From 6552f622df826549f585b8735235766429c0a502 Mon Sep 17 00:00:00 2001
From: rpotter6298 <rpotter6298@gmail.com>
Date: Thu, 14 May 2026 09:46:31 +0200
Subject: [PATCH] Add Decision Tree analysis notebook and script

- Created a new Jupyter notebook `EDA_Decision_Tree.ipynb` for exploratory data analysis and decision tree classification using the biologically selected dataset.
- Developed a Python script `make_decision_tree_notebook.py` to automate the creation of the decision tree notebook, including data preparation, model training, evaluation, and visualization steps.
- Implemented iterative tuning of the decision tree model with cross-validation and hyperparameter search for improved performance and interpretability.
---
 assignment/Decision_Tree.ipynb                |  912 ++++++++++++++
 .../{EDA.ipynb => EDA_Decision_Tree.ipynb}    | 1096 ++++++++++++++++-
 assignment/make_decision_tree_notebook.py     |  377 ++++++
 3 files changed, 2383 insertions(+), 2 deletions(-)
 create mode 100644 assignment/Decision_Tree.ipynb
 rename assignment/{EDA.ipynb => EDA_Decision_Tree.ipynb} (63%)
 create mode 100644 assignment/make_decision_tree_notebook.py

diff --git a/assignment/Decision_Tree.ipynb b/assignment/Decision_Tree.ipynb
new file mode 100644
index 0000000..e11aac3
--- /dev/null
+++ b/assignment/Decision_Tree.ipynb
@@ -0,0 +1,912 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1fd56de4",
+   "metadata": {},
+   "source": [
+    "# Part 1: Decision Tree Classifier\n",
+    "\n",
+    "This notebook adds the Decision Tree part of the assignment using only the biologically selected dataset `slice_1_significant_7.csv`.\n",
+    "\n",
+    "The task is a binary classification problem:\n",
+    "\n",
+    "- `0`: Healthy control\n",
+    "- `1`: Exfoliative Glaucoma (XFG)\n",
+    "\n",
+    "I use the iris tutorial as a template for the overall workflow:\n",
+    "\n",
+    "- define `X` and `y`\n",
+    "- split into training and test sets\n",
+    "- train a `DecisionTreeClassifier`\n",
+    "- evaluate the model\n",
+    "- visualize the final tree\n",
+    "\n",
+    "Because this dataset is much smaller than iris, I also use cross-validation and a small hyperparameter search to make the model selection more defensible.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "64ed7e23",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samples: 60\n",
+      "Features: 7\n",
+      "Feature names:\n",
+      "['HPRA000767', 'HPRA034083', 'HPRA006876', 'HPRA019035', 'HPRA003490', 'HPRA022019', 'HPRA017192']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from IPython.display import display\n",
+    "from sklearn.inspection import permutation_importance\n",
+    "from sklearn.metrics import (\n",
+    "    ConfusionMatrixDisplay,\n",
+    "    accuracy_score,\n",
+    "    classification_report,\n",
+    ")\n",
+    "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
+    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "df = pd.read_csv(\"slice_1_significant_7.csv\")\n",
+    "identifier_cols = [\"Internal LIMS ID\", \"Original ID\", \"group\"]\n",
+    "feature_names = [c for c in df.columns if c not in identifier_cols]\n",
+    "\n",
+    "print(f\"Samples: {df.shape[0]}\")\n",
+    "print(f\"Features: {len(feature_names)}\")\n",
+    "print(\"Feature names:\")\n",
+    "print(feature_names)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acec5b40",
+   "metadata": {},
+   "source": [
+    "## Step 1: Prepare Features and Targets\n",
+    "\n",
+    "Following the same style as the iris tutorial, I separate the predictors into `X` and the class labels into `y`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3c66b866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HPRA000767</th>\n",
+       "      <th>HPRA034083</th>\n",
+       "      <th>HPRA006876</th>\n",
+       "      <th>HPRA019035</th>\n",
+       "      <th>HPRA003490</th>\n",
+       "      <th>HPRA022019</th>\n",
+       "      <th>HPRA017192</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352329</td>\n",
+       "      <td>-0.074663</td>\n",
+       "      <td>0.442451</td>\n",
+       "      <td>0.210802</td>\n",
+       "      <td>-0.025678</td>\n",
+       "      <td>-0.923763</td>\n",
+       "      <td>0.028253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.184865</td>\n",
+       "      <td>0.467075</td>\n",
+       "      <td>1.272995</td>\n",
+       "      <td>1.857599</td>\n",
+       "      <td>2.709290</td>\n",
+       "      <td>1.367955</td>\n",
+       "      <td>0.220041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.519969</td>\n",
+       "      <td>-0.358698</td>\n",
+       "      <td>0.124177</td>\n",
+       "      <td>1.383003</td>\n",
+       "      <td>1.227493</td>\n",
+       "      <td>-0.128889</td>\n",
+       "      <td>-0.343792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.155189</td>\n",
+       "      <td>0.467075</td>\n",
+       "      <td>0.630671</td>\n",
+       "      <td>0.080847</td>\n",
+       "      <td>-0.025678</td>\n",
+       "      <td>-0.416995</td>\n",
+       "      <td>0.753164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.590507</td>\n",
+       "      <td>-0.074663</td>\n",
+       "      <td>-1.423605</td>\n",
+       "      <td>-1.591214</td>\n",
+       "      <td>-0.375025</td>\n",
+       "      <td>-0.165849</td>\n",
+       "      <td>-1.292029</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   HPRA000767  HPRA034083  HPRA006876  HPRA019035  HPRA003490  HPRA022019  \\\n",
+       "0    0.352329   -0.074663    0.442451    0.210802   -0.025678   -0.923763   \n",
+       "1    2.184865    0.467075    1.272995    1.857599    2.709290    1.367955   \n",
+       "2    0.519969   -0.358698    0.124177    1.383003    1.227493   -0.128889   \n",
+       "3    1.155189    0.467075    0.630671    0.080847   -0.025678   -0.416995   \n",
+       "4   -0.590507   -0.074663   -1.423605   -1.591214   -0.375025   -0.165849   \n",
+       "\n",
+       "   HPRA017192  \n",
+       "0    0.028253  \n",
+       "1    0.220041  \n",
+       "2   -0.343792  \n",
+       "3    0.753164  \n",
+       "4   -1.292029  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Target distribution:\n",
+      "group\n",
+      "0    30\n",
+      "1    30\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.drop(columns=identifier_cols)\n",
+    "y = df[\"group\"]\n",
+    "\n",
+    "display(X.head())\n",
+    "print(\"Target distribution:\")\n",
+    "print(y.value_counts().sort_index())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ab9a713",
+   "metadata": {},
+   "source": [
+    "## Step 2: Train-Test Split\n",
+    "\n",
+    "I use an 80/20 stratified split so both classes remain balanced in the training and test sets. The training set is used for tuning, while the test set is held back for the final evaluation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f3df4577",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set shape: (48, 7)\n",
+      "Test set shape: (12, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.2,\n",
+    "    stratify=y,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "print(\"Training set shape:\", X_train.shape)\n",
+    "print(\"Test set shape:\", X_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0990209",
+   "metadata": {},
+   "source": [
+    "## Step 3: Iteratively Tune the Decision Tree\n",
+    "\n",
+    "The assignment asks for iterative development, so I compare multiple settings for:\n",
+    "\n",
+    "- `max_depth`\n",
+    "- `min_samples_leaf`\n",
+    "- split criterion: `gini` or `entropy`\n",
+    "\n",
+    "For each configuration I record:\n",
+    "\n",
+    "- mean 5-fold cross-validation accuracy on the training set\n",
+    "- training accuracy\n",
+    "- test accuracy\n",
+    "- actual tree depth and number of leaves\n",
+    "\n",
+    "This helps distinguish a tree that genuinely generalizes from one that merely memorizes the training set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "806946fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>max_depth</th>\n",
+       "      <th>criterion</th>\n",
+       "      <th>min_samples_leaf</th>\n",
+       "      <th>cv_accuracy_mean</th>\n",
+       "      <th>cv_accuracy_std</th>\n",
+       "      <th>train_accuracy</th>\n",
+       "      <th>test_accuracy</th>\n",
+       "      <th>actual_tree_depth</th>\n",
+       "      <th>n_leaves</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>4</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.775556</td>\n",
+       "      <td>0.143398</td>\n",
+       "      <td>0.854167</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.755556</td>\n",
+       "      <td>0.148740</td>\n",
+       "      <td>0.916667</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>None</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.755556</td>\n",
+       "      <td>0.145551</td>\n",
+       "      <td>0.937500</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.753333</td>\n",
+       "      <td>0.146633</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.751111</td>\n",
+       "      <td>0.150817</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.158854</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>3</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.131280</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.131280</td>\n",
+       "      <td>0.895833</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>None</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.115042</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.731111</td>\n",
+       "      <td>0.149633</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>3</td>\n",
+       "      <td>entropy</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.731111</td>\n",
+       "      <td>0.132105</td>\n",
+       "      <td>0.812500</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>gini</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.711111</td>\n",
+       "      <td>0.110889</td>\n",
+       "      <td>0.791667</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   max_depth criterion  min_samples_leaf  cv_accuracy_mean  cv_accuracy_std  \\\n",
+       "21         4   entropy                 1          0.775556         0.143398   \n",
+       "27         5   entropy                 1          0.755556         0.148740   \n",
+       "34      None   entropy                 2          0.755556         0.145551   \n",
+       "22         4   entropy                 2          0.753333         0.146633   \n",
+       "10         2   entropy                 2          0.751111         0.150817   \n",
+       "15         3   entropy                 1          0.733333         0.158854   \n",
+       "17         3   entropy                 4          0.733333         0.131280   \n",
+       "28         5   entropy                 2          0.733333         0.131280   \n",
+       "33      None   entropy                 1          0.733333         0.115042   \n",
+       "9          2   entropy                 1          0.731111         0.149633   \n",
+       "16         3   entropy                 2          0.731111         0.132105   \n",
+       "0          1      gini                 1          0.711111         0.110889   \n",
+       "\n",
+       "    train_accuracy  test_accuracy  actual_tree_depth  n_leaves  \n",
+       "21        0.854167       0.583333                  4         6  \n",
+       "27        0.916667       0.583333                  5         7  \n",
+       "34        0.937500       0.500000                  8        11  \n",
+       "22        0.833333       0.583333                  4         6  \n",
+       "10        0.750000       0.750000                  2         3  \n",
+       "15        0.833333       0.583333                  3         5  \n",
+       "17        0.812500       0.583333                  3         5  \n",
+       "28        0.895833       0.583333                  5         7  \n",
+       "33        1.000000       0.583333                  8        11  \n",
+       "9         0.750000       0.750000                  2         3  \n",
+       "16        0.812500       0.583333                  3         5  \n",
+       "0         0.791667       0.750000                  1         2  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+    "rows = []\n",
+    "\n",
+    "for max_depth in [1, 2, 3, 4, 5, None]:\n",
+    "    for criterion in [\"gini\", \"entropy\"]:\n",
+    "        for min_samples_leaf in [1, 2, 4]:\n",
+    "            model = DecisionTreeClassifier(\n",
+    "                max_depth=max_depth,\n",
+    "                criterion=criterion,\n",
+    "                min_samples_leaf=min_samples_leaf,\n",
+    "                random_state=42,\n",
+    "            )\n",
+    "\n",
+    "            cv_scores = cross_val_score(\n",
+    "                model,\n",
+    "                X_train,\n",
+    "                y_train,\n",
+    "                cv=cv,\n",
+    "                scoring=\"accuracy\",\n",
+    "            )\n",
+    "\n",
+    "            model.fit(X_train, y_train)\n",
+    "\n",
+    "            rows.append(\n",
+    "                {\n",
+    "                    \"max_depth\": \"None\" if max_depth is None else str(max_depth),\n",
+    "                    \"criterion\": criterion,\n",
+    "                    \"min_samples_leaf\": min_samples_leaf,\n",
+    "                    \"cv_accuracy_mean\": cv_scores.mean(),\n",
+    "                    \"cv_accuracy_std\": cv_scores.std(),\n",
+    "                    \"train_accuracy\": accuracy_score(y_train, model.predict(X_train)),\n",
+    "                    \"test_accuracy\": accuracy_score(y_test, model.predict(X_test)),\n",
+    "                    \"actual_tree_depth\": model.get_depth(),\n",
+    "                    \"n_leaves\": model.get_n_leaves(),\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "results_df = pd.DataFrame(rows).sort_values(\n",
+    "    [\"cv_accuracy_mean\", \"test_accuracy\", \"n_leaves\"],\n",
+    "    ascending=[False, False, True],\n",
+    ")\n",
+    "\n",
+    "display(results_df.head(12))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bb8f566",
+   "metadata": {},
+   "source": [
+    "The top row by cross-validation score is a deeper tree, but it does not perform as well on the held-out test set. That makes overfitting a real concern.\n",
+    "\n",
+    "A simpler model with:\n",
+    "\n",
+    "- `criterion=\"entropy\"`\n",
+    "- `max_depth=2`\n",
+    "- `min_samples_leaf=2`\n",
+    "\n",
+    "has nearly the same cross-validation performance, gives better test accuracy in this split, and is much easier to interpret. I therefore use that as the final tree.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b680ad84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "depth_summary = (\n",
+    "    results_df[results_df[\"criterion\"] == \"entropy\"]\n",
+    "    .groupby(\"max_depth\", as_index=False)[\"cv_accuracy_mean\"]\n",
+    "    .max()\n",
+    ")\n",
+    "\n",
+    "depth_order = [\"1\", \"2\", \"3\", \"4\", \"5\", \"None\"]\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.lineplot(\n",
+    "    data=depth_summary,\n",
+    "    x=\"max_depth\",\n",
+    "    y=\"cv_accuracy_mean\",\n",
+    "    marker=\"o\",\n",
+    "    sort=False,\n",
+    ")\n",
+    "plt.title(\"Cross-Validation Accuracy by Max Depth\")\n",
+    "plt.xlabel(\"Max depth\")\n",
+    "plt.ylabel(\"Cross-validation accuracy\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fa2981a",
+   "metadata": {},
+   "source": [
+    "## Step 4: Fit the Final Decision Tree\n",
+    "\n",
+    "The final model is deliberately shallow. This trades a small amount of flexibility for better transparency and lower overfitting risk.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "babd4532",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final model parameters: {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 2, 'random_state': 42}\n",
+      "Training accuracy: 0.75\n",
+      "Test accuracy: 0.75\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0      0.667     1.000     0.800         6\n",
+      "           1      1.000     0.500     0.667         6\n",
+      "\n",
+      "    accuracy                          0.750        12\n",
+      "   macro avg      0.833     0.750     0.733        12\n",
+      "weighted avg      0.833     0.750     0.733        12\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_params = {\n",
+    "    \"criterion\": \"entropy\",\n",
+    "    \"max_depth\": 2,\n",
+    "    \"min_samples_leaf\": 2,\n",
+    "    \"random_state\": 42,\n",
+    "}\n",
+    "\n",
+    "final_model = DecisionTreeClassifier(**final_params)\n",
+    "final_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_train_pred = final_model.predict(X_train)\n",
+    "y_test_pred = final_model.predict(X_test)\n",
+    "\n",
+    "print(\"Final model parameters:\", final_params)\n",
+    "print(\"Training accuracy:\", round(accuracy_score(y_train, y_train_pred), 3))\n",
+    "print(\"Test accuracy:\", round(accuracy_score(y_test, y_test_pred), 3))\n",
+    "print()\n",
+    "print(classification_report(y_test, y_test_pred, digits=3))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f15a3232",
+   "metadata": {},
+   "source": [
+    "## Step 5: Evaluate the Final Model\n",
+    "\n",
+    "The confusion matrix shows how the model handles the two classes rather than only reporting one summary number.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e295be0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(\n",
+    "    y_test,\n",
+    "    y_test_pred,\n",
+    "    display_labels=[\"Healthy\", \"XFG\"],\n",
+    "    cmap=\"Blues\",\n",
+    ")\n",
+    "plt.title(\"Confusion Matrix for the Final Decision Tree\")\n",
+    "plt.grid(False)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16aa78b9",
+   "metadata": {},
+   "source": [
+    "## Step 6: Interpret Which Proteins the Tree Uses\n",
+    "\n",
+    "Decision trees only assign impurity-based importance to features that are actually used in splits. That means it is completely possible for a shallow tree to put all of its importance on only one or two proteins while the others receive zero.\n",
+    "\n",
+    "That is not automatically a biological conclusion. In this setting it mostly means:\n",
+    "\n",
+    "- the tree found that two proteins were enough for its chosen split rules\n",
+    "- some proteins may contain overlapping information, so the tree only needs one of them\n",
+    "- with only 60 samples, the exact split choices are unstable and can change with a different train/test split\n",
+    "\n",
+    "To make that limitation clearer, I look at both the tree's built-in importance values and permutation importance on the test set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3ab232c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tree_importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>HPRA017192</th>\n",
+       "      <td>0.675353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA000767</th>\n",
+       "      <td>0.324647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA034083</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA006876</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA019035</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA003490</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPRA022019</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            tree_importance\n",
+       "HPRA017192         0.675353\n",
+       "HPRA000767         0.324647\n",
+       "HPRA034083         0.000000\n",
+       "HPRA006876         0.000000\n",
+       "HPRA019035         0.000000\n",
+       "HPRA003490         0.000000\n",
+       "HPRA022019         0.000000"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gini_importance = (\n",
+    "    pd.Series(final_model.feature_importances_, index=feature_names)\n",
+    "    .sort_values(ascending=False)\n",
+    ")\n",
+    "\n",
+    "display(gini_importance.to_frame(\"tree_importance\"))\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.barplot(\n",
+    "    x=gini_importance.values,\n",
+    "    y=gini_importance.index,\n",
+    "    hue=gini_importance.index,\n",
+    "    dodge=False,\n",
+    "    palette=\"crest\",\n",
+    "    legend=False,\n",
+    ")\n",
+    "plt.title(\"Impurity-Based Feature Importance\")\n",
+    "plt.xlabel(\"Importance\")\n",
+    "plt.ylabel(\"Protein fragment\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5ba925d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "perm = permutation_importance(\n",
+    "    final_model,\n",
+    "    X_test,\n",
+    "    y_test,\n",
+    "    n_repeats=50,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "permutation_df = (\n",
+    "    pd.DataFrame(\n",
+    "        {\n",
+    "            \"feature\": feature_names,\n",
+    "            \"permutation_importance_mean\": perm.importances_mean,\n",
+    "            \"permutation_importance_std\": perm.importances_std,\n",
+    "        }\n",
+    "    )\n",
+    "    .sort_values(\"permutation_importance_mean\", ascending=False)\n",
+    ")\n",
+    "\n",
+    "display(permutation_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbff8bf0",
+   "metadata": {},
+   "source": [
+    "If only two proteins receive non-zero tree importance, that is therefore not necessarily weird. It is a normal consequence of a shallow greedy tree.\n",
+    "\n",
+    "What would be weird is claiming from this alone that the other five proteins are unimportant biologically. A single tree cannot support that conclusion, especially with a small dataset. The safer interpretation is that this particular classifier can achieve its decisions using a small subset of the available predictors.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6596d818",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(18, 8))\n",
+    "plot_tree(\n",
+    "    final_model,\n",
+    "    feature_names=feature_names,\n",
+    "    class_names=[\"Healthy\", \"XFG\"],\n",
+    "    filled=True,\n",
+    "    rounded=True,\n",
+    "    fontsize=10,\n",
+    ")\n",
+    "plt.title(\"Final Decision Tree\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf65611c",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Using only the significant seven proteins, the best final decision tree is a shallow entropy-based model with `max_depth=2` and `min_samples_leaf=2`.\n",
+    "\n",
+    "This is a reasonable final model because:\n",
+    "\n",
+    "- it is built from biologically motivated features\n",
+    "- it was selected through iterative tuning rather than one-shot fitting\n",
+    "- it remains interpretable\n",
+    "- it avoids the strongest overfitting seen in deeper trees\n",
+    "\n",
+    "The fact that the tree mainly uses two proteins is not by itself a problem. It reflects how decision trees work: they only reward features that reduce impurity through actual splits. That should be interpreted as a property of this classifier, not as a definitive ranking of biological importance.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "intro_ds",
+   "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": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/assignment/EDA.ipynb b/assignment/EDA_Decision_Tree.ipynb
similarity index 63%
rename from assignment/EDA.ipynb
rename to assignment/EDA_Decision_Tree.ipynb
index 7d6176b..e539f88 100755
--- a/assignment/EDA.ipynb
+++ b/assignment/EDA_Decision_Tree.ipynb
@@ -309,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "499aa028",
    "metadata": {},
    "outputs": [
@@ -337,7 +337,1099 @@
     "\n",
     "- The 7 significant protein fragments were identified as being associated with XFG in the original study, so they are likely to contain relevant information for distinguishing between XFG and healthy controls.\n",
     "- There is no clear separation between groups across any one or two features, which is good because it will let us demonstrate the capabilities of the decision tree and mlp.\n",
-    "- The features are not highly correlated with each other, which means they may provide complementary information for classification.\n"
+    "- The features are not highly correlated with each other, which means they may provide complementary information for classification.\n",
+    "- The sample size is fairly small (n=60), which is not ideal but realistic for a bioinformatics dataset. This will allow us to demonstrate how to handle small datasets and the importance of techniques like (repeated) cross-validation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6c99b0d",
+   "metadata": {},
+   "source": [
+    "# Part 1.5: Decision Tree Classifier\n",
+    "Now that we have explored the data, we will build a Decision Tree Classifier to predict whether a sample belongs to the XFG group or the healthy control group based on the 7 significant protein fragments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f3c144da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HPRA000767</th>\n",
+       "      <th>HPRA034083</th>\n",
+       "      <th>HPRA006876</th>\n",
+       "      <th>HPRA019035</th>\n",
+       "      <th>HPRA003490</th>\n",
+       "      <th>HPRA022019</th>\n",
+       "      <th>HPRA017192</th>\n",
+       "      <th>HPRA044540</th>\n",
+       "      <th>HPRA037260</th>\n",
+       "      <th>HPRA045626</th>\n",
+       "      <th>...</th>\n",
+       "      <th>HPRA029223</th>\n",
+       "      <th>HPRA011757</th>\n",
+       "      <th>HPRA046120</th>\n",
+       "      <th>HPRA030026</th>\n",
+       "      <th>HPRA009665</th>\n",
+       "      <th>HPRA001250</th>\n",
+       "      <th>HPRA026116</th>\n",
+       "      <th>HPRA038796</th>\n",
+       "      <th>HPRA017982</th>\n",
+       "      <th>HPRA001152</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.352329</td>\n",
+       "      <td>-0.074663</td>\n",
+       "      <td>0.442451</td>\n",
+       "      <td>0.210802</td>\n",
+       "      <td>-0.025678</td>\n",
+       "      <td>-0.923763</td>\n",
+       "      <td>0.028253</td>\n",
+       "      <td>0.556472</td>\n",
+       "      <td>1.468111</td>\n",
+       "      <td>0.104271</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.830436</td>\n",
+       "      <td>1.049801</td>\n",
