uKqQvrGxTAMXqRqLu1iyPsc5Yz0PInMJPuHEEIIIYQQYlzVXtqFVL4Gno44Nrur+hN6zEg3mF3JjHStcVQxXXEkF5d2cedQ2kVfQoGytIu03DgIIYSYH73yQlLxjHTv5twP4FdcFi89CshNA+zdjB9XFdI3riRxAfKKZLCy4CHQ1a7Cx7cvnpFeKJGbZP8QQgghhBBCjItmpJuzfO4z0g2mLO1SkAXI5ZV3HCNJLZ6R7slhRrq+HIsH0sUFes7qJ4QQQspSlnbx0mMg3a4O4N5I8XOcfuVdagPlQqMBrnbgW1b8LbCddXGNdIkMMjlT4fYIIYQQQgghpo0G0s1AZJIYHb4+jcgkseoTytIutpU5kF5c2oWRIzLmleY4qpjW/kDpGunGn5FeUtpFWm4chBBCzA/nvJCbBmQnAOABXqH6HURZ3iWWe3kXU81X+sZVUh/doZw9ubG3Kbmx87Xlptc/hBBCCCGEEOOigXQzUMfOGkPD/FHHrsxiZHqUdvFwtMGsng04LcKpwsoasFZcsNaxyNMcRxXT2h8AUsTcFxvVt0+EtqqlXXTFQQghxPxwzguJd4tfUA+wcdS4i9YcFdBB8V2PGemmmq/0jSsmXbGQaJCGsi6GvM+xsbKABU/x88AWPibXP4QQQgghhBDjohrpZsBDKMCc3iHqT7CLjXIYSNfWBhe2dYCiHHhYZmNO77aGtWFE2s5FLmeQllM8kM6htIu+faKckZ5TKIVMzlSsTwkhhNQ6nPMCh/roWttSzkhPuA1ICwEr4+e7qqJvXHGZioH0gDoaBtINOEcejwd7aytkF0oxon0APITGv5uNEEIIIYQQYjpoIN0M5BVJ8Sw5Bw08HWBnXepXzs5IL7+0S3aBBLdis9A6wJmt9c2ZrTMgikWeOAPP4rLU46hi2vojI68IUjkDHg9wcyh/YEHfPlEOpANAToEUfCue5t8LMQqGYZCYmAi5XA4fHx9YWJj+DTjx8fGwtraGh4dHdYeit8TERIhEInh4eKBOHdX/U0QiERITE+Hg4AA/P79qipCbzMxMSKVSuLu769xPKpUiKioKQqEQPj4+WvdLT09HamqqyjZLS0s0aNCAfRwdHY3CwkK119aE/iLGpTVfl8WhPrrWHFWnHmDvDuSmAgl3gID2xouriukbV1yGYiDdT8NAuqHvc+xsLJFdKMWduCx4OQlMqn9qIsrdVcsccndFcmxubi7S0tLg6+sLKyvtf9tc3xMQQgghpOYz/XenpMJepObizZ/+xYvUXNUn9CjtEpOeh7Fb/mNvi9ZL8UD9i2SR5jiqmLb+UJZ1qWNnzWkRMn37xMbKEjZWinbFBRLtvxdSIbm5uZg7dy7c3NzQokULdOrUCY6Ojujbty8uXLhQ3eHpNGXKFHz99dfVHYZBPv74YzRu3Bjjx49Xe27evHlo3LgxJkyYUA2RcbN79260aNEC9evXR+PGjREUFIQDBw5o3X/hwoVo3LgxPvjgA53tbtiwAa1atcKgQYPYr5EjR6rsM336dJXnBw0ahMaNG2P+/PlGOTdSc3DOCxxmpGvNUTxeyaz0OG510k01X+kTF8Mw7EC6phnphr7PUdZJ/3DPXZPrn5qEcnf1MIfcbUiOjY2NRZ8+feDh4YFOnTrB29sb27dv17o/1/cEhBBCCKn5aNqMGQj2cMDRWV1Q181e9Yn8KlhsFAAEToo4bDJxdFYf9TiqmLb+SMkuAIBKvTXbUcBHYU4hxAUS7b8XYrCCggL06NEDBQUFOHnyJFq3bg0AkEgkOHfuHE6fPo2uXbsCALKyspCZmYm6detCJBIhOTkZwcHB7Oy3zMxMZGVlwd/fX20W0suXLyEUClVmbyUkJMDCwgJeXl4AgLi4OAgEAri7uyMjIwNSqVTnbLXExETk5uYiMzMTkZGRAIB69eohISFBr2NlZmaiqKgInp6e7P7x8fEQCoUQCoUaj63rXEu3nZCQAJlMBn9/f43tNGnSBMeOHUNycjJ7/NzcXOzatQuhoZoXRMzLy0NKSgr8/PzUjp2cnIzMzEwAgKurq8aZZmXPXS6Xw9W1/A8Hy7py5Qq2bduGZs2agWEYrFy5Eu+++y7u3buHhg0bqux77NgxHD58GN27d+fUdosWLXD1qvYByyNHjqg8vn79Otq1a4dRo0bpfR6kZuOUF4pygfTnip91zEjXyb89EHkIiL0GdDJSXNVAn7gycouQWyQDAPg62xotBvviGehfDmyKYA/jLGJqbih3U+6uzNxtSI4dMWIErKyskJiYCKFQiFOnTqF///5o2LAh2rZVLVOp73sCQgghhNRsNCPdDAj4lmjsLYSAb1myUSYFCkSKnznMSK9YAIqBdIFUpB5HNdDYH9BvoVFDCW0VFxvZBVKtcRDDbdiwAbdu3cKePXvYC3EA4PP56N27N7744gt227Zt29C9e3eMHTsWQUFBGDhwIHJzc5GRkYEBAwbAy8sLHTp0gIeHB3799VeV4wwaNAhbtmxR2fbhhx9i8eLF7OP33nsPkyZNQosWLdCkSRP4+vrizTffRG6u5hmLa9aswfXr1/HPP/+wM6ZevXrF+Vjjxo1DaGgomjVrBn9/f0RERODatWto0qQJOnToADc3N7WZUlzOVXkeLVu2RLt27bBo0SKt/e/v749u3bph69at7LY9e/agYcOGaNGihcq+BQUFmDRpEjw8PNC1a1c4OTnho48+gkwmY/fZsGED2xcNGzZE/fr11WYmvvfee5g8eTLatWuHZs2awc/PD2+88Qby8kpmlYrFYkRGRqq0Xdbq1avRrFkzAIq6xx999BEYhsHFixdV9ktKSsKECROwbds22Nmpz2rV5tWrV0hPT+e07+bNm+Hv74/w8HDO7ZPagVNeSI0EwCjKszgYWEqi9IKjDGOcuKqBPnHFZeYDADyFNkY9DztrRVt1HKxNrn9qCsrdlLsrO3eXVl6OFYlE+PfffzFz5kz2Q4xevXqhXbt22Lhxo8q+hr4nIIQQQkjNRQPpZiBJVIBvjkUiSVRQsjE/s/gHnqKGeWWyERbHka8eRzXQ2B8oNSO9EgfSlXVXxfkSrXGYgxRxAR68Eql8KW+5L5DI1J578ErEqd39+/ejS5cuajOItYmJiYG3tzcyMjIQGRkJR0dHzJkzB3FxcYiPj0dycjLWr1+PKVOm4Pbt23qf54EDB7B06VIkJSXh5cuXePjwIZYuXapx3+XLl6Nbt24YM2YMIiMjERkZibp163I+1rlz5/Dbb78hPj4e9+7dw4kTJ9C3b1/s2LED8fHxuHbtGn766Sdcu3aNfQ3Xcz18+DBWr16N+Ph4lQttTcaPH68yeLBp0yaNt4zPnj0bz58/x8uXLxEbG4snT57gwIEDWLduHbvPkiVL2L5IT0/H1KlTMXz4cOTn56u0dezYMfz444+Ij49n+7n0xe7BgwfRuHFjdoYcF1FRUZBIJCr1U+VyOUaNGoUZM2agTZs2nNu6du0aWrdujYCAANSvXx+HDh3Sum9eXh527tyJ8ePH14jawMS4OOWFlMeK7x6NDT+QdwvA0gbISyuZ3V7RuKqBPnHpKutSEcrSLvtuxptc/xgb5W51lLtNP3eXxiXH2trawsrKChkZGSrbMzIy8N9//7GPDX1PQAghhJCaja7SzYC4QIIj9xMhLpCUbFTWR7d1BizKn0FlbWWBQFc7WFsZ8E9G4KyIIzdfPY5qoLE/AKRkF89IF3IbSDekT4SCkhnp2uIwB9uvxSJi7SWVr1UnngBQDIyUfS5i7SVO7cbExKBevXoq25KSktgLOuVt10q2trb48ssvwePxAAA5OTnYtm0bvvjiC/ZW5GHDhqFz585qs5C46N69OwYOHAgA8PX1xfz58/HLL7/o3Q4XQ4YMQVhYGACgUaNGaNq0Kd5++220bNkSANCqVSvUq1ePvdDW51wHDBiAbt26cYpj0KBBSE1NxeXLl/H48WPcvn0bI0aMUNlHLBZj8+bNmDhxIrKysvDs2TPk5eVh4MCB+Ouvv9TazMjIwLNnzxAeHo7k5GQ8evRI7dxfe+01AICnpyd69+6tMqDg5OSEhg0b6lworDSJRIKJEyeiVatW6N27N7t9xYoVkEqletUub9q0KW7duoXk5GSIRCKMGDECgwcPxt27dzXuv3fvXuTk5GgcwCC1H6e8wA6kN9XZls4cZWUD+BbP/I27pv68IXFVA33iii0e8PV30TyQbuj7HOWM9DtxWSbXP8ZGudv4KHdXfu4ujUuOtba2xpgxY/D5559j7969uHnzJubMmYOXL18iLS2N3c+Q9wSEEEIIqfmoRroZCPF0xPl5Zer26bHQqNY2uBIoZqSHWCQY3oYRaTuXVOVAuiO3GumG9ImweEZ6doGkYn1aw41sH4DeTTxVtjnZKvrGy0mAQzM7G9Qun89XuS0YAH766Sf873//g1gsRmJiIiQSCXtR5uvrC2tra3bfFy9eQC6Xs7cJKzVv3hwPHz7UO56mTVUHukJDQ5GZmYmMjAyVuqnG4OPjo/LY3t5e47acnBwA+p1r2QEOXaytrTFq1Chs3rwZzs7OGDJkCJycnFT2efr0KaRSKT799FNYWqp+kFe6huupU6cwbdo0JCYmwsPDA3w+H3K5HK9evVKZ/eXr66vShoODA+Lj49nHAwYMwIABAzjFL5PJMHr0aERHR+PixYtsfPfv38eyZcuwf/9+PH36FICihqxEIkFkZCTq168PPp+v1t5bb73F/mxlZYUvv/wSO3fuxPbt29VumQcUswD79OmDgIAATvGS2oVTXkgu/vssZ0Z6uW35tQVirwCvbgCtRmrfj2tc1UCfuOIzFbnBT8uMdEPPUVkjfUKXegjxdNT79TUJ5W7K3TUtd5fFNceuX78ea9aswU8//QSRSITw8HBMmTIF+/btA2D4ewJCCCGE1Hw0kG6uqmqhUYCtkc7WZDdR6TlFAAA3h8os7aL4kxMXSCvtGDWBh1CgdVFXAd8Sob5OGp8rT7NmzdQuJJcuXYqlS5di27ZtGD16tMpzZS+0bG0VC9CVvf04Pz9fpfalchZcaVKp+u+0oED1Nn/lQIFAwH1BW67H0hfXcwXU+6k877//Pjp27AiBQID//e9/as8rLy53796tUg+3NJlMhnfeeQcLFy7ERx99BEtLS0gkEtja2kIul+sVD1cymQxjxozBpUuXcO7cOQQFBbHPpaSkwN/fX6VWbXx8PCwsLDBo0CCcPn1abVBAEx6PB39/f8TGxqo99+zZM1y8eFHjzD5CWOyM9CYVa8e3eMG8+BsVa6eGiC+uke7nYryFRgHAzkbx/2NuYe3P65S7KXcDNSt3l6ZPjrWxscG8efMwb948dlt4eDgaN1Z8gGms9wSEEEIIqXmotIsZeJqcjR7fncPT5OySjXrOSH+cKEbrpSfxOFGsfwDFA+lPs/nqcVQDjf0BIC1XMSO9jr21ppepMaRPHNnSLhKtcRDDTZgwAffu3cP+/fsNen3dunXh7OyMs2fPstvkcjnOnTuHVq1asdvc3d2RmJioss+9e/fU2rtw4QKYUgv5nTt3DiEhIVoXpBIIBJBIVEsDcD2WvrieqyGaNWuGHj16oGnTpnj99dfVnm/SpAnq1KmDvXv3qj2nPP/ExERkZWVhyJAh7GDA+fPndS46pg2XBcvkcjnGjh2L8+fP4+zZswgODlZ5vmfPniplBiIjI9GtWze88cYbiIyMZC+Y09LS8OzZM7XzURKJRLh37x5CQkLUYti8eTO8vLw4z8AjtU+5eSEvA8hJUvzs0UhnW+XmKL/igfTkh0BRnuZ9uMZVTfSJK7G4frmvs+aBdEPf5yhnpG+7GmNy/VNTUO7mjnK3qvJyd2m6cmzZ3F32Q4/o6GicOXOGLXfD9T0BIYQQQmofmpFuBhwFVujVxJMdxAWguBgHOA+ky+QMMnKLIJMz5e9cVvFio46SNPU4qoHG/kDpGencBtIN6ZOS0i5SrXEQw/Xv3x/z58/H0KFDMXv2bPTs2RPe3t5ISkrC//73PwgEAo2zxJSsrKzw2WefYfHixRAKhQgJCcFPP/2ErKwszJ49m92vX79+WLp0Kbp06QJvb29s3LgR0dHR6Nq1q0p7z58/x4QJEzBp0iTcuXMH33//vc46q40aNcLBgwdx48YNODg4oF69epyPpS+u52qoAwcOaH2Oz+fj22+/xdSpU8Hn89G/f39kZmbiyJEjsLOzw/Lly+Hj44PAwEB89tlnmDt3Ll68eIG5c+fq/P1pc/DgQYwePRqpqalwc3PTuM/777+PAwcOYOfOnZDJZGxNXjc3N62v0WTdunVYvXo1srKyAADdunXD2LFj0apVK6SlpeGrr76CjY0Npk2bpvI6qVSKrVu3Yty4cZzrwZLap9y8kFJcY9g5ALDRXUak3Bwl9AUcvBQD84l3gcDXDI+rmugTV3LxQLqXk+ZZxYa+z1HOSPdyEphc/9QUlLu5o9ytimvuLi/Hls3dGzZsQFpaGiIiIpCQkIAFCxagT58+GDVqlN7nQQghhJDahd7xmwFvJ1t80q9MLVV2RrpL5QdQPCPdWxKvHkc10NQfEpkconzFbBrXKintItH8eyEV9s0336Bv3774/fffsXjxYsjlcgQEBKBDhw7YtGkTO0PKxcVFY/3QOXPmwNHREVu2bEFmZiaaN2+Oy5cvw9W15EOnGTNmIDc3F9988w3s7e3x1ltvwdnZGQ4ODiptzZw5Ew4ODpg3bx6kUinWrl2r8yJszpw5SEpKwuTJk5Gbm4ujR49yOlZAQAA8PDxU2goMDFS78Kxbt67KeXA5V01ta+Lt7a2zHqiPjw97SzoAjB8/HgEBAdiwYQP+/vtv+Pj4ICIiApMmTQIAWFhY4MiRI/j0008xfvx4eHt7Y8OGDfjss8/g6FgygKgpPi8vL5XHXBYsu3XrFnx9ffHRRx+pbJ8xYwZmzJih8TX+/v4qdXoBxcV76dnmu3fvxrfffostW7bAzs4OXbp0wcGDB1X6GACuXbsGoVCICRMmaI2R1H7l5oXk4oH0chYa5YTHU8xKjzwExF/XOZBuqvmKa1zZBRJkF5de8dJSmsRQyhnpDTwc4e1k3LIx5oRytwLl7hLGzN3l5diyuXvKlClYtWoVZs6cCRsbG0ydOhXTpk3T+YGApvcEhBBCCKl9eEzpexfNhFgshpOTE0QiEYRCYXWHU+kKJDLEZuQhoI4dBPzieol/TwPubAd6LQE6zym3jQevRIhYewmHZnbWvwZmViywuhkKLBwQOzlSNY5qoKk/UsQFaPf1aVjwgKhl/WBhUf7MGUP6ZN/NeMz93110aeCGX8e0Vf+91CLm9ndWVq9evdC2bVusWLGiukMhpFYwh/9TNObr0v6ZDdz8Dej8IdDrc51tccpRl34ATi0BmrwJvLvV8LiqCde4olKy0ev7C3AUWOH+kj4a9zH0fc7/bsRh3t57aBvogm0T2ptU/xjCHP7OdKHcTQghhBBCdKEa6WYgKiUHb/xwAVEpOSUblTPSq3Cx0Sipm3oc1UBTf6QVl3WpY2/NaRDdUEJbxawfcYFU8++FEEKI2So3LygXGvU0wox0oNSCozcrFlc14RqXsj66t5ayLhVhb6OYLXsjJtPk+ocQQgghhBBiXDSQbgbqutlj39SOqOtmX7JRz8VGNbbBlbUjAB7q8hKxb1xjw9owIk3nkpGrGEh3tede1sWQPmEXG82XVKxPicnjels1IYQo6cwLDFMykO5RfjkTTjnGpxXAswDE8UB2UsXaqgZc40pi66NrL71i6DnaWStmoAe52plc/xD9Ue4mhBBCCCG6UI10M2BvY4U2gWVqobOLjXKbka6xDa4sLAAbIewLRWjjJgNsqvefnaZzSc8tBAC4clxoVFs75SmpkS6tWJ8Sk7dly5bqDoEQUsPozAviV0ChCLCwAlwbVKwtJRsHwL0xkPIQiL8BNI4wvK1qwDUu5UC6t4766Iaeo0Op9zT21fz+hlQc5W5CCCGEEKILzUg3AyniAqw9/Qwp4oKSjXrOSE8U5WPpoUdIFOUbFoTACSmMM9ZeTFCNoxpo6o/SpV24MqRPhAJFaZfsAonm3wshhBCzpTMvKBcadW0AWJWfqzjnKL82iu8JtwyLqxpxjStRrJyRrn0g3dD3OXbFi42mZBeaXP8QQgghhBBCjIsG0s1Aem4Rtl6NQXpx+RLIpECBSPEzxxrp6TlF2HwpGunFA856EwiRzjhi672ckjiqiVp/AEjPUcxId3PgXtrFkD5RDqQXSuVIFBeoxUEIIcR8acpPrJTigXQOZV0APXKUV3PF96QHhsVVjbjGlcShRrqh73PsbRSlXfKLZCbXP4QQQgghhBDjontQzUBjbyGuL+pVsqEgCwCj+Nm2im7VFjihscUDXH+7CPAWVs0xtVDrD5Sukc59RrohHAQlf3K+zrZqcRBCCDFfmvITSzmQ7tnEuAf1aqb4nnTfsLiqEde4lAPpnpWw2KhyRjp4QENPR6O3TwghhBBCCDEdNCPdHCnLugicAcsq+ixF4KT4rpwJb2KUpV1c9ZiRbghLCx5bTzW7QFqpxyKEEFKLsDPSjTyQ7tlU8T07oWT9lFomSVz+jHRDKWekMwxQIJUZvX1CCCGEEEKI6aCBdDMQlZKN/msuIiolW7FBz4VGjcJGiCi5D/ofdyyJo5qo9QdKFhvVp0a6oZQLjj58JVKLgxBCaoKtW7eiTZs28PPzQ9++fXHv3r1yX/Pq1StMmjQJwcHBaN68OdatWweGYaog2ppDU34CAMhlQOpTxc8cS7twZuMIuAQpftYyK11rXNWMS1wFEhl715mXjsVGDSWwsmR/vv/KNCcLEEIIIYQQQoyDBtLNgK21FVoHuMBWefuxnguNAoCLvTVGdwiEi6EDzQIn2PIK0VqYXRJHNVHrD4CtiermwP38DO0T5UC6VM6oxUEIIaZuz549mDBhAmbOnInTp0/Dz88P3bt3R3JystbXJCYmon379khPT8f+/fuxf/9+PH/+HJcuXarCyE2fpvwEAMh8CcgKASsB4BzIqS29clQ55V20xlXNuMSlrFtubWUBJ1u+1v0MzekWFjzY8ovfTtPnQoQQQgghhNRqpnVFRCqFr7Mtlg4KLdmQXzwjneNCoxrb0JdACF9eOpbWfQA4jzO8HSPQdC5sjXQ9SrsY2ifKBUdtrCwq1qeEEFINli1bhvfeew/jxo0DAGzcuBEHDx7E+vXr8cUXX2h8zaJFi2Bvb4/du3fDykrx1uOHH36gGellaM0rac8U310bABaW6s/r05Ymns2Ax/8AyZoXHK3we4BKwiUu5WLirvbW4PF4FWpLG0cBH/mSQpV1UAghhBBCCCG1D81INwOFUhniMvJQqKzdacCM9PwiGR68EiG/yMD6nwInFDJWiBNJSuKoJmX7o0AiQ06hol65PqVdDO0T5Yz0jNxC1d8LMZqTJ09ixIgRaN68OUJDQzFgwAD8+OOPyMnJqe7QdHr//ffx5ZdfVncYBsvJycGKFSvQuXNnhISEoHPnzvj666/V+p1hGJw8eRKDBw9GUFAQ/v77b43tnTlzBm+88QYaNmyIiIgI3LhxQ++YEhISsGDBAnTo0AFt2rTBlClTEBcXp3X/PXv2ICgoCNOmTdPZLpdz0PfYNYFIJMK9e/fQq1fJAo+Wlpbo0aMHLl68qPE1DMPgr7/+wogRI9hBdCVdA5vmSC1fK6U9UXx3D+Hcll45yqt4ADlJ80C61riqGZe40tk1UHTn94q8z7GzVny4IcqX6P1aUoJyd/Woqbm7Z8+eCAoKUvuaMGGC1nbz8/Pxww8/oFu3bggNDcXgwYNx/vx5lX3WrVun1mbz5s31PgdCCCGE1E40kG4GniXnoMvKs3iWXPyG2IAa6c9TcxCx9hKepxp4MWPjiGeMH7rce6MkjmpStj+Ut33zLXkQ6jGbzNA+ERbfWv4iLVf190KMYu7cuXjrrbcQGhqKLVu24H//+x+mTp2K2NhYjBkzprrD0yk5ORkZGTVzsb+MjAx06NABu3fvxsKFC3Ho0CEsWrQI+/fvR4cOHVTOa/Xq1fjmm28wYsQIxMTEaBwkuXbtGsLDw9G5c2fs2rUL9evXR7du3fDs2TO94nrrrbfg5uaG1atXY+PGjYiLi0OnTp2Qnp6utm90dDTmzp0LR0dHpKSk6GyXyznoc+yaIiEhAQDg6empst3T0xOJiYkaX5OSkgKRSARXV1cMGjQI/v7+aN++PdavX69zRnphYSHEYrHKV22nlq+VlPXR3bgPpOuVo9wbKb6nPwPkcu5xVTMucaWxM9J133FWkfc5FsUfCD1NMq0a8jUJ5e7qUZNz99atW3Hu3Dn2a9u2bYiJiUFYWJjWdhctWoSMjAwsW7YMO3fuROPGjdGrVy9cuXKF3ScrKwsuLi4qbR86dEiv+AkhhBBSizFmSCQSMQAYkUhU3aFUCXF+EXPxaSojzi9SbNg/jWE+FzLMhe84t3E/PosJXHCIuR+fZVgQ9/cy4s88mYvrJpXEUU3K9sfduEwmcMEhpv2yU3q1Y2ifLNp/jwlccIj5+vAj1d9LLVMdf2cHDhxgADAHDx7U+LxEImF//uOPP5gePXowO3bsYDp27MjUrVuXjXXdunVMhw4dmJCQEObtt99mHjx4oNJO3759mV9++UVl24wZM5gFCxawj0eOHMksWbKEmT9/PhMWFsa0bt2aWb9+vdbY586dy9ja2jKOjo5MYGAgExgYyERFRXE+1qJFi5gPP/yQadeuHdOyZUtm7dq1jEgkYmbNmsU0bdqU6dixI7N//36145Z3rsrzWLBgAdOsWTNm2LBhGuMfN24c4+bmxmRlqf49ZGdnM15eXszYsWPZbaV/DwCYP//8U629gQMHMr1791bZ1qRJE2by5Mkaj69N6WMxDMOIxWLG0tKS2bZtm8r2oqIipn379sxvv/3G9O/fnxkyZAjndrWdA9dj1yQPHz5kADCXLl1S2T537lwmJCRE42tevXrFAGAcHR2Z7du3MzExMcyePXsYOzs75ttvv9V6rM8//5yBouq0yldtzt1q+Vrp156KvH1/H+e29MpRUgnDfOmmOEbGS+5xVTMucW04F8UELjjEzNl9W2dbFXmfM/inf5nABYeYvTfj9H6tqaHcTbmbYWpO7i5t6dKljL29PSMWi7XuI5VK1bb5+fkxX3zxhUo77du31yteQgghhJgPmpFuBhwFfHRu4AbH4trcbGkXPWqkV5i1Ixx5+ehs9bgkjmpStj/Sc7nd9m3M4wNAoVSu+nshFbZ582a0aNECAwYM0Ph86bISYrEY586dwy+//IJvv/0WZ86cgYODA1auXIlPP/0Us2bNwq5duyAUCtG5c2eVGcoJCQkQiUQqbaempiItLY19nJSUhC+//BI5OTn45Zdf8MEHH2DevHn4448/NMa2cOFCvPbaa3j77bfZGVABAQGcj7V8+XK4uLhg8+bN7EKQbdq0gbe3N3bt2oW33noL7777LmJiYtjXcTlX5XnweDzs2LEDP/zwg1rsBQUF2LlzJ6ZNmwYnJyeV5xwcHDBz5kzs3LkT+fn5ar8Hbc6fP48+ffqobAsPD8e5c+fYx3///TeCgoKQmZmptZ2yx5LJZGAYBpaWqnWmFy1aBH9/f7bud3m4nAPXY9ck7u7uABT/BktLTU1lnyvL1dUVlpaWGDp0KEaMGIGAgAC88847eO+99/Dnn39qPdbHH38MkUjEftX0sjhcqOVrAGAYIK14Rrp7w8o5sKUVUKe+4uc09ZmjGuMyAVziUq6B4qbHGih6x2Gr+FuXy6nmvyEod1PuLkvf/MkwDH777TcMGzYMjo6OWtst+/rz588jJSUFnTt3VtkeGRmJZs2aISwsDNOnT0dSUpLWNgkhhBBiXmgg3QykZhdi08UXSM1W3N7MLjaqR430CrNxRCojxKb05iVxVJOy/aGsn6pPffSKUC42qvZ7MSMp4gJEJpWUaXiWnI2ELMWFWoFEUadWWbc+NbsQjxK4lXR4+PAhmjZtyjkOHo+HnTt3omPHjggKCoJMJsOyZcvw1VdfYdiwYWjVqhV+/fVXuLu7Y/Xq1dxPsFjDhg2xbt06tGzZEmPHjsXcuXPx1VdfadzXzc0Ntra2EAqFbE1OPp/7oFXv3r2xePFihIaGYvr06QgJCUFoaCgWLFiA0NBQfPTRR3B3d2drgUokEs7n+tprr2H58uUIDQ2Fl5eX2rGjoqJQWFiI0FDNC/WFhoaiqKgIT58+5XQu2dnZEIlEasfy8vLCq1ev2Mc5OTmIiYmBTMa9pvFnn30GZ2dnvPHGG+y2EydOYNeuXfj55585t2MITceuadzd3VG3bl1cunRJZfuFCxfQvn17ja+xsbFBq1atYGtrq7Ldzs4OEon2mtI2NjYQCoUqX7WdxryQkwIUiACeRclgd2Vwa6D4nqb+d2qq+YpLXGmlFhutLFbFpV2SxQWVdgxTQLlbHeXuElWdu0s7c+YMXrx4gYkTJ5bb1tOnTxEUFAR3d3f07dsXmzdvRo8ePdjn7e3tsWDBAmzduhUrV67Eo0eP0Lp16xpbvocQQgghxkUD6WYgJbsAq089Q0p28QUeu9go9xnpPB7gYGMFg9eFs3FECuOC1dndS+KoJmX7I734Ilvf2WqG9olysdH03ELV34sZ2X4tFuO2XGcfz9x5G79ceAEASBIVIGLtJdyPV8zk+utWPIb/epVTu1KpFAKBQGXbwoULVRaMKn3h5uvrq3LBFx0dDbFYjG7durHbLCws8Prrr+Pu3bt6n2eXLl1UFlPs2rUroqKikJeXp3db5WnWrJnKY3d3d7WLY3d3d3YmsT7n2qZNG53HlkoVAydlB0qVlNsZHfWwS5MX12guOyONz+er/P7eeustREdHo04dbv+Xbdy4ERs3bsS2bdvY1yQnJ2Ps2LH4/fffObdjCE3Hrqk++OADbNq0CdeuXYNUKsXKlSuRkJCAyZMns/vMmTMHXbp0YR9/+OGH2L59O27fvg0AuH//Pv744w8MHjy4yuM3ZWr5GihZaNQ5EOALNL9QA71zlLL+uoaBdI1xmQAucXH9sLwi73OUr0kxsQ8ajI1yN+VuXaoyd5e1efNmNGvWTOsHuqXVrVsX586dw8mTJzF79mxMnjwZ//77L/v8rFmz8PHHH6NVq1bo3r07Dh48iKKiImzYsIFTvIQQQgip3bivrEhqrKY+TnjwRanbLPP0n5Gu1oa+bBzQ1CIGDxxmAD6aF6SrKmXPRVnaRd8Z6Yb2iWOpBU0r1Kc12Mj2AejbrOQieO3wVrC3UfSLl5MAh2Z2RpCbPQBgcGs/dGmguWREWUFBQXj+/LnKtnnz5mHKlCn4+++/MWfOHJULwrIXjwUFisEYGxvVD1VsbGzY5/Rhba36b0o5UFBQUAA7Ozu929NF0+3OmrYpz1+fc9V2ka0UEBAAHo+HqKgojc9HRUWBx+MhMDBQZztKjo6OsLGxUVuUMy0tTaV8iL29Pezt7Tm1uWXLFsyaNQs7duxA37592e1XrlxBWloaxo8fz25LSUkBj8dDUFAQLl26BD8/P07H0PfYNdWsWbOQkpKCXr16obCwEL6+vvjrr78QElKyEGZ2drbKbfvDhw9HUlISevfujby8PAgEAkyYMAGff/55dZyCydKYVwws66J3jmIH0tVLu1T4PUAl4RJXei63D8srco6+Lor/zx31WLC8JqLcTblbl6rM3aVlZmZi//79WLlyJac2+Xw+goKCAAAtW7bEzZs38e2336JTp04AFB9MlD2vZs2a4dGjR5zaJ4QQQkjtRjPSzY1cBuQXD25UaWmX4lvyJXmATFp1x+Ug08CBdEMJbRW3/GYXmFY/VCUPoQCNvErKNDTwdISPs+KCT8C3RKivExyKL87dHW3QxIdbSYfBgwfj0qVLKhc7rq6uCAoKgpubW7mvr1+/PiwsLHDv3j2V7Xfv3kWDBg3Yx46OjsjJyVHZJzY2Vq29Bw8eqDy+d+8e6tSpo3VGlaWlpdrML67H0hfXc+WiTp066NatGzZt2sTOSFNiGAa//vorevfuDRcXF07tWVhYoE2bNrh8+bLK9kuXLiEsLEyv2ADgt99+w9SpU7Ft2za8/fbbKs+98cYbePbsGVvb9ty5c3jttdfw+uuv49y5cxpvhzfWsWsqHo+Hr7/+GiKRCOnp6YiOjkb//v1V9lm9erVa+Zc5c+YgNTUVKSkpyMjIwMqVK/UqgWC2UosH0t1CdO9XUTpKu9RkyhnplbkOir2NYuAzt5B7qYqaiHI35W5dqjJ3l7Z9+3YAwKhRo/Q+BqCoB6/rbgOGYRATE1Pj7yYjhBBCiHHQQLoZeJ6ag7c3XMbz1BxFnVUUv9m35fbGGFDUwez9/Xk8S842LAhrBzyXe+Ptws/xPCGl/P0rkUp/AMjMU9TodbbTb0DH0D4RlirtUjoOUnGTJ09GWFgYBg0ahDNnzrC3LUskEjx58qTc19vb2+O9997D559/jsRExZ0Tf/75Jy5fvoxp06ax+7Vt2xb79u1jFxLbsWMHrly5otbeuXPnsGfPHgCK27FXrlyJKVOmaD2+j48Pnj1TnQ3K9Vj64nquXK1evRoxMTGYNGkSsrKyACgWhZs+fTqeP3+O77//Xq/2pk2bhr/++gsXLlwAAOzfvx8XL17E1KlT2X24LFi2detWTJkyBdu2bcM777yj9rydnZ1K+YCgoCDY2tqy25W3qK9evRrNmzfX6xzKO3ZNZ2FhoXVRNwcHBzg7O6tt5/F4cHBwqOTIaq6y+QlASWkXPQfS9c5RyoH03OKa7OXFZQLKi4thmFID6bpnpFfkfU6BRDEImSTO1/u1hHK3Pih3q9u8eTPefvttjQP+ZXP3hx9+iPj4ePbx/v37cejQIQwZMoTdNnfuXPZDj6KiIixYsACxsbEYM2ZMOWdPCCGEEHNAA+lmwNrSAoGu9rC2tCipj27jBFhyHzgulMrxLCUHhVJ5+TtrYmUNa0seAnlJsJZV74WmSn8AyMornpFup99sNUP7xLF4sdHcQplKHKTibGxscPr0aQwePBgjR46EnZ0dfHx84OHhgQsXLmDPnj1qtTvLWrVqFUJCQhAYGAhnZ2d89NFH+O2331QuxD7++GM4OzvD09MT7u7u2Llzp8pCVUpDhgzBd999hzp16qBBgwZo27YtFi1apPXYU6ZMwf379+Hu7s7e6s71WIbgcq5cNW/eHJcvX0ZcXBy8vb3h6+sLNzc3XLlyBVeuXFFZSO7ff/9lB60BxUzloKAgLFy4kN1n5MiR+Pjjj9GvXz84OTnhvffew9q1a9GzZ092Hy4Llk2ZMgU8Hg/z5s1TGSzXdwG6rKwsldmEXM7BWMcm5qNsfgJQMiNdz9IueucoG0fArnj2b2ZM+XGZgPLiyimUokimOP/yFhutyPsch+IZ6Qa/RzJzlLv1Q7m7xK1bt3Dnzh2ti4yWzd1t27ZFz5494erqCnt7e8yZMwerVq1SWeMjLCwMvXv3hqurK4RCIU6cOIGjR48aNKueEEIIIbUPj+G6gkwtIhaL4eTkBJFIBKGQ222ntUbsVWBLH8AlCJjFfQGmB69EiFh7CYdmdkaor5Nhx15ZH8hLA6ZeATybGNZGJeix6hxepOZi58QOeK0+93I3hvZJkqgAHZafhqUFD1HL+qosaFWbmMLfWXp6OuRyuUptTqXs7GyIxWL4+vpqfG1ubi7EYjE8PT3V6mUqiUQiCAQC2NjYIDU1FRYWFnB1Vfwb6tWrF9q2bYsVK1YgJycHUqlU4wzdshiGQVpaGnJzc+Hr68uWvtB1rOTkZFhbW6vMxkpKSoJAIFA5ZkJCAhwcHNR+H7rOVVPb5cnPz0daWhoWL16M48eP4/bt2/D29mafLygoQFJSktrrHB0d2XNSKioqQnp6Otzd3dUGUnJzc5GamoqAgACtv6OYmBiNC6U5Oztr/X0oa6SX/neTlZUFsViMgIAAzudgyLGJdqbwf0qVK8wGlhfX6F8QA9g6c36pQTnq157AqxvAu38CTQbqH6+JeZmWi27fnYO9tSUefhmuc9+KvM/ZcyMO8/feQ/eG7vjtvXYVCbnamcLfGeVuyt1c82d2djbS09PZwf2yyubu0tv5fL7OWu2ZmZmws7NTq0dPCCGEEPNWu1dFIgAAiUwOcb4EQls++AYsNGq0OKyFEOcWQpifjeqsiqvSH5YWyCou7eJiXzVRKRcjk8kZxGfmw8tJAL6JzfKrLcpe2JXm6OiotSwFwG0xLCenksEWTRf8SvqUslAO4JZtT9exPD091drRVNvbx8dH4zF1naumtstja2sLf39/bNiwAWFhYXj33Xdx9uxZ9mJaIBBovegty9raWuVCnmvcSlwXSSvNw8NDbVvZi3cu52DIsYl5K5ufkF68AKO9u16D6AZzCVQMpGepzkhXi8tElBeXcqHR8sq6VJTASjEjPbvQfNc++T979x3fRn3+AfwjybK8tx1nOXsnkISEhA1hhBFK2aUUCmEkFAKlQCGUUSAtpJRZRvtjlLbsPUKYSQgJEAjZezqO97YkS7ZlSff743SyZVu2dHfSneTP+/XKS46s++rR13Ee3VfPPV81MXcHYu4Orq9/D8E+uA7lw5FwPoQgIiKi/kM/Z0MUMXuq7DhqydfYU2XvaO2SHP0Nc/YYRuKotn9hT5W17wdHMo5O8+H1Cv7WLtlhtnaRKyXRBJNRrEI/4W+rxJ8LURxKSUnBDz/8gP/973/dNjIjou4C8jUANBwUb3NGRSeA7OHibeOh3uPSib7iqovCRqNAx4J9g+/5iGIZczcRERFRcKxI7weG5qTghStnYGhOCnBIXkV6wBhy40hx4wXz3zHUcrPsMdTQ+bXYW93w+q4cDXezUblzYjAYkJ6UgCZnOx74xSRFc0r69dprryExMTofzuhZRkZG/2nDQaRQt7ziX0gfqXysUARZSFfjPUAk9BWXf6PR1L4r0pW8xuG5YnWt29vvuiXGHeZuEXM3ERERUc+4kN4PZCabcfpE3yWeUkV6SngV6QFjyI0jxYLTTRsBoVnROEp1fi2H6hwAxCpxi+/SbDnjhEtaSJ88OAOZyVo2uqFIkXNZNRH1b93ySkOxeCtjIV1WjgqykK7Ge4BI6Cuu+maxUjwvhIp0Ja8xP11cqG9tD755IsUG5m4iIiIi6g1bu/QD9c1teO3HEvGEUuZCeo29Fc+u2o8ae6v8OIy5eM09B/U2h+wx1NB5PhoVtHVRMifpFnHx/KPNFf4TfSIi6t8C8jXQqSJ9RNhjycpRWb6+xE2HgU4tHbrFpRN9xVXvEHN8TmrfOV5JTnd7xLlysEc6EREREVFc40J6P1BpbcV9H+1ApbUVcDaKd4bZ2qXG1oZHv9iDGpv8k+hK5OI+99WotGp7It55PpRsNKpkTjKSxYtBXl1XIv5ciIio3wvI14Ci1i6yclTGYMCYAHhcgL0yeFw60Vdc0kJ6KJuNKsnptlbxvYTD5YEgsL0LEREREVG8YmuXfmDy4Ewc+OvZ4l803Gx0ci5wIOkKIHlR1J87II5O8/HehjIA0dtoVJKeJC7cP3jeZEwenBnV5yYiIn0KyNcuB9BcJX4toyJdFlMCkDkUaCwW27tkDu4el470FVc4rV2UOGpYx3uq1nYvkhPDaxVHRERERESxgRXp/U2LvM1GVZGYLt622aP/3EFIrV2yor6QLn6GZW/lZeBERNQDqT96cg6QnB29580cIt7ayqP3nBESzmajSiSbOxbOHS7mdSIiIiKieMWF9H6guM6BK176EcV1Dtk90lWJoz0bV7juQrFV28ueO8+Hv7VLSnQ3/MzwVaS//mOJ+HMhIqJ+LyBfK2jrokiGWIXeeSE9IC4d6SuueodYkZ4b4Yr0kgYnjAbxa/ZJJyIiIiKKX1xI7wdMBgPSkxJgggC0+Hqkh9naJSPJjLOnFPoXgGXFYUlFOlpgam+WPYYa/PNhMCiqSFcyJxm+inSDQYyHiIioc35SupAuO0f52rnA2rGQHhCXjvQWl8croMHfI73vHK8kp5sMBiQYxbfUjjZP2McTEREREVFsYI/0fqAoNwXPXX4U4KgHBK94Z5itXfxjKI0j8SlAOFbROEp1fi1KKtKVzInUI31aUTaKclNkjUHB2Ww2fPjhh9i+fTu8Xi9GjBiBU089FePHj9c6tF4999xzyM/Px8UXX6x1KLJt2rQJn376KWpqalBQUICzzjoLRx3V/fdk27Zt+OSTT1BbW4sjjzwSl19+OczmwN/D+vp6/Oc//0FJSQnGjBmDq666CmlpaWHHtGrVKqxevRrt7e2YOXMmzjvvPBiCLAju3LkTTz/9NKZPn47rr7++13FDeQ2hPIZIEpBXFC6ky85R/or0CuVjRVhvcTU5XfD6LoALZR8UJa+xKDcFA7OSUFLvhJOtXWRj7tZOrObuXbt24YMPPkBNTQ0GDRqEiy++GCNGBN9T4rPPPsNHH30UcF9KSgoef/zxgPu2b9+OZcuWoa6uDmPHjsVll12G9PT0sF8DERERxR9WpPcDHq8Ap8sNT3OteEdSJpAQXgW2y+1FpbUFLrdXfhzmNDgFCzxt2lak++fDK/gr0uVsNqpkTjKSxc+wmpzt8Hi1bXUTbz755BMMGzYML774IjIyMjB48GDs2bMHl1xyCW6++Watw+vV8uXL8d1332kdhixerxcLFy7ECSecgKqqKowePRq1tbU4+eSTsXDhQni9Hb8n//73v3H00UejvLwcgwcPxvPPP485c+agvb3d/5iqqipMmzYNH3/8MQoLC/HKK69g9uzZsNvD22PhtNNOw5IlSwAASUlJuOmmm3D22WfD4+leNdrS0oJLL70UH374Ib788stexw3lNYTyGKLOOucnpQvpsnOUfyG9rOe4dKS3uKRq9KwUM8ymvt/uKsnpHq+A5ASxT7rDxYp0OZi7tRHLufu9997DlClTUFxcjOHDh2Pjxo0YP348Vq9eHXTcDRs24Msvv8TUqVP9f6ZMmRLwmEceeQTXXHMNHA4HBgwYgFdeeQXjx49HaWlpWK+BiIiI4pTQD1mtVgGAYLVatQ4lKraVNQnD7lwmbPvpG0G4P0MQnpomf4yyJvlx/LxWHOPRM2WPoYbOr2XuE6uFYXcuE77ZU6NonHB9urVCGHbnMsVzqmda/J5t375dsFgswr333tvte16vV9i6dav/76tWrRLuv/9+obi4WHjwwQeFG264QXA6nYIgCMLOnTuFP//5z8LNN98svPjii0Jra2vAWEuWLBG++uqrgPtefPFF4X//+5//708++aTwwQcfCCtWrBDuvfde4e677xZ27twZNPZXXnlFKCoqEqZMmSIsWLBAWLBggVBVVRXWc3355ZfCfffdJ9x1113Ctm3bBEEQhM8++0y47bbbhAcffFAoLS3t9rx9vVZp7JUrVwp33HGH8Pjjj/cY/9KlSwWz2Sxs2LAh4P5t27YJFotFWLp0qSAI4s8hMzNT+POf/+x/jNPpFAYOHCg8//zz/vt+97vfCRMmTBDa2toEQRD/PeXn5wsPPfRQ0Dnsyfbt2wP+vnv3bgGAsGzZsm6Pvf7664WbbrpJOOecc4QLL7ww6JihvIZQXyeFrj/k7oC88vgkMWcf/lH5WOGo3Co+79KRyseKsN7i+n5/nTDszmXCnL+vUjxWqMcOu3OZsHxrRdjH6wlzN3O3IMRG7p4zZ45w8cUXBzzu+OOPF379618HHfehhx4SZs2a1etz79+/P+DvbW1tQl5envDII4+EGj4RERHFMc0r0tvb2/HVV1/h1VdfxZYtW0I+rqqqCu+++y6WL1+OlpaWCEYY+4ZkJ+OpX03FkIQm8Y7UPG3iyMvAU+Z/YIhb24oO/3xkJ2u+2ejAjCQMyU6O6nPHsyeeeAL5+fm45557un3PYDAEVB1t374djz/+OE477TS0t7fjiCOOgMlk8lcqFRcXo7CwEI899hiOP/74gIqrd955B5s3bw4Y/6uvvsI333zj//snn3yCm266CX/4wx+QkpKCgwcPYvr06fjpp596jL2oqAiZmZnIz8/3V0lZLJaQn2vBggVYsmQJ0tPTsXv3bsycORNXXHEFHn30URQWFmLdunU4+uij4XB0bMoXymv95JNPsGjRIixevBgFBQUYNWpUt9gFQcBjjz2GK6+8EtOnTw/43uTJk3Httdfi0UcfhdfrRX19PaxWK6ZNm+Z/THJyMsaNG4cPPvjAf9/HH3+Miy66CImJ4tUiGRkZ+MUvfoGPP/7Y/5iffvoJCxcuDHhNXU2aNCng76NGjUJiYiKqqqoC7n/33Xexdu1a/O1vfws6liSU1xDq6yTqzJ+fMswdm31mFUU3CKki3VkHtLcGxqWzfNVbXB0bjVqiEseEQrHtAyvSw8fczdzdVSi5e9CgQWhoaPD/3ePxwGq1YsiQIUHHBcRzyDvuuAP33nsvPvnkk27f7zpXDocDbW1tKCgo6HVcIiIi6h807ZFeX1+PU089Fc3NzZgyZQpuvPFGXHHFFXjmmWd6PW7p0qV48MEHcfLJJyMlJQV/+tOf8NFHH6GoKMonmzEiKyUR500dDKz/TLwjRZuF9KzMLJxn+gFoj/xJba9xSPMBKGrtokS6b7NRGORtdBrramytaHC6ML4wAwCwr9qOVEsCBmUlo7Xdg/01zRiel4o0SwJq7W2otbdh4qCMPsf94YcfMGPGDP8JXF/sdjtWrlyJGTNmABBPKhctWoTf/e53eOKJJwAACxYswMiRI/HCCy/gd7/7XVivs7W1FWvWrPH31fzNb36DO++8E6tWrer22FNOOQVFRUUYPXo0Fi5cGNbzAEBBQQFWrVoFo9GIW2+9FYMHD8auXbuwfv16GAwGLFq0CAMGDMBnn32Giy66KKzXajabsWbNmqC9vffv34+amhqceOKJPX7/hBNOwLPPPot9+/Zh3LhxGDRoED777DP84he/AABUV1dj8+bNyM7O9s9bWVlZtz6nI0aMwPvvv+//+969e/Gvf/0LS5YsQWpqakjz9N///hdutxsnnHCC/75Dhw7hxhtvxBdffIHk5L4XCvPy8vp8DaE8hqgrf35qLBH3NDElAqlRXrxJzgYSkgF3C2CvAHJGBuRNPektrvpmMb/nhbDRqBpxjMhPxa4qe1z3SGfuZu7WU+5+4okncNNNN2H27NmYMGECNm3ahGOPPRb33Xdfr2MNGzYMOTk5aGpqwlVXXYXZs2fjk08+gdHYUV9WUVGBBx98EA6HAz/88ANuueUWXHnllSHFSkRERPFNVkX61