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@@ -0,0 +1,1126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.io\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare Data\n",
+    "\n",
+    "Download [Xenium data](https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast) from 10x website and save them as `h5ad` file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Barcode</th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>cell_id</th>\n",
+       "      <th>x_centroid</th>\n",
+       "      <th>y_centroid</th>\n",
+       "      <th>transcript_counts</th>\n",
+       "      <th>control_probe_counts</th>\n",
+       "      <th>control_codeword_counts</th>\n",
+       "      <th>total_counts</th>\n",
+       "      <th>cell_area</th>\n",
+       "      <th>nucleus_area</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>847.259912</td>\n",
+       "      <td>326.191365</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>58.387031</td>\n",
+       "      <td>26.642187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>826.341995</td>\n",
+       "      <td>328.031830</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>197.016719</td>\n",
+       "      <td>42.130781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Unlabeled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>848.766919</td>\n",
+       "      <td>331.743187</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>16.256250</td>\n",
+       "      <td>12.688906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Invasive_Tumor</td>\n",
+       "      <td>4</td>\n",
+       "      <td>824.228409</td>\n",
+       "      <td>334.252643</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>42.311406</td>\n",
+       "      <td>10.069844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>841.357538</td>\n",
+       "      <td>332.242505</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>107.652500</td>\n",
+       "      <td>37.479687</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167775</th>\n",
+       "      <td>167776</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167776</td>\n",
+       "      <td>7455.475342</td>\n",
+       "      <td>5114.875415</td>\n",
+       "      <td>229</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>220.452812</td>\n",
+       "      <td>60.599688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167776</th>\n",
+       "      <td>167777</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>167777</td>\n",
+       "      <td>7483.727051</td>\n",
+       "      <td>5111.477490</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>37.389375</td>\n",
+       "      <td>25.242344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167777</th>\n",
+       "      <td>167778</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167778</td>\n",
+       "      <td>7470.159424</td>\n",
+       "      <td>5119.132056</td>\n",
+       "      <td>397</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>397</td>\n",
+       "      <td>287.058281</td>\n",
+       "      <td>86.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167778</th>\n",
+       "      <td>167779</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>167779</td>\n",
+       "      <td>7477.737207</td>\n",
+       "      <td>5128.712817</td>\n",
+       "      <td>117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>235.354375</td>\n",
+       "      <td>25.197187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167779</th>\n",
+       "      <td>167780</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167780</td>\n",
+       "      <td>7489.376562</td>\n",
+       "      <td>5123.197778</td>\n",
+       "      <td>378</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>378</td>\n",
+       "      <td>270.079531</td>\n",
+       "      <td>111.806875</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>167780 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Barcode         Cluster  cell_id   x_centroid   y_centroid  \\\n",
+       "0             1          DCIS_2        1   847.259912   326.191365   \n",
+       "1             2          DCIS_2        2   826.341995   328.031830   \n",
+       "2             3       Unlabeled        3   848.766919   331.743187   \n",
+       "3             4  Invasive_Tumor        4   824.228409   334.252643   \n",
+       "4             5          DCIS_2        5   841.357538   332.242505   \n",
+       "...         ...             ...      ...          ...          ...   \n",
+       "167775   167776          DCIS_1   167776  7455.475342  5114.875415   \n",
+       "167776   167777          DCIS_2   167777  7483.727051  5111.477490   \n",
+       "167777   167778          DCIS_1   167778  7470.159424  5119.132056   \n",
+       "167778   167779          DCIS_2   167779  7477.737207  5128.712817   \n",
+       "167779   167780          DCIS_1   167780  7489.376562  5123.197778   \n",
+       "\n",
+       "        transcript_counts  control_probe_counts  control_codeword_counts  \\\n",
+       "0                      28                     1                        0   \n",
+       "1                      94                     0                        0   \n",
+       "2                       9                     0                        0   \n",
+       "3                      11                     0                        0   \n",
+       "4                      48                     0                        0   \n",
+       "...                   ...                   ...                      ...   \n",
+       "167775                229                     1                        0   \n",
+       "167776                 79                     0                        0   \n",
+       "167777                397                     0                        0   \n",
+       "167778                117                     0                        0   \n",
+       "167779                378                     0                        0   \n",
+       "\n",
+       "        total_counts   cell_area  nucleus_area  \n",
+       "0                 29   58.387031     26.642187  \n",
+       "1                 94  197.016719     42.130781  \n",
+       "2                  9   16.256250     12.688906  \n",
+       "3                 11   42.311406     10.069844  \n",
+       "4                 48  107.652500     37.479687  \n",
+       "...              ...         ...           ...  \n",
+       "167775           230  220.452812     60.599688  \n",
+       "167776            79   37.389375     25.242344  \n",
+       "167777           397  287.058281     86.700000  \n",
+       "167778           117  235.354375     25.197187  \n",
+       "167779           378  270.079531    111.806875  \n",
+       "\n",
+       "[167780 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a =pd.read_csv(\"/mnt/scratch1/spatial_compare/public_data/xenium_breast/outs/xenium_breast_r1.csv\")\n",
+    "cells_metadata = pd.read_csv('/mnt/scratch1/spatial_compare/public_data/xenium_breast/outs/cells.csv.gz')\n",
+    "a['cell_id']=a['Barcode']\n",
+    "merged_df = pd.merge(a, cells_metadata, on='cell_id', how='inner') \n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(167780, 11)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "exp = scipy.io.mmread('/mnt/scratch1/spatial_compare/public_data/xenium_breast/outs/cell_feature_matrix/matrix.mtx.gz').toarray()\n",
+    "cols = pd.read_csv('/mnt/scratch1/spatial_compare/public_data/xenium_breast/outs/cell_feature_matrix/barcodes.tsv.gz', header=None, sep='\\t')\n",
+    "rows = pd.read_csv('/mnt/scratch1/spatial_compare/public_data/xenium_breast/outs/cell_feature_matrix/features.tsv.gz', header=None, sep='\\t')\n",
+    "# Extract gene and cell identifiers\n",
+    "row_names = rows.iloc[:, 1].values  # Gene symbols\n",
+    "col_names = cols.iloc[:, 0].values  # Barcodes (cell IDs)\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "exp = pd.DataFrame(exp, columns=col_names, index=row_names)\n",
+    "\n",
+    "#"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/scratch1/miniconda3/envs/squidpy/lib/python3.10/site-packages/anndata/_core/anndata.py:183: ImplicitModificationWarning: Transforming to str index.\n",
+      "  warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata = ad.AnnData(\n",
+    "    X=exp.values.T,  # Transpose for cells x genes\n",
+    "    obs=merged_df,  # Align metadata with cells\n",
+    "    var=pd.DataFrame(index=row_names)  # Add gene symbols\n",
+    ")\n",
+    "\n",
+    "# Add morphology or spatial metadata (if available)\n",
+    "adata.uns[\"spatial\"] = {\n",
+    "    \"morphology_mip\": \"/mnt/scratch1/spatial_compare/public_data/xenium_breast/morphology_mip.ome.tif\",\n",
+    "    \"morphology_focus\": \"/mnt/scratch1/spatial_compare/public_data/xenium_breast/morphology_focus.ome.tif\",\n",
+    "}\n",
+    "\n",
+    "# Add additional gene metadata if needed\n",
+    "adata.var['gene_ids'] = rows.iloc[:, 0].values  # Add gene IDs\n",
+    "adata.var['feature_type'] = rows.iloc[:, 2].values  # Add feature type\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 167780 × 541\n",
+       "    obs: 'Barcode', 'Cluster', 'cell_id', 'x_centroid', 'y_centroid', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area'\n",
+       "    var: 'gene_ids', 'feature_type'\n",
+       "    uns: 'spatial'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test Spatial_Detection_Score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cell_type` information is from 10x `Cell_Barcode_Type_Matrices.xlsx` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['cell_type']=data['Cluster']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame from AnnData\n",
+    "data = adata.obs\n",
+    "\n",
+    "# Ensure required columns are present\n",
+    "if 'x_centroid' not in data.columns or 'y_centroid' not in data.columns:\n",
+    "    # Extract spatial coordinates from the `.h5ad` file\n",
+    "    data['x_centroid'] = adata.obsm['spatial'][:, 0]\n",
+    "    data['y_centroid'] = adata.obsm['spatial'][:, 1]\n",
+    "\n",
+    "if 'transcript_counts' not in data.columns:\n",
+    "    # Sum up transcript counts across features for each cell\n",
+    "    data['transcript_counts'] = adata.X.sum(axis=1).A1 if hasattr(adata.X, 'A1') else adata.X.sum(axis=1)\n",
+    "\n",
+    "# Use the cell type/cluster information as the category\n",
+    "if 'supercluster_name' not in data.columns:\n",
+    "    data['supercluster_name'] = adata.obs['cell_type']  # Replace 'cell_type' with the appropriate column\n",
+    "\n",
+    "# Use the entire data as both reference and query\n",
+    "reference_data = data\n",
+    "query_data = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spatial_detection_scores(\n",
+    "    reference: pd.DataFrame,\n",
+    "    query: pd.DataFrame,\n",
+    "    plot_stuff=True,\n",
+    "    query_name: str = \"query data\",\n",
+    "    comparison_column=\"transcript_counts\",\n",
+    "    category=\"supercluster_name\",\n",
+    "    n_bins=50,\n",
+    "    in_place=True,\n",
+    "    non_spatial=False,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate and plot spatial detection scores for query data compared to reference data.\n",
+    "\n",
+    "    Parameters:\n",
+    "        reference (pd.DataFrame): The reference data.\n",
+    "        query (pd.DataFrame): The query data.\n",
+    "        plot_stuff (bool, optional): Whether to plot the results. Defaults to True.\n",
+    "        query_name (str, optional): The name of the query data. Defaults to \"query data\".\n",
+    "        category (str, optional): The category column to compare. Defaults to \"supercluster_name\".\n",
+    "        n_bins (int, optional): The number of bins for spatial grouping. Defaults to 50.\n",
+    "        in_place (bool, optional): Whether to modify the query data in place. Defaults to True.\n",
+    "        non_spatial (bool, optional): Whether to compare to an ungrouped mean/std. Defaults to False.\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: A dictionary containing the bin image, extent, query data, and reference data (if in_place is False).\n",
+    "    \"\"\"\n",
+    "    # code goes here\n",
+    "\n",
+    "    if category not in reference.columns or category not in query.columns:\n",
+    "        raise ValueError(\"category \" + category + \" not in reference and query inputs\")\n",
+    "\n",
+    "    shared_category_values = list(\n",
+    "        set(reference[category].unique()) & set(query[category].unique())\n",
+    "    )\n",
+    "    if (\n",
+    "        len(shared_category_values) < query[category].unique().shape[0]\n",
+    "        or len(shared_category_values) < reference[category].unique().shape[0]\n",
+    "    ):\n",
+    "        in_place = False\n",
+    "\n",
+    "    if in_place:\n",
+    "        s2 = query.loc[query[category].isin(shared_category_values), :]\n",
+    "        s1 = reference.loc[reference[category].isin(shared_category_values), :]\n",
+    "    else:\n",
+    "        s2 = query.loc[query[category].isin(shared_category_values), :].copy()\n",
+    "        s1 = reference.loc[reference[category].isin(shared_category_values), :].copy()\n",
+    "\n",
+    "    means = s1.groupby(category, observed=True)[comparison_column].mean()\n",
+    "    stds = s1.groupby(category, observed=True)[comparison_column].std()\n",
+    "\n",
+    "    # if you want to compare to an ungrouped mean/std, try this:\n",
+    "    if non_spatial:\n",
+    "        means[:] = means.mean()\n",
+    "        stds[:] = stds.mean()\n",
+    "\n",
+    "    s2[\"detection_relative_z_score\"] = 0.0\n",
+    "    s2[\"detection_difference\"] = 0.0\n",
+    "    s2[\"detection_ratio\"] = 0.0\n",
+    "\n",
+    "    for c, gb in s2.groupby(category, observed=True):\n",
+    "        if c not in shared_category_values:\n",
+    "            continue\n",
+    "\n",
+    "        s2.loc[s2[category] == c, [\"detection_relative_z_score\"]] = (\n",
+    "            (s2.loc[s2[category] == c, [comparison_column]] - means[c]) / stds[c]\n",
+    "        ).values\n",
+    "        s2.loc[s2[category] == c, [\"detection_difference\"]] = (\n",
+    "            s2.loc[s2[category] == c, [comparison_column]] - means[c]\n",
+    "        ).values\n",
+    "        s2.loc[s2[category] == c, [\"log_10_detection_ratio\"]] = np.log10(\n",
+    "            (s2.loc[s2[category] == c, [comparison_column]] / means[c]).values\n",
+    "        )\n",
+    "\n",
+    "    s2[\"xy_bucket\"] = list(\n",
+    "        zip(\n",
+    "            pd.cut(s2.x_centroid, n_bins, labels=list(range(n_bins))),\n",
+    "            pd.cut(s2.y_centroid, n_bins, labels=list(range(n_bins))),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    binx = s2.groupby(\"xy_bucket\").x_centroid.mean()\n",
+    "    biny = s2.groupby(\"xy_bucket\").y_centroid.mean()\n",
+    "\n",
+    "    z_score = s2.groupby(\"xy_bucket\").detection_relative_z_score.mean()\n",
+    "    difference = s2.groupby(\"xy_bucket\").detection_difference.mean()\n",
+    "    log_ratio = s2.groupby(\"xy_bucket\").log_10_detection_ratio.mean()\n",
+    "    n_cells = s2.groupby(\"xy_bucket\").x_centroid.count()\n",
+    "\n",
+    "    bin_image_z_score = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_difference = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_ratio = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_counts = np.zeros([n_bins, n_bins])\n",
+    "\n",
+    "    extent = [np.min(binx), np.max(binx), np.min(biny), np.max(biny)]\n",
+    "    for coord in binx.index:\n",
+    "        bin_image_z_score[coord[1], coord[0]] = z_score[coord]\n",
+    "        bin_image_difference[coord[1], coord[0]] = difference[coord]\n",
+    "        bin_image_ratio[coord[1], coord[0]] = log_ratio[coord]\n",
+    "        bin_image_counts[coord[1], coord[0]] = n_cells[coord]\n",
+    "\n",
+    "    if plot_stuff:\n",
+    "        if non_spatial:\n",
+    "            title_string = \"Non-spatial Detection Scores\"\n",
+    "        else:\n",
+    "            title_string = \"Spatial Detection Scores\"\n",
+    "        min_maxes = {\n",
+    "            \"detection z-score\": [bin_image_z_score, [-1, 1]],\n",
+    "            \"total counts difference\": [bin_image_difference, [-100, 100]],\n",
+    "            \"log10(detection ratio)\": [bin_image_ratio, [-1, 1]],\n",
+    "        }\n",
+    "\n",
+    "        fig, axs = plt.subplots(1, 3, figsize=[15, 5])\n",
+    "        if non_spatial:\n",
+    "            fig.suptitle(title_string + \"\\n\" + query_name)\n",