+       "      <td>-0.536090</td>\n",
+       "      <td>0.777445</td>\n",
+       "      <td>0.783611</td>\n",
+       "      <td>0.453935</td>\n",
+       "      <td>-0.368408</td>\n",
+       "      <td>-0.070405</td>\n",
+       "      <td>0.551598</td>\n",
+       "      <td>0.937847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.184865</td>\n",
+       "      <td>0.467075</td>\n",
+       "      <td>1.272995</td>\n",
+       "      <td>1.857599</td>\n",
+       "      <td>2.709290</td>\n",
+       "      <td>1.367955</td>\n",
+       "      <td>0.220041</td>\n",
+       "      <td>-0.341010</td>\n",
+       "      <td>-0.905002</td>\n",
+       "      <td>0.144433</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.152472</td>\n",
+       "      <td>2.007440</td>\n",
+       "      <td>0.913802</td>\n",
+       "      <td>-0.183163</td>\n",
+       "      <td>-0.620014</td>\n",
+       "      <td>-0.562501</td>\n",
+       "      <td>0.677817</td>\n",
+       "      <td>1.471449</td>\n",
+       "      <td>2.555079</td>\n",
+       "      <td>-0.262521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.519969</td>\n",
+       "      <td>-0.358698</td>\n",
+       "      <td>0.124177</td>\n",
+       "      <td>1.383003</td>\n",
+       "      <td>1.227493</td>\n",
+       "      <td>-0.128889</td>\n",
+       "      <td>-0.343792</td>\n",
+       "      <td>1.447048</td>\n",
+       "      <td>0.686725</td>\n",
+       "      <td>-0.318804</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.521932</td>\n",
+       "      <td>1.387472</td>\n",
+       "      <td>-0.811128</td>\n",
+       "      <td>1.465961</td>\n",
+       "      <td>1.298456</td>\n",
+       "      <td>0.803286</td>\n",
+       "      <td>-0.072726</td>\n",
+       "      <td>1.030176</td>\n",
+       "      <td>0.039371</td>\n",
+       "      <td>0.836374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.155189</td>\n",
+       "      <td>0.467075</td>\n",
+       "      <td>0.630671</td>\n",
+       "      <td>0.080847</td>\n",
+       "      <td>-0.025678</td>\n",
+       "      <td>-0.416995</td>\n",
+       "      <td>0.753164</td>\n",
+       "      <td>-1.367555</td>\n",
+       "      <td>-2.288136</td>\n",
+       "      <td>-0.981434</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.798731</td>\n",
+       "      <td>-0.260461</td>\n",
+       "      <td>-1.579115</td>\n",
+       "      <td>-1.708423</td>\n",
+       "      <td>0.151569</td>\n",
+       "      <td>0.692163</td>\n",
+       "      <td>0.651407</td>\n",
+       "      <td>-2.179294</td>\n",
+       "      <td>0.691236</td>\n",
+       "      <td>0.186323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.590507</td>\n",
+       "      <td>-0.074663</td>\n",
+       "      <td>-1.423605</td>\n",
+       "      <td>-1.591214</td>\n",
+       "      <td>-0.375025</td>\n",
+       "      <td>-0.165849</td>\n",
+       "      <td>-1.292029</td>\n",
+       "      <td>-0.402478</td>\n",
+       "      <td>0.385166</td>\n",
+       "      <td>0.018241</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.066913</td>\n",
+       "      <td>-0.624109</td>\n",
+       "      <td>-0.451182</td>\n",
+       "      <td>-0.138439</td>\n",
+       "      <td>-0.620014</td>\n",
+       "      <td>-0.973675</td>\n",
+       "      <td>-0.194778</td>\n",
+       "      <td>-0.807481</td>\n",
+       "      <td>-1.029396</td>\n",
+       "      <td>-1.601052</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   HPRA000767  HPRA034083  HPRA006876  HPRA019035  HPRA003490  HPRA022019  \\\n",
+       "0    0.352329   -0.074663    0.442451    0.210802   -0.025678   -0.923763   \n",
+       "1    2.184865    0.467075    1.272995    1.857599    2.709290    1.367955   \n",
+       "2    0.519969   -0.358698    0.124177    1.383003    1.227493   -0.128889   \n",
+       "3    1.155189    0.467075    0.630671    0.080847   -0.025678   -0.416995   \n",
+       "4   -0.590507   -0.074663   -1.423605   -1.591214   -0.375025   -0.165849   \n",
+       "\n",
+       "   HPRA017192  HPRA044540  HPRA037260  HPRA045626  ...  HPRA029223  \\\n",
+       "0    0.028253    0.556472    1.468111    0.104271  ...    0.830436   \n",
+       "1    0.220041   -0.341010   -0.905002    0.144433  ...   -0.152472   \n",
+       "2   -0.343792    1.447048    0.686725   -0.318804  ...    1.521932   \n",
+       "3    0.753164   -1.367555   -2.288136   -0.981434  ...   -1.798731   \n",
+       "4   -1.292029   -0.402478    0.385166    0.018241  ...   -0.066913   \n",
+       "\n",
+       "   HPRA011757  HPRA046120  HPRA030026  HPRA009665  HPRA001250  HPRA026116  \\\n",
+       "0    1.049801   -0.536090    0.777445    0.783611    0.453935   -0.368408   \n",
+       "1    2.007440    0.913802   -0.183163   -0.620014   -0.562501    0.677817   \n",
+       "2    1.387472   -0.811128    1.465961    1.298456    0.803286   -0.072726   \n",
+       "3   -0.260461   -1.579115   -1.708423    0.151569    0.692163    0.651407   \n",
+       "4   -0.624109   -0.451182   -0.138439   -0.620014   -0.973675   -0.194778   \n",
+       "\n",
+       "   HPRA038796  HPRA017982  HPRA001152  \n",
+       "0   -0.070405    0.551598    0.937847  \n",
+       "1    1.471449    2.555079   -0.262521  \n",
+       "2    1.030176    0.039371    0.836374  \n",
+       "3   -2.179294    0.691236    0.186323  \n",
+       "4   -0.807481   -1.029396   -1.601052  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    0\n",
+       "3    1\n",
+       "4    0\n",
+       "Name: group, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "df = slice_2.copy()\n",
+    "identifier_cols = [\"Internal LIMS ID\", \"Original ID\", \"group\"]\n",
+    "feature_names = [c for c in df.columns if c not in identifier_cols]\n",
+    "X = df[feature_names]\n",
+    "y = df['group']\n",
+    "display(X.head())\n",
+    "display(y.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3d7990a",
+   "metadata": {},
+   "source": [
+    "Because the dataset is small, we will use repeated stratified k-fold cross-validation to evaluate the performance of our model. This will help us get a more reliable estimate of how well our model generalizes to unseen data. First, I will just do a quick test with 100 repeats. I am using many iterations because the dataset is small and I want to try and stabilize the estimate of the mean. I might increase this to 1000 repeats if the results are still unstable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8fe30b97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6 0.14337208778404378\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score\n",
+    "\n",
+    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=1, random_state=42)\n",
+    "decision_model = DecisionTreeClassifier(\n",
+    "    criterion=\"entropy\",\n",
+    "    max_depth=5,\n",
+    "    min_samples_leaf=2,\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "scores = cross_val_score(\n",
+    "    decision_model,\n",
+    "    X,\n",
+    "    y,\n",
+    "    cv=cv,\n",
+    "    scoring=\"accuracy\",\n",
+    ")\n",
+    "\n",
+    "print(scores.mean(), scores.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5508c84e",
+   "metadata": {},
+   "source": [
+    "Okay, so that works. Now we will expand it and do a bit of a parameter search to see if we can improve the performance. We will try different values for `max_depth` and `min_samples_leaf` to see if we can find a better model. We will also try both \"gini\" and \"entropy\" as the criterion for splitting. We will then compare the results and see which combination of parameters gives us the best performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "398eef57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top parameter sets:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>criterion</th>\n",
+       "      <th>max_depth</th>\n",
+       "      <th>min_samples_leaf</th>\n",
+       "      <th>mean_accuracy</th>\n",
+       "      <th>std_accuracy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.676000</td>\n",
+       "      <td>0.128762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.671667</td>\n",
+       "      <td>0.108538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.671667</td>\n",
+       "      <td>0.108538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.671667</td>\n",
+       "      <td>0.108538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.667667</td>\n",
+       "      <td>0.129632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.666000</td>\n",
+       "      <td>0.129098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.664000</td>\n",
+       "      <td>0.120847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.663000</td>\n",
+       "      <td>0.119782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.662333</td>\n",
+       "      <td>0.119991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>gini</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.661667</td>\n",
+       "      <td>0.125842</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  criterion  max_depth  min_samples_leaf  mean_accuracy  std_accuracy\n",
+       "0      gini        3.0                 2       0.676000      0.128762\n",
+       "1      gini        1.0                 1       0.671667      0.108538\n",
+       "2      gini        1.0                 2       0.671667      0.108538\n",
+       "3      gini        1.0                 4       0.671667      0.108538\n",
+       "4      gini        2.0                 1       0.667667      0.129632\n",
+       "5      gini        2.0                 2       0.666000      0.129098\n",
+       "6      gini        NaN                 2       0.664000      0.120847\n",
+       "7      gini        5.0                 2       0.663000      0.119782\n",
+       "8      gini        4.0                 2       0.662333      0.119991\n",
+       "9      gini        3.0                 1       0.661667      0.125842"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score\n",
+    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
+    "\n",
+    "# Repeated CV setup\n",
+    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=50, random_state=42)\n",
+    "\n",
+    "# Parameter grid to test\n",
+    "results = []\n",
+    "\n",
+    "for criterion in [\"gini\", \"entropy\"]:\n",
+    "    for max_depth in [1, 2, 3, 4, 5, None]:\n",
+    "        for min_samples_leaf in [1, 2, 4]:\n",
+    "            decision_model = DecisionTreeClassifier(\n",
+    "                criterion=criterion,\n",
+    "                max_depth=max_depth,\n",
+    "                min_samples_leaf=min_samples_leaf,\n",
+    "                random_state=42,\n",
+    "            )\n",
+    "\n",
+    "            scores = cross_val_score(\n",
+    "                decision_model,\n",
+    "                X,\n",
+    "                y,\n",
+    "                cv=cv,\n",
+    "                scoring=\"accuracy\",\n",
+    "            )\n",
+    "\n",
+    "            results.append(\n",
+    "                {\n",
+    "                    \"criterion\": criterion,\n",
+    "                    \"max_depth\": max_depth,\n",
+    "                    \"min_samples_leaf\": min_samples_leaf,\n",
+    "                    \"mean_accuracy\": scores.mean(),\n",
+    "                    \"std_accuracy\": scores.std(),\n",
+    "                }\n",
+    "            )\n",
+    "\n",
+    "results_df = (\n",
+    "    pd.DataFrame(results)\n",
+    "    .sort_values(by=[\"mean_accuracy\", \"std_accuracy\"], ascending=[False, True])\n",
+    "    .reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "print(\"Top parameter sets:\")\n",
+    "display(results_df.head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a454fe06",
+   "metadata": {},
+   "source": [
+    "Looks like our top 2 results have fairly similar results, with the best one having a max depth of 3 but the second-best being only a depth of 1. Depending on which one that second one is, this might actually support the results we found in the study, where we applied a random forest and found that the antigen HPRA006876 was able to differentiate the groups on its own fairly well. A decision tree with a max depth of 1 would essentially be using only one feature to make the split, which could be that antigen. Lets plot them and see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "013fc803",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2CQoKCsKAAQOEnJycrzqO169fCwCECRMmFLh9w4YNgqysrLBhw4bPjvPo0SNBVlZW8Pb2/mw/d3d3AYBw6tSpAre5u7sXKbb69esLpqamwrt37yTa+/fvLygpKQlv374VBEEQzpw5IwAQatasmW+MvG1nzpwRBEEQcnJyhBIlSgiOjo4S5zE5OVkwMDAQqlWrJm7LO/+zZs2SGLNv376CkpKSkJubm++Yv/Tw8fER77N06VIBgHDkyBGJ8Xv16iUAENatW5cvlo+ZmZkJSkpKwtOnT8VtaWlpgo6OjtCrV69Cz8HXAiAYGRkJ79+/F7ft3btXACA4OztLnIcFCxYIAITbt28XOFZubq6QlZUlPH36VAAg7Nu3T2L7tm3bBADCggULhPHjxwsyMjLC8ePHvyrekSNHCiKRSAgNDZVor1u3rsR5SElJEXR0dIQmTZpI9MvJyRHKlSsnVK5cWdyWd/4HDx4s0TcgIEAAIGzevFncVrZsWYn3eJ6816Fhw4YS7du3bxcACJcvX84335ceZmZmBZ4DRUVFcR8bGxshPDy80PP16bF8HEeenj17CgoKCl8coyB5PxvXrl0Tt8XHxwuysrKCsrKy8OLFC3F7aGioAEBYtGiRuC3vOvz48eN8Y165ckVirjJlygj169cvcmw7d+4UAOR7r3xOTk6OkJWVJUyePFnQ1dWVeP+bmZkJysrKwvPnz/Mdk7GxsZCSkiJuz/sZ2r9/v7jNx8dHACAsXLhQYs5p06YJAISLFy9KzPXx9WT69OmCjIyMEBISUuAxHj58WBAEQZgzZ44AQEhMTCzyMRfk02v5li1bBADCrl27JPqFhIQIAIRly5YVOE7eNeHcuXMCAOHWrVvibXmv/afH9L0/H3meP38uGBoaChUrVvzq36t58l6zj487KytL0NfXFwAIN27cELfnve+HDBkibuvVq5egpqYmcR0XhP9ep7CwsALn/dL7sCi/G75W3pwbN24UZGVlxb+Dk5OTBQ0NDaF69eoScXzK0tJSsLS0FNLS0grtk3edLMrj42uCvLx8gccWFBQkABACAwMLnTMmJkYAIGhoaAimpqbChg0bhFOnTgm9e/cWAAijR48udN+EhARBSUmpwOuOra2toKSkJKirqwt+fn7CmTNnhFmzZgnKysqCm5vbZ88VERH9mVhzmIjoL/ZpfTl7e3vs27cv39e0d+zYgQULFuDWrVsSX49VUlLKN2atWrUkbqxjaGgIAwODfCtwAWDatGlYvHgxZs+enW/F6o/QuXNndO7c+Yv9Tpw4gZycHPTr1++LfbW1tfHPP/98c0zp6ek4deoU+vTpAxUVFYlVQg0bNsSSJUsQHBwMT09PcXurVq2+OO79+/fx8uVLDBo0SOLrn2pqamjVqhVWrFghro+cp2nTphJjODk5IT09HXFxcTA0NAQArFixotCyBR/T09MT//vcuXNQV1fPd6O59u3bY8WKFV8cCwCcnZ1RqlQp8XMlJSXY2NgU+D76HrVq1YKqqqr4ub29PQDA09NTotxFXvvTp0/h6OgI4MMd5MePH49Dhw7h5cuXEqun7927J3F+27Rpg7Nnz2L48OHIycnB6NGjJWo4FsWZM2dQtmxZlCtXTqK9Q4cOOHHihPh5UFAQ3r59Cx8fn3yr0Bo0aIBZs2YhJSVF4ri9vb0l+rVp0wY+Pj44c+ZMvm2FKej9BHw4Z3nXmp49e6Jx48ZfHKuwG8QFBQUhMzMTUVFRmD9/PmrVqoVTp04VaXVkYeVovqdMjbGxMSpUqCB+rqOjAwMDA5ibm6NEiRLi9o/fP19iZGSUrw6tk5MTQkNDixyXs7MzFBQU0LNnT/Tt2xc1atRA6dKl8/U7ffo0/Pz8EBISkm/F38fXgbwxTUxM8h2Th4eHxHXlc8f66XupQ4cOGDNmDM6cOQM3N7cCj+XgwYNwcHCAs7OzxPu5fv364rIynp6eqFSpEoAP791u3brBzc1NIt5vdfDgQWhpaaFJkyYS8zs7O8PIyAhnz54V16x+9OgRxo4di9OnTyMuLk5iZea9e/cKXAH6se/9+QCAt2/fomHDhhAEAdu2bZP4ffC1RCIRGjZsKH4uJycHKysryMnJwcXFRdye977/+DU/ePAgatWqhRIlSkicN09PTwwbNgznzp0T1+r/2vfhj/jdcPPmTUyYMAGXLl3C27dvJbY9ePAAVapUQVBQEJKSktC3b99CrxMPHjxAVFQU/Pz8CvxMlKdChQoICQkpUmwfXzuAz1+jPrct73dSUlISjh07Jr4O//PPP4iNjcW8efMwatQoqKmp5ds3ICAA6enp6N69e4HjpqenY8KECfj3338BfLgOKCgoYNCgQTh16hTq1Knz5QMlIqI/BpPDRER/sY0bN8Le3h7JycnYtm0bVqxYgfbt2+PIkSPiPrt370abNm3g5eWF4cOHw8jICHJycvD39y+wzl9BtQ8VFRXzfcUY+PDVdxMTE7Rr1+7HHthXyqsbWZQboxkbG3/XXPHx8cjOzsbixYuxePHiAvt8XLO2qHPGx8cX2rdEiRLIzc1FQkKCRBLn09cqL+Hw8WtlZWX1xa+eApBIQMTHx0v8Zz5PQW2F+Zr30ffQ0dGReK6goPDZ9vT0dAAf/nNcr149vHz5EuPGjYOjoyNUVVWRm5uLqlWrFhhn165d4e/vDwUFBQwYMOCrY42Pj4eFhUW+diMjI4nneV9lbt26daFjvX37ViI5/OkYcnJy0NXVFb+viqIo7ycjI6MilY0pLOFRvnx5AB/+sNW0aVNYWVlh9OjRBdZA/zSugo7l7du3+V7rr1HQvgoKCl98/3zOj3jvW1pa4uTJk5g1axb69euHlJQUlC5dGgMGDMDAgQMBAFevXkW9evXg4eGBVatWievC7t27F9OmTcs337f+rOTJe099LO9997n32atXrxAZGQl5efkCt+ddL2vWrIm9e/di0aJF6Ny5MzIyMlC2bFmMGTMG7du3L3T8L3n16hUSExPFx1XY/O/fv0eNGjWgpKSEqVOnwsbGBioqKnj27BlatmxZpNfve38+EhISULduXbx48QKnT58u8A8CX0NFRSVfwrOg93de+8ev+atXr3DgwIEvvm5f+z78ET8f0dHRqFGjBmxtbcU1eZWUlHD16lX069dPPFZRPhsU9fND3n0MiuLjP9AXdh3OS2h/7vqlra0NkUgEdXX1fIsBPD09sXfvXoSHhxd4U8Q1a9ZAX18fzZo1y7dNV1cXDx8+zHdTO09PTwwaNAg3btxgcpiI6C/D5DAR0V/M3t5efBO6WrVqIScnB6tXr8bOnTvFiaXNmzfDwsIC27Ztk/gPaUZGxnfPf/ToUbRt2xY1atTAqVOnYGZm9t1jfou8G1s9f/4cJUuW/Gzf770Znra2NmRlZdGpU6dCVyp/mgAsypx5/2GOiYnJt+3ly5eQkZGBtrb2V8dbu3ZtnDt37ov9fHx8xLWddXV1cfXq1Xx9YmNjv3r+39Xdu3dx69YtrF+/Hj4+PuL2T29alyclJQWdOnWCjY0NXr16Ja5R+zV0dXULPIeftuWt4l68eHG+hECeTxP1sbGxEqsss7OzER8fX2Ai5ntMnjy5wLranzIzM5O4UVtB1NXVYWdnhwcPHny2n4ODAwDgzp07Eqsg89rytv9tatSogRo1aiAnJwfXrl3D4sWLMWjQIBgaGqJdu3bYunUr5OXlcfDgQYkE4N69e39KPAW9p/Leu597n+np6UFZWbnQm459/K2FZs2aoVmzZsjIyEBwcDCmT5+ODh06wNzcXOKGXl9DT08Purq6+W4omCfvmzKnT5/Gy5cvcfbsWbi7u4u3JyYmFnmu7/n5SEhIQJ06dfD48WOcOnXqi6uUfzY9PT04OTlh2rRpBW7PWx37q9+HeWOnpKRg9+7dEp87Pl2d//Fng8IUpQ/w4Rs1tWrVKlJ8jx8/Ft/w09HREXfu3MnXJ6/tc9cvZWVlWFtbF/h7I++PvgWtLL958yZu3ryJoUOHFpjcd3JyQnBw8FeNSUREfzYmh4mIipFZs2Zh165dGD9+PFq2bAkZGRmIRCIoKChIJChjY2O/OrFVEDMzM1y4cAF16tQRJ4itra2/e9yvVa9ePcjKysLf3/+bEwifKmjVJPBhJVatWrVw8+ZNODk5Fboa7WvZ2trCxMQEgYGBGDZsmPj1SklJwa5du+Dq6iqxariovqWshLu7O7Zv344jR45IlMfYunXrV8//u8o7v59+vbuwshm9e/dGdHQ0rl69ioiICLRu3Rrz58//qnIqtWrVwqxZs3Dr1i2J0hKBgYES/dzc3KClpYXw8PACb1ZXkICAAInyCNu3b0d2djY8PDzEbT9i5faP+Np8njdv3uDOnTuFliPIY2JigsqVK2Pz5s0YNmyY+AaAwcHBuH//PgYNGlSk2P9UsrKyqFKlCuzs7BAQEIAbN26gXbt2EIlEkJOTk7ghYlpaGjZt2vTTYgkICJBYNZ/33v34ffapxo0bw8/PD7q6ugWunC+IoqIi3N3doaWlhWPHjuHmzZvffG1v3Lgxtm7dipycHFSpUqXQfl97TSjIt/585CWGHz16hBMnTkiUfJCWxo0b4/Dhw7C0tPzsHyal8T4s6LUSBAGrVq2S6FetWjVoampi+fLl4p+ZT9nY2MDS0hJr167FkCFDCr12fWtZiRYtWqBv3764cuWK+P2XnZ2NzZs3o0qVKvlKUHyqVatWmD59OoKCglCtWjVx++HDh6GmplZgSZ41a9YA+HBD3cLGXLlyJY4cOSLxXjt8+DCA/CXLiIjoz8fkMBFRMaKtrY1Ro0ZhxIgRCAwMRMeOHdG4cWPs3r0bffv2RevWrfHs2TNMmTIFxsbGePjw4XfPaWxsjHPnzqF+/fqoWbMmTpw48cWVfEeOHEFKSoo4aRkeHo6dO3cC+FC3Ny8JunHjRnTt2hVr1679bO1hc3NzjB49GlOmTEFaWhrat28PTU1NhIeH482bN0VayfUpdXV1mJmZYd++fahduzZ0dHSgp6cHc3NzLFy4ENWrV0eNGjXQp08fmJubIzk5GZGRkThw4ID4LvNfQ0ZGBrNmzYK3tzcaN26MXr16ISMjA7Nnz0ZiYiJmzJjx1WMCH5LOX8vHxwfz589Hx44dMXXqVFhZWeHIkSM4duyYONaf5cmTJ7CwsJBYyfwz2NnZwdLSEv/++y8EQYCOjg4OHDggUfs3z+rVq7F582asW7cOZcuWRdmyZdG/f3+MHDkSbm5uBX6ltyCDBg3C2rVr0ahRI0ydOhWGhoYICAhARESERD81NTUsXrwYPj4+ePv2LVq3bg0DAwO8fv0at27dwuvXr+Hv7y+xz+7duyEnJ4e6desiLCwM48aNQ7ly5dCmTRtxH0dHR2zduhXbtm1D6dKloaSkJK6/XFQlSpT4YjLjU+/evUPdunXRoUMHWFtbQ1lZGQ8ePMDChQuRkZGBCRMmSPSXk5ODu7s7Tp06JW6bOXMm6tatCy8vL/Tt2xdxcXH4999/4eDggC5dukjsn7di70srl39ny5cvx+nTp9GoUSOUKlUK6enp4pW3eV/3btSoEebNm4cOHTqgZ8+eiI+Px5w5c4qUmP8WCgoKmDt3Lt6/f49KlSohKCgIU6dOhaenJ6pXr17ofoMGDcKuXbtQs2ZNDB48GE5OTsjNzUV0dDSOHz+OoUOHokqVKhg/fjyeP3+O2rVrw9TUFImJiVi4cCHk5eUlVvJ+rXbt2iEgIAANGzbEwIEDUblyZcjLy+P58+c4c+YMmjVrhhYtWqBatWrQ1tZG7969MWHCBMjLyyMgIAC3bt0q8lzf8vORlpaG+vXr4+bNm1iwYAGys7MlVnXq6+vD0tJS/NzDwwPnzp0rUrmg7zF58mScOHEC1apVw4ABA2Bra4v09HQ8efIEhw8fxvLly2FqavrL34cAULduXSgoKKB9+/YYMWIE0tPT4e/vj4SEBIl+ampqmDt3Lrp37446deqgR48eMDQ0RGRkJG7duoUlS5YAAJYuXYomTZqgatWqGDx4MEqVKoXo6GgcO3YMAQEBAD58Jsj7ptbX6Nq1K5YuXQovLy/MmDEDBgYGWLZsGe7fv4+TJ09K9M37ps/HNZ6HDRuGgIAAeHl5YcqUKTA1NcXOnTuxf/9+zJkzB8rKyhJjpKenIzAwENWqVRPXD/9UvXr10KRJE0yePFlcSunatWuYNGkSGjdu/NmfZyIi+kNJ7154RET0sxR2p3RB+HDn71KlSgnW1tZCdna2IAiCMGPGDMHc3FxQVFQU7O3thVWrVonvqv4xAEK/fv3yjfnp3efz9n39+rW4LTExUXBzcxN0dHQKjOvT8VCEu3znHee6deuKcFYEYePGjUKlSpUEJSUlQU1NTXBxcZHY193dXShbtmyB+356h3tBEISTJ08KLi4ugqKiogBA4hw8fvxY6Nq1q2BiYiLIy8sL+vr6QrVq1YSpU6eK++Td3XzHjh355svbdubMGYn2vXv3ClWqVBGUlJQEVVVVoXbt2sKlS5ck+hR0/gXhv/P18Tn8VtHR0ULLli0FNTU1QV1dXWjVqpVw+PBhAYCwb9++fLF8zMzMTGjUqFG+MT89xwWdgzt37ggAhH///feLMRb0fn38+LEAQJg9e7ZEe0GvRXh4uFC3bl1BXV1d0NbWFry8vITo6GgBgDBhwgRBEATh9u3bgrKyssRrLwiCkJ6eLlSoUEEwNzcXEhISvhjrp3MqKSkJOjo6Qrdu3YR9+/YV+F44d+6c0KhRI0FHR0eQl5cXTExMhEaNGkkcQ975v379utCkSRPx69W+fXvh1atXEuM9efJEqFevnqCuri4AEMzMzAo9Nx+fy6L+/BUmPT1d6N69u2Bvby+oqakJcnJygqmpqdCxY0chLCwsX38A+X4WBUEQjh8/LlStWlV87jp37pzvGAVBEPT09ISqVat+Ma7CrgeFvX8/fb8V9PNW2Jg+Pj7i810Uly9fFlq0aCGYmZkJioqKgq6uruDu7i7s379fot/atWsFW1tbQVFRUShdurQwffp0Yc2aNfniKuoxCULBP0M+Pj6CqqqqcPv2bcHDw0NQVlYWdHR0hD59+gjv37+X2P/T3xeCIAjv378Xxo4dK9ja2goKCgqCpqam4OjoKAwePFiIjY0VBEEQDh48KHh6egomJiaCgoKCYGBgIDRs2FC4cOFCkc+bIBR8Lc/KyhLmzJkjlCtXTvz7wc7OTujVq5fw8OFDcb+goCDB1dVVUFFREfT19YXu3bsLN27cyPdz8LnfwV8r73wX9vj0XFaoUEEwMjL64rh5r9mnvuZ9//r1a2HAgAGChYWFIC8vL+jo6AgVKlQQxowZI/G6f+/7sKDX7EsOHDggfj1NTEyE4cOHC0eOHCnwWnr48GHB3d1dUFVVFVRUVIQyZcoIM2fOlOhz+fJlwdPTU9DU1BQUFRUFS0tLYfDgwV8VU2FiY2OFzp07Czo6OoKSkpJQtWpV4cSJE/n6ubu75/t9Kggffh+3a9dO0NbWFhQUFAQnJydh7dq1Bc4VEBAgACh0e57U1FRh5MiRQsmSJQU5OTmhVKlSwqhRo4T09PRvO0giIvqtiQThJ/9ZmYiIiIoFPz8/jB07FtHR0UW6+d+3WLZsGUaMGIGoqKivugFecTVx4kRMmjQJr1+/ligNUlyFh4ejbNmyOHjwIBo1aiTtcP4avr6+2LlzJ96/fy/tUIq15ORk6OjoYMGCBYXWvCciIiL6FMtKEBER0VfL+7qtnZ0dsrKycPr0aSxatAgdO3b8aYlhADhz5gwGDBjAxDB9kzNnzsDV1ZWJYfornT9/HiYmJujRo4e0QyEiIqI/CJPDRERE9NVUVFQwf/58PHnyBBkZGShVqhRGjhyJsWPH/tR5d+zY8VPH/xkEQUBOTs5n+8jKyhZ4MyT6sfr16/fbr6jMycn5bL1YkUgkcXMv+oDn7UOd6b/9Dx8f19stiIyMzE+te09ERPQ3YlkJIiIiop/o7NmzqFWr1mf7rFu3Dr6+vr8mIPqtmZub4+nTp4Vud3d3x9mzZ39dQH8Inrfi4Ut/RPvZNyslIiL6G3HlMBEREdFPVKFCBYSEhHy2j4WFxS+Khn53Bw4cQEZGRqHb1dXVf2E0fw6et+LhS9dS1lYnIiL6elw5TERERERERERERFQMsSATERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTHE5DARERERERERERFRMcTkMBEREREREREREVExxOQwERERERERERERUTEkJ+0AiIiIiD725s0b3L9/H+np6dIOhYoBJSUl2NnZQVdXV9qhEBERERH9ckwOExER0W/hyZMn6NmzJ06fPo2cnBxph0PFiKysLGrXro0VK1bA3Nxc2uEQEREREf0yIkEQBGkHQURERMVbbGwsqlatChkZGYwcORLVq1eHioqKtMOiYiA1NRUXL17EzJkzkZubi+DgYBgZGUk7LCIiIiKiX4Irh4mIiEjqNm7ciLi4ODx48ACmpqbSDoeKmbJly6JRo0awtrbGpk2bMHz4cGmHRERERET0S3DlMBEREUldjRo1oK+vj927d0s7FCrGWrRogfj4eJw/f17aoRARERER/RIy0g6AiIiI6NWrV7C0tJR2GFTMWVlZITY2VtphEBERERH9MkwOExERkdTl5uZCTo7Vrki65OTkkJubK+0wiIiIiIh+GSaHiYiI6Lfl6+uL5s2b52s/e/YsRCIREhMTJZ7nPfT19eHp6Ylbt26J9/Hw8BBvV1BQgKWlJUaNGoWMjIx84z9//hwKCgqws7MrMK6EhAR06tQJmpqa0NTURKdOncSx5ImOjkaTJk2gqqoKPT09DBgwAJmZmRJ9BEHAnDlzYGNjA0VFRZQsWRJ+fn4SfQICAlCuXDmoqKjA2NgYXbp0QXx8fIHH9fGjUaNGEuO8ePECHTt2hK6uLlRUVODs7Izr168XeHzSsmzZMlhYWEBJSQkVKlTAhQsXPtt/9+7dqFu3LvT19aGhoQFXV1ccO3as0P5bt26FSCQq8D1FRERERFQcMTlMREREf4379+8jJiYGhw4dQkJCAho0aIB3796Jt/fo0QMxMTGIjIzErFmzsHTpUkycODHfOOvXr0ebNm2QmpqKS5cu5dveoUMHhIaG4ujRozh69ChCQ0PRqVMn8facnBw0atQIKSkpuHjxIrZu3Ypdu3Zh6NChEuMMHDgQq1evxpw5cxAREYEDBw6gcuXK4u0XL15E586d0a1bN4SFhWHHjh0ICQlB9+7dxX12796NmJgY8ePu3buQlZWFl5eXuE9CQgLc3NwgLy+PI0eOIDw8HHPnzoWWlta3nGYAQGZm5g8twbBt2zYMGjQIY8aMwc2bN1GjRg14enoiOjq60H3Onz+PunXr4vDhw7h+/Tpq1aqFJk2a4ObNm/n6Pn36FMOGDUONGjV+WMxERERERH86fn+TiIiI/hoGBgbQ0tKCkZER5s6di+rVqyM4OBj169cHAKioqMDIyAgAUKpUKQQGBuL48eOYPn26eAxBELBu3TosW7YMpqamWLNmDdzc3MTb7927h6NHjyI4OBhVqlQBAKxatQqurq64f/8+bG1tcfz4cYSHh+PZs2coUaIEAGDu3Lnw9fXFtGnToKGhgXv37sHf3x93796Fra1tgccTHBwMc3NzDBgwAABgYWGBXr16YdasWeI+Ojo6Evts3boVKioqEsnhmTNnomTJkli3bp24zdzc/KvPLwBcv34dGzZsQGBgIMaNG4eBAwd+0zifmjdvHrp16yZOfC9YsADHjh2Dv7+/xOvzsQULFkg89/Pzw759+3DgwAG4uLiI23NycuDt7Y1JkybhwoUL+VZ5ExEREREVV1w5TERERH8lZWVlAEBWVlaB22/duoVLly5BXl5eov3MmTNITU1FnTp10KlTJ2zfvh3Jycni7ZcvX4ampqY4MQwAVatWhaamJoKCgsR9HBwcxIlhAKhfvz4yMjLEpRwOHDiA0qVL4+DBg7CwsIC5uTm6d++Ot2/fivepVq0anj9/jsOHD0MQBLx69Qo7d+7MVzLiY2vWrEG7du2gqqoqbtu/fz8qVqwILy8vGBgYwMXFBatWrfriOcwTExOD2bNnw8HBQRzT6tWr0bdvX3Gf3r17Q01N7bOPwlYBZ2Zm4vr166hXr55Ee7169cTntChyc3ORnJycL2E+efJk6Ovro1u3bkUei4iIiIioOODKYSIiIvqtHTx4EGpqahJtOTk5n90nPj4ekyZNgrq6ukSZhmXLlmH16tXIyspCZmYmZGRksHTpUol985KrsrKyKFu2LKysrLBt2zbxitbY2FgYGBjkm9PAwEBcZiE2NhaGhoYS27W1taGgoCDu8+jRIzx9+hQ7duzAxo0bkZOTg8GDB6N169Y4ffo0gA/J4YCAALRt2xbp6enIzs5G06ZNsXjx4gKP++rVq7h79y7WrFkj0f7o0SP4+/tjyJAhGD16NK5evYoBAwZAUVERnTt3LnCszMxM7NmzBxs2bMCJEydQsWJF9OvXD23bts2XfAU+JGCHDRtW4Fh5Pk6Wf+zNmzfIycnJd84MDQ2/qnTF3LlzkZKSgjZt2ojbLl26hDVr1iA0NLTI4xARERERFRdMDhMREdFvrVatWvD395dou3LlCjp27Jivr6mpKQAgJSUF1tbW2LFjh0Qi19vbG2PGjEFSUhJmzpwJDQ0NtGrVSrw9MTERu3fvxsWLF8VtHTt2xNq1ayXq/IpEonxzC4Ig0f6lPrm5ucjIyMDGjRthY2MD4ENiukKFCuLyFOHh4RgwYADGjx+P+vXrIyYmBsOHD0fv3r3zJYDz9ndwcJBIiOfNVbFiRfHN7lxcXBAWFgZ/f/9Ck8NBQUFo164dSpYsidOnT3+xVq+BgUGBSfOv8ek5+/Scfs6WLVswceJE7Nu3TxxHcnIyOnbsiFWrVkFPT++7YiMiIiIi+hsxOUxERES/NVVVVVhZWUm0PX/+vMC+Fy5cgIaGBvT19aGhoZFvu6ampniszZs3o2zZslizZo243EBgYCDS09MlSkYIgoDc3FyEh4ejTJkyMDIywqtXr/KN/fr1a/HKVyMjI1y5ckVie0JCArKyssR9jI2NIScnJ04MA4C9vT0AIDo6Gra2tpg+fTrc3NwwfPhwAICTkxNUVVVRo0YNTJ06FcbGxuJ9U1NTsXXrVkyePDlfbMbGxihTpoxEm729PXbt2lXgeQSAypUrY9WqVdiwYQP++ecfcZmN5s2bQ0VFJV//3r17Y/PmzYWOBwDh4eEoVapUvnY9PT3IysrmWyUcFxeXbzVxQbZt24Zu3bphx44dqFOnjrg9KioKT548QZMmTcRtubm5AAA5OTncv38flpaWXxyfiIiIiOhvxZrDRERE9NewsLCApaVlgYnhT8nLy2P06NEYO3YsUlNTAXxYeTt06FCEhoaKH7du3UKtWrWwdu1aAICrqyvevXuHq1evise6cuUK3r17h2rVqon73L17FzExMeI+x48fh6KiIipUqAAAcHNzQ3Z2NqKiosR9Hjx4AAAwMzMD8CHhKyMj+XFNVlYWwIek9ce2b9+OjIyMAldUu7m54f79+xJtDx48EM9TEBUVFXTv3h0XLlxAREQEKlWqhDFjxsDQ0BC+vr44ffq0ONEKfCgr8fF5K+hRWFkJBQUFVKhQASdOnJBoP3HihPicFmbLli3w9fVFYGBgvlrMdnZ2uHPnjkQMTZs2Ra1atRAaGoqSJUt+dmwiIiIior8dk8NERERUbHXo0AEikQjLli1DaGgobty4ge7du8PBwUHi0b59e2zcuBFZWVmwt7dHgwYN0KNHDwQHByM4OBg9evRA48aNYWtrC+DDjdTKlCmDTp064ebNmzh16hSGDRuGHj16iBPXderUQfny5dG1a1fcvHkT169fR69evVC3bl3xauImTZpg9+7d8Pf3x6NHj3Dp0iUMGDAAlStXzpdoXbNmDZo3bw5dXd18xzl48GAEBwfDz88PkZGRCAwMxMqVK9GvX78inSdLS0tMnjwZjx49woEDByASidCsWTOJes0GBgawsrL67ENOrvAvrQ0ZMgSrV6/G2rVrce/ePQwePBjR0dHo3bu3uM+oUaMkymBs2bIFnTt3xty5c1G1alXExsYiNjYW7969AwAoKSnley21tLSgrq4OBwcHKCgoFOn4iYiIiIj+VkwOExERUbGloKCA/v37Y9asWVi4cCHKlCkDOzu7fP2aN2+Ot2/f4sCBAwCAgIAAODo6ol69eqhXrx6cnJywadMmcX9ZWVkcOnQISkpKcHNzQ5s2bdC8eXPMmTNH3EdGRgYHDhyAnp4eatasiUaNGsHe3h5bt24V9/H19cW8efOwZMkSODg4wMvLC7a2tti9e7dEfA8ePMDFixfF5TE+ValSJezZswdbtmyBg4MDpkyZggULFsDb2/urzpdIJIKHhwfWrVuH2NhYNG/e/Kv2/5y2bdtiwYIFmDx5MpydnXH+/HkcPnxYYnVzTEwMoqOjxc9XrFiB7Oxs9OvXD8bGxuLHwIEDf1hcRERERER/M5Hw6XcSiYiIiH4xKysreHl5Yfr06dIOhYqxUaNGYceOHYiMjJR2KEREREREvwRXDhMREREREREREREVQ0wOExERERERERERERVDTA4TERERERERERERFUNMDhMREREREREREREVQ0wOExEREf0i5ubmWLBgQZH7nz17FiKRCImJiT8tJiIiIiIiKr6YHCYiIiL6RUJCQtCzZ88i969WrRpiYmKgqan502ISBAETJ05EiRIloKysDA8PD4SFhX12n/Xr10MkEuV7pKen/7Q4iYiIiIjox2NymIiIiOgX0dfXh4qKSpH7KygowMjICCKR6KfFNGvWLMybNw9LlixBSEgIjIyMULduXSQnJ392Pw0NDcTExEg8lJSUflqcRERERET04zE5TERERPQDJCcnw9vbG6qqqjA2Nsb8+fPh4eGBQYMGift8WlZCJBJh9erVaNGiBVRUVGBtbY39+/eLt//sshKCIGDBggUYM2YMWrZsCQcHB2zYsAGpqakIDAz87L4ikQhGRkYSDyIiIiIi+rMwOUxERET0AwwZMgSXLl3C/v37ceLECVy4cAE3btz44n6TJk1CmzZtcPv2bTRs2BDe3t54+/Ztkef19PSEmpraZx+Fefz4MWJjY1GvXj1xm6KiItzd3REUFPTZed+/fw8zMzOYmpqicePGuHnzZpFjJiIiIiKi34OctAMgIiIi+tMlJydjw4YNCAwMRO3atQEA69atQ4kSJb64r6+vL9q3bw8A8PPzw+LFi3H16lU0aNCgSHOvXr0aaWlp3xR3bGwsAMDQ0FCi3dDQEE+fPi10Pzs7O6xfvx6Ojo5ISkrCwoUL4ebmhlu3bsHa2vqbYiEiIiIiol+PyWEiIiKi7/To0SNkZWWhcuXK4jZNTU3Y2tp+cV8nJyfxv1VVVaGuro64uLgiz21iYvJ1wRbg05rGgiB8ts5x1apVUbVqVfFzNzc3lC9fHosXL8aiRYu+Ox4iIiIiIvo1WFaCiIiI6DsJggCg4CTrl8jLy0s8F4lEyM3NLfLc31NWIq9OcN4K4jxxcXH5VhN/joyMDCpVqoSHDx8WeR8iIiIiIpI+rhwmIiIi+k6WlpaQl5fH1atXUbJkSQBAUlISHj58CHd395869/eUlbCwsICRkRFOnDgBFxcXAEBmZibOnTuHmTNnFnkcQRAQGhoKR0fHb4qDiIiIiIikg8lhIiIiou+krq4OHx8fDB8+HDo6OjAwMMCECRMgIyPz2fIMP8L3lJUQiUQYNGgQ/Pz8YG1tDWtra/j5+UFFRQUdOnQQ9+vcuTNMTEwwffp0AB9uole1alVYW1sjKSkJixYtQmhoKJYuXfrdx0NERERERL8Ok8NEREREP8C8efPQu3dvNG7cGBoaGhgxYgSePXsGJSUlaYf2WSNGjEBaWhr69u2LhIQEVKlSBcePH4e6urq4T3R0NGRk/qtGlpiYiJ49eyI2NhaamppwcXHB+fPnJWouExERERHR708kFKUYHhEREdFPZGVlBS8vL/HK1L9BSkoKTExMMHfuXHTr1k3a4VARjBo1Cjt27EBkZKS0QyEiIiIi+iW4cpiIiIjoB7h58yYiIiJQuXJlvHv3DpMnTwYANGvWTMqRERERERERFYzJYSIiIqIfZM6cObh//z4UFBRQoUIFXLhwAXp6etIOi4iIiIiIqEBMDhMRERH9AC4uLrh+/bq0wyAiIiIiIioymS93ISIiIiIiIiIiIqK/DZPDRERERH8AX19fNG/eXNphEBERERHRX4TJYSIiIiL6IRITE9GvXz8YGxtDSUkJ9vb2OHz4sESfZcuWwcLCAkpKSuK6zEREREREJB2sOUxERERE3y0zMxN169aFgYEBdu7cCVNTUzx79gzq6uriPtu2bcOgQYOwbNkyuLm5YcWKFfD09ER4eDhKlSolxeiJiIiIiIonrhwmIiIi+sjOnTvh6OgIZWVl6Orqok6dOkhJSQEAhISEoG7dutDT04Ompibc3d1x48YNif1FIhFWrFiBxo0bQ0VFBfb29rh8+TIiIyPh4eEBVVVVuLq6IioqSrzPxIkT4ezsjBUrVqBkyZJQUVGBl5cXEhMTC41TEATMmjULpUuXhrKyMsqVK4edO3eKtyckJMDb2xv6+vpQVlaGtbU11q1b92NP1kfWrl2Lt2/fYu/evXBzc4OZmRmqV6+OcuXKifvMmzcP3bp1Q/fu3WFvb48FCxagZMmS8Pf3/2lxERERERFR4ZgcJiIiIvp/MTExaN++Pbp27Yp79+7h7NmzaNmyJQRBAAAkJyfDx8cHFy5cQHBwMKytrdGwYUMkJydLjDNlyhR07twZoaGhsLOzQ4cOHdCrVy+MGjUK165dAwD0799fYp/IyEhs374dBw4cwNGjRxEaGop+/foVGuvYsWOxbt06+Pv7IywsDIMHD0bHjh1x7tw5AMC4ceMQHh6OI0eO4N69e/D394eenl6h4/n5+UFNTe2zj8+VgNi/fz9cXV3Rr18/GBoawsHBAX5+fsjJyQHwYWXx9evXUa9ePYn96tWrh6CgoELHJSIiIiKin4dlJYiIiIj+X0xMDLKzs9GyZUuYmZkBABwdHcXb//nnH4n+K1asgLa2Ns6dO4fGjRuL27t06YI2bdoAAEaOHAlXV1eMGzcO9evXBwAMHDgQXbp0kRgrPT0dGzZsgKmpKQBg8eLFaNSoEebOnQsjIyOJvikpKZg3bx5Onz4NV1dXAEDp0qVx8eJFrFixAu7u7oiOjoaLiwsqVqwIADA3N//ssffu3Vscc2FMTEwK3fbo0SOcPn0a3t7eOHz4MB4+fIh+/fohOzsb48ePx5s3b5CTkwNDQ0OJ/QwNDREbG/vZeYmIiIiI6OdgcpiIiIjo/5UrVw61a9eGo6Mj6tevj3r16qF169bQ1tYGAMTFxWH8+PE4ffo0Xr16hZycHKSmpiI6OlpiHCcnJ/G/85KhHyeZDQ0NkZ6ejqSkJGhoaAAASpUqJU4MA4Crqytyc3Nx//79fMnh8PBwpKeno27duhLtmZmZcHFxAQD06dMHrVq1wo0bN1CvXj00b94c1apVK/TYdXR0oKOjU+Rz9anc3FwYGBhg5cqVkJWVRYUKFfDy5UvMnj0b48ePF/cTiUQS+wmCkK+NiIiIiIh+DZaVICIiIvp/srKyOHHiBI4cOYIyZcpg8eLFsLW1xePHjwEAvr6+uH79OhYsWICgoCCEhoZCV1cXmZmZEuPIy8uL/52X+CyoLTc3t9BY8voUlDjN2+/QoUMIDQ0VP8LDw8V1hz09PfH06VMMGjQIL1++RO3atTFs2LBC5/veshLGxsawsbGBrKysuM3e3h6xsbHIzMyEnp4eZGVl860SjouLy7eamIiIiIiIfg2uHCYiIiL6iEgkgpubG9zc3DB+/HiYmZlhz549GDJkCC5cuIBly5ahYcOGAIBnz57hzZs3P2Te6OhovHz5EiVKlAAAXL58GTIyMrCxscnXt0yZMlBUVER0dDTc3d0LHVNfXx++vr7w9fVFjRo1MHz4cMyZM6fAvt9bVsLNzQ2BgYHIzc2FjMyH9QcPHjyAsbExFBQUAAAVKlTAiRMn0KJFC/F+J06cQLNmzT47LxERERER/RxMDhMRERH9vytXruDUqVOoV68eDAwMcOXKFbx+/Rr29vYAACsrK2zatAkVK1ZEUlIShg8fDmVl5R8yt5KSEnx8fDBnzhwkJSVhwIABaNOmTb6SEgCgrq6OYcOGYfDgwcjNzUX16tWRlJSEoKAgqKmpwcfHB+PHj0eFChVQtmxZZGRk4ODBg+LjKMj3lpXo06cPFi9ejIEDB+J///sfHj58CD8/PwwYMEDcZ8iQIejUqRMqVqwIV1dXrFy5EtHR0ejdu/c3z0tERERERN+OyWEiIiKi/6ehoYHz589jwYIFSEpKgpmZGebOnQtPT08AwNq1a9GzZ0+4uLigVKlS8PPz+2yphq9hZWWFli1bomHDhnj79i0aNmyIZcuWFdp/ypQpMDAwwPTp0/Ho0SNoaWmhfPnyGD16NABAQUEBo0aNwpMnT6CsrIwaNWpg69atPyTWgpQsWRLHjx/H4MGD4eTkBBMTEwwcOBAjR44U92nbti3i4+MxefJkxMTEwMHBAYcPHxbf/I+IiIiIiH4tkSAIgrSDICIiouLNysoKXl5emD59urRDkYqJEydi7969CA0NlXYoxdqoUaOwY8cOREZGSjsUIiIiIqJfgjekIyIiIiIiIiIiIiqGmBwmIiIiIiIiIiIiKoaYHCYiIiKSsokTJ7KkBBERERER/XJMDhMREREREREREREVQ0wOExERERERERERERVDTA4TERERfQVzc3MsWLBA2mF81tmzZyESiSASidC8eXNph/PN1q9fLz6OQYMGSTscIiIiIqK/DpPDRERERH+p+/fvY/369eLn/v7+cHJygoaGBjQ0NODq6oojR45I7CMIAiZOnIgSJUpAWVkZHh4eCAsL+6p54+Pj0aBBA5QoUQKKioooWbIk+vfvj6SkJIl+d+7cgbu7O5SVlWFiYoLJkydDEATx9rZt2yImJgaurq5ff/BERERERPRFTA4TERER/aUMDAygpaUlfm5qaooZM2bg2rVruHbtGv755x80a9ZMIvk7a9YszJs3D0uWLEFISAiMjIxQt25dJCcnF3leGRkZNGvWDPv378eDBw+wfv16nDx5Er179xb3SUpKQt26dVGiRAmEhIRg8eLFmDNnDubNmyfuo6ysDCMjIygoKHzfiSAiIiIiogIxOUxERETFwooVK2BiYoLc3FyJ9qZNm8LHxwcAEBUVhWbNmsHQ0BBqamqoVKkSTp48WeiYT548gUgkQmhoqLgtMTERIpEIZ8+eFbeFh4ejYcOGUFNTg6GhITp16oQ3b9780OMriiZNmqBhw4awsbGBjY0Npk2bBjU1NQQHBwP4sGp4wYIFGDNmDFq2bAkHBwds2LABqampCAwMLPI82tra6NOnDypWrAgzMzPUrl0bffv2xYULF8R9AgICkJ6ejvXr18PBwQEtW7bE6NGjMW/ePInVw0RERERE9PMwOUxERETFgpeXF968eYMzZ86I2xISEnDs2DF4e3sDAN6/f4+GDRvi5MmTuHnzJurXr48mTZogOjr6m+eNiYmBu7s7nJ2dce3aNRw9ehSvXr1CmzZtCt0nOjoaampqn318vAr3W+Tk5GDr1q1ISUkRl214/PgxYmNjUa9ePXE/RUVFuLu7Iygo6JvnevnyJXbv3g13d3dx2+XLl+Hu7g5FRUVxW/369fHy5Us8efLkm+ciIiIiIqKik5N2AERERES/go6ODho0aIDAwEDUrl0bALBjxw7o6OiIn5crVw7lypUT7zN16lTs2bMH+/fvR//+/b9pXn9/f5QvXx5+fn7itrVr16JkyZJ48OABbGxs8u1TokQJidXIBdHQ0PimeO7cuQNXV1ekp6dDTU0Ne/bsQZkyZQAAsbGxAABDQ0OJfQwNDfH06dOvnqt9+/bYt28f0tLS0KRJE6xevVq8LTY2Fubm5vnmydtmYWHx1fMREREREdHX4cphIiIiKja8vb2xa9cuZGRkAPhQ2qBdu3aQlZUFAKSkpGDEiBEoU6YMtLS0oKamhoiIiO9aOXz9+nWcOXNGYtWvnZ0dgA9lLAoiJycHKyurzz4MDAy+KR5bW1uEhoYiODgYffr0gY+PD8LDwyX6iEQiieeCIORrK4r58+fjxo0b2Lt3L6KiojBkyJAvzlNQOxERERER/RxcOUxERETFRpMmTZCbm4tDhw6hUqVKuHDhgsQN0IYPH45jx45hzpw5sLKygrKyMlq3bo3MzMwCx5OR+fB39o9r5GZlZUn0yc3NRZMmTTBz5sx8+xsbGxc4bnR0tHg1b2E6duyI5cuXf7ZPQRQUFGBlZQUAqFixIkJCQrBw4UKsWLECRkZGAD6s3P04tri4uHyriYvCyMgIRkZGsLOzg66uLmrUqIFx48bB2NgYRkZG4pXKH88D5F+5TEREREREPweTw0RERFRsKCsro2XLlggICEBkZCRsbGxQoUIF8fYLFy7A19cXLVq0APChBvHn6t/q6+sD+FBX2MXFBQDylYMoX748du3aBXNzc8jJFe2j188sK/EpQRDEK6ktLCxgZGSEEydOiI8nMzMT586dKzC5/bXzABDP5erqitGjRyMzMxMKCgoAgOPHj6NEiRL5yk0QEREREdHPweQwERERFSve3t5o0qQJwsLC0LFjR4ltVlZW2L17N5o0aQKRSIRx48YhNze30LGUlZVRtWpVzJgxA+bm5njz5g3Gjh0r0adfv35YtWoV2rdvj+HDh0NPTw+RkZHYunUrVq1aJS5p8bG8shI/2ujRo+Hp6YmSJUsiOTkZW7duxdmzZ3H06FEAH8o5DBo0CH5+frC2toa1tTX8/PygoqKCDh06FHmew4cP49WrV6hUqRLU1NQQHh6OESNGwM3NTZz47dChAyZNmgRfX1+MHj0aDx8+hJ+fH8aPH8+yEkREREREvwhrDhMREVGx8s8//0BHRwf379/Pl/CcP38+tLW1Ua1aNTRp0gT169dH+fLlPzve2rVrkZWVhYoVK2LgwIGYOnWqxPYSJUrg0qVLyMnJQf369eHg4ICBAwdCU1NTXJbiV3n16hU6deoEW1tb1K5dG1euXMHRo0dRt25dcZ8RI0Zg0KBB6Nu3LypWrIgXL17g+PHjUFdXF/fx9fWFh4dHofMoKytj1apVqF69Ouzt7TFo0CA0btwYBw8eFPfR1NTEiRMn8Pz5c1SsWBF9+/bFkCFD8tUlJiIiIiKin0ckfFwkj4iIiEgKrKys4OXlhenTp0s7lL/C2bNnUatWLSQkJEBLS+uHj+/h4QEPDw9MnDjxh49d2HzOzs5YsGDBT51n1KhR2LFjByIjI3/qPEREREREvwuuHCYiIiL6S5mamqJ9+/Y/dMzk5GRERUVh2LBhP3TcggQEBEBNTQ0XLlz46XMRERERERVHrDlMRERE9JepUqUKHj58CABQU1P7oWOrq6vj2bNnP3TMwjRt2hRVqlQBgJ+yApqIiIiIqLhjcpiIiIjoL6OsrPxTbmj3q6mrq0vUOiYiIiIioh+LZSWIiIiIiIiIiIiIiiEmh4mIiIg+8eTJE4hEIoSGhko7lN/e+vXri1TyQSQSYe/evT89HiIiIiIiKjomh4mIiIj+Mh4eHhg0aFC+9qImcr/HxIkT4ezs/FPnICIiIiKiH4PJYSIiIiIiIiIiIqJiiMlhIiIiKpZyc3Mxc+ZMWFlZQVFREaVKlcK0adMK7JuTk4Nu3brBwsICysrKsLW1xcKFCyX6nD17FpUrV4aqqiq0tLTg5uaGp0+fAgBu3bqFWrVqQV1dHRoaGqhQoQKuXbv204+xKA4cOIAKFSpASUkJpUuXxqRJk5CdnS3ePm/ePDg6OkJVVRUlS5ZE37598f79+wLHWr9+PSZNmoRbt25BJBJBJBJh/fr14u1v3rxBixYtoKKiAmtra+zfvx8AIAgCrKysMGfOHInx7t69CxkZGURFRf34AyciIiIiIshJOwAiIiIiaRg1ahRWrVqF+fPno3r16oiJiUFERESBfXNzc2Fqaort27dDT08PQUFB6NmzJ4yNjdGmTRtkZ2ejefPm6NGjB7Zs2YLMzExcvXoVIpEIAODt7Q0XFxf4+/tDVlYWoaGhkJeXLzQ2T09PXLhw4bPxF5ag/RrHjh1Dx44dsWjRItSoUQNRUVHo2bMnAGDChAkAABkZGSxatAjm5uZ4/Pgx+vbtixEjRmDZsmX5xmvbti3u3r2Lo0eP4uTJkwAATU1N8fZJkyZh1qxZmD17NhYvXgxvb288ffoUOjo66Nq1K9atW4dhw4aJ+69duxY1atSApaXldx8rERERERHlJxIEQZB2EERERFS8WVlZwcvLC9OnT/8l8yUnJ0NfXx9LlixB9+7d821/8uQJLCwscPPmzULr5/br1w+vXr3Czp078fbtW+jq6uLs2bNwd3fP11dDQwOLFy+Gj49PkeJ78eIF0tLSPtvHysqq0G0eHh4ICgqCgoKCRHt2djaUlJSQmJgIAKhZsyY8PT0xatQocZ/NmzdjxIgRePnyZYFj79ixA3369MGbN28AfFgtPGjQIPGYEydOxN69e/PdzE8kEmHs2LGYMmUKACAlJQXq6uo4fPgwGjRogJiYGJQsWRJBQUGoXLkysrKyYGJigtmzZxf5vH2vUaNGYceOHYiMjPwl8xERERERSRtXDhMREVGxc+/ePWRkZKB27dpF3mf58uVYvXo1nj59irS0NGRmZooTxzo6OvD19UX9+vVRt25d1KlTB23atIGxsTEAYMiQIejevTs2bdqEOnXqwMvL67OrYU1MTL7r+IAPq5XHjBkj0bZ79274+fmJn1+/fh0hISES5TRycnKQnp6O1NRUqKio4MyZM/Dz80N4eDiSkpKQnZ2N9PR0pKSkQFVV9aticnJyEv9bVVUV6urqiIuLAwAYGxujUaNGWLt2LSpXroyDBw8iPT0dXl5e33L4RERERERUBKw5TERERMWOsrLyV/Xfvn07Bg8ejK5du+L48eMIDQ1Fly5dkJmZKe6zbt06XL58GdWqVcO2bdtgY2OD4OBgAB9W04aFhaFRo0Y4ffo0ypQpgz179hQ6n6enJ9TU1D77+BJNTU1YWVlJPAwMDCT65ObmYtKkSQgNDRU/7ty5g4cPH0JJSQlPnz5Fw4YN4eDggF27duH69etYunQpACArK+urziGAfKU0RCIRcnNzxc+7d++OrVu3Ii0tDevWrUPbtm2hoqLy1fMQEREREVHRcOUwERERFTvW1tZQVlbGqVOnCiwr8akLFy6gWrVq6Nu3r7itoJukubi4wMXFBaNGjYKrqysCAwNRtWpVAICNjQ1sbGwwePBgtG/fHuvWrUOLFi0KnG/16tVfLCvxI5QvXx73798vtETFtWvXkJ2djblz50JG5sOagu3bt392TAUFBeTk5HxTPA0bNoSqqir8/f1x5MgRnD9//pvGISIiIiKiomFymIiIiIodJSUljBw5EiNGjICCggLc3Nzw+vVrhIWFoVu3bvn6W1lZYePGjTh27BgsLCywadMmhISEwMLCAgDw+PFjrFy5Ek2bNkWJEiVw//59PHjwAJ07d0ZaWhqGDx+O1q1bw8LCAs+fP0dISAhatWpVaHw/oqxEUYwfPx6NGzdGyZIl4eXlBRkZGdy+fRt37tzB1KlTYWlpiezsbCxevBhNmjTBpUuXsHz58s+OmXfjutDQUJiamkJdXR2KiopFikdWVha+vr4YNWoUrKys4Orq+iMOk4iIiIiICsGyEkRERFQsjRs3DkOHDsX48eNhb2+Ptm3biuvffqp3795o2bIl2rZtiypVqiA+Pl5iFbGKigoiIiLQqlUr2NjYoGfPnujfvz969eoFWVlZxMfHo3PnzrCxsUGbNm3g6emJSZMm/apDLVT9+vVx8OBBnDhxApUqVULVqlUxb948mJmZAQCcnZ0xb948zJw5Ew4ODggICPjiTQNbtWqFBg0aoFatWtDX18eWLVu+KqZu3bohMzMTXbt2/ebjIiIiIiKiohEJgiBIOwgiIiIq3qysrODl5fXFxCP9/S5dugQPDw88f/4choaGv3TuUaNGYceOHYiMjPyl8xIRERERSQvLShARERGR1GVkZODZs2cYN24c2rRp88sTw0RERERExRHLShAREZFUCYKAjIwMZGdnSzsUkqItW7bA1tYW7969w6xZs6QSQ3Z2NjIyMsAv1hERERFRccHkMBEREUmFIAg4cOAAKleujJiYGH6Vv5jz9fVFTk4Orl+//stuyPephw8fIiYmBpUrV8bBgweZJCYiIiKivx6Tw0RERPRLCYKAffv2oWLFimjatCmUlZXRtWtXHDt2DM+fP5d2eFRMPX/+HMeOHUPXrl2hpKSEJk2aoFKlSti/fz+TxERERET01+IN6YiIiOiXyEsKT5o0CaGhoahZsyYmTpwIDw8PvHr1ClWrVoWMjAxGjBiBGjVqQFlZGSKRSNph019MEASkpaXhwoULmDVrFnJzcxEcHAxDQ0OcOXMGkyZNwvnz5+Hi4oLx48ejWbNmfE8SERER0V+FyWEiIiL6qXJzc7F3715MnjwZt27dgoeHByZMmAAPDw+Jfk+ePEGvXr1w6tQp5OTkSCdYKpZkZWVRu3ZtrFixAubm5hLbzp49i4kTJ+LcuXMoV64cJkyYgGbNmkFGhl/AIyIiIqI/H5PDRERE9FPk5uZi9+7dmDJlCm7fvo1//vkHEyZMQM2aNT+7X3x8PCIiIpCenv6LIqXiTElJCXZ2dtDV1f1sv3PnzmHSpEk4c+YMnJycMH78eLRo0YJJYiIiIiL6ozE5TERERD9Ubm4udu3ahcmTJ+Pu3buoXbs2JkyYgBo1akg7NKLvduHCBUyaNAmnTp2Cg4MDxo8fj1atWjFJTERERER/JH6KJSIioh8iJycH27Ztg5OTE9q0aQNjY2NcvHgRJ0+eZGKY/ho1atTAyZMnceHCBRgZGaFNmzZwcnLC9u3bkZubK+3wiIiIiIi+CpPDRERE9F1ycnKwZcsWODo6ol27djA1NUVQUBCOHz8ONzc3aYdH9FNUr14dJ06cwKVLl2BiYoK2bdvC0dERW7duZc1sIiIiIvpjMDlMRERE3yQnJweBgYFwcHBAhw4dYGZmhsuXL+Po0aNwdXWVdnhEv0S1atVw7NgxBAUFoVSpUmjfvj0cHBwQGBjIJDERERER/faYHCYiIqKvkp2djc2bN6Ns2bLw9vZG6dKlERwcjCNHjqBq1arSDo9IKlxdXXHkyBEEBwfDwsIC3t7eKFu2LAICApgkJiIiIqLfFpPDREREVCTZ2dnYuHEjypQpg06dOsHa2hpXr17FoUOHUKVKFWmHR/RbqFKlCg4fPowrV67AysoKHTt2RJkyZbBp0yZkZ2dLOzwiIiIiIglMDhMREdFnZWdnY8OGDbC3t4ePjw/s7OwQEhKCAwcOoFKlStIOj+i3VLlyZRw8eBAhISGwtbVF586dYW9vjw0bNjBJTERERES/DSaHiYiIqEBZWVlYt24d7Ozs4Ovri7Jly+L69evYv38/KlasKO3wiP4IFStWxP79+3Ht2jWUKVMGvr6+sLOzw/r165kkJiIiIiKpY3KYiIiIJGRlZWHNmjWws7ND165d4eTkhJs3b2Lv3r0oX768tMMj+iNVqFAB+/btw40bN+Do6IguXbrA1tYWa9euRVZWlrTDIyIiIqJiislhIiIiAgBkZmZi9erVsLGxQffu3eHs7IzQ0FDs3r0bzs7O0g6P6K/g4uKCPXv24ObNm3B2dka3bt1ga2uL1atXM0lMRERERL8ck8NERETFXGZmJlauXAkbGxv06NEDFStWxK1bt7Br1y6UK1dO2uER/ZWcnZ2xa9cu3Lp1C+XLl0ePHj1gbW2NVatWITMzU9rhEREREVExweQwERFRMZWRkYHly5fD2toavXv3RpUqVXDnzh3s2LEDTk5O0g6PqFhwcnLCzp07cfv2bVSuXBm9evWCtbU1VqxYwSQxEREREf10TA4TEREVMxkZGfD394e1tTX69u0LV1dX3LlzB9u2bYODg4O0wyMqlhwdHbF9+3bcuXMHrq6u6NOnD6ysrODv74+MjAxph0dEREREfykmh4mIiIqJ9PR0LF26FFZWVujXrx+qV6+Ou3fvYuvWrShbtqy0wyMiAGXLlsXWrVtx9+5duLm5oV+/frCyssKyZcuYJCYiIiKiH47JYSIior9ceno6Fi9eDCsrKwwYMADu7u4IDw9HYGAgypQpI+3wiKgAZcqUwZYtWxAWFoaaNWvif//7HywtLbFkyRKkp6dLOzwiIiIi+kswOUxERPSXSktLw6JFi2BpaYlBgwahVq1aCA8Px+bNm2FnZyft8IioCOzt7REQEIDw8HDUqlULAwcOhKWlJRYtWoS0tDRph0dEREREfziRIAiCtIMgIiKiHyctLQ0rVqzAzJkzERcXh44dO2LMmDGwsbGRdmhE9J0ePHiAqVOnIiAgAIaGhhg5ciR69uwJZWVlaYdGRERERH8gJoeJiIj+EqmpqVi+fDlmzZqFN2/eiJPC1tbW0g6NiH6whw8fYtq0adi8eTP09fUxYsQI9OrVCyoqKtIOjYiIiIj+IEwOExER/eFSUlLESeH4+Hh07twZY8aMgaWlpbRDI6KfLDIyEn5+fti4cSP09PQwfPhw9O7dG6qqqtIOjYiIiIj+AEwOExER/aFSUlKwbNkyzJ49GwkJCfDx8cHo0aNRunRpaYdGRL9YVFQU/Pz8sGHDBujq6mL48OHo06cPk8RERERE9FlMDhMREf1h3r9/j6VLl2LOnDlITEyEr68vRo8eDQsLC2mHRkRS9vjxY/j5+WH9+vXQ1tbGsGHD0LdvX6ipqUk7NCIiIiL6DTE5TERE9IdITk4WJ4WTkpLQpUsXjBo1Cubm5tIOjYh+M0+ePMH06dOxdu1aaGlpYejQoejXrx/U1dWlHVqR+fr6IjExEXv37pV2KERERER/LRlpB0BERPSjiESizz58fX2lHeI3SUpKgp+fH8zNzTF+/Hh4eXnh4cOHWLFiBRPDRFQgc3NzrFixApGRkWjVqhXGjx8PCwsLTJ8+HcnJyb80jk+vxaampr9sfiIiIiL6PCaHiYjorxETEyN+LFiwABoaGhJtCxculOiflZUlpUiLJikpCVOnToWFhQUmTZqEtm3bIjIyEv7+/jAzM5N2eET0BzAzM8Py5csRGRmJNm3aYOLEiTA3N8e0adOQlJT0S2KYPHmyxLX45s2bv2ReIiIiIvoyJoeJiOivYWRkJH5oampCJBKJn6enp0NLSwvbt2+Hh4cHlJSUsHnzZkycOBHOzs4S4yxYsCDfitx169bB3t4eSkpKsLOzw7Jly37acbx79w5TpkyBubk5pkyZgvbt2yMqKgrLli1DqVKlftq8RPT3KlWqFJYtW4bIyEi0a9cOkydPFl9j3r1791PnVldXl7g+6+vrIycnB926dYOFhQWUlZVha2ub7w94n9q5cyccHR2hrKwMXV1d1KlTBykpKeLtv/I6TURERPS3YHKYiIiKlZEjR2LAgAG4d+8e6tevX6R9Vq1ahTFjxmDatGm4d+8e/Pz8MG7cOGzYsOGHxpaYmIhJkyaJV/V5e3sjKioKS5Ys4dewieiHKFmyJJYuXYqoqCh4e3tj2rRpMDc3x+TJk5GYmPjL4sjNzYWpqSm2b9+O8PBwjB8/HqNHj8b27dsL7B8TE4P27duja9euuHfvHs6ePYuWLVsi7/Ypv+o6TURERPS3YXKYiIiKlUGDBqFly5awsLBAiRIlirTPlClTMHfuXPF+LVu2xODBg7FixYofElNCQoL4q94zZsxA586d8ejRIyxevJhJYSL6KUxNTbF48WJERUWhc+fO4rrmEydO/OFJ4pEjR0JNTU38WLRoEeTl5TFp0iRUqlQJFhYW8Pb2hq+v72eTw9nZ2WjZsiXMzc3h6OiIvn37Qk1NDcDPv04TERER/a3kpB0AERHRr1SxYsWv6v/69Ws8e/YM3bp1Q48ePcTt2dnZ0NTU/K5Y3r59iwULFmDhwoXIzMxE7969MWLECBgbG3/XuERERWViYoKFCxdi5MiRmDVrFmbOnIn58+dj4MCBGDx4MLS1tb97juHDh0vcEFRPTw8AsHz5cqxevRpPnz5FWloaMjMz85X5yVOuXDnUrl0bjo6OqF+/PurVq4fWrVtDW1v7p16niYiIiP52TA4TEVGxoqqqKvFcRkZG/LXkPB/fqC43NxfAh68sV6lSRaKfrKzsN8Xw9u1bzJs3D4sWLUJ2drY4KWxkZPRN4xERfa8SJUpgwYIFGDlyJGbPno05c+Zg4cKFGDBgAAYPHgwdHZ1vHltPTw9WVlYSbdu3b8fgwYMxd+5cuLq6Ql1dHbNnz8aVK1cKHENWVhYnTpxAUFAQjh8/jsWLF2PMmDG4cuUKVFRUAPzY6zQRERFRccGyEkREVKzp6+sjNjZWIkEcGhoq/rehoSFMTEzw6NEjWFlZSTwsLCy+aq74+HiMGTMG5ubmmDdvHnr27InHjx9j3rx5TAwT0W/B2NgY8+bNw6NHj9CjRw/MnTsX5ubmGDNmDOLj43/YPBcuXEC1atXQt29fuLi4wMrKClFRUZ/dRyQSwc3NDZMmTcLNmzehoKCAPXv2/NDrNBEREVFxw5XDRERUrHl4eOD169eYNWsWWrdujaNHj+LIkSPQ0NAQ95k4cSIGDBgADQ0NeHp6IiMjA9euXUNCQgKGDBnyxTnevHmDuXPnYsmSJcjNzUW/fv0wbNgwGBgY/MxDIyL6ZkZGRpgzZw6GDx+OOXPmYMGCBVi0aBH69++PoUOHiktDfCsrKyts3LgRx44dg4WFBTZt2oSQkJBCk7lXrlzBqVOnUK9ePRgYGODKlSt4/fo17O3tAXz/dZqIiIiouOLKYSIiKtbs7e2xbNkyLF26FOXKlcPVq1cxbNgwiT7du3fH6tWrsX79ejg6OsLd3R3r16//4oq0169fY+TIkTA3N8fixYvRt29fPHnyBLNmzWJimIj+CIaGhpg9ezYeP36Mvn37YvHixTA3N8e///6L169ff/O4vXv3RsuWLdG2bVtUqVIF8fHx6Nu3b6H9NTQ0cP78eTRs2BA2NjYYO3Ys5s6dC09PTwDffp0mIiIiKu5EwqeFFomIiOi7xMXFYc6cOVi6dClkZGR+2Eo7IiJpe/36NebNm4clS5ZAEAT07duX34QgIiIi+oMxOUxERPSDvHr1CrNnz4a/vz9kZGQwYMAADBkyBLq6utIOjYjoh3rz5g3mzZuHxYsXIzc3F3369MHw4cNhaGgo7dCIiIiI6CswOUxERPSdYmNjMWvWLCxfvhxycnIYMGAABg8ezKQwEf314uPjMX/+fCxatAjZ2dniJDFvsklERET0Z2BymIiI6BvFxMSIk8IKCgoYOHAgBg0aBB0dHWmHRkT0S719+xYLFizAwoULkZmZid69e2PEiBEwNjaWdmhERERE9BlMDhMREX2lly9fYubMmVi5ciUUFRUxaNAgDBw4ENra2tIOjYhIqhISEsRJ4oyMDPTs2RMjR45EiRIlpB0aERERERWAyWEiIqIievHiBWbMmIFVq1ZBWVlZnBTW0tKSdmhERL+VxMRELFy4EPPnz0d6ero4SWxiYiLt0IiIiIjoI0wOExERfcHz58/FSWFVVVUMHjwYAwYMgKamprRDIyL6rSUmJmLRokWYP38+0tLS0L17d/z7778wNTWVdmhEREREBCaHiYiICvXs2TNMnz4da9asgZqaGoYMGYL//e9/0NDQkHZoRER/lHfv3mHx4sWYN28eUlJS0K1bN4waNQolS5aUdmhERERExRqTw0RERJ+Ijo4WJ4XV1dUxdOhQ9O/fn0lhIqLvlJSUhCVLlmDu3LlITk4WJ4lLlSol7dCIiIiIiiUmh4mIiP7fkydPMH36dKxbtw4aGhoYNmwY+vXrB3V1dWmHRkT0V0lOThYniZOSktClSxeMHj0aZmZm0g6NiIiIqFhhcpiIiIq9x48fw8/PD+vXr4eWlhaGDx+Ovn37Qk1NTdqhERH91d6/f4+lS5dizpw5SExMFCeJzc3NpR0aERERUbHA5DARERVbjx49gp+fHzZs2ABtbW0MHz4cffr0YVKYiOgXe//+Pfz9/TF79mwkJCTAx8cHo0ePRunSpaUdGhEREdFfjclhIiIqdqKiojBt2jRs3LgRurq6GDFiBHr37g1VVVVph0ZEVKylpKSIk8Tx8fHo3LkzxowZA0tLS2mHRkRERPRXYnKYiIiKjcjISEydOhWbN2+Gnp4eRo4ciV69ekFFRUXaoRER0UdSU1OxfPlyzJo1C2/evEGnTp0wZswYWFlZSTs0IiIior8Kk8NERPTXe/jwoTgpbGBggJEjR6Jnz55MChMR/eZSU1OxcuVKzJw5E69fv4a3tzfGjh0La2traYdGRERE9FdgcpiIiP5a9+/fx9SpUxEYGAhDQ0P8+++/6NGjB5SVlaUdGhERfYW0tDRxkvjVq1fo0KEDxo4dC1tbW2mHRkRERPRHk5F2AERERD9aREQEvL29UaZMGZw5cwYLFy7Eo0ePMGDAACaGiYj+QMrKyhg4cCAePXqEBQsW4PTp0yhTpgw6duyIiIgIaYdHRERE9MdicpiIiP4a9+7dQ4cOHVCmTBmcO3cOixYtQmRkJPr37w8lJSVph0dERN9JSUkJ//vf/xAVFYVFixbh7NmzKFOmDDp06IB79+5JOzwiIiKiPw6Tw0RE9McLCwtDu3btULZsWVy8eBFLly5FVFQU+vXrx6QwEdFfSElJCf369UNUVBSWLFmCCxcuoGzZsmjXrh3CwsKkHR4RERHRH4PJYSIi+mPdvXsXbdq0gaOjIy5fvgx/f388fPgQffr0gaKiorTDIyKin0xRURF9+/ZFZGQkli1bhqCgIDg6OqJt27a4e/eutMMjIiIi+u0xOUxERH+cO3fuwMvLC46Ojrh69SqWL1+Ohw8folevXkwKExEVQ4qKiujduzciIyOxfPlyBAcHw9HREV5eXrhz5460wyMiIiL6bTE5TEREf4xbt26hVatWcHJywrVr17Bq1So8ePAAPXv2hIKCgrTDIyIiKVNQUEDPnj3x8OFDrFy5EteuXYOTkxNatWqFW7duSTs8IiIiot8Ok8NERPTbu3nzJlq0aAFnZ2fcvHkTa9aswYMHD9C9e3cmhYmIKB8FBQX06NEDDx48wOrVq3Hz5k04OzujZcuWCA0NlXZ4RERERL8NJoeJiOi3dePGDTRv3hzly5fHnTt3sHbtWty/fx9du3aFvLy8tMMjIqLfnLy8PLp164b79+9j7dq1uHXrFlxcXNC8eXPcvHlT2uERERERSR2Tw0RE9Nu5fv06mjZtigoVKiAsLAzr169HREQEunTpwqQwERF9NXl5eXTp0gURERFYt24d7t69i/Lly6Np06a4fv26tMMjIiIikhomh4mI6LcREhKCxo0bo2LFioiIiMDGjRtx7949+Pj4QE5OTtrhERHRH05eXh6+vr6IiIjAhg0bEBERgYoVK6JJkya4du2atMMjIiIi+uWYHCYiol8mJiYGXbp0QUJCgkT71atX0ahRI1SuXBkPHz7Epk2bEB4ejk6dOjEpTEREP5ycnBw6d+6M8PBwbNq0CQ8ePEClSpXQqFEjXL16VaJvQkICunTpgpiYGClFS0RERPTzMDlMRES/RHZ2Ntq3b49jx46JS0MEBwfD09MTVapUQVRUFAICAhAeHo6OHTsyKUxERD+dnJwcOnbsiPDwcGzevBlRUVGoUqUKPD09ERwcDODDauNjx46hQ4cOyM7OlnLERERERD8Wk8NERPRLTJw4ERcuXMDWrVtx+/Zt1K9fH66urnj69CkCAwMRFhaGDh06QFZWVtqhEhFRMSMrKwtvb2+EhYUhMDAQT58+haurKxo0aIA7d+5gy5YtOH/+PCZNmiTtUImIiIh+KJEgCIK0gyAior/bsWPH4OnpiR49euDx48c4ceIEypQpg/Hjx6N169ZMCBMR0W8lJycHO3fuxOTJkxEeHo66devC3Nwcq1evxtGjR1GvXj1ph0hERET0QzA5TEREP9Xz58/h4OAAOTk5xMfHo2zZspgwYQJatWoFGRl+gYWIiH5fubm54iRxWFgYdHR0kJOTg7CwMJiYmEg7PCIiIqLvxuQwERH9VObm5nj69CkUFBRgamoKHR0dAIBIJEKfPn3QpUsXKUdIRESU37p16+Dv74+8/y69ffsWz58/R2ZmJszMzPDkyRPpBkhERET0A/BuP0R/gOTkZNy9exepqanSDoWoUCoqKnBwcIC6urpEe6NGjRAVFQVzc3MAgCAI4v9olyxZ8leHSUREVCQlS5aEs7MzgA9/0BSJRACAJ0+ewNLSMl9/fl6jP0Fhn9eIiKj44sphot/Y27dv0bNHDxw8dBAZGZnSDofoixQVFdC4UWOsXLVKvEKYiIjob/b27Vv06NETBw8dRGZGhrTDIfoiBUVFNG7UGKtWreTnNSIiYnKY6HeVmpqK6m7VEP0oEgMbV0DNsiWhpqwAkbQDIyqAAOB9WibOhz3DwoPXUaq0FS5eCoKKioq0QyMiIvppUlNTUc2tOh4+joZjq4EoUa4G5JXVIOInNvoNCRCQlfYeL29dwJ1dC2FtUQpBly7y8xoRUTHHshJEv6mDBw/iZugtnJzSHs4WhtIOh6hIHMz0Uc3eBHXGbcGhQ4fg5eUl7ZCIiIh+moMHD+JW6E00n3cSelblpB0OUZHoWjjA2KEa9g6pw89rREQE3iae6Dd14MABOFkYMTFMfxxnC0M4mhviwIED0g6FiIjopzpw4AAMLB2ZGKY/jp5VOehbOvLzGhERMTlM9Lt6FRsLc33eKIL+TBYGGoiNiZF2GERERD9VbOwrqBqaSzsMom+iZmiOmJhYaYdBRERSxuQw0W8qNzcHsjKsV0d/JlkZEXJzc6QdBhER0U+Vk5sLkSwr9dGfSSQrh5zcXGmHQUREUsZPMkR/mP4rjuFdagY2DW4q0X4x/Bma++1C1Ire0FRVEj/Po6uuDGcLA4xrWx0OZvoAgKZTdyAo4gUAQF5WBia66mhWxRojWlaForzk5eFlfDIqDFkHMwNNBM/2yRdXYko6Rm08i6M3HgEAGpQvjRmdPaCpqgQAeJucht7LjiLs2RskvE+HnoYyPCtYYqxXNairKIrHCX/2BiM3nMHNqFhoqSnB5x9HDGteBSLRf4nyS/eeY1zAedx/EQ8jLVX0b1wRXWo7ibdvPHMH2y/cw73n8QCAchYGGNvGDeUtjSRijnn7HpO2XsSp20+QnpkNSyMtLOhR97cu5XEwJBIbTt/GrcdxePs+HWemdYCjmcFn94l4Ho8Zuy7j1uNXePYmGVM71kTvBuUl+iSnZWLGziAcuhaFN0mpcDQ3wLSO7hLnTK/jggLHn9CuOv7XuOJ3HxsREdHf4tyC/shMSULdMRsl2l/euYTDY5qjU2AkFNU0xc/zKGnoQs+qHCr5jIOuhQMA4ODoZoi9GwQAkJGTh6qeCUq7NUX5DiMgK68oMX7Km5fY1rMi1A3N4OV/OV9cGe8TcXnlaDy9ehQAYFa5AVx7ToeimiYAIP7xXdzauQiv7l1BetJbqBmUhH0DHzg07SVxDHf3LcfrhzeQlfoeGiUs4NSiP6w8WkvMFXP3EoLXjEdi9H2o6BjBqWV/2Hv6ircnREfgesAMvIm6jfdxz1C12xQ4NOstMUZm6ntcD5iOp8GHkfbuDXRLO8K1xzToW7sU5WWQGkEQcGPLbNw/vhEZ799B36Y83HrPhHYpu0L3+fh1/ljJinVQf/wW8fPww2txe/dSpCW8glYpW7h2nwqjsq4S+yQ8e4CQDZMRczcIEHKhVdIOtUeuhpq+6Y87SCIi+mswOUz0lwue7QN1ZQW8iE/GqI1n0WbWHgTP9oHG/ydkO9VywL+tXJGVnYObj17hfytPAADGta0uMc6WC+FoVsUGl++/wJUHL1HFpoTE9l5Lj+Dl2/fYPqI5AGDImlPos/wYAoc2AwDIyIjQoEJpjPKqBj0NZTyOTcSIDWeQ8D4dK/t5AgCSUzPQesZuuNmb4sTk9oiKTUT/FcehoiiPfg0rAACexr1D+zl70cnDAcv7NMCVBy8xYv1p6Kkro0llawAfksctXW1RycYYSvJyWHzwGlrP3I1LMzrDWEcNwIdkdsPJ21DdviS2DW8OPQ1lPHn1Dpoqkv/J+hqZ2TlIeJ8OQy3Vbx7jS1IzslDZpgSaVrbB4DUni7yPmb4mmla2xtjN5wrsM2j1CUQ8j8eyPvVhpKWGHZfuodWM3Qia+d85C1vSQ2KfU7eeYODqE+LzTkRERN/Gyz8Y8ipqeP/6BS6vHI2jE9vCa9llKKhqAABs63VCBe+RyM3OwuuHN3F+4QAAQCWfcRLjPDi1BRZuzRAbdhmx4VdgVKaKxPYzc3ohJT4GDSZuAwBcXDoUZ+f3Rf1xAQCAN5G3oKSpC48hy6CqZ4JX90JwcelQiGRkUbZxdwBA3L2r0DEvg3Kt/gdlLX1EXzuBcwv6QV5FHWaV6wMAkmOf4tikDrCt1xEeQ/zx6t4VBC0fCSVNXVhUawIAyM5Ig7qROSzcmiF4zdgCz8uFJYOQ8DQC7oOXQkXHCJFnd+LwuFZovfQSVHWNv+lc52RlIuN9AlS0f95igNu7F+PuPn/UHLgYmiaWCN0+D0fGt0brZcFQUFErcJ86o9YjNztT/DwjOQG7B3jAwu2/BSFRF/YgePVYVOs9E4b2VRBxdAOOTmqH1ksviRO/STGPcfDfxrCp443y7UdAQVUDic8e5PtDAhERUR6WlSD6y+lrKMNQSxXlLY0w2bsm4t6l4lrkf7VgVRTkYKilClM9DTSpbA0Px1I4cydaYgxBELDlfDjaVLdDK1dbBJy9K7H9wYu3OHX7KRZ0r4NK1iVQyboE5nevg+M3H+Phy7cAAC1VJXStUw4upQ1RUk8DNR1KoWsdJwTffyEeZ0dQBNKzsrGkVz3Yl9RD40pWGNy0EvyP3IAgCACA9advw0RXHdM6ecDGRAedajmgg3tZLD18XTzOir6e6Fq3HBzNDGBdQgfzu9dBbi5wPuy/41p04BpMdNSxuFc9lLc0Qil9TdR0KAULQ62vPsehj19h1MazcOi/CnuDH3z1/l+jTXV7DG9RFe4OJYu8T3lLI0zqUAMtXW2hKC+bb3taZjYOhkRiQrsaqGZnitJGWhjZyhVm+hpYd+q2uJ+hlqrE48iNKFS3LwlzA80fcmxERETFlZKmHlS0DWFgUx5Vuk5CWkIc4u5fE2+XU1SGirYh1PRNYVGtCUyc3fE89KzEGIIg4MGprbCu5QVL95Z4cCJAYnvCswd4fuM0avSfD0O7SjC0q4Qa/ebhWchxJD6PBADY1vVGtZ7TYezgBg0jc1jX8oJNnXZ4cvmQeBznNoNRseMoGNpXhoaxBRya9IRp+X/w9KM+945ugKq+CVx7TIN2SRvY1esEmzodcGfPMnEffWsXVOkyEZY1WxSYuMzOSMOToIOo7Dsexg7VoFmiNCp0GAF1w1K4d2TdV5/jN5G3ELRyFAJ9HfHowt6v3r+oBEHA3f0r4NxmMCyqNYaOmT3cBy1BdkYaos7vKnQ/JXVtqGgbih8vbp6FnKKyRHL47r7lsKnjDbt6naBd0gauPaZBVc8E9w7/dz6ubfZDyQp1UKXLBOhZOkHDyBylKtWDspb+TztmIiL6s3HlMFExovT/pSKycgquLXb36WtcffASJfU0JNovhD9HakYW3B1KoYSOOupP2IppnTygrqwAAAiJjIGGigIqWP23gqOilTE0VBQQ8jAG1iV08s0Vk/AeB0MiUc3uv6+3XYuMRTU7U4mSFrWczDBl+yVEv06CmYEmQh7GopaDmcRY/ziaIeBcGLKycyAvlz/5mZqRjeycHGipKYnbjt54hFpOZui66BCCIp7DWFsNXeo4oXMtx0LP38diE1Kw49I9bLt4D49iE1HX2RwLutdBXWcLcZ+ha09h56WIz45zaWYnmH5yvn+17Jxc5OQKUPokcaykICeRvP9Y3LsUnAh9giW96v2KEImIiIoNOcUPn1dyc7IL3B7/+C5e3bsKNYNSEu0xty8iOyMVJcq5Q1WvBPYNq4+qPfzEK1XjIkKgoKoBA9sK4n0M7CpCQVUDcRFXoWVqVeB8mSnJUFTX+mzMmSlJ0DK1ET+PiwiBqYuHRB9Tl1q4fyIAudlZkJGT/+x4AJCbkwMhNweyCkoS7XIKyogNv/LF/QEg9W0sIs/uxMPT2/Du5SOUrFgXNf43H6Uq1hX3ubhsGCLP7vjsOB+vzP2S5FdPkZYQBxNnD3GbrLwijMpWQ9y9q7BvkL88W0HunwxE6RotIK/04RtpOVmZeBN5C+VaDZDoZ+rigVcRIQAAITcXz66dgFOL/+HIBC/EP7oLdcNSKNd6IMyrNizSvEREVPwwOUz0Bzp+8zHMui2VaPvSzSTeJqdh9p5gqCkpoHzp/+rIrj15G5vPhiErJxeZ2TmQEYkw06eWxL4B5+6ihastZGVkYGeqCwtDTewNfoBOtT7UwotLTIGehkq+OfU0VBD3LkWirceSwzh64xHSMrNR36U0FnSvI94Wl5iCkvqSiVJ9zQ/jxr1LgZmBJuLepUBf0yxfn+ycXMQnp8NIO39JhynbLsJYWw3uZf/7T9TT1++w/tRt9GlQHoOaVsKNqFiM3ngWinKyaFujTIHnMDM7B4euRWLbhXs4ezcazhYG6FrHCS2q2kJbTSlf/39buYrLYRTGSLvgrxb+SurKCqhkbYw5e6/A2kQHBpoq2BV0H9ejYlHaULvAfbZeuAc1JXk0rljwfySJiIiKu+iQ41jfRvIzi/CFz2vpSW9xY8scyCurSdTVvXdkHe6f2Izc7GzkZmdCJCODar1nSux7/2QALGu0gIysLLRL2UHD2AKPLu6BXb1OAIC0hDgoaerlm1NJUw+pCXEFxvMqIgSPL+1DvXGBhcb8+NJ+vH4Yiur95orbUhPjYKopuVJVWUsfQk420pPioaJj9Okw+SioqMHArhJubpsLLVMbKGvpI+r8bsQ9uA7NEqUL3S8nKxNPgg/h4elteBF6DvpWzrBv2BWlazSHknr+zzUVOoyEY/O+n42lKPHmSfv/c6msJXlPCGUtfbx//axIY8Q9uIGEp/dQ438LxG3pSW8h5ObkWwGsrKmPtMQPc6a9e42stBTc2rUIFTqOQmWf8Xh+4zROTvdFo2l7YOzgVuTjICKi4oPJYaI/UPUyJTHb9x+JtutRsejjfzRfX6cBawAAKRlZKG2khbUDGooTrgDQupodBjerjOS0TCw6GAJ1ZUWJGrLvUtJxKCQSB8e3+W8fN3sEng8TJ4cBQIT/bhiXRxDyt0/t6I4RLaviYUwCpm2/hHEB5zG7y3/Hkm8U4f/bP7ohnUhUYJd87QCw6OA17L58H/vGtIaSwn+XvNxcAc6lDTG27YcPyU7mBrj/4i3WnbpdaHL46oOX6LHkCEx01bFnVCu42pkU2C+PvqaKxLn+GjsuRWDY2lPi51uHN//ifN9jWe/6GLDqBBz/txqyMiI4mRuglasdbj8p+D+LgefC0LqancQ5JSIiov8YO1aHW59ZEm2vH9zA2Xl98vXd0vXDjXWz01OhUaI0ao9cI5EEtHRvBWevwchKS8atXYuhoKIurt0LABnv3+HJ5UNoMuOguM3KwwsPTm4RJ4cByc9TYoJQYHtCdAROTOsMl7ZD860CzvPyziWcW/g/1Og/L//N1j4ZM69EWIEf2ArhMXgpzi8aiC1dHCGSkYWepRMsa7ZC/KPbhe7zKiIEZ2b3hKqeCRpN3Z3vZm2fUtbS/+aSC5Fnd+LisqHi5w0mbIVI5sM3sfIfplDg5+WCPDgRAG0zexjYlM+/8dPzCgF5n6CF3A/n2KxKAzj+/839dEs74lVECO4d2cDkMBERFYj/qyf6A6koyqG0kZZE28u3yQX2PTDOC+rKCtBTV4Z6ATdb01BREI+1vE8DuP27CZvP3kVHjw+J311B95GelYP6E7aK9xEEIFcQcP9FPGxNdGGgpYrXSSn5xo5PTs2XHM2rV2tdQgc6akpoPGUHhjavAiNtVRhoqSLuXapE/9dJH57r///KZANNVcQlSvZ58y4VcrIy0Plk9e6SQ9exYP9V7Pq3FcqWkvzQb6ilCptPyl1Yl9DGgZCH+Y4jT3lLI8zvVgfbLoSjxfRdcC9bEl7V7dGwgiVUFPN/PfJ7yko0KF8aFSz/W6WSd1O4n8XCUAsHxnohJT0LyWmZMNJWRbfFh1BKP39slyNeIDImAav78+uJREREhZFXUsm3wjUlPqbAvo2nH4CCijqUNPWgoKKeb7uCioZ4rFpD/LGzf3XcP74ZtvU6AgCizu9CTmY69g2r/9FeAoTcXCRE34d2KVsoaxsgLfF1vrHTk+LzJUcTou/j0JiWsK3XES5th+bbBwBi7l7CianeqNp1Cqz/aSuxTUXLQLyaVTzPuzcQycpBST1/ubHCaBhboPH0/chKT0FWajJUdIxwalZ3qBuWKnQfAxsXVO8/Dw9Pb8OhsS1hUq4mrGq1gXlVT8gp5v+j/feUlShVuQFafJTAVdU1RmrCKwBAakKcxIrjtMQ3RUpCZ2ekIurCHlToMFKiXUlDByIZWfHK5Dzp7/4bV0lDByJZOWiVtJHoo2Vqg9jw4C/OTURExROTw0R/OTN9DWiq5i95UBB5OVkMbloJU7ZdQktXW6goymPzuTD0bVge7T9ZTTtq01kEnAvD5A41UcnKGEmpmbgRFYvy/5/QvB4Zg6TUTFSyLvxO0nkLSDKzP9TUq2hlhGnbg5CZnQOF/68dfPbOUxhpq4qTlJWsjXDsxmOJcc7cfQpnCwOJesOLD17DvH1XsWNkC7iUzn836so2JRAVkyDRFhWbmK/e8sdUFOXRqZYDOtVywONXidh64R78dgRh6NpTaFLJCm2ql0F1e1PIyHxYvfE9ZSXUlRXENZ1/JVUleagqySMxJR1n7jzFhHY18vUJOHcX5SwM4GDGG5sQERH9COqGZlBUK9oNXmXk5OHsNQghG6fC0r0l5BRVcP9EAByb94V17XYSfS+vHI0HJwNRpeskGNhVQmZKEuIe3BCvSI27fx2ZKUkwsKss3ichOgKHxrSE9T9tUanTmAJjeHnnEo5P6YDKPuNh16Bzvu0GdpUQHXJMou35zbPQt3IuUr3hT8krqUJeSRUZ7xPx4uYZVPaZUGhfOUUV2NXrBLt6nZAU8xgPT2/DtU1+uLRsKMxdG8O6VlsYO7pBJPPh3uzfU1ZCQUVNXNM5j7qhGZS1DfAi9Bz0LD+sCM/JykRsWBAq+Yz/4rE+urgPuVmZsPLwkmiXlVeAnlU5vAg9B3PXRuL2F6HnYFa5gbiPvrUL3r2Iktj33csoqBsU/WbGRERUvMhIOwAi+r20qmYHkUiEtSdv487TONx+EodOHg6wL6kn8WjpaovtF+8hKzsHNiY6qO1khsFrTuJaZAyuRcZg8JpTqOdiIb4Z3YnQxwg8F4Z7z94g+vU7nAh9jOHrT6OKTQmU0v/wn6HW1eygKC+L/iuO496zNzgUEon5+0PQx7O8+OuOvv844Xl8EsZuPocHL94i4FwYAs6GSSRhFx28huk7L2NRj7ooqaeBV4kpeJWYgvfpmeI+vRu44FpULObvu4pHsYnYGRSBTWfuoGudckU6TxaGWhjV2hXX53VBwJCmEEGETvP3Y83JW+I++poqKG2k9dmHnOzXXYYT3qfjztM43H/xFgAQGZOAO0/j8Crxv5XbfZcfw5RtF8XPM7NzcOdpHO48jUNmdi5i3qbgztM4PIr9P/buMjqqq4vD+DNxdw9xJLi7JbiVtkhpKfWWunupe0uFttTpW3coUtwS3N0tAZJAEkhC3DPvh5SBkAABEgb5/9Zilblzzr37TlvYd+fMPsdMYxZu3s+CTfs5kJpJ7JYDXPfWROr6uzOyW8UfCmTnFTJt9R7TynIRERG5+CK6DcVgMLB95vekxW0hbd9mGvQZhUdIwwq/IroNYU/MX5SVFOMeVJ86rXqwdPwTpO5cS+rOtSwZ/wRBbfuYNqMrLwxfT2CL7jS97j7yMlLIy0ghP/Oo6dqHtixj7usjaTzoHkI7DTKNKcg+8UP3hv1uIyc1kZXfvURGwm52zfuV3fN/pen1J4qwpcVF5bHHbaGspIjc9GTS4raQeSjONCZx/UIS1i0gO/kAiRtimTHmOlwD61K/103V+pxc/MNoffNzjPh2Lb1f/BUMBua9fQvbZ35nGmPv5o1rQPgZf1lYVn9NlcFgoMnge9k0cRz7V8wg/cAOFn/yMFa29kR0G2oaF/vxg6z58Y1K83fN+5WQDv2xc6m8wrrJtfexa94v7Jr3KxkJu1k54UVyjiQS2f9205hm1z9I3NIp7JzzM5mH4tg2fQIHV8+h4YA7qn0PIiJyddHKYRGpwMbKkrt7N2f8jLXsSkyjQaCHqcB7sgGtI3j6+4XM2RDPoLZ1+eqB/jz/UyzD3p0MlLdFeO+2KNN4Oxsrfo7dyou/LqKouJQAT2cGtanLo9e0MY1xcbBl4nNDeOaHGHq9/DuuDrbc378VD/Q/8XW9EB9Xfn/qOl78ZRH/m78ZP3dH3r41qkKf5O/nb6KopJQ7Pp1RIeanr2/Ps0PL+861ivDjx8cG8eafy/hgyiqCvV14c1R3hnc+pV/eWRgMBro0CqJLoyDevS2aY7kF5zT/XM1ev4+Hv5lnen3P+FlAxXtLPJqFxUnt6JIzcogec2Ijmc9nruPzmevoFBnItBfLV6Vk5RXx5l/LOJSeg5tjed/pMcM7VViNDfDPyt0YjTC0Y4PaukURERE5C0trGxoNuIvN/4wn4+BO3IIa4FanXqVxIR36s+zLpzmweg5hnQYR9eRXrPjmBWa9Uv73f3C7fnS6913T+Lil0yjIPMq+RRPZt2ii6biTTxA3TlgPwJ4Fv1NSmMemiZ+waeInpjF+TTox6O2pADj7hdD3ld9YOeElts/4Hw4efnS85+0KfZLz0pOZ/NiJfSe2TP6cLZM/r3Ceorws1vz0FrlHD2Hr7EZYx0G0uWXMOa8+NhgMBDTtTEDTznS6910Ks4+d0/xz1WzIw5QUFrDsq2coysnEu34r+r32d4VVxjlHEiv1es5M2kfK9lX0e63qNhcRXa+nMDuDDX9+SF56Cu4hkfR9+fcKq4JDOw6k8/1j2TTxE1Z8+wKugRH0eu57/Bp1qJ2bFRGRy57BaNoZQEQuJb169sAlL4lv1ddVLkP3jJ9JlkMg8xcsNHcoIiIitaZHz14cKHahx9PfmDsUkXO2cOxoQqyzWLhgvrlDERERM1JbCREREREREREREZGrkIrDIiIiIiIiIiIiIlchFYdFRERERERERERErkIqDouIiIiIiIiIiIhchVQcFhGzaPnYd3w1e321xy/dnoDXqHFk5hbUYlQiIiIictwfd7di69Svqj3+0JZlTBjsTWFOZi1GJSIiIjXJytwBiMjVad7rN+Fga13t8e3qB7Bt/D24ONjWWkxGo5H3/1nJTzFbycwtoFWEH+/f3oPIOp6nnTN9zV4+nraa+JRjlJSWEe7rxgMDWnNDl4ZVjh83bTVv/rWce/u24K1bogAoLinl7YnLmb9xPweOZOJsb0v3JsG8NKIz/u5OtXGrIiIiImd17YdzsbZzqPZ438i2jPxxKzaOLrUWk9FoZP3vY9k19ycKczLxrt+Kzve9h3tw5Gnn7JzzM3ti/iTjwE4AvOo2p80tY/Cp38o0pigvh3W/vsOBlTPJzzyKZ3hTOt7zFt71WprGrPvtfeKWTCb36CEsrKzLzzPqBXwatK61+xUREaltWjksImbh5eJwTsVhGytLfN0cMRgMtRbTZ9PX8uWsDbx3WzTzXr8JHzdHhr77D9n5Raed4+ZoyxOD2zH7lRtZ9PYoburWmIe/mcvCzfsrjV2/L5mfYrbSONirwvH8ohI27z/Ck9e1Z8EbI/nxsUHsO5zBqI+m1fQtioiIiFSbvasXVrbVLw5bWtvg4O5bq/na5n8+Y+vUL+k4+l2u/XAuDu4+zHp5GEV5Oaedc3jrMiK6DWHgW5MZPHYWTl6BzH5lOLlph01jlox/jKSNi+j++OcM+XQRgS2imPnS0ApjXAMj6HTvuwz5bBHXvDcdJ58gZr0ynPzMo7V2vyIiIrVNxWERqXHZ+UXc+8Usgu8aT6MHv+HLWesZ/ObfjPk51jTm1LYSXqPG8XPMVm79+F+C7hxP2yd/YNa6fab3a7uthNFo5KvZG3ji2rYMaluXhkFefH5vH/KLipm0fOdp53VpFMTAtnWpH+hBmK8b9/ZrSaMgL1btOlRhXE5BEfd9OZuP7+qF6ymrn10cbJn03BCu61CfegEetKnrzzu3RrEpPpXEo1m1cr8iIiJydSvKyyHmw/v4YXgIv97WmC1Tv2L6C9ey4tsxpjGntpWYMNibnXN/Zt7bt/H9sGD+urcdB1bNNr1f220ljEYjW6d9TYsbHies0yA8QhrS/bHxlBTms2/xpNPOi37yKxoNuBPP8Ka41alHl4c+xlhWxqFNiwEoKcxn//LptLv9ZfybdMI1IJzWI5/B2TeYHbO+N52nbvehBLbojotfKO7BkXS46w2K87JJ37+9Vu5XRETkYlBbCRGpcS/9upjVuw/xy+OD8XZ14N1JK9i8/whNQ7zPOG/s5JW8cmMXXr2pKxPmbuS+L2ezcdxduDvZVeu6I96fzMpTirKnOvDdg1UfP5JFamYeUU1DTMdsra3oFFmHNXsOc3vPZme9vtFoZMm2BPYlZ9AxskuF9579IYbeLcLo3iSYD6esOuu5svOLMBioVEgWERERqQmr/vcSKTtW0/vFn7F382bdr++Rtm8znmFNzjhvwx8f0O62V2h3+ytsnzGB2I/uY8SEDdg5u1frurNfHUHy9pVnHHP7XweqPJ6dcoD8jFQCW0SZjlla2+LXuBOpO1bTsN9t1YqhpDCfstISbP+Luay0FGNZKZY2FXNOKxt7krdXnbeVFhexc85P2Di64BnWuFrXFRERuRSpOCwiNSo7v4g/l2zn6wf7061JMACfje5Dk4e/Pevcm7o2Ymin8n5xY27ozLfzNrJ+XzI9m4dW69rj7u5NflHJecWdeiwXAG/Xil+d9HZ1OOvq3ay8Qpo+PIHCklIsLQy8f3uPCkXmf1bsYvP+VOa9flO1YikoKuH1P5cytGMkzioOi4iISA0rysthz8I/iX7yKwKbdwOg+6Of8tvtTc86t16PG4noPgSANreMYdv0CRzZvZ6g1j2rde2uD4+jpDD/vOLOz0gFwN7Np8Jxezdvco4kVPs8a356HUcPPwL+u3cbByd8Ituy4c8PcatTH3s3b/Yt/ofU3etwDQivMPfgmrksHHsPJYX5OLj70v/1idi5nH5/ChERkUudisMiUqMOpGZSXFpGq3Bf0zEXB1si/M6+mqTRSb14He2scbKz4WhWXrWv7e9x4Zu3GajYI89orHzsVE52NsS8dTO5hUUs3pbAS78uIsTbhS6NgkhKy2bMz4v4+9nrsbM5+x+5xSWl3PP5TMrKjIy9PfqC7kVERESkKtkp+ykrKcb7pA3ZbBxdcA2MOOtcj9BGpt9b2zlibe9EwTn03HX09D+3YKtQuaWx8az52nGbJn1G3OLJDHhrClYnrRSOevxzFn/6KL/f0RSDhSVeEc2I6DaUtLjNFeb7N+3M9eNiKMxKZ+fcn1nw3t1c+8Fs7N3O/A05ERGRS5WKwyJSo4wYAc5rIxJry4pt0A0GKDMaqz3/QtpK+Lg5ApCamYufu6Pp+NGsvEqriU9lYWEg3M8NgKYhPuxOSueTf9fQpVEQm+JTOJKVR8+XfjONLy0zsmJXEhPmbeLQDw9jaVF+38Ulpdz12UwOHsli8vNDtWpYREREaocpvzKc5vjpWVhV3FDYYDBgNJZV+9IX0lbC3r18xXBeRioOHn6m4/nHjlarOLt58udsmjiO/q9PqtQKwsU/jEHvTKO4IJfivGwcPPxY8P7dOPsGVxhnbedYvpo4IByfyDb8dW87ds37lRbDHzvr9UVERC5FKg6LSI0K9XHD2tKC9fuSCfR0BiA7r5C45GN0igys1WtfSFuJEG8XfFwdiN16kGah5Q8eRSWlLN+ZyMsjupxldmWFxaUAdG0czJJ3RlV47+Fv5lEvwJ1HBrWpVBiOSznGlBeG4uFsf173ISIiInI2zn5hWFhZc2TPepy8y/OzorxsMg/H4dekU61e+0LaSjj7hmDv7kPSxkV4RZTvB1FaXETytuW0ve3lM87d/M94Nvz1Ef1f/Qvvei1OO87azhFrO0cKc46RtCGGdre9cuagjEZKi4vO9VZEREQuGSoOi0iNcra3YUTXRrz6+xLcnOzwdnHgvUkrylcSn8dq4nNxIW0lDAYD9/Vrybhpq4nwdSPcz42Pp63B3sba1AcZ4IGv5uDv7shL/xWMx01bTYswX0J93SguKWXexv38uXQHY2/vAZR/Hg2DvCpcy8HWCg8nO9PxktIy7vh0Bpv3p/Lbk9dSWmYk5b8eyO5OdthYWZ73fYmIiIicysbBiXo9RrD6+9ewdXLH3s2Ldb+9h8FgcV7f/joXF9JWwmAw0GTwvWyaOA7XgHBcAsLZ9Pc4rGztieg21DQu9uMHcfTwo+1tLwHlrSTW/fou0U99hZNvEHkZKcCJthgAiesXYjQacQusS+bheFb/8CqugXWp36t8z4jiglw2/vUxIe36Ye/hS2F2Ottnfk9u2mHCuww+73sSERExNxWHRaTGvXFzN576fgE3fzgVJzsbHh7UhqT0bOysL+0i58OD2pBfVMLTPywkM6+QVhF+THz2epztbUxjEo9mYXHSM1NeYQlP/xDD4fRs7GysqBfgwZf39+X6Dg2qfd1D6dnMXh8HQNSYXyu8N+WFoXRpFHRhNyYiIiJyivZ3vsGyL59i7hs3Y+3gRLMhD5N79BCW1pd2W6tmQx6mpLCAZV89Q1FOJt71W9Hvtb+xcTixSCDnSGKFIveOWd9TVlLEgnfvrHCuljc+TeuRzwBQlJfFmp/eIvfoIWyd3QjrOIg2t4wxtdEwWFhyLHEvexbeQUFWOnYu7njVbcmgd//FPTgSERGRy5XBaDyHhp4ictH06tkDl7wkvn1ogLlDuWC5BcU0feRbXh/ZjVFRTcwdjlwE94yfSZZDIPMXLDR3KCIiIrWmR89eHCh2ocfT35g7lAtWXJDL73c0o/0dr9Ggz6izT5DL3sKxowmxzmLhgvnmDkVERMxIK4dFpMZt3p/KnkPptIrwIyuviA+mlG860r/12XfAFhEREZHad3TfZjIT9+JdvyVFeVls+ONDAEI69DdzZCIiInIxqTgsIrXi85nr2Xc4A2srC5qH+TL9pRvw1CZrIiIiIpeMzVM+JzNpL5ZWNnhGNGfQO/9i5+Jp7rBERETkIlJxWERqXLNQHxa+OdLcYYiIiIjIaXhFNOP6jxeYOwwRERExMwtzByAiIiIiIiIiIiIiF5+KwyJyRXro6znc8vE0c4chIiIiIqexaNxDzHvrVnOHISIiclVTWwkRETOYvmYvH09bTXzKMUpKywj3deOBAa25oUtD05hx01Yzfc0+9hxOx97Girb1/Hl5RBfqBXiYMXIRERGRq8POOT+zJ+ZPMg7sBMCrbnPa3DIGn/qtTGPKSktY//v77I2dRP6xVBzcfanX80Za3vAEBgutxRIRkUufisMiImbg5mjLE4PbUS/AA2srC+ZuiOfhb+bi5WJPj2ahACzfkcRdvZvRMtyPktIy3vp7OcPfm8yy927F0c7avDcgIiIicoU7vHUZEd2G4BvZFksbOzZP+ozZrwxn6PilOHr6A7Bp0qfsmPUj3R/7DPfgSI7u3cjiTx/BxsGZJoPvNfMdiIiInJ2KwyJyQaat3sPYf1YSn3IMe1trmoZ48/Pjg3G0s2b9vmTe+ns5W/anUlxaRpMQb968uTvNw3xM871GjeODO3owZ0M8S7cnUMfLmU/v6Y2nswOPTZjHxvgUGgV58eX9/QjzdQPgvUkrmLVuH7f3bMZHU1eTkVNA7xZhfHxXT1wd7aqM02g08tmMdfy4YDMpx3KJ8HfnyevaM7hdPQCO5Rbw7I8xxG45SG5BEf4ezjw+uC0juzeulc+tS6OgCq/v7deSP5ZsZ9WuQ6bi8F/PXl9hzGejexP5wDds2p9Cp8g6tRKXiIiIXHnil01j/R8fkHU4HitbezzDm9J7zE9Y2zlyZM8G1vz0FmlxWygrLcYzrAkd7n4Dr4jmpvkTBnvT+YEPOLh6Doc2L8XJpw7dHvkEOxdPlox/nKN7NuIR2oioJ77AxT8MgHW/vc+BVTNp2P8ONv71EQVZGQS36UWXhz7G1sm1yjiNRiOb/xnPztk/kpeRgmtABC1HPEFY58EAFOYcY/nXz5G0IZbiglwcPf1pMfwx6veqnY2Qo5/8qsLrLg99TPzyfzm0aTH1eowAIHXnWkLa9yO4bR8AnH2D2bf4H47u3VQrMYmIiNQ0FYdF5LwlZ+Qy+vNZvHJjFwa2iSCnoJiVO5MwYgQgp6CYEV0a8s4tUQB8PnMdN30whVUf3I6zvY3pPB9OWc0bN3fjjZu78fofS7n3i9mEeLvy2OC21PF04ZFv5/LcjzH8+cyJYml8SiZTV+3h1ycHk51fxKPfzueZH2P4+oH+Vcb69t/Lmb52L2Pv6EG4nzsrdiZy/5ez8XS2p3PDOrwzcQW7k9L54+nr8HS2Iz4lk/yiktPe+8dTVzNu2pozfj5/PH0dHSMDz/o5Go1GlmxLYF9yBh0ju5x2XFZeEQDupymAi4iIiJwqLz2ZhR/cS7vbXyG0wwCK83NI3r4SjOX5WnFeDvV6jKDj6LcB2DLlC+a8dhPDv1qNjYOT6Twb/vyQDne9Toe73mD1j68T88F9uPiF0GLYozh612HJp4+y/Ovn6Pfqn6Y5WYfjiV86lT4v/kJRXjZLPnuM5V8/W6noetzaX95m/4oZdL7/fVwCwknetoLYjx7AztUT/yadWffLOxw7uIu+r/yBnYsHWYfjKSkqOO29b/zrYzZOHHfGz6ffK3/g17hjtT7LksJ8ykpLsHV2Nx3za9SeHbN/JDNpH66BEaTFbyV5+2o63v1mtc4pIiJibioOi8h5SzmWS0lpGYPa1iXIywWARkFepve7Na64Ovaju3oSMforlu9MpG/LcNPxkd0acV2H+gA8ck0b+r36J09e1960gnZ035Y88s3cCucqKC7h83v7EODpDMC7t0Zx0wdTeX1kN3zdHCuMzS0o5stZ65n8wlDa1gsAINTHlZW7DvHjwi10bliHxKPZNA3xpmW4LwDB3lWvaDnu9p7NuLZ9/TOO8fdwOuP7WXmFNH14AoUlpVhaGHj/9h5ENQ2pcqzRaOSlXxfToX4ADU/6jEVERETOJC8jBWNpCaEdB+LsU56beYQ2Mr0f0LxrhfFdHviQn0fWJXnbctNqWID6PW8ivMt1ADQf8jDTnulPyxFPUKdVDwAaXzOaxZ8+UuFcpUWFdH9sPI5e5flXx9HvMPeNkbS/8zUc3H0rjC0uyGXr1K8Y8OY/+Ea2BcDFL5Tk7avYOfsn/Jt0JudoEp7hTfGu1wIoX6V7JpH9byesy7VnHHO8PUR1rPnpdRw9/Aho3s10rNnQRyjKzeLvBzpisLDEWFZKm1EvENF9SLXPKyIiYk4qDovIeWsS4kW3xkF0fe4XejQLIappMIPb1cPtv5WtRzLzeHfSCpZsT+BIZh5lZUbyiopJOppd4TyNgk8UO71dHABoWMfzxDFXBwqKS8nOK8TZwRaAOp7OpsIwQNt6/pQZjew9nFGpOLwrKY2C4lKGvTu5wvGiklKahnoDcEevptzxyQw2708lqmkIA1pH0K5+wGnv3d3JDnenC1vB62RnQ8xbN5NbWMTibQm89OsiQrxdKrWcAHj2xxi2Jxxhxks3XNA1RURE5OriEdqEgObd+OfhbtRpGU1gy2jCOl+DrZMbAPnHjrDut/c4tHkJ+ceOYCwrpaQwn5wjiaec50RB2d7Nu8pjpUUFFOVlY+NQnqM5edcxFYYBfCPbYiwrIzNpb6Xi8LGE3ZQWFTDr5WEVjpeVFOMZ3hSAhv1vZ/47d3I0bjOBLaIJ7dAf34btTnvvds7u2J20yvdCbJr0GXGLJzPgrSlY2ZzIAeOWTGHvoolEP/k17sENSIvfysoJL+Lg4Uf9njfWyLVFRERqk4rDInLeLC0smPTcEFbvOUzMlgNMmLuJt/9ezpxXbyTEx5WHv5nL0ax83hrVnSAvF2ysLOn/2p8UlZZWOI+15YmdnA2G/45ZnXTsv3+W/ff1x6ocH3N8/smM/8377alr8XevuJrX1toSgF7Nw9gw7k7mbYxn0dYEhrwziTt7N+f1kd0qnQ9qpq2EhYWBcD83AJqG+LA7KZ1P/l1TqTj83I8xzF4fx78vDq9QEBcRERE5GwtLS/q/PpGUHatJ2hjLtukTWPvL21w7djbOfiEs+uRhCjLT6Hj3mzj5BGFhZcO/zwygrLio4nmsTtoM97+Ey8LSqtIxY1nZ6YMxJWqVE7bj8/q+/BsOHhVX81paly8OCGrdixu/W0/C2nkkbVzMzJeG0mjAnbS/87UqL1dTbSU2T/6cTRPH0f/1SXiGVdyPYvUPr9J86CNEdCtvf+YR2oic1AQ2TfxExWEREbksqDgsIhfEYDDQvn4A7esH8PT17Wnx6P+YsXYfDwxoxYpdSYy9vQe9W5RvTJKUlk1adn6NXDcxLZvDGTmmYu+avYexMBiI8Ku8OqR+oCe21pYkpWXTueHpN3LzcnHgpm6NualbY35YEMCrvy89bXG4JtpKVKWw+ETh3Gg08txPscxYu5epY4YR4nPmVhciIiIiVTEYDPg1ao9fo/a0HPEUf9zdkv0rZ9L0uvtJ2b6STve9T1Cb3gDkHEmiICutRq6bcySR3LRkHD39AEjduQaDhQWugRGVxroFNcDS2pacI0n4N+l82nPau3pRv+dN1O95Eztmd2D196+etjhcE20lNv8zng1/fUT/V/8ytbM4WUlhPhgsKhwzWFhiNJ6hSC4iInIJUXFYRM7bur2HWbwtgaimIXi72LNuXzJp2fnUDywv0Ib7uvHX0h20CPMlO7+QV35fir1NzfyxY2dtxUNfz+W1m7qSnV/E8z8t4tr29Sq1lABwtrfhwQGtefGXRZSVGWnfIIDs/CLW7DmMo601N3ZrxDsTV9A8zIfIQE+KSkqZuzHedB9VudC2EuOmraZFmC+hvm4Ul5Qyb+N+/ly6g7G39zCNeeaHGCat2MnPjw/Gyc6GlGO5ALg42NbY5ygiIiJXttRd6zi0aTGBLaOxd/Middc6CjLTcAuqB4CLXxh7Y/7Gq24LivOyWf39q1ja2NfItS1tbFk07iHa3/kqxXnZrPj2BcI6X1uppQSAjYMTTa9/gJUTXsJYVoZfo/YU5WWTunMNVnaO1O95I+t+fReviOa4BTegrKSIg2vm4hZ0+h/WX2hbiU2TPmPdr+8S/dRXOPkGkZeRAoC1nSPW9uWLAILb9mHj3x/j5B2Ie3AkaXFb2Dr1K+r3Gnne1xUREbmYVF0QkfPmbG/Lip1JfD1nA9n5RdTxdOH1kV3p1bx8pfAn9/Tmie8WEP3irwR6OvPiDZ155bfFNXLtMF9XBraJ4MYPpnAsp4BeLcIYe0eP045/flhHvFzsGffvGg58l4mroy3NQnx47NryDU9srCx4889lJBzNws7Gig4NAvj2wQE1EmtV8gpLePqHGA6nZ2NnY0W9AA++vL8v13doYBrz/YLNAFz71sQKcz8b3ZubulX8SqOIiIhIVawdnEnetoKt/35DcV42Tj51aH/nawS17gVA10c+YennTzLlsR44egfS9pYxrPr+1Rq5tot/GKEdBzLntZsozDlGUOuedL7//dOOb33z89i5erFp4icsTTmAjaMrXuFNaT78MaC8tcWan98kOyUBK1s7/Bp1oMdT39RIrFXZMet7ykqKWPDunRWOt7zxaVqPfAaAjqPfZd2v77D8q2fJzzyKg4cfkf1upeWIp2otLhERkZpkMBrP0MRTRMymV88euOQl8e1DtVegvFy9N2kFs9btI/btUeYORU7jnvEzyXIIZP6CheYORUREpNb06NmLA8Uu9Hi69gqUl6t1v73PgVUzGfJJrLlDkdNYOHY0IdZZLFww39yhiIiIGVmcfYiIiIiIiIiIiIiIXGlUHBYRERERERERERG5CqnnsIhcdp4d2pFnh3Y0dxgiIiIichqtRz5j6ssrIiIily6tHBYRERERERERERG5Cqk4LCIiIiIiIiIiInIVUnFYRC66lo99x1ez15s7jDNauj0Br1Hj8Bo1jls+nmbucKp0OcQoIiIil6c/7m7F1qlfmTuMMzq0ZRkTBnszYbA389661dzhVOlyiFFERK5u6jksInIGK8fehreLven1uGmrmb5mH3sOp2NvY0Xbev68PKIL9QI8TGMe+noOfyzZUeE8rSP8mPPajdW+7u+Lt/HwN/OqfG/H56PxdnWgXf0Ato2/hxd+jqWopPTcbkxERETkCjH8y5XYuXqZXq/77X02/DG2whh7N29u/ml7tc+ZnXKQDX9+yKHNS8k/loqDhx91o4bRYvjjWFrbAFCQlU7sh/eRfmA7BVkZ2Lt5EdKuH21ufREbB2cAfCPbMvLHraz4dgxlxUU1cLciIiI1S8VhEZEz8Haxx9XRzvR6+Y4k7urdjJbhfpSUlvHW38sZ/t5klr13K4521qZxPZuF8OnoPqbXNlaW53Td6zo0oEez0ArHHv56LgXFJXi7OpjO6evmiL2NlYrDIiIictWyc/XC1sm1wjH34Ej6vzHR9NpgcW652LHEPRiNZXR58ANc/MPIOLCTJeOfoKQgj/Z3vvbfOS0Ibt+f1qNewM7Vk6zD8Sz/6lkKv3iK6Ke+BsDS2gYHd1+sbOwoUnFYREQuQWorISLV9sOCzTR5+FvKyowVjt/84TQe/GoOAPEpxxj10TQaPvANIXd9Tq+XfmfR1oOnPefBI5l4jRrHlgOppmOZuQV4jRrH0u0JpmO7ktK4cewUQu76nIYPfMP9X84mLTu/hu/w7P569npu6taYyDqeNAnx5rPRvUlMy2bT/pQK42ysywu3x3+5O9md5oxVs7exqjDf0sLAku0JjIpqUpO3IyIiIleYHbN/5Lfbm2IsK6twfO6bo1j08YMAZB2OZ+6bt/DLLY344YYQpjzRm6SNi057zuyUg0wY7E1a3BbTscKcTCYM9ubQlmWmYxkHdzH7tRv54YYQfrmlEbEfPUBBVloN32H1GCwtcXD3Nf2yP2llcXUEte5J90c/o07LaFz8Qglp349m1z/A/hUzTGNsndxoNOAOvOu1wNkniMDm3Wg44A6St6+s6dsRERGpNVo5LCLVdm37+rzw8yKWbk+gW5NgAI7lFhCz5QC/PjkYgNyCYno1D+WF4Z2wtbbkzyU7uPnDqawcext1vFzO67rJGbkMfnMit0Q14Y2bu5FfVMLrfyzlrs9mMOWFYVXOSTyaRednfz7jeYd1juTDO3ueV0zHZeWVrwBxd6xY/F22I5HIB77GxcGWTpF1GDO8k2nF7/n4c+kO7G2tuKZdvQuKV0RERK5sYZ0Hs+KbFzi0ZSmBzbsBUJhzjMT1MfR56RcAigtyCWrTizajnsfSxpY9C/9k7pujGP7lCpy865zXdfPSk5nxwrU06DOKDne9QUlhPmt+fIMF793NwLcmVzkn50giEx/sfMbz1o0aTpcHPjjneLIOxfPb7U2wsLLFp0Er2twyBhe/0HM+z8mK8rKwdXY77fu5acnsXzEDv8adLug6IiIiF5OKwyJSbe5OdvRoFsKkFbtMxeGpq/bg5mhLt8ZBADQJ8aZJiLdpzgvDOzFj7V5mr4/j7j4tzuu63y/YTLNQb14cceLh4dN7etPs0e/YeziDuv7uleb4uTsR89bNZzyvs73NecVznNFo5KVfF9OhfgANg06sRunZPJTB7eoT5OXMgSNZvDtxOde/M4kFb9yErfX5/bH726JtDO0Yib2N/tgWERGR07NzdqdOqx7sWzTJVByOXzoNW2c3ApqVv/YMa4Jn2IlvI7UZ9QL7V8zkwKrZNB5093ldd8esH/CMaErbW180Hev2yCf8fmdzMpP24RoYUWmOg4cf14+LOeN5j/fuPRc+DVrR/fHxuAZEkH/sCBv++oh/nxnA0PFLsXPxOPsJqpB1OJ5t0yeYWkqcbOHY0RxYNZvSonyC2/Wl68Mfn9c1REREzEFVBhE5J8M6RfLE/+bz/u3R2FpbMXH5Tq7v2ABLi/IuNbkFxYydvJK5G+JJPpZDaamR/KISEtOyz/uam+JTWLo9kZC7Pq/03v7UY1UWh60sLQj3czvva1bHsz/GsD3hCDNeuqHC8es7NDD9vmGQFy3CfGn52HfM27ifQW3rnvN11uw5xK6kdD6/r+8FxywiIiJXvrpRQ1n6+ZN0vv99LK1t2btoIhFdr8fCsrzvbnFBLut//4CEtXPJS0+mrLSE0qICco4knfc1j+7bxOEty/jhhpBK72Ulx1dZHLawtMI1IPy8r3k6Qa17VXjtE9mGv0a3Zc/CP2l63f3nfL7ctGRmvzqCsM6DiexzS6X3O9z9Bq1ueprMxL2s+fktVn33Mp3vf/+84xcREbmYVBwWkXPSt1U4ZRNg3sb9tAz3ZeWuJN64uZvp/Vd/X0LMlgO8dlNXwvzcsLO24s5Pp1N8mg3TLAwGAIwntTEuLq3YI89oNNK3VTgvj+hSab6vm2OV563tthLP/RjD7PVx/PvicAI8z7yixc/dkTpeLsQlZ5zXtX6J3UbTEG9ahPme13wRERG5ugS37Yux7HEOrpmHd72WJG9fSYe73jC9v/r710jcEEP7O17FxT8MSxs7Frx3J2UlVW+YZvhvEYDxpIStrLS4whhjWRnBbfvQ9raXK8138Kg6h6nNthIns7ZzxD2kEZmH4s55bm5aMjNfvA6fyLZ0ffCjKscc72vsVqceti7uTH/uGlqOeAIHD78LiltERORiUHFYRM6JvY0Vg9pGMHH5TuJSjhHh516haLlyVxI3dm3EwP9WyOYUFHHwaBanS/s9Xcr78KYcyzUd23rgSIUxzUJ9+HfNXoK9XbCyrN4+mrXVVsJoNPLcT7HMWLuXqWOGEeLjetY56dn5HErPPm0h+0xyCoqYsmo3L91w5gcnERERkeOsbO0J7TiQfYsmknU4HteACLzqNje9n7x9JfV73Ehox4EAFOfnkJOacLrTYefiCUB+xokNeNPitlYY4xnRjP3Lp+PsG4yFZfUeM2urrcSpSosLOZa4G7/GHc5pXm7aYWaMuQ6viOZ0e+RTU5H8jP4roJcWV11oFxERudSoOCwi52xYp0hu/mgaOxPTGN45ssJ7Yb5uzFi7l76twjBg4J2JKzhls+wK7G2saFPXj0/+XUuwtwtp2fm8PXF5hTF39m7Oz7FbGf35LB4a2BoPZ3viU44xecUuPr67l6mlxclqq63EMz/EMGnFTn5+fDBOdjamoraLgy32NlbkFBTx/j8ruaZtXXzdHDl4JIu3/l6Oh5M9A9qce0uJKSt3U1paxrBTPmcRERGRM4noPox5b44i4+Au6kZV3MDXxT+M/SumE9yuDxgMrPv1XYxnSNisbO3xadCGTRM/xcknmIKsNNb9+k6FMY0G3MWuub8QM3Y0TYc8hJ2LB1mH44lbPJkuD31samlxstpqK7Hqf68Q3K4PTl51yM88yoa/PqI4L5t6PUZU+xy5aeUb7Dl516H9na9RkHXU9J6De/nCiIS188g/dgSvei2xtnPkWMIuVv/wOr4N2+HsG1zj9yUiIlIbVBwWkXPWtXEQbo527D2cwdBOFYuWb47qxiPfzmPAa3/h4WzPI4PakJ1feMbzfXpPHx75di69Xvqduv7uvHJjF4a9d2JXa393J2a8fAOv/7GU4e9NpqiklDpezvRoFmpqS3GxfL9gMwDXvjWxwvHPRvfmpm6NsbSwYEfCUf5auoPM3EJ83Rzp0qgOEx4aUGGl8kNfz+HgkSymvTj8jNf7ddE2Brati5ujXc3fjIiIiFyxApp1xdbZjcykvUR0H1rhvQ53vcHiTx9l2jMDsXPxoPnQhynKO/P+EF0f+YQlnz7KlCd64xYYQdvbX2H2KyfyGEdPP655bzqrf3iD2a/cQGlxEU4+dQhq1aN6K25rUG7aIWI+uJeCrHTsXDzxadCawWNn4+wTZBqz7rf32bPwD26csL7KcyRtjCHrcDxZh+P5/Y5mFd67e1r5t9wsbezZOfcXjn33EqXFRTh6BRDacSDNhz5aezcnIiJSwwzGkxtHicglo1fPHrjkJfHtQwPMHcpVaen2BK57exL7vr4P11oozA5+8286N6zDs0M7XvC5Hvp6Dpl5hfz8+OAaiKxm3DN+JlkOgcxfsNDcoYiIiNSaHj17caDYhR5Pf2PuUK5Kh7YsY+aY67jlt73YOp291dfJFo17CIDuj42vjdCqvF5Rbha9x/x0Ua5XHQvHjibEOouFC+abOxQRETGji/sjXBGRy0yzR77jnvEza/Sc2flF7E/N5MGBrS/oPCt2JhFy1+dMXL6rhiITERERufz8fmczFo4dfU5zDm9dQeubn6+liE5I3raCH24IYe+iSbV+LRERkfOhthIiIlVoXdef1R/cDoCjnXWNntvZ3obNn959wedpEe5r2nSvpmMUERERudT51G/F8K9WAWBtf24b/944YV1thFSJV90Wpk33zjVGERGRi0HFYRGRKtjbWNXKhnY16XKIUURERKS2WNna18qGdjXpcohRRESubmorISIiIiIiIiIiInIVUnFYRC7YwSOZeI0ax5YDqeYO5ZL3++JthI/+4qzjvEaNY+bavRchIhEREZHKslMOMmGwN2lxW8wdioiIiNQiFYdF5Ko3+M2/GfNzbKXj1S3kXoj3Jq0g6oVfavUaIiIiIleT4vwc/hzdlpXfvVTheHbKQX4cEcbOuT8DcGjLMiYM9q70a+0vb5vmGI1Gds75mWlP9+fHEWH8cEMIEx/swopvXyDzUNxFvS8REZHaoJ7DIiIiIiIicsWwtnei2yOfMPOloYR2GIBf444YjUYWf/oIvg3bEdnnlgrjh3+5EmsHpxPz7co3jjMajcR8cC8HVs2ixbDH6HDXG9i5eZGdcpCkjbFs/Osjuj82/qLem4iISE1TcVhEqqWszMj4GWv5OWYrSek5eLs6cFuPpjxxbbtKY0vLynjiuwUs2Z5A6rFcAj1duLNXM+7t19I0Zun2BF77Yym7ktKwsrQgMtCTrx/sT5CXC1sPHGHML4vYGJ+CAQPhfm58eGdPWob7XsxbrtLs9XG8/89KdiWl4efmyIiujXji2nZYWZZ/EeOLmev5ffE2DhzJxM3Rjr4tw3nlpi442dlUOtfvi7cxdnL5Dtteo8YB8Nno3tzUrTEAadkF3Prxv8RsOYCfuxOvj+xK/9YRGI1G2j35A7f1bMZDA1ubzrcj4SjdXviF1R/cTpivW+1+ECIiInLZM5aVsXnyeHbN+Zmco4ewd/Mmst+ttLzhiUpjy0pLWfr5ExzavJT8Y6k4eQfSsP8dNBl8r2nMoS3LWPPDa2Qc3IWFlRVuQQ2IfuprnH2CSIvfysoJL3J070bAgEtAOF0e+BDvei1q5d78m3Si8aC7WfzJI1z/aSy75vxMWtxWho5fUmmsnasXtk6ulY7HLZlC3JLJ9B7zMyHt+5mOu/iFEti8G0ajsVZiFxERuZhUHBaRannjr6X8HLOVN2/uTvsGAaQcy2XPoYwqx5aVGfH3cGLCQwPwdLZn9Z7DPPm/+fi6OXJdh/qUlJZx67h/uSWqKd882J/ikjLW70vG8N/8+76cTdMQbz64owcWFga2HjiCteXpu+CMeH8yK3cdOmP8B7578Hxv3WTh5v3c/+Vs3rklig6RgexPOcYT/1sAwDNDOgBgYQFv3xpFsJcLB45k8cwPC3nt96WMvaNHpfNd16EBOxLTWLj5AJOeGwKAi4Ot6f2xk1fyyo1dePWmrkyYu5H7vpzNxnF34e5kx8jujfl98bYKxeFfF22jQ4NAFYZFRESkWtb89Ca75v5M+7vewK9Re/IyUjiWuKfqwcYyHL0C6PnsBOycPUjZuYalnz+Jg4cv4V2uo6y0hPlv30qDPrcQ/dTXlJYUc2T3egyG8gwv9sP78QxvSuf7x2KwsCAtbisWVqd/HJ396giSt688Y/y3/3XgjO+3uWUMCesWEPvRAySuj6HLgx/i6Ol/5g/lJPsW/4NrYN0KheGTHb83ERGRy5mKwyJyVtn5RXwzZyPv3hrNjd0aARDm60aHBoFVjre2suS5oR1Nr0N8XFmz5xBTV+3mug71yc4vIiuviD4tw0yFzPqBHqbxiUezeWhAa+oFlB+L8HM/Y3zj7u5NflHJhdwi/5u/mV9it1U4VlJWhq21pen1R1PX8Og1bUyfQaiPK88N7chrfyw1FYfv69fKND7Ex5Xnh3Xk6e8XVlkctrexwtHWGisLA75ujpXev6lrI4Z2igRgzA2d+XbeRtbvS6Zn81Bu6taYdyetYP2+ZFpF+FFcUsrE5Tt59cauF/Q5iIiIyNWhKC+Hbf9+Q8d736F+zxsBcPEPw69RhyrHW1hZ03rks6bXzn4hpOxcTdzSqYR3uY6ivGyKcrMIbtsbF/8wANyD6pvG5xxJpOn1D+JWpx4ArgERZ4yv68PjKCnMv6B7tLKxo+PdbzL71RHUad2TetHDqxz3+53NKry+ccIG7Fw8yDy0D9fAuhXeW/HtGHbNK98vwsbRlZHfb76gGEVERMxNxWEROas9h9IpLC6lW+Ogas/5fsFmfondSuLRbAqKSigqKaVJiDcA7k523NS1ETe8P5nuTYLp3jiYa9vXx8+9vEB6f/+WPPbdfP5atoPujYMZ3L7eGVfD+ns4nfa96hrWKZLHT2mRMX3tXsZNW216vXl/Chvjkvl46hrTsdKyMgqKS8krLMbB1pol2xMYN20Nu5LSyM4vorS0/P3cgmIc7azPKaZGwV6m3zvaWeNkZ8PRrDwA/Nwd6d0ijF8XbaNVhB9zN8RTUFTK4Pb1zuf2RURE5CpzLHE3pcWFBDbrVu05O2b9wK55v5CTmkhJUT5lJcV4hjUBwM7ZnXo9b2T2KyMIaNGdwObdCO9yLQ4efgA0ufZ+lox/nL2xfxPYvBthnQebishVOZcVvmeya96vWNk6kHFgB0W5Wdg4ulQaM+idf7G2P5FP2jq5mX5/6urgFjc8TqOBd7F/xQw2TRxXIzGKiIiYk4rDInJWdjbn9kfFlJW7eemXRbw+shtt6vnjZGfD+BlrWb8v2TTms3v7cE/fFizcvJ8pq3bz9sTlTHpuCG3q+vPs0I4M7RTJvI3xLNi0n/f+Wcm3D/ZnYNu6VV6vJtpKuDjYEO7nVuGYt4t9hddlZUaeGdqRQW0qx2FnbUXC0SxuGjuF23o24/lhHXFztGPV7kM8+u08SkpLgXMrDp/aSsNggLKTetuNimrCA1/N5s1R3flt8Xau61AfB9tzu4aIiIhcnaxs7M5pfNzSKaz87iXa3/kaPg3aYG3vxJbJn5O6e51pTPdHP6PxoHtIXL+QuKVTWffrO/R/bSI+kW1oPfIZ6nYfysG180hct4B1v71Pj6e/IbTjwCqvVxNtJfYtmczB1XO45v2ZLPr4QVZOeJFuj35aaZyzb0iVPYddA8Irtdmwd/Uq/+XmVWm8iIjI5UjFYRE5q3BfN+xtrFi8LYFbfConzqdauSuJtvUCuLN3c9Ox/amZlcY1C/WhWagPjw1uR79X/2DS8l20qVu+SqSuvzt1/d25v38r7hk/k98Wbz9tcbgm2kpUR7NQH/YdzqhURD5uY1wKJWVG3hjZDQuL8lUmU1ftPuM5bawsKS07v81MercIxcHWmu/nb2bB5v38++Kw8zqPiIiIXH1cAsKxtLEnafNiIv1uOev45G0r8Y1sS6MBd5qOZSXvrzTOK6IZXhHNaDH8MaY93Z+9iyfhE9kGANfACJoGRtD02vtYOHY0uxf8ftri8IW2lcjLSGX5V8/RetTzeEU0o9ujnzHtmf6EdRlMUOte1TpHRNchxHx4LwdWziKkQ//zjkVERORSpuKwiJyVnY0VDw9qw2t/LMHGyoJ29QNIy85nZ2Iao6KaVBof5uvGn0t3sHDzfoK9Xfl76Q42xKUQ4l3+Nb4DqZn8FLOFfq3C8XN3Yu/hDPYlH+OGLg3JLyrh1d+XcE3buoT4uHIoPYeNcSkMald1YRhqpq1EdTx1fXtGfjiNAA8nrm1fHwuDgW0JR9iRkMYLwzsR6utKSWkZ387dSN9W4azefYgfFmw54zmDvF04eCSLLQdSCfBwxsnOGlvr6v3RbGlhwU1dG/HmX8sI83Wlbb2AmrhNERERuQpY2djRfOjDrPnhdSytbPBt2I6CzDQyDu6kQZ9Rlca7+IexJ+YvEtcvxNk3hD0xf3FkzwacfYMByE4+wM45PxHcvh8OHn5kJu0l89A+6kbfQElhPqu/f5XQzoNx9g0m9+ghju7ZQGina04b34W2lVj2xVPlxehr7wfAu14Lmg15iKWfP8nQz5ZU2V7iVOHdrmf/iuks/GA0zYc9Sp2W0di7eZNzJIG4JVMwWFie9RwiIiKXOhWHRaRanrquPVaWFrw7aQXJGbn4ujlye8+mVY69vWdTth44wt3jZ2LAwJCODbizVzMWbNoPgL2tFXsOZfDHkhlk5BTg6+bAXb2bc3uPZpSUlZGenc+DX8/lSGYeHs52DGpTl2eHdKzyWhdTj2ah/PbkYD6YvIrxM9ZhZWlBvQB3U4G8aYgPb9zcjU+nr+XNv5bRsUEgL43ozANfzTntOa9pW5cZa/Zy3VuTyMwr5LPRvbmpW+Nqx3RzVBM+nraGkecwR0RERASg5YgnMVhasu6398hLT8bB3ZfIfrdVObZh/9tJi9/KwrH3AAYiug2hUf87SFi/AABLW3uOJe1lz7t3UJCVgYOHL40G3kXDfrdRVlpCQXYGiz5+kPxjR7Bz8SC040BajXymVu5rz8I/Sdq4iOs/WYjB4kSbrlY3Ps3B1XNO217iVAaDgR7PTGDn3J/ZPf93Nv/zGWUlJTh6BRDQrCvt73qjVuIXERG5mAxGo/H8vs8sIrWqV88euOQl8e1DA8wdilzCVu0+xLVvTWTzp3fh4+po7nBM7hk/kyyHQOYvWGjuUERERGpNj569OFDsQo+nvzF3KCLnbOHY0YRYZ7FwwXxzhyIiImaklcMiIpehwuISktJyeGficq5tX++SKgyLiIiIiIiIyOXB4uxDRMQcLCzOf6MyufL9s2IXHZ7+kay8Il69sau5w6mktMyIhfrwiYjIFc7SwgJjae1viitSG4ylJVhaqCQgInK108phkUuUr58fm/dtNXcYcom6qVvjc+pNfLHFp2bRosGFbSQjIiJyqfPz82Xzik3mDkPkvOSk7Me/UwtzhyEiImamHxOKXKKuueYaNscnszE+xdyhiJyTjfEpbNmfwjXXnH4HchERkSvBNddcQ+q+LRzdqwKxXF6O7t3EkX1blK+JiIg2pBO5VOXl5dGlcycOxu3lkUGt6N44GCd7GwzmDkykCkYgJ7+IRdsO8un09QSH12XpsuU4ODiYOzQREZFak5eXR6fOXdgTf5AmQx4hsEU3rO2dMChjk0uQESPF+TkkbVzM1n8+pV5YMMuXLVW+JiJylVNxWOQSlp6ezr2jR/Pv9H8pLCwydzgiZ2Vra8M1g67h62++wcPDw9zhiIiI1Lr09HRGj76Xf6f/S1FhobnDETkrG1tbrhl0Dd9887XyNRERUXFY5HKQnZ3N1q1bycvLM3coleTn5/Piiy+ya9cu3n77bZo1a2bukK5ImzZt4oUXXqBRo0a88cYb2NnZmTukShwcHGjSpAnOzs7mDkVEROSiU74mytdERORypOKwiJy33NxcBg0axJo1a5g1axZdu3Y1d0hXtMWLFzNgwADatWvHv//+i6Ojo7lDEhERkUuc8rWLS/maiIhcblQcFpHzkpuby8CBA1m3bh2zZs2iS5cu5g7pqrB06VL69+9PmzZtmD59uh44RERE5LSUr5mH8jUREbmcqDgsIucsJyeHgQMHsn79embPnk3nzp3NHdJVZdmyZfTr14/WrVszY8YMPXCIiIhIJcrXzEv5moiIXC5UHBaRc5KTk8OAAQPYuHEjs2fPplOnTuYO6aq0fPly+vXrR8uWLZkxYwZOTk7mDklEREQuEcrXLg3K10RE5HKg4rCIVFt2djYDBgxg06ZNzJkzh44dO5o7pKvaihUr6Nu3Ly1atGDmzJl64BARERHla5cY5WsiInKpU3FYRKolOzub/v37s2XLFubMmUOHDh3MHZIAK1eupG/fvjRr1oyZM2dq52kREZGrmPK1S5PyNRERuZSpOCwiZ5WVlUX//v3Ztm0bc+fOpV27duYOSU6yatUq+vTpQ5MmTZg1axYuLi7mDklEREQuMuVrlzblayIicqlScVhEzigzM5N+/fqxY8cO5s2bR9u2bc0dklRh9erV9OnTh0aNGjF79mw9cIiIiFxFlK9dHpSviYjIpUjFYRE5rczMTPr27cuuXbuYN28ebdq0MXdIcgZr166ld+/eREZGMnv2bFxdXc0dkoiIiNQy5WuXF+VrIiJyqVFxWESqdOzYMfr27cvu3buZP38+rVu3NndIUg3r1q2jV69eNGjQgDlz5uiBQ0RE5AqmfO3ypHxNREQuJSoOi0glx44do0+fPuzdu5f58+fTqlUrc4ck52D9+vX06tWLevXqMWfOHNzc3MwdkoiIiNQw5WuXN+VrIiJyqVBxWEQqyMjIoE+fPsTFxTF//nxatmxp7pDkPBx/4Khbty5z587VA4eIiMgVRPnalUH5moiIXApUHBYRk4yMDHr37k18fDwLFiygRYsW5g5JLsCGDRvo1asX4eHhzJ07F3d3d3OHJCIiIhdI+dqVRfmaiIiYm4rDIgJAeno6vXv35sCBAyxYsIDmzZubOySpAZs2baJnz56EhoYyb948PXCIiIhcxpSvXZmUr4mIiDmpOCwipKen06tXLw4ePKgHjSvQ8QeOkJAQ5s2bh4eHh7lDEhERkXOkfO3KpnxNRETMRcVhkatcWloavXr1IjExkYULF9K0aVNzhyS1YPPmzfTs2ZM6deowf/58PD09zR2SiIiIVJPytauD8jURETEHFYdFrmJHjx6lV69eHDp0iIULF9KkSRNzhyS1aOvWrfTo0YPAwEA9cIiIiFwmlK9dXZSviYjIxabisMhV6ujRo/Ts2ZPk5GQWLlxI48aNzR2SXATHHzgCAgKYP38+Xl5e5g5JRERETkP52tVJ+ZqIiFxMKg6LXIWOHDlCz549SUlJISYmhkaNGpk7JLmItm3bRo8ePfD19WXBggV4e3ubOyQRERE5hfK1q5vyNRERuVgszB2AiFxcqamp9OjRg9TUVD1oXKUaN25MTEwMqamp9OzZkyNHjpg7JBERETmJ8jVRviYiIheLVg6LXEWOP2ikpaURExNDZGSkuUMSM9qxYwfR0dF4e3uzYMECfHx8zB2SiIjIVU/5mpxM+ZqIiNQ2FYdFrhIpKSn06NGD9PR0PWiIyc6dO4mOjsbDw4OFCxfi6+tr7pBERESuWsrXpCrK10REpDaprYTIVSA5OZno6GgyMjKIjY3Vg4aYREZGEhsbS0ZGBtHR0SQnJ5s7JBERkauS8jU5HeVrIiJSm7RyWOQKd/jwYXr06EFWVhYxMTHUr1/f3CHJJWj37t1ER0fj4uJCTEwMfn5+5g5JRETkqqF8TapD+ZqIiNQGrRwWuYIdPnyY6OhosrOziY2N1YOGnFb9+vWJjY0lOzub6OhoDh8+bO6QRERErgrK16S6lK+JiEht0MphkSvUoUOHiI6OJi8vj5iYGOrWrWvukOQysHfvXqKjo3FwcCAmJoaAgABzhyQiInLFUr4m50P5moiI1CQVh0WuQElJSURHR1NQUEBMTAwRERHmDkkuI/v27SMqKkoPHCIiIrVI+ZpcCOVrIiJSU9RWQuQKk5iYSFRUFIWFhcTGxupBQ85ZREQEsbGx5OfnExUVRVJSkrlDEhERuaIoX5MLpXxNRERqilYOi1xBEhISiI6Opri4mNjYWMLCwswdklzG4uLiiI6OxsbGhpiYGOrUqWPukERERC57ytekJilfExGRC6WVwyJXiISEBKKioigpKdGDhtSI8PBwYmNjKS4uJioqioSEBHOHJCIicllTviY1TfmaiIhcKBWHRa4ABw8eJCoqirKyMj1oSI0KCwsjNjaWkpISoqKiOHjwoLlDEhERuSwpX5PaonxNREQuhIrDIpe5AwcOEBUVhdFoJDY2ltDQUHOHJFeY0NBQFi1aRFlZGVFRURw4cMDcIYmIiFxWlK9JbVO+JiIi50vFYZHL2P79+4mKigIgNjaWkJAQ8wYkV6yQkBBiY2MB9MAhIiJyDpSvycWifE1ERM6HisMil6njDxoWFhYsWrSI4OBgc4ckV7jjDxwWFhZERUWxf/9+c4ckIiJySVO+Jheb8jURETlXKg6LXIbi4+Pp3r07VlZWLFq0iKCgIHOHJFeJ4OBgYmNjsbS0JCoqivj4eHOHJCIicklSvibmonxNRETOhYrDIpeZuLg4oqKisLGxITY2ljp16pg7JLnKBAUFERsbi5WVFVFRUcTFxZk7JBERkUuK8jUxN+VrIiJSXSoOi1xG9u3bR1RUFLa2tnrQELOqU6cOixYtwtbWVg8cIiIiJ1G+JpcK5WsiIlIdKg6LXCb27t1LVFQU9vb2xMbGEhgYaO6Q5CoXGBhITEwMdnZ2dO/enX379pk7JBEREbNSviaXGuVrIiJyNioOi1wGjj9oODo6EhMTQ0BAgLlDEgHKHzhiY2NxcHCge/fu7N2719whiYiImIXyNblUKV8TEZEzUXFY5BK3Z88eunfvjrOzsx405JIUEBBAbGwsTk5OREVFsWfPHnOHJCIiclEpX5NLnfI1ERE5HRWHRS5hu3fvpnv37ri6uhITE4O/v7+5QxKpkr+/P7GxsTg7OxMVFcXu3bvNHZKIiMhFoXxNLhfK10REpCoqDotconbt2kVUVBTu7u7ExMTg5+dn7pBEzsjPz4+YmBhcXV2Jiopi165d5g5JRESkVilfk8uN8jURETmVisMil6CdO3cSFRWFh4cHCxcuxNfX19whiVTL8QcOd3d3oqOj2blzp7lDEhERqRXK1+RypXxNREROpuKwyCVmx44dREVF4eXlpQcNuSz5+voSExODh4cH0dHR7Nixw9whiYiI1Cjla3K5U74mIiLHqTgscgnZvn070dHR+Pj4sHDhQnx8fMwdksh5Of7fsJeXF9HR0Wzfvt3cIYmIiNQI5WtypVC+JiIioOKwyCVj27ZtREdH4+vry4IFC/D29jZ3SCIX5OSHZj1wiIjIlUD5mlxplK+JiIiKwyKXgK1btxIdHY2/v78eNOSK4u3tzcKFC/Hz8yMqKoqtW7eaOyQREZHzonxNrlTK10RErm4qDouY2ZYtW+jRoweBgYEsWLAALy8vc4ckUqO8vLxYsGABAQEB9OjRQw8cIiJy2VG+Jlc65WsiIlcvFYdFzGjz5s306NGDOnXqMH/+fDw9Pc0dkkitOP7AERgYSHR0NFu2bDF3SCIiItWifE2uFsrXRESuTioOi5jJpk2b6NGjB8HBwXrQkKuCp6cn8+fPJygoiOjoaDZt2mTukERERM5I+ZpcbZSviYhcfVQcFjGDjRs30rNnT0JDQ5k3bx4eHh7mDknkojj+wBESEkLPnj31wCEiIpcs5WtytVK+JiJydVFxWOQi27BhAz179iQsLEwPGnJV8vDwYP78+YSGhtKjRw82btxo7pBEREQqUL4mVzvlayIiVw8Vh0UuovXr19OzZ08iIiKYN28e7u7u5g5JxCzc3d2ZN28e4eHh9OjRg/Xr15s7JBEREUD5mshxytdERK4OKg6LXCTr1q2jV69e1KtXj7lz5+Lm5mbukETM6vgDR926denVq5ceOERExOyUr4lUpHxNROTKp+KwyEWwdu1aevXqRf369fWgIXISNzc35s2bR7169ejZsyfr1q0zd0giInKVUr4mUjXlayIiVzYVh0Vq2Zo1a+jVqxeRkZHMnTsXV1dXc4ckcklxdXVl7ty5NGjQgF69erFmzRoAjEYjTz75JMnJyWaOUERErnTK10TOTPmaiMiVS8VhkVq0evVqevfuTaNGjZgzZw4uLi7mDknkknT8gaNhw4b07t2b1atXU1JSwrfffss333xj7vBEROQKpnxNpHqUr4mIXJkMRqPRaO4gRK5Eq1atok+fPjRt2pRZs2bh7Oxs7pBELnlZWVn079+frVu3MnfuXMaPH8/69evZtm2buUMTEZErkPI1kXOnfE1E5MqilcMitWDlypX06dOHZs2a6UFD5By4uLgwe/ZsmjZtavp/aPv27XrYEBGRGqd8TeT8KF8TEbmyaOWwSA1bsWIFffv2pUWLFsyYMUMPGiLn4OGHH8bJyYkBAwbw/PPPs2nTJgCeeOIJXnvtNTNHJyIiVwrlayLnT/maiMiVRSuHRWrQ8uXL6dOnDy1btmTmzJl60BA5R05OTnzzzTd069aNQ4cO4ebmRl5eHj/88AP6WaaIiNQE5WsiF0b5mojIlUUrh0VqyLJly+jXrx+tW7dmxowZODo6mjskkctScXExsbGxTJw4kUmTJpGWlgbA0qVL6dy5s5mjExGRy5nyNZGaoXxNROTKoeKwSA1YsmQJ/fv3p23btkyfPl0PGiI1pKSkhDlz5vD555/z/fff4+vra+6QRETkMqV8TaR2KF8TEbm8qTgsco7i4+NZuHAhd911FwCLFy9mwIABtG/fnn///RcHBwczRygiIiJydVO+JiIiIlI9Kg6LnKM777yTNWvWsGXLFhYtWsTAgQPp0KED06ZN04OGXNGOHj3Krl27KCgoMHcochWws7MjMjIST09Pc4ciIpch5WtyNTAajWzfvp3U1FTKysrMHY4IFhYW+Pj40KhRIwwGg7nDEZFqUnFY5BwUFRXh6+vLQw89RM+ePRk4cCCdOnVi6tSpetCQK9b+/fsZfc/dLIyJpbS01NzhyFXE0tKSnj2i+fqbbwkNDTV3OCJymVC+JleDr7/+mrfeeZeEA/vNHYpIJUEhobz4wvOMHj3a3KGISDVYmTsAkcvJvHnzOHbsGKGhoQwYMIAuXbowdepU7O3tzR2aSK1ITk4mqltXKMji3eGtaBfhjb2NpbnDkqtAflEpq/cdYfyC1UR168rK1Wvw8/Mzd1gichlQviZXui+//JIHHniAgE7X03bkuzh4B2OwUH4m5mcsKyXvyEGSFv3JvffeS2lpKffff7+5wxKRs9DKYZFzcNtttxEbG0tqairdu3dn7NixTJ8+HR8fH1NPO5Eryfvvv8+rL73I8pcHEOCu1VZy8R3KyKPj6zN4/c23efrpp80djohcBpSvyZWsrKyMgDpBWEV0otn9n+qr+3JJMhqNbP7yEUrjVpCUcBALCwtzhyQiZ6D/Q0WqqbCwkIkTJ5KYmEhQUBCHDh2iWbNmvPnmmyQlJZk7PJFa8e/UqUQ38lNhWMwmwN2B6Ib+/DttqrlDEZHLgPI1udJt2LCBlMOHCIoeqcKwXLIMBgN1om8i+VASGzduNHc4InIWKg6LVNP//vc/8vLyKCsr4/DhwzRp0oRJkyZx5MgRXn75ZXOHJ1IrUlIOE+rpaO4w5CoX5uVI8uFD5g5DRC4DytfkSpeSkgKAg2+oeQMROQtH3zCgvE2diFza1HNYpJrq1atH69atefbZZxk0aJD61slVoaysDEtLrUoR87K0NGgXdhGpFuVrcqU7/vehwVKP8nJpO/7fqHI4kUuf/kYRqaZevXqxdu1ac4chckl45KeVZOYX8eO93SocX7Y7hSGfLGT32KG4OtiYXh/n6WRL82APXry2OY3ruANw/bgFLN+TCoC1pQUB7g4MbhXE0wOaYmtdcXOVQxl5tHvlX0K8HFn28qBKcR3LK2LMX+uYs6X8q8N9mwby9g2tcXWwMY1JTM/luT/XsnR3CvbWllzfJpRXh7TAxurEtbYnHeOFv9ay4UA6bg423NqlLk/0b2z6+uYjP63kz1Xxla7fwM+FxS8NNL3OzCvinX83M2NjApl5RQR7OvHqkJb0ahIAQJuXppGQnlvpPHd0q8e7I9pU9dGbxfeL9/D5/B2kZubTwN+VN4a1okNdn9OOP/Xf+3FLXxpIPT8X0+uvF+7kxyV7ScrIw8PRlkEtgxhzbXPsrLWpjoicH+VrcjXb9NWjlORm0vrJHyocT9u+nFVvDqX3tzuxdnQ1vT7OxtkD1/DmNLhxDC4hjQFY+cYQ0nesAMBgaY29ZwB+Ha6h3tCnsLS2rXD+/LRDxD7WAQefYLp/uLRSXMU5x9j204ukrpsLgE/rPjS+7S2sHV0rjS3KTmfp870oSD9sihcg70gCsY+2qzS+7bO/4t28R6Xj6btWs+qNITgFRdL1nfkV48nNZNdf75KyZibFuZnYewfR8OZX8WnZs3zujhXETf+SzPjNFB5LodXj/8Ovbf9K1zCnC43xTJ9P/KxvODj/J/KPJmHj7IFf+4E0GPECljZ2pjEF6YfZ+fubHNkUQ2lRPo7+ETS750Ncw5vX2D2KyMWj4vAVpKioiMTERAoLC80dylXN1taWOnXqYGNjc/bBIleJ5S8PxNnOmsSMPMb8vY4bP49l2csDcbEv//9kVOcInh3YlKLSMjYeSOfRX1YC8OK1LSqc54+VcQxuFcTKvUdYve8I7SK8K7x///fLOXQsj98fjALgqd9X8+CPK/jl/u4AlJaVcfOXi/B0suXfJ3qRnlvEIz+txIiRd24oL8Zm5xdzw2cxdK7vw+xn+hCXms0jP6/EwcaS+3s1BODN4a148doTyW9JmZEe78zimlbBpmNFJaXc8FkMXs52fHd3F/zdHDiUkYeT3Ym/emc/04eyshP7wu44nMkNn8VwTcug8/6si0pKOZZbhI9rzayWm7LuAC9NXM+7I9rQLsKLn5bu5abPF7HkpQHU8Thzy5Hj/96P83Q+8TA5cfV+3pq6iY9HtadtuNd/n/MqAN4Y1qpGYhcxp4yMDFJTU7Vi6jJjYWGBj48P7u7u5g5FpNZ1+3Ap1vbO5B9NYvtPL7LmvZF0+2AJ1g7lP8gNir6Z+sOfoaykiGNxG9ny9eMARN44psJ5khb/iX+Ha0jfuZL0XavxaFCxiLvx8wfITz9M22d/A2DLd0+z6YuHafP0T5Vi2vLNEzgHNaQg/XCVMbd74S+c6zQwvbZ2cqs0pjgvi81fPoJn4y4UZh2t8F5ZSRGr3xmBjYsXLR/9FnsPf/LTDmFl72QaU1KYh3NII+p0H8H6cXef7uM7J2UlRRTnHMPW7fQ/XD8XFxLjmT6fpKWT2PXH2zQd/RHu9duSe3gfm796DIBGt7xePj/nGCteHYxHo860feZXbFy9yEvZj1UVxX4RuTyoOHwFOHLkCE899RSTp0wlOyvT3OEI4OziypDrr2Ps2LF4e3uffYLIFc7L2Q5XBxt8XO15bUhLrvloPuvi04hu5A+AvY2lqZhZx8ORf9buZ9GOZF689sQ5jEYjf6yM570RbfB3c+DX5fsqFId3J2eycPthZj7Vm9ZhXgB8OLIdAz+Yx96ULOr6uhC7I5ndh7PY8OZg/NzKN9l7dUhLHv15JS9c0xxne2smrdlPYUkpn97SAVtrSxoGuLEvNZuvFu7ivp6RGAwGXOxtcDmp9jpzUyLH8oq4sUO46djvK+LIyCti+lO9sbYsb/EfdEr/Zi9nuwqvP523nVAvJzrVO/cHh00H0/lzZTyT1+7n8f5NGB3d4OyTquGrBbsY2TGcUZ0jAHhzWGtityfzw5I9lYr3pzr+770q6+KP0jbcm6FtQwEI9nTi+tbBbDiQXiNxi5jLnDlzePvNN1iybDlGo/HsE+SSYzAY6Nq5Ey+8+BJ9+/Y1dzgitcbWxQtrR1ds3XyIvPkVVr52Lcf2rMO7eTQAlrb2pmKmvVcdDi2bzNHNi+Ck4rDRaCRx0Z80vuMd7DwCSIz9vUJxOCdpN0c2xdDp9Rm41S3/4W/Tuz9gxSuDyDm0F6eAuqaxB+b9SHFeFnWHPMGRTZW/gQRg4+R+1gLr1gnPENDperCwJGXd7ArvJcT+TnHOMTq++i8WVuU/wLb3rvhDeZ8WPfFp0fOM16iuzLhNJC75m0PL/qHu9Y8T1v+eGjnvhcR4ps/n2J51uNdvS2DnIQA4eAcR0Ok6ju3bYBqz79/PsfMMoPl940zHHLzPf2GDiJifisOXuYyMDLpH9yAu4TDe3e+kTv12WNqot5o5lRblk717NX9O/onVa9exbMlirT4ROcnxlgHFpVWvptuWmMHqfUcrFVKX7k4hv6iEbpG++Ls50H/sXN4a3hqn/1amro07iou9takwDNAmzAsXe2vWxB2lrq8La+OPEhngaioMA0Q38qewpIxNCel0qe/L2vijdKzrU6GlRXRDP96auomDabmEeJ1YWXLcb8v30a2BX4WY52xOok2YJ8/9uZbZmxPxdLJjSJsQHu7TEEuLyvvBFpWUMmn1fu7tEVnt3cdTMvOZuHo/f66KJ/5INr0aB/Dhze3p/V/bCoCnf1/DxNX7z3ie060CLiopZXNCOo/0aVjhePeGfqyNO1pp/Kl6vjubwuJS6vu58nj/xnSp72t6r12ENxPX7Gf9/jRahXqy/2gOC7Yd5oYOYWc9r8ilaubMmVx33bU093dg7DVhhHnaY2mhvu2Xk9IyI/Fp+fyxcQvXXDOIKVOmMmDAAHOHJVLrjrcMKCstqfL9rAPbyNi9plIRMG37MkqL8vFq2g07zwCWvzSARre+YVqJm7FnHVYOLqbCMIB7vdZYObiQsXutqTicnbiLvZM/otPrM8hLPXjaONd+eBtlxYU4+oUT2n80/u0rthlLiP2DvNT9NH9wPHsnj6s0P3XdXNzqtWbb98+Tsm4ONi6eBHS6nojBD2GwqJm2VgUZKRxaOonEJX+RlxyPd8ueNL3nQ3xa9jKN2fLdMxxaOumM5+k2dhH2XnVqJKbjzvb5uDdoR9KySRzbuwG3ui3JSzlA6sYF1Ol2g2lM6vo5eDWLYv24e0jfuQI7d3+Ce99GcI9RNRqriFw8Kg5f5n7++Wd27dpF01fm4RBQz9zhyH/cGnXDs921bHmtN7/88gsPP/ywuUMSqXHzth4i7PG/KxwrO8squfScQj6YuRUnOytahnqajv+weC+/LoujpKyMopIyLAyGSj13f1sex3WtQ7C0sCAywJUwbyemrDtoWtGamlVQaSUulK9eTc3KN43xPmWMm4MNNlYWFcacWpg+Pic1q6BScTglM5+F2w/z5e2dKhw/kJbD0t25DGkbym8PRBGXms3zf62ltMzIkwOaVIpz1qYkMvOLufEsxdGiklJmbkrkr5XxLNqZTPMQD+7sVo9rWwfj7mhbafwzg5ryQM/IM57T7zQtKNJzCiktM+LtUvEz83a2IzWr4LTn83W154ORbWke5EFhSRkTV8cz7NOFTH60Jx3/WxV9fZsQ0nIKGfzRfIxGIyVlRm7vWpdH+jQ6Y6wil7IXX3ietnUc+e2WSNM3BuTy0yHUhWEtvLnp5528NOYFFYflspK6YT5z7oiocMx4lvY2Rdnp7PnnI6zsnXCLaGk6fmDejyTE/IaxtISykiIwWND4jrcrzE2M+Q3/jtdisLDEuU4DHPzCOLxyKkHRNwNQeCwVGxcvTmXj4kVhZvmeE6XFhWwc/wCRI1/C3qtOlcVhK1sHGo56Fff67cDCQOq6uWz49F7K7v+EwC7DAMg9HMeuP96iwytTsDjNhn15qQfI376MgM5DaPvML+Qmx7PthxcwlpVSb8gTZ/yczqSspIjkNbNIWvwXR7cuxjWsOSG978C/42BsnCovFKo/7BnCB95/xnPauvuddzxVqc7nE9DpOoqy01jx2rWAEWNpCcG9biNi8Inn2bzUgxyc/xNh/UcTcd0jZO7bwPYfX8LCyqZCEVlELh8qDl/mJk+ZilujrioMX4IcAurh1qgr/0yeouKwXJE61/fh/RFtKxxbtz+NB39cUWlsizFTAcgrKiHcx5kJd3WpUKQd2jaEx/o2JrugmM/m7cDZzppBJ/XdzcwrYuamRKY9cWLFxbB2ofy+Is5UHAaoan2e0WissBK3qkW5RiMYTpp96pDjJe+q5v6xMg5Xe2v6Nw+scLzMWF6Y/nBkWywtLGge7EFKZj6fz99RZXH4txX76NHIv8Kq5qqsiTvKvf9bTqC7A5Me7XHGjeGgvJB7akH83FW8cSNVfxbH1fV1oa7viY3n2oZ7kZSRxxcLdpqKw8t2pzBu9jbeHdGmfOXwkWxenLge31lbeaJ/5c9H5FJ38OBBNmzazJfD66kwfAWwtrTg1jY+3P/3JhISEggK0lem5fLg0agzTe58t8KxY3vXs+mLhyqNXfhQ+Wre0sI8HPzCafnIN9i6nijkBnQeQt3rHqUkP5u4fz/Hyt4J/3YnVuoW52aSvGYWHV+ZYjoW2HkoCbF/mIrDQNXfiDopP9v1x9s4BdQzFXmrYuPiSdiAe02v3cJbUJybSdy/XxDYZRjGslI2fv4A9YY9hZN/xGnPYzQasXHxpOndYzFYWOIa3pyCjGTiZ3x5QcXhjN1r2fjZfdh5BtB+zN94RHY443hbV68Kn3Vtq+7nk7Z9OXunfEKTO9/BNaIVeSnxbP/pJfb842P6fIxlZf9tYPgCAK6hTclO3M3B+T+pOCxymVJx+DJ3MCER2/Boc4chp2HrV4+E+FhzhyFSKxxsrAjzca5w7NCxvCrHTnuiJ8521ng62eFsb13pfWd7a9O5vri9I93enMmvy/dxc6fy5PWftQcoKC6l/9i5pjlGY/lK5V2HM2ng74qPix1HsiuvZk3LKTQVR31c7Fi/P63C+8fyiiguLTOtjvVxqbwq9uh/5z21yGo0Gvl9RRzD2oVhY1Xxq4i+LnZYWVpUaCFRz8+F1KwCikpKK4xPSMtl8c4U/ndPl6o+vgpahnry4ch2/LUqnqGfLKRbpB/D2oXSv3kdHGwq/7V+IW0lPJxssbQwcOS/VdXHHc2uvAL7bFqHeTHppDjem76F4e1CTcX9RoFu5BWV8NRva3isb2Ms9FV8ucwkJSUBUN/7zD/gkcvH8X+XSUlJKg7LZcPK1h5Hv4rfQjrd5m4dXpmClb0zNs6eWDs4V3rf2sHZdK7mD4xn8TNRJMT8RlD0SAAOLZ9MWXEBy18eaJpjNBrBWEZ24i6c6zTA1s2Hwswjlc5dlJ2GjUv53hFp25eRfXAHyaOmnzgHMP/exkRc9yj1hz1dZfxudVuREFO+yV1Jfg6ZcZvI2r+V7T+M+e88ZWA0MmtUHdo+/wdejbtg5+aDwdK6QgsJp8B6FB5LpaykCAur89tU3C2iBU3v+YDExX+x6q3heDXpSkCXYfi16YelbeW/Fy52W4nqfj67/36PwC7DTMV9l+CGlBbmsWXC09S97jEMFhbYuvvgFFi/wvmdAuqRvHpGjcQqIhefisOXuZLSUgxWlQsttWn9M+3x7303/r2r10w/c+dyto8dTtvPtmPlcHXtYGqwsqa4pOq+XSJXk2BPp9NuTHYqa0sLHu3TiLembeL6NiE42Fjx2/J93N8zkhGntFwY8/c6fl8Rx6tDWtIm3Ius/GJTD1so3/QsK7+YtuHlKzPahHkxbvZ2UjLz8f2vlULsjsPYWlnQPMjDNObtaZsqFHBjdyTj52pP8CntJpbvSSX+SA4jO4Vzqrbh3kxee4CyMqOp0LkvNRtfV/tKheQ/Vsbh5WxboVfw6TjYWDGqcwSjOkew/0g2f66K551/N/P072sY1CKI4e1D6VzP13TNC2krYWNlSbMgDxbtTGZAixOFkcU7k+nbLLDKOaezNSEDH9cTBeX8opJKBWALCwNGwFi+Nvmczi9ibqWlpQBYWV4a/+22/3g9d3fw556O/tUavzw+k+E/bGf7c21xtdcjApz4d1miXE6uUA7ewVg7Vu/5zMLKmrrXPsKuP98moNN1WNo6kBDzG2ED76NOtxEVxm7/8UUSF/1Bw5tfwb1ea0ryskw9bKF8JXNJXhbu9ctbiLV6bAJlRSd+MH9s30a2fPM4HV6egoNv6Gljyjqw1bQ5nZW9M13fi6nw/oF5P5C2bSmtHpuAvXcwAO7123Jo+WSMZWUY/vsBfu7hOGzdfM+7MAxgaetAUPTNBEXfTG7KfpIW/8Xuv95l63fP4NduIIFdh+PZsJPpmhe7rUR1P5/SwnxTjMcZLCz/+xpdedHevX47cg/vrTAmN3lfjfdHFpGLR5mfnLOmL83Ewqb6q2Kc67ah9UcbsLR3Ofvg82Q0Gkmc9hEpi36lJC8T5/CWhN38Fg6BDc44L23tDBKmjKXgyAHsvEMIGvIsnq3611qcIlI9Q9qG8Pa/m/lh8R66NfBjc0IGX9zeiXp+Ff8cub5NCO9M28yYa5tT38+VHo38efK31Yy9qbzdxVO/raZ3kwBTi4Oohn7U93fhwR9X8Mr1LcjILeK1fzZyc+cI04rmIW1D+GDmVh75eRWP9m1EfGo2n8zZzpP9G1f6WuRvy+NoFepJwwC3Svdwe7e6fLdoN2MmruPu7vWJO5LNJ3O2cXdUxT+XysqM/LEijhvah2F1jl9FD/V25tlBzXhmYFOW70nlr1Xx3Pb1El4Y3Jy7o8pXdFxoW4n7ejbgoR9X0jzYgzbhXvy8dB+J6Xnc1uVEO6M3p24k+Vg+42/rCMDXC3cS5OlEpL8rRSVlTFwTz/SNCRVWRvdpGshXC3fSpI67qa3Ee/9uoU/TwCo37BORczNzdFMcrKv//1KbIGc2PNUaF7ua2ZCpKkajkY9iE/l1XQqZ+SW0rOPMWwPDaOBz5rxyxvY0xi5M4EB6ASEedjzbM4j+DT3POEdEal5A5+vZ9ec7HJj3I15NupK1fwstHvwcp8CKLQ79O13H7r/epcGIF3AKrI9382i2THiKJne9D8DWCU/j07K3aTM6x1MKwEXZ6UD5it7jxevExX9hsLTCNbQJGCxIXT+P/bO/I/Km8lWwBgsLnIMq/jDcxsULCxu7CseDe9/G/rn/Y/tPLxHS907ykuPZN/VTQvvdZRpTUpBLXnK86XX+kYNk7d+KtZNbtQqgjr6h1B/+DPWGPU36jhUkLv6LdR/eToMRzxPat/w6F9pWojox7vzjLQrTk2n+wGfV/nx8WvVh/6yvcQlpglvdVuSmxLP77/fxbd3HtNo6rP9oVrx6DXunfIJ/h8Ec27eBhIW/0OSused9PyJiXioOyzmzdj63ZNzCygYb1zP3xLxQh2Z9weG53xBx58fY+4aTOP0Ttn94Ey3fWoylvVOVc7L3rmX31/cTfN3TeLTqT/r6Wez56j5snpuMc3irKueIyMVhY2XJnd3qMX7ejvK2EX4ulQrDAP2b1eHZP9Yyd0sSA1sE8cXtHRnz9zpGjC9fGdG3aSDv3HBiYztLCwt+vb87z/65lms+nI+djSXXtwnh1etPbL7iYm/DXw9H8/yfa+n73hxcHWy4r0cD7jtl9W1WfhEzNibwxvCq/7wIdHfkz4eieXnSeqLfnoWfmwP3RDXg4T4NK4xbvCuZxIw8RnasvPq4ugwGA53r+9K5vi9v39CGY3mF532uU13XOoSM3CI+mrWNlKx8Iv1d+e2B7hU27UvNLCAp40RLkeLSMl77ZwPJmfnYWVvSwN+VX+/vTq+TVkY/3q8xBuDdfzeTnJmPp5MtfZoG8vw1zWosdpGrmafjuX2zzMbKAh/n8181Vx1fLD3ENysO8/F1EYR72vPJ4kRu+mk7ix9uiZNt1UXptQnZ3P/3bp6ODqZ/Qw9m7Ujnvr/2MPkuG1rVqfw1eBGpPRZWNoT0uYO46Z+TnbgLp8D6lQrDAL5t+rHtf8+Run4ufu0G0vzBz9n+44usefdGoLwA2fj2tyvNO5t9U8aRfzQRg4UlDn7hNLv3ozP2Ka6KvWcg7Z77gx2/vMLS53pi5+5HaL+7iRh8oidzZtwmVr051PR6xy+vAhDY7Qaa3/dJta9lMBjwbNQJz0adaHz72xTnZpxTrGdSnRgLj6WSn5Z0Tuete/1jGAwGdv/9HgXpydi4eODTqg8NbnjONMYtogWtHv8fu/58m72TP8beO4iGt7xOYJehZziziFzKDEbjWbaWl0taSHhdShr2J2To8zVyvtL8HOJ+fo70DbOxtHcmoN/9ZGyci0NQI8Jueh2o3FZixV2BhN82lozNC8jcFouNmx8hI17Bo0UfoPbbShiNRtY92Qr/XncTOOBBAMqKC1n7eAtChr2Ab9QtVc7b/dV9lObn0PDxX0zHtn98M1YOrtS/94saie3ApHew2jGLA3F7zz5Y5BJUNzyUAfUcePHaFuYORa5ib07dyMw9eeyN22/uUEROa+nSpXTt2pVFD7egrlfVrVpqSk5hKc/9G8fsnek421pyf+cA5u7KoJGfA6/3L2+/c2pbicBXVjB2cDgLdmcQuy8TP2cbXukbQp/I8pY6td1Wwmg00uqDddzdwZ8Hu5a3pSksKaPF2LW80CuEW9r6Vjnvvr92k1NYyi+3nPjB2s0/b8fVzoovhtevck5N2Xs0n+6fbWTJkiV06XL2nvAiF8P06dO55ppr6PnlZmxdvc0djshpFWYeYcH9zfj3338ZNGjQ2SeIiNlo5bBUsP/P18jeu4bIh7/H2sWbhKkfkHtgCw5Bjc44L3HaR4QMf5GQ4S+SvOB79nzzEK3eX4W1k3u1rrvj41Fk7Vl1xjHtv9hT5fHCowcpzkzFrXF30zELa1tcGnQge9/a0xaHs/etq9Q32a1xdw7Pm1CtmEVERETM4bXZ+1mTkM33IyPxdrTmg5gEthzOpZHfmdszfBSbyIt9QnixTwjfr0rmoUl7WPV4K9wdqrfKeNTPO1h1MOuMY/aMaV/l8YMZhaTmFNO9rpvpmK2VBR1CXFibkH3a4vC6xGzu6VCxb3L3CDcmrKx6gy0REREROTcqDotJaX4OR5b/Tb3R43Ft1BWAiDs+Yt2TZ2+x4N35BrzaXwdA8NDnSF74P3LiN+LeNLpa1w6/fWyFTQjORXFmKgDWLhV7Nlm7eFOYlniGeUewdqn403ZrF2+KsyrvpisiIiJyKcgpLOXvTUcYP7QeXcPLv5H10XURtPpg3Vnn3tDCm+ualudLz/UK5n+rk9mYlEN0ver9MH/steEUFJedV9ypOcUAeJ3S7sLbyZrEY6dvhXMkpxhvp8pzjvx3PhERERG5MCoOi0nB0QMYS4txCjvRe9PKwQV7v4izznWsc+Krfpa2DljaOVGSfbTa17Z1r95O2md0ykZRGI2Vj1Wacur7RuDS2GVcRERE5FQHMgooLjXSMvDEngoudlZEVKOVRUO/E73CHWwscbKx5GhuSbWv7e9ie27BVuE80rVK+ZqxivOIiIiIyPlRcVhOON5+ulLSfva21AbLU7+OaMBYVv2VJRfSVsL6v83uijOPYON24iuJxdlHsXE5/Q6w1q7eFP236tg0J+topRXIIiIiIpcKU1p2HvmatUXFSQYDlJ3D9iMX0lbC57/Vv0dyivE9aeO7o7nFeDmefiM8bydrUrOLKhw7mlNcaQWyiIiIiJwfFYfFxM47FIOlNTlxG7H1KN8opCQ/m4LUeFwadKjVa19IWwlbr2CsXX04tn0xjiFNACgrKSJr10pChr1w2nnOEa3J3L6EgD6jTccyty3GuW6b84pDRC5NbV6axj3R9bm3R2S1xi/bncKQTxaye+xQXB1OX7AQETGHUA87rC0NbEzKIdC1fCVvdkEJ8ekFdAh1qdVrX0hbiWB3W3ycrFm87xhN/MtXMBeVlLHyQBYv9Ao57bzWdZxZEpfJ6E4BpmOL92XSJsj5vOIQkUtTzCNtCe1/D2H9R599MJC2fTmr3hxK7293Yu1Y85uei4hcTVQcFhNLeye8Ow3nwN9vYuXkhrWzFwlTP8BgsKii/ULNupC2EgaDAf9ed5M04zPsfMOw9wkjceZnWNjY49X+etO4PRMewcbdn5ChzwPg3+sutr43lKSZn+PRsi/pG+aQuWMJjZ+bfMH3IyKXjtnP9MHBtvp/3bUN92LL29fhYl97q9KMRiMfzNzKz8v2kZlXRKtQT965oQ2RAad/uPl52V7+XrWfnYeOAdAs2IMXBjenVainacwPi/fww5I9JKTnAtDA35Un+zehZ+MTRZXcgmLenLqJWZsTycgtIsjDkbuj6nN7t3q1c7MiUqOcbC0Z3tybN+cewM3eCq//NqSzMBgw1HJrrAtpK2EwGLi7gz+fLUkizNOOMA97PluSiL21Bdc3O/GtrUf+2YO/sw3P9y4vGN/VwZ+h32/l8yVJ9I30YM7OdJbEZTL5rsYXfD8icuno9OYsrGzPvKnmydzrt6HnF5uwcqi9H4oZjUb2TPqQhIW/UJybiVvdljS+4x2c6zQ47ZzsxF3s/vt9suI3k380kYa3vHbGgvfeqZ+y+893CO13N41ufcN0fObIqp+PI296ifBrHjj/mxIRqYKKw1JB6IhXiPv5OXZ+chuW9s4E9LufovTDGKztzB3aGQX0f4Cy4gLif3mBktxMnMJb0uiJ37C0P9GPryj9EAaDhem1c9221L/3Cw5Ofp+EKWOx8wmh3r1f4hx+9g34ROTy4eV8bn9+2VhZ4uN69t6dF2L8vB18tXAnn97SgXAfZz6evY0bxsew/OWBONlVXZRevjuV69uE0DasNbbWloyft50R42NY/OIA/N3KH6b83R148doWhHmX/9n356p4bvt6CfOf62cqPL80aQPLdqfw+W0dCfJ0JHZHMs/9uRZfV3v6N69Tq/ctIjXjlX6hPPdvHLf9uhNnW0vu7xzA4cwi7Kwu7Ua8D3QJoKCkjBemx5NZUELLQCd+u6URTraWpjGHMouwOGlRQttgZ74YVp/3Fx5kbEwCIe52fDm8Hq3qaOWwyJXE9hxb+1lY2WDr5lNL0ZSL+/dz9s/6mmb3jsPRP4K9k8ex+u0RdP9wKVYnPWeerLQwHwefEPzbX8OOX1454/mP7dtIwsJfcA5uVOm9nl9sqvA6deNCtnz7BH7tBp7/DYmInIaKw1KBpb0T9UaPN70uLcwjcdrH+HS/2XSs1fsVewN3/C6p0nnajd9h+r1rZKcqx9Qkg8FA0LVPEnTtk6cd0/iZiZWOebYZhGebQbUZmojUopyCYp7+fQ2zNifibGfNg70bMmdzEo3ruPHmsNZA5bYSvg/+zocj2zF/2yFitx/Gz82eV4e0pF+z8sJobbeVMBqNfBOzi8f6NmZgiyAAPrulA02en8w/aw5wa9e6Vc778o5OFV5/dHM7pm9MYMmuFG5oHwZA36aBFca8MLg5Py7Zy7r9R03F4bXxRxnRIYzO9ct7tN/apS4/L93LpoPpKg6LXCacbC0ZP+zEav+8olI+XpTIza1PFEpWPV7xh91Jr3WsdJ4dz7cz/b5TmGuVY2qSwWDgyeggnowOOu2YiXdUXhE8qLEngxp7VjFaRC4HJfk5bP3uGVLWzcbK3pnwQQ+Qsm4OLiGNTatlT20rMXOkP03v+YDUDfM5sjkWO3d/Go56Bd/WfYHabythNBrZP/tbIq591FSQbXb/Jyy4vxmHlv9DcM9bq5znFtECt4gWAOz6463Tnr+kIJeNnz9I07s/YO+UcZXeP7XwnbpuNp6NOuPge/o2PCIi50vFYakg98BW8pP34hTWgpL8bBKnfQyAR4u+Zo5MRKSylydtYE3cUX66txveLna8P30LmxPSaVzH7YzzPpy5lZeub84r17VgwqLdPPDDCta9MRh3x+p9Zfqmz2NZuffIGcfEfzy8yuMH0nJJzSogqqGf6ZittSUd6/qwJv7IaYvDp8ovKqWk1IjbaQrYpWVlTFufQF5RCW3CTqzGaR/hzZzNSdzUMRw/V3uW7UllX2o2bw47//Y+InJxbT2cy96j+bQIdCK7oISPFyUC0DfSw8yRiYhUtuOXV8jYvYbWT/6Iras3uye+T9b+LbiEnLk9zJ5JHxE58kUiR77MgTnfsfHzB4n+dA02Tu7Vuu6a90aSvvPMm573/X5flcfzUw9SeCwVr2bdTccsrW3xaNiRjN1rT1scrq5t3z+PT8ueeDXtVmVx+GSFmUdI3biAZvd9ckHXFBE5HRWHpZJDc74iP3kfBksbnEKb0vi5f7B21sOGiFxacgqK+WtVPF/e3pFukeWF1k9uaU+zF6acde6IDmEMaRMKlK+u/W7RbjbsT6PHSb15z+Sjm9tRUFR6XnEfycoHwPuUdhfeLnYk/tcruDrenLoJP1d7070ftz3pGAM/mEdhSSmOtlZ8f09XGvifWFHz1vBWPPnbalqMmYqVhQELCwMfjWxH+7re53U/ImIeXy07xL60fGwsDTT1d+KfOxvj4Vh7vdJFRM5HSX4OiYv/psVDX+DVpCsAze4dx8IHW5x1bp1uNxDQqXwPmfojXmD/3P+RuW8D3s17VOvaTe/5kNLz3PS8MDMVAFvXivmRrYsX+UcTz+ucxx1aPoXM/Vvo/Masao1PXPwXVnZO+LUdcEHXFRE5HRWHpQLHkCY0e3m2ucMQETmrA0dzKC4to+VJG7K52NtQ1/fsG5M0CnQz/d7R1gonW2uO5hRW+9rHe/xeiFM3+jQaqfZWUuPnbWfy2gP881gP7KwtK7xX19eZhc/3IzO/iOkbE3jk55VMfqynqUA8IXY36+LT+Om+btTxcGDlniM8++dafFzt6X5KoVlELk1N/B2ZfV8zc4chInJWeakHMJYWm1otAFg7uODoH3HWuSf34rWyc8DKzonCzKPVvradR018K+rU7MwIF7BZe35aEtt/eol2z/+BpU319sVIjP2dgM5Dqj1eRORcqTgsIiKXJaOx/J+n5ufG42+cgZWlRYXXBgOUlZ193nEX0lbC26V8s7vUrHx8T9r47mh2Ad4uZ0/6v5i/g0/mbOfvh6NpHFj5a5U2VpaE+ZRv1NQixJONB9L5NmYXH4xsR35RCW9P28z3o7vQu0l5f+LGge5sTcrgy/k7VBwWERGRmnXahO3sUw2Wp5QrDIYT56uGC2krYeta3vO3MDMVO3df0/HCrLRKq4nPRWbcZoqyjrJszIm2jcayUtJ3ruTA3O/p99MBDBYnfvCfvnMluYf30fKRr8/7miIiZ6PisJjd3u8eoyQvi8iH/2fuUETkMhLq7YS1pQUb9qcT6O4IQHZ+MXGpOXSsV7u7V19IW4kQT0d8XOxYtDOZpkHlLXuKSkpZsTeVl65tcca5n8/bwcezt/HHQ1G0CKne5kxGIxSVlAFQUmqkuLQMi1Me0CwNBs6hNi4iV6HHJu8lq6CE/90Uae5QROQy4uAbisHSmmP7NmDvWf6D6eK8bHKT4/Bo2KFWr30hbSXsfYKxdfPh6JbFuIY2BaCspIj0HSuIvGnMecfk1aQrXd+LqXBs89eP4RhQl4hrHqpQGAZIiP0dl7BmZ+3PLCJyIVQcFjmLtHUzSZrxGQWp+zGWFmPnG0ZAn3vx7jTM3KGJXNWc7Ky5oX0Yr03egJujDV5OdoydsQULCzBUu0HD+bmQthIGg4HR0Q34ZM52wr2dCfNx5pM527G3sWJI2xM7UD/04wr83Ox58b+C8fh523lv+ha+vL0TwR6OpGaW9y52tLXC0a68z+hbUzfRs7E/Ae4O5BSUMGXdAZbvSeWPB8s3U3G2t6ZTPR9em7wRO2tL6ng4smJPKn+v3s9rQ1qe9z2JiJjbzO1pfLYkif3pBRSXGgnztOPeTgEMa65+6iLmZGXvRJ1uw9n56xvYOLpj4+rFnoljMVhYXFB7huq4kLYSBoOB0H73sG/qpzj6heHoF87eqZ9iaWNPQKchpnGbvngYWw8/Im8sLxiXlRSRk7j7v98XU5CeTNb+rVjaOeLoF4aVvRPOQRV/yGZp64CNk3ul48V52SSv+pfIm1857/sQEakOFYdFzsLK0Y3AQY9g71cXCytrMjbNZ+/3T2Dt4oVbkyhzhydyVXt9aEue/n0No75chLOdNQ/2bkhSRh621hZnn2xGD/VuSEFxKc/+uZbMvCJahXry50NRONmd2EwqKSOvwgrfHxbvpaikjLsmLK1wrqcGNOHpgeUrWo5kF/DQjytJycrH2c6aRoFu/PFgd7o3PPFw9PUdnXhr2iYe+GEFx/KKqOPhwPPXNOO2rnVr+a5FRGqPm70Vj3QLpK6XPdaWFszflcETU/bi5WhNVF03c4cnclVrOOo1tn73DGs/uAUre2fCBz1AftohLKxtzR3aGYVf8yClRQVs+/55inMzcYtoSbvn/8DK3sk0Jj8tCSxO5J0FGSksfaG36XX8jC+Jn/ElHg070uGlf87p+odXTMFoNJo25RMRqS0qDl9F0tZOJ2HaxxSk7sfSxg7H4CY0ePh7LG0dyInfyMF/3iX34FaMpSU4BDUm9MZXcQppapq/4q5Awm95l/RN88jauQxbzzpE3P4hVs6exP34NDnxG3Go05B693yGnU8oAAlTPyR9w2z8om4lcfonlORm4Na0JxG3j8XKwbXKOI1GI4dmf0lK7M8UZaZi7xtGnWsew7PNIABKco8R/+uLHNu2iNLCPGzd/Qgc+Ag+XUbUyufmGtmpwmv/3ndzZPnfZO1ZreKwiJk52Vnz5R0n/h/NLSzhw5lbuaXLiU1O1r4xuMKclM9vqnSePR+c+CZA5/q+VY6pSQaDgacHNjUVdasy+bGeFV6feh9VGTeq/VnH+Lja88kttfs1ThE5f9O3pfFxbAL70wuws7akib8j39/UAAcbSzYm5fDu/INsTc6lpNRIYz8HXu0XStOAE4WKwFdW8O6gcObtTmdZfBZ1XG358LoIPB2seHpaHBuTcmjo68BnQ+sR6lHe5/zDmARm70zn1jZ+fLI4kYz8EnrWc2Ps4Ahc7at+XDAajXy57BA/r00hNbuIME97Huteh0GNy1veHMsv4cUZ8Szad4y8olL8XGx5pFsgI1rWTtufTmEV88q7O/rz96YjrD6QpeKwiJlZ2TvR4qEvTK9LCvLY889HBPcYZToW/emaCnMG/Ha40nn6TNhl+r1no05VjqlJBoOB+sOeov6wp0475tSCr4N30DnHdbqicXDPWwjuecs5nUtE5HyoOHyVKDqWwp5vHiR42Bg8WvWntCCH7N2rTA39Swty8O40nNCb3gDg8Nyv2TnuFlq+vRTLk34ymjh9HCEjXiF0xCscmPg2e759CFuvYAIHPISNRyD7vn+C+F9fpOHjv5jmFKTu5+iaf4l85AdK83PY98OTxP8yhnqjx1cZa8Lk90hfP4vwW97BzjeMrF0r2fPtI1g5e+LaoCMHp4wl79BuGj7+C1ZOHhSkxlN2hl5SiTM+JWnGZ2f8fBo+9gsu9c9eVDEajWTtWEp+8j6Ch51/rykRqRlbEtLZk5JFqxBPsvKL+XDWVgD6Natj5shERM5dSnYRD07cw5jewfRv6EFOUSmrDmSb9l/KKSxleAtv3ggIBeDr5Ye55dedLH2kJU62J/pUjlucyCt9Q3ilbyhvzzvAQxP3EOxuy0NdAwl0teGJKft4cUY8v9zS0DRnf3oB/247yg8jI8kpLOXJqfsYMyOe8cPqVRnrewsSmLUjnXcGhRPmYcfKA1k88s8ePB2t6BjqytiFB9l9JI9fRjXEw8GK+PQCCv7rf16VTxcn8tmSpDN+Pr+Makj7EJezfo5Go5Gl8VnsO5rPmN7BZx0vIrUrc/8Wcg/txTWiJSV5Wez95yMAfFv3PctMERG5GFQcvkoUZaZiLC3Bs9UAbL3KiyaOdU48ELg27FJhfPit77Hm4UZk7V6Be/MTX4vx7jwCr7blq9cC+z/A1rcHU2fQY6YVtP697mbv909UOFdZcSF17xqHrUcAAKEj32TnJ7cSMuJlbFwrrh4pLczj0NxvafzUnzjXbQOAnXcI2XvXkLLoF1wbdKQoLQnH4CY4hTYvf98r6Iz37tv9FjzbXHPGMTbufmd8vyQvi3VPtcZYUgQGS8JHvY1b425nnCMiF8eX83eyNyUbGysLmgW7M/XxXng6XdpfUxQRqUpqdhElZUYGNPKkjlv5n2MNfR1N73cJr7g69r1rwmn07hpW7M+idwN30/ERLbwZ3MQLgAe6BDJ4wlYe617HtIL27g7+PDFlb4VzFZaUMe76ugS4ll/3zQGh3PrrTl7uG4KPs02FsXlFpXy74hB/3t6YNkHOAIR42LHmYDa/rE2hY6grSZlFNPF3pHlg+SKDIHe7M977LW18uabxmTfa9HOxOeP7WQUltP5wHUUlRiwt4O2B4XSLcDvjHBG5OOKmf0nu4X1YWNngEtaMDi9PwcalepvriohI7VJx+CrhGNQI14Zd2PRKT1wbd8etcXc82wzEytENgOKsoyRMGUvmzmUUZx3FWFZKWVE+hWkVV3CcXFC2dinf4MOhzonG+dauXhiLCyjJz8bKvvxhwdYj0FQYBnCOaA3GMvKT91UqDucf2o2xuIDtH1X8SrexpBjH4CYA+Ebfyu4v7iH34BbcGnfHo2VfnOu2Pe29Wzu5Y+3kftr3q8PSzolmr8ylrDCXzB1L2f/na9h6B1dqOSEiF1fTIA/mPdfP3GGIiNSIRn6OdAl3pecXm+ge4Ur3CDcGNvbE7b/WDkdzihkbk8Cy+EyO5hRTajSSX1xGUmZhhfM09DtRUPZ2Ku9lHul7YiNNLydrCkqMZBeU4GxXfu5AV1tTYRigdZAzZUbYl5ZfqTi8+0g+BSVGbvppe4XjxaVGmvx37Vvb+nLPn7vZcjiX7hFu9I30oG2w82nv3d3BGncH69O+Xx1ONpbMva8ZuUVlLI3L5LU5+wl2t63UckJELi7X0KZ0eXuuucMQEZHTUHH4KmGwsKThk3+QvXctmdsWkbzwew5Ofo+mY6Zj5x3M3v89TnF2GqE3voatZx0MVjZsfXswxtLiiuexPClp/2+jJIPlyf8Z/bd5kvH0Xxs0zaPy7rTG/+ZFPvoTNm4VV/NaWJc/mLg37UGr91eTsWk+mTuWsu2DG/GLvo3QES9XebmaaCthsLDA3jcMAMfgJuQf3kvSzPEqDouIiEiNsbQw8MetDVmbkM2ivZl8vzqZ9xYeZPo9TQl2t+PxKXtJyy3mtX6h1HGzxcbSwOAJWykuNVY4j7XFiRzr+O+sqjhWVnFaBQbTPyvna2X/9bn46eZI/E4pHNtYlW/M1KOeO6sfb8X83Rksjcvkxh+3cVs7P17uG1rl9WqirYSFhYEwT3sAmvg7svdoPuOXJKk4LCIiInIGKg5fRQwGAy712uJSry11Bj/O+mfakb5+FgF97yVr9yrCR72Ne7PyDZAK05MoyUmvkesWpidRlJFsat2Qs28dGCyw8wuvNNbBvz4GK1uK0pJwbdDxtOe0dvbEp8sIfLqMwKVeOw78/eZpi8M10VbiVEajsbzFhIhc9R75aSWZ+f9n777jo6i6Bo7/sskmm957rxCSQOgdEnoHQQEVbNgVUUGxoFgAFQSxYcUOyIMKCNIhoffeSUgghfTe2+7zRyAQUgiQsIGc7+fzvo97587MmUWHO2fvnFvMr89IqRkhxO3T0dGhvZsZ7d3MeCXEhQ6fHWLt6XSe6eLE3ovZzBriRW+/8jei4qKvdwcAAL/1SURBVLOKSM8vrZfzxmcVkZhdXFG64WBcLgod8LKuWg7Cz9YIAz0d4rOK6exRc+LV2ljJmNZ2jGltRwc3M2ZsvFhjcrg+ykpcT6PRUFxWSwZcCNFkHP12EqV5WbSd/Iu2QxFCiEZHksNNRE7UIbJO78AioCdKUxtyow5RkpOOoVP5IiMqOw9Sdv+NsUcrygpzuPi/GSj0a68NV1cKpQGRP72M++h3KCvIJXrxO1i3H1qlpASArqEJTv2f4cLS99Bo1Jj5dqCsIJec8wdQGBhh13U0MSvmYOLeEkMnPzSlxWQc24ShY/WLpcDtl5WI/+9LjD1aobJzR1NaQsaxzaTu/gvPcR/d8jGFEOJO+X1nJMv2XuDMpUwAWrpZ8dawVrTxuJqEyS0s4ePVx1h7JI7U3CICXSyZ8UAbWrtLLUAh7qRDcTnsiMqip7cFNsZKDsXnkp5Xgq9t+WxYDysVfx9NoZWTMTlFZczYcBGVUlEv5zbQU/Dy8kje6e9OblEZ76yJZmiAdZWSEgAmBro808WJ99ZdQK3R0MHNjNyiMg7E5GBkoGB0sB1ztsTQ0skEP1tDiss0bDqXga+NYY3nv92yEl9ui6eVszHulipKyjRsjsjgr6OpfDTE85aPKYQQd0rMlj+I376MnNizAJh7tqTZmDex8Gld0efcX58S+c/cSvvpm9vS55tjdzRWIcS9R5LDTYSuypTsc3tJ2PgjZQW5GFg74z76XSyDegHg8/g8zv/2Osfe74+BtRNuI9/g4v8+rJdzq+w8sGozkNPzH6E0LxPLoF54jZtVY3/X+15HaWZD/JqviEqJQdfIDGP3IFwGTQRAoask5u+PKEqLRaFUYerbEd9nFtRLrNUpK8on+o83KcpIRKFUYejojc+TX2DTYXiDnVMIIerLrnPJ3NfOnfaebTFQ6vLVxlOM+SqMbdMG4WhRXoP0lUX7OHspk68e7YyDuSF/7b/AA1+Esf2dq32EEA3P1ECXvRez+XFPArlFZTibG/Buf3d6+Zb/yD1vhA+v/3ue/t8ew8ncgDd6u/Hhhov1cm4PKxUD/a145I/TZBaU0svXkllDqr7ldcXrvVyxMVby1fZ4YjKiMFPpEuRozMTu5QsfK3UVfLQphtjMIlR6Cjq6m7LggZp/zL9d+SVlvLk6msTsIlRKBd42hnwxyofhlxfmE0KIxiz91C6cutyHpW87FEoDolYvYN/HY+kxOxyVlWNFPxOXZnR8639Xd1TUzw+EQoimTZLDTYSRky8tXllU43Zj90BavrOmUpt1uyGVPndeWLkOnMrGtUqbefMuVdoAHEIfxSH00WrP7TNhfqXPOjo6OPaZgGOfCdX2dxn6Mi5DX652W0NwGzkVt5FT79j5hBDVW3Uohk/XnuBCSi6G+roEuljy6zM9MDbQ4/DFNGb9e5QTsRnlCyK5WPDBqDa0dLOq2N/+hSXMGdue9Sfi2Xk2CRcrYz4b1xEbEwNeXbyPwxfTaOFswYJHO+NhW75o0pz/jrP2aByPdffls3Unycgrok+gE3Mf6oC5UfWvN2s0Gr7edJpft0eSnF2Il50prw4IYGgbNwAy84t5c+kBtp5JJK+oFEcLQyb1D+DBzjUnYW7HN49Xro0+7+EOrD4Sy/azSYzu6ElBcSn/HYnl12e609m3/I2O1wYHsfZoHL9sj+TNoS0bJC4hRFW+tkYsGt+ixu2Bjsaseabyf5NDrivFEP9+5bJcrpaqKm1dPM2rtAE82sGBRztUX2pr/n0+lT7r6OgwoZMjEzo5Vtv/5Z4uvNzTpfoLaQBTe7sxtbfbHTufEKJ6CXtXE/HPXPITL6BrYIiZeyBtJ/+CnsqIzPNHOLv0I7IvHEdTVoqZewD+49/H3PPqfW3NQ44ETviEpEMbSTu5A0MbF1o+/Rn6ZtYc/2EyWVFHMHVtQasXvsLY3gMon1GbdHAd7n0eIXL55xTnpmMX3Iegpz5FaVx96RuNRkPU6gXEbPqNosxkjB298LnvFRw7lj8Dl+RmcvKXt0g9vpXSwnxUVo54D38J15CxDfK9Bb9YebJT0FOfkrhvNakntuPSY3RFu46uHgYWVd/AFUKI2yHJYSGEEI1eUlYBz/68i3fuC2ZQKxdyC0vZez4FLi+KlFtYwpiOnsx8oC0A32w+w0MLtrLnvSGYqK6+pjxv3QneH9mGD0a25sOVR3nul124W5vwUr8WOFsZ8fIfe3nzfwdZ8kJIxT7RKbmsPBTD78/2IKewhFcW7eWNpQeqJF2v+GjVMdYciWP22PZ42pmyJyKZF37djbWpii6+dnyy6hjnErNY/HxPrEwMiE7JpbCkrMZrn7/uJJ+vP1Xr97PkhZ508qnbg0JBcRmlZRosLie3y9QaytQaDPR0K/VT6euy73xKnY4phBBCCFGYkcSRr56j+YPTsG8/iNKCXDLO7gXKx2ulhbm49HgA80fL31CN+u879s8eR8i8XegZmlQcJ3L5fPzHvYf/uPc4u2QGR75+HiM7d7yHTcTQxplj373KqV/eov3UxRX75CdGk7BnFe2m/EppQQ7Hvp/MyZ/frJJ0veLc/z4mcf8aAp/4GGMHL9LP7OHoghfRN7PC2r8L55bNJjc+gnZTF6Fvak1+YjRlxYU1Xnvkis85v/KLWr+f9lMXYdW8U52+y7KiAtSlpVXKI+YnRrH5+WAUSn0svNvQbMybGNm71+mYQghRE0kOCyGEaPSSsgooVWsY3MoVV2tjAFo4W1Rs796s8ky3Tx9sj9+hv9kVkUy/IOeK9rGdvBjetnxm2Yt9/Rn86UZeHRBAaIvymW9PhTTj5T/2VjpWUWkZXz7SCSfL8vIKsx5oy8PfbOP9ka2xM69cPzOvqJTvtpzlr5d60d6r/FVmDxsT9kal8NuOSLr42hGXkU+giyXBl+v5ulmbUJtHu/swvE3ts+EcLGqu43m9GSuP4mBuSI/m5d+ZiUpJO08bPlt3Ej8HM2zNVCw/cJFDF9LwujyDWgghhBDiRooyk9CUleLQfhCGtq4AmLn5V2y3CehWqX/Qk7PZ+FRz0k7vxr5N34p2lx5jcOw0DACvoS+ye/oQfO57BdtWoQB4DHiSY9+9UulY6pIiWj77OYbWTgAEPDaD/bPH4z/uvSozbUsL84le8z0d316GpV87AIzs3Uk/u4+YzX9g7d+FgrR4zDwCsfAKLt9++Xpq4tbnkYqYa6Kyqvsi6Gf+nInKygGbwO4VbRY+rWn53BcYO3hTnJVC5Ir57HpvKD1mh6NvalXL0YQQonaSHBYNynX4ZFyHT9Z2GEKIu1yAiwXdm9kTMmsNof6O9PR3YGhrt4rZryk5hcxefZwd55JIyS6kTK2hoKSU+Iz8Sse5NqFsa1q+6Kb/tW1mKgpLysgpKMHUsHzGsbOlUUViGKCdlw1qjYbI5JwqyeFziVkUlpQx+suwSu0lZWoCXcpnfjzW3YcJP+zgeGwGIf4ODGzlQnsv2xqv3dLYAEtjgzp+U7X7auMplh+4yD8v90KlvDpT+OtHO/HyH3tp9fZKdBU6BLlaMrKdO8djM+rlvEKIxm1yqCuTQ2tPfAghxI2YuQdgHdCd7W/0wiYoBJuWPXHsMASliQUARVmpnPtrNmknd1KclYJGXUZZcQGFaZXLEpq6XS2vY2BePkYydfWv1KYuKaQkPwelUfkP2Spr54rEMICFbzvQqMlNiKySHM6NP4e6pJB9H42p1K4uLcHMIxAoT/Yemv8kWReOYxvUE/t2A7D0a1/jteubWKJ/G4ugX+v8qq9J2LWCju/8je41i8TbBfe+ppc/Fr7tCH+lE3Hb/ofX4Gfr5dxCiKZJksNCCCEaPV2FgmUTQ9kflUr46UQWhp/jo3+Psfa1frjbmDDptz2k5Rbx4ag2uFgZY6CnYPDcjRSXVi7XoKd7ddEOHZ3y/1Ves5DH5SbUl8tVVEfnci+darap1eX7LXq+J47XJY71leXn6R3gxIEPh7HpxCW2nU3k/i/CeLyHL++NbF3leFB/ZSUWbDrN5+tPsWxiKAHOlR9ePGxNWfFKH/KKSsktLMHe3JCnFu684axmIYQQQogrdBS6dHhrKRnn9pN6fCsX1//Euf99TJcP1mBk58axbydRnJNGi/EfYGjrgkJPn93Th6AuLa58HN1r0hSXB2yKatrQqGuL5vL/r2bEdnm/dq//jsqyct10hbJ84oFdcG9Cv9hPyuHNpJ7Yxt6Zo3Hv9xj+D0+v9mz1VVYiavU3nF/5BR3eWoqZW8016AH0VEaYuvqTnxhdaz8hhLgRSQ4LIYS4K+jo6NDB25YO3rZMHhRA23f+Ze3ROJ7t3Zw951P4ZEw7+gSWzxiJz8gjLbeoXs4bn5FPYmY+Dhbls4cPRKei0NHB265qyYVmjuYY6CmIT8+ji2/NyVobUxVjO3sxtrMXv22P5P0Vh2tMDtdHWYmvN57ms3Un+fPFkIpyFtUxNtDD2ECPzPxiwk8n8M6I4FqPK4QQQghxLR0dHayadcCqWQd8R75K2EvtSdy/Bq/Bz5J+di+Bj3+EXevyGbAFafEU56TXy3kL0+IpzEhEZVleuiEz4gDoKDB29K7S18TZD4XSgMLUeKz9q19DAsDAzAaXnmNw6TmGmOa/cWbxhzUmh+ujrETUqgVErphP+zeWVJSzqE1ZSRF5lyKwat7xhn2FEKI2khwWNTr0ekcc+z6JY9+ntB1KjbLO7OLUnAcAsAzuT/OJP93R8xemxnJ4avmvv0auLWj13sY7en4hmoqD0alsP5tEiL8DNqYqDl1IIy23CF8HMwA8bU1Ytu8CrdytyCko4YPlRzBU6t7gqHVjoKfLxN/38N59rckpLOHtZQcZ1sa1SkkJKK/f+1wff979+xBqjYaO3rbkFJawPyoVYwM9xnTy4pPVx2jpakVzR3OKStVsOBGPr331K2nD7ZeV+GrjKT5ZfZxvHuuCm5UxyVkFwOVE8OXF+sJOJaDRaPC2N+NCSg7vLz+Ct50ZD3b2uuXzCiHujI6fHeLJTo481dnxxp21ZFd0Fg/8Uv4GRP/mlvz0YPM7ev7YjEI6zT8MQAsHIzY+1+qOnl+IpiIz8hCpJ7Zj07InBmY2ZEYepjg7DRNnXwCM7T2J3/EX5l6tKC3I5fTiD1BcUzbhdiiUBhz9ZhL+D79LaUEOp357B8dOw6qUlADQMzTBc/CznPp9OhqNBstmHSgtyCHj3AH0VMa49BjNuWWzMfdsiYlLM9SlxSQf2oSxk2+N57/dshLnV31NxLLZtHrxa4xsXSnKTAZAV2WMnqp8vY3Ti97Hrk1fDK1dKM5OJXL5fEoLcnDp/sAtn1cIIUCSw+IeETxzG0ozm0ptiVt+4dL6bynOTMbI2Q+Pse9j5ndzv6qe+eIx8mJPUpKdhp6xOeb+3XC//230L/8ibWDlRNt5h7m07luyTm+vt+sRQlRmaqhkT2Qy34edJbewBBcrY967rzW9A8pnCs8f15Epi/fT56N1OFsZ89bQlry//Ei9nNvT1oTBrVx5aMFWMvOL6R3gyCdja64598aQIGxMDPhiwykupuZhZqikpaslk/oHAKDUVTDr36PEpuWh0telo7ct3z1R86yV2/XLtkiKS9VM+HFHpfYpgwJ5bXAQANkFJcz89ygJmflYGOkzJNiVN4e1RHlNGQ4hhLhd2yYGY2OsrPj8675Efj+QRGxm+ZsefraGvBLiQi/fm0uwZBaU8s6aaDaeLa+T3reZJTMGeWJuWP6o42RuwOEpbfl21yW2R2XV09UIIa6nZ2hC+pk9XFj3A6UFuRjauND84ekVtXKDnpnHiR9fY8db/VBZO9NszJucWfR+vZzbyMETh/aD2D97HCW5mdgG9yLw8Y9q7O/3wFQMzGw4v/IL8pNjUBqbYeYRhM/wlwBQ6Ck5u3QW+Smx6OobYtWsA60nflsvsVYnZuMvqEuLOTy/8sQsn5GT8bt/CgCFaQkc+fJ5inPS0TezxsKnDZ3fX12x+J8QQtwqSQ6Le4LSzAY9o6sz71L3reTCn+/hOW4Wpj7tSdr6O6fnjyP4w3AMrJ3rfFyz5l1wHjwRfXN7ijMTuPC/Dzn7zdMEvfUvUF5XS9/cDt3Lv+YKIRqGn4M5f74YWuP2IFcr1k/tX6lt6HWlGJK+frDSZzdrkyptXf3sq7QBPNbDl8d6VD9b5ItHKteO09HR4anQZjwV2qza/q8ODOTVgYHVX0gDOPBh7a84Agxv68bwtrWXrhBCiNtlY6ysSNgCOJrr82YfNzysymcOLjuSwhNLzrL+2ZY0szOq6TBVvPhXBAnZRfwxvnzBqqn/nuelfyL59eHyGcq6Ch3sTPUx1q+fN0qEENUzcfajwxtLatxu7hFE1xnrKrU5dhxS6fOgxQmVPhvZulZps27RpUobgHvfR3Hv+2i152717OeVPuvo6OAx4Ek8BjxZbX+f+17B575Xqr+QBhD6xf4b9mn9UsMlp4UQTZtMCboHJYX/zoHJbdGoKxfoP/PFY0QunARAYfIFznz5OAdeacXe53059uEgMk9tq/GYhamx7J7gTF7MiYq20vwsdk9wJuvMroq2/EvnOD1/PHuf9+XAK62I+GEiJfVUR+pmJGz4AbvuY7Hv8RBGTr54PvgBBlZOJIb/dlPHcer3NKbebTGwccHUpz3Og14kN+oQ6tKSBopcCCGEEE3B7/uTaPvpgYqFLK94bPEZJv0TCcCF9EIeX3yGVrMP4DtzL4O+O8a285k1HjM2oxDn6bs5kZBX0ZZVUIrz9N3sir46Y/Zccj7j/ziN78y9tJp9gIl/R5Ced+fHNv2aWdHbzxJvG0O8bQx5o48bxvoKDsXm1PkYESn5hEVmMme4N+1cTWnnasrsYd5sOpdBZGpBA0YvhBBCCHFvkJnD9yCrdkOIXvIu2Wd2Yt6iOwCleZlkntxK84m/AFBWlIdlUC9c73sdhdKAlJ3LOPPF47Seue2mZtZeqzgziZOfjMK+x0N4jJmOuriQi3/N5Ny3zxDw2rJq9ylKi+fIOyG1Hte200i8HvmkznGoS4vJvXgMp0EvVGo3b9GTnMgDdT7O9UpyM0jd8w+m3u1Q6ClvvIMQQgghRA2GBFjx7tpodl7IprtX+dtPmQWlbI3M5JeHyme85hWX0cvXktd7u2Kgp2DZkRQeX3yGbRNb42xxa7XIk3KKGfXzSR5qa8/0/h4UlqqZufEizyw7x7LHAqrdJz6ziJCvj9R63JEtbflk6K3XKS9Ta1h9Mo38YjVtXasu+FmTg7G5mKl0aeNydZ+2rqaYqXQ5GJuDj03tC3YKIYQQQjR1khy+BylNLLEIDCFl74qK5HDagdXoGVtg3qIbAMauARi7Xn0AcBs5lfTD60g/sgHH3o/f0nkTw3/D2D0It1FvVrR5Pz6XQ6+1pyDxPIYOVVeK1bewp+X0DbUeV8+w7g8IAKU56aAuQ/+6GsRKcxtKTiTf1LEALi6bSeKWn1EXF2Di1Ybmk3696WMIIe5Orw0OqqjLK4QQ9cnSSEmIjwUrjqVUJIdXn0zDwlCPbpc/BzgYE+BwtXTV1N5urDudzoaz6Tze8dYWoPttfyJBjsa82edqKZm5w71pP+8Q51ML8K4mmWpvqs+GZ1vWelxTg1t7rDidlMewH09QVKrGWF+XH8c2w+8mSkok5xZjbVz1R3trYyXJOfKmlxBNgd/9Uyrq8gohhLh5khy+R9l0uo+oX6eiHjerfGbwnuXYdBiGjqK81lpZUT5x/84j4+gmijOT0KhLURcXUpwef8vnzLtwjOwzu9j7fNW6nIUpF6tNDuvo6mFo73nL56ydTuWPGg3o6FTftRZOA57DrvtYitLiift3HpE/TqL5pN/QuYVjCSGEEEJccV9LG6auimLWEDUGegqWH0thWKANuoryMUZ+cRnzwuPYdC6DpJxiStUaCkvUxGcV3/I5j13KY9eFbHxn7q2y7WJGYbXJYT1dHTytG2YGrre1IRuebUl2YRlrTqXx8vJI/n484KYSxNWNyG5x2CeEEEII0eRIcvgeZdmqL2heI+PYZkw8W5ETsRePMdMrtl/834dkntyK++h3UNl5oFCqOPfN06hLq3/Y0NG5XJ76mrJ4mrLSyp00Gixb9cXt/req7K9vYV/tcRuirISeqRUodCnOTqnUXpKdhtLMts7HuUJpaoXS1ApDB28MHX049Fp7cs8fxNSn3U0fSwghhBDiir7NLHntX9h8LoNWzibsjclh+gCPiu0fbrjI1shM3unvjoeVCpWegqf/d47iMnW1x1NUkw0tva6msUYDff0seatv1UUo7U31qz1uQ5aV0NdTVCSeWzmbcORSHj/uSWD2sKqTCqpjZ6JPajX1ktPzS7A1kTJgQgghhBA3Isnhe5SuviFWbQaSumc5hckXUNl7YeJx9XXA7Ih92HV9AOs2AwEoK8yjKDUOmlV/PD1TKwCKs5IwJhCAvJiTlfoYuweSdnANKhtXdHTr9q9WQ5SVUOjpY+LekqyT2yquDyDr1DYsW/e/qWNVVf6AVVMSXQjRuLR751+eCvXjmV7NtR1KjXaeS2Lk51sAGNDSmV+f6aHliKq6G2IU4m5kqNRloL8Vy4+lciG9EC9rFS2dTCq277uYzQPBdgz0twYgr6iMuMyiGo9nZVw+/krKKSbQsbwcxcnEvEp9Ah2NWXM6DVcLFXq6dZta25BlJa6n0WgoLtPcuONlbV1NyC4s43BcDq0v1x0+FJdDdmHZTdUuFkJoT9hL7fEY+BSeA5/Wdig1Sju1i70zRgFg37Y/bSf/ot2AqnE3xCiEaJwkOXwPs+k0kjNfPEb+pbPYdhpZaZvKzoO0g2vLZxjr6BC7fA5oqp+FAuXJZhOvNsSv+RoDG1dKc9KJXT67Uh+H0MdI2raYc989j9OA51CaWlGYdIHUfSvxfmxORUmLazVUWQnHfk8R+eMkTDxaYeLdluRtf1CUHo9Dz/F1PkZO1GFyo49g5tsePSMLClMvErviUwzsPDD1blvvMQshmrZd7w7GxlRV8fnz9SdZcySOiKRsVEpd2nvZ8M6IYHzszSr6vPTbHpbuja50nDYe1qx9rd8txZCeW0Svj9aSkFnAuTmjMDcqn0XY3suG47NG8PZfhyguLbulYwshqjeypQ2PLT7D2ZR8Rras/IaTh5WKtafT6NvMEh0dmLMlFnUteVNDpS5tXEz4ekc8rhYGpOeXMntzbKU+j3VwYPGhJJ7/6xzPdXXCykjJhfRCVp5IZc4w74qSFtdqqLISH22KoZevBU5m+uQWl7HyeBq7L2SzaLx/nY/ha2tEqI8Fr/0bVTFzeeqqKPr4WcpidEKIetdj7g4MrlnbJnLlFyTtX0PupUh09VVY+raj2YPTMHHyqfMx81NiiVz+GWknd1CUmYLK0h6nbqPwGTEJhV75WKw4J50jX79ATsxpSnIz0Dezwb5tf/zGvInSqPyHMEu/dvRecJRTv72DuqTmHxKFEOJ6khy+h5n7d0XP2ILCxPPYdLqv0jaPse9x/udXOfHRcPRMrHAe+AJlhbm1Hs/78Xmc//lVjn84EJW9N+4PTOP0vAcrtutbOhD45gpi/prF6c8eRlNahIG1CxaBIXClLMUdYtNhOKW5GcSt+ozirGSMnJvhP+l3DGxcKvrErpxLys7/0WZ21Zp7AAp9FemH1hC38lPKigrQt7DDIjAE32cWoFDe2grhQghRExtTVUUyFmB3RDKP9/Al2N2aMrWaWauOMebLMLa9Mxjja2bo9WrhyOfjOlZ8Vurd+v32lUV7aeFkQUJmQaV2fT1d7MwNMVTqSnJYiHrW1dMcC0M9zqcWcl9Q5cV03xvgwasrzzN84QmsjPR4oaszuUW1/zc4b4Q3r644z8Dvj+NtrWJaP3ce/O10xXYHM31WTAhk1sYYHv79NEVlGlzMDQjxsaCavHCDSs0t5qV/IknOKcZUpYu/vTGLxvvTw9uios/csFj+dySFva+0qfE4X47y4d21F3jo9/Lr7NfMkhmDGmpNCyFEU2ZgZoPS2Lzic/rp3bj3fRxz72A0ZaWc/d/H7Pt4LD1mb0NPVbfa6XmXItCo1QROmI2xvSc5cWc4/sMUyory8X+4vDSkjo4C+7YD8Bs9FQNTa/KSLnDy5zcpzsuk9YsLgPI3aA0s7FDoqyQ5LIS4KZIcvofpKHRpN+9QtdtUNq4EvLasUptDr8cqfb4+aWrk5EvQ26sqtXVeWHkBO0N7L5q98OMtRly/HHo9VuWarlWUGotZs841bjd28a/yHQkh7ozftkcyd+0JDs8YjuKabMX4b7dhYaTky0c6cyElh3f/PszBC6nkF5Xh52DGW8Nb0bO5Q7XHjEnLpf27q9j8xgACXS0ByMovxu+1v/lnUi+6+pXXRj+bkMX7/xxmd2QKRga6hDR35IP722Btcmd/FPrzxdBKnz8f15GAN5ZzLCadzr52Fe36egrszG9/dtwv2yLILijh1YEBbD6VcNvHE0LUja5Ch0NTql/HwNVSxbLHAiq1Pdax8j3u+qSpr60Rq54KqtQW/37l8Y6XtSE/jq2hltgdNHfEjWfWxWYW0dnDrNY+lkZKvhxVdUFkIUTDitn8GxH/fEavLw+io7j64/SBTx9FaWxOq+e+IC/pAqf/eI/MiIOUFeVj4uxLszFvYRNUfYmq/JRYwid1oNusjZh5lJczLMnLYuNTzek47W+sW3QBICfuLGcWfUD6mT3oGhhh07InLca9j76ZdcNf+DU6vLGk0ueWz3zG5meDyI4+ipV/zc+a17Jt1QvbVr0qPhvZu5N36TwXN/1akRxWmljg3vfRij6Gtq64932MqNXf1MNVCCGaOkkOi3vCwSntsGzVF79nFtR5n+xzewiY+vdtnffKgnqa0hIMneShRIj6MrSNK2//dZAd55LocTnZm5lfTPjpBH5/tvxhIq+olD4BTrwxtCUqpS5L90bzyLfb2PnuYFysjG/pvElZBdw3fzMPd/Hm/VFtKCwp5cMVR3lq4Q7+mdS72n3i0vPo/uGaWo97fwcP5jzY/pZiuiKnoHzBJQvjygtG7YpIpsXUfzA30qezjx1vDmuJ7TXlKeribEIWc9eeYO1r/biYWvtbJEIIcavazTtIXz9LFjzgV+d99lzI5u8nAm7csRZXFtQrKdPgayulJoSoLw4dh3Lq13dIO7UTm8DuAJTkZpJ6LJy2U34Fyte2sQvuhd8DU9FVGhC3/X8c+PRRes7djuE1b3XejMKMJPZ+OBLX0IfxH/ceZSWFnF0yg8NfPEPHaX9Vu09BahzbXutZ63Gduo0iaMLsWvvcSGl+DgBKE8vbOk5JQTZKE4satxdmJJK4fw1W/p1u6zxCCAGSHBZ3OROv1gTP2gGArurmkkFtPtlz2+e/dkE9hbL6Fb6FEDfP0tiAUH9H/jlwsSI5/O+hGCyM9OnerHyGb4CLJQEuVwfebw5tydojsaw/Fs+EkLonHq71y/YIglwteXt4q4q2+eM60nraSs4nZeNtX3X2moO5IVveHFDrcU0MlbcUzxUajYZ3/zlMR29b/J0sKtp7BTgytI0rLlbGxKTl8cmqY4z6fAsbp/bHQFm1znt1ikrKePbnXUy/LxgXK2NJDgsh6l1rFxN2vBQMgLF+3e5NV+yppZxEXV27oJ7+bZTeEUJUpm9iiU2rUC7tWl6RHE7YuwqliUXFZzP3AMzcr/7A02z0GyTtX0vSwQ149H/ils4bs+lXzDyCaDb2rYq2oKc/I2xiW3ITzmPi6F1lHwNLB7p9tKnW497sQujX02g0nP7jPSybdcDU9dYXQ85LusDF9T/hP256lW2Hv3yOpIPrUBcXYtemH0FPzb2dkIUQApDksLjL6eobNsiCdnXVUAvqCSFgVHsPpizZxydj2mGg1OWf/RcY0dYd3cuvLeYVlTJ3zXE2nrhEYlYBpWUaCkvKiM/Iu+VzHovJYOe5ZDxfqVpS5kJqbrXJYT1dBZ52t/cwcSNv/u8gp+Mz+ffVPpXaR7R1r/hnfycLgt2saPvOv2w6eYnBwa51OvbMf4/i62DG/R3kXiaEaBiGSt0GWdCurhpqQT0hBDh3HcnxH18j4PGP0FUacGnnPzh2Hl6xGHlpYT6R/8wl+fBGCjOS0JSVUlZcSGFa/A2OXLOs6GOkndrF+serJoHzky5UmxxW6Oph7NCwY52Tv7xFTswpOk1fecvHKMxIZP/HD+HQcSiuoQ9X2d5i/Pv4jnyVvITznF36Eaf/eI/AJz6+nbCFEEKSw0IIIRqnfkFOqBdr2HTyEsHuVuw5n8L7o67OIPtg+WHCTify3n3BeNiaYqjUZcKPOyguU1d7PIVOee1iDZqKtpLr+qo1GvoFOfHO8OAq+9dU17ehy0q8+b8DrD8Wz4pXeuNkWfvCJvbmhrhYGRGVnFPn4+84m8TpS1k4Hf4TAM3lr8d/6j+83D+A14cE1bK3EEIIIZoyuzZ9QaMm5fAmzL2DST+7F/9x71VsP7P4A1KPhdP84XcxsvdEV1/Foc+fQl1aXO3xdKoZr6nLSip30qixa9OX5g9Oq7K/gYV9tcdt6LISJ395m+SDG+j07nIMrZ1u6RiFGYnsnXE/lr5tCXpyTrV9DCzsMLCww8TZF6WJJXs+GIHPfa+gsqz+uoUQoi4kOSwqFKbGcnhqJ1pOX4+xW6C2wxFCNHGG+noMbuXK3/svEJ2Sg7edKa3crCq27zmfwthOngy6PEM2r7CE2PSaZw1fWVAuKauQoMuTak/GZVTqE+RqyX9HYnG1NkZPt26vHjdUWQmNRsNb/zvImqNxLH+5N+42JjfcJz23iEsZ+djfxAJ1Pz3VjYKSsorPRy6m8/Ife1n5Sh88bG98TiHE7YnNKKTT/MOsf7YlgY63Vi+9qVh6OJn31l3g9Jsdau3nPH03C8c2Y4C/Va39hBC3T1ffEPv2g4jf+Q95SRcwdvDC3Otqea70s3tx7jEah/aDACgtzKMgJRZqWKztyoJyRRnJ4FHeln3xZKU+Zh5BJO77D0NbVxS6dUtpNFRZCY1Gw6lf3ibxwFo6TfsbIzu3mz4GQGF6Antm3I+5Z0taPju/0gJ/tZwdoMZEuxBC1JUkh8U9oawwj6Pv9cUquB8eY9+raC9MjeXY9D64j5mOfY+HyDqzi1NzHqiyv/Pgl3AbORUo/ws+edtiknf8ScGlc2g0agysXTD3745D78eljIQQd9Co9u6M/3YbZxOyGNXeo9I2T1tT/jsSS79AZ3R04OPVx1FrNNUfiPJkc1tPa77ccApXa2PSc4v4aNXxSn2e6OHLHzvP8+zPu3i+jz9WJgZcSMlh+YGLzHu4Q0VJi2s1VFmJN5Ye4J8DF/n1mR6YGOiRnFUAgKmhEkN9PfIKS5iz5gSDg12xN1cRm5bHrH+PYWViwKBWdV/gxcO2cuzpuUUA+DmYYW4ktdSFELW7/+eTtHAw4oOBlcdHdU3k3o65YbGsO5POxuda3bizEKLBOHUdycFPHyU37izO3UZV2mZs70HS/rXYt+kHOjqcWzYbNNW/5QXlyWYLn7acX/UlhrYulOSkc+5/n1Tq497vcWLDFnHky+fwGvI8+qZW5CVFk7B7JUFPfVpR0uJaDVVW4uTPb3Jp13LaTv4ZPUMTijKTAdAzMkVXv24/1hdmJLLnw1EY2jjj//C7FGenVWwzsLADIPnwZoqyUrDwDkZXZUxu3DnOLPkQS7/2GNnWrZSYEELURJLD4p6gqzLG5/F5nJo7Bqs2AzHz64hGo+H8T69g6tMe+x4PVeofPHMbutf8MqxrUD5TR6PREPH9C6QfXo/L4BfxGPseSjMbilJiyDy1nfjVn+MzYf6dvDQhmrRuzeyxMDYgMimHkdclhz8Y1ZqX/9jLkLkbsTIx4MW+/uQWllR/oMvmj+vIy3/spf8n6/G2N+XdEcGM/iq8YruDhRGrJ/flwxVHGPtVGMWlalysjAlt4VhRluJO+WV7JAD3zd9cqf3zcR0Z29kLhUKH05cy+d/eaLILSrA3U9HVz57vJ3TBRHV1pvJLv+0hNj2P5S/3vqPxCyGEEKJpsAnohtLYgryE8zh1GVlpm//4Dzj+3Svsem8o+qZWeA19kdKC2stftXxmHse+e5Wd0wZg4uhDs4emsf+jsRXbVZYOdJ7+L2f+nMG+jx9EXVqEoY0Ltq1CQefOLjoZs+lXAPZ+WDkp3vKZ+bj0HAPAub8+JX7bUkK/2F/tMVKPbSU/KZr8pGi2vFh5Ec5BixMA0NVXERu2iNN/TEddUozK2gmH9oPwHvZifV+SEKIJkuRwE6NRq7m07huSti+mOP0SSjMb7HuOw2XIpGr6lnH+19fJPrOT4qwUDKyccAh9FMe+T1b0yTqzi4t/zaQg/iw6ukqMnP3wfeprDGxcyIs9yYUl08m9cAwdHR1U9p54PfIJJh4NM7vDrFknHHo9TuTPr9LqvY0kb1tEXuwpWn2wuUpfpZkNekbmVdrT9v1L2r6VNJv4M1bB/SraVbbumLfojqaWWYlCiPqnq1BwbNaIare5WZvwz6TKCc8nevpV+nzgw2GVPvs5mLNmSr9KbUlfP1jps5edKT8/3f0WI64/18d1PUN9PZa+GHrD48Sm59HF167O5+3qZ3/Dcwshbo5areGbnZdYfCiJS1nF2JgoGdfWnkk9q87yL1NreP3f8+yMziYltxgncwMebe/Ak50dK/rsis5i5saLnE0uQKmrg5+tEV/f74uLhQEnE/OYvvYCxy7loqOjg6eVik+GetHKWftlYjacTWdeWBznUvKxN9XngVa2vNTDBT3d8h/fvtt1if8dTuZiRhEWhnr0bWbJtL7uGBtUnQW49HAy88LjgPIyEgDzRngzpnX5/S49v4QJS84Qfj4LB1N9pvd3p19zKzQaDd2+OMz4dg482/VqXdAzSfn0+eYoO15qjYeVqqG/CiHuKToKXXovOFLtNiNbVzpO+6tSm0e/xyt9vj5pauLsR5cPVldqu5IkvcLY0Yu2r/x0ixHXn+vjqk5BaixWLbrUuN2l55iKRHJNrAO60uX9VTcdnxBC1IUkh5uYmL8/ImnbYjzGTsfMtwPFWckUJERW21ejVmNg6Yjfs9+iZ2JFzvkDRP36OkoLO2zaD0NTVsrZryZg1+Mh/J7+GnVpCbnRh+Hy7LqI7ydi7BZAy/Efg0JBfsxJdGqpCXX6s3FkR+ytNf6OCyJq3e426g0yT4QR8cNEsk6G4/XIbAwsHWvd51qp+1agcvCulBi+ls4dnjkohGg6gt9eSb8gJ757omu9HTO3sIQLKbn88WyP2zrOnshkHvx6K8WlZfQJvLVFVoRoyj7aFMPiQ0lMH+BBBzczknOKiUwtqLavWqPB0cyAb0f7YWWkx4HYHF7/Nwo7UyXDAm0oLdMw4c+zPNTGjq/v96OkTM3h+FyujFAm/h1BgIMxHw9piUIBJxPzK5Kv1Rn3+2n2xmTXGn/E2x1v9dIrhEdm8tLfkXwwyIOObmZczCjk9X+jAHg1tPyVaIWODh8M8sTVwoCYjCLe+i+KGRsv8tEQryrHGxZozdnkfMIjM/nzkRYAmKquJpHnhccxrZ870/q58/PeRF78O4K9r7TB0kjJmNZ2LD2cXCk5/OfhZDq6mUpiWAhRqy0vtsGuTT9aT/ymzvukn95Np3eXN2BUl89zZg/7P3kYdWkxdsHyxpgQou4kOdyElBXkkrBpIZ4Pz8Cu62gAVHYemPlWXwtOoafEdcSUis8qWzdyIg+Qtn8VNu2HUVqQQ1lBNpat+qCy8wDAyMm3on9xejxOA57F0NEHAEP7qgP7a3k9Ngd1ceHtXCIKpQqPse9z+rOHsQjqhW3nUdX2OzilXaXPbWbvRWliRWFSFIYO3pW2RS95l+TtSwDQMzKj7acHbytGIYS4VhsPa/ZMHwKAsUH9/rVsolJyeObw2z5OKzerikX36jtGIe51uUVlLNybwIxBnowOLp/V6mGlooO7WbX9lboKpvS6Wj/SzVLFgZgcVp1MY1igDTlFpWQXltGnmWVFItPX1qiif3xWMc92dcLHtrzWpZd17TUv5wz3orCk5vqfdfHb/iSWHEqu1Fam1mCgd/X17i+2xfFCN+eK78DdSsVrvVyZufFiRXL4qWtmR7tZqnitlxtvro6qNjlsqNTFWF8XXYUOdqZV66OPDrZlRJANAG/0ceOnfYkcic8l1NeSMa3t+DQslsNxObR2MaWkTM0/x1KY1s/9tr4HIcS9y8KnNT3n7QJAT3Vzi4eGfr6vIUKqwtyrVcWiezcboxCiaZMnvCYkPyECTWkR5v7d6rxPYvhvJG9bQlFaHOqSQjSlJRi5BQCgNLHEtutoTs97GIuA7pj7d8e6/VD0LewBcOz3NFG/vkbq7r8xb9Ed63ZDKpLI1bmZGb61Sd6+BIW+IflxpynNz0bPqOrDV+DU5Siu+QtTz8jimq2VZ9e4DJmEQ6/HST+0lvj/vqyXGIUQ4gpDfb0GWdCuPt0NMQrRWEWk5FNUqqGbV9VyVjX5bX8iSw4lE5dZRGGpmpIyDQEO5QlgSyMlo4Ntefj303T3sqC7lzlDA62xv5wgfbqzI6+tjOLvo6l09zJnSIB1rbNhHc0Mbu8CgfuCbHiph3OltrWn0/lye3zF52OX8jgan8sX2+Mq2tRqDYWlGgqKyzDU12VndBZfbosnIiWfnKIyyi5vzy8uw0i/ammJ2vg7XB3nGenrYqKvS2peKQD2pvr09rXkz8MptHYxZdO5DIpKNQxtYX0rly+EaAJ09Q0bZEG7+nQ3xCiEaJwkOdyEKPRv7jW51P3/cuHP9/EY/Q6m3u1QqIy5tP4bcqMOV/TxeeIzHHtPIPNEGKn7/yVm+WxaTF6CqXdbXIdPxqbjCDKObSbzeBixK+fi+8wCrNsMrPZ89VFWInXfStKPbCTorX+J+PElLvz5Hj5PzKvSz8DWtdqawyo7TwoSK5fZUJpaV/yfEEIIIcTNUClvbnGkf0+k8v66C7zT34N2rqYY6yv4ZuclDsfnVvT57D4fJnRyJCwik39PpjJ7SwxLHmlBW1dTJoe6MiLIhs3nMgiLzGRuWCwLHvBloH/145j6KCthqtLF87oZytbGykqfNRoNk0NdGehvVWV/Az0FcZlFPPLHaca1c+C1Xq5YGOqxPyaHySvPU1J282s+KBWVf+zX0Skv2XHFg23tmPRPJO8NcGfp4RSGBVhjeJMJaCGEEEKIe4Ekh5sQQ3tPFPoqsk7vQGX70A3755zbh6l3Wxx6PVbRVpR8sUo/Y/dAjN0DcR48keMzh5K6dwWm3m3Lz+ngjaGDN079nubcd8+TsmNpjcnh2y0rUZyVQvSit3G773WM3QPxmfAZJ2YNw7r9ECyDetXpGDYdRxDx/QukH16PVev+txyLEKJhxKTl0v7dVWx+YwCBrpbaDqdR+3N3FO/8fYiIT++vtZ/9C0v4+enuDGpVdWEsIcTt87QyRKVUsCMqi4fa3viH+n0Xc2jraspjHRwq2i5mFFXpF+hoTKCjMRN7ODP0h+OsOJ5KW9fyGf7eNoZ42xjydBcnnl92jqWHU2pMDtdHWYm6CHQ04XxqQZUk8hVHL+VSqobp/d1RXE7srjqZVusxlbo6qNW3tlhwb19LjJQKftufRFhEJn8/EXBLxxFC3L78lFjCJ3Wg26yNmHkEajscIYRociQ53IQolCqcBrzAxWUzUegpMfVpT0lOGvmXzmHfverK9Co7D1J2/0XmiXAMbFxJ2f03uReOYmBTXheuMCWGpG2LsGrVF30LBwoSz1OYFIVtl/spKy7g4rIZWLcdjMrGjaKMBHIvHMW6zaAa47vdshJRv03F0MEbx35PA2Di0QqnAc8R9evrtPpgS7XlJa5n3WE4aQfXcO6753Ee9CIWgT1RmtlSlBZH2v5V6ChubvaPEEJc6775mwlwsWDG/W0rtdc1kXs75vx3nLVH49jyVvU/0AkhGoZKqeCFrk7M3HgRpa6C9m6mpOWVcC45nwfb2lfp72Gl4q+jKYRHZuJqYcDfR1M4Gp+Lq2V5+YeYjEIWHUiib3MrHEz1OZ9aQFRaIfe3sqWgpIwZGy4yuIU1bpYqErKLOHopl0E1JIahfspK1MUrIS48uugMTuYGDAmwRqEDp5LyOZOUz9TebrhbqihVa/hpbyJ9m1myPzaH3w8k1XpMVwsDYjKLOJGQh5OZPsYGupXqHNdGV6HDA8F2fLwpBg8rFe1cpXSOEOLmlRbmseON3ti17U+L8e9XtOenxLLjjV74j3sP19CHSTu1i70zqq6H4z1iEs1GvwGUv2ERG7aIuPA/yY0/i0atxtDGBZvA7rj3nyAlI4QQDUaSw02My9CX0dHVJXbFpxRnJqE0t8MhZHy1fe1DxpMXe5Jz3z4HOjrYdBiOfeijZB7fAoBC35CChEjO7lxGaV4G+uZ2OPR6HPue49GoSynNzSBy4SRKslPRM7HCus1AXEdMbpDrStm1jKxT22j53oZKCVyXYa+SfmRjjeUlrqejo4Pfs9+SvG0RyTuXcmndAjRlpehbOmLu342AMdMbJH4hhBBC3Lte7umCrkKHT8NiScopxs5Eyfh2DtX2Hd/enpOJeTy37Bw6wPAgGx5tb8+WyEwADJUKIlMLWLb0LBn5pdiZ6vN4BwfGt7OnVK0hI7+UScsjSc0twcpIj4H+1kwOda32XHdSiI8Fvz7cnM/C41iw8xJKhQ4+NoY82KZ8gbpAR2Om93dnwc54PtocQyd3U97s48akfyJrPOagFtasOZ3O6F9OklVYxrwR3oxpbVfnmB5sY8eX2+MZ29r2tq9PCNE06amMafnMZ+ydNRqH9gOxat4JjUbDse9extKvPa6hD1fq32PuDpSGV3+M0r28Do5Go+HIV8+TdGAd3sMn4j/+fQzMbMhPiSH1+DYiV8yn1bOf39FrE0I0HZIcbmJ0FApchkzCZcikKttUNq50Xnh14RCF0gCfJz6DJz6r1M991JsA6Jvb0vzFhTWcRx+/ZxbUY+S1s+3yALZdHqjSrtDTJ/iDzRWfzZt3qXSN1dFRKLAPGY99DUlzIUTDUqs1fL3pNH/sPM+lzHxsTVWM7+bDKwOqvvJbplYzefF+dpxLIiW7EGdLIx7r4cvToc0q+uw8l8QHK45wNiELpa6CZo7mfPNYF1ytjTkZl8G0vw5xNCYdHR3wsjVlzoPtCXbXfo3x9cfj+fS/45xNyMLe3JAxHT15eUAAerrlP4B9u/kMS/ZEcTE1F0sjA/oFOfHuiGCMVcoqx/pzdxSfrjkBlJeRAPh8XEfGdvYCID23iMe+3074qQQcLAx5b2RrBrR0QaPR0Om91Tza3Yfn+/hXHO/0pUxCZ61lz/QheNjKbDshbkSh0GFSTxcm9axavsXVUkX8+50rPhvoKfjsPh8+u67fm33dAbA10Wfhg82rPY++QocFD/jVW9x18dfj1ZdjGNParkqiNsTHghAfixqP9XQXJ57u4lSp7f5WVxO31x/TQE/BD2Oacb1rv88rTr/ZoUpbcm4xegod7g+W5LAQDU2jVhO1egGxYYsoTLuEvrkNbr3H4zPi5Wr6lnH8x9dIO7mDoswUDG2ccevzKJ4Dn6rok3ZqF2cWf0hu/Fl0dJWYujQj+IWvMbR1JfviSU79/i5ZUUfR0dHByN6TwCdnY+EV3CDXZuXfGfd+T3Dsu1fo9tFmYsP+IPvCSXrMDqvS18DMBqVx1bVvEnavJGH3CtpO/gX7tlfLGxrZu2MT2B2N5tZK6AghRF1IclgIIUSjMvPfo/yx8zwfjGpNR29bkrIKiUiqfrEktQacLIz4YUJXrIwN2B+VypQl+7A3M2R4WzdKy9Q89v12xnXx5rvHu1BcpubwhTR0Lq9T9NwvuwlysWT22PboKnQ4EZeBUrfmV5If/DqcPZEptcYf/VnVH6puVtipBF74ZTczH2hDRx87LqbkMHnJfgCmDA4CyhdXmvlAW1ytjIlJy+WNpQf4YMURPhnbvsrxhrd140xCFltOJfDXxFAATA2vJpHnrjnBO/e1YvqIYH7ceo7nf9nNwQ+HYWlswIOdvfhzd1Sl5PCS3VF08raVxLAQ4q5UVKrmUlYRs7fEMjTAGlsTfW2HJMQ97+zSWcRuWYT/+PexbNaBoowk8hKqfzNAo1ajsnKk9Uvfo29qRca5/ZxY+BoqS3scOw1DXVbKwXmP4xr6MMETv0FTWkzm+SNcGeAd+foFzDwCCXz8Y3QUumRfPIlCt+qP51fs/+Qh0s/UvjB6/5/P17q92Zg3STm6haMLXiDlWDhBT85BZVX3somXdq/A2NG7UmL4Wjo6OtW2CyFEfZDk8F1OT1eXktISbYchaqApLUGpJ/+ZCVFXuYUl/BB2llmj2zGmU/msVg9bUzr6VD+rS6mr4PUhQRWf3W1MOBCdyr+HYhje1o2cwhKyC0roG+Rckcj0c7g6WyM+I48X+jTH16G8JrmXXe3JznkPd6CwuOy2rvGXbZEs2hlVqa1MrcZAqVvxef66k0zs53/1O7Ax4Y0hQXyw4mhFcviZXldnDrrbmDB1SEum/rm/2uSwob4exgZ66Cl0sDOvuhjUmE6ejGznAcBbw1qxcOs5Dl9Io1eAEw929mL2f8c5dCGNNh7WlJSp+WvfBabfF3xb34MQ9xpd3fL/hkvLZHZXY7fieCpTVp4nwMGYL0a61djvyp+lnozlhLgtpQW5XFj3IwGPzcSlx2gAjO09sGresdr+Cj0lfve/VvHZyM6NjIgDJOz5F8dOwygtyKE0Pxu7Nn0wtvcAwMT56lsThWnxeA15HhNn3/JzOXrVGl/QU3Mpu42F0QF09VW0GP8B+z95CNtWvXDuVv06EltebFPpc+gX+9E3tSIv4TzGTj6Vtp367R1iwxYDoDQ2p9dXh24rRiGEqImMdO5ybq4uHEuM0HYYogZFiRE0c3HWdhhC3DXOJWZTVKqme7OqizTV5NftESzaFUVceh4FxWWUlKkJcLEAwNLYgLGdPBn7VRg9mjvQo7kDw9u4YX85Qfpsr+a8umgfy/ZdoEdzB4a1dq11NqyjhdFtXR/AqPbuvNy/8mvY/x2N5fP1pyo+H41N50hMOvPXXW1TazQUlpSRX1yKkb4eO84l8fn6k5xLyCansIQydfn2vKJSjA1u7q/3Fs4WFf9sbKCHiYGS1NwiAOzNDekT4MSS3VG08bBm4/F4ikrLGNqm5oSKEE2Ri0t5yYhzKfk0t7/9e4VoONWVvKjOuZR8AJydZSwnxO3IjY9AXVKEdUD3Ou9zcdOvxIYtpjA1jrLiQtSlJZi5l4+f9E0sce4xhv0fP4RNYA+sA7vj2GkYKsvy8aPHwGc4/sNk4rf/hU1Qdxw6Dq1IIlfnZmb41iY2fAm6BobkxJ6hJD8bZTULoneavgI9lUnFZ6WxRcU/Xz832HvEJNz7PUHi/jWcX/lFvcQohBDVkeTwXe6+EcPZNnkK+ZciMHLy1XY44hr5lyLIPLWdkfPmajsUIe4aqmtmz9bFyoMxvPv3Yd4b2Zp2ntaYGCj5etNpDl1Iq+jz+fhOPBnix5ZTCaw8GMPHq47xv4mhtPO04bXBQYxs587Gk5fYcjKBOf8d57vHuzAouPrFm+qjrISpoRLP62Yo25ioKn3WaOC1wYEMblU1DpWeLrFpeTy8YCuPdPNh6pCWWBrps/d8Cq8s2kdpmbrW81dH77pSGjo65bWfr3i4qzcv/rqbD0a1ZsmeaIa3ccdIX4YQQlzL1dWV1q1a8vuBiwz0t6q1RI1o/ErK1Px2IJk2wa1wddX+gn5C3M0U+qobd7pGwp5/Of37e/iPm46Fb1v0VCZErV5A5vnDFX1aPTsfjwETSDkaRsKefzm37BM6vLkUS9+2+N0/Baeu95FyeBMpR7cQ8denBE/8Bof2g6o9X32Ulbi0eyXJhzbQ+f1VHF0wkdO/T6flM9dXjwcjW7dqaw4bO3iRe6lymQ0DM5uK/xNCiIYkT3Z3ufHjx/Pt9z9wds4orHuOx8y3Iwp9Q6QkkXZoNKAuLiA7Yi9pW3+nWbNmjBs3TtthCXHX8LIzxVCpy/azSbjbmNyw/57zybTztOHxHld/HLuQmlulX5CrFUGuVkzqH8CgTzfwz/6LtPMsH2h725vhbW/Gs72a88xPO1myJ7rG5HB9lJWoiyBXSyKTcqokka84GpNOaZma90e2RqEov+GvPBRT6zGVugrKbnExkz4Bjhjp6/Hr9ki2nLzEilf63NJxhLjXzZj1ESNGDOf+X04zNtgGT2tDdBUyKLublKk1RKcV8OeRVI4m5LPiq1naDkmIu56xgycKfRVpJ7djZPfwDfunn9mLpV873Ps+VtGWn3SxSj9zjyDMPYLwGf4Su94dwqVdy7H0bQuAiaM3Jo7eeA56hsNfPkfc1qU1Jodvt6xEUVYKJ39+E78HpmLuEUTLZ+eze/pQHDoOwS64d52O4dRlBEe+ep6kA+uwbzfglmMRQohbIcnhu5ylpSVbw7bw2muv8c/yn4lfNV/bIQnA1MycMfeNYM6cOVhaWmo7HCHuGiqlLi/29efDFUfQ11PQwcuG1NwiziZk8XAX7yr9PW1NWbb3AmGnEnCzNmbZvgscuZiOm7UxABdTc/l9ZyT9g1xwMDfkfHI2Uck5jO7gSUFxKR8sP8KQ1q64WZuQkJnPkYvpDGld8wyx+igrUReTBwYy7putOFsaMbS1GwoFnIrP5PSlLN4c2hIPGxNK1Rp+3HqOfkHO7D+fwm87ql/U5QpXa2NiUvM4EZuBo6URJgZ6leoc10ZXoWBMJ09m/nsUT1tT2nvJDBYhqjNo0CBWrVrNRzNn8NqqnbK6/F1KR0eHHt27ser7t+nfv/rFoYQQdaerr8J76IucWTwDHV0lls06UJydRm7cWVxDH6rS38jeg/jty0g5GoahnRuXtv9FVtQRDO3KS1rlJ8cQs+V37Nv0R2VpT27CefISo3Du/gBlxQWcWfQhDh2HYGTrSkF6AllRR3DoMLjG+G63rMSJH1/DxMkHz0HPAGDhFYzXkOc58ePrdJ8dVm15ies5dh5B4r41HP7yObyHT8S2ZQj65rYUpMaSsGclOoqbe7tOCCFuhiSH7wG2trb88ssvfP99MfHx8RQW3l4xfXF7VCoVzs7O6OvLytdC3IpXBwaiq6tg9urjJGYVYG+m4pHuPtX2fbSbDyfjMnj6p53oAPe1c+exHj5sOZkAgKG+LpFJOfxv7w4y8oqwNzPkiR5+PNLNh1K1mvS8Iib+toeUnEKsjA0YFOzCa4ODqj3XnRTawpE/nuvJ3LUn+HrjafR0FfjYm/Fw1/IFVQJdLXl/VGu+2niaWSuP0snHlreHteLF3/bUeMwhwa6sORLHyM83k1VQwufjOjK2c+0LtFzroS7efL7+FA/exD5CNEX9+/enf//+ZGRkkJKSQllZw79tIOqPrq4utra28uO+EPXM575X0NHVJeKvORRmJGFgaYdb70eq7evW5xGyL57k8JfPAjo4dRmBW99HSTkaBoCugSF5lyI5tG0ZJbkZGFjY4d7vcdx6j0ejLqU4N4Oj30ykOCsVpakVDu0H4TtqSoNcV9y2/5F6YhvdZm1CR3G1nJDvqMkkHdpQY3mJ6+no6ND6pe+IDfuDuK1LiVr1NZqyUlRWjlgHdKPzuPcaJH4hhADQ0ciUBiGEEDXw8fJgkK8R04YHazsUoWX7zqdw3/zNHJ45HDszwzt67hkrj7AmIp/IqAt39LxCCCFEY7N69WqGDh1K72+OYWBuq+1whKhRUVYKm59ryapVqxgyZIi2wxFC1EJmDgshhBCiRkUlZVzKyOfj1ccY1sbtjieGhRBCCCGEEEI0HFlGWQghRI0UCgVlZfKCSVO2/MBFunzwHzkFJbx7X7BWYigr06BQyJBFCCGEuPL3oaasVMuRCFG7K/+OyhhOiMZPZg4LIYSokb29IxfSYrUdhtCisZ29bqo2cUOITs3DwdFNqzEIIYQQjYG9vT0A+UkXbnshNSEaUl5SNAAODg5ajkQIcSPyE44QQogaDR0+nLBTiVzKyNd2KKKJupSRT9jpBIYOG67tUIQQQgita926NfaOTsSGLUaWDxKNlUajIS5sCQ5OzgQHB2s7HCHEDciCdEIIIWqUmJhIpw7toTCbF3r70dHbDpW+LjraDkzc0zRAYXEZe88n8/Xmc6AyY8++/TLzRAghhAC++eYbnn/+eZy63IdzzzEY2bqho9DVdlhCoFGXkZ8SQ9zWpSTsWs6CBQt47rnntB2WEOIGJDkshBCiVhcuXOCZp59i85YwysrKtB2OaEJ0dXXp3SuU777/AQ8PD22HI4QQQjQa33//PTNmfUTsxQvaDkWIKlzdPZj21ps8/fTT2g5FCFEHkhwWQghRJ2lpaZw5c4bCwkKtnL+goIBp06Zx9uxZZs2aRcuWLbUSx73u6NGjvPXWW/j7+zNjxgxUKpVW4lCpVDRv3hxra2utnF8IIYRo7DQaDadPnyYpKQm1Wq3tcAAZr90pjWW8dj2FQoG9vT3+/v7o6Mi7hkLcLSQ5LIQQotHLy8tjyJAh7N+/n7Vr19K9e3dth3RP27ZtG4MGDaJDhw6sWrUKY2NjbYckhBBCiEZOxmt3lozXhBD1RZLDQgghGrW8vDwGDx7MwYMHWbt2Ld26ddN2SE3C9u3bGThwIO3bt2f16tXywCGEEEKIGsl4TTtkvCaEqA+SHBZCCNFo5ebmMnjwYA4dOsS6devo2rWrtkNqUnbs2MHAgQNp06YN//33HyYmJtoOSQghhBCNjIzXtEvGa0KI2yXJYSGEEI1Sbm4ugwYN4siRI6xbt44uXbpoO6QmadeuXQwYMIDg4GDWrFkjDxxCCCGEqCDjtcZBxmtCiNshyWEhhBCNTk5ODoMGDeLo0aOsX7+ezp07azukJm337t3079+fVq1asWbNGkxNTbUdkhBCCCG0TMZrjYuM14QQt0qSw0IIIRqVnJwcBg4cyPHjx1m/fj2dOnXSdkgC2LNnD/379ycoKIi1a9fKA4cQQgjRhMl4rXGS8ZoQ4lZIclgIIUSjkZ2dzcCBAzl58iQbNmygQ4cO2g5JXGPv3r3069ePwMBA1q5di5mZmbZDEkIIIcQdJuO1xk3Ga0KImyXJYSGEEI1CVlYWAwYM4PTp02zcuJH27dtrOyRRjX379tGvXz9atGjBunXr5IFDCCGEaEJkvHZ3kPGaEOJmSHJYCCGE1mVlZdG/f3/Onj3Lxo0badeunbZDErXYv38/ffv2xd/fn3Xr1mFubq7tkIQQQgjRwGS8dneR8ZoQoq4kOSyEEEKrMjMz6d+/P+fOnWPTpk20bdtW2yGJOjhw4AB9+/alWbNmrF+/Xh44hBBCiHuYjNfuTjJeE0LUhSSHhRBCaE1mZib9+vUjMjKSTZs20aZNG22HJG7CwYMH6du3L76+vqxfvx4LCwtthySEEEKIeibjtbubjNeEEDciyWEhhBBakZGRQb9+/YiKimLTpk20bt1a2yGJW3Do0CH69OmDj48PGzZskAcOIYQQ4h4i47V7g4zXhBC1keSwEEKIOy4jI4O+ffsSHR3N5s2bCQ4O1nZI4jYcPnyYPn364OXlxYYNG7C0tNR2SEIIIYS4TTJeu7fIeE0IURNJDgshhLij0tPT6du3LxcvXmTz5s20atVK2yGJenDkyBF69+6Nh4cHGzduxMrKStshCSGEEOIWyXjt3iTjNSFEdSQ5LIQQ4o5JT0+nT58+xMTEyIPGPejo0aP07t0bNzc3Nm3aJA8cQgghxF1Ixmv3NhmvCSGuJ8lhIYQQd0RaWhp9+vQhLi6OLVu2EBQUpO2QRAM4duwYvXv3xsXFhU2bNmFtba3tkIQQQghRRzJeaxpkvCaEuJYkh4UQQjS41NRU+vTpw6VLl9iyZQuBgYHaDkk0oOPHj9OrVy+cnZ3ZtGkTNjY22g5JCCGEEDcg47WmRcZrQogrJDkshBCiQaWmptK7d28SExPZsmULAQEB2g5J3AEnTpygV69eODo6snnzZnngEEIIIRoxGa81TTJeE0KAJIeFEEI0oJSUFHr37k1SUhJhYWG0aNFC2yGJO+jkyZP06tULe3t7Nm/ejK2trbZDEkIIIcR1ZLzWtMl4TQih0HYAQggh7k3Jycn06tWL5ORkedBoogICAggLC6v074IQQgghGg8ZrwkZrwkhZOawEEKIendlcJmWlkZYWBjNmzfXdkhCi06fPk1oaCg2NjZs2bIFOzs7bYckhBBCNHkyXhPXkvGaEE2XzBwWQghRr5KSkggNDZUHDVHB39+f8PBw0tLSCA0NJSkpSdshCSGEEE2ajNfE9WS8JkTTJclhIYQQ9SYxMZHQ0FAyMjIIDw+XBw1RoXnz5oSHh5ORkUFoaCiJiYnaDkkIIYRokmS8Jmoi4zUhmiYpKyGEEKJeJCQk0KtXL7KzswkLC8PPz0/bIYlG6Ny5c4SGhmJmZkZYWBgODg7aDkkIIYRoMmS8JupCxmtCNC2SHBZCCHHbEhISCA0NJTc3l7CwMHx9fbUdkmjEIiIiCAkJwczMjC1btuDo6KjtkIQQQoh7nozXxM2Q8ZoQTYeUlRBCCHFbLl26REhICHl5eYSHh8uDhrghX19fwsPDycnJISQkhEuXLmk7JCGEEOKeJuM1cbNkvCZE0yHJYSGEELcsPj6ekJAQCgoKCA8Px8fHR9shibvElQeO/Px8QkND5YFDCCGEaCAyXhO3SsZrQjQNkhwWQghxS+Li4ggJCaGoqIjw8HC8vb21HZK4y/j4+BAeHk5BQQEhISHEx8drOyQhhBDiniLjNXG7ZLwmxL1Pag4LIYS4abGxsYSGhlJSUkJ4eDienp7aDkncxaKioggNDUVfX5+wsDBcXFy0HZIQQghx15PxmqhPMl4T4t4lM4eFEELclNjYWEJCQigtLZUHDVEvvLy8CA8Pp6SkhJCQEGJjY7UdkhBCCHFXk/GaqG8yXhPi3iXJYSGEEHUWExNDSEgIarVaHjREvfL09CQ8PJzS0lJCQkKIiYnRdkhCCCHEXUnGa6KhyHhNiHuTJIeFEELUycWLFwkJCUGj0RAeHo6Hh4e2QxL3GA8PD7Zu3YparSYkJISLFy9qOyQhhBDiriLjNdHQZLwmxL1HksNCCCFu6MKFC4SEhAAQHh6Ou7u7dgMS9yx3d3fCw8MB5IFDCCGEuAkyXhN3iozXhLi3SHJYCCFEra48aCgUCrZu3Yqbm5u2QxL3uCsPHAqFgpCQEC5cuKDtkIQQQohGTcZr4k6T8ZoQ9w5JDgshhKhRdHQ0PXv2RE9Pj61bt+Lq6qrtkEQT4ebmRnh4OLq6uvLAIYQQQtRCxmtCW2S8JsS9QZLDQgghqhUVFUVISAj6+vqEh4fj4uKi7ZBEE+Pq6kp4eDh6enr07NmT6OhobYckhBBCNCoyXhPaJuM1Ie5+khwWQghRxfnz5wkJCcHAwEAeNIRWubi4sHXrVgwMDOjZsydRUVHaDkkIIYRoFGS8JhoLGa8JcXeT5LAQQohKIiMjCQkJwdDQkPDwcJydnbUdkmjinJ2dCQsLQ6VS0bNnT86fP6/tkIQQQgitkvGaaGxkvCbE3UuSw0IIISpcedAwNjYmLCwMJycnbYckBFD+wBEeHo6RkREhISFERkZqOyQhhBBCK2S8JhorGa8JcXeS5LAQQggAIiIi6NmzJ6ampvKgIRolJycnwsPDMTY2JiQkhIiICG2HJIQQQtxRMl4TjZ2M14S4+0hyWAghBOfOnaNnz56Ym5sTFhaGo6OjtkMSolqOjo6Eh4djampKSEgI586d03ZIQgghxB0h4zVxt5DxmhB3F0kOCyFEE3f27FlCQkKwtLQkLCwMBwcHbYckRK0cHBwICwvD3NyckJAQzp49q+2QhBBCiAYl4zVxt5HxmhB3D0kOCyFEE3bmzBlCQkKwsrJiy5Yt2NvbazskIerkygOHpaUloaGhnDlzRtshCSGEEA1CxmvibiXjNSHuDpIcFkKIJur06dOEhIRgY2MjDxrirmRvb09YWBhWVlbywCGEEOKeJOM1cbeT8ZoQjZ8kh4UQogk6deoUoaGh2NnZsWXLFuzs7LQdkhC35Mq/wzY2NoSEhHD69GlthySEEELUCxmviXuFjNeEaNwkOSyEEE3MyZMnCQ0Nxd7ens2bN2Nra6vtkIS4Ldc+NIeEhHDq1ClthySEEELcFhmviXuNjNeEaLwkOSyEEE3IiRMnCA0NxdHRUR40xD3F1taWLVu24ODgQEhICCdPntR2SEIIIcQtkfGauFfJeE2IxkmSw0II0UQcP36cXr164ezszObNm7GxsdF2SELUKxsbGzZv3oyTkxOhoaGcOHFC2yEJIYQQN0XGa+JeJ+M1IRofSQ4LIUQTcOzYMXr16oWLiwubNm3C2tpa2yEJ0SCuPHA4OzsTGhrK8ePHtR2SEEIIUScyXhNNhYzXhGhcJDkshBD3uKNHj9KrVy/c3NzkQUM0CdbW1mzevBlXV1dCQ0M5duyYtkMSQgghaiXjNdHUyHhNiMZDksNCCHEPO3LkCL1798bDw4ONGzdiZWWl7ZCEuCOsrKzYtGkT7u7u9OrVi6NHj2o7JCGEEKJaMl4TTZWM14RoHCQ5LIQQ96jDhw/Tu3dvPD095UFDNElXHjg8PDzo1asXR44c0XZIQgghRCUyXhNNnYzXhNA+SQ4LIcQ96NChQ/Tu3Rtvb282btyIpaWltkMSQissLS3ZtGkTXl5e9OrVi8OHD2s7JCGEEAKQ8ZoQV8h4TQjtkuSwEELcYw4ePEifPn3w9fVlw4YNWFhYaDskIbTKwsKCjRs34uPjQ+/evTl06JC2QxJCCNHEyXhNiMpkvCaE9khyWAgh7iEHDhygT58++Pn5yYOGENe48sDh5+dH7969OXjwoLZDEkII0UTJeE2I6sl4TQjtkOSwEELcI/bv30+fPn1o3rw5GzZswNzcXNshCdGomJubs379epo3b06fPn04cOCAtkMSQgjRxMh4TYjayXhNiDtPksNCCHEP2LdvH3379qVFixasX78eMzMzbYckRKN05YHD39+fPn36sH//fm2HJIQQoomQ8ZoQdSPjNSHuLEkOCyHEXW7v3r307duXwMBAedAQog7MzMxYt24dAQEB9OnTh3379mk7JCGEEPc4Ga8JcXNkvCbEnSPJYSGEuIvt2bOHfv360bJlS9auXYupqam2QxLirnDlgSMoKIi+ffuyd+9ebYckhBDiHiXjNSFujYzXhLgzJDkshBB3qd27d9OvXz9atWrFmjVr5EFDiJtkamrK2rVradmyJX379mX37t3aDkkIIcQ9RsZrQtweGa8J0fAkOSyEEHehXbt20a9fP1q3bi0PGkLchisPHMHBwfTv359du3ZpOyQhhBD3CBmvCVE/ZLwmRMOS5LAQQtxldu7cSf/+/Wnbti1r1qzBxMRE2yEJcVczMTFhzZo1tG7dmv79+7Nz505thySEEOIuJ+M1IeqXjNeEaDiSHBZCiLvI9u3b6d+/P+3ateO///7D2NhY2yEJcU+48sDRrl07BgwYwI4dO7QdkhBCiLuUjNeEaBgyXhOiYUhyWAghGqno6GgWLlxY8Xnbtm0MHDiQjh07yoOGEA3A2NiY1atX0759ewYMGMD27dsrti1cuJDo6GgtRieEEKIxkvGaEHeWjNeEqH86Go1Go+0ghBBCVPXEE0+wf/9+jh8/ztatWxk8eDCdOnXi33//xcjISNvhCXHPys/PZ+jQoezdu5c1a9bQo0cPgoKC6NChQ6UEgBBCCCHjNSG0Q8ZrQtQfSQ4LIUQjVFxcjL29PS+++CK9e/dm8ODBdOnShZUrV8qDhhB3QH5+PsOGDWP37t2sWbOGjRs38vXXX5OUlIS+vr62wxNCCNEIyHhNCO2S8ZoQ9UPKSgghRCO0ceNGMjMz8fDwYNCgQXTt2lVmoAhxBxkZGbFq1Sq6du3KoEGD8PT0JDMzk02bNmk7NCGEEI2EjNeE0C4ZrwlRP2TmsBBCNEKPPvoo4eHhJCcn07NnT+bMmcPq1auxs7NjwoQJ2g5PiHvewoULSU5OZsiQIUyZMoVt27ZhZ2dHaGgov/zyi7bDE0II0QjIeE0I7ZLxmhD1Q2YOCyFEI1NUVMRff/1FXFwcrq6uXLp0iZYtWzJjxgzi4+O1HZ4QTUJcXBwzZsygZcuWJCQk4OLiQnx8PH/99RdFRUXaDk8IIYSWyXhNCO2T8ZoQ9UOSw0II0cj89NNP5Ofno1arSUhIIDAwkL///puUlBTeffddbYcnRJMwffp0UlJS+PvvvwkICCAxMZGysjLy8vL46aeftB2eEEIILZPxmhDaJ+M1IeqHnrYDEEIIUZmvry9t27Zl6tSpDBkyBENDQ22HJESTZGRkxMiRIxk5ciQFBQWsXr2aTz75BD8/P22HJoQQQstkvCZE4yDjNSFun9QcFkIIIYQQQgghhBBCiCZIZg4LIWqUkZFBcnIyarVa26GIm6BQKLCzs8PS0lLboQhx1yguLiYuLk7q02mZgYEBLi4u6OvrazsUIe46aWlppKamyritkVMoFNjY2GBtba3tUIRotOQ59O4kz6F3L0kOCyGqWL9+PbNmzmD7jp3IywV3Jx0dHbp368pbb0+jf//+2g5HiEYrJSWFKVOmsGLlSrKzsrQdjgDMzM25b8QI5syZg62trbbDEaLRW7FiBbM+/oT9e/doOxRxEzp06sybU19nxIgR2g5FiEaj/Dl0Jtt37JDn0LtU+XNoN956+215Dr2LSFkJIUQla9asYcSI4QS7mDG2nROeNsbo6epoOyxxE0rLNESn5vHngUscictmxYqVDBo0SNthCdHoZGRk0L17D2ITEuk5agK+bbqgrzLSdlhNWnFhPhGHdrH174W4Ojqwffs2mX0iRC2WLl3KQw89hFmzzlh3vh+VrTs6CllzvDHTqNUUplwkbfdfZJ/dzeLFixkzZoy2wxJC68qfQ0fQppk74wd2xdvFHj1duZ/dTUrL1JyPS+L3tTs5dPYiK1askOfQu4Qkh4UQlbQJboVRXjxLnmqHUv4yvquVlKkZ+/0BCk1dOHj4iLbDEaLR+eKLL5g8ZQrvLtmFk1czbYcjrnEp6iwfPNiFeXPnMnHiRG2HI0SjpNFo8PD0JtfSF78XFkpS+C6jUas59/UETDMjiY6KREdHJmOIpq1N62BMdYpYPudllHrykvvdrKS0lBFT5pOno+LgocPaDkfUgYwghBAVYmJiOHz0GI90cpXE8D1Aqavg0c6uHDpylNjYWG2HI0Sjs2LFSvw7hEhiuBFy8mqGf4cQli9foe1QhGi0Tpw4QczFaOxDH5PE8F1IR6HAPvQxLl6I4sSJE9oORwitiomJ4fCRozwxrKckhu8BSj09JgzvyaHDR+Q59C4howghRIX4+HgA/OxNtByJqC9X/iyv/NkKIa6KjYvD0au5tsMQNXD0ak6c3LuEqFFcXBwAhk6+Wo5E3Korf3YyThNN3ZX/Bpq7O2k5ElFfrvxZyv3t7iDJYSFEhbKyMoA7UmO4/cxwvt8WXef+uyLTcJyylqyCkgaM6t5z5c+ytLRUy5EI0fiUlZWhq6est+NNHRzIxkVf17n/mQPbebKNGfk5mfUWw71EV08p9y4hanFl3KajW3/3sVtx6PWOJGz8oc79s87sYvcEZ0rzZRHQK392cq8TTd2V+5lST7fBzxU4ZipfL9tY5/7bD5/BLORJMnPyGzCqe8+VP0u5v90dJDkshNCKtZM6M66TW537t/Ow5Oi7vTBTNdxrRhqNhk/XRxD8wRY831jPyAV7OZuYU+s+ZxNzmPDrIdrPDMdxytobJry/2HwexylreWflqYq2kjI1M1afIfTT7Xi9uYHgD7YwcclRErMK6+W6hBB3xrQ/wukx8vE69/dp1ZG5GyIwNDFvsJg0Gg0rv53F5H5+PNfZjtlPDSL+/Ok6779v/V882caMr159sMY+a36ay5NtzPhzztQa+/w2YxJPtjG7qeS5EOLuEPTOGux6jKtzf1OfdrSddxhdQ7MGi0mj0RC7ci4HXm3Dnme9OTn7fvLjz95wv7QD/3FkWgh7nvHkyLQQ0g6tbbAYhRDaEf7dNB4f2qPO/TsG+hDx91zMTQwbLCaNRsOsn1fiN2oydv2eY9Ck2ZyOrvuM278278Ms5EkefPurGvvMXbQGs5AnmfrlnzX2mTT3N8xCnryp5Lm4N0hyWAihFTYmBhjp1/2XYX09BXZmBg26WMfXYVF8ty2amfe1YO2kLtiZGTDm+/3kFtb8a2dBcRnuVka8PcgPO1ODWo9/JCaTP/bE0sLRtMoxjsdn80ofHza80oWFj7YmKiWPR38+WC/XJYS4M0wtbTAwNKpzfz2lPuY29g16X1v363w2Lvqah6Z+yrTfwzG3tmPec8MpzKv9hy+AtEsxLPtsGr6tu9TYJ/rkQbb98wsuvoE19jkctproEwewsHW8lUsQQjRySlNrdA3qnjRR6Omjb27XoPe+S2sXkLDhezwfnkHLaf+hNLPl1NwHKSvIrXGfnMgDnPvuOWw7j6LVexux7TyKiG+fJSfqUIPFKYS482wsTDFS1f7cdi19pR721uYNes+av2QdXy/byKeTHiL822nYWZkzfMo8cvJvPFkoJjGNad8so0vLmksMHTwTzS+rthHo7VJjn9XbD3PgVDSONha3cgniLifJYSFEvcstLOX5RUfwenMDrd7fwnfbohm5YG+l2bLXl5VwnLKWRXtjefyXQ3i+uZ4uH29l/cmkiu0NXVZCo9Hww/aLTOrtzeAgB5o7mvL52CAKisv45/ClGvcLdrPg3aHNGdHaCX29mm+peUWlvLD4KJ8+EIi5YeXXP80MlSx9pgPDgh3xsTOhrbslM0e04FhcNnEZBfV2jUKIW1eYl8MPb0/g+S4OTO7ny4Y/vmL2U4MqzZa9vqzEk23M2Lb8V76e/BDPd7HnreHBHNm6pmJ7Q5eV0Gg0bFq8gMETptC29zCcfVrwxAffUVxYwN61y2rdV11Wxg/TnmTYs29h6+JRbZ/C/Fx+fPtJHnnnC4zMLKrtk5F8icWfTOHJmT/WawkPIcSdUVaQS8T3L7L3OR8OvNqaSxu+5+Ts+4le8m5Fn+vLSuye4EzStsWc+WoCe5/z5vCbXUk/sqFie0OXldBoNCRs+hHnwS9h3XYQRi7N8ZkwH3VxAal7l9e4X8KmH7Fo0QPnwRMxdPTBefBEzPy7kbDxxwaJUwhR/3LyC5kw4wccBjyP78jJfLVsA4Mmza40W/b6shJmIU/y6+ptPDTta+z7P0/ww2+xZueRiu0NXVZCo9Gw4K9NTBk3mGE92tLCy5nv3nyCgsJilm3aW+u+ZWVqnpzxA289PgwPR9tq++TmF/LkjB/5YsojWJhUP4nhUkoGUz5fzI/TnkSp2/ClPUTjI8tACiHq3fRVp9l/IZNfn2iDrYkBs9dHcDw+iwBn01r3m7chkmlDmvHukGYs3HGRFxYfZf/bIVga6dfpvA/9sJ+90Rm19jk/q1+17THpBSTnFNGzmU1Fm4GeLp29rThwIZNHOte9BEZ13vznFL397ejhZ8P8Tedv2D+7sBQdHTA3lNu0EI3B0nlvEXlkLxM/+xMzaztWfDOTmDNHcfMLqnW/Vd9/zP2TPuD+SR+yZel3/PD2k3zy3wlMzK3qdN75L44k4vDuWvt8vTOh2vbU+AtkpSYR0KlXRZtS34BmbbsSeWwvPe9/ota4TS1t6D7iESIO76q2z6KPJxPUrT8tOoay+sc5Vbar1WoWTnua/o+8hLO3f63XIIRonC4sfZ+cyP00n/gzSjNbYld+St7F4xi5tqh1v7h/5+H+wDTcH5hG4uafifj+RdrM3ovSxLJO5z392TiyI2pPinRcEFFte1FqDCVZyVgE9KxoUygNMGvWiZzzB7APGV/tfjnnD+LY96lKbRYBPSU5LMRd5K2vl7L3eCR/zpyInaUZM39ewdGIGIJ8an+W+/jXVXzw7P18+Oz9fLd8C0/O+IETSz/ByqxuC7WPfH0+u49Vf0+6ImFd9aW1LiSkkpSeRa/2ARVtBvpKugY3Y+/JSJ4Y1rPa/QA+/m0VNhamPDK4O7tqOP/kzxfRv1MQoe1aMOf31VW2q9Vqnp61kJfG9sff07nWaxD3Lsk6CCHqVW5hKcsOxLPgoVZ09y1PtM4fE0TwB2E33Hd0e2fua12+qulbA/34aedFDsdk0at59b+CXm/u6CAKS8puKe7knCIAbE0qv2JkY2Jw27N3Vxy+xPH4LNZOqvnV7GsVlpQxc81Z7mvthKlKZtoJoW2FeTnsWrWYp2YtxL9jCACPv7eAKf2b3XDfrkMfouOABwAY+eJ0tvz5HRdOHCSwa986nfvRd7+iuPDW7kFZackAmFnbVWo3s7IjLSGmxv0ijuxhx8rfeXfJzhr77Fv/FzFnjjLt9/Aa+6z75TMUerr0fvC5m4pbCNE4lBXkkrJrGb5Pf4V5i+4AeD8+j4OT29xwX9uuo7HpOAIAt1FvkLjlJ3Kjj2AZFFqnc3s9Ngd18a2tvVCSVX7vU5rZVGpXmtlSlBZXy34pKM0qjzmVZraUZKfcUhxCiDsrJ7+Qxet3sXDaU4S0Lf9ResHUx2l2/5Qb7vvQgK480LsjANOfHMl3/2zh4OkL9O1Yc9msa3312qMUFBXfUtzJ6eVvUdhZVq7DbmdpRkxSWo377Tkewe//7WDnj+/W2Oevzfs4ei6G8G+n1djnsyXr0NVV8Nyo3jcZubiXSHJYCFGvLqbnU1KmIdjNoqLNzFCJt53xDfe9thavkYEeJgZ6pObW/S9ZR3PVTcVanetLSWnQVGm7GfGZBbyz8jR/Pt0elfLGr+iUlKl59o8jqDXw8cjaZ+UIIe6MlPgLlJWW4BnQtqLNyNQcB4+aa7tdcW0tXgNDY1TGpmRn1D3RYGnndHPBVqvyTUyDpurN7rLCvBwWTnuKR975AlNL62r7pCfGsWTOVF5dsAKlQfX33QunDrNpyTe8u3h7g9boE0I0nMLUi2jKSjDxbF3RpmdkhqGD9w33NXa5+raAroERuioTSnNS63xuA8t6qFFeZVBX873v6i7Xb9dw/T1UCNE4XbiUQklpGW39PSvazE2M8HV1uOG+19biNTY0wNRIRUpmdp3P7WRbt7cialP1lqWp8e6Tk1/IUzMX8sVrj2BtUf3buXHJ6Uz9agkr5ryKyqD6CUeHz17gm782sf2Hd2W81sRJclgIUa80mvL/rTq21txwXz3dyjV7dSj/S7GubqesxJXF5JJzirA3u5rsSMstrjKb+GYci8smNbeY/vOvvpZdptawJzqdn3fGcPHj/ugqyr+skjI1T/9+hNj0ApY920FmDQvRSFy5D10/aK7L/alqnV0dNOq639dup6yE+eUZw9lpSVjYXn0wyklPqTKb+IrkuGhSL13ky5fHVLRp1GoAnm5vyYx/DhIfeYqc9BQ+fPjqSt/qsjIiDu1ky/++59s9qUQc3kVOegqvD2pRqc//PnubTYu/4ZP/TtzgyoUQWlcxqLu++cb3MB3d6u596jqf+nbKSijNy+9vJVkp6FvYV7SX5KSif91s4sr72VJ8edZxxT7ZqVVmIAshGqfbGa9dX2dXB25qvHY7ZSXsrMwBSErPxsHaoqI9JTMHOyuzaveJjk/mYmIqY978sqJNffk6LXs9zcHfZ3AqKp6UjBx6PP1hRZ8ytZqdxyL4fvkWUjd+y65jEaRk5tBi9OuV+rz9zf/45q9NnFj6Se0XLu4ZkhwWQtQrD2sjlLo6HI7JwtmifOXqnMISolLz6eRdtxqbt+p2ykq4WRliZ2rAtnNpBDmX/wVdXKpm9/l03h5841fHa9Ldx5qwyd0qtb289Dg+dsa8GOpVJTEcnZLHX891wMq4bnWWhRANz87FE109JdEnD2LlUD6zpCA3m+SY8zRr07VBz307ZSVsnD0wt7Hn5J4w3Jq3AqC0pJizB3dy/0vvV7uPo4cf7/9vT6W25Qs+pDAvlwdf+wQrBxfMrGyr9Pn5vedw8PBj4GOvoNDVpfPgsbToWPn18c9euI9Og8fSbdi4W7oeIcSdpbL1QEdXSW7UEQysyutQlhbkUJgcjVmzTg167tspK2Fg44bS3I7MU9swdi9/e0NdWkz22T243/9WjfuZercl69R2nPo9XdGWdXIbpj7tbikOIcSd5elsh1JPl4Ono3GxK3/uzM4r4HxcMl1b3frzXF3cTlkJD0cb7K3MCTtwkla+5bWRi0tK2XnkLO8/c3+1+/i5ObLnp8pjuQ8XLie3oJBPXnwQFzsrbC3NqvR57pOf8XNz4JUHB6Krq2Bsv86Etq38tup9r3/G2L6dGDew8jOsuLdJclgIUa9MVHo80M6ZD1efwdJIiY2JPnPWR6DQAZ0Gfi3vdspK6Ojo8FR3d77YfB5PGyO8bIz5Yst5DPV1Gdn66mvdE5ccxcFcxduDygcYxaVqziXlAuUJ3sSsIk7EZ2NsoIunjTEmKj2aO1Z+1cdIXxdLY2VFe2mZmqd+O8zxuGx+m9AWtRqSs8trIFsYKdHXqzyjWghxZ6mMTeky9CGWzX8HYzNLTK1s+ffbWegoFDd8Rfl23U5ZCR0dHfo89DxrfpqLvZs39m7e/PfTp+irDOk48IGKfgvfeRoLOydGTXwPpYEKZ5/KDwlGpuU/mF1p11PqV+mjb2iMiblVRbuJhTUmFpXLUujqKTG3tqtTOQ4hhPbpGppg2+UBLi6bgZ6JBUpTG2JXfoqOjqLBXz++nbISOjo6OPZ5kvj/vkRl74mhnSdxa75EoW+ITcf7KvpF/PgS+paOuI96EwDHPhM48cko4td8jVXr/qQfXk/W6e0EvLH8tq9HCNHwTI1UPNS/C+98uwxLM2NsLUyZ9fO/KBQ6DT1cu62yEjo6Ojx/fx/m/rEGbxd7vJ3t+XTRfxiq9HmgT8eKfk/PWoiTjQXvPT0KlYGSFl6VF48zNzECqGjXV+pV6WOs0sfKzKSi3drcBGvzyovuKXV1sbMyx9ftxuU4xL1DksNCiHr3/lB/Xv/7BOMXHsRUpcfzoZ5cyizEQNm4k5wvhHpRWKLmzX9OkVVQQms3c/58qj0mqqu3yviMQhTXjC6Ssgvp+9nVRZu+2RrNN1uj6exlxT/Pd6QuErIKWX+y/DXGPvMqLwD197Md6OJTfd1PIcSdM+bVWfw+62W+mDQaQxNT+j8yifSkuBpr7jYWAx59meLCAhZ9/Cp52Zl4Bbbj1QUrUBlf/dEqLTGuPNEthBDX8Rgznajf3+DM54+ia2iK04DnKE5PQEfZuO99TgOfR11SSPQfb1Gal4WJV2tavLoYXcOrSZDi9Evo6Fy995n6tMfvmQXELJ9N7Io5qOzc8X3mG0y9brwAnxCicZj1whhenvc7o9/8AlMjQyY92J+4lHRU+o27XN/LDw6goKiYVz9bRGZOHu1aeLFizquYGl2918YlpVV6DhWiPulobqagpxDinrZjxw66d+/O9te742NncuMd6ii/qJTWH4YxfWhzHuroWm/HFTcWmZxL99nb2b59O926yatBQlzLy9uH5j2HM2rieze9b1FBHlP6N2f0qzPpPuKR+g9O8PeX73Fm60qizkdqOxQhGqXVq1czdOhQ2s47gr657R05Z1lRPgcnt8V9zLvYd3/wjpzzXlaclcLBV4NZtWoVQ4YM0XY4QmjNlefQg7/NqNcZq3kFRTS/fwoznx/NI4O719txxY1FxCTS9pFp8hx6l5CZw0KIenc8PovI5Dxau5qTXVjKvI3lD/b9A+1vsKcQQjROMWeOknDhHJ4BbSnIzWbVD+ULdAT3HKzlyIQQouHkXTxBQWIkJp7BlBbkEPfvZwBYBffXcmRCCFHV0YgYzsUk0La5J9l5BXzy6yoABncN1m5gQjRykhwWQjSIb8KjOZ+Sh76ugpYuZqx4oRPWssiaEOIutv63L0m6GIGuUom7f2umLlyHqaWUfRFC3Nsurf+WgsTz6OjqY+IRRMAb/6A0bdhFhoUQ4lZ9uXQ9ETFJKJW6tPZzZ92XU7G2ML3xjkI0YZIcFkLUuyBncza80lXbYQghRL1xa96Kdxdv03YYQghxRxm7B9Ly3XXaDkMIIeqkla8b275/V9thCHHXkdVHhBBCCCGEEEIIIYQQogmS5LAQ4p406c9jPPbzQW2HIYQQDean6c/y1auyIJQQ4u4VufBlznz5hLbDEEKIBvPsRz/x4NtfaTsMIWolZSWEEEIL/tgTy7KD8ZxNzAGgpYs5bw70o7WbRaV+CVmFzPjvLGFnUigoKcPb1pi5o4No5WKuhaiFEKLuDm7+lzU/zSU5Noqy0hLs3bzpN+5FOg+RhLYQ4u6QtHURKbv/Ij/+LAAm7kG4jnwDU6/WFX3i//uStENrKUiIRKGvwtS7He4PvIWhg4+2whZCiDr7d9tB5v6xhqj4ZErKyvB2tufFMf14sF9nbYcm7iBJDgshhBbsOp/GfcGOtPNogYFSwYKwKMZ+v5/w17rjaK4CIDO/hGFf7aGrtxWLnmyHjYk+F9LyMVfJrVsI0fgZm1syeMIUHDz80FMqObZ9HT+//zymVrYEdumj7fCEEOKGss/uxqbDcEx92qFQqohfu4DT