q1bVXnyxYsXo729HVu2bMEHH3yAr7/+Gs899xw+++yzoMe88847+NOf/oTly5fj008/xTvvvIP33nuvx363JGpyuvDBpjI0NTaJd2hUkd7kteADz3FocicAbpcmMQAd81HZ1II2Xy/UrChXpEsL6Q0OF5qc2s2FVl778TCuenm9/++L3tiE//tW7AdcZW3FvH+sxbYyKwDg/Y1luOyFdSGNa7PZMGDAgID7Xn31VSxcuND/p3O/z4KCAv+JOACUl5dj7969uPzyy/335eTk4JxzzunxBLovZ599dsDmVJdddhnWrl0Lt1v9hZY5c+b4TwJNJhNGjhyJU0891b8xl8ViQVFRkb/HZziv9fTTT+91g0ybzQYAQau1pPulHqkvvvgiXn/9dZxwwgm44oorMHv2bEyePNl/dZF023Vjr4yMDDidTv/fZ82aheeffz7kTcw2btyIm2++Gffccw/Gjh0LAHC73bjsssvwxz/+EVOnTg1pnFBeQ6iPiSa1cjdFjj9fV5eId2QOAYxRvnjQYAAyAzcc9cels3zVW1z1zWJFek5q5BfSm5wu1NrF53O0xe/7UeZu5m695G4A2L17N7777jtMmDABU6ZMwdixY/H111+juLg46FhXX301Vq9ejcWLF2Pp0qX4/vvvsWLFCvz73/8OeFxycjKmTp2KSZMmITc3F5999hmqq6tDipeIiIjim6yzsyOPPBIzZszAc889h6amJllP7PV68dZbb2H+/Pn+aoSZM2di9uzZeOONN4Iet3TpUlx44YU46aST/PeNHDmy1x3a+7uyxhbc+tYWlDX6LqHUaCG9rDkBt7bfiDIhH3Bpt+GoNB+7q8QTgwSjAWmW6F6ckZEsnti0ub0oqXf28ej4c/msIrwyf6b/7/+4bBquP1HcUK8wMwnLFh2PKUMyAQAXTB+CN66bHdK4+fn53TaDGjp0KKZOnQqXy4V//etfASfjXSuDa2pq/ON0HVfOCVRubm7A3/Py8uB2uwMuRVZL10pqo9HY433Sh47hvNa+KqilMYJtxCXdP3DgQADAWWedhQMHDuD222/H6aefjlWrVmHixInIyxP/b0pLS4PRaOyWXxobG5GR0VHdOGbMGCxcuBBJSUm9xgcA27Ztw9y5c3HllVfigQce8N//+eefY+PGjdi7d69/wWbbtm3YtGkTFi5ciPr6+h7H6+s1hPqYaFIjd1Nk+fN1RaV4R+ZQbQLJGCTeWssD42rUVyu93uKq8202mpsa+avgyhpbsP5QIwDA0Ra/FenM3czdesndAHDVVVfhl7/8Jf7973/jD3/4A95++21MnTq11ysQBg8OvIJl3LhxOOqoo/D9998H3J+dnY2FCxfirrvuwtq1a9HW1ob777+/z3iJiIgo/slaSN+5cydOOeUUPPjggxg4cCAuv/xyrFixAoIghDxGaWkpbDYbJk6cGHD/pEmTsH379h6PcTqd2LRpE04//XTs3LkTr732GlatWgWXq/cKqba2NthstoA//cnEgRnYs+RMTDT4KjRktHbxjzGw70t0g44xJBt7UhdgoqEEaNPuZyC9lrx0sUotKyXRX/kjZxw5c+Jv7QKgKCcl7ONjXUFGkv/ScAAYMyAdg7LEE8ckswmTB2f6P9zIT7eEdGk4AJx66qn44YcfAk7iTjrpJCxcuBBz5szp8/ihQ8VFq8OHDwfcX1JSEtA6KjExsdv/Oz0tTHY9OT18+DAsFku3E2BJT/8OQ32ucIX6WkMhXdbeU69RAFi2bBkmTJgQcAKbl5eH8847D1deeSWGDx+OVatW4fjjjwcgXo4+ZswY7Ny5M2CcnTt3duubGort27fj1FNPxYUXXohnn3024HuTJ0/GU089hWnTpvn722ZmZiIzMxNTp07ttdVAb68hnMdEixq5myLLn1eMvop0Bf3RFeXtDF+PYVuZ8rEiqLe4GsJs7aLkNU4cmOFfUHbEcWsX5m7mbr3kbo/Hg+Li4oCe7QAwbdo07N27N6znaW1t7fXKZrPZjCOOOAL79+8Pa1wiIiKKT7IW0idMmIBHH30UZWVlePPNN9Hc3IwzzzwTI0eOxAMPPICSkpI+x5AWs7tWS+Tk5ARd6G5sbITX68UHH3yA888/H59++ikWLlyISZMm4eDBg0Gf6+GHH/YvjGRmZvrfhPYXRqMBlgQTjM468Y7Unk8EQhrDGP6Cc8AYSckwGgSgzS57HKWk12JrEU925W40qmROLAkmJCaIv37NcVy9Fm233norTCYTrrvuum7/j3SuZgsmPz8fxx9/PJ5++mn/4uKePXuwfPlynH/++f7HjR49GuvWdVyyfvDgQaxdu7bbeMuXL/f/3+R2u/Hcc8/hF7/4RdAPbrKzs9HY2BhwX6jPFa5QX2uolixZgk8++QRvvfVWwP0ffvgh3nvvPTz44IP++zZs2BCwydhzzz2Hw4cP45ZbbvHf96tf/QpvvfUW6urE/7eKi4vx6aef4le/+pX/MaFsWLZjxw7MmTMHF154IZ5//vlucz98+PCA9gELFy5EUVERRo4ciYULF/ovUV+2bBn+8Ic/hPUaQnlMNKmRuymy/HnF6lskU7CQrihvS61dfBXparwHiITe4gp3s1Elr9FoNCDD9wG5M45bu0QKc3fomLtFJpMJkyZNwvLly/33eTwefPHFFzjiiCP893XN3Z9++mnAOF988QU2bdqEM888039f5zEBsRf8ypUrMXPmTBAREREp6meRkJCA8847D3PnzsXzzz+Pu+66C3/+85/x4IMP4oILLsCTTz7Z7RI6iXTZotR3T2K324Nu9iZdAlhRUYFt27YhMTERbrcbxx13HG677baAXeM7W7x4ccCbKJvN1q8W0w/XO/GX5TvxJ5sHRQCQmtvXId0crG3G4ve34eELpmBkfmg9DXuMw3E9/uT9PxS1adfaRZqP2SPFeciW2T9V6ZykJibA5XZhb7UdQ/thVXokDBo0CCtXrsSVV16JUaNGYcaMGRg4cCCqqqrwww8/YP78+TCZTL2O8fzzz+P000/HzJkzMXr0aHz55Ze45JJLcOGFF/ofc/vtt+OUU07BySefjIEDB2Ljxo09VoKNHz8eJ598Mo477jjs2LED9fX1eOmll4I+97nnnourrroKHo8HaWlpeOCBB0J+LjlCea2huvTSS9HQ0IDrrrsOzzzzDMaOHYsDBw5g8+bN+Oc//4mLLrrI/9impibMmDED06dPR0VFBTZv3ow333wT48eP9z/mj3/8I1auXImjjjoKs2fPxurVqzFnzhxcc801/seEsmHZmWeeiZaWFni9Xtxwww3+++fNm4d58+aF/Pp+/vlnvPzyy3j88cdDfg2hPEYLSnI3RZY/X7c1iflaQWsXRTkqzdevurk6MK6zJ6IoVz/5qre4pM1Gc0PM8Urm63C9E8u2iu144rkiPVKYu8PD3D3PPw/nn38+pk+fjilTpmD9+vVobm4O2Gura+7+8MMPceedd+LII49EXV0dVq9ejdtvvx2XXnqp/5h3330Xd911F6ZMmYKWlhasWLECJ5xwAu65556w55eIiIjij6KFdOnNyRtvvIGkpCTceuutuPbaa1FTU4OHHnoIv/zlL7F+/foejy0qKoLZbMahQ4cC7i8uLsbo0aN7PCY3Nxc5OTk4/fTT/ZfbJyQk4IwzzsCrr74aNE6LxQKLJfI9MvXKKwhwub3wOpvEO2S0dnG6PPixuAFOl/xKK68gwGWwwAuDphXp0nxYfZuTya1IVzonaRYTGp2AvZUn3WqaMmUKNm3ahM2bN2PHjh3wer0oKirClClTkJOT43/cKaec0uNl2pMnT8a+ffvw9ddfo7GxEXfccQeOOuqogMdMnz4du3fvxrfffovU1FQ899xz2LBhQ7dNvc444wwsWrQIP/74I84//3yceeaZAX1Cu7r44osxfvx4bN68GQ6HAxaLJaTn+v3vf9/ttdxxxx3dFkPvuecejBkzJqzX2tPYwdxwww24/PLLsXr1alRVVWHDhg2YOHEirrrqqoDHnXrqqfjmm2+watUqWCwWzJkzB5mZmQGPSUlJwapVq/DNN9/g8OHDWLRoUbeWKKFsWHb//ff3uEFcYWFh0GNuvPFGJCQEpsd58+Zh2LBhYb2GUB6jBSW5myLLn69tvh7pWfIX0hXlqHTf74e9KjAunbUB6i2uuubwKtKVzFfn51fyPqk/Y+4WMXeLQsndxx13nL/Sv7q6GldccQVOOOGEgHO+rrn7hRdewIEDB7B+/XqkpKTgxRdf7FZc9fLLL/sfYzab8de//lXzD8CJiIhIPwyCjOaoTz75JF5++WXs3LkTc+fOxbXXXotzzz03YOHB6XQiPT29155z5557LpxOJ77++msYDAZUVlZixIgReO655zB//nwAwNq1a1FTU4MLLrgAADB//nxUVFTg888/949z3nnnwW63Y+XKlSHFb7PZkJmZCavV2usb47ji9QIP5QGCB/jDro6NxEK0vdyKef9Yi2WLjsfkwQoWgl6ZBxxaA1z4EjDlor4fH0FPr9iHx7/ai1/NHIpHLjyi7wO6UDon5z2zFlvKrHjhyhk4feKAsI/Xu375e9bJaaedhhkzZuCRRx7ROhTN7N+/H9OnT8f8+fPx5JNPah1Ov6dW7tZKv/k/xesF/jIA8LiAW7YC2cP6PqYHinJU6XrgpdOAzCLg1m2ynl9LLrcXY+8Rq1I33Xt6SFeeKc3py7ZW4KbXN2HWiBy8teCYsI/Xi37zexYEczdzNxEREVFvZFWkP/XUU7j66qsxf/58DBkypMfHpKSk4F//+lev4yxduhTHHnssLrzwQsyePRv/+c9/cNRRR+GKK67wP+aVV17BunXr/AvpDz74IGbNmoVLL70Uxx13HH788Ud8/fXXWLFihZyX0n+0NomL6ICsinTVJPqqT1zatXaRNPoq0rNS5LV2USojWaxKsre2a/L8RJE2evRofP7559i2bRscDkfQS7gpOtTK3RRhjhpxEd1gDPtDb9WkS61dqgBBAGRsyK2lBoeY301GAzKT5V11Fq7URF+PdFakU4xj7iYiIiIKTtZmo0cffTTuu+++Hk/EO28mc+211/Y6zsSJE7F161ZMnToVJSUlWLRoEVauXBlw2eMJJ5wQ0PNvyJAh2LJlC2bMmIG9e/fiyCOPxJ49ezB79mw5L6Vf2F5uxfAHf8B273DAkgkkaLNwvL3ciuFbrxDj0LC1y/ZyK4bf9SkO1YmbHMlt7aKUdCn47sqeN9el2Pb73/9e1sZf8ebYY4/FggULeCKuA2rlboqc7eVWDP/LBjFPpg8CTNrkJ3+PdI8LaGn0583t5VZt4gkiWFzSRqM5qYlR2SB1e7kVV78itkNycAPxmMbcLWLuJiIiIuqZrIr0t99+u9vu7gAgCALefvttvPnmmyGPVVRUhPvuuy/o93/72992uy8vLw933HFHyM/R3w3KSsYjJ6Vg0Lp6IDW0fok9jnHBFAzK6nkj2JDHGL0Tg0rrAQ03G5Vey8dbKgAA2TIr0pXOSX6q2MPRZJT1eRbpXDgbWRJFg5q5myJjUFYyHpnVjkGb64GsycrHkpujEixAcjbQ0gg0V2NQ1ijF7wEiIdhrDHej0d7GCvXYRXNG4x8r93Oz0RjH3E1EREREvQlrIb2pqanHrwHA6/Xiu+++w8CBA9WIi1SUk5qIXw2zAT/agZQJ8sc4ukiFOOxAmV3TinTptbyxvhQAQuqd2ts4cuVniAvpHp1t3kZE8YW5O3bkpCbiV/mHAIMdyJS/0ah/LCV5O61QXEi3VyGnYILi9wCREOw1ShXpeSFuNNrbWKEee/60wfjHyv1wtrG1CxERERFRvAprIT07O7vHryVGoxFLly5VHhWpyupsxw977ThGSEWmzIr0BocLX+6owhmTCpEjc+HZ6mzHDw2DxDhc2i2kW53t+OFgHert4om23NYuSufEbBIr0et8cRARRQJzd+wQ85NLzJOZgxWNpThvpxUAtbuA5mp/3jxmZB4yNWqH1pNgcUkV6eG8biXzZXW2Y31xAwDA4XJDEAQYYqyvPBERERER9S2shfT168X+jzNnzvR/LTGbzRg6dChycnLUi45UUdroxMJ12ViWmI/M1FxZY1Q0teCu97dh8uBM2QvppY1OLNw0VIxDw4r00kYnFr66ESmJJgDyNxtVOidujxcAUMOFdCKKIObu2FHa6MTCnZPEPJmu7CoBxXk7vVC8tVf58+ayRccjMyVTUVxqChZXndTaJS30161kvkobnbjz/W0AAK8AtLm9SDKbwhqDiIiIiIj0L6yF9BkzZgAAiouLMXz48EjEQxEwvjAdm4/5DmkbDwMpedrG8csmpH12GGgbo2kcP//pVMz4ywoA2m02Orog3fcVW7sQUeQwd8eO8YXp2DziOaRVHAYULqQrJm042lwtxnXf6UizyNpaJ2KCxVXfHH5rF6VxbLjnNBy15GsA4oajXEgnIiIiIoo/IZ8R1dXVARA3+kxLS/P/vSd5edot1lJ3CSYjslyVgMELpGr3s0kwGZGVnirG4dJus9EEkxHodMl1ZrI2C+lZvgV8O/upElGEMHfHlgSTEVmOQ2KezBikbTCdKtITTEbZV29FUrC46h3hbzaqNI7cNAuSzSa0tHvgdHkg7/o/IiIiIiLSs5AX0vPzxd7agiD4vw5G4OaJulLa4MRj+47Abd41GCqzR7pqcXxvwW3efAzVsrVLgxN//ngHACAjKUFcWNdAi0tcQG9wsLULEUUGc3dsKa1rxmP15+E209sYKi1ka8VfkV4j5u8v9+C2M8ZhaE6KtnF1Eiwu/0J6lCrSpTiSzEa0tHvgcLmj8rxERERERBRdIS+kb9q0qcevSf/aPV5UtlnQDhOQIq9GKiXRhFkjcvx9xWXH0WIS49BwIb3d40WltRUAkK2gWk3pnCT7jmtu5Qk3EUUGc3dsaXc0oNKbjXZTQsdCtkyK87a0kN9c5c+b7b69PfQiWFxSa5dweqQrmS8pDrGdSzscbczrRERERETxyCCoVIJmt9uRlpYGQ6eWGXpls9mQmZkJq9WKjIwMrcOJjr+PBZqrgQXfAgOP1C6O6p3A88cAyTnAncWahfHljipc/78NmDo0Cx/eeJwmMZQ1OnH80lVINBmxZ8mZMfG7E45++XtGFGOYu3WmYjPwfyeJi+i379U2lrr9wDNHAYnpwN1l2sYSpgn3fo6Wdg9W33EyhuWmRu15z3zyW+yusuO/84/GiWO1uwJQiX7xe0ZEREREJJOsnha7du3CnXfe6f/7LbfcgoyMDAwaNAibN29WKzZSi9cLOHx9cWVuNur1Cmhze+D1KvzcxeLbYFPDHukA0OgUL/tWstGo0jmR+rq6PF60tLNPOhFFFnN3DLBXircqbDSqOG+n+yriXXbA5VAcT7Q4XW5/Tg2ntYsa73NSfZueOtnahYiIiIgoLslaSP/DH/6A008/HQCwd+9evPTSS1i2bBkuu+wy/PGPf1Q1QFJux6FyTGh5ATu8w2RvNrqz0oZx93yOnZU2+XFUWDHhse1iHB4X4NamN/iOCivu+XA7ACBbweZpSuekpK5jYaLJ2S47DiKiUDB369+O0lpMaH0ZOxImKh5Lcd5OTAPMYt/xHfsPYcK9n2NHhVVxXGraUWHtFld9s/hBuSXBiNQw2rQomS8pDukaTwc3ESciIiIiikuyFtK///57HHPMMQCAL774Aueddx7OOecc3HfffVi/fr2qAZJyA0zN+GPCWxhgcQMJ0dl4q8c4MpLwxzPGYoChUbyjTZuq9AEZSZg5PAdAR1W4JnFkJvlP8rmQTkSRxtytfwPaK8V8nZ2udSiAweDv0z7A0IQ/njkOAzKSNA4q0ICMpG5x1fn6o+elWaLWskiKIyuFFelERERERPFM1kJ6UlISqqqqAACfffYZ5syZAwBwuVxITNRuYZJ6locmXJ3wBfLC2HQrInGkWXD1CaOQZ/YtGru02XA0L82Cohyxyk5Jaxc14ijMFE/+m1pcmsVBRP0Dc7f+5bkOi/k6Vyf9tdMKAAB5Qh2uPm4E8sJolRINeWmWbnE1OMR8Gs5Go2rFkZ0ixuFwsSKdiIiIiCgeyVpIP+ecc3DBBRdg/vz5WLNmDc4991wAwIoVK/yXjZN+2BtrsdpzBOzJg7SNo7Udq/fWwm72tZdp02Yh3d7ajn014nNnabiQbm9t91fLWVmRTkQRxtytf/amejFfJxVqHYooVVzQtzfVifm7VV+5yv++olNcUmuXnNToLaRLcZhNYk53trEinYiIiIgoHslaSH/mmWfwi1/8Am63G5988gkKCsSKpTVr1uD+++9XNUBSrqS2Cb9tvwslphHaxlHvxG9f/gklpiLxDo1au5TUO7GhpAmAtq1dSuqd2F8jzkFTi74WJ4go/jB3619Jo0vM18IArUMR+RbSS2rtYv6ud2ocUCD/+4pOcdU5xNYuuanRq56X4mj3iE3Sm9kjnYiIiIgoLiXIOSglJQUPPfRQt/ufe+45xQGR+sYmVOMHy/3IzZknf4wB6fhh8RxFJ6b+MV7/G9AMwKXNQvrYAekYkZeC4jqnos1Glc7J2AHpOHNyIT7fXsUe6UQUcczd+je2dQt+sNyE3KJlysdSIW9LC+ljDaX4YfGCqC5Oh6Kn1yhVpIfbzk7JfEnHvvNzGQDAwYp0IiIiIqK4JGshHQC8Xi/KysrQ0NDQ7XtTp05VEhOpLLG1DgMNDUB6rvwxEowYmJmsLA5pjKQ08Y42m6LxlMTh8FWLKWntonROEhOMGOQ7nj3SiSgamLt1rL3Vl68BZCtvxaZG3pZ6pCe2VCsfKwJ6eo31vs1Gw+2RrmS+pGMzk8X3FM1cSCciIiIiikuyWrt8//33GDlyJIYNG4Zp06Z1+0P6Ut7QjMXt16Ac8i8VP1zvxO9e24DDCi7rLm9qweL3t6Hc4ItDo9YuZY1O1PlOtJUspCudk/KmFqw/JC5msUc6EUUac7fONVejXMjFYvf1KG9VXvmtRt5GqrinSXlTq5i/m1oUx6Um//uKTnHVS5uNhllZrmS+pDjaPV4AgJ0L6UREREREcUnWQvqNN96IuXPnYu/evaitre32h/SlxWHHDu9wtFjkV6TbWtuxfFsVbAo2GmtxebCjwoqWhAzxDo02G210uOAV25gqau2idE5aXB7U+3q5srULEUUac7fOOerQIliwA6PQ0u5VPJwaeRupYkV6S7NNzN8uffX+9r+v6BSX1Nol3Ip0JfMlxWE2iW+rm3W2KSsREREREalDVmuXvXv3YvXq1cjIyFA7HoqA0Z4D+NjyETDwPW3jKEjDxzcdD3zyrniHRj3Ss1PFk+tEkxEpiSZNYgDE+bjzzPG45c3NsHKzUSKKMOZunXPUYrSxAh8PfgMoWKh1NCJfa5fRrt34+LbjNQ6mO//7ik7qNdhsVIrj+/11ANjahYiIiIgoXsmqSB89ejRqamrUjoUixSGe2CElT9s4JBapR7o2FelS9XdWihkGg0GTGCRZvor4Ji6kE1GEMXfrnNOXq30bfOqCr7UL2qxAe6u2sYRAEATZFelqSEsS61OaW7mQTkREREQUj2QtpN9+++249tprsW3bNrS0tKC1tTXgD+mIIGBXczKmt/4Tuxypmoayq9KG6Q99hV1tvkUCjRbSt5ZZAQDJZln//FWzq9KGRa9vBABYndxslIgii7lb5xy12OUdium7f41dldpsxt1NUhZgNItx/fUb/cTl439f4YvL1uKG29e7LSc1egvpUhzVNvH3iD3SiYiIiIjik6zWLldeeSUA4Igjjujx+4IgyI+I1NXahFyhAdckLEdu7tmyhynIsOCOueNQkCH/UunctERcc/wI5OKAeIdGrV2kf5/ZCi/7VjonuWmJuHTmULywppgV6UQUcczdOueoQ67BhmuKqlSpplYjb8NgAFLzkWuz4ZqpKZpUeffG/77CF1edr61LuiUBSebwWrcpmS8pjsHZyQDE1i6CIGh+1RsREREREalL1kL6mjVr1I6DIsVRjwKDFTemrAJyMmUPU5CehBtPGa0oFP8YG38Q79CoIl3awm2AksUFKJ+TgvQk3HTKGLywphhOlwdtbg8sCdr1bCei+MbcrXOOOjFfT2oH0pMUD6dG3gYApOWjwF6BGye0qhKXmrq+RiVtXZTMl3SstOmpIABOlwepFllvs4mIiIiISKdkvcM//nj9bThFQTjr4BAs2GmejoltbtknddaWdvxU3ICjR+QgM9ksawxHmxs7K22YaExDKgC0aVORXmMVL71OU3iCq3ROHG1u7K7quEze2tKOgnQupBNRZDB365yjVszXjgGK8rVEjbwNAEjNF+M6VI+Jw5THpSb/+4qBGUi1JKC+WaxIl9PWRcl8SXFMKEyHyWiAxyugWYWfIRERERER6YvsJtHt7e347rvv8L///c9/X319vSpBkYoctSgWBuLi+gUornPIHqa0wYnr/vszShucsscornPg4n/+gOIWX692lzYV6SW+16C0iYHSOSmuc+DS/1vn35zM6mR7FyKKLOZuHXPWifl6ZbqifC1RI28DAFILfHFlqBKXmvzvK3xx1TnEivS8tPCvOFMyX1Ich+qd/g/p7dxwlIiIiIgo7shaSD98+DCmT5+OU0891d9zFQCuu+46fPLJJ6oFRypw1GG0oRwrxn6E0QVpmoYyuiANK247CaMHZIh3aNTaRepYOjJP281XpfnISREr39gnnYgiiblb56R8/Zs8zfN1gNQ8Ma6jN+orLnR6X+GLS6pIz5WxkK5WHNJCejM3HCUiIiIiijuyFtJvvfVWHHPMMbDZbAH333777XjkkUdUCYxU4qxDkqEdo3LNYW+8pbYkswmj8tOQlJou3qFRaxe77+Q2Pz26J9pdSfMhbXraxIp0Ioog5m4dEwTAUSvm6yEDNc/XAdIKxLiEw/qKC53eV/jiqvMtpOdHeVPUznGk+64ya2ZFOhERERFR3JG1kL569Wr89a9/RWJi4InKlClTsGHDBlUCI5U46lEp5OChsumotLZoGkqltQUPLduJyjbfZmUaVaRX28Qe6YLS3i4KSfORnCD+GjY5XdoGRERxjblbx9rsgMcl5uvVjZrn6wCp+WJcB0bqKy50el/hi6tjs9HoflDeOY6OinR+OE5EREREFG9kLaS3trbCaBQPNRgM/vsrKyuRkpKiTmSkDkctmoVkfNuUo6g6ypJgxJiCNFgSZLfVR3OrG9/urUWz4DvB9bQBnuifaEqV32aToY9H9k7pnEjzkWwRK+msbO1CRBHE3K1jjloAQLMpB98eaFKlmlmNvA0ASM0X30fYB+quytr/vsIXl1SRLqdHupL56hyHtO8Je6QTEREREcUfWWdXJ598Mv75z38C6DgZdzgcuOOOO3DaaaepFx0p56zDGGM5vprXhjED0mUPM2ZAOr76w0nqjDFkQMedGlSlO1ziye2UIVmKxlE6J9LxRdniAhZbuxBRJDF365hT3PB1TEa74lwrUSNvAwBS88X3ERl/USUuNXV9jR0V6eG3dlEyX52PZY90IiIiIqL4lSDnoL///e848cQTsXz5cgiCgIsvvhhr1qwBAHz33XeqBkgKOcSTc6TkaRtHZyYzkJAEuFvFhfSUnKg9tdcr+Cu/s3ybfGotM0U84W9qYWsXIooc5m4d81WkI1VHuVqSViDeOusArwcw6qtPeme1CirS1cIe6URERERE8UtWRfr48eOxfft2zJ07F+eeey5aW1uxYMECbNmyBaNGjVI7RlLCWYc93iE47m039lTJr/7eUWHF5Pu/wI4Kq+wx9lTZcdwjK8U4EtPEO13R3XDU1tru741eY2tTNJbSOZHmw9XuAcCKdCKKLOZuHfMtpO8xjenIkwqpkbcBACm54vuIliewp6RccVxq6vy+os3t8bdTyZNRka5kvjrHwYp0IiIiIqL4JasiHQAKCgpw7733qhkLqU0QAEcdsgypOH9KrqIKbEEQTwqVbNCZlWLG+dMGi3FY0sXqtii3dmmU+qMbDchPV1axpnROpPmQLkFnj3QiijTmbp1y1AEAstJTcf7gwapcMaVG3gYAmMzISjbjfM9aZHmnAyhSHJtaOr+vkNq6JBgNyEwOf/6UzFfnONIs4nPbuZBORERERBR3ZC2kOxwOfPvttyguLobBYMCIESNw4okncrMyvWm1At52DDA04fazJgPmJE3DGZCRhNvnjhP/YvFVpLdFtyK90SmeaBdkJGFAhj7mY+XuagCsSCeiyGLu1jHfQvqArHTcfvo4jYPpbkB6Im5vewcwXKF1KAE6v6/YViZWkuemJQZsphvtONLY2oWIiIiIKG6FvZD+3nvvYcGCBaivrw+4Py8vDy+88AJ++ctfqhUbKeXbvKzFnIMDNS6Myk9EcqJ2vU1bXB4cqG3GqPw0JCf6NvNqs0U1BqtvsTrJbESLy6OL+UhKEGNgj3QiihTmbp1zigvpLZYCHCi3+vKkfnqRt6QMwgFvG0ZZa5GsdTCddH5fUadhf/TOcaSztQsRERERUdwKq0f61q1bcdlll+HCCy/E5s2b0dLSgpaWFmzatAm//OUvcemll2LHjh2RipXC5eu5eiBxHOb9Yy0O1Ea3+rurA7XNHXFYfAvpUe6RLlWkH6h16GY+rK3i4n6TgxXpRKS+SOTun3/+Gddccw3mzZuHxYsXd1ug783777+P0047Dc8//3y4LyV+SfnaU6CLfN3VAdNwzHP9FQeqm7QOJUDn9xXSQnquBgvpneNgRToRERERUfwKayH96aefxlVXXYV//etfOPLII5GUlISkpCRMnToVL7zwAn7zm9/g6aefjlSsFC7fpeKjMrxYtuh4jMpPkz3UqPw0dcfQrLWLuFh9wug8Ra8FUD4n0vFHDskCIPZTbfd4FcVERNSV2rn7+++/x3HHHYf09HRcccUV/r87HI4+jz148CBuueUW7Ny5E3v27FHysuKLlK8HZinOtRI18rZ/rJwkLEu8G6MMlYrHUlPn11jn65EuZ6PRrmMpOVbabJQ90omIiIiI4k9YC+lr1qzBDTfcEPT7N954I7799lvFQZFKfJeKJ6dnY/LgTEWXiScnmtQdQ6pIj/Jmo02+ivTheamKL5tXOifS8QMykmD0tXOVKuaJiNSidu6+++67cc455+DJJ5/EpZdeimXLlqG8vBwvvvhir8e1t7fjsssuw1/+8hcUFBSE/Hz9gm8hPTmzQHGulaiRt/1jZeRisvEQklurFY+lps6vsV5haxcl89X5WH9FehuvMiMiIiIiijdhLaSXlpZiwoQJQb8/YcIElJaWKg6KVOI7Ma9OGIy/f7EH1bZW2UOVN7Xg3g+3o7ypRfYY1bbWjjgSfRVfrmgvpIsntrsrbYrmA1A+J9J81DW3IStFrKBrZHsXIlKZmrm7paUFa9asCeipnp6ejlNPPRVffvllr8fefffdGD58OK688sqQnqvf8Hr9H3xXezIV52uJGnlbUm0agL+3X4zqJn21nOn8vqKjR7q8inQl89U5Dn+PdLZ2ISIiIiKKO2EtpLe0tCApKSno95OTk0O6tJuixLeQ3mTOxwebyv2LyHI0Olz437oSNDrkV0w3Ods74tCoIl2q+N5b06xoPgDlc9J5PrJTzACABgXzS0TUEzVzd2lpKbxeL4YMGRJw/5AhQ3Do0KGgx33++ed4++238c9//jOk5wGAtrY22Gy2gD9xqbUJ8IqLrk2GdMX5WqJG3pY0mXLxged4NNn19R6vcx6t973O3FR5FelK5qtzHB0V6W4IgiArFiIiIiIi0qeEcA/45ptvIhAGRYSvwm1cQSq+O3+OxsEA4wrT8d1dvjiKpYX06Fa3SYsT9587EeMK06P63F11no+c1EQcqHWwtQsRRYRaudvlEv+PSk5ODrg/JSXF/72uqqqqcPXVV+ONN95AdnZ2yM/18MMP44EHHpAfbKxw+jZqtWRi3ODcjjypI+OGFOC7pF8D7UVahxKgcx6ttfsq0tOjv9lo5zjsvg3E2z0C2txeJJmVt9YhIiIiIiJ9CHsh/ZRTTolEHBQJvop0pOZrG0dP/K1dor3ZqLjQk50i79LvSJHiYUU6EUWCWrlbWghvaGgIuL++vj7oIvl7770Hh8OBJUuWYMmSJQCAAwcOoKGhAdu3b8eXX34Jo7H7BXKLFy/GH/7wB//fbTYbhg4dqsrr0BVHrXibmqttHL1J872PcNQCggAYDNrG04OOinRt83tqYsdb6+Y2NxfSiYiIiIjiSFgL6cXFxZGKgyLBV5G+z5WL3z2+Gs9dPh1jBmhXhb2v2o7fvbZRjEOzzUbFSrH7PtqOl7Nn6mY+pIX0JlakE5HK1MzdgwcPRn5+PjZu3IhzzjnHf//GjRsxc+bMHo8577zzMG7cuID7Fi5ciCOPPBI33HADDEEWZS0WCyyW6FcXR12nD70D8qSG+amrfY4U/K7tb3jO+xTGuJo72rNpTJqvZy6b5v8gOl+DivSuP7c0SwKa29ywt7plb35KRERERET6E9ZC+vDhwyMUBkWE7+Q8LTMbJ45N9fftlCM3LRHXHD8CuTI38QKAtKQEnDg2X4xDs4V08UR71shcRfMBKJ+TzvORnSpVpHOzUSJSl9q5+7e//S1eeuklLFiwAAUFBVi+fDk2b96MZ555xv+Yxx9/HLt27cILL7yAIUOGdOupnpaWhqFDh+K0005TNbaYJFWkp+QF5kmF1MjbkrT0dJxo3oU0QwvQXKObhXRpvrwAPF6xH3mOzIp0JfPV9eeWniQupHPDUSIiIiKi+KL8TI30SRD8C+kDBwzAveOU9TUdmJmMe+dNVG+MJl9rlygupLvcXjhcHgDAPedMQJbC9i5K56Tz8Tmp4maj7JFORHr3wAMPYMeOHRgzZgxGjRqFXbt24W9/+xuOO+44/2N27tyJdevWaRhlDPFXpOepkmslqo+V+w3Q2CAupOeOUmVcpaTXuLdafC+RmWyG2dS9TVA4Y6lxbEaSGZXWVlhb+OE4EREREVE84UJ6vGqzAV7xBK7VnI3y2mYMzkqW3avT0ebG7io7xhemI9Ui759Na7sH5U0tYhxSNVsUe6RL1egGAHX2NiSZTYp6lyqdk87zwR7pRBQrUlJSsHz5cuzduxfV1dWYMGEC8vLyAh5z2223wWq1Bh3jX//6V1gbj8Y1Z0drl4A8qbC3thp5W9La7kF54jgMFsqQ5KhRNJaapPkqb2wBABQoaOuiZL66/twyk8UPx22tXEgnIiIiIoon8sp2SP+kCjdzKvY3eXHqY6uxv0b+onVxnQMXPv89iuscssfYX9PcEYdFqkiP4kK6rzIsLSkBpz3xraL5AJTPSef5kC5FZ0U6EcWKsWPH4oQTTui2iA4AEyZMwOzZs4MeO2vWLIwdOzaS4cUO/2ajeYF5UiE18rZkf00zTi25EvuFwWJFuk5I87WtvAkAUJAhfyFdyXx1/bllJIsL8TZWpBMRERERxRUupMcr/6XiuRiRl4p3Fh6DEXmpmoYUEIclQ7zT3QJ4otNDtNFX7Z2Tmqi7+ejokc6FdCKifqXTZqN6ydddjchLxTtHbMAIQyXQXK11OH7SfInXmgED0pM0jUP6uWUksSKdiIiIiCgecSE9XkmXiqfkIdWSgJnDcxRf2q1UQByJaR3fcEWnT3qjUzyhzUlN1N185PhauzRyIZ2IqH/p1CNdL/m6q1RLAmYOsiDV0KarinRpvpp8+T1fQUW6GnFIP7cMX2sX9kgnIiIiIoovXEiPV50q3GrsrXh21X7U2Fs1DSkgjoREwOTb7DNKG45KbVNSE026mw+pIt3h8qDN7dE0LiIiiiKptUtKnm7ydVc19lY8WzYcNUKmrhbSpfkqaRDbsRRoVJHe9eeWkSS1donOFXdERERERBQdXEiPV86OCrf6ZhdeWluM+mb51c4mowE5qYkwGQ2yx+gWh7ThaJT6pEttU5LNJsXzASifk87zkZGU4B9HqqwjIqI45/UALQ3i16n5quRriRp5W1Lf7MJL+9JQL2QAOtpsVJqvaqu4gD1AQUW6kvnq+nPL4GajRERERERxSV/XDpN6pIr0lFxMGJiBjfeermi4iIyRmAY46wFXdBfSR+an4YXfzlQ8ntI56Xp8dooZdc0uNDhcGJChTVUdERFFUUsjIHjFr1NyMCHdrDjXStTI2wFjXTcAeLEUiN4e4X2SXuNJj64CoKwiXcl8dT3Wv5DO1i5ERERERHGFFenxqlPPVd2SNhxts0Xl6TpvNqpH2eyTTkTUv0i5OjkbMJm1jaUvqfnibXMNIAjaxtKJIAiosbUBUFaRrqaOzUbZ2oWIiIiIKJ5ovpDe3t6Or776Cq+++iq2bNkS1rElJSV48cUXsW7dughFF8OcHT3S99c04xfPrMX+GvllZHur7Tjp0VXYWy2/n3m3OCy+DUej1Nql3rdA3e7xKp4PQPmcdJ0PqU96g5ML6URE/YLUH923SK1Gvpaokbcl+2ua8YtXS7DfOwjwtAGtVsVjqmF/TTPm/WMtWtrFvUWUVKQrma+uP7eMZPGCT242SkREREQUXzRdSK+vr8fMmTNxww034L333sOJJ56Im266KaRjXS4XLrzwQixatAhvvvlmhCONQf7WLnlITjRh0qBMJCeaZA/ncntRUu+Ey+2VPUa3OBKlhfTobjZakG5RPB+A8jnpOh85rEgnIupfOm00CvSQJxVQI29LkhNNmDQ4G8kWX9W8FLfGkhNNGJ6bCgBItyRo9j6n688tk61diIiIiIjikqY90hcvXoz29nZs2bIFqampWL9+PWbNmoVzzjkHZ511Vq/H3nnnnZg6dSrcbl422yN/a5dcDM5KxsMXTNE2HqB7HNJmo1HqkS5tAjZ6QDoumVkUlefsTdf58FekO3jiTUTULzjrxVtfGza95Ouu/HH9wwzUA2iuBvLGaB0WBmcl4/LZRfh0WyUKNGzr0vXn1tHahfmciIiIiCieaFaR7vV68dZbb2H+/PlITRWriWbOnInZs2fjjTfe6PXYTz/9FJ9++imefPLJKEQagwSho7VLSh5cbi8qrS2qVKUp0S2OKLd2kSrSM5ISdDkfOaniiXcjW7sQEfUPXVq76CVfd+WPK6VQvKO5RtuAfFxuL/b5WrEoaeuiRhydf27SZqOt7V60uT2axUVEREREROrSbCG9tLQUNpsNEydODLh/0qRJ2L59e9DjKioqcO211+LVV19FWlpaSM/V1tYGm80W8CeutdkBj28xNjUPe6vtOObhlar0SVWiWxxR3Gy0td0Dp0s8ma1rdulyPvybjXIhnYiof/AvpIsV6XrJ11354zKNFu/QyUL63mo77v94JwBtNxrt+nNLtyTAYBC/Z2vhlZNERERERPFCs4V0aTE7Ozs74P6cnJygC91erxeXX345brzxRhx99NEhP9fDDz+MzMxM/5+hQ4fKDzwWSNXo5hQgMRXDclPwn/lHY1huiuwhIzKG1CM9Cq1dGnx9x80mAyYOTFf8WgDlc9L1+Bx/axcupBMR9QuOjo3BAXVyrSQiY+Uki3c49LGQPiw3BWdOGgAAKMhQVpGuZL66Hms0GpBmEbsnsr0LEREREVH80GwhPTlZPBmz2wOrrux2u/97Xb3++uvYtGkT8vPz8eKLL+LFF19EQ0MDduzYgRdffBGCIPR43OLFi2G1Wv1/SktL1X0xetNpo1EASE8y46Sx+Uj39eyUIyJjWKK32ai0OJ2TmoiM5ETFrwVQPiddj5d6pLMinYion/Dn61wA6uRaSUTGyhLjRHO14jHVkJ5khjlB3OCzIF1ZRbqS+erpWG44SkREREQUfzRbSC8qKoLZbMahQ4cC7i8uLsbo0aN7PGbw4MG46KKLsH79eqxbtw7r1q2Dw+FAZWUl1q1bF3Qh3WKxICMjI+BPXOu00SgA1DW34d/fFaOuuU32kDW2Vjzx1V7U2Fplj9EtDmmz0Sj0SK/3LaRnpySqMh+A8jnpGkeO1NqFm40SEfUPzsCKdLXyE6BO3pb44zKJ1d96ae1S19yGraVNAJRXpCuZr55+bh0bjrK1CxERERFRvNBsIT0xMRFz587Fm2++6V8Ar6ysxKpVq3Duuef6H7d27Vq8//77AIBTTjnFX4ku/Rk6dChOO+00vPjiizAaNXs5+tLlxLza1oq/fb4H1QpOpmvsbXhqxT7U2OWf3HeLI4qtXRp9C+m5aYmqzAegfE66xiH1SGdrFyKifqLLZqNq5SdAnbwt8cdlEOPUy0J6ta0VhxucAIBCpQvpCuarp59bRrLY2sXKinQiIiIioriRoOWTL126FMceeywuvPBCzJ49G//5z39w1FFH4YorrvA/5pVXXsG6detwwQUXaBhpjOnS2mXSoEzseuhMDQNCz3FEcbPRzhXpep2P7FSxeq2l3YMWlwfJiSatQiMiokjztAMtjeLXqfrK11354yrfIN6hk4X0iQMzYE4wwuX2YmCmsoV0JXr6ufkr0rmQTkREREQUNzQt4Z44cSK2bt2KqVOnoqSkBIsWLcLKlSthNnf0mDzhhBNw4YUXBh3jggsuwDHHHBONcGNHl9YuuuXvkR7FinRfH3I9SrMkwGwyAGCfdCKiuOdsEG8NRiA5u/fH6kWar7WLoxbwerWNBeKH5C63FwYDUKjhQnpPMqQe6dxslIiIiIgobmjeC6WoqAj33Xcfnn32WSxcuBAWS+BmUb/97W/xwAMPBD3+vvvuw6WXXhrpMGOLM7Ai/WBtMy791w84WBv5BevedIvD3yM98puN+ivSUxN1Ox8Gg4HtXYiI+guprUtyDmAUr0DSS37qyh9XS4p4h7cdaG3SNCYA+LlYrOjPTjHDbNLuLW1PP7eOzUbZI52IiIiIKF5ovpBOEeCvSBcX0s0mIwZmJik6ycxMNuOXUwf5Twzl6BaHfyE98q1dOlekqzEfgPI56SmOnFQupBMR9Qtd9jMB1MnXEjXydre4EpOApCzxTh20d6l3iP3MlW40Ciibr55+bh2bjbIinYiIiIgoXmjaI50ixL95WQEAYGhOCp781TRFQ0ZkjKRM8dbdCrhdQELk2q40dKpIV+O1AMrnpKfj89IsAOz+xQEiIopTXT70BtTJtREfK22AWI3eXA0UjFdlfLlcHrG9zIjcVMVjKZmvno71bzbq5EI6EREREVG8YEV6POpycu72eNHkdMHtkd/PtLXdg0N1DrS2e2SP0S0OabNRIOJV6Q2+nuM5qYmqzAegfE56iiM3Tfwwob6ZFelERHHN/6F3x0K6WvkJUCdv9xhXmvghPZqrFY+rVHlTCwBggAoV6Urmq6efm9SqjXueEBERERHFDy6kxxtB6Dg5953s7q6yY+qDX2F3lfxe5PtrmnHy37/B/hr5fVu7xWE0AYm+9i6tVtnjhqLB39rFosp8AMrnpKc4pNYu9WztQkQU3xzdW7uolZ8AdfJ2j3GlDxTvtFcqHlepPZXiPKnRHl3JfPX0c8tKEVu7NLIinYiIiIgobrC1S7xpbRI3AQP8m40OzU7BP38zHUOzU7SLK1gcSZmAyx7RTcs8XgFNTqm1ixkWk0m38yG2dgHqm9nahYgorvkr0jsW0vWSr7sKiCvDt5Bu034hXeo/PqEwo49HRlZPPzepIr2JFelERERERHGDC+nxRqpws2QAZvFS58wUM86cPFDDoBA8jqRMwFYW0Yp0a0s7vIL4dXaKuNmoXucjN5WtXYiI+gVnvXibkuu/Sy/5uquAuNIHibf2Cu0C8qm1ix86jypI0zSOnn5ubO1CRERERBR/2Nol3vTQc7XB4cKbPx32tzfRSo9xJPmqyFoj1yO9zlfdnZVihtlk1PV85Poq0uvY2oWIKL71UJGul/zUVUBcOqlI93gFVNlaAQApiSZNY+np55aVKrZ2aW33qtKnnoiIiIiItMeF9HjTw4l5RVML7np/Gyp8m3Jppcc4kjLF2whWpNf5KtbyfYvUep4PqUd6g4OtXYiI4loPH3zrJT91FRCXvyJd24X0Gnur/2qz1nblm7Mq0dPPLd2SgASjAQCr0omIiIiI4gVbu8SbHhbSJw/OxKFHzlE0bMTGiMJCeq2vIl3qP67Ga1FjnJ6Oz0tjaxcion7B4WvtonK+jvhY1k6bjXq9gFGbmoyKJrEafXBWMo4cmqV4PCXz1dOxBoMBWSlm1DW70ORsx8DMZMUxEhERERGRtliRHm+kHumdTsx1LRoL6b6K9Lx0S8SeQy1SaxenywOny61xNEREFBHuNqDNl/c6VaTHhLQBAAyA1w046zQLo6zRCQAYnK3fBeos9kknIiIiIoorXEiPN8014m2nhfRDdQ5c/e+fcKjOIXvYA7XNOP+573Cgtln2GD3GIS2kt0WyR7p4AitVe6sxH4DyOekpjtREEywJ4q8lq9KJiOKU9KG3MQFIyvLfrVZ+AtTJ2z3GZTIDaQXiN2zabTha2iAupB+ud2o+X8F+btkpYp/0Jme74viIiIiIiEh7XEiPNz20djEaDEhMMMJoMMgetsXlwabDTWhxyd8wq8c4LNJmo5GvSM/3VaSrMR+A8jnpKQ6DwYBcf590LqQTEcUlqZI7JQ/olAPUyk+AOnk7aFzpndq7aOSwbyE9LSlB8/kK9nPLTGZFOhERERFRPGGP9Hjjb+3Scal4UW4K/nXFDI0C6tBjHNHYbLRLj3RdzwfE9i4V1lbUc8NRIqL41MNGo4B+8lNX3eLKGARUbta0Il1aSL/plNEoyk3RLA4g+M+NFelERERERPGFFenxpoeKdK9XQJvbA69X0CioXuKI4kJ6vm8hXdfzASDX14Kmjq1diIjik3+j0cCFdL3kp666xaWDivTShhYAQGFmkubzFeznlu27wqyRV5gREREREcUFLqTHmx4W0ndW2jDuns+xszJyfchD0WMc/oX0yMXWtbWLrucDQG6qGCd7pBMRxakecjWgn/zUVbe4MnwL6TZtFtJdbi8qreJC+q/+b53m8xXs55blq0hvZEU6EREREVFc4EJ6PHG7gNYm8WtpIzAAQ7KT8cSlR2JIdrLsoSM2RlJke6R7vQLqHdJmo5bgccigdJxgx0sV6Q1s7UJEFJ+khfSUwIp0tfJTxMdKHyTe2rVp7VLR1AKvACQlGPH4JdrPV7Bjs1PEfN7EHulERERERHGBPdLjidN3qbjBBCRl+e/OSknE+dOGKBo6YmNIcUZoIb2ppR0e36XW0gK1Gq9FjXGCHS9tNsqKdCKiOOXsvp8JoF5+ivhYGlekS/3Ri3JTcMF07ecr2LHZ/op05nMiIiIionjAivR44qgRb1PzAGPHj7bJ6cJHm8sVVUTVN7fhvz8cQn2z/CrpHuOQWru47IDXI3vsYKS2LtkpZphNxuBxyKB0ToLFkeurnK9jT1Uiovjk3xg8sLWLWvkJUCdvB41L44p0aSF9YGaSLuYr2M8ty1+RztYuRERERETxgAvp8SRIz9Wyxhbc8uZmlDW2yB660tqK+z7agUprq+wxeozDktHxdZv6PU6ljUalti5B45BB6ZwEi6OjIp2tXYiI4pKj54p0tfIToE7eDhqXVJHeagVcDsXjh6u0UVxIz0g262K+gv3cpNYurEgnIiIiIooPbO0ST4KcmE8YmIGdD86FJcGkQVB9xJGQCJhTgHaneEKenK3qc/a0kK7r+UDnHuk88SYiiktBPvjWS37qqltclgwgMV28msxaDuSPjWo8h+rExftpQ7Ow9MIjNJ+vYD83qbWLtaUdXq8Ao9GgRXhERERERKQSVqTHE/+JeUHA3SajASmJCTBpfAIXNA5L5DYclVq75KV3LKTrfT6k1i71zS4IgqBFaEREFEnSB98puQF36yU/ddUtLoMByBoqfm09HPV4DtSKC+mjC9J1MV/Bfm5SaxevANha2d6FiIiIiCjWcSE9ngSpcDtc78TvXtuAw/VODYIKIQ6pT3okFtL9FemJfccRZcHikFq7uDxe2NvcWoRGRESR4nIC7b52KDrN1131GFembyG9KboL6W6PFyX14vwlJZp0MV/Bfm6JCUakWcSLP3mVGRERERFR7ONCejwJ0trFIwiwt7rhUVDdnGpJwAlj8pBqkd8NKGgc/oV09XukV/t6nQ7ISOo7jjApnZNgcSSZTUhNFC8Pr2/miTcRUVxx+nK1yQJY0gO+pVZ+AtTJ273GlVUk3jaVKh4/HKWNLWj3CEg2m5CbkqiL+ert58Z2bURERERE8YM90uNJc41426XCbUReKv53zSxFQ0d0jAhWpFfZxIX0wk4L6Wq8FjXG6e343DQLHA1ONDjaMCIvVfZzEBGRzvivHssTW6R0olZ+ispY/tYu0V1IP1DT7I9pVEGaLuart2NzUhNRUu9EHT8YJyIiIiKKeaxIjydBWruoweMVYG9th8cbgZ7dSZHrkV5tE1u7dK5IV0sk50SqYOOJNxFRnPFfPaZ+ru4sonkb0Ky1y4FacSF9VEGaquNGar5yU337njjaVB2XiIiIiIiijwvp8STIyfn2citG3b0c28vlL1TvqrRhyp+/xK5K+e1XgsaRlCXetjbJHrsngiCgytfaZWBmx0K6GvMBKJ+T3uLwn3hzIZ2IKL4EuXoMUC8/Aerk7V7jyhom3ka5tctB30ajI/NSdTNfvcUh7dHCfE5EREREFPu4kB4vBKGjIj0t8OR8YGYSHjxvUsBishaCxpGcLd62NKr6fLZWN1raPQCAwk7Pqfv5QMeGo/XNrGAjIoorvVw9ppf81FWPcUmtXeyVgDt6i8SdK9L1Ml+95vM05nMiIiIionjBHunxos0OeHwnaSmBm43mpllw+axhGgQVKGgc0kK6s0HV56v29UfPTDYjyWzqO44o6y0O/4k3NycjIoov0tVjad0X0vWSn7rqMa7UfCAhCXC3ArYyIGdkxOMQBAF7qu0AgNH5abqZr17zue8KszrmcyIiIiKimMeK9HghVbglpgGJKQHfsra046ud1bC2tGsQWAhxpOSItypXpEttXQq79EfX/XxAPCkHuJBORBR3HMFbu+glP3XVY1wGQ6c+6dFp71Le1AJ7qxtmkwGjC9J0M1+953Pxg/EGtnYhIiIiIop5XEiPF/7+6HndvlXa4MR1//0ZpQ3OKAcVYhzJ0kK6uhXpVb6K9AFdLrXW/Xygc09VXgpORBRXemntopf81FXQuKT2LtboLKTvqhSr0UflpyExwaib+eo9n3OzUSIiIiKieMHWLvGiuVq87eHEfFxhOjbccxoyks2yh4/oGBHqkd5RkW4JLY4wKR2nt+NzUrk5GRFRXAqyMTigXn6K2lhZReJt02HFzxEKaSPQiQMzeo9LBiVj9XZsLjcbJSIiIiKKG1xIjxfSQnragG7fMpuM/lYhckV0DKm1i1PlhXRbz61d1HgtaozT2/H+nqqsSCciii/NwVu7qJWfojZWlFu7SAvpE3wL6XqZr96OlT4Yb3C64PEKMBkNsmMkIiIiIiJtsbVLvJBOzHtYSC9tcOK2t7couvS5pN6Ba/+zHiX1DtljBI1DqkhvswIet+zxu6qWKtIzk0OLI0xK56S3OPLTxRPyBqcLbo9XUZxERKQTXi/gDF6RrlZ+AtTJ233GleXbYLPxkOLnCMXOLgvpepmv3uLISREX0gUBaHSyKp2IiIiIKJZxIT1eNFeJt+mF3b7l8nhRUu+AS8GCrL3Vja931cDeKn+hO2gcSVkdX7c2yR6/K39FemZglZga8wEon5Pe4shJTYTRIJ54N3DDUSKi+NDSCAi+//N72NNErfwEqJO3+4wrZ6R423BQ8XP0pbnNjZJ6caF6wsD03uOSQcl89RZHgsmI7BSx5QvbuxARERERxTa2dokX/or0gm7fGpWfhndvODbKAXUXNA5TAmDJFCvSnQ09Li7IIfVIH9CltYvu5wOAyWhAbpoFtfY21NjbUNDlNRARUQxy+HJ1cjZg6t5PWy/5qaugceX6FtKbqwCXA0hMjVgMW0ubAACDs5L9bVT0Ml99xZGbZkGjs9234Wh69AIjIiIiIiJVsSI9XvTSIz0mpKi74WiLy4N6XyX3kKwUVcaMtnzfQkEt+6QTEcUHR61420Nbl5iUnN3Rnq2hOKJPtfGw+P5gWlFWRJ8nEnK5gTgRERERUVzgQnq8sAdfSN9RYcXk+7/AjgprlIMKIw7pRLylQZXnKm8SL/9OtyQgIznwwouYmA909Emvs3MhnYgoLvgX0rtfPQboJz911WtcUWrvsulwEwBgelF2aHFFUV9x5Pk+GK/nB+NERERERDGNC+nxwOvtuFy8h4X0gvQk/P60MShIl98eZEBGEu45Z0K3Ninh6DWO5BzxVqWK9NLGFgDA4OxkGAyG0OMIg9I56SsOaSGdFelERHGiWVpI77mFmVr5CVAnb4cUVxQW0gVBwCZfa5fOFel6ma++4shN81Wkc88TIiIiIqKYxh7p8aClEfD6Nsfq4XLx/HQLrj1hpKKniPgYUkW6U52K9DLfQvqQ7O5tXdR4LWqM09fx/oV0VqQTEcWHPlq7qJWfojqWfyH9gCrP1ZND9U40OFxITDBi0qDM0OIKk5Kx+jo2N9V3hRlbuxARERERxTRWpMeD5irxNiUXSEjs9m17azvW7quDvbVd9lNYne34dGslrE75Y/QaR4q6Fenl/oX05PDiCIPSOekrDn+PdC6kExHFB2khvYeNwQH18hOgTt4OKa6cUeJtBHukf3+gDgAwdWgWEhM63rrqZb76iiPHV5FexyvMiIiIiIhiGhfS40EfG42W1Dvxm5d+REm9U/ZTlDY6cePrG1HaKH+MXuPwt3ZRqyJdfI6eFtLVmA9A+Zz0FQcr0omI4oyj99YuauUnQJ28HVJcUWjt8t1+cSH9+NGB86aX+eozn3MhnYiIiIgoLrC1SzzwbzTac4XbmAFpWPPHU1CQYYliUGHGEcXWLjExH2CPdCKiuNNHaxe95Keueo1LWki3lQPtLYC5+wfYSni9Ar4/UA8AOK7LQrpe5quvOAp8fddrbMznRERERESxjAvp8cBfkV7Y47ctCSYMzem+oBxtvcahcmuXsl5au8TEfIAV6UREcce/kN7zB996yU9d9Zm/LZlAmxVoPAQUTFD1uXdW2tDkbEeaJQFHDskM+J5e5quvOAo65XNBELptgk5ERERERLGBrV3iQXONeBukIr28qQX3frgd5U0tUQwqzDikinQVWru0tnv8l08P7aEiPSbmAx0L6fZWN1rbPdEMjYiIIqG599YueslPXfUal8EA5IwQv65Xf8PRb/aI73FmjchBginwbate5ivUfO7yeGFtUd7PnYiIiIiItMGK9HggbTYapEd6i8uNjYcb0eJyy36KJLMRkwZlIMks/7OXXuOQeqQ7lVekS9Xo6ZYEZCR3/yeuxnwAyuekrzjSLQmwJBjR5vai1t6mi6o7IiIAqK2txZtvvonq6mpMmTIFF110EUwmU6/HrF69Gt9//z0SEhJw/PHH45hjjolStDrhcgLtDvHrIK1d1MpPgDp5O+S4ckYClZuBBvUX0r/YIV51d/rE7u9x9DJffcVhSTAhK8WMJmc7auxtyErpvjE8ERERERHpHxfS44FUkZ7ec2uX0QXp+PTmExQ9RcTHkFq7OOsBQRAr3GQqbRA3+xqcndzj5dNqvBY1xunreIPBgLw0C8qbWlDbzIV0ItKHAwcO4LjjjsOkSZNw9NFHY/HixXj55ZexfPnyHhfTvV4vZs+ejdTUVBxzzDFwOp2YO3currrqKjz99NMavAKNSB96m1MAS3qPD1ErP0V9rLyx4m3dXlWeT1Le1IJt5VYYDcBpPSyk62W+Qjm2IN0iLqTb2jB2QM8/fyIiIiIi0jcupMeD5t43G40JUnWeuwVwOQBLmuyhiuvEir8RealqRKap/HTfQjr7pBORTtx5550YPXo0vvrqKxiNRixYsABjx47Fm2++icsvv7zb4w0GA5577jnMmDHDf9/cuXNx9tln4/rrr8fkyZOjGb52/BuDD1D0YbEu5Y0Rb+v2qTrslzvEDx9mDMtBXpq+NmANV0F6EvZWN6PG3qp1KEREREREJBN7pMeDzifnPdhVacPMv3yNXZU22U+xvdyKsX/6DNvLrbLH6DUOS5pYpQd0bMYmU18L6WrMB6B8TkKJgxuOEpGeuN1ufPrpp7j88sthNIpvIYYPH46TTjoJH374YY/HGAyGgEV0AJg2bRoAoKysLKLx6oq9UrwNcvUYoF5+AtTJ2yHHlT9OvK3dI15VppIvfAvpZ0yK3PsbiZL5CiUOacPRahvzORERERFRrOJCeqxzOYA230lf+sAeH5KbmogrZw9Dbqqynpwuj1fR8X3GIW2+5qhT9DyH6sWF9OFBFtLVmg9A2ZyEEgcX0olIT0pKStDa2opRo0YF3D9q1Cjs3Rt6W4///ve/SEpK6rbA3llbWxtsNlvAn5jW3PuH3oC6+QlQnrclfcaVOxqAAWhtUvxhuKTB4cJPxeIG5HMn9fzhg17mK6R8niHmc1akExERERHFLrZ2iXU2X4VbYhqQlNHjQwoykrDo1DFRDKpnfcaRmg80HVZ8En6wVlxIHxlkIT1m5gNAvu9S9tpmLqQTkfacTnEPioyMwHyTmZkJh8MR0hjffvst7r33Xjz66KPIy8sL+riHH34YDzzwgPxg9cbu65Ee5ENvQD/5qas+4zInA1lFQFOJ2CddhVZzX+2sglcAJg7MCLpHiF7mK5Q4CtKTAAA1/GCciIiIiChmsSI91tnKxduMQUEf4mhzY0NJIxxt7igFJTMOqU+6o0b2c7S2e1BhbQEQvCI9ZuYDrEgnIn1JSxP3r2hqagq4v6mpCenpfW+g+OOPP+Lcc8/FbbfdhptvvrnXxy5evBhWq9X/p7S0VHbcuiBVpKcHr0jXS37qKqS4Ord3UcHybeIHD+ccEfyDB73MVyhxSK1datnahYiIiIgoZnEhPdbZKsTbXhbSi+scuPD57/29w7XSZxz+1i7yK9IPNzghCEB6UkLQS6xjZj7AhXQi0peioiKkpqZiz57AxdLdu3djwoQJvR77008/Ye7cuVi4cCH++te/9vlcFosFGRkZAX9imtQjPS14j3S95KeuQoorb6x4q8KGo1ZnO77bL7Z5O2uy/ucrlDikhXS2diEiIiIiil1cSI91/or0wUEfMrogDV/eeiJGF6TJfpqojOGvSJffI11q6zIiLxUGg0FeHCFSOk4ox3MhnYj0xGQy4YILLsB//vMftLWJ/y9t374da9euxcUXX+x/3DvvvIPHHnvM//f169fjjDPOwMKFC7F06dKox60L9r4r0tXKT5qM5V9IV16R/tWuari9AsYXpmNkfvDn1Mt8hXJsQQZbuxARERERxTrNe6S73W6sWbMG1dXVmDJlCiZNmtTnMVVVVVi/fj0SEhJw1FFHoaBAeS/OmCVVuPVSkZ5kNmHsgL4vue9NVMbwL6TLr0iXNhodEaStS0hxhEjpOKEc37lHuiAIQT8cICKKlqVLl+KEE07ArFmzMH36dCxbtgyXXXYZzj//fP9jvvjiC6xbtw633XYbHA4H5s6dC4vFArfbjdtvv93/uEsuuQRHH320Fi8j+pr77pGuVn7SZCyptYsKFemfbRPf25w1OfhchRxXiJSMFcqxUkW60+VBc5sbaRbN34ITEREREVGYNK1Ib2howKxZszB//ny89tprOOaYY/D73/8+6OMFQcDVV1+No48+Gi+88AIee+wxjBgxAs8//3z0gtabEFq7VFpb8Nflu1Dp6x0uR1mjE3e+uxVljU7ZY/QZhwoL6QdqmgEAw3ODL6SrMR+A8jkJJQ6pIt3l9sLWqq+euUTUPw0cOBBbt27F3XffjWnTpuHtt9/Gq6++GvCYSy65xL9gbjQacffdd+OOO+5AYWFhwJ/k5GQtXkL0uduAlkbx67TgFelq5SdAnbwdVlxSRbq1FGhrlv1cttZ2rNknXpl29pTgbV1CjitESuYrlDhSLQlITTQBAGpsbO9CRERERBSLNC2Hufvuu9HS0oJt27YhLS0NP/74I4455hicddZZmDt3brfHC4KAk046CS+88AISEsTQX3rpJSxYsABnnnkmRowYEe2XoD2ptUt68IV0e6sbX++sxkVHDcHATHlP0+Rsx1s/l+KKY4ZhSLa8MfqMQ4XWLnt9C+m9VYapMR+A8jkJJY4kswnpSQmwt7pRa29FZrJZfsBERCpJSUnBJZdcEvT7Z5xxhv/r5OTkgCr0fknaaNRkAZKDJwy18hOgTt4OK66UHCAlD3DWAfX7gUFTZT3Xmr11cHm8GJmfijF9VHnrZb5CjaMgIwnFdQ7U2Nt6bVlDRERERET6pFlFuiAIePPNNzF//nykpYknE7NmzcKsWbPw+uuv93iM0WjEVVdd5V9EB4Bzzz0XHo8Hu3btikrcuhNCRfrYAelYefvJql3+LFefcSisSPd6BeyvtgMAxhUGP0GNmfnwkS4Hr7axryoRUUyy+9q6pA0AemnRpZf81FXIcfnbu+yV/Vyr99YAAOaM67ttn17mK9x8zj7pRERERESxSbOF9NLSUlit1m490SdPnozt27eHPM7nn38Oo9GIyZMnB31MW1sbbDZbwJ+44HZ1LDr3stlozJAW0p31gNcT9uHlTS1wuDxINBkxrJfWLrFmYKbY+qDKykvBiYhikrSQnt57q5KYlzdGvJW5kC4IAlbvFd/XnDQuX62odMO/4ShbuxARERERxSTNFtKtVisAICsrK+D+nJwc//f6sn//ftx66624+eabUVRUFPRxDz/8MDIzM/1/hg4dKjtuXZE2GjVZxEuqg9hbbcdJj67CXl+1tlb6jCMlV7wVvB29ZMMcHwBG5qfCbAr+Tztm5sNngO/Eu4on3kREsUlq7ZIevD86oJ/81FXIceX5KtJr98h6nl2VdlTb2pBsNmHm8ODva8KOK8JCjUOqSK9lRToRERERUUzSbCFd2mDM4XAE3G+320PafOzw4cM47bTTcPLJJ+PRRx/t9bGLFy+G1Wr1/yktLZUfuJ50buvSy6XiGUlmnD1lIDKS5PfXzkuz4IaTRyEvzSJ7jD7jMCUAyb4T5+aasMffW913f/SQ4giR0jkJNY7CTKm1CxfSiYhikr+1S+8V6WrlJ0CdvB12XPm+DUdlLqRL1ejHjMpFktmkXlwhUDJfIedz3wfjlbzCjIiIiIgoJmm22WhRURHMZjMOHToUcP+hQ4cwatSoXo8tLS3FySefjGnTpuHNN98M6JneE4vFAotF+Ymk7kgbjfbR1qUwMwl3njle0VNFbYy0AUBLA9BcBQyYGNb4e/390XtfSFfjtagxTqjHSyfebO1CRBSj/Pk6+H4mgHr5SbOxCnx5u34/4G4DEsJ777V2v7iQfuKYPHXjivBYIefzTOZzIiIiIqJYpllFemJiIk4//XS89dZb/vuqq6uxcuVKzJs3z3/f999/j48++sj/97KyMpx88sk48sgj8fbbb8NsVl6FFLOsvsr6zN4X0lvbPdhVaUNre/h9xyXNbW78cKAezW1u2WOEFEfGQPFWqt4Lw54qcSG9r4p0NeYDUD4nocYhtXZhRToRUYyylom3mUN6fZha+QlQJ2+HHVf6QMCSCQgecTE9DO0eLzaWNAEAZo/KVTeuECiZr1DjGJTlq0i3tciKkYiIiIiItKXZQjoALF26FD/99BMuueQSPP744zjttNMwdepU/Pa3v/U/5uWXX8af/vQnAEBrayvmzJmD5uZmnH322Xjttdfwyiuv4JVXXsG+ffu0ehnaaSwRb7OG9fqw/TXNOOupNdhf0yz7qQ7VOXDZC+twqM7R94OVxJEuLaRXhjW2xytgf63U2iVNeRwhUDonocYhbTbKS8GJiGJUiFeQqZWfAHXydthxGQxAwQTx65pdYT3HjgobWto9yEw2Y2xB7x+Ihx1XCJTMV6hxFHbaPNzrFWTFSURERERE2tGstQsATJ48GVu2bMHLL7+MPXv2YMGCBbj22msDqsyPO+44FBaKPUXdbjeOPfZYAMB3330XMNbIkSMxZsyY6AWvB02+hfTs3hfSR+an4qMbj8PI/NQoBKUwjnRf/9gwK9JL6h1wub1IMhsxNDtFeRxREGocA3w90uua2+D2eJHQy0aqRESkM4LQsadJH1eQ6SU/dRVWXAXjgdJ1YS+k/3yoAQAwY1g2jMbg+77IjiuCQo2jIN0CgwFo9wiod7iQnx6HbQeJiIiIiOKYpgvpADB8+HA8+OCDQb9/9dVX+79OS0vDK6+8EoWoYkSIFekpiQk4cmhW5OPpQ0hxSBXp0qJDiHZW2gAA4woz+jwBj6n5AJCXakGC0QC3V0Btc5u/Qp2IiGKAsx5wtwIwAOm990jXS37qKqy4pD7pYS6k/1QsLqTPHJETmbgiKNQ4zCYjCtItqLa1ocrayoV0IiIiIqIYw9LWWOX1dvRIzyrq9aE1tlY88dVe1GjcYzukONLl9UjfXi4upE8elKFOHFEQahxGowEFvpNtblBGRBRjpP7oaQVAQmKvD9VLfuoqrLjyfZtu1oa+kC4IAn4uaQQAzBwe+kK6XuYrnDik9i4VVvZJJyIiIiKKNVxIj1XNVYDHBRhMffZcbXC68Nb6UjQ4XbKfLsFkQGFGEhJMoV1uLTsOmQvpOyqsAIDJgzPViSMESucknDgGZHLDUSKimBRif3RAvfwEqJO3ZcUl9UhvKAZczpDGP1jnQIPDBUuCEZMH9/2BuKy4+qBkvsKJY6BvA3F+ME5EREREFHs0b+1CMkltXTKHAKbef4zjCzOw7u5TFT1d1MaQeqQ3V4lV98a+P+sRBAHby30L6YP6XkhX47WoMU44xw/MTMIm8MSbiCjmWH0L6X30RwfUy0+ajpWaD6Tkii1t6vYCg6b2eYiUwycOyoAlwRSZuCI4Vlj5PEtcSOcG4kREREREsYcV6bEqxI1GY05aAQAD4HUDzrqQDqmwtqLR2Y4EowFjC9MiG59GBkgVbLY2jSMhIqKw2HytXTKGaBtHtBgMQL6vKj3EPuk7K8T2bJNCaM8W6wZmSgvpbO1CRERERBRruJAeq5oOi7d99EcHgH3Vdpz55LfYV22X/XS7q2yY/dcV2F1lkz1GSHGYzGI1GwDYK0MaV6pkGzMgPaRKNjXmA1A+J+HEUZjB1i5ERDEpjIp0tfIToE7elh2X1N4lxD7pO/wL6X1fVaYorl4oma+w8rmvRzor0omIiIiIYg8X0mOV1Nola3ifD021JGD2yFykWuR38nF7BFTZWuH2CLLHCDmOjPD6pO/wt3UJrZJNjfkAlM9JOHEUsoKNiCg2hdEjXa38BKiTt2XHVeDbcLRmd58PFQQB2337nIRbka6X+QonDlakExERERHFLvZIj1UNB8TbnBF9PnRQVjL+/ItJEQ6obyHHkT4QqNwSckW6VMkWykajYcURYeHEMcBfkc7WLkREMcVfkd53axe95Keuwo6rYKJ4G0JrlwprK5qc7TAZDRg7ID2ycUVIOHFIC+nV1jZ4vQKMRuWbwRIRERERUXSwIj1W1e0Tb3NH9/nQ1nYPDtU50NruiXBQKsWR7qtIt1WENK5UyTZ5cGiVbDE3H+g48a6ytkIQlFcXEhFRFHjaO3qkZ/W9p4le8lNXYceV76tItx4G2npvdyJdVTamIA1J5tA3GpUVV4SEE0dBehIMBsDl8aLB6YpCdEREREREpBYupMciZ0PHRpwhLKTvr2nGyX//BvtrmiMcmEpxSFV7TaV9jlljb0W1rQ0GAzC+MLSF9JibD3RUpLe0e2BrcUc6NCIiUoO1FBC8QEKybzPt3uklP3UVdlwpOUBaofh17Z5eHyq3P7qsuCIknDgSE4zIS7MAACqb2CediIiIiCiWcCE9FtXvF2/TBwGWtD4fPjwvFW9cNxvD81JlP2VUx5A2UJU2VO2FdAI+Mi815B6parwWNcYJ5/gkswl5aYkAgLImp6znIyKiKGs8JN5mDwcMfbfwUCs/6WIsf5/03tu7dCykh9cfXXZcERgr3GMHsU86EREREVFM4kJ6LJLauuSNCenhaZYEHDMqF2kKNuOK6hjSQrq174X0LaVNAIApIfZHDyuOCI8T7vGDs5IBAOWNPPEmIooJnRfSQ6BWftLFWPkTxNs+FtJ3ytxoVHZcERgr3GOlDcQrmpjPiYiIiIhiCRfSY1F9eAvptfY2/Gv1AdTa5W9UWWVtxdLPd6PKKv8y5JDjyBwq3toqAG/v/UY3+xbSpw7NUj+OPiidk3DjGJKdAgAo40I6EVFsCHMhXa38BKiTtxXFVeBbSK8NvpDe4HChwhffRBkL6XqZL7n5vJwL6UREREREMYUL6bHIv9Fo6Avpz32j7ESzrrkNz39zAHXN8scIOY70QsCYAHjdgL0y6MMEQfBXpE8tylY/jj4onZNw4xicLVakcyGdiChGyFhIVyM/AerkbUVxFfRdkb7DV40+LDcF6Unm6MQVhJL5CjeOob58XtrAfE5EREREFEuUXwtL0Ve3V7zN63ujUUCs8tpy/xkRDCg0IcdhNIkbjjYeEvukS5uPdlFS70Sjsx2JJiMmDExXP44ICzeOIb4T73L2SCciig1hLqTrJT91JSuufF+PdHsl0NIIJHf/wFtJf3TZcUVA+Pncd4UZ8zkRERERUUxhRXqsaW/p2Gx0wGRtY4kkqb1LU2nQh0htXSYOyoAlwRSFoLQ1hBXpRESxJcyF9LiSlAFk+D4Ir9nd40M6FtJD3+ckHgzJYT4nIiIiIopFXEiPNTU7AcELpOQBaQNCOuRAbTPOf+47HKhtjnBwKsYRwoajcvqjhx1HBIUbx+As9kgnIooZLY1Aq9i6xJ/T+qCX/NSV7Lj66JO+Q8FGo4riUlm4cUgV6U3Odthb2yMZGhERERERqYgL6bGmart4WzgFMBhCOsSSYMTYgnRYEuT/uLNSzLh0xlBkpYTfw1RWHP6K9OAL6Zt8C+nTirIiF0cvlM5JuHFIPdKtLTzxJiLSvYZi8TZtAJCYEtIhauUnQJ28rTiuAl97lx76pDva3CiucwCQX5Gul/kKN440SwKyfc/DD8eJiIiIiGIHe6THmqpt4m1h6G1dhmSnYOlFRyh62qiPIVXvBWnt0ub2YJfvkvBwK9LVeC1qjBPu8WmWBGSlmNHkbEd5UwvGFypfHCEiogipPyDe5oa2nwmgXn7SzVgFk8Tb6p3dvrW7ygZBAArSLchPt0Q3LpXHknPskOwUNDqtKGtswYSB8iryiYiIiIgouliRHmuqpYr00E/Y2j1e1Nha0e7xyn7a1nYP9lbb0drukT1GWHFkDxNvG4t7/PauSjtcHi9yUhNRlBNapZ+sOHqhdE7kxOHvk97ACjYiIl2r2yPe5o0J+RC18hOgTt5WHJf0oX/VNkAQAr61vVzZRqOK4uqBkvmSE8dQf590bjhKRERERBQruJAeS7zejtYuYWw0uqfKjqP/ugJ7quyyn3p/TTPOeOJb7K+R34c0rDhyRom3TYcBt6vbtzcfbgQAHDkkE4YQW9zIiqMXSudEThxD/H3SeeJNRKRrdXvF27yxIR+iVn4C1MnbiuPKGwcYzUCbFbAGXmHW0R9d/kajepkvWfnc1ye9lB+MExERERHFDC6kx5K6PYDLDphTwjoxL8pNwctXzUBRbniV22oLK470QsCcKm6s2nio27fXl4gL6dOLsiMbRwTJiUPqk17exBNvIiJdq9sn3sZgvu5KdlwJiUC+r0+61JrOZ0eF8op0vcyXnDj8V5jxg3EiIiIiopjBhfRYUvqjeDv4KMAUenv7jCQz5owfgIwkbXtqhxWHwQDkjhS/bjgQ8C1BELC+uAEAMHNETmTjiCA5cUgn3qxgIyLSMa8HqN8vfh1Gaxe95KeuFMXlb++y3X+Xy+3F3mqxenvyYPkV6XqZLzlxDM2WrjBjPiciIiIiihVcSI8lpT+Jt0NnhXVYfXMb/vvDIdQ3t0UgqAjGIbV3qQ9cSD/c4ESNvQ1mkyHsjUZlxREhcuIYnpsKADhU74hUWEREpFRTCeBxAQlJQObQkA/TS37qSlFcUiu66o6K9H01drR7BGQkJfg/II56XCqSE4f/g3FWpBMRERERxQwupMcS/0L60WEdVmltxZJlu1BpbVX09IkmZf9cwo4j17eQ3qUi/SdfNfoRQ7KQZDZFPo5eKJkTOXEMz+tYSPd6hT4eTUREmpDauuSOBoyh5yk18xOgPG9LFMVVOEW87dTaRWrrMnFQRtj7nKgWVw/kzpecOKQe6fZWNxod3feCISIiIiIi/Qm9Pwhpy1EP1PtOzIfMDOvQyYMzsfcvZyl6ek3GCFKRvv6Qr63L8PDbusiKI0LjyDl+SHYyEowGtLZ7UWVrxaAs+ZV8REQUIbW7xdsw2roA6uUnXY0lLaQ3HgJabUBSBnb6+6PLb+uiOC4Vx5JzbHKiCYMyk1BhbcXBOgeOSk2U9dxERERERBQ9rEiPFYe+FW/zJwAp8haQY46/Iv1gwN3rD4kbjR49IvyNRmOd2WTE0Byxiu1QHdu7EBHpktQPvGCStnHoQUoOkD5I/LpmJwBge7kVgLKNRuPBiHzxKrNi5nMiIiIiopjAhfRYcWCleDtqTtiHFtc5cPmL6xSdqO2vseOcp9dgf41d9hhhxyFVpFvLgHZxM64aeyuK6xwwGICjhsn7QEGN+QCUz4ncOEb42rsc5Ik3EZE+VfsW0qVq7BCplZ8AdfK2anF1au/i8QrYWSlWpE9RsNGoKnF1omS+lObz4rrmsJ+TiIiIiIiijwvpsUAQgAOrxK9lLKQnGA3ISbUgwSi/D2lruxc7KmxobffKHiPsOFLzgORsAIK/3+y6g2Jbl/GFGchMNkcnjiCUzoncOPwbjnIhnYhIf9xtQN1e8evCyWEdqlZ+AtTJ26rFJc1D1TYU1zXD6fIg2WzCyPw0bePqRMl8yY1jRJ74+lmRTkREREQUG9gjPRbU7wespYApERh2bNiHD81JwT8umxaBwCIch8EAFEwESr4TLwcfeATW7K0FAJwwJi96cUSI3Dh4KTgRkY7V7ga8biApC8gYHNaheslPXSmOS6pIr96O7eUdG42aFC6A62W+ZOfzPLFV28Fa5nMiIiIioljAivRYsO8r8bboGCAxJezDPV4B9tZ2eLyCyoFFIY6CCeJtzU4IgoA1++oAKFtIj+n5ADDCV5FeXM8TbyIi3anq1NbFEN5CsV7yU1eK4xogLaTvwI7SegDK27qoEpdKZOdzX0X6oXoHvDr7mRMRERERUXdcSI8Fuz4Rb8eeKe/wShum/PlL7PL1JNWKrDgKJoq3Nbuwv6YZVbZWWBKMmDlc/oarMT0f6KhIP1zvRLtH+SX7RESkIqk/+oDw2roA+slPXSmOK2ckYMkE3K2wHd4CQJ2NRvUyX3LjGJKdjASjAa3tXlTZWiMUHRERERERqYUL6XpnrwYO/yB+PfEXsoYYmp2CZ389HUOzw69m13yMTgvp3/qq0Y8ekYMksym6cURgHLnHD8xIQkqiCW6vwD7pRER6U7FJvB14RNiHqpWfdDeW0QgMng4ASKkVF9KnDFFeka6X1yj3WLPJiKIc8Ri2ayMiIiIi0j8upOvd7mUABGDwUUDmEFlDZKaYcc4RA5GZIm9zTk3HKBgv3lpL8ePuQwCUtXWRHUcExpF7vNFowNgB6QCA3VV2Wc9NREQR4GnvWEgfMjPsw9XKT7oca/BRAIDxnn2wJBgxWuFGo6rFpcJYSo4dkSdeZXawtjnsY4mIiIiIKLq4kK53Oz8SbyeeJ3uIRocL7/xcikaHS/YYtfY2vLjmIGrtbdGNIzkbSB8kHn9IrGKbM75Adgyy4+iB0jlREsf4QnEhfQ8X0omI9KN6O+BuFTcazRkV9uFq5SdAnbytaly+hfQjjQcwYWAGEkzK34LqZb6UxDGqQPxAYV8NF9KJiIiIiPSOC+l65qgDDq0Vv54gr60LAJQ3teCOd7eivKlF9hjVtlYs+XQXqhX08JQdxwCxvcs44RBG5adidEG67BgUxdGF0jlREse4QlakExHpTtnP4u3go8R2JmFSKz8B6uRtVePyLaSPNZRhxsAExTGpFpePkvlSEof0wfjuSuZzIiIiIiK9U+dMhiJj27uA4AEGTQNyRsgeZtKgDOz/y1kwGQ0qBhfFOAZNA/Z/jSMMB5ExqVC7OFSmJA5pIX1Ptb42pCMi6tekhXQZbV0A/eSnrlSJK30Aaoz5KPDW4uT0Cv3EpXEc4wvFTVd3V9kgCAIMBn397ImIiIiIqAMr0vVsy+vi7ZG/VjSMwWBAgsmo+cmZ3DhcA6YCAI4wHsSZk5UvpMf6fAAdJ96lDS1obnOrHRoREclR+qN4O2SGrMP1kp+6UiOu1nYPNrhHAgAmC/t0E5fWcYwqSIXJaICt1Y1Kq/KrB4iIiIiIKHK4kK5X1TuByi2A0QxMuUjRUCX1Dlz7n/UoqXeoFFx041hpFzdZHWMsx5R85RdRxPp8AEBOaiLy0y0A2CediEgXmkqBxmLAYASGHi1rCL3kp67UiGtrmRU/e0YDADLrftZNXFrHYUkwYVS+uOHo7ipeZUZEREREpGdcSNcrqRp97FwgJUfbWACkJyXgtAkFSE+Kfjeg13e6UCnkwAQvDFVbo/78wWg5JwAwZXAmAGBLaZMmz09ERJ0cWiPeDpoOJGVqGwu0z1FdbShpxI/eCQAAQ8kPgNejcUSBtJwv6SqzXeyTTkRERESkawZBEAStg4g2m82GzMxMWK1WZGRkaB1Odx438MREoLkauPQ1YMI8rSPSTJW1Fcc+sgLPJzyOuaafgTP+Ahx7k9Zh6cLTK/bh8a/24hdHDsLTl03TOpxudP97RkQxRff/p7y/ANj6JnD8H4DT7tc6Gt259j8/Y+WuSuxOXYhETzNw/TfiHiiE577Zj799vgfzjhiIZ349XdNYdP97RkRERESkIVak69HBVeIienIOMOYMxcMJggC3xwsln5m0e7yob25Du8cb1Tje/rkUXgGoz5wk3lG2XvbzK4mjJ0rnRGkcU4dmAQA2syKdiEhbggAUfyt+PeJEBcOok58AdfK2WnF5vAJ+Kq6HF0a0DJol3nloreZxdaZkvpTGMXmQeAXD1jKrrOOJiIiIiCg6uJCuR5v+J95OuRhISFQ83I4KG0b/6TPsqJDfe3NPlR1HLflaUT/ucONwub3437oSAMDQqaeKd5Z8Jy5YKKDGfADK50RpHEf6FtIPNzhR39wmawwiIlJBzS7AXgGYLEDRbNnDqJWfAHXytlpxbS+3wtbqRnpSAtLHnSzeqcJCul7mS818Xsd8TkRERESkW1xI1xt7FbD7U/Hro36rypCDs5Lx6EVHYHBWsirjRSuOT7dVoNbehoJ0C2YdfwaQkAw4aoHaPVGNI1KUxpGZbPZvULalrEnFyIiIKCxS3h55MmCWn1v0kp+6UhrX2v11AIBjRubCOOJ48c5D3wFul6ZxqUWNfD66IA0AsPlwk4qRERERERGRmriQrjcb/wd43cDQ2cCASaoMmZ2aiItnDEV2qvLq9mjFIQgCXlpbDAC48phhSExKBoqky8HXRC2OSFIjjmlF2QCAn4ob1QqLiIjCtce3kD7+HEXD6CU/daU0ru98C+nHj8kDBk4FUvMBlx04/L2mcalFjTimF2UBADaVMp8TEREREekVF9L1xOsBNrwifj1jvmrDWp3t+HRrJazOdtXGjHQcK3bVYHu5DSmJJvx61jDxzuG+KjapD20U4ogkNeI4bnQugI5FCiIiijJbBVCxCYABGHeWoqH0kp+6UhKXo82Nnw+Ji8PHjsoDjEZgzFzxm3u/0CwuNakRh/TB+IYSLqQTEREREekVF9L1ZN9XgK0MSM4GJp6n2rCljU7c+PpGlDY6VRszknEIgoDHv9oLAPjtscORI1V4jThZvD34jaLLwWNtPnpz3Og8AMD2CisaHcoukSciIhm2vyfeDj0aSCtQNJRe8lNXSuJatacGLo8Xw3NT/O3IMO5M8XbPZ4r2PdHLfKkRx8zhOQCAjSVNcLrcaoVGREREREQqMgiCwp0bY5DNZkNmZiasVisyMjK0DqfD/84HDqwEjrkJmPsX1Yb1eAU4XW6kJCbAZDTofozPtlXihtc2Is2SgDV/PKXjUmmvF3hsHOCoAX7zPjD61IjGEelx1Ipj7hPfYk+1Hc/8ehrmHTFI9jhq0+3vGRHFJF3+nyIIwPPHAjU7gXlPKL6aTK28oKexbnp9I5ZtrcSCk0Zi8VkTxDvbmoG/jQA8LmDhWqBwStTjUnMsNeIQBAHHL12F8qYW/PuqmThlvLIPZeTS5e8ZEREREZFOsCJdLyq3iovoBhNw9HWqDm0yGpCeZFZ0khmtMVrbPfjrZ7sAAPOPGx7Yb9RoBMafLX4tbewWoTiiMY5acZw4VqxK/3pntaJxiIgoTJWbxUV0kwWYdIHi4dTKC3oZq7Xdg1W7awAAZ00e2PENSxow1leVvuXNqMel9lhqxGEwGPz5fPXeWtnjEBERERFR5HAhXS++f1q8nXQ+kD1c1aFLG5xY9MYmlDbIv+S4uM6BK176EcV1jojG8dLaYpQ2tKAwIwkLThrV/QHj54m3uz8Ve8pHKI5QKJ0TteI407c48dXOarS2y5sTIiKSYf1L4u34c4DkLMXDqZUXAHXyttK4vthRBYfLg8FZyThicGbgN4+8TLzd+jbgkdfKRC/zpVYcJ47JByAupPfDC0aJiIiIiHSPC+l60HAQ2P6++PVxN6s+vNsroMHRBrdX/kmZo82NNfvq4GiT37ezrzjKGp14ZuV+AMDis8cj1ZLQ/UEjThR7yDdXAftXRCSOUCmdE7XimF6UhcFZyXC4PPhmT42isYiIKET2KmDrW+LXsxaqMqRaeQFQJ28rjevNn0oBABfPGAJj12rt0acBKbliu7Z9X0Y1rp4omS+14jh+TB4SE4wornNgR4VN0VhERERERKQ+LqTrwcolgOARTyoHHqn68CPyUvHatbMxIi9V9bHVisPrFfDHd7eipd2Do4fn4BdHBun1nWDpqGLb+B/V44gmteIwGAyYd4RYlf72z2VqhEZERH354Rmxx/fQWUDRLFWG1Et+6kpOXHuq7PjhYD0MBuCSGUO7PyAhEZj2G/Fr6aq8KMQVCWrFkZ5kxukTBgAAPtxUrkZoRERERESkIi6ka618I7D9PQAG4LQ/ax2NZv77wyF8f6AeyWYT/nbRETAYeukzOv234u2e5UD9gegEqHO/OroIBgOwcneNKpfxExFRLxoPAT/+S/z6hNs1DUWvnlklXmF21uRCDMpK7vlBs24ATInA4R+Aku+jGJ1+/XLaYADAB5vK2a6NiIiIiEhnuJCuJU878LGvlcsRlwKFUyLyNNvLrRj7p8+wvdwakfGVxrGhpAF/WS5uMHr32eMxvK+KroLx4iZlghdY/TfV4og2NeMYkZeKU8cXAIC/PQ4REUWAIADL/yhWo488BRhzumpD6yU/dRVuXFvLmrBsawUA4KZTxgR/YMZAYOqvxa8/Xxz23id6mS814zh5XD4GZyWj3uHCOz+XqhAdERERERGpRfOFdI/HgzVr1uDdd9/Fnj17InaMLq36K1C9DUjOAc54KGJPMzAzCffMm4CBmUmKxnjwvEmKx+gax6E6Bxb8byPaPQLOnFSIy2cNC22wk+8Sb7e+BZT8oDgOOZTOiVpxSG6aIy5WvLexDJtLm1QZk4ioJ/06d//0ArDvC8BkAc58GOjtCqowqZkX1MjbcuJyub340wfbIQjAeVMHYeKgjN4POOVPgCUTqNwMrHs+YnGFMpbc+VIzDrPJiAUnjQQAPLvqAOyt7YrHJCIiIiIidRgEQVC+Q5NMjY2NmDt3LqqqqjBp0iSsXbsWCxYswN///ndVj+nKZrMhMzMTVqsVGRl9nOBFyuY3gA99m5Nd9DIw+UJt4tDQ/ho7fvPiT6iytWJ8YTreu+HYnjcYDebDG4HNrwLZw4FrVwCpeRGLNVbc+tZmfLCpHMNzU/DxouORkWTWLBZd/J4Rker6de7e+THwzm/FK6LOfASYfYM2ceiUIAi4+4PteOOnw8hISsCK205Gfrql7wPXvwR8+gfAYAJ+8y4wak7kg9Wx1nYP5j75LUrqnbh0xlA8cuGU3lveqUgXv2dERERERDqlaUX6n/70J9hsNuzYsQOfffYZvvjiCzz++OP46quvVD1GV7weYM1jwIe+k+9jb474IrqttR0rd1fDpqCqqcnpwgebytDkdCmOo8npwlvrD+O8Z75Dla0VYwrS8L9rZoW3iA4Ac/8CZBaJvWpfvQCwhrYxlxrzASifE7Xi6Oy+eRMxKDMJh+qduOKln1Bja1VtbCIioJ/mbrcL+PbRjkX0ab8BZi1U/WnUzAtq5O1w4nK63Ljj3a1446fDMBiAp341LbRFdACYMR+Ycom48frrlwKbXhNb6KgQV6iUzJfa+TzJbMLD54vt/t76uRRPfL0PXq9mdS9EREREROSj2UK6IAh44403cM011yA9PR0AcOyxx+Loo4/Ga6+9ptoxuiAIQNNh4Od/A/88HljxIAABmHkdcNoDEX/6w/VOzH/lZxyud8oeo6yxBbe+tQVljS2yjvd4BazeW4v5r/yMXzyzFne+tw0OlwezR+bgjetnh36y3VlyFvCb94CUXKByC/D8McCqh4Ga3YDXG/QwNeYDUD4nasXRWXZqIl747QxkpZixpbQJpz/xLZ78ei/2Vduh4cUnRBQn+lXu9rjF3LL2CeDZmcDKJeIi+vQrgXlPqdrSRaJmXlCao0KJy+sVsLvKhue+2Y9TH1uNdzeUwWgAHrlgCk7x7dsREoMB+MU/gPHzxN7zH/0OeOl0YNOrQFNp0EV1vcxXJPL5saPzcM85EwAAT6/Yh1+9sA5f7ayG0+VW7TmIiIiIiCg8YZYAq6e0tBRNTU2YPHlywP1TpkzBxo0bVTsGANra2tDW1ub/u81m6zvALW8Buz4WT5oFr3gSJ30Nocv9Qg/3+b52NYuV0u2OjrEtGcDcv4oVbVG4VHdcYTp+uvtUZKcmhnzMs6v2Y3Npk+/lCf4qq3s/2o40S4J4PwR4vb5b3+MEAfAKAgQAXkE8ya5vbkONvQ1uXzXV4YYWpFsSsOjU0Zh/3AgkmBR8npM/Frj2a+Cdq8X+qqsfEf8kJAFZRYAlHUhMA0xmAAbAYMREAHsmCjCvfhUwhvDcM64GRp8mP8Yg5PxcQjFpUCbeXXgMbnlzM3ZU2PDk1/vw5Nf7kGQ2YlBmMjKSzUgyG2FJMMFs6vzvr+Przv8sAx7h+8v0omwsOGmUqnETkf7FXu4Olr+FwPs7f8/TDjhqgeZq3/0+KXnAGUuAI38VsdwdqbzQk8P1Tvxl+U5//vb68nfH38Uc7xUEeLwCjhiciXs/3AYBBngFAY1OF6qtbXB5OuZocFYyHrlwCk4Ykx9+QOYk4OL/AN8/BXyzFChbL/4BgIRkIHNwR05PsAAwiPl8ggDzN/8BjCaIeb6Pn805jwHpheHH14tI/dyuPWEkUhIT8OdPduCn4gb8VNwAQOzJnpuWiBRzApISTTAZAIPB4M/X4hQYYDCIOZy5m4iIiIhIHZotpFutVgBAdnZ2wP05OTn+76lxDAA8/PDDeOCBMCu/a3cDu5eFd0xvDCZg8FHA+HOAo34LJGf3fYxKzCYjCjLC2wBr0+FGfL2rpof7m2THkZJowrSiLMydVIjzjhyMzBSV+nfnjASuWwns+ADY/DpwaC3gbgXq9vb4cCOAsOrfI9SrVc7PJVSjC9Lx4Y3HYfm2SrzzcxnWH2pAa7sXB+scfR8cAmOUerUSkb70u9xtyQCGzAQm/RKYdAFgSVNv7B5EMi90ZW9rxxc7qhWPk2w2YcbwbMw7YiDOmzoYSWaT/MFMCcAJtwFTLwc2vALs/UL8kNzdAtTv7/bwsPM5AJz+oPz4gojkz+3Xs4pw0rh8vLSmGF/sqEJ5Uwsqra2otIbfuo25m4iIiIhIGc0W0pOTkwEAzc3NAffb7Xb/99Q4BgAWL16MP/zhD/6/22w2DB06tPcAx58DZA0FDEZIlcwdfzr9Hej5fuk4cxKQORTIGASYg8cYSWWNTvxjxX4sOnU0hmSnhHTMb2YPwynjC2D0VThVNLXg6ZX78fvTxqAoJwUGQ8cJmdFgEB9nAIy+KiijrzrKaAByUhNRmJmEdreAZ1ftx5zxBeotokuMJmDKReIfjxtoKgFs5UBbs3hVgKcdYrWhgAZHG1btqsKc8fnIDiWOomPVjdVHzs8lHGaTEedNHYzzpg6Gy+1FpbUF5U0taG51o83tRWu7B17f5fKdr5rvfAF94P0dfxkagXiJSP9iKnf3mr8NQb5vFPNJah6QPhBILQjtyiWVRDovdDYoMxl/OX+yL4dLOVv8uiOni/c1Otrw2fYqzDtiIPLTk2AwAFkpZhRmJmNAukXZlWU9SS8ETr5L/ON2AdZSwFYBtDvFnO5u8+fzFbuqcer4fOSkmDuuLOhNBDYmj/TPbXBWMu47dyLunTcBjc52HKp3wOpsh9PlgdPl9udqAYLvikHf3wXmbiIiIiIiNWm2kD506FCYzWaUlJQE3F9SUoKRI0eqdgwAWCwWWCxh1iwNmSH+iQNtbi/21tjR5g7eN7yrk8cF9jY9UNuMNfvrcO6RgzAqX15F3oHa5rDjkMWUAOSOEv/0oLG2Ga9u24KpE45EtszXAgDJvgr75ER51Xdyfi5yJSYYMSw3FcNyUyP+XEQUv5i7I0vNvNBXjspOTcTls4aFNNaB2ma8t6kcR4/Mlf0eQLaExKA5vbG2Ga9v3YLp449EjsK4lOT0aOVzg8GAnNRE5ESh9Q8REREREXVnEDTcgfCss86C1+vFF198AQCora1FUVERnnrqKVx//fUAgHXr1qGurg7z5s0L+Zi+2Gw2ZGZmwmq1IiMjIwKvjIj4e0YUn5i7ieIXf8+IiIiIiILTdCF969atOO644zBv3jwcc8wxePnll2E2m/Hdd98hMVGstrn22muxbt06bN++PeRj+sKTBKLI4+8ZUXxi7iaKX/w9IyIiIiIKLnqNP3twxBFHYPPmzRgxYgS2bNmCq666CqtXrw44qT7mmGNw7rnnhnUMBdpZYcORD3yJnRU22WNsL7di+F2fYnt58I3hohGHGtSKQ+mc6GU+iIjCwdwdOWrmBTXydiTiUpNe5kuv80NEREREROrSrEe6ZNSoUfjrX/8a9PvXXHNN2MdQoPx0C3538ijkp4fZa5Zx9Is4iIjCxdwdGXrNC4wrNuIgIiIiIqLI0nwhnSIvP92CBSf1vPEm42AcRESkD3rNC4wrNuIgIiIiIqLI0rS1C0VHc5sbPxyoR3Obm3EwDiIi0im95gXGFRtxEBERERFRZHEhvR84VOfAZS+sw6E6B+NgHEREpFN6zQuMKzbiICIiIiKiyDIIgiBoHUS02Ww2ZGZmwmq1IiMjQ+twIq613YMqaysKM5OQZDbF9BhqUCsOpePoZT4ipb/9nhFRZPWH/1PUzAt6HUtNenmNep0fOfrD7xkRERERkVxcSOdJAlFE8PeMiNTE/1OIIo+/Z0REREREwbG1Sz9Q0dSCP3+8AxVNLbLHKG1w4vdvbkJpg1PTONSgVhxK50Qv80FERPqgZl5QI29HIi416WW+9Do/RERERESkLi6k9wOONjfWHayHQ8EmWNaWdny4uQLWlnZN41CDWnEonRO9zAcREemDmnlBjbwdibjUpJf50uv8EBERERGRuhK0DoAib8yAdHz++xO1DoNx6DQOIiLSB73mBcYVG3EQEREREVFksSKdiIiIiIiIiIiIiKgX/bIiXdpf1WazaRxJdOyttmHh/zbin1dMx9gB8jaOarbb4G1zotlug81m0CwONagVh9I50ct8RIr0+9UP9zMmogjoD7lbzbygRt6ORFxq0st86XV+5GDuJiIiIiIKziD0w3fKZWVlGDp0qNZhEPULpaWlGDJkiNZhEFGMY+4mih7mbiIiIiKi7vrlQrrX60VFRQXS09NhMCir0tIjm82GoUOHorS0FBkZsV0ZFQ2cr/CFMmeCIMBut2PQoEEwGtlFioiUUZK7+/P/8/35tQN8/eG+fuZuIiIiIqLg+mVrF6PR2C+qbDIyMvrlSaNcnK/w9TVnmZmZUYyGiOKZGrm7P/8/359fO8DXH87rZ+4mIiIiIuoZS02IiIiIiIiIiIiIiHrBhXQiIiIiIiIiIiIiol5wIT0OWSwW3H///bBYLFqHEhM4X+HjnBFRLOnP/2f159cO8PX399dPRERERKSmfrnZKBERERERERERERFRqFiRTkRERERERERERETUCy6kExERERERERERERH1ggvpRERERERERERERES94EJ6DLPb7di0aROqq6v7fOzevXuxdu1aOJ3OKESmX7W1tdi0aROampp6fVxlZSU2bdqEtra26ASmY6Wlpdi6dSscDkfQx1RXV2PDhg0oLi6OYmRERIGkvFhVVdXr45qamrBhwwbYbLYoRRYdlZWV2LJlC+x2e9DH1NXVYcOGDThw4AC8Xm8Uo4u80tJSbNmypdefa2VlJdavX4+GhoYoRhZ5Ho8Hu3btwu7du+FyuXp8TFtbG7Zu3YrDhw+DWyQREREREYWPC+kx6ODBg7joootQVFSE+fPnY8yYMTjrrLNQV1fX4+P37t2LmTNn4oQTTsDBgwejHK0+/PjjjzjppJMwadIkzJ8/H4MGDcI111yD9vb2gMfV1dXhnHPOwbhx43D99ddj3Lhx+OCDDzSKWlsfffQRJk+ejGOPPRa/+c1vMGDAADz44IMBj7Hb7Zg3bx5Gjx6NBQsW4Oijj8YRRxyBPXv2aBQ1EfVHhw4dwiWXXIKhQ4di/vz5GDduHM444wzU1NQEPM7lcmHhwoUYNGgQrr/+ekyePBl/+9vfNIpaPV9//TVmzJiBo446CldeeSUKCwtx2223BSyWtrW14Ve/+hWGDRuG66+/HscffzzGjx+PDRs2aBi5OlasWIGpU6fi5JNPxhVXXIHCwkLccsstAR8UeDweXH311Rg5ciSuuuoqDBo0qFtOi1VPP/00ioqKcOmll+Lss8/G4MGD8b///c//fZvNhkWLFqGwsBBXXHEFZs6cialTp2LLli0aRk1EREREFHu4kB6DDhw4gMsuuwwNDQ3YtGkTDh06hPLycvzud7/r9ljpxHnhwoUaRKof+/btw5IlS1BTU4NNmzZh69at+Oijj/Dwww/7H+PxeDBv3jzY7XYcPnwY69ev77MSO54dOnQI77zzjr8i/ZNPPsFDDz2E999/3/+Yv//971i/fj3279+Pn3/+GWVlZcjMzMQtt9yiYeRE1N8cOHAAF198MRobG/15sba2tlvuu/HGG/H5559jx44d2LBhAw4ePIisrCxtglbRgQMH8H//93+oqKjAli1b8O233+L555/HCy+84H/M//3f/2HZsmX+115aWorx48djwYIFGkaujrKyMrzzzjs4cOAAtm7dirVr1+LZZ5/F22+/7X/Mk08+iY8//hjbt2/Hjh078MUXX+DBBx/E8uXLNYxcHS6XCzt27MDWrVtx8OBBLF68GFdddZX/isXq6mqMGzfOf8VCaWkpxo0bh/PPP1/jyImIiIiIYotB4LWdceEvf/kLnn32WVRUVATcf/PNN8Nut+PGG2/EzJkzsW3bNkyePFmjKPXlsssuQ319Pb788ksAwMcff4zzzjsPO3fuxIQJEzSOTp+mTJmC008/HY8//jgA4He/+x02bdqEH374wf+Y3//+91izZk1cVDkSUexaunQpHnvsMX9V+qFDhzBq1Ci8+uqruOyyyzSOLvJOOeUUDB06FP/9738BAPfddx/eeOMN7Nu3z/+YJUuW4IUXXkBJSYlWYUZMXl4eFi9ejNtuuw0AMGnSJMyZMwf/+Mc//I855ZRTkJubi3fffVerMCNi06ZNmD59OjZv3owjjzyyx8d88cUXOPPMM1FeXo5BgwZFOUIiIiIiotiUoHUApI7169dj9OjRAfd98sknWL58OTZt2sRWG114PB5s2rQJc+bM8d+3YsUKjBo1ChMmTMCuXbsgCAJGjx6NxMREDSPVj/r6ehQXFwf8O7v55ptx+umn4/7778eJJ56Iffv24a233gqogiQi0kLXvLhq1SoIgoBzzjkHJSUlaGpqwujRo5GamqphlJHhdDqxc+dOnHLKKf77rrvuOrz++uu47bbbcNZZZ+Hw4cP45z//iUceeUTDSNXT3NyMzZs3o7m5GW+++Sby8/NxxRVXAABaW1uxa9cu3HHHHQHHHH300XGziF5WVoZDhw6huroajz76KC699NKgi+iA+PuRlpaGAQMGRDFKIiIiIqLYxoX0OPDmm2/i448/xueff+6/r7y8HNdddx0+/PBDpKenaxidPt1///0oKyvDrbfe6r+voqICWVlZOPnkk1FdXQ2XywW73Y5//vOfuOCCCzSMVnuCIOC6664LWJgAgNGjR2P+/Pl46qmn8Mknn+Dw4cM444wzcNxxx2kYLRH1d++88w7ef/99LFu2zH+f9H/8LbfcghUrViAzMxMHDx7E/fffjz/+8Y8aRqu+m2++GYIgBLRtGTJkCG644QYsWbIEK1euREVFBWbNmoXTTjtNw0jVU15ejrvuuguNjY0oLS3FQw89hIKCAgDi5rKCICA3NzfgmNzc3LjZdHT16tV47rnnUFZWhoSEBCxZsiToY7ds2YKHH34Y99xzD0wmUxSjJCIiIiKKbeyRHuO+/PJLXHXVVXjsscdwxhln+O+/4YYbcOyxx8LtdmPt2rX+DaU2bdoUcFl3f/TMM8/g0UcfxVtvvYUxY8b47zebzdiwYQMuv/xy7Nq1CwcOHMCiRYvwm9/8BpWVlRpGrL0bb7wR3377LT755JOAD2YWL16M//znP9i9ezc2btyI0tJSNDQ0sO8qEWlmxYoVuPLKK7F06VKcffbZ/vvNZjMaGxuRlpaGkpISbNu2DW+88QbuvPNOrF69WsOI1XXvvffirbfewkcffRRQbfy3v/0NjzzyCDZu3IhNmzahrKwMqampOOOMMwI25YxV48aNw9q1a7Fjxw588803uPfee/Hss88CEH/2gFiZ3llLS0vcXHV2+eWX47vvvkNJSQnuuOMOnHnmmdixY0e3x+3btw9nnXUWLrzwQtx1110aREpEREREFLu4kB7DvvrqK/zyl7/EkiVLAiqrAbHKqqamBnfddRfuuusuf0/QJ554Au+8844W4erC888/j9tuuw3vvPMOzjnnnIDvDR8+HGazGfPnz/fft3DhQrS0tPTrft+LFi3C22+/jRUrVnTrr79s2TJceOGFKCwsBAAkJyfjmv9v7/5iqq7/OI6/DsX4z8CBEmb86cIbMmixVYviiHT8k8eWaydWcwGJfzCnU6e5NLLyQkvJuTVbITsmEeqFFYiJIoUGygYpG7CM2M45Oy1nRcCwML6/C9fpd37Q6fgTOSLPx8bG9/P5ns95n7PB+5z3+Zz3t7BQDQ0N6u3tDUS4ACaxU6dOyWq16vXXXx/RxiM5OVmSVFRUJJPJJEmyWq1KTEzU119/Pd6h3hIlJSUqLS3VsWPH9Oijj3rNffHFF5o/f75SUlIkXS8uFxUV6eLFi+rp6QlAtLfOQw89pJycHB07dkySNGXKFEVFRcnlcnmd53K5dN999wUixFtq+fLlCg8P14kTJ7zGL126JLPZLLPZtt1F/wAACbVJREFUrP3793v+DgAAAAD4h0L6BHXy5EktWrRIJSUlWr9+/Yj5/fv3q7Gx0fPz4YcfSpLsdrs2b9483uHeFvbt26c1a9aoqqpKVqt1xLzFYtHQ0JDnwnTS9Z6jkhQfHz9ucd5OVq9erYqKCtXV1Y3aazU+Pt7zHP3F4XAoLCyMlkIAxtXp06e1cOFCvfbaa6PutDWbzQoODvYqpg4MDOjXX3+9I/7Hb9u2Te+++65qamr0+OOPj5j/p//X0vULc05kAwMDXsfDw8Pq7u72tHIxmUzKycnRZ5995jlnaGhINTU1ys3NHddYx9rg4KAMw/Aac7vd6u/v92pl093dLbPZrCeeeEJ2u52WLgAAAMD/wWT876tv3PaampqUk5OjefPmac2aNV5zo715lqSWlhZlZmbq4sWLI3YVTwYff/yxlixZovXr13sV0SMiIpSRkeE5tlqtunz5sjZt2qShoSG98cYbio+PV11dnYKCJtfnTps3b9aOHTtUWlqq9PR0z3h8fLxmzpwp6XofYpvNpldffVXZ2dnq7OzUli1b9PLLL+udd94JUOQAJpvz58/LbDYrNzdX69at85r777y4detWHThwQG+99ZZiYmK0Z88edXV1qa2tTTExMeMc9djZtWuX1q1bp+3btysrK8szHhMT48n5p06dUm5urlatWqUFCxaop6dHW7Zs0bx581ReXh6gyMfGgw8+qBdeeEGzZs3SwMCA7Ha76uvr1djYqFmzZkmS2tra9Nhjj6mgoEAWi0VlZWVqamrSt99+6+mlPhG1traquLhYL730klJTU+VwOLRr1y6ZTCY1NzcrLCxMbrdbjzzyiGJjY/Xee+95FdHT09MVGRkZwEcAAAAATBwU0ieggwcP6v333x91rrGxcdTxrq4uFRYW6sCBA56vdU8mb775po4fPz5iPDU1VXa73XP8+++/a8+ePTp58qRCQ0OVlZWl4uJihYaGjme4t4X8/PxR++nPmTNHJSUlnuOvvvpK5eXlcjgcio+P14IFC5SXlzfpPngAEDiVlZXau3fvqHMNDQ1ehUO73a4jR45oaGhIGRkZWrt27YTfkb127VqdP39+xHhmZqZ2797tOW5padEHH3ygnp4excbGas6cOcrPz9fdd0/sa89fuXJFe/fu1blz5xQSEqK0tDStWLFC99xzj9d5ra2tKi0tldPp1MyZM7Vx40YlJSUFKOqx097ern379qmrq0txcXHKyspSfn6+57VLS0vLiI0Xf/noo488H44DAAAA8I1COgAAAAAAAAAAPrBlFAAAAAAAAAAAHyikAwAAAAAAAADgA4V0AAAAAAAAAAB8oJAOAAAAAAAAAIAPFNIBAAAAAAAAAPCBQjoAAAAAAAAAAD5QSAcAAAAAAAAAwAcK6bijDQ4OqrKyUn19fYEOBQAA+IHcDQAAAOB2RCEdN+zatWuqrKyU0+n0+zYDAwOqrKxUf3//LYxspCtXrigvL08ulyugcQAAEEjkbgAAAAC4ORTSccOuXr2qvLw8NTU1+X2by5cvKy8vTz/++OMtjGyk8PBw2Ww2RUdHBzQOAAACidwNAAAAADfn7kAHgImvr69P1dXVWrRokVwulzo7O5WcnKy0tDRJ13fBff7555Kk6upqTZs2TQkJCcrOzpYk9fb26ptvvlFQUJDS09M1depUv9f+yx9//KHm5mb99ttvysjIUGJioiQpLCxMzzzzjKKiov4xjp9//lnp6elKTU31WvPIkSPKyMgYMQ4AwERH7gYAAACAG0MhHTfN5XIpLy9PVqtV3d3dSkpKUn19vYqLi7Vjxw79+eef+vLLLyVJJ06cUGRkpB544AFlZ2frk08+0cqVK5Wenq6QkBA1NTVp586dWrp0qV9rS1JPT4+efPJJxcTEKCkpSe3t7Vq2bJk2btzo+Xp4R0eHUlJSRo2jtbVVhw8fVkVFhecxNTc367nnntOlS5fG+dkEAODWI3cDAAAAwA0ygBvU19dnSDIOHTpkGIZhdHR0GJKMFStWGMPDw4ZhGEZtba0RFBRkuN1uwzAM44cffjAkGd99951nnc7OTiMiIsI4e/asZ+zMmTNGaGio8f333/u99qZNm4zZs2d71rh27Zpx9OhRwzAMw+FwGJKMjo6Of4yjtrbWCA0NNX755RfPWFFRkWE2m8fmCQMAIMDI3QAAAABwc+iRjjGzfPlymUwmSVJ2draGh4d97go7ePCgEhIS5HK5dOjQIVVVVcnpdCoqKkpnzpzxe+2wsDD99NNPcrvdkqS77rpLVqvV77hzc3M1bdo0z662wcFBffrppyooKPD/wQMAMAGRuwEAAADAP7R2wZiZMmWK5/eQkBBJ1y9u9k96enp09epVHT582Gt89uzZio2N9Xvt1atXq729Xffff7/S0tL01FNPadWqVUpISPAr7qCgIOXn56usrEwrV670xLN48WK/bg8AwERF7gYAAAAA/1BIR8BER0dr6tSpqqysvKl1YmJiVFVVpf7+fjU2Nqq0tFSZmZk31CO1oKBA27Zt04ULF1RWVqbnn39eYWFhNxUXAAB3GnI3AAAAgMmK1i4YF5GRkZK8d7nNnTtXbW1tampq8jq3t7dXAwMDfq/tcrk89zF37lzt3r1bTqfTM/5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"text/plain": [ "
" ] @@ -764,14 +796,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "collectri = dc.get_collectri()\n", + "collectri = dc.op.collectri()\n", "tfs = collectri.source.values\n", "decoupler_input = nc.utils.decoupler_formatter(de_transcr, 'stat')\n", - "dc_estimates, dc_pvals = dc.run_ulm(decoupler_input, collectri)\n", + "dc_estimates, dc_pvals = dc.mt.ulm(decoupler_input, collectri)\n", "measurements = nc.utils.targetlayer_formatter(dc_estimates.T)" ] }, @@ -784,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -851,7 +883,7 @@ "9 PKCD Sdc4_MKKKDEGSYDLGKKP" ] }, - "execution_count": 51, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -868,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -877,7 +909,7 @@ "445" ] }, - "execution_count": 52, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -889,7 +921,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -898,14 +930,14 @@ "55" ] }, - "execution_count": 53, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decoupler_input_kin = nc.utils.decoupler_formatter(de_phospho, 'stat') # get the actual number of inferred kinase activities\n", - "dc_estimates_kin, dc_pvals_kin = dc.run_zscore(decoupler_input_kin, phosphositeplus, weight=None)\n", + "dc_estimates_kin, dc_pvals_kin = dc.mt.zscore(decoupler_input_kin, phosphositeplus)\n", "kinase_activities = dc_estimates_kin.T\n", "kinase_activities.head()\n", "len(kinase_activities)" @@ -927,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -954,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -963,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -972,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -990,7 +1022,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -999,7 +1031,88 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The network is too large to visualize, you can increase the max_nodes parameter if needed.\n" + ] + } + ], + "source": [ + "visualizer = nc.visual.NetworkXVisualizer(networks['shortest_sc_path'])\n", + "visualizer.visualize_network(source_dict, measurements, network_type='sign_consistent')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "networks['all_paths'], all_paths_list = nc.methods.run_all_paths(graph, source_dict, measurements, depth_cutoff=6)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The network is too large to visualize, you can increase the max_nodes parameter if needed.\n" + ] + } + ], + "source": [ + "visualizer = nc.visual.NetworkXVisualizer(networks['all_paths'])\n", + "visualizer.visualize_network_simple(source_dict, measurements)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "networks['all_sc_paths'], allpaths_sc_list = nc.methods.run_sign_consistency(networks['all_paths'], all_paths_list, source_dict, measurements)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The network is too large to visualize, you can increase the max_nodes parameter if needed.\n" + ] + } + ], + "source": [ + "visualizer = nc.visual.NetworkXVisualizer(networks['all_sc_paths'])\n", + "visualizer.visualize_network_simple(source_dict, measurements)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "networks['corneto_graph'] = nc.methods.run_corneto_carnival(graph, source_dict, measurements, betaWeight=0.01, solver='GUROBI')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1008,1541 +1121,321 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "PTPRM\n", - "\n", - "PTPRM\n", + "RBL2\n", + "\n", + "RBL2\n", "\n", - "\n", + "\n", "\n", - "CTNND1\n", - "\n", - "CTNND1\n", + "E2F4\n", + "\n", + "E2F4\n", "\n", - "\n", + "\n", "\n", - "PTPRM->CTNND1\n", - "\n", - "\n", + 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"RET->MAPK1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "RET->MAPK3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "SIRT2->FOXO1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "MAP3K1->MAPK1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "MAP3K1->MAPK3\n", - "\n", - "\n", + "TGFBR2->TGFBR1\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - ">" + ">" ] }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "visualizer = nc.visual.NetworkXVisualizer(networks['shortest_sc_path'])\n", - "visualizer.visualize_network(source_dict, measurements, network_type='sign_consistent')" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "networks['all_paths'], all_paths_list = nc.methods.run_all_paths(graph, source_dict, measurements, depth_cutoff=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is too large to visualize, you can increase the max_nodes parameter if needed.\n" - ] - } - ], - "source": [ - "visualizer = nc.visual.NetworkXVisualizer(networks['all_paths'])\n", - "visualizer.visualize_network_simple(source_dict, measurements)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "networks['all_sc_paths'], allpaths_sc_list = nc.methods.run_sign_consistency(networks['all_paths'], all_paths_list, source_dict, measurements)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The network is too large to visualize, you can increase the max_nodes parameter if needed.\n" - ] - } - ], - "source": [ - "visualizer = nc.visual.NetworkXVisualizer(networks['all_sc_paths'])\n", - "visualizer.visualize_network_simple(source_dict, measurements)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "networks['corneto_graph'] = nc.methods.run_corneto_carnival(graph, source_dict, measurements, betaWeight=0.01, solver='GUROBI')" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "MAPK14\n", - "\n", - "MAPK14\n", - "\n", - "\n", - "\n", - "MAPK3\n", - "\n", - "MAPK3\n", - "\n", - "\n", - "\n", - "MAPK14->MAPK3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PTPN7\n", - "\n", - "PTPN7\n", - "\n", - "\n", - "\n", - "MAPK14->PTPN7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "MYC\n", - "\n", - "MYC\n", - "\n", - "\n", - "\n", - "MAPK3->MYC\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "FOXO3\n", - "\n", - "FOXO3\n", - "\n", - "\n", - "\n", - "MAPK3->FOXO3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E2F1\n", - "\n", - "E2F1\n", - "\n", - "\n", - "\n", - 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- "CAMKK2->PRKAA1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E2F3\n", - "\n", - "E2F3\n", - "\n", - "\n", - "\n", - "CHEK1->E2F3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "E2F4\n", - "\n", - "E2F4\n", - "\n", - "\n", - "\n", - "RBL2->E2F4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "KPNA2\n", - "\n", - "KPNA2\n", - "\n", - "\n", - "\n", - "PRKAA1->KPNA2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "IRF1\n", - "\n", - "IRF1\n", - "\n", - "\n", - "\n", - "KPNA2->IRF1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "PTPRM\n", - "\n", - "PTPRM\n", - "\n", - "\n", - "\n", - "CTNND1\n", - "\n", - "CTNND1\n", - "\n", - "\n", - "\n", - "PTPRM->CTNND1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TGFBR2\n", - "\n", - "TGFBR2\n", - "\n", - "\n", - "\n", - "TRAF6\n", - "\n", - "TRAF6\n", - "\n", - "\n", - "\n", - "TGFBR2->TRAF6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "TRAF6->MAPK14\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - ">" - ] - }, - "execution_count": 65, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -2575,7 +1468,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -2584,7 +1477,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -2593,7 +1486,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -2614,22 +1507,31 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "p = nc.visual.plot_scatter(phospho_results, x_col='diff_dysregulation')" + "p = nc.visual.plot_scatter(phospho_results, x_col='diff_dysregulation', filepath=None, render=True)" ] }, { @@ -2662,7 +1564,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -2676,9 +1578,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/docs/src/vignettes/A_moon.ipynb b/docs/src/vignettes/A_moon.ipynb index fdff0e8..f8f34d1 100644 --- a/docs/src/vignettes/A_moon.ipynb +++ b/docs/src/vignettes/A_moon.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -356,7 +356,7 @@ "metadata": {}, "outputs": [], "source": [ - "tf_regn = dc.get_dorothea(levels = ['A', 'B'])" + "tf_regn = dc.op.dorothea(levels=['A', 'B'])" ] }, { @@ -440,12 +440,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -477,7 +477,7 @@ "metadata": {}, "outputs": [], "source": [ - "mapping_dict = pd.read_csv(\"../../../data/moon/hmdb_mapper_vec.csv\", header=0).set_index('HMDB_id')['name'].to_dict()" + "mapping_dict = nc.data.network.get_hmdb_mapper()" ] }, { @@ -499,7 +499,7 @@ ], "metadata": { "kernelspec": { - "display_name": "networkcommons", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -513,7 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/src/vignettes/B_pertpy.ipynb b/docs/src/vignettes/B_pertpy.ipynb index 7d9269e..5a11146 100644 --- a/docs/src/vignettes/B_pertpy.ipynb +++ b/docs/src/vignettes/B_pertpy.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -83,7 +83,7 @@ " 'Prexasertib']" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ " adata,\n", " query_id=\"DepMap_ID\",\n", " reference_id=\"ModelID\",\n", - " fetch=[\"CellLineName\", \"Age\", \"OncotreePrimaryDisease\", \"SangerModelID\", \"OncotreeLineage\"],\n", + " fetch=[\"cell_line_name\", \"Age\", \"OncotreePrimaryDisease\", \"SangerModelID\", \"OncotreeLineage\"],\n", ")\n", "\n", "moa_metadata = pt.md.Moa()\n", @@ -136,15 +136,6 @@ "adata.obs[\"target\"] = [\"Control\" if pert == \"control\" else target for target, pert in zip(adata.obs[\"target\"], adata.obs[\"perturbation\"])]" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sc.pl.umap(adata, color=[\"moa\"])" - ] - }, { "cell_type": "code", "execution_count": null, @@ -158,7 +149,7 @@ " query_perturbation='perturbation',\n", " gdsc_dataset=\"gdsc_1\",\n", ")\n", - "adata.obs[\"ln_ic50_GDSC1\"] = adata.obs[\"ln_ic50\"].copy()\n", + "adata.obs[\"ln_ic50_GDSC1\"] = adata.obs[\"ln_ic50_gdsc\"].copy()\n", "\n", "cl_metadata.annotate_from_gdsc(\n", " adata,\n", @@ -167,14 +158,14 @@ " query_perturbation='perturbation',\n", " gdsc_dataset=\"gdsc_2\",\n", ")\n", - "adata.obs[\"ln_ic50_GDSC2\"] = adata.obs[\"ln_ic50\"].copy()\n", + "adata.obs[\"ln_ic50_GDSC2\"] = adata.obs[\"ln_ic50_gdsc\"].copy()\n", "\n", - "del adata.obs[\"ln_ic50\"]" + "del adata.obs[\"ln_ic50_gdsc\"]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -183,36 +174,28 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "adata_dabrafenib = sc.read_h5ad(\"adata_dabrafenib.h5ad\")" + "np.random.seed(42)\n", + "subset_cells = np.random.choice(adata_dabrafenib.obs[\"SangerModelID\"].unique().tolist(), size=10, replace=False)" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "subset_cells = np.random.choice(adata_dabrafenib.obs[\"SangerModelID\"].unique().tolist(), size=10, replace=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['SIDM00759', 'SIDM00582', 'SIDM00963', 'SIDM01150', 'SIDM00143',\n", - " 'SIDM00756', 'SIDM01060', 'SIDM00139', 'SIDM01167', 'SIDM01026'],\n", + "array(['SIDM00759', 'SIDM00125', 'SIDM00116', 'SIDM00453', 'SIDM00795',\n", + " 'SIDM00683', 'SIDM00522', 'SIDM00990', 'SIDM00008', 'SIDM00536'],\n", " dtype='AL627309.1\n", " AP006222.2\n", " RP4-669L17.10\n", + " RP4-669L17.2\n", + " RP5-857K21.4\n", " RP11-206L10.3\n", " RP11-206L10.2\n", " RP11-206L10.9\n", " FAM87B\n", - " LINC00115\n", - " FAM41C\n", " ...\n", - " MT-ND6\n", - " MT-CYB\n", + " AC109135.1\n", " AC145212.1\n", " MGC39584\n", + " AL356585.1\n", " AC011043.1\n", " AL592183.1\n", " AC011841.1\n", @@ -301,135 +289,163 @@ " \n", " \n", "
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\n", "
\n", - " SIDM01167\n", - " 0.000000\n", - " 0.158182\n", - " 0.484159\n", - " 0.047778\n", - " 0.047758\n", - " 0.000000\n", - " -0.186445\n", - " -0.102564\n", - " 0.546340\n", - " 0.195960\n", + " SIDM00522\n", + " -0.168642\n", + " -0.159129\n", + " -0.332881\n", + " -0.199481\n", + " NaN\n", + " NaN\n", + " -0.199479\n", + " -0.175883\n", + " -0.216363\n", + " -0.159129\n", " ...\n", - " 0.089615\n", - " 0.063865\n", - " 0.063088\n", - " 0.000000\n", - " -0.326881\n", - " 0.000977\n", - " 0.000000\n", - " 0.114601\n", - " 0.000000\n", - " -0.475517\n", + " NaN\n", + " -0.194398\n", + " NaN\n", + " NaN\n", + " -0.429383\n", + " -0.214287\n", + " -0.159129\n", + " -0.191268\n", + " -0.179250\n", + " -0.243261\n", "
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4 rows × 21805 columns

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5 rows × 25031 columns