+    "        else:\n",
+    "            fig.suptitle(\n",
+    "                title_string\n",
+    "                + \"\\n\"\n",
+    "                + query_name\n",
+    "                + \" grouped by \"\n",
+    "                + category\n",
+    "                + \" and spatially binned\"\n",
+    "            )\n",
+    "        for ii, plot_name in enumerate(min_maxes.keys()):\n",
+    "            ax = axs[ii]\n",
+    "            pcm = ax.imshow(\n",
+    "                min_maxes[plot_name][0],\n",
+    "                extent=extent,\n",
+    "                cmap=\"coolwarm_r\",\n",
+    "                vmin=min_maxes[plot_name][1][0],\n",
+    "                vmax=min_maxes[plot_name][1][1],\n",
+    "            )\n",
+    "            fig.colorbar(pcm, ax=ax, shrink=0.7)\n",
+    "            ax.set_title(query_name + \"\\n\" + plot_name)\n",
+    "\n",
+    "    if in_place:\n",
+    "\n",
+    "        return dict(\n",
+    "            z_score_image=bin_image_z_score,\n",
+    "            difference_image=bin_image_difference,\n",
+    "            ratio_image=bin_image_ratio,\n",
+    "            extent=extent,\n",
+    "            count_image=bin_image_counts,\n",
+    "            query=True,\n",
+    "            reference=True,\n",
+    "        )\n",
+    "\n",
+    "    else:\n",
+    "        return dict(\n",
+    "            z_score_image=bin_image_z_score,\n",
+    "            difference_image=bin_image_difference,\n",
+    "            ratio_image=bin_image_ratio,\n",
+    "            extent=extent,\n",
+    "            count_image=bin_image_counts,\n",
+    "            query=s2,\n",
+    "            reference=s1,\n",
+    "        )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_757836/775244361.py:71: RuntimeWarning: divide by zero encountered in log10\n",
+      "  s2.loc[s2[category] == c, [\"log_10_detection_ratio\"]] = np.log10(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = spatial_detection_scores(\n",
+    "    reference=reference_data,\n",
+    "    query=query_data,\n",
+    "    plot_stuff=True,\n",
+    "    query_name=\"Xenium Data\",\n",
+    "    comparison_column=\"transcript_counts\",\n",
+    "    category=\"supercluster_name\",\n",
+    "    n_bins=50,\n",
+    "    in_place=False,\n",
+    "    non_spatial=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def spatial_detection_scores_no_category(\n",
+    "    reference: pd.DataFrame,\n",
+    "    query: pd.DataFrame,\n",
+    "    plot_stuff=True,\n",
+    "    query_name: str = \"query data\",\n",
+    "    comparison_column=\"transcript_counts\",\n",
+    "    n_bins=50,\n",
+    "    in_place=True,\n",
+    "    non_spatial=False,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate and plot spatial detection scores for query data compared to reference data\n",
+    "    without category grouping.\n",
+    "\n",
+    "    Parameters:\n",
+    "        reference (pd.DataFrame): The reference data.\n",
+    "        query (pd.DataFrame): The query data.\n",
+    "        plot_stuff (bool, optional): Whether to plot the results. Defaults to True.\n",
+    "        query_name (str, optional): The name of the query data. Defaults to \"query data\".\n",
+    "        comparison_column (str, optional): The column to compare. Defaults to \"transcript_counts\".\n",
+    "        n_bins (int, optional): The number of bins for spatial grouping. Defaults to 50.\n",
+    "        in_place (bool, optional): Whether to modify the query data in place. Defaults to True.\n",
+    "        non_spatial (bool, optional): Whether to compare to an ungrouped mean/std. Defaults to False.\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: A dictionary containing the bin image, extent, query data, and reference data (if in_place is False).\n",
+    "    \"\"\"\n",
+    "    if comparison_column not in reference.columns or comparison_column not in query.columns:\n",
+    "        raise ValueError(f\"{comparison_column} not found in reference or query data\")\n",
+    "\n",
+    "    if in_place:\n",
+    "        s2 = query.copy()\n",
+    "        s1 = reference\n",
+    "    else:\n",
+    "        s2 = query.copy()\n",
+    "        s1 = reference.copy()\n",
+    "\n",
+    "    # Calculate global mean and std\n",
+    "    mean_val = s1[comparison_column].mean()\n",
+    "    std_val = s1[comparison_column].std()\n",
+    "\n",
+    "    s2[\"detection_relative_z_score\"] = (s2[comparison_column] - mean_val) / std_val\n",
+    "    s2[\"detection_difference\"] = s2[comparison_column] - mean_val\n",
+    "    s2[\"log_10_detection_ratio\"] = np.log10(s2[comparison_column] / mean_val)\n",
+    "\n",
+    "    # Spatial binning\n",
+    "    s2[\"xy_bucket\"] = list(\n",
+    "        zip(\n",
+    "            pd.cut(s2.x_centroid, n_bins, labels=list(range(n_bins))),\n",
+    "            pd.cut(s2.y_centroid, n_bins, labels=list(range(n_bins))),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    binx = s2.groupby(\"xy_bucket\").x_centroid.mean()\n",
+    "    biny = s2.groupby(\"xy_bucket\").y_centroid.mean()\n",
+    "\n",
+    "    z_score = s2.groupby(\"xy_bucket\").detection_relative_z_score.mean()\n",
+    "    difference = s2.groupby(\"xy_bucket\").detection_difference.mean()\n",
+    "    log_ratio = s2.groupby(\"xy_bucket\").log_10_detection_ratio.mean()\n",
+    "    n_cells = s2.groupby(\"xy_bucket\").x_centroid.count()\n",
+    "\n",
+    "    bin_image_z_score = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_difference = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_ratio = np.zeros([n_bins, n_bins])\n",
+    "    bin_image_counts = np.zeros([n_bins, n_bins])\n",
+    "\n",
+    "    extent = [np.min(binx), np.max(binx), np.min(biny), np.max(biny)]\n",
+    "    for coord in binx.index:\n",
+    "        bin_image_z_score[coord[1], coord[0]] = z_score[coord]\n",
+    "        bin_image_difference[coord[1], coord[0]] = difference[coord]\n",
+    "        bin_image_ratio[coord[1], coord[0]] = log_ratio[coord]\n",
+    "        bin_image_counts[coord[1], coord[0]] = n_cells[coord]\n",
+    "\n",
+    "    if plot_stuff:\n",
+    "        title_string = \"Spatial Detection Scores (No Categories)\"\n",
+    "        min_maxes = {\n",
+    "            \"detection z-score\": [bin_image_z_score, [-1, 1]],\n",
+    "            \"total counts difference\": [bin_image_difference, [-100, 100]],\n",
+    "            \"log10(detection ratio)\": [bin_image_ratio, [-1, 1]],\n",
+    "        }\n",
+    "\n",
+    "        fig, axs = plt.subplots(1, 3, figsize=[15, 5])\n",
+    "        fig.suptitle(title_string + \"\\n\" + query_name + \" spatially binned\")\n",
+    "        for ii, plot_name in enumerate(min_maxes.keys()):\n",
+    "            ax = axs[ii]\n",
+    "            pcm = ax.imshow(\n",
+    "                min_maxes[plot_name][0],\n",
+    "                extent=extent,\n",
+    "                cmap=\"coolwarm_r\",\n",
+    "                vmin=min_maxes[plot_name][1][0],\n",
+    "                vmax=min_maxes[plot_name][1][1],\n",
+    "            )\n",
+    "            fig.colorbar(pcm, ax=ax, shrink=0.7)\n",
+    "            ax.set_title(query_name + \"\\n\" + plot_name)\n",
+    "\n",
+    "    if in_place:\n",
+    "        return dict(\n",
+    "            z_score_image=bin_image_z_score,\n",
+    "            difference_image=bin_image_difference,\n",
+    "            ratio_image=bin_image_ratio,\n",
+    "            extent=extent,\n",
+    "            count_image=bin_image_counts,\n",
+    "            query=True,\n",
+    "            reference=True,\n",
+    "        )\n",
+    "    else:\n",
+    "        return dict(\n",
+    "            z_score_image=bin_image_z_score,\n",
+    "            difference_image=bin_image_difference,\n",
+    "            ratio_image=bin_image_ratio,\n",
+    "            extent=extent,\n",
+    "            count_image=bin_image_counts,\n",
+    "            query=s2,\n",
+    "            reference=s1,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>Barcode</th>\n",
+       "      <th>Cluster</th>\n",
+       "      <th>cell_id</th>\n",
+       "      <th>x_centroid</th>\n",
+       "      <th>y_centroid</th>\n",
+       "      <th>transcript_counts</th>\n",
+       "      <th>control_probe_counts</th>\n",
+       "      <th>control_codeword_counts</th>\n",
+       "      <th>total_counts</th>\n",
+       "      <th>cell_area</th>\n",
+       "      <th>nucleus_area</th>\n",
+       "      <th>cell_type</th>\n",
+       "      <th>supercluster_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>847.259912</td>\n",
+       "      <td>326.191365</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>58.387031</td>\n",
+       "      <td>26.642187</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>826.341995</td>\n",
+       "      <td>328.031830</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>197.016719</td>\n",
+       "      <td>42.130781</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Unlabeled</td>\n",
+       "      <td>3</td>\n",
+       "      <td>848.766919</td>\n",
+       "      <td>331.743187</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>16.256250</td>\n",
+       "      <td>12.688906</td>\n",
+       "      <td>Unlabeled</td>\n",
+       "      <td>Unlabeled</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Invasive_Tumor</td>\n",
+       "      <td>4</td>\n",
+       "      <td>824.228409</td>\n",
+       "      <td>334.252643</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>42.311406</td>\n",
+       "      <td>10.069844</td>\n",
+       "      <td>Invasive_Tumor</td>\n",
+       "      <td>Invasive_Tumor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>841.357538</td>\n",
+       "      <td>332.242505</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>107.652500</td>\n",
+       "      <td>37.479687</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167775</th>\n",
+       "      <td>167776</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167776</td>\n",
+       "      <td>7455.475342</td>\n",
+       "      <td>5114.875415</td>\n",
+       "      <td>229</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>220.452812</td>\n",
+       "      <td>60.599688</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167776</th>\n",
+       "      <td>167777</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>167777</td>\n",
+       "      <td>7483.727051</td>\n",
+       "      <td>5111.477490</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>37.389375</td>\n",
+       "      <td>25.242344</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167777</th>\n",
+       "      <td>167778</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167778</td>\n",
+       "      <td>7470.159424</td>\n",
+       "      <td>5119.132056</td>\n",
+       "      <td>397</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>397</td>\n",
+       "      <td>287.058281</td>\n",
+       "      <td>86.700000</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167778</th>\n",
+       "      <td>167779</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>167779</td>\n",
+       "      <td>7477.737207</td>\n",
+       "      <td>5128.712817</td>\n",
+       "      <td>117</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>235.354375</td>\n",
+       "      <td>25.197187</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "      <td>DCIS_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167779</th>\n",
+       "      <td>167780</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>167780</td>\n",
+       "      <td>7489.376562</td>\n",
+       "      <td>5123.197778</td>\n",
+       "      <td>378</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>378</td>\n",
+       "      <td>270.079531</td>\n",
+       "      <td>111.806875</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "      <td>DCIS_1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>167780 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Barcode         Cluster  cell_id   x_centroid   y_centroid  \\\n",
+       "0             1          DCIS_2        1   847.259912   326.191365   \n",
+       "1             2          DCIS_2        2   826.341995   328.031830   \n",
+       "2             3       Unlabeled        3   848.766919   331.743187   \n",
+       "3             4  Invasive_Tumor        4   824.228409   334.252643   \n",
+       "4             5          DCIS_2        5   841.357538   332.242505   \n",
+       "...         ...             ...      ...          ...          ...   \n",
+       "167775   167776          DCIS_1   167776  7455.475342  5114.875415   \n",
+       "167776   167777          DCIS_2   167777  7483.727051  5111.477490   \n",
+       "167777   167778          DCIS_1   167778  7470.159424  5119.132056   \n",
+       "167778   167779          DCIS_2   167779  7477.737207  5128.712817   \n",
+       "167779   167780          DCIS_1   167780  7489.376562  5123.197778   \n",
+       "\n",
+       "        transcript_counts  control_probe_counts  control_codeword_counts  \\\n",
+       "0                      28                     1                        0   \n",
+       "1                      94                     0                        0   \n",
+       "2                       9                     0                        0   \n",
+       "3                      11                     0                        0   \n",
+       "4                      48                     0                        0   \n",
+       "...                   ...                   ...                      ...   \n",
+       "167775                229                     1                        0   \n",
+       "167776                 79                     0                        0   \n",
+       "167777                397                     0                        0   \n",
+       "167778                117                     0                        0   \n",
+       "167779                378                     0                        0   \n",
+       "\n",
+       "        total_counts   cell_area  nucleus_area       cell_type  \\\n",
+       "0                 29   58.387031     26.642187          DCIS_2   \n",
+       "1                 94  197.016719     42.130781          DCIS_2   \n",
+       "2                  9   16.256250     12.688906       Unlabeled   \n",
+       "3                 11   42.311406     10.069844  Invasive_Tumor   \n",
+       "4                 48  107.652500     37.479687          DCIS_2   \n",
+       "...              ...         ...           ...             ...   \n",
+       "167775           230  220.452812     60.599688          DCIS_1   \n",
+       "167776            79   37.389375     25.242344          DCIS_2   \n",
+       "167777           397  287.058281     86.700000          DCIS_1   \n",
+       "167778           117  235.354375     25.197187          DCIS_2   \n",
+       "167779           378  270.079531    111.806875          DCIS_1   \n",
+       "\n",
+       "       supercluster_name  \n",
+       "0                 DCIS_2  \n",
+       "1                 DCIS_2  \n",
+       "2              Unlabeled  \n",
+       "3         Invasive_Tumor  \n",
+       "4                 DCIS_2  \n",
+       "...                  ...  \n",
+       "167775            DCIS_1  \n",
+       "167776            DCIS_2  \n",
+       "167777            DCIS_1  \n",
+       "167778            DCIS_2  \n",
+       "167779            DCIS_1  \n",
+       "\n",
+       "[167780 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reference_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reference_data=reference_data.drop('supercluster_name',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_data=query_data.drop('supercluster_name',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/mnt/scratch1/miniconda3/envs/squidpy/lib/python3.10/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n",
