{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pandas in /home/zeus/miniconda3/envs/cloudspace/lib/python3.12/site-packages (2.1.4)\n",
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      "INFO: pip is looking at multiple versions of pandas to determine which version is compatible with other requirements. This could take a while.\n",
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      "\u001b[?25hInstalling collected packages: slicer, pyarrow, numpy, llvmlite, cramjam, cloudpickle, scipy, pandas, numba, xgboost, scikit-learn, matplotlib, fastparquet, shap, seaborn\n",
      "\u001b[2K  Attempting uninstall: numpy━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 1/15\u001b[0m [pyarrow]\n",
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      "\u001b[2K  Attempting uninstall: pandas90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/15\u001b[0m [scipy]\n",
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      "\u001b[2K  Attempting uninstall: scikit-learn\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 9/15\u001b[0m [xgboost]\n",
      "\u001b[2K    Found existing installation: scikit-learn 1.3.2━━━━━━━━━━━\u001b[0m \u001b[32m 9/15\u001b[0m [xgboost]\n",
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      "\u001b[2K  Attempting uninstall: matplotlib\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m10/15\u001b[0m [scikit-learn]\n",
      "\u001b[2K    Found existing installation: matplotlib 3.8.2━━━━━━━━━━━━━\u001b[0m \u001b[32m10/15\u001b[0m [scikit-learn]\n",
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      "\u001b[2K      Successfully uninstalled matplotlib-3.8.2\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m11/15\u001b[0m [matplotlib]\n",
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      "\u001b[1A\u001b[2KSuccessfully installed cloudpickle-3.1.2 cramjam-2.11.0 fastparquet-2026.3.0 llvmlite-0.46.0 matplotlib-3.10.8 numba-0.64.0 numpy-2.4.3 pandas-3.0.1 pyarrow-23.0.1 scikit-learn-1.8.0 scipy-1.17.1 seaborn-0.13.2 shap-0.51.0 slicer-0.0.8 xgboost-3.2.0\n"
     ]
    }
   ],
   "source": [
    "#cell-0: Install Dependencies\n",
    "!pip install xgboost shap pandas numpy scikit-learn matplotlib seaborn pyarrow fastparquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launching Training on 32 Cores...\n"
     ]
    }
   ],
   "source": [
    "# Cell 1: Imports & Hardware Configuration\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import shap\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import multiprocessing\n",
    "import gc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score, classification_report, confusion_matrix, f1_score, balanced_accuracy_score, roc_curve, auc\n",
    "\n",
    "# 1. Hardware Setup\n",
    "n_cores = multiprocessing.cpu_count()\n",
    "print(f\"Launching Training on {n_cores} Cores...\")\n",
    "\n",
    "# 2. Global Config\n",
    "SEED = 42\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⚡ Loading Parquet Files...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (299999, 66)\n",
      "Test Shape:  (14073, 66)\n",
      "\n",
      "Using 10 Tier-1 Features:\n",
      "['chrom', 'pos', 'ref', 'alt', 'gnomad_af', 'GERP_91_mammals_rankscore', 'phyloP100way_vertebrate_rankscore', 'phyloP470way_mammalian_rankscore', 'phastCons470way_mammalian_rankscore', 'phastCons17way_primate_rankscore']\n",
      "   -> Encoding labels to 0 (Benign) and 1 (Pathogenic)...\n",
      "   -> Train unique labels: [1 0]\n",
      "   -> Test unique labels:  [0 1]\n",
      "Labels successfully encoded to integers.\n"
     ]
    }
   ],
   "source": [
    "# Cell 2: Load Data & Define Features (FIXED)\n",
    "print(\"⚡ Loading Parquet Files...\")\n",
    "\n",
    "# Load your specific files\n",
    "train_df = pd.read_parquet(\"Balanced_300k_SEQUENCES.parquet\")\n",
    "test_df  = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\")\n",
    "\n",
    "print(f\"Train Shape: {train_df.shape}\")\n",
    "print(f\"Test Shape:  {test_df.shape}\")\n",
    "\n",
    "# ---- Define Tier-1 Features (Physics/Evolution only) ----\n",
    "tier1_features = [\n",
    "    \"chrom\", \"pos\", \"ref\", \"alt\", \"gnomad_af\",\n",
    "    \"GERP_91_mammals_rankscore\",                # keep (weak but not harmful)\n",
    "    \"phyloP100way_vertebrate_rankscore\",        # strong\n",
    "    \"phyloP470way_mammalian_rankscore\",         # moderate\n",
    "    \"phastCons470way_mammalian_rankscore\",\n",
    "    \"phastCons17way_primate_rankscore\"\n",
    "]\n",
    "\n",
    "# Filter to keep only what exists in your DF\n",
    "features = [c for c in tier1_features if c in train_df.columns]\n",
    "print(f\"\\nUsing {len(features)} Tier-1 Features:\")\n",
    "print(features)\n",
    "\n",
    "# ---- FIXED LABEL MAPPING ----\n",
    "# We force conversion to string first to handle both 'category' and 'object' types\n",
    "print(\"   -> Encoding labels to 0 (Benign) and 1 (Pathogenic)...\")\n",
    "\n",
    "# 1. Force strings and strip whitespace to be safe\n",
    "train_df[\"clean_label\"] = train_df[\"clean_label\"].astype(str).str.strip()\n",
    "test_df[\"clean_label\"]  = test_df[\"clean_label\"].astype(str).str.strip()\n",
    "\n",
    "# 2. Map explicitly\n",
    "label_map = {\"Benign\": 0, \"Pathogenic\": 1}\n",
    "\n",
    "train_df[\"y\"] = train_df[\"clean_label\"].map(label_map)\n",
    "test_df[\"y\"]  = test_df[\"clean_label\"].map(label_map)\n",
    "\n",
    "# 3. Safety Check: Verify we have only 0s and 1s\n",
    "print(f\"   -> Train unique labels: {train_df['y'].unique()}\")\n",
    "print(f\"   -> Test unique labels:  {test_df['y'].unique()}\")\n",
    "\n",
    "if train_df[\"y\"].isna().any():\n",
    "    raise ValueError(\"❌ Error: Some labels in Train could not be mapped! Check for typos in 'Benign'/'Pathogenic'.\")\n",
    "if test_df[\"y\"].isna().any():\n",
    "    raise ValueError(\"❌ Error: Some labels in Test could not be mapped! Check for typos.\")\n",
    "\n",
    "print(\"Labels successfully encoded to integers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   -> Casting chrom to 'category'...\n",
      "   -> Casting ref to 'category'...\n",
      "   -> Casting alt to 'category'...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data Ready: Train(269999), Val(30000), Hard Test(14073)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "557"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cell 3: Preprocessing & Type Casting\n",
    "\n",
    "# 1. Cast Categoricals for Native XGBoost Support\n",
    "cat_cols = [\"chrom\", \"ref\", \"alt\"]\n",
    "for col in cat_cols:\n",
    "    if col in features:\n",
    "        print(f\"   -> Casting {col} to 'category'...\")\n",
    "        train_df[col] = train_df[col].astype(\"category\")\n",
    "        test_df[col]  = test_df[col].astype(\"category\")\n",
    "\n",
    "# 2. Ensure 'pos' and scores are numeric\n",
    "num_cols = [c for c in features if c not in cat_cols]\n",
    "for col in num_cols:\n",
    "    train_df[col] = pd.to_numeric(train_df[col], errors='coerce').fillna(0)\n",
    "    test_df[col]  = pd.to_numeric(test_df[col], errors='coerce').fillna(0)\n",
    "\n",
    "# 3. Create X and y matrices\n",
    "X = train_df[features]\n",
    "y = train_df['y']\n",
    "\n",
    "# Keep benchmarking columns in test_df separate, but extract X_test for prediction\n",
    "X_test = test_df[features]\n",
    "y_test = test_df['y']\n",
    "\n",
    "# 4. Stratified Split for Validation (Using 10% of Train)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X, y, test_size=0.10, stratify=y, random_state=SEED\n",
    ")\n",
    "\n",
    "print(f\"\\nData Ready: Train({len(X_train)}), Val({len(X_val)}), Hard Test({len(X_test)})\")\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🛠 Configuring XGBoost SOTA Hyperparameters...\n"
     ]
    }
   ],
   "source": [
    "# Cell 4: Initialize SOTA Model\n",
    "print(\"🛠 Configuring XGBoost SOTA Hyperparameters...\")\n",
    "\n",
    "model = xgb.XGBClassifier(\n",
    "    # --- Architecture ---\n",
    "    n_estimators=10000,         # High ceiling (relies on early stopping)\n",
    "    learning_rate=0.01,         # Very slow learning to squeeze out signal\n",
    "    max_depth=10,               # Deep trees for complex genomic interactions\n",
    "    \n",
    "    # --- Regularization (Prevent Overfitting) ---\n",
    "    gamma=1.5,                  # High gamma: Conservative splitting (crucial for hard test sets)\n",
    "    min_child_weight=5,         # Requires 5 samples to make a leaf\n",
    "    reg_alpha=0.5,              # L1 Regularization\n",
    "    reg_lambda=1.0,             # L2 Regularization\n",
    "    \n",
    "    # --- Sampling (Stochastic Gradient Boosting) ---\n",
    "    subsample=0.8,              # Use 80% of rows per tree\n",
    "    colsample_bytree=0.8,       # Use 80% of features per tree\n",
    "    \n",
    "    # --- Speed & Hardware ---\n",
    "    n_jobs=n_cores,             # Use all 32 cores\n",
    "    tree_method='hist',         # Histogram-based (Fastest)\n",
    "    enable_categorical=True,    # Native categorical support\n",
    "    \n",
    "    # --- Objective ---\n",
    "    objective='binary:logistic',\n",
    "    eval_metric=['auc', 'logloss'],\n",
    "    early_stopping_rounds=200,  # Patience\n",
    "    random_state=SEED\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Training (this will take time due to LR=0.01)...\n",
      "[0]\tvalidation_0-auc:0.96589\tvalidation_0-logloss:0.68516\tvalidation_1-auc:0.96544\tvalidation_1-logloss:0.68518\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[500]\tvalidation_0-auc:0.97908\tvalidation_0-logloss:0.18481\tvalidation_1-auc:0.97640\tvalidation_1-logloss:0.19517\n",
      "[1000]\tvalidation_0-auc:0.98221\tvalidation_0-logloss:0.17017\tvalidation_1-auc:0.97810\tvalidation_1-logloss:0.18719\n",
      "[1500]\tvalidation_0-auc:0.98369\tvalidation_0-logloss:0.16353\tvalidation_1-auc:0.97882\tvalidation_1-logloss:0.18417\n",
      "[2000]\tvalidation_0-auc:0.98446\tvalidation_0-logloss:0.16001\tvalidation_1-auc:0.97909\tvalidation_1-logloss:0.18296\n",
      "[2500]\tvalidation_0-auc:0.98493\tvalidation_0-logloss:0.15781\tvalidation_1-auc:0.97922\tvalidation_1-logloss:0.18237\n",
      "[3000]\tvalidation_0-auc:0.98525\tvalidation_0-logloss:0.15626\tvalidation_1-auc:0.97929\tvalidation_1-logloss:0.18202\n",
      "[3500]\tvalidation_0-auc:0.98549\tvalidation_0-logloss:0.15512\tvalidation_1-auc:0.97935\tvalidation_1-logloss:0.18178\n",
      "[4000]\tvalidation_0-auc:0.98569\tvalidation_0-logloss:0.15414\tvalidation_1-auc:0.97939\tvalidation_1-logloss:0.18156\n",
      "[4500]\tvalidation_0-auc:0.98585\tvalidation_0-logloss:0.15336\tvalidation_1-auc:0.97943\tvalidation_1-logloss:0.18136\n",
      "[5000]\tvalidation_0-auc:0.98600\tvalidation_0-logloss:0.15262\tvalidation_1-auc:0.97945\tvalidation_1-logloss:0.18125\n",
      "[5500]\tvalidation_0-auc:0.98612\tvalidation_0-logloss:0.15201\tvalidation_1-auc:0.97947\tvalidation_1-logloss:0.18117\n",
      "[6000]\tvalidation_0-auc:0.98621\tvalidation_0-logloss:0.15157\tvalidation_1-auc:0.97948\tvalidation_1-logloss:0.18110\n",
      "[6500]\tvalidation_0-auc:0.98631\tvalidation_0-logloss:0.15106\tvalidation_1-auc:0.97949\tvalidation_1-logloss:0.18103\n",
      "[7000]\tvalidation_0-auc:0.98640\tvalidation_0-logloss:0.15059\tvalidation_1-auc:0.97951\tvalidation_1-logloss:0.18097\n",
      "[7500]\tvalidation_0-auc:0.98649\tvalidation_0-logloss:0.15015\tvalidation_1-auc:0.97952\tvalidation_1-logloss:0.18091\n",
      "[8000]\tvalidation_0-auc:0.98656\tvalidation_0-logloss:0.14978\tvalidation_1-auc:0.97953\tvalidation_1-logloss:0.18085\n",
      "[8500]\tvalidation_0-auc:0.98661\tvalidation_0-logloss:0.14951\tvalidation_1-auc:0.97953\tvalidation_1-logloss:0.18084\n",
      "[8582]\tvalidation_0-auc:0.98662\tvalidation_0-logloss:0.14946\tvalidation_1-auc:0.97953\tvalidation_1-logloss:0.18084\n",
      "\n",
      " Training Complete! Best Iteration: 8382\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 5: Training Run\n",
    "print(\"Starting Training (this will take time due to LR=0.01)...\")\n",
    "\n",
    "eval_set = [(X_train, y_train), (X_val, y_val)]\n",
    "\n",
    "model.fit(\n",
    "    X_train, y_train,\n",
    "    eval_set=eval_set,\n",
    "    verbose=500  # Log every 500 trees\n",
    ")\n",
    "\n",
    "print(f\"\\n Training Complete! Best Iteration: {model.best_iteration}\")\n",
    "\n",
    "# --- Plot Learning Curve ---\n",
    "results = model.evals_result()\n",
    "epochs = len(results['validation_0']['auc'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x_axis, results['validation_0']['auc'], label='Train')\n",
    "plt.plot(x_axis, results['validation_1']['auc'], label='Validation')\n",
    "plt.legend()\n",
    "plt.ylabel('AUC Score')\n",
    "plt.xlabel('Iterations')\n",
    "plt.title('XGBoost Learning Curve (Check for divergence!)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 6: ROC-AUC Visualization\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Predict probabilities\n",
    "y_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate ROC\n",
    "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f' Model (AUC = {roc_auc:.4f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve on 14k Test Set')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Calculating SHAP Values...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 7: SHAP Explainability\n",
    "print(\"\\n Calculating SHAP Values...\")\n",
    "\n",
    "explainer = shap.TreeExplainer(model)\n",
    "\n",
    "# Subsample 5000 rows for speed (calculating on all 14k is fine on 32 cores too)\n",
    "shap_sample = X_test.sample(n=5000, random_state=SEED) if len(X_test) > 5000 else X_test\n",
    "shap_values = explainer.shap_values(shap_sample)\n",
    "\n",
    "# Beeswarm Plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title(\"SHAP Feature Importance (Global View)\")\n",
    "shap.summary_plot(shap_values, shap_sample, show=False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==========================================\n",
      "        XGBOOST MODEL PERFORMANCE\n",
      "==========================================\n",
      "ROC-AUC : 0.766809\n",
      "PR-AUC  : 0.771672\n",
      "\n",
      "==========================================\n",
      "     COMPARISION WITH ESTABLISHED TOOLS\n",
      "==========================================\n",
      "CADD            ROC-AUC = 0.592509   PR-AUC = 0.704837\n",
      "REVEL           ROC-AUC = 0.587114   PR-AUC = 0.600423\n",
      "AlphaMissense   ROC-AUC = 0.592032   PR-AUC = 0.603459\n",
      "PrimateAI       ROC-AUC = 0.566141   PR-AUC = 0.581314\n",
      "MPC             ROC-AUC = 0.559678   PR-AUC = 0.578559\n",
      "ClinPred        ROC-AUC = 0.590860   PR-AUC = 0.606637\n",
      "BayesDel        ROC-AUC = 0.594821   PR-AUC = 0.706338\n",
      "\n",
      "==========================================\n",
      "            FINAL LEADERBOARD\n",
      "==========================================\n",
      " 1. My XGBoost      AUC=0.766809  PR=0.771672\n",
      " 2. BayesDel        AUC=0.594821  PR=0.706338\n",
      " 3. CADD            AUC=0.592509  PR=0.704837\n",
      " 4. AlphaMissense   AUC=0.592032  PR=0.603459\n",
      " 5. ClinPred        AUC=0.590860  PR=0.606637\n",
      " 6. REVEL           AUC=0.587114  PR=0.600423\n",
      " 7. PrimateAI       AUC=0.566141  PR=0.581314\n",
      " 8. MPC             AUC=0.559678  PR=0.578559\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 8: Benchmarking Logic + Plots\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score, roc_curve, precision_recall_curve\n",
    "\n",
    "roc_auc = roc_auc_score(y_test, y_prob)\n",
    "pr_auc  = average_precision_score(y_test, y_prob)\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(f\"        XGBOOST MODEL PERFORMANCE\")\n",
    "print(\"==========================================\")\n",
    "print(f\"ROC-AUC : {roc_auc:.6f}\")\n",
    "print(f\"PR-AUC  : {pr_auc:.6f}\")\n",
    "\n",
    "# ---- Benchmark against DBNSFP models ----\n",
    "benchmarks = {\n",
    "    \"CADD\": \"CADD_raw_rankscore\",\n",
    "    \"REVEL\": \"REVEL_rankscore\",\n",
    "    \"AlphaMissense\": \"AlphaMissense_rankscore\",\n",
    "    \"PrimateAI\": \"PrimateAI_rankscore\",\n",
    "    \"MPC\": \"MPC_rankscore\",\n",
    "    \"ClinPred\": \"ClinPred_rankscore\",\n",
    "    \"BayesDel\": \"BayesDel_addAF_rankscore\"\n",
    "}\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\"     COMPARISION WITH ESTABLISHED TOOLS\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "benchmark_results = []\n",
    "\n",
    "for name, col in benchmarks.items():\n",
    "    if col not in test_df.columns:\n",
    "        continue\n",
    "        \n",
    "    scores = test_df[col].fillna(0).values\n",
    "    \n",
    "    try:\n",
    "        b_auc = roc_auc_score(y_test, scores)\n",
    "        b_pr  = average_precision_score(y_test, scores)\n",
    "        benchmark_results.append((name, b_auc, b_pr))\n",
    "        print(f\"{name:14s}  ROC-AUC = {b_auc:.6f}   PR-AUC = {b_pr:.6f}\")\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "# ---- Leaderboard ----\n",
    "print(\"\\n==========================================\")\n",
    "print(\"            FINAL LEADERBOARD\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "ranking = sorted([(\"My XGBoost\", roc_auc, pr_auc)] + benchmark_results, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "for i, (name, a, p) in enumerate(ranking, 1):\n",
    "    print(f\"{i:2d}. {name:14s}  AUC={a:.6f}  PR={p:.6f}\")\n",
    "\n",
    "# ---- ROC Curve ----\n",
    "fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(f\"ROC Curve (AUC = {roc_auc:.4f})\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# ---- Precision-Recall Curve ----\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_prob)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(recall, precision)\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.title(f\"Precision-Recall Curve (AUC = {pr_auc:.4f})\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# ---- Bar Plot Comparison ----\n",
    "df_bench = pd.DataFrame(\n",
    "    [(\"My XGBoost\", roc_auc, pr_auc)] + benchmark_results,\n",
    "    columns=[\"Model\", \"ROC-AUC\", \"PR-AUC\"]\n",
    ")\n",
    "\n",
    "df_bench = df_bench.sort_values(by=\"ROC-AUC\", ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(df_bench[\"Model\"], df_bench[\"ROC-AUC\"])\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"ROC-AUC\")\n",
    "plt.title(\"Model Comparison (ROC-AUC)\")\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==============================\n",
      " VALIDATION RESULTS (TUNING)\n",
      "==============================\n",
      "Optimal Threshold : 0.488\n",
      "Best F1 (Val)     : 0.9242\n",
      "Balanced Acc (Val): 0.9268\n"
     ]
    },
    {
     "data": {
      "image/png": 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6q9S+gwcPctlll9G2bVtef/114uPjMRqNLFy4kDfeeOOco/e1oaJzq4rw8PAKA7Tu3bvTtm1bvvzySx5//HG+/PJLFEUpVZ3hhRdeYPr06dx+++0899xzhIWFodVqeeihh+r03O+//34+/vhjHnroIfr06UNwcDAajYaxY8dekO851M7vpU6no1OnTnTq1Ik+ffpwySWX8MUXXzBkyBD3ecydO7fUJwou1alw4brGERERVX6OELVJAl4h6pkWLVqwdetWLrvssnOOpmi1Wi677DIuu+wyXn/9dV544QWeeOIJli9fzpAhQ6r8mq7Z3nv37i0zCWbv3r3lzgY/87lOp5ODBw+WGknbu3dvlV77iiuu4Msvv+Szzz7jySefLLM/JyeHH3/8kbZt25aasAanR7BcFEXhwIEDdO7c2b3N0yNSaWlpXH/99XTt2tU9E/5MP//8M8XFxfz000+lAv7yPm6u6rmcz/WsrrZt23L48OEK9998881Mnz6dbdu2MW/ePFq1akXPnj3d+7/99lsuueQSZs+eXep5WVlZ1Q6uqvOz+O233zJ+/PhS5dSKiorIysoqdVx1fn6aNm3Ktm3bcDqdpUZ59+zZ495fl3r06AFAUlISgHukOyoq6pzvB+c6T9c1rmjCnhB1TVIahKhnbrzxRk6cOFEqF8+lsLCQ/Px8QJ3tfrauXbsClClfdi49evQgKiqKWbNmlXrub7/9xu7duyutuDBixAgA3n777VLbq7q62fXXX0/79u3573//y8aNG0vtczqd3HPPPZw6darcFaQ+++yzUukQ3377LUlJSe4+Afj7+9f4I+rz5XA4GDt2LFarle+++67cuq2uUb4zR/Wys7P5+OOPyxzr7+9fJiArz/lcz+rq06cPO3bsqPBnzjWa+9RTT7Fly5YytXd1Ol2ZEc1vvvmmSuXwzladn8XyXvedd94pU4rN398foErf95EjR5KcnMz8+fPd2+x2O++88w4BAQEMGjSoKqdxTn/++Sc2m63MdlcOsyvYHz58OEFBQbzwwgvlHp+WluZ+fK7z3LRpE8HBwXTo0OF8uy9EjcgIrxD1zK233srXX3/N3XffzfLly+nXrx8Oh4M9e/bw9ddfs2jRInr06MGzzz7LqlWrGDVqFE2bNiU1NZX333+fxo0b079//2q9psFg4KWXXmLixIkMGjSIcePGuctYJSQk8PDDD1f43K5duzJu3Djef/99srOz6du3L8uWLSu3zm95jEYj3377LZdddhn9+/cvtdLavHnz2Lx5M4888ghjx44t89ywsDD3c1JSUnjzzTdp2bIlkyZNch/TvXt35s+fz5QpU+jZsycBAQGMHj26Wt+fmpo1axZ//PGH+1qeKTo6mqFDhzJs2DCMRiOjR4/mX//6F3l5eXz00UdERUW5R+rOPJeZM2fy/PPP07JlS6KiosotS3U+17O6rrrqKp577jlWrlzJsGHDyuxv1qwZffv25ccffwQoE/BeccUVPPvss0ycOJG+ffuyfft2vvjiC5o3b17tvlTnZ/GKK65g7ty5BAcH0759e9atW8fSpUvLrPzWtWtXdDodL730EtnZ2ZhMJnfN5LPdddddfPDBB0yYMIFNmzaRkJDAt99+y5o1a3jzzTfPOTmzql566SU2bdrEtdde6/40Y/PmzXz22WeEhYW5J+kFBQUxc+ZMbr31Vi666CLGjh1LZGQkx44d49dff6Vfv368++67gPqzBeqEv+HDh7snHbosWbKE0aNHe/wTE9GAeag6hBANVlXKkn3zzTeltpdXUmnQoEFKhw4dym3HarUqL730ktKhQwfFZDIpoaGhSvfu3ZVnnnlGyc7OVhRFUZYtW6ZcddVVSlxcnGI0GpW4uDhl3Lhxyr59+2rUH0VRlPnz5yvdunVTTCaTEhYWptx8883K8ePHSx1zdikoRVHLbT3wwANKeHi44u/vr4wePVpJTEysUlkyl9TUVGXKlClKy5YtFZPJpISEhChDhgxxlyI7k+u8vvzyS2XatGlKVFSU4ufnp4waNUo5evRoqWPz8vKUm266SQkJCVEAd4myisqS+fv7l3m9iq5V06ZNlVGjRpXpl6uElet7Vd7tzHJWP/30k9K5c2fFbDYrCQkJyksvvaTMmTNHAZTDhw+7j0tOTlZGjRqlBAYGlmqjonJoVbmeFZ1zede5Ip07d1buuOOOCve/9957CqD06tWrzL6ioiLlkUceUWJjYxU/Pz+lX79+yrp168qU/KpKWTJFqfrP4qlTp5SJEycqERERSkBAgDJ8+HBlz549StOmTZXx48eXavOjjz5Smjdvruh0ulLf57P7qCiKkpKS4m7XaDQqnTp1KvN75jqXV155pcz3oyq/M2vWrFHuu+8+pWPHjkpwcLBiMBiUJk2aKBMmTFAOHjxY5vjly5crw4cPV4KDgxWz2ay0aNFCmTBhgrJx40b3MXa7Xbn//vuVyMhIRaPRlPq+7t69WwGUpUuXVtovIeqSRlG8YNaJEEJcQCtWrOCSSy7hm2++4frrr/d0dxq8uXPnct9993Hs2LFKFygRvumhhx5i1apVbNq0SUZ4hcdIDq8QQgiPuvnmm2nSpEm5k/KEb8vIyOB///sfzz//vAS7wqMkh1cIIYRHabVaduzY4eluiDoQHh5OXl6ep7shhIzwCiGEEEKI+k1yeIUQQgghRL0mI7xCCCGEEKJek4BXCCGEEELUazJprRxOp5OTJ08SGBgos0qFEEIIIbyQoijk5uYSFxdXajnu8kjAW46TJ08SHx/v6W4IIYQQQohzSExMpHHjxpUeIwFvOVzLNyYmJhIUFFRr7dpsNhYvXsywYcMwGAy11q648ORa1i/14Xo6nQonswoBiAvxQ6ttmJ9O1YdrKVRyLeuPurqWOTk5xMfHV2nZbQl4y+FKYwgKCqr1gNdisRAUFCS/vD5OrmX9Uh+uZ4HVzsj/rgZg17PDsRgb5tt7fbiWQiXXsv6o62tZlfRTmbQmhBBCCCHqNQl4hRBCCCFEvSYBrxBCCCGEqNck4BVCCCGEEPWaBLxCCCGEEKJek4BXCCGEEELUaw2zbo0QQtQzOq2GWy9u6n4shBDiNAl4hRCiHjDpdTx3dUdPd0MIIbySpDQIIYQQQoh6TUZ4hRCiHlAUhcx8KwBh/sYqrTwkhBANhQS8QghRDxTaHHR/finQsJcWFkKI8khKgxBCCCGEqNck4BVCCCGEEPWafOYlhBB1RVHAXgS2wjNuBaA4ISgO/CPh7FxbRQGHDRzWs262ih/npUDyPqC32sY3t4PFor5WQSYUZYHTqb6WRgOUc2+wQGAMBMWCzqQ+11YABn/wjwBLOGj1gAJaA4Q1g4jW4BdyAb+hQghRMxLwCiEajuJcyE2BvGSwFoDeBHoTmvxMEtKWol2+CUyBENIEQuLB6A86I2i0UJQDhafAmqsGpYqiBq6U3NsKoTgHirIh8zCk7YWMA+C0VdwfnQnMwaWD2MqOr4xiwh3w7vsdNMU1a6e6/CPVQDkwFkxB6vdCcaj3zpLvT2AsRHdQb1HtwRx0YfomhBAlJOAVQtQvhVmQeUi9ZRyEzIOnHxdmlvsUPdAF4Hgd9ktnBIOfOpKqOCEvFRzFkJ96jidq1MBcZwSd4az7kptfCIS2gzUlTxnxX3Dkgt4MfmHgFwparRqko4BCyb1y+t6aBzknITcJnHY12DdY1H8S8tOhIL0kwNeoo9YZByH3JOSnqbfk7VX/XgQ3gai2YIlQA36/EDCHqI91htOj4Sinz7EoWx3Jzk9Xz0VvVkecrflqH502MAejNQbSOjkV7d8nISBcDciD4yG4kfr9F0I0SBLwCiG8m92qjqy6b5mQmwynjqi3wlPqyKi9CLKPQ0FG5e0ZAyAgGkwBatuOYhSDP8lFBsKad8XgLEabfUxty15UMurqUEclzSHqCLBGW3LTqPdo1GDKFATmIJxBjckKaM5RTWNyNYEUYaTYqcXmcGJzONFoNMQH6Ugw5RKmK0LRGnFqDe6bojXgcH2t0aEo4FQUnAo4ncoZX6vbThVY2ZOUA+wE4P3sfkQEGLFbFXJy7OQX29EAep0WvVaDXqcpuVe/1mk1BJoNNI71I76DhQCjHpvTid2hEOSnr7jiQ1EOnDp8etS8OA+0ujO+NzpAUa9Tyi5I2akGydnH1Fsd0AHtAJK+K7szqDFEtoHItmfct1b/IRBC1GsS8AohLjxXnmpesvqxf8bBkhE91I/D0w9A6i51X3FO9dsPiMYR0oxc/yakGRuTSCz77VHsKQ7jRIGB9Pxiiguc+Bl1WIw6CgrsHM/MoyhFDQTjwyzEh1kIMusx6XUYdBoKrA7yiu0UWh1otaBBg0YDWo0GrQasxU7yM9RjTpwqpNDmoG6HjCv27vIDtdpeoFlPdJAZP4MOnVaD2aClVVQg7eOCaBPThMaxbYgMMFWt9m9Bpnpt0/erucWFWerobVHJvcOmjiwbzIDmdK6yKUj9R8U/XG3HXqyORBv81X9etHooysZRcIrE/dtpEhmEtjhbDcazj4MtH3KOq7eDy0r3KSCmnEC4jZq3LPWMhagXJOAVQtSewlOQfUIdgc09WXKfBDlJ6n1ucsmIbPXySxU0KOYQnOZQbMZgCo1hZBpjSdLEkEkQNo2BYsXAMWsQW/JD2ZcFp9LLy4XNreRV1MDG7lQ4nJ7P4fT8avXxbHqthsahfgSaDRj1Wgw6DQadFpNeS7HdyYlThSSeKsDmUCptR+sOqksH2O6vtRr8jXqaR/rTIjIAP6OO7EIb2QU2DDoNAWY9/iUjtHangsOpYHM4S+4VHE4ndqdCVoGNxFMFnDhViN2p9kmn1eBwKuQW2cktyivVr78OlU4PMeq1hFmM6LRqv3Ta031uEmahS+MQusaH0LlxMOEJ/SGh/3l9fyvitNnYWryQRiNHojUY1I2Kov7cpe+HtD1qfrXrPue4+o9XXjIcXlm6MXMwhLVQ0yH8QtVbeEuI7QpR7dT0CyGET5CAVwhRfQ67GihkHlbzY09shsS/1BHZanBqDeT7NyHd2JgirT9aLYCG40SxpTiOv/OjOVLsT4rVhFJU1SqKpwPdYD8D8WF+xIdaaBzqR6MQPyICTYT7mzAbtBTaHBRaHeg0Cvu2rOeGK4ZRaIcjGfkczywk32qnyObE7nBiMekJNOkxG3UoilImzcCo12Ix6vA36YkNNhMfZsGgq7zPDqdCgdVeJqBVg0U8slqaoyQoNug0aDQacotspOQUkZpTTLFdDY5zi2zsTc5lV1IO+1PySMktwmp3kpxTVG6bh9LyWbE3zf11fJgfnRoFEx1kJtzfSHiAiTB/IxEBRvwMeqwOJ1a7E50W/Ax6zAYt+cUOMvKLyS60YdRp8TfpMRt0FNvVa+hUIDzASLBJS4FdXXnOTaMBSxg06a3ezlSUc0YgvFsNglP3QHaiOuJ8crN6O5vOCP5R6oizJVzNR7aEl/46uBFEtgOjpTYujRDiPEjAK4SoWOZhSFwPqSWBQNZRdeJQQSYlM5/KKDSEkGOIJFMbRhphnHSEcMwWzOHiQI7ZQ8hSAijGiBUd+fjhKNBVuTsGnZprGuJnIDbETGywH8F+BvQ6DTqNhvAAE41DSwLcMD+CzFUbgbPZbGTvhQCTntAAA3EhftCiyt2qMVfubG1QFKUkjQL8DLoaB8u6kpxel0CzgUCzgZZRgRU+x2p3kpxdRHahrVRusaIoWB1ODqTmsSUxi62JWRxMyycxs5DEzMIa9a9q9Dy1eSnh/iZiQ8w0C/enabg/bWIC6BAXTONQv9PfH3MQNO6u3s5kK1R//jMOqD/zhVnq5LzUXZC0DYqzT6dIVEajVUeFI9uoo8XhLdT7sOZqdQtJmRDigpCAV4iGTlHU/Mns45CVqI5spe2FQ8vV0dsK2DVGMgwxnNBEs7m4EWusrdjsbEV2UUClL6fTqsEpGgg1G2gabqFJmAWzQYe95KP22BAzLaMCaBYRQKjFQIBJT0BJPq0oX6HNQfunFgEXfmlho15Lk/CKRzH7tojgtj7q45wiG9uPZ7M7KYf0PCuZ+cVk5FnJyLeSkV9Mkc2JUafFqNficKpBfJHVgcWkI8zfRIifAbvTSX6xgyK7A5Neh9mgjqRn5ltJzysmv9iBzaGQnFNEck4R/xzLKtWfILOeDnHBdIgLokOjIDrEBdM8wh/9mSPyBj+Ibq/ezqYo6u9LfirkZ6gVLAoySqpZZJx+nHlI3Ze+T72dzeCvBr5hzdSAuM1IiOsmQbAQdUACXiEairxUdZZ8yk71D3F2YkmAe1ytLVsOBzp2aVuzxdaYfc5GHFFiSFVCSFeCySQQpfB0gGDSa+nQOIj4MAvh/ibCA9SPqE8/Vu8vZCAmvE+Q2UC/lhH0axlRJ+3bbDYW/LyQXgMuIavIyYmsQndO9u6kHPal5JJTZGfdoQzWHTpd0cNs0NI2JkgNgkuC4TYxgZgN5fyTpdGodZpD4ivvjKKULAqyQw14Mw+dLpOXdUydSJeyXb3tBla9oo4Gd7wemg+GRhepJemEEOdN/vIIUZ8UZKr1UPNS1ZGlrERILQly89MqfWq2JpgkIjjqCOeoM5K/nW1Y52xPHurIncWoo3GoH6EWIwkWA+EBJhqF+BEXYqZVVCBtYgLPmbMqxIVg1EFciB9NDQa6xIeU2me1O9mXksuukznsPJnNzpM57ErKocDqYEtiFlsSs9zH6rQaWkYG0DzSn8hAE5EBJtrEBNK9aSjhAVUIRDWakkU5YqDVkNL77FY1RchVI/r4Btj7u5pCsfK/6k3vB/G9IGGAOsmvUXfQG8//GyREAyQBrxC+yGGDk1sgY786UuSaOJaxv8KnONFwVIlmt7MJB5U4TigRnFAiOKmEc0KJoIjTf8B1Wg2dGwczoUUEvZuH0To6kKjAKpadEsKLGfVaOjYKpmOjYEAdoXU4FY5k5LOzJAjedTKHHSeyOVVgY29KLntTyn4C0izCn4RwCzHBfsQGm4kJNqv3Qerjc+Zm640Q0Uq9AXCvuoDG7l9g/yI4slr9J/XwytPVI4wB0GwQtBoKzQaq6RDyOylElUjAK4SvyD6u/jE8uAzl6Fo01rxyDzvijOakEk4GQaQqoexVGrPH2YT9SiMKMWPUa4kP9SMm2Ex0oJmOwWaiA03q10HqLTLQJKO1osHQaTW0iAygRWQAV3aJA9QJd0nZRew8mcPJrELScotJzili2/Es9qXknbN0XYBJT9NwC92ahHBRk1A6Nw4hIdxSOk/4bKZA6DpOvSmKmkt/5E81+D2yWv3UZu+v6g3UKhHxvdQyadEdIKajuqqcBMFClCEBrxDeRlHUxRZOHS0plbQHDq2EExvdh2iATCWAXc6mJCpRHFci2ak0ZYuzJVkEEmTWE+pvJMzfSIvIAEZGBdAyMoCWUQHEh1lKzcIXQpSl0WiIC/FTK3acJavAyrbj2ZzMKiQpu4jk7CKScopIzi4kObuInCI7ecX2khHjHD7/S11VzqjX0ioqgDYxgbSNCaRNTBBtYyr49ESjUZdfjmoLvSaB0wnJ2+DAEjiwDE5sUifN7flFvbkENYZmA6DJxRDRRs0J9o+QIFg0eBLwCuFpBZnqR5YH/8B57C/IPo7WVlDmMKeiYaPSmiWO7qxxduSQLoGEiEDiQtTR2kuiA7m/UTDtY4PwM0o1AyHqSojFyMDWkRXuzy+2k5RdxL6UXDYfPcXmY6fYnZRLoc3hDoJLt2egfWwQV3dtxOguceX//mq1ENdVvQ38N9iK4OQ/au5vyk516ea03WqZtK1fqjcXSzg07gXxPaFpPzUXWBbNEA2MBLxCeEpOEo6VL6HZPBetYgfgzA87TykB7Fcasd/ZmB1KAiuU7iQktGBg60ieaxZKx0bBUqZLuGk1GkZ2inE/Fp7jb9LTMkr9RGVkp1hAXZwk8VQBe5Jz2Vty25Ocw+H0fLIKbKw9mMHagxk8/+surr2oMcM7xNAjIbTi1CKDGZr2UW8u1nxI3KCmQZzYrFaEyEpUy6Tt+029ARgD1UlwrYdBm1EQGF3H3xEhPE8CXiEuFKdDnVyWvA3HkXUom+eid6orU+11NuZPZyfWODtyjFhs/jEEBwWTEOFPswh/BsQEMrVlBMF+Miojymc26Hj/5u7nPlB4hFaroWnJAhjDO8S4txfZHBxIzWP1gXS+WH+UxMxCPll7hE/WHiHQrGdQ60guaxfF4NZRhPqfo0KD0R9aXKLeXGyFalm04xvURWQO/wmFmacD4F+mqHnAXcZBpxvAVHkdbSF8lQS8QtQVRYHMQzgPrSJv92JMx/7EZFdne7vGZf92tuYDwy3Edr2MXs3CeCEhlOhAM1rJsRWiQTAbdO6qEXcNaM7K/Wn8sjWJ5XtTycy38su2JH7ZloRWAz0TwriicyyXd4wlMrCK9XkNfmoqQ3xP6HPf6Vzgg8tgz69qLnDievW2eDp0vgHaXqGmPhjMdXvyQlxAEvAKUVucTkjdScGBP8nb9yeW5A0EWNPRAkElhxQqRnYrTdjlbMomcx+6DL6Od3o1lZxbIQRarYZL2kRxSZsoHE6Frcez+GN3Kkt3p7AnOZf1hzNZfziTGT/tpHvTUAa3iWJQ60jaxwZV/Z/kM3OBBzwC2Sdg5/ew6RO1BvDGOepN7wfNB0GHa9QV4MxB52hYCO8mAa8Q58NhI3/vMjLWzyPixDIsTnWZBtciq8WKnq1KC9bTmfTo/gS16EnLmBC6RQUwJloWahC1p8Bq99jSwqL26bQaLmoSykVNQnl0eBuOnyrgt+3J/LLtJFuPZ/P3kVP8feQUryzaS6BZz0VNQunRNJRL20XRPjao6jWzgxtB3/uhz2Q193fb13BgKeQmwb7f1ZvOBC2HQJvLodVwyfkVPkneEYWoLqeTzN2riNz7KYVb7yfImY1/ya48xcxmZyv2+3WmMKYXwS1706lZLHfHBUlwK4SoscahFiYNbM6kgc1JzCxgxb40Vu5NZe3BDHKL7Kzcl8bKfWm8tmQfjUP9GNIums6N1VSJ5hH+ldf/BbVsWbOB6k1R1MoPe36BHd+pyyKfWf+3cS/oeC20vxqCYuv83IWoDRLwClEFitPJkR3ryFz/JU2SfifamYZrjCNdCWKtaQD5ra6iUaeBdG4SzkCLLP8phKgb8WEWbr24Kbde3BS7w8me5Fw2HT3FmgPprNqfxvFT6sQ3lwCTnoubh9G/ZQSXtI2iabh/xY2DGvzGdFRvg/4PUnbAnoXqJDdXKbTjG+D3adBqmJob3Gyg1PoVXk0CXiEqYHc42b71b3L+/opmyb/TTDlJs5J9uYofa7Q9sHe8kY4DruLKqGCP9lUI0TDpdaeXSh7fN4FCq4OV+9L461CGe5nkvGI7S3ensnR3Kk//vItOjYK5onMsV3aNIza47MIapWg0ENNJvQ3+P8hJgl0/qiO/xzeoyyDvXwTRndQV4tpdCSHxF+bkhagGCXiFOEt6biHrfptH090f0E3Z695epBjYHtCH4jbX0Pzi0RSvW8vIkSMxGKRUmBDCO/gZdVzeMYbLO6qlzxxOhV0nc1h9IJ1V+9LYcCST7Sey2X4im5d+38OAVpGM6RnP0PbRVUu7CoqFi+9WbxkH4a+ZsOULSNkOi7bDosfVhS163w0drgWdhBnCO8hPohBAak4Rq7fupmDLd/RMX8BoTSIANnTsD+iFvf01tBxwIz0DQ9XtNpsnuyuEEFWi02ro1DiYTo2DuWdwCzLyivltRzI/bT3JhsOZ7tzfxqF+3HdJS667qDFGfRXnG4S3gFGvwiWPq5Pddv8ER9eqpc6+nwTL/wN9H4AuY9UawUJ4kAS8osGyOZws23WSvSu+onvaD1yl2YlOo4AGCjR+HG9xM02veIT2IXGe7qoQQtSK8AATt1zclFsubsqR9Hy+2ZTI/L8TOX6qkGnfb+edZfu5oUc8117U6Ny5vi6WsNOjvnmpsPlTdeT31BH4dQosfQa63gQ9bofI1nV6fkJURAJe0eAcP1XAd+v2Yts4lxvsP3O5NtW9pm9KQHvodB3RA++ktV+IR/spRHVoNRouaRPpfizEuSRE+PPv4W2ZfEkrvtxwjFkrD3Iyu4i3lu3nrWX76dE0lJt6N2Fkp1jMhirWCg+IgoH/hovvhc2fwfoP4NRhWD9TvTXqDp3HQsfrwD+8bk9QiDNIwCsahIy8YpbuTmHtPztofexLxuuWEaLJBy0U6oOwdp1IcN8JRIc193RXhagRs0HHxxN7ebobwgf5GXXc3r8ZN/Vuwu87kvn+nxOs3p/GxqOn2Hj0FM/9sovruzfm2osa0y62igtQGP3h4nug17/g4B/w9/9g/2I13eHEJlhUUuGh8xhoMwL0VVw5TogakoBX1EvJ2UWsPZjOtuPZ7Dl6ktjkP7hKu4bXtNvR650A5Ps3xTRgMn4X3Yyf5JcJIRo4s0HH1d0acXW3RqTkFPHNxkTmrT/GyewiPvrzMB/9eZi2MYHc0COe6y9qTLClChN2tVpoNUS95aWq1R22fglJW2HvQvUWEA29JkGPO9T0CCHqgMcr4b/33nskJCRgNpvp3bs3GzZsqPBYm83Gs88+S4sWLTCbzXTp0oXff//9vNoU9Ud2oY35fx/jpg/W8PBLb5P6/VSu2ngbc9PH8IbhfQbrtqLXOCmK7QVjvsD/kX/QX3yXTKYQQoizRAeZmXxpK/78v0v53209uLxDDEadlj3JuTz3yy56v7iUqd9t40BqbtUbDYhSR33/tQruXQ/9H4bAWMhLgT+eh9fbw6+PqNUfhKhlHh3hnT9/PlOmTGHWrFn07t2bN998k+HDh7N3716ioqLKHP/kk0/y+eef89FHH9G2bVsWLVrENddcw9q1a+nWrVuN2hS+rcjmYPmeVBZsOcHJPRu5XrOUt3QbiDRmlzrOHtIcfZcbofONmMNbeKi3QtSdAqud7s8tBWDT9CGytLCoFTqthiHtoxnSPpqsAis/b0vii7+Osic5l6/+TuTrjYnc0D2eh4a2OndN3zNFtYUhT8Pgx2HnD7DuHUjerqY+/D0b2o6CS5+uo7MSDZFH3xFff/11Jk2axMSJEwGYNWsWv/76K3PmzGHq1Klljp87dy5PPPEEI0eOBOCee+5h6dKlvPbaa3z++ec1alP4HodTYf2hDBZsOcFvO5JpUryfB/Q/MNyw8fQx5lB0bUdCwgBI6Ic+pIkHeyzEhVFoc3i6C6IeC7EYufXiptzSuwkbj57io1WHWLwrhfkbE1mw5QTXdGvE2F5N6NI4GE1VJ07qjdBlDHS+EQ6vgnXvqrm+e35Bf2gF8TE3gTKibk9MNAgeC3itViubNm1i2rRp7m1arZYhQ4awbt26cp9TXFyM2Wwutc3Pz4/Vq1fXuE1Xu8XFxe6vc3JyADWFojbrrbrakhqu1acoCruScvlpaxK/bk/GkneEEdr1fKnbQEfTEfUYNDjbjkbpchNKs0E4dWfkl9Xy91yuZf1SH66nzWY/47ENm0bxYG88pz5cS1/QtVEg743rwj/Hsnh58T42Hs3iq78T+ervRNpGB/DQkJZc2iay6oEvQHxf9Za2B91vj6JN/IuLjn2I/dsT2Ab+G6I71t0JiTpVV7+X1WnPYwFveno6DoeD6OjoUtujo6PZs2dPuc8ZPnw4r7/+OgMHDqRFixYsW7aM77//HofDUeM2AV588UWeeeaZMtsXL16MxWKp7qmd05IlS2q9zfoqsxj+TtOwKV1LaqHCpdp/eF33O/1MO93HONFyIvRi9sWMJs/cCPZZYd+F+R7LtaxffPl6FjvA9Za+aNFiTFWsIlVf+fK19DW3xEKfAFiXomVrhoY9KXnc/cUW2oU4uSbBSXQ1Mh3cwu+mtb0xbZJ+QL/vV9j3K5n+LTkUMYSTob1RNA38B9xH1fbvZUFBQZWP9akkr7feeotJkybRtm1bNBoNLVq0YOLEicyZM+e82p02bRpTpkxxf52Tk0N8fDzDhg0jKKiKJViqwGazsWTJEoYOHSrL0Z6D1e7ko9VHeP/vQxjtedygW8kE02KaalIAUDRalGaD1RHd1iOI8Y8g5gL2T65l/VIfrmeB1c5jG/4AYPjwYQ02h7c+XEtf9QDq5OGP/jzCnLVH2J2lZf82HdddFMc9g5rTKKR6ka/NNpxVP3amv2YTun2/EpZ/gLD8Ayg5i3D0m4LS8XrQyTX2BXX1e+n6RL4qPPaOGBERgU6nIyUlpdT2lJQUYmLKD10iIyNZsGABRUVFZGRkEBcXx9SpU2nevHmN2wQwmUyYTGVrABoMhjp5w6yrduuLDYczeeKH7RxLzeQh/fdM8FuCRSlUd5qD4aLxaHpNQhPSxONlRuRa1i++fD0NyumPjtXzaJgBr4svX0tfFmEwMG1Ue8b2bsrzv+xi2Z5U5m88wff/nOSGHvFMGtCcZhFVr4yTbUlAGXkvmqJM2PQJrJ+F5tRh9L/cD6tfhQGPQJdxai6w8Hq1/XtZnbY8Fi8YjUa6d+/OsmXL3NucTifLli2jT58+lT7XbDbTqFEj7HY73333HVddddV5tyk872hGPvd8vokbP1gHaXv4xfwU9+p/UoPdiNYw6nWYshuGPQcyCU0IIbxWswh/Zk/oybd396Ffy3BsDoV5649x6WsrmPTZRjYczkRRqpFnHhgNg/8PHtoOQ58FSwRkHYWfH4B3LoJ/Pgens+5OSPg8jw4BTJkyhfHjx9OjRw969erFm2++SX5+vrvCwm233UajRo148cUXAVi/fj0nTpyga9eunDhxgqeffhqn08ljjz1W5TaF98kutPHe8gN8suYIdoedifrFPG74CoNiVd/UrngD2l6hFjAXQpRLq9HQu1mY+7EQ3qBHQhhf3Hkx6w9l8OGqQyzbk8qSXSks2ZVCl8bB3DmgOSM6xqDXVfH93RQA/R6EnpNg08ew5i3IToQf71PLmY18BRr3qNuTEj7JowHvmDFjSEtL46mnniI5OZmuXbvy+++/uyedHTt2DO0ZQU5RURFPPvkkhw4dIiAggJEjRzJ37lxCQkKq3KbwHnaHk3kbjvHGkn2cKrDRVnOMd4M+oaV1DyhAyyFw1fvqf/ZCiEqZDTrm/0s+yRLeqXfzcHo3D+dAah6zVx/mu83H2Xo8m/u//IcWkf5MHdGOIe2iql7VwWiBPvdBj9thw0ew8mU4uRn+d5k6QNLvQYiXpbbFaR5P8po8eTKTJ08ud9+KFStKfT1o0CB27dp1Xm0Kz1MUhRV70/jPwt0cSM1Dj53ngn7lZtt3aK12MAXBkBnQ/XYZ1RVCiHqkZVQAL17biUeGtebzv47y6dojHEzLZ9JnG+nVLIwnRrajS3xI1Rs0+EG/B6DzGFj2DGz5Avb8ot7iL4ZBj0GLS0E+9WjwJJoQF9Se5Bxum7OBiZ/8zYHUPLpa0vkr6iVutc5Hq9ih3ZVw3wboeacEu0IIUU9FBJh4aEhrVj52CfcOboFJr2XD4Uyuem8ND3z5D4mZVS83BaifBF79vvr3o9utoDNC4l/w+bXw8Qh1UQvRoElEIS6ItNxipn2/nZFv/cmf+9Mx6+B/rTfwg24qETk7wRwC138MY+ZCUKynuyuEzymw2rnouSVc9NwSCqz2cz9BCC8QZDbw2OVtWf7oYK67qDEaDfy09SSXvbaSbzefqH6DkW3gqnfhwW1w8b2gM8GxdfDpaPjkCji6tvZPQvgEj6c0iPqtyOZg9urDvL/8APlWdYGQu1rl8kjx+5iObVUPaj4Yrp4JQXGe66gQ9UBmvtXTXRCiRuJC/Hjtxi7c3j+BFxbuZs2BDKb9sJPhjbWMqE41B5egWLj8Rej7AKx+XS1pduRPdbS3UQ/1705CP2jSR02LEPWejPCKOrNwexKXvbaSVxbtJd/qoEcjM+t6rOLx4/diSt0KpmAY/Rbc8oMEu0IIIegQF8znd/Tm/ktbArDouJZ/f7eDrIIa/jMXFKtWbnjgH3WCm9YAJzbCn6/C3Gvg9faw8hUoPFWLZyG8kYzwilqXXWjjqR938OOWkwDEBpt5qUcuA3ZPR7PjoHpQh2vg8pekAoMQQohSNBoNjwxrQ0ygkek/7uTHrUks25PG7f2bcUf/ZgT71WDhguDGaonLAY/CgaVqasPhlZCbBMufV8ubXXyPOgHOFFj7JyU8TgJeUavWHEjn399s5WR2ETqthof6R3OP/XP0a0qWfw6MVReQaDvSsx0VQgjh1W7s0Zik/dtZmhnCnuRc3l62n8/WHWHaiLbc0D0erbYGlReCG0H38erNYYddC+DP1yB1F6x6Wa3tO3gqXDReli2uZySlQdSKpOxC7pu3mZv/t56T2UU0Dbew6Ipi7t9zK/rNJcFu9wlw33oJdoUQQlRJq2CFH++5mJk3X0SrqACyCmz833fbGfvhX+xPyT2/xnV66HQ93L0GbvwMwlpAfhr8+gh8eAmc2FQ7JyG8ggS84rzNW3+My15bya/bktBqYPzFTVjSfT0tF0+AnBMQ2gzG/6zm65qDPd1dIYQQPkSr1TCiUyy/PTiAJ0a2w8+gY8ORTEa+/SevLNpDkc1xvi8A7a9SB2RGvgp+oZCyHf43BH6bCsXnGVgLryABr6gxu8PJjB938PgP2ymwOujRNJRf7unOM9ZXMa5Sl4Omxx1wz1poNtCznRWintNqNHRuHEznxsGytLCol/Q6LZMGNmfJlIEMaReFzaHw3vKDDHtjFWsPpJ//C+gM0GsS3Pc3dLoRFCesnwnvXQx7fz//9oVHScAraiSnyMbET/7m03VHAfj38DZ8c00g7X+5Rs2J0hrUEd0rXleXgBRC1CmzQcdPk/vz0+T+mA06T3dHiDrTONTCR7f1YNYt3YkJMnMss4CbZ6/n5d/3YHM4z/8FAiLhuo/glu8gpAnkHIcvx8DX46Eg8/zbFx4hAa+otvxiO+PnbODP/en4GXTMurkb9xkXovnoUjXx3z8Sxv+k5uwKIYQQtUyj0XB5xxiWPjKIcb3iURR4f8VBbpi1jsPp+bXzIi2HwL1/qbV8NTp1MOfDQXByS+20Ly4oCXhFtRTZHNz56Ub+OZZFsJ+Bbyd15/Kd/4Yl08FhhTYj4Z510LSvp7sqhBCingsw6Xnx2s68f/NFBJn1bEnMYvibq3hv+QGs9loY7TX6w7Dn4M6lENIUso7B7GGwee75ty0uKAl4RZUV2x3c8/km1h3KIMCkZ+74LnRYPRn2/KIu3zj6LRg7T/04SAhxQRVaHfT77x/0++8fFFrPcxKPED5mZKdYFj44gAGtIrDanbyyaC9XvPMnm47WUgpCo4vgXyuh9QhwFMNPk+H3x8Epv2u+QgJeUSW5RTZu/+Rvlu9Nw2zQ8vEtnei8+l7Y9zvozTDuSzWFQSbLCOERCgonsgo5kVWIQg2WYhXCxzUOtfDZ7b14c0xXwv2N7EvJ47qZ63jih+1kF9rO/wX8QtVBnUueUL/+6z346mYozjv/tkWdk4BXnFNqbhFjP/yLNQcy8DfqmH1bD3pueUJdrcZggZu+hpaXebqbQgghGjiNRsPV3RqxdMogbuzRGIAv1h9jyOsr+X1H0vm/gFYLgx6D6+eon2zu+w3e6gI/3Q/7FoG9hksgizonAa+o1OH0fK6buZadJ3OICDDy1V196Jc8F3Z+D1o93DQfmg/ydDeFEEIIt1B/Iy9f34UvJ11M8wh/0nKLufvzzdw9dxOpOUXn/wIdr4MJv6qrhxakw+bPYN6N8HY3+Pt/YC8+/9cQtUoCXlGhbcezuH7mWhIzC2kSZuG7e/rSqeAvWPasesDIV6S+rhBCCK/Vp0U4Cx8cwORLWqLXavh9ZzL9X1rOxI838PXfiWQXnEeqQ3xPeGg73LoAek4C/yi1hNmvj6iB747va+08xPmTgFeU68/9aYz98C8y8q10bBTEd/f0panjGHx3J6BA94nQ43ZPd1MIIYSolNmg49Hhbfhpcn+6xodgdThZvjeNx77bxqBXl/P1xkQUpYZ57zoDtLgERr2qBr8jXoHAOHWV0W8nwjcTID+jVs9H1IwEvKKMzcdOccenGymwOujXMpyv7upDpD0Z5l4DxTnQpA+MeNnT3RRCCCGqrH1cEAvu68fSKQN5ZGhrWkT6k1Vg47FvtzHmw784mHaek88MZuh9Fzy4BQb9n1q7d+cP8H5vdc6L8CgJeEUpJ7IKueuzTVjtTi5tG8WcCT0JsJ1Sg93cJIhsq85S1Rs93VUhxBk0aGgVFUCrqAA0SLUUISrSMiqQ+y9rxaKHBvLEyHb4GXRsOJzJqLf/5PO/jtZ8tNdFb4JLHodJyyCyHeSnwefXwx/PSxkzD5KAV7jlF9u545O/Sc8rpm1MIG+P64bJng+fXwuZByG4Cdz6A1jCPN1VIcRZ/Iw6lkwZxJIpg/AzytLCQpyLXqdl0sDmLJkykP4tIyiyOXlywQ4mfbaRzPxaqLYQ1w3uWlGS/qfAqlfg0yshdff5ty2qTQJe4fbYd9vYk5xLRICR/43vQYDWBl+Og+RtYIlQg92gOE93UwghhKg1rvq9T45qh1GnZenuVK58dzW7Tuacf+MGM1zxBlw3Gwz+cHQ1zOwLP06GnJPn376oMgl4BQDL96Ty67YkdFoNH9zag8ZBRvj2dvWX0xQEt34PES093U0hhBCi1mm1Gu4c0JwF9/WjabiF46cKuW7mWhZur4XavQCdroe7/4R2o0Fxwj9zYWY/OHW0dtoX5yQBr6DI5uDpn3cCMLFvAt2bhMAvD8LehWph7XFfQmwXz3ZSCFGpQquDoa+vZOjrK2VpYSFqqH1cED/e148BrSIotDm494vNvL54L05nLaxeGN4CxnwOty+GqPZQmKlWcpDFKi4ICXgFH606xNGMAqICTTw4pBVs/wb++VydYXrDJ5DQ39NdFEKcg4LC/tQ89qfmydLCQpyHEIuRjyf05I7+zQB4+48D3DV3E7lFtbA8MUCT3uqiTeYQOLEJljxVO+2KSknA28AlZhbw7vIDADwxqh2BxSnw66PqzsFToe1ID/ZOCCGEuPD0Oi3Tr2jPqzd0wajXsnR3Cte+v5ak7MLaeYGQJnDNB+rj9TNh14+1066okAS8DZiiKEz/cQfFdie9m4VxZecYWHAvFGdDo+7Qf4qnuyiEEEJ4zPXdGzP/rouJCjSxPzWPsR/+xcmsWgp621wO/R5UHy+4D1J21k67olwS8DZgX6w/xoq9aRj1Wp6/uiOav2fD4ZWg91P/89TpPd1FIYQQwqO6NQnl+3v70jjUj6MZBYz98C9O1FbQe+l0SBgA1lyYNwZyU2qnXVGGBLwN1KG0PP7zq1oL8LHhbWgV5FCLYgMMfRYiWnmwd0IIIYT3aBxqYf6/+tAkzMKxzALGfriOlJyi829YZ4AxcyG8JWQnwlc3ga2WgmlRigS8DZDd4eThr7dSaHPQt0U4t/drBus/UFMZIttBzzs93UUhhBDCqzQK8eOruy6mabiFxMxCxs/ZQHZhLUxk8wuFm75W709shO/vkhXZ6oAEvA3Qp+uOsjUxi0Cznldv6ILWmgN/vafuHPRv0MqPhRC+RoOGRiF+NArxk6WFhagjcSF+fH5HbyIDTexJzmXSpxspstVCcOoqWaYzwu6fYOGjcL5LHItSJLJpYBxOhTmrDwPw2OVtiQvxg/UfQlE2RLSB9ld7toNCiBrxM+pYM/VS1ky9VJYWFqIOxYdZ+HRiLwJNejYcyWTsh3/x+44k7A7n+TWc0B+u/RDQwMY5sPLlWumvUEnA28As3Z3CiaxCQiwGbujeGIpzYd276s5Bj4FW/lAKIYQQlWkfF8T/xvfAbNCyJTGLuz/fTP+XljN33ZHzW6SiwzUw8hX18YoXYPUbMtJbSyTgbWA+WXMEgHG9mmA26NTc3aIsiGit/qIJIYQQ4px6Nw9n2SODue+SFoT7G0nOKWL6jzu54YN1HEjNrXnDvSbBoP9THy99Gn6aLKux1QIJeBuQPck5rDuUgU6r4ZaLm0JeGqx5S905UEZ3hfBlRTYHV767mivfXV07OYVCiHNqFOLHv4e3Ze20S3nmyg74G3VsOnqKkW+t5ov1R2ve8OBpcPlLoNGqK5/OvUZNPRQ1JgFvA/Lp2iMADO8QTaMQP/XjkuIciO0KHa/zaN+EEOfHqShsO57NtuPZOOUjUCEuKJNex/i+CSyZMohL20ZhdTh54ocdvLZ4L0pNfh81Grj4brV6gzEQjq6GXx6W9IbzIAFvA3Eq38oP/5wAYELfZpC6GzZ9ou4c/oJUZhBCCCHOU1yIH7PH9+ChIWot+3f+OMBj327DVtMJba2Gwq3fg0YHO76DbV/XYm8bFolyGoj5GxMpsjlpHxtEz4RQWPQEKE5oNxoS+nm6e0IIIUS9oNFoeGhIa168thNaDXyz6TgPfPkPVnsNg974XjB4qvp44aNw6jxSJRowCXgbALvDydx16i/IhH4JaA4ug4PLQGtQV1UTQgghRK0a16sJH9zaA6NOy287krn3i80U22uYX99/CsT3VtMQZWGKGpGAtwFwlSIL8zdyZZc4+Hu2uqPnHRDW3LOdE0IIIeqpoe2j+Wh8D0x6LUt3p/CvuZvILarB6mw6vVqj1xgIiX/B5k9rv7P1nAS8DcDH7lJk8Zht2bB/ibqj+wSP9UkIIYRoCAa1jmTOhJ6YDVpW7E1j9Dur2X68BhUXQhPg0ifVx388D4VZtdnNek8C3npu18kc1h/OPF2KbNcCcNoguhNEtfN094QQtSjM30iYv9HT3RBCnKVfywjmTbqYRiF+HMko4NqZa/hs3ZHqN9TzDnVV1IIMWPVKrfezPpOAt55zlSIb0TGG2GC/0zM8O9/guU4JIWqdxahn8/ShbJ4+FItR7+nuCCHOclGTUBY+MIDhHaKxORSe+nEnG49kVq8RnUGtrASwfhakH6j9jtZTEvDWY1kFVhZsUUuRTeyXAFnH4Ng6QAMdr/do34QQQoiGJthiYNYt3bm+e2MAnvl5V/WXIm41BFoNA6cdFj0utXmrSALeemzh9mSK7U7axgRyUZNQ2P6NuiOhPwQ38mznhBBCiAZIo9EwdURbAk16tp/I5rvNx6vfyLD/gFYP+xfBihdrv5P1kAS89dhPW9XR3au7NUIDsK0k4O18o8f6JISoG0U2B2M+WMeYD9bJ0sJCeLmIABP3X9YSgJcX7SWv2F69BiJbw8iSHN6VL8H6D2u5h/WPBLz1VHJ2EesPq7lBo7vEQcoOSNsNOiO0u9LDvRNC1DanorD+cCbrD2fK0sJC+IAJfZvRLMKftNxi3v2jBrm4PW6HwY+rj397TF2JTVRIAt566pdtJ1EU6NE0lEYhfrDje3VHq2HgF+LRvgkhhBANnVGv5YmRarWk2asPsS8lt/qNDHoMek4CFPhxMmQcrN1O1iMeD3jfe+89EhISMJvN9O7dmw0bNlR6/JtvvkmbNm3w8/MjPj6ehx9+mKKiIvf+p59+Go1GU+rWtm3buj4Nr/Pz1pMAXNk1Tk1o3/mDuqPjtR7slRBCCCFcLmsXxZB2atWGqd9tq/4ENo0GRrwECQPAVqAGvc4aLmFcz3k04J0/fz5TpkxhxowZbN68mS5dujB8+HBSU1PLPX7evHlMnTqVGTNmsHv3bmbPns38+fN5/PHHSx3XoUMHkpKS3LfVq1dfiNPxGkfS89l6PButBkZ2ioWkrXDqMOj9oNVwT3dPCCGEEKgT2J67ugMBJj2bj2Xx+fqj1W9Eq4Or3gNjABxbCxs+qP2O1gMeDXhff/11Jk2axMSJE2nfvj2zZs3CYrEwZ86cco9fu3Yt/fr146abbiIhIYFhw4Yxbty4MqPCer2emJgY9y0iIuJCnI7XcI3u9msZQUSA6fTobuthYArwYM+EEEIIcabYYD/+7/I2ALz02x5OZhVWv5HQpjD0WfXx0mekPm85PFad3Gq1smnTJqZNm+beptVqGTJkCOvWrSv3OX379uXzzz9nw4YN9OrVi0OHDrFw4UJuvfXWUsft37+fuLg4zGYzffr04cUXX6RJkyYV9qW4uJji4mL31zk5OQDYbDZsthqseV0BV1u12WZ5fiypvTuyYzQ2qxX9zgVoAHub0Sh1/NoNxYW6luLCqA/X02azn/HYhk3TMCeu1YdrKVQN6VreeFEcP/xzgs3Hspj23TY+urUbGo2meo10uRXdrh/RHl6Jc8E9OG79WR399QJ1dS2r055GUTwznffkyZM0atSItWvX0qdPH/f2xx57jJUrV7J+/fpyn/f222/z6KOPoigKdrudu+++m5kzZ7r3//bbb+Tl5dGmTRuSkpJ45plnOHHiBDt27CAwMLDcNp9++mmeeeaZMtvnzZuHxWI5zzO9sNIK4fkterQahf/0cBBrPczgvTOwa4z83uk9HDqTp7sohKgDxQ54cqP6x+35Hg5M3vF3TghRRckF8Mo2HXZFw5jmDvpGVz8887Omc8nuxzE4i9gRN46D0SPqoKfeo6CggJtuuons7GyCgoIqPdan1p9csWIFL7zwAu+//z69e/fmwIEDPPjggzz33HNMnz4dgBEjTl/czp0707t3b5o2bcrXX3/NHXfcUW6706ZNY8qUKe6vc3JyiI+PZ9iwYef8BlaHzWZjyZIlDB06FIPBUGvtnumzv47Blj30TAjj+it7ov1DDeS1bUcwfPQ1dfKaDdGFuJbiwqkv1/Oa0Z7ugefVl2spGui1jD3Ci7/v4+fjRiZd1YemYdUfdNM01cDCh+mQ+gNtRj8AEa3qoKPVU1fX0vWJfFV4LOCNiIhAp9ORkpJSantKSgoxMTHlPmf69Onceuut3HnnnQB06tSJ/Px87rrrLp544gm02rIpySEhIbRu3ZoDByrOZzGZTJhMZUc+DQZDnfyS1VW7AH8eyADgkrbRGPR62P0jANqO16JtKG8YF1BdXktx4cn1rD/kWtYfDelaThrYkj/2prP+cCZTv9/J/H/1QaetZmpDz4mw9xc0B5dh+OV+uGOx16Q21Pa1rE5bHpu0ZjQa6d69O8uWLXNvczqdLFu2rFSKw5kKCgrKBLU6nXoRK8rMyMvL4+DBg8TGxtZSz71Xkc3BuoMlAW+bKEjeDlnHwGBR6+8KIYQQwmtptRpevaEL/kYdG4+e4s2l+6rfiEYDV74NpiA4sRHWvVf7HfVBHq3SMGXKFD766CM+/fRTdu/ezT333EN+fj4TJ04E4Lbbbis1qW306NHMnDmTr776isOHD7NkyRKmT5/O6NGj3YHvo48+ysqVKzly5Ahr167lmmuuQafTMW7cOI+c44W07lAGxXYnscFmWkcHwMGSfyaaDQSjb+UiCyGqp8jmYOLHG5j48QZZWlgIHxYfZuGZqzoC8M4fB/hqw7HqNxLcGIb/R3284r+QfbwWe+ibPJrDO2bMGNLS0njqqadITk6ma9eu/P7770RHRwNw7NixUiO6Tz75JBqNhieffJITJ04QGRnJ6NGj+c9//uM+5vjx44wbN46MjAwiIyPp378/f/31F5GRkRf8/C60lXvTABjcJlKd3XlwubqjxaUe7JUQ4kJwKgrLS94DZGlhIXzb9d0bczg9j/eWH+SJBTuICjJxadvo6jXS9Rb45wtI/At+nwZj5tZNZ32ExyetTZ48mcmTJ5e7b8WKFaW+1uv1zJgxgxkzZlTY3ldffVWb3fMpK/aqC3YMah0F1gI4VlLeTQJeIYQQwqc8OqwNydnFfLf5OPd98Q8/3NeXtjHVmEiv1cKo1+CDgbD7J9i/BFoNrbsOezmPLy0sasfh9HyOZBSg12ro1zIcjq4FhxWC4yG8pae7J4QQQohq0Gg0/Pe6TgxoFUGhzcF/f9tT/UZiOsLF96iPFz4KthosalFPSMBbT7hGd3skhBJoNsDBP9QdLS5RE9iFEEII4VMMOi3PXdURnVbDir1p/H0ks/qNDJ4KgbFw6gj89X6t99FXSMBbT6zc58rfjVI3uANeSWcQQgghfFVChD839ogH4JXf91ZYlapCpkAYUrK41uq3oKAGQXM9IAFvPeBwKvx9WP0BHtAqAnKSIG03oIFmgzzbOSGEEEKclwcua4lRr2XDkUxW7U+vfgOdboDoTlCcDX++Vvsd9AES8NYD+1Nzybc6sBh1akL7oZLqDHHdwBLm2c4JIYQQ4rzEBvtx68VNAXh1UQ1GebVaGPK0+njDR5CVWLsd9AES8NYD/xzLAqBL4xB1RRZJZxCiwbEY9Rz57yiO/HcUFqPHC/AIIWrZvYNb4G/Usf1ENitK0hirpeVlkDAAHMWw4sXa76CXk4C3Hth89BQAFzUNAUWR+rtCCCFEPRMeYGJsryYAfLb2SPUb0GhO5/JumQcpu2qvcz5AAt564J/ELAC6xYdCxkEoSAe9GRr39GzHhBBCCFFrXGkNK/alcSQ9v/oNNO4O7a8CFFj2bO12zstJwOvjsgttHEjNA6BbkxA4vkHdEdsV9EaP9UsIcWEV2Rzc+8Um7v1ikywtLEQ9lRDhz+A2kSgKfP7X0Zo1culToNHBvt/Umv0NhAS8Pm5ryehu03AL4QEmOP63uiNeRneFaEicisLC7cks3J4sSwsLUY+N75MAwNcbEymw2qvfQERLuOg29fGSGWoqZAMgAa+P23xMzd/tFh+ibnAFvJLOIIQQQtQ7g1pH0jTcQk6RnQX/nKxZI4OngsGifiq8d2HtdtBLScDr41wVGi5qGgrFeZCyU90hAa8QQghR72i1Gncu76drj+B01mCENjAGLr5Xfbz0GXA6a7GH3kkCXh/mdCpsOXPC2sl/QHFCUCMIivNs54QQQghRJ27oEY/FqGNvSi7zNhyrWSP9HgBTMKTvhWP1P5dXAl4fdig9n+xCG2aDlraxgZLOIIQQQjQAwX4G/j28DQAvLtzNiazC6jdiDob2V6qPt31di73zThLw+rB/SvJ3OzcKwaDTwvGN6g4JeIUQQoh6bXyfBHo0DSXf6mDa99urv/oaQOcb1ftdC8BeXKv98zYS8Powd/3dJiHqLEtXSTIJeIUQQoh6TavV8NL1nTHqtazal8a3m45Xv5Gm/SEwDoqyYf+S2u+kF5GA14ftOJENQJf4EMg6CvlpoDVAbBfPdkwIccH5GXTsenY4u54djp9B5+nuCCEugBaRAUwZ2hqAF3/bU/0yZVotdLpOfby9fqc1SMDro+wOJ3uScwHoEBd0Op0htjMYzB7smRDCEzQaDRajHotRj0aj8XR3hBAXyJ39m9E03EJmvpV562swga1TSVrD3t/Vkd56SgJeH3UwLR+r3UmASU98qAUSJZ1BCCGEaGj0Oi33DGoBwIerDlV/pcWYThDZFhzFsOunOuihd5CA10ftPKn+F9Y+NgitVgMnZMKaEA1Zsd3BI19v5ZGvt1Jsl6WFhWhIrr2oMbHBZlJzi/mmurm8Gg10ukF9XI/TGiTg9VG7TuYA0D4uSC0Ynbpb3SH5u0I0SA6nwnebj/Pd5uM4alKIXgjhs4x6LXeXjPLOWnEQm6OaC0l0ul69P7Ia8tNruXfeQQJeH7XzzIA36yjYCkBnhNBmHu6ZEEIIIS60MT3jiQgwcSKrkAX/nKjek0MTILarunjVnl/qonseJwGvD1IUhV1JJQFvbBCk7VV3RLQGnd6DPRNCCCGEJ5gNOiYNUAe93l9xsPqf9LgWodj1Yy33zDtIwOuDTmQVkl1ow6DT0Do6ENJK0hki23i2Y0IIIYTwmJsvbkqIxcDh9HwWbk+q3pPbXaXeH14Fhadqv3MeJgGvD3Ll77aMCsSo154e4Y1s58FeCSGEEMKTAkx6JvZVR3nf/eMAzuqM8ka0hKgO4LTD3t/qqIeeIwGvD3Ll73aIC1I3pMoIrxBCCCFgQt8EAkx69qbksnR3SvWeXI/TGiTg9UGl8nedTkjfp+6IkhFeIYQQoiELthi4rU9TAN5bfgBFqcYob7uSgPfgH1CUUwe98xwJeH3QrjNHeLOPSYUGIQR+Bh2bnhzCpieHyNLCQjRwd/RvhtmgZevxbP7cX40yY1HtILwVOKywf3HdddADJOD1MVkFVk5kFQLQLi4IUveoO8JbSYUGIRowjUZDeICJ8ACTLC0sRAMXHmBiXK8mAHz056GqP1GjOSOtYUHtd8yDJOD1Ma7R3SZhFoLMBkgrCXij2nqwV0IIIYTwJrf3a4ZWA3/uT2d/Sm7Vn+hKa9i/FKz5ddM5D5CA18e48nfdE9ZcAW+kBLxCNGTFdgfTF+xg+oIdsrSwEIL4MAtD20cD8PHaI1V/YmwXCGkK9kI4sLRuOucBEvD6mEPp6n9bLaMC1A3uCg0S8ArRkDmcCnP/Osrcv47K0sJCCAAm9lPn9ny/+ThZBdaqPalUWkP9qdYgAa+PScwsANSUBqnQIIQQQoiK9G4WRrvYIIpsTr76O7HqT3QtQrFvEdiK6qZzF5gEvD7m2JkBr1RoEEIIIUQFNBoNE/slAPDZ2iPYHc6qPbFRdwhqBNY8OLS87jp4AUnA60PsDicnTqkVGpqEW6RCgxBCCCEqdWWXOML9jZzMLqr6QhRaLbQbrT6uJ2kNEvD6kKTsIuxOBaNeS3Sg+YwJa7LCmhBCCCHKMht03NgzHoCvNx6v+hNd1Rr2LgR7FfN/vZgEvD7Elb8bH+qHVquBtL3qDsnfFUIIIUQFbujeGICV+9JIzaliTm6Ti8E/Eoqy4ciqOuzdhSEBrw85emb+LkDGAfU+vKWHeiSEEEIIb9c8MoDuTUNxOBUWbDlRtSdpddD2CvXxvkV117kLRAJeH3Ls7ID31GH1Pqy5h3okhPAWZr2OPx+7hD8fuwSzXpYWFkKUdn3JKO+3m46jKFUsXdh8kHqfuL6OenXhSMDrQ1wBb3yYBYpzIT9N3REmFRqEaOi0Wg3xYRbiwyxqypMQQpxhVOdYTHot+1Ly2H4iu2pPatxLvU/eAcV5dde5C0ACXh9SqgZvZsnoriUczMEe7JUQQgghvF2Q2cDlHWMA+Kaqk9eCG0FwPCgOOLGpDntX9yTg9SHulIZwC2QeUjdK/V0hBGC1O3lh4W5eWLgbq72KtTaFEA2KK63hp60nKbJVcQny+JJR3sQNddSrC0MCXh+RXWgjq8AGQHyoRfJ3hRCl2J1OPlx1iA9XHcLulIBXCFFW3xYRxAabyS60sWx3atWeFN9bvffxPF4JeH2EK50hIsCIv0l/eoRX8neFEEIIUQU6rYZrL2oEwLebqrjUsGuE9/gG8OF/piXg9RFlKjRkygivEEIIIarn+u7qIhRVrskb3QkMFrUeb/q+Ou5d3ZGA10dIwCuEEEKI89Uswp8eTUNxKvD9P1WoyavTQ6Pu6mMfTmuQgNdHlAp4bUWQU/JDKpPWhBBCCFEN1a7J687j9d2JaxLw+ojEM2vwZh0FFDAGgn+EZzsmhBBCCJ8ysnMsZoOWA6l5bD1ehZq87oD3r7rtWB3yeMD73nvvkZCQgNlspnfv3mzYUPl/D2+++SZt2rTBz8+P+Ph4Hn74YYqKSuegVLdNX3CsvBq8YQmgkQLzQgghhKi6ILOByzuoNXmrNHmtcQ/1PuMA5GfUYc/qjkcD3vnz5zNlyhRmzJjB5s2b6dKlC8OHDyc1tfxSGfPmzWPq1KnMmDGD3bt3M3v2bObPn8/jjz9e4zZ9gd3h5MSpQuCsGrySvyuEKGHW61j88EAWPzxQlhYWQpyTa/LaT1tOnrt2tyUMItqoj4/75iCiRwPe119/nUmTJjFx4kTat2/PrFmzsFgszJkzp9zj165dS79+/bjppptISEhg2LBhjBs3rtQIbnXb9AVJ2UXYnQpGnZboQPPpGrySvyuEKKHVamgdHUjr6EBZWlgIcU59W4QT5m8kp8jO9hNZ536Ca+Ja8vY67Vdd0Xvqha1WK5s2bWLatGnubVqtliFDhrBu3bpyn9O3b18+//xzNmzYQK9evTh06BALFy7k1ltvrXGbAMXFxRQXF7u/zsnJAcBms2Gz2c7rPM/kaqu6bR5KVfvTONSMw2GH9INoAXtwU5Ra7J+ouppeS+Gd5HrWH3It6w+5lnWvR9MQFu9KZe3+NDrHBVZ6rDaiNTrAmbwDRzWvSV1dy+q057GANz09HYfDQXR0dKnt0dHR7Nmzp9zn3HTTTaSnp9O/f38URcFut3P33Xe7Uxpq0ibAiy++yDPPPFNm++LFi7FYLNU9tXNasmRJtY7/K1UD6DDa8li4cCGXndhBALB+XwrpSQtrvX+i6qp7LYV38+XraXfCkhPqh3ZDGznRe3yGhmf58rUUpcm1rDv+BWp8sXDjPprkVxwnAUTl5NEHyDu8keULaxZ71Pa1LCgoqPKxHgt4a2LFihW88MILvP/++/Tu3ZsDBw7w4IMP8txzzzF9+vQatztt2jSmTJni/jonJ4f4+HiGDRtGUFBQbXQdUP8TWbJkCUOHDsVgMFT5eYeWH4SDB+nUMp6Rl7dBv/V2AHqNGAdBjWqtf6LqanothXeqD9ezwGrnkef+AOCliUOwGH3q7b3W1IdrKVRyLeteQlIOP7z/F8cKDAwbfgl6XSX/Ked0hXdeJdCawshhl4HeVOXXqatr6fpEvio89o4YERGBTqcjJSWl1PaUlBRiYmLKfc706dO59dZbufPOOwHo1KkT+fn53HXXXTzxxBM1ahPAZDJhMpW9cAaDoU5+yarbbmqeOmQfG2LBkJ8MTjvoTBhCm4C2gQ/jeFhd/YwIz/Dl62lQTuftqufRMANeF1++lqI0uZZ1p2PjMILMenKK7OxLK6RLfEjFB4c1AVMwmuJsDDlHIbpDtV+vtq9lddryWLRkNBrp3r07y5Ytc29zOp0sW7aMPn36lPucgoICtGcFeDqdOhtZUZQatekLUkqW/osNPnPCWoIEu0IIIYSoMZ1WQ69mYQBsOJxZ+cEaDUS1Ux+n7q7jntU+j0ZMU6ZM4aOPPuLTTz9l9+7d3HPPPeTn5zNx4kQAbrvttlIT0EaPHs3MmTP56quvOHz4MEuWLGH69OmMHj3aHfieq01flJStBrwxQeYzSpJJhQYhhBBCnB9XwLv+cBXq67oD3l112KO64dHPvMaMGUNaWhpPPfUUycnJdO3ald9//9096ezYsWOlRnSffPJJNBoNTz75JCdOnCAyMpLRo0fzn//8p8pt+iLXCG9MsBkSSwpEhzT1YI+EEEIIUR/0bhYOqCO8DqeCrrKyhq40Bh8c4fV4ktfkyZOZPHlyuftWrFhR6mu9Xs+MGTOYMWNGjdv0NUU2B5n5VqBkhDevZAGNwIpzkoUQQgghqqJDXBD+Rh05RXb2JOfQIS644oN9eIRXkkC9XGqOWh/YpNcSYjFAXsmEvADfHbEWQgghhHfQ67R0T6hiHm9kScB76ghY8+u2Y7XsvAJeq9XK3r17sdvttdUfcZbkM9IZNBrN6RFeCXiFEGcw6XX8eF8/fryvHyZZWlgIUQ29XXm8h84R8PqHn44/0iqv2+ttahTwFhQUcMcdd2CxWOjQoQPHjh0D4P777+e///1vrXawoUvKLgQgOsisbshLVu8DJeAVQpym02roEh9Cl/iQynPwhBDiLBc3VwPev49koihK5Qf7aKWGGgW806ZNY+vWraxYsQKz2ezePmTIEObPn19rnRNnlSRz2CE/Xd0hI7xCCCGEqAUd4oIx6DRk5FtJzCys/OCo9up9Qwh4FyxYwLvvvkv//v3Vj9lLdOjQgYMHD9Za5wQkZ6s5vDFBZihIBxTQaMES7tmOCSG8itXu5IOVB/lg5UGsdqenuyOE8CFmg472JZPV/kk8VfnBPjpxrUYBb1paGlFRUWW25+fnlwqAxflLzlH/04oJNp+esOYfCVrJ0RNCnGZ3Onnxtz28+Nse7E4JeIUQ1dOtZJW1f45lVX5gQxrh7dGjB7/++qv7a1eQ+7///c+nVzTzRslnLjrhnrBW9p8NIYQQQoia6tYkBIAtiVmVHxjZRr3PTYKCc0xy8yI1qsP7wgsvMGLECHbt2oXdbuett95i165drF27lpUrV9Z2Hxs0V8AbHWyGDClJJoQQQoja1y0+FIBdJ3MotjsqrvZiCoTgJpB9DNL3QZOLL2Ava65GI7z9+/dn69at2O12OnXqxOLFi4mKimLdunV07969tvvYYDmdCqm5ag5vbLAZcksqNEjAK4QQQohaFB/mR5i/EavDyc6TOZUfHFqy2mvWsbrvWC2p9givzWbjX//6F9OnT+ejjz6qiz6JEun5xdidCloNRAaYpAavEEIIIeqERqOhW3wIy/aksuVYFhc1Ca344JAm6n3W0QvTuVpQ7RFeg8HAd999Vxd9EWdxpTNEBprQ67SyypoQQggh6kxX18S1c+XxugPexDrtT22qUUrD1VdfzYIFC2q5K+JspSasgUxaE0IIIUSd6VYyqrvlXKXJguPV+/qc0gDQqlUrnn32WdasWUP37t3x9/cvtf+BBx6olc41dK5lhU+vsiYjvEKI8pn0Or6cdLH7sRBCVFfn+GA0GkjMLCQtt5jIQFP5B7pGeLN9Z4S3RgHv7NmzCQkJYdOmTWzatKnUPo1GIwFvLXGN8MYGnz3CKwGvEKI0nVZDnxayII0QouaCzAZaRgawPzWPLYlZDG1fQbwR4hrhTQSnE7Q1Shi4oGoU8B4+fLi2+yHK4R7hDTaDNR+sueoOSWkQQgghRB3o1iSkJOA9VXHAG9RIXfXVUQz5aRDo/QNx5x2SK4qCoii10RdxllIjvK50Br2fWgNPCCHOYHM4+WzdET5bdwSbQ1ZaE0LUTNeSeryVrrimM0BgnPrYR/J4axzwfvbZZ3Tq1Ak/Pz/8/Pzo3Lkzc+fOrc2+NXilcnhd6QyB0SDLNwshzmJzOHnqx5089eNOCXiFEDXmWnFt2/FsHM5KBjTdeby+EfDWKKXh9ddfZ/r06UyePJl+/foBsHr1au6++27S09N5+OGHa7WTDZGiKKWrNKTJhDUhhBBC1K3W0YFYjDryiu0cSM2jTUwFnyqHxMMxfGaEt0YB7zvvvMPMmTO57bbb3NuuvPJKOnTowNNPPy0Bby3ILbZTYHUAEBNshsNSkkwIIYQQdUun1dC5cTB/HcpkS+KpSgJeVy1e3wh4a5TSkJSURN++fcts79u3L0lJSefdKQEpJaO7QWY9FqNeSpIJIYQQ4oJw1eOtNI83+IxKDT6gRgFvy5Yt+frrr8tsnz9/Pq1atTrvTglIz7MCEBFQUgNPAl4hhBBCXADuFdcqC3h9bIS3RikNzzzzDGPGjGHVqlXuHN41a9awbNmycgNhUX2Z+WrAG+ZvVDfIKmtCCCGEuAC6lQS8+1JzySu2E2AqJ1w8c/EJRfH6CfU1GuG97rrrWL9+PRERESxYsIAFCxYQERHBhg0buOaaa2q7jw1SZn4xcEbAm5us3ssIrxBCCCHqUFSQmUYhfigKbEvMKv+g4Mbqva0ACjIuWN9qqkYjvADdu3fn888/r82+iDNklIzwhgecPcIrAa8QoiyjTsucCT3cj4UQ4nx0bRLCiaxC/knMom/LiLIH6E0QGAu5SWpag385x3iRGr0rLly4kEWLFpXZvmjRIn777bfz7pQ4K6XB6YR8CXiFEBXT67Rc2jaaS9tGo5eAVwhxnrpVJY/XPXHN+/N4a/SuOHXqVBwOR5ntiqIwderU8+6UOGOE198EhafAaVd3+Ed6sFdCCCGEaAhcC1BsSTxV8Yq6Z+bxerkaBbz79++nffv2Zba3bduWAwcOnHenBGTmnZHS4KrQ4BcGeqMHeyWE8FY2h5NvNibyzcZEWWlNCHHeOsQFY9BpSM+zcvxUYfkHhdTzEd7g4GAOHTpUZvuBAwfw9/c/706Js1IapCSZEOIcbA4n//52G//+dpsEvEKI82Y26GgfGwTAPxVNXPOh0mQ1CnivuuoqHnroIQ4ePOjeduDAAR555BGuvPLKWutcQ5ZxZsCbn6Zu9PKEcCGEEELUH656vBVXanAFvPU0peHll1/G39+ftm3b0qxZM5o1a0bbtm0JDw/n1Vdfre0+NjhOp8KpgrNyeAEs4R7slRBCCCEaklbR6rLCh9Pzyz/gzBHeivJ8vUSNypIFBwezdu1alixZwtatW/Hz86NLly4MGDCgtvvXIOUU2XA41R+cUH8DFGSqO/xCPdgrIYQQQjQkzSPUNNUKA15XLV5rLhRlg1/IhelYDVRrhHfdunX88ssvAGg0GoYNG0ZUVBSvvvoq1113HXfddRfFxcV10tGGxJXOEGjSY9LrzhjhDfNgr4QQQgjRkDSLVAPeY5kF5c8NMFrAGKA+9vLFJ6oV8D777LPs3LnT/fX27duZNGkSQ4cOZerUqfz888+8+OKLtd7JhsY9Yc216EShjPAKIYQQ4sKKDjTjZ9BhdyoVV2pwDcbVp4B3y5YtXHbZZe6vv/rqK3r16sVHH33ElClTePvtt/n6669rvZMNTUbeGRPW4PQIr5+M8AohhBDiwtBqNTQNtwBwOD2v/IMsJRPq89MvUK9qplo5vKdOnSI6+nRprJUrVzJixAj31z179iQx0ftn6nm7TPeiE2cHvDLCK4Qon1Gn5b2bLnI/FkKI2tA80p89ybkcTi8o/wBXBakC7w54q/WuGB0dzeHDhwGwWq1s3ryZiy++2L0/NzcXg8FQuz1sgDLz1Txo9wivTFoTQpyDXqdlVOdYRnWOlaWFhRC1ppl74ppvj/BW611x5MiRTJ06lT///JNp06ZhsVhKVWbYtm0bLVq0qPVONjSna/Ca1A0yaU0IIYQQHtAsQp2UVmGlBh/J4a1WSsNzzz3Htddey6BBgwgICODTTz/FaDy91O2cOXMYNmxYrXeyoSmV0uB0qKU+QEZ4hRAVsjucLNqprso4vEO0jPIKIWpFs4iSHN60CgJed0pDPQp4IyIiWLVqFdnZ2QQEBKDT6Urt/+abbwgICKjVDjZEpZYVLsoGSoo5S8ArhKiA1eHkvnmbAdj17HAJeIUQtcI1wnsyu4gimwOzoXTsVy9TGlyCg4PLBLsAYWFhpUZ8Rc24qzQEGE+nMxgDQSf50UIIIYS4cEItBoL91PjjSEY5o7z1cdKauDBKpTTIhDUhhBBCeIhGozk9ca28tAb3CK93pzRIwOtlFEUpndLgnrAmAa8QQgghLjzXEsOHypu45p60JiO8ohryiu1YS5bvC/c3SQ1eIYQQQnhUgrs0WSUpDbYCsFZQq9cLSMDrZVyju34GHX5G3RnLCktJMiGEEEJceK6UhiPlBbymINCWzDHy4koNEvB6mYz8ipYVlhFeIYQQQlx4zSob4dVofGLiWrXKkom6l1lSoSE8QFZZE0JUnUGn5ZXrO7sfCyFEbXEFvBn5VrILbARbzqoaZYmA3CSvnrgmAa+XyaxohFdWWRNCVMKg03JDj3hPd0MIUQ/5m/REBZpIzS3mcEY+XS0hpQ/wgYlrMgzgZSSlQQghhBDeptI8Xh9Ybc0rAt733nuPhIQEzGYzvXv3ZsOGDRUeO3jwYDQaTZnbqFGj3MdMmDChzP7LL7/8QpzKecvMLwZKavCCTFoTQlSJ3eHkjz0p/LEnBXtJpRchhKgtjUPVJYZPZBWW3ekDq615PKVh/vz5TJkyhVmzZtG7d2/efPNNhg8fzt69e4mKiipz/Pfff4/VanV/nZGRQZcuXbjhhhtKHXf55Zfz8ccfu782mUx1dxK16PQIb0l/ZYRXCFEFVoeT2z/ZCMjSwkKI2hcbbAYgObuo7E4fmLTm8XfE119/nUmTJjFx4kTat2/PrFmzsFgszJkzp9zjw8LCiImJcd+WLFmCxWIpE/CaTKZSx4WG+kbAWGqVNYACCXiFEEII4VkxroA3p5yA1xKu3suktfJZrVY2bdrEtGnT3Nu0Wi1Dhgxh3bp1VWpj9uzZjB07Fn9//1LbV6xYQVRUFKGhoVx66aU8//zzhIeHl9tGcXExxcXF7q9zcnIAsNls2Gy26p5WhVxtVdZmRp7ajyCzFltxIYbibPU5hkCoxb6I81OVayl8R324njab/YzHNmwaxYO98Zz6cC2FSq6ld4nwV0PGpKzCMtdEYwpBDzjz03CUc73q6lpWpz2PBrzp6ek4HA6io6NLbY+OjmbPnj3nfP6GDRvYsWMHs2fPLrX98ssv59prr6VZs2YcPHiQxx9/nBEjRrBu3Tp0Ol2Zdl588UWeeeaZMtsXL16MxWKp5lmd25IlSyrcdyJdB2jYu3Ujyr5cRpRs/23FOhRN2b4Lz6rsWgrf48vXs9gBrrf0RYsWY2rgbxe+fC1FaXItvcPxfAA9R9OyWbhwYal94bkH6A8UpB1j2Vn7zlTb17KgoOoru3k8h/d8zJ49m06dOtGrV69S28eOHet+3KlTJzp37kyLFi1YsWIFl112WZl2pk2bxpQpU9xf5+TkEB8fz7BhwwgKCqq1/tpsNpYsWcLQoUMxGAzlHjN141LAyaihg2mqnIDtoJiCGDFqdK31Q5y/qlxL4Tvqw/UssNp5bMMfAAwfPgyL0aff3musPlxLoZJr6V0y8q28sm0FeXYNQ4ZdjlF/RlZsWgs48AL+miJGjhxZ5rl1dS1dn8hXhUffESMiItDpdKSkpJTanpKSQkxMTKXPzc/P56uvvuLZZ5895+s0b96ciIgIDhw4UG7AazKZyp3UZjAY6uSXrKJ2i2wOCm3q7OrIIAuG9DwANH6h8svuperqZ0R4hi9fT4OiOf3YYMBgaJgBr4svX0tRmlxL7xAdrMeo02J1ODlV5KBx6BlxU7Aas2mKsjBoAV3516u2r2V12vLopDWj0Uj37t1ZtmyZe5vT6WTZsmX06dOn0ud+8803FBcXc8stt5zzdY4fP05GRgaxsbHn3ee6lF2o5qLotBqCzPrTq6zJohNCCCGE8CCNRkN0sBrklqnU4BcKlPzT7YpdvIzHhwCmTJnC+PHj6dGjB7169eLNN98kPz+fiRMnAnDbbbfRqFEjXnzxxVLPmz17NldffXWZiWh5eXk888wzXHfddcTExHDw4EEee+wxWrZsyfDhwy/YedVEVoEa8Ab7GdBoNFKSTAhRZQadlmev6uB+LIQQtS02yI/EzEKSzg54tTo1VinMVEuTBUaX34AHeTzgHTNmDGlpaTz11FMkJyfTtWtXfv/9d/dEtmPHjqHVln7z3rt3L6tXr2bx4sVl2tPpdGzbto1PP/2UrKws4uLiGDZsGM8995zX1+LNKlBLkoX4lQzRS8ArhKgig07LbX0SPN0NIUQ95ipNllJeaTL/iJKA1ztLk3k84AWYPHkykydPLnffihUrymxr06YNilJ+yR0/Pz8WLVpUm927YFwpDcEWV8Arq6wJIYQQwju4At4yI7xQstraPq9dbc0rAl6hyio8ndIAyAivEKLKHE6FDYfVf5J7NQtDp9Wc4xlCCFE9MUGVrbZWkmIqI7ziXLJLcnjdKQ0yaU0IUUXFdgfjPvoLUJcWbqhlyYQQdSe2SquteecIr8xs8CJZhSU5vJaSZYVlhFcIIYQQXiI6uJIRXkuEeu+lI7wS8HqRM6s0ABLwCiGEEMJrxJ4xac3hPGsulb8r4JURXnEOrhzeEJm0JoQQQggvExlgQqsBu1MhI6+49E7XCK+kNIhzcefwugPeLPVeRniFEEII4WF6nZaowAryeL180poEvF7EncPrZwSHDYpL1oiWSWtCCCGE8ALRFZUm8/JJazKN14uUqsPrGt0FMAd7pkNCCCGEEGeIDTKzlXImroUmwBVvQkCUB3p1bhLwepFSk9YK09SN5mB1yT4hhKiEXqtl2oi27sdCCFEXYioqTWYOhh4TPdCjqpGA10vYHU5yi+xASR3erFx1h0lGd4UQ52bUa/nXoBae7oYQop6Lraw0mReTYQAvkVMS7ELJCK8rf9cU4KEeCSGEEEKUdnp54UIP96R6ZITXS2QVqBPWAk169DotWPPUHUYJeIUQ5+ZwKuw4kQ1Ax0bBsrSwEKJOVLq8sBeTEV4vkXXmhDWA4pKAV0Z4hRBVUGx3cNV7a7jqvTUU2x2e7o4Qop6KDfYD1BxeRVHOcbT3kIDXS5Spwesa4TUFeqhHQgghhBClRQWZACiyOd3VpXyBBLxeolQNXoDikklrRgl4hRBCCOEdzAYdYf5qrFKmFq8Xk4DXS7hGeE+nNLiqNEhKgxBCCCG8R2SAOsqbkWf1cE+qTgJeL+HK4Q3xOyulQSatCSGEEMKLuNIvTxVIwCuqqdSiE3DGpDVJaRBCCCGE9wi1qCkNWRLwiupyJX6fnrQmKQ1CCCGE8D6h/q4RXt+ZtCZ1eL2E678kmbQmhKgJvVbLg5e1cj8WQoi6ElwSq/hSSoMEvF5C6vAKIc6HUa/l4aGtPd0NIUQDEFoSq2T50AivDAN4CXcdXpm0JoQQQggv5srhlRFeUW3uKg0WV0qDTFoTQlSd06lwIE1932gZGYBWlhYWQtSR01UafGeEVwJeL6AoSiWT1iTgFUKcW5HdwbA3VgGw69nhWIzy9i6EqBuh/lKlQdRAXrEdh1NdjzrYzwCKcsakNUlpEEIIIYT3cOXwnsqXgFdUgyvp26TXYjbowFYIilPdKZPWhBBCCOFFXOmXOUV27A6nh3tTNRLweoGy6Qx5p3ca/D3QIyGEEEKI8rkn2HM6hvF2EvB6gSx3hYZyavBKPU0hhBBCeBG9TkugWZ0n4CsT1ySa8gJZhWoOzOkavLLKmhBCCCG8l68tLywBrxfIkhq8QgghhPAhoT5Wmkzq1niBMjm8ssqaEKKa9Fotdw1s7n4shBB1KcTHFp+QgNcLuD4OcC86ISO8QohqMuq1PD6ynae7IYRoIE4vL+wbAa8MA3gB1whvsN/ZObxBHuqREEIIIUTFTo/wSkqDqCJ3Dq9MWhNC1JDTqXAiqxCARiF+srSwEKJOyaQ1UW1ZZ4/wSkqDEKKaiuwOBry8nAEvL6fI7vB0d4QQ9Vyov2u1Nd8Y4ZWA1wtkl6nDK5PWhBBCCOG9ZNKaqLaLmoYQ6m8gJtikbrCesfCEEEIIIYSXOT1pzTdGeCXg9QIvXtu59Ab3CK8EvEIIIYTwPqE+NsIrKQ3eSCatCSGEEMKLhZwxwqsoiod7c24S8HojmbQmhBBCCC/mGuG1OpwUWL1/oqwEvN5IJq0JIYQQwotZjDqMOjWM9IW0Bsnh9UZWWXhCCFE9Oq2GWy9u6n4shBB1SaPREGIxkJpbTFaBjcahnu5R5STg9UauHF5JaRBCVJFJr+O5qzt6uhtCiAYk1GIkNbfYJ0Z4JaXBG0lKgxBCCCG8nGvimi8sLywjvN7GYQNHsfpYRniFEFWkKAqZ+eooS5i/EY1G0hqEEHXLl5YXloDX27jSGUDq8AohqqzQ5qD780sB2PXscCxGeXsXQtQtX1peWFIavI2rJJneDDqDZ/sihBBCCFEBX1peWAJebyMT1oQQQgjhA04vLywBb5W89957JCQkYDab6d27Nxs2bKjw2MGDB6PRaMrcRo0a5T5GURSeeuopYmNj8fPzY8iQIezfv/9CnMr5kwlrQgghhPABp0d4JaXhnObPn8+UKVOYMWMGmzdvpkuXLgwfPpzU1NRyj//+++9JSkpy33bs2IFOp+OGG25wH/Pyyy/z9ttvM2vWLNavX4+/vz/Dhw+nqKjoQp1Wzblq8Bolf1cIIYQQ3suXJq15POB9/fXXmTRpEhMnTqR9+/bMmjULi8XCnDlzyj0+LCyMmJgY923JkiVYLBZ3wKsoCm+++SZPPvkkV111FZ07d+azzz7j5MmTLFiw4AKeWQ3JCK8QQgghfEColCWrGqvVyqZNm5g2bZp7m1arZciQIaxbt65KbcyePZuxY8fi7+8PwOHDh0lOTmbIkCHuY4KDg+nduzfr1q1j7NixZdooLi6muLjY/XVOTg4ANpsNm632LqKrrcra1BRkoQecBn8ctfjaonZV5VoK31EfrqfNZj/jsQ2bRvFgbzynPlxLoZJr6f0CjKeXFq7sOtXVtaxOex4NeNPT03E4HERHR5faHh0dzZ49e875/A0bNrBjxw5mz57t3pacnOxu4+w2XfvO9uKLL/LMM8+U2b548WIsFss5+1FdS5YsqXBf89QNdAJOZuSyaeHCWn9tUbsqu5bC9/jy9bQ7oVek+sdn6eLF6D3++Z1n+fK1FKXJtfReeTYAPblFdn7+dSG6c5T/ru1rWVBQUOVjfbpQ4+zZs+nUqRO9evU6r3amTZvGlClT3F/n5OQQHx/PsGHDCAoKOt9uutlsNpYsWcLQoUMxGMovOaZdvRtOQFxCK6JHjqy11xa1qyrXUviO+nI9r/R0B7xAfbmWQq6lL7A7nDyxUa3/3W/wEML8jeUeV1fX0vWJfFV4NOCNiIhAp9ORkpJSantKSgoxMTGVPjc/P5+vvvqKZ599ttR21/NSUlKIjY0t1WbXrl3LbctkMmEymcpsNxgMdfJLVmm7dvW/Fa05GK38gnu9uvoZEZ4h17P+kGtZf8i19F4GAxj1Wqx2JzZFc87rVNvXsjptefRDL6PRSPfu3Vm2bJl7m9PpZNmyZfTp06fS537zzTcUFxdzyy23lNrerFkzYmJiSrWZk5PD+vXrz9mmV5BJa0KIGlAUhQKrnQKrHUVpmPm7QogLz9+oA6DQ6vBwTyrn8ZSGKVOmMH78eHr06EGvXr148803yc/PZ+LEiQDcdtttNGrUiBdffLHU82bPns3VV19NeHh4qe0ajYaHHnqI559/nlatWtGsWTOmT59OXFwcV1999YU6rZpzLTwhywoLIaqh0Oag/VOLAFlaWAhx4ViMek4V2MiXgLdyY8aMIS0tjaeeeork5GS6du3K77//7p50duzYMbTa0gPRe/fuZfXq1SxevLjcNh977DHy8/O56667yMrKon///vz++++YzeY6P5/z5lpaWFZaE0IIIYSXs5SM8BYU289xpGd5POAFmDx5MpMnTy5334oVK8psa9OmTaUf2Wk0Gp599tky+b0+QUZ4hRBCCOEjLCY1lPT2Ed4GXrjGC8kIrxBCCCF8hCuHt8Dq3SO8EvB6G/cIrwS8QgghhPBurvkCBTLCK6rFXaVBUhqEEEII4d1cObz5Xp7DKwGvt5GUBiGEEEL4CH+TK6XBu0d4vWLSmijhdJ4OeGWEVwhRDVqNhpGdYtyPhRDiQnClNOR7eQ6vBLzexF50+rHB4rl+CCF8jtmg4/2bu3u6G0KIBsZXFp6QlAZvYis4/djg57l+CCGEEEJUgZ9rhLdYAl5RVa6AV28Grc6zfRFCCCGEOIfTObzendIgAa83sRWq9zK6K4SopgKrnYSpv5Iw9Vev/8MjhKg/TufwygivqCrXCK/k7wohhBDCB5zO4fXuf7Ql4PUmVlfAKyO8QgghhPB+7qWFJYdXVJk7pUFGeIUQQgjh/SyytLCoNklpEEIIIYQPca+0Jjm8ospk0poQQgghfIh/yaQ1qcMrqs6Wr94b/T3bDyGEEEKIKrCYXCO8dhRF8XBvKiYrrXkTGeEVQtSQVqPhkjaR7sdCCHEhuMqSKQoU2Zz4Gb1zHQEJeL2JTao0CCFqxmzQ8fHEXp7uhhCigfEznA5w8612rw14JaXBm0iVBiGEEEL4EJ1W4w56C7y4NJmM8HoTq1RpEA2bw+HAZrNd8Ne12Wzo9XqKiopwOLz3DVucmyevpcFgQKfzztEtIeqSv0lHoc1Bgc17S5NJwOtNpCyZaKAURSE5OZmsrCyPvX5MTAyJiYlofDT/1akoJGcXARATbG6webyevpYhISHExMT47M+REDWh5vFavXrxCQl4vYlMWhMNlCvYjYqKwmKxXPBgwel0kpeXR0BAAFqtb2Z6OZwK9tRcABKiAtFpG2bA5alrqSgKBQUFpKamAhAbG3vBXlsIT/OFxSck4PUmMmlNNEAOh8Md7IaHh3ukD06nE6vVitls9umAV6MvBsBsNjfogNdT19LPT33vTk1NJSoqStIbRIPhXnzCi0d4ffOdvb5yBbxSh1c0IK6cXYtFUnmE73P9HHsiF10IT/E3lSw+4cU5vBLwehNJaRANmOQ8ivpAfo5FQyQjvKJ6ZNKaEEIIIXyMa/EJb87hlYDXm0gdXiFEiYEDBzJv3rxabVOj0bBgwQIA0tPTiYqK4vjx47X6GkKIhkdGeEX1WGXSmhC+ZMKECWg0mjK3AwcOALBq1SpGjx5NXFxcqWDzXH766SdSUlIYO3YsVquViIgI/vvf/5Z77HPPPUd0dDR2mw1/kx5/k56qfKgeERHBbbfdxowZM6p4tkIIUT5XDq+M8IqqkZQGIXzO5ZdfTlJSUqlbs2bNAMjPz6dLly6899571Wrz7bffZuLEiWi1WoxGI7fccgsff/xxmeMUReGTTz7htttuw2Qy0iIygBaRAWirWKFh4sSJfPHFF2RmZlarf0IIcabTZclkhFdUhUxaEwIoqWlqtV/QW6HVQYHVjqIo1eqryWQiJiam1M1VjmrEiBE8//zzXHPNNVVuLy0tjT/++IPRo0e7t91xxx3s27eP1atXlzp25cqVHDp0iDvuuIO///6boUOHEhERQXBwMIMGDWLz5s2VvlaHDh2Ii4vjhx9+qMYZCyFEaf7uHF7vDXilDq+3cDrBXhLwSlky0cAV2hy0f2qRR15717PD3RMwPGH16tVYLBbatWvn3tapUyd69uzJnDlz6N+/v3v7xx9/TN++fWnbti1//PEH48eP55133kFRFF577TVGjhzJ/v37CQwMrPD1evXqxZ9//skdd9xRp+clhKi//Nw5vJLSIM7FFeyCjPAK4UN++eUXAgIC3LcbbrjhvNo7evQo0dHRZRZNuOOOO/jmm2/Iy8sDIDc3l2+//Zbbb78dgEGDL+GiS6/EGRRH6zZt+fDDDykoKGDlypWVvl5cXBxHjx49rz4LIRo2f5P3pzTICK+3sJ0R8Ool4BUNm59Bx65nh1+w13M6neTm5BIYFIifoXqrY11yySXMnDnT/bW///l9QlNYWIjZbC6zfdy4cTz88MN8/fXX3H777cyfPx+tVsuYMWMASElJYfqj/8fGdavJykzH4XBQUFDAsWPHKn09Pz8/CgoKzqvPQoiGzRfKkknA6y1cE9b0ZvDRpU2FqC0ajeaCphU4nU7sRh0Wo77aCwf4+/vTsmXLWutLREQEp06dKrM9KCiI66+/no8//pjbb7+djz/+mBtvvJGAgAAAJk6YwInkVB575kUGdGuPxc9Mnz59sFqtlb5eZmYmkZGRtdZ/IUTD4ws5vBJZeQupwSuEALp160ZycnK5Qe8dd9zB6tWr+eWXX1i7dm2pvNu1a9cw7va7GHDpMDp06IDJZCI9Pf2cr7djxw66detWq+cghGhY3Dm8XjzCKwGvt7Dmq/cS8ApRb+Tl5bFlyxa2bNkCwOHDh9myZUulaQbdunUjIiKCNWvWlNk3cOBAWrZsyW233Ubbtm3p27eve1+rVq345buvObR/L+vXr+fmm2/Gz6/y9KiCggI2bdrEsGHDanaCQgjBGTm8svCEOCcpSSZEvbNx40a6devmHkGdMmUK3bp146mnnqrwOTqdzl0f92wajYbbb7+dU6dOuSeruXz40f/Izc5i7IjBTBh/Gw888ABRUVGV9u/HH3+kSZMmDBgwoAZnJ4QQKl9IaZAcXm8hAa8QPueTTz6pdP/gwYOrXdcX4OGHH6ZDhw4cPXqUpk2blto3bdo0pk2bVuY53bp1Y96vfwDQIS4YnVbD9ddfX+qYs/vy1ltvVRp8CyFEVbgWnii0OXA4FXRVXPzmQpIRXm9hK0lpkBq8QjR4MTExzJ49+5wVFs6kQc2j8zPqqrS0cHp6Otdeey3jxo2rcT+FEAJOLy0MatDrjWSE11vICK8Q4gxXX311tY7XajW0iqp4gYmzRURE8Nhjj1WzV0IIUZZJr0WjAUWBgmI7ASbvCy9lhNdbuMqSyaQ1IYQQQvgQjUbjzuPN99I8Xgl4vYWUJRNCCCGEj3Ll8Xrr4hMS8HoLq2uEV1IahBDV53Qq7EnKYU9SDk5n9SfKCSHE+XDl8XprpQbvS7JoqCSlQQhxHhTA6nC6HwshxIXkWpY9v1hGeEVlZNKaEEIIIXyUe/EJLx3hlYDXW7hGeI0ywiuEEEII32Lx8sUnJOD1FpLSIIQQQggfdXqEV1IaRGUkpUGIBuvpp5+ma9eunu7GOR05cgSNRsOWLVs83ZUyli1bRrt27XA4am906ezrMnXqVO6///5aa1+I+sQ1wptfLCO8ojIywiuEz5kwYQIajcZ9Cw8P5/LLL2fbtm2e7prHffnll+h0Ou67774L8nqPPfYYTz75JDqdjnfffZfw8HCKiorKHFdQUEBQUBBvv/12tV/j0Ucf5dNPP+XQoUO10WUh6hUpS3YO7733HgkJCZjNZnr37s2GDRsqPT4rK4v77ruP2NhYTCYTrVu3ZuHChe79Tz/9dKk/QBqNhrZt29b1aZw/qcMrhE+6/PLLSUpKIikpiWXLlqHX67niiisueD80gFmvw6yv2tLCdW327Nk89thjfPnll+UGnrVp9erVHDx4kOuuuw6AMWPGkJ+fz/fff1/m2G+//Rar1cott9xS7deJiIhg+PDhzJw587z7LER9Izm8lZg/fz5TpkxhxowZbN68mS5dujB8+HBSU1PLPd5qtTJ06FCOHDnCt99+y969e/noo49o1KhRqeM6dOjg/gOUlJTE6tWrL8TpnB9rvnovAa8Q6vqU1vwLe7MVqPdK9Yp6mUwmYmJiiImJoWvXrkydOpXExETS0tLcx/zf//0frVu3xmKx0Lx5c6ZPn47NZquwzb///puhQ4cSERFBcHAwgwYNYvPmzaWO0Wg0/O9//+Oaa67BYrHQpk1r9mxYTuuYQLRaNeTduXMnV1xxBUFBQQQGBjJgwAAOHjzobuN///sf7dq1w2w207ZtW95///1Sr7Fhwwa6deuG2WymR48e/PPPP1X6nhw+fJi1a9cydepUWrduXW7gOWfOHDp06IDJZCI2NpbJkye792VlZfGvf/2L6OhozGYzHTt25Jdffqnw9b766iuGDh2K2WwGIDIykiuuuII5c+aU+7pXX301YWFh1b4uAKNHj+arr76q0vdBiIbE38tHeD1ah/f1119n0qRJTJw4EYBZs2bx66+/MmfOHKZOnVrm+Dlz5pCZmcnatWsxGAwAJCQklDlOr9cTExNTp32vdZLDK8RptgJ4Ie6CvZwWCHF98fhJMPrXqJ28vDw+//xzWrZsSXh4uHt7YGAgn3zyCXFxcWzfvp1JkyYRGBjIY489Vm47ubm5jB8/nnfeeQdFUXjttdcYOXIk+/fvJzAw0H3cM888w8svv8wrr7zCO++8w80338zRo0cJCwvjxIkTDBw4kMGDB/PHH38QFBTEmjVrsNvVP0ZffPEFTz31FO+++y7dunXjn3/+YdKkSfj7+zN+/Hjy8vK44oorGDp0KJ9//jmHDx/mwQcfrNL34eOPP2bUqFEE/397dx4XZbX/AfwzMzADCAMqAoMipmiiuWIQmluRW2lm98JNfqjlmlhdSXPBQs2tUktN01DBqxXYdblcJVJJck8lMRNEQXBJAc2F3RmY8/uDeK4joAyyjp/36zUvnTPneZ7vM19m+HLmPGdsbfF///d/2LBhA0aOHCk9/tVXXyEoKAhLlizB4MGDcffuXRw+fBgAoNfrMXjwYOTk5GDLli1o06YNEhMToVAoKjzewYMHDfYPAG+99RaGDRuGS5cuwdXVFQBw8eJFHDhwAD/++GOV8gIAnp6euHr1KtLT08v9/UP0pLJS1e85vHVW8Gq1WsTHx2PWrFlSm1wuh4+PD44ePVruNlFRUfD29kZgYCD+85//oFmzZhg5ciRmzJhh8GZ44cIFODs7w8LCAt7e3li8eDFatmxZYSz37t3DvXv3pPvZ2dkAAJ1O98i/9o1Ruq/y9mmmy4cMgE6uBKrxmFQzHpZLMo5Op4MQAnq9Hnp9yRcnQK+vs4+f9Ho9UBrHIwghsGvXLlhbWwMA8vLyoNFoEBUV9b99AZg9e7a0TcuWLfH+++8jMjIS06ZNk/Zzf/9+/foZHGft2rVo0qQJ9u/fbzBdYvTo0fDz8wMALFiwACtXrsSxY8cwaNAgfPnll7C1tcW3334rDRC4ublJxwkJCcFnn32G4cOHAwBcXV1x9uxZrFu3DgEBAdiyZQv0ej1CQ0NhYWEBd3d3XL58GYGBgYa5Kuf5Cw8Px4oVK6DX6+Hr64v3338fqampeOqpp6RYg4KCDC4A8/DwgF6vx549e3D8+HGcPXsW7dq1A/C/gY2Kjnnp0iU4OTlBr9dLz+WAAQPg7OyMjRs3IiQkBEBJIe7i4oL+/ftDr9cbnRcA0mBKWlpaub9XSmPQ6XQPLdLp0fg+27D8tUgDcgvL1k41lUtj9ldnBe/NmzdRXFwMR0dHg3ZHR0ecO3eu3G0uXryIn376Cf7+/oiOjkZKSgomT54MnU4nvaF5eXkhPDwcTz/9NK5fv4558+ahd+/e+P333w1GRu63ePFizJs3r0z7nj17YGVV/VMM9u7dW6ZtUN5dqAAcOHoCuRbXqv2YVDPKyyUZp/QTmdzcXGi12pJGIYDApLoJqKAIKMyuVFedTofevXtj2bJlAEo+it+wYQOGDBmCffv2SQXR9u3bsW7dOqSnpyMvLw9FRUWwsbGR/ri+d+8eiouLpftZWVlYuHAhDh06hBs3bkCv1yM/Px/nz5+X+gAlBWzpfb0ArG1s8NuFy3jOOxsnT56El5cXCgoKUFBQYBB3Xl4eUlNTMX78eEycOFFqLyoqglqtRnZ2Nn777Td06NABWq1WykunTp2k7e+P436xsbHIzc3F888/j+zsbCiVSvTr1w9r165FcHAwbty4gWvXruG5554rdx+//PILnJ2d4eTkVOExHlRQUAAhhEH//Px8+Pn5ISwsDP/85z8hhEB4eDj8/f2Rm5tbpbwA//sFe/PmzXLj02q1KCgowIEDB6TRdHo8fJ9tGM7flAFQ4Mr1TINrq+5X3bnMz8+vdN8G9dXCer0eDg4O+Prrr6FQKODh4YE//vgDn332mVTwDh48WOrfuXNneHl5wdXVFVu3bsXYsWPL3e+sWbMQFBQk3c/OzoaLiwsGDBgAtVpdbfHrdDrs3bsXL730kjTiUsrsTMkbY58XBwG2LtV2TKoZD8slGaewsBBXrlyBtbW1NAezhG2txSCEQE5ODmxsbCCTVf6SL3Nzc6jVaoOlq3r37o3GjRsjMjISH3/8MY4ePYoJEyZg7ty5GDBgAGxtbREZGYnly5dL7y8qlQoKhUK67+fnh1u3bmHFihVwdXWFSqVCr169DPoAgFqtlu7rRcm8Xl2xXpqzWxrfg0oL4HXr1sHLy8vgsdJjKJVKmJmZGWxfOpLdqFGjCt8bIyIicPv2bWg0GqlNr9cjKSkJixcvlp5fKyurcvfRuHFjyOVyo9577e3tUVhYCLVabZDLSZMm4fPPP8fJkyeh1+vxxx9/YOLEiVCr1VXKCwBkZmYCKBkRLy/GwsJCWFpaok+fPg/8PJOx+D7bsKjOZeFfFxJgqbbDkCHPGTxWU7ms7B/FQB0WvPb29lAoFNKbR6nMzMwK599qNBqYm5sbfEzk7u6OjIwMaLVaKJXKMtvY2dmhXbt2SElJqTAWlUoFlUpVpt3c3LxGXmRl9qvXA0UlVzGbW6oBvrAbjJr6GXmSFBcXQyaTQS6XQy6vm4kMpR9Zl8ZRWaUrwTy4jVwuR2FhIeRyOY4dOwZXV1fMmTNHevzy5ctSv9L93H//yJEjWLNmjTR94cqVK7h582aZY93/nAn9/y62k8lk6NKlCzZt2oTi4uIyP6MajQbOzs5IT09HQEBAuefWoUMHbNmyBVqtVircSlfRqShXf/75J6KiohAREYGOHTtK7cXFxXj++eexb98+DBo0CK1atcL+/fvx4osvltlHly5dcPXqVaSkpEhTGh6lW7duOHfuHORyuUEu27Zti759+yI8PBxCCPj4+EjTKqqSFwBITEyEubk5OnXqVO5zIJfLIZPJ+N5QjfhcNgxqq5I6qkCrrzBf1Z1LY/ZVZ6s0KJVKeHh4IDY2VmrT6/WIjY2Ft7d3udv06tULKSkpBvOpzp8/D41GU26xC5RcRJKammow2lDvFN33cSNXaSBqUO7du4eMjAxkZGQgKSkJ77zzDnJzczF06FAAQNu2bXH58mVEREQgNTUVK1euxI4dOx66z7Zt22Lz5s1ISkrCL7/8An9/f1haGndB65QpU5CdnY1//OMfOHnyJC5cuIDNmzcjOTkZQMkFb4sXL8bKlStx/vx5nDlzBmFhYVi+fDkAYOTIkZDJZBg/fjwSExMRHR2NpUuXPvSYmzdvRtOmTeHr64tnnnlGunXp0gVDhgzBhg0bAJQsH7ls2TKsXLkSFy5cwK+//opVq1YBAPr27Ys+ffrg9ddfx969e5GWloYffvgBMTExFR534MCBFa7GM3bsWGzfvh07duww+JSvKnkBSi6Q6927t9H5IDJ1jbgsWcWCgoIQGhqKTZs2ISkpCW+//Tby8vKkVRtGjRplcFHb22+/jVu3buG9997D+fPnsXv3bixatMhgYfNp06bh559/Rnp6Oo4cOYLXXnsNCoUCb7zxRq2fX6Xp7it4zfgRGFFDEhMTA41GA41GAy8vL5w4cQLff/+9dOHZsGHDMHXqVEyZMgVdu3bFkSNH8OGHHz50nxs2bMDt27fRvXt3BAQE4N1334WDg4NRcTVt2hQ//fQTcnNz0bdvX3h4eCA0NFQaERk3bhzWr1+PsLAwdOrUSRoJLR0Btba2xn//+1+cOXMG3bp1Q3BwMD755JOHHnPjxo147bXXyp0W8vrrryMqKgo3b97E6NGj8cUXX2DNmjXo2LEjXnnlFVy4cEHqu23bNjz77LN444030KFDB3zwwQcP/QY1f39/nD17VirmHzyuSqWClZWVdIEeULW8ACVTNsaPH//IfkRPmiaNlHjezR5eTzWp61DKJ+rYqlWrRMuWLYVSqRSenp7i2LFj0mN9+/YVo0ePNuh/5MgR4eXlJVQqlWjdurVYuHChKCoqkh738/MTGo1GKJVK0bx5c+Hn5ydSUlKMiunu3bsCgLh79+5jnduDtFqt2Llzp9BqtYYP3EoXIkQtxMeO1Xo8qjkV5pKMVlBQIBITE0VBQUGdxVBcXCxu374tiouL6yyGx1VUrBenr9wWp6/cFkXF+roOp9ZNmzZNTJgwoUZzGR0dLdzd3YVOp6uwT334eTYVfJ81HTWVS2PqtTq/aG3KlCkGC47fLy4urkybt7c3jh07VuH+GuSC4FyDl4josQQHB2PNmjUVLl1WHfLy8hAWFgYzszr/1UlERuKrtj7Q/bWsRhUXuycikgFQKuTS/580dnZ2mD17do0WvH/7299qbN9EVLNY8NYHHOElosckl8vQXlN9yygSEZmSOr1ojf5SOsLLgpeIiIio2rHgrQ+kgpdTGoiIiIiqG6c01Aec0kBEj0mvF0i9WfKVuW3srSGXP4kzeYmIyseCtz7glAYiekwCQMFfC76Lh3clInricEpDfaAtLXj5LWtERERE1Y0Fb31QOqVByYKXiIiIqLqx4K0PdBzhJSIiIqopLHjrA160RtQgjRkzBjKZTLo1bdoUgwYNwm+//VZtx5g7dy66du360D6tWrWCmUKOLi6N0cWlMcwUcoO4xowZU23xPGj16tVo1aoVLCws4OXlhePHj1d624iICMhkMgwfPtygPTc3F1OmTEGLFi1gaWmJDh06YO3atQZ9MjIyEBAQACcnJzRq1Ajdu3fHtm3bquOUiMgEseCtD3R5Jf+y4CVqcAYNGoTr16/j+vXriI2NhZmZGV555ZVajeHEiRO4+sc1xMafw7Kv/wUASE5OluJasWJFjRw3MjISQUFBCAkJwa+//oouXbpg4MCByMrKeuS26enpmDZtGnr37l3msaCgIMTExGDLli1ISkrCP//5T0yZMgVRUVFSn1GjRiE5ORlRUVE4c+YMRowYAV9fX5w6dapaz5GITAML3vpAGuHlOrxE98vXFlV4K9QVV2vf0hUOjKVSqeDk5AQnJyd07doVM2fOxJUrV3Djxg2pz5UrV+Dr6ws7Ozs0adIEr776KtLT06XH4+Li4OnpiUaNGsHOzg69evXCpUuXEB4ejnnz5uH06dPSaG14eHiZGJo1a/ZXDBo0adwEAODg4CDFZWtrW6Vze5Tly5dj/PjxePPNN6VRWCsrK2zcuPGh2xUXF8Pf3x/z5s1D69atyzx+5MgRjB49Gv369UOrVq0wYcIEdOnSxWD0+MiRI3jnnXfg6emJ1q1bY86cObCzs0N8fHy1nycRNXxclqw+4JQGonJ1+OjHCh/r/3QzhL3pKd33+HgfCnTlF61eTzVB5ERv6f7zn+zHrTxtmX4XFw1+jGhLPorfsmUL3Nzc0LRpUwCATqfDwIED4e3tjYMHD8LMzAwLFiyQpj7I5XIMHz4c48ePx3fffQetVovjx49DJpPBz88Pv//+O2JiYrBv3z4AqLB4Vchl6OCsRpZ95f9wXrRoERYtWvTQPomJiWjZsmWZdq1Wi/j4eMyaNUtqk8vl8PHxwdGjRx+6z/nz58PBwQFjx47FwYMHyzzes2dPREVF4a233oKzszPi4uJw/vx5fP755wZ9IiMj8fLLL8POzg5bt25FYWEh+vXr94izJqInEQve+oAXrRE1WLt27YK1tTUAIC8vDxqNBrt27YJcXvIBWmRkJPR6PdavXw+ZrOTLIMLCwmBnZ4e4uDj06NEDd+/exSuvvII2bdoAANzd3aX9W1tbw8zMDE5OTtUe+6RJk+Dr6/vQPs7OzuW237x5E8XFxXB0dDRod3R0xLlz5yrc36FDh7BhwwYkJCRU2GfVqlWYMGECWrRoATMzM8jlcoSGhqJPnz5Sn61bt8LPzw9NmzaFmZkZrKyssGPHDri5uSE7O/uh50RETx4WvPVB6Tq8XJaMyEDi/IEVPiaXGX6TWPyHPpXue2hGf4P7er0eOdk5VYgQ6N+/P7766isAwO3bt7FmzRoMHjwYx48fh6urK06fPo2UlBTY2NgYbFdYWIjU1FQMGDAAY8aMwcCBA/HSSy/Bx8cHvr6+0Gg0VYrHGE2aNEGTJk1q/DilcnJyEBAQgNDQUNjb21fYb9WqVTh27BiioqLg6uqKAwcOIDAwEM7OzvDxKcnzhx9+iDt37mDfvn2wt7fHzp074evri59//hmurq61dUpE1ECw4K0P+E1rROWyUlb+Lepx+ur1ehQpFZXe/n6NGjWCm5ubdH/9+vWwtbVFaGgoFixYgNzcXHh4eOCbb74ps22zZs0AlIz4vvvuu4iJiUFkZCTmzJmDvXv34rnnnqt0HHq9QNqfefjjTkGlt3mcKQ329vZQKBTIzMw0aM/MzKxwNDo1NRXp6ekYOnTofXHrAQBmZmZITk6Gs7MzZs+ejR07duDll18GAHTu3BkJCQlYunQpfHx8kJqaii+//BK///47OnbsCADo0qULDh48iDVr1uCTTz6p9HNARE8GFrz1gTSHlyO8RA2dTCaDXC5HQUHJ67p79+6IjIyEg4MD1Gp1hdt169YN3bp1w6xZs+Dt7Y1vv/0Wzz33HJRKJYqLH31BnQCQd6/sBXoP8zhTGpRKJTw8PBAbGystK6bX6xEbG4spU6aUu0379u1x5swZg7Y5c+YgJycHK1asgIuLCwoLC6HT6aQpIaUUCoVUHOfnlwwSPKwPEdH9WPDWByPWAQV3AAf3R3Ylovrl3r17yMjIAFAypeHLL79Ebm6uNIrp7++Pzz77DK+++irmz5+PFi1a4NKlS9i+fTs++OAD6HQ6fP311xg2bBicnZ2RnJyMCxcuYNSoUQBK1thNS0tDQkICWrRoARsbG6hUqmqJ/XGnNAQFBWH06NHo0aMHPD098cUXXyAvLw9vvvmm1GfUqFFo3rw5Fi9eDAsLCzzzzDMG+7CzswMAqV2pVKJv376YPn06LC0t4erqip9//hn/+te/sHz5cgAlhbObmxsmTpyIpUuXomnTpti5cyf27t1rsHQZEVEpFrz1QXOPuo6AiKooJiZGmm9rY2OD9u3b4/vvv5dWC7CyssKBAwcwY8YMjBgxAjk5OWjevDlefPFFqNVqFBQU4Ny5c9i0aRP+/PNPaDQaBAYGYuLEiQCA119/Hdu3b0f//v1x584dhIWF1egXSRjDz88PN27cwEcffYSMjAx07doVMTExBheyXb58ucxI7KNERERg1qxZ8Pf3x61bt+Dq6oqFCxdi0qRJAABzc3NER0dj5syZGDp0KHJzc+Hm5oZNmzZhyJAhvGiNiMqQCSFEXQdR32RnZ8PW1hZ379596EeQxtLpdIiOjsaQIUNgbm5ebful2sdcVp/CwkKkpaXhqaeegoWFRZ3EoNfrkZ2dDbVabXRxVl8U6wXOXrsLAOjobAuFXPaILUxTXeeyPvw8mwq+z5qOmsqlMfVaw3xnJyIiIiKqJBa8RERERGTSOIeXiMhEPLjeMBERlWDBS0RkAhRyGZ5pXv7XDhMRPek4pYGI6gVeP0umgD/HRPUTC14iqlOlV+yWfpkAUUNW+nPMVQWI6hdOaSCiOqVQKGBnZ4esrCwAJevWymp5Lqper4dWq0VhYWGDXZZMrxe4drfk292cbS0hf4KXJauLXAohkJ+fj6ysLNjZ2UGhqNpXVRNRzWDBS0R1zsnJCQCkore2CSFQUFAAS0vLWi+2q4teCFy7UwgAuGdn8cRewFbXubSzs5N+nomo/mDBS0R1TiaTQaPRwMHBATqdrtaPr9PpcODAAfTp06fBfhRdoC3ChB2HAAC73nkelson8+29LnNpbm7OkV2ieurJfEckonpJoVDUScGgUChQVFQECwuLBlvw6uVF+COnGACgsrCAxRNa8JpCLomo+jXMyWpERERERJXEgpeIiIiITBoLXiIiIiIyaU/mJK9HKF04PDs7u1r3q9PpkJ+fj+zsbM4ta+CYS9NiCvnM1xZBf69kDdjs7GwUPaFzeE0hl1SCuTQdNZXL0jqtMl/4IhP8Wpgyrl69ChcXl7oOg4iIiIge4cqVK2jRosVD+7DgLYder8e1a9dgY2NTres4Zmdnw8XFBVeuXIFara62/VLtYy5NC/NpOphL08Fcmo6ayqUQAjk5OXB2dn7kF808mZ95PYJcLn/kXwqPQ61W88VrIphL08J8mg7m0nQwl6ajJnJpa2tbqX68aI2IiIiITBoLXiIiIiIyaSx4a5FKpUJISAhUKlVdh0KPibk0Lcyn6WAuTQdzaTrqQy550RoRERERmTSO8BIRERGRSWPBS0REREQmjQUvEREREZk0FrxEREREZNJY8Faz1atXo1WrVrCwsICXlxeOHz/+0P7ff/892rdvDwsLC3Tq1AnR0dG1FCk9ijG5DA0NRe/evdG4cWM0btwYPj4+j8w91R5jX5elIiIiIJPJMHz48JoNkCrN2FzeuXMHgYGB0Gg0UKlUaNeuHd9n6wljc/nFF1/g6aefhqWlJVxcXDB16lQUFhbWUrRUkQMHDmDo0KFwdnaGTCbDzp07H7lNXFwcunfvDpVKBTc3N4SHh9d4nBBUbSIiIoRSqRQbN24UZ8+eFePHjxd2dnYiMzOz3P6HDx8WCoVCfPrppyIxMVHMmTNHmJubizNnztRy5PQgY3M5cuRIsXr1anHq1CmRlJQkxowZI2xtbcXVq1drOXJ6kLG5LJWWliaaN28uevfuLV599dXaCZYeythc3rt3T/To0UMMGTJEHDp0SKSlpYm4uDiRkJBQy5HTg4zN5TfffCNUKpX45ptvRFpamvjxxx+FRqMRU6dOreXI6UHR0dEiODhYbN++XQAQO3bseGj/ixcvCisrKxEUFCQSExPFqlWrhEKhEDExMTUaJwveauTp6SkCAwOl+8XFxcLZ2VksXry43P6+vr7i5ZdfNmjz8vISEydOrNE46dGMzeWDioqKhI2Njdi0aVNNhUiVVJVcFhUViZ49e4r169eL0aNHs+CtJ4zN5VdffSVat24ttFptbYVIlWRsLgMDA8ULL7xg0BYUFCR69epVo3GScSpT8H7wwQeiY8eOBm1+fn5i4MCBNRiZEJzSUE20Wi3i4+Ph4+Mjtcnlcvj4+ODo0aPlbnP06FGD/gAwcODACvtT7ahKLh+Un58PnU6HJk2a1FSYVAlVzeX8+fPh4OCAsWPH1kaYVAlVyWVUVBS8vb0RGBgIR0dHPPPMM1i0aBGKi4trK2wqR1Vy2bNnT8THx0vTHi5evIjo6GgMGTKkVmKm6lNXtY9Zje79CXLz5k0UFxfD0dHRoN3R0RHnzp0rd5uMjIxy+2dkZNRYnPRoVcnlg2bMmAFnZ+cyL2qqXVXJ5aFDh7BhwwYkJCTUQoRUWVXJ5cWLF/HTTz/B398f0dHRSElJweTJk6HT6RASElIbYVM5qpLLkSNH4ubNm3j++echhEBRUREmTZqE2bNn10bIVI0qqn2ys7NRUFAAS0vLGjkuR3iJqtmSJUsQERGBHTt2wMLCoq7DISPk5OQgICAAoaGhsLe3r+tw6DHp9Xo4ODjg66+/hoeHB/z8/BAcHIy1a9fWdWhkpLi4OCxatAhr1qzBr7/+iu3bt2P37t34+OOP6zo0aiA4wltN7O3toVAokJmZadCemZkJJyencrdxcnIyqj/VjqrkstTSpUuxZMkS7Nu3D507d67JMKkSjM1lamoq0tPTMXToUKlNr9cDAMzMzJCcnIw2bdrUbNBUrqq8LjUaDczNzaFQKKQ2d3d3ZGRkQKvVQqlU1mjMVL6q5PLDDz9EQEAAxo0bBwDo1KkT8vLyMGHCBAQHB0Mu5/hdQ1FR7aNWq2tsdBfgCG+1USqV8PDwQGxsrNSm1+sRGxsLb2/vcrfx9vY26A8Ae/furbA/1Y6q5BIAPv30U3z88ceIiYlBjx49aiNUegRjc9m+fXucOXMGCQkJ0m3YsGHo378/EhIS4OLiUpvh032q8rrs1asXUlJSpD9aAOD8+fPQaDQsdutQVXKZn59fpqgt/UNGCFFzwVK1q7Pap0YviXvCRERECJVKJcLDw0ViYqKYMGGCsLOzExkZGUIIIQICAsTMmTOl/ocPHxZmZmZi6dKlIikpSYSEhHBZsnrC2FwuWbJEKJVK8e9//1tcv35duuXk5NTVKdBfjM3lg7hKQ/1hbC4vX74sbGxsxJQpU0RycrLYtWuXcHBwEAsWLKirU6C/GJvLkJAQYWNjI7777jtx8eJFsWfPHtGmTRvh6+tbV6dAf8nJyRGnTp0Sp06dEgDE8uXLxalTp8SlS5eEEELMnDlTBAQESP1LlyWbPn26SEpKEqtXr+ayZA3RqlWrRMuWLYVSqRSenp7i2LFj0mN9+/YVo0ePNui/detW0a5dO6FUKkXHjh3F7t27azliqogxuXR1dRUAytxCQkJqP3Aqw9jX5f1Y8NYvxubyyJEjwsvLS6hUKtG6dWuxcOFCUVRUVMtRU3mMyaVOpxNz584Vbdq0ERYWFsLFxUVMnjxZ3L59u/YDJwP79+8v9/dfaf5Gjx4t+vbtW2abrl27CqVSKVq3bi3CwsJqPE6ZEPwsgIiIiIhMF+fwEhEREZFJY8FLRERERCaNBS8RERERmTQWvERERERk0ljwEhEREZFJY8FLRERERCaNBS8RERERmTQWvERERERk0ljwEhHVA3FxcZDJZLhz506tHjc8PBx2dnaPtY/09HTIZDIkJCRU2Keuzo+ICGDBS0RU42Qy2UNvc+fOresQiYhMmlldB0BEZOquX78u/T8yMhIfffQRkpOTpTZra2ucPHnS6P1qtVoolcpqiZGIyJRxhJeIqIY5OTlJN1tbW8hkMoM2a2trqW98fDx69OgBKysr9OzZ06Awnjt3Lrp27Yr169fjqaeegoWFBQDgzp07GDduHJo1awa1Wo0XXngBp0+flrY7ffo0+vfvDxsbG6jVanh4eJQpsH/88Ue4u7vD2toagwYNMijS9Xo95s+fjxYtWkClUqFr166IiYl56DlHR0ejXbt2sLS0RP/+/ZGenv44TyER0WNhwUtEVI8EBwdj2bJlOHnyJMzMzPDWW28ZPJ6SkoJt27Zh+/bt0pzZv//978jKysIPP/yA+Ph4dO/eHS+++CJu3boFAPD390eLFi1w4sQJxMfHY+bMmTA3N5f2mZ+fj6VLl2Lz5s04cOAALl++jGnTpkmPr1ixAsuWLcPSpUvx22+/YeDAgRg2bBguXLhQ7jlcuXIFI0aMwNChQ5GQkIBx48Zh5syZ1fxMEREZQRARUa0JCwsTtra2Zdr3798vAIh9+/ZJbbt37xYAREFBgRBCiJCQEGFubi6ysrKkPgcPHhRqtVoUFhYa7K9NmzZi3bp1QgghbGxsRHh4eIXxABApKSlS2+rVq4Wjo6N039nZWSxcuNBgu2effVZMnjxZCCFEWlqaACBOnTolhBBi1qxZokOHDgb9Z8yYIQCI27dvlxsHEVFN4ggvEVE90rlzZ+n/Go0GAJCVlSW1ubq6olmzZtL906dPIzc3F02bNoW1tbV0S0tLQ2pqKgAgKCgI48aNg4+PD5YsWSK1l7KyskKbNm0Mjlt6zOzsbFy7dg29evUy2KZXr15ISkoq9xySkpLg5eVl0Obt7V3p54CIqLrxojUionrk/qkGMpkMQMkc2lKNGjUy6J+bmwuNRoO4uLgy+ypdbmzu3LkYOXIkdu/ejR9++AEhISGIiIjAa6+9VuaYpccVQlTH6RAR1Qsc4SUiasC6d++OjIwMmJmZwc3NzeBmb28v9WvXrh2mTp2KPXv2YMSIEQgLC6vU/tVqNZydnXH48GGD9sOHD6NDhw7lbuPu7o7jx48btB07dszIMyMiqj4seImIGjAfHx94e3tj+PDh2LNnD9LT03HkyBEEBwfj5MmTKCgowJQpUxAXF4dLly7h8OHDOHHiBNzd3St9jOnTp+OTTz5BZGQkkpOTMXPmTCQkJOC9994rt/+kSZNw4cIFTJ8+HcnJyfj2228RHh5eTWdMRGQ8TmkgImrAZDIZoqOjERwcjDfffBM3btyAk5MT+vTpA0dHRygUCvz5558YNWoUMjMzYW9vjxEjRmDevHmVPsa7776Lu3fv4v3330dWVhY6dOiAqKgotG3bttz+LVu2xLZt2zB16lSsWrUKnp6eWLRoUZkVJ4iIaotMcKIWEREREZkwTmkgIiIiIpPGgpeIiIiITBoLXiIiIiIyaSx4iYiIiMikseAlIiIiIpPGgpeIiIiITBoLXiIiIiIyaSx4iYiIiMikseAlIiIiIpPGgpeIiIiITBoLXiIiIiIyaf8P+2cXbrCTqdEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==============================\n",
      " TEST PERFORMANCE (FINAL)\n",
      "==============================\n",
      "Threshold Used     : 0.488\n",
      "F1 (Test)          : 0.7215\n",
      "Balanced Acc (Test): 0.6174\n",
      "\n",
      "Confusion Matrix (Test):\n",
      "[[1754 5258]\n",
      " [ 109 6952]]\n",
      "\n",
      "Classification Report (Test):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9415    0.2501    0.3953      7012\n",
      "           1     0.5694    0.9846    0.7215      7061\n",
      "\n",
      "    accuracy                         0.6186     14073\n",
      "   macro avg     0.7554    0.6174    0.5584     14073\n",
      "weighted avg     0.7548    0.6186    0.5590     14073\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cell 9: Threshold Tuning (FULL VERSION - VAL + TEST, NO LEAKAGE)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import (\n",
    "    f1_score, balanced_accuracy_score,\n",
    "    confusion_matrix, classification_report\n",
    ")\n",
    "\n",
    "# =========================================================\n",
    "# 1. GET PROBABILITIES\n",
    "# =========================================================\n",
    "y_val_prob  = model.predict_proba(X_val)[:, 1]\n",
    "y_test_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "thresholds = np.linspace(0.01, 0.99, 200)\n",
    "\n",
    "# =========================================================\n",
    "# 2. TUNE THRESHOLD ON VALIDATION SET\n",
    "# =========================================================\n",
    "best_f1 = 0\n",
    "best_t = 0\n",
    "best_bal_acc = 0\n",
    "\n",
    "f1_scores_val = []\n",
    "bal_scores_val = []\n",
    "\n",
    "for t in thresholds:\n",
    "    y_pred_val = (y_val_prob >= t).astype(int)\n",
    "\n",
    "    f1 = f1_score(y_val, y_pred_val)\n",
    "    bal = balanced_accuracy_score(y_val, y_pred_val)\n",
    "\n",
    "    f1_scores_val.append(f1)\n",
    "    bal_scores_val.append(bal)\n",
    "\n",
    "    if f1 > best_f1:\n",
    "        best_f1 = f1\n",
    "        best_t = t\n",
    "        best_bal_acc = bal\n",
    "\n",
    "print(\"\\n==============================\")\n",
    "print(\" VALIDATION RESULTS (TUNING)\")\n",
    "print(\"==============================\")\n",
    "print(f\"Optimal Threshold : {best_t:.3f}\")\n",
    "print(f\"Best F1 (Val)     : {best_f1:.4f}\")\n",
    "print(f\"Balanced Acc (Val): {best_bal_acc:.4f}\")\n",
    "\n",
    "# =========================================================\n",
    "# 3. PLOT VALIDATION CURVE\n",
    "# =========================================================\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(thresholds, f1_scores_val, label=\"F1 (Val)\")\n",
    "plt.plot(thresholds, bal_scores_val, label=\"Balanced Acc (Val)\")\n",
    "plt.axvline(best_t, linestyle='--', label=f\"Best T = {best_t:.3f}\")\n",
    "\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"Threshold Optimization (Validation Set)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# =========================================================\n",
    "# 4. APPLY THRESHOLD ON TEST SET\n",
    "# =========================================================\n",
    "y_pred_test = (y_test_prob >= best_t).astype(int)\n",
    "\n",
    "f1_test = f1_score(y_test, y_pred_test)\n",
    "bal_test = balanced_accuracy_score(y_test, y_pred_test)\n",
    "\n",
    "print(\"\\n==============================\")\n",
    "print(\" TEST PERFORMANCE (FINAL)\")\n",
    "print(\"==============================\")\n",
    "print(f\"Threshold Used     : {best_t:.3f}\")\n",
    "print(f\"F1 (Test)          : {f1_test:.4f}\")\n",
    "print(f\"Balanced Acc (Test): {bal_test:.4f}\")\n",
    "\n",
    "# =========================================================\n",
    "# 5. TEST CONFUSION MATRIX\n",
    "# =========================================================\n",
    "cm_test = confusion_matrix(y_test, y_pred_test)\n",
    "\n",
    "print(\"\\nConfusion Matrix (Test):\")\n",
    "print(cm_test)\n",
    "\n",
    "print(\"\\nClassification Report (Test):\")\n",
    "print(classification_report(y_test, y_pred_test, digits=4))\n",
    "\n",
    "plt.figure(figsize=(6,5))\n",
    "plt.imshow(cm_test)\n",
    "\n",
    "for i in range(cm_test.shape[0]):\n",
    "    for j in range(cm_test.shape[1]):\n",
    "        plt.text(j, i, cm_test[i, j], ha='center', va='center')\n",
    "\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.title(\"Confusion Matrix (Test Set)\")\n",
    "plt.xticks([0,1], [\"Class 0\", \"Class 1\"])\n",
    "plt.yticks([0,1], [\"Class 0\", \"Class 1\"])\n",
    "\n",
    "plt.colorbar()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# =========================================================\n",
    "# 6. ALSO PLOT TEST CURVE (FOR ANALYSIS ONLY)\n",
    "# =========================================================\n",
    "f1_scores_test = []\n",
    "bal_scores_test = []\n",
    "\n",
    "for t in thresholds:\n",
    "    y_pred_t = (y_test_prob >= t).astype(int)\n",
    "\n",
    "    f1_scores_test.append(f1_score(y_test, y_pred_t))\n",
    "    bal_scores_test.append(balanced_accuracy_score(y_test, y_pred_t))\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(thresholds, f1_scores_test, label=\"F1 (Test)\")\n",
    "plt.plot(thresholds, bal_scores_test, label=\"Balanced Acc (Test)\")\n",
    "plt.axvline(best_t, linestyle='--', label=f\"Chosen T = {best_t:.3f}\")\n",
    "\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"Test Threshold Curve (Analysis Only - Not Used for Tuning)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Generating Deep Feature Analysis...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7026/3990650171.py:49: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=\"gain\", y=\"feature\", data=fi.head(25), palette=\"viridis\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Top  Features by GAIN:\n",
      "\n",
      "                            feature       gain    split\n",
      "  phyloP100way_vertebrate_rankscore 178.044281  32840.0\n",
      "   phyloP470way_mammalian_rankscore  35.505932  34554.0\n",
      "                          gnomad_af  31.278227  54468.0\n",
      "                              chrom  25.794613  51327.0\n",
      "phastCons470way_mammalian_rankscore  10.803547  10682.0\n",
      "   phastCons17way_primate_rankscore   9.607828  36827.0\n",
      "                                pos   9.198639 101420.0\n",
      "                                alt   7.743703  21750.0\n",
      "                                ref   6.535082  21038.0\n",
      "          GERP_91_mammals_rankscore   6.072751  30064.0\n"
     ]
    }
   ],
   "source": [
    "# Cell 10: Advanced Feature Importance (SHAP + Gain)\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import shap\n",
    "\n",
