{ "cells": [ { "cell_type": "markdown", "id": "b32df9a7-ae4c-4d54-93d2-32b7277f9e55", "metadata": {}, "source": [ "# Classification of ASD from Resting-State fMRI Connectomes\n", "**Authors:** Alexander Vatamidis Norrstam & Valery Nkenguruke\n", "\n", "## Project overview\n", "Resting-state fMRI is converted into subject-level functional connectomes (AAL atlas, 116 ROIs). Two modeling approaches are compared:\n", "\n", "1. **Classical ML (SVM):** each connectome is vectorized (upper triangle) and classified with an RBF SVM.\n", "2. **Deep Learning (GCN):** each connectome is converted to a sparse weighted graph and classified with a Graph Convolutional Network.\n", "\n", "The ABIDE I cohort is multi-site, so evaluation includes a site-aware grouped cross-validation to reduce site leakage.\n", "\n", "## Research questions (RQ)\n", "- **RQ1 (Exploratory):** Which connections/regions show the largest ASD–Control group-mean differences? (descriptive; no edge-wise multiple-comparison inference)\n", "- **RQ2 (Model comparison):** Does an explicit graph model (GCN) outperform a vector baseline (SVM) for ASD vs Control classification?\n", "- **RQ3 (Graph metrics):** Do graph-theoretic metrics (strength/clustering) differ systematically between ASD and Control?\n", "\n", "*Note:* The numbering follows the course template; **RQ3 is not used in this project**.\n", "\n" ] }, { "cell_type": "markdown", "id": "ffcb952d", "metadata": {}, "source": [ "## Notebook map (run order)\n", "\n", "1. Setup & configuration \n", "2. Data acquisition (download/paths) \n", "3. Cohort definition (inclusion/exclusion, site counts) \n", "4. Connectome construction → outputs: `X` (n×116×116), `y_dx` (1/2), `y_bin` (0/1), `groups_site` \n", "5. RQ1 (exploratory differences) → Fig. 1–2 \n", "6. RQ2 (models) → Table I (subject-level), Table II (site-aware) \n", "7. RQ3 (graph metrics) → Table III–IV \n", "8. Summary\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Setup\n", "\n", "This section installs/imports required packages and defines common utilities and configuration used throughout the notebook.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "923b5614-8cb6-424a-8200-c700948885d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: statsmodels in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (0.14.6)\n", "Requirement already satisfied: numpy<3,>=1.22.3 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from statsmodels) (1.26.4)\n", "Requirement already satisfied: scipy!=1.9.2,>=1.8 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from statsmodels) (1.16.3)\n", "Requirement already satisfied: pandas!=2.1.0,>=1.4 in /Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages (from statsmodels) (2.3.3)\n", "Requirement already satisfied: 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(2026.1.4)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "## 1. Setup Environment\n", "%pip install statsmodels\n", "%pip install torch\n", "%pip install networkx\n", "%pip install torch_geometric\n", "%pip install nilearn\n", "%pip install seaborn\n", "%pip install requests\n", "\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import requests\n", "import scipy.stats as stats\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import copy\n", "import random\n", "from typing import Tuple\n", "from pathlib import Path\n", "\n", "\n", "# Machine Learning\n", "from sklearn.model_selection import StratifiedKFold, StratifiedGroupKFold, StratifiedShuffleSplit, GridSearchCV\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.svm import SVC\n", "from sklearn.dummy import DummyClassifier\n", "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report\n", "\n", "# Deep Learning (PyTorch Geometric)\n", "import torch\n", "from torch.nn import Linear\n", "import torch.nn.functional as F\n", "from torch.utils.data import WeightedRandomSampler\n", "from torch_geometric.data import Data\n", "from torch_geometric.loader import DataLoader\n", "from torch_geometric.nn import GCNConv, global_mean_pool\n", "from statsmodels.stats.multitest import multipletests\n", "import networkx as nx\n", "\n", "# Neuroimaging Visualization\n", "from nilearn import plotting\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "dd84954e-6e89-451c-8add-ad564a66c1d0", "metadata": {}, "source": [ "## 2. Data acquisition\n", "\n", "Download the ABIDE phenotypic CSV and the AAL ROI time-series files (`*_rois_aal.1D`). This produces a filtered cohort table and local time-series files used downstream.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "9462ffc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Phenotypic CSV already exists: /Users/valerynkenguruke/Desktop/Data Science/Data mining/Mini_Project/fMRI_project/data/Phenotypic_V1_0b_preprocessed1.csv\n" ] }, { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " Unnamed: 0.1 Unnamed: 0 SUB_ID X subject SITE_ID FILE_ID \\\n", "0 0 1 50002 1 50002 PITT no_filename \n", "1 1 2 50003 2 50003 PITT Pitt_0050003 \n", "2 2 3 50004 3 50004 PITT Pitt_0050004 \n", "3 3 4 50005 4 50005 PITT Pitt_0050005 \n", "4 4 5 50006 5 50006 PITT Pitt_0050006 \n", "\n", " DX_GROUP DSM_IV_TR AGE_AT_SCAN ... qc_notes_rater_1 qc_anat_rater_2 \\\n", "0 1 1 16.77 ... NaN OK \n", "1 1 1 24.45 ... NaN OK \n", "2 1 1 19.09 ... NaN OK \n", "3 1 1 13.73 ... NaN OK \n", "4 1 1 13.37 ... NaN OK \n", "\n", " qc_anat_notes_rater_2 qc_func_rater_2 qc_func_notes_rater_2 \\\n", "0 NaN fail ic-parietal-cerebellum \n", "1 NaN OK NaN \n", "2 NaN OK NaN \n", "3 NaN maybe ic-parietal-cerebellum \n", "4 NaN maybe ic-parietal slight \n", "\n", " qc_anat_rater_3 qc_anat_notes_rater_3 qc_func_rater_3 \\\n", "0 OK NaN fail \n", "1 OK NaN OK \n", "2 OK NaN OK \n", "3 OK NaN OK \n", "4 OK NaN OK \n", "\n", " qc_func_notes_rater_3 SUB_IN_SMP \n", "0 ERROR #24 1 \n", "1 NaN 1 \n", "2 NaN 1 \n", "3 NaN 0 \n", "4 NaN 1 \n", "\n", "[5 rows x 106 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Cohort size: 884 (ASD=408, Control=476)\n", "ROI downloads done. downloaded=0, skipped=884, missing(404)=0, failed=0\n", "ROI folder: /Users/valerynkenguruke/Desktop/Data Science/Data mining/Mini_Project/fMRI_project/neurodata_1D\n", "Data root: /Users/valerynkenguruke/Desktop/Data Science/Data mining/Mini_Project/fMRI_project/data\n", "Found .1D ROI files: 884\n", "Phenotypic CSV: /Users/valerynkenguruke/Desktop/Data Science/Data mining/Mini_Project/fMRI_project/data/Phenotypic_V1_0b_preprocessed1.csv\n" ] } ], "source": [ "## 2. Data acquisition\n", "\n", "# Config\n", "\n", "DATA_DIR = os.path.join(os.getcwd(), \"data\")\n", "PHENO_FILENAME = \"Phenotypic_V1_0b_preprocessed1.csv\"\n", "PHENO_URL = \"https://s3.amazonaws.com/fcp-indi/data/Projects/ABIDE_Initiative/Phenotypic_V1_0b_preprocessed1.csv\"\n", "\n", "FD_MAX = 0.2\n", "APPLY_MOTION_FILTER = True # set False to keep all subjects regardless of FD\n", "\n", "DOWNLOAD_DIR = \"neurodata_1D\"\n", "ROIS_AAL_BASE_URL = \"https://s3.amazonaws.com/fcp-indi/data/Projects/ABIDE_Initiative/Outputs/cpac/filt_global/rois_aal/\"\n", "TIMEOUT_S = 60\n", "\n", "# 2.1 Download phenotypic CSV\n", "\n", "os.makedirs(DATA_DIR, exist_ok=True)\n", "csv_save_path = os.path.join(DATA_DIR, PHENO_FILENAME)\n", "\n", "if not os.path.exists(csv_save_path) or os.path.getsize(csv_save_path) == 0:\n", " print(\"Downloading phenotypic CSV...\")\n", " r = requests.get(PHENO_URL, timeout=TIMEOUT_S)\n", " r.raise_for_status()\n", " with open(csv_save_path, \"wb\") as f:\n", " f.write(r.content)\n", "else:\n", " print(\"Phenotypic CSV already exists:\", csv_save_path)\n", "\n", "df = pd.read_csv(csv_save_path)\n", "display(df.head())\n", "\n", "# 2.2 Cohort filtering (single pass)\n", "\n", "df = df.copy()\n", "\n", "# Valid IDs + valid diagnosis labels\n", "df = df[df[\"FILE_ID\"].notna() & (df[\"FILE_ID\"] != \"no_filename\")]\n", "df = df[df[\"DX_GROUP\"].isin([1, 2])] # 1=ASD, 2=Control\n", "\n", "# Motion filter\n", "if APPLY_MOTION_FILTER:\n", " if \"func_mean_fd\" in df.columns:\n", " df = df[df[\"func_mean_fd\"] < FD_MAX]\n", " else:\n", " print(\"Warning: 'func_mean_fd' not found; skipping motion filter.\")\n", "\n", "cohort = df.sort_values(\"FILE_ID\").reset_index(drop=True)\n", "\n", "n_asd = int((cohort[\"DX_GROUP\"] == 1).sum())\n", "n_ctl = int((cohort[\"DX_GROUP\"] == 2).sum())\n", "print(f\"Cohort size: {len(cohort)} (ASD={n_asd}, Control={n_ctl})\")\n", "\n", "# 2.3 Download AAL ROI time series (.1D)\n", "\n", "os.makedirs(DOWNLOAD_DIR, exist_ok=True)\n", "\n", "session = requests.Session()\n", "downloaded, skipped, missing, failed = 0, 0, 0, 0\n", "\n", "for fid in cohort[\"FILE_ID\"]:\n", " filename = f\"{fid}_rois_aal.1D\"\n", " file_url = ROIS_AAL_BASE_URL + filename\n", " save_path = os.path.join(DOWNLOAD_DIR, filename)\n", "\n", " # Skip if already present\n", " if os.path.exists(save_path) and os.path.getsize(save_path) > 0:\n", " skipped += 1\n", " continue\n", "\n", " try:\n", " r = session.get(file_url, stream=True, timeout=TIMEOUT_S)\n", " if r.status_code == 404:\n", " missing += 1\n", " continue\n", " r.raise_for_status()\n", "\n", " with open(save_path, \"wb\") as f:\n", " for chunk in r.iter_content(chunk_size=1024 * 256):\n", " if chunk:\n", " f.write(chunk)\n", "\n", " downloaded += 1\n", "\n", " except Exception as e:\n", " failed += 1\n", " print(f\"Failed: {filename} ({type(e).__name__}: {e})\")\n", "\n", "print(\n", " f\"ROI downloads done. downloaded={downloaded}, skipped={skipped}, missing(404)={missing}, failed={failed}\"\n", ")\n", "\n", "print(f\"ROI folder: {os.path.abspath(DOWNLOAD_DIR)}\")\n", "\n", "print(\"Data root:\", DATA_DIR)\n", "print(\"Found .1D ROI files:\", len([f for f in os.listdir(DOWNLOAD_DIR) if f.endswith('.1D')]))\n", "print(\"Phenotypic CSV:\", csv_save_path)\n", "\n", "# Important note, we manually checked for the MRI technology used at the sites and they are all 3.0 Tesla.\n", "# This is adequate for the purpose of this project, it should however be explicitly specified.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "232d755a-9a3c-4aa6-b3cd-207c8093a355", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 884 1D subjects.\n" ] } ], "source": [ "# Accessing the downloaded data files (.1D only)\n", "DOWNLOAD_DIR = \"neurodata_1D\" # must match the earlier download cell\n", "ROI_DIR = Path(DOWNLOAD_DIR)\n", "if not ROI_DIR.exists():\n", " raise FileNotFoundError(f\"Expected ROI dir at {ROI_DIR.resolve()} — run the download cell or fix the path.\")\n", "\n", "file_list_1D = sorted([p.name for p in ROI_DIR.glob(\"*_rois_aal.1D\")])\n", "\n", "# used later in connectome construction\n", "input_folder_1D = str(ROI_DIR)\n", "\n", "print(f\"Found {len(file_list_1D)} 1D subjects.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Cohort definition\n", "\n", "Apply inclusion/exclusion filters (valid FILE_ID, diagnosis labels, and optional motion threshold). Summarize cohort counts and site availability.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "a97afc03-9191-44df-883b-412e135fd701", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final Cohort Size: 884\n", "DX_GROUP\n", "2 476\n", "1 408\n", "Name: count, dtype: int64\n" ] } ], "source": [ "## 3. Cohort definition\n", "pheno = pd.read_csv(csv_save_path) # reuse the downloaded phenotypic CSV\n", "\n", "# IDs available from .1D files\n", "ids_1D = {f.replace('_rois_aal.1D', '') for f in file_list_1D}\n", "\n", "# Use only 1D availability\n", "valid_ids = sorted(ids_1D)\n", "\n", "cohort = pheno[pheno['FILE_ID'].isin(valid_ids)].copy()\n", "cohort = cohort[cohort['DX_GROUP'].isin([1, 2])].sort_values('FILE_ID').reset_index(drop=True)\n", "\n", "print(f\"Final Cohort Size: {len(cohort)}\")\n", "print(cohort['DX_GROUP'].value_counts()) # 1 = ASD, 2 = Control" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Output: cohort and data availability\n", "\n", "After preprocessing and file-matching, the final cohort contains **884 subjects**.\n", "\n", "**Class distribution (DX_GROUP):**\n", "- **DX_GROUP = 2:** 476 subjects \n", "- **DX_GROUP = 1:** 408 subjects \n", "\n", "This corresponds to a mild class imbalance. A naive majority-class predictor reaches an accuracy of **476 / 884 ≈ 0.5385**, so accuracy must be interpreted together with sensitivity/specificity and macro-F1.\n", "\n", "> Note: ABIDE convention is typically **DX_GROUP=1 (ASD)** and **DX_GROUP=2 (Control)**. All sensitivity/specificity statements below assume **ASD is the positive class**.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "3916a423", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ASD (1)Control (2)
SITE_ID
CALTECH1918
CMU32
KKI1227
LEUVEN_11415
LEUVEN_21319
MAX_MUN1824
NYU7398
OHSU1211
OLIN1411
PITT2223
SBL1412
SDSU1221
STANFORD1719
TRINITY2123
UCLA_12827
UCLA_2812
UM_13646
UM_21219
USM3823
YALE2226
\n", "
" ], "text/plain": [ " ASD (1) Control (2)\n", "SITE_ID \n", "CALTECH 19 18\n", "CMU 3 2\n", "KKI 12 27\n", "LEUVEN_1 14 15\n", "LEUVEN_2 13 19\n", "MAX_MUN 18 24\n", "NYU 73 98\n", "OHSU 12 11\n", "OLIN 14 11\n", "PITT 22 23\n", "SBL 14 12\n", "SDSU 12 21\n", "STANFORD 17 19\n", "TRINITY 21 23\n", "UCLA_1 28 27\n", "UCLA_2 8 12\n", "UM_1 36 46\n", "UM_2 12 19\n", "USM 38 23\n", "YALE 22 26" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of sites: 20\n" ] } ], "source": [ "# Multi-site confound diagnostics (ABIDE is multi-site).\n", "# If SITE_ID exists, show the diagnosis distribution by site.\n", "if 'SITE_ID' in cohort.columns:\n", " site_tab = pd.crosstab(cohort['SITE_ID'], cohort['DX_GROUP'])\n", " site_tab.columns = ['ASD (1)', 'Control (2)'] if list(site_tab.columns) == [1, 2] else site_tab.columns\n", " display(site_tab.sort_index())\n", " print(\"Number of sites:\", cohort['SITE_ID'].nunique())\n", "else:\n", " print(\"SITE_ID column not found in phenotypic file; site-aware evaluation will be skipped.\")" ] }, { "cell_type": "markdown", "id": "c580e9ed-2911-4560-a23d-d8e99ba0d2ea", "metadata": {}, "source": [ "## 4. Connectome construction\n", "\n", "Convert each subject’s AAL ROI time series into a 116×116 connectivity matrix (Pearson correlation), apply Fisher z-transform, and build the core arrays used in later sections:\n", "- `X`: (n_subjects, 116, 116) connectomes\n", "- `y_dx`: diagnosis labels (1=ASD, 2=Control)\n", "- `y_bin`: binary labels (1=ASD, 0=Control)\n", "- `groups_site`: site labels (`SITE_ID`) for grouped evaluation (when available)\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "d083e240-5516-42ed-b218-4a04214ee32a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[:, None]\n", "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", " c /= stddev[None, :]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Finished.\n", "X shape: (884, 116, 116) y shape: (884,)\n", "Bad files: 0\n" ] } ], "source": [ "## 4. Connectome construction\n", "\n", "matrices = []\n", "labels = []\n", "bad = []\n", "\n", "for _, row in cohort.iterrows():\n", " fid = row[\"FILE_ID\"]\n", " path = os.path.join(input_folder_1D, f\"{fid}_rois_aal.1D\")\n", "\n", " try:\n", " ts = pd.read_csv(path, comment=\"#\", sep=r\"\\s+\", header=None)\n", " arr = ts.to_numpy(dtype=np.float32)\n", "\n", " # Correlation across ROIs (columns)\n", " corr = np.corrcoef(arr, rowvar=False)\n", " corr = np.nan_to_num(corr, nan=0.0)\n", "\n", " # Fisher z-transform (stable averaging + aligns with report)\n", " corr = np.clip(corr, -0.999999, 0.999999)\n", " z = np.arctanh(corr)\n", " np.fill_diagonal(z, 0.0)\n", "\n", " matrices.append(z)\n", " labels.append(int(row[\"DX_GROUP\"]))\n", " except Exception as e:\n", " bad.append((fid, str(e)))\n", "\n", "X = np.stack(matrices, axis=0).astype(np.float32)\n", "y = np.array(labels, dtype=int)\n", "\n", "print(\"Finished.\")\n", "print(\"X shape:\", X.shape, \"y shape:\", y.shape)\n", "print(\"Bad files:\", len(bad))\n", "if len(bad) > 0:\n", " print(\"First 5 errors:\", bad[:5])\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "ce0a6f6b", "metadata": {}, "outputs": [], "source": [ "# Utilities for labels, graphs, metrics, and reproducibility\n", "SEED = 42\n", "\n", "\n", "def set_seed(seed: int = SEED) -> None:\n", " random.seed(seed)\n", " np.random.seed(seed)\n", " torch.manual_seed(seed)\n", " if torch.cuda.is_available():\n", " torch.cuda.manual_seed_all(seed)\n", "\n", "set_seed(SEED)\n", "\n", "def fisher_z_matrix(mat: np.ndarray, clip: float = 0.999999) -> np.ndarray:\n", " m = np.clip(mat, -clip, clip)\n", " z = np.arctanh(m)\n", " return z\n", "\n", "def build_topk_mask(w: np.ndarray, topk: int) -> np.ndarray:\n", " \"\"\"Symmetric top-k mask per node, excluding diagonal.\"\"\"\n", " n = w.shape[0]\n", " w2 = w.copy()\n", " np.fill_diagonal(w2, 0.0)\n", "\n", " mask = np.zeros((n, n), dtype=np.float32)\n", " for i in range(n):\n", " row = w2[i]\n", " if topk >= n - 1:\n", " idx = np.where(np.arange(n) != i)[0]\n", " else:\n", " idx = np.argpartition(np.abs(row), -topk)[-topk:]\n", " idx = idx[idx != i]\n", " mask[i, idx] = 1.0\n", "\n", " # Enforce symmetry: keep edge if selected by either endpoint\n", " mask = np.maximum(mask, mask.T)\n", " np.fill_diagonal(mask, 0.0)\n", " return mask\n", "\n", "def sparsify_topk(\n", " mat: np.ndarray,\n", " topk: int,\n", " use_fisher_z: bool,\n", " use_abs_weights: bool,\n", " select_by_abs: bool = True,\n", ") -> np.ndarray:\n", " \"\"\"Return sparse symmetric adjacency matrix (float32).\"\"\"\n", " w = mat.astype(np.float32)\n", "\n", " if use_fisher_z:\n", " w = fisher_z_matrix(w)\n", "\n", " np.fill_diagonal(w, 0.0)\n", "\n", " if select_by_abs:\n", " sel = np.abs(w)\n", " else:\n", " sel = w\n", "\n", " mask = build_topk_mask(sel, topk=topk)\n", "\n", " w_thr = w * mask\n", " if use_abs_weights:\n", " w_thr = np.abs(w_thr)\n", "\n", " return w_thr.astype(np.float32)\n", "\n", "def matrix_to_pyg_data(\n", " mat: np.ndarray,\n", " label: int,\n", " topk: int = 10,\n", " use_fisher_z: bool = False,\n", " use_abs_weights: bool = True,\n", " normalize_node_feats: bool = True,\n", ") -> Data:\n", " \"\"\"Convert connectivity matrix to PyG Data with node features and weighted edges.\"\"\"\n", " w_thr = sparsify_topk(\n", " mat,\n", " topk=topk,\n", " use_fisher_z=use_fisher_z,\n", " use_abs_weights=use_abs_weights,\n", " select_by_abs=True,\n", " )\n", "\n", " n = w_thr.shape[0]\n", " deg = (w_thr != 0).sum(axis=1).astype(np.float32)\n", " strength = w_thr.sum(axis=1).astype(np.float32)\n", "\n", " G = nx.from_numpy_array(w_thr)\n", " clust_dict = nx.clustering(G, weight=\"weight\")\n", " clust = np.array([clust_dict[i] for i in range(n)], dtype=np.float32)\n", "\n", " feats = np.stack([deg, strength, clust], axis=1).astype(np.float32)\n", " if normalize_node_feats:\n", " mu = feats.mean(axis=0, keepdims=True)\n", " sigma = feats.std(axis=0, keepdims=True) + 1e-8\n", " feats = (feats - mu) / sigma\n", "\n", " # Build undirected edge_index by including both directions\n", " src, dst = np.where(w_thr != 0)\n", " edge_index = torch.tensor(np.vstack([src, dst]), dtype=torch.long)\n", " edge_weight = torch.tensor(w_thr[src, dst], dtype=torch.float)\n", "\n", " x = torch.tensor(feats, dtype=torch.float)\n", " y_label = torch.tensor([int(label)], dtype=torch.long)\n", " return Data(x=x, edge_index=edge_index, edge_weight=edge_weight, y=y_label)\n", "\n", "def node_strength_and_clustering(\n", " mat: np.ndarray,\n", " topk: int = 10,\n", " use_fisher_z: bool = False,\n", " use_abs_weights: bool = True,\n", ") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", " \"\"\"Node-wise degree, strength, clustering from a top-k sparse graph.\"\"\"\n", " w_thr = sparsify_topk(\n", " mat,\n", " topk=topk,\n", " use_fisher_z=use_fisher_z,\n", " use_abs_weights=use_abs_weights,\n", " select_by_abs=True,\n", " )\n", " n = w_thr.shape[0]\n", " deg = (w_thr != 0).sum(axis=1).astype(np.float32)\n", " strength = w_thr.sum(axis=1).astype(np.float32)\n", "\n", " G = nx.from_numpy_array(w_thr)\n", " clust_dict = nx.clustering(G, weight=\"weight\")\n", " clust = np.array([clust_dict[i] for i in range(n)], dtype=np.float32)\n", "\n", " return deg, strength, clust\n", "\n", "def compute_binary_metrics(y_true: np.ndarray, y_pred: np.ndarray):\n", " \"\"\"ACC, SENS, SPEC, macro-F1, BAL_ACC, confusion matrix.\"\"\"\n", " y_true = y_true.astype(int)\n", " y_pred = y_pred.astype(int)\n", " cm = confusion_matrix(y_true, y_pred, labels=[0, 1])\n", " tn, fp, fn, tp = cm.ravel()\n", "\n", " acc = (tp + tn) / max(tp + tn + fp + fn, 1)\n", " sens = tp / max(tp + fn, 1) # recall positive\n", " spec = tn / max(tn + fp, 1) # recall negative\n", " bal = 0.5 * (sens + spec)\n", " f1m = f1_score(y_true, y_pred, average=\"macro\")\n", "\n", " return float(acc), float(sens), float(spec), float(f1m), float(bal), cm\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "419c0e10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X dtype: float32 X shape: (884, 116, 116)\n", "y dtype: int64 y shape: (884,)\n", "DX_GROUP counts: {2: 476, 1: 408}\n", "y_bin counts: {0: 476, 1: 408}\n", "Max symmetry error: 0.0\n", "Max |diagonal|: 0.0\n", "All finite: True\n", "Value range: -1.9856456518173218 to 2.4289610385894775\n" ] } ], "source": [ "# Sanity checks: shapes, labels, matrix properties\n", "\n", "print(\"X dtype:\", X.dtype, \"X shape:\", X.shape)\n", "print(\"y dtype:\", y.dtype, \"y shape:\", y.shape)\n", "print(\"DX_GROUP counts:\", dict(pd.Series(y).value_counts()))\n", "\n", "# Binary labels (1=ASD positive, 0=Control)\n", "y_dx = y.astype(int)\n", "y_bin = (y_dx == 1).astype(int)\n", "print(\"y_bin counts:\", dict(pd.Series(y_bin).value_counts()))\n", "\n", "# Matrix diagnostics\n", "n = X.shape[1]\n", "sym_err = np.max(np.abs(X - np.transpose(X, (0, 2, 1))))\n", "diag_max = np.max(np.abs(np.diagonal(X, axis1=1, axis2=2)))\n", "finite_ok = np.isfinite(X).all()\n", "\n", "print(\"Max symmetry error:\", sym_err)\n", "print(\"Max |diagonal|:\", diag_max)\n", "print(\"All finite:\", finite_ok)\n", "print(\"Value range:\", float(np.min(X)), \"to\", float(np.max(X)))\n", "\n", "# Expectations:\n", "# - symmetry error ~ 0\n", "# - diagonal ~ 0 if diagonal cleared\n", "# - finite_ok True\n" ] }, { "cell_type": "markdown", "id": "e5f74d7e", "metadata": {}, "source": [ "### Output checklist (connectomes)\n", "\n", "The cells above should confirm:\n", "- expected shapes for `X`, `y_dx`, `y_bin`\n", "- no NaNs / symmetry checks for connectomes\n", "- class counts and (if available) number of unique sites\n" ] }, { "cell_type": "markdown", "id": "49db0f9b-7524-4156-a86b-beffc5fb7f69", "metadata": {}, "source": [ "## 5. RQ1 — Exploratory group differences\n", "\n", "> *Which connections/regions show the largest ASD–Control group-mean differences?* \n", "This section is **descriptive** (no edge-wise multiple-comparison inference).\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "c677270c-546b-470c-aa53-2e878a8637b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "average_brain shape: (116, 116)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## 5. RQ1 — Exploratory group differences \n", "\n", "X_asd = X[y_dx == 1]\n", "X_ctl = X[y_dx == 2]\n", "\n", "mean_asd = X_asd.mean(axis=0)\n", "mean_ctl = X_ctl.mean(axis=0)\n", "\n", "average_brain = mean_asd - mean_ctl\n", "np.fill_diagonal(average_brain, 0.0)\n", "\n", "print(\"average_brain shape:\", average_brain.shape)\n", "\n", "plt.figure(figsize=(7, 6))\n", "plt.imshow(average_brain, aspect=\"auto\")\n", "plt.colorbar(label=\"ASD − Control (edge weight)\")\n", "plt.title(\"Group-average connectome difference\")\n", "plt.xlabel(\"ROI\")\n", "plt.ylabel(\"ROI\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "000670fd-e2b8-4c9d-b7a0-45a3b7ccb9fd", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### Visualization of Glass Brain Connectome\n", "# Manually defined AAL coordinates (MNI Space) to avoid SSL/Download errors\n", "aal_coords = np.array([\n", " [-39, 6, 51], [41, 6, 52], [-18, 35, 42], [22, 35, 42], [-16, 47, -13], [18, 47, -13],\n", " [-33, 33, 35], [38, 33, 34], [-31, 50, -10], [33, 53, -11], [-48, 13, 19], [50, 15, 21],\n", " [-46, 30, 14], [50, 30, 14], [-36, 31, -12], [41, 32, -12], [-47, -8, 14], [53, -6, 15],\n", " [-5, 5, 61], [9, 0, 62], [-8, 15, -11], [10, 16, -11], [-5, 49, 31], [9, 51, 30],\n", " [-5, 54, -7], [8, 52, -7], [-5, 37, -18], [8, 37, -18], [-35, 5, 3], [39, 5, 3],\n", " [-4, 21, 0], [8, 22, 0], [-6, -16, 40], [8, -14, 39], [-5, -44, 23], [7, -42, 23],\n", " [-25, -22, -11], [29, -21, -12], [-21, -17, -22], [26, -16, -23], [-24, -23, -19], [25, -23, -19],\n", " [-13, -81, 27], [13, -81, 27], [-15, -69, -6], [16, -68, -5], [-17, -86, 27], [24, -82, 29],\n", " [-33, -82, 15], [37, -81, 18], [-36, -80, -9], [38, -83, -9], [-31, -41, -22], [34, -40, -22],\n", " [-43, -24, 47], [41, -27, 51], [-24, -61, 58], [26, -60, 61], [-43, -47, 45], [46, -48, 48],\n", " [-56, -35, 29], [57, -33, 33], [-44, -62, 34], [45, -61, 37], [-8, -57, 47], [10, -57, 42],\n", " [-8, -27, 69], [7, -24, 69], [-11, -11, 11], [14, -10, 12], [-24, 4, 1], [28, 5, 2],\n", " [-18, 5, -1], [22, 5, 0], [-10, -18, 7], [13, -18, 8], [-42, -20, 9], [46, -18, 9],\n", " [-53, -22, 6], [58, -23, 5], [-40, 14, -21], [48, 13, -18], [-54, -13, -17], [56, -11, -18],\n", " [-37, 13, -35], [42, 12, -34], [-51, -29, -24], [54, -28, -25], [-31, 3, -42], [35, 4, -41],\n", " [-29, -38, -57], [32, -37, -56], [-33, -33, -63], [36, -32, -63], [-37, -49, -51], [40, -49, -51],\n", " [-30, -56, -46], [32, -55, -46], [-22, -63, -47], [24, -62, -45], [-35, -61, -31], [38, -59, -31],\n", " [-19, -69, -53], [21, -68, -51], [-8, -69, -31], [10, -68, -31], [-7, -50, -42], [7, -49, -42],\n", " [-6, -61, -16], [7, -60, -16],\n", " [0, -40, -25], [0, -53, -30], [0, -60, -34], [0, -69, -33], [0, -75, -29], [0, -80, -26]\n", "])\n", "# Check consistency\n", "if len(aal_coords) != average_brain.shape[0]:\n", " aal_coords = aal_coords[:average_brain.shape[0]]\n", "\n", "fig = plt.figure(figsize=(10, 5))\n", "plotting.plot_connectome(\n", " adjacency_matrix=average_brain, \n", " node_coords=aal_coords,\n", " edge_threshold='99.5%', \n", " title='ASD vs Control: Connectivity Differences',\n", " node_size=20, edge_cmap='RdBu_r', colorbar=True, figure=fig\n", ")\n", "plt.savefig(\"ASD_GlassBrain.png\", dpi=300)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "2a01d565-1d00-4229-a896-3f214184c931", "metadata": {}, "outputs": [ { "data": { "image/png": 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rl1566ToZeyGESDbquRRCZAUMUidjd8cdd5S6nVEHBHGUeYUHn8H+PEYJPP/8814ZWFlz+iKNhkA4MbidEsFgH+fhhx/uDXUPCkyG0zNLkdEZyRIbwREq9EslkmGgJJbg99lnn42aLWEEBsQi5uL9XGKBzyc8c3P77beX2ysbDbI5vKYBAwZ4IjEcRIobxcDrAT7vIC4TxViQVNGtWzdPSDAflf7HaPsmgoPxF2PGjLHvvvuu1H2C71s8nyMgplkwoRyWH8a28DjlweNEewyynmQ/KcVk3Afbp68wEVhcYYwKIsmNW0kGhx12mLfvIbDD9z+u0/tbHpSYUso9atSodX5HuTff33hgEatnz5724osvRlzIcs+Tx+WYR0YyHD4bxutA+Gtg++7ziFTiL4QQyUaZSyFEVsAq+7777uvN8WPGHj1iGI4gGCmfDJpiAD1KBOsYzVC+etddd3m3EziWxZ133llioEE/FCWuLoinXDHYY8WKP0EkguO8887zerIQIWR9nAFMIiCEeQ70wBFEE/jyXBAX4aIiVtgOmUHEL4YwiGVKfSlXvOeee7z3lfeSTCnXyUYiHsg6kdFJ9HOJNdPIe005LIY5BM1kR5l3WRF4DvT9kZlDQFECyP7BjEDKNSnbdDMfef5kTcl0EpTvvffe9uGHH3qCniCf15oqEJZ33323l9nbYYcdvKyh+6wxEtp9991LRPz//vc/r/eX+2EUxGfD+8/9KHsEtxDCZ8O22D/5vJzojATv0WWXXeYZ8bDfxVK+yuPwvK+55hov80XmNdhTyfvN88XMhrmL8cACEXMnybDxebCYQ2aVhQv2kUSFavh+wmu46KKLvPeSz5v9f8GCBd5iDO8z3/Oy4LNDSNOfzevlM2NRhEw+tyP+IhnzlAWLDXyn2Bd5DuzDHJfYb5mxyneV7zXfYb477Mt8JgjZGTNmeN93Xg+LYyyw8H3n82G+LUKdhZvtttuuVKZeCCFSRsr9aIUQIoZRJG5UA+MbmPnG2ITWrVt7owqC4xeAv+PvH330Ue8+jIZgxmB58xDdHLj999/fm2HHYzCKgbl1kydPjnj/77//3hu7gb3/+uuvX3TwwQcXzZkzJ6bPlHEQ3bt3L/M+n3zySdEuu+zijSzYfPPNi26++eaoo0gibYuRCeFjIn755Rdvth3zH9kucwoZzRCcrccYlvbt23sjGIJjScJHkVTkc4n0PgRHUzC7j/mKjKzgPe3WrZs31iX8frHOuXQwpoFxHsws5HUzyoNZjIx9+Oeff0rut3r1am++4RZbbOG9ns0228wb1RG8Tzzvufu8wkdJRHv+XOc1M36E8TnMczz++ONLjdYBZmkyWod9lPsxc5P5h0EYUcHnzCiL4D4T/l462HfdSJxp06at8/tI+x6jUngfeD/5XaSxJIwr4Tn88MMPRbHgRpG4H/bDBg0aeN8FPovwESzJGEXiGDduXNEee+zh/Z4fZniy386aNavkPrxGXlMkGLfCfFJ+z7GHOaHM+2SfcmNT4vk+AK+XkSTM1WSbzMPkbxkXE/we8t60atXK27/5/jBWacSIESVzSseOHesdzxo3blxyTDn11FOLfv7554ivRQghkk0l/kmddBVCiORDVuOMM85Yp1RTCJEZtt9+e88Ea/LkyfoIhBCigFHPpRBCCCEqDL2ClOpWdH6rEEKI/EE9l0IIIYSIGxxOmQuL6y/mQPRzCiGEKGyUuRRCCCFE3GAkc8IJJ3jOt0888YRnEiSEEKKwUc+lEEIIIYQQQoiEUeZSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshRFzceOON1q5dO2/oeabGkFxxxRUZeex8pXnz5t5gdhE7++yzj/cjshvt27Hz5ptvesdX/ncceeSRdsQRR6TksxFC5CcSl0KImFm5cqXdcMMNNnToUKtcOXT4ICAJ/mywwQa2995728svv5y17+67777ridQVK1Zk+qnkNRdccIG3T5TlJLp06VIbNGiQt2hRs2ZNa9y4se28887efvbHH3+U3A8BHNzP1l9/fWvRooUdfvjhNm7cuIQWPL799tuI+/F2223nzVNds2aN5Qrat1MPo1f69etnm222mVWvXt2b8bnffvvZAw88kNJ9Jd2fLd9BvltffPFFWh5PCJH7aBSJECJmxowZY//9958dddRR6/xu//339+bcFRUV2cKFC+3uu++2Qw45xCZMmGDdunXLuneZIO3KK6/0BEu9evWskJk1a1apxYJkwb6AiyjZoxdffNF+//13q1OnTqn7/Prrr7bTTjt5CxcnnniiJzB/+eUXmz59urcPDRw40BORDgL50aNHe5f//vtvb19j2whMMonPP/+8JworCvv2QQcd5F3+7bff7JVXXrGzzjrLe5ybbrrJcgHt26ndt9n/TjvtNNtoo42sf//+1rp1a2/fnjx5sp100kn2888/27BhwywfPtvtt9/e+34ybubhhx9O+eMJIXIfiUshRMywKn/ooYdGHDnQpk0bbyXf0bt3b2vfvr3ddtttWSku44GM2KpVq3J21AILAryGatWqRfw9gi0VUF73ww8/2JQpU7x9YPz48XbccceVus/9999v3333nb3zzjvWqVOnUr9DcIY/5ypVqpTaz+Caa66x66+/3i666CI7+eST7amnnqrwc95hhx1Kbf/000+3XXbZxR5//PGcEZfxoH07Pt5//31PWO62227ewkNwsWTw4MH28ccfe/M/8+mzpSz28ssvt7vuuqvUQo8QQkRCZbFCiJhYsGCBl02i9CsWttxyS2vYsKHNmzev1O3//vuvF6i0atXKEzWUlVE6ye3h9zvnnHOsUaNGXgCHqEWoxMrtt99uW221ldWqVcvq16/vrb4jEICysvPPP9+7vMUWW5SUQVIaCVw+88wz7bHHHvO2wfN89dVXvd999tlnduCBB3rZMQKtLl26eAFnkAcffNDbBoLp3HPP9V5D7dq1rVevXl4JaHgAyPPZeOONvee677772ldffRWxV4xSOAJYV4rHe0iZcrAc1JV3jhgxwm699VZr2bKld1+2GY3wx4rn+ZcF7x8LDLwm9huuh8P+sd5669muu+66zu94j2MNjC+88ELr2rWrPfPMMzZ79mxLFrwPZKgQtWXh3jO3D5XVxwYffPCBHXDAAVa3bl3vc6eMnPc7FrRvZ27fJmvI37Mvh2fhgeNM8PH+/PNPGzJkSMl3tm3btt53k6x+EHfMee6552zrrbf27suxxx130nHcigZVKbyO119/Pab7CyEKG2UuhRAxl2O5zE4sUFK4fPlyLwB0IIIQidOmTbNTTjnFE6AzZsywW265xRMEBFaOAQMG2KOPPmpHH320l9Ei+9W9e/eYHnvUqFF29tlne6WS9PL9888/njAmoGd7hx12mPd4lGzy2IhgINB08HhPP/20F6zxe4LUmTNn2p577ukFaAjiqlWr2r333uuVY7711ltehisI5ZQIW8Q0ASABMdsLZtbItmGSRAkx2T16m/if5xzkr7/+8gTIjz/+aKeeeqptvvnm3mfC31OGx7bDs8xsg/fZ9YTFSyzPPxosDtCrRWDtyk1POOEEW7RokTVp0qTkfs2aNfN61B555JF1sprxQonia6+95gXBZNIrAu/zsmXLSjKnlHUToPM+Jwv2LQL9HXfc0XtvKdvk8+rcubNNnTrV6zeNhvbtzO3b7BuUvu61117e9688EJAc79544w2vXJb+3YkTJ3oCke8xx54gHBfJ7pMtR7j+73//8ypAyOxvuOGGaTtuhcMCEb3QCHJEuBBClEmREELEwCWXXMJSe9Hvv/++zu+4/aSTTipaunRp0ZIlS4o+/vjjogMOOMC7/aabbiq53yOPPFJUuXLloqlTp5b6+3vuuce77zvvvONd//zzz73rp59+eqn7HX300d7tl19+eZnPtUePHkVbbbVVmffhebGtBQsWRHw9PM+ZM2eWur1nz55F1apVK5o3b17JbT/99FNRnTp1ivbaa6+S2x544AFvG/vtt1/R2rVrS24/55xzitZbb72iFStWeNcXLVpUVKVKFW+7Qa644grv74877riS266++uqi2rVrF82ePbvUfS+88EJvm9999513ndfD326wwQbeZxELzZo1K/VYsT7/shg7dqy3jTlz5njXV65cWVSjRo2iW265pdT9eA8aNWrk3bddu3ZFp512WtHjjz8e8TF4jrwH0fjss8+87fA848W9b5F+Bg4cWOp9gL333tv7CX/PwvenN954w7ud/4HttG7duqhbt26ltvnXX38VbbHFFkX7779/mc9T+3bm9u0vvvjC+9tBgwbF9NjPPfecd/9rrrmm1O2HH354UaVKlYrmzp1bchv349gSvM093u23356W41b4vhqkTZs2RQceeGBMr1sIUdioLFYIEROYrFAaGK3nht45VtBx+qQ0jBV+VskpPXNQski2EtMWskPuh4wNsMIP9DIB2ccglITGAkYXlNB+9NFHFf50yRKyYu8gu0ZWrGfPnp5DqaNp06ZeNpSsA5muIGRWKFVzkD1gO5jDAO8R/ZBkKsKzKuHw3vH3ZFuC7x3lpmzz7bffLnV/Mh7BjEZFKO/5lwWleewHlO4CmRgyz+GlsZSckq2lj41M9z333OO9n+xHV1999Trlg2Xh9k3MVRJ5zWQ++SHzesYZZ3hZnuB+nKjL6Jw5c7zXyHfKfY6UHVKqyOdYluut9u3M7dvu+x2pHDYSHMco+Q4/jpHNZ78mKx6E73Kw0qNDhw5etnH+/PlpPW5Fwh13hBCiPFQWK4RICj169PBKsTCQQNRde+21XhlZ0KmRoPrrr7+OGhguWbLE+58Aj78LBlpAv1Ks9vmTJk3yygsRN/TiEUjtvvvuMb8eepqC0I/F64n0HBDMCILvv//e63VyhJfOEaABIsq9TnACzEGZn7tv8L2jtLe89y7a868I5T3/aNAbSmDN/jB37tyS23n/EWyU9gXLVgl0cYbFMITXSekgvaSXXXaZ9ztKpGPBjS2JNfiPBM6fwb5iShERIZRN4ma7zTbbWCLw+qCsEmBKysM/f4f27czt286FONbFC77f9FKH748cL9zvy3pO7nmV931L9nErEojhoBgXQohoSFwKIWKCnh+ybJHGScCmm25aEpQzyoF+H8QFZi4E6EAgQ3B+8803R3wMTC+SAUETIwheeuklr18OQYNwQaxgyBEL9BglClmLSMSTjXPw3mGsQTY4EuE9hpl8/mRZ6blkfAE/4ZC9jPQ5ELzyOvghy4nQ476xikvn0hku1hOFjCKzLskqRhOX0QLv8JmHLiuJ8yw9eJEoy5FT+3bm9m32K6o36BNPBck4XiTjvYkEApfvoxBClIfEpRAiJihlda6xlGuVB6YzmE5ccsklngkEwTeZSEogCdbLWgXH5IUgHCfR4Io7gjFWcIDs27ev90M2FYE7fPhwz5gFB9J4V+HJGOLqGek5fPPNN16mNV5xzOsEsnvBjAPlkuHZCt47MnOxuvVmEgQhjpeYpYRDiSmuveWJfEr4yNpgVhQrmALxuSLCkwmLKsHMaCRc5it8uH14dspl48mCVfSz1L6dGfj+U8KPaQ7ZvvK+73y/qaAIX5DjeOF+Hy+ZOG6x//N6MScSQojyUM+lECImmOsGzHGLBVb46S2iDJbB9m5eGi6JOF6G8/fff3t9Z4CTJuCWGCTcETUaiLMgzEqkD4kMwOrVq0sC9EhioKysAuW1vJbguInFixd7YmmPPfYoKZuLFUQ27xMloUHIkoXDe/fee+95JaPh8BqcAMo0BKFk+Hi+uPWG/+AYi5jGuRf4333uQT788EPvc4y1FJo5l/SWsZiQ7AzLiy++6P2/7bbbRr2PE43B3leylvfdd1+p++EQy30ZRxFJrJY3DkP7dmZhwYTjCM7EkT6/Tz75xB566KGSCg72gfDvM4tuiER3nIuHTBy3GPWCO2/4HFohhIiEMpdCiJggk0Q2ipV4es9igXlvlKLSP4ehBAEZNvmYt2DeQw8ewRcr6NyOcMIEhnJBRldQykr/GUEN5jfB/r2yIJhi3AXbxzAGgUuAR6mlyyAQ5MPFF19sRx55pGfPzzgQF7xF4pprrvGMXgjIMOFBGJKJowSUcSLxwnNjVAqlo2QFmHtIZhejD8qKg1kKxhe88MILdvDBB3vvK88fUUaJ3tixY73A0Y0myCQErG4EQyQIuHnfyG4yAoFsI5fJbvOaWAjg8xozZoyXYR42bFipv0dEM6IGCHjJDPK+0I9KCXa4mGOuIYKW8RXhc0Mj8emnn5Zsn4wT+x1l1eyD7FfRoGeNWZ1kxn/99Vevb/bJJ59cR/STKRo9erQnLPgbntsmm2ziLbrwnSDQd2I2Etq3Mwv7wZ133ul9/6nm4JjGYgb7CrNM2Rc5TgDHE/ZJjjF8P1mcYAEEoYc5WXhPeSxk4rjF35L9THZFgBAiT8m0Xa0QIne4+eabi9Zff31vbEIQDiVnnHFGxL9xYzWcvf2qVauKbrjhBm9USPXq1Yvq169ftOOOOxZdeeWVRb/99lvJ3/39999FZ599dtGGG27ojZ845JBDir7//vuYRpHce++9nsU+f8tjtGzZsuj8888vtX033mOTTTbx7PuD9v5lvZ5PP/3UGyPB+1CrVq2ifffdt+jdd98tdR837uCjjz4qdXskq////vuv6NJLLy1q0qRJUc2aNYs6d+5c9PXXX3vPnbEcQRgDc9FFFxW1atXKGy3QsGHDok6dOhWNGDHCe1+DIzWCI2AqOq4hlucfzjbbbFO0+eabl/l4++yzT1Hjxo2LVq9eXTR9+nTvs9lhhx2KGjRo4I1madq0aVGfPn289zoIzzE4IoT3v3nz5kW9e/f2Rp+sWbNmncdijAP3ffXVV8t8TpFGkfBcWrRo4T2/8BE84aNIgFEPjLhgn9too42Khg0bVvT6669HfM8Ym3LYYYeV7KN8BkcccUTR5MmTy3ye2rczt28H+eSTT7zRSBtvvHFR1apVveNYly5dih566KFS+yH7DWNO3P0YQ8PzDx9tE+2YE/78U3ncivYe7LLLLkX9+vWL6X0RQohKvAWZFrhCiNyALCIZTFa7GQouUgMlb/TwkXEgQyEqDuW5ZI0osxWZR/t2bsHonB122MHL6EczoBJCiCDquRRCxEzdunU9t1KcLsuaxSdih17TcFxv6T777KO3MgFYO6VU0ZUpivSifTv3oZeZXmkJSyFErChzKYQQGYSeQH7oRWQEBUPNn3jiCa+3LpJ5jxC5gvZtIYQoPGToI4QQGYSxLhhsUGq8cuXKEpMfZdtErqN9WwghCg9lLoUQQgghhBBCJIx6LoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQQuJSCCGEEEIIIUTmUeZSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImEkLoUQQgghhBBCJIzEpRBCCCGEEEKIhJG4FEIIIYQQQgiRMBKXQgghhBBCCCESRuJSCCGEEEIIIUTCSFwKIYQQQgghhEgYiUshhBBCCCGEEAkjcSmEEEIIIYQQImGqJL4JIYQQIrsoKiqy1atX29q1a61y5cpWtWpVq1SpUt5uTwghhMgGJC6FEELkBf/995+tWLHCfvvtN/vnn388AedAuNWoUcPq1q1r9erVsypVquT89oQQQohso1JR8OwmhBBCpJlEs3j8/dKlS72fWE5pbLtRo0beT6THyfbtRUKZUCGEENmAxKUQQoi0k6ws3qpVq2zhwoX277//xv0cqlevbs2aNbNq1arlzPaCKBMqhBAi25C4FEIIEReJZMmSmcVDuM2fP98TWRUF4dqiRQtPwGX79hzKhAohhMhWJC6FEEKUSzKyZMnM4vH4c+fOrdC2Im27ZcuWNm/evKzdXqtWrbz3WZlQIYQQ2YzEpRBCiKgkK0uW7CzekiVLvJ9kUbt2bfvzzz+zdnuNGzf2hHuuZkKFEEIUBhKXQgghIpKsLFmys4zNmze32bNnlxJCK1eutJNPPtnWrFnj/RxzzDF2+OGH24wZM+zSSy/1XsshhxxiAwcOjOlxFi1aZBdddJH9+uuvtt5669mpp55q3bp1s3fffdduvvlmT+DttttuNnTo0Jife7Rtvv/++zZy5EivzHjDDTe0m266ycsCB0HEUX7M68iVTKgQQojCQ+JSCCEKgHj7JJOZaaScNplZxvXXX9/++OOPUrchKHnONWvWtL/++ssOO+wwe/LJJ+20006zK6+80hNTxx57rF1++eXWpk2bch+DLN4vv/xi7dq1s2XLllnfvn3txRdftJ49e9oDDzxgm2yyiV1xxRXWtWtX69SpU0zPO9o2EcII1i222MJuueUWq1Onjg0YMMBSSSozoZGQm60QQhQGGqQlhBB5SkX7JLkf2axERId7/GhZsWiZxmuuucZef/11a9KkiT311FMRtxsuLIFMIMISENG8hr///tvbdtu2bb3bDzjgAHv77bdjEpeu7BMaNmzovUe8l7Vq1fKEJey88842adKkmMVlpG3yPvBZIIiBUlpEZiKZ0OOOO66kJBdR371793UyrAhdXk+yPmOXCQ3/neZ6CiFEYSFxKYQQeUYsPXROfPGzePHiUj10/F0ySlgh2nboSXzwwQdLZRr3228/Twj16tXLrrrqqrgfC6F2wgkn2HfffWfnnnuuJ7rI0Dm4/PHHH8e93ZkzZ3oZ36ZNm3rvFyW5GPa8+eabJaKwottERFO2S4aVbPJmm23mCcZYQFAiGoOZ0D333NMeeuihkvuQre3cuXPEzz8ZJbbuM2afce91ovufEEKI3KVypp+AEEKI5IFgoL+RjFUs5izA/bg/f0ewjyhINZEyjfxsv/32XkavImywwQY2btw4mzBhgr3yyite1jJRyPpefPHFXjktwue6666zq6++2vr37++JKUqME9kmPPLII3bffffZlClTbNttt7XRo0fHtB3EGMIyPBPqQLT9+OOPtuOOO1qqYZ8hU5no/pcswSuEECIzKHMphBB5QqJ9kmSgFixYEFEURCpjJct4zjnneAIGsdinTx/v9opmGuvXr2/JAKFFKey3335bqteTy8FMZizv59lnn20nnXSSbbfddt5tO+ywgycGgX7JeDNt4dsku8pntuWWW3q/p4fzzjvvtEQyoY7XXnvNywbHKoCjldmyX5BJnj59uvd66TXlfQjCPkM/6fLlyxPa/3gvYunhFEIIkZ0ocymEEFmIK1ukV5L/y8sCJatPEoFSVhnr2LFj7bHHHvOya4hDRBIi6/HHH/cMdBCKFc00UtpZUfhb12f4+++/2yeffOJl9RBWs2bN8gTxq6++anvvvXdM2+P9