+      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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hIiJ6lvGqo0RERP9Pp9Nh3LhxmDVrFo4ePWr0t/j4eClfH9NPzCxduhSlS5fGqlWrjJrxZ2ZmFnhMSUlJWL9+PcaNG4f333/faJk3b94s8HIfZN++fYiPj0e/fv0A3DlyRn9k0Ztvvqm8T+nSpR+4TP3RPKNGjUKHDh2UOcHBwQCAYsWKSc3fH4X+/YmLi5Mmt65cuWJ0pFFhqFSpErp06YLZs2fj33//Rd26dU16jp6enso+b/qjyO4f9/0XgDDnNXd0dMT48eMxfvx4XL161XB0W5s2bfDPP//kO0b9Y9y8efOhEz/t2rVD/fr1MW7cOMyfP9/ob+7u7tBqtWY933s1a9YM8+fPx9q1a40+J/lZunQpevbsicmTJxvFb9y4obyow/3MeW09PT2NLnChd/82xdz19P73+0Fj9fT0xMaNG5V/d3Z2Nvz/iy++iBdffBG5ubnYv38/PvvsMwwdOhQ+Pj7o0qWLIU+/7Snszw4REdGziEe0ERHRcym/RvX608b0R9HoHTt2DIcPHzaKLV++HM7OzqhZsyaAOzu+NjY2RjvA8fHx0lVHAdOPkNJoNBBCwNbW1ii+YMEC5ObmPvT+5rp58yYGDRoEnU6HYcOGAQAcHBzQsGFD/PXXX6hatSpq164t3fSTBPpx3v/cgoODERQUhMOHDyvvX7t2bcMOf4sWLbB9+/YHniqY3+Oo6E+vu/9iBrGxsThx4gQaNWpkykvzUAkJCVLTeD39ZJV+vTLlOTZq1AjHjx83OjUZAJYsWQKNRvPACxAA5r3m9/Lx8UHv3r3RtWtXnDx50uiKk/erUKECAOC///574Fj0pk2bhosXL0pX7HV0dERoaCh+/PFHo/c0Ly8PS5cuRYkSJVC+fPl8l9uuXTtUqVIFU6ZMkSbJ9TZt2mR4LhqNRvpMbdiwQToV0hLrc0REBLZt22Z01GdeXh6+//57o2UW1nraunVrJCQkIDc3VzlO/YTgvaysrBAaGmq4qvD966D+tNKKFSsWaExERERFGY9oIyKi51KzZs1QokQJtGnTBhUqVEBeXh4OHTqETz75BE5OTnjnnXeM8v39/dG2bVtERUXBz88PS5cuxebNmzFt2jQ4ODgAuLND++OPP2Lw4MHo2LEjLl68iIkTJ8LPz086Ba9KlSrYsWMH1q1bBz8/Pzg7Oyt3eF1cXNCgQQPMmDEDXl5eCAwMxM6dOxEdHW3SkTcPcurUKezZswd5eXlISEjA3r17ER0djeTkZCxZsgSVKlUy5M6ZMwcvvPACXnzxRbzxxhsIDAxESkoKTp8+jXXr1hn6hZUtWxb29vZYtmwZQkJC4OTkBH9/f/j7++Orr75CixYt0KxZM/Tu3RvFixfHzZs3ceLECRw8eNAw8TBhwgT8+uuvaNCgAUaPHo0qVaogMTERGzduxPDhw1GhQoUHPs79goODMWDAAHz22WfQarVo0aIFzp07h7Fjx6JkyZKGCcVHtX37drzzzjt47bXXEB4eDk9PT1y7dg0rVqzAxo0b0bNnT8ORSqY8x2HDhmHJkiVo1aoVJkyYgICAAGzYsAFz587FG2+88cCJJz1TX/PQ0FC0bt0aVatWhbu7O06cOIFvv/0WYWFhhvVbJTQ0FPb29tizZw/atm370PHUr18f7dq1U04+T5kyBU2aNEHDhg3x3nvvwcbGBnPnzsXRo0exYsWKBx7BZWVlhTVr1qBp06YICwvDG2+8gYYNG8LR0RHnz5/H6tWrsW7dOsMVXVu3bo1FixahQoUKqFq1Kg4cOIAZM2ZIR5JZYn0eM2YM1q1bh0aNGmHMmDGwt7fHvHnzkJaWBuBOvzag8NbTLl26YNmyZWjZsiXeeecd1K1bFzqdDpcuXcL27dvRrl07tG/fHvPmzcO2bdvQqlUrlCpVChkZGfjmm28AAI0bNzZa5p49e+Dp6YkqVaoUaExERERF2pO9FgMREdGTsWrVKtGtWzcRFBQknJychE6nE6VKlRI9evSQrgSpv3ri6tWrRaVKlYSNjY0IDAwUM2fOlJY7depUERgYKGxtbUVISIj4+uuvpatgCiHEoUOHRP369YWDg4MAYLhKoOrKoZcuXRKvvPKKcHd3F87OzqJ58+bi6NGjIiAgwOjKpeZedVR/s7a2Fp6eniIsLEyMHj1anDt3Tnm/s2fPir59+4rixYsLnU4nihUrJsLDw8WkSZOM8lasWCEqVKggdDqdACDGjRtn+Nvhw4dFp06dhLe3t9DpdMLX11e89NJLYt68eUbLuHjxoujbt6/w9fUVOp1O+Pv7i06dOomrV68+9HFUr3dubq6YNm2aKF++vNDpdMLLy0t0795dXLx40SgvIiJCVKpUSXruvXr1EgEBAQ98XS9evCg++OADUb9+feHr6yusra2Fs7OzCA0NFZ999pnIyckx+zmeP39edOvWTXh6egqdTieCg4PFjBkzDFe0FOLuVUdnzJihHJcpr/n7778vateuLdzd3YWtra0oU6aMGDZsmLhx48YDn7MQQvTo0UNUrFhRit971dF7HT9+XFhZWSmvfPv777+Ll156STg6Ogp7e3tRr149w5VKTZGYmCgmTpwoatasafS57t69u/jzzz8Nebdu3RL9+vUT3t7ewsHBQbzwwgvi999/l67YKYRl1ufff/9dhIaGCltbW+Hr6yv+97//iWnTpgkAIjEx0ZD3qOup/m/3P4fs7Gzx8ccfi2rVqgk7Ozvh5OQkKlSoIAYOHChOnTolhBAiJiZGtG/fXgQEBAhbW1vh6ekpIiIixM8//2y0rLy8PBEQECDefvvtB74XREREzyuNEPdcVoyIiIgkgYGBqFy5MtavX/+kh0L01Nm/fz/q1KmDPXv2IDQ09EkP55nRtGlTnDt3Dv/++++THopZtm7diqZNm+LYsWOGU4eJiIjoLp46SkREREQFVrt2bXTq1AkTJ07kZHQ+hg8fjho1aqBkyZK4efMmli1bhs2bNyM6OvpJD81skyZNQt++fTnJRkRElA9OtBERERHRI/nkk08QHR2NlJQU5QUWnne5ubn48MMPER8fD41Gg4oVK+Lbb79F9+7dn/TQzHLr1i1ERERg8ODBT3ooRERETy2eOkpERERERERERGQB2ic9ACIiIiIiIiIioqKAE21ERETPoL59+8LW1hZHjhyR/jZ16lRoNBqsW7eu0B5fo9EgKiqq0JZvrh07dkCj0RhuNjY2KFasGOrXr48xY8bg/PnzBV72lStXEBUVhUOHDlluwE/Q7t27ERUVhcTEROlvkZGRiIyMLNByVfe15Hqif49Xr1790NyoqChoNBqLPG5h6t27NwIDA5/0MIiIiMiCONFGRET0DJo9ezZ8fX3Rq1cvZGdnG+JHjhzBuHHj0Lt3b7Rp06bQHj8mJgb9+/cvtOUX1OTJkxETE4Pt27cjOjoakZGR+OabbxASEoJly5YVaJlXrlzB+PHji9RE2/jx45UTbXPnzsXcuXMf/6AsrH///oiJiXnSwyAiIqLnECfaiIiInkEuLi6Ijo7GoUOHMGnSJABAdnY2evToAR8fH8yePbtQH79evXooUaJEoT5GQQQFBaFevXqoX78+2rZti48++gjHjh1DhQoV0Lt3b+URgHRXxYoVUbFixSc9jEdWokQJ1KtX70kPg4iIiJ5DnGgjIiJ6RjVu3BiDBg3C5MmTceDAAURFReHw4cOIjo6Gq6urUe6qVasQFhYGR0dHODk5oVmzZvjrr7+Mcnr37g0nJyecPn0aLVu2hJOTE0qWLIl3330XmZmZRrn3nxKY36l6ixYtgkajwblz5wyxwMBAtG7dGuvXr0eNGjVgb2+PkJAQrF+/3nCfkJAQODo6om7duti/f/8jvU4eHh746quvkJOTg1mzZhnip0+fRp8+fRAUFAQHBwcUL14cbdq0MZqM27FjB+rUqQMA6NOnj+HUVP1z379/P7p06YLAwEDY29sjMDAQXbt2NflU1S+//BLVqlWDk5MTnJ2dUaFCBYwePdrwd/3rt3nzZvTp0wceHh5wdHREmzZtcObMGaNlbd68Ge3atUOJEiVgZ2eHcuXKYeDAgbhx44YhJyoqCv/73/8AAKVLlzY8nx07dgBQn/45fvx4hIaGwsPDAy4uLqhZsyaio6Nh7vW0zp07B2tra0yZMkX6265du6DRaPD9998/dDkZGRkYPnw4fH19YW9vj4iICGldVq2P+vVu48aNqFmzJuzt7VGhQgV88803Rnn613z79u1444034OXlBU9PT3To0AFXrlyRxmPKZ0u/3ODgYNja2iIkJARLlix56HMlIiKiZw8n2oiIiJ5hM2bMQKlSpdCxY0dMmzYNgwYNQpMmTYxyJk+ejK5du6JixYr47rvv8O233yIlJQUvvvgijh8/bpSbnZ2Ntm3bolGjRvjpp5/Qt29fzJo1C9OmTbPouA8fPoxRo0Zh5MiR+PHHH+Hq6ooOHTpg3LhxWLBgASZPnoxly5YhKSkJrVu3Rnp6+iM9Xp06deDn54ddu3YZYleuXIGnpyemTp2KjRs34osvvoC1tTVCQ0Nx8uRJAEDNmjWxcOFCAMAHH3yAmJgYo9Nmz507h+DgYMyePRubNm3CtGnTEBcXhzp16hhNcKmsXLkSgwcPRkREBNasWYO1a9di2LBhSEtLk3L79esHrVaL5cuXY/bs2di3bx8iIyONTv/877//EBYWhi+//BK//fYbPvzwQ+zduxcvvPCC4fTi/v374+233wYA/Pjjj4bnU7NmzXzHee7cOQwcOBDfffcdfvzxR3To0AFvv/02Jk6caMIrf1dgYCDatm2LefPmITc31+hvn3/+Ofz9/dG+ffuHLmf06NE4c+YMFixYgAULFuDKlSuIjIyUJh5VDh8+jHfffRfDhg3DTz/9hKpVq6Jfv35G64Ve//79odPpsHz5ckyfPh07duxA9+7djXJM/WwtWrQIffr0QUhICH744Qd88MEHmDhxIrZt2/bQMRMREdEzRhAREdEzbfny5QKA8PX1FSkpKUZ/u3DhgrC2thZvv/22UTwlJUX4+vqKTp06GWK9evUSAMR3331nlNuyZUsRHBxsFAMgxo0bZ/j3uHHjhOprxcKFCwUAcfbsWUMsICBA2Nvbi0uXLhlihw4dEgCEn5+fSEtLM8TXrl0rAIiff/75ga/B9u3bBQDx/fff55sTGhoq7O3t8/17Tk6OyMrKEkFBQWLYsGGGeGxsrAAgFi5c+MAx6JeRmpoqHB0dxZw5cx6Y+9Zbbwk3N7cH5uhfv/bt2xvF//zzTwFATJo0SXm/vLw8kZ2dLc6fPy8AiJ9++snwtxkzZkjviV5ERISIiIjIdzy5ubkiOztbTJgwQXh6eoq8vLwH3vf+9UT/Pq1Zs8YQu3z5srC2thbjx4/P93HvvW/NmjWNHvfcuXNCp9OJ/v37G2Kq9TEgIEDY2dmJ8+fPG2Lp6enCw8NDDBw40BDTv+aDBw82uv/06dMFABEXFyeEMP2zlZubK/z9/fMdd0BAwAOfNxERET1beEQbERHRMywvLw+fffYZtFotrl27hsOHDxv9fdOmTcjJyUHPnj2Rk5NjuNnZ2SEiIsJwyqCeRqORLqJQtWrVR7pqp0r16tVRvHhxw79DQkIA3Dl10cHBQYpb4vHFfac65uTkYPLkyahYsSJsbGxgbW0NGxsbnDp1CidOnDBpmampqRg5ciTKlSsHa2trWFtbw8nJCWlpaQ9dRt26dZGYmIiuXbvip59+euARcK+99prRv8PDwxEQEIDt27cbYteuXcOgQYNQsmRJWFtbQ6fTISAgAABMfj4q27ZtQ+PGjeHq6gorKyvodDp8+OGHSEhIwLVr18xaVmRkJKpVq4YvvvjCEJs3bx40Gg0GDBhg0jK6detmdFpoQEAAwsPDjV6L/FSvXh2lSpUy/NvOzg7ly5dXrl9t27Y1+nfVqlUB3F0XTf1snTx5EleuXMl33ERERFS0WD/pARAREVHBffzxx4iJicHKlSsxbtw49O3bF4cOHYK9vT0A4OrVqwBg6DN2P63W+Dc3BwcH2NnZGcVsbW2RkZFh0XF7eHgY/dvGxuaBcUs8/oULF+Dv72/49/Dhw/HFF19g5MiRiIiIgLu7O7RaLfr372/yqardunXD1q1bMXbsWNSpUwcuLi7QaDRo2bLlQ5fRo0cP5OTk4Ouvv8Yrr7yCvLw81KlTB5MmTZJO//X19ZXu7+vri4SEBAB3JlybNm2KK1euYOzYsahSpQocHR2Rl5eHevXqFfjU23379qFp06aIjIzE119/jRIlSsDGxgZr167FRx99VKDlDhkyBP3798fJkydRpkwZfP311+jYsaPyOark91rcP8ms4unpKcVsbW2Vz+P+XFtbWwAw5Jr62dK/R/mN+97+hURERPTs40QbERHRM+r48eP48MMP0bNnT3Tu3BkBAQGoX78+xowZg5kzZwIAvLy8AACrV682HN1UGPSTc5mZmYYJCQAP7VP2uOzbtw/x8fHo16+fIbZ06VL07NkTkydPNsq9ceMG3NzcHrrMpKQkrF+/HuPGjcP7779viGdmZuLmzZsmjatPnz7o06cP0tLSsGvXLowbNw6tW7fGv//+a/R+xcfHS/eNj49HuXLlAABHjx7F4cOHsWjRIvTq1cuQc/r0aZPGkZ+VK1dCp9Nh/fr1RhOwa9euLfAyu3XrhpEjR+KLL75AvXr1EB8fjzfffNPk++f3Wqgm0QqTqZ8t/bjyGzcREREVLTx1lIiI6BmUk5ODXr16wcvLC3PmzAEA1KtXD8OHD8ecOXPw559/AgCaNWsGa2tr/Pfff6hdu7byZgmBgYEAgL///tsovm7dOoss/1HcvHkTgwYNgk6nw7BhwwxxjUZjNCkIABs2bMDly5eNYvcfyXTv/YUQ0jIWLFggNft/GEdHR7Ro0QJjxoxBVlYWjh07ZvT3ZcuWGf179+7dOH/+vOEKofpTEu8fy1dffSU9Vn7PR0Wj0cDa2hpWVlaGWHp6Or799tuHP6l82NnZYcCAAVi8eDFmzpyJ6tWro379+ibff8WKFUanAZ8/fx67d++WrpZa2Ez9bAUHB8PPzy/fcRMREVHRwiPaiIiInkFTpkzB/v378euvvxodfTVx4kSsW7fOcAppYGAgJkyYgDFjxuDMmTNo3rw53N3dcfXqVezbtw+Ojo4YP378I4+nZcuW8PDwQL9+/TBhwgRYW1tj0aJFuHjx4iMv2xynTp3Cnj17kJeXh4SEBOzduxfR0dFITk7GkiVLUKlSJUNu69atsWjRIlSoUAFVq1bFgQMHMGPGDJQoUcJomWXLloW9vT2WLVuGkJAQODk5wd/fH/7+/mjQoAFmzJgBLy8vBAYGYufOnYiOjjbpiLjXX38d9vb2qF+/Pvz8/BAfH48pU6bA1dVVOh1x//796N+/P1599VVcvHgRY8aMQfHixTF48GAAQIUKFVC2bFm8//77EELAw8MD69atw+bNm6XHrVKlCgBgzpw56NWrF3Q6HYKDg+Hs7CzltmrVCjNnzkS3bt0wYMAAJCQk4OOPP5Ym9Mw1ePBgTJ8+HQcOHMCCBQvMuu+1a9fQvn17vP7660hKSsK4ceNgZ2eHUaNGPdKYzGXqZ0ur1WLixIno37+/YdyJiYmIiooy+XRZIiIienbwiDYiIqJnzOHDhzFx4kS8/vrraN68udHf7OzssGjRIpw+fRpjxowBAIwaNQqrV6/Gv//+i169eqFZs2YYMWIEzp8/jwYNGlhkTC4uLti4cSOcnZ3RvXt3DBo0CJUrVzaM4XEZPXo0wsLCEBERgT59+mDbtm3o27cvjh8/ji5duhjlzpkzB927d8eUKVPQpk0b/Pzzz/jxxx9RtmxZozwHBwd88803SEhIQNOmTVGnTh3Mnz8fALB8+XI0bNgQI0aMQIcOHbB//35s3rwZrq6uDx3riy++iKNHj+Kdd95BkyZNMGzYMJQvXx6///47ihUrZpQbHR2NrKwsdOnSBUOGDEHt2rWxY8cOQ087nU6HdevWoXz58hg4cCC6du2Ka9euYcuWLdLjRkZGYtSoUVi3bh1eeOEF1KlTBwcOHFCO8aWXXsI333yDI0eOoE2bNhgzZgw6duxodKpsQRQvXhwvvPACPDw80K1bN7PuO3nyZAQEBKBPnz7o27cv/Pz8sH37dul9exxM/Wz169cPCxYswPHjx9GhQwdMmDABo0ePxksvvfTYx0xERESFSyPuvwQXERERET0VFi1ahD59+iA2NtZip/k+Da5du4aAgAC8/fbbmD59+pMeDhEREZHF8NRRIiIiInosLl26hDNnzmDGjBnQarV45513nvSQiIiIiCyKp44SERER0WOxYMECREZG4tixY1i2bBmKFy/+pIdEREREZFE8dZSIiIiIiIiIiMgCeEQbERERERERERGRBXCijYiIiIiIiIiIyAI40UZERERERERERGQBnGgjIiIiIiIiIiKyAE60ERERERERERERWQAn2oiIiIiIiIiIiCyAE21EREREREREREQWwIk2IiIiIiIiIiIiC+BEGxERERERERERkQVwoo2IiIiIiIiIiMgCONFGRERERERERERkAZxoIyIiIiIiIiIisgBOtBEREREREREREVkAJ9qIiIiIiIiIiIgsgBNtREREREREREREFsCJNiIiIiIiIiIiIgvgRBsREREREREREZEFcKKNiIiIiIiIiIjIAjjRRkREREREREREZAGcaCMiIiIiIiIiIrIATrQRERERERERERFZACfaiIiIiIiIiIiILIATbURERERERERERBbAiTYiIiIiIiIiIiIL4EQbERERERERERGRBXCijYiIiIiIiIiIyAI40UZERERERERERGQBnGgjIiIiIiIiIiKyAE60ERERERERERERWQAn2oiIiIiIiIiIiCyAE21EREREREREREQWwIk2IiIiIiIiIiIiC+BEGxERERERERERkQVwoo2IiIiIiIiIiMgCONFGRERERERERERkAZxoIyIiIiIiIiIisgBOtBEREREREREREVkAJ9qIiIiIiIiIiIgsgBNtREREREREREREFsCJNiIiIiIiIiIiIgvgRBsREREREREREZEFcKKNiIiIiIiIiIjIAjjRRkREREREREREZAGcaCMiIiIiIiIiIrIATrQRERERERERERFZACfaiIiIiIiIiIiILIATbURERERERERERBbAiTYiIiIiIiIiIiIL4EQbERERERERERGRBXCijYiIiIiIiIiIyAI40UZERERERERERGQBnGgjIiIiIiIiIiKyAE60ERERERERERERWQAn2oiIiIiIiIiIiCyAE21EREREREREREQWwIk2IiIiIiIiIiIiC+BEGxERERERERERkQVwoo2IiIiIiIiIiMgCONFGRERERERERERkAZxoIyIiIiIiIiIisgBOtBEREREREREREVkAJ9qIiIiIiIiIiIgsgBNtREREREREREREFsCJNiIiIiIiIiIiIgvgRBsREREREREREZEFcKKNiIiIiIiIiIjIAjjRRkREREREREREZAGcaCMiIiIiIiIiIrIATrQRERERERERERFZACfaiIiIiIiIiIiILIATbURERcCuXbvQpk0b+Pv7Q6PRYO3atQ+9z86dO1GrVi3Y2dmhTJkymDdvnpTzww8/oGLFirC1tUXFihWxZs2aQhg9ERE97R5WZ4QQiIqKgr+/P+zt7REZGYljx44Z5WRmZuLtt9+Gl5cXHB0d0bZtW1y6dOkxPgsiInpaFaX9GU60kcn69u0LW1tbHDlyRPrb1KlTodFosG7dukJ7fI1Gg6ioqEJbvrl27NgBjUZjuNnY2KBYsWKoX78+xowZg/Pnzxd42VeuXEFUVBQOHTpkuQFTkZaWloZq1arh888/Nyn/7NmzaNmyJV588UX89ddfGD16NIYMGYIffvjBkBMTE4POnTujR48eOHz4MHr06IFOnTph7969hfU06DnHOmOMdYaeJg+rM9OnT8fMmTPx+eefIzY2Fr6+vmjSpAlSUlIMOUOHDsWaNWuwcuVK/PHHH0hNTUXr1q2Rm5v7uJ4GEWvNfVhr6GlRpPZnBJGJkpKSRKlSpUSNGjVEVlaWIf73338LGxsb0bt370J9/JiYGHHx4sVCfQxzbN++XQAQkydPFjExMeKPP/4QP/30kxg9erTw9fUV9vb2YunSpQVadmxsrAAgFi5caNlB03MBgFizZs0Dc0aMGCEqVKhgFBs4cKCoV6+e4d+dOnUSzZs3N8pp1qyZ6NKli8XGSnQv1hljrDP0tLq/zuTl5QlfX18xdepUQywjI0O4urqKefPmCSGESExMFDqdTqxcudKQc/nyZaHVasXGjRsf29iJWGuMsdbQ0+hZ35/hEW1kMhcXF0RHR+PQoUOYNGkSACA7Oxs9evSAj48PZs+eXaiPX69ePZQoUaJQH6MggoKCUK9ePdSvXx9t27bFRx99hGPHjqFChQro3bu38tcyer5kZGQgOTnZrFtSUpIUy8zMtNiYYmJi0LRpU6NYs2bNsH//fmRnZz8wZ/fu3RYbB9G9WGfUWGfoYQpSZyxZa86ePYv4+HijmmFra4uIiAhDzThw4ACys7ONcvz9/VG5cmXWFXqsWGvUWGvoQZ50nVF5mvdnONFGZmncuDEGDRqEyZMn48CBA4iKisLhw4cRHR0NV1dXo9xVq1YhLCwMjo6OcHJyQrNmzfDXX38Z5fTu3RtOTk44ffo0WrZsCScnJ5QsWRLvvvuu9AG8/zDrqKgoaDQaaYyLFi2CRqPBuXPnDLHAwEC0bt0a69evR40aNWBvb4+QkBCsX7/ecJ+QkBA4Ojqibt262L9//yO9Th4eHvjqq6+Qk5ODWbNmGeKnT59Gnz59EBQUBAcHBxQvXhxt2rQxKlw7duxAnTp1AAB9+vQxHMatf+779+9Hly5dEBgYCHt7ewQGBqJr166PdFg3FZ6MjAy4uBWHq6urWbcSJUpIsSlTplhsXPHx8fDx8TGK+fj4ICcnBzdu3HhgTnx8vMXGQXQ/1hnTsM6QXkHrjCVrjb4uPKhmxMfHw8bGBu7u7vnmED0urDWmYa0h4OmoMypP8/6MdaEunYqkGTNmYNOmTejYsSMuXryIQYMGoUmTJkY5kydPxgcffIA+ffrggw8+QFZWFmbMmIEXX3wR+/btQ8WKFQ252dnZaNu2Lfr164d3330Xu3btwsSJE+Hq6ooPP/zQYuM+fPgwRo0ahTFjxsDV1RXjx49Hhw4dMGrUKGzduhWTJ0+GRqPByJEj0bp1a5w9exb29vYFfrw6derAz88Pu3btMsSuXLkCT09PTJ06FcWKFcPNmzexePFihIaG4q+//kJwcDBq1qyJhQsXGl67Vq1aAYDhl69z584hODgYXbp0gYeHB+Li4vDll1+iTp06OH78OLy8vB7thSKLysrKQnbmTdRpshpW1o4m3Sc3Jw2xm+98vlxcXAxxW1tbi47t/i91QggprspRfRkksiTWGdOwzhBQsDoDFE6tKUjNYF2hJ4W1xjSsNfQ01Zn7Pa37M5xoI7M5Ojpi0qRJ6NatG3x9fTFjxgyjv1+8eBHjxo3DW2+9hU8//dQQb9KkCYKCgjB+/HisWrXKEM/KysL48ePx6quvAgAaNWqE/fv3Y/ny5RYtSgkJCdizZw+KFy8O4M7pCtWrV8fXX3+N06dPw8HBAcCdD+LLL7+MLVu2oE2bNo/0mKVKlcLff/9t+HeDBg3QoEEDw79zc3PRqlUrVKpUCV999RVmzpwJFxcXVK5cGQBQtmxZ1KtXz2iZHTt2RMeOHY2W0bp1a/j4+GD58uUYMmTII42ZCoe1jROsdaYVJo32zobfxcXFqChZkq+vr/RLzrVr12BtbQ1PT88H5tz/qxCRpbHOmI51hvTMqTOAZWuNr68vgDtHDvj5+Rni99YMX19fZGVl4datW0ZHtV27dg3h4eGP9PhEBcFaYzrWGgKebJ1ReZr3Z3jqKJktLy8Pn332GbRaLa5du4bDhw8b/X3Tpk3IyclBz549kZOTY7jZ2dkhIiICO3bsMMrXaDTSxr9q1aoWP2y4evXqhoIEACEhIQCAyMhIQ0G6N26Jx9fPqOvl5ORg8uTJqFixImxsbGBtbQ0bGxucOnUKJ06cMGmZqampGDlyJMqVKwdra2tYW1vDyckJaWlpJi+DHj+NRmvWrbCFhYVh8+bNRrHffvsNtWvXhk6ne2AOd4iosLHOmI51hvTMrTOWrDWlS5eGr6+vUc3IysrCzp07DTWjVq1a0Ol0RjlxcXE4evQo6wo9Eaw1pmOtIeDJ1hmVp3l/hke0kdk+/vhjxMTEYOXKlRg3bhz69u2LQ4cOGQ5Jvnr1KgAYzsm/n1Zr/IFzcHCAnZ2dUczW1hYZGRkWHbeHh4fRv21sbB4Yt8TjX7hwAf7+/oZ/Dx8+HF988QVGjhyJiIgIuLu7Q6vVon///khPTzdpmd26dcPWrVsxduxY1KlTBy4uLtBoNGjZsqXJy6DHT6PVGH7VMSXXXKmpqTh9+rTh32fPnsWhQ4fg4eGBUqVKYdSoUbh8+TKWLFkCABg0aBA+//xzDB8+HK+//jpiYmIQHR2NFStWGJbxzjvvoEGDBpg2bRratWuHn376CVu2bMEff/xh9viIzME6YzrWGdIzp87o883xsDozdOhQTJ48GUFBQQgKCsLkyZPh4OCAbt26AQBcXV0Np9R5enrCw8MD7733HqpUqYLGjRubNRYiS2CtMR1rDQFPvs48S/sznGgjsxw/fhwffvghevbsic6dOyMgIAD169fHmDFjMHPmTAAwnE+/evVqBAQEFNpY9IUsMzPT6DxvfePDJ23fvn2Ij49Hv379DLGlS5eiZ8+emDx5slHujRs34Obm9tBlJiUlYf369Rg3bhzef/99QzwzMxM3b9602NjJ8jRaLTRa037VMTXvXvv370fDhg0N/x4+fDgAoFevXli0aBHi4uJw4cIFw99Lly6NX375BcOGDcMXX3wBf39/fPrpp3jllVcMOeHh4Vi5ciU++OADjB07FmXLlsWqVasQGhpq9viITMU6YzrWGbqXOXVGn2+Oh9WZESNGID09HYMHD8atW7cQGhqK3377Dc7Ozob7zJo1C9bW1ujUqRPS09PRqFEjLFq0CFZWVmaNhehRsdaYjrWG9J50nXmW9mc40UYmy8nJQa9eveDl5YU5c+YAuHN56uHDh2PmzJl45ZVXUL9+fTRr1gzW1tb477//jFZySwsMDAQA/P3330a/NK1bt67QHtNUN2/exKBBg6DT6TBs2DBDXKPRSM0fN2zYgMuXL6NcuXKGmD7n/l9zNBoNhBDSMhYsWIDc3FxLPw2yIK0W0Jr4q04B5tkQGRkpHdZ/r0WLFkmxiIgIHDx48IHLvb9/BlFhYp0xHesM3c+cOqPPN8fD6oz+aoL3Xk3xfnZ2dvjss8/w2WefmffgRBbEWmM61hq615OuM8/S/gwn2shkU6ZMwf79+/Hrr78a/VIxceJErFu3znC4dWBgICZMmIAxY8bgzJkzaN68Odzd3XH16lXs27cPjo6OGD9+/COPp2XLlvDw8EC/fv0wYcIEWFtbY9GiRbh48eIjL9scp06dwp49e5CXl4eEhATs3bsX0dHRSE5OxpIlS1CpUiVDbuvWrbFo0SJUqFABVatWxYEDBzBjxgzD1Xf0ypYtC3t7eyxbtgwhISFwcnKCv78//P390aBBA8yYMQNeXl4IDAzEzp07ER0dbdKvR/TkmNOn4HH0aCN6GrHOqLHOkCnM7YfDWkPPK9YaNdYaehjWGdM9v8+czHL48GFMnDgRr7/+Opo3b270Nzs7OyxatAinT5/GmDFjAACjRo3C6tWr8e+//6JXr15o1qwZRowYgfPnzxtdoeZRuLi4YOPGjXB2dkb37t0xaNAgVK5c2TCGx2X06NEICwtDREQE+vTpg23btqFv3744fvw4unTpYpQ7Z84cdO/eHVOmTEGbNm3w888/48cff0TZsmWN8hwcHPDNN98gISEBTZs2RZ06dTB//nwAwPLly9GwYUOMGDECHTp0wP79+7F582a4uro+tudM5tP3NDD1RvS8YZ3JH+sMmcLcOsNaQ88j1pr8sdbQw7DOmE4jHnRsHhERPZLk5GS4urqiYaddsLZxMuk+OVmp2P5dAyQlJRXKpbCJiKjoKEidAVhriIjINKwz5uMRbURERERERERERBbAHm1ERI+BOYdPP8+HWRMRUcGYe5oOaw0REZmDdcZ0nGgjInoMtBottCY2BDU1j4iISM+cOqPPJyIiMhXrjOk40UZE9DiY8wvQc/zrDxERFZC5jadZa4iIyBysMybjRBsR0WPAU0eJiKgw8ZQeIiIqTKwzpnt+j+UjREVFQaMp2Mq/fPlyzJ4927IDMvNxNBoNoqKiCn0MRJag0WjNuhEVht27dyMqKgqJiYkFXoYltv+RkZGIjIx8pGU8DX755ZfHVofur3mLFi2CRqPBuXPnjPI++OADlCpVCtbW1nBzcwMAZGVlYdCgQfDz84OVlRWqV6/+WMZMj5e5dYa1hh6H/LZVlrZkyRJ06dIFwcHB0Gq1CAwMzDc3NTUVQ4cOhb+/P+zs7FC9enWsXLlSmZudnY0KFSpg6tSpDx0D962enOPHjyMqKkq5nvXu3fuB68ODNGjQAEOHDn2ksRUlrDOme36fOT2Sp6EYxMTEoH///oU+BiJL0P8CZOqNqDDs3r0b48ePf+ITbUXFL7/8gvHjxz+Rx27VqhViYmLg5+dniP3000/46KOP0LNnT+zcuRNbtmwBAHz55Zf46quvMGbMGPzxxx/49ttvn8iYqXCZW2dYa6go+fbbb3Hs2DHUrVsXZcuWfWBuhw4dsHjxYowbNw6//vor6tSpg65du2L58uVS7ty5c3Hr1i28/fbbhTV0ANy3elTHjx/H+PHjlRNtY8eOxZo1awq03IkTJ2Lu3Lk4efLkI46waGCdMR1PHaVnVr169Z70EJ5Jt2/fhoODw5MexnNHq9VCqzXxYggm5hHR86tYsWIoVqyYUezo0aMAgCFDhsDb29sobm9vj7feestij89a8vQxp87o84mKik2bNhnW6datWxu2h/f75ZdfsHnzZixfvhxdu3YFADRs2BDnz5/H//73P3Tu3BlWVlYAgJycHMyYMQN9+/aFo6Pj43kiT9DTtG9lyRrzsInXB4mIiEBwcDA++eQTzJ8/3yLjeZaxzpju+X3mz5kNGzagevXqsLW1RenSpfHxxx8r84QQmDt3LqpXrw57e3u4u7ujY8eOOHPmjCEnMjISGzZswPnz56HRaAw3vaysLEyaNAkVKlSAra0tihUrhj59+uD69evS4y1fvhxhYWFwcnKCk5MTqlevjujoaJMeR3V489GjR9GuXTu4u7sbDgVfvHixUc6OHTug0WiwYsUKjBkzBv7+/nBxcUHjxo1N+rXi3rHcf3vYYfHXr1/HgAEDULJkScNrU79+fcNRB3obN25Eo0aN4OrqCgcHB4SEhGDKlClGOT///DPCwsLg4OAAZ2dnNGnSBDExMUY5+kPYDx48iI4dO8Ld3d1QbEx5r8lyHrTeqG5ElhYVFYX//e9/AIDSpUsb1rUdO3YAAPLy8jB9+nTDttvb2xs9e/bEpUuXDMt42HZ5/PjxCA0NhYeHB1xcXFCzZk1ER0dDCFHgcT+oTuh98803qFatGuzs7ODh4YH27dvjxIkTRjn5na56/ykl586dg0ajwccff4yZM2eidOnScHJyQlhYGPbs2WN0vy+++AIAlHXg+++/R2hoqGE7XqZMGfTt2/ehzzc5ORmvv/46PD094eTkhObNm+Pff/+V8u4/HSswMBAffPABAMDHx8dQIzUaDRYsWID09HTDGBctWgTA9DoQGRmJypUrY9euXQgPD4eDg4PhuSQnJ+O9995D6dKlYWNjg+LFi2Po0KFIS0szWoZGo8Fbb72Fb7/9FiEhIXBwcEC1atWwfv166bn9888/6Nq1K3x8fGBra4tSpUqhZ8+eyMzMNOTEx8dj4MCBKFGiBGxsbFC6dGmMHz8eOTk5D32Niypz6wxrDT1Jpmy3AeDrr79G+fLlYWtri4oVK2L58uXKUwFN3aFfs2YNnJyc8OqrrxrF+/TpgytXrmDv3r2G2M8//4zLly+jR48e0nK4b3XXo+5bPWh/Zf/+/ejSpQsCAwNhb2+PwMBAdO3aFefPnzfcf9GiRYb3s2HDhlKtU60vGRkZGDVqlFHtevPNN5VH/Pfo0QPLly9HSkrKQ59LUcc6Yzoe0fYc2Lp1K9q1a4ewsDCsXLkSubm5mD59Oq5evSrlDhw4EIsWLcKQIUMwbdo03Lx5ExMmTEB4eDgOHz4MHx8fzJ07FwMGDMB///0nHYabl5eHdu3a4ffff8eIESMQHh6O8+fPY9y4cYiMjMT+/fthb28PAPjwww8xceJEdOjQAe+++y5cXV1x9OhRw4bzQY+jcvLkSYSHh8Pb2xuffvopPD09sXTpUvTu3RtXr17FiBEjjPJHjx6N+vXrY8GCBUhOTsbIkSPRpk0bnDhxwvBLlsr9k1np6eno0aMHcnNz4eHh8cAx9ujRAwcPHsRHH32E8uXLIzExEQcPHkRCQoIhJzo6Gq+//joiIiIwb948eHt7499//zX6ZW758uV47bXX0LRpU6xYsQKZmZmYPn06IiMjsXXrVrzwwgtGj9uhQwd06dIFgwYNMuz8mPJek+XwYgj0pPXv3x83b97EZ599hh9//NFwymHFihUBAG+88Qbmz5+Pt956C61bt8a5c+cwduxY7NixAwcPHoSXl9dDt8vnzp3DwIEDUapUKQDAnj178Pbbb+Py5cv48MMPzR7zw+oEAEyZMgWjR49G165dMWXKFCQkJCAqKgphYWGIjY1FUFBQQV4ufPHFF6hQoYLhFJuxY8eiZcuWOHv2LFxdXTF27FikpaVh9erVRnXBz88PMTEx6Ny5Mzp37oyoqCjY2dnh/Pnz2LZt2wMfUwiBl19+Gbt378aHH36IOnXq4M8//0SLFi0eOt41a9bgiy++QHR0NDZu3AhXV1eUKFECzZs3x8SJE7F9+3bD4+t3YMypA3FxcejevTtGjBiByZMnQ6vV4vbt24iIiMClS5cwevRoVK1aFceOHcOHH36II0eOYMuWLUZfsjds2IDY2FhMmDABTk5OmD59Otq3b4+TJ0+iTJkyAIDDhw/jhRdegJeXFyZMmICgoCDExcXh559/