Repository: cytomining/copairs (language: python)

Issue:
[bug] AP of 1.0 should have min p-value
When calculated AP is 1.0, corresponding p-value is too large.

For example, for 2 positive profiles, 16 controls, null size 1000, and AP = 1, p-value = 0.061938, which is incorrect, because the proportion of null to the right of the 1.0 should be 0, and p-value [should](https://github.com/cytomining/copairs/blob/11570eb5e03d908e344bb8f7f90940e6dac603e3/src/copairs/compute.py#L155) be `p=(num + 1) / (null_size + 1) = 1/1001 = ~0.000999`
Merge multiple filter queries and apply once



Discussion / hints:

Fix patch:
diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 2c6cf4d..7c95642 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v3
@@ -26,17 +26,8 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip build
-        python -m pip install flake8 pytest
-        python -m build
-        pip install -e .
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        python -m pip install --upgrade pip
+        pip install -e .[test]
     - name: Test with pytest
       run: |
-        python -m pip install scikit-learn
         pytest
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
new file mode 100644
index 0000000..c97fd13
--- /dev/null
+++ b/.github/workflows/ruff.yml
@@ -0,0 +1,11 @@
+name: Ruff
+on: [push, pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v1
+      - uses: astral-sh/ruff-action@v1
+        with:
+          args: "format --check"
diff --git a/README.md b/README.md
index ae7592e..dc8d731 100644
--- a/README.md
+++ b/README.md
@@ -1,119 +1,60 @@
  # copairs
 
-Find pairs and compute metrics between them.
+`copairs` is a Python package for finding groups of profiles based on metadata and calculate mean Average Precision to assess intra- vs inter-group similarities.
 
-## Installation
+## Getting started
 
-```bash
-pip install git+https://github.com/cytomining/copairs.git@v0.4.1
-```
-
-## Usage
+### System requirements
+copairs supports Python 3.8+ and should work with all modern operating systems (tested with MacOS 13.5, Ubuntu 18.04, Windows 10).
 
-### Data
+### Dependencies
+copairs depends on widely used Python packages:
+* numpy
+* pandas
+* tqdm
+* statsmodels
+* [optional] plotly
 
-Say you have a dataset with 20 samples taken in 3 plates `p1, p2, p3`,
-each plate is composed of 5 wells `w1, w2, w3, w4, w5`, and each well 
-has one or more labels (`t1, t2, t3, t4`) assigned.
+### Installation
 
-```python
-import pandas as pd
-import random
-
-random.seed(0)
-n_samples = 20
-dframe = pd.DataFrame({
-    'plate': [random.choice(['p1', 'p2', 'p3']) for _ in range(n_samples)],
-    'well': [random.choice(['w1', 'w2', 'w3', 'w4', 'w5']) for _ in range(n_samples)],
-    'label': [random.choice(['t1', 't2', 't3', 't4']) for _ in range(n_samples)]
-})
-dframe = dframe.drop_duplicates()
-dframe = dframe.sort_values(by=['plate', 'well', 'label'])
-dframe = dframe.reset_index(drop=True)
+To install copairs and dependencies, run:
+```bash
+pip install copairs
 ```
 
-|    | plate   | well   | label   |
-|---:|:--------|:-------|:--------|
-|  0 | p1      | w2     | t4      |
-|  1 | p1      | w3     | t2      |
-|  2 | p1      | w3     | t4      |
-|  3 | p1      | w4     | t1      |
-|  4 | p1      | w4     | t3      |
-|  5 | p2      | w1     | t1      |
-|  6 | p2      | w2     | t1      |
-|  7 | p2      | w3     | t1      |
-|  8 | p2      | w3     | t2      |
-|  9 | p2      | w3     | t3      |
-| 10 | p2      | w4     | t2      |
-| 11 | p2      | w5     | t1      |
-| 12 | p2      | w5     | t3      |
-| 13 | p3      | w1     | t3      |
-| 14 | p3      | w1     | t4      |
-| 15 | p3      | w4     | t2      |
-| 16 | p3      | w5     | t2      |
-| 17 | p3      | w5     | t4      |
-
-### Getting valid pairs
-
-To get pairs of samples that share the same `label` but comes from different
-`plate`s at different `well` positions: 
-
-```python
-from copairs import Matcher
-matcher = Matcher(dframe, ['plate', 'well', 'label'], seed=0)
-pairs_dict = matcher.get_all_pairs(sameby=['label'], diffby=['plate', 'well'])
+To also install dependencies for running examples, run:
+```bash
+pip install copairs[demo]
 ```
 
-`pairs_dict` is a `label_id: pairs` dictionary containing the list of valid
-pairs for every unique value of `labels`
+### Testing
 
-```
-{'t4': [(0, 17), (0, 14), (17, 2), (2, 14)],
- 't2': [(1, 16), (1, 10), (1, 15), (8, 16), (8, 15), (10, 16)],
- 't1': [(3, 11), (3, 5), (3, 6), (3, 7)],
- 't3': [(9, 4), (9, 13), (13, 4), (13, 12), (4, 12)]}
+To run tests, run:
+```bash
+pip install -e .[test]
+pytest
 ```
 
-### Getting valid pairs from a multilabel column
-
-For eficiency reasons, you may not want to have duplicated rows. You can
-group all the labels in a single row and use `MatcherMultilabel` to find the
-corresponding pairs:
+## Usage
 
-```python
-dframe_multi = dframe.groupby(['plate', 'well'])['label'].unique().reset_index()
-```
+We provide examples demonstrating how to use copairs for:
+- [grouping profiles based on their metadata](./examples/finding_pairs.ipynb)
+- [calculating mAP to assess phenotypic activity and consistnecy of perturbation using real data](./examples/mAP_demo.ipynb)
 
-|    | plate   | well   | label              |
-|---:|:--------|:-------|:-------------------|
-|  0 | p1      | w2     | ['t4']             |
-|  1 | p1      | w3     | ['t2', 't4']       |
-|  2 | p1      | w4     | ['t1', 't3']       |
-|  3 | p2      | w1     | ['t1']             |
-|  4 | p2      | w2     | ['t1']             |
-|  5 | p2      | w3     | ['t1', 't2', 't3'] |
-|  6 | p2      | w4     | ['t2']             |
-|  7 | p2      | w5     | ['t1', 't3']       |
-|  8 | p3      | w1     | ['t3', 't4']       |
-|  9 | p3      | w4     | ['t2']             |
-| 10 | p3      | w5     | ['t2', 't4']       |
 
-```python
-from copairs import MatcherMultilabel
-matcher_multi = MatcherMultilabel(dframe_multi,
-                                  columns=['plate', 'well', 'label'],
-                                  multilabel_col='label',
-                                  seed=0)
-pairs_multi = matcher_multi.get_all_pairs(sameby=['label'],
-                                          diffby=['plate', 'well'])
-```
+## Citation
+If you find this work useful for your research, please cite our [pre-print](https://doi.org/10.1101/2024.04.01.587631):
 
-`pairs_multi` is also a `label_id: pairs` dictionary with the same
-structure discussed before:
+Kalinin, A.A., Arevalo, J., Vulliard, L., Serrano, E., Tsang, H., Bornholdt, M., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2024. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, pp.2024-04. doi:10.1101/2024.04.01.587631
 
+BibTeX:
 ```
-{'t4': [(0, 10), (0, 8), (10, 1), (1, 8)],
- 't2': [(1, 10), (1, 6), (1, 9), (5, 10), (5, 9), (6, 10)],
- 't1': [(2, 7), (2, 3), (2, 4), (2, 5)],
- 't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)]}
+@article{kalinin2024versatile,
+  title={A versatile information retrieval framework for evaluating profile strength and similarity},
+  author={Kalinin, Alexandr A and Arevalo, John and Vulliard, Loan and Serrano, Erik and Tsang, Hillary and Bornholdt, Michael and Rajwa, Bartek and Carpenter, Anne E and Way, Gregory P and Singh, Shantanu},
+  journal={bioRxiv},
+  pages={2024--04},
+  year={2024},
+  doi={10.1101/2024.04.01.587631}
+}
 ```
diff --git a/examples/finding_pairs.ipynb b/examples/finding_pairs.ipynb
new file mode 100644
index 0000000..d8fa818
--- /dev/null
+++ b/examples/finding_pairs.ipynb
@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matching profiles based on metadata columns\n",
+    "\n",
+    "This example demostrates how to use `copairs` to group profiles based on their metadata properties.\n",
+    "\n",
+    "Specifically, this is used in calculation of mAP for profile strength and similarity assesement.\n",
+    "\n",
+    "Citation:\n",
+    "> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "\n",
+    "from copairs import Matcher, MatcherMultilabel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "\n",
+    "Let's assume you have a dataset with 20 samples taken in 3 plates `p1, p2, p3`,\n",
+    "each plate is composed of 5 wells `w1, w2, w3, w4, w5`, and each well \n",
+    "has one or more labels (`t1, t2, t3, t4`) assigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(0)\n",
+    "n_samples = 20\n",
+    "dframe = pd.DataFrame(\n",
+    "    {\n",
+    "        \"plate\": [random.choice([\"p1\", \"p2\", \"p3\"]) for _ in range(n_samples)],\n",
+    "        \"well\": [\n",
+    "            random.choice([\"w1\", \"w2\", \"w3\", \"w4\", \"w5\"]) for _ in range(n_samples)\n",
+    "        ],\n",
+    "        \"label\": [random.choice([\"t1\", \"t2\", \"t3\", \"t4\"]) for _ in range(n_samples)],\n",
+    "    }\n",
+    ")\n",
+    "dframe = dframe.drop_duplicates()\n",
+    "dframe = dframe.sort_values(by=[\"plate\", \"well\", \"label\"])\n",
+    "dframe = dframe.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting valid pairs\n",
+    "\n",
+    "To get pairs of samples that share the same `label` but comes from different\n",
+    "`plate`s at different `well` positions: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'t1': [(3, 11), (3, 5), (3, 6), (3, 7)],\n",
+       " 't2': [(1, 16), (1, 10), (1, 15), (8, 16), (8, 15), (10, 16)],\n",
+       " 't3': [(9, 4), (9, 13), (13, 4), (13, 12), (4, 12)],\n",
+       " 't4': [(0, 17), (0, 14), (17, 2), (2, 14)]}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matcher = Matcher(dframe, [\"plate\", \"well\", \"label\"], seed=0)\n",
+    "pairs_dict = matcher.get_all_pairs(sameby=[\"label\"], diffby=[\"plate\", \"well\"])\n",
+    "pairs_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting valid pairs from a multilabel column\n",
+    "\n",
+    "For eficiency reasons, you may not want to have duplicated rows. You can\n",
+    "group all the labels in a single row and use `MatcherMultilabel` to find the\n",
+    "corresponding pairs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>plate</th>\n",
+       "      <th>well</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>[t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>[t2, t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>p1</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t1, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>[t1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w2</td>\n",
+       "      <td>[t1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w3</td>\n",
+       "      <td>[t1, t2, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>p2</td>\n",
+       "      <td>w5</td>\n",
+       "      <td>[t1, t3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w1</td>\n",
+       "      <td>[t3, t4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w4</td>\n",
+       "      <td>[t2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>p3</td>\n",
+       "      <td>w5</td>\n",
+       "      <td>[t2, t4]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   plate well         label\n",
+       "0     p1   w2          [t4]\n",
+       "1     p1   w3      [t2, t4]\n",
+       "2     p1   w4      [t1, t3]\n",
+       "3     p2   w1          [t1]\n",
+       "4     p2   w2          [t1]\n",
+       "5     p2   w3  [t1, t2, t3]\n",
+       "6     p2   w4          [t2]\n",
+       "7     p2   w5      [t1, t3]\n",
+       "8     p3   w1      [t3, t4]\n",
+       "9     p3   w4          [t2]\n",
+       "10    p3   w5      [t2, t4]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dframe_multi = dframe.groupby([\"plate\", \"well\"])[\"label\"].unique().reset_index()\n",
+    "dframe_multi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matcher_multi = MatcherMultilabel(\n",
+    "    dframe_multi, columns=[\"plate\", \"well\", \"label\"], multilabel_col=\"label\", seed=0\n",
+    ")\n",
+    "pairs_multi = matcher_multi.get_all_pairs(sameby=[\"label\"], diffby=[\"plate\", \"well\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pairs_multi` is also a `label_id: pairs` dictionary with the same\n",
+    "structure discussed before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'t1': [(2, 7), (2, 3), (2, 4), (2, 5)],\n",
+       " 't2': [(1, 10), (1, 6), (1, 9), (5, 10), (5, 9), (6, 10)],\n",
+       " 't3': [(5, 2), (5, 8), (8, 2), (8, 7), (2, 7)],\n",
+       " 't4': [(0, 10), (0, 8), (10, 1), (1, 8)]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs_multi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "map_benchmark",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/mAP_demo.ipynb b/examples/mAP_demo.ipynb
new file mode 100644
index 0000000..23a8478
--- /dev/null
+++ b/examples/mAP_demo.ipynb
@@ -0,0 +1,3012 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from copairs import map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This example demostrates how to use `copairs` to:\n",
+    "- assess phenotypic activity of perturbations' replicates against DMSO control replicates and\n",
+    "- assess phenotypic consistncy of perturbations htat target the same gene against other perturbations.\n",
+    "\n",
+    "Citation:\n",
+    "> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# these imports are only needed for showing Figure 1 from the paper\n",
+    "import requests\n",
+    "from io import BytesIO\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from PIL import Image\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAKACAIAAABhX4dIAAEAAElEQVR4AeydBWBd1f34n3vy4q5N3YW6o8WGy4AxGMPGjMGMwXxM+CEz5o67S9G6u6WStkka9+S53f/ne26SpmlaWmQD/r19fbnv3qPfc87Xz/cYNS2oGWwJg8lk0AzxsBaPa1ZnbWtnfXtjUUpatictYTTErKa4UbNrCVNMM1gsBqPhxHUCAicgkEgkjEZjPB63mC2sHs1kCBlkcTgMhlgkkTD5DeaY1ZhsNJhPwOoEBD7OELAc2jjNZLEkjKannn/ppw/c990vfen2G79oimsWY1wzMM9N/DtxnYDACQjoENA0loXBbDa3tnf+9aFn24NdEUPE7XKPKBl6xvw5DodTS4Rs5hNM04n58nGHAGQA1N49U40mU0IzdoVCr7y9pDPhePGdlTd87mq3JWEyGyxGs2Y0xU3GE7P64z6kJ9r3X4QA0gBXu8/3+0eebGhv0gxRBGerZv3KTdd/+2tfMBkdLCjTCULwXxyRE1W9DwigCjrI4cdicaPJsn1v1bbdlSZXxpa9Bzbu2m2y2YyaZkbmNRiiRkPifVRyIssJCHwaIWAymaAB9ExDcWqz5JeW/ub+e+/++c8Kigr//chju/cecJgsxh4e69MIgBN9+pRAADIAkjeKcMt/IzempavWt3QFTj/1NLPdsWjVanRECL4GEvH/U9LrE904AYEPBwKYBygIeqAZoyke+4UL5t9y0YJZc6Z1drW0NdYqInBi0Xw4oD5RykcHAVC82IaV2KoZjaa2QODVtxZZXZ7PXbygMD/jpbffbotEDSabQTObNASH+EfXlBMln4DAJxQCIhNosc621mWLlj3x8htr1q8dMXbU8OFlhlhUsVef0G6daPb/LxDAw0HJtUocMJut23ZsW79l25DRk6aPHTK0OPfFha+t27HzjHGjheMxRCyGuFH8IA7qkf5/gdOJfp6AwJEhkEBOtjmbmluvv/6rCbO5y2T42c9+nJqREYtELZYTi+XIgDvx5uMBAeaoogAGlP4mPm8vXh4IRUPB4AO/+L+KndtD0dhr7y6LkUilUtJAt5DLn76fj0d3jrsVvV3Qcx76k19HuQ5N2wONo2T4H73S29m38t6W9970ffu/uu9tTM8Nc47bT8KFOB3VTGkZmb/85d0/+ckPho4c8/s//m3b/lqzzXr8zUfLJN3WoaCy97mV3x8IKN2l95R78KcqFYh3A/2QStTj7hbpOXpe9/ztbpfK3PdZn+byWP/01N2TrvdFvxtVpl61yiJlUXt3KT25u1/1y6v/7M3Wk6kP7FR+viixHxD6VtFbrCqhJ3uf0lX7eoDWU5/+9/BXqs7+iRRulTc4nvVW16cGSX8wo0qoJ+4GhLxXl0qkZ+z97nn3Hn8tBhNyq3gBxQzGZn9k0bodZodr/+5d/9i8HN83oydjyepNTV2RbBs6IWPcYjeLqeBgA1TV8lPmu/5DVzD11Pvf9JLQHfj0mnXbXU8rDv4VSKsLWweX+sU0MKLclQlhlImGJYROirJMXpsHMvJRDHsouPT+dXN8WFa4+292WZpwtIs2osfD5COu6z09hqzzUG+z3l6xCanu/M8EvYRyPqCF0siDy4pfA8L/aH3+77/DDmyMxTOzPBedO9drcTT6Qj/66a82r984uTiHN8czIzRDIoq9IYG3kQlJPWEy8EEEZ0baKEe5aOgDJUOmxpQJO9DVx/Wj9zVzPmyIs1QtqoyYsXseWDR8BI1MgYQxIs1NWKVa8QxJ4DaCp1Mixt4InhgSWiJuZLcEtm+bFjfEMI2YTFaz8h+kzATe5bJoKKTvpZ5QgQ4KSkGHppJwC/OpY3fJ0JtPElhYi1qE4hIGvBVNUocWj8alRla3Fk9YLTStOwsTWvWpuw69dt6ZxHgTkxllNCdMFpKxEugoPQFCQIDFwIXHJDABBfBQb5o0S73iBkBbNYBDS0AUZBfcwGiALuJGymBc9I9yClPZwB0xYyxhiJvYaRWTrghuMcVtgJHseqsTJNXr0cJxgSrNjNMvvHJk2nSvW/Zs0QHgapauJOhAlP7EDGY8OGkzlwBEmkvLo3rTFCil+6oQKUqvUfXo8C/ZNwDI+eZu3/79eyt2uyzad27/RlmaszYU/cH9v9uze1fFrt2540cyOZXHkLRQVSql6YXLfFSd7/6tnusPJNHH6wKW3ReN7wEOVnKhb2IekZ5wq4ant589Wfr+VXn1xL0FymLqm+bjca9PbL0tveOkt7z3Z/cA/u8aDF7rHY6P9QQaAEQyT8wY0fzBSMxpaWpqMpltJosVpNGzMAbIdPgjIBBLGM1meyIeN2tCDgR3mm2xeAxXPYVjeibsQVD1rPve4nQ49ibsfa5uzCBREoAOQYhgRDVf4YFk+CULf3ou9SwqhCkOvmV4+Eh7jEKY44m4IFa8YRUFoXl6XpzKJb+qXZ923AqC6oeHepoH+eFd9/66PmlUA8lJrbTOiAtjp98fDwWSk710gaYootRdERWaIUo99R6C86R60rKcpUr+q5qlt1QM0tOxGTCR+cdPdcOt7hnPjZAamiEIgY7wXl/gqhgprbvRpOAR31KIfCCkkJiYoGqyxA0mM5QEodHe20vgI6VIHipJAEq2IkKfgK80hspUWdxSt6JAwhDE4HaBptkIPgb8UK5uvaMqzayGVpooxcolRUlB3TBWz/p/kZ6PNIxUq5cu6WqoPnnquGsvOevCM0654jML5k4cm+hsXfru20ILIT6qTIGHZBOaaDYkqFh6SRFS2cHrsAcHX/0P74TpN9JaACrUXT50jWFWIwxHwBhIP6UzCjiHdqqn5YyTmamoPt1d/3j2VzWYpvVMCr1TMtqql93d7O7WwH3t6fN/5y8N7W6rDtCPQ5veq+cgFLNmqqqq+/z1X7rsc9c9/MgTY0YPP2nKxKjIlsfRAWbdU6+8/e+nnrdAQuAmDfFgOPKLXz/47OuLFL9HOyjt4JrtLlrhax1uAjr1tBuGh7VcVrugZ/mAX9VsVxl4IXMCVCBol4XN7DYZzCs2bvq/B/+g2ewPP/nstp0VJrM9znJHFDDBXMMWmzoDwYgGU6yYaLCZUBS1sKQU+VCRojX6lJPGU7LIOLqYo7hKOq7jX/lWjeOPZEW6NpoimvHFN9796b2/efGNd+rrG80WJAOIpeDnnrlCp1jF8M005OBHeGvZ7YQwYaWvlEe3JI/RlOCX0DajJW40x+VG9VpV3tNyHlE9MOFbcYlgcumMvFeDQNstMOVCT2moQJDx1qsX3l50JJIXKh6MhBIyoAIMfaQE3FKUSWQUUiFfmczBcOixRx+LR2PU0ptO4VsFHkYMJGw0+8NRiIHZZlm+etXjTz1FOXql9A3uAYp4EA7gOuQtoYJHmhHSEKhgN5+BgLLg5HmP/+sv37/tK7ZEJBLu9BgS3//ql578x5/PWnBqQpONxDIFpStyK10QyQoiTB1SzcHu6Ul6fvfV1fCm76W/OkqCvokHvD887+FP+mTU2yTfOupXDdcpmuqWQEymn8ww1dU+eQ+5FdFQJpWel0kohEQH9VEbcEghH/0PfS7RF2keDaONzAsEShGJZehl9HUGh98ytke+jrFf/ZL1+3mk4lmUaiHRCtAQPKJYqno4ryNl+rg8t1lMU0cNnTB8iDmRsBu1C06f84dffm9QTrqmHbenUHlVw5//9WhbV5fgD5Ohtb3j2VfeWL5hW29XIzCMfS5+hNjuoyMgNYLBaBQuVH9CQlYmaRh3LrVWFX7iPhEHF/M8GIsxFwQvSxLwTzd/JKrDRKLN76+oqQtphrEnTU7JzIrEDWarLRhBhQEqNvqj4b/8458dvgCJQXyMYSwWE3xokUJ0goWaif1GlKywkdyFEwkpnA+/0FMlUDSRGIwlP2lSOBajDHUJ/oXePPP8y2MmnnTpZy/NKiogb8xkDMbjglZhuVGQoPkxW4XfFqRt0wzWQAzEZE0YrHpdwvpREkogGRERZNCBB0SjpRpJlYDCaAiyNQrKDW+rAMJD1C+UAFSEz5WlIukE8ZE4FpflJM+lG/wP80R+GyLSO2avk088bl21aduf/vOoyW4HaorMSR9BnSAZBRPGyBiHBiTiXZFIfUc7wpeMsqKbooERimCkcOiE0WLyRWIP/u3vrT4fI5tTWDR6wni6BOioNyRNhfTQcQQ1ov7YIgZzQJpp1evtBuphf0QqYpKo1mmjhpTxIY0If8ikhviYkoLxJQXyREM6RBmmoSyk9ZSMPGI2IcXIS0XReSSohf6JGzXfCgHQVmrgiQi46uIxl2SRYuWm709SIhnxUApRYCZZv0svjYcko1g9JYm5erNbiH1EW+gEQywa++5LzXypmsxUzh8q6WkL6TVIt82KyUTAAuZkxHuySnVUof9kBrFKZSnJU5SnUQoxmy3IaKTQm8Sb3rxkpF8856F+6U+419Nw07edvRk/4I2aH0hsQIDVJjJdXImToH/G0gyU0FjSyUSCwDgqPA6zr/vSO9v7zQ0XsRNoKje9vSC1/kR/KIKtesJDbvQs3SX2/OEhl55AT6xWhbzWX3TTrIRmEa6vu5ye3JKmNy/3PAd0vROsN9lHfUMb9GbkZKb963c/YebbWCFawmJ1MPkShqgFVCBL/uA0OHqTSJqRnZ2dn79izbqzT55N4bv37hs2arTV4QLxwiQ+/cLL6zdtyc3NvfqKy7JSk/ZXVj30+JMtbW1DBg++8sorQEhPPvX09h3lVovl/PPOGTZ06IoVK+fNn2e3WLaUl2/esuW8889/4rVXC3Ny33z+pYmTTjrl7NMfeerpXTt2pHi8F1166dhBxfEISNwEDt5TVf3wf/5tt5o1b6rV6WKA91VWO4c5AtHoU489s3371qRk95VXXrV2/cZnX3ix0+f/7MUXpHm9zzz1VG19g81uv+aaz2fn5Lz8ymtJyd7lq1Z2dXR+7pLLRo0cAdJYv2HD0888C7W49prPjxw+7JXly159440kl/uKyy4bXFi0dU8Fbzs7OseNHn/FxZ+xAk+Tednqdfuqa95+d6nb6UxNc0UioaVLlrocrq/fcsvqNWteeulloDp7xpwFZ5xSU9e4as3aUDi0YdOm7Jzs8ePHr1j0TkdX6LLLr5g2frhFLA1xzehu9Pv/9fTje3fvGZxfdM3ll2empizfuGnr3j17K/d1NbTMmTnzzLPOZOK9tXjxm2+9xeiddsopp86eTeQ1VpDJZnxp0aK6psa95btincFLL7144sTx4Wj8ueefX79ps91pP+/880aPGvnk64uTHY61yxYlp2XUtHSsXrfBH49feu6Z+6srvd6kKePGBkLBxx57csG55+ytqmnu6Cqv2NrU1HDdNV/sikX++uijO7Ztz8nKue6az2d5XOu2bntx4UJgOCiv6KorPvv28hVPPPN8R3vH1CmTM9LTfT7/0KFDQ+HI4088WV5enp9TcNlll2Rkpm3cvGPz1q0H6mqbWlqmnDTp6vPPtSo0yHo5HM+g1gCG3dMUIq3oNKQO1MhGAQQ/wWrIVgpb6uwZibVoLAoeDEaiEYxKBosvHIMHgXeIYFGCnHZzcVKMaJN6sIbUpK7eduiveE7jmBmsZC79LU+4wCl8k0BPQ3r9hm89r/5Wfy6JYHHN8GRy8YrnfV+p93pnhdmEtYFoB+OJMITNYiQMGNyCiexUKC1XFFthZ72c3gJ5VlFZtbfyALspoNKMqFB2M9KfAEGvkW/S653iRu8IN1w0g+bxTU/167BGqpZ+4C/VD0DGQMTpjwAPW2ZCi0Dt4CAwc8fikShWJ4EJJB75tu9FU2mn3hG9wfo9z/UG630hi36jp6dH3Oj3fPctUE+pXnYPjV6OaDkVwGUKaFp9Q8Oeir00VWiYovR6M8je2wC9KKCqN+xI1fWr/SP6CWH0mg1eq9FpMbmsZpshRJQ5i1gmpcnHValRi5wyf97bi5eENXNXVFu1fkNBXo4WD1pMplffemvp6rU33nJTWDPc+7s/wM8ardYzzjrrli9/ZcnylUtWrGROHqhvuOWWL1106SWZ2TmBUBjExEpmoOsbm998+x28Gp5+8YV/PfLognPPnTpz+sNPP7tz/76vfOMbQ8eM/sUDv+kMBMzoWhLGYCTyk3vuGTZy5OWXXLJn956Az08flq9cVd/UvGzVynWbNn3l619bsOBMp8NZVFSck51z6imn5ObmM83Gjhv/xS/fUjh82MKly1gar731zj8fevjCCy8cOWbsL+7/bcigbSnf+av/u/8zn/nMZy+/PCczc9OWrX976OHLr7qqaMjgX9x7X2s4DJ87cuzYL95w/ZDhQ2k2y4oBLikqzMvOmDxx3KQJo9auX3fv/Q+cNHny+ed/5kBD/bMvvnjxpZdcdsUVjzz/wgtvLYqaLf/32wc1i/WmL32p6kDjiy++cuVll86cOeO+3z7oD8Gjs+gSSBL3/enfDfUtN99wIxz93ff/uj0a3byj/N13F114znnnX3zxXx96eHdl1erNW/792GOXXHb5xZd/9i///vfWnTtg4RLYfwyGZWtWvbV86WevunLO3Lk/+eV9jR1dby5Zsnzd+utvvH7G7Fn3PfBAqz/45rKVf/jz32fNmHH+OWelZGcVDh284Oyz8nKyl69cvXtPBdOYfr26cGGnP7B1Z8XP7rk/Lzv/c1de7fG4y/fs9qal3njLl8JG7Z7f/Z4hczpdp51xxk0331xVU/fia28OHj6qoHTQqaeeNmnMmP379q1atQqD8j/++a89u3dff8P1YaMR7VlUM9KFJ55/6fQFZ15y6WeffOa5LVu29V07/eaksMyKEgjE4fD5CX5USxKFF3wjcw31v6B23sMAR6JRk8Vktdoef+rpdxYvM9mcGLVYqnZT+MYvfGHc6NEwyDLxwShRYeoxZ8PlgidkJasbHvJeRxDc45wajUaTvV4SIF7wCjzCdxzBEIxOK0CmWMx0JhQiIYYtyc5FSqmbjPCMiBEIpAYtEAhSgc1m47mehvQ9l3RShG2ls6usrfnVvfcLD2+ze9OSLz7/vFGDy0QQAARSibLQK0Smt1narRrP3+deftVqtX/txmse+OMf8nOyr7j0kmg0ZLMBT7S63ciPXFar/GTIaV4kHMbMlZqWRvaehglqBpXpXeb5h34hicmQCkFgDSADGBqa2++5/zfxQBeWJdDoFz5/9ZRJE+iwLhOIaNODufRW6b0BmO1tbbB4ZKFfXFA4vmWAqEB1kMEgc28uuoZ/h2Tn0wM3sugFirSphq8HFNJ1yqPyFatWLV+58ud3382EAeZAj1cUS16K4ok+B7p/qhXFW13+k1L+BxctjKBhw6eF5YJWS8EQVoNlILPmGC/pWiQwbuLExe++sRMmw2BobG4tLS1urG8IG7Q3Fy1Kz83fuXefJyNj8fJlzV1d6VkZDW3te/fudSd7QfSjx46FHd6wdfvUKVNyU5MONDSIpiWBR42wczaHg2+71XH26QtOnjbZnzBs37R16KjhOzZtddjs++tqqmrr0kqKmSh7q/YHYtGzzj4r02E/bd586AfDgFsO45ydldnU0rhmw/oz5s5zOxyMhtPhGlpW5nI4kp2OUCi8t6oKxiLoR7+CUtp01smnji8tLcjMfvnVN7uisbeWrJgwecrMKScBLzyfHn708WRPcktDk8vq2Lt3f+2B2oLsvG0bNk8YPnL8+NGJaJSJw7rPyUxNTnIOKsopS081x7RzT1tw3rz5sAiPPfXMoNyi6aNHc3/2OWe++c7bQ4eUlQ0qPvv0UzM8znlTJ0fDwdFDhqRm5T798puBYCDNJqPR0uVbu2n7gz+9a0hxVuoVV371G7c1tbbbjJaTJ8+cPGRIwGAoKx5UW11TXV2dmpzW1dpO4SA8KNbkYSMtFjv9QnwePW7c0JKSUSUlLy1asnrr9r21td7MjL1VlaizOv2+nbt2olA4fe7MU+fNJH2K25KRljS6LN9pNNrsABtlkaAAl8cl7oYW68jR4z977jl0trm1dURx2Rlz53tczisv/+ztt3+zrbOjuKTEV7EHyc9ssyF4Zefn2F2eIYOHFGem2qwOu9XeFYyuWbX2W9+8vayk+NprP/flr95a3dAIFhs7cvjk0cMA9dhhw2tqDkwaP5Z1KqvmsIuhpSF81MRFkSM0gN8wYrAFvcnlrf4LeRNemLJmTJ9+w43Xn7Zgwd6qaqjxddd+obCosM3vQw9Y39x4oKqahQv8auobN27ZhtzAeMLY4w1XW9+4Y9eeUERUa+D3fzz66LOvvNrc1goj2OEPwuPs2F1RzQy2WDp9ge07djW3d7DEaRVNQA+4tby8qbWVZQ8G7fL7Q/FEXWPzlu3lKMsMVqs/FL7/N799+Y03GpuaoxASHQ319kNulEJfLc6G5uY9+/dfcfXnLr/y8qKy0j/89a+0SunMDbsq9lXsr8QYxRUIR1r8/laff+OOHQ3tbXphwjYBPINh/qmnTp42DYxosTnbOnybtm6rqKyOxuKQoJaW1q3byw80NKJJofFvLFr8i/seqDpQ1+Xz8zMYjZXv2rtnX5WSP5gDH/7FkCk0JNpMmXcJ4Kd1dPlQLFxy8UXXfv7a6669tqSkmInCgtu5t6K+uQUoizCEojMS2bF7956qKiYOuB5o/OL++yuqqts7ulA+N7a0CGI2mYKhUHNbG5ShrcuHFrW1s3PHrp2hSJgsDU2tW3eUd3b56KzeN1rQ3NICswkB5xNCE1rfAKQ6fL4dO5noNQwLSc0WVJkoeTR/KNTS1aG4AQvh2yicpkIbWrt8m7Ztb2hu4RVVMz1qGho2bt1WC6g/EkDqzT/Kt9A/pi4b7rG7iHZZ/A9xT9RZrKNkPOxVNJSRkjxz5qwXXl346htvzZs/3+N0GOMRVlxXMEAtXaFwenraLV++iQV1369/zXxjdkXi7FSL5GWk3nDDDR2dnb/+zW8ff+4FpqTFajPDiAilBDAQYIPTYs/LzkEANMUkbryVaGHM1VjilptvzM3JEXbZbImEo8JXWWXRpaQki1sm44GKPxIePXLkXd+/0+fz3X33L9as32jG+hqL62q73/72wVdffS3sD1jUQxiiZLszKzlVVkk4bnHYWO/tvi53UhKINRqVWRYJRexma9Dnd1rt3/zK1wqzsm778s0zT5r0ysuv/+xXD/iD0D5TTLhJUyIW0WJhSnIYLbmpGTp8DcGoXVhL8XL0OK3hYCeMm8cBshAdvCkaTrJbhAcjErjVjKICMywFdgUiBqMlCchoBpcNR3hDOBi2Gc3JFhtYD1MAkHGYrTD+DqMpGAxGgsFLL7hg1qxZsGsoCGgDbKPZZqcX/HYmJ/sjIQYaDrQrEAyHw9df98Uhg0oxr2aluGFhxGoYDVkTSIfwQ+jUYaNRHaN2x7WMfPAKWnZmBo8AkMNgcmL5lfljsJmstKpyf9WdP/jhjl27o2FwkwbZQD+iFikWDqXnFbxIk8xuj4e143TaXU5LNNhl0eJeB54Gst3XjvVEWGRQwAAaIZ7rSnBu1PJTq5VKuARvyYqUe7g6lMvyW35C5+iOVlZUwCcrvT7VbZw0ekimy8mg/fg3v03JTFuzdNHJk6feeM0X//XPJ1949x2L1TJqxLCv33K93W576ZW3n3rmOWZhSkbm3XfdumHL+t88/ZzX661qrLnxupvu+sVvMtKTd+3Z2enruuyiS7asWd9UV2d1ue6869uDsjIPNDb94r7765ubWQBfvemmmRPGLV69+vmly6KYsXxdncbY9793h7+24T/PvFA8uvzd5Su//fWvF+XlChNMR1TXuIV4iHs0NiqUfCajJylp0thRHpNx5PDBm5evTIRlBdz/xz+s2bwRnHXRmedcfdGFe5saf/aXv6e7kqv27AgmIrfdcuuCSeOwZ+pixZtvL8rLzS0uLobT+fNf/wptZ7Xd+a3bzYn4L++51+ZJamjvuuqqq6aMHHrfvx5pau9q/uVvP3vhOcMmjLzr7ntCbf6oIXL6aSd//tJLkLiswFiIp7pUg/XbD/LNehAdnyi7IHBGK/5NRoPT6z1p4oQ0NzNEnNpacUf53e9279zJrL3k8osuPeUMSzTxmz/8ZVXFjmZ/1ylTZnzri9c/9do7z61eVxm897wZc8ePGvHXxx/68ffvSrXaNmzZ/J8nnvz5Pff88ann/V2du3duNcdDv/r5z59/fQl6ao/bASn99je+Ori0xIgh0Gj8+78fcqWmoBemr0+98DK+yFdfd80P7r4bzrGttfWUU06+6QufZ36DMlxm0zMLF+5rqrvj+psBxkMvv9TZ0fXtG65ft2XXD39zn8NqRdF22SUXnXXqKf9+5JHX33jDCo/k8vzkm7cW5GSD9KiL64OA7njyUpPYA1gfzArxH1EXf463BRg5Ua+ed9aCm2/5ChLtFz/32ccff4KHFDV2zJimxqaz58+EQIZDoUAgsHLNugevuc5itna0ttnNFjS1xfn544YPevo528K33p0+fVZDIzGw/U67Ze2GLV0+nFVAIiw+FoDBbjUVZmcZIpH582Zj14pHgl4nkj2oxTAov1Drim4rrxhWVrx45dJwIhxju4AGujdHAsHC9KzxF138y5qm5SvXlZYNRT/c1umz2eysuO/dece4sSMeefK5uGKDqC0SRwEJ+8zmA7TF8fHjRj/75NOtl10E+jZEzaNHjN7+xpuzZs22w7uGw163A85p3vx5w8eNvemb3+z0+bI9UCY0htBXpgQYE+282LaBKp4+kyaOf+jJp+r8nOtgWrR46ZRJUwjuLbKiBS0dOgxi5DPdzVaqinagpIppLjB3RpozJ9v6wtK3r7jg3EWrVjrdztKCnA1rtAhWZMYPLXcUwc4wqKxs9bp1J02alJLq9QeCLsRamCWMFYgxMXNNRS3eOlU1Nbu3bLn68ksjXV0ttXWnz58H2gxA6hwuwfGMiNJvJDlc/ua2QCTmslpcScl1jU3oVFZs3FJRXWd3OEwQiTjqOAzpWtho2LpvT/mBqhFlpUuWrsjOKqht69i9VyiBHSXbwrdB53ZMv6FAa1tLXnoKhnAU2y6HNbcg77U3X7/mqitXrFwd9IUKcwvKt26PovWW8ZTxFkvmka/u+do3wSETV/1QLNrBJDqdQFKnZLThjHJUvu1wGA2NLWs3bPjlj+4amlewYt3Gl9588+e/+mlRXuZPfvqLFStXnjJ3zuQJY2bNmGpzOW++7fvLl688ff7sk2fMHzFy6HXnnB6MJXaU7zl15qQ/3HvPs68tvP/e+x/8v3umjB/1vR/fs2jxitJLPvPgX/6aXVD4s1/c/fbSFb/5/R8m/fF3DE9rY+Nvf3K32+n4zr33PvfCS9+/6caZ02bOO++sM6dPc9tsEGNd0UTrabbecsWi6bdGbDs1NfVJbvsLb7yBzJXkdj/+zHM7K/b+5ne/83V03vGN2+eeNBFxesPytfd++46Tv3LLwwtfueeBByb+6U9QeStGeXQsjc2epOTGtq577n3glltunjd3ZsjnS3I6oyGw4U/Tc7KXbdj6/HMvXnTyzAsvOHflhs0PfP9Ot9X8gwf+r2xQ4bdvvqWqueW3v/tdS3t7TlqqNJB2HTIAB8H+/u70wvS+g5+YC/B54Wh4bfn2nGSvKW4oGVzy0GNPBroC//rjH/bv2/+dH/5w7uiJBSkZl1986edy0quaG7/3tW9ddfr555x++pbKXb/48Y/y7Y7NGzY2NdViOwLjhYLh5qZWim2ub1m5avkDv/jxoLwcBPBnXnzxez/43tBBRY888dQ/H3nkR3d8xwqJMxgmTzzp8eeeZ0nhC7HorXcv/Mx5malpd333exnZWQzEbbd986JLLhZ/DzhKgwFxsL29k1z0ItAVggxEQ/H77nngymuuWjB3Vvnu3fffe+/4kSOfePTxn93989FjRra1d6U5UDbIhfLqv6smEprTd+j63utNOpbvJI/bbjHlpKdMnTjO7rCnO2zJbleXxwM0rrz00gf/8Idv3X47StQZM2aiwzzt5JN/dNcPsrKzJ4wekZaSUlff+OCDf8TJD7Ph579wDVJFmjf5e9/9XkpqijfZO2zoYDwtM9O9Tge+NDLRsDPf97vff/2222BAB5cUfvn6m0W1ZjSmJXuuu+qzf/z9H1IyU7MzU+35+WifM1JT0FTsLN/18EMPMZFwgP/yV76UmuycMXXKz3/2s5tvuunSiy/8/e9+n5Kelp6WNnTIYLj4FC+9cQAXq9GUmZJsjEVPmTWjctfOu753pxaJfeFzVy9YMH97TfW3vvVdr9NakJXxtS9/+fFHHq2sru4MBufNnp6VkQ67KWQ1oaWnJiWxm9Vg8Ho9KIiAJNYtmMvBZcXfu+vOsJYYXFRy6QXn+wOBzLRUcSswGKhbF2xhOHIy0mxCGwST2Uymb3755gcf/Mu6NWuIl3DrV7/stlqAOiQAmGDVz8nKsJo01Prbt22+88473J4kCrz1lhuS8+EphQLZLPbaqr0//ukvW2qrLj7vnMFFBXmZKds3b77tG7fZrZbc9Ixbv/q1jFSvE0qrGJqZU6ctXbTk9tu+dcMN15937pk//8U9X9u4saCwYPrUyUSdSnY5s1K8MJ3wlWaLcczYUU89/WRnW1ew0085eQVZ77z99p3fuSMpOTkfspCTmWwxz5o66Rd3333m6aelpmckKyHg+uuu/e1vfnvbt76FHuIrX7op1WV3OeyZ6akIdEAwMyPd6RSeD/Fd198KKPpcA5CBPm8HuNVzC1lWxIG2C6FnpJAy4wlL3HTO3FPHFw2Cx1y5eVPQZly5cumaRKy5oap8q3PB3Dkuh2njprX1rZ3oDbB9u1DKm2xJCZPbbg9Hg8z7U6fPzHA5hhUWDi8pmThqlMPOMnC2tTR3RKKrN2ycd8qpTz7/kr/Lv39vZVNjKz5icydNzE3xYAEdVzZoS/luq9FsN9oQdb0eFwZrhCAds/bttoi4DDj8gsFY09j8q1//rrmxxq/F7/vp3TiuYQpzOd1vv/YG5lNfl3/7nj1Fo8cWeVNnjxrjMtrOm3/Kw48/Wd3YAjbV1xLKPlzodu/dm5aRMW/ebI/FmOR1wbo43M6du3YtXr7yQEsbPAyaVbs5keQwJbvsUb9v49rlk6fNeuaZ5xA+Wpoam5ua8tJS4NylbXwdMkYDjMLxP2JCygymdNBxR3vLg//6m8tkTnUmffUrX127dm1mZubrL74WDQQ7G1t3bi8vnjsnFom9/cobbf6ueDAWDoSTHRaXQfPabQ4rEjbqBqRgbG52LOOiMEUDktBOnz1v3LChlF9TeYBtMGu3bNqwaX19U31NbQ3aP2Qdmj154qQ331ns9wXqauqDXaHZ02c4rdamhqblq9eDvNgiivmEcRENhnQSkc2mA8Nutjktjo5WX3tLa11V1TPPPhdDdAsEkWDmz571lz/88Vwk9+nTmP291ovjh9L/OAddPnfB6QA0Fo18/cs3o/JCiXr2GafhxAVWzUjx3vXtb2M8d7mcGRmZ8Hhfuu4LVfur0tMzXG6nMM1m061fvbm1rR0nmbS0FHR3t37l5uqaWpvVisInGAqiAfne7bc7bXZhsQ2GvNzsn//kh5V1NaFolAQWECUrA8VFNHbavJmjRg9hFedn5yB5sLZv+Py1YEOqKMj/VjtcS06OBwSkGb76peurq2syMtInjx89Z+ZUws8U5OeRxWYy0gWb9CWKXutH3/q2y+Om3q/ffGN1VbXVZElPT0Vte9O1VzU2NSVCwZysTHDojTdcV1dXjyIrLTcb4kFzMC2yp/nO229DvQWGufAz50Co0PSwRqKR6GcvuWjW3JnoLYsKCll0LkvSHbd/Ays9COrkOTOZllK7w/6Db38bLyPuWQb0sKyg4Jc/+kFjY2NGWipoFDQBIy+rQ7wzE7d97RY7yqJE/Cs33VhTVwdPk5qSmi6YGuOpFMDEmzNz2tmnz7PGoznZuYgnXo/7zu/cXl9fT66stHS7zfylG67DLkiDEUwx8v/0R3cxvfECs9isP7nrO6iF83JzWO4wqWeddgqrHq0e9M7jsH7lhhvYLNLS1JKZmg7lQ0d79w/v2r+/EjKYnpGFbgcB4MZrrzn7tNM8bhRsSawsHCuKc/N++sMf1jc0AtXk5CSmzSnz5s6fNVO3q33hmqvYBK6vC9rfFxnqM/64yYCe7eC3KBykZGYMVNcS1zLcXmYYswwf2IjVEoGgJxJTZ82ePG5sZX3N9+78AdLW2EmTTXBtyjFFJDdxT5WN3UabBUFYyhMdFnhLUAFb1tlJHcSHCRaSkURnZ7NBw3HzAiKwTgw2dUBIJZukZ0+IWDgQhBg5sLnN3s0h9jRb1cBwxrX8/ILv/+COpobGO+/+GbQSXV0kmnA4EMyj6OGuvuLKMSNG1QWDFo/L4LCiQjMnzE60YiG/BNVQFBXqSr3hcMhut4spQhA5ihfTT3/2C18wPH3ObEeS12BpQL/OnEAZSMNghMOUjxI2isbSdOZpp+ZlZ9NW1f7uYqWJx3bJ7DyUth+ST0iLOGjrBfIdC4dy0r0//+GPcrxeQhaEYjEUDNFEDDc7ptT1139x8PChIOU//v2fM0+fl5mZZbSYIWLxYCgBwRffMcbDHDPZ4yaYMmMMxTTtplyTISlJmFbggV0BWTWKasBgQJF90uWXgUF01ONJchSWDtqwvXzz+g2jx49LSnU+98pbz7722pxZM9LT08XawLjJYpP1BuFiXCmTH7ST+kOqTG7CeA1YTJddcVlqWso3brtt8ZIlz770Ck579/zgrqyMtEMg0PPjPQDVk+x/+9fjZJepeD04McCyLOKxJKdQWUz4OIwCmeFlZQpXizDOqA4uLWV5swIEwkZDTnZmVlYaeIGzMdGLm+1W1AuyCgCXw84opbpRtglPCJwZN9heVLuIiGKNlQGWVZyIRywW26D8XGYr6hwnom08boO5gkQYDRkZadlZGSw12kZJWA6GlBSC7MBGeVmZsuhQvTvsFo6tdWMTFVcC4uuB0sjOPIlHwoMKC6gcxQgdsZsMBVmZqEToK7PY4bCVlZWCOyNx3G0R6fBIZDWb0pGH0H8kYkkeBzdaXJBmPBa22qyDiwqpUSa2Mn05XHaaQnsc6NiBm8loN5jSLdQOkhB7JB10gH4d9qSiQrqMLwq9dtqEb0bEACGmJLl1kAKi4vwcikINBfND02Wm04VomKaV5mZhsqAxNB6FF60dVFSAkp02g/GSwRigAk02YAOZzLSU7Ix0hhXEmJuenpOWpla7rFyH2QbhhagLMBkJYOJypJcUgdTi0C2TEQv/yCFlUi/Ez24ROBiMZcVFlM6wuu02ASxWCqu1rKgQSg1doEcW/EOtHBwMYkYwcpFAwC9wGuB6/2RAoCpULAFtZEKgcKR48DKwpyqQ2tjRI99dvXbm3JOLMlK7QhGP1fL8Sy+1+kO//+53UZHf//BTCTFBiV9FZ7CLhc5NzJSIMJqgLTOsJohSXAZDWsxpikPShwwuddvsV19yAdnCvrDTiSMvvo9MCBTfRoKlhBMhQB9ORGEZmdYmpBOIoMD24CXQZ9IAJyDOpNc0p9U2bnDpBeed89e//e1Xd9059aSJazdvOvvMBSlulxaMuq3WugOVFW0NSyq2zZ004c3ly+LR0JC8gneFegvlkWkfj5aVlrY2N27asG7ySZPQpOK4986KdX/5+99K89OffXv5ho1bITBmu7u5M9gZTrgc7uFjJpqdSZdefiltCEeicNB49RObRQZLpuORxutgR7hjUpGOKSt9IS9ZgbxCIkwpGXXpqj7wcsuKAsGYmJqJWKrRmmq2Ilk77KYx40Y3dnacecn5WEuCoTCg/vUf/jRozIjPX3rh8m07WnztEUvCbnYhp3IQBbyGPdnbGoh3hJn6sXdXrAiAn1iEeKLSfNmiY0jPzYE8z5g0YVBpCbwSmICmoqBjXYGxSoeX/f2R/8SCoTtuvRUt7OLVKz/3hatnjx+zdkt5Z1u7DYQnLZegBEkuV1VdnS+uhf3+N5e8M2XKlBSE4Exv0ZDS06dPE4IUkZXZ1N4yY96cqXNn33vfr/fs3ZOVPhkmCID0BZYsbHUBEB1WOmRAUnpKXvbL0jf7f+eeoQLlUZfy2+KvUMSeEURjIDOZQRYM3tM7iK2azUrfJ0uPqYPDJNmYcDoEBL2ywY1S6SO0mTGgTEaE/qIooUCIBntYSU8i0LHDiU8RuAvYMmMgSyZGkyHhhqnGJFPTXlrX0zbZk2u2SnXgLslFwfxRlyBQWkA1PAI9ySyVMuxWdjmBJ2ULLayDIEiZq2LRpVzyCPalG7IbQwoDFCRguaG44iFFORFQab/SfstjzEWqFhhmzBgy2hIyKMGyguMTFk0sLELn5JW0QcpjWUjpAh8uHksvpAp1kUhApHqDcCb9M2jnn3s2IJIyEiJegH34B+XB/IvDuDCI7NdSBQrQAAZWei6hc9BdcZuU/ptYFoorFHMraErqxQDO8ErLGBOAYIN/FblHQYxZIXc0TvJzkUDBWUYB+MtzVresci4qYYgBGisP1b1V+TipbAN8HYIiB3h/2CO9AVKrhGXQ7EZzXmomjRWPAYPZm4RCRSmhEvGTp03HDebb3/1+TmZ6Ihr+0g3XTz9pysuvvHHrrd9NSstgn1qyS1LOmj/9r3/9eyJYf9EVV2WnpqBxAKfAAHhTvYrQGNJSUAuJZuvWL33pvnsfuPWrt+GZXVZa/JUv3Qjld0bdAg8mutWamuplIp+94OS/Pf34/k2bvnTNtQW5ubzs24nu9pPHkHDZrVkpHptg4Ohl537m/p17dm7bceUlF1TXVH3rW98CB6XaXHfeeivLz5WS/PTChc88/+SB+spbbrrBm+TwOLHkC/RwUYCVgDW4+spLH7j/AawXwWD4C1+4du78k391z/04Wtic9qElBRjLpk6e/MxLb3z7W9+/6pLPfOOrt/7knvtu+cp3sKFmZ6bc9rWv0GU12GrMD2ly3+YfvGeYEbdYyGjAGW9eyDpRFw95xU+4d1lGqlzBHopC2yxGdh7Bp8kEY8zMxuuvvfZn99/75a9/Lcnh9thtd37ve2edd9af/vLXr91xJ9Lw2LGjsbalelNT0lK/e+cPzps57zPnnzV+3Pi77vpRlt1ldlqK8nJAKskeR3KygzoQsfPycs469eSf/fhHmdlZkVBo3uxZl51/ocxzVrvBMGrk8ObGukkTJgwfNpj2Tp8x+cE//O7V3GK30zV+zAhEfofFnOJxw7nNnzXr5SWL77jjRwTkGjykzJviAdq33fbVn/3xty+/8JwpGsvJzLjl5pvuv/++EHFvHA7oUWlpKWXqy0C/USCRpaeQniwPWSV91gkJ9Id6yv/1t4y9LGt10c4+7ZHJ2/cBjCr4C20QEwATIusetKpkbLUk+uTsvQVrytQHmQEQgKBeMGqgYrkFs8FjigwNLRHkJPWpLHpLSEk62iRP+zalp4LeBquSSa5S61+qK30TKHyMkx21YA0SQVNojZAMiTiHy0QoBv6SVabQKcpnvWqaexAsyuSkqlcDK4kJ7SF/xAkHHAxPgAMCzYVtkUbLneqWlEFCatdvVD+5Vb/lb89FC1V3SCf9Hj9mOIyPCtiA2C/NhslAwFAkBMwLlZJFp+DTDeHekugbZeidZLyYkyohj4CVtKO7DrmjDFVtT+bevwpEPTX0SaMXy6pX2UQU4oPEICRQTfveEvrdyED0e3SUnyQVyOotxN2G3ic0PFvZF2c1AwlLa1cQf61kJ4CALDoQTvZU1rS3tWalefOzs6HDwXB05+69GVlZKampjrjf5bR2GJ1V1dWuaGdRYWlL0OR1WYlYGIhFgl2+7ORUkEKrr9NgMaPzBeNFw9G9eyvBbwUFeW63wx8KhrR4tiOJPjbFUKmF813JDMyO2ipfR9eIQYNdNmssHIF0q+FDXySTGHnFxKZo1FZRrcsfSMOtDE2J2RXs8LnMbNh0wB/u3F/BrpnCzOzsjMz1NTXf/tFPf3PXXcH21vTCzNyMXHMo5gv5mIset8vn98MmuBxOUG5zc+veA9UpqRn5ebmssV3lu2AKSorymCAOk91nt3d0Beq27+EgB09Omj+uVe3YG45H2ByTlpyEzMe6U8IF05epJHPiKBcDx6XzdLip+f3+ri7Rw+HixtwqKSlBluc13CV8NV68TCqYJYxmwTgOSl2Z3iybQEKE5IAWD2na/oq9Wiicn5+bkp7OxidUtB0tbE8dEkC3Z7Om2m0NoSCK6WJ3Sm5uTpfFsH3Hjmy7J7sgLxgO4tfd5fOxNtEXIyDDHcET1Tc2HqivQ5GN3tZpkw2hPGRS0phW8aZwusQgaQobEjv37UWUGzliSDgccdodaMbpBXZN1nVdINBQvi8vK8OcnoS0m4+GzWQ8EOyq3l0JehhUVOhy2X1dvgN19UiRxUVFWd4khQ0FJQgd7EFVdJQy9+/fj7oP9V1qaqpLXcBK4KhWAemPAvD/zivVkgGHvv86pWcQgNcXL09LSx0/evimjVvKSoqwCYuiA6ypON/D2ozsKJAR1ACOF9TTi3eE0QerSayRGGa1aCAQSklORg7o25reRggTMdCluGzJwRzu+55Hejl9S6PCaCwAIkaNi08TbcvKyEBrZEfDhTBvNWI5C0XDKRY7tI52w3vrygZVMiUJry24kr8gXjXW6hZvT3bIm4PRyPMLX58zkUivuRQQVTYDXFmE00T50tsguenumdAbLv2Veqr3kwdCKdGBEJ4BX1tmVwxvVxHygSg0bOP2bZjozzj11P2V1ehI2L5Ag1U7D35BQ6i2+zd/IVRokIQwgLSUxHCw5t5cR4Bzd+t7k3Xf0P7efm3YtBlxY+zYMSBj0HL/1vTJetxkgHIpTqitqB+FPItABNCtUWIkRQhbgAgVj4g9MIHmWZzrEUA1fNlwmJWlibcCQQugyYgrATbcB8xuEKhHC4lmz+gG/0XZ8C0SjmgQcHhlOGOIioSiEjKrJHoqR2LkiVkwpi0skIpi1NESdgwIWBHEqYrdZFAq8KC0UEcHzHQ+PWSACmVvhFmLEro1AqKGkMvY4A9mhKeCkjJmsBJbcN39zg+e/eOfXUkOzZLwazF6JYG3AQX9YC3q4y1cjBiRoMKI6nAxoi+SnRIR9DCGuC1itcNDuHCFDvrDhrDV4bYRqAs4YTMIs/UMmZz5RHayUfXBUdORVHctfQYPpN/S0sIOcmhAcjK2Lg8IDsOU2+3GGCVIjYJE56rsLsJuiSaUEREzjAmLtTi381b07PhCUyHQgUIirIotEKig8lOCOWery75YbDQWS4St1rReBhJujSAHiK5oY802FZUYGoCQBBghtdQi7kSAhQ+iKXDGMRAYGcwoNJFgonHGV2zWGBJ5DhpQwFQMIKlEisaX0SmqTkOQGAdoKBhj4jRiQ9JXJfNCFqdI8SxQhStk9clAKDLQCy2eQABqamrQAkEPuAd03OPpW1BQAPQOB29v3v/mDeDi0sceBRo9516HoTyna4KP5OKGV7fc8cMxo0de/dlLf/qjn15+8YVjCNjAKMOZKhSvp1HJBW2TcVdFZSgUHDFsKOgCJrEXNwBUKRwdI4YfzfD7P/11+NAhp82bazuo3ZEa9YsbPsLH9jRDL1z9RCuB9k9fHt2tpV5FZLq7o/dOzxyPB+mmxepcvXHLM8+99J1vfsPBZi0GI6YFzYYlmzdMGTsu3W4XzYNMZ+kydakb/QuMLNOj+yKZ0dDY0rq3svKkSRPYo3vVjTf9+NvfnTxmBHlCsFp4NRChhAWgmwtR6AJkJQrp3ZGi5RLU3H2rBFz4Ub0WHupBoe0UhkkUbMeaN5r++eijGzdvuf/nd//+L//MSEu5/MLzxD9awZxKubjvrkIViE/8rvKdkyZOlNVOQkE43Zfq2MF7PS/fent6O8ts1xtJRh7ylieCbnsy/OeJp3jx2UsuBrOKxam7yAH+iMB17BcFdaMn7gisJy2Xwi2CdkW8ceiTBhuMJNC/eC/Ou3g3S0WqMcJh0GoNQdbh0We15kRwkdIYILa00GhVhQqiKkGSmCryiDqk7/xGMc8mI1WkTcALMpEqxWsf714ZQjhxhQ3khX6RTrWY/CQRJMgFZWJuYEeSdgqOEm0gU01lkwBaGRbz1BFleEFBQMDTbiK/U31PqdD3nlvpE/eMDpqabrAb0XXLRmIKRfMuWj06QTwUozi9SXE0iZ06TmdvKWrhy8veS9fwUCDN06VIENmePXtwdYCjRQ2Cnw+ovzf9ITcKleichtQPl8j6l46DJiUhX+rkdJVJAGlEG6C3noA+BqXwlb5gc5P+ATnAJQpjoYJ2K/YpeSv7VCDIijgKOOQ/reW/vOUf/h44qOiXArssPnZ1crypoqayn8HSLbrKKNA4gRkgZyAFrk7dGR8EozagSgUy5fmr5rAOSUZPngxwkRLvA11lpL9GkAKAEAYcpXg7aNAgYvWQhtr1SxUuaXWYD1Doh/2I5drcFcCYvn/X9oKiIra/NrSySbiyrDC/KDsjkjA0trWy+a62ri7J4Rw5eAjSFf4zTB9mxrXXXYfMLUQ9HNq5cydasrLSsrSU1IbWlt3VVQ6XbeywYayrf734ohaJXud2ZWemY8jdsb+ypbVrcMmgrIxkE27rolk27NlXvWnT5muv+CycFA3YV1WFjW3U8BHoXYEJwAH1tsWjzTv3aaFI6uCiUDya503rbGtDL5dqdYS1mD8YTLbgVebsNMS37Sz3Gm3DB5chlbb4fTaLtaO+qauzMy0rIzUrE9kfJFxXXzdi5IiOR595c/GKC06bSzQYGP/NVZW//+u/U2+4yVpaaEt1swFi/8693oxUDE4oZLCfMkPgHtjJxWbG3ZWV6e6kkcVlrOe3Fq1YsmqZJz0NYd1u94Y089qt5YloZNSIoTBJjKYvGti8aUdGanppcXHMpLV2+ix2a+3e/ekeVxGGcXZaWWyd/jArobx8J8YwkhEfYtv27TkZ6YPKSthFQUQAfyJaUbGHAxaGjxiRxMYHk5ldC5hTzrvgPBQXHBsBN7mzuqKpsWFYyaDstMxgILxrzx5jNDF09AiD0/7aypUvvfzSN9PTC1LSMlNS9tU3E+e/pCgfrQlD4AsEt+7YCQc8aFBJepIHVQvcD8Gqapub8KtOcbpHlgyGKWvyd8DnHqhBPPaNKhua6k4OYB/WtPLy3Q63PZCIJ3mTWTxYSRVnMPDSYBarJXQ8s/lgSXJ38Jd+3/1bX4oHXzJ5Dv7oro0HslujpwgdJ+nv+qTuue2TX5WkP+8uVH8iZcld31c92fVy5bs7i/rbp1BpibzteaQwpCAyBJ3crKzvfOt2AmbpmfU69PSSpfdOssuPnjJ6Xx5E7IoykqC7ut4UffNIEX0u2qArN0BbIKnW1tZdu3Y5ZB+/hskUj7H3UGXoTeopUHWwL/skLw52oTtx94ND0knOQ8qSn/zr01tVVp/C+r3qacPB+vpUp5fT9/tgbX3LOaQJh8D60J70VjbADaDjKXDD65ELmQCoQg+am5uB8DDQJTotVakOfL77d3OAUj+ER0yPB//yD+ThgvRkrDKVdVufennhyKGlbyx8fe6MaSfPnf2Pf/4bfre0oICQc6/b7V/+0s0QYFisUCzxwO9+f+MXr8Xc9Zc//6W0uAjeAoppGWx5653FMYe5uqZm4etvXHPl53ZU7HNbrJu2bp05bepLLzzf1IbrZ8FLLy38zDmnTpswGikCZ+DnX3qZwHNpXo+vvWPR0hU4ttXVHKjYux/HfMWBshsr+ps//yEjZinIzNm9csmabZv/es8Dv/3dHydMm3zBaaf5/ZEf/+KXP7z1m8ZA+L6H/5GVk6O1dL62cOF1X77xnZUrnnnq6blTpg8qzP/Xs0+duuD0U6ZM3bxl24N/fPBn//fAgrMWPP/cy585bY5gJaNhy44d7LTasWNncXb62t1bnn3hpYnDxzd1tDqc9ms/d5VoV7CR2p3PvfwybHhJadGGts7y9B0Lzjp7Y/mO6ob6zRs3Tpl0UiIae+nlV0cOKWmuq1mzetUNN3xx265djzz2xKAhQ2oqFxXk5lx21eVPPf9s+a6dw4tKZ4wbV5SfA0OLjuFf/3m4vcvHnoxxY0bbLLWPPv9Sfk4mYU7QVV575eVPPfV0dXXVsGFDG5pbH37siW/dfqvZake4pVGPPPZ4cUH+eQvOuOf+XztTXMlOp9fmTHN73166pIN9DW1dyzdu/OzVV2zbtr2xoXnDxk3JU6a+/c7i8srqISX5y5YvnTRhPNtRH370sRGjxuA8ghCdPXEi0jZUIJqIbN2yrbO9Y/Hu6lettq987aa33n5n/ZZNY8eM9bW3P/XQk9/4xq2axfLAvQ+MGDokMz+H+EuzZs8Clan5e3BhHj5Tj5sMHF7Ep/sJGEG3wb4Htv0ooaCjITBUhbpGjBgB+0+r9CYhGXAvwYtOXMcGAXA6IIWsklyHbZa60K1t27btrbfeEpeklBRe6dd/DbaQp/bOzisuPn/qmJE4yP78m9+bPGPmqafOJ4TX7//+90nTpgT8fnzYZp00qb7LB/s/d8EZxE9Gp4fxrJN9AbH40889m5adc8P114fjUXbN4vd57nnndsSiFfv3/9/Pf3XjDbcMGzY8JyX5wnPOau7s2rp915XXXJuWmtbS1PHY409PmzQexVo4HiNm4jlnn8PGXbsn+dSzzyaoAxF2Vi5bhoMnAhgiGltYa2vqvnnHj7we+ytr1768ZBGxdEiGowxKPtxIiX0RiseXrVi+a3/VFdd+AVvQ7d/55qxzz4yYTa0B30VXXJ7pdrZqicdeeGH+jBmvvfXmqHFj05Lcg0qL2jpaMddZ3U62SoyfOD7/jbcuuvR8t1m75c5vfPZz17C/vaaj68abbp560uTxY0dDK9o6Ov/90CM/+9ndhEclyNId3/vB9NNPGT5xVNgcvfT88yJBdkAHpp407pzT5hEE5Z577m33BZ947lVXatbJZ5yxZ+eeX/z0ZwvOO6ezKxANx26+7jo0jQgYIoqaTIQqGTps2OcuvRD0+cCf/oYoM+/006oqD3z/e3eddvrplDZy+OjLLvpMUDPc9q3vLVmxBqEfxSojyPZhNKXvrlq7r6bugdvv8bD5IxBihZ664MzWgJ8gRf956BFc/MaUDeuob7vqwgsaO/3/ePyJb9x++/Ahg5I2bVq9etXcObMJt3P+xZeOGTNCXIUi6GiJ0IEW1jhn5mz2Ue7K3HPPn/90dTzR5QsWZeZefcEF6J3u+tHPN5WX76utMdjM191wDZxmdXM95lLIAFIm9Okg+3XYMjlBBg4DSZ8HvcwpN2CN/yYlAAH11k69/Fy0aBF867Rp05AA+EkzdXQGoeLq0+oTt+8BAR22Ogx1MIqvQyKBhg0CgFiwevVqbAZlZWUQAJ1avEeJH9prLSU1LTU9nRhBgVDoQE1tbOWafXsrg76OkeNGM//YgZXqTcPL1u12pedmN7Y2Y+E3oUwT9Gz2BYO7KvZefPHFIWQdNvFYLWvWrP/Ho09klhRHUWNjoWfzClsA4CzZvBJL1DU1PfLkk1h8Qr7goNJS7MZYp0Bh/mDIkZREn9BL/PGhJ3H3wmGfXbZiSBNkYgz4fWy4QWURjxmyc3KTUtNElwS/hNaIOLsYGDA7OByVNbWNzS1//ds/3RGttKiEvaLYiwaXDmE3cjhuYG/tk88+v3Hn3k5/8KxzzwaRuZPY+G8hZrI52a3bAlTc96gvHMaEVlRagsrF5nClZ2YdqK2bOG4szDduEWaLne1dvLLb7OyTaG5vC7OtAFMkayQWdztsxYX5UHvkGJvTyX6WhtbO9q6aP/zjX2i0xk2cgJYUKjtm1Gi4qhjBgVRgWCSplJS0ocNHQQtBo7U1tXvq633BUCQUHjV2LDmczuTC4lLoIsaVwqIiwhkgoAMEZcAQUOzYW5FXUowGKhqKEEyuurb2/n/8g7vMlHQkMPFnDcdN5DcYmtraGtvbXn3jzTfe0NjVzMboqVOnXH/99X/9x79xOLr5+msnDRsi5WLzaGv57R//7LCzC8/sJx6OuPhaygqKoV1o2V0eD5777b6O4kElgvrZAOFNplOMNT+Pfp0gA0eHj6gOSKEj4l7EwRMdR79H5g/wGoyv18s3Mvjy5cuxAE+cOFHnTHtr5wYp4QPU8/9jVh16fYk6I9tLSvPz8zMyMlauXIm9HUoA8EmpZ4FawNb1Av9Dhx3MeDwSweoCx03oSMJ5nnfuWWfMmx4KRy2yGUojhCcHVPGWGB5EnM9MS+eoKT6wCeA9tpXm5OWh2hKmAEST0B5+5NH5p5x6+tmn769v2rZuA/ZQdh+xv1fhBa2kdNAtX/mym5366I9Aw7EIbDB2O3zM/D4f1oX7Hvj13LPOufyis2sa2l554XkaBnZGX8ZWAAJAcSwH7EdHVycOe5AEkBFoC7QIsPwBH/BEfzJ86LBv3fq1ZLY9KQKyZdMmLLT46iCLFaalzJkyZeFbbxM2uWxQGdgKX7dYRHaEUY5sJ8UAoPxK2EmH6wNRCI2DhuFHQJSXnOxsoeXEhnO58GIIBIMOQzJ2XxwiUjCQiMuDHPYYoSkcxQUDB1E0QbRidruZYM5jx4/9/OXnIbg4ldkZzzSzQzwk2JDIllXAi68UY80eMVoNrLIz04qHD7n6isvZzIZfN87rqGtaW9vIEzQY6uvqS0uK0N5grkAkx/kHnSKxeHfu3kVH2EeNcvWJx5+kAbd/85vY+X77f/dRLU40YtLB49zjzszO/OIXPp8vQSxE2MIt48yT58ybP+fl199+9pWXRw//GgPG57HnnrUmub7zta9u2lW5uHyLGNWQaPkofY9gDJMx1ZvcWFOrgM0kCTGLaD/wRu49ijig5sOHPp0/jQV+dIt/QGiBbnQ+FGfQt99+G9w0derUvphrwFwnHr4/CPQbXAgAYgE2GGyt0F39LcPRSyreXy3vmQsUANqSbZSGhNtqv+AzZz3x2GOr121csXTpprXrHMRzDYceevKx1Zs2PfinP6UnJY8ZVKaFgkY2tbI9CuyTiF/8mXNffO65lxcuXLx06ebt250uV+V+ovjsfv7pp8Od7UTjIx7ZxrVrtuzYjctGLBb+y9/+snNn+ZuvvV2+bQdOZjhVOC2W/Lyc/ZWVIHmHw9pYW7Nl87YnHnm0vbWF5gleJSJmVk5zfcMjDz26aeO2Z557NhwKkLW0uPD1V15Zt2HT628sbKitjYfDZ51+alXF7ueffXbL+o1vv/oGe14RRmLBAKp3vCzwCDllzpx33lmSkpzCDlh4mZrKSnaGpCS5xBpqNkmou1BgxZJlXW0d5yw4+/HHH1+1fvMjDz3MZhEC2kMPwLiEEjj95Hl//sOD67ZufuKJx4ty8nLcnoK09D3bd6zauL6to40tXXigClpMxCSgZCJ+zoKTX3/11VcWvrFm+Yq333wXYomsw145hUvxMcCTDrwK+82OYtlLhlPngpPn7ti8admyZetWrXpr4Zt4UoVj4SeffW7Z+q3PPfdS1f7dMyZPikXwXZdCiHIa7Ow4Y86s+uqqhx59ePnqlfv27cPO7Otoryovf/6Jx5sbahnlnJyMvft2L1+2Eloye/rU3/36gfVr1y1bsnTr1q17du185vkXK3buJqgqEg4EUpzgiNjq9SDJEEj8+eee1QJ+OQ4UL0O1X4+phY89KGPu9Jlb1m145Y23Vq5b//ZbC2k/F7ICfKu6HfhLlKQDvznxVEHgSPDphzg+dGjp0gDfa9aswftTDhgKiYYRnw2ZpSeujxgCBOQhRhtrEmF/zJgxvbV9pJQYB+lVm7YMLS1MT3JwGlBIsyxft6lq3z52OI4fOXr4qOG/fPDB1NwsPHSdmunkqTMKsrJWb1jnTU0eOmTooqVLhw4Zksk2l42byneWhxORSWMnZKRkcBwNGvlBJUUcbD957JiOaGTR2+/GQ5HZJ88NGrVFS5fgIEwMmCnj2M03iC2JBotjzY7dv33wz/f+9AfxYGDhO4uJmoVcUliQD88rDnaCkkzb9u9f9/ZSYhn5vY4nX37h7z+/162ZX3vzzWjQb0pLTktLmzJ4lDc5aeuBSg5a8UYNaRnemafMO1BfW3PgwLypM0C1UUPCn9Cu+uLXv/uNL88YN4LAKt/94U+nTp1xybkL2F9rSIQ7rNa1m7dXbdhyxrzZ3pK8d1esadrfwPahWbNmZKWn4ssNf40DOduF3nznrZbOVo/Vcca8U51uN3E633jnzUBXx9xp03ft2Tdm9Gji4kEPtm3fNnL0WFzW1qxbv2tnucNkLi4snDZr+pqtmwiVMbZsKJ5qYHEoFH7LENH83LyMNK/IE0YTQSG3l+/EHSg3L2/mzJn/fOhRt8tts5rCAd/kSROHDxu2e89u5INpU6asXb8+Jdk7fPBgDn55d9VS5Ltp4ycV5BQufOddzuwkPFxaairpQSwrV6ysa6ifM3sOm6gWL1vW3tqG/DF+3JhkT9LKNWvR2uF+PnX6lNKcPCvEymjsjIRefftdIqoNKx2M3DZh/NiqfRUEsCspLpaZs3Z9dm5uUWHhhjXrdmzbZnM7MziKrCB/SEERDorir3hktHEkMtBLG46ctXdl9L/pJ30oYtY/DcXqgkjfxNyT+EiXItLdL/Xm6W0bsPwjFaLXS3Y+egNI2be0/hmPTAZIKW6vZO4mtFKMeC/0L+J9/YYAgHE4WggJnbP0+KlrJ4joKzV0N1louPjryB+kev3q14Den3oe0vTe9OQ4pr+9tZKae6BHOUcZr/csVG9Yb/N6b/o174NU0a8NVNFbS+8rqtNr5FX3nGTQg0F2chCM0rRkyRKMMQwBGVAUfKTmYtqBKjAeJgYcu1ItUaNdNxCJ1i/KKbqJHz5w77mXXDSqpAzlA+797KPGUZrNGWz1sHFEcDCEfp4AbSRHV469A49sekWZwtWiOCJmvYVwouy/oTxCr2BaJnCbdFtso+GgzCI8XkzWv/7z36OGDpk/YxoTWryDAFG3uwn8D06PhHDRPLIv3LCppvLbP7jrX/f+JsvjFY0GChMaG485OFSC2CQ2E76UeGbDz7IzCDHHiqc3+4Y0A+ctP//6wvUbdv3fz+50W0wbN254/a13vvzlr2CnZashLv5+TssxWZJQ08SiQTA0sQUpnX5J0GuMBSoSA7u3QNtWa8SQIFg/MGHXRBRlDjGNhF4RlwIZCaVIXAUYFUBhAu7rUEF0ZAlJTZZYnFCVTBA0OfSa2BTiOC6bHQXDaNIPmR4sMz6/+eNfpk2bOmX8WN0xg/hIZMQXlm13DIHoxyJx9EEIF8gsSCEWYl30bOgj4hYbYuiWnXMLpJHo82K6apc2s4mIb+IB6fNepiaxpIitzZYb2WptRZdFbDKkBPZiicuhRIugyey94ZwFtG0GiVehJjLjjpnEFI0AUsJfHgU1HW4b6K49JttJEAHx56ZIGV0FDpkO3A90kexgy3vWGxmxshyeBUBJHB0FUmxPAn2N4LVo8/CGHfBiDHglphLGiDLpsawFNcn1egfM1u8hsAQcLCAmol2ixsoOAMDF3B643iNw/fSIODni0M6coeWi/8MdHy0kgy3Qej9XX2sk+gd2BuC7gi6IspQcIPscWIEWbIXMRCAm+/DYJm8VjaaJhQ8cZF70AJ/kNKW3Nfzkw3Xs4FLJ5YtCKBZAkZcbvP0ZBX1FyE6LfpdeTb+H+k+lpqQ4aZjy9ddz698qH+Pb3U71U3DQh3Cx8KRGUJO+RLqrYuxQrer9shIHlgQsItSzLol0Qk8Ns2bNWrhwYW1tLXq5j1QUoC6aaEW9blWUXvfmVhpwZmmcIyAMpmJUCTYXVEGab+HUcQI5sTeRlSr772wSPK6bHWBtsKFAYCkJZcRldqEyF6yDJgFG0+DuQUwCfeya7NJSTAxz6JZrrobvoDQFNb5ZJ7IpQ4qjZEGuUiAASTGYpxSXOcXJEnzHMiAoLAnYESSJiQ2tFw4iBo9yzABsLeEGfJHwL+79DTvPb7vlSw7BhAb2q48ZM1a2D9M2prFRAgYphMxWFatD9USvnsJBazIDuVi36oYM8padFGaDXeiOXDRTvsW0w1DyDKsGuFl/qVKAPWXbEO+wiugoXbTwvatemGjSU3ZPJgqi0EE5WWmEz1Nl8IU+Tb+1q0KAFVupyMEHdGAyEdm7J7+YslWgb+mmAEeaTDQkdU8hbJzVi5LuQH2oGrqunumNkKAzdoESkaNUSjEUK6AxtZWxWDJKcbTTBQQAnoKNXuyA37IJtmdw9QQyY0Rbxh5bOsA/gHkQcNKZgx06pEia3ftGtULegrtlJAa4pJcYdViBdJY0OBGAlcESPZuM+uaRxKB+/qjh7F63TAUGSd4d6wUgGRDxZcAAJao/utc9wsoUfKzlyM5f9i4znAwic5V9NxAoGJBuJ85jLueQhKwrGU51oQJavHjx9OnTUUr0qqdpexiVpYrJKsYvGBimr8b+aUVHDynsw/+h5hIDpCafmtfds6t3tPU61c/eqdCvHTzXk+vDdnDwoM2yaa/vRR1iS+v76H3fM7eol2UiNXYb1bqbopajai+wl9koiEBxut21NTU1IZadcsopsGwftXlATdBDJrUCEfNCdARsdu8J9UPbIMMMB7w10waMDDbou9Z0HEF5MMgkQy3AW9kijkMP7CPIEfTYC8+Dd72PjnpDsVx6kigBrKgcbpRaBl5HpKS1XEIGwph+mckSgNhOYHbZXq6wPyXoBX7Mv+mmvqOTBr8nhv2Y90Vv3sBwZ8lYTaBdcLSsHQnFdCitOHLf+k4n8vIT3qXvw96sLG+KVVNZUDw4nZQEakVU7Z5evUnVEwrpWw73rGYE0MMT98nX/5b5B9fM9Md5ACJk0WLwIsiCgkYPKb5/xn6/obqcKsehxzBBMGNITjA0xPBxC7g+wMUq0pmsFStWFBYWsjdYmLiei65yBBKiLwfYQRfhgETIhij2cBAKGipVT5Ye+PSC7v01jzJpBt96dv1bL5OHx3X1ptcnlf6Toiizdwz0ktWgiC/Gh3DBQB4EDtRAR2GC9lkCIH6RbGBKGVeewJdoBphavRkGdmngMtTGUczs8PyIL8gV1epcvF6VUCmAJCIgBF8JgvyUEZD/qGdgPeiA7KoVlk5vM0VQhrrnhvWMdgIFR5SjDCPJyQjECJAHe8Jtn18Hnx/9Tp+r4EQogGoJEtdRiuFV99DTIlrMifa0noHQ6Qd5KeqoJRy9Of+9t/oKpfufiNYeC1wGJgPEbw50bk/EOxELFMcgPKAaYbxQQb7dw3loBcwrRBXYN+YUafVkKuxGz/AfTI9IqtkIPAxjzpmhIpUn4ArcRs2mWToU1TmYVr+TV1AjkK0QDHHeYKMG+FfnSntT69Pw8Pb1PEdqRbHoUbIa9AZWe7jFlqdWXm8Zx3KjRQN1wWA1zCFKRVBz3Bi12nKdruEKrRxLCf3TsAaYXvQLzqhKtu9bhgwZImusz54AFrdFM4f9DfFAFUefGoRRRo8hp4UkBIwSoIhbHd9REtnVQ90CpJCBUt+q50dbsv0ap7Am3nfEptP5TZAmMBYdnToZsD+8BdrdSOhgSTIv5LkaMf6KNkbYb5AvTSX4nuKBeUAqkT9FxJUEbNARTPi+r+56RUTmFl9z4AOgpBZpjjynETCp8t9gdicll2jGNIOBj3QWWKFPzsvLW79+PUYCrvfdkvfMqLdPsItqn4BKCTEsKBrN7iHoFNhHGi4kgGFnr7CdLUIHDtQMGlQqHZE+cYFmpZ/6LzycEAjwhtywbv2b77z7zdtvl3BXoiISiHDp3+r2OL6ADI3hG2c2ZulR0aLeENVo8d2U0FKC9vGIQi6he2pfznHU/T9NKr6kil3ruzb/py36oJUPSAYYs0ioeUMocEAhEYnspRYsswbFDQM48LIUvKM8dJlXjHECmxbyhChwDplm/GDZmbWQZrTG0G5a0D4FmA7muNuccBO2vl/5Kj1cGslCiomxJ+LoQJHbQRABY8Kr4yYaLSnVfFNIpLvWPs+ZdwQFDMTDmUR4i1o6Y+akjByv2Z6D1lVHb8cMzkSoY19Xy1qbMWKOuQBHxBxyeUe4HCUGk+uYCzkkoSwFdXgA6wo/xZNOOkkXOfnJcz0pf6wGu6+9LdiwxWZtipvD0F4FTOxGIE3kElwGBNOJylTkfjOHyuBcRNA2faHiACMx43qLO6QJR/yBO7sog7GmiNO5SELgRrPZjY8iB5Zg1FI50YHq9EawD6n4dOMh9Vp+kZU/Mj8YHUgypi8UxcwvaCAmcfVXMB3lCDT4FkpzEACqoOP80usVxMm04MR4GH+ZH1Qm4emjCSKYSWwrqsJWyIHnSc48sxVjZA/M0SubTByXja2+srJy9OjRunzGNwN0nG15j+RAgaN7Cc0dj3BiF1G2JBafn/A1oSDUCeUgmtpIIoqSsK29A8NmUnIS4vPqzTs5C/62b3yVeCf0zGY1+3wBliLR2WHe2jvaOcuJ88qhKW0BX0tHFzSc3gUDflzdMYEQjAJYqPhg79G8w18DQB7iX3v4q35PVEJJzCxVcQsVeBVs5aGSCfpl+dj+1JW0et8/to08roYNOI9BLQmHuctkahdWHeWDsOEsTRIjS7KQZGX1rQaIiEqQgJ1qcwmvRGNuCAsxIFysYl36pleLG47MG4q6QsGgHUOGMah4yxhsdTe72CeDoAMOU5KtG7gCYFLmaGn8D4hIz1qghfA9csnAyEyTqxd1ct/znH6FLVq72ZzO7hCTodVoCplNAXLQFJp/SJdUIUf+go8J2U1NdqPfDNsoumSf2eiT2vvD5shlDPSGGVZdXU2DiYQ8wHtVPp4TVmOHw9ScQMsq/SR0K2BJVn2X0cEyBgMrngjE7LZZcZwG54b8wZgWc7ucGOvUeGHQprhjuiDMYCh8MTjJPBEPg/1wpwz78JO2Ol1ErAHuAjxBsKoR/JA5wnVoDQyAIHVFpoQicM/+e/LJWSjgOE4wIio4niwQKhlLIQu6W/Sh5aiij+uL+YgKDcEDNwTKoiEcMCf7ThPYVyIJHChRmJgdUHSmrNcQ84iuqGdCQAaA5/Dhw9etWzdy5EhALol1Y2YPhT6u1vRLTCd5QrGY4vZW7/v1/b8GKKWFxbfc8qXte3b9/T//IbKMLTnlpi9en5eVft//3YM7ZnVtXUNbx0mTp1x2/kV//89je/ft9f/i/nPOXrB5y3an07V0yeL5MydfdcXlv/7t7/ZVVTLeU2dMv+KSS8Vt0EmIW9Nb7y559ZWXkC6GDxny+as/h4nMYuVwl54O92vfh/oT8IubTHeZMlP0vn+olXy0hemA+u+A66PtSU/pA5IBXoL4idwLbhTszpqWsZK1LpJ8T15JJutcDSWEQuDCwYphdlqyTyWOLQsnNBH+wJLdl56aHPg3OLR46oGq6Euvv/v56850uXyIDihF5By87iqkvh48YjHEkwxmG0GCrebUJ59cgqvsSVOLCVktmnPqVesI1km/ozba05NXlpdev2a0he15TZXudWs2nXJunvQKlhSvNMFPPW08pr+QQ1PMHLEZgwYTnnGWhBmvLlry3mzRUYoH3QAsROxRo0bpqIHE/WabyNGWcNzqT5hx7+OfHvCuu4MKtQritFqd7e0Rh90ZDqFb1+wOS0dXuLa2auLEcfgKgl1pf7+Sj9IwwASsQmHB37JfJdQFbxqOGvZX1JYWe9JSPTqDjPDRW4jMnZ7RU42TEWJExINOxkNcQcKxeNAfsjs9gUA02ZEWDAQqDzTlZqdnZCYrUVKJNeIUNfDVPZkGfilPDyagXtGlyB+ZGEYLaDxItBWjvcPXnOxOj0RijS2N1mRHam6ueKzxpqdYBoILWBGMmgHq6Ogg3JAOuuMBYE9xR/0LLXr40UcvvPiiyaNH49GJk/BDDz18yWWXjBox8p9PPvvnv/79rm9+Y8Wy5XPmzrnjW9+samr74U9+dtFZZy04df7iJdY7vvlVghz85U9/YQv0j35wR2Zq8qJly+nsD77//YbW5h/dfffg4cMIGYsfIztg//L3f9x4wxdnTZ6IwYNJB0n70PvSr6PMH6G/B69u1k1p3nqBffD1ibv/JgQG9G4UPixu8CRs6ehhrHJsCcgW71TFSKENRhuAhwpacWLvG8IWE6ZR9nFA43H3ci5atHTQoKKhZcXhSCfLLWGFW2flE65EYhITJk8FYEI3gDIh6DP6moONcUfE4MLAFYyFJFwKqZjQqJ6VO4MlGvPbbZZduyqSUowFhd5gV9uEqWVJ3qSYvTlubLdasjRON+AE0AS7YWgnWib0IQgLyg6mcRay2h+o0BJC//rtvpee25IIRaeePsQpvrmcdy0o4jgv0X7FzBosN2YO4b6ZyZxSwN0HmNI6ukHV0CsK9FucMFEw3HEz57RGOPQAT+W4RiRoO0tbM2MboDuAmjMF8Ci2NLQ17tu7MT3d63I7Bg8uJdhhrIFjAaJ4WkscX2Hv9bbyV1yUulUxAEJgJShT3pOKL37BNZvMa1evcHkshLMtSypKzcyrrGyOJMKaxSm+KJKjG2UzgShOQENerPJCy7CgUBx4CVddIUJEqUZ23LZ7ZygSS/Ym52a0FRbkOXzxkLElbkO3heUGyQa03aufOQSL0CpVXN9x07sjTwSSwhNAvhT+kQaRgUZCWjhDgqAKrs3rN+Nvm+Q1FRdY0lIzDJylaemImFoT5g7NjGyK6HOwQJ1Cg2EJ7oe+jv5w9a37g9x3F6Vpre3t1dUHiEGf7HIjRFdV1ja3tI4cMcrpdp1/zjl3fPfOprrGVI/3tHknez2eUoe7OK8g4ve5bMZklzXNmwRZ5TC7884+rSQvG2K3deu2cePGp6akeFNSZkyf8c6SxZOnTMUgYLNb5s6e9ejDD9utxlnTp7O/AJr8Qdp/bHmZC72ov5dA9x/TYyvqRKoPGQJIAyxdWfZ9CpbFnDC6l27Zm5WZtX9tua+jY+TMoUOGZVgCzWw5XPRmTV2DPzXbO2NeVnJypHxjh8teVNVWW7Vt/5hRJ722vKK0LrBne8PsGRP2NNZkpydnpLvjprbtO5oSmnX46OzVayoKC0avWrvFbygvGzYq4XaV72zYs3UHh6bMOXl4ioVTaDp2lVdv3XEg0OUZPWryyAkZ+2vrn36lIi3FWFyUmH3SnFAX5xcEvCYtEPcuW1TZUu73ZpjmnF6YbTW1BCzrKqry0jJXLltntNmnzx5ZkGO0RUPmWDICCG4VTbsa5582bPXqneao2R6DZtgTVuwdYmU9BAZ9wHHoLVMZWLHThA00qJbQsbhjgYgtBRVLp9EUwFX30PTH8Qt0ECASbVsbLuoDYhkwPEy5IZokRzWYOZub9duKXGUx2eXAH9y7hG2NolNBCsvKyW9sCY+fOJhQNEQqo9FECsBZXEv4xe8UQkavVV80CzYGNiK5iFXOYaIahyKbPGiMcO0TRCoiFkf5BNxOd1qKK78wMysXj8NAIhoinc3C1n/0eWJAUgocQbWUis7HpKG5xumZDTog9GTcBDUzRiDIF8Q+DOVx2bNTXFnObDvh7o2anDkv5xXZPUaRFHGFApEzLjIyIHbmqMIjoHV5oHYAyB0VyWt9/kJ9xBuYw4cgajQdVy6G3Wk22qPQFdFDCf2xmGwJk9VL1AKna0RpVjDeFSPKl8lpjPttbIqKJ8GIkBQiJBWpixsujitYvHgxCiKdKugPP/i3Tv6lOlpIxAXFN7MIodXi1CZoWtfdc8CJ/HPY5bBPCREHNGHHzMaQUUPow22ZGPdOj4dxjYr8RvvZWCBu0TjzILjyO6j50dfefuMXlq4e+/qa5a+tWnb79Tfn2Nz9ccB79WrA+XmUTCJfKS9+lUYNaXdqfeSOkvXEq48cAkwntZQOqUgNUty8bEn5E4+/6XIQrSjvX/95e//+kNWWuWpddVVt85hxpTWVtc8+uk4Lji7favvnvxfu3rOjpDCNYx5YZa5klzedvYCRjZsrAgE0+DGrxbttS8eWTQ0GLWfJkvqH//MmgVBKC9zsKGk5ENq+tnpY4WR/S9aTD21DY2sxOw8c6PKmeXMK8p548rUD1c1yMIvF7fXmJCengaEWL9qyZ3dXQit59qnd2zdVjRk5iNBbTzy5NhJPj8ZTX3xh62svbRo6tCwes/7nX6+GgjgmsI7wVGaqG89eMGn0eE7naCEsI6dwsL5UbxWreggQen70nbE9z1hd8Vhy2J/zr7+ufPLhFcsWbQF3mGMphkSSUvMeTHe8d+wXQ+1wpGhx0lwMJPGMRDCn9oCpqYmT9pJbWiyVFVoUQwxYE/0WNlVB3VqXr4NdLX5fSLfbB/1RX1c0EjIlYmxNByCyPdKYYMOmJx5MMsXz6yqdtfud+BP6A6YDB1o6OrqUDlfClqE3s5qdMSQrbBAxYyQYj0WIYWMM+LDsgF5pl65i6lnS8OAJQziYOFDT3t6FlGerrGptafSBnAAdMAHhYbENBwMcScn2zmCgi/Be7KUMByI+XxBhhjaLOQrfTV3M0DX60i15rj4IFSoNQycfXlEs5kcOtTQFOm37KtoIM8yoH6hraWyKKBxEKdTO1lB2UOH5HZB4ln7IuSka1fw+NFTifmnA2ME+J0Vd+g0f0f2IZebz+UDc+tUvwfv+SWnkTU9NKS4qXrxkSUNT0579lVlZmfkF+Rs3bexoa3vkkYeKONo0JzMeY2copnmQP7t7whDo9LSM6gNVxGnw+broIQHUKAy7+9TJk9avWdPW0rqjvHz54sWnzp2biGJliBAlY9OaTSOHDp03a/aypUuDPt+HJ9gcIwBkFHo+x5jlRLKPEAKKx+pfvixUuDItYhozatCpZ5aeed5IDlhYtbIiFErdscM2e97ZpWXec86YUr2zuqkGQ5vRnt51+aVzT5k/cnBJptedMmrUkGlzSlwpSN/Y3CJmezs6BS2eYrQ4glFbJJ6SV5R3+Xlz504e5IhHPCbPmafNmTWn8OxzZhyobCWYajDqnD33zJOmjp80bXRWdsH+yob84vSywUkTJhZMmTbczAk/TrZa2qtrIju2t1x18ZnTZyaf+ZmZe2sim/bUGJ34z1mmTZ42c8aQC847yxBLrj8ACmBnQFi4VDCFpSmRaLCgl2YJsdRBJfK0W5vRHxJH+W2y1ddH62riM2bOnz17sjERsuLCTzSqD3YRHhJO88hlgBlxtI1a7KFVa1Zu2FgV0/LLK9pef/td2cMJclW4UfboaFFCm3PC34ED9cgs0Uiivq6RUDT79x9gbGHi1SLsrsdqxaRhrqmpfe21d3x+V01d+LkX3yGoLmZUNmyjasMn1RCz+bqIbJ9oamoJBRM2i6e2phEc1N7RSjRgeG8xSuvoRNA3W0k94aD9nSW7qputEUvBW4vLN204YDfhryKaAZwV2TsZjvgi0c6mxgONDXV4QzY1N4QJSePvbG1r5pAo4AByPIihFIsOl05ePoqtV8gf/N+TSA4ri3sIkdnS0vnKq6vr65OjsSFvLSrfvmcfDo3UC64VdGviiC4/TW5taWhobMWPprWp3d8V6Gjr9LX7TRaO9DmimgTDQGdnJ8p03RzSDcEP6Q8OAldfeeWbCxf+8Ac/ePLxJ4DAtVd//pWXXrn9m99ua2r8+i03Ewp0zKhhyR4XkhfS34jBZRjUifowtKTsx9//4e7tO8YOH5XidmMLRxSaMnFCWlLyD++86/e/+e0lnzl/0qjRKU7X6CHDUKG+9cab3/vOHf/8y9++esNNuemZSqf3IfXhRDGfQAgcCW1hnkLkN6Znm+OWHURIyC3w7NzWGIzEa5rqNm5ZaTeX2+K2cWOybPb2UKI2q4hD7uKar4VN+OxyFZ6Rk4ciLE83MInj4R5PxcE5Zm4y21ElBweNyCM6rJYIsH49SU5XWrjDsMWSmmNPDTW1trjchU89/bI1uamgYGiYvQFGN60xJjrj8bYI2ndT2Gj3mVy+prZq4s9mpthD4S0J84iktIy69tosE+f8xLKyOFy41hC32y2pkQhuF7QkKLsNiIOL8hmUiaOH5pTAEsJmorIAN4EmjoJ/+41t3OIKVlTuGDtx6KBhHmN8R8zSbDC2GI1dBoO3L4btl+3oP+kqKAY/EIX+hBj3u0TRTSutTUbn1jMvGFm+Nfmxx94lntil18y3mpH+sdagdRfbAAaLoWWFJlMS92ywwE97zJghZrMLnhvFgGjKxe8LZEffIwZCocRDJ81IKxg0ftnyDelpmVd/4VqChsVjjQQfEEWTAVtEKDnJMWXKGCqJa352IhUX5QwqHoSLFGw1Ldexts7iiSIj3ulNLTjz/AWL1izmuKVZ82YNyU+JRSulWqG+KOkTDqdl8mT8L7E6s3EklpeTVZBbIjFV5MySoJSJP7HAQgaQXNIQXfIQQoI5hn+8RnCQi1tVsi2SaCgcZro08/S1q9vWrKufMW9OXhG7poK4c1GCmgz4OFomjh+JTgVLbCjUlZubTpgGzdAZT+AFG1ABXWC4B7jA1Ehs+gtaOECK439EOXSDfPwfVFr8q5/fHQqHmdsw9gV5OT/6/l0IR7irgr7Rld15x3exHiHPuK2WG665msNTmbs/+O53ONiKkHOzJp6kkqFyE0vd9ddd+7kwnl0W4kxEYrEJI0eOHz3aqhlv/eatBHLQnESNc+BNrOZDH7P48XfhRI5PNASORAbguvAvTLS3oBAoMGvW9uadyUkuh6MrNbNlwVkXJFmKHRpxPuxhlmrcZUrksn0KLBuXIA0I5mzNRUGB8prw5lnoiE2mrK7OeluKHDKqNgT54qhVoBAWS3sg2BWOODPdXcFEp1gLM1ev2hoJ2q+47gqHPXnbmjdNKG9iGKqdVi3VwvG1ZjvBThJaKCUlPR71h302T0qOT0vtbI+keHMlOgW+p5agzRxXoXZgUcVLRGcrxXAZ83I6vBmeETIg+6FAIiBKdO7H4uQja1Vd8MHh6gO1U8eP1owtmrELeNksjrghZBYy8P4vMAIu6kfGL9TLmuVkvZjdYSWGbjQCQtYcDrcxBgUCrbOYOW9YUAGbRhMxbPhsOWbjMVgXfl0Cyqut1yj9QXN0B0TGSODWhScosVucOKuAg/AlF4iZUB+xIwGHYc4+ol7O4kbvDyZGSiCzn4gaVCPIudfeLKQUnIaA1amZO9huEIm3II0YrT7YV7M5FItj1XCi9oH9B5XHYnjZkoVBghhEpFKkD+RQqUVpt5hJIgcI/pf9a3QGs7icWoxXPSIIzrLkZMopmDMvTB0m2RGiWV3GUKQhFOUILifSjhYG3YlPjI4fqRzVCu4D4iuJ9TvRpSrsQkw02/B/wJKMTDMAlsdHnjgfVKYj7vc/0ofmPDjiGiYTOx/ei32bS/cFU0OFwlQ+QsPFbkBgaOgh4OOvLckjUKfRAiwkMlHT2TCD2GSTIZeJBSo8meSEGCanJvNQZgAqUwlp8//XhTCnw5xvXbDjhuv/Lyj09FZnpHp+9f6FO7Hi92Jes6ph7/aczSvtm9ccmD5ldLI7zLndbzy3svmAu3JvfNOWHaFwiwVNZchiMzrQttpsEY8nund3RUstURvMaanmDWsPNNR4N62vQTI3x13WhNXEmWpRfHQ0XIVYa63tbWtW7auvyti0OupyFOcXFHNcRDgSaG8JrlqxoqpqoyHR7rC4bcb0inJfS70z7Es3JewE8CvNy81KTX39jbX11d4d6xrdJuuY0tGWiAsPQ1ATEXNRE8e1Vs0MjoZRhvEX5yYzBlUDFkhQAiZLNEUQI64jwKEXIP1vzMF2S0tdV152Ftpto5amhcs62lLZlabbFfsnP+bfoBj4viMmRzXOjqtYpjU2YdWiuo7WpquuXOA0ud59aZdJw/YLt2vlkCnQglKpQ+Gw3LLwFctP7Dkx+WJ65SeYjp8MAloUtymRo4UL6/Ylvfv6PhRcubmeN996PRzGAsmBSjiGwkbL6XqYE2AOoLJKh4bOHnEvwD2kR6l6elEJSAbUkxsKpGxctW/80GkXn3nJuuV7du9Ebkgj8pjPD7p3yIndoiCidKwZrD+WJU0F/8IoUDg4kBoZF8FqICt8TJtbOvbtr0MhuHNPDWed7Nl7oLMriCs89EqHGKEcmVJatCjYMvT1F3eOHj3i3HPnbF69t3x9wm7JsFrYn+Iw4deUsHBYIvQQ7loQo5E2QE4iJjH7AyKmBJUOfCH09HjHHu+cGbjAvk9BQgyeEnmgenL+ichNdB960J2OSgUaCiDqEUtVZAk9jXyL8CR960/FyEk2GSsIrejbSCz0QK7DEqunn9ov+s4gcgkU1MU9vUWG/dT2+agdG3AqMzVQAjjw6Bs5vmjxkreXLHnj7HPGDxnsivo7T58+1RCIPPvMi6+8/tb+A/utjkBxvmFYqRUpXrgzQ9OCz4ypq61648U1XW1ds+YWtnTufe6F5yqq1535mVElxanGeNvoYfiFp6LfJVKg3Ro677zJLkv09aff3r991zVXzLOYAzNmDc3OsT//9HOtzbUXXzIlO4cTHVpHj8ut2LeZPS84wpeVFmZnWD22zs9eOitgqHvm+dc2b1zxhasmJ5vbnVp8+LACpxOvSAf6n2EjUz2pHDyHpZBYBezJhM3kiIxg2ZAMi60DHQzqCxUvE/eeAUFxJOCZQz7D6OFDk7xxsyWQiLnWrG199aU1LCu1So+U672fwx4fdW8q2raw2WjzdRob6lpSkuGLmzz2KGeAtLV04n2/Zs2min0HTGaJhwNCEZ45QdhbTobig4Sg38gTQSbqv+6ggpcOO4944nQF3J5wa2vTlq3leD3trqjbzBamvVVIfgR/BV9LIQmbSf+pl9DbLUFZ+gerggd30s6mTq/F6oglUl3e9et2BiP2qgON5Tv3bdtagaUBjlWwkiAj4eaFHsB7GPiwBwPaAEbX2TNpK7HLDtQ0rN2wOxo3LFm+udMfXrpyS2t7F8S8l4kTZl+suyn7K7qC/rjNFnXa4Avc1eW+WNi8Y8eeDRu3bt5Svm9/NTIAIpVFIISYIwE50H6xGUQQr6BipGRaNPClIwvowcCvP/hTutvvQ2MEBnwBFZwahKHXf9PkXhKh18xPXIn7FqA/17/B+yA8EcT1/unFHrGvfbN+eu71CYO4xCByj6VHX3S9E+nT09Vj6wmMBFOCCd07EZgkfALNlUt++ps75swddOrYoVo0GLVzDkTYibe6DQV0SoTdvDhWSxybLms0nfxhrcOuBRKWWMieEo9kekJwXi1BV7PBmBYOsUWoyxhNY/OgZmiwmzMSYZsxwR6oTg5/s5q8eA1pURuRnuO2Bi0ii9BqKY5EYfSETTOaWlDiEwOBg/lQIrPBGRyUMFRyFpMhkRtzm+0+YpITLeGAC4WGITnk8piDflfMEtLsmscc0VqtBFGPJMNsGo0+a9zS6YCrzbVG2uGdkwo+a3ZPpPwjLmrWSfeK6/0LaENdNUv9nWtNtr02rTPUkbx8ecBvHXTZF75qtaYxr44N+AOkWrx4MYec9O4b6JcCj8xY3NdZtz/Q9pTb3iR43hAwx5NsRleEk1XjhtZ2f2NT7dixZZyAZKG7mjWOTkHM43iIgvoRidgpIsKBbNQT9MfQAbqoxHoFCGaHlugimdGQBMvM3GhsasjNL1q3eu2EkWVOKKkgEHCMrmcAjLj+AB39AzYXzKgQSyJqY6TcbFFgx5Wgd0MacyVm8kWDbCv3Njd0RiNdZYMzE+z2SNgkYJTZpwgBoBM5Q9jUhF02rsuucqQD2UNOQ9kAyDfYmm12Ek+PXYpUSCAK0R2JPBE3BePYkNk1gq4nhh3IiFe9bD7QTDt270KnlJ6RhlIrLdnuYl8KLqomR9wUwvAu294NgZhpWGrJVUZ7oU5/+sGfn5wk1draOmnSJJjIjwRrSDMOv4S119G9/lpfrjpO11l67rl60/TKAt0pe4tkUBXvI8uM1OQSxVLPEPYm+7Tc9AGXDiC6qoPEUF/fyCFrEh4VG1pyUklJSZLHo79Ug6uDoDvxpwUeA/eDlXQ4zoIVA2Vo3rjLGfDGw6nRmJWDDoxGj9/A9iNhvwwsLhYnK9qYyunS0NREwgsKBqVoogSOhYmsgP0tkAH7aDMkaQHwCxWxcPJCCN9EqJEARHgrgn2Yxm54HHzPtUiKuPAbzEQqICmxanA/NRgyyKikeIcwQaKNRuOfgTu1UXNqnZiiwWtghEzRUIBx/NC2ZNxc4IkSAXBGGuMflfIRl/ExcpuigaiJczPy4qZMhwTz9hlMSbKP7AgX2XX0RyNBKxLRUwthr4gHTHZDjmYUlVXukJTKWrw20tUkY1lxCU78cC8gY40nm3ETjadhHYkRVcmYYokJbMNmi5HwMPjrmpMNhsxoNKB6zICA+h3ogkRTIPfAATWIfuQerWPagw1B1uJQL3vFjJngByI80X6j5irKz96+rTwnc7DZkYKjIq/1IQDcsoEQ9E2B0lNEDdJLdylQ/jAt2FUHmjVn8YtkvCQAlN3tbG7u3FlZO7h0UCSejXUBpCSIiJ0YcilgU5BQFMqnUTxU2ErsqDRXnH9JBU/AfFGDwU+pUa1X1i9kQy7+mo0eHovSBzJiTTHbC3GrbO7qnDhxSFTzhcSzHpxoikk4eGIwIUGmxuKpgkqZlN3B3FVZfb6oQJcDqLrP4w/vFop5hEt/cchrJbx0d1296H2rA6H358Ei1cEaAkMuXqsUH/5M1cv/X38DBJSMdJEOMs2wJBHZn1mzdcfOrZt27ijfzWh6klzM/XAkxC7xUSOHjx42JDc3R/kp6GeOwn8IjD6tINKHSHgpfSr0HTJ2eLqzZlz+xfvSU93uZE4tCHNOCwlUWjVx+qZW9wBLwYskfPSLhcLaVeXr3z0vSAJ5QDZV5w2AnoQaYQhUGKQ3e2/qnhvBONROPexMJhmN5wnLXM+iXvWkHfAv8jOxxQzmYMLEaQGOeCLZbse4jW0Nk6JC4ANmA4lJdcIvCnJAgQj3mDU4w5svlkYtwNnSzRXLRs2aGdCiTjlO56O68POP2DvNObaU9FOl1xhyxbWeTVIhB/TSaO9K1GY4E+78IY4oPA6tFlysB4ZSMxlTqfIOgiwOrPegm4q/B6dj3Y3Ey8v3ZA4Zk5uXK+feQ7QFDlz6txANNej0WI21eqO6D8S6OVOVvvsLQoyNuDjXYUyqxpvYW1KGtVtNA4qiAv0iqxSoPtQk5ug+l161/uCQF9IoeSAk4bALHG/xavXjB7k9XgnIZsYeIPoUeJUA0oMmB3nQkLDRbIs44DK6nIYkAHdYOZ+YB4eCpn+zj/62f+pP8m/FOjAvQPiwAo7mlpa//fshzF4pKTlDho86qANg510stnd/zf7d+/JyMk9dMB9fMtVvKeCTDIBjarve1cOTJmwuw4jRQxLxIBpTWKWe1cXCgGsbAC4sKLVA+74STq6bXzm8BkFDkBYslsJjKqGEkkHr4OnuBd0nE08E6fR50nurKun9dbQbCqH90mWz1IusYUUPhYRzDFocqYX/WJIEvRIbw0ZQjExDHA9ueu4vKKgvKU42mOowjSqMpoBxtMa8n3dUFTaELE6705FCoDc2vuJ4aTK0Gwx+K8ePRRK+QIIw7rjLOJLTiBauAEarlVlYYCv7A6Tj8gHtHo7mGMSAgjNMqaWuudIX6cxJzW3pbCsoKFKu9xSl41kGixL08aIv9Ffvcu9wDIiODQ3VNTiwhsKx4uISiwMZnGSMON8URd7eQnrhw8Njv3qb0S8Lbkh+E0H2EtGszEy1+ElJHBGIP1CCBCCDcnahH/IXMFiIft4v/4mfn0QIMHVkG7qS25CC1m/Y8Ke//CM7r7Bs6HA2tiPOSlgRSAQ6DPgjsxW3dwTY3RVVB/7x8KWXXpSe5mXFKZ0Zvf80TwnxzjsUu7IgAUxXsHlRxN8AMjDGQiwRMToJ89RjmzpsUuhOfQAToAvllcUr+F3XF/dLLlw5imnR1rPtH98MnG04DxQFLrv88T8ZAINQsRQo5gV01hILjgXMcQUw6P060K+uvj/pAsoTDocR7ajFR96U1El2e4lgNBOak4ExDlMEnQUzSrTBbM03BwPtu8Jd66jcHEuyEPCANrMfDV9YZ6Yn+zSDwa3Q2fuZN+9hGwBfanF/c0W8Y4vN3oEzFt6cssVX5rIAOxzF4wWEKoHjul1acL0XKDHrLUSJAOXhiEWUCDIqutYHPFICAwOiBw58TDikmiw2iaYpB/ShAAT+IVUU+h92Xbgk/qCZLb9i12V0FPiUckmMu3oIqj7lS6H4gGJPQI+GYpDlh6YRB06wLmClVXQDFxaAx5DCfODIxMzzKJnvkHKO+oO8MlcOXvJLM1mVcymBJXitYXCiZDF0Q4A0YpsaAyYtbCEKRTzLlTPbYElj/9uAs2Hv3r1s854wYcLB8k/cfYwhwF5LwRUGAm77vvTlr48dd1Jx6WD2NOFywLoWTCXrGuwjsj6zFn0obrgVFeUlJflXX3kxruSKR2EiDsz+foy7fhxNO5I0EI90VEZ8B+DQUPQDRFmPAiVWGIxkzzI7uFKEr8atG4YUq7uAFp8PnMrFbCfSdr9LFhjaZ5MDpT7rM57owPhpiTtMbN7pQwbUIHVnFanCxOkWmANRzLghIQnNxyY1jo4Rgq1fB9vT/UDJgrS6JwFRQY1dxnhWwmiNWprY36ylFGimbFxM2ZEgfT3sYp7Qi94uCABQMsb9oc4tDmMIZMopNoKcE3ZioFo4FltcNQ8r5UN6QOVmAssFfeGubcbYnoTZF9ccJoJYxNg4KkyPDCcmG0Gj4g2tjNWQScgqyiu8uUSQCEXbLVa05QPQWsnN4DLYauXwh4JQvVEeSFo8acxMBuGjYhIDwh1DYYN9l1DkOOXHI1icofoyT2SqDFw+JgS08BKSTx9dVYc0mtPEuajBzHG8gFS2O1Czbt09VvgJ5HvHqm8mOYRBkD6x0MV+xFkXMcLsQQEctkQwGFCOD/Fo2Gm2DjOE27REklGO8O5bwon7TyIEmIZMYeaE+elnXxw8fExBSRmhDNk/whn0LBgVZoYFg9sLJh/oAfyCJRKPF5UOCYe72rsCHrccsAF2IpzNJ7H/x9jmAckA5DMRs4eicfA4244kMAugBL2BH1S4se6V1rtMuGEvKITA7CKGlzhi4aMJGQB05AUN92sNgAVJxOJJ7Z3GPTt3j52QZzGHNRWnBnQgbv649Aoq6s7IvQV3b0MgigifcAe62Bhpt7mCca3NTKw05aBCFX2r0TG3qpnHeoPB4RE3fGfMlUjYg6YW0L5JAlN42eXEbd/shzZYtaX7EQ2X7UVxcyJmiXBjNOCEA6Ns1cIO4h19pMIjWJajyWP2LnZGm4l5IEINpgjOqGdmQxHFaCFdhQbQN8QmMd7DlSdw1tq9qyoes2RmZeXlewnxlkjIHqjDLlA9aF+HRC/4hYaIFIY1HZsqR0KA5G1a5YHtLc0taWmZhUV5uLFiFJZYNtQt4hoiCqhcB3vfSsgqJFkYMRpJ6+3Es2M12hDRDtTUNTW08mLIUFT4LkgPAp8JA/4A5fQts989lQ5Qr3iF4lBE1xCG5LgexFC8i2EV2VJiq27oaqxtNcTsg4YVJ7l9cWu7ZkwXserE9QmHAFMSpF99oL6qprFk0OAoEc6kR7hRG1pamjs7u1wuT3ZWjuykZmIL4kI8RKfBTLE2NDYnDypCH6J8Jz7hgDhq8wckAwpOuFegOhB8gp5BZ9RYwKxfMeQqzKpjd/WQBDaO5iB6jebrJGKXDV7KZGcvJlhbEnejaJVNYXcwadRi8Ta1Bl5buHbwqPOJkoLZVkYC9EAy0SUpJl5f0UZLIBxEP4F7jMPqefbFtwaVFE2dOcgCeyeOR7JcGW+VU4iBMLRSq+y55aH+XB5jkralb98W2Lhu7dmXDSG4jrif8jmirksK4b9qv5SofgoEzESajnPYE1w4fiY8wUKL60kfUUml/tC/lJQKBnWZoilATLopqA0CIFvhRHBhBhus0Viiq8sPsQxFuhB/AWV6jjc3u3j58mUZeSNsaGCINS2g6r2kIOmmGOp7eiqijzxmRwi+RL5g2O9j2y27uKMpqe6owTdu8tCtG/ZkBr2cZcLJboyaIiFUhx4RoUEHXW8V6kbJU0Jo5GwcrbG+Psmd0dLSkpnhjUb8o8YMb25sbmqs8SQVEIVUnTfHwhyonENL7fOLxP3TK7CECC3FAiclg4bjW0tLl9uVUVtXn5VTwL7gEaMmtLeEdnOCwniiq6I965Eg+xR94vYTBgEGHoWo3bRnb1VqehanoRBxRZzijImtm9dxvlNSUkogEBo/blJuTj7YC24nhpSgdhMgBtfUtAwpLRJuQBaEvig+YQA4xuaCvA5PKYvZHksyJVJZNMR/YM897BpRZbC3WPGw1lyRaMJiRdsTiicIn0AoLithGQWACfuyRe8MGzZk+ODiaLCTffnmiH5SNiojmEQWKLyfaPYtVlMgnGIJexyxbFes2BHutBBCAJkC7YMxTqwTZHZODeAgKs4msdrcu7bvy8i2Z+ckJ3zuC+Zeynl7johPiyJvpMRDcYsNVQT+ppAuIeNEiBT/RUGIhK30g5MUWZFjEV5buWnpW+0WzX5KYhgbH2R4OTiAr6OyfjgzdWMXwYwgsTi7YB1GazxmB0DE2FGGinYLEgbupALT/pjocCi/jyf0g0MorVEXwaTxZ9GI7YOiBgEE11sN9ll6I8wMB/QYHW1tres2bh48NMflthUWFhndjtp9zdnuvKREciLQZU24RLijneRQZF6RUToKTRTuR7TnojhSg4Zm1GLXwsYt67fbnZCTNHdS6tAsd7yTbcE5KRrB+tHXMQ2QjWCv4S0oA4rYPbkoAkrM4MtfMbXprIHFnLA37txR4esoLCowOkKjiksQsqoamvPyswg3Ice/ILpxQvXAQ6MPCI3se1GDzu71GQQhk2wfJxizhfkpTqhiFHbs376TyHtl+XZLODikIIXw5rtrdhbnzdVCXpMhxeCUXCeuTzgEiBxgiYUTHNtgs9lFMFDR1ffurdi/f+/pp53udiUT0RauhVgAIH+mFNNXOT4jbVtbWtpCoajTAU/wKZ8KrFiEd1Zm337CfeFP7Xp9TXlaSvaeFTu72jvHzh88aUquLdwSCIZffn5bS3vMk+I6+czS7JzEmkWVNmv+9saamt3VU6ZMfH1txeaqwKBVVeecOW3z/o1lRXnZmSkJY9u6NXsQwSdNH7Tk3c35uaOXrtgcs8UmTJkRctiWri6vLCcGZPSs8ydzVEDMXLVyY/mWjW2d7Y5Ro4fPO6Vke0Xjk6/sSkmJ5udp55xy6s7t1Wk5rkFpaW3+7Jdf3eyvq3ekBM84d2yh19UaiS1ctS0/acTmlVvZbjr35DFjRnsMYZ8pakdRETP6nHHDhZ+b8/qrK6FH9kiqQeMcgjgygUz3vjDomf6CuSBMYkknOzIjpMOCTAlJDITQkqQEwrXJaaIdQsAhlA+btQ5mHbDEntfv6y+8TQeauogx6oAQYSngWE1qiXpj+MmDwWXXBQYXFGuhlAxPXmnhmNFDUM8lDLb9Bxpa29pGjxoqyjoD28rAjmInFa96UxBLjWbG68mKQGY1edjSpfbKwkHj1w/+5nGnJyknPT0zryAnM8sajHRqcc+OLTVFxSmahaqVFCJlUiB7FICV0Hy7UY5AgG6yttgAiEhuF0MO4OIULLvN5vWmpuWXOPMLUqPxQDAe37G1Ii09KyUzDWFGKAesA9RcQUror34jUMXSLMfX8EDqFLIjYIfNl6PZBNFbbQ5EoiAWKqsxCf9ebOKURygObNCyTYKgg+nu1OT0wTneYKI9ojm3bilPzU7KKk5JxANmJ/qBIwrKqhUnvj4JEGCKmDjd2dja0WIyOyECsunIkKis2D1q+CgnB2zEjcSXZS8KggDHUDCDWBhMI8wDZqsZHgudNlywzC19Fn4SOv0+2ig9V5++eTEt4kuXuW5dw0uvvDtxyohpsyc8/cKSHeVhi3nI8hVdNk/JOeefm+TJeOw/S0L+ov0HTA899bzFFDjnjMmlhbluh2fUiMFTpwxCz7N3V1W4K2wgWKnRXbkrWLnXZzHmb1zX8fwziwcPTjr51EKLpaW5obajpeWMU+fnZg15/OFF0TALlZgHtqlTJ51++qlLF63btXN3drY3vzhrxNghE04ahFl3845tVXUH2AH1yOPPxyKt5509p7Rw1GMPr4mEsmOR7OWLK9avXn/62RMHj8x/4rnnWzs78ACU2SA9Nc2ePikzxx43oipBLywYU3TQ8mqAq/up/OlzSylme6fP9eCfXnzk0Xc3bao1oWUGEcWTtViWckgduLQBKjjOR1KLlkRYMEIm7NhVV1GBpFXG0cVbttYRQw0CZCLcgzD0YgBp62gmknNDc0MsHt5ftW9Pxc7svPSGpgNKd6R75tB3MCaWEm8inrN3t2H9uk5f0Fbb2LF+S3lTe5vYvyXOD+sCjyGTL4DNrL2zqwk3MpMlumz5YpvDFor4w2zWhTKiu+neyyt7+Awap0w7Nm6pb+50heKZW8tbdmyv4xBpEVZoHsWaEuGIPxju7PQ1tbTXsu133Yb12NvZBtfS1sRJmRAvwfBmvA5kn0cc6xEfxlE+iH1ynLSQHUAiqjASywf5xWxJb6x3rFpV19KGWtezceuBin0BE3KjqM6YCQTaY/twwO9r8gWaWtubkYc2bNpEtD6Hm+jl+42YeCwiBh3n4JxI/jGEABNIdMJupzh2i3ZRyaSRCIEwbVZO75HNiJz0w8xhIcDLoLSQmFHi4SJeEMgS6DyYzJ/y6SB0boBL1CcSx2H6NDys3JOmp06bMnnV8j1+v3v9tg6n113buCY9LdJY1dZSa42Z3LmDU88786SxZa6idFuq1VWSnz5ksNVi89nMbjz0rZZ2vNodhgwboSXD1njYO2niuLPmjhte5HIbYmn21FNmTx48xHzqqRN9bYmqfVVWQ8aowVMlJLS/0WNztDe2Z6bbstINRUUpZUPyUflg6bO4XBVVBItvu+qCCcOKAyfPnh4KJG3auNeSSHbEk86cP2bwkPCcecMddk9DfQesuoFzr8ACCUJmcixJEFUTVgVxjtSjrQmCGAAMR3qEjmT3njaOgTr73JNnzRhP+F88JsFIojNTzOmRMn7g58LninnTGmhub1q7tiISyti8qbqxqU184cUaTEeYt8zkqM1uzMlOi8dD+Ed6vY4Rw0sM8RDhegQVqoMhJSWXeMvhVMqEt8ERNzfFK/a2VNc2WbA9o9jBp1eWiITX50yWgsJMonVimohGI2WDi1LTXUiTVitedsogrGNOoQbIAZwVZNt7oHVnVVuz37h6Yzl2YJyxYMn5krNC2ckXD2VmJScl4V5FvLxwUXFudo40GG2RGLlR60oUWj6CvvnIQch0TX2zegE1LD6lSYHdw8eKtUQxIzmNO3ft378v1NbmWbNxZ5hNA0Sy4FwEiiLsLWQg7CvITUtxs5WAmCWJvNz0/Dz2pQdcBB5kUzV4Qe/LBx6wEwX8ryFAvCB7sjuZuSJkAFc0o3XosFFbtm7fum3bjh3lbCZAHRTwde0q31G+Y3tHZ4faJZ5Ac+2wybEXkASZC/pi+V935iOqf0DJl/UfM5mbbVbChRI8og5LAfrlmgMNoXhTZ6Sx1eeKxPZbIsbTTxvlcgcjxpakTA47ChKXOh63iscQopclZIg7YwYXDhoxc6dmyIiZrRGTL2HzG5xhT1Y8FoJ9ZuuWw2HNdLitQeOuuD3H6ORcSY6Lij762Ku5xc78nDKTBXzilAA4hoApEQ2DItD7E6xMs/j8qMlTHJyHaKgNaplWr71Dq45ZC6z2uNsdxAkkEU43RDlRNpkh1Ex++EaUA5gBWOjgF84kE8uusMOCQY8TvpbqA21DRqXmlLRFA60mmFN8HImKY2kRYvORXcKuaCHN0hI3106dNYSI1P/890MnTc6fPGlEItqEGoe+0K2oiLSm7PQUQ7o1IYc6aCnJzrSUZNhr9J9xcQDDGIahB2AgQ4QM4jmTKBmWclHelJee25STm7fgzDO1BCx5JwhavE7ZcKAZXU6bJ99FB3HZoqriolycpZCEIpzxKVwVMBQUT++hiIlEm83hPu3smS8tXLlqy/qLzplTkG7VQu3EBxWYaxZgD6HKz0sDdOzqgm7l5mRwRITk1QIcnEnALxmy7o0tFMtCZARl9warGa6NceNOjzItj/iPyo5O2VrScrQLLpv5xmtVW7asPe3sk7PyfJx7bVYyCzqmBCYdq7Ekn+iw+IQQZzxQmJ+JCwmWqIgEz5KYqZ92bfBHNkc/XgUzbZgzhsyMdEIuOp2cc87e+FhZ2VCX23mgugbVX3FxKXHlQkG/2+lk7axcuerUU06z4bkY8mdnpWNmhE9SfZKJ/Wm9BiQDdBZ9GEd/xBrrOFRrJDGBG2vrM9NSktzRjEzttFPnp7pbXLIgDf4EtkHcgQi1z7HsMYym2Hk51tZos0bDbAwzh41ZMVyAtLQDjbvS8i0RtgXYQ3ErMbwIN4PDjbGlq7MjGMrJcvkC8bZgyJ2etXj1Rk9K/oWXnY3mYuXKGuIOcxo6qJoD0czGJJMoMdDxJbLSM8N+Y2dXpiudrUH2pq665OziGK70Zg5ptZg4+DKRbDF42Gegmzo5pFf8HhMOIwdycIaX6BRgT9lYi8MPo61jsGMZaABjqqsOzTs9LW7cy24Js5aLjAkuQxWj6tIL+fDnDbgJ7ze1k9kcCkeamrry8nIDfj9HBHDWLFiMhkk/2LoBv6+J2xKirjDgYFrRl4A05VwaQXLyS81vJAhws82G305LczvHLBJ7hXMWk5MhISBbAvQT/Qm9KWWCNuVMY/AvG685DVGc7OJAE7Teq0WhdHh03DEsFqutobWLYwGyszObm1vyUnOQwUXnzqkunLgrcajIHqIVrFSK5WRL4eohqJxPwBN4N4zDbPIS0YAqkNBx9UGG0DhUncNhOHWAExRknwqsh9AAIfciCkEo2C7U3gGs0tPTGxuacvI9CUgjoZiYAFAK9MDEIqU6mRBQFuIIBDgNgdOVYWdg/5SKQBE0Cj1xfZIhAE9B8zGJbdmynZNaOXQBRolVAK+TnZPP2MPlsLEwIz3D52tv72xLS09nBuFJ5nW7C/NymJxKN6mIAVPlU3oJ1hjoAs8maybvxvW1q5e1v7uwcueWyuknTXGxu6Yk99F/PLN9Q9ua1TXvLFoajfqIVmkOu+EK5fgAWywlw7J2/eYdWzmq0JCTn/zGG5u3b40vfGP9/v3VhoTTwr6tUAjm3mjt0CxtBKaOxBveeuud3dtDb72+tbiwLL8wJzXDWVtftXXr7jdef7WpZZfVwrlRZpfDtWr5pn176/1Y9jl829BZnOvBFPHIU6s3bIq9/fbmdG/qyLJRbEOK4iwZ91pxc0LrH+0wmNkrxMoXFxE0A1ZR/mmxECiAdc7oAoEjAeFQwKg5IHgGtr+jM9AZLcwpMCHQhDPb6t0rl+1ftaaq089h8ZCWj+oSRjhuM8UzE8HsxW9ux9X1nLPOTvFkvvrCkhgEyMDp7+LnKwKPxLzAVApCZCjpoeBy4a51ykDHRSGGszTb/txmLS8ezN+12bD07ZpZM6eUlWa9+DxRvjvxm8S23NLpJ561pBTyAYrHCgDcYP8BBboakbQFCx9cI1QCvk7taDFsWL5j8qhp58w/t2J7zZpVe8JhD176ra3+cBj/KrROUgKp8eHBI1UZe4VoURIymvL0pyOyAULujbaq6oaNm3dG4sZlKzZ2+UJr1m5tae2kbtUsgbloiWhcPK9+X9rCF7eMGT34jDMmVu5uWL+kzWrJoC9mW5JmdrN5UAIaig1a9MHkEwUwnI/Al4v6qfdgf6ToE9cnEwLwj7Avbrfz9FNmQ+ZVbEjxmUD8xHkUvgZXQnYJMBVqDlRX7NpdNngwA8+LQUX5OVlpzAf4Fbga4Zw+vdeAGJDFgfcl+pz45BkjODygqX7/5z43Py9HS4Q6Tp42atigjA0b127cth3fUastMXpo7sThBVow5OAAkHjrhZ+ZmGQ171rbYPTHZ0zOyUg1bFizzWJu+fzVs8YOzbfF2udPGVSalmkkZrWmpaear75q3phhhRuWVLgS5isvnamFG2ZNLZoxuXj72o0ZLtsXPze/MAuv09C4sSPhGjdvKte04IQJJUUFeMPXXHrxjPzB7nUbNoV9DdddfmZyzJlqNs2dNTjVa4Xym82ts+YXeDNDMcKJxt2ILDIlEkG7PTFl6jjOd9IM+IphH1YO78cwyiAMUId8maKz5hSxwwllst2QV19ba7S2BxOxN97cge/sRzdbBPuiY4tbYn67VcPPBo1JjTcF9Gbt6gywUWDjxm27du9HC68C6CM40BZBr6LfV7gVRleIHx86Ixe/zawEmPq6usbMzAyructi9qWnJlVXN5nM7q3lFZW1dRu3bcN2axaVkuBKZcNXZw+IBqy7IL0wyuMDaiUGbFt7Kwx2kjloi3XkpLlbW5o5bqaSTWJ1bfv21DU3tsOXiRlBmieWOkoQEiBNpYU0W2F/oTSYKMSu6/N31Te1Ir3UNWL9jtXXtwUDEYvsOpbKoU3kZ+8aG+v8XbGMVK/D0caJ9LmZrmiXIRq1bNq8Z/mqzes379xVcYBdLSIXSWgNJoUMqlIOUj23QkSlxBPXJx4CKFHFtoQSmE2Obpe5sbHWbAFrIePKsAvLZDI2tzSFgoGxY0YXFxXW1dUjNSIzlJQUytQWLoEp8Sm/WDeAiUnf21W6zdoONO5f+PMHfzh31vDTxo0hekHERjhnn5OjbM1WgzlLdvOa7Zw+bDR02IjNS7zoaMJt6IqbAn57qkUb5PQ7NXNdyFVvMKQgY5mtOG2kxfHWMTbZzWnxkFMEM4c/ZibKa5IpHjFrdjZuJGwNYGxU7WYtX6K2GfAGYltpiyGRE8ZIAE9qCkTRKnGGlLUhHvGZjEVBe8QRQtngRMZ34uVri4ScRluY4PThEOfmuKzhRCvHJlvDqbLCTW1oOkIObySR4oi1J+JJ3tILzO5ilOfiHDXQxXQRdloC1srpChxsbjZ0+Zq3BtteNGqVhqDTqQ3SbEEtuWn5htbG+vFXXvddQW4yg94PKll89PMGUO7EcavZF2xa7vXWxRL+SNwnXqrGnFiCTfIGnz92oK5m9MhBBqJgo/aX8ycFUyMKgO3oHwMguzvwJxKEDqqlmZheI+jLzNZUNgEmoh0WI0rSJFQz4WjIF+x0epI2b9w6eeRQu+BbqCYlASsmDBUQlEK/9PmjqIvcIpXJRjeX1SGHzOGIYQU94yWK/G2JBM3bNu8sLs7Oy3dClQ0iqNmI9KlKRvtEg9XmXmrhYwpAVtjEyGYI0SYJjRA/bi2C64Ei6xKOXOzzQkOYakI2bEQ3JyFWEB5YCC1ucKM2Kt9TgWrYwdHXqV6ot5ndMCSWnXGEJmcXIWEwAnHziPSSKw32AmZL75Lo7qL6cyKmUF9o/FfuZd72XIeNCWj64LODdz3ptSi7nfCjxkHMZPX5Ao89+fT2ir2jxkwwceqqmjLMp86Otpamhlg0zF6ojOyCWDh48XlnFBfmIRPIZGdaoUc4vOyeOj4Ff5XQfUg/6C4PrUaH02NJ4kgTYlYCCyPLV/MQ4T8CHSX4hvjOs1K8Cc1DRGPBeGajX0sCZQIzjuT1OTlLnmg2OfCOcNBR9lqxjK3A0+1nRTptuGtqWopBTqNkB5QLUwEx5uJastEOc8r+Y46jEdWDSUs3GjJk8bNVC6SVcBLVGb90Y4I9gZlydBRoRcQ2VLumuJtDE3HzNYTZisbypl8xGuqBv4yqsLFGQkdgSRTnenvQUqhZ091sgyNoPpiHTg90iXaClvIRZApHDPdKkDUU6B67udDkxLUQI3Xy2iUtu/fEzr/0JCEBMjE/qlkDVMFyMZsnnEgDPFaLG68bn+x78Ij9OxY029hNlx2Nh6wOzPWgORA31hoaJMtJXzQwvXQDdkh6LHCS3oE+0YtrGAnEDEB8IKvR4klLLdi6tbyocJLJ4wpLUGgBR0/vuGclSXYpR790fpoHaNs4gDhMeWwvj8mRk7xielhdIb8vbHJFzN6IOUvtMqNAiKznYDlCnfilKjImqee9lfKOe4IBcniQJGKKqN80SnLRTeXlo7pLE8QPAJKHvj/J4gjj5sDm5Pz8UmhcnKDXBmzjTNBkUqABi2r5mqVA7cjj8CNxa5IaTlz/CwjokxJyr3h5UIYwY8SbOgQdC6sO+4d9CGlS7F7YowThiFVYLqaK1SJbghTnYEzyeq695qq333lne/nucNzmTUnHFYg0yUle/NWCwSB8RXFB+oRxY7OzM0Q5SLEgOSr5+E0EmeuHXdJMwXo9Vw8j+p49AL0NWKAhHOzqbG10uq12mzj7YCjplroVnuup58h/AV7P8lSJaOHBisCqUWUGJDwnkAZzMxgkJw9O5RaTB5ykxcNqWx/5ZIzF2etgAXq9h5SpPxrou28y6mFLFLOK3lgSmsNoTjGZvPChaiINlBsKgeQie4PN4lXML1xrEv44RkV44Xhkz/4Dr76w0OHJPPmMs0tLBnHeuogd7/d6D2mAfdtoZ9jOG2k2G/wSzkiUGTZoLwsGn5ntW7babdaSoUOxf+D/IGDlDK9jao+CEpAx+WTiCxK3NDe179qxOzunICszzZ1shyXq3y2SyVE2/Z/zQA6FtzoQXrDHCGIWYQvuKurvaIdDr6urTfK4C0qKhfrIsDMelNMzbftX84F/0yNzonxbucPhwTAo+0IxjTCOoA6ioEgDFI9hsMeZDNa0hMlOYLkBW3NCGvjAg3FMBchecNgGWfPMHgxH1nAoUlvb2NBQ5/N1UQRHdqsjw4qTk5MScQR18SmDkSGDThVIo8o4WJ3aESlvm5qad+6t2rx5K9Q/Eo2keFPFSpyROXHiuKLcTKiIjhWVBvhg9o/ZXV9839s04e/0S+GEnvv3ImQD88ACPmtSej4GTyRrduvIAhXkoNi5/tDtbcMx30h4Cs1vs0Q7Aw0Rf5syzaEZcIG7UONpxnaCjyZ7s+12F0pmgjwTscfMbtBjLv+ICYXagHNENcIHIUIdbUj9Is+8Z/mARTAhmpFwpLWN0Ekc3hg6UF2VlOlyJlkqDmz2Znoyk4uOoaQjNvDoLwRb0lKTy+IoUHSR9uPbKww5hha/ryuk+cIRc2tHZnZONuuH1+DWw7D0USohA3iZiwVl8UX3E3zBF0sKN1uHpQ9FFpKhP/w6DHAQ9FCoubG13oYXD9GqTZiaIKXW5OTszqC5Fq+zjJE5pSUGuK+eC/r/0cGNZmMOTNiibcFYlrXE6IJnBHIi3BBkkLcyA3Bbg9Dh+KykiY/Q0N/T5RN/jwoB1JhMCcaF6WVevHTFk08818WmTY/bm+Ilalk4EiQ2nNPhOPXUU049Zb7LgdkfgxRTTy4dg/ebmDzEXw7Z1OtNnjZ5zLQpY9rb2leuXDlnzhy7jT3ttkhEzIQIhsxYmSGfvIue63SArvPpXl/94HB4twYmAywJkxkjHA6f6AkwpIA1daAAZhbPexZ7eEWHPCG/KGsTvqqKZfXVG5wmPAWJMZmsJZyoJWKogy3JYyZcaLWWEtBUtkRx+DAq7EPKeD8/4IuxSHRrfQVHooPGNxGBBHi9txJAlP6Ipmw5C7Tt3/SskXhKpkSKJTqhhE3poWCoJdDAwbf5vdB/P018jzxQxC7Fqjs5ZYAxQVYRx1ewXCzuSnJPmjqFAqCyUdRBMFN0WJRCxz6hWUWw7Wr2aImC4vwiCa0FKRapXPRIfUeeUoUMHV4+0Ix1VK+s2PWuw4azAa6s2ClCkYR18PD5JWWnFJYU0Ubx1pBY8OqinVLnsbdTz3Yc35CZYaOHojoEMognitrBccJnEkcA8gYbqRO5qFUOwIB0uj/KcTyOlv//mpSpLbTa5w/98lf3LV2y6owzzpoyrRiVj3oOpkaUi6HJWbJy7eYduy676LzBpUU4KwqukonUd6YKCEHr0ACdEoiqX0R7s7+rIyczPdmTjFTAohFrk2QUQvJJADtN7d9N/ORof68CQASqY7gGJgOUYtHYQkHsTLl0mOj3uFZ8QAhJw5WunXhf+WnEp8myGlopFBdu8d/HpEgwGLPJbWcflA0XeJHz2AcgXf5Ag0MBFEQ0AlE+CaT4ovsgUZROCmVKX9/jYn5ACe1289BSGHDMIOA5bIxE13ZEo5zY6ZVKPrILTQ2HTcoYSw9kyrOnwiRm27hFdKA8hZfH/QXHeH2YeCK7gY+jTZKWmcTFDegSxRKu+/zU0b2ArfuSlLyAfKqpJnXIfzU1tVSXNnqo22rwWWNOFmDUEo5qdjenjRmIaCTFCuPNr+6LO8Ji9/7sefxh/FWFMmyC3CH8nFah6qWP0gxMyvABJukFjkmwCWL5AWAfSVM+jO78/1EGUwZGBvnM9te//rOpqePKq69xOj0SDpo3DBO4AyJuNDvdySNHj21taXn08eeu+OxFBQW5OAXZMZLJTNQ9ILoBxqCjJpWnQgQIOo6B0NTS1JmRnseMVRNAjTmDf3BafoyALaSpB//RQl395fP7G/GaC+EkY3I4nWnpaU7so0x2Go59T5FMGC5+HlnrLX0cmAwoJCnGNX0xyDf/ZdkLIv3AUKI3JgzL4KeQKYYew2HtAp1xTKDGmVbEdrE2hc2RJBs+gsTRZ1REryFBa8Q6obdImn78lxBJ5YZIVjCpjkx7cJfu/HJMhWLtjkWsjQTFMRFWk4BpaOxN7mjCTiQCBaljKuX9JIIHwiFLGq9yi9sSXBFkjAFXcSz6gEeGS36KFffY4MbgKqKoF663rxchinVkwIuyGRpqUtkYW2HstYg1GjQ3J0wdRAdizkRtbeG4F6M1m/VMmAEksQ5/1WK+RObUyxmwlg/ykH4x7mIq5BJQSEv1TsJ3IN3qvVYtiXPqGHYBZuixwUyVeeLrw4YAY4R1175s+dpAIDZ7zvxoXMMQB4JTs12QELy9TDrYFM2QkpKGWvbZF1696sqLvR59oEkxcKN6sLwxEo7VHKgfOmy4mgtq9AfO8bF4SrMJ8E5TrFYYFePu/dUrV61paGgIhcIoM6029sPG3R7P0BHDxo0bw0ZfOBzl6CSqTnZOHL0PRyAD8N0I+6KuVYwei0ewAI1goZKl++HRiz7aWyiJCC+dRmMALx5KxgZA1AI5NRre2hwwmX2ydYFURlz7Q0ZjquJzqf0DXkgWPe1n3PnoBFYqOp55ILngjmk2PAWt7UTIwGnVaAooEH3ARh4tO1hWXtMPoCU/YHnYCC0jpL+Re/3SOyQDd8xwQ/JCKSSgoIPd+XuKEx1U/0twN8OnXgh21VtHflhsxDisAkFMxCipDEb8PjlklDKl6T2F95THI/RGx97OnnzH+lfv1yEAUr2ThisHBFqOIwCNk74zvY9vOhxrM06kO2YImEwWbEjLl6/Kzy9mYIRngx1k8ydHRDFXYljEWAD4rcgCYPrYbC6bPbynYu+EcSMU2deXthrlwyoVvtqUaGips7rMRJWQUnoSKoPEYRk+Hg/E9K1pHJPwymuvVzd2ZmXnpmTk8pDJivWbb0Lm7dpdvX9/XU52+sxpJ6UmudDmQgN6XaeO1I+ByQBg5WAoZWcRYPeAiEXDp5urOlKJx/Cc8nAUB7+zWcBoiNjtRDZGGYRgrjlsEYJSBEyWFEuEncnQPkX+kQO7NVQK4xxDHQMlkX5IFPvukPTMHgRL1j4fHFlcA+vRujvfAwMpVyakPZxs1oLih8N+WEJgonWOxYhkOlC9H94zFgPHaCvPepqhVgIAEkGGUxV5clhNNBvWm+vwV4ellXEWpZlKzL3+IRk36hIUefiliJCUz3LsmSucVBZOdgY9DlPIEpMjaaxasimejIwHtJGf9OIE4Qph4OL72NupchzHl1ShKtWBIE3t/oj0IyZ0oIcjqTjDSR/REBNuRCnfjqOWE0k/TAjAh76+8C3sgYQkISg5MbJQ3xF3pq25A3ViVlaW1UY8K2GFZPUiSMJlOF1VVdVjRg/V9XtHaQ2DTNbahgNDhpVKRNFuLSjTomdiHCXzf/eVLCmZv1y4aJiJhff1r986euyEEROmo+iRNyo2hoi73FrY+ca+Tm3FivULX19453dvy05PxY1K3nUXoko67GtgMkAWOZ9Rv7q5TNE8qAfvWeZhlQzwgEKcrHyOlomacQzlL4Za9jChDrRjyY8bM4S1xDbBcLH/gL09wndzHbU3A1TU/5HSQesPgY3OCcrPw3jp7owKR1ApPKyemIT4MqFaxKbYbEwIXuOI9YTJhvqbOPYKh1KyDEp3ER/qH7gV1VRpjIKFaM3UPZs5jgKdY4QbZYlnp2r8Ucvr7lRvsaoNPRMWihDhmBewa9wVtHdCBmxRJ8Jp3AqNof1g2J6rN0v/AnsSfEh/qVT1i+KoXLom/7lVTTkoI3ZXd0Il9CHB/f0W4wuHy/ftGzxkpER9YTQSxB7evWPbFtyX/f7A3ory2XPnoiqGpwBP8iG2IYFyw8FgLBg3O+X8bZ3BODjTDmkJYcrZBmTNTM1mkhJzlJc90/0IOQ7J/t/7weEYcJ1iHzWynTP657/9e+yEWRMmTglGQe5iXyN2jsxhWWyiIDawUc5sGjpsaMXexH8ee/TrN98Es4h6CM98xeIM3PKByQBpBwJG97OBXg1c+hGfChHHyIE4xoYvTgNWZ08KomfDcQdR7TRTmsEcZOsTU0DxiUds5xGrOOKLXsRzHP1givQBCa1HR+/CrMRpVqqFWLfZnoz9Atp2HMUesY1He0H7B6yit19Hy3wM73qp14C1HEMBkoSZi5GaTcLsJQwCPXzANKNDYVtG+YOUfIwNODwZlfbWKze9P9Q9vw7yBIdnPvHkvwyB1tYWTh60EKGSKIQGsz/g37N794zp0zMyc9iWTtwUjKKYBXQaQNuwHKtvg9/ny0jz6sPbg9kHaDtxRlO8KS6XG/dQMy4Ch86HATL8Tx4pvEPfENK51q3f5ElKGT5ibFQFDqNFyAcSCRFTXJTtuqIassg05sBwoqiWdXW1dPp8KRz88l64oXfN/7d7KQ0z4rSDKkNcRzX89ugFXqFmP3EFOA9LjBPdFFr8+f7b7TtqfSAyxBdLgjCVfIuPC6y47F9kWp64BAJQeQR6jvkSXgbhSSniMPZgEDoBohNT5L0hEPD5PG7snHFUP8SKCYVCGVlZmTk5ckSMZvQkJeHfxTIEf8E78hGKAFpE4sQwegwXiJJdAhStZzyGHP+zJNIxjKiB4OrVa0sHDQbj4ykL44+2Z2/Fnk0bN3CmJqAgVJ5+OAK2YaUlM2A2319ZiZVFbaw7Ggr9ELns4wETTWIPpxHLqo8DYTTOKuBiTEVK4EhbZDq8DFEW6wzax4wISEdhc6NxS9gI9cUSil3DbMNopZlDH6Gd83gA/L9Oi+WO0xd8bPUXT02kdolBwt4cn4LPCb77fz0+H/v6HXZcEnDtF8M9ugL8Hf3BEDQAnxjwt+zwgg7AhnHIqIWY5zCRcpA5eCUmRxW997V//362EXzcyYDwm6h0IG8co1QRCsUybY4IzjQWYzQSXLVmNdQxr6AAYkCg+/z8fHokcrjY3/gv/m91tU3xEZzoJ1qvo1z/IzIgLbJoUHqjOxIhCljcZuZUaEgBXqTJHPiQsKcotTutx5wowdA+IlX7UUBz+CtgKyRV1HDsPnCajW6TOYnDB4xmu7g6aWYPx/l+NCaBwxvz8X6CCMC+E48hjkiHcC8ubcR7MRixJwsIT1wnIHB0CGRmpNk5MSQeQzEEckN709HeuXT5Cm9KRm1t7dSpU91u966duzhsw+V0lA0ZzBZFCmRnQEpKypFKBr/oHjWgy8bGxkmTJgne/HhfsqdZtXB/1QGXJxlCCF3ECrx3727wJBuoQfHDhg6jH8g3aNHwzYYQAjSlL7N0dPg45TU1LfnovfwfKYXg+qPYCTPN8axH/7PihWc3uuxlC1/funp1pcmc+vd/Pv/226uJBan0B2iPGKqP1WgRkDP+t7+9UlUZr6szvP7aNpMl95ln161cVaklvEqC0Qfu6JD/dL/lQO/sNSsPbFrftHdX8N0390YCKc8/vaK9BRpwQhT4dA/9h9I7zeV0lRYXxqKcpMe275jV7pw9d57bk9zl9w8bMcrtTuJkJCJJDCotYe9YTXU1tbIvPTUlxZucDLo/UiNEwWQ2BwIBCIbH4yHlx5wSECEDQtDlC1ZV1xGkC0skp8ViTK2q2jd08CCLxRSNcPqTBgGQ3UGyTZr4eiwxkSEIxERQR7EhC+t6JJDI8/+RNICwg6KADcNaZNq0cW57LBQNDB5aTDiEmKFryozhKdnFCQkSSgv5TyM/VrhDxK7JE8dkpBBszjykLF+LBEYOzfWkpGqavlX1aBD//+EdQxuO+DipWCK+Oo3eNBdWgZy8dLuLcWQ+yrieuE5A4MgQIDCieezoUU8+93pmrpsDADgQw5OUPGbsOIKNi60pzvHa3rQUbygQhEfEioDnD2dmZGSmczL2e9qfWltbXS6XxF782EsDcMGt7e2rV28EodudotsRuUeCn5qQBjAQ4DXLjlsO+yMsUlQO4xO7HHQQvykchAAjMTMkThveVke+PgIyoJOdo1Uqpm9MwRxAGTe2DB+dZTGh9msqHZaO8icSaZo4eXDEkBbX/FYx/tBniWGJpmvA6wiPB0yrHnY3r4c4ijeWPD9Ke6FZfd6TkdMeLSdNKjFou4lGxBm/4Vjt+LHZhC02mVrVNrf3nohHbt8xvaERkNH+10B9ODyVnmugtP3L6/v7SOLYgPCHLeEgnOKyFEJdR2yxvJI8Q7DzpBlDzU5sJ1jwBsj0YbWzb5sPudcrGLjbvZV3A3XgVIcUd+LH8UMAMB8C2SMOCYieYBKFhUWcIdze1pKelQ8ZiMai4D6YY1hIWZKasb6hbvfOnUPKyrIyM0KxWGNDbVbmDHUq6iHV9DYUwQJpgJ9VVVWlpaX6c9Zyb4KP4U31gQNbt+6YOXO2w5W0edsuu9Mp3p8m0/CRI9avXdve6cdvCiPwSSdN83e2bS/fwdFg7NwcNWq8x52CJOUgxISLnV54VA0Mk24gHKnnSlqSDWTCuB+1CL2E/nJYz8oaMKtCrGzZ9xhtOXFLgK2BjK9sWTNFLYS119hQlms14X/JQbU4lcri1Hcc9WstPcOOPABC7Jeu709xLOODoAQngA1dNs2yi8wqocUHuFT7+RJxRAzYksSVMKYmbByMoygupzHb8Rhyc8pxwpSKL6mC+ICFDVD+cT4iOg+71cxhTc5QRhGID45Uh2xFhfLhZw/Glr1RGNcOioM9YyIJZZfBMV8UDrwOz0E58CcDwd9oTWT4DSM4VoDzqjH6mDxJkYg9xZAl8FEFUSYN128HLkM1jyoGvg5vzcDpep72QkWvV5VLGeyyZMOKSZ04EdOsGBnpjoXwhuaPNXbo6dUn5q9EAkJlIVMSFxfAi4kXT0dWITMR5CHaDJ70TEz4fZnWnzl7we//+HcOoHUnpYRxHGXF4VwOs5vQNqzfwHl2w4cNTUtJj0eigY7GSaPLCnMy1BkbEm6BsvtNHv1hZ2en3+/Pzs6WBOr6OACx12ihW635DoVD1ZXVDfX1p86fT1zVosLMdRvXqngKFswDeQWDHC5vdVWl22EtKMg3WaN1+/e5Paay0gmRiGax28McPGWKpWekOV1E7YH3Ahj94HGw30ea66BIP0o0xbgxWPqNXsrA60+BXS/3aMl6ambrpi1uSE5NH5Oazq4/oe0qvATbxpkKBAHF1goRo14WJluN447eIBA9RfT8JWPP7Xv/1bGknoGKmI4ITHCnNF8Q2nsWoLgJo8OVac0/jyMucNg1EOvcBDGyagmbCn/B/Ud3IfC1xww4RyQp9A9y5oPDKiPNuHDPeOsOc/QJyqY76euv9IGjjww6QOvBi8fQWApHIlMJdRAdCqhDf+nJUjOmpmZONJjRkiG2A2oKcEksP+oVt9FuWKus0hJFaPUq+MVN731vWm56L9pP1gEq7k3R/6aH7qnpwmzjQtY0yxk7tE1CYtA2KRG+UwUaYu4dT/n96zvx+xAIiBJYQiEokydSv+IGwPsK4OINZOG0aTXoDDx3WD6xCaR4vddc/dmXXn2ztSWYnJJObvHRM5kIfhwO+1NTkxsb64g0Om7c6NkzJxQW5AtxoUyZGwNcPEcLRBweaICdQ2ilooFTDpD5o3/Uq6GiVc3NzUTAHjJkyEmTJ+vKq7JBpePGjq6qaeREE3WSRzQ1NTMjIwuzdywexeTR2tZOZ7Zv2zF46FDB3KxZq2XUyGHH0vCByQCUe9feBsLXMWzi4CGXouNy03d9ym/9AiOqG1oi49ibTK2snkQ9f0G8nPoBK8uZuQRK5mh1nqDR4pkZ7oDdz6bOhNYumwnMHCgRAMHZYy7hbA+9hLPow+0e+nKAX7RMmAwVWBRWBM1aaUl2spv9dSAmhRwHyHTwkT5pJKaVyVzRFGhs4aBzO3ykMQGyi6Z5XXJwnQrBcTDPh3wnm8YjbLCsrI7GjDbAxSGiRmucLfdaRM5nggYARsFyinKz60Qmuv5cQvvICpOIqhAJQb7HeMnIMCdE4qO0PsMr8O/WqvUtCiCInAB82TAgi1vWr0FrVSs0TCa9HBW9Q96TWuaPomxKrNTnkhQp+fuj455m9H/etwn971VMbh5SWveMoVNwpwFhdiTKOc9VKJDEqMGFNiAqjZTFdOL6cCAgseAEyCj6KTAsR5Pi1xjFi0w8yUQO0NkXFrQsc7w/ZS9AIpaXm/35qy/buHlrbUOjz+9Dyc3GAKvNOGP6SSiOOD5s+IhhZaXFLgdoHSHh4Mw5vNmUBwlpb2/Ht/Lwt//bJ924RbV/165du3fvnjZtWlpaGrMUCzmGATTRU06aWFP7mt1m4TccHrZjca6UHiMfmEoGDbNbbW3NTRvWr+UEBdJ4U1LT0lJE+oqzpeBo/TsCGTAYK2qMdY3sj8LVjwGkkWq1ynql3t512KdocDRN7t4fRGokN2rmw9j3vSSvdM0UFmVMFJzEXlMVsxmne5OfmMYEAZVzIjn0TCoFEViwiCAUSLz7nos7EBzDLpGP9Me0S1BG9yUt7bmXv91vQVCyYxn8yV9jPJSZZkl2OiTQ0BGUQoeWIUWimGPC1vk7tlfWGI1JBEbC9VWLtQ4uTi4uydDVVH1zfZj3+E8b09paOrfu6Epw+AwdhpYSFA0fagapG70CBTbFyHjxFZYDhGHABQCwXqwt9QIFlgzDMV4khN+QcuQCzjzgI7ychYNJDysHnBvnXASJ4Cwnv0smDic2sbKjEtEXn2+y801J8g6VWiJMXAApSGarIldywwM7c69/+QdHWXIf2yWkRp8fVKo3SaZCImIPWbQoohxMBlXZLdFhg0rgrUh8bAWfSHU0COhYihRgdAVVEx4vm7dura1vjssBIponyVZQkDds6NCcrExBGjLH+AB8wTmi9NTinBY3Y+pEfzjY0NBYV99SV1fXWF8/csRwLAd5ebnpKV4mDMeHUYvk6YMEDm8ZpKirq+soTqWHZ/nonujAocHcoBSC6+dm06ZNbW1t8+fP53g1qqb/JjmwAzYskZ6SXJiXtfCNxePGTYQOgP1YYEJbiYcVjnBWYE5WFtg/yQ3HHO9obZw5dTzYO6bOfToUO/bv08BkANRqTSlwC2aUJmJrZn3qnB0FCIPfDWtZoKoz5JDtG5AB5eNPLlKgqYBb1Yn8wYpVt8FNfrrAKFuI1E9IIRCXJaCZOEgduoVVgFAEUAQb2IMKIQAhO6WzfGXntIy0CJegbpMlLufIAiaa0TsDuGG8+WbmMf/0expNKnPCbzHZDXEimRKlzW8020QupUyZee9xHSwf+4A1JyMlTTMks9mNjS7meEuqC3zGHoj3Luc9qjnaa8HhTBhvyqCE1UNv+CAKyMkzJlRnAhsBl+yPFyTNrhq7mmSaAXMR64SQ63a25AAqiRnF1Di2i3TEdmS0mFUc30QmamLFRkNhh9XNCAN/HvbCR94bOdZRaPnBSuTgoJioh0mncDEiPA2GOFAsJxYbLaaAP2i1Wc2cqCP0QOiETtCOrZlHSwXxlxZSqNAe6gV2bE0ieEnEhso5nggFw1a73WENMKBqMkiPTlwfBAKgJIUcZHpwmF1tQ9Oy5SvLd+7m+EiXK0lOyDAa/eHAzj17y3dXlBYVjR87NjMjVSYqJjuLA89ItaiZTEIMPHZbclHxoKKSrkBg1aqVU6dM8jhAlGAS9AlGHIRY7kdvLc2or69nzHUMe/TEH/VbuoZiC9TPaqBJkAGuzZs3A7T58+fLElEITSIfSr9ESIrEIvPmzNizc/fype9OnDrDYkOZAmMoRk4ctIkxuWvnjtQU19hRI5rqD8yePXPEkEH0wij7dY5KG5UmZID+smKi5kgwwWZrEC9FIN7L5m0aCigZUqVf6C6cKoRBFiEvptTlqHEYx//H3n8H2ZkkB57g01qk1loBCSRkQqMAFEp1dXWTzR4eOUvObC9nhittbcfObm1uz+yE2Zmt3e3+M7s3Z7vLGw57SQ57hkPN7q6qLg1UFbTWIrXW4uXT8n4e8d7Ll7ISKKCquhofEt/7RHwhPDzcPdw9PBhlDhUrSHErXQj5CpFkIMLdoL/JqdmRa1euvfLKmywvYopAWJ5MqhgRnzTMB5n/kUYoviHJRunyK9Ux9fT2+H3+svIyUCDKeLawmTjkRbLW7YVPQrOYyggWUhmhlzjbwnIgYnODQ2PJpLGhvtbNd0STZEqawYMFjrhVJQDdYklYDOyZZrLqcNjskIOZALFWibPrgPTZPNJ00RJLmpbwS8IlAOSAbKWQZCXwtbSf/yjToKBgl9VmsoSNVhylI0u9D+/xat/e7mSCpb2GmF3Rw61Vi5KMsbRLYhcCWvoFxE2MjmLBGt657xAGPJBCeK3qXZ2lituJBMCf9Iti5UgvdCqUXzErwgXqIHOibyI4X3p8YGxwcKijfXtpaSU8XzgEO1KIHXxtLcEkIetbPMiB2FoklsJFxODEtiNIKXieWNAxTE1MszyntqGuqd6D8KXYaRZZt1jEi2RrIZAdjIxQs/kXH3z83vsfN7dCxlvBWwQU2L9R3N3p/pTVZO0fmLh9+/6Jo4d27eq05OJOibyge00jNnvs4W3ABjQu1+LivNvBPtvgiErzRTxA8jEa8RGqqqpaW9Wv5YlQJ3VQMazWly5dwmixZ88eDbeCsySDEcqOaQbj7//T37t67dqZcxfZZMbucCbiYDL2rXR7S+P29hYsqR6X48Txg9vaWxl9jCx0H/K5gHHDY/3ZAB9B1+1sx0RMD1mZhhJKovBimoRvAXq7VQw4jJbsaKQv8O+RbbyTI6ODLPArLipPJqJmiRGKKK9aK/xNJHShFyKmgwyJSGx6eOSu2XSajehTCacF76CMLQlxRqoW3x00iLiapIkpG56bc1itdjSAGePAw3vVNTW1FcVE35PF5XjvK3SRVkpRkCMJswcLhfTDPKTQVBJquDA7/el7f0PRsWT8s3PhH37/DYu1CuZiQgGlFilsCKc1L0wERDKFhW0x6UErY1hMsdzR5N8qJ1mT4dYe0K3wHaY/KAtFuyVWDagr40lpUqT99B3TLHNyeLhXOOOSyW6x1NXXbNvW3PO4B62son7EGaEztlYmAE1nXBbrUmBhenoK1W4iEWtqqm9rqErHQ5lE1GJwSI0Ux12Ro1iD9Ryfx3Q/VlqmL7JvAWY9iG0kFBgZG/Z5HIHFQG11XW1deTwRosfFYEvdhHAz4cuNlRVZc0POWz0UJ5GeYebI4ny8TeLx8OjIoNfrm5+P1VTWVNdUxGVWAj7pOEh06JZcBrZag1/hdMjpf/AH//pvfvrBr//Gb7AvioiL9B0kwGrGDRRbgdFM/Lg0NMNhc/7VX/5dNBJ++eWTQjfUYFaQY+YqyMpHkA0Gdm1N7cP7D2oraxQaaDz+YmxG+mbhWFdX1zehNxi6mKmpErL19evXqRJLozEGMBsorF5OImGMMxAgY6LlP3bk0N59XfOL88yepyantSGQ3PjcW+z1u11MjVLJqMBW3LH4+wLpdH02AKCtTOcDMa/bPTYyBTVlqMgy5kTGbXcuLC5MzY9TpM/nw0qNWspstqbjifnJBZ/Pfv3Mxda2Dkujy+cpZccgBr7oBIUOI7KjQLGwn7vN4ZyPTceiC2w0aTOlEuFAJEy4aW9JmTdhDFvtcL7k/OwcK+iKSkrZmD4Wj9958LC0pKysrNJf4jv6ymvoDRLsY2UxRBeW5hcCfq+PeIEskpB5aCpBjPLpmRkAV1VVCaohbYJ3RILzWUqOH/pufWNNxpL6D3/xb69fv/fmiWNIh5BQQa4vRqTlDhJ2gwLLRoAPM4YanHTE1I18uZzkeVxRRZsYRNJooAhxSm+DH4j+Vtg2XAEuIeiSTMAdKjwV5y98vrv7FK7DYYs1kkiGLY6gySYBGwntgA5muYKCYWo2BdElB0oRvspTDRKu8Z6xekomHvWVlhZBsTHiw2njBrsl7bAl7eAGXzFR01mKFAF3FDYFQZf44WgZmeoh/VMoIgOEmHme31Y0Hh6emZtt294RtztCJnvU6mR+G7HY8eMEueHmNmRGgfWqA04v84VVT9UtVZbnNEdaxVRQFIPUQ9wRtNcZTMhrtgwtxsenR9t37nDY2CoiaDKz2pKwvjTez860TE/Xy/zFsyeAADIY84DxicmPPjnzgx/+JoogeiaFg7WSJsUyTC9CIcAkmR+kLWbrjp17Hj7oO3Cg2+NB4UM/0ptZk74ULB0rGFpVWXn71s1IJIRfPDKvYg1fXDEkbrbu+oZohNRAFZP1lStXHA7Hvn37aABbK1ND4KbGoG5RHsmR9YRnIvMBRbfT5nZVMkZYSywSIIMFlU0KWEiAS5bXaWABwOx42BQ8BaSgIB2s12KJf/bu37Ck21ZZGgpFbn125rXX3rCW+gZ6bj14eKekvOjuvUlC/h0+cOTK+YvTEzPFpWVGh7u5tiQ08nDJ4xgw+9paffcvfNiyc095WYvRtHj18oeZuP3kiUMffPjvrRa/xWet8rWWOstMgdTtG2cd7tr5qYjH49598mUktr4H92OBqURi4erM1OnXvw8qzA5fyyy1pWMZb5Hp7GfvNlRuO7i7+9rlD8enB5xlzZmesM/m2nXsTWMy/M5f/riovsXt8c7PjN3JJF5+7TsZuy+Zhnoa7S53RcO2cCrqtJmZ2YCGSUMyzmYNyo4BvLZ4AHb04YZ05Ma5zyNpt6+opWtbSzodRZGFiknh7hZzeuJkzKDBC8IuJpnRJGIuhzWeIOSgwYECiHkbkU+F2GHqtKG8aWtoL/cRshtrUpwXLDcUYRcXSSizCaOClrFEZgcS4fAi6ewOTyzO4gTYpt1mI56H4i3wOPHXTqF5rKwodjqY90EyCXSYSqQhsFSGpT1mJoy6PTAAGfDJRDIWxrGBYR+LLsWZUnggBKJZkgkNs0tUum5XWWW53enFOsDakGQkjioT072V6iGdGywo9wSROZb9xASxkdxziK7LlLMMHnqAmVosRF+gMAUmoTjTVpPD7hKdlEhGwp7oJnZGrmtsdLiLxUiSZEV+HOHDknEIVWJDN9JsHSGWq/CrfqWI1vI8kwA3H50537XviMvvj8XY812UsGg2cN6fn5svLi4qKSkHFUAVWRqGMGZzIqX0DU3t3tEseCIEsZAmysSU+b3DZqutrhkaGmlpaSENwoUyTq0DfJUDkpFkQhwhJFcoL8Li17VqTJsBVJ0tTE3Onz/PFjpMUITCq2B5BQxAmqO0zABVBCy1fJiGaGEL/NcImpe+cMMiHQ9B72W3dVLztzku6xzXAR9jbGlxlngPR48fPPXKS4lE6MrVcyZL8v6Day8dO/DSsSPHjx29cP78wjwW/+DE5OCewwe6Tx4vqav2FHubO5r3HDlo8zhC0YUYxNFmRDk0H55ZjCymzInJudFoOn7k5Ctd+/enrZlQMtS8bduh4ye6j+69cO2D2blxmyXT1dn+0rFDr796Ih4NDPQ9qiwvq69p6Wjp2re7G8VuJDSbSS4uzPZf+PwXu/Yd6T52ev+RQ5dunO8fe5ixLE3MPCwqt7906vjp0ycH+ntmZycZ+wxq6BkKh3QmbLcb+/v6Jidm9u3ej95IAShLv9YFxNqHfIJP7vzMzOXz50v9RdVV1RLyiRI2Qsa1WTz1E4YFnCCTPvPxB3duXnVYDf0999/5+V/je4eBAPsxtnzaG43FJqen5hcWCUKLg2ksGhsZGiYe4cjwMPwPagmdFpjQzUbMx8SiIoThg5///O9ikcWZ6ZG/+9u/WCAAkFgYJIEglymzuLgYWAr09w+GwrjwGkfHJhYXl6ZnprFcQXBZzC8Wf2oHnEWFmIyEAx988E4gMGk2Rj/66J2rVy7AOoRDqGTAKrAUDCyFJianR8cnUDPOTk8vLQamJyYWZmdkUoU3NDxA9AGSpwpFri74XMKSZ8vKvlVpRP9I48yGqemJv/qrP0eJxW7WH3/47u1bVyTOimouXMBksYZC0UAwPDg0Mjo+yVx2dm5+aoqmTOOkIeRf1jdsPnCeuv++5R8qNpBtI6Rtbm5heGSsrqGBfXTR0zJKkHZv37kN+ZuemuI80D+AvoCZAfSRz9ACw4UnJueUu6gIy4XwgnpyQCg54/RJZ5GAI0cZC9Nmrwup6uDgYHV1NS9EAPyaDl1/CmcvybNnz7KYedeuXaLDYOrKoFof60DF7F9BSzVt3whLCz75Ih5AuevPBvR4YXjU19eLXGdMN7U09A8OLAZnh4YfnT2LZ6dDdKhsuBVF1Eo2NFfZ/P6IiS62E1aYyKHheEQGnihbES3jNvw6ieBBY+k0m7WupTVlL1pKJCOWBWep3V9evRQ2eIt8Ln8ysDBYX1k6MPhodOBhIh6gpxHVU/E0miSzzcOWO2Yk4UyCDSCXFoac9kxJaWvMgOuBz1XkmFkcrq6odXnNNbXl4WgU7SPWSzyUxboNLJAJ8VyxhmPRxNlPPnnt9JslvkqjGFOTqFTgVFsAVxZ3yA3OMj0+3dHWuWvX3ljGGY8FjUixyNBbzyWb2RP9QPzELYqp9bHDh+7evDE9Ohyan3715DGIu0wTFA1jckialtZ2MIWpNs2GQre1bwf7k8jvmNlEJOYlH4hsjqYOdf/+/d3w64nh/kgsefql4zWV5aTNisRCdE1+f+mePSXkDLKiaquurq+pbWTsou3lCZlrVFbtQfFjKPYV79u7Y2jwfiRUWlVZsmfvMVmFRsmKVIhMZ3ft6tojqJ8xsI1qaWlpeXmZcvygblIO3G4FOMXS8AUHnIBEDY0NR48ewoadiEXKfO6De3czbVGdI2iQTqTwCdq77wCxGFFOR+OxysqqktKKlDHlMBOVQC3RVjD6gsJevN4UAvTs+NgEOmsh8UqGRbDASNvX2/vGG6/7PN7FxQX8hYRDKKQSEknHm82I7cGlkNcr25fKw5XEkSekLy4uvnHjBnsG2GxE01ydJl8vZYkkA/bpjSN34yoqOW6cPv/hc7rAEkBN4AHAAWMAam6ENMbOqjY+p9I3ypbxtv7BCNTwEjZgNsbRllpRB6c9Xsfevbv37WNkHfqPfvtHFeXV8ViUVoj7JbvGiD+PqG0xm4pBUNTYTOzTXGTobAaoyHBGu5W1AoIcFlsynYqkY0aH0Z2KxmzmqM0cf/zw1i/e/WltXfWhw4eqq6swSViwJhlTTNrxz2dKiUMfVcHXyG71iioMR0RWfhriZlajSOgpRwYnQJMNrSOCMfKFkDsOkkJH0sEP3//pvr17tnXspHxUwLhCykbI2UTrQ2PVU/KzWaxT45M1VY1oueMJ9k5IYIfG1elJslmV65ZumQGq9TWZ0pKi7R1tf/NXf15TWVpTVY6cBa9Dq8MfOEXfYUpmkyJ8pKROdIdF1OxIwsIqlP1cWepE5YLdCQU6bgB79+7r73mM/qetpQlxXlkLcroYGBzcVD7HskdV1bVME6iQuAlRYr4BVNJidoLxLc0NkWjg3Pmze/Z2WZiygkyoeug7ukIYB89sKgCiSN9qtSSEQJZAsF6GiolVKVe+ypyHJKCgjfiBEBImN9D3zs5t0XDw87MfHzvc7bCJuCNyAPWG0NDZcBvxN8aXzoDwQshYWUyCw4HVTtZiU3hxPAsIzM7OAE6tgQFJ6FDIX0trK14kkD/CR6N55CEoRGkqAVzAzKul4BKUEaTiYb4imlaSmOdQf+SGBw8ebE5AectBJhgG4AG6JoW4ms/8q7mgaRgDsE+8+uqrcDLGiDYGfDWlb1TK+mwAnoxfDZrf3r5eBkw0lnjc21deXePy4qhZarW56urF8ausooZQ+4wlFCLWtBlHGRtemgljcinuMNhZ/2vD/ycYsTNvCMYm+kecJpsdeRDzYpKUrBY3Y68MTKNZWODzyEIguhCqLi8f6O+rrq3ZsWsPwjy+xojYiMBpC0LqkplFRsQlFX5jLy3bFgxa5iaHnKZkKrQYnptvqGgwxZzpsNuWKWXdHEjH4BZ1BrMetTQDLfq7b//cbmPlYWc8DlOhoeoQAXWZim0ErMLnS+HQ2Ph0aUm1IoNMM5lP4H0qknJhsmd9DVHEByDBuJmZnX30uOcHP/yNyZnpyWk0G7KQHpaG+zCsmFvRFAIvKJrAACYNpVUaEyWQAxVqKlRVDgg+w8968+adxsYW4CKbFoEATOFR7JOnchUFE/jD9sA4VM2kpdJYdS3n/KGgmWR6MDw84nJ5Dh06wqIYcegSozbknj8ZyRBkJhLCnyQTTQV4TyUBpHh1ipMclRTXWAriC2QT0XpBw+Wd2meKh/lyJRfR/0vT791/4HS5Dx05dv3GLXYcJBlCgE2Up6ihpSnCUmB+WBOwb8vSVuQWC4wcmYa2q5Y96977VctPzeag8stKDzzV8CgxmaF9qtdUp0pXiDCh+5FuVcgspIknmnDnIccrnYwnHR0dXHNRyCryKfWF4IySUYghQTVIv0niVd9+mVvK5dA5QPqZ8dAuJgEfffQR9gy0LLqZ0lL0I6wAUA35MiV+mW/XVwpRfeptt9uQEy9cuDwfDJaU1ezZeziRsu3tPnHhys2SsjlkfRz29+3d6fKVp5NJDGvkZTNY9u3a29fTP2+8sYNe2t515fbdxXlTPDVbUVHt9pQkUoby8lqb1ZWKwx6gzf6O1l0D/b1Tk4aF2aETL33P4y7e1tF16eK1Tz69xOYuVfVNLm8Rc4yaxtq7d+6wA1H7tjJfUbnNXWr3VZ945ft3bl30Tk+ng1PHjrxcXNyQiSVLShuMZiIbMzGwlJZXOBwuZNNYPOG2W3t6BsdGZ1vaSj+/cBGVs9du3rvjVTQVoiN5km4A9YLRJU9paXFZFXIri7MmxvqDI4E99a8a2DVJMPP5HMK3YDZmnIWvX79ZV11VVd+4lEh8fvnaqVMEak2ODQ8VFftLy8rFmRdzsdBpJUwJ+4Dycg+VhOkxh5J3HCArSiFmqvfu3Z6emn755PGZuflzF6/tNzhq6+rHRkeZaVkttpLKKqCkWqWwGxpawDhXQQ8Cjkvr1MzMo76h7u69BAQe+PCTi9cud+8/HAtHWcZJFuwpSJwSxaMkIyWP8B2mAC2aUBo15w2HarXF3NfXPzY+evjQgU/OnOk+eOTatRudnTvwU1Bp5CQjT9a4GUbHxu4+eHD65AmMkGPnFm7cedC1e/8sZoxIBDbqdHmKSsrEmAEsBBpqEiUcShZZ6yLzeb64eHoIGI1ul4uBBakTyIoskmpvb7t35y6km051Oh0N9QTNt4yOjmp/RxIIR0CgYVGmOqRLNxibfOL1eqFUWqBeNxlEHzrLQQyJhoYGnee6KZ++mRt8qUuh/pROuwgRARs4fvy4VgTxcIPvvobHMu1aWywqknN3p//wX/6bA4cOl3Z2xJLGYpMbwhq0sn88zkixkNgEJMw3rJnwfoidBrMzYbFbU8FSc3A+lJgy+NwOV2l6KZhIxeL4fMYdznQiRiaJjDHM9iz4o9riTjPKfussXizBsNNmsnpcosg3GV3RaCoSDUHAERqIO2G3ORKZQDhgx9XQ5Y0wm0jFfSajB1/FZHx2JmJxm9J+hzNoK0qGg27Wopq8Jqvdmolm4kGLzR5B+WN2GBMJG5JfIohfbhSkNOPBHjnZ3Vjnt5tZ+iUOgprMrYXH6ifQp4u9LEMj9EVJ0mRdiE7duPRerdvwL/7L38NhZ8vZrM6W+7Nnz2IyYra4zjt5BO3N9I0GLj2YjxPHAoOGzRRJxdmdz2f3onabmZoIh4Jt27azbx2QhcChKBN5GpunJq/QVlbkyaJEJtvKbYb1O9gS0Jejo0Q0liUYsvMfxlamTSOD/Y21VY8f9TZt284i23WxZZ2qipUYNVUqFUs6MaZbLMEoe1LKnCyxFHXanVOzc5ForLW1HWWMmpJh1hUT+5p60l76RbMEECmKK6fdYUO8MlvcqH6AALRAVUD1nYj27M0tXkpAQ8Jh4X4aZdZnRAh4+OCu3WIuKSJWZaqkrDJjdiD5m2gxCk3mjkwFMim3Nfjy/pJiO1VhlK6DD319fZjKtXvfOg3/lX/E0EDyyAHOdP9Bz89/8UFJRTWzPng9kjkzy7mZ2d7ex6BFe1ubz1fS29t/69ZNQNrY2IjeHJrOOqEf/vpp0E8T8Y2oNvh1+/bttrY2dCxQ1XWT6RkACPP+++9/97vfzXOXdRM/p97DDfTixYuUeOjQIZmmAwhVW339nAp9omzXnw2IbQ6Bk1gB0bCdKbTMnw1sbgBDTyRQPZt9bndKzIQsBWARMwELsLGKSpbQjIFY2ORyO4wudAsxwsiyJsQJdY/HIAywHFE/4NfIwIykMKqipbYQnzlk8zIeLRBjQyKKHwj0wucrSqbD+PYw1DE/MNmw21w4HybSYSZ2GXMyY4qG4hGb0eLxFhljkRhR6NiGlAkMcw8Ty5pwgiSCDTN+BEwagJ6AEDIpm8WLMhxlSdIQZll00shaCKojyvJNj7xYKqm4wd0GQgdU0JIUOe27trWHxvtZErlpJl/6pdSCUcYyAVmlC2KxHINC2biVDrNZTT6fOxxcYOUERpS0WlOA2QPdB7M7FVGOrzEkM0o50xn0qvzJkCA+Kh6nyGsie9HjqFBYYZZqaWoMLs4T2g9HANb4aOmc5PlDelJuNHyyj4Wem/E+YgWgA4YbT+CH6kwZWKoddzst+G49uH9/z969rGQQhxD1EfVBasSVSLErskT5g66IEsWvVc0M0nYb+iBhVQ7COClaw9BPJYlVt1wuah6+4q3FhgIRCwaLk6yYIdgIp7jIzfrBhcV4fVNTUjYIAnp0PWoz/sgW91JmTPBIjnyW2Zxf/GwRAisAlzHU19cx10RyRLmjkBdhLFlUXHTkyFFQF1TDilPCpLq8HMU9cj0qHzhBY0OjA1WEzAxEThX8XO/QCqLe3t7du3dvlIbn0FzCEGFIQPeiNUIbJV6vkCd4lq9q/oKP8Qq9evUq1ult29gqUkxoz6N0hg9IC5jkR1VZD6t87deHYO71+myA0dVe6/+d332Txc0lFV6ZKAvVERMv8WsY47ooKVuoiDIlEk0MqiSGRKd6DS1hUHmJeUDDTaYilRjxkxsXQ5hRR4gHocAmr8Ho5hPMhoxe5c6B1CDFGQweofiqOCg6pIWKsNELdF3EWXJiIs+1tJ/A1HzEtQMTskIfMmdZCrFLJYUkkfrYWBYtbi8ogaXaDp9TtCO4i2xEvxXlU0tkyV/qB/0VStNU7C3uwBBOboJpNxfMDUcPK/l7c4DnAP+UvyJm+72urlZiiIi5Q+5NxijiL9tUGzOsiW2srK2rc0eJOW7Bhwd2pYAmxemKyVnBRICVP+hxJDWVRIYrTYI8swVgKBiPuK17d+xyuBxgcDa94Jsc/MhyBH0jZ5VAdQnMVx285F4wAvduuRHC66uvMHjclqoauxjV+QwOQGfK1yq9yjhbTek5SSInOSvZX2dJf0qVBDX1a1Ko1Nk7wKOekDNAck54/fPlGUSUmno/2CVCCXqJDAvbwAmRFOLwS6YNZAbXe2EeADpPfqjOyvY93NXjdrz28rFPP79g9TJCec5Qkz2/4qjpBfzYhjION1HFEm6vj6UeaUPCX+TYsbOFd18oLJMPDpd4nXZ2dkLiN6ksrkcEbSbBc1XFwLc4dLWFKJhMd+7cmZ6eZn5fXl7OvATplueb1PMpXgFFRoFIUzLuwHNNjbMDhgxJgOSIvWyTgjdgAwZDZZnje985Qi4ssmctjwxWNUaRr6W41QeNExcLJb/JOzqcyQ8FEw5ISH9BelUt3jv0M3Vb8Dpb7+UnOoHObflp7opC+SvMnze6fvrMrX7LraoVyaF0+Ea6uIKosyRNILSBv6guPZ8lvQiH4qHPbyn2e7ToyCzE6U20dlYniFVpfALlUq4RW/4VAX+hqJiF9x5ADUWkRVzo61g4cuHBY1bWVNlLfRXeLWe6KiGZachZ7t2/O9vbz6Q7ERrrbN6Bwjb3CnjxpyG6PgqtyjR/e/8ufsDxUkN4R3N9UZE7/5wLXWrhE91xhU+e/prd7Abmin3W7TuaMXrp4kAA9J+glgrPRfBzaZUxGRQXMyN1k/n7i+PpISASXKa1peWDDz5KmQLsHSOxvkQfIvyb7oYuEFJiYnLU7nAgrsCYmZS1NTeU+Ny8/0KKSQL2E2YagVu53kZmbVWhxSyXQS3DppVr3z7zJxQHc+KMGopJAFzh2LFjLBLmIv/qC9u1pVrp0YIEpMch5QJRAVqe3IHdcqxHrvWb5fP6Y5gMZxZiLPtmO2jREoisrIckHZgvaDkXVRMJ/qNJJDXkAxGz5av8t4Xp0S+BA4Im2UNupIgCk6bKVZ6RK7mhB5G0ulUirKpGM7HkYT4bSaGPta0nEYUSVR5PQdbhEsE7ky52E0qA3KF969Yzl5v6pYO1zBqKxgPBiNBLcYvFhpps3r5rASV9Mlbhx531OR5AYTG8NBMOp/BZVcAh0htzFFcCn96kv7QawMwuJELxoMShQschk7HV9UEQw4VIxLHVr4Aaj9RTY8Jmr29sKg2gzXcWT0whQOtFLrzV8CYDXHnQ10lX5MoQNOSFENL18rc4ioORpbKqykDYGGaxhRQlNUQ4VPXMfyM14eUG9cyVtsVfmD3ryO1lmIYmJkN2h4p+qHSFSyzRl3AiguZMCFCX1RURfTb2ggVsEbSbJ0PPAxF867tv/psf/9vyqtrKquoYDsICakYzAw+9I2PIwv5Z0JhoNLwYmq+pPCbLBkUZ8AWHYiUGDGlI3BuxAboUWw47D+etAl+Q6Zd4TVm0F3LPGumzZ8/iIsMUhIeaMWjGJij2DA81XMiRP7grpkDyVkNRWwI1J9AEYDP6tj4b4OtHA8m5hRjqf8BHnzDMIdMwb7z19ZinPVIeL6Rh/OA7j4KAZ1K2KGQgrFRGrRjiyXJ6ucGTJM5AR/sjTsCizSEgj1SUcajaIW2SfORGCEQM1CCdpACepIVfEOHAiAeq6I1U/pwlsdyRtagbyETBXVM3amaMYCsFYkK/U+b00r6dpppybPZRh6iVBIibHLoJTMBmZ4O3HwYMFhdwp5R0MuGwm5PRcGN1cblfwPG8DuCZLp6bDdx8PJ8wYZ8HCizMNcuefibkWjrKL3Qcy0gyhKsnjpIm9qaRPmKKLeAV2MlEPDfIFKyytZVuJL/8tEiuzWY8PQjukDLM61kdPUUiyUfytMA/ERdECScdJeds6zXPlocFh4xtI1EDK9LjjBaQSy1NUFnRX3wpOnzxBAWo8DhqLl0ox8p8CrLc0iWl4kBkMXkAUHoALGC/VslU4SthkRJWZgVKKWUzRKq76zIWh0LGLWX+ItEmEAAxoIytrS3/5Ee/+5Of/Lk5kywqq8A6SLeKFzi8N2OsrKhAtzk3N42nxtFD3Sz6Ky8rArc2yVa/EiREb1FZee7cOfRCGymRBgYGmDRs9PYLS9l6AhqL2gcFFPGiu7u7tVeo1gUx6MhHhskzO0DjLKcMhyMzs/OECnARmqW0FLOKGjaUBYC0ZP4Fpa4Pa8Zy1OAIsqGKXVEXsmK2JlrdjNmqLMPCfGA/cqj5Dvo+j9BXWRekh6wpnFCeiClscXwvpEP+qwHNxJDNe1lUTHwxbHxioGPZjtgkQQuhq4BLSIrkpAi+wRiDaiOl4TeOmZe9f4mSkyD6qbAZFQFHaiLsQegdmIdwR14AQkEK6kmJInegJItjukRFCfFGE8RER01Ft0RmZFon8wgpK2nwGzNCVqS+JvxUcct3JtjZ+PkewAV3fgJke9Ky1YG0Dqd34kikUL6Jh6XgGuMLG00Mw6iLQMqyyIAXMzNzkUissqLUwo5pwmwViFfXFtxCNZKDBngmzRP+qDsOXgJaM63nWSwWmR1fKPWXuZ1SE7pG70tBDfgeZeTag+dYVnhO9cRwgS8A/cCCMrFKQ/2TU2Mj6AeIIciMjQ7H/19mM2szetInIqvQe2CIyAeQIMFmJf3LljcG4g6lidcI5Jpr/Pg0s6HPi+PLQwCIc2hivW17x3/73/7zTz879+BxnxlnLwcu3awoYAGwRJgglsTuHe3d+3d73c7zn57pfdzX1rGNzoK2QFjJZJPK4DMKiWcjgZqaGgamFr3z6WFCc3NzO3bsWPU8n+BLXmDQ1m1EXGZojIyM3L9/H68nKkPOVIyDi82bsMU6AEmao8ga+9ASwSH+8NGjc+cvTk5NC+kFnGYz/uIN9fVNzY0NdbUYERMxJfEUOOCuW9b6+C4dYIqmLUSqVMSCaARQaHEkgStAdkQYJGo9BUudxNcPdQGcAvVUnLA2oqXCPdBOEFAcSbkTisyZg0/kR+J4xZ1Oy/TYyPnPPvv1X/uB0eJmNGLnZH4AhZA1sDJQJSB5FOJqMvvweBE7u1C0axevs71ae8c2FHBJjL3ixyiJdU/HmWE4GOt4tPJQJgXqJUEMaWwivfTw8aNHqbhlW8v22rJib9LlSWEnoPZP4ujJB5aweJeQv9BE5GtIK5EJALL2X1wX2l/6obh7Yt+Opy0hAqcJcwOWQIlQUkjkQr7lT+3sao7HY/jJMpWj22ZnZ2NTM+zWNHn/zrbt2+AlSVRiknjVwRNF91c9FtlN+QaoGT1zDRBupKe/uKh4of9xRVub6ljFxWVGBRswQr6zgnxBVuSuTcdSVRZqMSnDs5V5XgL7k/FRzyO3xxGenokkQpUVdXGCxFnt2BKB7Jc/JBO0eAgHsCvhbrKWjQlHNJPAVXh0+KHd6QhHg8NsS7L7oGyhbMR2tRn1+fJV+hXJQY/9eDzq9rjeeuvN40uEICMOVcrhdMnKMtzJ0daV+hwOtKlpFnjuPXDowoWLkVhi586dUD1NRteFFTnzHJLCVIDFyUQZgm3oh/n0LBfAKoBS6DmxASgPtgcqiSIIYwB1eOWVVza3V+fr9qQX2sBAm9m389rN23/8x3/qcnuqa+prG9tY/c5YRw4mCvTg2NSNO/fHRgZ/+7d+ePL4UVzpxP9i02N9NsBHDmPSZ8d5i6UZxOaEACO9xRm2eNcI4WcYM3pxOEdrRMAgyADrgGU37/hwf4/X46moqY/EYoQeku3F1LIR5YyD6M9YRMgzsqEmcSMy0fnQ9LDLEGUpKdPyKDvJ4SBIoB+LmAKYcLC9CQPYaTWHJketZmNRUSm0NhMJsi4FcZR16JB7fGRwIacOiJZMVdieFE8EpHyTBBAWr3kYGN5p7F86Ozl59hc/rSgtRqX9l9cu/uCtN/fuLIcYSPyFtURrM8Ax71BB3GR6AwugzkAIoZyVaM/7IPID0yiII5yS7oCughgmAj6LEC4sj+dJayY5NtYfCYdxgPR6vGVl5du3NwGEqckorpMkgktDFFfVVXiZBAJcjTQ0kmW9i4GF4aFh7KtIHU1NTe2125wux+PFuWQqarMSixQzjVKsSLb4TGW9S6lUvhiy17M9UQwzE4xG+/v7cH6Fndc2tDU01Lk8lqGhSDS+xC46BICTKKFYDfLfr6rupre6I/hUX7Aygj4CRDJbZeucZOJRTw/1XwpH6+qqmxrqLU7n6MRoODhlszEzwQ/ixfEsIQD+SPw0Q8zjdnW0NS+DV9aD81xGPb8iuZltBw4f+/zsJ2hyWEmwSSU0xYcE4Y7J4iwdpXkVG2CWwFvINMSK/Fe93STzrbwiQ5gQqxZgQnfv3t27dy+TAOYE0qDncJAr03FE7zNnP/1f/vWPuw8crq6pZYUyozmJ2wshsZjlGiw2d1FDUSmeV3/703dZqtbZ0QYBF6lsGeirK7c+GyCVLeUcufegqMj3aHQ4HkvU1zVWVdXFhe6HH/U8hAHanfaWlmZWCU6MDKBnmJ0NEICyoa708/c+bGxonaoMN7d3zs6PFpWWmG1ocUyjQyP0AYHCR0aGkQuGhoc8NrvPbnEbLOO9I9PzOO45Wne0O5xeloSNDw9NT09AtkrLypobO2bH5s6dO1PkLy4urerYvqO8vtHjLmElWSwaGeh/GAqHEHi3d7S73H5TIj7SO+DyugfHhlLJeFNjS2UlHQMtYK8Zc5G76o23/nlRiYdw+3/913965dGDN35wyoHjKhvpKJ3VavBk79fCDwZlR7MNUYmL7osgxsxT4lnf/A1yeRaPqQmxmNi5xckiO5QmCNK4xqNpSRgVWZfpAmsC4izS8JVV9t+8sf/AEdDS7PPMLi2xnt5f35BwuCWGs46lrKgkmYIi+g+pX7Qlajs5pZVTbQeNzClXqTs6EvMWeWAqxO2Gy/aPDzkrahJ0GVgoLAT5ABaichJxG5SVFUMy/GBY6A8hv1JDcQ2GH1id3pTFSQC79h17YnBQu6Vvapjd0mpq6pfw3DIxy0OLrH2CAZ7uBX3mVrKAB6+Aqq4snwnrR4YQVkQlIDVEFIdfk5j5E3ybqPMJwhmFU9vb9rF8gOnU+PDM/GJyV/supjoGs1tyXpn3ioJe3DwhBJCSQCp2FED1B1xBEbAChqwmn6CIyBAEPTSZ7eIBbTIcPXb8zMcf+b3+krISihIUAs9zSKAL54l0McTKZiNIEf5CUGG4QmHVmAezqoAngoyU+owOqbu0QlY7w4FgA6dOnYJvUTqvKOsZlZPPhlzJVjSbPb19/9P//K9OvPZWWXlFNEaoN5ZtIeYyuwbCTP6FCcXwFiku7fJ43v/gDFywjI3pN237+tVlCLA67PLZD376lz9OGGfiprmf/8WfLYxOIPT13Lkz3PuoqNj5eODOz9//C5sjeu/OZ3/753/Q33PT6LTHraw667Pb4ilzUcLsvHP3vcW5CXZCtBrS925cvX/tfJE1cfnjn37wd38WWpiyp9wOg3kh8ODG/ctOd9n0xNh77/x/U8Y5RIOZqSlWJ1uMoXd//pOJsfs2ayidmrdYiaLvIhrRxUsfDfc88qaSl3/x1/dvnXX4HZGlsU/f/Q/pVNQUC3z203937uOP3C5fcH7+3b/596noArSImMUJyL3d7HEXJePpZDwaDy9UFXutibSDaQN6oVUEJQd/kV+FfiBzoyAXHQwHN8Zw8P2f/dn77/3pzZtnZIuSNOIwwNQepLmPn8Mv44Y6UKfA7NzS3DzENBgOjk2Op9KhjIUldMyK0amBMBb8vKqLK9hYjU0j5qan7t+7VVris7MvES2VsSP2UVkQgfmADSyN8cDC6NxUbyq+GAnPj0+MBiMh2UWe4UpaCDdoloi67Ba2AGQtLiuX792+EQsHXVaXEYIPnRcYMTVhgQfAZtuAdCQeHBsfjMVxB0rMzU9MzQ0rvwAIsQxoYAgVZ8sC4j2wKJxgsY/v316cWagqqc3EmA0SDQ4rITYCvPlx6aWGaCjZVEN1htQJk7jsaaP+RJZUHA3VYZyFAKHo/OzMIPtVpNKB+YXRpaUZHBtwOmItC7+yWjCDw7Kxtrw0gT+009Hb82huZryxriKVDLFcEiYvtXtxPCMIKHJpIT4VwgBKbaGSyAUwavoZvmDF643BabSwAFJ0yQYcLnwez5GDxy59fikejqaJEpxgd41EDIlmZZXICkmf/BF7Hz58qEmwpsVwCDRCuAlBnbnenA6uzHWzO4ogfw6kfnRWFy5c6OnpOXDgAA5RfEZlWNb+DNkA7VVNZiBSJDFxkn/wh3+ya9+x0vK6GCMAPQfLJ9mfw5xwWRn9EXM6Lju5g+mENzC7fP6Ka9fvsdx+FdxWtXADNoBawRaMm8Kt2zr3Hz196MTrtU1NV+6ei5tn+wfvvvbqifb21lMnXx4fnRwfG8fhzuOxf/ett7r2dZdVVbg9zoam+t37umwOuieBgIhSH/JgcVjwWpEuNSSq6qpPfec72/ftT2IbcpiPvXaic2/X4RPHx6YHxsf72H7nQPfeI4e6Dx/cX1VROj01Ruyc2srW9qY9u3ccFoueOW41h+dn+/r7rn73136jfee+IydOjk2ND08NE6HCZAvvPrht195dL59+GZQjkozooxj+stQKvU3Y5kjduns9GI7s3LVXYh6gydDKjFWwKbxdCUUUVtOzw8MDfZ3bdu3fdwCLCDZsoUTKZlv43TO/RnBCpcZwunzp/P27N3F3Heh5+PnZj6CJIAr6byQkCdyWSi0tLKJsGR0ew4+VFcAuh3N2enZyfAL6L3sYCXZl/xDKWBQ9Pjb80QfvGRLhmYmRj957NxoKKtaoRTfIr2kpEGYHucmJaYL1E26OkYX4Pz42hrwPuRZxX0RoxWTQkqGYi0RuXL0YXJw0m6LnPvuo9+FdHgoHFTaAwGdcCgT5dH4+QJg8tnyR+YHZPNg3GA6GIBVgjc5Qi4BcU2GRT7J/2bJyhcot1cAcgo5vcX72nZ/9fWRpDvJy7vOzfY/uyzwF3iOWHDGWBBeXUKNOT89Mzs6yOwIr5JEoR4eH2VmPdomMurLHn3k//qpluIEcDsN1WNIuc9ophCCD74eVkJCsyUymoxU1xZ27Wh/cvwMLYD8PtIyCPBsADqU8KgrCiPIeWqxTYbBl8TC3z5AukzPDigwp68yZM3CX119/nbmInpc8K2aTb6VCQznxH453/cZNtsho69guuqAcimIR6O/rZT/6eCyC5lxROwCF8w7j2jIxPjUyOrER3HRB6yuFGE9pS5hJeUllLVFhkB0rGuof911fSo2Pzzx++51QxuyJiVbAS1wGnELYGBiVTgxznywRtENu43E8Qdl1EB0/VAedFVIqhkBsxhL4s7a5OWw0hcSIabO4is0OVxjdvcvqcHrCwUAmGSVu2sToYCYTmZueNbVbEzG2HLDHwxIgh3GMoGg0x+YWh9x+JvclYcwTZqKFlY5MTZSXNmTsaXexE76JkI6sIDEwFLwgE7IFrhXnqrlr1669+eavWx0+hEkcVwmtxqZIedB/4QXSzPhEX0tbZ3vrIdG/4F1jimKMYNfkL/z2yyWgc1WHptOvvnzq9vVrD29fjwXnv//Wa1YgTzMVLYakctnQ1Mw0OEngPGOaeOCE9qNoZDBUZCAx6j1lDRY5gIcs8e/atcdhNffcv81Szl9/6w1vUTE6HCG8avKLX4+PHUHLKpGDEIhA+u7uQ4SXgN7znlse6pScEdmh1SX+okMHdg/0352Zdrc21e7aeygGq6L/VELyYFfCzs4uCkDAglN1du4U67ZaqEIp0lIpXKYE/KjaaiFd3a5HEBiEslmrwdBc12g9/Vp/T9+0d6qupqq7e7/YtlVbICUU5vb4O3fuhraw2hqxrnPHLqIaMNlzWiPi4UAJ/ClIq6JfnJ4PBJjX6lkc2QNtpStU6APRwEUg0dBaf/9O+Mr1q4cPHQPPEA7omvV63oAwzorimZkZrMG6riADswGsx5pA8xC0//Jkmhwoi5kHZbFrGMuDyZZpgcb/5wMmyZVhg+791s2bDQ31jAg4mwgrjLNU8rNz52B17MfCllDHTx6z2Z2yxRuNxRUvFWOEv/vOu//57/+IvX02qt76swGBVsKSiqEGQC6L2wlAGV2ysjugweL1OV99/eVXX3v1rTe+90//498vI9KyyRqXXYrNBGXJEHA6jdoavw8ivRDey0QcalsGRm9Ox6KydzB6ajb7JrAccXGoKhdJu8VAQLYYNN4QZysC6/27t25cu3rk8KHvf+/7VZXVEATRuhjT7AyAOIl7CcGOUHqYrE5mFrI5ImZojHrRuMPiMhudbEVsTGOaxi8dmZNAMcLqZFLEbiKIydH5s++//frJl1tqW80pimYRFhSJ2oJmWz1ArKnZ8bq6ZmwKKhhe0mIj2BkEaH14bjXfLaTDHIHQT4gIj9vZUF934dynlWXFpUVe2gDvphVMbJgtMFrQhbPfAK2CsCIhIVtjgBdxXA6hvYBU0Vali+U+bWxvbZ8YH7WZTUzCWF+HeC4Ih+zOcjMLahVzjOjcys6Kqx97VaoxKSp48AVEzI8x6DfSAb6qNVVl7DT94N6Njo5m6C9KdzoCZFTdIVhGzYjQin6Kl2osUaCav6OG0mNWJlj0oJBknuANwRbQ1IWDYDWSA5XIH2CVkUBGZsTHpsameCR2+8aNrs7twkhkqiCpZamKMBhiYIn2TEJMS9HwUIgQhkSxc6OCFXb14njuEKA/WKkXNViiGf7w2zJF0qYYoQdSaTaBYKt6S/u2Llj4nXv3RI0phiup04pOV5XkCUpwjYFcc8HkAJaARkgJPU8wulc1Ol+WvuBMPDsWBxw8eJAtLUF+DorgWPXhU9zmy1rzLRhL/uzJEoXxgL142TJSGHRItA6nA8sEm9gcOXoEQVwGhCA4ondSVn2l0/ML89jP1+S5/GD9qgNqe6LIlXb13bs5PnxzqP/q47s3u1r2uS01FdWNj3r7CJ4dCIaGR8dYDE4gt4zJgbVaImpjrLR4B4bHF4Mz8WTU7SwbfNgTm1/su/tgfGDUlrGiC8QHxETwR8K7JVMYGmPzqQc3H4QX5nru3rEbPLVVDYGFIHKizeaUPQInJk0WG+TNZE+OTvQEgjOKeIEftvLKHQ5X9dWLn0SXJnru32LKAWU3xtjnwGdI+gi/RlUwoYBoekwTQn5hbv4vf/ITwgwZI4lHt+6P9w+zKw2Br6yaSyyDZYMrBVzeRaJR1mtUVNegdLOY7WzxODoyNDDwGCr83ImH0GWh4YS6HBke+uFv/ubc4sKdu0ycxfIGo2a9rKak2HnVoBGqKvQa3sGWPIpmiieMXg7ON2iRBEbm2ZnZS5eudnXtIUz/lctXQ2GZ+TK9TsYRKGQGIPYpdUAp5VZGpFBhLtS1YF7+oDymDHfv3isrqzh16vS9u/cnJyax+bOuOZEAIyXoP4nJgSyZ7kKPVTA7aD1sgjrTxOx8RefJ87n5mcGhAWgBU2C8A3p7e5aWAjpOy3K5VNXAXnXRy5cvsePgq6+8duP6rYnxSTJE6LAzaUJpJtXH4xmOpkUq1NRwGiszHt0Y1G4C4hfHc4YAelpsOWyQHTeIGwcLIdmjJIm5gHWLqImUlIZMeejwUWywQ8O9hH1CuIDMQNpkvlhw8AS1HuI/PQtGcctL4kygqYdWQg0LxZSC7za7JCvy4cx8EUUQ1yiCiFRKtDhCRJAn0wLQj5w5U7rC580yXPedLoXMOWiUPngozxlciqTjbhsKLg0NDTKIipimq40TaCb1GZ+Y2NG5g8SU7nZ71HDIIa9CYQYYEjy70lI6RZBybTXWZwOkJAwz0m1jTdX1K+c+O/NR9/6DXZ0HU3HPoSPfGZtaevvd9z85+/HYxBgSYXlVY3l1CzYcKAai9+mX3xoZnXzvo3eC0fD+A6cmx2fe/vnP+x73HzpwhOj4bB/T1LTd4ylFBiP0GevTjh0+nYimfv7O3/b09v7g136XuAX7uo/6/OU//dkvhsYmjp485fB5Y5l06/bWnoHH7334i1g8Xl3X4i2usbnKv/eDfxRYWnj77b+7/+Du977/fYzjbIPT1LLT4SjChg9Pam3tcDpFbYfeA1mSzWYtFg9urNdv3L5+/erD+3fZV10mUCIjA/AvOjQNJU51NNHSuqOkpDSeikBc+npGBvumBvp6Ll06s4VcvqiUTd6D4tBgo+zo/eDhY9kFqLTcX1F551EvYCEO691bN+FHLHADKWR1lki4Yi1AfFKoIdo+uVVWBD0VoMLgcSwWv3Pn7sJioKSizlNS2T8y/rhvgKIGYW4PH4yNjMjIIwuVl2QnsyeZLFBZjf3qfbbqEGKmYYMjQ/0D45VVLaWlDeFw8trNa0nmYvNT/X2PHz+6F44GoP8ogZixqHwUwqsMwH9VNylQ/YniAO4wMTHOnhNMGq7fvBEMBW/fvjs/L8M+DzAZObg5WTJDI4PDo8NsaVBSXpFIss1yPwxraKD3wYPbfX0PRkcHhf6zFI5xwUJAiastExJaotpFfrrofMYvLp4TBBBGPMm0kwhOzOOZmltRDBjRDZhtRtw20Corm5PRcOrlkw8e3GOLac3vNdUrrBPkD6JMJFFswjxntKO0AVUK0zzFtS6O3GAnzAA++ugjNi1gHsCQITdwXiP/U+SsP9ENIX99QbZck6ceTfCeoZGxCxcvXjh/gT3LxscnEPyR8RkOwiJENSTBgOFSfKXqI+Ka5KbkGXKDqdiI9I51XlV4o9qunFPnWsOw+/zm2B/9T3/YfeBgbfeeWNJUZPCGoumok96JoEaBOwIEZQmAKmEMEOktylqDeKREnGXiS05kTLuLPSXNMQK5wCyZdVNDGLP4+JsIJZF2IQDGWJdgilvioUTYaXUbkyi4mZPrHbSIRB3GsBxGeW9EIRXOxP1YmlPGKSurwxJFyYTdbInZLNEQqz7ji06zIWAuNqfYfiwUsuB8YnVkYg4DswHW01rxaJTVVsSKQSkgy1bhc2w/ENm/o7qq1GnMxFl8sS4nEHpAvSEvsgZVQs9Q+f7hmdv9sRTObbIjI2ZIqpkKzD2cHLn5L/6L31ufteZgu/nv2c33G6AxmXTvaOB6TzBlLUJ6QDeIBZ4dlpldEWR/aXF+fm62ta0DbZk4cQK4TBj6pjTsql7CA+ghzojcOMgjBQNw8Tiz4ViJL4ZI/qhJmEsKY5yfma4pK717725jxzarHW+oZZq7SUOopTj2oLxCKYiSn0qjosOCQoHRFEtIJ2dmmE22trZhXRBrMZVgvAsl5pC66UMRZTUxEBuF2LHpAYYJEiFBKTD6gOI4kDCNkPT0EgpDUxQ3IKRB5AzJiQUVspcAk7Y0bAApv6y0hDKtTrfsNyAKoBSVwmCFNpqTy7p0el9FsR0jBviwji71xX4D2b55Fj9gE51IIHAQb2FxKrQwMTc7FonOs2lokcfr81fafVVuX6Xofs3mhfnZSxcvnDr1Wt7zR9M+XRHoHZSOTe7gB6w74yFDiWUHm6882LwRjHcOTZSJSIE94OjRo5ROQfoVn3O9EW3dPHPe6kyEaquDC/gW4hwHLAf/1/mFBbvT3dxYX1bqJ0QE2z7+wb/+N7Gk2VtUji4aSctizty+eR1ZjSjWE6MTrW3t1bXVeBMxDIi/3dbawqI8Qzo+Pzv9n/6zf+xmrdUGhxo/a94JM7GGjI5EXMYJeudMmG38LAQNjzMYWQKGqh/bajodY8vZeIK1WjbWAkERsVPzWMRQbIcMS4M1lomb7cZoIiILWgk9T28xIhG+0jGoEcGmE1AI+ISDe4wHXmJEMIxlvYOZJeY4vERxMMJqh5MfGcpsHbJA+IhMCDUxXpJ47UPpbQ4Ha46SRlb2Rh1WIjssmo1ePBfTbHrPfBDOiVAsMYjScfKRCMy4IcZNhji7puHtjymbdq4BgzyQpwWkjy4XARUhAJOjIYqSGsZtMfrZO/X65UtHD9Z/GR6wbgVWPKTt4monRC2VjkpVxOKOZMuchg0jTSm7KcQTQwLTPN5AyAZs0QztVKRfEzVYPDRUelgRfG6hmWKowYsWvYxglxX1CA6bQp4bayumx8ed1oyTZf8SNmpFdeSGYbLyGYXJnEGikFghrgIx8IBxLH0Xs7tt4+PDD+7c23/goDHDlg/SIpgGczJeq5zybEDqLau9VAphD/ggoNsR/gRKco0gFMcNQKYlkohS2ZAZqSHNviVJnJZxOZEZi2x5TIQRl8M4NjwYDU53bO9k3XsS8YCP1LpmMVpg3TCCCTB6yUv9Sa4vjmcCAUG4bP9mx5PubDYZu3zug5/8yf9WVZU51N3s92bgwaz4WZrzProfvPVgrKHt8A9++M/MltKiovLuA8fu3b23b+9u4sHIxFRQTWonVJSBmMnU1NaiIN0OKuNjmkxVVVTlRq8uTSVW7Vm+V7f5EznlybrkigZYbRpTUlLy2muvaVW7TkOywhlwPoetX5ADB7I8uizWN3Awy6+rq8PkwJI0NFq7fT6HUzb6UjFXoJfG6qrqS1dvl5bVKOcMlvMndu7YWVxUxJKghsYGtKCosRw2+8VrV5gMwQbAf+bx23d0stHbJhUTCXfta55NBUMDfUOA3ukVNsAoYaYvorAsFBJXQW6lD7gUcsFrJkgyghDJGNcwAdH6ImHJKlCBebYcrqVIab8EqoEr8KmIqDyGwEG+4AFiDJKshVrxT2ZJ8kA0ImSOGwxjG7pNuTyl+3kqiVWFhF0oQiEuKfxJzlRUlatotOQhTeCZIUM8dDshRtPQKY1Ra4AhuKZwjCpQTWpoMIajycUATJFmgiWJW9d77RZjXY2rpbmiyFdEojW5bPXB5rMBqkIc13A0EYrwSynSfTJtFvsmE8DM3Ny8YH9VGTBB383KLOG5Gx26mgJC4Kfyk2sxMQnA0yxCiS/MLwL00lI/jggarutkJl+teCykW9CPNdVYdtBkMlVhkQvVDCdSaafD3T8wAPeqq63RaiU+Bgn4SB25X5WroJS8lbPGBqkbl1Jp6RKjrFjQh9SbVBzql0oJaVD9TQLj1PQUWl1WsbKeRlBZsiA9VZAf7sBl9Gl+rx0Ti1hZdK4rzy9mAyvhsaU7+gu+y6RZ3A4IEwlNYFcQ9Hex5Mdn/+pv/92/+t5rbd37K012xDsEM2I+It+4kWMiokucCIVrXvnef1VU2UqH9d+9t7Awc/DYsRhZoRIRpKKnyFyChuEL8+GHv+jevz8dTw/1jR48ejBjSii0IJ0e/YqyiGf9OhIwmKP1PPoMiqJlIkQEsjbhQrfUVJVIUFDKEVwUdFXX+nOkIX0Bd4FS9/f3wwaYwRAZiRh5nPE9pVydRp2hMRwgI3KsYXBw5H/9X/9w957uNEpvkFhIl+yhxxQZozHVxksHN9loNIbWdM+ePcjTTrfzlVPHK4s9Kp/1T+vAQicsdrtK9nRihgGyVEGPNH2Rv9Up87dccORT6mvcO1U/qXfqpBMUJtPvNLnKP4f3UTlgRrb8aWColOImogvNn3mSL6XwQuecP5N+1QEoASRgX/tqOaVQDKmX7IeOCiVjJhqSw8kCbtFN/OVfvnPh/I29u3e4PVUeby3ZqWAby18/2yvw1OO0+pxYPoQrkrmuOQ0JhSL3Rx6y82JdpauivJgao5fb3OFFfyttKzh4yBOAf+32o96HvU1NdYMLw0cOd6NfLEi1fLnqc16QA/uHMBsQAUb+GNf6Y/utW/fZMxOD1b69e3xe2RRo3Trkc//Ct6tKX5WeW10Emq7+RxOUtm1XO4bifLkUlM+BCwascNX8o3w9Xlx8OQgUQhRiy0JXZL6LF35x/cKP/8U/P1XmiSRTc+mkJWXAMuxG4c2ms7B3kPD4oeaRiczlCz95+c3ftzmqW7fvOPf5mVs3b+zedwD1pRq+zGKjGJil7zLO2tLK8YExCKu/CB0IfpwiKCgBVYsc6FFFBMyRlhWt0iQbYqptwuyQPDg4yM6R+CBpgr4i9cY3edKvtfYQaaHTRiMWXY6xsTG240bvj72BVW8YG7Spmfw0+ynIWFBS3VJjCH26qam+a2fno4f32nZ0kSfzeFqH/CfBdERNYMKM/ODBw5qaqsBSAObLRoQdLdtLijbjAeS/glMVFI8bn8j5cBgUMWRPXfQAK0zzZa4R6RmZCwEijLNXLGRCzfXpH8S0ZNxhZ98brwfXRV4oOMDLv3wdyEFmGgX1FpmPxoFwMrdY7xAxGQ4FvIW3Q9qABFUenFyARyGPhCPssGnCLdJhy9RX+6qK/F8GVGc3tw3ADul5meXIQa9TfUWz4A70WCoQWAI5mFEyexXEg2nJ3xN1nTBcBaJMJBImqBQTT/aC9np8zO70C1X4ZidqOD6/EAixuy8BnsNSftzBfKusxOSz2+Zn53x+HxFGNuIrm2X95O9oC+UzIMORCOY1r9cnDtSqhSoz+ldgSBo91IGa/NvgeDEb2AAwmz0G2Fjj0RMgvSPEx1JhqyU+8Oj63UsfnjhUZkqM2QyLeAnhOppi1og+mBm/JWKU4MH4YLM/qn025BoY8b/25n+TYnfbZPKzj99ta2hqat3O1rhMKwxmWWRONFKTwRUIxG7debgQXDz20iGPhx3OhaTIQJAeR02AZMIf/Q2TWGfIgyegATI7jpioaI4cOQKxhjozoDZr4XrvNDphtCAfFD4wFcR/lEvQfRa14cyKc5EiKYJ4eQagGUYuPyqtBWAZyEl8nBlJicT/7f/6/6xqaMJTEdqv/CsgkxzC7EKLC8l4/M7dW7U1le0tjYSReOX0SRb+CzHY+NhwNkAkFwkqCaET+68aGhvn8hRvaBYGutGJxf6xeeIdsDSYWzoHOY25otuW9BO/GKd/btUYFX61WUO2VAUZ6koBJamxNooZVJqmhv06OLEqU1l7JdzRFI4kH/WwnxAISKxNp2ybnnIY0gG7zVhV9KVruarUlbeiy5Yaa/4NvQUq1B88Qr1mKC8p1nfyQvgDGjm6eGtVIrkkBKME0mTLRmYOjf3CcPL6m5UVWu+OTpuaMfYNENzCDosnO5NsjhPb3mGpbPQ4qyrgWJRCnjI2n/Oh2sKIT3tdoh4Vf1TR7uUP9JcCQLkHmNlLWr/h0Mh/+eLiSSCAyCJYCZQxwsRjS++9/affObEtk5rljsd4bietzrSJpTwZ7KQYYY0pi8PkjqXmMql5n6c4HV0afHS7ruMwKw2PHzt67sNPnHZ/ZUM9eo+0EZu+iR0emaDjaDI13x+JiqzNNFQYgNjIQGg0hAgyuluZE6w/KEAALLTsIA/dxxlfIYXCipVNFWoEqmxwYOnlQPAn3BB6SGpClNPW1lZcjPTqNjU2RfYnA/LhgPFoTrAyZ428UhDPw5GwA1chh+P//N/9n37yF389vzDjdntZUyZ4K94XVMjoKypGjm5qaizyupKJ2PGjB92gfZplspvRgY1xHfiJk6VUQJOHDZr8lI8ZjEwICMnJirSU2YWXHyChGewRbCFQAcZDveyL0vV0YHO9zZZrAURpjxwC+jzpzz7TbzY6qx5CVJFeYx21xD0zZbAyC2KLzsOZMq7WgG2U1dM+B5Gps9SWSqhMsmeLWZmAeKOawlP9Qu5zV19QKMnk82WUsKguUDkJN95qPiJlkdpM1NmM0c2WOGRqMruF5OspF6iV0y9tsWpfUPMtvLawqgx8W4Zb/htdBTmjjM4/fXHxbCEAZBWIZZRbjfbPz58dHbpX7K9PZoaILsPa0IyJKADJs+cuua3oAsJvfaeLRaCy3YBZnBVimURbQ/WlT9+ua9pNYCuTyXfk9Gvnzt+oTxrqW+vpt2QoceaTXqwMLAABAABJREFUn9+88jN7ZiIenCI0xd8+OmO2Ftft3b3/+Mtub0UybWVuii5IyCULndTWfbqNWnXDNYOLAEHsZYadlkMNakmiqTbnVReMwbzwjpIHkZ+AptB99Pt6ZVlVVRUKJTT+uqD8WQ/e3BDOPs4Xl0+mLrI4SaW9Xo/+pKKi9B//o9/u7RsksMTM7CIcENyladBP1kRCIfbv21Ve4m+sqylBLoQHqIGte2Bl5tm75TG/5nW2+E0+XvPJEzygVdSN6RzTwFTGgeclciiTD6YFhIuWjISPQ3rEIweaJ7Rl63Ros4pszMQ3+2rlO6Zi5hDhEPGJghmoONWIu0tpC/3tXJn02d7pTsl3TT5zgVH+5ktcSCZCK1dnsebB6gRr7s1BlGbixWqJIG6L2jK9ZBBe5V4n+zVfP4cHIv+t14z1nj2H4l9kqXQysAG0K5nPzlzY19VkMwaIPoCfGhFzjaai/qFHV67f+Ue/9d2WGj8jX03VEbIsiTjxB2zFfqvdOh6cGymuaUAz6vD5dx3ofvCgt6ml+u7Fcz/9X/5Hv3X+5D5PTUm02O4wp12J4OxCKHDx4r2L7//Z67/xoz3H3kIowdZJzritr0Vy5PfLly8zFdCKoML+gvii2+EMG0Bmx/DABQyAxCh8UPXAAPD2YQIB8yCmBdoeOAFpVhH6wjyf4lpopoxOIYtFXs+Bfbu6dmwfGhqbmp6OEqcFg3nGWFpWXFlRXlbiteG1iaidxEmEAfcFxHNDNkBJT1HRrX8iDULvJGIjXQIeUFOgJrMkRSaEcav2CjLwhEmBPPjShzCX/CFzRX2sTyFyb9f5lcpTKVHHcKbaWDoklDE5PlfAbQAH1IIadKuqCh4+SXVEOSI9k4dLLrsny0c+V3NUAQ1OmKojhQoIsDiepEq5Gjy33wJ8WC7jiaC2/NmLq00hQO+Lv044EgsF5re1NBhTAWvGbZCVN460wT7U2//G6f3HumvTsekkT4zEjBFXL4uJLeGYWYaKSsLh0EyxoRFFAos/fF5nW7XzT/5f/8d7F9/+0WstXa2N5kTAkIiYCQ+VCRjdYb/fWdngH1lIv/2T//fMWP/pH/6+xexPyV4Yyj85V1coO347rM9Ccc8+ZUjlsAQwPi/pk5BrnZwLBH92NEPhw4X27GR1AuGs8wofeEZew6MZRq6oL/+rhrkaVwSRYyf1zo6m7e1NEqMb+RnqIMubpBRU3ghf4i4oun3xttyk7E3YwCZfPZtXqrYauMBYSChUiMqqvyxF1S2i1Vxs1o6t1Ujl8BTZqBoJfZRv+RFanMbEBPSY0VB3WpFQEXhUsJutVebpUgGK9T4EdqJtX/OKxE9m2iK245fPR+qRZvE26jy2Mpb9cBCeGLb4tq2t4po6f8UP1oHnOo++4kp9O4uDhIlqkC2yQ0uhIr/XkJ40GQMGFhalfYmYcWFq+tSRbgNRJ5OsJkbvGmXdUTLJihaCoKAdStrtYYkACI1FbDMaMSPM9F0JPnjv//FPuq3mmWRsmnilGUORWuNpIChR2hi0hmdavGW/993914fuDt0527TrNVxL8VxTywOzUMZv5/PPP29ra4MHoCDiQN6HExR2gib9uOcHg0FeoaBnq2EUPqwmoyZavIH0a6Kf5x+8Kszkqa6hLXkJD+BJhhQIAJiYqBEPuTex/Bpbp0jMEmtGqiOXysNdsQw4wmaFb8gGKDxb5mafP/07qiwuLyIfQC24pNryxxIeFtUJ+GRqwMoDgsrpV+vTv63XYG1zttJJ4pUvFZPSqSu/gstyj/iP5xmzGInvTROYHqB+5s1zPTboF4rdsCufsD7PIB/AxBGzYqFmdx5ZpRW3oA4SFkb9v+HHWjz5hlf4m189EEA0E7mKsl8ha4SXQsESN5HoxUkwaYguhFnxFUGdgWealWWkEetkcDblXGLVt4UF/ylXJspyT7fZ7MWniCcGNMmReO+tM//R9zqLDNNRTKBmnzETzJjnIhm3KVVmjyQdEtvYakpGPcnpvTXOualPEoFdZs/2tDGCYx3xZsBJHHhYe0ysUPQ50HcYAGSBCQFngtNB2dH5YC3gDHHHyQfBH13/KschTUny9CR/QYsLr3MAeNLf/KDRA0s+NzHHBqCCrPzIlfxmYZytji4GyCvCpe/WP2825iXb53gIQ5MasnBVNCpcQ0lpFgyCJ6rBUoNsy1XiL1Wbp2uOwFmK5aRqAkR1rUT61lkyHWDaJdxB5jRfqo5b+niDIjZ4vKUsCxM9m3xUn6LQS1rY0wbYYRdkIbdMm77px7Np/ze9lV9X/WRa73LaG5qbxydH68tdbJ4q68EJTBlf6mhtddgRBBOpTLh/dLx3cHFyIdLabH/pQH0mRcwCj8FYV1ReBTYRBAzD3Cfv/4XXMutDqxTLWNNhUzKIN5rZRQgdR1KCyy0YDQFTqjYZC1pMUUKJBcLjQ/fONu9vFPdwk4noQ9evX8d3kzhxnBFJoftI/UwOhoeHURPBGAATGeHd397ejtVXQ+1ZUPang38BbupLOS9fbZTpFxLPzdjARpk+i+cgA7QebRaGF4zA4r8lgQzgBzgKCcFVVgM5o6OAdvAHV/tmHeK7JHo4SD9nfch9QV/lHv+K/QIB+KIogNBL4nAs/hmciVf0jevEX7Ge+Rqbq0eGTK7xun7pxInP3v2XB3fts6SIecVGefHa8qL67x4xGKPJVNBsDddsM9dte2l8wHHuk3dO7q9J2+YjiSqjv95e5JEIQ2ljIrw4cf+Dg+0RK/6mSQcaEbM1brBWTwfKrvZPzaUnmppLwhPBhuJkSw37w0bMSVup1TA4fjmydMzp7FgMBG/cuIETJ9I9ll5svKwIgQEwD9DqflYOY+ZlcqBVPTAJDqYL8I9VU4GvEabPquiviw1Qf9GsCLknYB7x4mDubHIiMjWeA8IYICHqVp9JCSdQX8hXX/tBbD/RCqGgo06wAVxPRbMl5o0Xh0CApf2y6SBLKuCWbDaI0xA+Hy+g8yuJHQwUNZwZKGrAmEzbOnfdvtISCDpKnFgK5mR1ihG1f9RscRBwko2PzM6iidHZa9f6Dh1oZZEwYWCHpyP1HfvEcTBjtprsodmhMvt8mXOOqOIGcxMOe0F7bCBR/ue/6AmMDnvKnJ/es8yOWv/L71ka6mEVUWPC4TJEii3sUDo4NV109drt5sZGXPvPnz/PGaMuun5Iv156SXU19dfdBenXVl8u4BP64bfp/LWxAZY76SXe7/3iXYev/I033vrg/Y/KSot37Dnw13/+t52N/hMH28STSEi/GBC+EQQEeUB1PkFy3nnn7T3dBwmJ++hR39GDx8+eOVPsM3W2Hv82IceXaQurmAd7e+MGZ7nDwk4G9Tt3X7l8YVvpfoPB/2WyffHtLykECBrAINZaQTapMpkdR0/+4NKnf/XKMS9rhzJG0cKbzC6igiM0mM1lN66EHj3uO33yYLmfuJvxuUVbKlFfVn6QvT2gyOyuMTc97rMTojpM+EnxgjBmonbLX1y8fndx9r//x/vqKv1/+EkkMBuQjRHNPcqtnggVSz6X98a1C+eGbjU3bw8sBrw+b1NTU3FxMSZf8qAOnMlf8lMWAkY8FxxMC3j4bT2+trbR19gD2J8aJy2bq5jNcstKSlm2Cs0vLS1zuRzsP6RUWtD//N83phfUHi5MCJgCsF2aaKzMRCRH/c3li0MgQOfOBQI2pzWYjC8GAtWp9MLiEj4YL6DzKwkBDMHEfFS2M/G0Rr4zNbbuDc7NX7r9zt495YbMvNnE3iWyAZHFZr90+e7Pfvqo6+C2e/0XD3fvykTLRiesKUfJuc8vEmeCyLF2qyvc98jNVoTsSkCoOoxzMZfTYJrr7630+ltLMvbkbGxmoLXYSngt9rliB1XZ5kgCIthDwYS/xO/xuiH9NTU1iP/0iDAhdXChryH9XCgW8I0QQZ8r2nxtbECmh4ZMPBY/dvQEcUKSyfS2jh02mzkST58+/ZozvZhgk1+2NwB5OPTq0+cKiS1nzoSA3R7efPPNaJy9x9jhqBx0OX3qVDw2k0yIOfTFAYiIING1a2/K5HWnIo3NrRGr6/Srr3t8T+a9+gKS3zYIgBkICARskC2j7du6XnV5i3tGLvvd6bJSgkiwl4ktHI+1dTT+V/95R9QcSdiJfl7sLe46cHxb0uI2W81OkzdhJzKCLdZQ9PCnl1LJsMUwxyYTOATZ4paKVPH92ejVx45I0nZ3bOBEa9Rvm0/FMTD6sDewC1M0Yjt4/K1jzXtjoSQB+nEARdGPWRi9ENoeYjsjleIIxLWe+VNVukDPCb5tfVHQHtbUyZHjd/qu4P3zuhQ1DxoWl8NuSodM7C6LhVhCiOPjRWjkpJ1OtWoWlavashl2i3XKf7gq/fptBDlJt/ablU8UUpAMDWeKLXV4iUGAmQAeukmbKWVlRfF6mayqwZe/XYGX2S5cv10bl7WyZRuny73ZKP8N8pHY/UmbNc0en6hy2QjZYmO6rzPbKKtcUSt+N8h/RZqt3GxS6KoidMpVD7dSxIs0m0BAaXYVUMXxH4RgTbnV3dL+anV9Z9/j98em+kuLfZFISLTvDmeRzZe0uOLWkpLyfT73NkOS9WVsEpe0ZGwEkiNUtdVXbHSVJY0zFiOr0xMB90xp1H5yX3XPx4/+f2/fTJd1j2SqyxqjhnTEnLawvSX+CemM0+ys8ZTWxiyZ4iJ/aVEJ40iE/3RaW4nhCnfu3GENMI5DHLAErMTwCc0MNmlb/tWKgZl/+twucsUV4jbXedTNX3xBDSSSDwfDUyZqEmpNqeJ5JOYc9e65nNAAsneysb3OW1/B+gvoqPZ7lYLxDCOyqNtBpXhKq6SOmkyvV5dlEBT6RbFeab3EZC+KvzUHsYyE/ys9D0E5UPcwgZVUuYXNXEqkHFWdTInTdmpvOS/5RGWFo7EhmfC7Xc99dsV2pVSE/VjhmzKLZV2D7ie1DENVZksnuBf1X5tUAjjxV/hGbumI9fU5KhhwYWqylAHfWl9SV43alnmcHepPbESzyeG0Az9t/F9b8vpPNqrn+qk3eKpwed1+p7JUHhBKjEYCtosjC6sdcElEY70CChtk/eLxFiDAwDQbkAKAKwPK4DBaHRbWO8m0gLPLW9u1/3cT8cVIZC4UWchk4lYL+3cU+5w+u8tnNNIRok+Shb8MavQ0BizKRouzxFHdPDZzp5HEEZPT4EnHTCfbzVXlpQPx0sG5ssR7N+qK97Cq02JeMKfHjRlHON1k8m0zEzCR7lUSHRWA6XBA66H4LAvAMEBQIByH2PsLt1HYgw4HXVtbq/cdo7mac3AhFEOtMOCChzIcswRBdiXbAmCePgnLhtllgFgREhdNkQDFqxirIDJntgdgLZMazQrDWQ6xSWHLZIuVsVlpjfSrCMEmGTztKwYlHWG3m2wSII8BJ7WUC/mFpGlyxA0iN1YEebOqUvp2A2K/yRBe5wuywhQhFAGlf1YBBUekdP5WFEs96Gn2+ZI9zXKFMCNQci4brKhmPC1MtvKdcEv+m9k/FxdM2XZDqinEVfwzufzCY5NkfC4GmVW5ZAfvhvocMlx5CBhtNpMy9KgqqRpKMjyCt1ZJ0m5Sz5XFbeUO5FlG9fwHAkpVQeEBKJjpXLg9Usk3SQmZr+0v9YWGswI2CKYwTKNZFtnYYbHUbPP5/LoraCsxeQparL/gjBgnVA9fc0tt24nz196raC1xOxaMmTmj2ckuuNsqzaU2+6M7H+2umeyoWrQmIgZDCPfDqMEdtpb6a9vTLn/CaGfbwGzJBYVwCSlHKcSB6Rj3UNgAa8fYIYBdIeEQPIcZsIBAn0kPNdAHjYIT6KaRycpcn/0dBbArAogqi1jFu5KikVwFZuwMoBZrS31kZwfie7O8VXYnWbfFUjegCdyl0oTrvnr9diQSwy9WPlXfbPzh0zdMKDr1EblLCAxFCV5wlpEPUyVGknqQxQKFC4JE63BXvpGqkkS+Ljz0o8IXKp816fQ3AIutGxrqa5qba9QTYJL/Nn9BWQgCKavVMjY+9bBvUOCmyqUOdDxxRMpLi3d3thbW45lf09kzc/N3Hz4SgQagIcfChGADIhU8waEWmq9OvxqK6n2u/fTRSjjTW6oDV+ci97J5mfQiH2lBSfw/JPUGPbBeHurZuvXcMPUGL6RdIouuOFRdMixokGWsgoD4uCbZ9/TAvj1Oglo+aUVX5P3i5mkggLAvQcnVsNJeghp/OOeyQziLIGNwyxAtqdlh9b185vrfvHLMZo3ZCfpuMDoz4ZQjHvkHx3baMrW+xCSb46ZNlpTVPxl1WutaXXUdUZMLD1MJTpDLtPAX0sktSMuZcjEgs4iMtQU8Z3EZkSTgCmxKTEQ5dnzExQjdEU5EzBg48jOAPD8ozPmZXjMsJOoRg8xqtZNzhK2AY3HZfAYXbTNCqsUFDlswvsZgsCqu+2b0AXSX8cofOQwMjwWCEaOZzzDgCDVWVReQKLYjoJGRLAASmpi/UAQhC1XpOaHn8jZ/yEP9gSbcROxjtyAli0Ef2D1OZcmH9IE8lw9x0pIAqipbeSSamlwevJV/kqdiI9wI31MZ8sNHlJivpH6ubwuvc+1T1Uyl2FylpblGcVeeUAfdhkJsEdrLO/aKftjTSxgJuck1jcqwG9IuQ2vhByrrZ3miTmzm3ts3lCAiNzNrukbaTcvXsEnqoWGRBQh4QB05pGGq37PQ3bx+kljmIELEpZws1ATCfCg7sq1CMAU2ifsiCeDrwiNVyVKqjh++XKIkyd4JbAGmBGZZfi8lCIwLnzzZtcBAYXg+D3ITIIisA2KLvEQgbPCLHWwxSu3ZtRvl1ZOV8SL1M4AAaJQNgEaHIMfqsabRTHUWnQLasp+SjaSxVGh6ZMns6bo/8XH5WGRHCeGm0fkvYJ5zGUN2R9zKeuSkM2PzRNO2hUxlsrijcvuJjLUYLRHeRRt1cF6Qz/MDiDulc0ZrxMFWkWx5D0sgvIQOL4pCiSXHsAfmCiw+II32PnoGIFmTRQ4OihqKFG++cOny+x+dZdWt1eaQwDamtJXQeLFoIhp+9eUTRw4dENyXucJGLZYyZKasBoUkEsuA0SxLuZjSQ9Jkj0WZJdE5whjExZNBKvGMsvNmSCaeYCoNzzksKPw51CjT4OMhkym73QZfAl6qOKEg7J0rkxgKJWfp1uxQh2io+vKG9eDyGAssfkTssI7jMTlTBH6HVIY9p80y25FKUwSmZc6iL6e2OJvKnmlGNirizCGt42syVVIzhSId6uecw6FQNBQkESlAQJFb1zvIW+Uh9TeaWbMu/FKueU4zuab5As/neIiODD0fe2xIsFyBm+zJKrQSNqBKVlUU5iqO2EJZuaCbCIvCdUVlhQJXEuGcbwUiWziEXiKniSJVtJ/khwS0MM+/hcrqSrw7pNOFqqoKqJPaMEgKkHJ4zmtVkK6zLpP0oo5XPQLYg+HIzOwMwwwbXUGl5Lst1rPgq4JL4V9Sp2wFVD1RqcH1QQ/YGJwtFI4SLLi6shTs2lyLWpDvi8tnAgEGuaAxh0Kc7GW2yxCtlBc/Q086UEabfWkhdL//8vzScJGjzVlc8Vv/xX9vylwLjN2JpQc9rkWzIZpJhzNpazruzhgqwpbypLvGVbW7puOIwVbKcztZgwrkV1CeLlXqoHEYZXSBcj//MFs5NUuA4ue/giJhPEB3NDQ0hGkBZIcZEIKCgwtV+ewIICVvQTI9XsiZQ8hXQXH5bFddMO54QmI20WPTbEbUH//pv/3zv/zrbTv3dO7YbXOwe7NF1t4iVqcSI4N9P/7jfzs1OfnWW29azRa+yZeo61OYeVZhqkeJdoRHxM0wPNgZEusHsFdaJYEZ/FlgRGw1OAHUWtRSAJNkrObgFhM/iUgCXYTwECiEPc15a3c4g8GlK1eu7t+/n6mTzcZmynwodIp8SEbIATkpQiPx/zhoolhpyYpC8CMiU1gADEqW89lkKYeaHpCM96KoscJt4I0y/aEmRkM0EmJRAgQFXQ1f85VuPHnCD6RQUROIjcVmtw/0909PjJx86TAlZxFOcIQ/qYo6rzlRjsUMLIQjSmXNUjIZr0n4zB/APSmRymMPUNUT5KAz9CEcloO9iVllk04AECYNwI2GL8zPV1dWiFlJ6syD3DcFVVR6noJ7dSldKgzYjPjDFdyXCgBeVKfICkaMf2LOkfl8wSFdkz0UC8i95Wn+Mo2LbToqHSR7UsK6EsloOOy02WlB7uNc4tz90/3m28Wog5GBhmyxKd7soA2bxol0ZVwKLBV72KppVUOersAXXz05BHJdzS8oB8VkMNJf+sxtYDE41DsxPjVU2WA7dui4w1HRc2e2pr7W6WnLNB1bnDw3O3E5HZlLRReT8aTP3eoq63LU1rmqtxlddUmDHakekqu2YYGA5Ap78mqu/QKyjoKILcaoKqSGuQL7z7A1/L1795gZ4HHEoe0KQqlkvivozYVunaI5a3Nd/UQzDL5lROMk8q//6MdXrtz4nX/0n1hszlgcMZ2cGdMMRjP/Wtu3t7a03rzzMBKL/x9+8x9Aq/gwf5BVYe6AQwiIwB2ziyFtI6aDRM5gia9Ml6U3SC7DJc2mtFBVAgRAWCH4iXhUTdxwsYD5JMVmajYzOaJJyMlIeKlk4vq1y7t27YJYetyO7n27lfaMtDGagaVbaD38nbDZJmM8EUMzyxZrzA0UPcHuwS+Mh7IR2nDegOywPovEKLyikDXkNqmaAAVhnEiFMBcEeRBHtBCXL53v3L69vLwc4oWlBzGW+qhWUppMQ1Cm0RyMvWlck00wvigb4QlwpIMEHrm/dTxMwE1RvKUyan8ukZHpe0gamjQB13M8qJyos2mw9IkwScU2pZvw5FGFC4eQLu8b7KmtqaUvpybHCKJbVuIPLS0AKBG+5WNquaayPJA+X31AN+nEqclRmol0MzYxSoZej8PlsILLFCb9Ih2W/3D5Kv9ILqiv6P3koI/m5+ZD4RDaVRz1ODOK3Gg0mSeKll5morSQhOvUU2fxRGcjKJQ9QO1gYB5vkMrKqum5WcSUmtqaOGFnMmiXRdyiRVJo/oPchy9+nwME6F99ZBFDbhhGQrLl4A4cRuUCSQVtW5tbu/Ydt1gtiB+RUCJjDZiclcm0w1DU7PJ7PU3H4wvJnpvXL1/82e/8sx85i7cbHeyE6kV0ZJzgU2FkjCMEscXec+hcIQLQL7OZjQeIVsQB4WJFApsTjI+PE7OIiQKvGDssUIAfIBPTOr7iE2n1Fx0MapIITIzGv/75O/PByOnX34T0496C1I6sK0RbMpERSTxumFNja0cgFn33o7Pfe/0UlcnPQlYVlV03IJ9SoWT8ysXzLo93cmIqEU8SVLWzsxNly/jY6O3bt0UDYzLu3bsHzjYxOXrn9h1aQjAm6EJn546S0pKrl68tBBYZwOy/Q3SOB/fuDPQ9ZrwxXWppbbl9+9qurq6bN2+xIScb4tAXQyPD0Ku6uto7t+5MTE5QfkfH9oa6DholEi2aB6txYKD/4aN7QMrjLj5+/CXMINeuX6NQIEERmG5IefXqZZfLqbd/Q6Wwb9++sbGJseFBtGNw4IMHDzx+/Biu0T8wAKF56djxiYlxmkOToUR79+5lozgwRMXrh/dwCFET6qNJ2XqEXelFWNxgHx4ZScQTJSWl9Ct6KjiRoh+Sy3M7GC3QKexeUkPkCrQuAJP19VkSrthAMBJyOx1+nwfQMTrYlRRuxwewcbBBzedAJ91CqanipsJWk2m2UWNeB9cUqWW59Zl0JBysrakBj30eFxEfbXYHmMd4VURTp8wyAg1GcgXt1NjIYLOiCLUyXLSIHBQeDi2VFBexV2pTQ72ohlh7IVsm0QvSRkkjSflbrqd8mTvWBTWlyPwM8d7M7iOoBKWXYZZJSIjOS/S8llAwQLx7n9fttDuYpsejMc1taA5ILmxyra0lV+6L32cKAd2NuouzHQ0agZ10HAxgcAg6MYwIuG1bR3VNtdWCIZFoo6w3sgwPDAdis2ZrAwJqwhCjx62uWkST0qCrJhK5OTLZXbcLYRYFgwxsyZsZO+KFFfyA7io0fJZNAdt1dlrMpwnQd0gQnkWQRF5BuNikDJbQ29vLuIByMprwUmU9M9+uzww0eCRfUcaKlsVsunr9xq07Dzq27yAeE/iOtYSSRTchkReEBEFCIc7EvKEGzBWu37zTWFvZ1bmN0QHHWFuQmIhV1Rkv/DMOj46Vlle+dOrU/PTcZ599BgVnUnPp0uXu7m4s4/fu3b185cLrr7966dK5jvbt7e3b+vr6Z2fnKiqqEqlEbUPDrtLS+bm5c+fONeBv1doyMj6+r7sb9yr0E1MzoylDRyC4MDI+1t7WkUxEBvsfYWwZ6OsZGx159ZVXAoHFj8+c9fpKiUVOrWA6s7Mzly5eOnhgP5Q6mWBDw/Sla1cSsdjpkyfgq2fPnmUQQ/enpydo3unTp2G8H330cWVlOQ/9fm9X186SsjKb0zkxPQOxPnD4CFBnW+eLF88fOHCAicKDBw/OnfuM9cDQJKVW0wopjR5CTgCo+CvnDjqBy+wkNRP95KMziWTG5nDdf3D/5OlTNrs1LU6kz/2AzsOpenoeWu1OvJsmx8fmZqfb23diLZDqEfMulXI6PJPRmfGxKTCe+SONnZsLYAGZnw+Ae0xmoHYyLuQ/00m4bmp4sB/dXXNbE95iqDjrauuLS9ClkkCbHsxuj39sYhrcBc4N9fUzs9gGlthN2qm0n+grZWTlxhZQC4cjeF6DQnwCqBnVsG3GM51FplTU4/GPj0+XJw2E/C0uLeHbmTnZyc9qdyEoSTLyk8S5TOWz7AEEVC65e/UrXyiiPzc/Pz4+2tzURHiSvoF+9vGuqW0UtRU5kSht8rh8MxhL0qal4EJxaRmsYXZuAWmguNgr7FT4yIvjK4AAyss4cgnKa/i1GDLZJDJjCIXCU3NjC4uLNpsTYnrg4CGf1yPzSIk+iQkQPIcZpEfHevd37zFlXIRzscv+ppiU1Hh1GUpq2+0O2/jweFVVJd4oVpsECsW92mxktyjp2+fRv1RVg6yQzuYf8gqWwIFRAdxm6TIqVhRHg4ODYDtjFudUnkO7kCmhunzIrewlg/iGd7hodTCkoYFP/+Ef/mnjtp2yoxTyP4Q9I+IO0m00FsGjtbi4zG5i+DBsRbeDhbuirOqn737Q2NTkZrmuVJJRDXHT9E2qDPtaHmYQODQtzS2tDoerqtpWVORfCi7F4zH4D7QDyzhlDA3HVVDuGHMCFKy0am52gRoj7pWXl6EUIj15slkcnyNYOZwulO/hcJwG2B32ltZWSENLc1sQeSywSIY3b96oqqwk7AxhhaAUTAt2lJXGoxFY6dDQcGlZOTxDmGAmQ+bjY2OvvfYaMOIAXj09vdXVNYz8lqYmPHmpDCbQYChUh4mb4FXomJy61407u7rICgAM9vXQSXwL+92xc+fjnp7FhUU1l1qFGAKWtYRGdzPtunnjOpbk77z6Oo4yRMsBRAqw+v1zPWO8F4EfOC8uQY5rFhbmopGwaLoUFspLvCUy6fr6xsXFBcBe4i9itgoo6usbaBQyE4yBvmYg6TYrhDNArPv6eqpqqxBYQsEQEyyhmIwXJZpxUVFRyZoakBVdCtkw86mpqTMxvUAlqFVTeRBSk0wG5KbL8LGjI5jUIxioIZFNRJf6/UVwhUBgyV9U7HJ5QImSkjJwACnG6USVBz6oJuXACQ7kLkUhxvXyvXqh58SkokR0PhVl5c5SOxqn5mYpWrpTOIt4pnk9PrLAPODxuF1OZ2Apwtgj5gwkSbryuVCJfN1fXOQhwLRW1L7MZwV/TQZ8Mvv7Bx4/flRWVtLe3lFUXGy1Kn8B9pkR4iU6ApNJaNnI+IjDYS8pLqe70fMqASSrWoEBQIcOHDjEnmIetxcdAAphRr2U8c04QEZQlAHC/mUQN7BO0zeGXj92yulpJhC8AieLPMVur0ukfZn5iwHg6pXLw8Ojx175TjwBriIbs4zJ8tmnZyPhsNfnuXVrsqtrT2NDKwABokAM/THi78BQaHBouGtbG742CgYytPPDh9xJqvGekcVBFAeR1xKpOEu4+AReGo6F7z28B5km0+rqWpfT3VDfdP36DcS90dGxurp6RtjY6PD9+/dpGNOcdCKu7czC38mL7sOEmMJvJwMlIiQnpGF4dBQpzO5yL4Uj6dk5vERIhPhZUlwKUCQyccYQi8WhTaLDAAPMlhj7bbJxkd2h1MYGvoUqoTvAKGKxOxJKgyEzIrQkohagZP5oq0QHZOUg/BYXWngYmaBJieOjAqtwucOxmBgFs9RpS2gSTyQmpiaPHT9F0VgIfF4fQRPpToTqLX3/JRJhglC2DeOuXbsfPuo5f/5Csd9z8OBhou1KY4X8iloDug+a2WzlwEEqJnoeObiWPsYYK3qPbD14yFelJaWHDh26ffeO0+U6fPiw8ixQCYzZT+gX0JLE6kihzecCWw1MhYvCNnErFhiTiV39WJ0PZm/fvh2vCbqAgvIpZdbidILxUHw+gZHT3XxFAoUDgjukXr9v1It8VvqC5LSOg3xOvHTixvXrQ0MDFF1ZVRlPZMsFRDSXohEakEKwRDKcoBSylV8mZbfCCtIZm7CL9ctdVeSL2y8HARZimm1o6jPzgQXUv/Nzs7V11cdOHiwtqmBgC8IQEkhQQv6kV5CAZN5g6O8frqtrElqGwg9OoKbCui7gOUISaIC0d+nSpVdffZVrEEyj1per77P8mirRQGrFQZ0xVTK+kJZQdUAhIW4TE5MDwUEULW6PAxzGCu21Ou7cvU9EPFqkhzMmWaYUNrvt6JEj8D/sEEg5AIy8lVwI2sM7jXyLr6Bhewfzi7WInVVmSePQHmCXUywBQoFJQNxN0imHA8nacPjwIUqCfUBVUdgxYhn4ZWXltbV1sDWcT65cvrRrV9fOnV1jY6M9jx/SV1hfId/IVlRXDU9mc8aSkqKqyipmAHMLc9QbzRUthxCgpQEWdGcsAWgwSEoN/D6/UutD08VbFB4D4CanprCxAD6kPO2zpYGY7Wnh+aIGAX94jrEawiN0T/7QU6chXvfui7EBAoQuAogziVgKBgvJ01a6GlmY1RqkhKYAFKk8BFrx3618/tRppBWiHzSHQrD/aHlZRTS6BD+22twiEfGfCbPCLSiphglnpHKgp6mkgAZAr9F9kwwccrnc5ACzZCoFA+ehxjY+4lrnSWNJAxootiMcgFeFLeJWlwU2o+NBMuACyKtknBR9FwovinvJRy7kIRdUj7NKKSxNisgdsHOdhoewMU3Xs09yafgFScCrxcAiRTscbiQsilamdCmaBPpMMtV7bCQirsYilbLpLbaJbIHL5Rbk/eLyWUPAmILY9fUPLgQCtfV1L3V1szeZkIAM/iDIfiLnc+D8wSo/elyMhinZJmx+LnDwYLXQCjwKIHkyM8x2mUYqtC4QCm2bxY4ImoEquuufdRueMj8qw+DirHFYIaSMCEgTRJXZAPmmWB8RCY9PjCBL9fT0oJhly8z6hgb9iWqRaXhoqLmliayYqSOSKimH2Q+DRQDCGCJnxgKeMtzLJzlA5esNG9CcVsDIjAMdOkRTRp845rNhZ6akrKKsovKdX7wHmwKyVoMZv09cidDtoNWh0vX1dViSy8orHj7qRVc+NT0lPuzYSw0EkXFfv3m9rrauvLxCYh9kMDxmOjo6/u7v/66yqqqkrByxumvX7k8//TQciSItzszMdu3ah3ZHPEWSqYaGJhRnn3xyhlcom/bu27tn777r128GloLUBExBfyXkmGkKrp8pcWbnzOwB6yWGmWvXrjc2NTc2NyNsKtInEgFtqatvuHTpSll5GTympraupLRsbHycVwBIKAtTA2jBpgd8u6m17fzFizt2hCfHp5saGxsaauCZYyMjqfQ+5Zy56fdP/xKcAYzMq9I3b9wqKa9ob2vBGn7hwoVDh1+CgjM8SAAcKEG3hguaRoWldWoYZM+CCtJcearq09PbMzo6fOTYS5BsJhl79uwBERk88/OzbrcHaQsIaZTVOfARWSguuxpcJGP4ovoEcZkQIHUjlMFjyBMTQjAYwkUL3qBzU4WrquirHJmmFLArP3Hlmm1j4f1Y9S9evLi9c/v9Bw9wxkA5WZgP1/j+oye9e+/OoQMHETsuX7r44MH9ju1dYBSQgeJL9UTmEPkA0RK2oCGQrQnlfmO0BzmQ/LL+5pBLkBCw61EG8DXNArUeP7wZi6fatu3aX1UjykxYc4Lt6FVQalw7E9FUImrFQdHio49QR+Dhi2fg0OBgbW0NEqqCi3CCQgBB8iCIlEUpR44cefvttyEg4AlpdDU0ahV+8rVcA4fCcqlt4a2+Rhz34ctQ1Llte2cyxVJhNSNXOIzjg8Z8KA+JBauNCFVKlYrTqPIQ1SIfzxm85A/xAPXXloLuJS/BCu0/dPgIBBR+C9D37On2+30MiqNHXsIlhkmKxWyrqqicmprWo5HlcrFY9Oq1K16/7+CRow8ePo7Ek9t37mpqbTdL+CbL7n37+/t6qRlsb//+Qy6nl2pBFE6ePOH2+FhQCjUvK6t47bU3IPeBQLC2tl4JoaLPgr9D106eODU40B8Mh0pKy/m2tbXd7fayTAPrxcmTLzudqIyS8IbiomL4GdDo3LHTgYIokTp46FBfby9qJbLavm0HX2lAMXHZv/8AFktE6d279zK9ItuqqhqnSwnLIArAXcMtNeAAtL5gncT2zp1FJRVTE9NMPKuqKthH+86tm5UVJRt9uxb0T/sE/33cbZOYOux2p1iQjGb0cpBdeAAiA+QVhq2J3apq6/pnz7RR1ZUhiRjMgdIPhq24oRG7gh5IKFUg2QuLC/V1TaQkwzwQ9EVWWlvTGCpA5zIV5QJego2aDLnA+sekc2FhnlcceljyddZMsSb/PIehOIa30iAlOEPQ0eRohldYK6VNgspYME3jfQRMUHZBPPgcLkId4IgcOB3wlSYH4L0uVq10ICFcYUMcWNPQFw++GALAmQPAapiDpRgaUQOwuKe5uaWyuhaJUVS/mbQNvh+PXT53/sqts3Zn2m1noWzSbHcuRQxllR1HXnq52OVGgzg+OXzk8EsbFQyegFcgHgm4Pnr0KJ6B6BtQFUrn5mqy0effhOcaVtmaiJDCuJRlW1jsOnfsuHTlBo8wZGHKpaHsk3z9+tXFxiasa8hAu3Z3MZofPr7PvjqVlTVVlWiQMOGZRYUDrq/X/AKlEAPcYMJJNE6gfzz2DaaqqjrKRqVmsTrr63F5aqdIu9ly9fJlLHsdHdvg7slU/M7dW5jazHZXR2cXDIc0XqVOirFgw+XZtXs3KhMWCTU2NDP+EcIYqOTFttNx7DYE+04knG7Xzl1ddI+YATK4XdJuamtg9RkW5u3bOmkE6wKoTCQWKynDlUmmS9SMhcSM12oh5aLehyuWi8lFlFlOh3tHZxerGaA92DZJgDcVOfAdM83OnTvIgRJ5RCYePAc9+PBqmpOnPCRZcVAleaf/G43VNXUN9S1kgToBmaW6qtImrmgbfr4ir6e60RhMreHHLS0tKHYgrMLJkmXxBPM+G1oyTKMMANq7eQliGUDBJxgmukL+sMgxfWTVoM9bhPEW9wMUrDA5l9v5+HEPBbG/DhVYlW2efK96zoo/0I7PobzAhB7X0Jac02k4BM+lllnVzjIbWJVPvkDSItBpma5zeyc+r8z0dUuVQkl/J7SGAxJTUloMitDdMEXKolC7w8FEkwk3WKq1W5B7se2ItgH8UQ64pJM6gU0cz7ErVzXzW3mr0VXgqA4UsKgQkOFQeoC9CAFI8Snk2zjan4TZhrdQfGrs8f/2L/+H6bHB7oP1R3Zut5miLqcpmghMLsQ//uz8xx/+0Q9/8z+rrd3mcJllEZHQiXW6CdIPAtCPvKMOiCAUjbn49ddfpyI8BG3yAgS3vxQH9Wa0IMofPXL4088v0TDaAq8DwxllcILJ8QkcohpbWpH9b96+xZypsbF+fHyqsqIaGmi3Oxobm2i9jHDmwCuP5WUUvPF6XGZTzOBGlU9kH8ToNLp1tDPQCBRWwJzPsdrs3bPr4qWL9+/fxRN0YXGuqalx587ONHzAJVSJYOEUoekF6dmEFg2zwYX6NWN0sN6UGbcds7NdfJ1k1RVjmM6U+RHdChcQuoJWRpi2uJIrYyP1wfwq/i3UQYX4EOLLa8arsojC7nSvkw/VhroRORoGKFOKjIPPmS+BFayNZttIWgYhoGjgSKNgqkpXEMMbSimGOC1T8yymCdQoSrRPrKJVlnCnzYb7vBAQPHbNJufc3CTOUc+TC2S7Dg9jv99Db8KqrSZHKhGzOOHzAjExf6bEHZ42qhpnP1n7A/g0G5DBJIlh3WoxNtkKbKU3AA+9Pzkx3thQi+0+T5HzF2SrumtN9lBWoaQusrKzIExFtgDmKp2RCUEiHqmvqwF/dK8BZKlu/qCbc4c2C+bu9CgG5LJKWpYKKhSSt+oT0FMuBdlFkhFdsqogzkDAilWH4+NjyYSs39FoIy01JmAAafwUQUhMxMSzJ5oEdS+og+T/4tgYAgCTlzKaFG3KJ8z2FsvXFxZwQWbGj3IGlTKTOV4BfBn+ZrsoUZFAjYm7dz75sx//D4f31B/9vTfdjkg6NWcxxjKpiN9hLvE6O360a2Q6fu7Gu5cufPrbv/Wf4aZIqfwJFZC/FQciCKQfZkCVKAiuo+uAk75+siL1N/kmj4dCWYygfXNzQ1trM2FXcKgRBFaG9BZWZjW3QFKBRDSOtBwvL68FAtu3d/IQTWxVJY6cRahJxPoFtBSdWs4bci8DHkBA+tHeMH4U0WRFmgwlIZSFY4KvJQOUEtEI2vkUGgNUQyLVCdXgjfpclaH6hu8lb8g0LziU4Mav5KLPvJTC85iEX4r0SrZS+QzVNzyV+qgMNdVT3+r663z0WxiJIm18ropWhUCx+UjlTm4aRwUgqqIQeFZhIO+yiAmrNuQeugBt4q3SpZEQeED1jTFgD58QxiyHVNyYCQfDf/RHP/693/tRaTHkUjvhiGo7S98ECoUTL/XleqezZ8+y7hpBab2X8oyac4RjUUzoAFEioinhVSzrmfS9e/fRucGYdeuo6Cq16XK2utmc1UF3CEpJr3ASAyyccGx8jKWbHR3t5Olnlw/RmAvwVxy5HFY8lBudMv+aWkvtFxeXMH+x3ASRsK21CbTWHxbmu5IGL79RCCMZCg7Qg2KW56wwSeeiESR7zYeqQaRU0x4MFajRKisrUAqpJCoTY5y+wSBJjyOxYJKUFfBAgWyzaJzNTv+wFhTPgn379q14+it8Q79CzTUAuFY4j2I6q+bGPoSrGNYmHacTtUw+pVwYDZF0GCriMVv7Hl67ee1/7+o0lxWb0viLm0MZM0E+