8x6i1YdbMLB0BCDr3B4cQh/FxDMYjbqUmH8+4dTchwieEY6ugZGWr0AIIWpnaWrMlPGD8XNzQKmnx7rdx3j+45+xtTClT4dAbYcn7hDJMAghbsvqownM3RjJhdR8DPV1CXQ245fH2mBkoMeRmEw+WnuO4/HZlKo1BDiZ8v4wf1peM+vVccpaPhkVwMZTyeyITMPF0pDPRgdhbaLP5P+d4EhsFi2cTPnqwZZ42BgD8On6CNadTOKRzm58vuk86fnF9PG349MHAjE3VFYbp0ajYUF4NL/tjiE5uwgvW2Ne6ePNkFblA/vM/BLeWn6SredSyS8qw9FCxUu9vBnbwaVBvrcFDwdX+vzpA0GsPpbI9og0RrdzBuDrsCicLFTMH9uyop+rlTxkCHGnHdi0glXff0xybBT6KkPcmrXixc+WYGBoTPTJg/zz1QfEnj1KWWkprn5BjJn8Ee7+wRX7P9nGjPFvzefotrWcObANKwdXHp/+NSaWNvz64UQunDyEi28AT874ATtXLwBWfjuLI+H/EXL/BFYvnENeVjpB3frz6DtfYGRqUW2cGo2Gdb9+zta/fyIrNRF7Nx+GPPU67fqMACAvO4PFn0zh5O4tFBXkYWnnxKAnptBt+LgG+d6at+te6XOfh55n1+olRB7ZLclhIe6wtAOrif33MwqTL6Crr8LYLZBmE39G18CI3OgjxPzzMXkxJ9CUlWLkGoDH2PcwcQ+q2H/3BGe8xn9M+tGNZJ/ZiYG1C96PzUXP1JqoX18jN/oIRi7++D71JSo7DwBiV84l/fA6HEIeIW7155TmZWAR1Bvvx+agZ1T9G1AajYZL674hKfx3irOSMbT3xGXoy1i3GwJAaV4m0YumkXlyK2VF+RhYOuA8+CXsuo1pkO/N9+nKr4J7PzaH9IP/kX16B7ZdHgCgxSuLKvXxeeIzDrzckrwLxzBr1qlB4hJCVLUi/AAf/7qKqPhkDFX6tPJxY8nMFzE2NODgmWg++OEfjkbEUlpWRpCPKx+9MIZgP/eK/c1CnmT+q+NZu+so2w6fwdXeiq+nPo6NuQkT5/zKobMXCPBy4Ye3n8TL2Q6AWT+v5L8dR5gwPIQ5v68mPTuP/p2C+GLKo1iYVv/cptFo+PzPdfz071YS07LwcbXn9fFDGBHSDoCMnDymfL6YLftPkldQhJOtJVPGDWLcwG4N8r11b9280ufn7+/DkvW72H08UpLDTYgkh4UQtywpu5DnFh1l2pBmDAq0J7eojL1R6Wgub88tKuOBds58OKIFAN9tjWbcwgPsmtoTk2tmv87fFMl7Q/15b5g/M/47y/OLjuJubcTE3l44Wxjy6v+O89byUyx+qn3FPtGp+aw6msivT7Qlp7CUycuO8+Y/J6skXa/4eF0Ea44n8vGoALxsjNkTlc6LS45hZaJPF29rZq87R0RSLouebI+1sZLo1HwKS8pqvPbPN5/ni83na/1+Fj3Zjk5eVnX6LguKyygt02BpdDW5vf5kEiHNbHnqt8PsPp+Oo7kBj3ZxZ1wn1zodUwhx+zJTEvnhrScY9dIHtOk1lMK8XCIO70KjKb/TFebl0mXIg7i/NhuADX98yecv3c+sFYdRGZtWHGf1j7MZ/eosRk+exd9fTOeHt5/ExtmDQY+/ipWDK7+8/zyLP5nCy1/9U7FPcmwU+zcuZ+L8pRTm5vDLBy+w6OPJPDVzYbWxLv/6Qw5t+Zdxb87D3s2bc4d28eO0pzC1tKFZ226sWDCDS1FnefmrvzGxsCY5NoriwsIar/2/hZ+y5qe5tX4/k778G782XW74PWo0Gs7s20rihQhGvfT+DfsLIepPcWYSEd+/gNv9b2PVZiBlhbnknNsLl+9jZYW52HZ5AI8HPwQgYcN3nJk/ntazdqBraFJxnLjV83EfMx2PMdO5+NcsIn54EQMbN5wHvYi+lTPnf36V6EXT8H/lj4p9CpMvkLp/Fc1f+oWyglzO/zKZ6D/erpJ0vSJ2+SekH1qL1/iPUNl7kn12DxE/vISeqTXmzToTs2IO+ZfO4f/KH+iZWFGYHI26uOb7WNx/XxD/35e1fj/+L/+BmV/HOn2X6qIC1GWl6Blb1NinND8boNY+Qoj6lZiWyRMf/sAHz45iaLc25BYUsutYRMV4LTe/kAf7d2H2S+XJ4C+XbuD+qZ9zeNEsTI1UFceZ/dtqZr0wmlkvjGb6d3/z5Ic/4OFkw6sPD8LV3ornP/mFKZ8v5p/ZL1fsExWfzPLw/SydNZGc/EJemP0Lkz9fxMJpT1Ub64cLl/PvtkPMe2Uc3i727Dp6jqdm/oiNhSndgpsxY+EKzl64xN+fvIy1uQlR8ckUFhfXeO2f/vEfc/9YU+v38/fsSXRp6XfD71Gj0bD10BkiYhN5/5lRN+wv7h2SHBZC3LKk7CJK1RoGBTrgamUIgL/j1WRIN1/rSv1n3x9I83c2sjsqnb4t7Crax7R3YVhw+QzeF0M9GfLlHl7p601oM1sAnuzmzitLj1c6VlGpms/HBuFkUX7eGSNaMH7hAd4b6o+dmUGlvvlFpXy/NZplz3agnYclAO7WRuyLzuCPPbF08bYmPrOQQGczgl3LZ7LcaIbuI51dGdbKodY+DuaqWrdfa+aasziYq+h+zXcWk17Ab7tjeLqHBy/19uJwTBbvrDiFvp6iYnaxEKJhZaUmUlZaSttew7B2cgPAxTegYrt/h56V+j/y9ue8FOLG2YM7aNVjYEV712EP077fSAAGPPoyHz3WhyFPvl4xg7bPQ8/x83vPVzpWSXEhT3zwLVb25f+9P/T6HD6f9ACjX5mFuY19pb5FBXlsXPQVU75dhXer8iSHrYsnEUd2s/Xvn2jWthvpiXG4NWuJR4s2ANg4uVObnvc/Qbu+99Xax9LOqdbt+TlZvDagOaUlRegodBn3xjwCOvWqdR8hRP0qzkpGU1aKdZtBGNiUvxFl7OJfsd3cv/JsNK9HPmH/xBZkn9uNZau+Fe22Xcdg034YAM4Dn+fErGG4DHkZi8AQABz7PEnkz69WOpa6pAifCfMxsCq/V3g8NIMznz+C+5h30Te3q9S3rCifSxt+IGDKUkx9ymfQqWzdyYncT9LWPzBv1pnitHiM3QIx8WhVvt2m9h/M7XuOx7rd0Fr76FvWPp671sW/Z6Fv6YB5i+7VbtdoNFxc+j6mvh0wcmlebR8hRP1LTMuitKyMYd3b4uZQ/jwV4HX1DdCebfwr9f988iO4DX2JHUfOMrBLq4r2hwd2ZWRo+YSklx8cQJ8XPuL1R4ZUzKB9blQfnv/k50rHKiwu4ds3nsDZrnxS0JyXHuKBNz9n1nOjsbeu/JZEXkERX/1vI6s+m0LHAG8APJ1s2X08gp9WbaVbcDPiktNp6etGm+YeALg72tR67U8M68l9l2cd18TJ1rLW7Vm5+TS//zWKSkrRVegw75Vx9GoXUOs+4t4iyWEhxC0LcDKju681veZuJ6SZLT39bBjS0gGLy7NfU3OKmL0+gp2RaaTkFlOm1lBQUkZ8RkGl47S4JqFsa1Ke2PV3uKbN1IDCUjU5hSWYqsqP7WyhqkgMA7Rzt0CtgciU3CrJ4XNJuRSWqhnz/f5K7SVlagKdzAB4pLMbT/52iOPx2fT0s2FAoD3tPWr+S9TSSB9LI/06f1e1+TosihWHE/j7uQ6olLoV7WqNhlYu5rw1qBkAQc7mnEvK5bfdMZIcFuIOcfULwr9DCNPHdCagc28COvWibZ/hGJuV3x+y01NY+c1MzuzfSnZ6CuqyMooL80lPjKt0HBffq6/lmVmXJ0ScfVtcbbOyo6SokILcbAxNyu9LVg6uFYlhAK+WHdCo1SRejKiSHL4UdYaSokLmPT+iUntpSTFuzctL04Q8MIFvXhtPzJmjtOjUi9ahQ/BpVfNsORNzK0zM6/b2Q01Uxqa8u2QHRQV5nN63laXz3sLGxaNKyQkhRMMxdm2BuX83jk7vjXlATywCemLdbnDFzNaS7FRiV8wh68xOSrJT0ajLUBcXUJQWX/k41ySUlWblP+BfmwBVmtugKSmktCAHPcPycZyBlXNFYhjA1LstaNQUJJ6vkhwuuHQOTUkhp+ZVXrRSU1qCsVv5PdQ+9BHOLXiKvJjjWAT0xKp1f0x92lMTpYklSpPakyJ1Fb92Aal7VxLw+jIUyuonAEQvepv8uNMEvLG8Xs4phKibIG9XQtr40/mJ6fRuH0Cv9gEM79kWS9PysoQpGdnM/GklWw+fISUjm7IyNflFxcQlp1c6TqD31YSynVX5eKyFl3OltsLiErLzCjAzLn8WdbW3qkgMA3QI8EKt1hARm1glOXzm4iUKi0sYMXlepfbi0lJa+pZPQpgwPITx737D0XMx9GrfgiHdWtMxsOYFLq3MTLAyM6lxe12YGqnY8eO75BUUsfXQad76eikejjZVSk6Ie5ckh4UQt0xXocPSp9uz/0ImW8+l8tPOi3y89hxrXuqMm7URk5YeJy23mA+G++NiaYi+noIhX+6muExd6Th6uoqKf9bRqabt8v+qNdToyn46Fb2vurLf7xPaViz2doW+Xvl5evvbsv/tEDafTmFbRBqjv93HY13dmT60+r8Q66usxDfhUXyx+TxLn+lAi8uJ6ivsTA3ws6/8F72vnTH/HUus9ZhCiPqj0NXl1W9WEnl0L6d2b2bzn9+x/OsPeOu3Ldg6e/DT9GfJzUhlzJSPsXZ0Q0+pz0eP9aG0pPLrf7p6V0vG6Fy+YV3bduUmptFUvj9e68p+1d3nNOry/V76YhkWto6Vtin1y38wC+raj0/+O8mx7es5tTeMuc8OJXT0U4x+ZWa156uPshIKhQJ7t/KZMW7NWpIQfZa1P82V5LAQd5COQhf/yX+SE3mArJNbSdzyMzHLPyHo7dWobN2I/OkVSnLS8Bj7PgbWLujo6XNi1jA0ZSWVj6Nb9Z6lo3vt4+Tle1Mt9zFqu49d3q/5pN/Qt6g8m1ehLP9B3jKoF21m7yPj6CayTu/g5KdjcQh9FI8x71Z7uvoqK3Fp3bfE//clLab8ibFri2r7RC+aRsaRDQRM/adSQlwI0fB0dRWsnPsqe09EsvnAKb77ZzMf/LicLd+8hYejLc9+/BOpmbl8/OIY3Oyt0Vfq0eeFjyguKa10HKXu1Yk6V+5TldvKqWt5MK0Yr+lU81x6eb9lH7+Eo41FpW0G+uX32H4dgzi59BPW7z5G2MFTDH11Lk+NCGXm86OrPV99lJVQKBR4u5RPPGjp68bZiwnMXbxWksNNiCSHhRC3RUdHhw6elnTwtOTVvj60nxnGmhNJPNvTk73R6Xw0MoDe/uUzQ+IzC0jPK7nBEesmPrOQxKzCitINBy5motABb1vjKn397E0w0FMQn1lIF2/rKtuvsDExYEx7F8a0d+E3T0s+XH2mxuRwfZSVWBAWxfzN51nyVLuKchbX6uBpSWRKXqW28yn5uFgaVukrhGg4Ojo6+AZ3wje4E0OffoOpgwM4HLaafuNeJOLwbsa9MZeW3foDkJ4YR25mWr2cNz0xlsyUhIpk7/lj+9BRKLB3rzp7xMmrOXr6Bvy/vfuOrqLq+jj+Te+995AQeuhFQKpIkw6Cgg1EUBAVpQhKUQEfRYoVKyIqiCBFOjYE6b2HDgmQHgghpOe+f0QuxCQQIBB48/us5VrPnDlzzh5Yz+HOnpk9SdFRVKxT9AdLHFzcadypD4079eHvBTOZ/+GYIpPDJVFWogCDgaysouvmicidYWJigmNYPRzD6uHfaSg7R9QnaedKfNsM5OKRLYQ8MQmX6g8BkJF0luxLSTcYsXgyks6SeT7GWLrh0vEdYGKKtXdIgb62PhUwMbciM/EsThUbFjmmhYMbng/2wvPBXjiG1ef0/AlFJodLoqzE2VUzOLvsQyoP/dFYzuJaBoOBk3PeJGnnKqqOmI+1R+B1xxORO8PExIQHwsN4IDyM15/qSNVeI1m2fhcv9mzNpr1HmTL0Cdo8kPc21Zm4JBKTL5XIvFGxSUQnXDAme7ceOI6pqQnl/b0K9K0U7IuVhTlRcUk8WLNikWO6OzvQp11j+rRrzMxf/2bM5/OLTA6XRFmJ/zIAmZklc90u9wclh0Xklu08fYH1xxJpVsEdd3tLdkVeIPFSJmGeeU+7lnOzZcGOs9Twd+JSejZvL4vA2sL0BqMWj5W5KS//tJexHSuRkp7NmMWH6FTDp0BJCQB7a3Oeb1aOcUsOYcg1UL+cCykZ2Ww/dQE7SzN61vPn/VVHqO7vREVvezKzc/n9YJzxPApzu2UlPv3rBO+vOsKnfWoS4GJL3MUMAOyszLCzyluaBzQJpuMnm/nwj+N0quHNrshkftgcxeRHVf9J5G45sW8bh7b+TdWGLXFw8eDk/u2knE/Ap1ze0xeeASFsWvETQVVqk556kfnTx2BpXTI3cCwsrflm7PP0HDqBtEspzJ08gnoPdy1QUgLyyje0eXII86aOIteQS1jNhqSnpnBszxasbO1o3LEPi2dMIKhyLXxDKpGdlcme9auM51GY2y0rsWLmFIKq1MLTvxzZWVns27CGTcvn0mfUtFseU0RuXsqJnSQf+gfnqs2wcHDn0omdZKUkYeMbBoC1ZzDxm37BLrgGOekpnP55AqaWxf9uwvWYWlhxbOYrBPUcQ07aJU7OGYNbvY4FSkoAmNnY49tmIKfmjcdgyMUxrD45aZdIOb4dUytbPBv3JHLxZOyDqmPjWwFDdibn9/6OjU9YkfPfblmJsys/I2rxZMKe+wQr9wAyk+PyYrWyw8w674GEkz+MJmHLYioOmYmZtf3VPjYOmFnqhr7I3bDt4An+3nmIlnWr4uHiwPZDJ0lITqFCUN4N9hA/T35as4naFYO4mJrOmM/nY2NVMiUCrS0teP7db5jwQk9SLqcx4uO5dG1er0BJCcgr3zCkVxtGfTKP3NxcGoaHkXI5nS37j2FnY0Wfto2ZMHMxtSoEUSnYl8ysbFZt2mM8j8LcblmJKT+uoFbFIMr5epKVlc2aLfuYu3oT04b2ueUx5f6j5LCI3DJ7a3M2n0jiq3WnuJSRjb+LDeM6Vuahynl16Kb2Cmf4/AO0nrYBP2drRrWrwFvLDpfI3OXcbWkf7sUTX2/nwuUsWlb24N1uRSdNR7YNw93eko/+PEFk0mUcrS0I93fkpZZ5rztbmJsyacVhos6nYWNhRv1yLnz+RM0SibUwszZGkplj4LnZu/K1v/ZweYa1ybvIqRnozMxnajNpxWGm/XaMAFcb3u5cme61VW9Y5G6xtnPkyM4N/D7nM9JSU3DzCaDn0ImEN24NQN9xnzJ7wku83ftB3Lz96friOOZPe7NE5vYMCKF2y458OKQHqRfPE964NX1GTS2yf5dBY3Bw9WDlt1OZfeYUtg5OBFaqwSP9hgFgbmHJwo/HkxgdiYWVNWG1GjHg3W+LHO92ZaSl8uO7r3I+7hwWVtb4BFfg2Xe+on4bff1a5G4ys3bg4pEtRP/2NTlpl7By8yOo51hcwvM+Dlm+71SOzx7B3rfaYOXmS2C31zn98zslMre1ZzCutdtxaPpTZKdewCW8JSFPTCqyf0DXEVg4unN2xSeciI/EzNYRu6Bw/NsPAcDUzILIX94lIzEKUwtrHMIaEDbwsxKJtTCxf32HITuTIzMG5Gv37/QqAZ1fy+uzdjYAB9/vka9PaN+peD7Y647FJiJXOdpZs2HPET5b8DspqWkEeLsx8YWetG4QDsCnI/vy0gezebD/2/h7uTGuf1fenDG/ROYO8fOkY9Pa9Hj9Q85fTKX1A+FMvU5idcyzXfBwcWDqjys5FT0bJ3tbaoQFMuyJRwCwNDdn/FcLiYxJxNrKgkbhYXw7dkCR492u1LQMXp32I+fiz2NtZUGFQB++euNZuresf8fmlHuPicFguE4VTxEpS/755x+aNGnC+hFNKH+dp2ZL2werj7LqQCy/v1r0q9OS51jcJZq8v57169fz4IP68xK5VkhoeSo160z3IeNLO5QClnw+id1rlzPupw2lHUqp+eXj8UT8vYQTx4+Vdigi96Rly5bRsWNH6kzdjaWTR2mHU0DUkikk7VpFjfG/lXYo96zM5Hh2vFqTpUuX0qFDh9IOR6TUXLkO3TF7AmGB1y/1ci+Z9O0Slv+zmw3fjCvtUO45RyNjqPPUm7oOvU+UzPvdIiIiIiIiIiIiInJfUXJYREREREREREREpAxSclhE7jvD2oSppISI/L/W+fnRZbqkhIjc/wI6v6aSEiLy/9rovp1VUkL+X1ByWERERERERERERKQMUnJYREREREREREREpAxSclhE7rp6E9fy5bqTpR3GdW08lojPsJX4DFvJM9/uKO1wCnU/xChSVo18pBq//fhpaYdxXRHb19O/tiP9azvyyauP3/X5E86dNs7/1mON7/r8InJ9O0c0IPq3r0o7jOtKjtjIpmf92PSsHxEf9yvtcAp1P8QoUlZV6zWST+ff2+Vv1u+KwLF5fxyb9+fxNz656/Ofjk4wzt/42bfu+vxyd5iXdgAiIveyf0Y2xd3e0rj90R/HWbEvlmPxl7A2N6NusDNvPlKR8p72hR4/fMF+ftgcxVudKjGgabliz3vg3EU++fMEW0+eJyk1E39XG55qGMhzTYKNfeoGu7BnbEvGLDlIRnbuLZ+jiJRtExbtwNHVw7h9ZMcGVs3+kNOHdpOcEMPgKXOo1aLDTY2ZcO40y756n4ht60hOjMXZw5sH2vXikf7DMbfIW1NdvfyZsuYoq7//iENb1pbkKYlIGVNz4josHN3ztcX8OYtzqz8n80Ictn4VCH7sLRwrNCj2mOkJUZxZOp2LERvITI7H0tkLjwe64dfhJUzN89ax1KgDnF3xKSlHt5J16TzW7v54NXsSn4f7G8dxKF+XOlN3cWruWHKzMkvmhEWkzNnx/QQ8nB2N21N+XMGv63ZyNDIaaytLGlQN5e2BPQgL9L6pcSd/v4zVm/ex71gUluZmRC3/ON9+f09Xjv4yhY/mrWbtjkMlci5y71FyWETkOtztLXGysTBubzqRRN/GgdQMcCI718D/Vh7hsS+3sW54E2yt8i+pK/fHsivyAt6OVjc9794zybjZW/Jx7+r4Oduw7dR5hi/Yj5mJCf0eDALA0twUT0crrC3MlBwWkVvm6OqBrYOzcTsjPZWACtVo3OkJZgx/4pbGjDl5hNzcXJ58YzqeASGcPX6I794ZQkb6ZXoOnQiAqZkZTu5eWNnYlcRpiEgZZuHojrmtk3E7YesSTv00nnJPTMKhfD1i//6eQ9OfoOY7a7Fy8yvWmGnRx8CQS8iT72HtFczls4c58d1wcjIuE9xrLACpp/Zh4eBG+ec+xsrVl5Rj2zkxewSYmuHzUF8ATM0tsXTyxNTCWslhEbllHs6OODvYGrf/2X2YAV1aULtSMNk5ubz99SK6DJ/K1lnvYGdT/OvPzOwcujSvQ/2qIXy//J8C+83MTPFyc7qpMeX+o7ISIlJsszdFUuvtP8nNNeRrf3rmDl6auxeAUwmpPPPtDsLH/0Ho6DW0nb6RdUcSihwzKukyPsNWsv/sRWNbcloWPsNWsvFYorHtcEwKfb7eTujoNYSP/4MX5+whMfXu/8Ce+1w9etXzp6K3A1V9HZnWK5yzF9LZc+Zivn7Ryem8segAn/augbnZzS+1j9cPYEKXKjQKdSPIzZYedfx4rJ4/K/bFlNSpiEgh/l4wk2FtKpKbm/+Gy8ev9OKbsQMBiIs6wSdDH2Noq1AGN/ZhwhPNOLjlryLHvFI+IfLwXmPb5ZQL9K/tSMT29ca2cycimD6kO4Mb+zC0VShfv/kcKecTCxvyjgpv3Jqug8dS56FOtzxGtcYP0++tGVRt+BAe/uWo2aw9bZ58iZ1/Li3BSEWkMLFrv2f7a3Uw/Gcdi/joGY598zIA6XGniPi4L9uH1mDLoDD2vtOeCwfXFTlmekIUm571IzVyv7Et+3Iym571Izlio7Ht8rkjHJr+JFsGhbF9aA2OfjWErJSkEj7DG4te8xWeTR7Dq2lvbH3DKPf421i5+hKzdnaxx3AJb0H5ftNwrtYMa48gXGu2xqfN8yTtXGns49nkMcr1fgenig2x9gjCo2F3PBr3ImnnijtxWiLyr5m//k3FHsMK/F7rNfpjBr77DQAnzsbx2BufENp1KD5tB9Ns4AT+2n6wyDGvlE/YezTS2HYh5TKOzfuzfleEsS3i1Dm6j5yOT9vBhHYdynMTvybxQkoJn+GNLZo8lD7tGlO5nB/h5QOY8XpfomKT2H3k9E2N80bfzrz4aGuqlvO/Q5HK/UBPDotIsXWs4c2YxQfZcDyRJmF5r+5duJzF2sPxfNevDgCpmTm0rOTByLYVsDI35eftZ3l65g7Wj2yKv4vNLc0bezGdbjO20KdBAOM7VSI9K4cJyw8zcPYuFrxQ+OuBZ86n0Wzy+kL3XdG9ti/v96h2SzFdkZKeDYCL7dWni3NzDQyZs4cXmodQ0dvhtsa/1sX0bJyvmUdESl6dh7swd/IIDm9bR+UGzQFIvXieA5v+YMj0eQBkpKUS3rg1XQaNwcLKio1L5/DxK72YsHAHbj4BtzTvhfgY3u/fjqZdn6HXq++SlZHGgo/G8sXIpxn25bJCj0mMjmJsj/rXHfeB9r148o3ptxRTSUu7lIydo0tphyHy/55r3Q6cnDuWixEbcKrSBIDs1AtcOPA3lYbMAiAnIxWX8JYEdB2BqYUV8RvmE/FRX2pNXFfsJ2v/K/NCLAfe645X094E9xpHbmY6pxdM5MjnA6k6fH6hx2QknmX3mObXHdfjgW6EPPVesePIzc7k0um9+LYfnK/dqUozUo5tL/Y4hcm5fBFzO+fr90lLuWEfEbk9XZrXYcTHc1m36zDN61QG4HxKKn9sO8C8SUMASE3LoHWDcMY82wUrSwvmrNpIr9Efs+P7CQR4ud3SvDGJF2j38vs806Ep7w7qRVpmFmO/WMDTb33BsmnDCj0mKjaR+k+Pve64vR5+gOmvPXlLMV2RfOkyAC4OeiNLbp6SwyJSbC62lrSo5MGiXdHG5PDSvdE421oat6v6OlLV92otpNfbVWDl/ljWHIgzlkO4Wd9tjCTcz4nR7Ssa26b1DKfOhLUcj08l1KPgP4Dejlb8/ur1P3DkYHV7S6DBYGD8rxHUL+dCJZ+rSeBP/jqBmZkJ/W/xfAuz/dR5lu6J5vtn65bYmCJSkL2TK9UatWLLqvnG5PD23xZj5+RC5fp52wEVwgmoEG48puvgsez6axl7/l5By8cG3tK8axd8TVClGnQbMs7Y9sy4zxjRrjIxp4/iHRRW4BhnDx/Gzi34+t+1bOwdr7v/bomLOsGf877k0X9LSojInWNh74JztebEb1lsTA4nbl+GuZ0zTlUeBMAuoCp2AVWNxwR2G0nSrlUk7V5jLIdws2LWzsYuKJzA7qOMbaF9p7BzeD3SYo5j4x1a4BhLZy+qj1tz3XHNbW7uRnt2ShLk5mD5nxrEFk7uZO2Pu6mxrpUed4qYP78lqGfRSZ6UY9tJ3LaUSi8X/wllEbl5ro72tKpfjfl/bDEmhxev3Y6Lgx3Na+dth5cPILz81Zv2Y/t3Zdk/u1ixYQ8Du7W8pXm/XrKWGhWCGPdcN2PbZyOeoXLPERyNiiEsoGC9Xx83Z/75+vrJYUe7W3uI6gqDwcDoz36mYXgYVUJu7QaflG1KDovITelWy5fhC/bzbrcqWJmbsXDnOTrX9MHM1ASAyxnZTPntGL8djCP2YgbZuQbSs3I4eyHtlufce+YiG48nEjq64MXDqYTLhSaHzc1MKed+Z++ajl50kIPRKSwZfPXp5T1nkvn6n1OseaUxJiYmJTLP4ZgUnvl2J0MfLk+zCu43PkBEbkuDdj2ZPeFl+oyaioWlFVtW/ky91t0xNTMD8p4c/vWL/7F3/SouxMeQm5NNZkYaiTFnbnnO04d2E7F9PYMb+xTYF3/mZKHJYTNzc7wCCyZb7jUX4qOZ/mI36rTqQtOuT5d2OCJlgvsDXTnx3Uhyn5iU92Tw5kW41++EiWneOpaTcZkzv07l/J7fybwQiyE3m9zMdDKTzt7ynKmn9nIxYiNbBhVcr9LjTxeaHDYxM8fGq/gf7L05//kdZjDALf42yzwfw6FpfXCr2wGvpr0L7XP57GEiPumHf8dXcK7a9JbmEZHi69mqAS9/MJupr/TBytKCn3/fQveW9TD7t6RfaloG//vuV1Zt2ktMwgWyc3JJy8zkTNytl+zafeQ063dF4NN2cIF9J8/FF5ocNjc3I9Tf65bnLI7XPpzDgeNnWP3xyDs6j/z/peSwiNyUh6t6kjvfwO+H4qkZ4MSWk+cZ36mycf/byw6z9kg8YztUopy7HdYWpjw3exeZOYV/MO1KAtXA1TrGWf/pm2sw8HAVT958pCL/5eVQeGH8O11W4o1FB1lzII5Fgxrg63z1Tu+WE0kkXMqk7sS1xracXANvLY3gq/Wn2fZG85ua53BMCj0+30qfBwIY2qr8LcUqIjenRtN2GAxD2Lt+NeWq1uboro30fHWScf/86W9yYNMfPPrKRDwDQrCwsubzEU+RU8SHhkxM/q07bri6zuVkZ+frY8jNpUbTdnR/6a0Cxzt7FP7V6fuhrMSF+GgmD3iE0Or1eerNj0otDpGyxqXGw2AYzvm9f2BfrgYpR7cQ3Ovqmwmnf36HCwf+JqjnGKw9gzG1sObIjAHkZt9oHbvaZsjJv45hMOBS42ECe4wucLylc+GJkTtRVsLcwRVMzci8GJ+vPetiIhaOHsUe54rM8zEcmPwo9qF1CHnq/UL7XD53hIMf9MSraW/8O75y03OIyM1r16gGQwwGVm/eS+1K5di49yiTBvU07n/z8/n8se0AE194lBA/T6wtLXhq3OdkZuUUOp6p6ZXr0quyc/L3zc010K5RDd4a0L3A8d5uzoWOe6fLSgz7cA4rN+xm5Ucj8PN0vaUxRJQcFpGbYmNhRvtwbxbuPMephMuEuNtRw//q16G3nEyiZ11/2ofnJTNSM7KJSkqjYREPt7nZWwIQdzED/n0D5sC5/AX9w/2dWL43hgAXm2J/3O1OlZUwGAy8seggK/fH8ssLDQh0s823v0cdP5qG5X+69/GvttGjjh+96t3cKz5XEsM96/oxql2Fm45VRG6NpbUNtVt2ZMvKn4mLOoFXUHmCq9Qy7j+6axONOvahdsuOAKRfvkTCuUgq1il8PAeXf2u0J8QQSA2AfB+nAwisVJOdfy7B3TcIM/PirU33elmJ83Hn+GDAIwRVrknf8TMwNdV3kEXuFjNLG1xrtyNh8yLS405h7RWCfXB14/6LR7fi2fhR3Gq3AyAnPZWMhDNQ8D488G/CFchMjsWOvBvrqZEH8vWxC6pG4o4VWLsHYGJWvHXsTpSVMDW3xD6oOskH1hnPDyD54DpcarW5qbEyzkdzcPKj2AVVp3y/aZgUso5dPnuYgx/0xKPRowR2e/2mxheRW2djZUnHJrX5+fctnDgbR3l/L2pVDDbu37T3KH3aNqJjk9oAXLqcTmRMAkUtdO7OeWtNTOIFaoQFArD3WGS+PjUrBLLk750Eebtjbm5WrDjvVFkJg8HAsA/nsOyfXSyfPpxgn5u/+SVyhZLDInLTutX25emZOzgcc4nudXzz7Qt2t2PlvhhaV/HExATeX3WUXIOhiJHyks11gpz5+M8T+LvYkJSaxXsrj+Tr07dRID9ujuKFH/cwqHk5XO0sOZmQypLd0XzwaLixpMW17lRZiVELD7Jo1zm+7VsbeyvzvKQ24GBjjo2FGa52lrjaWRaIxcPBkvKe9sWe53BMCt1nbKVZRXcGNi1nnMfUFNztC39aWkRKToN2Pfn4lV6cO3GIB9r3yrfPMyCEnX8upUbTdpiYmLD4swkYDIW/HQF5yeaQ8Hqs/HYa7r5BXDqfyOLP3snXp2Wv51i/aBZfju5Hm6dewsHZjbioE2xd/QtPj/nYWNLiWneqrET65UvERZ0wbsefPUXk4b3YOboU+4N7F+Kjmfxce1y9A3h06ERSzicY9zm539lXK0Ukj/sD3Yj46BkunzuMxwPd8u2z9gwmccfKvCeMTUyIWjQZrrOOmVnaYB9Sm7MrPsXKPYDslCSiFuV/ita7xTPErpvDkS8G4dv2BSwcXEmPPUXC1iWEPjPZWNLiWneqrIRP6+c49vXL2AfXwD60DnHrfiAj6SzezYr/ZF7m+RgOvt8DS1c/gnqOISvl6qvolk6eQF5i+MDkR3Gu2gyf1gPITM6raWxiaoaFw6198EpEiq/nww3oNepjDp08R6+HH8i3L8TPk6XrdtKuYQ1MTEyYMHPx9a9LrSypVyWEaXNWEuTtTmLyJd75ZnG+Ps91acmsZevp986XvPRYG9ycHDhxNo5f/tzKx8OeNpa0uNadKivx6vQfWfD7FuZOfBEHG2tiE5MBcLS3wcbK8gZHXxUVm8j5i6lExSWRk5vL3qN5CfEQP0/sba1LPG65Nyk5LCI37cHybjjbWnA8PpVutfInh9/uVImh8/bR8ZNNuNpZ8mKLEFLSs4sYKc/UnuG8+vM+2n64kfIedrzZoRKPfbnNuN/byZpfX3yACcsP8/hX28jIzsXfxYYWFT0oJC98R323Ke8fy+4ztuZrn94rnF71/Is9zss/7SUqKY2FgxoUun/pnhgSUzNZuPMcC3eeM7b7u9jcdGkKEbl5les1w87RhZhTR2nQ9tF8+3q99i6zxg/mf30fxt7ZjbZPv0JaakoRI+V5ZtxnzHprEBOeaIZXUBg9Xn6baYO6GPc7e/jw+rdrWPDhOKYP7kZ2Vgau3gFUa9Sq0CfV7qRTB3fxwYBHjNs/T817RbxRx970e+tzAJZ8PomNS+fw3vL9hY5xYNOfxEWdIC7qBMPbVsq37+udF+9Q5CJyLafKjTG3cyY95jjuD3TNty/4sfEc//ZV9r/bGXN7V/zaDSYn/dJ1xwvtO5Xj377KvnfaYe0VStCjb3Jo6uPG/ZYu3lQbtZjIBZM4NK0PhuwMrNz8ca7WHEzu7jrmXr8z2ZfOc2bpNDKT47D1q0jll7/Hyv3qb7WoJVOI3/Aztd/fUugYFw78TXrcKdLjTrFzWP4PAjf8Jq82c+L2ZWSnJJKweSEJmxca91u5+Rc5roiUnGa1KuPiaMfRqBgebZX/uurdwb0Y/P4sHn7xf7g52fPK421JSb3+d3A+G/kMg96bRbOBEwgL8OLt53vQZdg0434fd2fWfPI6475YQLfh08nIyibAy5VW9asZy1LcLd8sWQtA+1cm52ufMbIvfdrlvUE76dslzFm1kf3zii7NM3HmEuas3mjcfvC5twFYPm0YTWpVKuow+X9GyWERuWlmpibsHlv4F14DXG1Z8EL+f5j7Ng7Kt/3f5GYFL3uWDWmYry36g3b5tkM87Jj5TO1bjLjk/Deu4igsmRuVlEaj0KJrQg1rE8awNgU/6CIid4epmRlT1hwpdJ+7bxDDvlyWr61lrwH5tv+bNPUNqcjo7/7I1/bfJKlXYHkGT/nxVkMuMZXqNrlhAjcxOpKKdR8scn/jTn1o3KlPSYcmIjfBxNSMulN3FrrP2j2AqsPn52vzbvlMvu3/JjdtfcMIf2NpvrYrSdIrbLxCqDj461uMuGR5t3ymwDldKyMhCseKDYvc7/lgLzwf7FXkfoCAzq8R0Pm1Ww1RRG6TmZkpR36ZUui+IB93lk0blq9tQNf817D/TZpWDPLlj8/y102/uDb/mlbe34sf3yn4Qbq77b9xFSYyJpEHaxZRL+hfn4/qx+ej+pVUWHKfUvE3EZHrqP3OX7zww+4SHfNSejanEy/zQvPbe41y84kkQkevyfdksYjIzRretjJfjup7U8cc2bGBLi+8eVvzJkZHMbixDytmFn5RJyJSXDuG1eXIF4Nu6piLRzYT0HX4HYro2nm2sGVQGAlbFt3xuUTk/6/Kjw6n79tf3tQxG/Yc4c1nu9zWvFGxifi0HcyUH1fc1jhyb9OTwyIihagV5MzG15sCYGdZskulvbU5O8a0uO1xagQ4GT+6V9Ixisj/fyHV6jJx8S4ArG2LXxMd4H/L9t32/Nd+UM/CUrXUReTm2YfUouakvHXEzPrmvjVR+73NdyKkAuyCqxs/unezMYqI1K0Swq4fJgJgb3NzNYD3/fS/257/2g/qWVla3PZ4cm9SNkFEpBA2FmZ35IN2Jel+iFFE7l2W1jZ35IN2xXWnPqgnImWHmaXNHfmgXUm6H2IUkXuXjZXlHfmgXXHdqQ/qyb1FZSVEREREREREREREyiAlh0XktkUlXcZn2Er2n9UX6G9k3rYzVHzztxv28xm2kpX7Y+9CRCJSEhLOnaZ/bUciD+8t7VBEREpEekIUm571IzVy/407i4iUgtPRCTg278/eo5GlHco978eVGwh4ZMgN+zk278+y9bvuQkRyL1FyWETKvG6fbWHMkoMF2oubyL0dH6w+Squp/9zROURErif98iVGdarOvCmj8rUnnDvNi038WLfoOwAitq+nf23HAv8t+vRt4zEGg4F1C2cx6emHeLGJH4Mb+zC2R33mTh5BbOTxu3peIlJ25aSnsvP1Rpz6aXy+9vSEKLYOrkjsujkAJEdsZNOzfgX+i1z4nvEYg8FA7N8/sm9iR7YOrsiWQWHsHtOCk3PGkhZ78m6eloj8P9P+5fcZ+fFPBdqLm8i9HZO+XULjZ9+6o3PI/UM1h0VERETKMGtbe/qOm8GUFzpSq0VHKtRuhMFg4NvxgyhfowFNuz6dr/+ERTuwsXM0blvZ5tU+NxgMfDW6H7vWLqd9v9fo9dq7OLp6kHD2NAe2/MXybybT763P7+q5iUjZZGZtR/m+Uzk4pReutdvhWKEBBoOB4zOH4lC+Hl5Ne+frX3PiOsxsHK4eb3V1XTv65WCSdq3G/5EXCX5sPBaO7mTER3Lh4HrOLvuQ8s9Ov5unJiIiUuKUHBaRYsnNNfDZ2hP8uOUM5y6k4e5gxZMPBPBKq/IF+ubkGhi+YD//HE0kPiUDPxcbnm4UyHNNgo19Nh5L5J3lhzkccwkLMxMqetvzae+aBLjacODcRcYuOcSeM8mYAOXc7Xi/RzVqBjjdvRMuwpoDsXyw5hhHYi/h5WhFz7p+vPxQKOZmeS9ifP73SeZtO8PpxDRcbC14uIonYzpUxM6q4HI7b9sZpvx2DMgrIwEwvVc4ver5A5CUmknfWTtZezgeHydrxnWsRJuqXhgMBhr9bx1PNQzgheYhxvEiolNoOfUfNo5sSrA+VCdS4nJzc1k9+0PWLZzF+dizOLp50rRbXzr0H16wb04Osye8RMS2dSQnxuLq7U+LR/vTqvcgY5+I7etZ8OEYzh2PwMzcAt/QSgyY+A1uvoFEHdnHTx+8zqmDuzAxMcEzIJSn3pxOcJXad+TcKtRpTMteA/l2/AuMn7eRdQtnEXV4H2/N31ygr6OrB7YOzgXat635ha2rf+HFaT9Rs1l7Y7uHfzkqN2iOwWC4I7GLyK0z5OZybtUMYtfPITPpHBaO7ng1ewL/Di8X0jeH49+N4GLEBjKT47Fy9cW7xdP4PNzf2Cc5YiOnF0wk7exhTMwssPWrQNhzn2Ll7k9q1AFOzR3HpVN7MTExwdqrHCFPvYd9cI07cm6OFR/Au2Vfjn37KjXG/0bcuh9JjTpIjbf/KNDXwtEdc9uCvzMTt/5K4tYlVBzyLa41WxvbrT2CcKrSROuayF2Qm5vLhz+tZtaydZyNP4+niyN9OzZl+JMdCvTNycnlpSmzWbczgtikZPy9XOnfuQWDerQy9lm/K4IxXywg4tQ5LMzMqFTOl2/eHECgtxv7jkXx+ic/sevwKUxMTAj192T6q09Ru1LwXTzjwq3cuJt3Z/3KoZPn8HF35vE2jRj+xCOYm5sB8MnPa/hh5QZORcfj4mBH20Y1eGdgD+xtrQuM9ePKDfzvu6VAXhkJgBkj+9KnXWMAEpNT6P3mp/yx7QA+7s5MGtST9o1rYjAYqNlnNM92as5Lj7UxjnfwxFkaPjueXT9MJMTP807/UcgdoOSwiBTLpBWH+XHLGd7qVIn65VyITcngWFxqoX1zDQZ8nKz58smauNpZsu30eYbPP4CXgxWdavqQnZNL31k76dMggBl9apCZY2B35AVMTPKOH/zjHqr5OfK/7lUxMzHhwLmLWJiZFBlb76+2seXk+evGf3xS6+vuL46/Dsfz4ty9TOhcmQYhrpxKvMzw+Xl1+F5rHQaAqQlM6FKFABcbIpPSeH3hAd5Zdpj/da9aYLxONX2IiLnEXxHx/DywPgAONleX5alrjvFmh4qM7VCRb/45zeA5e9j2RnNcbC15rL4/P207my85PHfbGRqUc1FiWOQOWfjxeNYtmkWv194lrGZDkhNiiT51pNC+ubm5uHj6MvC9Wdg7u3F8zxZmT3gZJ3dv6rXuRk52Np++2pumXZ9mwKSZZGdlcfLAdq4shF+90Z/AitV5YtQ0TM1MiTq8DzNziyJjm/5iN47u2nTd+D/dEH3d/d1eHMe+Db/x9ZvPcWDTHzz5xoe4ePre4E/lqq2rFuAdHJYvMXwtE5Oi13ERKR2Rv7xL7Lo5BD82Dsew+mQmx5EWfazQvobcXKxcfKjw/OeY27uScnw7J74bgYWzJ+71OmHIyebwJ8/i2bQ3FQZ8Sm52FpdO7jKua0e/HIJdYFWqP/k/MDXlcuQBTMyKvhw9NO0JLh7dct34G3x29Lr7A7u/zoX9f3H0qyEkH1hLyFPvY+Xic4M/lasSti7G2js0X2L4WlrXRO688V8tZNaydbw7uBcNw8OITUzmSGThv2lyDbn4ergwa/xA3Jzs2bL/OC9PmY23mxPdWtQjOzuH3m9+ytMdmjJzzACysrPZfuik8Tq0/4SvqB4WyLShT2BqZsq+Y1FY/Jt8LUy3EdPZtPf661D0qk9v+dyv+H3rfp6b+A3vDXmMRtUrcPJcHC9/8D0Ao57pBOStR++/9DiB3m6cjk7g1Wk/MuaLBUwb+kTBuFvW4+DJs/y+dT+/TnkNAEd7G+P+/323lLef78E7z/fgi0V/0n/CV+yf9x6ujvY82f5Bfli1IV9y+PuV/9CoepgSw/cxJYdF5IYupWfz9T+nmdi1Cj3/fao12N2OBuVcC+1vYWbK8DZhxu1AN1u2n7rAr3ui6VTTh5SMbC6mZ9OqiocxkVnBy97Y/+yFNAY1L0eYZ15biMf1k51TeoaTnpVzW+f43cZI5mw5k68tJ9eAlfnV0uwf/n6cF1uEGP8MgtxsGdE2jAnLDxuTwwOaljP2D3SzZWTbMEb+cqDQ5LCNhRl2lmaYm5ng6WhVYH/Pen50rZWXmBndrgIzN5xmV2QyLSt58Fg9PyavPsquyAvUCnQmKyeXX3acY2yHirf15yAihUtPTeH3uTPoPfIDGnfsA4BnQAhhtRoW2t/cwoLOL7xh3PbwC+b4ni1s/20R9Vp3Iy31ImmXkqnetC2eAXk3eXxDrv7/NynmDG2eegmfchUA8Aos+JbGtZ4e+wmZ6Wm3dY4WVtY8Nux/TH+xG9UaP0zDRx4rtN/wtpXzbb+3fD/2zm7EnD6Gd1BYvn0/TR7J+sWzAbB1cGLyqojbilFESk5O2iWif/+Gcn0m4Nm4JwDWnsE4htUvtL+puQUBXYYZt609Akk5tp3EbUtxr9eJ7LQUctIu4lKjFdaewQDY+l5dEzKTzuLb9nlsfPLWMxuvEK4n5JnJ5Gam384pYmphTfBjb3FoWh+cw1vi0bB7of12DKubb7v2+1uwsHclPfYENt6h+fadnDuWuPVzATC3daTOBztuK0YRKVrK5XRmLPidD17uTZ+2eU+1hvh50rB6WKH9LczNeaNvZ+N2sI8HWw4cZ9Ff2+nWoh4XL6eRnJpG24bVjYnMikFXb4SfiUvipcfaUCEo7yZSeX+v68b3yfCnScvIvK1z/HrJX8xevj5fW3ZODtaWVx8K+OCH5Qzt3c74Z1DO14M3+nVm7BcLjMnhwY8+bOwf7OPBm892YejUHwpNDttYWWJnY4W5mRlebgXfmujdtjGPPtQAgHH9u/HFwj/ZcegUDzeoxhPtGjNx5hK2HzpB3cohZGVnM++3zbzzfI/b+nOQ0qXksIjc0NG4S2Rk59KkvFuxj/luYyRztkZx5nwa6Vm5ZOXkUtU3r0ali60lver60fur7TQNc6NJBXc61fDGyzHvlZeBTcvx2vz9LNh5jiZhbnSs7n3dp2F9nAq+KnOzutXy5eVW+X/8r9gXy0d/XP2A0t4zF9kTlcyH17Tl5hpIz87lcmYOtpZmbDiWyEd/HOdI7CVSMrLJyfl3f0Y2toWUlrieKj5Xa9/ZWpljb2VOwqW8Hx9ejta0quzB3K1nqBXozG8H48jIzqFDjeI/DSMixXfu5GGyMzOoXL9ZsY9Zu+Ab1i+aTWJ0JFkZ6WRnZRJQMRwAeydXGnXsw7TBXanSoAVVGjSn7sPdcPbwBuDhPoOZ/c4QNi+fR+UGzanbqosxiVyYm3nC93r+WfI9lta2nD12kMspydg6FLxgGPnNKqxsr97Qs3V0ubrzP0/RPdJ/OC16DWDnn0tZMXNKicQoIiXjcvRRDNkZOFV+sNjHxKydTdy6uWQkniE3Kx1Ddha2gXk3wC3sXfBo3JNDU/vgXLUJTpWb4FavI5bOeckVn9YDOPHdcBI2/YJTlSa41e1gTCIX5mae8L2euPVzMbW04fKZQ2Rfvoi5rWOBPtVGLsLU+upvTXNb52v25l/X/Du8jHfLviTtXMnZ5R+XSIwiUrjDp8+RkZVNszqVb9z5X98sWcvs5euJjE0kPSOLzOxswssHAODqaE+fto3oOnwaLepWoXmdKnRrURdvN2cgL8E6ZPJs5q3ZTPM6lenSvO51n4b19XApcl9x9WzVgGFPPJKvben6nUz5YYVxe/eR0+yMOMUH3y83tuXk5pKemcXl9Axsra1YtyuCKT8sJ+J0NCmpaWTn5O1PTcvAzqbgg0jXUy3U3/i/7WyscLC1Jv7CRQC83Zxp07A6P6zYQN3KIazatJeMzCy6Nq9b1HByH1ByWERuyNqi6FdpCvPr7mjG/3qIcR0rUSfYGXsrcz5be5JdkReMfaY/Vp1nmwTxV0QCv+6O5r1VR5g3oB51glwY1iaMrrV9+f1QHH9GxPPB6qPMeKIm7cO9C52vJMpKONiYU+4/CWh3e8t82waDgWFtwmgfXvAOsrW5KVFJaTzx9XaebBjIiLZhONtasvVkEq/+vJ+s3JuvSXeljvEVJv/GcEXv+v4MmbuXtzpXZt62s3Sq6YOt5c39XYlI8Vha2dy40zW2rVnIvCmj6Dl0IqHV62Nla8/q2R9xcv92Y59+b82g1ePPs3/j72xbs5BFn03g1c8WE1q9Pp2fH02Ddo+yd/1q9m/8jV8/n8SAd7+ldsuOhc5XEmUltq7+hd1/r2D0rN/5eswA5k0ZRd/xnxXo5+4XVGjNYa/AUGL+U2bDwcUdBxd3HF09rju3iNx9ppY3d3M9YduvnPrpLYJ7jsEhtC6m1nacWz2DSyd2GfuU7zcNn4ee5cL+v0jY9iuRi96nymtzcQitQ0Dn13Bv0IXze//gwr6/iFoyhbCBn+FWu12h85VEWYmErUtI2v0b4aN/5ejXL3Hqp/GU7ze1QD8rj4BCaw5be5YjLSZ/mQ0LBzfjfyJyZ9lYWd640zUW/rWNUZ/OY+KgntSvGoq9jRUfzVvN9oMnjX1mvN6P57u34vet+1n41zYmfLOIxR+8Sv2qoYzu25lHWzVg9ea9/LZlP5Nm/cq3YwfQsUnh33woibISjna2hP7nCWUP5/w3sXJzDYzu26nQOKwtLYiMSaTHyA/p16kZb/TrgoujHZv3HWPw+7PIyr75N2wtzPJfU5oAhmuuZ59+5EEGTPyGd1/sxQ8rN9CtRT1srW8uAS33FiWHReSGyrnbYm1hyvpjifRxs71h/y0nz1M32IVnGgcZ204nXi7QL9zPiXA/J156KJQOH29i0a5o6gTl3X0N9bAj1KMcA5uW44UfdjNv29kik8MlUVaiOML9HTken1ogiXzFnjPJZOcaGN+xEqameU+Z/Lrn+skYC3NTcm4hcQzwUGVPbC3N+G5jJH9GxLNoUINbGkdEbswrMBRLaxsObf0bj67BN+x/dNdGQqs3oEXP54xt8WdOFugXWKkGgZVq0L7fa0x6+iG2rJpPaPW8V7q9g8LwDgqj9RMv8uWovmz49Ycik8O3W1YiOTGOOe+9RtdBYwisVIN+b83g3WdaUffhLoQ3Ll7N9vpte/DV6GfZtXY5tZo/cuMDRKRU2XiVw9TSmuRD/2Dt0fuG/VOObMUhtA7eLZ8xtmXEnS7Qzy6oGnZB1fB7ZAj7JnYkYctiHELr5M3pHYqNdyi+rQdw5ItBxP8zr8jk8O2WlchMjufkj28Q2HUEdkHVKP/sNPZP6oRbvQ64hLcs1hjuDbpw9MvBJO1ajWutNjc+QERKVKifFzZWlvy94xDBHW58o3nj3qM0qBbKc11aGNtOno0v0K9GWCA1wgJ5rU97Hho0ifl/bKF+1by3SMMCvAkL8ObFR1vT9+0v+WHlhiKTwyVRVqI4alQI5GhUbIEk8hW7Dp8iOyeXSYN6Ymqa94DRor+2F9r3CksLc3Jyc28pntYNqmNrY8U3S9by25b9rPxoxC2NI/cOJYdF5IasLcx4sUUIE5YdxsLMlPrBziSmZnI45hK9GwQU6B/sbsv8HWf563A8ga62LNhxlt1RyQS65j15F5l4me+3RNGmiidejtYcj7/EifhUHq3jS1pWDu8sjaBDdW8CXG2JTk5nd1QyjxTytO4VJVFWojiGPlyep77Zga+TNR1reGNiYsKh6BQORafwersKBLvZkp1r4JsNp2ldxZOtp87z/abI64555cN1+89exMfZGnsrM6yu89GDa5mZmtCznj/vrjhMsLstdYNv/7UmESmchZU1bZ9+hQUfjsHcwoLyNR4g5XwC505E0KTLUwX6ewaEsGn5T+zf+DvufkFsXv4Tpw7uxN0376ZZ/NlTrFs4i5pN2+Hs4UPM6aPERh6jYYfHyUxPY/70N6nTqgsevkEkxZ3j5IGd1Hmoc4F5rrjdshLfT3wZr6AwHn7iRQCCq9SmzVOvMHvCy7z18+ZCy0v8V/02Pdj551K+HNWX9n1fpWrDh3B08yQxOopta37B9D9vQ4hI6TK1sMa37WBOz5+IqbkFDuXrkZWSyOVzR/Bq8niB/taewcRvWsCF/Wuxcg8gftMvXDq1Byv3vN+C6fGRxK77EdcaD2Pp7E1azHHSY0/g0agHOZlpnJ4/Abc6j2DtHkjG+WgundqDW+3CP2AJt19W4sTskdh4h+LTegAA9sE18G37Aie+G0GNt/8stLzEf7nV70zijhUc+WIQfu1fxLlaMywcPchIPEPitqWYmGpdE7mTrK0seOXxtoz5YgEWFuY8UK08CRdSiDh1jqceaVKgf4ifJz+t2cTvW/cT5OPOT2s2s/PwKYK83QE4FR3PrKXraNe4Jj5uzhyNiuHYmVgeb9OQtIxM3pwxny7N6hDk48G5+CR2Rpykc7M6RcZXEmUlimPkUx3pOepj/Dxc6Nq8LqamJuw/foYDJ84ytn9Xyvl6kJ2Tw+cL/6Rdoxps3n+Mmb/+fd0xA73dOR2dwN6jkfh5uGBva42VZdEfP76WmZkpfdo2YvxXCwnx86BB1dAbHyT3NCWHRaRYhrYqj5mpCZNXHyX2YjqeDlY81TCw0L5PNQzgwNmLPP/9bkxMoEtNX55uFMhfEXl3bW0szTgWl8r87bs4n5qJp6M1fRsH8eQDgWTnGjh/OYshP+0lISUDVztL2od7M6xN4R8duJtaVPRg9rN1mPbbMT5dewILM1PKe9gZE+TV/BwZ36kSn/51gkkrDvNAiCuj2lfkpbl7ixzzkeperNgXQ4/Pt5Ccls30XuH0qudfZP//6l3fn4/+OM7jN3GMiNyaDs+NxNTMnCUzJnEhPhond2+a9+hXaN9mPZ4l8vA+vni9LyYmJtRv253mj/Zn/4bfALC0tiXm1BE+WzqH1OQknNy9adlzAM269yM3J5vU5CRmjh3IxcQ47J3dqN2yI52fH31Hzmvjsjkc3PwX4376x/i0CUCnga+zZ92KIstL/JeJiQkD/zeL9QtnseHXH1j13YfkZGfh4uVL5XrN6fnqu3ckfhG5df4dX8HEzIyoxR+QeSEWCydPvJs/WWhfr+ZPkhp1gCOfvwAmJrjX74xXi6e5sO9PAEwtbUiLPsbhDfPJTj2PpZMn3i374tXsSQy52WRfOs+xb14m62IC5vauuNVuR0CX1+7IecVvnE/ywXVUH78mXwLXv9OrJO3+rcjyEv9lYmJChec/J27dj8RtmMe5VZ9hyMnG0sUHp8oPUrXXuDsSv4hcNfKpDpibmTJp5hKiEy/g7eZEv07NC+37bKdm7DsWSd+3vsDExITuD9Wnf+fm/LZlPwC2VpYciYxhzurPSLqYirerEwO6tqRfx2Zk5+SSdDGVge/OJO78Rdyc7OnYpDajnyn65vzd0qp+NX5+dwjvzV7Khz+txsLcjLBAb57+N0FePSyQSYN7Mn3uSt76aiGNqocxbkA3Bk76psgxOzetzdJ1O+kw9AMuXLrMjJF96dOucbFjerJ9Ez74YQVPti9+3Xq5d5kYri1gKSJl2j///EOTJk1YP6IJ5T3tb3yAlLqtJ8/TfcYWdo5pgYdDwTpPx+Iu0eT99axfv54HH9Q/3CLXCgktT6Vmnek+ZHxphyKF+OXj8UT8vYQTx4/duLNIGbRs2TI6duxInam7sXRSXe/7UWZyPDtercnSpUvp0KFDaYcjUmquXIfumD2BsMDCSwnKvWXzvqO0f+UDIua/j6drwTfMjkbGUOepN3Udep/Qk8MiIvehjOwczl1I5/1VR+hYw7vQxLCIiIiIiIhIScnIzOJMXBITZi6ha4u6hSaG5f6jIkkiYmT271dJs3P0QsG9bvGuaB58bx0X07MZ06FSkf2u/F2am+teoMh/mZmZkZOdVdphSBFysrO0dolcx5XfbYYcrWP3qyt/d1rrpKy7sp5lZd/5j4zL7Vnwx1bqPPUmF1Mv887AHkX2u/J3qfXt/qC/JREx8vfPq1t7JPYSlXwcSjkauZ5e9fyLVZv4SOwlAPz8/O50SCL3nQB/f6JPRJR2GFKE6BMR+GvtEinSld9taeeOYuV6ex+llNKRdu4ooN9pIlfWs4jT56gSov8/3Mv6tGtcrNrEEafPAVrf7hd6clhEjAICAqhVozqzN0eRlZNb2uHIbcrKyeW7TVHUrlmDgICA0g5H5J7TpUtnDm1dy7kTh0s7FPmPcycOc2jrWrp27VLaoYjcs6pVq0ZgUDli/5qFIVe/2+43htxcYv+aRVBwCNWqVSvtcERKVUBAALVq1mDmr3+TlZ1d2uHIbcrKzuabJX9Tu1ZNXYfeJ/RBOhHJZ8WKFXTp0pma/o48VteXcu52mJuZlHZYchOycwycTEjlp+3n2H3mIosXL6F9+/alHZbIPef8+fM0adKUqOgYmnbvR1itRlha22JiojWvNBgMBjLTL3N010bW/TKTAB9v1q9fh4uLS2mHJnLPmjdvHr1798axYkPcGvbA2j0Qk39fz5Z7kyEnh/SESBI3LeDi4U3MmTOHXr16lXZYIqUu7zq0C7UrBvFku8aE+nthbqbnGe8n2Tm5HD8Ty/crN7Dz8GkWL16s69D7hJLDIlLA6tWreXfSRNat/wctEfcnExMTmjZ5kFGj36BNmzalHY7IPSs+Pp7hw4ezaPFiLiYnl3Y4Ajg6OdG1SxcmT56Mh4dHaYcjcs9bvHgx7773Pls3byrtUOQm1H+gIaNGjqBLly6lHYrIPSPvOnQS69av13XofSrvOrQJo0aP1nXofUTJYREp0vnz54mPjycnRx8GuJ+YmZnh4eGhp+1EbkJmZiZnz54lPT29tEMp06ytrfHz88PS0rK0QxG57yQmJpKQkECuSkzc00xNTXF3d8fNza20QxG5Z+k69P6k69D7l5LDIiIiIiIiIiIiImWQCriIiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEFKDouIiIiIiIiIiIiUQUoOi4iIiIiIiIiIiJRBSg6LiIiIiIiIiIiIlEH/B89bmFbwzX8QAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_2 = results_df.head(2)\n",
+    "\n",
+    "for i, row in top_2.iterrows():\n",
+    "    model = DecisionTreeClassifier(\n",
+    "        criterion=row[\"criterion\"],\n",
+    "        max_depth=None if pd.isna(int(row[\"max_depth\"])) else int(row[\"max_depth\"]),\n",
+    "        min_samples_leaf=int(row[\"min_samples_leaf\"]),\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "\n",