RlZWFmxtbREfH4+6detCq9Xiww8/RNmyZRETE4NJkybh3LlzWLhwoWlvchHDJtX0rDB1uz1//nwMHDgQr7zyCmbNmoWkpCSMHz/eaNLdXEePHkVISAisrY13g6tWrWr4e3h4OIA72yxvb29DndTjvpVl9630VPsr586dQ3BwMLp06QIPDw/ExcXhyy+/RJ06dXD8+HF4eXmhVatWmDx5MkaPHo0vvvgCNWvWBJD/kWz6Wrt161aMGjUKL774Iv7++2+MGzcOMTExiImJga2trSE/MjISI0eOxI4dO9CmTZuHPo+ijHXGDIKKvNDQUOHv7y/S09MNseTkZOHh4SHuXQViYmIEAPHJJ58Y3f/ixYvC3t5ejBgxwhBr1aqVCAgIkB5rxYoVAoD44YcfjOKxsbECgJg7d64QQogzZ84IKysr8dprrz1w7Pk9jhBCABDjxo0z/LtLly7C1tZWXLhwwSivRYsWwsHBQSQmJgohhNi+fbsAIFq2bGmU99133wkAIiYm5oFjuldOTo5o166dcHJyEgcOHHhovpOTkxg6dGi+f09JSREuLi7ihRdeEHl5ecqc3Nxc4e/vL6pUqSJyc3ON7uvt7S3Cw8MNsXHjxgkA4sMPPzRahjnvNT2apKQkAUC0HnhQtH/7lEm31gMPCgAiKSnpSQ+fipgZM2YIAOLs2bNG8RMnTggAYvDgwUbxvXv3CgBi9OjRhtiDtsv3ys3NFdnZ2WLChAnC09PTaJsWEREhIiIiHnh/U+rErVu3hL29vbQ9v3DhgrC1tRXdunV76GP26tXL6PmcPXtWABBVqlQROTk5hvi+ffsEALFixQpD7M033xSqr1Iff/yxAGCoO6b69ddfBQAxZ84co/hHH30k1byFCxdK76V+m3/9+nXpOTo6OhrFzKkDERERAoDYunWrUe6UKVOEVqsVsbGxRvHVq1cLAOKXX34xxAAIHx8fkZycbIjFx8cLrVYrpkyZYoi99NJLws3NTVy7dk31EgkhhBg4cKBwcnIS58+fN4rrX/djx47le9+iqCB1hrWGHpf7t1Wmbrdzc3OFr6+vCA0NNco7f/680Ol0D6xDD6pTQUFBolmzZlL8ypUrAoCYPHmyIRYSEiKaN28u5XLfyrL7Vvntr6jk5OSI1NRU4ejoaFQrv//+ewFAbN++XbrP/XV+48aNAoCYPn26Ud6qVasEADF//nyjeFZWltBoNGLkyJEPHV9RxTpjPp46WsSlpaUhNjYWHTp0gJ2dnSHu7OwszcivX78eGo0G3bt3R05OjuHm6+uLatWqGU4vepD169fDzc0Nbdq0MVpG9erV4evra1jG5s2bkZubizfffNNiz3Xbtm1o1KgRSpYsaRTv3bs3bt++LR2J1rZtW6N/63/JuvdIiYd56623sGHDBnz//feGX0+EEEbP/d7TWOrWrYtFixZh0qRJ2LNnD7Kzs42Wt3v3biQnJ2Pw4MH5Hmp78uRJXLlyBT169DA6TN7JyQmvvPIK9uzZg9u3bxvd55VXXjH6tyXeazKPFlpoNSbeeFY/PWbbt28HcGd7ea+6desiJCQEW7duNWk527ZtQ+PGjeHq6gorKyvodDp8+OGHSEhIwLVr18wakyl1IiYmBunp6dK4S5YsiZdeesnkcau0atXK6Bd4c2pEnTp1AACdOnXCd999h8uXL5v0mPr34bXXXjOKd+vWzaT7m8PcOuDu7o6XXnpJWkblypVRvXp1o2U0a9bM6LRkvYYNG8LZ2dnwbx8fH3h7exte09u3b2Pnzp3o1KmT1H/u/sdt2LAh/P39jR5Xf+Tfzp07H+GVeXaZVWdYa+gJMXW7ffLkScTHx6NTp05GeaVKlUL9+vUfaQwPOp3t3r9duXLFqN8lwH0roPD2re7fXwHuXCF25MiRKFeuHKytrWFtbQ0nJyekpaUpTzU2hf7o7vvXwVdffRWOjo7SdwedTgc3NzeTa3lRxjpjuuf3mT8nbt26hby8PPj6+kp/uz929epVCCHg4+MDnU5ndNuzZw9u3Ljx0Me7evUqEhMTYWNjIy0jPj7esAx9T4ESJUpY4FnekZCQYHT1NT1/f3/D3+/l6elp9G/9IcLp6ekmPd6kSZMwb948fPXVV2jevLkhvnPnTum563vorFq1Cr169cKCBQsQFhYGDw8P9OzZE/Hx8QBMe130zyO/55qXl4dbt24Zxe/PtcR7TebhFXroafaw7cr920+Vffv2oWnTpgDu9NT5888/ERsbizFjxgAwfduqZ4ntoSnjzs+j1IgGDRpg7dq1yMnJQc+ePVGiRAlUrlwZK1aseOD9EhISYG1tLT22qoY/KnPrgOo1vnr1Kv7++2/p/s7OzhBCSMu4/3kBd15X/Wt669Yt5ObmPvS7wdWrV7Fu3TrpcStVqgQAz20N49Xg6Flg6nZb/19VK5NHaW/i6emprA03b94EAKM2MOnp6UaTaQD3rYDC27dSPVa3bt3w+eefo3///ti0aRP27duH2NhYFCtWzOzvFXr6Wnv/DzoajQa+vr7K9cPOzq7Aj1eUsM6Yjj3aijh3d3doNBrDRM697o95eXlBo9Hg999/NzovXU8Vu5+Xlxc8PT2xceNG5d/1v2TrN2yXLl2SfiUpKE9PT8TFxUnxK1euGMZmKYsWLcLYsWMRFRUlNbeuVasWYmNjjWL6guTl5YXZs2dj9uzZuHDhAn7++We8//77uHbtGjZu3Gj0uuRHX8Tye65arRbu7u5G8ft/ubPEe03mYY82eprdu125/0v6lStXTNp+rly5EjqdDuvXrzfaMVm7dm2BxmRKnXjY9vDecdvZ2SEpKUnKK6xJmXbt2qFdu3bIzMzEnj17MGXKFHTr1g2BgYEICwtT3sfT0xM5OTlISEgw2mFR1fBHZW4dUB0B4uXlBXt7e3zzzTf5PoY5PDw8YGVl9cAaqF9u1apV8dFHHyn/rq+5zxv2zqFnganbbX2equ/Zo2wTq1SpghUrViAnJ8eoT9uRI0cAAJUrVzbEvLy8DBNwety3Kpx9K0CuM0lJSVi/fj3GjRuH999/3xDPzMyU3hdz6Gvt9evXjSbbhBCIj483HJV+r1u3bln8+T6LWGdMxyPaijhHR0fUrVsXP/74IzIyMgzxlJQUrFu3zii3devWEELg8uXLqF27tnSrUqWKIffeX6DvX0ZCQgJyc3OVywgODgYANG3aFFZWVvjyyy8fOP78HkelUaNG2LZtm2Hjr7dkyRI4ODhY7JLVGzduxOuvv46+ffti3Lhx0t+dnZ2l521jYyPllSpVCm+99RaaNGmCgwcPAgDCw8Ph6uqKefPm5XuVvuDgYBQvXhzLly83yklLS8MPP/xguBLpg5jzXpNl8Ao99DTI79dl/SmBS5cuNYrHxsbixIkTaNSokdEyVNtljUYDa2tro9Mt09PT8e233xZorKbUibCwMNjb20vjvnTpkuGUF73AwED8+++/Rk20ExISsHv37gKNDzDt13pbW1tERERg2rRpAIC//vor39yGDRsCAJYtW2YUX758eYHHmB9L1IHWrVvjv//+g6enp3IZ91/l7WHs7e0RERGB77///oEToK1bt8bRo0dRtmxZ5eM+txNtvBocPQNM3W4HBwfD19cX3333nVHehQsXHmm73b59e6SmpuKHH34wii9evBj+/v4IDQ01xCpUqID//vvPKI/7Vpbft8qPRqOBEEKakFywYAFyc3ONYuYcPadfx+5fB3/44QekpaUZfXcA7kwsZmRkSBfFeB6xzpiOR7Q9ByZOnIjmzZujSZMmePfdd5Gbm4tp06bB0dHR6NeA+vXrY8CAAejTpw/279+PBg0awNHREXFxcfjjjz9QpUoVvPHGGwDu/Br0448/4ssvv0StWrWg1WpRu3ZtdOnSBcuWLUPLli3xzjvvoG7dutDpdLh06RK2b9+Odu3aoX379ggMDMTo0aMxceJEpKeno2vXrnB1dcXx48dx48YNjB8//oGPozJu3DhD35YPP/wQHh4eWLZsGTZs2IDp06fD1dX1kV/Ls2fP4tVXX0WZMmXQp08f7Nmzx+jvNWrUyPfXqaSkJDRs2BDdunVDhQoV4OzsjNjYWGzcuBEdOnQAcKfP2ieffIL+/fujcePGeP311+Hj44PTp0/j8OHD+Pzzz6HVajF9+nS89tpraN26NQYOHIjMzEzMmDEDiYmJmDp16kOfhznvNVmGVquB1sRfdUzNIzKX/kv9nDlz0KtXL+h0OgQHByM4OBgDBgzAZ599Bq1WixYtWhiuOlqyZEkMGzbMaBmq7XKrVq0wc+ZMdOvWDQMGDEBCQgI+/vjjAh8ha0qdcHNzw9ixYzF69Gj07NkTXbt2RUJCAsaPHw87OzujH0N69OiBr776Ct27d8frr7+OhIQETJ8+HS4uLo/8ek6bNg0tWrSAlZUVqlatikmTJuHSpUto1KgRSpQogcTERMyZMwc6nQ4RERH5Lq9p06Zo0KABRowYgbS0NNSuXRt//vlngScrH8QSdWDo0KH44Ycf0KBBAwwbNgxVq1ZFXl4eLly4gN9++w3vvvuu0U6rKWbOnIkXXngBoaGheP/991GuXDlcvXoVP//8M7766is4OztjwoQJ2Lx5M8LDwzFkyBAEBwcjIyMD586dwy+//IJ58+ZZ9PSpZ4U5dUafT/S4mbrd1mq1GD9+PAYOHIiOHTuib9++SExMxPjx4+Hn52fUpxgAjh8/juPHjwO4c2TZ7du3sXr1agB3rq6tnyRp0aIFmjRpgjfeeAPJyckoV64cVqxYgY0bN2Lp0qVGPxZFRkZiwoQJuH37ttGP2Ny3sty+1YO4uLigQYMGmDFjBry8vBAYGIidO3ciOjoabm5uRrn6IxHnz58PZ2dn2NnZoXTp0sqWBU2aNEGzZs0wcuRIJCcno379+oarjtaoUQM9evQwytfv7+l/DHuesc6Y4UlcgYEev59//llUrVpV2NjYiFKlSompU6carvByv2+++UaEhoYKR0dHYW9vL8qWLSt69uwp9u/fb8i5efOm6Nixo3BzcxMajcZoOdnZ2eLjjz8W1apVE3Z2dsLJyUlUqFBBDBw4UJw6dcrosZYsWSLq1KljyKtRo4ZYuHChSY+D+66MI4QQR44cEW3atBGurq7CxsZGVKtWzWh5Qty9Ms73339vFNdfae7+fNV987vdfyW/e2VkZIhBgwaJqlWrChcXF2Fvby+Cg4PFuHHjRFpamlHuL7/8IiIiIoSjo6NwcHAQFStWFNOmTTPKWbt2rQgNDRV2dnbC0dFRNGrUSPz5559GOfldgU7PlPeaHo3+Kj0dhx4XXUdeNOnWcejx5/YKPVT4Ro0aJfz9/YVWqzW6Qldubq6YNm2aKF++vNDpdMLLy0t0795dXLx40ej+D9ouf/PNNyI4OFjY2tqKMmXKiClTpojo6Ghp+2jKVUf1HlYnhBBiwYIFhhrn6uoq2rVrp7zy5OLFi0VISIiws7MTFStWFKtWrcr3qqMzZsyQ7n9/3cnMzBT9+/cXxYoVM7wWZ8+eFevXrxctWrQQxYsXFzY2NsLb21u0bNlS/P777w99vomJiaJv377Czc1NODg4iCZNmoh//vnH4lcd1TOlDkRERIhKlSop75+amio++OADERwcbHj9q1SpIoYNGybi4+ONXrs333xTun9AQIDo1auXUez48ePi1VdfFZ6enobvLb179xYZGRmGnOvXr4shQ4aI0qVLC51OJzw8PEStWrXEmDFjRGpqqnKsRVVB6gxrDT0uqm2VEKZvt+fPny/KlSsnbGxsRPny5cU333wj2rVrJ2rUqGGUp9/+qW737y+kpKSIIUOGCF9fX2FjYyOqVq1qdEVpvdOnTwuNRiO+++476W/ct7rrUfathHjw/sqlS5fEK6+8Itzd3YWzs7No3ry5OHr0qLJ2zJ49W5QuXVpYWVkZPe79dV4IIdLT08XIkSNFQECA0Ol0ws/PT7zxxhvi1q1b0hh69OghqlSp8sDnUNSxzphPI0Q+56cREdEjS05OhqurK14ddgI6W+eH3wFAdmYKvp8VgqSkpEc62oaIiIq+gtQZgLWGnk2JiYkoX748Xn75ZcyfP7/QH09/tc9ff/210B+Lnj7Jycnw9/fHrFmz8Prrrz/p4TwxrDPm46mjRESPAS+GQEREhYlNqqmoiY+Px0cffYSGDRvC09MT58+fx6xZs5CSkoJ33nnnsYxhypQpqFGjBmJjY5VN8qlomzVrFkqVKoU+ffo86aE8FVhnTMeJNiKix0BjRk+D57koERFRwZhTZ/T5RE8zW1tbnDt3DoMHD8bNmzcNDfjnzZuHSpUqPZYxVK5cGQsXLiyUqz/T08/FxQWLFi0yukLt84x1xnS86igRURExd+5clC5dGnZ2dqhVqxZ+//33fHN79+6tvDLQvV9cFy1apMy59ypbRERU9AUGBirrwZtvvglAXVMK+4qEVPS5u7tj3bp1iI+PR1ZWFhITE7Fx40azL7LyqLp37442bdo81sekp8OwYcNQt27dJz2M505R2KfhRBsR0WNQ2JfCXrVqFYYOHYoxY8bgr7/+wosvvogWLVrgwoULyvw5c+YgLi7OcLt48SI8PDzw6quvGuW5uLgY5cXFxcHOzq5ArwERERUec+uMObUmNjbWqA5s3rwZAIxqRvPmzY1yfvnlF4s/RyIienIKs87oFZV9Gh4DSUT0GBR2j7aZM2eiX79+6N+/PwBg9uzZ2LRpE7788ktMmTJFynd1dTW6LPvatWtx69YtqQeFRqOBr6+v2eMhIqLHqzB75xQrVszo31OnTkXZsmURERFhiNna2rJeEBEVYY+jR1tR2acpshNteXl5uHLlCpydnQs0k0pEJIRASkoK/P39odU+2gHAWmigNXFbpMWdvOTkZKO4ra0tbG1tpfysrCwcOHAA77//vlG8adOm2L17t0mPGR0djcaNGyMgIMAonpqaioCAAOTm5qJ69eqYOHEiatSoYdIynwesNUT0KJ5UndHnA6bXGr2srCwsXboUw4cPN9ru7dixA97e3nBzc0NERAQ++ugjeHt7m/ks6H6sM0T0KJ6lOlOU9mmK7ETblStXULJkySc9DCIqAi5evIgSJUo80jIKckTb/duwcePGISoqSsq/ceMGcnNz4ePjYxT38fExqXlvXFwcfv31VyxfvtwoXqFCBSxatAhVqlRBcnIy5syZg/r16+Pw4cMICgoy6bkUdaw1RGQJj7vO6PMB02uN3tq1a5GYmIjevXsbYi1atMCrr76KgIAAnD17FmPHjsVLL72EAwcOPHDSjh6OdYaILOFZqDNFaZ+myE60OTs7AwAWakrDQXN35jbix/eU+bmOrlIsx8ZBmZuz7jspZuvtpcyNjzkqxRJOJyhzE4+lKuPPm/IHfjA5999ar5h8f1UuFa5K+1dIMd3CGcrczD7vS7HfzpRV5naLlw8bFsHVlLlCq5NiA1eql+vgZHyefnZmKtbOrWvYnjyKgky0Xbx4ES4uLob4w3ZW7v+lWwhh0q/fixYtgpubG15++WWjeL169YyaWdevXx81a9bEZ599hk8//fShy30e5FtrFvZV38FNrhW5Dur16/yML6SYR1kfRSZw+KvDDxsq3Ye1puioErtUisW/9bYyt9gXX0qxpfsClbkDzgyVYo4vRMiJAITiinTt5qg/r/fKzbmNA1tffex1Rp8PmF9roqOj0aJFC/j7+xtinTt3Nvx/5cqVUbt2bQQEBGDDhg3o0KGDyWMimX7dWFutKhytrAzxitP+p8x3qttKit088qcy16NKfSmWtnuNMtcxvP1Dx0rGzpw5a3KuOXWmTJnSBR4TPdiVEb2VcTFghBTzuHxEmZvoX1GK+QVVUebe/lN+j3Ps5XkJAMixlvuJnbFTX33XSiMM/5+WmoJWkVWfqTpTFPZpiuxEm/6NcNBo4aC5W5RcHO2V+bmO8qRajq2jMjfb1kaK2dmrV5ZUnfwSZ9xTJO+VpVHHnzfmbAQcFK9ZfvdX5VLhcnZykmI6W3niCwBsnOVce0cXRSbgovi8CcVnGACElfx4Ohv5se6MTb19sMSpGuY0BNXnubi4GBWl/Hh5ecHKykr6pefatWvSL0L3E0Lgm2++QY8ePWBjI2/b7qXValGnTh2cOnXqoWN6XuRbaxzyaa6qqEG5Dup110lRP5zz+fxw+2Y+1pqiQ1VrUhUTX/nl2tqrt7POiu97TvnVGsXjWevU3yNVHnedufcxTa01AHD+/Hls2bIFP/744wPz/Pz8EBAQwHphAfr3ydHKymiizSWfddFJ8V7mOKnXRdX7bpXPch1NXEforsKqM6Z+Xsl8Kfl8zxKK2pHfZzBXlZvPe2atmoNwUH9eVRNtTnbq5d470ab3LNSZorRPw6uOEhE9BlotoNVqTLyZt2wbGxvUqlXLcBU4vc2bNyM8PPyB9925cydOnz6Nfv36PfRxhBA4dOgQ/Pz8zBsgEREVOvPqjPm1BgAWLlwIb29vtGolHzV1r4SEBFy8eJH1goioCCnsOlOU9mmK7BFtetXfrGl0BECuo5sy71/PF6SYm9UtZa5/vVAplly6pjI3ILCMFCt+8bwyV2snH6Wj8S2uzFW5vHytMv7PitMmL+NpcCK4xRO9Pz2YnY/8S88Ln/VX5p7WuEmxgf8MUC93bJIUy0jdpcxdhpekmHuM+vTtN94oL8Uat1Rv+jb+9I/Rv7OzLHc6d0GOaDPH8OHD0aNHD9SuXRthYWGYP38+Lly4gEGDBgEARo0ahcuXL2PJkiVG94uOjkZoaCgqV64sLXP8+PGoV68egoKCkJycjE8//RSHDh3CF1/IpzQ+73zCPOB07xEtHupf3f72l3dOi+luKHPLdmokxdKrN1TmvlRjuxRLOvKPIhOwcZJ/PXUoJ9eq/Gzvv1gZz8uWfz19mrHWPHsiP1OfgnhGU0yKDXWdql7I8KuKoCoGbEEvOfiXerFvv/+iFHupY44yd9vqGPVCHlFBjzQwVV5eHhYuXIhevXrB+p7tXWpqKqKiovDKK6/Az88P586dw+jRo+Hl5YX27Xm6oaVUfa8LXBzuHhWd5uipzLt56oQUs7FRHyGT+NdWKZZeTN2vKPvgFimmS09WZAJCI+9d59qqj/7R5sqfE+szcusdALi0Tq51Ogf1WUWBX69VxgvDeutgZbx1zkkptjesrsm55jyeOfd/1vzzahMpdmHXFWWuXx25Hjh4qs9kKVZdXtddXlNs9wHc1Mj7Drs8OipznXOzpNiZ4ynK3AQH+ZRhb8fbytwSVhelmL0mU5mbnnv3c5ErLHcRlcKuM0DR2acp8hNtRERPg4L0aDNH586dkZCQgAkTJiAuLg6VK1fGL7/8YrjiTlxcHC5cuGB0n6SkJPzwww+YM2eOcpmJiYkYMGAA4uPj4erqiho1amDXrl2oW1f9JZGIiJ6cgvbOMdWWLVtw4cIF9O1r3IPSysoKR44cwZIlS5CYmAg/Pz80bNgQq1atskhPICIiejoUdp0Bis4+DSfaiIgeA63mzs3U3IIYPHgwBg8erPzbokWLpJirqytu31b/agYAs2bNwqxZswo2GCIieqzMqTP6fHM0bdoUQshHrtrb22PTpk3mLYyIiJ45hV1n9IrCPg0n2oiIHoPCPqKNiIieb4/jSAMiInp+sc6YrshPtLk1CDfqZ3C6mPrwwCtJ8lUwyuKAMvd2SfmSvbGZNZS5jbRyMw9N1TrK3Iwdv0kxUauxMjfVXu5HFT+qizK3mksfKVaslXyuOwAIG/lqJnHLVitzz/8u95oLaqHuUZAanyjFbp2XYwBg4yD3AIv7I0GZ61pR7jmRdDxNmasS2NJfip37RX3O/+PkXkXdS0C1sUo5q569f3GWvD5o8rkCy7Ude6VY8mV1j0L30nLvg27bWytzry78VxlXyUiVn4ert7r/iKuXfNnrwCB5XACQfFu+itOe3XHK3P6vG/dquJ2ajE2LlKlm40Rb0VahVwujK42eKqnedp+Ol9fdSlY7lLnZleR+oJtT1I1g22l3SjG3purt/KVFq6SYdetXlblJLiWlmM3eYcpc+171pVjNd9U9tTSKK2rt7D9fmZudnCvFPKqpT0e7eVjdA4UKT/h4uW+glb36qrt/fS33V0o9laHMVdXBZr80Vw/iF3U/wsIQWLmsMn49Ud5u59eLbcC7DQz/n56WjH0WOhiMO0BFW6Z/EDLvuXrobTt3ZV6GVtGHMzdRvUxbef8nw1rdz809Xv6ueLt4iDLX/toZOddV/s4NADnWco+123XUyy1pK29bMgLk/TIASDog71fZnDmizE3/V77yoH2Q+rMu0tOlWOQXcp8tAEhbMFaKlWup3me8vXiCFNvWb5kyt1RTXymWvnK6FMu8IPf0AoA/RsuvjSV6vJ16Te5d6lNX/f6oenflZcm9zQCgxPC3pFjJofJ3AwCwunZJiomcbPUY7OV1/bCL+nvWiUvy97cM9XARXFwem+oqoADg4yjvt1pp85S56VZyXczJVU/nFLO+2//XzsqCPadZZ0xW5CfaiIieBhqNBtpCvBgCERE938ypM/p8IiIiU7HOmI4TbUREj4FGY8YRbc9xUSIiooIxp87o84mIiEzFOmM6TrQRET0GGs2dm6m5RERE5jCnzujziYiITMU6Y7oiP9EmrG0hdHfP/RdC/W6rVoK9VhHK3Ir28nn8omJlZe4WRazu4W/VY0jPlGJpDt7KXAF5wCV1l5W5bj17S7HMP1UjA2wDA6WYd70q6uW+854U++bsi8rcrCz5XPOhvureb3n//C3FAhupe7SldRkqjyvxnDI386fvpFjyxetSrNGinsr7x23dI8cOqfu51RrbQ4plX1HnWleoJMVyjqt7SKDeS1JId0ndBy0vVe5V9IXdCGVu96GHpJjb55PVy82R38uKdeU+TgDg4iH3Ejh10PQeEEnX1O+7Kt6oSQllbswB+XU4c1j+DAPAmbrGPS8ybmsfNkSTabUaaE38BcjUPHqK2Njcuf2/PKFed7SKHh2brdsoc0Odjkoxp1B135qtiljYwWhlbvotuR/iLddAZa6q1pS3P6vM9Zr1rhS7ukze7gKAV035eVQfUFuZa/9KVykWta+BIhPISJf7sMyssFKZm/THbimWePaqMtdx4idSzP3qCWXu2RlzpdjFHfFSrN4oubcZAMTOlvvtZSXkKHMjP5N74KWclvunAoBbmPz63vxd7rkEAG4dO0oxcfSgMjc7MUmKjbf9SJk76nu5tv3TTX1VsVzF94ayNcorc88cUnwvU1wd0xIq1/BTxjevM722Hf3nbo+nTEW/p4Iyp87o8+nZkWtli1yru/s0GqjXcVX8lr16vXXOkr9PxXWWt7kAcMNR7qFc8qvPlbn2Wrk/rqoXGwBY58j7P/ZQ99tMK1tTzk1U7//kOLhJsTy/AGUuguTt4ynHCupchcCkQ8q4dZrc79ihtHo7llBS7t1W75C616rjFbn+aBR9yLQN1ftwjVYESrHUr0Yrc7UVqirjKj5Dh0gx6/RkZW6Gi9xnTpeVT5/tHLkZ2mnvF9Rj8JB7tLmel3umAwCy5XXP1Vq97gX7yt/rUrLU63R2rrz+i/z6rmXLfQdLOqn3fzLy5NwLyW7KXOFyd/ueKuTPbkGxzpiuyE+0ERE9DTQajcmHTz/Ph1kTEVHBmFNn9PlERESmYp0xnVmHa0RFRRleXP3N1/fubLQQAlFRUfD394e9vT0iIyNx7Ngxo2VkZmbi7bffhpeXFxwdHdG2bVtcumQ883zr1i306NEDrq6ucHV1RY8ePZCYmFjwZ0lE9IRp/795qKm35xXrDBFRwZhbZ1hrWGuIiMzBOmM6s8+LqlSpEuLi4gy3I0fungYwffp0zJw5E59//jliY2Ph6+uLJk2aICXl7iGYQ4cOxZo1a7By5Ur88ccfSE1NRevWrZGbe/cyuN26dcOhQ4ewceNGbNy4EYcOHUKPHvKpeEREzwqN1rzb84x1hojIfObWGdYa1hoiInOwzpjO7FNHra2tjX7x0RNCYPbs2RgzZgw6dLjTM2Tx4sXw8fHB8uXLMXDgQCQlJSE6OhrffvstGjduDABYunQpSpYsiS1btqBZs2Y4ceIENm7ciD179iA0NBQA8PXXXyMsLAwnT55EcHCwclyZmZnIzLx7nnVy8p3zwU+WagEnJ2dD/NgVD+X9nezl86a9HNTniZ/NDpRi7lXkXlQAcOtIqhRLtiumzPV4tY8Us8uW7w8Ab85zlWIt25ZT5lb095ditq3DlLkV4jZLMeFfVpn74w25R86//9xU5qanyefAL3LtrMytE9lMilU5tliZ23b4NSk2Y5r6nH3nXnLvg7jb7lLM0eaYFAMA90tyj7Ujb65R5oob8nit3eT3DACSisv9E2y8AtXLtZI/slZJicpcTfFSUizIQ90fwPWK/JwvJ6v7xuTl5EoxJyf1psTZzUF+LG9PZa69k70Uiz8j91kAgMavyuvvwjm7lLn1W9dVxlVWf/270b9zsvPpFUGF6mmtM0D+teZYpR5wdHIxxHf/4yLdFwDcneVf9oq7qz9rRzNM7w+jctNR3bcwcILchyUzQ73tfnmUvO1u01O9jQ0NkXvfOLzbUpnre2q5FHMJVvc6nXVG7lHz9+7Dytz0ZLlmTvHuq8xt1vM1KVZ1p7o3ZbN35X4pEz5qrcz1/DhSimUkyt89sh0PKO9f9p8LUmx333XKXKsr06SYnZebMjclRH7fnAPlHqEAkGVtI8Xyrt1Q5jqEyP2GavjI234AcP5X7nWackruGQgAedlynylXd0dlrpuPXFduxavHa47yteXP4Ppv/1DmlgwpLcUS1K2jsHtDrOH/WWeenKe11uRXZ1IcfQGnu/sbGZC/NwGANeSejtZC7t8FAKk28rYpqIW6r9fRlfI2K0+r/v6X4is/N22e/P0RAC5ay/svjlbqz0WWg/yc0+zV3ytdUuQPYK6TOjfeKUjOzZP7bAFAtiJ+2U1dv+zd5Ofhkajuc3omW96G+Nqrt2NppeX9lzSN/L3DM0u9EdK5e0mxazXbKXOLJci9oLXZGcrcLHs3KZZtp/4+lGMl1xldhrqfW56N/L7ba9XfnWwz5b6hmiz1eEWW/B1H5HMckrVWXn/d7dT1KzNX/lykZcnPF1D3Y/srTt1TMcRH/q5mZ63+XKXe0/stLVvucUeFz+w5xlOnTsHf3x+lS5dGly5dcObMGQDA2bNnER8fj6ZNmxpybW1tERERgd277zQcPnDgALKzs41y/P39UblyZUNOTEwMXF1dDQUJAOrVqwdXV1dDjsqUKVMMh2W7urqiZEl1c3Yioifh/lNUHnZ7nj2tdQZgrSGip5e5dYa15umsNawzRPS0Yp0xnVkTbaGhoViyZAk2bdqEr7/+GvHx8QgPD0dCQgLi4+9cUcvHx8foPj4+Poa/xcfHw8bGBu7u7g/M8faWr7Tp7e1tyFEZNWoUkpKSDLeLFy+a89SIiAqV/io9pt6eV09znQFYa4jo6WVunWGteTprDesMET2tWGdMZ9apoy1atDD8f5UqVRAWFoayZcti8eLFqFevHgD5yhJCiIfOZN6fo8p/2HJsbW1ha6u+xC4R0ZOm0dy5mZr7vHqa6wzAWkNETy9z6ow+/3n1NNca1hkielqxzpjO7B5t93J0dESVKlVw6tQpvPzyywDu/Hrj53f3vOJr164ZfhHy9fVFVlYWbt26ZfQL0LVr1xAeHm7IuXr1qvRY169fl35ZMkVmjg7WOXfPib58Td2jqnflQ/JjWhVX5too+hEED3tZmauxs5Niv9yQe2cBQG0P+Tzxc5nq3O6vyQX49CW5nwkAJHnKY7hw3VmRCVwr1kGRq1Pm1iiVKMWWfXVKmesdIJ9r7uGsHq8/5F/uGi2U+6sBwE9T5PPdD2arz4Gf9rncYy2khtw37WIZ9WN19ZPXv5BP1D16rjrJr3n2bfn9BQDfm7ekmLZGqCITyq1VXrq6R4FVgty/rqHHVmXupcAXpVjJ9nLPAADYVOotKVbHRn3uf+nicu/Cpi+q+05NHf+nFAuuW1GZe+ZfuWeFraJvBwD8uX6fFBs2Rt1jqrbXf0b/TklJRdVNylSzabQaaEz8VcfUvOfBs1BnACAt2xbIvrtdPvOfur/muCaKfog26j6YdlbytjdoYiNlrpVi/V99Xu6ZBgCNS8rbjGOp6jH0GyaP4e+j6ueWUFLuy/XnWfX2+GLpgVLsn7PqmvBSNXm8G5aqx6Di462uYaW1x6VYs1+aK3PXT5S/O+zIUD+3D8fIy1X17zpZp5by/gPKy7313Bupt5u7S8hjSL+k3h5Xv3Jdijm3bKrIBHRWcg+ixFR1LxrrS3IPoA7F1f1LL1RrL8UazJQ/iwCwtpLcL6+pg7oXzZVKcq1QtP0EAMydLvfz1Nmq38t/9/+jXojCxRNy36VeQ+RetgDQtPTd70pPqs7o8+mOZ6HWCGggcPc9y4O6h5hTlvxZz82nl5qwktcB53rq76BhoXWk2GVr9f6EjUZ+vByt+nPmqpF7aqXnqT/AVoo+WelQ927McJVrUp5Qn8zlKOTeYEm5co9rANBArlVWGrkvHgDY5Mj16y87+Ts3AJS3knuhpcJNmXslRx6br05e1+KsA5X3d7I9J8X8j/6qzIVO8b7lqJ+vg6J3W5arel3X5MrL0Oao95VyFT2qPW+r+zinOshHkVp7q78zXC8WIudC3c/QxSpFilnn874fSpS/f5V0VfefS86W198S+fTuvZkh55ZzVvfhc0m/ux+YIizXC5R1xnSPdB2IzMxMnDhxAn5+fihdujR8fX2xefPdZvpZWVnYuXOnoeDUqlULOp3OKCcuLg5Hjx415ISFhSEpKQn79t3dOd67dy+SkpIMOUREzxqtxrwb3cE6Q0RkGnPrDGvNXaw1REQPxzpjOrOOaHvvvffQpk0blCpVCteuXcOkSZOQnJyMXr16QaPRYOjQoZg8eTKCgoIQFBSEyZMnw8HBAd26dQMAuLq6ol+/fnj33Xfh6ekJDw8PvPfee6hSpYrhij0hISFo3rw5Xn/9dXz11VcAgAEDBqB169YPvBIcEdHTzJyGoM9z41DWGSKigjG38TRrDWsNEZE5WGdMZ9ZE26VLl9C1a1fcuHEDxYoVQ7169bBnzx4EBNw5PHLEiBFIT0/H4MGDcevWLYSGhuK3336Ds/Pdw4pnzZoFa2trdOrUCenp6WjUqBEWLVoEq3tOU1i2bBmGDBliuJJP27Zt8fnnn1vi+RIRPREaLaA18RhizSMda/xsY50hIioYc+qMPv95xVpDRGQ+1hnTaYQQ6qYoz7jk5GS4uroi9uAxON1TFEvG7VHmZ7jKPcSuO6j725S4dkCK/W7fUpl7I1nuDZOcz2nSzYPl/h7pQt2jYNdpXylWqlg+/QGs5d4yu/9Wr/UVysj9HjKz1TPRFXzlngr/xMs9zwDAyV4eQ35r3pUb8tiaVJD7qwHAqhj5PHxHR3XPCpU/tsg95Tp0Kq/MLVNMfuNqJG1R5mqOy+uIpnxlZe7My69Isbcq7FTm2p4+JMVOVuupzC23daY8hirqnkCHPZpJsUCrM8rc1aeqSrGbt9TrXmqK3CvIWqde937/We6lZg5nTzdlfNhQubeRh72611Cp3NNG/05JTUVQ6EtISkqCi4tLgcal3xaNmp8AOwfTlpFxOxlTBng+0uPS46F/f/cdPGFUawJP/aLMz/KW68oV10rK3MBz8vblZ4ceytwrcttCJCSo+4z0aSD380jNk/spAsCPMW5SrHxpdc8zB1t5O7/ht5vK3Jq1vaTY7XR1UahbXu5Vsu9fdW10d1E1Hlem4r9zci+Y1yLlnpkAMHul/Lukk4vcixNQ9yPZt0muCe37qvv0VC0jv28v3vhOmZu6fZsUy6+/0tBjnaTYjMjdylzs3SGF/mk2RplaMnqIFHN9KVKZG1O8mxSraH1CmftZjPy5iLucT4+bm4reOTr1b8mqXmqWMPwDufenv4v6C1/5nCOG/09JTUPp8GaPvc4ArDXPCv37e/xADJyd7m6rndLU/Q1zdHJvsgwbdS81+0z5u/xNB3WP6lyh2EcQ6os2eOXJV1VV9TYDgGQrDymm06jrV56i61FarrpHm51W7hcm8umaZAf5e2G2Rt1Tzgpyn7jcfPrlqXrC2efTLytZ0Y8t/9dBfryUHPl9t9Wq7+9kJfcsc0tXXwnXLkXu+Zxtr95enHOoIsVKZJ1WZAJ2t+XvB8muco9SAHBNkPdJMp3k7xEAkKLo0WaXLdcIALhuLa/rOcL045Du7Zl4r3+vu0uxQA/1GNIUvcWtterPSik7+fubdZ66L6t91t16mZKahjJhTVhnHrNHuhgCERGZRqM1/Ved5/nXHyIiKhhz6ow+n4iIyFSsM6bjRBsR0WOg1WigNbFPgal5REREeubUGX0+ERGRqVhnTMeJNiKix0CjuXMzNZeIiMgc5tQZfT4REZGpWGdMV+Qn2kruXwEX+7t9VP6pO1CZV2qV3HvEt9WrylyrDPnc+hDvc8pca0f5vOktl9X9ePz/WivFFjoNU+ampsm9cNJd1f0BSjgnSrFyAerz2qHonxDoJffHAQAHK7n3wdmL6j4JVy7JfVVe76A+lrSsh9z74FC83EMPABrVlHsqlLRV93P76Du5B8TQwSWlmK1W7lsAAOk58sdl0Po6ytxL//pIMY/DxZS5Y99OlGJxWrmvGAD4BsmvWYWjy5S5OdXkPj1/u7+kzC0r/pFiiVbycwAAV0Urp9vp6veyQz35tfzrstw7AQAiJ4ZJsdXr5d4hAFCytJsUO/inuqdc/du/SrE0G3X/EZfjvxsHbsvreEFxoq1o81k3Cy52d/tsHGk7QZnnOq6LFCsx7C1lriZN3m6GBqp7ndh4ytvpZcfkXikA4P3rPCn2retHytykRPkzkJqu7tFWwVv+vFaq6qnMVakUqO716Goj19x/jqn7LJ49dk6KTRyr3p5WKyk/3s5/5f6nANCpjVzLgx3Uvb76fCC/b6PHh0sxO2u5RxwA3M6SX9/Gs0spc4HecuiwOnPmDHk7fd5a3bczoJ6cG7JjhjLXqmljKbbHV/39qZo4KMWu6QKVue7uih63Sep+UG93ljea24/LNR8AXmkv9wCaO1PdI7RstbJS7MTe48rclilLpdhth3LKXNvffzb8f2a6ej0oCO4AFW1u107CJe1uH66bPhWVeV4X9ksxnbv6e7Sqn5tDjroXok22vN1NsFN/n3JOlntJXXKvpszNU/S5UvVBAwCdkLfFdlr1/o+qJ5ytohcbAFgJuR4k5bkpc1V94jw115W5ujz5851kpd4Hc9DIY7PPUff1uijkfq++VnKPtRytus9ctpC3r/9q1b2koWi/rdXI+6EA4KOV+7mJfM4dTHOW9zOcU+KUuVmO8vY82UG9n+KQlWjyGGw08vqUX981F438HScd6v3eF72OSrHLQt7nBAB3O/l9z8xVf89yS70kxbJ16jHY3r7bczbztnq9LwjWGdMV+Yk2IqKngVargVbRJD2/XCIiInOYU2f0+URERKZinTHdc9yejojo8dH/AmTqrSDmzp2L0qVLw87ODrVq1cLvv/+eb+6OHTug0Wik2z//GB/d+MMPP6BixYqwtbVFxYoVsWbNmoINjoiICpW5dcacWhMVFSXVC1/fu0eACiEQFRUFf39/2NvbIzIyEseOHSuEZ0lERE9KYdaZexWFfRpOtBERPQZajXk3c61atQpDhw7FmDFj8Ndff+HFF19EixYtcOHChQfe7+TJk4iLizPcgoKCDH+LiYlB586d0aNHDxw+fBg9evRAp06dsHfvXvMHSEREhcrcOmNuralUqZJRvThy5Ijhb9OnT8fMmTPx+eefIzY2Fr6+vmjSpAlSUtSnvhER0bOnsOsMUHT2aYr8qaPxO2KRZnP3PGe/Ws2Vedb2ct8Pqz2blblbh6yVYnUPf6vMdT62U4o1rJHPy35D7iVQvZS6X1h2ntyPoFrWbmWu3T8npVh8qdeVuWG5O6TYdfsgORGAW7rcC6B6BXXfgYga9lKsyrH5ylzYybmuwY2Uqal5zlLMJlfdU25kZ/nL3tyN8mMd3C6fVw8AXQfUk2LdO6n7rrnbyefLn76uaHIAIDj+Oyn2j6/c8wYA7G4oNjBW6t4UQtGzIjlT3d/mt6TaUszbVe5bAACdLk2WYquKj1bmfrpKnsuf2fw3Za42Xe4FJVq1UOY628q5b1VR98fQ3pT7Euiy1etIbsIN439bsneOVgONidVGn5ecbNwnxdbWFra26vdw5syZ6NevH/r37w8AmD17NjZt2oQvv/wSU6ZMyfexvL294ebmpvzb7Nmz0aRJE4waNQoAMGrUKOzcuROzZ8/GihUrTHouz4sDn+2E4z2fuZotuynzUl3kbU7qDyvVy5wl99kJO1hDmavbLv8q17llf2UuFG0sG1RX90fLypXXt/oZ65W52j/l8Z6t+KEyt7lYJ8XiXaoqcz1TzkmxF+pHKnPbt3STYlV/H6fMtXKUt9Ne9TopcxPz3KWYbY7cOw4A5k+W68KI2eel2NVzcg8jAGjetb4UGzr6RWWuj4u8LTt8Tu65BADVzsg193CZzspc3aV/pVhufrXGWu4lczNNvZ1aHCf34gzwk7/7AEC/k29LsQXlP1PmDh8j91hbP1Tdr1Wbrtj+D++qzHVX9Nmt3TJbmau5JH9fs85SryNpF+72Ibqdqa61BWFOndHnA6bXGmtra6Oj2PSEEJg9ezbGjBmDDh06AAAWL14MHx8fLF++HAMHqvsjk3msbl2FVebdGmLjGajMEzr5vbO9IPfiBQDX9kOk2NXjscpcu7QbUszNSt0DTPUd1E7RgwwANELeBrimqnt1WeXI3/+uuZVX5rqny8vIzKeflXWu/H3PzkbRlBiATiNvA9xvqnt25lnJ28c8J/W2NE8jx7V56tpcQivvD1zJk3uApWTYSTEA8LeX30sfW/X36Bxh+nSBy+2rUizJUd371D3lohTLy6ffXq5iPcvVqMd108ZfiukUvdgAwP+63NT0ipe6l2BCrryPWzr9iCITyLOWx+tur+47bQX5PXbKvqnM1ebK656wUW/ztdl3PytaxeemoAq7zgBFZ5+GR7QRET0GBTnMumTJknB1dTXc8isuWVlZOHDgAJo2bWoUb9q0KXbvVk/A69WoUQN+fn5o1KgRtm/fbvS3mJgYaZnNmjV76DKJiOjxK+gpPabWmlOnTsHf3x+lS5dGly5dcObMnYsQnT17FvHx8Ub1wtbWFhEREawXRERFSGHXmaK0T1Pkj2gjInoamHP4tD7v4sWLcHFxMcTz++Xnxo0byM3NhY+P8RWYfHx8EB8vH3kKAH5+fpg/fz5q1aqFzMxMfPvtt2jUqBF27NiBBg0aAADi4+PNWiYRET055p6mY06tCQ0NxZIlS1C+fHlcvXoVkyZNQnh4OI4dO2aoCap6cf68fDQnERE9mwqzzgBFa5+GE21ERI+BOQ1B9XkuLi5GRenh9zN+ACGEFNMLDg5GcHCw4d9hYWG4ePEiPv74Y0NRMneZRET05JjbeNqcWtOixd1WDlWqVEFYWBjKli2LxYsXo169ev+/PNYLIqKirDDrjPH9nv19miI/0XZu23mjvjl1qy5V5mWlyn0DbMuUMflxrPPp+fRnBbnHiDZD3Y/Eo6TcY+C7LerHa9tQXinsLp5Q5maWCJZivx9Uj+G4h9wbrLK1uh/JheuBUuz6zTxlrq2N3COgajm5LxgApNu5SbHLGerz+5108jnnVzXFlbn/XZd77ByPlfuxuXp7KO+/5LNdyripmnQKV8ZP1Wsoxbb87abMzazYXorFp6j7TUTayOPNyVOfLZ6mOHX/hlbddyP7utzX4etVpr82P9Zvqox3TpglxZxC1D0VRo48KMV+ez9BmZvjKL/v+cmrFWH879Q0ADNNvv+T4uXlBSsrK+lXmWvXrkm/3jxIvXr1sHTp3W2kr6/vIy/zeZF9MwdZmrvb1fQvP1Hmpd+U+zh51QzJZ6lyzzNdprqx+LbwaVLMOlm9nX+pcnUpNudLdYPZ1/vJdVB7RN04VlSuI8XW/qz+pXBPCbnW1K+t7iVz4qzcd+bK5WRFJmBnJ3+tqV/vJWVusqPcS+1UWillrput/B3hgkb9HeHvs25S7Oo507eRG1f8KcdMvjcQXLeiMn68c0cptuxXdU243WiAFDt7Te4vCACvOvwqxbKz1V9ck1NypdgVnfqraOpFudfP2j/zv+rY/eZefVkZfyN+lBRzrS+/NgAwboy8rm8ZckmZm+shbxdVvacAwKl1W8P/56XdBmavUuY9zRwdHVGlShWcOnUKL7/8MoA7Rwz4+fkZclgvLCv70mVk39NT2snTT50o5O/idopebPlxTFL3N4zzlntXWQv1PoKtldyPKjFX/X3MXSv3o9JlqntU59go+pzmqnupZdsFyMvNp1dXjqKXWnY+vcmyhPz92MlR3aM6x0o+aifTSt1HUyvk7WO2lbouqvq55eTIMWcbdW+um1luUixXqLfbN9PlMZRwzqcG28uvQ1Ku/FgAoHGSt4+q5wUAzhnyvkeuUOfmKOPqfRqrDPk71blU9Tbrdpa8Pth6qPsDBiQckGI6O09l7unb8veOmkL9GczRyeuOVT59/DId7z5eplCvR0+jorRPwx5tRESPgUYLaE28aczcMtvY2KBWrVrYvNn4Ai6bN29GeLh6glflr7/+MtpJCgsLk5b522+/mbVMIiJ6PMypMwWpNffKzMzEiRMn4Ofnh9KlS8PX19eoXmRlZWHnzp2sF0RERUhh15mitE9T5I9oIyJ6GhTk1FFzDB8+HD169EDt2rURFhaG+fPn48KFCxg0aBCAO1fXuXz5MpYsWQLgztV3AgMDUalSJWRlZWHp0qX44Ycf8MMPPxiW+c4776BBgwaYNm0a2rVrh59++glbtmzBH3/8Yf4AiYioUBX0lB5TvPfee2jTpg1KlSqFa9euYdKkSUhOTkavXr2g0WgwdOhQTJ48GUFBQQgKCsLkyZPh4OCAbt3UV2AmIqJnT2HWGb2isk/DiTYiosdAo9GY3AegIP0COnfujISEBEyYMAFxcXGoXLkyfvnlFwQE3Dl1Ii4uDhcu3D09MCsrC++99x4uX74Me3t7VKpUCRs2bEDLli0NOeHh4Vi5ciU++OADjB07FmXLlsWqVasQGhpq9viIiKhwmVNn9PmmunTpErp27YobN26gWLFiqFevHvbs2WOoMSNGjEB6ejoGDx6MW7duITQ0FL/99hucnZ3Nfh5ERPR0Ksw6o1dU9mk0QuTTQOIZl5ycDFdXV+w8cBZOTneLfLkNk5T5WU06STHHK+qeZ9dLh0mxA7fyOUfbLVGKLfhZfU551xbyvGd6jtwzAAAuJcjnmqekKVNRvbTcP27CR8eUuf5l5f5mjRure0CU9JTP+z98Vn0FESvF5UmKualXvWIucv+E03Hq5bo7y8sI9lL36krOls9rH/W+3APJElQblfw+aqPHy4es+jqq+zDNiJZ7Vlhbq9ent3rIPSustOoeepeT5S/CTrbqc/6XrL4lxf77619lrneAvxR7uWM5Za6jnTy2nTHqlbpZA7lHx61U9e8GB4/Iy8i4re4p4l/C+b68ZEx+3RNJSUlmNfC8l35b9MnqJNg7mraM9LRkvNvR9ZEelx4P/fu7NfY8HJ3uvlf+n/VX5ju9/qYUs/tXvR26Ub2lFNt6Wd1/q4Kv3C9l/HT11f6GDZX7dqZlqj8/py7KsZs31T1uImrL26JJH8Yoc1Xa931RGa9QSu5bszNW/Rm2tpbPUSjhr64fxYvJyz38r3o77eMlvz61AhOVuTfT5VozYeweZe7j9NZI+fUt7amuNe+OOGTycidPkXvz2Vip68fJeLnWuDvL7wMATB8v96ozxyv91OuTi5Ncn9euOq7Mfa2X3D8xXm4nBQDY9dt/Uux2srqGBYTc7TuYlZGCxRNKP/Y6A7DWPCv07+/xAzFwdrr73c79qno/Jc1D0ZssW70uptvLfYlTrNS91GyQKcUS89S5rlaJUkyr6B0HAFkaeRudm09/NBuNPIZrWer+aNZaedvibKXu/aZaboZQ91JT9QCz1qi3Y1aQt4XZUO/b6SDXNTsh9wcFAKE4H2/npSAp5ueurtfJ6fLr62Snfg6OOnkZSRnquhrsdlmK6YR6DLfy5HVPA3UNdlH0/MuvN1mWVu5H5pCj7imXbCX3TYvPUK/Ttqr1Saf+XNlr5X3v5Fz1NtZJsU7mt/6n5srrpDaf10ynvbs+paamoH7NINaZx4xHtBERPQaFfeooERE93x7HKT1ERPT8Yp0xHSfaiIgeA060ERFRYeIOEBERFSbWGdNxoo2I6DHQX33H1FwiIiJzmFNn9PlERESmYp0xXZGfaLudbQNN9t3zyHUhlZV543ZUkWIDm5dQ5l5Jl3sBOObTz6pM6l9SrG6tRsrc84rWYpV81f3GLuTJ55SfPavuO2BvJ/fqUvViAwBHZ/m89gz1qfVIz5ZXn7g4uccBAJQvKy+3rJd8vj0AHI9zk2I3bqr7BtxOlz+9/q5y/y4A2H1cHoOrt/w6Jl1Tv+b9hjWQYkvmqvvuZGfm86IppGbIvR42/+eqzE1LuirFVnU7rB7Dr/LYLrcdocxdulzuu5Z4TY4BQHqyej1TKVtR7tFmq1P3EvB0ktcdP391f4xvV8g9IFq0KqXMbf6C3AvjWpKjMtfHzfh9S0tVr3cFwSPairakTHvk6O5ue6q+WFeZ122BXFc+UvRMA4D/Er2lmKujusdN5cQdUuylNm2UuScuyCtYWFCiOjdH7ql14q8LikzAyamsMq7i6CovN+22+vOm2kZePBOnzK1WW65tVQPUPVT2nJS3L/GX1XUpJUnui1rOT11rNuxU10FTtev9ghT7adGjXxUrKU1+35fFmr6x2TpQ3Q8qfs4sKZY84itl7tcz5e9EluBXrqQUs7NTPzc/D/n7WskguVYBwOfTfpdiL/dR937r3qOMFDsfpx5Daf+7dfB2qhaLlVnm45EGz5ccB/V3xXgbuUebl3W8Mve2tdwzSQt1nXFKl78fZ9rJ360BIEvIPbyc8tTb10xFT63MPHmb+/+Dk9hZqb9zCyj6JasWACAPcp3Jbwz39r4yjEGj7qWWKeQ6kavo8XZnDPLYrLXq/cu0PPl7bAVf+fW9nqb+vlu12CUpFpdeTJmbcFt+f1LT83kOrvJzyK+PX3aevIwqRxcqc+07DpdiF06pa1JClvx4tzTqz0pmpryP4GCtXp+sNerPhYoW8vcZq3z6+KUovmfZWqm/R3hZ3ZBimVB/Bu1wt0+cBqbvuz0M64zpivxEGxHR00CruXMzNZeIiMgc5tQZfT4REZGpWGdMx4k2IqLHgEe0ERFRYeKRBkREVJhYZ0zHiTYioseAE21ERFSYuANERESFiXXGdEV+oq2M1Rk4W93tUbbbv5syL/OofC70H+fUPdpa/fqaFMu+rT6X+tTQBVKsokOiMtfBOkOKrfzdQ5nb6YVrUmx3jLrvQHa23KPNzVOOAUCrhvJ53kLIvQgAIDNHfry61dT9DBxt1ctQSVKcRp6drT4vPiFdPt89T6hfh+Lecrx4WV/58fPp0fbbxotS7NMpck8WANBp5J4Ky36Xe/sBQLCX/Hg21ur3/UY1uQ/Zd3kVlLmOYZ2kWOYV9dbuxZfk2E9L5T4A5rp9W37f22b/pMydENNeivn7qXtAVKyh7qejcvg/eTM3RDNHmbu/WD+jfwud6b32HoanjhZtlW2Ow9nm7nZ1Szm5nwgAZGw6LsXWHiitzG38mdyrK+OWutYcWb1TitVzUfeMcbGR4598q+4dMqy7HP/t+5vK3Mws+Xm4+6q3e737ydstIdQrflqGHG/cVO7JBQDODnIPSI1G3RcyIUF+LTPS1Z/5lET5NcvOVfezCQyQ6+tRB7lPT+btdCkGqPuxff5JJWWuqlfQtEXq51snUO67aaNT9865ovj+81mWujeZa6eBUizjpDIVDdrJfXJ2/bRXnWyG1FspUqxv1lxlbp9VbaVYYJD6vQwJrWjyGHbukXsBTraZoszdU2ac4f+1OaZ/P3oYntJTtNlk34ZN9t3vssnO6n7Lqh5gqdZuytxSJzdKMaFRf4+OD4qUYg5C3ftJK+TacVOj/py558nfN5OF3LcKAGxg+ufF1UruWabqg5Zf3NFK3d/T2owxZAm5B1h+Pdo0UG+7lWNQ9PuyUfRzu5Wq3tW/Zi/vZ5S1OaN+MMWu3TVXP2WqnVB878inGX4e5P5x56p3VuZqTsv7YNCo92VV+9PpuXLPQADIUewz3s5Q91/1spfXh5IpR5W5F5zl3u/59WgTipkoa6h786n6sfknyt8rASDJ5W4dt8pnX74gWGdMV+Qn2oiIngY8oo2IiAoTjzQgIqLCxDpjuke64OqUKVOg0WgwdOhQQ0wIgaioKPj7+8Pe3h6RkZE4duyY0f0yMzPx9ttvw8vLC46Ojmjbti0uXTK++smtW7fQo0cPuLq6wtXVFT169EBiYuKjDJeI6InRaIRZN7qDdYaIyDTm1hnWmjtYZ4iITMM6Y7oCT7TFxsZi/vz5qFq1qlF8+vTpmDlzJj7//HPExsbC19cXTZo0QUrK3cP6hw4dijVr1mDlypX4448/kJqaitatWyM39+4hld26dcOhQ4ewceNGbNy4EYcOHUKPHj0KOlwioidKo7l7uPXDbs/zrz/3Yp0hIjKdOXWGteYO1hkiItOxzpiuQKeOpqam4rXXXsPXX3+NSZMmGeJCCMyePRtjxoxBhw4dAACLFy+Gj48Pli9fjoEDByIpKQnR0dH49ttv0bhxYwDA0qVLUbJkSWzZsgXNmjXDiRMnsHHjRuzZswehoaEAgK+//hphYWE4efIkgoODTR6ry/VTcLntYPj37+fVfU4+aPOvFBNQrxnu9etKsVt/7lPmulonS7GF232UuU3ryeePt6qn7sej6kPWpKGnMveWon3CC6Hq89rdbOU+J/kpn3VYim3PqK/MvZwg9ygoV1runQAAdcrKq2VSpvrcetV53ycuq8+trxWYKMX+tDJ9rrnLq3JfsHFzzipzb1y6KsXeGqHuZ7DlmPy+xcepe/eE1ZLPzQ/xjFPm6jRyr6HD19V9jWJj4qVYfv2DVGo1rqGMe3s7yMEL/ylz/xd6QIp9ekj+rAHA2dNyX7teoep196qftxS7nVlTmVvhu2FG/07JtFyPNp46ap5nqc4AgOvZg3C5pw/XT4fkfooAsHCE3CdDaNQ9PpxfayjFji/brn58XaIUi/pavSJ17yz3puzTSd2/I0fINejVvvWUudduyMto3VHd48rbWe7jkl+Ptuo2cs+y7281UeaeuST/clrTR913s0moXItvpql7CGm18nL3HlX3Dm1aS+7jslEn1zV1dQdeH95Air317q58smU93pLvDwDLtsiv+fl/1T15WraV+4/WLSXXNQCw1crPZNdZ9fr/qP3YPPzl7TkAlFD0W7195Igy98vO8md7+Aa5tgLAyX1y75uxndXf4S4Ey33t8tIjlbnFx7c2/H9qtroXT0HwlB7zPGt1RpeZAhvd3e3OTVv190q/7PNSTJOj3l5le8p93qyT5D7QAGCTK38vvKGVP3sA4GQl73y4CPX3futcuXZ46tTb7bQ8ef/FTrENAgAN5Odsn6vuXWqfkSjFbjiot2OZkPczXHPVfY01VnLtyNOqe7TlQo7fzlN8jwbggkQplibkbVMxV3VtL2Yj9+w8mxmozFX1MStuf12ZeyNH3kbn15vMXiv3UnPKS1TmqqRpXZRxVQ+8tGz1fqSTTh6Di059BJZWI69P1pnqPn5+NvL+4WWbssrc2zly/fHQqdcnVe/DTDu59ykAeJ2PNfy/TZrp+3QPwzpjugId0fbmm2+iVatWhsKid/bsWcTHx6Np06aGmK2tLSIiIrB7924AwIEDB5CdnW2U4+/vj8qVKxtyYmJi4OrqaihKAFCvXj24uroacu6XmZmJ5ORkoxsR0dNCX5hMvT3vnsY6A7DWENHTy9w687zXGtYZIiLzsM6Yzuwj2lauXImDBw8iNjZW+lt8/J2jYnx8jGfUfXx8cP78eUOOjY0N3N3dpRz9/ePj4+HtLc+Ie3t7G3LuN2XKFIwfP97cp0NE9FhoNQJaE/sUmJpXVD2tdQZgrSGip5c5dUaf/7xinSEiMh/rjOnMOqLt4sWLeOedd7B06VLY2akPswcAzX1Tl0IIKXa/+3NU+Q9azqhRo5CUlGS4XbyouAwwEdETwl9/TPM01xmAtYaInl480sA0rDNERAXDOmM6s45oO3DgAK5du4ZatWoZYrm5udi1axc+//xznDx5EsCdX3D8/O72Dbh27ZrhVyFfX19kZWXh1q1bRr8CXbt2DeHh4Yacq1flXiDXr1+Xfl3Ss7W1ha2tfP61NuEqtOl3i2jDGupz1c/lyv1IztxQ9zF7JW+LFHPq3F2Z63RD7hESUVvdU6Fe/CopFh8Yrsz977bcays3T70mR5aTe3gVT1L3BIrJjJRiLjbq3gcnbapLscqOl5W5ae6OUuxCmvq9dNDJ71EZZ/Uvf+uOyD1RAnzVM+fpOTZSLDND7iFh66Du8dYob6MUW2FXRZlbKUzuS1TT54Iyt4aP/L6dKa1eR46ckXOreKnXac9U+fEaOlxSZAJz5DY06DxQ3ecnLChRir0fdVKZqw2rIMXObtuhzHUM7yzFThxSr09uxeS+DDkauQ8gAGTnyb0adBnq0zBOdZtm9O/U1BRg6rfKXCocT3OdAfKvNTmXLiLH/m68Y2t1z4xj2fK24eh59Y5eP7FCilWa8q4yV3NZPg3plfavKnMbnftMisVVbaXM/TuxtBTLUbdbQcewm1Ks9PU9ytyNKW2kmIejuifiQdsXpNgLgeptWVIJZyl2/JZcJwDA2VbedlYupu55Gf2bmxQLKqfunZOSJdea9FR1XyCVTlr5u8DXUNcER1f5+b5UTl1rGpaV68eRWoHK3D/2ybXxhQD1++OTKG//O3qcUuYuhLydbtAuVJEJtAmX35//jTyozNUq+q3unq/uZ1i583AppurFBgB2TvJ7nK1V9/rJzJW/UlunyX2QACD5o9WG/09LTQbWBSjzqHA8q3VGm5sD7T39zOyg7ruUZS2vtxla9fbK6ZY8iZfqr+6t6Xhb7suV6aJermei3P/xtqO6x2KGtbyPkC3k7SgAuAm5d5vjbXU/t2RHuX9cnkbdHy3FQR6bS45c0wAg20rxHUCrHq8G8j6JbT594m5pvKSYlaIvGADkaOTH0+bJj5XfPlyxVLmHWIKNuyITcLCW+5jphLoeFM87J8WSdPLzAoAMkf8k9/1scuTXTKdRP7erufK+h6+9eltsp5GXezFd7skNAPbW8uNZXTqtzM2pIm/TVb3YzKVazxxy1e9FQkBtw/+npCoatlOhM+uItkaNGuHIkSM4dOiQ4Va7dm289tprOHToEMqUKQNfX19s3rzZcJ+srCzs3LnTUHRq1aoFnU5nlBMXF4ejR48acsLCwpCUlIR9++5eYGDv3r1ISkoy5BARPUv4649pWGeIiAqGRxqYhnWGiKhgWGdMZ9YRbc7OzqhcubJRzNHREZ6enob40KFDMXnyZAQFBSEoKAiTJ0+Gg4MDunXrBgBwdXVFv3798O6778LT0xMeHh547733UKVKFUMz0pCQEDRv3hyvv/46vvrqKwDAgAED0Lp1a7Ov0ENE9DTQQn2V3Pxyn1esM0RE/9feeUdHVa5dfE+f1EkjDQIJvYReAyog0hSwoxeMckVQARHBq2K5IFdBUQFFxXIVVFCwcRULvShSBUIn0pNACoT0MvV8f/AlITzPwJnIhBCe31qzFnnYM3Pqu8/7znn3qRqe+EyZ/npEfEYQBKFqiM+ox+OHIVyOZ555BiUlJRgzZgxycnLQtWtXrFy5EgEBFdMbZs+eDb1ej6FDh6KkpAR9+vTBggULoNNV3M67aNEijB8/vvxpPkOGDMG77757pRdXEAShWtBAYacQuNMK7hGfEQRBoHjiM2V6gUd8RhAEgSI+ox6Noii1cu3z8/NhsVhw+tcFCPS7ID/Azf2LWiuTc2Dn5zwjn87ZtzVqy0o/PNGH1Ia0Pc1q9RoH/30MAdazpKZ18cE5OT402yWs4ASrvf9NmvfSvT+fQ/ZIV5rLsq+4Kav9YWURqd07kJ+r3jX3J1I7HkUzegAgsziI1FqY/mK1n+2OJ7WfvthIap/OpplEAPDlJpr1cHeCm/wVO82s+O8Sug0AILIezXJ6sdUKVtt/Bs1PWPlMFqvNC6f7YlV6G1Z75CSThdNyDasdOI2u2/CxfJ7bg5YfSe34q7NZbdEbP5BaHSOfu/HFJppReHc3XhviotvHsn4xqy05VTkLsMBqQ6OZi5CXl4fAQJoLp4aytmjR2nPw9Vf3GcWF+Rh+c8jf+l6heijbvxnfvo3AC/MddXwOjKaI5mQoVpp/AgB2JttH2/lGVvvin9RrHr2VzyLUa+j57u5CKLiY+pXGyXtVlj/NOo3O5bOvBr5M2/8WXflcoJfvpblpm87R9hwA/jtvD6k98WQrVtsnawGp/dX4dlZ7Ii+E1Lr67Wa1r/zamNR2rUsitU9m0+0FAK99QXPMxg/nMyjPldJso6kvbmOUQEwL6m2f9l3Lam95O5bUVo/ls2jyGrQntS+T27HaPbtoe/zeLb+z2n4zaWbSzfcksNrn69FcuzX9X2G1gbtpzlu0mffR6V/SrN7xNE4UABDhopmi2gWzWO2ZAxW5WIV2BxKWb6x2nwHEa64Vyvbv8Y2/INC/4pzXOXjv0DBdO5eW9yS9lV6bFgfymZDpxlhSi3DweZnc93HLBQBaF/Ukd32aUiPtp5js/PX1MS29a5DL2QL49Sh0k1mW76TnSqiG5tcBQGAR9a9CJg8O4PPjTEw2GQCc0dMcsfRiurzx5kPs+9NBr6OD9LmslsuUS1fqslpuFzey89cBO1ydSK2jhj4FGACsJrrfz2ppvwwAHAq9j6hhURKrXe/oSWqNg/l9GVl6gtT8DtB+JABkdqDXEnoXP65wTlOH1IIV2s8HAIOTHr/+ufzDUnRFueX/zi8qQeQdj4vPVDNX/I42QRAEgaLVeDB19DrOMxAEQRCqhic+U6YXBEEQBLWIz6hHBtoEQRCqAY1GgUajcuqoSp0gCIIglOGJz5TpBUEQBEEt4jPquZ7z6QRBEKqN6nhCz/vvv4+4uDiYzWZ07NgRv//OT8kCgO+//x59+/ZFnTp1EBgYiISEBKxYUXnK8oIFC6DRaMirtJSfriIIgiBcPbz5NLgZM2agc+fOCAgIQHh4OO644w4kJ1eOEBkxYgTxi27dul3htRQEQRCuFtX11NHa0Kep9Xe0FQfHQOdfkbGR+8q/WV10rw6kZs/JY7XGUDoHfqeJzxDr1JTOx8530MwPAAgy0DydVcn1+c9tSJchyMjn8ZyzBZHazyl0bj4AfDd+OanZTTRTCAB8D9J8mi6xNL8OAHq2o9kuzsJQVsvlsS3+nebjAMBNdLfBt4ifW/9Q232k1iKuB6l9ucnFvr9HWzoivzONz1loFE7zIm68KYLVtqtHc960mXyGRPve7UgtN/wkqw3MTSE1u4PPaHuiUxKpGU7TfCgAuPPhB0mtVT0m4xDAYSPdvs2f4o/Tw3ra0J0s5nNCTqfRzziWF8ZqnS8MJTX/vu1YrTm88mfYSvj9UBW0UKBVGQiqVnchS5YswYQJE/D++++jR48e+PDDDzFw4EAcOHAA9evTduS3335D3759MX36dAQFBWH+/PkYPHgwtm7divbtKzKXAgMDSWfKbObzFa9nSqKaQX9Bds7mDiNZXaeJXUitKJPPegyoS9vI7ebbWO0t9FRDtpuY0TpG+n0L/+CzTvp2om1cHZ9cVptRSn3pkz/5TLmVz/xMak4f/mJH9zvNQOnfjubeAMCgQTSjDbl7WS2Xx/bWF3wu0J2D6JWify6fTfTiQFpb0YHmWM78is9guf026ndr9vFXqu0b05182wPMwQCgV2vqS5rj/Da3hNNjLzeW5okCgCWTZgCVWnmveetuei2gTaZeBQBdB9xKaj34OFzsNQ8itVu+4c+rgwbqV4dy67HakweS6Hel01whAEjrQ69dWj3UnNWGNKq4HjBYaT5VVfHEZ8r0atmwYQPGjh2Lzp07w+Fw4IUXXkC/fv1w4MAB+PlVtH0DBgzA/Pnzy/82Go2qv0O4NC6dEU5dxfb0PZbE60KZ6003mWeKnu6fYlMQq/XT0DbEqqe5vQCfR3VOz18z+zKfa3S6yS7VmUitSMvnPjUtov0UjZXfDjo7/T6tL59HGmY9SmoOI83LBPg8Ni6TCwD8tHQ7+DpzWW0d0ExIkz+9Zs108llq3HeVKD6MErBraUao3s3xFKSj7a7i4O/rCTXTZSgFvy91Lrov9Dp+Geo46fWBouGXoYEll9TMGv7YKzDTfoamOZ8byuHQ8m2h1cmcgzqaSQcA9ZN+ITUl3E1ensF0wb/5bVUVvOkzZdSWPk2tH2gTBEGoCXjyq05Vfv2ZNWsWRo4ciUceeQQAMGfOHKxYsQLz5s3DjBkziH7OnDmV/p4+fTp++OEHLFu2rJIpaTQaREbygzCCIAhCzcHTuwc80S5fXvmH2Pnz5yM8PBw7duzATTdVDCSbTCbxDEEQhFqKN32mjNrSp5Gpo4IgCNXB/2caqHnh//MM8vPzK72sVv4OO5vNhh07dqBfv36V6v369cOmTZtULZ7L5UJBQQFCQirfTVNYWIgGDRqgXr16GDRoEHbt2lWFlRcEQRC8jgc+UxWvuZC8vPOzPi72jPXr1yM8PBxNmzbFqFGjkJXFP9FVEARBuAbxss/Upj6NDLQJgiBUA2VP6VH7AoCYmBhYLJbyF/crDgCcPXsWTqcTERGVp4tEREQgIyND1fK99dZbKCoqwtChFVNtmzdvjgULFuDHH3/EV199BbPZjB49euDw4cNV2wiCIAiC1/DUZzz1mjIURcHEiRNxww03ID4+vrw+cOBALFq0CGvXrsVbb72F7du34+abb1Y1cCcIgiDUfLztM7WpT1Prp46emToFJYaK1SzI4PPGdEaa6/Jmqy9Y7eiedJ6zzs7PP96XRue7t2/A53GkFtF5/MdPFLPanDya89anHT9u2qpoM6ktTad5MQCwvM4AUht0+hNWC2a+u27Vt6x03+CXSS3GdYzVljjpXOnYGH5eewoTx9bdj875BwDjj4tJrcs/niC14A5N2PfP+5zm6Ux8mJ9Df66UZjWczuTnxw8v+ZrUXNn8L8B39R1CasccDVntaWc8qe3eW8Avw7kNpKYx8XPWezSn51AzWxKr/T6F5jNlhN7HaltoTpDarmw+zyM8kh57rYPp+wEg6hGaw5TclubMAUCz1MpTY/RFfPZcVdBAgUZlTkGZLjU1FYGBFXkVJhPNJqn0vovuz1YUhdQ4vvrqK0ydOhU//PADwsMrtnm3bt0qBVn36NEDHTp0wNy5c/HOO++oWpfrhW03joWvRndZ3e7/7iC12UO+YrUvP9iA1PSlfIbkxt10P9/ckV+ev3KjSe3ATj7r8UwGzV277zZaA4AueTTj88NjfJ7Vokb3kFpiymus1qWl5/u5D+ex2tNPUd9u6qL5nABQZKf+3LwVn1Fz8Dg9dwfU4a8nsl9/htT6z5hJauGDWrPvf+XfNJPu9deZQFIA6XnUa44d4bNSnyx5n9SKUmjODwCMGfMwqe218sfesVIanLZp9QFW+0Tm/0jN5ctv80E30f3e0bae1b63+2ZSOx4zjtV20/xFautS+RzYuk1odluPejSfCQDqzKFe81ff51htqwMLy/+tLy4F5n3P6jzFE58p0wOee824ceOwZ88ebNxY+Vi9774Kf4+Pj0enTp3QoEED/Pzzz7jrrrtUL5fA47f3N/j7VlybuZx8hphWS/1gf/PhrDZUR69tXW7yrGwKPS6M4POsSvT0+tjh5LudRRrap9Fr+L5SYAm98E818tftOf70/A3LpTmRAKB10Ey5gNN8O5YVSx/w4S5TzsVcFxjdrBu3ffUO/jrUP4u2Q0p0K1LTucnmOl1Kr6+jzHzGtYu5L8eh8NcXliKaj2Yo5bOZ/UKZnDgN36/itk2Bg88HjMk/TWpOJosQAIK0NFPO15rLajMM9JrM6sv3lYLsdFsWGvlrJ5OWHnv+brL5HLF0H+cEx7HakDMVWWQuA+/fVaG6fKY29Glq/UCbIAhCTaAqGW2BgYGVTMkdYWFh0Ol05JeerKws8ovQxSxZsgQjR47EN998g1tuueWSWq1Wi86dO8sdbYIgCDWQqmbnqPUaAHjiiSfw448/4rfffkO9evxDJMqIiopCgwYNxDMEQRBqCd72mdrUp5Gpo4IgCNWAJ3kGGo1nT+gxGo3o2LEjVq1aVam+atUqdO/e3e37vvrqK4wYMQJffvklbruNf5rlhSiKgqSkJERF8U+DFQRBEK4envqMJ16jKArGjRuH77//HmvXrkVcHH8XxYVkZ2cjNTVVPEMQBKGW4E2fAWpXn0buaBMEQagGPHkcdlUehT1x4kQkJiaiU6dOSEhIwEcffYSUlBQ89thjAIDJkyfj1KlT+PzzzwGcN6QHH3wQb7/9Nrp161b+y5GPjw8sFgsA4OWXX0a3bt3QpEkT5Ofn45133kFSUhLee+89j5dPEARB8C6e+EyZXi1jx47Fl19+iR9++AEBAQHlnmGxWODj44PCwkJMnToVd999N6KionDixAk8//zzCAsLw5133unxugiCIAg1D2/6TBm1pU9T6wfaTIE+MBsN5X+vGbWW1d279h+k1rS5hdWeKqDz3bfv5w8irY7mJ+wCP/88kJlqnp/HZ60UF9P5/X+Y+dsxSxrTPLY7b+LzATILDaT2V2OaOwIAxQ46Lz0+kmbLAECgjmaDHXE2ZbUHUmhWg4OPGMAPX+4mtZw7BrPaQQ/QrKCsUjpfPsDI5yE88wjNwlmzjz9G/jf/d1LzCaTrBQBPD8olteSEMaz2o//SOf9xTfkcM5OJbrQX+yczSkBziO73xYF8vk2MneZQWE38sdc2JpfUjpzht9k7m+qSWqvmrBTtWtCm64+0Rqz2g8/pfn9uCp+TsGD/HZX+trrJlagKVZk66gn33XcfsrOzMW3aNKSnpyM+Ph6//PILGjQ4nymRnp6OlJSUcv2HH34Ih8OBsWPHYuzYseX1hx56CAsWLAAA5ObmYvTo0cjIyIDFYkH79u3x22+/oUuXLp4vYC1H56uF7oJcm3Vv7GR193zRm9Rad+PzZY6codlVq1Znslq9gealbNAGsdrgIKrNO5vLaovyaA7ZzwGNeW1X2vY++iDvNSk0FggHO49mtXk2uh26Nl7GakuM9JxNKm7PKIEte2k7YrfzOSarv6FZp2f689448k2aF5ZUGEpqIT58