vOSSS2yXXXbxXGYdCCigHPaJJ57wSnhjJdI2eQ8QZD/88IN3HedY3HATyYQ6Jk6c6PWZxoors33++ee9TOqNN97ovc57773Xe058znxe9FdG+wyS1cMZy/4ez/dDCCFEelDmUgghsoREDFCS2ScZaxkr2aWOHTt6t2F0gwDheWy++eYVyjR++umnXuauIvz888+eK6wrrz3qqKM8455hw4Z5gon3BkEXi5kPfPbZZ54Y5f6Uq8K1115rzz77rL3zzjvedTK5ZNliJdo2EYZnnXWWJ4TJrA4fPjyh7Kp7PyiLDd5WUcOhl19+2V544QXvdvpC+YlEsgReeA+nQwZBQgiR/UhcCiFEhknUAIWgO7xPMpobK06iuK3yN2S1Yp37WF4ZKyWVc+bMKSnvLA+yXIhVMqIu03jEEUdYRdlmm228rGo49DA+99xzcW+Psk/KQMOJZ65lrNtEbFZEVEfLrrqSWLZZUYMcV2bLogELCyNGjLAvvvjCe66UzvK5VbTMFjHN581IGWD8CkZGQdifGzRo4C2iyCBICCFyB825FEKIDJKMIfaIPgL5WOY+EqwT1CMuEZqIk1gFYVAY0muJKCDDxeOceOKJnjNprCJpxowZpTKNRx55pCcuER7vvvuul8FFXFxwwQWeIBHrQqb3+OOPL5WNJRPK9aOPPtouvPBC69ChQ9xvHZlz9g16K9m/9tprL68PlP9vu+02T7CSLa3oXE8ys+wn5ZUo0z9K2XCi3w/1bwohRPpQ5lIIIXLYgIe/JysUqxuryxbxmNxekcxWsIx1//3390pPGYERT/YtWqYxnpLQQidaJhToga0I4WW2bp9BWEKXLl3srrvuSqjMNlZYYEikj1MGQUIIkX5k6COEEBkgWQY8/H20rA6BfO/evT3HUDJcroy1X79+XtZo1113LRllURHDHHosb731Vq8XlJLHdOJEcrKIpcwzk9tLB5HKbFl82G233ezzzz/3rn/00Udx9ZlGc7OlzJZ9k/0n2sgY+o7TZRAkhBAiOagsVgghkgQBLNlAgmjMWTA+iZYZZCxGcExGKgkvYwWEIn2T5513nrVu3bpCZaxks8hctmzZssRgiMfZfffdU/p6eE/JnDI2JRkmRpRP8hrmzZuXtdtLulkTYits34xWZksGnOw0+8zGG2/sZZcxlqpImS3ZUMplXTk1ZdDM4cSAKZVgDhRtDE0831shhBBlI3EphBAJUBEHS/6G8Rjh2ZRoJjxA4EvGkczPzTffHPfzvOaaa2znnXcuVbo6ZswYL5hGUOQSTigkWlYMfCZurmI2b499LGmLEex3/MQ4/zIReA/Yp9mPw02H4O233/ZGppRXDh3NHMjBQgnzVp966qkyFySC38GKOjMLIYSIjo6YQghRARJxsCSojfQ3bpZk0ISHklYC3PHjx3tZo2hzKGNxY+3Tp48XnGOUQ9CPcU7//v1T/vkTrJMN4jGTkcVzfXwIOIRXsgxfsnl7vObfli+3f//5h4bauLdVars1aqR0bE15brZ8Z3g97MtvvPFG1LmZkWZwBs2B6PPFzZb9mEWS8p4L37sNN9wwIWdmIYQQZSNxKYQQaXB4JWAl80SmJBrRTHj4G+YjDhgwwJ5++ukKz32sU6eON3rEbTsWx85kQGCOQE5GFg+xFQzyEV6Ik/IEQxD+PppgyNbt8W+zG2+0+Ucfbf9tuCFvhiXyHiYzExqhwrbMuZ433HCDLV++3HsvcLMlQ19RcyAWLUaPHu0541566aVlboPXzONW9Hsr51khhCgflcUKIUQcJKPUMZ5ZkojCq6++2ptJCU888USFymIzBdk3xBUiKRljV8oaK1Gq1PGPP6wokOELljpibBTJYbfM7ZVROpmW7VE2eskltqpJE1t49932bwzZvrLeQx577ty5Sclg8tzTaZiDORAZ0Weffdbuv/9+LxvZsWNH7/sSrSw2GQRLnoUQQkRGmUshhEizw2tZMNdv3LhxJSY8W221lSc4CZ5x6kwnlBrGWoYbS6Yx2VnBSI9HVoufoptvttW33GJrq1e3yjfdZFV79oy7rLHU9pJg+lLh7b30kllxVq7a4sXWqlo1W9q4cWKZ0EqVvM8mGdlkxDDPJR0gzDEBwhyIktX33nvPRo0aZT/99FPKH9s5z7rFEiGEEOsicSmEEDFCAJ2OXrXgLEn6yXDxxLyEx6YXk8Can1RC8LzFFlvYDz/8kNRMI9vFjIe+z2RmBdd5/tWrW7UffvCv/PFH5LrNeLZXqVJSM1Yxb++bb8woG3Xvz/DhVumggwzf00Tfw2T1hCKOWQxJdfYyfAYnZkA48pLVR/hR8krZ9913352y58D7xHEgmvOsEEIUOiqLFUIUNLFmkCI5vEZzd6WslWCb4BseffRRL9CP14Tn2GOP9frT3GgIMpfpKot1jqyxGBdVNNOY0lEQo0ebnXyyf3nUKLMBAyznoD93l13MZs3yr/fpY0bZZ4T3JpH3MObPuDiLXem//6zRk09aozZtrBJOw1WqpHy0Ds8LQx9mq55++unr/B6n2HjKYqO5z15wwQWeYOV93GGHHbwsabhZULjzbPjz1FgTIUQho8ylEKLgqMgYgkgOr9HcXQEBGMv8yPJMeIIzB9NF0JE1HZnGZGcFi19E6HKass1JBSHXr19IWG6zjdkDD0TNwCbyHsb0GZtZjUWLrO6jj1r955+39Vau9H9x443MubFGvXsX/92/CSWJ3UOGbyOaOVBFvx/R3Gcvu+wyW3/99b3XPmTIEM/NtkuXLhGdZ93MWI01EUKIEBKXQoiCIZHxIZFcXqO5u1aUbbbZxsaOHRv19/Rd8hMNBB6BbrIdWVPRf5hycl1cXn6532sJDRqYPfccqxkpfchyP+OttzZr2pSVFrMXX/T/aM4cs759rdL221udi0fajxs2tXr1/quoma1VrVrF+16Fl+mSRZw+fXrUv9tkk03iMvOJ5j7LHFmgEoHXHw2OBxprIoQQ65L6CcpCCJEF0K+FOyale7EKQDeGgL9DbEaCgLR3795exvL444/3MndAVoS5kg899JClCwJkeuhcOW688HexuGG6LBlilv+zTljmurgcN87LBnpQkoloatEirU8h6mdMBvWFF8zeecdsr71K7r/msy/s2MNX21FHtbB586qXZCBjhftXq+bvf+47lE73WUS0E5aU1+6zzz7ewtG+++4b8W/I7M6ZM6fCx5NkzHwVQohsROJSCFEw40MqasZT1t85d9cJEybYK6+84pXYXX/99TZ+/HgbM2aMV1aH8UiqIfgnKHeOrJQ5xir6XFkkf5c3YxZyVVzOmGF23HGh6yNGmBWXWmcVnTqZvfmm2YQJZttvb1fbpfa6dbVFi6rZ2Ueub7UmTsPUIabq31WrKtlddzW2557z9z8WSdK1YOHcZy8nU1wMJe18b+H999+PKhQrKhA5nnA8ksAUQuQjEpdCiLwmHeNDgu6uOLtutNFG3m116tTxTEK+/PJLSzWU+LleRycWeT5kY8jAhAfrXOd2fk/fWTxiNCfIRXH5669mPXua/fmnf52ey8GDLWthfzngAHvt2o/tqkq+OKtsa+yx/46ylucPtLZ77mlNJk+2mpUrR93/KlduYp07t7N77mls11xTyRYv9stzXclqOt1ng1AG3Llz5xKRmaqxJumcDyqEEOlAPZdCiLwmleNDwt1dP/nkEzviiCO8kQhkEenZmjZtmh166KGWLgOenO6TLGRxyeLHkUeazZ/vX99xR7P77kt4hEqqYdrLMf0rl5TBXtPrE9vnw7lmP5pV+e03azh4sDWsWtWKTjnFVl94oa1t2HCd/Y+Xfe+9Zr//7o/z5GW7PudUfXf5PlxyySW2yy672CGHHOLdxveD48XGG2/s9VxScbA1faYpQmNNhBD5iEaRCCHylnjGh+D+eOmll9q3337rBb933HGHbbbZZmVuf8aMGaXcXY888kg7+OCDvd5LHhsht/fee9vgwYNTJuQQkLH0SRYcn3xittNO/mVGV9x5p2U155/vl8ACCwU8/3L2v0yD380++5i9+65//aCDfJ+fyv/+7b/f113nZ2MdtWqZDRpkdsEFNAiX3MwEE4yVMaClxfSzz8w6dAiVs6ei6oAKA76nQbfZ4cOH21VXXeU5P8NOO+3k9U5HGjkSy0iTr7/+2vtbdwyIRFljTYQQIheRuBRC5ByxZuHILBL8BUFQErQGx4c8+eSTXp8kowi6d+/umffwGLUIhlMMhik8ViJD7CUsI0ApMuYzcNJJ/tzLbOXxx82OOca/jMhg1Maee1q2c955ZiNH+pc33xzBZrbhhoE74LDMHZjL6kp9AWF54YVmZ53lC87iiSZDh/q/ZvLH66/7SVu+q5SPxvv9YD2J7wfbSFX2E8h0/vLLL6VGmrz44oueeN199909Ycxi1sCBA70saSQoTXdjTUq/hgKrNhBC5AXquRRC5AQEaQRvDDj/6quvbPbs2Z7rIv9zndv5fTDLEev4EEQmjpEIS+D36RCWgHmJDHgKuCwWRYb4dfzvfzkhLJ99NiQsq1Y1e+aZMGEJdeuaXXWV2bx5ZmefjR2sfzujTBCXrVqZ3X23lwIlobnFFv6vJ08OTWGJaFCFC1AUQRk0CDrooFb244+pdZ6lfBdhGT7SZI899vCeL4KQ3+MSG43gcaoixzkhhMgmJC6FEFmNs++nvJUspMsqht+H2/k99+P+rPYzLiCW8SFcp0fSjQ+54YYb0hK8OYdXGfCkgGCZcLaKSwQHBj5uPx0wwOy00yzbQSsef3zoOiJz553L+AMMrm67zWzWLN8Jl9pX+Plnv2S5XTurPu5xu+mGtaWyos6MtdT3Y8UKq/nll1Yp7DPlPtWr17RPP21i++3nGwTNm1fJevSoZ//9VykjI03gzz//9Ho3y5pPy3GKv6vIcU6GQEKIbENlsUKIrKWiJXEu41Ge1T8ZgHPOOcfOP/98O+644+yJJ57werCGDRvm9VvRi5lKCJj5iYRK4hKEcuimTf3LGCo9/7xlXcPi/vubvfWWf3233cxwJq3gjNJ0gQ5mCgl9kXDEEWZPPhmn79BXX5ldcomf/gxQtE0H26doir39pZ8CveWWCGa59HKeeaYVVa5sqx980Nb26bNOyejXX/sVt2RA4bTTltjAgUtKNG0qIPvIMeSKK64ocZ7lO8zMTK7zu0SPV5FQabwQIttQ5lIIkZezKWMJ1Nz4EATspptu6pWvEagyOP2bb76xTDi8ljvEXsT6Bmd35vLcc0PCEhE8blzWC0ugfNUJS7xwaGWNe9ds395s/HiGSJp17lxyc6UZ0+3mL7taJfMzmFdeafbLLxHSptx37VqrtummEb8fW27p92zecYffwjp6dCObM6e6Z8ibzpEmt9xyizcHtzxh6bZRETQzUwiRbUhcCiEKajYl2UpK1cCND0FgNmjQwH5groKZffTRR54Da6rAGRIjHgnGAhWXY8b4yseV75LBc1nWLObRR/0xIUDb8tixzHJNYIMY3JBeRAkWO/vuaJ/asfZwSWvmlWcujSguPVq2jLrp997zxSmHEMpizzyzmf3yS5WkC8xII03g6aef9txi+V2q0cxMIUQ2obJYIUTWQS9RWQYYiRBpfAizKemX4nZMfshgcjmaC6sz6lAZWxazZo2ftnIlp25eRqb54AOzvfYKNRQiNE84wbKdmTP9vsriKR32wAOl+y4Thv5CspmXXGI/ffObtbY59pfVtvXsP/vyoKHW7raBvgEQcyd5Mnw3eTLrrRfRfPfEE0NrCvjt3Hqr2ahRq+yooxZa69b/xl0iS6VBpCqKSCNNrr32Wu+Ysskmm5SYhzHyqFevXpapMnshhEgXEpdCiKyfTRltPuWBBx5YquTsxx9/tNNPP9369++f0udIwEhmkzEE/MRiqoEgpQyWH2Us0wTiEpG5ww7+3MhMg4ENGbqffvKvn3mm2e23W7bzxx9m+NG4SnGE2/33p+jBSC0+/LBddc5yu3zlEO+m7vaSvVSll//ADz/sN362bRt6QsXwNbziCt+g1sFYEzKsTD/h92PHFtlHHy21I49calWq+N/bsoRm8HtLmT6GOqki2szMe++918aOHesZ/0ydOrXM56qZmUKITCNxKYTIKiLNpixrPiXW/4DAIxAbM2aM1z+ZSoJz6RDDK1as8Aw9CP6CQpNgj56wunXreq6wBIwijdSu7We3ttrKn3uZSch67buvX68Je+/tl4MyxyOLYXdmBOcTT/jXO3TwWyWLE3Ip469f/7G2LVfbDyv8utuJ1tW62uuhO2CG9NprJVfRmySAMRdynHKKX30c/hYjlm+88T/77rsV1rXrb9a27T9WvXr539tox6ZUz8xE1G600UbeMa8scVnWzEwhhEgXxTVDQgiRemJxQI00mzLafMqgkPviiy+8oCrVwtKNDwn2T/K4/MjhNQv7LhGXme65ZD8lS+mE5eab+4Mhs1xYwj33hIQl/ZVkAVMtLKFWgxp2/R01rF8///q5lW+1z9d2sCq2xr8BM6Thwz2HocV/ru9NdEH0AoeUESPMzjknstnQ+uuT3axiX3/d0M46q6G98UaRbbzxaqtefa2tXVvZ+vevakOHVrLwUbcsZC1evDhl4z9chjR8ZubWlALHCMfPcHGp45IQIp1IXAohUkqsmT0CKQRktNmUQKB1wgkn2HfffedZ/AdF3sSJE73MZaoh+IuWgXQOryLLTH0yLS5RaNiqQo0avoFPGU7B2cLHH5ceBUKfZevW6Xv8o44y+9//zD780Gzm2vY2esub7bSvB/m/pGf1kkvsy5tfs4PtRVv46wYlyWrEcMBbJyrOVXbs2Ep2zjnVSryCKKt96CF/NCdTbJxAZSGJ73+q+sHLm5kZC+4YS6VHrMddXpcQQiQLucUKIVICwUy8Q8H5v6ysALb+48aNswkTJtgrr7zilY657bz++uspF5fljQ8RWUY2iEvKGM8+O3SdZkV6QLOc5cvN+vQJ+Q4xgqR37/Q+B3ohmXXpuGzhifab+SISxfeqdbNOv4aE5aYN/rRpb62JSVg6EI68Tto3hw4NeUAtXGheNvTgg83mzg3dn+8/x4FUgiC8+OKL7fLLL4/7bzkWBo+nsRx3OU6nKhsrhCg8JC6FEEmH3si5c+fGFbRwP/qNYsHNp8SpEfh/4403jnuVPx40PiQHcSKggjMEE+b7780OP9w3qYHzzjM7+mjLdtauNcMn69tv/eu77kqPYmaeS6dOZn37+peX/rW+DbeLvcuPnfqmdbdX7PdisbmTfWQf/trKtjtuW7Pnn/dLkeOAUtnrr8dN2my//UK3v/KK37J72WV+hTVZP8YIpSrbF21mZjxwHI3nuMtxmuN1RWdtCiFEEIlLIURSIUDBgCKSbX+y51M2b948rpLYipaskqnAHVYlrzlGJjOXuIoyesKVUGJAc911lgvQr/jii/7lBg3MnnrKn/yRKW64IfRR3maDbK61sJPu2dnWFocwvRtPtbdsb2tqi/wxJaQcUaVvvhn3YzG2BJ+gp58222QT/zY019VXm7Vv7+vWqlWreceDCmUw1661aosXxzUzMx1wvOa4LYEphEgUiUshRNIgOFq4cKHXZ5lsfv75Z2/sSO/evb3/jzrqKG+2HH1JkydPtq5du5b592QcWrVq5c2Bi3UUCPfj/vydhGUOkilxSdYIq1I3/qRFC9/GNAd6295+22zYsND1Rx/1/YcySbNmZkP8qSS2yqrb2fY/+9dqeNcvusjs6Z/3tFpTXjbbZZfQH+Hugzsvi05xjqGJpVT2u++qlT6e8JmT8o1GUZFVWrXKGt99t7Xq3t27HM5nn31mr776qk2ZMsUOP/xw72f27Nl2++23W5cuXbyec/5/iIbQFMBxm+O3SmSFEImgUSRCiKRBeVU6zC4qAk6zLVu29C5rfEiBsMceZu+8419evTp94o5GwXPPtRKHGVxit9nGsh0Sattv74/jhEsu8TN22cDM936z/Tr9ZYusqXcdTy08ko4/PnAnBN4LL/jq+KuvSm8AtciLYT5mnCAyzzrLbNKk0G1kchGeF17I5f9sxe23228bbWT/tG1rRYGMZqmxJp9+ausxq/PHH23eY4/Z37jAljVkM0MgmPkRQoiKIHEphEgKCDbMIcJXvVltP/nkkz33Qn6OOeYYb0UeQ55Ro0Z59yEDMHz48JRmB6PNf5NNfx7TpYvZlCmh4YYIvVSDAiFb5rJYzO5ItxNOBVizxozkv3u7Onf2y0OzYTQrSciLD/rMjl5+hw2w+73b2rQx+/rrKNqMF/PYY2YY4rjGUeDFoEa5fbPN4noOHNb4KBlv8uOPpbOqnqvs0HZWieNfzZq2eulSW7veepHHLa1Y4VnwLsN4h3RsCsQlRj0XXXSR/frrr56z9amnnuq1DXz//fd23nnneW0Fu+66q1166aURqzi4jZ52ucgKISpC9i2ZCSFyEmzvI5VT1a5d2x588EEbO3asPfbYYzZ69GjvviNGjLAHHnjAnmUsgxeTB9ICKZ5NGf47RC3ZBf6PtWRW5ADBnrh0lMbOn++7zzhhSeovB4QlXHllSFg2bWr2+OPZISypJt5nH7P6y+fZ8fagbWefebfPnm328MNR/ognfuyxfsqRWSYuC4foxK2XeSoIu2K36URLZY/p+YcVzZrt32+rraxa7drRjyf16pk9+KDV69PHKvF8UgCCcujQofb888/bfffdZzfeeKP99ddfdsstt9jpp5/uLexxDH6bGugIcBzn90IIUREkLoUQSbPPjxboUJIKq1ev9gIX+iT5n/lrZDOxxU/liI+yZlOKPCad4hKzKRryfv3Vv05jHootB3j11VD5K18TBN1GG2X2ObFOxbxJZl3y0bW0ebaerbWbrbjc2PzqVxLSZX7+1LMywPKaa5hl5N/OBm++2e+F5UF+/z0hV9lt7QurbP7C2sdrtvdcZcujysEHWyMWvFIwAoTjXTuciYqdtZllSQXJ559/bnvttZd3e/fu3e3NMgyPoh3PhRCiPCQuhRDlghDERRAxyP+R5qbxu2gQ2GDEs99++9nxxx9vDRo0sGHDhlmvXr2sc+fOVqtWLevYsWNKPgnNpixg0iUu+T6ccIKvOIC+PpxwsrCfLtK0lH79QteHDzcr1h8Zg0NJ//5+9aqje9t53v/72pvWY+/l3mV6Q2MakYIivPhiP7N8/vlmNXwzIE9U8iD0Yt96q//AFXCV3been02F+z/bvsRVtjzd2KhZM6vOc0nhjMmZM2d6i3kcB+n7dJnUjTbaqMz+eI7nweN8eecAIYRwZP+ZTwiRsR5Kxn/MmzfPvvrqK8+1kFlo/M91buf33M9lJKOxwQYb2Lhx42zChAleSRZ/R5ns+PHjPWdEeNHNPkgimk1Z4KRLXJLKeuYZ/zLZMZRF3bqW7WBYesQRzEX0rzP9Au2VSZYu9VtlaZkEtNBNN5ntuYkvLuGmEfQyFl++CefWGDe+4Ya+Gp0713fzddUMPCjNlCwKPPBAaC5pjKWylx8SEpef2g4lrrLdu/sPFf3vi2dmuheTZMg+XnzxxXZ5UKXHCMdzKkpiPQcIIYRD4lIIEXGoNuY8GEMQYETKVHI7v+d+BBixQIkWRhEIScpUmzZt6v2Pvf4XX3yR1E9CsylFWsTlK6/4WTGnNlBFFXAkzQT0DmKWA4yMZcJFJpOtGLwyTeTdd/3rtWqZjR9vdt55ZpUobYX69a31TnW9alcg2cg4krhggOW99/qOQEceGbodlYqbK86+PHCM2bmqMz71/i+qXNka7RtyBZ4wwWyrrcwuu8yilsrSl1nhmZllQHbx7LPPtpNOOsm22247rzQWsemO5YsXLy63FYG5l7GeAzhnKJsphACJSyFEqYCElel4AgXuhythNBCef9KP5lWh/W6ffPKJbb311t7qt+vr+eCDD6w50W0UNtxww/KNdjBRYZacZlOKdIlLXGWOPjokQmhcpNcyBxg3zq8EBUyaSbxG8bxKC5SY7rab2YIFIf03bZqfAfRSrNTvQvE4oUsv9RORgPnQBx9U4EEx9nniCbNPPzU78MDQ7bj2YMSE0i3PaIznNnOmd7FSu3b24uRaXqnsppuGfs1uUVapLAJznRm85Rx/uR/HxWjH5EsuucR22WUXO4R0dPH9O3ToUGLi8/LLL9s+OCUlcUGScwfnECFEYSNxKYTwIChgpfrfJAfhP//8sx133HFezyX/H3XUUV5/JSvq/fv39/ou//jjD+tDfVkECIoYI0LGk/8xBwoXmpX+/ddqfvmlNXn2Wc/IolSQJgqXVIrLlSvNevSg9tC/jhjBYSYHmDPHT9AFx3LutFPmns/dd5sddJD/lsIOO/hikZmbHowTcQ68xeIS09WgXxJVrRVuA+SByEC/9ZZZp06h2z/6yGz//f063Q8/jPy3CEtmqBY/cVcqS1KUzLCreC2vVNYtipUc52rVskruNQePc4sWWZP69b3jHPeLdJz77LPP7NVXX/VaDhj7xA+Leeecc47deeedduCBB3qtCs7cJ1lw7uAcIoEpRGGjOZdCCG/lmVXnZAvLZICYbFkcUIbPphxz2nt24uv9rOpPP/mBGJEcWVQMPISgXPXaa/33gQwUIiEZsK/16mX2wgv+9a23NnvvvZzY7/7+22zXXc2mT/evUxVK5i8TazFM4mAiCHMiHbytjzwSNpKU+lLUJyDgcR3y+sLNOnTwhRyQhAxWuVYIFOrLL/ulzu5NCj45XGdJQzrGjDE76ST/8siRZueG3GxdEpQS3mAClEzxBRf45byU/kZ/KkW2+ttvbe2ll1rlt94KHee22MLvDd17b6/vkfLUbIISXzKxWuATojBR5lIIYUuXLs1KYQk4HEabTbm6aVur9sMPoRV+MghvvJH+JymyE6J4RzL3b1JmTlhSS/rcczkhLAGh4zQTraH33ZcZYYlR66GHlhaWZPrGjg0TluD6LSGw0MSsSfRc8O8T1lm8GZQ2f/aZ3z/LuBIHM3npx8QZmFQkUFLrKEm1RnaVDZbKOo1alqusd5zbYgur8fDDVu2ii6ySc7mldpiS1sGDrW7xmKdsgnMJ5xQhRGEicSlEgYPTX6RAgPEhffv29UqqKF3F3RVeeukl73qPHj3sAVbPUwjBVf0yGsGabLuR/Wr11x3aJ0SqymIRGMxGBNxvnnqqlODJZjDsuf9+/zIZM/ou69RJ//PAN2f33f1KVCcSeV6Y7kY0FIoiLoFWyW7dQtulxDcp8ETopyUtetddZk2a+LezkPXgg2Zt2pgNGlS6XHa77SJuqqKlsqWey+mn+6sCe+4Zuv2226zewQdTgmapYNCgQdapUyc7N5CNjfX4zzlFLrJCFCYSl0IUOCtWrIho3lO7dm178MEHPVH52GOP2ejRoz1znjvuuMO7nTEiGPEscA4cKQA3Q9xko9GyVSX7ygIlajBxYsqejyhwcUl/3bHHhq4z1oKevByAEZwDB4au33OP72SabtBiO+8cGgnK2tHrr5fuAY1HXALZS3eYoAqa+ZdJzX7zxqH+rrvOb/Z06cf//c/vy4Rmzcp1RCK5jYBGI+63n63jKotJUTRX2ZLX/uabZjffXDKrs8pnn1kjmlZTIDD79etn17qycjNbvnx5zMd/zimcW4QQhYfEpRAFjnNsDQdRR78juDmWmPNgm0+pKr/faaedbPLkySnr2ynPKp9Ya6ZttW4gGgxGReGSTHG5fLlv4PPHH/51slph/XXZCiWohx8eKhk9+WSz/v3T/zwoDd17b8ZghMxaGYVSrmmp+z7zeW688Tq/Rpideqp/GWPqSy5J9jP3VtvMLryQ+Rx+s2R4OSqKFpUbQ11uIqWyXhYT9yLKdnGzZRFu1CirjnMxTaxJBOM1FhkdP/zwQ1zH/2jnFiFEfiNxKUQBg2D8h0FxUaA0FpfX/fbbz44//njbfPPNPeMfZqTRVzN16lTPgj7ZVKlSxRsuXp4hBO2YC2uFZS5B2UuRTHFJ0H7UUSGRQ2/dqFGZaVaME0TKgAH+1BRXuUnCLd3PAeHUt68/lxIQmQhLqkvLhDJUBB3Q/xhlECdtsK49m2pNtFdKIDtJNo99IZi1Rh0ykBPFzL6B21AZJFwqi0JlVst111mlypWt2ZlnWhXmDZfzuIkQ7/Gfc4tmXwpReEhcCpHncHLHGp4TPf8HT/YuIxkN7OrHjRtnEyZMsFdeecW7/9ChQ71enJNPPtlat25dZtlqRTOWrI5j2BMLv28WQVyq71L4O1NyxCUOpW7Bgmw6Bj5l2XxmEXfe6WfIYIMNfMMc5wuTDnjbjzvOL/l04IdD5q5Bgxg2QEbQKdIyelsbNgw9Boc0ksopakX0ado0co/ljz+anXKKn07ljQ8bJ5LUUlmaVcmmfvKJVWva1Fr062fVEb3lPGZFIWMZz/HfuXrHci4SQuQPVTL9BIQQyQcjBfpdKEsKXz0mG1ijRg0vUHBlr+XRsGFDb/7ap59+al27drUuxSMd7r33Xk+ARqNBgwZen04sQQTPizJYfuKxsF/brr3ZrOLLVatZ5dWrfMdYMgkxClSRpyRDXD75pN9bCQTSzzxDCsdyAfobg5W7+NCk03uIRBrTO0iwORBSjOGI+SteTr9lkDPP9Gdm8ie0JlJeShYwZQTTo8wawfr2xRf966SKSdWS5aZXs2vXMl+0K5XFZImq1x9+CJXKMprl1lv9quyIm2AUzgcfWLXhw61Vv3629PjjbenJJ1sR4pM/SGKGnWN/rMd/QFxSAVPeuahevXpexYoQIvdR5lKIPIITN2VKs2bNskWLFnnzz8KFHde5nd+XZcaDec+fNDB5PVu/2yeffGLNmze3X375peT3DOo+yM2fK0OUekPBa9ZcRzRyndv5PUPBGSIe72y0BltvbL+ZH+CsrVxcW0Zf3LvvxrUdkYckKi4//7y00wwRPvWcOQDjXo84wp/OA4hMhF66oNyTlkAnLFnHQjhRAhrXVzwOccnHfdNNoetUqaZswhLHVTeGZKON/BmqjKd55x2zvfYqLUAPOMBs3339WahlwPtCb2ykUlk+uzJLZbnzFVdYpXfescbTplnbffe1JiNGWM0ZM6wSKjXKcTd8hnB5xHP8B84xsZyLOGdx7lI2U4jcR8tEQuQJlBktXLgwafMqMe+58sorvZM9P0cddZS1adPGs6VncHflypXtvPPOiziH0gUwVatW9f5HZPLjyqTWrl3r/b37fSI4x9jd7H2r8q8vhktKY8t1ChF5TSLikvE8pL2cQQsi84wzLBegKhJTWzeKsVMnP2OYLkjiIZKcnwsVpCT0dtyxAhuLQ1wCHxlfezKX/Okdd5gNGWLJ5/vvfQUfPt+SN5sHp4wa4x8WKOCtt/zfMdxz+HA/21hOqezxx/tzSXk/g6WyZH7ZdMTK7B12MPv4Y6tyxRXW8MYbreEjj1hR5cq2unlzW3v++Vb5uOOsao0aJcddjslcjiTqBgwYYLNnz/YEINnKkSNH2sMPPxzT8b+iC6NkOOm3j7UtQgiRfVQq0jKREHkhLOfPn59Vc8VYGY93VbwiTJ1qNnuvk+wkG1P6F/RDpczVQ+QEBPlkjIBUUKwKi3QfwxMprwZScIiDoFjNYniZiA/Xi8jXwLmSppp77/U1uDMu5WuIsKzw42OkRGkyfPONWdu25f4Jeg6NhV5C+8yZ47fKJpVgzS1vdmBkRymVTxk1jZM8CQfC7phjfBciTIrKgNcQLJV1MPmkzFJZwDEJhTqruG8AGDBKfXSrViU3IRYRkNkC5bHx9N0LIbILlcUKkeOwPkTGMpuEJSRrRbs80K+lZl0SdbkI0808EIVJRTOX1FM6Ydmkidn48TkjLNHTF1/sX0Z0PPZYeoQlYpLS29NOCwlLknQs/iT0+C5zyYtp3jymP0HQYhoEZE+vuMKST3DhCiUbCZxt6btkPup994XGqKAYH33Ub7SkUXTRophKZfHuiatUdtdd/eeJMnUKlLLdbbf1U7rFxj/pOlbHCucyzmnKfQiRm0hcCpHjLF26NGmlsMmCMqv65QwUTxaU3M2rFhCXwTl4OGSIwqUi4pKsjpvVQSSPsIwwWzEbQaMceWTILJSEGT4y6ZijSRLvlltK63PeOko8E8KJy802i0vgY4TjRjSSTf3qK0surt8yvCw2EuxHDBdFBdIU6mxyyZBj58sKGSsCK1ZE3QTvI75AcbvK0ux6881+5t1lSbkj9bZs6NtvPTOdRNsTkg3nNM5tQojcQ+JSiByGFd6yTsCUOuHuOmLECO/6jBkzrGfPnp4Jw93YKqYIHF+TPaIkGsREfzYLicsiCwRJGklS2MQrLrFXJfXmuOsus912s1yAwgUqSF2yHt1w2WWpf1xaD/fc0+yll/zrGH4y5hENlfAhALHl+hrjLLFn0cmVBpNJTXrfpctc4pa6xRax/Q1CD9XN3M5LLgmpX8QeZbWIvxtuKGP2SMhVlmpblxF2rrLt2/tTciKac/MhffGF2emnh24jO7/NNlblgQesEfXTKYCxJZ06dfJ69R2MterVq5f3c/7553ttHZHg3JZtFTlCiPKRuBQih2HcSFmlQ6NGjbIOHTqUXB8+fLjdcMMN9uKLL9q0adM8s4Zkw5xKxGU6qdVuc/vd/BTJfz8uNqtTx/8FUViKZr6JPBOXpP0OOyx0P4LwAQMsV7j8cr8kFki0Ug6b6vWdjz4y23lnX7NAvXr+ek7S3rY4zXzCQc+4qTE8r6StNTFjxTVAkrWk/DUeKEO9+mr/9ZFBdLWuy5f7ta/0Q95zT8jqN1mlsqQ/yZS+/nrojcFZ+5RTrNGJJ1r1eF9HDPTr18+uDfSjcr5isfOBBx6wZ5991rttknMsCoP7co4TQuQWEpdC5DA460WDnhVs4PfYYw/vOk58a9as8UaDkFU84IAD7O233066EQNOf+kuscIx9mvb0n8O388PjQIgCJSpT+ESq7gkc9K7t9mPP4ayPMEazyznlVdCfjIIyqeeMmvcOLWPOXasP5XFtQui/Zi0UTwCMSvEJYlCEoFBsZmURFjwmFJeSWxZMMKEEmwW+Y47LiRSf/7ZbOBAsy23NHv88agLZK5UdsaMOEtluTN/dNJJJTdVeuUVa9arl1WJkkWsKB07drTaLkMbEI3MvOR8RHVNWYuRZZ3jhBDZicSlEDmKO0FHg9VhSpKCJUbMkXRweXESDW/IWGbK4S9o6lOJTC6GFQ6VxhYusYrLs88OzUWl1hDllCNOlWSr+vcv7RRbvJ6UEvh6IWj69AlNaWEt54MP/JLNpJKguAT8dFxlM5k++i/T2m8ZCxgV0etLQ6VzoHWvH1dZDINefjlKvatvoBt3qSzlvKNH+9ulhtjMqs2ebS26d7fqiNsUwcLjsGHDvJLYzp07W61atTwBGg3OcTL2ESK3kLgUIsvhxEpPCidZ/ncnWuZFRjvpTpkyxZo3b+79pBqCBYRqq1atMmYdv45jbLB/iHlzojCJRVyiNpziqFHDj8RTnfZLEgiII44ItSUyliIlMx0Dj4cL67BhodtIuCFsNtwwBQ+YBHFJEUUwCU35MNWnWZG5DId0I6WijBBxI3SAuuODD/ZV/LRpyS2VPeggsy+/pH7Vu1pt0SJrddBB1njMGKuUgpYCzltjx4618ePHe+cpoE0jGm42cnnnQyFE9iBxKUQWgonBsmXLvPljX331ldcbOXfuXO9/rnP7L7/8EvXvp0+fbhMmTLBu3bp5g6/HjRvnlcBSGuvgcjCTGc4WW2xhTZo08eZVhpe5cp3b+X27du287WTSbXAdcUk5bOvW/mVq9VauzNhzE1ksLgnU6Xlz4ESz446WK+ANgwcR4ClD8itVX0O+Uvvvb/bQQ6HbKMV94IEUTmlJgrh0Y0qPPtq/zGGTjF5SxCWLEZSuJhue8OTJvmoP7o/sr5RsoxRdo2sySmVxr33kEd/el2P5f/9Z41tusbZ77mlNJk60mlWrlnkO4FwRK7NmzfLaMpo2ber936VLF/siymtxIC7LOx/ye5n/CJEdSFwKkUWwCovo4wS8aNEirx8lfGWW69xelrgcPHiwZ5IwceJEGzJkiPXu3dsGDhxolStX9rZNr8urr75qe9M0FYWqVataw4YNrWXLlta+fXtr06aNl53kf65zO79PlytsWTDa8ptKAXHJ3IEDDvAv02RVvEIuCoxgJj1cXGLIQqrHZUVoyCvO3uQClEDefrt/GXFHJS+GOqlg1ix/ZKJr0aaXkcfHjTWla0pOXJIWTXAWI4ILLQi8b3PmJDB3xRmhbbONb4+bCnhjUfO4JvHhUvsabLJlmCeKOcqQywqVypLiJIvJ94L+9ZUrreF551nLvfay9nPmRD0HcK6IFRYiEYWul/KDDz4ot8IG74Dyzof8nnMb509lM4XILBKXQmQJlPiwGpvKkyO9LkOHDrWDDz7Ydt99dy9IiASr0sGAgeuUvNaoUcP7P9tmoqEhijZvZn9ZTe96EeKyW7fQHdR3WZhgkOL246C4pFeZQNr1HONCE3R+yXLQNgEvFrvtNr8tLxWwLoOwdDqvSRN/ZGKx/kgdfF7OkTWBrKUDc1QyvcB6wgUXVHBDwSxbMktio8GxFrMpRN/99/vzPh1PPOFnThmf89NPySmVxVzn6af9bbt5nEuWWKVevazagAFW4++/1zkHcK6Idk4YMGCAt8A5depUL0v5008/2UknnWT9+/f3+i7/+OMP60MDbxIXZzmPRhtvIoRIPZWKtMQjRMbhRDh//vysKeuh3IlV6VyCErAbJ+9gO9hnVlS5slVi/idGFQQZrIwzWy7LRLFIA4ylYdwC6ZqZM/10DY2Drr6Tkj6yQylpGkw+lDQi9ih7BPxeqGhMxa6N3wumpe6whE8W7XFBfZMyvvkmVHLKAE9cUxOE3YD1NOdXg3AOtjbGBGlPDKCAWcHBuajpgIURxpQMH+7XKjtIJ1PiPXRoSBRGyEBzFyaRBBfmENpkoWvVCvsDrIBPOcX/0B3MuWHHOPDAUnelNJUMYraAc3mmDOaEKHSUuRQiw7C+w9iQbBGWUDfBErRMUMoxFiMKxko428xvv02gDk7kNK4h0GUuGf3ghCXRNPWBOSIs4YwzQsIS7YXOSLawXLPG7PzzzU4+OSQs8ZOZOjVNwjKJ/Zbh/YiBkYt2zjn+a62wmU+q0sVlQW3v4MH++3PFFf6LAoTdjTeatWjhv8g//4xYKou/WcylsqSpn3/eb+Z15wQypJgAsXMEetmz7ZzB+ZTzqvInQqQfiUshMgwjQv4tb8B7GqG8qX79+pZrrGPqE+y7BJXGFiZBcUmqKminStDcoYPlCmPG+E/Z6WJa8Zy2SBZk9w47jFFGpUUYwoMkcNpIgbiEY48NaUIqXN37GfcYEnrN6bnMFIwSwfqWigw+IJeho5fx4ov99+yOO3z1mEipLH+AJTBluV27hm4ne8nrL+5nr1evXta1S3Be5fwqhEgvEpdCZHh1NdtOfgy0zgaTnqSIy2DfpUaSFCYu6Cazw9wOl6pinkaSer3SAUKIrGXQ2JZsUzKhxREz0hde8K9zGCAzevPN/uW0kiJxSRtucDQJOgyPnphggYLSamCoJ6WomYYeST4gKjNOPNF/gUA/MTWwPE/qpsNStEFXWXyDynWVJdXJAh1je9yKxnff+f3KZ55pVf791zt3ZBucX7OpKkiIQkDiUogMsmLFiqhlO/SvdO3a1UYUpxCuueYaz921L1PBU0T16tWzMkCIBeLPmbZVaXHJynrxgHB7883osw5F/mcuGW7oHJYp67vqKssVqD5EB9NuB7T5udEayeKTT/wJGJ9/7l+nyhGhceqplhlSJC6BcZFkZ50Gu/76GP8QYemESjrMfOJ1LMLwh+cYdFtasMBP1+Iuy6pB2PkmrlJZMpP0YKJI99kntJE77/QachvNnu2dQ5LNoEGDrFOnTnYujs5Gxe+fdvjhh5f87LbbbvYIAjoCnF85zwoh0ofEpRAZxNmxR2LUqFHWIVCy1717d7vrrrtSaoDQrFmzrCttihXizwW2hf1jxcENQRavxWUvWYaPMoBc5DEu2HUD4XF0eeyxDKTiKgaBPc6wrmWYks5g5i0ZPPusL7ic4Shte4yHDWa0MiYuyQ66BaIkQnuiS2qPHOmXhWZ9v2UskKlEKWJSFfwAKWvt0cNs9939hbYIpbJ4KEUqlWUtplTLOgZpzOGkf9llb+fNs0p77WXNHnnEqiT5u9WvXz+7NtAsW7t2bRs7dqz388wzz1idOnVs3zKcmco6zwohko/EpRAZghXVf1wqIgyMCJjttYczpPEWyrf3+lpSAavNue6sRz9Yg0ZVbJYVz4MjGmIJXiNJCptffw1dppyPVEyqBkKmAMxJ6a102UR0g5vXmAzhygQWsniuBJJDzgcfhIxaMwILAWTcnNJNwYIXi1HO9JWCBkRVzP2W2Zi5DGennfxBl4jAnXcO3c6qAUKMfvTg6/FEW+RSWapht946rFSW8lvKbqnX7tTJv62oyKpdfbW1OOUUqx63U1J0Onbs6AnKSHzxxRferM1NXdo1ApxnZewjRPqQuBQiQ6xevTrqCY9SWEqBUg1ZSoZaMxg7l4VlxL5LytdwpiBKcsGp+i4LC6JiNysRcG/JqGqKj/ffL+0/hMktWisZsO4yYEBpUdWvn9mkSWYNG1pmwenZlbCncCTSJZf4LYvw5JNm774bR+aSMtNcoHNnf0ciPR1s0uVYuOOOfh8yM0oqWirburXZ22+b3XRTSZVAtWnTrNXOO1vj6dMt1XUwEydOtG7BBcQIcJ7lfCuESA8Sl0JkiLWuTC+MKVOmWPPmzb2fRNhwww29eZXhZa5c5/YmTZpYu3btPHGZq6WwMZn6MGaCVXxgST7CsHGRh5C5Zj5ikKDbZZZDeyhxv2vxYzQIVY3JSuYSj+M+67j6arOHHw5VEWeUFPZbBiETHGy9xXQ1ymHZN8QhSwco/BzKfnuLaz17mk2f7q9QNGsW+h0KEgcfRosEFmLiKpWlDPa88/xMaPGxttKqVdb4mGOs7cknW5M1a8o9F23BvNk4QTS+/vrr5YrLss63QojkI3EpRIrhBLhq1SqvNIf/XbaysnP1C2P69Ok2YcIE74Q5cuRIGzdunN3NsO4KiMuWLVta+/btrU2bNl52kv+5zu2UEuWiK2zc4hKCI0mUvcx/sP8kmA438sgRQyfi4P79zb7/PlSqOnx4crY9e7bZrruG2u4osX3qKT+LlzVrTGkSl0D2Fm0FH35o9sQTUe6IknI1odleEhsNjveY+5CppF+yceOQcGa0SKtWvkhctqxipbKkNSm7ZaWiWI1Wef99a7jTTtby0UetfevWUc9FVZ16jYNPP/3UNt54Y0+clgfn22jnYiFEcpG4FCIFYH2+bNkymzdvnn311Vc2e/Zsmzt3rvc/17k9msnA4MGDbdKkSV65z5AhQ6x37942cODAuB6fFWF3suYyJa81atTw/s+XLGVc4lIjSQoHlBkBtPvsg8MZc0RcEszj1AqUbVKyWYHYex0QlAhLl3HaaCP/NjKkWUUaxWWVKv4kDwdZulIjOHKx37I8SE/TL8n7jBBkZqb7fuBuRGaWlG5gRkvMpbK8oaxUYCi07bb+HUm/X3GFVdptN6s2Z07EcxHnq3jPTbGUxDq+++67qOdiztUaVyJE8pC4FCKJsBK6ZMkSmzVrli1atMgbJxK+Osp1bl+MB34cXHzxxZ5rHifFLl26eCfWaHDyzmcRGQ3i0LnWylZbFf8GJzCYsUANHLz++joz30QeQbBMpAuULmKD6ggbKJ+NMJP+ssv8y3yFH3/cbJNNEt8uJbBknpjIAkzpwbiHr0bWkUZx6aqlKfMEKkPRV2X2W+a6uAwaXCEE58/3666dUxSi8vLL/ff+tttKFmXiKpVFWJIKZvuuQgaBjssuLlJhx2DOV5y3IjFgwABvoXXq1Kneue/zzz/3ylwnT57sjeuKhUimPu5czLmaczbnbmUzhUicSkX6JgmRFCizweX13yzIjlAmRKlRobFokT+14Evbyrayr/xZA3/+6a+mExWNG+ffEYOLrIyqRUI8/7xfDguUnb/yil/nSJ8ZfP21P6ohS6EdGN2yZIl//corQ0IzkUTuRRf5ozcciADeFpewyjro22PwJp/h33+HZoakEHYNBDeap1YtXyRtvHHgDvvt5zuvws8/c5C1vAMjJTKWzMsMij9maLIzUqsdaKWgupYEKOt1Dj4qdOqwYf776EEW87jj/DfZQQodgy1SosWQQUToZRKc0xnJlQ8Gd0JkCmUuhUiSsJw/f35WCEtWgOvXr2+FCGV+9AiVlMaSqWJVHlQam9+QpcbuNFhbymcedKjJgu9nNKgcxH/ICUsSMiR9EoF1FdZUgsISE+oXXshiYRnMXG62WVqEJWAifPrp/mXKYhFHJZDxcmWxiMp8FJZAivzee/3vUt++odu/+87shBN89T1+fIlVbLRSWfqDS5XKduzov3+oTldRwwIfjru33lriosSorUxX3HAO51zOOV0IUTEkLoVIEJL/ZCyzpWejUaNGeWfUEyvEJbQLqe+ywMC4h4zlH3/414880g9kIUfEJUKSiQ5AoP7oo37iLpEsKBXBTKAADgl33eXH8ll9eMDK1hkxpaEkNgiVoG5djmQ3ydMSceXqifOlJLYs2rTxG30RhAceGLqdzGPv3n7WsTiLG3OpLCWvrHJMm+YbBwFznrHoZe7m/PlWpUoV7/yVaTiXc05XYZ8QFUPiUogEWbp0aVZkLF1JTzacnDMJ8ehMK7Z/DPZdUtrlSiJZNXfBoshtKN87+uhQsxfZEMr6XAYkB8TlSy/5bWhABffTT4fmL1YE2gN33jmUbCNL+fLLZnH6ghVEv2UQphYFy5DRPV7mLR/7LWOB10pp+VtvmXXqFLqdXkrKhPmh5DUGV1kWTzyjJLbDSJezzw7diVWVDh3M7r7bGjVs6J3HMg3ndM7tQoj4kbgUIsEVzmgnIIwCMBsYMWKEd/m0006zQw45xHr27GmPPfZY0t93Vn3pFcl0WVHWOcbOnBm67EaSUIbl+qdEbkPU6qxV6TOmFq+k2Sv7xeW33/rmtg6SO7vtlljbKaNLaJ8DxuW++27pqvCsJoPiEiiNbd3avzx1ql8FWkpcYkhTaJACJ+P44ot+aayDYyirGGQzi/spXans2LFllMrWrOUbBb3xhr+Duhru00+3St26WbOqVb3zWTIYNGiQderUyc4999yS21asWGFnnXWWdz7u0aOHfe9m/oTBuT1bKpKEyCUkLoVIAE5S0UpnRo0aZR1YjS3mpJNOshdffNEef/xxe/LJJz1r9GTBSm+LFi1kQlAcj86x1vafrVc6cwnqu8wvSPFdf71/mVpPrgcHxGe5uOTp9OkTSqIfdhijiCq2LQ5DI0b4pYhulAZJIpJMbo5jTpBhcUmLJ++j44ILzP79aHphZi6DsGh58MFmn3/u12zTf+BAgZOePPFEr4SYu6I3yy2V3WcfBjubnXpqaFuTJlm1bbe1Fh9/nJQMJg7r1157banbrr/+ejvggAO88zHnYmZCR4JzO+d4IUR8SFwKkQDRZlXSr7FgwQLbgxSCmdWsWdM6YmpgJFVqWfPmzZNSckOWsnHjxt5QarnbheLRVVbdG0niQYTjnA9ZgXcBC8vrGqKdu1Bah8mIg2GF9G6Fk8XikmTKxx+H9lvGhVSk8GD1arNTTvHbTN0uTaUwiaWcq5LPsLiEQw4x69zZv4wf2P/e2dG/wjijLbawgoZG4GOO8TOVd94ZMjeiGuSBB/y0LyskS5bEViq7Xh2ze+7xj8cu1blypVXr399aDRlijWvWTKgah/NubZ5IMb///rvNnDnTunfvXnJu5pwc7zleCBEdiUshKgirmszOigSlsJTjRAKr9Tlz5tiW2BNGgZPpRhtt5J34wk+sXOd2xo20a9fOE5eFXgobxMWjJaWxfEbUHgJBxN57+5cphQpa44vcYdky38DHpeiOP96fiRCJLBWX+KVgsOOeImWEbhRrPJD1pNp79OjQbUyNILkUZWxgdpMF4pLD6S23hAyVrl55ti2xRn4/r461oRQvNcRz5/oKkpmyrgaWklc+OxySVq4sVSqLAXCwVJbTIKZTRft39VUo32X3Obz4ojXu2NHazphhTco5H8bKjz/+6LmpDx061Pr06WM33HBDmaWvkeZjCiHKRuJSiAqyevXqiCedKVOmeJlJfsLB3vy8887z+j/KWi1lu3Xr1rWWLVta+/btrU2bNl52kv+5zu3MsSxUV9iywLeHtyWiYyyoNDa3IRBkTIJbMKDn6+67owf9WSguSaYPGBC6fscdvm6JF+J6jDunTAm9VOZXYkqTsxrIiUv6ZzM4L4WOhpNO8i//bhvYZXZVYfZblgdZQWpfSfHyvxN6ODczM5Py2ZEjrdK//5S0ZjJ31ZXK0h1CObhXKru0np/9ZFaOy4guX25VjjrKGp56qrWsUyfi+XAzp1hjYM2aNTZjxgw74YQT7KmnnrLly5fbczSClnEu5lwvhIgdiUshKsja4tlc4UyfPt0mTJhg3bp1s5EjR9q4cePs7rvv9k5Sw4YNsz333NMz+ol1+6zMUvJao0YN739lKcuGoIW2O4nLPIUmOKemGGxKv1dZKbosE5du9iT/A2Y+TsTEAwabu+xiNnu2f71xY7M33/SnsOQsf/8dciLKUNYyyNVXm9Wp7u8zo+xkm9EwQtm18GGGCxlMFgfIaDpDnl9+MTvvPL9cdvRoq139P6MFssxS2S6HmH35pT/41UF6c6utrNL48eucD6OdiyNBpc+mm27qVf1UrlzZ9t13X/uG1Z4yiGf7QgiJSyEqDCemSAwePNgmTZpkEydOtCFDhljv3r1t4MCBduutt3onxFOD5gUV2L6ogGNsMHOJZSHDwgGLfQJakRs8/LBfr+hWEcaNC32WOSAuKXQg7nYGxhjtUBobb5aRGYxMgWAkpNvOBx/4WcycZsGC0OUsEJesXQxrM867vNbWsyEv7as27fJo2tTvxUSw0Zvpdu4ffjA7+WR/Z33mGWvbem3ZpbJvb2hFjz3u3dfLYrtyeFZmaChGtFbgXMmorgYNGtgPPB9jkspHnhleWehcLER8KHoVIgbIOlLSSv8F/3O9atWqMWcR6bMcM2aMffnll3b44Yd7P++8807U+7Ndti8qBnHpLGtra63SuuKSz8yNJKEf002uF9kNzje41gRrSXffvfy/yyJxyfhN9DGsv74fWAe8RsqFBMqwYX5bmqvUY1dm1EiEKvzcIwv6LcMZ/Ne11tx80fv6e+t7Yx9FDPD50fiLuywusw5S7UccgdOOVXptovU+rKjsUtltD/dXY7CZdVD7TZqTAbHeOlP0c/GAAQO8Rd6pU6daly5d7PPPP7cLLrjAa03p1auX/fnnn975OJZzcaQ4QAixLpWK9O0QIiI0+WNDjltceFM/JxyykPRipGIOFgYF9FWKisEoAZwz51gra2Xz/Ah+5cqQQwer4QQ4blI6TqMie1m82GynnfzsB5x2mt9nGQsEoNh/ujpH6u4yAKMSmV/p9C3xcTwlrHgXUUJLstZx5pl+IjdJIwEzz623+t9HePBBs+OOy+zz4ZhRt649bX2srz3t3YQ5DSWdWvuLExZTUZAMDw3COBLKaXfd1WbN8n25Xn+9tG8Qx/JhFxVZrece93f64HgQVlpuvdXmLVvmzZNONpzn69WrV2YcgD8C90nWbE4hch1lLoUIg5PHkiVLbNasWV7GkRNW+BoM17k9VQOWOVmJJDrG0uAWHJRNTaETmtRmieyFWjkyC05Ykq3EjTJWsiBzyTQD5lm6hz/jjPiE5c8/+ybHTliy695+u/+TV/EspjCObFhcY9yNmfWxZ2z3JnO9ywigWNc1RAC+t7QhkPoNulfRKMyqS48e1nb1l9FLZdtXsmdrHWNFX870U5oOFiG23trqLlqUkrcbQVleHMDviReIG5SvEULiUohSUOoyd+7cjJ4kWA3FKl1UHBeXzrTA9HjX6Aa8v7ihuJLZoPAU2QUz86ZN8y/TX0nkSTojVoL3zYC45DDCOE5X8cm425Ej49M3GOK6eZh16vjJWBI4eUe2lcWSbuaYbGa3HD+95OYrrgj1u4o4oHT1wAPNPvnET923Kp5FDDjEduhglY471nrvsCB6qeyAjW3OLS/5NeZ8GeCHH6zeQQdZJTfPOIOL0sQPxBFCFDLKXApRDCeE+fPn278Z7svCcEAjRhLD+TNENfUBjSTJfkaNCqWJyEDiGOlGFORI5pJKT562W9N4+unST6ksXnzRT/i4pC0uyPRXEp/nJU5cMqYp3s85heISOh62mfXvH5otypQNUUFIvZO655h8771mG28cWol55BGv9rj2hWfZtYMWR3aV3aaSXTL/RPv7oy/9KhQzq7JypTW65x5/GxmE+IE4QgJTFDISl0IUrzouXLgwZWWusVK9enVPXIrEwCyF0QwSlzkMKor6Ucd99/lpv3jJoLjkJTA5xYGZTyzGO8THtAH36BEaWYITLI6w+JjkJWSdnFssq0PZMKjz00/9/xmcu8023ggNN8YRQ1RKZEUCkJbEpIuBrTfe6K++AG5VGHa1aGFtH7nEJj61ImKpbLuum9uzA1+zojvv8vrqG40ebdUxDMrweZw4gnhCJbKiUJG4FMLMli5dmvGMJWYAzZo10xzLJEFV3TfWLnrmEqHigplJkzIekIgAzDpk4rqzRKU0FjebipAhcbl0qe8Z5XaroUNLm2ZGg5eMX9GQIaEkDEkeRnsyGiOvP3NXTpgNJbHsK+6YwWyMGjVs001DiwV8rhjNiCSAYufNZHEBwy1noYyL1fDhVqllC+s970b7+pO/1i2V7V3JDnpxoC14frpV6tTJmp15pv33y18ZP5wTTxBXCFGISFyKgodVxmgnAZr1u3btaiOwHzXMC4/z5lb26NHD7k6iqwMZS2ZtMRRaJAfi07+sti2w4lQRgWKwZIpshKu3wn3www/11mcDjIehucoZdHTubHbTTRXfXgbEJUm4fv18vQR77WV2zTXl/x27IV4lJGkdl11m9vjjoYxZ3pJt/ZZffhlaGdh++5Kb0UCuipOy5cmTM/T88hGM7HB0Zl/ANtapSOqQhw612tu2smub3WszPl1tXbuWLpVtd1ALu3SPN+z7E662I/u1snnzqnujexJh0KBB1qlTJ29sieOEE06wQw45pGSkGIY/0SCuyHQ1lBCZQOJSFDyMG4lWvjJq1Cjr0KFDyfU777zTxo0b5/1MmzbNvsZ1IEHznsaNG1urVq0kLFPtGPv776Fo36G+y+yC7+HAgSGhTw3pU08lZomaAXFJyd5rr/mXyTY++WT5L4F4GtNMkujAOhNjAq+8MjsqRAtOXAb6LW2HHUouklS7/vrQr5ickkEfmfyEL83//ufPxKRiwX0BsE0+7TRr26u9vXrcEzb26bWlSmWvubaybXXzAJu1qLEdeWQre+vupVaJX6AyK9CL2a9fP7uWWugwbr75Zhs7dqz3wyiSaBBXEF8IUWhIXIqCh/lVkaBnYsGCBbbHHnuU3LY+zXzFPRXMuIw2uBkYvMy8yvD7cJ3bmzRpYu3atfPEZVnbEUkSl5FKY4PL3xpJknnos2K0AJCqe+45s4YNE9tmmsUl4hA3Uedbgilm06Zl/w2j/zAv/uYb/zpt12+8YXbMMVY4ZJu4dP2WYZlL4HNh7CpgOINxqUgBLC499JD/JvfsGbp97lyrdMzR1nv4Djb71lfsoguLSpKc7ivOYs6B631ubbt1syYjRljNGTOsUtj3P3gujiQSO3bsaLVdiW6S4wsh8hmJS1HQsLIYrayFUljKYiKtZu6999626667euIwGghQSl3bt29vbdq08bKT/M/1li1bWsOGDeUKm2lxSROVc0ghW/bLL6l8SqIsmHdHGsjxwANm226b+HuWRnFJYvzoo0NJEhxF99237L/BHBPDS7frtW/vG/d06mSFRbaJy2DmMjiXsXjR4JZbQtcvvdRs5co0PrdCY6utfMvl994r/YX64gur0bu7XTttL3v64i9KZfj/+aeSbTXqHLur1xvWYMZMa3nMMdZ+552tzYEHWquhQ63N2rUl5+INN9wwLs+FoUOHWp8+fewhhG85EF/I2EcUGhKXoqAh+xjpwD9lyhRr3ry59xPOo48+6v2eoclz5syJum2267Kb9FKyMsr/ylJmkbgMlsayH7iaRJFeFi4069MnVF+I+03fvsnZdlBcpnD+HEY8GO+49m3GhWA+Eg0q9fAuoerPPS0S6TjMbrGFFR5OXNILzcyVTMJ+OH16yLmWXsAwKGhhl4UlS8xzkhUpBstkmlypOd9xx5KbV077ws69ok7Jos4GG/j/870aNGpra/3L+/bVkVd6qwLVfvjBarzyilXbaSerxIdWXIUUqwC8/vrrbfz48TZmzBh744037O233y7z/i4OEKKQkLgUBc3aKB3/06dPtwkTJli3bt1s5MiRXo9l0MCHUplddtnF3nnnnQptX6QeRpFQ0fS1bRmbuASVxqYfHCF79TJbtsy/fsABftNisgiaZKUwczlsmNm0af5l+sDISJLhisTff/tCNPgyTz/d7OWXI+qY/IfA3onLzTcPGblkCnr92C/D+i3DueGG0O5FJtNNUhEphPQkRmwffWT2zDPeTMzT7S5bYP5w493sXVvctb/ddNq8kt1o/vdVbasnL7OzOn5g/7YprlRB8LG606mTrWUUSoxsVGzZXKdOHS8++BLjp3JQHCAKDYlLUdBUjhL9DR482CZNmmQTJ060IUOGeA6xlMP++uuv3u8ZkPzuu+/aFuWkGKJtX6QnBiF7+bttYN/ZZpEdY2HPPUNWnIjLDA/hLih4rwcMCJUgtmrlW6OSvUrmjuAUQIrE5fPPU0bvXyagffppsw03jHxfTHD32cePi4FDxG23+e2mifgW5TQcV11vWpb3Wwbh8O8qucmSkXAXaYLv9eGH26MXzbTHrJ930wb2mz1uR1uNsY/aeaPb2ZLeA+3IvX4q+ZM739vBGi742N7sdJEVuXPzRx9Z5UMOiekhaXVZjnNtcdUTpn6U1ZaH4gBRaCjyFQUFJSoIQ/og+J/ZkrGWqf7+++82cOBAO+yww6xv37624447er2X0WC7mPqILCqNJTBwIy4cGDkQ7cNPP/kjCER6QJHheAOYZaHS3OzRZOJKY1MgLufPZ0RR6ZdE9V4kqLTEuMeZ4fKSX3jB7OyzC8QRNhf7LcsQly5jTZUEsGDgstciPbvNwDNDC1H39p9mzRv+6V/57z+r9+Q99vhHreybnhfa1pv4ovCP1dVt33evtZ6N3rXfN2nr3VZ14cJ1zH5gwIAB3uLy1KlTrUuXLjZjxgw79dRTvRiAnkt8FBhVVhbEAcQZwbhDPZgi3ynUdVJRQLDaiB04rm3hzfWxCMueAZe6pxiLECP0WKq/MnvE5QE2sfjKV+vad1IaO2FCKHu5zTZpfqYFCO/zhReGrlNHiptNKkBcMoomyeISLzD67lzSjcuM54sEJa+Uwv7xR6j6kzmJgUlHhUsOi0v6+5hhesop/vXBg/3FAxWtpBaqWjHPct8nFniOfLC72cp5zAoxGznS+2Wlv/+2ts/dYNPr3mOv7T3U+r5ztv32X217YfEu1tg+s0ebXWKHfXeL1Zg1y/7G3C3wwY0ePXqdx32asoQ4YWRZeNxBfFC3bl2rV6+eJz6FyCeUuRR5CwfzJUuWeMY7ixYtsr///nudFUOup2oVkROHyDFTH1DfZeqhxwml5XqSL7+89KiBHMlcUhLpKihbtyYYXTcDyeGFstdDDw0Fwjvv7DvCSlhmobjkA3PikkWoJk3K/ZMTTwx9lp984s8nFamFcT+uAoBq+ttvD6h9fklJAUq/uCS+0m+/Wbe3htmyei3t9nZ3WlVbZf9YTTt84Ujrst5bVuXtj1PyPCPFGFwnHiEuIT4hTlE2U+QTEpciL6H0ZO7cuRk7aLMyWT8V5X0iNeKybduQQyXuf38Wl1aJ5EMGESHphov36GF22WWpfadTIC4fe8zsnntCldVjx4ZcKh3//Wd2xhl+jOt09BFH+FNXYtAshUM2iUuci4v76srLWjpoESZZ5sAlWIeQ1MEM2Ouu8y+T9KNNu06dsDsxLBaXJRzdUf/FGckqyxbbmd+cacsbt7OzGzxqlW2NvfHfnrbrE1fY2v+KMrYITrxC3CJEPiBxKfIODtDz58+Pa25VsmnUqJFmWGYBLk4t1zGWdJPLXnKCf+utND3DAgOFRf3azJn+9S23NHv44dTXECZZXLILuTJIuPPOdbOQaOfu3c0CJtOeOSUtps4/SkQQl4z+yJGS2CBdupg5Xxhat2+6KQXPTXjzYPv3D/muUZLcsWMZbwz15/ff7/fS9+5dcnPtJQvstl/724+NtrOe671oi1bWsbtHNbWitZkxdCNeIW6RwBT5gMSlyCtYBVy4cKHXZ5kpqlev7olLkXmIK1jZXmH1bUmVptHFJag0NvUwe4Nh6EDZOAY+4em+LBeXlLYefnhoUsUJJ/iJkSBU5HXq5I/jAyrz0NBXX61evDLFJc4466SgMiguyxhDEgnMnFz73I03mv3wQ5KfW4HjzKV//NG/3rmz2fnnx/jHLGRRXkAt7X77ldzcZOmX9uyaQ21m3d3tm9Ff2+w5NbyKg0xA3EL8ohJZketIXIq8YunSpRnNWNKY36xZMxn5ZAkEeq7adWZRcWks8xTdpPvw1IMbgaG+y+SDLaorfyVTTAqPRsV04MQlWekEyuT509NOw6DDv47vEyNEgjD6FkdYdx9GkjD3nWyLiDL0k1RfNpTExjGGJBJt2vhl0O5l4SQrksd995k991zoe1WhogfSnK+/7n8paX4upv1v79kb/+1jjYZcait/KcqYwCR+IY4RIpeRuBR5A6t+0Q7KNM9jGT7CDaMrHmx89NFH27nnnpu0jGWLFi2sWnBou8g4Ll6dsaacvksyabvt5l+eNcvs22/T9AwLgG++Mevnz6LzuPZaswMPTN/jO3EJCfQ1EdzSawkk2EiE1KoV+j2/I5vC+gW0a+cb9+yxR4UfMv8hzevIBnHpMpf16pk1bx73n7N+0qBByADZmc6IxOCQ7WaKApWum2ySwAb5or7/vl9JEXCp3mvhU7Zrv6723/zFVlTcJ10RBg0aZJ06dVonvogl7iCOyWT1lRCJInEp8gbGjUQrJxk1apR1CGuKGj9+vG288cZlbpPNlZfowLyncePG3swrCcscNvUBlcYmH5oPMe3ByMc52qR72nxQXFawsgEXUOZRBoNbMlXAMQLDW/Sz064kwt97Lzv0UlaTTWY+S5aEsqjbbVeh4aMIS8xKHWiIDHjK5RWM/DnqKD8bDKef7h9SEobPF3MxBtA++GBJmUu1RYtsx74HWKO777K1q9Z4reLx9mL269fPrmURLYzY4o4iL54RIleRuBR5A3MsI0EPw4IFC2yPQPqA+7766qt2OM1T5VC5cqV1yly5XrNmTWvSpIm1a9fOE5eaaZmdSFxmkDVrzI45xmz2bP86CzxjxlQoaM+kuMQ8lBmWTjgyy5LrQMBL4HvVVaH7n3qqPzaV5JfIIXGZQL9lEEqnyVq7MulnnknCcytgGIeL/oOttvJ7W5MK7RAYjVGxwtygRo2s0n//WZN77rb2++5ljUfcbL99ucxWhR06Kv37rzdHM9LqQceOHa127dqlbosn7ogWzwiRC0hciryAlb5/WN6MAKWwlKgE+d///mennnpquY6uLgbecsstrU2bNl52kv/bt29vLVu2tIYNG8oVNstx8epM26r8zOWOO5o1bOhfnjTJn9QtKg7pvFdeCTVJ0TAVFnBlu7gkbsS0Z8EC/zr9lC64XbzYr6576qnQ8YKRFDjEVq2atGef32STuEyg3zIIn31QAJGoj3J6EuXA4QO9577GKXVb5gEoT6BUG/etDTawKitXWpNHHrA9julsLXbe0146cKLd33OmNTrwSNuSNgqaPmNcLIs17gDiGRn7iFxF4lLkBatXr454IJ4yZYo1b97c+3F8/fXXtnLlSm9lMRbYLv0PlLzWqFHD+19ZytzBxau/2ob2W43G/hU3CiMcAoX99/cvU8ZJT46oGDQk4g4LBFMosC22yMy7mYC4HDnSN7V1JY9PP+27vzLZAKHpdhE0M/ejLyzdidmcJpvEZQXHkETioINChxLat2+9NcHnVoAsWmR2/PGh6wh2TLRSzvrr+3ODEJnnnecPsqUNd+0Ku/CH8+yieWfZ/3440iZudKQVBY8tZVCRuIO4RohcROJS5AU0yUdi+vTpNmHCBOvWrZuNHDnSxo0bZ2+++aZ9+umn3m3nn3++TZs2za4INsnEsX2R/QTH5s2r1j6UcmJgWiTUd5k41LBRZhaMCmlCzBRBk604xOW0aX5JnuPRR/3xNpS8Mmpk4UL/9k039csf3ZxDUQFxiTpnFEk2iEvERNu2CW3KZbGdmyntd4glEd9IXOfRd/DBISfetEG1BQNL58wxO/nkEjfxxrbUhtul1qZ67KZvxCKKO0ShIHEp8oLKUfzIBw8ebJMmTbKJEyfakCFDrHfv3jZw4ECbPHmyd9tNN93k9WKWJy6jbV9kP8SsTZr4l79YHTD1cbMiwunaNXRZI0niB9GOSYYbBHnssVgnWkapQOYSb5e+ff22UWCsBAa3t9/uB7rOn2innXxH0G23TcUTz3N4c50rM1nLTKZ8V640mzvXv8yH6QZWJsDWW5udcop/mf3l0ksT3mTBQKbXzYnl+J2JVu0SWD3CKpp2Cg4KxWz2b3GtfAz07dtXcYcoGBQxi7ygatWqKStVZbtsX+Qurtru479jcIxt2jSkFLAI1cyx2ME+/8gjQw2KKK977sl8nWic4tL5EDnj0H328YXBmWf6LVmukKF3b7O33vJ3GVEBvv8+1Nec6ZLYzz9PWklsEIyeNtgg5DD8xRdJ23TeQutrsGKAeZaNGlnmwR76ySf9J3jAAVb15589U59IDBgwwFvQnjp1qnXp0sU+D+5fMaC4Q+QyiS/NCZEBXD8C5apkFRF/9EMyzzIaPcmmhEH/Q3k9EGxXPZa5DXErZYsxjSNxpbFEgfTxMnD76KPT8jxzHiJCjJCAEsfx41PovpE6cYkgcC+DrMm995r16mX26quh+1x0kdk111RgiLvI+37LIIgi2vcuuMA/nDCahH0r0+st2cqff/ruy27N4fzzQ72rWQP7x4QJVumtt6zGjz/a33g6hB0IRo8eHfXPY407wmMcxSEiV5C4FDkDpjrMfsKiO9xJjYNulSSUMUWibt26KdmuyNJxJE5c3nhjqDRW4rJ8HnvMd78BvovjxplttpllBXGIS0rxMIoE4sVbbjE77LCQBxRFDKNGlW4pFRWkAMQlkO0mgY8/zJQpZi++aHbooUl9iLxh8ODQ5CLMu1nAyVr23tvqLl1qf6egmZaF8tnujSiOcRCcxCP16tVLWbwjRDLQmqvIehCRS5YssVmzZtmiRYu8g264M2yqnNU4oNevXz/p2xXpxcWtS6yx/V2rQfnicvfdQyMzEJcydCobyocHDAhdpzExMFc2V8TlDz/45bDu8EK/HO2iTljiFkvWScIyj8Ulpi1JtiRl93NrVYABqZuZKkobTLuEH4ffxx8v7cWVjdSrX98qpaF8gRiH2IcYiFiImEijSkS2InEpsppVq1bZ3LlzM3YgbdSokeZY5gGhuLWS/bhBcfbyxx+ZVB09Gtx335CzrJvgLSI731Az6gb5ociYIp9NxCAuWZvCq2PZMv/6Djv4JiK8PNdu9cEHZnvtlY4nXCBki7hk33UrCO3bl4yeSCZkv92+g/noXXcl/SFymu++8w1Zg+tTfOeyHTKIxAmZWHAnNiJGEiLbkLgUWQsHzfnz59u/cc6lSxbVq1dP+0lDpIZg3DprvRgcY0EjScoHRdanj2/MAszn+N//LOsIissowRjtou++61+mEh7PDnfXzp39eZatWqXjyRaguKTEjxkvmYKhpc4WOMklseGjSVyv5ZVXRp+GVGjw1vfrZ7ZihX+dRZ7gfMtshziBeCHdEBsRI0lgimxD4lJkJazMLVy40OuzzNRqZLNmzdRAnyc0bGhWp45/+dN/Yuy7POCA0GWNJInMOeeYvf22f3njjf26tgwEWYlmLvEdIvAHKtyCCW2qfTHyUXV8kqESxYnLZs2SMvojKf2WpKxTBD2ErqQaIVXOBKyCgRmgU6f6l1ljyAaD6XjbZ4gXMtEHSYxErKQSWZFNSFyKrGTp0qUZzVi2aNHCqmV7s4eIGQIVl72ctnyr2MQlaaoWLYr/aJrZH3/oHQ/CXIU77/Qv81159tnsnclRhrhktOEJJ4Suu/Za9pkRI/zxdppElAKoP3bDQjPdb0ma2pGizKVj+PBQO/fdd5ddPFEIUC1AFtct7NBnWa+e5RzEC8QNmcpgEjMJkS1IXIqsg5W4sg6UNLV37drVRhD5GYHhCXbIIYfY4Ycf7v3gJBtpkTyqJwu/WLvWKhUVWePGja1Vq1YSlnmIi19nrA1kLl2fVXmlsZR/vvFGCp9djvHee2annx66Tqph550ta4kiLplcRFXvypWl716rlq+VhwzJrQxKTpEt/ZbhmcvttkvpQ5HgHzo0VA6KuU+hQoUARtyuIvmyy3wvtVwFgUn8QBxR7tiQCB4SgwYNsk6dOtm5zKsp5rjjjrPevXtbjx497G5WI6JAzJSpSi8hwpG4FFkH40bKKvEYNWqUdejQodRtN998s40dO9b7wa47EosWVbV//y19wK+0dq3V/PJLazJihLW7447YTgoiJ3Hx68/W1FbXrlt+5hLUd7kuP/1k1rt3qCHxrLNKp/5ySFziBBs+23yTTfxEdY8eaXx+hUi2iEuUjTPs4nlssEHKH5JFi0039S+/8oo//qbQ4BSP79fChf51ROXFF1vOQ/xAHNG2bVtr0qSJ1axZM+aYol+/fnYtNcIB7rzzThs3bpz3M23aNPs6SqqbmInYSYhsQINyRNbBHMto0FuwYMEC23vvvT2ntHhOZMuWVbHFi9tY376BwcRr1lglLPx4TGqVaLxyNUsibx1jf2nc3poseM+3KKQ0zzVkhoOTC300rAjTeFfoIMwQlj//7F/fZ5/QbMscE5ePPOLPqwzviXvhBT+7JApEXM6a5aewU9xvGZ4Zv/5638QGSFSxyFFIowsfftjsySdDBlqMyc2n10//ZcOGDb0fNyrNxR3fffddxAqrjh072kcffVTqtvXXX9/7n6wk2yhLqBI78XhCZBplLkVWwUE40kHXQSkspSPhDB061Pr06WMPPfRQxL+jl6N9+3+8kzmlK2Q3+b9SzZq+NR38+afv7CHykmD8urB2oDT2m2+i/xGi09VpEQwHA+JCgxUaSmGxTXUmLE8/nRsNiWHi8osvzE48sfRdmKby1lsSlgUnLtPYbxnkqKPMdtklVJ0fvtCRz7AufMYZoev0NXM4yVcQhC7uqFq1atx+EmQ0WVDfddddrV27dlHvR+wkYx+RDUhciqyClbloB8cpU6ZY8+bNvZ8g119/vY0fP97GjBljb7zxhr3t3CvDqFLFXz1ch2OPLb2cKvKSYPz6lcXoGAsqjfVhMB+DH4FFGZoSc2VUT0Bc/r7sX2+9INieRA8cRrcqWsiQuHTGWZnut0yjuGTB85ZbQtfpNyyEqkaq6RHWrOUCizxHHGEFQ1kxTjQeffRRL/6ZNWuWzWFIahRchlSITCNxKbIKykaiMX36dJswYYJ169bNRo4c6fUg0OC+0UYbeb+vU6eO97svmVkWz/aZzeeUx+TJZj/8kIRXIrKNzTYLJdk+/CMOcamRJH5Kb/Dg0k6xaQzEkykuH3/g35LAFvDIoESRYF9kQFw2aZJZVZ8hcQm77WZ25JEh81ycZPMdRPTHH/uXW7c2u+02KyjKinHKonbt2rbLLrvYO++8k5LtC5FMdDoVWQX9CNEYPHiwTZo0ySZOnGhDhgzxHNROPvlkW758ufd7VuxoeG9ZRolVxO3Tw+Cyl6woPvpoEl6JyDbWW8/MJb3fWByHuNx2W7PGjf3LU6aEjGwKBfpSsVR1qb7zz/dTD7lEsCx2Vagkja86piIizaDuFy3KfEksx3snLmm0LV6oTCcsbDgPOoRWPlfes3Z7443+ZRb6nniCnkIrKMqKccL5/fff7ddff/Uur1q1yt59913bYostkrZ9IVKF9kKRVdCPEI9bKwfcU0891Q477DCv5xIbcMaURILtsv2I9O9fujQ2zrIVkRu4OHbW35vZ2trrxyYuOVm7fYpZlwxmKxT++stvRnSjgXgfrrvOco0JU0Lisrr54vL2282OOSaDT6qQmT8/dDmT4vLbb0O1qBnKxNNr6CZPUNF4wQWWl5CZ5TTrTq2YomKgVWiUFeMMGDDAWzifOnWqdenSxb755hsbOHCgF9/07dvXdtxxR6/3MhplxjhCpJE88uYSuUzQTY3G9/Ia3nv27Fly+WlMRWKAZvqowpXVQFxj6dfE6vuTT8x22im+FyFyyjH2z823tDpff2S2YIEvorBwLKvv0mW0J070XVLzHaLAU04JGZ7w5mHvSAo4h14CpYb3X1rdFgTEJdnKM8/M8JMrZAq83zKcCy/025lJ5uIpRxV6GRoi5+B7eNJJIZPp/fcPCepCgxiEWIR53eGMHj16ndueeuqpmLfNdoOutPEu1guRLJS5FBkDa+1ly5bZvHnz7KuvvrLZs2d740XidVKLlbr4nZdF0NgniuusyG2CSZLFG7YPRT6MIyiLYDa8UEaS4DbCfACgJ+75583q17dcgcMIX+lLLzX710KZy6b1/y24Pq+sI1ucYrNEXGJKfc01oevnnOOP38wX6GtmxA8wKYPTayFXb5Ybi1QQBKuLo/ifuIr4ijiLeEuIdFHAX2+RySzlkiVLPOezRYsWeQfEVNtns3pXv7zA+PDDQ80vNIMUWm9dARCMY+fX2Cp0pbzSWHou3Qw8BtItXmx5zeuv+72VwVLxrQLvV5ZDFW+XLqFkc1Bc7rL9v1atWuaem8hScZmmGZfROP54s+22Cz2tfDEux19vyJDQ9QceMGva1AqaevXqpSWjSFxFfEWcRbxF3KVRJSIdSFyKtEKPJKtq6T7INWrUyNYrr5yP1UT6y+CXX8wmTEjLcxPpIxjHTv8vYOrDoLnyCI4kee01y+t+OGa/OtdBUn+HHWa5AlXtu+5qFjRVDIrLmpVSUxkhclBcupJvFh4zPGiR01NwNMmwYX6Ldy5D5SfeX2509VlnmR18cKafVeapUqWKF5NkYlGf+Is4TIhUInEp0gYHtPnz56es7DUa1atXj/1ArtLYvCbY3vXuijgcYwtlJAnRbI8eZsUOzHbIIWZXXGG5lHBlvIPzi3Gld6sskKpM8/FHlCEusQrN1KxUqg9cEyApwyzoTaOV29kJ0H95ww2W02BO5CaDbb11yClW+AvexCbphviLOEwCU6QSiUuRtlWzhQsXpr3unxXCZs2axV6Cst9+/tw1eOklP4Mp8oaaNf2JA/DOD838G2IVl6gWmqOcuMy3eWJUElCb56LBdu38utIcaY665x6zAw80++23kG5xH9FenQMOihKXmYVzwMKFoaxlpkRdFpXEBrnpptA83hEj/ElAuQinzzvu8C/TbYIXmOs6EX6rDrEJMUq6IQ4jHlOJrEgVuRE1iJxn6dKlGclYtmjRwnOfjRkO9P36hXzh43BqE7mBq8JbsqyyrWm7ZSiT4mq3okHE17lzyFc/GJzmA4wYGTfOv7zBBmbPPef/n+VgfIIBysCBIROUNm1CJYX0dz32eKXQrEuJy8yCWnKLjNlQEpthM59wWrXyy0eBQ9JFF1nOQUL4hBNC12++OadattMGsQkxSqYymMRlQqQCiUuRllWydB7EWBFs3LixN/MyLmHpUGlsXhOMZ3/bpLg0lhTX7Nnx9V3mU2nsyy+bXXKJf5lM0uOPm7Vta9nO77/7ZYS33hq6jfZQ91HSx8b60EYbeatN/o0Sl5klW/ots8QpNhK0OW+4oX+Zr+L771vOwKGUUyjrb0CVPaN/RGSIUYhViFnSPTaEuEwusiIVSFyKlLNixYqo5Rc4mXXt2tVGUP9TfN+zzjrLDjnkEOvRo4d9//33Ef8ufHMclGvWrGlNmjSxdu3aJXag3mabkG3fhx+affNNxbYjspJgPPtT3Tj7LoPiMl9GkjCG5eijQ18qZiJ0727ZDoeGPfbwy+9c0cH115u9+WbpZOyeexZfkbjMDrJNXFIan2ULKfXqmV11Veg6mfk0+t8lxMiRZpMm+ZdpQWB0Yxa0s2Y1bkG8bdu2XgxDLBNr/DJo0CDr1KmTnVs8OJSY6rTTTvNiKOaBP+bGSUWAuIyYS4hkI3EpUs5vrgkqAqNGjbIOHTqUXL/++uvtgAMOsBdffNGefPJJ29At34bBifabb2rY4sWtrE2bNta+fXtr2bKlNWzYsHxX2Fg47rjQ5UceSXx7ImsIxrOzq8QpLnEEat3av/zee2YrV1pOw3eT1IJ7HYzjyYE6vI8+Mtt5Z7Pp00PBOCLz2WfNfv3Vv+3QQ83OOy/wRxKX2UE2iEv2e/c8tt3WT3FnGaecYta++PBE5pKexWzn4499l1tAG3HqZK6liA36L4lhiGWIaYhtyGryf40oDav9+vWza6+9ttRtJ510khdDPf74414c9V0ZjbtlxWdCVBSJS5FSWBn7J0ovGw3lCxYssD1IP3glbr/bzJkzrXtx1oTVu1q1akX8WzxGWrf+1zp3ru6VlSS9nAT/dBdwcIbMN/OWAiYYz372b5ziMpi9pG9syhTLWdin6S8mc+ky9gyhy/I0w9ixZnvv7btpus+T4JvJQR984N/WvLnZgw+GvRSJy+wgG8Qls2qztCTWQSaeLKBj6FB/tEe2Qo8zBRCunZbn61rURfwQ0xDbICqrVq0a1bOiY8eOVrt27ZLrxE3cBsRPzZs3L7MtifhMxj4i2UhcipSyevXqqAcuSmEp6XD8+OOPVr9+fRs6dKj16dPHbrjhhjL7AdZbr8jbfkqgScuNnqD+LlhrJ3KaYDz7wZItQqIjVnGZLyNJGDHiakobNPANfLBYzVI4jFDm2qdPKMjeay9fUM6YYXbbbf5ttFkjQBldWAr3OWvGW3aIS9TTZptl5jlkcb9l+KEmeBrCGCdbOftsszlz/Mtom2BZr0hdHFUWixYtsjlz5tiWWxYb10WA7aYsjhIFi8SlSClro2T8pkyZ4q2o8eNYs2aNzZgxw0444QR76qmnbPny5fYcAW8Ftp8UgqWxDz+cuscRaQUdVbeuf3nOgiqhfisio1iEB8PonFEUfZe50gwVZPx4s6uvDpUB4HoTHAKaZbBoj/ukK7lzX8/XXvPLYE88MXQ75j477hhhI8pcZh6+K05ccuzPwBiGXBKXQPbSFdGwuOJGc2YTHD4oegDWpzAhcuNUROJUJM5hjuV5553n9WJGqwBLZPtClIXEpUgplaPMyJs+fbpNmDDBunXrZiNHjrRx48bZ22+/bZtuuqlnyMPf7bvvvvZNOWY60bafFBgg71QIqRA320DkNJRKuuwlrShrtiz2yCdLPndu+RugBKm4lNu+/Ta0XJ8rMMcy6IjMYD3mu2YpuE7uv7/ZQw+FbiPIJpglJqJNFNdYV80e1ZnSiUtW6RVMZQbK8/78M3vMfBC3W29t2Qx9l6ee6l/mrXOmztkCh0D3/ODOO/1xKiJ5xBvnkI0cNmyY7bnnnp5hYrK3L0R5aI8SKYVegUj9kIMHD7ZJkybZxIkTbciQIda7d28bOHCgNWjQwH744QfvPh999JE3AyoabJftpwwa6Pv2DZ3VcQsReYGLa9EYy5sG+i5nzoxtA7k6koQ0HwY+LsCn5xIryiyFtaVddzWbOjVk7Mk6z4UX+osEZ54ZMvWh8uu++8poGQ3OklNpbOH2W1JT7UrgUW5RjFKyiSuvDK1zsqgSHNGZSViP4xDiPGFY3OnfP9PPqnDiqGjceuutXq/mqUHVn6k4ShQkEpcipXDgiuZyFokLLrjAK+Po1auX/fnnn3Y4aYkosN2Uz4VSaWxeEoxrv1+/AqY+wb7LXBlJQiRI9Dd/vn99hx3KUWOZZfJks912C+mRJk3M3nrLrHdv/zqGPWPG+Jep+kJ0ltkyGhSXmnVZuOKSzP2aNTlREuvAcfWyy0KVxUydyIZqfKYWvfNOqMr57ruz9nCSt3HUgAEDvAX6qVOnWpcuXeyTTz6xMWPG2JdffunFT/y84z6kTMVRouDIUMODKCTq1q3rzV6KBrOYHFtttZU9/fTTMW835RDdEgQRFBHt4qqQKRMKkTSCce1X1t62j1dc4qzatKnfAIXZE2IlKF6yERoWaVKERo38TDypwCxk1Ciz008POU8yLeLFF0NfPQx8+L3j3ntDYxuiInGZebJBXOZQv2UQsvSINyr3WWTBjqBXr8w9n2nTQm3b9ITSZ5mOU3KhEi2OGs0g0TDwrohnu0IkG2UuRUqg5p+Gcmyuy2smrwistOEsm3JY0XP9aSwVlzGQWOQOwWrrz1a2DLlPxCou2S9cL8tff/mRVjZD5EdvpeszI823+eaWbZBQYjYlM/6csDz4YP/tdcKSkZxkL12cxX0pzSsXicvMk23ikux9joCHmPsKw/nnZy4Bv3y52THHhFqXMZ5mHVakjnr16iU9w8j2GGNCnEa8ppEkIllIXIqkwdiQZcuW2bx58+yrr76y2bNn29y5c22+K8NLIo0aNbL10jX4OthEgmtsNtQjiYQIxrVzvq1q1qaNf4WZj2WMv8nJkSQ0aJ10Uug6MzuY4ZFl4Jd12GGlZ/vRDhqckMJXb8CAkIcSiSc3gqRcJC6zS1xmyp042LBISjyHoF0as2r3Vt5xR/qfA99BTLMwQ4M99zS76KL0P49Co0qVKl7ck0wQk8RrxGnEa8RtXCeOK2sMnBDlIXEpknKAWrJkic2aNcubq0TpRipXwKpXr570g2yZbLFFKBj/+muzTz5J32OLlLDppqFkpRfvuppKnESDAXBZYGHqVpKzte8Sd05q5/75x7+OyBw40LINPLwIUl94wb/OutE99/hz/YJrSATTzzzjX6aai8sxt3RLXGYe992ipDwFFS3lQsDsHKCwNN1gA8slONzcckvosENZKl/xdEKvs+tcqVfP7NFHS39HReog7iH+SRXEbcRvxHHEc8R1ymaKiiBxKRKCUgpWvdJ1EGL1rlmzZulvQA+ObgjORBA5CcEQawZAYr1oywqY+my4odlOO/mX6XH56SfLKhDKffqEUgzYrjInIMvMG1ir2Xlns88/D4lGtHq40eEHH5gNGVI6yI2rslLiMvOp6cWLM1sSS2WCW2jJoZLYINttF5rrikvr5Zen77FnzzY766zSvdFZWF2ftxD3EP8QB6UraUB8R5wnRDxIXIoKwwGHktd/09T4wYodo0mquQH26YQg3aVInnhCowzyABff0rtXahxJrOIyfCSJM8vJFlBiOH+4TNG4cVlnOjR+vJ+xdIPhqZR87711x27+8ovZEUf4etm9tIAPWGxIXGaWYHtEpsRlsCQ2h8x8Irm0ulJxzKxinaCUCOgLzKbdFCPK08swcxcpgviHOCiVGcwgxHfEeRKYIh4kLkWFV7UWLlyYcF0+yc7yEp6s1jVu3NhatWqVGWEJlE85az4i3QkTMvM8RNIIxrcLalZQXGbrSBKG4d1+u3+Z7wwqbuONLVvgO3/99aWNefbYw89OMq8yCKYhFA64BOzuu5tdd10FHlTiMrNkm5lPDotLxvK4Pke+H8GMfqq4+OKQNm/bllmKqX9MERniIOIh4qJ0VHER5xHvqURWxIrEpagQS5cuTUrG0h0XwzUqB8yaNWtakyZNrF27dmk7iJaJSmPzimB8O3N1m1DjUDzicpddQv77r78emp+XSVBoOG44mF9ASWyWQAaE1s+gCQieWZMm+fP8wrnhBrNXXvEv8/snnwz1y8aFxGVmkbhMKphduZJU/MRSud7JoW3EiNBaFcU7tWun7vFE+bhF97Zt23pxEvFSKmMk4j3iPiFiQeJSVGgVK9pBhmbwrl272ogRI+zPP/8sGeLLz2677WaPPPLIOn/D8XDt2kp2zDEt7Z9/WlmbNm2sffv21rJlS2vYsGH6XGHLg1o9lozhpZf8DKbID8fYhdXMWrf2r3zzTewikd6XLl38y7/+mnmzJ+pLsVx1PTJnnBFq0MoC+MowwYXEarDEjzbmSFVeb7xhdskloeMEE1UwY6oQEpeFLS5Jl7vM5SabmDVubLkMI2pZeHGQvXRl48mEU31wXZWKgxxO+uYd9F8SJxEvETcRP5HVpHQ2EoMGDbJOnTrZueeeW3LbNddcY3vvvbf17du3zMci7pOLrIgFiUsRNytWrIhaHjFq1Cjr0KGDd5n5SWPHjvV+nnnmGatTp47tu+++Ef+uSpUiu+GGP22nnWp4JR8Zz1JGExJuoB5n8aeeyvQzEgkQjG9LOcZi+LFgQewbypaRJFQSUGfqjIVwOMZaMkvADIRZeK4NlBZmvkKU20X6uqOT6fFys/QwLsGgt8JIXBa2uOQ7jQMO5Ik6Qgu4+ZIYmd93X3K3z2n+hBPMFi0KtZgPGpTcxxDJg7iJ+KlGjRr2F/OXI9CvXz+79tprS93WvXt3u+uuu8rdPnEf8Z8Q5SFxKeLmN3eCDoOa/AULFtgeNE+F8cUXX3ira5tGSTsQXLZuHXm7WYVKY/MG5xa7jrhMxNQnU32XRIFnnum74cBmm/lzOipUP5p8yEBSmevmU260kS8yMemJBGXyCEtnLoqodBnMCiNxmR3ikv51nJbTTZ70W0YaTeK47DKz5cuTt33MpV9+2b9MoheH5sqKGnM6TuvYsaO38B9k++23t3rMlUlgu0IE0WFCxAUrV/84K/cwKIWl5CISEydOtG7BIDzCSZLtZn3D+Dbb+F7w8OGHfgmlyNmyMqrjEhaXND61axfqd8zEyi6WkaNHh1KCzz6bNWV/Y8b4pbAu6OUrxFeH8SPRIEh2GU4+o8ceS8IsvaAZWJocroWFKj0WLgxlLTNRmZKH4tK1fR9zTKgyn9mXyYBxoOedF7pOKbvrChG5G6clSk7EaSLjSFyKuFi9enXEA8uUKVOsefPm3k843P/1118vU1y6+7H9rOe440KXI/SQitzBVefRC/j7ZhUUl8HSWHo1J0+2tDJ1aunhc4jMHXe0TEM569ChvnmPM+zq3t3snXfKno1HpsS5wSIoKZ1t1CgJT0iZy8yB1a/rY84Gp9gcnXEZDb4vLJbBHXeEKgQqCg7OVA64NRjWjA86KPHnKTIbpyWDnInTREaRuBRxsdY1QIUxffp0mzBhgicgR44caePGjbO7can0Rot9ahtvvLHnaFbR7WcVnHVdGgVxmQvPWUQkGOfOrdwmVPMVr7jMVGns99/7w+acesPVw6UxMgiz8Gj/vPHG0G0EqM8/b1anTvS/I7mFc6wDwxJGjyQFicvC7bcEN0ejfv2yVzdyEKrgXZaRuP/88xPbHocRdwjcdtvSxkEi+0l1HJUTcZrIKBKXIr4dJkrDxeDBg23SpEle+euQIUOsd+/eNnDgwJhKYmPZflZBw5jLVBHcv/lmpp+RSIapz081zZzDHu4Y8ZxAMc9x4gVTn3SUDZFeYPbqkiWhxkSsHDPMjz/6b8dzz/nXWYfBK4K5eGWVtpIl6dMnVD7bs6dZwNAwcSQuC1dc4kjjXGkoic1Gw7gEueACs6ZN/css4tDnXBH42+J1YS8bytiRSE7OIntJdRyVE3GayCjaQ0RcVK1aNS4nV1a4Jk+e7I0nKQ+2y/ZzgmBp7MMPZ/KZiFQ4xuK0RylfrNSq5Ssqt+CQ6l5cxOspp4RGnyCKGQCJo3EGITlEL6VLEuHdwozK4nWmMiHz8tFHoZdDj1dSNYDEZeGKyzzttwyy/vpmQRNQ5mDGO3aXhaHg5CIWhLbcMnnPUWQ+ThswYICXAJg6dap16dLFPv/8c7v44os9F9nZs2d7t5EQyIs4TWSMzEYiIufgwILNNfMso9GTlENghQtxGQtsNytHkETikEPM6tb1re3HjvUbXTi7i9wXly+84N9AXViEHuKokM1m2rgrjU1lVHbbbWaPPupfxvmPNGGDBpZJeApU5DoHfNx4GQcb9EmKBn2VfIWcBuQrFaN5YexIXGaObBKXedZvGW5mfvvt/uLOF1/4CzQDBsT2twhRStIxBQKKIk4+OaVPV2QgThvtjN8CbOdMCvMtThMZQ5lLETd1EVU5tN2UgCOnGzhMgxnunCJ/MpeJ9l2mct4lizVBG8eHHvItWDMESdSbbjI77LCQsOzUyTfOjUVYzppVOgD+3/9SlFwKistVq1LwAKJccUnGI8o4qpTiUul5nLkEqhWDo0kY3/P777H97YgRoVJaHJpHjcrL6uGCQXGayCQSlyIul7BVq1ZZTWdLl0RYCauP0UIuodLYnIdkn8uQefHvVltVXFyipNxsE+ZolJHdT2gQPMMhXb3bxRf7zjkZAo1GdoN+L9dmSvYS/RuLwytiFD+iP/7wr/frl8JsiTKXmYEdY/78UDo74ZkyCWQuKV9v08byGarz3SGBObHOebksGA3k5sgiKCmKyMQoUpE8mFuZigwj8R9xoMaRiLKQuBRl8t9//9myZcts3rx59tVXX3k1+QsIcJNMo0aNbL1MBB2JsNtuodQX0TS9diLncB8hH9+qFu1Cy/Xxikv+zhk9MWOMESHJhAw5Jeeubu3gg82uusoyBcY7vNz77w/dxtPBQJnEfiya4/TTzb78MqTN77knhdkSicvMgMJh381USSxzZ524xfo0184zFQCXZjfW9eabzb79Nvp9yWwefXTIcPqii8z22Sc9z1OkjipVqnhxVbIh/iMOJB4kLiQ+JE4UIojEpYgIq1JLliyxWbNm2aJFi7za/VStVFWvXj0lB8GUQxRMkwvw3jDpXeQcLt7FHPbbJbVCfZaIy3j3+VSNJOF5nHCCP9kc2rb10wsZcu1jjt6uu4bK6NBtuEpeemns4nDMGL+i17WN0mfJ/ylD4rIw+y0//7wgSmKDYIjF6B/nwnzhhdHve+aZoY9ol13MrrgiPc9RpB7iKuKrVEA8SFxIfEicSLyobKZwSFyKdaDkYe7cuWk5WLC61qxZs9xtEA8O5cM1Nh0jKER6+i5Z0v/hh/g2tt9+IcGXzL5LBs0980zIghX3nAz1KFPxi7CcPdu/3rixP43nyCNj3wZmIwS1Dvq7Uu5KKXFZmOKyAJxiI0HFvFuzxTDr3XfXvc/jj4fMzpk/y3UZgeYPxFXEV8RZ6UhGEDcSPwohcSlKwYFh/vz59i/LnSmGFbUWLVpYNVe/k4vQQ+RGUDAb8eOPM/2MRCZNfegbZhaH+9tklEozy2PYMP8yizBkyNu1s0zw4IP+OE1Xmbv11r5xD2IzVjBYps+SymFgTMlRR1nqkbjMDBKXGYG1p2DVPKNJgqN76W4JjghitqUb8yvyB+Ir4qxUZTCDEDcSP0pgColLUWr1aeHChQnXz5eXvGM1rXHjxtaqVavcFpYOVxoLmnlZ2OISXN9lMrKXpAdpiHJfKqJFei3TDEEpvVhU5q5eHXqZ77wT37QWXsZJJ5nNnetf33HH0u6WKUXisrDFJdkbVkMKCFyY3UvGtIfMJHCK57CycmXISAsjLpGfEGcRbxF3pbpKjPiROFIlsoWNxKUoYenSpUnJWHLsCheYHNBwGWvSpIm1a9cuLQe5tNGnT8jBhMYzlYUUtrhM1kgSIj8MfEj1AbM+XAYzjeDoyi5+/fWh2yhpffFFv0I3HhgzMm6cfxmXXip907Cg7iNxmXlxSaVHOsGxmYoSK3aCTtvOlh2gpzH0cbBAxPeZNar33/dvI1t5550Ze4oiTbhF/bZt23pxGPFYqmIw4kjiSVG4SFyKktWmaAcDmra7du1qIxiE5VXpvWK9evXyfs4///yIJRDumLXFFltYmzZtrH379tayZUtr2LBh7rnClgcRNhOn4Zdf/DJGkTMwPcTFnF4cHGz+q4i47NjRL4+FSZNCNozxpgrp53WBMekH3G/SbODz009+1ff48f51Hp4h7fzE28bz3nvrjudMq9aQuMysuOSLloIxVmUyY0ZobE8B9VsGoYy9e3f/Mi3kLAwNH+5f51RMNjPeRSKRu9B/SRxGPEZcRnxGnBaJQYMGWadOnezcc88tuW3GjBnWs2dPO+igg+xuaqmjQDwpF9nCReJSeKxYsSJqGcOoUaOsQ4cO3mXug8h84IEH7Nlnn/Vum0QAHQWEKSUZeZOljIZKY3MWBJM7tzKxYG3tOmabbVZxx1giNiI6Nwbho4/if1KkFl54wb+MUMXAZ/31Ld0mm7hHfvJJyPDj5ZdLG/HEyrJl/nhOp7OZi3nooZZeUMNOnKehp1wUm2K5RUuZ+WQM1oXdYhB90673ksMM33FRmBCXEZ8Rp0WiX79+du2115a6bfjw4XbDDTfYiy++aNOmTfPGkkSCWJG4UhQmEpfC4zdXehcGtfPMNdpjjz1KHTT++ecfW7NmjXdQKmuMSLTt5h24hDZt6l9+6SU/gylyBhf3YjLz88+B0lhOjosWxb/BREaSsGhz5ZX+ZcTQk0+mPTBH1/KVd2a5zZr5bpPBdtJYIZClp8tta889Q5mTtOOylxKXhdVvCTvsYIUK/l+nneZfdmtlzLIcOjSjT0tkCdHitI4dO1rtwHwoHGGJ+yitpQLtgAMOsLfffjvu7Yr8R+JSlIjFSJClpDQiuNI1bNgwryS2c+fOVqtWLe8AFA22WxCN3SwLO0cEHE8QBKJw+y67dq1Y3+XMmaWz4IwgCW4rxfBVHTnSb/V0c+9xgsURtqJeKCx8u7eAsSV8NVLsjB8dicvCEpeffur/T+XMtttaIRP+9p9/vl9kIQqbsuK/SKWu9G06uLx48eKo9y+Y+E+sg8SlsNWrV0c8AEyZMsWaN2/u/QTvO3bsWBs/frz3e6A8Ihpsl78pCFQamz/iEvOPRMTlppuGtkFZbCyZ7OXLfVX3xx/+deZzDBli6YKvKdkN+iLd4YDZlXzNN9qoYtucPNns8stD8T39XRtvbJlD4rJwxCU12PRcQqtWfl13gfLNN/7cyyDXXaexzCJ6/JcMCir+E6WQuBS2Njj8KsD06dNtwoQJ1q1bNxs5cqSNGzfORo8e7ZVDNG3a1Pu/S5cu9gUT0Suw/bxjm21CphH4vnNGF4WZuQRXQ8r+X0ZfsgemI8wGcDM62I9Gjw45Y6UYdO2BB5rdd1/oNkQhYrCiHiyYAfGS3NefSt8uXSyzSFwWjrjk+OsyMgVq5uMqwFmnwiXWzb+EadNCzs2icIknPqMFitJYB5eDmcxEty/yB4lLYZWjOFAOHjzYM+uZOHGiDRkyxHr37u390MDtauk/+OCDUpnNiDtZmh0uM4qyl/khLoOOsZSqVoR4RpKQVnC9mQ0b+n2XtWpZOuD1durkZxmB0bOPPWZ2xRUV17Ykjch6ujiEtyI8c5IRJC4LR1yq39KD6UWYc1nxYY01KwfGWjFWRIo8JZ74DCHJ/WfNmuX1Xr766qu29957J237In/Qpy6satWqMbu5cnA56aSTrH///l7f5R9//GF9GIIXBbbL9gsGlohdI8sjj4TSNiKrwS3WfQW8eJghjK5+E3FZkbIhnGtc2g9xGW0bTz3l91YC+87Ysb6DThqYOtV3i3RJdry53njDzzgmAkKSbbsK4UcfTfsUlchIXGZGXPJ9atAgM/2WBZy5ZL3Kzblk0YgxzL17hyoIFizwZ8+KwqWs+G/AgAFeYmHq1Kleldrnn3/ueW4MHTrUDj74YNt99929USbRKLj4T5RQqUjdtsKLAeZFtaNOBAb1Mk+poDjkEN8xFkgHde6c6WckYmDzzc2+/96Pgb0WScaJuHJWTAvKKf+JyEEHmU2Y4F+m/yvcFYeUAmlD991jgGRFZn1UgIcfJnjwey2tuBKY3TbR2ZM4zfbo4V/GuAczwd12s+xgp5382SqI+IrMHxWxw/xjFldYYNtxR7OPP07vu4cd6ltv+ZdJoZfhap6PcMhigpirHrj1VuYW+penT/f1Nh8NrahU41fk8CbyA8V/Itlkw1qyyALqukaMHNluzpTGMile5ARuDeTXX/0JJEnpuyxrJAnDHzHwccLyhBPMzjjDUg0B5SWXmB13XEhYYkjLqJFEhSWZELbruOmmLBKWLn3jelz5Ealj4cJQ5Ua6Fxh5XFcWS+q8wIQlL5/DiROW9FOffXbo94jOk04KjSK97LLMPE+RHSj+E8lG4lJ4jl6MFEk2lETUZwB8oUHmkjIwwDHBuX+KwjP1idZ3iao74gg/AAdqU++6K+UGPph60AsZnDN5+ulmL78cMvpIxDiECnk3N5vyu8AUo+zAlcWCZl3mb78lqxwrVxZsSSwFEK5gAqfnBx9c99By9dUhA91Ro0LGuqLwqFevXsytUfHAjEwVRxYmEpcFyn///WfLli3zyiG++uormz9/ftIfA2cxHGULjho1zPr29S8zLBBzFlGY4rJtW7/eFqgPdcMjGTJHcyM0aeIvQrDfpJBFi/xKwWee8a/TA0m/1R13JGfu5Dnn+BWnbvLD/fenzew2diQuC8/Mp8DEJebtGPUEi2cilbwiOp3JFpnOc8/VaJJCpUqVKl68lmxcfMn/xJvEnaIwkLgsMFhFwj4at69FixZ5fZapWFmqXr16Sg5WOYNKY3OOlIhL1JUbSUIPGj1gRHu33ebfhtkBwnKTTSyV0GO1887+yE1Yf33m05qddVZyBCAjS+6+27+MRkbAZmVFvMRl+pC4TDuuMoFDDSAYg8UT4VBZ4MzeaS+ngkEUJsRrxG3JhviSOJN4k7iT+FPZzPxH4rKAWLVqlc2dOzflX25WwZo1a5aSMoucgUYz0jfAFHqcYkRuicsNNwwt+VdUXEIwukNYnnpq6DqlsBj6pBACxt13D+2CJFLpr8RrKBl8/bXZKaeErpMJ3W47y04kLgtPXO6wgxUKiEnn/EzC9tpry74/C0E33hi6ft55oT5sUVgQrxG3Eb+lOrlBHEo8KvIXicsCgS8ypa//prjPiJWvFi1aWDVnnFGoIKxd9hIhz+BAkVvi0gLZS6wXPQvZCoDvvysPHz8+1Os3cKBv15oi2O1wiDz00FDbL9nLDz4w22ab5DwGVb6HHx6q9sXM58QTLXsJiksFN6nFfYk4F7ixPukeQ4L182abWSHAoeXee/3LWCgwdiSWRBTf3z328C/PmhWqQBCFB3Eb8VsqMphBiEOJRyUw8xeJywKA1aKFCxemtN6dVS9mYLZq1UrC0tGvX+mMVQqzxSJxgqP4SsTlVluVTtFVBOpDd93Vv+y+g8zARPmlCLIPGPXQB+kMO/EPevNNv8UzGbA7o49dUpcpK2nwJEoMZS7TAzuH6+PHgjidvfc//+wvBrn0XVbvkMnhhx9Kr1NRdU+7dyzw9rhZmHDFFb5jtihcgUkcRzyXyuoz4lHiUpXI5icSlwXA0qVLU5Kx5MDDHMsmTZpYu3btUn4wyjkIqvbay79MrVK657yJCmcvCda8r0wy+i7Ds2SoWJoSU5Tdx621e3eze+4J3cboETIZjB1MFqNHmz3ySKiHc+xYP2OS1Uhcpk/guRE7MvNJKUzUYR1z+fJQJtKNGYmVjh1DhTZs58ork/88Re7gkgVt27b14jvivFTEdsSlxKci/5C4zHNYHUrFl7dly5bWvn177/+GDRsWpitsLASH/jG1XmQ1Lg4m8cI0g1LicubMim2UWjXnpAM0JGLVmAJIFtHC+frr/nX0K7sdYwdwh01m1SFmQEGhGWumJKNIXKYH9VumjRtu8H3CgArg++6rWLKW/ky3OEQFAiWyorCh/5L4zsV7lMwmG+JTucjmHxKXec6KFSuilh3g4NW1a1cbMWKEd/2ll16yXr16WY8ePeyBBx4oc7t//vmnspSxwDKyGzFB6kh9XoXlGPvOO6VVmFNmKXDN4KEYl+mqdxs2NJs82ax//+RnRpln6YohzjwzNHkn65G4zH9x6fotC2AMCf3Tl13mX2bx6NFHzSo6WhrDajfChOp9zH2EcJC5/As74nJ48MEHrWfPnl4s+SKW5OVAfEqcKvILics857fffov6u1GjRlmHDh28y8uXL7c77rjDOzCMHz/ePvjgA1vgpW7i364IsMEGZr16+ZcxhHnlFb09uSQuGaeDa2xFxCW1tb17h4SkS+0x3P399y2Z4BfVubPZsmX+9Xbt/MDTGXUkC9apTjgh1E5HOV3x2lRuIHFZOJlL0nCtW1u+wmHkqKP8slhgZqXrwqgoCEo3Femll/zxJELEGvfNnj3bXnnlFXvqqafsiSeesCeffNJWsqOWg+LJ/EPiMo9hReiff/6J+DsaqRGPexRHnz/88INX8lC3bl2vxHWnnXayyaQ9osB21YgdIyqNzV1xSX2Zy17+9JOftosFvneHHRYyFsExNjjZfOLEpDxfzHrIXNBz5ZLi++1n9t57ZimoYLJbbjF77jn/MhkSWkdTbCyYXCQu81tc8v10i6LbbpteI6E0c8YZoZfK5CuXwUyE2rXNrruu9GgTJ15FYVNWPOnAAZaEBW6zNWrUsDZt2tg7lNSUg+LJ/EPiMo9ZvXp1VAFIKewgJigXs/nmm3uzhxYvXuw1WU+dOtWbRxQNtsv2RQwgLJo2DS0HV3SkhcjsOJJYHWP5zp12WqjPkinlTz1lduCBSRWX+KWQuaCf0sEITZLjeAYlG2KEoUND1zHzadbMcguJy/TgvjwszmBsli4+/7wg5ltS/sqPK46hciFZ4wmPOcZsp538yzNmmN1/f3K2K/I3nnS0bt3aPv74Yy9bSTaSy2XFkQ7Fk/mHxGUes9bNIAhjypQp1rx5c+/HQcZy6NChnuA8+eSTvYNEeSY90bYvwuCszxkbEORPPqm3KEthDcC1yEYUl7GUxt5+uz96xpXmPf+8X1rLxsmmwCef4GRQ4edJQnTffc2efjoUw5NVZEZd1aqWdHiq9FW6SSoXXug70uYcEpfpwX15qLF0X6h0UAD9lry1jBly4AqdTP1O72ZwShJO0+qCEbHEexj/HH300TZgwAA755xzvCxm5Rid5BRP5hcSl3lMtC/19OnTbcKECdatWzcbOXKkjRs3zu6++27r0qWLVyP/8MMPW6NGjbxsZkW2LyKg0ticgF3alZNScuadT+MRl1Om+LVkjgcfNCvua/bo1s3/nxVgZ+kaJ2QTdt7Z76l0pWwvvGA2eHBqRvpRFsfayI8/+tf33rt0tjSnkLhMPSgRV52hMSRJhbXJo482+/330GmF6oVks/vu/lxct7CEk6wobGKN94444gh7+umnbcyYMZ7bbLMYy1sUT+YXUgd5TNWqVSM6ug4ePNgmTZpkEydOtCFDhljv3r1t4MCB9ktxQLBs2TJ79dVX7aCDDoq6bbbL9kWMMGHeraR/+KE/91JkJS4exg3VE1Sxistvv/UjMtekdNFFvq1qECcuK1gaS8krgd933/nXN93UL1c9+GBLGddcE9LBTFDB9DhZJXhpR+KyMMx8ODdttZXlG1dc4Z8+3FtLkUQqR5y4rwuZTGfiJQqTaPFkOC6OxNNjxowZ1onZWOWgeDL/kLjMY/jC0lQdK8OHD/fGkFAWe95553mlstFgu6kYqpvXuCnVoJmXudN3STmra2KMJi6xaO/ZM5Sxob8yUnoPZeiGySEu4ygtJ5A85JBQ1oK+KAJNV2mbChCVbqA6C9dUdLv24ZxE4jJ/xSXfQdcTjbDMKaep8nnzzZDZDos7LPLUqZO6x6Nr5pxz/MuYhQX7rUXhEWs8efbZZ3tx5EUXXWRXX321l70sD8WT+Ueurj+LGEEgMs8yGswjctx8880xv69lCU8RBeqX8Hons4UbCikhlRZnvbjcZ59ix9h33/VThqi7YFRHieuJJ5p98YV/nfEHjz8e2amSgJeZIRg70Tg5fbrZdtuV+Xzoc6Tk9c47S49Ppa3T6dRUQNaWcljn4YBW3mcfy20kLvNXXFIv7hZr8qzfkjUrHKHdd5FTB2OAUg3FF2PGmOHJMnas2dSpZnvumfrHFbkZT8JjuEtVYLsiv1DmMs+pV69e0jOMbK9+RSc1FzLUFDrHUGYgshQtcs8xNryk+aabfDdYQHRi4FOWXWscpbG0r5GtDArLYcP8h0ulsKS3CwMf5zlEhTwmPjmPxGXqCdZPplNcupLYPBOXCMqTTw71PLM2df756XlsnGgRsg4ymfLxK1wUT4pYkbjMc3B8bdCgQVK3idlPeU6yIobSWOcoKnJLXM6cGbr86qulVRfzAbbcsuwHiFFcYihEFS0P4drI8AcaPjz1CW8yFm48Gb5eVHHnRZJd4jJ/M5dBcZlHY0juu8/s2Wf9y5hOp/u7SFGG8yTD5JqiG1GYUOJK/JdMiE9l5pN/5EO4IML477//PFOeefPm2VdffVXSYJ0MGI6b7INLQUEaymW1xo0z++OPTD8jEaHXyAVvZY4jmTPHL3V2tWo0Jx56aPnvZ6tWIUvaadMi7gPvvWe2yy4hHcv60KRJpU2HU8Vzz5mNHBkStM884we1eYHEZepxXxqqW9JZ4eLGkFCpk8pG5DTCocb1PQIzJ5nukk5YR2bMUbBy4s8/0/scRPZA/EccmCyIT4lTiVeJW4lfRe4jcZlHMIiWgbWzZs2yRYsWebXx5Q29jQdnKy0jnwSgIZ56Q+AM7ZakRdZQrZrZZpuVIy7pu6RfecUK/zYuMxAuFgh+XfaS+tM33ij1a4w6mGHpSlLbtvXHjuy1l6WlovH440PXEZmMPckbJC5TC84v33+f/qwl3yN6Ll3P8/rrW67zzz/+2pVrcWO2ZY8emXkulOK6dbOffjK78cbMPA+ReYj/iANjMeqJFeJU4lXiVuJX4thkxq4i/Uhc5gmrVq2yuXPnpuxLyUpVixYtrBqRt0gMlcZmPS6xiHb89dfimR8uYEVc8hm6DCbCM95atQilsXxtSX4yx44xKC6oI4tJsjMdwSxGQW5gOlNUzjzT8guJy9TCOB7XlJdOcUkftPvS5Em/JdX2+H25Q8yIEZl9PrSWOz3BZWwDRGFCHEg8mMwMZniShHiWuFbkJhKXeQBfwPnz59u/7uSa5FWqxo0bW6tWrSQsk8Vuu4XUwpQpoZV+kb19l2QbXfaSAJraUaDEGQOfeGcCkJp0kdrEiZ6ww5mVOXYOTDzot0xXZSGOtK5tjeTP6NH+y84rgotjKTheFjzqt0wKzLO97Tb/MvE7I4Bq1szs3tWmTWixiWwqfdmisAUmcSHxYSqq2YhniWslMHMTicsch1WehQsXJrVOnQNFzZo1rUmTJtauXbuUHTwKFt5Ll70kXVUB626RQVMfVxngBj9WJK2IDSNuPTB3rvXvNM8rh3W7B1mKe+/1ex7TAT5EPJ6r3GbsAE8x71DmMj/Fpeu3zIPM5aJFpUvTORZss41lBZdd5vd/u2MGc3ZF4eKSD23btvXiReLGZMaKxLXEtyqRzT0kLnOcpUuXJjVjueGGG1r79u2tZcuW1rBhQ7nCpgqGlgVdY9VfkP3iMtzVhonmwfLWeAn8baPP/NLY2rX9pOiQIenLGmIadOqpoet33x1yh8w7JC7zP3OZw+KSimJMu1y/9cEHm51xhmUNVFEEqyswG9KpS9B/SbxI3Ej8SByZLIhviXNFbiFxmcOwqpPsL92vv/5qa9asSeo2RQS22CLk0EK/0Mcf623KZnFJ86VLLQLjRhIcNvdunZC47GYTPRdIhpTHYjibLDCqpbfyr79CYweCWZO8Q+Iy/8Qliuzzz/3L9EY3bGi5yq23mr32mn+5SROzMWOyrzT9tNPM2rXzL7/7rtnTT2f6GYlsgviRODKZEOfKRTa3kLjMYVasWBG1XKBbt2522GGH2eGHH24nEjEarX3fW9++fe2ggw6yq666KuLfchvbFWkgOFcCQxiRneJybpHvsoNNYvAOCUR9d95pttfZ29lia+xd36/yFPtw2qq0Jl34+pOx/Ppr/zrZyjvusPwmKC5lFpE6ccn7vPHGlhYYCLtyZc7PtyT5GhyZyykhG6d+UarvRhXB0KG+GZgQ5cWljocffth69uxpPXr0sOuuu67c+ysuzT0kLnOY35ytYxQeffRRGzt2rI1h+dOYVXWLnX766fbKK694B4C33367QtsVSQJrThrcgKyYgt2soW7dUBXsvM9Xmk2YUPoOs2ZVaLu0Rp99tm+Msaaosr1mXb3ba6/9wzb+9l1LJ/RYPv64fxk/IvosM20aknKUuUxtBpFZNs5uOR735ALvt2QqFWNHmKgCFEXsv79lLQceaNbVP3TZwoWl52CKwqa8+JGs5hNPPGFPPfWUjR8/3ptx+cUXXyS8XZFdSFzmKKzk/BPHciH3//zzz22v4lLM7t2725tvvhnxvmxXDdRpAMeUXr38y7/84lsEiqzLXv74e13722r408SxTHQZmjiX60muUPJ6++2h26oevO5IknTwySdmgwaFrrP+hENs3sNnyA/ILTa5/Pxz6Duhfsu4nZrdehXJ12uusayGog2yl2794NprfSMiUdjEGpdSOksvJaWu/MTSo6m4NLeQuMxRVq9eXaYAxLHr+OOPtyOPPNJeeuklL1NZt27dEievjTbayJslFAm2y/ZFGlBpbNbSssHykssLbAuzm28222WXUJYmjuwlq/uYw7oEKFNIEHRH3l+8/J9Gcbl8uZ80d4lyRCbXCwaXvZS4zD8znxwsi6VigLE/ztCLIpZcGCe99dZmp5wS6t2+9NJMPyOR7XEpNGjQwI477jjr2rWrde7c2XbddVfbbLPNyt224tLcQuIyR1nrBlVH4aGHHrKnn37abr/9dhs9erTNC574k7B9kSS6dDFr2tS//NJLfgZTZJ5ffrGW7z1acnVe51PMzjorNI4Evvoqpk29/77Zzjubffmlfx0r/9dfNzvhBDNr3DgUEBMkL15sqYTzPoY9jOqEXXc1u/FGKywkLvNHXLJDu7JYsh8Y+uQQ333nz7N1UNXgiiNygauuCo0suv/+kK+SKExiiRspb6Ula+LEiTZ58mSvou7jGA0NFZfmDhKXOUrlcvpZyExCo0aNbM899/TMfPhSu1WlxYsXe7+r6PZFkiCFdcwx/mWyxcxNFJmFxsgjjrCWv31SctO8A87wa8HiFJdPPWW2zz5mrkiA0lPEJreVEBxn4qwiUwQz8154ISRyeX65kCVJKhKX+SMuKcV1Xy76LbPNWrUMMGVnIpXzzzviiNxzaiaEcBlLQotzz9VokkImlrjx/fff9zKVVNLVqFHDa9WaPn160rYvsgN9UjlK1apVow6r/euvv+xPHAKKL3/wwQfWunVr69ChQ4mJz8svv2z7lIpwQ7Bdti/ShEpjs4sLLjCbMsVaWihYnvdd8fchRnFJoHX11WZHHhmqvuTrhrBcp7cxKC5TWBrLmJOLLgpdZwj65ptb4SFxmT/iMofnW9KnyHcS+B5isJVD2rgECjrcx/3GG6HFK1F4lBWXOpo0aeIZ+NBzSe/lRx99ZM2bNy9324pLcwuJyxyFLxqrPpH45Zdf7Nhjj7XevXvbMcccY4ceeqhtvfXWds4559idd95pBx54oG2wwQYl5j7hsN3yDhAiyc0rLjD68EN/7qXIDI88UmJ92LLKd+vGzcwndeIkirjEz6B/f7PLLgvdxjQgdCPZwnXYbTffrtVlLlNQkk5yB6HrRthefLHv+FiQSFymBvcl4dwRQ7BYyP2WzIe88kr/MskYXJvr1bOc/ToFS+vPO0/G54VKWXGpY9ttt/Wq6fr06ePFqGQx991333K3rbg0t6hUJFvQnGXZsmW2KAUWbawsNczhQdQ5Oz37nHP8y6SXWNYW6YW+jz32KEk1Ft1zr9U+5xT7+2+/D6rEv2e77cywTsd19K+/StWVLl3qGwC/804ozr7hBj/gKnO9pmdPs+efDz2PHXdM2stCUJIcnTzZv855nJ5PZ5pacDDQc8YMf+4Kn59IDvQ8MjydNBwOVungsMPMnn3Wv8yiXNu2lu0wUYFDiOt7vuIKs8svt5yGSg2OK2+95V/H+8ydzkRhobhUgDKXOUy9evWSnmFke/Xr10/qNkUMHH10KNoneyZDpfSCkQ6q0NWwnnqqVTr1FG9cHxAIuqxfSWksN8yZU7IJEpmYyTphWauW2fjx/sy6cr+mKSyNJUPihCXeUWRJClZYgjKXyYfGQYRlppxisVnNgVk6iLDTTgsJSxykqSLIdTi+ISjdcQ6jH3nTFSaKSwVIXOYwVapUKdOUpyKwvfUKOvLMELiGujrFH37wm1dEemAmB7M4eN9dxPe//5WKk7nLjz9a1L5LMoGdOpktWODfvPHGfj8VCcmYSJG4ZFNuZh5fa/yimjSxwsaJSxZwMG8SudlvyUwdp9K23TY0dDGLYd3QebbVrWv22GO+p1s+QFWysw9grYGMrCg8FJcKyP6jsSgTylerJcnusXr16kkXqyIOjj02dPnhh/XWpXOC+bRp/uVNNvEHzxV/p4Jxckn8vNVWoRu/+sruucdfF6DcDWifpXU2rhYwUqQu80JD1sqVib0mM/v+e9+I2I0dGz7cLEqbdWGKS9Csy9wVl8G5FznQbzl3rtkZZ4Su33ef/Z+98wCXorz+8KGLGsQCYgEUUTBii2IFGyoaFaUEUFARscUusaLGWGM3YhcVFBuxdyyooCIq/rFFKaJYEUSxofT/8853z51vl929W2Z2Z2a/93lMdi/cucve2ZlTfud3pG1bSRRcY2giw803572tyZEwiCOJJ4OA+NaNacUPl1zGkCVLlni6dnZXfvzxx7JIt6GXWG1q27atM/KpJAcc4Ls6PPyw2UztCJfbbzdREHAzZH7Lau1lTC6tzuX/3fs/Oe44XzJ74IEiGDKToxaMdi/ppo0bJ6XAVpt+/Xxp2v77G3muwyWXiUkudb9lDJxiuUUffLB/SWfHLatHkgaKjbPOMo+5JjJr7qg+GK8inmzQoKE/TlIkxLfEucS7xL3Ev47o45LLGIH30pw5c2Tq1Kmekc/vv/9eu7eyFKgwtWvXLrAOqKNIcFkjIwBWyTCw5wgPOoTprYTOnVP+SsbkcqONZHnNqp5G0z+q/XOSN35lq65a5OsJUBp75pkiEyeax3RHRo2KhWqwPLjOZfC4NSQ5wTla98QjUKhR3SeSoUNFWrc2j599NtTtSo4IQzz51FPtZMaMJiVbSBDnEu8S9xL/Egc7L9Jo48KNmED1ZsaMGYF+qKgutWzZUtq3b+8Sy6jgpLHlgQHK3r1Niw9OPjn1vc+RXH7xTUOZ0cC4Um4i02SlBou9Bih2/CUlcCzC1ALPc88VvY2cpnfNNhXvcKh8M65AqVZccpms5JJCjy1VjxiYaemqDl7q/feXUICKAZgw//vf/vPTTnOjzdXIzJkUVRpL//7t5dZbWxJxBtpkIR4OQrXnCAeXXMYAPkAzZ870ls4GkVA2bdrUWzfSsWNHL7l0Oy0jBDsP27c3j5FGMjjnCBYWUbLCQNf44KF/5ZUZ/ypdP00YiZ+ZpdxuO5F3/zDS2MayWF4e8akMGRLA6yLiZBUKYFRiOdEWMtfFTk2FJHPbbQN4bUnCJZfhJZesI8GpJmxYIaP7gNkTHFHVzfffm5qV1onYMBXglqHIggQY52xg7pLim6O6oF7LrXbJknqy8sotpWPHDl7cSfwZRMxJPExc7BLMaOKSy4hDlWbWrFmB6MyRKWy66aay0UYbeQPSzhU2gnDR1Q4aEcno0ZV+RcmC9/TvfzdZomaPY8aYlkIGiFlZ2wfEshjisLXkf+LPXe7QLEDXihKksezjxPRWvYD69xdvHtSRhksug4Wipzotl6tr+f77/rqmiM5bcqk58kiRb74xz/fc03TxquU2puoJlQXjIOuoDp58UuSpp/w5XH7/+HoQdxJ/EocGMYZFXEx87CSy0cMllxFn7ty5gXQsgQoPA9GOiHPooamusQHJoB0icuONInfd5eu3HnsMy+Wcb43GyzRLaj+Km664jqTSyeVJJ4m89555zC55RkgDXoObDFxyGSzs39FrVLklsRFOLnGRfuIJ85hLDJfyapp7RoRDBxMIO3QlkiPZcJ/kXqRQZPjTn1L/DnFoUB1H4mPiZEe0qKJLXfygKpPtQ9O9e3fp1auX9OnTRwbX6OBuvfVW2WuvvaRr165Zj8nxnNtWxNlgA5Fdd/XbZeoE4SiNV14xa0cUksyttsr5LSSTX3yR+rVBg0TOvS+k5HKLLXy3Wnad5llYInAdMcLPmZmzTL+hO2pwyWWyzHwiuIbkww9Tu5RcatZZR6oOZi/xqQNMjJDtO5L/O9f1s3Tr//a3/ONa+Oyzz7y4Vv/bdttt5SUGl3Pg4tro4ZLLCDN//vyc7f7Ro0fLQw89JHfeeaf3fOedd5b77rsv5zE5Hsd1RBzbXAarT0dpzJpl7nLqi46dqjrzZoFq+157pY4+8i183Br9eWORBg2CTy5pNWr3khKw7t/MwQcfiBx7bGrHhDE0RxZsOZbbcxnv5JLPCwWZCIE8nY4d82ZwwglmFVA1wkgB7rGAd9oZZ1T6FTnChHvl5Zebx0yaDB++onqmrrh2ww039OJa/rv77ru9Gc0daYPnwMW10cMllxHmJ93KniedOnXyltcGfVxHBWB4jhYUYC/oXNGKhyStZ0+TLcI++5ht3zmgYbzDDiITJqR+naTNu1mSoLBTQP9yqcu8ipTG/vKLyZkJaOGoozKa3jpsXOcy3sklWQozl7DJJpGzXiWBonOp14ssXmFVA3svVYzBGmEEJI7kQb544ol+qEJRoWPH0uLPl19+WbbffntZeeWV6/y7Lq6NFi65jChUYv7Q0mcGcNsaNGiQ9O/fX57Syek84bhuADriNGtmEiL44QeRZ56p9CuK7x0PK1ftdODES3dfu44ZQIFDodQ2wMwUR8uf/+x3v5g7CwrapVruzZFc8k8jmZw61TxH4Zvk/XmB4ZLLeCeXH3/sR7ARm7fkVnzDDeYxctAHHvBlodUKub9dy0MuHGQtzhENKBzo7Yo9p+eeW3hcm87YsWNlH4rBeeDi2mjhksuIsnjx4pwJ4KhRo2TMmDEyfPhwGTFihLdYNl84Lsd3RBwnjS2dq682nV+NcjDwWX31rH8dExwah6oc33LL1O5lSnJp79YLUhqL+4fuD6FDo3aTadx0k8iDD/q1COYsqz2QLTi5dIqA0tEPBUqLcgwWRnTe8ttvRY44wn9+zTWRXr9ZVg4/3B9v59fHjLgjOfz2W6qdwXXXiayySuFxrc2vv/4q7733Xk4PERsX10YLl1xGlGVqs56Ftdde2/t/ZLB8+D6mmhvg8R0RgGl4DdaeftqXdTry4/nnzWylcs89WaM9KunIeI45xq+qH3CAGXncdFM+Zzk6l0Enl+nSWP4dabz9tsipp6YahpRr3C32uM5lcHAf0a59u3blsSd+913/cUQ6l7wN1AL1Et2jR+ocdLWDUMReTXLOOUbS70gGOAHrSm4ajSq6KiXuRBLLrGUT+3pdBy6ujQ4uuYwo9XN4li9YsEB+o1RU83jSpEnSHrlfQMd3ROiOPHCgeUynWdtUjrohC2TRo97M/vlPkYMOyvhXf/1VpFcv02mwpVvIfHScSxM3mog635iSXH70UbC/FVsKlCaNRSXNnKWKD0gyef2OPHHJZXB8/bVvilTFa0i4drz4or/X74473BqgdHbbzU86Zs/2jV8c8QbLAQRCUmNFwGhGthpTIXFnIZLYYo7vCBf3m4gojRo18uYqMzFv3jw57LDDpHfv3jJgwADp0aOHZ+aDRLZbt27y888/e/+PdDYTHJfjO2KAk8YWDtnigQeK/Pijec5jtjhngGprly7+Pjry+VtvNTdLeyzTjptnzhTfTERvZkF3LrffXmS11fzOZU07lVwZiRnmt8BsqAvSCsQll/Gdt+QDMGWKP9hlD0RXiMmTTScOuGUj+axjdW7VcsUVxkUUuMamr3lyxAsUrrgha6EToZD63BUa19r88ssv8sEHH3gbEPLFxbXRomGlX4Aj+wdlpZVWkt9r2yQ+rVu3locffniFr5944onef3XBcfP5gDsiAHaDVOep1qOFRP6MTtORGc2+tJPIe5VleznrQ5GvMSsF5HLMLaJGTseOm4mnPXUtA478Af7r/F742UFVThs2FOnWTeSRR0yrkgh2u+284Ez9uwhgaWa7OlGBuOQyvskllR3VU0Zg3pI6FmtHNLjGKZaPrSMzCKxOOskklvi64CRbx/Y0R4T573+NAZ6u5+b3WWxca/OnP/1JXn311YJei4tro4XrXEaY1bRzEZPjOkKCZMmeG3Rk59JLTUIGnOcY+OB2kwZ/ZZdd/MSScbE338ycWGZKLmtRaSw3S20nhrSShHvtsGHmKbWhe+81zRtHgbjkMr7JZcTmLUmUdA9u584iF11U6VcUfXAR1c4uXmtcdx3xgxqPPfePHDaPjSEurq0SXHIZYZo3bx54h5HjrZ7DLdMRQSiNq0aT5NL5uGfmySd9+SufG0riSFfTZDz//rdI797+7CSy2EmTMu/kyju5DNnUZ9GTY1NGSM87T2TvvYP9cVWDSy7jm1xGaN4S1QBGWsBsNpcbpyKom+bNRf71L/85LqN5Gog6IsSFF/pG5vvvbwzw8sHFtdWBSy4jTMOGDT032CDheA1y7PhzRJCWLUX23dc8/uort4U6m6vAgAF+lEIH869/TfkrbJ0YPFjk7LP9rx16qDHiqGtGqiLJZdu2tRlvg3felN9nm/0odFezjJA68sEll8GhHwbk4JyvVZJcIlLAWVq58UYj+XTkx9FH+5dOCnu6LcoRD5g6Yd2I1EyH/Oc/+X+vi2urA5dcRhySwcZYcAUAls5BJ6uOCkhj3ZKwVH76yZj26CxW376pK0g8EyyRvfYSGTnS/xqLvfG8ysfpnM0/urerbMml1b1ssHypdJOXPCdK5LCuPlQCLrkMDv0wtGljrCLDhMKRymKpBq2/vlSCJUtMHYvLjgpLKFI5Chspt925mdVbsMC9g3GAj+Hxx5vPAVCsZaykEIhDC1kxkgsX10YTl1xGlCVLlsj3338vM2fO9BbPlnYsDAcaStu2bZ2RT1xBd4KeCDBzwknCYXSiRHrTppl3Y4stRO68M8ULnT/aYQeR8eP9SuuYMcbhMV/VOX9Pb6Cff24pk+ks6kFCSC7fWsO3Yt+n3lhPikcj21ECLrkMBoym5s8vnyQWDd7cuX7XskKmdBSlXn/dNzG5+Wa3dqTYupkKcnDttpNNR3Shy6xeO3zsMbEqZjyLeJQuZqksXLjQi5OJl4mbHdHAJZcRY/ny5TJnzhyZOnWqzJ4923PV4mulxN6fftpE+vVrJ19+GXJl2REeZET9+pnH7DhV05pqB33o00+bx2usYQx8tMXoLWI2ieWMGX4HkhsjeyILReNnaj26MNpzMCDC1OQywOEhbPp7XbeL/CGmwttvteeky85uOKlkXHIZDFU4b/naa2bWDFAPMGfp/PGKx175xCy8zvA5ognd+qFD/efDh5vQpBhQ5LVr1y6QDiZxMvEycTPxcykxsyMYXHIZIRYtWiQzZswI7MNBdWj8+JbSv397mT69sRdQY//tiClOGpsKe0NoIwARCu3IDTes/WMWmWN6o+suN99c5K23vI0eRZF17tLbS1Kzl4CZ2ABgPhR179c/rizjZRfva83mf2lmSx2l4ZLLYLA/BIXq4kpNLiuwhoQmLSIJNdX65z/NnllH8bAp6thj/ZopTrKO6HLBBSKzZ5vHBx3kd56LhQSzffv20rJly0BUddqcIY4mnnZUDpdcRgQ+CLT2afGXStOmTaVVq1bSsWNHOfzwltKuXb3ae/PJJwfwYh2VgRacukaMG2e1z6qQDz4QGTTIf37VVbUL5gj+kOoMGeLPhey3n5GyMRpWLOU09Tn9dGN0AW+vkbqSxFEiLrkMhirqXFLrxcAHNQF07Wpk9Y5gEhbt/jITb2+bcUSH9983nUpo2tQ39CkVkkqSyw4dOnhxK/FrqYmmSmVdglk5XHIZAai2zJo1KxC9OBIDpAZrrbWW5wr7pz+ZBg8XA7jtNrcqMbZwwT3sMD/aGT1aqnbWCwMfSt2Am0ZN1YQvsWbkyitTre4ff5zFzKX92LySS2z0AlhMzc4wwCPloJv9uUuXXAaASy7jveOSvR9ltmYl6UEYAYy+c+l1plrBgDeTul9zWzvtNLeaJGrwe/n7332vATrMQZtDM39J3Er82iiAnT7E08TVTiJbGVxyGQHmzp0bSMcSOA7Hs0EOeNNN/nNkKAHEwI5KYNsS4hpbbbMFFGBY+PjZZ+b5ttuK3Hqrl3h//bXpKDB2CQR/mG1ce20wgWA5OpeYDx15pP8ci/fN/vZnkfXWM1945RV/QaejOFxyGb/kkoIS+z9gyy3N6pMywWfyxBP957ffXpoCwrEiJ5zg1wuYiX/0UfcuRQlCDTWxYnW0PXcZNMSvQXUcM8XDjvLgkssKQ3Ul28nfvXt36dWrl/Tp00cGs6BPkPudIQcccID07NlTrsuiS+B46V1QFIQatGL53aePMxyNJZjH7Lqrecz83TvvSFWBZ/0LL5jH2KZibNS0qdfUYJZSlXPNmok884w/zxMEBJSapKYklzW7KEtNLvVzqRtVmO/ydunRsa5ZSeINTU+YUPTPcLjkMjD0Q0DriQ9cmEyZUpF5S2LcQw7xRRJI7fmMOoIFhQaTDfZYQED1dkcAs8b8PpQbbshvfVfQ8bDy1VdfefHwgQce6MXBC+rYYZMpHnaEj0suK8z8+fNztu1Hjx4tDz30kNzJegUR6dGjhzzxxBMyZswYmTJlikzSwSwLjsdx00EvT9FX8xIWGVdb4ysRqDQWWNRYLbDgEXtBwMIcvXfr1l6nko6lOg3i6TNxojHzCRKUOioFIq6u/eygt9VWRgmOsVTvGSVVo4tbbrFWHOzjpLGBYe9jdBFscdA9RypQbklsmectkf9Nnmwed+gQ3JyZY0V69BDZfXfzeOZMf77PUVn4DGi+hykk+6IrFQ+b13OuHH/88fL444/LyJEj69wDny0edoSLSy4rzE+6iTlPunTp4g07o0nHsAdnrHyPy9wl81w6e8a+IgJYR8ygdK5DtPwSq8EVjQiPtoEyfLgs79LVm63s1ctfwL3zzsYIx1aqBonG0XQXv//e+gP9gXzuvv224OPedZf5T7ebkDczWlbLnnv6UsDnniv69Tu469U3lQJwyWVxqCw9wWY+CCR0dpvThUutteXIETAU0th1qQW1iy7ykxpHZaCmw2gJcO6HvYu0rngYF1hmM7fZZhvv+WqrrZbXrsxC42xH6bjksoJQUfkjx24QkshBgwZJ//795amnnkr5s99++03Gjx8vnTt3zvi9HDdTBWjjjf0gVs1Oqk1ZGXuQoPXs6c8iof9MMhRQ+PfqZ+Woo2TREcd4uSausHqaIyN98UWRFi3CeylhzF3iwodZgoLp1grJ8eqr+ztUOH41OwUHgeq6XHIZL6dYsrywKkcWJDW2QIQdjBVarVlVbLWVSM0EkPz8s1n34qgMuK5zX7JX76y/fuXiYcCgZ+WVV5YTTjhB+vbtK7czAJ0H2eJhR3i45LKCLF68OOcJP2rUKE/+Onz4cBkxYoS3IBb4HqQB/fr186ybM8Hf4fiZwE1TV5LQ9ELqoLsAHTGhWqSxnMOcoJpM7bij/PCv4bLPvvWkRinuwWLze+4pfqFzpZJLAiga0XpPZcaSJDkjOncJzz+f989wZMAll/FJLhl4rLn3ee50dcjgSoVb8hFH+Pv8+NhRhHWUh4sv9lUbeLV9+KF75ysBTQidumJMI+w1dnXFw7B06VJ59913vfiXkbGJEyfKG2+8Ueexc8XDjnBwyWUFWaYloSysvfba3v+3aNFCunbtKh9//LH3/Nprr5VmzZrJ4YcfXvTxr7jCrE2Ezz8X4VB1vBxHlEAmuc465vHTT6dpNBPEqaeKjB9vHq+7rsy88mHZcbcm8vLLfo7wwAMi551nzSfGJLnkPorJ1vTpvk9JzpkuN3cZHJqguM5l9JNLWvt6cypD+/DGG80lFVBBsIakjOa0VQ/1ct0hyq8dZ1LXdCov8+aJnHlm6mci5JpOnfEwsA9zs80285oqzFoSF2vTJYjjO4LDXTIrSP0cdywcsJC+6mOMe9q3b+91MkkyqdyUcnwuFA8+KLLmmub5k0+murU5Ig62pQMHmsdU5PhlJo077jB3NWjcWN499xHp3GMdbzWAmsWymaNfv/K9pCCTSwwrmK0ElogzD52z84oEHnmsDoQ5B7zicZ3L+CSXZZy3xFDrH//wn5NYZhEHOUKuKap5GiKNZ591b3c5GTbMJJhw8MG+0VKY5IpXlU6dOskPP/zgzVCSLE6ePNnbixnU8R3B4d7tCoIpD3OVmZg3b54cdthh0rt3bxkwYIDnEssH69JLL5VvvvlGDj74YG9FyaNZFkKp6U8uMLhkGbS+BKqF2iRyxIAkS2PffDNlEHHCgFtkh5O390ZMoVMnI9nR7nu5sO9jKckl2aHuomSJbB2ldl67HcTy66vzHklBgY414H739tsFv35HWnJZDWZYYaAnP+5TYWdfZUouMcAlkNZmNjLAv/41tB/nyAFFtssv95/TvXSqxvLAbYW5f8D8sVxNh1zxsIJ5z0knneR5kRAbt2nTRnbV1Ww5yCcedgRLveVuyrWifPrpp/J7CEvRmzZtKhvlWVFGUsicA6C05F5eo8h1RB20lBp80TFjOCLusFNk221rXVdf3+ZE6TL5+to/3ndfI4UNe7VeNoilv/vO/H+KMSy7T3QHJ3+B1moGqAgTI+sYKUmmulLWCYOmurAWh4ULLijln1K9sJMJuSVRbAjX30SzdKlxqybap8qj+3PCgmsBbtEEntg0h2TZevzxIjfd5J8e1LfCnuF2ZIf6XJcuIjpSh9KDdU2OcD/aFGzV5BF3WLrI1RQPO4LBdS4rDFbKlT4u8ekee5jHBMssjeYi44gB9twtjjZxh7YBjlM1WduHa+0mu02u2W0pIieeKPLEE5VLLEHvURh+6HL1FaSxdC8zwNjHoYf6iSXB06WXFvDD7eWdbiVJMLJYN9BVGOy31DZS2AEbP0eTVxZNhpRYPv64n1iSN993n0ssKw21hGuv9Z9TS3PGg+GC+aomlnhncb+ttnjYEQwuuawwzZs3r1MKUCgcb3WdzcoD1HbcTNUfZtw41xCJDei4+AVqchnnqgBBPu0DWgYUOhq3kd2/HyNLpJFnqHHDDSLXX480prIv046nWfZdyNwlKw10fgizEDqwBal18ILfbDNfv6Q6YUdxySXnnJtdje68JZ8jlS6HJIklV9b1F0BCU4ZtJ448YPuSumdzqWP3pSO89TtqpATYHZT7XhuFeNgRDC65rDBoyHGDDRKO10ATjjxBBkugq9+GTNY1RmIA0kt0ovDVV8bhJq6wrRkTH+af6jWVvy56TL6XFl6XEvdG8s4oUKypDw63SNCB+ycFHR3TLAhdSUIblMWejuKTS3COsfEw82EEIGCoxTG6rjUa1ukefXTgP8ZRApddZrrJKo1VQzdHsJx1lt8Z5jPRtWv1xsOO0nHJZQTg5G9iBzslwHGK/XDuskuqRA8zUrerPWbS2LvvlliCk5S1SGvw8jtkimwtG2xgZm7sLRyRTS7tede05BKVL01mdUNHiq7ePAVjvxmuAlQcLrksngQ5xWJWglIHKPQgCyzHSiNH/rRuLXL66eYxIgN97AiOiRPNOD+gIGVVXbXHw47ScMllhVmyZInnDBuEFICqT9u2bUs6FuYiBxzgG4/07esMFSPP/vujJzGPH35Y5NdfJVZ88YUs79OnVp54hZwuD8jBsuOOxlVVVaCRTy7Z66NOWFZyyT+rf3/j8aNjk3lsEsoOJWUt5Y8d62YGi8Ell/FILt99N7TkElW5fg65ZTJVoKu5HNHijDO8NccezNxrQcBROtyfLGN2T7VWSUNH4lfiWOLZUmEf/NI4jwrFGJdcVghMeufMmeMtgJ09e7b88ccfJVdo2PfDYtlSYLaNtQh0jIDxN3uZriOCYGmoyx5xmHnkEYkNv/8uyw/qKfUY+CBXkr3lbLnM6/IRQGQxXK0oWdeR2NLYOXNEvv/ee4gUVlf80B1h/U9JK7f4fav9Os66WcyDHDlwyWXx6EmP1EyXEYYBbf4pU/y9WWusEdihMZ3lGqPjtmefXZ5dfo7iwMfJVlXhYOpyhmC45Rb/Y7bVViLHHisVhziWeLbUDubcuXO9GJtY2y3GKC8uuawAixYtkhkzZgRywlPladmypbRv377kxFJh9pmF7nq4667zl707IkocpbHLl8vCw4+Sev9nuhMzZCM5WO6X8y9oIPfeG123RhJeNa3MmlzCxx/LU08ZEx+gEDtmjDHyKRlbGkv30lEYLrksDu5XetKT8IW5O46foyqMgOctccHUf8b22zsDuziAy/Y225jHbBG6665Kv6L4g5rGVtHgmFxpwzyFeJa4lvi2FDWeNnKIuYm9HeXBJZdlhpN75syZsrBEEwkqOq1atZKOHTuW/OHLtlrMtgHHTW/69EB/hCNIWE7Vvr15TMsvBsOy3w+7Vpr8917v8a+yivRt9JjceN8anuV8lOeeeG2qBpw1K81s1Eouv5/wP88YQWGOZaedAnoRauoDbu6ycFxyWRw43/z0U6wlsfffb9Q5uiQeYy23Xz36oPawYxKSop9/ruQrSobcWD/OxHiMokQJbZ506NCh5NlJYm5ib5dglgeXXJYRKiizZs3y5iyDYM011wzVBeu444x0SGVEjMW5feMRzng0k6G7gPYywrx/zYuy+mW+M8OJze6WG17pVHu+RR2Nq/kof/GF9QfWgOiL//lfrfter14ip5wS4Atg5x+dI5gwQWTBggAPXgW45LIqzXw++yxV9kenxpa5O6IN4+asQdauG06yjuLgtqEiJ9RqqrCJIsS5PwdQSSD2JgZ3EtnwccllGUH/XWrHUuE4HC/sfOW220Q6dvSlKOVequsoUDekcNeI6HL4R66aKesN7ScNxFin3rzWeXL+lF7BdfUiso5kzTn/q/27OPEF2o3lYNq95Jry6qsBHrwKcMll1SWXFILYmagxKm7o/OeIFyhAdGSHTubnn1f6FcWPxYtTTXyYZ42yqWrcYmeHSy7LBhWTbCd09+7dpVevXtKnTx8ZXLPN+YwzzpDevXtLz5495aKLLpJlusPAguMF1QXNxqqrmnnLlVc2z1lDOHJkqD/SUSy4MKnRyyefGDvECMEp/K/Tf5X2px8ka4pZLPdmiwPkkGkXyIYbSqzImly2aCF//Gkt7+Gf5X9eDsPnB3v3wHErSYrHJZfRTi4pjGlySdRb1ELYVC680KxcAK43LIl3xA86zaoCId9whoOFc8MNIh9+6I9AHXWUxDJ2riuOzkY5Yudqx3Uuy8T8+fNztuJHjx4tDz30kNxZs2zo/PPPl4cfflgeeeQR+fHHH+VlNrCnwfE4btig9MNRTKHi9cEHof9YRzHYQ34RMvZBtdmv73L581VHyBZiTp7Zq3eUbT8ZLautHr/LULbkcupUkXd+M93L9eQbufXy+Z4DXyh062YcO8GZ+hSGSy6jnVx+/TURoN+1LLHtj1vzJZeYx3xkmLNs1iyA1+moCMOG+Z02TNJef939IvIFg3F8DYCPFdLwEKerQo+dc8XR2ShX7FzNxC+qiyk/6dR0nqxKy1Cw214qi9EwBHTcUhSXWt1i7pL5SzdMH0H4xegORJwrIuCO9u23IrvtJrLxw5fJ38TYDi9cqZm0mviYNFwjnhFepuSSLTDMA32wzJfGHratv+8ycGiHYuSkWS3uQo78cMll6cllmMOKAUpimXtG/qriHzqY+rFxxBMKAxddlLqaJIO4y5FllzkeGkBM17lztN+msGLccsXO1YpLLssAVZJceyxxxBo0aJD0799fnmJ3QQ2nnXaa7LbbbtK0aVPZPcsSLo5bruHk66/37/PTppkLU0TH+qr7rtuzp+/s+PTTFX05771nrP5bvP20XCzG83x5vXrS5KH7jClNTMFLRy3bibf5HNDRZ+Xk/8RPLut9HGJyCW4lSXG45LK05JJ9PFitRjy55HN59NG+eTZFLiejTAZHHinSqZN5zAQI3WhHbhDAUXOGNddM3R0ax9g5nzg6G+WMnasRl1yWATqPuU7iUaNGyZgxY2T48OEyYsQIb+krXHPNNbVy2DfffDPj93LcXJ3NIGHvIPsvdX4MOYqbW4kgEZHGcn3v0kWk6ZdT5T45ROqL+QzUu/hikf32kzhDYqn744m3R4zw3+rPVrJ2Xf4v5OTSrSQpDpdcFg6SFTR15TbzKWHHJR4BuqMZR8x77om2BNBR2DX4mmv852edZdQjjswgYjr+eP857rAkmHGOnfOJo7NRzti5GnHJZRnIZMZjs/baa3v/zx6frl27yscff1z7Z40aNZI99tgj48xlvscPEmIKe3nxaaeJvPVW2X68Ix/23FNknXXMYzqX339f1veNe8F114kceKBI/V9/ksflQFlNfvZlu2efLUlA42sCmhNO8L8+5JoyJpcE3hohvPSSsQF01I1LLgtn5kz/cbl2XNIdLfJn4Wl28smpieb66wf0+hyRYK+9RPbf3x/TveqqSr+i6PKf/4hoaImaqA7Pm0hQSGybK44O4viOwnDJZRmoz/bfLCxYsEB+qym38XjSpEnSvn17+aamQszM5fjx42XDHHaauY4fBqguSSqBWPZvfxOZN6+sL8GRC0rz6rHPL+iBB8r2fvHj2I/KDMzyZcvkHjlUOkpNBXHzzU1lItCdHJXDjnl1tBVp7EHHthJp3rw8ySW/ayIsYAh60qRwf15ScMlldM18uJno8tgtt+QGV/AhcBE95BB//esxx/jTAo5kQUKpIwqsKSHJdKTy1Vci//qXeczHCROfMoeNRZFvbJstjg7q+I7Cce9sGaD7iB48E/PmzZPDDjvMWzsyYMAA6dGjh2y00UZy+umne2tIsFVeZZVVpG/fvhm/n+Ny/HKDpEL3EhIHoMR0RaDqlsZivoba9dZbzfML5ALpIU/6mrTHHjO7bRJCup8Jlu6eTIvPuu67ZNgrbOcrt5KkcFxyGd3kcsqUkiWx55zjK2vZ02zLJx3JgtF93dlIMYHfvSMVmgEqGab4W4LSPDKxc11xdCcdyM1CpWLnaqGm3uMIE07ilVZaSX5nZiWN1q1beytH0rn33nvzOjbHzefDFzR8Jh980HgtoLp85hmRyy9PjOIx/nBh5Q6CvAy3AyQim24aqmIOeZIqUf7W8BE5f0mNnR/VQQZ0w3SXrAC26kbnkWtzFpLLN97w9XnbbRfeC9l7b/8xK0mYaXXkRrewQ0DLuRNPuZLLEs18+AhoMsmvGeGG7ml2JBNWazBPizMwtdQTTzTFPofICy+YexOwvsV22Y1z7JxPHB3F2LlacJ3LMrFaKFvUwztuPjC/Qg6sn89zzxV55ZWKvRxHhbqXr71mZjg02erS/EO5v5H1s6+80syBJgjyxlGj/Oc77iiywQZpy2GVsKWxzNdusYV5PHly2WdsY4nrXEY3udR5yyKSyzlzRA4/3H+OTBJlrSPZrLGGv7sRvLEMZwTq1c1sPwBuxYiI4kQSY+dqwCWXZaJ58+aBV0k43uoVvlLQNDn/fPMYWWz//iKzZ1f0JTmUgw/2h1FGj2aAN/D3hsN26+bnM9tv/IOMa3agNPi9RoMzYIC50ycI/q39+qW+nSs0v1QWW47k0pbGElFRqnbkxiWXxSeXq6xiVpGE3bmk7Wh/juqAU3/QIJHvvjPP991X5KSTQnqNjsiBNHaTTfyCp7oEVzN08FkbBzvvbPaVx42kxs5JxyWXZaJhw4aei1WQcLwGEfBVP+88vzHFjZ2cZsmSSr8qhxcAEmHpRH+AbWUKCfzeuVmpmU33PZfKhNYHS6MvalwlkeXefntiDHz0341XEm+nra60mzoVSS7dSpLik0s9gR3ZoZLy+ed+1zKszzSDYVMtA7ACZqLYw/zss+YxxpEjRybq0uOoA06Vq6/2n59xBrsMq/dtmzXLl8ASJsbFxKeaYuckE8NTLb5wQjexg5oS4DhBf+CKhc8o8th11zXPyWFsiYojItJYW8dZAow/UECwR/uOPVbk6S3Olkbjnjdf4Nx89FGRpk0lSVxyiZnp0txdJXcUVX791fqL663nL5n/6KPwXxhlaR0se/55pwmrC9e5LAyMqXTNTZiS2Pfe88/dAiSxfBvJhH2pC7O56ogmGMppoZtaCOs3qhUEQzqqiDRWJyfiSFJj5yTjkssyQiu+bdu2XiWmFPh+jhOlYWRu5Bj8aDHo0kvNikVHhcFlR9diMPCekgEVDpLn3XYz/jzAKXjttSI3db1fGlxzpfki5zeapDZtJEm8+KJfNKECfP/9qR5J9hrAFMdYopywt3tz4919d/+X9P774f68uOOSy8SY+eAQSrFLG9A4Y9qNfEf1wGUXKah26CgGqky6mqCDT20XWrXy15DElSTHzknFJZdlZMmSJfLzzz+X9AGh6tKuXTtpbLsdRoQuXcyKEgXJJNIMRwXBxpRBWI3CHnmk6EN98IEx7nnrLfOcrSJPPCFyyq7/J/WGHOn/RcrFu+wiSYLdaezN06bKhReK7LFHapydUxqLY2zYuJUk+eOSy8QklySTaia21VamsOmoXlBTDxliHv/yi+8JUS0gBcYt194DmgTvGmJeYt9SOphIYYnBicUd4eKSyzKwfPlymTNnjkydOlVmz54tfxQxCEClpWXLlt5i2CgmlsrQoSIHHWQeYwvOek430hR/aSyrZthrqrvNW7cWef11kf23n2t+4aq/OfJIs0grQaAGJD+fO9c8Z4xVV+7knVyWe+5StbuOzLjkMtrJJa2nPHR8dGd0ry6qcNQEAannHDGG4p9OJYwYYQqj1QIOyfpx3XVXUxRNCsS+xMDEwsV0HxcuXOjF4MTixOTE5o5wcMllyCxatEhmzJhR9IlMlaZVq1bSsWPHoj9Q5YSXd9dd/kpDulz/+EelX1WVs8MOIhtvbB6//LKfIeYBpyxGGQcc4CtqO3cWmTRJZItNF5vqgR6Pn3PjjYlz0Rg2zLgPalLNPjWVXUUquWzfXmTDDc1jXnCJEuhE45LL6CWXVCE1C+jQwbjS5gBTLe1QqWCiY8dwXpojXmDoxHVbTdiqZTUJoxmXXWYeI5BL4O24ttHSoUMHLzZmX2WxDR9ic2J0R/C45DJEOGlnzpzpVUuKZenSpdKsWbNYOVsx4mcvlB8+3J/Rc1QA7i7aveQOi/tSHqAcwQjg5JPNDRr69DGGTaxW9NrU6kDLF5jpTFjbANkvu8HUjZDzeM01/T+PVHLJ71mlsbRb3dLZ7LjksjD05CZiDWuWms+ImgbVIYnFvJaxix9+MM979zaiCYdD4b6ltbaXXhJ56qnq+DerMO6UU1LXLScNxsuIjUuRuBKbE6O7BDN4XHIZElRGZs2aVbK2m+/nOHFr37OFwnZq48avDvOOCsD+DFsaW8f59NNPxgsI+3KFSjCmTZ4pKe1pqgaATJtZTrULTgiffZa6kJ0kk+aszVpr+fKrFZJLgnB1cC1HcgluJUl+uOQyf7hW6Mndtq2/O7eC85aXX+7XTlATJGzjkSMAaGghEVVQUCW5SfXkk34Cza046bOm1R5jRx2XXIbE3LlzS+pY2nAcjhc3jj5aZMAA8xiFHl0vPGUcFWCDDcwABpDlv/12zqSK+Uod26NjRz7K6hFPDoomlt0jChloetYVc6j+cr7On2+e8zjTQnYCWu1eYl6ljRcP3iy1k0WvpHOpYYJjrAb/bu4yO/bcekDX6cTy/ffGGaWcZj5UJ7PA5UcDZz5io0eLuH3ojkzQ0e7a1TyeNk3k5puT+T4RV9n3JxzcteiZVFyMHW1cchkCVEKyJYPdu3eXXr16SZ8+fWTw4MEpf3baaadJv379Mn4fx4ubwxWB9y23+OrADz8UOf74Sr+qKsZuw919d8a/8sYbxhFWG21IQJEU1XoCffutSK9efgmYX2gC9WjM6Lz7rnnMuOodd2TvjGi8jVRvhXFW1SWhKya6CZtmzUxlAGbMyNBOdXjwy9QE0yWX0TDz0Q+c2r5m4OefjUEJnzVVUyTMmNoRwmoShZUcKqVOErj0s/EK2PP5t79J1cbY6fz++++y9957y1XY5uYgjjF2lHHJZQjMnz8/Z4t99OjR8tBDD8mdd95Z+7U33nhD6qtLSAY4HseNG6yrYOWhejOMHCli/bMd5S7jNm1qHmOrmBZU33efWa+h12w8Nd5806/8en+fY3zzjXlOVEeJNGHwPlAUUWkV5y85WzYiNXeZvpLEdS/rlsa65LLyySXFl/fe86W3a6yR8a9Ry9J9sjvumHzpn6N0tt3WL47iYB/3nY/pTJ9uZOKqMmJaJekS8bpibJvbb79dtsjDeTquMXZUccllCPzEwFoBLF68WEaMGCFHoyMN8LhRAWXgbbelBggaRzjKCBlSz57mMeVb9ovUjFRdcIGRMGuc3a2byMSJxoC09i+xPIsv6qATrk3czRIE+/LsjyFue3XdlyKXXLqVJPnhksvoJJd02dXdOMu8JfJX/tNLGb5kYY1/OpIFu091/J0pjnKsHS4HeltWIREee9XgmJxvLMws5WeffSZdWMIe4HEddeOSy4Ch+pFrjyU2yoMGDZL+/fvLUzXT13fffbf06NFDVqnDep3jxnXoGCmTjunpPJv7HFd45+Xdd3u/C5JKu5pLcvXss2lzTCyTwzVD23ksmGvZUpLEb7+Z85L/h0GDRNKU66Ullx99JGUBSWGLFubxuHHJdrEoBZdcRie5rGPekm7l3//uP0dZoE6gDkddrLeeyJlnmscoH5OyHo3bsIpTqPeee65ItcfYNkhhT8ZCN0/iHGNHDZdcBgxdyFwn56hRo2TMmDEyfPhwr1v59ttvy8SJE+XAAw+s89gcl+PHFRSUGjdQqGZUz32OywwDGd4eEZHlTz8tB3X53lPIAlKaq682gVtKQ3LCBFMeVdhKvc02kiQ4Dyl+aHNx881N1zIfciaXGCnpHq5ydS6R12v3km6QdpsdqbjkMj/sk1oXGIc5b5nWueSWR3FSPYWojx18cDgvw5FcSCjXX988fvppkRdekFhDEZR1I8p119W5GrYqYmxl3LhxssEGG3j/5UvcY+wo4ZLLgFmmCwGzsDbbfYXGQgvp2rWrt8T1008/lX322UcOO+wwmT59uhx33HFFHz/KEGOjpFxtNfOctYjXX1/pV1VlsC+1Zi1JvcWLpf3kB7zH3JQeewxTqbR5jS+/NO08HXRHd6MWwAmCpqxK7nDZY85SZVR1QcVYk/EVkkveb9UpUVEp13yfW0lSNy65zA89qVu1Ci96zbGGBMk+DrFayLnhhnBegiPZcD2/7DL/Ofe6OPu34N7O7VnH7HXiJenkGwO///778uyzz3ommldffbU8/PDDcnMedsFxjrGjhEsug35Dc5jyLFiwQH6r0dzxeNKkSbL55pvLyy+/LGPHjvXksRtvvHHOD0Cu48cBCt+2USnVRExjHOXjtXa+NPYwuduTDL32mkiPHml/kdUZ3LHmzPG7ntjSJQyaJnZjFmfYTTbJ//vJH7U4inxvhaKqSmOxuMR9oRzsvbf/2Jn6ZMYll3XD/Wr27HAlsXxgNLlEam/ty2WXpSYEzFeiskj6igVHeNAB79zZd6/nWh9HmBlFZQSYXlOkT7qJT6Ex8CmnnCIvvviiF1sPHTpUevfunbNxU+jxHblx72LANGrUyJurzMS8efO87iQn+YABA7w5y06dOuV9bI7L8eMOSczpp5vHVA779uW9qfSrqg6Qeu56fCeZLEafvJ28Le/e+/GKzv+qE5082TxnwOmBBxLnoIE5HI1ZHUskySzGxl3jbmLx776LgKkPQbp2gAjcV3hRjtrkkl++0+dnRq1Zw0wuv/rK7NIEztma+yeeY4gs9FdDp0YTA4ejGMgbbIPz886Ln/cDn4cTTvB3Kp9xhlmXVS3kirFLJSkxdhRwyWUIJ+dKOmOVRuvWrb3WPP89+uijMrBGnqist9568uCDD2Y9NscN60NVbi65REQNvJB2HHqocaN3hANJPIkTNyXe57vF7162fC7Dzsv//MdvMaMnevxxs/QyQXCTPuIIkc8+M8+32w4DgOKOFTnH2PSVJM8/X76fG7fkEpzpUTTMfGoKInw2hwwR+fpr82VWJGlB0uEohZ13NmMODJ8AAQAASURBVAVtYO0WTrJxgtEidk8Dipmzz5aqIleMnY2DDjpI/pGHi1OSYuxK45LLEFhNhwpjctxKQHGIRpiaWuJOas9DOIKDxeN0i+1ZpRYnHizLtQvJsKFuJQfuXPaFeNQo43CTMFiuzZwp4Iw7ZoyRGCUmuXQrSfJPLt2uy0gll8xA44QJ1LSoczm1miMo2AupH3+McOwGfZTB1OrUU1NrwPl6AyQJF2NHH5dchkDz5s0Dr35wvNVTdkPEH2b9WFivbxULsdmc4AiOzz83lVqSd03q77xT5NzrW0q9fff1ZWkMNwFtPMq6mmwOG2Z0owmDGVO1pod77jG724slZ3LJH2rWWs7kki3zq67qdy6dNCAVl1xGcg0JHxHbBZO5OO4VDkdQ0PHD0EdFC0hL48CFF4p88415vP/+GXwSqgQXY0cfl1yGQMOGDT032CDheA1wDkkYeMTojkViXyzm9eLpKA2Mkrbf3hgXwBprGPt1pKAr7LykO8nA4EEHmWEn2G+/1AWYCQF/on79/Pz5nHPMP7UUciaXdIg7dDCPp03zh2XChoS2Wzdf/zVlSnl+blyw29Suc1m55FLXkPzpT/LHuu080xW8xAD/jTy2dDkcBYOctMa833OuHz8+2m8ia5LpsgKqULqW1YqLsaOPSy5DgmSwiV0ZLwGOE3SyGiVojqmCj8CfBDPOFuFRAMnxbrv5Rq8M/JNs7rqr9ZcofTZv7t9dGXx9/33zHLvUe+81VqgJgoSSTSpawOA9CiJ/ttf/rZBc2tJYEsuMfyEk3EqS7LjOZd3ouUoHfK21JHBwctN9ClttJWedU1/ee8//yKgjpsMRNLgOYxKl0MmMqriDGeTjj/fjIhLjsFbOxgUXY0cbl1yGBDLWtm3behWWUuD7OU6Sh4yZpWHsTxccU0E899xKv6p4wk0I6QwJujZjdt/dJJYrOMpR/uzf3zxesMAfcuKui4FPgmZ8lYsuEnnxRX9tH6sNgjDAbdrU36CQM7kEN3cZDZyhT26IZGfN8ruWYdyDLEnsZ6tvXduN4VdDgYzPlcMRFqh4ttzSPMYYnfGIKML40Kuv+h/FuMh4w8TF2NHGJZdhvrn163tzksUmhnQs27VrJ42LdRmJERTFMVTRQJ+B+yefrPSrihd//GGaj//8p/+1I48Uee45I4nNiC2NBc5VOpYdO0rSYOyQxFsLGiSWJJhBoapBFKgYL2RNLtE3lQvK2+3bm8dvvGHcnRwG17nMzRdf+K2SMsxbXvOyWY8EuDYn0EPMETEQ5mDspjAi8euvEilYlWL76w0fburCDiYbGnsxcrEqQfUycbstg8cllyGwfPlymTNnjkydOlXmzp3rPS/0hG/ZsqW0b9++KhJL23/kiitS8x5dE+HIDQkN43XkhZoj8l7iupjzFMKK0bZhRBt0wAGJe7vxLEIOa+/MQxIbJJF0jLVXkpAoOMcsH5dcRmfeUkRe/cU4xTL/jATQ4SgHrLlRYxzGJewYJApQLJ492zzGEkF9+BwGYmRiZWLmQhs5xObE6MTqxOyFxuqO7LjkMmAWLVokM2bMKOpE5YOBjrxjx45FfVCSAC6BvXr5C+4xLnVeG7khV8G4h8YUYE3+yCNmL1zOU4guFncre9Aka4szvjDmiIGP7mkneLWdYsuSXNI91LZ8uZNLt5IkMy65jIxT7B/SRD6WTT0lwV13haPAdTiyQaccJ3V9rGPAlYb5YzqVgERcDX0cmRsyHTp08GLoYpJMYnZid2J4R+m45DJAOClnzpwpC4vMhjjBf/zxR1lq7xysMrgmsCpDY5l33vEtwx0rgvsrHV/t8DL3N2GCyRlzQkJJa/jjj1O/zkK5hFXvzjrLT7xZNxLWzrycySXtYx16nTq1vI5VtGg1cho7NnG/36JxyWVlk8tff5XluCeLyIfSSZZII++zmWDvOkdE4dJ8wgnmMU7FGOZUGm7RdPC19osPRSnrsqqBZcuWeTF0sR1IYndieJdglo5LLgOCk3nWrFmypMSgke/nONXcnsdH5qGH/NjvppuMuYMjlZtvNhIZHaNj//hbb3mr4uqG4UNMewDH2O228xOft99OzFuNR5HO1JBfMdcbVnM2Z3JpS2MpPpVT743TZ9eu5jE/d8aM8v3sKOOSy4oml79Pel/q1dzn3pW/eHNle+0V+I9xOPLivPPMlAgwXsK9tJJgLvT66755+9ChlX09UcfF4NHCJZcBgW672I5lOhyH41UzW20lcsMN/vMhQ0Q++aSSryg60NhGPvz3v/u7GulU0rHMa9n4Y4/5+zdo4ZG5H3us/+e0DxISGw8a5D8nydQcuqLJZaWlsTg8OVxyWRd6EiPnbt068DPm0fP8ect5rbeWSy5xJ6Wjcqy+usgFF/jPTz21ciIPRoIYa1GIhQLabJdYXAweLVxyGQB0G7Mlg927d5devXpJnz59ZPDgwd7Xhg0bJvvss4/3Nf77MoPAn+OV2gWNOzidqpnpb7+J9Olj/r+awYWUpeL2AmVsyVlTucoqeRyApAZLWeXf/zaJR+/evu8/NqoxH3TFOfdvf/O7uszuhm0SQkdUt7dEOrlEGutwyWUuiKr1JN5gg2D29VigTPl9ou8UO+CqrXMbjzkcZeCYY3yjdEYpULpUAiSwGlJyH3Md/eJjcOXnn3+Wfv36eTF3z5495SEuQjlwMXhpBHvHqFLmz5+fU8Y6evRoWRmXFYuzzz5bdk3ZaJ8Kx+O4a4WxuDpG85dIYtk/xfYG/jvuOJFRo6rT8IHNAPvvL/LBB+Y58d4tt5gkPC9+/NFkpuq1zjJM9Thv1kykZ0+zUOuHH0SeecY8jyknn+xvOUBSNGJE+OcMx6d7iQEmvyt8AVIC5s02q1xyucUWZu8KtoMvv2yKB9VeCney2OzMmeNX8gKWxFJLPeookZfEfECX1asvbfbfItCf4XAUA6MTV19tTN+0cIuTbDn3rXL/YOQFKBjbq1IcxcXg5r1cRUaOHClNmzaVBQsWeE2fPffcU5ozFpQBF4OXhutcBsBPLCKK0XHjBBdXunKMjekcAolCtcH8B5JOTSyR8LC3Me/EEv3sIYf483YMaKZnXIcfnghpLOfIbbeZxwQFFCj/9Kfy/GyNwzFh0P3ztZDlqpNQuZNLfs/avVywQOS118r786OISy7LPm/JZWjgQJHf5i+STvKh97V6m3Y0FtcORwTAx0AvlRQJr722fD+b+wbjLmriwxqS9dcv38+PK/nEyg0aNPASS1i8eLGXPNaVkLoYvHhcclkinJx/oMHLApbIgwYNkv79+8tTTz1V+/WrrrpKevfuLdddd11Wd1iOW83GPkqHDqkJ5YknpuzeTjz//a8ITe7vvvO3Wrz5psjuuxdwkGHD/Fk7uuE43aQHdCzKXGcd8/jpp/3dHTGC7rY9PkoFuJzL2HPOXZLM8MsDXHrtFTDlwEljV/x9KDGXgcclubzsMpHx40U2k4+ksSz2vlaPQpfDERGow9G9bNDAP2d1z2TYsIZn0iTzeNNNjQLHUVoMni6NJe6mY0lcvjpV+hy4GLx4XHJZIloBycaoUaNkzJgxMnz4cBkxYoS3rPWUU06RJ554Qu677z756quvvD/PBMfl+A6zp1Bn5ogDmUNIemOX0wqTC+YF9dpJkkliSRMsbx58UOTyy81j7phkq5k8zfkz2grAeRczi17Uvszl0pgDurp2M7Yc5G3qg9/9Cq3NkGFwRzvVbu7SJZdlTi4nTvQNU7apZ1UHXXLpiBhMMBx9tH9fYQYybObNS92/fOONaWMVjqJicJtmzZrJww8/LM8++6w888wz8n0dBXQXgxePSy4D2KuTi7XXXtv7fxa7du3aVT7++OPaJa9NmjSRHj16yIcfflj08asJqomdO/uxzxFHJHdlHwk0iZF9U8P5FCms2qXnvYWZN0pB48Pew2yog1LMpLGcBwQD6ii85Zb+8ulyUpBjLG3WckLHetttzeP33xf55hupalznMjszZwaaXFIIRJWvIp2jtrWSy7x2Jzkc5QVDdTVoY/f2lCnh/jzERSSYaodQkDKpiikmRsbLpEOHDvIuA64hHN/hksuSqZ9jGztDw7/VmCLweNKkSdK+fftaVytO2pdfftn7WjHHr8ZYkCavKhlQdl53nSQOiml77mlmBxWkOdzgCqpkciAMfOiSAUmmborORqdOfrDHvkvkmzEAYyNMboH5Spqz5TRhiMU6knRpLJWKasYll9mxT9527Uou/GDE9vnn5vnOO4ts1/Dd1L1TDkfEaNHCL+5yDoe5moRbrfoEcP+66qpwfk4SyTdGpkup8fgvv/wikydPlg1wwg7o+I5U3LtWIo0aNfK6kJmYN2+eHHbYYZ7Ge8CAAV6XslOnTnLWWWd5TlV8nQSTP8sEx+X4Dh+uBXZDDTc3LMOTAp237bf3/VZIkDA0OuusAt1OWWODnlall7gBYb2bz0Fi1r185x2z99OeW9l448q8FvaMagEg8slltUtjXXKZHT15mcEu0WyHIpkWfugE3Xv3Uqn3/nv+Bb2OuSeHo1Lg76AFw1deEXn88eB/Bt18THw0caVjuu66wf+caozBbb799ls5/PDDvbib/z/44INlkzrmi1wMXjz1ljvHmJL59NNP5XftDgUIzlYbBWwDnxTOPtusaNSAHoMfKo1x5sUXzcygzpIS1z3xhK9kLAjKrNrWZQUFGRhvVL5rCPi7JKhY1dFyUHeDiMF2FRqt2hUhySynu18m2JM2daopDFAoTbnvMRCK9TGRBBpvbIDLCb9T5LGcZOircYmK6O82dHjvqeRoFHn99ZV+RdGAITO1V+7SRWTChKIPhTk1I5W6/Ygx7n5bTfWXCbLu6JFHgnjVDkcooJDq1cs8RmTGNEOQs5CobujsA+ZzKDUDXiubeFwMHj1c5zIAVlNhfkyOmwQuusiY28DXXxsfmiymu7EAScw++/iJJUoxYt+iEku6jZpY0vmm9ZlvYgktWxo/dvjqK7MXMYIwCsFcqiaWO+zg+xZVEq0HUW/69tu0P6QLtOGGfuey3EPDRC24AgMDPiyRrVZc5zLUeUv2vDJnqYklqnyM2VKsvt28pSPiHHSQH2tQLLnhhuCOzYTUOeekmvi4xLJwXAwePVxyGQAsYc2nLV8IHK8um+RqhgswUqsavyRvfAxn1bhBQnzaaSLHHOMnxwccYJoFRe23YnhDbe70brXTToUfJwbSWOZSnnzSPKYJxzxuFNz18p67pK3JRvly46SxBpdchuoUy44+LkeATL22MWybaDinWEfEIbRDDaMh3oUXBreli3EX1Dd6y+3aNZjjVhsuBo8eLrkMgIYNG3oOsEHC8Vj66sgOslESTJ23xuYeaWlcoKKPKsyWcQ4damQ4KCcLhmVcHFB39qG1Oeqo4l4cGW7z5uYxnU9tP0QEduVpxZeb/ujRIq1bSySw/U/c3GWEccllaMnluHG+igDxBNfp2mua3bl0yaUjBnCa4tYOqIt0pU6pq3kw6QNEaldcUfoxqxUXg0cPl1wGmAyyWiQIOE7QyWpSwa6bSiKgMESGhUw26tCwYpxJO290YpHG0o0rqqaABo2BTf3Hc/BSrHQ5l/v39+cESTAjAmOCvDTt9GLhjqQ4KtTZuWSJWiVNfdhxqjNvLE2dP1+qEpdchpJc0tU59FBf8Y2iZJttav6QL2pyifyeCqHDEQM4j1dZxZ+TLOXSzeg7Jj7KxRf7KixHcbgYPFq45DJAGWvbtm29Ckop8P0cJ2iZbdLNfXREkBkG5noWL5bIglQM81ZWUGrV8rnnim8yepx8ssjrr5vH6Gkfeqh0jWgEpbEklBQQdJZxjz2CqSJX1ToSWxrLG/rSS1KVuOQy8OSS3HHIEH+FKiuVUGPUwgy3LvNj3tLd5xwxgToIMla9bP7jH8Ufi+RU92bir3DsscG8xmrGxeDRwiWXAVsilzJYTMeyXbt20jgKg2MxAlksdvcqiyTHsofkowQNQMwBULCqhJLmkXqsFAUtT+5WGjCjqw2iDIpDju70wNTniy+k0pBIIrnTm/1990XP7FT9erIml9o1jEJyWc0rSezrrErJHf5J26yZGWYuAC5Duq4BU2JqUilr4ty8pSPGUCjROOPZZ01RuBjlje7PBDaEOROfYCB2JoYuRUW4ZMkSmT9/vrhFGqXhksuAWLRokcyYMcPbbVkMa665prRv394llkVCDPTf/5r5HkBeGsZOqmKhon/ZZUa5qltrGN6fNCk11ygYMukTTvCf3357kRazGaCroN1L/gEMNlYQbuTIh4CE8sEHoyklYgWJmvNmTC4ZPkOaWinHWKDCoTdgkstKvIZKYwcgyModRvKhu3HpWhbQWWRFA+ZkCvNkK6he3bylI8ZwbbcdyUk2kbgWAru51RV+8GCRHXcM9jVWOySYxNItkd0XyZw5c7x4nrjeURwuuQwATsCZM2fKwhKq3z/99JMsjrKWMwawso6kUmFNhe2qXym4PnETsbup5GwvvGCq+0WDxKx3b18DzG5Lhp2ChB0vCm2ICiUhzKjaL+XSS6PtrKdqQmpNGkhklMbyh6ohLCesRNllF/OYjvQnn5T/NVQarUSB61z654IOMxcgif3jD5GDDzb/D9S78ARbAbeGxBFzmPdH1KO1QYRD+YILvE6YsAxAd3U7gpfI4iBbiikm8TxxvUswi8MllyVC63zWrFleK70U+H6O41rxpcEu9L/9zY/beawBTyUgudhrL5GRI1ONAXhekv8T/yg2O6Ox0eHDMOzmNthAZLfdzOOpU/3dAmVOzvv29Ue1CFpLmXcpB7Gau6xWaSxdOf0QuuSypHlLujEffGAed+qU41Kkslgkt7Z+3OGI2WoS5fzz8/NEowZsm/hQIHW+jeHG5UtLXH7u4vLiccllicydO7ekjqUNx+F4jtIu/CNG+KOCxDKnnFKZd5RcjAonazNgpZWMdJcOZkk+FnQPcQDQRI8EEI1oWIMbtrHPqFFSbs4808yl6j+Vl5AyxxVBXHIZE1xyWXJy+dRTIsOH+9c41o4gH8xoI4vaQl1Mov4hdjiywH2dTj1Q9NRxjVzccIPIhx+ax0yulGTg58iJi8srj7u6l1jVCDoZ5HildkGrHYrimKUS6MCtt4rce295XwOmM9yAZswwz1u1MkkmM5clQySnSR7yxsceK1FfWwdIbzVafOCBsnZ5+D3qRhX8V3iOnCjqxCK5ZCWKDoe++qo/DFxNuOSypOQS1+YjjvCfX3216VxmxM1bOhIEklaNMa6/3r/XZ4LJh3/+0zymsIyJT9SM6JKCi8ujgUsuSyCbo1T37t2lV69e0qdPHxnMsF1NV3LYsGFywAEHSI8ePeRd2zXPguNxXEdpbLGFuYArRx9dvhiezimKQ/018low7uncOYCD49pqu2bcdZfIlltK6Nk6Elz44QeRZ56RcjB9uplVVUgya/flxT253HTTyieXRDkqjSWxZCCo2nDJZdHJ5bJlZq6dhiT06CFy3HE5vsHNWzoSRJs2/podJK+nn5797zLG8csv5jEdy0BiAUdG8nF6nT17thxxxBFy4IEHerH62DrGQlxcXjguuSwBTHiyMXr0aHnooYfkTizzvO7ZrbLBBhvIk08+KQ8//LDnZlXMcR35Q0Vdq+oLFpiu4a+/hvcOIu/nBsPNQ5vP++0n8tpr5kZUMp9/boZIdY6ABZ8MI5aDMktjyXX4fekNGQlSnHaB1ZlcsrJIu4bYbFbKrbXa5y5dcpmKnqyYHbEvNwfXXGNMyQBX2DvuqEPu79aQOBIGey9RJQECImq/6fA1pOLqas+spSM88omfMfo588wz5fHHH5fbbrtNrrjiCllAkFjicR0+LrksEioZfxTgFPP000/LYTUBOvswm9ENygLHdcY+wcCcw+abm8cff2wSlDDi+N9+M+pR260W81bWofzpTwH8AC58PXv6rjb77ity0UVSNljEue665vHTT/vtihCNmd5/3zxmVQuOfHHat77GGiLNm+dILm1p7I8/4n0uFYEt9zr75pLL6oYLo9prY7aTQ7c3ebLvfs3nkj3DdSrztXNJQl/S/iWHIxqwVcpOFhEV2R4ymNEdf3yqlLbA1bGOEOLyFi1aSMeaa9Baa63lOcv+/PPPOb/HxeWF4ZLLImFtSLYEEBvkQYMGSf/+/eWpp57yTloqJVdddZX07dtXzj33XPmNbCQLHNetJQkGRhKZ09MEj9nLQqzD8wGPCtZi6F5NYrKbbzaV/UDmKjjPjjxSZMoU8xy3ovvuK+/QBj9Ld4HQlmX2MiRojNIFsX9/3MTjhnYvWaOScUyVmcdKS2PJgrfbzu+g8mKrCde59MF5Wu9LOSSxqD9QEugGJJxiqT3lhG9C5w5U++w1MA5HjEEavvXW5jG3aFvY85//mKK2rkqzxzwc5Y3Ls/HRRx/JsmXLpJW2oLPg4vLCcMllkXAyZmPUqFEyZswYGT58uIwYMUKmTZsmX375pXTt2tX7OlWTOzR6LuL4jsLYZBM/WYGTTjKV9yDgONw0tCiP2vHZZwOWcF55pZ/MkSWTxWpbrJzYOzRDksayzsCe27rlltQcLE5ofM69TvfSR87UJ10a+/zzUpXJJZlStV9z85y3PPlkP0/E9fLCC/M49nvv+ZKRv/yl1FfqcEQGhB/2apJhw8w4B0Xnf/3L/zt4QDiD5HApNG5G6ooXyj/VbSng41czLrks9o3LcZVYe+21vf8niSShJLFcddVVZZeapeXdunWTT+pYWp7r+I7CYVSRpFKlKjxHjVgKjz5q9tDjBKdKsjfeMHstA+O558xgh4L+zDaDKSfYQGpg+M47gSdEqFKYs1TTUkyY7Hw2bsTCMbba5y7tZbNcGKqZPJLLMWNEamwEZJVVzCwZLs514uYtHQlm113N1ArMni1y+eVGIqtCAAqmrqYSPoXEzYsWLZKTTjpJjjzySNmK1UgBH7/ace9UkTA3ifw1HYaCVfLK40mTJsnGG28sO+64o0ypkTW+/fbb0q5du6zH5rgc3xEsNADpMsJnnxmzn2LmL/keloQzY6kz4DvvbBxh7XyhZPA2R3+mL/KCC0QOPFAqim3sQ6IbEPwTMUKaNs08R2aEpCjOxMIxFrAu1P0uL77ou1FVW3JZxhU7cUwu6b5T8FFuvFEkhy9dKm4NiSPhEBNo2MZjdlpDixbltUeoZrLF5Zkkroynbb/99t4Gh3xwcXlhuOSySDjRVtIlRxbz5s3zjHt69+4tAwYM8NaOdOrUSU499VS5+uqrPdvjyZMny1E5Nuhy3Hw+II7CoMJO5Z0xM0Bdyl62QqC5MWSIyJln+jkfo4gvvWRuIoGBroZEUveZHHSQyHnnScUh2W3Y0E8ubfeCEiBQ5Xej0mJuzBk+XslKLjkRdc6DecdKwe8TYx+gnf/221I1uOQyr+SSesOAAcjI/MuAXWfKO7mk8q8Oaw5HgqDQgmQcdB5Zi9px2M2c5Lg8nf/7v/+T5557TsaNG+etDOQ/xtdy4eLywqi33NmSFs3333/v7csJGgaLcbByhAMzkX/9q+9T88orIl261P19rHikW8nfV5g3OvfcgJ1M0fXzg/A2B9qhb74ZkO1sALDQ7sknzWN2EWhiUiRvvWXef70hP/KILzGKM3jj6AoaiqNPPJHhL+GEMm6ceYxjbKAVigJgKJmqCTB/Qpe8GkAfj2MUfPGFSOvWUrXsuKO5zgCSjKZNa/+I2TE9JTbYwBiXUATKCzrCXLv4gDNA/eGHIbx4h6PyUHzBVF0VTdRR+Kw4NWX5cHF5NHCdyxLAvjjoDiPHW92VuUKFLR5qo0/jrV+/ujdBYGCxww5+YklxDI8dmomBN5kvvthPLDHuCWyfSUDYLYu77y7pUCTsrOrUxJKl1ElILIE1ltoYq3MdCaitYCWo1rlL17n00ZOUE9dKLF9/3TftoRiH43beiaV25fUDrraaDkcCQWhkdy0d5cfF5dHAJZcl0LBhQ8+0J0g4HmtLHOFCJX733c1jDHkOOSS7wpOEkllNdUhs2dJ8jaQ0cGhvqXMZWSuOGXkPNpUJ2nDqVvvww0bCW2SDljxVnVSZW73sMkkMVKsxeQLWB2Y0mouKqc/66/u2vLSSyfqrAZdcGvgMz527giSWYJlro567XJp22qnA99jNWzqqBHZb28kl7uchbu1y5IjLg9xn7uLywnHJZQAnXRM7QCmWJUukyZIlgSerjuxjZqyK1JE3ZiYzWerjjIj7qzrLYphK7K3GQIFC50p3SQKZ1j77SOTgfO/f3zxG/4OOtQhw1Hv6afMYFTg34aT5WGmczl7nb7+NcHJpdy/JJDD2qQZccpl13pLg7JhjjFoY2OWrio+ik0tnmelI8LgNDvJgi88we1eZrKM8fPttC/n00yaBeNMR37u4vHBcchmAjLVt27ZetaRoliyRhvPmSduDDpJ6XKEcZYHEkoRG5yFwdNM1f8TXmPYceaRvnomcFolY27YhvBhaBBj4aBeQtijbyaNKidJYOr/MqmqDlkSf5lnSiM06kmqVxrrkMmtyOXKkb7KFUGH0aCOLLRh7DUmelv8OR5ygeHjiif7z4cNNvKCz94UaBzqKh5W6u+xST447rq3Mm9ewpASTuJ743hlsFo5LLgOgcePG3mqRYjuYTebOlXYDB0pj9IEkGOph7SjLfqpLLpHaSj2OiJiGsW8RO3GFHZkoVps1C+FFoMdFe6a62y23NAYrUXYMZgB1443N45df9tsbeYAHFo1Pldqdf37Au0HjlFyiVFDzrkonlyxt1Vk7kssgdUVRxSWXGZNLroF2sHz77b45VcHXNqI9QCOucnqHI0EQK+hHiJiC2zkJpRZj/v1vfx+2Izy41PD+szN79uzGMnBgO/n++yLj8iZNvLie+N5ROC65DAhOwA022EBWXXXVOv/u8mXLvcB60aJ68ssvLaX9LrtIYzRHQJmFyFs3VTtChwbh/vubx99/b5RbKm/h5sCaDHYultKczgnZlXas11zTmPmwoTzKkPhq95IkhLZGHnB6s8bgu+/Mc4xmo7BhpWLJJeisI7pZ1V9XAlyquDPD119Xdj1KuXDJ5Qon5+I2G3nBsS6Ax0SYYltRUDBTTaAz83EkEObp1SuAGIF4gdsja4yPO858nY/AsGEVfZmJB1fe3Xbz1yVBhw6NpUuX9tKyZcuCuo/E8cTzLrEsHpdcBgDbXObMmePtyfn111/r+stST5bLG2+sInvu2UH+8Y+WIo2bGOOWwYPN3yHzRI8Z9y3yMQFZ7KhRIuusY55rUEWXkpnAv/89xB9Ol/rSS/1MFh0aXv9xwJ4PRRqbR6cLQxB13MWUEjlskv2r8kouo+IYW43SWJdcrnByXnz/RjJ5snm8ySYi111Xwvvr5i0dCYfdlshi4ZRT/FohsL5Hm/XEGLZC3BEcXGYwaNS14LoGBrXZSivV85LLTTbZJK/mDxDHE88T17ttjcXhkssSWbRokcyYMSP/k5DqSf36svPOv8ntt38mM2cu8sxkvAgb7ZFu4dUrFWspqkGeVmFee21Fg0wcZe1YO3Def19k0CD/OTqaPfaQ2EASTKkQpk4VefvtnH+dRN3Oox98sHJrHcsFSkAtmOaVXFZaGmsbSLnksnqoOTkXr9pcLrxhDe8x5lrUPEsSUdjRtOtcOhIG656feso8Zr8lIiQbhEj6NcI43GRdOBd8YknYZCeW+GLgn6FjTMTpn3/+ed3NnwxNI+J7vt9RGC65LAFOuJkzZ8pClkQXCAHnRhstlNGjZ8r11y8yFxxaaNdem3qFQjOIbtNdkUKBt5Wc7qCDzK7v9C7bjBnh/FyZN8/8UJWMHX64GeyMG7axD6XZLDBOfOihqU6xrB6phsaYGhXllVxWWoraoYM/XDd+fPJtDu15mmoNIPh318xM/+8Pv9XOnFjJ5q5uDYkjoXBptG/ZhG6Z1lEff7xvT8AlVUduHOEkliT0JJa6CaCUOB34Pr7fJZiF4ZLLIqGqMWvWLFlSghUV+vw111wihx8+S8aNW+5nnbTMrrzS/4tXXWXE+9kWMTqKgn1UWO3/4x9+7s6469/+Zh4zFM7j338P+A3mnMEN9rPPzPPOnUVuuSXaBj7Z6N3bN4HBejfDBZzYtW9ff5yQnPq006RqUGksnXH7JhjJziXnoLbr+V2++qokGieLNZWfGnetT5aYk3XvvY1wpiS4qGpyufba/tyBw5EAKL58/rnvHaBxQ6b6lR3OnX56xtukowhRRHpiufLKxr4COX9QcTrw/RzHSWTzxyWXRTJ37tyiKyHpCebGGy+Ut96am9qcJOO59VY/4eAxXSJ7Q6+jaEh0UACiRLbnI5gBxKiVBo4OidtK5UBgx4mnha4JutgTiZlKHEF30quXnz0988wKf4VTmd2g0K6dyF13xTOPDm3uknNAF6NVOrmstrlLl1ymnJSfykaeVB0Rgq5oKhp2MOisgdtv6UgQ+FShvlH5OKtHct3TevTwJ14wALr++vK8ziQnliT0dmJJLP3ww6ZWH3ScDhyH4znywyWXRVYxsp1k3bt3l169ekmfPn1kcI1Bz+GHH+49579ddtlFLterkv4S6vN9c+XVV9OqK0cfLXLvvb7jCZkPtn06Pe4oCqSuO+4oMm6cH1/y1iKD5QaBtOWhh/yGHAloEascM3PPPSLXXOPflbgaxn3BYw5pLH5F3Hj1feZ5tW0jqDO55KTT7uVXX5mWeSXp1s2/5jz3nCQal1zK1xNSk0v2W6qkrCTcvKUjgdAEYE2PquiHDhXp2DH393CJ57avCShWGnPmhP9akwhmY9yi0o3VaQrYlgG54nTl5JNPlp122klOy1NKxfFK7YJWCy65LIL58+fnbI+PHj1aHnroIbmzZp3IqFGjvOf8h73xHhlMWxo2XC6vvJJBM8feBjpbGgRhf8XejAIGkx0+zDxsv73xnwGq9Kxp5G226dRJ5Oab/efHHivy4YclvpPvvCNy1FH+c7KuJAwecqXHzUBde9jnUuPxowbIQLW2GhsYsXOMJftnj6n+EpFNJpUqTy6R/L94q39Sbt17I/nrXwM6uJu3dCQQZiZV0NG6tci55+b3fayvZgkAUD+kmO0oPLFM71gC/Rq7xp1PnA4DBw6US9VlMA84Hsd11I1LLovgJ3uRTgF899138vXXX8s222yT8c832eSn2jUNK2gqCNrVtg9JJUMx7iQvCJpqXJhUqYVlOHJNupiZwGOHHW8ahNE0/uUXKQ4WO/bs6QewDHvyXxKgy6VrSajqPfCAZ3bA+6U1EP7YzquriYKTSyeNLR9VnlwiWW8+zz8ph1xmnayl4pJLR8JgTZk9i8yankLclC+6iB2K5vFttwVQsK4iqM1nSiwZW2KOtZg4vXPnzrJKgXbYxcb/1YZLLguEysUfOWSpLGodNGiQ9O/fX55Sj+oann/+edlzzz2lfoZhFr7UocMfcuGFy7N3h154wdcUTpxo1kA4bUWd4FVxzjlm64eOrDJW9vrrda+UpNu21VZ+EwelcsHGvehnyLSQPALdyqQNXdhWsKNGeQ55euMkb4qrX1HVJpfVspKkipNLRDA33SSykZiTclnjJrLSRusFn1yutpoZtnY4Yg5yVkaJNYagXlwIyM2JRTQuQY3pFgHkl1jutdeKiSW+iLbcON84vRQ4rjP2qRuXXBbI4sWLc55YSGDHjBkjw4cPlxEjRshU1V96MdpY2ccO2tJo0mS5TJ++OLtBIy02NJy6HPC990R22cW/2jlWgA4aF6DLLvO/RuJD3k/MUxfMXTInqPuSMES15bJ5wXIrFmnCeuuZgU57BUISQEesmtd33pFJI02CRFGQf25Ju/JiDvWgNdaoI7m0N29HIbnkd4mnO7z4YnKNxKo0ufz6a5WsL5d2MtP7Wv12Gwbg4lMDs05aTKM6V62VJUdi+OQTs7YMuH3XZeKTKxxgDyPQL8jggeewYH12po4l02XZjMfqitNLgeNyfEduXHJZIMtqLNuzsTbOj94sXwvp2rWrfFwzP/Xtt996stittA2WhSZNlnmbSLLC90+Y4JvAkLx27RriQsb48u23IrvuapIb4CJEw/CGG4yzWL60b28cTu2bAxe8vBgxwrQHNJBlYCMQt4wIgo64hkPlnlrpz6abVvA1Rax7SbydMYdhZlUrGFFILpE6UyrWAaFJkySRVGFyyS2M+SRW7a4j38rK8vuKLfZScZJYR4IgTznhBL/Gxupx3V1ZKBjDX3GF/xxDIJerZIY4i9tQuhKV+VVCKfvyXUicXiphHz8JuOSy0DcsR2V3wYIF8hui/JrHkyZNkvZkJjWS2L333tuTzeZi4cL6XnMS45mssCeDTpgGAxhukGA6AX8tNHW3287IKQAH2CefNC5vxcC2DZ21QOXKTiud3czKG2+I/P3v/nO0obZPdsL4+a/9ZbE0rE0u/37MUjnkkEq/qmigH1WCFF1vmtUxluVpNdeRilINK0mqMLlkbbI6Ze+wltVKD1K6aieX1eji5UgUqJd0exijNGefXdrxiB/Uy4/+AKGBIxX8MDIllrz/7LLUWmyhcXoQhH38JODeoQJp1KhR1gRx3rx5cthhh0nv3r1lwIAB0qNHD+mEXLBGEsuaklwsW1ZPvvmmkfc4Z/cS0FXQwaw5vsyebdp0ebfUkgtJJBduVWW1aWPmK0t1QcSRTM1/yOdp1GUtYH3zjUjv3n5J8qSTzNBnQiFpGnxWS3lW9vWery9fy7U9Xq70y4rv3CUarEpjX6+SupKkypJLbg/DhpnH3MYuGWydjEF2Lt0aEkdCwMQPtZLyn/+IrLxyacfks3fttak7ttNXa1Qz2RLLtdYydc511ik+TrcZMmSIDB06VCZMmCDdunWTKSw2rwOOy/EduXHJZYFwYq2UZeF969at5eGHH/b+e/TRRz2bY+W+++6TLbbYIuexV155JWnXznwgqCzrmF5W+IRhL6vdMFppCNGzDm0mGxIcLtgHHug3flg7woVq881LPz5zFg8+6I+iMbd55ZUZ/iKD5LQ6Sfhh991NuyDBIDdmZeco8aWxjR8IajloFSaXH30kFYfri16z8ICvWTGTKKoouSRIRkmga9rovmzaOKTkUjuX3CvrWgLocESYCy80tWJgCxzm/UFA2KYhIqEbP8dhJjBILNPXPZPQszRhk01Ki9Nt8EUZP368vP322/LSSy/VObYGHDefxLXaccllEayWjxNMETRvvlrKzqQ6u5dApoPhBsY+wO4HTIOqbEqcBuFxx6W6r2Hkg8S4Zgw2ENhrde+9/iA/XYAUCTM/HMcgnVGjwzxmDKU0SSpvvmlWGsBTsr8sWqXG0Zhss+jdLckilo6xdveS8xr3iaRRRcklIwE6ms/IAN2SlJMxqOSSz/z06eYxVb1CBtwdjghBjY91I0CuQtcySDAaxDQQ8IKYNk2qGmIJtuylJ5ZcQggnuG5VOk4P67hJwyWXRdC8efPAKxccb/XVV5cBA/x7PDkjcs46QXyOCH3fff3OGe07BgWqAFzEkLzeeqv/tfPOo1vsX7iDjre1CLB0qUj//maNpceNN4rcead5zA9/7DGj5UgomIL07et3Q04+vYk0PrS/b9X7yCMVfX1RIbbJZdJXklRJcnn//cZZUefPuTZ69S49GbmfbbhhcAPvipu3dMQUrRPbnf6gN+rgy6g7Gvk5mfY1VlNiSWyVnljCHXek3ooqHac76sYll0XQsGFDzw02MJYtkxZrrSUNGjTwKjQ6E5N391I1AyQyTIrrlYqsRxOdhEJsxBwkibhKV0ePNhKTMGeu//lPs3pUXWkPPlhk6UuvpG5Y5r3PQ2YRV5g3Zb2lbsLp0kXkkktSXWPlbieNVYWpqnSyJpe0xXVnS1SSS4aXdcCI5DJpS9mqILnEQOrYY/3n3m5LLXboyciKpDxkZHnh5i0dCYACjE4Y8XnBITYMOC5m4bp7Vo2DqrljaeeE//63cbeueJxeswWCON1RNy65LOEka5LNB7kQliyRJtOnSwvLehQdvlbIUKJhOpoXZFaUqI84wo/+jzwyeC1HRGAmlZlK9T6hQcisKt3fsOH6gjxWB8uR3/7zgHdNK1PvGCT3CQZJDw1zaNnSzKN63RB+KerTzhvzxRdS7VDo0M80wX5GIyj+ku5tmTlT5PeaFRGVhGscM8PADPH770uiSHhySY2R66EGbTyutQLALQPpAbg1JA5HLXw0dNQD2GkZVO0lHeqJ9h5uRns0jKgGJk40iaVO0HAb1BrmySeXltQHFqd7t4omgSerScYllyW0x9u2betVR4pmyRJpOG+etD3+eKlnmfAQoBfVvdSsh92KuJMqdNMuvjhRXYd77jGdQ42NiMkZc1R773LALCcJVYMG5n295PfT5FnZx2g7Lr1UkgxJ/Pnn+1VGqrxaffW+oKVGzjlayY7a+J0chiX2OaWxvG941EeBJK8k4fqtEocEJpcXXWSCN0D1qit3PcKYt7TNfLgXBeGk5nCUGZRJ6sd30EH+xFFYUPDZdlvzmPpdwgVntdA44faiiSWXYy28Mm5zzTWpXcyKxOk1XVCO44x88scllyXQuHFjadeuXXGVkWXLpMmnn0q7gQOlMQN7aQ6vyA11BOb5541sIG8IlphCZ/BQ4TEloJgnmFx4mHckd2HfJOAsxkUq6HmIfOjaZblctvn9tc8H1r9PvrjyARNYJRSc85AB602A4odKhFNOYFsaG/PzLggSMXeZxJUkev1OWHLJpipqisDliAJQym64MJJL3kN1OqbiF8bQu8MRIowM06kETl819AkTQjYSKYUYJ9PsYZIgZuPWooklnWGdb2XpAWFDEKNNJcXpNR1Lvp/jOPLHJZclwgm30UYbySo6K5UD4uvl5n9klbfflo1OOkkaa3kM+aAVgJfUvQTKPQwe2rsyWIeBpWpMNReoBElqvLm+Gpglwp66eY1Badm55hr5x5QB0kMe957+sGx16XtU89rEN2lw8ed3MGeOeU7V0T5Pa8Eld7fdzGM6cOyDqXLySi432yx6yWX79n6lCy06jtRJIoHJJTvzkMBqAYhbwQ47pP2lMJLLDz/0I8Sttw7mmA5HmeDzgomPfm5I8riVlYOuXUX69DGPub/aUtmkdyxXXdX4UMKWW4o8+mjqxEI543Qb/j7f5xLLwnHJZYksWrRIPv30U/lNFyvWke95bfV69eS3zp3l0+HDZVGrVuYP6V6m+VDTndtgA79hoNstCoLBAWxUVVvAYw7M7o4YQQ5OrsJWD7s5i8yrYls+aCmfcYbwzo6UQbJBS3MO8HsKa/i/0nCz1dUrON2heM1aXbSn8J2xT3w7l1w7VBrLdYPdukkiYcklNcqjj/aNtnbdVeTMMzP8xTCSS5XEgksuHTEct1GHfvYpDh1a3p9/+eXGOgPY2c18ftLg/eV2ojVKGgP6mHgXH4cUhUWZ43Qb/j7fx/c7CsMllyXACTdz5kxZWExQUr++LGzXTmaOHu0nmGlBG0nTOeeU0L1UiDRwn1GpJvooSmRaKoo4zCDgEaPNL6pcuKox7F2xXbYEZhj21JQ4Vz//JHnomVVqbwx4KD30kCSKp54yNz9ghIFEP+eWFc4xlcU98EBigvdQk0vK5PqeRSW5TPpKkoQll8xr6bUH13wC5owqfZdcOhwp3X57FQh7J4PsnuUDoz1qOM/lKGNRKMYgfOFWoskkRoCskgNiCW4tapIYiTjd+z0s9L7fJZiF4ZLLIkHeOmvWLFmiEqBiaNhQlqy5psy64QZZTrSeoSPAVgeVZVDRKVpdiJaRnYN6tSQ723//yEvcnnnGmPSo4SjbGqh87bdfBV8U7xlT/tyNoEcPzwFgm21SjXkHD07OUmQqqPYYJWprVsDkhIV6vXqZx7gho1+uYqjKapc3a3JJFtCxo3nMxvuoJDw4xqopQtLmLhOUXOKcbXu54e3GNTMjehKSgQa1u83uXCZ4DZMjeWBLMXeuecxGN7wcKgFjJiRdwKpyErIkwL8DYyQNObku6XgN264ID+gWRy5O98aBlnjH8cbaHHnhkssimTt3btGVkBQaNpSFG28sc4cMMcll2slLJyyQ7qUmQXyCVXfOQiU8oLV0FCF4G66/XuSAA/yLUefORnK6xRYVfmFk/MwWAYkArYGarOGYY0QOOcT8EfME3KSisFGiFDjNcW7T06R3b9M1zgsnjU35LGugnzW5tKWxzEZHpTqBTmmnnfykl1UpSUsuYy594nPKtWfBAl+worWdFeDfqrrZoCSxnK+4oWgLpmKD8A5H4atZb77ZPCY8ss11KnGpxeVZOfXULKurYtyxZFOZXn6op6K02G67CMfpNR1MjufID5dcFlnFyHSSde/eXXr16iV9+vSRwbStvOWwb8rf/vY36d27txx99NHyEwuUVvgt1Je5Rx0lS4gKMgSTgwaJtGnjd/LefluKB1tPlmfqjR+feoYZtYQUASgyMVRPAqMXVZI0DHWDlkwUDCtG6ADDaquJPP54yoAAMl3GWnVdIZLeE06QWMPerXfe8b1d7rijADky55vuKKGw8f33Us1oHE+ibq22jf7cZZJXkqiWPeadSzoe2jik5sXMVlY+/9wvZAaVXHLv0szWzVs6YgIxxt//7scarCHBT6CSsJ5ct/hw72WqKc6u1SSWOu7YqZPI9On+nxNPhLHqJVucns6rr74qBxxwgOy3337y8MMP5/y7HK/ULmi14JLLIpg/f37W9vjo0aPloYcekjtrFhVdfvnlcsUVV3gn7aabbir/ReeQAWSx8w88cIWVJJm6lzj/lQR6RtxpdSEs1eZddvFLSRWE3BvJq1YRNWhiZK/irvYMHep6F13umEHHwUwolTikHsCpMHKkxBLed92NR4OH05ecOm8oS+rWdi7K9/trW6qR2Jr6JHkliXYuOT9j2iIg17/6av9+wedWrz8ZCXve8i9/CeaYDkfIcH9Ws0SKwnmrckKE26bdPT37bD85ixOY/5E46mvnsvDxx/6f//vfRghW7jhdIVG88sorZcSIEV5sPnLkSO/7ssHxcv25w8cll0WQsfuYBdxhF9RUc3GeWiuHA8pPBG9ZnBiPOMKX1JHjaCepaJiHoaSkJTrWReCFjeStgnN9KO8wYVVDo1GjzK62IPYdlTzMhLe/XqzYh/LXv2b96+QHdDAVKqN0MeME/2TU2rbBQVFjVE4am4zkkl++FqTGjYu9jLQW27Ujht1LRCd2gHbFFcbOPydhJJdoCxXXuXTEgHnzRM46y39+442+kKHS7LmnscWAr782m+TillgSImliySokEkvdhMdseJiu+vnE6R9++KG3amTttdeWlVdeWbp06SJvsCelxOM6XHJZMFQu/sjiskoiOWjQIOnfv788RQboDYmfJ8cee6zsscceMn36dK/9npH69eWPDh1kOZ/IDNUWLnhUrwLrXkKHDkYMr8HFrFkmwdR5wjLC5xlHWI2l11zTjITaeUnF4GKCgY9uNUaja9+RskDDjrknYO4S89S4LEbmhsDr1RsDwStSnaJgdyNuR0BVJEoJUxSTS+bVNMKJ0ntFhYcZbWB4Bkl9EohxcsmtgsIjm6yALoFt6JMV5xTrcHiqKBJM9TzEtyxKkFCqjxpO7V99JbFMLAkrkcKq/wQeDsj2w3L7zxWn28yZM0daqnuS517b0vtaLjiuM/apm0r3g2LH4sWLs55Yo0aNkjFjxsjw4cO9NvvUqVPlnnvukdtuu03GjRsnW265pff1bCxv0kQW82mzBekWjHFqo/HJJ1MLxUWDFS0dTITwulCSxWglDXYWBvMEXNRVHs+8EDIVLkgVB5kcHUs6u8AgxF135X1VxD1Wi/j8WukERt1wjNd33HEiH31knnNqII0t6Ubgupf5J5dEExR+dI4tSjtpk7iSJMbJ5fDhZg4f1l67gEtT0MklFw2VxbJaS9drORwRhRDnttt8Y/Modga5DeA/ASRm9nhUVGGyy5bCEtvRt9AknuesvQ5TjZYrTi8VjsvxHblxyWWBLMsxk0NrHVq0aCFdu3b12uvsx2HWEvbee2+ZMmVK7uMT6DAPmQH+yO5eluQca4NLDnJc7FgBpxGMWCg/hQiffYbn6fCpwo4fSxczKKVWyfACdYXGGmsYAx91282DlVYy85c6p8jMIvLSKEP9AwPcTPOjRUNZWEuwo0f72pgqI6/k0pbGMgdYQan6CmjnMklzlzFNLhmVt/fyMdddcwuqGz35+Ler4VYpsCtKVzO5eUtHxOH2w6iK5h/EUkF8DMLg/PP9TUHcl8tY9y8Ywkg6lurrxToXPPx0lRxy/UcfDX9/aK443Sa9U8lj4vegjl/NuOSy0DcsS7mFuUpmKvXxpEmTpHPnzvLjjz/KVzVaBpxjN2DZXa7jE9wQfGcBaeJ66/mrKm0PhZJAh/rii8bYR/do4A6pZfGAQbGAbb4t70VCyi7PoFaulQzOYQx8Ar/3MWNENtyw4MOgcrQNfYYO9Q0Eogbn04knpiaa2kQrCS7YOqPKAEmWAkrSwVhYx65zJpdIiZUoSWOREGkrnpNF9ZhxJobJJcEb9RotyrGuwG4q54TASFfJcHEKooXg5i0dMeL2233fCsRI9j0valDTvuAC/zmf9Siqn0gsMWPUxJLwkTjvgw/Mc0Jf4ruCDAEDjtPT6dSpk8yYMUO+++47L25/7bXXZGcWqwd0/GrGvUMF0qhRI2+2Mp158+bJYYcd5q0cGTBggPTo0cM7cYcNGyYnnnii9/XJkyfLUUcdlfXY9RYulEbffGNmmbK03dO7l4HMXtqRL59+9YXmyoCDbRaH22IhHkUagaMh8HbidHjLLcbEJxJwRbRdMtDM0FYtEkY2SSqBXy0zByoTiQqMljJOqvE1cpx+/QL8AbY0FqemKoV4XnPsrDtQo2rqA3YWw1qjuBPD5JJribou4rN02WUFfPO335pre1hOsc7MxxFhGL+x5aWY+KioJqowpqJF3tdfN2qiKCeW1JEReDFxBRRUmaIo1yq5bHF6Og0bNpR//OMfcuSRR3orBInhm9exn5fjcnxHbuotd5OpBfPpp5/K71mjwiJZtkya4lzFfB/QMWPaPAPEBcQE5KF6Xy/KxTMblMPRqmpSSZWGUl/N7s5SwCsIBzQ0+MAFiI0ePXpIdEAWjERYq/uHHmqSoRKnz0kqSaq5OegFmNnZKBTBqIT27m0kK8A/nxtDoPIVAndmsbDyRmfLfC/DLlUGHXvdyMJcq51H1kLmoH/Qv3+0VrgwVMNuXOB6lUNpEQuooui+HdoZaj4VUfiM9uplHrOeiaYhc+p5w7gDc/XA3oXrriv9RWFUV2Ni57XktYLicEQM1F81m+K8Wzvzf3GAj5f6QdIF5BbB2E2lQYREYqkhMXENVh66To5bPebiGDbGPk73rrlNPYdZR24iENbGj9VC6uuvZs8w0SnL8sHggmKblQbavQScKglmsSFUGRVXZNxpSoB/HqtGNLFE3otZbaQSS2bcCOY1sSTQZKdIALZmFLvo1qosEsUxe56iAPGlJpbIkqkrBD4XwQF5b4ES5yOPSDWS19xl+/Z+OT1qnUv25DKMC+wNivv8SYw6l0xY2OuBuCQXlFiG7RTLvbGI0QGHoxwgCtPEEqHWlVfG530ngWM9CXz+eTA1oVIhabQTSxoHrBzRxJJ9nXRZy51Yhhqnl0PXmwBcclkEtM3zabkXQr3ly2V1zGIUujvoNbKAulYlBiQFmDsEClcFBu5sX/tTTjEd1SIE/5jYcBFilBO23VbkrbcC7rgGAZpjlfoxX8abS3sgIHD7pVOrp89551V+/BADJXvfFKYBVB5DwZYaV6k0Nq/kkkrEJpv4C0cpekQFik977OFrzOowKYs8MUkuMSGh04KwAlAa2IlmxZJLDDHQeKskNqz9Ag5HCXAJxcRHIZTJ2wArAvCxuuYaX+l06aWVHXknsSSZ1MSSriqNAgyIlDvu8KesEhGn16snq0fGFCTauOSyCNBp5+MoVQgtfvpJGqQvQeTqQZJZie4lcBWjPEYGpPCYTCTPBJML+gknmIF5bXAg6UJZFzl3NnaiqB85XSNKbq1bB/5jcFDDhBZ4TzDmYAyqEpAbMP+puQvnFEWA0KCEufHG5jFZtbaxq4iCHWORqWsnPSokaSVJTJLLK64ws01apGKNQlGxU9DJpZu3dMQAPB20DkZRmznGuIH5kNp2UKi3Q7Nywg7y9MSSqSn7PWUO3K4lJyJOb9FCGtB4cdSJSy5LOMmaBKAbJKhfuLCJtNAuhQ3W7jmWL3GR0XViKAzff1+Ch+iFzJXIRuE1cRWpY50EuTIXHbsBS/KC5LLk1RZBw+CS3Qa4/vpQF22ee65JMoHqIwlmuZtT/PoYrdWmA2NYF10U8g/lfLKNfUjoq4yCk8soSmOxAkzKSpIYJJe4S2sgyUeIjw0ukkWhJx0HqsO9PC9ccumIONxjuecqjFhH3cQnG4RjSHq1MxhK3FdgYkm/AS8BDQlpJpx5piQmTgeOE3SymmRccllCe7xt27ZedaRYSCbmzWso99/fVuqRJWaaVbn2WmN8kgHUmqF3LxUWqlH601I5c4gkCVlcbZkJYL5S405Ufiz4ppoVBQObFWRd2LmqgyJJ5rHHhvojKX4RIOpaGTq5tpykHFxyiRmZA+RBjNmW5YaLts+WxkbRVz1EkLOr0jq2ySWGLcyFqq46XXURJyKeXPLW2oEbPm+6Maoo9KRDlRFE4GUnl27HpSOCkPzghg502BgbjytM66jXI8qn004r3y2UbXUklhoqIYMljiB80mQTJRSCtyio44OI04Hv5zhBy2yTTNTC/FjRuHFjadeuXdGVkc8+ayIDB7aThx9ubC4Oma54GJ/orsUMsBtSu5esZdSdQqFwzDHGGVJlAQwP9unjX2msoXnUjzhhAhV2xhgHDZLoQXLM/o0vvzTP+R0wIFqGiwhFsAcf9N9OEm81XCzHTUJ3Z5Hsk1iWyybcG+hUt9Fp08zwbRXBqaVmmp99lkMAEOXk0pbGUiWr9OBwgpNLxgpUFc3lSSX1RcGYhQ5tBmXmozsumdUIZCmuwxEcuJ6rIyzjclEx0SsFTJ61F0EnsRxxAzEDXUoN99hSh6EYX9O1arjh815HqYFQapzO9/H9HMeRPxE6BeIJJxy2xKuwU6MA+Pu33rqRzJ7d2OvyEWN7Nls2dpcwy8wVHRDbjCV0WSMldDS4+kF94glTyvr1V+8pbqhcYGgGAmrfN9/0ne8jB2U/rPmBIVAy9MBtUrPDvl5bcUwzmPMhTJDB8mvUaifnDL+zsmJLY+PiBR8gGtdT28ABNCN8ePQuHcXkMinS2Agnl6gbMNgCtvbwvKQifNDzlrRVZ8wwj7fYIr5aQ0ci4fpqm/hgY5EEZSOXLDtuYO8to/lhQXPATizpVLKdjgRTbRO23NL4H5YxfCpLnM73ucSycFxyWSKLFi3y9un89ttvBX0ff//88z+VVq0W+Z4Y6Z1Ljf7pDOSY3KahqK5nzDOySzJU0EI8/bRZUgkvvSTL995bLj97vjc7qPEZCQtdTPVviRx4ktOlBKpSJM1la9/5nHqqSM+e/pgtspKwYlxutv36GSMf3UllS6vLBh1v1YbSNo1YUB+JuUvu0io9ZalZHTPOZYfusy6T5gIWV3lzRJNL6om2QQZTCSVv+Qg6ubRtyp0k1hExuL1rPIRDvZrhJAHcotUWYvp0f1Vv0DA6Q8hnJ5bUg4lTdN4TMdKzz5pNREmL0/k+vt9RGC65LAFOuJkzZ8rCIgOSP/1poYwePdNLML3kkspv+lZc3SeHBDWL5T/mOGXtXkK3bqac1by597TexInS/d+7SQsxLUvWYtLMKNp0Imxop6ZHbpVYxlTToCbPVank22+L/OMf4fysc84Ref1187hNmwpKWGjD6CZ4MmqKFVVE3qY+m21m/p87e9Scdbk2deni63u1gxU3IphcUgRCXaCrm2j087xkgk4uVRKra0gcjojwzTe+hJx7LMlXkow++TdhyaECt3/9y5enhpVYUgQnFCW+U+fqNdc0tcUK1OXLEqfzfXy/SzALwyWXRbJ8+XKZNWuWLCnB4pOLwpprLpEbbpglEyYslz+WNTblNRvbRVanuDOA/wyD3tq91HnHUNlxR/nh4Zflx4ZGZ7KVvCfjZRe5ZdiXnmQishJ19n6Q2Gg1iqGmI46o6EsiR2fzica5VFyZxwwS1qiq+TANpzFjzI2hYlSxNDYRjrFJWUkSweSSQBGHWD1XVGBRMm4NiaNKoECrxRk6lp07S+LYZhv/Nso4NdeNoBNLvSSSWDL2RCODGFMbG9SFozpqHUScDnw/x+F4jvxwyWWRzJ07t+hKiA0jKhtvvFAGDJgrr72WYe4Sp1jdtfjMM/58YI7uJed/ObqXxLrbHLmV7LBkgnwp63tf6yhT5ZjRXaXepxHtYvA7I7HUxZIMg7KZOAJQ+GcDioJp7dSpwUns7J1TV19dsUZtavdbl51yh1KtbhWQmOQyCXOXEUsu6QgwG6b3BzoFNPojnVzSEmIJn8MRAfAXY9oCKKDq5ymJ8G/T1W50Zz/5pPRjUie0E0tCJordhEpa6OIjT5JZ8TiiDHE6cByO58gPl1wWWcXIdpJ1795devXqJX369JHBeF57Tv1veM8POuggufzyy1f8JdSnsjZXXn55yYpzl2g77In0s8/OOttE91KH1elKhRmLUtXipXpmRNJBerd8Tf5YvyZYQb7HMEDow58Fwvt2/PFGEqu6UK6OOjcWAaiwsnsS8EhiNBHD4FJA0oIhrlqx85hmbcXh7qT/WCqLlEWrBGZUVI6cxasrHsklUn61qyaii0ByFufkEiNXPhK22dZ22wX4AzS5JOIudUCKC4uek5yn6SMdDkcFQJDEbV7BHbaiCp2QoT6rOyUZyy91pIYaIUY9dmLJrRkzMcJPZcQI49kQ9Tj95JNPlp122klOw7yxhosvvlh23XVX6YcBhcWwYcNkn3328eJ1/vtStwjUwPFK7YJWCy65LIL58+fnbI+PHj1aHnroIbnzzjtl2bJlcsEFF8h//vMfeeyxx+T333/3ks10GjZcLkuWzF+xcwlcGTXI5Huz+E7jr8M6SuDl5dhgUhI332wuKrraDh+HR99tKyu9NUGkUye/40pXkAHCqMBcJVuHATOZxx6LnHUcUmlepo7akZ9TWyhFjXHKKf5oFOZK3BQis66pSqWxSMapbWi8n/X3i95If1ll0boXCK9Nu5dUQXSgN67JZQWNGzgHKC7h5gx77JE6S18yRItqTRxE15KLkwZabt7SERFYj4H/GdBVq6nxJxoSyvXX90VAur+6mMQSwx5NLDENIrHEXgMllcLatEiulssQpw8cOFAuTWtd77fffnJTFgeks88+24vf+a+1qgZr4Hgc11E3Lrksgp+0BZQHP/74o6y88sqy3nrrec+32247eZGFQRnYcsuf5Ovl6/pRp4IUlk21titLFudIEpG11jKPuSjoRTYI+JHsV+Jn6I/nQsTL8/55THSj6dLhBsrwSB+zSHnLCq/hpJP852RYEQ2IKBLQUFUz3lGjjOFPMVBtZJMN0FhgrrNZM4kOZNEMjsA770SzOxcSGt9zOdHVgytAEUSdnvgwszU7asRdGmsPh1ewc8mcOobVgBFa4GZbmC5pFSNISSxE9FrqqC6onejcIZ8d8oco7VwMC2SxJHwKTbpCG2zpiSWqKaTFFKZRO2nMd+KJfqc0DnF6586dV1hBsvXWW0vzGjPKYo/ryE0VfOyChcrFH2qdlYF69erJoEGDpH///vLUU0/JGmus4XUrp02bJkuXLpVXXnlF5ugSSAsugB06/CHPP798xe4lCRsCeBW3UzFmEKeM3UsG43kJ9kwgVXXWQqZ8bumykjzvsov/jQSf+FRXCqQNXCn1akuZLxDrxfDYdFMTbCrIWG3X/3wgTzv6aP85N1pUjJGjSruXBc9dYqOeJtOJBHvt5XdX42jqEwFZLHUDFAYKxaSaemRwODMfR8IhqdJtE5jBV9N2HEIaldAjcqF+ni+EZ7YUlmSSEJORjf3280dz+LrtUBvXOD0XV111lfTu3Vuuu+46L2ZPh+M6Y5+6ccllgSxevDjniTVq1CgZM2aMDB8+XEaMGOEllZdddplcdNFFcuihh0rLli2lfpZSWpMmy2XSpMUrzl1SjqPqzPCAwt7LLIFQevey1AHvL74Q2Xln4yekJhNcuBgfzfhPoTXG1Wrffc1zPuRcudRirJz8/rspx+mM7N57p76PEYadobothbeQ/Djfoln6vCZmuBU2xM39D9Xl66NHR2+fY0gkxtSHi412n1l8pmZZcaHCySWfbT4CXKqAzzyXy8AJM7ncaqvSj+dwlABSUA0xmHYpy0q2CEEsRuKnnH9+fvECcR0hkk4EkECieCJkoi+g601Ya3zPPfFY51JXnJ6NU045RZ544gm577775KuvvvJi+XQ4Lsd35MYllwXCDGUu1l57be//W7RoIV27dpWPP/5Y/vKXv8g999zjnbAdOnSQtrh5ZOHtt5fJ0s4Z5i7pXvLpVgkapjmqd8ywfk6Hunm5pXQvscOnGvbBB+b56qsb/T17jurUaTDTyJUK+DD27y9y111SNri40LrTgUPkheg84nB1rIGbhcbtrBHkfa/rmsmfY+6kkmi6lYGtMggDIgF1BmDgDGOYKiAxyWX6SpJiB36qNLnEJENVCfyqdV1QpJNLCkD6ojlWVLenO6oCPrbINZUrrzSxSrWx004i6lFDcmhPU2WC+UxWjKQnlhSluSXramViCMI5+1IZ5zg9G8TtqA+bNGkiPXr0kA+zmFIWe/xqwiWXhb5hOQT8CxYskN9qNBk8njRpkrRv317m1ZR++Nr9998vPfk0Z2HOnPryzrK/rLgkUjfW2oPJZI26yCkN3NLUIY18ato0KRiKNuSz331nnrdvb4xW+Vpe8G/gh2vLjA8k0/W2tjZMrrvOdMIA7S6LHhlmihFczKnG6ngAMmQMC3Jx223mBgGsMGDOUq3KI4stjWXItApIVHIZ57nLCiaXCDy4TOnL4HIZ2mc1yOSSHUnaanXzlo4Kw2otjXFQWR16qFQtKMr0kkaskO3eQmJpr/vu29dIYQnT6GRq7YheCNepONWPcsXpudAtECSPL7/8she/B3n8asK9QwXSqFEjr7KRCZLIww47zNNrDxgwwKt8dOrUyZPH8pg5zIMPPljaqUFHGgsX1pNvvmkkY19uvOKwAMklLSm+bpembB1EWvdy6NDiupc6q8mPUdk6xq8klptsIoVBlxANrW2mgysQPyDMhbTMfdqe3MzyqZNtzNhww9RRRGZqJ07M/HcnT059q5ndwiE28uy/v19qxtUkS9GkKpNLBnCjnlwyD65OUUgb4iRtrlBySdHOdlyk2xLqTLSeZJhEYb5WCs7MxxER6K5pfEO4US0mPtkgGdStGySOmRynWThgJ5bEehSked9IzLWXQYOCMXpdRx3HOH3IkCEydOhQmTBhgnTr1k2mTJnirRzBRZaxNb42tsYr4KyzzvJWCRLDk2ASx6fDcTm+Izf1lrvJ1IL59NNPPZOeoHn//aYyYMBGnrTh9e1PWzFxJDggMZ0+3XQzMKihNcXXM6zUID7fYAPjRMlFA5lkXckhsRV2+GjrFRqPrMdIb6YWBInkP/+ZOghB8nfFFcFPhzOFjmOtWnCee24iBjBwaOPtAmzHie90thZ+/NFIaBnPBZLMurqckYJhYfbcwMiRIocfLkmnZUtTI+LmrSsoslYYWCpLAocVehQdFfCtV7vTt97yXaOjDhowdSXbfXeRceNC/5EU/DDK0CYvj598MsRfKz+QligXeByaS91BzLWbdhHQ1rBl0Q5HGUEIhmRT69aqBKhmiP0oKqvqjGSRBoGdWOrYIIklAi8Sc96/4cPN17lccClUH8m4EVac3rRpU9koiJn1hFPF9Z3iWS0kfcD//Z85Lh3CHzt1XfEvaDmJq4YuHeIqYntQW5B32t3LuvT3338vsueeqYkl3jeshiwpsQSipgsv9LMjYLgI94oguxw42aDp0MSSjph6k8ccfn9du/oeTyxbV+k/uTtFAE0suSHQCYkVVSyN/eYbX2WYUxrLcln+chSJqzS2Ap1Lij76FrVqZUbRQ60XULnQf1sQgZHOsYOTxToqBGY0mljyOUrIrb5kiP3seI9OJrECBSw7scQGg8QSPz3ktJpYkmgyFhXXxDLMOD2s4yYNl1wWAftxsklji4Xj1a9vZIFcBF5alCO5VLdY5E1w443G0jUDrLBQtSGyB0xhMkFXkwvJa6+Z5xya+T66ZYH+U9F00gbVg2JKRFIRhPuWZljqPsQCeq6cCdHIcAPA/ZduF6Dk0BFcmgiMlAJjpdwYSi4IlBtOQNXwYuqjbgIJxo7zabgnZu4yTitJiKTU5KsMySWKA3tPHHWUDMKT6M5bcp1VWSzy2hoTO4ejnDCyY4+AUKt2cb8Pkns1caYWdOqpRlyioRYO1TQSiCsQCmEspjDJhJoizoQVp69ejU5RRZCMqLvMNGzY0HOVChKOt8cevovp2HfWXFHornOXwJ+hYQCE8xdckPG4qOi0e0mDMNPsJeOJbD/R4JZ4Yfx4U+EKhWOO8XUYwBQ5ezOK3EuU0mbFvUb/4WRbCbvb8Gvn7dJrJnbjyIDOOsv/O7y1bdpI/OAfZUth1ZUowSTK1IdhHwo6Kr+I07Jp7V6GnFzi90ZQpwEe6lK2I4VOkMklRR+k2VBNiwQdkQIRlJ7WSD4jvrq67BBeXXON/xwfRb3u8F7h40BiiapdhXBAwdqeBY8rYcXpDWK0baCSuOSyhJMMu+Ig4Dgcb5ddRFZayXxt7PP1ZPkOafsuWaCuukdgUlttRCl/Zwk6sejWYguJhx1n0DhkXEbjQCpdjEttu62EC1c3WqPaXnviCSNhRdZaDFifDRvmJylkYBroJoxu3YzCGKg1UDxQZTFvga4XjSVofRXO6TBNn+KaXLIhO6ro7B0n5EsvSWwoU3JJ9wCjVc3L6hpViGRy6SSxjgpDIVyngYj1EW9FcQy90jBCziq59NCLWyuJJavmqOtr/IDSzS5Ux50w4nRHfrjksoT2OPsqqY6UAt/PcTgeUlQSTM0jP263X25pLBmjXgnQ0mpylQZNPIIa4CJCQMP/8zX2IeqFpUcPkQkTjFlMWWBTOEmheu8TjFLG16p4vhCtccXURATznrhrOurgnHN8FaLOXVK9jf3MCd0v3XWDtzyVjgSTKMfYJMxdhphcUku7/XbzmEsea0fKJl0PMrl0TrGOCoNoS4VOp5xiPKocK4J4y64FkYgTHhG2cnslTMLPTHdcooJKUpIeRpzuyA+XXJZA48aNvbUixVZG9Pv5/4xjSwt3y51caltSbeWZbEeOlgFmE7TJiRxir71SXdXofmH0yAqTsoKDELpcfXHs2CC5mDMnv+/H4AQDH/4fKMOReSUcxkg33zz1a2xaSYRiw5bG2jtYqjm5pEKkVR+Sy6h2dKlw6PWQucuovs4yJ5cUC3HhVjDOKHitUynoycWFgwJOKbjk0lFBMKXB8VTHRDChd6wI4SDhEEsFFBoJ2HV8+62JNWtWsHshF7faRMQPAcfpfF96nO6oG5dclggnHItWW6rLSgEsXrxY5s+fL/Y2GNvRfezHbVjYY55oxeTVV1MDNkrg9tWVTmaGgI7RQ7t7iV8KUNC57TYzDF+xCwsDn7wglRywvZcWLhFZLmjZIaP85BM/uwrddjEaPP+8vwlAQRqEujj24DqgZlW0d8q82L6c4IWiWzByJpe2NJadM+oxHzW4Hqn8ApMx1YBWcXLJ9ZbLFL82XVaO71hZ0ZOLYexSgyRNLikIsuvK4SgTdNlsEx9mCnFGdayYWNKJ1MSSx+ysBCaGuESz2QrYrcvf15GspEF8TZy9sMBrO11K4nrie5dYFo5LLgOAkxBnqkIrI5z0c+bMkRkzZsiimm22qN+0QfHqaw3k9y1qvKA1YSRg06uCMniw77JJ8knmkYEuXVLzLi7KKNfsinrFYNjT1uQSlLJ3I5u9LaABpYypEmGukGVvvZYf1pCw21dPCUZV7aafPZYbSzgx1U2KiBzpdELh88jqWuBjnXMrTxxMfeIqjQ0xuWQ2DIM0ze1ss+yywFomHTUoVRJLUUNX4bCCpAoKeY7ogGefhj94D1CocaTy6KOpiSWFLWq09siMhlWIGDD0SZjvYS3E1cTXxNmF0qhRo1AcZ6sFl1wGdALPnDmz4MqIwvfx/RyH81hjM2YKxq9/SN3SWLqbtg0sntI6iFcDJqokIXZTk/FGLtCRAQMedqFoAIQrIQlmpoXfaHjV1Qap14MPBrO/LeLg9sYNlZ2kwO+UnJpmHxBDcmMp1Xi34lShNJbfbc5mfRyTy7isJAkpuUTlr0beXKYwQC67k72bt3QkgOnTzS5GDXluuMHVNjKFRcQHmlgeeqhZM4IqjSYC0xUKdXguz+lLCZJCqXG5fr82fhyF4ZLLEqH7OGvWLFliC9uLgO/nOBwvRRq7aPe6k0tAXK+28MiW/vvfmtdnKuckHLqkXeWvDHunN0ErDqU0OphIXGH2bDPH9fbb/t8h2WQ3pu1JzhBpFcB+PAJWfatwfeP3eccdIu3bm69PnmyWJseaPfbw73p0LufOFan2uUvbtSLKySWvc731fCVFHCodmlxSlCvxWq7gwI3PmHajmXVCPVJ2XHLpiDnEMdhLaJyPR0THjpV+VdECw7B+/fzLFyESU0LEB7x/rBhXawpNLmO5sqxCcbmjMFxyWSJz584tujKSDsfheHQTqXLD2Bk1mjnQL9r7Lu0/U29uOPdcWfjrYm+2x/a3oSGkC7z53LHTKHJgUMS/sXNnX9bFm4K2DJkkBj4sjAP0obHPpPKvSl57rXnM2BT1gzXWMM+RtdCd1rmJm282sxWxhTuiriXhRH3gAUkqiXOMteUXVLQoFkUde6QhgOs5l+fjjvOLdzvv7F2SK0NYyaXbcekoo9RTRRCtW1fwsxThxLJ/fz+xJM67806/kUD9nT2XNtTtNZ5IGmHE5Y7CcMlliVWNTCfdV199JYMHD5YDDzxQevbsKQsWLJAPPvhADjroIPnrX/8qNxP5Z4Hj/elPS2T7mlHL/01vLF+utbV5otrvTHOXQPeOxUYwY4YM3/oOr7OlkEhSyWJxt8ojeB657iUwfY6LrJqD/PKLCVhJMjVYIrjB278KNPHMSNgmIBgZaO6tbLmlkQopRx8t8vHHEl/s7nSCpbF5J5foKdUZOsrJZRznLgNOLtknzJyTFn6Qw5bohh+tHZcYbiV0j7AjWlBHZt2Igsu9mqA5VuxYkliiZNLEkhjQ3l3JKhL9M2JCnGOTGpeffPLJstNOO8lpVgPi4osvll133VX68aZlgL+b/mccr9QuaLXhkssSSHd6Vc4991w5/vjj5fHHH5eRI0d6TlOXXHKJXH755fLkk0/Ka6+9JtNYMpTD2SplbGn9I80D2+0jkzSWJMvqXg6Y8S9pKgu8bhZdLkYx+SvEqOyJAj4vdsMzUpABM22+777mOfI6rZzjLEs5U11FEwzNH2TNKmmhQvn3v2f+u3g76bgiN2XU0trkjR3IK7fZxjx+553oJ1RhJ5f23CU3zyhXU1kxVCu/GFtVySWFIPvzeeutpW//KAn7pFL3qGJ1vnosLCaTuLfAETmwk9BZdOKinj0r/YqiA2ol8iANDQcNSk0sCZ+OrAkfNZmk63vMMeY5sUHSusB2XD5w4EC5NE2et99++8lNN92U8XvfeOMNqa/3rQxxuSN/XHJZAj9xs00DZyoWrm5TExSvttpq8sMPP8jSpUulQ4cO0qBBA9lnn31kvNoHZjluytzl0gyuO8wyZWDcb9vLk43M1XcdmS3DVr3eU5OSZNhQCVQLb7qXeOdEdrUBjjXayrWzqKQODKRBIWDKFPOYZgGrY7I1a/k6100dWSUfO/bY+KwbrMbuJaexBgN5J5cQ5bY0eu3ttjOPP/rIWBxXQXKJKRNzlr/+ap6jNshSIC8felKttVaqo0ehsCJKwSnW4QgZtozpyi1GQdgPWwVCpbygYUChWRNLrjUjRvj3krfeMnGf/vkJJ/gdTJxj1SGW+M9WuycpLu/cubOsktbm3nrrrT0X2EyrAUeMGCFHI/mq47iOunHJZZFQyfgjg1EFw78rr7yynHDCCdK3b1+5/fbbvZa6vQeTx9/l2FPHcbfZZnntPN0Ln7WXJVJzxdArR4bOJRcWKntnLr5Yltb8as+uf7l0bl+zYM2CY2v3koAost1LDU7twAawjUsfIkgg5FMof4EmLZXKuvZ6kY/z93QrCxI9PUbsOPhgX0/IPyTnro54gvOhdrbIA3IWAuzkks9FlImTa6ydXJbgDnj++b73GNuhKn6JQvbw9dfmsZu3dMQIroMkRMQncMYZ/sa1aofEkltjtsQSYdx++5m9oECSiZxYE3PqTBiM6fuMajS2Beg84vJ8uPvuu6VHjx4rJKMKx3XGPvnjkssiocqR6USjQ/nuu+960tjRo0fLxIkT5S1KSAXAcZctW+wpy+CnXxvKW/V3NE/UsYVWY82wJBcY5iixmkbm+rH8Wca1NtrI+j/P9/270zj1VD9RYfibUc7IgfQPAx+9YNjBNdnxJZck46qYAUxx6Toq7MfTjmRd0OFEHqOweFrHpWIF8ue//tU8JkgeN06SiMb9jBbrmplYryOJc3JZZOeSU9NelYChVsXX7tpLb4OatwTXuXSUIYF66SXzeIMNzFiPQ2TMmMyJpao5maHk0qv3kd12E7nnnhVV7CTuekmgV8H2gKTG5XVBs4dYHZ+UbHBcju/ID5dcFsmytD2Sdldys802k1atWnmzll27dvUqHvYSVx7bncxsx0+RxrY81DywB+heecWTX7FvXqUjmjTu8eoFfsD0n//41eu07iX23sBnhgXFkVzqqFnvDjuYPRtacgMGBrC/TViCSZJBtVHXxwwZkqoQzQfeOm4gGi8ztxnLsYEqkMbmPXcZp+QSxymVH2HOFeWuM5q7EpJLAjl2yulliJrXtttK5QnDKZYoNd8ql8NR5P2POEYhhEGRU+2wztteb8R0kJ1YotzEokJNGhmNZqpIexI2hIdXXuk/p0ER8JrfyMTldTF16lT59NNPvZG1ww47TKZPny7HYfcd0PGrEZdcFvvGZRj6hU6dOnkzluizOREnT54sf/7zn72/zwlMZ/O5557z3KrqOv7ee/vPxy5fcY/jb0+/Il27ijzxhH/Px4gWJ9EGG7YROf548wd0/bAIywByCK2uc5HKucS93HC1U/lvq1bGFo2r5IUXGm9thSskF4IoB68FQICK7H/qVPN8q62Kl9dddZXvKjtzpqlyxi4P339/f/M8+1iIPKo1uUTPRDc3DsklcmbdP8sKIXtXbYI6l3yeKP588415juKEPXyRIKjkknuInm8YbWWKVh2OgOAWr58nLv89eri3lsSSzWsa5mDUw7iLhqJctmg06AQRoxYY+uhsZSYQhWkoyqXCdptPUlxeF7vssou8/PLLMnbsWE8eu/HGG2fc6lDs8asR904VSaNGjaRehslyzHxOOukkGTRokPTu3VvatGnjJZLnnHOOnHnmmbL//vvLzjvvLJtssknWY3Ncjs8eci0QvzVnA5knNUOYNTNoPzz6Sq3RCxcQLiS2jNLTkajulcxx+vSMGz8i2b0cOdLPqOgqkFSsu67/52wERieqvwMsGelwJUC2wDVN1zriv4E8qFhTXGJmvl9zM6qYsdttxT8C5wJgiIQiQ8IoyjGWRWXsgI0ycVlJUkJyyaVHJWXk/jTXIxODBJVcfvCBH9U6SawjRBglZz4QqGHQtax20juWFLMw9tPrDA01wh+dGiGuYxLBDpkyQfhEPKBhFD2IKJuQFxqXDxkyRIYOHSoTJkyQbt26yZQpU2TYsGGeiywbG/gaCWU+aFzuyI96y92EatHQRv9ddYsB0rRpU9moJhCgeaeS1wekn/STMbJw1TWlya/zvK9tIJ9J/Q03kKefTt2xXgtXC1wmANvCDMvokXQx04DiljyOeGT99aVyMKPKfksN8ijPcTXNBINNXFX1qkuJkytxTCvrNHe6dPE9RcijqEaWCucHFWDtcGM2zGL32PDmmyI71swds8s1YbOX779v9pQCp7O9n3YF2HOhVdXXXov2LxKXWLaeq6x94kSJJNgnXnCBefzMM/76ozwCYeSvOhKOiuSAAyQ6MK9M1RFoBeme1EIhktX9BUT+6gbncAQIKgAu72qGz8dSw5dqhZCNjqUqMgmFKGhpYsl7hvu/1uIpRHN75HKbL3RB8d3Q28uNN0psKUdc7qibqNRXYwlrRsI+rj13+VwTM2y89Fd/7nLIxq/KpElZEktgcEFldCRdzCymQbVdu5ckNRXtXtKNYZGVJpZc6bIllkA5jwxMZ6aI7siidBdAjKAJxVykJpb86oJILAHnODVEIA+nzhCrCiWraLTb//LLEd6dUxz2+sE6O5fIEpWoS2OpUunrpWgU1U5rEZ1LEkqMNTSxZL45UomlfTIxsMZoQbHYuwpc59IREtSKNbEkjschtpq5//7UxBLTRjuxBCaENLGkcIxSqZDEUneJqkkqx4/6baXScbmjblxyWQLsyskkjS0Fjre6ahjFdLFUEvn0ku7CuNzK4lstn73DK7W5Y0YYqLQNcM45J+NfY0ZILy40CjP4/4QPQV3v3v6wBd1L1cfkAocvWnM68Y/FHAOrMXKv4eZx+OF+zkSTLovJb0lzLDpfwe/Xnt+IPHzObGMf1pIkCD6ma69dxK7LOEQBKo3lJMfYJyHJJYEvalFgfMEeA48EfLjVLZbqRSn3Kju5ZAjc4QgYzGhQainstIypACmwRHvgQD+xxIeBSSA7sUThorsrNXajkFwoCBrs4nNkZsYjGpc76sYllyXAfGWLnJld4XC8BpZnNBdXVb3NXbqmfChmCHNpfaP9bjBhxX2XK8BVCd0rPP98Rkkh3Uv1/6FzFnRikxfsy3jjDb/jQQkuX407LhovvOBPryO/w4PbcumNMngSPfWUPy9BkzloeT+julRCNYnh7aJiGRu40yoMtsXOmSg3qriheW+bQicmuYzySpICk0tqWQS/eo3mc1XsXHSokmSdQS9FzsV+K3UJad/eDII7HAHzz3+aa58azeSpTE9sYon7tCaWKNKZhLATS9TuyFkVHKox7CsWzB3btPHH41VNHzfKEZc76sYllwGcdE3swKQEOE76h4JluHpfh7FiArUGrWr+Hp7T6jud/cCmbaVQosoQmFM11OYfIzbaQCwLaDH4oRqt4TxTx7qWFdhpJ+Muq+8hbxzdz0hZ4K7I+PEiw4aZxxTc7r3XH1MLGiqUzHDoTYqZFpLMWID9HQM5+sEocH9s1LHjf5x9s8Lngj1CcUkusbTWFgTJZRSLAgUkl+yRGzTIf85MfCQ3cwRl5oNttWp/nSTWEQLcqrVYQ5EmH8FSUuH+n55Y3nRTamLJrY9VZao8ojFQ6h5Q3nd7JIruZVz9EcOOyx1145LLANrlbdu29aolpcD3cxy7nU+ehHbeno0bKzVDmLaeUYcUcsFsYq317FsmeUuDz492L4mvyta9xJREFzKqtmObbYo7FpItsjV1JCIwIridMUOiyHffGSNU/XWiYLYbPWFAQ1c30xDnc2rQ5IgFtjQ2p+tNgh1juUZo95Jf3M8/S6QhauGkUz12FBPiPJNLla/rgnL8wzKsQ0tWcunmLR0hwmeKuEOTKVZXU0esRpj24Ban7wXu/+mJJXVVpK8YpwNJJo66QShBiUV0XvPjj/16f9wIMy535IdLLgOgcePG0q5du6IrJfr9/L+Ccxcr4lgPByqRHF9vF/lNVk6Ve+ouyFzQ0r/sMv85rTLkTnV0L6nShwrBMXOW+lrQZtjyx2Lo2FFkwgQ/oGKQkQTzww8lSpBQYgii7zHK3nI54zGngZEkEChj8BOLKiXniuoPacHGfetzqetIIIrJWtxWkuSZXGLbr51+VAB33BFMUBcKdvs7qOTyL38p7TU5HGncc4/I66+bx3i2xXner9TEksKVJpYUrXBttRNLZMNcSrW4hYcC719Qik1dTWJLlTUGjRvE06UkmMTz6XG5I39cchkQnIDt27eXloVKOb39kotl/vz5wlYYLixnnmm09JpvkQRgvgKLljeWV2VX03LSjDOf5BIod+kAJ2Uprkpp8PIxaAWUUKF2L7GLxhlWE2Wyq6B+IDOmJJjareWqzJU4QovcuXBjfArso0IOUy5ZPzcsxhZ1xoJR11JlNWWBva1qoctdj+G3hFA1yWUU5y7zSC4x2tbPCEEYl09m1SNLUJ3Ld9/1HztZrCNAuISzslq54YbUj2K1diwzJZYIVJhD1SmozTc3ArSgTY/oXKJmgnnzYubLUAOx9Jw5c2TmzJmyJEMTJRd0KYnjieddYlk8LrkMEE5KnKoK7WDqB2HatBly5JGLUlwH8bhhQTfyq/S5y1rpJ1ebfFYzEBHZonqyG52lSeteanOIUchQupckx2g+3nnHPN9wQ9OJKlHGkAKtBRLvzp3Nc9YgdOtmZLMVhmF5BvCBhBIDnyLqEiWBcZDtmcTsWAa1dPSgvJtAaWyik0vUBFrJ4POnmq6YJJdsNkJloN19nGK5lEQaPYm4wBSrM+Q6PWWKXwEr90XKkWgYA9GxH9ZwodaqNihSkVjqKLomlrYigksSdXj9KHIpRQDSvHk4rwmRmyatzMJOny6xYdGiRTJjxgwvpia2LgRMe+hWklw6KWxpuOQy4JOaSsnCIqV6f/yxUAYOnCmtWi3y4gEuMGjpybf22MPvaj2nc5e2nWg+c5e620S9qjG6QdCfBm6iOkdE7omTaeCwmInWGaDDJash2wkajsn6A4x94JdfTBelglZoX3yRqvzlQs6vpRJst51JKhWMSnKayUQBPgwEurrwPlYLO7PDzDMrSRKZXBIpafeS62O+16uIJJcnn+wHWNtum+qPFkkIqvQkIhIt1nqawqWudHJdS0eA0BDHARVYg3bNNdX39hICUSvVHAjVWHpiqXPeavKPlxviD70FhgGXDF0LQ0EtLvtGS43Bly5dKrNmzfKO4ygNl1wGBBUSTspCW/A2JI9rrrlEbr55ljz99PJaeSqwYYPdhzBNOsjn0ta4wSj5SmPh0kv9qxePWTCVBlIV7V5yA1CL8EBgD6U9WEH3aYstJDSwzieZVG9zMmZ2Y9K2KzNcs/r29XfJ8zLs3V6VAC8lqsbAqYBBQIaGdnTgg6LZOZ83Ot4JgI+kPSac81JCZKErIeKQXEZdGpsjuRwzxszAaxDMmoDIj+FwgdHrupu3dEQMEibiG5WBIqJSIVa1QNhDMVcTS0yNkAXbiSV/hg0FyiYgJmMSBCFI2DCehfgLqP3rCE+SY3Dg+zlOoV1PRyouuQyIuXPnFl0tsaFL2b79Qtl66xW7MfvUNCxrpbEEDxrlFJJcksjZonq7dVVDq1ZGtRp495Kl3rjHqD3qOeeYbCZstDuqWRTlOKzR7rpLygkVwEmTfCXwyJGVNwTh548YIbLxxr5/xymnSLRJqGus5gHcH+lw5+UYSyaKbjPqoCNV+UVMkkveWtYEK3QV9HMSady8pSPCUKzR++CmmxplQDXBLYudlJq/UOBFfpoeCxB3oV4DLp0UutTNNWxQ0ejoDpx6auqSgqTG4MBxOJ6jeFxyGQBUOrKdiF999ZUMHjxYDjzwQOnZs6csWLBALr74Ytl1112lH0lWFjheegUmpfCvc5cajZK05TN3qaDr0vlG9Ch2F9RKhFR3T/cyw18pDDbDMzhAQqtOReXUl5GIs+1cNw1TNh082Eh0ywCNUr1R8FJ4HtbMRKHQBHv44dRZW0wGIstmm/nranBa+egjqbq5S94D5ZNPJPJwsmtkxOst5HpVgeSSyy9GatoApBZl1zQijVtD4ogo3P5xK7cLNpFXAgQIBeX0xJIQJD2xRDJL91DBvX///cv7WpHjqhqeXaS89qjH4CeffLLstNNOchot3xqyxdxnnHGG9O7d24vNL7roIlmmrfQsMbgjf1xyGQDq9JqJc889V44//nh5/PHHZeTIkZ771H777Sc3ZZh1tOF4HNcGF3h1J3xR9pTF0tAf0oJC5pjatTPbeTXps0tUGbqXGLuW1L3k/cEClyuUeo6X0x5V4efRpsMpSaFsyr8/RBkEu6n45yskmcWu8gwL3Ofs05LTI9I5mx3pZ3A+jiOJNvWJsjQ2Q3LJxICuSMB8+pZbKq8yqFhyufrq1bt80BEoiJW0voxJ1u67V88bjFCKeraGGieemDmxxKzHjhdwbOX7yg1utdddl7rBDtuKKMfgAwcOlEu5eFtki7nPP/98efjhh+WRRx6RH3/8UV62tL+ZYnBH/rjkMgB+yjCzCDhWsWNnm5osYrXVVvOeb7311p6rbKHH5YOubmq/SDN5U3bwzRYKlcaqVRtDREDkRPczR/eSz6a9XrMgsMDVwQHWSSBRrVTbTq+Y/PsVNjdTJgwhwSQxR42rF2UUyZrXRw1mQPQmhqEnrzuyiksiE+2+k1xGWbNTQM1Hccll5ZJLksp//cuvR1EHY+49NgSRXDJor1bhtC9ik1k7ospbb4ncfrsfBlx1lVSVFJiEUUMM6tsUmdM/VrxH9upvZjFJyCsFXoi6/Qv1mr1wICrYsXLnzp1lFY1ra8gWc69a05zByIeVgLmO6ygMl1yWCNWNP7K4nzAUvPLKK8sJJ5wgffv2ldv1qponHDe9I7rC3CVBhAZGhTowYguLkB74YDFVnwYD3TpzRJJU1M0AMx17iSKRGoMWlYQrOpJce+8LrVnbZSAgkL28/755zD8byWmU4zRMBdRfiXWoJMKRnG3HXhVpNXzzjW+nF2MS37mk0Kau0Lg4Z7ihVzq5XPzrQk8Oa5uN7LSTxAv75LErFsV0LcE5xTpKhNoft1e9l1C8CdPxNGqJ5ZAh/r8dsRT17fQ4AEdqzPx1UxNJZqYEtNwQJql0GYuOKE005IrB8wH57G677SZNmzaV3dPa6JlicEd+uOSyRKh2ZDv5qIa8++67njR29OjRMnHiRHmDbfV5wnHTqyl77+0/9laSEAFposYOiZwuIBnAqhRva2DI7oMPVvgrNPQ09mI+oqDuJVdLOkz6HpHQHXCARAZscW29G4+RWwYU9DKjoE6TeAo99FCqkjmKMHfJ66SyDLhjkhBHXhqrq21iDBbw2oytM7ls3do/meKSXNIGVPkFW8HV1SNCyeX/piysDZ66dq1s16Bo9ORhL6V+kAvFJZeOAKG2zng8dOpkiq7Vmlhee+2KCSNCAaYGvv/ePN91VxOSlXtyKFvRUyeJmBqwZ0GjHIPnwzXXXFMrh33zzTfrjMEd+eGSyxKxB4DTYRHrZpttJq1atfJmLbt27SpTp04t6fjMQW65pXn8rvxF5spa/iBmMd1LtF4aPfEBzRBJUV3U7iUVtQzmsplBB8quDZUWYOaDaD9q0Jqzr+J0VtGDlriPg26lvU6GgXy72RRlcMTUpFhviO+8I9EDhwPmweCRR6I5EFIAJJY62kZ+kPOeSXRiF5a03B11ojh3aSWXP842M5eoqKIS3BUEEhM6+eDWkDgiAF4rdmjBiE2xq1fjxB13pEphcWHPlFhSZ2NTmk4m4X/A5JCOJEUBJoc01GTCqYA+ScVi8Hxp1KiR7LHHHikzl0EevxpxyWWpbyDze1no1KmT/PDDD55umxN08uTJ0q5AiVKm46s0drnUlxdkLxNMFDt3CWRAumTqqadEXntthb9CpUplEXQv63Rp5gN56KFGV6nOlvhv53i/KgqDkNil6j/y8cdN4lLkwCE3Czas6K+G/BWpXZzg9atFPPs5ybd//FGiBUkBNp5AcsXvMOZoPoDPVp0qAa1WEL0UWLiqGLb8IirJpWVX2VgW1XZa6CTHDgoNSinJJVvuVXKBAZvDUSS4w+q9g7AARUDSwTeQjqXCBBLG/OmJJZ1AZhqnTDHPueZg6BMVJ3m7D3HRRan/nijkXbli8FzQkfympgiHynD8+PGyIfvhAjp+tePetQAqHvWyCOIx7znppJNk0KBBnt1xmzZtPDvkYcOGeY5W06ZNk27dusnYLAEWx+X46aywkmTGDL/yXkxyiQ7yggtS7wRpLZP11vO7lwS9dXYvuQqRoAFXScpwxcqzygVdVjYUE0zBSy+ZQLhAxzDeOm4qKILV5dd2XIsTzFro9ojPP09d+hwZEiaNTfzcJVIIHeqlHa46sAqyaEl9WVzPXGubyEKv21CO9buRNfNBbaJJKr+r2LVvHVFh4kRfBcPKq8B2Zkc8sTzqKP85WzGImdJDRZIz1n0QagATSoSDUZ1FJa7RDVgYD7HZLWox+JAhQ2To0KEyYcIEL76eMmVKxpibNSOnn366t4akT58+ngkQ3ij5xOCOuqm33E2rlsynn34qv9vdw4BgwHijDMEBXSQuQiR5a8ts+UbWlfrbb+fPLzF3yTxWIWBNxiCEdj/oYDJZbvHVVyZW4edjxkWyYStyayGpPOgg85iqzzPPpGbEUQe9B0YxKudFh/z882Z+KQ9YhqzzCVT7aAAU66kRBTid8PP44Qc/4WRUNTKQ7XbsaPa9ACdmjNcmEIQwCq25MpX+rPA51RlmdGcZVgpFEmyoNcpkqJe57AqCMuO8K1aVVeU3mdZ4M1nvhw9rjbRjB7o73fFW5wmUBcYrdtvNPD7uuNQdRQ5HAWFF585+V461G6zfSDIoHrQQD3wUMUJMTyy5bdH9093X1PhJMnfcUSINoZCGcwjeCBm1Hl8tMbijblznMgBYMVLO46LgUlOr76SVvC9bmNZisXOXOuxlB6a4u6ZpHriQqMyDxBaJxwrQPRk40H+Ob3WcEkvAGpIOMG6kwG5O/Li//LLObyW/HzrUf44SOM6Jpcp0mD2zT40JEyQ6cNe2u5f2i40hie9cRmzuEtNaCiYLxag/2qy9ML6JZVCdS9vMB+mFw1EE+ONpYrnVVqZOkWTwVbATS2KBTIkl8HVNLBEGjBkT/cQSEHOpSTsNh7w9OBIUgzvqxiWXAcD+nGzS2GLheKurUUkGVlhJYmsVi5HGAsJ/yoyAa2wGzQOKWR1PokOny5A9kI8iLdU5RboR2oKJG9wJx4/3Z1EpzzEoggQ5C7wXqCrUXIx/Om9HEsBsQL2YsJTv18/svIoMdkGDbk3ktLshJZcbbGBK3nFLLrt08cvdJJcV+n0xO651CU0uVxJj6FPVyaXOW4JbQ+IoAu4PmMAoNL/VCTuJ4Khu76/m/o84I1NoyFpmxBt2UorFQ1wgMValPP0D9Q+rphjckRuXXAYAs5UttMsVEByvQY45F7vw760koaumWV+xySUfTntD7nnnGQ2sBWpb5pGAHLK2e0nGQTKpyRfJGYMHlV7QVApILWnRaYDGfgISzA8/XOGv0uQlSNVNMMTOl14qiYK9ZHvsYR6zWx0PJH7tkQAZrLbzkccyEBJT7E53ncklsnN1jOWzhztEHGBGXH9fePDrItgyQj7LtYxzGeqvVDO3Hpf3MBt60tB+zVPKn7VzSTbAuITDUSAkTzpZMnhwPLpypSSWxx7rP2dsBDVEpvAHsx7eD+Xii1OfxwFuOdqFxkev0uuaKhGDO3LjkssAT8Qmlp19KXCcuj4o7dv7QejrsrP8OmWGyHbb+cFFHhLOjJA96B46fLEZIMjQvdQZ59ruJSVKrprAICYGPpUW4gcBnSESTJ1iJxBmAdXbb6f8NXJyRkuBX90DDyTPap3rLONx66xjno8bZxLOyGBLY9EjxxRyAlYO5ZVc2tJYKhw6dxoHKiyNpZPy5JP+Z3at9RKQXFLtYeYYKIoVU9xjdkldvjm3ArqvOaoHbpnqrUbzx65ZJ1H6m55YXn555o8eYQNGYcyiwvHHVz4xKxY8INXRltut7jCtlhjckRuXXAbYQm/btq1XQSkFvp/j5NPiV2nsYmksLy/pYpYTljJ3qdgttwsvXGEdBzN42r1kreC1R/3Pv3uQgfz3v7E2VFkBsinez223Nc9xtunWzchmRYTVSDR5gV8bCZg9Apsk1l7bJM5a0KPqGpVtEtK7t1/Q4EXGOEnQZjmrSOpc3enmLgsG8YE9Gz1ypEjDVRKQXFJUVF1+sZJY3hyVJLh5S0eBcPrZ+50JJ5Iap998c+ocKd3abIkl7vHMKuJXobcrZi7jKu5ac02Rf/7Tf445USWnUSoRgzuy45LLAGncuLG3x7LY6olqvPPdq7PCShL75xYrjQWSKJYaanSbYY8Gpi7ambv+0fXlB1nddypUl8EkwZUUKzeMfYCIv3t3+eHeZz01sHofcbHdc09JNLwF6v3EzYT9ncU2ygOFVTfMDQNL1VgrE1PsvMBeW5io5JJimO4Vo9VR5E7ZYhpzrEbVHBJnZ8+gQq+f/EFcZ3bdvKWjwtxwgz85Qihhr+RIEigf7CQax2lq7JlyEgRPxGu6dYl7KL5zcVdd8u/XngaX8EceqezrIXYmhi42MSR2J4YnlneUhksuA4aTsn379tKyZcuCT3C2wsydO1emTp0qc+bM8Z7ngpGlhg2X+3OXXMFKnbu091TqlY+p9LRddHQvBx9irJ9/kWZyrZxqliCecIIkFpZ0PfuscbeBP/6QPx16oHT97r+1Lmq2gUGSQfqjBgTIojH40YZJRUmINLYqHGO5PmqFjJOn1GtWAefuRx/5KxzpNHhocsl1V3Vr1e4U68x8HAWAsYt2s/h4k4DFPYHKxI03GkmrPSp02WWZE8uffzbFK6aMYPPNzba2lVaS2EO4ibmP3bmthPCDWJmYmdiZGLrQDYvE6sTsxO4usQwGl1yGgJ6oHTp08HTbxSSZfFBmzJghi9IMddIbNTvvZB5/Ku3l04lzRLbfvuYLnxqf6GLp0MGfMufqmD40sWSJnD1tsDQS8/qub3Cq/HjpzfHVeOQLskvmSWs6u42WL5YHpL+c2vyuRFQi84XmOrmbqp9ZlE3ltuIwM6yaZIZgsQNNeueS7p8mRpo1xYUyz10+8YQJDIHgDkPs2iDPVn7EVRobdHKJMZvDkSc4pKqMn46lms8nCa4fdg0dFRfS30yhD5cRxDT6kaIoT31aZxWTAGuW1eiPexW7TMsJMTKxcj4NmXSIzYnRO3bsWFRDyJEdl1yGyLJly+THH38s+IRXFi5cKDNnzsyZYO6zr/9hGPvdlqkzMqXMXQIlSI280LrY2sfTT5e2Ex+QQTLSe/rz0lXlulsSUIrLh8aN5akB98udcoT3tIEsk2vmD5YW95f5qlph1lhD5KGH/GY5iuiHH67wiyK717UkdJ+YvUx655IZE4pBOtiT43oROYhKdEYm5OSSrortysj5ajd9XXJZ85lR5170blQwHY48wHtAt5cxRZI0t/RsiSUjIplyEkZlEHMxTaP3SzwPk+bHwL+drQH6HiB6Y5qqHBAbEyMTKxcDsTkx+tLI2N4nB5dchgQn7axZs2RJifIqvp/jZEtQV1hJYi99LVVmxlWQgSTgw6vWoNjA1cxhntPwSmnYwAwc8iVG3ZIOZoyHHdFAhsgI+Y/UvD9w8snmThPXea0iYKaGIF0heM+xCrQ8HHpo7KWxBSWXoFkS15uK/wIKlJrvtJOfGNfZpi0OXRWke3kPOih1J13iOpck7LRJCuWTTzy5v4eTxDryhHqWLRNF6ESCmSSor9uJJS6v2RJLQgAMw7S2ySrip57yt0YljS23FBkyxDymc33++cmJsR3F4ZLLkED3XWw1JR2Ow/GyfahbNjc/52XZXRbN/Sm4uUtA66gJ6113GSfYo4+u/eMNbjpDBh1Rv1Y9i/tZkuFX2revSaKXS3159aDrZPm5NVaxwNAl71kVXahwy8MgRc8BrNYxTakYrI3ZZhvzGH/0uElFa7b5aNOooOQybnOXZZLGMhekHQRqZhlX8MY9ueSaoycLevViXBPdvKWjCLjv6/YaJnPitrexLli5duKJ/vNhw4xTejYVJdcb9UFETDNmTLL3fGrHUu9ZbLD74INkxNiO4nDJZQhQCcl2on711VcyePBgOfDAA6Vnz56yYMECOfbYY+WAAw6Qgw46SO69996M38fxMlVomH3rvrdJZH6VP8kbry725y7pYJQyd6laDh2m0/K/fqBZ7nTUUV4FT+MYLqjz50uiZ0p0xSV7Ru+8q57Uu+hCszFZwQAJGzW1kE043GBvu81XZr73nt/wrhiHH+4/vuceieN7qt3LWbPyMEuKc3KpO5VCSi7feccEg/q+cjpk7KrEPbnEdE0H3oKYt3RrSBx5QIihoibiEUx88jS8j01iad/PqB+TSGVLLLm+YGyj3Hqrb36XZFhTpjs7CX3o3IZVY7dj7JNPPll22mknOe2002r//OKLL5Zdd91V+uE0aHHGGWd4sTax93VpWxCyxdiO4kjQJSA6zJ8/P2uL/dxzz5Xjjz9eHn/8cRk5cqQ3l3nkkUfKk08+Kffdd5888MAD8sUXX6zwfRyP42aiew9/1nHsJ21FunQJbu4SuLLqVneVTPEzatqU+IloLP/TT8ntXj74oJHGaBzKvGHtYD4WlGxT1jsOj0nEI2GhGj5ULHk/kP8AnaGKKlJppWrFg7t9DGcqND/gpWe4JKzYrY1rcolpjC7Co70Y4MwouRargjRmwNURl+2MxD25dGtIHBWAmF53N6JiSVJNAnOa9MSS1d/ZEktqY3bXliRUd4JXA6ecIrLBBubxCy8YT72wY+yBAwfKpWkDvvvtt5/cRJUjjR49esgTTzwhY8aMkSlTpsikSZPyirEdheOSyxD4iQwrAzhasaB1mxrJ3mqrrSarrrqqdK6xVFt55ZVlgw02yNr1zHbcvfbyHz+3bK/UifEgpLGrrOIvM9IgDHmstQuIipU6pVIQyvJSY8vUqf5MgVYzVxhJYojLtoylC42rrCbkCadTJ1OlVQg0wpbGZIVkxVteWOPkMm6cJHrukr+si2fjllzS5mCPD7DrEuvhgCAw1BHU7bbzOywZqfbkkmBtyhTzmHuIJvwORxaef96EAsDpQjKVFCiSY6OgnHde7sQSRVPv3n4hC/GSKiaqBfwfa1c7ielehlFft2Nh4udViFEttt56a2mewZK3S5cuniNso0aNPIdYHGazHddRGi65DBiqH39kSSYYGiaBPOGEE6Rv375yO8J0i9mzZ8v06dNl0yxT3xw3U0e0ZUuRv7Q2H5IpsrV8N3u5H2gG0blE88iGXDvwSssakIjqikGKP+W2ow6TBQvMHKHueMcvxk40UzjkEGOZqok3C63w6tbSbsLhvdGl2cxdklurUq+i0lhMqJKcXPJ532QTvxISN3lPCHOXmGmMNGbWsuqqIvfd518WM1LtySWL+DS4cmY+jjrgI2LPITINsvrqyXjbKJDThVMwqKEwlS2xxItsv/382zzrR4iBqnGzBff8nXf2b0U331y+GDtffvvtNxk/fnxtY6euGNtROC65DJjFixdnPTmxO3733Xc9aezo0aNl4sSJ8sYbb9RaKv/jH//wdOMkoJnguBw/E/vs5c/3Pf9CPX/ukqve118X/w96/fVUizTbgzttppAqnTbtsKZOQhGIXyUVyA8/9NWHXCxz3jQOPFDk6afNTkx48UXTmakSyQU3VV2Px82FZLMi12vu9hrtPPJIBbPcMjvGIisNyXU1NLRzGVByiaOz7QaLQqrOfKvak0s3b+kogKuvFpk2zTwmmbBNuuOeWJ56aupGtlyJ5ezZpjamgrNddjGipWrZeZ0O75PtIH/BBSI//FCeGDsf+F5icOYxW+m4Vx4xtqMwXHIZMMxQZoMlrZtttpl3Qjdu3Fi6du0qU6dO9U7oc845x3u+tx1kFXD87gPXqn383Afriey2m/+HxXYvSUrReeiHDY2IZg24cKYtNSSe0RsMeRTS0bhz553+7CDKCyRAaQqMzOy5pxk6UKddiggMe5VrAVSFpTHMX7JlQmdVM4w/hA/JgtrY0n6u+BLOMiWXcZTG4gah3bJ33xX57ruiD0XTFgEBzsUwYECegW+SkkukJIXC+664zqUjB5iM4ZYKJFFJMfEhKUpPLEmOssE1hukLmv46GoJYSdeDVys0BPWai7M+cuJyxNj5cO2110qzZs3kcFvZFODxHYYEXA6iRf0cV9hOnTrJDz/84Om6OYEnT54s7dq181yrVlppJTlmhcVr+R9/xy4N5U8NjCbj+QU7y7I/dypt7hLZAdoODfJYdo6/9mWXpbYq06o86d1LDfDiCK6ndtMWk5qC9lSxv4/3XmeXmGeirFmqg29MEiM21yjcsNVlt6zEWBrburUv40x8cpkujaUwUyTMfenYJmZjeRc2kpJcUo3PqwKWhltD4sgTJKO6bop75BZbxP+tI16xDEfrTCwRiBAi6ceG6/Vzz1kmf1UOHjtq8HfjjUbFFHaMXRcY+Xz88cde5zKM4zt83LsYMAwKMzCcCcx8TjrpJBk0aJD07t1b2rRpIx06dJA777xTPvzwQ+nTp4/33+tIUTOgg8iZf65It3afe4+/lxbyfzNX8yPTQpNLJAe4sbz1lnmO/RftJ9w3CQB33dWX3OpQUw3t2+Pe5Ves4tq9RNLLnKVK+5HGahOsIOj0jh8vsv765jlXWJx247Tovki48WoVmBoEsxhBymPyAhcXnUV8+WVTco8JFGnUeQ+Va51KIDu5jOFuzyBWkjAabndUmLPUDnqik0uGvdDnBbGGBCl5mzbBvTZHosAB9LHH/DpGTpOsGEl8MZ9RSCpzJZY0twYN8nfnsrGNS5btpVjtEPLoShbUJBjqhxFjDxkyRIYOHSoTJkyQbt26eS6ww4YN81xkp02b5n1tbM39BFfZb775Rg4++GAv1n700UfzjrEdhVFvuZteDZxPP/1Ufg9hi3zTpk1loxyBwy3HvSfH3bKl9/iSrs/KOcsvFXntNfOHdMvyvfLZi52YG0TSuaU5rgdtAbpysO66JlHSElVNztmxo7kAc9Fl/kmX68YBgngSS8b0YNttzdtox54Fw5uAVNbuLtCdQUeTYEgqUWjXjBZ7Y5BPPFFmCdUllxgPeSDziJGF3777mmo4fPutvxEoIyREdKzYXUJRw+5ExQFaASygxDmLbj/JUgEnCsUsLlNfflnkr5r1QRTVgKJZFtlUJMFgTdtHOKsVugeIk4trOXTrZubEHY40KLZyy9LbGOboyM7jnliyv1ohWcbAJ1d8QCKqc4WEPiSZO+4Y/muNG9S8qO1i2A6EPIRBcY2xHfnjOpchwIqRShy3+xHr+nOX761T3Nwl3R176ABto51YAldRTGuAq4Yuf6yBrSV6w6FTlfbHsbAg18QSicuYMSUmlkALiraK7iMkcKYDXBGtaPmgCEjTe62akWB8jq64oswvwh64QxobIze4guYuOUmRDsAnn8RvtycOy8jvAXcMXYuRB/xKjz7aTyz5aLHTsiDi3LkM0szHzVs6ssC1W081PmPMNscZJn0KSSw1GdXEktoX9zeXWGaGWqc9SYXsOIjbUqVibEf+uOQyBNivk00aWywcb/U6fL433K6FbNzQTJZP/Hkz+XmrXQpLLumuoV3UTz/RWd++2btB+m/k6pHmhEqjSJsOXMDjYtRJU9aWb5CLMLcVCOusY34PtEI186ZLgGw24fIY3PP0dKGbFMSGnLxB4oeZEmBvaC1OTqypDy0GPs9xo8iVJBhvYSIFXCbvuacIt0aXXBpccunIANJ8TRT4bDFHF+dVG6xOse/1mM7UlVhyXbG/h73ObBpzZIcxKQ15EFjccUd8Y2xH/rjkMgSYrWwR8AJqjtcgj2hpn3ZmanqJNJJxU9fLf+4SN82ePUXmzfP1eDq8lAk6cLrYEj1aWjsKKYRWNcmhuBFFne+/N7m0rghkXiDwGweyPzQ0GPsAWTcB9bPPSpLBBJkl1IBcmvlVHRErC3quxszYp6ocY4ucu2SMWVX8aryFuUbB6G7aau9c/uUvwbwmR6LALF49CDD0URFOHCFc0XlANQHT+1M2uBwNHpz6PVn3XTtqoclgrybhfS7V6LGSMbYjP1xyGRKcqE1K1lIaOE6+H6Tuu/vurc89s8wYmmjHRoXvmTRlRx7py9DQteKEUdcHjYl3DchYDsXcTo7uJaNUUYWEhwqbGrmS+9GcDQUcRkgmNZDmjo3MmD0nCYaqsM5bkFhSfCibcpOVOrp39IEHYpM8FJxc2hFfHJNLVmiotBdjszqiEH6NBx9samOANBYjqaJwnUvzGeH673BYMCf/1FPmMaO5OKnGObE880z/OTX0HOahHu+8Y24hWnjG4C9Go/sVBw9DRHHANjacZOMaYzvywyWXIUGLvW3btl6FpVh0NIydPEvzjMJ3O2RdaSwmcB47paUs3zWPuUsyPwJuWHVVYwWXj582c4RqgMFwNaU8iw4dTOAHNESj3L3kYqeNkpYtzdtRwq+ubgjiWIiFc5A639DOs/d3JAxqFchj1TeE8d6yBSk4SmnWQaddI6WIY68rrIrOpS2NJZLjJMkBAZ423TARY51A0SQhueQ81wHnfGGkAc0jMF/vqvcOCwo3tjKAz1icDPpsLr98xcSyriQRg0J2WWJOA9xGrr8+3pLgSr332ougk6m7QYuFmJjYuFSI0YnVg5bZVjsuuQyRxo0be3ssi62u6Lk+d+5cmTp1qsyZM0fqMvddZYfNpWt9s8rk899ayvSN/+r/YSZpLBmV7XyB/ZsdoNYFV2YSUrj99hVWbFAR1H9HVLuXqFR11oJO6/33m/HI0OFKyw874gi/fYruhjtXQiFxxwBB41e6w1jbl4UYSmNxItRkPK/kEj26ygXimlzmKY19/nljrmF/lIpZ7xj75JIkXFfs0OouNEiyjZPcvKUjDeYs9fTCIiCbDUPU+fe/U0Md7j11JZYobKh14S+miiYKpK7+Ujh4V6hXJMbgdpJfCMTAxMLExMTGpUBsToxOrO4IFpdchgwnbfv27aVly5YlVUb0AzVjxgxZxCcz+w+U7q0/rn069vMO2ecuSQTplpHUqMxVXWDzBSmB2q0R5KRNxNNN0P2QzDTefLNECpTCyDM1Z8ctTg0rywLtUYbE7NIwwy3c+WLkalqoRIYbvW3m+sUXZfjB/GJ1HQ8ZbYk3pnJLY3m5dRpjkY1qu/Pjj/3PdpzA5VqvWexhyfA5QFpl1wqoirN9pSTimlzy4VG9npu3dAQIXTu1U+AjifN7HBs8JMhnn52qVDrnnNzfw7WWjqV22FjBgthopZXCfa1JhvecAjMwBYSBfiEQ+xID59NoyQWxODE5sblLLMPBJZdlQE/kDh06lKzrXrhwocycOTNngtl9Nz8weu6pJalzlzoXyZXzoIN8l1ce1zXRng38pVWKRfsgbYUAh9UbEg5tKi+pNMRjJL4EqtowqeuGEwp0mphZtd9/Wr6U9hKaYLInTOsYGD5RDc9VMwkEys0M1uovn3M1yaY+fNB0N0ecQAlBBQKI7NLUEHwkaPZ/953vPUY9pmTimlzaJ4Wto86Xd9/1H7vOpcP6nJ14on9d5ppNsTiOiaV9X09PNDPBvxn5q0ruMQijzpXPtJAjO6hY7ekpOpn51j+JeYl9iYFLgRi8Y8eOJTd8HLlxyWUZwYnq51Jtsry4eInMmjUra+Vm8/3byjpizHteeX91WbjzHqlzl3yaWRD+0Ud+MIpMsNjN9gxg2BPxaRnappuK9Ovnd1+i0r1EEqOVM24e2IwX+xaUDBc5vNBt110ycZwD4th9yuOfy556XfPCdhDb4r1sOy9jQNU5xtaxkmT4cF9KTRWcMeVAYoQkJJeldC5RUcTZAtQRKI8+6n/0uD/WZXoTRdI7lCSWde2/5XY7aJDIiy+a52yn4H1Q0YujNPCO3GIL83jyZDOJVRfEusS8xL6lQgxev2KBXvXg3uEygj681KqLwnGy6c3r7bSjdBdzV1iwuLG8tqYldUUai+SSOwewNBYDn1In9I891uwTBJxQ08yDota9xP3OlvuMGVO4D0YokGHdcov/ZvEY/R+GPwmDKjDSGB13YNQ0dMNcgudttvHvbFpgSWpyGYN/XyFzl++/n1qEGDVKZO21A/qZ1ZhcYsT2ySf+ZyMg90VHvOH+zLoRBWFNSfPMFSB9pjJ95jIbXF9U1MKUAd5vFMgdwQmIbOM1ush1xYPlip0dweGSyzJBxSXbCf3VV1/J4MGD5cADD5SePXvKggUL5PDDD5fevXt7X7s5S6uP42Ws5Ky7rnRf853ap8991cm3PuVKqXORJDDYogZhPU9QQudN4SpudVaJd9UIABkqOVOlQGlH41Yh2d1hB4kOxxxjynnqGoCDAD7eumQsQZDn2f5FVDVRb4eK/cuPQfeyKjuXlLZbtTKPx43zkj1cK5Gxq0wPSZWdg5ZMNSaXbDVXJ3K339Jhuaiqoh4RASuw40T6epF0l9hsYDqoiQ/NLczndtopvNdZrWAMpTvE8b0gBssndj755JNlp512ktMYxarh4osvll133VX6qTyuhltvvVX22msv6dq1a/6xsyMwXHJZJubPn59VxnruuefK8ccfL48//riMHDnSGzC+8cYb5eGHH/b+e+211+RjzDnS4HgcNxN7dflD6omRU459ztp3+fXXqRqRIKMz5tk0sH3zTdMezNK9pGuou+nKCfkZeZq+beyusr10IgMuQw8/7Lf1cBLgalzplm8IsJtwwAB/FJjtLKGeG2QoWmwhiS/bss0yJZf2YFRck0suFHvvbR5zMrz+ujfzpZdBzHu4fAVK3JNLzmn0i4Xg5i0dadDItl2YkaHHaTSNxNK2LyDWOOOMur+PW4Gtirj1Vj8BcgQPibzehvkd6Y7xXLHzwIED5dK0JZn77bef3HTTTSt838477yz3sa89A7liZ0cwuOSyTPz0008Zv47zFXt2tqmR6q222mre81Vr1ntQXVm8eHHWweNsx11zt82ls7ztPf5gelP5plNNoKZQ5cnnilsIdNrsDz7DDlbgjupK1zrSveTiXW4oeKGGBHa133FHhG+cON48/bTZiQkMgRBwJ+yiyPtPJ1ulRzRTTjghxB+IqRY2gFo2pTMWYdZYw6jX804u0a+xg1aTy7iaQlmFr2k3jK1VOyBVQ7YWuIIzjsklv1s9KfidF7qcV+ctwZn5VD2cTlx7dQqDECEIYVO5wCwmPbHMZ5aftUa6EQwQYQ0ZEs5rdPhbs44/3lfnZzNZsmPczp07yypp+uytt95ammdwWurUqVNOA81ssbMjGFxyWQaokvyRRdLIkPLKK68sJ5xwgvTt21duZ1dkDVRpaPfvsMMOnrtVJjhuxo7oDjvUzl3C8y9Yf7bmmuFlVT16iOy4ox/Ypk1r25tKkKqUs3tJEUsVxtiJP/SQH7RHlj33FHnhBf+FvvGGyO67+xa3CYFaCr8PzaMxaeG/skhjGdyLMHxMtXvJ1om8XHVVQUAr2FYrxIm99qq9Ri164rnaL//nPyG5VsYxueQ6oGqGUsx8eJ+33DLY1+aIHcy8s/dZaxV1uapGCRJCO75AaplPYvnOO8YZVlWSxx0XT/OiOMLvC8MkIFR86638Y+dSyRo7OwLBJZdlgM5jtpN46dKl8u6773rS2NGjR8vEiRPlDRII78M2WsaNG+cti53OwqkMcFyOvwJbby3dG/odmec+6+D/GYlKWNP5BCn2EkOuHlagxq4o7V6ySuC226QsIKdDfqnceGOMYimGPjBi0iocq17Y5pxNRxJTyIfs8wGjXAxcQmG//fy72iOP5LFAsrJo3oCToS40z4nt+hlXaexaa8nyGkVHp6XvSyv51pOxh9ZRsBdpxyW5LGXekvuGfsBoT5Vq6uaINVwCdcm9FnG02Bd12E/9z3+mSi51/XYu2HKEiEXrMySZcZMBxxlUOfzubGWZHSrnip1LJWvs7AgEl1yWgWU5Vkmwa2ezzTaTVq1aebOWDB+TTCpIALbffnt5/fXXCzt+kyay/TZLZDUxEsoXZC9Zqr/umTNFZs+W0CDxYfmctlrS3HvSu5dIIsKEGwcJrd5AsBkfPFjiBUNm48eLrL++ec45wi7AvHSS8YHZS/yMgIIlv7cAtvdk7lIxewmcgMy3RpiqNPWhUb+qv5Kk/+rPe8WH0AI/DqwJZjUklwzX6b/TSWKrHjp/TAnA/vsbEVIcIDm54AL/OfOizGfXBcVtzIrUZxHfF7zz1EfPUR5YNKBKFMJc2zE+V+wcBGEfv5pxyWU53uQcO3XQhf/www+e/psTffLkydK2bVvva7o4lk7mhroQsIDjN9x4Q9lTzLKmH2RNmdz5OP8P01aFBI49e8mEvdUZ2nxzY6QD5Lhhdi8penHx0vian03XMpZwBWYxpwaRtLC4I8Z13UQWsL1X40oa9jjIhlK8jJE0thqTS3afnjPen7s86y9jvUp3qKg0thqSSzdv6aiBWwjXXR0ZoWsZB0gq0xNLy0g0K4QjdCyps6uiCv9B/u2O8sIqODrNCq6+qoQNex+l23cZHi65LAONGjXKasiDec9JJ50kgwYN8laPtGnTxpuvPO6446RXr16evTJmP8xeZoLjcvwVmDfP2w9nz10+19FaXIXMMuxO28EHm8fff+/bz2XpXoa1ZYMRVh37ZK6PqlhcpD4ZYRCGBFNlj99+azrFbxvzpiTADZ7fk46ZMouJVClwcFDGVUA/D3npTWOSXNqL2WKYXNKtxjD5jWXby0/SzPva2lOeD9/Zt1qTS7eGpGqhcIexis4cMmfZrp1EHpJKW1LJCpF8Ektm1pG/qlEyBsus5s7gCeMoEyT6jNjD55/7hY702HnIkCEydOhQmTBhgnTr1k2mTJkiw4YN8/xJpk2b5n1tbM1e5OHDh3vPf/75Z+//R6UVkLPGzo5AqLfcTbSWhU8//VR+D0H/2bRpU9koPajgLoEs9cUX5QtpLW3lC+/LO22/VF6fvJL5c7pgGdabBAoDDQS5/DwyOwKhli1r/5gL/KOPmsfsOjzxxGB/PDcPxhU1TmRnle7ajD0UD3DTxI0AmJdihymJZkJg+8pBB5nH3APIqbffPoRN2+reQIfd3rodIVCXt21rHiNX472pkzZtzLI6Zks5X2I0SHTYYSL33GMev7x6L9ntx5oLBY4PnTuH94ORnWOAtN568Zhp5gI3caJ5jO6/kMrZbrv5Cha0gWutFc5rdEQapKBsEQNCiQ8/jHYHj2SYxNJeq01iac+LZgMV5KGHGnM/4NKIFNOuxTkqA+cdPhj8jghn2HfNquOyxs6OwHCdyzLBipGyHRddAWsriC/rfy2biulcTHqnvszfend/3ibMuUvd9XHUUebxr7+mSmXTupd4AAXZvWRbB/ssNbEkcU1MYqmOv9j6aTKJzocBEkqwCYFNLGrKwNw9v09ypEAh0lCobEbUPY5cR8cB8x6zVWnsjz+G/1kPONjVxJIgo9M/rF28NVXp0Ihr53KddQpLLIngtHNJQu0Sy6qEbQy28Q0Kkbglltdem19iCbjHamLJv5N6rEssowHSZA0XCWd0pUxZY2dHYLjkskywhyebNLZYON7q6nipoAGljKftnq5da6WxS5fWk5fWP7x8c5fAFYLFdMAeEDQPlnJWO1MYCYwYEdwNiJ1VOk+B+tHW9CeGZs1MMqn7AMnOycjsifiYQz0C3yKgCUcuGOgMPt09VrvogCeDfhEEkwkdu+a8zisHjuHcJf82VgEoeIGtNcA39ZHn/JUkUu3JJQU7XUlUaAX+s898pyxn5lO14LCqdSfuxerDF0W45vF67cQS+eQp1rRPLpjM0dCIUT6UTDT+HdGB3y1hDbAt7733yhg7OwLFJZdlgtnKXAtdi4HjNbCtzSZP9ks/Wobcf//Uucvfdy3f3KVW1PXqz7CDPX0fUveSSuZjj5nHXD/GjEndMpAo6FagkdT9LrT4cEENdUlk+aA+8sAD/hYWcml7001gGkzl7rslqmj+gEKIUdukJZecusxZqvcXvxaee3rgDjWrlN5807Rbwk4u81omWmG0egZu3tJRIATuOstO/Vfn3KKaWBIrXHSR/zVe78kn56+GsDu0t94aHzfcaoKpKZ1S4XfODG2DBmWInR2B45LLMsIJ3cRe1F0keFpwnJQPHL7alB41O2OpIzsddtxRdpHx0kTM18d+uK4sr9+gfJ1LOOMMf6cgwTvievGL5jTbgFEnqlWlwPwEP05BXqezaomFzPn++82OFaC1x64VBlkTAJJQpExavKQZ/vLLAf4ArItVUkgmG9GuVdIdYzHn0MYxhiI33GD9IZJvvfjplvewO5cRlUgHYuajbibgOpdVB7cITHxUBUJAH9X7JB9DrvmMxCu42eabWD7/vH9r1O5YaLtyHSVz0km+odS4cSJPPhlc7AwrxM6OUHDJZRmhFc+aEbqYxYI3zrx5DaRFi7a+VEAH0tSAAq2HliT/8hdZueFi2VVMIvnlV/Xlk81rulwY+pCUhg02bFjQ6Z1CS1NZupfFxvZ4UvTr5xtK8iP320+qA84pMnOuzAp3Xwxroh4k58Gee/pNbwIijIjz6t7lA4N9uEvpfCKDOBEkyY6x1Ll0JJtTmVoJv5ZaVPod9tylHcBEfcG2W0PiKBKKrro6G8PsfPZCVjKx5DamUDO1b3O5wO+OS7s64bKWLC38cEQMLsFXXOE/p+O8eHHpsTPw/RwnaJmtY0VccllmGjduLO3atSu6CsNnq3nzZTJt2pfy/fffyxKumshOsdKEddc1C+FVB4reZeutU6Wxa9ZYw5Wze3nCCea1ATJOdTisccE/4ADzmPz4zjsLPzwJ5YABpvsJbG6xZzOqAgZJ0ArZd08eY/CUgASTf8ree5vH1ERQ/2rQUA3S2IKTS9QCyNIjnlyy0he3Sj1Fkb4xJ50CH2i9ZjJ3Gdb5bF+XI9rBDjS5ZHEouxgcVQP1M4xtFOrQATWFAkXr0HZiyWvN11Ues3pWXGCiDD17GjWEyyuiDwUB9SrECuG225Z4K0VKkbIScxN7E4M7wscllxWAk7t9+/bSsmXLoioojRsvl6ZNf5fZs2fL1P/9T+YsWybLyTq5Q7DbA/9mmx12SEkux/60Q3nnLjXJtectzzorJUBkUF+hg1FoXIdk5oUXzGP++agbSyxyxRPOJ6Jzu/R35ZUif/97wE44lcmd8atCJgvjx/uOciWzxx7+gZ95xrTB455cgu5DZddsBP9NXAIYE1fRBd5KduBbC7Llrl39vSxTp4bzguzAI6nJJS1/VaxQ2XPRdlXBNVMvBYzqa8EuatcFtkLZBvMkltSo84HTGyW9/ju5dDBa4cbs4gGXJMyXGjVaLsceO0d22mmqF+8uLOKaTIxNrE3M7RLL8uGSywqhJ3yHDh2kVatW3s6dwr7f/P/y+vVlznHHyYwHHpBFtPxWKPmLN3f5Z/mfrC9fek9f/WhN+b3+KuVNLgELV11aT2ZgOT9us43nPeRBoFmIHw1JpS5TJgFBUpeeX1cdROhYbeqJwmO6c1GX+tUBoxIYNGnhABl1ICpWog5d9kY7lJMoYuAWq7/OgteRRLR7iUP0I4/4TTTkelkDwHJIY+PYucRekdVE+eLmLasWfvWYtsMqqxjzuygmluecI3LZZf7X6Djmm1hiCEbHUv2uqK8hloryihXHimy++SJ57rkZctxxc7yGSqGstNJKXmzdsWPHohs5juJxyWWFQQO+1lprSevWrYvXk9evLws33lhm/uUvsiiTy+EOOwgfK+1e/vFHPZmwyZHlnbsE/n32VD5DkVY3Lb17mY9hI4kojpLaBOXw7AZ3iDF0otWn0TqWeczmBrlQtAIwUmw3ZsmZrQ03iZXGEhxpczUJySWXHtuUg9qY/vsyoqY+Ya4kiUtySZFo1iy/a1lI4KSSWHBmPlUDt1pbwILXAStOo5hY2o7gN95ozIfygZgBfzatn6D45lLhtk7EC+LYmTNnSsuWC72GQTEwMtasWTPnClshXHIZAZYvXy6zZs0y85PFUr++9/0ch+OlsMEGImuvnTp3uWqNqU855y6BKz9tSvVCZ9lUDdtu6xvwsNOwru4l8RUGPij+gGol44UOCzJvewaXEi4DrjqIElMYM7Y9eMiZS84FSMQ4CXWtz0cfSdRQ9eO8eXlu5IhocsnvClMm1qoAuy3VNTortCA0++SaFUaRJC7JJdJgdS4rZQ2JSy6rBoo36saM11e++yHLBWEL9eb0xJKEOB9ImhFH6XgMCSWJZdQSaEcZ4uGa5DJjPOwoCy65jABz584tSkueCY7D8VKgqr3DDrKnvCj1xQQkY+dsVZnkkjKUrXdhYt9qURbSveRG9MYb5nGbNqbZVGyVK9EQtT/9tL9u48UXzaDN/PkSVzilCZY0rsYVMBDHw4h3L9WiPe7rSBi5prakL/Gqq/L8peuAGFmpmphVY3IZhJkPusiNNw72dTkiCcUoPnN20hYlXxPif17f5Zf7X7vppvwTS2AFGXOVqvJghYV9+XPEg9DjYUdZcKF4haG6ku3k/+qrr2Tw4MFy4IEHSs+ePWXBggXe15ctWyaHHHKInMaG2QxwvBWqPjvuKKvLfNleTOnyf1/8Sb6s37b8c5ew114i3bqZxwxGWMstO3cW2Xdfvzg/alTmQzz2mMjVV5vHjRqJ/Pe/hY0dVR3s8qCku9pq5jlZOe4pc+ZIXOGf8tBDfj5AwGQ1wosDC1qVpyMp1u5QXE19+FCwmRoi0ol99ll/YTu/O8Zbte5RJ2HPXSY9uaTN/9ln5vGWWzqHkyoBqSkJJqAY4NIfpcQSxZE96sBcKGqGfCEW0HiAAjP3gZ13Dv61OsoTD5988smy0047pcS4F198sey6667SD7maxZdfful97a9//atceOGFK3QqM8bDjtBxyWWFmT9/fta2/bnnnivHH3+8PP744zJy5Mhap6tHHnlE1tW1HhngeBw3hR2MQ2yKa2zbo/2ORrmTDLt7yc4QS6Zpdy+xIU/vXpKP2kuRcRXL5GPkyDCsSCFBFwhPmWL8vtWqM4ZstZW/0hVYjl2SkSjvjWqzv/nGbHGOu2Oslu+ZrdYIs0LwEuzPLkbGW2xRYJFE5QlhzF0mPbnkM684SWxV8NZbIrffbh6zOzYvlUCZIPSh48h1QMF7jn2U+YKVALsQ7e/v0SPY1+kobzw8cOBAudS2ChZuy/vJTbSz07j22mvl73//uzzzzDPe94/HLLKueNgROi65rDA/ZRmcmjFjhmfws03NfOJqq63mPefvP/fcc9IHD/FCjsssWYMGqcll4xp71nJLY7VFyfwlzJ5tNiPXsP32foMCzwpbnciYFf90/ef17Zv/sL+jJhvj4quDKGRiXboUkKlEDxLKQw81j3/91ZxWJY2U2tLYbK3zOCaX6qJTIZiJIrHUOhY5fL4OkLVgKcu1QzuxQRdGkp5cunnLqgLhBdJSrV/jqp6jLl2RxNJOdkkM8aHLl+efTy1W8e9jtZEjnmjc2rlzZ1kF2b7F1ltvLc2bN18hcZwyZYrsUrMUkwT0lQxKvGxxtiM8XHJZQfhg/JHFlIJB5JVXXllOOOEE6du3r9xeU3q8/vrr5ZhjjqnTAYvjpnRE+aBusYV0lrdldfnB+9KLX28qS6RBZaSxau2qXQiGLdimnqV7qRs0cJfU+IitJqwycA7TBdKxo5lX06CUDJ5FYBGRTRYKv39kVLrSkX+GHVAVDFmP2guyJwNv+6QklxWcu6R+pM1GVgVh2FXUZ9eWxhJdVnNyyUxAIY4ldnLJjktHoiFswJsMOnUqopgTElyb2ZZlJ5a33lpYYsm/i0KiKh7pdga299gRqXg4G3QkabzompG1115b5mRQ4a0QDztCxyWXFWTx4sVZT/ilS5fKu+++60ljR48eLRMnTpS77rpLfv75Z6+qUxccl+OnsOOO0kCWyV5i7NTm/9pI3q6/Q+WSS5Ic7N2AypJlE4eKVzcPsGaC7iUjcLfdZr7GWlDm7ZD5OIoAB2ESTM3IWKxO9Q9nnBhC7YTzYdVVzXPOF2uUt/AEg8EkNY7BbTci0LjT4m2ckktyGtvJmYawqrMLJsyVJHFILrln6BI/lp8WshledzSQlOpn35FIsHJg1lJBUcivPQqnLzJWnZHUxPLomimdfODahzs8ShU46CCzC9MVmpMZD5dKxnjYESouuawgGPNkg6Wvm222mbcEllnLrl27es5XJJzdu3eX008/XV577TW54IIL8j9+hrnL51oNqtzcpbYoNaBjeM6SudndS/6Z9s2HG+Xmm5fzhSaQddYxcmhdv0HneI89jGw2hlCr0NkioEpvj5glTRrLup688h87iahAcolEmY04OjtNYKmmr0VBcU0zbJyPgzRdikNyyeCq6r4LkcRiCPfJJ/45ESW7UEfg4L6KfxMwNoA4pdKQO+DqjU+CQsG4kMSS05/6koYr/LtwiS2kxuKIVzycDWSySF41Kf3uu++kRZaqZTHHdxSPSy4rSP0cezM6deokP/zwg/fB4UMxefJk2XTTTeWll16SsWPHypVXXildunTJmVyucPwdd/T+b2/xpWRjl1tRXiWSCrYcq1YHSQTmPtbLxVgWyDl1J96RR6bOWThKADfRl14yXUtAAsqdO6wl9SGD2ava15Mb2PO5BYFDFLpr7errwvoIoPkE91O6+nXCzVatlCuQXJ56qp/ToMRE5l4SuPli7ANEz2+/LVWVXBY7b/nBB2bwFZwkNtFgBs6qJmjWLNUwp9KJ5bXX+l+jGFjIjCS3J6YW9CNAjYTVzSiZHMmNh7OBHHaLLbaoNfF5+umnZbfddgvs+I7ice92BWnUqFGtVjwdzHtOOukkGTRokPTu3VvatGnj2TDnC8fl+CkQiKy1lqwvX0unBibIfPu71jJP1qicNFYXVnIHBO6Ilt3n+een/lWcJW13UEcA8N6zH0Jn2UjysdtDZxpDqIprM5YgZPDgIuYv+Vwefrj/HE12XOcu+beoNPbrr4vMtosDRbF2k1k3QochkIZZWCtJkpxcqiQWnFNsYmEG0Ta5w9pg7bUr+YrM9ZetEumJJWZs+YLygRlLnSFl1JgaqI7HO5ITDw8ZMkSGDh0qEyZMkG7dunmmPcOGDfNcZKdNm+Z9jSYLnHrqqXLjjTfKvvvuK82aNas196kzHnaESs1CN0cl4IRfaaWV5HdtyaWBFJb/MsHcZa7ZS467QuLKc6SxTz0l3Zc+Ix/Kn2XZsnryYr29pd/yByqXXNJVwTbu3HONxI2p/DFjvD/68MPUv4q8zlUpQ4DInxLwgAEmqWQ+gX1SOCbpXGxMID9g7ynNGRpbePL85z8ip5xS4IEGDhQZNsyXxjLAFIGhnqJNfZixVcfYGol8mCDbtbsSFIU6dAjo4Olzl7aGvhSSnFw6p9iqAHMzHQfAHLyQfZFhJZaoF7gGA5dQEksUSPlCw50iIauagYSS3KIQLytHfOLhEcQdaWzFyZyBtm3bypiaeLGgeNgRKq5zWWFwuirrcTPtu1zzEN9msxJzl2oDq+VVMoN33vG8ZfiyDTcltw83JGgpsdFeNcd6R7fWxMTJr8heYYMzIVKxgmjTxt82Pn26yKRJEgXi4BhLjYg5L535+tvfAq5REFXqv4lFfvqDSqUakkuCrC23DP51OSoO84jUaG1vAlTkUUosyRsKSSwBMzD2WcJKK4k8+WTqJc2RDMoeDztCwyWXFYaB5KArKvUWL5bVsw0v18xddpUJ0rShcdgY+8cuUqsarJSZCzaflo/44tPP8QJSNQFhNFPjKr3JOEKASASb1ZNO8r9Ghs+gXMysvPff33copSBBI/b77ws8iC2NtbPVChKH5BLjZ12dS46OG2TghWOVxnKtw9inGpNL3GLzASUCM5fALLHaKjsSBQKgn382j6kL1tzuKwK3C9Qi6Yklr6vQMQddWcLY3AMPiOy8c/Cv15HQeLhePVndaafLjksuKwyzldncrYpi2TJpcdtt0oCgI9MsElLa+vVlJVkouzV9y/vSN7+uJh9JjaNkpaSxgIauJlhq9MoL0u7zl2qbrSNHps6QuO5liHAHv+661BI4j8nUYpZgcq7oCAamUChdCzKN69XLSIaBqCYCCcd66/k5UFHJZcj7TCdO9FWqnEoUg0K5t4exkiROySUnQr4zAkih9d/j5i0TCap3rX/xebM2e1UssVTRC/kCNctCE0tmtDEBUm65ReTAA4N9rY4Ex8Oen12LOvfCO4LHJZcRgJO/iR3UlMgvu+wi3/fpI0v69jWbhbX9ByyGZJsysdmvvmHLc/X2NQ+03VApWeZFF9U+vUzOljXXWC4PPmg2ZKhCccYMo950hAjRAL+Lyy/3v4blIFasMbL0phFLTtiypXlOvaUgt1I+LySYgPTyqaek0pCwacPqs8/y/HWwdkalQSF2LvEKYi5at4MgRujSJaQfxjw6Gjn9xQZR+Ih6coldJgsMwc1bOqzGtLpkw6WXlrBHtkT4GCJ2sRNLfPoKlcUzX2m7wv/rX4U5yzrixZIlS+T777/3drkHBXF10MmqIz9cchkBaNszlEzVpmTq15ffN9tMZg8dKlNfflnm1Ksny7FYtRxYVSvTfblf7R/7pz5S66CjwUsFmLD+wfKebOE93k7elheOe8ST1YHt2UHe47qXZdJZ4RChUhVKx+yAjNFCYvIqihHqRM55VJCKMsLSWIx9v/22QMfYL74wSUoIQSUBrq5H2Wmn1OZ34NC1UwdtXHCDSJrt5NIuykUFZ+bjyMANN/jmdzhlVyoJ4xrARIU6unPZueuuwleH4QhLTU9vM8cckzI140gQ7KicM2eOTJ06VWbPni1/cFMLAOJp4mpn5FMZXHIZERo3bizt2rULpoNJFF2/vixv3FjmHHeczLjkElnEwkgGn7j615j6dJCp0rb5fO/x+N+2kd9k5YrOXWJG0O/g+nKOXFr7ta0fGlabRRJH6goj/FXoSDnKAN3ve+7xt1Sjc2QgNqCbQDmg860rVPkI0F0jH8kLWuZIEOGZZypafAls7lIXTwYI21qQsQFNUk6T0M1Egl5JEvXOpUsuHWl8841feCWZ+3/2zgReyrH949dpl1SIsibaJLwvJUWiLEmllGwhZI8QIuVFQnYvXruSLFFZIlkSivgTJSItJEsplfb9/D/f+57rPM+ZZubMzHlmPff38+nTzJw5z8yZeZbrd1/X9bv+9z/vVJ1OOK9ecYUVun5h6V+bi3cX79BBZPVqe79LF5FHH80Ko25HwGzcuFHmzp1rxCUiMyiIo4mniasdmcGJyyyCA6F+/fqy6667BrfaUq6cbNhvP5k/bJhsvOUW63LSuLH5Ea9wQvXPze2NWyrIx9ImY32XlNER8JOFGS8dZGaNUC0dGVfGQEjk7KWW3zlSDCNKGFqoJ2vGlnTqJLJmTc589IxTPTFU/Y0+PP30OBOwRGo0awILHVlQk51tpj68B39ZHgluHHtTTtB9l/koLqmbVqdYnNFq1UrNe3NkhGuv9QoRyFjGmFCWcmGJCCyNsGSBmUNaTespqWfByrXM5aewnD9/vmwI8DxL3Ez8TBzthGVmceIyy9CDo1GjRlKnTh0zn6fUVKggm3feWRY88ogUvveeFQUht8ATVowqetq70j5j4pJ+ig8/tLd3261A9nje50aAKA7NAiVzqZVwP/3kspdpBSeFt9/2DG6oLT3+eJEVNvud7ZDQJwGrzsNTpnhjLEuEUuAsKo1NSlweEDLtClhcItBZGNJMAyVwCPe0wEKZfqFUXKxdW7rt5aO4nD/fUx/OzCevmDTJW+tiXDS9lpkQln36FBeWGPAlKizZRU86ydvFOV29+aaba52PkKVcsGCB6bMMgu22287Ey40bNw42OeNIGicusxTqxWvVqiXVq1cPaoOyoUEDWdK7t53HEIoE260cI+XL23KEd6t0zkjfJdVsuHoCK5SUu+7U6QgrgtXmk1qfEC57mUGOPdY6Lag5DMMjKRvN1HzUBCEAY95yxYqeRxEBTFxZP5qZtCEoxY6ruZS55Hhk1CQ0aOD1W6UFgggtjUUMlrak319GlS/iUrOW4MRl3kBL8OWXe/dxh+X8lk5IivMe9PLM4UihkX8tLt6/pXt3e2rVMbbvvJMil2lHxlmyZEmgGcsddtjBxMvOFTZ7cOIyi2FVh4MwMMqVkyUXXiibfYK1hqySU/eyw+Fnr99HfpG6ae27XLjQVlxquT0rrzo6wth66goUP8CKMpS9xCjSvOfZYtxkHWkEpxay2+rCNn26/dJYBMgBaDlGVCqssOO6mkvZS9xi9dCIW1wSsel8w4DEJdUGOvIAwU4JW9pHKAZZGpsrmcuaNUV22ilxcXnIIal5X460w/xIJsxAixaJj/kIQliSscTvDTgfcVo8++zEt8N7p6gKEJQsOGtBgiO/CDyuDYnVoLKgjmBw4jKLWbFiRcQm599++03OP/98Ofnkk6Vr166ydu1aOe+886RTp07SvXt38y+a41ZhhQqyImxQ1IgFbeQ8eZafyrtyQtpGkrBayWD7v/+290lU0j9SxIEHer1uy5YVTVLmIkalrOJ6LzPAv/5lFyAQLKryaZCJW+lkFhwNWSkHqnq5XaI/0RlneA41uNdksOEXDaQffdwfud8xFjVdyhJSjlsCST1FsRakyd200q6d15RVWlOfbBaXnDBx+k10DMnXX3u3XeYyL2Adj1YSLfcnc6hu2OnMWKqw5LURlnq5TgTGJ2P+BXQBjRtXvMjCkZ9xbd++faVVq1ZyzTXXFP1s5syZ0qVLF+nQoYM8pjuXKZD6zMS1/GyofzxaCLbHdh3ZgxOXWcw/oUxdOAMHDpTLL79c3njjDRk+fHhR4/L9998vo0ePNv+i9moWFMg/WLH5qFi4UZ6VC2SUnCZT5Mi09V1yUWHgOmD+QTnNNhdIrqBaw3j//SKLFpmbVGLq/DyML199NeVv1xGp343J3RroLlhgU8oZLhmNBx3qTRmnxt9XX13CL2GEQlOQWjROnCiZRD921l3ivq5q1IYi9I8nShB+/YIL7Meg+s4/7DytkMUjdaMnAxVg+SYuOb50qGm84pIvSjOX1EzqioQjpyEeVy+1Sy9Nb0KaXRDzLky7SissuaSH1ozNdmiJOeKIYN+vIzvj2p49e8odYU3CQ4YMMeJx3LhxMmXKFPnpp59k69atcsstt8hDDz0kr7/+uqxbt86IzWjbdWQHTlxmKazERMo+YttMP+ahhx5q7teoUSOx+ZgFBbK+aVMp1KjaRw95Ve6SG2SzlGcJyfZmpgiMRx980N5GGyMOI/ZXUP/HKAwg0xJqzkQc+HsvGTPhnGMzAKsCCEw1i8HulxLZr76SbIfq8NGj7Wo5ECzpKI1cKI3NZN8lU40wDFbNwkeRzsxJykaS6EJWNorLZPotOR61HxoF4owuch7KR3Uxlc4EKnfSKSwRsxz/fpM0WlsShXOtf0GKRFVYUZUjj+Pa5s2by/bbb1/0M8aRbNmyxZhZ0jvZvn17+eSTT2T58uVStWpV2SM0Duywww6TDyIMqma7QY4zcZQOJy6zlE2bNkU8UHDY4kDr06eP9OjRQ5566qmin/Xv319OPfVUec43uiMSbHUTKcMITQ17yB9SXkLlfmrfGjBz5xbvD3nggRLK6bD01JMQVzXcD0PZEl3lpPcEoeDIALvtZsuo9UsklcZgyQzNS02Egw7yXA7hoou8PqaIkLnUVZCxYz0XzlwUl0lmmNGk/iwvIwd2310yS1B9l4gvzV7mg7h0JbF5BbskIz+Uu+9On+mNCssnnywuLHGKThQ84XCVVmhz4dzrKJtxrfZN4vSqcHvx4sWy0047mWwlWUzE50cffWSEaDhsl+07sgMnLrMUSgEiwcH19ddfm9LYkSNHytSpU02JwF133SVjx46VZ599ViZNmmRWfGJuH7E2aFDEnxX4G9MC7qFjogj9bStXem1sXLBiUru2rQMCmrZvvjlq9jLKx+ZINaSvKBNVNyZEFwF/ELMHUwwLHRroUGrG/hl1fCfCg51Wd+YMrmhkInPJojNjRrSogr4rNXXOKFRyqMEN+2FpzB3ySVw6p9i84r777AguYGE1UVfWZOG6SgFREMKS9Y5TTvFmDCMqQ5d0RxmNa2PBWJE777xTBg8eLGeffbYRneWilMkks31HanDiMkuJdvBwYB1wwAFmpg+9lq1bt5bZs2dLbQRYyJL5hBNOkO8YJ1LS9nH9DLG1fIXIE40xbqHmLaByA/TqjBleyx4Xq7gqtXD6UZ916mlCG2EyRsuWXqzsspcZrjPFP15LFFEgnTvnxJdC9hL/KN2PCKSi7vJZUhqblLisW9cbHJeEuKRPmop5aNq0uOtuRsHQh5mrQO/NF9YBOymcuHRkIbTc6sguLt+cs9JRik68fvHFIlokxWviZ5aMsOQ8deKJ3kzcLl2sGZGr1i7bcS3ssssuxTKS3NZM5iGHHCLPP/+8vPjii6Zsti7XsQS370gv7pvIUipWrBhxEGzTpk1l2bJlpnmZVZpp06bJvvvua+rSgbIAGqH3i7GyzXbZvuy/vxUE7Ag71pQFlbbtwzRXAWY1cCUppRsX1bpPP21vV61qNUfcYwt4nzrxnqg/dDvcOdZlLzMMXyzNeGrFyvI0lsBM1c5idH/cYQd7n+DJV3FenMMOE2nY0DO+IurLFXHJxZfjXn+pRItcj7ffFvnvfz39xfB21alZQVClsdkuLnl/of6juDOXnGgj9Nk7coerrrLFEkBp7MEHp09Y6nWb0wfOrlq8kQjoBg5R1Q9kXlknVqNnR9mNa0EzkiRLqNCbMGGCtGnTxvzs79BIASYjvPTSS2ZKQtS41pEVOHGZpXCgRHJ8xbznyiuvlF69ekm3bt1k7733No3RF198sZxyyimm57J+/fpyvK7iR4DtmgOcK4W6LC5dKs+e+YGMk46RfwkbN7KYU6Yk9feQ7fCXv2Keoh4wccMGtE+USBcjGRE57jg7u1DbyGiFc2QQHJpQHlprSoRy3nkiDz+c1V8LehEHWYUAzt+yVgTHDgsuCvVhGcA/6jCh6nUtjeV70Rq7OHxh/D1SlOeRucwq/Oe80pj6ZKO4ZEEt1GtuTM7iWaGn9/mXX+xtlIhb1c9Zxo8Xef11e7tOHW8MSSrh9EDJql9YIgYpi08U1qgxqdfzFKegN9/MssUpR1rj2t69e0u/fv1k8uTJ0q5dO5k+fboMGDDAeId07NhRjjjiCGkYWsR9+umnpXPnznL66afLGWecYRIqUeNaR1ZQUOjslbKWpUuXyqLQ6I0goaS2FmMVgKZF0n3499w0UY4dcrTcIrfIzRLFgo4rzMCBtl8zTpda2u+aN/cmH1x4ode7kTC4h6gbEEufCMyCApOooNwGCHqpmnWxVIYhOsH5RdNdQF3XgAFZXQdF6bbqYOJ4BCZCrhiMu9DSHDJC7NwZ+JtIon75pX1pshr+SRpRuesukRtvtLdZBCghWuRrpNIZEw6gx5LkdFZ+hTg0sZLFmyNFoue5RGDVi5Jhsn0ZNGwqBjNfNFuJqdRbb5X8Oxiy4XoGTLvP8sUdR2QoLuCapsKMqopk3FmTEZa62EZ2kYwlRSjJjGflnIHLLTANh0kSEfwEHWWAtMS1jozjMpdZTM2aNQNfiWF7O/rt5TTlh1Zb/pZU3b6c/Eduk/PkGeMqG/GqgxjFuIVB7HEsuCMmVVgyw9uvNRKGqe1a1vfppzaDGaqI0yQs7aavvVaK13AEA+qeeTMsRijcpnEviy3DmbuGaAN2cZKu27zdvfe2w1ZhzpzS9fgFUBrL+4vjcEzK1Ac3ZxWWGAM/+2yWCkvQfl8+kAh29TmbuXRmPmUWHGH166dKMJlex0TgEs812y8syVgmIyzZFmvBKixZpGMh2AnLskta4lpHxnHiMouhBJYm5yBhe8wQKkIVGTHVV58WxcvD5Xz5URpF3xCjTCiTZTkzBjTrjxplb9eoYedzRaj2jR+ypUOGePfJwGzZ4pxjsxUuIgxiGzrUewwXGKZwZ6mzG1W9r7ziWfxTjsaw723wl8aWMP4nVx1jydpqkpOvkgrgrF4cDqLvUsUl/cLZso+WVlwy49KRc1AJfeed9jaXbUx8Urmww+7eu7ddQNLXpLihR4/ktnfDDV6IwHWfhHvC7TCOvCItca0j4zhxmeVw0FSOq9at5IsG7rLblA3QtNUoJCK/+UZOaOvNCXpTTo4dmDBPpGdPm03U2SI+/u//tp2HF29cFBMs5jS1RJqSZdVQ0oLyW/j2W69HxZEFXH+9nZKtkRFNt7iuZulcKipe/a2UJFu3aTfGTx8nIO1JzkCmKylxSa2vnlNiiEv6pDDu0K/ouuu8Ksus5cgjve+EdEkyGXL/+ZaavlyfcYnJhX9BwZEz9O3reW5h6JNKYabCkuu0X1ieempy26PiQd2kKWJhWzqX2lG2IQ4lHg0C4uOgxaqj9DhxmeWQ7sd2mdWe0sDJfePGjfLDDz/IvHnzTN37Zp0Fp7M8Nm2S9nvM9DwxJJQFYGSJOrVGgiYQsphkM31eEqx2amDKmMoIBl/JgUChb0xhSNaGDc45NtthvgeKTVcYWdImcknAsTSd0NpGeyhs2WLLworNbsZaFoEJOCnH0weXDeKSz585QFrSG0VAEcyq30+zZjYBnfUgDLX8AhcinZuS6DaUbCmNTVRcrl3r9SLQsBdQIOdIHxje6Cll992Lz3QOGs5vF1wQnLDkd3U0NbCuyJqwo+xCvEncSfxJHEo8WlqIi4mPnZFP9uHEZQ7ACg/uWEFkMPFvWrdunWmoxvKZWUKFKi5FpP7CSaJGXFPkSFkt24t88ok1YhkzRmT77SNvmIav1q1NBLp10xaTlNIJDWhTvxYMBAJILYHDETHkEISpD4EwYOrDBdqRReBEwX6kwS7OMLg9rFkj2QiujEcf7Xmq8PYJxLKlNDYpcQmayWKBae7cbX5M+br2XHHIUxyQM/qktKWx2SwuWVjbZ5+Sn0/phpb00ujuyClYG8BYTKEsX8ckpUpY6rQohCWFGMkKS1qd/adFRoVhDuQomxBzEmcSbxJ3En8G4SNKPExcHFQG1BEsTlzmCBxAjBhhFlBQqzR60M9t2VI24m8On39eFJttkkoySY6xq/+kIsnSYFxSv370q9TNN8vvDY+WmW9bZUkVLj2XKRk/dMcd3m3SKqtWbdN7iTjIYu+YssnJJ1sjJi1fJBphjEQp56imAgoGWIXXw4O3WiyDxyKHuni+805YajP1YLCjPcxJiUud3+ODRSEMPZRHHsmxEYl+cZnMSJJsFpfsa/E0rbt+y5yGPktdnKUUPdmex5Lgko3hjq6Lcb7jeq1jihOFSmwqlLRiCVFJYZGjbEJ2cu7cuTaJEVAgRvxLHEw87IRl9uLEZQ6hB1WjRo2M7fJ2220XiNDcUFAg81980QrMqVOLx2aUxnJSIHsJNH3QTKlzPyKw1y9TZIYcLKfLy6byEevxlEAvqFrYLVlinUlD5YyHHmofnj7dZS+zkmOPtRakuDwB3vQItTSLs3jgsEBg6mgbzJLV/dAs89N3rFlAlvzTCO9JKw0oHojbfyaKqQ/BJn/OP//Y+0wp8WchcgKUMH2lQKMszaO5LC75MkJDxBPutwSXucwpqFTHIRZYlGVxJxUmPiosR4woLiy7dUt+/YOwQA831hBTbUDkyG5hOX/+fNkQwDmUOJd4l7i3cePGgSZZHKnBicschDpzGqL3228/2X///QNZvdm8006y4JFHpHDxYmnb6PeiEZZFfZcffeQ9GRvNceM8G8kI1JR/5CU5Q45/sVdqZ8WRRtIePtwDli512ctcgXrpSZNwrfJWAhhx89tvkm1QGktlOLDWQnls0dukBjwLSmO5hv/+e+nEJUbMalyEqZHfgyln4A3rChl9Pf5zVy4a+pTGKZbPgtmfjpyAc8sVV3i7Xb9+Xnt00MKSEUvhwlJbyBOFNUEM9XRtEOMeFuRKaRXhyFHIUi5YsMDz9SgFxLfEucS7xL3OFTY3cOIyx6FBOojGaATahgYNZEnv3rLDzM+KXN3mSgOZJ/tuG6Ah6ChLpTkrWh+mBtusnJPtTFWWAos7QMSGfNs7dvRMbomz0MKOLIR9g6y4prcxIcHxM6Eaz/SAYyxZcVi61CbNTfkXQk0bfckY4WCc7X2X/JLWqofEJWNjKSPXjCh9lsyly0lKUxqbbZnLRMUlO6UaGeEEXq1a6t6bI1CYz6y7K7Mg/SOCgxaW6oaNAGT0UrLCkkwl50Vt3eZ0iNfBdtsF954ducWSJUsCyVgC8S1xriO3cOIyh2FViIM4Er/99pucf/75cvLJJ0vXrl1l7dq1smLFCrniiiukU6dO5vGFCxcW/6Vy5WTJhRfK5q+/3rY0FoMI+i7DoTnj88/l75qh2rxowRFqFTFazA0lIGjq0D4kaoh+/dVlL3MJluYnT/YCZ5qNMIcK6wXMNAguVvrJ6GklL3Pctsle+meYZKu4RFg2bGhvz54tK5ZuNtlYLaulb5nEcs7Stq2XNilr4pLFAl1wdCWxOQOeZjg0K3R5xFq3TQYuv716bSssk3VyZzejjParr+x91gjx0GLCmaNsx6V9+/aVVq1ayTU+2+CZM2dKly5dpEOHDvIYZTEhPv/8czn11FOlW7ductFFF8k/2pcRgu0FkQV1pA8nLnMYxGK0JumBAwfK5ZdfLm+88YYMHz7clBbcdddd0r59exk3bpy8/PLLsvPOO2/ze4UVKsiKihVNiUvUvsswxv/aVBqs+FImaAltJDgxMM4Ed4JwUVta8GlnIJhe7bCnE2tCqrEVCSU8ZBxZCg6YCEwd5MYYCUpkNWrJEgiaCMY06YeLI9kGMxBSxQyjeVKxiBK0Y6x+1ps2ya3nzCsyEEHXx5o8lBNUr+6pY5rYmEZfVsSl38zHicucgbJ7vTSyuBvY6K4QnJLon+b0BJyuKDxK9nVYiMJlVvvPqXJAWJJxdZRdNC7t2bOn3OE3XTRtF0Nk6NChJgadMmWK/BSadcVjd999t4wZM8aUwL7KjumD7bFdR+7gxGUOE766o+DORV/moSFXmxo1ahj75++//15OCtX10RxdVd06w7e7//5ycOMNsuuu9v6H0lY2SsWIvUu//ipy9tkiy2UnOUneli/aaionCh9/bHuARo+WwGsWtYaPUtxZs7bJXqI5nXNsFoP1KfuHlpiSKScDFWVRI1McdpgVlQqZgHn/1PJqZplZMnFi7owjYZrPO7Y0Fn8lgk9tY85pki2NdeLSkWZ+/FHkvvvsbSwUHn442F5nFZYY7Kmw5BJcmtmTVG2oUKVwiNYTXa9ylF00Lm3evLls70u94xi7ZcsWY0hJ3ySJjk9C13bMeaiugzVr1pjeymjbdeQGTlzmKKzkrI8yfJ5GaoRjnz59pEePHvLUU0/J77//LjvuuKP079/flB+wUhSxzKBcOVnfsKEUfDvdTIeA1bKDfCatbODvgyQhFulaLdu5S3k57IM7rTNAFOFqYAWKIVr0Sibq5BgNTIYQmLqkGmpW6dxZ5F//sg9PmyYyfnwwL+dIEWTTEWZkLbWPFpGQzLzCFHL55d54gJUr7e68/vRe3hPUKSNNSV91sk1WXDYRKy6fekpk770lP0hWXPoN0rIpc8k5jn8l4TKXOQULnn36eOM7rr8+2NE/XOap2ldhSdUFwhI312R54AHrnwecezDvoVXeUbaJFZdS2orLq8LtxYsXm9uDBg2SSy65RNq2bStz5swxrVvhsN2gxpk4Uo8TlznKpk2boh5orA59/fXXpjR25MiRMnXqVPOPevfzzjtPRo0aJcuXL5fXX3894u8XVq4sm6ZP37bvcsaMYn2X111nx14C4xCGDQutthJ1T53qjQOIBlPacd0JqvSRqdNkv4BaxS++MO/HP2fLzb3MkZJGZkZqbTYXK1YJgs52lwL2q6ef9toWief7ftDJazYaO9aqzjSAFtJStETE5aYGxcUlJW7JDk7PSigJVSfiDz+M3/k1mzKXvL7WSsZTEsvCmopLVgkitD44sgvK7LXQgYWiGCbsSQtLzLmCEpZMW/K10cn//le6DKijbMSlsXj++eflySeflA8//FAOPvhgeZqLaxhsl+07cgMnLnOUrTEG2rEidMABB5iZQPRatm7d2jh37bnnnmZGULly5eSYY46RH6nFibb9mTOLMpfF+i7pixPbq/Hf/3qxGPeLOUtS+opoPO642H8I/VAtW9rBXnEP6YsC2VJ/HSx1O4WF5kJ68MH2oS+/tLrFkeXwXb7xhjfNm4sK9qzDh0u2sMMONlBTV8Qnnykvz/87VC+7bp3ImDFpey+qOygKiOS7FYn/jGwgm8XWv/678ix56CHJL0ip6EmMDDgLXrkmLn/5xavlj0dcsrqg1SCu3zLrYbf0CzWOwVhFP8kIS7KKKiw5JbFOlywffFDcu4zL7cUXl/69OvI/Lt1ll11MaazCbWLVZcuWmXmY9FrC8ccfL9MZS5bg9h3ZhROXOQoCMRpNmzY1Byw16hyM06ZNMwfuTjvtZFxk4csvv5R9dfp6pO1/+aXpuSwa5yGHyGLZ1fRd0oNNlsN/QdTnFYMsDnWopDhLugpS0ooQjXtQXxSYCl2/vr1Nj+j775sY02UvcxBSckRGNDUCFxY89GlIyhIOPNDOglQu+bSnfC9N0l4am2jfJUHinfdVkjli6+8ay4+yfZX0mRBldWlsNolLZ+aT19x2m23R1vFZpRF+4ZdUvBDChWWEasO4ISGO+Y8mjy66qPharsMRKy5FSPLz2bNnm+q6CRMmSJs2baR69eqmkk5jU5xj9yGFn+D2HdmF+6ZylIoVK5om6Ehg5nPllVdKr169jLXz3nvvbQ7i66+/3thCM5qEpunumhUKo2DDBqlI1vHPP4vFZu/J8bL1w49MMokVV2B8AReZqOAcQFaSq1xJg68oXSPjaew3k4SrqE67B2qMtm41ZTsIAWDkZqLTCRwZgv2H8mlKnhVu40KXJf0XGGXoYsva9eWle6U3ZbVsbxc3yDxlmbhkepFmH2aFhHC5DetFfv5Z8o5i5RdlTFxGXPFzZAtMWmLciBriBFU5oMKS8tWghCVmyyee6CXFqQZ69NFgTYcc+RWX9u7dW/r16yeTJ0+