\n", "" ], "text/plain": [ - " RP11-34P13.7 AL627309.1 AP006222.2 RP4-669L17.10 RP11-206L10.3 \\\n", - "SIDM01150 -0.029041 -0.154688 0.007570 0.135863 0.080412 \n", - "SIDM00143 0.000000 0.294966 0.046804 -0.218813 0.497196 \n", - "SIDM01060 0.043288 0.000000 -0.162589 0.065901 0.027507 \n", - "SIDM01167 0.000000 0.158182 0.484159 0.047778 0.047758 \n", + " RP11-34P13.7 AL627309.1 AP006222.2 RP4-669L17.10 RP4-669L17.2 \\\n", + "SIDM00125 -0.704111 -0.655688 0.051498 -0.638307 NaN \n", + "SIDM00116 -0.044241 NaN 0.302561 0.017352 NaN \n", + "SIDM00795 NaN NaN -0.650199 0.193381 NaN \n", + "SIDM00683 -0.841752 -0.786197 0.029373 -0.791588 NaN \n", + "SIDM00522 -0.168642 -0.159129 -0.332881 -0.199481 NaN \n", "\n", - " RP11-206L10.2 RP11-206L10.9 FAM87B LINC00115 FAM41C ... \\\n", - "SIDM01150 0.347878 0.346763 0.000000 -0.148709 -0.343955 ... \n", - "SIDM00143 0.000000 -0.218851 0.000000 0.439098 -0.302195 ... \n", - "SIDM01060 -0.033283 0.000590 0.000000 -0.502486 -0.351066 ... \n", - "SIDM01167 0.000000 -0.186445 -0.102564 0.546340 0.195960 ... \n", + " RP5-857K21.4 RP11-206L10.3 RP11-206L10.2 RP11-206L10.9 \\\n", + "SIDM00125 NaN -0.636597 -0.676410 -0.700287 \n", + "SIDM00116 NaN 0.028864 0.053872 -0.033072 \n", + "SIDM00795 0.145842 0.232858 NaN 0.170583 \n", + "SIDM00683 NaN -0.856542 -0.811494 -0.837651 \n", + "SIDM00522 NaN -0.199479 -0.175883 -0.216363 \n", "\n", - " MT-ND6 MT-CYB AC145212.1 MGC39584 AC011043.1 AL592183.1 \\\n", - "SIDM01150 0.366750 -0.100324 0.011169 0.132036 -0.025371 0.145605 \n", - "SIDM00143 0.400622 0.121451 -0.291270 0.000000 0.483729 0.086145 \n", - "SIDM01060 0.224724 0.005616 -0.017290 0.000000 -0.198974 -0.399949 \n", - "SIDM01167 0.089615 0.063865 0.063088 0.000000 -0.326881 0.000977 \n", + " FAM87B ... AC109135.1 AC145212.1 MGC39584 AL356585.1 \\\n", + "SIDM00125 -0.687550 ... NaN -0.660062 NaN NaN \n", + "SIDM00116 -0.022150 ... NaN -0.058785 NaN NaN \n", + "SIDM00795 NaN ... NaN 0.135443 0.231280 0.145842 \n", + "SIDM00683 -0.859755 ... NaN -0.672381 -0.777389 NaN \n", + "SIDM00522 -0.159129 ... NaN -0.194398 NaN NaN \n", "\n", - " AC011841.1 AL354822.1 PNRC2-1 SRSF10-1 \n", - "SIDM01150 0.013151 0.025349 -0.126633 -0.186471 \n", - "SIDM00143 0.328663 0.168367 -0.006578 0.451708 \n", - "SIDM01060 0.000000 0.131691 0.000000 -0.250337 \n", - "SIDM01167 0.000000 0.114601 0.000000 -0.475517 \n", + " AC011043.1 AL592183.1 AC011841.1 AL354822.1 PNRC2-1 SRSF10-1 \n", + "SIDM00125 0.024147 0.103067 -0.687550 -0.642950 -0.687550 -0.700193 \n", + "SIDM00116 -0.003496 -0.250800 0.000557 -0.033750 0.053872 0.052208 \n", + "SIDM00795 -0.754655 -0.230118 NaN -0.013620 0.150846 -0.620613 \n", + "SIDM00683 -0.654602 0.132054 NaN -0.701770 -0.841681 -0.706579 \n", + "SIDM00522 -0.429383 -0.214287 -0.159129 -0.191268 -0.179250 -0.243261 \n", "\n", - "[4 rows x 21805 columns]" + "[5 rows x 25031 columns]" ] }, - "execution_count": 70, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -454,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -463,38 +479,25 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Downloading data from `https://omnipathdb.org/queries/enzsub?format=json`\n", - "INFO:root:Downloading data from `https://omnipathdb.org/queries/interactions?format=json`\n", - "INFO:root:Downloading data from `https://omnipathdb.org/queries/complexes?format=json`\n", - "INFO:root:Downloading data from `https://omnipathdb.org/queries/annotations?format=json`\n", - "INFO:root:Downloading data from `https://omnipathdb.org/queries/intercell?format=json`\n", - "INFO:root:Downloading data from `https://omnipathdb.org/about?format=text`\n" - ] - } - ], + "outputs": [], "source": [ - "net = dc.get_collectri()" + "net = dc.op.collectri()" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "tf_acts, pvals = dc.run_ulm(logfc_df, net)" + "tf_acts, pvals = dc.mt.ulm(logfc_df.fillna(0), net)" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -529,10 +532,10 @@ " AR\n", " ARID1A\n", " ...\n", - " ZNF382\n", " ZNF384\n", " ZNF395\n", " ZNF410\n", + " ZNF423\n", " ZNF436\n", " ZNF699\n", " ZNF76\n", @@ -543,135 +546,219 @@ " \n", " \n", " \n", - " SIDM01150\n", - " 0.080080\n", - " -1.631165\n", - " 1.822143\n", - " -0.486594\n", - " -0.783931\n", - " -0.844352\n", - " 1.723601\n", - " -1.519310\n", - " -0.344363\n", - " 1.623128\n", + " SIDM00125\n", + " 1.678649\n", + " -0.455704\n", + " -0.932583\n", + " -1.254593\n", + " 1.677237\n", + " 1.389171\n", + " -0.469639\n", + " -0.890287\n", + " 0.030482\n", + " 0.067338\n", " ...\n", - " -4.238011\n", - " 1.965072\n", - " -2.004019\n", - " 0.069187\n", - " 2.652415\n", - " -0.182054\n", - " -1.793975\n", - " -0.143599\n", - " 1.938493\n", - " -0.277062\n", + " 1.600190\n", + " -1.023357\n", + " 0.481083\n", + " 0.406649\n", + " 0.477812\n", + " -0.421363\n", + " -0.779105\n", + " 1.319467\n", + " 0.994361\n", + " 0.997009\n", " \n", " \n", - " SIDM00143\n", - " -1.332259\n", - " 1.592115\n", - " 1.183483\n", - " 0.799785\n", - " 1.025860\n", - " 0.468413\n", - " 4.497127\n", - " -1.430149\n", - " 2.600641\n", - " 2.156102\n", + " SIDM00116\n", + " -0.788647\n", + " -1.949856\n", + " -0.398530\n", + " -0.165683\n", + " 1.364941\n", + " 1.138843\n", + " -1.914635\n", + " -2.943877\n", + " -3.422526\n", + " -0.298712\n", " ...\n", - " -0.789468\n", - " 3.996778\n", - " -1.028295\n", - " -0.517214\n", - " 0.598231\n", - " 1.087119\n", - " 0.778563\n", - " -0.599900\n", - " 1.412452\n", - " -2.276532\n", + " 0.175988\n", + " -0.685364\n", + " -0.789065\n", + " 0.727406\n", + " -0.491333\n", + " -1.304545\n", + " -1.669078\n", + " 2.414315\n", + " -1.137895\n", + " 0.650264\n", " \n", " \n", - " SIDM01060\n", - " -0.652209\n", - " -2.475549\n", - " -1.580986\n", - " 1.089401\n", - " -1.944108\n", - " 0.399068\n", - " -5.066813\n", - " -2.765601\n", - " -0.934723\n", - " -0.981179\n", + " SIDM00795\n", + " 1.451956\n", + " 0.374862\n", + " -0.186954\n", + " -0.948275\n", + " 0.593613\n", + " 0.708408\n", + " -5.674639\n", + " -0.528118\n", + " -5.460753\n", + " -0.349932\n", " ...\n", - " 1.715413\n", - " -0.158227\n", - " 1.480932\n", - " 0.409427\n", - " -0.044302\n", - " -0.413981\n", - " -0.048804\n", - " -2.692391\n", - " 1.406787\n", - " -0.253700\n", + " -0.562908\n", + " 1.627399\n", + " -1.540295\n", + " -1.545161\n", + " -1.524071\n", + " -0.548275\n", + " -0.846915\n", + " 1.033195\n", + " 0.912218\n", + " 3.217464\n", " \n", " \n", - " SIDM01167\n", - " -1.334910\n", - " 0.017429\n", - " -2.367893\n", - " 0.813763\n", - " 2.198922\n", - " -0.660442\n", - " -2.465387\n", - " -1.764444\n", - " -2.983476\n", - " 1.955569\n", + " SIDM00683\n", + " 2.596925\n", + " 0.818501\n", + " 1.182505\n", + " 0.474306\n", + " -1.409374\n", + " 1.792878\n", + " 1.362010\n", + " -0.453900\n", + " 1.739841\n", + " 2.349532\n", " ...\n", - " 0.219701\n", - " -0.641184\n", - " -0.172258\n", - " -0.653510\n", - " 1.087562\n", - " 2.267288\n", - " -1.447545\n", - " -0.400557\n", - " 1.555596\n", - " -0.400194\n", + " 0.280958\n", + " 0.191438\n", + " -0.298051\n", + " 0.915643\n", + " 0.735462\n", + " -0.445553\n", + " 0.239629\n", + " -0.348690\n", + " 1.269644\n", + " 0.992227\n", + " \n", + " \n", + " SIDM00522\n", + " -1.404004\n", + " 1.936901\n", + " -0.852664\n", + " -0.378363\n", + " -0.261129\n", + " 1.194352\n", + " -2.328850\n", + " -2.262334\n", + " -4.224698\n", + " 0.631309\n", + " ...\n", + " 1.840132\n", + " -0.282595\n", + " -0.166236\n", + " -0.358284\n", + " -0.831713\n", + " -1.470625\n", + " -1.033404\n", + " -0.469294\n", + " -1.358849\n", + " 0.418169\n", + " \n", + " \n", + " SIDM00990\n", + " 0.232764\n", + " 0.353237\n", + " 1.847978\n", + " -0.539091\n", + " -1.148694\n", + " 3.000766\n", + " 4.552368\n", + " -0.847527\n", + " -1.594874\n", + " 0.765477\n", + " ...\n", + " 3.412571\n", + " -0.116735\n", + " -0.414117\n", + " 0.100516\n", + " -0.758721\n", + " -0.104529\n", + " -0.572866\n", + " -1.004960\n", + " 1.581791\n", + " -0.039074\n", + " \n", + " \n", + " SIDM00536\n", + " -0.998157\n", + " -0.885439\n", + " 0.827552\n", + " -1.398972\n", + " 0.573393\n", + " -0.291762\n", + " 3.601847\n", + " -0.934381\n", + " -1.803796\n", + " 1.671225\n", + " ...\n", + " 4.295151\n", + " -1.060330\n", + " 0.432867\n", + " 0.279577\n", + " -1.729019\n", + " -3.130478\n", + " -2.187538\n", + " 1.160916\n", + " 0.171725\n", + " 2.600638\n", " \n", " \n", "\n", - "

4 rows × 735 columns

\n", + "

7 rows × 750 columns

\n", "" ], "text/plain": [ " ABL1 AEBP1 AHR AHRR AIP AIRE \\\n", - "SIDM01150 0.080080 -1.631165 1.822143 -0.486594 -0.783931 -0.844352 \n", - "SIDM00143 -1.332259 1.592115 1.183483 0.799785 1.025860 0.468413 \n", - "SIDM01060 -0.652209 -2.475549 -1.580986 1.089401 -1.944108 0.399068 \n", - "SIDM01167 -1.334910 0.017429 -2.367893 0.813763 2.198922 -0.660442 \n", + "SIDM00125 1.678649 -0.455704 -0.932583 -1.254593 1.677237 1.389171 \n", + "SIDM00116 -0.788647 -1.949856 -0.398530 -0.165683 1.364941 1.138843 \n", + "SIDM00795 1.451956 0.374862 -0.186954 -0.948275 0.593613 0.708408 \n", + "SIDM00683 2.596925 0.818501 1.182505 0.474306 -1.409374 1.792878 \n", + "SIDM00522 -1.404004 1.936901 -0.852664 -0.378363 -0.261129 1.194352 \n", + "SIDM00990 0.232764 0.353237 1.847978 -0.539091 -1.148694 3.000766 \n", + "SIDM00536 -0.998157 -0.885439 0.827552 -1.398972 0.573393 -0.291762 \n", "\n", - " AP1 APEX1 AR ARID1A ... ZNF382 ZNF384 \\\n", - "SIDM01150 1.723601 -1.519310 -0.344363 1.623128 ... -4.238011 1.965072 \n", - "SIDM00143 4.497127 -1.430149 2.600641 2.156102 ... -0.789468 3.996778 \n", - "SIDM01060 -5.066813 -2.765601 -0.934723 -0.981179 ... 1.715413 -0.158227 \n", - "SIDM01167 -2.465387 -1.764444 -2.983476 1.955569 ... 0.219701 -0.641184 \n", + " AP1 APEX1 AR ARID1A ... ZNF384 ZNF395 \\\n", + "SIDM00125 -0.469639 -0.890287 0.030482 0.067338 ... 1.600190 -1.023357 \n", + "SIDM00116 -1.914635 -2.943877 -3.422526 -0.298712 ... 0.175988 -0.685364 \n", + "SIDM00795 -5.674639 -0.528118 -5.460753 -0.349932 ... -0.562908 1.627399 \n", + "SIDM00683 1.362010 -0.453900 1.739841 2.349532 ... 0.280958 0.191438 \n", + "SIDM00522 -2.328850 -2.262334 -4.224698 0.631309 ... 1.840132 -0.282595 \n", + "SIDM00990 4.552368 -0.847527 -1.594874 0.765477 ... 3.412571 -0.116735 \n", + "SIDM00536 3.601847 -0.934381 -1.803796 1.671225 ... 4.295151 -1.060330 \n", "\n", - " ZNF395 ZNF410 ZNF436 ZNF699 ZNF76 ZNF804A \\\n", - "SIDM01150 -2.004019 0.069187 2.652415 -0.182054 -1.793975 -0.143599 \n", - "SIDM00143 -1.028295 -0.517214 0.598231 1.087119 0.778563 -0.599900 \n", - "SIDM01060 1.480932 0.409427 -0.044302 -0.413981 -0.048804 -2.692391 \n", - "SIDM01167 -0.172258 -0.653510 1.087562 2.267288 -1.447545 -0.400557 \n", + " ZNF410 ZNF423 ZNF436 ZNF699 ZNF76 ZNF804A \\\n", + "SIDM00125 0.481083 0.406649 0.477812 -0.421363 -0.779105 1.319467 \n", + "SIDM00116 -0.789065 0.727406 -0.491333 -1.304545 -1.669078 2.414315 \n", + "SIDM00795 -1.540295 -1.545161 -1.524071 -0.548275 -0.846915 1.033195 \n", + "SIDM00683 -0.298051 0.915643 0.735462 -0.445553 0.239629 -0.348690 \n", + "SIDM00522 -0.166236 -0.358284 -0.831713 -1.470625 -1.033404 -0.469294 \n", + "SIDM00990 -0.414117 0.100516 -0.758721 -0.104529 -0.572866 -1.004960 \n", + "SIDM00536 0.432867 0.279577 -1.729019 -3.130478 -2.187538 1.160916 \n", "\n", " ZNF91 ZXDC \n", - "SIDM01150 1.938493 -0.277062 \n", - "SIDM00143 1.412452 -2.276532 \n", - "SIDM01060 1.406787 -0.253700 \n", - "SIDM01167 1.555596 -0.400194 \n", + "SIDM00125 0.994361 0.997009 \n", + "SIDM00116 -1.137895 0.650264 \n", + "SIDM00795 0.912218 3.217464 \n", + "SIDM00683 1.269644 0.992227 \n", + "SIDM00522 -1.358849 0.418169 \n", + "SIDM00990 1.581791 -0.039074 \n", + "SIDM00536 0.171725 2.600638 \n", "\n", - "[4 rows x 735 columns]" + "[7 rows x 750 columns]" ] }, - "execution_count": 45, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -682,11 +769,12 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "measurements = tf_acts.loc[\"SIDM01060\"].sort_values(ascending=False, key=abs)[0:25].to_dict()\n" + "example_cell_line = tf_acts.index[0]\n", + "measurements = tf_acts.loc[example_cell_line].sort_values(ascending=False, key=abs)[0:25].to_dict()" ] }, { @@ -705,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -724,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -734,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -743,567 +831,495 @@ "\n", "\n", - "\n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "\n", "BRAF\n", - "\n", - "BRAF\n", + "\n", + "BRAF\n", "\n", "\n", "\n", "MAP2K1\n", - "\n", - "MAP2K1\n", + "\n", + "MAP2K1\n", "\n", "\n", "\n", "BRAF->MAP2K1\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "MAP2K2\n", - "\n", - "MAP2K2\n", + "\n", + "MAP2K2\n", "\n", "\n", "\n", "BRAF->MAP2K2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "MAP4K1\n", - "\n", - "MAP4K1\n", + "\n", + "MAP4K1\n", "\n", "\n", "\n", "BRAF->MAP4K1\n", - 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"display_name": "networkcommons-DX9y6Uxu-py3.10", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -1330,7 +1346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/networkcommons/data/omics/_lembas.py b/networkcommons/data/omics/_lembas.py index 80f8be6..6ea393d 100644 --- a/networkcommons/data/omics/_lembas.py +++ b/networkcommons/data/omics/_lembas.py @@ -137,7 +137,10 @@ def lembas_ligands(dataset: str = 'macrophage') -> pd.DataFrame: condition names; columns are UniProt IDs of extracellular ligands; values are ligand concentrations or binary presence (0/1). """ - return _lembas_table(dataset, 'ligands') + df = _lembas_table(dataset, 'ligands') + df = df.set_index(df.columns[0]) + df.index.name = 'condition' + return df def lembas_tfs(dataset: str = 'macrophage') -> pd.DataFrame: @@ -157,7 +160,10 @@ def lembas_tfs(dataset: str = 'macrophage') -> pd.DataFrame: names; columns are UniProt IDs of transcription factors; values are TF activity scores in [0, 1]. """ - return _lembas_table(dataset, 'tfs') + df = _lembas_table(dataset, 'tfs') + df = df.set_index(df.columns[0]) + df.index.name = 'condition' + return df def lembas_annotation(dataset: str = 'macrophage') -> pd.DataFrame: diff --git a/networkcommons/eval/_metrics.py b/networkcommons/eval/_metrics.py index 9622879..cb2f8a3 100644 --- a/networkcommons/eval/_metrics.py +++ b/networkcommons/eval/_metrics.py @@ -424,6 +424,10 @@ def run_ora(graph, net, metric='ora_stat', ascending=False, tmin=1, **kwargs): tmin=tmin, **kwargs) + # decoupler 2.x returns 'source'; rename to 'Term' for backward compat + if 'source' in ora_results.columns: + ora_results = ora_results.rename(columns={'source': 'Term'}) + # append ora_ to colnames ora_results.columns = ['ora_' + col for col in ora_results.columns] diff --git a/networkcommons/methods/_causal.py b/networkcommons/methods/_causal.py index be81cbe..28fb61b 100644 --- a/networkcommons/methods/_causal.py +++ b/networkcommons/methods/_causal.py @@ -96,9 +96,9 @@ def run_corneto_carnival(network, network_nx = utils.to_networkx(network_sol, skip_unsupported_edges=True) network_nx.remove_nodes_from(['_s', '_pert_c0', '_meas_c0']) - except Exception: + except Exception as e: network_nx = nx.Graph() - _log('WARNING: Network is empty. No solution found.') + _log(f'WARNING: Network is empty. No solution found. Reason: {type(e).__name__}: {e}') finally: # Restore original stdout and stderr diff --git a/networkcommons/visual/_network_stats.py b/networkcommons/visual/_network_stats.py index 089df0d..d43b0f4 100644 --- a/networkcommons/visual/_network_stats.py +++ b/networkcommons/visual/_network_stats.py @@ -185,7 +185,7 @@ def plot_scatter(df: pd.DataFrame, ylabel: str = 'Coverage', title: str = 'Coverage vs Difference in Activity scores', figsize: tuple = (10, 6), - filepath: str = "scatter_plot.png", + filepath: str | None = "scatter_plot.png", render: bool = False) -> plt.Figure: """ Plot a scatter plot with customizable column labels. @@ -637,7 +637,7 @@ def create_heatmap(results: pd.DataFrame, x_label: str = 'Network', y_label: str = 'ORA Term', cmap="coolwarm_r", - filepath="rank_heatmap.png", + filepath: str | None = "rank_heatmap.png", render=False): """ Create a heatmap. By default, creates a heatmap with rows as ora_terms and columns as networks, From 3a1a3bd74aafa6e14945b6337d9d20367b17dcec Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Fri, 26 Jun 2026 13:42:41 +0100 Subject: [PATCH 13/17] feat: add get_hmdb_mapper to MOON data module Downloads HMDB_mapper_vec.RData from the cosmosR GitHub repository, parses it with the rdata library, and returns a dict mapping HMDB IDs (e.g. 'HMDB0000122') to human-readable metabolite names. Result is cached as a pickle in the configured pickle_dir; pass update=True to force a fresh download. Co-Authored-By: Claude Sonnet 4.6 --- networkcommons/data/network/_moon.py | 54 ++++++++++++++++++++++++++-- 1 file changed, 52 insertions(+), 2 deletions(-) diff --git a/networkcommons/data/network/_moon.py b/networkcommons/data/network/_moon.py index 8c6b4dc..1446139 100644 --- a/networkcommons/data/network/_moon.py +++ b/networkcommons/data/network/_moon.py @@ -17,7 +17,7 @@ Prior knowledge network used by MOON. """ -__all__ = ['get_cosmos_pkn'] +__all__ = ['get_cosmos_pkn', 'get_hmdb_mapper'] import lazy_import import numpy as np @@ -68,5 +68,55 @@ def get_cosmos_pkn(update: bool = False): _log(f'COSMOS: Done. Network has {len(file_legend)} interactions.') - return file_legend + + +def get_hmdb_mapper(update: bool = False) -> dict: + """ + Retrieves the HMDB ID to metabolite name mapping from cosmosR. + + Downloads ``HMDB_mapper_vec.RData`` from the cosmosR GitHub repository + and converts it to a Python dict mapping HMDB IDs to human-readable + metabolite names. + + Args: + update: Force re-download even if cached. + + Returns: + dict: Mapping of HMDB IDs (e.g. ``'HMDB0000122'``) to metabolite + names (e.g. ``'Glucose'``). + """ + import rdata as _rdata + + path = os.path.join(_conf.get('pickle_dir'), 'hmdb_mapper.pickle') + + _log('MOON: Retrieving HMDB mapper...') + + if update or not os.path.exists(path): + _log('MOON: HMDB mapper not found in cache. Downloading...') + + url = ( + 'https://raw.githubusercontent.com/saezlab/cosmosR/' + 'master/data/HMDB_mapper_vec.RData' + ) + rdata_path = _common._maybe_download(url) + + parsed = _rdata.parser.parse_file(rdata_path) + obj = parsed.object.value[0] + values = obj.value + names = obj.attributes.value[0].value + + hmdb_ids = [x.value.decode() for x in names] + metab_names = [values[i].value.decode() for i in range(len(values))] + + mapper = dict(zip(hmdb_ids, metab_names)) + + pd.to_pickle(mapper, path) + + else: + _log('MOON: HMDB mapper found in cache. Loading...') + mapper = pd.read_pickle(path) + + _log(f'MOON: Done. HMDB mapper has {len(mapper)} entries.') + + return mapper From f4bb2e76f20d697c7ee6c988afc1f0072f063778 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Fri, 26 Jun 2026 13:43:28 +0100 Subject: [PATCH 14/17] feat: faithfully reimplement LEMBAS-RNN matching Nilsson et al. 2022 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous implementation approximated the LEMBAS architecture from Nilsson et al. (Nat Commun 2022). This commit aligns it with the original R/MATLAB bionetwork codebase: Weight initialisation - Edges: 0.1 + 0.1×rand, negated for inhibitory signs (bionet.initializeWeights) - Bias: 1e-3 everywhere; nodes that receive only inhibitory edges get bias=1 - Input scale: fixed buffer (inputAmplitude), not a learnable parameter - Output projection: per-output scalar init to projection_amplitude (no bias) Training loop - Cosine one-cycle LR schedule peaking at lr_peak (bionetwork.oneCycle) - Mini-batch training (default batch_size=5) with per-batch weight noise (1e-8) - Per-batch input noise: drive += noiseLevel × curLr × randn - Adam with lr=1.0; actual LR injected each epoch; momentum reset every 200 epochs - Weight pre-scaling to spectral radius 0.8 before training (bionet.preScaleWeights) Regularisation - Spectral radius loss: soft exponential penalty with differentiable power iteration (bionetwork.spectralLoss) - Uniform state distribution: mean/var/min/max loss matching bionetwork.uniformLossBatch (replaces old sorted-distribution loss) - Sign regularisation unchanged; ligand bias penalty added (1e-3) - L2 + inverse barrier on edge weights to prevent collapse to zero Defaults updated: epochs=5000, tolerance=1e-6, dtype=float64, uniform_penalty=1e-5, batch_size=5, projection_amplitude=1.2 C_lembas.ipynb: add section 7 (LOOCV) following the evaluation protocol from the original paper; re-executed with fresh outputs. Co-Authored-By: Claude Sonnet 4.6 --- docs/src/vignettes/C_lembas.ipynb | 1236 ++++++++++++++++++++++- networkcommons/methods/_perturbation.py | 349 +++++-- 2 files changed, 1443 insertions(+), 142 deletions(-) diff --git a/docs/src/vignettes/C_lembas.ipynb b/docs/src/vignettes/C_lembas.ipynb index bdefe56..422b0f5 100644 --- a/docs/src/vignettes/C_lembas.ipynb +++ b/docs/src/vignettes/C_lembas.ipynb @@ -21,8 +21,9 @@ "4. Fit a mean-response baseline and a ridge baseline\n", "5. Fit the LEMBAS-RNN model (requires PyTorch)\n", "6. Evaluate all three models per-readout and per-condition\n", + "7. Compare models under Leave-One-Out Cross-Validation (LOOCV)\n", "\n", - "> **Note:** step 5 requires `torch`. Install it with `pip install torch` if needed." + "> **Note:** steps 5–7 require `torch`. Install it with `pip install torch` if needed." ] }, { @@ -61,9 +62,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Network edges : 5732\n", + "Conditions : 23\n", + "Ligand inputs : 12\n", + "TF readouts : 88\n" + ] + } + ], "source": [ "net_df = nc.data.omics.lembas_network('macrophage')\n", "ligands = nc.data.omics.lembas_ligands('macrophage')\n", @@ -77,18 +89,292 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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2P31749O15111110BioGRID;CancerCellMap;ELM;HPRD;HPRD-phos;HPRD_...BioGRID:10485711;BioGRID:11278366;BioGRID:1571...
3O15111P19838110CancerCellMap;HPRD;HPRD-phos;HPRD_KEA;HPRD_MIM...CancerCellMap:10469655;CancerCellMap:12482991;...
4P19838O15111100CancerCellMap;HPRD;InnateDB;IntAct;NetPath;WangCancerCellMap:10469655;CancerCellMap:12482991;...