ButbM9uR2uJVVAcA21dRr3GHvSfd6EfufZ3VzvgXfbJXbDzfxpp8ac7Kgif5tlPzO/Wa90NohisANGUy4Ur86HYEgN5t6LbceZy/1pq4hC5Dpx78NdxNN0WS2g9urve+XUF/QX+8De81L6+oeJqa3cZn/VWFqk7pUcO8eedzEXv16lWpPn/+fIwYMQI6nQ579+7F559/jtzcXERFRaF3795YsmQJAgL4fSF4iEF//vX/nOpG+y4AUO/wGlLz1fHXtg4NPR9KFT53iuswF2n46z8daP+Hy/oCwOY95YLPTXSZqX8Fg+8rWZn1yA2sz2odWprhZQng/Van0HUr1dM+AgCUgK9z5NmZ/o9fS1Yb5EfbJruWtsUa8J5W35hGv9/Ft1dnS+k+1mv5z+Xy2IoC+TuGcuy0P2DU8rmDToU2VvVxgtVqmLy91GA+59QIJh/NxXc6/bQ0U47LjgOA0zqa52ZW+IfC+GtpH7lQE8Rqj/jS669YpDBK4K+wipzsQjOf0V0VvOkzZdSWPk2tH2gTBEGoCXhy+7Snt1mXMWbMGIwZww/SlhlNGevXr7/s582ePRuzZ8+u0rIIgiAI1Yun03Q8nTp6KXx8fLBixQrVnycIgiBce3jTZy6kNvRpZKBNEAShGqjKU0cFQRAEQS1VfRqcIAiCIKhBfEY9MtAmCIJQDWg1519qtYIgCILgCZ74TJleEARBENQiPqOeWj/QZs0vgcFQkREzoEUqqwvR0dycmBB+BDbMl2aB7NvBBM4AuPPuhqS2aVseq3U56Xz3c1m8dvwj9PG2Kdn8kRxkosv7x2E++yCQiRKwBtD8BgCwOenhs9HAP0q3V8o3pDZ+QStW68ssRNbJ06yWQ+fmWbpm0O3Q9LNnSC24542kBgD9P21NarcN78Zqn3rhBlKb/SqfX5e4bSipdXCTrRTAlHdvPsJqv7uZZhhlGO9htesjx5PayeN8ToKPieYR/PdEPKs9c4Zu8zat+OO0c1uaw2Q28hkQK9bR86JNWz5bYvQkmlFYbOPPbYej8vc5Hfz3C8LFOItdcF4QRPHPPnxmTAMfmuO0J5oe+wAQE0LPn8M7+ZzFe0bRdmvl0n38strpuV2Sz+dE/fs/tI07lEqzbACgjh89L5dt5tctJJieg8Xh/OdaHdRrlunvZbW376ehto/8tw2r/bvo3Fw9+oFuy9Dxd5Faq5ED2ff3/bYnqXW8hc+Z++eTtH2b//ZvrPbWDXeTWhsX3xaafOl+O7HvKKv9+cZvSS3HTD0FAL5rNJ3Ukrfx108BfmGk9u9Vsaw2/cQpUku4mc8+vLk/vS7z4SOpsPjTXfRz+/F+d/dIeg4W8rFYcFxwDjqY81EQ1BBopbm9AOAMpFmGBg3NogIAg4vmRuUrfO5aqIZ+3zmFnqcAoGWy3+wumq8GAKE6+rlWLe8dWiY/K99NnpWOySdTNG46CQx5PrSvBQChecdI7YC5K6vlps0V2PhcL5uDLptfIN+I6F10f4YnbyA1ezifSfenby9SCzLyuaH1fTNILaWYZsQBQJI//VyLnmabAYCe2ZdWJ9/nbJu9nNSKQmgOGgCkhncmNYeLH/LQaegxkmLgM8RdDur5PjqaWw0Avlq6LXUaPvstx0H75O4+N8qHOVfAnyt6V4W36DXiM1eDWj/QJgiCUBOQqaOCIAiCN5EpPYIgCII3EZ9Rjwy0CYIgVAPV8TAEQRAE4fqlukKqBUEQhOsT8Rn1yECbIAhCNSB3tAmCIAjeRO40EARBELyJ+Ix6av1AW0zvdgg0V8yF12ZsY3WOYDrXfNtuPs8gpDvNkanbsA6r7R2xl9TmbchhtVGNY0jNaefnc8//lmbAtGzNz9G+ITqb1BZ/wGf3cNxybwJbb9OMZi1sTeK3WUJnmnPQpjs/Bz4wkG7f4xFBl1jCyoRY3GTVFaaRWup+Wgvuzb9//LM0qyjEj2ZbAMCLk7dfahErf18dmoXx88I/VL+/fkuaNwMAJ7o+SGqFTl9Wy0UNxTfmcyxmvfI7qbW+gebXAcCbN1Htaj3NqAKAjHP0eDqaymekJXSleWwmA9+QfziHnvO2Un6/te/djq1fCeSOttpNm1FtEGCqyBYJOULzRAAAdWNJadWvKaw08h/RpBYSHc5q/9EkidS+PVvAL4MHTJ/2J6m178Vnnt3RnOa4rPs+U/V37e3UnK3fcIM/qa10s81uu5vm0XDeCgAh4RZSS95+iNVqtLQ9jIziPTfs3GFS27+JZvbFj+bzih6eQHPXIoP5fJXpU/g8Ng4ud23P73tUv98daXc9T2r5NrrPAEDDeE1CAp/x9Okcum5+lgBW+8PDB0ltiYY/nk6k0W2ZcZo/V/rd3Y7UfEz8NcKX89Tvi8CwCzzMdeWyQOVOg1pOSATgV3Eem0v4/oTLQDPAilxMCDOAINDrIXd5TgFFtD0/aeDbV5POztY5zrqorxnAv9/PRbNAs+20LQcAnZb2oQIMfAZYgCaf1PKd/OcG6Wmoo5+Bz1LjBhn0zHIBgJ7J8OJqAGCy0X6gUsJnoXFEmumxY9Lw63CkkO5jrZu2g9vvOVa+3d5zknpSt8a5rDYnogWp2XR8uKZGoW2qRcd/7oniuqTGZZsDQKztL1I7q+OPfxeov5c6+eX119F9qWWy4wDgrI3mueVb+cy/cN8KX3Mq/PVGVRCfUU+tH2gTBEGoCWgAqI3gvY4f0CMIgiBUEU98pkwvCIIgCGoRn1GPDLQJgiBUA3JHmyAIguBN5E4DQRAEwZuIz6hHBtoEQRCqAcloEwRBELyJZOcIgiAI3kR8Rj21fqDNUDcGBt+KOdF/RdzI6iLnP0NqUd0Gs9o12+n886dvPcVq9XaafTDuWX4ZTqbTA/HPTSdZ7T2D6RztphY+s6bOui9IbeWd6m/63NWpA1uf9wXNSZh7/zFWa/prN6n9K4UuFwAs7r6A1BQ3GSbcdqgXcIbVrsmmuTe3PXw7qa2P/if7/g0baPZB40Z87gBHw7ZN2Pq4e+m6jf+TPzWbdmpGau80+YzVLj83gdTMBj7rYdmvNHejeSs+dzC8Ac2Ncrn4RvSjTHoOPV4yk9Vmdr6L1ByN+SyNt74LIrXEQfw263dvZ1L76YuNrLYwr3I+hc1ayuqqgtzRVrsJbB6HQJ+KnIxDDWnbAgCaJ+hxXv/Ou1nt4u+ySG3eJP4cNlhptsr9j9E2DwAOH6F5VDtW72K1j0+iGZ0dIlNZre/810nth47qc0GS7nyXrU95gWZf/TqFPzeVLTTf8t/Js1jtV61WqF62x57qSmpNwnJZ7ZLUgaR2/zsbSO2nRpPZ9//wWTKpteoUe+kFVMGb/2lEak9M4vNazf40z/N/Pb5htd+k0OMswIdvwz57h+7LNjfymX8cLoX/3JcO3UFrpU+x2tzBj5GaXctn3Ix9mV5XPfM09WEAaNuzLant3kCvfQCgIDu3/N8Ou/pcpcshdxrUblxmX7jMFedmXiCfD1XnJM2mNbjJecxXgkitbgnNmXRHfRPf/ykEvT4ucvC5lqG6s6RmdPJ5YUGZNEezg5HvK3HkhvC5xul2em3b2EpztgFAb6PnbNOsX1ntydibSa1EcZOphdOkVqrh8y7P+NA80rr1ae5aRmhL9v15TI5mmJHP2c4uoNfiMSF8uxVsoH3DIgefLdulEdU2z9vMat2tB0cOk9nnq+evGQxMXp6i8JMdU420H9c0i2ZRA0BBSCypaRT++i1LU4/U/DT89g0w0Lq75XVckMvmUDyZ7HlpxGfUU+sH2gRBEGoCGqj/Ved6zjMQBEEQqoYnPlOmFwRBEAS1iM+oRwbaBEEQqgG5o00QBEHwJnKngSAIguBNxGfU49F9hPPmzUObNm0QGBiIwMBAJCQk4NdfK26VVRQFU6dORXR0NHx8fNCrVy/s37+/0mdYrVY88cQTCAsLg5+fH4YMGYK0tLRKmpycHCQmJsJiscBisSAxMRG5ublVX0tBEISrTFmmgdrX9Yr4jCAIQtXw1GfEa8RrBEEQPEF8Rj0e3dFWr149vPbaa2jcuDEA4LPPPsPtt9+OXbt2oVWrVpg5cyZmzZqFBQsWoGnTpnjllVfQt29fJCcnIyDg/Fz9CRMmYNmyZVi8eDFCQ0MxadIkDBo0CDt27IBOd34u8bBhw5CWlobly5cDAEaPHo3ExEQsW7bM4xW0paXAZq6YC78ziJ8nbrnjU1JLCKA5NgBwJtqP1FanNme1K36h+R5pyfx87m/foHPK/Xxp5gcAtAqk+QmRu39htahD19mZRTO5AKA0g+abdTq2iNWOe3AYqdlAtw0AbGvyBKn1DOXzXnrG0GW4tUUxq617fDldBiWK1TYEzctLa0/zklYupxl8ADC0L7351WLgt2P67TTPJ7Y+n8mwYg/NaAuL4Y+9d7oxmUIKf0z31q8jtQc/pDkAAHBjv6aktn83zYcCgA8fpxkQJ/VhrHZPGt1mRY17str1qTT7YMXyNEYJpBzYQ2rbW/B5VHVojB/enNme1eZZzZX+LirU4ysaO1UltFCgVWk2anW1kWvRZwAg7+BRuIwVOSZrwpkDD0DIszSra1A4n0WTeo5mxny5N5LV/rCAyx2keVgA8N1Mms0yN6Abq00I/4vUQld/zmo1sbR9KTrGZ4fmHqdtZ/ekN1jtKzMmkZoVtA0AgPUdXyO1wTHvsdp7W9Csk5G96TYHgLq75pCaU8Nn/bTQU7861f9xUlv8znH2/U+OaUBqYT75rDblKM1da96aP0Y+X84fZxw/37+FFl28f9zr+xOpDXg1kNV27kszX/90kw+47BXqH4cM1KsA4Lf9ZlJzNrmT1X69l37G/+bz12UcK/gIIdSLoefVPdO7sNrsworrgeKifGyllzJVwhOfKdNfr1yLXqMtLYZWV7HPSrU0SxEA0uN6kFqAk167AYBdR69Ns/347LdcOz2vHTa+KxmnPUpqBj3vi2Z7Ian5FvF5y3a/YFLTWfk+gq6UtvEWPX9daQ+m28Hl4HOCz4TQnMYQA22DAMDfRXPIAjT8vvArputsYjK53JEbHEdq5+xBrDbEmEu/y8lvxxbh2aRm1PJ9pRIXzeEzMTloANCklGZYOg18XymkiO63vwx8vmeokW7fc7Ygfhm0NBPVruH3ZYGWHntcFhsAFBro9xU4+VzvnBJ6Hut9+W1m1NAcvWhjOqt1airOTYOO71dWBfEZ9Xg00DZ4cOVg81dffRXz5s3Dli1b0LJlS8yZMwcvvPAC7rrr/ODFZ599hoiICHz55Zd49NFHkZeXh08++QRffPEFbrnlFgDAwoULERMTg9WrV6N///44ePAgli9fji1btqBr1/ODFR9//DESEhKQnJyMZs34EFpBEISajAYeTB29jk1JfEYQBKFqeOIzZfrrFfEaQRAEzxGfUU+VH0HhdDqxePFiFBUVISEhAcePH0dGRgb69etXrjGZTOjZsyc2bdoEANixYwfsdnslTXR0NOLj48s1mzdvhsViKTckAOjWrRssFku5hsNqtSI/P7/SSxAEoaYgt1l7Tk3zGUC8RhCEmotM6akaNc1rxGcEQaipiM+ox+OBtr1798Lf3x8mkwmPPfYYli5dipYtWyIjIwMAEBERUUkfERFR/n8ZGRkwGo0IDg6+pCY8nE6FCw8PL9dwzJgxozz/wGKxICaGv+1ZEAThaiCmpJ6a6jOAeI0gCDUX6QB5Rk31GvEZQRBqKuIz6vH4qaPNmjVDUlIScnNz8d133+Ghhx7Chg0VmTMaTeVMDUVRSO1iLtZw+st9zuTJkzFx4sTyv/Pz8xETEwNHfhHstop55N1i+Ln5dUpojsymIpqzBQA3adaS2uclfVltz1vqk9rh2FBWuzKD5pu1jeXny0ekbCe1vG07We2fQz8htZsDvma1zuOptJbFz/3ebaBzzX8vbMdqufwgy8zBjBIwKXTe/7TP+PnyhTk0h+LzYfsZJXCqDs3l+vM0vXh5sO9Z9v3NDy4mtV+jxrBak4nmrkUE8/Pt+zY6QWo/LDjFavdG30pqzX55mV+Gdp1JbfEwmrMAAIruAKmlJPLHv5H5ZbXpCn4Z4pll2OSiuXgA0DSCZnR8fOAYq/3mzSBS+5bGFgIAbA5ae/oZPhOow82VjxFbaSn/oVXAE7O5nk0JqLk+A7j3muIzBdAaKiz11tZ8JyqqkOaB/HyuF6u9Q/8Dqc3IG8hqb7k3gdT2bDvJahceorleN3Xg81YiDqwitUPfc3lwwKF/rye1O+rw+WgZSd+SWvExfnl/09GcnB+yW7Ha7ato1lbo9EdYrZ+L5nY+NInPlAM6ksrqCbz2RIObSW3VQZr99vwYPqen+e/TSW1Jw2ms1uRL/ap+tI7VDu9Kc/ES6e4FAGxv/E9Sa/DuQ6w2fAA9Z9Y8bmS1ip7mNh29lT+mjWe2kVrdBQ+y2gkDepPaKgfvzx2b0P3+P1bJ5xm+/QffRlit1Pdfep6uAwAER1bkmtpt1PuqiqedGvGamuk17nwGdhtgrzi/LVY+S1fvpDlOub58fnFQ0WlSy/KlWV8AEGSg139cJhcA5GtoHptZ4fs05pJzpGbI49ftdCy97g8p4L1DV0zz0bQ2PquyWKF9MKuZX7ezpTRTG3z8FjSg7UKOg8+qc/rQPklDxyFWazPQXK9CbRCpRWn4Ppwll/b3zgTRrGQAcDnpfTlGDW1HASDARffxWSefZ5rvT4/JiGP89YU1jPanm7t2sFqXnV4zmHzcZJQ66HkYlpbESv1CaE7p2YBYVmty0e8rtNdhtd3yaM7pMZ8bWa2GGbpJtfLjCmZ9xXVdoZO/LqgK4jPq8XigzWg0lgeHdurUCdu3b8fbb7+NZ599FsD5X2+ioipOnKysrPJfhCIjI2Gz2ZCTk1PpF6CsrCx07969XJOZSS8Gz5w5Q35ZuhCTyQSTiQ9QFARBuNp48jjs6/lR2EDN9RlAvEYQhJqLJz5Tpr+eqaleIz4jCEJNRXxGPVXOaCtDURRYrVbExcUhMjISq1ZV/Dxqs9mwYcOGcsPp2LEjDAZDJU16ejr27dtXrklISEBeXh62bav4FXDr1q3Iy8sr1wiCIFxryG3WVUd8RhAE4fLIlJ6/h3iNIAjCpRGfUY9Hd7Q9//zzGDhwIGJiYlBQUIDFixdj/fr1WL58OTQaDSZMmIDp06ejSZMmaNKkCaZPnw5fX18MGzYMAGCxWDBy5EhMmjQJoaGhCAkJwdNPP43WrVuXP7GnRYsWGDBgAEaNGoUPP/wQwPlHYQ8aNEieziMIwjWLTB1Vh/iMIAhC1ZApPeoRrxEEQfAc8Rn1eDTQlpmZicTERKSnp8NisaBNmzZYvnw5+vY9n0/2zDPPoKSkBGPGjEFOTg66du2KlStXIiCgYuL67NmzodfrMXToUJSUlKBPnz5YsGABdLqKucOLFi3C+PHjy5/kM2TIELz77rtVWsFtr/0OX03FZ7c6wefmmLu1JrWe7fin/BjO0jyD25rT3BEACCqkWmc9PruE45yJ5roAwNl67Ujt9Ia3We3NnebSYjA/T1yjpTc5Fp+g8/gBwNCA1sbr32e1YwbRjIDC+f9ltedGv05qUx7is7KOFNA8g/QA/rD+8o9IUmsSS3V2hX+/4ksDGML8+Dn/t3ely1vk4KcBhOSdYKo0XwAAWp1ZTWqaDl1YbWa9TqR22sZndOTb6LL1PPIFq9W4aOhZyc23s9pzPjQ3wOjgs+p0WlrXG/h94cPkefwjdiur1TLLq3viJlZ7Z93K2YcFhYX4iFUK3uJa9BkAOPrDyUpeY97Yj9XV+SfNLbyzP58LqU05Tmoje8az2tBcmmfobONm6hGT2ZLlx2fyZDfvSWppa/jtNOj2ybQYVZdfBC3NRcnYyfuoqRX1pVd9Z7LaghYnSO30hOdZreGD70jt8zl86PiuM9TwUuvwma8zF9J2q10HqrM6+XZeGxhIavXD+Dycx4fS78qz8to6Zw4yVfpdANAp5StS09zah9VmNqeZdEdLmQsEAOeK6PXPbX++xWoVG12PkJGjWG1OIL1W8uECOgEYGK9xh18Bnfb3r85FrFbL5GLp9b1Y7Zj4LeX/LigsQtznqhdJuEJci15jS94Pm7miXbdY+cwzl4XPbOIwF9AstCAjzSYEAJOV9oscej5DmYPLFQOAIj/6wAjTAf6aLtw/mNScBjfLoKXtI5fb5o5653az9QbFuaSmcfLtSkaDbqQWa+PaYuCMD80hcyi8j2dqqLeaQNsgu5Z/v4vJPnU3IBKmoceIFXx+ndFBj8n8Ut7rAl20T14U3YLVWg1Mhp6eP55coHlkYfl85rOiodcXeVH8Mri09HP14PNtuc/VamheHwA4fGnmX7id73srGroMThM/QTHUUeFfBcqVywIV1OPR1NFPPvkEJ06cgNVqRVZWFlavXl1uSMD5wM+pU6ciPT0dpaWl2LBhA+LjK3cKzGYz5s6di+zsbBQXF2PZsmXkaTohISFYuHBh+SOtFy5ciKCgoKqvpSAIwlWmOm6zfv/99xEXFwez2YyOHTvi999pMPyFbNiwAR07doTZbEbDhg3xwQcfEM13332Hli1bwmQyoWXLlli6dGmVlk0t4jOCIAhVozqm9HjqMzUV8RpBEATPqa6po7WhT/O3M9oEQRCEy+NtU1qyZAkmTJiAF154Abt27cKNN96IgQMHIiWFfzLi8ePHceutt+LGG2/Erl278Pzzz2P8+PH47ruKu3w2b96M++67D4mJidi9ezcSExMxdOhQbN3K/9IsCIIgXD283QHy1GcEQRCE2kV1DLTVlj6NDLQJgiBUCy5oVL7APA7+csyaNQsjR47EI488ghYtWmDOnDmIiYnBvHnzWP0HH3yA+vXrY86cOWjRogUeeeQRPPzww3jzzTfLNXPmzEHfvn0xefJkNG/eHJMnT0afPn0wZ86cKm4DQRAEwXuo95mqeI2nPiMIgiDUNrzrM0Dt6dN4lNF2LRLWOQj++orV1D5H878AQHuE3o7o3ETzsAAAzCO5gwv4udQnfWmejk7Dz+OPKdxPatFr32G19psGk5qlHp3j7Y61wfez9Zt70ZPh1Gdfs9qQQVRr33+S1eYNGEFqof7LWK3fuSRSW6Ph847+3Ee3ZY9m9P0AMKQrzaIxaOnc+iYpK9n3Z//4M6kVPjyM1YaaaI5F8918Jl3xEZrD9MGsl1ntSU0TUguz8jlBEVu+ITX/djRLBwD+cNDcqPz6bVjtaT3Ncmq6kckBBMClEbZrSbPjAGCrb19SmzeTz406p6EZeOFnD7Da3wz9SU3n5icGc0lOpb9tJXwWT1Xw5HHYZbr8/MrHkclkgslE8zZsNht27NiB5557rlK9X79+2LRpE/sdmzdvLs+MKaN///745JNPYLfbYTAYsHnzZjz11FNEIwNtl6fpenr+AYD/tv+R2tnFS1itpSnNuaqT/RerPWRJIDUj074BQMNs+utdwKdPs9qAB0awdQ7FRY/v/4WPY7V3PEz9Y+1weps/AEQ+RU/Y3I38dsCYF0ipUTjf9hrSN5Da186hrHbNGpprd1vv31jt6KE0s9Ksyya1FvsWsu/f+To9HrLnTmK1ET65pNZ05RRWe3p7Mqm9P4vP4jwCGtQeXXKE1VqWvkdq7fvdxWp/svYiteJWPVjtUUNLUmu4+F+slksA6t6L97tV/nQfz5vVnNVmaakHRKVtY5TAj3r6uTod38G4MBfLVsTnbFUFT3ymTA+o85qq+IxwZdFbLND7VOyX3Kb8ueNTTHM/fc7x/RSnD7021jv4XOQScxCpaRX+GDfa6bkTkr6P1RaFxtKimc8Ag0KP72xLQ1Zax0k90Jh2mNXqmMEAXWkBq82NakVqljP85waW0HyzHF8+f7vESdc53MZna1n8c0lNx2QSBxZS7wIAnzTqB0qLWFbr0NIWNiozidVyGXhdYvhs8mJNCKnpnXzGaGAO7V9a/cNYbZ4P7adbTXweqY3JeQs5Q7cNwOfaGd0sQ64PzQWP09H+HgAU+dPlNbnZ77m+NGtby5wTQOVz0Gi/NnwGqF19GrmjTRAEoRrQKIpHLwCIiYmBxWIpf82YMYP97LNnz8LpdCLioh8BIiIikJHBPwAmIyOD1TscDpw9e/aSGnefKQiCIFw9PPUZT7ymKj4jCIIg1C686TNA7erT1Po72gRBEGoCnuQUlOlSU1MReMETCLlffiq9T1P5SY6KopDa5fQX1z39TEEQBOHq4GkeTlW8RjxBEATh+qU6fAaoHX0aGWgTBEGoBs7/qqMup6Ds15/AwMBKpuSOsLAw6HQ68qtMVlYW+fWmjMjISFav1+sRGhp6SY27zxQEQRCuHp74TJkeUOc1VfEZQRAEoXbhTZ8BalefRqaOCoIgVAPefEKP0WhEx44dsWrVqkr1VatWoXv37ux7EhISiH7lypXo1KkTDAbDJTXuPlMQBEG4enjzaXBV8RlBEAShduHtp47Wpj5Nrb+jLWbuLAT4+5f/vaOgES+MoPXIhnwIZsudH5HamQA+iNPp0JHaB0tpDQAeGkID6dvH0AckAEC+TzCpxf7zHlab3OROUps+kQ8TTH/yQVK7s/FGVrvqN7p9Ot4+ktXOWEpDIWf07cJqtet/IrU31vqx2vsfu4nUdCX8fit20DBOfwMdkU+OGcC+v2mHo6SWnsNFMAM7DoWTWpeWTzFKoM6nt5Ba1mC6fwGgq4s+tGOH3k0YbocWpFbi4E/5mS//QWp9h/INz/Dup0hNo+c/19m8I6n9pqcPPQAArZM2xL7gw0C1LvoQjBMh/EMW/jpIz7dTaXwo6Ir2lZet2EEfalFVNHCpv6OtCk/omThxIhITE9GpUyckJCTgo48+QkpKCh577DEAwOTJk3Hq1Cl8/vnnAIDHHnsM7777LiZOnIhRo0Zh8+bN+OSTT/DVV1+Vf+aTTz6Jm266Ca+//jpuv/12/PDDD1i9ejU2buTbhOuZjps+quQ1a7NomDwAIPZZWurAt1mBv9JQ+/QQ+oAdAHDY6XH+wn/44N3Jz/UhtS7x/OPN8wNo8G6fBdQnAOBA58dJbe7TtM0CgGMP0Ick3DWIPnAGAN75bA+p3fL8S6z2kem0bfjmcf5hOtmfLyC1j1OoVwFArzu7kZomP4dRAgVhdDqEzkSXa2+rh9j3N7vjIKltTOcvVNf9Tn2tf+/ZrNbwHH24gHViKKvt76I+vMZwG6v1H0Db+WIb740fvEkfILGuC10uAHhuGG1/dSY+VNt4Q29S+1F7N6vV2GgtUEMDvAFAx4Sp/xVFzx8A2LGWBsgfT+azXxYOrLguKym6Oj5TpveEy/mM4F3yuw2GcoHPFOn5h6AVG+ldIz5+9LoUAAKYkHmbgQbEA4CTCcXPBg20B4BAPT2nTIYzrNahN5OatSH/QC4no82y8YH0SgidEhbNhPUDQDHzIILcCP4hKWe11CdMFv561SeHPrDsgLYtqw028dcCauEGNAp9+f1uCKHbwabwU/msLtruOiPasdp6O+iDoJz1+T6CgXnwQbGRP6ZLwugxrVPowx8A4HQpXecgI39MhzlpG+0y0GMMAGw+dNm4By8AgAJ67Cka/v4mLfMQi2JTEKu1Ksy5wuwfADjnW7f834XOv3dsXYi3fQaoPX2aWj/QJgiCUBOoSkabJ9x3333Izs7GtGnTkJ6ejvj4ePzyyy9o0OD8kyvT09ORkpJSro+Li8Mvv/yCp556Cu+99x6io6Pxzjvv4O67Kzqn3bt3x+LFi/Hiiy/ipZdeQqNGjbBkyRJ07drV4+UTBEEQvEtVs3PUcjmfEQRBEGo33vYZoPb0aWSgTRAEoRrQKB7c0ebBL0UXMmbMGIwZM4b9vwULFpBaz549sXPnzkt+5j333IN77uHvlhUEQRBqDp74TJneUy7lM4IgCELtpjp8BqgdfRoZaBMEQagGvH1HmyAIgnB9Ux13GgiCIAjXL+Iz6qn1A21al6PS3Ofvfs5ldUEhdO52ozg+J6uVmWqjztAMGQBYfnYQqTVs7G5kt4iW3IwCpyn1SS0k7xdWe87qz9Y5imnECHzq16VFAI/eQINOSrX8HPiMlLOkZiji821K8ug88n7383lhJaV0+ziDA1jtruM0561/83P0M100pwEANAE0H6BOIM3dAYB+9Q6RWpozhv/c974ntZMn+fn2N2VtJ7XW7fgnuDz7DZ3KMeZ+/nM5bunIHAwAVibTzx2cwP864FdKt29XzZ+s9rvTNAMpwETzoQDg1y10PRLa8tkHwQE0J6FpN3477EqufDyVFlftVxiO80/pUTnQplIn1Bx0Tjt0zoo28YPZfOZZSFQdUmvVibbnANA6gLbdMWl8lsSCI3eRWvPOjVkth+Lg27IjTvoZbbL4nJ2sYvVeU1REv69OfCyrnf6PeqRWqOVzFguyj5GaPieT1eafoh7UMqEV/7kFNEtGqc9nyazbTs/fh3rR/J4CJ7+9TKFBpBZdh2/fhsfT/J8jdj5fxnf/FlJbsZNva/qkria1G/vz10S3/5su28wZfL4Sx7Dbec/+/DfqbY/dzf+6rSmi+7ifhs8HnLufXk+E+vPn4ILF2aR22618rl04E4XUsXUsq93wR275v22lVzCjzQOfKdML1w56hxV6R0VOWj746z+DlmYL2vR8/lYAaEabX1EWq00PpNm/BvA5WWxGlZbPqHZqaXdUy+QjAnxGr415PwCUMnlWThOf+RwJ2pY6NW7yhxW6Hhonvx001hJSCzLyeW56Zlu622bFCl2PYBfvzRwuAz0etOCvA+raqa9awW/HrPaDSa1Uw/cNo3NojqzOjwnRBHBcT3NvI3V8BqZRR7ejv4bPKMvW04w1l4X3W7ONfoalhL++OGOmnmLV8duhANQD/XVuMqoVuo/caUtdFcd/qcKfT1VBfEY9tX6gTRAEoSZQHVNHBUEQhOuX6prSIwiCIFyfiM+oRwbaBEEQqgGZOioIgiB4E5nSIwiCIHgT8Rn1yECbIAhCNSB3tAmCIAjeRO40EARBELyJ+Ix6av1A2257a/jZKzIM3ridZlwBwK+5PUht3hu/sdrif44jtUf1q1itiYmC6tucn1P++/FoUmtVn+YhAECbU8tILXvTDlbbLWgRqSWOe4TVZp5lMgYaNme1uUz2Wz3lMKt9eAR9dK728Hes1q9xHKklxPMn6cLvaPbbmgY0Fw8A8vLpvPbVf9HctBIrP/LezI/OoW9gofktAGBeNIfUtPe/wWoXrqZz9qd0+InVlmyjWRrOjnzeWN36QaTmUmhWBACsfuIoqd05k74fAAzMQa25kz9G7ov+g9T8T+zitXVpRqHxDN2/AHAk7j5SK3Kz31pG5dLauQ2sVt9iYOXPLOTzKqqCZLTVbn4v7QJfXYXX/O/5dazus1M0i/Drj3ivKej7LKm9auC9xteX2vnwXnwO5v+205y4Lq07strOyf8ltZ2L+Jy4XhFvkdrWoU+z2lOpeaTm15Ffhswi2vYmIInVjnnmZlKzbV/KaqO70Xbrtj5BrPaNabQt+7bdg6w2+wzNplz4Ow3wKi7mM31eDaLL0CqS95r8yRNJTfcffn1f/4AeD18M5rNdU3+kXm4ZxOeXxsbTHFeHi8+4WZ24jdRueYGVsuh0CWz9qXh6XWVO4s/BSc1zSU1/8hSr3dXpMVLLL6bZUwCQ0Jzm5HQ8TTNYAcDQ84HyfxcXAZ+yKs+R7JzaTZ4pHE5zRXsYV7iP1Z31p1m6Z60hrNZRpwupRRf+xWq5DK9AB23vACBPT7MMnSY+o8qSm0Jq+nPprNYaTtct3Mx7nRO0HeKyyQDAqTWQmo+V+hQA+PvQfDKDm6xFly/N0bOAX95TdtqWanz4fGcuJ65AR3M0rQq/vn46msNncfA+E3D6AKmVxNJrGQA466JeF2s7yGp1JXSbKf5M2CUAs45mt7k0fH5ds2KaBX3ATPuhAKDR0DbQYODzzAxaml1tLuH3ZRhSSU3noFmvAGDzp1m43P4FADNoPy6gmM/mO+dbcTw5NHzudlUQn1FPrR9oEwRBqAlo4IIGKu9oU6kTBEEQhDI88ZkyvSAIgiCoRXxGPTLQJgiCUB0oANT+qnP9/vgjCIIgVBVPfKZMLwiCIAhqEZ9RDX9vvyAIgiAIgiAIgiAIgiAIHlHr72hLKPoVgajIBMiMasfq6rjo3O+b7+GzQG5vS+ddJ5XewGrzC+kw7pLN/Pxzk4neWukK5edo50W3pFrH/1itM+U4qQ3utZfVZsYx+QDHaHYWALR20jnwPslbWW2fcJotVpR8hNWeS3yJ1JYv57Nsju6iORL2W/ntq2eO9rUrjpHazf0bsu9XmEyGuov/zWq1TI6Zw8Wfbq/U/4LUzoTcyGpDevUjtcOIZbUWC82b+OMQrQFAi3p0v4fX57dj/Tia87FrJ5/rcDq9M/2uxjQPEQCGOH4ltUNRfVntzl8LSG3CIJo3AQB2he6L/G8Ws9rgR3tW+tvgKGZ1VUEehlC76ZezCIG2igyr9CY9WV1dG923jdo3ZbWP30qzPzYW3sZqz52j+RuzvuLbHLMPzZJyxfE5LsVNaQ6Lo/QzVpu/l+awPDpiP6s92YlmcSq7+Yyb7q71pKb9YyWrvTv2EKkd/Y33O/2b80nti7nUq9xh5WNcoDdQ3173/RZSc3eNoQmgmT6+Lw1ntTpfut+sDn6/f96e5rVm17uT1cY8TFfuT4XPjA0Np9c5P/7O/47boXUjts7BnRcbf93Nao8faUVqnbr0YbUjHd+S2u4mw1jtb7OTSe2df/Hb1wozqe156T1WG/HJXeX/LtJcHZ8p0wvXDmEFxxCoVFyLFvuGsTo96PnrZ+AzekMdmaSW7x/Fam3M9VS2PoLVKgrNMnRp+HPH6UPPHYOL9rUAwFCcS2qBfvw1KJe7pnXy/Qkuj81o5T0p1En7jPpiXptTN57Uzin8fiu20+UNYvwEALTMdLw8B80zDdDz7YtLR/elJZP6JwAoBqq1aek+A4BGBTtJrciP708gpD4plRip/wH8kytLweeGOoxMhp6O3+8GDa0Xuej7AcBqpPmARhN/roSW0tzPQjfbocRFt2WkksZqHVq6L/yO8BntJa0sFe9z8X35qiA+o55aP9AmCIJQE/DkcdjX86OwBUEQhKrhic+U6QVBEARBLeIz6pGBNkEQhOrAk1+AruNffwRBEIQq4uGdBuI1giAIgkeIz6hGBtoEQRCqA0Xx4GEI1++vP4IgCEIV8cRnyvSCIAiCoBbxGdXU+oE2bXEBtBfMv/a18XPoA4x0vn2qm2yy+XaaR+Dnx2/KvDyamzOwB80tAIBVW2n9til8pta//t2W1LoF+DJKIHsnzc0x32hltfW/fpbUSpm8MQDwMdI55fk7+Swc4/2dSM0URrO+AODXA3TOfu65s6w2rnVjUtuwmd9v4RF0+2Qco3Pg64XTOfgA4GSyD7YPnstqD5+i+y26gB/Rz2vYkdSCztHsOADY6EfzmXoeW8Bq19hHklpWDj3OAeCub2heRGwL/phu2IAeDynH+bCi+vWotlMUn7uRZL2J1Bq7+By//Vvo94X1OMdq17huIbUBtw5gtb+kBVX6u6Toyj0vRjLaajkFOYCzIgMnoIRvs0L8aB7isaTDrHaaneZFBgbzeSvZGbmkljgshtUu/i6D1Aa8yueijJrYldTaRvuz2gPfJpFam+F8exzw0l2kdiqAz3ypdw/NYTm4jM8DbfzqzaQWHFeH1b6zjtazTyWxWo4fvuHzbKJi3eTRXETTWN7fFWcQqe2ZQDMsASDpIG0LG53lPbttB9oWhqbxmWe/BCaS2q27XmO1X9nGktrpE/zx3+/nUFIzmvl8wJat6XbkclkBoGkLui/7Nj/Nav8oGkJqbV17WO250/R8C8/k86CWWGne3T8m3c9q5+yruB4pLeazg6qCZOfUbjQuJzQuZ/nfJhvN2wQAJ5OF5gSf9cVlrOnBH5Mu5hl6gaDZZgCQDwup7TfQ610AiDDTc8oP+1itLofm8epC+Wzl4HT+MzgMetpuGnNpfh0AFEXQvofLxOeFndPRdszmJkcz0ET7jMVO/nMNWtr2F9ioh4YaaNYrADiZjLaz0bRvCQBFGnp9oFWcjBIo9qNtsdlN1l22H71GiciluZgAkBcYTGp2hffQv7Q0F8/E5BYCgI+WZhdyWXcA4KenfWc/F79uBT60X2Up5o+nFF09ulw2vk9zNoDm2xbH8futUFtxDhZprk5/pkx/vVLrB9oEQRBqAhpFgUblrzpqdYIgCIJQhic+U6YXBEEQBLWIz6hHBtoEQRCqA8WlPqfgOv71RxAEQaginvhMmV4QBEEQ1CI+oxqP7iOcMWMGOnfujICAAISHh+OOO+5AcnLlWzwVRcHUqVMRHR0NHx8f9OrVC/v376+ksVqteOKJJxAWFgY/Pz8MGTIEaWmVp/Dl5OQgMTERFosFFosFiYmJyM3NrdpaCoIgXGXKbrVW+7peEZ8RBEGoGp76jHiNeI0gCIIniM+ox6M72jZs2ICxY8eic+fOcDgceOGFF9CvXz8cOHAAfn5+AICZM2di1qxZWLBgAZo2bYpXXnkFffv2RXJyMgICzs95njBhApYtW4bFixcjNDQUkyZNwqBBg7Bjxw7odOczBIYNG4a0tDQsX74cADB69GgkJiZi2bJlHq2gK/UEXD4V+R+GyKas7lguzZxp096P1bZvTOfQL/ofPwd+zFA6lmnU8jlZd9xA5+H/9gMrxcoNNJehzz3/YLVHH32J1DrsWcdqM0/RXBWNls+USxpM80ga5nzLakvnvUNqdYbz2SW5qTQbws+fz3BJ3naA1Lo8SrO+ACA6jJ7odZ+iWr2Wz6bQHviT1L4+1Y3VnjxwgtTe/w/NwQCAwBN0HUYs78VqX3/sJKm5/INY7RNhK0ltwEL+mL75ngRS2/Ebn4VT2onmDnTtFslq76nzG6k5S/l9WWykGXpbcluz2g49aU7C4QB6XgJAMhe904DmDwGAxVn59mbDFXwktSePw76eH4V9LfoMABTuPQDNBXmWxsbtWd3eUzRDpdsAPremdzeaofLmjB2s9rX/NCc1Hx2fJfXYA0Gk9txzrBRLPqe5m0NfGsdq1978b1IzrOY9ISOJydnx4X/7OzzxUVLzSfsvq/1zGF2RhDljWG32Hpof5x/MZ9XlZFBvjO9Ms1IAIK4BbeMaNr6B1Ax6/jwvWkf9+b19fAaR3UqvJxJn81r/PTTXrs98/jj9chbNDdQE0Xw1AHijwSpS6zeTz6lr0qEZqR3eyWfylJTQDCDOqwBgfGO6DI4i3u8KzdRrfj5Fc2QBoG6TE6S2J4TfZkkrC0hN2+YpVhtyQVxQieEKZud44DNl+uuVa9Fr9KUF0OsqzguHkT/GS3S0T2NW+GxmH9B2MM8VxGrDwGRMuTmELNpc+rlwc0466fI6A/n2RnOM5k775fN5jJoSum7uOv3ZMfR6vm7GUVbrn0IvLIvrtmC1Dhftauu1/DKcK6XbJ9yHz8Dz1dL8yAgf9eezuYh62mkT7x3FdnrdEqNL4T+3lC5vsrEdq63rOkVqipbPEqxfSPP2Dpo7s1p/A902JU6+7+FkhkJ89Xx/IqyYrrPdwOejczmJZ3xoBjkABCg0Jy7fEMVqrQpdj+wAPlvciIpz3qDhz/+qID6jHo8G2soMooz58+cjPDwcO3bswE033QRFUTBnzhy88MILuOuu80HHn332GSIiIvDll1/i0UcfRV5eHj755BN88cUXuOWW88G8CxcuRExMDFavXo3+/fvj4MGDWL58ObZs2YKuXc8HMX/88cdISEhAcnIymjWjF2qCIAg1Gpk6qgrxGUEQhCoiU3pUI14jCIJQBcRnVPO3fkbLyzs/ah0Scv7pkcePH0dGRgb69etXrjGZTOjZsyc2bdoEANixYwfsdnslTXR0NOLj48s1mzdvhsViKTckAOjWrRssFku55mKsVivy8/MrvQRBEGoKZeGhal/CeWqSzwDiNYIg1Fw89RnxmgpqkteIzwiCUFMRn1FPlQfaFEXBxIkTccMNNyA+/vxjdDMyMgAAERGVp8hFRESU/19GRgaMRiOCg4MvqQkPp9MOwsPDyzUXM2PGjPLsA4vFgpgY+shgQRCEq4aiVPwKdNnX9WtKF1LTfAYQrxEEoQbjkc+I15RR07xGfEYQhBqL+IxqqvzU0XHjxmHPnj3YuHEj+T+NpnKml6IopHYxF2s4/aU+Z/LkyZg4cWL53/n5+YiJiUHGxiQUGipWM6xzH/b92cwU+PwCmg8CAB2sdJ1xB81fAYCmZ2lOVnIonyHWtHQXqd0+gtf6+9Ex0u+LB7La7i3nktqet/ncHEcpXee2o/sxSuCsgc5hD7+pC6vN+XM3qZUG8rlesXXpYTmiC5+/cLhPV1KLN+xktf65aaRmDaMZEAYrzVkBgMK/jpPatIeZvAoAX+7tQGpZVjoHHwD0X3xDam++wOfFhP5OtRojnzvgak5zn9r3bsdqfcz0eBr6QCtWG+BLbwHeuZ/PtTvdlGYiRhfy2W/1XYfocoXwuQN1bgoiNXfZh12a03rKWZo3AQCNwivneRQxGRhVRlHUm811bEoXUtN8BnDvNTvmbIevpiJbpOcd97HvP3Watpu55/jjrGcpzZ3SPn8rq22T+iWpJdW7i9W2K6LZiR1v4T0sOJieK+9nx7Pa5qAZbb/96xdWy9HxKb7dO2mmbWfzf/LemLyU5pDZIvkstWb2EFJ76d4zrHZnbg9S6+FDczsBwD+T5pvZGtKMTn0Jf5fK4c20jfzyHT4H5rXltJ1OKeI94eQjH5Ha15sXs1rzl2+RWqkP327qb6DXVZZwPl/Jx48u2x3/vJHVBgfS83D9Wv5a4ER36rmxufy1QBMnzTn0r8/nK9UfQXNyfPX8fuvbgx5Ph1L4trxto4qQtqJC3ruqhCc+U6YXapzXuPMZbcphaC84D13t6rHvL3XRc1UB/10RpTQvU+Mm68uvkLaPuf78MvjY6XliMdLMNADQgl5Xnori87fqZ9Frec1h2scAAMXJ9ONiGrFavYueh45gvp+iy6P5ZjY3eXlc7nOIk25zALD4BZCany2X1RqZvorNRLPu3GXS6YtoxzcsiB/4dehpXqyi8MeTbwrNUqvXjPeD4LNHaNFNm2TzDSY1d1lqPlp6zWDQ8P0UI5Ndlu/ks1qLzXQZjA7++s1sp/tHr+PberOO8VY3TbNZQ9fZxuS2AYDZVXG+GZjju8qIz6imSne0PfHEE/jxxx+xbt061KtX0cBGRp5vkC7+hSYrK6v8F6HIyEjYbDbk5ORcUpOZSQcwzpw5Q35ZKsNkMiEwMLDSSxAEoaaggQdP6GEuOq83aqLPAOI1giDUXDzyGfEaADXTa8RnBEGoqYjPqMejgTZFUTBu3Dh8//33WLt2LeLiKv9KHBcXh8jISKxaVfErvM1mw4YNG9C9e3cAQMeOHWEwGCpp0tPTsW/fvnJNQkIC8vLysG3btnLN1q1bkZeXV64RBEG4pij7BUjty4vk5OQgMTGxfFpKYmIicnNz3ertdjueffZZtG7dGn5+foiOjsaDDz6I06cr313Sq1cvaDSaSq/77+efLuwO8RlBEIQq4qnPeMlrTpw4gZEjRyIuLg4+Pj5o1KgRpkyZAput8l0VF/uFRqPBBx984JVluhjxGkEQhCpQQ3wGqNn9GcDDqaNjx47Fl19+iR9++AEBAQHlv/JYLBb4+PhAo9FgwoQJmD59Opo0aYImTZpg+vTp8PX1xbBhw8q1I0eOxKRJkxAaGoqQkBA8/fTTaN26dfkTe1q0aIEBAwZg1KhR+PDDDwGcfxT2oEGD5Ok8giBck5T9sqNW602GDRuGtLS08qeujR49GomJiVi2bBmrLy4uxs6dO/HSSy+hbdu2yMnJwYQJEzBkyBD8+Wfl6XOjRo3CtGnTyv/28fHxaNnEZwRBEKqGJz5TpvcGhw4dgsvlwocffojGjRtj3759GDVqFIqKivDmm29W0s6fPx8DBgwo/9tisXhlmS5GvEYQBMFzaorPADW7PwN4ONA2b948AOdH+S5k/vz5GDFiBADgmWeeQUlJCcaMGYOcnBx07doVK1euREBAxbzz2bNnQ6/XY+jQoSgpKUGfPn2wYMEC6HQV+TaLFi3C+PHjy5/kM2TIELz77rser2Dduwcg0Ldiw/ApWQAXPWLz09EigJTANqTWKXsNq11ScjupDc35idXa/Gi+xz9a09wqAPhiFzXnrDP8iPFi4+uk9v2re1ntvro0/+e1TXw2zIv755Haoc6PsNqAbnSuutaWwyiB/hE0V8U3102eQSC97d70/SestuAczSOwDBpMl4vJLQAARwDdDgdsDVntEwGfkdr35/7Jajv1TSC1n7L5vJgBLWmGkeEUk3EAwLXyf6TWJL4vq83IpBkFvTvwnxtQTPfF3F8MrNZooss7oTl/7B0wdSO1vcdpXgUA3B9Msw91pXy2XoovPQfb1+NzmHKtlfMtXMrfejBzZTx5HPb/6y5+0pjJZILJxGcxqOXgwYNYvnw5tmzZUv4UtI8//hgJCQlITk5mL/wtFkulX+wBYO7cuejSpQtSUlJQv3798rqvr2/5lJuqcC36DADcNPdeBPpU7Bs+4RMIDKTnit3Om/eRMJoL1vM0n685JyeR1J40LGG11iCaO/XsgBOs9tVl9Ukt9aSdUQJLB9IMsDUP89k5O5vR9vDZxYWs9v31r5HawdunMUog7G7qK9YS3j8earKN1Hwzaf4PAESE1iW13FdeZrUpp6iHtHr6QVJTcrPZ9/uGUK/ZVdSS1b4eNYfUPk59mtUmPtOb1L4+2ZrX9qK5a64D/L5Mf+9DUmvT63tWm3qMrvN9rflcIEt+KqnN38u3f3O/oVl1s3tRXwOAXT49Se337TSDCACeqv8jqWlP89cuBwPoMX1zq1xWm1Vc4TUu16XzvjzCE58p0+PKe82AAQMqDZ41bNgQycnJmDdvHhloCwoK+lueUVWuRa9xNIyHw6+ifdC4CXIyMblTTjddvmJTEKkFFvHnZJY/zTerU0jzi88vHD2uI0DPaQDI0FGfsbr44+/P5iNJrW3h76w230Lz485oqP8BQOOz9AmwOaGNWa0mlG4HnYvPAOPy2Ew23uusOtr2W9L2sFoOJYIur87B55hxOLR8Oxiesp3UTjboxWpDQui5XKCl2WYA4OsfRmrGYr599clOoVp/fv8UOGnfIdp5ktXqnfRcOaVxEx9ioPX6zmRWmutLj7NiF9+fji6m/S13g1Nn/Wl2tb/C91vtuopzyO4mx7pK1BCfqen9GcDDgTZFxa1/Go0GU6dOxdSpU91qzGYz5s6di7lzaUh/GSEhIVi4cKEniycIglBj8eQR12W6i580NmXKlEu2rWrYvHkzLBZLuSkBQLdu3WCxWLBp0ybVv7Dn5eVBo9EgKCioUn3RokVYuHAhIiIiMHDgQEyZMqVSp+RyiM8IgiBUDU98pkwPeMdrLiYvLw8hIfQH5XHjxuGRRx5BXFwcRo4cidGjR0OrvYI/crlBvEYQBMFzaorP1PT+DPA3njoqCIIgeIDLdf6lVgsgNTW1Ugjy372bDTgf7BweHk7q4eHhJPTZHaWlpXjuuecwbNiwSss3fPjw8lybffv2YfLkydi9ezf59UgQBEHwAp74TJke3vGaCzl69Cjmzp2Lt96q/DTb//znP+jTpw98fHywZs0aTJo0CWfPnsWLL754Rb9fEARBuELUEJ+5FvozMtAmCIJQQ/HkaWNTp07Fyy/zU9nK2L79/O3/GmZKh6IobP1i7HY77r//frhcLrz//vuV/m/UqFHl/46Pj0eTJk3QqVMn7Ny5Ex06dFCzGoIgCEI1o9Zr1PpMp04VsRGnT5/GgAEDcO+99+KRRyrHi1w4oNauXTsAwLRp02SgTRAEoZZxpX0GqPn9mVo/0OYIiaqUZ/CXuT2r++yd30jtmSk0HwcAft1Th36PYyCrTT2ZS2rJlltY7Ysd6TLc/zw/5x8466aujltm09yC86jPAigdOIrU9Nn87f43d6EnVqw/n4F3rIDOa4+08PPPtcytq6buN7FabKLb90x0O1Ir0vCNQFwRzVQ4V8SPxh9vOIDUIor5vJj0hPtIrZXCj8T/UdiP1Pya9WK1bY49T2qP2t9mtVk9+5Na8LmjrHatjq5bt/58ZpNBTxs6d7cbO1z02Klfh/9cjY0mYOWEN2e1q1fS7d7/hiBWW9+/8nnlY+dz36qEJ0/eqcITesaNG3fZJ+LExsZiz549yMzMJP935swZRES4yaX4f+x2O4YOHYrjx49j7dq1lzXMDh06wGAw4PDhw7V+oE1TtwE0fhVZa0l+fDv065d/kNpjT/PaBcvpcWAt5b3m2D6afZgUznvYp3fQvJVBL7mbqsXnpailz6dt3fwPzeJ0x4gCxmuO8+fmfffQ/JKWIfylzt6zNHetQR03Uw2YH29j/kHzHwEgbQkN4c1qRnPB8sDn1sTmUr/7PYdfh8PtafZb/Vw+ITCnxVOk1t1F2wIA+CX7XlIL6nEHq228k7Y7Lzr4DL2cfz5AaqGn+dzO70C9MaYFn/Gk19PrCXcZN1Yn1TaL5Y9/jZ160Lm4zqx2yVzqmcOG0ywnAGgeVpHbVIir5DNleg9Q6zNlnD59Gr1790ZCQgI++ohmOF5Mt27dkJ+fj8zMzMv60fWI02CG01jhMzYDn/l0xkbblrpG/royR0NzsnJ8aQ0AXMx1Wpoff+1Vt+QwqW0t4v3gRDo9J5vW46//CktpW2gMplnHAFBoowHc7g75wHCa81jg4q9x/LW0P2BW+Ny1Yj39DJuOz2XVu2iOljUshlECxgLaD7Qzx0OJOYh9P9c+ustoy2zQldTc5QOeYzzU4uT7rNm+tC9q8KF3KAFAROkOUovKP8hqi/1oP93oJhcv25/mAwY6i1mtVkO3maJRP83dX6O+rS818w+FKXDQqYtaPe91Po6K77M53aXUV4Ea4jPXQn+m1g+0CYIg1Aiq8DAETwgLC0NYGH9xfCEJCQnIy8vDtm3b0KVLFwDA1q1bkZeXh+7du7t9X5kpHT58GOvWrUNoaOhlv2v//v2w2+2IiuLDhwVBEIQrSBVDqtWi1mcA4NSpU+jduzc6duyI+fPnq8pd27VrF8xmM8nKEQRBEGoINcRnroX+jAy0CYIgVANVeRiCN2jRogUGDBiAUaNG4cMPzz8tcPTo0Rg0aFCl4NDmzZtjxowZuPPOO+FwOHDPPfdg586d+Omnn+B0OsvzD0JCQmA0GnH06FEsWrQIt956K8LCwnDgwAFMmjQJ7du3R48e/J1VgiAIwpWjqiHVV5rTp0+jV69eqF+/Pt58802cOVPxpO+yp7gtW7YMGRkZSEhIgI+PD9atW4cXXngBo0ePvuIZcYIgCMKVoab4zLXQn5GBNkEQhOrAy3e0ecKiRYswfvx49Ot3firykCFD8O6771bSJCcnIy/v/BS2tLQ0/PjjjwAqcnTKWLduHXr16gWj0Yg1a9bg7bffRmFhIWJiYnDbbbdhypQp0On4aeKCIAjCFcTLdxqoZeXKlThy5AiOHDmCevUqTw8re9qnwWDA+++/j4kTJ8LlcqFhw4aYNm0axo4d65VlEgRBEK4ANcRngJrfn6n1A20fnR4Is2/FvNtlMzayOi2z4XwMfM5Jo3p0s337fTqrtZXS+fbt2jVktUX+3HxiPpvs73L3yBvZ+nef/K76MyIiacZAfgGfKffyi5tJ7Z9P8rlEA+rvJzUuQwIA7ArdF0eie7Pa2L40s+Ksi2YBbDrC5+YMbUWXt6ORzzH7dm9jUnM4+BH9G3xWk9qJ5rexWoeTZp6F+fPHSEkWzVZylPB5Bv4HD5Ga9qY+rDY4hGaevdiKfwqLy0B/lS724+fN19enkVq+MYjVlhjo8VCg5fdbk8Z+pPafl2heHwAMH1t5H5cU0fdWGUXxYKDNe3e0Aed/tVm4cOFlFqFiGWJjYyv9zRETE4MNGzZckeW7Fvn3gUEwmiu8ZufbNE/EHf4+/HHRupU/qX06hz92Obr3p5kzAFAYQnPMAD6T5O+SMLATW9/865+qPyOmEW2nc87weStv/od6/KDEG1jtg22p15zR8NMCOK852PQeVtv0Mbq8exw0D+6nzfxdOxN70uy3Pj407wgA3llBP9du4/dlv5AvSe1Ej0cYJWB3UK+J9OXz+s4dO0Nq1nw+D0a7MYnUNA/cxWrDo+j10/x+61ity0zb6qIgmr0DAM2Nx0gtx5efOlJSQrdvno6/HmnbleYCzZnOX1P1HVoxrcVaYmA1VcITnynTe4ERI0ZgxIgRl9QMGDAAAwbQzFfBPWkBreDvX5HRlGfjr1H0WvXHgK+Wthfn7EGsVgFtF4INbvopTBi5zc5PH24QSftb+0/wXVRfH/q5LeqUstozxdRD/Y189lupQvPNXAq/vKdK6XVsmInmwQFAiI1m4zm1/Lo5tbQtyPOnbRAA+Ouof9iZmrs8OB8jPXb8bLmsNsdA15c7FgAgoIiub5EvbRvdYXKTj6ax076HsTCb1RqKqFeVWnhv1yv0eIix/sVquTw2d/vS155Pag5m/wJ8tp5Lww/ocPmAJ4r4dYv0rTgmC7X88VklaojPADW/P1PrB9oEQRBqAjVl6qggCIJQO6kpU3oEQRCE2on4jHpkoE0QBKE6cLnOv9RqBUEQBMETPPGZMr0gCIIgqEV8RjUy0CYIglAdePI47Ov41x9BEAShinjiM2V6QRAEQVCL+Ixqav1AW2GhA3ZnRW5YRCw/111voHOhZ87cx2rr1KP5K33712OUwBfztpLa53NpFhUAPDicaoGurPbv4kkWmzt2bj1Fav4WPgsgLCaS1Oa/zWcN1Z2WQGomPZ+X56Onc+vr6Pi8iBX2vqQ2+PBHpNbKxX/XOusoUos10mwagM+QWPSem6yWN4eSWrQjhdXuOULn4d9c/DGrtUfSzBl9kIXV6oKpNqdOE1bb4RzNY7P78p+7vLAnqf28lM8zbNWOHiMxkXw+Rsdouo+W7abvB4BmDfj9ybHovcrHpMNepPq9l6UGPQxBuPLkni2EwcRnllwOLlfMHYMf5PPGln1OP2Pl4k2s9qms/zHVO1Qvgyd4ksXmjm0raN6dyZf3Go6fvuC3b8N6NKvUz8S3F34m6jXR2nOsdlHxnaT2wNFXSK0N+EzTnwqmkFprH5p7AwCBgUZSW/U1v993z5xAag1tyaz2t010+/Yr+TerLWhI83cC6vGZPKZIWs+L7cBqbzr9Nak5AvnP/fzsQFL78nX+GqN972ak1qRJIKME+rSgx8NHP/O5WJ3b0UtqPr208j66aj5TpheuGZyKDk6lor9iZq6BAUAL2rHNcvDnjl5Dj4EQQy6rPWOj14rZNj4ft17OFlI7ncd7ZN06tK51Y6fB/nTdtp7kr/8CfanW4eI/2N9AM6ycCq/lttnp4hBWq/Ol7bwRNG8MABQm187k4DPLzvjSrNW62bvpZ+r4XLCzwY1IzexQ3xbl2Wn+HQD4BNBsTKOTz+wsddFtHp19nNUqTOazi9lnAOAw0TbaoeczUQNLsuh3MVlsAHDWTPv6BU4320FHcwNNGn6/c+Rp+ePJR0O3ZZ6bnM8iW8U5X1x4BZ/kLD6jmlo/0CYIglAjkDvaBEEQBG8idxoIgiAI3kR8RjUy0CYIglAdKB5kGlzHv/4IgiAIVcQTnynTC4IgCIJaxGdUIwNtgiAI1YFMHRUEQRC8iUzpEQRBELyJ+Ixqav1A2/Ae6QgIKCz/+6GlNFcMAHwC6Rzrm4e0ZbUpJ/JJLSubP4h0RjpveuiDfO5adjOafYBF2ay2JpBy4Njfen+j9k3Z+jvvHCK1Vp1plgAANG8aQGqnA/j8lM076bz2DzZ2JLXxk9qz72/gl0Nqf56KZrXLluwltR9fpTmAALDsNM23SHLxmRcrv6GZF/ubPMJqn32YHnuNMvjMmvUBt5Paf/61mdUmDKRZdy8m0OUCgK07ad5DTEPmOAdw9C+ad5Rygm+iEu6mOQd1w/lMhfffoZkVVwWZOlqreS7RDv+AirycERPUv7fNjW3Y+tE9NKvkVJr6DJUbh3Rh6wU3Me3LWtq+1WSsxXzmiye8M4PmZkY1jmG1HbrR3JljYXyO5fIfj5LaZyduIrXRk2gNAFqG5pLaigN8BtGqr+k6/Pwyn9v08UF6nbPByeej7fmd5toNwQOs9t23WpFaxNGlrPanoJGkNucZPr80qnEPUpt//wFWu/Knv0gtNp6/bjiwlebSHU6iWXcAcNsUmj/apDG/Lz54k/fXakWm9NRq6ijpCFAKyv8+Ym/M6kw62gb4G/isL5dCr50uzIG7HO7y3IpCmAwxI99XMunpcWg289d0oQE2UosO4rM1C6z0OjiAydsEgD8OU1+MCuXPj0Af+hkhPvz25TLsuP0DAAH6AlKz6vhr5mInzdHcHUBzkSNMfD/S6KIZYrk6Pscv3077VY2dfFucb6Q55sV6PgMzt5R60l9hNDsVAOqA5pT6lvLXLbl+tG92uIDPUq/rR/sekdYTrNaq0Jwzs5bPXbO76LHHvR8A/JVcUjPq6HEOAFm2MFLLK+LPlZCAivPiijb14jOqqfUDbYIgCDUCTx6HfR0/ClsQBEGoIp74TJleEARBENQiPqMaGWgTBEGoDuSONkEQBMGbyJ0GgiAIgjcRn1GNDLQJgiBUB4riQUbb9WtKgiAIQhXxxGfK9IIgCIKgFvEZ1dT6gTbj3BdhvCAn7b5Hv2V1XRrR3DWNppBRAp8cowfMiq+3s9oZ01qQ2nuL0lltu7iGpNa0Ez9f/q8/aY6ZOwYOozknv375h+r3e4uiPD7P4JHHaN7LviP8CT3/bZqJ0vuubqy2YxtfUnuwbyyphYHftqEn/yS1gIZ8xk7s03RfJjn5zIt9B+l26NWVz4uZ/CLN09Fp+Qbs8YlbSe2tmf1Ybc/TS0jthuf5TIWscHr8prroPgOA7at2ktr9j/HbTKvV0GVoT2sAMP83ui+7tuYzOgpz8kjNXQ5T+pFUtn5FcCnnX2q1wjVFxv3D4aerOMdveorPOBzQjbZlWg3fvr28mx4HO9fuYrUzXutEapOf28Zqe3VOoMug47UuJ39ecXBZc3t+36P6/dXNwxNoW7RlK59n8/NC6pnuckZ79afZYAlNaM5OXfDbJnjPalIL6XAnq231CvW73+2812z9/QSp3X13LKud+OINpObOa8ZNottm+ozhrHbwX++Q2qAJfAZRemxdUjvq5DNuz6bSvKDWnflrAY2G+srtg6NY7bTPMkmtP40prTl44jNleuGaIeTIZgT6VmRzFbTg83z9XLRPozBZbABwxhVBakUumv8FAFEGmpN1zsWfvwYfmiUVZ6DXYwCw+xRdj2B//tgM96HrllnMX6+W2uk6h/ny+WidGzlILbuY3w4BRpqf5XTx2zfYSJfX6uKv8Q/n0Wyxuv65rNZfR/uoITqaF+Zjo98PACYrret9+FwwXwP9riJDCKvNttN9GWTgl6GemfYnNG4GZY6UxpFaIx8327wwjdS6gL++tzloTlypkeZ/A0Cxw0xq/nr12YcWPX/8Zzqp1/mDH4M4W0T7P00jeW3OBcevju9OVQ3xGdXU+oE2QRCEGoE8dVQQBEHwJvI0OEEQBMGbiM+oRgbaBEEQqgN5GIIgCILgTSSkWhAEQfAm4jOq4e+5FARBEARBEARBEARBEATBI2r9HW3H1x2Fn7Yir+ThxB9Y3VF9d1KLth5jtR060IyQG7vzuSG/HaCjuLbSXFZ7/AzNAkg/fphfhpvbk5q77J5BHXNILeVEPKtN3k7zyRx2mlsA8DknigeBhxnH6Bx6AFjwKc0ICAzh58u37dmW1JrE8dkH78+keW4PPkEzetau5Ofb/+fx3qQ2YXoBq83L2sHW1dK2FZ9jVlRKa1+8S9cL4POSPvya+QAA74TuJbXs3X+x2uCGNEMiujX9LgDofdc/SS0xag2rzW5Oc+22Z8SyWkWhGRtdfPnjn8OrWWzukKeO1moKDhfDqanwmn/H0txDADhgGExqcSX0/AOAGwZQX7JY+Pyt79cUqVlMAMDeY9T63WWxhUSHk9q501ms9pHb6XH77G6afwIAJfl8pkh18ukcvu3kCAgNIrV2HWi2EQAs+ZB+bs79dF+uXJzLvv+T2UNJbeQE/noEbnJn1JJxLpat5xfQa5eVizexWm7bvPgizecEgCVxW0jt4NL9rLbBjfVJrWlfPnetYduRpPZc8+WsNvOm1qS2PJkPsHE56HYYEMRvh/fZajUjT4Or1TizMuH0MZX/HVmPv04r9KNtk9nK50P5mmm+WaCezzErcFGtovDnjk0xkZrdxftX67q5VOvku6ihoP7j9OE/N9BEP6PYwfcRjDra1wny4a+Z0wtoTlaAme8rKaDbRwP+vIv0o30KXx3fJ0krpt4c7XOW1LI0fNZ3ffMJUkt1NWC1Ngfdjkcy6TYAgOgQeuwE6nm/L1Lo9cGZUgur9WGOyWyFX7fQAuopunw+f9XsH0RqDl9aA4CzgfS8irIeZ7V5PlTrcnN/k0ZDj4egEpqHCABOhWZM/5XBX2fd1OBE+b8L9Hx/tUqIz6im1g+0CYIg1Agko00QBEHwJpKdIwiCIHgT8RnVyECbIAhCdSBPHRUEQRC8iTwNThAEQfAm4jOqkYE2QRCEakBRXFBU/qqjVicIgiAIZXjiM2V6QRAEQVCL+Ix6PB5o++233/DGG29gx44dSE9Px9KlS3HHHXeU/7+iKHj55Zfx0UcfIScnB127dsV7772HVq1alWusViuefvppfPXVVygpKUGfPn3w/vvvo169euWanJwcjB8/Hj/++CMAYMiQIZg7dy6CgoKqvrYAMj/8L1v/t18LUps/2spq+zU+SmqbTzdite0a0zn7DevWY5SA3UFHfBvG8/PlzT501z03pQerPVVIP3fobXwez0dnaP5Wlxv5ZUhJoXPuTx07w2rPptK55vPf4PNt3l0eRWrbV/H5W+++0ZTULBo+1+6P7jSXbvs2mmeQlnyCff/BHLp987L+YLWN2tPlGnZXKKv9z0ubSe3IST4fo3VTmkPx+usdWG20KZPUkvNp5g0AlC6l2+HAQprXBwBRN9Ccg4YmPvOiXitaX2OnWXcAcNNno0gts/MnrLZza7odAlP2sFqgMan4B/MZEE3bV86Js5XmY9sKNx/rKYoHvwBdx3kGwLXvMwDw29DX2fq0HrGk9usUPgfmwe4nSe2ng7zX9OxG81LiW/JZj1Yag4nAsGBW6+NnJrXH/8V/7l9n6HH7xFM0RxMAZr5M286EgZ1YbfLuFFJzlxPH8cWcumz96Tm0lnniFKt9dTLNRQnT8vlIvwTSvJT1/1Of27n9FHeNwGe0cVmpT06+gdXOmf47qe3ZxXt2Qnea//Pyq3wWbSNfmhO3LYvmoAHAuddeIrWSNOaABHDoqyOkpjcbWG3jXs+R2pLiIay261M3k1rq4F9Ybd9+9JrId/8iVgvw24cjODKs/N92Gz3HqownPlOmv065Jn1Gqz3/+n/MKQdY2eHm9LqwoYvPQvR35ZJaiY7PfPLX0Jwnl57PR3MotJ/C5aC5I9hEr0sBwMXkvAXpclltppVmeIWa+ayqUie9XnWXpRZjySe1uho+LzMDtD13lxMXpU8nNaOdvz6wGGl2db6D7rezxX7s+0Mt9Dq4qIhfrmAzzYnr2YD2MQBg3fE4UovwpRnkAOCvo/3IWF8+zy2ghB4PucZIVqstpZm19mO07w4A+gjaF9VF8ce0IYj2zc6a+T59g+NrSe14bF9W66ely6tz8Ps92If6ZZgf36fXwsn++28jPqMaj586WlRUhLZt2+Ldd99l/3/mzJmYNWsW3n33XWzfvh2RkZHo27cvCgoqGrYJEyZg6dKlWLx4MTZu3IjCwkIMGjQIzgvCmIcNG4akpCQsX74cy5cvR1JSEhITE6uwioIgCDWAskwDta/rGPEZQRCEKuCpz1zHXiM+IwiCUAXEZ1Tj8R1tAwcOxMCBA9n/UxQFc+bMwQsvvIC77roLAPDZZ58hIiICX375JR599FHk5eXhk08+wRdffIFbbrkFALBw4ULExMRg9erV6N+/Pw4ePIjly5djy5Yt6Nr1/C+EH3/8MRISEpCcnIxmzZpVdX0FQRCuCorLBcWlcuqoSl1tRXxGEATBczzxmTL99Yr4jCAIgueIz6jH4zvaLsXx48eRkZGBfv36lddMJhN69uyJTZvOPw59x44dsNvtlTTR0dGIj48v12zevBkWi6XclACgW7dusFgs5ZqLsVqtyM/Pr/QSBEGoMZQ9Dlvty4vk5OQgMTERFosFFosFiYmJyM3NveR7RowYAY1GU+nVrVu3Shqr1YonnngCYWFh8PPzw5AhQ5CWlnZFl/1q+kzZOorXCIJQI/HUZ7zoNbGxscQznnuu8hTflJQUDB48GH5+fggLC8P48eNhs/FTiasT8RlBEAQ31CCfqen9mSv6MISMjPM5XBEXzXeOiIjAyZMnyzVGoxHBwcFEU/b+jIwMhIfTfJDw8PByzcXMmDEDL7/8Mqn7RfrCX1+xmhFdWxENALzfh+axKVZ+jnbAZ9NJrcH9b7Pa1HN0bny7SDoHHwDCSun8fv9efGaNv5leiNT3oTk2ALDlNJ0vv2Z9Lqvl6NiEnyc+uDX9jLf/x+eQzR5Hl/eZD/ksgDpRdB554/ZNWG1GEc0YMPrz+WYdO4aQWnYO/a7ePfn8oQg/eqHz7Rt81le6lq7b7M/5vInJU7uT2pY9/Oj/6g15pPb4XXzmxWkrzR1YsIg/9tqPHEdqUZv5rDtzoInUTg15htX2Am2Uwgv5rCHfzvRY79iIz2pwMhkdRVto1h0ArHmcbp9V4SNZ7cGTlfOOSouvYKaBSwHU/qrj5Sf0DBs2DGlpaVi+fDkAYPTo0UhMTMSyZcsu+b4BAwZg/vz55X8bjZWP8wkTJmDZsmVYvHgxQkNDMWnSJAwaNAg7duyATse3p55yNX0GcO81F9Pmn+3Y+rejaJvhKubbrJJJj5Jaq2k/sdpDaTTnqWdTfj2iC2m2mOWffLsX7E/Pn+aW46z2l0PUa76dtZXVcvTuxnvCI/1o7syEV/jO+MfP08ua+yfwuWsh0XT/a90cp8dz6H4zhfE5rj1vo7mQp9NoLlCLlkHs++uHlpDadzP5zKSTGprZN/FfNIsNAMY8Q/fx8hW8J3z7Gc1FnfFSLKs9Wkzz696ZwS/D0tdoduGJX9RPo8t/5kO2fq+GZs3Vy93LavV39iS1WzrSrDsAsDvpuXlsDh/cufo+qv2u/vOs9s+kCm+zll7BQRRPfKZM70WmTZuGUaMq8lf9/SuOY6fTidtuuw116tTBxo0bkZ2djYceegiKomDu3LleXa7LUVN9Rms0QHuB7yoB9LoWAGLtyaSmaPm2LewoHfA73bQPq7WBXv/5O3JZrc5FvUNn5K+pdArVGpx8+5qrpf2MfCvfPuq09PsCtPz5VsdFc7JSFT6juq6WXtumufgMZK2GnmMmHe/5di3dvty2AQB/Lb0+LnHRLLQGgXzfg/vclkZ63ACAkxkuOG3ns097x9HrgwInzZMDgFwH9dVIzWlWW2im+z3bxmfLBka3pLVM/nPB5JxmR/BjBYFKLqn5WGm/DAAcAXR5/RT+2FOY+57M2fygTocAen2QHUKvOQCgRKkYg7C5OY6qRA3ymZren/HKU0cvDudVFIUN7L2UhtNf6nMmT56MiRMnlv+dn5+PmBh68ScIgnBV8ORXHS/++vN3prKYTCZERvLhs2qm0VxJrobPAOI1giDUYDy9e8DLpuLxKwAANGtJREFUd08HBAS49YyVK1fiwIEDSE1NRXT0+YdOvPXWWxgxYgReffVVBAYGenXZ1CA+IwiCcBE1xGeuhf7MFZ06WrbAF/9Kk5WVVf6rUGRkJGw2G3Jyci6pycykTzM5c+YM+XWpDJPJhMDAwEovQRCEmkJZpoHaFwAydcRq5X/h9YSqTmUBgPXr1yM8PBxNmzbFqFGjkJVV8eRHNdNorgRX02cA8RpBEGounvqMN70GAF5//XWEhoaiXbt2ePXVVytNC928eTPi4+PLB9kAoH///rBardixQ/1Ter2B+IwgCAJPTfGZa6E/c0UH2uLi4hAZGYlVq1aV12w2GzZs2IDu3c9Pj+vYsSMMBkMlTXp6Ovbt21euSUhIQF5eHrZt21au2bp1K/Ly8so1giAI1xQuxbMXgJiYmPLcAYvFghkzZvztxajqVJaBAwdi0aJFWLt2Ld566y1s374dN998c7lRqplGcyUQnxEEQXCDpz7jRa958sknsXjxYqxbtw7jxo3DnDlzMGbMmPL/z8jIIINNwcHBMBqNV9QzqoL4jCAIghtqiM9cC/0Zj6eOFhYW4siRI+V/Hz9+HElJSQgJCUH9+vUxYcIETJ8+HU2aNEGTJk0wffp0+Pr6YtiwYQAAi8WCkSNHYtKkSQgNDUVISAiefvpptG7duvz2vBYtWmDAgAEYNWoUPvzwfB7H6NGjMWjQII+f0GOJtsDfULGaGf0fY3XBC2nu2on7aZYIANS7/3FSS8+jWWwAsHMfzTf76yQ9KADgH93o/OkTJ/l5wL98e4TURoyKZ7URFjpinLztAKvlmPkWzZYBgIdH0++7fyCdOw4AO3NpxtoLo/jcgGN5vqS26jf+ttNZsw6SWp16dVhtXvYhUjt3OotR8sS1pnPgj+/l5+YDtG400+wFAJgxlc9C44iNp3k8YyYeZbXvz2pBamnJJ1jt3UzE2tz3/scvQ9KnpDb9jzBW27sDzcc4aItmlEDzFnT7btnLn1c3NKfHpH+Xzqw2rVk/Ulu3nP8VZcyAysdDQUEBprBKz1EUFxSVj7gu06Wmplb6Jdtk4o8hAJg6depls8O2b98OoGpTWe67777yf8fHx6NTp05o0KABfv755/KnsnGomWpzMdeaz3AUjXmVrdufGUVqJ1/5jtU2+c80UtuYTrPYAGDD6hOklpTE56I8PZRmqR3cyWeerV+6hdQefILPc6sfQc93h119Lsj0Kfwvhdz3TZjA3w2y5pSB1N6fRfO7AGDPaeoVX32+n9XOfnUjW+ehGXjs9/MxZljiwTcBu1Ur35/5m0effDHjn+Yzz+a+xV97cNz5HL1GmLWPz/FrseI/dBkW8tcC9wyh+3JbAW37AaDjDW1J7ZeVfHbU7T1pm93wnptZbVrCcFL7fg7vz69PqLguLSwowAfPsjKP8cRnyvSAeq9R6zOdOnXCU089VV5r06YNgoODcc8995Tf5QZUzYuuFNeiz2h8A6DxrfCAM/XasbqwtCRSy4rpyGodsbTucnMfRqlC/adUy0+/CtXQ62vu/QBQaKfX/RYDn9Fr1tB+VXIB36/S62h7YfPhu74R5mxSC8M5VlsCmgkXoeX7Ey4N3ZbZDj7POsNKr6UNWj6HzOWkn5uWS5crxI+/3g000X1hdfH7EkyTomOy5wAgpYR+hl7Dt0lmPc2q213QlNW2DqRtqdVJ/R4AdtppGx/fmR5jABCUQzPlcsFnHwZoaMZajk8UqzWbaP5cgYbP9fbV0HxAWxB/jVPiS4+Rcy7+eKrrqMjENtj586kq1BSfAWp+f8bjgbY///wTvXv3Lv+7LEPgoYcewoIFC/DMM8+gpKQEY8aMQU5ODrp27YqVK1ciIKDign/27NnQ6/UYOnQoSkpK0KdPHyxYsKBSuNyiRYswfvz48tv2hgwZgnfffdfTxRUEQagZXPCrjiot4NGUkXHjxuH++++/pCY2NhZ79uyp0lSWi4mKikKDBg1w+PD5geILp9Fc+CtQVlaWx7/ci88IgiBUAU98pkwP9V6j1mc4yp7qduTIEYSGhiIyMhJbt1YeZM3JyYHdbvfIi6qK+IwgCEIVqCE+cy30ZzweaOvVqxeUS4TaaTQaTJ06FVOnTnWrMZvNmDt37iWfKhQSEoKFCxd6uniCIAg1E8V1/qVW6yFhYWEIC+PvLLyQC6eydOnSBUDVprJkZ2cjNTUVUVHnf827cBrN0KFDAVRMo5k5c6ZH6yI+IwiCUAU88ZkyvQeo9RmOXbvOP8m2zDMSEhLw6quvIj09vby2cuVKmEwmdOzI3311JRGfEQRBqAI1xGeuhf7MFc1oEwRBEHgUl+LRy1tcOJVly5Yt2LJlC0aNGkWmsjRv3hxLly4FcH6KzdNPP43NmzfjxIkTWL9+PQYPHoywsDDceeedACpPo1mzZg127dqFBx54oNI0GkEQBMF7eOoz3vKazZs3Y/bs2UhKSsLx48fx9ddf49FHH8WQIUNQv359AEC/fv3QsmVLJCYmYteuXVizZg2efvppjBo1SsL/BUEQaig1xWeuhf6Mx3e0XWuEd26OwAuysYwHV7C6w9tpTlYDhQmuAqA4aJZHqxF8Ho+mHb11cflqfs7/rjj66O51y/l8j9gWVHv4JD9inBlA50D3vqsbq133Pc3jMZiMrNZqp/OUGxhTWW2Eic6NX3OEf1R5bh7dvrvWJbHa1je0JrW9G/kcmV530nVev1R9RtsbD9Kcn3c2dmWUwG8/0MwZW+nff4rXiX388cAx/ln12W8cT0zax9bnvPkIqa19ejOrva8HzVqI9aVZBAAw5yeasXEuM53V9mlFj+k+85qz2tY30EwEg5HP4/nij7qV/raW0DyGKqO4AJf37mjzBDVTWZKTk5GXdz5rUKfTYe/evfj888+Rm5uLqKgo9O7dG0uWLPF4Gk1tJf7hVggwVuSFhP7B38Gw9gfaRsY5+UyI04zXJMzmE7x0t8aS2qIP+eyrdR1pu7V+KZ/f5R9Mz59du3IYJRAcStv5hm1pPicAHNutvn3ims6Wfm680Z9m1CzezOfOZKbnklpeFs3pAQA/C827K8rj80u5dfZkfb9/nW7HiV/wOTCeeIK3cOcVapn4Lz5n7s2ZNCHz8DO7WG39B2j73zKI3z9PzqS5QOdOJ7Pa+/u0IbVbltzAav2W02s7ow+fQTN9YcXxZCtVn2N4WTzxmTK9FzCZTFiyZAlefvllWK1WNGjQAKNGjcIzz1RcV+t0Ovz8888YM2YMevToAR8fHwwbNgxvvvmmV5apVmAwAIaKa3IuXwoANLn0ejUc6p/k6qrL5y46GS8vdvHZVyU62hYX2X1YrU5Lj0Ori+97lIKeU42C+X5VgZvv43Ap9N6TQDvvBzY9/dx8TRCrVVy0r1Tq4LPFAgw0w9LdOnBas4Hui8Nucl3vqE8zrrMMdRklcLqI5sQVlPBDCNyYyik3Xa0QC902UcF8XuwJa31SK7Tx29Ggo8fTMRvNpgWAhkwE3qlCPhevgymF1KwGepwDwDmF3pFlc/LbLFCXS2qH/LrwWj3tQxkU6mkAkGmo2GaFBt4Pq0QN8Rmg5vdnav1AmyAIQk1AUZRLTlO5WOtN1ExluXAZfHx8sGIF/yPFhaiZRiMIgiB4B098pkzvDTp06IAtW+gPtxdTv359/PTTT15ZBkEQBOHKU1N8Bqj5/RkZaBMEQagOXB78AuTJL0WCIAiCAHjmM2V6QRAEQVCL+IxqJKNNEARBEARBEARBEARBEK4Atf6ONn2bjtD7XTBnPY+fbx/Wgs5LtxfR+e8A2Ano9dfwj+o+0PY/pHbqKJ87dSguiNSi4+qwWi6z7JCZzwKZPyOE1A7WjWW1u7fQnKzONzVmtb/+eorUfgvkl/dfQ+nc8Pp1+FySvXsK2TpH3RgamMsntAEbf/pT9edyWPU0+2BAN36U/rcfaM3sz+dYBEeGklr6ET7rzhOadaaZNekn+KCEgXdSbR0Lf6tvZiHNjfIJ5DMKJr+RS2rTJvHHSNt4moHUsh8rxZINfC4Dx+u30P1+zsJnNTzw1OlKfzvsfJ5cVfAkENSbD0MQvENY/94I9K3IUnG5yReM6E7b49Jc3mtcnNd88i9+AW75jJTc5UL+uf0sqVnCaTsE8Jll7nIwv5xFM0m3tKQ5jQDwDpNZ1qo7nwv09Uc0P+5HN+3pvFdovUksf6mzedVJts7RoCXNhzmweT+r9SSPjaPESHPxHhhKvRkAXqkBGW1c+1+Sz/v4LfcmkFp0JN+en8hW3w4+PvEQqc16oy2r7d6Hek23lg1Y7ez5/DUjx/djaH7P2fCWrHb4UxXLe7V8pkwvXDtYI2Jh9fcr/1vj4vNmlUAmY8pdThLzGYHnTrDSgjr0c93lThVraFts1PLX/VwOmUPLZyE1dh4gtTwf6j0AUOyg+WS++lJWm+eg7UK+hr+2jUQGqZk1bj7XSdvzQhufP8flruWV8n07Dn8zzTfzM/MZb04t3W/+Wj7HS6+l69AgOI/V6jS0TbH48tsx5Qxt+zU0tu38shno9jVo+ePfj9mOBg1/7JUw+zjYzF+THUMzUovGaUYJmLV0eUPB5z6XaOkynCvkr3Ea6+l1h83gxyiB465G5f8usl+5LFDxGfXU+oE2QRCEGoGiqA8E9XJGmyAIglAL8cRnyvSCIAiCoBbxGdXIQJsgCEI1IHe0CYIgCN5E7jQQBEEQvIn4jHpkoE0QBKEaUFwuKCoDQdXqBEEQBKEMT3ymTC8IgiAIahGfUU+tH2izbd8Cm7liLnzp4BGsLqiEZmR8Gfo0qx1ePI/UXBY+32b56nOkZvbj58tv+HEXqXlycPYf2pmtf7mDjiS3asQIAditdH7/sK40dwQAfvCNJbXvPvmd1Y7YRGujJ/GZKHt+38MvHMPyr/4gtS79O7LabSt2qP5cjhLQOfBWJ58hwVFaWMzW88+o/wxPCLTQbIr9p/mMtkXv0bq77XhfH5qJEBJBc6cA4NRheuyczGvCaj+dQ3OYNG7CGm4YzB/rHAtO9yW1/GQ+1+GN1yMr/V1UmI9+l38KtDpcCpvv6FYrXFNk/LASRcaKvBH/pyazuuYFNLvq3cjXWe2TRTTjU1snklECiz7cqmYxAQAHt9KMG0/oeEt7tv7aUnpede/qJnCF4ZmhfH7Jh8H0fN/083ZW+88JJ0jtzodjWG1hDp8xw8HlsYU3iGa1WSf5zBa1FIJmBZXaveMTV4KgcJrb5C6jbfU3m0nN3Xac8BjNzvWEg+k0VwgAfvqCes1Pbj6jfsuGqr/vlf39SS17PZ+/9p/pFcdkUWE+hlwNnynTC9cMxtxMGC/IMysO5y/mNQG0LT4d2obVRuT/RWouHZ8hVuikWVIaJpMLAIqY3DV3Wg53WWqn9fScDHLRvhYAcD2oUPDXwbk62o8rcvD9tUPFdBnq+/GfezyHtkNmA9+323I8jNS6xPHrVuyg+8jlpH4bEUT7dQDg1NJ8NBd4n8kroVqni/d2X4Od1ApK+OGGQF96POQV85mdQSb6fam5fDZZoA/NtYvw4/PnwkG3b4me3++Hs+kxEhzGL0OWlfaLjFqaKw4AQVp67aPT8udKug899lwK/2zLerq08n8XGNTnn18W8RnV1PqBNkEQhJqAorigqMw0UKsTBEEQhDI88ZkyvSAIgiCoRXxGPTLQJgiCUB14kmlwHf/6IwiCIFQRD7NzxGsEQRAEjxCfUY0MtAmCIFQHigtQOxX8Ov71RxAEQaginvhMmV4QBEEQ1CI+o5paP9BWmnUOBlPFfO+g1N28MJTm3txSP5nXHqQjsy49nQ8OAIe2HSQ1xc1jbmPjadbCiX1HWW3322hmTd1wfo72B2/SPJK2L3dntY3bxJLaL4f4OeWbNhwntR6DurDaP37aRmofvUWX60rQoxM/t97ppLlCzZvRLJxF7/HL9chTR5gqV+Ph9i8AWEtpfkJRHp8lwNG2Z1u2/teeVNWfwe23p3ryx//zS6JI7aEH6rHa6VNoRltBsfqsoW9fp3kgALD6DM2fG/R6B1Y774tTpObuvPrrQOX8OJv1ymUayFNHazdnD2ehVF9hqZH7+HZEE1Of1IbH04wcAFB+p8eBYubbN1upVc1ieky9ZrGkFhfHn5fffkwzOnv2uJHVWsJp1smC3/isrk0/byG1mBZxrDb1IPWlpZ/y2aF/l9vuaMzWf/jGQWodb6DaVV8z4aUAHn2KOx74Y6Q6CQgNYuvpR9R7DXc8vfMwn2009EVaH/csfzy9+zrdxzkF6tvRpa/x59VXx6i3jRxeh9VOeUF9TuJLF0TVOex8jltVkKfB1XIcNuCCvEa9nc/+dRrp8Rxky2S1GqYT7NLxOVlFDtrXKbDyeW5hvvS45nLFACDERK+1zBo+oy21OJzUAvz4fE+jlrbFRW5ysgrtvqTmo+N9tVBDt0N6KZ/VHWCmy2B38v21MAvdF4E6d+tGr4NdBvq5qQV8hvJxK83Jdpehx2XKBZlLWK2i0Cw1iy/NbQOAI+n0eOgUm8tqHS46ZOGmO41I5niIsR3ml0HXktRCjPwycPtSAb8v80vpunUz8Rnk+UbqKc186bUMAJxz0Ry/Qjs9FoDKx0Ohwo9TVAXxGfXU+oE2QRCEmoA8dVQQBEHwJvI0OEEQBMGbiM+oRwbaBEEQqgFFUdzezcppBUEQBMETPPGZMr0gCIIgqEV8Rj0y0CYIglAduDzINLiOf/0RBEEQqognPlOmFwRBEAS1iM+optYPtF18e+MT2wazutdvp/kne/P5TC1LDM18ygxowiiBH16hGVVPL+W1Gi0/z5tj08/bSS0igs8uue/Rm+hyrTjHaqPr0eyCuEg6Jx0Auj8WRGo+uhxW+0RvOqf8t0w6Lx4A/txNcx38A9xkQITQHInZr6rPftuxWrX0b+MuF8wTRj5F9+Uns/n1rduEZkG5g8vQ69ruBlb7158bSa3gFv7Ym/JKN1Kb+/4h1cs19gMLW793KP11pIGenmsA8MGdJ0ltzV39WG1RaeX8uOIiP3z95uWWUh2S0VbLcSpQNBVec+t39FwFgO9foLkmf2Q2Y7WDWnYitdSwdqz255dppuLtr/C5U5zX2K00KxIA0pJP0GWIo+05ANx0e1dS+/jdHay2QctYUmvVhM8Fum0mzdf01/PZRMEKzclZergVq137K83YtITS3E4ACI+mbdH8t9V7zaqv+RyymsrtI2j7/8MC2vZ7Cnc8/ZzBnysOO92+5/Jp/g8APP0SXd43/6M+m+/O5/i8oeFjaa2Fic/LW/WvDFL7xjWU1eZdEElVWpyPbSsuv4xqkOycWo7eCBgqroePBnRkZc0y15Javj+fgal30ra/xMjnmDVzHCC10/58XqbVSdtzq4Pvdp510XY3jLcvhJlphnGWjc9NNOvouuk1fJ8mykDPX52Lzxarr9D87dNmvm9X5KArYtA6Wa1OQ+spxTRDHAAcLsbHmey3s/n8Ng+ow+x3O6/NZ7KV9Vp+Bxl0dFDlUCrfhxvYgl6frztGs+MAoHU9ut91brrNpU76fdnmuqw2I8eP1CKCeb+u60/7zmesQazW10iPs0PaeFYbibP0/Q4+my/GSbPmcnxodjYAKKjwS5eWv2aqCuIz6qn1A22CIAg1AcWl3myu4wf0CIIgCFXEE58p0wuCIAiCWsRn1CMDbYIgCNWAPAxBEARB8CYSUi0IgiB4E/EZ9chAmyAIQjUgU0cFQRAEbyJTegRBEARvIj6jnlo/0GYtKIHRWjFP+u271rO613beTmr9O5eyWl2hldQunAd9Idl+MaQWWsef1Zp96O5o157PLrmxaTap+ev4jKoAG9U2q8fPEzfraR7B8i38JPgne5wgtaQS/nM7a4+R2sAAPt/GtwvNWjl9ll+GL+fRz5j9RhtWu/dUEKl9Okd9xk7XATQvaUI/fpv/Y6L6PB5LOM0Uysui+wwAvl20X/Xn3jYkltQys/ncNi57Jy44l9X2Hdqd1JrV4TP/opw0f+GLx/jP3WVIILWzBWZWOzjjXVLLCeXPlVMhrUmtKJ3mTQBA/+Atlf4uMNC8wKoid7TVbgrSiuHSVhxXv/x7L6t7fCnNCX3gbj4fTZdLMzUU8G1hRiDNeavTgM+X8Q2g2Sp16wez2jtvoNlVwYYzrDa4JJ3UNrTuwmr9TDSL5oslp1nt7Ado/bdsPpvoFuM+UnsoPJVfhrsGkNrxVH6brf12M6m9/noHVvv7Pl9S++kL9flmkQ3rkdrbY/kMsfsm8V7xd/Ekj+3ukTSj88TJQkYJ7Fi9i9RaReex2mZdaI5r54a5rDbOQTMKb3mRZvoAwG+G/qSWdpa/HH7g5EukVtDgTlZ7Ippuhzw3kaQP1Ku49sgvLMKzvMxj5E6D2o3GWgLNBZcvccV7WF1aFM3LNIHv0+ht9DrHrOGvkYpNQaTmYrLCAMCooxlV9fz59sqkof0qg8L7op6pFxh4/9IxeWzFLto+A0C4g+ZvW/W8lqPBOdq2AUBGKG3HSlx8vlmhnX5fjG8mq3UoNAPvVDHtT4T48/1TfwM9HlooNP8LAFa46PFk1PM5c3oNbVNC+Mg/nLHS5W0WxXudn57Wm4TS4wYA8pntaASvjQmkWWh6F3/shbrovgjR8/09q44uQxH4/n9k5m5SKwym4weAm3NQ4c/BIFvF8mptvCdXBfEZ9dT6gTZBEISagKJ4cEfbdfwobEEQBKFqeOIzZXpBEARBUIv4jHpkoE0QBKE6UJTzL7VaQRAEQfAET3ymTC8IgiAIahGfUY2bB+MKgiAIgiAIgiAIgiAIguAJtf6OtkNf/gXfC7IGclPeYXXPj6ZZW0UumkUFANi2npQaRPJBHPZUmg0T12wWqx22ZxypJXebzmpb7P+S1M6s5vPGgvr1JLWWLfjsq7GzaC7D56PTWK3pFM0zqBcbxWpPK41JrUny/1jtzVF0Hv2rqTT7BACeeI7WG9k2sdpMSze2fjGd+/K5Ow/3p/P4lx5szmoTBtIstPv68HP+Jz5Lc9fGPcuvb/8AmptjKuQzLzIjj5Kaz7J/s9qxz95FajmgeUsA8K+4daSmTeOzD4698ympxQ7iz6suzXNITdHRDAoAKNpHt1lAOj0eASAomGZ31I/is+ocJZW1hhKakVVVJKOtdmPNskN3QTbJmsFvsrq5s2iWlMN1G6vNXbqU1GKa7GS15/YeIbUW3b5itSPW0vP91H1LWG3LDW+Q2q73lrPa6PF0Pbrd6Mdqhz5Nz/cVz+eyWn3ycVJr0jqW1Z5Q2pJa0y0fsNr7GtHMl4f38tlvw8fSDMj40jX8MkT2YesXExYTydb/PS6A1N5ZG81qIxvSrJ+nH6vDap9+hmYI3f8Yn235YOQqUjNm89cC6Y3qklr2rERW+/rzj5BajiaI1b7X+U9S0xzkczNX3/seqXV9vgervbk7k3lkMLLalNXbSS3wEM2cBYC6dcNJbXTjJqzWWVChNRVdHZ8p0wvXDuYBI2EOrAi84q/kAT6xzB1N/8YSAcVH+Guv2K2fk1pqt2GsNiozidT02Xxmpz0iltR8fPicx2QdzWzu3pK2rwBQsGUbqSlBfE5WoS9tY+u35dtS/dpFpHa0/s2s1s9APcnfnstqc/S0vXEpNI+tu2kr+/5cHe2vpRj4Y6FRHZp3GWbks5nTSuhy9Yim1ycAYHDRvoNfMZ8B6wD1uoATSay2tC5td4Na8b4c+ucKUrOZ+VA5v1SaAWuvw/cn/BXatvr4R7BaXQndvoEOfpv53nQfqeXu4q9FtI6KfqddfOaqIHe0CYIgVANlT+lR+/ImOTk5SExMhMVigcViQWJiInJzcy/5Ho1Gw77eeKNiIKZXr17k/++//36vrosgCIJwHk99xltes379ereesX17xeAl9/8ffMAPjAuCIAhXn5riM0DN78/U+jvaBEEQagI16Y62YcOGIS0tDcuXn78zafTo0UhMTMSyZcvcvic9vfIdjr/++itGjhyJu+++u1J91KhRmDZtWvnfPj7807UEQRCEK0tNudOge/fuxDNeeuklrF69Gp06VX6C+/z58zFgQMUTgC0Wi1eWSRAEQfj71BSfAWp+f6bWDrSVPeGi+KJbNwsd9FHPAJBfTB9zXFzIT1GwltBbXfXM+wHAXkq1JcV0CiIA5JfSqYVFhW60zPcV2Oys1lRMb0UuKOQf82u30amj+W62g6mIfm5hAb39FQAU0NuZ85nlAgAr833WEn47FBeZSK1Awy9vsY1+hsPOfFcp/12FBVRbWsw/+tvKTDksLOSnjnLLUFzkZr8z6+Zu2kkBsy8cVv4YAfMZ7o4RHaPV2vipo4V2er7lM+ePu2VwN3XUxpwr3HkJADoTPVcUN9vMoVQ+ngr+X3clnpjjya863vz15+DBg1i+fDm2bNmCrl3PP7L9448/RkJCApKTk9GsWTP2fZGRlae5/fDDD+jduzcaNmxYqe7r60u0tRl3XuOOfMYTHG7a2FLufHVznHPtv7t2k9O69Rrm+4qcTtVad+0I1+7luzkvOX8tLHTjNczUmfwS3p+5dsBu5T+3hGmT3Xkjp+XW127jt01hIb00s5bwbQK3vEWF1BfdLYPb6xFm3YyM5wO817i91mI+w73XUK2mlN+XxQo9JgusvOe6uPUw8Mc052EaNz7qYM5t7voLAJzGimOv4P+Xp7p9pkzvDYxGYyUfsNvt+PHHHzFu3DhoNJXP0aCgoOvKM6pC2bGRn8+fr1cTd9f9XD/FrZZpi/Vuzh17IdNuO/nubJGOabfz+WO+gFmGIr2bvpKOtgHu9k0J87nutoMTtA/m43TjEzra6S8qptfMBVrepwqcTLvt5Ce62V10+5oN/DoUldIJzQV2fh0MCm0zncX88jr19BpLcXOMlDL+pXWzfzivszvofji/bPT77G6uA7gHAJSA3w4axpMUA++hDmY93F2LaB0Vx+nV6s+U6b3BtdCf0Si19JmraWlpiInh59YLgiB4QmpqKurVq1el9+bn58NisWD38AEIMPIDhxdTYLOj7aLlSE1NReAFeSwmkwkmE9+JVsunn36KiRMnklurg4KCMHv2bPzzn/+87GdkZmaiXr16+OyzzzBsWEXmSq9evbB//34oioKIiAgMHDgQU6ZMQUAAn4lSGxCvEQThSlDdPgN412su5LvvvsPQoUNx4sSJSu2lRqNB3bp1UVpairi4OIwcORKjR4+GVivJNhciPiMIwpWgNvnMtdCfqbV3tEVHRyM1NRUBAQHk1zPh/MkSExNDDnqhAtlG6qjN20lRFBQUFCA6mg8i9+izqjB19OIL6ylTpmDq1Kl/azkyMjIQHk7DasPDw5GRkaHqMz777DMEBATgrrsqh+oPHz4ccXFxiIyMxL59+zB58mTs3r0bq1bRYPXagniNe2pz23Alke10eWrzNrpaPlOmB7zjNRfyySefoH///uR7/vOf/6BPnz7w8fHBmjVrMGnSJJw9exYvvvjiFfvu2oD4jHtqc9twJZHtpI7aup1qo89cC/2ZWjvQptVqqzxiez0RGBhYqxoSbyDbSB21dTtdqbyYqkwd5X79ccfUqVPx8ssvX/Jzy0KouQt1RVFUX8B/+umnGD58OMzmylMERo0aVf7v+Ph4NGnSBJ06dcLOnTvRoQP/NN9rHfGay1Nb24YrjWyny1Nbt9HV8JkyPaDea9T6zIU5bGlpaVixYgW+/vpror1wQK1du3YAgGnTpslA20WIz1ye2to2XGlkO6mjNm6n2uYzQM3vz9TagTZBEISahOJS4HJ6NtDmidGPGzfusk/EiY2NxZ49e5CZmUn+78yZM4iI4B89fiG///47kpOTsWTJkstqO3ToAIPBgMOHD9fagTZBEISagic+U6YH1HuNWp+5kPnz5yM0NBRDhgy57Od369YN+fn5yMzMVOVHgiAIQvVSU3zmWujPyECbIAhCNXD+FyC1U0c9j84MCwtDWFjYZXUJCQnIy8vDtm3b0KVLFwDA1q1bkZeXh+7du1/2/Z988gk6duyItm3bXla7f/9+2O12REVFXX4FBEEQhL+FJz5TpvcEtT5T/vmKgvnz5+PBBx+EwXD5TJ9du3bBbDYjKCjIo+USBEEQqoea4jPXQn9G0kavU0wmE6ZMmXJFw25rG7KN1CHbSR1lt1qrfXmLFi1aYMCAARg1ahS2bNmCLVu2YNSoURg0aFClJ/Q0b94cS5curfTe/Px8fPPNN3jkkUfI5x49ehTTpk3Dn3/+iRMnTuCXX37Bvffei/bt26NHjx5eWx+h5iJtgzpkO10e2Ubq8NRnvOk1ALB27VocP34cI0eOJP+3bNkyfPzxx9i3bx+OHj2K//73v3jhhRcwevRo2c+CaqRtUIdsJ3XIdro8NcVnroX+TK196qggCEJNoOwpPdtv7w1/g7qbiAvtDnT+YR3y8vK8khFx7tw5jB8/Hj/++CMAYMiQIXj33Xcr3UWg0Wgwf/58jBgxorz20UcfYcKECUhPTydZD6mpqXjggQewb98+FBYWIiYmBrfddhumTJmCkJCQK74OgiAIwnmq4jOA971m2LBhOHnyJP744w/yf8uXL8fkyZNx5MgRuFwuNGzYEI888gjGjh0LvV4m3AiCINQkaqLP1PT+jAy0CYIgeJEyY9o2uJdHA21dlq33WudHEARBqD1UxWcA8RpBEARBHeIzniM/GQmCIFQDnjwO25PsA0EQBEEAPPOZMr0gCIIgqEV8Rj0y0CYIglANeJJT4O3cHEEQBKH24WkejniNIAiC4AniM+qRgTZBEIRqwOVU4NKqMxtPHpstCIIgCIBnPlOmFwRBEAS1iM+oRwbaBEEQqgG5o00QBEHwJnKngSAIguBNxGfUo73aCyBUjRkzZqBz584ICAhAeHg47rjjDiQnJ1fSKIqCqVOnIjo6Gj4+PujVqxf2799fSWO1WvHEE08gLCwMfn5+GDJkCNLS0ippcnJykJiYCIvFAovFgsTEROTm5np7Fa84M2bMgEajwYQJE8prso3Oc+rUKTzwwAMIDQ2Fr68v2rVrhx07dpT/v2ynv4+iuMpzDS77Uq7fPAOh5iA+UzXEa9wjXuNdPPIZ8RqhhiBe4zniM+4Rn/Eu4jPqkYG2a5QNGzZg7Nix2LJlC1atWgWHw4F+/fqhqKioXDNz5kzMmjUL7777LrZv347IyEj07dsXBQUF5ZoJEyZg6dKlWLx4MTZu3IjCwkIMGjQITqezXDNs2DAkJSVh+fLlWL58OZKSkpCYmFit6/t32b59Oz766CO0adOmUl220Xmj6NGjBwwGA3799VccOHAAb731VqVHI8t2+vuU/QKk9iUIVxvxGc8Rr3GPeI338dRnxGuEmoB4jWeIz7hHfMb7iM94gCLUCrKyshQAyoYNGxRFURSXy6VERkYqr732WrmmtLRUsVgsygcffKAoiqLk5uYqBoNBWbx4cbnm1KlTilarVZYvX64oiqIcOHBAAaBs2bKlXLN582YFgHLo0KHqWLW/TUFBgdKkSRNl1apVSs+ePZUnn3xSURTZRmU8++yzyg033OD2/2U7/T3y8vLOn5s3dVV23NxD1WvDTV0VAEpeXt7VXnxBKEd85tKI11wa8RrvURWfEa8RairiNe4Rn7k04jPeQ3zGc+SOtlpCXl4eACAkJAQAcPz4cWRkZKBfv37lGpPJhJ49e2LTpk0AgB07dsBut1fSREdHIz4+vlyzefNmWCwWdO3atVzTrVs3WCyWck1NZ+zYsbjttttwyy23VKrLNjrPjz/+iE6dOuHee+9FeHg42rdvj48//rj8/2U7CYIAiM9cDvGaSyNeIwiCGsRr3CM+c2nEZ4SahAy01QIURcHEiRNxww03ID4+HgCQkZEBAIiIiKikjYiIKP+/jIwMGI1GBAcHX1ITHh5OvjM8PLxcU5NZvHgxdu7ciRkzZpD/k210nmPHjmHevHlo0qQJVqxYgcceewzjx4/H559/DkC205VCbrMWrmXEZy6NeM3lEa/xPjKlR7jWEa9xj/jM5RGf8T7iM+qRp47WAsaNG4c9e/Zg48aN5P80Gk2lvxVFIbWLuVjD6dV8ztUmNTUVTz75JFauXAmz2exWdz1vIwBwuVzo1KkTpk+fDgBo37499u/fj3nz5uHBBx8s113v2+nvUhYKqlYrCDUJ8Rn3iNeoQ7zG+3jiM2V6QahJiNfwiM+oQ3zG+4jPqEfuaLvGeeKJJ/Djjz9i3bp1qFevXnk9MjISAMioelZWVvkofmRkJGw2G3Jyci6pyczMJN975swZ8mtATWPHjh3IyspCx44dodfrodfrsWHDBrzzzjvQ6/Xly389byMAiIqKQsuWLSvVWrRogZSUFAByLF0pFKfi0UsQagriM5dGvEYd4jXex1OfEa8RahLiNe4Rn1GH+Iz3EZ9Rjwy0XaMoioJx48bh+++/x9q1axEXF1fp/+Pi4hAZGYlVq1aV12w2GzZs2IDu3bsDADp27AiDwVBJk56ejn379pVrEhISkJeXh23btpVrtm7diry8vHJNTaVPnz7Yu3cvkpKSyl+dOnXC8OHDkZSUhIYNG1732wgAevToQR6j/tdff6FBgwYA5Fi6UihOwOVUVL0U5+U/TxC8jfiMOsRr1CFe43088RnxGqGmIF5zecRn1CE+433EZzzA649bELzC448/rlgsFmX9+vVKenp6+au4uLhc89prrykWi0X5/vvvlb179yr/+Mc/lKioKCU/P79c89hjjyn16tVTVq9erezcuVO5+eablbZt2yoOh6NcM2DAAKVNmzbK5s2blc2bNyutW7dWBg0aVK3re6W48Ak9iiLbSFEUZdu2bYper1deffVV5fDhw8qiRYsUX19fZeHCheUa2U5Vp+wpPas7dlA2d+ms6rW6Y4fr9gk9Qs1BfKbqiNdQxGu8R1V8RrxGqCmI11QN8RmK+Iz3EJ/xHBlou0YBwL7mz59frnG5XMqUKVOUyMhIxWQyKTfddJOyd+/eSp9TUlKijBs3TgkJCVF8fHyUQYMGKSkpKZU02dnZyvDhw5WAgAAlICBAGT58uJKTk1MNa3nludiUZBudZ9myZUp8fLxiMpmU5s2bKx999FGl/5ftVHXKjGllu/bKHx07qXqtbNf+ujUloeYgPlN1xGt4xGu8Q1V8RrxGqCmI11QN8Rke8RnvID7jORpFUa7fibOCIAheJj8/HxaLBStat4WfTqfqPUVOJ/rv3Y28vDwEBgZ6eQkFQRCEa5mq+AwgXiMIgiCoQ3zGc+Spo4IgCNWA4lKgaNT9rnE9PwpbEARBqBqe+EyZXhAEQRDUIj6jHhloEwRBqAZcTgUuqDMb13X8hB5BEAShanjiM2V6QRAEQVCL+Ix6ZKBNEAShGlCcChSVxnQ9PwpbEARBqBqe+EyZXhAEQRDUIj6jHhloEwRBqAZcTgUulZGYruv4NmtBEAShanjiM4B4jSAIguAZ4jPqkYE2QRCEakCxK1C0ktEmCIIgeAdPfAYQrxEEQRA8Q3xGPTLQJgiCUA24HApcKo3pev71RxAEQaganvgMIF4jCIIgeIb4jHpkoE0QBKEakDvaBEEQBG8idxoIgiAI3kR8Rj0y0CYIglANuJyK6l91PMk+EARBEATAM58BxGsEQRAEzxCfUY/2ai+AIAjC9YBid3n08iavvvoqunfvDl9fXwQFBalbfkXB1KlTER0dDR8fH/Tq1Qv79++vpLFarXjiiScQFhYGPz8/DBkyBGlpaV5YA0EQBOFiPPUZb3qNGp9JSUnB4MGD4efnh7CwMIwfPx42m62SZu/evejZsyd8fHxQt25dTJs2Dcp13HETBEG4mlxrPkOWvxr7MzLQJgiCUA0oDuV8roGKl+LwbifCZrPh3nvvxeOPP676PTNnzsSsWbPw7rvvYvv27YiMjETfvn1RUFBQrpkwYQKWLl2KxYsXY+PGjSgsLMSgQYPgdDq9sRqCIAjCBXjiM972msv5jNPpxG233YaioiJs3LgRixcvxnfffYdJkyaVa/Lz89G3b19ER0dj+/btmDt3Lt58803MmjXLa8stCIIguOda8hmO6uzPaBT5WUgQBMFr5Ofnw2Kx4GtdI/hqdKreU6w4MdR5FHl5eQgMDPTasi1YsAATJkxAbm7uJXWKoiA6OhoTJkzAs88+C+D8rz0RERF4/fXX8eijjyIvLw916tTBF198gfvuuw8AcPr0acTExOCXX35B//79vbYegiAI1zNV8RmgerzGnc/8+uuvGDRoEFJTUxEdHQ0AWLx4MUaMGIGsrCwEBgZi3rx5mDx5MjIzM2EymQAAr732GubOnYu0tDRoNBqvLLMgCIJQmWvRZy6muvszckebIAhCNVAMF4oVp7oXzt9mnZ+fX+lltVqvyrIfP34cGRkZ6NevX3nNZDKhZ8+e2LRpEwBgx44dsNvtlTTR0dGIj48v1wiCIAjewyOfucpes3nzZsTHx5cPsgFA//79YbVasWPHjnJNz549ywfZyjSnT5/GiRMnvL6MgiAIQmWuJZ+5mOruz8jDEARBELyI0WhEZGQkRmQc9+h9/v7+iImJqVSbMmUKpk6degWXTh0ZGRkAgIiIiEr1iIgInDx5slxjNBoRHBxMNGXvFwRBEK48VfUZ4Op5TUZGBvGU4OBgGI3Gcs/IyMhAbGxsJU3ZezIyMhAXF+fVZRQEQRDOcy36zMVUd39GBtoEQRC8iNlsxvHjx0nA8+VQFIVMi7nwV/2LmTp1Kl5++eVLfub27dvRqVMnj5bjQi5eHm4ZL0aNRhAEQag6VfUZwDOvudI+w3nDxcvD+Y679wqCIAje4Vr1GY7q6s/IQJsgCIKXMZvNMJvNXv2OcePG4f7777+k5uI7A9QSGRkJ4PyvPFFRUeX1rKys8l+FIiMjYbPZkJOTU+lXoKysLHTv3r1K3ysIgiCo41rzmcjISGzdurVSLScnB3a7vZKvXHwHQVZWFgB6R4IgCILgXa41n7mY6u7PyECbIAhCLSAsLAxhYWFe+ey4uDhERkZi1apVaN++PYDzT/rZsGEDXn/9dQBAx44dYTAYsGrVKgwdOhQAkJ6ejn379mHmzJleWS5BEASh+riSPpOQkIBXX30V6enp5R2elStXwmQyoWPHjuWa559/HjabDUajsVwTHR1d5Y6WIAiCUHOpTf0ZeRiCIAjCdUZKSgqSkpKQkpICp9OJpKQkJCUlobCwsFzTvHlzLF26FMD5W6wnTJiA6dOnY+nSpdi3bx9GjBgBX19fDBs2DABgsVgwcuRITJo0CWvWrMGuXbvwwAMPoHXr1rjllluuynoKgiAIV4fL+Uy/fv3QsmVLJCYmYteuXVizZg2efvppjBo1qvzJdMOGDYPJZMKIESOwb98+LF26FNOnT8fEiRNl6qggCMJ1Tk3vz8gdbYIgCNcZ//73v/HZZ5+V/132q866devQq1cvAEBycjLy8vLKNc888wxKSkowZswY5OTkoGvXrli5ciUCAgLKNbNnz4Zer8fQoUNRUlKCPn36YMGCBdDp1D8GXBAEQbj2uZzP6HQ6/PzzzxgzZgx69OgBHx8fDBs2DG+++Wb5eywWC1atWoWxY8eiU6dOCA4OxsSJEzFx4sRqXx9BEAShZlHT+zMapSxVVBAEQRAEQRAEQRAEQRCEKiNTRwVBEARBEARBEARBEAThCiADbYIgCIIgCIIgCIIgCIJwBZCBNkEQBEEQBEEQBEEQBEG4AshAmyAIgiAIgiAIgiAIgiBcAWSgTRAEQRAEQRAEQRAEQRCuADLQJgiCIAiCIAiCIAiCIAhXABloEwRBEARBEARBEARBEIQrgAy0CYIgCIIgCIIgCIIgCMIVQAbaBEEQBEEQBEEQBEEQBOEKIANtgiAIgiAIgiAIgiAIgnAFkIE2QRAEQRAEQRAEQRAEQbgC/B+k1Emf9jtD3gAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = spatial_detection_scores_no_category(\n",
+    "    reference=reference_data,\n",
+    "    query=query_data,\n",
+    "    plot_stuff=True,\n",
+    "    query_name=\"Xenium Data\",\n",
+    "    comparison_column=\"transcript_counts\",\n",
+    "    n_bins=50,\n",
+    "    in_place=False,\n",
+    "    non_spatial=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "squidpy",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}