+    "    # Fit on the full dataset only for visualization\n",
+    "    model.fit(X, y)\n",
+    "\n",
+    "    plt.figure(figsize=(18, 8))\n",
+    "    plot_tree(\n",
+    "        model,\n",
+    "        feature_names=X.columns,\n",
+    "        class_names=[\"Healthy\", \"XFG\"],\n",
+    "        filled=True,\n",
+    "        rounded=True,\n",
+    "        fontsize=10,\n",
+    "    )\n",
+    "    plt.title(\n",
+    "        f\"Rank {i+1}: criterion={row['criterion']}, \"\n",
+    "        f\"max_depth={row['max_depth']}, \"\n",
+    "        f\"min_samples_leaf={int(row['min_samples_leaf'])}, \"\n",
+    "        f\"mean_acc={row['mean_accuracy']:.3f}\"\n",
+    "    )\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "048cb116",
+   "metadata": {},
+   "source": [
+    "Exactly as I expected, the decision tree with a max depth of 1 is using only one feature to make the split, and that feature is indeed HPRA006876. This supports our findings from the random forest analysis in the original study, where this antigen was identified as a key differentiator between XFG and healthy controls. The decision tree with a max depth of 3 is likely using additional features to make more complex splits, which may explain its slightly better accuracy. However, they are very close in performance. Lets see if we can tighten the estimates of the mean accuracy by increasing the number of repeats in our cross-validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a0649548",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_repeats</th>\n",
+       "      <th>mean_accuracy</th>\n",
+       "      <th>std_accuracy</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>n_scores</th>\n",
+       "      <th>se_change</th>\n",
+       "      <th>se_improvement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>0.131233</td>\n",
+       "      <td>0.018559</td>\n",
+       "      <td>50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.688000</td>\n",
+       "      <td>0.127325</td>\n",
+       "      <td>0.011388</td>\n",
+       "      <td>125</td>\n",
+       "      <td>-0.007171</td>\n",
+       "      <td>0.007171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0.676000</td>\n",
+       "      <td>0.128762</td>\n",
+       "      <td>0.008144</td>\n",
+       "      <td>250</td>\n",
+       "      <td>-0.003245</td>\n",
+       "      <td>0.003245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>75</td>\n",
+       "      <td>0.674667</td>\n",
+       "      <td>0.128131</td>\n",
+       "      <td>0.006617</td>\n",
+       "      <td>375</td>\n",
+       "      <td>-0.001527</td>\n",
+       "      <td>0.001527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100</td>\n",
+       "      <td>0.679333</td>\n",
+       "      <td>0.131045</td>\n",
+       "      <td>0.005861</td>\n",
+       "      <td>500</td>\n",
+       "      <td>-0.000756</td>\n",
+       "      <td>0.000756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>150</td>\n",
+       "      <td>0.678000</td>\n",
+       "      <td>0.130531</td>\n",
+       "      <td>0.004766</td>\n",
+       "      <td>750</td>\n",
+       "      <td>-0.001094</td>\n",
+       "      <td>0.001094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>200</td>\n",
+       "      <td>0.677750</td>\n",
+       "      <td>0.129564</td>\n",
+       "      <td>0.004097</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>-0.000669</td>\n",
+       "      <td>0.000669</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>300</td>\n",
+       "      <td>0.679444</td>\n",
+       "      <td>0.130326</td>\n",
+       "      <td>0.003365</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>-0.000732</td>\n",
+       "      <td>0.000732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>500</td>\n",
+       "      <td>0.678267</td>\n",
+       "      <td>0.130401</td>\n",
+       "      <td>0.002608</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>-0.000757</td>\n",
+       "      <td>0.000757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.675800</td>\n",
+       "      <td>0.129379</td>\n",
+       "      <td>0.001830</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>-0.000778</td>\n",
+       "      <td>0.000778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.676933</td>\n",
+       "      <td>0.129712</td>\n",
+       "      <td>0.001297</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>-0.000533</td>\n",
+       "      <td>0.000533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>0.676087</td>\n",
+       "      <td>0.129500</td>\n",
+       "      <td>0.000819</td>\n",
+       "      <td>25000</td>\n",
+       "      <td>-0.000478</td>\n",
+       "      <td>0.000478</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    n_repeats  mean_accuracy  std_accuracy  standard_error  n_scores  \\\n",
+       "0          10       0.700000      0.131233        0.018559        50   \n",
+       "1          25       0.688000      0.127325        0.011388       125   \n",
+       "2          50       0.676000      0.128762        0.008144       250   \n",
+       "3          75       0.674667      0.128131        0.006617       375   \n",
+       "4         100       0.679333      0.131045        0.005861       500   \n",
+       "5         150       0.678000      0.130531        0.004766       750   \n",
+       "6         200       0.677750      0.129564        0.004097      1000   \n",
+       "7         300       0.679444      0.130326        0.003365      1500   \n",
+       "8         500       0.678267      0.130401        0.002608      2500   \n",
+       "9        1000       0.675800      0.129379        0.001830      5000   \n",
+       "10       2000       0.676933      0.129712        0.001297     10000   \n",
+       "11       5000       0.676087      0.129500        0.000819     25000   \n",
+       "\n",
+       "    se_change  se_improvement  \n",
+       "0         NaN             NaN  \n",
+       "1   -0.007171        0.007171  \n",
+       "2   -0.003245        0.003245  \n",
+       "3   -0.001527        0.001527  \n",
+       "4   -0.000756        0.000756  \n",
+       "5   -0.001094        0.001094  \n",
+       "6   -0.000669        0.000669  \n",
+       "7   -0.000732        0.000732  \n",
+       "8   -0.000757        0.000757  \n",
+       "9   -0.000778        0.000778  \n",
+       "10  -0.000533        0.000533  \n",
+       "11  -0.000478        0.000478  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "def check_stability(results_df, iloc, X, y):\n",
+    "    best_params = results_df.iloc[iloc]\n",
+    "    best_model = DecisionTreeClassifier(\n",
+    "        criterion=best_params[\"criterion\"],\n",
+    "        max_depth=None if pd.isna(int(best_params[\"max_depth\"])) else int(best_params[\"max_depth\"]),\n",
+    "        min_samples_leaf=int(best_params[\"min_samples_leaf\"]),\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "    repeat_list = [10, 25, 50, 75, 100, 150, 200, 300, 500, 1000, 2000, 5000]\n",
+    "    stability_results = []\n",
+    "    for n_repeats in repeat_list:\n",
+    "        cv = RepeatedStratifiedKFold(\n",
+    "            n_splits=5,\n",
+    "            n_repeats=n_repeats,\n",
+    "            random_state=42,\n",
+    "        )\n",
+    "        scores = cross_val_score(\n",
+    "            best_model,\n",
+    "            X,\n",
+    "            y,\n",
+    "            cv=cv,\n",
+    "            scoring=\"accuracy\",\n",
+    "        )\n",
+    "        std_accuracy = scores.std()\n",
+    "        standard_error = std_accuracy / (len(scores) ** 0.5)\n",
+    "        stability_results.append(\n",
+    "            {\n",
+    "                \"n_repeats\": n_repeats,\n",
+    "                \"mean_accuracy\": scores.mean(),\n",
+    "                \"std_accuracy\": std_accuracy,\n",
+    "                \"standard_error\": standard_error,\n",
+    "                \"n_scores\": len(scores),\n",
+    "            }\n",
+    "        )\n",
+    "    stability_df = pd.DataFrame(stability_results)\n",
+    "    stability_df[\"se_change\"] = stability_df[\"standard_error\"].diff()\n",
+    "    stability_df[\"se_improvement\"] = -stability_df[\"se_change\"]\n",
+    "    return stability_df \n",
+    "\n",
+    "stability_df_top = check_stability(results_df, 0, X, y)\n",
+    "display(stability_df_top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1a66cda7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_stability(stability_df):\n",
+    "        \n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "    axes[0].plot(stability_df[\"n_repeats\"], stability_df[\"mean_accuracy\"], marker=\"o\")\n",
+    "    axes[0].set_title(\"Mean Accuracy vs Number of Repeats\")\n",
+    "    axes[0].set_xlabel(\"Number of repeats\")\n",
+    "    axes[0].set_ylabel(\"Mean CV accuracy\")\n",
+    "    axes[0].grid(True)\n",
+    "\n",
+    "    axes[1].plot(stability_df[\"n_repeats\"], stability_df[\"standard_error\"], marker=\"o\")\n",
+    "    axes[1].set_title(\"Standard Error vs Number of Repeats\")\n",
+    "    axes[1].set_xlabel(\"Number of repeats\")\n",
+    "    axes[1].set_ylabel(\"Standard error of mean accuracy\")\n",
+    "    axes[1].grid(True)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "plot_stability(stability_df_top)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bde5ffa6",
+   "metadata": {},
+   "source": [
+    "As we would expect, the standard error decreases as we increase the number of repeats in our cross-validation, which gives us a more stable estimate of the mean accuracy around 0.676. Not very good, but better than random guessing at least. Lets check how the single feature decision works with the increased repeats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c3b54380",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_repeats</th>\n",
+       "      <th>mean_accuracy</th>\n",
+       "      <th>std_accuracy</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>n_scores</th>\n",
+       "      <th>se_change</th>\n",
+       "      <th>se_improvement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0.650000</td>\n",
+       "      <td>0.120185</td>\n",
+       "      <td>0.016997</td>\n",
+       "      <td>50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.666000</td>\n",
+       "      <td>0.107753</td>\n",
+       "      <td>0.009638</td>\n",
+       "      <td>125</td>\n",
+       "      <td>-0.007359</td>\n",
+       "      <td>0.007359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0.671667</td>\n",
+       "      <td>0.108538</td>\n",
+       "      <td>0.006865</td>\n",
+       "      <td>250</td>\n",
+       "      <td>-0.002773</td>\n",
+       "      <td>0.002773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>75</td>\n",
+       "      <td>0.668000</td>\n",
+       "      <td>0.112538</td>\n",
+       "      <td>0.005811</td>\n",
+       "      <td>375</td>\n",
+       "      <td>-0.001053</td>\n",
+       "      <td>0.001053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>100</td>\n",
+       "      <td>0.677500</td>\n",
+       "      <td>0.113929</td>\n",
+       "      <td>0.005095</td>\n",
+       "      <td>500</td>\n",
+       "      <td>-0.000716</td>\n",
+       "      <td>0.000716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>150</td>\n",
+       "      <td>0.677667</td>\n",
+       "      <td>0.114420</td>\n",
+       "      <td>0.004178</td>\n",
+       "      <td>750</td>\n",
+       "      <td>-0.000917</td>\n",
+       "      <td>0.000917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>200</td>\n",
+       "      <td>0.676833</td>\n",
+       "      <td>0.115383</td>\n",
+       "      <td>0.003649</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>-0.000529</td>\n",
+       "      <td>0.000529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>300</td>\n",
+       "      <td>0.676556</td>\n",
+       "      <td>0.117908</td>\n",
+       "      <td>0.003044</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>-0.000604</td>\n",
+       "      <td>0.000604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>500</td>\n",
+       "      <td>0.676833</td>\n",
+       "      <td>0.117966</td>\n",
+       "      <td>0.002359</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>-0.000685</td>\n",
+       "      <td>0.000685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.676250</td>\n",
+       "      <td>0.117538</td>\n",
+       "      <td>0.001662</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>-0.000697</td>\n",
+       "      <td>0.000697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.677600</td>\n",
+       "      <td>0.116135</td>\n",
+       "      <td>0.001161</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>-0.000501</td>\n",
+       "      <td>0.000501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>0.677893</td>\n",
+       "      <td>0.116187</td>\n",
+       "      <td>0.000735</td>\n",
+       "      <td>25000</td>\n",
+       "      <td>-0.000427</td>\n",
+       "      <td>0.000427</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    n_repeats  mean_accuracy  std_accuracy  standard_error  n_scores  \\\n",
+       "0          10       0.650000      0.120185        0.016997        50   \n",
+       "1          25       0.666000      0.107753        0.009638       125   \n",
+       "2          50       0.671667      0.108538        0.006865       250   \n",
+       "3          75       0.668000      0.112538        0.005811       375   \n",
+       "4         100       0.677500      0.113929        0.005095       500   \n",
+       "5         150       0.677667      0.114420        0.004178       750   \n",
+       "6         200       0.676833      0.115383        0.003649      1000   \n",
+       "7         300       0.676556      0.117908        0.003044      1500   \n",
+       "8         500       0.676833      0.117966        0.002359      2500   \n",
+       "9        1000       0.676250      0.117538        0.001662      5000   \n",
+       "10       2000       0.677600      0.116135        0.001161     10000   \n",
+       "11       5000       0.677893      0.116187        0.000735     25000   \n",
+       "\n",
+       "    se_change  se_improvement  \n",
+       "0         NaN             NaN  \n",
+       "1   -0.007359        0.007359  \n",
+       "2   -0.002773        0.002773  \n",
+       "3   -0.001053        0.001053  \n",
+       "4   -0.000716        0.000716  \n",
+       "5   -0.000917        0.000917  \n",
+       "6   -0.000529        0.000529  \n",
+       "7   -0.000604        0.000604  \n",
+       "8   -0.000685        0.000685  \n",
+       "9   -0.000697        0.000697  \n",
+       "10  -0.000501        0.000501  \n",
+       "11  -0.000427        0.000427  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stability_df_second = check_stability(results_df, 1, X, y)\n",
+    "display(stability_df_second)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "5eaad8e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAAAAAB7k/wPaydedgtMjDgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_stability(stability_df_second)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e99eba3",
+   "metadata": {},
+   "source": [
+    "Our single feature decision tree actually slightly outperforms the more complex tree, but they both have very similar mean accuracies around 0.68. This still isn't very good, but it does suggest that HPRA006876 is a strong predictor of group membership on its own, which is consistent with our findings from the random forest analysis in the original study. "
    ]
   }
  ],
diff --git a/assignment/make_decision_tree_notebook.py b/assignment/make_decision_tree_notebook.py
new file mode 100644
index 0000000..3e7e1f2
--- /dev/null
+++ b/assignment/make_decision_tree_notebook.py
@@ -0,0 +1,377 @@
+import nbformat as nbf
+
+
+nb = nbf.v4.new_notebook()
+
+
+text1 = """\
+# Part 1: Decision Tree Classifier
+
+This notebook adds the Decision Tree part of the assignment using only the biologically selected dataset `slice_1_significant_7.csv`.
+
+The task is a binary classification problem:
+
+- `0`: Healthy control
+- `1`: Exfoliative Glaucoma (XFG)
+
+I use the iris tutorial as a template for the overall workflow:
+
+- define `X` and `y`
+- split into training and test sets
+- train a `DecisionTreeClassifier`
+- evaluate the model
+- visualize the final tree
+
+Because this dataset is much smaller than iris, I also use cross-validation and a small hyperparameter search to make the model selection more defensible.
+"""
+
+
+code1 = """\
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from IPython.display import display
+from sklearn.inspection import permutation_importance
+from sklearn.metrics import (
+    ConfusionMatrixDisplay,
+    accuracy_score,
+    classification_report,
+)
+from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+
+sns.set_theme(style="whitegrid")
+
+df = pd.read_csv("slice_1_significant_7.csv")
+identifier_cols = ["Internal LIMS ID", "Original ID", "group"]
+feature_names = [c for c in df.columns if c not in identifier_cols]
+
+print(f"Samples: {df.shape[0]}")
+print(f"Features: {len(feature_names)}")
+print("Feature names:")
+print(feature_names)
+"""
+
+
+text2 = """\
+## Step 1: Prepare Features and Targets
+
+Following the same style as the iris tutorial, I separate the predictors into `X` and the class labels into `y`.
+"""
+
+
+code2 = """\
+X = df.drop(columns=identifier_cols)
+y = df["group"]
+
+display(X.head())
+print("Target distribution:")
+print(y.value_counts().sort_index())
+"""
+
+
+text3 = """\
+## Step 2: Train-Test Split
+
+I use an 80/20 stratified split so both classes remain balanced in the training and test sets. The training set is used for tuning, while the test set is held back for the final evaluation.
+"""
+
+
+code3 = """\
+X_train, X_test, y_train, y_test = train_test_split(
+    X,
+    y,
+    test_size=0.2,
+    stratify=y,
+    random_state=42,
+)
+
+print("Training set shape:", X_train.shape)
+print("Test set shape:", X_test.shape)
+"""
+
+
+text4 = """\
+## Step 3: Iteratively Tune the Decision Tree
+
+The assignment asks for iterative development, so I compare multiple settings for:
+
+- `max_depth`
+- `min_samples_leaf`
+- split criterion: `gini` or `entropy`
+
+For each configuration I record:
+
+- mean 5-fold cross-validation accuracy on the training set
+- training accuracy
+- test accuracy
+- actual tree depth and number of leaves
+
+This helps distinguish a tree that genuinely generalizes from one that merely memorizes the training set.
+"""
+
+
+code4 = """\
+cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
+rows = []
+
+for max_depth in [1, 2, 3, 4, 5, None]:
+    for criterion in ["gini", "entropy"]:
+        for min_samples_leaf in [1, 2, 4]:
+            model = DecisionTreeClassifier(
+                max_depth=max_depth,
+                criterion=criterion,
+                min_samples_leaf=min_samples_leaf,
+                random_state=42,
+            )
+
+            cv_scores = cross_val_score(
+                model,
+                X_train,
+                y_train,
+                cv=cv,
+                scoring="accuracy",
+            )
+
+            model.fit(X_train, y_train)
+
+            rows.append(
+                {
+                    "max_depth": "None" if max_depth is None else str(max_depth),
+                    "criterion": criterion,
+                    "min_samples_leaf": min_samples_leaf,
+                    "cv_accuracy_mean": cv_scores.mean(),
+                    "cv_accuracy_std": cv_scores.std(),
+                    "train_accuracy": accuracy_score(y_train, model.predict(X_train)),
+                    "test_accuracy": accuracy_score(y_test, model.predict(X_test)),
+                    "actual_tree_depth": model.get_depth(),
+                    "n_leaves": model.get_n_leaves(),
+                }
+            )
+
+results_df = pd.DataFrame(rows).sort_values(
+    ["cv_accuracy_mean", "test_accuracy", "n_leaves"],
+    ascending=[False, False, True],
+)
+
+display(results_df.head(12))
+"""
+
+
+text5 = """\
+The top row by cross-validation score is a deeper tree, but it does not perform as well on the held-out test set. That makes overfitting a real concern.
+
+A simpler model with:
+
+- `criterion="entropy"`
+- `max_depth=2`
+- `min_samples_leaf=2`
+
+has nearly the same cross-validation performance, gives better test accuracy in this split, and is much easier to interpret. I therefore use that as the final tree.
+"""
+
+
+code5 = """\
+depth_summary = (
+    results_df[results_df["criterion"] == "entropy"]
+    .groupby("max_depth", as_index=False)["cv_accuracy_mean"]
+    .max()
+)
+
+depth_order = ["1", "2", "3", "4", "5", "None"]
+
+plt.figure(figsize=(8, 5))
+sns.lineplot(
+    data=depth_summary,
+    x="max_depth",
+    y="cv_accuracy_mean",
+    marker="o",
+    sort=False,
+)
+plt.title("Cross-Validation Accuracy by Max Depth")
+plt.xlabel("Max depth")
+plt.ylabel("Cross-validation accuracy")
+plt.show()
+"""
+
+
+text6 = """\
+## Step 4: Fit the Final Decision Tree
+
+The final model is deliberately shallow. This trades a small amount of flexibility for better transparency and lower overfitting risk.
+"""
+
+
+code6 = """\
+final_params = {
+    "criterion": "entropy",
+    "max_depth": 2,
+    "min_samples_leaf": 2,
+    "random_state": 42,
+}
+
+final_model = DecisionTreeClassifier(**final_params)
+final_model.fit(X_train, y_train)
+
+y_train_pred = final_model.predict(X_train)
+y_test_pred = final_model.predict(X_test)
+
+print("Final model parameters:", final_params)
+print("Training accuracy:", round(accuracy_score(y_train, y_train_pred), 3))
+print("Test accuracy:", round(accuracy_score(y_test, y_test_pred), 3))
+print()
+print(classification_report(y_test, y_test_pred, digits=3))
+"""
+
+
+text7 = """\
+## Step 5: Evaluate the Final Model
+
+The confusion matrix shows how the model handles the two classes rather than only reporting one summary number.
+"""
+
+
+code7 = """\
+ConfusionMatrixDisplay.from_predictions(
+    y_test,
+    y_test_pred,
+    display_labels=["Healthy", "XFG"],
+    cmap="Blues",
+)
+plt.title("Confusion Matrix for the Final Decision Tree")
+plt.grid(False)
+plt.show()
+"""
+
+
+text8 = """\
+## Step 6: Interpret Which Proteins the Tree Uses
+
+Decision trees only assign impurity-based importance to features that are actually used in splits. That means it is completely possible for a shallow tree to put all of its importance on only one or two proteins while the others receive zero.
+
+That is not automatically a biological conclusion. In this setting it mostly means:
+
+- the tree found that two proteins were enough for its chosen split rules
+- some proteins may contain overlapping information, so the tree only needs one of them
+- with only 60 samples, the exact split choices are unstable and can change with a different train/test split
+
+To make that limitation clearer, I look at both the tree's built-in importance values and permutation importance on the test set.
+"""
+
+
+code8 = """\
+gini_importance = (
+    pd.Series(final_model.feature_importances_, index=feature_names)
+    .sort_values(ascending=False)
+)
+
+display(gini_importance.to_frame("tree_importance"))
+
+plt.figure(figsize=(10, 5))
+sns.barplot(
+    x=gini_importance.values,
+    y=gini_importance.index,
+    hue=gini_importance.index,
+    dodge=False,
+    palette="crest",
+    legend=False,
+)
+plt.title("Impurity-Based Feature Importance")
+plt.xlabel("Importance")
+plt.ylabel("Protein fragment")
+plt.show()
+"""
+
+
+code9 = """\
+perm = permutation_importance(
+    final_model,
+    X_test,
+    y_test,
+    n_repeats=50,
+    random_state=42,
+)
+
+permutation_df = (
+    pd.DataFrame(
+        {
+            "feature": feature_names,
+            "permutation_importance_mean": perm.importances_mean,
+            "permutation_importance_std": perm.importances_std,
+        }
+    )
+    .sort_values("permutation_importance_mean", ascending=False)
+)
+
+display(permutation_df)
+"""
+
+
+text9 = """\
+If only two proteins receive non-zero tree importance, that is therefore not necessarily weird. It is a normal consequence of a shallow greedy tree.
+
+What would be weird is claiming from this alone that the other five proteins are unimportant biologically. A single tree cannot support that conclusion, especially with a small dataset. The safer interpretation is that this particular classifier can achieve its decisions using a small subset of the available predictors.
+"""
+
+
+code10 = """\
+plt.figure(figsize=(18, 8))
+plot_tree(
+    final_model,
+    feature_names=feature_names,
+    class_names=["Healthy", "XFG"],
+    filled=True,
+    rounded=True,
+    fontsize=10,
+)
+plt.title("Final Decision Tree")
+plt.show()
+"""
+
+
+text10 = """\
+## Conclusion
+
+Using only the significant seven proteins, the best final decision tree is a shallow entropy-based model with `max_depth=2` and `min_samples_leaf=2`.
+
+This is a reasonable final model because:
+
+- it is built from biologically motivated features
+- it was selected through iterative tuning rather than one-shot fitting
+- it remains interpretable
+- it avoids the strongest overfitting seen in deeper trees
+
+The fact that the tree mainly uses two proteins is not by itself a problem. It reflects how decision trees work: they only reward features that reduce impurity through actual splits. That should be interpreted as a property of this classifier, not as a definitive ranking of biological importance.
+"""
+
+
+nb["cells"] = [
+    nbf.v4.new_markdown_cell(text1),
+    nbf.v4.new_code_cell(code1),
+    nbf.v4.new_markdown_cell(text2),
+    nbf.v4.new_code_cell(code2),
+    nbf.v4.new_markdown_cell(text3),
+    nbf.v4.new_code_cell(code3),
+    nbf.v4.new_markdown_cell(text4),
+    nbf.v4.new_code_cell(code4),
+    nbf.v4.new_markdown_cell(text5),
+    nbf.v4.new_code_cell(code5),
+    nbf.v4.new_markdown_cell(text6),
+    nbf.v4.new_code_cell(code6),
+    nbf.v4.new_markdown_cell(text7),
+    nbf.v4.new_code_cell(code7),
+    nbf.v4.new_markdown_cell(text8),
+    nbf.v4.new_code_cell(code8),
+    nbf.v4.new_code_cell(code9),
+    nbf.v4.new_markdown_cell(text9),
+    nbf.v4.new_code_cell(code10),
+    nbf.v4.new_markdown_cell(text10),
+]
+
+
+with open("Decision_Tree.ipynb", "w") as f:
+    nbf.write(nb, f)
+
+print("Decision_Tree.ipynb created successfully.")