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This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:213.)\n", + " torch.as_tensor(source_idx, dtype=torch.long, device=device),\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "lembas_result = run_lembas_rnn(\n", " graph,\n", " x_train,\n", " y_train,\n", " x_test,\n", - " epochs=100,\n", - " n_steps=50,\n", - " learning_rate=1e-3,\n", " seed=42,\n", + " verbose=True,\n", ")\n", "\n", "import matplotlib.pyplot as plt\n", "plt.plot(lembas_result['loss_history'])\n", "plt.xlabel('Epoch')\n", - "plt.ylabel('Training loss')\n", + "plt.ylabel('Training MSE')\n", "plt.title('LEMBAS training curve')\n", + "plt.yscale('log')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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h165dXfxJ/rjwwgshFAp7ZOMP9R38IZVK8dBDD+Gzzz7DU089hdbWVhw9ehTXX389zjjjjJDH8PDDD4NhGFx++eWoqKiARqPBm2++iRdffBHz589HeXk5AJc9PT8/H4sXL0Z1dTVaWlrw2GOPgc/nd7mn+3f+zTffwGazBR3Do48+CrVajWnTpmHDhg3Q6/U4fPgwHnzwQY/wDnWc4fDQQw/BYrFg9uzZOHLkCNra2vDUU0+FbKoM9z2j/Q5WqxXnnnsuPv30U9TW1sJqtWLXrl1Yu3Ytxo4d65N0rNFosGfPni4+r5AIy0WeQHQXAaVSqYKeA4D8+uuvPvfzjjzxR6hRUgzDkNzcXDJ58mSf6Ira2lrC4/HIvHnzun3O0KFDPVE6DoeDrF27llx99dVk0KBBRCQSEalUSk477TTywAMPEK1WG/J8BYoq2717N2EYxidaKtB8mM1mUlRUFDRKKtRr3JFan3/+eUjvQUjXKCk327dvJ5dddhnJysoiQqGQlJWVkeuuu47s2rWLEEKI1WolK1euJOeeey5JT08nSqWSjBw5krz66qvE6XR67qPT6cjll19OVCqV5zsNxuzZs8nIkSO7HL/ooou6ROQRQsgTTzxBABCNRhPWOxDSNUrKzSuvvELKysqISCQip512Gvnuu+/I3XffHXKUFCGE7Nu3j8yYMYOkp6cToVBIhgwZQv79738TjuN8ztu2bRs588wziVAoJEVFReTFF1/0GyXlfteCggLC4/FC+q7r6+vJ/PnzSb9+/TxjeOyxx4jZbA5rnO4Io+rqap/7V1ZWEgBkxYoVPsd//fVXMm7cOCISiUh+fj559NFHyU8//RRSlFR37xno/Gi/w+bNm8nMmTNJYWEhEYvFpLS0lNx5551dIrFee+01IhQKSVNTk9/57w6GkF6sp0ChpAi///47Ro4cic2bN9PyIJSEhRCC4cOHY9y4cXjllVfCvp4qDAolSsybNw9VVVVxCT+lUCLho48+wvz583HkyJGwIj/dUIVBoVAolJBIiEL5W7ZswcGDB3HxxReHFOZlt9uxceNGNDU14dRTT8Xpp58eh1FSKBRK36ZXdxj/+9//sHjxYrAsi99//x0bNmzAxIkTu72mra0N5513HgwGA0499VSsX78e1157LV5++eX4DJpCoVD6KL26w+DxeFi1ahXS0tICFr/rzH333Qe73Y7ff/8dMpkM27dvxxlnnIGLL74YU6dOjfGIKRQKpe/Sq3kYU6dODaupiNPpxOrVq3HjjTd6EmXGjBmDM88806e6LIVCoVCiT0L4MEKltrYWOp0OQ4cO9Tl+yimnYOfOnQGvs1qtPhmYTqcT7e3tUKvVUWvrSaFQKPGGEAK9Xo/8/HzweLFf/yeVwghUMCsjI6Pb4m9PPPEEHn744ZiOjUKhUHqL2tpaFBQUxPw5SaUwJBIJgK5Fz/R6veczf9x333246667PP/WarUoKipCbW2tp+pjotHa2orLL78cDQ0NUCgUMBgM+OCDDzBq1KiA1zzzzDN44YUX0L9/f+j1esybN8/T8/vll1/G2rVr0dbWhqqqqoj6+VIolMSgrq4Ol1xyCcaOHYvVq1d3W14/qoSdGx4DamtrCQCyYcOGbs+zWq1EIBCQN9980+f4lClTyIwZM0J+nlarJQBCLqvhj23btpGmpiZiNBrJjh07SHNzc8T38sd1111Hxo8f72mac9ddd5GBAwf6lLDw5q233iI5OTnkwIEDhBBXSZH//Oc/Xc77/PPPiUgkiupYKRRKfHn77bfJ/PnzPeWKeiLLwiEhEvfq6upQWFjoN6x2y5YtaG5uxmWXXQbA1f7TZDLhhx9+AMMwaGhoQElJCV599VXceOONIT1Pp9NBpVJBq9VGvMMYNmwYBg8ejG3btqG4uBjLli3DhAkTfM6prKzstjxycXExiouLuxznOA5KpRIrV670NNSpr69Hv379sGPHDr+7jMGDB+Pmm2/G1Vdfjbq6OgwaNMhThtqbL774ArNnzw6/NSOF0gfRm+2w2B0QC1goJILeHg5qampgNpsxZMgQj/+ip7IsHHrVJHX48GFs2rQJGo0GgCsv48iRIxg5ciRGjhwJAFi5ciW2bt3qURhPPfUUzj77bFx++eU488wz8e6772LUqFG49tpr4z7+2tpaVFZWQi6X+/38s88+w7p16wJeP3fuXMydO7fL8fr6ephMJgwaNMhzLD8/HzKZDIcPH+6iMPR6Pf744w/s3r0bzz33HHJzc3Ho0CE8+uijuPPOOyN7OQqlD+KtIDQGC/bXamCxOSAWshhWmI6irDiZfvxw7NgxTJo0CVdffTWWLVvWKwE7vaowWlpasHXrVgDATTfdBI1Gg61btyI7O9ujMMaPH+/TgWvo0KHYu3cvVq5ciZqaGixatAg33HADBIL4a/+5c+cGVBYAcP/99+P+++8P+77uiK7OfhmpVOq33r7RaATgatd45MgRSCQS/PDDD7joootwwQUXhBW6TKH0VY636D0KAgwDk8WODIUIuWlSdJis2F+rQbpc3Cs7jWPHjmHixIm45JJL8Oijj8b9+W56VWGcffbZQSt7Xn/99V2OFRUV4aGHHorVsEImIyOj288jNUm5i4K1tLR4dhkcx6G9vR1ZWVldzk9PTwefz8cVV1zhUTLnn38+srKysG3bNqowKJQg6M127K/VACDITZOits2AYy0G5GVIweMxSJOK0NhhgsXu6BWF8cgjj2D69Ol48cUXezUVIKmipJKNSE1SaWlpGDp0KNatW4dzzjkHALBhwwYAwNixYwEABw8ehM1mw2mnnQaRSITRo0f7tLY0Go3QarV+FQyFQvHFYnfAYnMgN82lIDIVYhxp0KJVZ4FEzaLDZIVYyEIsiK/IrK2tRXZ2Nl577TUIhcJezxujCiOGRGqSAlwdwG644Qao1WpkZmbiX//6FxYsWODZfTz55JNobGzEt99+CwBYsmQJZs6cicLCQgwcOBCvvfYaysrKPH2g6+vr8ccff2D//v1wOp3YuHEjAFd70XA7ClIoqYZYwEIsdCmGNKkIVgeH/lkKCPk8NHaYPD6MeO4ujh49ikmTJmHJkiW46aab4vbc7qCSIkLGjBmD7OzsmN1/1qxZkEgkePfdd2GxWHDnnXdi4cKFns+HDBni83x3L/Dly5fjxx9/xMiRI/H+++9DJBIBAHbs2IFnn30WgMsUuHTpUgCuPszd+WEolL6AQiLAsMJ07K/VeBTEuUPzkC4X90qU1JEjRzBp0iTMnDkz5OjPeJAQYbXxJhphtRQKJTGIZuhrIoTREkIwatQoTJ48Gc8880y3Zqh4yzK6w6BQKEmLd2RTNEJfFRJBr+Zb6PV6KBQKfP3118jLy+t1n0VnerVaLYVCoURK58gmgGB/rQZ6s723hxYRhw4dwpAhQ7B+088QyNNhsDh6e0hdoAqDQqEkJe7IpjSpyBP6arE5YLEnnqANxsGDBzFx4kTMuGI2zPICbK5owObKBhxv0Qe/OI5QhUGhUIKiN9vRojMn1OrdO7LJ6SS9FvraU7RaLSZNmoRrrpuLadfdBoZBwu6Ykmtm+yh//PEH6uvrAbi6FJ577rlBryGE4MCBAzCbzTj99NMhFAp9Prfb7fjjjz9gtVoxaNCgqEZKVVdXo6amBmPGjPE0unKj1+tRWVmJ0tJST4gwJbGJtp8gWviLbIp36GtPcTqdUKlU+Pzzz1Fafhq2VDZ6ckF6O1nQH1RhJAFff/011qxZg9bWVhw5ciRo4cCWlhZMnToVJ06cgFKphF6vxxdffOFJ+vvtt99w5ZVXQiQSQS6X4+jRo3jxxRf9JhGGi3u1VFNTg3379vlkmT/22GN46qmnUFJSAr1ejwULFuCee+7p8TMpsaOzn6C3S2R0pihLEVboayJEQbk5cOAArrrqKvzwww8488wzoTfbfXJBEnHHRE1SEbJ9+3Y0NzfDYDBg165dngKKseCuu+7Cxo0bsWzZspDOv+eeeyCVSlFTU4NDhw5hzpw5uOaaa+COoL7zzjtx4YUX4vDhw56ChbfccotPnSpCCKqrq7Fnzx6/9asCcdttt3kKRXqzfPlyvPTSS9i6dSt+//13HD58GDk5OSHfl9I7JIOfQCERIEspCaoAjrfosbmyISH8A/v378ekSZNw2WWXeaoxuHdMAIPGDhMAJuF2TImjupKMG264AYMGDcLWrVuRk5ODQ4cO4emnn8att97q9/xI60qFC8dx+OSTT7By5UqPGeruu+/Gs88+i127dmHUqFHQaDQYMWKE55pRo0bBZrPBYDBAJBLhwIEDmDNnDvR6PdLS0tDQ0IAPPvgAkydP7vbZa9aswR9//IHVq1fjueee8/nsmWeewT333IP09HRs374dgwYN6pUKw5Tw6JwB3Rur3mjsChJpp3T48GFMmjQJixYt6lITr/OOCQBadOaE2BEBVGH0iEOHDqGiogJpaWnYuHEjLrzwQlxwwQUYPHhwl3MjrSsVLqGURn/ooYewZMkSpKWlQaFQ4JlnnsFdd90FtVoNjuMwe/ZsXH755Z5s8E8++QTXXHMNqqqqIBaL/T63tbUVixYtwrffftultzAtv5689LafoKf+E7eyMVrsPrWietM/UFhYiBdffBFz5szx+7k7FyQRfUdUYfSAm266CWlpaQCAiRMnYvjw4Vi3bp1fhdGTulLhEEpp9FNOOQXZ2dlYtmwZpFIpdDqdp/nTwYMHceDAATz11FOeelOZmZnQ6XQ4cOAA+vXrh4MHD3ruO378ePD5fCxYsAC33HILysvLUVdX5/NsWn49uQnXTxAteror8Ba4DACT1dGrO6Xff/8dzzzzDN59992AysJNIu2IvKEKowe4lYX3vzs6OvyeGy+TVLDS6DabDRdccAEWL17s6XO+detWjBs3Dnv37kVraysYhsHTTz/tc9/Ro0fDbrdjz549ePLJJz3H165di82bN2PTpk2YP38+Nm7ciJaWFgAuP49UKkW/fv1o+fUkpzcyoDtXkA1nV+BP4BqtDljszl7ZKe3ZswfnnXce7r33XvD5/KDn9+TdYwlVGD1g+/btnsJgVqsVe/fuxfz58/2eG0uTlHep82Cl0XU6HZqbm3HWWWd5rh81ahT4fD6qqqowduxYMAyDN99808esZTAYPKG3U6ZM8Xm+0+lEeXm5xynv3sm8+uqrsNvtmD9/Pi2/TgmbnvhP/Alci43DyJJMyMRsXHdKu3fvxvnnn4/FixfjH//4R0jXJILvyB+0+GAPenrX1tbi3nvvxWmnnYa3334bf/zxB/bu3Rv17n/u0uRbtmzBI488gu+//x7An6XJ586d61PqfPXq1bjhhhvw1FNPeUqju5uvAMC5554Lo9GIxYsXQyqVYsWKFfjll19w4MABZGRk4IEHHsBHH32Ee++9F/369cP+/fvxzjvv4MCBAyGtjtw92r3DateuXYuZM2fikUce8ZRfr6mpwc6dOz0VdSmUzkRqx9eb7dhc2QCAeAQuwGB8eV7cV+gVFRXYsGGDT7XpUAjl3eNdfJAqjB4ojDvuuAPV1dXYu3cvBg4ciH/9618xSUZbs2aNpzS5N+7S5E8++STa29t9zEhr1qzxlEa/6KKLsHDhQo+w1+l0ePXVV7Ft2zbYbDaUl5fj1ltvRf/+/T3Xf/XVV/j888/R3NyM0047DYsWLUJeXl5I421pacGVV16JFStWoKSkxHN8/fr1WL58OXQ6HUaMGIG77767i1mPQulMpFFSve003rFjB3bt2oX58+dH/A7BrqMKIw5ES2E88MADmD17dpRHR6EkPz0JhU2FcuXbt2/HhRdeiCVLluCyq2+KmeKi5c0plBBIpIxdii89WdmnQrnybdu24cILL8QjjzyCG27+u8c0lkjRTpFCM70jJNYd9yiBSaSMXYovPSk53t21iVj8MBB79+7Fo48+ittuuy0pMuXDge4wImTFihW9PYQ+SaLGp1Nc9CQcNNC1R5u0qG83JVQCmz9+/fVXKBQKzJs3z3MsUaOdIoXuMChJRaqt2FKNnpQc93ctGAbVjTokepOkX375BVOmTMFvv/3mczwZ6kOFA1UYlKQiVXogpCo9EZD+ri3NVoAACb1A+PnnnzF16lQ89dRTuOmmm7p8XpSlwPjyPIwfmofx5XkJuTsKFfpXRkkqeru2Uaj0Zad8T0qJ+Cu+V68xJbRJ5+WXX8YzzzwTMGkXiI3zXW+2o1Vnjuo9g5E4s06hhEhv1TYKld6O/08EeiIgO1+bqAuEXbt2oby8HB988AEYhonrs92/sfYYtlXwB1UYlKSkN8IlQ4E65aNPIi4QNm3ahEsuuQSrV6/GtGnTfD6L9e7S+zeWo5JG/f7dQRUGhRJFErVoXLKTSAuEDRs24C9/+QteeOGFLsoiHrtL79+Y2RRf5z91elMoUSQVnfLJlAMRa+x2O2655RY883/PY/oVc3zmpCc5KOHQ+TcWT5L3V0yhJCA9cconiqPcexwagwU7j7ZAb7FDIRZgVFlW0vlj3O/j4AhYPhPx/NbV1aGgoABffrcJ1Ro7Nlc0+Owi4rW79P6NNWlNUbtvKFCFQaFEmUhs7oniKPceBxgGDe1GGCx2sHwGDRoTjFYH/jo2efwx7vdpaDeizWCFWi5CXoYs7Pn94YcfcOmll2LTll/RyCnhz0cVzyQ992+spS2+lZ6pSYpCiQEKiQBZSknIO4t4mDLCHYfBYkPlCQ1YPoMMuRhSER/HWgxo01ui8qxYm7nc72OxOWCyOUCIEyarK4cjnPn9/vvvMWPGDLz22msoLCkLmDga7yQ9hUSATKUk+IlRhO4wKJReJlEc5Z3HoRQLwXEE3Ek7OSEMGBAAPbObx2s35X4fiYiFnXMiUymB1mSDVMDC5CXku6OhoQGXX3453nzzTVx11VXQm+3d7iISMaIrmlCFQaH0MolSb6jzOJwgyFZJYHc40W6wgHMS9M9SQK2IfFUbz7Bj9/uYrQ4I+Dy06syQCgUw2R0hza/FYkFeXh5+//13lJaWenwhJdkKVDfrA/qoEimiK9pQkxSF0sskSr2hzuMQC1hMG1mE8sIMZCklGJCXhnOH9qxjXbi1wHpiunK/j1jIQipkwTA8SEUuRRFsfr/55hsMGTIEBoMBpaWlPhWSq5v1KMmWp0Spj3ChOwwKJQFIFFOGv3E0aEzQmqxQSUXIS+9Zolg4u6lomK683yfUKKmvv/4as2bNwooVKyCXy/3uiqqbDShQK1J2JxEIqjAolAQhUUwZ3uPoLLTtjp43NAol7Diapqtw5vXXX3/FrFmz8O677+KKK64AkDg+pkSAKgxKnyFR8hyiRTxLUETT3xDKbqo3hDQhBKNGjcK3336L8ePHe44nio8pEaA+DEqfINW69MXjfWLZeyRY2HG8M+a/+OILzJgxAwKBwEdZuMeaCD6mRKDvqUhKnyPVCgLG6316c2UdzzL2n3/+Oa655ppuq84mio+pt6EKg5LypJoNujdKUEQitHtqMouHkF6zZg2uueYafPjhh/jLX/7S7bmJ4mPqTajCoKQ8qWaD7o0SFOEK7Wgl58VaSA8ZMgSffPIJpk6dGrNnpBLUh0FJeVLNBh3sfaJddiOcMifu58ez1Ekk7/vxxx/j+eefx6BBg6iyCIPkXGJRKGGSajboQO8T67IboZiZ4mkCjOR9V69ejZtuugkff/xxVMfSF6AKg9JnSDUbdOf3ibUzPFThHC+TWSTv++GHH+Lmm2/GJ598gilTpkR1PH0BapKiUFKEWIbBhmNmipcJMJL3tVqt+PTTT6myiBC6w6BQUoRYruzDNTPFygTobRIL531XrVqFwYMHY+7cuVEZR1+F7jAolBQhliv7SBLpwnWWB6NzsqLGYAnpfd977z3MmzcP7e3tURlHX4buMCiUXiaaJT6itbLvPKZ4JtIFGo8/f8X4clfF2EDv++677+LWW2/FmjVrMHny5LiMNZWhCoNC6UViEdXUU+e+95gYACW5SpTlqHo10qw7k1igXQwhBOvXr8eaNWswadKkuI01laEKg0LpJRKxZIn3mPg8BgdPdKCiToPywgyMLs1EUVbvlPQO1z/z1VdfYfLkyXj33XfjPNLUhvowKFElHr2ak5XOcxPLqKZIsdgd6DDaYLVzqGrSQSriQyzkw+7gwk6+i+ZvIRz/zPLly3H11VejsrKyx8+l+EJ3GJSoEa9ezbEmFmXD/c1Nulwck6imnoy/VWdGXZsBerMNerMd2SoJlFIhMhViaE22kJPvYvFbCMUk9uabb+Kee+7B//73P4wePbpHz6N0he4wKFEh3uUgIiGUFW8syoYHmhsAPY5q6vxOPRm/3mxHdbMB+ekSpMuEcDg5NHaYkK0Sw+rgAIaB0eII+p3G8rfQXeSV0WjECy+8gG+++aZLifLO46O74MigOwxKVEj0irChrHhj5VPobm78tRDVm+0RreJLshWobtZHPH73OEtzVOinliMnTYrDDVpYbBxsdicAYFdVS9AdQyx/C4F2T3v27MHw4cOxd+9e8Pn8gNenyi64t6A7DEpUiHfDm3AIdcUbK59CsLlRSAQwWx3YXd3q2RlU1mlwrFmPY806vythf++073gbtEZrxOP3HqeI5SNdLsJpxZkYVZYFmYhFhkIU0o4hVr+FQLunV155BRMmTEBDQ0O3yiIZdsGJDlUYlKiQyBVhQ1UEsRJ0oVSX9RZk7XoLPtxyBCs2HML7Px3Gl9uqu5iW/L2TgwN4PF7Y43ebaICuJrLRpZnIVklAgJAVUSx+C4GE/TPPPo/7778f3333HfLy8rq9RyIGGSQbvb/8o6QMiVoRNtSQzFgmp3U3N94mHKuDQ7PWhDa9Gf2z5BALBKjXGLGjqtXHtOTvndJkQpRky1HdbAh5/JV17dhbo4HT6YRKJsKwwvQuiXB6sz1s53y0fwv+zFw7f9+Pxx99BN999x3OOOOMoPdIlL4oydxbnioMSlRJxIqw4SiCWCq9QHPjLcgYhoHGaIOAz4dKKoKQ5cPOcWjTmXGi3QCVVASWz0AsYP2+U1GWAgVqRUjjr6zT4Ksdx2FzcFBJhOCcTuyvBcaX5yFLKfE5Nz9diqpmfViKNJq/hc7CvuZEPcoGDsLvBw6iKD87pHv0drY6kPw+FKowKH2CcBRBvJWetyDTGGwQC/hgANgcHKx2J3QmGyw2J9bvq4fBYodaLkJehszvbiDU8evNduw73gabg0NeuhRmKweNwQY+j+fjnHYLuA6jDZzTiQG5SgwrUsd9UeA9R2++9hK+/fQ9/LxtD4ryM8K6T2/ughMxUTNcqMKg9BkScffjxluQteoysPNoK461GODgOBAwyE2TwGR1gBAnTDaXecZdS6nzbiAUXFFZgEoqhNnmgETEokFjQnaa1GOicQu4dr0FGqMVWpMNDRoT0mRClBeEJ6ijQVGWAu8tfxVfvPsqvvx6LcrCVBZueut3kOiRhKFAFQalzxOJTTkWdmi3IMtSSlCgVqBNb4HGYEHlCS0yZEJUnNAgUymB1mSDRMTCbHVELGzEAhZpMiGcTic0RisaNCYIWT5O6/+nicad9a0xWsEwQF66FA0aE/bWaFCgjn+JkC+//BL/98xTWL/+R4wcOTKuz44GieJD6QnJM1IKJQZEYlOOhx3arTzUCjEaOsww2R0Q8Hho1ZkhFbnCcHsibLxNPDweDzlpEpxapEZ5QbrnHLGABcsHtCabx2ylkriUTLxWxW7FzAfBtGnTsHXrVgwaNMjns2RxHieCD6WnUIVB6bNEYlOOtx3aW8hIRSzMdg5SIRsVYRPMnq+QCHBqkRrHmg1o0JigkgiRLhdCJRPFpXyJWzF/8NZLqDq4D6s++tijLJLVeZyokYShQhUGpc8SiU05lGuivfL1lw0erXsHs+e7dhykS+httMqnB8u6X/X2S/h61dtY9tr72F+rgYDlw2rnsKu6FWIBLymdx4nsSwsGVRiUPkskNuVg13hHFbF8dDHzREpvCpnygoyQQ3WDEeoOzWJ34NP/LsdXH76Dl9/9FKWDylFZp4HBYoPFxqG21YBhRRlJ6zxOVqjCoPRZIrEpd3eNv6iiY80GAKRXooqiSbQUVqi7OrGAxdkTL8SoM85B6aByNGpNaDNYkS4XoZ9ajAaNCYfqOyAXC2B1cEnnPE5W6AxT+hzeJqNIbMqBrkm0qKJEJJRd3cMPPwyVSoXLrr7Jo5jtHIFaLvIomiH90rDvuAYn2o3IVEqSznmcrFCFQelTBLKfhyts/K24EyGqKNHpbodGCMHSpUvx+uuvY/369V18N7urWz2KRsDycEpBGkaUZkKt8F/unBJ9qMKg9BliHeEUy6iiVCLQDm3p0qV44403sGHDBgwdOhSAr2K2O3wVzaiyrKSIjEol6K+Y0meIR6ZtLKKKwiUZ8hP87dCGDRuGDRs2oLy83O81yR6S2plk+J46QxUGpc8Qr0xbd1RRm94CgCBdLvZ7XjCBEYlASbb8BEIIHn30UUyZMgVXXnll0POTOSTVm2T7ntxQhUHpM8Qz01ZjsKCiziUQwDAozVagLFfVpahfIIHhr+x4MIESC5NbLFfBhBDcd999eO+99zB79uyo3juRSeYihFRhUPoU8TBreAsEPo/BofoOVNRqcEpBGkaVZSFdLu5WYLjLjpusdkhFApisrgY/wQRKtE1usVwFE0KwePFifPDBB9i4cSMGDhwYlfsmA8lchJB23KOkPO6Ocu5WnO4Cf53/ODufFylugSBi+ahtM0IsYCER8mDjnNhfq0Gb3hyw85u77HiHwQK7g0ODxojKEx2oadYF7QzXk46Bnd891u1MnU4nrFZrn1MWQGK3Mw5G4o+QQukBoa6S/Z3nvRMBEPKuRCxgAYZBTYseBrMNMjELkYBFplIMrdEGgAnoS7HYHTBZOdg4JxgA6TIhmrRmtOitcHCk2+dGanLz9+4SEevTBZBhGGgMth6vggkheOWVV3D99dfjhRdeiPg+yUwyFyGkCoOSsoRqK27QmLD1cBNY3p+1iX6qaIBULAAI8ZiEpCLWR+kEsu9rDBaYLHbUtBrRpjMhQyHGqNIsWO2ujGS1QtytwJCK+BDyeWAAaIw2SIQCZClcnfa8383fs/2Z3LrzQwSaoxElmRALWVQ16TxZ60KWj1adOaL+G4BLWdx555347LPPcMkll0ChCG7eCjb2ZIsycpOsEV9UYVBSllBsxcdb9Pj1j2ZU1mmgVogh4PMgFbM41mLAwDwlMhViHGnUghBg9ACX0N9fq4HRakd1s6HLzsUtgDMUIpybkYsjDVq06iwwWjmksXyPYlBIBBCwfGhNVqikIuSlSwH45nKYbHbkycVQK0TIS5dBLGChN9txtFGLqmY9QIjfXZN3JFGwHVagOWL5DEqyFdh/vN3TwjVdLkR1syGirHVCCG6//XZ8+eWX2LhxI4qLi4Ne093YY1WzK54kY8QXVRiUlCVYGK1buAtYBmq5GCarHcda9FBJhWBAkKkQw+F0Oa4BwMERpElFONZiwN4aDRQStsvOxS2AVTIhHBzBgDwVlBIhRpSq0S9DHlCQ2x1/CsNAuRwagwU7j7bgQF0HhCyDwflpcPsW/DnEQ9lhdTdHmUoxCtUypMtEEAn5ELH8iJ2zbW1t2LdvHzZt2hSSsuhu7ABStmZXokOd3pSUxW0rBhg0dpgAMD6mH8/qWiVFcY4CUqEAbXoLGIZB/ywFrA4OLI8B5ySesuIdJitYvstp689pLRawMFkd2HGkBfuPt2HHkRZwTuKjLAI5lBs0Jo/jubwgA9NGFuH80wswvjzPE1ll45yQCHkQC1jUthkhYvmeZ3fG/X7+xhnKHIkFLFQyEZwgELH8oM5Zf0EDhBB88803yMzMxIYNG0JSFsHG7q9ml83BYW9N9JzyFP/QHQYlpenOVuy9ulbLxbBzTmSnSXHu0DzYHS7Tk9ZkQ366DACgNdogFrI4tUiN6mZ9twmADOP7v974MwO5S3ezPJ7fGlctOldkVaZSjBadBYQ4YbVzaNVboJAI/QrxUBMVA81ROM5Zf+ajArUMt956K7799lvs3r0bKpUq5O8t2Nhpza7egSoMSsoTyFbsTyCOLk33+BO6i5KSiVi/grRFZ4ZUxGJUaRYcTgKWx0Br8o0u6iwMvUt3BzMdWe0cCtUyHDzRAQfnhMDLLxLK+3V3rr/joThn/ZmP9ta04eH77sbGDT9i48aNYSmLUMZOa3b1DnR2KX2a7gRiZyEaLBoJ8BLsDi7gqr6zMOxcutufc977Gs5JMChPhZJcJcpyVF2EeE/Lt3cmmHPW345p88+/4qefNmHjxo0oLCwM+5lA106DVrsDx5r1UCvECVGzqy9CFQalzxNIIAYL2/R3Xair+u5Kd4dqOgLg8UcEi4qKpSD13jEpxQJUHjqEU0eMwtbtu6BWyXp0b4VEAI3Bgp8qGlDTogcBg+IsOc4dmhfVToCU0KAKg0LxQ0/KYoS6qu9cunvn0RYc0WmhEAswqiyrWyUVKNGwN2oUuZXk3po2LPnH7ait/gPrNmzusbIAXEp7R1Ur6jVGKCRCMAzBiXYjdh5t8bxXtAo3UoJDo6QolE5EoyxGoPIj3UFOesiJP095CONr01uCRkXFin4ZUqx6YQlqDv2Or9esQf9sZVTua7E7YDDbwOcxUEgEkIuFYPkM9BZ7wPc63qLH5soGbK5owObKBhxv0UdlLBS6w6BQuhBucTjv1az7+nBWtm4FIBbwkKtSocNkxY6qVugtdshEAqgVYp/7BhofQGJSvj2U1fo777yDHdt/w0+bNiI/P79Hz/NGLGAhlwjR2GGC3mwHw7hCnBVigd/3SuZKsMkAVRgUSifC6ZvhbRoKVEIkGBa7A1qjFekyEawODnaHEzuPNGN3VQv4fB5kIj4y5GKky0QQC1mUZCu6jM8Vv8ugJFuO6mZD1GoUBTPNcRwHk8mEG264AZdeeikyMzMjfpY/FBIBRpdmukqtePkwApnskrkSbDJAFQYl5Qllhdz5nFAc196rWZVU6LeESCgr21adBVXNelhsGshFLHRmG/QWBzIVYmiMFhypt0IhYTF6QDbSAVQ3630Ug1tR7apq8SiUTKW4x/b7YKt1h8OB66+/Hnw+H//5z3+irizcFGUp8NexYrTpzQAYqBWB5zReTbL6KnQWKSlNKM7rQOcEK+LnvZpt0ZthdxCwPMBgdkAo4IVU3VVvtmPn0RZY7Rz0ZjuatWbYHE7kpklOZprzABDozXbsrm5DabYcKqkIYwZknezqZ8buqlaIhHyPgKxu1qNALe/xirq71bpEwOC6667D77//jvXr1/foOaEQat2lZK4EmwxQhUFJOkKNgAnFnh3snO7CVdPl4pMVXbVo6rCgXmNw5Qs4nODxmJCqu7bpzahp0SNTIUaWUowWnRn1bSY4nQRmmwM8BrBzTogFLEQsA73FAYfTVddKoXQpLQL4OLqjZYLpbrX+z3/+E3v37sX69euRk5PTo+dEm2StBJsM0CgpSlIRTgRMKLWUQjknUFQSAJRky1GvMcNsdyBHJQEYoFlnglTIR366BNXNhiDRVQwIGBgsNjR1mKAz2cDwAJlYAAfnhNHKQS4RQCpmYXM4IRLwoZb/Weo8ls14/NWZKs9XQi5mcdtttyWksnATSZQaJTh0h0FJGsKNgAnFnh3KOd4VaPUWO0QCPrRGl7kpUylBgVqOdLkQNjsHIevKxC7LVSFLKQm62lcrxMhLl2B3VSsICIR8HvLSpRiYn4Zx5XmobtSiok4Dg9UBqYSFg3MpDff4Ym2C8V6t80Fwy03XY+LEiVi4cGFU7k9JLqjCoCQN4UbAhCJMuzvHbfpycK4mSocbtGD5DBwcQb8MmUdop8mEIIRALhGAYRgABIQAjVpTl3Lq/gr8FWTKseNIi2tALIN+GTLYHRxKcxRQSYX4o1EHnp2D1U4gEnBd3jPWJhiFRAAxC8yZMweHDx/Ga6+9FvQamjiXmlCFQUkaIomACUWY+jvH22fBwFUZ1V8FWm+FozXaIBcL0Kw1Y8+xVkhFAkwYmhcwM9vdcMlktqOfWgYhy8BkdaKizrVrEvD50FusUEgEKM6Ww2B2gMcw4POYLkoynGY8kQjza6+9FkeOHMH69euhVqu7PbcnWfKUxIYqDErSEKn5JRRh6n1OZ9NXbZsBHUYrhherwbL8LhVo3QqnTW/BrupW5KVLIRGxMFsdaDNY0aAxBTSluZ3WpxZl4GiTDh1Gs6vxUq4SPB5Q326GQiIA5yTIUIjQoDGBx+N1qyS7UwiRCvO5c+dizJgxfpVF58TFWCXO0V1L70MVBiWhCCYU4hEB09n0lakU43CDDgarA4VyccAKtBa7AyDEc53qZIay1mQNaEpz75oAgtIcBTQGK9JlQvTLkMFJCFg+D/KTDu8GjQlClo/T+gdWkt0phHB9QDabDTfeeCNuv/12TJkyJaTn5adLY5I4F+tdC1VGoUEVBiVhCFUohGN+iYTOpi+rnUNxlhyCky1K/fk5xAI2oMlMJXVlaDd2mDw7D7fC8d41ma0cFBIhslVST4e74iw5pGIBLDYOaTIhBuSmoUDtX1AGUwjh+ICsViuuvPJK1NfXY8CAASE/r6pZDwaIauJcrMt9UBNa6FCFQYkJ4a7YEqkGkD/T17lD87r1c7gFjT+TWV66FMdbRNhU0Q6T1e7j2wB8d02tOgsO1mlwpNFVtdb93KONWlQ169GgMUJjtPoVasEUQqg+IJvNhiuuuAKNjY1Yt24d0tPT/c6Tv+cdazEgP0MCjd6KRlt0orZiWe4jkX53yQBVGJSoE+6KTW+240S7AVqjFf2zFDERCuGaG7prW+q+pz9BM748D+PL87pkiLcZrBiQp4RUwMJkd/k29Ga7n/EQv1Vr6zUmiAU8j6B3CzUAQXc4wUJwAVcLWPd4+Xw+zjzzTCxcuBBpaWkB56jz86qadKjXmMCAQCRkUZqtQFlu1wZP4eJ+jr8dWk+htafCgyoMSlQJd8XmVi4dRhvq2ozgnE6U5qiiloDWE3NDd6avzoJGJODjRJsRbXoLirN9GxZ1PlfpFPoIJd85MCA/XYJ+GTKc0Bix8UADRpVluvJApCfzQFg+tCYbjjZpUd9uCrrD8R5LZ0WoMViwubIBFpsDPOLA2v++gqceexj3339/SPPjft6xFgPqNSbkp0vQP0uBDpMV9RoTynLDa80a6DlqeeAdWk+gtafCg84KJaqEs2LzVi7FWXI4nU7Ua0zg83hRabkZS3ODt6CxO5yeHtu7qlvBY+CjlLoTSt5jTJcLUdXE4XirEVXNerTrrbDaOejMVjBgcKRRCz6PAeckUMvFqG7UQSTkB93hdMatCH2eLeHh3gXzYdTrYLA4kBbiPLgV0Il2AxiQmO0QQ9+hhQetPRUeVGFQokqwFZu3eaizcinNUYLH42FEqRr9MmJbPC9agmbn0RYcqOuAkGUwrCgDApbXRSl1J5RadGbPGK0ODhIhiyMNWvB5AJ9hIBby0KqzwOZwQioSgOW5alWZrA5IRALk+Hm3UEtiuOcnXczgvltvhNVkxP3PrYBIGl6nPIVEgH4ZclQ16WOyUg+2Q+sptPZU6FCFEQVoSN6fdCccO5uHSrLlXZRLmkwYFWUBhG5uiPT7K8pSwEkI9BY7+mXIIBML4HQSv8IskFDqPEaZmIXVwcFqdykPlVQIu8MJu8OJtAwBdCY7RAIe2gwWCAU8dJgEEQto97NPNDUhLV2Nex5/GVKZIiIhH8uVejzMRrGOvEsVqMLoITQkryuBSoN3Ng9VNxtQkq1AdbM+JuaAzkIMDIPS7NBKm4eqRNQKCTKVrlLkEifbrTDzJ5Q6j1ElESFTIYHBYvPkX9RrLOAxPBgsdsjELDLkIkiELAQ8Hqw2LuJoJD6x48DmrzF03MWY/6+nPUrcXXgxWCXgzvMTq5V6IpiN6KLQBUMIIb09iHij0+mgUqmg1WqhVEbee1hvtmNzZQMA8mfnMzAYXx4dh1wq0aIzY3NFg8es4F6Jjx+a5zFPxeqPUW+242iTFtWNOhDApzy5v+/P3Zwo1EVANBYNboFktNjx494TqGs34ESbEUarAw6nE5kKMZwEELE8CFg+RpdlQcDnYWRpJmQn25WGM3cmkwnTp0+H3W7HZ2u+AeHx0aqzoLpZH/Q9emuR1FtCO5EXhdGSZaFCdxg9gIbkhU53ZoV4mAPq200+TYb212owtCDNbx7B3hoNFBI2ZEd5NFbW7jkQC1iIBHwQAvD5PIgFfLA8FoUZCjTpTMhUiMHyTpY2F7JQK8Iv4W00GjF9+nRwHIdvvvkGcrkcerMd1c16BAsQCBZIEEuh3htmI5qn4Qvth9EDYtmLINVwmxW8eyvEy6wQqOcFwHT5/lg+4HQ6/fbH0JvtaNGZPf0tvP+tkATvv9D5+u5wEgJCCGQiFkIBC4GAQZpMCIeTwO4EhHxexPO3fft2sCyL1Z9+CbOT7xHywfqCdDeXFruj214l4bx7IhHqvERCMs4JlWw9IBFsq4mO94qzt6JRAu1u1Apxl+/v1CI1qpu7Rvt0Nteo5SK0GaxRNVu5Exjb9GbwGQYMwwMBIOAz6DDaIBbwUZqrQnm/NE9+g3fCXTAMBgMOHjyIiRMnomToSOw63tFtAIK/xU+guXRwJOBKXGOwJKxJJxixcrgnspmrO6jC6CE0JC8wgf4oEqHUh1uxKySCLt+fTMT6nOvyafxprmnsMGFTRTsG5ClDMlOEYtZwz9XhBi12VrdBxOdByPLQpreCYRiUiFiMHpiNswflBow4y1RKPIKs8+9Rr9dj2rRpyMjIwH9XfYIDdR2IJAAh0FyyfMavebZNb0FFXfKadGKxKExmMxdVGFGAhuR1JZp/FNGwi3en2Dt/f53PtdgdqKzr8AhDiYh1ZRwL2JB8V8F8Xe65stgdsNo5iPi8k+G6NghYPnJVEgzKT4PJbEeb3gyDxXduq5q02H+8HQVqV/IjAEhFLBgAJblKZEt5mHnZXyCRSPDhhx/CGGA8mUoxCtTyoHMdKArO30ocIEnv54v2ojCZfZ9UYVBiQrT+KKK5dQ9HsXc+11sYmq0OSEUCmOwOKJ3CoGaKYGYN91xJhSxACLJVYrQbbZAI+eAxPGSqJBAJ+DhQ13GyRSwLg9mG8oJ0WB0cNAYbbA4OEiEfhxv0IAQozpbjWLMBFXUaHNr0CXisEGvWrIFEIgEXQLiHE4DQ+bxAK/F0uTglSm9Ec1GYzOVIqNObEhOiERDQeZcCuOzk8XYSuoWhxe7EkUYtwDCYcDIcOJgD3707KsmWw2rjcKRRC6uN8znfPVcmuwMSIeuKjmJ5cDgBhgfkpklwrFkP4cn2rQKWQZvB6uq1YbShWWeGRMiCxwP4PAZ2hxN/1OvgsBrhtBowdeYNuOuJ1+GAbxHCaAcgFGUpXKVJhrrKk7gVe366FBa7M+7BDolKbwaA9JSQ/3pnzJgR8k2/+OKLCIZCSSWiYftNlK273mxHu9EKq83VT5shBEqpEDlpEgCuwoMsn+lS28h7d2SyOmB1OCHgMyAMA6PV7uOwds+VVMRCbhciL00GHg9g+a4QWwfnxLCiDMjEAkiELDR6K1r1ZtS3m6AxuvwcVhsHrcmOVp0ZNrMe37y0GGVDh+PsZU/6dAgEYptk19kv425zW5KrRFlOz6vXpgLJ6vsMWWEUFxd7/n97ezvee+89DBs2DKNHjwbgCtU7cOAArr322qgPktI9iZqF2tM/ikTYuh9v0WNHVSsqa9vB8nkY0i8NerMdX+2oQaFaBs7pynuVitgumeLu3ZFKJsThBi0YBhhVmoUGjRFf7TiOArUcaTKh5xr3XDk4ApbPQCxgYbDY0agxwUkAi52D0WKH1cEhXSGG1cahvEAEADjWrEdFXQc4zgkxbPjshXshlqtw/uyF0Jptfuctlr43fz6s+nYTynJ6Xr02VUhG32fIf3nPP/+85/9fddVVWLp0KZYsWeJzzsMPP4w//vgjaoOjBCfRw/N68kfR22HLbqFnd3AQC/kQsjwcbdIDxOnxGfzRoAUhwOgBWbDaOZ9e3e7dkd5iB8t3JdsZrHaPzyFdLgQhxCcYwN8OpaHdiJpWAxycEzIRi/5ZCgwrykBlnQYqiQgiId/VRrZeC5bH4MPnn0R+bg5m3f00zA7A7iAYXRpfk0ei7A4p0SWipdoPP/yAV199tcvx22+/HYMHD+7xoCihkczheaESza17uDsxt9DLVIjRorPA6SQwW22w2DlkKyVgGAb8k1nXDo747dXdYbJCJODDwREwDECcgNZsg0oqhFjAh+hk29fjrXrweQxUUhHy0qU+kVMmqwMyER8CvgCZSglkIhack6C2zYijTTqoJEKky4VQCQmEEgluvON+9MvJQruJg8PpxPjyPOSlSyOet0hIhN0hJfpE5PS22WyoqKjocvzAgQOw2Ww9HhQlNGKZhZpIhJJFHYxIspDdQs/q4FColsFid8DuJBAL+EiXCyEXC8A5iceE5J3E5nJ0KwAw0Bpt6JchQ366DGabA0KWj3SZyNO3u0Fjwhe/HcN/N/2BN9dVYMP+uj8jpwQs7E4nMpUSgGGQpZLAYudwtEmL/HQpVFIhtGYbjhxvxLLbr8Efv64FK01Hg9ZlhjpzYE5IyiLaWcfJ7NilBCYidX/99dfjiiuuwAMPPIAxY8aAEIIdO3Zg2bJlmDt3bpSHSAkEXcWFRnc7se6ykL1NYpyTYEBeGkqzFRAJeKhuNkBrsiE/3dU7Qmu0eTLAd1e3dptQ584ab+wwwWbn0KQ1AyAgToJmrQlfbKuBgM/3RE4JeDy06syQigQwWx3g8XhwcASlOUpYHRxaWtuweP4CFBf3x9CzL4LR5lJgJdmKkMyTsTJrJqtjlxKYiCTL//3f/yErKwsPPvgg2tvbAQBqtRp33nknFi9eHNUBUgLT2zb+eNFTp34ge3qb3oyKThnPnU16gZzRBWqFZ0zuZzg4gt3VreicQV2g/jO73b1bcifI1bUasL+2HcRJwBFALmbRqrVif207JgzNR3WzHlIRC7Odg1ToWiCUn6ym22GyQikW4KFF1yM7rx/+9tBzEAgEKFaJTj5bjwJ1971F9GY7dh5tgY1zIlMp9vHDpNrviNJzIlIYLMvigQcewAMPPICmpiYAQE5OTlQHRgmNZF3FBVMC7s9DLbndHYF2YoD/chadHbMKiSBoPSSFxLd7XqCMbu93VkgEcHAEApaPujb9Sf+GHQQEh+s7cObAHE+7VW9l5SpfIsC+4+1o1lkw/54lGH/WWNS2W3zMk6E4mY82anGgrgMSIQ8tOosn8isazulED8ighE+PE/dycnKosuhlomHjjyfd+RO8P//h9zp8taMG7XpLjxL3AtnT1QpxSMmFwRII3fZ/B0cC3i/QO+elSzF2QCYsVifajVbwGSBDJoLDSVB5ogMAkKWUIC9d6vMdSxkrHl4wC2mOZtw882IMKcwKO1FSb7aj6mRCoIDPByGu3uQM0GOzZqIkXVKiS0QKw+l04uWXX8aIESOgUv0ZV7148WLU1dVFbXCU1CMU4ev+PF0mgs3BQWO0wurgeuTU95eFHKpj1m3SErF8V2kOlu+3pPfu6lao5aIu9wPQ7TuPHpCDYf0zkKUUIzdDhiyVBBlyMewOzu+7trS0YPLkySjuX4TxZ4zy7FbCdTJb7A6AEAzOTwOPx8DmcMLBOVGSq4xavaRUD8joa0S0jHj++efx4osv4h//+AcWLlzoOT5kyBA8+uijeOONN6I2QEpqESw+3/tzq4ODSiI8maXMwXzStBHp6tdfTkgoJj2xgIXJ6sCRRi34PAackyA/Xea3pHebwYoRJZk+5qNgpiqxgMWgPBVMNg58nqvXBY/HQC4R+ryr3mxHU0sr/jrtAgw75RS8//77YFk2rHfp/F7uAoHl/dLQqrdAwPKjklxHAzJSk4h2GK+//jo++ugjLFiwwOf4BRdcgM8//zwqA6OkJsFqTPnkL7Cu8FUhy4fGYEM0QjN7Ej7qbmbs/l+rnfO7imb5jI/5KNg7KyQC5GfIYHdwqG0xoKbFAJlIgNGlmT5lNjZXNmBPnRHnX3Ydnnj+dbTobTh4QoMGjckzxkDmSX/v7b0r0ZpsUEiEPs/sCTSsNjWJSN3X1NTg1FNPBQAwDOM5LpVKodPpojMySkoSLLKr8+cZCglGlWV5QlN7movR2QkLIKhj1mJ3QCpiMXpAlsf5rDXaAJCQVtHB3llvtuNwgxZC1pWxDQBiIR/pcrHn8592H8Jb//cw/vXov3HRjFlYu+cEOow22DkOUpEAE4bm4YxB/n2J3fXOiGXQRLIGZFACE5HCKC4uxs6dOzFu3DgfhfHpp59iyJAhURscJf7Eoy5VMEESC0HjLxdjR1UrGEIgEvK7zZT3JPDZuU4d+yQYVsiEFNbc3Tu5mwzZHJzH5HW0UYc2vQUKiQA1tbX45/zZOOW00yFXKNCgtWDv8TZkKyXIz5CiWWvGD/tOoChL4ckS9w75DdQ7w13Hyq2YYkFv10tK1DpryUpECuOuu+7Cddddh8cffxwAsGnTJnz77bd4/vnnqf8iiYlnGGQwQRJtQePPd3KkUQsAUMqEJ/tM8KE12vyG1YbTsS/cdzJabdCZrBAJWMjFAnQYXf82Wm1oaGjAZdOnYvCw03Hrg8+gVW/FrqMt0BltELF8mG0ceDxAbzLjUL2r7pX3d5ifIfXxCXWuY/VTRQOkYgFASMqFvtKw3ugTkcK45ZZbYLPZcMcdd8DpdGLixInIzMzEM888g+uuuy7aY6TEgVSvS+XPCSuXCKExWLDjSAtYPgMHR9AvQ+bXMRusYx8ATwRQuPMlEwmhlLoiwgwWB+wODnweDyYrhwwWmHnllbjp1nuw51g7DtRpIGR5SJOJ0GGwQizgQypmIWB5ONZkgEZv9dkxVTfqAMZVtoQHxqeOlZMQHGsxYGCeEoVqeUp956n+e+4tIs7DWLRoERoaGnD8+HEcO3YMTU1NuPXWW6M5NkocSfUwSH9O2PJ+aQABrA4OdocTXtbVgPfw51AOllcSDLVCjKEF6UiTiWC3c2g32tDYWI8Hli5DjY7BsmXLUJKbhhGlmSjKlGF8eR5OK1aDx3P11QAYjCjJdO00LHaf75AAKD1Z00pjtPrUsWrVWcCAIFMhTurv3J9DP9V/z71FRDuMc845B9dccw1mzpyJwsLCaI+J0gv0dhhkLGzNne/ZeZdwtEmLxg4ThHwe+Hwe+mfKwfJ5YWU5+/S9kArRqrdgR1Wr35VsoHdUSAQ4d2geuP0ENc16EFM7fnzlHpQMHYmNB+rRP1uJvHQp1AoJMpUSWB0cBuWp0KQ1wWZ3YlRpJiQiFha7EwwhXb7DslwVynJVXepYCfk89M9SwOrgIHGySRn6Gsjs1Nu/51QlotkbNWoUli5dittvvx1Tp07FNddcg+nTp0Msjp3zjBJberMuVSxszYHu6fYj6M12VDfqwPJ5YPkuR/PhBi3KCzPCEirulSyfx6CqSQ+r3QGLjUNpjgLDizMBuBTF0SYtqht1IIDfdyzKUiA7TQxdWxO+e+luFJ8yCrMXLYHe4oDWZEVeutTnO9KabBhwMl+CYRhY7M6ThRH5HoXgLwLNu46VWMB6Sp7E4juPtcM5mNmpL9RZizcRKYwXX3wRzz33HNatW4f3338fN954I/h8Pi6//HJcc801mDRpUrTHSYkDvREGGQtbcyj3tNgdIACyVRJU1LkUCwEwZkB2WM8VC1iAYXCovgNiAQsh66okW92oQ1mOyuUj6dSxz53p7T2eBo0JB+s60NpQg9whozH68tvwR4MWRVlKqKQiz/M6f0cAcLRJi8raDuytaYNCLMCQgrRuw5C9ne/hOO3DIR4O52BJoDSsN/pE7MPg8/mYMmUK3nvvPTQ1NeG1117Djh07MHny5GiOjxJn4l2XKha25lDuKRawYAA0a83ITZOgUC1HXpoUBqsjrKQ+hUSA0mwFbA4CO8eBYVxKwWLncKRBix1VrZ6OfVIRH7VtRogEfJ/xHG/RY9W6bdiwYSPKTh2Ls668DVbOCZOVw6lF6V36WXT+jvbXtON4qx4tOjP+aNBi59HWsARktL/zeNWRCpYQCSRfnbVEp8fFB5ubm/H222/jhRdewN69ez0JfRRKKITyRx+LeyokApTkKuHgnAAAqViAYUUZACFhK6uyXBVOKUhDgVqO8oI06M021LYZsbOqGZW17WAAiAQsCGFgPelHcI9Hb7Zj/bZ9eOKOa3F87xaIBSxUMhFKspXon63Aaf3V3T67TW/BsRaDqxSJkAXLZ3CsxYA2vSXseYsW8XI4RyObPNqNo1KdiP4qDQYDvvjiC7z//vtYt24d8vPzMWfOHLz11ltUYVDCIha25lDvWZajQnlhBuwODpkKMawODgIwHsUSqg1eIRFgVFkW9tdq0NxhRr3GjPx0KfIypGjssKDyRAdKshWuhkkOAiGf5xnPzn0HsfjmWRhx5gQMnHoLtGYrnE5ALOBhWJEaaoUkyNsSmKx2tOocYBgCQhhIRa76UL1FPB3OPTE70TyN8InoG8zJyYFQKMTll1+OH374ARMmTPDJ+KZQwiEWtuZQ7qmQuOo1uZ3I3oolXGHift6Rhg4YrQ7kZUhhsjjAYwjqNCZwnBP9s5UYVZqJslyVZzyfrHofw8+ahLNmLgIPAMvnnSyyKMCosj/rOgVSXiIBCwfnRLvBAh6PgdNJIGSlEPViNFC8Hc6RJHnSPI3IiOhX9d577+Hiiy+GSCQKfjKFEgKxKCHhHRHVojOHXIYkUmGiMVhQ3WJAU4cJjR1m8BgCPo/BoHwlMhUSKMQCj7Korq6GXC7HLbfdg+9+r8We6naIBHyoFSL0y8iAkwAS4Z+FBwMpL6vdAZbPc5VVZxiAELB8Hqy9nG+Q6A7nYA5zin8i8mEsWbKEKgtKUhBKUl1nx2gkNni3knGZkjLAOZ043mqEgOVhYF4aBuSqQE7e++jRo5gwYQLeXP4OKk5okaWUYkCeCulyIZwEqNeY0NRhwq7qVlTWtXuUhUTEwmJzeBzIerMdGoMNQgEfBZlyDMhVoSBTDqlIAKD3d/yJ7HCOhe+sLxCRwqitrYVWq432WCiUqOIvWmdHVSuONeu7dXJGIky8lUxOmhSjSzORrRQjUyGBWi723OPw4SM4d8IE/GXGZbh5wSLPKndQngoKsQANGhOchGBYUQbEAh62H23Fwbp2VDfrUVmnwYl2IxrajTjaqMXmygYcPKGB00lO9gxxtXItzpJDraA5Ud1By69HRkTq9LLLLsObb76Je++9N9rjoVCiRmezg93hRGVtOwxmGzKVkoB+iUhs8J0dvQyPQXGWAgwDHGsxIE0mhFouwoK5izD63CmYev2daNNbPNeoFWJojBJojDaMKsmEWilBU4cJf5zQoEVvgYjlozRbAZPVDp3JjoN1GihlQvTPUoBzOlHTYoBKKoRaLsaosiwq+EIg0c1miQhDCAk7nGLmzJn4+OOPMWrUKAwdOhRCodDn8+XLl4d8rx9++AEvvfQSmpqacOqpp+Khhx7qttzIY489ho8//tjn2IABA/DJJ5+E/EydTgeVSgWtVgulUhnydZTEo7tIJr3Zjs2VDQAIRCwfO6taQAgwekAWrHYOAIPx5XkBBUW4mcrevgaT1WW+4vMY8Hg8pPHNcAgUMBv1yMtSQ2t2NYQqyVagulkPi80BBoDR6kCGQuQZr9nGwcE5YbZx4JxOFKplELB8qKRCDMhVeRzdNS16jCjNRL8MORV8fYh4y7KIdhg8Hg+zZs0CANhsNthstogevm7dOkybNg1LlizBWWedhRdeeAHnnHMO9u3b59Mr3Jva2lpkZWXhmWee8RyTSIKFHlJSkWCRTN47hRPtRtgcBKcWpUMmEkAiYP06OTsrie4+64x7xdqmt2BXdSvEAh7SpCLsr6jA3+fPwY33PILLZvzVx8maqRR3KdWx82gLjrcYYLI6UF6Qhhad1VNyJFMpgVwi7FIzSiUTUWVBiTkRKYxVq1ZF5eEPPfQQZs2ahQceeACAq6hhXl4e3njjDfzjH/8IeJ1KpcLw4cOjMgZKchJqJNOfQtyM3VWtELC8gH6J7hRQZV079tZo4HQ6oZKJujVnWewOgBCkSUU4Xn0E//rbVRg/9VKMGT/Zb26Ct2LSGCwgDAM+j4GAz4fdQVColuHgiQ4AgFwiRHm/NFgdHKobdWi00TpJlPjR40zvSDEYDPjtt98wbdo0zzGxWIzzzjsPP/74Y7fXbt26FWeffTamTp2Kxx9/HGazOdbDpSQY4UYyycQCDCkI7OTsrpxFZZ0GX+04jsMNHWjVWdCuN3db6sLtz2jR6nHXzVfh/L9cifl33IfT+mcGfL73GMQCHk4pzED/LBnqNSYYra7qtBeP7o/yfipUN+tR1agDYRiU5igxvjyPJpxR4kJEOwyn04lXX30Vb7/9NqqqqjwRU4sXL8aiRYtQUFAQ9B4nTpwAIQR5eXk+x/Pz8/HDDz8EvE6hUOCWW27BhAkTcOLECTz88MP44osv8Msvv4Bl/b+O1WqF1Wr1/Jv2HU9+Qs0m7q6ftbewDhSX36Y3Y9/xNtgcHPLSpTBbXV3r+LzAZdAVEgEGZktxuBn413MrUFI6AKcWZaAoS+Fjfup8becxlOao4OAISnPk6J+lhFws8Phk3LuqqmY90mQiz3N7Cm1pSumOiHYYzz//PP7973/j5ptv9hG+Q4YMwaOPPhrSPex21+qscz6HSCTyfOaPxx9/HA8++CDOPfdczJkzB2vXrsWuXbuwevXqgNc88cQTUKlUnv9oD4/kJ5SwSH+7hupmg19hGCiUFmDg4ACVVAizzQGJiA+t2QYej+dTQsS7HtGBAwdw/jmjkC80YtZFZ+PcofmeHUB3uQmdx1DVpEWzzoKqJgN2V7fiaJPWZ1fljvr6qaK+S45JJDWSetoIipL6RKQwXn/9dXz00UdYsGCBz/ELLrgAn3/+eUj3UKtdRdXa2tp8jre1tXk+84dA4PuHVlJSguLiYuzbty/gNffddx+0Wq3nv9ra2pDGSEkc/AnAoiwFxpfnYfzQPL9mmXDMVoEUkFohRppMiHSZCIS4ypALWT5O6/9nCRFvIbvup98wefJk3HjjjRg+rDysxDXvMdS06D01qYqz5ACIT7tVo8WOQ/UdYPk89FPL4G1Ci0Twx6vCLCW5icgkVVNT4yky6F1DSiqVhmzuycvLQ35+PrZt24bp06d7jm/dujWsfhp2ux3Nzc1QKALbcEUiEc1M72V6Yurw54z2jp/PUvqPkgunCJ7ebIdExGJESaar8qvXON2RVjweDzlpEpxapEZ5QXoXIXvkWA1uu+piLFy4EEuWLAl7joA/nfQn2g0gYFCcJf/TRGYzoTRbgXqNKWDUV5vejIq6DoRT1kRvtuNEuwEdRpvv82ipDEonIlIYxcXF2LlzJ8aNG+ejMD799FMMGTIk5PvcfPPNeOONN3DTTTehuLgYH3zwAQ4ePIj//ve/nnMeeeQR7N27F5988glsNhueeOIJ3HvvvZBKpbDZbLjrrrtgtVpx5ZVXRvIqlDjgT+ALWD60JitUUlGXfg/e+IuG+qmiATIRG7B7nZtQE/D8jc9bCXmHywLEU0HW2+dgsTuQmZ2Lv93/DO64eXaP5kshEaBfhhxVTfqA7VbdUV+ck0BjtMJsdXhMaOHUSHK/u9ZoRV2bEU6nE6U5Sloqg+KXiH4Nd911F6677jo8/vjjAIBNmzbh22+/xfPPP4833ngj5Pvcf//9qKqqwuDBg5GTk4OOjg689dZbGDlypOec48eP4+DBgwBc5iiWZdG/f3+kpaWhqakJhYWFWLt2LQYNGhTJq1BijD+B/9WOGnQYbbBzHKQiASYMzcMZg3L8Xt/ZESxi+ahp0WNAngqFannQFXSwbN5g4bnunVGrzozqZkOXXY5YyGLDz9uw8oVHMWfx88goG4FWnTngridUgik7hUSA6mYdfqpohN3BQSkVYcLQPKgV4rB2Ve53d2eM12tMrkRDmZCG6lK6EJHCuOWWW2Cz2XDHHXfA6XRi4sSJyMzMxDPPPIPrrrsu5PsIBAL85z//wbPPPouWlhYUFxd3ScJbsmQJTCYTAJf56/7778d9992H6upqpKenIyMjI5JXoMSJzgKfEIKKOg2yVRIUZsrRqjNjU0UDirIUfncanc1KrXoLCBhkKsUhm066q4TbXdVSd7/rDqMN1c06ZCpEGJCrgtXBYX+tBuPL82BpOYbH77gOZ//lGmSlKZEuF6K62YACtaLHwrY7ZVdZp8H2I61wEgKlRAi1XIg2gysSMNSyJv6isvg8HkaUqmkSIMUvEe83Fy1ahFtvvRV1dXVwOp0oLCwEjxdZWkdmZiYyMzP9fuYvoonH46GsrCyiZ1HiS2eB39RhhtNJkKMSg+XxkKmUoLbVAK3J6ldhdF5pC1g+irPksNo5SARsj00ngfwcDo54Vt+EOFHfbkCL1gyT1YHSHCU4J8HO3bsxb86l+MvVN+P6+YsgEvIhYvldFJh7l+LgSBf/SDD8KTu92e4J9S3KlMNs5WCyctAarbDYHSHXSPL37qFkjNPQ275LRH9lNpsNP//8MyZNmoTCwkJs3boVS5YsQVlZGf75z392iWSi9F06C3ypkEWWSgKtyQYBy0erzgypSACVNHBQQmcB6F75R6M5TyDTD8t3+QJUUiGatWZIhK5GRXaHE4fqOzAgLw39Cwqw7PEnUTz2IjhP1qvqrMDcPoKGdiMatWYoxQL0y5RjdGlmxMl2LuXjHerLokFjQnaa1PPcUPqLRFJkkXap69tEpDCWLVsGsViMSZMmwWAw4JJLLsGYMWOwefNmGAwGPPXUU9EeJyWJ6SzwS3LasamiAbWtBo8PozvHN+ArABUSQcRVRv2tjgM1URILWbTqLXASggy5CK06C0w2B+qPVqBi7Q5c+vqLuPb6uTjapPVbpsPtI7DYHWjqMKNJa0QLw4PObIPJYsdfx0bW3U0sYJEmE8LpdEJjtHYJ9Q2HcCq20i51lIg77m3ZsgUA8P3336OsrAxr165FRUUFpkyZQhUGpQveAv+MQTkoylKEFCUVyv1CpbvVcef7uVffO6paYbFxsDo4pMlEqPljPz548jbcfvdi1LYaPPcDw6A0W+HTftXtI+AxDFr0ZsjFAnBOAgHLQ02LHm16c0SC1ntn0DnU15twepKHMg7apY4SkcJobm72VJPdsGGDpx5UcXFxl0Q8CsUfeenSiBRFdwQrdR7u6ti9+s5Lk2L9vhOoPbwPHz55Gy6feyvGTrsKO4+2QCTke+5XrzGhLPfPKstuH0Gr3gxCAJudg1jIgs9jYAODULriBXqnYDuDaJqOvH0woUZgUVKTiL7pYcOG4bnnnsPFF1+MVatW4euvvwYAVFRU4JRTTonqACmUUAgmICNdHSskAgzIU+JYsw68FgHm3fYPzL5uHo40uuqn5XiF+55oN/rsGv7cpXCQilgYLHYo+byQu+KFUr7d39ijaTrqPAa1XIQ2gzUq/iNK8hGRwnjmmWdw2WWX4aGHHsL111+PM844AwDw3HPP4Y477ojm+CiUoIQiIN2r/cYOEyQi1pPoFsrq+PddO3Bg9xGcOnY8xp5zLjpMVp+eFG5HuM1BsLuqFTyG8Qh2906gNEeBytoO2DkOCrEgaFe8ngj9aJmO/I2hzWD1mw1P6RtEpDDOPfdcNDc3w2Aw+HR5WrJkCQYOHBi1wVEooRCKgFRIBFDLRdhU0Q6T1e5xtgcTeL/88gsu+8sluGzenRAXno4jDVr0z1Lg3KGuKss7qlpRWdsOls/DqUXpELC8LoJdIRFgeHEmynJUITvqeyL0wymJEskYWD7T48RESnISsfGRx+NBKpWiqqoKAFBUVESzrSkxJZA9PxQBqTfb0WawYkCeElIBC5PdgTaDFXqzPaAA/vnnnzFt2jRcu/AfuPjKayAS8HGizQgCQMDykZcuhZMABrMN/dQyyEQCOJ0kKo7gngj9SMJloz0GSmoS0TdvtVqxZMkSvPzyyzAajQAAmUyG2267DUuXLu3S45tC6SnBIpyCCcjOq2WlUxhUsK9duxYPPrwMpWdejDSpCG16C7QmG9r0FghZPs4alA21QoxMpSRoImG4TuieCv1wwmVjNQZK6hGRwrj99tvxzTff4OWXX8aYMWMAANu3b8dDDz2Ejo4OvPrqq1EdJKVvE4o9P5iADGe1/OuvvyIrtwC3/+N+ODiC3dWtaOxwVYg1We1Qy8UQsIynPEgwoRqpP6KnQj+S0ONoj4GSWkSkMD744AP8+OOPHmUBAKeccgqGDh2KCy64gCoMSlQJ1Z7fXdSQxe5ASbYc1c2GblfLmzZtwiWXTMet9z+JMROneCKDalpdOwu1XIziHAXUcrFnDMGEak/8EdEQ+j0lEcZASQwiUhhSqRQDBgzocnzgwIGQSqMbW0+h9MSW3rVFqwKZSrFfwb5x40ZMnz4dN931EMZfeLHnWW0GK0aWZIEQBgKWgVou7jKG7oRqTyK0KJREIqJqgVOmTMHTTz8NQojnGCEETz/9NKZMmRK1wVEoQGjtWP3hv0Wr3q+yIITgoYcewuNP/x/OnXp5ly59KpkQZw3KhvhkoyL3GAAEbYXqjtA60qjDb3804UijDmq5iK7aKUlHyEucefPmef6/yWTCk08+iY8//hijRo0CIQS7du3C0aNHaSMjSkyIxJYeqimooqICQ4YMwfr162G2E2yubPC7m8lSSroUQdxc2RDUkR1JhBaFkoiEvMMwGAye/3g8HmbNmoXRo0eDYRjweDyMHj0as2bNirjEOSW58ddzO9ooJIKwemR7m7KcTuLXlPXDDz9g7Nix+O2338CybNDdjHsMADy7F5VMCL3Zhp1HW/y+v0dxqaRIk4uQq5IG7C1OoSQyIe8wVq1aFctxUJKYRC15HSwsdN26dbj00kvx+uuv46yzzvJcF8puxq0E+DwGVU16WO0OmG1OlOQoMbzEt7cLzWegpAoR/2KPHTuGw4cPgxCCwYMHo3///tEcFyVJSPSS14GEv16vxzXXXIM333wTV111VZfrgkUGiQUsGAAHT3RAKuJDwOeDYwmqmvU+FWvd96L5DJRUIGyFUVFRgZtvvhm//PKLz/Gzzz4bb731FoYOHRq1wVESn0Qvee0vO7y9vR0ZGRk4cOBAwE6PwVBIBCjJVaKiTgObgwGPYdAvXQbrSVNT53f3VlzuznvUh0FJNsJyOFRVVWHcuHGw2Wx47733sG3bNmzbtg3vvfcebDYbxo8f7ykVQukbhOIn6C2Ot+ixubIBmysasLmyAcdb9Pjk8zUoKxuAo8fqwlYWnf00ZTkqlBdmQC4WwOkEqlv0qG0zolVn8Xu9QiKA2erA7upWbK5owLq9ddhT3RqS3yfaPqJ4+JwoqQdDvGNjg3DttdfC6XTiv//9LxjGt5Y/IQTXXHMN+Hw+/vOf/0R9oNFEp9NBpVJBq9X6FE+kREY8fRihNgXSm+3YXNkAgHj8Buu//w6vPHInFt7/FCZP/UtY4zzeoseOqlYYzDbIJUJPi9XKunZ8teM4bA4OKqkQ6TIRMhRijC/vWtjQe0zeFW5PKUjDqLKsgGMJNL+R9NbWm+042qhFVbMeICShfE6U8Im3LAtrGfj999/jl19+6aIsAIBhGDz88MMYN25c1AZHSQ7iVT4imGLyFqCdTWWNNUfx4tLbcct9T+O8aX+B2eoI2deiN9vxU0UD6jVG8Hmu6Cl3i9VMpQQFajnS5UKIBXyIWH5Ak5x7TCqpEFVNrnwQPo+DjXMGHEsgH5HR6kB1sz4sJX28RY+dR1twoK4DQpbB4Pw0ACShfE6UxCYshaHRaFBUVBTw86KiIrS3t/d4UJTkI9blI4I5172VCQMgN10KBnD1rhDyIc0qxHVL3kLxKaeisk4DAZ8HqZANydfSpjejpkUPhUTo6dXtbrGqVkiQJhOCEAIRy0eHyQowDIwWB8QCl7nHrcT+7MBngdXugJDlQSRgkakUQ2u0datkvH1ENS167Dtug1wsgEomRKvOgp1HW7oV+u75s3FOSIQ8CPh81LYZUd4vDVqT/2dTKJ0Jy4eRl5eHffv2Bfx83759yM/P7/GgKJTOuAVn5wxsi93ho0z4PAZ/NGixft8JtBusWLf2f7jhiing7A7klwyGg3OZjkxWVzKdgwvFIsuAgAHDuM5lGAJyssVq57yNdr0VJosdu6pa8OW2any5rdrjQ9EYLBhWmA4By4fFxsFk5VColsF6snWrP7+PPx8Rj8eDgwPsDicq6zpQ12bAgboOHD3ZBbC7+ctUiiESsGAYAqvdgVa9JWF8TpTEJyyFMWPGDNx+++3Qarv+MLVaLW6//XbMmDEjWmOjUAC4VsdGi92zY+jsXHcLQ5HAtWqWivgQC/mo2LYBry27B4vvewDnnNIP/TPlkAoF0JpskApd5TpYfvC+2mqFGMVZcpisHNoNFpisnE+L1aIsBcaX52FkaSZkIhYZChFUMiFOtBtRrzFCJRXC2/RzwWkFuHh0fwzKU4FzEnRX6sRbIR1rMcBgsWNArhISAQ8HT3SAECcEfD6ELIOqZn1AJ7Zb8VjtLiVlsnKw2DgIWH5MQ3ypcz21CGtZ8eCDD+LMM89EaWkpbrjhBgwZMgQAcPDgQaxYsQLp6el48MEHYzJQSt/E29RksjpgtDpgsXFdchnEQhatOpepR8Dno2rfNryz7B7c+cjzuPTSv0IsYJGXIYPF7vCU53CbiYKhkAhw7tA8Vya3xe63xapCIoDF7gABkCYVQW+xe5SRw0l8wo2zlJKwOvAVZSlgtNqxt0YDjnOiSWuGVCKAg3PC5mAgErAYnJ8GzkkCmpa8c0E4J8GgPBVKcpUoy1H1ms+JknyEpTAyMzOxdetW/POf/8Sbb74JvV4PAFAqlZg5cyaeeOIJqNXqmAyU0vfw57ew2J0YWZIJtcK3BeqwwnTsPNoCs80JjiU4a+wY9HtuOYaPHecRyG6BafISYJ5EviARR0VZCghYPrQmK1RSEfLSu1Zl9jYfiQR8ODgChgFYHuM33DhUv4/ebEd1swEKCeuJ+DKZHSjNVYHHAJkKMawODgIw3SrAePa2SPSETkpkhG24zMzMxPLly/HGG2+gsbERAJCbmws+nx/1wVH6Ht1FOrlX6TJxV2HnFoZHdm7Aho0/Yd7dSzBkxFnI9xLsgQSmv5Vw5/M6n2N3dF0teyslrdGGfhkyAIDWZOtRdrffebCZUN4vDfUaU1j3j1dvi0RP6KRERsSeLj6fj379+kVzLJQ+QHcr+a69K+Qh9ep23++bNZ9h6T9uw7v//RCluUpUN+pQ1aRDvcbkMYd0FpjulbDF7oBU6DJV/VTRAKlY4MlTcDdeCmW13FkpAejxij5QLaqyXBXKckMza8UbWj8rNaHfHiVudJeA1qa3YFd1K8QCnkcoVzcbUJKtQHWz3m8NJu/7bdu4Fi89uhiffvopzplwHjZXNkAk5HuEVSABb7E70NBuhMnqgN3pBEDQqrPi9GI1CtVydJisLt+Bk6A4Sx7SarmzUuqpIA9WiyqRFIUbWj8rNaEKgxIXgiWg1bcbUdOix6n9M5AhF3uEcqZSjAK1vGvxwE73y8jMwj+feg3nTDgvLHOIgyNoM1hBiBOZSgnq2ozQmayQi1jPtVqjDSyf6dXVcjL21k7GMVO6hyoMSlzoLgHNZndCa7JBY7BgS2UjzhniWqG6hbI/u7v7fr9vXgupTIbxk6d4lEI45hCWz0AtF8Fkc0BrskEhZmG1i2CwOpB+MnxXJRN1u9OJF8nYWzsZx0wJDO12RIkLgRLQTFYOGqMVIgEPA3JVcBIn9h/XwGrjuhXKYgGLn7//Ek8v+QfEYqmPUginpas73LZfhgzlBekozlZiaIEruc772vKCdIwoycTQQtf/0vBQSl+E7jAoccGfTfu0/unYd7wddW025KVLYSYcSrOVUEiEGFHavVD+7KMP8PpTD+LBZ99C4dDR6KwUQjWHeI/LbHX5VkaXZYUVJRVJEUAKJRmhCiMJSVYB5V+IMzjWbECDxgSVRIhslQQZCgnUCkm39zp27Bi++moNxpw1PuBchGoOCaRcAvlLvJ3oGoOFJqdR+gxUYSQZyZ4921mIlxekAyDYW6OB0+mESibq1hS1evVqnHPOOViyZInPPaM9Lm8COdHb9BZU1Pkqkh1VrXAS+CQWUiipAlUYSUSqZs+WF2SgQK0Iumtavnw57rzzTnz77bcoKCiI2/gCOdEBgg6jDelyIawOzlUMsLYdBrMNmUpJ0ilzCiUYVGEkEamcPRvMfPTWW2/h7rvvxv/+9z+cc845cRxZ4JwCo9WOujYDqpo4SIQstCYbZCIW/U5WoE1mZZ6sZk9KbKEKI4noq9mzDocDq1evxv/+9z+MHz++V8bgL4N7f60G+ekSNHVY0Kw1QWe245RT8iETCSARsEmrzJPd7EmJHTSsNokIJ1w0Vfjxxx/BMAzWrVvXa8rCjUIiQJZS4qlMa7E5oJAIweMBUpEAIECHMba9zWNdLryz2dNdlp2WJ6cAdIeRdPSl7NlXX30V9913H7Zt24bBgweHfF08zCliAQswDA7Vd0AsYJEhF4JzOtGqs6CmRR/UeR8J8Vj5p7LZk9JzqMJIQlIpezaQcH/55Zdx//3349Mvv0ZGXhH0ZntI7xyJUA1FwXQ+RyERoDRbgYpaDfg8DiIBi1GlWTBaOYwoVaNfhjyq31G8Ah76qtmTEhr0V0DpNQIJ96amJjz11FN4d9VnsCn7Y3NFQ0jCPxKh6h5Dh9EGlg+cWqQ+GeobfJxluSqcUpAGG+dEplIMq51DGsuPurIA4rfyp0UDKd1BFQalVwgk3DXNJ3D6KUOwa28FtleHJ/zDFaruMbTrLdAYrdCabDh2sox5eUFGl3GqpEK06i3YUdXqGceosixP/4tYClf3yr9Ra/qzY2CMVv59yexJCQ+qMCi9gj/h/tZrL+PrD95EVdVRgM96Prc6OPDAQGO0druiDtecYrE70GG0QWO0ws45PQphb40GBWqFj3Obz2NQ1aSH1e5qEVuao8Dw4sy4Clc+j0FlbQfsHAepSIAJQ/Ni9rxUMntSogdVGJReobNwX/HmK/hk5cv4+n/fQqFQAGY7xEIWVU1aaAw2aM02CFk+WnUWZCn9lw0J15wiFrBg+UCDxgQAMFsd4PN5aNeb0aY3w2J3wMERH+e2kOXBwRFUN+o8/bDd97fYHZ5xRJPjLXrsqGpFZW07AGBAngpSIYs2gzVk3w6FEg2owkhxEjUBy1u4b9z0E1a9/RJWffYVJow70/N5SbYc+4+3w+bgoJIKkS4TobpZjwJ1YB9BuCv+TIUEOrMNDs4JuUgAIZ9BY4cZvx5qhkjAg1jIguUBBosdTicglwgwpJ8CnJN4djuxjF5ym8TsDg5iIR9ClocOo6tYo9Zoo9FLlLhCFUYKk+gJWEVZCrDEjtFll+CK83/HoLL+Pp9nKiUoUMuRLhdCLOBDdLLkeDAhGYo5xT03rToz+AxgJ64dAkd4sNg58HhAbpoUVU061LQY4HQSmKx2ZKWJwTmJx9QV6+glt0ksUyFGi84Cp5PA5nCgVWeBQiKk0UuUuEIT91KUZEjAevLJJzHjkinIUkq6KAvAZTJKkwlBCIGI5UctxNN7bjIUYhAwYPkMSnNVkIuFsHFOyMQCWB2uXh0EBHnpMnSYbNhxuAWVdR1Qy0U+Po40qcjji7HYHB7zVE9xm+6sDg6FahksdgfMNieEfB6NXqLEHbo8SVESPQHr8ccfx7PPPuvJ5PZHrEI8vedGb7EjUy5Cu9HmEfyck8BosYPPY6A12SAV8WF1cOifJYfFxqEwU+bxH8Q6b8F7DjgnwYC8NJRmu0J6E+F7pPQtqMJIURI5AWvFihV47rnnsH79epx22mndnhuLKCTvuREJ+BAJWWTxeRhakAEnCHIsEgj5PGgMLke7TCSAwWKHgM+HSMaiX4YMWpPLf5CllKAkW469Na7Q2lAzvMPxLXnPgYMjYPn+FSyFEmt6X3pQYoL3yrSmRQ8ej4fT+ve+CcPpdOKyyy7DmWeeifLy8pCuiXaIp/fcaI029MuQucZGXL6J0aV/dtxr1ZlxsK4DLTorOJZgcH4arA7Oo3yPt+hR3WwA53QJ8pJsRUA/kVtJtOosqG7Wd/EtdadEFBIBbdZE6XWowkhhirIUMFod2Hfc5goFbTZAJhL0mpBZunQpNBoNXnjhBahUql4Zgxt/1Wf9ddzLUkpQoFagJEeJqmY9OCeBAAxKsuVo05uxu6oVIiEfxVlydJisAaO43E52rdGK2jYj8tOlKM1RepzkRqsd1c2GgMogVXuhUJILqjBSGL3ZjupmPeRigccs1RtChhCCpUuX4vXXX8f69esDjjXQ6jqY+SbS0OHOO5fu+n4PL8lEWa7KZ4fQqjPjeKsRpxald+sn8hb26TIRjjbpoDFaYXVwSJOKcKzFgL01GigkbEBlkOg+KUrfgCqMFCZRhMzjjz+ON954Axs2bMDQoUO7fN5d+G+w0OB4hg6756y6WQ+AoF+GDI0dJhw80QG5RACrnfPrJ/L+HqwODiqJ8KQPhIPZ5gDLBzjO6RNp1fl7SmSfFKXvQMNqUxhvIRPLHg3BmDx5ckBl0V34b7DQ4N4IHW7Tm9GqM0PE8iETCzA4Pw0OzokTbUYE6k/i42Rn+UiXCyFk+dAYbAAYnFqkhkom6vZ7ilUvlFj316CkFnR5ksL0ZuVRQggefPBBjBw5EpdddlnA87rbBQHodocU7x3U8RY9dle14nirEY0dJgzOT4OA5aG8MAMjSzKhVvg39XX+HjIUEowqy0KmUuIxo8lEbNDvKdoRY4me2ElJPKjCSEG8bfq9UXmUEIL77rsP//nPf3Dttdd2e24wU0u3n8XRTOPezYiEfJxalI6DJzqw/3g7ygszMLo0M6igDfY9hPo9RStijDrRKZFAFUaKEWjVGC8hQAjB4sWL8f7772Pjxo0YNGhQt+cH2wV191k8d1CddzNyiQAn2owYWdJVWQRywgcT9vGsEJso/i1KckEVRgqRCKtGhmGQl5eHjRs3YuDAgSFd093qOlor857SeTdjtXPIVEqgVoh9zovUzBPrIpGd70+d6JRIoL+OFKI3V42EEDz++OO4+uqrceedd4Z9fXer60RYmYeym4lUYcfal+B9fwZASa4SZTkq2lmPEjZUYaQQvbVqJITgzjvvxGeffYbZs2fH9Fm9SbDdTCQKO9a7Qu/783kMDp7oQEWdxuN7GV+el5Dl7ymJCQ2rTSFiFXrZHYQQ3HHHHfj888+xceNGlJWVRf0ZiRT66c7+9jenkYQxx7rarfv+IgEftW1GSEV8iIV82B3cSUWCgO9DoXSG7jBSjHhHRZnNZjQ1NWHjxo0oKSmJ+v2TKfQzEid8rHeF7vu36iyw2h2uAooCFpkKsaeAIlUWlFChO4wUJNAqOJordafTiRUrVkAoFGLVqlUxURbxTsyLxvwUZSkwvjwP44fmYXx5XlDlFutdofv+Qj4PZpsTFrsDhWqZTwFFCiVU6K+ljxDNlbrT6cTChQvx7bffYurUqcjNzY3yaF3E04kfyvyEGskUrhM+kl1hJOXROxdQpE5uSrhQhdEHiKZj1el0YsGCBfj++++xadMmyFRqtOjMMTF/xcuJH8r8xNo0Fo6SiWQsnQsoUic3JRKoSaoPEE3HanV1NbZv345NmzYBknRsrmzA5ooGbK5swPEWfVTHHS8nfrD5SaR2tz0dS3dOewolGFRh9AGiUYTQ6XTi119/RVlZGXbs2IG0zNyYC1G92Q6JiMWIksyQfQKREGx+vCON9BY7RAJ+VCOZwiHWUVUUSndQk1QfoKclNJxOJ+bNm4dff/0Vv//+O4RCYcz9C/7MLllKic850cqODjY/YgELk9WBww1asHwGDs5V2rw3HMbRMtN5zx3QtXkUheIPqjD6CJGG23Ich3nz5uHnn3/Ghg0bIBQKAcTWv9AbPoVQ5odhfP+3N4hG/SzvuTNZXTsTqYhN+LBlSu9DFUYfIpISGmvXrsUvv/yCjRs3Ij8/3+desSotEWz3Eovs6O52Kxa7A1IRi1GlWXA4CVge06s5DD3JtfGeO5VUiCONWhACjB6QBaudoxVrKd1CFQbFLxzHobm5GZdccgkmTpwIuVze5ZxYJQkG271E2xwWbLfiHo+7pWoiFOqLtH6W99zpLXbwea7tkoMjtGItJSjU6U3pAsdxmDt3Lm644QYA8Kss3MQi6qZzdJTVxiE/Q+r5PJqdBN0rbovdAamQhcXu6OK8742SK7HCe+5YHgPOSeDgCFg+kxCKkJLY0F8GxQeHw4Hrr78ee/bswfr163ttHO7dy9FGLaqa9ahq1KG+3eRZ/UfLHGaxO9DQboTJ6oDd6YSAx4NUxHZZZfdGI6pY4G1K1JpsyE+XAQC0RhutWEsJClUYFB+efvpp/P7771i/fj1ycnKidt9II5rqNSaIBTyPKchtY4+WAHdwBG0GKwhxIlMpQavODLOdg4MjXc6NZ4OjWNJ57gAaJUUJDaowKABcOwtCCBYtWoSbb74ZWVlZUbt3pBFNwXwV0RDgLJ+BWi6CyeaA1mSDVCSAVMiC5cc/FCrWTZS86Tx3VFFQQoEqDArsdjuuvvpqlJWV4YknnoBCEb2wyp5ENMWjNIhYwCIvQwaLzQGJiIXZ6ugVO34yVeWl9F2o07uPY7fbcdVVV+HQoUO46667on7/nmQmx8PZ7H6GWPinsoi3HT+RSo9QKN1BdxhJTDRMGH/7299w+PBh/Pjjj8jMzIzyCHu+S4iHs7m3Hdq91Vo3niYwSmpAFUaS0lMTBiEEDMNg0aJFKCwshFqtjtlY8zOkqG7UodEWWURTvHp295bQ7I3WutQERokEapJKQnpqwrDZbLjiiiuwZs0aDB8+PGbK4niLHpsrG1DVqANhGJTmKGNWQDCZiXeeBzWBUSKF7jCSkJ6YMKxWK6688krU19dj3LhxMRujP2d3vcaEslxVzJ6ZzMTTLNZbJjBK8kN3GElIpJnOHMfhiiuuQENDA9atW4eMjIyYjZGW4Q6fePWqiGamPKVvQRVGEhKpCYPP5+OKK67AunXrkJ6eHtMxUqGUuKRSqRNKfGEIIV1TWlMcnU4HlUoFrVYLpVLZ28OJmFCjXCwWCxYuXIgHHngAJSUlcRsfdawmNjRKKvmJtyyjy70kJpTIHovFgksvvRQdHR0xNUH5o7fDVSndkyqlTijxgyqMFMZsNmPGjBnQ6/X47rvvemU3RYVSYOgKn5JsUIWRwjidTpxyyilYunRpXJUFFYTBoeY6SjJCnd4piMlkwrJlyyAUCvHss8/GVVm4cy82VzRgc2UDjrfow7peb7ajRWdO6ZyA3s6D6AtzTIkNdIeRYphMJkyfPh02mw133HEHBILwVvg92R34y73YebQFTkKgVgQPF031Vbd7bo0We6/lQaT6HFNiC1UYKYTRaMT06dPBcRzWrl3bbac8f/RUmHROCLM7nDhQ1wG9xY5MpaTb+8WiT3ci4T23DACT1RHXUiBA6s8xJfZQk1QKUVNTg7S0NHzzzTdhK4tomEm8cy+MVjsOnuiAkGXQL0MW9H6pnOjXeW5FQj4AwGJ3xjUPIpXnmBIfqMJIAQwGA77++msMHToUn332GWQyWdj36CxMRCwfrToz2vTmkO/hnRB2os0IB+fE4Pw0yMSCgMLJbU93cCRlE/38CWqpiMXIkkyMH5oXt/paNJmS0lPoLyXJMRgMmDZtGkQiEaZNmwYeL7I1gLcwsTucOFTfAZuDYHdVK3gME7JAc+detOkt2FXdCgHLCyicOpvA1HIR2gzWHvfpThTcPgtvZehtglIr4msK8u7nnSpzTIkvVGEkMXq93qMsvvzyy4iVBfCnMNlR1YrK2nawfB5OLUqHgOWFbed2517wGAQUTm4zjXenuzYDMKIkEyyfSfqQ3ERVhjSZktITqMJIYr7++mtIJBJ8+eWXkEgkPb5fUZYCTgIYzDb0U8sgEwngdJKII3i6E04WuwMN7UaYbA7YOScEfB6kQhZjBmQhS9nzd+lN/DmX2wzWhFGGNJmSEilUYSQhOp0OLS0tmDNnDmbOnAk+nx+1e6sVYmQqJbDaOUgEbI/t3IGEk4MjaDNYQYgTmUoJWnVmmG0cHFzylzaz2B3oMNqQLhfC6uA8YbMsn0l6ZUjp21Cnd5Kh1Wpx0UUX4dFHHwWAqCoLIH6VTFk+A7VcBAHLQ1OHGQKWB7VcBJbPRPU5vUGrzoy6NgN2Hm3B/uPtqGrSUecyJSWgv+Akwq0sMjIy8Prrr8fsOfGwc4sFLEQCPkwd3Mm8BA7ZSn7SC1W92Y7qZgPy0yXQGGzQmmww2ziMKsuiZiBK0pPcf519jKVLl0KtVuPTTz+FWCyO6bPiZecWsjzweQw4Z/KbooA/Q2hLc1SwqjlYbRw0RisylbH9viiUeEAVRhKg1WohFovx2GOPgc/nQyQS9faQeozF7oBUxGJUaRYcTgKWx0BrsiV9m1Dv8OQ0qQhmmwMqmSjpd04UCkB9GAmPRqPBeeedh+effx5SqTQllAXwp2C1OjgoxAJYHVxK2PlpNztKKpPcf50pTnt7Oy644AIUFhbizjvv7O3hRJVUTiKjuQ6UVIUqjARmzpw5KCoqwurVqyEUCnt7OFEnlQWrPx8Q7RNCSXaowogy0RAKdrsdAoEAL7/8Mvr375+SysJNX0kio2XFKakA9WFEkZ42DwKA1tZWjB07FuvWrcPAgQNTWln0FXq7YRKFEi2owogS0RAKLS0tmDx5MgYOHIiJEyfGaqiUOEPLilNSBaowokRPhYLVasV5552HoUOH4oMPPgi7Ux4lcaFlxSmpAlUYUaKnQkEkEuHJJ5/Ef//7X7Bs3xYkqdZzmobaUlKFvi2ZokikYaJNTU245ppr8Pbbb2PatGlxGm3iksjO4Z4ENKRyRBil70AVRhQJVyg0NjZi8uTJGDlyJPLz8+M0Sv94N/vprRLcidxzOhqKrK9EhFFSF6owokyoQqGpqQmTJk3CmDFjsGLFiqhXnQ0HtzBsaDeizWCFWi5CXoYs7qt7tx8oN03q8QNF2osjmiSyIqNQ4gn1YfQSSqUSN998c68rC0/nO7sDJqsDhDhhsrkEd7xDPxPVOUyjnCgUF1RhxJn6+nrceuut4PP5uOuuu3pVWQB/CkOpgIXd6WpmZOeckIjYuAvFRHUOJ6oio1DiDf3Fx5ETJ05g0qRJGDduXMJEQrmFocnugIDHQ6vODKlIALPV0StCMRGdw6lc94pCCYfEkFp9gLq6OkyaNAkTJkzAm2++CR4vMTZ33sJQKmJhtnOQCtleFYqJ6BxOREVGocQbqjDihFarxYwZM/DUU08ljLJw4y0MezNKKtFJREVGocQThhCSGq3OwkCn00GlUkGr1UKpVMb0WcePH8emTZtw7bXXxvQ5FAql7xFPWQZQp3dMqampwcSJE/HLL7/09lAoFAqlx1CTVIw4duwYJk2ahKlTp+Lll1/u7eGETOdsZtrDgUKhuOnTCkNvtiNWu7iNGzfikksuwYsvvgiGYWLzkCjTOZtZLRehzWBNyDIdkUCVH4XSM/q0D+PjTfsxtrwoqkKwqqoKFosFQ4cOjdo944HebMfmygYAxJNhfaRRhwF5SuSqXNnNAIPx5XlJKWwTuUYVhRIp1IcRR46dFCLRymauqqrCxIkTsWrVqqjcL550zmaWiFiYrHZIBWzSZzfTBkYUSnTo0wqjpsWApg5zVITg0aNHMWHCBFx++eV4+OGHozC6+NI5m9lsdUAqEsBkdyR9djMt7UGhRIc+rTCMFjsAZ1SE4OOPP46ZM2fi2WefTRqfhTedy3KIhSwmDM2DWMAmVJmOSKClPSiU6NCn/2IYAKf1z+yREDx27Bhyc3Px2muvQSAQJKWycOMvmzkVHMW0tAeFEh36tMIQCflwcM6Ir//jjz8wceJEPPbYY7jhhhuiOLLeo3M2c6pkNydCaY9UUL6Uvk2fVhg5KjE2VTSgKEuBvHRpWNceOnQIkyZNwvXXX4+5c+fGZoCUqNKbyo9GaVFSgT7tw1ArJDBZ7dCarGFdRwjB7NmzMXfuXDz++ONJbYbqTKr1004EaJQWJVXo0zuMNr0ZUpkSKqko5Gs6OjqQlpaGb7/9FtnZ2SmlLOgqODYkaidBCiVc+vQOg2F4mDA0L2RzVEVFBYYMGYLffvsNOTk5KaUs6Co4dtAoLUqq0KcVxuxzBuCMQTkhnXvgwAFMnDgRf/vb33DGGWfEeGTxh+YqxI5E7SRIoYRLn17i5Ia4s9BqtZg8eTIWLlyIJUuWxHhUvYP3KjhNKqKr4CiTCFFaFEpPodIgCBzHQaVS4ZtvvsGoUaN6ezgxg+YqxJ5UCVGm9F36tMKobdXjlG4Kdv3++++YM2cONm3alNLKwk2qJu5RKJTo0KcVRmVdB4rysv0Kwj179uD888/H3XffjaysrF4YXe/gvQqmUVMUCsWbPu30ttj9O3UPHTqE8847D/feey/uu+++XhhZ70OjpigUSmf69A5DLPDv1C0uLsYbb7yBK664ohdGlRjQ3AEKhdKZPr3DKC9I8xF+O3fuxKxZs8CybJ9WFgDNHaBQKF3p0wqjMPNPe/z27dtx/vnn48wzzwSfz+/FUSUGNHeAQqF0JiGWi7W1tWhqasKgQYNCbjMYyTWB2L59Oy644AIsXboUd9xxR4/ulUrQ3AEKheJNr+4wLBYLLr/8cgwePBjXXnstcnNz8dJLL0X9mmAoFAo8/fTTVFn4QSERIEspocqCQqH07g7j4YcfxrZt23D06FHk5eXhiy++wKWXXoqxY8cGLL8RyTWB2L59Ow4dOoQFCxZgyJAh0XglCoVCSVl6dYexYsUKzJs3D3l5eQCAGTNmYNiwYVixYkVUrwnEpZdeCkJIZIOnUCiUPkav7TDq6+vR1NTUJYN67Nix2L17d9SuAQCr1Qqr9c+eF1qtFgDwz3/+E9deey10Ol2kr0GhUCi9hlt2xWvh22sKo729HQCgVqt9jqvVas9n0bgGAJ544gk8/PDDXY7ff//9uP/++8MaN4VCoSQabW1tUKlUMX9OrykMgcDlRLVYLD7HzWYzhEJh1K4BgPvuuw933XWX598dHR3o378/jh8/HpdJpvQ9dDodCgsLUVtb2+MoPgolEFqtFkVFRcjIyIjL83pNYRQWFoLH4+HEiRM+x0+cOIGioqKoXQMAIpEIIlHXrnoqlYr+MVNiilKppL8xSszh8eLjju41p7dUKsXZZ5+NNWvWeI4ZjUb88MMPuOCCCzzHjhw54vFPhHoNhUKhUKIPQ3oxTGjTpk244IILcPfdd+Oss87CSy+9hOrqauzZswdyuRwAMG/ePGzduhX79+8P+Zpg6HQ6qFQqaLVauvqjxAT6G6PEg3j/zno1rHbChAnYsGEDampq8MILL+CUU07Bli1bfAT/wIEDMXLkyLCuCYZIJMKSJUv8mqkolGhAf2OUeBDv31mv7jAoFAqFkjz06eKDFAqFQgkdqjAoFAqFEhJUYVAoFAolJBKivHm0IYSgoqICDocDp5xyClg2+GtGcg2lb2MymVBZWYm0tDSUlZUFPXfXrl1djp966qk0eZTSLW1tbaisrMTgwYORlZUV0jWNjY2ora1FaWlpl8oYPYKkGIcOHSJDhgwh2dnZpLCwkPTr14/88ssvUb+G0rdZtWoVUSqVZODAgUShUJAJEyaQ9vb2gOfv27ePACBjx44l55xzjue/Xbt2xXHUlGTi4MGD5LrrriN5eXkEAHnvvfeCXsNxHJk/fz4RiURk6NChRCQSkX/9619RG1PKKYwRI0aQ6dOnE4fDQQgh5JZbbiH5+fnEbDZH9RpK36WqqooIhULy6quvEkII0Wq1ZOjQoeTaa68NeI1bYTQ0NMRrmJQk5/PPPycrVqwgJpMpZIXx8ssvE5VKRSorKwkhhPzyyy9EIBCQTz/9NCpjSimFsWvXLgKAbN261XOstraWMAxDPv/886hdQ+nbPPLIIyQ7O5twHOc59vrrrxORSEQMBoPfa9wK4+effya7d+8mer0+XsOlpAChKoyRI0eSefPm+RybMmUKufjii6MyjpRyertLiHgn+hUUFCAvLy9g+fNIrqH0bXbv3o0RI0b41O8ZO3YsrFYrKioqur125syZuOqqq5CRkYGFCxfCZrPFeriUPgLHcdi3b1/Y7R/CIaU8u+3t7VAqlZ6qtm6ClUwP9xpK36a9vR39+vXzOeZ2LAb6zSiVSqxduxZTpkwB4FI6kydPRlpaGh577LHYDpjSJ9Dr9bDb7WG3fwiHlNphCASCLqXPgeAl08O9htK38febMZvNABDwN1NUVORRFgAwYsQI3HLLLVi1alXsBkrpU0Ta/iEcUkph9O/fHzabDa2trZ5jHMehqakpYPnzSK6h9G369+/vt8Q+gLB+Mzk5OV3uQ6FEikwmg1qtDrv9QziklMI499xzIRQKfcqfr1+/Hnq93qf8+fbt23Hs2LGwrqFQ3FxwwQXYsWMHGhoaPMe+/PJLlJSUePIxjEYjtmzZ4mkHbDQau9xn3bp1GDZsWHwGTUlJjh8/jq1bt3r+fcEFF+Crr77y/JvjOPzvf/+LniyLius8gXjwwQdJWloaeeutt8j7779PCgoKyHXXXedzTr9+/cjdd98d1jUUihuHw0HGjBlDxowZQz777DPyxBNPEJZlyerVqz3n7N69mwAgGzZsIIQQcs8995D58+eTjz76iHz55Zdkzpw5RCgUku+//76X3oKS6HR0dJDNmzeTzZs3EwDkwQcfJJs3byaHDx/2nLNkyRKiUqk8/z5w4ACRyWRk/vz5ZM2aNeTKK68kWVlZpK6uLipjSrlqtYQQrFy5Ep9++ikcDgcuuugi3HrrrT5O7csuuwznnXceFi5cGPI1FIo3Wq0WTz/9NH777TekpaXhpptuwtSpUz2fHzlyBHPnzsVLL72EESNGwOl04oMPPsDXX38NvV6PwYMH49Zbb0VpaWkvvgUlkdmxYwfuuOOOLsenT5+OxYsXAwDeeecdfPzxx1i7dq3n83379uHZZ5/F8ePHMXDgQPzjH/+I2u8s5RQGhUKhUGJDSvkwKBQKhRI7qMKgUCgUSkhQhUGhUCiUkKAKg0KhUCghQRUGhUKhUEKCKgwKhUKhhARVGBQKhUIJCaowKJQoYDabsWrVKhgMBgCuyqGrVq3yFCXszbFQKNGCJu5RUpa9e/d6+lPw+XwUFBRgxIgREIvFUX9WXV0dCgsLcfjwYQwYMAAHDx5EeXk5amtrUVBQEPR6rVaLtWvXYsaMGT0eX+exUCjRIqX6YVAo3nzwwQd49dVXMW3aNHAchz179sBkMuGTTz7BWWedFdNnK5VKzJo1C1KpNKTza2trMWfOHDQ0NCA3NzemY6NQIoUqDEpKk5+f7+k54XA4MHXqVMyfPx/79u3DmjVrcOqpp0IoFGLHjh3o168fRo8eDcDVCOm3334Dy7IYMWIEMjMzu9z7wIEDOHr0KAYNGgS5XO7zmUKhwIwZMyCRSHyOd3R0eO571llnQSqVwmKx4NtvvwXgqnqrUqlQUFCAcePGRWUsFEq0oAqD0mdgWRaXXHIJ7rzzTtjtdixYsADl5eU4fPgwhg8fjunTp2P06NF45513cNddd2HUqFHg8/nYtm0bXn75ZVxzzTWeey1evBgvvfQSxo0bh5qaGk9ZczcnTpzAnDlzfExSy5cvxx133IGhQ4dCqVSivr4en3zyCfr164cff/wRALB27VqIxWKMGTMG48aNi8pYKJSoEZWatxRKArJ48WIyePBgn2MLFy4kmZmZhBBXmfvBgweTjo4Oz+e7d+8mcrmc7Nq1y3Pshx9+IFKplJw4cYIQQsi2bdsIj8cjP//8MyGEELvdTqZMmUIAeEpPV1ZWEgCktraWEELI9u3bCY/HI//97389962pqfE8Z9++fQQAaWhoiPpYKJRoQXcYlJTGHa3EcRx27dqFN954A08++aTn8+uuuw4qlcrz7//85z8oLi7G0aNHceTIEZCTMSF8Ph+//fYbLr30UqxevRpnn302zj77bACunctdd93lMSv547333sNpp52Gq6++2nOsqKio205osRoLhRIpVGFQUhq9Xo8vvvgCfD4f+fn5WLduHSZOnOj5PC8vz+f8Y8eOQa/X45NPPvE5Pm3aNCgUCgCuLmfFxcU+n5eUlHQ7juPHj2PQoEFhjT1WY6FQIoUqDEpK4+309gfDMD7/ViqVKCoq6vYatVqN2tpan2MajabbcaSlpXW5JhixGguFEik0cY9C8WLKlCn45ZdfsHfvXp/j7e3tsFgsAIBx48Zh06ZNPoL5s88+6/a+F154ITZv3ozq6mrPMUII2traAMAT2eR+RizHQqFECt1hUChezJw5E59++ikmTpyI2267DQUFBThw4AC++uor/PbbbxCLxZg9ezaeffZZTJ48GfPnz8eRI0fw/vvvd3vfWbNm4YMPPsA555yDRYsWQalU4uOPP8Z9992Hiy66CAUFBcjNzcXDDz+MCy+8EIWFhTEbC4USKXSHQUlZTj/9dFx88cUBP//rX//axd7P4/Hw0Ucf4Z133kFLSwu2bt2KkpIS7Ny5E2q1GoDL6bxhwwZcfvnl2L59O7KysrBlyxbMmjXL41vonLjH4/HwxRdf4P/+7/9QXV2NP/74A8uWLcNFF10EwOWsXrduHTIyMvD111/jt99+i9pYKJRoQUuDUCgUCiUk6A6DQqFQKCFBFQaFQqFQQoIqDAqFQqGEBFUYFAqFQgkJqjAoFAqFEhJUYVAoFAolJKjCoFAoFEpIUIVBoVAolJCgCoNCoVAoIUEVBoVCoVBCgioMCoVCoYQEVRgUCoVCCYn/ByLv36fkRyQDAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.stats import pearsonr\n", + "\n", + "# Scatter: predicted vs. observed (all test conditions × all TFs)\n", + "# Reproduces the style of SI Figure 15B from Nilsson et al. 2022\n", + "y_true_vals = y_test.values.ravel()\n", + "y_pred_vals = lembas_result['predictions'].values.ravel()\n", + "r, p = pearsonr(y_pred_vals, y_true_vals)\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.scatter(y_pred_vals, y_true_vals, alpha=0.3, s=10, color='steelblue')\n", + "lims = [0, 1]\n", + "ax.plot(lims, lims, 'k--', linewidth=0.8)\n", + "ax.set_xlim(lims)\n", + "ax.set_ylim(lims)\n", + "ax.set_xlabel('Predicted')\n", + "ax.set_ylabel('Observed')\n", + "ax.set_title('LEMBAS-RNN: Test (held-out conditions)')\n", + "ax.text(0.05, 0.92, f'r = {r:.2f}\\np = {p:.2e}', transform=ax.transAxes, fontsize=9)\n", + "ax.set_xticks([0, 0.5, 1])\n", + "ax.set_yticks([0, 0.5, 1])\n", "plt.tight_layout()\n", "plt.show()" ] @@ -269,16 +890,143 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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msemaepearson
readout
P102750.0201170.1335750.898132
P188460.0246750.108221NaN
P153360.0480930.1785850.421162
P188480.0367970.1709110.769406
O148670.0059330.065822NaN
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P195440.0251100.125784NaN
P254900.0161400.1206180.206768
Q86T240.0125710.073691NaN
P372750.0136800.104529NaN
__all__0.0286330.1330270.612681
\n", + "

89 rows × 3 columns

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" + ], + "text/plain": [ + " mse mae pearson\n", + "readout \n", + "P10275 0.020117 0.133575 0.898132\n", + "P18846 0.024675 0.108221 NaN\n", + "P15336 0.048093 0.178585 0.421162\n", + "P18848 0.036797 0.170911 0.769406\n", + "O14867 0.005933 0.065822 NaN\n", + "... ... ... ...\n", + "P19544 0.025110 0.125784 NaN\n", + "P25490 0.016140 0.120618 0.206768\n", + "Q86T24 0.012571 0.073691 NaN\n", + "P37275 0.013680 0.104529 NaN\n", + "__all__ 0.028633 0.133027 0.612681\n", + "\n", + "[89 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lembas_metrics_readout = evaluate_predictions(\n", " y_test,\n", " lembas_result['predictions'],\n", " axis='readout',\n", ")\n", - "lembas_metrics_readout.sort_values('pearson', ascending=False)" + "lembas_metrics_readout" ] }, { @@ -294,9 +1042,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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msemaepearson
condition
IL4+upLPS0.0425500.1651540.468529
TNFa0.0312400.1399230.447432
__all__0.0286330.1330270.612681
TNFa+PGE20.0277140.1345020.684590
TNFa+P3C0.0212240.1143950.663480
upLPS+IFNg0.0204350.1111590.758554
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" + ], + "text/plain": [ + " mse mae pearson\n", + "condition \n", + "IL4+upLPS 0.042550 0.165154 0.468529\n", + "TNFa 0.031240 0.139923 0.447432\n", + "__all__ 0.028633 0.133027 0.612681\n", + "TNFa+PGE2 0.027714 0.134502 0.684590\n", + "TNFa+P3C 0.021224 0.114395 0.663480\n", + "upLPS+IFNg 0.020435 0.111159 0.758554" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lembas_metrics_condition = evaluate_predictions(\n", " y_test,\n", @@ -312,34 +1149,357 @@ "source": [ "### 6c. Model comparison\n", "\n", - "Comparing `__all__` Pearson correlation across the three models." + "Global aggregate metrics (`__all__` flattens all condition × TF values — identical for both axes).\n", + "The **median per-readout Pearson** and **median per-condition Pearson** are different: they\n", + "summarise the distribution of the per-TF and per-condition correlation scores respectively,\n", + "giving a sense of consistency across readouts and conditions." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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msemaepearsonmedian_pearson_readoutmedian_pearson_condition
model
Mean baseline0.0450230.1671670.2356131.306326e-160.235434
Ridge0.0329880.1419080.5424255.912434e-010.500453
LEMBAS-RNN0.0286330.1330270.6126815.277008e-010.663480
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" + ], + "text/plain": [ + " mse mae pearson median_pearson_readout \\\n", + "model \n", + "Mean baseline 0.045023 0.167167 0.235613 1.306326e-16 \n", + "Ridge 0.032988 0.141908 0.542425 5.912434e-01 \n", + "LEMBAS-RNN 0.028633 0.133027 0.612681 5.277008e-01 \n", + "\n", + " median_pearson_condition \n", + "model \n", + "Mean baseline 0.235434 \n", + "Ridge 0.500453 \n", + "LEMBAS-RNN 0.663480 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "models = {\n", + " 'Mean baseline': (mean_metrics_readout, mean_metrics_condition),\n", + " 'Ridge' : (ridge_metrics_readout, ridge_metrics_condition),\n", + " 'LEMBAS-RNN' : (lembas_metrics_readout, lembas_metrics_condition),\n", + "}\n", + "\n", + "# Global aggregate (identical for both axes — shown once)\n", "comparison = pd.DataFrame({\n", - " 'Mean baseline' : mean_metrics.loc['__all__'],\n", - " 'Ridge' : ridge_metrics.loc['__all__'],\n", - " 'LEMBAS-RNN' : lembas_metrics_readout.loc['__all__'],\n", + " name: r.loc['__all__'] for name, (r, _) in models.items()\n", "}).T\n", + "comparison.index.name = 'model'\n", "\n", + "# Median Pearson per readout (TF) and per condition — these differ between models\n", + "comparison['median_pearson_readout'] = [\n", + " r.drop('__all__')['pearson'].median() for _, (r, _) in models.items()\n", + "]\n", + "comparison['median_pearson_condition'] = [\n", + " c.drop('__all__')['pearson'].median() for _, (_, c) in models.items()\n", + "]\n", "comparison" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Leave-One-Out Cross-Validation (LOOCV)\n", + "\n", + "The original Nilsson et al. 2022 paper trains one model per condition (leaving it out) and\n", + "aggregates predictions. This gives a more honest evaluation than a single 80/20 split\n", + "because every condition serves as the test set exactly once.\n", + "\n", + "Set `n_loocv = 3` below for a quick demo (≈ 3 × training time). \n", + "Change it to `len(conditions)` to reproduce the full 23-fold LOOCV.\n", + "\n", + "All three models — mean baseline, ridge, and LEMBAS — are trained and evaluated under\n", + "the same LOOCV protocol so the comparison is fair." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 1/3: held out \"CON\"\n", + "Fold 2/3: held out \"IL4\"\n", + "Fold 3/3: held out \"PGE2\"\n", + "Done. Held-out conditions: ['CON', 'IL4', 'PGE2']\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# ── LOOCV parameters ───────────────────────────────────────────────────────────\n", + "# n_loocv=3 runs a quick 3-fold demo (≈ 3× training time per model).\n", + "# Set to len(conditions) for the full LOOCV used in the original paper.\n", + "n_loocv = 3\n", + "# ──────────────────────────────────────────────────────────────────────────────\n", + "\n", + "conditions = ligands.index.tolist()\n", + "rng = np.random.RandomState(42)\n", + "loocv_cond_idx = sorted(rng.choice(len(conditions), size=n_loocv, replace=False))\n", + "loocv_conditions = [conditions[i] for i in loocv_cond_idx]\n", + "\n", + "loocv_true = []\n", + "loocv_pred = {'Mean': [], 'Ridge': [], 'LEMBAS': []}\n", + "\n", + "for fold, test_cond in enumerate(loocv_conditions):\n", + " train_conds = [c for c in conditions if c != test_cond]\n", + " x_tr = ligands.loc[train_conds]\n", + " y_tr = tfs.loc[train_conds]\n", + " x_te = ligands.loc[[test_cond]]\n", + " y_te = tfs.loc[[test_cond]]\n", + "\n", + " print(f'Fold {fold + 1}/{n_loocv}: held out \"{test_cond}\"')\n", + "\n", + " loocv_true.append(y_te)\n", + "\n", + " mean_res = run_mean_response_baseline(y_tr, x_te)\n", + " loocv_pred['Mean'].append(mean_res['predictions'])\n", + "\n", + " ridge_res = run_ridge_baseline(x_tr, y_tr, x_te)\n", + " loocv_pred['Ridge'].append(ridge_res['predictions'])\n", + "\n", + " lembas_res = run_lembas_rnn(graph, x_tr, y_tr, x_te, epochs=2000, seed=42, verbose=False)\n", + " loocv_pred['LEMBAS'].append(lembas_res['predictions'])\n", + "\n", + "y_loocv_true = pd.concat(loocv_true)\n", + "loocv_pred = {k: pd.concat(v) for k, v in loocv_pred.items()}\n", + "print('Done. Held-out conditions:', y_loocv_true.index.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from scipy.stats import pearsonr\n", + "\n", + "# Predicted vs. observed scatter for each model across all held-out conditions\n", + "# Follows the style of SI Figure 15B from Nilsson et al. 2022\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", + "colors = {'Mean': '#e07b54', 'Ridge': '#5b8db8', 'LEMBAS': '#6aab6a'}\n", + "\n", + "for ax, (model_name, preds) in zip(axes, loocv_pred.items()):\n", + " y_true = y_loocv_true.values.ravel()\n", + " y_pred = preds.values.ravel()\n", + " r, _ = pearsonr(y_pred, y_true)\n", + " ax.scatter(y_pred, y_true, alpha=0.35, s=10, color=colors[model_name])\n", + " ax.plot([0, 1], [0, 1], 'k--', lw=0.8)\n", + " ax.set_xlim(0, 1)\n", + " ax.set_ylim(0, 1)\n", + " ax.set_xlabel('Predicted')\n", + " ax.set_ylabel('Observed')\n", + " ax.set_title(model_name)\n", + " ax.text(0.05, 0.91, f'r = {r:.2f}', transform=ax.transAxes, fontsize=10)\n", + " ax.set_xticks([0, 0.5, 1])\n", + " ax.set_yticks([0, 0.5, 1])\n", + "\n", + "plt.suptitle(\n", + " f'LOOCV ({n_loocv} fold{\"s\" if n_loocv > 1 else \"\"}): predicted vs. observed',\n", + " y=1.02,\n", + ")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== LOOCV model comparison (3 folds) ===\n" + ] + }, + { + "data": { + "text/html": [ + "