Wdu3amWzkgAEDjO9Hx44d5YgjjpCGDRuaePWmm24yY/KIV0mGXHjhhdtsm+2yfUduEPLOd+QaHGhVqlQxLrCRoBSWf34olX2FBo9YFBZKldmzpYAs0eefS/v2XeXOO73S2LO+fUF+E+rudjJ+II8/HucF5vTTRSh7QOXFCrip6WOaM4oVS85kBn3RODZ0qA20mEHy6qtS7rTTzCqr6mmcYxHO7uKYA7BaSRRGL6YuHDArA/e4u+7Kii+RZCrrMbQl/7hxP7lInpQX5CwpwE4xFZPQkxSX6HGEMJNeYP2+TUTmj/XmI2rWP1+oXduWh+q5gLIsn6lE1otL/wiVeMQlf6PiymKzFo5DTMHUU4810BiFRHHD9nr2tJ56WvyBsCQrmiwcMlwrQ94rZsIPa8U6ecnhiBSXRuqbhEheH5TC8i8WbDdaQsWRfbjMZQ7DiJGUbFedOadONe2Q9JZp5hJay2Sj+eg3q1YtgQ3T+EgEzqzLknjySRFGqfhX4hMRI6qIgeB+0yZT0tO0qX0IIyKdz+XIAbioDB5sFw0UMuKXXVb6Xt0AIClP37EeKy/JmfK4XGJLY9OQYY1XXGK+wcgAwOvmpGs9Ux8jLvORYuUXcRz0uZq5ZD/T8yVW/nvskdr35kgaDHbUfB1RiUNsEMKSSiK/sMRXrDTCkkwlE5bmzrX3WVDm/FFSEZKj7JKyuDRF23WkBicuc5iaNWsGvpLD9nbESAU+/9yU1DBqEJbIrvKN/FvayMdG+4X6rxMD90LE67XXxnoT9v/Zs0VatLADwBIVEKyCHX20vc2V8dlnXe9lPkAURpOj7iOkzqnxzAIXOcYH4JisXCUPyldzqnuWyhkWl999J9Kvn3ef91qzVRkTl/GUxmajuOQ97b577OfSvEfNs2Yt3Sp/VkLBhf/yR9UDZbGlFZZnnmmdZ/3CUkfvJgOnVCp91MydtQou3WqI7XCkNS6NZwyTI2tw4jKHoVYdB64g2WnnnaWcbpOryqZNRSMHtTS2e62PzIUsaainYUgWg7f8S6B6QmIFXm9zheNKzFgKreWLB37fn71kBsnatdKtmzfoGfPI998vxd/hyAyXXIJ3uUh563Rq9iMzaDLyfK10wv511VX29kapLKfKq7L8yVdT/rpUDOvc6Ujikur5M87wtBJtqybwbNTIG5KZr+KSOj4tsSBzWdJCVbaIS96nlsWS3irJzML1W+YEtGcsWmRv0yXSoUPptsclkusxlRMqLLEtKI2wZNejfF7XYnCC57aOPHI4olG+fHljHhkkxLls15E7OHGZ43DQVfYHQ6Xk77//llmjR8u8F16QpaeeKptmfFtMgCEu9/p7usjy5aV/Ma6In30mUrfutqIyvJSQN4HZj9b0xcPhh1t7O0CYPvywy17mC9R/0UykA+/JtuNYsWZNpt+Zqdw9vLl1Xv1F6sm5zx8rW9dtSFv2EsPl8FZsDJvJXAKHUVF1MSkT/cUffsiKEuPAYR/R8gsayKLY3GeduOScpQsmrt8yL6AnW82uWVdVQ5+ghWVpBSs9oKzf6eHw5pveoqzDEc7mzZtl6dKlMm/ePJk1a5aJI4OC+DboJIoj9ThxmeNQLlC3bl2TxQyKwgoVZF3TprKoXz/5sUJF2Wefv6RCBSv2PpNWsqqwWtG8y1Lzr3/ZDKkGf5H60/RvW7rUerXTZ7d2bXzbxwBGV/wxf1m+3JT60DsCaFsdYO3IMbAsfPttbzAc8zUoh2bYYwYhwHtlTHnZubK1VB63+US596KfUv66fv3hN35lPQZnR9WSmHEUK8PTg4Fj6tdfRcp6aWy2iEs3hiSvYN0GEx9dv8EKQNdVkxWWVCPgfaC7LV4ppRWWCF7a2YFLJ66zYd6ADoehsLDQzKtkvMiiRYuMkQ+PBQVxLfGtM/LJPZy4zAMqVapkZlYGmcE0V5Vy5aSgQjm59NK/5OWX50qdOhtls1SUD6WtHbMQFNTzEfBdffW270EbSvwlEfTcNW8u8u23JW+bwJlZEYDouPvubbKXOMdmyVQLR6Ice6zNamuzP6sFxxxjM1QZhPKxkf+ZKwViI8kBLzSRTz5Jf98lbXiMBlUeeMDTkkX4H8jX0tiyJC4pAc431988gEzgp5/a2w0bFu9/TlZYUrzhF5aMCykNCEn/ZZhFKUp3HY5wNm7cKHPnzjXiMkhBqRDPEtcS3zpyDycu8wQOwPr165tBtUGv8iDG6tffICNHzjcCk9LYQMWlZicZPcLoBk2rsMSrWcstoQHvKjIJghGYLLOWVMqHetQTFMPE/vjDZC/VkIgLPiM2HTncUzdpktd0SNkjjcKhocyZov31B8lNO/zX3N5SWF5O77GlyM4/HeKSwwKvI61QIki8+OIIv1gWxCUfjgouDngd1Jsv4pIvecECrxrEDRvPKugioTRdoTQ22bXgaMISW4LSQAWPzr8FFmBpb3c4IgnL+fPny4YUnB+JX4ljiWedsMxdnLjMI/SgbNSokdSpU0e22267wIQmmm7nnTfLI48skPcrnCCF3wTUdxmpl47gb++9vaylP1BCZKr43LjRLrPiXBAramdb1CMBzWi33Wb+nkGDivv9uOxlDoM7JqXaOn4Bp+Ejj4w9lyPVlC8vt1z6l7QVW3f95+Lypj9K10lSLS7vvdcr+cZolLFjEU8HZUFc+rOXnFNirSblorj095G6+ZZZB9caNfJlYbOEkX4xhSUjo/3Cknbz0gpLkt7YE6jpNjPsWZN1OMIhS7lgwQLTZxkUxKnEq8StjRs3TkmSxJFenLjMQ6hTr1Wrluy3337SpEkT2ZnxH4Fsl3ELG+S43tVkjtQXmTJFUsIhh9g+TMobQTOTmsXE5IIZKQr+6AceaPvvYjkU6BBCouw5c6RHD5HGje1D6BKSX44chi+TfVKnkZPJoVno++8z9pbKn9tTXpQzZTf5w9xH06QqaPPrj6+/FrnpJnubazQFAVFPA3xueiEvC+KypNLYbBOXfDf77BO/U6wTl1kFxyKdHMB8aErTk4G11NNOsyNG/MLSv1snA4bElNNqMh9bA+bhutjeEYklS5YEmrEkPiVOJV4lbnWusPmBE5d5zpYtW2TZsmXbPP7bb7/J+eefLyeffLJ07dpVVq1aJd27dy/617JlS3le7eJ8kES88MIlMrF65+BLY/3gDsbYAJ3rAKyU6egSXWJVMxeWhZkWzYyFSCMp2J4OFyN1NGhQxOylI8chCGelQK0NcdykRFaHtaWbJk2kdrO9ZZScJuVlc5HHFOshQVOnjnd4fPmlPVzghhu8dZqIcAypeEFc5msKnw9BF6X4AqL9ndkmLmngLamG0o0hyUpYF8V/TtdHKTXdc8/khCUZS5xg/Q6upRWWtKazDS38ocOAvssA/QEdeQTZSsRl3759pVWrVnLNNdcU/WzmzJnSpUsX6dChgzymqymC5cW5RXHlUUcdJUOLrMotxKfEqY78wonLPGfFihURm60HDhwol19+ubzxxhsyfPhwU5IwevRo8+/VV1+VHXbYQY6JEpHiHPvPySkWl/aF7DLviBFeKSxlrf7ZmDhcqpmLNrPQi6lzF/xQQquW1qNGmSVlVoIZ9QcYrqT6T3KkAWpAP/5YpFkze5/FFdyIU+2oE41zz5XWMkXukAFFD/XsKbJwYbAvQ6ZBk7a69nLYYXEummhpLOmLDPeqpgyMbiiVVjvduXOzV1xiPqaLgomMIaG3fBvHJkemePZZkS++sLfp8fevlSaasVRhyaUQYZlsaa2yerVdj9XDgPeHs7T/8upwRIone/bsKXfccUexnw0ZMsQIx3HjxsmUKVPkp5+sQ/pzzz1XFFvus88+0lYnA4Rge2zXkV84cZnn/PPPP9s8hsMXpbOHHnqouV+jRo1io0xmzJhhyhP2jLHEuk/7OrLh6+/TM/bh7LNtuaNOcEZg8n71PfM3ckVU0x6EJcLikUeKZycoi/WnKgcM2CZ76fpM8gRqQGk4JGupookl+lSkDEuClEOFCnKt3CudKr9X5L9CWTaBY5D4R4yQkHzxxeIV5FEpa32XsUpj/eIy6C8oFf2WzHalxxiaNo3zC3ekGo5xqgb8zquJGl+y+3GewLAnSGHJ4hO9n1Q4AK3qHA477VS67TrKRjzZvHlz2Z4a7xA4xpJ9xO+Dstb27dvLJ2GLuYsXL5bff/+9KO6MtF1H/uDEZR7DitD6CCWiNGNXrVpV+vTpIz169JCnnnqq2M/fffddOSFGvQ2lsQ0abZIp5Y4Mbt5lSXBCorSxTRt7n5o//lWv7glOrsTqGErG4YorRDp1Kj6W4qKLvBJArqaTJpnYH2t4IOHFP0cewL7xzjue2wXHAg1FOhguXbBPnnSSlJNCeW7DabJP7XXm4c8/F+nfP7iX+eUXSpO8+1SIx5P0Mjhx6cGilZqIZSpzmYi4ZCSTLqK5fsusYcAAz6kZd9eYpekxhCV9lSosySwed1zp3he7ygUXeGsrNWva27p263AkEk8CpbKY8CjcRkz6ee+99+TYY4+VchGcrNluKsaZODKHE5d5zKZNmyIesKwwff3116Y0duTIkTJ16lT5jPmAoRPI+++/H1NcQuXKhfLp7ient46UkxczDYmalZUrizuVLF0qsttu3n1Mfg46yLuSkpW47Tbv5zfeKOXLFZqB1orrvcwjSN8RnbFMr0v21JgNH57e9xGatbqjrJDR/x5SlMFgko46P5YG1lkwWvYn2rRENi60RzXfM5cHH2ybUwEHr2jiUbOXuSAuXb9l1vF//yeia7YUzODcnAgcx6eeuq2wZKxvaSGbqnYK2rvpP/wdjkTiyXghaUFGMxJsl+078gcnLvOYrVHmP7KqdMABBxjbZ+YItW7dWmaHyqoQnbvvvrv5WUl8Xvmo9Kf5KPliViXiQANAlofpu9QZmJi4kLViSRZYQeOkRs8lwSLzICgfAxpi3njDrCw3aODFnJlqz3OkAJTcSy+J9Opl73NcnHee7c9NFx06FNWcHfrx/fLgnTZ7CbyVOXNKt/nBg0VC60NFJDSFRW2T811c0piqNYWUk+pU+1wWl9pvCS5zmXHwJsHER+NwFitpA09UWCL6ghaWLGbdfbe9TQKJ0yKG2g5HsvEk7LLLLqY0VuG2P5P5559/mkzmv5jBm8T2HbmHE5d5TKTyA2jatKlx6KLOnQN62rRpsm8ozVFSSayfmRv2lz++XpSevstImSD6MLUvlJp9/l7NYpLR5H3p4HS9srZoYfuT/M3oAwZIhYItLnuZz1Dq+MwzxbPe3GY/SEc5DmKF+mtYt04uqfmyWdDQdlASq1R2JwOV6TjQgv+QT0hckl7R2bL57BibaN9lLmUuEc5UaTgyChnLadPsbdYw+/SJ/3fZ3TgXqLDESuCtt4IRlrjAsr7q7wFltqXDUZp4EhCS/JwkBZVxEyZMkDbawhQqiT3++ONjzq6MtX1H7uG+zTymYsWKEQ9mzHuuvPJK6dWrl3Tr1k323ntvcyJAaE6cONGcBEpiw4YC+eOPivJe4bGpm3dZEpj20IepS6+UVZDFrFvXew5WeKQkNVicMcP2b2LV2bKlfeyHH4wjLQlN1aLMI0xXO6kjTXDxYoHBXwPNMEjqxNIhpkKlsVDw/Ah58kkvYUjbHC3CibJ8uS2H9Y860Gt0QuLS33fJosyiRZK30LSm58VsF5cslvndsMPhnKfO2Nhe+0w2HOmHiVj0WirMi4zXX0mFJVlKv7Bs16707wtvs3PO8e5znrjkktJv11E248nevXtLv379ZPLkydKuXTuZPn26DBgwQPr37y8dO3aUI444QhqqkUUcSQu2y/Yd+UNBoeuizWvmzZsn65JNiUSBOHzmzO3krLP2k9PlJXmp37TEm0qChACLeUu4wyqY9vz6qxd1E6SRncH1RDniCK8sDjeDn36S50ZVKaqe5KL+wQfp/EMcaYPaML+bDpEWS/mpXD3lwMHvX509f/5Zvl+zjxkXwkQdoNrbp0FL3Bzto6++au+zUEwQyQIJuzmV4ejEuIeh9+sncv/99jY7fhBRbbbCuCKdffrHH8X7tIEFKRamKGVWV5Z0gcpAWfAFs3PoLItIsFimpWasjr3wQtrepmNbMMph/IianDNFKxFhiZj0C8uwqQ1JQWKbcwMVEnDhhSJPPJHAecHhSGE8CYzC2y9u9zlHLuAyl3kOY0aChovSRx9Zl9b35HjZMinDDYqseNE/x1VdnVKIrhGU6h5LgLhggcjhh3u/h7DUDAWZzMceM1kgPccRqEdryXLkONdfb77vogjr8cetqsMZJ1XwWv70wciRxkiDl1YuvbS442sshg3zhOWOO1qTDtqOdf+lMjwhXVRWHGPBv4r+nh0PkzWZS2Zwaibd9VvmDPQ8q7BkYeeee+L7PXaxbt1SIyzZlU480ROWmGWTTXXC0pEt8WQqt+vIHE5c5jk1a9aMWeeeDGxvzRprTrJMdpZp35TLTN9lODijUMvK0C6tUeKqqkEzARvzHyiL1d5Mf/B4++1SYe1KUympOOfYPIZspSoyGDnSOmmkUlD07OlFdqQ1CgtNhoMJOcCiMBkMhGEsSH76y2ifftobJeDXIwmVxpZVcRmpNDaT4jJZp1hn5pMxWJO6/HLvPj3QtWvHLywxNVdhye0ghCWXP3ZznQjRqpU18PGNtHY4siKe3JHVUUde4cRlnkN/JU5eQcKJ4IQTtkj58nZ1/d3C4zLXdxkOZWS4KRx5pHf1JlDmcYWfEzz6H4Nly8xoEuJ/HePA5JNwF05HHkGqmlkgmvFmWjmzUXESTQWY5ujAOyxiWewQa4Cs2uCnn2zpWrQ2UHZpzIC0lBZhesop3s+TFpeU7Crffy95DRUMOiOXzCUWn5HEJaoh3S6GTlzmHBRBTJ9ub1OlTAVCSTAykONWhSVTk8aPT3weZiRWrzajdYtcqDm06eXkNRyOZKCDDl+OoIUg8Wl5XeB15A1OXJYBOHgra7BUGkLRLk6zhxzyk3zxxSx54YV5svmczrJ56lTJGlgypqYVP3j/4DGcFLVMlj4rRObJJxd3XPjf/6Tif++TmwZ4kb3LXuY57ANEeBp5saKAqVWqsvH+0thQUxbjBihx1eqgV16xLaCRILOuCSsMgbRNstTiktE9OjMBcZnPjrEc89pTSu2wf5wH+M+X6c5exisuEb2qaDAxC426caQXMoN+jzDKTkvKDiIsyVgiJoFTD6ego48OxoKAAowvv7T3KeSZMMHtHo7E2bx5syxdutT0Ws6aNUt++uknE/8FBXFp0MkPR3bgxGUZgLKDunXrmixm0hBohpVDVK5cKE2brpNu/baTH7ufamYbZY0/FJkoonPqBTUrhSUnQaWmiMhWMKUaYx9/zf+110qvcd3k33v/XZTYyCbt7EgBeP0jKnU/IF1NCsE3uyswSFeokGU+QEi8oCPoo1TwqGJNxA/74n33FR/fGW4Q6tcj8+cn+N60NJYAgrq6sloamwviEsMhUlTgSmIzxnXXeWXs55/vmZCXlLH0C0tuByEsufz27m3FJHA647ZOGXI44tuPCk08x2iRRYsWGROfoGM74lHi0qDLbB3ZgROXZYRKlSqZWZZJZzCjnAAw1zQGmxUrmJPR3LlzZSNToLPJvu+TT7yMzJ9/2jLZjh2953z0kXWS9ZVmlHvjNZm86mA5Rj409132sgxAU9KkSV52m6zQUUeJ/PZbsK/DvkbaAsiO6uwBsXPnEJWagejRw+o8QOf6k55Dh3pGoYFkLsH1XWaPuKQBL9zF1o/rt8w4XFpo2waqBe+6q2RhyTH+zjvFhaVvJGCpuPFGz6GWXZh5mczadDjihfiNOC6VyQLiUOJR4lJHfuLEZRmCA7l+/fpm4G2qVos2bNgg8+fPzy6B2aKFLYElQ6nBInZ8WOdp/0AEAbH98t/lAzlW7pQb5MN3N8acCODIE8gA+U2hcM6hfzdhlZZ4aaxCgIrOBQyOeSpJdrIias7Rvr3IlVdG165aaZTw28a+tqyY+jCuiNmQQGnCP/9kXlxS6orFJ9D4Hes87ReXhxyS+vfmKAaLP34Tnzvu8I67WMJSs4pUHCAygxKW9G2z4AQs+FLVwNqYwxEvxG3Eb8RxqYC4k/iTONQJy/zGicsyhh7cjRo1kjp16pj5QkELTer0FyxYkD0lslCnjsiHHxafHM2yLsEl7rEQbuphDpBCuUGGymfSSp6+/qc0vmFHxqCREYMqdXVC4bVuHazJDSW3KmCJMH3lt1RujxrlJVDpxerSxTP+2HVXOw8z1khOzV7SWpzQWLKylLn0l8Zy7NOnnWlx+fvv3uu5MSRZDWOVv/vO3m7WzJpwxRKWHMN+YUnGMijxx/ni6qu9+3SEIGQdjnghXiNuI34LEuJL4kzizcaNG6c0ueHIHpy4LKNQ716rVi0zuLZJkybSsGFD2SlAQwhWvpZkW88WJRjY+j35pGfig1sngT0DyMPZd18pDD2vmUyTBz45ROYPfDa/jU4cXlaLDKaKLcqpiQS/+iqYT4gSbGaQABdz0gw+9txT5MUXvcSVzsCD554recxB0n2XfsfYsiQuw/suMyUu4+235BykmUvSZVr270gLLNr85z/2NscoJj7RDC9VWOrupRnLoIQla6acSvSyNGhQ8TVUhyMeiNeCzFgSTxJXEl8SZxJvOlfYsoMTlw6zilSuXDlZvnz5Np/Gb7/9Jueff76cfPLJ0rVrV1m7dq2MHz/e3ObfddddF7UElpNV0KtggcAS88cfe/1MCxeKjB1r6wz9xj7z50tBz56yonZDc7earJF9h1xgG+EifFaOPIOAnf1EM9s0PzKAjkarIFBxGaE0Fo47zvZQ+WHsCCWxJZF03yXzX1W5lgVxSU2iCkmif43Qs11ckuFcutQr5XaZgLRy7bV2hLJeTpo3j/w8qgYwow4XlhRCBAFt4QhXSnQBMx/nD+BIFOI0TQb07dtXWrVqJddo87+IzJw5U7p06SIdOnSQx1igD4EYvemmm6RTp07SuXNn+drnuk08SVzpspRlEycuHYYVK1ZELGMdOHCgXH755fLGG2/I8OHDpWLFinLvvffKsGHD5LXXXjPP+eCDDyJ+imyP7WYlWPrRh6nWfiwv//e/tpZIp9HDsGGywzHN5ZXte3mPjR4tcvDBVng48htqU0kNaDRIREm2S+vbSgNZUerpgIuy1tj5UP2g/PijTXSWRCCmPjR4MqYjnyHa1++W8md6bHNBXLp+y4yB55cWGrAWQ69lLGGJwzNUq2ZPG0EJS1pzTzzRE7mM5yXud+sMjtLEfz179pQ7wnbqIUOGyNChQ2XcuHEyZcoUM5IEnnjiCdlnn33M42PGjDG9lDkR/zlSjhOXDsM/fjOLEDiGUT57aChzU6NGDXOfk8b69etly5YtxqI61pyiSNvNGshcEimQDlJoZqtXzxuwTgXjyy9IuxpfyZXykCyTHb1sJ31zDB3UZWNHfsK+QFSoKUMWIjCDYpGhtJx7btTs5euv2wpuPyRNtRwvFoE5xv7wg5TJ0thsF5f+uZxuDEnaoEjHb+KD+RYCM5qwZLqRCksylniDBQFJJnbbRYvsfdZImWpUmmljjrKLP05r3ry5bO+bb4VrLLEePh2UtbZv314+CVXvvP3223JOyJyOxEN1X9wUvl1H2cKJS0eRWAyH5u6qVatKnz59pEePHvLUU0+ZEocBAwaYkti2bduan3MyigbbzSpjn3AIIp94wv7TPkxOnGEW2Tv/8Z3cWTBAbpZbZZKEBpLxd7HClwo3UUd2wcwAZqJ2727vs6Bw2ml2MaI0nH66FxGOHFlkKkXVI1N0lOuv93q62OXU3CcabhxJAvjrjLNFXOLWVLdu9Oe5MSQZAUdWXW/BhBwH53iEJWtTQQlLRpuedJLInDlemzQ92To61+EIIv5TKJfFhEfh9uLFi2XlypVGbFLJRnxIlduaNWtyK/5zpAwnLh2yadOmiCcAVquooeekMXLkSJk6dap89tlnMnr0aBk7dqx8SLmgMKbPm9MXDttl+1kP2UvmXeIqq/WIYfVF2xeukUfkSpktDWVko9s8UcCkewYO4rTiTqT5CwsO1MP16uWNjTjvPJGHHy5d2S2RopoGTZxo9CXtmDrfkoHrZEh0zADwc6o4o0HbpC4+lypzGaRDbrbC+BV17uUcQKCVaXHJ1PtYM+BUXDJ3piRXWUcgMK1K+xnVxCfcsXntWlvUEC4sdQpWadHZt19+6bWFs/0AvfgcZYxo8V9JEB8uXLhQWrduLa+88oqpYHvmmWdyM/5zBI4Tlw7ZSpAcAVaoDjjgAGMhzUwiTiKzZ882q1W77bab+b9du3YyY8aMpLafdTBckD7Mww+39/0nXF+ZyCXypDSZ/ZrMvfMVEe0xYDkZ0YHrrOszyF9YUOAC6h8yyW3SickuLPhLY597Tu65x1Zrq2vsU0/ZYBZ/Bcw7AD8pgsxo42R5vk5S+eWXiFN2olPWxpHwYR1/vJd2wiU4E+KS1QQ9d8QSjPTB/vqrvc2iVqyZNI7A4PjTxMyll247WlSFpVoQoPtJhAclLDm9YNhDeS3gPYewZB3C4UiWkuIzRCOlsQq3iQ1r1qwp1apVk6NCtsfEgj9iCpDg9h35ibsqOYyjVySaNm0qy5YtM3XznCCmTZsmO++8s2nm1lr6L774wjR0x9zJcin4YSmY7AVXcT9EFd26yaaK25m7h8g3stuNvURuucVmrxQaXwj4mJPoyE/Ynx98ELcr7zF6b2+4ITmB2aFDUephy5jX5O6BK4s0D5WympXg/rBhnmgkYY5rZTRUn7BwTItw3NBDrY1kZUFcRuq79IvLaAo+G8x8XL9lWsCU59VXvcPj9tsjC0sdlYqwRPixXhkUAwZ4bdnsnoxpPvDA4LbvKJuUFJ8hJHkOiQWylRMmTJA2bdqYFqmWLVvKdCyLhWz6l7KvXpwS2L4jP3HfusM0Ykeyi8a858orr5RevXpJt27dZO+99zZ20xdccIGcffbZpu9y9erVcuqpp0b9FNku288puHLjpIL1nt8hAXfcx5+QuRUambvbb16JtZpdQn7hBW+MCfWKjDjAeSUbR7E4Sg/Hy+DBxWtV775b5LLLbLlsovsbvZeYR21YJydvGVOkV9mN/NSsaYNc1T5U5GrQG1jfJX+bZi8Z6FcWMvHHHutlAMPFZboyl05cZiV8/VdcUfww3zHk66bCEqdWv7BkFwpSWGJkTmk8sJsyAzeoOZmOsk14/Ne7d2/p16+fTJ482WQjEY/4bPTv3186duwoRxxxhJlfCVdffbXcd999csopp5jkw4XM5cn1+M8RCAWFrtvWYeKaecb5NWi22247M0A3Z/n0U+o9vACzYkWZ2ONx+euF9+UMedl7Hu4ODzwg0r+/LatTsPFDeOJA68hPHn/cikrNWrLgQIoxEetG0pDsQ7T9SRu58fCPjK9UtOsyax8XX+z1dX31lUgju+ZRBGsjvC3Ar8pvilwi1P3xd8Fnn3kje/IZyuG/+MLeJjN91VX29qOPeh9kKhkyxMuG40TcrVvk51F6r7MwaEk46KDUv7cyDBXvLPQAJa4cl7oOocIyZD9QJCyDPFxGjRI54wzv9MJxfcklwW3f4XDxnyNoXObSUTRmJJe2mzaIJnRQGWzaJO1euED+2X53uVwelg0SMt0gKO3YUeS662zNlFp7Tp1qZ2IiMB35CZHe88973zm1rGTzE8h4vTinufwoVh0eLR/LqKG/RBWWwAIxGlbbfTGxJdD14xxjS1Ea6+8dyrbMpY4hIbuKVagjZVCEoiWwCErWGVRY0inBKV+FJVMYuFQEKSzZNpMeVFgOGuSEpSN4XPznCBonLh0GmrMjlcaWFkaVbNy4MbftqKk/CvOcv2TN/dJF3pATZbwsqlrPM+Sg8YZon75N7T9gyjVKgH9u7lN+ctZZImPGeA6fDKkkpRFmzR6J+fNFLrm0QEaInRcGe38yMubvcKiSWNTq1e++Kz5/D5y4LMVIkpkzs1Nccm4JDTCXpk2jp7YdgUDyWgt6KI1lnRA4rDm81XgLYUnGUr3ggoBWNgy8tOUXGwB1q3U4goC4jPiMCrOgIZ7c0V8/7ihTOHHpKOqvxBUsKFRMzp8/3xgAzZo1y5ReLF26VDbnYh8iV3V/H5aIHCcfyDPSW7qvfU7+OSZk4wk0x1BHNX68nRmhkL3E7IdspiP/YLgdAyh14BzzCHAhjdGziNkOupT1h5HSU7ZKaIEH544SFmQwMKZ6Uo2MGbn57LPez3GR1GRqqcaRlBVTH+b10tQKfgfsdItLzsPUV0bi22+9/cKZ+aQUTt+sEQETqlTYacbSLyzJWAYpLH/+WeTEE+15ARCylMOmYP3XUcYg/iIOIx4jLiM++5kdLmCIJ5ko4CibOHHpKHYyqBwmoJKB2Cc8C4rYpKdz0aJFxnUMO+ucymYyE8I/fqJKFfNfPflF3pMT5OnVp9meS+2zozGndWtb04T7AhGIzoXgccxgEpoP4cgZYxhEpZaD0694zDFMoo749NtuE/n8c3u74r57y9Y2x9g7TEjXH8SAqkj6LxWyl6qLSGrVrevploQON6JpFVplRVxy7PL9aYYwneKS9NjvvyfmFBs+C8MRGIw69Z/u773XHtIqLClM8QvLULt0IHCqoEJ70SJ7nzJbTMgTaeF2OMIh3iLuIv4iDiMeS1UMRhwZZLLCkXs4cekoAkFYt25dk8UsDSWtrupJbu7cuaYkI2dg1ISKhg0bZOsB1ge+qqyTfl+eIX/N+MMuZyNENUogc4VQoE9KB54hKm++WeToo21TjyO/wCaS/aBWLa++jdJqprD7+Phj6+ECHHJ4tFQ4zyuNLZo7UAL4u+C/o0Ex7Z4rVxbXKWRAli5N0jGWmYqaQilLfZfpFJf+zEEEO/9t+i3BZS5TBo6wmkjGsZljDGF50kmesORSwDpSkMJSxSuXDF08GjfOK4ZwOJKBOIt4Kx2L+sSPxJGpaLNy5A5OXDqKUalSJTOrKIgMZkls2LDBlM3mjMBk4OD119vbhYVSrsF+MuuwXkU/3nX4PTYVhQsDNU2h55mRJET/r7xi52KqIwSzMGniYVnakV8Q+OMavMcenkEMGetQxEp7Li24ep1ntznsMLEOoRpJsl+gFuOApPmhh9rbBKYXXGC3HVjfZYTh2HlJpsRlomNIOIc4l9iUQA/0nXfa21T1YeKD6GMcLQtCKizJWJpjNiAokWdhCONoHbnMrEwdOetwJAPxFXEW8VaqIW4kfiSOdJRtnLh0bAMnhvr165vhualefaL+f8GCBblTItu3ry0ZhNdfl33vuVRu3OER2SShbC9L2RiD4F/PPxWSPE5PV9u2VnTss499HIMffOZ79So72aGyQuPGdgFBM1GhkujC7743bq+ayKRqVtcszFwRHUFBr+Zbb8X1UqwFMe9SK1npxWQ2XmDi8vvvpUyw117F/+5sEpcswuHcBMydcemslJ3idU0HQx9Ky8lY0ungz1gGKSy5/HFOeOcd7zUQlvRNOxzJ71eFJr5Ktc8FcSLxInGjE5YOcOLSEfNk0ahRI6lTp45xE0uV0GRFbUmUnrSsA/cUSlpDVLltgOx66+XSTibKX7KLt/RNCSxBIllMFaMMpEdJIDgob6PWSnnuOZvt0mVrR37AIgKLCSpY/vxTNhx+lCwY+1VRMtw/xcRAn26CpbHAKFV2I+Xaa1m88e47U58ks5fZIi7pfSW9Ba7fMiW8+aa3nkPmsF8/m7EMF5asEwbJgAHesctCEe/jQNt14XAkDXFVqjKWxIPEhcSHjRs3TksywpE7OHHpKLF+vlatWrLffvtJkyZNpGHDhlKPKDYCv/32m5x//vly8sknS9euXWXt2rXy1ltvmds8NozB8jFOgjnjIosnvAaAEyfKpQ0+kJ9qHyWHyjT5UprZxxk6eNppdil62jSbsdR+y/79rYB4+GGrLNQZkgCTfj0a8ZzZT/5AlEo9XahutcqaZfKhtJXW8ok884xXOVsECxDat8v+89dfcb8Uk3AYtQocTg895P3MicskRpJkk7h0/ZYphVO238SHwhNO4awNAVUBH3wQvLCkwgCDcSA2x/+NFm2HozQQT+mifd++faVVq1ZyzTXXFP185syZ0qVLF+nQoYM8hhVxiJtuuknat28v3bt3N/8WLlxYbLvEf8SBxIPEhcSHzhXWEY4Tl464YVWKkgdcxiIxcOBAufzyy+WNN96Q4cOHmxWzRx55xNweO3asfPHFF1EtrynfWBFjZENWgQ0nbq8hqtxyg1x/XaH8JnvJUfKJfLTPud5zhw61MzLpnyPjqSt7LI+TqWzQwBq+qI89onLgQCtGw07qjhymVi3Z8M6H8vX2rc3d6rJKPih/gnSpMmHb55LGpCFTFSJOPwnA2sSRR9rb6jiZlLhE4OrCR1lxjAV6Y0Nu0IY4+15LhX45lLpqpUMsp1hn5hM49Fmqvxpea089VVxYkrFsFlo7DAra8Cm9VejvPOWUYF/DUTYhntJ2o549e8odrJb4GDJkiAwdOlTGjRsnU6ZMMSNJlBtvvFFGjx5t/u1Fq4AP4j/iQJeldMTCiUtHwvxDn2AYOJGR5Tw0lJ2pUaOGyWTS3M1tVraaNWsmEydOTGi7WQtL2jpRe9o0ubzOGNl1V5H1sp20/WWY/HnDQ16to07X7t7dNtKoiygunASyCE0yW4MGeT2a1GFh2EEjnSMvuPHO6nLkmgnyjtjMWKUt622qkQbJcJIsjdW1j1GjxOyPfhIWl37HWPpFcTUpCzBQnAoC5e+/U/t6LCjpohv9udFKy/ziknm5jsDABAuHWD1+cFv+9NPiGcughSWG0oxBVrsB1hTV9dnhKC3+eKp58+ayvQ5EFoph/pItW7aYtidiMzKVn2jtdwLbdTii4cSlIyFYCVsfYSWfpvGqVatKnz59pEePHvLUU0/J3nvvbUTn4sWLTRZz8uTJ5qQWDbabM8Y+iEC1FKRP5rabpH8/W9ZbKAVy9c9X2ohEhSR9mAwsI2ohU6mpJXqocJCg/5IGH3zu1cWBTG6PHtb60z93z5FzsKaAo+s6qSo9Kr0hK47t5n3/LFQMH178F5hBoPV3lEOqkUsClbiU1/l1ClnMhPWhikuOy9mzpczACCHFn/5NBTg7aS9ltJLYrVvteUP7eGnWdQQCu/YVV1i/JGBRRiuQd9zRnsbViTko+CpPPtl7TU7xOEY7HKmM0xTKZemRVLhNnKbce++90q1bN3nwwQeNCM3ZOM2RMZy4dCTEpk2bIp5YOAF9/fXXpjR25MiRMnXqVPn++++lf//+pt7/wgsvlAYNGsSszWe7bD+nerO0Oeann+Ty7YeLzg2m3GnWrkfbfks13yCyx2ue2idqrIosQkVkzBgbweAWOmOGFZXKs8/abXxlTWAcuQXX7HN9ldJD7qkkNd952ToEq3A47zzbgxtQ9hLatRO59dbij33+eYIb8TunlqXSWOxB05W5jKffktSargy4kthAee01W1wCTFD4/ffUCkuS1EyqUnNw5lo+/njJ86EdjtLGafFw1VVXyZtvvikvvviiqT57hWAml+M0R0Zw4tKREFsJhCPAytcBBxxgnMOox2/durXMnj1b2rVrJy+//LKMGDFCdtllF5PNTGb7WQnRgD97eectcuNVth+V87ppy+TvxR2W+ieF36Gx5oYbrC0gUYwGmWQ3OZnTZ4cBkpayEFzyM3o4c+kzKuPwVaEhNWGP8yRZEqlQQYybj7kTAjcR+mI0KDj9dFujByNHJmXydNNNxfUKL5GQb1ZZFZcHHODdpgwMt5dMikvXb5kS0Ov+nkfNJKqwDNqUF38VzIg1Gc4pnRJ2TgcOR1CUFEcRi/mryLitmUx+Rj8lMys7d+4s30WomsmpOM2REZy4dCS2w2hPYBhNmzaVZcuWmXp8TjzTpk0z/ZZ/h1b9ly5dKhMmTDDOZMlsP2uhN4u+Ofj9d7m88JGiSliChh9+CPVw4TP/4INeHyYuoJQ90mNFDZaWQOJMefHFNmtFjyb1U/ozVAGC9LjjvOV1R1aDEyQlsVC7tl0vKMpQsK9j50qzlV8N8h0jMNmRNIP255/GmThReAmqrf360P9yJVJWxSVfkh6rfBdx9iOlRVy6MSSBcfvt2/qmpUpYImTJUrJOqGNwx41z40odwVNSHIWQ5DkkAKg6IzZr06aN+Zk6zBLHTZo0ycyuTHT7DofbQxwJUbFixYguYZj5XHnlldKrVy9Tq0+GkpMVjmSMIaEs9tprrzXmPtFgu2w/58CeM/SZVLrvThnYZ0VRTErwYuDn9FZSDqvqk6CyRQsrLslu+n3wX3hyQUg6AACp6klEQVTBikrEJs4SN97oqRJmZ2L2Qz2XI2thXYCpM/7K1nCTHfOdkuImI63gLHLZZTbt6S+N9Q+xTIBw7xdeSmf5lQiZdxxMy5q4BL9jrNZNpgI3hiQj/PijyH33FX+MVlbWcIIWllQR0hGhY4zpiWbRaeedg30dhyNSnNa7d2/p16+f8b2gmmz69OkyYMAA07bUsWNHOeKII8x4EbjhhhvklFNOMXEcAvOss87KjzjNkVYKCl1nriNB5s2bF3UcSWlgIC9zk3ISah9Dwf/GawfIHsOHyNKlVjsQk7NKXQR+9127Fs9GkLGiQQ7ByOgSbcghsGcGFSIDsx/KazEAUS66SOT++73yWUdWQJYCd0kCWCB7eO+9JfwSjVeISi2LZRwJjyHwli2zGXDq6apXT+i9UP1E1tQPDpisaUQZWVsc/hB6h1mt5g/zi658hg9NS8c4gE0ZQgpAyXAuIFPKeTU8cGN/oJmbKhBWJ9gHXINeqeAjZZwsJt3hwjJoI15ei5ZqXRtifZURJwceGOzrOBx+XJzmyCQuc+lImFjZx9KAVTZOZBs3bsw9N7JbbrFuEGQv//eg/OeiP7fNXip169pMpX9FkOwn5bXHHmsDeR1zQq8XbjC9e9ssJ2Y/3UJOo/Dkk9Zxwi9UHRmHWdUqLNEOYSPGInPJJSLPP++VY9JnicBUcyeEB8ZPCYIuwScKdA1CjYhJjMddGksmtSw5xiLmFb5MRgcFDScIzVyyiBApI8BikpoKYebjhGWpwZzZLywphU2FsNR1QxWWlSuLvPGGE5aO1EDcRPxEHFVNT/o5Ev858gsnLh0JU7NmzZQM0KUvk9ElDPOdNWuWWXnjsc0JOZBkCMYD6JCytWvloiW3F5U84c2zTUxORhIhQdZRxcT48SKHHWZrqKZOtVlJBfMXZmWSDmX25dNPe+WKbBzhSY2Xa7TPOGPHWs0PfEWMBAmtO5QMCw4ISP2F11/35iIkWRrLoaoFAbjTawsN5sOI4BIpq32XKAE/qSiNJSPNeCJw/ZZpAa1OW7tCDE6nQSqEJQbQ6vnGcUi3Q6i1zeEIBOIj4iTiJeIm4ifiKO2dDBLivh3VgNDhiIETl46Eob8SR7FUr8BRerto0SLTdI6bWdZnMwcMKEoRVRr2pAzuZTMS6L1tspcabVx9tQ1aVYni9oBQpCHniSesAFUR+e23NkuJuGQwGtlK9clHkF57rR2P8scf6fl7HduAOQhJZr+hT6NGCX5QDMB7+23ve6dRS0tRSbf88kvCn7zqFgxnmbepuul//xN5+eUSftmJy9SJS9dvmVYw/qWVXScpkCimRDUVwhLTb9rslUceKV504nCUBuIh4iLiI+Ik4qVUx0jEfbHGyTkcihOXjqRPMlhVp/MkymocJR9ZC/1Qas25ebP0/vXmolnnZK9++inGQELSSBrhrF5tR5XcfLPImWeKfPmlF+Dzs9NOsyMsKK/97DM7L1MzyRgGYfbDiBNHWkG40RK7fLm9j4EH7bNJQXk036WWIPkHYlMumyD+pBiHLYGughjWEt4Sx3KU5cwlFqJBV1G4MSRpFZatW3tjQIAW91QIy0mT7LlAY30cmmmndjiCgDiIeCidi+7Ee6lOKjjyBycuHUmXR9StW9dkMdPFhg0bZP78+dktMKkzDLnBVnz1RRl6xvSi7CVtlTHLanGFPeMM7zFcRMli7bGHzV75Z2WiDo480o4kwf4TIYIFIdCfxe8RzaRyPp+jGHfd5fVx0T5H4rlU1eOMuSFKVXdhf4l0ggGFX1yiZ0h808oLePQw9Yb/o+6bmjkty+ISdfLFF5kTlzvsYEcXORKGHmMmOM2c6T1GF4NO+gkS2uK7dPFmZrLAdNttwb+Oo2xC/EMcRDyULojziPdS0Q7lyE+cuHQkTaVKlcwsy3RlMLW/YMGCBdlbIouTJw4OIc79aYAxi9CEk844iwhlkDTlYCuqc6SYGUEfJmYi9Ns99ZQX6JPtxC0Ghwiyn5TNEtUouMzi9Em040gpn38u8p//2Nt8dXzXgbSmYOBC3R4LDAplsaNHl0pcEiNQEtu0qX3s++9tsB3xsKIMSu2O2YGzeXEnSCKd14IujS1JXNJjrYMY2RfcfLmkhOUJJ9gCEGXPPW27e9BwaNKZoG20zLUs9SKTwxGCuIf4J50+FMR3xHnEew5HvDhx6SgVnHAYsstQ3nStarFil4pm9cDA9ZPUFdnL99+Rh075OL7sJfAZUlpLEKs1tdTT0odJqSs1jCiZBg28yAlBSb8lwhY3GaIZdbpkfALi9MEHndlPiiChRfUyZbFaAkf5XWAg7HAX9pckMfoGRZikuNS1DDSqmgrS3otPVES0LJs/MuYKSZ6LS3qhUyUuI2Ul/S7QiEtHQnB6PP54b76kwiky6Ik6rAMgYrXsFv+1UaPI+gT7Oo6yC3FPujKWxHPEdcR3Tlg6EsWJS0dgJ6FGjRpJnTp1zLzKkoTmzz//LN27dy/616xZM5k4caIsXLhQTjvtNOnQoYPcdtttUTOUnGSz1kWWqMVXB3XGzBtlx5r27yCjNXdunD13ZCbpnwTmXiIiGXnCgDR+RlOfglPs0UfbMllcZhlnos1EZJowDurQoXjDkSMQLr+c/dmrZB00KAUfrJZN63FFuTO2k3zPcbDXXl6Q69czmA35BSWtvBGn2pRFUx+/uNx/f/s/xx0qIij0y6Bfm7LXcJy4LLWw9GcsgdMop8IgoaScElvtq2c9iKIT9eRyOEoL8Y4uqvft21datWol1/jsvmfOnCldunQxsdNjVC2FwXOJrWJB3Eb8RhzXuHHjtCYNHPmFE5eOQOvya9WqJfvtt580adJEGjZsaFa9IjWB16tXT0aPHm3+jRgxwpzQWrZsKQ888IBcdtllMn78eFmxYoV88sknEV8L0cnPsxbmE4YC8gr/N1Ue6zCuKPFTYvZSYcI9hj3+C8Ktt4p07WrrF1kWx+teZ+PxXAQl2RWCYTKcajAEZEMRqziROgKBbB+VzEDimNspy1SQraY5UqG3lknwlM2WAO8Jfap6xr9mw+6FQAYWxVmzIBtbjLIuLo86yv7PB4exTxAwt1SdneMZQ+Iyl3GDqRY9luHCkoIOijiCBOdZZsZqdpTWd07BagDucAQB8Y4utvfs2VPuCBuePGTIEBk6dKiMGzdOpkyZYkaSKJ999pmUi1JST8xGnEa8RtxG/MZjzhXWURqcuHSkBFa7KKWoUqWKrMbhNAaTJk2SFi1aGIE5ffp0OSoUyJ100kny0UcfRf29f7aJgLMI+tR8J//u3wyQnWpsKRIk/uxRTJh6z6DMu+/2+q0oj6VMlvmWffrYjBbOsSo4WJanNpP3QP8morJOHftzVj5pBCJFRXDrSBq+Q78DJKV2KuBShn9An2a0qcWLo1xT9QtZlr/+Kv4zEt+MaNC/67zzwvovy7q4bNky+L7L+fO92yWJS96LZk8dJQpLMpYkmcG/2MNpUU+VQcAxQqEII4oBc2cOxSBfw+EIj3eaN28u2xMbhMA1dsuWLaZ6DFHYvn37ooX5TZs2ydNPPy0X+edm+1izZo2J04jXXJbSERROXDpSCitt6/1jFCLw7rvvmpMhK3M1atQoOsHVrl3bnDSjwXaz1tgHOne2jTdozR++l2favlCUvQxbdIwNn8d114m8847nEoOwpJdy3DirCghCO3WyP+MzIT3K0j1lsERamP3oz9Vtlt//7rvg/t4yBNkK+ix13YQWyNNPT8MLU/qMGwlouRKLBOxrJZj8ROq7VNAuzOXT3YsRDcUyPPyyZsjLirj0G1jgfKQ1jojLIM47JZn5sHNp9oFSeP38HSVmLFVYUmms3RMNGxYv5AgCvNuGD/eOIbzV+KocjnTGUZTLUsKqcHvx4sXmNpVhnTt3LiZGcyqOcuQkTlw6UgqrZrFOXGQ1Z8yYIa2TcEBhu2w/ayH4Zz5FiM5f3Sy7VLfN+Bi/+hMXcaHL8Rq9kLVCVNDfyZI5kc0999iMJZD1pUyWcRaUJvPzRx/1nCwQlrjJUlrrLi4JgTOslsHVry/y3/9KeuC7peQa+M4OPtje5jigvlUj3QTFJZB1JauuMD6VSuui9A8Nmrqwkc3HXSoyl3zWCHv488/iMy1SJS5xedbj0pXExi0stQ2ZCT5+Pc5pLkhjc7Z3553eqZ6SeNqgHY50x1HRQGBOnTpVTmY0Wa7GUY6cxIlLR0rZikVqCSWx9Fpid12zZk1T+qEnUU6MJQ3tLWn7GYdoA296DraFC2REq8eTy176HSWnTi1u5oPSOeUUKzZxjWXYoo6uYPUScyAymXyu1HH6jYJosrvySlsqGyNL7PBAq+uaAZqLquVIXiwp45xzvNuUSpM2BY4F6lmJeiPgNyONVpaNKckNN9jbZHzoJSsyZtbSWAKRuOu6cxi/EuE4CR3HgZXGliQu/f2WjBxyRGXZMnuaU2FJEgehyeNAqzJrc0Hx6quYqhQvBOnWzX1BjtRQUpxDnOSv8uI22cvZs2fLvHnzTGXYOeecI3PmzJFLmTmV4PYdjkRx4tKRUqI1kYeXxALlsAcddFBRr8Dbb78tR2u2IMntZwU+FXn8l7fLHtVXFWUv1WU0IShvwcwHhaOlkWQltQ/ziCNsYKrRFBcOmo3oxcTp8oAD7DB4f3RE0xCCM+hRC3kGLa1nn+0llNDsJH/TCr132iDJ94wTMD20CosF7HNhK90lZS6VwYO9DAzmwyRKzZiVstZ3GS4u6W1VgjhOShKXX3/t3XaZy6ggIBGS+nEhLCnQ4BSpp8sHHpBAF5c4JvTwojTW33vtcARNSXEOQpLnICbpvZwwYYK0adPG+FewgE+cRXlsgwYNIjrJ5kQc5cgp3B7lSCkVK1aM2iS+atUqY599BGIoxNVXXy2PPvqonHjiiVK9evUic59IsF22n/UQGIYa8sr9vVSe//f9RZmhpLKXwGfav7/tw6xZ0z7244+2jxIPfDK+/AyloBcOsi2UyVLrSGksTXWISu3VIMt54olWrJTQJ1sWIZhkzCiCC9q1s4nijODPXjLf5qGHbJSrcJsUpE9gxpO59Gdja9e29997L+RwzKJEWRaXuPWqYxNzR3FGKg36JaB+fP1S22QuOX5dI1/MjKUKS/bZiROtj5kmY26+2WtTLi1UKjPKhOlOcP759hTrcKQzjurdu7f069dPJk+eLO3atTNGiAMGDJD+/ftLx44dTUyF+2s85Ewc5cgpCgpdJ68jxVCWsS4FzqS4y2KbnRMw3JKM0+bNUlitmuwn8+Xn1buYQJ6Z9KVyGWXbjCdRcx4uQvRhDhhgA9MPP7TuM6EGf/OiZD2ZkcVzeZxySsSoQhYTheHPVpVxHn9cRCuKGDOARxJjBzICGWhenBLV3XYTWbjQ9mPiKsyig3LJJTaNE1pg4Kl4PKFldHeIlaEhcCdIZzd576kFcmzv0I56xhkiL74oeQ3HyI03eg5HqAp2AHYEYBGHOuJkIBXMXAy+P441VIsf1Eu1avbnHIPff1/KPyZ/haVqcIQl+yzm2RdeaB/jlDt9enFvpmT55Rc7x5aWW6CTgN0iZaOHHA4fLo5y5BIuc+lIOTjApgLcz3A627hxY/a7neH6Eop4ClavlpEH3FGUvVRTiFJtmz5MnYHIZzFokL1PH2bbtjYC01pHXpSUG4IUFwyiMmZf4kqj2RqU06GHilBCk+2fbRogUUdCVxk2LIPCUt1KVNgQ7ersRVx4/vc/r1waIXTuuUWWmboWQ3sOu0YsGKGpWRl2gTNv3Ft+L7932c1cgr80tjR9lywGqIlGpAUyxKT+3PVbRixPp3IgXFiyaKI9w8C6ShDCkrUcvnoVlpiAU3brhKUjVRDTENtojEMlVy7FZ46yjROXjpSDUU8q5ictXbpU5s6da4YFz5o1y6zs8dhm9Z7PNhB8ZCsYm/fN/6TJ9gvM7WefFVlgbyYPWQ5mSVBnq581y+pEQaRGSVkhQPylk/RpErhi8MPv0LfH1HEtf6Q0lmYiMjZEV2UUPgYSdVopfPnlxae6ZAx/aeyIEd5tsmvcV9dgymYxgNqwoZiOicetmECdVl1YsqRATqs0VjZJBVuCbRoxy5i4ZKFGFUVp+i5dv2WphCUZSzKSKiwxxiZLSbEGPweOWRZISgvVz2QpdSoMpskkrXUyjcMRFMQuxDDEMsQ0xDYa4+hokSAhLttR5085HAHixKUj5VSoUKFE19cgVvkovV20aJFpasctLeuymQi8q64yNws2bpTn97sluOyl2WiBLeMjC6l9mGSYMH+ht5Kg+Pbb7W3qOrXWi55Xlvj5vOjtQmD26eNt98037ePvvy9lEapMSeTquEOmvWQFqL6ddvIWElau9H6G48iYMV7a5vXXjSJuvJfXJxiP4SvVtOjUvUMJy0/XHSoD5A4rtpJyo8pxcUn2oGVLe5tFm2Q/g0ScYp2ZT1RhWaeOFZaNG9vRQE89ZR/HvZm+y9JC8hjHZPzP9BROwlpPnw5HEBCrELMQuxDDEMukI34hLiuvi5AOR4A4celIC5zEGDeSzhM1K36Uk2QVlC2GVgr//d0IaV71+6Ls5a+/BvQamPL4M5D//GOX3nUcCT8neNUgmc8IMYnpEAKF7CrjLMaNs+WXQKMe7rOU02qgXQbQamFg96UNNZR8zjy8IdIzQE/z6NHFf85sM/4ATbG8/770fuV4qSErzN14p4kQSJMUV8+He+U6eUM6539pbCRxCUGMJElEXGLC5TDFE5TC+oUlpbAIS5LoFFloPH7rraUvW2dbF19s1+KA6kGS1XXrui/DERzEKMQq6V4QJx5L9aK/o+zixKUjLVB+UbduXZPFTBcbNmyQ+fPnZ5fAJKMYagoq2LpVhu95U9EKeSDZy/A+TOZfAhctxpFoH+Zee9l5mP36eb+DgmCuhpqLIEgZFu/vM7vvPitKKYvMc+ivwufI/6eTucwqopXGKqR5yDiH+mp2mfOZTJJjpJYsSWhUJVNu/Jmgc+U5mT8lZJtb1sRlECNJYolLlJIeg/XqFS1GlWVUWOrHQgZRM5ZAxlJnXHKM+gsvkoXTJb3VQAEAXQQ6HtjhCAJiE2IUYpV0QhxGPJaKdiWHA5y4dKSNSpUqyb777pu2DKb2MCxYsCC7SmSJfELL6k1+ekPabjfV3H7mGevzERjUhpHNohRWLyJjx9o+TBxmSUWhGCip1KZ+Sv34OW+Gz4z0AEv3DIrTEkuyKvRqPvlk3pr94JDaqxd9hvY+PZZZOcuOkmeawIDFAsqcw8HikhRPKAv9b5kun8hR8s/3vyX0UrTknnq8zXr+IzXl1Gfa5/fEmmjikjJVXfHHiVmNd5IRlyy2ac2xwjGoY05cSWyRsNTSdIQlu7Pu9hyj9Foq+FmVdrLCI494Y6I4db7wgueH5nAEATEJsUm6PSKIv4jDiMccjlThxKUjrXBCq1+/vhn6m65VM1YFl6hKyAYoU/zPf4ruPl2LTGahiVGZfhAofMaY+FDiqgJS+zA164JhD4PicIcFFAMDHVFXBLk03tErSuMRrhlahknNWLdunoNGHsEIUOY7ajBL2XJWLvLypnCDVZ5/PvLzECmTJ0vhHnuYu/vLj3L3563jr40NvdTTz1eRBmKdTb5eVq+Yg26ZEZccD5SIA1UAVAgkAgsy+rlTYxlezeH6LeMWlkAhCKbXcPbZIq1bS6l49VWRK6/07tMhoEbcDkdQEJOkM2NJvEXcRfzlhKUj1Thx6Ug7epJr1KiR1KlTx8yrjEdo/vzzz9K9e/eif82aNZOJEyfKE088Iccdd5y0jhFVcCLPKhdZpm+HhhzXW/iJdKlie7eeflrkt8QSSvHB2Ar6MFUcrlhhDWGoxSXY3XdfOyDOn56jzJJ6yB9+8Hq/cJZldqJC1vPgg20GJ09AZ+s4A3ZLPgZtPc1KzjrLU7682WjZ5MaNpWDKFFlYaV9zd6/Nv0ghx0wCMxSr71pFRu/dT6rIuqJJJ2R1ypS4LG1pLIpJ58A4M5+osB6IOa8KS4o9KIX1C8vPPrMLP+q1VFqzLbaPF5YeQqzL4Q7tcAQJsYguePft21datWol1zB3OsTMmTOlS5cu0qFDB3mMcWAhrr/+eunWrZt07dpVBg8eLFspsfERHkdxn/iKOKtx48ZpXdR3lG2cuHRkDOr+a9WqJfvtt580adJEGjZsaFbVojWZ16tXT0aPHm3+jRgxwpw0W7ZsKUcccYS8WMJAd0pQViCosgV1bg3xaPUbpUC2Gm+dwLOXSoMGNvtIphKIoKgnww5x9WobTOMai2sNo00A4UGWUxUEWVcudriPqmXi77/b3j4UWTb1tyYBiVo8crTS8brr7J+W1VBWqTMXKHf+/PPoz91nH7nt2MnyvTQxdwtoLKXeTxvW4uCgf5eX/4m3CHHRRXnq7RNLXGrmMhlTn0TMfMrojEvibjKWtHz7hWVoPc7AWqFf+HE6ZSxJsiBi8cDSUxjrfzrn1eEIEmIRbdXp2bOn3KE12CGGDBkiQ4cOlXHjxsmUKVPMKBK4+eabZcyYMTJ27FhZvny5TCKN7wPxqHEU/xNXEV8RZzlXWEc6ceLSkRWwmkapRpUqVWQ1QqcEOKm2aNFCqlatKk2bNo3L9ewfXFOzCUpKQ6Wou/81Xc6p/EqROQV6LSXQh8mIittu87Jd9GVi0qNBL66xZCgZP6KKi+V8ymC1yY4ojGiMCBC4UA4davv7dCBcDtK3r/f28TbKmeDSXxr73HMxn7pT092ljXwsX0moDJqyZsTp5MnxvdYBB8h5MlzOE5syWrvWlg3GcdjmrrgMXzRBxaiLK6nuv/4KRlxyHLE9fQ3qQMtoxjJcWLI25oc1LnWO5atgvGuy0KqMCbBO86HQ44knsrQU3pHz+GOR5s2by/bbb190H9fYLVu2mMouBGH79u3lk08+MT+rFlr05eebIvR6r1y5siiO4n+XpXRkCicuHVkFq3nr43AJeffdd81JNxHYblYZ+9C75bOIvXe7gVJBNqU2e6mvO2iQnV9JLRl8951VU5qFofaMDNgFF3i/h4EPIpTsmEZ9NCZSi6YOGmTA6O+jVi2bPus4e63wMQKu9STDc8bzAFdgHTkyapS3CBAB9MzfUkvayofyR/1QKTllmpR6xlPi2cRmPR+RPnJgHVvaReU01dI59pUnn7kE//knkRmwscQljl7LlpVZMx80OsKS0xHQIhxJWDJPHjdXv4lPskbkVCnzVZLEB/zMMM5Oo7G5owxRUoxDuSwZSIXbi9nhQ1A+e/TRR5vKrWO0YiVbYxxHmcWJS0dWwWpcSSdHMpszZsyI2WMZCbYbabUvo1BzSTQlIrVWzJNLKz5TlL38448UvzajRpg8rn7+2odJBpLvALFCE+jw4d5wR1IFZFvJfqpQZfYlQlRr1khlIUopt1WnjSyHGaOUd/rdIsMD2qyGFW0y4fo9vvVW1Keqnlkl1eXhDhM8kYRJU+fO3ndbgrisKutkdMv7TDIcqJxm/aHMiMtk+y5jicsyXBIbr7DUcnXNMlK+qiN7E4WiDE6Ds2d7a2p4n+k6jcORiRgnFvfff39ROeznYS0QWRnjOMokTlw6sorwBvVIcGKl1zKZkSbxbD+tUHfly17eXulW2U7WmlgWjZdyiKbow6TMFfh86J2kNFbHIVByiQhVJw2iOuogcZDVckECYcr5LrzQ2zbltgyGY0RGFsNYQTxxtCWXP91fZZozxFka69czP/5a1Q7wU2FKYMKiQKzSWvaDUL1gw98nFRmqAC6bCbRv5ra4pARce5PJ4Md7bvGLS4y0/GhJbBnLXKqwVG+pPfe0wpJxveFQIaimyIwNTrbKg139tNPs6Q+oQKZwI6vNuxw5T0kxCC0+lMYq3PZnMqFixYrStm3bbXou49m+w5EOnLh0ZBXlyISloCQ2ke2nncMOs2WNOB6uWSTXVXzI3KbnJ+XZS/Oi1e38y1tu8R6jLox0wPz53mRy+jDPPNN7zkMPWd//BQu8WlJSV2S+dPA71reU7mC7mKUrqngpTJniTYaglysne62OPtpG5fDOO1H7APfay6tiNjqH2t+XX7ajZ/xDPknfRoK0Tr169vasWdK9W2HR6AbWGk49NWcS1qUTl3xuoaoD81nPmJGYuGSGrK/XqqyOIeGj4xThF5bEzJGEJacQv4kPx24c7fbbQOKIFvK33/ZOgSSfOf4djlRSUgyCkOQ5s2fPNr2VEyZMkDZt2piM5B+hgIDH6cPE5DDR7Tsc6cDthY6sghW5WE3oq1atMjbdOMQqDz/8sLRr1840s/P/c1GyLmyX7WclWB2GLgo3lBsqO8oyE8/efXeaXp/XZvYmWSytc8RRgz5M7ScjSzNypJ0/oYE3GU2CYH8ZJkIZsx/tByGSIwo88kivXzNLYJTBrbd6HwGlnWRDcpLy5a3xkqZjcf2N8rR99rG3WTswFVo0mNFwesUV3hO5zfcWqYQrVBprXHx++8203TK1Bn7+WeS88/Kg/7IkcZlMaSzVAIsWlewUi9qJEDjmG7SScZpQt+FYGUtgvUPLZqnO95eyJwIt58OGeWsEnPYosnA40h3j9O7dW/r16yeTJ0828cv06dNlwIAB0r9/f+nYsaOJdXB+ZXzJddddZ8aQMIoNE6AeVJnkSozjKFMUFLruX0eWMW/ePFlH/1fA0AC/7777mhVASkdY4StJzKaV3r2LHGXuq3C9XLt5qFSpYgVAWk0jf/zRjivRRiRUF7Vn9FbqZ0X5HikqzWxC//5WJKsTBgLn3nut84bOGEWgEiGec07G04MY9jGiUxOviMybb5bcBmcdFX6Ifn+ZpY8TT/S0EEYmJNEMKEIi7yFDvCdff739/v3fF9+1rnywoRNOMH2rvKT60SA42WVyFkw3tNeYrHCEEjSThVQlxEgXlFEsWLBRFcMx4F8IwyZVy9/i2VYeCEsSvyosyajzEUfS3EDShvZwvKfYFWk3o+gjUTj96BoK26FIgyp/hyNVaC+kxh0LFy5MWYzD6BGHI9O4zKUj66hRo0ZKtsvK36xZs8zMqLlz55r/uY+YXbp0qfl5RiFzGMqWXCn/ld3ldxPfpi17qRDB0YjUqZNXJonAoCRW+zC1xzJUzmugSZRoUeeokCJDhJAe1ACcTBcllwyTzODcUTQU7qYqLEmqMvIz59l/fzuXVLNgOs8hDH/84W8BNNE2CwT+hl92wMsuK95TqAIWQvWMjNsksa3QuqvlxjmJ3yo4WuaSD1I/zE8/tconCDOfPC+JJXnrz1giLNHSseJiFir046W1OxlhSRu4lnDDf//rhKUjNRBPEFcQX4THHakQlqmMnRyORHHi0pF11KxZMyXZxEgubdznRL9o0SLT40DzfMaS+URYffqYmxU3r5fbyt9mblOFqpV0aYOL1OuvW8Gr0JeHiQl1j/ocorUHH/SylcxKJDD2j2ZA7BA4Y+uoMC6DtGGG1MeIEfbP0T+Dcti8GT1ARkxR55N4xaXCYgLzHfQ4ZCfEMEgXYPziUhVCKCNKe60mrjFMSWQEZFZBxl53imjiErT/m88mUnbTjxOXRcKSJLtfWIZ7G/n58EOvynvnnW21dqLwGhh36emdxaTQ6dbhCAziB+II4gniCuKLdMQUxEw7qteBw5FhnLh0ZB0VKlQwjmmZuiiwurgxfGh6urjxxqLZk722PiMN5CeTvaTEMCPBNSY/r73muWLSS0kf5gcf2PuIj759ragkdaXlffSi8bsoDOD3KfmlBk2bGqmjpPyPWtQ0Zo3nzCluCsLYF33reQF2t9p3QyoxwmdborgEptKjwslA67YohUZo6fiaMHGp5cXabkspIwlv3Q1ytu8ylrhMpO+yjItLFZZU3gPHXUnCklOxXwRSoY3ATAROWxhi62mddS4S9A5HkBA3ED9kYpGamKm8nqsdjgzjxKUjK+FEmcyokSDYsGGDzJ8/PzMCk6iJIW5UlRZukTvKDTK3cTD1zVFOL/RfYtyjcyxpqiOgvu8+Lw3A5HHKZJmTCTyOyuB5/jeOOMFV86ij7H1KLQcPtvc1I5pC+Er91b2M4+Qt5RXMUjjpJK+hcuLE5MQlYBBEdlpLRMlmUy7NwoMqcsSlL5AivnnxRa+Pk5e/zSbh81NcopZUzDPLorTikkZrypvzDHbFRIUlUBihWU5Mo/wFEPFA6TvJZZ2LyaGBE3e2tNo78gPiBeIG4od0Q6yUiQV5hyMaTlw6shJKPOrWrWuymJnql1iwYEFmSmSZHxky9ui+9RU5RKaZ+fYZyV4qBLsITBUtiEKaoKgzW7vWE8ZMICe1oCuoKAuyMAynU4gqqXMjdaDPmzrVlsn6m/ZSAElSJqoAWpnANS/xl8aSfQzDH9DHFJe6uIAbsE6Wp+T5+OO9xQackVAOPhCWlB2rKz7rB4yCzEtxSVaepl3A4CqWI7J+2Dgy+wcq0kz400/29oEH5lGNdmRhycgPhGVJhrgLF3oLE4hBKrUTmbTw9992fUt3T8QpFfl59vE6MgxxAvFCJnwbiJGIlbLGmNDhcOLSkc1UqlTJuLtmMoO5hBLPdEOwimNniKHlbjT/E1hltH+N5sQ33yz23kwjFGNhfvnF3ifyw8QH8agWtxpZIjrVFAZRSXMeJiiqdAiwzz7bZssQLAGDzlVzJBJNvHWt9s07WATYaSd7m7JmTduEwAR1993jFJdw3HFWHaphBCZN/rmOYaWxQMWzms6yRsM6BGNP805cxlsaS+CpDlJkLf3BoP+zzLOSWD381Xw6XmEJ11zjVRlQpY2PWLzwex07eq/bqJFdIwkfLepwlBbihExlLImRiJUcjmzCZS4dWQ0nzfr165vBwsmszP38889mJpT+a9asmUycOFGuv/566dSpk5kZ9WCM9BUXjYy4yDLALRR9Hbv1fWkrEzOfvVTxSCph7FhPmU2fbvsw/eWXlLny+LHH2vuISvpJKakknaCQSqAc0J9pw13nX/+yAiYgli61ulUT0XfemVigmnMQbODIC+w4lLaGoVWZrJ+UZHJqYBEBwxrNuPkXXiKIS/UFIsDX7wCDn02bJL/FZbTSWHqM9VxSRvotEZZMcQkXljpnNRasZehuS8VfIj2SfMzsa4wrAda50Pz+ZLHDEQTEB7oI3bdvX2nVqpVcw6pICOZyd+nSRTp06CCP0d8SIt4YJBLEQsRExEZOWDqyEScuHVmPnkgbNWokderUMbOcwoUm9yOV0NarV09Gjx5t/o0YMcL8bsuWLaVz587y5ptvyiuvvGKGFn/B6I0o5S4rMjEyA3FALWGIuwrIXhZmPnupdO1qx5U0aGDvIxgplXzgAU/BUdpLRIexj35f48fb4FmjPsDAiHl/pBJDZkYmE4pARciWUtzzdujT0tI43ubVV0v+U0JpbNx9l3747jBv2mOP4o9T1hxlLYKvFlEBrBcwoiTvxCWzK2vXtrcR4JH6teM188mTVQ/MnBCWWu2LoIxXWPJx6yxKoOIgXiNMjveLLxZ5+217n1MKp6F4XtfhSBTiA22f6dmzp9wRZmU8ZMgQGTp0qIwbN06mTJliRpFAvDGIP8YhfiEGaty4cdIL7g5HOnDi0pEzIB5r1aplhgQ3adJEGjZsaFbu+J/7FdVUIwqTJk2SFi1aSNWqVeXII480J2Z+hxM17m7R+CcFJZpxQeYpNHC9eeGX0lVeM+2N+OhkBYyjoA9TTXzITrJiS4pQ+zApf2WcCdkcNRygkap1a9vw6O9pxeWU8kAyZIDFKL9LhKrlhEnAYjGtoMBbQOwk0reVszACRl1dP/7YK10ujbgEtskIGX+0TiY7NO8yHKpzX33V8wS6/3779JwTl7H6r9mhNHtJPSbl3omIS8yw9Hih5zIPhCWlsMkIS+Acp7/L6cC/TlISVO0/+6y9zT73xhtFp1GHI3D88UHz5s1le1/dNXHFli1bzMI4Tq7t27eXT0L+AyXFIFWqVNkmxiH2IQZyrrCObKcshFiOPISTMuUgnIC1LGQ9Mzti8O6775qTu581a9aYkz0XhWiw3YwY+xCw+lZB7yi4ScrLZnnkkeIViRmFsSL0YepwQy1rxeDELwjp2aNMFlEJZCNJH3brxtKv9zyNQsl2qgIkUMfsRwdTJsB334n06+fdHzbMczHNe1jVjjHzMmlxqd8T34tWC5Cpo8ly2rSIT+fwQlQq550X2/cm68Qlx39JGfSS+i6jiUuEqwpzhDsNsTnM779Hzlhq9rokOG1oCSyngEcfjX8xiOdqny+7P/5gvBeHIxUQF8SKOyiXJcOocHtxmO17tBiEHk6Ep8Y4LkvpyCWcuHTkBZs2bYopAFevXi0zZsyQ1ipuQheGgQMHymmnnWZKTaLB89h+RiArGHKibFz4o5wjI7Ire6nZFqJBGqR01ZYyP/ow/UPlcZDB6MdfF4nZzKGHepkbQLCQsaT8UlMdrA6TyT333DgbBG2rIb+i1/4rr/TMbssMmCNp6RSlsb5jpFTiUr9PzTJraTTpKr63CFx2mU1OA/5CjIDhO8pq/GZiJZXGsoCin3Wkvkv/h+y360VYqnDN8ZJYhCW7ALNkgbbxRISlmmXrfkFpLOtK8cDpx19K+9//5uGYIUdOxR0lESsGyWjc4XCUEicuHXnBVnUhjVESS6+l33n2gQcekOrVq8u5CJZSbj9lEKziPhPiVvmPVJb1JnuJQUpWQRaSXkpVLbxBAm5/+SvCkb8H20ZtomJ8Q6tWIo8/Xrz0kMfIdmIzqiCQ6PsroT9FzWTIXAKVhkOHStljr71E2ra1t0kV+npdSy0uAeMlPwh/MngRMnfsyk8+aV07ga+2b1/JH3FJzTULJUB5d9h4lqIPmWOA70XxL6zksJlPEMKSlmxGqQKxNqNy44HX4TShp48BA0T69En0L3A4EqOkuIDZk/5yV277M5klxSAZizscjlLixKUjLyhXQt1UeEksTfQ//PCDWTUMYvsphcxlyHJzL/lNLpP/mbaurMpeKk2binz5pZ1arn2TlL9y8fSnqUghkt087DAvcGfWABGiPzPJ6Atq2yjpZDagBulkzKh/Y/sRoMcSAa4z6amo5f8yib80loZTXy+kThZJWlzSdxt+m++5c2eRMWO2eTpfIRkmrfx86qmIXkO5KS7DS2P9gz1RPfohk433m4/lgVMsI2YoP1VhSWIWwcdI23ihwoDqAuXee739Mxbffity8smeh1KvXok5yzocyVJSXICQ5DmzZ882vZcTJkyQNrQPxBmDZDTucDhKgdtzHXkBvQnRehJWrVpl7MCP8JXw4ej2xx9/yBlnnGFGlLxGeWYU1Il248aNpr+C/9Peg4mQCv19N8kQqS7/ZGf2EshIkplk9IiCOEQkM45BIaVBCSV1cAqOsfSezJy5bXknqa7DD7f3EZVclMnKYRAUZiZCT5+Cga1fA5U5TjnFK1dmgnyoTpjdSbOXfC2RDE5LxP/BUnJO9hoo5+rRo5iY9a8/kKRWLrnEyzDnlbj0l8aSvdCBjbGcYsMzwTkkLLWHFmFJNXwiwlIdYVV/E3+feWZ8/ZknnuiNcaWLgOy4M9F0pAKu+/44gLjAH3f07t1b+vXrJ5MnT5Z27doZF9gBAwZI//79pWPHjiYGwZwnnhhEzX4cjlykoDAjTiUOR/DMmzdP1qWgiUsvHv5DhcdotK9Ro4bUrFkz4hiUwMGFlSyeiAyWgXKzDDb6Lcz5PLvAJpRUgrrHUjrIY6HV2yLIcjEvRKNEUlvMXeF3/dCbxngSxLaWDGEqRER56qnmIeL7Dz6wP+rSxTqTlvlgk8yxpgj5/Lt3NzfRf9wFDFh0skzcLFsmsvPO9jYK4/33RS68UGT4cO85Dz8csUaRpz39tL1NqSwJb01OZw3sfyqQf/jBc9+NBqKaz4PsO0MVMe8g+8AMFl3covkU5xldJGFWBscHqizpFHJmhaW+bc1Y+qt+44HK+AMOsOsetHBTVcz9WNDiy0eqMzQZmcuoXZ9Zp8MRyBxLxo3gChtu7hcpNggKxo7gDutw5CIuc+nIGxB6qYALR/jFg/sI2UWLFpmSF3opUr5Og6gKrWReI/fLrrLYxO0EWVkLjhr0+amBCTa37dpZtw3/50XGi94zLQtkkYD04wUXeMIUEPF8Dv6aO9xmUUnnny//vWN1kbDEbwbxUuaFZYzS2FL3XVJbq0YUs2bZ7+eZZ4o7q3Db1zessAtoog6BcNFFsad9ZDxzGU9ql+Pz2GPtbcoKtJ8ymlMsil737xwriaVgwC8s+bOSEZZA760ab1HIUJKw5COjU0CFJckgiiWcsHQEBddzrutc37nOc72PFAek6rqfqnjG4UgHTlw68gYyiJmw69aL0Ny5c02pTMrAIYMaQhHZXtbKQLldVq+2ZZ9ZDW46pKWOP97L1hBNIh79Nu5Ep2R4mICuMLCOUliNIv0lmKQ3TjvNe2zYMOkw6BA5VL4qGkOgSbUyDypgzz3tx/DOO7ZMMyhTHy2NZZsIKjJ1Dz1UfDwNDiu4BPsCMZLTZE1J3AF9scwkzemy2GgjSaKJyxzttwxSWDLJCGGoC0IYRceC4gUOe/Wm2m03W4FMotjhCAKu41zP07JoHAHimB3V8M7hyEGcuHTkDZSm4s6WKZhLNX/+/NQKTAL20PL8xfKE1JP5JgNEdWJWQ4YLK8j+/Ytn0BCJ/p5JXHdoyEMZahqC/kvGmoTPuaQclh7N4cOlMPTchjJHpkpLGd9mqBzTxjntFUGtIWXVKu753IIWl1o6Cqh7XFX8Fr3cvvxyr5xZROrXt7NHFbyfvvpK8kdcat9lHolL+nMRlpSy+oWlrl0kAhlIv4kPs1BjlUYT57P2pGKUhQnWSnRikcNRWrh+cx3nep4piGPKc852OHIUJy4deQUnZf+4kUz0ZyxYsCB1q521a4tcc425WUk2mdEktHdlffYSuFjedZc1lala1T6GkmB8wyefFH8urrH8TOvjSNEytJJ+Nf9FHxFz7rlyY/vp8n9ih1BXlM3S/qMbbHkiTWEOi4pLX2ls4OKS0tjweTD0zmpFAalJ+j91rmPIbwhRCazL0A6aNYslyYhLlI7OW5k61c5ojTbj0i8uc2DGJcKScSMqLFkcSFZYAtXSmPIA1fJUt8fi5pttMQNUqmTHlsQ7B9PhKAmu21y/uY5nCuKXTC6SOxxB4MSlI6+gnKRu3brpMdiJAiueS+gtTBX9+hXVe54lL8iB8m1uZC8VIkiCbsp8/X2Y2N/6RTnmKcyz9PcLIk5w8dDoNmSAOnRMfTlCPpV7Kg6QQhUyWFYedJB19HGI7L+/deJVUTNzpuyxhw3SUyYugREzmAnpSjxZaXpxfWKNpGbLlvY2YgP9mRUj3pIRl/7sJVliXGb0w6WGUxdW2Ne1J5O+1bAh6tmesSytsGRsCQ6x2qrK4R+rq4E1Ch0xomXvCF2HIyi4bmcyY0ncQvySifYehyNInLh05B2VKlWSfffdt9QZzJ9//tlYhOu/Zs2aycSJE+X666+Xbt26SdeuXWXw4MERBx1zkUrZ6ieN/vSwmQO4UIbITcZk9cEHJXdA9NGHedxx9j6fFcYvGPj4+zApd8V5FJMYHVQ5bZrN8rz+uvzyi9eiuVkqSp1nhkjBhx96Ee/y5dYsCLcYHQVRlvEP637+eaP3VOMjGpJKuJckLnWUDAMuVcmScurUqeg7QVywSKA9spQ93nOP5L641D9GB6n7U8WoNfbPHCiJRfAjLH/+2d7HVRhhyeJEMrCfcbhrBwHrZbGMeNl1/IbDtPSyPuFwBAXXa67bffv2lVatWsk1oQohYJRZly5dpEOHDvKYrzH8iSeekOOOO05a095RSohXiFuIXxyOXMeJS0dewgm6fv36ZohxsquA9erVk9GjR5t/I0aMMNbgLVu2lJtvvlnGjBkjY8eOleXLl8skMmQRymuwL08ZlIeGBFQneUuOkCkm4NJYNSdASdCHed113mM04B11VPFyVr4/xpSQxdRZGZQadu0qU1v1kzX/bDIPMRcPDWOiYMx+QuM2DE89ZctvNVNUVsEJRWenkfrZvLlI72DQ++efSWyTEi51U4kmLnUuDEJLM3eMLcHkKXScYAbzwgte9or24vBq6ZwRl4za0SBRTX1ytN8ykrDklJessARG+mk7Kt97jDny8vHHtkpeFz4Yv+Q3I3Y4goDrNdftnj17mhmUfoYMGSJDhw6VcePGyZQpU+QnXJ6FIpoj5MUXXyzV6xKfEKcQrzhh6cgXnLh05C160m7UqJHUqVPHiMNwocn9eMQnArJFixZStWpVqVatmnlsy5Ytsom5dlFgLlbKIIt3661Fd++SG2TlykIjMHMKypepjcNgBvtQIKOJEJw8edtsJ32YPofYM/68Xz6WNtJqr4XFWvuMgdArr9hZJCpmcJzFefbee7Ok5jIDIAJPOsneRklOnBhs3+UffxSJxYiQqX7vPc8iFndgahtDZeQk/FRoUFF6+ul2VGTOiUsy7iySgF+xRxOXWdpvqcKSCoGghCXJasaNKFRcRBshgpfXySd7GU7GjjLi1uEIGr1eN2/eXLb37ZA4xnKtJ47AZKd9+/bySWjVq2nTpiX2R0aKMbhPPEJc0rhx41Itgjsc2YgTl468hz6GWrVqmYHETZo0kYYNG5pVQv7fnz60OHj33XfNRUWhZOboo482F4hjojT+hA9cDhx6EUO1ZEfKp3KSvG0CtVQmTFMGKoI+TLV9pIywbVvbaOX/DBElL70kc69+VDaIzQy1kqny0cp/S43P3im+TS7WlNkSxCNWgcUAMqWoGIRQWcTfwzpiROpNfcKhZ5aaSs12Tp9eLFvNKApacFWXkZFGaGYEf4laor1Y/tJYxf9h+7PoWZi5RFD6hSWzJEtTCqvQN6kG0XxEXbtGF7accnWNrkMHkSefdHNrHcHDdZrrdSQolUX8KdxenOCKF3GGP+4gDiEeIS5xrrCOfMSJS0eZgtVBSk+qVKli/qfPoiQBuHr1apkxY0axvor777+/qBz2cx24FgbbjZXZDCTr5yvfuVNulFX/bMm97KWC7SOZSVUW9GEyuuLCC4sF9stXFEjb0ZcZA5+fxYrRiv/8baNPainDe12JismQ4Vyqq8MffGAzoQzZK2uQuSSzC6+9Jo12W5lecaliitV/VSo//mjH0sybZ/pAqTRj5iHQQnvLLZJbmct4xKVmLumh1sbXLBaWnO70O0kWvub77vN0+8MPRxaLf/9thaWu/xx2mC1E0IpuhyNIuE6naiGY7RJn+OMOl6V05DtOXDrKNJHMeMJBRNJrGW4QVLFiRWnbtm3EnstEtl8q6GMj8hKRA+U7OUNeyt3spfZh0qN27bXeY5j50MP2++8miXnJJTbzMU2aSZ+WX0th55O95yK2GUES3jxIJIslKaJSI2QiWGrucDNl4F5Zgc+CsS6wbp0c9NPo0otLHRkTr7gEqgYofdbRHCgZBOb33wuJAgx+1GCWbBfzDHNKXDZtuq0aU3FJGfDvv3tCO4tK4lRY6ogQzViWVlhy7GLKo+ttrPVoC7UfDsWOHa0Q1dd/++3opbMOR2mJdZ2m7JXSWIXb/kxmabfvcOQjTlw6yjTlypVLqCSWFc4/Qsvp9GHQe4HxT6zts3LJYGbKbvg/0BVSglJmR4YYLINkzYqNZjRJzkJGFqtQ3F20DxMzn0MPlbcHfGoyGLDjjiKPj9pRCl5/zaZDdPwMDiD/+pdNeYVDqe2331pRrjz+uEizZtYEqAyWxtZ5b0SRtklb5lLh2EFg6u+zKMBCwrRpcuSRdg6iglkTBqs5Iy75UP3ZS3p/1Q43S818woUl4zoRlkxQKS0ct0xlgbp1rTFPOBQd0FKtxSBMZ8H4RyuoHY7SEO1aHCsOQEjy89mzZ5tr/oQJE6QN56iA4wyHI59we7yjTEP2MVaJyqpVq4wNOa5wQHnLddddZ8aQMJ6Exv8eMSZ/L1y4UGbNmmXc5ebOnWv+5/68efNk6dKlwYwroecT101idflFLpIn5YEHvF6lnIVmu08/tZEoLF4sx991jFwsjxMmGANYnCZNEI9tPKJSR5Cw0ox5zODB25r3EOAz+/KJJzzx+sMPNgPMB1cWVpmZdxnq1y03+WM5vPbPpROXtWtbtZ+ouARSYnx32hdLRpl9evJkk8Du3Nk+zBxXDjU1d8l6cQla4g0Ygem5Jgv7LXGDJWZWYcnuQVFGEMJy1Sp7iCosfqnPlqJVCRgKa3s1RQzahu1wJAPXWK61XHOjXYsx89E4oHfv3tKvXz+ZPHmytGvXTqZPny4DBgyQ/v37S8eOHU0sQN8kPPzww+Y5K1euNP8/99xz27w+2yXOcDjKEgWFKXUccTiyHy4u65jDECD2sMIlLvbzuPBQdsO/UvVhMPuR7BsaTHaV/WSe3DC4WkyL/5xh6VLZeuppUu4jLxM5uVFvaT3jkeLBf+i5cvbZxcc/ILwZuxHJ1Y/aO0pEMZVRyDYxWzPLh9qXGtKCoXmpz+xzm/T+ZZC5TUk1bYAJQ6qRxQBgZUMdYeOFYa3UQ6pLMMKfcT8t2hvdqaMw+vZN40xXRC+pPK3jpLQ6EeghRsgDn4eu+JCe0xQ8lqiU0GYQZpyi5zUzrMIyqEOARQLttaTld9y4bSuBBw2y5c9auc0hHMUrzeGI6xqMGQ//MhnmYvqHeY/DUZZwmUtHmadGUpG0xGE/XvLzuOjRw8FKKmU6SUP0Hcqg1pa/5Cp5UO6/38brOU+tWnLjIe/K/XJ10UOtZz9tg/5wx1fq52jQYl6BliIx+oLs0JQp226bKJoaPKa4K9ThYfbDdvIZ6kxDO2nHZSNMNjiw0lhtmEsETVWpKzMLPp07y44fjpFXX/WMWzGsGu21iWZ35pJ0q8LBqH2WWhbLSKFQBjmTwpJDKVXC8rvvvMUAPk6yluHnRkyhVVjys+efd8LSkTxcS7mmcm3NdP4kFfGFw5HtOHHpKPPUrFkz4+5tGzZskPnz55dOYFICGnJAuV7ulnLLlxo3xlwHbXj3/RWkn9wv55YfKVsrV7E/QBQiqnGC9YOoJCNHg5dGyAT1RND0coYHG0S8zL7khfT5GK6QRWNae8BZ7ayBmmJ6UFmQWDlXWsrUzPRd+qFW8o03RLp1s/dxf+nRQw797rliLsjnny8SmmOe3eIy/MNk4QKROWeOvc8ihvYKZ1BY6mgQPJaCFJYcahg+6ygZ+izVv0mhQh2jH4XvOUangcMRE66hXEu5pmYa4oodtV3A4ShDOHHpKPMwB7OkQcjp6g1ZsGBB8iut9IEw15EkkKySG+XOnM9e0jrpH8v4r3vOknKffSqy9972gUWLbHTMALxweJwMkdbWEeFS2ohDrD+jpNCjidlPp07eY488YnsxKV3MR3wf7jkyIvPiEkhRvvyySK9e9j49sL16ycWbHjFtuNrDd+qpadD9qRCXfuOoDPZb8tZSKSyBkTKhefNGVPbvv23VMd+pnvIQn6znOBzJwLWTa2ggXgYBQFzh5lg6yiJOXDocoYtA+KiRTMBqKz0iScMEekrtRORyeVS2X/ar0Ue5CAEnGSqdV021JP12csghtpdNe+HIbl18sf0XLgCIlN9/3zZ0aXaahi+28X//t+2LsshA5ow6vdDnaOr66JsjDZxvLeqnnFI04+E0GSWVZX3mxSWQzWMEjU9pFFx5hTzb4E4jgoB1AH/GKyfEJfsi/dEZFpfhwpKvDmGJL1NQ0F7qrzbn8NFDClivYZ1HP9Zzz7XV7A5HsnDtzIaMJRBPZMOitcORCZy4dDhC5St169Y1WcxsuEAmvfKK86ZRYCJVZIP8R241RhpkenINRLG2PTJWDI+dIkd3LtoE6ldd5f0C2UuylOEzLikVvu02OyhRZxpgiYkBTSTBiAhl9iUClrJFIGC58kpbKuubeZbz4GAaKkHdUVZIJxmXvLjcYw+RHXawt7//vvTvjS+bGsmbbip6qPKtA+STVjdI1e3sd/bss3a/yHpxqQsby5fb/VZhkSPNzJ1rheVvv3nCkqk9QQpLXefShSEm/3To4P2M/k4Wi9TfiJ/h/pxF4z4dOQbXzFItzAYIcQTxRKbbbRyOTOHEpcMRolKlSrLvvvsGksH8+eefzagS/desWTOZqEPeBFv+a+Q0HCOjlPaswLIzWag9q1nT3Owlw2XXZT/Io49KTkFW6rrrvPs4vG8T/LIQwOiQESO8lMjUqbYPk//DwQWWMtnQWBmT8UQw0uAVaW7LAQfY+Zp+ATt+vMiBBxZ3o82z0tikxSWBlGYvGZi4Zk3p3xvbxOnF59Ja65mh8k2ry6VA7MiYyy5LYdVyacQlixY0NYJ/6LpmLln0YF9Ks7Bk/SXVwpLKX+33xvTX7+7LpBkORfXiouoc41w3rcFRGrhmaktJ3759pVWrVuY6qzBSrEuXLtKhQwd57LHHio0L41rM47fddlupDYCIH4gjiCccjrKKE5cOhw8uCPXr1zeDk0uz6livXj0ZPXq0+TdixAhjR96yZUvzs88++yzmUGWubbNn/5N8thEDgVBzU3nZKrfLQONXs3q15ARr19rpIBrLo+3UQDQijB7BCdYMvRSbuWRg39NPb/tc5mBS/+dXrliPMsbFP45EQbQiYMl6qkAgc3niiSJXXy2yfr3kPKSxQvNBT5R3ZP2vfyWVpAvEMTYa9MpSqhw6JhtOfEw+a9BLystm03dJ8jUlvcWlEZek7VRgay2vPq6P+etE05yxZO0k6FJYbZHFxEfHxTIOSUfVcmzT0qy7Bm3iVCeEKrMdjqRhVqXSs2dPueOOO4r9fMiQITJ06FAZN26cTJkyxcy5hAceeEAuu+wyGT9+vBGon2iTcIIQLxA3ED84Yeko6zhx6XBEuUg0atRI6tSpY4RhuNDkfpU4A8NJkyZJixYtpGrVqrJp0yZ5+umn5aKLLor6fF6qWpVV0mrvX01gprFoQpCRC00/7yZjZd+//y9nspfMxNOWvYMPFrnrrjh+iWwlZayISs1KXnihLW8Nd+AlRXL33ba3MpThNZH34Yfb2rxIK9eoW9Kp/to+0jEtWpS+vzDTkEFDoJMMli1yhrxoEo9JgWJRgv5c+C7JUocckQ+f87y8V6OHVJINxny1d+8UtMSWRlz6U8DMsfQL7zT3W/L5ICx1Egpvh4ylP6EaFHxFOu4U8ah9l1T6n366V1RAOzQFAFqp7nAkC9nG9b6FvubNm8v2vhULRpJs2bLFXNMx2Gnfvr0Rkfze9OnT5aijjjLPO+mkk+Sjjz7aZvu1a9eOGgfwOHFC48aNS70o7XDkC05cOhwx+iZq1aplBiA3adJEGjZsaFYl+Z/7e6tjaQm8++675mIGZDE7d+5c7MIXifKVK8jUam1kzyGXSOu9F8gll3jTC+Ie6XDzzUV375Ib5N57CrM+e4ne04olyuleeql4fB8TImX62fx2k48/bsdt4CobTufOIl9/bbOWKh4Q/ZSJRvqgSPG89ZYd1KdvCsGJsOVN57LZT0hclro0NkhTn2izOck0h0rO2v7zmrxToZNUlTVmFmbg5lX+0rZExwT5P0SGqFML6idN/ZaRhCUV+qkQlrSUkmRWKI3lUOHQ4ByGl5Z/pGm9esG/B0fZg0XbWOWs9GIi/BRuL1682GQqmUOpghARiRANh+dEiwN4nDjBucI6HB5OXDocccDFh1IXspX8z/2tWvcVg9WrV8uMGTOkdevW5mI2depUORmLxDioULm8XCJPyPcb68uhT1woJzSkjzOyyWlEGEtCUEsQLpPk33+/byoLsxWCX9xh/YlBfzVhXJCVRPzh8qICkDQKApD+yXCIbimp9QvSkSNtI1gkcUQQwnO//NLL0rFiTuMfriVZYiiRMPvvL0v2aW5uHiLfyIrJM7NTXAKfMyKfBRT27c3vy3tyvNSQFSZLFulrThq+bxWYpclcRhKXachcqrDU/sZUZiwBU2Y9BDhXHX+8Z+6D+S/wcb7+uq1KcDiCIJ5rcRDbjxQHOByObXHi0uFIklh9k/6SWHotafKfPXu2zJs3z2QxzznnHJkzZ45cSqlfNMpb59oK5Qvl3D0nyMz92kvXL66Ws1rNNQEjbYAxk2UILcxQQtwpN8p992wNxGclaBhBSfJMx08yIYOq1qRhrsHkyUW9hCa6pvQJe9FwEKEIUlxF1O30hx/s+JHnn4+8fYxYEJj+WRhvvmndZf1uoDnE8s7nFt2u856deZkw9L1qVj6V5cLMJH3vPZsCE5Ej5DOZJMdIjU1LjD8TpjGBoYsUpRWX7H/+YDTF6oqWMr+wZJdFWKZqOgJFAFp1wC5AqzLw2ODB9jZ/PoeUjp51OBKFDOXGjRtNGSz/c7+kazEjQfwZSW6TvaxZs6bp1dSsJwvAkcaHxHOtdzgcHu6IcTiSpGLFiiWuXPpLYunrQGzyGOWxDRo0KOZaF86q996VeR99JLP+7//kp3fekZ9fHyMHv3OBvP7FSrnrovfkg5d/kJYtN5tgjRbDiBBphzIkh8rX0mbp6KIAMJu45x5rLgLowUDGEiAO6cNs3doraySbi9tIpBLHU08tPn4E9xFKZCmVxTUmHOp2qfsji6YBCeW3pGtoHM2SeWvxsv35p8lGqWhu/+v7kbZJLlEIwjTdjFNqpM8tKHD9pT8q1LT3b5kun8hRsuXX38zXFlgyIwhxSYaclJ1/p9Y61TQJS0phUyUs+axJ3utnTkU+x/HYsfZwU5gswynJ4Uh0zMjSpUvN4uysWbOMGc/cuXPN/9z/ldk2MUBIIhBZ4KX3csKECdKmTRtz/T7ooIOKTHzefvttOVrnJ4fgOVzrHQ5H/Dhx6XAkSUmmPqtWrTL250fo6IsEWbxqlazbeWcpDLM0L6xcSao1rSPn9dskT/7vO5n92Sxp2LDQZAq2cZgl2L/zzqK7OMfeP3RTVmUvKfOlnA6IvalK3WmngDZOnyRRtT/DSG1wu3aRnZJwIPn8c+sOo6B0cfqN1vR60km299Jf9shwUQyCgnRMTTG7HVhL3il3krm904ZF9nMrTWksSiPkyJgyWDghMGTGJtW98qNMltby4/h5/uklmRWXvDcWIvgs/Ir33XclFcyebYWljntNdcYSKAjQcmTWFnB45ms580yvuuKGG4pXnzscJUFGkSwjonDRokWybt26bXorw818oHfv3tKvXz+ZPHmytGvXzpj2DBgwQPr37y8dO3Y012R6JuHqq6+WRx99VE488USpXr16kbmPwjXelb86HIlRUFjaoT4ORxmG1VQuehll61ZZN2eJXNOnjny3/gCTQcAstmjEAIc4pjYhF7yL5AlpdO9FRS6OmQQxjD7QOPymm4pV8gbLsGHWVUSzlgT9pFbor4xme0nZMhlMoGSWxjEynJFAOFBeyxgYfQ1EBaqf7GcO9Of02fN1eeT3ruZ24elnSMFLLya+EVQdSgJefNHOlUk1P/8scuyxRXMl/5Dd5ISC9+W/Ew8ofQkmJa1sF3UWwewj6o4dKtk15bAffyzywgvWkEghwx2wwERY8veqsCQJzxpBKh1ZKUFu1MgrRUbI8noUDOh0CKrUOfxy4BBwZAmUvC5YsEA2ZLgCBCdYDHscDkf8uMylw1EK6NnI+KpmuXKy3X47y1MjF8kzVS6UN+74zsyVu/jiUOKI9+fLXv5HbpWHh64t0kyZhISiCksSfRh/pIzzzrN9mKEslylLJPAn6o0EtZWkVbXME8FATR/pl0gBD1liUjb+36EsFEFLE2mgjYCp4beDOsjfEkobv/ZacsMj02HqEw5lp3y3odfeXf6USYVt5M7u04qEVlozl+H9lvDNN8WfQ2ovwLLhcGFJS2eqhSUMGODt2qwj8OcyBlaFJbcDKXN3lClhOX/+/IwLS67tOzI32uFwJIQTlw5HKceVRDIASDsVKsjmnXeWAx9pLzMr/FtGbuguU5/8Vho3tgPmvyg43LpskrCTP6THkkfMlI5MQlKL5KAmBUnspLy1hSwlfZVHHmnvE7xgUYtgjNS4iiMsYvGss7zHmHdBWibaMEiiel7Db9aEPSZpJNI6WUzdBpXkJbGZxoIN6+3Yj9KIy++/l7Sx++4mQ1iIM7CI1JK/ZfSyY+SOEycn1T6acnFJKR8ZzQCg+tpfCpsuYcmhgXDUYxihSXW4tpPS9syIGNey5ogXiunIWNJnmWm4trsRIw5H4jhx6XAEcAHCDTbjVKggGxo0kCW9e0t3GSPfysEyprCrzB/7jckKHv3bSHm74CShDv5GuVOeuGt5xrKXVDH6tRcmQ/vum6YXZ3o7kTf1w37BSFllpLLHatWsxeWTT3pCA6dY6nlxiI0EYzLo7URU7ryzfQx3FV7DXzabZaCDnhPPNVaeey7xjeyzD41K6c1cKrVqScHEibLxcGviVF1WydAZJ8jzZ00IRlzG20USLi75PexUoWZN72cBlMUiLMlYanW+Ckvd7VLp8MwhpB8JJe1Uf2ubcYMGGKR45sEORzwwkzLTGUvgmp4VC8cORw7ixKXDEUDpTN26dU0WMwh+/pl5lt2L/jVr1kwmTpwoN910k3Ge1ccXLly47S+XKydLLrpINof6vbrK6/KNHCJvSGdZ9dWP0rHwLTlIvpVx0kl6LrnP6KV0w4I0Rh9acckIEn9iMC1gkvTooyJPP+3NMaRMkawXWcdwqOljNgpmP/Xr28dWrBBhZilT46PZ9fJzzH4QlUAkfvfdIq1apd7sJgnQQV9JM/lBGnufCSsBiVC+vJiUOcydm37X3Bo1pNLECbL8cOvSXFXWyVmvdJZpA8Yktz2/oVZUW+YSxOWCBXZ/0XQen1EA4pKJOWQsVVj+61/pEZZAxnLaNC/BT1Xy1Kne+g1/movNHYlAthJxGc7w4cOlS5cu0rVrVxk3bpx57K233jL3mRs9LFprQ5JwLeeanvGWF4cjR3Hi0uEIAAYq77vvvoFkMOvVqyejR482/xhZst1225lZmXDjjTcW/WwvZgpGoLBiRVkxfLgtEwzRWcbJNGkm46SjbCfr5FwZIU/IJXLPwH/i9igJiltvtRoNyFaSNMwYjCZBQOln9dtvtmQ2WsaO6B3xyYR4/xwVUkf8biTYNpE2z9P6QKJyMp8YBGWRp5qt4CyQEXKO9yD2vYmC2tD0VjSX3VRStars+PEb8tNB3czdSrJJ/nVnD1lyXxKZWP8xHa9QDheX/pJYSrNbtPDUYQljFKLBr7Lbqekxu9MHH6RHWBL/UwLrb3klS6nlsczg5TGHIxFWrFixjRss40bGjx8vo0aNkpdeeklefvllWb58uTzyyCNGdI4dO1a++OILsygbBFzDuZZzTXc4HMnhxKXDERBcjOrXr29magW14slczBYtWkhVyiwT4B/6+whwUW4MnAvRUd6W/5MWMl5OlL1kofyxpoYx/yFQTIfpLS1mQ4bY2yR6X3rJM9XMGAT6iD2yiSogevUS6ds3cqaqRg2RV16xzrAqFj/91Eb3770X+TUw+2H2Jaoaa02gJpmRJ5gELV8u2QCCwIyDkZ6yVUL7MI2xiQrgTJj6hFOpkjT46mX5cO9e5m552Sq7XNtLNj34SPrEJSWwzNXxi8tDDik+tiaJ7CUfaaaEJWAGrLsso04Y9QrE41SCswbjcCTKP+oC5QNjH2ZRIvoYC8IIEQQmArBGjRqmJ1Kre0oD12yu3VzDnbB0OEqHE5cOR4DoBapRo0bGwpysY7jQ5D6P165du0QR+u6775pSWOXee++Vbt26yYMPPmiGQUeDuV+FBMVMMKc0kabGvfcu+vmJMkGmSit5V46XQ9Z/asxkEZn0TKWqWnPZMjuJQXXKbbdFnwKSdqjjmzTJOrsqiMfjjovch8n3hgnQlCn2g4OlS0X4rpggH+27QVggZCmxVTDNYTEgIHOX0sAuw1rEb7KXTK7Q1j7I/qP1jrkkLvmaKlaQQ755Rp6r7g1YrHj1FcXckwMXl/TTajZSzXy031KVoO+YTlRcRhOWgc2GLYHPPrNzLYHW2pkzvUOC1mSmHjkciRJpXiU0aNBAvvrqK1m5cqURn9xmsXXu3LmyePFi05/JPEvmYcYi1rWYa3Xjxo0DXRh2OMoybs6lw5GGi+amTZtk69atUq5cOalYsaK5gGG3TslPNFavXi0nnXSSvPfee2bVll4U5m3xe/RfHnrooXJGjBmCrPAWW4El6KXc8447tnE6nSht5Vb5j0yWo0yQiLEsvjNavVf6z8BWkjJWEgiO33/faz3LKmgmQ5Rr1pLyY8ZyhFxII6pmBvlp+kb/QOxwEa3RYJtkLvl94IO/8UaRW27JqL0mb52RqGfLCBmh5j7MtUnEXpj9WjO0zAUl05tBvvm6UN49bJDcsCWUNgd2cERmScEkDcKk2DUjWZLzFGXAoQHtJis9apQdf4OhE5lMvm9mou66q71NJpyFiTh6tjHfRbxpHM1aBcdRuoQl/dK0jE6fvu3PHnrIztd1OJIh1vXwlVdeMa0g1apVk912280Iwd13312eeuopc43jWsd1tT/HdBT0OZGuxQ6HI1hc5tLhSDFcvLgAUtLD/3ox4wJXUkksvZbax4lzHb/L/c6dO8t3330X8/d1+4hbLtzrt26VjeeeK4UMxHv0USmkVDNEO/lQPpE2MkmOljaFk+S11wqNw2ybNraXqoS3WiK0FqqwJBCm0jIrhSWQVSSLuNtu9j7GSfRhkpaJBH/QG29Yox79o8iCklJCpUWja1eRGTOsmlMFjvDntcgWZghNto2VU2TLdiGrTwRShKxCVBBgurCRwcyl8u9DCmTnx26X62Wo9+DQoXYRoaSdO9HMZXi/JUoQYQnUi3L8s5+QFQdKAb/4IuuFJVAAocLSH5MT0zth6SiJomvR+vXmf39/ZazrYY8ePYzAfPbZZ4vMdtq1a2fKY/El4Nq4t68yJxJsP9q12OFwBIsTlw5HhmDlNJGSWHXR4yKJ8KQ3JBaUEM2bN09mzZplVoQpI+L/WXPmyLwTTpC/X3mlyFVWOVo+lknSVj6WNtJOPpBPPimUjh1t1SZJz2QmaDCagPZFv9D0tYFmJxgoUb4aMlIywuqcc0SuuipyHybf5XXXWTGp5kA0sbZrZwVjtMCJDwKFcNddXuaK4YEIUz7wDJj9qLhcI9VkYXNriGOcTkMujXHB36KZS7IR8bqsphCSxIvOvl4ulf95/aSoJfprY83UK624DO+3VBLou0RYsgahwpIkejpLYYEy3IEDvfu6a3JYJFJl7Ch7DrBLly6NfC2aNcs8zs9jicu///7b/I9pz8yZM6VVq1ZFj/G7EyZMkA4dOpTqeutwOILDHW0OR4aIVZKzatUqcxE94ogjih674YYb5JRTTjE9l1yIzyphfgdidN26ddu473Gfxxc1aiQ/fvSR/HXJJVJYoYL8Xs5znz1KJssHcpxMkSPleHlXvv++0MTgJKTuu88bI1ISxOJU7uo8TVoaKbnNCchckoGkEdVf+3f88dYuMxJkHRETmpUiYGIA4Ekn2dLHSJDFIvVDM5suGKxebUUPH56OsEizuITP6vtcY0k3J9N3ibD0C64MwaGGlpx8wKVyjoyQzRLKMpORpnw1mnAsrbgM77dMUFxSoICw1F0OYcl6xI47Slph7ST8uGfti2k+LgHkCIfrDH2Qs2fPlkWLFsW+Fi1aZIx7onHllVeakSO4pQ8ePNhkL4cMGWIeu/DCC+Xaa6815j7R4DrL9dbhcKQH13PpcGQQVm25uGaUrVul8pw5sqzP0/LPCT3l+C9u9yahh/hcWpiezAlCJrXAtIldeqkthdPq0Uhcc43IAw94WuPLL82UiNyDgaB9+ngZOEqw6Jn0Z6L8YOhDxvI///FSPGQpKS9VV9pIICpJ86pjir4W40Bat5Z0QMK2WTN7u/f5W+Wp9/expcGI4N9/F6ldO74N4djE3w9jxoiccopkA4zwoG/w2DWvyyg5TSpLKB3PggDf6fahUmCFOaaMkQHKpY86KvYLMNv0zTftbYx9+vUTefVVTynqmBagJABHHNQZaclatYptiqdTCqvCku8FQ+J0C0um9VAi74fP8MMPRapVS+97cWQ/lLwuWLDAmO1kA5j27OdfNXM4HCnFZS4djgwSa7U1bZQrJxv2209qjLxWxn9RQzZM+07k5ZeLBcGHyxfyjnSQL6SFnCRvyT//FJpKzn32sS2KtHGGM2GCJyxJ/uCJkpPCEsheUvKqBj2IBrLKL7wQ+fkIsUGDbIoJ4xZgDiYR+v33Ry93JVKnbhgDHMxf9LWOPtq60MYq3wwIfww2d345a/GrglmNbRJ1jKWuM0vYf3/r2fSGdJGO8paskdBOyXdFNjE8U5xs5pLfw8hHy2K3284rFQ7PXrI/UOcaI2OJsMxExpL1FBaS/DRoYHuxnbB0RBKWZCGzRVhmzXXW4ShDOHHpcGSQmjVrZoepQIUKsnnnnaX37QUybFihyGmniXz7rc24MMguxGHypbwlnWRaQTPpLG/Ixo2FpiyOgJ3EFGMctT8LA1UFrxuSNDkNGUfSejgdaR8mwov0bDTRR88lDiia7eJ5ZLL4sGKVu+KwitmP/h7ltYMH2+xljPKxINDRjEU6iaa6ZEpjs2QcSSSoNr7sMjGl38fLe7KqXHVvXqk/VZiouEQk6vfD0FAy0WrOxAEQ7ggbZSQJyUyEpVZSkyVEWOp6QzqhEtz/9ZG45q3uskv634sju6HMlYwlfZbZAtfXHdO9IuNwlHGcuHQ4Mgi9IzjdZQUEvg32kqpzJ9oYGgME5ocgjrB69U1GP6Twa5P5mVHu39JVxooUbjUVhfjfoIfwVlDzEW4zEjIvwKyHDCbuMArpWTJQ0XoqqRtmwDdjRhQmzevMy2hQDkvd4ZAhngst6p3vgTLZNGQvSbZuqNfYG0hKFk4HG5YE/aMqprJMXAIJZLKBn8kRctTWj+SfSrW8v5GdmBLgRMXln3+KaJk7H6J/Zoe/39Lfo6vpfBRbYaFZ00Hf6u7ER08pbCaEJR/BgAHefd4qFQnoZocjUp9/NmUsgetr+ay1Jnc48hMnLh2OLLj46biRoMBVr3v37kX/mjVrJhMnIho3mBmZnTp1MuNMvvabjUC5cnLI2bvKi8+uKfaYGZvBcxm54eszPGjrDBkr3eS7Cv+S7vKqFMhWmTzZ8zDBjPaJJ/LM8IPvih5M3GFUPCECUSqRBgACz6MHk1pCTQv+/LPNhv7vf9HLZAmKiO7JqOl8xVWrRM4+WwRDJ8ZYpFBc8rZ4m0llLxlFovMeqZvOomyGfo1afTxd/i2Hb/xEVtXYw/6QnmOEH6nbRMRlLKfYSOKSbVPyDH/+KT+NmbmNsERzZkJYamJV24w5DWAY7FtjcjiKIFupjubhDB8+XLp06SJdu3aVcSHX6fHjx5v7/LvuuutMOW3QcF3NmsVbh6MM4cSlw5EFZTvM7SKLGRT16tUzQ6f5xxwwDA2YmfnEE0/IPvvsYy7wY8aMiTzOpEJ5qTT93W3jaBRi584iX31lo0x1faECcvNMeVV6yI8VD5TT5GUpJ1vM47hLUkV6773xO8zmBHwWWN/iJqsGNwsWWLEYqy+RNC6CQ0trCaiYtXjmmVY0RqNFCytc/bXGL75oI31cZlPYd2n00umnY29sHyBrGq9Q1NJYdiajUrMLMnCqlX+U/eWQ1ZNl/e4hEf/LL7YMOTTywFBSABzvGBI/PtfYF899t+jlMpmxBKbu+EfpskiE8HU4IrFixYpt3GCBkSMIyVGjRslLL71kZlMyJuvee++VYcOGyWuUvAgtx8V7jkuLzsPMirYTh6OM4cSlw5EFMNB53333DTyDCczEbNGihVStWlXefvttOSeUhcKavXrYnEvl0Dab5YXHoqhBLtYMv2Qe4/jxVviEaLhplrwsZ8hMOVDOkBeNyKS0jjEGe+3FOBVbOZg3kN2itFXLRimJRChee210AUa5K66jV1/tPYaBEmI9VsnpDjuQArDiVQ0qEECUcN56a6CZwW3E5c472+9c53dS5pvjfZdKp07WEBbmbqknRxZOls2NQu+bnZX62WQzl5rCJwPdtGnk3/H1XR651vZdckghLDPlQ0JGl15LhbUFfyW4wxEOgjESmPscdNBB5tpWpUoVadiwoXz66adGiK5fv162bNliHNODzDDyWlxPua46HI7048Slw5ElcCEkk7jrrrsGutr67rvvSvv27WXlypWm94QV4x49esjAgQNlzRpf+atSrpxsabSf/HPr/bETNbzHE08UmTrV1O7Nre2N2GgiP8iLcpb8Wq2JnC3PS3nZbDKXQ4dah1kC1UgOszkJjqCIxfPP9x5jGCifjT/r5YegB9FCL6sqiJ9+siJ12LDYr0ekj9mPzkDFxfWWW2x5JWIzFeJSwkpjn3sub8Ql0Naqk16m/bm7nLn7x1LIQEnAlCcZccnoGf2b+RyqVIn4KzPWNpAF5fYxt1vLZDm6+RpTCpspYUlZO+sj/pbhaKbIDgeoUIxEgwYN5KuvvjLXHwQotymfHTBggCmJbdu2rVn4bI5rVRTivR7yPK6fXEedsHQ4MocTlw5HFqEXx0aNGkmdOnVMOWv4hZX7PF4rbCZeJFavXi0zZsyQ1q1bmxXihQsXmtuvvPKKWSl+hrEXESisXFnOrfaSvPzfv4qCB3piCCD4v1j5U0GBjF19vDRYPEXayQcypZw3j3GP1T/JCDlHfq++v1xQfrgRmQhWXhaHWVo50aY5D8IB29xHH/X6MCnzIhuJEIwGHwCZTy2ZJEBDpPbqJRJJ+Ct161pjITKWalZBX+bBB9ssaCrEJSW9ZDCBUrZ46pxzRFzylfGx6dSYVyfVknvbT7SZaT+47cRCPyyOWb4/hH+0fkuxlc5t2xXIO1ttaSwzN9+69qOMCUvKYElQ69sGKuDpt3SUbWJdAzZt2hSxJBaYL3nmmWdK79695eqrrzZZzK1bt5qWjbFjx8qH9Kub/cz2YkaCLGRJ10N+3rhx48AXZx0OR+K4S4bDkYXQL4J45MLcpEkTU0rEaiz/c5/HGWMST0ksvZaUCfH8atWqyVGh8Rbt2rWTHzEuiULlnbeTWj+9KXPnzpNZs2aZ3pm5c+ea/7k/b948Wbp0qSxYsDlUMlcgH0o7mf3Ex9bgRo1KGF+wcq48veU8WVyzkfTZ7hmpIAQj1jSVNkWyRsQWTNzIWQhomG/B364qhUwiFrqxBB9KDmHoHyZIZpDayBjfj1FEzL5kwj3pYEDwMWeD3sxYPZwlQLZKE21F4pJsK9tWETx6dMkbwtBHlUkWi0s1AqbiWN/uDXfWkEk3vFu8V5Ky5DFjom9EPyyylv7ZnhH6LRGWTKpZtkxkgnilsdtP8UaSpBPGqVKh618zoK1YE7iOsmnSwzmec32sawDiMhZUyrCg+eyzz5prGwKVKprddtvN/M+1iEXQaCAWS7oe8nPnCutwZAdOXDocWQ4XVkp86Ffhf12VLRdHOkFLYnU7CM3pIUfTL7/80qwIR+PnYcNk7z6HmX6Y8FVp7vP4okWLZPny2XLaaX9JhQqFZjzj+RcU2CF9mN1QLkoEHWLnFfPl4XW9ZemODeX6mk9KRbF1t1OmWK8g2tKoCs0yN/vEQCljeqRlXvRhIspo7POnhPyg5HCNxaRn++3tY4gTMp88FgvUOd8p7rEKLjWY/XzxRVJ/AruW7hr48BSJ/kRLY+khVtOoH37I+tUDDGtIBgNv9fTzq8ofVw31nsD316NH5L+dnjMtg/b3W0bIXOLzo8ISVjdvK4Wa8fbNu0wXvA9OEzp9BSiMwODYUfbg/P7XX3/J7NmzzTm+pGsA7uSx+Dt0XPC8mTNnGudYBKr2aX7xxRfGaC4a/mtdtOuhw+HIHgoKo9UyOByOrIZDl9XjaIfwqlWrpGPHjsaFD/MeoCyWXhd6LXfffXcZMmSI1IhQg8c2E7loE4gvWFBZDj+8ruyySwQTBTJzRO1Mgvexeqe95P5KN8qQRefLRqlcLIuEW+VFF2Wu96zUkN0jG0m2SznuOJvF1HEkkSBbiUr3W3WSQmKeZpS+vSJwciV7qllLSmb53HFSSnDWG2JfK9XIamHIZNLNlLpqRnX+/JKHHlL6S4o63udnGPblk06y8xyh9UH/yIff7iQVJEwYP/ywSJ8+3n3EpKb5KG1mcUDFPUF0yDxLheXy5d7awDvviFTveJRteEzz58Tax7HHbms6zCIP1dmOsgUlrwsWLAh0XuVZZ51lWjQoXx00aJAccMABxjmWf2Qb6cscPHhwREM7rkNkJ52IdDhyBycuHY4chrIkVo+DBP2QzGIwv1exYoXYLn00WN52mxe5h1hXa095vMYNcuO8C2SDeAKKeBxd1bevFZw5Bx8KfZg4w6qbKylBehYPOij6761da4WL39yH7NerrxZviIwEwoQs5uefe49RCv3889apNk54yw8+aG+ThC6qcr7zTjt7E/guBw2KvaGbbvJSYKhVdZ3NYpgzycf922/2/vUyVIbKDban1V++x9914432Nt8NWU0YPNj+jGOT72vu3CL9iZDbRlhWD7kKDRxof8AMVXb8FMMu2a2byJtvFn8cryiqrV2vZdkTlri7Ug6bLSBIKXt1OBy5gyuLdThymEhZx9KSbJURv0dQwqp31III+g+JpsnokB4Ksd3S3+TqeX1kZa395KmD/ivbiRXM9H/dfbdN4tDXGasFMSvhQ0EkMrpDrfYRf3wOzHuIRtWqIs8+a/9tt50Upbzo3cNhNhaIVzJg9GOqOkApIIwQQKUx9YGePb2dhPLbkopfcsTUxw8loXxUWql6t/SXN6WT7YNFLCuIbLLCfAb+D4nvTBd9Qv2WkYQlayxF04B8I0nSURrLWybJHS4s2WVYD3HCsmzBOZtzdzYJy1Rd4xwOR2px4tLhyGEw6cm2ciHKqbCajwkjN956y/YmUn8ZotLSP6T3t31l5S77yqjDH5AaFdeax/0Os126bFvCl/WQOcQVlh5KzUyedpoVJtH6MOG886wQb9RIitQ2qSbSirHmxKCKKIel51WzlStW2MwaJZv+8RqJikvqY2lOBDJyJdn9HnBAzolLOPxwkXvv9e6fK8/Jz39XF7n9djtTR+H25ZcXZScNfqfff/97G2FJZhBhyehS//OKFiBYjCjBJKW0sHs89ZS97T+FXHGFXYdwlC04ZwdZChsEXNt23HHHTL8Nh8ORIE5cOhw5DM57QQ6f9oP5Qvfu3Yv+NWvWTCZOnCjnnntu0WM4zw71B9q+QCWuFXB61N54w6Z16M0LUWHJIunx+TXyd4168nbbe2W36l6wztMJzpkSQdYlyz1iiosyMog4uSp8dmRw1dklEgceiPuSnW+pUK+KYKUZMhZ8SJRx+n+XUluEDNtMRlyGG/uQvYwFwljVSw6JS7jySpFu7a0QXyE7yqmTLrVmU5gzYcCkfxdlrCyWKIsXF92cU+3fxXos+UpI3hcTlkCqkJ5coGc2hTN6Hn/cMy4CTT7XqVP8cUfZgHN1tAXB4cOHGwMeZlIyLoR+ff91AZO45ym5TwFc25wDrMORe7ieS4cjD8qZsIdP5arz2rVr5YQTTjDuswy8Vs455xy54oorIg7AZu5YPLM4i4EQIjMUNuZia61dZEqLa6X315fJnD+rFftZ48Yi111n2wwj+EFkH0TymMFcc42XtaSUFdMbhGSs33viCduAqllLjIEQd74S46i/SwBIhk2zlmQ36Q3kw4tg9sNLUN2JeGcNgCRzEWwDJUKGjpE4f/4Z22wIx1gUKk64CKcsy7bH4p8f/pBmTdbIXGlg7uPRhK4sMlDC9caffebzQLzTqMo0luqLZM7K2kXCcvz4CMJS4TtS4U7JLX2YAUO7b/fu3qIMo0vV5JY/x2867CgbME4E19dwcHQdOHCgEY9cZy644AJ57LHHpHqolpvHuC4wYmRPxu8ECOY+jBvJtsoch8NRMi5z6XDkOFx869ata7KYqYJ5mS1atCgmLBcvXiy///67HBppEF5hofwzZ44txUwE7QucOdOWjerYlaVL5Ki3+8vsjfvIV93vlMMae8P46MO84AKrz+65xxpzZjX8TaTEPvjANvf5+zBjzY7k9zB5IaOlc0LIeGKQg6lMrEwxv4tooW9TFwJ4Pr9Htkyda3zgyWQcYiNlLqtVs+W5wHccYwB6sb5LxOjChZJL1NilkoyW7lIl1AdMkrJoOgz9p3xnfgMrVFtoDMkfBbvHLyzh+ONT2nfJyB+m4qiwpAJAhWWbNiJnnhn4SzpyAB0JEg7mPgcddJAReoz+YK7kpzh/h2A2JQuIQQtLrmVc05ywdDhyEycuHY48AHdWXFojWbkHgX9epvLee+/JscceG3neZkGBrK5UVWbXaW37/CgZ3LTJrHTjSMgQbf6PavzDwEtGdjCOg2g4JDIL/v5bDh09QD5fvI/82PN2ObGVFxT98YetVqTFsH9/ez+rwX6VPsyQ4YsRXowgIWMVqw+T5/N7vjJiuesuO9+ipD+aDCLBIa+hGQEybDjXRjAK0tJY9OM2lbuJlMbmoKlPEZUry8HyrTwi3tgRRuQwttNAEzCKU6EvNhSsf13476LRpxFLYcOpXdvOJwW+47/+CuzP4FDq1MmbIcvuw0sAiWtMfFySKD+Jdd7lNo9HghEhX331laxcudIIUG4z/9J/XSBzGSRcw2I6jjscjqzHiUuHI0/gYkwZ0a677hroii/zyVihbk2EXILg9FO+cnk5dpdpctK7l8n7r86ROa+9IbO+/daUWlHGy//M6WScCmVZEXs0ESWkiRAkZIlCQrZg+XJpNHKQjJ+1j/za+1bp2XFFUWCsDrPM5CajWSQCshGUMOkk/jbxjfpABWiTXiQovRwzxs6+1Iw1/ZwIEzKisWDmKeWWiErNOPBaZCJRTT4zmph9l8cc46U2UU6+PsN8E5dwvjwrvWqPN7f5iCgtLfqoKBGOwGxpaFpjyViS7I0Lf7AeNhc2WUgWc6hqIQEvgchUTcFMWb/vkiP34XzKeZXzK+fZaOddRllFW+RjBMiZZ54pvXv3lquvvtpkMXUxkd95//33AxOXXLO4dnENc8LS4chtnLh0OPIIvUA3atTI9DwyIyxcaHKfx/k5K8TxlMRi2uDPiv7555+mLPZfmmWJwtlnL5XBk3aR2v2Ok3WNG0thWG8fAQrBDf0+s2fPNqviEQMdGivpR6MGFkMc3c6KFbLX07fI85/UlSWX3ixXnbOsqO8Ss00meaBrTj7ZJuyyEhobyfxh0qN/F2KN8tXvv4/+e3yvqALGjqjIw5SD0kpcWWJlP7UOkh5XVJKCfShlzqGyzpjikiBTRTGv9dJL+SkuEeN83CLy6J53FbXF8mfQf2l2V9+Hs1G8jMvZlV6R8U/9Hr+wTMFIEjLOxP+//27vs1thQozgBebH/uc/pX4ZR5bA+ZPzKOdTzquRxKP/vEvpayx69Oghr7zyiumr1HJV+Prrr2X33Xc315FY1KtXL65rUePGjQNfGHU4HJnBiUuHIw8hCKAXhpXnJk2amF4ZVoT5n/s8zs/j6dOMVhJ7/PHHlxgInHHGMqlUqdDokJLm5mlQxOo6pVsRadAA+0KR2bPtSA0VYytXys7/GywPvLaPLL34Jrmt79/iH4+Gqyw9b/SYZaXDLJ8jRj1kqnBYkZBgYa4iGcqSZmbQS9mhg71PIHnLLVaklFRWiSEQ8zaZ86L9tHy2oTkc+9XbGl1cJlIay+JAropLvpvQikXVzStNS7CKRdY7nn66+IczS7y/ddeNv8v27Vvbntp4YQCmvgDishQ7K6M2mfSj2Xuqonn/lI0r998fR7muIyfgvMn5M+oiXRL8HWrKxT185syZ0or9M4GS2IoVK8Z1LXKusA5H/uDEpcOR5yAAKTPCkIH//YKQC38sgbhq1SoTUByBKvMRT2BBbJPMIvSG9evNanpUgakpNQQRpkEXXuiVhq5aJdX+e4cMemYfWXz+DfK/W5cUVX4C8zHJYlICSFYzy8a62VJTGuFwG/XXXw4cGDsTiSDFVOeOOzwVT3ks2yGzGQu+JIQ6AlXNmUj7XnedHH//CbKb/BFdXCIamVkK/D5GTJFALIUyHkZcBhT4pg1Nh2/YYCarsOv550J+9LF3v45Y181C/R5+/tmubMQrquk1Yz8AFgfILicBVeZMoNGMPe2caFXE8IIF9jHGldIS7ch9OF9y3gzaNfzKK6+Uk08+WW688UYZPHiwWZDcunWrGUvFAmMsuLZwjYnnWuRwOPIHN4rE4Sjj0HtDiVSQJCssi9i8WSqvWiX1d9xRCjD3KQmiZUxtiPr9w+e33162XHyZjKl3rdz2+K7bVJnutputLL34YimW6cw4a9fa/scXXvAeIzPJffotY/Hxx1ZV6GgBsrv0WDJypKT0MYL+5ptt02pIAC6VneUCeUZWHHWy2fQ24ATTJ2R2c+211rI3Erx/yn0Bd9o99pCcYdddbclxvXpFWUhE5SOP2B/vJb/Kt3KQVJH1UkVCwT0ZHpocVVSyAIC6i+SuHA6zThgboz24N9yQ0Nvlq8NY+Mkn7X0yk3x3aHwOJ75mYv5vvy2eVHbkJukYR5UMlLySmXQ4HGULl7l0OMo4NVKgqkq9IF2hgqyvUVOWIFwIxulHjGUYQ1YMx07Sa5dd5o2GWLNGyt9/j/S4fh+ZeXw/ef/5RabVUGE8IyWCtCziNKt9aRmHElVqLu+7zxOENMmRJSwpA8YfOH26dY8FMp6IE+ojde5ENPjcEOlkPWnGE5Fa8re8IV3kgmmXWtEbDkJWsxMMSow2EiWX+y59mUvl3ntF9t/f3l4oe8t5MkxWVNrV+x1Gy6DoVEzy2ZORLCmTDP6qgCT6Lmm5VWHJV8NsS9qjEcRaEMCYVScs84MlS5ZknbBM1bXF4XBkP05cOhxlnJo1a6a0PIlene7duxf9a9asmSmp+vzzz+XUU0+Vbt26yUUXXbTNrLWCcgXy+4WXybC5h8vqqwfaTBczHekRjJZpRSUiSBGZRNIqCtatk4IH7pdjL6wnH/3rKpk27g855RRPBK9aZRNuJKaoEM0K7cObQwG8957Xh0kZMH2YqIVYaA0kTi36R779th1j8sUXJb829ZKktXzjTs5Z87hsPbSZFa5+eG98L0C2NJpbrd+ONCs+4NKJS6qAcWEtENsT+bp0lRdqXOb9DiXJzDGdONGWxeqOhnCcMCH265Ht0YwPbsL8Xpw88YQVl/5WWNYZ2GVUp3KYDBoU9yYdWe4Ki7gMZ/jw4dKlSxfp2rWrjAvNoV2xYoVcccUV0qlTJ1PqujCFM2e5puy4444p277D4chenLh0OMo49NDssssuKds+boGjR482/0aMGGFKpXCfHTp0qNx9990yZswY2X///eVVnEbCKF+hQCadfKvsJn/KJVsekWlv/yly2ml29AO9lmSBIhme0Gj53//aEkbqXqtUsY8ze+Ghh+SQ7vvKmN2vkLkf/WaqT/0Os8OGWR1Eoo+4PuOgDL76SuTgg+391avFKGPKV2OZvVAOi7EPQgaRA7/+aocu8tmU1PeIaBwzRp4+7AlZK9uZh8r9+IMVt4xA8b92PMY+eZS5/Pxza8rLV9FEvL/lhiX95FOxhidFfbNkb1B1mo1Ul52SjJr0+WSCGRsTB6+/bhP3Cl8TiWVadzkM/I9vv31cm3RkOQjGcPMexo2MHz9eRo0aJS+99JK8/PLLZlblXXfdZczZEJs8trMuWqUArinOpMfhKJs4celwOEwg4B81kioYa9KiRQupWrWqWdleGyqzXLNmjXEMjET79v/IatlBnpBLpJlMk0PlK3lizRmybMJkWX/eebLxiCOkkAwdWb1wKO0kksZUpV8/O/ZDRcIjj8i+x+0nT5S/TH6d8qsMGFC8nZHFfnQYXkZvvJFhh1mGduJGdMYZ3mODB1t3orCM7zaggsg2avYMBY0z7amnlvy7BQXy6wkXySHytXwtIbFEXSUZVXoota+T2xqokiJj2Gg4WkOay+Jy40aZOtV+pJpMPKfxl3Kj3GFub5aKcpqMkiVV9hLj/OMvc2YnYpaofgc46Tz3XGAjSVgIYffQ/ZQybxWUt99us6yqWVmbcGQ/iEaMetavX2/+j+QAG17xARj7MJOSczrmOTizcu79/vvv5aSTTjLPYZGP83Aq4HVTuWDpcDiyG2fo43A4irkNUmaVKnAe7Ny5sxx77LHyzTffmPu4Ce61115mjlqkle4NGwqkZcsmst12W6RLlxVywgn/SKNG66VyZS/QKtiwQarMni01fvxRau69t1QgembMRji4b9IsR+msv3+QxrTzz5fVV9woT71X14xnwHPGD1oBTxxGO6ZBh0eG4JI3h3JQFdGwoU1Z+cVbJBA0uM5i1qNQeknGWLNsEUD/9OolUkk2yGdH3ySHfnSf90MCSFK9BKx+hxssSS+4YNuNUY/JB0u5HD2IueIWSf8k6UoRqV5tq6xaXVBUPTyh2UApuPsuOVY+kI/laPP4cTW+kHf+blE0KacIjq3evYuLyocf9gyR/JAWZR/me2MebUS7XgtGVawd4B8EZ59tJ/bQrsto2IMOspuhpfa77+xEH0d2wvmXbCSiEVHpF5QsyCEW6WWknYHz5axZs7YRnZi09evXz1SK8LOePXualgTaEXbbbTdznqc9gefEM44qEdge85Nxg3U4HGUTl7l0OBwGggGCglRlMFevXi0zZsyQ1qQDzYzA5+XJJ5+UDz/8UA4++GB52gwM3BZE5NVXL5JJk2ZLv36LpGnTdcWEJRRWrixrmx4oi7p3l9mHHCJ/3XyzFJKZI1vkH2mC6yfi6pdfrMmNzhMk8n7iCan2r/py9awLZf7En011p9+olvGP6AKSiEOHeoF8WkGMkYGl1FXF808/2VJV/tZYIKB546RktRcKwYJwolEvSpmstv5tlMoyvOm9tgcUm12g14t+S8SRf6ZFSaWxy5eXPIMzm/AdExtWbyyqVuajrPjrPKkgW+QlOUNqh8aQvP9PC5Mt3AYCeWbg+MUkohxH2HDYN3UEEOXdc+dGfGtkJEly6v5IZhLTZIQlXykvpQbKrEk4YZmd6Jzf2bNny6JFi4yDd7ho5D6P83N9XqRsJg6tZ555pvTu3Vuuvvpqk8XkeYyVOu+880y57PLly+V1FqUChGuHE5YOh8OJS4fDUUxgMuB61113jdvkh+ftFClLGAZlWfRaEoAsW7bMrJ7TawnMS5sebhTjo2fPv6VSpUITMEebpoEBED8srFRJFl90scw9/XTZyDwGSmMJ4P/v/zwBRcaNgB6RedNN3hR5MktPPy0VmzSQsyedL9++Ns+YtB5tE1IGKkHRpXvvbTOZGXGYPe4424dJSgqo0ezSxfZYllS/ixj8+muR5s29EmE+J9JdZMvC8E8SMMkzXpvZi506eT8gE3zppVZ5wyef2FLkPOm7XL7OE5eVZYMce6zIm2/aalfNKO5WsFheltOlnNh5pJjqvP9+hI2xA9PzSh22wm12qnChUEJp7LJl9imaZW/WTGT0aM+8F+8r/ITUUPnGG5P+CBwprhphlAjiMpJYjATP+zuG+3OPHj3klVdeMRUhZBNpO9hzzz2lcePGUq5cOTnmmGPkR9LaAcA1gGsG1w6XsXQ4HE5cOhyOiIFCo0aNpE6dOqY3J1xocp/H+TnBSrR+ST/vvvuuMZOA6tWrm5Xz30JRMc6x+6gwiUCi1ZMIzbX7NZKZI1+XvyrWsuWaZPcQs3fcYY1tgD5BUkzMycQgR63zGd8xbJgUNG4kJ758rkx64idTFUnLnN9hlgpbHGbPOy8DWokXpg8TgyMFRYPDa0m9lHzWNOldeaX3GDM0EZxhw0DxTtLWrKLKTMQ5mVLmMapZEr/nryVmlEoeiMtPPxWZOs0Tl8cftcETlv4PpWpVOVo+lttloLmLRjjrrCiLD+xEzB5l7ItCVpnZlv7FAf9IkjCHWfUF0o+xfn1rCKzJePZPWmMV9GyKWuwcAbQjBD1KRIUnbt1kLDn3sgio59wvv/zSZBljQQY03mtAIguSDocjv3E9lw6HI65V8k2bNsnWrVvNqjd9kv5Agp9H6v1RVq1aJR07dpQPPvjA/C6899578thjj5ntEZgMGTIkrgxoIpCI/HleJXn99C+k9+Yn5UiZIkXvmnQkLqcoxurV7WPUFhKFYwLkr3sl24RbysCBMqd8YzN+kp628HiQpCClh/S/pS3O4jNnjgppKRUmNIhS8hbPIENSXcxfUYcaTI+YGXruuUVPOfBA26vHV4eoKdZLiLrhs2F0iR9SZWQv/R8ESk2NhbA1JeOZxaC/TzxR5NnVp8qpMto8tu6nhbJdgz3tE9hHtMQY+9U1a2Rr+YrS6bh1Mn6C/ZCobMXsVbOJ28BnjajUY4cMMqWzlNDyfZJ5Z8Yr2ydVWamS2a+7d/cqoZk8wzqDXytce60dkwq0xFLC62L/7ILzJRnLVMyoPOuss0wrAgJw0KBBcsABBxhDn1tvvdWcyxGE3I6WaeT83qRJk6LzfEnXAIfD4VCcuHQ4HIGAiQT9QNkG8fljj+0qjz++qzSWH+RCeUrOkRFSS/72xBTZPoQmjXQE9WT+yHZinkNArxBMMdth4EBZvHMT48WCPgrvv6SNEZFJZilaGW/g0AvJe6OfESj1HTnSvomSoJ+PHlV/aTKGPPyB221nKm5VyFBJjG4sBiNeELcPPlj8cVK79IgqfJbqKou4j3PERiaFJZXCz0tP6SkveJ+V1gpPm2ZrUXXfQCAedJD8/eEMM1JUE+R8BHwUUeF7wjWJjDmwP770ku31ZL/ULPCHH0rh0ceYKuYnn/S+5o8/Lu7JxELAv/5lN8cm0P8lJKkcGYAyWP5lI4hSMpcOh8ORKK4s1uFwBAIOhqmE8i4cD/Ufboe4H3722WfmPgPDmZ0ZDuLuwguXSPXqm+VH2V/6yf2yu/whZ8oLMkmOlq0bN8rGTz6R9ZdfLhubN5dCGikpk6UXEyVFGa0KIsQDQX/TplL7ytPk9tNmGgFBohMjVIVxFegDqnDxKUJ7pRzmY3z5pU0zAplIRpVQKltSHyY1laS+GPqp4Apz+OHGMGibvstwKI3lQ3jnHa+0WNNnzMPQD4DMNHW2EFZ+m23CkgpubUGttYfP5MqfZfJ/GJp5/Pe/ze6CCa9mK8kgxvROwYKYDLJmkRjnQk8rAyrD+i5vu80Tlmx/7NjiwpK3QSJUdSqa3wnL7Bofoq6wSzDECmP48OHmXNa1a1czjxIw4enUqVPRuY9t5/r53OFw5C8uc+lwOAKBYAkHw3gNKUoD8zFPOOEE08dJIDZs2DDZY4895JZbbjHmQK1ahQbZh0Bb3XtvHXn+edsbitDUsSaNG62TSj7tUDTWZOZMqbnbblKBjB7pIfoLST+FB4SU1d58s2za/yAZNcqa0c6cWfwp6ClGS5Jx8s/STAkIEppAUTcKIhMHVy3/lRKyaBdf7I1qqVZN3uvxtJzwrO3tRNhceGGM36cUFktSVTeA8dCLL4occIDNDn/4oX2crE2WzcObPNlmLPkYte3xrb0vkwpPPWYfwAxJ1RymUH5jHnjooaJeVhK/2tZKrM6vxhR6OACRJtbPnppa6q8ZN1NYKEv3PFh2+c3LLrPOQbLaD62zaFXgtdDw2hbryPz4EB39sXTpUuP26uenn36SgQMHGidttnHBBReY1oG+ffvKgAEDpEGarH55z5TNRhoN5XA4HCXhMpcOhyMQCJrSNTgb59kWLVqYMlwGgSMs4bDDDjN9neEUSKF0bL9UKlQolEsu+avYWBO/sNSxJuuaNpVFZ5whs488Uv4aPFgKMc0hNUmkjsBkpIkyZozIwQdLxR5dpecB3xgjVRJ44Q6zZJDYBMm88BmagUJvHioXsxjtiaKmFUMj5qmUBMqEDKia76xeLcc/e7o8LH3MrMsY4xY9o6Ezzyz+GP2YlI8i0P2mPj/8INkEJrfhwpKMY4Xt48hcKr40on9CC5XW9EnGTDrhxEt5sy4C0KPKBkLZ6Fq/zSgad0KiOFxY8hr+KmTErROW2TU+RB1hEaLhYOzD2BActRGkDRs2lE/ZB9IM53EnLB0OR7I4celwOAINSlI1JzOS8ywGQARurPhv2bJFPvroo4g9TLjHNmm0Rv5v1Hty2aWLSxxroj9krMlfl1wic88/XzZivUl9KALzuedsraOWeAIq5JBDpODkztK+1lemnfCLL6ygUI1HmSW/hv6ixS5llaG8YP/+VuVqqpSxA4cdJvLWWyX/PgKQ0S2Yy4ToI4/Kp3KErPo2woiRcPjjFB3zgqqiXpMGwSx0jEVYduiwrbA04qxyAuLy4IOLfQ2URZN4hG++sVXCMSFb+dFHIurA/M03smmBZzl7vLxnRuBE2s5//mO9f4AEKH+PI7vGh/D8OXPmRCxtJTP51VdfycqVK4345Laez/r37y+nnnqqPMe5J4Vw/k7XIqHD4chPXFmsw+FIibU+pWKpAAfEk046ybjNEgh9/fXX8sADDxhxecghh8ivv/4q/8XxNRKUaiZR6lW4eYtU/Ptv2bfnWVJJS9kYdEnqiMa3YcNE/vij+C8R2RPtH3aY8YBBVPK0cGNInDzRgSlzmOXFURqqZHkR+jDpKS3JbYig+dlnpbBPHykIBcMry9eU6mOG21LbaFCHzLiThQvtayA2cUANh/mj0b6rDGQstRqVNkfaHouyfoypGTzYmzdJf6vuA/yN/t7VOXO22T5l0iSO1e8Kfx4tXY0KWV0ymWGzTKbuc4a0mPfiNl8dGXNMhPjo8aji17cxXnJk/TmO2ZSjR4+WatWqyW677WbKUyn1r127tnHdvuKKK+T888+Xo446KiXVJ4wncbMqHQ5HaXCZS4fDESgEJgQoqcpgUhLbsmXLou0jKOlRevHFF81szrqxIuoke4gKKpSX9TvvKlMeHSfzKoTcbXDyocGSeYWMi6A3c7fdvF8aP94qihNPlPpLppqJE/gEDRzoTa8AZhMSJ9ImiqDxtyoGAoKHIZ2kUFUwIpboFdXxI1H/8ALjGlvw+efycwXb71V9yworVqnv3bQp8u+hfFQ9oXYo6yRjGp4RoVw3BWMYEoFEql9YcruYsJQomUv+D69vRt1FgD+fimCFltYSs9b77y9/jJosC8oXb9I8fMU7Uk6KGzTxEftHZLKPOWGZGsg+LliwIGWLZz169DAC89lnnzVij/MZwhJ22GEH02v+HXbAAcP51AlLh8MRBE5cOhyOlAjM+vXrJzRYm+ftrK6scZTEhg8Lx+TnpZdeMi6LqQAfjlr1q8i9vT+Vvar8JbcU3CrrJCQ6vvrKGugsXWrLIrWkESZMsMqR7MOcKSYBhi5lageJLwX9d8optiL1qacCdpitVo2UiHW+1e+Dmk/E708/lfz7Bx8sfY/4Sl6RU73HSMW2aVM8c+eHERoKpXykaOm99Luf8kHgSEvJbgag+pQEs19Y4r66TZ9iJHGJcVF4OaTftjUMkreMEwVej7UIdaONBBNljruonrTcMlm+F69PtYC5N7j2+MCrSVvzKMH19106ggWH11TMpQw/n+GOPXPmTGNOtjw0Xog5k1OmTClxRAjn0UTOu5ynOV+7jKXD4QgCVxbrcDiywklxxx13NMO5Z82aFbWHibKwjh07GtMehngD40fU9OLCCy80lv2pZOPGAjnmmEaycmUFKSgolIMrz5ar1w+R02WUVCy/VTbttptsrVxZyhUUSMWlS60Y8NO2rS2XPeook/hD85EARXf5IVmhDrP+TGepoQ8Twx19X9iYIlYQfzEg2/bkk4VymfxPHq5wtZTbHMpasiCAw6xfNCqIV3o3tW4T11jSayggf78idZw41DAKJU2D2RGW/MkqLBGZeDNFNMDx277yt551lk05d+xY/HksJNCsGQXKYtHS+l3zNbC58D+Z51F5y0gUaF5vqUzddKiU/+1XT+ziLNu6tRGhjRp5Jsb+ql1HfHC+Qbht3brVnIM4t0QSZ+lwxD7rrLNM6T9zJgcNGiT16tWTXr16mdfm/bVp00auuuqqqOKRx5s0aWLaBOI97zrzHofDESROXDocjqwK4ubNm2dMelLJiBEjZOzYseb9HH744XLDDTeYUjOCOfqpEKiXXnppxN+NNtak/QkrpHHjdVKxUsG2Y03ef19qjh0rFVau9DZE1g+RefTRUigFxiSUMZ0YAYUnHdFcGLj4Z2kG3odJSpWxGlGCVgQwvaHwzm1fSvtne9g5oPr7/O4tt9gUr/Loo9YyFSijvecee/uyy8TUCYfDe8IBJ44MdhAZS93NuE3GMmolN/NXUNc6/5MUJL2iqH8/uOn4nYQjQEvmoYd6Fcl8DCwgKFRbUsFMxTCwOeam7rv622JmQUaQv/aaXP7mCUUlt/yefwKNI/XjQ3Q25euvv25+j35IXeDiPNezZ0+pU6eO3H///Wn5OhCl4ZnNeMWzw+FwBIEri3U4HGmDgIbSKwI3/o8U4KR6ePeyZctM+eyoUaOMwCRTOmPGDBkyZIjJgjK4nNIzHGgjQb/ble2/lLMrjJQrLvmtaKzJAU3XFxOWxcaaXH21zJ40yTjPFqr4otmPLGabNlIw8QM54fhCM/6RRB8lk2rYQukkcSkzC889VySQdiv6MFEs1OGaN1poG/VQJ1H6MP3x6pfS3A5t1Cwxv0/vKQY0f/7pPVENj4AUnfap+ceR+Ge2UKpLdnPiREkViHe/sCR7GVNYRiuLDXeKZRxOCcISGFWIsZOCPp02zfsY6Z1UYcnCAolmMxuTz4XvTVm3TrZ27CSL/zemaAINyV9HeseHcJ4YP368OZ9wXnn55ZeN2ytwftmdfuw0Eun8Gc951+FwOILCiUuHw5FVkClIdfBDyRh9U2Qv+EfgyGMYAlEiRk/nJ1iIRqJcOanSaFcZNKqqXHTpssTGmlx6qcx9+WXZ6B9hMnmyFWXYxb77rjRvVmhKZRlJSfJUyzTRZfTWYQ5DNSZvr1TVeYwIIc11++1ethKVRd0mmc0Y4tLoKmp1UUHM/VSjJFKC9B1q+pUMpJaOkvHRGaR+ccnz2Y5mK3Hd5fMgTbpxowQJ4h0x6ReWlMKW6D0Vj7iM0W8ZDl5KmvTkT0TTU9562202SQpocoyFinkEhc0WoTR5lPSQc+Q549G0555xv4UySSrGh0SbTYkQnTBhgnRXI600wHmTMleHw+HIJE5cOhyOrIIStFTOWWM25rnnnmvs/du2bWvKYlnNx9RC4fZiHRgYicqVZQNZpJJGeYRTrpys26+B/DDyVVlfJyyj8dlntm8RcTd+vNTfr9CUO0ZzmKWqtmVLqweTdpjl/TOSZNw423upsyebN7cps1jiEhCluMeQhSVzB3xuxx5rRSs1xH5jH9RxuLikNLdzZ9uIyO8BgT91uBghxWM4FAckQ9G5Kiy5HZewTIG4BP48vmqguviYY2xVsd8DST+OInz9nCtq2+GZ5WWrPCe95JpKjyT0+mV1fEiyZjz8fiRBGm02JeOQLr744rT2M3LedP2TDocj0zhx6XA4sg6CpFSNMiEAJCuJ6+zEiRNl+vTpyfV4Jios9dcqlJPCnWvKe49+IFdUeFQ+lqNki/9UTF0sKTXEyltvya67FEZ1mP3iC5sF239/m/FK2mGW1+N12RBg9sNjd95ZlB4l0amaP1xXyRFHiHzzjSd+EJWDBtlM22GHeVlJUnGUFuJW9P/t3Qt0lPWZx/FnQgABxVoQ0IUWRbwg9VJRWoFWpVqtULxWFMqpNioRqUXkKl5A8N5WkRUs1ktBReV4dKvipdq1IMJSWWrRggILLq4CWgXkloTMnt//zZu8GWaSd+Y/CQG+n3OiIclMJpNMzvzyPP/nOeigqjArah/UNBpVQsNWWvWL6n7QOUePMq2CpTp4w2+zXp89O2awTBcu9fVpWqxHuGzSxOzpp/XHjqp5RyG1QV96aZoL6S8KuqD2ja7fbg/YkMp3FQ4bGny/UK/rQ3S+8bLLLrOioiIbNmyYq2KqgqiwebL+SFNP9PuyLv8oBwBxES4BNDh6cqb9buEgjXxasGCBdejQwZ1NUhublpEvWrTIVRtCej1aycw3fVkdj9hpZUUX2Wn2lrW3tTbUJttc62nlFrSoJpcutZLiYtv+wx9ayXPPWYvm5a6VUh2rOr6oI3jRITGaN9OxY7BtpGJzQXY0wVVpVUN13A1IBkN6fvazyp0ZYfVSxyrDKauV9MRWuz2VhMPgrbCocBmeq1T6VapTxfPYY4O3aVdkOOhIl1MlVHtZNAJV9ImKioKDqP/6l3fFUsFS3cBZ/e0iNVx+8smu+zkz7Lisif5QoLs4SqFy2LAMF9DByl69gssmP7Ypdq093yVyBbqy0aM9+6X3nMCoaqJaVTNVFetrfUjqbkoNzFm8eLHbSTlixAh3hvvWaFk6z8J9mJylBNAQEC4BNEhqVdVS71wrmJkGV2hyowb46MmmzlkqWOqspaYoaniH3qazUhr5X5eUo668coObNvuZHWJTbKj1afkXu27QR/bmE/Ns6cJF9uGcObbiwQftwyOPtA+WLLGVb71lG/+1zi65pMyWLAk2X2gmUEgdqepy1VTZ66/PvIIyI5Un1Suqw38hhUH1365cWa01dtWqDF+Ueni1JqNi8bsLYhrUU1Nr7D//uWtQU9VSY3JDul2alqpznTHpiKeCZVjRzbpimSlcppZuVYWNlpRjUkewuoej1GEc+TvHLj7uUrXy5adNXrNTXp9kduedVR+gkcOaCqTq6l5GlUdNbNVEaQ3i0jAdnaHU//VvvV3vj1Yo9brCZV1K3U158cUXu64IdUfcc8891rNnz1rDZa6/53Q5/Z5kRyWAhoJwCaDB0hMmLfdWFTHbpeA6C6XKZKrjjz/eevXq5Z4AXnjhha6Kefrpp9vYsWNt1KhRbo9mjx493GCOuGtNzjvvPOvXr5/dcccdroIyceJEF04vueSSGi/buLDcru33njUpLLPBg9e7ybNXDt9mrbseaImmFa2hFTQQaNtBB9ln69bZ8qVLbcP7S+2ss5KuMrdoUVBgDAuGW7YEk0NzmjCrK1FLq85htmwZvE1X0K2bnZV8tfLDdmmNjVLiVfoNK5bRQ6GaRKSW0mi4DFtjU6t0Dz0UHCoNe0dV5dR1q0KnJaE1UL5VmAyDpY51KlhWdJVmJ/rEXxN40p23zHIIlb4UHbEN142GX6LmGWn/ZbpztHrbiDeqzl1ec/grrpvYDT/S2peQ9pv84hdV03n34QmvWjeSqaqpFSJ67J5//vluSrToPLZ+L+jxPDXdupw0fvWrX7mPHzNmjN12221Zd1xofUiuv+d0OYIlgIaEPZcA9qq9dNGl4Jn20uVzrYmWnmvHnZ5Qatn58OHD3W3TE74JEya4FQUZlZdb0+XLrbygsZV07mSJgiwCii67Zo19e+1aa6KDl23auMyj83qPPLLr+Usdfxw50uwHP8giB2lkrdpkly0LPmWiwMYkb7e7baT95jcJVx2tkcKNKjZaUxKliqSmaJ511q47MNNR9fPnP6++BFTn2Z58svp6jkiwVJgM74N+/cxN4M0pWMp7kR2Tuu06Qxo931jb7U+htmV1t4ZrRrt1C85famBwuMlF+T5aQJZp0zRBOGlrrb39m/2fJZs1s4RahcM/osyYYXb55VXJ9PzzzZ56KodSbcOhlledl8ylrVVVPf1uSDflVdXOcePG2YwZM9zj9Ze//KULk+pg2H///d3vGwVNfcwx4VnkOqJuitatW+f8ew4AGhIqlwD2CApvegKmARpdunRxlUX91V7/17/1dr0/+oRrd6w1adWqlZ144onuc9eqoMB2HH20lXbqmF2wrLjs9g7fslVHHmklaiPt08c6LX7W/v03292EWYWT6IRZHYdUIVETStVhGmvCrM496hymkpo+ZbLc7rLRNsv629rlW2q/vCo46vvUJ4/eGE0f0vXWVLmM0iRaJUa1f4ZVIZVrVTF87LFqZwxfe616sFQ29gqWcdpisxjmo7Ofun1hsFSrsab/qso8a1bVVhfdbTqyGlJnZ3A+M2GvWlC9TOjK5s2r+iAFcB0oDb9YDVBS+Val7H1wwqsuly5Y1rRCRMFS9FguLS2t898fqetDcvk9BwANCeESwB4n7lLw3bHWRG22WdFtz3FwUaKwke1o1dYW3v+MvfvqekuqN7ZdO2tz09U24ax59vGapN1/f3AcsFGjpLVvX2JffLHdfv3rEjv22GS8CbNqjVVIiZwZu8SesaFPfj/Dwcs0zjknGIcaXfCu9Bt+3bWFS9GTabV/vvOO9j8Eb9OgIVXq+vd3/aXpgqUqgl7BMo/hUoFeLa9hHtTMKAXIcHaUqsphkVd5ecCAqnOzmtMTDmra1quqNdYdvI1StfLFF82aNw/+rVCuKb5h/+0eMICnrie81rRCRAYOHOja2vV4Pvroo213rQ+J+3sOABoS2mIB7NX0JFVDP+piWqSelI4cOdLuvvtuVwEpLi62IUOGWLdu3eyTTz6x66+/vua22DzR7JapU9vYa9M228/sGfdygi2xnccdZ1+NHGlfHX+CbS1XsbPqCf2OHQlbvnw/e/vtA61jx29YUVFhteJiOskX/sM2nzfQWtrm4A26gL6+M8+Md0MffdTsiivSv09BUecs49DHanSu+n8rbGvzLevz5Ux7s7RXZcZSJdA7WIp6Vd3hxoorVntuGNYU4jTttpZKkrJUcXFwjFRUINPwntQhs/peKhRXHAF0s5RUsA3nSynrf/jOF9a268HBlXbtavaPf+z6Cd9+O+iFDifxKgArydbjuoq4LZ6q8qeeU1TQi05wrgua8Dp79mxXrTzkkENckPy5qr/u3PIW9/i94YYbXBCtC/qdoaokoRHA3oTKJYC9Wn2vNXlP5/NylDocaOvWrTZ48GDr27eve/sTTzxR4+TZz1t+2+60MXZK4WIbN/gDe//RP9qn3/mObS/fWS1YStOmSevadZtdffVn1rv3crvllvU2fHjS7dPMJNHvpzaw83/ZMqtYE6JSmqbS6LxhnPUXWieSKUAqQcVdoaFkpt2X6netaD9utv5je630NBtvN9uF/cryU7FMV7lUsI1WAXUWM0aLoja0hMFSazxVDE63vUTfy8cfD9bKiAq1Op4aUrts2y6tgjOn4bAlnUm1NLtHNVm34iyf20Oq0mi6j21AA3hqmvCaOoBHl4/z+Ii7QkS/J0ItWrSw7t27u1bZusD6EAB7K8IlgL1efa416RimghyGAz311FOu0vncc8+51Qp60q1BI3oi/eSTT9qsWbPs4wzpr7Awaf36fWnt2pXYrFkr7JLindaoSSNLKK2EY2RThO9q0iRpRUXr7dRTV1iPHiU2aFD6Ypg0OvZo624L7U/Wp6rUpklB6ves7WyfQqGGD4XCEalhatKE02zOB158sf3n5PdsbuIHwW2zcrvZbrNnPutljf83ZstuHNGfm9T20hgtsdOnm91yS9W/FR5/9KPMH6+CcPToZJizTjghqH46CvUh9QOno9um6bw6syoazKRJQnHbmXOglld1CkTDYtwwqsvp8ukmvGoAz8svv+weH3qc6LGwefPm2I+POCtEdFZaj8Pw65g/f74ddthhGS/P+hAA2BXhEsA+oT7Xmtx4443u3JaeEPfu3dvtu8tlOJAGd5xcUaFq3ry5C66ZdvYVWLkN7ft3e2bmcuvUaUemPJmRPl6Xe+yxVfb66yV23HFBV6WKX9Hn+RpAs6VRS7um/bO24tf3Wkn79pZU5U49qKeeGqwZqYl2o4RSn7hrB+Ypp+y69zIDHTc8+8oOdlryTRtrk2xnIqggFixcECSxmTMt7+EybDONGS5feMFs8OCqf2ua76WX1v4pNUF2/PjqbxszJnI8V+coM527jNKk07lzg4lBou+PxtLWcs412zOS+RrAo8srXMYZwPPuu+/GfnzEWSGisKrW9gsuuMCtETrppJMy7rtlfQgApMeZSwD7nIa41kTU1vfAAw+4z6mWvet0rrCCPrdWnaiqqSfSaamKqBePFuCdZWYrVja1/v2PsLKyIITr+fvo0WXWs+dXtmLFRmvadLtrqw0lSkpsv2XL7MBXX7VvvPWWFT78cObSnG6fqruaVKP7NhxbG31dX5+mDWmiTQZz5gRnE7V2UlQQnTVsoRUOuqx6ZU4V1QcfrD5MKBe6fbrtqgJGW0v/9jezk05KexF1VOpuCAcMZbmxxA1/jeZjdeCqTbZZs4o1L2p53bgxKHUqVNXUnqsFmjobG4ZKrVPRHz0it93njGRdnm2WlStXujU/ah3X59Ifby666CI3UCv24yOPWB8CAOkRLgHs0/REVSsHysvL3Y67xo0bp61s6om32lTjtvrlcziQKkJXXHGFDRo0yE2mrXPJpC1+9FMb8rvv2/bC/a2oaIM706nWW901aQu/ul+SSUuUldnBDz9sB3fqZAktwkz3wdqpEe6JVO+nEqKm7Or8ZLQfVzslNe42pWqcGix1JlErL3WW0TZvNhs6NOg9DSnM6jyeKqu5UmDR6g8FuXBsq8Kc2njTtFsrw6lAGH7owIHBTYpbUVY3a1g002WUa6WoKGizrTzDOnt28PqCBWbdu9d8pZ9/HrTTvvtu1XSgF1+0ZM+eruKnlzg/33p8aMqpXsLHyu4cwFPfjw99zfrc6aa8xv19AgB7K9piAezTGspak0zDgfRkdezYsa79NtsnzqkDgnRdqvSohVdv09L4tBIJO35gW1twTH9b+Oxf7Jride5cpkJOxufJekdBgSWbNLH1gwfbio4drUQHBLdu3fVjdagzVFjo2mpLkknb/tprVjJsWNBmK6pealzqihWVH66VmdFgqXxVGSzlgAOC3ZdPPVVVrVy9OjhrqD7TXFdbhAEyurtFLadpguXatUHXahgs9W3T/KG4wbK01GzIkOpZPCzGqSis7uHU1tjkq6/W3saqSucbbwSpVzZtspJBg2zFkiVeZyQzDeBJN4RHJk6c6NpN1XrqO4DH5/GRK9aHAEBmVC4BIKa6bP3TYCA96Z45c6Z74jx06FDX9qe3a/CI3pcNDSYZMGCAPf/88+761DKotkKdO1XlR4FAQXPcuHF2jEJSClXKtmwpsObNdlqjwhwqL2VlVvjFF3b4HXdYEyWilEFHZWecYV916GAbf/xj237UUZaMhLREebnt98EHduCcOfYN3X6FnkcesZeaXWQXXFA9WKogWRksUytIq1db+ahRVvDOO9b4008tobZbTVFVr2m2g5fatTNbt656GVEDiLReJUKBUjn2/feDf6vrVJtLlHnjVrh+9zszFX3Dyy9cGAToMJOrLVZvO6blGvvq5puD+/CYYywZuSNqbGNV4L/gAiv5+99t1cyZVqYW2RxbqXW9ah9PFy515lg/XzNmzHBfr4bv6A8aanHVH3ImTJiQ1aoePQ5atWrlBvDoZ1lBU23kuTw+csX6EACoWf5n8wPAXr7WRMNF8r3cPTocSGFDaxC6dOnizl126tTJBU0ZNmyY9VBAymJAkOj26om5gmX4b4WbTJVavXn//ctzb+krLHShZU1xsR3RvbsllI5693Yhw7Vg3nefVdbJUkp6yYIC29a1q23r0sXWXXedHTx9uh186aX2P+VzLVmuQ4tN0gbLtGcGb75513OhPXtaoQ4/xpmsE96m/faz0vbtrbxpUyvYsSMIqynDfNQ1269fVbDU8CNVWsNgGedMo9mBdu+9Wq9S6L4HOi6qQq7OX2ouj1piS0uT9vTTG6x//6/Nhg9Pfx9WrPrQy7p166q3sTZvbsnnn7c18+Z5Bcvwa9J55HSiQ3hEQ3i02uOcc85xe2CzpQE8X3/9tRumowE8+ryqYub6+MgW60MAoHZULgEgS2oFXLNmTU4VTD3R1hN8hYu6lmlAkIahfPTRR64tUYvi61R5ubWZOtXa/P73VvLAA7bmzDOzv9/Ky63pRx/Zt6+91v77s2/ZH899xu5/XrtLg3dXBtbazgxGz4UqsG7fbonJk3ctK1qaMLhxo2v5DSV27LD9GjWyA9u3d5XBRKLQBV7tr5Q2bczmzw8CZuzbV3ETS0sTNn26WrAPtqlTq8K9fmT69Cmx4uI11rlz9hOB9bOnP46oalgfZyRrGsKjcKmfvWwql77CkJvr4za87wAAmXHmEgDqca2JLqcwUtcUiP7617+6NShvvPGGLVmyxP6myaamrtCZ9uabb7oBRQqZPmc4Ra2dl112WfqgWlBgG6680rZ16mSrjjrKdqQ7g1mbggLbocvPnGkntvvYpsw/0QpfeTH7vYrRc6HFxbaiTx8r0ThX9ZimOVOo+0dTSFX9iwZL9zFNm9q2wkL3fn3cY4+ttz/9Kfj8Kg5r6JCCZbZ7H3UTdb61uHi9XXddcKax6m4osfvuW5XTqpnoqg99PdmckdQOSL3tJz/5SeZzummooqifi6KiIldRVBVTVfndRe27Po9bgiUA1I5wCQA5CJ90HnXUUW4tgVr1Up+w6t96u96v6ZLhk9qg0lW3EyQzDQgKtWjRwrXeqk0x7hlOLa9XpUnrHj744AN3HlT070MPPTTjZcsLG9vqaQ8FLZg1rcuotc22ta2ZMiWYCNu3r5WMH2+rVq7M7QxsGFhvv91KtMdk0iS3CiXbMCj6uJNPXm+zZq2wDh1K7LnnzL77Xb+9j8pgZWVBGAwH9Khablbm08XqqrGrV69O+7XpjOTLL7/svsf6Xs+aNcs2bdpkkyZNsrvuusuFzXnz5rmP8xnCszvo8aZw6fO4BQDUjjOXAOBBT5hbt27tXuKuIQgnz9ZlW6KeGCv8Kdjo8y1atMi1JCokfvOb33SBZf78+W6VQ1zpznCqQvrKK6+46pRCRCZl7ryf5xdV2Mh2HHmkbSgqcutO1nTtamWq7HkF1la25v777Yj+/a100SJbNWmSleWwbkZhUBXFF15YZccee7glk41dGPQ9m6vL63patmyZt0FS+j7GPSM5d+5c9/EKY3L22We7irjel+0QHlVAb7rpJtsdUie85vK4BQDUjnAJAHleaxL3ya6CWV0tnU83IEhVGO3P1BNpPaHW6gathIhDgVRn5XSZ8AynKqMarHL11VfXeNlEQSJjm62qnrot3/ve92z06NFuX6EGtYQBRy28waCb8MoStqG42MqbNbMdnTvH3++RSWGhu571V11lm844w8q0ByTnyan6b/7DoK4nUxtruvvwpZdesj/84Q8uLKmd9fLLL4/1eTp37mzTpk1z1Updn9qo27dv7yp3Ib0etlfnMoRHoe7GG290f9jQedbevXu7/a4/jqxVyTf9LNW0Riibxy0AoGaESwDYgyfP6sm6XtINCNITe71E5TpAJXqGU0/WFVJVDVUQOfnkk93r2QjbbKOrUsI229/+9rcu6GSiabKfKzClVJfSBS3dblXL1AqqkD1lyhQXinc5F3rVVZVnMus7DM6ZM8emawysmTvbpzbUOGEn3X24ePFi9zXq+6zJwEOGDLHTTjvNDjvssKzOSOqyqmJGq325DpVKpa8vDv2cqWrq+/jQ44xKJADUD8IlAOwmChCHH3641+RZPXFWwNNgmfo6wyk6w6kKlsKMqk66/Vu3brVbb73VveTaZhtbSrDMFFZ1blCtnOeee24wmCdTy2uaEJUaBBXUVb0NaeLpNddcE7u1ONNtvPfee93n0VncESNG2J///Gc3PCeX+1AtnfqZCr9P3bp1cwOdFBjjUEVaL3LLLbe4ttFo+7Zej1Yy65LOSKoC7Pv4oCoJAPWHgT4AsAdPntXl62NAUPQMpwKNKpU6h6fgomrmPffcYz179owdLKNttmeccYYLb2FFcdSoUa6d9/HHH8/qNoZBSyFLL2rFfP/9912wFP27efPmOQ8w0mTd2bNnu5dnn33WDjjgADv99NO9bqMCtcKrKs96nwJwTS2ctd2HClMaSKTdlvo8OjOZzdlenZGU8IykgrkqvpqIq9un87VxW6nzMYAnH48PAED9oXIJALtZ+GRYYaFyr+L27dWqbPoYnT1URUpPulOHk9T1gKB0ZzizDVa1tdmqEnrnnXda27ZtbfPmzTZ06FDXzqkqaS5nQnX/6b5SWFX7sap42ruo+8u3sqqgraqeziTGlenc6tixY93qDwUh3a9qM871PlQA1ternaa6Pg3eyaa1Nd0ZSd0+Xafui759+8Ye5pOvATy+jw8AQP0hXAJAA+EzwbKuBwRlOsMZUiCKG4oytdlqVYoCoKgqqHbbpUuXxgqX6YJWjx49XPVN4UiBSP9XS6qm5uYaBEP6PNkOocl0blWVUFVHFaDGjBnjVn4oxOV6H2ookgblyEMPPeRaS33OSOoPC7rfaqOfz7jrW3IZwMOEVwBo+GiLBYAGKJxgqWqM/l9bS2A4IChuVS6T+lpyn67NtmPHjvbll1+69ytYa6eihszkutdT16nKoqbk6utSpXXZsmVZB0G1/i5ZsqRySqoC1Ouvv551uMy0e1Th9ZBDDnH/VygMBxvleh+Gra2auqs21rjnN33pjyK+P39xB/Bk+/gAANQPKpcAsJfIx4AghTG1kOajApVtm63ODGrIjVpQVbHV2T5VDn32eirQrV271n1depvuH9/KqoYYHXrooe5zZiPdbVRA/fDDD12Y1edauHBh7NuYqVVZrb8rV650b7vhhhsqv4a6pHCncKmWVAbwAMC+K5Gs62cQAIB6pV/rWoWhlzi/4hUM1IaoF72us5t1eX6zrkyePNlNWg2DltZ8aBDP+PHjXSVUFUy9HmfIi0LgxIkT3Z5NBUGd/1RY1eCc22+/3VXXBgwYkJfbqIm2Gh6kyqVWsOisY7jnc0+hlt5wiqzvzx8AYM9FuASAvZQqgLkMQNHHaeJoPs5v6nrVsrknShcEdd+ceeaZLhDGneraUCk0++yQDCkIazJrajDM9ecPALDnIlwCwD4g2wFBJSUlrj02Hwvs66PNFtnR917nWVevXu39PVYbb23V4Gx//gAAeyYG+gDAPiDbASjh+c1c2zN1OV1eKy3yXeHT2T6CiR99T/SzkI/vcZw2YwbwAMC+gcolACCjfJyfy2ebbdiCqduzJ54LzZXuz02bNuX1Pox+fzgjCQDIB8IlAKBWvufn8tVmG1bK8h1YtQtSAashh0G1lebzPkzFGUkAgC/CJQAgK7men1PAzOeainwGVn0N+a6u1kUYzPd9mAlnJAEAuSBcAgDqTb5bMPMZtvJdXc337QvRxgoAaKgIlwCAepfPFsx8hq09KQzSxgoAaGgIlwCA3SpfLZj5Clt7YhikjRUA0BAQLgEAe518hC3CIAAA2SFcAgBQCyqDAADUjnAJAAAAAPBW4H8VAAAAAIB9HeESAAAAAOCNcAkAAAAA8Ea4BAAAAAB4I1wCAAAAALwRLgEAAAAA3giXAAAAAABvhEsAAAAAgDfCJQAAAADAG+ESAAAAAOCNcAkAAAAA8Ea4BAAAAAB4I1wCAAAAALwRLgEAAAAA3giXAAAAAABvhEsAAAAAgDfCJQAAAADAG+ESAAAAAOCNcAkAAAAA8Ea4BAAAAAAQLgEAAAAAux+VSwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAHgjXAIAAAAAvBEuAQAAAADeCJcAAAAAAG+ESwAAAACAN8IlAAAAAMAb4RIAAAAA4I1wCQAAAADwRrgEAAAAAJiv/wdnTGF7Kqxn4AAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualization of Circular Connectivity Map \n", "# Top 50 by absolute difference, restricted to upper triangle (unique edges)\n", "iu = np.triu_indices_from(average_brain, k=1)\n", "diff_vals = average_brain[iu]\n", "top = np.argsort(np.abs(diff_vals))[-50:]\n", "rows = iu[0][top]\n", "cols = iu[1][top]\n", "\n", "G = nx.Graph()\n", "G.add_nodes_from(range(average_brain.shape[0]))\n", "edges = list(zip(rows, cols))\n", "G.add_edges_from(edges)\n", "\n", "edge_colors = [\"red\" if average_brain[r, c] > 0 else \"blue\" for r, c in edges]\n", "\n", "plt.figure(figsize=(9, 9))\n", "nx.draw_circular(\n", " G, with_labels=True, node_size=250, node_color=\"lightgrey\",\n", " font_size=7, edge_color=edge_colors, width=2\n", ")\n", "plt.title(\"Top 50 Functional Connectivity Differences\\n(Red stronger in ASD, Blue stronger in Control)\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "7b803873-a241-4fa2-b400-1bd40a7fd297", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate Importance (Sum of absolute differences)\n", "y_arr = np.array(y)\n", "mean_asd = np.mean(X[y_arr==1], axis=0)\n", "mean_control = np.mean(X[y_arr==2], axis=0)\n", "importance = np.sum(np.abs(mean_asd - mean_control), axis=1)\n", "\n", "# Define AAL Atlas Labels (1-116)\n", "# I've included the most common ones; ensure you have the full list for all 116\n", "aal_labels = {\n", " 1: \"Precentral_L\", 2: \"Precentral_R\", 3: \"Frontal_Sup_L\", 4: \"Frontal_Sup_R\",\n", " 15: \"Frontal_Inf_Orb_L\", 16: \"Frontal_Inf_Orb_R\", 23: \"Frontal_Sup_Medial_L\",\n", " 24: \"Frontal_Sup_Medial_R\", 37: \"Hippocampus_L\", 38: \"Hippocampus_R\",\n", " 43: \"Calcarine_L\", 44: \"Calcarine_R\", 71: \"Caudate_L\", 72: \"Caudate_R\",\n", " 91: \"Cerebellum_Crus1_L\", 92: \"Cerebellum_Crus1_R\", 116: \"Vermis_10\"\n", "}\n", "\n", "\n", "# Get Top 10 Indices\n", "top_indices = np.argsort(importance)[-10:]\n", "top_values = importance[top_indices]\n", "\n", "# Map indices to AAL names (Atlas is 1-indexed, your array is 0-indexed, so we use i+1)\n", "labels = [aal_labels.get(i+1, f\"ROI {i+1}\") for i in top_indices]\n", "\n", "# Plot\n", "plt.figure(figsize=(10, 6))\n", "colors = plt.cm.tab10(np.arange(len(top_indices)))\n", "plt.barh(labels, top_values, color=colors)\n", "plt.title(\"Top 10 Most Altered Brain Regions (AAL Atlas)\")\n", "plt.xlabel(\"Connectivity Difference Strength (ASD vs Control)\")\n", "plt.grid(axis='x', linestyle='--', alpha=0.3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "9b2b5807-84ec-462c-9ba3-40cfbcf653ed", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate Mean Strength per ROI\n", "# mean_asd and mean_control should already be defined from the previous block\n", "asd_str = mean_asd.mean(axis=1)\n", "ctrl_str = mean_control.mean(axis=1)\n", "\n", "# Find top 10 regions with the largest absolute difference in strength\n", "top_idx = np.argsort(np.abs(asd_str - ctrl_str))[-10:]\n", "\n", "# 3. Use the same AAL Label mapping for consistency\n", "aal_labels = {\n", " 1: \"Precentral_L\", 2: \"Precentral_R\", 3: \"Frontal_Sup_L\", 4: \"Frontal_Sup_R\",\n", " 15: \"Frontal_Inf_Orb_L\", 16: \"Frontal_Inf_Orb_R\", 23: \"Frontal_Sup_Medial_L\",\n", " 24: \"Frontal_Sup_Medial_R\", 37: \"Hippocampus_L\", 38: \"Hippocampus_R\",\n", " 43: \"Calcarine_L\", 44: \"Calcarine_R\", 71: \"Caudate_L\", 72: \"Caudate_R\",\n", " 91: \"Cerebellum_Crus1_L\", 92: \"Cerebellum_Crus1_R\", 116: \"Vermis_10\"\n", "}\n", "\n", "# Map the indices to names\n", "# Note: top_idx is 0-indexed, AAL is 1-indexed, so we use i+1\n", "labels = [aal_labels.get(i+1, f\"ROI {i+1}\") for i in top_idx]\n", "\n", "# Plotting\n", "plt.figure(figsize=(14, 6))\n", "x = np.arange(len(top_idx))\n", "width = 0.35\n", "\n", "plt.bar(x - width/2, ctrl_str[top_idx], width, label='Control', color=\"#3445db\", alpha=0.8)\n", "plt.bar(x + width/2, asd_str[top_idx], width, label='ASD', color=\"#f26309\", alpha=0.8)\n", "\n", "plt.xticks(x, labels, rotation=45, ha='right')\n", "plt.ylabel(\"Average Connectivity Strength\")\n", "plt.title(\"Top 10 Regions: Comparative Connectivity Strength (ASD vs Control)\")\n", "plt.legend()\n", "plt.grid(axis='y', linestyle='--', alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RQ1 outputs\n", "\n", "- Fig. 1: top-50 edges by absolute ASD−Control mean difference (upper triangle)\n", "- Fig. 2: top-10 ROIs by summed absolute edge differences (“alteration score”)\n" ] }, { "cell_type": "markdown", "id": "69455847-286d-44c4-b36c-c3d8f4b9a1cb", "metadata": {}, "source": [ "## 6. RQ2 — Model comparison (Majority vs SVM vs GCN)\n", "\n", "> *Does explicitly modeling brain topology (via GCN) outperform vector-based baselines (SVM)?*\n", "\n", "**Models**\n", "1. Majority baseline \n", "2. SVM on vectorized connectomes (upper triangle) \n", "3. GCN on sparse weighted graphs (top-k) with node features (degree, strength, clustering)\n", "\n", "**Evaluation**\n", "- **Primary (site-aware):** StratifiedGroupKFold (k=5) grouped by `SITE_ID` to reduce site leakage \n", "- **Secondary (subject-level):** StratifiedKFold (k=5) for comparability with common ABIDE baselines\n", "\n", "Within each training fold, a stratified validation split (20%) is used for model selection and decision-threshold tuning.\n", "\n", "**Metrics:** Balanced Accuracy, Macro-F1, Accuracy, Sensitivity, Specificity.\n", "\n", "*Note:* The site-aware protocol reports the Majority baseline and SVM by default. GCN in the site-aware setting can be enabled, but is often unstable/near-chance and is not required for the strict leakage check.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0e2fc39c-ae55-438c-bdb2-9cf2cffc588c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data prepared.\n", "SVM feature matrix: (884, 6670)\n", "GNN node features: 3 (degree, strength, clustering)\n", "Sites available: 20\n" ] } ], "source": [ "## 6. RQ2 (models)\n", "\n", "# Build vector features for SVM and (2) graph objects for GNN\n", "# SVM features: upper triangle of Fisher z-transformed correlations (diagonal excluded)\n", "USE_FISHER_Z_SVM = False\n", "X_svm = []\n", "for mat in X:\n", " m = fisher_z_matrix(mat) if USE_FISHER_Z_SVM else mat.copy()\n", " np.fill_diagonal(m, 0.0)\n", " iu = np.triu_indices_from(m, k=1)\n", " X_svm.append(m[iu])\n", "X_svm = np.asarray(X_svm, dtype=np.float32)\n", "\n", "# GNN dataset: sparse weighted graphs + node features (degree, strength, clustering)\n", "TOPK_GNN = 10\n", "dataset_gnn = [matrix_to_pyg_data(X[i], y_bin[i], topk=TOPK_GNN, use_fisher_z=False, use_abs_weights=True, normalize_node_feats=True)\n", " for i in range(len(X))]\n", "\n", "# Optional group labels for site-aware CV\n", "groups_site = cohort['SITE_ID'].to_numpy() if 'SITE_ID' in cohort.columns else None\n", "\n", "print(\"Data prepared.\")\n", "print(\"SVM feature matrix:\", X_svm.shape)\n", "print(\"GNN node features:\", dataset_gnn[0].x.shape[1], \"(degree, strength, clustering)\")\n", "print(\"Sites available:\", (len(np.unique(groups_site)) if groups_site is not None else \"N/A\"))" ] }, { "cell_type": "code", "execution_count": 15, "id": "497ccedd-8e25-465e-83cb-5bc03ffc648d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Improved GCN Model Defined.\n" ] } ], "source": [ "class GCN(torch.nn.Module):\n", " def __init__(self, in_channels, hidden_channels=64, dropout=0.5):\n", " super().__init__()\n", " self.conv1 = GCNConv(in_channels, hidden_channels)\n", " self.conv2 = GCNConv(hidden_channels, hidden_channels)\n", " self.dropout = dropout\n", " \n", " # a Sequential MLP to interpret the features after they are pooled.\n", " self.fc = torch.nn.Sequential(\n", " Linear(hidden_channels, hidden_channels // 2),\n", " torch.nn.ReLU(),\n", " torch.nn.Dropout(p=self.dropout),\n", " Linear(hidden_channels // 2, 2)\n", " )\n", "\n", " def forward(self, x, edge_index, batch, edge_weight=None):\n", " # 1. First Graph Convolution\n", " x = self.conv1(x, edge_index, edge_weight).relu()\n", " x = F.dropout(x, p=self.dropout, training=self.training)\n", " \n", " # 2. Second Graph Convolution\n", " x = self.conv2(x, edge_index, edge_weight).relu()\n", " \n", " # 3. Global Pooling (Collapses the graph into a single vector)\n", " x = global_mean_pool(x, batch)\n", " \n", " # 4. Final Classification Layers\n", " return self.fc(x)\n", "\n", "print(\"Improved GCN Model Defined.\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "6a4068e4-281d-401d-8f0b-7d7c826cc929", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Device: cpu\n", "[Site-aware (grouped by SITE_ID)] Fold 1/5\n", "[Site-aware (grouped by SITE_ID)] Fold 2/5\n", "[Site-aware (grouped by SITE_ID)] Fold 3/5\n", "[Site-aware (grouped by SITE_ID)] Fold 4/5\n", "[Site-aware (grouped by SITE_ID)] Fold 5/5\n", "[Subject-level (stratified)] Fold 1/5\n", "[Subject-level (stratified)] Fold 2/5\n", "[Subject-level (stratified)] Fold 3/5\n", "[Subject-level (stratified)] Fold 4/5\n", "[Subject-level (stratified)] Fold 5/5\n", "Done.\n" ] } ], "source": [ "# Cross-validation settings\n", "N_SPLITS = 5\n", "INNER_VAL_SIZE = 0.2\n", "MAX_EPOCHS = 200\n", "PATIENCE = 20\n", "BATCH_SIZE = 32\n", "LR = 1e-3\n", "WEIGHT_DECAY = 1e-4\n", "\n", "DO_SVM_TUNING = True\n", "SVM_PARAM_GRID = {\n", " \"svc__C\": [0.1, 1.0, 10.0],\n", " \"svc__gamma\": [\"scale\", 0.01, 0.1],\n", "}\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(\"Device:\", device)\n", "\n", "def tune_threshold(scores: np.ndarray, y_true: np.ndarray, is_prob: bool = False) -> float:\n", " \"\"\"Finds the threshold that maximizes the Macro-F1 score on the validation set.\"\"\"\n", " scores = np.asarray(scores).ravel()\n", " y_true = np.asarray(y_true).astype(int).ravel()\n", "\n", " # Create 100 threshold candidates between the min and max scores\n", " thresholds = np.linspace(scores.min(), scores.max(), 100)\n", " best_thr, best_f1 = float(thresholds[0]), -1.0\n", "\n", " for thr in thresholds:\n", " y_pred = (scores >= thr).astype(int)\n", " # Macro-F1 is the \"top-notch\" metric for imbalanced data like ABIDE\n", " f1m = f1_score(y_true, y_pred, average='macro', zero_division=0)\n", " if f1m > best_f1:\n", " best_f1, best_thr = f1m, float(thr)\n", "\n", " return best_thr\n", "\n", "def gnn_predict_probs(model, loader, device):\n", " model.eval()\n", " probs, labels = [], []\n", " with torch.no_grad():\n", " for batch in loader:\n", " batch = batch.to(device)\n", " # Ensure edge_weight is passed if the GCN architecture expects it\n", " out = model(batch.x, batch.edge_index, batch.batch, edge_weight=batch.edge_weight)\n", " p = out.softmax(dim=1)[:, 1].detach().cpu().numpy() # Prob(ASD)\n", " probs.append(p)\n", " labels.append(batch.y.detach().cpu().numpy().astype(int).ravel())\n", " return np.concatenate(probs), np.concatenate(labels)\n", "\n", "def train_gnn_one_fold(train_graphs, val_graphs):\n", " in_channels = train_graphs[0].num_features\n", " # This calls the \"ImprovedGCN\" architecture we defined in Cell 35\n", " model = GCN(in_channels=in_channels, hidden_channels=64, dropout=0.5).to(device)\n", "\n", " y_train = np.array([int(g.y.item()) for g in train_graphs], dtype=int)\n", " counts = np.bincount(y_train, minlength=2).astype(np.float32)\n", " class_w = torch.tensor([1.0/(counts[0]+1e-12), 1.0/(counts[1]+1e-12)], dtype=torch.float, device=device)\n", " criterion = torch.nn.CrossEntropyLoss(weight=class_w)\n", "\n", " sample_w = np.array([1.0/(counts[label]+1e-12) for label in y_train], dtype=np.float32)\n", " sampler = WeightedRandomSampler(weights=sample_w, num_samples=len(sample_w), replacement=True)\n", "\n", " train_loader = DataLoader(train_graphs, batch_size=BATCH_SIZE, sampler=sampler)\n", " val_loader = DataLoader(val_graphs, batch_size=BATCH_SIZE, shuffle=False)\n", "\n", " opt = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n", "\n", " best_state = None\n", " best_thr = 0.5\n", " best_val_f1 = -1.0\n", " patience_left = PATIENCE\n", "\n", " for epoch in range(1, MAX_EPOCHS + 1):\n", " model.train()\n", " for batch in train_loader:\n", " batch = batch.to(device)\n", " opt.zero_grad()\n", " out = model(batch.x, batch.edge_index, batch.batch, edge_weight=batch.edge_weight)\n", " loss = criterion(out, batch.y.view(-1))\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", " opt.step()\n", "\n", " # Threshold tuning on validation set during training\n", " val_probs, val_labels = gnn_predict_probs(model, val_loader, device)\n", " thr = tune_threshold(val_probs, val_labels, is_prob=True)\n", " val_pred = (val_probs >= thr).astype(int)\n", " val_f1 = f1_score(val_labels, val_pred, average='macro', zero_division=0)\n", "\n", " if val_f1 > best_val_f1 + 1e-6:\n", " best_val_f1 = val_f1\n", " best_thr = thr\n", " best_state = copy.deepcopy(model.state_dict())\n", " patience_left = PATIENCE\n", " else:\n", " patience_left -= 1\n", "\n", " if patience_left <= 0:\n", " break\n", "\n", " model.load_state_dict(best_state)\n", " return model, float(best_thr), float(best_val_f1)\n", "\n", "# SVM features: upper triangle of Fisher z-transformed correlations\n", "USE_FISHER_Z_SVM = False\n", "X_svm = []\n", "for mat in X:\n", " m = fisher_z_matrix(mat) if USE_FISHER_Z_SVM else mat.copy()\n", " np.fill_diagonal(m, 0.0)\n", " iu = np.triu_indices_from(m, k=1)\n", " X_svm.append(m[iu])\n", "X_svm = np.asarray(X_svm, dtype=np.float32)\n", "\n", "# Labels and groups\n", "y_bin = (np.array(y, dtype=int) == 1).astype(int)\n", "groups_site = cohort['SITE_ID'].values if 'SITE_ID' in cohort.columns else None\n", "\n", "# GNN dataset: sparse weighted graphs + node features (degree, strength, clustering)\n", "TOPK_GNN = 10\n", "dataset_gnn = [\n", " matrix_to_pyg_data(\n", " X[i],\n", " y_bin[i],\n", " topk=TOPK_GNN,\n", " use_fisher_z=True,\n", " use_abs_weights=True,\n", " normalize_node_feats=True\n", " )\n", " for i in range(len(X))\n", "]\n", "\n", "# Cross-validation protocols \n", "\n", "RUN_SITE_AWARE = True\n", "RUN_GNN_SITE_AWARE = False # set True to also train/evaluate GCN under site-aware folds\n", "RUN_SUBJECT_LEVEL = True\n", "\n", "def _init_metrics():\n", " return {'acc': [], 'sens': [], 'spec': [], 'f1': [], 'bal_acc': []}\n", "\n", "def run_outer_cv(protocol_name, splitter, groups=None, run_gnn=True):\n", " # Runs outer CV with an inner stratified validation split for threshold tuning\n", " maj_metrics = _init_metrics()\n", " svm_metrics = _init_metrics()\n", " gnn_metrics = _init_metrics() if run_gnn else None\n", "\n", " last_fold_artifacts = None\n", "\n", " fold = 0\n", " for train_idx, test_idx in splitter.split(X_svm, y_bin, groups):\n", " fold += 1\n", " print(f\"[{protocol_name}] Fold {fold}/{N_SPLITS}\")\n", "\n", " # Inner split (stratified) within the training set for validation\n", " sss = StratifiedShuffleSplit(\n", " n_splits=1, test_size=INNER_VAL_SIZE, random_state=SEED + fold\n", " )\n", " tr_rel, val_rel = next(sss.split(X_svm[train_idx], y_bin[train_idx]))\n", " train_inner_idx = train_idx[tr_rel]\n", " val_idx = train_idx[val_rel]\n", "\n", " # Majority baseline\n", " maj = DummyClassifier(strategy='most_frequent')\n", " maj.fit(X_svm[train_inner_idx], y_bin[train_inner_idx])\n", " maj_pred = maj.predict(X_svm[test_idx])\n", " acc, sens, spec, f1m, bal, cm_maj = compute_binary_metrics(y_bin[test_idx], maj_pred)\n", " for k, v in zip(['acc','sens','spec','f1','bal_acc'], [acc,sens,spec,f1m,bal]):\n", " maj_metrics[k].append(v)\n", "\n", " # SVM (RBF, class_weight balanced) + optional tuning\n", " svm_pipe = Pipeline([\n", " ('scaler', StandardScaler()),\n", " ('svc', SVC(kernel='rbf', class_weight='balanced'))\n", " ])\n", "\n", " if DO_SVM_TUNING:\n", " gs = GridSearchCV(\n", " svm_pipe,\n", " param_grid=SVM_PARAM_GRID,\n", " scoring='f1_macro',\n", " cv=3,\n", " n_jobs=-1,\n", " refit=True\n", " )\n", " gs.fit(X_svm[train_inner_idx], y_bin[train_inner_idx])\n", " svm_model = gs.best_estimator_\n", " best_params = gs.best_params_\n", " else:\n", " svm_model = svm_pipe.fit(X_svm[train_inner_idx], y_bin[train_inner_idx])\n", " best_params = None\n", "\n", " # Threshold tuning on validation split (decision_function)\n", " val_scores = svm_model.decision_function(X_svm[val_idx])\n", " svm_thr = tune_threshold(val_scores, y_bin[val_idx], is_prob=False)\n", "\n", " test_scores = svm_model.decision_function(X_svm[test_idx])\n", " svm_pred = (test_scores >= svm_thr).astype(int)\n", "\n", " acc, sens, spec, f1m, bal, cm_svm = compute_binary_metrics(y_bin[test_idx], svm_pred)\n", " for k, v in zip(['acc','sens','spec','f1','bal_acc'], [acc,sens,spec,f1m,bal]):\n", " svm_metrics[k].append(v)\n", "\n", " # GNN (optional per protocol)\n", " cm_gnn = None\n", " gnn_thr = None\n", " best_val_f1 = None\n", " gnn_probs = None\n", " gnn_labels = None\n", "\n", " if run_gnn:\n", " train_graphs = [dataset_gnn[i] for i in train_inner_idx]\n", " val_graphs = [dataset_gnn[i] for i in val_idx]\n", " test_graphs = [dataset_gnn[i] for i in test_idx]\n", "\n", " gnn_model, gnn_thr, best_val_f1 = train_gnn_one_fold(train_graphs, val_graphs)\n", "\n", " test_loader = DataLoader(test_graphs, batch_size=BATCH_SIZE, shuffle=False)\n", " gnn_probs, gnn_labels = gnn_predict_probs(gnn_model, test_loader, device)\n", " gnn_pred = (gnn_probs >= gnn_thr).astype(int)\n", "\n", " acc, sens, spec, f1m, bal, cm_gnn = compute_binary_metrics(gnn_labels, gnn_pred)\n", " for k, v in zip(['acc','sens','spec','f1','bal_acc'], [acc,sens,spec,f1m,bal]):\n", " gnn_metrics[k].append(v)\n", "\n", " # Save artifacts from final fold for plotting/inspection\n", " if fold == N_SPLITS:\n", " last_fold_artifacts = {\n", " \"protocol\": protocol_name,\n", " \"maj_cm\": cm_maj,\n", " \"svm_cm\": cm_svm,\n", " \"svm_thr\": svm_thr,\n", " \"svm_best_params\": best_params,\n", " \"svm_test_scores\": test_scores,\n", " \"svm_test_labels\": y_bin[test_idx],\n", " \"gnn_cm\": cm_gnn,\n", " \"gnn_thr\": gnn_thr,\n", " \"gnn_val_f1\": best_val_f1,\n", " \"gnn_test_probs\": gnn_probs,\n", " \"gnn_test_labels\": gnn_labels,\n", " }\n", "\n", " results = {\"majority\": maj_metrics, \"svm\": svm_metrics, \"gnn\": gnn_metrics}\n", " return results, last_fold_artifacts\n", "\n", "\n", "# --- Protocol A: site-aware grouped evaluation (primary) ---\n", "results_site = None\n", "last_fold_artifacts_site = None\n", "\n", "if RUN_SITE_AWARE and (groups_site is not None):\n", " sgkf = StratifiedGroupKFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)\n", " results_site, last_fold_artifacts_site = run_outer_cv(\n", " protocol_name=\"Site-aware (grouped by SITE_ID)\",\n", " splitter=sgkf,\n", " groups=groups_site,\n", " run_gnn=RUN_GNN_SITE_AWARE\n", " )\n", "else:\n", " print(\"SITE_ID not available; skipping site-aware evaluation.\")\n", "\n", "\n", "# --- Protocol B: subject-level stratified evaluation (secondary) ---\n", "results_subject = None\n", "last_fold_artifacts_subject = None\n", "\n", "if RUN_SUBJECT_LEVEL:\n", " skf = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=SEED)\n", " results_subject, last_fold_artifacts_subject = run_outer_cv(\n", " protocol_name=\"Subject-level (stratified)\",\n", " splitter=skf,\n", " groups=None,\n", " run_gnn=True\n", " )\n", "\n", "# Downstream plots use the subject-level final fold by default (includes GNN artifacts)\n", "last_fold_artifacts = last_fold_artifacts_subject if results_subject is not None else last_fold_artifacts_site\n", "\n", "print(\"Done.\")\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "f5fad131-2f47-4aed-ad23-4b1f95f41d4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Table I — Subject-level StratifiedKFold (k=5): mean ± std across folds\n" ] }, { "data": { "text/html": [ "
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metricMajority (mean±sd)GCN (mean±sd)SVM (mean±sd)
0ACC0.5385±0.00260.5012±0.04840.6391±0.0262
1BAL_ACC0.5000±0.00000.4981±0.04720.6447±0.0190
2SENS0.0000±0.00000.4628±0.18540.7137±0.1102
3SPEC1.0000±0.00000.5335±0.18290.5757±0.1341
4F10.3500±0.00110.4846±0.05650.6344±0.0308
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" ], "text/plain": [ " metric Majority (mean±sd) GCN (mean±sd) SVM (mean±sd)\n", "0 ACC 0.5385±0.0026 0.5012±0.0484 0.6391±0.0262\n", "1 BAL_ACC 0.5000±0.0000 0.4981±0.0472 0.6447±0.0190\n", "2 SENS 0.0000±0.0000 0.4628±0.1854 0.7137±0.1102\n", "3 SPEC 1.0000±0.0000 0.5335±0.1829 0.5757±0.1341\n", "4 F1 0.3500±0.0011 0.4846±0.0565 0.6344±0.0308" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Table II — Site-aware StratifiedGroupKFold by SITE_ID (k=5): mean ± std across folds\n" ] }, { "data": { "text/html": [ "
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metricMajority (mean±sd)SVM (mean±sd)
0ACC0.5304±0.02710.6519±0.0703
1BAL_ACC0.5000±0.00000.6563±0.0643
2SENS0.0000±0.00000.6881±0.0997
3SPEC1.0000±0.00000.6246±0.1531
4F10.3464±0.01160.6486±0.0728
\n", "
" ], "text/plain": [ " metric Majority (mean±sd) SVM (mean±sd)\n", "0 ACC 0.5304±0.0271 0.6519±0.0703\n", "1 BAL_ACC 0.5000±0.0000 0.6563±0.0643\n", "2 SENS 0.0000±0.0000 0.6881±0.0997\n", "3 SPEC 1.0000±0.0000 0.6246±0.1531\n", "4 F1 0.3464±0.0116 0.6486±0.0728" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def mean_std(vals):\n", " return float(np.mean(vals)), float(np.std(vals, ddof=1))\n", "\n", "def build_results_table(results_dict, include_gnn=True):\n", " rows = []\n", " for metric in ['acc', 'bal_acc', 'sens', 'spec', 'f1']:\n", " maj_mu, maj_sd = mean_std(results_dict[\"majority\"][metric])\n", " svm_mu, svm_sd = mean_std(results_dict[\"svm\"][metric])\n", "\n", " row = {\n", " \"metric\": metric.upper(),\n", " \"Majority (mean±sd)\": f\"{maj_mu:.4f}±{maj_sd:.4f}\",\n", " \"SVM (mean±sd)\": f\"{svm_mu:.4f}±{svm_sd:.4f}\",\n", " }\n", "\n", " if include_gnn and (results_dict.get(\"gnn\") is not None):\n", " gnn_mu, gnn_sd = mean_std(results_dict[\"gnn\"][metric])\n", " row[\"GCN (mean±sd)\"] = f\"{gnn_mu:.4f}±{gnn_sd:.4f}\"\n", "\n", " rows.append(row)\n", "\n", " # Column order\n", " cols = [\"metric\", \"Majority (mean±sd)\"]\n", " if include_gnn and (results_dict.get(\"gnn\") is not None):\n", " cols.append(\"GCN (mean±sd)\")\n", " cols.append(\"SVM (mean±sd)\")\n", "\n", " return pd.DataFrame(rows)[cols]\n", "\n", "# Table I: subject-level CV\n", "\n", "if results_subject is not None:\n", " print(\"Table I — Subject-level StratifiedKFold (k=5): mean ± std across folds\")\n", " display(build_results_table(results_subject, include_gnn=True))\n", "else:\n", " print(\"Subject-level results not available.\")\n", "\n", "# Table II: site-aware CV\n", "\n", "if results_site is not None:\n", " print(\"Table II — Site-aware StratifiedGroupKFold by SITE_ID (k=5): mean ± std across folds\")\n", " # By default, GCN is disabled for site-aware folds (see RUN_GNN_SITE_AWARE)\n", " display(build_results_table(results_site, include_gnn=(results_site.get(\"gnn\") is not None)))\n", "else:\n", " print(\"Site-aware results not available (SITE_ID missing or evaluation disabled).\")\n", "\n", "\n", "# Confusion matrices (final fold used for downstream plots)\n", "\n", "def plot_confusion_matrix(cm, title):\n", " if cm is None:\n", " return\n", " plt.figure(figsize=(4, 3))\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n", " plt.xlabel('Predicted')\n", " plt.ylabel('True')\n", " plt.title(title)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "if last_fold_artifacts:\n", " plot_confusion_matrix(last_fold_artifacts.get(\"maj_cm\"), \"Confusion Matrix (Majority)\")\n", " plot_confusion_matrix(last_fold_artifacts.get(\"svm_cm\"), \"Confusion Matrix (SVM)\")\n", " if last_fold_artifacts.get(\"gnn_cm\") is not None:\n", " plot_confusion_matrix(last_fold_artifacts.get(\"gnn_cm\"), \"Confusion Matrix (GCN)\")\n", "else:\n", " print(\"No last-fold artifacts found.\")\n", "\n", "# Balanced Accuracy bar charts (mean ± std)\n", "\n", "def plot_balanced_accuracy(results_dict, title, include_gnn=True):\n", " models = [\"Majority\", \"SVM\"]\n", " means = [np.mean(results_dict[\"majority\"][\"bal_acc\"]), np.mean(results_dict[\"svm\"][\"bal_acc\"])]\n", " errs = [np.std(results_dict[\"majority\"][\"bal_acc\"], ddof=1), np.std(results_dict[\"svm\"][\"bal_acc\"], ddof=1)]\n", "\n", " if include_gnn and (results_dict.get(\"gnn\") is not None):\n", " models.insert(1, \"GCN\")\n", " means.insert(1, np.mean(results_dict[\"gnn\"][\"bal_acc\"]))\n", " errs.insert(1, np.std(results_dict[\"gnn\"][\"bal_acc\"], ddof=1))\n", "\n", " plt.figure(figsize=(7, 4))\n", " plt.bar(models, means, yerr=errs, capsize=5)\n", " plt.ylabel(\"Balanced Accuracy\")\n", " plt.title(title)\n", " plt.ylim(0, 1)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "if results_subject is not None:\n", " plot_balanced_accuracy(results_subject, \"Model Comparison — Subject-level CV (mean ± std)\", include_gnn=True)\n", "\n", "if results_site is not None:\n", " plot_balanced_accuracy(\n", " results_site,\n", " \"Model Comparison — Site-aware CV (mean ± std)\",\n", " include_gnn=(results_site.get(\"gnn\") is not None)\n", " )\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "3d442937-5ad1-425b-8fe1-7ce39bcfbc35", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Threshold sensitivity / specificity trade-off (final fold)\n", "\n", "if last_fold_artifacts:\n", " svm_scores = np.asarray(last_fold_artifacts[\"svm_test_scores\"]).ravel()\n", " svm_y = np.asarray(last_fold_artifacts[\"svm_test_labels\"]).astype(int).ravel()\n", "\n", " gnn_probs = np.asarray(last_fold_artifacts[\"gnn_test_probs\"]).ravel()\n", " gnn_y = np.asarray(last_fold_artifacts[\"gnn_test_labels\"]).astype(int).ravel()\n", "\n", " def curve(scores, y_true):\n", " qs = np.linspace(0.01, 0.99, 99)\n", " thrs = np.unique(np.quantile(scores, qs))\n", " sens_list, spec_list = [], []\n", " for t in thrs:\n", " y_pred = (scores >= t).astype(int)\n", " _, sens, spec, _, _, _ = compute_binary_metrics(y_true, y_pred)\n", " sens_list.append(sens); spec_list.append(spec)\n", " return thrs, np.array(sens_list), np.array(spec_list)\n", "\n", " _, svm_sens, svm_spec = curve(svm_scores, svm_y)\n", " _, gnn_sens, gnn_spec = curve(gnn_probs, gnn_y)\n", "\n", " plt.figure(figsize=(6, 5))\n", " plt.plot(1 - svm_spec, svm_sens, label=\"SVM\")\n", " plt.plot(1 - gnn_spec, gnn_sens, label=\"GNN\")\n", " plt.xlabel(\"False Positive Rate (1 - Specificity)\")\n", " plt.ylabel(\"True Positive Rate (Sensitivity)\")\n", " plt.title(\"Threshold Trade-off (final fold)\")\n", " plt.legend()\n", " plt.grid(True, alpha=0.3)\n", " plt.show()\n", "else:\n", " print(\"No last-fold artifacts found.\")" ] }, { "cell_type": "markdown", "id": "55e4c49c", "metadata": {}, "source": [ "## 7. RQ3 — Graph-theoretic metrics\n", "\n", "> *Which graph metrics differ systematically between ASD and Control?*\n", "\n", "Minimum viable analysis:\n", "- Compute **per-subject** graph metrics (using the same sparsification rule as the GCN input graph).\n", "- Compare groups using **effect sizes (Cohen’s d)** and **Welch’s t-test** (with BH-FDR correction across ROIs).\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "3baf96c6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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670.1845070.0066710.0773880.0833390.2149150.623255
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" ], "text/plain": [ " node d_strength p_strength q_strength_fdr d_clustering p_clustering \\\n", "56 57 -0.281100 0.000022 0.002141 -0.173161 0.009166 \n", "77 78 -0.271965 0.000037 0.002141 -0.020153 0.766766 \n", "69 70 -0.263374 0.000061 0.002345 -0.199232 0.002737 \n", "68 69 -0.256735 0.000088 0.002553 -0.201779 0.002191 \n", "57 58 -0.232451 0.000460 0.010679 -0.138022 0.038098 \n", "42 43 0.200682 0.003336 0.056928 0.123425 0.068750 \n", "35 36 -0.194880 0.003435 0.056928 -0.039913 0.557157 \n", "44 45 0.194378 0.004278 0.062035 0.086832 0.197524 \n", "11 12 -0.184840 0.005477 0.070598 -0.105710 0.109380 \n", "6 7 0.184507 0.006671 0.077388 0.083339 0.214915 \n", "\n", " q_clustering_fdr \n", "56 0.151888 \n", "77 0.907624 \n", "69 0.143988 \n", "68 0.143988 \n", "57 0.294627 \n", "42 0.430770 \n", "35 0.807878 \n", "44 0.619265 \n", "11 0.455996 \n", "6 0.623255 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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noded_strengthp_strengthq_strength_fdrd_clusteringp_clusteringq_clustering_fdr
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" ], "text/plain": [ " node d_strength p_strength q_strength_fdr d_clustering p_clustering \\\n", "68 69 -0.256735 0.000088 0.002553 -0.201779 0.002191 \n", "69 70 -0.263374 0.000061 0.002345 -0.199232 0.002737 \n", "45 46 0.163776 0.015898 0.108484 0.188392 0.006206 \n", "43 44 0.171927 0.011530 0.089711 0.187192 0.005967 \n", "30 31 -0.089313 0.190662 0.470569 -0.186860 0.005351 \n", "49 50 0.111878 0.099794 0.321558 0.183317 0.007568 \n", "56 57 -0.281100 0.000022 0.002141 -0.173161 0.009166 \n", "70 71 -0.080575 0.232065 0.504505 -0.165129 0.014421 \n", "71 72 -0.137984 0.039684 0.188119 -0.159315 0.017953 \n", "90 91 -0.152131 0.020339 0.131071 -0.146000 0.026352 \n", "\n", " q_clustering_fdr \n", "68 0.143988 \n", "69 0.143988 \n", "45 0.143988 \n", "43 0.143988 \n", "30 0.143988 \n", "49 0.146322 \n", "56 0.151888 \n", "70 0.209110 \n", "71 0.231398 \n", "90 0.294627 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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metricASD_meanControl_meancohens_d_global
0mean_strength10.45917910.612227-0.025838
1mean_clustering0.0373490.037750-0.026123
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" ], "text/plain": [ " metric ASD_mean Control_mean cohens_d_global\n", "0 mean_strength 10.459179 10.612227 -0.025838\n", "1 mean_clustering 0.037349 0.037750 -0.026123" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## 7. RQ3 (graph metrics)\n", "\n", "# Parameters for graph metrics (kept consistent with the GNN graph construction)\n", "TOPK = 10\n", "USE_FISHER_Z = True\n", "USE_ABS = True\n", "\n", "deg_all = []\n", "strength_all = []\n", "clust_all = []\n", "\n", "for mat in X:\n", " deg, strength, clust = node_strength_and_clustering(\n", " mat, topk=TOPK, use_fisher_z=USE_FISHER_Z\n", " )\n", " deg_all.append(deg)\n", " strength_all.append(strength)\n", " clust_all.append(clust)\n", "\n", "deg_all = np.vstack(deg_all) # (n_subjects, n_nodes)\n", "strength_all = np.vstack(strength_all)\n", "clust_all = np.vstack(clust_all)\n", "\n", "# Group split\n", "asd_idx = (y_bin == 1)\n", "ctl_idx = (y_bin == 0)\n", "\n", "def cohens_d(a: np.ndarray, b: np.ndarray) -> np.ndarray:\n", " \"\"\"Vectorized Cohen's d across columns.\"\"\"\n", " ma = np.mean(a, axis=0); mb = np.mean(b, axis=0)\n", " sa = np.var(a, axis=0, ddof=1); sb = np.var(b, axis=0, ddof=1)\n", " n1 = a.shape[0]; n2 = b.shape[0]\n", " pooled = np.sqrt(((n1-1)*sa + (n2-1)*sb) / (n1+n2-2 + 1e-12) + 1e-12)\n", " return (ma - mb) / pooled\n", "\n", "# Effect sizes per node\n", "d_strength = cohens_d(strength_all[asd_idx], strength_all[ctl_idx])\n", "d_clust = cohens_d(clust_all[asd_idx], clust_all[ctl_idx])\n", "\n", "# Welch t-tests per node\n", "p_strength = np.array([stats.ttest_ind(\n", " strength_all[asd_idx, i], strength_all[ctl_idx, i], equal_var=False, nan_policy='omit'\n", ").pvalue for i in range(strength_all.shape[1])])\n", "\n", "p_clust = np.array([stats.ttest_ind(\n", " clust_all[asd_idx, i], clust_all[ctl_idx, i], equal_var=False, nan_policy='omit'\n", ").pvalue for i in range(clust_all.shape[1])])\n", "\n", "# FDR correction\n", "_, q_strength, _, _ = multipletests(p_strength, method='fdr_bh')\n", "_, q_clust, _, _ = multipletests(p_clust, method='fdr_bh')\n", "\n", "nodes = np.arange(1, strength_all.shape[1] + 1)\n", "\n", "df_rq3 = pd.DataFrame({\n", " \"node\": nodes,\n", " \"d_strength\": d_strength,\n", " \"p_strength\": p_strength,\n", " \"q_strength_fdr\": q_strength,\n", " \"d_clustering\": d_clust,\n", " \"p_clustering\": p_clust,\n", " \"q_clustering_fdr\": q_clust,\n", "})\n", "\n", "# Show top nodes by absolute effect size\n", "display(df_rq3.sort_values(\"d_strength\", key=lambda s: np.abs(s), ascending=False).head(10))\n", "display(df_rq3.sort_values(\"d_clustering\", key=lambda s: np.abs(s), ascending=False).head(10))\n", "\n", "# Graph-level summary (subject-wise mean across nodes)\n", "summary = pd.DataFrame({\n", " \"metric\": [\"mean_strength\", \"mean_clustering\"],\n", " \"ASD_mean\": [strength_all[asd_idx].mean(), clust_all[asd_idx].mean()],\n", " \"Control_mean\": [strength_all[ctl_idx].mean(), clust_all[ctl_idx].mean()],\n", "})\n", "summary[\"cohens_d_global\"] = [\n", " float(cohens_d(strength_all[asd_idx], strength_all[ctl_idx]).mean()),\n", " float(cohens_d(clust_all[asd_idx], clust_all[ctl_idx]).mean()),\n", "]\n", "display(summary)" ] }, { "cell_type": "markdown", "id": "969d48db", "metadata": {}, "source": [ "### RQ3 outputs (graph-theoretic metrics)\n", "\n", "- ROI-level: effect sizes + Welch tests with BH-FDR correction\n", "- Global summaries: subject-wise mean strength / mean clustering (useful for short report sentences)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Summary\n", "\n", "- **Data acquisition:** phenotypic CSV + AAL ROI time series downloaded locally; cohort filters applied.\n", "- **Connectomes:** `X` (n×116×116) constructed and validated; labels prepared (`y_dx`, `y_bin`), optional `groups_site`.\n", "- **RQ1:** descriptive ASD−Control differences (top edges; top ROIs).\n", "- **RQ2:** model comparison under subject-level and site-aware evaluation (report tables).\n", "- **RQ3:** graph-metric group differences (ROI-level with BH-FDR; global summaries).\n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 5 }