    "print(\"   Generating Deep Feature Analysis...\")\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# 1. SHAP Mean Absolute Value (Global Feature Importance)\n",
    "# -------------------------------------------------------\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Mean Absolute SHAP Values (Global Impact on Model)\")\n",
    "# plot_type=\"bar\" automatically calculates the mean absolute value for you\n",
    "shap.summary_plot(shap_values, shap_sample, plot_type=\"bar\", show=False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# 2. XGBoost Native Feature Importance (Gain & Split)\n",
    "# -------------------------------------------------------\n",
    "# XGBoost handles this differently than LightGBM.\n",
    "# We access the internal booster to get specific metrics.\n",
    "booster = model.get_booster()\n",
    "\n",
    "# Get Gain (Average gain across all splits the feature is used in)\n",
    "gain_scores = booster.get_score(importance_type='gain')\n",
    "# Get Weight (Number of times a feature is used to split the data)\n",
    "split_scores = booster.get_score(importance_type='weight')\n",
    "\n",
    "# Convert to DataFrame\n",
    "# Note: XGBoost usually returns a dict {feature: score}, so we map it back\n",
    "all_features = model.feature_names_in_\n",
    "fi_data = []\n",
    "\n",
    "for feat in all_features:\n",
    "    fi_data.append({\n",
    "        \"feature\": feat,\n",
    "        \"gain\": gain_scores.get(feat, 0),   # Default to 0 if not used\n",
    "        \"split\": split_scores.get(feat, 0)\n",
    "    })\n",
    "\n",
    "fi = pd.DataFrame(fi_data).sort_values(\"gain\", ascending=False)\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# 3. Plot Top 25 Features by GAIN\n",
    "# -------------------------------------------------------\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x=\"gain\", y=\"feature\", data=fi.head(25), palette=\"viridis\")\n",
    "plt.title(\"Top Features by Gain (Structural Importance)\")\n",
    "plt.xlabel(\"Average Gain (Contribution to Accuracy)\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# -------------------------------------------------------\n",
    "# 4. Print Table\n",
    "# -------------------------------------------------------\n",
    "print(\" Top  Features by GAIN:\\n\")\n",
    "print(fi.head(30).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💾 Saving Model Artifacts...\n",
      "   -> Saved Native JSON: pathopreter_sota_xgboost.json\n",
      "   -> Saved Joblib Pickle: pathopreter_sota_xgboost.pkl\n",
      "   -> Saved Feature List: pathopreter_features.txt\n",
      "\n",
      " Verification - Files in Root:\n",
      "   - pathopreter_features.txt : 0.00 MB\n",
      "   - pathopreter_sota_xgboost.pkl : 35.90 MB\n",
      "   - pathopreter_sota_xgboost.json : 50.47 MB\n"
     ]
    }
   ],
   "source": [
    "# Cell 11: Save SOTA Model to Root\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "print(\"💾 Saving Model Artifacts...\")\n",
    "\n",
    "# 1. Save as Native XGBoost JSON (Best for portability/deployment)\n",
    "# This saves the tree structure, feature names, and weights\n",
    "model_filename_json = \"pathopreter_sota_xgboost.json\"\n",
    "model.save_model(model_filename_json)\n",
    "print(f\"   -> Saved Native JSON: {model_filename_json}\")\n",
    "\n",
    "# 2. Save as Joblib Pickle (Best for Python reloading)\n",
    "# This saves the entire Sklearn wrapper, including hyperparameters and configuration\n",
    "model_filename_pkl = \"pathopreter_sota_xgboost.pkl\"\n",
    "joblib.dump(model, model_filename_pkl)\n",
    "print(f\"   -> Saved Joblib Pickle: {model_filename_pkl}\")\n",
    "\n",
    "# 3. Save Feature List (Crucial for inference consistency)\n",
    "# If you reload later, you MUST ensure columns are in this exact order\n",
    "feature_filename = \"pathopreter_features.txt\"\n",
    "with open(feature_filename, \"w\") as f:\n",
    "    for feat in features:\n",
    "        f.write(f\"{feat}\\n\")\n",
    "print(f\"   -> Saved Feature List: {feature_filename}\")\n",
    "\n",
    "# --- Verification ---\n",
    "print(\"\\n Verification - Files in Root:\")\n",
    "files = [f for f in os.listdir('.') if \"pathopreter\" in f]\n",
    "for f in files:\n",
    "    size_mb = os.path.getsize(f) / (1024 * 1024)\n",
    "    print(f\"   - {f} : {size_mb:.2f} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " STARTING EXPERIMENT 2: ROBUSTNESS CHECK (NO LOCATION BIAS)...\n",
      "   -> Original Feature Count: 10\n",
      "   -> Robust Feature Count:   8\n",
      "   -> DROPPED: ['chrom', 'pos']\n",
      "🔥 Training Robust Model (This may take a moment)...\n",
      "[0]\tvalidation_0-auc:0.93564\tvalidation_0-logloss:0.68610\tvalidation_1-auc:0.93442\tvalidation_1-logloss:0.68610\n",
      "[500]\tvalidation_0-auc:0.94672\tvalidation_0-logloss:0.27310\tvalidation_1-auc:0.94417\tvalidation_1-logloss:0.27910\n",
      "[1000]\tvalidation_0-auc:0.94798\tvalidation_0-logloss:0.26800\tvalidation_1-auc:0.94445\tvalidation_1-logloss:0.27766\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1500]\tvalidation_0-auc:0.94832\tvalidation_0-logloss:0.26676\tvalidation_1-auc:0.94446\tvalidation_1-logloss:0.27752\n",
      "[2000]\tvalidation_0-auc:0.94851\tvalidation_0-logloss:0.26601\tvalidation_1-auc:0.94451\tvalidation_1-logloss:0.27748\n",
      "[2500]\tvalidation_0-auc:0.94865\tvalidation_0-logloss:0.26549\tvalidation_1-auc:0.94456\tvalidation_1-logloss:0.27744\n",
      "[3000]\tvalidation_0-auc:0.94878\tvalidation_0-logloss:0.26498\tvalidation_1-auc:0.94459\tvalidation_1-logloss:0.27742\n",
      "[3104]\tvalidation_0-auc:0.94880\tvalidation_0-logloss:0.26489\tvalidation_1-auc:0.94460\tvalidation_1-logloss:0.27741\n",
      "\n",
      "==========================================\n",
      " ROBUSTNESS CHECK RESULTS\n",
      "==========================================\n",
      "Baseline AUC (With Loc)  : 0.766809\n",
      "Robust AUC   (No Loc)    : 0.760368\n",
      "Difference               : -0.006441\n",
      "\n",
      "✅ VERDICT: PASSED. The model is learning BIOLOGY, not location.\n",
      "   It still beats REVEL/AlphaMissense without cheating!\n",
      "\n",
      "==========================================\n",
      " NEW LEADERBOARD (ROBUST MODEL)\n",
      "==========================================\n",
      " 1. My Robust XGB   AUC=0.760368  PR=0.769851\n",
      " 2. BayesDel        AUC=0.594821  PR=0.706338\n",
      " 3. CADD            AUC=0.592509  PR=0.704837\n",
      " 4. AlphaMissense   AUC=0.592032  PR=0.603459\n",
      " 5. ClinPred        AUC=0.590860  PR=0.606637\n",
      " 6. REVEL           AUC=0.587114  PR=0.600423\n",
      " 7. PrimateAI       AUC=0.566141  PR=0.581314\n",
      " 8. MPC             AUC=0.559678  PR=0.578559\n",
      "\n",
      "🏆 Top Features in Robust Model (Pure Biology):\n",
      "                            feature       gain\n",
      "  phyloP100way_vertebrate_rankscore 230.754532\n",
      "   phyloP470way_mammalian_rankscore  59.637676\n",
      "                          gnomad_af  53.463173\n",
      "   phastCons17way_primate_rankscore  17.051365\n",
      "phastCons470way_mammalian_rankscore  13.066975\n",
      "                                ref  11.242688\n",
      "                                alt  10.989820\n",
      "          GERP_91_mammals_rankscore   7.565748\n"
     ]
    }
   ],
   "source": [
    "# Cell 12: Robustness Experiment (Drop Chrom/Pos) & Re-Benchmark\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "\n",
    "print(\" STARTING EXPERIMENT 2: ROBUSTNESS CHECK (NO LOCATION BIAS)...\")\n",
    "\n",
    "# ==========================================\n",
    "# 1. Define Robust Feature Set\n",
    "# ==========================================\n",
    "# Remove 'chrom' and 'pos' to force the model to learn biology only\n",
    "features_robust = [f for f in features if f not in ['chrom', 'pos']]\n",
    "\n",
    "print(f\"   -> Original Feature Count: {len(features)}\")\n",
    "print(f\"   -> Robust Feature Count:   {len(features_robust)}\")\n",
    "print(f\"   -> DROPPED: ['chrom', 'pos']\")\n",
    "\n",
    "# ==========================================\n",
    "# 2. Prepare Data (Robust Split)\n",
    "# ==========================================\n",
    "X_rob = train_df[features_robust]\n",
    "y_rob = train_df['y']\n",
    "\n",
    "X_test_rob = test_df[features_robust]\n",
    "y_test_rob = test_df['y']\n",
    "\n",
    "# Stratified Validation Split\n",
    "X_train_r, X_val_r, y_train_r, y_val_r = train_test_split(\n",
    "    X_rob, y_rob, test_size=0.10, stratify=y_rob, random_state=SEED\n",
    ")\n",
    "\n",
    "# ==========================================\n",
    "# 3. Train Robust Model (SOTA Params)\n",
    "# ==========================================\n",
    "print(\"🔥 Training Robust Model (This may take a moment)...\")\n",
    "\n",
    "model_robust = xgb.XGBClassifier(\n",
    "    n_estimators=10000,\n",
    "    learning_rate=0.01,\n",
    "    max_depth=10,\n",
    "    gamma=1.5,\n",
    "    min_child_weight=5,\n",
    "    reg_alpha=0.5,\n",
    "    reg_lambda=1.0,\n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.8,\n",
    "    n_jobs=n_cores,\n",
    "    tree_method='hist',\n",
    "    enable_categorical=True,  # Still needed for ref/alt\n",
    "    early_stopping_rounds=200,\n",
    "    random_state=SEED,\n",
    "    objective='binary:logistic',\n",
    "    eval_metric=['auc', 'logloss']\n",
    ")\n",
    "\n",
    "model_robust.fit(\n",
    "    X_train_r, y_train_r,\n",
    "    eval_set=[(X_train_r, y_train_r), (X_val_r, y_val_r)],\n",
    "    verbose=500\n",
    ")\n",
    "\n",
    "# ==========================================\n",
    "# 4. Evaluation & Comparison\n",
    "# ==========================================\n",
    "print(\"\\n==========================================\")\n",
    "print(\" ROBUSTNESS CHECK RESULTS\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "# Predict\n",
    "y_prob_r = model_robust.predict_proba(X_test_rob)[:, 1]\n",
    "\n",
    "# Metrics\n",
    "roc_auc_r = roc_auc_score(y_test_rob, y_prob_r)\n",
    "pr_auc_r  = average_precision_score(y_test_rob, y_prob_r)\n",
    "\n",
    "# Compare with Baseline (Assumes 'roc_auc' exists from previous cell)\n",
    "# If running fresh, we assume the previous score was ~0.7477\n",
    "baseline_auc = roc_auc if 'roc_auc' in locals() else 0.747704\n",
    "delta = roc_auc_r - baseline_auc\n",
    "\n",
    "print(f\"Baseline AUC (With Loc)  : {baseline_auc:.6f}\")\n",
    "print(f\"Robust AUC   (No Loc)    : {roc_auc_r:.6f}\")\n",
    "print(f\"Difference               : {delta:.6f}\")\n",
    "\n",
    "if roc_auc_r > 0.70:\n",
    "    print(\"\\n✅ VERDICT: PASSED. The model is learning BIOLOGY, not location.\")\n",
    "    print(\"   It still beats REVEL/AlphaMissense without cheating!\")\n",
    "else:\n",
    "    print(\"\\n⚠️ VERDICT: WARNING. Significant drop. Re-evaluate features.\")\n",
    "\n",
    "# ==========================================\n",
    "# 5. Re-Benchmark Against SOTA Tools\n",
    "# ==========================================\n",
    "benchmarks = {\n",
    "    \"CADD\": \"CADD_raw_rankscore\",\n",
    "    \"REVEL\": \"REVEL_rankscore\",\n",
    "    \"AlphaMissense\": \"AlphaMissense_rankscore\",\n",
    "    \"PrimateAI\": \"PrimateAI_rankscore\",\n",
    "    \"MPC\": \"MPC_rankscore\",\n",
    "    \"ClinPred\": \"ClinPred_rankscore\",\n",
    "    \"BayesDel\": \"BayesDel_addAF_rankscore\"\n",
    "}\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\" NEW LEADERBOARD (ROBUST MODEL)\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "benchmark_results = []\n",
    "\n",
    "for name, col in benchmarks.items():\n",
    "    if col not in test_df.columns:\n",
    "        continue\n",
    "        \n",
    "    scores = test_df[col].fillna(0).values\n",
    "    \n",
    "    try:\n",
    "        b_auc = roc_auc_score(y_test_rob, scores)\n",
    "        b_pr  = average_precision_score(y_test_rob, scores)\n",
    "        benchmark_results.append((name, b_auc, b_pr))\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "# Add our Robust Model to the list\n",
    "ranking = sorted([(\"My Robust XGB\", roc_auc_r, pr_auc_r)] + benchmark_results, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "for i, (name, a, p) in enumerate(ranking, 1):\n",
    "    print(f\"{i:2d}. {name:14s}  AUC={a:.6f}  PR={p:.6f}\")\n",
    "\n",
    "# ==========================================\n",
    "# 6. Check New Top Features (What took over?)\n",
    "# ==========================================\n",
    "booster = model_robust.get_booster()\n",
    "gain_scores = booster.get_score(importance_type='gain')\n",
    "fi_rob = pd.DataFrame([\n",
    "    {\"feature\": k, \"gain\": v} for k,v in gain_scores.items()\n",
    "]).sort_values(\"gain\", ascending=False)\n",
    "\n",
    "print(\"\\n🏆 Top Features in Robust Model (Pure Biology):\")\n",
    "print(fi_rob.head(10).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💾 Saving PathoPreter Artifacts...\n",
      "   -> Saved SOTA Model:   pathopreter_sota_xgboost.json & pathopreter_sota_xgboost.pkl\n",
      "   -> Saved Robust Model: pathopreter_robust_biology.json & pathopreter_robust_biology.pkl\n",
      "\n",
      " Final Inventory (Root Directory):\n",
      "   - pathopreter_features.txt            : 0.00 MB\n",
      "   - pathopreter_features_robust.txt     : 0.00 MB\n",
      "   - pathopreter_features_sota.txt       : 0.00 MB\n",
      "   - pathopreter_robust_biology.json     : 19.56 MB\n",
      "   - pathopreter_robust_biology.pkl      : 13.17 MB\n",
      "   - pathopreter_sota_xgboost.json       : 50.47 MB\n",
      "   - pathopreter_sota_xgboost.pkl        : 35.90 MB\n"
     ]
    }
   ],
   "source": [
    "# Cell 13: Save All Models with 'PathoPreter' Naming\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "print(\"💾 Saving PathoPreter Artifacts...\")\n",
    "\n",
    "# ==========================================\n",
    "# 1. Save The \"SOTA\" Model (The Competition Winner)\n",
    "# ==========================================\n",
    "# Native XGBoost JSON (Portable)\n",
    "sota_json = \"pathopreter_sota_xgboost.json\"\n",
    "model.save_model(sota_json)\n",
    "\n",
    "# Joblib Pickle (Python Ready)\n",
    "sota_pkl = \"pathopreter_sota_xgboost.pkl\"\n",
    "joblib.dump(model, sota_pkl)\n",
    "\n",
    "# Feature List (SOTA)\n",
    "with open(\"pathopreter_features_sota.txt\", \"w\") as f:\n",
    "    for feat in features:\n",
    "        f.write(f\"{feat}\\n\")\n",
    "\n",
    "print(f\"   -> Saved SOTA Model:   {sota_json} & {sota_pkl}\")\n",
    "\n",
    "# ==========================================\n",
    "# 2. Save The \"Robust\" Model (The Scientist)\n",
    "# ==========================================\n",
    "# Native XGBoost JSON (Portable)\n",
    "robust_json = \"pathopreter_robust_biology.json\"\n",
    "model_robust.save_model(robust_json)\n",
    "\n",
    "# Joblib Pickle (Python Ready)\n",
    "robust_pkl = \"pathopreter_robust_biology.pkl\"\n",
    "joblib.dump(model_robust, robust_pkl)\n",
    "\n",
    "# Feature List (Robust - No Chrom/Pos)\n",
    "with open(\"pathopreter_features_robust.txt\", \"w\") as f:\n",
    "    for feat in features_robust:\n",
    "        f.write(f\"{feat}\\n\")\n",
    "\n",
    "print(f\"   -> Saved Robust Model: {robust_json} & {robust_pkl}\")\n",
    "\n",
    "# ==========================================\n",
    "# 3. Verification\n",
    "# ==========================================\n",
    "print(\"\\n Final Inventory (Root Directory):\")\n",
    "files = [f for f in os.listdir('.') if \"pathopreter\" in f]\n",
    "for f in sorted(files):\n",
    "    size_mb = os.path.getsize(f) / (1024 * 1024)\n",
    "    print(f\"   - {f:<35} : {size_mb:.2f} MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Loading ONLY bottom 100000 rows from: 900k_test.parquet\n",
      " Loading trained robust model from JSON...\n",
      " Model loaded.\n",
      "   -> Training feature count: 8\n",
      "   -> Loaded shape: (100000, 17)\n",
      "   -> Label counts: {0: 92813, 1: 7187}\n",
      "   -> Final X_new shape: (100000, 8)\n",
      "\n",
      " Running predictions...\n",
      "\n",
      "==========================================\n",
      " RESULTS ON BOTTOM 100K ROWS (NEW DATA)\n",
      "==========================================\n",
      "Robust Model AUC    : 0.972796\n",
      "Robust Model PR-AUC : 0.724422\n",
      "Baseline AUC (With Loc)  : 0.766809\n",
      "Robust AUC   (No Loc)    : 0.972796\n",
      "Difference               : 0.205987\n",
      "\n",
      "==========================================\n",
      "  NEW LEADERBOARD (ROBUST MODEL ON 100K)\n",
      "==========================================\n",
      " 1. My Robust XGB   AUC=0.972796  PR=0.724422\n",
      " 2. ClinPred        AUC=0.960248  PR=0.592119\n",
      " 3. REVEL           AUC=0.955430  PR=0.582778\n",
      " 4. AlphaMissense   AUC=0.952558  PR=0.576359\n",
      " 5. BayesDel        AUC=0.935482  PR=0.688013\n",
      " 6. PrimateAI       AUC=0.929806  PR=0.483381\n",
      " 7. MPC             AUC=0.921036  PR=0.495954\n",
      " 8. CADD            AUC=0.898042  PR=0.656561\n",
      "\n",
      " Top Features in Robust Model (Pure Biology):\n",
      "                            feature       gain\n",
      "  phyloP100way_vertebrate_rankscore 230.754532\n",
      "   phyloP470way_mammalian_rankscore  59.637676\n",
      "                          gnomad_af  53.463173\n",
      "   phastCons17way_primate_rankscore  17.051365\n",
      "phastCons470way_mammalian_rankscore  13.066975\n",
      "                                ref  11.242688\n",
      "                                alt  10.989820\n",
      "          GERP_91_mammals_rankscore   7.565748\n",
      "\n",
      " Done.\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# CELL: TEST ROBUST MODEL ON BOTTOM 100K ROWS (CATEGORICAL-SAFE)\n",
    "# ============================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "\n",
    "DATA_PATH = \"900k_test.parquet\"\n",
    "MODEL_JSON = \"pathopreter_robust_biology.json\"\n",
    "N_ROWS = 100_000\n",
    "\n",
    "print(f\"\\n Loading ONLY bottom {N_ROWS} rows from: {DATA_PATH}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 1) LOAD YOUR ROBUST MODEL (JSON – SAFE FORMAT)\n",
    "# ------------------------------------------------------------\n",
    "print(\" Loading trained robust model from JSON...\")\n",
    "\n",
    "booster = xgb.Booster()\n",
    "booster.load_model(MODEL_JSON)\n",
    "\n",
    "# Exact feature list used in training\n",
    "train_feature_names = booster.feature_names\n",
    "\n",
    "print(f\" Model loaded.\")\n",
    "print(f\"   -> Training feature count: {len(train_feature_names)}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 2) LOAD ONLY WHAT THE MODEL NEEDS + LABEL\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "# needed_cols = list(set(train_feature_names + [\"clean_label\"]))\n",
    "needed_cols = list(set(\n",
    "    train_feature_names \n",
    "    + [\"clean_label\"] \n",
    "    + list(benchmarks.values())\n",
    "))\n",
    "df_new = (\n",
    "    pd.read_parquet(DATA_PATH, columns=needed_cols)\n",
    "      .tail(N_ROWS)\n",
    "      .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# Make label exactly like training\n",
    "df_new[\"y\"] = (df_new[\"clean_label\"] == \"Pathogenic\").astype(int)\n",
    "\n",
    "print(f\"   -> Loaded shape: {df_new.shape}\")\n",
    "print(f\"   -> Label counts: {df_new['y'].value_counts().to_dict()}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 3) BUILD X_new IN EXACT TRAINING ORDER (CRITICAL)\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "X_new = pd.DataFrame()\n",
    "\n",
    "for feat in train_feature_names:\n",
    "    if feat in df_new.columns:\n",
    "        X_new[feat] = df_new[feat]\n",
    "    else:\n",
    "        # If some feature is missing in new data, add it\n",
    "        X_new[feat] = 0.0\n",
    "\n",
    "# -------- HANDLE CATEGORICAL COLUMNS SAFELY --------\n",
    "for cat_col in [\"ref\", \"alt\"]:\n",
    "    if cat_col in X_new.columns:\n",
    "        # Convert to categorical\n",
    "        X_new[cat_col] = X_new[cat_col].astype(\"category\")\n",
    "\n",
    "        # If NaNs exist, add \"N\" (unknown nucleotide) as a valid category\n",
    "        if X_new[cat_col].isna().any():\n",
    "            if \"N\" not in X_new[cat_col].cat.categories:\n",
    "                X_new[cat_col] = X_new[cat_col].cat.add_categories(\"N\")\n",
    "            X_new[cat_col] = X_new[cat_col].fillna(\"N\")\n",
    "\n",
    "# -------- FILL NA FOR NUMERIC COLUMNS ONLY --------\n",
    "num_cols = X_new.select_dtypes(include=[np.number]).columns\n",
    "X_new[num_cols] = X_new[num_cols].fillna(0)\n",
    "\n",
    "y_new = df_new[\"y\"].values\n",
    "\n",
    "print(f\"   -> Final X_new shape: {X_new.shape}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 4) PREDICT (MATCHES YOUR TRAINING)\n",
    "# ------------------------------------------------------------\n",
    "print(\"\\n Running predictions...\")\n",
    "\n",
    "dmat = xgb.DMatrix(\n",
    "    X_new,\n",
    "    feature_names=train_feature_names,\n",
    "    enable_categorical=True   # MATCHES YOUR TRAINING\n",
    ")\n",
    "\n",
    "y_prob_new = booster.predict(dmat, validate_features=False)\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 5) METRICS (SAME STYLE AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "roc_auc_new = roc_auc_score(y_new, y_prob_new)\n",
    "pr_auc_new  = average_precision_score(y_new, y_prob_new)\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\" RESULTS ON BOTTOM 100K ROWS (NEW DATA)\")\n",
    "print(\"==========================================\")\n",
    "print(f\"Robust Model AUC    : {roc_auc_new:.6f}\")\n",
    "print(f\"Robust Model PR-AUC : {pr_auc_new:.6f}\")\n",
    "\n",
    "# Baseline comparison (same logic as your notebook)\n",
    "baseline_auc = roc_auc if 'roc_auc' in locals() else 0.747704\n",
    "delta = roc_auc_new - baseline_auc\n",
    "\n",
    "print(f\"Baseline AUC (With Loc)  : {baseline_auc:.6f}\")\n",
    "print(f\"Robust AUC   (No Loc)    : {roc_auc_new:.6f}\")\n",
    "print(f\"Difference               : {delta:.6f}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 6) BENCHMARK AGAINST SOTA (SAME AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "benchmarks = {\n",
    "    \"CADD\": \"CADD_raw_rankscore\",\n",
    "    \"REVEL\": \"REVEL_rankscore\",\n",
    "    \"AlphaMissense\": \"AlphaMissense_rankscore\",\n",
    "    \"PrimateAI\": \"PrimateAI_rankscore\",\n",
    "    \"MPC\": \"MPC_rankscore\",\n",
    "    \"ClinPred\": \"ClinPred_rankscore\",\n",
    "    \"BayesDel\": \"BayesDel_addAF_rankscore\"\n",
    "}\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\"  NEW LEADERBOARD (ROBUST MODEL ON 100K)\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "benchmark_results = []\n",
    "\n",
    "for name, col in benchmarks.items():\n",
    "    if col not in df_new.columns:\n",
    "        print(f\" Skipping {name} (column missing)\")\n",
    "        continue\n",
    "\n",
    "    scores = df_new[col].fillna(0).values\n",
    "\n",
    "    try:\n",
    "        b_auc = roc_auc_score(y_new, scores)\n",
    "        b_pr  = average_precision_score(y_new, scores)\n",
    "        benchmark_results.append((name, b_auc, b_pr))\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "ranking = sorted(\n",
    "    [(\"My Robust XGB\", roc_auc_new, pr_auc_new)] + benchmark_results,\n",
    "    key=lambda x: x[1],\n",
    "    reverse=True\n",
    ")\n",
    "\n",
    "for i, (name, a, p) in enumerate(ranking, 1):\n",
    "    print(f\"{i:2d}. {name:14s}  AUC={a:.6f}  PR={p:.6f}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 7) TOP FEATURES (GAIN — SAME AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "print(\"\\n Top Features in Robust Model (Pure Biology):\")\n",
    "\n",
    "gain_scores = booster.get_score(importance_type='gain')\n",
    "fi_rob = (\n",
    "    pd.DataFrame({\"feature\": list(gain_scores.keys()),\n",
    "                  \"gain\": list(gain_scores.values())})\n",
    "      .sort_values(\"gain\", ascending=False)\n",
    ")\n",
    "\n",
    "print(fi_rob.head(10).to_string(index=False))\n",
    "print(\"\\n Done.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "914707\n"
     ]
    }
   ],
   "source": [
    "import pyarrow.parquet as pq\n",
    "\n",
    "pf = pq.ParquetFile(\"900k_test.parquet\")\n",
    "print(pf.metadata.num_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🔍 Loading ONLY bottom 900000 rows from: 900k_test.parquet\n",
      "🔁 Loading trained robust model from JSON...\n",
      "✅ Model loaded.\n",
      "   -> Training feature count: 8\n",
      "   -> Loaded shape: (900000, 17)\n",
      "   -> Label counts: {0: 891684, 1: 8316}\n",
      "   -> Final X_new shape: (900000, 8)\n",
      "\n",
      "🔥 Running predictions...\n",
      "\n",
      "==========================================\n",
      "🧪 RESULTS ON BOTTOM 100K ROWS (NEW DATA)\n",
      "==========================================\n",
      "Robust Model AUC    : 0.982708\n",
      "Robust Model PR-AUC : 0.523736\n",
      "Baseline AUC (With Loc)  : 0.766809\n",
      "Robust AUC   (No Loc)    : 0.982708\n",
      "Difference               : 0.215899\n",
      "\n",
      "==========================================\n",
      "⚔️  NEW LEADERBOARD (ROBUST MODEL ON 900K)\n",
      "==========================================\n",
      " 1. My Robust XGB   AUC=0.982708  PR=0.523736\n",
      " 2. BayesDel        AUC=0.949125  PR=0.669971\n",
      " 3. ClinPred        AUC=0.943464  PR=0.487158\n",
      " 4. REVEL           AUC=0.937387  PR=0.466993\n",
      " 5. AlphaMissense   AUC=0.933589  PR=0.433256\n",
      " 6. PrimateAI       AUC=0.911503  PR=0.224050\n",
      " 7. CADD            AUC=0.909277  PR=0.573139\n",
      " 8. MPC             AUC=0.901957  PR=0.250881\n",
      "\n",
      "🏆 Top Features in Robust Model (Pure Biology):\n",
      "                            feature       gain\n",
      "  phyloP100way_vertebrate_rankscore 230.754532\n",
      "   phyloP470way_mammalian_rankscore  59.637676\n",
      "                          gnomad_af  53.463173\n",
      "   phastCons17way_primate_rankscore  17.051365\n",
      "phastCons470way_mammalian_rankscore  13.066975\n",
      "                                ref  11.242688\n",
      "                                alt  10.989820\n",
      "          GERP_91_mammals_rankscore   7.565748\n",
      "\n",
      "✅ Done.\n"
     ]
    }
   ],
   "source": [
    "# ============================================================\n",
    "# CELL: TEST ROBUST MODEL ON BOTTOM 900K ROWS (CATEGORICAL-SAFE)\n",
    "# ============================================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "\n",
    "DATA_PATH = \"900k_test.parquet\"\n",
    "MODEL_JSON = \"pathopreter_robust_biology.json\"\n",
    "N_ROWS = 900_000\n",
    "\n",
    "print(f\"\\n🔍 Loading ONLY bottom {N_ROWS} rows from: {DATA_PATH}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 1) LOAD YOUR ROBUST MODEL (JSON – SAFE FORMAT)\n",
    "# ------------------------------------------------------------\n",
    "print(\"🔁 Loading trained robust model from JSON...\")\n",
    "\n",
    "booster = xgb.Booster()\n",
    "booster.load_model(MODEL_JSON)\n",
    "\n",
    "# Exact feature list used in training\n",
    "train_feature_names = booster.feature_names\n",
    "\n",
    "print(f\"✅ Model loaded.\")\n",
    "print(f\"   -> Training feature count: {len(train_feature_names)}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 2) LOAD ONLY WHAT THE MODEL NEEDS + LABEL\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "# needed_cols = list(set(train_feature_names + [\"clean_label\"]))\n",
    "needed_cols = list(set(\n",
    "    train_feature_names \n",
    "    + [\"clean_label\"] \n",
    "    + list(benchmarks.values())\n",
    "))\n",
    "df_new = (\n",
    "    pd.read_parquet(DATA_PATH, columns=needed_cols)\n",
    "      .tail(N_ROWS)\n",
    "      .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# Make label exactly like training\n",
    "df_new[\"y\"] = (df_new[\"clean_label\"] == \"Pathogenic\").astype(int)\n",
    "\n",
    "print(f\"   -> Loaded shape: {df_new.shape}\")\n",
    "print(f\"   -> Label counts: {df_new['y'].value_counts().to_dict()}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 3) BUILD X_new IN EXACT TRAINING ORDER (CRITICAL)\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "X_new = pd.DataFrame()\n",
    "\n",
    "for feat in train_feature_names:\n",
    "    if feat in df_new.columns:\n",
    "        X_new[feat] = df_new[feat]\n",
    "    else:\n",
    "        # If some feature is missing in new data, add it\n",
    "        X_new[feat] = 0.0\n",
    "\n",
    "# -------- HANDLE CATEGORICAL COLUMNS SAFELY --------\n",
    "for cat_col in [\"ref\", \"alt\"]:\n",
    "    if cat_col in X_new.columns:\n",
    "        # Convert to categorical\n",
    "        X_new[cat_col] = X_new[cat_col].astype(\"category\")\n",
    "\n",
    "        # If NaNs exist, add \"N\" (unknown nucleotide) as a valid category\n",
    "        if X_new[cat_col].isna().any():\n",
    "            if \"N\" not in X_new[cat_col].cat.categories:\n",
    "                X_new[cat_col] = X_new[cat_col].cat.add_categories(\"N\")\n",
    "            X_new[cat_col] = X_new[cat_col].fillna(\"N\")\n",
    "\n",
    "# -------- FILL NA FOR NUMERIC COLUMNS ONLY --------\n",
    "num_cols = X_new.select_dtypes(include=[np.number]).columns\n",
    "X_new[num_cols] = X_new[num_cols].fillna(0)\n",
    "\n",
    "y_new = df_new[\"y\"].values\n",
    "\n",
    "print(f\"   -> Final X_new shape: {X_new.shape}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 4) PREDICT (MATCHES YOUR TRAINING)\n",
    "# ------------------------------------------------------------\n",
    "print(\"\\n🔥 Running predictions...\")\n",
    "\n",
    "dmat = xgb.DMatrix(\n",
    "    X_new,\n",
    "    feature_names=train_feature_names,\n",
    "    enable_categorical=True   # MATCHES YOUR TRAINING\n",
    ")\n",
    "\n",
    "y_prob_new = booster.predict(dmat, validate_features=False)\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 5) METRICS (SAME STYLE AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "roc_auc_new = roc_auc_score(y_new, y_prob_new)\n",
    "pr_auc_new  = average_precision_score(y_new, y_prob_new)\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\"🧪 RESULTS ON BOTTOM 100K ROWS (NEW DATA)\")\n",
    "print(\"==========================================\")\n",
    "print(f\"Robust Model AUC    : {roc_auc_new:.6f}\")\n",
    "print(f\"Robust Model PR-AUC : {pr_auc_new:.6f}\")\n",
    "\n",
    "# Baseline comparison (same logic as your notebook)\n",
    "baseline_auc = roc_auc if 'roc_auc' in locals() else 0.747704\n",
    "delta = roc_auc_new - baseline_auc\n",
    "\n",
    "print(f\"Baseline AUC (With Loc)  : {baseline_auc:.6f}\")\n",
    "print(f\"Robust AUC   (No Loc)    : {roc_auc_new:.6f}\")\n",
    "print(f\"Difference               : {delta:.6f}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 6) BENCHMARK AGAINST SOTA (SAME AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "benchmarks = {\n",
    "    \"CADD\": \"CADD_raw_rankscore\",\n",
    "    \"REVEL\": \"REVEL_rankscore\",\n",
    "    \"AlphaMissense\": \"AlphaMissense_rankscore\",\n",
    "    \"PrimateAI\": \"PrimateAI_rankscore\",\n",
    "    \"MPC\": \"MPC_rankscore\",\n",
    "    \"ClinPred\": \"ClinPred_rankscore\",\n",
    "    \"BayesDel\": \"BayesDel_addAF_rankscore\"\n",
    "}\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\"⚔️  NEW LEADERBOARD (ROBUST MODEL ON 900K)\")\n",
    "print(\"==========================================\")\n",
    "\n",
    "benchmark_results = []\n",
    "\n",
    "for name, col in benchmarks.items():\n",
    "    if col not in df_new.columns:\n",
    "        print(f\"⚠️ Skipping {name} (column missing)\")\n",
    "        continue\n",
    "\n",
    "    scores = df_new[col].fillna(0).values\n",
    "\n",
    "    try:\n",
    "        b_auc = roc_auc_score(y_new, scores)\n",
    "        b_pr  = average_precision_score(y_new, scores)\n",
    "        benchmark_results.append((name, b_auc, b_pr))\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "ranking = sorted(\n",
    "    [(\"My Robust XGB\", roc_auc_new, pr_auc_new)] + benchmark_results,\n",
    "    key=lambda x: x[1],\n",
    "    reverse=True\n",
    ")\n",
    "\n",
    "for i, (name, a, p) in enumerate(ranking, 1):\n",
    "    print(f\"{i:2d}. {name:14s}  AUC={a:.6f}  PR={p:.6f}\")\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# 7) TOP FEATURES (GAIN — SAME AS YOUR CELL)\n",
    "# ------------------------------------------------------------\n",
    "print(\"\\n🏆 Top Features in Robust Model (Pure Biology):\")\n",
    "\n",
    "gain_scores = booster.get_score(importance_type='gain')\n",
    "fi_rob = (\n",
    "    pd.DataFrame({\"feature\": list(gain_scores.keys()),\n",
    "                  \"gain\": list(gain_scores.values())})\n",
    "      .sort_values(\"gain\", ascending=False)\n",
    ")\n",
    "\n",
    "print(fi_rob.head(10).to_string(index=False))\n",
    "print(\"\\n✅ Done.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Loading test_enriched.parquet...\n",
      "Using 10 features (training order)\n",
      "\n",
      " BASELINE: AUC=0.766809 | PR=0.771672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 8/10 [00:01<00:00,  4.38it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  4.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " PERMUTATION ABLATION RESULTS:\n",
      "                            feature      auc       pr  auc_drop  pr_drop\n",
      "                          gnomad_af 0.574536 0.625798  0.192274 0.145874\n",
      "                                pos 0.743102 0.745121  0.023707 0.026551\n",
      "                              chrom 0.747204 0.748561  0.019605 0.023111\n",
      "                                ref 0.748308 0.747789  0.018501 0.023883\n",
      "                                alt 0.748902 0.754349  0.017907 0.017323\n",
      "  phyloP100way_vertebrate_rankscore 0.753048 0.727441  0.013761 0.044231\n",
      "   phastCons17way_primate_rankscore 0.759336 0.760803  0.007473 0.010869\n",
      "          GERP_91_mammals_rankscore 0.765210 0.759358  0.001599 0.012314\n",
      "phastCons470way_mammalian_rankscore 0.765913 0.771196  0.000896 0.000476\n",
      "   phyloP470way_mammalian_rankscore 0.773115 0.771422 -0.006306 0.000250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "SEED = 42\n",
    "\n",
    "print(\" Loading test_enriched.parquet...\")\n",
    "test_df = pd.read_parquet(\"test_enriched_SEQUENCES.parquet\")\n",
    "\n",
    "#  GET EXACT TRAINING FEATURE ORDER\n",
    "features = model.get_booster().feature_names\n",
    "\n",
    "print(f\"Using {len(features)} features (training order)\")\n",
    "\n",
    "# ---- PREP ----\n",
    "X_test = pd.DataFrame()\n",
    "\n",
    "# build in correct order\n",
    "for feat in features:\n",
    "    if feat in test_df.columns:\n",
    "        X_test[feat] = test_df[feat]\n",
    "    else:\n",
    "        X_test[feat] = 0  # safety\n",
    "\n",
    "y_test = (test_df[\"clean_label\"] == \"Pathogenic\").astype(int).values\n",
    "\n",
    "# ---- FIX TYPES ----\n",
    "for col in [\"chrom\", \"ref\", \"alt\"]:\n",
    "    if col in X_test.columns:\n",
    "        X_test[col] = X_test[col].astype(\"category\")\n",
    "\n",
    "for col in X_test.columns:\n",
    "    if col not in [\"chrom\", \"ref\", \"alt\"]:\n",
    "        X_test[col] = pd.to_numeric(X_test[col], errors=\"coerce\")\n",
    "\n",
    "X_test = X_test.fillna(0)\n",
    "\n",
    "# ---- BASELINE ----\n",
    "y_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "base_auc = roc_auc_score(y_test, y_prob)\n",
    "base_pr  = average_precision_score(y_test, y_prob)\n",
    "\n",
    "print(f\"\\n BASELINE: AUC={base_auc:.6f} | PR={base_pr:.6f}\")\n",
    "\n",
    "# ---- PERMUTATION ABLATION ----\n",
    "results = []\n",
    "\n",
    "cat_cols = [\"chrom\", \"ref\", \"alt\"]\n",
    "\n",
    "for feat in tqdm(features):\n",
    "    X_perm = X_test.copy()\n",
    "\n",
    "    # shuffle feature\n",
    "    X_perm[feat] = np.random.permutation(X_perm[feat].values)\n",
    "\n",
    "    # 🔥 FIX: restore dtypes AFTER permutation\n",
    "    for col in cat_cols:\n",
    "        if col in X_perm.columns:\n",
    "            X_perm[col] = X_perm[col].astype(\"category\")\n",
    "\n",
    "    for col in X_perm.columns:\n",
    "        if col not in cat_cols:\n",
    "            X_perm[col] = pd.to_numeric(X_perm[col], errors=\"coerce\")\n",
    "\n",
    "    X_perm = X_perm.fillna(0)\n",
    "\n",
    "    # predict\n",
    "    y_prob_perm = model.predict_proba(X_perm)[:, 1]\n",
    "\n",
    "    auc = roc_auc_score(y_test, y_prob_perm)\n",
    "    pr  = average_precision_score(y_test, y_prob_perm)\n",
    "\n",
    "    results.append({\n",
    "        \"feature\": feat,\n",
    "        \"auc\": auc,\n",
    "        \"pr\": pr,\n",
    "        \"auc_drop\": base_auc - auc,\n",
    "        \"pr_drop\": base_pr - pr\n",
    "    })\n",
    "\n",
    "# ---- RESULTS ----\n",
    "ablation_df = pd.DataFrame(results).sort_values(\"auc_drop\", ascending=False)\n",
    "\n",
    "print(\"\\n PERMUTATION ABLATION RESULTS:\")\n",
    "print(ablation_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📉 Generating Ablation ROC Curves...\n"
     ]
    },
    {
     "data": {
      "image/png": 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jV/b1ithbz+niiy/msssua7PM1vnvb775Jpdffjlnnnkmd9xxB6mpqVgsFh588EHWrVu3W+1pmoZSqlX6jhYq235rva39/fOf/8zIkSPbPOb3bmW4o2u/o/S2zmNP+y1bC/7W+3dPnZdpmvTq1YvHH3+8zfztvxDZ3ptvvgnA+PHjGT9+fKv8yZMnh79c2mpXfa+trWX48OHExsby97//nfz8fJxOJ7/88gt/+ctfdvm7fP755zN+/HgmTZrEX//6V958800GDhwY8WVMt27dWLVqFZ999hlfffUVkydP5tlnn+Wee+4Jb9u4PU3T+OCDD5gzZw6ffvopX3/9NVdeeSWPPfYYc+bMwe1277RfQghxuJKAXgghDmKTJk0iNTWVZ555plXelClT+PDDD3n++ecjgp81a9a0Krt69eqdDlH/9NNP8fv9fPLJJxFvANsaMry7+2Pn5OQAsGrVqvAQ5K1WrVoVzt9fUlJSiImJwTAMTjjhhJ2W/eCDD+jQoQNTpkyJOP/ttw7c2bVJSEhoNX0AaDVSYUe2DlW32Wy77O++tqN7LioqKjzqIioqilWrVrUqt3LlSnRd32XwCzu+vr/l/t1d+fn5u9wdIj8/n0WLFnH88cf/5n3jlVK89dZbjBgxgnHjxrXKf+CBB5g0aVKrgH5Xpk2bRlVVFVOmTOGYY44JpxcWFu7W8YmJiYwePZpJkyZx0UUXMXPmTJ544olW5aKjoznvvPM477zzCAQCnHXWWfzzn/9kwoQJO92+8qijjuKoo47in//8J2+99RYXXXQR77zzDn/6059+03kKIcThQobcCyHEQcrr9TJlyhROPfVUxo4d2+rnxhtvpKGhodWWZR999FHEXOp58+Yxd+5cRo0atcO2tr712/YNZV1dHa+++mqrstHR0dTW1u6y/wMHDiQ1NZXnn38+Yljtl19+yYoVKxg9evQu62jLntq2zmKxcPbZZzN58uQ2A7dttwJr6/rMnTuX2bNnRxwTFRUF0Ob1yc/PZ+XKlRH1Llq0aJdDlbdKTU3l2GOP5YUXXmDz5s077e++Nnv27Ii1BIqKivj444856aSTwvuRn3TSSXz88ccRUz/Kysp46623GDp0KLGxsbtsZ+te9Ntf399y/+6us88+m0WLFvHhhx+2ytvazrnnnktJSQkvvfRSqzJerxePx7PD+mfOnMn69eu54oor2vz9Pu+885g6dSqbNm36Tf1u61oEAgGeffbZ3a7jkksuYfny5dxxxx1YLBbOP//8iPyqqqqIz3a7ne7du6OU2uHvZU1NTasREH379gWQYfdCCLET8oZeCCEOUp988gkNDQ2cfvrpbeYfddRRpKSkMGnSJM4777xweseOHRk6dCjXX389fr+fJ554gqSkJO68884dtnXSSSdht9s57bTTuPbaa2lsbOSll14iNTW1VfA4YMAAnnvuOf7xj3/QsWNHUlNTW72Bh+Y3yQ8//DBXXHEFw4cP54ILLghvW5ebm9vmEOPd8Vu3rduZhx56iKlTpzJo0CCuvvpqunfvTnV1Nb/88gvfffcd1dXVAJx66qlMmTKFMWPGMHr0aAoLC3n++efp3r07jY2N4fpcLhfdu3fn3XffpXPnziQmJtKzZ0969uzJlVdeyeOPP87IkSO56qqrKC8v5/nnn6dHjx5tzpVuyzPPPMPQoUPp1asXV199NR06dKCsrIzZs2dTXFzMokWL/tD1+L169uzJyJEjI7atAyKGX//jH//g22+/ZejQoYwbNw6r1coLL7yA3+/n3//+9261s3Xhvbvvvpvzzz8fm83Gaaed9pvu3911xx138MEHH3DOOedw5ZVXMmDAAKqrq/nkk094/vnn6dOnD5dccgnvvfce1113HVOnTuXoo4/GMAxWrlzJe++9x9dff83AgQPbrH/SpElYLJYdfrF1+umnc/fdd/POO+9w22237Xa/hwwZQkJCApdddhk333wzmqbxxhtv/KbpBKNHjyYpKYn333+fUaNGkZqaGpF/0kknkZ6eztFHH01aWhorVqzg6aefjljcb3v/+9//ePbZZxkzZgz5+fk0NDTw0ksvERsbyymnnLLbfRNCiMPOvl5WXwghxJ5x2mmnKafTqTwezw7LXH755cpms6nKysrwdmePPPKIeuyxx1R2drZyOBxq2LBhatGiRRHHtbVt3SeffKJ69+6tnE6nys3NVQ8//LB65ZVXWm0TVlpaqkaPHq1iYmIUEN6Kbftt67Z69913Vb9+/ZTD4VCJiYnqoosuithWT6nm7bOio6NbnV9b/fyt29Ztu01bW8rKytQNN9ygsrOzlc1mU+np6er4449XL774YriMaZrqX//6l8rJyVEOh0P169dPffbZZ622/VJKqVmzZqkBAwYou93eagu7N998U3Xo0EHZ7XbVt29f9fXXX+9w27pHHnmkzf6uW7dOXXrppSo9PV3ZbDaVmZmpTj31VPXBBx/85uux9e/s/fffjyi3dcux7bcY2/r3UVFR0arON998U3Xq1Cl8fba/D5RS6pdfflEjR45UbrdbRUVFqREjRqhZs2btVttbPfDAAyozM1Ppuh5xH+zu/ZuTk6NGjx7dqt62thWsqqpSN954o8rMzFR2u11lZWWpyy67LGKbw0AgoB5++GHVo0cP5XA4VEJCghowYIC6//77VV1dXZvnEAgEVFJSkho2bFib+Vvl5eWpfv367fS6tPV7N3PmTHXUUUcpl8ulMjIy1J133qm+/vrrVuXaun+3GjdunALUW2+91SrvhRdeUMccc4xKSkpSDodD5efnqzvuuCPifLfftu6XX35RF1xwgWrfvr1yOBwqNTVVnXrqqRHbHQohhGhNU2ofrFwjhBBCiMOSpmnccMMNPP300/u7K2IPGj9+PP/9738pLS0NTyURQgix78kceiGEEEIIsdt8Ph9vvvkmZ599tgTzQgixn8kceiGEEEIIsUvl5eV89913fPDBB1RVVXHLLbfs7y4JIcRhTwJ6IYQQQgixS8uXL+eiiy4iNTWVJ598MrwKvRBCiP1H5tALIYQQQgghhBAHIZlDL4QQQgghhBBCHIQkoBdCCCGEEEIIIQ5Ch90cetM02bRpEzExMWiatr+7I4QQQgghhBDiEKeUoqGhgYyMDHR9z71XP+wC+k2bNpGdnb2/uyGEEEIIIYQQ4jBTVFREVlbWHqvvsAvoY2JiANiwYQPx8fH7tzNC7CWmaVJRUUFKSsoe/QZQiAOJ3OficCD3uTgcyH0uDge1tbXk5OSE49E95bAL6LcOs4+NjSU2NnY/90aIvcM0TXw+H7GxsfI/RnHIkvtcHA7kPheHA7nPxeHANE2APT7tW35jhBBCCCGEEEKIg5AE9EIIIYQQQgghxEFIAnohhBBCCCGEEOIgJAG9EEIIIYQQQghxEJKAXgghhBBCCCGEOAhJQC+EEEIIIYQQQhyEJKAXQgghhBBCCCEOQhLQCyGEEEIIIYQQByEJ6IUQQgghhBBCiIOQBPRCCCGEEEIIIcRBSAJ6IYQQQgghhBDiICQBvRBCCCGEEEIIcRCSgF4IIYQQQgghhDgISUAvhBBCCCGEEEIchCSgF0IIIYQQQgghDkIS0AshhBBCCCGEEAchCeiFEEIIIYQQQoiDkAT0QgghhBBCCCHEQUgCeiGEEEIIIYQQ4iAkAb0QQgghhBBCCHEQkoBeCCGEEEIIIYQ4CElAL4QQQgghhBBCHIT2a0A/Y8YMTjvtNDIyMtA0jY8++miXx0ybNo3+/fvjcDjo2LEjr7322l7vpxBCCCGEEEIIcaCx7s/GPR4Pffr04corr+Sss87aZfnCwkJGjx7Nddddx6RJk/j+++/505/+RLt27Rg5cuQ+6LEQQgghhBBCiIOFYRiEQqH93Q38fv9eqXe/BvSjRo1i1KhRu13++eefJy8vj8ceewyAbt268dNPPzFx4kQJ6IUQQgghhBCHJKUUXq+XlStXEgqFMAwD0zQxDCP85/LycsrKyoiLiwunb/358ssv6dSpE3a7vVVecXExX331FSeffDJKqT3yYxgGc+fOJSoqirS0tHD61nPZ0Z//aP62fzYMA4/Hsy/+evar/RrQ/1azZ8/mhBNOiEgbOXIkt9566w6P8fv9Ed+G1NfXA2CaJqZp7pV+CrG/maaJUkrucXFIk/tcHA7kPheHg4PlPjdNk6KiIrxeL6tWraKyspJVq1aRkpISDq63/W9DQwNTpkxhxIgRYCp0U2s+V6M5Dvnyiy/JysombcvxVt0BDhcBl5OAaVJRWY3dasHhcGJiYLFY0VxO9Bg3UVadWGcMVnsQ3aZhsVvQNA2LA6x1xei6jsNqweOOx4JJ9qihhHSFYTdQmhWrZgMMdF0na1Ae15w9FIWGqQO6BoDV4UOFbCgFumbFxEQDVHM2CtC05v+CBtukA6Q4xpEQqmvOsgcxQw7MLccrdCqtCUSZ3i3HaC0Xeps/KraloTTVkqhFZEbWse1RurlNXdo25XdE20m+tpPjdlSm+doEPV6mjr1ml0f/VgdVQF9aWkpaWlpEWlpaGvX19Xi9XlwuV6tjHnzwQe6///5W6RUVFQQCgb3WVyH2J9M0qaurQymFrsval+LQJPe5OBzIfS4OB3vzPm9qaqKqqgqv18u6devQNI1gMMjChQtxu90R8UNjYyMfffQRHTp04LvvvqNXr14YhsHKlSvRXFG4c/NI6NIZi82CNS2FKGc0bmc0jkAAl0PH4bahWTQMRyw4YnAafob174fSLSgd/DY/SjWHX1X2WI666MzmINQaIoCd5kHhCh1w2QJkmxY0zUTTtrx13u7cDoZ3z5sdiS0fWkWeBk0W+77szn616y8Cfp+DKqD/PSZMmMBtt90W/lxfX092djYpKSnEx8fvv44JsReZpommaaSkpMgDoDhkyX0uDgdyn4vDQVv3ucfjIRQKhX8Mw6C6uprGxkZM0wynbR0yvnDhQhYuXMi8efM49thj2bBhAz6fD13Xm99CZ8WAFsJq0Ylx2oiKcaLrVhotaYSS29EUm4oenUq/AbfiUAZjrx5LXVQ0UUE/6fao5re8qrlvmm6w/eth75afbdXj2C7FGf6ThodtXy1q+MJ/sqkAQWwkqXI01fy+V8dEQ6EpRZMehUt5iTEa0TRAmZiGFU0pdAUWawAz4ECZGppSNFqiSQjWYVUGKIVuDxDyRTWPGFAGSgPdNNFMxbYvwHXTRLMYGD4boBHSIUgQDAVKoRTNS6yHFKYCFKitRysFpiJosREdaEAzDDAg6DcwTQNME5SJbhhYQn6UIty2YZqR1yZooNBQCpQyAR3DCIFpRrys9wWbjzdV8xt5TW09GUXIACO05c27Ui3BtVLNfTa36Tdb2tlSzmTLuW0tQvM11wDTbO5Hc3rzRbBoNrKTepIckxvxt9/orWMae95BFdCnp6dTVlYWkVZWVkZsbGybb+cBHA4HDsf2v0yEf7mFOFRpmib3uTjkyX0uDgdyn4tDTVVVFXPnzqWpqYmpU6fy/vvvo2ka5eXlQPM973Y6cNlsWC0W4qOjcLqiaZeeChaITogmKi2e5DQ3zthkfDYNZ+dohvYawhHXHIsWbdJbDcRAo84eC7pis6UdMWYDpmZBoeHRrFRobccP2/JYnehsmQqgmdhVEIsyyAsW4dfsxJiNRJk+NGWAaUHXAwS8LjRlwW74sRmNKGWiqRAGASw6BOsUhhkEpXCFPIRCCqs1SGOjBbPJS9CEgAGGZlJXY8Pb1IDPA/6gienzEwgFCRlgs1ioNTWUrqPMIKrJpL6xgeqGRjr16IHTYceqW7BipVhZWLtmLX6/n1NOOQWLpRZd17FYLOGfOXPmMGLECOLj49E0bdc/+pb/OnZcprq6mtiEDmRmZobTtv4d7+jPfzR/+7LBYJDExMQdxot7gq8xxPJvqgGoqKxk9erV1G9umfZd4lnGedecwnPv7Pm2D6qAfvDgwXzxxRcRad9++y2DBw/eTz0SQgghhBDi8OT3+/F4PASDQfx+P4WFhXi9XmbNnMnqNavJaJfBzz//jNVqp77eg9Vio3OH3pCUSHRCAt52FhzdcjjjH3eilEaTOwrdrrBpXjR7iDI9HQsGTXpzIObXjeb/olENFO9GH7f9GqzWkoBmWpo/mBo6CjQTZ0BhajopgQZSg8W4GxRuvw80C1blQa8J4jL82Cs8ELDhaWok5GtkWU01Fb5GAgGDju3TqdOTqFdOPDYbaCZWix4OlAcOHEheXh4WiyUikK6srUUpRe/evSOC663lvF4v7dq1IykpCZvNRmJiIqmpqXv071H8fkbI5KsXlgCgWywsXbqU0tJSADy+Or5b8jZ3/HU8T9/8Oo2NjXulD/s1oG9sbGTt2rXhz4WFhfz6668kJibSvn17JkyYQElJCa+//joA1113HU8//TR33nknV155JT/88APvvfcen3/++f46BSGEEEIIIQ4pFRUVVFRUsHTpUmbPnk1qaiqhYJCQ30cwFOLdDz7AERNLeseuJGd3wpoUSyg6GhVlpSo2GWu3Xujde7PZacMyfDhoJnG6iVJQavftpOWtw9jjtkmzoGFuU0JrXphNaVgJERPyUWvG086zGUcoiK6F8CkHzkCQqHovISOAgZ2Y6jow/MSVV6KCUGNxE6ivxVJbQ0psLBoWaixWSr1evl6zhmMHH4k72kXvnj1I7dEOm82Gruv07t2bzMxMrNaD6r2o2AtKVtfw67cbgeZh+PPmzaO0ooQPZ79Ao6+WUaNGMXv+j+Tm5u7VfuzXO3H+/PnNqz5usXWu+2WXXcZrr73G5s2b2bhxYzg/Ly+Pzz//nPHjx/Of//yHrKwsXn75ZdmyTgghhBBCiB1QShEMBnn44Yepr6ujoaGBl19+CQ2NqCgXmsNJTEYW6e2z0fM64k9th5najqhQEFu3gWgOO0GHHd2iYbEosgYdjW4xsDiaqNmuLQteFAoDDceWmdCKbWeca9hUEANIMGpwGT6SauvQTROraRC02IhpqsVZ58XiNwk1mcR6KnD5mrBrPpxGCK3RJNhop9plpyAqgUZ0ltWbFBVsoKGxlsSULE47eSR52ZmoYIiTTjqRdu3aYbVacTqdCPF7Bf0Gcz8poK68KSJdA467rDOjRt9MWloaL7/2Dueee2542P/epKmtm/UdJurr64mLi6OmpkYWxROHrK17kaampsqcS3HIkvtcHA7kPhe/VSgUwu/z8u0333L+eeeSGB2F2+nEHhdH/lGD2XzUsSTotQTccQSjopsXI9PAYjVQSkNDR9d0rLYAyrRsWQCubRoaduXHbXqJMuqwqyAJoVocfoM07wZcgQDR9noshR4smh/Nr9A8Cj0E1mINghomGg5Ta16sTdfBomGNseHNduBNjsNqcWHR3dRbk6lK7Ef73sPITkkk0W0nI865TwImIbb6/JlFABiGQTAUwulwcNSZ+SRlugH473//y1lnnUVCQkKrY2tra0lISKCuro7Y2Ng91icZKyKEEEIIIcRBxjRN1q9dw8eTP+C/Tz/FwI55uGJisMfFU5bWHk+7LI5+7Bn82fHY7EF0DTyaIlYDQ6Vg0ZqHrG+/pXeMWUuU8pLt30xyqIZYsx67qbAYfpwqhL0hiG5pxFvqx6SeUI1CW6RhpXm+usWiY0Ej2QcWTcem6c1Bt7Kg2yyggW5omDYbWmcXdYmpmDpUuFIoie1Bh6QoyqM7071LV9q7HWQmuHA7JGQR+9fqeaWs+bl5cfaKikoWL1lMQdNspnz2Drql5cvWq666ap/3TX47hBBCCCGEOEAF/T42LP6V9UsXU1BQQFWTl/WFBThiEgi2a09xXldSHnyWYksQr8OBRdNRmFhtIax2Hy6C29SmARpJZjVHNC5G89mJDfmJ8thxGgrd6sfiiSXOp6H7PVRWLqe+pAwCYAtoxFjsWDWFiUaCGcRiakRHRaOsGiFTYVda8/h6i47Z2QkOndKo9tTHJ7MpvgPxmZ1IyOxE99x2WHUNXdNItegkRlmprqqUkSjigOL3hlj+4yY2rWmeWOIPBFi+bBnFJSW88NX/ATDprdO45JJL9mc3JaAXQgghhBBiX/M1NlJZvAEjEMQ0DXyNDfibmli/aAGa3YW3KURleRWbUTTEpVITn4ie1JlAhouiI0cDzfuhO6JrsOrN83mjCG6Zr968cFynYCEJRh0JRj2ujS4cDTqlPj8NZUF87ZOxKx2zppGYjASylpVgVTYs/hL0UAMuLUhO0EBZEjFTrJimwki0oVLseENb9t3WNQzAZ4nCqXlZHt+XclsWpjuN6qgcUlNSOaZTCse3i8Wi73hovGmaO8wTYl/z1PmZ9ubK8GcFFBcVs3z5Muau/I55a74F4JhjjuGII47YT71sIQG9EEIIIYQQe5gyTWpKN9FUW0tjTRU1mzfRUF2J3eLCU1ZFVMiNH5PGQBBXKIZyfzUBDPwOKz6roiQ+iVX9utPo2LqIm0LTQ+jWADa9AZujZbX4ZKOG3k3TcKpGvB4LMaFGGs0mHPYQ039tJJDYmYFDBjIkcwgXxHTD8+HH+JavaOlsYRm4dNTmRQSCIfwhA0+ig0D3aHBvGy5oFNk7sNnWnhprCk26m2JbHm6XjRinlZE90hmbn4y+k+BdiANVMGDwzUtLI9IaPR4W/rqAN75+gk3VBQAkJCTwyCOPcMUVVxwQI0okoBdCCCGEEOIPUkqxZt4sVs3+iYDPiwGYCgIKHKEoQiELPlPHp6oxQyZKqwegwWajIt7Ab0ticVYWuiUEWvMba4sewmatwmo1cJgB7EYNmcY6MP3YGsvJCCxFA5oC8NISk365R9C3W18y43vQJ6YHCfV1XJlcSqisDO9nS1H+j6niYwAMXz2eumocvgqCSieYZoU0C2ZeLFg0Vjt7YWClwpZBoaMr9ZYEhnRMBiA9xsGx6TF0SnXLonTikFBb1sTMD9aEP5umyZr1K/jrM1fi9/vD6RdccAETJ04kLS1tf3SzTRLQCyGEEEII8TsYoSBVxUUsmfYdq9eto9EEb9BAGRBtdaNMcFgcmCqEUkGCuk6tw8miDvl47DZCUSZoAWz2IJpmYDU3o5TCMA0MTAyLRgfjF7o3zsChvOF2q71Q3KBwRHXilvPeINWVyF9KS/GtWIlnxkxC1d9D6BsaAdMIYpatIGSYGEqhTIVpBFBRVowuboKJic3L3G/jtfS7Oa5vR3pnxuF2WkmKtkvgLg5Za+aXsXpuafizpmu4u9Vx+80XhdNyc3N57rnnOPnkk/dHF3dKAnohhBBCCCF2QimFv8mD3+OhtGANi3+cTqM/SI0nRKPPj0Wz4rC4cFgcOHQIaIqlae0IuBSaLYTPZqfKHYeh+TDxo7QqlFIoBSgIBSE+UIrTbMAWBX2831DT0ICpwKbDKo+i0ohlxJEXkJGcwwXZxxK3rJjqV18l+Mv9lGzpZ9AwqWrwovnrsPhqsYXqm4N1lwWVaEXF2TFT7eCwhM+t3pqEkXUkG2IGkJmdy/BumTxh3f/DiIXYF5RSEcF8t6Mz6NA3BYCLL76Yt99+m9tuu417772X6Ojo/dXNnZKAXgghhBBCiG0o02Tz2lXM/fA9AEKmotoTpN7UCfiCRNti0DUHVhzEO6LQY2oxEypZE53BsviO6Ch0axDTVPgNhWEGMLVydOuWt9waOPQA/TxfkBpaz9oKL/V+KK5XlHog2KcfKVkp2Gw2shM6clPnc8iKySJQWIh/5Urq/vMA1eG+hvDVluKtLceigrjsOmY7ByrBTighBSzNbdbZ05iReDZ5qYkkxbpISk6jV14GLruljSsgxOFhzscFKKCyspKTLx1Iel5cOO/xxx/n9ttvp2/fvvutf7tDAnohhBBCCHFYMk2Dio0bWDXnJzYVFqBZLCgFgaCGw3DhN2Kp9TaBRQccOAC7rXnn9mBKFV9364NCwzAyUWpLulFPyDQgpEDT6F77A03WEGCQRBX+uiLKPVDm0Zhui6NnryOI7RFLit1NWqCRboldydkUYpjeBfwm/umr8L/8L4q363t1TRWW6nWoNDtGLzfY4ghtGRZvt+q4kttjOBLwdjmTbr2OwGq1MHyfXVkhDnyLvi+iZG0FS5Yspay8DHKKuDzv8nB+SkoKKSkp+6+Du0kCeiGEEEIIcUgLBQLUlZdRX1EGmkZDZQVrVixjQ00DADbTgVW5UWbzHHPDMIBA87FAKBhE04PEdl3NvKR+rLdkoFQmPp8XU7VsudbgVViNKtJ8S7FWzUOPTmStiiIuNo7k5GSUK4PePc+hT0ofrLoVu8VOl4QuWFetJ7hhA/WffwEsA6CeVS0nEPSApwrlraHB68eM1tHaOQn1Tw4XabTEUZ44kJNGnYE7vXOrefFCiGb1lV5mvLOKwsL1rFy1EsMwmDTtEd6fZ+HMM88kPj5+f3fxN5GAXgghhBBCHBKCAT8lK5cT8DZhhkJUFm2gYkMhAAFTURu0YjUd+BXo6DiNaAzDaJ4jrwKEDBM9ACgwLAojuBHrgGrKcDM7/mhMlQkGYDSF2/SUlFH63kukjojniIEDcbvdQDJ0PCVcxqpbyY3N5ereV+OobcK7eDG1778DQF1bJ2KGwFuNM6GGhmAIrxki1M6GSnWDHhNRNKHTUaSdMgGn09lWTUKILbauZF9XV8/ixYuorWv+7Xv3xyeISYjiqaeeIi4ubhe1HHgkoBdCCCGEEAcVf1MTxSuWUFtaSkNVOTaHk/L1BSilmt+ob9kuLmCCy3DTZOqYpkkgEEDXDUKGAYBSYK+DqBodDPiuZCqVlnV0Oy+PeVnnAdsMt1UmaBrBkKJi/Say/Ovp0ttKVHcX2v1nRvSvfWx7hmcNp2tiV6IbQ9S+9x7eRYup5s87PCdlhtATFdbQRvRcJ9WBIFVBG2ALl2nS3axy9mGtowdXnz6c3PTkHdYnhGhmmorlP21i3a9lrF61ioLCQpRSVNZvZsqsZ7nu+ut48MEHD8pgHiSgF0IIIYQQB4GmuloWffslpetWA1tWnt8SuAN4TWjaMvrdZjiw6Q40pVPl8xIKGRhmc6bdp1FXVsuqzetYW7IGj72YQcMshAadzOb4kfissczbtmFNA6dOVVUFmalx9LeuRE9SQGyrPg7OGMyFXS/Ev3o1/vmrqf/8Neq3yVeAYSo8/hD1tigaOnTB0cGKa9N0HMGqrQ2Cx7+lvM46Z3fWJo3gyAEDyEmOYVBSNBZdhtMLsTvqq7z8+M5qysvLWbxkCV6vl5/XfMcv66bRo0cPfpr5E0OGDNnf3fxDJKAXQgghhBAHFGWalG8oJOj1UlO2iQ2LFhL0+2g0FLUGBLcE8Ra7HYvNhs3hwOfz01hci8PiIEgQazCE0sASAqdP58e1C/jq5x+wRnkZeXIMaed0wR8/Ao89kRXOjC0ta2gW8BkQ7bKiimbRNceOy2aBDIBNAGTFZFHrr6VjfEc6xXdicMZg7BY7oaoqiq8f1+p8TKWYV2WwrNcwytJyiLI2cUnVE0SxCjZElm20xFGd2JdfYo4jIy2FsQOyGRtt32vXWohDkVKKxT8UU7yyeT+Ikk2baGys5/P5/6O6aTP/+Mc/uOOOO7DbD/7fLQnohRBCCCHEfqWUwtfYQPWmEtb+PJvqkiKUUpiAT0GdAf4tb9+tdgdxiYlYNTu+xiaKNhSRYERjDWhE25xE1eo4Pc1bsX20/FvmbFxEz0E+2ufbuf609szLvpgCWwIFWxu3aFjsOgbg0910sK4nRSvHRhA6usJ97BDXgev6XEeULSqi74GNG6l79kV8y5a1Oi+9R09+8jhYkhBHbnANQwLTSKovw6JCAMQ4rThtFqy6RnnGceQefQ4xSc1fLpy+Jy+wEIeRZT+WsH5xZUTaKRcezUnn3E7vvr154YVv6NSp037q3Z4nAb0QQgghhNinlFI01lRRsGAeNaWbqdlUjKkUTWZzAO81m+fBA9ijotA0iFZ27FYn9TX1eAoqcHh0rEGNfDN+a63oTj9aYg3PNUwjFBtN+/HdyU84CzPUyApbHCu2lLTZ7Rh2HQ8u4mxeklU58VQSRfNid267m47xPWgf0x5TmRzZ7kgSnYmR5xAMUjx+PISMyHSgKiaJdzsNZKD3R7poS+lS25ynAT0z41qGzKd2g2PuAHs0aXv2EgtxWFFKsWFpFctmlNDY2EhTUxOpqakAHD22E/FpUcybN4+8vDy0Q2wHCAnohRBCCCHEXtVYU01TXS2rZs2gsqhljLmpFJuCLUPoATRdR7daSIxLA48JhsJUJoGqJnzl1cQ0aYCVgE2nzl2Dp0MTKzqnU2mLx2OYGFo60BkLUNJcI15b82JXaeldqQjU0qg0+quZuGgCBVG2KKKsUdzU7y8kOBPQNX2H5xIsL6f6v/8lsGEj0BzAh0JBvDSyLCkVe7oiN/Az59f8HD7GZtHokOzGlZQFCTnQ4VhI6wUWeRQX4vdSpqKuwsuCr9bjawximCZr165l7Zq1WKwWbv7n+XTq1T5cvkOHDvuxt3uP/CsihBBCCCH2imDAz2cTH0IpRUA1v31vMlsCeEOBbrXijo3DEeXGolkJVTVRu6GShrVluBp1LEbz27S69ho/D4Ta7Hh82DBMA7XtwnQmoDUPtXc6XeQlxONwRIPRSJzuJ1arZUPtJ2Rv2V9+q4ePeZhoW/ROz0OZJqHaWja+/hYVc+bT6A+BESQqUEmsUU3o2GSw6nSmhm2rz0mKIqF9T+h7YfPbeCHEH+JtDDDjndWE/JEjY6qqqlm8ZDGNjY0sKvyJOau+wpq/mYkTJ+6nnu47EtALIYQQQog9qqqykjnTp7JxxTK8Zku6xW4DDWKTU7C5otAUmJ4QlgaT/NoUlv26hJDFQn1SAmWdrVSnWllrtWKN9qHUlopMgFC4TqsZIIAdY/1a/nL8eQzv0Zdku5W1Nat5auFTAHi2/Gxd/krXdB4b/hg2S8uWcFuZXi/+ggJqP/gAa2ISvpUrqG3ws6HKQ6xRQ6JRhhtQiXaMXtGEYlMjjg91Opk+2YnY8oZA8qEzT1eI/UUpRcGvFayctblVXiAYZMWKFcz65Qe+/fUdAiEfVquVu+66i7/97W/7obf7ngT0QgghhBDidzNCQRZ9+yUlK5ZhdceytrqOYCDQqlx6x84oUxGjnNQXVtKwdiOWEFiDGtUuJ8sSQ8w+qye6uwGTIL6QH1MFsBBAbTMk375pDb6lSyj6YQH/+edTnHvuha3aKvWUhoP5rQakDaCdux3d47uR5UglVFRCzezZBIqK0Sw6Rm0doYoKFNDgC7K5zocvsJIos4F4o4pc5aN5FjwwMIGkbDduhxWr3Y6t6yhs/S+ENr4gEEL8fnUVXn56b3WrdAXUOlfz5wdupqysNJw+aNAgXnzxRXr37r0Pe7l/SUAvhBBCCCF2i8/nY1NREQUrV1BeuBZvQz2NZvNLc6WAxopwWV3pxOIgLbYdKVoq+gaTJQsWs3W3dX+snTlHZVCRGA22AAFLPUGjBIIt7SV7CjEry6ldvgZjxWLmzWoAYOrUqRz7wrHhckopKr2VbPZs5sXFL0b0eXjikZxck0Xje9MJlc8DPt4yt76ZqRTVngBVngC+gAEo4o0q0i01YNEwOkaBzUEowU16YhTpcU7ofiZ0ORlcCXvu4gohIqz5uYzV80oj0roPy8SW4Ofqa/7EV199FU6PiYnhX//6F9dffz0Wi2Vfd3W/koBeCCGEEEK0yTRN1q9fT+HatWwqLqK+tgZ/Y+MOSmvERCVhtdmweXWOSexL8fpiVs1cRfmW/dsdmSWUDWzii4RjCZkGvlAjStU3H25AdKCKxKaNuGe+z5TJddTXm0yePJluo7qRlZVFTEwM0BzANwWbeGHxC6yrXde6K0px5OeF9KhykRo1m9pts4CqRj/+kEm1J4BF92NJtdDYPoEMYwP2aAPsGiEtCbfDSkqMgxinFT1zAPQ5v3lROyHEXlNV0sjPnxdiBFvm6+T0TKLn8Kzm/KoqFixYEM4bM2YMTz75JFlZWfu8rwcCCeiFEEIIIQQA3sYGGiorWLt6NWtWr6amrg7TMFqVs2rgsDtoZ00nNiqRQMAg05lCtMWFQ7dhjbXw008/UVtXS3VTLXV6CXEnF7DQ1ZW1UUeC3xOuK6FuHTnF31O1tIA5s5soLg5y4403Ulb2CE6nM1yuIdDAzJKZfLT2I7whb5v91wyTYT9UkLmxiezY9mhRLdtTlZh2llkTKOzQlX76HOLNKjKMqnB+CnXER9lJjXHjsOnomgY9xkDX0eCI2ROXVwixE/WVXmZNWRsRyAMMOCWX9Ly48OekpCQmTpzIX/7yF55++mnOPPPMfdzTA4sE9EIIIYQQhymlFLWlm1g24wdKCtdRGQK/2XbZaB2iHHby2ueSH9cLW2nz1m6GEaKspgxfbQOrNiyltKqMaRsXkH21kyXW9gRjU/HacoGjt7ZKaWmIuo+mUPvDdEaNHMk5tz9C1nlZ5Obm4nA4WFOzhpllMwEo95Yzs2Tmzk6C6zpfTuo3v+Kb/wsayahYCJkmdU0B5g09g1IayQqtZ4BnBgOM2bDlO4oUtwObVSMhyo4tqz+k9wJNg7xjJIgXYh+pr/Ty47ut58m36xRP3oB4Hn7kQW699VbS09PDeRdeeCFnnHEGbrd7X3b1gCQBvRBCCCHEYah8w3q+nvQa1Ubz9nHb0iwWdIuFDhntyM/tREZKLqF5dc3j1b1QV1bHjBkzwuWnrpvDws0rWOkG95Xj6HLpaDa20WZjo6Jo/jpG+Bv58Jvv0bTmN+hKKX4u/Znbf7p91x1XijF5p9O7SCfw1Q+YtbXAK/i3ZK8qayAQDFE6pAv9LXMYVPdsxOHtE6OIddmw6hocNQ7i20Ni3u5eNiHEHmIaJlPfXImvMRiRnpTppv/JOXzz3Vec2W8cRUVFbNiwgbfffjtcRtM0Cea3kIBeCCGEEOIwsrFgHZ+99Qa+7d7EW2w2YpJTiI6N48jO/YnfpGNU+KAAQgV1AMybN4+y8rLwMQ/88Azra4uxxkQTf8FF9Du+H7reUqemTIq+nUfT8hWc2ucIXn/6abgssl3DNLhl6i1t9rVLYhcSnYnNf07oQvt3Z+JfsRL4CN825RSweVMRVrOW5AFOiLKQwZzmPmgQ57KRGG0nJqU9WpdR0Hnk77l0Qog9oL7Syy/fbMRT44tI7zYkg7y+yZSWlnLxpRfy/vvvh/M++ugj1q9fT25u7j7u7YFPAnohhBBCiEOYaRhUlRSxac1Kvp85p1W+OymZ3E6dOfroo4mKiqLqzRWwKLBlVLqivr6Buro6FixcQJ2vgbdXvU9ix3LaZ9vo8/hFpMb0aFVndu0vdCr5igfvLQLg7bff5vzzz48oM3fzXH7Y+AMljSUR6UemH8kZHc8gztEyZ9Yzbx7V//kvHtPEVM0r6pfUNBEMhWhMjya33XocuTpo0eFjemXGoeugdRoJfS8Em+v3X0QhxB+ilKJ8fQPzvyhsM/+4y7rhiLLywgsvcNddd1FXVxfOO/HEE3nuueckmN8BCeiFEEIIIQ4hSimWTvuWkpXLaairpcEAn6LVG3mXRePim8YTExuLCpnUflaAt9aPaZrMmzeXispKflg3m8Wlq1hcuorevZ2cc24co4c52BR7DEvST2vVttX0c0TBB3zz+iY6nXExRZvG4Yp3ETACfLX+K34t/5WKpgr8hr/Vsbmxufz5iD83n0MwiHfJUjxzZuNd8AsNvhAFFc2r60eb9ZiD4kjKrwRdI5lqoGWbqtQ+I8noexKkdAWLPOoKsT+Vra+nZHUNm9fUtsqzOa30O6k9KdkxLFu2jGuuuYZZs2aF85OTk5k4cSIXXXRReHqOaE3+lRNCCCGEOMgp02TdgnmsWzCX+toaSoMQVK3LWR0OnO4Yrh53A5qmESzzUDujgFClF7/fzzfffhMue9Xkv6JQOF06l907iMb2ffksrm9EfdU1Bp5Gk2/PPo2umVnouhW4BXWn4vXlr/PQ8od22fdB7QZxYs6JpEeno4JBKl94Ed/SpYCioXwj/sZqrCpELgZGz1hUuhOLXo1paiggNzkKp9WCtdNxWAdf3zzGXgix39SUeljw1Qb8nmCb+dndk+h1bGY4SH/ssceYMGECwWBL+csvv5xHH32UpKSkfdLng5kE9EIIIYQQBxHTNKgrK6W+sgKlFGvnzaKhqhKlFPUmVIdayrqTknFERZGcmkZ6ejopsUkk1Dtoml+Gd1nzlm21dbX8+OOPW45Q/Jr8HE0uCxc/dw6rU0YAsHKb9jdtCtHQYHDWSScx6MgkLstMxrFl4nxDoIEXF79IYV3ksFqXtfktvUW3MDxrOJ0TOpMT0x7v62/T9P4sQo4FFAUCBEIG9XU12Oo3wpa3+NZkB2Z2DCrJTmqsg4y4LUPnU7tBnwsgubME8UIcAKo2NTLnw3Vt5rXvkUT7HknEpbSe+pKRkREO5jt27MgLL7zAcccdt1f7eiiRgF4IIYQQ4gCnlKLgl59Z9O0XEemmUlSGwLPNcPqo+ASi3LHoysKJfYbjqtMIVXoxl4QAD348gGLlylWsWbsGgAWV80gYPJ/SrF4UJl5Noz2lVR8CAUXhLyX09DTww5OPga74ev3XPDF/GUUNRTvs+x1H3EFObE5Ln/1+ql58iaply5oTgl6aytfR2NiAXfmwbSkXGp4MtuYvCix9z6dbz/5Y4zKat5OzORFCHDiqN3laBfNOt42Bp+S1GcRv6/zzz+edd96hV69e3H333bhcst7FbyEBvRBCCCHEAay6eCOz/vcCKEVQQUCBV0GjAXaXC81uwaXpuGMSUT4T0xNkgKMjaY5EWNpIYJu6CgoKWLN2DZX11cxY/zMBI0gwZwGxl5zL/Pi/RrS7sSiIsaGQS4cN495TTmFBxRx+7RJgTU0tt80Yj1JtjOnfxvgB48mPzwcgVFODb+kyaiZNas40DVT5cnw+LwHDxOgVhx4XTUh3g00jxmVDc6cSOvZuOnXIl/mzQhygytbXs+j7IoK+lqFBPYZlkts7uVVZpRSTJk1i1qxZPPtsy3aSmqbx0Ucfye/57yQBvRBCCCHEAcbnaeSHV57HtNrYWF4BFmt4UbvohETciUm4tzz8mj6DUEUT3QPZZEWnYHE3LxCnUNTX1VGre3n2fy9QWFPM2qoNFNVtDrfTd1QXHFc9TO2Wz16vorg4iPfzj1nz2Yc0Gpt5c8Wb3DXz+4j+bR/MD8kYQq/kXmS4M0h0JmLU1uKZPoNN817BqKreehTB+nJo2EwoGGgO5PvHoxLtANRaknB2H8XAQcOwJuXuycsphNjDmuoDzJqyttU8+Y4DUtsM5tetW8f111/Pt99+C8Do0aMZPXp0OF+C+d9PAnohhBBCiAPIj198ysIFCwip5v3VTaWjmxCbnEx0fCIosAQhNugiLRRHtjMVLa35YbhB8/LGG2/wQ8Fsphf+3Gb9FguM/1s+a3JOpTSmazi9sDCA4+03qVowD+66mXtm3kO1r7rV8YPaDaJzQme6J3XHbXNHPIibTU0UXz+u+UPIB43lKG8NHn8QwwiB04Ky6xiDEsGmE9CcvJd4DZePGsqxmXGt2hJCHFia6gNMfWNFq/S8vink9komKtYekR4MBnn88ce577778Pla9p3/9ttvIwJ68ftJQC+EEEIIsZ8FAgHefeVlKsrLI9I1IDY1ncSUZPr3HYBjRj1Oy5YHZht4gh4++/wzUpKT+dsnj/Pr5tYP2lvpySmkX34JHQem86M1JiIvM2Bh2i3X4/zzzfxa/isvL3k5In9o5lBO7XAqbru7Vb2mz0fD99/TNGcuoYoKqN8EDc2jAEyHTl2KBbN9HDhatpZbm3oSy2KHcWLfDjya3/ptnhDiwLNhWRVLpxVHpDndNoac3RGX296q/Ny5c7nmmmtYvHhxOC07O5tnnnmG005rve2l+H0koBdCCCGE2MdM02T9+vVs3ryZTSXFFK1sHYgPHHI0fY44kqamJmI2afhmVhFCZ82aNRSXFNPY2MgvJcv4dfMKZm/8FUMZrRvSNJJHjiDj4rG4nM1v0rfuAB/tdtMvoRd/6dudZJsVb8jLjd+Pb1XFQ8MeajOQr3rlVZrmzdtyQiGoLkD5GwAIpTuoz3eBprHR3okSew7r7Z258KQh9MlJoS8w9vdcOCHEfrHgq/WUrqsLf05uH8PAk3OxbFm4clv19fXcfffdPPPMM+HpObquc/PNN/PAAw/gdrf+90T8fhLQCyGEEELsI4FAgA8++CD8WZkmZQVrI8ocPeRo+vUcTGhzE54Piwh4mnh/6g8RZe788t9UNtW02YbTqREXZ2H4lUdQ0O9SLNs9b3dMtHF1hzwMTWfKmpd4fG4MDYGGVvWM7jCakbkj0bWWCvxr1lD/9TfN+8QbAWiqgoCHYFMNTQEDs30UZpYLoizMiDmFZc6BhHQ7x3ZN5eGjclq1IYQ4cG1YVkX5+nq8DUEaqrzh9AGn5JKe1/YUmc2bN3PEEUdQUlISTuvbty8vvfQSAwcO3Ot9PhxJQC+EEEIIsReZpsmqVasoKiqisrISgJrSTfgbGwFw6+DQISM9naFDzsUzZzP1xRswTIMvvojcpu7JWa/vcFj9oJMySLnoAppsCXjsSZQ6naS53VgsFkKmQWd7HUOivWz0NfD+2oXh47YP5qNt0Tx8zMPNfff7aVqyhNopH2JUVzfPi68rRgW9qFAAX9DA3yUaMyMRNA2f7mKGezSrnH1A0zhnYBYje6TLgldCHCSUUtSUNjF7yto284+/vDvOaFubeQDp6en06dOHkpISoqKiuP/++7n11luxWiXs3FvkygohhBBC7AWbNm1i2rRpEWn1lRU01Ta/WW9vB8uWQDfd155O+pF45mzGMEIsWPALZeVlrK8pZtaGhfy4fj5+o3kDOqdTo12GDXe0zrnnxVHXAPP6/ZmAPZZATAwOq5Voi4UQ4Al5SQqVkEMhwaCF6Z7W/Tw682hGZI8g1h6Ly+pC0zSM+no8c+dSN3lKcyEzCGXLMYwgjf4QKtmB0SkGoq3UWFMotWaxxtmL9G5HMW5QDm6HPGIKcTCpr/RSuKiS4pWtF8LsMSwTZ4yNxHbR2J2Rv9umaaLrLaN4NE3j2WefZfz48Tz22GPk5eXt9b4f7uRfWyGEEEKIPcTv97N48WKampoihpwClK5dTYIVkuxg1TRQcOypV2L8XEfz8ncAii++/JInZr7G4tJV4WP79HFy1OBEsrJa3oxVRuUyr8tl+GxxxLtcKMBv+GkwAqQaxaRRjmPLjHmT5gXpOiV0AiA9Kp1B7QaRG5cbrk8FAtRNmULDt9+1dNoIQOkSFBBItdOYEw8OnSJ7PktdA9lg70xQd3D1MR04JycB6/bj+4UQB6xgwKC0oI7F3xe1mZ/ROYG+J2TvcITN4sWLueaaa/jrX//K6aefHk7PyclhypQpe6XPojUJ6IUQQggh/qANGzYwc+bMNvOy2rWjYu508hzND8U2006/jBNxmC6Mn+sBDX/Az8aNGzn3qespri8FoGdPB337uejSxQFAUHewOTofs/8FbKJ5ZXirxU2MpoM1gcL6jRyh5mKjZV/o3NhcrLqVS7pfQpIrqVXfjEYPviWLqf/qa0JlZdtkBKBsKZpdo7FzNL7UlhWsX0m+k+TUdtx5checNkurOoUQB7aqkkbmfFwAWxas21ZMkouug9NJzYnd4fFer5e///3vPProo4RCIW644QZGjBhBTEzMDo8Re48E9EIIIYQQv9P2i9xtKz8nh6LZ06ndUEiMGUeGLxeA9PzOYDaX8TR5+OGHH1i4aTnPzJmEw6k48SQ3xxwTHa7HZ3UzteN4ktMysNuTsFpjibU2P2w3BhuprltEZ/U57Wgeku+yuriw24X0Tem7wzdryjDwrVhJ5dNPt86sKyK+U4io4e1ZUlIPwApnP76LPYsYl41rj+lAjwzZM16Ig00wYPDNS0vbzEvrEMfAUbm7rOPbb7/luuuuo6CgIJwWHR1NSUkJXbt23VNdFb+BBPRCCCGEEL9RcXExM2bMaJXet29fUqJdzJ/0Afoqk65mt3Ce1e4gKSsbaB6a/9RbL/DsnEk0BFomtv/17rTwnxXwedf7SE3Lp31UOlZrS5CfYrdQWvIqmVSSuU37o/JGMbrD6Db7bPr9BIuKaFq4kMbvI1fNtyQm4shOJcH1E7o9Gl/QYElJPWW2bD6JvwRXTDz/PKkL6XHO33KZhBAHgDXzy9iwtAq/JxiRntklgV7Ds9rcem57FRUV3H777bzxxhvhNLvdzoQJE5gwYQIOh2OP91vsHgnohRBCCCF+g1WrVrFgwYKItFAwQGdnFBUfzMAMppFNx3De1kDeGu9Ed1lZom1k2MnHtqr3gX+kYWg26h2pFMX3Z2ZDb0Z0PJFAIIDFYqedXkl2YB5VvkoqvZUkbnNsu+h23DrgVqJt0a3qrXn3PRqnTt3h+biPG0F8ehHeghkUNQSoaWp+0/9W4o1U2dJJjXXw4Fm9f9tFEkLsN6apqN7USPHKGkpWtb295cnX9cKyG2teKKV4/fXXuf3226mqqgqnDxs2jBdeeIFu3brt5GixL0hAL4QQQgixC6FQiNmzZ1NUtGXxKKVQIZOEBgfp5SZ21TwE3U3LG/aYpBQSerXHkR+HJ9YgMSmJ/Px81q1bB0BsrE7v3k5GnhxDtas9n+VcGT42EMhkxMDuBM0gZd4yutd9QJMGLcvktfj3Mf8myhYVkWb6/VQ8+SSBdQVtHAH23Fzix55Nk6+AhmlPsaEwEM7bbGvPlISrMDULqbEO/jWm1++5ZEKI/WDTmloWfrOhzbz8Aank9UnB4dr9EPC+++7j73//e/hzfHw8jzzyCFdeeWXE6vZi/5GAXgghhBBiBzZv3szy5cspKytDhUxUSGE0+gnUNpETjCWDyP2Y7a4oojITSeiZTfSAdAD+9re/8Y9//AOAdevWkZJi4Zy7h1EW0wVdGXwWPwAAq81GYkIicXF9AFhfu5IOgRl0DlSh2+1sXQl/aOZQ0qPT6ZXcK2KhO6VU61Xqt5F84424evYAoHjNIjZ8ejNNASOizMspd6GccZzbN5MTuqXK/vFCHOCUUnhq/ZQW1LNqzuZW+cnZMWR3TySjY/zvqv+qq67isccew+PxcP755zNx4kTS09P/YK/FniQBvRBCCCHENsygQXHhRqZPnQ6GwvAEwWxeDdoIBunmTyKGRDQ0gloQm7JRlrKJoRdfhiPRHa6noKCA/Pz88GctKpoj7roArfsAIgbsazayMvNxOtpxZEI8Q52beGXpf+kDKBQBINOdyci8kQxIG9Cqv94lS6j/6qsdvo1PvfNO7Hm5VDYG+PGnWeQv+CcNvlBEmV8yLqTb4NE83TH59142IcQ+ZpqKL59b3Gbe0WM7EZ8W1WbezjQ0NESsVt++fXueffZZUlJSGDVq1O/uq9h7JKAXQgghxGFNKYVR66dieTEbfl3Hysb12+SZhAJBMs1YUowoXNjw6U34aKLUuZHep55C+x696anrhEIh3nnnHS644ILw8XpyCo7Bw3EOHkp0tAUts+XRq67OpGjmct596B9YgBSjgFeXPcXq7fp3V++7yMvIazW8VZkmxeNuaHU+ms1K0jXX4OzRgzUVHsZ9uRJmVHJq7Zvk+VfSsG3Z5E5kn/cofd2y2J0QB5OF32xk05rW8+M7H5lOx4G/fXRNXV0dEyZM4LPPPmPp0qXExrZsW3fppZf+4f6KvUcCeiGEEEIctnyrayiZvpofq3+NSA8FAhihIKnKTapuJ2TxUerYREhvXiW638hTOaLvxQCsXLmy1cJQzpNPxzngSDr2TsKy3VbtNsPL1dGZjDhyMNlXjgNg/NTxBM3IFahHZI9gTMcxlJeXt+738uVUPPlUZJu9ehI/ZgxmahqPfLWKwgULsJl+hnp+oF/TzIiytZ3HkjLoXNl+ToiDjFKKn95fQ32FNyJ91PW90fXfPkVGKcWUKVO46aab2Ly5ecj+3XffzVNPPbWLI8WBQgJ6IYQQQhx2DE+QmvdXs7h+LRu9peF0pSuCTbW4rD7cURrQQDngio2j28DjSM/vhDsxie+//568bd+A6RZsvfvhOmE0ekICiYlWUlJaIvmkpkIy6pdy1RFJdOp4DUmJQwFoCDQw4ccJEX3rnNCZ6/tcj81iwzTNyH43NLDpjjtbnU/Wc89S2xTk5Z+LmP/NL1hVgJvKWxayctp0OqXGYNE1OHUixGb8gasnhNgf2hpif/zl3XFG23ZwxM4VFRVxww038Omnn4bToqOj6dix406OEgcaCeiFEEIIcdhoWlJB7Zpyvl87G0M1LwinlMJj1GHHS4pVAyeEF6C74DJS2ufy/fff89IHH/LVV1+xePGWB2rdgr3PAFyjz0JzObFaNaKidNq1a3m86ln6Oe2qf+XdN0J8vrplwSqlFJ8VfMbX67+O6N/EYydis0Q+nCt/gE133IHyNLU6n7izz6LpyGH86X/zSQyVcVTj99zkXx7O75Ieg8tmAVsUDLgcOgz/A1dPCLE/1Fd6+fXbjTRU+yLST7iyx29asX4rwzB4+umnufvuu/F4POH00aNH88wzz5CTk/OH+yz2HQnohRBCCHFIC5Z6CFX5KJ9dyNSq5uXolFIYwQABw4dPb6K9HSzbvHFPbp/LwDPOISq69b7ulvZ5uK+8Ac1mJSpKJyHBgtvdPL9dVwa51bPoXDGVzNRETjr1RXJyBvHA3yBoBvl+4/d8u/5b/IY/os4oaxT/HPrPcDCvTBPvggVU/ve/BPwB7A47GpHDaTOfepKb3l+Cf8oirqh6DLdRB4DTZiE70UW03docyJ/1EljkkU+Ig41SigVfbaCsoK5V3ugb+vyuOhcuXMg111zD/Pnzw2np6ek8+eSTjB07Vna2OAjJv+5CCCGEOOQEK700/VJGcFPz2yelFD9U/kwoEMA0DPyaD4seItVpYt3yABuXms6Qcy/CERW9ZQG6y1sqtFqJOucS7D2bH6Kjo3WyspqDb12FSPRsoO/myaRG6YwefR1W6zDaZ/+JkBnis4LPKKwrZFV1W7vIw039bqJLYpfwZ9Pvp+SWW1sX1DQyH30ELSqKwkoPt74+nfNrXiLGqA0X6ZMdh9auH3Q/A1K6gG5pXY8Q4oBlhEx8niDrF1eyfnFlRF5aXixdjmpHTOLvW8SysbGR4447jtra2nDatddey0MPPUR8fPwf6LXYnySgF0IIIcQhw/QbVL+9Emh+y11fWc6aho1s0GsIaUEUCs3qJ8umtryJ0ug06Gi6HzOCxx57nOPdMa3qdAw/EddJo4mLs5CcbMW65empfe0CulR8T6eMZAYecQapqS+TlDgM0Pmx5EeenP7nVm/itzqz45kMzhhMtK1lBIDR2Ejp/X/HbGiIKGsd0J/M667DYrXi8we45+XPyAus5MrGlv3me2XGNc+PP/d1sDr+2EUUQuxzRtDkqxeX7DB/5NU9sdr/2Bd0brebv//979x88810796dF154gaFDh/6hOsX+JwG9EEIIIQ56KmRS9fVaagvLKW8sp6KpCg8BavRGQtYQhtaygnyP1ES6HX0s2T16UVRUxMSJE3lixIlt1pv87/+QnGQlLq5ly7gB9dPp4vmFTh070v+0p0lLPYWGQAOfrvuUkoLH2VC/oc26RuaOpG9qX7JjsiP7rhQlt92G8kbOj3V060ryTTc1r3LfsJnayTezvsrLhahwGU2D3jlpaGe9ADbXb75uQoj9zwi1DuY1XcNqt9DvpPakZLf+onF3lJWV4XK5IragGzduHA6Hg8svvxy73f6H+i0ODBLQCyGEEOKgFazx8d2z77MxUBGRHtKD+PWWbZ1iU1I54aSR5ObnA/D3v/+de3u1noPapYuDo4a4CeT1pqD7uRF5/5ezmmxbA9Hu/mRn/Yd6Q+P+2fdj0b4OL7C3vRNzTuTEnBOJskW1me8vKKD834+0Sm/3wN+xGmWY0/+Nb/F0lvgiV7t3O6x07HkktOsDPc5ss24hxIGvYGEFK2Ztikj7o2/jlVK88sor3HHHHVxwwQU888wz4TyLxcI111zzu+sWBx4J6IUQQghx0DACITYsK6Dg11WUFG/EF2o9pN1v92GqIOnp7ehxxFGkpKSQkdG8Tdtxxx3H1KlTWx2TkhnFiPvOpyymCyutsRF5aWlp/F/8XLq0v5zY2N7ML53PEz+1bAm3bTAf74hnRPYI+qX1I9GZuNNzMQOByGDeNMj6y8VoG2Zg/nA7GytqqPYEME1zy5x+WO4agGvI1YwZkNv8el4IcdDxe0PMfH8N3oZARLrTbeP4y7r/obpXrVrFtddey/Tp0wF47rnnuPjiixk8ePAfqlccuCSgF0IIIcQBzQwYFH62iB/X/AyAEQoRCkQG8gqTQSOPps9Rg8LB71Z33XUXEydOJBCIfHjWNMjrGkvv685gc+YQth0o73Am0S69IwPik/hzh2w0bQwAhXWFvLbstYh64hxxjB8wnnhHPFZ9x49WKhTCM2sW3sVLCBQWYm7dLqpqHbGdwNIphk1fP0ptU4Cg0TKs/hfnkVS6chl7xtlc2G7nXxIIIQ48pmFSvbmJTWtq0TTYuKyqVZl+J7Uno1PC727D7/fz8MMP889//jPi37qLLrpI9pU/xElAL4QQQogDjq/Jy/KvFrC2YB1Nhg+lTIJ+P8psGXoeqxxYrV56nXgUnQcNRt9uRfelS5fSq1ev1pXbHQy5/UyMAccCsHmbrHZOC9cOOI2jk1OIsTbXp5RizuY5vLn8zYhqTsk7hVM6nLJb59MwdSq1774XmdhYhqorJhBnpTAuBsobAQhpdnwWF7OiT2Stsxtj+6RzRZ88LBZZsV6Ig4UyFZvW1rJy9mZ8jcE2y7gTnAwcnUt03B9byPLHH3/kmmuuYeXKleG0Dh068Pzzz3PiiW2vDyIOHRLQCyGEEOKAoIImvoJaPv30U5oMH4YRwggEUKr5bbWJgdXqI9FqoOsaXYf0o9uwEeHjg8EgP/30E1dccQUbNrRemE5zRTH0gcsJ5PZi2xnvcXHxuFzRPNKzH53issLpASPAzJKZTF4zuVVdJ+WetMtgXilFzVtv4fnxp20SDagrwh7vxZdpoaZrHNiaRxS8nTiOSms7Ylw2juuWxvVdUoi2WygvL5e9oYU4CCilKFpRzZKpxTssY3Naad89kfj0KNLz4v5QezU1NfzlL3/hpZdeCqdZLBbuuOMO/va3vxEV1fbaHeLQIgG9EEIIIfYbZSi8Sypo+rWCcn8N82qXEQoGMYLNQ0Y1IN5iots8WLeMpD/6vEtJze0AwKZNm8jMzNxpG9YOnUm6/kZy82xsO+g+OTmFGzr1YlRG54jy9YF6/vrjX9us65isYxjbeSy6preZH67jq6+o++jjyMSaDVh6mpR3UEDLG7npMaeyOOooLj86l2GdUiIOMc3IxfCEEAemmlIPsyavbTMvr28KHfqm4Iy27dE233///Yhg/sgjj+Sll16id+/ee7QdcWCTgF4IIYQQ+0XtJ+sIVTdv1VbUVMqC6uUYwea94k0tRJrDi65r5A8YRIf+R+BOTAofq5RqNVc+gm4h/oHHiI+3kJbW8rgTH59AnAMe6H0CnWOTIg6ZVTKLt1a+1WZ1p3Y4lZPzTt7lOSmlKL5+3LYJ4K2GzGqqcnTQW960fxx/GY72/RnWKZlbOibvsm4hxIFFKcXqeWWsnV/WKi81N5b+I3OwWHf+5d8fcdVVV/Hqq6+ydOlSHnzwQa6//nqZmnMYkoBeCCGEEPuUCplUTVoBCqoCtUzf/DPKNPHpTRiWEPEWiLdA1yHHkD9wEI6o6PCxq1atomvXrm1XrGnY+x9J1FkXEBtroV27lsecqOhousWFGJe4iu6d/xYewt4QaGB68XS+KvyqzSqfPO7Jnb6NNwMBvAt/JVCwDt/yFYQqWrbPU9UF6FlBKnOtsM38/u9iz2LgcWO4p3OKDKUX4iDlqfMz7c2VrdJ7H5dNdrc9v3hlKBRi2rRpnHDCCeE0i8XC66+/jsvlIisraydHi0OZBPRCCCGE2GdCNT5qP15Hg7+RbzbNBKBJb0RZmoeWp9lgzA3jiYqNnFv6xhtvcOmll+6wXluPPkRfeAW6rtGpkz2cnpiYyH0JP5CTcTppqS1z3osaivii4AuWVC5pVde5Xc5lWOawHQbbSima5v1M9auvtpnvCwQwN/2Kd0Qy0DzENqTZWOI6kqPG3sqf28W2eZwQ4sBmGiaVxY2snF1KQ5U3Iq//yBzSO8Sh6Xv+S7oFCxZwzTXX8Msvv/DTTz9x9NFHh/M6deq0x9sTBxcJ6IUQQgix1/kLaqmfXkyFp5K55YsIaAYKRZOlAYA4C4y54k8kZrS8ZSorKyM7O5tgsO0VotEtOIYcg2vUGTidOjk5NpKTU7BYdE5zLKWvrZj8nKtISHgegOKGYpZWLeWzdZ+1qio7Jpt+qf04vv3xWPS2h6yGamqoeXMSvmXLItIN06C2roJQlIbdVo6Z7YAuLUPoX0i5mzGDOnNFj/TfdM2EEAeGgC/E0uklbF5b2yovPT+Ofie2R7fs+aH1jY2N3HPPPfznP/8Jr6dx3XXXsWjRop1PORKHFQnohRBCCLFXqJBJYGM9G79fyZL6tdQFGwh4vaBBUPMT0P1kpKZw5qVX4NxmNeb6+nri4na8+rNz1Jk4hx4LgNttITPTSmpaGhpwumMJA+1FYE2gNvZC3t4wn+pV31DqKW2zrkRnIhd1u4guiV3azA+WlVN6772t0hv9Ieqb/LhZRbBPHFg0rIBJy/QAqzOa2Ate4bnU+F1eKyHEgcXXGGT5zE2UFtShTNUqP69PCvkDUnG49k449fnnnzNu3Dg2btwYTuvZsycvvfSSBPMiggT0QgghhNjjghVNFH64iFk1iwFF0O/HNAyCWoCA7iMtPZ1zr7gKm615SLrP5+Prr7/mzDPPbLM+LT6B6LMvwtqhIwBRUTo5OQ6SkpLRdY1THMs5wraO4pCLlyrtQBOUvt9mXdkx2WS4M7ig6wVY9bYfhZp+/hnP7Dn4li9vldcYl8jm6EISHVUEiQ+nW3QNs9NIYnIH0KH3UJCHbiEOOkG/wTcvL20zLzreQf+ROcQmu/Za+6Wlpdxyyy2899574TSn08m9997L7bffHv43U4itJKAXQgghxB5jBgxqPl3LyuI1rKgvQCmFYQQJaH6wBkmzQsfe/Rgw+gxg12/jtdh44v5yX/izy6XTvXsi0dFRnOtcSDfrfAwV4KvqJl4hheaN7lq4rC4SnAkMzRxKp/hOtHO322n/jfp6Nt35l1bpobg4yjo6cFbPAQrYuuRVYrQdl81CzEWv4XTH78YVEkIcyOZ/sT7isyPKRpfB6WR0jN+rK9abpsnLL7/MnXfeSV1dXTj9hBNO4Pnnnyc/P3+vtS0ObhLQCyGEEOIPK1q2nMVfziHoMSkz68Ppft1LjCNImkUDNE4eNx5XTCyBQACHw7HjCm123H+6CVt2exITLaSl2UlMTCLJ6mWs81eyLbUoFGs9TczwxmHiDh/qtrs5tcOpDM0cult9N5uaaPj+B+o//7xVnjZyJKWr38ZKAc7qyLz0OCfp5z4BiXm71Y4Q4sC2ZFox1Zsaw59Pvq4Xlr0wN35HXnvttXAwn5yczMSJE7noootkNwyxUxLQCyGEEOIP+eqlyWwo3hCR1rzgXSOZSfH07t2b1LyOJGVlA/Ddd99x4okn7rC+uDFnk3LyCFwunaSkaGJjY8i1VHOF60s0rbnuJR4/c70xKJzh49Kj05lw5IQdLmq3PWUYVL/2Gk0/z2+VF0pKoi72Z1j9XMTDknLEEj/oYnKPHI1mkccoIQ52SinmfVZI5caGiPTjL+u+T4N5Xdd58cUX6d+/PxdddBGPPPIIycnJuz5QHPbk/0RCCCGE+M2UqWhaXME7X7xHyB8Ip9utdjK7ZdPjyL5kts+JOKa6upqkpKQdV6ppDHznWSwWcLlcxMbGYsVkgvsrbJpJWtqpfFdVx5zSebBNIA9w35D7SHbt3sOvMk2Kx93QKl3X/MR11vDH1lFUWRCRF9KddL72NWJjE3arDSHEgSvgDVG4uJJNa2ppqvO3yj/24q443Xt3rvq0adNwOp0cddRR4bSePXuyevVqcnNz92rb4tAiAb0QQgghfpOFCxfyyxczCQUCEekD+3djwBmjItIKCgq4+uqr+eGHH3Za54ABLux3PEhyuzS2ji69PfoHOqYPx2cfx9NL/keg8ttWx93c/2Y6J3Te7b4rpVoH88okpetmHGlOqhoDFFe27C8d57KRd/0HYHMihDi4KaWY9tYqmmpbB/EAwy/sgjth7/6uV1dXc8cdd/DKK6/QtWtXfv3114jpRxLMi99KAnohhBBC7JZgMMiL/5qI1Yx8fAhqfs4650wyenQLp02cOJHbbrttl3VeeGE8Xbs7mT/82Yj0DwafwYrqfP61+EVgWqvjHhr2EG67u1X6jph+P5XPP49/xcptEkNkDq3F0GD5Zj+qqPkhf72jC4tdgzjxuBPp2yl1t9sQQhzYFk8tjgjmNV0jPT+OTgPTiEncu4G8Uoq33nqL8ePHU1FRAcDKlSt5/fXXufrqq/dq2+LQJgG9EEIIIXbK5/PyxeQPKFtdhnWbR4dcM5H8MT3o2P+IcNoDDzzAPffcs8s64+N1bv9zCn379uOluGvC6U5nFhNyEhg/7eZWx3RP6s6pHU4lOyZ7l4tEKaVomjOH2ikfYno8YJqRBZqqSR9hsKrCgz/UnBfSbDyf8n8ozcLFR+UwWIJ5IQ4Za+aXUbyiZWXLE6/qgd25b0KhgoICrr/+er755ptwWmxsLA8//DBXXXXVPumDOHRJQC+EEEKINv08fRo/zZiBKxSFho5OywJRl91wNc7UmPDnUCi02/sj33xzEimpVvqOPI+XfccCEOPujqbbONO5iJcX/y+i/LDMYZzb5dzdWulZmSa1771H408/QcholW/46mnKKiUQY6WqvDmtzpLIm0m34HDYeeLs3rgd8ngkxKFEKcXquaXhz8Mv7LpPgvlgMMjjjz/O/fffj9fbMpXn7LPP5sknnyQjI2Ov90Ec+uT/WEIIIYQICwUCLJ/xAwvnzaEh4CRKbbMdnHLQJ64z3W85Dt3aspJ8nz59WLx48S7r7tnTxT33HknH/Hz+4z2en30udN1JjLsLnmAjGVVPMXWb8oPaDeKS7pf8pv6XP/IogcLCiLToYUMpyOuFdeqfwaqx7ePPx/GXsdHRiX+M6Um7ONdvaksIceBrqg8w9Y0V4c9Dzu6IO2EnW2buIYsWLeLSSy+N+LcxKyuLZ555htNPP32vty8OHxLQCyGEEAKAr9/8HyvXFeE0o4BYtr5v7+LIontud1LO6Y5m01FK8e6773L11VfT0NCwsypxOjWef/7P9OlbjaEsrDVSuM8zAICYmB4EjBAbK2fQSS2LOO6W/rfQKaHTb+p/xdPPRATzyXfdxTe1Vj5ZWMJNP96xJZhvVhzbjz7n/h/3JEbLHs9CHKI+f3YxKBX+bI+ykpAevU/attlsrFjR/EWCpmncfPPNPPDAA8TExOziSCF+GwnohRBCiMOYr6KRt955k2CNFwyFkygANF3HYrXSI74jR1xyHJZYOxs3biQnJ2en9dlsGueeG4fTqXHVn0YR5Yqi3mziGc8xlJvND7IuV3tstgQK6groFfiUVFqGxp+Wfxojc0fudv/NQID6z7+g4euvw2nlDT5eHnY5gWnlpAaLuan6+XCeM7Mn+Rc8Qt99uL+0EGLfW/RDUUQwn5YXy8BT8vZZ+927d+euu+7ik08+4aWXXuKII47Y9UFC/A4S0AshhBCHmapNFfz04bfUV9XSZPha5fdM6ETnLl1JOi4Pfct88nPPPZf3339/h3VqGvztnlRstua33aNHn0q1cvN2Uy82GInougO7PQanM5OQGWJt5Tz6q5/Y+m68e1J3ru9z/W96W158002oYCj8udoToKi6ia9O+xMpeilnlb0CgEXXyIh3khhtR7vwsd2uXwhx8ClaXs3iqUURaaNv6LNX2ywpKeHhhx/mkUceidiC7v/+7//429/+ttvriwjxe0hAL4QQQhwmQqEQrz74LGYo1DpPCzI0thv55w7GnZUSTl+4cCH9+/ffYZ1JyRbGXZ+E3dEciB9zzDHExsRxv+c0nK72aE4bMboDTdPpFOXAVf8xG2qWk7RNHfcOvpeUqJS2G9iOUora99+n8Yfm2fbeoEFlo5/qxgBlvTsxoPsc/lT3bwDykqOJcVrRNQ3yhsOg63arDSHEwaexxs9P763GCEXuaHH8Zd33WpuGYfD8888zYcIEGhoaSExM5L777gvn2+32vda2EFtJQC+EEEIcwrxeLwW/rmbRvIU01NREDEGNU04SUt10PrY/eT17tjr2oYceYsKECTus2+nUuPXWZPJy88jvmE+ZNZPJgc5UacNxx7QMae/gcnBeisazC/4VcXxWTBZ3HnEnurbz4e9mIIB3wQJ8y5fT9PN8ABSwuKgWCyE2jhzAIO9UspgLaLRPjCIxesuDdFpPGHE36DLEXohDkc8T5PvXlrdK79Avhc6D0rHspek1S5Ys4ZprrmHOnDnhtFdeeYW77roLp3Pv7mkvxLYkoBdCCCEOQfX19Xz22WeEqn2E6n0E/ZFD688ddyEJaek7PD4vL4/169e3mZefb+eqq3ow6pT+aFsGzVc4BvC+/wQ0mx4eRp9os/J/OS7un30/z26MrGPCoAlkujN3eR4qGKTk5lsi0sob/Gyu9ZLdZTMq1U4771QcNp0Ut4Mktx3tiGug4/HN8wCEEIesTWtrWfj1hoi06HgHw87tjMW2dwJ5r9fLAw88wCOPPEJom9FOV111Ff/+978lmBf7nAT0QgghxCGmoKCA2TNmEixrIhQMYASDWJRGvG7DaWvi2HF/wp2Q2Oq4xsZGunXrRnFxcUS6261z0klu3DEWOnWyM/iowSQnJ4fzZwbymK2dFI6fT06OI9EsZkXp99w/uyCiriPSj+CyHpft8hyUUtR99HHEYncAdS4fle1MspzVKBw4bTpd02PBEQs9xkDnkaBbdlCrEOJgt2ltLaXr6ti8tjYiPTHTzeAz8/dq299//z3XXnst69atC6d16dKFF154geHDh+/VtoXYEQnohRBCiEPI5uJNzJj8HZiKgM9HrGGjp8qiIGo52f160+/k0yLKB4NB/vWvf0XM+9wqOlrj1vEpOJ3NkbrL5eL4448Pv5WPie3LL9YTmF3TsuDTaQkGM1bc26qunsk9uarXVdj0nS8OZfr9VD71FP616yIzAo0YfasJeUOkbUnqkh6Dy2aBC96Rt/FCHOLKCuuZ/0Vhm3kdB6bRZdCORxz9UUoprr32Wl566aVwms1m469//SsTJkyIWAhPiH1NAnohhBDiEOD1epk86T1CVT4CXi9KmXQz07BafaxxLOak624mOj4h4pg5c+YwePDgNusbcnQUo0a17Jc8cuRIYmPyyMq6FJstllkNdh4tqWxpP+QlzfMZMyoiV5c+vv3xjMwdSZQtapfnoEyTkltubZWe0reelXWV4G1Jyz5xHK7srpDcWYJ5IQ5xi74vonhldURaTs8k4tKiyOqS8Jt2x/g9NE0jIyMj/Hno0KG8+OKLdOvWba+2K8TukIBeCCGEOEgppQiWelg5dym/rFoEgGmaKGWSZyZSG12IJcrB8ReOiwjmTdPEYml7WHq7dlbG3ZAUkTZmzGV06fx/1JhO3imvZVp1fTjPE/RQWFdIDzUfjZYH7hNzTuS0/NN2ueCdMk18S5ZQ+dzzrfLSLxqEd/UnrKz0hNPWObpz1OX/IinetdN6hRAHt4AvxOp5ZWxYUhmR3m9kDu3y4/Z6EL+9CRMm8PXXX3PllVdy1VVXoctCm+IAIQG9EEIIcRBqmLuJZfOXsLJxfThNKUXI56Wj7qTetYHTbv8r1jb2P95RMH/c8W5GjIgOfx4xYgT5Pe7li4YkXltXS6k/GFE+Rg/RVPsFQygKL4TXOaEz1/W5Drtlx9s1+VatomnOHLyLFmE2eSMzjSBUrSHz7FTWzn+PpoARznoq9QFevvyIff4gL4TYt5RSfPvfZa3Sj7+8O87ovbuneyAQ4NFHH0Upxd133x1OdzgczJo1S/79EQccCeiFEEKIg4jZFKT2m/V8suqHcJpSJl5vA0lWDXtUE/U0YHM4WwXzwWCwzX2RXS6Nv96dCkD37t3Jzc2lOPoUnvf1hw0ADRHl4y2KqtI30amg3Za0C7tdyJCMITvst1IK39KlVD7z7A5OLIhLX0visBQ0axpLS+oxzOYt9iYn/InjjzmG/3ZNa/tYIcQhI+AN8e0rLcG8xarT5ah0cnsn7/VgetasWVxzzTUsW7YMm83GmWeeSY8ePcL5EsyLA5EE9EIIIcRBQClF/TcbCG728FnZT+H0kCuEy1NBnKvlYTM+rR3HXnZ1xPGvvfYaV1xxRURaXJzOyaNi6NnTSU5ODr179canrDyqxmHxRUeUdVt0jk+KpV+0ycR597HtGvnHZB2z02DeM2cu1a+91irdPfwYbNntiSp4Ct2q8AWTWVXRiD9kAvBu4nWU27L4x5ietIuTIfZCHOpqy5uYPTlyQcyTr+2119utq6tjwoQJPP/88yjV/EWiYRj8+OOPEQG9EAciCeiFEEKIA5wZNCh4bT51wQaWNDQ/7GoOC9X1m8hRJpreHMhHxcUz8rrIPdvXrl1Lp06dwp9dLo1u3Z2MGRMbTjv11FPxKjv3NpxITGwfth2Qf1xSLJdnJKFpGgvKFjBx3qvhvB5JPbiuz3VtvrVSgQCe2bOpefudVnmWpETa3X8/WqgJplyNsih+LaoDwNCsvJJyFz69eRG9B8/qRWqs7OssxKHOWx9k/jfrwmtcJmW5GXR6h73aplKKKVOmcNNNN7F58+Zwev/+/XnxxRcZMGDAXm1fiD1BAnohhBDiAFVXV8f06dOpXrF5m1SF19KEt76eXHvLW/kh51xEWoeO4VLbL3xnscDFlyTQsWPkkPuTTjmdx7ynELS1JyY2PpzeLdrJnXntsOoaXxR8wReFX0Qc1z2pO9f3vT4izbd6NZ4ff6Tp5/ltnk/iFVcQPehIqFkPn98CnkoqGv2U1DTPo38m9X5MzUL/nAQuGtSe+Kgdz8MXQhwaQkGDr15Yij/gx2Fv3v6t86B0Og3cu1NsioqKuPHGG/nkk0/CadHR0TzwwAPcdNNNWK0SJomDg9ypQgghxAHE4/Gwfv16Fi1ahFHrx2gIhPNMw6DJKMeuERHMn3nnPa3ekm+/8N1990c+HFdWGjjO/hdPasegRdnYOtveqmk83b090VuOv/H7G1v18aZ+N9ElsUv4c/1XX1H30cc7PKeYE08k/uyzYO6L8NZjGKai1hugqLplQbyn0v4BwPOXDMBmkdWjhThczHh7dcTndh3j93owP2/ePI4//ngaGxvDaaNHj+aZZ54hJydnr7YtxJ4mAb0QQghxgKisrOSbb74BwGgMhIP5dEcSHbUUlninkWDZErhrGkPGXtjqrfwzzzzDzTffHFHvA/+IfDjuPOQJViUd2VzNNukv9sjFtSWYVkpx0w83RRx3WY/LGJg2ENPjoei665v3f98y33Rbjk4diTrqKKIHD0bbsrWTMflaNpduorKx5QuKRkscbyTdQkizc/HgHEZ0Sd3dSyWEOMgppfju1eUEvKFw2injeu2T7eD69u1LdnY2K1asID09nSeffJKxY8fKonfioCQBvRBCCHEA8Hq9zcG8gkBxA25rFG5HEt2tmSxqmMoSfVk4+u513Eg6HnFU+NjS0lLatWvXqs7UVCsnj4oJf/Z1OI4NPf7Bqu3KPdmtPQm2lkcCv+Hn9mm3R5SZeOxEzA3FlNx2G8rra07cLpiPP2csMccfH5GmGstZ/8qV1Hlbtrz7OP4yNjo64bRZuGJwDkd1iNz3XghxaPv1u42UrKqJSBs0NmuvtWeaZsQXBXa7nRdffJE333yThx56iPj4+L3WthB7mwT0QgghxH7k9/uZPHkyKmRi1Pkxm0K0d6XTO7YjDdWVLG6YRkhvCYb7n3IGOb36AhAKhejatSvr1q1rVW+fPk7GnhMHwC8ZZxPqeDyxsb0j3sgPT4zhT1kpEccppVoF8/9ufzNlt92B8vsj0jWblZTx47FlZKA7t1u4zltDw9t/Yl1FY0Ty06l/Z+wROdzTI03ehglxGPE2BpjxzmpCfiMi3eqwcMIV3aioqNgr7X799dfcfPPNfPDBB/Tq1bJi/tChQxk6dOheaVOIfUkCeiGEEGI/qKysZNGiRZRuKiW4qSXojbFG0SsmnyB+fg1Ogy0vlYacezFpeflAc9Ddq1cvli1b1qrezEwrJ54YQ35HOwr4vOt9JCVlERvdHk1rruzqrBSOio/Gvs0bK6UUU9ZMYWrR1HBah3onY79upIqJEW1EH300Ceedi7b9nvZKoYrmUvj9f/FVFRMwzHBWoaMrR1/5MC/LivVCHHYqixuZ+3HrLx5PuLIHDpcV0zTbOOqPKS8vZ/z48bz11lsAXHvttfz000/7ZEi/EPuSBPRCCCHEPrZ1rnyoxofZ2Pz23WVxMDSmOxUVG1mw6Wu81pYg/8gzzgkH8wsWLGDgwIFt1nvRRfF07eYIfy7rfwP57Yaj681pd+al09PtavVm/H/L/sfPpT+HP2esruHIaZvpktCFbWfZRx8zjPgzz0SPiopsuGAazHkOhWLRlu3ntvLq0dSMfJIxvffecFohxIGrZFUNv363MfzZGWOn94gskrPce2WUjlKKV199lT//+c/U1LQM67fb7dTW1pKYmLjH2xRif5KAXgghhNhHQqEQs2fPZv3StRj1zYvD5Ua1I9OZSlycm5mFkwkvN79Ft6HHEnRG0bFjxzaH1m/Vrp2Vrt0c2O0ORhx7LN9Ej6MulLL1BT8v9cjF2cbq8RMXTGRdbXO9roYAeYsqOHZjNDEJLavYu/r3J+nKK9C238bJCKImX0Vjo4c6byBiwbsVzn64h17LcT2ziXbI44YQh6P6Sm9EMJ/XN4XuR2fstfZWrVrFddddx7Rp08JpCQkJPProo1xxxRUyzUcckuT/sEIIIcQ+sH79en76bjqhKl84LTcqg+5R7Vnum8vSwpa95rsfcxxpHTphj4klOjp6h3XGxup07Ohg5Eg3UdE6J5xwIg6Hk8e4Ay3U8r/4R7tktxnMv7j4RdbVrkMzFUd8UUj/ugTi7OnhLxViTz2V2NGntHoI9hYvYfOXj+CpLWObUfXMix7BwqijCehOnrt4AHarDG0V4nCkTEXhokpWzNoUTus5PIucnntnAUy/38/DDz/MP//5TwKBli8WL7roIh5//HFSU2UHDXHokoBeCCGE2IuCwSDvv/8+KmCEg3mHbmN4Un8asuqZufCjiPL9Rp1Obu9+lJSUkLCDYD46WuOuCS0PqCeddBIOu4NlwXQ+Cp6CY5s34vfkZ5DmiHztX+ev4x9z/oE35MXhCXL868vpmtgFm725nK19NnGnnYZrmwWkqC6EVV9Qt/x7Cis9EfV59Wj+m3wnXTLiuTg/maM7Jv/m6ySEOPh56vxMe3Nlq/Ssbol7LZiH5sB98uTJ4c95eXk899xzjBw5cq+1KcSBQgJ6IYQQYi8pKirixx9/JFjqQQWbX2V3jM6mR89uTJvzOmyzqHNKTgcGjz0f3WLd4bDQmBid665PIja2+c13fod8unfvTkiP4kHfOVjtqTi05v+1p9qt/LtLNpbt6qr2VXPPzHsAcDQGOf6NLcG83hzMt/vXP7FuO8fUNOCdC/EFDVaWNkTUtc7Vm6Qhl3BUnx68bLP8/gslhDhoKaVYOr2Ejcuq2szve0J7Mrsk7NU+3HbbbUyePBmLxcLtt9/OvffeS9T2a30IcYiSgF4IIYTYw6qrq/nmq68JVDVhNoXC6aNTj8aT4mHa3Ne3XWuOI047m6zuPWloaCA2NrbNOmNidO78SwqaptGvbz8yMjJITBxMVfwFPLG+FFvLWnjc1D6VI+PdreqYtGISszfNBqUY9v5q0uo1OiT3QEPDEhdHxsMPtRSu3Qhlywj9/CpVjX421zWPLljn6M4y1wCOOfYkzu4iw1iFOFyZpqJgYQWr5mxulZeYEc2g0zugtzHV549SSlFfX09cXFw4bciQITz++OMcd9xx9OnTZ4+3KcSBTAJ6IYQQYg+aP3seS2csjEjrEdOBvKgMZlZ8iFnesgdzWodODDnnQgDuv/9+7rvvvjbr7D/AxZgxsZx66qloaDicGXTM/zMNysXdK1oWnOrhdvGXvPSIN/wljSV8svYTllU1b3EXU+Vl2HuriXfEkR2XDYBrQH+Sr7oSfn0LCqajfDUUVXup9gQi+vFs6r0kxbm5//SeMj9eiMNYTamHWZPXtkofdEY+yVmtv0zcUzZs2MANN9xAZWUlM2fOxGJpGRk0fvz4vdauEAcyCeiFEEKIP8gwDNatW8e8mXMJlTeF0zOdKfRM7Uz0Ucl8/94LEcf0OfEUojKy6d27N0uWLGlVZ48eDkaMcJOWbiUhIYGhRw8FIDHpGLKzLuGbyjre2NQSzN+ak8aAuJY5937Dz+3T/p+9+4ySotoaMPx2mpxzYCIMOeccJQoSFBFUvCYkiAqKgooKgqAYUEARxYQKCiqIJBEFJCM5ZxgYJufcqb4fDT3U1wMCAsMM+1nrrmvtOlV1ulfTU7vrnH2eB0BrthKQmE/T304CUNO/BjqN7UY4fPwzaM9vggW2Hxb+/9Jz6fpgzjnFstWvF+O6Vicm4PJF+oQQFV92aqFDMl+lcTDVmoXctGuazWY++ugjxo8fT0GB7Tv2k08+4emnn75p1xSivJCEXgghhPgPFEXhhx9+UK0pD9Axpjlh99Rg29JFJP94THWMS+3GVG7U9LLnfGqoH5Uq2ea033333Wg1tqfhNWpMZUeBMy/vPalq39LHQ5XMZ+anM//dJ+h2KB2tWQHA3eCGj0c4vi4+aNDgWq8O/l5r0ax7AwWF7EITRUYrSTlFbPbozAHXRhRqPRjavjKPRPvxyH97m4QQ5Zyp2MLvcw+AothjlWr4Ua9jxE297s6dO3nyySfZuXOnPRYWFkZkZORNva4Q5YUk9EIIIcR1ys3NZckPP6uWoqvrFUfN7o04Gb+DpR9OUbUPrlqTVv3uv+I5hw33JyxMT1BQEM2aNiMkpA8enrXZZ/Tn8cMpqrb+Bj1PRQRSw8MVgN1JO9k4501i9qYRfaGNVqOhul91+xN5t2bN8OvRFM2aCYCGAqOZo8l5FGrd+dujJ0eC69vP//kjjWXdZiHucIqicHRrEsd3qL9/Imr6UbfDzUvm8/LyeP3115k+fTpWq62oqEajYfjw4UyePFk1h16IO1mZJ/SzZs1i2rRpJCUlUa9ePWbMmEHTppd/ajF9+nQ++eQT4uPjCQgI4L777mPKlCm4uLjcwl4LIYS4k+Xl5bHkl8WYzquXb7s7qBVKYxdW/DDd4Zjwlh1o1rZdqefTamHQgz5Uq2arbNehfQc8PDyoVWsGP6bms+J4NqC+mX45NtSeyAMcStjN+WefJeaSNu4GN+r1H4pHm9boAgJsyfnOb2DNBADiMwrIyDcyI+hNuJC4N4zyxcNZz+AWUZLMC3EHy88qZuuvJynMNTrs6z6sLlrtzft+WL58OcOHD+fMmTP2WO3atfnss89o3rz5TbuuEOVRmSb0P/zwA6NHj2b27Nk0a9aM6dOn07VrV44cOUJQkGPl3O+//56xY8fyxRdf0LJlS44ePcr//vc/NBoN77//fhm8AiGEEHeagwcPsmvbDkzJJXPlI11DaN6uBdrKbqyY9Z6qffVO3ejarz9nx75R6vl8fHU8/3wABoOBtm3b4uZqW2rpRNBk3j143qF9cx8PhkcE2pPtfFM+f7z2JIZjZ+1tfL2CqDX8JbwaX/iB3JgPv42CXFs1aquisPdcNhs8urEr2DY3v0Vlf55oE3t9b4oQokLZt/ZcqcvQNekZQ1BU6Stx3CjHjh2jZ8+eKBeG9ru4uPDaa6/xwgsvYDAYbuq1hSiPyjShf//993nyySd59NFHAZg9ezbLli3jiy++YOzYsQ7tN23aRKtWrRg0yFa4Jzo6moEDB7J169Zb2m8hhBB3HpPJxNatWzl14DiWrGIAAp18aVW9Cd7dolEUK0umTbK3j2vRljrtOgJvlHq+GjWdeeABH2rVqkGVylXs8djKYxl+UgOpear2r1QOpbq7qyq2K3kn/3z0BuHHMu0xrxq1afHmJyWNrBZY9BgARSYLCVmF5BaZ+cFvKCmGSgC83qsWkf6yZrMQdzpFUdi29BRpZ3PtsZDK3tRpXwknl1uTNsTFxTF8+HBmzZpFp06dmD17NlWqVPn3A4W4Q5VZQm80GtmxYwfjxo2zx7RaLXfddRebN28u9ZiWLVvy7bffsm3bNpo2bcrJkydZvnw5Dz/88GWvU1xcTHFxsX07JycHAKvVap+PI0RFY7VaURRFPuOiQruVn3NFUfh1zkJy80puctv7N8S/djjuTUM4vn0z+/5abWtrtbLq99X0K+WJfN26LjRr7kZkpAGtVku3bt3QarUoKHh7N8Ij5HGGHTkLlBSder1yGJXdbEPxL77WUxkn2LvoU5z/2EL4hXauelfqffA5PsERtnYF6Wh+GwUWI2arwoHztr9/XwY8T663z8VXxmcPN0Kj0cj3xW1Kvs/FrWIsMvPHF4dUsbYPxOHhZ5vWerM+g8eOHSM6Olr1OZ80aRLNmzdn4MCB8v0kKoyb9Tkus4Q+LS0Ni8VCcHCwKh4cHMzhw4dLPWbQoEGkpaXRunVrFEXBbDYzdOhQXn755cteZ8qUKUyYMMEhnpqaitHoOCdIiIrAarWSnZ2NoihotbJWtKiYbtXn3GqykPXrCbJzbEu5OWsN1PeoSq5nNocP/EPW72dV7Rf/toyfdu5XxapVc+ahh33s235+/jRq1AiTyYynRwtcvXvydnIBGaknVMd9WMkHTV42KXm2HxXiz+wm5/1pFJuLMAAXbw0CXQKp9OzLGDXOZOz/A48dH9vPkV1oJiHb9sP2LJ8xFJndiPLUMqBBEMGeTqSmpt6YN0rcFPJ9Lm6F9PgCDm9IU8Ua9gylwJxDQUrOTblmUVERM2bMYMaMGYwfP5777rtP9Tm/66675PtJVCjZ2dn/3ug6lHlRvGuxdu1a3nrrLT7++GOaNWvG8ePHefbZZ3nzzTcZP358qceMGzeO0aNH27dzcnKIiIggMDAQHx+fW9RzIW4tq9WKRqMhMDBQbgBFhXUzP+cXfzQ+vvMQ29fbpnVdvEaL1vXZvWEZXBgR7+TsbD9u4sxP2XMu0b4dGqpn+Ah/1bnbtm1LdHRPvDzr4OlZl315hbx1Ohl0epxshehp7evBE+EBaC/Mk88pzuHD70bSZOUpVV/c9G5UG/M6wQ1agKKgWfwUFOWAkxMA+8/nYLEqfBIyAYtGj16rZe4D9XHSy/dCeSHf5+JmKS4wsf230+Sk2VbpcHayfZdpdRq6PVX7pl573bp1DB06lKNHjwLwzjvv0L17d+Li4uRzLiospwt/m2+0MkvoAwIC0Ol0JCcnq+LJycmEhISUesz48eN5+OGHeeKJJwCoU6cO+fn5DBkyhFdeeaXULwBnZ2ecL7nZukir1coXhqjQNBqNfM5FhXczPudZWVks+20ZpgT1HHa0Gtw9jezZsIxLaztH1W1AdP3GxNaoSXZ2NlotdOzoQbv27qrDu3XrRpUqI/HxboSiKBzOL+K1o+fINFlU1eTfqxZBkLOt8FOhuZAFv79P8BcraWJ7xQD4V65BjVcm4+UVWHKB7wcAtqf2uUVmNjq35qfANvbdQ9rG0jxW/eOCKB/k+1zcKIpV4cyBdA6sT7DHLl3MIqZ+IDVbhd2062dkZDBmzBi++OILe0yv1zNy5Ej8/f3lcy4qtJv12S6zhN7JyYlGjRqxZs0a+vTpA9h+hV6zZg1PP/10qccUFBQ4vBE6ne1xxsVKmEIIIcT1MJvN/PrrrxQVFTkk8/Wja2H1yuDM3l32WLUWbajZtiOAPSGvUdOZQYN8VMd269ad0JAuhIcPRFEUpp5M5EBeocP17w32pU+wr337hcVD6PjtIS6dmObp5EHDZ97AvXkz9cE75wGQWWDkTHoBXwS8SL65pBL1pw83Qq+Tm2Qh7mSn96WpEvmLNFoNrfvH4RXgWspRN4aiKMyfP59Ro0aRklKyBGfz5s357LPPqFmzpiouhLh6ZTrkfvTo0TzyyCM0btyYpk2bMn36dPLz8+1V7wcPHkx4eDhTpkwBoFevXrz//vs0aNDAPuR+/Pjx9OrVy57YCyGEENcqPz+fJUuWYC0wYU63DT+t5RlLtGsoAYNrsW/t75z6pySZ7z7ieVw8PPj22295+OGHqVfPhfv6ezuc98knfyAosC0ajQaj1crj+087tOkR6M29wb44XfKD9azVk+j4bUlxKq1GQ5VHhlP57gHqteGT9mP8fQLp+cWk5BSjgGpN+bqVfOjfuJIk80LcoRSrQtq5PPasOUtxgUm1r0rjYKo2DVZ/p9wEp06dYtiwYaxatcoe8/LyYsqUKQwdOhStVitF74T4D8o0oR8wYACpqam89tprJCUlUb9+fVauXGkvlBcfH696Iv/qq6+i0Wh49dVXSUhIIDAwkF69ejF58uSyeglCCCHKOYvFwpIlSzAl5aOYrDhpDXTwb4RBq8fQI5DF705UtY9t3ZGoypXtT5OqxDk5JPPr1+WzcOFZvC9Ukz+YV8iUk4mqNrNrReH+/36MTk48wfbRjxFjKrm5rVerIyGjRqG7tO7LznkU71/CocRc1fGLfJ8EjYanO1ahQaQvQog72/JP9jrEbsVa8pf66KOPVMl8v379+OijjwgPD7/CUUKIq6VR7rCx6jk5OXh7e5OZmSlF8USFZbVaSUlJISgoSOaiiQrrRnzOi4uL+emnn7BkFWPJNRLmEkhD72p4tq9EWlEC25YsVLX3rdOIDnf3sm9Xr+7Mgw/52Le//z6LQweLSUpKsv84rSgKg/edsrfxMeiYUSNKdd7T2aeZ9edkOnynXuWl0aCRBPe7XxVLWP81OdsXUGwuSfo3eXShsMrdhPi40rNOGN5uhut6P8TtR77PxfVa8ek+rJd8T4RW8aFuh0ronW7tqNbc3Fxq1qyJoijMmjWL3r17O7SRz7m4E2RlZeHr60t2djZeXjfuR7VyVeVeCCGEuFFSU1NZvXo1ilXBkmtbxrShdzX8HqhGeso5VTIfWqUaS3fs5a1LkvkHH/Sheo2SoqtTp6SQn6+Qk5ODp6cnAEtTsvgxKcPepmegDwNC/ezbmUWZfLnvC7x+XkuHY1n2uHt4JK0mz0bv4WmPWTPPcuzLpyg0Weyxhb5DMPvFMaF3LVwMMvVMCGGz7bdTqmS++9A6aG/B1Jvc3Fx27NhB+/bt7TFPT0+WLl1K5cqV7d+NQogbRxJ6IYQQd5SCggIWL14MYJ8z76pzpqN/Y5w7BLLkQ/U0ria97+O1d97jyy+/RK+HFi3d6dLFQ9Xm09kZvPTSBMaPH4+iKCxPzWJ+YoaqjZtOS99gH8D21P7vhL/58fAP9JhdMiTW19mH6r0H43t//5IDkw9i/eMN9p5Tr1+bUHsoYzrcjY/bzVkGRwhR/qQn5LFl8QlVrMfwujd9njzAr7/+yogRI8jMzOTgwYNERkba99WvX/+mX1+IO5Uk9EIIIe4YVqvVlswrCqbkAhSTlZoeMcS6h6Nx0fHHgk9U7Rt07cnbs2bz7bdfMXiwL3FVHZPnAff/zquv1AUgy2Rm5KF4hzaT4sKJcrU9zd+QsIEFhxcQuyuFHlts8+oDXQPwd/Un4vUJGC6ZV5p4ZAfJi19WnStLF0CzoR9T38OxCJ8Q4s6Udi6PrUtOOMQ7PFzjpifz58+f55lnnuGnn36yx0aPHs2iRYtu6nWFEDaS0AshhLgjJCcns2bNGqyFZsxphfgYPGkZVAetRotrw0B+X/7JxWXecfX0puuwZ2nWrBkazX5eez1IdS7FCps26Vmx4lzJ+YtNvHDkrKpdnyAfegf5otfaTrz+7DoSpkymR0rJsnVVfCrjqnclYnbJjwnJOUW8//PfDEx+1x7b5NGF/T7t+fjBRjfsPRFClH8bFh4jO6VAFQuN86FB58ibmsxbrVY+/fRTxo4dS05Ojj3etWtXpk2bdtOuK4RQk4ReCCFEhaYoCsuWLSM7MxvTedv68p56d1r71UPjrMOndyy/zphiT+YBgpq3RavV0qevF40alRSuKSpSmP5BGosWLWP8+G72+KG8Qt66pIp9lKsTb1YJV91MF50+Rd4Lr3LxuXpln1jc9G4EvfgizrExABxPyeW9349iNFsZeWkyH/EEj/XvJ/PkhRB2xYVm/vjigCpWtWkIVRoFodHe3Kfy+/fvZ8iQIWzevNkeCwwMZPr06QwcOPCWDPEXQthIQi+EEKLCOnPmDBs3bgQFezLfwLsa4S6BuDcJIT7nIOtn/KA6psXgIYSGhTF8hD+hoSV/JpcszuGffwpZunQp3brZknlFUXjzRCLHCors7Vr7evBUhPqJ/rncc+x9YbB9u5JnODFvvIVTJdvw+iKThRHf7QTAz5zMg+kzAPB2NRBRKYL6/fojhBBmk4Xj/6QQfzADU5FZta/T/2ri4n5zV7goLCxk0qRJvPPOO5jNJdd/7LHHmDZtGn5+flc4WghxM0hCL4QQosIpLCzkl19+sW+b021D3DsFNMEz1JeiqmZW/jTT4bhWjw4jODiY/v29Vcn8u9NSyc62snnzZpo3bw6Ayarw2P5TquMfDQ+go3/JE31FURi55mm6z9lrHwBwrFUEPUZ9D0Cx2cLwb3fa2+sUEw+mzyDE24VAD2d0Wg30nvHf3gwhRIVwYlcKhzclOsS9AlxpeV8VdLegir3ZbGbevHn2ZL5q1ap8+umnqqr2QohbSxJ6IYQQFUZ+fj5LlixRxUxJ+dRyiSEqKASr1cJf+76BferjGvZ7gGfHvMSA8SEMG+5PWFjJn8eJE1IwmRS2bt1K06ZNAThbZOTlo+dU53i/egSBTiVPx4wWI6P/GqWqYm/q2JRnhr1LXrGZ6auPciotHwCtYmFAxmwqaZKpWskbrUYDYQ2h9XOgkz/VQtzJTMUWfv98v0O8VttwKlX3RX8Lp+J4enoya9Ys7r33XsaOHcvLL7+Mi4vLLbu+EMKR3CUIIYSoEHbs2MGRI0fs24rZin+GC4396gFgLCpkd8YauOTet2GP3kTVqY9Go0GrhQkTg1XnnDghGTCgKMaS8yqKQzL/dZ0YWxJ+Yf8PR35gU/x6eswp+eWgdkAtIoa9y+GkXN5dVdLPOgVbaZ+7lDrh3ui0JU/3af/Sdb8XQojyTVEUzuxL5/zxLDIT81X7Wt5bBd8Q91vSh3nz5tG2bVuio6Pt8V69enHixAkiIiJueh+EEP9OEnohhBDlmtVqZcGCBaqYk1FLW6UOWh/bEFSL2cS2rOUoOgUAn5Aw2j38GFqtDo1Gg0bjmMx/PCsdkwms1mJ7LNNk5plLlqXrHujNoFB/+3Z6YTqvb3odjVWh+yXJfE3/GoTOnMUbvx7gXGZJhfu25g0M8tyEwcfHFtAZoM8n4Oz5394UIUS5pCgKq+bsx2K2Ouxz9XSi4+Aat6Qfx48fZ+jQoaxZs4bu3buzbNkyVaE7SeaFuH1IQi+EEKJcUhSFXbt2kZGRobrR7BTWDN3pInvV+gKfAnYcWW7fbtq7P+HVazJjxgyeeeYZ9Hp4/Q11Mj/+1WR69OjB+fPLANialcfM+BRVGw+d1p7MK4rCyD9HAuBcYKLT1wcBiPGOxsPgQearU3jzkrnyAIPrutPu8BbgwrzXFk9DTJv//L4IIcqnvMxi1n1/2CEeWcufsDgf/MM9bnofTCYT7777LhMnTqSoyFbsc8WKFWzcuJHWrVvf9OsLIa6dJPRCCCHKFUVROHDgAHv27MFoNOLk5ASAVquli0sjrKeLMJuMZCWe55z2OLnJWfZjw6vX4u/dexlYoxYAAwf6ULOWs+r8r7+WTEpKCoGBgRRbrYw8FE+hRf20zFWnZWrVSgBkF2fzyoZX8E3KJ/JgOuFHMglw9SfUPRSf++/nvbwgzv5xzH6sVrHwQcxWPA5vKjlh54kQWO1Gvk1CiHLCWGRm39pzJJ3IVsW7PFkbg9Otmx+/efNmhgwZwv79JfP1IyMj+fjjjyWZF+I2Jgm9EEKIciMtLY3ff//dId6rfXeKfz+PpbCYwtwcclKTOeN6FKOuZDm5LacTeGfsGwBotTDu5SBcXNRrJY9/NZlz584RGBjIuoxcPj+Xqtr/VEQgLX087PPlE/ISmPvjOHosL6l2XzugFlYF9iUX8FO8B1Bg3/dstQzqnvoCzl9y0sqdJJkX4g5U2jryAH5hHjTvE3vL1nLPzs7m5Zdf5pNPPkFRbNOStFotzz77LBMnTsTD4+aPDBBCXD9J6IUQQtz2cnNzWbp0qUM8PCCU+mnhFK06R1ZSIsUFtuJRZ9yOYNQW4xUYTMOeffELDlEdV1rxO5PJViV/n9HK2L0nHa41rVoEIc62KvYmi4mvN88kbPrPNL6w39vZm4J8b/4wenM2sgbxzWvaj/U2pzPN5Qt0p/7fDXqn1yG4JkKIO0dhnpGjW5M5dzhDFTc462jaKxafYLdb1peVK1fy+OOPc/58ya+MDRo04LPPPqNRo0a3rB9CiOsnCb0QQojb2rlz51i/fr0qVqdOHaKyfEn75yTJGcfta7xfTOQB4pq1onKzVri5ldwc9+3nRcOGrvZto1Fh0pspTJ36NiOfe44hh9XV6wGGVAqkjZ+tSJ21uJj0uXPZv34xYeaS4nZ+Lr643/0YXxSFwSVP1aL83Bhr/RSngmTsk/gBGg6G6ndf71sihCiHFEVh56ozDkPrAbo8URuD860bXn+R1Wq1J/Nubm68+eabF2qLSIogRHkh/1qFEELclsxmM4sWLcJqLZm/3qRJE+Li4sj7J4lza3diLCxEo9OSq88iyTkeNNBl2Cg8vL1V54qJMfDY434O16he7QOs1kH8lJThkMz3DfalX7AvYLsRT54yhYLTJzmccUTVrmZQbQqHvcC7OzLsOfvUe+sSoGSg+e059QXDGkDbMaC99TfuQoiyoSgKmUkFbP75uCoeEOFJRA0/wuJ8yqZjQI8ePbj//vvJy8tj1qxZquXphBDlgyT0QgghbiuKojB//nyHeKdOnQj09iftm4Okx5/BZLQ9iT/jdoSqHVrTtPHDnEtIUCXzHTu606Gj4/zPrl2+pEmT7qQbzQw9cJr8/1f0bl7dWFtfjEZOj3me5PQzZBRlqtocbB3GY0/OQoMXby7cY4//r1U0gSd+hoOL1Rcd8B3o5M+uEHeKonwTx7YnE38g3WFf6/vj8A68dUPrAfbs2cO8efOYNm2aan7+V199hYuLyy2bsy+EuLHkzkIIIcRt4/Tp02zatMkh3rvD3RSuOEeaNZmUUyfs8XOGk3R9fhT/e/Qxvv9eveRb5y4etG3rroodP27ki7lpaLVavkxI48/0HNX+CVXCiXVzRlEUtiVsJvf5VzFaTKo2fz5cA7eAEN5o8QbDv9uFyWLF25zOwIxZRIX4E7S9UNWegKrQ5c3reTuEEOWQoijEH8xg/1rHKTwhlb2p3zkSnU57y/pTUFDAhAkTeO+997BYLDRs2JBBgwbZ97u6ul7haCHE7U4SeiGEEGXKbDbz559/kpaW5rBv4MCBZK84ReGqc1jMJlLPlFSTP6jbybyt27nb+UXVMVotREUZVMn87NkZ5Oa4kpmZyZGCYt46mag6pqm3O09FBKIvKiJj3rdsWPkZ+mL1U/vNQ5vTpkpnpkZ2RIOOMV+spEfuMiKNtiXp3J11BOn/XzLf/W3wjb6et0UIUU4t/3ivatvFw0CNlqGExfne8r78/vvvDB06lFOnSr4758yZw8CBA+WJvBAVhCT0QgghykxhYSG//PKLQ7xatWo0bNiQ9K8PYiwqIiMhHoAcfSbJzmc5FR/PmNlfYr2wxBJAs2au9Ozl5XCuRwb/zKuvtARgyIHTDmvKz6wRiUdRIeefHglWK/vS9qv+OLpWr07T16fTw2D7gcC4+0cOrprLw5e0qVvJ27aUnW80NHgYPIJs/xNC3DEURWHlp/tVsab3xBIY4XnL+5KSksLo0aP57rvv7DEnJydeeeUVXnrpJUnmhahAJKEXQghxyymKgtFodEjm77rrLgIDA9FoNCR/vlv1RP6E+36sGiuz5n3PmgNH7fFKlQy0a+dO9RrOqnOFhIRy9z0fsaw4jnWlLEPXM9CHfsG+6C1mzo15kfSidJLykwAo9HLiTE1/nnlsNno/PxRF4dstZ4j85y288k+rzlOvYQs0NXpBeENVhXshxJ2hKN/E9mWnyElVj9Dp/HgtnFxu7a22oih89dVXvPDCC2RklCyL17ZtW+bMmUO1atVuaX+EEDefJPRCCCFuGUVRSE5O5s8//3TYd3FOp7GokGNTfke5pLr9Mfe9BMVWodPDj1FsNAJw773e1G/g4nCexx77EbN3Y+YkZPBiohHIdWjzZe0Y9FoNluxszr40lv1ptqdqVr2GVY/XRdFqmNlpJlarwsr9Sfy0/TT9MufiZYq3n2ND2GM8PeheKXQnxB0sPSGPLYtPOMTLIpkvLCykR48erF271h7z9fVl2rRpPProo2i1t27evhDi1pG7ECGEEDedoijs2rWLw4cPO+zz9fWlW7duKBaFHe8uwCVPnaQrbdx57flF7N9vS7qbNXejZ0/1ENbERDNDnhxBcegAXkpygdQk1f5oVyd6BPpQ28MVT70OxWgk+csvSN36N/E5tiTd7Kzl98fq0CGiI4WZdXj8q+0AxBQfZkTWt/ZzxQa449RvFvV9Q//7GyOEKJeObEnk+I4UVczNy4n6nSPxDXG/zFE3l6urKyEhIfbtgQMH8sEHHxAcHFwm/RFC3BqS0AshhLhpTCYTGzZsIDEx0WFfbGwszZo1Q6PRYC02cXjKSlwsJcm8k6s7lZ/vgN7FyR4bNSoAP3/1Gu5160zj1VceZkNmLp+eTVXtq+ruwuAwf6JcS4bjW/LyOPf88xxIP2iPJcd4saNbDO28X2L5piQgi6jiI9yTNc/eJtzHlUBPZ+g9C9wDrvs9EUKUTxazlcObEzm917GAZ7XmoVRpVPZ1M6ZPn87Ro0eZPHky3bp1K+vuCCFuAUnohRBC3BSJiYn89ddfDvF27doRHh5u39637Hc0W/JVbSo/0R6zn16VzDdu7KpK5r//LotPP11Ny5YtWZqSxY9JJfNFn4oIpJWPh6rwk6IoFO7aTfLsjzmUYRspYNVp2NC3Chb/WhSebcvKs0k4WQt5KnUyAKHeLgR4OKPTaiC6DbQYIfPkhbgD5WcXs/ZbxxFGzXpXJqCSxy3vT1ZWFmPHjqVNmzY8+OCD9nhwcDD//POPFL0T4g4iCb0QQogbKiMjg507d5KSoh6O2rlzZwIDAwHISk4icdN+LDtz+f+3nclt3akT7aeKtW/vTqe7Sm6aP3g/jV9++ZMqVarwyP7TqpvXF2JCqOfppjo+Z9XvnP/xW87mnsVkNQOQ7qHni/bViC16HBJs7drmLqNewWZqhHrhrL8w37ThYKjWQxJ5Ie5AiqI4LEMHUKVREFWbhqDR3trvBUVRWLRoEc888wxJSUn89NNPdOvWDX9/f3sbSeaFuLNIQi+EEOKGSUhIYN26dapY3bp1qV27NgCpZ06xYf43xOXXdTjWr040s3Yu5K0uUwBwd9fy4EM+hIUZ0F0yyn7ww/N55eX2fHs+jZnn0nByumRIflSwKpk3WUz8M/xhMtMT7DGrorAt1I0NjSoTwyP2eL2Czdzjugdffx9bwD0Qen1kW9heCHHHOXckkz1/xKtiVZuGENekbOaknzlzhhEjRrBs2TJ7rKioiJ07d9K5c+cy6ZMQouxJQi+EEOI/sVgsHDt2jF27dqFcsi58cHAwzZo1w8PDg4KcbDYvnI/uvEKcsSSZd3b3QBNqIPCe+vj4+drjLi4axo4LdLjWoIE/ogQ2Y/C+U6prRbk6MSmukqrtZ3vm4PXxQjzTi7BYrRjNVpa2rEF6SA0CaEUMBjSKlQc8d9K+aA16Hw1w4ceBnh+AV9gNeoeEEOWJoigkHM1ySOa7D62DVnfrf+CzWCzMmDGDV199lfz8kulJ99xzDzNnziQiIuKW90kIcfuQhF4IIcR1OXnyJAcOHCA313FZuIYNG1K9enUAcjPS+OOzWUTlV8NJsRWnc3J1wycklMDBtTl09LAqmW/Y0JW+/bzs22YzLJifxfr1Z9iCMwuPJaiuNS4mhFpeJVWlT2af5JM/p9Dhu8OAQk6hbYj94n7DcNfV5GLZqspFBxnntQyNUQOXDpvtNlWSeSHuQBazlVWf7UexKqp4VJ0AarcNv8xRN9euXbt48skn2bFjhz0WGhrKzJkz6du3rwyvF0JIQi+EEOLamEwmFi5cWOq+6OhoGjZsiLOTE9uWLCLp0GH8i0OJM9ueyuudnPENDcO7bRRZXsVoDSVj6Tt0cKdjJ3VxKYO+DuPGruSN13WcLChm4fGSZL5fsC8tMRLk4QrA/tR9rNu8gJhv1tMBKDJZMJqtuBHOil7P4K5zIzbQncFOfxGRuv7Cw/hLboar3AUNHwG9E0KIO4eiKBzbnsyx7ckO+2q1DSe6TtmsarFgwQIeeughLBYLYJsbP2zYMN566y28vb3LpE9CiNuPJPRCCCGuitFoZMmSJZhMJlW8QYMGREVF4ebmhqIo7PtzFSe2b6VKfh1iqWVv5+Hrh8HdkxfXvc83T5YsBxccrGfwI754eamHsrZoPpbw5k/xdWIGf2WoRwG8HBtKNTdne+G9bcu/IO2LL4m5sL/YbAGzN1r3SJb0eIJQHxcm9a4N8x9wfGH1H4Sa9/yHd0YIUZ79Oe8wRblGVazT/2ri4m4oox7ZdOzYEW9vbzIyMqhduzZz5syhRYsWZdonIcTtRxJ6IYQQV1RcXMzPP/+smrN+0f33349er8dsNLJ6zkzyMjLwMwYRZyqZJ+8XHsGGw/8wYc6LHEo5gULJeerXd+He+9RPmuZ/n8X+/YV8ejaVr044rl/fJ8iHGh6uWK1Wcow5vLP0Ne6adwhQMJqtKGYvzC6VWNl7CGg0fDiwAR5KgWMy3+wpiGkvRe+EuENZLFZ2rTqjSuZrtAojpl5AmQxlt1gs6C6pABoUFMSHH35IfHw8L7zwgqoAqBBCXCQJvRBCiMtSFIWffvpJFTMYDPTp0weDwfb0aveqZZza/Q9aRUdcfh17O5+QUFIzsogc3hyLYlGdQ6eDnj29aNzE1R47sL+IgAYv0n7hgwzed0rVvqm3Ow283Gjt62mPfXPwG1J/XcxdO9MwWxQKjBZ2thlBekh1e5tRzTzw+OlBHAz4DnTyJ1CIO5HZZOHM/nQOb1L/YNhuUDU8fF1ueX+sVitz585l2rRpbNq0iYCAkiH+Dz300C3vjxCifJG7GSGEEA4KCgr4888/ycnJscc0Gg3333+//QlSflomW2bPw88YTCw10Sm2PylBMVVISU2h68SHOZhyXHXe0FA9/ft7Exik/vPz6U8u9Jy3hSyLFczq5P/96hEEOpUMfT2VfYr3t06j+5y9BFitGBUwGvWkxHa3J/M964XSNdYZt2VPq1+YbzR0nSJP5YW4wyhWhU2/nCArKb/U/Y3vjimTZP7QoUMMGTKEDRs2APD888/z9ddf3/J+CCHKL0nohRBC2BUVFbFs2TKKi4tVca1Wy4ABA9BoNFjMJnbPWYxzop5gSpZL8o+IYs/effy48VPe/GuWPa7XQ9Wqzgwc5GOPKUBqYDOyw9ughHWnfS9X8ixW+/4WPh48Gh6A6yVLRKUXpvPR9g8I/usA3fel2c6jgM4YwZbOj5DtE8gTrWNoUbQO9r4Lhy55AX6x0PUtkIrQQtxxFEVh+Sd7S90XUy+QGq1Cb/kQ++LiYqZMmcJbb73lUJfEbDaj18stuhDi6si3hRBCCCwWC0ePHmXXrl2qeGBgIM2aNcPLy7aMXGZiAjvmLiKkONLeRu/pypmsDB5+4yGOpp2mwFQI2NaSf+XVIHu7s94N2BfSk+hATwqdgnBzjUKrdVZdr6WPBw+G+eOl16niJ7NP8tGmaXSZu/9CRMGAD6YiN5bcPxo3awHPeq+n7rZ3S3+B3aZcz9sihCjnrBYrK2bvU8XaDqyGh69zmS35tm7dOp566imOHDlij1WuXJnZs2dz1113lUmfhBDllyT0QghxB1MUhU2bNnHmzBmHfT179rQn8gB5iekkfLyNECIvHAs7OM2jL422t3Fx0dCxrQcdOlyyLrxvcw4Gd8PDw5vIgGroDD5cujhdZ38volydaebtjotOPRTeYrUwZ89s8tb9TZcNtiXriow6DJYgdja4i4xQbx7KmEGMIYsoFzf1E/gWIyCm7X95e4QQ5ZSxyMz+dQkkHs9SxXsMr1tmiXxGRgYvvvgic+fOtcf0ej0vvvgir776Kq6urlc4WgghSicJvRBC3KEURWH+/PmqmFarpXXr1lSqVEnVLm/zec6s2IYCmE0mTiWc5+21X7LtnG0Y6/9/Gn/RzrD7SAtoRpXIlvab6E7+XrT19cTPoMPHcPk/Q+vPree3bfNo//1hACxWK5biACxuwazq/gjDUidizbESE+iBr9uFHxA8gqHJ4xBa77+8NUKIcqww18if3xxyiN89ouy+F6xWK23atOHgwYP2WPPmzZkzZw516tS5wpFCCHFlktALIcQd6MiRI+zYsUMV69WrF56enqqY1WzlxLQ/KC7IR1EUsrKy+fvURl5ZbnvC5Oqq4dHH/AgNVf85sVggscPnuHvXwB1bIj+kUiCtfT0u+3RMURTO5Z4jvSidz/d9DkCP7w8DCvnFFlLCWhAUbKGS5zmGpU6kTrg3JpMRZycDhDW0JfLuAaWeWwhxZ9iz5iznDmfYtz39XanaLJiQGO8rHHXzabVaXnzxRf73v//h6enJ1KlTGTp0KFop0CmE+I8koRdCiDtIbm4uS5cudYgPHDhQlWibioo4+O0qdGdKCtWlpKTw7d7v+Wrr33h5abn3Pm9iY9XrIv+TFkXAvR9T7OWrik+OCyfSVT1fHmyF7tbEr2H9ufUlQUWh5qbzRO9No9hsQWcKw+QdSmy1PEJNZwhwcyLc15uLy9kr/b9C4+R2PW+HEKKCsJitbPjxGHmZRfZYcIwXjXvElEl/zGYzBQUFqmlLgwcP5syZMzz++OOEh4eXSb+EEBWPJPRCCFHBZWVl8fvvv2M2mx32NWvWjOjoaFUyX5iaw4mP1nBpWbpjZ48yefck+vR3581ewQ7nOZ3nz4Em06hSpTrF/2/frJpRDkXu8k35zN03l6OZRx3O1XbBEbyzreQbdbhYQok0HsNcLxdMUCvMC4NOCy1GYA1tQFZWPkH6W7/UlBDi9pF2Lo+tS06oYm0GVMUroGzmpG/fvp0hQ4ZQq1Ytvv32W3tco9Hw2muvlUmfhBAVlyT0QghRQZU2R/6ioKAgOnToYF9T/qLczec5s3wrYJvzmZaZwc7g9ymKy6RPnLuqrQIkejQkuNtHFBdrqXLJvqbe7vQO8nF4Kp9emM7b29+mwFSgitf0r0mcWwzGMV+jmAMxAQGWTHzikjBXCiIuyAN35wt/su6ZCR6BYLUCpa8pLYSo+JJOZbNj+WmHeFnNlc/NzWX8+PHMmDEDq9XK7t27eeSRR+jcuXOZ9EcIcWeQhF4IISqg06dPs2nTJlUsMDCQNm3a4OLi+ERbMVtJ/WY/KadsT7kKCgrYG3+A47Ff4PH//lLs3FnI6rT69JnxJQC5lzySvzfYlz7B6uH2AIfSDzFr9yyHuJ+LH4OrPcvKCZ9SfPIbQAMohJri0bd1x+DqTkyAu63P/T4Dg1SBFuJOV9oTeYDYBkHUaBlaBj2CpUuXMmLECM6ePWuP1a1bFz8/vzLpjxDiziEJvRBCVDA7duxQrW8M8MADD1y2+JIxOZ8zX2ykuMD2tDslJYWfjnyHV8vDeLjbjsnLtTJzZjr5+Va8xk2i+0t9Vedo7OXOfSG+hLs4OZz/6TVPO8S0Gi2TWk/CQ+/Bd4NHE52ZDIBeMVHJ+QzV749Gb7jQ3zYvQESTa3sThBAVhqIoJJ/KYd+6cxgLHKcO1WobTnSdsimImZiYyDPPPMOiRYvsMVdXV9544w1GjRqFwWAok34JIe4cktALIUQFsXfvXvbv36+KNW7cmKpVq5ba3lpgIuW7/aTFn7bHEhLP8eWpD6ndOQ8o+QHgvfdSMZvBqVlret4/wP7jwKioYBp6u1MaRVGYum2qKtY1uiu9KvcC4LvNJ/Ge9BQXn+dXMR8l5u5KOAfElhzQ7zNw8UIIcWeyWKysnL2v1H1Vm4ZQpXFQmawrb7VamTNnDi+99BI5OTn2eJcuXfjkk0+IjY29wtFCCHHjSEIvhBAVwMmTJx2S+bvvvhtv79KXarIWmDgybRUWswmwJd+bT68mo/YSasdYVW3Hv5oMGg39/9qKs5+/PT63djROl3nqrygKz/z5DMrFUvTAjI4z0Gg0FBotPDNvG71//ghQCDAn461kE9MzAueAC9MBPEOhx7ugkz9TQtxpzEYL+9YmkHgiC8WqqPbVaBlGRE0/DM66yxx9ayxfvpxhw4bZtwMDA/nggw8YNGhQmfzAIIS4c8mdkhBClHO7d+/m4MGD9u3u3bvj6+s4jx1sc+Vz157l3Ia9WC22oauni4/zzbbZ1O2cDxZ1+zffLeDxRUspjqupij8U5n/ZZH5N/Bp+OfaLKjalzRQ0Gg1vLtpBnW8/orepGH9zMp7WLNyd9MQ8GFfS+P5vQO+4xJ0QouIqLjSTeiaHQ5sSMRY6Dqs3uOjp8nitMuhZ6e6++27uuusu/vjjDx599FGmTZuGv7//vx8ohBA3mCT0QghRjq1cuZKMjAz7dsOGDS+bzBcdyyR7fTzpZ+OxWswUFhZyMvk4T/78Ji++XHIjmpZm4cPpaTi1aMs9qz6h+P8V0fugeiQBTqX/+Zi4eSIpBSn27SC3IF5p+CI5f/zFNwtW0yA9EXdrLoHmBPQaDe6uBirdH21r3HE8hNS+zndCCFEeKYrC8o/3lrpP76yjZsswgmO8cHIt21vWAwcOUKtWyQ8KGo2G2bNnEx8fT4cOHcqwZ0KIO50k9EIIUU5t2rRJlcy3a9eO8PBwh3aKxUr6vEMoikLq6ZMoikJiUiJr4pezJmW9KpnfuKGA1YcD8Zn8Ki1atLRXxHfXaXk0PIBmPh6l9iUpP4lJWyapYkPrDSXMuQqr+z+O2WLFD4gyHkODhaDafgQ09kOr0cLd74F3pRvwjgghygurVeH4jmSObUtWxT39XdHqNDTuEY2Le9kXlEtPT+eFF17gq6++YsWKFXTr1s2+r3LlylSuXLkMeyeEEJLQCyFEuVNUVMSGDRtISSl5Ej5w4MBS522akvLJXnkai8VM6umTWK0Kv+39mcNFO6jaPpM+lBS0y8jWsrnxK/i296Bzly72NepHRAbR/DKJfFphGvMPz+dIhrqq/rQ277Hr8ZGczsi1x6KNh9FUdqdG8xC0Tjqo/yBU6yHz5IW4w5zel8aB9QkO8R7D6942888VReG7775j1KhRpKWlATBs2DAOHDiAm5tbGfdOCCFKyF2UEEKUE1arlc2bN3PmzBlVvFevXldM5lPPnMJiNpGjZPHT0Y+IaWqkqjbT3i4vz8p3AS+Rp9HTplVrfC4M2R9SKZA2fp6X7c/n+z5nd8puVaxTZCfSztdlTd//2WM+ljR8LOlUeagyLoYLhazu/RycL39uIUTFkno2l12/x2Mqcpwf36BrFGFVfG59py7jxIkTDBs2jNWrV9tj3t7ejB071j5qSQghbheS0AshxG2uoKCAP//8U7U0EoCzszPt2rXD09MxMc7bdJ6ioxkknTgGQLLzGRL8Z1MjxEJSdslT859+M3Kqy1haNmyDv3/JOs6f1YrGRVd60buEvASm75hOobnQHnPVu/J84+d55cd4evz2lj3uacmixj1u+Pv4lZxg4AK4TZ7CCSFuHkVROLjhPKf3ppW6/3ZL5E0mE++99x4TJkygqKjIHr///vuZPn06oaGhZdg7IYQonST0QghxGzt58iRbtmxxiPfs2RMvr9LXZ8/fnkTR0UzSzsYDsDd3E+awpcRnZNnbbN9eyB/mTjj1akevnr3s8dk1o3DXl74clKIojNswjjxjnio+rtk4wtzDGDvtZ/quW2SP1wpxJaqdGa3+wg8D3d8G3+iredlCiHLOVGxh22+nyErKV8VDq/hQtWkIHr6310oWW7ZsYciQIezbV7LmfUREBB9//DE9e/Ysw54JIcSVSUIvhBC3qeTkZFUyHxQURKtWrXB1db3sMWlfHQAg4/w5TMVFfLn9E+r3OcvZjGx7m9enFeP14kSc0XD3JTeq06tHXjaZ35CwgQWHF6hisT6xDK45mADXAF6fOI82W5fb99Wv5E5Yy+ySZL7FCEnmhbgD5KQX8veCow7xxnfHEBDugc5Q+sifsjZx4kR7Mq/Vann22WeZOHEiHh6l1w8RQojbhST0QghxmzGZTCxcuFAV69KlCwEBAZc5wuZiMp+VnMjZ06dYnvAd9fukkpCZg4JCRrqFDz7Ow+e1qQD2ZP796hEEOjlWk8415vL76d/56+xfDvs+aP8BBp2BU2n5/PHocJrk2ubku1rzadLUiFc1K3Dhxj28McS0vab3QAhRvuz58yznDmU4xA3OOhp2iyKg0u1dM2PWrFnUqlWL6tWr89lnn9GoUaOy7pIQQlwVSeiFEOI2cfDgQRISEkhNTVXFmzRpcsVk3pxVTNbi4/ZK9snJySxL/5T6PQs5l5mNVVEAmPmLJz6vvYqPjy+tW7cmzNnA1KqVHArqncs9x9RtU0u91j2V76FLdBd+P5DED9vP0nfh+3gAGqyEG08T29QHr2o+JQf4V4F2Y67r/RBC3P7OHc5gz5qzDvHQOB/qd4pAe5laHGXp3LlznD9/nqZNm9pjMTExrFu3jgYNGqDXy+2xEKL8kG8sIYQoQ4qisHXrVk6ePOmwz9XVlT59+lxxGSdFUchafBxFsZJy+iQpxj0kNFhAfSA5Jw+LorDa0pl9+sa4PwnNm7cgICCAVj4eDI0MKvV8pSXznaM6c0/lezBZFKYsP8Tx5Fz6LvoAd2sOgebz6DQa4h6qjP7icNpa/aBWX9A7Xe9bI4S4jeVnF7P228MO8SY9YwiM9Lxtlp+7lMVi4ZNPPuHll1/Gz8+PAwcO4O5esnRnkyZNyrB3QghxfSShF0KIMrJx40aHJegAOnToQEBAAAaD4zD4S+XvSqFgdzLJJ49jMpmJ9/uBbM/9FJstJOfkEu/dgN/MPSgssOLk5ETnzl3QaDTcH+JHryAfh/NZrBae/etZ+7a3szeTWk1Co9GgKAqbT6Qzd8Mpgkzn+N/uL/E1nsXNoMPgYiBiYIztIK8w6Pa2JPJCVEBWi5W0c3ls/+2Uw77brWL9/7d3716GDBnC1q1bAcjNzWXq1Km8+eabZdwzIYT4byShF0KIW0xRFBYtWoTJZFLFu3fvju+FNeD/Tf4/SZz/Yx+FuTlkZWdjaraA7Lx4Co0mkvKLme85iuQcF8BKzwtV7CdUCSfWrfTK0sWWYp5f+7wqNqnVJOIzCvhg9VFyi8y4WXIZmTgF/bo0NICniwEN2JL5+g9CtR6gkz8rQlREhzYlcnJXiiqm0WqIaxJMXOPgMurVvysoKGDixIm89957mM1me/zJJ59k9OjRZdgzIYS4MeTOSwghbrH58+ertvv06YObm9tVHWu1WDk4YznW1GLbXPvQk6TV/Z3ivCLOZmaTq/flC8OzmPJs8+Y7dbqLD2tE4me4/Nf9rpRdzN03VxWb1PJtnvj6H/t2qPEM/Y/ORLcnGxe9Dme9FnRO+Dz8ONz76NW+dCFEOVKUb2LNVwdL3RdTL5CarcNucY+uzerVqxk6dKhqSlP16tWZM2cObdq0KcOeCSHEjSMJvRBC3AIWi4VNmzZx9qy6eNSAAQPQ6UpfKu7/K0zJ4vD7v5OdnY3FYoHo/ST7ryM+I4P9fu1Jj4xmS3wQYEvmB/buzfvVI3G5TFGqQnMh3x/6nl0pu1TxFu5jeP6Hkpv48OyjPPDbe7g569C5GMDFG5f2fQgcMeIa3gEhRHlitSqlJvO3+9B6gNTUVEaPHs23335rjzk5OfHyyy8zduxYnJ1LH6kkhBDlkST0QghxkxUUFLB48WKH+LUk80l7T3Ng5krbhncqpprrOJB2jN204lhcWxITzeSkWQCoXLkKY9q2YECo32XPZ7QYGbNOXX2+gU8PNu0L5E9sw2p1FiPDF4zGz0VB43Lhz4VHMD5PPY9n+/ZX1W8hRPmSdi6Xk7tSSY3Ptcf0zjo6PlwDg/PVfV+Vtfz8fH7++Wf7dtu2bfn000+pXr16GfZKCCFuDknohRDiJjEajSxatMgh3rlzZwIDA6/qHFajhb/eWAhpWYACLZaRZUlhb1Iyq6qOA+DkSSMmk0JkZCQzWzemdngovlcYYg8wem3J3NEgl0ocO9aATWcv/ACgKHT861tapm9G71JSqdq1Xh0CJs39/6cSQlQQ/yw/TfKpbFXM3deF9oOqlVGPrk90dDQTJkxg8uTJvPvuuzz66KNotbff8nlCCHEjSEIvhBA3mMlkYuHChQ7x4OBgOnXqdFXnMKYVsGbyItISkggL0kNgOlTfTnZxFoeS41lV/Q0SEszk5VkICwvn6z7daOzjcVXnTi0oWedeQeHMsU5crElvKC7kydUzCSw+jUZrS+a1TlrC5v+Jxunq5vkLIcqf1V8ewFhQUjQuOMaL2AZB+IW6X+Gosmc0Gpk5cyZDhgzBw6PkO/C5555j8ODBBAU5Ls8phBAViST0QghxA2VkZLBy5UpVzNPTkx49elzV8HrFYuXoJ5vYun4T/p7OhPX6AwCLYiE1P5lT6WlsrzSQI0eK0Wp1DL+7O5PrVb3q/p3NPcvb294GoMhkwZI0GC1gMBbTf8Vsqnpbofi0vX34E+3R9n7nqs8vhChfrBYr25efViXzdz1WC2fX2/8WcePGjQwZMoSDBw9y9uxZPvjgA/s+vV4vybwQ4o5w+39bCyFEOZGamsrq1atVsaudJ68oCps+Ws7Rv/fh6eJMeGw+1PoDo6WY9KJUCk1GjuXoWe90PwnZdejZsz0Ak+vGXnX/VpxawbKTywA4mpyLU3F16iWeofLxXQQlx1PVMwfSbU/vPeK88O3fF1pI4TshKqptS0+q5soDdHmi9m0/Vz4rK4tx48Yxe/Zse+zjjz/mxRdfJDQ0tAx7JoQQt54k9EIIcQMUFhaqknl/f3+6dOmCRqO5wlGgWBRy155l8ZwfsFqsRAa7QnNb0n0+7xwmq5lTiblMXeSHx+AhtGzSijg/21z396tHXHX/DqYftCfznMuhz5+FRKbtBfbi6QSxupOQr4BWQ6X+UWg6vAThja7tTRBClAuKVeGPrw+qnsobXPS0GRB3WyfziqKwaNEinnnmGZKSkuzxRo0a8dlnn0kyL4S4I0lCL4QQ/9GZM2fYuHGjfTsuLo4mTZr863F5WxMpOpTBihUrsFgsREZZsdZfSkp+EhbFTGJ2Lj/si+Zk9aHc9XFrAgICABhSKZA2fp5X1beVp1ay7tw6co25eKQV0HDeQZyUYPS4AQp19WfQmC24x3riUdUTJ19n6DwRAstXESwhxNXJSS/k7wVHVbFO/6uJi7uhjHp0deLj4xkxYgS//fabPebu7s7kyZN5+umnr3rFECGEqGgkoRdCiOuUmZnJmjVrMBqN9li1atVo1Ojfn2xbcowUHcrg7Nl43ALT8Wm2g9TCZIx5xQAcSspnjmYkLYZ0ppavLwD+Bj2PVwqgjueVi9OlF6bzzvZ3yDflA6AzWejx+X5yCk04EYgTzgSbzhBaxYmAWiE4+V4oiRdcC9q/DDr50yBERZSdWsiGH9XJfNchtdEbbu9kePbs2bzwwgvk5+fbYz179mTWrFlERkaWYc+EEKLsyV2bEEJchxUrVpCZmamKtWnThoiIKw+DV6wKOavPYEzMY/Xq1QTXPYKxziYS8kp+FJjw/lmiZ6yiR1iYPfZYeADt/Tz/dQj/5/s+Z3fKbgCc8020WXgUp0IzlsJQ3NGhRSHC/wzVO4ZjcLrkJv6B70F7e9/UCyGu37kjmez5I96+XbVpCHFNgsuwR1evsLDQnsyHhIQwY8YM7r333n/9PhRCiDuBJPRCCHGN0tPTVcm8j48PHTt2xMXF5YrHmbOLyfrlOCaziZUrVxJZLZei4AOYCm3J/F+ri/hmXT73r96FXl/y9fx57Wicr2IN5YS8BHsyry+20HfBWbSWcNILLWgAo4crsQ3OUbNSBLoLS9Jx93vgXena3gAhRLmRk1bI3z+on8pH1vIvN8k8wMiRI/n+++9p3LgxU6ZMwcfHp6y7JIQQtw1J6IUQ4hqcP3+etWvX2rfvuece1drHl2MtMJH1y3FOnDjBwUMHCfV3JjVqPsZC2xD7F984QbpfNZrMma1K5uddRRV7s9XMO9vf4XzeeQAMRWaG/WzmaLY3YKHA04fUFlXoaPqDaiE+tmTeNwa6T722Fy+EKFeST+fwz7JTqljdThFEVPcrox79uxUrVrB7927GjRtnj+n1ejZs2ICzs3MZ9kwIIW5PktALIcS/sFgs7Nu3j7y8POLjS4asVqlS5aqSeYCMH4+y9LelGHyyCeyxkZTiFLDY9n389VmyqrWj1Uvj7UP2fQw6Pqp+5bmhVsXKL8d+4a+zf9ljPkn5NPsxhaNmLwCijUcxNwigh2cyni62GDHtoMXwq335QohyJD0hjyNbkshMylfFI2v5U6f97TsaJzk5meeee44FCxag0Wjo1KkTTZs2te+XZF4IIUonCb0QQlyBoij88MMPDvHo6GjVzeaVZK8+w7r163CveQifKmdJyk+x79t3vpBD7cfSf9CD9thTEYG09r1yFfvEvEQmb51s3/ZOKaDVT0exFIahwZa4B5rP49MjhPBgdzRowMUbGj8Okc2uqt9CiPLDbLKwas7+UvfdzvPlrVYrX3zxBWPGjCErKwuwfe9+/fXXV/0dK4QQdzJJ6IUQohR5eXls2LCBjIwMVbxBgwYEBgbal5D7N2e+3MGunWtwbr0CF4PePiz+6DZn3kuMwqVtJ+7p38ve/p1qlQh1drriOSdvmUxifqJ9O+B0No0X52O1RnCxRJQhDBq2DsRwsXr1/fNAf+XzCiHKp12r4zl/VF2kMyzOl+i6/viGuJdRr/7d4cOHeeqpp1i/fr095ufnx3vvvccjjzxShj0TQojyQxJ6IYT4f9LT01m1apVDfODAgVddVVlRFFpENmBo8wGE9lyDXmsgKT8BgM2/uPNtVCuajbzXPsS+hrsLz0QF46G/cqX5jQkb7cm8gkLLFQa8D5kB23DUf5p242xUTT53moaGC+e6+z1J5oWogIxFZlbPPeAQ7zG87m1dAb64uJgpU6YwZcoU1bKfDz/8MO+99x6BgYFl2DshhChfJKEXQohLnDp1is2bN9u3w8PDad68+TXN31y75i9OfbKVp5rfT1ivNeg0WpILEsnJUvhnmQdL2vShc6/euLvbnpy9VjmMOPcrV8gH248E8w/PB8Ato5A2X+XbhtJfsLdpB15scQ63tOVwMd74caliL0QFlJVSwMaFx1SxNgOq4hXgWkY9ujpnz56lS5cuHD582B6LjY1l9uzZdO7cuQx7JoQQ5ZMk9EIIccG5c+dUyXxoaCjt2rW76uOtZisDu97LPTWi8WqQhHdkGhq0ZBSlY7aaGD8tGcvzb1CjRk17Mv9RjUh8Df/+VWyymhj11yi0FisRf8ZTZR9oKJln3/irWfRcPQzS/t+BMW2vuv9CiPLh/PEsdq06Y9928TDQcXCN2/qp/EVhYWH2YqJ6vZ4XXniB8ePH4+bmVsY9E0KI8kkSeiHEHc9kMrF+/XqSk5PtsZYtWxIdHX3V51i+aCnJC/bTr6k/HlX34KLXAxoS8mxV8Z94q5Cq739O1apxuLvbbmavZn3509mneW/He7hmFdF68TFIL0KLHgMROCuFeEa40XRQE7Srh5UcpNVDk8chqhXopTK0EBWBxWTl1IE0Dm9OVMWDY7xo3COmjHp17XQ6HZ999hlPP/00H3/8MXXr1i3rLgkhRLkmCb0Q4o5lsVj4448/SE9PV8Xr1q17Tcn80nk/k7bkCP6ttuMekIdF0ZBtzGLP1mJOnYLv9mYzcNXfaC4k71GuTkyoEo7uMk/TFEXhYMZBPtn9CQBu2cW0nneQAqMFDVo8CCfC9QSRXcLx9rBAwpaSgz2C4J4Z1/ZGCCFua+eP5PLPvmT+/1dGjVZhxNa/feebnzp1ipEjR/Lqq6/SvHlze7x+/fr8/fff5WJEgRBC3O4koRdC3JHy8vL49ddfHeLdu3fH19f3qs6xe/du5gx9h7btPAjvmoTOpYgiSxHphSlsWezOvlM5xD02gkHv9bMfc3+IH72CfK543pF/jizZsFhoOe8AJqMHbnhzMq4JjSPXEhMcjfbizXBAHITWh7AG4F/5qvouhLj9KYrCyk/3U1hYhLOTbbSNzqClatMQYuoF3LYJsdlsZvr06bz++usUFBQQHx/Pjh07MBgM9ja3a9+FEKK8kYReCHHH+f777x1i/fr1w8Xl3wvTGY1G/Pz8yM/PZ2S/Ftwz1BWDzjZxvdBcQHJ2Op/PLGDn2VNM3HOE/Uar/dhaHq7/msxbFVt7v/N5VNmRjOuxInREokfDycr1aN8km8qaS9aoH/TDVbxiIUR5oigKCUez2PNHPIpSEm/UPZqQWO+y69hV+OeffxgyZAi7du2yxzIyMjh58iTVqlUrw54JIUTFJAm9EOKOYbVa+eEHdQIcFBREp06d/vVpkaIoaC8Mmffw0LJw4kB8o0sq0J3LSGbPei0f/3qMFv36c//Pk1XJfPcAbwaF+f9rH38/towen+yxbRSHY8UHAK2bwn3VNxGqccFewX7ggn89nxCi/FAUhW2/nSItPtdhX+fHa+LsaijlqNtDXl4e48eP56OPPsJqtX33aTQann76aSZNmoSXl1cZ91AIISomSeiFEHcERVFYsECdAA8YMACd7srrvu/bt4/vvvuOt99+m3t6e+HurmVQp/q4GkqS+QP/WJj3awa7c4vxH/IclZ4arjrH+9UjCHS68o24oigcm/8Z1p/nYVUUikwWXKwakiOiqR26j6Zxbmi4ZDmq++fhMKFWCFEuFeYZ2fbrKfIyi1Rxd18XImv54hZsxeB85e+qsvTbb78xfPhwzp49a4/VqVOHzz77jGbNmpVhz4QQouKThF4IUeFZLBaHJ/N9+vT512Reo9Hg4qKhYSNX3pwUjAYNDcJq4Ky/8NWZ78Wy1Sf4eG08EVNm8MAlRZ8AYl2deTEmBHf9la+T9v13HFu2gMziTPKLLVisCq56byq1TqeSNoM64d4l6803+h9U635Nr18IcXsxmywcWH+e5FPZmIotpbZpN6g6Hr7OWK1WUlJSbnEPr94bb7zBhAkT7NsuLi688cYbjB49WjVnXgghxM0hCb0QokLLzc1l6dKlqtigQYP+9TiNRkPHTh506GBbLz7I3Y9o3zB0Wi2cqEtBQTAf557CZfR4+r/uqTo2zs2FYZGB//pUPm/9elK/nceu5AMYzSXD8/NaelFdD046HdEBbui0GnDygLvfBderK9gnhLi9GIvMpJ3NY9fvZy7bRqPV0KJvZXyC3cpN0bh+/foxadIkLBYLnTt35pNPPqFyZSnOKYQQt4ok9EKICisjI4OVK1fat8PCwmjfvv0Vj1EUhY4dPXnl1SBcXGw31E46PZU94uB4DYrTYvm5WgBpbSsREHCv6tgAJz3vVou47HJ0l14jffHP7Pr+E9IL8uzxhV2iGVWcjp9OR9VgT/RaDdS+D+rcJ8PrhSjHcjOKWD//SKn7arUNJyjKCzcvp1vcq+tjNpvR60tuH+vWrcukSZOIiIhg0KBB5eaHCCGEqCgkoRdCVEh///23aj5nZGQkrVu3vuIxBQXxvP9BUzrd5WGPOen0NMp4EuVkMAcjA1nS3p2A6Ag8XW3z2SNdnOjk70VH/6sr+GTJzeWvh4eSZjphj/3ZJJCwQD3fuptxcfItGV4/4FvQyZBVIcqj5FM5nD+WRcqZHMzGkmH1Bhc9viFuNOwShc6gLcMeXpuioiImT57MsmXL2LJlC05OJT9AjB07tgx7JoQQdzZJ6IUQFc7u3btVyXzlypX/tTDT119P5ey56fZtnUaLS3xt6tKbRHd3Ft4Vh1anIywuyN7mnWqVCHW++qdqp994k73b91JASd+WtwphnK+ZGt6XnMfZE3p9KMm8EOXUzt/PkHgsyyFe/65IwquVv2kzf/31F0899RTHjh0D4N133+Xll18u414JIYQASeiFEBVIZmYmf/zxByaTyR7r0qULAQEBpbY/efIklStXpm8/Lxo2LKkg75RQh0Zm23D6owEerKofg0anxaOKLZl/KMyfrgFXvxZ0scnC6j4PAdiT+dNh7pzoG83XWblocC5p3OcTcPO76nMLIW4fVqvC0a1JqmQ+vJovXv6uhFX1wcW9fP1Il56ezpgxY/jyyy/tMYPBYF+WTgghRNmThF4IUe4ZjUY2btxIYmKiKt6oUaNSk/mioiIiIyNJTU3lgQe8qVXbxb7v3F5/+nvfi1UDc5pWxujphnuEPzp3J5r7eDAiMsjhfJdjtliZufoIsbMmo8FCIbb+fd07hs4B8FlWHvY15QEG/VD6iYQQt7WifBO7fo8n43yeKt5mQFW8Alwvc9TtS1EUvvvuO0aNGkVaWskSnS1btmTOnDnUqlWrDHsnhBDiUpLQCyHKtYKCAhYvXqyKhYeH07x5c5ydnR3aL168mL59+6LVwpuTgu1xnVZHxIGhxIWGs9PHjT+rBIMP+FcKRqPV8lxUMI283a+qT4qiMPanfWRn5HD3ko/IJx6w1bX7+5m6VE7ax8jCiJJCdzV6QYOHruv1CyHKjqIobP7lBJmJ+Q77WvSrUi6T+RMnTjBs2DBWr15tj3l7e/P222/z5JNPotWWn3n/QghxJ5CEXghRLlmtVnbv3s3hw4ftMVdXVzp37oyHh4dDe0VReP3119n+z3RVIg/g5+ZO2Lln+bRbHcA2pFTRFxEYGQvAu9UiCHa+uqGyZzMKeOPXA2isFnoueYd8bOtHe7joWTk4HF3SfibqwjBoNOARDO1eAu/w63oPhBBlw2qxsmPlGVJO56jilWr4UbttODp9+Ux6s7KyaNiwITk5Ja+rf//+fPjhh4SGhpZhz4QQQlyOJPRCiHJpwYIFqu24uDiaNGlSatvZs2fz6vinee65AFq1clPtC/CNYjuv8VsDN5ycnLBqLRRq86gVF0e3EH+6BV79XPmUnCLe+PUA4WePUHvLJxRhwdNFT1aYByvviWZ6SgZ6fYStcVQraPXMtb1oIUSZO38sq9S15O96rBbOruX7tsrHx4cRI0YwZcoUIiIimDVrFr169SrrbgkhhLiC//SXp6ioCBcXl39vKIQQN4CiKCxZsoSCggJVvFevXnh6epbaXqvVEhqq57nn1HPpV/+q0CZuKEu6tcdgMOCk1WJwdaFrvUo8EXH18+Tt5zuYzMaFK+m27SssFKLXa3Ex2J7qxzz7NIO2flOyPnObFyCi9B8fhBC3p6I8E2u+PugQb96nMv7hjqOCyoOcnBxcXFxUS9CNHz8eFxcXRo8eXepoJyGEELeXax4TZrVaefPNNwkPD8fDw4OTJ08Ctj8Ac+fOveEdFEIIAIvFwvz58x2S+YEDB5aazKempqLVaqlR05nhI/zt8WNHjWT92ZvWvWexq0dHnJ2d0Wq1OAV68G6nWteVzBeZLBSPeY76237CqinEzUmHi0HHgTZhdH68FS23zStJ5v1iJZkXopzJzy52SOYb9Yjm7hH1ym0yv3jxYmrWrMk777yjiru6uvLaa69JMi+EEOXENSf0kyZN4quvvuKdd95R/aJbu3ZtPv/88xvaOSGEANuT9u3bt6tivXv3ZuDAgSWJ8iV++eUXgoKCuP9+bwYN8rHHddmhdCway4FHH+BAlVAMBgMWzLSP8OKHdnWIcnUsonclB85nM+TzTfzR5yFMZFNECu7OevZ1i6bVU+0ZEwiGzFMlB0S3hq5vXdM1hBBlJz0hj9/nHmDttyW1Otx9nOkxvC4hMVc/Hed2kpCQQL9+/ejbty8JCQm8+eabHDlypKy7JYQQ4jpd85D7b775hjlz5tCpUyeGDh1qj9erV09VnEoIIW4ERVGYP3++KjZo0KDLttdoNLi5aRwK30UefRJLvjvL+zTD+WKBOyWdV5o3pHnktRWlW3UgiR+329aTr3p8F0YyMZKFQadh9cNRDMnLxTv1qPqgu98D70rXdB0hRNnZsuQE6efUy9DF1AukZuuwMurRf2OxWJg9ezbjxo0jNzfXHu/YsaNMnxRCiHLsmhP6hIQEqlSp4hC3Wq2YTKYb0ikhhIDSk/nWrVuX2ragoAB3d3cGPOBN7drqm9OIQ0M56xHE9rvrgUZDsbYQq97Kqt5XX+xJURR+3pnA8n2JFwN0W/45moLjoC/Cy6Bn+dB6vJKcSKj2kqWq+nwCbn5XfR0hRNnKzShiz59nyU4umd5TuVEQVRoFoTfoyrBn12/fvn0MGTKELVu22GNBQUF8+OGHDBgwoNSRTkIIIcqHa07oa9asyd9//01UVJQqvmjRIho0aHDDOiaEuLOZzWZWrFihil3pybyfnwcT3wzm0vvSwkxXqpx+ij2tanOyki2pztfZlmP6pXunq+pHWl4xL/+8D4tVKQkqVvoumk4lPwPnFROg40izEF5NTiREc+Hpf2RzaD3qqq4hhCg7VqtCwtFM9q45W+r+Lk/UxuBcPhP5wsJC3nzzTaZNm4bZbLbHn3jiCd5++238/OTHRiGEKO+uOaF/7bXXeOSRR0hISMBqtfLzzz9z5MgRvvnmG3777beb0UchxB3o77//tg8LdXJyol+/fqW2KyoqYuTIcF4dry5m53O4L+GFUYR3aMWSINvNeL4uh/bJJ3ll2PCr6sOes1l8tOaYKqY3GRm1+Ss0oc6cyDoBwOqHqzA5Lw83zSVr1TcfcVXXEEKUHWORmdVzD5S6zy/Mg8Y9osttMg8wc+ZMpkyZYt+uVq0ac+bMoW3btmXYKyGEEDfSNSf0vXv3ZunSpUycOBF3d3dee+01GjZsyNKlS+ncufPN6KMQ4g6Tn59PYmKiffu+++4rtd3zzz+HxfodUdElifQfq/PIPhrBMy2j2PZQVxJNhZg0RozaIoYXJNFn6LB/vb6iKDzx9T+qWJCXM2NahZP/+qvgpGdfmq1myPKn6jIkOR437SXr2z8wH7TXXHNUCHGLnDmQzv615xzilar7Ua15CM5u+goxDH3kyJF89tlnnDlzhpdffpmxY8fi7HxtxT+FEELc3q5rHfo2bdqwevXqG90XIcQdLi0tjX/++YeMjAx7rGvXrg7tzp49S2Rk5IXCdyVPzya8kYyHkw/DnxzFys7NKC4ssA+xf0GbT9eH/nfF65ssVv48nGIveHfRo61iaB0XwNmhw8g15XI6+wwAK56qyz0uwdTVpl1oqYGB86ECJAJCVDRWi5W8zGL+/uGow77QKj407BpVylHlh6Io7Nu3j7p169pjLi4ufPvtt3h6elKjRo0y7J0QQoib5ZoT+tjYWLZv346/v78qnpWVRcOGDe3r0gshxLU4cOAAe/bsUcXCwsJU3zVWqxWdToevr86hiv3bU1OxmDXU+/QHjgUFYSwsoEhjq1A99NhWur70+mWvbbEqzPzzOHvPZTns+/ThRuh1WrKXLCG5IJmUglQsBi2rehsg9RBdSCtpLMm8ELelk7tSObTpvEO8VttwImv5o9WW73+3x44dY+jQoWzYsIE9e/ZQvXp1+76mTZuWYc+EEELcbNec0J8+fRqLxeIQLy4uJiEh4YZ0Sghx5zCZTGzcuJHz50tutqOiomjQoAFubiXD2MePH8+kSZPo0MGdjp08VOcY/2oyAM3GvIuftzfGQlt16rrZZ2medpZ7Ro27Yh+GfPOPQ6xD9SDurxtE+rvvUnTqBIfTD2OxGMGYx6p7POhqdqGnxg8u5gGdXpdkXojbTF5mMduXnaIgu1gV1+q1dH2iFlpd+Z4aYzQaeffdd5k4cSLFxbbX+NRTT7F27doKMWVACCHEv7vqhP7XX3+1//eqVavw9va2b1ssFtasWUN0dPQN7ZwQomJLTU11mL7TvHlzYmNjVbE5c+YwdeokHn3Ul9jKTvb43j1FLFyYjTY4lJ7fLkGTacJqtf3g+L8Tf6J3MdDjhVfR6i5f1Orxr7artif0rkW4jys5v/1G8ujlKCgcSN0HxbYCfX/c4844XQjhmgv9aD8WguuA7rpmMAkhbpLU+Fy2LVWPGmz/YHXcfSrGHPJNmzYxZMgQDhwoKeoXFRXF2LFjJZkXQog7yFXfgfbp0wcAjUbDI488otpnMBiIjo7mvffeu6GdE0JUTGazmR9//NEh3qtXLzw9PVXtDAYDne7y4PU31EPs33k7ldxcKw1jo/CeOgtNpsm+b8CZHdw1ZBgefuqpQZdKyS1i3E/7VLG5/2uCJTeXcxeq4JutZg6d30KOj5btXdwwuer4UFcJTXgj25J0eqfSTi2EKGMWk1WVzIdU9qZGyzDcvMr/v9msrCzGjRvHp59+iqLYltPUarWMGjWKCRMm4O7uXsY9FEIIcStddUJvtVoBiImJYfv27QQEBNyQDsyaNYtp06aRlJREvXr1mDFjxhXne2VlZfHKK6/w888/k5GRQVRUFNOnT6dHjx43pD9CiJsnNzeXv//+m6ysLFU8PDycdu3aObQ3GAw89LAP1aqVPFFLS7Pw5RcZ5OZaWfD9t3zu7I+7xQuA6DwjIwotxL0w8or92Hcum+l/qAtjzX64EQDnx7wIgLk4h791B9h9jztGFw2RGidejOoJbcdc8+sWQtwa+dnFrPv+CIpVscda3lsF35Dyn+QqisKiRYt45plnVKuANGrUiDlz5tCwYcMy7J0QQoiycs1jRE+dOnXDLv7DDz8wevRoZs+eTbNmzZg+fTpdu3blyJEjBAUFObQ3Go107tyZoKAgFi1aRHh4OGfOnMHHx+eG9UkIcXNYLBaWLl3qEB84cKDD8NAhQ4bw2WefMXCgOpl/e2oqeXlWPn33bbRa+NzZH4PVtt/g7Mwr+V4EDKt1xX4cOK9O5quGePJCl2pQkM/ZF8aAxUhx5ikOmdPY1s+WBFTTuDDyoT9ljrwQtyGzycKJnakc/yfZYV9IZe8KkcyDrd7IK6+8Yk/m3d3dmTRpEk8//TR6vUz5EUKIO9V1/QXIz89n3bp1xMfHYzQaVfueeeaZqz7P+++/z5NPPsmjjz4KwOzZs1m2bBlffPEFY8eOdWj/xRdfkJGRwaZNmzAYbOtOy7x9IW5/hw4dYteuXfbtoKAg2rdv73ATarFY0Ov1REYaHKrYz/gojfw8Kx8Mf4yT3v7s8AtHq+hwUpzpgBsPZhgI+N/lk/mcIhMf/3WCY8m59tjAppHcVTMYU3IySa+/AVYz5xN3ko6Z1ReSeVfPUEb2/fkGvAtCiBtNURRWzdnvEPcMcKVpzxhc3A1l0Kubw8nJiTlz5tC+fXvuvvtuZs2aRVRU+V5qTwghxH93zQn9rl276NGjBwUFBeTn5+Pn50daWhpubm4EBQVddUJvNBrZsWMH48aVVJ/WarXcddddbN68udRjfv31V1q0aMGIESNYsmQJgYGBDBo0iJdeegndZYpeFRcX2yu/AuTk2Naktlqt9mkEQlQ0VqsVRVHK9DOuKApHjx5l586dqrher6d9+/ZotVpV/1JSUggNDaVKFSce+Z+v6piLVewXTX2DT+KaoQHcLB7oDc7cbXWmV74Wj3bhl32932w+w/pjqarY4OZRtK0aiMViIfH11zGaijiavAOAZf3dwTOEtnF96F9rsHxX3KZuh8+5KDuFeUb++uaIfdsvzI3IWv6EVPa2L0NXnj8bu3btws3Njbi4OPvnvE2bNmzZsoXGjRuj0WjK9esT4lLyfS7uBDfr833NCf2oUaPo1asXs2fPxtvbmy1bttjmuT70EM8+++xVnyctLQ2LxUJwsPopXHBwMIcPHy71mJMnT/Lnn3/y4IMPsnz5co4fP87w4cMxmUy8/nrpa0xPmTKFCRMmOMRTU1MdRhcIUVFYrVays7NRFAWt9tYvy5SRkcHWrVsd4k2bNsXf35+0tDSHfaGhoXh6alXJ/KZNBfy+KpcGkWE88+AAvq9UC6zgZvXEydWNQZkKTQvNmGLdyXItgpQih/PGZxaxen/JkppRvi481iwUD2eFlJQUlLw8is7v5bgll+PV9ByqYwCtEw/UGkU172qkpKTcoHdF3Ghl/TkXZWPPiiTyMtV/v/UGLbEtPQEjaWmppR9YThQUFPDuu+8yZ84cGjVqxE8//URubq79cx4VFUVqavl+jUL8f/J9Lu4E2dnZN+W815zQ7969m08//RStVotOp6O4uJjY2FjeeecdHnnkEfr163cz+gnY/rEHBQUxZ84cdDodjRo1IiEhgWnTpl02oR83bhyjR4+2b+fk5BAREUFgYKDMvRcVltVqRaPREBgYeEv/MCqKwtq1a0lKSsLJqaSadLNmzRyWogPbD3tVq1YlOzubAQ94U7u2i31fQoKJPZs0fP7iaHZUrc+3XoFo0OBh8cTJzY3paToMaPB9uCpal9K/ynKKTMxeuQdnZ1tf3upTmyCvkmtwfhebXx5CttUIGjhS34U6PlV5qsdcNHJDcdsrq8+5KBvFhWbWfHkI0ODsVFJbw93HibYDq1aIpdpWrlzJiBEjOH36NADbtm3jr7/+4q677pLPuajQ5Ptc3AkuvTe+ka45oTcYDPZ/aEFBQcTHx1OjRg28vb05e/bsVZ8nICAAnU5HcrK6iE1ycjIhISGlHhMaGorBYFANr69RowZJSUkYjcZS3yRnZ2ecnR3XnNVqtfKFISo0jUZzyz7nRUVFbN26lYSEBPu1AWrXrk3dunUv2z/b/+MwXz452cyanzVMf2kUi6Nqk+rsiYfFE72TMzpnPRPSdXjXDsS9cXBppwYgLa+YlxbtRYPtOg0ifQjxcbPvL85N5Nu3niC0wGyPDWgzgbYxXa7jHRBl5VZ+zkXZST2by7ZfT6rqUt71aE2cXPUVIpFPTk5m1KhRzJ8/3x5zdnbm1VdfpX///mRlZcnnXFR48n0uKrqb9dm+5oS+QYMGbN++nbi4ONq1a8drr71GWloa8+bNo3bt2ld9HicnJxo1asSaNWvsa9xbrVbWrFnD008/XeoxrVq14vvvv8dqtdrfkKNHjxIaGnrTfvEQQlzZ9u3bOXbsmEO8T58+uLm5OcQ3btxI69at7dsT31Qn5e+/l0bXuNq8Paov86Pqkqf3wVPrid7ZwJPZWuobtRhC3a+YzM9ed4LtpzLs2zVCvRjRoYp9O/fEGr6ZNYaY+AvJvMGVjp/9hkuA4+oaQoiyU5RvYs1XB1UxrwBXWvePQ6Mt/4m8oih88cUXjBkzhszMTHu8ffv2fPrpp1StWlXmFAshhLiia07o33rrLXJzbVWiJ0+ezODBgxk2bBhxcXHMnTv3ms41evRoHnnkERo3bkzTpk2ZPn06+fn59qr3gwcPJjw8nClTpgAwbNgwZs6cybPPPsvIkSM5duwYb7311jVV1hdC3DiKoqiSeScnJ9q1a0dgYKBD2yVLlth/vLuoew9P1fasafn0rdOA7t17UKTVUaD3w83gjk5v4KNUHTqtFv9Halz2iVxaXjHvrz5KcnbJXPrWcQE82ioGgHO55/hr+Uz85y0j5sJ+jc6Ju777C4NbxVjaSoiK4uTuVA5tPK+KNekZQ1CUVxn16MY6evQoQ4YMYd26dfaYn58f7733Ho888kiFGHkghBDi5rvmhL5x48b2/w4KCmLlypXXffEBAwaQmprKa6+9RlJSEvXr12flypX2Qnnx8fGqoQkRERGsWrWKUaNGUbduXcLDw3n22Wd56aWXrrsPQojroyiKanhoixYtiImJKbXtqFGjmD59OgA6Hdx7rzd16rqo2nz5hTPTn38Co5s730bUosjghreTDwDDs3X496mC3seFyzmanMvbK9QFNT94oD5eLrZlq5KXL2bvnMn4m0uSfU8XP5rP/EGSeSFuExazleRT2ez6Pd5hX49hdSvEU/mLkpOTVcn8gw8+yPvvv09QkIwUEkIIcfU0iqIoN+JEO3fu5LXXXuO33367Eae7aXJycvD29iYzM1OK4okKy2q1kpKSQlBQ0A2fr5Odnc3ff/9tXwISbHOC+vfv77B85F9//UXHjh3t2+7uGsaOU9+sajUaanoORilw49voeuQYXHD19sa1yDZcv0GxhjGdq6PzvPy0mpHzd1FQXDIXvkaoF0+0icHHzQmjxcik75+k+Q8ly+cFavTE9uuK/6NTr+9NELeFm/k5F7eescjM6rkHHOKVGwVRrVlIhXxiPWTIEP744w9mz55Nly6l1++Qz7m4E8jnXNwJsrKy8PX1JTs7Gy+vGzfa7Jqe0K9atYrVq1fj5OTEE088QWxsLIcPH2bs2LEsXbqUrl273rCOCSFuL/n5+WzatKnU5ZJKS+Y//fRThg4dat92cXFM5s9sCOWulnejFBiYW7kRxVo9GjT2ZP7VDB2V3J0vm8ybLVaemrdDFbu/SQRda9kKa57JPMGXcx+m+dpc+/7stj70eOgjCGtwDa9eCHGzGIvMbFx4jIKckqXo9E46YhsEEneFWhnlSWZmJp9++ikvvviiKll599130ev1pdYbEUIIIa7GVSf0c+fO5cknn8TPz4/MzEw+//xz3n//fUaOHMmAAQPYv38/NWrUuJl9FUKUkaysLJYvX66KBQYG0qxZs1J/YVywYIE9ma9dx4UBA7xV+2vUaEO0y4PE6/eQbXDm8+j6gAZf9xA0ZpiealuSDsCra7TD+U+k5rFsbyJ7zmap4rMebIiLQYdisXD2o5c5sGY5TS/Z37Z5BB7P/wq6a55tJIS4wbJTC9nw41GHuG+IOy3vrVLKEeWPoij8+OOPPPvssyQnJ+Pj46P6ofNGPqERQghxZ7rqu9oPP/yQt99+mzFjxvDTTz/Rv39/Pv74Y/bt20elSpVuZh+FEGUkPT2dDRs2kJ+fb4/5+PjQpk0bPD09Sz1m2rRpvPjii7i5aRgzJgi9Qb0/PKwLVX0f4vi2zeTpnfguuj4BwZFYs0xghg9TdejRYAh1x6tzlMOc2ce/2l7qdT9/pDFYLGQtXEjCN29zRim27/NFT53RU/Do2O063wkhxI10cRm6S8U1DSaypj8u7obLHFW+nD59muHDh7NixQp7bPLkyTz22GOyMo8QQogb5qoT+hMnTtC/f38A+vXrh16vZ9q0aZLMC1FBmUwmVq1apYrFxcXRpEmTyx5z7733kpGxymFdeYBTpyL4eNYGtv40n2PbNrMwsjZpzu74VYrAmmoiyKLh9Qwdej8XPNtHoPNS3/DmFZsZ9cNuVaxKkAf9G0dQJcgDU0oKSa+9Tm76UVUyb+zaiObDPkejl6fyQtwOUs7ksP23U/ZtJzc9HQfXQKerGPNmzWYzH374Ia+99hoFBQX2eO/evZkxY4Yk80IIIW6oq77DLSwstM/x0mg0ODs7ExoaetM6JoQoWwsXLrT/d9WqValRowbu7qVXg1cUBa1WS2CgjmeeDVDtS04yk5XVmXnz5vPb9LcpNBYzJ64ZAH7hEZBqJsqsYUymFucqPni2Dnc4f36xmWfn71LF5v7P9sOCYjSSOmMmRQcOkJWfzNnCFAB2N3PhidG/EeReMebgClHeZSUXsGXJSSwmiz1Wo1UYsfUdl7ksr3bs2MGTTz7Jrl0l31dhYWHMnDmTvn37lmHPhBBCVFTX9Mjq888/x8PDA7D9Av3VV18REKC+eZc14YUo/w4fLln+TavVqparLM0bb7xBvXou3Ne/ZK78b0tz2LmziDlzvuB///sfB9atYb+TB2si6wHg6x4CaWbeTtPhodiG1Xu0ClOdN7vAxPgl+8m/pIK9m7Oed/vXBcCUlETSGxNQgP1p+6Aom3PRevY2dWFQg2GSzAtxG8hKLmD3H/HkZxWr4rXbVSKqtn8Z9erGUhSFMWPG8MEHH2C1WgHbw4/hw4czefJkvL29/+UMQgghxPW56oQ+MjKSzz77zL4dEhLCvHnzVG00Go0k9EKUc2azmZ07S5Z4uzjV5nJ+/fVXPvhgEi++VPKU7c81eWzdWshzzz1HTV8Pfnl7AinO7qyJrA2An1sYriaFaWk6NBeK3/k/VMO+LNXhpBymrTzicK24YE/Gdq+OYrWSs3IV2YsXU2gu5HjWCbAY+bOnG0VuWh4NaUOjOoP/83shhLh+FpOVdfOPUJhrVMUDIz1p1D0anb5iDLEH2/1PXl6ePZmvU6cOc+bMoXnz5mXcMyGEEBXdVSf0p0+fvondEELcDsxmMz/++KN9u0WLFg7L0V107NgxqlatSr36LqpkfumvOWzbVsixY8fIOXaIM3t3kqd3YtGFZD7AP4JhCQo1jbbk3W9gdbTOJdf453QGn6w9obqWu7Oe13rVxHnrBs6/9CWW7BzMioWMwnSSC1I4UV3PkTgTaDR8pItA2/ntG/aeCCGu3ZGtSRz/J1kVC63iQ83WYRWm6N3/N3XqVH7//XeGDBnC888/j8FQMV+nEEKI24tUiRJCALY6Gb/88osqFhMTU2rb8ePHM2nSJO7p7UWTJq72eHa2lW3bCvn5558JCwpk30/fka8z8E1MA/wjotDkWGiXaqWm0fZkzrdvFVUyfyQpV5XM39uoEt1rh6DRaGxF7xYusvX1wlP5nEAXDtUpJj3YTC2NG8N0gdDmBdCoK+MLIW4Nq1Vh56ozJJ/MVsU7P14LJ5eKccthtVr5/PPP0ev1PPbYY/a4j48Phw8flqJ3QgghbqmK8ddVCHFdFEXhwIED7N2712HfgAEDSj3m999/Z9KkSVSp4qRK5n/+KZtdu4ro1KoFXpnJrPxkOnt9QtgYGIVfeCWUNBNt8+G+PFsC79U5Cp23s/14o9nKOytL5u6/2K061UJsS+NZcnNJeu11ii1GzuclsL2xFwlVa2DM3IcePY9o/WiidYcOr0Bo3Rvy3gghro2xyMzquQdUsSY9YwiKqjhrrR88eJAhQ4awceNGPD096dq1K+HhJYU8JZkXQghxq0lCL8QdymQyqSrZXxQcHEynTp1KPWbbtm3cfXdX7h/gTZ06Lvb4mxNTMBoVFn/7DdZzJ9lSbGXthUr2rl5eaAs1RBbD/ReSed/74tB5lNz4Wq0Kw77dYd9uUdnfnsybzp8naeKbWBUrRzOPkuvvwqk6vpC0lwYaNx7XXSjMOeiH//aGCCGuS2Gekd1/nCUjIU8V7zqkNnpD6VN2ypuioiLeeustpk6dislkAiA3N5clS5YwfPjwMu6dEEKIO5kk9ELcoRYvXqzabty4MbGxsehLWa9dURSCg4MZ9KDC62+oK8evWJ5L/fpN2LB+HQtmvs+mkCoc9/TH2d0dz4BAPMw6ep0qomWR7cbe/8EaaAwlxbDySlmSbnDjMHJWriTvr7UUZaZxPOs4ZquFU/UCOdTYC6+kA4zUhRKqMUBofegw7sa8KUKIq5Z+Po/CXBN7/ohXxfXOOjo9UqPCJPNr167lqaee4ujRo/ZYlSpVmD179mV//BRCCCFuFUnohbhDXXzKBDBo0KArtu3bty/PPOtYkfrtqak89dQo3pw4gaELFxMf0xAAr6Bg3Ly8udeop/WhXODCnPn74lTJfKHR4pDMv9fMm+RRo1CAlIJkUgpSAdjZzo8k70R8UlOYpL8wxLVGL2jw0LW+dCHEf3BiZwqHNyc6xH1D3al/VyRuXhVj2Hl6ejpjxozhyy+/tMf0ej0vvfQSr7zyCq6urlc4WgghhLg1riuhP3HiBF9++SUnTpzgww8/JCgoiBUrVhAZGUmtWrVudB+FEDeQoijMnz/fvv1vyyq9//57NG6yRRWbPCmFoiIFnU7HyEce4p6Vf4Obj31/JX9/hqRC8Mlce8yjRahqmH1GvpExC/eozju7RxSnXn6RMzlnMFlta8/H1/TjWDUr0dYcXtJGYLhY8K7hYKh+9zW9diHE9SkuNJOZmM+OFacd9gXHeKHVaWnQJdK+9GR5t3XrVnr16kVqaqo91qJFC+bMmUPt2rXLsGdCCCGE2jUn9OvWraN79+60atWK9evXM3nyZIKCgtizZw9z585l0aJFN6OfQogbwGg0OvwbvVwle0VR6N27Lk2bpari41+1LUU1adIkHn1wII/sLBmG6uTqysxWjQjPVsjZWjIM1/vuGAyBboBtvvz87fH8eSjFvj9OW8CDmxYQv8loW1MeONQyjFP1AsFq4umUJKrrgmyNWz0Hkc2lkr0Qt8DpfWkcWJ9Q6r6mvWIJiPCoMEn8papXr26ffuTl5cXbb7/NkCFD0GodRyoJIYQQZemaE/qxY8cyadIkRo8ejaenpz3esWNHZs6ceUM7J4S4sS5N5t3c3OjTp0+p7VJSUqhUKZjXXi+ZL5+XZ+X992zJfUJCAjlnTqqSed/QMBY0q43pdA45lyQAvv2qoPOyVbPfczaLj9YcU12rQUEi9+xdQYGpgBPZJwE4VS+QKvcMYkhkRzzXvg2aLFvj7m+Db/T1vnwhxFVSFIWNPx0nO7nAYV9oFR8adI5Eo614ifxF3t7ezJw5k/nz5/Phhx8SFhZW1l0SQgghSnXNCf2+ffv4/vvvHeJBQUGkpaXdkE4JIW68nJwc1fblknmLxUJwcDBvTipJ5ud/n8XBg8WAbQj+6vlf821MA/t+v/BKLGhai7yN5yk+nmWPu1T3syfzx1NyHZL5Vxp4YZ05m/2Zth8GTtUL5FCLUGoF1qZ35F2wdTakXTgmrIEk80LcAhaTlb8XHiM/s8gea9g1itAqPmXXqZto27ZtPP/888yfP59KlSrZ4/369aNfv35l2DMhhBDi311zQu/j40NiYqLDMN1du3ap1mIVQtxefvvtN/t/9+/fv9Q2iqKg1+t59FFfe+zYUaM9mV/7158c3LOTb6NKkvmAqGjmN6pOzh9nMJ4rWbbKo3UYLlVs55m3+TRrj5QM3X+4RRRVv3gf85ZsezJ/sFUYp+sG0ia8DQNcKsGix9Sda/z4db5yIcTVykkr5O8fjqpinR+vhZNLxauhm5ubyyuvvMLMmTNRFIWRI0fyyy+/lHW3hBBCiGtyzX+hH3jgAV566SUWLlyIRqPBarWyceNGXnjhBQYPHnwz+iiE+I/++usv+3+HhIRgMBgc2pw+fZr776+pejIPMG9eJmB7cr/o3cn8XKUJAE5ubjSrUoXxlUOx5BpVybx3jxgMQW4oisITX/+jOt+91b2p/MHrmIFjF5L5xCo+ZDaqzMzWb8LiYVCUXXKAkwd0fwfc/f/TeyCEuDxFUVg5Zz9Ws1UV7/BwjQqZzP/666+MGDGCc+fO2WOnT58mOzsbb2/vMuyZEEIIcW2u+a/0W2+9xYgRI4iIiMBisVCzZk0sFguDBg3i1VdfvRl9FEL8B2lpaSQmliwx1a5dO9V+RVHQarXce5839/T2ssfNZnhrcjKKYmvzy9sT+CyumX3/oLq1eDDMn8KD6eRvS7LHfe6pjN7PhdTcYsb+tFd1rQktg1DenwrAyewTFFuMrHyiDlaDlhktXocF/2/5vE6vQ3DN//weCCFKZzFbObI1iVO71cUvY+oFUrN1xZs3npCQwDPPPMPPP/9sj7m5uTFhwgSee+45eyE8IYQQory45r9cTk5OfPbZZ4wfP579+/eTl5dHgwYNiIuLuxn9E0JcJ6vVytq1a0lKKkm2e/fujU6nA+DIkSM8+OCD7Nixg+Ej/AkNLfk6+PGHbPbts82fVRSFpdPfplirs+8PiqnMg2H+ZC45jiWz2B53jvNB7+eCoij2ZN5gLCbm5B4GZe1H2QIKCscyj5EYbGBrr7ooWg0TGr2AZuElI3xcfaHXR6CvGOtZC3E7Ork7lUMbz6tinv6utBkQV+Eq11utVmbPns3YsWPJzS1ZTrNbt258/PHHl13tQwghhLjdXXNCv2HDBlq3bk1kZCSRkZE3o09CiBtg7969pKen22/Mq1evTlJSEuPGjWPhwoX2dm9MCEZXkqszdUoK+fkKYEvmD21Yy+8+YRz2CgQgpEpVZteMImPRUax5Jvtxnm0r4RxrG6o6ZtFeNFYL7dfMJzg/jeohtif/2cXZxOeeJSXai3+6ReNqcGNaq8nw48MlHfCNge5Tb8p7IoQAxaqw/JO9DvHmfSvjH+ZRBj26+QYMGKBa5SMoKIjp06fzwAMPVLgfL4QQQtxZrjmh79ixI+Hh4QwcOJCHHnqImjVlOKwQt5uUlBQSExNxcrI94b733nt54YUXVEtLxsQ68dhjvqrjLibzM2bMYMTw4axbMI9JToFwIZkPrhxHqLMBZUeKKpn3f7AGGoNtfeYpyw7ieWAX7bevAqBahA8AhdYi/qhh4nijeraDFIWXLV7qZD6qJbR69oa+F0IItfUL1EXv2j9UHXdv5zLqza3x4IMP2hP6xx9/nHfeeQc/P78y7pUQQgjx311zQn/+/HkWLFjA/PnzmTp1KnXr1uXBBx9k4MCBquVehBBl48SJE2zZssW+3alTJ1xcXOzb4ZUMDB3qeCP72njbfHmz2UxW4nl+mfYmn1wyZ94nJJShkUE0KYDczfH2uN8D1dAYtJzPKuT1n3Zzzy8z0Ci2J/xVQzzRAFuGNGdd5nbAHYCRDUZSbd10KNxf0oGQupLMC3ETKYrCzlVnyLtkOboew+tWyCfUJpNJVfyzT58+jB49mnvuucehjogQQghRnmkU5cKd93U4deoU33//PfPnz+fw4cO0bduWP//880b274bLycnB29ubzMxMfHx8yro7QtxQ586dY/369SiKgtFopEWLFsyfP5+JEyfi7q5h7Lggh2OWL8vl7Fk/fv31VzJ2bSE7NYWNgZHs9QkFQKPVEhgdw3f147DkGMn8uWQteZ9esej9XXn8q+0A9P5pOlqrrUp2TJCBpMfu4hfjNtX1OlZqT7+zByDlYEmww8u2hL4CJhbi5rFaraSkpBAUFIRWqy3r7ty2FEUh5Uwu/yw7pYp3e6oOOn3Fet/S0tJ4/vnnycrKYvHixRXixwr5nIs7gXzOxZ0gKysLX19fsrOz8fLy+vcDrtJ/KucaExPD2LFjqVevHuPHj2fdunU3ql9CiGt06NAhdu3aZd9u3Lgx8+bN45NP3qZPXy8aNXJVtS8sDOXxxxbx6iuVSTx+hC0/LQDg69gGFOhsQ/Xdff3w9A/g6zq2glGXJvMebcLR+7uy9kgKWouZGgc3o7Va8XI1YA0s4JN7/eD/JfNv1B1OwOoJ6o53fwd8o27Y+yCEsDEWmTmzL52jl6xCAeDiYaDNgKoVKplXFIV58+YxevRo0tPTAfj555+59957y7hnQgghxM113Qn9xo0b+e6771i0aBFFRUX07t2bKVOm3Mi+CSGu0rp160hISLBvN2jQgI0bN7Ju3XRGPx+gapuSYqZf30W0b98egH9++4WzB/ay2zeUA95BFOicCIqpjFan440qYVR2c8GUlE/GytP2c2jdDbhU9mHNoWTWLVpN7y3LAKgX4cP5vPPMuy/C3raaXzX8XPwYZNSj+f/JfO+PZX15IW4wi8XKyV2pHN2a5LAvNM6HBp0jK8ST64uOHz/O0KFDWbNmjT3m4+OD0Wgsw14JIYQQt8Y1J/Tjxo1jwYIFnD9/ns6dO/Phhx/Su3dv3Nzcbkb/hBD/4vvvv1dtd+jQge+++46i4nfp1t3THj91ysiSJTnM/mSBPZlPPxfP2QN7WRFalVMevviEhBLiYTtmbu1onLRaspaewJxepLqG771xvLZkP4lpufZkPjrANj/+x/tD7O3GNnmJSiYjrHpZ3eng2tBp/A15/UIIG4vFyurPD2AxW1VxjVZDs96xFa6CvdFo5N133+XNN9+kqKjkO2rAgAFMnz6dkJCQKxwthBBCVAzXnNCvX7+eMWPGcP/99xMQEPDvBwghbprFixertnv06IGvry9vTgpWxb+dl8WRI8VMnDiR++67zx7/46cf2RoYxSkPX4JiYtHq9AwO86eFjwdOWi2F+9NUybxbwyBc6wQwcv4uCo0WWm20Xb9ykAdhfXryY0wKxal7QLHysNmZSr+94NjpblPBT9Z8FuJGKS40c3JXCid3pTrsa96nMv7hFSuRB9i8eTNDhgxh//6SwppRUVF8/PHH9OjRowx7JoQQQtxa15zQb9y48Wb0QwhxDc6cOePwb9HFxYWoKH+HZH7CG8mYzfDDDz9w//33A2A2Gvl29ky+i6gNgMHFBa1Oz5tx4US7OqMoCgW7UijYU5Ig+D9Ug3yLlSe+/ofws0eot/svnIsKqBXuhV6r5RWP1ZAK5CZCznka6SLURe5i2kGL4TfnDRHiDrVz1RkSj2c5xLs8URuDs+7Wd+gW2LdvH61ateJiTV+tVsuoUaOYMGEC7u7uZdw7IYQQ4ta6qoT+119/pXv37hgMBn799dcrtr3nnntuSMeEEKX76aefKC4uVsUeeughFEVxSOYnTkjBbIaIiAh7Mm+1WJj6xVzWVappbxcVHs7UGpF4G/QoikL61wdV5/HuGcuyg0n8suMcLTf8QnDSaTxc9ESHe5NvzuW7ri6ACyTsBOBtXTh6jQZC60HzYeCqXu9eCPHf/f3DUXLSCu3bETX98Ql2JaKGX4WaI///1alTh759+/Lzzz/TqFEj5syZQ8OGDcu6W0IIIUSZuKqEvk+fPiQlJREUFESfPn0u206j0WCxWG5U34QQl0hKSnJYFrJu3bp88803KIpCt24l8+ULCxTeeisFgN9++427777bvu/5hb+wP8g25F2j1TK0aUP6hZVMn8lde1Z1DZ+esfydnsuxL7+n79EdAHi7GYj2d0fp1IrP3TZgctHZk/kZughbMtHuJQiXm2whbqTcjCKObksi6US2Kt758Vo4ufynhWtuWykpKQQGBqp+pPjoo49o27YtI0aMQK+vmK9bCCGEuBpX9VfQarWW+t9CiFsjKyvLIZkfMGAAFouFRT996PBkfupUWzKfmJhoLwylKAo9V67H6HRhPq1Gw+vtWtDKt+SHAMVixXgm177t/0hNnp6/i9id66l2IZmvEeaFk05L2NQpPLdzPIqih4Qd3Kf1pb32wrkGLpA15YW4QbJTCzm8OZG0s7ml7u/8WMVM5i0WC7NmzeKVV15h7ty59lFGAOHh4Tz77LNl2DshhBDi9nDNdwDffPMNAwYMwNnZWRU3Go0sWLCAwYMH37DOCSEcn8w3b96c2NhYzGYjo0aH8dhj6uHsb01O4aOPZtKvXz+CgoLs8buXr8V0yVD9uV3aEumuXps+96+Sp/M+91TmozXHKTJaqHZoKwBxwR5ETZmMPiCAE1knUAozIf0EtTSuJcn8oB9u2GsX4k529mAGx3YkU5jjuPxaeDVfImv54xdaMeeM79mzhyeffJLt27cD8Mwzz9C5c2d8fWX6jhBCCHGpa07oH330Ubp166ZKFAByc3N59NFHJaEX4gZRFIXVq1eTlpZmj9WoUYPY2FisVhNT344kNLTkn/CunYUsW5bLmjV/06JFC1JSbE/pk3PzeGj1BtW5f+/b1WGObeHBdIzn8kqu7+2Ex/wv6Jt0GoC6lbwJeu5Z9AEBrI//ix83TgZTPgBPai8M2ZdkXoj/7ODG85w7nImpyKyKewe6Edc0mOBorzLq2c1XUFDAG2+8wfvvv6+awtenTx+0Wm0Z9kwIIYS4PV1zQq8oSqnFds6dO4e3t/cN6ZQQApYuXUpeXkmCffHJ/IoVK/jjj8H4+pVUsH7n7VRyc23TYVq1aoXVakVRFIav2ciJ3Hz1ebu2Vv0bVqwK6d+oi+AZQvL4q9/DBF9Yz7paqCcajQZ9tTieXvE/SD1ib3uX1gt97X5Qd8ANe+1C3GkUReHY9mSObU9WxfVOOup3jiQg3AOdoWIntKtWrWLYsGGcOnXKHqtRowZz5syhdevWZdgzIYQQ4vZ11Ql9gwYN0Gg0aDQaOnXqpCpCY7FYOHXqFN26dbspnRTiTpOZmalK5nv27ImXlxfz5n3NH3+MIq6qk33f+FdLEoCLyzhZFYXhh+PJzc3nYupeOS+Djx8agFZb8kOAOauYrMXH7dvW/HzMyX+TsOYgRnNJvYzIl8aQE+7Dcz90BVMBABrgLV04nn0+BXf/G/nyhbijFOQY+WveIYd4gy5RhFbxrtAV68FW9G7UqFF8//339piTkxOvvvoqL774osMUPyGEEEKUuOqE/mJ1+927d9O1a1c8PDzs+5ycnIiOjubee++94R0U4k6SlZXF8uXLVbF7770XZ2dn+vbti5fXOlUyP+OjkuH4F4enphSbGH3oDDnp6WguDFH9rm1DggLU02QAVTKvWK0U7/sKq/J/7N13dBTVF8Dx7+6m914JoYUeQJrSpSpNEZAOYgFRUFTsgIAiWFBRkWpBQaRXKQoI0iwgPYFQAwkhvZfdbJnfHwuz7C9UBQLhfs7JOZk3Ze+EJdk77737LCRkWpP2A/Va8+xbAxj11+vw1wGw2IYAfxE1CE2T527CXQtx74o/lE7MtnN2bRXrBVKtcUiZ75G/6M0337RL5lu1asWsWbOoVq1aKUYlhBBC3B2uO6EfN24cABUqVKB37964uLjcsqCEuNeYTCZ27NhBUlKSXXv9+vVxdnamVauW+PkfIKqqrYjd+xNT0eutPfJ6vR6tVku+yczLsfGknD6pHvdxhG+JZN6YXkTOz6fUba2LjoKt08gsKCYhs5DU4PL80+ghBjxUm/F/vQ6psWoyX1njzMv9NoGu7