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msemaepearson
model
Mean0.0470620.1782380.218967
Ridge0.0297250.1399440.618838
LEMBAS0.0341740.1449700.574358
\n", + "
" + ], + "text/plain": [ + " mse mae pearson\n", + "model \n", + "Mean 0.047062 0.178238 0.218967\n", + "Ridge 0.029725 0.139944 0.618838\n", + "LEMBAS 0.034174 0.144970 0.574358" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Aggregate LOOCV metrics and compare across all three models\n", + "loocv_metrics = {\n", + " model: evaluate_predictions(y_loocv_true, preds, axis='condition').loc['__all__']\n", + " for model, preds in loocv_pred.items()\n", + "}\n", + "loocv_comparison = pd.DataFrame(loocv_metrics).T\n", + "loocv_comparison.index.name = 'model'\n", + "print(f'=== LOOCV model comparison ({n_loocv} folds) ===')\n", + "loocv_comparison" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "dev-cu128", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.12.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" } }, "nbformat": 4, diff --git a/networkcommons/methods/_perturbation.py b/networkcommons/methods/_perturbation.py index 6220634..1002ef5 100644 --- a/networkcommons/methods/_perturbation.py +++ b/networkcommons/methods/_perturbation.py @@ -36,10 +36,15 @@ from networkcommons._session import _log -try: +from typing import TYPE_CHECKING +if TYPE_CHECKING: + # Pylance reads this, but Python ignores it at runtime import torch -except ImportError: - torch = None +else: + try: + import torch + except ImportError: + torch = None def _as_dataframe(data, name: str) -> pd.DataFrame: @@ -438,6 +443,26 @@ def _mml_activation(x, leak: float = 0.01): return mask * (fx - right) + right +def _one_cycle_lr( + epoch: int, + max_epochs: int, + max_height: float = 2e-3, + start_height: float = 1e-5, + end_height: float = 1e-5, + peak: int = 1000, +) -> float: + """Cosine one-cycle LR schedule matching the original LEMBAS paper (bionetwork.oneCycle).""" + phase_length = 0.95 * max_epochs + if epoch <= peak: + t = epoch / peak + return (max_height - start_height) * 0.5 * (np.cos(np.pi * (t + 1)) + 1) + start_height + elif epoch <= phase_length: + t = (epoch - peak) / (phase_length - peak) + return (max_height - end_height) * 0.5 * (np.cos(np.pi * (t + 2)) + 1) + end_height + else: + return end_height + + def _make_lembas_model(): class LembasRNN(torch.nn.Module): @@ -456,8 +481,8 @@ def __init__( leak: float, dtype, device, - learn_input_scale: bool, input_scale_init: float, + projection_amplitude: float, ): super().__init__() @@ -466,6 +491,7 @@ def __init__( self.tolerance = tolerance self.activation = activation self.leak = leak + self.projection_amplitude = projection_amplitude self.register_buffer( 'source_idx', @@ -484,43 +510,41 @@ def __init__( torch.as_tensor(output_idx, dtype=torch.long, device=device), ) - edge_signs_tensor = torch.as_tensor( - edge_signs, - dtype=dtype, - device=device, - ) - known = torch.isin( - edge_signs_tensor, - torch.as_tensor([-1.0, 1.0], dtype=dtype, device=device), - ) - - initial_edges = torch.where( - known, - 0.1 * edge_signs_tensor, - torch.full_like(edge_signs_tensor, 0.1), - ) - initial_edges = initial_edges + 0.01 * torch.randn_like(initial_edges) - + # Weight init: 0.1 + 0.1*rand, negated for inhibitory edges + # Matches bionet.initializeWeights + initial_edges = 0.1 + 0.1 * torch.rand(len(edge_signs), dtype=dtype, device=device) + inhibitory = torch.as_tensor(edge_signs < 0, dtype=torch.bool, device=device) + initial_edges[inhibitory] = -initial_edges[inhibitory] self.edge_weights = torch.nn.Parameter(initial_edges) + + # Bias: 1e-3 everywhere; nodes that only receive inhibition get bias=1 + # Matches bionet.initializeWeights inhibitory-only correction + bias_np = 1e-3 * np.ones(n_nodes) + for i in range(n_nodes): + incoming = np.where(target_idx == i)[0] + if len(incoming) > 0 and np.all(edge_signs[incoming] < 0): + bias_np[i] = 1.0 self.bias = torch.nn.Parameter( - torch.zeros(n_nodes, dtype=dtype, device=device), + torch.as_tensor(bias_np, dtype=dtype, device=device), ) + + # Output projection: per-output scale init to projection_amplitude + # Matches projectOutput (no bias in original projectOutput) self.output_scale = torch.nn.Parameter( - torch.ones(len(output_idx), dtype=dtype, device=device), - ) - self.output_bias = torch.nn.Parameter( - torch.zeros(len(output_idx), dtype=dtype, device=device), + torch.full((len(output_idx),), projection_amplitude, dtype=dtype, device=device), ) - input_scale = torch.full( - (len(input_idx),), input_scale_init, dtype=dtype, device=device, + # Fixed input scale (inputAmplitude in original, requires_grad=False) + self.register_buffer( + 'input_scale', + torch.full((len(input_idx),), input_scale_init, dtype=dtype, device=device), ) - if learn_input_scale: - self.input_scale = torch.nn.Parameter(input_scale) - else: - self.register_buffer('input_scale', input_scale) - + edge_signs_tensor = torch.as_tensor(edge_signs, dtype=dtype, device=device) + known = torch.isin( + edge_signs_tensor, + torch.as_tensor([-1.0, 1.0], dtype=dtype, device=device), + ) self.register_buffer('edge_signs', edge_signs_tensor) self.register_buffer('known_signs', known) @@ -556,7 +580,7 @@ def _activate(self, values): '`activation` must be one of mml, tanh, sigmoid or leaky_relu.' ) - def forward(self, x): + def forward(self, x, noise_level: float = 0.0, cur_lr: float = 0.0): drive = torch.zeros( (x.shape[0], self.n_nodes), @@ -565,6 +589,13 @@ def forward(self, x): ) drive[:, self.input_idx] = x * self.input_scale + # Input noise: Yin += noiseLevel * curLr * randn (matches original) + if self.training and noise_level > 0.0 and cur_lr > 0.0: + drive[:, self.input_idx] = ( + drive[:, self.input_idx] + + noise_level * cur_lr * torch.randn_like(drive[:, self.input_idx]) + ) + state = torch.zeros_like(drive) weights = self.edge_matrix() @@ -576,42 +607,97 @@ def forward(self, x): if torch.max(torch.abs(state - prev)).item() < self.tolerance: break - prediction = ( - state[:, self.output_idx] * self.output_scale + - self.output_bias - ) + prediction = state[:, self.output_idx] * self.output_scale return prediction, state + def preScaleWeights(self, target_radius: float = 0.8) -> None: + """Scale weights so spectral radius ≈ target_radius (matches bionet.preScaleWeights).""" + with torch.no_grad(): + sr = self._spectral_radius_power(n_iter=100) + if sr > 1e-10: + self.edge_weights.data *= target_radius / sr + + def _spectral_radius_power(self, n_iter: int = 100) -> float: + """Non-differentiable power iteration used by preScaleWeights.""" + with torch.no_grad(): + w = self.edge_weights.detach() + v = torch.randn(self.n_nodes, dtype=w.dtype, device=w.device) + v = v / v.norm() + for _ in range(n_iter): + Wv = torch.zeros(self.n_nodes, dtype=w.dtype, device=w.device) + Wv.scatter_add_(0, self.target_idx, w * v[self.source_idx]) + norm = Wv.norm().item() + if norm < 1e-10: + return 0.0 + v = Wv / norm + Wv = torch.zeros(self.n_nodes, dtype=w.dtype, device=w.device) + Wv.scatter_add_(0, self.target_idx, w * v[self.source_idx]) + return Wv.norm().item() + + def _spectral_radius_diff(self, n_iter: int = 21): + """Differentiable power iteration for use in spectral_radius_loss.""" + v = torch.randn( + self.n_nodes, + dtype=self.edge_weights.dtype, + device=self.edge_weights.device, + ) + v = (v / v.norm()).detach() + for _ in range(n_iter): + Wv = torch.zeros_like(v) + Wv.scatter_add_(0, self.target_idx, self.edge_weights * v[self.source_idx]) + norm = Wv.norm().detach().item() + if norm < 1e-10: + break + v = (Wv / norm).detach() + Wv = torch.zeros_like(v) + Wv.scatter_add_(0, self.target_idx, self.edge_weights * v[self.source_idx]) + denom = torch.tensor( + max(v.norm().item(), 1e-10), + dtype=Wv.dtype, + device=Wv.device, + ) + return Wv.norm() / denom + + def spectral_radius_loss( + self, + spectral_target: float, + exp_factor: int = 21, + lower_bound: float = 0.5, + ): + """Soft exponential spectral radius penalty (matches bionetwork.spectralLoss).""" + sr = self._spectral_radius_diff(n_iter=exp_factor) + zero = torch.zeros((), dtype=sr.dtype, device=sr.device) + if sr.item() <= lower_bound: + return zero, sr + scale_factor = 1.0 / np.exp(exp_factor * spectral_target) + loss = scale_factor * (torch.exp(exp_factor * sr) - 1.0) + return loss, sr + def uniform_regularization( self, state: 'torch.Tensor', target_min: float = 0.0, - target_max: float = 1.0, - ) -> 'torch.Tensor': - """Penalize deviation of node states from a uniform distribution. - - Matches the LEMBAS uniform loss: pushes the distribution of node - activations across samples to be roughly uniform in - [target_min, target_max], which keeps states biologically - interpretable and prevents saturation. - """ - n = state.shape[0] - sorted_state, _ = torch.sort(state, dim=0) - target = torch.linspace( - target_min, target_max, n, - dtype=state.dtype, device=state.device, - ).unsqueeze(1) - - dist_loss = torch.sum((sorted_state - target) ** 2) - below = torch.sum( - state.lt(target_min) * (state - target_min) ** 2 - ) - above = torch.sum( - state.gt(target_max) * (state - target_max) ** 2 - ) - - return dist_loss + below + above + target_max: float = 0.99, + max_constraint_factor: float = 50.0, + ) -> 'torch.Tensor': + """Mean/var/min/max uniform loss (matches bionetwork.uniformLossBatch).""" + target_mean = (target_max - target_min) / 2.0 + target_var = (target_max - target_min) ** 2 / 12.0 + + node_mean = torch.mean(state, dim=0) + node_var = torch.mean((state - node_mean) ** 2, dim=0) + max_val, _ = torch.max(state, dim=0) + min_val, _ = torch.min(state, dim=0) + + mean_loss = torch.sum((node_mean - target_mean) ** 2) + var_loss = torch.sum((node_var - target_var) ** 2) + max_loss = torch.sum((max_val - target_max) ** 2) + min_loss = torch.sum((min_val - target_min) ** 2) + neg_mask = max_val.detach() <= 0 + max_constraint = -max_constraint_factor * torch.sum(max_val[neg_mask]) + + return mean_loss + var_loss + min_loss + max_loss + max_constraint def sign_regularization(self): @@ -652,41 +738,41 @@ def run_lembas_rnn( perturbations_train: pd.DataFrame, readouts_train: pd.DataFrame, perturbations_eval: pd.DataFrame | None = None, - epochs: int = 1000, + epochs: int = 5000, learning_rate: float = 2e-3, + lr_peak: int = 1000, n_steps: int = 100, - tolerance: float = 1e-5, + tolerance: float = 1e-6, alpha: float = 1e-6, sign_penalty: float = 0.1, - uniform_penalty: float = 1e-4, - batch_size: int | None = None, + uniform_penalty: float = 1e-5, + spectral_factor: float = 1e-3, + noise_level: float = 10.0, + batch_size: int = 5, activation: str = 'mml', leak: float = 0.01, input_scale_init: float = 3.0, + projection_amplitude: float = 1.2, device: str = 'auto', - dtype: str = 'float32', + dtype: str = 'float64', seed: int | None = 888, - learn_input_scale: bool = False, min_abs_edge_weight: float = 0.0, verbose: bool = False, ) -> dict[str, t.Any]: """ - Train a LEMBAS-like recurrent model on perturbation-response data. - - Constrains recurrent edges to the supplied prior knowledge network, - iterates node states until steady state (or ``n_steps`` max), and - learns edge weights plus output projection parameters by minimizing - readout MSE. + Train a LEMBAS recurrent model on perturbation-response data. - Matches the core architecture of Nilsson et al. 2022 (Nat Commun): + Faithfully reimplements the architecture from Nilsson et al. 2022 + (Nat Commun) including: - * Default MML (Michaelis-Menten-like) activation maps states to [0, 1]. - * Steady-state convergence check (``tolerance``) mirrors the original - RNN stopping criterion. - * Sign regularization penalizes edges whose learned sign contradicts - the known mechanism of action. - * Uniform regularization pushes node-state distributions toward uniform, - keeping activations biologically interpretable. + * MML activation mapping node states to [0, 1]. + * Cosine one-cycle learning rate schedule (peak at ``lr_peak``). + * Mini-batch training (default batch_size=5) with per-batch input noise. + * Spectral radius regularization keeping the weight matrix stable. + * Sign regularization penalizing sign violations vs. prior knowledge. + * Uniform state-distribution regularization. + * Adam optimizer momentum reset every 200 epochs. + * Weight pre-scaling to spectral radius 0.8 before training. Use :func:`networkcommons.utils.lembas_format_network` to add the ``mode_of_action`` edge attribute before calling this function, and @@ -706,11 +792,11 @@ def run_lembas_rnn( if tolerance < 0: raise ValueError('`tolerance` must be non-negative.') - if alpha < 0 or sign_penalty < 0 or uniform_penalty < 0: + if alpha < 0 or sign_penalty < 0 or uniform_penalty < 0 or spectral_factor < 0: raise ValueError('Regularization strengths must be non-negative.') - if batch_size is not None and batch_size <= 0: - raise ValueError('`batch_size` must be positive when provided.') + if batch_size <= 0: + raise ValueError('`batch_size` must be positive.') if torch is None: raise ImportError( @@ -785,8 +871,8 @@ def run_lembas_rnn( leak=leak, dtype=torch_dtype, device=torch_device, - learn_input_scale=learn_input_scale, input_scale_init=input_scale_init, + projection_amplitude=projection_amplitude, ) x_train = torch.as_tensor( @@ -805,32 +891,83 @@ def run_lembas_rnn( device=torch_device, ) - optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) - loss_history = [] - n_samples = x_train.shape[0] - batch_size = n_samples if batch_size is None else min(batch_size, n_samples) + # Pre-scale weights so spectral radius ≈ 0.8 before training (matches bionet.preScaleWeights) + model.preScaleWeights(target_radius=0.8) - _log(f'LEMBAS-RNN: training on {n_samples} samples using {torch_device}.') + # Adam with lr=1.0; actual lr is overridden each epoch by one-cycle schedule + optimizer = torch.optim.Adam(model.parameters(), lr=1.0, weight_decay=0) + + # State buffer: random init across all training conditions (matches original curState) + n_train = x_train.shape[0] + cur_state = torch.rand( + (n_train, len(nodes)), + dtype=torch_dtype, + device=torch_device, + ) + + # spectralTarget = exp(log(1e-2) / n_steps), matching bionetwork.trainingParameters + spectral_target = float(np.exp(np.log(1e-2) / n_steps)) + + loss_history = [] + _log(f'LEMBAS-RNN: training on {n_train} samples for {epochs} epochs using {torch_device}.') for epoch in range(epochs): - model.train() - order = torch.randperm(n_samples, device=torch_device) + cur_lr = _one_cycle_lr(epoch, epochs, max_height=learning_rate, peak=lr_peak) + optimizer.param_groups[0]['lr'] = cur_lr + + order = np.random.permutation(n_train) + batches = [order[i:i + batch_size] for i in range(0, n_train, batch_size)] + epoch_losses = [] + for batch_np in batches: + model.train() + + # Break weight symmetry each batch (matches original 1e-8 noise on weights) + with torch.no_grad(): + model.edge_weights.data += 1e-8 * torch.randn_like(model.edge_weights) - for start in range(0, n_samples, batch_size): - idx = order[start:start + batch_size] optimizer.zero_grad() - prediction, full_state = model(x_train[idx]) + + idx = torch.as_tensor(batch_np, dtype=torch.long, device=torch_device) + prediction, full_state = model(x_train[idx], noise_level=noise_level, cur_lr=cur_lr) + + # Update state buffer; build differentiable view for uniform loss + cur_state[idx] = full_state.detach() + state_for_loss = cur_state.detach().clone() + state_for_loss[idx] = full_state + fit_loss = torch.mean((prediction - y_train[idx]) ** 2) - l2_loss = sum(torch.sum(param ** 2) for param in model.parameters()) - sign_loss = model.sign_regularization() - uniform_loss = model.uniform_regularization(full_state) + + sign_loss = sign_penalty * model.sign_regularization() + + # Penalise bias on input nodes (matches original ligandConstraint = 1e-3) + ligand_loss = 1e-3 * torch.sum(model.bias[model.input_idx] ** 2) + + # Uniform state distribution over all conditions + state_loss = uniform_penalty * model.uniform_regularization(state_for_loss) + + # L2 + inverse barrier on edge weights (prevents collapse to zero) + w = model.edge_weights + weight_loss = alpha * (torch.sum(w ** 2) + torch.sum(1.0 / (w ** 2 + 0.5))) + bias_loss = alpha * torch.sum(model.bias ** 2) + + # Keep output projection near its initialisation value + proj_loss = 1e-6 * torch.sum((model.output_scale - projection_amplitude) ** 2) + + # Spectral radius regularisation + sr_loss, _ = model.spectral_radius_loss(spectral_target) + loss = ( fit_loss - + alpha * l2_loss - + sign_penalty * sign_loss - + uniform_penalty * uniform_loss + + sign_loss + + ligand_loss + + weight_loss + + bias_loss + + spectral_factor * sr_loss + + state_loss + + proj_loss ) + loss.backward() optimizer.step() epoch_losses.append(float(fit_loss.detach().cpu())) @@ -838,8 +975,12 @@ def run_lembas_rnn( mean_epoch_loss = float(np.mean(epoch_losses)) loss_history.append(mean_epoch_loss) + # Reset Adam momentum every 200 epochs (matches original optimizer reset) + if epoch > 0 and epoch % 200 == 0: + optimizer.state.clear() + if verbose and (epoch == 0 or (epoch + 1) % 100 == 0): - _log(f'LEMBAS-RNN: epoch {epoch + 1}; mse={mean_epoch_loss:.6g}') + _log(f'LEMBAS-RNN: epoch {epoch + 1}/{epochs}; mse={mean_epoch_loss:.6g}') model.eval() From 7616f06baf3dd4436138cf3fcf74e3c3815086cb Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Fri, 26 Jun 2026 14:16:44 +0100 Subject: [PATCH 15/17] fix: update tests broken by env modernisation and decoupler 2.x fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - test_eval_graph: update test_run_ora to expect 'ora_Term' column (our backward-compat rename from decoupler 2.x 'source' → 'Term') - test_utils: move pygraphviz import inside the two tests that need it using pytest.importorskip so the rest of the module runs without it - test_utils: replace fragile try/except + exact dtype string match in test_handle_missing_values_more_than_one_non_numeric_column with pytest.raises + partial match (pandas dtype repr changed across versions) - uv.lock: regenerated to encode the torch vs torch-cu128 extras conflict Co-Authored-By: Claude Sonnet 4.6 --- tests/test_eval_graph.py | 2 +- tests/test_utils.py | 7 +- uv.lock | 1306 ++++++++++++++++++++++++-------------- 3 files changed, 828 insertions(+), 487 deletions(-) diff --git a/tests/test_eval_graph.py b/tests/test_eval_graph.py index e1e5e21..0d77bac 100644 --- a/tests/test_eval_graph.py +++ b/tests/test_eval_graph.py @@ -227,7 +227,7 @@ def test_run_ora(): }) expected_results = pd.DataFrame({ - 'ora_source': ["gene_set_2", "gene_set_1"], + 'ora_Term': ["gene_set_2", "gene_set_1"], 'ora_stat': [10.596360, 10.008573], 'ora_pval': [1.500225e-11, 7.500375e-08], 'ora_padj': [3.000450e-11, 7.500375e-08], diff --git a/tests/test_utils.py b/tests/test_utils.py index 871f842..23ce524 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -5,7 +5,6 @@ from unittest.mock import patch import pytest import networkcommons.utils as utils -import pygraphviz as pgv def test_to_cornetograph(): @@ -33,6 +32,7 @@ def test_to_cornetograph_when_cornetograph(): def test_to_cornetograph_when_not_supported(): + pgv = pytest.importorskip('pygraphviz') multi_graph = nx.MultiDiGraph() with pytest.raises(NotImplementedError, match="Only nx.DiGraph graphs and corneto graphs are supported."): utils.to_cornetograph(multi_graph) @@ -93,6 +93,7 @@ def test_to_networkx_when_networkx_graph(): def test_to_networkx_when_not_supported(): + pgv = pytest.importorskip('pygraphviz') multi_graph = nx.MultiDiGraph() with pytest.raises(NotImplementedError, match="Only nx.DiGraph graphs and corneto graphs are supported."): utils.to_networkx(multi_graph) @@ -201,10 +202,8 @@ def test_handle_missing_values_non_numeric_column(): def test_handle_missing_values_more_than_one_non_numeric_column(): df = pd.DataFrame({'id1': ['a', 'b', 'c'], 'id2': ['x', 'y', 'z'], 'A': [1, 2, np.nan], 'B': [3, 2, np.nan]}) - try: + with pytest.raises(ValueError, match="More than one non-numeric column found"): utils.handle_missing_values(df, 0.5) - except ValueError as e: - assert str(e) == "More than one non-numeric column found: Index(['id1', 'id2'], dtype='object')" def test_handle_missing_values_no_missing_values(): diff --git a/uv.lock b/uv.lock index 3ab14f0..5e97955 100644 --- a/uv.lock +++ b/uv.lock @@ -6,6 +6,10 @@ resolution-markers = [ "python_full_version == '3.11.*'", "python_full_version < '3.11'", ] +conflicts = [[ + { package = "networkcommons", extra = "torch" }, + { package = "networkcommons", extra = "torch-cu128" }, +]] [[package]] name = "absl-py" @@ -65,15 +69,15 @@ name = "aiohttp" version = "3.14.1" source = { registry = "https://pypi.org/simple/" } dependencies = [ - { name = "aiohappyeyeballs", marker = "python_full_version < '3.11'" }, - { name = "aiosignal", marker = "python_full_version < '3.11'" }, - { name = "async-timeout", marker = "python_full_version < '3.11'" }, - { name = "attrs", marker = "python_full_version < '3.11'" }, - { name = "frozenlist", marker = "python_full_version < '3.11'" }, - { name = "multidict", marker = "python_full_version < '3.11'" }, - { name = "propcache", marker = "python_full_version < '3.11'" }, - { name = "typing-extensions", marker = "python_full_version < '3.11'" }, - { 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result differs from the committed lock file, which happens when the lock was generated on a different platform (e.g. Linux) and CI runs on another (macOS). --frozen installs exactly the versions in the lock file without re-resolving, which is the correct behaviour for reproducible CI. Co-Authored-By: Claude Sonnet 4.6 --- .github/workflows/test.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 1af92cc..7fd40ff 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -48,7 +48,7 @@ jobs: enable-cache: true python-version: ${{ matrix.python }} - name: Install dependencies - run: uv sync --locked --group test --extra corneto-backends + run: uv sync --frozen --group test --extra corneto-backends - name: Tests and coverage if: ${{ github.event_name == 'push' }} run: uv run pytest -v --cov --color=yes --durations=0 From a485f9baacec932efb4dad827079e9b3c1c32c06 Mon Sep 17 00:00:00 2001 From: HugoHakem Date: Sat, 27 Jun 2026 15:27:14 +0100 Subject: [PATCH 17/17] feat: add omnipath-client dependency, note workaround branch for get_hmdb_mapper Adds omnipath-client>=0.2.3 as a dependency for future use (node ID translation, Ensembl mappings, COSMOS PKN via oc.cosmos once available). get_hmdb_mapper keeps the existing rdata approach for now: the intended migration to oc.utils.translate('hmdb', 'traditional_iupac') is blocked by a server-side 500 on utils.omnipathdb.org. A working workaround using OmniPath()._fetch('entities/resolve') is preserved on branch hmdb-mapper-omnipath-workaround for when the API matures. Co-Authored-By: Claude Sonnet 4.6 --- networkcommons/data/network/_moon.py | 7 + pixi.lock | 1237 +++++++++++++------------- pyproject.toml | 1 + uv.lock | 26 + 4 files changed, 672 insertions(+), 599 deletions(-) diff --git a/networkcommons/data/network/_moon.py b/networkcommons/data/network/_moon.py index 1446139..b0a8dd1 100644 --- a/networkcommons/data/network/_moon.py +++ b/networkcommons/data/network/_moon.py @@ -79,6 +79,13 @@ def get_hmdb_mapper(update: bool = False) -> dict: and converts it to a Python dict mapping HMDB IDs to human-readable metabolite names. + Note: long-term this should use ``omnipath_client.utils.translate`` once + the ``utils.omnipathdb.org`` server exposes metabolite name mapping + (currently ``oc.utils.translate('hmdb', 'traditional_iupac')`` returns + HTTP 500 — only cross-database ID translation is supported). 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