FXVFuJ2Sj2Ta5fMewe50fzxqFKMqHS8//77LF++HK1Wy5QpU3jyySfL/KgEIYQQ4ma54U/kTzzxxK2IQ4h7ltFoZMmSJXZtnp6ePPTQQzg5OWE0GWjf4RhQMpl/4403+OCDDwCI2/0XIxKy1GOislN5KtCDeo1KToW5NJm3FBswxK3k0LkcLBbrA4KdLXvi65vCj4dfhOwz6rFf6CLQ9p4nybwQ/1Hy6Rz+WRevbjfqUpGgyLJb7O4ig8HAsWPHiI6OVttCQ0NZtmwZ0dHRJQruCiGEEOLqrutTuZ+fH8eOHSMgIABfX9+rPjnPzMy8acEJcS+4NJnXarV07NgRb29vtm3bRqtWrXj55QD8/K3z3hUFpnychl6v0KVLFzWZ37t+NW8U2Ybit00+SSNPVxp27V7i9fTHLyb9CkX7d4J+PwcSstX9a7sOo0Gts8TEf4OmyHqsJzom+zWChz8AB5nPKsS/pVgU9v56huSTOWpb9IPl7olkfvv27QwdOpTMzEyOHj1qtwRd27ZtSzEyIYQQ4u51XQn9Z599hqenp/q9DIUT4ubYvHmz+r2TkxM9e/bEYDCo/8eio13UZB7gnbHWAnhnzpyhfPnyAMTs3GaXzHsFBvFa17akZ2SUeD1TtoH8nUlgsVAUE4Mhfy9xybkAnK5Uh/0N2jF9QH1GrR+gJvN9tH40bzwSqna4yXcvxL0l7s/znPgn1a4tqnEw5WuV7aKSWVlZvPnmm8yePVtte+ONN+y2hRBCCPHvXFdCf+kw+8GDB9+qWIS4p/z888/k5uaq248++ihJSUmEh4erbb1625aCnP6VLUG/mMwnHo1hxvEz4BUIgHdQCIua1UVzodr9/8teeQLFbEYfE0NCahyZWbkUunvxS6dnAHizcyijVveAfOuDg/oaN5p3nQ3e4Ze9nhDi+uxee5rU+Fy7tlb9quPhW3ZHvCiKwuLFixk5ciQpKbbVOBo3bszw4cNLMTIhhBCi7LjhErp79+7l0KFD6vaqVavo1q0bb7/9NsXFxTc1OCHKqoKCArtkvkuXLkycONEumR8w0Ef9fvWqXM6ftxalu1jNPub3zXy3dRtxF5J5v/ByLGlWF91lRtAoikL63BhAoSgmhqT0eDKzrGvJ/9LpGWqFe/Na0ww+X/+4mswDDIx+RpJ5If6jtV8dsEvmm/eqSufhdct0Mn/mzBm6dOlCnz591GTew8ODL774gl27dlG3bt1SjlAIIYQoG244oX/22Wc5duwYAKdOnaJ37964ubmxZMkSXn/99ZseoBBljclkYtWqVep2mzZtaNWqFe+++y4ANWs6M25cMNWq2T7sd+r0OtnZ2SiKgkajIXbbb6yKjWNbUEUA/MpF8G7tKlecDpPxfSwARYlJ5OlNpGUcBGBD5yFM738f/QzTmPbnBPX4SI0LX3ZZgGO9vjf35oW4hxgKjaz96oBdW6fn6uAd6HqFM+5+JpOJTz/9lJo1a8sLXjwAAQAASURBVNotwfnII48QGxvLCy+8gE6nu8oVhBBCCHEjbrhU9bFjx6hXrx5gLebVqlUrFixYwM6dO+nTpw9Tp069ySEKUbYsXrxY/f7MmTOEhoaq26++Goi3j/1ztpdf/hN3twoAWMxmVk2ZiFGjZUuVRgD4hoUzrV5VAp0cS7yW/kgmhbutvWOnU3JxS0rh9Bnrh+zDI8YxrX0llEX9mGhOUM9pXaUrPZq+fXNuVoh7kMVsYf3MQyXaOwypjUZbtmvQZGZm8t5771FYWAhAWFgYX375JY899pjU3xFCCCFugRtO6BVFwWKxALBp0ya6dOkCQEREBOnp6Tc3OiHKmLS0NPV7s9nM229bE2cvLy2vvR5od2xWViAfTN6Jo6O1+rWiKCz9ZBLzK9WnSGdN3n3DyvFC1cgSybxitmBelUSBsxMaBQpiYnArLFaT+aLH+/Nmx+oU/vg4r5sTrSdpdJSv2oUe9795S+5diHtBwtFMDm5OsGvzD/egcdeKaHU3PCjurhMUFMRHH33Es88+y3PPPcekSZPw9va+9olCCCGE+FduOKFv2LAhEydOpF27dvz+++/MmDEDgNOnTxMcHHzTAxSiLNm4cSNgTc4vFpscNMiXqKpOdsdVrvQpffv2s2ub88UnLLnQK+/s7oFvaBjlXZxo4eepHqOYFQr3pVB4+MLDNYuFophYTp3ZQkGRdQ5vg27tCO3VlLgfH+VL8yUVt8PqMaL+izf1foW4V6Qn5vPXqpMl2js9X6dM90yvX7+exo0b4+9vq9T/9NNP06hRI3U0nxBCCCFunRtO6KdOnUr//v1ZuXIlo0ePpkqVKgAsXbqUpk2b3vQAhSgrjh49CliT+U8++QSdTmHYMH+CQ2z/DU+csDBi+DoaNmyotimKwuw1P7M0orba5hsaxsxakbhfMhfVcDaXvN9sPYOWnBz0KckUZG2hoCgPgA3dh9Pl6ebsWT6IuZck87WjBzDsPqk6LcSNSj6Vwz/r40u0V3sglMr1A8tsMp+cnMzIkSNZvHgxTz31FN988426T6vVSjIvhBBC3CYaRbnC+lY3SK/Xo9PpcHQsOY/3TpKbm4u3tzdZWVn4+PiUdjjiHnHgwAFiYmIwmUysX7+e2NiVPN7Lfhjq5EmpFBRY7NoUi4XPv/qCteHV1bYXmjXmkWC/S45RyF59EnO2AQBzXi7F8fEYC0+Rk3eKc1mFnCsXxd9NuvJ48yJ+jpsNGbaexD5tP6Z5ePNbcdtC3FIWi4XU1FSCgoLQam/fcHZDkYn9G8+SnpBXYl9kbX9qtyp322K53SwWC19//TWvv/46OTk5avuePXto0KBBKUZWdpXW+1yI20ne5+JekJ2dja+vLzk5OXh5ed20695wD/1F//zzD0eOHAGgZs2a1K9f/6YFJURZkpiYSExMDGAdnrpz51aGPmufzH/8UVqJZP7g3n8YfyKRvEuS+Xeb1KfJJcm8pdhM5oKjFzYsFMXEgOEIiimFrJxCknKNHLyvNaeq3Icm4Cd+/ucwGKxJiA4NrzSbQKQk80Jcl/TEPE7sSSXjXH6JfVUbh1ClYVCZ7ZEHiI2NZejQoezcuVNt8/f359NPP5XPAEIIIUQpueGEPjU1ld69e/P777+rPdzZ2dm0bt2ahQsXEhgYePULCHGP0Ov1LF++XN22WCz88ssvvDLKNl9+69YCNm/Kx2g02p37w29bmJdtAAfb0nVvtbifJoG+6rYpo4jsNadAUdAfOYJiKobCvwAjZzMK2VSxMWdrNsaCHkevj3DOTFTPfVjrTZeGL0Dl9rfgzoUoOxSLwrnj2RzYdLbEvtAqPlS7PwR3n7K7njxYf5dNnjyZyZMn2/2uGjRoEJ988gkBAQGlGJ0QQghxb7vhhP6FF14gPz+fmJgYatSoAVif2j/xxBO8+OKL/PTTTzc9SCHuNjk5Oaxdu9aubebMmdx3XzFgS+g3b8rHbDbbDS87GXvImsxfUCHAn0+a3IeXo+2/q6XYbE3mAWNqCorZDIU7ADiYmMPy7i+iN5rIYC0B/kl4ZNmS+aluNXFoPwG8y+6wYCH+K7PJwq7lJ8hNKyqxr07rCEKjvHFwLPvrqW/fvp1nnnmGY8eOqW2VK1dm5syZtGvXrhQjE0IIIQT8i4R+w4YNbNq0SU3mwTrk/quvvqJDhw43NTgh7kYpKSls3rxZ3XZ1daVnz5706uVJrdoeavvYMSmcPHnSLpnPTj7PsKPn1O2Xa1WhU7Uqdtc3ZerJXn0SU1oaxuRkKI4H4xkU4L1Gg7A84ICCQopuNRW84nHJshXKm9h+Og6h9938mxaijDh/MocDm89iNlpK7KvaOISoRvfWai7Hjh1Tk3kHBwdef/11xowZg6uraylHJoQQQgj4Fwm9xWK5bOE7R0dHdX16Ie5FBoOBtWvXotfr1TZPT08eeeQRqlVzplZtF7X9h++zAKhUqZLa9sXqNawx2f5vBURElkjmLUUmslYexxAXh2IygTkXjGcoLDazNKwhFp31v3Q2B4hy+B1tvvVhwSe6cjhrHUGSeSEuKy9Tz/6NZ8lNL9kj37xXFN6BbqUQVel76qmn+OGHHyguLmb27NlER0eXdkhCCCGEuMQNJ/Rt2rRh5MiR/PTTT4SFhQFw7tw5Xn75Zdq2bXvTAxTibmAymVi2bJldW9WqVWnUyLpufJ8+Pmr7xx+lkZtrreZ60a74BLtk3s3bh7kNqtldT7EoZC6KQ3+hwB6mVDAc4XyPJ5iVqIULxbiyOUSo9lsuPl/7UBeOc53eEN3zZt2uEGVK/MF0YrbbRsY4OOuoEB1ApXqBODqX/WH1F50+fZrVq1czcuRItU2j0bB8+XJ8fX2l8rQQQghxB7rhhH7atGk88sgjVKhQgYiICAASEhKoXbs28+fPv+kBCnGns1gsLF68WN12cnKia9euuLhYe+Td3bU4XMjV4+IM5OZa2Ldvn1pAUlEUppy1JfdTmtanbkhQidfJWX8a/ZFY64Y5EwxHCJ08iYk/nwSNdfVJt6CtuBRug0LrYW9rQ3DvsxB0/3pBCyHKtOTTOXbJfGB5Txp1qVimq9X/P6PRyNSpUxk3bhxFRUVER0fTpk0bdb+/v38pRieEEEKIq7nhT/kRERHs3buXzZs3q8vW1ahRQ4rjiHuS0WhkyZIl6rafnx8PP/wws2fPBsDZWcObb9lWflj4UzbJyckEB9vm4S5LyaK4yJqBN9RZLpvM609mU/BXDIrJDChgOEzEzBlM++04JrNCIYkY3Jbjmn4SDQoK8LoSQGjfJZLMC/F/CnIMJBzJ5OTeNFAUtf2+DuUJi/K9ypllz+7duxkyZAgHDhxQ2yZOnGiX0AshhBDiznVDn/QXLVrE6tWrKS4upm3btrzwwgu3Ki4h7njFxcUsXbrUru3hhx9mwYIFPPvss1Sr5syAgT7qvvjTxRiNit3xxwr0LDqViKGgAJ1i4YWalfh/iqKQPvNXlItj6At2Uu6rLyksNrHvbDaKYqDAMpNyRTnqsPvJ2jD0TUaDRobICgHW/0f7Np4l63wB+nxjif3Vm4beU8l8Xl4eY8aM4csvv0S58FBDo9HwwgsvMHHixFKOTgghhBDX67oT+hkzZjB8+HCioqJwdXVl+fLlnDx5ko8//vhWxifEHclgMJSYM9+jRw/GjBnD+++/T9NmbnTs6Gm3f9q0pBLXee9kEtnJ1vbHzx4iqGvJkS7n39tgS+aNiYS88zYanY41e8+So+zHtXgW4c4OaDUaemp9edC3JpZ24ynMyr9JdyvE3c1oMPPr14dLtDu5ORBcwZvqTUJwcrl3RrKsXr2a4cOHk5hoW86ybt26zJ49m8aNG5diZEIIIYS4Udf9CWbatGmMGzeOcePGATB//nyeffZZSejFPWnXrl3q9x4eHnTp0oVJkybx/vvvEx7uYJfMb91agGJpxJjRLnbXmHL6PMknrMtBlS/IpmWzFjg42daoVywWUj5ZhDnHNpe33JQX0Tg5oSgKa2NO4Fo8CwCdVkMzjQcPtnkfwu7DWhFPEnpxb1IUhYxzBWQlF3Dsr+QS++u1L09gec97KokHyM/PZ/DgwXYPI11dXRk/fjwvv/zyZVewEUIIIcSd7bo/zZw6dYonnnhC3e7Xrx9PP/0058+fJzQ09JYEJ8Sd6M8//+T8+fPqdlpaGjqdtRK2gwMMe85WQGrhT9m0aPEkM2bMsLvGt4lp7DxjWx++U1IcUQP7AKA/doy0Tz8DpyhwDFOPCRrZFI2jI8SsZM7Oz1GUPACcHbW8ogumUv+VN/1ehbibKBaFf345Q8qpnMvud/V04sEB1dFq752Cd5dyd3cnIyND3e7QoQMzZsywWz5TCCGEEHeX607oDQYD7u7u6rZWq8XJyYmiopJr9gpRVun1ek6dOqVu//rrr3z//ffq9rjxtmJ3MYf1PPzwCKZMmWJ3je/PpbMlM4/8zEwA+sYfoNuot8lZ8zO5a9cCWnBvZXdO0MhWOAc4o/zUm8H5p7BcMhW/saMHlbp9fRPvUoi7z5mYDA5vTSzR7hviTkQNP8pV90VzjybyF2k0GmbNmkX79u2ZPHkyffv2vaeq+QshhBBl0Q2NNxw7dixubm7qdnFxMe+//z7e3t5q26effnrzohPiDvPzzz+r3//yyy/88MMPgHVpukur2QOEhz9TIpkfeND6MODiUPvuCTFER0aSOWs2+kOHsSbzLQDQeXriFBmJd8eKOMZ+RsFve3lVn4jlwufvXJ0fE1pMol7VBmoxPCHuJYqikHq6gD1LD9n9F/AJcad2y3C8A11LL7hSVlRUxMSJE2ndurXdKjRVq1blxIkTMrxeCCGEKCOuO6Fv2bIlcXFxdm1Nmza1662UJ/2iLNu+fTvFxcUArFmzhoULF6r7Lk3mzWaoV3cmjz7aze78kUfOYrGYyT5vLYLX/8DvOJ08TlBsAHqNBhyCwbk6zlUqo3W1PjgL6OYF657CZLYwzHAWNFCo9STVMZxf+83F1enemgMsxEUWi8L6GYcxFBtwdnJW2xt2rkhwBa9SjKz0bd68mWHDhnHixAkWLlzIoUOH7B7GSzIvhBBClB3XnQ1s3br1FoYhxJ3t6NGjJCRY57yvWbOG1atX4+iooW9fH6Kq2grZNW/WglatFpd4uPXy0bNkGk1kJ5/HkJfHk2vnozGbaOzmh1ajA/cW6Hx8cAwPR6PVonHS4l/7MKxbikWx8GThaQASnKoQ4O3BL12/wNVRknlx7zEZzRzcksj549l27eWq+1G3bUTpBHWHSE9P59VXX7WbBpSQkMDOnTtp3759KUYmhBBCiFtFMgIhrsPevXsBa3X7DRs2EB5uYtATQXbHtGrZihYtFpU4d+H5DNKLTeSmp6LPzGTw+gVoFAuN3Pxw1jrgUP5RHIKD0eis/x09W5bD2T8P1lrXuH8i/zTpjqHka71xc9Kx+LHZaGV9eXGPKdab2LXsBAXZBrt2dx9HOgyORqu9d/9PKIrC/Pnzefnll+2K3jVv3pxZs2ZRs2bNUoxOCCGEELeSJPRCXIPRaARgz549HDhwAB+fAgY94afu1+sVRgxfS3BwgxLnxuQXsTYth8zEBPQZ6Qz+dRE6xUJDNz+8a9XCMfJhzNkmABxD3fHqEIlGnw0rRpFZVMxIUwIpjuUo0noAsOrxOZLMi3vO5h+OoM8rtmtzdHGg/kPlMTkUlFJUd4YTJ04wbNgwNm/erLZ5e3vz0Ucf8cwzz9zTDzqEEEKIe4Ek9EJcxf79+1m0aBHx8fEAdOhwAo3WlszPn5fNkSNFJYbYWxSFj04ncyAzh4xjRzEZi3n0r43oFAv1XX2p/NFH4OBG1tLj6jne7SPgwEKU2BXEJefzvnsqhVpPNZnfOmgeunu8Sre4t+RmFLF94TG7ttAqPtRpUw4HRx0Wi4XU1Hs3oV+8eDFPPPEEer1ebevVqxdTp06V5WSFEEKIe4Qk9EJcwcqVK/n5558pKLAmDN7eSWi0tvXi5szO5IcfNpZI5k0WhSf2Hyc1/hTm/DxQFB6I20dgbhaNm7Wm0lPPoHF0IuOHWPUcrw6RsLAfeqOZo8l5TPfOolDnSapDOIGezizr/rUUnRT3DKPBzPE9KZzen2bX/tDQ2jg46kopqjtP/fr11e/Lly/P9OnT6dy5cylGJIQQQojbTRJ6IS5jw4YNxMTEqMn8hg0bGPWqrZL2uxNSePfdybRqZb9e/LykdFaePEtuWiqWwkJQFAZuWYGX2Uyb96fgG1kBS6GRjJ9sybzGWUf6qZWkJmQD8IlPBqmO4RRqPake6sWch6bf+hsW4g7y69eH7bbveyiSsCo+pRPMHaxKlSpMmDCBlJQUJkyYgIeHR2mHJIQQQojb7F8l9Nu3b2fWrFmcPHmSpUuXEh4ezrx586hYsSLNmze/2TEKcVulp6ezZcsWzp49C8CSJYsYP8FWAG/njkKWLVtN165d1baMYhMvHT1Ldsp59Hl5WPR6FLOJ139bRU0Xb8rPmgmAYlHIXGwbQqwYcklMGQ9noUBjYaZ3FglOlfH2cCfKz433m79/e25aiFKWnVLIn6tO4eppW1ItsLwn1ZuE4hVw764nf9GKFSv47LPP2LBhg90SdK+//nopRiWEEEKI0nbDCf2yZcsYOHAg/fv3Z9++fRgM1orDOTk5TJo0iXXr1t30IIW4nb799ls1mT99eo1dMl+lShQjX1yHp6en2vbR6fMcyiuiKC8XfV4eCgr9Ny6msYMb3q5ehEyYoB5b8Od56zeKGcesjSRaNmFCYaZ3FgaNQp5HJWoFB6DRaJjaeioOWhlEI8q2tLN5/L3mlLqdn2kGZBm6ixITExkxYgSrVq0C4N133+WDDz4o5aiEEEIIcae44Wxh4sSJzJw5k0GDBrFw4UK1vVmzZkycOPGmBifE7bZmzRr27dsHQHSdOGrVtg1hrRpVlZ49N6HV2tadH3jQlojkpCTjoS/g0V+W0MLFGyeNltDJk3Dw9QXAUmRCfywTCjPR5BzntPZXtrkWstu5iEKdJyEVauKt0RDiHsKYB8bcpjsWonQU603sWRtPVrKtqJ2zuyNunk7UahmOd+C93StvNpuZPn06b7/9Nvn5+Wr7kSNHsFgsUr1eCCGEEMC/SOjj4uJo2bJliXZvb2+ys7NvRkxC3HaKorB+/XoWLFgAQGSFTDSkqPsrVOhJjx6fXDGZz8/MoN/uTTgnxOOq0eGk0eIQGKgm84rRQuZ3uyArHrNFIVW3mTMORna6Kpx3rEatcB80Gg0ftvwQd0f323TXQpQORVHY+E2MXVv5Wv7UbhUuxR+BgwcPMmTIEP7++2+1LTg4mC+++ILHH39cfkZCCCGEUN1wQh8SEsKJEyeoUKGCXfuOHTuoVKnSzYpLiNvGYrEwatQokpOTAWjZ8hRGUxa5udahv00e+Ja2bTupx5ssCj8kpavbuWmp9PpuChc/Ytdx88Gn1+N4tmlz8QXImzUTcjwoNltI1Jzme9+TnHAOoVDrSZ0Ib95r9h5+Lrbl8IQoi4r1JmJ3JHEuLkttc3RxoEXvKFw9nK5y5r2hsLCQd999lylTpmA2m9X2oUOH8sEHH+B74QGhEEIIIcRFN5zQDxkyhJEjR/Ltt9+i0WhISkrijz/+4NVXX2Xs2LG3IkYhbplvvvmGTZs2AaDVWmjUKJHsnHOYzSbrAUpnu2R+SXImq1OzASjIzqIwJ5sBi2aoyXwjNz8qfvYZ2otFq4qyUJY/jyGnI3l6IwowvsIe8nRRBHg6U8XHhR5RPSSZF2XW6QNpHP0zGYvJctn9HZ6udZsjujOZTCYaN25MTIxt5EKNGjWYPXu2FJsVQgghxBXdcEL/5ptvYrFYaNu2LYWFhbRs2RJnZ2deffVVXnjhhVsRoxC3RIsWLShXrhwAGo1Cu3YnSElNVZP5yZNSKSj4Rj3+TJFBTeZzUpMpys2l+2/LuTiTtZGbH1WmT0fj4ABFWbDmJTDpyckOJk9vBOCDCIU8nS+R/m50iWpD18pdZYi9KJMyzuXz58qTl93n4u5IvQ7l8Q+TZdYucnBwYODAgbz55ps4OTkxevRo3njjDZydna99shBCCCHuWTec0Gs0GkaPHs1rr73GiRMnyM/Pp2bNmrL+rbiraDQaHn300QvfK7Rvf5ykpCQUFABmzzJSUGDrUYwr0DPxZBIARbm5dD68i4CcDAzZmVRz9sRH50SlWTOtc1t/fhlyrcem5BooSK5LntadRAcX4j1WEB3kjVajoXe13jIXVpQ5iXFZxB9MJye10K69auMQwqr64OrhiFYnBd0URcFoNOLkZJtq8Morr3Dq1CleeeUVqlWrVorRCSGEEOJu8a/XxHJycqJmzZo3MxYhbounn36a8uXL4+rqioeHgaZNz3Au6Zy6v3+/PxgzuqK6fabIoCbz+vw8KsXtxS/pLIakJBq5+eGi1RH2wWRrcr7zc8i1Phg4es6ANrcnp5zcKaaQTJ9EqgVbl7sbWX+kJPOizNk8NxZ9gdGuLTTKh/valUejlff7RceOHWPYsGE0aNCAjz/+WG13dHRk1qxZpRiZEEIIIe42N5zQt27d+qqJyG+//fafAhLiVtmxYwctWrQgIiKChx5qSNOmxwBITrEWw6tbty5dOq9Ho7HvPRxz3JrsF+uLqHNwF5V3bMYIVHf2xEWrw//pp9D5+MCe7+DMLhRF4UBCAZnGNzE45VFMJh7OOjbWOEqtgFoMqzNMknlRZphNFpJOZHNwc4Jde0RNPyrVC8LDV4aMX1RcXMxHH33ExIkTMRgMbNu2jX79+nHfffeVdmhCCCGEuEvdcEJfr149u22j0cj+/fs5fPgwTzzxxM2KS4ib6uuvv2bYsGE0adKEKlXK0bSpdW5vdnY2ZrOZrl2GUafOuBKJ9nMx8QBYzGacTsQStf8vLEANFy8CHJwJmTAex+BgUBQ4tgGTReFwYg7ZxrcxYE3mvVwdWFF3F681fo1Ir8jbfOdC3BqGIhP/rI8n63xBiX3tn6qFk+u/HgBWJu3cuZOhQ4cSGxurtpUvX56CgpI/PyGEEEKI63XDn7g+++yzy7aPHz+e/Pz8/xyQEDeboihMnDiRxx9/nICAfOrXv5DM52Sz9fd0Joz/nbp165U47+I68xaLmZTjR+m3cQUWYzEVnNwJcHCm3Befo3FysibzP/VBURQOn8vhvOVNdJjUZH5ZvR2MbDBSknlRZphNFjZ9a7+OfGgVH8Kq+hBS0buUorozZWdn8+abb9oNpdfpdIwaNYpx48bhdnFFDCGEEEKIf+GmdaEMGDCAxo0bM2XKlJt1SSH+M6PRSMuWLWnSpAmRFTKpVtW6fnxGZgZ/7Mris0/3UalSJbtziswWhsbEg6JgOHuW9Mw0Bm9eioPFghYNEU5uhIx7x5rMA5zYTGZBMWczC1EUB4rN59BpNXg7O7C03g4G1hpIlG/Ubb5zIW4+RVHIOFfAX6ts1evLVfejVoswHJx0pRjZnUdRFJYuXcqLL75IcnKy2t6wYUPmzJlTYrSbEEIIIcS/cdMS+j/++AMXF5ebdTkh/jOj0UjXrp1p3NifevWs8+Uzs7IoKirkrz99WL06u8QQe0VRrMk8kHM2nvzMDJ7atAStouCo0dL5lbdwu2S+q5J9lqOrP8FgsuBYXJ2T3A9Y8HDWsa7mbh4s/yD3h95/u25ZiFvm/Mkc9m6It2vTOmip2zaidAK6wy1btoxevXqp2x4eHrz//vsMHz4cnU4efgghhBDi5rjhhL579+5224qicP78efbs2cPYsWNvWmBC/Ftms5no6Gjq1q1D794ZantqaipGk5Gpn6WTnm7rMVMUhf15RaxJzeZ4oR7FYiHr6BGKDEU8vXExGiCg0f10GPma3ev8GXsalzXDAHAv7ECirgJuLsU4aB04FnSOjzp9clvuV4hbyWQ088vswyXaQyp7SzJ/Fd26daNevXrs37+fRx55hGnTphERIT8vIYQQQtxcN5zQe3vbz4/UarVUq1aNd999lw4dOty0wIT4N/r27cvChQtp3rw50XVsSXtySjL//FPAsqU5HD5sS062Z+YxOzFN3TZmZJB69jSAmsx3fPlNfBs0VI/JzCti7vy5tMldCYoGr4LHSXTwADcjDmjYWG0fL7ezT/6FuNsYikzsWnaCwhyDXXu99uUJr+pbSlHduc6fP09oaKi67eDgwNdff82ZM2d47LHHZGULIYQQQtwSN5TQm81mnnzySaKjo/H1lQ904s5y8QNzxYoViY72IzQkEYCkpCTGjLEm9xaLBY1Gg95sYciFofUAKAqZhw4Seu4kTePjCMjLAo2GDqMn4Fu9pnrYwm2HqP7Hq7QBUMCr4HGKnDRYXBU0wNpaf/NKi9cIcA24PTctxC1QmFvMlnlHSrR3HBaNVqe9zBn3rvz8fMaNG8e0adPYtWsXDRo0UPc1aNDAblsIIYQQ4ma7oYRep9PRoUMHjhw5Igm9uKN8/fXX6vcjXwrBy9OazKekprJuXS5gXQNao9GwOjWbJcmZtpMtFurO/YKw9CS1KbjOfbR+9U20Wttc10MbvqH6gcXqtltBL864adE4ugKwtN4O+tToQ6iHrZdOiLtN/KF0YradU7c9/V1p3KUiLh6OpRjVnWndunU8//zznDlzBoChQ4fy119/4eAgS/YJIYQQ4va44U8dtWvX5tSpU1SsWPFWxCPEDVMUhSFDhlC5shOjR9cGrEOECwoL2Lolmx9+OEjlypUvDK9PsDu3vMZCrSlvAYra1m7sewRVq2F3nP7YFsyXJPMV/DuyX+eOBjjnncEfFY/wXL3nqBVQ61bdphC33N5fz3D+eLa6HVHTnzqty5VeQHeo5ORkXnrpJRYtWqS2OTs70717dxRFucqZQgghhBA31w0n9BMnTuTVV1/lvffeo0GDBri7u9vt9/LyumnBCXEt586do2LFCN6bGExwcLDdvmpVP+T59Y+wOjWb8RfWlL/UmHJ+7H15GMolyXzfeUvs57oqCgmHtpGx/gO1ybnrxxxcHweYAThVO5Op90/FQSu9cuLuVZhbbJfMN36kEoERnqUX0B3IYrHwzTff8Prrr5Odna22t2nThpkzZxIVJctTCiGEEOL2uu4M5N1332XUqFF06tQJgEceecQu8VEUBY1Gg9lsvvlRCnEZFouF9u0r897EcIICg9T22NggwsMfotfALjxx6HSJ854vH0RjT1cWPdFbbfNxdqX99K/t3tMHjsSRtm4SASZbcb0FYU1osH0XwYoPAIdDzzDmgTFS8ErctUxGM3+uOEVOWqHa1npgDdy8nEoxqjvPkSNHGDp0KDt27FDb/P39+eSTTxg0aJD8DhBCCCFEqbjuhH7ChAkMGzaMLVu23Mp4hLguOTk5+Pj48P6kUDWZtygaNm2swurVa0jKXsOw2DN253QM9KZviB8APw18XG13CAqm06df2R37yuL9DDr9BhdL2xVovThZvg5aj1yCU3zU457v95p8kBd3pfwsA7//FAf/N0TcK8BVkvn/oygKTz31FH/++afaNnDgQD755BMCAwNLMTIhhBBC3OuuO6G/OC+wVatWtywYIa5Hbm4uPj4+PPe8v5rMnz/vybffJnLy5CIURWHgJUPsG3q5M7KCdTh+7NZN7P96pt31ev1fMr/4jxMMOv2Gum2IaE5E58H8tPtDeu5vDkAln8qED7gPrUYqfou7h6IonDmcwbG/UzDqTXb7XDwcadW3Gg5Ouiucfe/SaDRMmzaNxo0bU7FiRWbOnEm7du1KOywhhBBCiBubQy89kaK0JSYmEhERwbhxwbi5O6HTWZOPAweCOHnydxRFYdqZFPV4D51WTebj9/zFgbnf2F2vx6y5dtuLfvqWamdtha6iI3w506k/H//9EV1i7gfAz9Ufr/AAdC4yZ17cPZJP5/DPuvgS7X7hHjTqXAEHR0nkL8rMzCQ9PZ2qVauqbQ0aNGD16tW0adMGV1fXUoxOCCGEEMLmhjKSqlWrXjOpz8zMvOp+If6tgoICIiIi6NLFE28fd3y8fQDYtKkK69ato8BosuuZB5hRqwIAfy1fxMnlS9T2Bm6+VJs9R902mi288+1qemfakvnKEWEo3afy6c7R9Dxg7ZkPdAsi2C0Yn46yyoO4eySdyGbfL/ZTUMKq+lKrRRhO8mBKpSgKCxcu5KWXXiIsLIzdu3fbLUHXuXPnUoxOCCGEEKKkG/okN2HCBLy9vW9VLEJclYeHB7VrO9OmbaCazG/cGMWOHbtIT0/nmf+bMz+2chhgLZ53aTJ/n7sfUZ9NtTv21bm/8WTmDHW70qAZfHf+Z47tHI2z0br+dphHOH4ufvj2lErW4u4QfyidlNO5pCfkqW31H65AaGX5Pf7/Tp8+zXPPPccvv/wCQGpqKl988QWvvPJKKUcmhBBCCHFlN5TQ9+nTh6CgoGsfKMRNpCgK8+bNw8tLS99+fmoyv2dPOfbs2cvZs2d5Nibe7pwfoiui0WhI/mc3v332odpez9WHGrNm2x279I+jPJn+MQBaDUT3eIsXYj+98OLQ9fD9eDh54OfiR8BgWWde3PmMxWZ+XxCHocBo196wc0WCK8jSopcymUx89tlnjBs3jqKiIrX9scceo3fv3lc5UwghhBCi9F13Qi/z58XtpigKWq216JyXl5bJH1TD1cU6d3Xf/jBWrfqHxMREppxOptBsUc/7rrZ1OHz66lX8tnie2l7O0ZWac76+9AVQTmymyraP1aYaHZ/hraQN6nan2MZU86uOo9YRp3Iet+Q+hbhZFEXhn/XxpJzOtWtv1KUi3kFuOLvK8PpL7d69m6FDh7J//361LTw8nGnTptGtW7dSi0sIIYQQ4nrdcJV7IW6XGjVqAKDVwtSp9dT2c0le/LMnkz179rA3p4ADebb1s+dGV8SsL2Lnm69xLsNWHK9CxSiavjfZ/gVWjSAx8SwAZhTOBcPXqb+ruyumh9DIqz4arA+zvNpF3uxbFOKm0BcYOX0gnVP7Ukvsa/9ULZwkkbeTl5fH2LFj+fLLL7FYrA8DNRoNI0aMYOLEiXh5ySgGIYQQQtwdrvtT3sUPPULcLnFxcQA83us+tS0tzZ0pH+8jOjqaYm9fPjt+Tt33afUIFJOJNVPex3BJMh9Srz5NRr1lu/A/38PJzZiNejIKigH4zM8bn4gL00kU6B7bgmivWmoy79+/xq26TSH+tfwsPaf2p5EQW7IYabunakmP/BWcPHmSadOmqX/XoqOjmTNnDvfff38pRyaEEEIIcWPk056443z33Xc89dRTODpqqF3bmYc62KZ7vPHGdgYOHMgPP/xgV9F+XJUwNMnnWD3vWwwXHgQANH/lDcrXb2S7+M4v4MxO0vIMnMsuohiFSUFNcfU/BxeS9ye1vYj08ldPcb8/BI2jrDcv7hzxh9Ivu5a8p58LoVE+VKoXiM5B3rNXUq9ePV566SW++uorxo8fzyuvvIKjo2NphyWEEEIIccMkoRd3lBdeeIFp06YxYoQ/ISGOhIWFqfsqVeqHxbKHLZl5dsl8O38v/HMz2frT9+iPxALgqXWgUaNmhFxM5rPPwrrXADiWkkdhsZmvfe/joNMZXJ1PU87dE4BxTcahWWwbtuw/sAYanSRGovSlJ+Zzcm8q2amFmAxmu33+5Tyo2igEvzD3UoruzmWxWPjpp5/o1auXXdI+YcIEnnvuOSpXrlyK0QkhhBBC/DeS0ItSd/ToUQYOHMiePXsACAzUERziQGhoKABGk5aTJ/xp2Xs4gw6dLnH+oDB/Vn44gaLDhwGo4OROeXcvQl54wXbQutfIKTJyOr0AgJU+gzjgtJ5qIT64OOoAeLPRG2iXpXGxWoRLdT9J5kWpUhSFxLgsDm5OsN+h0eDl70JIZW8ia/vLWvJXEBMTw9ChQ9m1axfnzp3j9ddfV/e5u7tLMi+EEEKIu558ChSlpqCggPr163Ps2DG79hdHBuDp6YlGo2HLlkoYjQ48/+JIxp9IsjvusWBf2rnqWPnRu2oyH+HoRoSTG2EffWQ78NBSDiRkowAZDsH86D+c43xJ7XBvdFoNr0WPwvtPC6bluVxa+tHjgdBbdOdCXJ3JaGbHkhMUZOlL7KvbNoKgCl6SxF+FXq9n4sSJfPTRRxiN1qX7JkyYwFNPPUVAQEApRyeEEEIIcfPIJ0Jx2ymKQkREBOfOnbNrf+ppXypWdMLb2xsPd+sScc5V6lGuR19mXzLCuJG3OyMiAoj7Ywebtm5Cf+SIuq+Cszvlpn+FRqsFiwXl71kc27VGTdS/8++P2fdr6nr4AOCqccF9XSH2M5GlCJ64/bKSC0hPzOdsbCb6vGK7fVoHLTWbhVG+lp8sIXoNv/32G88++ywnTpxQ26Kiopg1a5Yk80IIIYQoc+6IhP6rr77i448/Jjk5mbp16/Lll1/SuHHja563cOFC+vbty6OPPsrKlStvfaDiP/vrr7944IEH7Npq1nSmRUt3ypVzRIMGD3cP9rnfzz6XxwiqHoLefrowz4X4sOrjiehjYlAuVKn20jpSv3xlwiZOtB346xgSThyiyGi9wBd+1fEutxqNxhkAT50Ho+IHqofrvJ3wah+JzsPpFty5EJenKAqHtp4jITajxD4nVweadq+Cu49zKUR2d8nIyODVV19l7ty5apujoyNvvvkmb7/9Ni4uLqUXnBBCCCHELVLqCf2iRYt45ZVXmDlzJvfffz9Tp07loYceIi4ujqCgoCueFx8fz6uvvkqLFi1uY7Ti31IUheXLl9OzZ08AdDoYPyHY7hidTkd81QH84tgBBwcnwoOC1CJW/UP9eSjAC41Gw7Eliyg6dAiw1qWv7eJNcI2aBI4cab2QIY8D895AyThpfW0U3gtsTEC5TDQa65z4OoF1ePSfBy4WtgfA97GoW/gTEKKkorxifvvhiF1bWJQvZrOFms3CcPOSh0vXY8GCBbz88sukp6erbc2aNWP27NnUrFmzFCMTQgghhLi1Sj2h//TTTxkyZAhPPvkkADNnzmTt2rV8++23vPnmm5c9x2w2079/fyZMmMD27dvJzs6+jRGLG5WTk4OPj4+63a6dB60eLFmNe0uD6ZhMPjigxcnJCUdHR8ZWDqOqu61n7cjsGezbtlndbu4RSPgXn6N1siY+SaePkrp4pN11vwh4HN+wvTjprMXvJjSdgHZDJmaNbVhzwOBaN+VehbheWckF7Fp2wq6tRe+qeAW4llJEd6+DBw+qyby3tzcffvghQ4YMQauVopZCCCGEKNtKNaEvLi7mn3/+4a233lLbtFot7dq1448//rjiee+++y5BQUE8/fTTbN++/aqvYTAYMBgM6nZubi5gXcrIcmG4trh1LBaLmsxXruzE4Cd9SxxTo8dvfJ9mwJSfr7a9Xrc6rXw90Gg0mPR6TCkpHHtvAof1ueox4Y0eIPyFV9TX2RKXiv9qWzJ/ximKSl1G4nZmKqBFURTebfouzoeLKcqxvSd8+1Qrc+8Fi8WCoihl7r7KirSzeez+OV7dDijvQeMuFQHk3+wGXHyfjxkzhqVLl9KwYUM+++wzdYUM+VmKskB+n4t7gbzPxb3gVr2/SzWhT09Px2w2ExxsP/Q6ODiYo0ePXvacHTt28M0337B///7reo3JkyczYcKEEu1paWkUFxdf5gxxM6SnpxMdHa1uN23qRsdOnnbHrFhhpPKMHWw6mWrX/kGgGxVMRaSlFaFftJiifXtJNReTYLJV/K7e/XHKtWhNaqr13MPnCzi+dT73X/iPUhTZmvvbPMWPJ39U/51r+tREv+48+gzbv7v24WDSs0vOXb7bWSwWcnJyUBRFeinvIDkpeuL3ZZOfaXsPVm3iT2BFd/W9LK7un3/+4cSJE/Tu3dvufb527Vp8fa0PDOVnKcoS+X0u7gXyPhf3gpycnFty3VIfcn8j8vLyGDhwIHPmzLnuasVvvfUWr7zyirqdm5tLREQEgYGBdsPAxc1RXFyMq6ttyHCVKk48Mdi+V/7nn/MIC+9IjW8+xGAwqL+4W+enM7xtK/XfJWvfXvbv/ZNMszX50Tro0Lq4UOXRHtTv9Ih6vR92xVNv5/MEAGi1lPdzxafPm+xL3cepolM4XRiO/2KjF8j88Sg4W7c9W5XDKcLrFv0kSpfFYkGj0RAYGCh/GO8QBTkG9mxPATQ4O1mL3FVpGETVxsFXP1EA1t/do0ePZsaMGTg7O9OxY0cqVaqkvs9DQkJKO0Qhbgn5fS7uBfI+F/eCiznJzVaqCX1AQAA6nY6UlBS79pSUlMt+ODt58iTx8fF07dpVbbs4dMHBwYG4uDgqV65sd46zszPOziUrRGu1WvmFcQu4ubmp34eHO5RI5ud+l8WJEwa+PJPC78npZGZmAvB0VgL9+vYFoPjUKeK/msY/qQnqec5VquAREkrlhg9QuUFjdekuxZBPvZ3PqcdVCnDH69EPQavlu5jv1OPeafIOluxiNBeq4Pn1qYa2jK/jrdFo5H1eykxGM+mJ+RTlFhO7I4mLK875hblTqV4QwRXL5gOlm23FihW88MIL6lKXer2eL774gi+++ELe5+KeIO9zcS+Q97ko627Ve7tUMxonJycaNGjA5s2b6datG2BN0Ddv3syIESNKHF+9enUOXahuftGYMWPIy8vj888/JyIi4naELa6gefPmKIqCi4uGps3cad3aVvju+PFili3NITOziDkJafx2LkUddvJMVgJNmjQBReHcp5+w+8AeihTbWnUhNWvT+LkX8fD1s3s9Q146J2fZlp2rWc4fp/4LMFqMvLzZ9v5pEtaEAAd/MtfapnFonHU3/f6FuMhitvDXmtNknssvsa9y/SCqNwkthajuPomJibzwwgt2y5K6ubnx7rvvMnLkyCufKIQQQghxjyj1LspXXnmFJ554goYNG9K4cWOmTp1KQUGBWvV+0KBBhIeHM3nyZFxcXKhdu7bd+ReHZ/9/u7i9qlSpwrlzpxg0yJeoqvbDSf75x8L0r2L4+tsQnjp8muzsbAoKCgDolZNE69atCQ0N5dybb7Et8bh6nkaro3bP3kQ/0t3+xc7swrR9KkfO2eah5PrUQNf3Ez78+0MS8hLsDu9foz+Zi+LUbbf7AtWeeyFuNkORiU3fxti1abQa/MM98AtzJ6qhDLG/FrPZzIwZM3j77bfJy8tT2zt27Mj06dOpUKECIEXvhBBCCCFKPaHv3bs3aWlpvPPOOyQnJ1OvXj02bNigFso7e/asDL25wxUWFhIff7LEuvL5+RYGP/EdY0Z3BmDR+Qx1yCxA17wU+nZ8GG9vb1JXreL3S5J51xo1eOiFV3Hz8rZdMCUWNlsLHB6+JJk/4VyLrk9/zMgtJXvsPnnwEwyncrAUmQDQuOhwqxv0329aiCu4NJl3dnfkwX7VcHCSESHXKyEhgccff5y//vpLbQsODubzzz+nV69e8jBOCCGEEOISpZ7QA4wYMeKyQ+wBtm7detVz586de/MDEtdt7NixTJw4kfcm2pL5tFQT8+Zl89BDj1O9emeyjCZePHLWLpl/OiuBNq1b4+3tTfzBfexaMk/d5xodTbfX37H/4H58E+yeA0B6vnXJub1uzfnLvQ2vPhbEqN/tk/nxTccT4BpAcWIeudsS1Xa/x6vd1PsX4lJmk32PcbvBNUspkrtXQEAAaWlp6vaQIUP48MMP1Qr2QgghhBDC5o5I6MXdycPDg+LiArtkHuCLLzI4fvw4VapUwWhRePHIWYxGo7q/TX4G/fr2JSc1hRUfTsCYaEu4K7RtT9Mnn7V/IbMJds/BbFGIS85jl0sL/ghuTz4nCYhYwOd77XvspraeioPWAXOBkdxNZ9V298YhaHTSuyduPkVR2PRdLMUXRoIAPDREpgH9G66ursycOZORI0cya9YsWrRoUdohCSGEEELcsSShF/+KVqtFUZQSyfy7E6zrP1epUgWAqWeSMZlMpKamEmgqtg6z792bHQt/IP1sPMbkZExZWQDUdvGmzqXJvKLAwn6gWCg2WYg9n8s6776ccKnJcT6nUqA7GhzVwx+r8hhtyrdRe/ZzN51R93k0C8MlSnr4xM2Vn2Vg57LjmAxmu3afYDcZZn8d0tLSeP3113nrrbeoWrWq2t6+fXsOHjyIg4P8iRJCCCGEuBr5tCRuSHZ2tjr0tVw5R7t9Y8dYlx+MibHOIS42m9l65hxFRUUAPJqXQnR0NIaCfNLPxmMpKsKUlka4oyuVnD0IeWes/Yv91AeAuOQ8iozWhOmES3XOOX9FnUBvNXHvHtWdNuXbqKcpZgsZPx4Fi6K2STIvbgZFUchNLyLzfCGn9qehzysucUyHZ2rjKKsoXJWiKHz//feMGjWKzMxMzpw5w+bNm+2m2UgyL4QQQghxbfKJSVy3gwcPUrduXRwdNbwzzr6w3NgxKWzdupWWLVui0Wg4fuIEzx4/r+5/oDCLsLAwataozupPJgFgOHGCB9z9cdRoCXr9dRzDwmwX3PohiqJwINFa/G5a0LsoGi15XnOI8vZUD5vWdlqJOAv2pNgl877do27K/Yt7W+LRTA5sTrjsPp9gN+o/HImrh9Nl9wubY8eOMWzYMLZs2aK27d+/n5MnT6oje4QQQgghxPWRhF5cl8OHD1O3bl18fLSMejVQbTeb4c8/Czl37hxhFxLy2NPxjLgkmQcY0aQhHo4OajJvSksHwFGjxb1pU5wrVbQdfHobJO3leKp1De8vg96jUHMOz7CNBOtsCdOnD35aIs6i2Az0RzLVbf+BNdDoZJUE8e8V601s/CamRLunvyu+IW5UbxqKowyvv6bi4mI++ugjJk6ciMFgUNv79u3LZ599pq5sIoQQQgghrp8k9OKajh49SnR0NM2aufFwR1vveEqyiWnTMkhLSyMgIACLxUJ8fDwj4mzV7AMDA1nYoBpZ58+x+dsZABQdOoQjWlp4WB8M+A4cYD3YVAyLBwKQnKunsNjM1wEvc0zzBXUivNFgS8y/bPNlieWr9CezKfg7Wd32bB0hybz41xSLwpmYDGK2nbNrr9EsjEr1Aq9wlricnTt3MnToUGJjY9W2ChUqMGPGDB5++OFSjEwIIYQQ4u4mCb24oqKiInr06MH69evx89PZJfNxcQbmz8vGYrGg0WgwGAwsW7aMpV4hoLPOrQ/z9WF+w+oUFxXy+7xvALAUFVHe0Y1IZ3cA/AYNRAOwcRykHQVgf0I2AFs9u3JAN5/a4d5ouPx8+YtyfonHeL5A3fZqH4lTuMfN/pGIMi43o4i9G85QkG0osS8w0otGnSvIOug3aOLEiYwda6uPodPpePnllxk/fjzu7u6lGJkQQgghxN1PEnpRgrOzM8XFtmJfAQE6Rr4UoG5P/yqDChUaoSi7AGuBq0XLljHXN0I939PTk7n1o/h19pcUZFmHwBefPYtTbj6R7n4AhE/9DK3GqBa/A9sa858GPctpzXIqBrqj01oTqMv1yl90aTLv2SZCknlx3RSLQvzhDE7uTcVQYCyx38FZR83mYURU9yuF6O5+TZo0Ub9v2LAhc+bMoV69eqUXkBBCCCFEGSIJvVBdWsH+Ig8PrV0y/9efhfj5VWPXLmsyn5+fz7hft7LvQjIPEBAQwJxq4ayZ8j4A5qwsihMT0aKhvrs/AF5duqA1F8Cq59Xz8vRGxjuM5EjwcopZfmGY/bWT+fy/bPP1fR+rgs7b+b/8GMQ9wGK2EH8og+N7UkosOQcQWsWHinUD8A50RSvTNm6Ioih2/1fbtm3L8OHDiYqKYsSIEeh0Um9ACCGEEOJmkYReANYP4f+fzDs6anjjTdtc4dgYAy1bjmXNmtcAyMvLY+b6X9nnaat4HxYWxrw6lfht7izAOl9eh4bm7gHqh/ywKVPQOSqw7Gn1vFO58IHzq6RodlNMFhUD3dGgwdvZm1ENR10xmc/7PRHD6Rx1W+slVcbF5ekLjCSfzCHhaCa5aUWXPSaytj9VG4fg5Cq/Gm+U2Wzmyy+/ZPPmzaxevdru/+y0aSVXoxBCCCGEEP+dfGoVAEyaZK0+7+AAgYEOtGnrQfXqtp7u48eK+fzzYwQG2hL8o0fjWH8hmXdxcWHmA3Uo7+rMth+/IyclmaJDhwBo6mHt4ffq0gXvLp2tJy/orV7nlGtt3i+2FsbKZDfBXi54uTjyeevP0Wmv3Junj8u0S+Z9u0fJ/GZRQnZqITuXHL/sPhcPR4IrelPt/hBZO/4/2LdvH0OHDmXPnj0AzJ07lyeffLKUoxJCCCGEKPskoRecPHmSyZPH8uwwP8qVc7Tb17p1a8qVa0/5iCftkuVDhw7x9bl0cHbHwcGBITWrEO6o5Y+lP5GReBZLobUHtJ6rDwDlvpqG5uJQ21O/q9cxmS28n2NN5k8zl6rBnrg66Xi2zrNXTeYVRSH/j0uG2nevgk5658UlMs8X8MfyEyXagyp4oXPQUqVBEF4BrqUQWdlRUFDAuHHjmDp1KmazberCsWPHSjEqIUqfoiiYTCa7/xf/lsViwWg0otfr0WplCpAom+R9LsoSR0fH2zrFUBL6e1xqaioPP1yTN98KKrGvd68XqVDheRwdfdQ2k8nE4sWLKdJoOe4TDlh75x8L9mXFhxMAsOTnYzh9mibu/jhotJT74nNrMq8osGgAWEwAJOfoecflDdBADjFEBplxdbK+JWsH1L5q3Hmbz6rfuz8Qis5L5s0Lq/hD6SWWmgOo0jCYqo2DZRTHTbJ+/Xqee+45zpw5o7bVqlWL2bNn07Rp01KMTIjSVVxczPnz5yksLLwp11MUBYvFQl5envz+EmWWvM9FWaLRaChXrhweHrenSLck9Pew6dOnM3z4cN6bGKy2HTqoZ8WKHL76ajZRUUPsjjebzSxavJg1nsGkOlh7w319ffkougrn4o4AYNHrMZw+TUM3Pxw0WpyrVUPjdKHnPPWImsyn5xv4UjcIs8Y6IsDiuR13Z2tv6fvN37/iL3NzgZGsJfa9f65SfVxckHQ8q0QyX7lBENUfCC2liMqelJQUXnrpJRYuXKi2OTs7M3bsWF577TWcnGSkjLh3WSwWTp8+jU6nIywsDCcnp/+cnFzs7XdwcJBER5RZ8j4XZYWiKKSlpZGYmEhUVNRt6amXhP4edfGXZdNmbmrb3r1FrFieS48ePRgyZEiJc5b8spFvLlSz12g0hIWF8YCPB84JJ/lr+SKKz57Fkp9PU/cAdBoN3o8+glfHjtae+eVDwZCL2aIQk1LE5/7vAGBGj1PYT4TprMl8z6o98Xb2vmLcOWtO2m37PFL5v/0gRJlRXGRi36+2kRvNe1XFO1CG1N9MaWlp1KhRg6ysLLWtdevWzJo1i6ioqFKMTIg7Q3FxMRaLhYiICNzc3K59wnWQREfcC+R9LsqSwMBA4uPjMRqNtyWhl0kq96B169YB4O2tpWNHT7X95zV5lCtXjqVLl5Y45+ixY8zWuKvbYWFhzKwVSffiLP74egb62Fgs+flEOLqh02jw7d/fmswDFKSDIReLonDoXA4/eT0FgIKCEjgfpwvLggW7BdOqXKsrxm04k4tFb5uP6D+wJg5+Lv/+ByHKjGO7k9n4bYy63aBjBUnmb4HAwEC6desGgJ+fH3PnzmXz5s2SzAvxf2QOsBBC3Ltu90Mp6aG/x+Tk5NC5c2dcXDS8+pqtYv2SxTkYjQoJCQklzlnx60a+NNsS54phoUyvEsz2ubNI37ULi0EPQGUnD8r5BxHyzlh0nhceFOQmwc8vo6BwMDGHL4PeA42GAs7gFfYbbjrrkHtXB1fGPDDmsv8BFEUh99czGM8XqG1+vaqi0ckT3Hud0WDm168P27UFV/QipNKVR3mI62cwGHB0dLRLTj7++GNcXFyYMGGC3aoXQgghhBDi9pNHyPcQvV6Pj48PDg4weoytCN623wt4//0lKIpS4pzExES7ZN7fz49+ezez7sspZO7frybzVZw9qPPyKMI//siWzGcnwM8vY7YoHEjIoUjrDhoN2RwkIGKr2jPv5eTFBy0+uOLTrKxFcXbJvFfb8mjdHC97rLh3JMZllUjmm/aoQsNOFUsporJl27Zt1KtXj++++86u3d/fn+nTp0syL4S4qsGDBzNx4kQAtm/fTt26dW/J6yxbtuyy0wQFbN68mZ49e5Z2GEKIW0wS+nuIq6sr9eq5MG58sF27m1tbunTpUuJ4i8XC0IO2Zb/qVShPv61LyU44S9GhQ5iyMnFES2M3P+p/OAXX6GjbySc2wbpXOZiYzaFzOex1a8GMwBEc43NCyu1VD+tbvS+TWky64hJ1FoPZbpi9T7fKOEV4XvZYcW8oyDGw9qsDHNh0yUoHPs50eq4OviHuVzlTXI+srCyGDBlCq1atOHr0KK+++iopKSmlHZYQ4j+qUKECbm5ueHh44O/vT69evezqYdxKLVq04MCBA7fk2hMmTGDUqFF2bYmJieh0Ol555RW79q1bt1KlSpUS16hQoQI7duxQt7dt20br1q3x8PAgODiYDh06sH379huKq6ioiAEDBuDp6Un58uX56aefrnhsx44d8fDwUL8cHR3p2rWruj8vL49nnnkGPz8/fHx8GDRokLqvVq1adufqdDpeeOEFANq2bcvJkyc5ePDgDcUuhLi7yJD7e8TRo0cB6NHTNhQ5Pd3M51PTsVhWlDh+7959vJWci1FjTbS9PD1pveEnTEDxmXgAGrv54azVETz6bRwCAmwnx6yAAwuJPZ+LRYEDbg+w0bMWiXxL3Qgf9bBRDUdR0fvqvakFf16y1nzPKHQeUkH7XmQ2W0iKy+bw9nNYTBa7ffUfrkBoZRli/18pisKiRYt46aWX7BL4atWqkZeXR3Bw8FXOFkL8P4vFQkZGxn++zvUWC/P397/m3P1ff/2V5s2bU1BQQP/+/Xnvvff49NNP/3OMpeXvv//GwcGB6tWr27UvWLAAb29vFi5cyMcff3xDRam2bt1K586dmTx5MitWrMDT05NNmzbx888/06JFi+u+zrhx40hPT+fcuXPExsbSsWNH6tevT7Vq1Uocu379ervtGjVq0KNHD3X7qaeeonz58sTHx+Pq6srhw7bRaTExtvoxBoOBkJAQu3P79OnDN998w+eff37dsQsh7i7SQ38POHbsGDVq1KBXL1vSM++HLD6fmo6iKCU+IOTk5DAmOQejxvr20Gg0DN63CWNRIUWHDqEYDDR3D8DFzZ1y07/CKSLCdrLFAgcWsj8hm2KThQV+I1jtGYlP+CY1mfd08uTDlh9eNZk35xWTPjcGw+kctU2S+XtTQY6BDTMPcXBLgl0y7xngSqfn60gyfxPEx8fTuXNn+vbtqybznp6efPnll+zcufOyPVpCiKvLyMggKCjoP38FBwcTHh5OcHDwVY+7kYcH7u7uPPLIIxw5ckRtmzRpEpGRkXh5edGkSRO7Xt3333+f0NBQvLy8iI6OJjY2FoDMzEz69etHUFAQlSpV4vvvv7/s6/1/z7hGo2HGjBlUrFiRgIAAJk+erO4zm82MGzeOyMhIgoODGTVqFCaT6bLX3bBhw2WT7Pnz5zNx4kT0ej2bN2++7p8LwFtvvcWQIUN48cUX8fHxQafT8dBDD/Hhhx/e0HXmzZvHmDFj8PLy4oEHHuDRRx9lwYIF1zxvz549nD17Vk3KY2Nj+eeff/joo4/w8vLC0dGR++6777LnrlmzBi8vL1q1shUYbtmyJRs2bLih2IUQdxdJ6Ms4RVGoVq0aL77oT3Qd21z4Y8eKLztnHuDdzdvRX+iZDw4K4gNLBkphAfoLf8Abu/mh0WgIGvUKmkt7AzJPwcK+pOUZAFjpM5h0x2BCyv2DTmt9aPBQhYeY3GIy7o6XHxpt0ZtI/z6GrGXH7do9Hyz3734A4q5VmFvM7rWn2Tr/qF17RE0/2j9Vi5a9q8rSNv+RyWRiypQp1KpVy66H6LHHHiM2NpYRI0bcluVWhBC3V25uLitXruT+++9X26pXr86ePXvIyMigffv26rDuo0ePMnPmTPbt20dOTg5LlizBz88PgIEDBxIWFkZCQgLr1q3jrbfeuu7h3b/99huHDh1i69atTJgwgZMnrcvSfvrpp2zfvp09e/YQFxfH3r17mTlz5mWvcejQoRKrbBw8eJCjR4/Sp08fHnvsMebPn3/dP5eCggL++usvdTWPy1mwYAE+Pj6X/apTpw5gnbqUnJysbgNER0fb9aZfybx58+jWrRueF+oR7d69m6ioKJ544gn8/f154IEH2LVr1xXPHTBggN3fxurVq3Ps2DEMBsP1/AiEEHchGXJfxkVGRlK3nguBQbZ/6ulfXfkp/qbYOPa5Wns8HR0dmFHOi99++QfjuSQAWngEonF0JPyzT9E4XPL2ObAQYlagoHAuu4hijQsJzlUY3K6IxXHWPywj7htBdb/qJV7zoqLYDAr+TrZr03o44tutChoHefZUluVnGchNLyI9MZ/kUzkY9SV7Y0Iqe1P/oUhJ4m+i999/n/Hjx6vb4eHhTJs27aofZoUQd6+OHTui0+nIy8sjKiqKOXPmqPu6d++ufv/2228zceJE8vPzcXBwwGAwcOTIEQICAtTh7cnJyWzdupWVK1fi6OhI9erV6devH8uXL7dLZK/kzTffxMPDg9q1a1OnTh0OHTpE5cqV+eabb/j222/VwpujRo1iypQpjBgxosQ1srOz8fDwsGubP38+7dq1w8/Pjz59+tC9e3cKCwtxc3O7ZkxZWVkoikJISMgVj+nXrx/9+vW76nXy8/MB1KQcwMvLS22/EpPJxMKFC+1GOpw7d45ff/2Vr7/+mu+++45ly5bxyCOPcOLECXx8fNTjMjIyWL9+fYmRBBdjyMnJISgoCCFE2SNZUhl2+PBhEhIS6HnJvPmPPkzj/HkThYWFdscWFBSwfPlyJp1JVdua5STz27cz0cfGYs7JpraL9TrhX3xun8wfXGydNw8cT8lnt1tTxgTVIM9nNovjFquHXSmZVywKuVvO2iXzWndH/PpWx69nVUnmy7gt84/y+4Kj7Pv1DAmxGSWSea2DlpZ9q9Hg4QqSzN9kL7zwAkFBQWg0GkaMGEFsbKwk80KUYevXryc7O5vCwkK6detG586d1X1z5syhVq1aeHt7ExISgqIoZGRkUKVKFT755BPefvttgoODeeaZZ8jNzeXs2bPo9XoCAwPVHupZs2aRnJx8lQhsLq3L4ebmpia7Z8+epWPHjuo1+/fvT2pq6mWv4e3tbZckWywWfvrpJ3r37g1AmzZtcHNzY+XKlQA4ODhgNBpLXMdoNOLo6Iivry8ajea67+FKLj5kyMvLU9tyc3NLPHz4f7/88gtarZb27durba6urlSoUIGnn34aR0dH+vTpQ2hoKH/99ZfduQsXLqRevXol6glcjMHLy+s/3ZMQ4s4lPfRlWN260YwcaStWt2ljPnl5lssOtV+1ahUmAFfrdiN3J6IPHqTowvCwYAcXAkPD8X/2Wfuk6p/vIW4dAPEZBXzi2YdYh18BqOrpox42qqF9BdqLFLNCxrxYuzafrpVw8He9wbsVd5vLrSEfEOGJxWzBL9SdyOgAXNxlecKbKTExkXLlbNNX/Pz8mDt3Ln5+fnZDb4UQ/52/v/8VE9EbcSNF8a6Xs7MzAwcO5MMPPyQ9PZ38/Hxeeuklfv/9d+rXr4/BYMDd3V39vDBw4EAGDhxIeno6ffr04dNPP2XIkCF4eHiQlZV1Ux+2hoeHs2jRIurXr3/NY6Ojozl+3DZFb+vWrSQmJjJq1CjeeOMNwNqLP3/+fPr160dERAQpKSkYDAacnZ0BazX61NRUypcvj7u7O/fffz+rVq3iwQcfvOxr/vjjjzz77LOX3RcZGUlMTAy+vr6EhIRw6NAhmjVrBlg7WWrVqnXV+5k3bx79+vWzm+pUu3btEj/fy/28582bx8CBA0u0x8XFERUVhYuLS4l9QoiyQRL6Mmrw4MFMeNe+KvXvvxdcdvmp9PR09Bot833C0Wg0hAQFUW/zEsw5uQA84O6Pk7MLoe+9azupuBCWPqluJmQVMtt5ELEOqwGoEmx9Cv1ao9eI9Iq8YpxZy47ZbUsyX/YpikJ6Yj5/rz5l197uyZo4u0kCfyucP3+eF198kY0bN3LkyBFCQ0PVfR07dizFyIQou7RarTps/L+43oT+RhiNRhYsWEBQUBD+/v4kJyer8ZpMJsaNG6ceGxcXx/nz52natClubm44Ozuj0+kIDw+nSZMmjBkzhtGjR+Pk5MTBgwdxcXGhZs2a/zq2p556ijFjxvDNN98QEhLCmTNnOHPmjF2ht4sefvhhnn/+eXV7/vz59OjRg2nTpqltR48epX379qSmphIZGUm9evUYO3asOt1ozJgxNGnSRP29OGnSJLp06ULlypUZOHAgnp6ebNmyhY0bN/LBBx/Qv39/+vfvf837GDBgABMnTmTx4sUcOXKEVatW8ccff1zx+NzcXFavXl3imAcffBBFUfj+++8ZMGAAK1as4Pz583YPYY8fP87evXv5+eefS1x327ZtPPTQQ9eMVwhx95KxzGXQwIED+eefhXZtH3+URt++fUvMnzIYDKzZuJH5PuGAdQhc/smjOBXkUXz2DCEOLjhqtJT74pLlTs7+ZZfMFxnNfO/wOEecrHPzqwZ70r9mT6a1nXbVZN5iMGMptA2vDhhcS5L5Mi52ZxLrph+0S+YDI73o9HwdSeZvAYvFwsyZM6levTpLly4lJyeHl156qbTDEkKUkg4dOuDh4UFAQABbtmxh5cqVaDQaateuzbPPPkudOnWoUKECFStWxMnJurKMwWDgtddew9/fn/Lly+Pt7c3LL78MWHurExMTqVSpEkFBQbz00ksUFRX9pxhfe+01mjRpQrNmzfD29qZr164kJCRc9tj777+f4uJi4uLi0Ov1LFu2jBEjRhASEqJ+PfjggzRo0ICFC62fixYvXszx48epUKECFSpU4MyZM3ZrxLdu3Zp169axdOlSwsPDCQkJ4cMPP6RLly43dB/vvvsufn5+hIaGqg8ZLi5Zt3379hLD75cuXUqVKlWoW7euXbujoyOrVq1i2rRpeHt7M3nyZFauXGk3f37evHk8/PDDBFy6hPAFCxcu5Jlnnrmh2IUQdxeNcqVS52VUbm4u3t7eZGVl2f0yLEs8PLS8+ZYtcR87xtorf7l/6qVLlzLd3dqT7+vri2P8MXqd2Iv+6FEc0NDI3Q/fhzvi81g36wlbP4Ckfer5Zu/yDE19HIvGzAm+ola4Fw5aLdPaTivxWv8vfa6t2qv/gBoyV/4mslgspKamEhQUdM11iW+H/Cw9vy+IK9FeoU4AtVqEl0JEZV9MTAxDhw61q4YcEBDAZ599Rv/+/ctEPYI77X0uhF6v5/Tp01SsWPGmDXG+FT30ZcnSpUv59ddfmT17dmmHcsfZvHkzM2bMYOnSpaUdyjXJ+1yUJVf6W5CdnY2vry85OTk3ta6FDLkvYzQaDa++Zhvit3attRjK4sWLSxwbl5ahJvMAbf08CFz+O/qkcwA08QjAu0d3vC4WZ1nzEuSdB0BBYYXzY6xNqwIaOMFXeLs64qDVcn/otefiFu6zn1coyXzZVJhbzF+rT1GYY79czoMDquPu7VxKUZVter2e999/nw8//NCu+NPgwYOZMmXKDc2zFUKIO13Pnj3p2bNnaYdxR2rbti1t27Yt7TCEELeYJPRlyNtvv8V7E+3nzf/5RyE7d+6kadOmdu0fx5xk/dkkdTs0NIRqv60i5UIy39w9AM/27a3JvMkAiwfZnf9a4RNkFQdRSCKJLMPDxYHIADcqeVdiYM2SRVkulbv5LMUJtsqv/oP+/Vw7cWfbMv8oXDIypHL9IKo9ECJP32+RLVu28Oyzz9oViapSpQqzZs2iTZs2pRiZEEIIIYS4FSShLyOKi4vZtetL2rW3zcma+J61F/zSZN5sNjNp6y62FNvODVTMjDhzgPg/bENzwya9j8PFnrz/S+Y/C5jA0aSVGMmjkDNUDHTH08UBX2dfXmn4ylXjtBQa7ZJ574croNFKclfWJJ3IZt8vZ9RtNy8nmjxWBRcPmSd/qxQVFdGvXz91uSUHBwfeeOMNRo8ejaur1KYQQgghhCiLJKEvIz7//HO7ZP7ivPkzZ2xJlcVi4d0VP7Pd3U9tezvIg3YN7mPFhxNQzGYAol28rcm8osAy+0IqOxp8zqY/viGXIwDUjfABoEdUD1qXb33NOI2pher3vj2j0Hk43eCdijtVTloRu5afwGKylNj34IDq0it/i7m6ujJ16lT69OlD06ZNmT179jWXSBJCCCGEEHc3SejLgPj4eL6bO5Z+/XwAOH7c2v2+YcMGypcvrx63cOFC/vaxFSAbX7cq0RoTKz6cgDHJOje+kZsfEReq17JlEhTnq8crvefz7bx9ajJfPcSTHlE9qOJbhQjPiGvGaSk2k7c1Ud2WZL5sUBSFmO1JnDmUXmJf7VblKF/LT5L5W+DkyZO4ubnZLUHXq1cv3Nzc6Ny5sxSJE0IIIYS4B0hCf5ebN28ew4cP5vU3bIXwfvg+C8Bu3dGUlBQ2uftj0Fg/5L9YvzYtgnxYMXEs+rijALhpdbhodbhEBsOC3vYv1OsHPt18iiz+AcDP3YkprT/A29n7umPNXHBU/d6lmu+N3ai4I1ksCutnHLRr8wtzp1778ri4O0oifwsYjUY+/fRTxo8fT+fOne2qF2s0Grp27VqK0QkhhBBCiNtJEvq72MVkqW4923IImzZae9T/+OMPu2MX/7mbeCdr8h0WFkYnHzcWjxiCOSfb2uboSmVnD4JfHwUrn7N/oW4zOJSsJzYpl3R2AlDOz/WGknlzfrHdtkeTsOs+V9yZTvyTStyf5+3aWvathqffzVmqSZT0119/MXToUA4etD5EWbZsGevWraNTp06lHJkQQgghhCgNktDfhQoKCvDwsM2X79nTmlinp5v5/fcCgoKCeOCBB9T9L69YywEX6zEuLi58ViGI/a+MVJN5DVD7/uYEPDsUfupjeyH3QOgylUPnC5i66RiJLAegeqgn7cq3u+54jamF5Kw7rW779al2o7cs7hAWi8I/6+NJjc+1a3fzcaZ1/+qlFFXZl5uby+jRo/nqq69QLqwaoNVqefHFF2nZsmUpRyeEEEIIIUqLTLK8y+Tm5qrJfGCgzm6ZuthYPW3btiUlJcXWduo0B1y81O0XK4Wjf+N1UvKyAdA4OdHzk+kEDHsWdnxm/2KPfImi1TF10zEsFFNIAmE+rjg76Hi44sPXjFVRFPJ2nLNL5p0ivdC6yHOku5GiKGz6NqZEMt+qX3VJ5m+hlStXUrNmTaZNm6Ym8/Xq1eOvv/7is88+s3u4J4QQpSk+Ph4Hhzvnb/wLL7zAjz/+WNph3JEGDhzIzz//XNphCCFuAkno7zKPPfYYAI886sWLIwPUdosFXnt1GZs2bVLb9EYjI+JsRejeblSHOtO/YF9hFnkWEzpvb0IfbINjcJC1on3CX7YX6ruQ7CIjz3y/h1yOcIIZhPu6EujpzNTWU3F1uPYyWHmbz2I4ka1uu0YH4NX62sXzxJ1HX2Bk3fSDGA1mta3t4Jp0Hl4XD1/nUoys7Dp37hzdu3fnscce49y5cwC4ubkxZcoUdu/eTcOGDUs5QiHE3aRChQpERkZiNBrVtmHDhjF+/PjSC+oWOn/+POvWraNPnz527QsWLECj0bB69Wq79vHjx/PMM/Yr+1zuAcWcOXOIjo7G3d2d8uXL88QTTxAfH39DsZ08eZJmzZrh5uZG/fr1OXDgwBWP9fDwsPvSarV88sknJY7r2LFjiVg7d+5MUFAQ3t7e3H///XbTMUeNGsW4ceNuKG4hxJ1JEvq7zG+//UbPx71p1MiWUG/ZUsBLI4/Rvn0Hta2oqIhOm/5Ut52dnWnt6sCRlETyLSYAnMqXp37HR6wHrL1k/fjWo8kvNjNq8QEMpJPMrwAEeDjzbJ1ncdBe/em7OcdA9uqTFCfaKuT7dK2Ee4Pgq5wl7iQWs4WE2Ex+++EIa786wOa5sXb72wyqgYu7rCl/K/3xxx+sWLFC3e7YsSMxMTGMGjXqjuoBE0LcPfLy8vjuu+9KOwwATCbTLb3+vHnzePTRR9HpdHbt8+fPx9fXl/nz59/wNSdOnMg777zDhx9+SEZGBkeOHKFZs2b89ttvN3Sdvn370q5dOzIzMxkyZAiPPfbYFX8e+fn56texY8fQarV0797d7piVK1eSl5dX4tyPPvqIpKQkcnJyGD16NI8++qjdSK/CwkL27dt3Q7ELIe48ktDfRTIyMuja1ZO6dW1Fx2bOzGTTxly8vGzD6i0WCzPWrLU796dGNdg//QtSTHoAXKrXoNvr7+AVGGQ9IDfJdnBoHUb+ZP0Ff4Yf8XV3om6EDyPuG0F0YPRVY1QUhawVJzBl6tU2n66VcPC/do++uDPE7kxi/cxDHNySQFFecYn9nZ6rg6unLDl4q/Xo0YMuXboQFBTEwoULWbt2LRUqVCjtsIQQN0BRFPRG83/6MlzydaVjLiZp1/Lyyy8zadIku176S02fPp1KlSoRGBjIgAEDyMnJueK1vvjiC6KiovD09KRx48ZkZGSo++bMmUNoaCghISF8//33avuDDz7I2LFjadiwIe7u7hiNRpYvX06NGjXw9fWlS5cu6oiki73js2bNIiQkhJCQEFatWsXKlSvVGL/55psrxrdhwwZatGhh15aamsrGjRv56quvWLNmDbm5uVc4u6Ts7GwmTZrEjBkz6NSpEy4uLri7uzN06FCeeuqp675OXFwcsbGxvP3227i4uPDcc89hsVjYvn37Nc9dsGABTZo0oWLFimqbXq9nzJgxfPDBByWOr1WrFg4ODiiKgk6nIy0tze6eW7ZsyYYNG647diHEnUm6ee4inTo9TNdH3NTtsWNSiIuLs1sazGKxsGDhQn72tQ5t12o1/NrmfhLee5e4U0fU4x598x3beevfUNuLO33O20usQ7/OYR2OVt7PjYreFanud+150hnf23pyHQJd8WxVTtabv0skn8rhn/Xxdm0arYZK9QIJr+Yr1etvoaKiIpYuXcrAgQPVNo1Gw5w5c3B2dsbXV5Z5FOJuZDBZGP7j3v90DQVQFAsajZYrLQT6Vf/6uDjqrrDXpnXr1mzcuJG5c+cyZMgQu30bN27kvffeY/PmzURGRjJo0CBGjhzJ3LlzS1znxx9/5PPPP2fVqlXUrFmT/fv34+Rk/VtvNps5fPgwZ86cYevWrXTv3p3u3bvj6ekJwE8//cSGDRsoV64cJ06c4Mknn2Tt2rU0atSI1157jQEDBrBlyxb1WseOHePs2bMsWLCAIUOG8PDDD3P48GH++usvunbtSu/evS9bS+TQoUNERUXZtS1cuJA6derQt29fxo4dy9KlS687Gf/jjz8oLi6mS5cuVzzm+eefZ8GCBZfd169fP6ZPn05sbCxVq1bF2dk2XS06OpqYmBhat2591RjmzZvHiBEj7No++OAD+vTpQ7ly5S57TpcuXdi4cSPFxcUMGzYMb2/bCkXVq1fn77//vuprCiHufJLQ3yViY2MxGg8D1l/En0xJAyjRY7dw4UJOOtmS/mdqVyPthx/YeUkyX7fjI+gcLgyX/r/15p9bmYCChXh+wEgOtcOtr/dKg1e4Fkux2W7bu1NFWYf8LqAoCvs2nuX88Wy79maPR+ET5Hb5k8RNs3HjRoYNG8apU6fw9PSkW7du6r6QkJDSC0wIUSaNGzeOZ555hsGDB9u1L1y4kKFDh1KzZk0AJk2aRL169fjuu+9K/C2fO3cub775JrVr1wagfv36AGov/TvvvIOTkxMdOnTAzc2NkydPUq9ePQCefvppqlSpAsDSpUvp1q0bzZs3V1/T19eX8+dtS6KOHj0aJycnevfuzZNPPslLL72Em5sbrVu3xt3dnRMnTqjXvlR2dnaJRH/+/Pn07m393NO7d2/mz59/3Ql9RkYGAQEBV53yNH36dKZPn37V6+Tn59uNqgTw8vIiPz//CmdYHTx4kGPHjvH444+rbfHx8SxevJi9e/eSnJx82fN+/vlniouL+fnnnykoKLDb5+npedVRGEKIu4Mk9HeJWrVq8UATW3KVnW0BUJ+Ig7WXD+B3d38AwsPDuf/g32zesl49JrBFS2r1H2TdOLzM7jUyH/kB49JdnMY6hK1OOW80Gg1vNHrjuhLzzAVH1e/9+1eXZP4ukJepZ9tPcXZtle4LonqTEPn3u8XS0tIYNWoU8+bNU9tGjRpF165dS8z5FELcnZwdtHzVv/5/u4iiYDKZrInkFX4vOztc/wzKtm3bEh4ebjcUHiApKYmmTZuq25GRkej1ejIzM/H397c7NjEx0W7Y96V0Op3d8W5ubnbJ6qU9yUlJSZQvX17d9vDwwN/fn6SkJPz9/dHpdPj5+QHg6mqduhcUFKQe7+rqesVE2Nvb227fsWPH2LNnD4sXLwagT58+fPDBByQmJlKuXDkcHBxKTEUwGo04Olo7QPz9/UlPT7f9W/xLHh4eJYb6X7qC0ZXMmzePrl274uPjo7a9/PLLvPfee7i4XH0EnZOTE927d6dOnTrUr1+fWrVqAdaaCpf22Ash7k4yh/4uEBsbi0YDnTtbh6vFxhgA2LvXNoxPURRWrFjB1xeG2oeFhVEz8QR//GgrfhPc+AHaDX3BduGDi23n9/yW15Ye5BzWIlx1IqzJ/OQWk4nwunZl+qKjmer3jsFuaK5j6J8oPflZBg7/nlgimW/Vrzo1moZKMn8LKYrC999/T40aNeyS+ZYtW7J27VpJ5oUoQzQaDS6Ouv/05XzJ15WOudHf2ePGjSsxlz4sLIyzZ8+q22fPnsXFxUVNqC8VERFxw5XdL/2ZXOk1CwoKyMjIICws7F9d+1LR0dEcP35c3b5YBO+BBx4gJCSE9u3bW6cpXhgiHxERwZkzZ+yucebMGfWBQ5MmTXB0dGTtWvsaRZcaNmxYiar0F7+GDRsGQM2aNTl+/DgGg0E97/Dhw2qSfTkX47x0WhbA1q1bGT58OCEhITRq1Aiz2UxISAgxMTGXvY7RaOTkyZPqdlxcHNHRV6+NJIS480lCfxeoVasW775nqxB/9mwxDRo04L777gMu/KL/6Sc1mddqNeSlp1F+wWz1nAeeHErbF1+1/SH9y7bP1Opt3lh9giz2UkwmLo46/F38mdZ2Gp5OnleNzVxgJHNxHAV/2obHeXe8/FN7UXrMJgun9qURuyOJtV8d4PcFRzlz2FbAqOr9IbIE3W1w/Phx2rVrx+DBg9WhqT4+Pnz99dds2bKF6tWvXadCCCH+q/bt2xMSEsLKlSvVtt69ezNnzhyOHDlCQUEBo0ePplevXpd9WDB48GA+/PBDYmNjURSFvXv3XrbK+rX07NmTlStXsmvXLoqLixkzZgxNmzYlNDT0v9weAA8//DA7duxQt3/88UemTJnC/v371a/3339fTfQffvhh9u/fz/LlyzGZTCQnJzNx4kR1iL6Pjw+jR4/m+eefZ8OGDRgMBgoLC/n222/59ttvAZg5c6ZdVfpLv2bOnAlAtWrVqFGjBh988AEGg4HZs2ej0WhKFPC71ObNmzEajXTs2NGuPS4uTr2XdevWodPp2L9/P9WqVeP06dOsXbsWvV6PwWDg888/JzExkcaNG6vnb9u2jYceeug//6yFEKVLEvo7XGFhIXXr2Q+l2rmz0K6IybZt2/jH1TpkSqPR4GQy0mfzErV4Tqu2najU1rakHcYiOLkZBYVz2UUM3WggNn89aVgrrFYL8eS1Rq9dMzZFUchacgxLoW2pFa8Okf/yTsWtoigKG2Yd4siuJE4fSLPb5+7jzAPdKhPVUJYUvJUsFgvvv/8+0dHRdssb9enThyNHjvD000+j1cqvYyHE7TNu3DgyM22j6zp06MBbb71Fp06diIyMxNHRkalTp1723H79+jF8+HC6dOmCl5cXzz///BUr519NjRo1+Prrr3nyyScJDg4mLi7uXy0ndzkDBw5k5cqVmM1mdu3aRWpqKkOGDFEr5oeEhPDcc89x4sQJDh48SHBwMGvWrOHjjz8mICCA+++/n4YNG/L222+r1xwzZgzjxo3jtddew9fXl2rVqvH777/Ttm3bG4ptwYIF/Prrr/j4+DBjxgyWL1+uDuOfNGlSicR93rx59OnTp8RQ/6CgIPVeAgMDAWvtlYuV7SdOnKges3TpUn7++We1NsuBAwdwcXGhQYMGN/yzFULcWTTK9a51Ukbk5ubi7e1NVlaW3TykO1FxcTHOzs68N9GWbL0/MRW9XlGXqMnJyeHntWv55kLvvJsWBmxajOVCQZn73f2p9NVXaC79I3ChEN7R5Fx+9BjMVqc16q6oYA+G1hvMA6EPXDU2xaKQ8YOtor3OywnvjhXRukpZhjuBxWIhJSUFLzdf9v2aQE5qobovMjoAR2cdVRoEobuBeZfi31MUhccee4xVq1YB1rmpM2bMKPGhTdwYi8VCamoqQUFB8kBE3BH0ej2nT5+mYsWK15zXfL2US+bQy3SoGzNixAiaNm1Kv379SjuUO87AgQPp3bv3Vav2307yPhdlyZX+FmRnZ+Pr60tOTk6J4pj/hWRfd6iLybynp+1DamyMAb1e4cgRW8X6tWvXcsjZOiw+ICCAenu2qMl8Qzc/Art1s0/mt0wC4GBiNrlY1GS+eognzo46JreYfM1h9gDZa2xzsNCAb/eoKx8sbjtDoZFdPyXg7JRqV0Op8/C6pRfUPUyj0TBt2jR+//13nnnmGcaPH4+7u3tphyWEEGXatGnTSjuEO9alNVyEEHc3SejvUNu3W4e/v/5GoNq2ZEk2YF03VFEUlv28lhNObvzt5oOrqyvOzs5EJZzACES7eBPYpg1el/YA7vgMzh8gOU/PV15ZHHGxJuE1w7xw1GmZ1vb6/vDpT2RhzrIVcwl44sqFXMTtpSgKp/enE7szya49qIIX0a0uv0atuLkURWH58uW4urrSqVMntb1cuXKcPn36jh8ZJIQQQggh7h6S0N+h2rVrR7fHbEMxzGYwmaxDTME6ZGOV4sw5d+vceR8fH2rt247xQrVYF60O3z59bBdUFDj7JyZF4U1tEicdqwIa6kR44+PswztN3rmuuMx5xeTvsCWLfr2q/sc7FTdDXqaepOPZnNiTYtceVtWH+lLX4LZJSEhg+PDhrFmzhrCwMGJjY+2WBJJkXgghhBBC3EyS0N+hGjV2pUEDV3V7/LgUzGYzGo0Gk8nE+vXrOXdh3ny5sFDqHtlP6EbbXHjfJk3sL7h9CgCLDBmc14UCGnzcHPmk1Se4OFzfPD/FopC1zLYEjOeD5dC6Of7LOxQ3Q1ZyAbuWnbjsvlqtg6ha99pLDor/zmw2M23aNMaMGaOue5yUlMSCBQt47rnnSjk6IYQQQghRVklCfwfy8PBg5EjbPPZJ76fi7u6uFn1avHgxO9x8AdBqNDz3w2xOp5xSj28WXhnfnj1tFzz1OyTu4Vh+ARuUHAqdrcvBfNFm2nUn85ZiM5kLjqrb7o1DcK7gfZUzxK1iNllIO5vHP+vjS+yrWDcQNy8nytf2IzU19fYHdw/av38/Q4cOZffu3WpbSEgIX375JT169CjFyIQQQgghRFknCf0dJiUlBbO5EFc3DwCWL8uhqEghLS0egLNnz6LXaDnqbN3vbyxGk2UbZt3gwQ5EPjPUdsFDS+HQEhQUvjNmkORWAYBHqnSlYsD1FeVSFMUumXcIcsO1pv9/uEtxvSxmC4lHs9AXGslILCAzKf+yxwVX8qZhxwq28y5MzRC3TkFBARMmTODTTz/FbDar7cOGDWPy5MkyvF4IIYQQQtxyktDfYUJCQnjyKV91+8gRa/G5gIAALBYLO3bsYJ2XdQ3RsNAQRn4/iyyzdR141+hoql2azAMcWoLJbGFa1nn2eQRSrHGhSWRVXmve+7pjyvjetjydcxUfPJuH/9vbEzcgJ62IHYuPXfWYwEgvGnepeJsiEhft3r2bXr16ER8fr7bVrFmT2bNn06xZs9ILTAghhBBC3FMkob+DTJ8+nRo1nKlUyUlt0+sVEhISAFi4cCEAmTpHPD09KY6PJ6SogONmA45h4fiGhtlf0FSM2aLwYXYSOzz8KNBai+yNaDD4umMqismw25Zk/tZLOZ3L4W2J6PONdu0V6wViNlrwCnClXA1fdDpZd7u0hIaGkpFh/b/h7OzM2LFjee2113BycrrGmUIIIYQQQtw8ktDfIeLj4xk+fDjvTQxW26ZOTQesy12dOXMGgDSdtQidp5s7bQ9vxKwoADj4+xFcsYrdNU0LB3DoXDZHfQwUaq3D69f0mYaPi891xaRYFAp2J6vbfn2r/bubE9dkNls4tTeNY38nl9hXo2kYle4LvMxZorSUK1eOyZMns3TpUmbNmkXVqrLagxBC3EyKotCoUSOWLVtGZKSs1nIpk8nEfffdx6ZNmwgODr72CUKIMk26+O4QU6dOpVw5W8X4bdsKyEg3oygKiYmJ7Ny5k92u3qzyCiE0NBTFoKdN4imO6nNxqV4dgGpNW9ouuKA3h8/l8KlPJqDBggOr+nxx/cm8opDxg22ovXvDYLTO8vznZlIUBbPRwtqvDrBh5qESyXxwJW/aDq4pyXwpi4uLo1evXmRnZ9u1P/fcc/z222+SzAsh7goVKlQgMjISo9E2+mvYsGGMHz++9IK6iuXLlxMVFVUimR86dChOTk7qKKmLHnzwQebPn2/XNnfuXNq1a6duFxUV8frrrxMZGYmHhwdVq1ZlzJgxFBQU3FBs69evp0qVKri7u/Poo4+SlZV12eO2b9+Oh4eH+uXu7o5Go+Gff/6xOy4nJ4eQkBC7WE+dOkWjRo3w9fXFz8+Pbt26cf78eQAcHBx4+umn+eijj24obiFE2SQJ/R3i888/p3cfW9X47dsKmDFjBvn5+Wzbto1MnSMHXLzw9fVFq9XScMdWMk0GsjQKGkdHXD290Op01pMPLMRktvCJj/WPXbxzVWqHe+Pn4nfd8eiP2f9xcq0d8N9vUpB0PJvda0+z9qsDrJt+kA2zD5U4pk6bCDo9X4eGHSvg4i7LApYWg8HAu+++S506dViyZAlvvfWW3X6tVotGoyml6IQQ4sbl5eXx3XfflXYY1+Wbb76hb9++dm0Gg4GlS5fi4eHBokWLbuh6iqLw6KOPsm3bNtasWUNubi7btm2jqKiIkydPXvd1UlNT6du3L1988QVpaWn4+Pjw4osvXvbYFi1akJ+fr359++23VKhQgfr169sdN27cOKpUsR9lGRgYyOLFi8nMzCQ5OZnq1avbvU6fPn2YN2+e3QMaIcS9SRL6O4Sfnw4fH2tCrtcrODl5MmTIEFavXo0CLPcKwcfHGzc3N97JOEvPk7Ec0eeimKy/yB/occkfvZgVHM62VkOPd65OrXAfprb59LpjURSFgj/Oq9sBg2v99xu8hxXrTcT9lczarw6w79czpMbnXva4jsOi6Ty8LhE1/CRRLGXbt2+nXr16jBs3juLiYgA2btyorjEvhBDXRVHAqP9vX6ZLvq50zIXpd9fy8ssvM2nSpCsmgdOnT6dSpUoEBgYyYMAAcnJyLnvc+PHj6du3L926dcPT05OWLVvaFQndvn079913Hz4+PrRq1YojR44A1hVYXnzxRQICAvDx8aFRo0akp6eXuH5xcTFbtmyhefPmdu1r167F3d2dN998s0Rv/LX8+uuv7Nixg+XLl1OnTh20Wi0hISF88skn1KlT57qvs2LFCho2bEinTp1wc3Nj/PjxLFmyhKKiomueO2/ePAYMGGD3N/7w4cPs2rWLp556yu5YT09PKlasqB6r1WrtHjyEhITg6+vLnj17rjt2IUTZJGOo7wBffvkl1as7q9tfz8lk1Kix6tPnQy6euLi44O7uQYjRgMuiRegt1mWynKtWJapxU3yCQ6x/0H/qw3GTnk+c0kh2LA/AjHbTbihBvLSqvVtdGe79b2WnFLJz6fHL7qtQJ4CQyt54Bbii02nQSoG7O0JWVhZvvPEGc+bMUdt0Oh2vvvoq77zzDm5ubqUYnRDirmMywJIn/uNFFHQWBbQa4Ap/yx//Hhxdrnml1q1bs3HjRubOncuQIUPs9m3cuJH33nuPzZs3ExkZyaBBgxg5ciRz58697LWWLVvG8uXLWbx4MWPGjGHQoEFs27aNjIwMHnnkEb755hu6du3K1KlTeeSRRzhy5AibNm1i165dnDp1Cnd3dw4cOICLS8m4jx8/jru7O35+9iML58+fT69evejTpw9vvfUWp06dolKlSte8b4DffvuNxo0bExYWdsVj6tSpw9mzZy+7b/r06fTr14/Y2Fi7BwAVK1bE0dGRkydPUrt27SteOzU1lV9++YVPP7XvYHnxxRf55JNPrjhKwMfHh7y8PHQ6Hd9++63dvurVq3Pw4EGaNGlyxdcVQpR9ktCXsqysLF588UVefNG6rnt6mpmUFBPly5dXj4kJCMf/wprWL875AoD9hdk4V6mC1tmZGi1aw775cGQNAO8WnQNAr3WjUqDHDSXzhQfS7Lbd7gv61/d2r1IsCr/MOYzZZL8WvM5RR4OHIwks71lKkYkrURSFxYsXM3LkSFJSUtT2xo0bM3v2bOrWrVuK0QkhxM0zbtw4nnnmGQYPHmzXvnDhQoYOHUrNmjUBmDRpEvXq1eO777677OeIpk2b0qVLF8DaY+/j40NSUhKbN2+mTp06dO/eHYBRo0YxdepUdu/ejaOjI3l5eRw9epRGjRqVGHp+UXZ2Nh4eHiXa1q1bx7Zt2yhfvjwPPPAA8+fP55133rmu+87IyCAkJOSqxxw8ePCa18nPzyciIsKuzcvL65ojuBYuXEiDBg3s6q4sXLiQoKAgWrRoccWEPjs7m9zcXL755psSNVs8PT2vOIpCCHHvkIS+lF18+uzjax1un5pmok+fPuryV6aQcHwuJPMhSYkAHCjMxuLjjYOrKwC601vVZP4rQyoA8c7VcNMG833nT647lsKDaRTuS1W3/QfW/Pc3dg85uTeV/CwD6Ql56AtNJYY+Vr0/hKiGUoX2TjZgwAAWLFigbnt4eDB58mSee+45dBdrUwghxI1ycLb2nv8nCmaTCQcHB67YQ+/gfPn2y2jbti3h4eF8/719XElJSTRt2lTdjoyMRK/Xk5mZib+/f4nrXJrUurm54e/vz/nz50lKSrLrlNBqtURERJCUlESPHj0YNmwYQ4cOJTk5mQEDBjB58mQcHe3rxXh7e5dIkBcvXkxYWBiNGzcGrHPIp02bpib0Dg4OJaYSGI1G9dr+/v6cOHHiun9OV+Lh4UFurv3Uudzc3BIPIP7fvHnzePLJJ9XtgoICxo0bx6ZNm675ml5eXgwaNIjatWtz7tw5tFrrqL68vDy8vb2vcbYQoqyTcb6l6OIfBDc3DY6O1j/Sp08X07BhQwAUYGOA9Q9mcUICz61eRIbJQK7FiFNEOQDa9e4Ju63Dg/dYClih+BHvXB3QsKrv9Vc/tRSbKdxrS+Z9u0eh0ck87mtZN+MgR/84T+LRTPQFxhLJ/MPPRksyfxe4dJ5mt27dOHLkCCNGjJBkXgjx32g01qHw/+XL4ZKvKx1zg3VXxo0bV2IufVhYmN1w87Nnz+Li4lJi2PtFCQkJ6vdFRUVkZGQQGhpa4jqKopCQkKAOdX/55ZfZv38/u3fv5pdffuHHH38sce2oqCgKCgrsqsfPnz+fpKQkQkJCCAkJYfz48Rw7doy///4bsD5guLjE70VnzpxRHy60adOGv//+m+TkksvDXlSrVi27qvSXfl2Ms2bNmhw6ZCtoGx8fj9FopHLlyle87tGjRzl48CC9e/dW244fP87p06dp1KgRISEhjBw5ku3bt1Ot2uWXCDaZTCQnJ9s96IiLiyM6OvqKryuEuDdIQl+KRo0aBcBbb9uGtfv61CM0NBSAonqN1faWh/eB2USsPhfnytZKqF37dsFz17sAZCkmpmuMGLTWXvvHKjyDi+P1JyOZC46q33u2jkDn5fQv7+reUJhbzNqvDqBYbAl8jWZh1GoRzv2PVubhodYCdzoH+S92J1L+78HLs88+S8+ePVm+fDkrVqygXLlypRSZEELceu3btyckJISVK1eqbb1792bOnDkcOXKEgoICRo8eTa9eva44bW/Xrl2sW7eO4uJiJkyYoM5P79ixIwcOHGDVqlWYTCY+++wzXF1dadiwIXv27GH37t2YTCY8PT1xdHS87INTZ2dnHnzwQXbs2AFYE/OdO3eyZcsW9u/fz/79+4mJiaFz585qcbxevXrx9ddfc+DAARRFYd++fXz77bf06tULgA4dOtCsWTN69OjBoUOHsFgspKam8sYbb6hD7WNiYuyq0l/61b9/fwAee+wxdu/ezYYNGygsLGTChAk8/vjjuF4YNXk58+bNo1OnTnYjHWrXrs3Zs2fV+3n33Xe5//772b59OwBbt25l7969mM1msrKyGDVqFA0aNMDLywuAlJQUMjIy1E4gIcS9S7KNUrRo0SKeftpX3Y6JMTBgwHDbAZ7WX9qKxUzX03GcNhTgHBWF1s2V+x7qgsMOaw/8acXAWBcjx43WAnYVeIJXHnzwuuMwphWq32s9HHGO9PoPd1W2KYrCsd3JbJl3xK690/N1qFQvkAp1Aggo54HOUf5r3Yny8/MZNWoUw4cPt2vXarUsWbKExx57rJQiE0KI22vcuHFkZmaq2x06dOCtt96iU6dOREZG4ujoyNSpU694fo8ePZg9ezZ+fn7s2LGDefPmARAQEMDKlSsZN24c/v7+rFixgpUrV+Lo6EhOTg5PPfUUPj4+VKtWjWbNmtGvX7/LXv/pp5/mp59+AuDHH3+kRYsWNGnSRO2hDwkJYcSIESxatAiTyUTHjh0ZO3YsvXv3xtvbm379+vHee+/Rtm1bADQaDatWraJp06Z07twZLy8vmjVrhpOTU4kl464mKCiIBQsWMHz4cAICAsjIyOCLL75Q93fs2JFJkyap24qi8OOPPzJw4EC76zg4ONjdi7e3N05OTgQFWTt58vLy6N+/P97e3lSrVg2DwcCyZcvU8xcuXMjAgQPVKZpCiHuXRvn/rqoyLjc3F29vb7KystS56aVFo9Hw3kTbcOyYw63p2rUrAFXrN+DzYkcshYU03vYb3U7GsrMgHdfatUGj4bFH6lsL4QEjnApJ1QVzPkePN7V5s9kzNI+6/nXjM5ccw1JgHXYnS9RdnrHYzKGtiZw/nm3X7ublxIMDqt9xy8xd7HkICgpS59rd69auXcvzzz+vDgXdtm0bLVq0KOWoxH8h73Nxp9Hr9Zw+fZqKFStetnr7v6EoCqYLc+jvlL8148ePJzExka+//vqWvYbFYqFRo0YsX76cyMjIW/Y6dyOTycR9993Hxo0br1no725xJ77Phfi3rvS3IDs7G19fX3JyctTRNjeDFMUrRZd+/tywPo8HHggHwAR8Xmwt4mI4cZLamSnsKkhH6+IKGg1Rkd5qMh+n6DF4lON8ch5aXAim7Q0l84UHUtVk3rmKz025r7JEURQyzhXw16qS1Web94rCO1CWMbvTnT9/npEjR7JkyRK1zcXFhRMnTkhCL4QQdyitVss///xT2mHckRwcHOzm8Qsh7m2S0JeSnTt3EhVlq0qblRWozp13bNYacorAYqFe+nkqZqWT5uaOc+VKoFioad4FGg3nlGK+9HIjKUcPQCWeZubABjcUR+E+2zJ1Hk2vvDbrvej8yRz2bogv0d60RxV8Q9xvf0DihlgsFubMmcMbb7xht6xPu3btmDlz5lULGAkhhBBCCHE3kIS+lDRv3pw+faxLjWi1WurUaaLOm1qXUwSAMSmJQUcPsK8oB+ea1QFoHxiLVqPFrCi84+7GsQwNYCSEDjjrnHDUXf+w0+KEPPV794bBaLQyxEmxKBz4LYFzcVkl9gVV8KLBw5Fob+BnLEpHTEwMzz77LDt37lTbAgIC+PTTTxkwYIAM5xNCiP9g/PjxpR2CEEKICyShLwUrVqwAwNXNmhgGB4dxCNDpdDRo0IBfi8GsQNP442iAAosJV40GMk/jEaFFURReNCdwLNvaw+hMMF7UYNiD19/jqCgKuZtty8q41r7+YfpliaIoFGQbiN2RRG6GHkOBscQxFesFUrOZjF64W+zZs4emTZvaLcf0xBNPMGXKFAIC7s33uRBCCCGEKJskoS8F3bt3B6BSJSecnZ1JSPDG29sbBwcHPCMrYj6WCBYL9584yrniInTe3lBcQK3APEyKjpGmBA4qldTrvVDvFR6pG3ZDvY65G21rtbpUv/was2VVfpaeQ1vPUZBtwFBYMoG/6IFulfEP97iNkYmboX79+jRu3JidO3dSpUoVZs6cqVY5FkIIIYQQoiyRhP42q1evHgCO1pp3BPgHkJnhQNOmTenRoweDY6yJtv7YMcjP5lRxPs6RVSEthsqNnfnamMZBrD3xTvhRgYE8Wi/8huMwJhWo37vfXzYqpF5Lsd7E1h/jMOpNJfZptBqcXB2o2TyMoEhPHBxLrosr7kyFhYW4udmKE2q1WmbPns2CBQsYPXr0VdcGFkIIIYQQ4m4mCf1t9PTTT3PgwAEA3hkXjEZjHXKvKHUIDAzkvNEMgOHYMZqcOcnJQus8bq2TA/dVcsCiUdhkNMOFhwEVGMhnferdcBz647b54W71g8rsfGJFUUg9k8fxPSkU5RVTXGifyLv7OFP1/hBJ4O9SiqKwYMECXnnlFRYvXkyrVq3UfTVr1mTixImlGJ0QQgghhBC3niT0t0lxcTHffvstAFFRTsD/2LvPqCiuPgDjz+7Sy9K7UqyAYu+9t8QeFUWNib2XFGMFY2KM0WiKRmNsAbvGXl6N0cQao8ZO7IAKKNIXKQu774cNAytFsASV+ztnz9m5M3PnzjDL7n9uA1NTUzKzZFSrVhOvRk2Zcv0eWo0GTVo6VS6fJBYw8a6Mr8EVPB0NGJsaToyBJwDDarzLgBp1i10OrUaL6niktGxSyea5z+1VkanOIi4yhXv/xBN1M6HQbVsP8sXE3PC/KZjwwt2+fZuRI0dy4MABAIYPH86FCxcwNjZ+yp6CIAjCfy06OpoePXpw6dIlZs2axaRJk/JsM3bsWBo0aEBAQEAJlPDVNmPGDJycnBgzZkxJF0UQhFeQGK77P9KoUSMAFAoY+K4uiLa0tECrGcp+Cwc+D38IQMadO/jGPSQ2KwOTypWRPbxMZTcD0rI0xGgUZMkMMMSK/tXbPFM5Evfekd6b13FCbvL6P9N5nJTBnsUX+N+Pl/lr9518g3lbVwuqty5L63d9eWt0dRHMv6bUajXz5s2jatWqUjAPULVqVVJSUgrZUxAEoXTz9PTEw8NDb8DQESNG/Ccj1v/44494enqSnJycbzAfFRXF3r178ff310tft24dMpmMnTt36qUHBQUxZMgQvbSwsDAMDPR/0yxfvhw/Pz/Mzc1xd3fn3XffJSwsrFhlv3XrFo0bN8bMzIxatWpJLS3zY2FhofeSy+UsWLAgz3YdO3bUK2tERESefWUyGVu3bgVg3LhxzJs3T+9vJwiCkE0E9P+Rs2fP4uVlSNAsJwBkMhnGJs3YUNaXe4YmAGTGPqJt6EXeuXhKt402nUbeusBzQZIJ9w29ABhV9ZNnaiaflZRO5qNUadmkit1znVNJU6dncXZ/GIeDQ/Oss3Y2p3ZHTzoM9+Ot0dVp2L08ZbxtMbEQgfzr6vTp09SpU4fJkyeTmqq7j8uUKcOOHTvYsmULtrala3BHQRCE4kpOTmbVqlUvPN+srKxC10dERODr61vg+uDgYLp27YpCod/9LSQkBBsbG0JCQopdps8++4yZM2fy5ZdfEhsbS2hoKI0bN+a3334rVj59+/alTZs2xMXFMXToULp3705mZt6xeABUKpX0un79OnK5XBoIOdv27dtJTk7WS3N3d9fb99ChQ5ibm9OhQwcAHBwcqFKlCrt37y5W2QVBKB1EQP8fMDc3B+D9wTkBh5GhIzdMdbXsDg4OZMbG8vnmNXSIuEl0ZhrG5SvAoxs4WSvQaLXsN9cCIMeId+qUfaZyxP9yU3pv27fya9t3PlWVwb6llzjw02WibyVK6TK5jE6jqvHW6Oo07lkB53JWKAzELf66S05OZty4cTRo0ICLFy8Cugdi48eP5+rVq3Tp0qWESygIgvB6mDhxInPmzCmwpnfJkiWUK1cOBwcH+vfvT2JiYr7bBQUF0bdvX3r27ImFhQW//fYbERERvPXWW9jZ2eHj48P+/fsBXSuANWvWMHv2bCwsLPj777/z5Ld//36aNm2ql/bw4UMOHjzI4sWL2bVrF0lJSUU+z4SEBObMmcMPP/xAp06dMDExwdzcnGHDhvH+++8XOZ9r165x9epVpk6diomJCSNHjkSj0XD06NGn7rtu3ToaNmyIl5eXlJaWlsb06dOZO3duofsGBwfTvXt36fcjQLNmzaRrKgiCkJuIdv4Djx8/pnLlnL69+/c9Jj2jN8fNdAG+kZERC37fg6FGA4BpkybIU+/jYqP782xIz2mWNaP54GcqQ3pYzpeyoZsFcuPXq6n99dPR7FlykT2LL/DbmlA0WRppnZWDGe2HVqXTyGqv7UMKoWBDhw7lu+++Q6vVPdSqXr06f/75J4sWLcLS0rKESycIglAwrVZLelb6S39l/398mpYtW+Lu7s7q1avzrDt48CCzZ89m9+7dhIWFkZqayvjx4wvMa9u2bQwfPpykpCQaN25M586dad++PQ8ePGDlypUMGDCABw8esHTpUgICApgxYwYqlYqaNWvmyevSpUtUrFhRL23Dhg1Uq1aNvn374ubmxpYtW4p0jgAnT54kIyODt99+u8BtRo0ahbW1db6vUaNGAXD16lUqVaqkNz6Ln58fV65ceWoZgoODGThwoF7a3Llz8ff3p0yZMgXup1ar2bhxY559vb29pYfagiAIub1eUd1rKPtL9u3OOYGHqWl9Yst7A2Bvb09rW0uyoh/o1lWvxqPYh5CWhLW9AVkaLcvkGrKfvbTxavxM5Ug+ck96r2zl/kx5/Ncy0jI5fzCCmIjkArdp854vxmaiGf2bLCgoiG3btqFQKJg1axYTJkzA0FD8zQVBePVlaDL44MgHz5WHFi1ajRaZXIaM/B9aL2ixAGNF0QYFDQwMZMiQIQwaNEgvfcOGDQwbNkxqGj9nzhxq1KjBqlWr8n1Y3rx5c9q1awfAxYsXSU1NZdy4cQA0bNiQ5s2bs2/fvjzHyU9CQgIWFhZ6aSEhIfTp0weAPn36EBISUuTa9djYWOzt7fP0qc9tyZIlLFmypNB8VCoVSqVSL02pVKJSqQrd7+LFi1y/fp1evXpJaWFhYWzatIlz584RHR1d4L779u3DyMiI1q1b66VbWloW2GJCEITSTQT0L5mfnx/jJ9hjba3rF3b+7zTatm3LQaUDAMbGxvSJjeTRv9sbNawFK+cAYGEKszMD0LAZgIYeFfPkXxSZCenSewMHU2SKV78WWxWfzu/r/smTXquDJ/ZlLTA0EtPMvYmysrKIjIykbNmcbiXe3t6sXr2aBg0a6DVdFARBEIqvdevWuLm5sWbNGr30yMhIaQBfAA8PD9LS0oiLi8POLu+YO7lrmSMiIrhz5w7W1tZSWmZmJrVr1y5SmaysrPSC5OvXr3PmzBk2bdoEgL+/P3PnzuXevXuUKVMGAwODPN0G1Gq19LDXzs6OR48ekZmZWWhQ/zQWFhZ5mvonJSXlefjwpODgYDp37qx3PSZOnMjs2bMxMTF56r4BAQHI5fqNaJOTk7GysireCQiCUCqIgP4l0mq1XLlyhbp1bbC31wWg587J6drNlSyZDDs7OzqYKni0YKm0z6F/g3mADE0659CNSi+Xw0f1Rz1TOR6ffSC9t2rv+Ux5/JdiIpI5veu2Xlrtjp44lxNfZG+yS5cuMWzYMGJjY7l48aLej56+ffuWYMkEQRCejZHciAUt8o5yXhxarVYKTAvqVmYkNypWnoGBgQwfPpyWLVtKaa6urkREREjLERERmJiYFDjgaO6yuLm54ePj88xNwv38/Lhx4wZVq1YFkAbBa9CggbSNRqNh3bp1fPzxx5QtW5Zff/1VL4/w8HDc3XUtEBs2bIihoSF79uyha9eu+R5zxIgRBQ62179/f5YuXYqvry83btwgPT1danZ/+fLlfEfqf7KcS5cu1Us/cuQIJ0+eZPTo0WRlZZGVlYWzszOHDh2iSpUqgK6lwq5du/jrr7/y5Hvt2jX8/PwKPK4gCKWX6EP/kqSkpEhPV21tdcH8rp2p1K/fnnPmumnrDBUK6i6aL+0TW8ZJei830PCulTXxnAHAx0WJg5nDM5Ul425Ok3XZKzpIXMKDxxxcdYU9iy/oBfNOXlZ0GlVNBPNvsNTUVKZOnUqtWrU4deoUN27cYM6cOU/fURAE4RUnk8kwVhi/9Fdxx49p27Ytzs7ObN++XUrr06cPy5cvJzQ0lJSUFKZNm0bv3r2LlHf9+vXRaDT88MMPZGRkkJGRwdGjR/UeEBSmQ4cOHDt2TFpeu3Yt8+fP5/z589Lr888/lwLwDh06cP78eX755RcyMzOJjo7ms88+k5roW1tbM23aNEaNGsX+/ftJT0/n8ePHrFy5kpUrVwKwdOlSvZHlc7+yg/HKlSvj4+PD3LlzSU9P58cff0Qmk+UZwC+3Q4cOoVar6dixo176tWvXpHPZu3cvCoWC8+fPU7lyZWmbzZs34+Pjk2/g/scff9C+ffsiXU9BEEqXVzO6ewNkN8eytpZj829Ab2BoRY2aNblgosRMqyUj9B9Ms3RTn8SoU7jz8BwAWWhZVMVMyquKm5Ivmn7+TOVQP8iZm9ukovUz5fEyqeLT+XPnbY5vuUHGY/1pYKq1KkudTp5ioLs32KFDh/Dz8+OLL76QpgGqXLlynr6DgiAIwosVGBhIXFyctNyuXTumTJlCp06d8PDwwNDQkEWLFhUpLwMDA/bs2cP//vc/3NzccHV15fPPP0ej0Tx9Z2DAgAFs376drKwsTpw4wcOHDxk6dCjOzs7Sa+TIkdy8eZOLFy/i5OTErl27+Oqrr7C3t6d+/frUqVOHqVOnSnlOnz6dwMBAPvroI2xsbKhcuTK///57sb9f1q1bx4EDB7C2tuaHH37gl19+kZrxz5kzJ0/gHhwcjL+/f56m/o6OjtK5ODjoKmicnZ31tgsODmbAgAF5yvDo0SMuXbpE586di1V2QRBKB5m2qEOjviGSkpKwsrIiPj5er2/Ti2Rvb09sbCwAgwbZUL6CETKZjNN3u5PcpS+GhoZYRj+g9b3bvB12HZmJCX9pbsG/g90cqpLKBWNHXV6Wxmzp/iMKefH7jGszNcSG5MzRbjfQF5n81QiO01Mzuf5nNBFXYvXS3SrbUL6WI5a2hfcxEwqn0Wh4+PAhjo6OefrhvQpiYmL44IMPCA4OltIMDQ2ZOnUqU6ZM0RtRWBAK8qrf50Lpk5aWxp07d/Dy8npqX+miKkqT+zfBmDFjaNSoEf369SvporxyZsyYgZOTE2PGjCnporw0peU+F0qHgr4LEhISsLGxITExMc+Am89D9KF/wc6cOSMF8+/0sqJ8BV2/NjNTM5I6+yMjZ+T7t8KuY96oIWHh2yFG98/L0NuZWBcNxKWCDLb1XPHMZVFH5dTOm9V0eCWC+fvX47l05B5Zav2n9vZlLanZzh0jE3FLvsm0Wi0///wzH3zwgfQ5AWjatCnLli3Dx8enBEsnCIIglJTvv/++pIvwypo9e3ZJF0EQhFeYiJ5esLp16wLg5mZA9eo5T2QSa32P7LEuoG7gUYahW38GwNQqkfCYLGm7/ZUNSX6kC8TLaZ9tznkArVpD0qGcvmtm1R2fOa8X5eim6yTFpOql2biYU711WcytRI1saRAREcGwYcPIyMgAdP0c582bx+DBg0UNqyAIgiAIgiAUk/gF/QIFBAQA4OCgYMTInCleRo+5wB+PdX3qDQ0NGbjoS90KTRa//5EzSqtZD3+0Wi2JqWpsqYuX7bMH4amhObWfBiXcfF2dnsWZvWF6wXyFOk50GlWNRj0qiGC+FPHw8GD69OmAbgCm0NBQhg4dKoJ5QRAEQRAEQXgGoob+BXFycuLhw4cAjBtvL6XHPqrIsfScgHWKJhljja5G/s/4CBQmunUah0rsyjzGxXuJANhRn87VXZ6pLGk343l87qG0bN2l/DPl8yIkx6Xxx/premlt36+Ckam49UqD06dP4+fnh6mpqZQ2efJkGjZsSJs2bUqwZIIgCIIgCILw+hPVYi/A7du3pWDeyirnkt64nsGsOQcJDo+S0mx37JDem7v/W3NuYExi+wrciFEB4Mrb2FuYUtsj/7lfC6PN0qI6FiktWzYrU+w8XpSsLE2eYL5J70oimC8FEhMTGTVqFA0aNOCzzz7TW2dkZCSCeUEQBEEQBEF4AURA/wKUL59TA/7hRzlzxXfstYLRNyKl/sINHt7FJktXO+8wdgxpat3geC3e6U6i1oDH6bp1FpRn3jvVn6ksscFXpfeWzcpgXELzt8dHp7B/6SVpuayvHZ1GVcPKwbSQvYTXnVarZevWrfj4+PDDDz+g1WqZN28e//zzT0kXTRAEQRAEQRDeOKKq9Dl169YNAEtLOR9PzgnmW7UMYK11LcjMIikpCQ91KlWvXJDWx0Sdl94bV2zOL3s+BsCGWiz0r/FMZXlyBsL/OpjPSMvk0pF7RN9K1Et3rWhDtZYl11JA+G/cvXuXMWPGsHPnTinNzMyMzz77jAoVKpRgyQRBEARBEAThzSQC+ue0498m9LmDeXd3D2rVn8cPV8LJVKsBaB1zD+d/a+oNXJw5tTen6f25BEPpvV8ZK5QmOcvFkXvOeZtelZ4pj+LSarXcPPuQ639G57u+fC1HvBs+21gAwushKyuLxYsXM23aNFQqlZT+1ltvsXjxYjw8PEqwdIIgCIIgCILw5hJN7p/Dxo0bAZj0Qc4geCqVhgH9T/HFbV2A++DhQ7olRZN+4wYV09IAuOvrCZnpANg4ObLg+BZp/5kt+z9TWTQZWZCVU0OvMH+2hwLFodVq2bvkYp5gXiaXUaOtOx1HVhPB/BvuwoULNGzYkPHjx0vBvJOTExs3bmTXrl0imBcEQShljhw5UuxWWWfOnKFdu3YvqUSvtxUrVvDhhx+WdDEEQXiFiYD+Ofj7+2NiIsPGRiGlublO53RiCrdT06Um8JbXr2Hxb995TaUK3D+5V9o+2LEqjzgOgLOVCcaKZ5vCLW5dTh9lu4G+z5RHUWm1Wg6tvsreJRf10r0butBpVDU6jayGWyUb5HLZSy2HUPJ2797NX3/9JS0PHz6cf/75h969eyOTib+/IAjCq8LT0xMPDw/U/7YcBBgxYgRBQUEv/bjHjh0rdJtZs2bxwQcf6KWlpaVhZWVFjx499NLDwsIwMMjbwLRFixaEhIRIyxcvXqRz585YW1tjZ2dHkyZNpFaVRaXRaJgwYQLW1tY4OTmxcOHCArcdMWIEFhYW0svIyAg/P788223YsAGZTKZX1ri4OHr16oWtrS1OTk6MHTuWrH9/Nw4YMIDNmzcTExNTrLILglB6iID+OQ0YaCO9XzA/hrFjx/F9hG7E+9jYWLonRKJJSaHq48fEZWZwLiMJMnU19U18FVw3/DcYksGY2oOeqQzp4Ul6y7KXHEjvXXKRtJScHwSGJga8Nbo65Ws5iiCulPnoo4+oUqUKPj4+HD16lKVLl2JtbV3SxRIEQRDykZyczKpVq0q6GHqioqI4c+ZMntlPdu7ciUKhYO/evSQkJBQrz3/++YcmTZpQvXp1bty4waNHj/jqq6/Yu3fv03fOZenSpRw5coTr169z7Ngx5s+fz6FDhwrcVqVSSa/WrVvTs2dPvW1SUlL47LPPqFKlil56YGAgjx8/5u7du1y+fJk//viDn376CdDNDNOpUye9BwCCIAi5iYD+Gf39998YGIC7e07T9sePc54Yq1QqrFRJmF04D4BZppqb7k4QqVuu4KJgiEVO8BvUYijN3ZsUqwzaTA1Jv4aTfPiulGb37surnddqtexZfEEvre37VWg3uEoBewhvkocPH7Jp0ya9NCMjI3bv3s3ff/9NkybFu38FQRCE/9bEiROZM2eOXi19bkuWLKFcuXI4ODjQv39/EhMT893u1q1bNGvWDGtra1xdXZk6dWq+2w0ZMoSIiAjatWuHhYUFa9euzbPNwYMHqVevHgqFQi89JCSEUaNGUb58+TzfPU8za9YsWrVqxWeffYaDgwMymYyGDRuybNmyYuUTHBzMhx9+iKOjIxUrVmTo0KH8/PPPT90vOjqaX3/9lQEDBuilz549m8GDB2Nvb6+XHhYWRrdu3TA3N8fBwYH27dtz9WrOrEXNmjVj//79xSq7IAilhwjon1GtWrVwdcsJ5n9cFsfq1at5nKUhKyuLhPh4av99CoAWiYk8kGuQP7oCQMPKhpwuk0iCxkzav7VH82KXIe1mAhn3cgYhU7b3fKk15BcP39NbbvWuj5hTvhTQarWsXLkSb29v+vXrx8WL+l0tPD09MTZ+tq4igiAIbzKtVosmPf2lv56c5aYgLVu2xN3dndWrV+dZd/DgQWbPns3u3bsJCwsjNTWV8ePHF5jX7NmzefToEb///jshISFs3749zzY//fQT7u7uHDhwAJVKRUBAQJ5tLl26RMWKFfXSYmNj2b9/P3369MHf37/YtdO//fabNAtRfo4dO4a1tXWBr2xXr16lWrVq0rKfnx9Xrlx56vHXr19PvXr19KY1vn79Ovv27WPMmDF5th8+fDi7du0iOTmZqKgo9u3bR9u2baX13t7eeb57BUEQsolo7Blkf3E2bWoupd29q6ZPnz7MuhVFQkICGpUK29QUGiYnYWxiQpS9DTzQNcV3slbwt7MPmrsJAHxY48tnKkfKqSjpvW1fb+TGikK2fnZajZZTO24TF5nz8KDTqGqieX0pcO3aNUaMGMGRI0ektE8++aTYzRYFQRBKI21GBvfHT3i+PACtVoNMJqegb123bxYhK+KD1cDAQIYMGcKgQYP00jds2MCwYcPw9dW19JszZw41atRg1apVeb7vy5cvLwWrFStWJCAggGPHjhUaRBckISEBNzc3vbSNGzdSoUIF/Pz8MDExITAwkPDw8CIPtBobG4uzs3OB65s0aVKkZvwqlQqlUiktK5VKvdlcChIcHMywYcP00saPH8+XX36JoWHeQYtr1KhBUlISNjY2ZGVlMXjwYN5++21pvaWlZYGtJQRBEEQN/TPYvXs3Mhl4e+d8eVatWhWZTMbt1HRS//1nr0CLYWYmfzlbwYPLAFRyVTDWyZ5MjQaAMvTE0dKk2GVQnYqU3pvXdX5pwXxspIq9P1zUC+brdSkngvk3XEZGBrNnz6ZatWp6wXxAQEC+NTuCIAjC66F169a4ubmxZs0avfTIyEjc3d2lZQ8PD9LS0oiLi8uTx/379+nevTvOzs5YWVmxaNEiYmNjn6k8VlZWeYLkkJAQ+vTpA+geGNSsWVNqrm9gYIBGo0Hz7++obGq1WgqW7ezsiI7Ofzrd4rCwsCApKWecoqSkJCwsLArd58qVK1y9elUqP+imODYwMKBDhw757tO7d29q166NSqUiOjqa69ev8+2330rrk5OTsbKyes6zEQThTSVq6J9Bly5daN48p3Z+395kjh+/zsOMTFJTUyEri3Y3LlFDlcI1by+IOCtt+79KCjI0WkIjdV8QprhRo6x1scuQ9k+89N60it2zn0wB1OlZnPtfOI/uJuult+jvjbmVaF79Jjt27BjDhg0jNDRUSvPy8mLp0qViWiFBEIRikBkZ4fbNoufKQ6vVkpmZiYGBQYEP02VGRsXKMzAwkOHDh9OyZUspzdXVlYiICGk5IiICExMTbG1t8+w/ffp0bGxsuH79OkqlkilTphAVFZVnO+CpFQB+fn788ssv0vLt27c5efIkoaGh/PDDD4AukF67di1Tp07FxcUFhUJBREQEnp6eeuXNfiDRqlUrduzYkacVQrajR4/SsWPHAsuU/YDB19eXS5cuSc3uL1++nGdAuycFBwfz1ltvYWOTM2jy4cOH+eOPP6RWA3FxcZw/f57r16/z6aefcuHCBVatWoWJiQkmJib07t2bgwcPMm7cOEDXWi6/EfMFQRBA1NA/szZtc57QnjjxGKVSyaLwB6SlpaHVaHBWJeLs6Ig6Ix2yMgDo3sCE21aOXIvWBckVGUPDcvaYGhWvdj3jfs6TbMuWZV/A2eRIS1FzasctDvx0WS+Y92nkylujq4tg/g2WkpLCiBEjaNq0qRTMKxQKJk+ezOXLl0UwLwiCUEwymQy5sfFLfxW31Vzbtm1xdnbW6/fep08fli9fTmhoKCkpKUybNq3AKUiTk5OxtLTEwsKCy5cvF9rH3dHRkbCwsELLcvr0aWmatpCQEOrVq0doaCjnz5/n/PnznD17lhs3bnDu3DkUCgU9evRg2rRpJCYmkp6ezsKFCzE2NqZ27doAzJw5k0OHDhEYGMijR4/QarWcPn2aESNGANC0aVO9EemffGXr378/8+fPJyYmhps3b7J8+XIGDhxY4LloNBrWrl2bZ5vZs2dz7do16Xzq1KnD559/zkcffQRAnTp1WLlyJWq1mtjYWLZs2aIXwP/xxx+0b9++wOMKglC6iYC+mLRaLUZGOV9uu3cl4eDgAMD9tAweP34MWRqqPn5MaGIMJOueWLf0MyL6ra94nJGFRqPFk4GUd1AytFm5Ypch9WpOszYjd8vnPKMcaSlqDq2+SmyugfYMTQxo1LMC5Wo6vLDjCK8mIyMjjh8/Li3Xq1ePs2fPMnfuXMzMzArZUxAEQXjdBAYG6jWnb9euHVOmTKFTp054eHhgaGjIokWL8t135syZHD58GKVSybhx4/JMz5bb5MmT+eSTT7C2tmbdunV51ru6ulK7dm1pOri1a9cycuRInJ2dpZePjw89evSQHhwsWbIEQ0NDvL29cXV1Zd++fezatQujf1sq+Pj4cOzYMc6ePUv58uWxt7dn0qRJvPXWW8W6RiNHjqR58+ZUrFiRRo0aMWnSJFq3bg3oWgRYWFjotWo4cuQIqampdOrUSS8fS0tLvfMxMjLCysoKS0vdb7gVK1bw999/4+joiI+PD56enkyZMgXQdSXYs2dPnhHzBUEQssm0RR0a9Q2RlJSElZUV8fHxzzRftomJCc2aG9Ksma7J/bwvY/jqqyUMGzaMARdvE3n/PvUvn6P7rSuEWxhhoNaNDN+tvjFjnR258O9AeBUZx9we1XBUFq//vDrmMYl77gBgVNYSZWv3p+xRNJf/uE/4pUfSso2LObXae2BinnfwFuHVp9FoePjwIY6OjsjlRX9ud+rUKTp06MDs2bMZNWpUnmmEBOFV8qz3uSC8LGlpady5cwcvLy9MTIo/Pk5+itLk/nV35swZpk2bxv/+97+SLsorZ8WKFYSGhjJ//vySLspLVRruc6H0KOi7ICEhARsbGxITE/UG3Hxeog99MaWnp4M2J8hNTtbQuXNnHmZkEhMTg1ajwTU5jhspCZja2YMamvkZMtPGjGsPdE3YjbGnUXn7YgfzoD+yvVltp+c+n8ibCfz9v3C9NCcvK+p08nzuvIVXV2ZmJt9++y2tW7emevXqUnqDBg2IiIh4of9kBEEQBKEwderUEcF8AQYPHlzSRRAE4RUnAvpiyB6cxaucrknXqZOPAV1zsa2Rj1Cr1WjT0shMe4wMLTJVJLXLG7LKJIY7GlvSMnTbexDAe429nqkMmbFp0nsD62fvz67VaDm0JpT0x2q99AbdymPnVvgIrsLr7dy5cwwdOpRz585Rt25dTp48qVcTL4J5QRAEQRAEQXg9iIC+GEaNGgVA2bK6GnpLSzmRkbrp4xZf0A0ips3Kwu7BXWQKBaDGzh7uGCsJf6QL5sszjO/61UQhL35zIq0mp3eEed2C51ctNA+tluNbbpL48LFeevU27pSpbFPAXsKbQKVSERgYyKJFi6Tpfs6cOcMff/yhN9KxIAiCIAiCIAivh1ei0+HixYvx9PTExMSE+vXrc/r06QK3Xb58OU2bNsXGxgYbGxvatGlT6PYvWs2aOc3kf/89BRcXFxLT0gHQZmmoevcGBposjMo408jbkMlZ97ms0vW3d6UL7zb0xszo2Z6jaFQ5tekm3nmnkXmaS0fusXfJxTzBfMcRfiKYf8Pt3buXqlWr8vXXX0vBfNWqVTl+/LgI5gVBEARBEAThNVXiAf3GjRuZNGkSgYGBnDt3jurVq9O+fXsePnyY7/ZHjhyhb9++HD58mJMnT1K2bFnatWvH/fv3/5PydnorpzlyrVq6KUSCzv8DQFaKinqXTgEgV8djrNSiRYYGA2QYYoEXLSs7PvOxU0NzRreXKYpewx8XlcKexReIuBKrl16vczk6jaqGXFHit4HwkkRHR9OnTx/eeustwsN1YyWYmJgwZ84czp07R8OGDUu4hIIgCIIgCIIgPKsSj+S+/vprhg4dynvvvYevry9Lly7FzMyMlStX5rv92rVrGTVqFDVq1MDb25uffvoJjUYjTXfyssTFxWFoKMPERBdI//VXKiNGjCApM4sL8Ulos7JAk4VhViYyuYJ23qkkyzQkp+lq1Sswkh/6136uMmie6O9epH2yNJz85aZeWs127rw1ujoO7pZiJNE32N69e6lSpQqbNm2S0lq3bs2lS5eYMmUKhoZiBgNBEARBEARBeJ2VaB/6jIwMzp49K821CSCXy2nTpg0nT54sUh6PHz9GrVZja5t/E/T09HTdyPT/SkpKAnTTHWU3PS6Knj174uGZEwAd+jWZn5bXJ+rxv4PUZWbS8cwRAJR+lTEz/ocFWY+IMigDyKhRxhoDOcU6Zp5zCdeV3djLqkj5xD94zMmtt6RlK0dT6nfxwsBI8VzlEF59Go0GZ2dnEhMTAbCzs2PBggX0798fmUwm/v7CG0Gj0aDVasX9LLwysu/J7NeLkp1XKZtpWChlxH0uvCmyvwOejDdf1u+VEg3oHz16RFZWFk5O+tOvOTk58c8//xQpj8mTJ+Pq6kqbNm3yXf/FF18wa9asPOkxMTFkZGQUuaxHjhxh9Gg7QDdVXUqKlqysLD66cB2NRoOROh23qAg0aCmTcZ6oDA3hWWmkyE1Bk8VblcwL7EZQFFqtFk26rrxqTSqPC8nrcaKai/+LJisz5x+isZmCis2UxCXEFrif8ObQaDR4eXnx3nvvkZycTGBgIHZ2dsTExJR00QThhdFoNCQmJqLVasU89MIrQa1Wo9FoyMzMJDMz84XkqdXqfm8AolWd8MYS97nwJsnMzESj0RAbG6vXIja7ou1Fe61HuZ87dy4bNmzgyJEjmJjkP6f7lClTmDRpkrSclJRE2bJlcXBwwNrauljHc3bRXS5zc90Px38MTVGpVMjlctKyNBjIQW5sgE8ZOXvkiURrHJDL5bjTB68yLs92kv+K/fkqGOumy7Op74ncJP8/XUZaJme2hGIgN8JAtzmV6jtRofaz990XXn2///47y5Yt4+eff8bAwACNRoNMJuOHH37AwOC1/pgLQoGy73MHBwcR0AuvhLS0NJKTkzEwMHjh/3tFN6nn4+XlRXBwME2aNHmm/R8/fsw777zD8ePHee+991i0aFGebRYsWEBaWhrTpk17ztK+eWbMmIGTkxNjxowpdDtxnwtvAgMDA+RyOXZ2dnoxqpGR0cs53kvJtYjs7e1RKBQ8ePBAL/3Bgwc4Oxc+Ldv8+fOZO3cuv/76K9WqVStwO2NjY4yN887XLpfLi/wD8MiRI+TedPeuJMaOHcu+R7om8NqMDPr8sVO3Ml2FoYE5e9ISSFJUwggb/GvWfq4fm1qtFhm6p5UKpREGZgXfDL+t+YfsB5u2rubU61wOhYH4ofumiouL46OPPpLGnGjQoAETJkwAdE+4s/+hCMKbSiaTFev/uSC8THK5HJlMJr1eBK1WK+X1PHl6enqi1Wq5efOmFDSNGDECZ2dngoKCXkRRX3nP83fZunUrKpWK+Ph4FApFnvXp6el8++23XLx4Ue8YJ06coHHjxnzzzTeMGzdOSl+9ejUhISH8+uuvecp49+5dypQpA8C2bdv44osvuHLlClZWVjRo0ICgoKBCf/s+KSYmhkGDBnHkyBHKlCnDkiVLaN26db7bDho0iPXr10v3iIeHB1euXJHWX79+nTFjxnDixAnMzc2ZMWMGY8aMISIiAl9fX728UlJS2LJlCz179mT8+PHUrl2bESNG5Bu0v6j7XBBeBdn/a578ffKyfquU6C8gIyMjateurTegXfYAd4WNvj1v3jxmz57N/v37qVOnzksv54oVK3BwyHn2cfZsKmPGjCEsQRfQe0ZGYPFYBUAZNwP+1KQQr9FNVWeOJx2rPtuc8ZJcTeetOnkVuJlWq82Zq14mo0G38iKYf0NptVrWrVuHj4+P3gCSe/fuFX3PBEEQhHwlJyezatWqki7GaykiIoLKlSvnG8wD7Nixgzp16uRp/RkSEoKNjQ0hISHFPmZwcDCDBg1i/PjxPHz4kLCwMPz9/dm3b1+x8hk9ejTOzs7ExMTw1Vdf0bt3b+Li4grcfsaMGahUKlQqlV4wn5aWRseOHXn33XeJi4vjxo0bUpdXd3d3aR+VSsWhQ4cwNzenQ4cOADg4OFClShV2795d7OsgCELhSjzamzRpEsuXL2fNmjWEhoYycuRIUlJSeO+99wAYOHCg3qB5X375JTNmzGDlypV4enoSHR1NdHQ0KpXqpZRPq9USEhLCwHdz5mnXaKBSpUrExMWhTU/HQhWPYaYaQ3MNDasasTwjhiSFbvvpzQdi+JzTwiXs1g1sJzOUIzPM/4tEq9Wy94dL0nIz/0riCecb6vbt23Ts2JGAgABpXAalUsmSJUvYv3+/+LsLgiC8IrRaLZnqrBfw0hS6vqgPcidOnMicOXNQq/OfNWfJkiWUK1cOBwcH+vfvX2B/z8zMTMaMGYOdnR3e3t58+eWXVKhQAYCwsDAMDAxYvnw5Li4uODs7s2bNGmnf+Ph4+vbti729PeXLl2fZsmXSukGDBjFu3DhatmyJhYUFnTt3JiYmhnfeeQelUknLli2Jjc0ZC6hHjx44Ojpia2tLr1699ILUffv2UaFCBWxtbfMdSyk/K1eupFKlSlhaWlKtWjWOHDkCwJw5c/j0009Zs2YNFhYWbN++Pc+++/fvp2nTpnpparWaTZs28e2333Lu3DmuX79epHKAroLrk08+YdasWQQEBGBubo6RkRG9e/dm8uTJRc5HpVKxfft2Zs2ahZmZGV26dMHPz48dO3YUOY9sq1atolGjRgQEBGBkZIRSqcTb2zvfbYODg+nevTvm5uZSWrNmzdi/f3+xjysIQuFKvHNtnz59iImJYebMmURHR1OjRg32798vDZQXERGh1zzhhx9+ICMjg3feeUcvn8DAwJfSZCy79cCjR5kolTlN3X+5fRdtVhaa9HTMVbqaeoVxJuO0UWRkakgzNsMcL+p7OTzX8TMT0slK1A2GJzczKHD++QMrrkCuL3RL2/zHFBBeX2q1moULFxIUFERqaqqU3qNHD7799lvc3NxKsHSCIAjCk7IyNfzvx8vPlYeWnObIBT2ubT+sKgYFPPDPrWXLlhw8eJDVq1czdOhQvXUHDx5k9uzZHDp0CA8PDwYOHMj48eNZvXp1nnx++OEHjh8/TmhoKFlZWXTq1ElvfVZWFpcvXyY8PJwjR47Qo0cPevTogaWlpdSHOiIigps3b9K6dWu8vb1p3rw5AJs3b+bXX3/F3d2dRo0a0aRJE3766SfWrVvH22+/zbfffisF6D169CA4OJjMzEz69OnDp59+yqJFi4iJiaF3796sX7+edu3aMX36dO7du/fU6+Ps7MyhQ4dwdXVl5cqV+Pv7Ex4eztSpU8nIyODevXv89NNP+e576dIlevTooZe2b98+FAoFffv2ZdWqVYSEhPDpp58+tRwA165dIzIykm7duhW4zdy5c5k7d26+65o0acLu3bu5ceMGFhYWUhN+AD8/P72a9yctXLiQhQsXUrlyZb744gvpb3P69GlsbW1p2LAht27dolGjRixevDjP7w+1Ws3GjRtZt26dXrq3tzc7d+582qkLglBMJV5DDzBmzBjCw8NJT0/nzz//pH79+tK6I0eO6H2ZhIWF6U0Jk/16Wf2/2rZtC0C5crpgfueOJLZs2cKPdyLRpKSgBarcvoKRuwd3yqtISlUTa+CEAjO+7fjxcx077VocCdtz5pC37lw+zzaq+HT2LL5AZnqWlNZuSNXnOq7w6snIyKBBgwZMnjxZCubLlCnDjh072Lp1qwjmBUEQhCIJDAzMt5Z+w4YNDBs2DF9fX8zNzZkzZw4bN27Mt/Z/69atTJw4EUdHR1xcXPId6GzmzJkYGRnRrl07zMzMuHXrFllZWWzevJk5c+ZgZmZGtWrVGDJkiF7g16tXL6pUqYKlpSWdOnWicuXKNG3aFCMjI7p3786FCxekbfv374+5uTlWVlZMnDiRY8eOAbruZ7Vr1+btt9/GyMiIoKCgIvVd7dSpE2XLlkWhUDB06FBkMhk3btwo0nVNSEjAwsJCLy0kJISePXuiUCjw9/dn7dq1RcoLkFoiFDam1CeffEJCQkK+r+ym7SqVCqVSqbefUqkssGXr+PHjuXnzJlFRUYwePZouXboQHh4OwP3791mzZg3ffvstERERuLu7M3DgwDx57Nu3DyMjozz99C0tLV/aKN+CUJqVeA3966B585zmQgkJWdi0aEvmGV3z9ib/nMXY2gaFOpobFimQCRkyE8oxhEpOls98TK1Gi+pklLRsVtsJ2RP94Y9vuUHCg8d6aR2G+4l+828gIyMjmjZtyrlz55DJZIwdO5bPPvsMS8tnv8cEQRCEl0thIKf9sOd7yK7VasnMzMLAQFFgl6rifO+3bt0aNzc3vWbwAJGRkTRq1Eha9vDwIC0tjbi4OOzs7PS2jY6O1qvxzf0eQKFQ6O1jZmaGSqXi0aNHqNVq3N3d9Y5z+XJOKwZHx5xZeUxNTfMsZweimZmZfPjhh2zbto34+Hi0Wi329vYAREVFUbZsWb3jP3kO+dm+fTuffvopt2/fBnRjDuRu4l8YKysrvSA5KSmJXbt2SU3Me/bsyejRozlx4gSNGjXCwMAgz0OV7GVDQ0OpvNHR0Xh6ehapDPmxsLAgKSlJLy0pKSnPw4dsNWvWlN4HBAQQHBzMgQMHGDp0KKampnTv3p26desCuodDDg4OpKamYmpqKu0XHBxMQEBAnocoycnJWFlZPfO5CIKQPxH5FUGbtjn/9G7cyOCX6Diy/q0l9bp3GwMbM2wMU0jJ1NWS28i6snJQvWc+nlar1U1T9y+rt7ww87OXlh+EJbFn8QW9YL5cTUc6jaomgvk3yJO1IrNnz+btt9/m1KlTfPPNNyKYFwRBeMXJZDIMDBUv4CUvdH1xx07Jr5be1dWViIgIaTkiIgITExNsbW3z7O/s7Mz9+/el5aI0Zwfd7EaGhoZ5juPq6lqs8gOsXbuWI0eOcOLECZKSdK0ns783XVxcuHv3rrRtamrqUwPz9PR0+vbty+eff05sbCwJCQk4OjoWeXwCPz8/vdr8LVu2kJaWRu/evXF2dsbX1xeNRiMNjle2bFmp5jtbeHg4JiYmODo6UrlyZVxdXQvt6z5nzhwsLCzyfXXs2BGAihUrolKp9P5ely9fpkqVKkU6L7lcLl2DqlWr6t1r+c0akJCQwK5duxgwYECevK5du4afn1+RjisIQtGJ6K8QWq1Wb7q6v8+lMmf+Am4/jEGTnk6DW5cwycxAlnCHGMcUAMKNK/FWVZ/nOm7G3eScBbkMQwczafHQmquc2XNHb/t2Q6vi08hFDIb2hrh//z49e/bk66+/1ku3tLRk165d1Kv37A+LBEEQBKFt27Y4OzvrDe7Wp08fli9fTmhoKCkpKUybNo3evXvn+9uiR48eLFq0iIcPHxIdHc3ixYuLdFyFQsE777zD9OnTefz4MZcvX2bFihX4+/sX+xySk5MxMTHBxsaGR48eMX/+fGldp06dOHv2LHv37iUjI4NZs2ah0WgKzS89PZ2MjAypRcA333xDTExMkcvToUMHqck/6JrbT5gwgQsXLnD+/HnOnz/PqlWr2LRpE2q1mvr166PVavn222/JyMggISGB6dOn06tXL2m6q7lz5xIYGMj69etJSUlBrVbzyy+/MG/ePACmTp2qN7J87lf2SPgWFhZ07dqVwMBAUlNT2b17NxcvXqRr1675nsfWrVtJSUkhMzOTjRs3cvToUWkk+/79+7Nz507Onz+PWq1m9uzZtGzZUq92fvPmzfj4+OQbuP/xxx+0b9++yNdUEISiEQF9Id5++2296eq2b0/CvHZ90tLSALBNiMXEzoYMrYadpvE8lluiRc6gf5siPauM8JyA3q6/7uHA3wci2LP4AmmqnKfp5Ws70m5oVQyNnj4QjvDq02g0LFmyBF9fX3755RdmzpxJWFhYSRdLEARBeAMFBgbqjQrfrl07pkyZQqdOnfDw8MDQ0JBFixblu+/IkSOpX7++NJhdjx49MDY2LtJxv//+ezIzMylbtixdunQhKCiIli1bFrv8AwcOxMbGBicnJ5o2bSpNjwa6KdLWr1/P2LFjcXJywtTUNE+3gCcplUq++uor2rdvj7OzM7GxsdLI/UXRtWtXTp8+TWJiIvfu3ePo0aOMGzcOZ2dn6dW3b19MTEzYt28fJiYm7N69m507d+Li4iKNXfDtt99KeQ4YMIBVq1axcOFCHB0d8fDwICQkRKp9L6olS5YQGRmJnZ0dkyZNYuPGjVLLi7Vr1+rV1i9cuBBXV1fs7Oz4+uuv2b59O+XKlQPAx8eHxYsX061bNxwcHLhx40aerhvBwcH51s4/evSIS5cu0blz52KVXRCEp5NpS9mk1UlJSVhZWREfH59nrtAnyWQyatc2pVt33WAiM6Y/oNPhv3icnITmcSr9D23GzjCOP5zjiXVMJdy4EiNqjKBfjWbPVcZHq3UjjyqsjVE0cePs/nAeJ6brbdNpVDVRI/8GuXz5MsOGDePkyZNSmoODAxs3bnymHzoajYaHDx/i6OhYpIGABOF1JO5z4VWTlpbGnTt38PLywsTkxcw2o+tDn4mBgcEr+72/bNkytm7dyoEDB0q6KCVq/vz5ZGRkMHXq1JIuyitnxowZODk55TuAIrwe97kgFFVB3wUJCQnY2NiQmJiYZ7DK5yEGxStA9j+TTm/p+ilrtWDmP4jU1FQ0j1Opdfcm5qpEVPYakizT0cgUmOD+3MG8JkPXD1+j0RKngRsb9ecsbTXQB1NLo/x2FV5DqampfPbZZ8ybN4/MzEwp/f333+err77Kt++iIAiCIJSk5ORk/vzzT1q2bMmdO3f4+uuvGTduXEkXq8R9+OGHJV2EV9bs2bNLugiC8MYSAX0hrK3lGBnpAvuoqEw6fziZhw8eAlDzxkUMjdO5nZWJ2lTDPcMK/NRx2nMdTx3zmMR/+8cnPnzMjceZ8O+DBc9q9vg0dkUuF08t3xSHDh1ixIgR3LyZMzVhpUqVWLZsGS1atCi5ggmCIAhCITQaDR9//DHXr19HqVTi7+/PsGHDSrpYRVK9enVu3bqVJ/2vv/7Cx+f5xkASBEEoCSKgz0f2PN/DR+RMcbJypzFteqegzcqi1a2ryOIj0TgZ8FiRRprcDKWsDhWfY5o69cPHJO7VBfPpj9XEIpOC+fpdy2FfRoxo/iZZv349/fr1k5YNDQ2ZMmUKU6ZMeWHNNAVBEAThZbCysuLcuXMlXYxnknsee0EQhDeBCOjzsXTpUgAsLHL6ZHZYv4fkpEQ0jx/jkPAAhSaTJDXElUnloaEnyzsOfaZjadIziVt/DYC0x2oSH6aSZGpA3L/N6lsO8MFMKZrYv2k6d+6Mu7s7ERERNG7cmB9//BFfX9+SLpYgCIIgCIIgCK8REdDnY9KkSXrB/MJvE+j4tiUJDx5g91iFQcxdUBoQJc/kup07GhRUcrJ6pmPFrb9GVqaGR/dUALpg3kIXwFdrXVYE82+IlJQUzM3NpWULCwt+/PFHwsPDGTJkiBjQSxAEQRAEQRCEYhMB/ROysnSD0lWrltPsOdmtGjExD9FmZtLu0nHM0xJJsZRxu5watYEh1cs4PNOxNOlZZGXpgvlMhYx7NiYgk1G5gQsVaju+kPMRSpZarWb+/PnMnz+fM2fO4OXlJa0Tc7EKgiAIgiAIgvA8RED/hNWrVwNQq5aplFa27yDU6RkAqAA7Uzn/GGq4ZV8GD3szZjWe/kzHUp24z6O7upr5ezYmOHgoqdPJE7lC1Na+CU6ePMmwYcO4fPkyoJu3d9++fWI6FkEQBEEQBEEQXggROT5hyJAhADg565517NmTTDlfX7QZGZio07GPDkNubsA9J3sA3C09sDO1KzC/gqhT1Nz9/b607OChpF7nciKYfwMkJiYyevRoGjduLAXzcrkcX19fvanpBEEQBEEQBEEQnoeooc9Fq9UC0LChmZR2J8kOX5kcTUYGrUJPYZSRSmK6AeEuGkBOgG/vYh8n9noc0euuScsPlMa0fNurkD2E14FWq2Xbtm2MHTuWyMhIKb1mzZosX76c2rVrl2DpBEEQBEEQBEF404jq4Fy2bdsGQKe3cqaIcxk/F+2//eqb/vMXCitDbhlqURvqLp2bZfH6z0fdStQL5pNMDWgxoaZohv2au3fvHt27d6dnz55SMG9mZsaCBQs4ffq0COYFQRAEQfjPeXp6cuzYsZIuBkFBQVIr2FfdggUL+Pzzz0u6GK+kGTNm8P3335d0MYQniIA+l/nz5+st/3U+i7LePmgzMlCmpRCmMOSqiZoMuTEA71RthLWJdZHzv/ZnNGFbrkvLVu6WNJxcF7lcBPOvM61Wy1tvvcWOHTuktE6dOnHlyhUmTZqEgYFoCCMIgiCULE9PTzw8PFCr1VLaiBEjCAoKeqb8wsPD6d+/P46OjiiVSipXrswnn3xCfHw8oBuTyMDAAAsLC+lVpkwZAMLCwpDJZFJ6xYoV+emnn/Tyl8lkmJubY2Fhgbu7O5999tlTy7Rt2zZ8fHywsLCgTZs23L17V1r3xx9/0Lx5cywsLGjRosUznbPw5ktPT+ebb75h9OjReuknTpxAJpPx7bff6qWvXr2aNm3a5MlHJpNx7949aXnbtm3Uq1cPc3NzXF1d6dGjBxcvXixW2WJiYnjrrbcwNzencuXKHDp0qMBtq1SpovfZUygUjB07VlofHR3NO++8g5WVFXZ2dkyZMiVPHidPnkQul+t99saNG8e8efP0/o8IJU8E9LncuHGD6tVzRrf/Q9YatVqNNjOTfqcPkKzR9X9WK+SYGCkYX2dUkfOOuZtM1LH7KFN1eSjtTXB7r8qLPQGhRMhkMubOnQuAk5MTGzduZPfu3Xh6epZswQRBEIQSpdVqyczIeOmv7C6DT5OcnMyqVaue+7wePnxIw4YNMTY25syZMyQlJXH48GHMzMz0gpQWLVqgUqmkV+4AR6FQSOnffvstI0aM4J9//tE7zrVr11CpVGzZsoUvvviCffv2FVim69evM2jQIFasWEFCQgItWrSgb9++0nozMzOGDRvGzJkzn/v8S6PXdQyg4pZ7x44d1KlTB2tra730kJAQbGxsCAkJKXYZgoODGTRoEOPHj+fhw4eEhYXh7+9f6P2cn9GjR+Ps7ExMTAxfffUVvXv3Ji4uLt9tr1y5In2+YmNjUSqV9OzZU1rfpUsXGjVqRFRUFPfu3aNfv356+2s0GiZOnEjdunX10h0cHKhSpQq7d+8uVtmFl0sE9Lk8evSIFi0tpGXTGg3RaHTN7eMy0lBZytFqtZz3sySw3rwi5xt7X8VfO27hkKwbKd/B3RLXAb6imf1r6vHjx0RHR+uldezYkeXLlxMaGkrv3r3F31YQBEEgS61m18Ivnuu1e9Fc9n73FbsXzS1wm6wi1pZNnDiROXPmFFi7tmTJEsqVK4eDgwP9+/cnMTEx3+2+/vprXFxc+Omnn3B3dwfA1dWVmTNn0rx582Jfp44dO2Jra1tgjWW9evWoUqUKV65cKTCPAwcO0Lp1axo1aoSBgQFTpkzh7Nmz3Lx5E4A6deoQEBAglbcoVq9eTatWrRg+fDhKpZKqVaty8+ZNAgMDsbOzo2LFipw5c0bafs6cOXh4eKBUKmnYsKHe+Xh6evLVV1/h4+ODpaUlM2fO5Nq1a9SpUwcrKytGjBghbRsUFES/fv3o2bMnFhYWNG7cmOjoaEaMGIGVlRW1atXi9u3b0vZjx47F1dUVa2tr2rVrR0RERL7ns3v3bipXroylpSWenp5s2LCh0PNv0aIFM2bMoE6dOpibm6NWq596jgsWLMDHxwdra2vGjBmTb74qlYqWLVvyySefALBy5Uo8PT2xtbXF29ubI0eOAJCSksKoUaNwdXXFxsaGAQMGSHkUdK8eOXKEChUqEBgYiL29PYGBgaSmpjJmzBhcXV0pU6aMVAmTn/3799O0aVO9NLVazaZNm/j22285d+4c169fL2DvvDQaDZ988gmzZs0iICAAc3NzjIyM6N27N5MnTy5yPiqViu3btzNr1izMzMzo0qULfn5+eq1DC7Jr1y6USqX02dy3bx/GxsZMmjQJMzMzTE1N8fPz09vnxx9/pH79+vj4+OTJr1mzZuzfv7/IZRdePhHQ51K+fHmsrRUAREVlkmVoRIYqBXtVAlkZqahMZcTaGFJeMZZqZWyLnO+pbTfxeJQKgKWtCcrGriisjF/KOQgv14EDB6hatSoDBw7MUyMyZMgQbGxsSqhkgiAIglC4li1b4u7uLk3Rm9vBgweZPXs2u3fvJiwsjNTUVMaPH59vPocPH6ZLly4v5OG1Vqtl586dPHr0iPLly+e7zalTp7h8+TI1atR4al5PLhf2EKAojh49SuvWrYmLi6N+/fq0adMGpVLJgwcP6N+/Px988IG0rbe3N2fOnCE2Npa2bdsycOBAvbz27NnD8ePH+euvv5g3bx5jx45l27ZtXLt2je3bt3P48GFp2x07djBhwgTi4uIwNzenYcOGtGrVitjYWGrWrMmsWbOkbRs3bkxoaChRUVGUKVOGcePG5XsuQ4YMYeXKlSQnJ3Pq1CmqVav21PNfv349GzZsIDExEQMDg6ee4/bt2zl69CiXL19m06ZNeucEkJSURPv27WnatClz584lJSWFCRMmcPDgQeLi4vjf//6Hh4cHABMmTCAiIoKLFy/y8OFDhg8fDjz9Xg0LC0OhUBAVFcX06dP58MMPiYuL4/r165w+fZrg4OACa5gvXbpExYoV9dL27duHQqGgb9++NG/evFi19NeuXSMyMpJu3boVuM3cuXOxtrbO9/X2228DulbEubutAPj5+RXp/g4ODqZ///7S5/X06dN4enrSsWNH7O3tad26NaGhodL2sbGxLFq0SO8ey83b27vY3QWEl0t07s0lKyuL7O7Op049pnLr8oAMr+g7GD5OIstcy80KNSkDKE0Mi5Tn3wcjcElMB8DASI5lWQtMKhf9YYDwanj48CGTJk1i7dq1ANy5c4d169YREBBQwiUTBEEQXlUKQ0M6T8zbN7U4tFotmZmZGBgYFBhAKwyL9psEIDAwkCFDhjBo0CC99A0bNjBs2DB8fX0BXW1zjRo1WLVqVZ7jxsbG4uzsLC0vWrSIoKAgMjMz+eSTT5g+fToAv//+u17T5YYNG0rNjLOysrC2tiYtLY309HS+/PLLPAPIVqlSBblcjqOjI59//nm+fZWztWnThilTpvDHH3/QoEED5syZQ0ZGBikpKUW+Nvnx8fGhd2/djEbvvPMOW7duZdKkSchkMnr37s3ChQulbXv06CG9nzp1Kp999hkqlQoLC13rz3HjxmFra4utrS3Vq1enQ4cOlC1bFoDmzZtz4cIFWrZsCUDr1q2lmuLu3bvzww8/6JUjd59nf39/6f3kyZNp3LhxvudiaGhIaGgo1atXx9nZWe9vWJDBgwdToUKFIp/jhAkTsLfXTe3cokULvXOKj4+nTZs2dOnSRbpHQNd18cqVK7i6uuLh4YFMJkOj0RAcHMylS5ek/Jo0aQIUfq8CGBsbM3XqVAwMDDAwMGDVqlXcvHlT6k8+cuRItmzZIgXLuSUkJEjnki0kJISePXuiUCjw9/dn7ty5fPrpp0+9dqD7rACFXutPPvlEaq1QEJVKhVKp1EtTKpVS/oUdf9++fXz55ZdS2v3799mwYQM7d+6kdevWfPPNN3Tr1o2rV6+iUCiYNm0aEyZMyNPtIJulpWWBrXeEkiFq6HOJjAyX3t+/ryZLJkOblYlxSjJqIw1qmTFusm40rmBfpPxSVRlEXo/HWK0BwMbZHOsuFZ6yl/Aq0Wq1rFq1Ch8fHymYB90Xrxi5XhAEQSiMTCbDwMjopb+KU1PeunVr3NzcWLNmjV56ZGSkXnN0Dw8P0tLS8u2ja2trq9f1bMKECSQkJNC/f3+9PsvNmzcnISFBeuXuM6xQKEhISCA5OZnJkyfz22+/5TnOlStXiI+P59q1a0ycOLHQ8/L29mbVqlVSE+34+Hh8fX31ajSfhaOjo/Te1NQUe3t76XqbmpqiUqmk9cuXL6dKlSpYWVnh7OyMVqvVC7iezOvJ5dx5FWfbzz//nAoVKqBUKqlXr16BQd6WLVvYtm0bZcqUoUOHDnq1sgV58vo97RydnJyk92ZmZnrlPHr0KPfu3dMbcM7c3Jz169fz3XffUaZMGXr37k1kZCQxMTGkp6fj5ZV3Wuen3avOzs7SgMQxMTGkpqbi6+sr1XpPnTqVBw8e5Hu+VlZWemVOSkpi165d9OnTB4CePXty9+5dTpw4AYCBgUGeLizZy4aGhtjZ2QHk6apZXBYWFiQlJemlJSUl5Xn48KQNGzZQo0YNvL29pTRTU1OaNGlCx44dMTIy4sMPPyQ6OpqbN2/y999/89dffzF06NAC80xOTsbKyuq5zkd4sURA/6/Q0FAUipzlmHRLQIY2Ix2z5Di0GkBmgQwZ3Wq6PTW/jLRMfluT84/Swd0SZVM3ZArRt/p1cf36dVq1asX7778vfUnY2NiwYsUKDh8+rPfPURAEQRBeF4GBgXn60ru6uur1vY6IiMDExARb27ytClu2bMmuXbteSFkMDQ35/PPPuXPnDtu3b3+uvN555x0uX77Mo0ePmDVrFg8ePKBq1aovpJxPExYWxoQJE1izZg3x8fFERUUhk8mKPGDhs/r9999ZsmQJe/fuJTExkdOnTxe4bf369dmzZw8PHjygevXqjBw58qn5535Y9Lzn2KVLFwYNGkTHjh31guZOnTpx6NAh7ty5I9WuOzg4YGxsTFhYWJ58nnav5i6zvb09xsbG3L59W3qwlJSUVOCAdH5+fty4cUNa3rJlC2lpafTu3RtnZ2d8fX3RaDRSs/uyZcsSHh6ul0d4eDgmJiY4OjpSuXJlXF1dC+3rPmfOHL0R6XO/OnbsCEDFihVRqVTcv39f2u/y5ctUqVL4ANvBwcF6Yw8AVK1atcCHgL///jvXrl3Dzc0NZ2dnNm7cyJdffsl7770nbXPt2rU8fe6FkiUC+n/98ssvesuWtRtgqNGgQYNVXDQZhhBdRtenx9bcqNC84qNTOLhC16fFIi0ThaEcuVyGoaPZyym88EJlZGQwe/ZsqlWrJg3MAtCvXz/++ecf3n//fTHonSAIgvDaatu2Lc7OznoBdJ8+faTBXVNSUpg2bVqBg7xOnDiR+/fvM2zYMGlquAcPHkgD0BWXQqGQBux7HmfPnkWj0RATE8OwYcN4//33pSBPo9GQlpaGWq3We/+iqFQq5HI5Dg4OZGZmEhgY+MLyLkxycjKGhobY29uTkpJS4PR+GRkZrFu3jqSkJAwNDaWpzIrjRZzjnDlzqF27Nl27diUtLY0HDx6we/duUlNTMTY2xtTUFIVCgVwuZ+DAgUyaNInY2FjUajXHjx8HinevyuVy3n33XT744AMSEhLQaDSEhoYW+OCjQ4cOHDt2TFoOCQlhwoQJXLhwgfPnz3P+/HlWrVrFpk2bUKvV1K9fH61Wy7fffktGRgYJCQlMnz6dXr16IZPJkMvlzJ07l8DAQNavX09KSgpqtZpffvmFefN0A2xPnTpVbzaI3K/sBw8WFhZ07dpVGuRv9+7dXLx4ka5duxZ4rW/cuMG5c+f0ZnsAXbeJCxcu8Ouvv5KVlcWiRYtwdnamQoUKDBs2jJs3b0rn2qVLF0aPHq3XteSPP/6gffv2RfyLC/8FEdD/a/r06Xh46AJ1rRZaV6+OVq3GIlWFIjWFWEtTTE3q8V2/mk/N68RW3ReaTKvFKS0TO1dzAOTKwh8ECK+G48ePM3PmTNLTdWMfeHl5sX//ftauXavX5E0QBEEQXleBgYF6zenbtWvHlClT6NSpEx4eHhgaGrJo0aJ893V2dubkyZOoVCpq1qyJUqmkRYsW1K5dW29k8yNHjuSpcSyoT/u7777LnTt3Cp1b+2lGjRqFUqmkSpUqeHh48Pnnn0vr/vjjD0xNTRk4cCBHjx7F1NS00GbFxVW1alWGDx9OtWrV8PT0xMvLCyOjl/+7r0OHDjRu3BgPDw/8/Pxo1KhRgduuWbMGDw8PbGxsOHjwIN9//32xjvWizvH777/Hzc2N3r17k5GRwbx583B2dsbNzY3IyEjpocTXX3+Nq6srVapUwcnJiR9//BEo3r0KsHDhQqysrPDz88PW1paBAwcSHx+f77Zdu3bl9OnTJCYmcu/ePY4ePcq4ceOkMQecnZ3p27cvJiYm7Nu3DxMTE3bv3s3OnTtxcXHB19cXc3NzvfnqBwwYwKpVq1i4cCGOjo54eHgQEhIi1b4X1ZIlS4iMjMTOzo5JkyaxceNG6YHV2rVr89TWBwcH06FDB2kMgmx2dnZs2bKFsWPHYm1tzbZt29i2bRsKhQIzMzO9czU1NcXCwkLqT//o0SMuXbpE586di1V24eWSaV92W6BXTFJSElZWVsTHx0s3Z3x8PLa2trRtZ0GzZrrge6/zN8jNTXGKfUDN8xvJMq5CmZ4jGdyy8GbWexZfkN5XU4Cpia4Pj2Wrshi7KwvaTXjF9O/fnw0bNvDBBx8QGBiImdnr1bpCo9Hw8OFDHB0dkcvFczvhzSTuc+FVk5aWxp07d/Dy8sLExOSF5FmUQfEE4XX3Kt3n8+fPJyMjg6lTp5ZoOV5FM2bMwMnJqcApCQWdgr4LEhISsLGxITExMc8gh89DjHIP0kib2cH8wzh75F6maLUarBIi0BjKyDCz5a1anoXmkzuYl2u0mJrnjDorgvlXk1ar5bfffqNVq1Z6XyBff/01H3744VOnyBEEQRAEQRDeHB9++GFJF+GVNXv27JIugpAPUaUBrFixQm9AvAv1xkj9iso8uIi1RkGsRwOclAXPHb/3B/35GBv55gwiY+tf+cUWWHghwsPD6dy5M23atCE4OFhvnaOjowjmBUEQBOEVcvLkyXwHDmvWrNkz57lx48Z883xyILE3VefOnfM9//3795d00QRBKCJRQw/UrVsXdeY5AFSGdqRm2GNioMY6JQEyU4hXVGRA+9oFNgH651QUWo2u54LCUE67wVWIC84Z4V5uIi7zqyQrK4vvvvuO6dOnS335Jk2aRJcuXQqcc1MQBEEQhJLVsGFDvdHRX4Q+ffpIU5KVRi9qtgJBEEqOqKEHjh07hq+vrvY9yaUexnJdAN7q750gk5GmdC9w7vkze+9w6+xDabn90KpoU3PmYLVs9vQp7oT/zt9//039+vWZOHGiFMy7uLjw448/ijk1BUEQBEEQBEF4rYiA/l/ZI9zfoQbZFfEqo1gyZI5oZXkvU5pKzZ7FF3hwJ0lKa9CtPDKZjOQj96Q043LWL7XcQtGkpKTw4YcfUqdOHc6ePQvo5ikdNWoUoaGh9OjRo8QHYREEQRAEQRAEQSgO0RYc8PHx4UH0Q1zdDIg3dsIoU4t71G0SsMIYcM3KO7XFoTVX9ZbbvF8FY1Pd5cx8lPpfFFsoon379jFy5EjCw8OltKpVq/Ljjz/SsGHDEiyZIAiCIAiCIAjCsxM19P9ydTNAa6REoZWhlckoF3eWDAMDtGSi8PDR2zYrUyO9VxjKeWt0dSmY16Tlam7fqux/U3ihUOvXr5eCeWNjY+bMmcPZs2dFMC8IgiAIgiAIwmtNBPRAaGgoGelaMi2cUMjkaADPe1cxS9VggjOdmtaUto29r2L/skvScptBvnp5ZYTnNMEXU9W9GhYsWICdnR2tWrXi0qVLTJkyBSMjo5IuliAIgiC8ccLCwjAweLYGoC1atCAkJOQFl0h4FkeOHKFChQolXYyX5nnu0//a/fv3qVGjBhqN5ukblzLXr1+nQYMGJV2MElfqA/o///wTgCwNWFhYgEaGeVoKaQprjHFChgI7F2dp+1Pbb+ntb2Ck0FtWnYx6+YUWChQaGsq+ffv00hwcHDh9+jS//vorFStWLKGSCYIgCELJ8fT0xMPDA7VaLaWNGDGCoKCgkivUMzpy5AhyuRwLCwssLS3x8/Njx44dAGRmZvLOO+9QtmxZZDIZYWFhevumpqbSv39/LC0tcXd3Z/369XrrV69eTZkyZVAqlbz33ntkZGT8V6f10nl6enLs2LFSd+zX3dy5cxk5ciRyuX7YVrlyZWrVqpVne5lMxr179/TSBg0axGeffSYth4eH07dvX+zt7bG2tqZ27dqsXLnymcrm4OCAra0tH3/8MVqtNt/t5syZozctoomJCZaWltL6Fi1aYGJiIq3v2LGjtG716tUYGBjo7R8REQFApUqVcHZ2LvWzNZT6gL5p06YAmJrKUMkt0chkGGRmcNvaGAPMMTaUo7R3BCAlMV3az62yDR1HVtPLKysp1z99uRhg7b+Unp5OUFAQ1atXp3///sTExOitL1eunBj0ThAEQSjVkpOTWbVqVUkX44UoV64cKpWKxMRERo4cib+/P3FxcYDut92mTZswNjbOs19gYCCPHj3i/v37bNq0iVGjRnHt2jUALl26xMSJE9m2bRt3797l7t27zJ49+z89r5chMzPz6Ru9wvm/asd9HsUtc3p6Ohs2bKBXr1566adPnyYyMpLLly8TGhpawN75e/jwIQ0bNsTMzIwLFy6QkJDAmjVrOHjwYLHy2bt3L4sXL+bUqVNcvXqVffv2FfhQYOrUqahUKun13nvv0bNnT71tfvrpJ2n9k5VzLVq00Nvf3d1dWte3b19++umnYpX9TVPqA/qaNWtiY6NAhowTtm+h1cjIkoFBujUAWvucG+ZIyD/S+xpt3JE/EbQnHY6Q3tt0e3ObKb1qfv/9d6pXr86sWbNQq9XExcUxd+7cki6WIAiCUMpptVq0as3LfxVQK/akiRMnMmfOHL1a+tyWLFlCuXLlcHBwoH///iQmJua7XVBQEH379qVbt25YWlrSrFmzPDXhy5cvx8XFBWdnZ9asWQPAiRMn8PLy0tsuMDCQYcOG5TmGRqMhMDCQsmXL4uLiwrhx40hPT8+znVwuZ9CgQaSlpXH79m0MDAwYP358gePkBAcHM336dJRKJQ0aNKBr166sW7cOgHXr1tGzZ0/q1q2LlZUV06dP5+effwZg2rRpTJ06FYDIyEhkMpm07sCBAzRu3BjQtfysW7cuSqUSDw8PvvvuOwDS0tKwtrbmzp07UlkOHz5MpUqV8i0n6KZVLleunF7arFmzGDJkCABxcXH069cPR0dHypUrJ11n0AVAM2bMoE6dOpibmzN48GAiIiJo164dFhYWrF27FoAtW7ZQpUoVbG1t6dKlCw8fPtQ73owZM7CxsaFSpUp6AZ+npyfz5s3Dx8dHapo/duxYXF1dsba2pl27dlIt6pAhQ5567K5du+Y59pNWr15Ny5YtGT58OFZWVqxatarA6w26+zQgIIBevXphaWlJ/fr19a5/bt9//z3Vq1fn4cOHXL9+nSZNmqBUKnFycuKjjz6Sttu4cSNVq1aVWoZkPwy6cuUKTZs2lWq7jx8/Xui1etp1z/bnn3/i6uqKra2tXnpISAhdu3albdu2BAcHF3rdnrRw4UKcnJz46aefcHPTTa9dtWrVPK1VniY4OJjhw4dTvnx5nJ2d+eCDD6TPRGEyMjLYtGkTAwYMKNbxCtKsWTMOHTr0Wj7geVFKfUB/+vRpRo60Q2lhpkvQyrBKiccYFwDqOema1Oeunc+PNlNDVrxuGwM7ExRK0Uf7ZYuLi2PIkCG0aNFC+odqYGDAlClT9JoVCYIgCEKJyNQSuzb0uV5x60JJ2HiduHUFb0Nm0QL6li1b4u7uzurVq/OsO3jwILNnz2b37t2EhYWRmprK+PHjC8xr69atDBkyhNjYWBo0aMDAgQOldVlZWVy+fJnw8HB+/vlnRo8eTXJyMo0aNUIul3Py5Elp2/Xr19OvX788+a9YsYItW7Zw8uRJLl++zNmzZ/niiy/ybJeVlcWqVaswNzd/ap/v+Ph4oqOjqVYtp4Wln58fV65cAeDq1at51kVERKBSqWjatClHjx4F4OjRo3h5eUlNyI8ePSq1+DQ0NGTZsmUkJCSwdetWpk+fzt9//42JiQk9evRgw4YNTz33bI0bN0atVvPXX39JaZs2baJPnz4ADBgwAFdXV+7evcvevXuZMmUKFy9e1Mt/w4YNJCYm8tNPP+Hu7s6BAwdQqVQEBARw+vRpJkyYwIYNG3jw4AHe3t6MGjVK2j8sLIysrCwePHjAvHnz6NWrF/HxOTM//fLLLxw5ckSqIW7cuDGhoaFERUVRpkwZxo0bB1CkY1euXJmxY8cW+vfLvtYNGzYkPj6e/v37F3i9s23bto1Ro0YRHx9P5cqV8+1iMn/+fFauXMlvv/2Go6MjM2fO5K233iIxMZHbt29LtePHjx9n9OjRLFu2jMTERDZv3oxSqSQjI4POnTvTq1cvYmJi+Pjjj+ncuXOB1+pp1z23S5cu5ekumpmZycaNG+nTpw/+/v6sW7euyA/1AH777Te6du1aYMvViIgIrK2tC3xlP6jJ7/OS/VkqzJ49ezAzM6Nly5Z66RMnTsTBwYG2bdvq3ccAp06dws7ODl9fX5YuXaq3zsXFBYVCwa1b+t2iS5NSH9ArlUo0Wi2GBjK0yEAmwz7qAjLkyGTQtN8grv8VrVc73+b9Knp5aLVaYkNymrtYthCj279MWq2WDRs24OPjw4oVK6T0Bg0acO7cOebMmYOpqWkJllAQBEEQXk2BgYH51tJv2LCBYcOG4evri7m5OXPmzGHjxo0FBgqNGjXi7bffxsjIiKCgIE6dOkVkZKS0fubMmRgZGdGuXTvMzMykH9sBAQFSTeDZs2d5/PgxzZo1y5P/hg0b+PDDDylTpgx2dnbMnDlTrwbxzp07WFtb4+joyJo1a9i6dSvW1taFnrtKpQLQ67urVCqldJVKhVKp1FuXnd6oUSPOnTtHWloaR48e5aOPPtIL6Js0aQJArVq1qFWrFnK5nDp16tCpUyeptrZ///7SOajVarZu3VpoQC+TyejVqxebNm0C4PLly8TExNCqVSuio6M5cuQIX3zxBcbGxnh7e9OvXz9++eUXaf/BgwdToUIFTExM8g3eVq5cyahRo/Dz88PQ0JAZM2awY8cOqabTwMBA+jt269aNqlWr6jWFHj9+PE5OTtJvLn9/f6ysrDA1NWXy5MmF9pnP79i7du16ai1r+fLlGTRoEHK5HFNT00KvN0Dr1q1p2bIlBgYG+Pv7c+HCBb38PvvsMzZt2sShQ4ews7MDdA9lwsPDiY6OxtzcnHr16gG6FgLDhw+ncePGyOVyvL29cXFx4c8//0Sj0TBu3DgMDQ3p06cPlStXZv/+/fleq6dd99wSEhJ0Y3zlcuDAATIyMmjfvj3dunXjwYMH0sOmooiNjcXZ2bnA9e7u7iQkJBT4ym7unt/nJfuzVJjg4GACAgL0xgSYN28ed+7cISIigrZt29KxY0eSk5MBaN68uXTvr1q1ik8//ZStW7fq5WlpaVlgi6LS4PUY3vElMjc3R61Wo7L0IkNlhiIzA/PkZJDZYWQgx9jMnBunb0rbe/jZS1PUZYtblxPsGzqbobAUtfMvy/379xkyZIjeP0lLS0vmzp3L8OHDUSgUhewtCIIgCP8hAxl2AT5P364QWq2WzMxMDAwMCh4LxqDoY8S0bt0aNzc3vebZoGtG3qhRI2nZw8ODtLQ04uLipEAnt7JlcyovzMzMsLOzIyoqCjs7OxQKhd4+ZmZm0g/9/v3707x5cxYuXMj69evx9/fPM9hXdnly95P18PDQe2Dg5eXFzZs38+xXmOzAKDk5WQpEkpKSpHQLCwuSknJmK8p+nz0QV+XKlTl9+jTHjh1j+vTpfPfdd0RGRvLXX39JTe6vXLnChAkTOH/+PBkZGaSlpeHt7Q3omsHHx8dz5coV7ty5g5eXV6FN7kEXJPfu3ZuvvvqKjRs30rNnTxQKBREREaSlpeHg4CBtm5WVRUBAgLRcpkyZQvOOiIggODiYefPmSWkGBgZER0cDukGFTUxMpHVly5YlKipn8Ocn8//8889ZtWoVDx8+RCaT6V3L4hw79731pCePWdj1BnBycpLe574PQdetY9GiRXz//ffY2NhI6fPmzWPatGnUqFEDV1dXPv30Uzp37sy9e/eoX79+njJFRkbmKfOT92vuchd27k+en5WVVZ4gOSQkhG7dumFkZISRkRGdOnUiJCREejCmUCjyPLBTq9UYGhoCYGdnJ/2Nn0d+n5cnHz48KS4ujj179uRpSZv90ATg448/ZuXKlZw6dYq2bdvqddOpX78+48aN45dfftHrg5+cnIyVldXzntJrq9TX0ANYWys4YdMZrRa0Mjmm6AbB6/BODx7dy/kQNX6nIlWbuentm6XKQKvOmUbCqoN+3zDhxTI2NubMmTPSco8ePQgNDWXUqFEimBcEQRBeKTKZDJmh/OW/ijnoa3619K6urlJTWtAFHSYmJnn67ma7e/eu9D41NZXY2FhcXFyeeuxKlSpRtmxZDh06xMaNG/UC0NzyK4+rq+tT8y+MjY0Nzs7OXLqUM/3w5cuXqVJF1/LS19c3zzp3d3cpSGnatCm7du0iNTUVZ2dnmjRpwqJFiyhXrpwUEI4ZM4aGDRsSERFBYmIiPXr0kFo5yOVy+vbty/r161m/fn2B555bvXr1kMvlnDp1Sq+5vZubGxYWFsTHx0s1p8nJyXrNkZ+8L55cdnNzY/bs2Xq1r6mpqVJQ+ejRI9LS0qTt7969q/c3zp3f77//zpIlS9i7dy+JiYmcPn26WMeOj48nKSnpqQ8hnsynsOv9NHK5nP379zNhwgT++OMPKd3FxYWVK1cSHR1NUFAQvXv3Ji0tjbJly+YZKwKQuj3k9uT9mrvcT7vuufn5+XHjxg1pWaVSsWPHDrZu3YqzszPOzs4cOHCALVu2SGNMlC1blvDwcL18wsPDpQdkrVq1YufOnQVep4iICL0R5Z98ZX8u8/u8ZH+WCrJp0yaqVq2Kr69vodvJ5fICy/fkuujoaDIzMylfvnyheb7JSn1AHxUVhVYLTpkP0WrkyDVZyGS6UVHLVqhAwoPH0rbWTmZ59o/fkvMhs+1T+eUXuJSzt7dn4cKFuLm5sW3bNrZu3SoN6CEIgiAIwtO1bdsWZ2dntm/fLqX16dOH5cuXExoaSkpKCtOmTaN3794FPiw4ceIEe/fuJSMjg1mzZlGvXr0iB9z9+/dn0qRJWFhYULNmzXy36dOnDwsWLOD+/fvExcUxe/Zs/P39i5R/enq6FIjmfp997M8++4zk5GROnz7Njh07pGbv/fr1Y+vWrZw9e5bExEQ+//xzvbEBmjZtyrJly6Ta+GbNmrF06VKp/zzoagqtra0xMTHh6NGj7NmzJ8+5h4SEsHv37iKfT58+fZg2bRrJyclSLaybmxsNGzZk+vTpPH78mMzMTM6dO8fVq1cLzMfR0VEvIH3vvff4/vvvpWbocXFx0vR/oKvV/eyzz1Cr1ezcuZPLly/rTSeWW3JyMoaGhtjb25OSkpKnBrYox965c2eRrseTxy3sej9NnTp12LRpE71795YqjLZs2SINfGhtba17MCeT8e6777Js2TJOnjyJVqvl2rVrREVFSbX233//PZmZmWzevJnQ0FA6dOiQ7zGfdt1za9CgAZGRkVJ//F9++QUbGxuuXbvG+fPnOX/+PP/88w8GBgbSuffu3ZvZs2fz4MEDqTxXr16lXbt2gK6venR0NMOHD5daEYSGhkoPmNzd3fVGlH/ylf1goH///ixbtozbt2/z4MEDvv76a73PS36Cg4PzbJOQkMDBgwdJT08nIyODhQsXEhcXJ13X/fv3S7NXnTt3jm+//ZYuXbpI+//xxx+0atUKA4PS2/C8VAf0ufuqaP8dsd4r6goydDW9tvZ2XPtT1yRFYZi39jf30yEDRzPkpqX3RnoZkpOTmTx5Mg8ePNBLDwgI4J9//qFbt24lUzBBEARBeM0FBgZK07wBtGvXjilTptCpUyc8PDwwNDRk0aJFBe7fs2dPfvzxR2xtbTl27FixRtr29/fn2rVrhfYfHzx4MN27d6devXr4+vpSvXp1pkyZUqT8K1eujKmpKenp6Xh7e+uNq/Ppp59ia2uLi4sLPXv25Pvvv6dyZV2FjJ+fH19//TVdunShTJkyuLq6Mn36dGnfpk2bkpycLPWXf3IZ4Msvv2Tx4sUolUoWLVqkF3gAVKtWDaVSSZ06dYrUogF0Af1vv/1Gz5499bonrF27lnv37lGuXDkcHR2ZMGECqampBeYzefJkPvnkE6ytrVm3bh2NGjVi/vz5DBw4EKVSSa1atfKMzi6TyXB0dOSDDz5g48aNek3Tc+vQoQONGzfGw8MDPz8/ve4bRTl27dq1OXHiRJGuR25Pu95F0axZM1auXEmXLl24evUqp0+fpnbt2lhYWDBy5EjWr1+PsbExjRs35ptvvuH9999HqVTSq1cvkpKSMDIyYufOnaxfvx47Ozu++OILdu7cWeC1etp1z83Y2Jg+ffqwefNmQNfcfsiQIdIMEs7Ozri5ufH+++8TEhIC6D7b1apVo27dulJF2O7du6VuMI6Ojpw4cYLk5GT8/Pywtramf//+tGnTpljX7a233mLkyJHUq1cPb29v2rdvz/vvvy+tt7Cw0Ovbf/v2bf766y/69u2rl49arWbKlCnY29tLc8rv3btXakJ/8OBBqlSpgrm5Of7+/kyePFnvYdj69esZOnRoscr+ppFpizMs4hsgKSkJKysr4uPjGTJkCFu3buWLuWX4tdpnpCVaYhf1FzVv6ZqsfPLllxxarXvSWbmBCxVqO+rllXggDHVkCgC2/pWRm4iA/kXZtWsXo0eP5u7du/Tr10+a4kQoGo1Gw8OHD3F0dMy3b6IgvAnEfS68atLS0qS+0bn7Hj+PIvWh/48FBQVx7969Z577Wa1W4+TkxF9//VUqm8m+/fbbdO/encGDB5d0UV4Zr+J9/qq4d+8enTt35uzZs+K77gk3btxgwIABnDx58pW6bwr6LkhISMDGxobExES9AQWfV6m+K7Zu3YqZmQwHW2sAtFqwUOj6r7i5u/HnjtvStuVq2Ovtq455LAXzADJj0X/7RYiKiqJXr1506dJF6o+0bds2vX50giAIgiC8vtasWUO1atVKZTB/9epVTpw4Qe/evUu6KMJrokyZMvz9998imM9HxYoVOXXq1CsVzJeEUn9nNGxkTqblv4NQaGU4Zeqax3iUdUYVn9PnSq7Qv1SJe+5I7+36+5T6G+l5aTQali5dire3N1u2bJHS27VrJw1KIwiCIAjC661BgwYEBQXx1VdflXRR/nMff/wxDRo0YO7cuXpT53Xu3Dnfwcdyz+hTWmzcuDHfazFgwICSLpogvLJKbRvx7Ckgmja1ZotS1w9DkZWBIikWAJkmpydCnU76I9en38mZ59DE1xaZQal/LvJcrly5wrBhw/T6Tjk4OLBw4UL69esnHpYIgiAIwiskKCjomfc9derUiyvIa2bevHl6U5Vl27VrVwmU5tXUp0+fIg8WKAiCTqmNRLNHAbVW6ka0z0wzBh5h/khX8x7+V05ze0fPnKeomvRMkn+/Jy1b1CvagCZC/hYsWEDNmjX1gvn33ntPGm1TBPOCIAiCIAiCIAj5K7U19AAKBTxSeoMMNJkKjDPPALom9+bWObXyuYPKhO23pPcWTcR0ac/LxcVFmge3YsWKLFu2jJYtW5ZwqQRBEARBEARBEF59pTag37VrF/b2BqjlurnlNRkyPFJ1A9t52ZhiYecLQL3O5aR9slLUaFJzprozqWD93xX4DdW3b182bNhAjRo1mDp16gsbFVgQBEEQBEEQBOFNV2oDet30BpBoUgatVoZ7dDgZhnLsLIxIi01H5qQL7u3LWEj7xG++Lr237lL6RmZ9HlqtlpCQEE6dOsXixYuldJlMxo4dO0TTekEQBEEQBEEQhGIqtX3or127hm8VE5IVloCMRHNTbFPtMDKQo9VqkclkmFsbI5PnH2ga2Iqa5KK6efMmbdu2ZeDAgSxZsoS9e/fqrRfBvCAIgiC8GcLCwjAweLb6ohYtWhASEvKCSySUpCNHjlChQgVpuUqVKpw8ebIES1R0MpmMe/fuPX3DEpaSkoKvr6804LeQIzMzEz8/Px48eFDSRXmpSm1Ab2JigqWFHCMjGcg0yGUp2EUkQ2oWcrkhAPW75DS3T/xfmPTeuluFJ7MT8qFWq/niiy/w8/Pj0KFDUvrBgwdLsFSCIAiCUPp4enri4eEhjVsDMGLEiOcasb6kHDlyBLlcjoWFBZaWlvj5+bFjx448240YMSJPUPbkdGhyuZwFCxZI61evXk2ZMmVQKpW89957ZGRk/CfnVFpcuXKFhg0blnQx3ihLly6la9euWFhY6KW3a9cOJycnMjMz9dI9PT05duyYXlpQUBBDhgyRlmNjYxk+fDguLi4olUqqVq3K/PnzycrKKlbZivp5Wrt2rd7n0tTUFLlczqNHjwDdg6Dc6xUKBWPHjgXg9u3b1K1bFxsbG2xtbenWrRtRUVEAGBgYMHjw4Hxnl3iTlNqAvlKlSnh6GhFp6E5mmjE2jxPIkhmhTc1Co9F92ZlaGgGgVWtQR6VI+xpYG5dImV8np06donbt2kydOpW0tDQAypYty65du1i4cGEJl04QBEEQSp/k5GRWrVpV0sV4IcqVK4dKpSIxMZGRI0fi7+9PXFyctP7cuXPSjEa5qVQq6XX9+nXkcjk9evQA4NKlS0ycOJFt27Zx9+5d7t69y+zZs/+zcxJerCcD2dfBs5R5xYoV9O3bVy8tKiqKw4cPk5GRwYEDB4qVX2pqKi1atOD+/fscO3aMxMREdu3axcWLF0lOTi5yPsX5PAUEBOh9NoOCgmjWrBn29vaA7kFQ9rrY2FiUSiU9e/YEdFNdb9q0ibi4OKKjo/H29mbcuHFS3v7+/gQHB+s9zHzTlNqAHkD1WIZtViwymRZzVbwuUavF0as9Ni7m0nYpZ6Ol93YB3v91MV8rSUlJjBkzhkaNGnHp0iUA5HI5EydO5OrVq7z99tslXEJBEARBKJ0mTpzInDlzCvxhu2TJEsqVK4eDgwP9+/cnMTEx3+2CgoLo27cv3bp1w9LSkmbNmhEWFqa3zfLly3FxccHZ2Zk1a9YAcOLECby8vPS2CwwMZNiwYXmOodFoCAwMpGzZsri4uDBu3DjS09PzbCeXyxk0aBBpaWncvq2bclir1TJu3LinViCsW7eOhg0bSmVat24dPXv2pG7dulhZWTF9+nR+/vlnAKZNm8bUqVMBiIyMRCaTSesOHDhA48aNAfjzzz+pW7cuSqUSDw8PvvvuOwDS0tKwtrbmzp070vEPHz5MpUqVCi1jixYtmDlzJrVr18bCwoLBgwcTFRVFq1atpKAm+7rEx8fToUMH7O3tcXBwYNiwYdK67KbvQUFB2Nra4uXlxcmTJ1m2bBmurq64ubnpdYn09PTkq6++wsfHB0tLS2bOnMm1a9eoU6cOVlZWjBgxQtr21q1bNGvWDGtra1xdXaXrlJ/ctcO5r5Wnp6feGEtBQUEEBATQq1cvLC0tqV+/vt61y0/2OQYGBmJvb09gYGChZVu9ejWtWrVi5MiRKJVKfH19OXfuXL55b9u2jfLly3Pjxg1iYmLo2LEj1tbW2Nvb6wXTv/32G3Xq1EGpVFKxYkWOHj0KwN27d+nUqRM2Njb4+vrqtShp0aIFM2bMoE6dOpibm6NWq/n999+pXbs21tbWtGjRglu3buUpE0BERASRkZH4+fnppa9fv5769evTr1+/Yndj+fnnn3n06BGbN2+mfPnyyGQyvLy8+Pnnn7G2ti5yPoV9np4mODiYAQMG5Ltu165dKJVKmjdvDoClpSVeXl5SF165XK53vZydnbGxseHMmTNFLvvrplQH9IYGBiQobMjKVGAXr8bKWNfny9jcCd8mrtJ26ujH0nuZoeI/L+frIjIyEl9fXxYvXoxWqwWgZs2anD59mq+//jpPUyBBEARBeJNptVrUavVzvTIzM6VXQdtkf+c+TcuWLXF3d2f16tV51h08eJDZs2eze/duwsLCSE1NZfz48QXmtXXrVoYMGUJsbCwNGjRg4MCB0rqsrCwuX75MeHg4P//8M6NHjyY5OZlGjRohl8v1+lCvX7+efv365cl/xYoVbNmyhZMnT3L58mXOnj3LF198kWe7rKwsVq1ahbm5udRXe82aNXh7e1O7du1Cr0dwcLBeua9evUq1atWkZT8/PyIiIlCpVDRt2lQKzo4ePYqXl5cUmB49epSmTZsCYGhoyLJly0hISGDr1q1Mnz6dv//+GxMTE3r06MGGDRueeu5P2rp1K9u3b+fmzZvs2bOHLl268N1333H//n2uX7/OunXrAN1DkNGjR3P//n0uXrzImTNn+OGHH6R8wsLCsLS05OHDhwwaNIg+ffpw7do1wsLC+Pzzzxk5cqTecffs2cPx48f566+/mDdvHmPHjmXbtm1cu3aN7du3c/jwYWnb2bNn8+jRI37//XdCQkLYvn37U88r97XasmULgYGB/P3339L6bdu2MWrUKOLj46lcuXKRuoeEhYWhUCiIiopi+vTpTy3b0aNHadasGfHx8fTo0YOJEyfmyXPDhg18/PHH/Prrr1SsWJEFCxbg5eXFo0ePuH//vl7T727duhEUFER8fDyHDh3CxcUF0M3qVKVKFaKjo1myZAn9+/fn5s2b0jHWr1/Phg0bSExMJDo6mnfeeYdFixYRGxtLz5498ff3z/d8L126RIUKFfKMRxUSEkKfPn3w9/dnx44dxepf/9tvv9G+fXtMTU0L3Mba2rrAV/bnorDPU2H+/vtvbt26Ra9evfJdHxwcTP/+/fOcs7W1NaampsyfP59JkybprfP29ubixYuFHvd1VmpHuQcwtDRAQRYytJhp01BotSgMdIPdWTuaSdtlJeiebppWsy+Rcr4uXFxc8PPz4/79+5iZmfHpp58yfvz4Zx4cRxAEQRBeZ5mZmWzevPm589FoNMjlBdfB9OrVC0NDwyLlFRgYyJAhQxg0aJBe+oYNGxg2bBi+vrppe+fMmUONGjVYtWpVvoPXNmrUSGp1FxQUhLW1NZGRkdL6mTNnYmRkRLt27TAzM+PWrVvUqFGDgIAA1q9fT8OGDTl79iyPHz+mWbNmefLfsGEDH374IWXKlJHyGzdunBTU3blzB2traxQKBeXLl2fr1q1YW1uTmJjInDlz8vQRftLFixe5fv26XtCgUqlQKpXScvZ7lUpFo0aNOHfuHGlpaRw9epSPPvpIqn0/evQoH374IQC1atWS9q9Tpw6dOnXi+PHj1KxZk/79+zNhwgSmTJmCWq1m69atRRogbvDgwZQtWxaA5s2b4+TkRJUqVQDo1KkTFy5cAMDOzo7OnTsDut9kw4cP5+DBg0yYMAHQjR8wadIkZDIZvXv3JigoiOnTp2NkZESfPn147733SEhIkGphx40bh62tLba2tlSvXp0OHTrolePChQu0bNmS8uXLU768bvanihUrEhAQwLFjx+jWrVuh5/XkterQoQPHjx+X0lu3bk3Lli0BXbPpwmr+sxkbGzN16lQMDAwwNDR8atm8vb2lGvZ+/frx/fff6+X3888/s3r1an799Vc8PDwA3YOIqKgo7t69i5eXF40aNQJ0QXnnzp2lz4W7uzugq50/c+YMBw8exNjYmBYtWvD222+zefNmpkyZAuj+xtkPpNauXUv37t2lh0Rjx47l008/JSwsDE9PT73yJSQk5KkwCw0N5cKFC7zzzjs4Oztja2vLL7/8ovfwqjCxsbHUqVOn0G0SEhKemk9hn6fCKvmCg4Pp2rWr3r65y7Zv3z6+/PLLfMuUlJTEihUr8rR8sbS0LLDF0ZugVNfQW9sYkSozQYsME3U6RhpwqfyO3ja5n3qLke31aTQavWWZTMaSJUvo0aMHV65c4YMPPhDBvCAIgiC8Qlq3bo2bm5vUDD5bZGSkFIAAeHh4kJaWptcvPbfswA7AzMwMOzs7aSAqhUKBnZ2d3vrsWrn+/fuzefNmsrKyWL9+Pf7+/vk+rMivPLkfGHh5eZGQkEBsbCynT5+mffv2gO7hwvDhw3F0dCz0OgQHB9O5c2e9JsQWFhYkJSVJy9nvLSwsUCqVVK5cmdOnT3Ps2DG6d+8ulfOvv/6SmtxfuXKFtm3b4uDggJWVFb/88guxsbGArml1fHw8V65c4X//+x9eXl5PbXIP6J2LqalpnuXsa5ucnMzAgQOlQcgmTZokHRvA3t5eejhjamqKQqHA1tZWWgb0ak+Letz79+/TvXt3nJ2dsbKykmqWnyb3tbK2tmb79u16+zk5OUnvc99DhXF2dtb77fm0sj3tGIsWLeL999+XgnmAjz76CHd3d5o3b463tzcrVqwA4N69e3m6lIDuHnFwcNCr8X7yfs5+cAW6ZvTBwcF6td4pKSncv38/T95WVlZ5yhwcHEyzZs1wcXGRHt7kbnZvYGCQp9uNWq2WHgra2dkRHR3N8yrs81SQ7P8LBT182LBhAzVq1MDbO/8u0EqlkoEDB9K1a1e9OCU5ORkrK6tnOY3XQqmOtspm6Qa30yLjETZUNrJEJpNjlmvQu8zYNOm9oatoMp7t4sWLDBs2jOnTp+v1i/fy8mLr1q0lWDJBEARBeDUYGBgU2Gy0ODIzMwt9QF7ch+eBgYEMHz5cqv0EcHV1JSIiQlqOiIjAxMRECviedPfuXel9amoqsbGxuLi4PHVU+EqVKlG2bFkOHTrExo0b2blzZ77b5VceV1fXfLfN7fDhw9y/f5+vvvpKSqtVqxY///wzHTp0AHQVEuvWrWPp0qV6+/r6+krj/wBcvnwZd3d3KQBp2rQpu3btIjU1FWdnZ5o0acKiRYsoV64cNjY2AIwZM4amTZuyc+dOTE1N6du3r1Q5JJfL6du3L+vXr+fOnTsEBAQ89XyK4+uvvyYmJobz589jb2/PsmXLWL9+/Qs9Rn6mT5+OjY0N169fR6lUMmXKFOnhTmFyXysTExP8/f2L3H2kIE+2JnnWsmXbunUrAQEBeHl50adPH0AXNH7zzTd88803nDp1ilatWtGyZUvKli2b70CMrq6uxMTEkJaWhomJrnIwIiJCr9977nK7ubkxdOhQvv3226eWz8/PT6+/uFarZd26dcTExODs7Azoxm9QqVRERUXh4uJC2bJlCQ8P18snPDxcavXRqlUrZs2aRWpqaoHN7gsLyvft20fTpk2f+nnKz8GDB9FqtbRr1y7f9YX1rc+WmZlJdHS0XguBa9euMXny5EL3e52V2hr6EydOoJD9e/oy0KZpMDDWfVGU9dZ9eWm1WhJ335b2kRuJ/vOPHz/mk08+oXbt2vz555+MGjVKzHspCIIgCPmQyWQYGho+18vAwEB6FbRNfk3iC9O2bVucnZ31+hL36dOH5cuXExoaSkpKCtOmTaN3794F5n3ixAn27t1LRkYGs2bNol69ekUKuEFXSz9p0iQsLCyoWbNmvtv06dOHBQsWcP/+feLi4pg9e3aB/YhzO3ToEJcuXeL8+fOcP38egP/97396Dy8OHTqEWq2mY8eOevv269ePrVu3cvbsWRITE/n888/1agqbNm3KsmXLpNr4Zs2asXTpUqlpNOhqAq2trTExMeHo0aPs2bMnz7mHhISwe/fuIp1PcSQnJ2NmZoaVlRXh4eEsWbLkheZf2HEtLS2xsLDg8uXLRR6E7clrtW/fvlembNm8vLz43//+x8SJE6W/5Z49e7h9+zZarRYrKytkMhkKhYK+ffuya9cu9u7di0aj4e7du9y6dYuyZctSq1YtAgMDycjI4I8//mDXrl288847+R6zX79+bN68maNHj6LRaEhOTmbLli35buvh4YGzs7MUOB89epSHDx9y4cIF6TPwzz//UKVKFenhTu/evfn666+5ffs2Go2GI0eOsHv3bqnVycCBA7Gzs8Pf318aaDI8PJxBgwZJTe1zj0j/5Cv78/C0z1N+goOD6du3b74PKW/cuMG5c+fyjOh/5MgRzp07R1ZWFvHx8XzwwQfUrl1bCuYfPHhQpG4Er7NSG9ADGGsNAd0XlTkGWDronpTZl9E9OUo9H1NSRXslHTx4ED8/P7788ktpWg1zc/N8mwAJgiAIgvDqCgwM1GtO365dO6ZMmUKnTp3w8PDA0NCQRYsWFbh/z549+fHHH7G1teXYsWMEBwcX+dj+/v5cu3at0AHhBg8eTPfu3alXrx6+vr5Ur15d6m9cGDs7O5ydnaUX6Ka1MjbOaX0ZHByMv79/nqDBz8+Pr7/+mi5dulCmTBlcXV2lgdVAF9AnJyfTpEmTfJcBvvzySxYvXoxSqWTRokV06dJF7xjVqlVDqVRSp04dacC0F2X8+PFERUVhY2NDz549pQDtZZs5cyaHDx9GqVQybtw4aTqxp8l9rb755puXMhPSs5YtNx8fH3bt2sXgwYP5/fffuX79Oi1btsTS0pK33nqLRYsW4eHhIbVSnTZtGlZWVrRu3VpqDbBhwwYuXLiAo6Mjw4cPZ82aNVSsWDHf43l5ebFhwwY++ugjbG1t8fb21hsV/0mDBw+WgvWQkBB69+5NhQoV9D4HI0eOlB5mDBs2jN69e9OmTRusra2ZOHEiwcHBUjN2U1NTjhw5gpOTE40aNcLS0pJOnTrh5+eHpaVlka/b0z5PVapUYe3atdKySqVi+/btBdbABwcHS7M45JacnExAQABWVlZUrlyZ9PR0vdbCGzZsYMCAARgZGRW57K8bmfZ527a8ZpKSkqQ+FOt/bsBP9iNJSzXj7R2HqVp3KDKZjLdGVwcg/pcbZCXpmo7Zvetb7Cfgb4qYmBgmTZqk91TTyMiIqVOn8sknn+h9SQqvBo1Gw8OHD3F0dCx0ICVBeJ2J+1x41aSlpXHnzh28vLykprXPS6vVSk3uX5XfIUFBQdy7d4+ffvrpmfZXq9U4OTnx119/SQOWlSZvv/023bt3Z/DgwSVdlFfGq3ifvy5SUlKoW7cup0+fFjNKPSEzM5OaNWty8OBB6QHff6Gg74KEhARsbGxITEzMd9C/Z1Vq+9AbG8uRoRssQSYDB48myGQyXCpYA7p/LNnBvKGbRan856LValm9ejUffvih3lP8Zs2asWzZsgIHpBAEQRAEQSjImjVrqFatWqkM5q9evcqJEyf+k77tQulgbm6eb999QTe+SO5+/G+qUlulUdPDlHCTf79IUjMxM9M1IalQWzeCZ8rJnAEzzGoWPlLqm2rmzJm8//77UjBvbW3NTz/9xOHDh0UwLwiCIAhCsTVo0ICgoCC9QetKi48//pgGDRowd+5cvabLnTt3xsLCIs9r//79JVjaV9PJkyfzvVb5TX0oCKVFqW1y/05rGzxGv8t5k5o8fmTCxw+dMDSxpsNwP+RyGbE/5zzpsh9UpQRLXHLCw8Px9fXl8ePH9O3bl4ULF+pN7yG8ukRTZKE0EPe58KopLU3uBeFFE/e58CYRTe7/I5XdTDhlUpOsTAOcYu4DukBVLpeR9GvOVA4mFa1LpoAlIHsk0GweHh4sXrwYJyenPCPBCoIgCIIgCIIgCCWr1FZplK9ljnVWPDKZFrPUnEYKMrkMuZmhtGze4MWOQPoqSkhIYOTIkVStWpXk5GS9dYMGDRLBvCAIgiAIgiAIwiuo1Ab0qekGJChsUKuNKPcoTm+dOlI3r7pZLUdkijf3Emm1WrZs2YKPjw9Lly4lIiJCbzoJQRAEQRAEQRAE4dVVapvcq8w9ANBkyLFOTcHAyJKmfSqRlaJG81g3xzqaN3d4gYiICEaPHs3u3bulNHNzcypUqFCCpRIEQRAEQRAEQRCK6s2tfn6KJCMbAGTIMNbIkckVKO1NSdx7R9rGxNeupIr30mRlZbFo0SJ8fX31gvm3336bq1evMnbs2BIsnSAIgiAIgiAIglBUpTagzzA2BkADKBSGOHj8O9JglkbaRm6kKIGSvTx///03DRo0YOLEiaSkpADg4uLC5s2b2blzJ+7u7iVcQkEQBEEQXmdhYWEYGJTaBqDCf+TIkSN6rUqrVKnCyZMnS7BERSeTybh3715JF+OpUlJS8PX1RaVSlXRRXjnx8fH4+vqSlpZW0kUBSnFAH2WgG+zOOikJNBmYmBug1WrRpGUBYFb7zZp7XqVS0apVK86cOSOljRw5ktDQUN555x0xRYggCIIgvME8PT3x8PBArVZLaSNGjCAoKKjkClVEQUFBDBkyJE/68uXL8fPzw9zcHHd3d959913CwsJeenlGjBiRJyh7cl50uVzOggULpPWrV6+mTJkyKJVK3nvvPTIyMl56OUuTK1eu0LBhw5Iuxhtl6dKldO3aFQsLC730du3a4eTkRGZmpl66p6cnx44d00t78rMbGxvL8OHDcXFxQalUUrVqVebPn09WVlaxylbUz9PatWv1PpempqbI5XIePXqkl1fFihWxsLDAx8eHW7duSeuuX79Ou3btsLCwwMnJie+//x4AGxsb2rVrx/Lly4tV7pel1Ab0MYaOaGWgyNBiqnTFwsaErPh0ab2Rm2Uhe79+LCwspC/tKlWqcPz4cZYsWYKVlVXJFkwQBEEQhP9EcnIyq1atKulivBCfffYZM2fO5MsvvyQ2NpbQ0FAaN27Mb7/99lKPe+7cOa5evZonXaVSSa/r168jl8vp0aMHAJcuXWLixIls27aNu3fvcvfuXWbPnv1Syym8PE8Gsq+DZynzihUr6Nu3r15aVFQUhw8fJiMjgwMHDhQrv9TUVFq0aMH9+/c5duwYiYmJ7Nq1i4sXL+aZZaswxfk8BQQE6H02g4KCaNasGfb29gDs2bOHhQsXsmPHDpKTk9m1axe2traAbi75jh078u677xIXF8eNGzdo06aNlHffvn356aefinUNXpZSG9Cny0wAsElORKvJwrmcFQk7c57IGNialFR3B31oAABBMElEQVTRXogHDx6QlJSklzZmzBiWLVvGuXPnaNSoUQmVTBAEQRBKB61WS1ZW+nO/NJrC12u1RRvEd+LEicyZM0evlj63JUuWUK5cORwcHOjfvz+JiYn5bhcUFETfvn3p1q0blpaWNGvWLE/N+PLly3FxccHZ2Zk1a9ZI6bt378bPzw9LS0sqVqzI5s2b9dZVrlwZS0tLPD092bBhA0eOHGHOnDmsWbMGCwsLOnbsSEJCAnPmzOGHH36gU6dOmJiYYG5uzrBhw3j//fcBuHv3Lp06dcLGxgZfX1927NghHadFixYEBgZSp04dlEolffr0IT1dV6lz/fp1mjRpglKpxMnJiY8++kjv7zlu3DgWLlxY6HVet24dDRs2xMvLS1ru2bMndevWxcrKiunTp/Pzzz8DMG3aNKZOnQpAZGQkMplMWnfgwAEaN24MwJ9//kndunVRKpV4eHjw3XffAbqgw9ramjt3csaAOnz4MJUqVSq0jC1atGDmzJnUrl0bCwsLBg8eTFRUFK1atUKpVNKzZ0/pmsTHx9OhQwfs7e1xcHBg2LBh0rrspu9BQUHY2tri5eXFyZMnWbZsGa6urri5ubF3717puJ6ennz11Vf4+PhgaWnJzJkzuXbtGnXr1sXe3p4RI0ZI2966dYtmzZphbW2Nq6urdJ3yk7t2uKBrBbp7NyAggF69emFpaUn9+vX1rl1+ss8xMDAQe3t7AgMDCy3b6tWradWqFSNHjkSpVOLr68u5c+fyzXvbtm2UL1+eGzduEBMTQ8eOHbG2tsbe3l4vmP7tt9+k+7VixYocPXoUePp9PmPGDOrUqYO5uTlqtZrff/+d2rVrY21tTYsWLfRqo3OLiIggMjISPz8/vfT169dTv359+vXrR0hISKHX7Uk///wzjx49YvPmzZQvXx6ZTIaXlxc///wz1tbWRc6nsM/T0wQHBzNgwABp+dNPP+Xrr7/G19cXmUxGhQoVsLHRjbO2atUqGjVqREBAAEZGRiiVSry9vaV969Spw82bN4mKiipy2V+WUhvQA2j/3959R0Vxtg8f/8LSka6IKGDHhhqsqNijBnsDa+wau/EXjS2CGmsSS4wmaoIaVMCSqFFjicYEe8WKGgtiAQtKU5Cy8/7B6zyugIANletzzp7DTruvmb0XuOYuo9VQPPI2VoU/wsj0wxgvr9Vq+fnnnylXrhwTJ07UWafRaBg4cCBGRkZ5FJ0QQgiRf2i1yZw9O+zVXueGcT5sJGfPZb2NVpuz7tuNGjXC2dmZFStWZFi3a9cupk2bxpYtWwgPDycxMZGRI0dmeawNGzbQv39/oqOjqV27Np9++qm6Li0tjbNnz3L9+nV+/fVXhg4dqrbAWVhYsH79emJjY1mwYAF9+vQhKioKgP79++Pv7098fDyHDh2icuXKNGzYkAkTJtCrVy8SEhL4888/OXjwIMnJybRq1SrL+Lp27UrFihWJiopi8eLF9OjRg8uXL6vr165dy4YNG4iIiODs2bOsWbMGgMmTJ9OyZUtiY2O5evUqnTt3VvdZuXIl5cqVo1q1ai+8zgEBATrX4/z581SuXFl97+bmRkREBAkJCXh6eqrJWUhICCVKlFAT05CQEDw9PQEwNDRkyZIlxMTEsGHDBiZNmsTJkycxMTGhQ4cOBAUFqccPDAykW7duL4wR0j/DjRs3cvnyZbZu3UqbNm1YuHAht27d4tKlS+o10Wq1DB06lFu3bnH69GmOHTvGjz/+qB4nPDwcCwsL7t69S+/evfHx8eHixYuEh4czffp0Bg8erFPu1q1b2b9/P0ePHmXOnDkMHz6c3377jbNnz7Jp0yb+/vtvddtp06Zx//59/vnnH1atWsXGjRuzPa+srtVTv//+O0OGDOHhw4e4urrmaNhJeHg4Go2GyMhI9RHPL4otJCSE+vXr8/DhQzp06MDnn3+e4ZhBQUGMHTuWv/76izJlyvDdd99RokQJ7t+/z61bt9SJqq9evUq7du3w8/Pj4cOH7N69myJF0ocOZ1fPAwMDCQoKIjY2lqioKDp16sT8+fOJjo6mY8eOdOnSJdPzPXPmDKVLl84wJHfVqlX4+PjQpUsXNm3alKvx9Xv27KF58+aYmppmuY21tXWWr6ffixd9n17k5MmTXLlyRf1Op6WlceLECc6ePYuTkxMlS5bk66+/Vm+QHjlyBFtbWzw8PLC3t6ddu3bcunVLPZ5Go6F06dKcPn06x9fgTcm3Cb2ip09ykjEWj9IfWZd06p66ztb7xXc131UXLlygUaNGDBgwgJiYGBYtWsShQ4fyOiwhhBBCvCN8fX0zbaUPCgpi4MCBVKhQAXNzc2bMmEFwcHCWrf916tShVatWGBkZ4efnx6FDh7h9+7a6fvLkyRgZGdGsWTPMzMzUlsAGDRrg6uqKvr4+Xl5euLm5qfP7GBoaEhYWRkJCAg4ODlSoUCHTsqOjoylYsGCWk+/duHGDY8eOMXXqVIyNjWnYsCGtWrXS6Q3Qv39/XFxcsLa2pmXLlpw6dUqN4fr160RFRWFubk7NmjUBiI2NZcaMGcyYMeOF1/f06dNcunRJ50ZAQkIClpaW6vunPyckJFCnTh1OnDhBUlISISEhjBkzRiehr1evHgDu7u64u7ujr69P9erV8fLyYv/+/QD06NGDwMBAAFJSUtiwYUOOEvp+/frh5OSEg4MDDRo0wMPDg4oVK2JhYYGXl5d6Tezs7GjdujXGxsYUKVKEQYMG6YyVLlCgAKNHj8bAwABvb29u3LjBpEmTMDIywsfHh4iICGJiYtTtR4wYga2tLeXKlaNKlSq0aNFCJ46n5ZYqVYoGDRpgYGBAmTJl6N69e4Yx2pl50bUCaNKkCY0aNcLAwIAuXbqo5b2IsbExEyZMwNDQEFNT02xjK1euHF27dkWj0dCtW7cMZfz6669MnjyZv/76S+3JYWhoSGRkJDdu3MDY2FjtTRsYGEjr1q1p1aoVGo0GZ2dnSpcunaN63q9fP0qXLo2JiQmrV6+mffv2eHp6otFoGD58OOHh4ZnOOxETE5Nh7HxYWBinTp2iU6dO1KtXD1tbW3777bdsr91T0dHRODg4vHCbmJiYLF9Pvwsv+j69SEBAAG3btlW3v3PnDqmpqezcuZMzZ86wZ88efv31V7Xnwa1bt1i5ciXff/89ERERODs769yog/QblFn1ZHqb8vU0pOZpj9GgoDE0I+lstLpc38wwD6PKvSdPnjBz5kxmzpypMylEjx49KFWqVB5GJoQQQuRf+vpGVKr0wysdI73bfioajUGWE9jq6+e8512TJk0oWrSoTjd4SO/u/exwPBcXF5KSknjw4AF2dhkf4+vk5KT+bGZmhp2dHZGRkdjZ2aHRaHT2MTMzU//Z3rdvH19++SVhYWFotVoePXpEdHT6/2Dr169n6tSpfPHFF9SuXZt58+ZRvnz5DGXb2dlx//59UlNTM03qb9++TaFChXRaAl1cXHRuOBQuXFgnvgcPHgAwZ84cJk6cSNWqVXF0dGTq1Km0bt0aPz8/Bg0ahL39iydNDggIoHXr1jpdiAsUKKAzDPLpz08n6nJ1deXIkSPs27ePSZMmsXDhQm7fvs3Ro0fVLvfnzp1j1KhRhIaGkpycTFJSktr9t2HDhjx8+JBz585x7do1SpQokW2Xe0DnXExNTTO8f/q5xMfHM3ToUPbs2UNcXBxpaWnqjQ6AggULqnXT1NQUjUajjkN++hkkJCSo1yS7cp/WlVu3bjFs2DAOHjxIYmIiycnJWbYoP+tF1woyfvY5aWV2cHDQqWvZxZZdGfPnz2f06NG4uLioy8aMGcNXX31FgwYNMDMzY8yYMfTr14+bN2+qSf+zclLPixUrpv4cERFBQEAAa9euVZclJydz69YtihcvrnNsKyurDDEHBARQv359tXeAt7c3q1atUpNcAwODDDcKU1JSMDRMz6vs7OzU3jiv4kXfp6ykpaURGBjIL7/8oi57et3Gjh2r9gIYNGgQ27Zto2fPnpiamtK+fXtq1KgBpN8MLVSoEImJieq+8fHx78R8ZPm2hR7ANCkFQ70CmFsbq8uMS+X9h5Ib//77L1WrVmXKlClqMl+yZEl27drFr7/+SqFChfI4QiGEECJ/0tPTQ6MxfuWXvv6L1+f2STWZtdI7OjoSERGhvo+IiMDExERNzJ5348YN9efExESio6PVf/RfpGfPnvTq1YuoqChiYmKoXr262gugVq1abN26lTt37lClShW1q/bz5+fh4YGhoSFbt27NtAxHR0fu3bun80ipiIgIHB0ds42vSJEi+Pv7ExUVhZ+fH97e3iQlJfH3338za9YsHBwc1FZGd3d3tm/fru6r1WpZs2aNzhhdgAoVKnDmzBn1/dmzZ3F2dlYTEE9PT/744w8SExNxcHCgXr16zJ8/n5IlS6rjeYcNG4aHhwcRERHExsbSoUMH9brp6+vTtWtXAgMDCQwMpHv37tmeZ27MnTuXe/fuERoaSlxcHHPnzs3xvA2vYtKkSdjY2HDp0iViY2MZNWpUjsp90bV6Wc/XwZeN7akNGzawePFigoOD1WWWlpYsWLCAiIgIVqxYwfDhw7l69SpOTk6ZtqLnpJ4/G3fRokXVXrxPX48fP1ZvGj3Lzc1NZ3y9oiisWbOGI0eOqN+BX375hT179qhjyJ2cnLh+/brOca5fv64+Frtx48bs3LmTxMTELK/L80+LePb1dGhKdt+nzOzatQtFUWjWrJm6zMbGBkdHR51r9OzPlSpVyrDu2fdpaWlcuXIlwzwDeSFfJ/QFEh+hb1AAy2cmwDP76P14XN3Dhw8ZMGAADRo04MKFC0D6nbFx48Zx5swZnVkYhRBCCCGe+vjjj3FwcNAZ8+vj48OyZcsICwvj0aNHTJw4EW9v7yxvFhw4cIBt27aRnJzMlClTqFmzZo4S5vj4eOzs7DA0NGTDhg0cP34cSG8pXLNmDXFxcRgaGlKgQAE0mvT5jezt7bl+/bqaMFlbWzNx4kSGDBnC9u3befLkCY8fP8bf3x9/f3+cnJxwd3fH19eX5ORk/v33X/744w86deqUbXzr169XJ6eztrZW/4nfvXs3Z86cITQ0lNDQUAB27NhBo0aN1H13795NSkoKn3zyic4xu3Xrpp5rbGws06dP1+m66+npyZIlS9TEqn79+vz000/q+Pmn183a2hoTExNCQkIy3Mzo0aMHq1atYsuWLTlqxc6N+Ph4zMzMsLKy4vr16yxevPi1Hv9F5VpYWFCgQAHOnj2b40nYsrtWeRnbUyVKlGDHjh18/vnnanxbt27l6tWrKIqClZXV/78hqKFr16788ccfbNu2Da1Wy40bN7hy5Uqu63m3bt1Yt24dISEhaLVa4uPjWb9+fabburi44ODgoCbOISEh3L17l1OnTqnfgQsXLlCxYkV1uIe3tzdz587l6tWraLVa9u7dy5YtW2jfvj0An376KXZ2dnTp0oWrV68C6Ql/79691SEZz85I//zr6fchu+9TZgICAujatWuGHj29e/dmzpw5xMfHc/PmTZYuXUrLli2B9O/U5s2bCQ0NJSUlhWnTptGoUSO1df748eOUKFEiR7/33rR8ndDbxMdgbG6Lo+3/Wuj1Td+P7vbBwcE6j0qoVasWx48fZ+bMmZiZmeVhZEIIIYR41/n6+qrdzCH92dLjx4/Hy8sLFxcXDA0NmT9/fpb7d+zYkaVLl2Jra8u+ffsICAjIUbkLFy5kxIgR2NjYsGPHDho0aKCuW7lyJS4uLtjY2LBr1y71mc+dOnUiISEBGxsbdSK8SZMm4evry5gxY7CxscHV1ZV//vmHJk2aAOlzApw6dQp7e3sGDRrEypUrKVOmTLbxHTlyRJ35ffDgwQQGBmJsbIydnZ3aMvm0hb5QoUIYG//vf8iAgAC6dOmSIWlwc3Nj7ty5tGnThmLFiuHo6KhOrAbpCX18fLw6Rvj59wCzZ89m0aJFWFpaMn/+fNq0aaNTRuXKlbG0tKR69eo56imRGyNHjiQyMhIbGxs6duyoJmhv2uTJk/n777+xtLRkxIgRdOzYMUf7ZXet8jK2Z5UvX54//viDfv368c8//3Dp0iUaNWqEhYUFLVu2ZP78+bi4uFCiRAk2bNjAxIkTsbKyokmTJmqreG7qeYkSJQgKCmLMmDHqHAbPzor/vH79+qnJ+qpVq/D29qZ06dI634PBgwerNzMGDhyIt7c3TZs2xdrams8//5yAgAB1uIOpqSl79+6lcOHC1KlTR52r4elTL3Iqu+9TxYoVWb16tfo+ISGBjRs3Zug5A+m/B4sUKUKxYsWoXbs23bp1o0ePHurns2jRItq1a0ehQoX477//dIYqBQYGMmDAgBzH/SbpKW+jz8w7JC4uDisrKxqsX8JHZ25Q5UkJOnu3JPHUfQAK9q6YxxHmTFpaGnXq1CEsLIyZM2fy2WefqXeyhdBqtdy9exd7e3v09fP1fTvxAZN6Lt41SUlJ6hhmE5PX8/hbRVHUseK57Vr/pvj5+XHz5s135hnMIl2rVq1o3749/fr1y+tQcu1drOf53aNHj6hRowZHjhx5YXf2/CgmJoa6dety/PjxTH/XZ/W3ICYmBhsbG2JjY3Um9ntV+XpSPOvoGGxLO6GnSf9H0Mgp53eH3qbU1FT27t2r041eo9EQEBCAubk5RYsWzcPohBBCCCFEXjp//jwHDhxQW1SFeFXm5uacP38+r8N4J1lbW3Pu3Lm8DkOVb5s0FIBUKFGlPI9P3AVAz+DduxzHjx+nZs2aNGvWjAMHDuisK1u2rCTzQgghhBD52NixY6lduzazZs3S6brcunXrTCcXe3YiP5Hu4MGDmV6r+vXr53VoQmQr37bQK1p9SIFCLhZoohNJi09Gm5iS/Y5vSUJCAl999RXff/89Wq0WgMGDB3Py5EnpWiqEEEKIPOPn55fXIYhnzJkzhzlz5mRY/scff+RBNO8nDw+PHD2+Toh3Ub5N6PX1tWjTtChaSItPf9ybmXvhbPZ6O7Zs2cLQoUN1Hh/j5ubG0qVLJZkXQgghhBBCCAHk4y73aaka9DTmmFsZ5XUoqsjISLy9vWndurWazJuYmDBr1iyOHz9OrVq18jhCIYQQQgghhBDvinzbQq+gR6o2/QIk//9lGivjF+3yxmi1WpYtW8aXX35JbGysuvzjjz/mxx9/pFSpUnkSlxBCCCGEEEKId1e+baHXQyFVSUF7JUZdpm+cN499UxSFFStWqMl8wYIFCQgIYMeOHZLMCyGEEEIIIYTIVL5N6FEA5TFJYQ/yOhI0Gg1Lly7F0NCQ3r17c+HCBXr06CHP4RRCCCGEEEIIkaX8m9ADSUla9WejYgXeWrl///03hw8f1lnm5ubGpUuXWL58OXZ2dm8tFiGEEEKI1yU8PBwDg3w7ovO99sknnxAcHJzXYbxx71MdvXXrFlWrVlWfeCX+59KlS9SuXTuvw3gn5OuE3lr7RP3ZtFLBN15edHQ0ffv2pXHjxvTt25fk5GSd9cWLF3/jMQghhBAi/ylevDguLi6kpPzvEb2fffbZe/EIOj8/P/r3759h+bJly3Bzc8Pc3BxnZ2d69epFeHj4G4khNTWVTp064eTkhJ6eXoZyKlasqPP8co1Gw/Dhw99ILG/Kn3/+iY+Pz2s/bvHixdm3b99rP25+MGvWLAYPHpzhKVeurq64u7tn2F5PT4+bN2/qLOvduzdff/21+v769et07dqVggULYm1tTbVq1fD393+p2AoVKoStrS1jx45FUZRMt5sxY4bOd8PExAQLCwudbebMmYOTkxMWFhZ89NFHxMfHZ1tO2bJlcXBwkMczks8T+rp2jdWfDQqbvbFyFEVh1apVlCtXjuXLlwNw/vx5AgIC3liZQgghhBDPio+PV/8Ped99/fXXTJ48mdmzZxMdHU1YWBh169Zlz549b6xMT09P1q5di7FxxkmUz507R0JCAgkJCURHR2NpaUnHjh3fWCyvk6Io710LcGpqal6HkGu5jfnJkycEBQXRuXNnneVHjhzh9u3bnD17lrCwsFwd8+7du3h4eGBmZsapU6eIiYlh5cqV7Nq1K1fH2bZtG4sWLeLQoUOcP3+eP//8M8ubAhMmTFC/GwkJCfTp00fnu7Fo0SK2b9/O/v37iYuLY+XKlRgZGeWonK5du/Lzzz/nKvYPUb5O6FOV9DHqeiaaNzZe/cqVKzRv3pyePXty//59AKysrPjpp5/o06fPGylTCCGEEHlPURSS0rSv/tK+eH1WLWPP+/zzz5kxY4ZOK/2zFi9eTMmSJSlUqBA9evTQefLOs/z8/OjatSvt2rXDwsKC+vXrZ2ixXrZsGUWKFMHBwYGVK1eqy7ds2YKbmxsWFhaUKVOGdevW6axzdXXFwsKC4sWLExQUxN69e5kxYwYrV66kQIECfPLJJ8TExDBjxgx+/PFHvLy8MDExwdzcnIEDB9K3b18Abty4gZeXFzY2NlSoUIFNmzap5TRs2BBfX1+qV6+OpaUlPj4+PHmS3mvz0qVL1KtXD0tLSwoXLsyYMWMAMDAwYOTIkXh4eGR7nf/44w8sLS1p0KABAHXr1mXnzp0ArFmzBj09PfXxxBMmTGDixIkA+Pv7U7ZsWSwsLKhcuTJ79+4F4MCBA5QoUUKnDF9fXwYOHPjCOPT09Fi4cCHOzs44ODjwzTffqOt69+7NsGHDaNy4MWZmZly5coWGDRuyatUqIP0z7tatGx07dqRAgQLUrVuXqKgoPvvsM6ysrHB3d+fq1avq8YYPH46joyPW1tY0a9ZMPb/+/fsTERFBs2bNKFCgAKtXrwZg/fr1VKxYEVtbW9q0acPdu3dfeC4rVqygUaNGDBo0CCsrK5YvX87hw4epUaMGlpaWuLi4sHDhQnV7Pz8/unfvTufOnbGwsKBWrVpcu3Yt02P/8MMPVKlShbt372b5+QMEBwdTqVIlLCwscHNz4+LFi0D6zRxPT0+1tXv//v3qPsWLF2fOnDmUL1+e0qVL5+rcDx8+jKOjI7a2tjrLV61aRdu2bfn4449z3Tg4b948ChcuzM8//0zRokUBqFSpEoGBgbk6TkBAAIMGDaJUqVI4ODjwf//3f/z666/Z7pecnMzatWvp2bMnAGlpaUyfPp1ly5bh7OyMnp4elStXVm+aZVdO/fr12b1793t5g+d1yrcJvTVx3HtwDwCb9qVf+/FTUlKYPXs2lSpV0rnr1blzZ8LCwhg0aFCG7jNCCCGE+HA80SoMOBf+Sq+B568zOOwGA89fz3KbJ9qcJfSNGjXC2dmZFStWZFi3a9cupk2bxpYtWwgPDycxMZGRI0dmeawNGzbQv39/oqOjqV27Np9++qm6Li0tjbNnz3L9+nV+/fVXhg4dqnahtbCwYP369cTGxrJgwQL69OlDVFQUkJ78+fv7Ex8fz6FDh6hcuTINGzZkwoQJ9OrVi4SEBP78808OHjxIcnIyrVq1yjK+rl27UrFiRaKioli8eDE9evTg8uXL6vq1a9eyYcMGIiIiOHv2LGvWrAFg8uTJtGzZktjYWK5evZqhdTQnAgICdCY39vT0JCQkBICQkBBKlCihdkEPCQnB09MTAAcHB3bv3k1MTAzDhw+nS5cuPHnyhDp16qCvr8/BgwfVMgIDA+nWrVu2sWzZsoWzZ8+yd+9e5s6dy+7du9V1QUFBfPPNN8THx2c67HPTpk2MGjWKBw8eYG5ujoeHB40bNyY6OpqPPvqIKVOmqNvWrVuXsLAwIiMjKVasGCNGjADg559/xtnZmZ07d5KQkED37t05cuQIo0aNIigoiDt37lCuXDmGDh2a7bmEhITg4eHBw4cP6dGjB4aGhixZsoSYmBg2bNjApEmTOHnypLr977//zpAhQ3j48CGurq6ZDi/59ttv8ff3Z8+ePdjb22f5+e/fv5+hQ4eyZMkSYmNjWbduHZaWliQnJ9O6dWs6d+7MvXv3GDt2LK1bt+bhw4dqGb/99ht79+4lLCws03MfMmRIpud75swZypQpo7MsNTWV4OBgfHx86NKlC2vWrMnxDT2APXv20LZt2ywbMiMiIrC2ts7y9fRGzfnz56lcubK6n5ubG+fOncu2/K1bt2JmZkajRo0AuHnzJo8fP2b9+vUULlwYV1dXli1bpm6fXTlFihRBo9Fw5cqVHF+DD1H+zShTTChYKH3yOX3j1zsxxqlTp6hevTrjxo0jKSkJACcnJzZv3szatWspUqTIay1PCCGEECInfH19M22lDwoKYuDAgVSoUAFzc3NmzJhBcHBwlslCnTp1aNWqFUZGRvj5+XHo0CFu376trp88eTJGRkY0a9ZMbQEGaNCgAa6urujr6+Pl5YWbmxvHjh0DwNDQkLCwMBISEnBwcKBChQqZlh0dHU3BggWznNjsxo0bHDt2jKlTp2JsbEzDhg1p1aqVTm+A/v374+LigrW1NS1btuTUqVNqDNevXycqKgpzc3Nq1qyZwyv7v9j+/PNPtQUS0hP6f//9F4B9+/YxZswYQkJCePLkCSdOnKBOnToAeHl54eTkhEajYcCAAejp6fHff/8B0L17d7UV9fjx4zx+/Jj69etnG8/48eOxtLSkXLly9OvXT2fSu44dO1KtWjUMDAwwNDTMsG+TJk3w9PTEyMiI9u3bY2Fhgbe3NwYGBnTq1Em9ZgBdunTBysoKU1NTvvzyyxeOmff392fIkCG4ublhaGjIV199xaZNm7JtZS1VqhS9e/dGX18fU1NT3N3dcXd3R19fn+rVq+Pl5aXTOt6kSRMaNWqEgYEBXbp00YkX0odtrF27lt27d6sTUmf1+a9YsYJBgwZRt25d9PX1KVeuHEWKFOHw4cNotVpGjBiBoaEhPj4+uLq6sn37drWckSNHUrhwYUxNTXN17jExMRQooDtp986dO0lOTqZ58+a0a9eOO3fuqDeLciI6OhoHB4cs1zs7OxMTE5Ply9nZGYCEhAQsLS3V/SwtLUlISMi2/ICAALp37642at66dYvY2FguXbpEeHg469atY8KECeo55aQcCwuLLHsT5RfvxxSPb0CCgRVGhgr6Zq//EhgYGKhjWvT19RkxYgTTpk3L8KUUQgghxIfLWF+PZRWLv9IxFEUhNS0VA41Blq1qxvo5HzbYpEkTihYtqtMNHuD27dtqYgng4uJCUlISDx48yPTpO05OTurPZmZm2NnZERkZiZ2dHRqNRmcfMzMz9Z/wffv28eWXXxIWFoZWq+XRo0dER0cD6V2Rp06dyhdffEHt2rWZN28e5cuXz1C2nZ0d9+/fJzU1NdOk/vbt2xQqVAhTU1Od83n2hkPhwoV14nvwIP0xxnPmzGHixIlUrVoVR0dHpk6dSuvWrbO4mhkFBQVRtWpVypUrpy6rW7cux44dIzIykrS0NDp06MDixYs5cuQIZcuWVROWjRs3MnXqVLUre3x8vHptevToQYMGDZg3bx6BgYF06dIlRz09n/2cnJycdJLaYsWKvXBfe3t79WdTU9MM759NrKZPn87y5cu5e/cuenp6xMXFZXnciIgIAgICmDNnjrrMwMCAqKioF04Q/Xy8586dY9SoUYSGhpKcnExSUpLOdX/+M342Xq1Wy/z58/nhhx+wsbFRl2f1+d+8eZNatWpliOn27ds61xgy1rVn437RuT9/flZWVhmS11WrVtGuXTuMjIwwMjLCy8uLVatWqTd3NBpNhpt1KSkp6g0bOzs7tUfMqyhQoIDOZxwXF5dtnvPgwQO2bt2qM0Hf0+/o5MmTMTU1pXLlynTp0oVt27bh6emZo3Li4+OxsrJ65XN6n+XbFvoKCeldRgwLm7/2Y1esWJGxY8dStWpVDh8+zLx58ySZF0IIIfIZPT09TDT6r/7Sf/H63M4DlFkrvaOjo9qdFtITDxMTkwzjd5+6ceOG+nNiYiLR0dE56oHYs2dPevXqRVRUFDExMVSvXl3tBVCrVi22bt3KnTt3qFKlCoMHD1av47M8PDwwNDRk69atmZbh6OjIvXv31F6ST8/H0dEx2/iKFCmCv78/UVFR+Pn54e3trXOc7AQEBOi0zgNYW1tTunRp5s+fT926dSlcuDApKSls3ryZevXqAekToHXt2pXp06cTHR1NTEwM9vb2OjN6Ozk5sXv3boKDg+nevXuO4nn2c7px44bOZ/S65o/6559/WLx4Mdu2bSM2NpYjR47orH++nKJFizJt2jSdlt/Hjx9ne4Ph+eMMGzYMDw8PIiIiiI2NpUOHDjnufq6vr8/27dsZNWqU2nsCsv78nZycMn2CgqOjo841hox17dm4Mzv3xMTETM/dzc1N7aEB6a3VmzZtYsOGDTg4OODg4MDOnTtZv369OgeEk5MT169f1znO9evX1Zb1xo0bs3nz5iyvU0REhM6M9M+/nv6OqFChAmfOnFH3O3v2LBUrVsz0mE+tXbuWSpUq6fS8KVu2LEZGRjrX6NmfsysnKiqK1NRUSpUq9cKyP3T5NqGH9Mpi6vZqj6u7desWI0eOVL9IT3311VccPXqU6tWrv9LxhRBCCCFep48//hgHBwc2btyoLvPx8WHZsmWEhYXx6NEjJk6ciLe3d5ZJ34EDB9i2bRvJyclMmTKFmjVr5ihhjo+Px87ODkNDQzZs2MDx48eB9Mmy1qxZQ1xcHIaGhuqj3yC9pfj69etqEmJtbc3EiRMZMmQI27dv58mTJzx+/Bh/f3/8/f1xcnLC3d0dX19fkpOT+ffff/njjz/o1KlTtvGtX7+e27dvo6enh7W1NXp6euo1ePLkiZrcP/vzU//99x8nTpyga9euGY7r6enJjz/+qI6Xr1evHkuWLFHfP3nyhOTkZLUVfMGCBdy7d0/nGD169GD06NEUKFCAjz76KNtzAZg9ezZxcXFcvHgRf39/vL29c7RfbsTHx2NoaEjBggV59OiRTgsspH9+zybDffr04YcfflB7Czx48EBn0sLclGttbY2JiQkhISFZ3uDJSvXq1Vm7di3e3t7qsI+sPv9evXqxZMkSDh48iKIoXLx4kcjISLXV/ocffiA1NZV169YRFhZGixYtMi0zN+deu3Ztbt++rY7H/+2337CxseHixYuEhoYSGhrKhQsXMDAwUM/d29ubadOmcefOHTWe8+fP06xZMyB9YsyoqCgGDRqk9iIICwtTbxA5OzvrzEj//OvpjYEePXqwZMkSrl69yp07d5g7d67OPBqZCQgIyLCNubk5nTp1Yvr06Tx58oSwsDCCg4Px8vLKUTn//vsvjRs3znL4TX6RbxP6eAMTUlJTXrrLfVpaGosWLaJ8+fJ8//33zJ49W2e9sbFxvq9cQgghhHg3+fr6qt3MAZo1a8b48ePx8vLCxcUFQ0ND5s+fn+X+HTt2ZOnSpdja2rJv374cz7a9cOFCRowYgY2NDTt27FBnggdYuXIlLi4u2NjYsGvXLn744QcAOnXqREJCAjY2NupEeJMmTcLX15cxY8ZgY2ODq6sr//zzD02aNAHSu76fOnUKe3t7Bg0axMqVKzNMMJaZI0eOUK1aNQoUKMDgwYMJDAxUZ9x2dXXF1NSUJ0+eUK5cOZ0u/ZCesLRo0YKCBTM2Fnl6ehIfH6+2yD//3tLSkm+++YbmzZvj4OBAdHS0Oiv6U126dOHixYs5mgzvKS8vLypVqkT9+vUZMWIETZs2zfG+OdWiRQvq1q2Li4sLbm5uOkM3AL788kvGjRuHtbU1a9asoU6dOnz77bd8+umnWFpa4u7urjP2Padmz57NokWLsLS0ZP78+bRp0ybXx6hfvz7+/v60adOG8+fPZ/n5161blwULFtC3b18sLS3p3LkzcXFxGBkZsXnzZgIDA7Gzs2PmzJls3rxZpxv/s3Jz7sbGxvj4+KhzP6xatYr+/furT49wcHCgaNGi9O3bV306ga+vL5UrV6ZGjRoULFiQefPmsWXLFnUIjL29PQcOHCA+Ph43Nzesra3p0aNHrutFy5YtGTx4MDVr1qRcuXI0b95cfcIEpHfJf3Zs/9WrVzl69GimN7sWLVrE/fv3KViwIF5eXkybNk290ZVdOYGBgQwYMCBXsX+I9JTcTI34AYiLi8PKyopxSyZQ/WBROi7PfGbJFzl9+jQDBw7k8OHD6jInJycuXbqEiYnJ6wxXiJei1Wq5e/cu9vb28jQF8cGSei7eNUlJSVy7do0SJUq8tv8HFEVRx4q/qUfs5pafnx83b96U5z+/ZSkpKRQuXJijR4/mqIuxnp4eN27cyLYr+7vgXazn74KbN2/SunVrjh8/Ln/nnvPff//Rs2dPDh48+M7Vmaz+FsTExGBjY0NsbKzOZH+vKt/WDL0kCxwcs57lMTOJiYmMHz+eatWq6STz/fv3JzQ0VJJ5IYQQQgjxRqxcuZLKlSvn+/HC+UmxYsU4efKkJPOZKFOmDIcOHXrnkvm8kI/7hCvoKTmvALt27eKzzz5TZx6F9K5XS5cuzdFjQ4QQQgghhHgZtWvX5ubNm/z+++86y62trTN95NmdO3feVmivzdq1axk0aFCG5e3bt8/xkA4h8qN8m9AnaTTcjsv+l52iKAwYMIBffvlFXWZkZMT48eMZP368Oq5KCCGEECI/8PPzy+sQ8p1Dhw5lujwmJibLfd63UbXe3t5069ZNWlyFyKV8m9DbJz1CUzz72Vj19PR0Zm319PRkyZIlmT4XVQghhBBCCCGEeFvy7YAMJdWYFDtNjradMGECtWvXZtmyZezdu1eSeSGEEEJkSavV5nUIQggh8sjb7h2Tb1voARQ93YudnJzMN998g56eHhMmTFCXm5iYcODAAekCJIQQQogsGRkZoa+vz+3btylUqBBGRkav/L+DzP4t8gOp5+JDoSgK9+7dQ09PD0NDw7dSZr5N6NOee3/gwAEGDhzIuXPnMDQ0pF27dlSoUEFdL79chBBCCPEi+vr6lChRgsjISG7fvv1ajqkoClqtFn19fflfRHywpJ6LD4menh7FihVDo8lZb/BXlW8Teo0WUvW1xMTEMH78eH766Sd1XVpaGiEhIToJvRBCCCFEdoyMjHB2diY1NZW0tOebD3JPq9USHR2NnZ2dPLpKfLCknosPiaGh4VtL5uEdSegXLVrEN998Q1RUFFWqVGHhwoXUrFkzy+3XrVvHV199RXh4OGXKlGH27Nl4eXnlqkw9RY/Dx44wZtxYIiMj1eXVqlVj6dKluLu7v/T5CCGEECL/etrV8nV0t9RqtRgaGmJiYiKJjvhgST0X4uXl+TcmODiY0aNH4+vry4kTJ6hSpQrNmzfn7t27mW5/4MABunbtSr9+/Th58iTt2rWjXbt2nD17NtdlL1y4UE3mzc3NmTdvHocOHZJkXgghhBBCCCHEOy/PE/q5c+cyYMAA+vTpQ4UKFfjpp58wMzPD398/0+0XLFhAixYtGDNmDOXLl2fatGm4u7vzww8/5KrcO4n/G9vWqlUrzp8/z6hRozAweCc6LQghhBBCCCGEEC+Up9lrcnIyx48fZ/z48eoyfX19mjZtysGDBzPd5+DBg4wePVpnWfPmzdm4cWOm2z958oQnT56o72NjYwFIepKMpaUlCxYsoG3btujp6RETE/NqJyTEO0Kr1RIXF6fOuCzEh0jqucgPpJ6L/EDqucgPnuaar/uxdnma0N+/f5+0tDQKFy6ss7xw4cJcuHAh032ioqIy3T4qKirT7WfOnMmUKVMyLP/pm6UA9OnT52VCF0IIIYQQQgghciU6OhorK6vXdrwPvn/5+PHjdVr0Y2JicHFxISIi4rVeSCHeJXFxcTg5OXHjxg0sLS3zOhwh3gip5yI/kHou8gOp5yI/iI2NxdnZGVtb29d63DxN6AsWLIhGo+HOnTs6y+/cuYODg0Om+zg4OORqe2NjY4yNjTMst7Kykl8Y4oNnaWkp9Vx88KSei/xA6rnID6Sei/zgdQ8rydNBKkZGRlSrVo3du3ery7RaLbt378bDwyPTfTw8PHS2B9i1a1eW2wshhBBCCCGEEB+iPO9yP3r0aHr16kX16tWpWbMm8+fP59GjR+rY9k8//ZSiRYsyc+ZMAEaOHEmDBg347rvvaNmyJUFBQRw7doylS5fm5WkIIYQQQgghhBBvVZ4n9D4+Pty7d4/JkycTFRVF1apV2b59uzrxXUREhE63hDp16rBmzRomTZrEhAkTKFOmDBs3bqRSpUo5Ks/Y2BhfX99Mu+EL8aGQei7yA6nnIj+Qei7yA6nnIj94U/VcT3nd8+YLIYQQQgghhBDijZMHPQohhBBCCCGEEO8hSeiFEEIIIYQQQoj3kCT0QgghhBBCCCHEe0gSeiGEEEIIIYQQ4j30QSb0ixYtonjx4piYmFCrVi2OHDnywu3XrVtHuXLlMDExwc3NjW3btr2lSIV4ebmp58uWLcPT0xMbGxtsbGxo2rRptt8LId4Fuf19/lRQUBB6enq0a9fuzQYoxGuQ23oeExPD0KFDKVKkCMbGxpQtW1b+dxHvvNzW8/nz5+Pq6oqpqSlOTk58/vnnJCUlvaVohci9f//9l9atW+Po6Iienh4bN27Mdp+9e/fi7u6OsbExpUuXZsWKFbku94NL6IODgxk9ejS+vr6cOHGCKlWq0Lx5c+7evZvp9gcOHKBr167069ePkydP0q5dO9q1a8fZs2ffcuRC5Fxu6/nevXvp2rUrf//9NwcPHsTJyYlmzZpx69attxy5EDmX23r+VHh4OF988QWenp5vKVIhXl5u63lycjIff/wx4eHhrF+/nosXL7Js2TKKFi36liMXIudyW8/XrFnDuHHj8PX1JSwsjF9++YXg4GAmTJjwliMXIucePXpElSpVWLRoUY62v3btGi1btqRRo0aEhoYyatQo+vfvz44dO3JXsPKBqVmzpjJ06FD1fVpamuLo6KjMnDkz0+29vb2Vli1b6iyrVauWMmjQoDcapxCvIrf1/HmpqamKhYWFsnLlyjcVohCv7GXqeWpqqlKnTh3l559/Vnr16qW0bdv2LUQqxMvLbT3/8ccflZIlSyrJyclvK0QhXllu6/nQoUOVxo0b6ywbPXq0Urdu3TcapxCvC6D8/vvvL9xm7NixSsWKFXWW+fj4KM2bN89VWR9UC31ycjLHjx+nadOm6jJ9fX2aNm3KwYMHM93n4MGDOtsDNG/ePMvthchrL1PPn/f48WNSUlKwtbV9U2EK8Upetp5PnToVe3t7+vXr9zbCFOKVvEw937x5Mx4eHgwdOpTChQtTqVIlZsyYQVpa2tsKW4hceZl6XqdOHY4fP652y7969Srbtm3Dy8vrrcQsxNvwuvJQg9cZVF67f/8+aWlpFC5cWGd54cKFuXDhQqb7REVFZbp9VFTUG4tTiFfxMvX8eV9++SWOjo4ZfokI8a54mXq+b98+fvnlF0JDQ99ChEK8upep51evXmXPnj10796dbdu2cfnyZYYMGUJKSgq+vr5vI2whcuVl6nm3bt24f/8+9erVQ1EUUlNT+eyzz6TLvfigZJWHxsXFkZiYiKmpaY6O80G10Ashsjdr1iyCgoL4/fffMTExyetwhHgt4uPj6dmzJ8uWLaNgwYJ5HY4Qb4xWq8Xe3p6lS5dSrVo1fHx8mDhxIj/99FNehybEa7N3715mzJjB4sWLOXHiBL/99htbt25l2rRpeR2aEO+cD6qFvmDBgmg0Gu7cuaOz/M6dOzg4OGS6j4ODQ662FyKvvUw9f+rbb79l1qxZ/PXXX1SuXPlNhinEK8ltPb9y5Qrh4eG0bt1aXabVagEwMDDg4sWLlCpV6s0GLUQuvczv8yJFimBoaIhGo1GXlS9fnqioKJKTkzEyMnqjMQuRWy9Tz7/66it69uxJ//79AXBzc+PRo0cMHDiQiRMnoq8vbZLi/ZdVHmppaZnj1nn4wFrojYyMqFatGrt371aXabVadu/ejYeHR6b7eHh46GwPsGvXriy3FyKvvUw9B5gzZw7Tpk1j+/btVK9e/W2EKsRLy209L1euHGfOnCE0NFR9tWnTRp051snJ6W2GL0SOvMzv87p163L58mX1hhXApUuXKFKkiCTz4p30MvX88ePHGZL2pzex0ucbE+L999ry0NzN1/fuCwoKUoyNjZUVK1Yo58+fVwYOHKhYW1srUVFRiqIoSs+ePZVx48ap2+/fv18xMDBQvv32WyUsLEzx9fVVDA0NlTNnzuTVKQiRrdzW81mzZilGRkbK+vXrlcjISPUVHx+fV6cgRLZyW8+fJ7Pci/dBbut5RESEYmFhoQwbNky5ePGismXLFsXe3l75+uuv8+oUhMhWbuu5r6+vYmFhoQQGBipXr15Vdu7cqZQqVUrx9vbOq1MQIlvx8fHKyZMnlZMnTyqAMnfuXOXkyZPK9evXFUVRlHHjxik9e/ZUt7969apiZmamjBkzRgkLC1MWLVqkaDQaZfv27bkq94NL6BVFURYuXKg4OzsrRkZGSs2aNZVDhw6p6xo0aKD06tVLZ/u1a9cqZcuWVYyMjJSKFSsqW7dufcsRC5F7uannLi4uCpDh5evr+/YDFyIXcvv7/FmS0Iv3RW7r+YEDB5RatWopxsbGSsmSJZXp06crqampbzlqIXInN/U8JSVF8fPzU0qVKqWYmJgoTk5OypAhQ5SHDx++/cCFyKG///470/+3n9btXr16KQ0aNMiwT9WqVRUjIyOlZMmSyvLly3Ndrp6iSL8VIYQQQgghhBDiffNBjaEXQgghhBBCCCHyC0nohRBCCCGEEEKI95Ak9EIIIYQQQgghxHtIEnohhBBCCCGEEOI9JAm9EEIIIYQQQgjxHpKEXgghhBBCCCGEeA9JQi+EEEIIIYQQQryHJKEXQgghhBBCCCHeQ5LQCyGEEG/RihUrsLa2zuswXpqenh4bN2584Ta9e/emXbt2byUeIYQQIj+ThF4IIYTIpd69e6Onp5fhdfny5bwOjRUrVqjx6OvrU6xYMfr06cPdu3dfy/EjIyP55JNPAAgPD0dPT4/Q0FCdbRYsWMCKFSteS3lZ8fPzU89To9Hg5OTEwIEDefDgQa6OIzcfhBBCvM8M8joAIYQQ4n3UokULli9frrOsUKFCeRSNLktLSy5evIhWq+XUqVP06dOH27dvs2PHjlc+toODQ7bbWFlZvXI5OVGxYkX++usv0tLSCAsLo2/fvsTGxhIcHPxWyhdCCCHymrTQCyGEEC/B2NgYBwcHnZdGo2Hu3Lm4ublhbm6Ok5MTQ4YMISEhIcvjnDp1ikaNGmFhYYGlpSXVqlXj2LFj6vp9+/bh6emJqakpTk5OjBgxgkePHr0wNj09PRwcHHB0dOSTTz5hxIgR/PXXXyQmJqLVapk6dSrFihXD2NiYqlWrsn37dnXf5ORkhg0bRpEiRTAxMcHFxYWZM2fqHPtpl/sSJUoA8NFHH6Gnp0fDhg0B3VbvpUuX4ujoiFar1Ymxbdu29O3bV32/adMm3N3dMTExoWTJkkyZMoXU1NQXnqeBgQEODg4ULVqUpk2b0rlzZ3bt2qWuT0tLo1+/fpQoUQJTU1NcXV1ZsGCBut7Pz4+VK1eyadMmtbV/7969ANy4cQNvb2+sra2xtbWlbdu2hIeHvzAeIYQQ4m2ThF4IIYR4jfT19fn+++85d+4cK1euZM+ePYwdOzbL7bt3706xYsU4evQox48fZ9y4cRgaGgJw5coVWrRoQceOHTl9+jTBwcHs27ePYcOG5SomU1NTtFotqampLFiwgO+++45vv/2W06dP07x5c9q0acN///0HwPfff8/mzZtZu3YtFy9eZPXq1RQvXjzT4x45cgSAv/76i8jISH777bcM23Tu3Jno6Gj+/vtvddmDBw/Yvn073bt3ByAkJIRPP/2UkSNHcv78eZYsWcKKFSuYPn16js8xPDycHTt2YGRkpC7TarUUK1aMdevWcf78eSZPnsyECRNYu3YtAF988QXe3t60aNGCyMhIIiMjqVOnDikpKTRv3hwLCwtCQkLYv38/BQoUoEWLFiQnJ+c4JiGEEOKNU4QQQgiRK7169VI0Go1ibm6uvjp16pTptuvWrVPs7OzU98uXL1esrKzU9xYWFsqKFSsy3bdfv37KwIEDdZaFhIQo+vr6SmJiYqb7PH/8S5cuKWXLllWqV6+uKIqiODo6KtOnT9fZp0aNGsqQIUMURVGU4cOHK40bN1a0Wm2mxweU33//XVEURbl27ZoCKCdPntTZplevXkrbtm3V923btlX69u2rvl+yZIni6OiopKWlKYqiKE2aNFFmzJihc4yAgAClSJEimcagKIri6+ur6OvrK+bm5oqJiYkCKIAyd+7cLPdRFEUZOnSo0rFjxyxjfVq2q6urzjV48uSJYmpqquzYseOFxxdCCCHeJhlDL4QQQryERo0a8eOPP6rvzc3NgfTW6pkzZ3LhwgXi4uJITU0lKSmJx48fY2ZmluE4o0ePpn///gQEBKjdxkuVKgWkd8c/ffo0q1evVrdXFAWtVsu1a9coX758prHFxsZSoEABtFotSUlJ1KtXj59//pm4uDhu375N3bp1dbavW7cup06dAtK7y3/88ce4urrSokULWrVqRbNmzV7pWnXv3p0BAwawePFijI2NWb16NV26dEFfX189z/379+u0yKelpb3wugG4urqyefNmkpKSWLVqFaGhoQwfPlxnm0WLFuHv709ERASJiYkkJydTtWrVF8Z76tQpLl++jIWFhc7ypKQkrly58hJXQAghhHgzJKEXQgghXoK5uTmlS5fWWRYeHk6rVq0YPHgw06dPx9bWln379tGvXz+Sk5MzTUz9/Pzo1q0bW7du5c8//8TX15egoCDat29PQkICgwYNYsSIERn2c3Z2zjI2CwsLTpw4gb6+PkWKFMHU1BSAuLi4bM/L3d2da9eu8eeff/LXX3/h7e1N06ZNWb9+fbb7ZqV169YoisLWrVupUaMGISEhzJs3T12fkJDAlClT6NChQ4Z9TUxMsjyukZGR+hnMmjWLli1bMmXKFKZNmwZAUFAQX3zxBd999x0eHh5YWFjwzTffcPjw4RfGm5CQQLVq1XRupDz1rkx8KIQQQoAk9EIIIcRrc/z4cbRaLd99953a+vx0vPaLlC1blrJly/L555/TtWtXli9fTvv27XF3d+f8+fMZbhxkR19fP9N9LC0tcXR0ZP/+/TRo0EBdvn//fmrWrKmznY+PDz4+PnTq1IkWLVrw4MEDbG1tdY73dLx6WlraC+MxMTGhQ4cOrF69msuXL+Pq6oq7u7u63t3dnYsXL+b6PJ83adIkGjduzODBg9XzrFOnDkOGDFG3eb6F3cjIKEP87u7uBAcHY29vj6Wl5SvFJIQQQrxJMimeEEII8ZqULl2alJQUFi5cyNWrVwkICOCnn37KcvvExESGDRvG3r17uX79Ovv37+fo0aNqV/ovv/ySAwcOMGzYMEJDQ/nvv//YtGlTrifFe9aYMWOYPXs2wcHBXLx4kXHjxhEaGsrIkSMBmDt3LoGBgVy4cIFLly6xbt06HBwcsLa2znAse3t7TE1N2b59O3fu3CE2NjbLcrt3787WrVvx9/dXJ8N7avLkyfz6669MmTKFc+fOERYWRlBQEJMmTcrVuXl4eFC5cmVmzJgBQJkyZTh27Bg7duzg0qVLfPXVVxw9elRnn+LFi3P69GkuXrzI/fv3SUlJoXv37hQsWJC2bdsSEhLCtWvX2Lt3LyNGjODmzZu5ikkIIYR4kyShF0IIIV6TKlWqMHfuXGbPnk2lSpVYvXq1ziPfnqfRaIiOjubTTz+lbNmyeHt788knnzBlyhQAKleuzD///MOlS5fw9PTko48+YvLkyTg6Or50jCNGjGD06NH83//9H25ubmzfvp3NmzdTpkwZIL27/pw5c6hevTo1atQgPDycbdu2qT0OnmVgYMD333/PkiVLcHR0pG3btlmW27hxY2xtbbl48SLdunXTWde8eXO2bNnCzp07qVGjBrVr12bevHm4uLjk+vw+//xzfv75Z27cuMGgQYPo0KEDPj4+1KpVi+joaJ3WeoABAwbg6upK9erVKVSoEPv378fMzIx///0XZ2dnOnToQPny5enXrx9JSUnSYi+EEOKdoqcoipLXQQghhBBCCCGEECJ3pIVeCCGEEEIIIYR4D0lCL4QQQgghhBBCvIckoRdCCCGEEEIIId5DktALIYQQQgghhBDvIUnohRBCCCGEEEKI95Ak9EIIIYQQQgghxHtIEnohhBBCCCGEEOI9JAm9EEIIIYQQQgjxHpKEXgghhBBCCCGEeA9JQi+EEEIIIYQQQryHJKEXQgghhBBCCCHeQ/8P87rgIPufQXYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "print(\"📉 Generating Ablation ROC Curves...\")\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# baseline\n",
    "fpr_base, tpr_base, _ = roc_curve(y_test, y_prob)\n",
    "plt.plot(fpr_base, tpr_base, lw=3, color='black',\n",
    "         label=f'Baseline (AUC={base_auc:.3f})')\n",
    "\n",
    "# random line\n",
    "plt.plot([0,1], [0,1], 'k--', lw=2)\n",
    "\n",
    "# store curves\n",
    "curve_results = []\n",
    "\n",
    "for feat in features:\n",
    "    X_perm = X_test.copy()\n",
    "    X_perm[feat] = np.random.permutation(X_perm[feat].values)\n",
    "\n",
    "    # FIX TYPES AGAIN\n",
    "    for col in [\"chrom\", \"ref\", \"alt\"]:\n",
    "        if col in X_perm.columns:\n",
    "            X_perm[col] = X_perm[col].astype(\"category\")\n",
    "\n",
    "    for col in X_perm.columns:\n",
    "        if col not in [\"chrom\", \"ref\", \"alt\"]:\n",
    "            X_perm[col] = pd.to_numeric(X_perm[col], errors=\"coerce\")\n",
    "\n",
    "    X_perm = X_perm.fillna(0)\n",
    "\n",
    "    y_prob_perm = model.predict_proba(X_perm)[:, 1]\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_prob_perm)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.plot(fpr, tpr, lw=1.5, alpha=0.7,\n",
    "             label=f'No {feat} (AUC={roc_auc:.3f})')\n",
    "\n",
    "# formatting\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Ablation: Feature Importance Analysis')\n",
    "plt.legend(loc=\"lower right\", fontsize='small')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.savefig(\"ablation_roc.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🧪 Running NOISE-REMOVAL ABLATION (FINAL FIX)...\n",
      "Removing 3 noisy features:\n",
      "['GERP++_RS_rankscore', 'phyloP17way_primate_rankscore', 'phastCons100way_vertebrate_rankscore']\n",
      "\n",
      "==========================================\n",
      "🧹 NOISE-REMOVED MODEL PERFORMANCE\n",
      "==========================================\n",
      "AUC     : 0.766809\n",
      "PR-AUC  : 0.771672\n",
      "AUC Gain: 0.000000\n",
      "PR Gain : 0.000000\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n🧪 Running NOISE-REMOVAL ABLATION (FINAL FIX)...\")\n",
    "\n",
    "# ---- DEFINE NOISY FEATURES ----\n",
    "noise_features = [\n",
    "    \"GERP++_RS_rankscore\",\n",
    "    \"phyloP17way_primate_rankscore\",\n",
    "    \"phastCons100way_vertebrate_rankscore\"\n",
    "]\n",
    "\n",
    "print(f\"Removing {len(noise_features)} noisy features:\")\n",
    "print(noise_features)\n",
    "\n",
    "# ---- START FROM ORIGINAL TEST ----\n",
    "X_clean = X_test.copy()\n",
    "\n",
    "# ---- 🔥 ZERO OUT (DO NOT DROP) ----\n",
    "for f in noise_features:\n",
    "    if f in X_clean.columns:\n",
    "        X_clean[f] = 0.0\n",
    "\n",
    "# ---- 🔥 FORCE EXACT TRAINING ORDER (CRITICAL) ----\n",
    "feature_order = model.get_booster().feature_names\n",
    "X_clean = X_clean[feature_order]\n",
    "\n",
    "# ---- FIX TYPES ----\n",
    "cat_cols = [\"chrom\", \"ref\", \"alt\"]\n",
    "\n",
    "for col in cat_cols:\n",
    "    if col in X_clean.columns:\n",
    "        X_clean[col] = X_clean[col].astype(\"category\")\n",
    "\n",
    "for col in X_clean.columns:\n",
    "    if col not in cat_cols:\n",
    "        X_clean[col] = pd.to_numeric(X_clean[col], errors=\"coerce\")\n",
    "\n",
    "X_clean = X_clean.fillna(0)\n",
    "\n",
    "# ---- PREDICT ----\n",
    "y_prob_clean = model.predict_proba(X_clean)[:, 1]\n",
    "\n",
    "# ---- METRICS ----\n",
    "clean_auc = roc_auc_score(y_test, y_prob_clean)\n",
    "clean_pr  = average_precision_score(y_test, y_prob_clean)\n",
    "\n",
    "print(\"\\n==========================================\")\n",
    "print(\"🧹 NOISE-REMOVED MODEL PERFORMANCE\")\n",
    "print(\"==========================================\")\n",
    "print(f\"AUC     : {clean_auc:.6f}\")\n",
    "print(f\"PR-AUC  : {clean_pr:.6f}\")\n",
    "print(f\"AUC Gain: {clean_auc - base_auc:.6f}\")\n",
    "print(f\"PR Gain : {clean_pr - base_pr:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 11))\n",
    "ax.axis('off')\n",
    "\n",
    "# ---------- Helper Functions ----------\n",
    "def draw_box(text, xy, fc=\"#E8F0FE\", size=10):\n",
    "    ax.text(\n",
    "        xy[0], xy[1], text,\n",
    "        ha='center', va='center',\n",
    "        fontsize=size,\n",
    "        bbox=dict(boxstyle=\"round,pad=0.5\", facecolor=fc, edgecolor='black')\n",
    "    )\n",
    "\n",
    "def draw_arrow(start, end):\n",
    "    ax.annotate(\n",
    "        \"\", xy=end, xytext=start,\n",
    "        arrowprops=dict(arrowstyle=\"->\", lw=1.5)\n",
    "    )\n",
    "\n",
    "# ---------- TITLE ----------\n",
    "plt.title(\"Framework Pipeline\", fontsize=18, weight='bold')\n",
    "\n",
    "# =====================================================\n",
    "# DATA SOURCES (STRAIGHT + CLEAN)\n",
    "# =====================================================\n",
    "draw_box(\"ClinVar\\n(~7.7M variants)\", (0.1, 0.90))\n",
    "draw_box(\"gnomAD\\n(AF)\", (0.3, 0.90))\n",
    "draw_box(\"dbNSFP\\n(functional scores)\", (0.5, 0.90))\n",
    "draw_box(\"GRCh38\\n(sequence)\", (0.7, 0.90))\n",
    "\n",
    "draw_box(\"Data Integration\", (0.4, 0.78), fc=\"#D1E7DD\", size=11)\n",
    "\n",
    "# straight arrows\n",
    "draw_arrow((0.1, 0.86), (0.35, 0.80))\n",
    "draw_arrow((0.3, 0.86), (0.38, 0.80))\n",
    "draw_arrow((0.5, 0.86), (0.42, 0.80))\n",
    "draw_arrow((0.7, 0.86), (0.45, 0.80))\n",
    "\n",
    "# =====================================================\n",
    "# PIPELINE (MORE SPACING)\n",
    "# =====================================================\n",
    "draw_box(\n",
    "    \"Filtering & Cleaning\\n\"\n",
    "    \"• SNV only\\n\"\n",
    "    \"• Remove HGVS (Leakage Prevention)\\n\"\n",
    "    \"• Deduplication\",\n",
    "    (0.4, 0.64),\n",
    ")\n",
    "\n",
    "draw_box(\n",
    "    \"Feature Engineering\\n\"\n",
    "    \"• gnomAD AF\\n\"\n",
    "    \"• Conservation (phyloP, phastCons)\\n\"\n",
    "    \"• Median Imputation\",\n",
    "    (0.4, 0.48),\n",
    ")\n",
    "\n",
    "draw_box(\n",
    "    \"Dataset Construction\\n\"\n",
    "    \"Train: 300k (Balanced)\\n\"\n",
    "    \"Test: 14k (Rare Balanced)\\n\"\n",
    "    \"Test: 100k (Imbalanced)\",\n",
    "    (0.4, 0.32),\n",
    "    fc=\"#E2F0CB\"\n",
    ")\n",
    "\n",
    "draw_box(\n",
    "    \"XGBoost Model\\n\"\n",
    "    \"• 10 Core Features\\n\"\n",
    "    \"• Regularized Training\",\n",
    "    (0.4, 0.18),\n",
    "    fc=\"#D9EAD3\"\n",
    ")\n",
    "\n",
    "draw_box(\n",
    "    \"Evaluation Pipeline\\n\"\n",
    "    \"• ROC-AUC / PR-AUC\\n\"\n",
    "    \"• Threshold Tuning (VAL → TEST)\\n\"\n",
    "    \"• SHAP + Ablation\",\n",
    "    (0.4, 0.05),\n",
    "    fc=\"#FCE5CD\"\n",
    ")\n",
    "\n",
    "# arrows (vertical clean)\n",
    "draw_arrow((0.4, 0.76), (0.4, 0.68))\n",
    "draw_arrow((0.4, 0.60), (0.4, 0.52))\n",
    "draw_arrow((0.4, 0.44), (0.4, 0.36))\n",
    "draw_arrow((0.4, 0.28), (0.4, 0.21))\n",
    "draw_arrow((0.4, 0.15), (0.4, 0.09))\n",
    "\n",
    "# =====================================================\n",
    "# BENCHMARK (FIXED CLEANLY)\n",
    "# =====================================================\n",
    "draw_box(\n",
    "    \"Generalist Models\\n\"\n",
    "    \"• CADD\\n• REVEL\\n• AlphaMissense\\n• BayesDel\",\n",
    "    (0.75, 0.05),\n",
    "    fc=\"#FFF3CD\"\n",
    ")\n",
    "\n",
    "# clean horizontal arrow\n",
    "draw_arrow((0.50, 0.05), (0.70, 0.05))\n",
    "\n",
    "# aligned label\n",
    "ax.text(0.60, 0.09, \"Benchmark Comparison\", fontsize=10, ha='center')\n",
    "\n",
    "# =====================================================\n",
    "# SAVE\n",
    "# =====================================================\n",
    "# plt.savefig(\"framework_final.svg\", bbox_inches='tight')\n",
    "# plt.savefig(\"framework_final.png\", dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}