HOyIYpYV/nHW6BPGIJbyzE066uvfLKo+lDQ7E4aUzZBA1iS2aHakqgIuX76MZYt1Kro4zkxAr1y5gmqIuQL1zNcwn+Cbf8GkVhl+Mw8fP/7zv3+nobFFqA/Kd3bs4lckQRGuw5HQ9NRkZU3lyOjw9OzUkcNHJsZHf+c3fx1hDkmacc3fKtyGNslAlLGlvYPEGZN/EAMhf8uDOAckoQUZg82BFRarozxVQzEbjS2fPpectDxT2cs441ZuuCg8eChPaIlEutGXUlOdv85TnjLec9+ue6FTShFC7ZcL0c858weT0d9y5tCJqKRYhcEOCDyp5APeCxfglWQoaUWDBFyAttBfEF1lIa+QmjPp7kOHiESO+Azl5UDWkY+yhy4td/clfoVuqgg8wi3lEkonPUJ1goEoKxgCSyGvz19eXiK4jrOWtEZVf1Wha2gc1c7XUhYmGwzBUNhktk5MzkxNz+3d48dwl0+QzUw1nuu1BaxKqRPQodMzbOixACfALxBuCSmXrKiimr7oa51f9pOCfiSpzjbXa/BAeZIvXV1rrJGHOrFgEteZ9GIglEwusrKElmruo5FMksH1qQsdhxigpt451JAavTg2hwCYRgIQkUHBGa4AwcJFB/Ef9QDkGK7JBJpBISNMWW6FP0sHyTQUvjs/O/3v//Rf/cPf6vB5xjOJsMngmF/wT80EpibH66vK2xurM+koA4++sVrCVWXeuakHxInzVLaD30wMGb4UX1hJ5vfUofAJFUDyuH79OkZjXeHCt78U14p1CQg7t3V07x2+fed+fV2jiOCs/DeyRRpaHPgAXWAJx3AKMiZjMR/z4GT01EuH4QH0CwiuIb+qvXq/ASCIX1Dq888vBcMx/IXoVUAuZLkAtnp4CSmEVrIIATMAV0IYYS9K2F+ZPlsSRHN5nK4qfe0thE0wQxmuRTKVb7OjWVVqq1mpxHytv9XlqFuh4TzkuuCgabSksbZm1442YbDymf4SgpOVa0guBNdompqYuHXvPusWVU5UlRcCf4KjvffBmfqqst27u8gf+MjTZ30wllgUfe3mLQw4YrNlUiKOWcxRkswPoKVQxht3HirMgEYKo9BtkV9gmauRzE7WxQhVYZ2KWRErsdhJng7+6MzFHExosXovPU+ewg4L8WSTFlM6KkvGpyWSmPj0MvXJVQEIwjWlv+VQ+WbzfAL8oVZSIZ1H/ix1pYaAiX+ZzKfnLuvWSTm8YYbHc+lZbtJOm/nY4QNulwR7eXFsBQL0oKb+wBaChPMIq5/AUuZM+ATiqov0racLnEmwUikvsblsxtSPf/L/YWlHsT/GIiUjO3ybPY96Js6dv7Bze73X40yjCxE0M4k9NM0CmbApM/XhL/73X/uH/43ZXmphMW0CyQhkWq4vFH8VG+Dd7t27mRDgHoImXZHU5fTf/CvAC6ixaSkAGr/76ukit/vx4z7WTLHwAtgK9sP8kkmHy4m/5MVL51lRdPKlI8y5Ozq0FlQ052RC76xqryzz4nv+GA0Ts/MLixGz1QFNFBICUaFsmXTLV1IMP2qiwA9QFy28PJFU5FLQC9m0POKlJFMTEa6y+fC1fMAXuax5oQ/9TOcl2ejSpbRsgmzeuRQqA14JQVGFcc0zVe9snbNf8FAqqRmBOAXSQv0JcEF7VVVWLtMlET8pK1fcMhuQNAAmFI5MTEwbTVAKUubzFrsTmbvtqO7JWcDyPA5qgOZvfHKWubLMr2T6IpRSVj+pVWMCBmUpwWcA/yXkM11HgI8SjzGpUQG/u01qmO0cWqjEB0EFWcdPq6Q7eabbp2z+NoFZ/oNN20w+zKYEEcXXW2QInScZazoslluKVNqeHLt+EjgKJNQsbc1HamMeWVBC5rqOUrpMopGeiNUK/icJysJOA7nAcpu25MVLBQHVofSnrCXCEUsvIGW9HpZ8NG/Sj4o36AuSieyoZgMafqCqJWMdHXlw8fO//7//dz8wJCfNBsRQVv6bphZm3vq1U7u2l6Ti03gGMbG1pK2CdhAyQ6TY70zGBwf7r7Zue5nFfng5LAtrKmtkfzRRq3qJahAU54MPPoBJ5PVFugmrUn4zbwEdPEBVGA1+/NRLRztaWz785DMiudnsrDS2J5IxEB8LaOf2DpOhtamxev/eLuJmATUZakIj5VjbOpRCmizKK1kzRgx9GScQD1Gf8A0sHvAxfqQGar5Ml8tYFSWCJCBb2Lxkk9UMwrtlsFFdkgiFUI5NSAf4MmoyJKhAeDlFKoUS0LUqPWkZk0Kb5RBCrDPH2KsQgJoIv9HpIbtUCdqMoUKjF59b7CJ6kICFw1SACzITwqjmqtkAZ3BUFOukVjiKgotok8HA3I72FkWXhJbkDgGeOhQUgZBMg6Dz7IuNNlk1QRm7SGO12LDWioQuJUru2U+f6Q8QoAghXia7tE1oGhWWwN2UR7M401hFSDMEHULapTKAaHxiHKqN1YEhCinHwLpJBfMQ0G2Ay+NyBmBZlyztkian5maJ3LMg+y8Remhr7RVLkbBSpQACzbBASGxwOkt8AhYWFrUgWVNdzTDO5Zml2luGomaLK5NTLpCCDLE6XfE2+A3dKGRIZkWmpcWFmbkpeFN9XTmyShLlg6BMHgwrc/tVvRNBR1Arz8JFSmBlIsofDlALIovXyirwaDCuC0xBJZPp3p0btTW+0mKi34bwfeFZLJGcnJ06cWxbIjxtsgQl8K/ZxvogG+gjbD5qtSw11fvnZ8cYCMLKZaXqigPKQBeveKRQlOcohT799FNUVWgmhVawp6zaB2VV4m/aLQAUCpylsRkW87KEorq66h/+1m/0D7MCb45oCMFgGCT3eJw+l7u2pryuqlTUnKIy4gBEG+Izo0Le8Z8/hqf4Egn5Fj0HQx16ITmg8ZBgzmQnkSyJA0VtcF+iDCUgCuUWYiQUSjKBVDErQZU8Mz2NczdyAfaZu7dus/AH6gXhJiH8iTIhaDjq8IQikA55gnVAVUhd85ThK+sgoGVY8LBbKrcTVWcIPYsAkOPsWsIV6wgLTiQ3HJxYzAxeElNMnJcUrXFabISzhu7QFpoJ64Kco9O3mY1jY8NjIwNvfec0OaCcUIROI1YheglbVGDiIZWUJtAaFiVRoiCNzIlUzeTmeR2UpOgcv9RHaTooWWqWLZoLegcw4eyPgY5+cnvkHws+6AhCG/q9Xvg97F01Zkv1pLUCLqNpfHQMT3yEKUY7sTlLW1qIJpLwuPFVBQiKvFP+xge9CpTUe9XnJswOOA5BkcvKiunHtpYWHF7ZFwEvWEE/kisM3DjHtW/4aM2hn2WLFv8nu8WK8prFknAkAhkRd4M1gHOzU7PTc6oTN23Fmux/RR6AV9Jr6gwDYD0gKwDYpJcxjm8+fgSMtVWgIP2qJ4W36qVxZLi3q6sW7T8Ux2q2E0NylmBY81GbwWVNRRj0FlfxUszkg1jHgth6EfwMmXiRzzOPk5fBiUDDSCjMlmstbq56yC2Vx4kIVzFsyMePH9+8ems//9qf5CoMVDVgMZZY2pvr+GORNCMGQEDdFHmCUHAgYeuUm3WEpORLSQJXFc1bxJJJhhZnw8EFxj7TZP6gmbFoaHFxLs1WAQjmEuzBRGzeRfafmp2LhsKs3JXHyehSYD4SXiIOBRthxolEdPcWAeRikajNYu3etx/CkYonhHpCt2T1fiYZI/oxtNe4tBiIhtlllJooikZlqBCTD7AjHAoszrMbCeyHlIlYPLCwyB9voec8JH+ekyC0FCRnHlLKvVvX56fGE+EgETDgXTZDOhyYT0RC0AB4RTgcWFyciUaWbFbkj7gVsV5ZtgADPE3BGsjwVEGSpxxZ8sWV0F8KEpaWMUBQSCS0Q/2ppM/vRF+l9H4DwnpViYBIVU4xIR7iHaSgNDczy1KG0uKi8NKSl2jONtvI0CC3AthUgj5li4It/tFbzAOmJ6eAbXVlFV1A9/k83tDSksNucRATJpMAsDDqL8pQNC55WC3MzSNVEGUaHy2wx2m3EUtlYY6QcKUsZiMlVS2Ebf7DDS/oJJEY1raLh/lysUlaQF0cXOrr6pAJiMLtULGEZmema1hWljawTU9uvD2/rvzlyxkaCvVHymZm/9FHH7F2HQZw8uTJY8eOQVvhAUzsnqJVwXCQABIy8gwOhEyUG0OjIy63zYXSG0EnYXv3vat/9h8+fO/jqxmTAymO6ThCvNlkT+EixAila/EMBycKDmaTa20DVI/K0wqiHiCY4sMqaomcU1PB178Ml0BLJkacUbojryfZLcZqTLFtJCCTgHMpWDKSNXSCP9FbbHLgLq1oL50An0+nzn/+qcdbRDwWFqQRo/XAgYNIeYiQxF2AwbIeeO8eiTg9MNB3986d0rIyZvFMtPft31da5r925UqItbnRKAnwDh7o7xkZHoxFIjU1tRiLLl08hyvklatXXzp+HKRBHsdWw6I45L5z58/TK7ir1tbVEx4PAVA6VcTVDLsasOUA0zdCyp04eYp1cYQl0LjIh8SpRrq/cf0KfUmIglAoSM4nTpyYmpqcnZ66l0qApmxvcPPGDaYaU5NT5RXlh44dGRgcfPjwATMsCqVKjQ2NkDlhMdBHERc0GwAsPFnvAK5EwolF2LOMYESEpmCCKdCj1tR4+YuCSwHyMzzgoEzPDTaZ5ci+mxAtwkuo+ksd0BHFYxG21HHiHWG3wn3ZJmx0ZJRF6QAEWInUho6eClIvIA2JzB7wVhnJpBFFn85R/aSTBvg6K6aZctptVYydpQBrgWfr6mtREaFfQRbhEDgopkjOWtYDnmQAxEiglUHZehpMst9ACfNWY21NNQFPcLqdmZ6qZyW5zY7+iq9U/TgXQlI93uiULVs1R3CIEtEqi95Jm7H0d/RSLBpxu9xSdHUF9CcSC6PaZkJA8EHe5nBgo2K+rueAkhZqaCDjPVukWt2otUBgosb6L6R+4ubjic86bfAEwopqhf7Vx9qvVudbcK+HDC68s1OPTYbGFLsImM1ohBpbqyvr602m2Uw6kMyY2ttaD73c/pd/+ifj7WXVNWXMD4Tbp4wuRzF0kNk7Wk9iLuewRQpg4kutOKihLlA6VbExfcFUAK8hNen8JeUEy/2vopnJwGNNJZiuNN70SBbQiixlrzf6kTDf+p0etNOz86wDe/W1NwILc++/94uO1jb46p2bt06fPg2xw4v85vUrFWWv3rxxsbt7f3NzK3H+pifn6qrrk6nInl17CEQBrnz44YedHZ0tjS2jQ6NHjx8lYiWcObA0j9HIajUODfdDvtHeoIfpPtDd8+g+QQRff/VlNgR+9/33q2trUV+L6sBoRGPw8MHD06dOlRF6WvYOS165etFf5CUQJisj3n///YqKkpra2sXAHK3/zne+A3X7xS9+MTw6WFdbS8zVPfv2VlZXQyQXQmEI3atvvYWtAsPRzWvXXnrpJXwY8AG/fOFiVTmxDIUSKnhRrKiPVx5KxkHcFf0PquR0KBz4/OwnKNmdbs+du7cPeY5BYHHlhHIyB5Nhyn9l0xbmAIQJ/J3rlZU5P+mdMCc6Gjp989Z1l8fX3tZGIKPhoUFidWBFk5KFHhOxwz8/vzg8NMwsR1RgAHN2Gh/f4ZEhVhozaUQjKmXTZuwKojQ39jx+PDc/s2tX59JS8FFPb1v7trLyKjiyJBGvvgyrvSYmx1xLTtgnSqGe3odAYGRsgkXa8GmhuLrtpAarMhlA/fDhQybgrN29cf0GS0yQwpSOUkqmSiz1nZycCoU8MPCKivKxsUmiok5Pz7HhATkTe4IxLLrg3EHTcpfyef668IKuYmuqyYkJRBcWGxOn/vqVa6xXamntBCwqJfM/g89bPDLCZmrpcGSBhc2j41MWq31qajYZC7PJdGGG35RraTykDxRjIDDGrWa0dHTNs8ErQRtAyjjirFBILvIHezqx6gIJD+ThoLsh+hoyEFz9LXLVE8OKzcAMRBU7+PN/d96QKDJnFtLpIGJukc9uSxMUciHoiKbs/qKE78bHF1qra/xFZdgl2e+VQHPJVKyiojYdM6ZtzEPZjEQE4/xB9aA5NCfPBmgLVdUJqDBkDQrAhAapEW6hm8BZUG4D1Mpn/g27ED23rjIQyANB03Oquv44WdmG5Z6T1ABA7Q3CcEGsZvAwkhnzPGYLCAR/wIT4yYEIAEmVzR1lGSfiAGYD4UTDw0MMadgSsYbYdEeIq5AgkAysMhAluq2tFXeCnTt3oP2LhEM+go4NX2SxH+SJTEk2NzdbXlHJngZWm5mNhyA3cGyiGrBOmpyRP48cOQydYcUv5pG+/r7aulq+2rZ9G6SNQMUlJcVkq0yOEoJGq3gQMTp3dLHnGzMMEJrnmA2oFkvv7t69Oz0zo3AaAGwKMRElRM8GiG7duskK9YOHD0M/I9EYhkastSJwykxNZ6IpzjLZUoxhJeyf8g53F0oz+ov8c/MLQB6IEVJJ8wCypC10Ex2EsIZtgDufz8uqQqIqQajdLi9uAATjo/kQaxHdhWJjfktUVlTOzk7T44HgktvjgRArVx64GmlNOCGgQyeIEz1LMHCifW3r2I7lmSk8hgHoqS5XGIs6gJSK3OBC9U/oEcYkQR0UhgmVIQlEGRxjNCJaEuEH94G6OlnaTRqYAW8ZkCRU2Kyz5LxpB0nZ0DLZrsDn87N6HlmBKRFF093CztShERXzBvCJxsIYuJ0uT4PVSaBywmf4i5yUIfl8UVn5On0VF9Qd5ELOE9cGCb0J+c+6zqmbL18HwYcCLqupIQAkiDEGADq3q6uLNcAk00dhiTwpvH2Sa5Fg29u7Pd7G0ZmFqsqAwThrThZbkqXpTChjS4pWmvgE9KUd3CA4DRvF4hFvi6Rs4birvaYeqQwCg89oYX/RECpMNUAnIQJrDt0EVBTYh9BJEP2Q8UKqQgis+eib++BLwD/bqGU2AAg4yBFUg96xqlb0bVB5iBy7tRJzh32CU6kjR44R/Q1lETaW0tIBu829Y0cXALx64xqRhYj4yCYJomARtz0RjslSsSihj4QMImbZ7dt3Jyam0DI1tbXbnK5oIsHZ4fIga5w4+bLL44cfUDtILr2IiMGZTmXqh7qCjal4oy54w+YBS5JQAifJOjrlIgThkBXENETGfQ6zSURPI5AiEcoKOewHvAZ9cewR3x7x8f8CXJa0goswv7HxiVdfeRm2h4MUIgZiMCFRKQ+G9LyRhQYxRAn9ytpxm2P8nXfe2bVzB7vuiK1cHaobs7s/Mq+iKyHcbAOBHZ0hwVtlk2dpCM1VEEKMkk6XyFN4ely5dhkGs2fvXogpCQSI8AvOEmTCQD78IQEATKfTK55odIC4jIlKaLnt1FK5ZqFzu3btGn3NBgaIkHRKTiSXtHwF9CAudDHXLDFlPietU7IGF7qG+Wxpi7pWdRJczXdvNgn546UGB7Lb7N37u9lv4OGD+0cOs3eVj9i3QjxVoSSgCKIRuNxMYqKURt3E8cvoRIGEUIPgmC/0G3FBB6ieoCv0vFJaIk9A9Lzk8aVqCvz5XlNPwIj/DxIS5l90ALjbI/TwSqdRnbgsZX+5Ug2mJPv+efYceuPvP/gf/8k/bTAkwvZMsSlmS9qx/7qikfgH7zNf346oAUaxZ5fNiFuIfyHgdRS1Wb1FDEeJsMWqgZWsiFshGrnlzWsrSVtIAFp+9NFHSDwgIWlAuVX5rP3wW/lEeGbuEHFAjz3Gqu5yxhpyExMCxglbC9TW1aHMRRrFSQCNP2r3PXt2e7weNl3pH+irq6tpampgMLGaGRxFR0LXQHog2ABX0EyRMKRCtHIIqqwvpxQ2LQhHIuAZ2oOi4iKRBMXXSFQrPCQQlZ6O8C01gWoMDg4hHdDBLFShSlzAtxjVcALdhZypvpASHEORBQS9RUks8ZGSqbLy8ih0MRZjTzT0VxSNrlND4IsxAKsAoxC8tZiXQiE2wAHZomwdZ5RoTcI82SxeoaOGXg6wz/iXpkGIMdMxStkaGvmda1154MYh7VVuvoBBNu802bAcUG3YMLH4eANHU1DJVoxPqDA54PZHIG4YJDtUELuLz5njEfJLrMpCQ/lQJt8qZrhcKOotMw+KWwE9CLSKbo20BeYgdiFRUgQHyVQds4jHhzzUFYYH8IoO1WmylUPoLfhTTJwPoPWCYDIHE1KY+5PVdJIzHJmYuMwwtnVsQ50dT8RJJsZ/sf9TILMXVtsBJWE/TKqpBl9pJiOhROQohJB68HWeqGU0bY6nAQ9dgvOGKZk2RzLmsGLET1kzDXn9MTDn4JrYn1evXj1z5gwd98orrxB9gbmafsVbfaGB/JSlFnyGCC/iYtpw8PgJf8WOh/1mo6UVNzeLOShrU8NWj7H02J5j0flgKhw5tG+vrDnJEBvK63Du3Nb1OgMvnmGbQnhBQabqUtdTd+vqd+peIUEKSkLw8xtiO1QIsJKXrPvht/Lh8mwACGhBTAngaWJ/4s5JtxOOv76h8WMV9D8ei/k9viOHD1st9kcPH6JgYckxapu9+/Y1N7Vcu3FzYmqGNEwd6F+LzU6Yx88/P4e2gVkClmSGL+MOi8Ktm7dramuKikvQTuj9Bt5+512sl0wI9ncf8vuKICLQIBjG2NjIu+++W1xcxAjGqrNnt8San5+TrRDZXq6psVm7yZA5X4CdEDj+HHZneXkFir+q4REEUp5LeFHV0+x3SLilTz/7HJUX9d/ZtYutj1Hr8xUdzNAXMinLHVYf4A2TIZkrWay7unZdv3FzfHwSFtXS0sZSlJEh9GGjTBRqKl7jSyz3zC+eE1LRHLQxsNKqWrZ/bhw1GoHJkWMnkaYJBi4zaCV36wZAdblQygTaJlRYjxA5q4m00FQlNN25c3t2fhYTOqzxs88+3717D3G4cN+ampikazwuwsAJZVz+HD4A/4CwZuln9pUuFzDCZR89eoSzAPMMdAvY9pltwDKQAMAxRDDpF9Upkk+ubkoQKSxF5yeCKq4K8H6Ej7Nnz+7bt/fWrdvEO8JqratEOpBGamgwst/A/fv3jh056vG4rl0Zv37tOngFdqkeIbkoDCmaW+HdmhMISxEOBP3hyYrGZKvwVf9o4FAqXC1hJMijAZ9djVQM1pSymqDoBGU1BPR581rqPEkpbVTiggY4kdeQKsBneg35DD0JUpfOisQ6zeY5P+VbWI8lAa35j//J/+XiuZ/PzYbK3BMp8yTIRTBjKlhb5W6o3QORjsQWofhxg89gramu7TbZSpNG/AzjyAOC3rRGs3FVDxBMU7ONapVvEaYO2g7nA680Em70ybf4+TIb0GiB7dRfVESDGa7d3QfYmYtxum9fNzvA4IeOH0VFSTny+NjYOJKC3WlD2Dx75rOyivL93QfLK6vxSKmorGRG6XLiC2TbtWvf2PAIm5agwEGbxH6z5MaE/bvf/Z7NQXx/UXOjs37llddAQUYpW4+iUKZ0VRlBvhMnTkyMjaE+9kmAYgMbA7z22hsILLxCSIEiQ5vYPwAKqDRCabYQYMaARxNhTKAFiItMRrp2drk9XslTDBi4i+2kUGRPLmAGMAj4XFl5sXSzKHZkeKw9FLFDDhOlEPzJV1KKyIxtlFkzXo9oGHZ0bmM/sP6+fuoJDiKxkdnafL7kE6rHSOZg8mSx22kFwxWTOw6dsEamBUAGzCZBrqCCRTQI6dmnUAHF6jS5k0wzJZjMykuVwGzet28/e5GiHxkfG4cHENrFVleveEk+A50V+eTKKfglP1aNQD4AFAOS6YKEFldO5aKvt6HNi9KJzPb4qBDghde5/LINoUXsHWK3S8bt7a3o91pbmzF7qAzyjRVOh+7O6/FCyOAcZJINKilmoTHqAKpQJeXcBf9jlQktyP7BDqk3DcsztlwdvrZfAKJaQUh3mZTZll0NkIHtCa0TEotIlqx/YUUBo2a6pOQrbrH5of+hX3D+QaABl/QritaJSfaF2T5lAjKWNXxsEMpe2dUnXvpn81N3x8be8bhR+iSYs+FHbCQSfCIgqx2t5mjC5vPvrKo8ZjDVJBDJUDOIj/I63UWd6WUIEdi7bt0KG0WTUXFjAyfIxLqJv/UPl9kAWEGvQ4ghlMyZUXT41Z4hTPhBg6LSkvKqSlxhzBkLjpI8gWrAqyORKOgCTOOJVG1tIwOJo6iIQMeZWJzVoXa2lQDtkDKQ/pQhEf98Axa8FE6NyQTrmFCvW2xW9p+jAnzFiBSnIFS8EiYB9We6CltwVRVhqsmZrBB1m5qamc4j58bYK81ghJRDaDh4CxdhbONkAhWDGhKGhJGPsZpGScRs8J4mWMz1jQ2UhDaAEBxgu4sgMg5RR9Df6Cyow7qYT/7kI3RHtk1m34MKCKXonpLJEoKnG02z05NVFXX6W0n0rIcPeSK9StsNcMRKxgZGAppc7PfHJQoDm69W4SxLQ3Q9FfpKo9YcqFEkmISol0XHwHTBWF1Vzcav+AO42a8d8z4tS9Gn1XgBLM7NEC5MKdtW5sSjZd+EglfA1CB9gW0W6zQvtO4VQGGuQCc3M4tZu0jVc5mCF3xfeLmcAPUdEwuwFHFV1a2WdFwI0ugDasjsh90mnC6f30MT4NmK2WTwp2UXI3oQhs2ECZYJV+ND+Vw6VALbgm+a8PFkHdJSWKmv5Jra6hpC0Zym+PTk4PRET18vk7YZu9Pf3LK9oWl7Vc0OwepVSrmNq6fz5BNyhvAxSyMt3BpfDErh0BDgzHMSk3LjzL7sG7qNEaqUjYBfSH153baMPTM+fiu4NOrAmmXGcAWzg3X5jOZSX2mTp6gjZfTKSlIUY3xktAvZgBmsDichddPeB5vXEjiAlhjAb926BVbk50Cbf/Ute0sfZLuZ7q6sKC+KQzVAPiadAl3wIIdhiAYimJgz5vLSkw8e3mZTMKAPUf7hD3+9uqY2JiZSjTI544waScLsczDT+Cp5QibQp+N0DGXXggxn0cagrxUxE9U1DEBqgGlSBgPUW90pwYd8OCSJGv78oM9lPJOzLkqokIGIEWZ4GA+VqRquIYxEDX5FWaissoWAB+gBiAOBt6viPTSDfKUt/KkS5JdSqDbWaMTJqsoK3NF5jyMKe2vhmD/KfqCD/ZWVZTt37JCkUgFxxMw2XeeVg8NT/5Ifw9PldGGmga6xHxA1FfFV6oYazzo7O8eCuLraKqwggEiqj9SoG5Hrad0izO1UQwFRvI2pMNMmGAy8VBovdBD9mom1e7PT093d+6x22rvi0Lmn1/GvlQLpM4G8UvRn4ag/UDKB08EUIYXPPgWvyHSdGyRe1Q7OVGyZ5IEPqh+zn2RfIy4ofEA3CAaRQDoDvKDnMJDTIu6Z9qjHUkMSSGVxQgRcmZRTtqMAdCs8T9ap1PN6RGWEK+sxKFVXmvKxgbt3bvw0sPCgpspZ4g7bMkGLzZkKz1z45Ext82vbd3T7i6qotXBkMZYAMBkIfKtwYEVdaTKCP4skYAAgDOZf5DOSQQo5c+RTk5KxzyDNP3keF9RWyAxU3RA1mKJsI1VctstfujuTnEvFAyzwwAMjlUlYbVSyPJVxJKQDE3YqJU4ZohIzmERE5EfarVpNw7mg8pD1rdSZlmJGgpSxSRnO6Fv55FuWBjlCQAb0ACHQg1YKZJeRQb1TKfQznZprNu0C0IgPSME81M9VwuypMA+uGZGc+VuVUifTOax9q/Na9WH+E97qT/QTbslHX+vi9DlbIfWTT8kFb/MH17iTwwDEGVvYAAc/ckVKBRSaC0Mx40MF3uQHBxeffPr5hx988NrrrzU1NLIwlVoonoRSC9TUWXHOf5Evc50LtN4YMxCi13mnHqWSDBo1E1Yt4aRGPO5PwqevXb9eUlzC7tCMXkJl8FZxVV0HDRn1mWpXvgj9SLc0m1RlOzA8grqge/9+HpaX+VXAUfkox9l12uV25b8tzDn/UJcyPDqMsy+bA7e2tFZVVDOmdeJ8HYBY/hP1StqnD57nAcoTPlGfa3aXTVOQT/aJTkna+w/vOZzOhvp6dgniId+SOEs75VYOXhDaRrrbBJvUmakXudNz3W8AuSphCBKvypCwpY024oRZzfFP3/3Tx5fePXqwsqmD7faWzMZSo8GeNC4iqWVSpfORDBs0lledrq06RRjOjG0JtzWjwS92Em3qUIMU+s490jH+kShgccJGV6anaLmWfT2/GuwK0FzKeIVr5zFMdYsepvS8DEb+VK+p76QD9WP50Q2gmQxURijGMyg7ukH9fJMzo1WAlUox+tALMTdChcAIIpNNvvo2vULEUwBFaEynUfqLOkjz0iwpXN1Y3Q3yNC84rBq2uS+yKfEcyj1Z+avL5Vm2AtkrVXzhJ7l8hKht8ZAcZYuYHGqoz1blw22BOEneFhYxVFSWiHU3S+fAshVQYCyh8A4EgxOT02CsxhG+HBufxJB+/cbt6elZm8VWVV0Bm8jxEl1lqdEzOcg2HI0NjY6ptonoCxPmzNQD56Xe3v4BC3tNzJWJyg7ICm/Q5Qr0FC3gqRL6BACbVIn0wyOj7F5w9tNzqFFYp1YoGOo+19MdciGxzovnqiAyZvSuyF8lM0qeiwG0cNMzwYXFfg1pXuX6QtH0HJ9R1ZOXG9Rz3edSfuGhE+HL8OgRLkOJSIRwI2woJI8h9xIZSuoKCIEKUlCmqa6apRArcacwv+d6DeFxC3tiomLGGT723nt/MvHww3/w67tt9lAyNYwCQ/x9wS5zjGvofJHLiX2kt/fnNy89euPXf0SwHSZxSfygQAzV3QhqWH11zD48CFCsYdVD9aGH+XNtzFYyL+gtLoU95+mK+jz7UGfFTS69+s3e5J7pD+hEpUxDlmKKoz/c/AwoOPgKFwb8m1E8spKJbzFibf7ht+Ytsi/DQNgv21ddvn47GMYLi3kx8F5WJqjWAmthvjJW5C3P5Ik+K6IAJFd1ofouI8FBJbk+8Sv3/Bf3/cIj+zkRcVa9yCXK5ZPNYuXj5ZfqiqpQf11DnVBd63ececdYUxdyw0XasKOzo7KyRJqxfgUEV0g7v7Bw/uKVdAqLXVZYwFJdXtUMCVkKRfsHBqtrKiTP53PAiKZmZ89dvMi8RGnuUAOgwZXZAJWz2YgbkXnYM3LvwWBWlkE4XCbN1CkPdOCz2UFjoSAsUGTX6FgyfeHSffk2C5scnSZnBQMBLjxH1GaqACmEWdOK/AV2RHwys/LDvBSOsXQEhNIglYxFESnZy5WupfzwHzyRxysPnqx9qJOsluBIJyvlKFni0dovXn5ANXUxaJjQUQnVlRmtBFNxWE01lVU2wg2uLO+rusN2hWpOJnzoOi5++lc9t9/93e/vdqSnkzjYE9c2gWOFPZlCu0UCgq7FTWmrwRxsbbY9fvjBz/8+8oPf/n1cggm+JtvHWawI/rhXYSlFD4ZPAQe8HLGXg3H8VTXq6ykHZRf87wvLBt307IHxwif4j+LSdurUqV8pIwGzAZmI8QfusOtbioVfFuWExroMPaZluKmRKcKFonwysFYOQ8Eo9XTVc+kEZWbSOIfQCo7KsJahJ2d5rrPL0WUd6kYylDdZYq1uhRrwcR59VaU0BUHnS858wVtJS9aygQKjW+74iLdaL8wLkZL4URRM0ki72NlCLMwkZiqoqsRl4UF6yZy5ApKaZGwiEjKZS3lxvPJpGjpMdsyQJVqklf9ScvZC3T2LE8ULDGW5HKsxrDLkxU5LMRJKEHot/jwIRCpYlIx2nH7lrcx5CajJckxsAGrOx+e5CgkwCq6zlzQV5b6Ain9GwnypBquGSdwSJs6s7rbZYRUiN9CjYqZbbrcqmKxymStiDppJnsK4EP2EregPwDoVnQanSDbEYcmCyk8xAJqTTZOrov7NJll+qAvSafPNyVYA8iqdrKhnLkNJjyJQntJKZsNwHIuLPbdBgMLvl0t43leUqmooPGBx6uGn7/3kH/6DPbb0rDETStPXKVaA+8UoaoimMhExBwErap0gROvs6683/s3fXFyY/EFRVWM0GUf+7+3twzWDRRvE+EKwRVEOvRN0EYRfF6TPu31fUf56UCPEbLGZeuCTmBkAcwi82pgTnDhxgurqrL6ien99xQjJkNYK4kPaRBJHsYbW38LGAygqFaHOQZOUUAIsMhh0RMDS+AQQITGceS18lVWg4nIjiSFH4Bv0l8k4Skk80rCvihcSeCjFKsmPXKRQIeICdKhwnjaguJXtwrO4SymwEQatJJP6yldIN5BFoYOIxxg5FW/nzmqTJWYytoURUQ1iSqv6kLvYulkxhGeCrIYl0k7fo0eLsxPbO1qkUqpi/OYOYKP/pL1slgSRMgmVzS4MgLwAE1ERUYrSqNEegcbqfHL5fblfaCfuVWIUhy6w2BXHIVGmSiVkZBMAUsDLlUAVvoSGAN/KcJi9amI4lY6MDzXU18ESySHbB5JU+GK2vtKN2UOaJpjBE6DL/BonKwv8hD5KGxOBMEGibNMLs+IlJQZzqiB1kG9EzGeiKWxV5UwJKlsEbmitxIShR6REOgiA0lF0ZjC6xDXxYnnFrgh4KglKKjjnalT4KwhQeK86TsAgOQsspF3qR6rDnvPsI6DxEqBJGrVTBa7MgG8pEMS1LBlLTIanyIe3y59LFl/ZgZgfpeJ03b37F0+/1Fnuxp2SfoAv2oaHo/duPgYsbnfy9de7gZzwf0YefBNrkWXppaM7iNQ7MdvfP9JDaHG8P3GpopkK/2XBdr4ZPMlff4svCtWYGzUT+GiGobEFggDjRIfGgkeMBEJDVESKbzfEIGcMVH3I8iihl0J2Icy4VDMMcchTtzKmhCrAG8R1hzWiVlmxJZIpg5pfJWGCagjFKJrIEUcdggLVVNew0ztuR5FQECMsV2g0gam4bMsWVDIwGXVEooaAK9mTb/Xwpl5C87WKkxJFZS/oK6yCxLylSizcVXuIK15BZaQp4oM02NeLgo9ZHkIQi25SYjeDSOqAPKJPgZhL7RlGKh4nW6jgRyjQQMhaMUZ4RlkaSKK7VNWjzlBYUR0IRRWCJgkUyVNJnudJKiGKniwnpqukMlIB1XriLCcTrN2em51dCgYIluBw2sVobEwTkYkofoAZ1bICYL6d0gBFM2mX8sxRjZHs5L9mMbDv1MjQEHMAVvbXN9QXF7nxOYmGg6aSIpbzs1YDCUDkezlEzahYO8BUXcYzARS4RHBT4ATchIsjtLKGAGSorqkp9rtZubYUmMVly8KCP9keUuaO/Kk8V540vFe9kYylo3VbVM2zXwGVbB/xITzcZMJbhhEeTWRYjOLzF8cjYfY5KPG6mFdSddB3ZXlfzR34LFrZJF4yi+N7Wnzp+BT1SBmdJovt8aOhK1cevvX9I61tHom7ZyIySkKiMRhcsFOsAn5f8uHdi66SXYcOHBRvKFn9I/4/XOjzV9OGr70URapE178VNqBruwo+uGkgtgJDjMwgCW8LmejX3sBnXgHmlNkDZIEuRkMBm8XJrlJIzzhcA0gZxOkMa8eIEYZ9iYAzEB2G+cz0BLHeCDGGkgEHDLfLRaTn2ekJPilXATqI6P/g3m3ichYXEb3Lubur0+myBxbngCxKbbQUyQShDhIC6HSKcI8I/jgvqy7MVwkin2RTg1AwSLAzAqjxQi90pI9ZTGC1iKIzFFyCguvId+yExVYzsVjo9q1r4iSQkVjTodCi0+GYkpjYJh3BZmZ2KsJqNRbHsj4uyV40wuBkzFCAEgZzaKFoRrY6uR8hQITkhFCw9x7DTIiOlh9NOVqTS/qsfykaGZupB/oAWLRwZnCUzeAkepMI9DQAnmo1sT8EoG5obIDCBgKLOL9YjPZYOElMaC6gv4pVZztfPqKmcBNNL4TFKFKdrz5U22Rkl1m301VSWweo4arIUMJ7mf7he5o02UwOcaTKHYjemiUoPi7LNXhDJkrOFvZDXeEBsP+apuqZmVkyJJIArjCJWNLsI4AkmxrJjI1IVJJ21SFsi0OXsOKdsnXBPqRBIsHptsidPOA/31Dzyckpdh1oqGucmV+U/hMel4xHw2avg56llmuKVBk8/xNOkLi2BtjJYSlgZrEzcZczhphMiVLDo5OvvP7awSM1KcNwOhZKp134eaYsMUPKa0z5DcYA2yh0dOxs6NzPbC+Zwt8JAEiTfzUPcFjEvCc5GMX6QDWEoZhYWDjU/irAkLm8HhtqqGVSn396psjLHN88F5hFh4CCDNHv5q2bI6MjUOH5+bm27dtZwX/nzt3B/n5mnX0DA0yrDx8+lLKZb9+8Bn1BQgSCL710Ymx0aH5uur/PGquuqW9o+PTTjw8SkO7K5SNHjhIcAi3C9eu3oNG1tTWffPIJ632i0QiFsHRZETPpPbrk2rWrxA8gNBi64+PHX+L60qWL1CQWj48MDRCoAAZw/doVxj1O0MGlIAsR3nj9deLgEw1/oL93ZlpiD9y6eV3U2LE4a0crykse9z5mxwk+vLmw0Nm5fceOnbAARZeEHEIx8nqRLPVQRDJPjCBkUKilwOK9u3dlv4G6urbWNoLbKSIK2uUJSO5raUr+odx8qYNcqYCsnkTtwqoolEAJK3vDAS1lCRUCJkYvVp1CRWBVFp/HiHqPOVl767a7d+9FQjFCf2fY2ptDEvMjWjKIISwcSIpOT+XC62wbIKYZaHTaU+SDNpUUl6XwKEikykoq5meWwkssufCqEZeHHOvRAKlSuCsjjQT1Y1AysxR8E2gAw0Q85aJyGXOxv4QaxyJsKVVC/gT5KCkqY/8J2Iaa+GRrIZ/pQ+g/kkT2ee6pbg0xlKQIzciFwjPvlIYwg5QMeEJv4+3M2kYy8Ps8CTRTqYTb5WxpaZ4eHSRvMRjzjqRf8SFTcQfNSMQNMG+TpSSZDkvQCLMtEovPL87X1JUkDbMZ44LR7IVxEnSayEImAx0s03psxSx9MWQChrRPDyJh52ug9BW36aspTg1chVhgMz0tfccWVYKEhYdOxpM8kSl8y1eICIiDYCNB0aFLqIYIhPOth6FMfUEggZmoktOhaLSsynns6NHg0vzbb/90ISD634HBvjfffJN5QF/fwJVrN1jE++j+/ZPHX2pobKqqqO0fHCktrUEoP3bsMDwDpzSC/rNV2fbt7SxROXLkuNvlCYdDTGOR051O+/DwYHlZEVR5cnyko6353t3bPq/71MmTyD8//dnPa+uaqqqq6Uno0mD/4PDQyFtvvsnuNKgqoAvXrl5tbWmDRaPqefudt8fGx5ubmiPhCDT9peMnmBkQcXN4eKS5uYmFtfv27CZQGlJzLJZgBemp06+BG/Oz0wTP+e5330TzQOCzs2fPQMdlziPL5aCGEIxlDBHqt8wUZGsXEwZRMzwg8OknH7OrQdOOznsPHhK/qKS8gpkBy9HQzAuSidQLSwC0IB5U6RmZCiRvg0SHM6YuX71A3Ne9e3b39gwNDgweOX6KYHAK+4X4urylhrlgb/+wFX8SI0H6KoaGR6G8TqfV4UTKjhtlvwEoHk0kS6izkb0BxsfHjh7pJgb47bv3d+3em99vAMjgF1VSWjQyMgQ+IB/BvAmpRLgOwvR7vW7VXtVyTZlFI2QKhQI3bl7r6tqBbPXZZ5/ZbU6ikii6JDIaeZaWFLNXaCCwAI9mIhgILNGD6IiYjMs6QvQcMDTYRo4c69YJCORzEV9WHRQuWkSLhT127t69xYoHv9935eLF4pLSts4djO1sevzqS/2IAuF4KB5h46DG2dlF1oOzr05tRTkqd/LOlbmqhOd+y5ooVrKx902R15UwJ9lri10X7Rnn9BzcK1NbazcaJo0ZnEprEgZbxqyD8UVptXBYsz1ljDAxTKYcFvZhUdhSCLTnXvuvqQDwJGUQsQYDHeMXUSPDLB1nk7hoLsSZTYFC3CvUqARP2LJjeZwXVBtwIVBKViYJYvbBBx+gFMGiqTkBKESCb9/8gNmABpGChJIHa+trYwR/ttkI+aD0toTMSVy6chldP7H1iVsGIJD3x0ZHuR4fG0OQh5igMr579zZTAagkIGM860jFyIDKaiDElWPPnr2ffXZ2Z1fnxPgE0MQri+W3EPGzZ8/yFYVCsiFn8ViczoNMExSePhATggp+i0SvF3fAtKsqq4YGh4i3TD5wbBpA0X6fH62R9JbQOKFyfEu2bW3tXCNdotAgQ+oM1WZuQLwj+JYQQzVoBFvAEYU0/PKnbtS9wgxFSYzXr19D245vGebayppa/ExkvYnJihQhX2SPXAa5+2fyK1VVs92O9rbRicne3p5QMLRr126ZjqgW85aBEEvE6xoa2GOLHelAa/gc0WHpC6dTdJ25Rul2iehEZwFY2O3AwABklAzRnimDN4KxID7bOLPLTUMjEjTbYbM7WIItfRLxpM1ix0GIPHMKn2wrUfRhpexo72ACNzc3D5VvbKCnZLYB1yERoHI4XM3NbcSWsNns9B2aRWKTUFviPQgakJh0NCcPOMVo5E5aqhqRf6UvhKmZCBBCzxoMXbhLEmSJYVxVXcMbeakOVEYoXlrbtoVDQVdlGV62JggoPNxo8LrYsDrtyG7jsSr3538r2qgwK9ccNpPXU4SaKpECvrB38/Rk//Zt9bi3MpWxmH33H48PDI2Y7Ut+l+/g/p0GY5gIvKZMscFYjOcTZHA96Dz/+n9NJdCvBLnRhSt0AaXF5pdMoDqLEndSvVJaVCXYcbsuD1hVffTVKJaJ2/jyyy9rBsBI4ViV7Ftwm4VdtiUy4uGnImchfbNGm+hNiGNur//AwcN6ZbbNgUbSyOby5z/9lHDNTrevc8c2Zv+XLl1mhLMUG86BEll8B8X/Rw/YLHGE1hCnmmGOsIY3W3NrO46PRETgAsladAapjNMtCzeg39AeaH0kEoLKi6ZCubFTMUVxpIbQI11tLjh4DikhE858QreJigPDsgqTQtHaxUj5CMEPxJmMNMz/xFdEMQyd2yZnGg6lSCRii4GlbRKFSqyJHGRkIcSpWJup9iYZPINXVFupbjKI2LFk6sK583t374IjRmXblGxlICaAAP9QdnPE4A30ob1mi0Mc42XTPrpY6KiipQIG7lRARgMy/oWLA0jQxHES5i3WfqVQIWeiM7EfIPs9iCsu6iM7spXNLrHOpEpC37PdoQECEQeDsN+MT4yyLPHll0/Tm6QRzYwsDZGDTsW/1unAO0BkBUzuDrubbkJZhAqLt8KAlvuZL6QI+kpJfPSGTOAko+WDrmA/UWE2TPKuTIwj8r984iWH00WQFNkEQw6hAOiKUJe5HG72UY7HiTviAKWAmFIn0MmCvcu5fnVX9E8EwclOZFR3RWB+tLK0yJSJJJNL2ztKd3TWmYisnGE7HevQ0PS+g8estoglSmthDUGz2Tc35/TZi00sLzAQS9XxNTXhqwPWckkMOzb4yx4ymFktA9mIJaOoTAlzoEBBhyoRSSfbQveSDbIRMRAJlIvRWEiKIixQmFxZ35JfoafZgxEvjaTt2aFFs7nFiBpYWkKqQo+PagX1LlBgdNXUVO/es6ultTGNzjjNHlhzSO7oiElJBHwywYmQgQpOK9gLmYb68C17ft26dQfzcrU4EaFeqCPMPSQfLY3NAe5K6TLEjcaq6irUxDOzM0ijfK4mKD50F3zFihhe0UlUkfScecgnXEtBrJFJYzoOyn4Dyl9C0yAoW1VlNWyG2lITdFZMHQijRpqtHLosHEaZvoyMjRPgExMFqgyyogqJWDQeY3q+BfzaSmEbpKGVzLPwbiS+KYL2K6+8At8dHBxQ+nBRavEnDjuyrBjtUBLLKxScM9uuSKA9QuwxMVaWACkB/QdSk8AQHpxkY7imFnazYU4wCCIISNnnGmjSKIkmBUeRD8T3X0lVcBQUiYCdHKTDcm2HesNwAMvg0CDgxXSE3wWaH5UhJxBDTNwcoJOcZfYGKlISvrAgCdotQCpcVbARHbj6Y+VDIsncPx1P4FQK0WYlLVWQyuf+hHMIBmcy4AlLqQ8dPMgYBltwjqJEXSiCDnxL2R6wT8AD0MGo0hEbhY9/fSohIGBkfS+bZDhb2g9MzaLqcWP+MZhDdkfMZFxEa2sxuacmwn39I1euXf/ZO59i12aebEKhnbHMLVhdzmaT0ZXOEIdiFYPcAJ++HY9BU/WHjAEiypZZmLJMBCl1JNNWwmfh+IUxSdAol2wr7YaSgJmIvJAaKB74zK3Grq18/kuUpmA2oNAGpQGDWYYl8pjdAeCI8Lx7z97Pz19Al4J+v7ys4tCBbgSuvr7B6dkpyIy/uPTo8eOdO3ayrdjg4DAuQ5WV1YrgWyHxn5z5pKqykgB+zLAY0QSmrqutl/0G6mrcXi9aFD68fOXKO+/+Qil/MocOHyvyFxNPP5nI4GzKtiGfnv2UvWigTSdOnMDei5b5vffeY2qCuIeNWrOHLJVXEwKt2iNkDTHE+4eGDx85QtwoWkRKaARqKIJRnzt3AUoKAT186IjLSeBJM7dCQURwhjBtRMplK1gkDTYtIiL/3//0Z/Q0G94W+Yt6eh+PE9c6mWyrr+YhZcEansdABIYQS9yc7t67i64D3hwMLBIc0eMtwvA7OTvjsNvZXFdJvGLmYFToaYIQc1qHixMUD1wWgifNheyxuhal1o2bNwBIS3sre81dOH8R1Upzc0skHJyZnistK3d6vULl+RN5XnnyKrlc8lScYBXS2x1WjEBshrx7TxfCQU9PH/PFgwcPQ8jREbH6DGM9VRJYIb/LRZZ8U4rUTokC5C1AVL3B9KJ/sA8vz6PHjn7+6Wd79+xjvwHseEw4SKzSSbMsuCQbjazlHugfOHLkEEhF0+7cubOv+0goGCGBQhgrWkFCSvAZ8OChUhnLCkQW6EpmX9uBCOsWlmQwVDftiicm5hYvl/rZIg03XxSQcARkKdviAvaMqre+992//du/ejRwo6xuWyJtWgwZaup3+4oaGLtiP//VOoihgXwgmwrB72XOyw/LKpB+EIJAWEFbRAakDonOizjgMMlenpsfmpKQ5uWXXyZCEZpGTWc4b/7hL91bhhzyFIcplkj+7J0PxiZmmEGjtxHJK5Fgd28GFVCEQCDmI4/jMjo5Mfbowd3TJ47b7NZILPGzd97dvXcvXiiLC4voeVH7xOIxjMGMN1YGYCsmdwhTFLuCDR4jo5SpAN/yloO3DD+cfCgCydHh9AgRENKFpoW36cDCAq9wGOWP9Eh2GJPpCdYEaJs+Z91hvCUl1+SMiyIud6hNsDcSjxr9L3IrBQppVLuls2UmtYI5wXBAjlg82r1319GDu5Fv8aqnVlILNSIRlwVjqJC8MvcOjZ39/BJregjn6bSLgZTScHMiROHs9EyJ3/Of/Oh3xCSRxsihBnTWRPyFWEcZhrNfFFoOuAwOj334yWehSIKYmXarjVkIfYWyDg7FFIGKEjWM4iU7qbWi3fom+0RJ8tJEnUYEefwLAR1nh9vHnCkWwepus5qtTDhKioolGFl9o55a5XMiA+gNTsYCqVWNE6qcicXCkShuY07yj0bjDEAsMSKYm0165xm09rl68r3I/ivyUQWwJ4XqChgQmWDyiLs9bvYJIWD1IrGJXC4wgVe6VoI0rHEzm6MYGSJhnH/kmgUrBqPLW8QUh/kAjIHEElWNKsqMB90TqnQERnhm0mU3/dr3XvW6sMSufzzX0HJSJOBUkjy8KZOa7nv8i0TwflmxbAGaMkzLApxE6dhY+OLVK3sP7P/k7MVXXt5ZU18eDKOV7Ghu+4EhU85SMwJs03dremX9Fn0LngKrUErwKpGIOmR5pzEYXMBh+uyZMx3b22pq63y+UqfTxywhTghTmYyanE8YRBZJC80BrongDEiVR7lvAfRowgq2xl59SmYHjdAmwEoRlPRgSVvtNvaWQWpGKcBDKD6zJJfHyVb1VpupuEQU+pBpZG0GNrvTiC4eM006TYh4Vr0iYmMGZDIhvuYpbINOid4gcgvYzYplCzHuWXrAkEZRyzMRWbHzy+KXDLKk1+dlsDIDEL6hQn9QCkQBqs4FT0SDQXEpcRulJsoNIE19PGbZkRxVA46kNIVaQeG5lbiybK7EVuxSJfFPp+HUBIjQzRuRAFokNENsFYjD5orKKoxQAiGDwW6x3L93L7A4/9u/+ZtyLy6YigfI9bM8oHgivBoMLMVgXgC/xGBrtjP5JWKasbK8nAms7IKg2YAQFFWdlVXgEQBT5FXeou2naYDEYHLFWaVnTLHhAIwMKs+S4/nZOVGW01WikFlxkDmHcIM1MOML1pTA7GGHZE6PE00I+LvctqWlIOs22EWCAMJC3OSgotRnLeCpXpafkUhWqMj6xHSRzw+C+mRvCTg0Kx11JmSA8gj0oU8t8HgVNtvEJInJRyqTdLvtszMzoAyT1EQ8QgdRMRCDGYGwbeFDKdAWwYiwAhgdeavq9tWeKBO3HwnJBFB9Da2vv/ez0fPn77x8rKOkDFbKRCpTW1N80Ny2GJh8/eVXSmvKJ2cS/uKuqtpuI6Em1OxK4d7XUfmvFlQFpRmt7Jdoyjisxvt3Pnv84IKFVRTGiCU+O/Tg/MwwgqDVV9K8a9fJ0or2eALNqaIQBd9/4SVbFn788cdMRpGxvjDxL10C2EAWXaC829s7guGYhJEQsoo0Am1Vy33F3MfwEpJhNlg6Wpu6trf19z4CUdHMvPrG6y4GZFwSMPyydBkiklUuyUBVxEiJeyLVMeqgpqK51iMNUgKDITdGNZfCA5SIhm2Hkczn+gGlk1IpENC6kD95CamFLIL3UP8c5WUkw0g0gZNEquIYIMXAwExBxro6yI1M+ApSFo1HKqvKVbibbCfKeCRzuVO/iqSSHq60vbND6I+8Q/qU9Wsel6trZ+dHH37o9/lIA71TMMvCNpvjs/gBvHA3Fm9g9BVNsgixNFfMITQHr6eSYi/77dBS1bpszVeXLFRb101xPowFwhQAh0yiYZiEy+CWP+wP+E8eOdRNOt6vzod7CeEnh2IF+fYidmOoJB5mDC7LW6ojrITHJuz/6eamaqYFjQ2NTATV1zRC9ELqeuUJ3NGdIJgjCagGBBwhghLRFNFS3ZvySvpDFYL4AEykDSCfQkVDGttWa2s9vESmAiqfbIaiTFCevhk2XiZPSsBG/bXoVeCp2WV30hcpq9Na/cZb//Wlz965dPWj5lamyymPjemOoaKqzuGOBxYc41OOmrrjlVX7zBYc6qBvbMYCK0SKWgnGb8mdIKUYb1S/ci0KHtXxBCdIpyLv/P2/GR++cuRgY02NR/nIleIUhrIglfKNT009vP5uY0e4pm6/ioaj5p8KLDoHGUhZcUY/IHPJX5IgO1qMBw7sO/f5ebTiTgQmqQAnhb1QEcFMHum0nBWm6vtfhjPStK69EucUhLNNkUauOJYbuma86pSFCQq/FKAqIBUmKLwuTLzudWHi/LW+yN/yYf6ai02OVbXllvQQM0ExDOCYWFXXk4NSF8hbBQxGF+Kr8ByxfGQRRN6ev3SZJdPEruh53PPDH/wGyyOSyTjzHoieVOOZKoVQ1sHQcHuBA5J7rqZSDoLt9Zs3mJA1NTd7UMpRfRqQQ0gSQ9+5E4IvyTlyv3KhwcDDrLMh6YdGR+7eu8dGOqLKr6wiSFD+A/W5PgE24REASWZwyy+k8QW32RdjEyNUaXp2pgI/zvL8fgOk1dxahvW6H655SN7ga2GJ2fqo/sxfcyHJWDGGrZjhSsRsAkOpislzDULNi0T2oMppQmmxQAQ2VgjdbP2fu1JIwUzaRt1EDqOGckSCeGg/GB17PDk5zjs0t15PeVVVW0vrTnYiE7ombJKTtIwuUTfqy2/RiWlr0hC1sHwaDb+FDfewIeIma7SZU4HAwCfv/nG5a66zw2+wRGOpKAZdvOVQDpszPnPGi1AQMSSmliwm18HW1jdsFhfxIIkxI0YEJdIRZIsMMSQwRJAscPxLogUmpB8CDZoRgrkajWPDo6NDk2y4i2cZyoyUIQzmsD2mSVgzC+uBOw8JACWyT7bnfhngL0oVVU/Zfuizz86hIEY3gmi5/q5ST94kgWcOHgXiJCi+zhhTyJ/Q9VFgZFTqZJx1Pbdag+yIyBUtnzFAhJAh82oiINfZh6IUMLU0N3V2ttCPCNm574jSINVXBDAl+iWLbXxm5uqNW3jpi5lBhqo0C3MFS3ZlX7DK6r17dyohFxdVVdtnywbSKVyxLly6LIGmKR8FmthWQDzCzOH3InozmZPh9yNiMqibhTNphbjQ3dI2tVBWGr/qUO2mXaTAYcWKGCURrNGA5cQFSS8NVsDkkn5BJ4txhTrIbExloGAmheRus6WQRk8BldJPYPf/Z+88ALQqjgf+9XK9N65wcPTeqwKKsWvsJSYmGo0m1lhi7NHYolGjMRp7icaosTcsIF0ElN454O44rvf7evn/Zve7jwMOpJf87/Hx7r19u7Ozs7szs7Ozu5EIiv4yLBOP/giISBrR79se9d+tWe+I/7Yxo/HBUo0hGD3AH8UQpLJWgLG+0EYRfmSMVcE8fswIPMEocqQU24I88GJg2/y2eUOOuwNBpoJYIu6wWuIw6kpVqEEtEf8nWX97AiAG/Aa3NRirxACnm/rChjh4trd18/NP3zJ2cEbfAqbTa4OsmhcLAj9GdQFj0G4O2g3WgDvcEnKkL14Rqq3PuuCCG9D8fJxRyO5iiskw/eI3BtmvkWaAaZAm4fXy12BjNb7F1lLbMnPGlA3Fi5YsXsIhrQXd87v36jt67FHJKdnhIF5n0lhYsscWT6pRkvU29vb2pTgMn7eeNwBRquvZoJAtbVF3sQbvh0ZFG42yb6EuMLehwbZvfJLOKXp5myKmWJhwJX5tLGMbCB28kFjzebm3z0G9ite74k4KN2FoxJFswgYWtfJKQIRx7gAbexTckCVsHC9jCNlgc9FCYUPwscy/tiY9JR0IXIqd6ccdAO1DAFXT1NK6pbqeyQDxzYe7ISpxwZLlM0owIHiZGmDnHHi3Kp4mOo+gK+YUpu7FR1Pae0fXVpxJIqxZG1XE2iLRBaRwHqGbNBIMeHjnqfmVKD9SYLfCUa9CWC7UDrWcR2incVCQBKLOTuEmBp827LaHo6FtW7VtYTv5C2RVsXpsBGCBSaDSUTw0FyUGpMYcFqMvqDZu3abp7ATuQQyWIYq0VE5CYJ0imFvY+xFiynwofg9SH/9PrrZRsGhh4pdstRpffOWZwuxwn8JYU6AWMzYKicMax5ZlbK4VDDcwWR4Oe4wGj82ME0T9gB5d/v3mtDUrx3XvPUzxOMUOIsSjbaDg+TDrmhhxWeyydNZb8eUnb039eC574PYd5PzFLzIRLTXVxRvYmGX+J1kZ/U88/cKkzHxOTJalSZwXJxNLzEZ3rEYcnpUE74jou7QjjrMNc/a17GVPl4eTyDhf9RbVc3RLUzqc6khtbET+SjvkL3+gLHTV7/yRYvOdm3IElAiRxPIxCkJ4TaTjkURHkVQKqE4vmagobV8lovoEUMCKMif9W8bROpeIaqkwVokkhdg8iBeRBSoPhn/4yOBpEGGjOnDbu4AWDGXXEXZTQGmQ1bOSnxRasudUTqu4OcFn0YspEGHbwtgfb+CNVRwHV/Ra4fuUiJykvvhHfpjjgxYbs8XC7sFQ8XGwlm/MHMjcaZLs5MGwpiN0KGTbqEsgKq5tZkygBoiMkxAjMgKRB8pNXu5WfMNkG1fplOrYSwW2HZy2bFQNSCejGmFdYl2zAiRSh2o7BORCuLaull2mtaiIgGqDsM1f0eAAuRuXZExs3SaFFKpVSjukEFyCuXiah6trqrMzUiGueBa3IbYbGRyUKFK34C7MRV2MU6lUVUkHJf/DJJNIlfOHGmI5o9lUXrJ29Yrpp14z3mBqZD9dM3tsmGKWrS6322MryjYOHJCTFG9n+yX6KkLDGHTFWpuPn9Bnw5q5Pfr0ZwVkWycVtiDLBrnJ6gJxFQj5w031VTOmP9/ctPjiXxQW5KUaLfW+4BZj2NO7W5x5ZH5rU8ySheVffPjY8GNOy+8xKmiME+dUZrnQK6ioI+faet4AXUF6ofQtC9okKwPE1oCtRHqsjKmFo6iVPorrwGfkIjY8UBuWMETAZUhLCDGpJdqpsEjR7cLzvpvHymxcQiWV2KmF7/MkvB2tTCmeSlGN0k9SCctgWak6GpRUvMNqYNpi9wC+cGEioKSLc5FifAz5JT8bzgBiYxAziCBMh2EILQb7MAZF9j4iMvBQnHFoWbe+eMum4mFDBoIQY8noCEagC6dXP8Xm1FhPYskMHjqk8HsKIFMLrIgGT5ULeUnxVUIFY//dQE4PSsRQI6iKQQMeGpJFM5Hp9ABbBoEOyo/HJb7TcllY07exbF1mZlZcstMAY9dVvT1i4IwY4C41Kxc7SIfZQZpzDX0yx67EDHsOsncIJGWLIYcjNhkHFbPM0zKz2iZD2sHZmoUGq8wyqLJsMy7x8c7BxIE8QyqYy8vKODEqNolDwPAWhjlTMK2mbIXShp6UvX3orp6lqSkxEIlEe5CKwzXOjjjkk9FUW1O9qaQ0M51JRZmC3hW0Q/FNDe3oUJrAQt6IO7Iigu5ThwKvg5yn6nq6MqUdiN6x5Ie5vXql2ePCPo8H9xa/IaamxfDCG5/17d2rMDfdbI4PBtCKZNBqNMWgvoV97i7pyZsr69ytVfb4rtAUNiE9mEaiOAojDKqfeM31tc89cedPfpLbY3wfn3ej11ttcGOL4wgIc9DnNZvq4q3+o8YmNfhDGyqmZOazunaA3493BUihXh92TWgXVSVzZfzkUjoaW1IxMUJZsaCK64n0B3hlkPPNleoE25TF2ZqrEqQYt3hV0F2tcnQf3nYiQjgylY1sVq9Zw9ZvrCFgr6GUpEQHnqQWLG5eu92h7OyKI+DfycjWKqdZ2W2WIB6YXEJDrFOi75Av6cgINGgA9F+rg2GebKCGyz9OiXAf5vGZO0U1wCSOuzuJcN9kYREnorBdBJjQt1UBYX+cT4AWzQQAxZMys2aAXfiZGpF8O7iky7URSYsoGIqwPHYqRsRQcpDS6QiGAh3A2K9Biv/pXKS5atg0c0FSyVQoxtOW8jKvx8OyW7xjs3OyGxuqMzNSkJ60b6mniIvOjpiJVNv+Ei/bsM/rZpcncoRWRT2KWpqb2B4iJSnO75UNP5Qsl7U5bVeHcPguo0ypeEOI9SVsTkfrYWYlv6CL1caKHn9aaiIY0t7QPVRxKFabTGoDLSFS3j252qFD+/T5POVbyun/rU3enNxcvIdZasCsNbufyzI7GV61K8qe5HNg4tJShU8p3ULnQAOQNnDEcZx9ow+VovugKj47owTDxWuX9ShMDrIbtwyB6dvOqvr65LScM888I5GNbzy1sv27GCBxeSMtfRVbPyzOW1WxKS+hUKlDQKP78xX2xQbdOLijQtX/4x93DOxnK8q3+JvKWVhitmQaA9kBL02jhZOKZH5Cppgr7VZ/YW5CWfHX+V3j2L1M5sjUUs19K+lBTd3+vAEGnMGK8pKEuOSSklKjjeM4MpJgo2pLfUJY8sM+NqzsZcE/TBwX2uamZhZ1QcLkJCIm11RVsnEb+8zk5OTAenBpWLVyCbv5pySnpqalZqYnw3+rKstTklMCfrfdZnK5WhAJSclJTY215eVyxnph127Mswp35seuIMiigH9TGVk3slyI7cnw7q+tqdxSUYFej68qK5aDAXdjUxNegJs3lyN7crvkslKhoaVlxfJFfl+PpkY2su5SU93AGehsZMQChYKu+Wi1a1avpAg4DiIqZM8LaSjSqXa4VGvbLlRFRFAihuAaiCg2aNBR1Khgu9j79ZWsGbhiTpFxjjBD/orOTCbMDUiYcAoce1xuF/updOvaw+tzMe3f2uhqaXThy+Rz+U1hddoXgLZe8qygIN4ELu/CcQSeuhBvoXDllurMtGw8keDanHdSWV4dFxff3NicnpZOTJLIzpxbL2FaomBJSISRK4gRoFRuXU1tUgKNI7WxqZERxqaNG+Pi41hayNE4OH+QCnGOkBWUtrsE3x1D2wcKG9cJ2woTBYF8MTbWNzhsjrwuec0NrWartWxLOadpNtTXGQ1pUgYc942H2xSfkv7RQshDhxTYJsb/3os0M1X76AhSxaFQRXn1mGFMGrvNqINy5p3B3djQuzA70ekPuxs4YBBLEdYHGgTqh1opxbCAnTlaG+rKCxQRVeuknYlN2GDymw3s52H8+L13/P4N448aZ/ZV0qUChszlK2o3l1Q0NjaPGtOlMD+GszE4CNpkcNpEyWy1+qvXLJk5eHSBH+FklBOid1BeDt/aoG1Jsxfi8iccXPjdvPnfzjZy6NGWsm+mfc1BHJgB5s2dvWHdWhYQLV/KRPn3hCxetHDtmpWxMXTd4uL1a9HdmEkpXr+G0042l26aO3smO760Njf6ve7mxgZXazOK5Jw5M1naN2vGNxxCgArOKphlS79vaqptaqiZ/s2XHiLW13w7d5ZabSS2OQwdrPGZ8c3UdWtWAa26cguDiY3F62bNnO51u+pra+bOnuX3sX2g4fsF80hI1o31tV9/NcXV2sRGKwEfUqahubGOrfhXrVw2e9YM5AdoEH/Bd99WVZSzZHTB/Hk8ozqLRUI1LiGCJoV6aOtn+q/+KMyF4UtV5ZaZ06dNm/rlggXzAqIhgDPDgj1UUVUue3EDZ/YvwnWJykMUYesXghnoAxTXiukex3d861iYYTU5LUb8XxzJiemtzV6/bMNDTVo5PwAjpjR9VCQQD5t83oDX4wcOQs3rZt8CADLqkp8xjM8cgs/BAjKSMzwDQlJCGvC9buYuiWlm5xtpSyIJuKPMW3G8AI6IyKDZ5wnyLPu5RSJITA5NFw+jIGfPOVl3kpKcAVhXC0cZyAiQVeThEEiixEm5QEbhLK9YRASO5Cs/1YblLmUhaw7oDbAQ2i/MPCybzrNOXMZpKrIsXxBjAnBMbCjE7qsYQ1kpzegDeYlhgaUqMvd+eF3Cwhi6bvsDSX6RZnl44XtgsIGxmqTS6Y/S2hgKoHazDNRqwUmTIxcwijLSpQ23FGTH2Tl3z+SjDRHTYmfxovILpZfShFk/ZGcNvjQeurPSTtRgWtAWIzK6b3nZ6hMm9zaEKoKBRiYEw6a4ZSs2Llq2oPegxKSsZq+hJGytDVtbsDWE/Xaz35CEISXU0tK42Yi/HlrSEXVJe490IwiEtTQUTOuSNWjEsKPGH41PQj2HM9U1VldUjxw+smte1359B7DFPwcLry9eO2TYoH7sRzx4aEpyZkJCOn1vzMgxg/oP6turX0NtY1N9c/eu3VMSUgYPHFJU2ANPbThkrMPJPMsGxIYpBIOuqarIycxYumRRdmZ6755FvXt0h7duLi+TDstcGNsWbdhAxzzumMnDBg8bM3JswBtY8sPSYYOHjxoxeuyocTCvdWuKWUSPNT85IXn0iFGTjp6YFJ9YW1WbmZYRH5fYu1ffQUPY/og9hE3ZXQrGTTi238AhqLSbN5WPGTluyMBhx06cXLapHGzhMnBxpRQgC9uYv2gKdL8ol8F8hDFbpj04jOKH7+cXdssHj/ikBJfXY7BafWKbx14vyRnQHBBdQDRcTplH5oTXF69buXIJZrBNG1YvW7oAnoaB3sCEhckfMvqccbak1PhmV4PL0+LxueISYrNyMvilpifjXsHBwuAnVlHu8BaGgYZAXUPNvPlzfP7mii2bFi9Z4Pa0wDYRj5jP2NuMoWJaRorX73F7W71+9jIzpGemZmZnJKQlhi00G3+QCRoGSACXO3MluIZ6Vq1c3FBfYTH7f1g4t6K8VJoYYxkVDVUqNZN1sf5md0uTq9Fn8KdmpqZlpXUpyLE4WAQV5DwzlpoTWSxw2G8BKPMgiBV5wA0wRI7iliGv+oc2KON+ND23a+H38+vqq7Gl/LBwXlVVOWWUEQ0xMRIbAvFJ8aw6bfU0N7XW+0Lu1PQUTowoKOCs7HhZiLenFqcD3+dphSLH2v2klxz4fA+3HNROouxk5qVjwlUgSHaXzM3lbns4gbblsfhd1tbKxsouuVlYkQ1huyGAdSH90y/WzVmwkXXtLAox4TwaSm7xW9Oy8mmPNC51SDWtiK6Ap4CZgxf9gdrsNGOP7CxOhmZ5stdqQKnYUlZ/3lmnDx2QkhLjtRmcHMYaMHv8jmbG5uaQHfN6TLy7oXm1xRBwGNjN90iqHBn5RvHF2o7pPT0jw+PzYWBwOmRTfgxd3NU+0ixQCnC+LyHs6rZi+UqPO7i5bEtqSqbyYmxZ9MMPsEAGX8hT7lSDCFbZi1+mLpmmJC/2AuNkGPaBKC3dnBCfzKY49bWNYNDU9C3VwZ4zMhnA5KFshxBuqG9g2xlQUgBD7OCJdOAEFWWnMmZnZVdVVhkHDICvdOmSxxwAecU45ZhW+jrMGoBkreZHLTlduuDWghLIvmYYrPCt5xPn4YhZo1n2Q9X2fUgBEE0R/gpHl5coheSVCCtXruhR1LOwsDsTrYmJqfhseD0BM4czyy4A7SNL+v17QQGfVIqpV6+ea9atX7p0EbM2Q4cMEZbdZoRhYIO4SuXAAGU6iuVIKlF4jCz2JiHTJ8hYiCZlEdmHYQlHIDmQAFqtW7eWChg8eKDVzkaVAlIlFTdeNhRJz8yEVgkYl1iSTzuwWmPMcTIvLLMjyBNFMCGBTLtxekGfPr3WrFnFkptu3QuzMuVgBplMUQMvmDG7u7AqjQl/Zwz7S5v86sT45JQ0mhn4q13AJCo9VWio6Mon8pUBvngfSEsDCz6ClcQRCoiYSUpKHDBoAAfkcawNJ0NgGJRFx6QilsRgOGBJT89kkoPl3+DjZ19/mz0tLcNmxcxATgLzcLsUAbYitd3r1g//w09Un9SzatZSnTJKzC/otXbZqrFDswMhJy0j6LKOHjw8MS4GV9GgudloN6/d2Lp09ZqRwwfITLCFDcTMDKK9QWdiUg5TATQnNRBQjZJxA0e0hrEk1MpGNGgz9G3am8leU+EicmZaQtBXHgw6zeF0zl4NBBvQQplmY9Nyzr3hrIiy8uK8/EnoL0fUsoF2yNJDdDdWPFe4sX6gg2FTnjRpkvQ94XOIYHNhQfdly5Y01DVgFy7s2p1uO2/ed+npaUOGDKFrffrpp6SS5EBh+G1hpwfmd3EJCbAjJs5CbES6qaS8V6/eYY76ccT06NGT8//4Ssc2aR8e+rLRwvQvm4hhNRZ/RHRWtErWR/l8rO6BYWFEZqpAmcrp3tpEIONEkxE7BsxOthhjZRNo03DAUHEKOehG1AQxSYh/EZMTWNJ30wgABHgOJgbYK1vs8caMOXkEsC2wa43SgOGAB7gbSj8QTExWtrVYsWw5q95kvyY5FFn6CFMFUB22HGSLfUQy4gGlWTRc1YGkWTOpLm8RRGW8QyQDhjROl1y3Zo0ctxQfz+ZO0s9UIninMkfIdsZUaNCH2GBGRAGhb8m8PQSX1Wq67EAmLRUKtcmdTQl79uxDXdCRxLghMLnULDUtgAl84fpYc2QxFMCpETDkArLi3BKb4gFfu8cIgIi2xeSBfI1mLE5TmHXCIXYiJGt2FC8q7CpwWGcr9iihkUDFrEC3ld0oEYo0dclMmojolwe6BgWLzmsvKSCVw38se2ryJmgYMuyoaV+9Xt1an4gLj89kDVriYxK8vhaz08TWMaWNrWtKWrt3G1+Q1d0U9PjDvqA9pqyqOT65yBGTgYUTRi+rZZV5jVaBmYhjUNmXkHlJhvcMvGHy2FTLN5UkJdpjYn2MQy2GLp7WeE+wFadxR8Bok2blhccYjDZXaysN8ohrQJHuo6uE7qc6M12PTiFhmnHDcFk8CQvmnBlmSNDjyjdvsdudyACOesfpArZDTOW0E163bh1TiKRlFhc4jY31MoMKQ2nr0T179l70w2KvN5CSms7yPBlYrFhVV9tA1TY0NHLuGDyF6iAtk8Cc+bB+/frWVhcwgc+MLju+wrtxgcdrpVu3bqKEyv5HDD7gK2KSwC7AIIPeXlFZgaYJHH2BAFveZ2dn88pUNgizvT7PaelplFoXXMq8k4uYsBU+Ui7ORl69eh0jALfLW1nBSffi18Ie+eJIj0leONxOoOyHYMWIg0H0XNjrpEmTKOmaNaspHUQDE3glXJWF8DRoIaNinkhHHqhV9Cg4Hm9t4kpSCTfkeA5fYOWKlb379LE7HUuWLqUEFBmuCl9laTzPRJMOIxYTWcVPWSkNIUqgAlCy0z8Br+DSbJjYZ19uNnJgQ9a2r9IYwFOTFEzEkRfPfYQBlSebI0lqvYZRlYAM+YU9XhfWRiRFbW01CNfXN4CzyDzJTv0QTKqApF+xYgVZjx49Ggsew0qg4TXLj1JIZCmeLpTY+ohP7wWm7HeoXgnpvA47CkglU1moGziVyYjN7wslp+f/7JLf17XiRU5LYvKv3hSqdzD3GHA0N8S9/eZKS7B/yeqgv8ls9mMyZHv5GIMxf8iwE8Ih9lFWDUcARloF/j9oRXYHHD9GWjq9A400aGJb4549ks3WeroXO3C+//7X38z4dvbsBa2NHtRi9r1kEooukBCfgq4sOEqbOmKubdwh6Jpo62whgOoOX8NiAudlY17OFGOf1bVr19JRc3LykwYkoFm1NDYuXcJR7xDSPPnYyYMGDVywYIE6jiYH7kxC4BQWFs6dOwfezQatOIRIbw8bOW2G3R8xVMfGxbO7dc9evZlsmD5zFklQHoePHOHk5A36ZjDIBt/Dhg2D78MdEhNTjj76aF7nz5//5ZdfQmCOgyAC7IjhBXnRQogGtrJXaCiI9WnZ8qUbNpZMmDiRgmiZpCMMVzDh6eQ4ZswYK87Gdpu4PMkFYFFyhatseyluJS2Pbxx7MO+7hVO++IKxxuDBg5iUBBremd99O7cgK0nsHrBa/JRFgd0B0LZg9/wN7Z6VzN7iDcWZWdlQjI2XVyxdnpmVh2DmTFCGSqjziCxwkNk02iT/qDDh41IsCZfWrRprJHuKHFq+bAnHGBT17E7bX7V6DTQp6t6TLTlramrT0tKTUtPgkyRnfEUiGUJIWxdaCWQBuLUovCMxOE2orGzzsGGDqRHiLFq0aOy4o1wiwhtIlJOdTa0JoUEVAaaIJZA1NAEmlRGFCsDi0jJkP63x+4ULhgwZQZtkJQonVysiq5oDLdyZzdYN64vZHLuwawHUoCGtW7t2+KhMZD9kQfyw5pl2oo1LsBWoonJiOIJMYaCDSU1vci5I7HgdgDrdMZPOkJ1QQG3vIu2LFifzPbRlS+8BEzascTe3rE5PaAgZK0IhPBxsRnNs0B0e0ncIS8A8nmKHk2PJYWtJ5mBRz26TY+MKGB3K9g/S5KRzi54kTYE2bYyJTQoabLQVpto4BMkf9I0fN8DqrAoEq4zGpC2VVZ6Q+5gJk2Lig7Ey19Qk/YsDDMxxaWkFjFjVBPZO8D8sgym16HRodZw//OmUaXUNLWKbp+ysofPLFsH0Fngoz/hRoFA5bLFYxrdUlE2ePFH0+7Dx3Xff56jxbt27E4FoWN650/PF3mKx4K8DhJiYOPZg4IGBPwpXAEcWbGp4dYjqh6XFTGRsRuxoz5mR5Mkz3AYmRgSf2yUClr2+1GHo8H3sQoTzKlUmGi78jtYgOiZfSa5MB0EWB2FuxiWcP9Qlp/UKW5SJCtHrwRYeCptgpgF3FW/A179Pr7EjB+30vAEyYKhhNJaUV06f9R0YcUaCzWLhoB48cvCmZeuvpsaGs3562sRJ45S/QQCkhGUI143Ypn60Dcz48fMGQhs2lX39zSymbTm02WaxcuCwhVpSVhp4NsSJnjcgjJoso6x0a/ZUXbTVw/YhJHJXZlfMdhsGPBCHRgCvranFG7i0tDQnL4/ciLgVhnoS2IS1z0LlSu2yxITWpV2oGY5IZVpsuCPxVFdbxyoBpDgDF0m7HYRoHiApGlnkgqETHwXD4/WxfTRCiwYmowHVEIkEGGWrleNjaCY2ZmuQyapcNntsaWmZVhpoh/g3U2Yu2pq2d4EH0txuDZ960jHxMQ5GD23ZbvOX8Q11jQqyTWjny0GiADXMaJuqUVqIylSGf2wj7g5sWD9nzep/DxsaH2d3Bf1YaWjCJps9va7JXVlV0at7nrcJc0ZOZuHZJnsPQ4zwF9RSWro0fxwn6KnAhRmIePG89+aDffPd+TJ/1Bo2O/HMMNmq2NPJFM4pKfGjkSSkJZSVVx8zvE9SDMqwy2tOdRl7Z+YfG+PME2c2NWV1kKiyz9lsHQ3Ql2DfdDQZ8+OsGZQzwelFXISLABAtG4JxnnDS8hXfL1jwLXoofCchMSY9XfYn4CtcFTYEV4DJghskZHgFdTnjBSdLgAdknS2jLjqfrNXDZE802emPURmue+y0rgz3op6pWgECK36pJ4IxQzDvy11CxF1MsNEbG4Ct2KyVpZ55f2a4MSxhAqAyZDKTMym5ROiLo4rMMBkNoEqRMTdhKkD7Q4gIMVWTaM/TJLDdxSfs40wvk8TukE0b/LhZtjYvW7K4b5/ezY0cwkzrE46iuFO7lPvjkfJywcXkYh9EhiA+tjLFZoM1jJ1HjcmJcWjBrIYjms6wo7Io4kJr/Rd0RYKG8bqDKXLMFeRCJqALQKnMzHSWdNgsSFFKvuN5A2qAQE6R3FSe0FkMPiwDFk5KBUFnJAB/eeb0YlYJVFdv6d69e4h5ACivxxHtIWjUpTKEkPqNP7JLkkAMw985hxKFAV0FlU0iCN2pVpPXE2ZZOMVHS6CxsaAE8UZZoInDZq5pqg8GvN27dfN7XTQYpCdNkm4vrAR6qhWIohjJjDhgKXPndVhSQLcWacIwBtERmeHv1mdMSfXaf7372YA+2f165NvDjU4OH3HDgqzJeV0WrG2Mt/bs0ecEQ2wWe9KZjHaZNTNw+ACwlFyRGke789MezGan1ZH97fyvck/rbTC7/QGXDXOiiIz4kB/jgv/oYwb7jTGLl35R39jEBuZGE5sKx5gdGU5nJuDCYZfJEHsEtR8tBqQj0WkHDuzv9fphnWjfsm8l5BUzPQ/odjBW1SuEmfYfM2ZwadkmpLDdNhCfGTipGkYLd4JFstcmXFc1HyDRcwWOMAdMvNQaXRuKy8ZFMAFC5UKnk82ZYRZKztNDRZRL1xbGLD1dGADVLmMVFUy/hSEymJA5AcIxVcuYnv/EldlpWD/6ODzNgpZLFkQQJ3ExmgtWwqSEb6g3lIBQIDkpQQSEEEMu/urHSAAkUN4tLHMdMWwQVABHGBOIYnOor6ksK9uIC39qajJkgV7kqOHsxzswoUdCQvzIkUMNJhwVhFEyLwHHhXqUqKKisiA/Oy8/lwauebOguQMG0lIjhZM2C11VZB4QA1g4hSgczOBqdVVsqchI7ZOVkc5QgErZDhLJxE60wwVI1h+w0ADDEyhibZeKIwxZHvI5nbaNGwuomvz8PBrADqm3CZCBeruL4jAiABPcgcBZGo7CPhoFFwHqjekCyZI2pMSRqotQeXkFJWNNe0JigjQwSSNuRWo0IDUGoqxGZ2ML0jF+i8LsfDicKEC9RKuGNiANSPw8jS34hUw67tz+gyYtXvDdkqVbku3hWBs8PYFthmr9vtT0cQMHnMABUSGrL2xyMZ0r6YStACA68hMlUS6D+Zjjznj8z5+uK67u1o2JBjdegOyTgBpjMtgXLpgTsLlDJqfD4kxLT2DP10DYGTanZ+b084fwXpFuqsAeTmTbJS7SXXYZ4cB9pALYzVFYhEgPqkLxIy/BVE8oaEWZxzt9a5UfOEx2F7JqIKIsgyksGJ6iGSNDFpjpl199mZ+XN2jgQGFAEY4JbaNNdrdymTt3Lt6NuzzhqGOYZNrS6poxfTroMU/D7KiowHua/bY4Ll+xctmy5V1ysikEkygA3Pb7rt4YQcLoRanmHsDsTtfA/S68ZNkyhoYM6caNG4uqvisQ+/UbdTd37rdoJ8zriB1vV5fuER1XHB4QGDD79esHQNVsdwWo89sBoED7Ri011VZbyANRYbQOzmpW9hdQvmiy9w0DRJMpUulEQ6NTM8zEb0utlQl6LkZh2A/KldFQXbFq1tfPDe5vSYhtMjESFgunMCmfP1hWVgmLyssttMc2elknYOubmnuM2d49EGb5O92f6eIj6RDQQyUG4FrYdAzfLVy0YWMpaixDM9aCU6l+JgvZ6d4SnjRhPBtIwEbEbNdOXh+AhrW7ILXIhMVXVVVPnzmPMQaMUdgBNS8ersbBgwb16Fawu+C2jaeFx7x583jAv2Xbjz/yJtTEIoZhSBZ8hLC/YUBXlhmZXPmRxDv/jH0PsDB05nKZR9l5xA6+IEKWr1yFk69swMegnY1dfJ5BA/p1KyxkkztcXcWsvydypYM89iQI4nDeGXfKgnlzT5JuE3fx4sXMfvXo0UPq/SDivw0SnS97QQEYfseSfVtYrIZnNChWTTU96i6rLJkV9m+Mc3CgCDorUw4IBJPNGmsMOzxuvMft7kBiZuF4Z2KfkDFOBtRsXCG/I0kMHDx1bFtiq9V6RoM3EG52y1nBsH44KiyfbbuxdXNUscxTymANEYDo3gM9dNuM9vMbbBH0kGAtbg4iU5YfGQBie2AGxdfQ3LqP+eGphXF/T4EggTRLYj6cZzjU7jX5XeWjZR4zKFy7ireTby6Pv76Zxcbs38koAh9WHxPpHMkAF8YpCxmAjNkXdryTbHcVrLODPruK9GPfGPkhw6DwPsL5sXw6v+9vCuxWtWtZQc/GysR6RpPVnp/f/ZTWpg0rlnxcXr4uMzvFGZOAjuX34xnoddhScjJH52X3MtnjsQspdgCE6CBjfxfhgME7VGJADcbokOJlZcPnBdKpkRqzdQ6ZSGZqIeKqQe1BVvjaYSEJ4GgMC/kZQFvaDGgziGEYyIIWFrfvDcfUlQtbgfOyrIEz5RE2e6rFk1ZLAh70BUAdsneNh+QaB5DZC1B0ILPVgUueeOFTlexGwTkWsqyBVQLMC0WcxHjdO/T2NBU5koTs9iLHaFoeWM9YVFS0L4TdU8w74x88CggDl3PXsUqgkZptVo4V4/RxZ1LfgcOzUso2lpasq9ncQJdnCTqm28yMfJudebgwC18xJaHIklLtmtXhlNzBK8ee5nTIxAC2YrEByRwik32gIUTEzCITwzLzh9uo8unlg5oFOkzEQISPiAURzPA1ZsbbKtvh4UeDy8I+yCraEQwXjRVHBPyscGzfo7psLzbaP+8RkO0iazh7x/KYDVZVxlCArY7kxFdctugpEFCD3V9Ibofzzl73JTuqBvWfO+YstkzHKERNkdG+wNwZnp3hh5gC9GtppgwIsA5xtCv+c1gjjM7EnB78+o3dFj0xDbLWROYYZGM7JhRY3nxYKKzb4vkjb4dODGhdWjR9pe5zU+RjbzJx5JHF/6K+6YtqaHs8LP6CjZiCRBIozEA3ogPsPXpawODmyDq7vYdy2KQU+sgl9UufQshDqiOvf6gyaBMQ4pAF7QUFBVi0MA1RX+pj5+1/kwLKHk3rxSzBZIA+YVjXOC0bIUF7lgEmtmz1LANN2vgR2iYOUccU7zzxQmUXCPnJjIrsTMYVkj0y1U+NsBSvBcnDjLwismQAKWMZFj1EHpjbEB14ry8ZWHJOhsfDWQ57DeQwSQhNEOhQSTb62EqfwwS7vUED1s+OJogBLRU6hwJ7Q8TDPA0tFV8gHIXE2mtn31A5UYCDKmWSkmVr/MQCDPeCIym1VfyVZRmUQaKxVIkPJMeBVTGuw7y0W9E7RGJAEBA9UYlSISoyAEkqxjVxuREpS41sRfPwe1LTFTD9CKrIAx4pyF5fWgbAYpglZgceHgBF4F4DPLQJFSlkFiVSudJpjlhdSXkEMSuAkNYWIerlyK2aQ9swDu/caa+yo5zMELASGGYuP07XYM2xM/ILO4yhdj+EBAKAZTaSRCusSAsGEEfSdYjEgHhUcUaPgV2Dsa3B8GSWhb37DCFbiGMwrSY/u01GcdPc9nAiqwgBBoGsPhU3Y3R4Ob5FCbG9xlIbGeD+nN3Glq4MCACMO80Rym5kizZowaZ1UIllycz4MLt+ZAoCqoCLPZFY0oGnk772bspkr5tHZ8KDQgFUezR6OxN+YufHZM6PkYC4q2gWL4YJbf2hLasH2cxRabLyif9yPhIQ9kknPChlbZdJlNW2Czs4jzIzLPtDkBscA30LP3KUR3aDgZiy1bBMwkYvpVxG3w71A5ixSouNTKhri+yBQeWLWw7TiPuCWpSzsCUfy5SYh4yG7AvYQ5IWGUm+MqaT5ZdCFqHN4VWNe0AYhgJwf7WjrdT1HqTsjHqEUYDK1b+2vx3WdluUjgrHt0PHVztC6EfDDhm68H0hLxMDuATJdnJG2VoOX0I5UB4TC343IiHUdciQbENgh79i/5HtFhSuOMSzLzk2cASYWHL25cLizIAArZNtU7///ntetVPKvsA8JGlxe5I9ncTExxmBbNCAX81hbufbFZ3YWGnQoEFIZYYFu4rX+a2TAkcgBQ4Zh0VFRAywG/XSJYtXr1oB89+4YUNZaQk7NCz8/rsNG9ZzlMphS08GLUuXLGlqbnS5OIlhHfxu1eqV7IGs97zba7Sjaiasny2U2bYPmO0HBEcQD0KcV1ZWNHE8RXPD5s2lrLYsKy31c1rnEXK1JzXbp7OYA0deJHS0jo6QcnSi2UmBH6fAoRED9DE0fmaCxWYQ4pxyUbJk67YQu8D5MCfwFWd8BgttJTg0eLblLn9BEPx0CAOXlhaO3+GYdc4h9qDwultdnAwvx9zu2wWXge8zCGDtLocrcIQDO9jgo4JgYFMHrn0Df/BSh4J+TqRobmp0tTQ31NVwIE9leTnH/hw8DPYhJ+oaUuu9NFgrsGzZMtlSaR+25dgHXDqTdlLggFPg0KwbgNkp10r2wPEOGTSQLQc4wozDAuGA/nBg8OABMTY5MlcZ5/A20fajA06LH80gqgnaHY6jxo31Y7gyGjj2lrO+hgwe5GfX7OD+ZNNsD9ezZ8+FCxdyepdISSyOymXlR/E8HCIgI3v0KAriAmY0dsnJob6HDx8aGxNzOOD2ozhQ0VxIfYTB9OnTGZnt6ZZKP5pFZ4ROChw+FDhUWrbo/jB4No4XC3Io7LQ7sagwS2y1W1nDp/gdjG9fTe37kdCaNWh2zB0MtclbTq6RJ5NdtkSVedH9dcGGEAMwIE59YX5SywCNwP7K4sDB4QRpqk/Vb2QnViHPkTO5iu4Pqb/99lsWCjBjLwPVziVjB665dEI+pBRgMjZqeDmYiJAt9hNTUzM7tInzJd2MLTHBwIfLrtlg4UhxJ5yEwQp2o8NlNACSYAg7wJrV1ILxii2U5aRJCqNFl81qjo/ZP1Ma6KHwfSCTHbtaYhQaMmQIwoBZdF1PkulhzFU9Xn9zq4f9NuRwaNk3UJYKxjhsMfuJPvu3sUaJGX1gpRgyADGMGID41ALVwdVpGtq/lO+EdjhQ4FCJgcOh7HuJQ5RT7GX63Uumc9F31hVz7i6MCesQy5dEDikZgEjQY5TdA9kZqwMKQEw4uxbwsHtiwOsbGxuxxfXv359jtCE1IYezxO2gVJ1BnRTYEwp0ioE9odYhiqv5/saNG1evXo3bIu6kjBVgTJ1iYN8rRLN+eL0GhUhgCTd0Zn4eGYB46BwB7DuROyEc5hToFAOHeQUJeogB7nAoXEiXLl3Kfvd60hIOpbHXgwP93HnfIwrA6BGomoCc9IBTELv7DRw4EPdQAvVXLW73CGxn5E4KHEEU6BQDR0BlobFqdZU7FwYiGBYHYGVkZLAx9c6OhSFmpymjfe3ujCCcSoZvaFkZCxtKe/funZubC90QAFx6JqZzPqA9GTuf//co0CkGjoA61fyr/b21tbW2tpbZAla3pqSksA0Rq47hVogEzfo1F6NsmpEdAYU8wChqUaqpwehKr8aAhizQY6MI7ZHFmT+a4+9MYBxgHDvBd1Lg0FCgUwwcGrrvda6aQ8HltcbK4iYYGXcEAyotgViKEAbarYjXTjEAqSEasynMtP/www88Qz0kAZPtmNc43QE5qkdUnVr/XjfLzoRHNAU6xcARVn2wMC0JwBsNF86l1X/4Gp/gdAwRkASwNrxLicOzjnCElXN/o4sYgD6o/8hFLg53gz5QRtOQO8+dYmB/U70T3pFBgfZiAG+JrY7orO1i38zIbg5tfymT+PZ3XoeOAsgAfcG24GsaER40U+OVr3zirp+j08iHDuVDnzPUQEzC/TUqUfrwigDQJCKQax9wle6j/Y3adhiU3VKAuC9A9wGfzqSdFNhdCrDIn7YKt5c1vSqRnKfDk8/gt4YNZtn12cCmPwH21JbttTmLYZ/6yu7i1RlvTyigWf+epOiMG6HAfiIdAtkbMNiCsjm9wWrwGA1s/+QIGawOBmSdxO6kwOFNAbg6PJ+fOlVZcIXLC+vn1Be2z5cvnBtlNNrkoHh57hQCh2GF7psaexgW6OChtL9Ix/F59KXIIXpqVCBnmXcOBQ5eTXbmtPcUsDAOgLsrqw8PovILr8dLPQzjB668yAExaizAm5IRe59fZ8pOCvwvUoBRtdkkZyuo/sMBSkaOz5PeI/2FX+fVSYHDmALGQNiPqUeJAdos22mh+m8dxcqypRAjAykBkRgycHZI59VJgU4KtKeA6h9buT0Haoc5XxXliUOV2CGrs8u0J1bn8+FHAT1pptupEWcTts7yBkPzFy4p21LONmn9hwzMSU5hd2BLUM6R4rBgom6VEodfeTox6qTAQaMA8woytWAwNHm8s1etCbhaneFwUkpybvdusQ5OLffZAl675cjYW/ugEa0zo8OQAoiBNq7O4cDhYGOr+6Ennnn9zbf9HpfPYcrrU/SX2+84dsBATKjsZ4YYsHXqNodhNXaidCgogAwgW1ZqVNXW/ebmO1tqKuP83qAx3GfY8Pvv/3O/LplhOTOj8+qkwOFOAXalwTUIs5DMCnCMyiNPPfe3F17uM2zEs/948rdXXLly9Zo7/nRvfX2TMcCAgL2C9fD3cC9VJ357TYGIV4BUdGdd/wgV0Y20zy5LDlxeY3ZOtz/dfedVv71q+coNt9/9kMvls+A41NllfoSKnZ8PPQXE7K98hcQfqNHrnzn/e4PN9rNfXHD6pDGDx4x8+/OPN5eUbFi7PnPYEKMnZGNb/Yjv9d6jrrmLtkO15zQ6ZFvuI2HROG0RIlm3wVGMa/tv2t2pQySj8Lb7qmbE28K2g9cWLH/b8o2G0dEj0dWf7b/zHvm89aktrYTo+G0hW/+2A6YDtw3QMKOJI1lsTR552jZC9I2vkgLaMeMjFJTFBuLsGCHcNtBJpd4FVZ1KgKsgBVDsIpH3Nuq0z0g+dnSpVB19OFLCGA1EN59gI4+E2ISzTvtpot1Y1WJ4+oWXFn6/7MQxQ/fYtU7EhqqEyIOmcHsXvQjdImMR7bqnEmmiy2f1GiWjWK7ats+LBqoHgcGnbQO3vmmASDKJo5o5bYXJQ2JszWtrdP0USdTWICKfdxpfirldXIBLFjugpWLKTX1p+7xTyDsguW1MDU1IrcMjxWxLJVhte7VluG0ob4oy8pfOrGhFzAjNVVxJSB7iaaNhRKKpj4fFzcKpiXaD1xDmrPDYimbP5npvkjOmR6KcnZLstL/82GO+1oaighxDyG02OcLsVgCv6IgeypmorZTtTg2TOec2MvOZxZoyc8ZkgxRfmoBOKIMR+TEqCSq+LgGqNYjNimiKYdGWJZm+FPMJm/HKU5/bgtv+8qXtcetfKl3yb/vS9peM9US4DlCYqDDJVjilmM7k7GSFpcJPoIMuFSoIcwldiMhRagq+Rl7fpZy4Esql7+qx7cYe9x2EGiA0xNj2C9kFyVVdEVKoRkwsiiUkkxN6ZJkxGfIG5goAN2b3VZAESBi71XEmTADqGVsNJq/N6PC3mpuqGltbKwJGoyMhKSEhKS42ht1Nw2BikjOB5KQgKtNspc0AzUpSCQEY/xWqKjNVpVvRVHGJHiG7WqGicBNy4XAgiKlLAB1xl+7tQmqWoRm95rDbYIjp1j0vEDavLtty4p46VEAdH1uIyxBdaKkqECekEP3GzNkSQjd+bTsNCq+xWC0SlVEJnUtxaEbtQmw1yCdQPlLVLD4P4szEwX4CVtNZLUgPIcl4MAFfh/KxrVEBJkjmHMnALntWszfkp9XYTBbpQ3K6nDT6tgYlRwuprqgnEOmqUr8apMSSNIJ8W+bqTT6o9q8yVQ63EXjS9znMScUKBSlBkAP+pOcF6PG0PEhEwzRSQDm2iqasGqIUQReurQhAp6uqlAYvR50b4Ty4wgdMVjoHzAA92OQO+E0c7hcISqe2ybGpqvtEcJcc5IKgckkZVBbcieny+GwmdiP3h40Wv8kOpnQVs1DcIAfSihXdZIYuoYCBc3UFNm3C5PeLtdBqFzargW2lFEEH/dK6PZhALqPP7xfDD9PEJhaMhWLDxpHdu0MDc7hFrJxmv8HI/gSaGh1gChT9TRERgnMpikFvzfDlg1BC1y6fAa6oIPcIfaXp86iDNTwFSOLK0rZ2X4hDi+jgbDI+KA6ooUry6KWMYG1vKideJL6EkSiatwogGHzEYia5y0cVVfi9oMaLtEMFRtqbwNAx5LuUPVoARY72ASouN2H3hLe72lowjZR+rducLg5IqEug6v8qBPx0LIIBpXGWJ0K3EXs6tXSKgD/cEA4l2Qx2szXR11Cx6PvPild8YzdsyTa0Gi3xTYZYvyUup6h/1/5jnWm9mkOmWGkGlBU+gB9Bm3ySzg4KkQKoYlC9EmJkEZVCJZKn1J3gRo8Ac/ox4cI3/icuimYN+yxhn8mgurcpbDX4w35Ewp5dEMcDKIvZYjL6GWoopcsXBKwIgTZYxmAo2NzSarNxJLaDZscCBdUcpBZUFUQi6lc2p61rqI91OJ02uzRK1RUlBvDgh0HpQtQJjTtyqXx0ZgR6WaDOKXjC1zh6zSY8V/Qh+S8QIvmpP9LYNFzuohULwEgE4pINQTuwOxVN2kTbpVNEOgFQZDEGmFqgrNloClqlH1LqgM9vh3nzSbqncJYIjDZQ/CUhd2mP/KgUk9knszVsKcXCPoWK6jdmi8UfCtowkCPLRNQIiDYwugh0xUhRBKzq29yF4mazxWZlvypqSzCHywuPg17gTGsAfSm06g4WX8DD1o+iOsg5uxFSSKpDfeHhzEWxkIoUUDyfA2FLwGxHSqNWhK0sJab89rDJFzT4YFlmo5XYO6JN0LahUnn8NI80IbaV2g6pkMaqwQgNFV+XaJCET0JI8ogQnI/qUedHchUx0rykWqF1WA8vdsRHrYfQ6bf7qMC25RH9hhbf1lJ1mGZtRKbJSSOSotBIIshIs1CFE6ykyUZEBZ8pg74iRZeSidosAHXL4bOmlaI5SbZSjiedXnqaZM1H+Uo29D/QsCiKShyVSFUfGQBc4kU6j8Qkd1VS1cuJLk1PopACFNF/3NZAitFj+OHrz0pXvJ8d7xmW0ZgaXx3vcRv8LQFbksvgrt4yc1X1WkOX4d2GHG9ypgf92EAsSC0zwsCICgRUUXQ0BdpQkkoRrBV+5CcvRJQHQZD6lz+KXgTqT+r7EXyjbVvCfjm8WMgLhwmaQ24rCqBcUQKot13eiBqw29dt3jx75qzyslL4b78+vX9y3HEWdHVVcRFwFsvDjz02YeLEycdMoonIrlIM2ISW0jylkao8dVt1ez03/eEPN15//YA+fZUeTdVIU0R/QqSrc+yoVEm7XW3otsSeVYwmzGZrc6v7jXffmfyTyXmZWejlkV4ULQ6xJQGIiJ1ZwZdvBHJBFMXzCN4mEzppqE0n1BjAbNscbGm4AfxtBVl2BgwZl69ZM23OnIDFwOnf40aM7FXYjS0OrJSaFtd28IbAb3cBU35SJ9yNlZWVr73+rysuuSwhIc6PNiN9C+IZ/L7AP5577vhJEwf26QOK2uolZZCsuQtGqhwChwav4MkzBfOGwx+/++7RY4enZUAW0/Q5811NzSdPPkb505hWrF49e9Y8e9gS57QNGzsyt2tu6YaNrc1NHGghdaAuBY2XaA6R8IP5R9UmdFJiIM7BwfAmrz/Q5OOM3XBzOPT0a+/f/9gL6zZUMViC1niN/ihyxNCMU9eBkFF++iZMk380UwVJMqb8QmRpRDxDE+6MURj66RCdXgEgb8CoS5ocQpWOJ/EAKInRP0WctAMBcxe2pX/0VdkMg24S4kcgbY5GpH76AZjgrtEHjgWlVTBWAlyy1cKMloBTlUKXYpC1NMRIi5FvChHulEWLNAVDZ6fyJWsYBz+Fnr6DpDwgL7ijJgNWaCGlC2AoIwRaqRAyF4AqSwVZ7AEUCrSF70iQEFjSKiprkikrgwKp6Ic0iQ26go/fe+fUt24YUbhmaM6WXENrbIsl6Ik1mOLQmhJ8TYWW5t7GSsOijz548OqytUssVvElC4sC6TeGGfKGfQqW1FM4YAbJMKiKdJAqUPhDZ4uEg79UFtFFVYr8hOYKAAiT6Mi+oLnfaPGY0JcNjc2tVEBCYqIiPcTY3QuetHLthgf+8mgoGBw3ekz/Pn0tGBj8IZsJEWNgF1lVuWJZbfF6m70eLWeo+NaW1gC742EvogHQMlQz4IH4gWCotr7B40MxRXM1ub3uVrebRxDmmIWHHnuyvKqGXfd0K+GhqdXVhi7th+rGRmhC7f7sy6+ee+Vf73z0iRE1VjRZaXWqf9DkVX8QGWBscnvcfrELEQp6/IDc4vGEaDbSrw3NbaUANzQVdyhU19rKZ4YdqnRCrpZWF8mZh8QOKYUwmmxm0/xFS7/6ZnZhQTfEwIN/efjjzz+HX5MTeoXb5/H7fboILW58HKVhim1UMmdAE2x1uXi1xMYMGjUqjPIuwZYml1cbZ8C/X/9+cUnJgRD9zegOhxtcLmEoor0qW4DZwMaN0I1kumiqi0l/fPuDD39YsSI+MYkRW21D0wsvvfbiy/+qrKlhoyqLyTR/4Q/zFn6f1z0/aLHcc//DX8+Yk5ya+u4HH5du3gJwgADwcLgwLIIMHEYYVnpibH5W0vq1qxatWHf0sP7zN2y467GnTL7QT46ZYMIULPUuOmXHeFNhQhbVNMKGuoaGqqpqs8Vqj7FlZ2RYKXIwQGPBnkDDUlCoIwLQDpUqorgapstWl3fqtKkTjj7aaXeQFYMnugSCQpqd2ULdBELS3MVGTV3KGYFiY+LMcAZZn38xhX2DBw8ezH5hKFB8Qf0xqQN7RQ8SohPRLa1PjKQyXsP0CRjpP0biG0IBP03fhO7j8k6bMWfiscfG2hl4KvaH6ZAYSt6QRE4oV3yWsSAYCgGFT1ta3N633nrt1JNP4a20pGTggP5kxbiFC2ObCC06FXbIAL2CBo6IYZdQ7YElzZazz+VV9BQb1KQbhKAbCJttSpUT7Ru0/WItlbYcCgRtJBDRoHujKOwy8giFEKQwX6ldhhAEgIAY5ISUxpA53pL+4UfP1JW8esOvuiWF6o3NcQZ/PKXw2Kweep0xYA22msLNsYamIRkp1pDvvZfuv/L2x6zOdBFMQflPLwGVIGtNVEYKWxoBG1ExLmZjBSoqgBWZmCATZlFtiAGE7e1PphQWFowc0B+EhZ6CoTSbI/GCmFJn0JPLbPaZ7Y1BQ2Vt05ffzElJTevZuy+8xymtbHcLR6V+9smUc089/bTJR9Mh5RKF3TB71sL/vPNmXX1dUY8eV117VXx8HEYM2SjVYFi0etXrL79cvm5jXkH+tTfd8M777x03YWLfop7k+sGHn+Tk53E8EYybjoNd/513/vv1tK9cbs+48eMuvviX77z73n/efqtkc3n37t1uvOoKDrZ7/qVXauvqR48ccenFF8XAepU+go5I2vmLl1957bWffPZpRWNTWozz73974vxzz8ntkkfnmTV7TtBoGjFq5Ktv/HvGtOlWq+Waq68a2K/PoiXLFi1eDCazZs54/P57KyoqX//PO8UlZQMHDbz8ssuSYmNKt2z5+wvPl5VvobCZ6RkXX3RRUbduzz373MxZs/Pyu1xx2SU9CgvpEZjdabZMSuXmFZ42aSLbNPmb3F9++dXkyZPnzv+urrHuP2+88YszzznnzLOef+2Nr6bOiIuLPf/cMyeOH4so+Gj6tPffea+1rmHchKNOO+esuUsWDRg8GJnx0suvz581Jz02/tJLL+05qO/3Sxbn5uWHzaaZS5b+6/XXa7ZsnjBu9OWX/NpusX85c3ZdXf2Gdat++H7h2NFjLrv017ExMbRzZHZdc8uHn3x29603OZ3x9P8Zc+ZldOmSlpz40ZdfXv7zC6SHWyxdi4qOPfYop8GwuaZq2vSZZ0w6Ki4hadrM2ZdceJ5EUFxJBKpwp0N20QPh0PAdNOlwqtN2wak/ibeZnv7nM5ffcffNd/+5rqVp9FEje/QqELZCmSTaLq6ITkG7/+97H930x9sf/uvjt/zx9muvv6G8otJgtqrpBQObYTLYo934gjJsRswqezNS1xgIhl1+/6xvv0NOw89ovsKczSaf3weXC4YwuIaZi4F/MIeB6MZkunL1qtVr18Jh4XYzZ89ZsWqVzWq32OyhsBEzoNFkRQuhj6LPKPUcxoiKLNZtCC/aB4oMooVujB5HX8EQKcLJ1OzyfDN3HgoGH5BCZMfolc4ArcBWyQIEiRWFhQ2MzRY70ibA3KnJ5A4a3v1kSm1jy4q1699+7wNvIOQHURAQPg40QWPduo3StkX00pakChAM3GWeTzAR1RnIjMVp+8JnxOCIbmGn45spETv+gYbZ4geC1c7kNmJI0dDMmQNfz5nb6PYiHim3H4pBKEVtQPMTvqL0OE9Nbfnq/1x6QXKCodHQmG4I5AasjkBsGD8Ai9Pic1gCsfawDasgKmdN13zzmCKLq2Ip6hdEDTEhhyRQVPAhl0wYs5kwVGKRYplsPrBhDo6YIiL9gYAfUWWyWjyBwOdffbNmXTF4EKxYqBSP/0fopVRYqhU9wr9hY+mV19/xs0uv/mbq1+edeWr/Xl0plBK7u1s4t8dTVrxpyIABtIYgmrkvgJob9AfjE+PPv/ji+//yoMnh+G7hD3wFMO2vNeB/6NG/9urd97777/vZhRdZLdY5337b0NwKf4Gka9cXV9XU0MSARKOinecW5F//hz/+4fY7Pv1y2twF3w8ZMSKnS5ezzzrjzDNOa/F4X3z9zZ+ec+5fHnm4pKLq+Vf/ZbKgYND96CnhuqYWa0zMccf/JC0nd9bceQ6rtaqu/uMpX9KiaWP//ejTgNHy34+nfDP3u9vuvP3iyy9/+d//qW1119TXP/vcc02NTTdcc218QjIq4XEnnfzAgw9srKj64PMv0NjvfeAveXmFf77rbiYdc/IKunfNf/+TTxta3X978ome/Qb++S+PNrd6pPNKBxTDD6oM/cftC61ZsbqoW3eb1fbhJx9/t2DhzTf/8ehjJ89dtGRdyea77vvTub/4xRPPvLRhc8WMb799/KmnLvz5RXfde8/o8eNcPu/ced8arZb3P/xw+ep1Dz38yMW//GVKapLH6523YH5NY/3GLRUPP/bk5ONPvP+BByqqq//58osw5w2VFQ/94x8jR4+55+575i/4fsqUL8UsTjM3GpavXM1gq7CgK9WBAeWjTz6bcOyk0ROPXllcDCujklCF4Jv080ZvYNWKFUVd8wgtLOq+8IdF8Ja2Zt/2V6r10FyaG8oITszsfte5p0y+4arLYi3mL9/9pHz12lOPHXfPLb9NRP4aPWGDD8VvFyjr3kwElL+GxqbTTj/j74/95e9PPBEXl/DJ519a4F9mS2V9w5J1xdVNzaKXWsxNLhcVvLmqcs2GjR4UZIsMJMOIG8X9qbbKqiq4hNWGe4IVhYvmXFJZva60HHUYeymM5r+ffv3N/MUltY31nqAhJtlniS2pa1hVUuEKhr24bqDg2ywV9Q3L1hfXtLr8ZkvI6qx3B70Ge3l966bqRrfRXuMKLFm/af3m8lYPc+Ci0iMeLHan0eqEn9KyqmobwKfF7WtqcTM8YYiDfGpqbPa4vNI4TTYsaAaLbWNV5dJNmzyQ0hrjDRsHDh12xVXXmmw2i91e39jyw6pVlczrhUKlNTX//uADIlc1t6L5NweMq8sq15fXeo12v8XpNzsbvQbKsnjdhpLa2oDVFrLbEJweg3F9ZeXiNRsRMH64vtlcWd+4av0GxBWkYCDGrAGStaKu4Yl/vrB87Ybqhkakl9FiLa9rWrK2pKqpGdlJLGXywmkhtH7B1KFdTdlOs6E53mhI9tlb3TGBja0xH83c9PbU4oVl5o8XVi+tMHitqdQaI4OixBZf9fIQYprRjdnKCFvcRoKhepd7+cZN5XX1fsQkjCkUbmaY7/MvLi5p8OPeEcNQ3OxI2FLvWrK+rDUEUeOhHhRWnErzK2lUu2hXh6Zn7F6uIr/F/BXOiLfGGP1Lf1jobqj99c/OuuHynzvC7NErRovdv1B3PF4PMOF3ovOgCVgMXmO454CeqV1yNtfVxSQm1TQ0AFCUI4OhvqExIyvr/AvO796j+9ChA2NiY50OZyAoA0q4EMqQqDFMgFqt6ARMOw8ZNtwdMje6falZuWVbKrNzckjRo3vX3nm5JZsrrLFJ1tiUTVuqu/cbNGvefCw5qGgMd9BBFi1e4oxPDAb8ffsP/Pyrqd5g+LjjT/pm5iyXP1haUVNcWp7XrfCTL6cNGD6q2uX2mS3rS8sqamphKhkZWZddcsmgfn0RUV27dy8o6rmlodEUE0tDbQ2GSyurhwwa3D0ro1u3Ih9ePDYbWkJCevrasrKcbj3WbChbU7wR9Ckv3N9qsaxZs+aev/79uj/e2tDafO4559D84iz2ay+9fPSgwalJSTO/XRCTnFbZ0IqjG55AGJGmz/725ONOOGnMmH7duo8bMMAaCNuhSsiQnpS2paphY+mW4SMGFxTkQSSbw2m22ad89U1hTsEpkyb06JJ7ynHHz5j2TQsblAdD/QYOOnr8uAE9e0w4ekLxxo3C4lSlcuJTanKq3WbFGrZy7brKmtrsrl2TczJdhnA9x37ARmzW2XPm3P/Y33//+xvjbKYzTzkR4ZeakV1dXQt5lThXgA71jcG79EARb5SNhmsw/f63l51+6mn1ZRXxCbGFRflOG1ZdNzoxTUrmd4nbwaU7snwSaOj1oZDdYY+z2/iNGDmmrGQjhPvy62lvvfuuNTYO/nTPH36fmZH+wiuvwmFLSzZVV1R079rtlltuwYZDk8UdDTo++MCDo4YNOfunpzFgwLIB0331zbe+/moa/SQ9I+WW318Xdvunzp5vcziWrlx39TVX+a3OKbO/m7VgcXNjQ3ZGyh9vuTE5If7jqTNef/11pwM3CdPvrryid89eDz72dLeibt/MnDHu6PH9BvR/+rnnWPrvras/94zTzj79VDgTA5egwV3f1PzXx55uratqaWo8/8Lz87t0ee7pfz76l/tiY5wen++5F1+56PzzcjMzpMwW038++uiN/76TmJyWmZbjY8thk2nBD4s/+eiT++++ffaCBS+98mrQZqFFXX/tNV9PnfbJtK9Xbtxw+mmnnfSTyY89/Vzxho2YfYcNG3b5ZZc0N7ue+sczGLWqq6traqvOOfO0888+y+Xz/O2Jx1etLsZAcNSQoVdfc/nr73303icf4yjStUuXm66+OtaK9Vi0ptlzvluxZu0//vnc8P59r/7Nrz/54suX//1WXFw8E1a/+82vxwwaiJCi8QU8DZtXzByYnxRCCoVTwsbakHVLrT/7xf+WV1W5nSmJ01eWNNdsufiUPn3z480UB15kaqipL/a21BjiUhiWWeENYeOK5SseefE5znD3NTfecM01w/r0e/ODD1YjcZtaqirrY22WP912c0Fm+pLly+77y18TkjNik1I3b6lkpAB3EU9H1ZRoMEfuhailsrLS01575gmcRRgUJsXGZsTZHGbmeHxii9lpl+mg0FDSEef0M4SSoZuMHtnjHe3+70+8uKWqtrBr18ryzZkZadSgrFcIhT0ud1pqOkoSEhn9BQun1xew2GyRIQhGSOwnTBrIKNZY09j80CN/cxktHFlaUV1jtloQGDKwVbGbWlpLy8s/+PRzBg0hn2fsuKNQszC0ooIz/vti6rR5i5cvXLHc4/VtLl67emPJ0GFDn/hHcNX64tWr1xcUdktKTqqqrV+5el1VVYU/4Cvq2TM2Lg51JTMz02azMh72+QNPP//KojVr8rsXlW2pysnOcVqMx/3khBeef3nNuFFr16y78re/afYGG1qav1+ydNPmLYGQeez4CarpgqHQkWI4Y53d+/YYO2EMA6Z4u83n9uQkpyVY7BgWglZzdW39+rKK6mZegvndu+fm5y/+fmHvoh7Cm+BHDKhDRpS7oMt3wjGTvHbn088++2Fi4rXXXJGckaYsz8bq2rqMpORYzAOBYGZKepBZDn/AYrJkpqShKcvsnAVNVFgcVMWD1uf1O9k1RLHE2bNm19XWPnD/QyFLqKZi89KVa44eMxzCp6el9u3d4/ijxw7s09uKSodDsdPp9fsY5BkwFcl16HsAjh+RC3uF2Wah4YBU/7xMA7/IRQDcn3aGYWW37JxKozd9u3AhMzu1da3rVq+86OzTsNGMHT549KgRHJj72BNPfzZlyqU/v6hkSw3eW3++7bag33ftjX+Yt2jxiEED7BbzytXr33zrnaNGjzn15OPDIR9zkwaj4+PZ33w8+8tn/3RvUlLiw++8dvffH33m1nvGDB86rF+vU06YbLPbA2FXTkHGvVdfbfD5fvuHW+d/v6hLTjaWvltuvrmoMO/Djz778IOP+93Sq7R0g9Hge+TPd6Wlpd36p3u65Xb5w/W/dwTCPg9zZnBwscOg8nqa6668+MLBRV2Xrlt/6yOP3XnHXfhQzZm94IQTJny/Znmj2ZeVnc5g1Wwzr62tmrpg7l/uvbdHZtZX334/e8YMRs1MxdXUVhuspg+++LxoQO+rfnNFY0tLjMN55qkn120pv+6aq5PiE0yh0CUXnu+IjSmrrLz51ttPO/3kpITETcUbb7j26sG9C+ctWnTfo48fe8JJb7z+Zn1N/dMP/jnWZsMYM/fbhVM+/+LO227Lycl66u9Pvfv+u7++6GfILYvBMWrIoPzCvDtuvbFHWmpF2ZavZ81+9J6bszKyZy384fG/PdH1r3/NTE6kR7XW1xoNW+LtYSvbSBnqLAaLI1Sw6NtNG6oqrjzlqG4Dkt/9rGZpVbAw124wrQ0ZEsNMFsDuvJt9DeviY3ogp3E0gkH06tbt4TvvjIl1fjNjxlfffDOib//mpubli5Y88vBDaGe33nUPpuTLf/Hzt9955+pfXzJ+1OgNpeWX3XQH/EUxKkxedArp4oe+H7S19T36qxko6iq/3l0LdVrKAoUj+qLoe+pNf/uxO+rIwH59//3Gm9dcfYXD4pB+FDLMnD57+Q+LX3rh2QSH7ZU33woHZP7TL7tWGGLi4zYVl9bUN2cmx8sMqc1uMtt55bHFF0A/7d6zJ0iKg73J8u38hTl5uVdd/ksGrCt+WODz+vD/8foZ44vJBZ2sW17OdTdeESe6rUzXmP1+/OGYalq4as3KjeufevLhxNg4FpA+89JL/37n7fv+cOMpp5z2n/c/baitu/i8M5Os5j5du2AHv/bSC3EeYA6W6d01uA0EfIh8ZtXmLFn+0VdfvfDis12TE2667yEmvjEjZmallacmZWRk3nrLTT265TIv170gj5mDC884jZaB04OZecQw9hVEkZkFAwWFeT8/8XgICmXUXZlmYFpmo81o6FGQXdQj/2fnnaurAJ/8hTOmL1y89KwzTxNvUEJR7xjfWIy+UOD0iWMmjx/55BNPPvncs3fdfht7piEVh/br9fbbHzR6gyl288riTUnpXWLtDksw5GCukeSKYfuMYfTRsFVGY2nJSY1NNcxyMw86c86cJx++v2tBAfJ7znfznn/zzSFjhof9wX49is498XidWsZWBkN11ZaEuLgYJwjqaTH1UTrBHjQVwWb/XW1rRgQiSIhg2wEXAiLCYodPUURU2rY3aUZGY21NbWVl7Tvvvn/t735z1MjhyOh164qnfDM9aHIsWrI8LzuDcmOtHjBgQEZCgjsUzM0vqKtvYOjX1NR8++13HTVx0nk/PQW3JbUuSkYY8374vv+gQV3yshiSHXfiCX+8+Wavm/mFoM1uQ9kXKobDPYqK8HeyOOxdcnIaGxuamps2lZS8/fY7aOKtzS3QnrSoJwzxcjMzaf0/P//cp194+cZbbj9t0qSTTvgJ08kwRhRmZlSzsrO6FhVa7Y6+/QckJyXU1VadfdZpn3782XHHT3jvk8+7dS8Uw6nwMUPZli2JiUlFeflU7PARQ1LTUtEC0KSodX4XnHfu3/7x9D0PPHjyyScNHzw4xhlDwrjYOIfV0uxyT50xc9XatcR3uVx+n59xbk5mRp8e3Zw2W8+iHjHO2IbG5kVLl5110omZqakY2Rnhr1q3tryy4l///k8w6CstLnYY+qkVNNJx1KIPizMGetiL1xY7YuO6FnQPhAJjRo95/qXX12/YlJ08EC8Rt7s5ztGCH5vBz80GH8DGtnh9XVxu3x4DMk0xTW5vc2p6bFpqjMFnN/qTjSJ9mmX5S8DHDICfwqEPGC2bSsve/eJTj99bUb4lKzuHKqDqRw0f1j0rkwaRk5EJzTE6uz2+ocOGwVGKuhX0KOpOg1dNvq01qZe2tnPE/qUUFCvSfSiaSIC9uM454/Trb7jpqquu7dWrl6vVlZGZMXrkcKPf/+pLL9KaVq5cddxxxwGd1WpME+UkJ+dlZd72x1uHDB5U39DArO/ECUe9+MLzpSUbqqpqayorGHDxMwb8jN4KCvI/+ujj5194pba+PuBptZsMyTGOrvl5f8P39OijTjv55Fi75ZYbbxw0dFB1+ZYRAwecddIpwoHD4SmffTpu7Kg++bmUD35x0bln3XzbbdV19aedfOIFv7w8Pydr5NABuJpcfvHPbr/3wZqKjempKRyGev0113AAtIVRi2oVXbLSkxLiXnnxxYT4hLLiDXmZmXxobm7BkQbfJIo2ftzYM8849eKLLnjk0ccqNpeFfEaHxXTJxRfGOs3i5UE/8wdZ2QQ01eeETZFKTztJYDB82gnH33rvnzZvLomLi6urqr76st+cd9YZ1996x+13P5Cenkp2J514Aj3bbrVNnTpt1vxvYdlrVq884fjjEZL0AGYmfzLhqFmz5tx915969uzONMDll13qQNnx4kLtE9moshHLhGTN/J2hT+9eTQ31jU2N3347Lycne+SQgSqSYeLYsf986bVlK9ag54V9TJ/hUIEazV9Be9PGTQMG9IuxasmiUxziu9jd9ssltaIASYXxEAycdOLky845q0t21ueffHTqcccUr1vzp/sfvPx3Vw0bOfyNt9+H8Uj3l5lPlqVJ94EJMuaCw7S63Oeff+5nU76e/f2SUUMHiveJDF0N8c64RlcLsKkAd4s71hbDgetGqyxqlQwJR11nele9MpFAV7RabN0Kiy76xcVG3FaCobSUJFyFQn4/XJjoLJcb1K/fXx96cO5381966bXyqsqrLvsliwCpY4bzjGS9ONVhDgNQ0G8L+ccdNea/b701dwlOfWUXnX8O8QJ+Jj+tMTYHWaOhEBM/YhzUmOIFY6ZzmQft17Pn/X+65+PPPr///r9cj5rfvz9T1FIkg+Gd9z/4bNqsG268Ljkpce36DaTDgIuSLFYB8JPpJQhkJQsMBaJkKT0Ii0xe1/xf/vLnfq8XF/6c1FQ1tyzlZdgKdSxmTKlIO5QYTpFjLh1vL0wIBjmokg9omKGA2egyB+PCAWfQZPWYmkLoQg5nVV14dbnPH7St3+TvlR9vsbiNftbcMBSrMLOEhPEifB7lEuFmsFXW1P3h9ntPOuf0yZMnrC/e+O3cuTRzaQPM5qsHWZyKaQSRYbV58ZkBOymUH88pKZ1UP3/5L0jJ/yP62k+FSE2Kf/Kxh7/77rvSstKctOTBQ4b079fngXvvhtFkZKSff9bZ0nBC4ct++UvsLVhpb77+GqzPJZtKeg0dlJkU//Pzzu6Wm71x46YTJ0+66Nwz7SwxMxpuueHa/KwMxtA/O+eMdevWnfKTyReecyaKWsjvu+3m62fNnO3AwyIYuu63v50zf97Gkk1DBwwcNmhw0OdHJ8M8Mmn8uK5F3cQ3CbXcaER/uvG6a/A4jkuIvePm6+lTMJGQ39unR/fHH7l/3rx5rU0NR48eixPBoL59M5JTxLU/GO6WlfHQXbfNmj07KSX11LvupPK/W7Z67px5N958Q0JCfH1d7eOP/61vn56D+/a++sorli5ZarE6B/TpHRsXgxcyxcR94tjRo4f06UvbijYVh9122mmnYmNBJ0HvTk9Ouf3mP6CVo1GNPf6EtKRkh8366EP3z547t7mpcfDAgVlpydf97grk34ghg3ArbWpu/t1vrhgyaBB+GtdddVXX3FyH1XbXrbfMnDWrZNOm6373m/59+6KEHjvhKM+IYdq9cfKkSR7cVZRZE9mR1yWnX+/e07/+euKYMaMHD6JmxCnOaEDZv/eWPyTHOk+aNKG1tYUKExIgCYy44wdLS0tOP+Vkca2QokiPUVe0WG0BB/HvfhMDCmfV0XU9BQNhrxiVzzzl+BlffTZ9+jTqJiYu7pjJx8L/N5WUDyjMIQlKDTeYFpxGIARpmYH0tIwLzj07MyPzsceefOpvD2elJAV8Adw2xwwb9diTT2yuqE1LS/ry0ynd8gpjYq3wtPrmFiQFFkzx+mSeVOCKI4rf5xsxbNiLz7/sbmkc0LcX7I1aEjmDE57XQ93DOBkvWB3OyeNGe0OGaZ9PweqNWQxWh+11Q2np+pKS+J69li1b3lhV07db13iHdejIYY/+/Z8FBT36dS1kkSC6Fgp/XlZ2c2192eaKbjmZPyxZysSRKMvUM3PHwVBdU1NCfCxiY+Xy1WtWrR05dBg9yuMP4P68YtXqvgP6D+1VNPXbBaWbSuHpiCi0BiX54Lcmv9fHBNSAfn2nfPHlScdOtFmtOJP0HTzgzXff8fu8PQoLAh4P07XKrCI3Zgi9Lm9jfX04LraoW1fPFHdZdW12euqc6XMwEHfrWkAvotQx8UnNXie7BVrNbty1EMVw5eNG9y3/ev0rb7ybktK3pdVU0DXWamowG2L4FDYz75tiNMYbzTFIIvi60WQv21zHfrTHTJ6ckZ70zTezWlpc0qgDrBKQlk3DCnj8MXZnEoMzi3Xp8hXjR43YVFq+Ytmi4yeNlbqWCue/+iMiQcI6L0z+KQmxJx13rKYHhMG2369njwG9etA4UWLgPnwaMWig2EqCQYfZdNyEo3CPhnR+VJxwCLZlnnA0FY2JHGUZQ++ksWOwjQd8vonjxhw7fhwNkyoCDGpQdmrKz84+U9KyOsFhP2HiBBIKW0eWs2QrLMcOjhs53IPwZgMGGe0wCRUaO3SYMeT1+LzjRg2n6XBkLW7TdMH87MzuPz0N9OjVaHV4iufnZIOUyPxQsHe3rr27FZIXzhwYo1/819ugNP6osfjbMax02K0yBg8bBg/ojz+xFJyoOIJTYBF84e65XbDfkltEcaAlm0x9ekIWcZCm6cFMCrvkdjtXjEIwAbQovBho/Of/9FRQIpB+NXrEEKIxcXj26aeRHHoRk6/jRo7ADID7NfQ86dhJkjMcHW0sHC7qmg8CLGAjYo+CrmRGVuiKAV/QYrdcdN65T/79qROOnpCT2wXVFlhkRITRgweIciaVhc1NznSR9AYjs6RZqalDBw2EHWEPIK5GVj0cstv+EwNqXSEkgKYUPQFzBrOybFRkZmR30VdfffWLi3/19Tcz/nDzrTEJyQGvOyk+lrzjY5xxTidMHOWCEJ5hq3FOB5NUp58wad3KFa+8/Mr1V/1WVoH7Q6MGDz5+4qR77r/fbrc1Bn03//739ISxw4e+8q+X1yz/4aqrr0lLTEh0wqe4wsmJmFItRblZv/n1rx556OHsrCyvxztp4sQzTj05JSkJ2z14oma//Z//LF6+IiUtY0N5+Xk/PQPuTVofmYXCRQVd3/rPO2/hCV6y6ZcX/Cw/Kwfb5aRjJ/3j5X9feN45TjTukBdxwZg7NT6ejnH3HXfl5uZZkxMH9e8HN2QYnp6c4DAb38Q7bflyqzPW09J6wuRjnDZrl8zMP95yC64OPz39tIf+9o/f335PRlrahLFj7GaL02pNiItlERmNhr6YmsT8QeDCc874W3nJ72+6iZ0f+gzse+mvLj3vrHMeuPc+ulnI77n0op8N6N9b5gwDzEcljRox4oH7Hzh+8LBfXnrpuDGj77zr3qz0dOabmSJOj4+R8hmNMSldWq09arybs6wtYkcKxITdvrG5senndq/xd1lbHFdTvbRLVpLJ7zN401hoFgrXe4xsL5Fmjc9RNiEau72oR8/x40bffdfdWdmZ9N+ueTn0Wyox7MdwJO0gJTHOaJfB9MknHP/3p/7x2SefMh4f0KenneEwfZZBisxMCVOTntN5KQrIIhq5pP65eKMhqVeZrmWsKLZ+2CLexDJw5BUOCf0kPvxUeQDwqrgbi33sWOC5QuwWYAaQAIe501d5wjzCNJhOixel6KbCqtSksWgGaF5KWsD2rFQVahOfyZdj1mQFG8ktLBekCtGc8BamR8H9uXgDOl8BJ/Z9eDKrfGwsXuE7cdmHgC4WnDxxAv3i7jvvdDhsdbX1uEIM7tuHEmiOyfwTmDAzIbYUcZ0WZMBayiY3uSgF+MvON7yI+icDy8g3KEAReCVYqCOrGmW7B0AgMvnLPwQeb21ApciEiZ1XiAlQOrcqikqlrUI6Pp/xmyFx2NC/T58/3HCD3Ypjukwht0EgAjvBgIsCRfkBSCo2aktMuOC8sxEA4t8nBIlcUiGH7hI67p/cxcYj5hoqCf0F3R8WGet0IlEhTV19I9Zzt99fvLEkLj4hKyPd6HM5YuKqm1yY8mM5+NgYrm1ywStpPe6WVkcMpjMznpFY3lKSEiAxhFaraU0bizeiCBcUdo2xsshJRqmlW8qbWup69ezlavHiVZoU72Q2s97jx8eUUSG8vr6+oRR1w2bLz8vFVOJqaUbFdjisdCAcLrdU1TB526VLbnpyEg1F1HuWscvCLObZvBs3lxdkZOSmpVA+quqL+fOf/edLTz/614x4hBy1CMczMb7BCrRhU4nL48ntXoh3AToF44Cg3x8TE8Pws2xLBZpBfn6ew2nH4sPsXPGG4gwG+enpm8or6hsbexYU0LORW7SWphZGD/EWI1tdBRtaW2J4tpjR7MrwRnW5cwrysa7SAiur6qorK5IS47tk463EKgKz2W/3Q2pjcOPaNdkWW25B1yaLCcfUmsoqxgFpSQm0NNUWaZqmVd9P9Sx9oU9CCUMfEydL+Jn3cgUTHM32zP+8v3Ht+pLfXzk8w1htcXcJGexu52aXtcCWc0p8r+PDdgc9zBh20HC8weCakk1oPV3z8+HryG+f18svMT6RxWst+MfSj53EDNfX15eXbynqWmgWXiX2WUzbiC7pH+qCu0We/r//EcbR0aX4mfrQvs/CCTuKvNdhmvHpbNoyUzsl0XLEk5rcYMzMHbOAx2Bl5Qo81cxyclk9ag2K2TByAYiexEsEQZ1YrctBzRIfDKOZYXFp+WYcltJZ9pmUpPgHPUpYEqN6jYoqNnbICFj6p0JCXrdtM1vpE4na9keQ0JxJsNc/YItA4oP8VzEkFlc0J/269S5ySMXeGkQI1luKHGDeAojikw0rEPxF1GxzabwZwGEgAlWBheTS0oiIyjaxTYKD+bIfxQBoq5IbjXiMUcuyMFuKR/llCRW2eVzZqGIMjjQQhpb4hIVYZITZnd2KoA8O+EhYli/Q0sRMwZBKdB8vE6cMF2CscArGcegyMjeLdytepPjEscsRsj7kDfjYKI2tVwKsCjBjjoGRWhl1QmiWNUJwLp/PhwqlVl/CtdgiibYp68IEWw9Nmmd86PBrYM2rtHMyscTQsmmFppKK6s/nzHz9P/++6uJfnXPiCcBjZCglBDmKJgs1ZQUc69xoSBQHygq3lP6BrQruZ6WtICYVu6MIIiZoLwIBZYcuhRsDZiuLmYGqdB1ZIC3l8wXg7QYbm8rI+lyWsIEULQ1kRY/hx1wHi+pQFc0BR4D1DSwpQ4HHgmk0NbHkzGByyOaJsoUYxhlAcZFpINi8/L0/5wdXxdpDTItb/fWsTfabHEFLysbNTlegpWdhU6yh2ehL9YZDnqRwZbBv97G/Dzu7BS1Mh4RMAZbHgkWQXSeZYcO8QGUxlwcxQEr0KnYtUquWZYpfeIXUBwT1hbzQhHXaFMemBsXSP1Q7kT+dl9IfOyLDVjYHkaMRpHvtn0szWp2LDOf40VpozGi5jPP4wOYqXFQwthL2UKKxYqIinG31tBhgco9XxLwk5Cd/2y7BU1ClgUg3pPXSO1j6yKJ3cQcnnXQgIjAmIZ24GrVJEJWnvKjhShtAYqq+pN/RxlTarV91fgJRIa+AbA1T1l9lQFaoSiQddSdioEP4JMJ4JhlTI8KvaPv8kckxumO08IQKfHUF/CzSB3H+K1KpQMqrhlWROAf/z/4zCrXhjrYLVeBVUkkQhVeqlNlSC9sJoDQj6L3C3eGSwkpFYVBEwXDn034F7OiA9Rk4RKDRwCzQhUUMSPNnLxtAYkRSPrwABIBYBRkHyHQ/DJJ9cGHAIutlWwg+AhnDnvBNlkXiYM2sFxlrew7CCcGNmwDDCGG9yqSv0MIfgMWy1KkHbzWEV2ure0tl1XXXXHPi2NFMDRstbLeH1Q8pJO0AKaalHQWVZgzeVL20EVkNjZGWQREDW+m/fKOrIxyQW1bkItDpWML7lXRTuwUrFg/UYEDmMBiuIjTJByYKbjJ1QWHRRGSoxZJewVR86lRDVo0SgomihlgjlcgjyVhMvSSNNHuzNavnqB/e/WFg/xy7vQGJZg3ZzEanIbilKDczyA68plZj0MZeQSF7Wlm1N6Z3f7MzQ0sAaC5QGAtZkORUFkKOugnC6KU2wE2ELVlDeuQ3lgcRedCCto9EBguqnMkjSEZZNNq6OwgphAXIJX/Vq3r7/3LT7GbXpRUCtl2q9bW97P1faR9civZbodOTNTOTGlOfdd1IJUn7Vv+l/qhY2q9qegpO5CbxIhcdsO2N1ZD0NQqhyqocb2hRTIuJfiaSQMCqVsGzFkgE0KNEZVI6eRvQrX8j+UhiFaiLE/2uwhVoCeKB8kpEHT+SKgJDctzxghSR723fiMZPxrZiwRVupgL4I/jrr9SOfo6kVaGwOwbQ0hl0QQWgkO4QXoq17u/8RSWMlFBxPNm8QbbgFuJIWxBN2Yj7M+s50B9xxYH9IkJh5bJrkKi9kEgIKYSihag2yjwASVGWSS4jKLWpN8pKGPWSvWtk4wPqQoYSMHSRN+AAc2LxrB6nKoDEV2Dg4FJ1qmGSD7ghV6RxSHVJAK1NGhz/xJIpK3Nk5Qgf9GBSzV75cYWB4YWCVuLyaOXwBtGdiE8dg4MMEiUFz5HGwR9pyPIuBZYGycRUEJOOaMd4BUn2quAqETAEGyKrRiY0FYYOmpKjZMNfikycYIhpOiSOGYEiYGibjBJIaMIzghikgm0LaMEHQ5aIJffSWR9O//C5Y4dndo932TxVHJ/LmCwoexBxAgH7ecS2+lKXVdg86QPGn/s7kyURiSJ5QxChEHgBEJduVtKrPb6QwGx0IdoiPk4+c4g6MSNO/fwTkWJhUyE/ox2hLSgCQSQGcOgrTM9FejybHwABwStY0hxgDVIrndcBpoA0NHVFqK3f9cvWZ55011RtqS088lf/AchuVNi2SbemaQtXqEQBqjcNdruwdl/047b3bcC1+7Sz8HZRtnncMf72IVvfeZKOwdURHfQXDbyj7/rLwb0fEDGwTRFUqbkpMSAag7C5EI6E9HSYhoXtmXAf7tIlG/814aCIAen3ghjsPgqKeXm05cq6GkKZUCUcHgEvkY3NeIE/wf7wb2NAwPZHCAAERVtyLZMjoIgOBqopC4+VOPyHXavde3gRboToEaapZsXw4VS1KgYQ0V+RBn6Xz4oCbDXhAUC+NqSKWAnDrT6vw8bEmxzSIn4C+hL0pNMoGSOoqmCFNCIA1xq2YcAbDx6uDCvkTSSRYIyHxK2JS8QDl3wQAKIjiaQU3GWCAkMZ+nZtXR0vmenpZaVlcfFO5R2L+s4OfYqM0EVWp8ol01cSiA7WULruu/lT3sy0u/ISLfHWoBXTECMUEb5sY2lqCaen9Dkue8CxZnuiwkRACT2E6MK+t2yp8AS8bEsAunaL7P+ubAhsLIqhj+3KxSTsx5LHCgXmHDnYg/kc1Do1KCELkWOqUDjcim9p2FDTWIchLzk5mb2T8FBkDwEpZufVSYFOChwwCkT4ywGDj66IjR3Gh3eXdGdhHzxjLOYIC8WSli9f8cq//gWL8Qv/Q5EVwzKvjACF5bVhJjYds/HLL774/PPP8H4AlDhNoP0agzJ8MJrLm10LVq0TMQJzMsmucrBKOJWw/K1weEMTRw0nczgQLAttFcdkdFzxIJC8+Y4VxcS6Q7s8kEIzcSDCvEK4u4X+/sJzS1eskE3gYdyRzY4MnGjxz+ef31BSQrYYi8TEg3TACq84o8pGCkWglEnAUn6z0WJjBX95ZQ1qL04PkIdg4fNi2MLlmklUJRwVGjBvISUBEqaoEBERrGq0TJs+6423/wvB2blhxtx5GJMwkAFPLgErALnLSMRsXFVcvKG8LGhy5vcee8qv704d/rPmnJOqkyd5M8cYCo/1ZE9uyj7O2u/cXidfnTf8tKA9WRGTKpGf1KGQVhB46+PPXnzjvxCB3ZMQLsg08qJGlIwStR8SW/FMVU4ZIrvkqBBWhbPfCIVEhlMwgYYVSdC0mKZNnz7l6y8hNeJNNjNU1BKKdV6dFOikwIGhAGziAF5i02DGOBjCHyA+Jla2T7CYPV5/dXVdfHxMYmI8LBKrOQxKZkuZIzUaKysq4cFJqXjmYPEPsv4FOyJbcGA7Yd8s+LfaoVlwbmhprauvTU+NS3ImwU4+njZ17ZribldcnhxjYxKgORDYUlGVmpKcFBMD24JlKYlibPKFWKDb1FjX0tiQ1yUPTxa8fT0ef2yspbqmgY1WWOsIo95cWQ2f7pKRIeZ/xfVqGpo4LyIxOdUSF3v6hednxcWDLdu4l1ZUxpptrJw0Wm1nn3d+ZnIqAxI4LZu8s5VcekYarlDMinqDAWYmWj3e2uqanIyMWAcbjopNpKK2buZ388dPnNji8dkdNhbdykYxacnY6VkPUV/b0CU7E5dqmWUwGdyBYGllFT5nyXEcDWdgY/WqmuqUdNapxIiwYZtohhVGdjnF8K426RN5BmWFcbPli9fnAxpVzu7s//zXG4MG9T9l8rGy4jgmt/ewPCVMjc0Vy9mtLje/h4HZZQxJwVB5TUuTy1OQlY7bFQYfTGws3a6oqmYDnPTU5CbsURZ7i9dfV1uXnSPAGYYp8z/DIyM7v+OgVVNb47AZ4+NTsB2xtpxVbOlpKYgrmdvAYIiv1+bypJTklLg4qhXpgL0KGYA1r6GhOTaWNT2yGq7zOrQUQEirEeShxaIz9wNCgQMiBpT2KdPE6MrF5Vue+sc/N61blRIfd/edd8TGJ//10Sc3lmz0GYLsOT5p7GjYLAYNeH11Y9Mz/3h67bKlTCTcfNsfS8pKf1i06LabbmaS6tNPPlm6Zu2dt9wsbj6iLBu+mT3nX/96neMm4hLt91x/a0OL59W332Hd4wO1jXffemN1XfMjTz5VtqkUMXDN1VcP6FEkCiwZhcMPPPF0XHz8+hXLaraUjxgx8sbfX+f2Gx585Im+fbp98OEHP//5z08+bvIzL78yY/Ys2OK5Z59z3vEnwGGnzZ7NOnhPQ9OY0eMuuf6qx555+sKTTsVf/oHHHm1uaIStXnzhzyb+5Ji/PfWPKy7+Vd+iwo8///KFl1+WncJiY6666qpB/fq8+sablVu2uFpb1q9ZW9Cly5/uuIP1AQxmFi5eYnUiEeOefOaFxIT4ubPmpKWm3fmn29/5/Ms3//0mMwa9i7r/4aYbOG9wzfq1jzz+WENtY0Fu3r133YU4efzxxxsaG1G3r7vuuhEcbIDiLEMQlGi0cxRpOb2JGQI2tX7jnfemzpgtG9gNGXL9VVd+NX36p9/MWrR6XU113VWXXgxt6ONI7H+//vbX076udflye/a//trL85PjPvrg7c8/+cTjCyVn5dx++21I7k1l5Y//7W8bN25My0j/8/0PmJ2ODctXPXD/X1evWZHbLf/mm27okpTMSSlIuEZ388NPPde7V69P337z0l9cOHLsuHseepi1xziSXXD+BccdM3HZ2jX/fvM/CYnxSxYtYuXzH2+4cVj/vrhaienNYJg+a87n731wyx9uzMhMj1rYDkgn6AS6cwpEvHrodDI+1bZEib0zkaBrSsPTkoM7geIcrI2TO8+r88uhosABEQO6MKLjh0IPPfoE+0k8989nPa5mVnt7/aGLf/XLrJzML2bOeu7lV0ePGikNSmwFxlf+9e+6+qZnnn7a5fXGJcQvWLyYjVvRCkGRXZ2ramsBiwBAP4UvFxR0ve2O29n2557HH/3o4y9+/eufHzvpqObmprsu/63N4nzwb4/k5Rc88Kd73/v448efeOIfjz2GmzrmetTvtRvLGAE8dNedjAke+stfv5j57bijxy5hU8zGKjZt79Kly1ufT5mzcOGjjz5W39Jy+213jeo7GGeZh5782903/2Foj96uZjaSCrMzNi7z73/8aXxS8hOPPOJpdfncvlaPb7OsDwg1ujwzZs2+7fbbuhV2/WjKlPsefui5Z/5ZU107fcasJx9/ND8359lnn3/3ow8v+flFImBmTh8ybBjlKt9S/tUXSx+498+FXQvYcPGVt9669567e+Xn/v2Jp6bNnDV2/Ni7H7p/woSJF/30rMb6JggWnxB39XXXgvAzL7366n/eYeEl/F+TCGjSA0UxZ0iFf7dp9Nixx554CgOvyy/7zeTJx44fP753v8/OOu2U0yaMYbJY7ExW2/JlKz6ZMuX2Bx9MzUi55+GnX3v9v7f97hfDBg2cfPS4+JSMp1554/vlK8aNGnHfgw/17dv7zjtu58Sr5LiYQNC3sXjtHWxal51x7Z23ffD5p1eed6Gy6DEKMS1euYpxwyN/eZgt7VzB4M9+9au8rl0XLl36z2eeGXX0UVT0lK+/uveeP/3+t7/758uvPPvCS39/7GE2kDEZLLOWLH3+5VfuuvGG9Ix09oPqZCKqbg/BTey3DDTVpYaWtCza104venM0AqN54mnh0SkDdkqyw+CDZh37GRHdDqh4DmPbUFZ2wc8vik2Iz0hPs9vtsXGx6P5ffjlt+Zq1rXpFAJkbjU0e38xZcy/8+c+TkhLSUlNZiITjDaNQtHj1HbdacdRhXyCtkLDwikOL3p8yZV1VfROrxqxWhx2rkYmDhxqa3PNXrrfFJbz32ed1jc0bNm5q9XrgiTReWrTDZD5+wng2qEiPixnQp2j1ymVqytVw+kmn9MnPZyZgzsxZqUmpc+bMW7hwcUtT68p1a2cvmJdT2HXQkKHsYpSelSZ2bLMsPes3cOCqtetefOPNhqaWtPRkXGLZOhMD/OrV6+yOmP79+1qczpNOPhlb/Kp16zlN8Oix43v36M6SOiZU6xobwYfNKoo3rGetL0VjV5bjjj1mQL+eibH2xT/84HTErli+6pOPPm9tchevLt60rri1vvns036aEBuT1yXDZjHFJ8RzPOwX02aUVtY0uGR/SXHOF78hxj3cpa8qg5B4rCYmJCxZvuLLqdPNdmdNXR3ktYeDSXZLAovomJAQR2fDirLNlrT0b7+b88XXU80G/6Y1ywGVlJ713cpN/5kyY31FZZPHXVpVs7Gs7Oyzzo6Ljc3NyrTLUoHgqGED+/YsTI2P7dOvL/syUZtM8HAzy2l/wbPOOTsvNxvSsC22yW77cvqMJUtXuN0+bGI46RZm5x07Zhz7OPbvP7CuoRHHXrZ9h1wPPvLX3119Ve8+Pak3OBEtSiMpraHzOogUiPZlOi8MnYvq2MWl4+g7vZJLi5CobDiIuHdmtbsUOICjAVCAV1qw7LIAmL075Lix4IsvvcKmQL3798G6A+9huCCma6Z0A0HOALI7OeBG9Af5hIdomwqB4YhnvjCbCKS6huY//+Xh3K5d0zPTUDktdtlAwhTy2+HibO7G6RhhkzfAvuI+R4zzyt9eyR7UANSTlpZwIFaWLYmHipl9QdzNZMJBk8mxccxkYNf2NrrYZSjgZgupAPuPsrHXe1O/kDW6Mj3Leil4q+DNWtljJx6VnJYyY+bMp599ftSI4ZPZTpadRgJBNpNiJKRKB3Ymq9WGWgRfdNgdMGdMN2KsV26sH3z66eBBg3LT0rBWsdY/jX3PmSXlvECPN+D2QRROtunbvfuQAX3rmhqclJOZEiXP3IHA408+FTZZi/r0hrP7Q3VCHQArTU2ooMSA+D+ZbGzg/ty/3ho8YozNEQOR6cMY920sFBCaUEdQFDdcQ1PA4DbjXRr2tjT27Zbb+5hx7FT89LMvFA0cEZuagkWAWfXm1hYqjgqVhAyuKIvfl5gQJ46yPFNMvbJfKpWVAUEOQkHws78e4a+98fqSktL+/fuzSg7zP5uWh72B1LgE1l8ADq9ZZZcSW2L55s2M8xYvWTJiYH9WXUdtCbCSthYhJey8NAUOKFlg6ORCFt9888369et5ZXDGRSB1QThXtFL0K3dh/+z56PcXFRVNmDCBCNE47WuNmBpO+8CD/9y+CPue+3bQDpMy7rpcB2Q0QFvRtZ6Vlppot3095Su4W7M/XNPqeX/KV+MmHX3RGSenJyb6W92cc89m9/gKxjudffr24RACVyDEoWMunz85IbFySwVnQ1f5fN8snI9zDCWBveJas650y9JlKzi59Oenn5ZrsbT4m8V2xAFa1V6OG4tNi+ubkZbgdJ531k/PPefM435yHI6kNDfSc+ck4qnfftcSCLFf+4aSqp69+uGzyewEjRqzEXvbjh49kpCTTz7xovPOOOPM01IyUwf361+2cm15RYXLaqgLcMpk0O73xeDl2ewa2K3olksvOf9n5363ZGGr3+1ncZsx1KN3z2aft6KpBb+XH35YzER2365d2eRLJIDS0kVdRph5fN/P/+HC087QotgbZvWzsELkR79+fVkVPW7UqEvO/+kF559R1Ks7G8e3+owz5y32GkxyLGRT85Svvzn7gp+ddQp757IgTzazFeAsjpNiWlnczgN7ZjPT+82c+WaH81fnnTFi1FCPuxX+bTWZPGFjndtNHHVCoci2osyscF3t8ccec9FZZ5171k+HDx86e953rR7Xb39x1oUnT4pj9sbnKyrIY5/0mXPnMOvQEkB3N9gCFlaDe8TdyuAIWWI8Qme2JcWOA3N3ej12duUwG1uMxo+/mjF50uTLzjx9QN8e/oBbDtk1hz1CTyYDwnYWg7OugE4TDPxk/Li/3nXn1K++fP/jjyGIknBil9h1U/7/+RVWC1OGONwhHg/oHFya+2ia6EDu7QN3n1ykogd9/fXXv/71r6+44oply5YxMoDLE6i4PcfeyMUzMJubmzkjjIOebrjhBiJzcYASSLbPGvTAVqGJHhhBWOMcjUa4vqIhu4/w7sRsAy9/o1nzvDtpdxYHVDVY/aDrRT/rwkYz0hD4RIhOsjOYByf8wI4GaCnXXPmbxx5/4ocF39Fcb7zx96ecfMJLL77w9Vdf0IK6dsli+x22jcMwAWO84tcX33PvfdffcCMt+rLLL+NEaXxD//jHPzo5AdphT4hR26LBwiymPt3yiwoL77j19rS01JCrJbUwH3Z2zPij/nTPn//84F+uuebqm6664oFH/7Z4wfcsJO7fu+cVl/xSayOIAafD1tjUdNsd99pkCbvhJ8dMpBtxkpHThsOiDF/OPev04uJ111x7XVJSCjaPm39/7fB+fU878cR77v4Tim1Bfv6VV1yR4BSU0Y+mfPV1alY6mxodc9xkp8WC7MFdNSs1ZUD/vrfeehv72ZUWr7/ysl+nJ8azeBb/UsgNU2PJQXyMY+F333FsBdvbIoQoPn5Qss+OaF6GkcOGHHP02FtvvSMvIznobb3kkksG9h/w2ysvf/65F7766L8YvthH75hjJt1/372Z2bm4rOZkpAGWtc0O2XTCwLQHnVK4J7v0BkPHTJr0zX0PXHPbnUlJyX1790QEsdrumIkTXn/9X+66ql+cfxZqPGJ01KC+M/v0uuGGm7KyMqurq845+5xJE4+e8sXnt9x2O92enpzgtMdarTded90TTz4xY/o3iHpOi8NSwFiKrMiOMZaYiZTvL46yDGvibGYOEUKdJ84JJ/zkxedemDlrOqvEu+Z2IZTV0fg40RdIzrPElF3MMGWF02JibrnphocefHBIr569e/YU+damWxycjnGk5AIT0fwX+kA3rX5Ja28bQ0UDeZBqagvfzQISH1YF8N/85jeffvrp999/v2rVqgceeIDNsnYBwe12b9iw4c0333z22Wc//vhj5Ef7yABkSBrFhweN/I7oReO0T34gnnUZ9x2yLosuiCY1FaQfOiwjn/TXfc96XyBsnc/ZFyg7S0u7w/GDfeVWrlqVlp7OlCll5hlloFtR95am5vS0NJRFd6srITmJ7RXcbu+q5SvYf7xbt25oGy6Pd9nyZUkpKZwh42p1p6Uk4xhDA8IK4XJ5fli0OD4+trCwkBNc2Uuds+K2VFRs2FDMaY4xdntjq+f7RUvgrH369Epkz06xm4sL/yXX3XjyyScP6lFUVVrat3fvxKREzk+vbahPjWUb7FjUKmyfeLhyFAx7sjFbm5OVifcNBqr169bV1df16dOHbUyra2oYrCD7V61ezXEf2V1yuhYWstNRTU1NZlIifBkP0XUbN7CtW9+ePXPSU9F3axoacXNic1N0MpfbxXZaiECkX3JcPAmx5Vc31HNea0pCAgeRw8VdIcOmktLqzSVpyfE9e/Y0We0sheDA2LLi1V2ys7vk5tN1Fi9bYbU7u3br5mpqzEtPwD8V1TouIammtj4hxp7gtKGjY+jC/3JLTe3i5ct69uiZHB+P3s0W7R6TefnyZTEmQ98ehep8ArbZQEwZ16wtrmTHuqSkou7dMKbV1NSuXrU6OycrKysbHY4zc9AB6+vrlq9YlZeXm5uTxeZ3sBb2dqe5N9U3INI4dY9OJW5KJjMnnNgdsPoYtFOcfdetZ6e+siFDBhEtKSE+4OXwykYWA7KMjZOoXW43gqq1tZVGwkmf2NOqKytj7bb4uDgqnV50OHSYnTX1QxIO0+HSXBUEovSJPkSxonY0G9rxUzROhw8kJAtSkXz69Olnn302jfzGG2986KGHyJdwqoaExIkmJ5BXnd3UqVNfe+21J598Mj4+Xkfgk1aTvco+iTghhFxoV1EIGqDGeU8Rbg9kj57JjvigvUep2kcGgi477lXIZk0EIujA7crIV/1JF7M9nIP/fIDFAHueKcmP6geNMRaj9rILm2wwI8RhT7OAjcOHoQQmarbDZV9ZQlEnWR2gGhONTIzXajtZ2BCVRASSA4RospGt2oFdjgy1QHdZkYs/DMdp4j8vC9ZYyaXap9aB2IjiV1ddc/IJJ154ysnQGlbMgFCtALPITnNARKkV+CSUyQDEBuedEkylytSY7OMmTVayVniiw5KG8S2RmfBm41I4r9vrszic9ANmGvga9nuVK6ccAyX8Uawc+LwKWqypZUt0vDzpZ3jIAJsPbFwBGuxvh/hh1hcAYMqAgXhAYDdHIRZzDOj9zM+qF0ph4/BbDogxWrD7Q0Y5QTbkAzl2ycP2T0EQQmj00Fe22mAHItncjZV3QRPeumL3R5cEIEXEaYtREcexBsiOQkBGhhS6TTOU512qgwOCGDzJfomyXQdSDbOS0Fs214A25AdSgoksWJPFeaAuZGCTQWEQrJqGdFKt5M8SAmgBtYRcPIMeAQJKNsbgLv1TIbP3XVQg/w9dVAEE4f75558PHTqUDTp51pwUHsTZMpSVEH0R3rVrV81wSbVHZKDFUl9Ugep5RnyUr7/+eo73YiiMvkUjIZwrmp2OqSUEmOAXgCTghBwOGYzmSxyaIpiPGTOGfXZ55drEBroeD3F4Bm0S5ubmgjOgtKSJJt/3B2AWFxeDAw/kBU2QUvl4iOyDDAArAAIBgCtXrgTz7t27UxZNcF0RuFlTRv3MncjZ2dm67va0XvadCO0hHFgxoHKSPhy56Ny6p7eNXtW7kAUq6Dvdn8hbQ8SlXb7CViQ8AqANoIoZjc8D4InDXz2XoLPjrgLF7P7cq69xDhFn7GFDEUkiEBUDijglkVYhoO68wJHa11AUMZnylowiOegMVGQ5/wYdnGQEqlEIqjF8TZh4WxklITyUUmEakReuaPcUPig8lczVTIHGnc+oXcATM2sEJVh620e1Wlm4rZqEFk5KWunyEgFGrFZn8yzhKHHseopfJjmJt6i6FKeWXGnHoCVh7R4kx0heOnrkLsJbCkqbjtZUBD1dXyQCJsCBqYokdGkra7uaVflGygUfiBQQCihUthZ5m9z/n77AfCk5TAR2c+GFF7766quDBg3SYhtyscc7avvtt99+1FFHnXTSSXA67PUYc4YPH37mmWdup3TvKQXhYkD+61//+pOf/OSVV17BYY+q2RlMPnHBCuGPTmfk/HVy5PXDDz/My8sbMWKEZvoguWjRIgYNmzdv/v3vfw/fpxRLly7lJE5wRiTsKZ4dxgcZ3ZwYiMydO/fuu+8+7rjjEGaE19bWlpaWnn/++VgXOky7O4G6sCDPdMgnn3xyzDHH4M8dTUgFUUZIBynIiIIjJhcuXNivX79zzjlnZzSMJj+wD6B+gC+xB3Twk3lB+cnegttEUMqqxknU1rbfNnG2AmRuTHTSHX5boYhuIZeGxK7VHh9Hk3GxgzJHRuK5yKkF4ICiz34WGp+t2VJbHV8KoIYcuUsG/BeAKom8qR9zdz5VWI1C5M44hl8HlNmmpLLBT9tPlZoN4+QHztullWjtikBZSMuP0cP2+HBAqiqpjhCBr9NuU6J2Lx0TAbozHtv210ZDoWTbJ4xq+kdNyWasmjIKPNTQtbk1s/Z5bQ3tfGqjAOxDmq/X+/e//x01/8svv+QLnFQatZooZjRA+FdffdWWIowW37t3b9hTNGQvHjT8lpYWTENwpV/84hcwayprT0HB+5566ikNDZHAgwZyxhln3HTTTVFo//rXvxAziLpoyD4+6Fy4QytIccIJJzAEASav3BGoDHT2MQtdCwCZP3/+P//5T/KKFlN/Ov300xGf0VyIdtRRR+1jvUSh7fWDKGiH8EJJRFXmJzqrukRzVj8JiSiqkU8d/SGGKLA7/jC3yA81vA0If1FK0ajxecHjE2gqa+zy6ofKvPXXUVbbhgm0thDgyU80f8YUYmCRjzpUwoFMtipQEkV/gnZbrDZYGlTbm4wqJA4/scByRZ7Fbyqav4STL76s3OVFvZIpP3Fw3QEfpaETS2/bIAl12h2RUcB2faOkUpAo9QDFhhBqTwgdKDRRxW/DOYK4jC5k5CNDJamOzms3KcA4AJXzhx9+wKSATl1eXk5C9FyYjoaA7s+sPnYYzWQJRIuHH8GJdjOLDqNpLoMP8D333IOdB3egZ555RuvXu4i/4yd4H0MTSkFa7oDloaKiAuk1duxYXjWeIExaXneEsHchUVTJdPXq1Zi2MMhAIsQquXCPEnDv4JNcZwE0hC6H0cLfAUWgvpA6SDWsTxRQZ8dADQT4unc57q9UEa6xv8B1BCfKAjp4UPxIwqMPUQgRlrSVPpobbncXU4aOud19qywRLrP1wrShyiwiAnMQyfEVVb0HOOqLirsVGsYQ4e/b/IQDtwHVf7m35YitRVDSl3rYGZISrvY7paVv81OBwvFBURVQqBB5VTydfkO44r86H/UqaEEfjZqkpWjCfzvAR5dUebBuzULSqowEwva/bSmwlSAA55NgE7k0EA1HBfFlaymisUgFzVQu+mu7HNvl1Ra/8+9WCsBl6urqlixZgks+8oBnvml2qRnZggULMKewSyumBi6Y0UcffXTBBRekqa15twLa8ydygW3hKMG2K/CyP//5z7Nnz94ZGM3dSMIVjUNyvAA0KySQOBSBBw6kxL8IE40OwTr08ssvX3PNNaw8iKbd9wcw0Vghiph7QKTxitFp8eLFjGyuvPLKfcwC+iNjAAJk5hsQ1bzqSiEjzFwUlhLxLNONJtNbb73FBAnSKJpve1pFAw/0Q5th+kDnsyN8xTaEV6lP+s6j5rXyUycAAQAASURBVE88REOiT1tD2kHbmRzVPH37JBjj2QQa9VhmRGFEMKCOLxLyqS1529/2cdvC9F/uGhQwI8nUh21jtU8vW65ydYgnINoSRnHQaZV9PfJN/4kQrB3d5DFarp3jI8n5H42pMpBZ3A6vtlLt8BGWDRx13+Hb1gCd0VZkIgWUYiICtwO+3etWKP+Pn6KshHYLK5kxYwYcMyWF7fji4JiaMLAVLtRMPRPQ2NjIIKCkpARD/MSJE/G91hxQRwbO2rVrcftBThC+HfcBMlZywgEYpToxo88sC5g5c+a8efMw5f/3v/9lXEJMgLSPT+RojnzSX0EJ/JlB5c7XaLkABTdEYmGj19dll12G9YlUlAjJweAgISEBX+goTEJ4JkJ7xDSGUXbM12h8nqOvSEoGNKyB4HhUssO99Y477kB2ItsQVCjpsGn85aLl3dkD8SmyzmK7sjP7zYI7PukLCN99911iYiK4VVVVQfmpU6dStFtvvZWaQgghthFIfIUyO8vuAIVvrdcDlMGPgt2O72z3+qPJdxZhV3DUN+qGtLuItotPHWa6p/E1kB1Z3t7C2Qap3Qey+zG3yaDdi4bwo3B2FmFHCrSD3fkoFIB5Re/wGrg8Pic4CMG/aMbc4R3E0fwI1gN/P/XUU4kGl4GpnXfeeUOGDAECPEvAqYv4GCiYGiUtLE9bYPQnQmCIBQUFAIzyzUiytj/wUNYE/OxnP4O1/eEPf8AOjv6r8WyLss3fKCtkSgODFfyOEI02D8iGadOmYRHiE8+IBOZR2ZMR/k4cioA9neECMwpEjsLlmRyJj4UnyoI1wro4yEgKEk0CKP0MC6bsiEZCSA47ZnzDfDWpgIPIefDBB0l4+eWXR/Pa2YOGAG4IsBNPPDGKBvGZ2FixYgUPZMoFo2demqEAkcmIQjGxj6ijyC+++OKUKVNeeuklKoiYO8vrwIUfejFw4MrWCbmTAv9LFIDBwWVQVFmQhVIJ8+JVq66we1g5LEkPFNArf/nLX2qLB3FIyKftSAG7OeWUU3ameEaT7IwrwbAGDhwI9z/ttNNwgGlqakLB31nk9llrJKP6O0nAEGUc6YXrDm4zRAZ/FvcgvcgCTBAJxEG5jqYiDuFcPKDOs8IZbk4cHc6dXPgKz+3Ro4cO1580hiAMr8eNKicnh3BoyJgGkUYWUJJBANGiCx2IsOsLoxxyCByYc24fU9eIDgEgLqqsr8bPCg8icCMEYUB2oEdeZE19EVkXqj2cg/C8T2JAF+YgYNmZxZFOAd24dSc8oGWJtsnowwHN7uAAh25RAuKwf+yxx44bNw5OBwdhZvX999+HocB0NL+bM2cOmiyKMEm4tADQd7AlJFoL7733HkuCMbPoQO5E4CssHkUVl1AAto+/Y2HRZFnp+cgjjzA4INXOhIrOF1AABE/YrlZ7eeWBrxhn4PU42mscGM3A1onJJ2BSOhg34xviwzdBWGOiKYA9Cv+iaKH0J+4aVPSVB10cHpjPYHICbZ1ngOjBBBkRgRIh0srKypCj5Mu1a3dVkjBLz2CLEQyg2lMA9R/RqPPlzvQD4k321FIXCJOWMoInUwgjR44kBBmvx0k7FgcIB+7aJzFAsUFXY6zvBw7RIwgy9Qo1uOu2rjFv3z6OoLLsEar0Gd0Mtius7pC0Fh74tH+biqZ2FM8ocJChmxFOvmiR0fBozCPrgWJSCmw48EfcbGhahFBA5jmxLMMcte6PNycs9Ve/+hXlJQ7U1tFIux2hKD4zzHAfHaE9NaAYow2AA4Qr+olwnqMAmXWA/6JHH3300QRuV+nE1O0fODprDQpMKAKXNiLpQCxLffv2RSPWwDFnwUCZKmhoaMCIBGfEmI7dhhEDc908IKJARgswMmqPZBTbHQM1GtzJbtSoUbBggPDKdDEjAPChRAgVJCs4Q4Fvv/0W93+c+uHyGhqRgU8qCqupQUxCCKew0az1A8jrdRU6LVMpuoyaLDoOn5gYx4OIWRCkEQUnhFUF24E60K/7JAbAmPJz5zrQiB4p8DVBuHPpjqGb2pGC/77jSWPA+okZlAd0Ug2QzSiwvWpNZ9+z2A4CKi26GPwCjRJtDkaDchplcJoVHulNFPwxNGNExo4RbVEwRwKxdLMiF8M0ko/ZYHgZUoE7a5d0TN0Ot6MAr1od3o6Yu34FoBYP8Lhrr70Wi9C5555Lku2AayCaRZKk/VcqiBB4nzb1oDJTd1jGUZNxpIHFw52ZoCYapYBBs5MKc+C8koQCMnGtweos2kPeNeZ8JSHjDFgtF8MdHG1pJwTiQIVzJ7nQbjGU8RUEUMwZ6CAVtLABZy46tc4FTHgmrR6ydJh1ZWUlhQJDpg1YKsEwbvTo0TjFMhAhPuEaAgYxmi7PkAI3U147hHZAA/dVDEAILQZ52KMqOaClOoTAde1y5wINmovuhIcQpYOWtS4yvQWPjnfffRfz6/3330/xUf0wZ+Mbd++999LB9i8+ZIriRh/GTv23v/2NETraMYZaFh/ddttt2vgb7b37N+uDCU3PATAOgLb0OAQqdIZRoqhScDDhmcaGVouo4BM0hzLR7tkhqrrn6lprH4EQTTEeuNp/0s9w5LvuugtBi4clmUatNNvFBAjJ9T36icio+czQMoYAAY0D7YQIGmeQxyBDHCQcYx2M+08//TStCMvJpEmTWH5FjlwdIhbNpcMHMCEhqslzzz1HBCgGEEIY0zDRgkpOiQhHLEFtRh7HH388HqukwkxEIDE1WHCGsOBJdSAnOswLKQKViEA0xnCweKadyQ7JTaE0xYBDCMYixACyB4IgBvR0yF6UrkM0djeQQm53gbcOiT7wuuMzIRALpQO9AALxyhWNqZ+3g7z7rxBIR1ZQI/h0mHwfM+oQZjTr3fwaRVI/QA0SMnNFw6IgXNEI+lN7sHuB/y6S7OzTjuE6ZMfw3Sl7e/zbP1NSmgQXjR77NZ4kfNVZ4OQOh2KLyvbx9+Mzm52dddZZ5KWzw3lGu7KQBTTnin7aj5keTFBQVWdHQSiOIrOsv43ioAsefeVBiq1i7vhJR4u2TE2c6J1U+lMUmoag4EmO7Ihw33338UA0rclGY7Z/ID4JaQyw0fbhuDkBAfsVjBgI0U9Eplw6dwJ5BjiygeXKyPV33nmHrXbpVkDTMaMJd/OBvACuI+sSEaLFJ4E8cMciBPdnWPCnP/1p2rRpTP+CJ2ig0/AAc9d3cOAB3DQcxjG//e1vo8CBw/iMORseCGwfTgjlIhVZ6wcE6n/+859Zs2axFAOYsFMNk5gH7bJQTuhLqZC3TKwjb7HKgTfiCCQQehoVQrR5kTvYI2SIzye2AcF2xnifQF5JRRK+8kyRuDMJrmNSNtJGpROvSEs0REZMDKwwGrCMghXwhBPyj3/8A28tAgEIvYDAnWf9Chyiadz0A2BBQIto7joCgcTXWGkg+pUQLhICViC2lZQHjTmfiMAzQzkm3HRMZDWal86Or5pEKrVgSAjPANQ48EDLwLB4zz336DiEI/C/+OILFqzzzEUqjScRdHYa4eidQKLpO3EI5yIVd0BNnTqV5GTEYlEmDFEP0YjZPwCHB1JFi0BCDUSnJQkA+RoFqB90iH7WyJCKS7Lckws4aDrUIBfkiibFR4KsYc3R3CnILgbU0YS78wCjoaawF4M/YKEJZlYeGNTr7CiILlS0aLsD9rCKoytOVxAFpCD6OYpk9DX6qcNo0fg87Kx+SRiNRoXyDEx98Ynto+Fxb7zxhg6nEnUcnSSKBq9UAa80VMxTgwcP1hG444qKjo9+TZ+Kxgc+zxRTRwOmtD+TCYkOj6KR6zrFZsJAAY2+faZRyLt+AJouRTRTlUOkkevWiNWI8RZaOTYrWArqC1ljktKTvTvC12wHIQEL5SItwFH/wZPdKXQpotnp5LqMBPKV5LReRgBgAm2xTSFaMCXtmNEBDTGBBEKPoTp1wwQ6O4lTJLIELegCZlzQDtRBmgfCIQqfdNkQodGyEchXHY1Aqv/SSy9lzp2YXHyKlgQ4PJMWIc+8ENyfsRIcE75JOByN0Rl3njUa3DUQoLHuQ4MiI3DTSOpnzZphQ0QAYR0NIKTV0QihaRKZV0oRbXM6Dnf9QHxdZJQCRqY0WZBk6A19CCc5cMhF04RXUvGsSLV1/pOY5MWdi/jcMcUySAQ48TUEjT/PBGqEuXMRTiDRVOqIOZJXHcInrOH0CjwrsIGgdDzxxBOUnZaKICeJhqaBA40QEoKMpowGriHzFWg6JneVg+CmL/26R3cIS3wEEro/Vl2eQQYNDksFm87j6s4rWYPMHoHddWQ4BRe0JRqlwDOPTQ5wZh8/fjwZRempawoEdg3tsP2qMW9/7xDV9gVs/9xh5F0H6kbCHThcUJIpHzQ/bDjY0zusRB2o41MXCAzc4bU1PJoXFYFvJXfYJTF1ePSBV3LUgbRDtGN23aE50dppP/BNlAldrVGAu/+gESN+++zaJ4cp4UhKvqzOY9KFYmorXBSl9pEJJCZzAHAJdFYKS4mIADdj0zr6IxG42idp/wwOFIR2Sy+mUChzNN2dWZnaJ9zvz9SsBYWdiRE43YQJE6677jrkGOKInowxDqIzaYP/GRkTDh/BD4GRERKScAJJrnsXBhDIobfro3hQh7oneXT9entyEIFUkICsmWVCCPGMT9hjjz2GHIYoqA9M3AMfLRKBzAOT7AD84IMPUA1oGYw/qB6SYJVCYLDwhFcQg+MgxkhFcpwBCARDkiA8qBsYOloJuZMQ6ySGY4YgNFZCyCJ6wVI1T6QUFBMM+QQcBBsZUSgoA9+hmnlFa2BYQ4tBoAKTVyaCKDVAiEmpkTcQjdolPjkCh4GnTgLR8CFD5kEHskDHxyJJNHJHNEIKAtujF6UhCFMpP/3pT4mAzovZkdxhweTLJ8CSNRRGR8YQSRkpCHBAj/bKAy2PZget0D4QTvRtqgANi3kz0AMCMUGbjgf83b+iZKSpkBwhChHoV1AbGl588cWUjikyagGdIxp59+HvLCYSkWE7xZk+fTr+GOhTuK9QauITzlcaM0MTtEhCoOF+zHpnKP1vhEMoyMVFcWg2aMroapiDohJXF7M9PWlpBNITiYyZAXmMeX27CSEAAo2+oyPvSKtovrRhOqCeaIVBYSEgLamIwLVjwn0PYYACENgRzBB7huZChHSYHYFgSLtiiQPP4EYf5E7H4QEgPNOhdoYVBeErG+ppOuAgBBC6/87iH7jwyChJG7ng42Cv8cCmARXow8zPwDsoLYNBLgLp2yzng7WBFuFwYfga5i1NCI0rBYN9w3NhLnAoyMHX7YpBCJQia00vRAiMD42bNoTGAa9HNpIRLQamCWeBvyNs6dhcwGcIBjvmK5hgRCILMkIvxs0LDMmdZ2KSEL9mikAEYBKf2Us8pqlgYvJMEUCMr1H0iKNf+URGPIMhWMGseYUC8DUeyJcsSMWcJM0dZko06AYycGEoqVkqXhDYaoAJB2RIQamJw2Qm7IlAbIL0Fh5ACWTgzvBKnqNY6QeNG2nJlzvx4Xr0NFgtLFs74WCHwVGETwT+5S9/wTMBHBCc//73v0kOHNDmKzR89NFHKQXVjfwgU2QYXxmZURDgQ0boD7QoQXbzAUJpWjHrRW2SO3oD87c4w7FaknYCfPgIHhqgQczdBPuj0SAvonfSpEkMrlEYqXraMHVBMwAHhCJrdmgt0A1QhP8owM4IUQpQTTQJmgeNll2JGI4jaO+44w5Iyp1JeC4e7rzzTu6cRsfSX9jZxIkT4fKYFqh0ah82Eq1u/UAtoChQO9GMtnsgUy5qkKZC7rrWSEIgIboqt0uy76/gpiGTLzmiRAIzGrgjfD5xgQ/FpCza94loUdYPtrtGVQqpBCdlBAgQDkn7lNNE4ZXwevCg/6OyyXleamSkuQMaJQwL9R/8kJBayYJMqMP0OpLD3TAUUvfadK6JBTScc2kBSAi4LZARehQySkpdfijIxTPU5BPKMpo7aaEdF6wKZofyGDWWwVzgsIAiDqkwOsFZkMYvvPACogIMYToEApNuz7QhX+ERRGCUo7MG7c8++4z5HNRkRpr4DEyePBltheza46bxoWLYxZf9s2CdDPqYoaLgQMMvG/g8sPsV+YIJMSEdXA9pQV7EJ5BWi4cMBKR70KSASSAXGTEIID4PlAg2TRVgKoW88DI0eoY4ushgpTGJ4qYfAIJ4e/vtt6kjyggoKoJAoZoSFVAAOtAcKSbw8eoDQ+5EhqR6NAN6YMt+7pAdsEhZxkz4HeKlh/AGse0y/dFXjSq1RtUzw6Z1RgJBifoiOdwZMqLfdVioH4UfjUBy6lqXlAZDA4P7QDdyOfbYY8macRsjGx2HTCGmnnYCAmm5SBuF1vmwCwpoQtG0UKTo77QrlI9dEJBPtDoIjgpIdcAusA1qfUjnomsNOLvINBqTDsWzbtg6kEb7own3OgK4gRhF0OjxGmXoHcIkAhhqavDM1b5c7Z93ljwaJ1pGHjqMfEADLfA1ikptUR76PwYKSgWrxd8OiwGcDj6uSY8uDPPS2ESpgy4PVwWIZiWkpRjc0SXhUHATdDF0fG3Q1/TSEDTVCKGvcgcCjAwORRaQBiCE05hwHWM7EZg7wy7kAYHE1BDgZai6YKUNIJrRkBzIRACmlnBwZMwmhJAQyAgkeC44Ew3kYUkEknY76msgiEMsVMw9Tp8+HTuGZjQMAtCgGUwAh4SgxIUgAQ6ZkhGUJAQS4RFBn4Ex7ahWaHIRWZMLVJEBMC8eULvobxoBIkQfeI5eYA6bw/RBobTfhfanJl/igEZ7l3AQ46IeoSTQeEYYkBGlY/BBFROfcCCwdTBCUc/dESGa3W4+QA2AU+PUPuKHV9CDDlHyoqdDRkjHeIU6hba7CXm7aGDLpdFm8ER2NBWdC6/a0kUSHQExjBIDYtpUSAvZi6Jth8D/k1cIqEtKK0WHYDSwFwWnTUbh6OTU3R7B2dP4ewR8x8h7mp2Ov6eptst3H5NvB21PX0V/pFeg+jFpQ21hwaDfYplhXM/yELRmOhj8QsON4qrrVVcwujm8g4TEIZyLRsMQAXWVbo8MgAvThkgbTa6hEZMHsiM+rFPPRjCkIBpdmjuIHXPMMXfddRf8gmkD0CCQ+KTimREAA08cbxiIoOoSCBzupOVOTNADAsogkkB/JZCYMGXKxWD23nvvZSIRWQW30sgQbbsLfs0cAy5rDImQUogc7D8IS/LFEwD4ZKoZkIbQ1nGEFEBGM8UUg3bcHiyfwESH8AAEwOpJC4iGDKC/7QwfnRBywdcoC1kwLQwCWFpIoikA5GhyzubklcgY9OC/bAx59dVXwwqJQCB3Lo0J+jIDAkxb4ICNRZNRf9rNO3PbIIBujt6AdAECWWg4OpfvFy6kdBijEKVYDKLtajfht48GHTQpyI7iYI3VZacVUU2INEZp5AJhETnQFmWWB2aqaT86YXtonc87o0B7WlGVqr1sbTM7popG4IH49EENgdcdI3eGHCYUEL6pzSk8MFGMngtDgS3C4AihI6F0a1yJFm0TRKCO6Xjc0Shx2YZLapWfiic+UoSJb2Z1cCrQ0iLKoTQ07kBDq8XaiA0EbyViwl5hEwCn9xKBSV2UO6LRqzWnhv2h62F24BWw8EGe8UaHf2n4hGskQUwnwVgMp0AgYQABIFO1IIydBI0YewLTD8QHrE4VxU0/gAasihZMEp2KXBgEYMRrbXV9+eXXDRw0L2f3QgfN2Rnc4OwsxxdzPLLNamMi95JLL3nvg/e9Pg+HqwAPyD4vU8c6BwmhILBLTPzkhRSEaCCm8dmx81AuUlI0mB0sD9JRZQTCBwnkFDU2bg5SORzRzOnFBvqhl2OWIQZoI3Ag+DfTp23YWAxk8uWVjLiAgEShyvDrQHIz7tkxa4UxeIMAkKUE+kU/cK+pa3j2+Rc+/vQzk9n61dSpKIEGThJWhwdQ2S1u1/JVK00Wc3Nr61ETJ5x3wQW4eUUhcDqm/ARkJEydwaCp1MEdhMGf4SBWO4x7zK8w8UM8hCLeBCj+DOAoFxoADYaqpz0zLNP70nQArjOoIwpAQB3MA+0h+kog9NdXNN12r4SThP5CNZGwfdpoks6Hw4QCMjeA6gcLoBaxM6DXw4/wtIM/YiKAz+KHjj2XGsV+ggVGtwacdhjUUwaGETxg9MfLCm0LCLq+kSUIFSBjiNA+o1gDdFpdcp7JGjUNSwgKGhyQLoruz1fCUcBBiQ5MP0edh8tj/kbHJDt0VRADOGYi5lcRVCBDZBocMbVTEy2PVwQAIYwY4Ji4oqILM6rAZeU3v/kNc7DMZ1BkuCcyhgfiR6tEJwdDxjFkzVcKhWmICVheGRnMnv3tqpWl2VldTjz+JIvBkhCbVNRVvHpYg8Jp8KNHjbDbbGkpqSOHy6LEocOGl27ZXLyhOCMjbfiwIXC53Jw8m5Ul9YiEQGxsDLsqwqdg/Wi1M2bMIAkFZ6ih51rIOoobKHGBD8yOCoL36T7GLEhaWip9tl+v/rDovKxcV79+YaM3GHInJeAX1I9efMZZZ7z7wYcvvf5qcnL8Rb+8IDYuxmyyaMdtgACWfKkvBKQO1CEE6ovvcjiDnGTjISRkcATbDkRTjFu27LY5neMmTDjuRIaVKpHFEiQRh7vB303GNes31DQ01TY2l22p7Naz99FZ2YG2U8c4GcccdHOgfciE8sG5QKJJhI1WsmDoF2FFGo+2OzThYvoB1YEHdBTqGpypTaZMcKilVdAY8OFD1GGfxHRGC6R90tIQup1cqY2QP/IXWukYEHm7qFCbi3ZLx6dBoifx2j6aTkt33i5h5+vhRgFZS60rj47BA3fN9UCUDqOrUPcZfScOEfiq70TgQdc9oAin7mErHJONOyNiA+0S5otxFlnSfjxOZA2QJMDUph4NMwocsETj4ivPxCcmr5pNo8LzTAiyigeNBq9E4yIOqXgFGhd6Pc/gBsJ85RntmHCA6DjR5k5gFA1JqXxLSMKDxgewLHU0wLFsHGHGycOYPhzhEGe+c5ZWIGSwsDLBhnUEPibTJAY/B0iaTQGfzExwDq8yk8iYgEKbDFb4JK4PmOOxcXG0rJ6Gwc8H4z4T0eAGMlHceOWisDpEv/IMYoqVB030WbJUp0KKbm0IB4zkCEpkZuETpbbbKD4nbzIAko6tAZILspyBBZZ0DFm8apoTQV0ADYApz/qUmLAc3CYM2hgMSI7qg6jzzOnRPIDMKEBP4givD5scxn8+9kxFZeUtf7zl/vvu/81vfxPw+PO65WvCAihk9AVMcmomR6BZ5SAaiMnxmTLU6vAC7UjabT+DOeH6E3E40wPuj9kQYkJV1AUGOhRNR9g2aefbHlBA90EojK/E3XffjQKHggXxaYp7AKUz6uFBAbGz6y5BvcJQdC+idrk0c9F9hldi8pW7/hTlTYRoxkRMPuk4F110EQwXmMzxwnpgMRoCkfUFcB2TBxBQXEzYNKAI4eIrcgiY2mJDcj4BkxDuGhrxSQuL506ILgjweQCCftXlIonOjlci8FUDIZCEvEbQUqXTwLnrMka/6hAgYASH4yEDDCY3bFGO4TVakQNGkxUDkc1s9Lhqgv5mdb6Y1WyKtdjjrDZzQJi/BX5qMns5q90cSjGEzJzhyMXsNxwKN01tEcKmwfSARjhaKF0ucidEk4IIUIM7YslsAlTAE/Ko89uMNkOMOWxBdw/ZUclFIqKTc/Z7jM0BWc0WOzKKw+GBCUAKSGdGYDMnxP4qRCYQsNGCQxWjwcd/Q9huCFPL8HmoDZunuNKE9AUpSBehu8kfsiDARD6BG+CWFi9irGmKCcVnOoo3r0F5z7flyrgCqIawXz1AQzMwySlsVn/4ahJp09EFhlHi8AAZCeGKtkzGAYxEsVhSIsQqIzlKxBVN1RHUzrDdpQCkhuYYA6hi2i3Pu5uyM95hRgHhv5rzbtc3qFQu3W2ob9DWEbgTnzufdCAQojF54BP36NCbPklyIuu7BkJC4ugQHrgI59LPhPPcHj6vOgmB+plXopG1DuEOVgTq12hePBAYvXRGvOoH5IfwR4UbD9FogCUXDUQHRhPqsouSKgzKHza5/Sj4phhDkDlbtHt/Xc2mlSvmNtYV+9zV8bEOo4QnJiRldR3QNym5T9AfpxTlFswYhmCcyeAMy6HIwrlwd2H5GOYLTEMYsriDBhcIIAijuIEVyPCq6cCdV0gJ00SieGCmzKKbEAN+T1NTa31No6eKeYKkpPTExEyrBbOJ3WhxkqXRDAEZQkkVkzti4Ouvv2ZWAEMKmRIC1HY0IUfmNpBjDkMQjz2ESE3YUKuGHQ5yFHu+XNiNwISLsVBEsorwCImw+eKrL4YMHpKV2WXVyqXNLc0D+g90xsa0cY5g2NBKRGPYYTTEG4z8kJckxaq0M7OQZCNlb6OGrjLqVAt4yoUYmDp16sSJExkvMnGFOsICCwrFpaknIDqvvaKAbpkQnykZRlqs70HQQnztGN1J3r0i6iFLJEyEzKN9ST//aC0SnzjwRB5gGaSi1+1YCB1N33f8urOQncXfMXzHEA1zu/DtXnfM90cjtE8SiQx/hnjovGI0NzMQaGmqmf7Vu/7W5QUF8ekpZrvZZTczUrAFfA6v31jiqo9PGtqt60k2R0rIiF7ebAwzuOFYJXycFOdsn0fbM3nx+KPVAaMN+txh+L/VDl4lG5ZsXDvT27w25K60Midrsfl9RqYwUrJ79RowLjapMGCwmYMMXuQCvq7Btjzlbwf5hpkEbw4bGGFwire3qWVmc+MKRAMCEANQNC3gNGcPU0hpIxqaSApGcYp3IHjQ9BlLIc8kXJmZTF48WQMBu9EetmTG5Q0PWbMQaBaVbhf0iebb+XDwKUBtUsH4X2DMxL+DSTtm8tgyBGVCVzw8IdICDj5ynTnuCQUiKnD72mr/vDNQxIFTUM00hfZa83bxNajdAdg+4c7i7xi+Y4iGs134dq/t8+ow/o4R2odEoMG/hIfZMF6jDG9Yu/jV5x8a0j95/Ig0i7k17G+2Gr1G/HYMVovVbrVZeySZ6ltWfvbRsn6DzyrqNSJsQBFuDYVbjGHmKrYORNpnxPOPYh6JD+s1hfEGqqsq//Dd10P+stGj0pNzw/GxSSafFUZtMdtb3MZ61+biNV/5LBlFPUekJHTD1k/ddZjF9oHAZ/JDxL3PYIxlltjnLfG7Sx2GFB82IlHbIcXWixDmRGRsoC7GTiJXZAyJZBBrf0RUKAkgUTABhRzmYKs5WOvyNoeC+QFrUtjgtIiFaxvIGmDn/ZBTQI9KUSAYZmERwscBdw9WyUSXSWpN4pDj2YnA7lBgpwzoRxPTp5EBDMCJybCgw9HAjwI5giPA44RBiShorK987qn7jj0qZeSwJL+3ChON2YKyi/lcLOghI0azkM1rTo8N9O8XeuOtG089/a4B/Y8xm+PwmIUt7jsR4Kro6bVVa5557I4xg7PGjsozmeu8oWa/D50cPELhQHMM8ighJmCylVXX//vZT3/x60fjEzKoNfrzbiEgnBuzm1pgIbPbNmsoJiNjaCihiFFIG2Nvg4RcCFspvnIuUh81M4/q/xJRB8kdc1Yo7HKEylyb5vtcPqa1Ia4pZBVvU2SPjtgGu/Pv4UABuj8XViDWnGJzw3EZT0L2/AE3uIFWDYlwOKDaicOPUmDvxQCg0QVwDGWlGCZXng9craNZ7DtwrZ7sOxxFU9iUD1cWpnzRkz/78LXxo7JGDo0L+IrDMkucEDRYWa2LpSgQag2agqaw1eJ3BgPVGeneU08vWLb80wH9xhvCtpDBLoJiXzuLqNgej/vfrz81eWLuiP6xYd+6sB9vJYcnaPabWeAWMDHBG/RawgHmantkJtXn219/9YnLrrzNbHGguQufhnFzRdi2KuION1R+UfPlj9Xgsxt98YZgpsXENK/4J6nkCojUFuIR4hCOOMS6A1MXLyn1IE0uHGJdyNaC47fkMhht5tigozTgr2SW3WSwWYCC3NnD8YBuKvulwexAgM6ArRRABWSKha1ZWLjDMkwWY+KChdsoImFHf5CtyTqfDksK7JMYYD4TmyCOw3job+3T+6+cWlHVXZq88P1nR4RJkybhKo7GQT4os4gfPOjZxx/PcaQRnu8EsuQNx0ceaKxEwOUfp1U9jKXt7g8EMe4b/UGjzWLcXLLIElo9amgmK7pCJrvR4PZ4HFX14i/qa64v6o5vU2vQmxxgyjQcG/J4u6SY7P2SA+56e3xmEB98I/ssibVlty+Ud7itMFwoI3wWo48xNHfmmwP7Wfr0N/mCm81hvDYTcNO0IAWILvEsQaNZ7DFGnz9YPWRwYt2C4vXFM4t6HYeIYGbbFFKz0CY4dQfqNwwZnyZzKBFx4zciAQw2v8WL65YjaGMoEI7g7/eJy5byWZKTpL6bNw9H4Xg2eTWb2ecuLTW1/4CB33+/0Olw9OzVS8qAqFAyENLEUQq/EyqFjU14HMvKMuYcbLgA/AhxKJxe2KhrlhpnRh0PBRDQy1x0+9Hk1a2UJrHb1O6M2DEFVNuTAzn0chxojiMWizT1Mo6O03SGHq4U2HuLBP2NxQHstYBPy4GQAe0phhcN8ga9A3nD4iBmpXSfJ1/GIpxkhLclE1Psl8CCMrg/a5hpoLgugB6GS5zxwRaA+0kGAEkc3REF2MrXrV3Qu1ealfmAEE5DFqsltbQk9NQ/3pk2fdaWLZVmJlR9ccYQqi2rkc3GgNMUDMU6fC2tpeJsxLSrYujtC7vLZ6W5a9UbCaDsORChpqbM07JhYP/scLAF3MIGRyhsY4ygnP216w5SSZyb1LQsAsIzqG92ZelqYwgpEBSHnB+7sNsIuw+zXECED28CTEAKMfRPxhQmY6vb5fP7n3zy7/fd/8Dm8gpmhltaXdOnz0hMSp4xY8Ytt/xx7rfz8KpifCDLCyKJkYRBNSxhmAUyAknEl8pm16hRfDRQuBK7t1L71DUNgH2ZWIpB22AnEnRVLhZDsNIQAaD5165hdn7dHQpASbx+OXUKgrNbDJoZSzSQCruTtjPOYUWBfVKN2ZwSRYx9lbVuvt8LRg/nAiz7dLJhwO9+9zteaXOsnmXPCcaePLOXEUuOWaZANDj+9OnTJ06cSDhSQR9bCuunvYIhgdpkuR/wFKu/2WoO+zy1zY2lvXIsAX+zzIMarKGwY31xbX5e7lnnjUrCg9RtCAUsZqvPxCKDoDMcchpDLTaLK+grDwV6WS0MBaSAe3dBDZxmWRK3ZNHCtGQU/wbYZigAs7YwfQ93h5eK3isse5sLV9FEm6EB1xxfk80RE8UAHhx93ibB7r1AYVCCEcBwYccwBTGMqfMPWNNLLcCmGbFpYHqgxtfdg72rWIxHWVjOiJCsAUhG7OM0fvx4FmNjqTjqqKOod7xF2UdEawNE2C/57gqn/+lvEJCLIRfjbEhK52JHMu6a+Jq2ROgk8pHSCvZ+NIBixcJ9ejuS4ACVVjU24eDsCwb7oFXRn9nqDgMR4wOYDp8YijJEIJzZKhZhEYc9RLljKWI3OjaAY6tUvQyN9grA/YMqHDYk3i3kHwo2c+g6696Y5mQNAiOC0s1lAwYX2Jybg+GNFkvAzKJYc1PIyEIzE8sI4NBmQ2vAz27+XnG23AcxwHpqShoMcVTe93ZzE2DF7mO0h4wsGUMGiI2+wwLDnHFkSnIyx+cBbXH4RP9Gvd8N7XvXBNRVhgxm2SC4aYKzsQ/WIbjwzTffjGygpgCy3+pCnfeALQKWRJMAMvtQIRhoDLB+MOGBrJEK7HZAOwGrXReh8+uPUkDTkA6l+5RWsEhFuP60Hyv3R5HpjLDvFNin0QCVTZdmSH4gjK26Jel+SzvTLkm6qRHIxbOOE218ugmCElucs4scR0sTwgEvJGdRrv667yRT+aqsscpjfHGwnEqZd9g8J2T0+kwNtS3Z2Rkm42ajrOONMZqcYm/HaB+QlbHiP8TaXitrvMxiotmHixIp6wnbetfbjAGWEbOhHBtU4JjEumYRBDLB2tGFgAj5E+NjZagknJEBhMgjbupvR0l2Iyxqc4PgXJrg1BTehOybRCMhEMahw4m8v2qEjaG4MBVqHNnPld1SNXzmk5iWAAEcGRlNcqodbWZ/5bsbJPmfjcJkAFd7YrZnAp0UPrIqfu/FgGbBlJaOrZny/i05LYksuGhemCBh6+TCK10absJkANkhGwhkr1D9iVlBFEDEEscncGl82Opk2rRpWDABSML9hCTmE7ismXW5WEFYphs22kLsD2Gx1pS4HVZnVmqG0dti9CeuWF6zumR90O4bNbxHWgJORC5kRzjosNgScak0WfdprlI4LeUxhmNjHBb2hgiwiIEteVjQwY7cyBvqxrJT1Vfc901h2WgIF68IWaRM+4lA1AgEp21QO0zsa3dyDVvzCH3fT7kJGA2QFoItiG0KCUE2sJOE3rqcmYPXXnuNrTJoA8TZ77nvx4IcQaA6yXgEVdYuUN0PbBHOu4sM9v0TTY0hP7oeGx4gEvBTxhgFW8EYheLPjnUzZ86E3aCbEAfxwAObpHLxlQ6PiYC970GDZ659xwcIwn0tLKgy2RzJRlNSqxsf+RiEQjAciHEGjz++R2xcQxjhGLT5De7Bo/skpGZ//c2KkNkeNHthvKEgJuxMoykmIJuM7j1KeNEobmvOyckO+tjtJy5ssMtOQSaPyehGz7cgBjpi7LKnMysLmlp9bIUnm3rKRj5SLjEPyTBrv1zBkCwrYWtVTPZUSiSL/SVnOkKR5oG6wDhAnzWGGJg0aRL+Qux7g/GaxU20mU7O1RHlOsP+X1Ng70cDdCdM89hb2QKa2doDQUW4NrobnI7OjNcHO68x08jhVuRIV+dAXWb/mAd+/PHHmabGYYmZSXaBBjFmDj766CNMQ0gC1rhjDYAfaamwn/BEnmBNYRMJa35Bv9INUxKLYoxMAht8GRmG9Byz37jezF76IXuPAYmhWNP6Km+sk43kEB7eAPLBEBebWBDEI9KMsxEK++6zRmLqny6HzOlyxkDffoP8TbVhozMYboWPi4kHfVfki8g9mZPQk79IiMgQy+g3WZvcfpOZrSxYBiyCTSWQx52QiBgyBsLQhLQIsRWSBNB+lC1puzTECLKGOQEhtGLZyoEDBwgSykJltzoigw9Br+1i3MDWEXLDD1h9EHMX8CW/3byoX5oKi1qRAdpeQZthWZPeexzfAaQCw0qaB9duwjzo0Sg7MlrN61NTUnhIwK9jdY3YahMnGdkx9FPxpXYU2iTBmZgohMh3FZk2tztll+bdjvQkEZhqoYz82R4E70Dn2v6DCmy7KbQAq6pVIms8SQuqAOVVtq1VsHZRQ0Rro4aObWTDcnLG4kqjEZh6JCztkgAiq88CePcuQCgkJbagy00g7LJsuwf5sI0VmcTbO/xg0wehR+lcuHNqFQMCBI/e1BC9jwc6NkMBlrHABVgcgEVIl4WpY+KDHtOD0cC9K2bHqRRaqp24ln3/YYx5U6Kj1hRoMBkdrH8NGVvDuOqYY/zh2HmL1q5dWXnhhJMTkhq8jurmYLw/NLpbr/ODMpfrshA/2qw7zulHQlH//Z6G5QtfyU5vMYc228NBSzAmxCJesyvIz2hnvzZTiA2c2QLPEwr7sWUZzLFVLbFG+4juPU4JmSziWGTwMGmBnUq2+OzogqOYjF7ZXS4UbzR46qr/01y7Ljf7Z+aE3h1Fl1N3qqtrkxITN28uL+zeVbbdU72xsa4Z+ZmUkigSSrMviGgyuJhFMVS1bnm3vqk0q/C8kK2PLWwy+q3sOr1r5kLuuoWwSzYOC3gToAqgExDOORPscoOzAC2EOWqOydOuRAeh0XZIk90IxGvWz+Z9MLCghaEibs4uRnk2tm5q10hwT9CgQmhiJo85lCynNVgaQ8bEQMhuNnqZGDKFHAazR61gZwsnFAMra0OCBo89bIu2tygcxZvltAfZGJC1I2ZvwOCXHWqVy5kxyHboLkAFQvF0eNmcBNcGURw0n0Vk4JEQYc3KsThSUFmnErnYYool7awFcaMNWGVTRXi+m2SoEyZ/jIh/S13YlOAzsCjFi6XSpDUMlTyKp9IW3IZwgkx9kanPQbZ+ax0ksxkSTazHNLpQxcK+RPAzmRsM7ANvZOpOo6FKqfERERGhIQGRN/7QEI02t8nHU4zPyX66ATvN1GcV0cNwNlocDeV/575PYuAgk0H3djKlV/PMgEA/M2LYsWNHI0cf9je20jSU0uRtbdm4aN5/u2aHY62NplClaA+BWLMxscVt+PzrOQGjfdKk8VnOlqCxOWCNr2xMSc89KTVjIE2OTm41IrfatJu9QlGURmOwfOO3tVXf5KRXmfw15oDDFI6DR/iNLShYxrDNhBMTdiCL2lPUEBsyJJXVxPQadLbd1o3dTcOsLTO4+WOAd4BLR62dgYTByDZBbJQUB1Ooa3itqW5pl7SzQs4irT21xx2UEE5sx01NCbtoK6Due0xgYC/i3paEDmkJ+eNtwbpgzZdNrpqUHmcHHIWmcMDC6jRR8toi7uSvykUioQog/uH1WIdoElzUPjNGfCKQsYJuDPtvimgnCO1DsDAnNSKibTS5SlpbNzsCrKKzixtwGx0oRSQHI5uW+y2eHGOo1ZLktyf2DZnTIBdrBoVPml2traUtDdV2qQRzCKqb4aHqNAeVPgqHJhKyBCwBqyWIeTPsMfnN7JbuCzOEdaQUmNjz1YBawx4RieysHvJtbKlp8ENaUZLlJy4Jcgk7tbCuMopdG99kyGoOeoJGR8jhT0rJsxqKGPsFzB54v5npKY8p6KtsdS3yBlnvEms1uNg8Hf/uNjCSgYIvGVhYxUgDttajuBjd6eGQ1ZJsdySze0qsaBMiBgCXHPBUNbd8H/Q3YbNo4/hR+ikJgPxRUDWOmrSUyRb2u50Bg9Ea40qJje8dSswLGOyOEJscAlxj8T9433uj0EEmhjKCi8MobYLurSf6eNZdWjcUwqNYEaIDCWkfHo2wrw80IhnG0ljNztiMjNzB73/88phhXbvnJYQDPovNQcvfUl5iN4cK8jKrKtcn9rT6gwnLl4X7DD4OGcBhLSZryCK78WzFeU9RooBSNBpvyJyTN7Jsy8qV61f26xETNPrYZg7eynk4ISM9AfbN4gn6NapYgteXtGGTu6jvJJs1FYVbdZKAUoiUv9CukGCggHJOx8cQhUXMu6Xy24aWuRwutmMZYM1aTkv0tm7MMzWnfJOEQdMNdUKTwWL3JMaFgumJDTYzp/lwUoMZMSZuVe3qVLLu6AISDQPWT2NgdEi+pCKEuDwgABAPGDCJxieVbUdQDoMw6iLIMUXmoIXzizzFTZVf2UJ1ssTbYPOY2R9Kc9ut7Zl1f7ZAOOTJMlvcLV6XxW4wxw3i6AvWXlPVBkNlbe1Ug2dL2G+10NaAbAx42e2qjU9HaYujsM/ksbB2O2gLG2ycahR2hWxUqyfRnHCm3YIYQLGHrCBQU1k7y9dQgWu0tsBoQUBmmlXjGBBt0VFSY9xz+lnlE+dranYFKhPTM0LGFHoAu9yGGRA7TM0N3zY2TWVyyxRIMDIKCoXcNrBt4/5tbYCtfH3+RBprwF5mNraY3ekmQ0LAm5hhO86SUEgrFNMQSg9L5QMbmhq+NYV8MiKJgFEVrJ6lzQvwrR800qwtJAO/jf0BAgav0xBsjklMMBiy/rdlAHQ5YsQAPVzLAB5ovrQwLh6izxSGZ1XVctPh2wVGv+7zg25Dkl8QTTbo6N5z9HGn2L/8jBNxE4q65dpsLRZLS27v2Lx+I0KGeJ8/vLmxOhzM7Dfk+OzcwYgyo4WdlRmDY5/ZivPeYSVyAA0yZO034PiP3l9cWrLxmHGD6LGcNIxjFActy1CXAb7BEgjb3V7EQHZBj17pXUaEGUnLhACKnWwC8SODElncy6VU+KDdHM4KBZOpkiynz8R+czugrrkwrJmH9vUiryILIkOBiKaGNppYZ/KZWlhRYUqPF/NEkDoGvR0AdxAAfC7agwaLBIrCJ1yPFVQUkQ08dADi8AhSzIhze7ygiJ+t2bjFaGi0OrtwVFyM1e4P+pGo0ISjl7C5URALrmder9ViCPprDSZ2ZsVv2E+dw16R02asGeFqo6XSZslhjybkq8losVlwC5Dz+4BDLkhOHvBds1udZo4QYsiAGwMDRyrDWxsM1IXCDQZjHgFiIMId2QLMBoOh1m6NBxGWLoIFXD4IzBCjGAOWR2oXPNnmRZNa6gVm7DUF3G6LqdEQrkNnDxuTmMPgzAth0kaX2VprttagtqDIW0KcmGSMc+B9J/7oETzV/h9mozXkZ3vEYMiOWclm5iRXf2041GwI1xgMuajysAZT2AHIcKjJHGqMj0mz2LtZWKQpdBO2TwNgJKpqW7AjGHZCK+X0c56t0McXyzRiIFweDGxkYAFNxEIkI7EOmo1mQQpaxzcdYcdoO4bo9DsL7xj6tqH7kvaIEQMUOdq3eaYOpRrV1f5Zh2wXIRq4Px+2tgqskE6YbM9eE7p3H75w/ozlG9abTa2pqfFWe3zAb2h10SXtObmTunY9ymrNZrBuMLfIkDbMsnsbbx01sN3CNEoBINCl7DFZPz3rzg/fefnrbyqGD81zxjQbYSUhDER2djgNGawev9lnSE3PGZ6aNRA/J7WagaT0YgoDX97FsBfuST9hKCBdAsNCQsIwKwN6Hwdx0lEjWupWpLcTCwI/GrSVcCq+godVwN7KFkJAdJhTjbZUJQO86thLtdnRVtAdPNEwoqSgS+v+EO0VOoRkxIkGdgBl34JgrLAY3NKYssKxjTlqsMJZln1N4GXky9YmrGXTIyRe9YPOk4RcxIdFmQ2O6obG2rqW7l1ywuFkkz09Ke/ETeWNNaXVAwcN4uA9ksyfO3fgkCEwzRUrlxX2zLeHrJUlU0PB6qA/xeBPES7sDXHWKqqBzZ8KM03OPcUXTFy27If6ap/DYezZuygzJ5PBEVVeVlrqdbkKevRctWZt5ZYy+GjPXr2zcrOMpvrGstlu72YWOaKao4bTwiCzIRRjCtjMhkRn1vFun3PF2uLGBjmsu0+f3rkFOageVrOtrrLK5/W4WrxbNm/2BQI2ixVNaeDwgs2r5sc7EPgM8hBUmCDVkUvhOGM4JuxPDgUyY5JGJSSOQaFhZ9mVS7+nXfXr159qM1ksG9aubW1qcnt9Xi9izG+OibUY7AN79gyFl7TUb+CgDc4qDAYYzphZRY9HME2SKbGYmN5h5+Cli1f5fF5oziC2oGthfvfuYfi8IVyyYUN+YSEO6KtWr+nTd3BsfHxFWcWWisb+wwdYAqsaW6upjzXr1md3YdCMZVeNg1WF4W+G4wn76NG0aFTYIdm8BNsjr9wJx0mBiFKhSu0gZ565q9QiinRz5YGTpmgkOLOxTTczW8RhQgtoOgmpGN2yYwKNisO0yYhpTn3EOu2NpqWdJxnp0rr69u0L/Oj6Kp3Xbt6PJDGwm0U6KNE035SsMJQoNspoOmy2xA8fM97rGR7AEIo9EV2Ddb1WE63Tao0N+FhbwPwaSpNP6UHbMcS9Rxw2S6eHt5jN6edecFNrc20gyOK1zU1Nm0DQYk6kpxlt8akJSVZnusGYgros2pVo5NqeQzNAAxME2mTrDsiI/Yg4fjlzLcyWetnxSTLxGDI5KeUOsbeKtij73zFONATuQidjwzqmKFDRjNigxakJAaPNUD9CqKgM0AD1a/vA6HP0IZr1/nqABeDDduedd5IFO1joPsnmGZ9++il7rrCO/eWXX4ZB/Pa3v6XTbocGvV1LEWjFGpRXXnm6Z59R3fP6YaqxWpIWLq3+02MvxIUcTz9xfEJMbPG6kjkLa4v6Z7/67KvTZk6797H7euZktDIwYOpWrHqhZYuX5efnJaQkM8cAU/SLit/lzfdmlFes+cn4nxavW/uvt167+eabCrsVMBJ8/+PPxx817rMv1i5atvyE446u3lL+p/teu/q6q/6PvfMAsOuoDvZtr79927tWvffuJtmSu7HpODQTMAQCSUggpIeeEBLgDySE3jsGDLZxL5Jl2ZabbEm2uqzetb28esv/nTu7TytpJe3bfbJle6/lt7fMnTl35sxpc+acGTMadbPUMw4ROQuC2tVFQK9MA5yJEIRuyNDiptb0i5/dmstqF110aU938kv/8du/+KsPTpg0PhAIPvXc1urqqpUrn2av/zXXXJFCh4GTaGPJveR4ncwHzFLg3/79e50ee/ykGVjCRCERj7UK12wkcPGd9zz08x/8sr6m5itfuYZsHXg133737YsWLsxkkho1WdamLbtX3b/m/75yWSC2xTZ6dCODFTGbSbW1dtaWj2NMSaXhkDBVq99/OP317/7qxhvfiiaE8aPHjuQcdnRqXd09z28/YkTqvvLf/7Nv796vfOUr6YOHv/rVLztGz5fmfj1kuBm9K2TGb//tnUuXll64CFe347rp1q1bN27cCFOnIYaSfSr4JS5btgzXeSg7hyL6kHIOJYVQjINLgc3nCpx897vfhQdcfvnlq1evxg8e3JAX/Mg3PKWeAwcO0BYb77/1rW/hJQ+rgPTfe++9RMoCYb73ve8RR4cdkZwjatAobw3tGGEDQ+s33lKdLoJwH5USKcRzjEi4VAsHcz3ElSDkqEnOGXZ2sSYq2ekJ4iCkDWMrMxZR4jhuDRkO/0XAEHEUDZ3lrXBpwkf62ljFTIOUBkaJ5sagtIABrER1zhlOWFKAwQN4ERUe+oEqwMIi+vKAKIH9SklDEIU0NEcucV+RxUHcKIaOf+qred9gDRSqxd5q0cQ54Ji4DxJxuq93VdHz9Zd5+MMf/pCN6zitccAGmM+IdQRBueKKK4Aap2rimiAtItAhxBEQRX0Ksx2JjxiITGb6s6X10NY9z9z0gfd7XmtQP2rZxqMPPfj6G657YtXqDPFI9OjaF56aOmfiMxuedEO5WYtnSg5TL6MbnVag3bQ6dTP98GP3X524oqQqrFtdutnm653OtdddUVpycUSvmb94xnNbn1m3Ze3YSaMOHzu69+jOsZPfMWZy02XXLikhGoA2e+O259dtWDdjxrXZbCdOR6ArIumxY3v2HjzUMKpBM3OOQYj1rGZ3vPedb4xFK4xoKUP0xMp79257YcbM8alMz5ZdLyy85Kb0yu6JM8cuv3IZlI8hzGm5vd1eKC7Gd/RQ8OyFjds6Dh0ZP2W6hqXIbDWJNU7eIYrt3XXfPbe8+31vu/e++3IsgJvmgSMHDjTved/8m8riCbHCatpz3/n6ogvnVzeWtjQfNYnQhd4TiXZ2Htu9e3v9JeM0rq1OVF9b4r1bkZLYlddcEwmgfEOGCQBpB4zgcxuetEKRB1Y+vHDxhfjOZXLu2nUbLrp4yfr1q/GnoHBSk3RR48dNXrV6zYWLpkoqvD5UxBsNeR+yy9gxagw0w8fQ53FTcXroPowBvUExAIox9JSEMVASRkL4y//8z/8kEhoc5e/+7u/IQE7OBjY5XXXVVaoqNjzi5VxRUfGxj32MX14cP378X//1X5NhG69IFEqy1fKKKkz9wMMvzeUhGeTJgHN+kO8KWxtCk4Ot/TwuB2Yr5BarPJRU+p1/9IbhdJtbtj9x+OiGrs59QRaICYMRKq+pnjht+vxwtAKzpG+R9/dS8bJoBsfRa8hfLEKG+INCN3HUcAlkSlVdnfbRw0fau3aEg+Vl8XEV5aOipfGcm7R19hjjboGjKgoAnIBVNaHjkBOJSCrqzUBo1OtnwRdkEF41N53r3utKed+82wu6dEffMVAl8myAMuQqlmwlbtSxUlgAwsGpuhYFQpZGVc/21Xn+/l25ciW7Vf7xH/+RIHfMUgCFMUDrL7vsMs4hFgQ/R6xT+94xRDz++ONMWh5BMhDocIPmnP2AzW3d4yfOKk/UG11ponkHPesv3v+JXc09j61YjRkvlctufXHHO9/+jvq6+ouWLPnqf38tkCXvaDiQNS3M2l1eNpdJ5wJtPbnDnR3lMrlxEsO/M1hTWulphzETdaa6m9taq2tqUSDXv/DChClsdYxJXiQJjJKBSx08eGzxwmWaFzFcpIeomwt0pDoxR3Sn2ts62gOhVhd3UiOZNu1IdWUy4+7dvrX5WFs2Hp44j9Vpc/fB3QgjpaVlmIMOHD78k1//Ys/ePZcvv2LJogvirhVzWHk2kj3pbrezixR2rt7e0RGJ5HTotaxjx1n8ffSelW+++trSxgaS1wAYbmbbXtwxdcaMaDyezOWiZuDAoT1rn3zy0//0JWQXkw352VItVdGV7Wpv7UqlksjXARiV0e1aPQgSbk7PpbT771mJWE0/o5kFQ0HX0bds3H7RxRctuXgJ508/8Rxi09vfduPWzdteeHR9NFNueBkjFdEjwRkzZ9+76pbO9LFEvI7e5KCLMNGwGxEND06ghH3u3HLLLbgms0mFR5h3KMn4YuFhoIXB+5fsdYXoYzCEbLKpBYqPMYdHOL5zsL1RSQ8KMUASkAoVgSbgAVTCCfld4CXE2VSEd82aNYRLYS8t26fQQalqaIdgypAP2IDiBKAyrOm1wxKgZGSUJ2OLaeP2bKXR4zTXcpPPP/HwwW3PVNYcmjDKDTWCCuSGz4Eura3dzz6xuWnMJfX1F1ihUM7N5rQMKYId2wgNawTyQ0eMOJuUZ0E0D8fZunHdvp3P9rRtjka7IzVElCjbu9FL9cQnTFk8dd7iQCyeBfc9gvv7piAvE8RpFS1amJJ1Ou9ocdphsPHE1hIhSjY/1d3+cBI7Dh3AbWGCJxiUhDP5N/1fueIQmt5PVMmXwfbqBDJarsTTuvVILDSqTDOnIttY2GQH1k7yH/6yndAZTFcOJicUH1Wd/QoYednhyG527iPys3PloYce4hIOgUH5E5/4hCoM0X/729+eB50+UaIluyu6Us01FbGwwTJlLKuVGEasNBY3mrvEvGgG27q644nSuoYm6sHInoV0Y2EhyajGAm90/dpN67dAyja0p/YFykredMlFk8rMNIK7q2EbZ4aaRvinP/9ptKR09rz5Kdfdc+DAvIWLxV0HVy2dZXntlt/+ipXehYvns+gcsEK5rHn44P4/3v5oT7LtSPeLW7Ztmztz1LzJccNIumYAV5+udOrnv/ntgQOHLrnk4vrRY4ilvva59eMmTITHNI0ZR/qB6TMTM2fP+eGPfh524+UBgp+D8BWPP73xgbWbOjsPZlrsI8nmyy+eNW2cASvQzOC+3fszjnX1DW955OmnzFDECASznrt95+65c+chNKG1smPhnrtumzCmZvzkJs9BW4ixNNLSfuj2+1a2tidTuX0b1m+dPnHWkitZG+lm6040HE/E40cOHZk3Z84DDzx45OChv/n4X3cTDD2TGT9xfDwW7UqmsEmxTBwK4nVFjRlmMlYmRtV1knUNla6Z7Ui2wQYYZfoQ8o32Bl3mUtlhiFzAcMPgCVtyzz33sGPxox/9KFWCIWxaykczU6ii7qvh5kWZUkwdlt0CATBEnUPxYRXwANgG+10gIAw3d7j/4IMPolzCP2gdug+O0Si2R+Lwf/7znwf9qD+PV4M/GRYRokm+jcaG1vbgoTwPSzJnxCAvFh7OnO7Og7f88L9LjY6rLh8bjQXxedczpSBXVjscKumMxnTWiVuPrt66ce2CC64vrRyF9cZh8cAnjMP/OuAIWHEQ99CB5//w22/HrNSFC6ZWTa33QxhFTNzAG1BP020dq7et3xWrWNA0brHJJgHdyIK+OmuqAgc/MtN8mE4CiZu4kLDl2S8jErqX6WE5NxQp8bebCQ5wKFqvzvv/oqmqCQO2sJjb/5E6B390OxrQ4pp2MGknIXBsYvOVFdSa02gnp9bykt9h9gI4n4Z2zxSFRnCCNIoSwJdu2bIFQ9Cll14KXBAOFgaQ+NQrCI+ExaaMfLdtYyjADsDrPMVbMowLPJ1kJDPBlBfpKA01Z63WkJOJ4b9y9NgF02Yx0h6LsY4X8excsC0TMjLBdtfonId8e8XS73zve8uuuAxjSyjT1nNwrWvmbLPLDbEzK/WLn3573/Yd//r3f1dmhbo6O+KONr1pLNIMqGBnjd/f+vudz27754/9Q0kkrNntFsqF01M3quwjf/nh3Tu37zy0cfllb9DczuTRe7xcW9jRQppeGy/9zMf/HuPnl770X0888OCy5csObd3xzne9G2Xz/Te+I/fGt4ZjbA3Tju7e++CTv3n98pqk0axr5Ysuvmruksvvf+C2dIt24zvfYnjdPW234wTkBnvuWfX7vR2Hf3/3r3du3NNz4PDK2+9cuGhh8vDROeMnBNEkTOPwsdYnnnzqwx99H3o1jqEm7FLvqmwM/dmfv2ff/rb9BzZdfMFV3M7k/hDU4rgv1Y8q++KXP4lhyzKtKTOaWLzp6PqTjZs21jeiBWGbSrlaN1YpFi0ke7aZzgVDKdRqnJQwrupmSFy/Q9kexHkwV4abberYcBgphlVRP4j1pz71KSg1pJzIBVBkQuoi5oMYCO8PPPAAJFtpgagC2I7UXIBkI++DNlxSGzyAO9SpMAr6zqoSggVYQRluEjiZfCpgC4I/d2jun//5n1E7eB3DI9nfMFWRdEshWKFTYbhsAGgU6ABaaNuv3PLwAFaEJToCZyiQyWPf+NonZowKXX/pTNc46DjdDltiobIYadgJ6+Eeh4ul3lSfcHL7v/uNFW+/6dNN4xfhls3eKLBp+B0n9mHN2Lvnxe9849NXLokvmDXKsQ8hKWJfsdLIgEnPOBI1k6HKuO3Gdu1+/LH7V7z9A3+LtAURg4PwDTKIYiPqNf2cMjRCKnxur3Zz4emBCGyWV882Y4vw8DmJBeAfKPNEZop/yBfSBNdy00cVNY96n0vl+K06WtfRh5zkTlGumHJSXFjt+XnwFRxMQlaGf/KTn2AKYOryVejssFxmOHlvWMFTbIBP4BHkgBMmNnLc2972NvVd3OdQ55h2I2Y82SmLBJBvtqHbMGlsShjoWWt0tX0v7pk5a6b0IZeYtNHhDLaQsYIgiypGkCENVpXXRAOlUSNsQKgJK4VfZ8A91HL4x7/8VmPZpE998tORQNDJ2R3NHfXV9SVRMol6rW1t3//eL8vioX/4138PB8NOJm2GWFtmi7IWi+CGY0Yi0Vi4zNRZqiFvkuk79Bp33Xr7tBnzxk+YxuilOrrbW9p2bN0+urZxVH2Tm7GfferpRFnZpCmTsfIdO3iockrUDrhJxwuaZiIUxDWzLB5JpfH6xNcsbOqxHJ4+dua666892NwpskKnsfm5TVMnTV33zHPjm8bH4liopJMeuPehyeNmzp21OOflgAPgDStCf6FpBMOBWLQMqZn+SaWjGrt3ytzDB9rXb9i4fPkyIxzcu7MlbFYZXsnaNZtveMPr8a2jQjSUoBsPeeyND+ik0xP9nrSCxOplDPRsOuOl3dKwmG6wuxEkEcWOaJVy6ZM+KDJ38BAjliXjyEoA48uhOARKw5ve9CYu/UEWYQh+oM5Z7yWDFnIDSEJMXJBnwYIFGN9AHt664447EPl5RP0crAxjbEQzUHF0qAGu8MgjjxA+HQMRa1HAwFs0qqACvIKOobMBcJoYPphBYXHkAGCTTkENF1qYL2fWnfmtwZQ5cw2Df8qcVIQT0rnigV9OatRuWD4ukN2XtNmsm4AEm6E0thNAdgkq5AbCRsbJHJ0wOpG+sGHNyjtHNc1nejg4c6Donu27zgoVPgLJVMtPfvjF1185YdYEqEIrnuGuxd76nG52ACHJkGWHvcl8S48do+3au+W2X3/tT27+OJYESK3frdgEsA0IPxjw8Ik6HuIyCj7TwYiA+b7C0mtY/+h9pW+7MI59kZA4esrMpbSniZuf68WR+LGuZhC5ArwD91KHEHw3Z+k51wxD2bBh8FAqw6vw9J3DcPP66bDidMhwuvu9oBTyh6aZeLfeeisWYSJVqFe5gxcgYiBTg4nNTaZxHkgl+kELlPk4/wonAIbffKKi7PCxZsdJIUNYuZKQUepmrEA6aARzR7v2Pbvp8bkXTc3gLK9MUmYykNb1zkAgWWLFEd4128m84a1vCAej6Ama1w0bZTnUsLt//Z1bnnzk0QWzjM+u+4d0tudd73o34mcgZmWcNtD4F7/93j2r7148f+6/f3ktLkGvf/MNVyybkwoamVw4lHXjAae+vqGqroRoWIyk43XpVpLoWD1O+5e++pnGxjHZTFaL2pdcsfDWW3+7YN7CrNcOIPtbtt//y/ura6ohguls+oalN3YceioQqjXsSt0O5YzshRctMnIJx8noeOVoBJHAIKklyioaEk2GFcx1tCfqA40TKp7acOCCxRdmcq2yUpDTju5/4XWXL2NjVybZYgVDTjpgZEp0J4qbXE1NbU1luURLEUOlcE47t8dLhR+4/dcr7vwNhjiI6U3vfe/RPVuc7qPVMa376B66HQ9UK92abdufbg9p3QdjuX3ZlmdyJTAM7ELalp2b8LRLxKrYqIEzFmv+qG6EKsmPKQPKByK5Y67hHH4ApYYiqwLIBCiCapTVEIMe6hKySU4OBAi8jFAf3/Wud6FV/OpXv8IERDjk1atXv//971c8AxL/5S9/mRFnDeC//uu/eP2v/uqvMDdhnlKGI7gIyTZQRPKV51sc5IkvtQ2yrF8M6s/XgtZE7iXHEzk9sIVh5PqXf/kXxQkAlwKFVDmosnQrjI50IhhhmUIYyOgUbvIyw8CsQyiDwdLvaGSsnMBd0ZXQxCmgAB5UM4MthAZJhDgJu3Lk8OY/3vbFNy4fldCOGm46rUcdtxJvaWynnd2HShIRVp/I42vJvuFuG5qgNR462jBlwY1WrJJ0viFJX+zT4cE2fbwcXa16G0Hz9tu+HLOOLp5WZaaaAzgmodWSXIx9+jj2uMQZYnMmpgZTNhhbxIcxH3io46Ll7xszaYmDIIUkKbNHdwB1IO0ESk4AF5YemGF4pQS1bHrfA53tm8rGLw/HZoheBLEXsu7u27//tj/8YfOWLVMmT37nO95ZWVWlNij98Ec/nDtnbl1d7e133L5x46bGhgYU3qZRYzwhLTIxcA4nME7zkdu6kpsbR78pELjA91/qtbod/+a+MzBezTRGnxPMLNxh+oEb9AmlUJ/BFpCE+5yDA/zyFEPN0CSmvpaP/6VdKkcWwwKARk+7itzDAGiX2ctc7U8Fjr95+rP2ru5/+uTH//UfPl5X2tWy5w7i1nZ01LT1GMdyR+MlUT5zzNix/l4nhi3Q3tpWG67QeqxE6TGzvDNStTReucyFtWPqt9Oau69536261uLZo17c1srqEZbvdCSbdXN1tXXd3V1MWwgNS6Ytra3tmQ4D/Y7ts+zsqqpIJAIVMbYv22VNN8QDiyRsHRs7CC5ktLQd/I2T3J3SJ3SnjJbm1t2795BnlOG2Asa2bdtZBifZHl2BVE7/0xVsMp81dWapHu1s3VU/2tJDE6vG/QnLPgQ4guyzyhbQct1H7uzsXG14De1HSwNuEJ+FFr2jJ5NkqblNFrSrocIy3Lg99XTWx+rSXlfawlU51hTJZpye6NirQqXzXI3AVTYrJeTSSHavbD7yQIhd9F55e0fPhuc30GmgB0mwe7p7MNyNHd3I2DEO8FPAZkUnEgnb6dyx7UdqJo+2Yt0xJ1lSPu0n9xxIZuv/5s8/bGPm1XXWmZG+4QQnDSAVYpOBNuLYw0I0NQ+SBtI/cA78f/D8AT9ZTOIz4RC4igKSQlQYg3Sjn+WNaulbOhlCx1PcjXBX5VIROqDKCxwnQXjmy4LZANCoGQVjxGuKnkV8+OQnP0lg96VLl/IUOAbZBWeG7KSnfDxdQ9phPhhBhnO8MuDw9Dh86Etf+hL6FH647e3tnDP9mJkwgw9+8IPstgAkjpMqHN4ldgu2waBu64+s/GnM2DyhPqVnmw3USlPraK37zS8fj5eYGGDf+IblITbnEznNLiF+liSG0UpTdn20ZlFZ3eycFrR8Z5ghAyOorBvNR7Y/98SXF86q19JdEhWIhVVoupnmH5Ynn0z7sj7NSD/witneWXKks3LxpTcTKJs0ahJpQLYOMJEGOHCzI9ZYgJBnupaV7PU9qQP3tbftKBt/XTQ6K29JSnanvvjFL+LBNm361McefWzPnr2f+vSnShLxzo5uHJ/ff/P7fvzjn1bXVC2YvwC3GRZOsaKWlZci5EmWBCtpOD2th+/sTm5tGP0WM3iBrApg/RgQIF/EBiUUVuCIybiDe9BcXOswuXAfJPnRj36ERx3yCvehSqjbWGax3hQLGeh8qqItQa9+CCZg+cKQmiz9Hw3Quf1uwb6gtz/46XdLIvY73ji7efddQadF08I5LawRIk1Z7KjZ3yKHRR5HIoedw3aNpvd06z1ljddFyi5xNNmWSPgoT9t3YO8fndSuoGNELbgjGoaBVRBnMGG90D/4pQ+qFSKiEL4/WsDB/TcA+oDdCC6keU5MenvQmMp2Bi3Y4XkRTe88vP/2XMfWqBERsyeyMA4+wg1R9oT28Vf0P1+YkKeWRRwPlCbdyWqBbFJLxRMXlNbdyGpwjmURTExahtjoHUfv6ei4HyuW4UbDWtpysdPHxA+IJRA/pwjL8dSJpphjUaIriNLYY+WCmUQ8nSWhX3zcVeHoIjpGYl4hsbhsIdh+8MAjpt4ewDsaMBkdtEvDyPjLrXQ5RFXJLoyOApv+CGrhRKq8VU97eF+RvSM8/r9/8fA7bvr41KYJ9BN7rRVd7i9G8L3UoIZbjSTnnAxm0HlXNa1+VVW8qN5Vv/74nCxYSyf7Tagy/uf0rjFQlQKjoN+CjUI0A9eiI1ihBholYaEHqI8vqO2CClM/0SJhtkSNpl1yit12220f/vCHAYDUxHBOFAKgwjEDHvC3f/u3nKOpsIau2EBBbQ2isPhYgleZbFdP675pbCfMdjlWNInpL4ifXMeBw+3vufZK2Dk7bTATkrfVMbpwq/FJGykHmrOZXZ47AbfI05G5QcAgK1R0C/F3Nm54vjYUjnjot6mMm8FCTKgw6D0UGylOPIB0LFRYWljICLBOAR8oTWSOdrXaue5AEImM3QD4DgnZPZ1ZCIIsT+VAA8t4BCs1bdHFet+RZ1bQ/OuP/3V1FYZUb1TTqI9/7GNHjhxKlE3Zs3fX6DGjqmtrPvIXf16SgB3q7DN6cMUDe/fvKa+aS9wIWQKgQ5nQdsSyo6gvXIpnElCdZomYCcDnQ2Xw0mNnzete9zowk5zVLL2++93vpluw0iKhKyJC8ksUeYQ13uJXzMdFOmgIMKg2TxrUHX7VFFW/g20Na7Sbe/21bzhwcJcRHFVSfZmb2a9BxT3yXkedLEsA7LCVlSCZ61BJLR0IdeqpRlChPOpFEjNwukUYZt+Z7MUzSksq5rmZJhO7khRnN3NPMIfLANohEokALQSMsXfRXiWUkSGh5dj46EcRtwkWURIgP5Ks04MyIAnLuOXlVZdkg6O0bDd+yXy162AeABIfAZBVfXINdFzjUhAwDAzwSixxjFzQyEZKZrKJAoB5VdoWqSUQDU83Amy5pGFcllr5ujCbvExCR0hQIH8BTSg5ukraxKJYSXRSPdgZcsrC5I8gaGC4gZoQUfytOXQ9C7tT6+oThJxn/5zoizQnsLlltCq712SXsbhECFYL2YadCrMgHlNWL484OfogE7dzide9vmnMmCaNvQ5wOwnoKp/cfzTpRb8C8Q/mPpfg4SAHnRfhc7ylfnkRXJKu85EKsNU5tdEuNynAwQmHaov7POXof59HhR4FswE192gmj/1sjwQauALAAVChEAyyPEYeTHvwAFWelTd266jd1R/60IfYecElvYmjLk4XgAF4rKiwNw9lBQ19kK0UUIy4vB6O1oRA6AmS3sV0swTqNOKGq+/ctXH2ggkTZ0Ts3E4vx175hIHp1GL9kARlEc/EB6ErmT7E9nexxdNhQ+0zOpzhR1t+4fmNV05nRz7iXLcbYHkiZTpx2YsligBiPoIhVF4+TuKM6rSr5dzu8tLREpW3l7qDf0raHKAP/Fflfh+w8BUinWF6kknvwy+TAWQsr6zI2Pi3GasffwzbQm2jqM8t7a1z5s9jtTxWWpLJ5phIK1c9jA8JM4w+RBPhkPU6NjyQkMeNkLEZWFwDsImQc8Ksk6L+wRAzTxhldFBoPZOQatEUWa9Sk+Sxxx5j1U7NEHAA9RlOAJZyp6+O4f6lKtqiXRBPPt+vmV81J7l5Esk4a3vUp7vp2orKmqo66EOkpB7V0YXSkljUTUchE8IA1Ej6lECUB9eIseyJEI8LaIzdi0aAMKIEFsQ7nn2E83giDqEs/RCRh7UiCQoCDiiZka5gAIEbE40sRojXKEzByEHWTWwswoxlyyFjxzqNRIEgCGBoarh6HBwasxx8xK9BvkwhCejUv3/9bjEwJmL8B1uCWlLDHywnfmCwfbYgypsQ39DEQLja0WMSc1RjZRqkYPT55WPlV4pJE+J6YWQTmpWOsekM1yGbIOl6t1Hmfwb8CoRkUZ3oKVY0NkXTwXA27AO//73SGhDJQSiUfhMPJiLkleFkE6Wns5VdC4dLCM84s2KCIXzOI2QSuhSHItk+OPIDBqrh7j/W3Ox/mS980onCH24qzAEAVT+/oDSX1KOeKhxTr1NYleeSkqpM/pE6KfS3YDZAAzRMXygIvv3tb2Mdwl0prxDkQSwUlDOXVz2bt/wi6AEDBx2k/PB4nUsYEsZf1aF0JZYBLGtnrnkIT0EnTOlMStFALTsVTOG1ZjqxUDaVDQf3Hdk3f+YM14ZDsCIad4Ise7LLHD9Lk3i7RJmQvcUG0VWGT5BAFCFABw4dyMyKOyHEPfYwgBqsBKC/4yCEpSXB5mEms4/jaNuMDxIEXhtWIloStFjmxRYEJPxHRzFbBiS7fDFii8w1vwy/TBj4GRkIZJLJ7EKiwhAlSzXmrj27f/u73yGDJ+IlqWymuaV11uw5OSRQoncFA7v37Lnlt7/9yEc+IhK6WB7Y4YTIBgGC4iBS8SvYL+vDADNQJ/HJjDUYCOAcnPOLzYd1IxwzOGd9CNM8i2boCqy/4ZIBfrI9B5Xx+uuvp8AQBv3UV6hHVaUEt3wBdV/95m8O7gQCEOerhRzJC6yQ48EiQ0KuOq77iLcMhF/AHxGZxPReCV62uPKI+cgUbxlddgjSu74aRxUQSAm+74+h/OFQAypnMurUKK/zX6SXLsg5COI/10stn3mI1kiuPV6R+8cPH57jl5zJHf+ugpW6AxrRt9RWEK7ifu8FfIcdvrSCerkrX+0fqsL+1fIlcsmWAPFZYq2LFXCq0/0zTmRHpDQJ0RdfBA5mgUJP/8qvix/565/7d3t/5IbffMh/mX4LRPyeByi6jjf8zziJvqtL9ah/bYM5B4FPLZa/SZ392zoJx9SLlFHHqfUUdKe3xwf/DjOQhoEJCQtbPOtjn//85zHEMxWppCgwDQgMSyI0zVRX85+1FGCA4qvCihConsJXD55BYVgCd2AGA1Y4zJsQH5CWWEHBQJntYA4h+wf7t8Lt7XbLkY6mq8e6GXbblh87qu09sI+NYpMmjA6FWL1Kis1WD1qhBHZT+H0v5g8emuMzl3fob88yrNKymBFACkdeqRL5gDKI+Qh3XokvzUFVMfhgAYA6gM8QTURCkVrhDWgolhARZhCvyZunHv6UkUd+Ca4IlxRAVOQ1H5HlNhMU5YP0ygcO7Pv+d777/ve+b/aMmQxE89GjXe3EeoyyTZU321o6fvbjn/zpu2+6+IILbbbPyXxkAR3+hIgqwas9M8U/DFmIr76m4jd+CkzMFgadeaKYAf5z3/jGNyilgvmwlIeliKesvOEqziIWsgJOBF/96lfxsYP9nFJf8W8wNoVXyitCXPu/yq1TKsrf8E/yV/Im//tjIuS2j8qoAnLnpGPgGyfeZZBVvX1Anfj4zDX2L8t5Lwi9Ffaxkd5CvXdPqvDES79o74+oFOro+86+v9zte9Rb4qTL3rsn/ukroz5Y1ZDvyr6HJ75yHlwNCc1Ohrtfx538aOBrReghsugBjzzyCCo52gD+UixYM+d5B+LL04FfHsZdKD4zHFs/lJ2ZzwIjmzg4pznaBSr1y6IxZSD9PMI+wNpgnlUMo/FTX+VL8XPATBn33ISbrfSy5abs5NScZGrxvCk1VXjf5Fg027htC/FOtm3rWvnwNtOMswkXRdjKRmOBOmimzWKUSNNDPKCB6qsbG9kplsWtW7cBBrUjpxuSYMQFNgPLO0trIcOJWey8J00HO8awupix9q5kzs4gQgq1FWsz4J8WH4SmQAdkNvB/CPM9Cw/oEXIpv+wUgCVYG1/Y+PnP/fvkiVMuu/Qym/jarrd107byUnHgge3sfHHnp/710/V1jUuXLM2kMygYwkt4XYwP4hvKYqJtEQkgIzHsYFen6RyGGxwDDRh9AMIjBY86dvmzJqS4PpmoWROic+AEeDNjFaQ8DhhgBYvJQ+zukddGeuBV2gMFawPMQKYfxhakLSwzaNzKV4/FWx6padlflyliv6Hvk38Y9w/sPOzjYGs+859VQfbls+inOAHmYCS+73//+/h1rV69+h3veAePIAE8LSIkCL9SHWZtM1xVO37PnienTWDLYcr1Oqsrzeuvn55O7YMQQ+IvWDKRhM2heGLPrhdzXnfQSuu5mOklgqFRth0g0aCEIR3qgdivSOGUqdMyXTvxucTvwnOItgiVxGTFY+gpC4YBYQNY3sU0RKQ5CfWbsUs7utmsi+0V5YAtxSJbwU5PwwcUB0CPQdPAikpoOSpE8YBYE+8MLBLl4mjrgf/+3y8QDPhQ864v/ffnuPOe97xn76Gtl1x8McHIOrqP/s83vtTa2XKsreKLX/ksIcPe8fZ3TJwwEQcM6jTEGYaFFbYqowSI7uJrMKeIdX19pVQBhhXBHx6AXwAeqOiIqAUgIYiB1wC8B1GAoCs4COE+xPIA/OBUb7++Kkf+jvTAa7QHRKoq1qerqopNcHuhU6QcUY49+rAZNuwwpREA8RzFbZbJz8zHAsAjfHghDawZcjlq1CjgOQcgYc0RJxaM2LbTtuGZ39SWHo5Y+yyti/3DUGFEbKzeRjDY0pm5974X2tust751SUVlt6l1adlaO7egduIbvVAio2dDiPCnl8EHGBqG6wSOhsjr5bJtW57+QUNdt+4RMp5dBOTZCLvwIfYPC5AxS9JSsle+09Z7sDi6RtnBo7Vm6ZSJM5Y7XmmQfMUsPoonBsExBmCYKAt8L1YvFws863muk9p9d3fXurKJy0Kxpb7hVCDNsR+1pQ0AWaMRvqEb5WWlLa3tdbVVbHLlaVt7J0tLLOBQmGJVlZUB2f0qh0cCBjfTdvju1q4XRo15Syi0gCY5VLQOOTvxgA1wg5HFLRijPzYfzrkJMoAqGAaXL19O14AGRLahAIILtqBly5YhH8A7zwFKnAjfQFdqgqgnLwsAAwE1cm+kB0RsLBobOKfdqeDklzlMQ8oYwgnTifP8TQWDuuRcvXVOppxvt4YiExiZjVRbn79tdEPKcFotXHRwcsAaoSc6kt1mlFAjdbf+buWceaHxk6s1I9beXlZRsax61CUO6ZxIPA7HKOg4hQ34N7LNh547fOC+2qoWPdtlSSY/8qe2e4F2z4mzZQGtwzRSttHC3vm0E3HdhkNHR09beJUXTOhaPKTjvi17B/wkCb10uT9QNIFmI1v3CS2pBSI4a+y7J9n5ZLBulq2zAID/pWARva0UQRkdLETiQsN6L3xJnAqELYizxXEmJqQc9BOOQT90m8QHSL/Qnj5YP+qtwdBM2JLoJ/jDDnTQljoY3PxwKxiU6x6Q9H8ESJRXJc8JPgwE5En3+F6aBgzVSyc9Hbkc6YGXqwcKNgq9XIDSLjOZ3/wkV9Rf3VQTjKfMMX4xC6g5359AcL9oh08XmdEikWqBaMkEPTT9rntvv+ziiZXRlOMSHC1saiXtLa0Ht20yQ4nKsmBjVYOXrdhx0KgZfWHF6AWsdJN5QFz/+HecMBYGIN/oi+5Y4I2K+untXYc3b10xe/IY3e1m6UKsQbYE9lKr2X7kLCNt457duH9fbPqs5eFwbRJ6LUsCWOfRBpC/6WGgGRAgyDGfyz/hW5IwVkt2tG1k+YOROQ437MLfRiTf5pN4NUAyEHCDE2WO3tHxm3bcsJVLBAJtuN5CJbE9EbRbtjvg7HGKtsSHq0MRdM7zACisUNSW+nmkAOhfJl/4JT4BHhAVeNBi8Vx6iVsfaW6kB07XA68YNqCmN5+hZjgnTCpuqkt+OZhjirKoX56qyc+j033/MO5D+4SEE9sKf+1589+cTZp/vOt3y5aYY8dECK1l292jxlSU140iLtis6aOzabO5JVFePrNp3LI0tnWdHT1ErwKwArWBEz5FyB90Htt6zgmOm7Dshee23nHb09deOSkSJt479pcgKxbscpOlArI+6WXpbLS9u3b0+CVlFROgSCwNkANEusev6YS6+3XNiffF09+IlXupUuJoh1AhlPnG58EyKKhC6AK+DxLV4ngnTFoGSsR7gbfv4IwBgoajZtgB+EoG6h8IlBBSxl+QxiGVtYdwX/ET/qqh5xbv9x9facYf7vzoA5LfygnFTqjrJbxgjRqLJXZLElWeCnyxARH2LFtH/E73OyrP5tUosCrDQYHizhBqVZKB1CxcXlrhjlwKROqpeCUolJDHI8fL2AOvGKPQGfroJELQv+QZHvUvNoRz39cTDOdV0fMFvYnxdWzP02t/n+7ZV1Gil0bMWFhyjXV02Bk7Fq6a1jRuZmnFWKKtiE+NzAlx7CxsYeC0gIrZhQnnZtP33/mLvS8+eskl9RWVScPsATb8qT035jjx5nZDDxN/ZGmifByMQYEuIPiHfIrM2N5LdfPEX77TF7z5XDfFxgjZXk/aSBo++aDj+5MWNfmlkP9+b2lFr+WmECtqUZuRAlagrB/1V7Sj95X+f848uCc9Pemyfz0v2TnsEEXnpz/9Kc7WbJsAJHVHSS1FBkM+OMPSlSt7qSSQHzY/chEJz9ej3JFUdCabB6HObGGxpOuLcfhrTOxhhqkjhWD2JL4hrsIIPQgHBLQgKwnrRrix4ZrAXuKg7KwvUtPFAP81WsergQ28LEOHAEu7kK4+HFYUkq3qBqGrst1Hcpk2TNtBk9By0VC8NFwehiVgFpbdL+JaIweTsFhsgLRibMth/xbB0rtajiR7DgVDBHY8ZgUI3wiMITabGmZFomqca5RAdIlyMeQDCqNovP/t7As9lQ0UXLdfBfUBFqsH8uv/K7ie8/wF4l+xjwEXps997nNElBQ96cS1jeLAT2/KPmC2efew+4s0l2wPZtsse2lE+pB9ySIFsHGF5pRB0M8bUYTG2bIuGwAFJUiMQ7ghpggMiVgOoAz4j07GI/EgEB9lWMNp1n6KAMpIFYPugVeMUWjQX/TSFRR8VgdILdq23Mho2WhpIlZaCr3nEmxnThCzBUd4zmEAJ4jDQuyKcDDTMLrwA1ehsUhVXbi8jhAEJRZbPfFogiTgoo/xis3PlARWoBi6/KekeKa0sDFhaVQ4/EP1lnTSq5UH0Ecq9i0diP8Se1y4k1eJht+DJ9QAp8bgh8UPcithOUA8+Cvb9CQOJ8v2oIds+qbXCTllEHwC49vwsZEFJlk9ssmq5K9bBdgNQkqvVDJJnB7TDYYM0yK2XVginBApxEyFZF/x8Ns94dNHLgrtgRE2UGiP9ZbP8wDmkW9KESaAsSdgZZLZDhK6HjnaJlkHiM5VHhvV0FAZLvepL1YXQXpInRyK9Knz4fwSjBHXIM8mAhk58xw9RYiJYCCasnNuWhxm4mF8gVi6Jf5iTnOJNAl1KBHJrBeOobTtvyxJUQpe3hi4Nb8muof+EZbWV2gYEPZVcb78JcTF7t27ly1bxv4GNjEsX74cG5EvIBf7I8FFor1RKzFBVYcadjbTtnPHpp6u9lDILYnF6uqmRuJj4eBgAZIB6FEUIGThCMkA5ADR9dS+vesO79p4eM/uYx3tmpWrrq2qqZ02c+bVsQRRUeE/8Iz8SJ8vw/QahGPEKDTEQRdppnfi6NmszU4B/CNT6fTatU8/9dRzPYSqjZSSbZjtsdlcKhyxlly4YNqUSeEQMcLEPoowZhqozA7BLvp4whAh4TURy9mPLJM/R1jGTK5nz8H9O3eTcHB3e0syFonXVFeNHt00eeKEmqoKGic/MkKhSbhK392Wdcuht32O3lTEoSiU6RxBWHi1RLOgqwlzxBIxWy8JeaQUgnOwNoD430poOccucXJkqPa2bV21cdPd0VCyNhoOh6z2rvTRZm/O4jeOn35xV05WeBLYDIuBiOR9JOqlFcYg1f7gA9/val03bXRFRUmMeNQS4tCzDh7KWYEJE2ZeESurI89wyIxiFSu8L0feKGYPjLCBofUmZhjsPBBgiS9HfEniqT237vnf3HJrZWldbc0oMxzJIWdJojoJb8sESGZaIhFr7pwZc6ZPFbpt5yTEA6mA/SxdQwOi/1tOGt8kmIq5bv0L9z2wSidvYKw8HIkGiZyL92g619OTClqBCxfNnz5lbCwWdgjaI2GGRW4bDhkqliz36iL4/Ufm+DkjBBtYuXIlS8REuFOuz0S/GE7/H6/9hDOw75hNckevIptO3XPXjypLj4xuyiZi6WCOGB5Ek46n3eCRTjtUOa266WIr1BAjSdkJNQzxgkQF7C5x7eSPvveZUHDbDddN0HM9ZKshQiq72XEIM62SnkykO1PVMPGqSGwCsTSHz32GCOvIa309UJSh76vsNfZX2YWI2cueqF/++tYv/MeXR40ZXzeqEVegjN7pBLucUHvaa3X1JDJ3PF6OGvDr3/zuG9/+tg0PEMdKmEdxwt7hJkR4Iitq/s83v/P//uf78bKmUaNnxBN14WiZYRHfNBgqKasgCXdV5TMvPP/NH/zoybXPAIDvwl4Us35xBx7OAlTqX7G4THEhHEptGH/y5B72TxWwBMQEsZ4U+6Apx04EAwnXS37pv/7u8MEHZ0yxSwMtwUy7ZyWh08QECRptjTXNbnLN+tW3uR1JwgIO/0ApzWkZMu394ntfTx7e/KbLZwQzzabLhnoWkYj9ZZBkyXNaQ9EXIyXbNz3/gGSvLP7XD/87XnM1DJENyOqgyLQeG2Hwdnhpuk01mm/rpMv8/Zf2RLB4wwsb77n3vmuvvyFRVpnTAqx8kfOQDOuOBNUkKSrZIOkkVuisqVNmvLBpy5133cN+WuDHxb4o0ELT9VDgJ7/+5eadO6684ZqS6khKb3VDzVnziG2mvaCT1ZMZoztjdgXLvbLG8Hd//s11G9YBgGmiqjOUQ6cB6mX127tNo++TuCm0XD6zt/7eP36B3lf8tvuGMk/61UN+h2I7VrX11dkHzXnwF21AQQE/EHOcQw4TSRfhDwHf3r97hgWu+OQYcRjN/Q/ctmfPyje9YazpHgiyTETIDtKsk3CdpWMvE9BaaktSduvOp+6/yyfHCgb1mx+Cwk4w8HR2H35h/cq3XH9RyOkxcngLBT2XPEhR14vapJNh7SDQbUZaOrtfXL/+8WF958jLReqBgo3ChHVDjQWhCSx6yy23EKoFiQYNl+TAgMTc4+BOkcA7Xg3tUjMCFOFiOGETZu9c8neQ0SIpaPDD4wUoLuDxVEECtOcCHqwpUDdmMFrw3fetnL/oEhYDWJHF3IN7jmuT6kkOQpwS7xNii/zHRi3cqOcvWHrgSAcZ2vHsFEHQt8sc/85CzvhAVRya8uz6bWvXbbt4yXKWn52cY4pLEoNLC5I2hFDSwCp6CwTAjEyfufTXt949dvyUitIgPoSyqIAboazLEtqiMPEMOYKX1Ts5ljr8HCydXV1Bsq6Hif/uh/0wjK7ubvL/1dbUECqcEQxHIvFYDAooBdTrgjN5oaT3u/yvGxge5WrJL4PLcBNqkLhSRJECB7jJ6HPgnUlEOVoEeSiGDM5wgDlgUSHdXJyyjJGKfir72vy0D/hr4sRFhoGMQ56Ko2wFt/RKMlPLaq3PJ3z20DfCJ0AxcJ9AYJWZnfS/WRDLNp95/OGbbpxfEuj2kjjuBx0zg6sQ4QU1Ly2JJnNR04rOmFr++BOPZlNXE/xc0NGPV6h0FIbBh4ErH1X9kVJj08+95zgw3Ax5sWMtz1+wpKK0riOb7dENkkaS2KvHskOOlcwS+NaNmpkGvr2ywtu68+HF3jI65oSPG7l4yXug4PnA7IL6gMqkkyXRK9GeWfj62c9+hhM0OWjV03PxFYqUr1ixgtQikNcFCxa87W1vIwmBaov799xzz2c/+1kCzxGCVCWigUVRBiDPBTx0AlMDurVx42ZYQkVFpc+PyMSAwd1+ft36w0cO0xuEMwNUMcBDZH2yHQiSdyvS1tZVXZGQiG3DOKhUSJ6u96Qzjz66Zu6c+UiWrACLEyc/QlWhDMx7WcXgDE4gjzW9qqqe82fWPnfV8iUA6ZNigaM/9R08XHkywAlYceutt0J5wZDly5dfd9110Fy+m1GjK4j2yjBxCV1+/etff/nllys6Lr1zwnHS5QnP1EXvtxvGjh07QD8qpEUeffzjHwcVOWf0f//7319yySX/93//R/xRwOAO8JCcboDqXtpb6nMVyeZTc+nsN7/1P81H2ky3zvcd0EnAgKWRr2BEVF8g4HOmZAZUyVM7CHrNEKNeyFMTh7CuAAppsnX6xBlO+oDpkfyLjKRop7Be3HzBAmrDdmSTNDtRZnzh376SqBjt4NYpmMJ/clAI0YGuplL/nsgdfY9k0YsndCzSBTflEX6pWra1+Ymll1TjJI0soiLX+nVhdkIioRQpMLGFOdWViQOHyCDSEw6XSYGR4+XrgYLZADgBdjL2733ve9UMx+HhkUceIeI0uKIEsXPxObS7bt263/zmN3//938fj8f/8z//kwijhJ4GGHIPKEIAfQEMnhJ3nvu8gmyIqFjE3LP5T6Mt5gBNbN68laCVqlvwkgiG9SeffLq9vWP58sssK3jw4GHM9oiBbJpkBvgHlrTM4cNHa6vKMBox0fJ1FnrCN/IKc3Tbtu046gWCwZykeCSXOJK9UshOYDMMEGBzMPnJCXzkyFGWB3BbknaHDsVxqOnqZ5555sorryTXI8LBV77yFeJ9zp49G0UN9xjigT/44IM33njjjBkzUCWJB042GOKT8z4g0YHHKzrbmXyCrwfwFnHOFy5cSChpzok6/sADD4CZfClRRfHDAQFIOEF2PBWPVg0TT8/Wwjl5rr4RIaqvt/lkoaA7t24z0t2Htm8I6YeyJuZyhSp5kivACAFWN5SOIAyhH5DySHpQFA0h3pD7FHuEZ0yqiARIXywZhnlDdg8It+BA5UAx5A+SQzISddeve9iwiL0qzxScciYCwvG+4n4eJkZMjRo3FR5KaUKQ60k7s2P5kjeR8ZgQIYJYxDCnHtbCZMMKFzbXLnZKL8fmMtLZy4sjx8vaA0NhAwDM2COeM5kR/dDxSQjDVOS+IjTn4ovAOXLQM9vhOtRP/uFvfetbV199NZOcgxzFP/jBD/KkH2mUANTIgBUVFbxYdHhkDvpkK5XJEOA6npBWmIFku+kgoMSeF6+++lqgYk1sdNNY+BFP/cnrT0DmomkdOHhw5tQJvhQ1dOjU9/I+KZdFcMSWwLYdFiF8cj+gqgHYPuREDyBjTzadScfJ4NafoAwdHBn997///QgH1KEyQOASwznO8owFCSo++MEP0hNI7qhxcGto9HEKUki7fAJt8S61LVmyhFfhQLSLHkDCGdUtMCSST1CAkliHgAFkIJJPnn8U0mCRywK+XyO7t0TIbju2d9LY8JI5y52k5kXTtter2QjW9B2QXU75n1t4BMifUw7uYRCVT2Z25iwCMnW07sdJTRMSnMN3GXuP1CFVqSFnN3HO8FjU7b72ioqqmnpeF9j62pW2SFOM2I+8INUi90ir0rgYrsTOhr+DXPuQoYAaWnV7q5ZOthh6xPFwpFYxU2S/MroIF1gn8a6jPB8Rj5SEwr0JBE/5mpEbL10PFMYGwDA1x/jlYCbPmTOH/K779+9H48bqyvzsj7tF/A7mOckGyBmi6iftJaYhOBDWXqwuTHIFEnMeQvDLX/4SWoMEevPNN2MWKCIYqiomg2C+bmSzkmIMAZxJor7dpzJWLBoDHsOwsplcMBgWIhWwcjm8uZliCED+jmLRJYYFGl2hGn3xxRcr68ejG3CHyel3EfSF1jjUr3/WN715REdFglE/rhwlBQ7+9xV/KTmEQwGDcKBOMP5A5VEFqApOyQktcs62qe985zskCiaoDqZ8eoab6lGhjSpzv6oBHgDLIQveO9/5TupBKeRA2wAz29raUBEwST3yyCPg6lve8pbh9nuhgPaV91EC7gUP9gVkyf8uDMHT20bVZ2dMsNiDLqabPlt5P+xgLI+PY99w9dV7/K8/8lxCsXEFMoyn1uxklgRJP6GTBMn2HLbscoC+spMYWU7YgqHlsh2XX1Q9qrE6TToJ/8hXiYeDNO033u8J1cho+gjlszJuUEZ2hAWT3TVPP7nanVZFjFjXyxhWQBJmS4ZhaZxUxiZxJgy9rdWua5rCSlW+rZGTl6sHCmMD/VGESUiCJ0Q8UO9rX/vak08+ecMNNyhEP0cfA4WF9AODmvmcqIYAgHbVfcjNxz72MRIQIm8+9thjzPxFixZBj4oOEpIRVFdyXcIDUG5lbZVNYWzVibI4unXr9pqa2nQ6W1ZagYoAAWxta43H4mgRTE7b8TATCe3rpdRDh06NCN0i688ioOUH9Ey6Nj1Gn4XDUSW5D735fm8CCV/EQIAYu3btuuOOO5T5jlFjtxTrRpSl1cbGxg984APr16/HQER6SFZx+tUx2FMffvlRddI0IsL//u//sieLxSrAgNnAAxBT+P3CF75Ao4gLmI/+/d//HZsVasFgWzq35XziCmUkX6nW4bqdpoPtBNiESZx8HDclnfzk1Gt6xjV78FSIJ7Tm1vba+hI9wJ7BlGnEJKyIkGNsUYEcrgRBft3uzq6KcbV2VxtIecIUFnGnd5bRiupwvzkeCJDqGf2vYBCzj5EriUbKE9GD+w83NJZh4LIlB7UftYL4JaKIBGyXZQsrUtbYNGmx4h7q9ZHfl6sHhKUXeoANzO0VK1Yg5XGChwaSF9OMevohSqG1nqU8Rga4DsId5SBeLAxC6JVRnjtKMEQDQEtA/Ifug5rYoIFK8Yyz1F74Y98I6wFVKETMODGPguBQwlikZPmyK1tbO1ghQEcheIvtZNatX/vEE2t4DuQqllY8XiKdJQJ84W33vSGMxD9kUVTm64lO6MxJscYOcKh5SzZg0eiLejAQDA1iAWY6+p9PRGMDPSD3oAoHlBoNkoSREG7i6lAeYAodIwU/fQ7sdAL0nbUi8AHGQw/TCurR3LlzaZ36QRsMdNxEWwUxUFuL+sVDqkzYP5pcmrxDvO8Rz8flX0jzgoyiWN1P/oc5h9X/wf7DCgRqgHhNY8Zu2XLQduK2w57xCNFG+sDFQkSRgGVGd+85UF1Vy05DZhHGJDoUcQJsAj6IOl3s/3LCzX7/BE4pc8I/8mwbOSx0c2Ys2r71YFsbLkkB+I2TQ3fAbQFzk0Q7MaxYT9Yqq56kBav7s5k+2Eb+vtQ9kBceB9swM5CJxy8UGU8MVuFQuhGvWBtg1vGIXzVLB1vjoMvh48F6ANI9Uh5rEkuXLmXOk2yWhJTUwXoAhAClBGchHEKY8Pfddx/AQAIG3UIhBZF8WAywAnV1NTt27quqrsvmcP4z+cUZ8pJLlsIrqY5pAt0Jha2q6mriRkCMmF+AXVlZBiUYjhGGylVvczJ50qTNO49ySf2oKdK0b0WAM/n6fK+wJvAIq+YvdEasusq8K9zELyLCnzqjSIEHXc0bq1at+uY3v8maDZwJnQD0ILw+Y4H2g9Hme9/7HtZ5xgtW0dnZyYIBpJwXIdYFtiY8FDTje0EAVpuR98EH1gPwEKUfCNozfvx4CsAPfvWrX4E5FHjqqacwDVVWVhba1jkoT1/hNoNt0Hd1c0OuFmP11PWy+HT6S6kntQm1HJijn1ROXfob1MNYZKKx0obGcZs27p6zoEZWBvQImgcb4CHXbHFHbGhp625r7YIxO0Z3zknj9MNKQP/5Sy+DLD7eULcMsX/46CKivcKXXkSGeeEES5yUoBldOP/C5zc9OWXG+KryauEBBJLDSETUUy/c1aUHysaUVU/NuCFCYPfpEqrmkd+XoQcYuPzQFtw8llYEPYgyAhekreD3C3kBgRFiwVooIiS0AxeUK664gs1rDz300PLlyyEHuCS+8Y1v5M7q1asRDymD9IdLIrSmP1oX0uaZysqKMM4qAetIS+sdd92Hf7SOa4SovewQ7iVSilTR+t69+zq6k7NmzSLYop3LVJXHr77i0kiIkD6iQJypmTM+o34OqN6hQ4dvu2dFOJyQEJLiM4rKD+HA7O575nPpDzLqAvpHwAzAJ7Kp1jFjRl1x+TIsWhJsXma0IjRQZH96n7HpUx8CCZwYyQByD6lFM+MOi7S4DLGiM2nSJEYQW82mTZvgi2AL5jsciihDVUMYIGGo/ouQfvgNSIhljAMrJZXDY1AKqZxiFGCJmBNUQ9aT0ReH0Nyp3zuEO8BD0yAqgF977TWexoowaqu58v4fh/Qnpo8L6lmsQoyfoq8ntVDAPKUdhASaIy8Q7nKbN221gub4cRMCViRnd5tmJ4sEOTd2sP3wps075k2bUV8rKAtHlp6hHTX+SBBnRoS+ksdL+UqEgU4DZ7e8ViIKPb81Fqutqh6TKNOtQNjTK3SjoaR0Umk1olspEREF/Y+/f9Inj1y+RD0wLDaQh3HIkzlfw5lPqJ8ZzvSG5EFf+OWciQ2t55yD18FginFwCcXhkfITpdgQhM0zwyNPkWLlR0zhL2zd+egTT4fDpbmsxFXvJzQJVMystc8+W1NfX1tXi4nUzqXmzZq2YO5MEcl9if7sbZ2mhPpePhA945HHntq2fTfzHB28T24UncBhgrMgwf/0D+nGxPjL5jL4QOd111xVV19D3QKi8IDhsgHFqoHKr7AXaNX/6qa6z1CqoTmp5Gm+cuDbVMsDRhlM6N8cdSqsABiFGPnRV80Np9GBQRn0XdV0ng24socLUQA28ENhA+OD5InBn6d3QXbQ1Q5YsI9p0DcS9H/7tp37DxxpHDWmNFEVLwnFYxPcYMnGHStGj6qMWUaIPecu9qKCdbIBmhaLkgUzYcoSpqgn47a1m8+uO7hi1f7rrn/Hm999s5MhmXUJ6wVYFSjKmtYIHxigG1/aW0MXRYGToeZgQjLl+k/Fon8ClTOZaUsRGurv3yL3mfmKLnCOTEoBqANloDhFB0ZV6Eux0DvPdp2JE8aWxKJ7du8KEugZZuSr0eqXpThxtdO0irKygKmnkl2lifiUqZNE6pIDyts3YYcKKF9KXRcsnFteEsllurEMCXsSMDgzXY3pzdZReo/NRYiahH/U2tpb5syeVV1T5Ztk8gAowWyIslkeAdQJI6I+CFrsf6n8MEaMCEM5TJyhKtUKv1TFQXPc5JdBV3IAjzjUOQVol6f8UmyoPf1Kek/2iMk/vDRty/SmT5+0ZMmiSNg4dKy1K1tTNvr1lXXL5sxdwgqxqafxXaOvivd5wsioD4ktEAjW1dVW1zR25hqc8BTTqtRC5Q4L1paZM7IEICoKzyse5K/RmobFBugzJhUzjeOczi4q51CzmragIxzqjoIhj8Sc8EhdqpP8o+KOsNBZn4ILzde9qy9fmupufe7Zp+CJsqkSKws/koYX12l93ry5oaCRTfUEA8bSixfHo2EIF4D5bGDo048eyH9UOGBdfMH8Iwd3NzcfDErQRjiB6Bqyl9gvJTtIsQ0ZbK/tamqqmzV7ht9wvgbFA4aFD3Q4pFYdDJMaGoDkjjrnl/tqRFSBPPyFnlCn34GCFbSrauOcg0dcqkbzLap2+e3faYU2+sopz7Dyj26nl2CQsMBMOKSPHVc3e/640RMmG1a9ZtZlMugeGXz5ERTOZgAa9KeDVb5vArMDFEA8A+scL2MHDmWtfWRD0ywWP4hzTnBr5Jdg76rCoKsfKXguemBYBn016wBLzbFzAZ+qk4ZE5j3xYPIrTqAmtgKGX4DJF1ScIH9ZxBO/FeaPUGK2CJeXln72U/901133bXlxT8zfGKXgIXCQL4nmAqY7a/q02bOnh/3I0viZ+jL7sNTw418qe7LSRLh+29ve/O3v/aC9o61x9ATDJHiAKN5MRWkLO5Ds28zOnTV9/rw5QQHgpGPoDElVpNCAD1eXjIuCME95eaSG7KSGC72kHoUPqpPV66rd/Ihz0v9+oU2cu/KoJgo2BfC5aUitOIky4PMDYZaggaflHK3TNtrFI8kVnzVK4FXkEvizaHComuTjcKKGDyAGoMbrejJk5XBfCkhUO4yjfYxHiR9Fa32koqH0wMm0dSh19BqXh/bqsN5SE+kM0+kMj4bVcL+XmUisEPAbtKy3vvl1h460HiZIA9EjxWUoDB1MpVPhUGhUY115WYI1C3ZPiluefxQNPH/OkSB+0uQJn/70P2/Z9mLO1jo7U+3tXY6TjYQlw0xpSXltTWV9XV1lZSm5QZiJfa0f55r9PmuIp311yuv58/xJ/5tDbODE1/rXrJ6ceufEN14zV2rDcN/nKu6Ll2hAfJRTHlK5GUBVJA+lOPDokhJZaY19bwz5b56hyImIBPgt4ZmabTQyjZqWoKnehnyvWWTIIbc08mKxeqA4bKBY0LyC6kHn9dEZXFeqN39ZivDqqsvraytO/RDWLjCOUIj5pl7kQubIqUWHeIfFNsN2szjHX7hoHlElsraTSmXRRTCZUGVYtjjgwqHZOYzFWGaAnH/9JqFM22IC5Fc38vNy9IDYKRlZkEuNsoIBQ2YgZJdoTsTTMpgN2SCgOUHiDLlm2nD92KZFAJYWFVKrpnt/SXDgerjS+T5rPBe845HPfoo4CYoA/2uxiuGygaLo+MPv+JcFDH8ZDIwWO7xLxGCJJCzr0vv278eFcdvW7XhGxkviTU2j2cs6dsJYYi+L1M5EkB2hnPpTYvgf79fAgolYYzEBySIKE0wTs08okM6w71oCLLNpRyxD7PJFCJQZKOs6IqzJufwZOV5NPdCHnP43+aMsw4wOaJfrTgmOxcgiOKaKXw+OxeJbjCtBP5lgOH0hq1LqH1BgkATPUEPavEArvh2OOCvgucAhs4FlrBHkG05nF+XdYbEBSJ4CApqWN8UWBaxTK8GiKpRTcEeOfIH8fYha/iYnrBAqkPIn/Z8O/xwE558hMwc/TJom+7yBV/zDq57pSWWD4aChx8n5BQ3eurt1865j1Vu2Ll44b2JTIx6jMh8xI0n6AUkFUISDiUbeJ/iR6xHIujuV2bZj2wubNh86coR1AUxSzPnG+prF8+eNGT0KJuHmbCcHBRCrAK+KugAkvpJyvGeLANZIFQP0ABir/GUHeFasWwyxuAn1HX3yBplHc+G2kJFD9of7u1xa6aAWMuz48cJ9Lw3pryCio0elfq1H95eCJbYu+mcuEvRist/fl4GYO742fOoC1ZCaHXlpeD0wLCKkXDIgysfXKocHzeneViSehjiB96j0Haqw8AT/fp7c5wtz53QVFvU++2JCmXT6Jz/58QsvbJw2c3F9WU2OlB+GhRsjtD4EsTe0np7uFStWb6kqu3LZpZFgEPchYBDFvWgHMhgqgbVixSOrH3/CsEIVldWlZTUBP4qnk8vsP3j04MF75syctmD+vERJCRvZ6Dd/r5kQiSJRgaJ9zEhFw+0B5sqpVcAeDHJo+xQY5VHkdJYKmD+sFBVjssg+FcR/gkcL0iMcir+aF/bNQVwCkPq/72wE8U4do5fjzrDYgPLNJ3IO0g1mj3MHPwQr74XSn+VA8ZGt4AqqAAAot3TK8Iib3FFcoeiwIXlL7b4eTY7J//7q10khsHjxJR6hnt0s8PqucphoZMqJGkyWyHBo184DX33u/z7wgfeylYzdFhLvvSh7dmjPChDJ7uvf+L8Xd+6ZNWdhaVm1BD+VMHbiwBoMEUMshD0KR6ZNW1+cOX3akosWA7Z4b/LP7yg+xv+ionfVSIWvlR6A8biwEyPra8nE02UaElMIAxTWJ5JgYDyQIj6iiQQ0gm/nCWYMURyFtnJAbYkN8IlPfIKsL+p7uFn0D1N1EqGMcMF/+MMfiFGcb0JRfwzxpCLggCHBElR5Up2wXZOwQiqUGDfV/fy7wzvhM8FpccQElR9a+ciBQ80XXHSpS+pHYTzioa982TmB1IorkXjIBcsr63Oeeefd92K9wSqrLKfDg6T3bUj+t777vf2Hj5KHMpGoZLHaMHDKDhhmgEfkINDMkMP+zVh5IJb4wU9/tXrNUxiCsGax/Q0DQlFgyFdyqh5G5596M1++6Cf+aBcfFYsO56uuQrYsBywXIShnYQnySERMNG2CV/dIMCFROMB7yYzBh0N6RtjAeYIABWsDTGbmmLLMQGphAMQQVnfgCmqqY5TPC5jD+U7VFlURuYhwwcSNIYYdOcg++tGPcsJ9DlLffPvb3yZaGed//OMf/+Iv/oIo00S7+/KXv4yywkG4aRITknyK2oCwWGsY8qU0qZnH2toeWvnw3AULWGhjT1iAgF2m0dLWRjdFJPFtLEeyAQKs2MR9E+RvGjfByfb0pFIlMeynQ6e/eUonvaDrj6x58sW9BxctvpA1FCgu2gGzDfou5/AilrBlJCRoDWaqxRdecuttd02cNKGyopzQSJKSSp76oYeGKBjI+2AFPQ/3Je4TcYSI7D9q1CjuA8CKFSvg0Jdeeilgkzf4pz/9KfmrCbIkrw3pULhBbQwxnH7btm0ICqAiiIGCyH1qJcggEe6IgIuOqNaxCDxHAdBgSG0W56U8eFIdYL6KaKHEhiDdjXwT62U5tiaQThPNnMCPyBoYKhF84AZ+1GlZHKMDwI3idOtILcPogcLYgCI9/EJMwWYEcMK7M7UIH8/UEnLTZ4oZBkgnvKqqJbkgdPyTn/wk1ifCyhPLnixXQohJbBoKQeVVSjJSVBJFkiw0v/71r4k2SnkKfPrTn6Y8iQmLCpuYPsF0PC/Xrd9Q19Bg4YsJFTX0dCa55vHHSQFFAGoys19+xZXkV/JpMQqyWEyJQBeKmC2t7Ym4xNcczizgixRFa+/sWvPU2jnzF9myji7zSliAZqsFCNQTmXFKUWOZwtMi0ZKx4yY//ewL111zmcSbAy7++azghN4v/OIXv/gFRJkIr8QQhXP/27/9G0E9ARLmfe2111If4D377LOrV69myIbDBvh2DmojXN2Pf/xjagY3iDtLtgmy2dCxUH9SGhDQ9Bvf+AbhReEWcILLL7+cBHaFf9bIG4PrATEDoQDkDEmlHPP0qOMl2rutgy1eV8rr6sn6+xnR18VOKtvawcvhTIDBATVS6qw9UBgboDomGLOag+TjUH+mOjE+mY3cYZpxoo6zNnzWAv40F0sCkUkgHG9961uZ59wkKC7Jxd7xjncQQZpLlc8EcQ9hk1j2RLEHDEgDWah4CjDLli2DDbzrXe8qFmA+5Cpcu57J5Q4dPEy72N9zjm0GzEdXPV5Wmpi7YCGrtc0tLZBXzCG+zEegNyHQSEW5jHOspX1sU4Mszfky0Vl749QCfA43qZ2e3759B/YoMxjh20XXlpURlBP0buK7U6wvZCWxA3x3INauI9FEe2dP1vGCAoKAWRTRlG4nvCg0d/ny5WvWrMFmyAkxyYGWtMP8EvlZ3WRYT/2owd/hCzmUKgDXWbp0KZWjiNx///2ktUFMoSGCiSI9oJAhEHCC4AKe+KyXstJ7I0dxewDsciyJ6USIPDNUeeio+egTe491mHpw7EVLZu3cdfh7P/r5RRcsmjZlIsjph1wpbvsjtQ2xBwrTjtXk4ZfwbbfffjsT+9FHH0XQI7AzGcABQaZXkSaYqkfNc2IXo3Oo+jlRvIFLqB5zmwMeQFz7u+6668ILLwQ2ZjvFQEcqgQpgLiAAdbHMQX09Td1GLktqrW48hTA6gNnJdFqZJqD2RJQuKyuFddErcAIWEpDNfalbss80N5NtCjrWV9mQ/vKBfDuvPvPMWt0M2JyikAlFh+XIP18nEPoOBDyQZW2R11iVlvz1lMPBya/BVxeGRxj5GHqEbldfRRoAwEMMBzwWb4gpDdsmDRlWRCKEMxbD/Hja4gABqHnZsmWcgypoHmAC7YIATzzxBOZBzmkLYFBTQA/KKziH1N8jL52tB3Q0UDdtZrKhwPodbXeu2KPF59RPvLZ6zKUTJ80ZN34y6c5u/+O9v/7N77HS+YbI4U2As4Ez8nyQPVAYG1CVQoWZS6jeRHVfu3btIaLdHz5M7gHmm6LLg2z7rMVUQ2x9UsyAS16BlDC3ucM5J0BCu6gL//Ef/0EWTHQF7nDAOXxCoWOjgDRwqNrO2ujgCkBsyRkilndUIM8hanzU00K+TQbmYyKZi4JkBhwyektuNGLKuZ6TFhYJzKzNsoUMixBTYRisAApOD0DlscAQqItVYJaiDTLZiiOqaeCk4RHuCJuPUH7/9/hwww/CbCuGg2G9hUFIjBmd5bxhHnwUQ8OiEbY7rEAICtyBB5NvAPb885//fOLEieQFY3QYI4UtQxgX3pXe83ercK5ijyOLIJqQ4YBPIC8eqxTYnWDDAAN6MAaPP/44eiEnw/zGYb6uIB9mJefn6wSKCLsx9jBuO9i+4ql0Zf0bQtGxjpFzzawte1W0aLy0YczEg8c67r5/FQFN2L5czA85iaecdFnMll5tdRVsFKIDmH7MLqztzCjI0O9+9zsUArRy8Js7PC1WJ0FQoHRIkWSSYiaTd4yaWQzEOZXlYgUJjeJBhIxJinNFAhD/yT0Cf2KRgDLARjZEAIZeFAsw6kHRgP6yOYAMw9hYJD+gZUTCbNUK79i1s76hMUlylWCourahvbOT7C+WnYrFEmgJ5Gbl3QBRQMUKAycY+kyg8/3elg6XvcEYSSSaF5/pL8Kd6XPlWTBIKmWGUJgA16o4v0MbPzXuDBnk/gc/+AFjwRIxEEKFIdBLlixBXFi1alVNTQ2L9piJWL+FN1MmT9ABafAHmAYXoVEOGoXo//d//zcr0qAKlSD+wwO4T0Kk//qv/0JLAItYFWCtglzE5EQbfEMjJQffA8gguWyuPRlZ/eieMRPfZjgRxCR2JxKOXeG5pKbX9Krq2n17D+7dd3BcUyPC0uDrP0vJkxD3pMuzvPyaflwwG+gjPSLiMoG5xCeEqUgvQmc5uF/EHmUm08rb3vY2JjOkn+TDWJah+IiT3/3ud7FEsz6BUxBUZt++fVCW6dOnsyr4J3/yJySnpTzvbt68+V/+5V+AE6rBb7Fg45ORr8NhIxyyMnjnsDhgmBjkl1566b69+17cuTMajY+fUJPLZDrb2+LRyJGjR9vbd8xffFHAJHNOOh4LE3kCngBTHT5IoxobbDasiUOeKBi+U5Dvygq7UtT9pDaklJ3JJAmApwVwWCLcl8Sk9N8/qWgBlww92R9JQsm6/Z//+Z+DDLx84MABFDLuQHyhwmgGjAKiAwsqJAhTtcPsC0Ib6Xwf0xQG4iiM8gGz/9CHPkSFKAeoRySeVJWDM2oZiV+a5hV1f+S36D3gaqSYDK9Z01ZdiTtA3DU7dSequVHd6L8OxNC52EuPHDkKGyg6DCMVDqEHCmYDeYrPdGI8McKShZiG1eyCag8BiDO84luuNSwJZBtnAZCMhiz2IuzDBqDy0A6oiVoNxkLF/IfWUxu2iL/9279VuYj/6Z/+ifKAB+RAi1pwhuYG/cgPxiLaj9XUVP/02vXV9Q1EVYeShqKxiZMncx9/fGqji5pGNRB8NNPF6nUnzkS2nQ2ZelNjI92H+jzoFgcoKPPJN8JMmDB+8459mIFYK8FSgt+eLAL0soOTm+AtWA8B6NFL8GQl5h3MUQ3rMDkS5P4rX/kKehhcGd4MxWclH4qM9gb9RfbHNKQ48T333MOgIEAwvjQ9BLRRA8q7rAF86Utfgv1zrFy5ktUIsAJkUMsSIMz3v/99ViPIRYpCSYtwhQG6cuRWUXrA8Lpz2ebW8rL6+Tm9zdDbTSdiOGHZOCYySu8hsl0mc+xoczaXC1tIISPHy9wDBVNtNY2Bur/45lOWYdKQATqCSZ5vhZ0BHPlC8AAl+mFkyAuV+aec4EGknIjUTQV2Hvj+JYd2julf0udp2sUXLdhDqttcxrCCkFP2Sspdn+tAqtjljJlsz65du3fuWrR4sQRcce3KyvK6mvJcziH623B6jc+hCVpbuGDBgSPNmpu1dBaK2ZgGJ5C8Nr1zz1cIFKFnpHgLup91MpUVFdiECImkdAgBm2M4AGna+973PriyYsZUhgbAnMd5Xyr2RXhF92+88UZsd9zMjy/nBR2qNn7pAXJQUxuGJtqFzYMbF198MfoHj7ARvfnNb4YVoabAh26++WbuF9RQEQurIQAqFMciVnv+VMUHEs/Ksas0rcpxOkBGH5+UofI4mLJ9XfMyWbIEDg/bjlfZ/4w5JxIqwKiDZ2qaDBnZ+tf+qjwvmA0M2At094D3z7ebRYVTSdluKBi89tor7rr7fkiw5PWQiG0WWAjuQYrJPrZ508ZUMjl/wYJIOMLCsJ1NTxo/Wl72dxez22zIvcTnQNGhfQ0N9YsWzFm/YTPZmokcwVoFlfvVAgb/ekeHycBMADSyc6K2LZo/F6utwMGPHFi5+B3iUPLJkGAsM35VvT/Ahv8oQPLUb1rUFw78C4QXDeOgQnUgBKB85GtSbIaeUU+5zyoFhyrAU8Uq8uVHToraAwYuE6aO9TWJJMLame+twEZLxQ+OjzijD1ceIqoNDHGvtgF6syABsvk4wE2ZXwr3Bn5v5C4kYKQThtcDQlRrqivGjm7cv29PLkPy1TA7dR3fEQhCt37duhdf3MFa7N69+yCBnZ3tJP+aPHE81iLUCWEWwzgUSQXFoeBzZ82oq6nMZtLkO4M7yAad3ur7aDx6gGmKU41ppFLJBfPmJkrj0EogET9Wn/z38YOhwMSsAx7oLHVifMtlSXUgFn8uFZzSRB91Hv60hI5wUKFqURF9fvN3VFs0Ct0HAFWAm0MwQA2lO16b77BhLBCzzC7LPGZqOd0L+kHriH0rptr8ocYIZwo86vI3i3UiQyxOw5YvI/ELbrO9X2zCxWri1VdPcbSBV1+/nO2LoK2KvApysT/ssqVLUj2Z+x9Y1TRxck1tPZQPx03XyS2ct0ibO5/1AOhiOtUzc/qUhQvmQJSE4IKYw0ZNavAJnMv69CUXXfjt7/zM07saR4/HP09aoA3Pd5FiGwHkmLAWhplNJSeMa5o+fTJeSyI3kaKeglJafdGZPx3xCr7BR8vas8ywfsUh7hBlbsh39fs0LiHZikZzot7In/RWoBrPwwBnUkD1q7//KbWpy5P6EADUHU6g/hB9LhUPUOXzL/avbeS8KD3AREhEo4lEezqz14gTpyRMBCE4tcyRXvSSUWMIcKioqigTN7WCjt4xP+UdQRV/T7LI/vaxw3tbWg4lU13hULi6tqmyos40w4hm/kqcILCPIQU2fUqbr6YbI2xgaKPJxuAUmTp0P267yN6ad8MNV8+eOXPligcObt0QCVYEgwkTL1XdzeZ6NCMzZmz94oUL6uvroL2BgMjIChl9+js0GGTbRN+bWFp7EonIjW+9/nvf+1VbSyvxgjBN4ZjqOTHDSmsm2ShzxPsKmsFF82fMmjWeOaGT80OOPLntR7n76s3/9SOlUpZ4AIQuReGISnAYw7VkcvXyAjGH+SApUqvIMSoI6gb1+CoHdqfe5vwZ3TutZZdzhr0NGK0g4vBIf/uzIakRmdb9OU0eHl8HEoKiWsnfV3SfS+7nBf+TyuQLv8QnClqYE2rMS9z0S9McY2caySlTIque3lAXX5b2Aqbl6HbWsi0bqQiWYHigjpdxw5Y1fnSt7iXxIzrNCA8M8ukYQVbLhPTg3q2bV933u7B1bNyEkBHqTKe9F9vKX9Qa6+qWjBu3CE3VJguCQYyvsKGHC2p3YGheLXdH2MDQRhL6y+YsUWlFsDfYEonfjTN6bM1N73tbc3P7jh17MmmHTW6kdzGtqgmTxjfU1skE0HCj9lssKmXC3iNZZj1t9Jjaf/7kR55b90JPmtwm8ZbmVtdpD4TS4agXDJSEg1XTJ02rrCp13DQz4fg0GARRkvCQQq358BDbzGAi/sYHMTz5qs0A3aiYgXrQywnkwmcJ/ZkAt+CjAS1HfhKDOHf0EABRK5uLoPK+sjJA9XLr1F7M38mfnObVl/f2IHr85QVwaK27GvrgtNFlO1882nJofaL2onQuzGZKR0+BN3yzKGiOFw1HZs+cWlVdr3lpzyQdZgGHvzH+5PJi79RCKx66ZcNTv12ycOyEplG6lsYZQwukbd3t7mle/+zPn3vmsRve/H42cmp6ENWYRgtq9+QmX13XI2xgaOOJIZxwiaCSrAq7WhaiBZV3vAxST3lN9SU1tb6/Jhoo+E8ETxZtPeIY+LRJFs5YQMZp028bcloEhDTNKKzI8ZKBiHvx0jmOG2RXczZDvEfyDeTYXGboIc9lbzPgiveeUNgC2oVOY96FmAfwRg0AOEYn5jCGJrLr9Lm9nuEz+GCx90gJ9dX9ut1nJDkDPSMDIyAct2km/KVF9hzl/A5UnLPfK6/MU8WZoIRnUrtemZ+moBbjHyw913LdZU2337uje38kUb+wmwATGCbtLCjv2k7ACs6bP2vi+AY2FRtGWASjU1HitJ0gc+7Ug6BFqx966PFHfnXTu8eWRjq8ZFKzE2yNzGXtgNmdCPdcsCh2170PrH+uftEFf8J0RSsISCDUkaO3B4bCBk7VxKlswJuv5m72CRsO0ezWIvMjYrJgKKQXRx07faT5xebmvSyAEdEhEauurB1lhWWTvS2ZYCyfObBoKQaQonWRaNx4ZwgzwENJ87IBKxeIsFcionthMcKyt8dhn5gnsbAl9aVMg8GPGnwAa478Sog8J9W9desLj2JncogXSdP+Z/g94H/RSXTO53+9T8V/PP/V/ntcGXoGgU7P0UHlZeMnz7zCMBQneHXO1UIYcNEQ5CWoSAhskA0zBBZyX3/llCee2b99z77EqGk9GXA+UhqvGtUwamzTqHFj6wnAgvsO7tNKqxw8bCcghMIjYnkluzc884cb33BBWN9jZzrCZommpWQBwjUDEmQlZQaOXn1548FDu21Sc1sEA1ZIeEJlg4fh1VeyYDagluDwvsAwraQb9QtBUUf+/quvs074IlAIWxDbJjUzl7NMEC7ITvquLS889OK2Z6xAEhfqkmjYtY19GSsYLGuYNL9pwkwrUJrLQhT9pTHfTg6eFuNgcS4FuTfcQNDCYdRp69jx/PMPdnUf0rLBkFVqaJHamtETp82KJspzDlZaBPpe3+r8OuoZwYBhsQqAbO4fVk86t6Gi6rBpR2yz05O04+oABcRQAw7gmQmngR3wv28NN1gUgU+5xBXom30Kc3iTKBharizsZS3Xdo2jXrrZjZXAr/xFF9SpV8lB76hPViayV8lX9fsMti2mAikyGwdz0YDRueQCfXoP0r7z4CP7/vDwhg9/5O+uWrqIvS6a1obRRtJwG7hQF6gNC7L1kn8MsaAXkRo3rL13+tRMdSmuGGWWEXWk8gwKpemF9GwYXUHT26LBTEO14WY7A6Eqj+SYoo6/SrTMfiMwxNOC2QB4DCdQtD4/jXuRGxrwmjkwqfimHuTuIDwgHNYP7dj4wD3fb6ptmz+9JhohQz1hMFIGpm2HLZOth5sf39S2rappTuPoBayE2mQH0y1fGegjisPoOuYCi47sUCbS9c6tm9Y8emdJrKO6OlPfaIeiPQEj2tHstjU/+cSqeyfPvLy+6SLdqEQMo0GGMr+Oeqb2Ie6YvcgbBUmHlmPqDbaaVls8XKNbo3golNx/nwVhLhSGcAOPKSaqkD+EBj9jKPoRyCMzGZIofwVuZiwaRdBp1pOHk3Y7HFS4lAThg/cUoX/O9Gkv4bNX/TRhIDH8eGJ8Dxtaqxk4VFWBh0BNfTQbC3phC8lcT3XtzWR26HoncQ9lfRhZqt8G47ONBljWJ3MojCPBWSja3bm6obrbcLuCWomdCwkSGj3CICTUFzM0Ti5WXW/R9bZMujVYWjWAZfJsDb+6nxfMBpDscNIAofOijeogLlERoCn8sjHk1d1rfB342GvdgFxpztOrVz74x69ed1XTxLFljtvuOBlMM5A8lAVsNYFwoKm2pDOVevzh50prXrj8yrcbYsqXPsS4rijy8HoMMhrGF/T3v/v2xnW3X3vF9AlNJQFioMIZjAzZ58uqwmNrAhk3d6xt9Zpd28trFk+Zzo4q2aQNCD5dPkv7vkUIbx55Cf6H1xFB/0oq5xml7BfrE9gh3RBtqLvyhGGiqn8wA7ar+XE+REdXh0xj4R/yi8ZAAtvctuyBxzJdSbgkpja8kMTkhr3t1cMI5MvpcP/De7vh1fTHcI1INuQT6oyD14IW8hAzUAPtDt3LhkQPsJMtu3u6nwlYR3W8zGADuLEd1ybP2hnQdSqRQ9BCkFK2JbrpTbpTJt5mZkZ2aJNlw4t6ela3kp7VqWWbNA/zKOvXnelcSwlIKn4HI8fxHii4O+h3dRAWhugxUHxEP0bipptuIooLj/IqwvFGhn2Wr1Zxmnx9+ftsV6JdJdjmb1JYcSxVPn8///pwTpjKoBVUKmgY+w9t+eOt//OuP5ncUNvtZFK6FTWsKFloLJaNjZztZXz/x0Ml8YqLFtX/8tY7oaRXXf0eYCbGM9kih00UhKxYgeAffvejrVtuu+mmmRWxpJZsgYy68GWj0ZCVgLRndJhmV0UC6anzp7/493e+998mTponLKzPFfRMYIjqw6STaYwubWhBw0tIvAq3yjBjPFLUHJsApl78tTs6OwnqGQ7GWItDKEMnIPBfaVlZNBLBOAvrozp4TyTS661ET3Zr8XhgXMbcnDbTaAgcwi4Etj62IfdOOE43oPn7CjNPeMenwucCRU9q5bV5iadvJMuqr5sze1j5ddwKl20rejiLioAaKciWtQxk9pag1oozsGf0yDTqxcCT+szHOLmXlwIE0fxgWf46XO8DFEYzEdBDerntZPVgGykBNbtaJ56dqAJp10yykmV4IXgDftqakUKQ9YyiBBY7CeBX8GXBbIDZq0y9xPJlOhE8gFnHQVYALpl4RZxjMmAiOsG9daKH3nLLLSQbIHgAMWSUGMt9YsWQe3Lv3r2UJKIcgefIM0WsY7jUI488AlTvfve7CS7EU6WmFAs8EURcy/Es2+t84N6vv+F1TXW1Jrk0TMNJ2yGy8GGST7ZnIuGspncYVqmpx7ScE7ba3/z6ic9v2eJkbSMQtY0OQ8Td01K6gTALysjEkLnBRzHl2LTLZpznn31y97YH3/uOxTGzTU+in1Q4lsR5xzUbB39RpUUCiuHPVx1pf+s1dXf84Qt/8TffDUdr2WhmwKi8iPjtQ8d7JfkTWmYMmH6mW0peqayeNbVgOF2WdZqdULclDKI3MhIus3fc9QcyEXGPrDtvfctbL7zoIjo82ZP8xS9+/Z733PSNr3/z6LFjBLgGT8aMGfNXf/lXinPTmTHNMXMRG2OuAdHAkQnHUQ+zwek87MENhWwMKwNN9mMi2YGN5JygV8BSLslNTQC7Z555hpJ8D6+QABkkAXlO+LyX6gLA6A0oooRyUkeexL1UMJzrdlDiHN10TBw1rYAd8CyS0Zs5PWRqYd2IIgBpwW7DTAU8HO0sCvvwnIr/kjgjD6qP61yxQYVlNck1BMIEA0En7QTMWECLjq6dRj5Yz8toLhkJsT+CNZSxPZe4UpUWXMPu0rLlulZf3liWM5M5LUIQl1dd3+c7rOCTgtkAeMwc42ACE6rlsssuU20yNmr6FYvOUi1VqdoIVkzoyte97nWzZ88mtQDRwV7/+tcTF4EgNuTCJLAlEc2Y6mSYgvoT2ow8UytWrCD2HOHGCEH8uc99jgh0ec5RcCcN9AJGEVN2qHpbtzxVXeFOGF+F5ivaqGkne8xbbr+9rNR0O9w/edNVgUg5lNY12FsfMMjNGvVmTq/KZg6FrdG4GIkJpYADmss/Dn9qyKlQFqJZb9pw1w2vmxXQD2u5JAYiNmDZTAMzhSosq7u9rzBuWi7TM6ap7MJs6cb1jy2++C2ITMKHxEaPYEbML2oeYIL4TWHflxUCGRkXryQiF0mQgLzVJtmd2r1rzzvf8e6pU6c89OBDP/jBD6dMmVpbU0teoprqmrra+v37D77pTW8kDwTYAijEYVVtMatN4O39MuqXlQP/23zAB/rxUUOMWnAdQs+Ch5wz6AgBxBMFFQknB90Hc1avXk0GU5rjkljTA1U2cq9IPQDnRrZnOP1xJb8S7gPsH4OC9+KY4CvjiwYA2pMgtdfH7JTmMXLi8WyjTKOACr5pLCQgGVhZww4G9KCdJcWZYZZ15Sqf2NS15pldYypyb3rj/JyWdLSUYXRrIv6D1tiCIPittt5jaKMdu1E3mzQX4SKrj2wf69fphbEBZhfvql8m1c6dO8kl0tLSgoROenHuMznV035NDPGUemhCCW6EjCaLCJOZushpRRKryy+/nCmNXYVYkiQSUasRzc3NKA2oAuQiJysOmcg4sFxxiU5QLMDke8AwHbKCS1r6wN71kyZU6G6XqbnI3YYV2n8guXtn6zvfvXhy3cSQUYZ5VGweHoJ5EA823egMB450d2+MxGsDWjxPQ4fQTUwOIibgDLp759ZRdamyaCagp0QOQuLUsmxdJu0x8lG+ZgRo4d+IUXZ2fFPp3kMHc7As8g1I1jERtbTh+enHS+J/9Vd/xZCBBgsWLrj197cyFnAJ0s7MnDlTWJDmEWmOCKCApHIA5GEr6IShVGIHJyAe6cxU+GjiSJNiDNwAABJR0ChyAOI/+SdU/WAU4gKQUKCgFkcKv7Q9wBi1+iOEjM9efdYagxrSjKeHJEKUoYVrOrPWps0ta559YvuBXEdnqjl6eOGiifWjSG+Q1j3WG8A1diuw9pXN6d1GPLBnb2ckXlFnVXuuYzFNBNtHcKB3VAtjA4rKowcwnaDLiFrMam4ioTPTyA7Wn3YPE2+oloOGqJPUMUrt4Jzg9SQbIO0lMYQpQL4BgssjEvJLIky0fmY+1AfwKEwBFIinnnpKDPE+eRomVPnXISV4vmSzbbnMsTKcG50eCCzrvY4b2rOrfc6sRXPnTnTbU6ZdyuZY1+iR1PCC0Civ6XAwl0nudu2kgYNzn3kgX/PgT0joCmlF3lm37unJ1e1RK+zkHIQmVkl8fo2MzyaZ47hOb6gBYitbSaAnZpGIOIOjhZQQ/o6Y1ueGMXgg+pWkfrqFEP+rVq0iIzy2O5aLoNeY8sgFBP1lNEkPwCPGiBwSqHSKzfero4BT2uIoLS3lo9asWQNKIAcsWbIEMDjhEpwkRTYnJKckLSWFaRGGQYECmhkp+pL3AMPjzxcWezHysPsSsYuNJe3oFHG3JqWVr9+VuX3V8zv3tnipdGXQvHLB6IsvvHDTpqctc3RdfZVsVUOowSuUmSfbPBu6u8PdTmlTw3S0D1YkxIV05OjXA4WxAeabmsxMpDe96U2cq2nMeiAZ4S+44AIK9Kt8WKeqKhqCdiDsK3mfm0xm6lXCIK3n73CTTIQSQdOX9fhVNcC0OCnyzIekY0on9XmGoCgZHOF8QyQ2ipDjhA7sOzJtTmMu124J7kb0QMox0louEiCiCjn5vBxbueAgvvY8rC5SQi3LA1s2b51VR9ZjBB1WGoLYmvzlAKYPE0m6Sx35ToAMsqemtIQIj+wnw6OJYnCC/PO+Fwb6yxQV1kKf9o71cZLKSNHtCPssxoAbaGZXX311MplEG6ioqODpBz/4QU6IMg3D/vSnP03OSHIWUQ/mckA6XtFA7fa/B6AMPRVyk3NqQDGFtZDZB4sQNzEVogSgcJCjgvskPEBpQCkk65lKU9G/tpfsXPUvjjMFfOpLBlzRGwI9GBp/jCTkrrilgYpKzmCoz0govIBrV8nU17s1q0c37ayTC4Ui2XRgx4vaqqefXrfrcHtOi8eCs6eOumbhxAlN8azVVV09Z+2ade3HaseObYjgI2qkrEhIS9Zk0mVGdMyFF8zXrVK0ZBLHGi47yASPRw7VA4WxAfCYec704yCXE4J5dXU1kxABEJssNTL51aLf8PtXTW8q5wSdg0SD1Enr0BRax9bPL3cowGxnYZBzpH7y3JLShFcwVaEoUJ4XARKogI37FCvOARphGDJDQVxlJDyARFhgl24unWlp3j1+7CzNPgTstpayNbxH4wE3gHAO4xD8tyNhs5J3fTvM0EFiNOAssGYUZcNgNYyAjtJf0FXBcaVNy56vU6cclNeLl0QICcpiBmCIEzezVvjZ0OGhY+nkKv9ANfyLv/gLDPQqTxz9j0KmskkzcKQIRlFATucVGeIhNcrg8jr8gGFlQYhL8k7ffffdEHoSVqsVgnnz5qEOqlbQTr72ta+xkgTCcGfkOG97AH8EVgHw6rH1dtYBdD2RzTZuXe8+vmb3hn1bbTtZGXWWzmi4cNGkxoYEAVTSRjNB3OPRxNJLLtq759D69Rtt0wmXJg4d7Z449ooFF94QCDfCf1yN+EX8A8MFc0aOfA8UxgaYsbypZHAMNT/5yU9Q7bu6unDUId8vj9TMzNdelBOaQ8/44Q9/CK2HdkDosQUzk1H8yTm1evVq8gtiAgqHw3gNQWvgGVziJYKdGkkQwfAv//IvFWxCcYrECSCcLK6aFhaVmB/MJ6AJPdW6u1rmzamvrPK8LCpswgzEdh048tvbV12+dMbC2awVp2CkXq4sFBut6zHPwJ8Bq9AQkVJ6W3yu9erqCtttkw1YrNmignjsopSdBBL4SLbnnMwGeArwPdlc3CPoEBq0Sc4cph5uS8ORkWC9JKNnJR9JnCUZbEGoiZBm1oTpf3LTkzWeZSTo8vPPP8/Q5NPJYS1iuX3w2MI4qoPRfPLJJxFBpk6dql5H+QAM3ITgNJRRa0UgDE9RFkGbc4Gig4f8NVZSjenJ6NevE9SjU4eeNHLHYAWGXtfabDzzzP4tmw8fPugRMLFmUuTihTPnjK1oSKDqdjrOEZcU4LINnWmVwSV04qTYqPHjjnUbD63adff9Wz7y1+8IxGuJZcTUt3p9JVi6FpFn5Mj3QGFsQL3G7ELowzWT7K+7du1iaqHso3cj5eUnZ76BIZ9QFe+qCqEdyJVf/OIXIfFMY+g781/5DsEDSDbLfUDCJvBnf/ZnFGBB+Otf//onP/lJyAELyCwUK9iKxgOwvsjOsEAoWBoIVBw5tmdMfSST69Asu7I8csMbLujJtuEWj4fl3v1tz64/kiiZmSipc/DsDGC7D3puuWZWaxKiLU3YoUK6CORV/+Ql6SJhbMb4CROy9pO4wLH2i0Odb+XH8oA8JaV6uQBKgqgFzACMpnjqBds7UzWSNdNlRxeLGxIcD0dNmNfABy/idUrseAg2rcrw+I4eaibLVWlpYtq0qT/80Q8CVsCx3T//0J8nShIdHZ2s4qB9oJzNmzf/D7//A/8Yjve9930TJ02SfIGyMdl0NNvXRHwIgRsopBH/5BR41Dgy4viMMeg/+tGP4Ci4jSGagABwIJJf4ivMe/Ae0AO2BKtg0fhP//RPX8YklKd8x6vvhiCPyEeMG5KGl5EVMY2AhngrSDYYwtNKWiSxP9p47CCmqOUq2Ugsd30nI8YdGcasOHIkuOHp1HPPNLcc69GCXY3jjesvHL1wal0sHCRVBhZQ3LOpnQAREZMJGZaGzQ4CFCLS1NROqKwMhiO6Y1aio7usCbNO5sXFTIoCLR5uiF+nQ/VX37ic5YsKYwNMP2RzqlS/GHY58i0UV86iLQ5VOSe4hOIBggZAWnOmNHTkb/7mbzhhhRD2gFWKOxidMT3zCkrDZz/7WZYrUBcgAdzJV5WHdngnoDd4TKwSa9KMJds37Ws0o1a2PeSyg8XI5VrwfsfDoT3d/eT6jbMWXLpi5eOVZZV6rlOWrTz21NREyypxpENuD0PFCwPleHHi9sh36frMGQsOv/hsTksH2UXJrjWnSnw5g62uedSyE7oTpBAGG91ykJsyYhsryWUiXhbTaYnrxnAo8sR3gok0MD7ASJihrtlKLFXDqyOPrBMiqKpmokj0W1UOBow3v/kt1113dVdnZ0k0Ho4n3Fzm5ve8K8Y2MZ+iX3PN1csvvaSjrT1RXhaKiBYlztsglOGlZAccPlVp07NcI0ocPKa4yZZUFvOOf/HxngIDFRJec801eASxbwA9AEMQ/IATxfj5TvATtQAGgM6KkYqS8M5iI8NxqM58ppoGUYs7U87c6Ev5FB7ObgCECkY25/UY4YBjlHd1VR1tbWvPpJ9a+0y6q3lSkxvFHGq0a8juuTrBKNDIzHACLrPiphmhrpS+em3PqofX9Ryliuio+rL5C0ddeHE8FmvX7Ha8mv2GfOKADquzTxkcMRwzkwOf7Li4mLppL9dDXQHc89jtaMo2FJkE8o92RnjACXgx8LQ/ocj5ccEUYsMRByesDzOUEH1A4xJmwAJgHkw1zbARQQt4yqWiF/kCwz+BdBPfHFEVmaRxzIR0ekFz+/qKcDnGR5FokFBwJbWircdakx25NSufObb3UEfrrorSmE1MlXRADyQC0TI2HhCdZzgiCdSEA4F69Lhp2a6Lk6kd0XDGIg2s7BWIIOUTE9o2CfVumE6cvWG6AxFPhRGXrNCx1uCYKfOYcv7GetYS/NAu/vQaUEhCfZCtAqyLi4TueIGcHkz2tG5q3nWAPXTK7uT3tqzhszu6lZjbxNXzt3Yf2iPbhqVa0Udw4jPbj7HIDzvyp6TcIvBFpiSXqi85HHKynp3Rglkes+Q+IDAnjSBcn0PdREXAhxhXMcYdJKEtlpGuuuoq9VRhTtHx4SR4znrpc+WzlnolFkCQd4jolmXLSqi6Kx1+/MlDR45C5RuvvmZmd9K+774nnoq1XnJhYMqEulAQB4pjGSPFbpqA7QQRqrSaQx3xxzc0P/38oZbDKYSSUaO8CxaWz5peWV3BhpcOnU1pIJK4tB0/hLr7DAQk5KHsxxQ0gxNlYStBS0UjwfPIZwO974GLI8fxHnjFsAFAhsrwyzxX0pya59zkkpuc+FSx1yWcO1xS5vi3FvOMlQEJrWOwiVizx024aM2qfV7Cqixju9YxvPlRidlEMKahfNRbr35u/e6a8sD4iQ2O2d2dC3Z5sbGjpuIATZxpS6yfQ8dIPtD/anTo0Jix123d9HszmDasQ5rWbZLjDPdoL54JMNmYIER7x4iKHEVvxTvb9GjZ+NKa8eziQisX7U7kozMd6Cx4a+O+LZUxCoTNc3sc50Ur0I1u778pVTMjAYlrwwgG/fVrH0KZp/0OHZ0+FDou5DN0YSeYoNOSXZZVgscrtjMUGt/j6YQ3+1XSeyrN+RRfXXOJJVB5BOBdphDAhwF+KUgiML5Mh2oaGE7qjpcJnOI3S9e6TkonwIwRTmbqVz5yJJWZWdMw0QxE0m42UFfiNGHA71i1ds3Rtuwli2KWdQAU1vRYzqto6Qivfe7ImmfWHWwDwSKNJebSpbNnz48nStlAcFB0bzdisA6HfakX304LP2DQx7B8SuQ9DF+2UT8tmOfRg1cSG2BKK0JP/6lzmVE+ezhpbqvZnv8tfn+DukgcYsok9QpB8kvHTbn6lz/7wqQJ2kULSi0v5bgsEEN5U4Fgauq0gGGOy+SincnQlr0tCy64JBgfk0NdgGRi3uzNBDkUGPnA3m8kcnSksm7sJQ/du3rh7JL6irAjgj/RIcxALoL9X9d7yMRENB/Nq+3pqj90LDR31qWenoBWQ7J94VSxAfpzoPnCPSHaePwhWzHHoqHw2HC8kzU3wyWUI9mc+r8nfSOWWLkrB3RancivP0VxdRW9wB8+/xHrFVErR91Vpl5hmOXMX/8+bk5nWbVm6MEKv3DvzIcHcBORn1+/h0Q+4IQ7/MIMXl5toN9XK6hfPb8ilhvooiT9Krn7wS1W+MJE5ZQ00xZjpBPpcVJaGKW0pGHylQePPXf/wy+84Zrxbre3Y2965fpD2/enjxxtqS5xL5pTesGcmsbGYCyOtHFUTyGqYK4MWF6QxUeJjzWIQxgSWCfK6Fnkm0FU9uovMrhOPQ/6IT/VFcVXl+oc6PIn+fP8nXM066BYEHI0TUkkY4YaGqf+2Uf+bcWKHz/6+PZpkxoTcUl2Aebik2NEsGJZx45WBBMNl102O1IyJsdsYLoIF8HPhyGgsqEc+T7h5azjVjdMmrXwXb/51XcuXtA0Y3KdoXcGjJz4seLDJEsZ4YwWau0oDYSnzVy6NBAm7pvs0RE7qwDAbBFj1ukE1b6kAuzkhAUGo9GZ0ShJBKG/sbzx3qf6vaRfdjL3UfkT5G/1vP8Xc0c2L5ChAZ0/qnsJwtCbYtIFFhYfzt4/+bHOi37ySX6r/AIGvxzqZv9O407RD5qD07BwzWoE/AZixC+GKWBTYPSCUvSGz4cKGXHXdPWSDRvbbLMpVjY6zf4tM+XpGcurMAw7q3c6ZNhwSkrK52Q69UefTG58bsvmncc63YAVj1580czLF1ZMqe2Om8cydCI+1l6MZNqCnDJ6WTLd+aHlzvCpYKf8J/54/rjnWb66PMObr+VHrxg2MORBUvN/yK8P/KKQTagX/0Liug8Bc71E6ag3v+XvD+3ZsWv7Cx2dXaFQLowRhUQc6bQZKm2YeFlV7URcTAk/F5Rgy+ArPEARxYEbGfxdBGvdInanM3X68uq/nPTUo3ftOebV1ZUm7daI2YMg5mmV3T1We9aoHTutrHqyQ2oOD6OML5nTDB8g0j7AABInpx75R6og31+heSVMN+KIycw78ZB52O9uHzvoV+jE7+Yqo+c8LW0YMRaILWrFsE/MAFb8qKjA46QRP+mywMoKLk5zmzZtYt8y2+g4h/qgnbznPe/h9zgHOrnDCm7l/HyBjwWwdDq6ecuR+skzsO67ZqfJ2i+Ks6R5YUOwS2BadlGSpi9j1377J780o3ZpTF84uuTSxROnTyo3jXbbSXV6UXLX4FUgG2/E4xlGgvsRyAmSsoRwlu4TIDAVwgbAZlbx5OrlNAYK2Of38epnA+em/zFcp/39WSFwzPWQZHHWxCEn1jB6TsOY2bLi6iZ9NzhsGiAiSfgC4s8gYRUdk01kLFvIYzG1nwWpB/cBHikASOjumjWNE66/8YO2066z0b67JRRJmVrcc8qqAqUoBoFwiEjwrk48RtqFVLO2AaUFQqYcl8zVAQ9uSxl5yn40ARnqTwxhKdyf4nOpyDYT/2yCm7zbd8BMJKcxfcJcNzXclmgu8gqdu2xWuO6669isAPVBqFVGKmXGBE/EWPEqPRj0QMA4sj8VCU1yiTJtZTQ8FOyQkWOLjKSeD2RLcdyUFHOBbLQ8Hi6Lz5xsXH7h+GlNsZjRrrsvEt7EC5RIRFJmCdGxyDyh40QmVn4RUEhPdPauAzt11q7ofEQ0FcyVbj/7e6/hEsNiA69hHovYgwc94a6ggWAb8QFAW9gAEd3c1ubDz29Yv3nzxp6eTDRe0UDyydmzRo+uIdW677FPuDdkHMqGJDZdcfBT7PAgPUb6HIEZTCto1pIXxg3UH0smMY/GwqF4mBzE3LOxtxD/V+YFoqqfDdYn8TIJpI7TTgboMgckLCPSFSErJHI1C+XCTU49WJiAiPdhiM9rRCKj4AAt0ImWRyaDrKPhGoQKQBMBz4yw1I45YIAXTm3vfLqDPxsmIKg/QCk7FcwAVUApB34XvOK+aZD9C/LZ3clcMFip6eigxPW0DafcsMud0BHhEdkENBreYGsdrpWcP6/pA1dVxUI9Oa85baTxEga9sKaiQmtWFqnEVymxvZLDQCywPhCnY6JMRcpIMcqBa2xIYXuMxJwe+BgIawcuyd0zVCIKtA/naRGV2cJUUNoJ5qrTNvLyPRg6G2CG9xdwjiu8L9/HvIQtIwvHwTc0TqwkuRzhggKETSZwxYOrn2g+1pbKpNOpHHpsRyZ9sHnTzn0EwqubP3vm+IYGn9IGoZ8SWoI5oajr8ECnGpyOsIciTRPp6Ghb+9PPPnfoSHNbW2cmbbN5gkAWpSWR6VMmzpk1I0AgoRw5MKHeQqWZp+wh5jPkPxH5Bz765gF/JRIDhXHKljnLDfXMxwfwnSvwHtxQIpjfiiCLvOUvDav7/KoTaU8qYX5AI0Q70rVSVY85uNxj1M6hajtep+9HkL/Mn+RLSrvn5sBZmdwYbGWAB9AP/LKHkZB2qml+X62ThflAbEM9YGbdzrDWbegs+kNtwUqwIIi3QtBhE0tnJpQjHUzYtaJeygp05rxOBBdxKmA3OyMC0RTCqXwEBE19zOT39ISbF1hGIsWYTWIDplbOMUJZVqbJdCA7U/zlYpA7n+ZBxv107ESenXJQyYDkmwBRPYSL1M0wQHZ0HNyz/YVkaxtIHK9K1DdMrKweqxmSGxypUdIRel7IKnyn0CnQFP3G0NmAmlfgNEd+jhUdvvO4QlC7FzElF4Zm3PHH2+65576Zcy+urW2E1lvshsImgBiDa79rHzva/tCDq/aMGbVsCZlYMAmJ+wxY70sKw/1KKkELhgGAa3fcdefqx56oqWusrK4pq6g22ass5hmnO5l8bM3a5ubW+XPn1NRUoQfgtSlrvD4JHjQElO7PKmSOqYPZCgxIPXwdhFw9ULiRZwm0qIRiXsGfT0nKfRVIzX3V9aJlXzV9RU7z93QYCFrStHop3+5LgKtsVCbkNbvYFABQH1nvPAf7V07THy/fbdyxAlqixEwnj5ZCFmR1N+MSZV0noxzZdsi7odnBFPtWDCOa7jwU0tOBcLnEAwWD+8g90Mu4n0jzT74++ROlMWQPNguAWeCgSXz1bAzl2/I5y0CD3odrJ1c14PXAhflcN5cAWDY1rHjwlq1bHxzbFGmqLw2aVle3uWXTM6lMePrMyxqb5jpOANXf15AHZCcDNvrS3Rw6GwDG/NRiHr50IJ8fLeHk46MG+ImhPfiDH/547drnLrzwMs0KQ+FZ3CTWKViJmJNDQEKEDpYQ/3bdus0vbt3+9re/jQ2R7LJBRvaN4EX4JHSRTCb37e/+4NCRY9OmzQlH4pmsY+hBcmGKpO0aoVginkjsO9zSsvLRCePHzp87k2390CYG0Wdn8jUDI/vgoBPS73vpBYO+SMfH+Yd6G1LIFed5iQzKOLiKz17K/4ReH+L+pZn8NNf3jfJEgaTK9y9Z3HN2MhNbhTgrsDp8hLAREeqqf28Ut7nzpzZZ77LtqirCRhyy062BcJPtIfS3alaL7kTY3p7DWch0bTsRYcN4tnPKRJYK0BjEq2fYXwHxR/YndKMfpgLcz6CLRZmb1AwmnNJEccgxm5i8XPLXP/qa3fP8G64YHYunbe0YokcZDhSG3tKeemzV8wsWfWDcxMsQG+kfJXcN+2OLXMGw2AATjABh4Dp7OFGEiwyaXx2Ddyonz9/Mn5yh6cGUOcPrp3kE1kLFIJtIvsaq1Y8/9cz6JUuWQXYYZiycNiZ4M4AJBLuQCMciC+HGHqquadyyecMfbr/zvX/6LlKn+oZMXhg6+e39OjwrHe//fe3rqXR25uwFWKQdnK01jD9EABLfVNzniB0kBDEQYdv+j352y5GjR954/TU5jLGyLRjb0NBhyHeREgsIPcsaKaSQNVJFcAHyoYceItgnsUePHTtGZhg2fhNtVK2d5l8v9IRqeQX0gKMQVZDQoRBfLGCqHj6WCKYEF2FHMQUoDDA8JfRQoQ0VWp6gqgTRAwAi6xELi13NBEBVoCqYC63wlVIegu7mgpFQbtH88jXPPdXQVKXbCQdbH7sFnCiho9KmzWqSJOXQM7Z3dNKEUM5m3wmy1DDZAAgeYlnCJdWwhvjFXhnWyHpMPQf6n7ueZ2KzMejW275l2Ov/5PoJptPuZuwMOTglblKPoSfrK/XY3JqN61dUlE8pKasj4euJyvT5MrAFswFmFMPGQXif73znO8QWJbQcUcM+8pGPoAgLofGXworyfdRGcxALJg8HYSmJU0/cCMJIwHtoiJs0RBnmNvSFaU9sUcrzCK9tktAy7SmPkg7AFOaXR0WBzTd2CxvAP/y2O+5aeMHFEsEQ7RB/OABjk20qFY6SA4/crBIWATpL++yxHzdxSneyo6snGSMBgai+/BsKCZYe8Q8ZDN24694HWjt65s5bgGHGYa7J2rNP/qlfdt1ALrH/GKxTMT5zFiy6+94VkyZPmjhxXC6XAWA/bT1ciQ8aevfwyUT0gyIT7+/hhx+eP3/+zTffjOWHlC+QfhLCEP/5S1/6EkID0jGRR4kKBejqKwodF3CDg3dZj/npT39KE6ABePiJT3wCZsM3gA94bSKJ//rXv1bZDijwgQ98IM+chv6dZ3uTgOdEXSTpDR+1b9++//mf/yFpEl8NtLwqqCuR91+FB7u1SGpt53omTwzuObC/9fBjdQ1XZNwSxw57WbJgsGJrhwKpgNbRfmB3XemRWCxMMlcJDzrMA6unGzJDpk1YIexvSF9uxgl06uEMOjfjDqoUimAnQcTYgavUwy+P1CXIe/Dgxt07V9z85tla9jAZZR20H/QPCAFaDt/s2ImgN2lMYs+LT8xedA2xjYY830+Cp7iXQxkA1QVk8AChv/zlLxPKkUkO8VV9pLqpKFBSlTIm0CKBpokcyTobicYgH0x1NbQ8ogzxA4CEVFPf/OY3oUGkRft//+//oaDADAguRlxJUEHZB4oCGESVZvnFrrL++Y01tfXhSATsE2nIzT391FPZXK6kpLSjq3Pu/AUlJQlZIhASIFuq2GsWjiVaWttLmuoJwMXNoYFE5/DtIDe/7R0dW7a9OHX6LBJCcx/9wzc3UbEfhcvHW2lFqJAwhXA4NmnKzBc2b580abzvSkEJFBZ5OpwDSC688ELCSkOOGYvPfOYz+E3CsyGFJIsHQ771rW9dccUViMZgDjnIKJ8fxELb5V2FHkQVJMvNG97wBsb31ltvJd8AUQipFo0EHgBiIJITaxZBARxAZKEh9W6hLQ6+PBxIMRta5POJZ0eM63zUo8HX88oryZh4Bk7QttN2xZKmtc+1b9lyR2nVzFC0PGBFs3o4l7W727e7rTsXT2maOmWMo+3S8Q0b9oFkY3hWJseqMDs24+QodvTSFLFGtWwghFLeG0fkxHEvbInY11gE5RTSAjJmWGbMvp17lixe5DoSNRJeg8cgEdyBxnCIbpogTAAuSxVlaS151LGPmcGGoqjdw+6wkysomA2oPkWaI/0L5JW5hwp86aWX0kHcpHo6gwl/cjtDuqZO3uMXOwNpAz772c9iaYUf/PjHP/7Hf/xHGuIRMw15k2T0hBolmxX0hfsqEQLpR5iQn/vc51ARyFjAI0U0VbVDguj4S1hbQD5CaO3dc6Ciqhoyj8t9wDKeX/tCJp25ZOkSclK2tkuAe5qTxSvxl5DXceckMl5LW+fYpgbY3JDZAFXxOfQ5n795y9ZQOB4IkrGPvHswPIz0ytWaRSkxOvkUXiRQrD+c246TKK3oTmbTOQcPJ9nAIFyKfz6IAmbBB01DapctW8aiCAcRnon+DScGSDAE6Rh6TaJQoryxu4qbKkMAg8WLXBbanhp63gX36AE1pliiUBm5SW2ckHISzAEGdnLBD/gFXYHtXMeaVlxw3LhxNIREwvcSHLcoWFdoL73E5fFU0MwkG8TYSWwaqYsXVDXW5V7Y9vixY3iRRgOR8aXlJROr3OmzaupLXdsi1hxhqk7r0Tl44JEmdKxRIaPbIaFeRU+yvLUtdqw92p5MY/ak56FIqAQnordC+EE2wryQ/aLi2oeUdRxd9WzX0Ya6gBHKZXNswZEYva7kHMQ2hWoQ1V28xO2seyAUcdOplnigaZDtvcTFCqbXatIyyekOZhop6dHHIcFve9vbELuYZnwAZYqC9IrQ83vvvfdiYCWIPNUy7f/jP/6D1CJQGYggbZGIhkDzH/7whzHIUgB4SElIjkNGC80AuWzVqlVYohV1KFb/ghdsB7PtbEd7V7S0NIcdJhhKpVPYKC5deilO466no7Kg8sJ+sM3L1gKYlu8QYQaCzcfaWS9iy9ZwaK/qH75r/YYNEuaT8EQeYSMgrLTVtxFMmmA4fCIvH69mncE+3WAgZGOlleA/fRNEzoZ+MPoAA7lnJQCbz5vf/GaGDIcZRgSdDGWOpw888ABWI8x38OybbrpJuVFyv1CE4dtpDiGAGQ4arF69mjrx0cQ4yX1gYEkAARwwaA7rEOQYBvCWt7yFuNND/8LBvYlK9N3vfhd4YDygIml2VDYOELLQzxxcg+dNKbCObfI5zD9h3z2yZWxToGFMWXfGyNpjauqvSiQqsi0P2y2rTa0FE74ViLKgO3TRI//duEAECElLpNuKZ59v2bWjrSdVVVY5Z1a58cCDj+wct3fK5AmjRtVqWgozs0SZFud90tb76O5jvwjpIun3B0ZNBnWL8kSBxLHPaGvv7upKtrS2pjPZrs7DB3Y+OWlina21sGsom8XsFZDt0sRONLtQUAy7xHOzViitO+3IbBiKpOUifHD+y4tzUhgbYLqqZpH74ARMchyiwWxS0jPzSUTDtBzClD7zp1Ch0rJpiJkMy4EEsDIBG2Be4Y/xq1/96l//9V9hQpAD5C8eQQgUfaFmhDJID7QYmBXdPHNzg3sKoSWzAB7BkteIxAPsWXcyZBjWbcRzoln5dA2Ahf6ybozA68kuXxMvUgxHHhcaFnzIwnBwQpFC2tqyeXP9+DkwGtohajP4LPWKHkDrfUjX+xcxRe6CrJYRCEKXcGTzl9GEb2DV8h8P4YdPVegBfX/729+OaYhxYVkYNoBJBAGZoQE9LrroIggxyPPP//zPpKe/9tprucm7BZFIhWP91TtsLyw7E8kHsktuVGqG8TDiUGRMQ+SoACU2btz429/+9u///u/9URnCJ57pFb6CT5CBNgwwk68DaflMUnajEQIqjapDlTxTXa/gZ9jo2UMjnhEi8IBrWbIJ2Ql2dcWciopKZOq0g4AeyJEo3stFMnGCSxSy2xx11kdgX8ft6yeZXRkr194dv+OBvVl9Ymn11MpoaRXdjeNS2t7wwt7bb7vt5vctGdXQaXhtLFZrdoLcxUhlVIdTn6+rg4EWbtf4Uoh4AfxckiWKj9DNTDZLctm2ttymTc3Prt/X3J61ybnhprPp1lkN9nXXNJDuybBKmAC60SFh00ktZTV7RtTTKkiS6zHTCbuKnCZmpL752Af9+fC3MDagIKZ7wXXIMdIWKM45yjjkmKdqJhT3w2iO5QdIPNUyndDuaUUZeTkh2SQUH4WA+xgfHnnkEdIMUBiGoWDDEMHrUARFO4oFm3QCWKZbYvqHwENPNR1KlygpYSF05qxZXdCAVLq+sQlgMulkRHOj0RLJtYW0rrmhUCCHkQqOMQzE4PPlc8iDTMIXfz0AkODUvgCjpP7Tfi7oHwiwjswhPAMolNgzDHA0vhShG7JLz2MQhyizpA9XBk9ohnyl3FeEko6iMOcKPp6eFtDTPIAFqqqoB6MQhJ5KsBliHsQACBjYYSC4UGQ0QlUHCYgYE7QEkOE0tQ7rNgCglaIOkm3twQcfpBXuKKIPX4QRDuEzhwXQy/KyL3yAUj69Ax0Ft9hlS64wkB+sY8hldy8COZTW5xTDBxMK0dFd/se7N8UrFlnxSVkzTFxr9pFBlx0rW1FfEY8suvePD7/5+sbq8nYvkw5q0ZBsMxY/Ptk7EyD5nfBvMS1ZuR5Hy2VD3d3W0aPZ5mP2ocNdLc1d7Z2d3d3Qfb5ItiKEzFQ0mIrXGqVxK5PMxeMVbi5nCT/BEYNfR8/VGpJbLeUZpNMhpkW5FSgXm2zBmD787jl7DYWxAfCYuceBJs7swkZPzj/8Q2677TYWA1VrqsDwMV7VQ50MD5McUQ5TLwSd9UY4EBOeR5SB3PAIcoPUyQHpwS0EczCpx0hdSRmewqWoRAmPCsjh/8qiFLnDQlYwaGSEogGnbAa76JJL9uzes3X7di4rqipB9LbWZpwV2ro6epKpmXMXICxhRkyUxCLk/urb3DQ0eOhkiCDkZuzoMd12VppnpoGGPi1XViEAGOigmJfJJMn9EpJIvKLHMkPFbjVQ6UHeg+P+7//+L9SWlQC8ZSD6aAYwZtZvoYYMGQ4zuBJdf/31DAp3cCWiDxUzKwhhVGG1JHDXXXdhdCIBMv2wcuVK3NWQSBDoYACAjXzA8sCNN94IFca0iNIACg3ycwoqpkDio5A/YFGcgG9K+1QfSG18bEGfWRAAr73CPpaLFMQKWejBFV2h2JJYYmKSbEuYRsUoKhxHD2azGTMcrRjVMO/xVU/dcE1TIkjiDXb2MYODjhbsSHpHm9NHWpNHW7rbOlM9KYMNN8luLJkd2YybzbG0YESjsWhpfOKo0VXl0frqeCLshq1MSVSvqCzZd/TggX2Hpk+tx1wk7br4xZL8NaNn63Utp5kdRHu33QpNq9WNShn/XqjPr+EqjA0o2EFl8Ju5/fOf//w///M/QXecMqG5SkDjKcfwvzJfD5SOdcXPf/7zeP6xrsi0xxIFH8ItBP8TznlKc6gCf/d3fwc5gA1w55e//CXTD/ss0ijZB5mWRYEq/10IoyCZZVi1NZXrNmxqGNWUzWUxiYWj0RmzZnLCGhnTHqNRXU1VsqenNdnNFTlWoQ+4UjTUVQvNliyMQzbDCFnhG+n2iRMmPLtxOw3mciyDE78UaQvw1P6sE+pXlIiuQK3KZQ2JdSqMQ43XcGWV6upq0kRDbVetWoXBEP8cwIMcYxmnReBkAQlzOU+5yYI/ZRgXygBVvmMHeQKhB/GollUfrE/gIS8iH4AD2CcV4wdzwEycC/AioxWEA9xJaYujuMhA09TJB8L/aJFLTtSHsIiFuVI9BWZ1c+R36D3Qa+rMm4YkV2k6oyfd6vIachuQ8Yb1Ylu0DnbqsEhB+C4tQgzHWGnj0WOlz+90xzdWtbUePpJMHmk+dLSl6/CRrrYObAysXiDNW+FMJBa2IrFATVlNRaVZVROqb0jU1pZFEph13KAFk8mw7hdgxZidCW7X6KbQY4/sHl1XE8cmhFhHpiTRdrpMIykLxSaqRUV3d3VN03xdwwIG2Ey2IpDHoXfgQG8WzAaYTtQDFWYm47iJAMglZtmBKh/uPdUWMxarAiZd6D72lqVLl0Lrkbmg7/iD9p/PyhwMdXjjG9/IjoH777+fmf+3f/u3yKHFnvmYxWRKU+0Fi+dt3b41k+4xAyHcFNB8CTrNQKuhJgded3fnjm3butpaL0Y7IZG9a6MJVFWywtErCA+5m/L9M3vO7B179jl2im3DkHUOKCtijK+fAuUJLfhdoeXsVH1tLeMoGgn0n0NZhoaBo8DDSgyLtPn2aAv3UO6r/ucElY4jX0BRxv6DmH905hOqQvynWgT8f/iHf0AhgApDcKkKJwKYDa/zdazSgwBgKbYgnnJHQXLmyofwtD+JZ30C6o9vAgvFLFZjI6ITQEiaVsdJIzKE5l7Dr4CrxxFaBK9gqKcnm6grzxqSmExiPqqQ1FqQlPdmBuVPtm92e25i9PRf3XN3yMwmk6n2DFMgR9YksuBFAoGqykBZIlJXWza2xhpVXxmNWLGIGQ6zfibZuz2n3cS8g/enhEyidYImBfELIjJkyMrOnjVx7VNrly6bxqzDjTRHICw94AVaxSKoRXq6Kq3gnJLyGeKcYSR1Ccl13kkDBbOBk/DvHDGA/q0wsZk8rPsxn5n8HEq0/+AHP0gxJj93OMEE8f73vx/2QGFeQSHARZ376il3+tdZhHOxo1CnW5qIvf6Gax9auRpTmUOITAv8EHxA1odLtLY3l8TiF1yw6InHHu/saK+pqU5nkpNmT8GWjyowjBVZ4UAcfAhsr6G+bvHCues3bLbIdKMmSe/3UoB/vd9OeagVPQaPiISMCy+YT8/6HQhe+vqDmDWLiaN0u+p59VuEbj+xiny1qIDqCZ+jzEF8rHrKSX8szb9yYk3FvHrhhRdQR9BCMGDisYb4wgn6AU37na9WYYrZ4murrl55RZYXCNMDAThy7GhXz5FYRbmEsPbJhY/zlENZxnkVlZMtBZ4RiXdkvGx3V1VZeGpNoKq8srIsVldZ0lBVUl4SiAaNCJFMQ8dst1vqgWxD8/G8Q+dmA7RXJsI+G6ANkmiLFVWCQ2o2UYLrquP2hDGPrX56yrTxVfVlphd19ZAezmaybiZTEau8sLrmMrzoJSkJmXP8yIzn23gNiw0oMqQ+6ZzOLipXJIxfZUZQd1Sj3OQEAgdBFPHWP9RNTvMn6n7RfkWCZsmL6t1RjXW1tZWbNu2oqWsiRDrEmbUmUIUl2EMHDm7r6iiJx7EOJUpLMhk2WI6dPnUynIti/Dd87sSHA8S8ObNamlsPH+3i0vHX3k7dD0Yv+V0UYDlnyUXzKiGdaCQozcJNxGdImEFR2UDRenvQFfVHCfVS/k7/y0HXN5SCGKM4sH2xcIVeAk72t0meK4QcCqSvxHfyMwY3PXbn46pvpFO+8Md0dJn+2GzYrY9Zlt90jowFIgohduFoaE8bXXHR/MnjGo2yUCYWCbLLxiCjOPmOXLIzOV4Wpx4UijC+c2LbEcsvPFuasSVKKNJd0PVwcmJ229j8xdIvGzQ7R4+pSlRcuHHzc7sOHYiTYtaubU86CxZfOmbiXEOvJv6o78UHqQhJItfz7xgWG+BzTppj5+4DEeqZP/wiT3GoS5pTwr6aWuo+IHHJef7pOYFKYaNv/uF0+aWXshdr1aNPTJwyO8Y2JbRCjJS2M3PGrO7ODhZjo+PG4kxH+IaLL1oEIgAejEs0hqEeJ/Y8Sfz0y5df+v3v/7qrp6Nh1BjUV79PmAGsHAOgmIhAXxTTbCY9d/b0aVOmsIiJb6mqpw+K/Bzru3HyXwWwzKw+0IuC1jKhfG6kxDhOew1VJ7c/0LUafZ6oceeL8uihbqqXuK8KKJwZqKbh3lOQYA766le/yhDjQo3bKN4KKKZcwqHzkAy3pdfu+yCKwlJBaNvJhQKhUfX1xw5WODn6NwRhl3EWNyE4gesYSSL56m4kiDqQ6p7aEFg6yzLdPV4u4WZyZOgglbgtAn5GJy03k4QkOQQFEuxmg4HfEKIne4PDnSIqsTfYDePiBDESjJUXCPBOuuXuWDhx4QUXN3cdaO+K/fGWF/YcCsye91dmYBLbOfHk8IVDDLBYhM46xV6GoR0WG8hTkPzJufuC/BRSc/ikFtWlMhb7g9NLCE4qVkTwBAdAAkJK+EJC2LLe/MYbJk0a9fTT6/Zv3x2PVkcj5QEzxEYCNEoEjMryxNw5M8eOHS1YIIiKp+hwbEKqeb5P6gNRM5lu04xefeWlP/3J79cfPjJrziwUDtnNYOO8DKRsliFdGukA3UsWLJg+fYL4k/qaE5RLUFOqUVgvNZ96iBcRd0VPRizyhRuh2/AAUEieDOegebJN9eaYwtEvYOOzh75vItz5Fr8zV54fZU7yeNL/Zv51bubv528O/4Q+VPK+Qk4WA1ipRg9gZQL1C+slq1PoBzyldZEAht1jw4f5FVqD0H5BaBBPOlPDQux2lJVFkuuaS6rqHSvDGi5LxOCmycIA4Z11K0tYIbDITHV0bJw7nqtOImgRZwL88mk5FfKY/W7igszQ+DOCJuTc7yV+Qc6E4DsnojT3yUACCWpCiEgWhtHjZcMVJYloWVWotCvcE9aDUSiEyCQSLICibBvy6zv/fs5XuE7fUzL2pz/6P+1/fvo3hvpEhAHEDRyOZW8hGIMOOX361ClTJx46fHTDhi0kn9HJSh+wwpHQlEkzJ0ycxHKxbGJElqBNcLgXyYpgi4f66bKR3Rs3oeEf/uVDqx55fM/eQ/FEOc2ZkSNQ+0wmxe6F0pIEO5xrKmtY7PKNP9KTg+2l3mU35ggAC9oAvwSrgz/0TYqhdiX956SsDvyxWVmjSsffzyORT9GWgPFMAz7kNov8oqId+KoSc5d1LCIa0bHqJtIJq9N4KxS5ydd0dX04wTTUvVAYweRwOlNnGWNcTPOS5K9Hs1jUTWp2DB04YOT07DHTPjp14qSe1OFQ2PInoRgM5BDNU1hKX6UnY5xM1VPi3wkL4AUZY9P3smCPmFB7kosQrEVyL2Mu6j2k/r7z8/HvK48NnCe9KFxAEvlmET0cj7Bl7BOEbLFgpY9qGD22YbSH17AvPvhStinyie8bBHHwHXhknckvUBw90SSbEyZRLxMIO1defXEmh74sclEwmEElztri24o0GgiaOY8QuFDwqOyqH+yBYZWU8aAyiq28BYYLXgueD76S0zaGDS9NikJ0Z6R5yThGnYjMaB5kJIF6ntdTiK9SrBSCQA/jqsCB6yr6AaoJN2EA+SWr03bByIOh9gBkmHgiF10WvfO++xpH3RAKTU3jL4pdB3cetm4R4wf7a7b92K4nrljUGCf+iB7KOhnJwpqn0kNt+sT3fCzweT8VsyuBEDIUGKyYdWJdL/HVCBsYaoeLygi1EomP9HIyz0WmMMIIIT3dO/Y8/+LO53EVjcfKmkZNHDd+ary0HPM82ZAQVCCh6JbIEcISinQQuwiE0/WwgQsDuQ5kdzLWG+h1KcgY87fN4r+GqIIOTHATNgwAQoGH/4J432ENgofxD0otuD7Mg3pjWpzexC0b322qhAmQi5yddhousMXgNMOE8MyvgwMcdDtxMlAF8GNmZztxT3mro6MDn1HQo//awJlrG3laYA+Aj6lRdR1vuLb6wQfvC4QPl1dOch0JlYXfT9rNRAKRhurE/FETJ9Yk9VzSC1qsBMjULbCZQRTvqxJhkMNnA4N46+UvMsIGhjQG0Ff5FxDboi9W4GFAXIhk19Hnnl7V1ba3o2t/S/s+9i92h2M9h6pb9oypGTNr7KTZsVhNhmQzFsZv9hhAQNEci4KP1JP2HMJaEZ6CNS6ttW3Xnr1Pd3YdctIRU4sEzEhN9ajaUePipVWIp0wPeJAEtvAPZdE+Y0f4SrGvyOBt5GlJ12lN9xzUvKTaP3HGd+F3vQ1RTPSQAQ58rIOUQoD2zHKzpEm3ojZO27L20je1BnjrfLnFlMfyk4eGfcvsYyCuLXeIccLutr/8y79k1z2sQpWROCQjR/F6gFUwMto0lYRvvHbKthcPuEbnrr2tmSQR3esnzb9sysRZDZWZjr279J49ph7Joc5C9sQ7o5ijcHxwEQ2FPIicWLxPPLc1jbCBIfXv8fElqCdIRbjm3Ia1K1bc+7PLFlbPmVpiBiJWcDwrQqwFYJ+37Z7W7k3b1u2Olk2YMOUiz42TqEDWCECVM63LDh42sA6TUzpkGUcP7H7myRVdHVsI5FNV5VnEOnc1O23u3p7cu6th6qxrqhvmBoIxpYjIcuVglQIfWlEEUDuS3R07Wo9sCBtJw2XZuZe6nQ7c/AyhwIDNwUtZMjedVNDDiNtQEbuGACK2FsDjD+X6eGefroHz7D571wlshXKAXYid0uwYYKsjq8TKNFRIn59nH3begoNBMV0dMKyElVk0O5x1O2dMIaFNaTBcVzV5qqmVas4hz0w6VtqwI6w5WWKwhfQVFbP6KmMqcvjKQN+t87bf+gAbYQN9PVHYX5aFMLlA/iDn4JX+8IO/e+KhH934lkU15RnXaXZJfsRfyUYs7jpBM1iZ6CAuyfM7dh08fGDpshs1nT2lxeEAAI5RmsiOlpF+5pnbVtz7iwvm1S9eUh82cq6d9MI5102aMa+pOtoD9T62urXtYP3ohZXV43BigVQNSJdP6Qy+lNUNEeRBbVQDouWJ+1uA14nfCxadhRMIkKefdugyWYPuaPOyrU6uS/M6CU1JnF7/nTO8dwqY58cN9q8RY44N1ZADIrGr8FbKTaiPI75iCMT50aNngQKl3LGI7UDO1Yzmpkyd5AY4iOqBUIfpgU5VUOW0Tuhftn9ZdD2BIHxkHlAxPUtbZ34s4hhWYjzaxSaE5emVcQydDSiO1/8rT73T/+lLeX7uIUF+zYgMq7EurG/dtOaRB39x87suKUsQ4S0FUTbNEtvN+B6b+CTnNCdtBTpDVnLOzNF33v9wT8q5/oYPkqiC0Aa6FxyCkf7EzhTRIxiIPrzittWrvvknb5lTV+Zp6b16xgpoUbuHCAoxzWvXjBQrBIZldWScn/7wD2995ydHj56OR4PPxSC1TIkz0iY20QjfY00A7A6x4zLrmCVV483EfHZw++sEJwLlL34cvyU6B/981plvxyeK/l2WTCKmfqTzwGNECSHgl5R2s+JgOSSpLY8AfgsDqyDHYSv2GSFOv/3tb3/84x8njB1GIaJvqSQHDBOqgAKp2G2+tuvDTSN0CLmKjMRo2MR3c7yA7sU8Jyj5l9BWcfhErWR7me/0g9v02cWWQfUo+Ml/BHTEaVBy0IrMQnQAUbLh+yewGYX6edwfVPUvVaGC2QB6Lp8HKqtDfSr4zaV6xJ3BCZgFfCKVM3/UC/n6uanmlbpPAR4BA5eccPRvQBUuHmBkkQky8PgJ6bnWB+799o1vnJ6IpzK5bo0UdFaFG4iRZIwg5XouHAQ5xYcsygKt5u666ora9c8dyHTlAhHCYLXqeilZy/qDerZz+kGIJMVAb/zTWKelx3duf371ql/efNOFUaNFS5mGEyfpRU7L4rAk9FsWWgMY3y2tszaWuXi289tffeYjH/1mIEKEO3LDkjc1Jn5vsqXYr/pEIHjf1Vh1xj6Tzcpuh2DATppe1rEaTL2RmpWsb9s55CCVBpmtamgbwaDkGKDbCelDnA9caNQw4ckaCIgxHW8KturIDAVIu1p61aQP0aJC3PS3a5594tCEqhblpq9+QQYqpVG1m4RzHnGACRhnuDzxE4t2RRPU/7GPfYw0nKwQEG+VVQG6gnZVGxQ4Z40X7SteYRURyNHGwQypCu84yfvIaBO/C9Fck6D/9DyOB3FROI0OWRvz4kX5QOy6OLh5VqttdWq5BuQhQ2+3yKuWE283OAOt8CM2Ad8tkHnEbphzhXnD+KSC2YDM117L1wl0Vs12HoHlzMZhgDTAq6pRHjC3/VnEDTloTrWoHgFDnvpzn5vqV5k+1PkAtQ/hFgxIfH4IO06ywyfHjY7V1oSy2TbImhmoaz0WefyJDXqou7xcu2TRBU7KCQUjtpaRlVI3F7LS0yY35FKHw9HREp1ElNTBH3yUfNdxWk2lHglsUmse/c3rrpoZMjs1MmB47FuBP6RdK0l0rV5JX4zs4ubv2qnpUxoPd7Ru3vjE/EWvZ6IIVfJrZY32NIyA5+K7g6jjQ8A5M41zGIBiHOorYAFqCUH71je/QwCvj370r0IhyUP51JOPHTxwYOeunSoaICMCX/jIRz4MlWRHtYwl5lqZNlQvCdRUtSfqDqftJVCCZ+zPUuE8Oad+kIF0x2zgwi7P0HNQDHMNj4hQfdq6hv2AT6F+DEEEOiXrDtFVSddKyCP5xJHjnPWAmBAx9ohkqLAUlJAdjyKhi28bmCVihex/lNVh5gIXwz5gNmYX7g2WZB4OWmJrynpOpWtXMbHx2aNVNI8+wUow4PxEgsLYgJpLihaD5QTMAuOViRlFGNcIpiKiEMWKjvSKvjO3aVFNezWGtMUJc5sTfjnvf6LK8Fb+UXEAE4lY5A3H7j58aOPUyTWW1gHxwk2f+FVbtjY/8+S+t7/7kvpRxChJBcy4myW1RYptLo7N9qh0KHzEdrZpWrWllRTivK++pv8vH4+vurn7xe2NNblxDaRVyliIPIL2xDpn5zAxF1nE9hm2MAzFoQ3ioS6c1fji/t12Jo0BSzNRpfkgamYBABI5eFw9YS7Rghom0gmsX79enYP7xNZeu/aZZcuWTZ4yWbFqYnDeeeedKkMcBYi+rTKg9f+2wZ+rwf3BD35A0rGvfOUrkH7exSgPimKNIRw6mXC4A3KSrZ7g5IOveQglQTDmxR/+8AfAYDcZnOB73/seUVfZSaBwdQh1jrxyfvYA2I8UpdmlAezAxDPTkzktmSPjGUkxUa3ZBykCjQS1E/jV7wnC8/nyWYWxAaBWPABcVzjNCUo3uV7xiCAZgOIB5+LjcL5etWoVWWWY2ASazu/JBAAOGAMkYM2aNUS0x3GbnASo5GQaARISXWGc5ZwyijAVBTxoqhXQu3vaNLe9JOqwsMn+LIkeYnbv3rfp0ssWTp9R5xpb3Ey7Z5ThF+SQtNKLB0Aat9Myj2ZzuwgHZxhCsIZ8kBovIGnHrE0b14+KJzH68ImWjhZM0HNQFGquRPUTWsA+E4kETaerIlbl2j2mlcDzVUn6PhsoGCVU7eAD5JgeJsMX9Jdo0mTg4hF3oMj8gh4q/jM3V6xYcfXVV6tYC/4AAiccqICD5nhRvUDlDz/8MBgCX4HWq5skyiZbEZmIkMc/+9nPgqX0jjr8FgfP6gqAShWFCxLqHAvYI488wicTCpu0SGAgcBZc18gL53MPiGtGGeqf5vWYGpvWMDbG9ZDmhI9h4bQkoRM46mPa+R1ApDC8ZP6AytB6Jvw111zzgQ984D3veQ/Tm5lGjlnGSxGC/Pwc5ggy1REeqQTt/nOf+xyhWt797nejYv/f//0fs51HFOCgUYL6kgGNjORYAGidnFPch1uQ6paMV/AMLocJzAmvQ2Wpj0pdduumwQCTNSJS1GtaJptq7ThYVx/F3cUgMA4RzQkxhxukbBhjP68knjHFVN4uMadOqLTgC0VW2DxMZOOQRTxdsDLi5LC/E+yaPQQhImr1yiB+3fQMdBB1CvFc93piETQJibbiyyxSohdl/cKF/lA5/cG4fOc732GkoH2oiRzcRPZnLxUEUdHoZ555Biatsg4oXIKVFdocH6Iq50Uq/N3vfvfnf/7nLMkqQg8rAhlUOlLQA78dJAPuUJgCxcLP08HMJ6uuVsIKpiou+VJ1k7dG+MHpuu6Vdh8ywF7lLjfQ6oXcjN2wf/+o57dkWpLdLR2dG7fs6E6mNN2CVglaKtfCotKhYnVXYWygf6vguj/N7TvuuIPE30rn5Wb/MsM/p0IO9ADc75YsWUJ+K0R7BD2mlppL/B45cgTFn0SDaCRc0uFQOty0OYAKegQhAJJiTn6olsSU47DCwRK81WR5SuKTe5muUKoz11hXwXqUlq20M43pdHnaDhLAPEfANN01kdeJd2iVGSb5UIfVQ7IYi2HStbt7Ulg78Vfw9JCDOgpBFsNQwHAGjlTBKNGr8QQxkNnAliMkry+ywKj4kKHDROcj/5KAfvny5RBiiD7sji/EW4aQy7SohgCxfdmyZYTZGfLHUxWjzC8HCuLPfvYzUk1g+eEmdQIGjAeuA7agC5IcGF0BJME4Q2pMcGPI7Q7yRXje73//+9WrVwPG1772NRaK0VABdZCvjxR7pfQAWynNYJsX6MqF9R3Nqd/fd/ChVcFt22vnzn3zzDkX33Lrnf/0r5994ulnsbtKLEbfaWIY0+sc9soQLQBAxJSGvKL/EkuLTZJ5GItIbZk5TGl+MbYyyamZec4qH+o2VldUEC6B4Te/+Q0Tb/HixbfccgukRwHw5JNPUgZzBGoKliLIgaIRxQIPwZuFpkAg7pF91NEdI6BDfg2zqz01a+rkivKc53S5bvyJx/eESgLhREssoI9vKLOJ8YmpUC8NBeoNM4L6AB8eOt2V1V2yGgRrasodvZXI1qxWSKQjL8NyGE+w9vjhdk8mQCh1hFxJkkPZZVd9CPHYIlEGDjvinTlIyQCoTwYc/QzTHJunvvCFL0Cdobyk3yIDKMGWERTAENO0sNJs377jXe96J8M60FicXGcer/qfqBcVepDUhUFncJEVWH8mphuIgfg/e/ZsClx88cUsWSFDQP05B1VYxDrXnIB1b0xe9913H3ISuZFvuukmTFJqUaT/V4ycvwp6ABHQcRqefH7vzj2ZePyiusaJKbPbNkh7aTeNdQ7s3fPLW24rLS2bNGEs1oPz9nuHzgb4JGbj3XffTepHxHB1WfTvVLSb+czUpTku+VW8QbW4bt26vXv3/uu//iu5iBU5oACkH+sQaYrJPsiaIX4aGIiYk/AM3i0GkPAAxFEtGExYwbKjzc1jG2O20wOcTfWV45vKHHe/bqY9r2bn3p0LLp5b09RYESnXMrJNikTYWrbcNOs9PeCIPVEMiIMGSRFfKe9DgBMCSogxduy4jH1UnKNZIibcIT5zoJwI9rJCJQ4SHH2+Ecqg5Wrhjq50HbxLsBOSTHx21BTfUcgvftKPVEZF8ArxsmANIu2S889/t59Nyf3whz+kXlz33HO/+MUv/uwDNzcfO5JKdkdI7CThfs1nnn6yq7Otob7WlfDwhIpQ3+7DK6meiCWHVsNSBcK+8DKaHfBQ4whtxdjCkgAmQe4cOHAAPgQDQDS5/PLLGQ7MRCwmgRJUAkUGB3AhPddsACYEJyDvGM2ptjjhANQBv2Xk5rB7gPF9OYisZwaM+jVPelsOjGqYNMfNVidJbamnc4LCpC8wG0ePq69rWL9xa9PoxjBxXsQt0J+Zw/7g4lYwLLzEwgvGQ4KVpKMmWxHhUxUyeTDsoBBQM4SDtQGSZ6Hvc8nMJ34LMuA3vvENOAQLcYhg2B+uuOIKNf0QDMkBgun8uuuuU8bZIoEH8czoXoQI4hOnL3j2iecaGkrxkJfkAnius1qAbcqz0nZHvNw+cPjwI48fuuaSJRPGWexvdHOVudSYiujEnIdHQbulVWCKKQSqXjbmUzYMjhKmbtq0Bc27N6IaWV4yYAfdXAUk1A62umabZUdJyeRTU6GsjqgBGmagTE8ol7SC7GbIxXQLF+c0YelOjaarAAN5/Y0x0GgnhEe/3p2KHEhmW8rhMgTW62UGsq+MtH4+V7OqqsoaG2qrq8ufe/bp8eNGYxjHrQrS/+zaJ+fPnw39p5vkRX/yohfYnhHUe4JGJ7zI0UjomsPHO+jK3oIBD2EhNO55rE5BYUEVhIBPfvKTN998M6YYnmIPBD1QCjkQCLARrVy5ErWAnMADVjjMmwoGKqFRtB/40CWknvbdk8BMsFHxAJ4qUWaYzY28fkoPMC9OlPBw5RMxqPgHWEeloBwy6ZGezIYdqYbJV2SyiFSya4G9jzprgb6Els0hppgd3clNW3YsmDODp8zW4gM07BoLIkAntEZf3HvvvchcEydORMICv094XLwLGrr++us///nPswixYMEC9HrEPWxEGAHQQt7ylrew2MjUggqwjLx8+XLm+Y9//GMIAYvYGIWefvppEoIDDsPGUSS4GE0LX7FsRqurnzV1xutau5+riqaCZidTnkUh0l/rTihmhN/0upmRktoHHlq5d8+GCeOn2EagM016mHIrXpIRi9awoiirL4IVTJw0I9s5szu5pyTCgk2bhaeaF3PtCNhq60FGxoD+ezZJMU3XzGUdsnC3Z8IN46brgQi7qSCnoiqIB+zphBWeEmiaSRXA/qXh5+rWapmkmav29PCpU41qpsxa8ImJM3J6aNElywPBYFYW8ukx70//7C+x6WXEzZXAfL1Lw5SXiKW5EsNBZ0pp9J6wRrxdaetMpnzVA2qDGHjC+hDeR7AB5AAQEpQAYXDRwTyI6AAPQE1UG8qKhAbHq1H0XWknYOAPf/hDJBI1KVgoxmqK4AK0AMlRNDQ83v7I2YCoC2aduwNfwdDmFw8lquY6RtgRQYq5gcVC4mD5mV3Fpo3sBeZv2bJ12qTxsfBpxZpzB+Vgah46GwCnEcSY0qA+LXE5mPYKKqPkJqYNQj2e16wEYoPCC/DP/uzPuMlyHytvKo4jrQMGLuGsHHB+6aWXEtbxi1/8IoQAZgBRyFdVJDghm1je0U7YRxUYM275U4/udrXWqkpCOHcZpKlzSk0vfORwx7Yd68qqyvfvXr/0ktE4i3QkS3r0mnFjJ3p6zvKCAa/E37tVUK8cL6xID6KJYYTHT75q57bbgtHuSKhbYGCxgi0tXjjDhgrCNWN9Yh8LCfq8cMCNJFOWFqqvaZpKbiahSUJtRbg+4wGd5mtZU6YUG6OD7CJOJ1/MEkBJifQnvkyHMxc6usWIl83ouW72+9sADDdIZgWSvk0M/muyLMFSSdrNdhvwLSKF9cIzqGlMWwKTaSp3NVaJaFSNOPiJv9D8+fO55AAAxHafEBcZXVWdiPw0AU5+5jOfASFpEUgADLMk9wGSYhwny60ndt3I1fnfA4ym7/pjNLfrVqgaKceGB+iYfEXzlmW544qIYFrOtjs6O0piGM8Z/SLj3vC7a+hsgLaVYz5orVB8+NCcVANTiIPpxO+sWbP+/d//nQUAGmVe4Xzy0Y9+lPvMNC6Z28AAq+AO8MAqPvWpT7FKiTKOaKZmo6ICJzUx1EvM4wIaabOQpnUzNn3OG26/4wtl5a0XzKsltohEHHJaqhrM5s5se9eLly2b1lhf39oWOdZVMW3+tVa0ysUIjtMQu8kYgaFiheoZyDiEJZoYP2r85U8+/s1p48sropbtuCacRpARwwtsgKgSuLYhjJSmcyXN7drkC5e5RkIYgLSeZwOnxVEJy+LrDZJqyejSzWY9uLMbit6OlstbJx70jWwEkHULWQgmsFc2Q/QIgcEXGjhhvGQFQ2gieWPdXFDPuEHL7fLMSjacwXBo8XRGqhMbE9rKHX9mshFNVgtg/3mc5ClDD4aot8AWH7qT6ijCpf+hOtIJWjJe1OipyjJJ1Yol0LRqZqgDXgQgR6oYfg/04o/gld6d1MOlCRvqjzdGFoEYAsSKFwMtOOkfkEemiK8IsmZwzqwmfc0N5e+w2EAerYfS8iDeUTNZGVUpju6PVKXeQ8pTg8Ek54Qy/PaHh8u8S6K6T22KXgyi5bMVkdAKkFBM6p0SZ9pNlFWOfsObP3PffT95Ys3+iWNLy8rx1iQWvzt93hzbDqeTxoGDpCadPH3OZdiIXC2Hc6mojZIYZug0ge/q6wRyjWnx8qkV1Vf85tafLl08cerkWts+5rnd2CtBQoeU2GY4G0i0dMS0QFPTvLmh2GiXrAN8hKArMPiIy8VptDqBUhZsMXnCWcygVW+adZobViv3A/SXmgU+V6ZKP6QEM0Fkdhky1Wbfa0L0iYfqBexAmW5WmwGsQ8ym/mkB+4oO9JeRVf3ArzpAFU76l1WGo5Nu9i8wzHM+CjDYO80y1fLly9nJiE/zhz70IYXDCiqFqzTUZwwbZpsjr7/cPSCKNIiuJgWCoYKHP8dxT2iOklYlGLFohOfhMSw28DJ+T34+q5P85ZlBGmSxM1ciTxllGVCYPxTNdvWU7UZLEw3vePs/73/xxc3PP7rvwD4rkI3GoE/hrk6rJNEwafL8ytpJnh5HKjUDIXGJgZ6Sm5ItiEM9jn+OKCd4+YQWXnBjZeWUlff/vCPd2tQUioYN1icgqOmc09pld+aC9WPn1jYtMKxyvJwQ1HtlFjEM8T0gtHJhPRUgNkHyiM3JeAthDSqJhJeH66ZqXtiPoCU2meEdbF0g7l5Ms9KaCWCVdDFLEYhQSFZnrfl4P/CaPxf738m/PuDN/NNhnlA5Bxun8Ul7xzvewWrEl7/8ZVKP4cVEzUoEUSyBy7N/0jChGXn9XPZALztH7jS0soTTlTmqB0NiDZIJxaxiOhyfEWAFnhmliUQoTMiW04pZ5xLes9f9SmUDZ/+yc1sCw0hSvPLdcvGhJLQsXmKEsnVDjeMmjBo/IZft9JxurEaE9nRxHTDDxNp0HYg+cd9YDkVWhfrnJMmKiOHDlxEQRbOYx1hZbRo36+a/+HRP175Mus0ynXg0aOhmsicbdYhFXRkrqcvaRJ3gJvQSpPRNJRBbPwIXnOm0FAoUh03g0SNOPRFdKzVMXG4sW5ICKMl/6D1O1cwoVi4cE+sqXhZoOTJjkLLomtOCNPQGi/8mpAG7E+vPbFqkdhU6AtWHg8u8HoB5yhcPhz/ixf+EkRoH3wPKzswUbqw1n31hT1VZQ0oW3vCHY36j7eL6zN/eUWbIR48ZU1qaYDkB38LBt/KSlTwfYXrJPn4YDbFnIIOpg/i1DLfsEtS6sEsh/LtepqOzZ+umXRs3bOlJpkOR8IQp46fNnFZdXUOIEV3rwUFfZFyybRksIhQn1IjgHNH5xXmJQFaIIoFo2eSIY2SzbrvdiaNCtDxSYiGMaLmMHQr4sf/k45X4z0meKuVPTuobKvW1FvEX6vHYmwDfIXSdJHLiK4ZNqMW9ghXkEEliJQKHsBkgKUbNJ33HObtE0mcBABJP4BNMl2ydw21UgnyEQqwQTJkyBatUP5ZwzuAYqfil6wHdzmbGjS59bsORnq4DoZIazyW3q6zGsdYl+3mAREzWXmVF+dTJE2RxQGATYeqlg3FwLY2wgcH108mlyPheRpBzPOUZVUQDTQ9xcfDQwTVPr1/HdpGt2+Ox0miUPWXOqjVP1zc2LLvy0sXzZ9eWlXou+WpEJhROwN7dXrP2yQ0Uek3oCIkgyuKqFeroTm3cvHHv/oN79+5vbW0PhYL1NdUNtTUzp06dMK7JdtJoDRhc4FrsfwZRcW+hOawwcLTToGivmV7XyGxM8hokXFBZwlnLi6d5p6BPYLZQL0vdsDRVIVuaAeu8mzEDfRUTXB1sVWP/2qZNm0AJPESJdIR+kEgkcGdobGxUr1JSkGbkeMX2gCjS/gGiloWM5UvKVq1b09Uzp7ZsHGmQjZDt2cT1Yq+YZ7i55qMHL5o/oyQSlHjwDjtYz0eSez7C9EpAD/AATuAbA9EMCX9tBVesWPmb3/5uysxF4ycQ22Iu3oL+Y7FucH5g/9E7Dz0we+rkhXNnQQgUJhWFgPrdhSgq7slQd8C454EVNbUN8URpdV1DbX0TJImcaAcOH2FP07TJExbOn4e4ioXKJvCGIQ6OsizsSypwgtPS3V7MhzILz/APMXcU8aCFPj9RqbV4nVNEGAeuSo0mfgqsCrz97W8X4P2Dnmes+eURv/m1gYFrGbn7iusBNt+n3NG18euW1T/66JZD21ur6yvR9UPmBBvf51R3Z0frpZdcMGnCGDykJYKBOfSFwHPaNyNsYIjdK9438io/RjgYuvPu+277wx0XX7I0GCnNZAmpRmgjcYnBY1JIgU6ZuOdkV6587NjhI9dcfQVO/L6HscjkQ4TghNfERoOb2u9u/S2hrKbPmFuSqCC/JJENbXYMGFYwFCf1DdmKd+0/0trx6IJ5c8aOaiQjpf8FvTzghPpGLgrvASg/Zh9MQ3k/UeoAB/jlJisH/e8XXv3IG+djDxApMpNuLQ/3vO3K6v07vO17N+w8kHt20/poReU733njjOnTSkviftBH1olJQtArTJ1vXzJEGgRyK/zu/z2n3un/dPjnA9Y/4M3ht3W2GpjbOHuKpxAln3hm3R/veuCiSy4LhhPZHGQXni+7toiK4JuLfa3BwyIfaBw17rE1a2+74048Lm0HH3oVludsrZ3+ed/nI0YHvv7Nb2/cvH3R4kui0dIs+xbwSsVt1SRZpuTDYz06ndOsUKw7bf/ft3742BNPYhSysSNJdiTl2OCL46dva5BP+kCS4v3PB/n60IqdriF1/3RPh9bWgG/BA5D3Ifec5AvQLofSA5RPc/7RyMmroQfYGskKWSBjkEQke2hCXe7yJY3Ll6EKHInHjIXzZ5WWRNHEkbNk6wAO68c16fPr6wtjA+C0kmv45VB4zy+SjrrknDLn4hOpX7XCb74VNcdojqfsGOLgDgUwyCrYuH+O4BEY5GO1rmTqd7f+YdaceSaZJv0Qzwh9PAUMZj52ABESwQCchjST3MXjJk05cPgYq8fiRDI8P2L1sYBBD9y/YtXOPfsnT5sJ++GjaVQ5KQOJmPwBgSiiBmvIFh6ZE6ZO/+Wv/3Dw8DH/GSoNq7xisB7m0Kkx4sOBh717xHqidXWwcIr3JABznwSNBP5To8ZT3uKXy6Ed1Mm4qxo4VweXnPCrgMmTZtoCN3g0tLbO8BYDzXgy9Ax6vpg/CDzpPbhP08Awsm8g30Wv6BNsQjmzx0Hk08k8FRZyYKRcs9UId7Fnk7RkuL8RR8w3u/JbpGXAc9Blx1F2kJWDxyA3v3yVIri8SNwukJs752KCKcBoS7XLZR4Gdc4lM5CnRLcHBia/goSbFAAwdaLqKdKvMHhMALD4tWvXRWIlifIyqJ+DL6Xubd78Qk9P0rICwDNr1hyWDYBBuT7SeVYgEo2XHznaMn5sIzxrmPCIjKlpHZ1djz3+5MzZ8xkWWWCVXYtQ+BxMHk8mNSgio/rUDxji8cTosZPWPLn2TW+81hdefTbgswMpc1ycLRg6Oh+XGGK+fupTn3rXu95FJA9aJ54PgUDY4004WIJ8EGOHOzx605vexFMGruBmTnyBGhhxAgsSf5B4U8onh85/+OGHCXjFigitABjFWLTBkX/4LZ7Y/lmuXuLmzgLNyONi9oD4AxFE0nEtW4+Sg9Y1UkSdx1coQNB5yBLO4cyt4hh+iwn3SXUVxgbUfFMzCvmOZC9kF+Fy+vTphG9TuZ+YkOcC76F3bMtEiiSonAoSqRiPYgDEuEfMJKyYkEWfKDDzCTH2/9k7CwC7iqsBP7d192yym2w27u4ECO5e3EopUFpKW4q0eNBixbVYcCcESSAhgbi7Z5Osuz1//3dmdl82BlmBUv69LC/3zp07cubYnDlzhs3GaWlp+/S5Qx7lhJag0R807NpVlJWVzWyIBQCr07p62YqiwsLRo7HMRJSUlBLyzmK1EVsT7qodK9lxYDTbKqrruoknvuKAbWWD9JELCKxbvyEuIdlqI06cCgPdtG5BR7VYkKVfudQT9+jKyakZbj+hHWQbpAroLK/bJQFYKleHPfD7+eefw4uZDahaAdGuzMxM8OTxxx+/7LLLOD6ICcE999xDgBDYNOk6W9t+9XBTKafK4KLzyCOP4KIDWKiUPVzgJ/GuOe0AxGAeQCxC7dfftro6v+qEQEsIcLSUw8upIXaCrrPlBeILmTwmv8XsiTSHcHpGB0MTbSI+PmwvgbWsu0PvWycGdNXwHVjwtGnTiP9M4BRYM6dCEr5xsorgRh4ok6sD20l1nGDOCbeUiRggtBxhhHUVBBeiJXjpsWEHhgKXQeOjSS+99BI6KTJp0qRJhCDt2PaokcUWbHB7vJWV1a7omIA40hs97oZNm9ePHT0mItIpTCeVnUScDhYQP0wVElqb4ZlDlJdXYwuRgDtEUWvrBbOjy5iWVq9ZzYIw0oVIznrFgq2MTeWyh0XLH1HymSIod2YZHdqPgYLjk1jJYJGjvReNoQi4//Tp0xHJaAY6hUSc6In6h8EE10nkATcIbO55RfsZXHh0e6pnlHHNxEOfwy/1rJHh5mRKKgUTwMzrr7+expBIdeFWtafGzm87IQAE2Dlq90YEjFavleByjSaRBCFzINrsjTATi1dNAuTMjF/81Rby04QE6cKRcT1kjo+SpROhNGiyo3oN0WrShcjnzZvHwQZwf+6ffPLJ6667jop4S/RpuAzBRGErhJXWIurRRx+F3UydOhV+dO+99zInIOK8ZgR80hHNY2w9RrOVtQBPMGDHPGSyCk9nJSLgc+EjbPBbiJNmDPnYdcsirT/gCLFGi6WGMNSyOdbr92GtEf/79l0KPqF1a9fHZ/T0cdCliePuYMc0T8xWLcreq9fkYKOuxc52Gsq7AAEAAElEQVTMFRhqI1DTzoD2WIRgxATVIbYao/Pcc8/B7mkAXJ7Df4jxSYinG2644dlnn+VQRl4Rb4cIgGjxjGMbWDPsXo81eEhIZw4dwzf/gQceoEZKo0YOw+CQiS+++AJFAZzh1AHCvemDwFqA5ee+DZPJz11xZ30/FQQI48JufRfs3mjwsVxsMDQYTB5C9wop8deCCvciwp+qPW0pt0UbD/lzjcq4SH/55ZfXXnvtVVddxUSbmT7p0CSMieuQC/uRjLquxYsXc4IgwgZGw2keUDishC95i8oPiyfltddewwqBCQhhgDmY0C5opjxy6KA+oZ5W6dJ+pMpDei2RkdHF6ajTxbHvMt/D58Zus0e4ojZu3uJj+25VdWFhkdlixT/fajP7Al4fpzIIYERrJsCIRCThUZ7afsEK+RilXmkehLXgSWxNEp6zCQ2pY7/hQB4ZDA6HHfElS8M6Q7ONqm2tAbycCIRNhujf2HwYBYxyWGZYGUZOIwNgzbBjRgR+zYl1xAznlZblbaiR6rTiv2nTJo6gQKigIjC+FMjF9l30ACaIrASQDalz3HHHsXjw3nvvtaGuzk86IXBACBA0wmcr99rL/NaqgLkhgHbHieOExyIgCpxVkV3H8cIDNqFjEls9G0BDp2bobenSpSwGjB8/HvJesWIFpM7kgPSOaVdzKZraMbDocKGUT6U8Uimyh8ZwYYnmhFvSueF8D6QFlK9DelEMH6IPktJOy0Nzi9S/cExYbSjosFmdHDPLCVxo+ESXNRrx11yxcuWyZasaPe6U1JTUND/MtrR496oli7O75uTm9eZ0Mp/fExMdie+O1tr3Krk1D1qwgWxyvGKAsNJiJAL9ZIT4VVgIm9//4g2n0Pjc9exsNIrO3iwJ9s/amhQOo8faw6IRrJlz55EEWP+ZpTE0yG8M91wE4GRpB5MRIwUKTZw4kRFkxqDlWWtqk2Cl5OcAan7R+sEKlog/+eQTlqloAKfOUSxKw7Bhw6gR2UBEB+aRoIpeW2pVXZ2ZOyGwPwQgG5/FFzB4kAcsC5g4mi+IlyBLdA426+yf/xeb0uq2QlrwYkgOhQsVTC/AQslY57UY4L4jGa46TgS7E3yckmF8eIDwqw0OgJWWsBhw8sknwxRmzZpFgN9TTz2Ve6wNcB8yIAO0cAr/8nm7x4PtX8L3cctPio9Zt3FrSmoGDSRKZkxs0sQJR/j8XnYOWK1yImXR7sKS4sKICBdLl0E/seQMnAKWnBBHe5hAtNMupKHdM6/Hqg3b0ex9PqLXIV/kLK9mi/8BJnxEsyCmhc/js8iGFg2N9sPEwAG8t99+O91iHsYKMJEVmKj95z//wXuH1tB9fhHVTBxBHux1nCZE+7ng0dLe1lx6EKkLQYJxiUJACerlYlKIywDYCBogmcjDtECXDbaQ2Jp6OvN2QuAHIMCRIXEBYkeaON7JYvK7gl5mApzxJwf9oZX9wJe/qFetFgOweOgKIkQerFy5Ei2PGxRA9D46pl91YA81j4ChYPzFzkPJaHz4p2qeQnVodqxPaNomGwuDzANoFXYJzEHk37BhA2ogPIKiaDZXRzSPYqysNsK9hg8dsmb1mpDPg1c+LBincNmQJexVlNyK8vLt27b0yu+1YfU6uw3kkG0NToc1MZ5j6/m8vY2hHQABPrt6/Saiy3HoNXJTDrdgXkBP9b6wZmwkp2a4iB+/t8ERGWOzWrAosbEFmCA5WFJoD48My2ZKQyTA5XHUgd2zSAvwecQc9M4778ycOZPVGnQIjPXklCFp/aDQEd0dfeQv5WjVBIUAEyKH0IEGVMpZ2UwXEBWYjGbPns3MADlB5v/WRZv1csh/qwGd9XYgBLC9WvAEtNq8wQYL1BwkUC5T/DqjoZJYvGA1HIDfNmg5HdjIQymq1WKAXmnyY20AumL2DbHBhiZNmkR9vOXxUCo+lDxUxEWZp5122sMPP8wCIIyetT4W+uD7rANPmDDhhBNOwM6ACQLdn1domqh+tA03QdrGEgIG6AsvvJDqKKoDx0PGVhinISUp/vhjpny3YKnJjhYuR4/CfWGr4rsZMpWXVQa8wZXLV1aWV+TU1TvtTr/X3b1XToSL899h1Rw+02pFOAw6IMNFv7IyM8aMGrZmzSaHM4ZZCmJI/EblQgLw1yRsyMno8MxLu9U0esQwmL5a5VB54avyb3slkyrLcOKJJ6IxwPIYLMQ2idQ+aNAg1AXmZ7xCEuicdEHftPY3/CGd4mJAOBWViSPWQvxQKY3a4fvYqVhAwhaEGkHtUGYHokFr29yZ/9cEAVi+2dwYCNWbrHUc+GoIsW+AHwzQ9Ta7FR4FsnEJlbYVyX8ecLVODGg+QpcgJEwxaNnaRKOtQHSYdK6OarqGHRSO7v/HP/6RRUVWHc844wzOGUenhtrR+iF49iWxWM2ugiOPPBLBQDP4RfvDRoRIuOaaa2AEtJxW8dtR40F5MrLIPGOwV37PjVu2rt28PSW9C2kqPoNEy/T7grndenTvlrurYGdSfFJSYpLP587OTB3QtzdNEYfRdsNK9wj2zqSktqa+pJSNuywXW5Vqr7h6i8HQMzl2fHv8njHDBmVlpBPnREpo0Qy+aSNXblERt8wMUM/51Ys6pAB56oI2kAq8Ypg02uz9XVueKBZIgid4iFIsNiIetf2HSplzgKuk6waAsdTbUWjQluZ2fvNrgQA8IGgOBUxBtguHjBEedzz7l5asry2tj9i+Y/fs2d+wQAUL+uUjW+vEAMOn6Y0b6Ary01NsTYekcJGBq6MGGghqRY9p/vnnn6/rJYV0QjlSLzVyAjgXibxFPFA7iRxEzhiQQh4ubkjXN9x3zCWigJJYZ/VNmjDB7Z+5YtW65PQuUVGRoo+LrUXiUNus9rT0zKDf7W6o75XfffiwQRYOYlT7DPi2Y1qCJcgUGj929PsffoFZPCUjG5aLgFIsHWmlWol4wkzF4oTJNGzowN752QG/lxYSDVeMQTJiHdYY3Sk4PsMEzPUv8NdDySOvZFQ6UipL4ymfodfV6bp0ikZI7hEAYCwZOgrsneX8v4YA7moGm88Q7Qklbt3UsGVjWVm9PxjZd8KRp5jdJR9+9OnHn8w4/fTThg4d3IxwYGnz7S8JcK0TA9APlyaq/WlY81kydFQHdUX6N1wmHIRLP1JX+F5n29/yKy3uuCY1NwMBwJarJr5pMpsinbYzjj8mIzFh0coNuyrLXE4Xa8K0DRtMTWUjQY4y4mP79e/Vu3c+cJPYooINYp9pzxXuGlycAy0ddtOYUYNeee3NlSsKs7t1j4iKIcgdRqdQUKLgmY0Bj7ueo4OZN+Tn91AHJajKm8SEbHHTzWpPk8LfMhzh5pGosSU8lB2rj+u6dBXMP/Rwt5xq6Ff6t2V6uLU/zw0N0BVhPfx5auys5SeFgNHoY5eAP5j1yVfFVe7o1NSeKQkJXgJLGAL2+Kzk9EyPz/fxl3PMzogBvfNQnFksINTjL1AStE4MtITpAXnrARNbfvWruUfLVX0RzonjDdZ2dvCOHT188OBBOwp2LV26rLComHeIpfjo6D69+vfrkw+HkslK02Eq4c87AiQiU2Qy1KVL5l+uv3bh4qWLlqyoKKljeYKoJhw74/W68Q/Nzso44oiJcbExtFUdHdPcg+YmUEwHXi2RoeU9Vezz2M5KdWktf/cp8Ade7ZPzJ33c0+uOBfRP2ujOwg8OAXwxbI6YebM31TSmp2TnE0uFsL74iZjNHjlHmzm6xZaV3XXd+k05XTKjXeJN007PwIO3pV1v2i4G2lXt//zHsjgknUCBlk1kwobZJBwI+e0Wc37PXP6CXk6jIb6gxWgTIPt9ft4aJeA4AgQrjIiBDuQGbEvG1sSpeJhgxowcNnz4kKpKnHRqAsSPCHEMsTUxId5uQxXmaBomB+30CZKud15tgAATAq498qANRXR+8suBgNFaUGLcvNOX3DXfJweMy8jiKIheCLUzF5ezBQ2m2rr6BYuWTBw7QqzEHUn0HQaITjHQZlCqeb0wc5Yc/PzLYitRho24bAZCxcUl69etr66tiY6Mxl0yJT3Z4rBimBdvYrnksw684CwcPeYN+GD3ajHEJxsaEuKSEuJb1kIEblkgOLhZr2XmzvufCAIM1k9UcmexPy8EsO46NmyvtUflhKwJvoDbtic2l1iriZ9uCLGnkxUpKwEFGho8URHiH6iMrz9vS3+stk4x8GMQ+rH3otgT4p/BttrrGtwrVq2Z993C+d9/j7ciPpFQPC4rvfvkn3DCkfn5eZEOvMmYNxLgTWYCLFparfYfq+EQ3ks0I842svqJeGvGOG6uq6/ftXMn+3gbJeqnKcLlykxPT01N4aA0msSpqFin0Vw0Swqb7A+hps4s7YIAI94pBtoFwV/Ax3oyxzhiWy0q9Tgj030BK44p+IpIaBYTYR0J2ssCkNiFJIQvuhcHk9TVxcbgOd2BJoAOg0WnGGgzKFkiFh8cpvg+X8DmsK1Zt+Gll1+Lis9ITkk74riTLZwzYLMTWJ8pAk5Ci5et3by1YFD/fhxMivOT3+fFt0ctFYIq7cUMNAy/L2SxcSKepa6+cdY3s1etWuNwuiif8yfZ0oKr/lzvwvi4mKOnTElNS0UhATc1NuvfNkOh88NOCPx/gwCyHKrhgrlziFFUjKvR57fY0av0ph1TgEAnAhQVyYVEnPFwIxH/yfZS+k8E6k4x0DbAikota8PiaGmxO2xzv1/0zAsv9+3bPyU920MIOXGODDQ01LFahPoPxtjtMXV13g8//mI8QW4G9SM9FPSCG8pA1F7koHzWn5E3azdsevrZF12R0UQvMjPPQNCwVBUMREQxSzA0NtR+POPL/v16Dx480EZURBXJp1M5bRsGdH71/x0CGAHMJpbbUMEsZnYOcNhkAJYgW/gNxBcVlw0uZQIUIhMR8UuVA7Ch1l37cI19HltXVvty71/1/intq+EHv8YMIyZ+3GeNW7bufPW1t0eNHJeYlEq8NjNLwUGfmVjToAWnDfvdbNmVI+jNjqTEtHfe+/irWXP4EJVcocgP1nKIL9nAYbUuWLT0sX8/1bVbjx49+xpMDo/f7AtaAiHiXdv4I/Kh2epyuKJWrl3/4EOPFhYXK39WkJVe8NcxF0Pws47Cj7U63Jj9b37s0873nRA4MATCDsr4B8ZG2D31NTaTJRTkJEEfx1IS0pGJtphcmR2IgicWg5iYqOiY6A6j9wO3q+2pbREDzIlwfOSCtOgqlaNXkshFik5ve4sO9CXFUoWq04/NXV86kUrDtZOu73Vm/Yp7cuq2cXOg4tuUZrQQQYSzhT0+/6tvvNM1p7vDGWmSsyaY/VmwzuAhgF0ItwGzBQMMLsN+9pmxo6xrbt7q9Vs4tdhkEm0dELapevko3Hdwbv3WbU8893yf/gNj4+OCAXQTFJKgmR3FIY8x5DUEOY6GLcMgqM3pjHN7jY89/RzLBio2qjQMJKapTQpMWxvEAAFtDFA0TDePEQHmOl2PTrhsPS681Vc4vQ03FKULp6IwTLjRFwXyllfsHyaFzG2ooqM+EdYgwGn7oHdUSzrLaTMEmjAW1hfyZaZYG2srCM7iN5h9ZoLDYC8iOgBaH+o/0eYMdg7/Nvqzs9I4hgQexrdtrven+7DVYkA3hc6A0IR7I4D7woULITOF3MKYtGDo8BZTsng7NocB4AZ6pi7Im1/kMxdO+iSyJEvtZCAFhZdLt7Zjm6TYt/iDLV2xuq6uLjU1TQlB0QN27d65Zu3qtevWbNq0mQVb5o7BAGQvejfyiNhHHAVcVlGtjIW0ve0cgW81wAHCh59M75HXMzJKNA5ZcqDWJmYD2knVdB9QoKV4vf4eefm19Q3zFy60muVkmDBTaj+GAn9GgQA+IAaRPxgC2ka3iShHbHBayylyDz300LfffktOXnXIoAAHqqBAyudGl8kN4aQ0PlCRlgS6MR1SaXsK0Q4C7Smh89tfAASIHNfYPYfTmwrcdVsdJps1kGgOJIgVIKjcQMQtO0gMsdiYqLzuuSAk5NAeev/putzGtQGYLIHd3333XaK1zJo1i6C+hI/HOq2ZdYd3lQKh59mzZxNelBNLCB0DVQNTDVm4MGeT4RXDSSO8JcA9ce2JJkYeJQXM48aN0yHm+aSjQBnmNxs2bNSBzKRkY2jDhrVbt24ZNmwEcQe3b99RW9sQFRlNZsWphEPBqWhGRXlFZmo8S0xiGGqrJADaUqfRuH3HTvSM9PQsoMRcRIkAaU5La4+GFUkIRyQlQU8LC4u9nOHV5C/UdmmkKmr60QDn6Hki+3MkkYZMaWkpIoGzwIgGyLj07duXg+m54cx6Gkz7uVoW0tp7ugbuffTRRxx89qc//YmIs0CGENOIIs7AIbQ1LltURDYCUumo162togPz04yw3O3AYjuL+vkhwGKA01px9OFd3v98rj/QNz6qr8/rMFrdHoPXQMQss5V4LUWFu3t2H+lyOXEL4ThCs+G/GeD2YCBqtRjQFAsfIZwnZw3CkQkmfNdddzEngNsKinfcRWm6Omj4qaee4igrwoRx8jjErINIw0917WTj1cyZM/mEAHMffPDBtm3bCCcJVyIPYUfJoJlmx7VOlO7aBjear80RSb+py+1uXL9hzbixYwkj5/UG+/cbCPNRRghZp23WukUMlJaVhYK5WCjUjpI2NopOUSkfc/SjRBUizLUo/toNFL1/r7HQkAQIANNiNdvtLiQHnqNNu5qFD5OfvzZyZD30tIcDiDgEhphOulekwPGJQkjEb44nevDBB2HTSAUOJJg4cSLnVdAe3Ys2QCFcKQGlGX2QAcwkEQhTnT7yjOMHCD5ICulU3YZaOvAT2sAlHmad1/8+BCQuu8cb7aw8dkrSlzMXlOwsi4vta7XZLfagu9HXWNfQUF83sF/vIYP6C/NB+necGtqxwGu1GNB4jP0XtkLYZ1qDok1MR0I9IwY0sWmO0/6GUpcuBEsCRxogbAhOiabJebbMQggoT0VMSki86aabyMmUHwdNIM6HBBkl7DD3NEmnkEEosH2KZ7hTiBeGFaba6G60O6MoOxhA3ouFirUgt8dtMlp9Pq/N5sSVkzqRGfQG+zuowNqK1+PBWM+kqs1sV7eE6ugUsx9bVBLcj3okqt1eXKYJhrr7/FIp+xwFErKXjZULfuUb2QDBP+24ADVC8Yknnpg0aZI++JPCAD7TNaSCPjdUG4IIDUsiRkXOTdN5WisJ6AAlc9Ed5ogoB0QXZ0JACgUyEWRmoM8eAD2QBxyQSXRCLHJ82I4udsCnNEB5iHVAUZ1F/HchwKm7pkCi0VgTFVF74nHJW9bVbNz89Y6Nge0lDf36jZ00fkJcXHxWRnJTI6HO9lLYT9XdNooBSAvby5133jlq1CisMdAz1hjaCBF2oCSAxwmhB4OcVshcnkq5x84AK0HqaM2OG2LKcwD61q1bOX6SiQKRXVEwOWzg8ccfxzKAPOBkEviCtlm1lt0cDPDCTYxGYkXgosM9TEkYqsUC0y8rrUxLy0AtJX4Em3uVCqDXMGStAlmAnOIGlt10LMzB6vixdGmDGHlM5aVlGVFpLEtpqfDD2Ka/4kx6u80haulechEeKtOLNlxUDdhffvnlnJwc1Pyvv/5ag5qFWTAE9MjKymLskOWEg8Vqz8EAyAwqov1tqE4grmQAGgkHnHEYKocZYAWiGVwIBkacGpmzMi/kRAqyIYeuvPJKFIg2VNf5SScEDgQB1h1dRgPOgVWWkL93Xkx+fsrshTULFq87YmLCgL754jOx5/jb/7L+caD2N6W1WgxAtFA7v5zlMmPGDM6CJ54z9AbDpci2kfTB2gepQ9K8he8jA7ihapgLi5AwFx7JQI00g4kI5I0VGH3w4osvJn4DDcNgxeQAaxICADZEIWQOl3mwSg8xnXJg6PgCOZ1y1CXtpG3c98zrvXz5qqKisurqqvT0DFq1Zs3astIyzvmKjorNyc2F+fOpikKu2HVbeOCeNmr4JCajcUi8aJELh2pvaPZYlU86Rk+B4U6fPv2II45AKnMP50XZRwGHI+u1GQz3zBJYKz7++OMR0gBhT09aeacxgY+YDmItvOSSS1iBACuIo4Q2gPUJkyDDPXjwYDBn9OjRLA9wMh3TSsxTWj61ssLO7J0Q2BcCBO7lhJmgGQfxSHPAYQp5zfZSu8XvYNemxeRj/5DsIeDiR/27bwG/lOdWiwHIj7bDUpkN/OY3v9H9uPvuu3Vwf7hSR/FZSkbdo0CYOMfOcKoMKdxjTMAETO088haSnjx58rHHHssjtikYENwHcxDNgAuQgenCN998w4xBN4wUcrb7kkIwrdjlWJXYLdt2xSekiFgIBHrl98npxnzFZ7Wxgdfmbqzfvaugb98+TkeEzeows2krSMR/A37EUgI2ItmA1sYr3Besc7tKPRTODIRet8A5vWdFJ+ieNw0QDz4vByNrLG1aSGgnaJif4SkA/OkPZh9YPxdW+4yMDIaJ6REDipDg7erVqzkXAY7MPYmqza0Dgh5csBHFn5Lvv/9+fnfu3PnKK69w1CWrERxJjfhh/sGla2EWwslFsq+byEudVycE2g8B2TZcLztJg5HGQLTRUCdneIDPQTYKsaPfAm5jPW5Bj+2v8icpodViAGZKQ6Bq3IRYfEPn4jB6SBq1SzcwzJva316KEjiazViWIW8kAeZdOAizAZyCIHvoGdbDscNHHXUU2cigm4dFiNPHEB4wAtRD7A8UpUeFG64OaBswUCt9A/r3WbFydXxisjLxGE0Bk9PqNDlcOO+z2TzgD9pZNS0srKiuZXYSb49lZ5nLZs5KT2UtAVjqXedtbo9WivG9Kfh6gR8TP5vWgn7sUBSo9izyw+ZG+kufVZwTnJg5nICZqqfOYGSKIqdmysHJLF+pFQKFsm2ED2PB+rzuC6Z5ZmA46rz11ltnn3027QT+L774IoYg/Mo+++wzJgScTkxmBq4NI8JXfEuZ2HnABO7h77fffjuTVEYcUUThiAo8F1g4QUsgPwYizqEDGXQL/yu/4Ke+/iu1d1basRAwhYx2n81vwT/UFzDXhIweg8WBHYigAfgEYQ1mC0HLGmGdbSStlqX8BPetFgOQFiQNKqNfc/Y3qwKooqeccgpHQmqWxKuOaqfW+Chw5MiRSBp8EGHuTAUuu+wy7AnwFITBcccdx5nj//rXv0jBOIB1CCGBVvj222+zhszUgcawVMgv5bSB3RysLyywUiKzom5dssaNG7Nm3WanK47JiVQkxiuZx8jeMbNlxMhRnEGzbMWKgoLt8XFRoYAvNTOVWG9o4jr/wao4xHSmIH1656/furu2roHqqB9GT19phvJMlH3t0nHBQX4JgRuwEsQiGOjbpyfrGaxg2OSrjsRPQH366acjsxksrPZwZMQ53eHg6CVLloAnV1xxBQqE7mA7B4VpBxc1IhJwCaOuWbNmYQWC7yMemIjgMsSuBRpAHg611vLjEGHb4dloA1c7u9zhreossG0QwMvDZ0EAgFo2Q8iJ3zKrfj5Trd/SYLAIwmMcEaL7xV+tFgMag0FlWDBqne4ghM0F14bYOhDFFcnAvUTp48hJpv8sNiJ18DJEzccWRDrcH6mA5aesrAyRgDGalkycOBHVG6mA6pebm4tg0A2jbR3VPBYoQQL8UWkk8f2LSyqKSypjYxIDwtwFKry12Ryr16zCXyg5Oam4eHevXvnIh8iIiKFDBrE4LM6a6AttXZLVwNE9IqbckRNHT5/xlS/gYwMzTqpBIyOLskJTBIAiclSAIUxSmC293sbsrhnEPUWg6wzNvyqvNL+9FxM4hRQBnAhoKvf8IhjYPaCHAAna3jrU92E4MDVE3sPlQQzuqZFBZ0aI6ZLJASIBCxVfkPhflARNre1QodshYOwspA0QCJpCPk6YMfnNfpvVazMGrSELAQO8Pmut0arQmyhzYvVtmhP8YgVCq8UAeAzzhZa4uOdXW1ohLR4BJb8dxWoph/IpE3YJ32chGhlAChTOK+SQrpF7eD0OKloOkcjF9B8WQAqZwwPMPY0PP7bnRrqKak3zxF/IPGn8qHfe/aSocEdCUrLFKpuZYbsenzunew6LpaVlpb3ze8THRCYmxIwZNTwyEpMR5xC10SdHN5te0zvdWZSOmEjXuFFDvvjqa2KgW+wujz+IEUjhn0Q8lwmBzF7YJWBwe2qTEhNGjByqVZUWAAFZVbfaAxf1LW2jYXrsAAX3pOgLdgyqaGzR6e2sjWKpiKIoh2L51cXyqzETeYMewKUxQedpZ6Xt/Jw2t7OEzs9/ERBA1zOwzuQxGeqtBJJBCATtKpCQiW08tFDNyYX/NC0BgqW/yKFvNU/UVAQDkk4qkcBNmLQ6Fr8pLcykIGkeqVFXTaVcmuD1va6atzpD+JVuW8tm6/zt/BU9XkzwMCBs6sHkxLiLzj/zw48+W79lK1OQqKhoXFNoid8fyuma7W5ssBh9ud2yRo0cabdipWF+o2SAcvJvc0vosu61dNYUzM7O6p6T/cbb7+f27JOUnEZ4HbBU7EJEGWXiYmSOwKHIDYmJ8ePGDI+OFgen5ivM/TuMPdEwzYWbq2j6FwOOvjvg230yH+KjHtzwiGutX0OmZQkMRzhPy/Sf8x6JJQ3rMDD/nG3vrGtfCLAAYA24gsF6s6kBCrRYYuyWiIDb4a23hYzihuBFBKCOiH4lmiuaET7l+5byC3huV5v2obR9Hju2d7rwfarY57FljT/wqmW2tt+H7S0SyjmEy4DVbD7r9OMLyypYk1y6bEVJYa3d7kRl54+TiIcP6SeHAIt5xI8pqaneDmIHdBYGxwa2cePGJiQmvvfBpwX19TFx8SwVmKwcRGbyB7w1DfVWs2Ho0IG9e/W0W23ipNRBtbcdhh36ZXjEwzf7F/8Dr/bP3JnSCYEfhgA6vsnPNMDFDlEOINu5K7Byw+qVW4yuiMx1GwqSkpfn5nSNjY6GB2CLVEUJmf4CkbBdYuCHYfTrfouijYqNlGdCoNipnCFDwIjkhJjU8WPGjRnZUF+vDRWEE7HiJ+prJAKJTCLl4gv5iIeOY8Us9nKmmLlPn97du+ftKNhZVlrKdMAdkH1zhDxlTTgrKz0rI52F64Df19wM/m2WSSqp86cTAp0QOFQIMJsPEkTX2miO/2ZWRXmpKyaxf27/1NxhGe5Gz5Jl62Z9PTfCbrno/HOiIyPEl5SAL79I5atTDBzqiO+fTzNxeD0KPgHdGF+WLeScF2OINVtbdEz4E8QDw68XEkCEZv7fcSKAErFQKecgYirbbeaePbryh5zxK6skNfEXxME5RCw5jHiqFU3tQ08JS4LwTbjtnTedEOiEwEEgYGIFjgO+oz/5akdNfe/ULiODRp/X4vEb2DYkZwGmJLuWLVkw/bOvzjr9RNwCxXCwR/Nrum1WDVEruTSlyl2LnPJ4oIssZGspWFrS8oG+OEhapxg4CGB+LBnoE9EZrskoMuETk4y4oEjoiPLKCiJbbN681eP2skJADGp2MKUmJ4gkCBHW32/Wgy3yogUH/rEaf/i97FJRGMGKqNio8EIys7E55JEQ50GHg7DnzAf8/Kn1DPFi+mUqJj/czf/1t3qZuhPy/9PjGDbsBEL+kM353Wym+oPT03oEfDZsQ0YT8dbcshRgthNPdPCwMbsLd27dsqNbVhcJL0nQL8heMfCmcuD8IgK0J4ts4ZH1PH7Jw74irZhheVCPpCuuLx+I/onHYYhzDMXd0MRpVybOlmLtTT5v1dUpBloFrn0yC7gZbmIJcd6Jnf0KBbtmzZlTVl6hhgh3UnyIGnfsLFm1emO3LpmDBvZPSonHZROOrQxKrR6tfapv+ajsPIJ6mIBYGy4sKlq0aHFZWWUjR2CqFem0lKRBg/plpKcyX1H4F1b8wzeU10ZtomVLOu87IfCrh4Dm4BarfeOOqsKShMQuPT0cNWhk7i2zcjPxpEN46JnVgkAoOi5q846NWV0S2SgZCtpkWhAm/SaWD8OQCw+YPayfZ2ETAZlCqP+08wtGXcljxGWGU21EgmD7FYWUkGISJpKSwqWrQg/hp1MMHAKQDpQFSCtgy7+y/GM0f/jR9K+/ntOle4/YhFSLxUbYZ+wzwphNVnTw7btKi0pmde2aPnr0MBVCAqThq+ZiDlRFq9JABqQR1a1avfbLmbNraus5DS06NtbucrIw0NjYsGHL9q07Cvr17T2gf9/EuDhxIW1VBZ2ZOyHQCQEFAahMxAAKuMm+ZWdVRFyuJ2T1mwO4YpjYNoxTXsAizqMo6HBlDEdmY0llUUXNjuT4aGMQbR2ui7yAx+NurkoUg65H0qRUpSKSrCxIPjEoi66JiPBjcRDjA2cFImwcbA7iLbtjOPycmX7I7+Vg5OSU7DaM0v+eGNByWKCkVmC5aRoVYamSKCBTl75v+avTO+5XRLgc9mU2PffMcwsXLh8zdoLFGcHg+uS8R3xb7XIqoxLeVkckQzd/0cqVq9deeMHZkS4bkZlkyDVOtLtNzEmo9+VXX/nm2+/69B/cNbdnwED8IkFFkNYVZY+KjsVNadPWnTt2lTAzOGby+BZ1Crq2eGz7bUv4U8o+j20v9we/PNgQk853YXz4wTI6X3ZCoBUQENQimmTQWFFtNLviQ2Zb0NSI2xCBe9k3YDRw8CyEh5coyjwLca7a2opXX3tzYP8MzkPiyFQst+wexdkcSzJ3QX/I53FyRiHpcHri1ZPITikMul6fA9MPq4+yGVLtiAwQhYatOH4T0Wq8+IAHvSHOQA4FvO5gn74D/3L9X+Azrb1aJwboPI3RVMcMBRaHyzZkRiKPqp1i4epAl/BwfwAKVehaSNQUrt/SDNJpSbht7DYgAzAlnTxkoJFc8r1KCRfbthtwQEAhXEakwLfzvluybNW4iZMJfooAQCtnKwWvkdoGk5nFW5UZjh9KTstat3b5W++8f8FvzpTgg+znYkNXWy+K5aJfiteZX37l1bnzFowZN8Fij8RpCI1BvRN0Q9yATETCtjsiwbsPPpiO5eiIwycSSYIdDLIHokkMtEsYsDVMNQYtScDOfbiFYVTRKbxlsPglJynctBln+FwPNDfhwdVVUwUXiEH5XCTySDbQQ7dTp/+cvxoVw036OavurKujIADygEJM8VHRxTuP6b7Y6MFn/kA2CBJWDH6LMcAYspsMkRiJ5n7//pK5M3AoBN8FCxXa8wtOMmUwhNTJ5MJXkBvCGmS+Dg0TpU4cTqUkab/8y6MWMTKxwOhgsnphNR63zWGJbMrWyq62mgdpQuJ4LzbuElUUCiTOMyF8CfBAGBli/kivfoILCod4iArAjd5TpisCaqTARIgsxj1hjaE0WiXgUsGfecUjn5BOhg5pGiNCURRI4ZU1NR99PH3o8FEGk+wZMYbcJoO/traOkXI4IvDVZJzhdj4lEtADevbq666rbPR4I5xEINHrQm1plG4AfeRj+PiCJcvnfL9k3KQjME/56SjOP6Kv8FJkANbFpvUIUVWMgwcO/fDDT/r16ZOcmoiGIYglOds1cLSHxjAW/IaHhnstG/QxkBp5dDaCQnPDbjI+aee4UAIl86tvpM+autTGxpY8lwyaEZOn8+qEQNsgoFFdUVfIbPGGgvVGQ6QU1XT+N0wGiyvkABeCXzM/MIc8XnuwMSaGSUI8WK8ORRTmLigLgpoNZlsthn3YhSIgxIzRTBR7c8hh8jKbUKuMZFQ6m/wYiAhjNrosJqfJ4jNYG402WyiYnJmVjzyCH7S2X60TA9AtLJVovRz29Oc//5kuEGeUCPIE+GTPFEeN//Wvf4XgW9uIQ8nPbIC6CGYHo+cMW0JH6MEAhMSvJ5AcUUW5P/zwwwkrRjhSAhDB+mkh0c2OOeYYTp7RrIEUenEoNf5wHhitOOcYDStWrExNS4+Iivb5WREipKjnu3nzkUkMr99HdKNJDjtWPCz36ONBh83GRl5XZGxlVW2EM4mdtpTTtubQCy46xeXx+r75fvGgYaP9EtGQBD3lUbMAYe7Kjik+CtyKY5PT4czr0Wvp0pVHH3uYhBraqwUgcVvgQyHAH7AT65shmDJlihYGHAVDFEJeMUYcEEY4EKIEcjoNx4ERAIrgo8T8UC3epxk/DP693vI55e/evRsHLcKa6khzICohRQlAQjAPMvABKEQ8O04j2OvjzodOCLQeAuA2CA/jTow3l1aXWRzRMGssQiIJCNoroafxGvIZjF7mBVaDv75u60nH9RvQl3kDsgG+ISSHEFCURgIyQ4SHRCSgFFn6VUYXQ8Amy8yiyQnl8p+6mNuLjMFPnZUDkztgafAE3b5AREwsUa/ZEsQsoXVXq+UG8RoJ9UzUeLQ8Ak1zjgcnfkDzxIwkzjNxfVtX/yHk1jQMNyGiNUfKENH63nvvpS7N0PnlTEqOqLz88ss5eow4xqSMGzeOOMZEmhs/fjzigegCmknBLA6hwkPKQoFcLAJv3VYQn5iCTU99Zly8eIHdZpo0YfyRh08eMKCvikbNvBG0wZeLKNBM80w+f6ikpALtmb+28VzdRBrADZ1av2Ejk1NHRDRBzkNGZfGQxSbMkxgniX9FTWQUXQVeK9vcgsGIyEiWkcWblFdh7BJ0kzLbdoESL7zwwqpVqxAGum3EfCbaK9WhIsybN0/PDJhKIgA4FYCwgAwlWER1On+r6tWIwYdcMH0C0D7yyCOca0Y6MOHIAY4eIqAph2Jyeh0H0XBiM3Xpr1pVUWfmTggcEALsCOuZk1hbvs1pIX68UFfIHAiZ4P5s0MEtnKMGUbK8plB5Q93a3j2tsZGlsbbSGGtZjKU02lIaZSqOMBVFmgojDCXOQIUrWO0KVLuCNU5/lcNX6QxUOvy1mP+NnFfbyEbQoMlj4M/o5p4bG38SzchtNTYwN3CY3Q6TL7oNUwG61moxwNEuf/vb3wjchq6NSEDz0sd6MAngFZQGFz4gyNqQCPvggm7RH7/66isOFufkEJg7Uex5pAFckDfq3l/+8heC3aP6ofhTEU1CK+RUQsKOEupSH4kDa9CSow0t2f8TLBlwH48/UFXnCRiIzWCGufoba8uKi/v37m8h7LjflJLa1WR1+kJmP3HJjZiHxJaEMOCvuKyEaKBqV+H+ZR9qiuZ3NGPhgoUGQtXJaQOa40sJUhvsH1nAQgUqiuAmk1VaahQvJZud2axXjskkHy9QPzjRvtVikjZwYeGBxaPjE3SWIwf0wAFwTh1gLGghsT85Eo7xIhYso4nAJidnAxB/icOlST/UPjfno1JYP7+6Ls6YY/RBDx6pjlxUzbhTPtjCzPWPf/wjU1XaIM1Vk4Pmkn7Wf5mRUB+N/Flr7azsJ4CAIJHflxNvzo6rrC3aZDFHNJp9jba6gAlfUZQzHzEDgkYUUMPObd8N6GVOjvVAgYGQxY/vhvrzhwhAalF/JqxIiA5wlz+IWBJDVvllYVh8RvkDaTArYMNl0oDZAWOQ0WOt9zFdCEYw8zBZa5iNtIGlt+UbbDIEa4QCYakQf0sChhGjjHcgjemiqAjGAW2z/CAUHArB35FA3NCBlStX0iTONnnwwQdvuumm1157jSbxChMwxIapCrGh+UJHYgJVB7G8sD8ryBleMreTEZRdxDZOHbPbsfqxXAzP97FdTCaPJjn5HQ4l7l5cPrFQ6xlhu5sFI1y/YYNFXJLFPq54YLhQRHIL7V4xfHlHmuREJqj5ZvN0QADaygu+RiHUy1yNMOBwdu6BPL9YxmDxI0aM4JQxwk1r3sfkjHoZIyDAygqqA0dWtG2AEDO6IqakmJgIKA1akkJ7OBKV2QAmINqAzfD111/n3GysVXpBom3VtRIwndn/X0AgGKg7fHJOyLvcU7EhKuS0eVJt3iyjL45g70gJc7CxdOfqrCTjwL4Zfn+jl+VJRZ/CxwFPi3tlGpBpu34V/lW2fjEWhf9EZRONjszICy88hsxsHEMoKOfDtoC91Zq7MA7lewO9YWZlzVYrOCSi6HHoRxNzaUtjDvpNmG6Fe6qFX5qhWwIrgY9w/BYa380338xJZBiI4DJkw1IMM/pJjiCX8RMeCnOPiYpsMrqjAhDmPhTasWOnx+fnAPSammorXkPBgKeRCEO1uOigvGJDREVHS0U0KFvNQXt9iC+wMlEse4ZF+Ti08VRGxqDdzuljSkjwqRynJyPb2gsIAwcWYF599VUO+QIfYLUMExwZqaA2UafySLH8csG7uecrfRNOaW29VMoQUw7snqnhb3/7W7QTGREW0MxmxD9xyEnRZ5Bxw+IQOTm4mE9aW1dn/k4IHBgC8AGT0WouP+Go9Mjg0vqCbx2Nu/31293uLb5gjc1mzM2IGjMgceLILLuxAbfQAG6VoL/QWvv+RBIwoeQPmuVedhOrFB7bQsVtmYxDflAaJAelYXb/5ptv0LiheRQ9jiHTCtqBodbKVCriCwrEBgVzwdCMTZkUGD1Va3rGFKCVTR7xU4LsOW0GLyYeWcomG3KildUeYnYCNoQ4eJowDV6fx2pzABCrxTpp8hGbNm5eunSZwxFJMziWZtmS5XFxMRHOiJra2lzOxTWKjT4mOlKMMWoj2SHWt382DR/S6TXexmLzR9uQqYli7vt/sCdF21LEG1nFvQ6jDjc/+u2eUvQdHWepBobOnIwULX0nT56MaR4rjVb8GUTNtVEdtJzQYoDBwny3b4mH9ky9ZOTsOeag06dPpwGUhh/B+eefz2SRKSNvmYsMGTKEqhFINObf//43i0YcUn9oNXTm6oTAD0MAzMIV0B9jrT1xckpZVW1B6TeFlbVGR3xOj9O7ZfdNifPW7d7tr9zECS0YDXxGn7DrHy7yUN8q/78m1U3TL7/65lCLCOdrnRiA8DTt0XsmASwMcPIXpIU+jp8GRmE4ss4QrqA9N2HFjaMEYam4mlx66aWsBGBquP766zlgElMAyj4t+fzzz1kNZiUA8xHnW8F0mJpgpsBM1J4GHPRbDNNs+giFCCCRlZq8bMXaxORU+BwKqisisv/AgewEwa8VLl9SVFRbU5VCQCFDKA8rOScj+rCkmZIS4ilcpoDtQwp6Cj/F5L1heyH+CXigmpv2o4V1XirYFznAINycfQTF4h1qtSj0zBBofqvXBsAEGoD/z9SpU+HvcFu8yGD9mP5Z1cc1SAsAjTBkYBzhwpjyGDhWbllAZkylDUrkHxTg+72QFisxgJqP2xi1MP+g3ri4OCxCIAZrEggGUIJXaAMUAJ6gE4SRar8if44E3c3WdvbnaFlnHW2BAMZnnykUQbhps6kmNbE6MdXUJ5RgcfWNT+kbMsQaQgXBQKnFVG0IEnPeajB5Zc24nTQvmhqKHgSDJqn966RMaEjcwtt0tU4MgL5QEfSGhygkB1Vz1uOtt96KH+eECRNQuyA8reW1qTE/9NHvf/97zA6wGJYisQL37t0bhyXm+FdddRWiiBum/Nhh0P5gSbQB/jJp0iR4gWYWHUx48D7sTnDdYIil6C2bt+AGarIzIWBhn5Ue7A5mEIQTCIqLC7HZs51ww/p1DfUNWdldGMLIyIjUFLwk2fOht4+1URTA4DTIhg8fVlQ5k4kTAgZmZzFzNi8MXhV7EFMP2w9drmiHgzNUxWIjokKy81nrGqO5KuyVGRugBs4MDfBnfsAcBWEQhn///v31whL6OOOFkGZ6x9ix5ANGtWHSRtXgGyOuUY45ASZBdJHvvvuOMpmnUizaCSoCKIHKgrICSmjbUQfjg4LgofxoEP23aj+UFnbmaQ0EoDF2DsOCHcTuDQXqJaSLL9oWikfLEsWGUBIsYHEyPTEkglZDqEGoUswB7btEaaN0HblInAFxTBLLgiS2jn51O1onBvgGwgOVMc7Ag7igNA6j1/453EOQbSPpH4YKNXK+PC6GuANC/Bh/YHbM8bUXCgsDrDQik6AubO5kpg2cYj927FhaqAnvh8tvw1sGQuAdCsVERx1z1JQvvp4LC2Qdh2REpRoSDC+IhFBOTre87rkNdQ119bXIzvqGun5d8oQZBdlz2y6EABS6gxnp6X365K9dv5GDbhRfRjxovf6gOAGssMYgR9juqLj/vjOGQ4QJ5QBhLgUMsTWhIvDIALGdkLdaB6fjcHzyMEzMAxg4VhEYLOBAnkOsq2U2Xa/qbNOqA1WwQsBUA9kzfPhwcIBm4CfGXIEZJAvFCAymlT+dptKyeQe7p3ZabsZfYN9L5HaLv31fd9xzy4FuWWPL9PbUdrByDpbenrr+y9+ig6ugEW6/VRx4jAamBQ6rjzU3r0kcSFkEs/uMJnHMCyInRG3R/tvtbbdM3YVuEUDYmIXbsGVJftvIT1otBiA8TfP0RBBaeWtwo/GbRKixvZ080PdUwTwAT3Nq16II/VEzQR7hJpqhkIcWkpmLYnRr9f2BSm1fmnBAIjoF0zNS0tOSFy5bk5aZzfQMgzuSQKSBxZSanrZpw5q62uq6Wk/PXr08DQ15ubl9e+UpryEgyFC2/VK9VFzYYCRmXFFpWW1No1VOihaSE4VBZiJsV2HGKJMCNXPkX3i0t2+vHinJiWLH2qM+HDI/bkHRFCzg5U9NBTTAeW+xWvX6A+OFKAZPSMRLCvRg1Kw2q10ENpgrLv8sGAsUyNEkEcIVHFRC6L4z9NTIpyAD8oadaKTjIEQ6j1TKhZkIHzbygBs0RqOHVPffuOi7brmqvKl3jJdyDSScgLhdAacDN20PfA78/kdS1ec4LDJQqHOAOhi0+IM2iwQ+gVT4Pwz2vUtqVb34yzdfohSpC6hLBGalCcMNSdWv1E3TffNHbfxX1aSUEQoX1KccvWoKaorTDo8kSydxvmxuWBsrC39GQSGiCbn9lmpo3hhwWoguZy5h1dhgHKEqsQVM8biO+01JfGQ0eINyOOUeEIVLatWNUBtuoixLGPFWN4dMVoORZjjxS29VOeHMrWbZmur4PnwD4oLZ4cdw0R14Q/nQs66Ce0rmMdwGnaKrQxjsU2/Lt/u8as+j9jfBM4XuNzZ6J4wfVVRYuHDeN/36DXRFx7Lng72A7kAwLjm1b6TD5/F0scUwc0uMix47dKDDxrAhK8CTA6iFh94qDRaVP+Rw2UeNHPbcc68lxKXHxsaxsQXGS4w78SoTJk3l7F1E+Q96vfWJCbGjhw6QfY9yCSS5Uw86RSXv/aMoS1q85099Iyte8pGeHHGnalO5GCQ+YEe8ei3FyXoa2VWnKYlRJdHMkAnhqp3Wkgnc0nTL24Mu74fRIDy+DL1GRcoAVZhqcNPy2h83Wr79Oe8l2JTAkQHg15ednWv0O83OkCnCBxSagdmiReRqGqAWiW24Ff5hMju6KBZpcET2CRrtVvQ6zJu0ZX9VsvX1ajzQTVNfyy3+w1ZHFykejPRHuY0ReFJiLncbInGs7DCmTPnC/mUWTlwvUTJktu0wBtlzVc3GGncwhuhtIBgxP81BWJ8gbvsuWB/tt4T8OB3gNUNkCbMP/dwaYTXBkTmg2JiYfIwh8XCTVEeF4tzZEfUq9JHmUx66HKRNM/ilGW3pVKvFgPRi7ytMh3snd/zT/hXtn9LxtR60RBlQaYA6ex2xdO5vzhjQr+93c5cUbNoaFZdosNgYHXixhSC0AXdkVHDIwP45XbKYwrGWq8ZKA78tw7ZPo+DxDkNjepz9tOMmPffsq7ExKTk5PQM+o8UcaTJYRAsSDZC4hfX46w+RWNN9JBJG03VIDZBpg77E16FZ34KTSaLgYtPbH/7nQLnUPIUC/QaJsqdlEgUquUVpB/okXMk+CBB+DN+Ec/5CbhgIGQvpFKKObrKbJNS9+3CxGhsbJNEULxtQD3gdMpgP+LUaJRhgQ8gQ6Q9EMIgRcUMi47pL+IEAZuuIpsHc/+NDrHf/bE16BoPI+HoDfofZ6HDFDjS5Io3GGlPIZghEhlg1FS/4jrwQRUzGsVibLdFBg0QQMBmS4xLHuj29RATQGgLB0NoOuBhLzvNAuDJkImJRdMTQagO8EUq8YQCMFSuxQJ9L5HAHVCuoowkyTB70ESBLFW0ovwPEQBtq/bV8onkg0GeuL8uVffv16tun55ZtBRs3ban3eDF9MCZkys0d0CU3wcFB8BKVEIwBF9k0oAasLaO2P/zYemj11tUOyM//x9//OHfeQo8n6HLF1NQ2+oI+9i5gM7FbnHZbRL++PTIzktiRuH8RP5ICitFUUToIbAvdCi3BxUBrkjWWt7ErijIEg9WmGACGDYlSRcCSehCW+COt/aW+BlFompLBYhDTnDcYIgyXIxSEX7DZ0LY/IQvgO6JHgDNojERtxG4Np/SH7FZjonibEAe3mVG1rKcN9VKFDKW6lLTTd0x/0JHtfp/JFpnhMMdgojMYIgwBO4c4ttM02lTZXv8oaKFvQRYKcizhOaKiHVG6Q6AVTWs9CexVhX6grwJLNY7wYQGrGOxV+S05vmqQfBIGzgEKa02SVuKkwhZfKYxq8XzIt51i4JBBtW9GPRAMuBisGHI2RDHKQaMnJy+5R166X1R+MYhbCDVhwEPfLZNUuBq2USNgZ/hgoLxvKmff4lv1DL77HBF2u98bSEhMPOGkY3x+H5KprrEhEKymBpvV6bAQj9BmMVt9XnxM91DoIdXThLz8gxhg4ZvWE0vVKuguaNj0+pCKOkgmbTsWR1bmUGqlSxUKr+wI+Byk0v9WsoKaIj06CUMS1ZW2kMLu60alJezVNHIBhZYEv9frVjxQBjsGmSaywcQPoJl9mA3YlNFq3e2vl3a2ZK6w/uamgfSUz3zPCc4ricOpLGIU5LdjSKC5pvC/YoE00MdqQSFM5yGcEcTGSO2y0bIj8YouqD8hBqYeor3szZHDcFCtU6/D7WzbDQeZQMvUJ0vFSuw0Iwhprb46xUCrQXagDwSzZR7KP0bUOiytousR5AeOicMKCwXGgEPWNkTB1chCMbyEakDNtozcPs0gVql4rrFMRbnSACsPUVExHH0D4YkfAeFIWMinRivrEgY7S3aKhe9TzkEfZaci3eR7RcAGZvRyulIIc0LzN+Gb5oRD/ZeJEcsB0h522IjRREVONKPNeYwSLvHXIAnolgYH3cTbTjqlEtAFGDtZy5ehIyQxkSn3w4imTw8VngfLR1WKRxGNBmUcNzbInz+NkyxQK57c8uNW1gsey1Dtjc7SG+m7WhpVVmyZ4hHXSpZtO0SFaNnive4ViOUwSAPoSgNUU7COKm2m4wQr5Qqg+J8y5U4AIOteewNCNU29Unft+ykrLWXbDc5+uEVQD9iFq4VaQG1LuZ1ioC1Qa/kNK1F+P76qqFUIZzQQziUOVVeWEODS425wOqPTM3JiYuI5EoIACxhSCEVqt6NHaxwRpGn/hWbntVZTDkwTi6jRyO5mgpQbvG7kkYNUq1MmIUHss2ALDs6CraIVHmrVwpkgWvKD3EizgIUz80KNEDNOLmpOo0s65AL3rhjgGQ0OQ7DeYGg0mFxGQwJTK9VgtLl91/z3/vR/5gnuD61yYTy06Z3tCloKppxOC4TRCRgTWOR+ndo/Zb8sh5oglTCICNfmQpvwAB1lvzL2T9kvy48mSBn8zxopXeQGs4nSyvWHrVJFfrSuA2QQT01dr/qVFghGSas68Org4n68ZbjpIwAeffRR4ifiHM89WqbYpves+f14IeEcnWIgDIo23oDHFqvEOMPg4/ZUb14zt7BgbX3NbpvZbbdbGoyWnetDkTEpmd37ZHXtZXMkGkMOiQUteCgstUMQUkojrBAL0pidrfZgwLtr+/Ki4g111SWGOnHhtUY4o+ITMrK7xyZkWM02CXDVavqjqVoGoL+63Q1ba8vXmkPVMtFGxVMXPK5Zu6N33B/qhasDO2wCxrqgNWiyZMYnjjZaEpkLEI61ab57qCX9ovNpScDel7fefpvt1uIrIM7menGliTEpw/IBQNdBS5rYgSI4FsmEaR6bXiACX92ApT5kqpOAuQfija2qV4b/gAwRkR7CiQVEYcaDOFQII333Ny2u/jTjJjQm62BE8sIxTzbxmHBdJrIP+kvTZKzDK9ZWMWa0aq7T4cWrAiE0cAnPN9b8XnzxRTZjsjdzwoQJbOZtW4VtFwO6KbpWfd8ypW2t+eGvDrH8Q8z2w3X9+FtQXjBe7EBE8kGb/fKLD+d///GoQeb8XKfT5nOYgyZjI54ZgaCtwVtYWlvPMS9BQ/LQ4ceZzUkyGedLvmtioT9e4Q/mMFsNiWIV8gdnzfpozcpPc3PMaZmmzJSAKwUborPRay6rCC4vnG+NzO2aMyY9s6eons2UoClXz2wPyAtU1bKqwY0wZ0ODz721rnaJw+o1B6IJsi7pCiAiXRgAYd5SqrqRr5tvVPHC5dT/TTwevgCcMGS5vX53IFQTHZ1nNSVIdCTWSw/Em1SBQglS9CFf0iyKa+VXh1z8IWVke/NJJ51UVFTo8dbJB3RC9pyL4QIw8uSzeIDw/mWJ4Ybk1vV4/2KosYYdOEgCNjRxWJ9wYmt10Nxo9scYUdj3K78V9armCRfcpxBJx+ZVqZxqRFkxBO2c2A7tUPk+eQ/U4jamNUPLz8auoIlfOD/R4K3mgA33zYBJYrJ3RO26DGqTbvKjukvnOoawD9Z5tH6OONy4cX1KSpI6SSlYXV3pxyjQphBqMlE9WE0HTGdXTnj2gQqs/ffZmEP1FAWNcbVtYnLA6sKJFM5sWhfObzidRN0encIr3Qz9SyI3upHhTzrghnUZNycDsUOpHr83h8H13svPffLJvy773fgemTjDbTMHLEZfhMnIzgGb0RzpN0J78W6D//3pK62OcedffIMv4LayxyTkaNuwteiC0B0c3BeEZZqee/jRNUumnXdeXl6PCK8H9HcZzOWQnDnkNJnZtWwoKAotWhIcPurivv0GB8wBIuEyXFbIA3Op0R8w+qz4We9Lx5QvKwtmvxVB4BFHvxpP4efV5SsjM4Y7I7oJOSs4g0lmi1lOzJZBQTZA/ZzSZ+ZRiQGGDS9xOWqV/LKnTAUzwv4D+hotxGAKVpd9W9+wO7XLyWbbIGiUfh1sMVtbQsNwCKMEw629ccAKjQP6MZyTm47Hh5ald953QuCnhwDBsgidGR0dzdF+RG1pJ0q3ejYAvWm60hVr1sz0JExs3PxEYgDjhibyln3WfJ8U5JO+1/RPTt0MLSfCbKJjBgjVDB4uygQ+ocYdHHL13ds3X39OZGy1kX0xJnvIEOcP2q2OGixFuAjZ7Ba/r95iMp547Jhp76z68vPXphx9ho652er2iLKx70ckWI3+D955pqr6yxtuPM5mqPTU17HOajCjguGJYoXHh4KNRnN9dmZ8TFzG669NtVr/nDdgIEYJ2LH22BBOLqrMgSpQkkbXymuZRwTQdgirmmK2ZSvTlqSqLzkYmnUEH4YoMEFjC86qvOQIb+pC4SSbuCjyTsS5fGS2sONXDtUzGlzqfOawTqmKlLL3ujR/17jHC0afFC5GXFeqH8nAW2ohg07nXusxquq9yux86ITA/woEhFBDIeI6sz2+Q/htW2YuumLOGpw2bZpWyggp/OWXX2rS+olkAMUShYa49i0JGFgwclA76RwwgpzQHIE83BO+hgt1u+UnHTPSgE20Xs78sQV8jSuWfHDBecNjIxuMPjcnkAW8CW5vhtccX+HxedldwvFAbI0ySuxPS7DxxOPyY6NKg74GM3o3dhzhg+2/jFvXzA95l5x3fqbJsgmDjzmEUYVl4BL8mkW7V/9xUl7AWxxh33H6KTnLFr/TULeD+Uqzui3yQibs+wuZvVsH55aMws35jyc8o1yhoJNNoZ4G0/ZtpZs3FW7eWLhpw86qSjfTHaOB9NCaVZsNIaffaykvqw8yq8BQVucvLqzkJhSwsbKNYJX/xBDUsg0HBg4DGh5TsJGhBzeYknKjtQHegjCEruORi3Ti0fKrzygNf7t3zzqfOiHwvwEBSDUhIQEZAPvVuk47293q2YDm8sSPfPzxx6ErwoetXbuWE2WJdUxwR1qjWXM7m7XP59DtZ599RqhIaJ5zjwklHa4F+ufEMQIX8wkx5jjinDCWNHLmzJkfffQRicSzPOyww/YpsN2PGLI9Ymc0WTauWe6ylyYn2k3MA/ChtBl37jJ+OuMbR5zf5Nh22vETlbsojM/N3iCzsT7KVU2woaC70uxMxBrc7pZQgNHnrd2w4svBfWIthh0wZo4zwBhFLbIoJo6eKMVMXsw4+DB5CYTqEqKt3btGr183Z/DwE3yyvIFZRzi65u+H0CTVbMZALq1JyKfLV6x68MF/ZWZmMV5eX+PZZ585cuQo7jdv3bJk6dL8XvmvvPLKvHnfPfzII4Rev/ueeximf9xyi8wLxFMI+7jESZE5lrTlxy9KRuoTN5RQ5/q4aajixBNPZK0M1g+qEOkajzrQA1JBLeCXM7RBnh8vujPHzwIBsIdBbFnV/ikt33beawgAJZAZ0EFB3LcfLK0TA1TJRd2c7sSqNMe/QGxE/4fwYMc0i6v9bQqXoLtKjQgAzhU599xzCS0HVUPzOnooXIBVcsILE1oSOn/zzTdnzJjB0TdMTT788MPzzjuP9sB6+J00aRKsgfw0Plx+O24APcOAL1qoYNvqnl0iTcYi5U7vCpmtWzZXbt9WdMrYYZldu2E18gdZTmGs1JDhEBlqsBoiqyp3JkV0o2vtaAOVY3Xh17BqxaKYiKpohzXoc8JQObIa51UJWuSPxyFfnVRHC9i3HG3w+vFSwkSUnmrcsHubbGpjM6e4gwgDxsn0R8ePrMp2pNdaIWNiS+iPWFvwJCbF3nbbTTa7zR/AFwUzvRxpUFFR3H9A748+fn/TlvXsGnV7aj/55OOGhmor4UVlcQJmQKmyjQrpKl5De4ByYPgANxCP0SQjoUPBvWuuuUavshCJFrThHhQlA+ZKzh7g9GzxzAmFwB8+7CAc2NPKzrtWQYABIr/mFS05BkPDKz2s4QLJoIdME8s+jySSwi9fkY2v/j8MLl3W2M5NGFDtuWkdT6RWoAy4OeOF02VhrAwbCjiPemjb05T9v9UDjIr3/vvvw9yJHkyMYkTOBx98QNXwfVrC8TLo+8S4J+40woBHDETvvPPOCSecwOIJyiAnI/I5FgMQRWPS/hW1PgW4Ub/R46msryt0uSwsfaJ14xvkDVq3bS8dO6r/kP6pUVaz0eswW2xeNgwZ7diGeE9MK5vJUl+72xeoAZoH5nOH1iAEgO7R8uWLXS63BRlnjAsEnCGzN2CqQac2BmJMAXYz4Hvp46Rsdo4RYiVI9Jgg+4nro50GjklQWytRx6FM3OnagFWyr1iFlzCw7m22hpatXPz2u2+s27DWTNAyQ8Djc+8u3tWla9bQoYOvvOoKh5NgCUG08mOOO5o4d2QQjz6RZfgttTQHAQIlcQ4ECo2H4TeoBcyRa2troQ3EgIYJ5xkQZZpBIvQ0XtWcRQHqcigCONNxaBBuQudNKyDAQGj+xQFBX3zxBZocp0kvWLAAIg2/Cg8TN2TmYtT0wIUJGZ5DCq/4avny5Qwxp04h/lvRlM6sCgKtEwMAnVHhQ+Cuhyc8bD8FPKmO8jE9oe7haUeNDDwkDcGDMdzzFgPZzJkzwQDsVEwCiouLsf8iJMivWwvlYxfWRuGOayRSmBAR3mCowmr3Wiz2YDAiEHKwDlDnN5eWVedkRxm9WyKDAYs3QuLB2nGKd+K1JvYZPIgCkU5XwGypDUkwtTZw3qZ+ABDYJ445O3fuZinaH8LbUtzg1B9KukQLMAVdRvHG9wXNbr+IB+SBDzlhNjQkxiOnrLLNTNrAsLbY/N86SDV1ISIisqysfPGSpazUPPLwY19++TXrxdXVdfV1HqcjMj29i8lIEFBsQNaMjC5+NjSL8za2IIBAG0DFZtOU3P/4hX6gM3H2JCZK5oJ33HEH3AS+wBSBSQCaASwD1vDEE09gILrttttmzZolHKWDdKgfb2JnjgNBQBMmxIs54cknn8ScsGjRIk6Uev755xVKC2dHfjN24ZGC2HnUKeShVD5H6pONewpE1eOkKQ5ARaIcqM7OtB+CgADx0C9GBYjrsWFIuNffMkjhQsIZwintvGHUqQu+T72MPSyeAnXtpHDiDRsobr75ZuQBeyjYXEcerjCusEpMftBFY1g7G6M/p9voKCF23pg5Yh6/GDiZQwIImS2lRXUE8szKdFhCNSG/w26O2FK4o8pbmJ3SO8LJ9BYN2BkkkIO48aBHt34y0EJqqOFgBmIm0jXMlOVVn7/BbBW9nvAVPImtJSQRY4jkRShHLIpAjgLEX5t9Wvh0Et2IlyQ1D+VB4APjVLtMlas10wtEGrG0mT8QW0KOrlGGoZ69+02978HklGSHydotN//lV1+ZNHnKqrUbomITiW6MXCLYugTWZxmAxQBSeGSDmGydIA4rMkC8yKUiJQYUcKQDqn37tkv1XZQSeD3nEbEsxDwAlZCTSjkFDzshmKDPHL722mu7du3KQUkwnaeffnrw4MHk3Le4zuefEQKab/ALLXMw3F/+8hcYOqcH3nDDDZMmTWLGz5lxTOtR+DieCFomJxdqPhN95v3Y9xh9jhIiG2cK4S7JW7KhFoAP2lryM/bm11BV68QAPWYAwv2GNcOOAT2JjCg3ejDguS2zhfO39obR5RNGnePG0Ok4ToQU9H0eqZdaED8gAZZffEKIL88rMAPi55PCwkLy8zkTBSzCnEHIt1ytbcPB8vuMBFSwm3yxVl8kR5wErI1yCLDXHqrzDBocZ4/2ugMpRnP8vO8X7C4pM9ttRTuWjRmTb2FWYAVdvWZTlDEYy9FFEmWurZcCsmhMKUmJZn+5JeC0ovUbq0PGetkp4483sU3UTAq2F3xtdSwg2CoMV2qub/AQ+F4s8SIn4LfiqXPAtvBC4rPLarPHHrKzAOyJqGhs9IQCUSyKqxD5FMuZKYHYxASrSUJlRCfE+EL+Bm/jmg1rJ06cJGFDsVEZgz52OJiDbB7lBuHJlEBUuyAhOFjEZktKLcIihIercH/k94HbI41V5mBACU+Hd+hmIxKYOCL4N23apI+kZ9yxWIKZoEpWVhZfMVHoFAMHHOWfLREuQV3gLTdcWqWD6cPBYSmEynnjjTeYzUO5iIRTTz0Ve8Bjjz3GwGH94+1FF12EKYmjRjlQiCVATMHIEkqjHH5/tl78mipqNRNi2LTuD6uFrqBD2DEDgHEWuEBvPHYUgCBaCmSVD1efr776CkSBqplC4pLEeM+ePRvNDuRATUApgP6ZGKIsIA84B5Hpv1YTvv76a5xDtMzouLZh2qC3nHkZF5+SU12/xpFow+3dHPB2zU7qmgsHRD13NriDO0tLh44cGxsXZwuxVavCYmsMBBsCfndERJycBKDWBtqMucAHOHD1zM8Pmuf4DV7h7l5WiZ2ys5dzkSQuNCxTds+zcUBzetG1Jbq/rcHDSy0BaAKUuY9pfp9hpDI1zxALPhtQCQFkMwXMVkSMGJQQIpb169Z/9un0QYMHRbhcX82cedTY8bWFRca6hvzMLJPXa7HZHP6Aw+tz+ILsFLN6vOa6BlLg+0zufFa3xVDstZf7vG6arZrCdlOiY/wQeEA8nJUZYjAB5YDjqTmBEgMgSgOLQyAPpkJWklgkgPUzG8BCyKxxn451Pv63IAA9sg0K7w/YCJY9dH8Y+nPPPXfGGWf07NkT2+/999+PaGeSB1Ffd911tBNbHwgPvSMDuDAIwwdwHoEWdC/CN/+tTv0v1tsWls0wQGC5ubl//OMf9caxzMzM0047jUQt2BEVHQILXRrVYflhjv/uu++CLrB49DsWl+bNmwfe0AAwSdt8x4wZM3HiROYlOJVC7eQHJ1hbZqEY0aWb1yENE3Va4niaOIA6b8CotSu3xWEQClZbjG6f7NjyEV7CZotrqGwImurXb128/ZuiSQMn9+rl9PjrjRanyRITEZXJoqiKoHnQeAk/3FQtA+gX+D981Jgtq1e4DV6bwW+HQXsiid7lt9QFLZyRjUhA49cuofBrzHr8WBsanEZTvM0WJXHmROlGAUdYHKxOZt3o5mSFNZPRbvZHWwPYw9gZwLPIFarond8nITqODSXFVcWnnXRa70EDN65aNXniZLvTxcmcLAazde23l10RYXciUIYOGJKVloX0YCsZaxwBi1iE/EY7fyGjRNCV2cnBl6wZTRCD7sM4cFoDAeAayICLL74YtQAtEs2Rt/B9Ym+xXIxqiRpxySWXkH6wTnam/8wQYBD1XP8///kPpjym9QRcQoTD2XH5g7QZYlKY28FemCuQn1kCw4rpjyk+Oh+ZQYCOYjg/c/d/OdW1UQwwPPBoPPTpCfyIYSAF2c44dWDftGDXv5wnDqLA4sEbUrD8YFLU1Z111lnEaaF20kmhJbSB/NA/N0wgEB7c0Eje0mzu233hWSlmciL22qPTYlP7VVQvTXPBfOuVwZ8jh5yGgMNf7+/Xc1C/oQO++GrG1u1Luvfsa7S5GhpijebuZlMWzC5gaJB4MqKGt/qim7qz9DoqOskZPbiucU28qyHor7MabZxQGjB5A5Y6s08OWjIZfCwXk5PlWSUQ7DUNEWnd+4cMdoJOSygJ2nBQGcBbcenB+VUEBdlg+oSxNjSYLSxNpykRIsLEag5ldk/O7D4ZQREMeQPB8q75KbQzZKimlwyLPcowaGSvYMAXMtUkZkYlZkb6fDWEkcCE5SRERTDS7o1tZOknKJGXQ7RZGnZglYJiuWANDCvew6NHj6Z34AA8At2fzxh0rUbgMEYIRqwNYAJvOw4HqKTzaiMEFCrKQiM8hOGDpxMsE22PKR2Sm91IqHGMFIPIPIAFZFYCsPIx1qTA+pk6PPDAA3yLvQhTMJhAUW1sSudnKtp468CgqE8YBjf6S33DCPHYQUy2qUm6NI0xJEHkXNxQI/TPja6UG3CFX65wZu4he5UmH+r0DsUVQiDA4Phz5nQb+dHb35bYqob07mLyOXDDxJZuDDmTY2KXLZq7ZeMuv6F29LickD1QWRVRsD1izPjDCPQom7b4WlTsNl50v3kUnF27HjV71sacLqGUKKinJhD0Bk2NLGKzMkwQB/Hul6U23ELxGnJs2lLmiumbmJLnJVSQbD5ARsho8nNgpiu8H4s/SjoiBb2ewNrI2/r6upUhTzVLvk2osHc/wjBvbmTTa1YRVP6mj+QRbyp8TbExNey2y6EJhFfzUQ2nhZDpgIWHMU2jRPiRG62d6MqoWteukSGcbe+Wdj79rBAAMZDHYC+/iGdYOc4drORhDmKVmEBpTA4w5DLjx/OH3T/4fCMkmBwwk8Md6PDDD2c058yZwydMFNALab0uCjWRm5+1M7+KyjqlaNuGEd4NtrE6Ko7/xpCnaPeaF5+6Mz83ZuywVEOo0mLhfFcmTNGVVd66RndsUpTZFayuMW7dZBo67MKk1L7wNgnAbmw0GTgW5iC895CbhibEbq2SkjVffnbHsAERyXEO6IyDZzhgIISjHS3EdoQJy+jyGqJrvbbyKuOQoRdbXAlEdGMWh+sr3YA3cy7NQYzxmH6oBOMSpwt4jKFARek3FRVzWHxg3nGgQPWH3HQlBsVmZfTgdRUw13rMsak5vzHYcgGsyUBU5GZ1oxVFdmb9RUMAMaAnsij1+HSNGzcOkYAXEGs82Hth8awAw+6ZIjChZ5EAAcBe8blz5yIwMPziFVZQUMBiIVMHJg0sAZIyf/58rEPIACYHzP9+0f3/5TWuUwy0eUwwf4mZnAVYUbfNgZraos9mvJ4cU9kly2oxV2I352BFkzHKZHL4jebaRofF0rVr1lizLUE89eXwV2KymtXsps1taPoQjunz471qWLdm9pxZrw3ok5iVarWE6mxmDoUJMGUIetGsHb5QrMeUXBeI7Z43ym5Mx6e00eSzGMw2gn7KVACxgb+OHJ6zX4MQA9iUMBxxllp9KGD1ewrrapfbTCGLzw0T3y9/6xKYDviIX8rWNkOtwR7rjBkfMsbj1WpmYeNg04HW1dCZ+xcEgbAxE+7PPS1DMOiJGo/MD5jcc8NbXvHIL5KAPLoPvOLCHqBTuGfCpzOToid/Omfn7yFCoFMMHCKg9s2mtWdRkXHEJEYzjjgwSdhZY60xWFZZvc7bWBrlinI3Gm1W/k0wW+MslhSLmd1kPgn8afCFQvHsIRAGvD/X3be2H30O+oL14lRltjfU19RUrfXWbXAZq/2NNTiMYmQhhJ3VGuOKzY5I7h0yp/qCTkcIn/2g18RZZSabsupgHwoY/JycfEAxINt95fhJxAAhtl0S88FUTtxowqn+aON+NAO2Nb+BAxow9DdSRcAQCzmzR9vot4mnUAfA50eb0Jnh54MAjBt+DePWXBsVPsy7SeEiA7Y+ErmnWWRASKg3Ta5x3POWi6UC5IEuQRfIq07TX2vHslMMtBZiLfMr9UQZurmThz32HUxGrEtrVbaJ02vUVd/7kB4sdsBLO4jHURpaFR47mmly7w14G92NDf6gh0BKVgvEwqYuVlBkRRq3JvIJicniRJPVRTWJ5hyM6erc/FIHBCwyT/VFFdESKq2/pzD9Ef+oZsijrEM0daf1JXZ+8cuGgObj+7dxn3QedR44/v6ZO1M6CgKdYqCjIPkLKgfa0TNlpTCFG6YpqomuRGZ0Xp0Q6IRAJwREG2yWt53Q+NVAoOWYsn8WRYoULu1U09zNTjHQDInOfzsh8P8bArJC2Xn9yiAAx8dOyoSAX7V4JvYfEjtnAL+yge7sTicEOgQCnbOBDgFjZyGdEOiEQCcE/lch0GkZ+F8duc52d0KgEwKdEOgQCHSKgQ4BY2chnRDohEAnBP5XIcDaACbjfZ2xSNKp+kVzjuZ/29pZvufaU9k+5TU/6mwHrGTPt+HX++RuztEyuTlNfbNfLfpty/zhsvf6MJzafNNcUvNz0797ksNliu9ji37v88FP/rhP5fs8/uTV/7+ooMm5Vf4Rz919MAeQa6iH03nkCj+q9zpPi7RmyP3QiOmCdM4DfNpcRFv/pUO6mT9B2Qdu0w91Vn+xT47mR/7d51Jt3j+ZXD9bb/Zp0S/0ETFAiEkcyWXHHts21m7b8Z8PPq81OIgIbwr4TJ66/rldLjzzZGLMy1YpcEIOCTnQJZEmm8DLamQTmOVfOdlQvgxDnsrIqEdnnzFik5JEqzzARSIFqOLYNMiTBLCX5ui8LQonidc47etPpK7mP0niTwXPobmc2M5bFU9HYu7sXyv1qIia6hvyNlM3z9pnnk/IA2ya6+eNBJlQ0Rl0VWThS2IwSEyH/av4OVL2qXafx5+jBb/yOuCV7Lxj+5tsqDZ6SxqCz778WVlVpclS65Hj4AwTRw49fuI4MMzMeyUxOBmOS46AELKSVM4xAqnULu4mcCm0a8Ii7vW46V/9incq+LcgsVzqdw+hNRVDrqbsJBAQsWWxByiTPC0wRB8modJUHNnmkvavRdfW8tvm+n/w32ZCokDhMU15aSUvaIdsrNGJTY3S+ZvzNWcXvrH/pb5vrqDFa0WyB/qgRZ7/V7d6NqAYvNrdtH3Hjsefed5tjeieFmsz+ix+j6Gir+WskwUoRhU/56DgaQlWKbBp2GQQ5VawTycxrooS9i+JLPztd8mXhO9R3JYRDCOCcF+F1U0fhV+o2gQhmyuUjE3FhlMFxSSgmvSKYoQcdfYW9dNoYms2lULxFANeSntUdl2qIHCLb3jbRCPNqXzYXHuLfJ23vzYINA1y0O33vPjKGyUVFRkZ1iizzd/QkJ8YZ5s4jn3aYApajFJkWjJMMEVIphlhmwAjqXsnNWOUIJzkV/ioc0EIgsRNnyJOQMLmJ122vJICQFcpVpcVxm5JaipJZ9MlNWtlUhTlyyuVU32tv9EZf+RX17Z/JilXXTqDbrP65Y1unG6prhdhSe59C1Nybt/EpnL3y9yc3vnvHgjscRhlqxG7uDnTC10+OyPt7afvSY52WCQOsonINJxWqJkZzP6A8G7ifE1vwTNBStE9GEvR2+UrzWhleAlRJoMsV7g0Hrk/YPkUp0uSOYB8xI8oUBpNdDn8hv8oR+veunAV9EbJCIX/1KMzqFmBbofE2FRYLqXvdekiwkkihMLdCb/TTdCZSOQRdNVvdaPCOcMFdd78OiHAuPt9AZvNGhcX+8LT/+qdlBDy+u02TlzzmCyCY2o+wC5uCcsqeKO4HMcuKF62L0xa4A2Ix1M4QZCQK6AoIvytRkT1St/uU2ATNkpBze/5VxclqK3vNAbLE1pPcz4oTiM+qYpZqKLDb5srIk6EuqWk8Dvp4sGsCKr7TZCQD/lI/TXl5765WSpnoEn/asqqa+G3VXNt8ndeLSGwRwyQio85AOKcZ26j4hMTXHYZEOLRyAAcPAixGjfGag8Hh+8qxCTIvIwh2o8eWalDMI0nMvOr7iWrwivBLtLlRfMl74TZk5GBlkskgvpQM3GMLTwqp3hpPdXpC6pqQjB5VlmU7q/mPCAydamqRBxJv6lAxFTzJXYtkYt44Osjlzm6hSx8grWM6lXOpjy0TsCosFXSRfIJzaiypNzO6/8XBHRIHIPHE0mQzAinIULhX8AnmAY7FPzQ+CZgAW3U2Yl72B6I4w8EJNS+TWyxconCo1FbrEp8r8yTCncFJ4U8mi9BO0FkXYO6a34l/wonB6l1JskGUguei5rFpX5UstTAXFiSpHbVaFGVJJfOp5LlWROLSucTaQCXumnKTQwhDoiUxGaygrh0Lg4ukQ/kfxEgMp9R/2CkVR/I5kcFJH55p2zXkluKb6Ja1Rpy8deyJapUFFjmRZp4SdD9k5vOqyUE9oiBPRAMharqGh5/5YPEKDtR00499oi0mEixbhNBjU+NEuh//6ukpqaivIJDD10OR3JSkslirmmon/bWmyeecHRSfDxTCQKvBTjG3GT0BwMEuMG2IjxVBQdnl5PP5yVIoJWDazhSiqNqBe+EOxM0SuYoJs5OYezh+WaP38fwhzjfy+912O2cTAEmBWSvLKFuyKVOQDOQbKivbygpLerePReU8fmJsCZ4I+dcmaTXLFlQJPEN6TgHYFFgM+cWXJFwmkRoBi9VUM1QMNTQULtpw4b+/fraCW3IZ4gcCY5IrebNWzbZHc7E5CTiIPrlgBa/laBXXh8hc/lQ6JyjCRRW7w+3zpRfHwTsNhu4Aep+O//7LcmJPrdvaN/eydFRMHXhZCAEuIyarbQWePl3CzlWZTsol5iYMHjwoPjYmO8WLvzk449v+8c/wFXhg2ILCSBE+B8k93p9HNXD8QzyhgmBX2L3S9nUqgN2yixBFCHSJTFElFn+k1N/AzBGcFvawBstEBSHDkohfq+cKcthGV9981V0TOyg/gP4mro0g1a8mwrB/ZBfTr3jpCnZpSiR3YRQDGaC3PKPujjgR7WOFnIIqtnt4/xtNKqgxWyVo38gC0IVmi1vf/heTrfcAX16k0aYRbNiBSaz1WMMwh+sACnoh/dTl7B9IWBI2ycd8QsbkZIQLRxdQPchNtVQaaeSNxw+YLYqHoIcMhltlmbepXjJrw/x2tyjPWKgafAYdau1tr7hmRenWYI+jtA9cvyYtJgo9ZZx5ToACBn5Dz797PXXp/XM7mbwBewO22+v+l10fPzbH300YvzotMRUjEsBr4/hEoWIU+xN8EVZBAJ7wSF0DLvdJehOcGFDyO2W+IJMq8EpyMALmvllAa3O7d60fnv/vn1oASgtbNgfspjtSJgggfNtouIQLl/4sAy/aXvB9sVLlnTP6+Flmc7iAMOowUwNQUgPpFGIJXSmLFSshamItfw6iVkIIZHNZF64bHVu95yUSOe6wpJp77zXM7+Xw+4KcLK6CB6ztMNomTFzVrec3KMyMxasWN2jR26M00k/kXw+Kg6GrA4Xpe2hDwXEzp9fKwTgeuAqnKixoeHGG280cfaRP/jCYw8fNmK4dBncbSYjEB6EBPuff+mlmNjYbpmZ3y9Y+MZ7799379Q6j6+kspqJOYfvqFkwdllihZs44M3jD0Ib6E/wTLQdnzqfy+Px2i3ED5SIm6FA0Ga3NfgIPR50WEHDAC8+/vSz1JTUoUMGesDFYMgmeCs6Ew31UyykA1v2+e0OB6yT5vvExQKma/EjTTjf2u3BSGCBhKQDXEYCmFvNlnqPF6qCWN1ej8vu8MixFgG71eb1wakxgcG95dL+GliWTRYOLzK40eRogMmMBmcwW4kizX532uXxeikfPZJXH375jcsVcdioIbAJYiNSYyOH6Zk4nhTlLOjz0kcOzqMHRjrrk1NfiYBLNF9bQ2Mjr2D6UrWckUQ/mHDAFZo5l+6B6kbnj4bAHjEgQINbiTT2paTHv/Cvu1NiIiwGf3pirMQpRo1W01aFugeAXnVNzaTJh193xWWmYODNtz/68OPPLrr8Yld0TGld3UvvvGPyMas41uXk/FswLDhj+ozi4pIjjzg8MzPdGwgU7CggqviiRYsR8kccPjk5KRH8LK+q+Wb2nN27d0+YMK5Hbi4o8sQLLy1eueq000/LSk1JiHDlZ3cFK8trapeuXN2vX99ta7c7nM45c+dyavGRh092GYPJackDhwzywN+N5t27i7+a9Q3tPnrSyIzUVLAPFPL6ffOWLFu7aaPd5Tpy3Li05EQwvrS8as6cOcxsOBCJLv/lH7efcsqpvXrm9OmVP+WY46sa3PMXLx4xfLjDJprKdwsWZWZlDxs91hURMXP+4jum3nviKSd2z0xLi4sb0Csf8gAXt+3cXVldPbh/7wNArTPpVwcBIRB03WAQlvrPf96dHhfDYdN9euRaQ3LYsjB+MWzoO0lAvzdbraedccaY/v2IrH/rw49tKig0wVJD5penvbvwO05Tyb7sovPjoh18Nuvb76a9+R4K1rlnnjykd8/i8rJVGzYtWb5i08ZNCbFxl150UXZmOp5/b7393sw5cxFIF5x33sihA2d9u+DBRx9HDEyePLnO1zAK9O3TB31+xuczUc5dcVHFFRXFhYWLFy/mMM9zzjzLju5icxGLBC5bWF755jvvLl+8uEduznV/+EOk3YZwoBdlFVX/eW3a5s2brDbbZZdcktej+2tvvzvn27nIocMPn3zicccihd56593c3JxPP51eVVV91hmnjxs90hsIffTJJ19//Q1lnHTiSRPGjwla7UarjfDir782beH8BRzQceapp0XHJ9z78NMpKUlfz/rq8vPPsVvNL7/62sZtO1Mzsy678Pz0pKQvvpoJ91+yZOnGjRuOOOKI4489Fqnm8fge+fdTq9esychI5/CyjRs2nn32GbS1rrH+Py+9dOG55yVERyvId/7sCwERs/wJPqoLYwdy02Gz5KVF985IyMtIjpBzoUWso4xw3ODBRCk4brbbImzWCIeDYRCLCicKuRtf/s8rpRWVS5atvO3Oe1E0CFd//8OPgc2Ihz/ddPPKzVu8ZvNjL7zw6HPP1Xgal61Z+/ebb61rcNc1Nv7tplvmfDvPYrE/8NC/l67dUF5Xt7WgoLKqurSsYtOWgnvu+xdTSyaDn8z6+rV33y+rb7j+tjuefPGlBp//5Xfe/ddTT6PKbNtZ8OnnX6BJrFi1+rrrb9i6dceugt1VFdUQIlNJdPkdu4o++mQ6k+Hla9fdcsdd6DLlNfU33HjLd/MX1je6dxTs2rmrqNHrLywsLi+v2rpj56NPP037H3/qhdXrNjK3hXjunnpvZVXVux98OG/h4p3FZRXVtYW7C0vKK97/5NOZc+aZOX7GYn77nfdmz/0uDOFmSHf++6uDgIwxU0zUd5SMgM1qGTN8+LEjhx8xcnh6TJRFTJ2cTuo1mPgTvQoRwP+oq0YbZ7jJ1egP1VTX2JwOpqErVq+pqau7/PJLNm/b9sxLL1vNZnSgJ5598eLfXT546JCp9z9UVlVdU9dw90MPJ2RkXnrl77xG49RHH/ObTa9+8MEr77z3m9+cdfrpZzz1/IvrthVkZmXEJyaPHDUajYqJ/rsffCyqscn85dezLXZbwa7iu+97MCIm5uJLLp3++ZevvD4NbRyCXb9hAzr7Hfc9uGXHzksuu3TM6DEIADWjxpYbevjJ53YWFp5/zjknn3hiVEzs6+98+NZ7H5591pkXnH/+jBlfzl+0BMX/k+mfP/3MC5MnHz5+/KR7H3qsrKZu4eLFr09788KLLjzx5JM5UVIE25xvdxYWff3N3C9nfnP1NVefd+GFUbExqakp6dldBg0edNIJJ0ZGxzzw8CON/sDV11wTHR3zytvvMHeZt3w5He81aOCJZ5zx9CuvLFm3zmgxv/ja65z0d8Vvfzth3PiU5JQPPv50046dtPm7+Qvmz1/YdE7twfiXgv//2x9LKGhFEDAXQyuB13s4J91obvR6VuzYVVhdzSQzJjKyS2oyswYTawP8sRx6IJYGM91RXPTt2rVVxcX/eeaFc37zG1gtpsbTj5xy2lFH7Sgrv+J31xRV15YhElav//ejD6ZFOW594KG33373r3+5zuN1//bCC0b27eP1Bs+99KoNOwu3bN/gDTQ+eNcDMNL4lLQnX3nt6fvuPGzU6DRnzLXnnF5Y1/Du268uWb165KABq9atPmHCRKfNWeVrOPPE4yaOGHbcsUddfvWfzjvjdKNZFA3mii+/+uJRR4678qKLGOagrL0FsJLSn25dM++/65/gYnFN7WV/uG5racWXn82ITYh/aOrt9Bc7I2z95Xfe//155+blpM5evbLR4I2LjZs0/ojpX343etCABYuWJ3OeZM+e74SYCBtPPmHKxx99ePX552enJb3w4acfzpx95OGH1VY1LFi06Pob//r/FsP+f3RcDIzQBVQUNNj8IW/I1GgM+YJuxd5Ropqs0i2YEBmxaKgF0rpQ8KX33v1+8ZI1i5ZG2E090lN3bN6cm93l8ovOizIbjz/hmPc/md5gNLz32Yy8fv3iEhIGRMe9++Y7S5cs79Ete0j/AeeddALnSBhPO+O2e+/dWVv38Vdfw2cnjRhK3TO/nfXO59Ov++3lCcmxvXtk53dJP+XYo6+/6Z8F1fXYTcwuy4iRI6Z98Gmv3oMvOflkDv2pP+esl19//bzfnB2AKzhsm3aV7Ni2+/knH8mMjxKip18hxI21yt24obj8kdtvzI6LILkmYPpiztzzL7jgsJFi+ArVNa5esXLUiGEYkY49aspR40Z7/Ib3P/tqV1E5c+iAxw3tTRk7GrrDhMRMCEtShMXqcTdW1VYPHTo0iqP0DKHkVGev7KRR/fLXrtm0ZMOGW6feZXXZJg4f/uIbb1Qxiw8Gx44dd+IEfHANc+fNX7N5Y25e3oxZXz8y9a68rAxBuaABS8CidWtyu3X58JPPjjhyiovzy7TC22IQJGfnpfavKK4uoGF1ycTxWUa/u3Tntksuv5xpgN/dOG7UiOceexTtnjVehvhgQHNabMuXLn3J2xhjsYJGcMAKjzvC7sztloMWZLPZLXarxx/YumNHUXHRgw89FqyvLS4vzeiahQ0qMSE+MyNdqjcYEf7+YHDz1i2DBw1CBvj9wbz8ns+9/U6jB+0/wOFYnpAhPtI1bvjIz7/4su+Aft6Qf/TwYZU+X1RcfE5uDhbUlKSkuNj4bdt3xEZHQoB1Xu/2HQXn/eZcFHmvx4211CKLDrLtprSi4s233uaoMIRbVW2Nx+fZvGXLkMEDQdBG7KRmE5N7WQRAkiAhIRurmcn+4YdN/Outd+6sbZw7f8HhU6agOmHCkjVlNMBAoNHjZmo/dtiwd994d1tp5fo1ayJiovrk9TgY3DrTfy0QkG2SUIj8BfwGX70B+zgHqXHtmWwLL1WXpiO+gORk9dZmNsdERJ5x8okjhw1k2QxDf2x0tFq6ZaELggi5A8GKysqyioJnn38ZbTwlKTEpIQlsTEtO0oZdjDUY71Fy4Kdo05iGkDPk2ryjgMpAepgnN92zs7OzshcsWJicEJ/dpUuCEwt7MCY2gcViWGpSUjIGd25YmGMBtqysFAXcxoIBJXBUHW+wDBuNDW6vIyLCbrPTOJwvAmYHPYmLi4NaWVeIT0ysXb7ch2XJasnKEKbsd3M0dyjg8w0dOODaP1z92ONPR8VEXXP5hT1zcuStPzDxsNFiunnl1aeff+bPV17drzcW1JDH7aEPxaWl1XX1r739dpTRaq5xZ6WnI13oZkZmpoa2jYVEk7m6vIKlbEdUhMfns2C4sFsOmzTh+zWrC6vrdhcWTZo0iU6pVRYN/87fvSCAZw7MV0sCbkMZCbHHTRwVwmDH2hXeNkF/77w8/QU4xJxwr69bPJj8gWMOm3zt73+LORwNAQ8gq9vIShQ4Lrlk0YylnpDVYsrKyrjkonMRzaFQwBUdhRHKqsqFWFj4AncDvkCEzVldXiYfWkwNjQ3WYCjSbHIH/PU2k9docBkMZx93yo133fXN8hWJLAWkJpQXljV6/G6P32wzB9wsNHgiXC7Fl/EQYAXM6vWK26vN7jQGA7w2W20sUU178x1PIHT+RRcylVn5980iB41B0FHXS9O9Eq3fJB4MgAZahNn7fT26paelxL370fSCwqKrfn8FTN+EawXQEfgZ/A4rCNc9LWlQnz6fzp69ceuG0RPGReKDK+CTgjuvXyUENJfRjCnKYZk0eji+EvERza4p+/VZo4MyIYWs/uAZxx4/adAAcvmDuNMxZ8X3AcqRXCAwf6jqcVFRXTK7XnvNFRAJK61OY2jdug3MbXXZZMapxmGzwZ4Ltm83DugD1laWVuakZjqFEeNRx2qvAXlz3JQjP57xVUZa4sA+PUkBLYvLit1BQ7TZsLOo1GJz2IwmHDpw3oiNiaqsrS6vrol2JVg5q1S8jASJnXart7EBo1RqRLLVbEddQmKVlpXCj3m7s7TQHmGHrSgPJckPATlYHEYyBEOHjx83dMzYx5545vZ773vq0Yelh6JkGU+aMvmIKZM//+rLF19+Zeptt6F/4UtCIRB4bFTc7y79bW5SEg7siDefrJRDchQsvAtLBkbelJgYr7uhpqE2NS6WsUCY9c7v+enMWR+890Hv/Pyu6akyU5NPlKyWTzuvPRBoXiKGTQPQUKBvXs4L/37Qh3sBrgRADC8uEBGFV4YKmAvaHPAKNjRwrq5LxhT9WVRjmHrQ40FhIT+/sGZPY8PIoUPemPbGt7NnT5kwfntBYXLQH5GWGvTA24WRMkXGQBT0uo+adNjNN9344edf9szr+erLrxw1aZLDYkxOTVv+xfTlm7cMSe3SJTO9/8DBdzxw/9UXnw8imMy2mrr6V6a9cd5xx74+fXp8XGyfvO6r1m6gSFjwSSee+MSTT9ssV5kCgdSE6MysTNYtmCiwrJ3bsxf3X8+aXVpU6HLYzz79tDvvuCO/W9fUlBTQK7NLNnoaqlOccyTOD57GRnysbVbTb846+Q9//+dZZ52ZkZxAs32cmh2QSAIYcL/89hvHiLG5yWlnn37KZbffEpkUff0Vv8OLoUkcHhB2nYm/BggotiSrw8Zop+P+O2+xcegDuK/43D79I02JDRbdZLXNgq0dPcUXxJ0zxOKB1SFucH6mvhAT+3Z8OAShyV507rl33H3v88+9GhvlKi8pO//sM6ywQ7UbRhFvKOj1OC3mP/7+yofvf7CitorEqoqKqy69BFnUK6/na2+9bbbbDx81ZuKY0e+899HcOXOvuOg8WuJ0Wrdu3fzQk88mxUV/8OFHv7/ycgdfqrl3z65dxk8Yc8s//3nMxLGehrqzTjs1LiYWB7q4CNfQ/n1u+cdtx00YjdLVb/gwFrGfeuzJ+tIKs8M048svr/vznymAvodkRQSmYPC5G6MctlXLV34+6+su+b2Ky8ogLg5CCvr8dG3houXfz5+f2S1z07btGRmZLKv07pH3/nsfRoaMI0aPOe6oo+78x21TJk0yeP1JifFHTzkcJ1k6S7FQsQG/o2Ag3uk467RTHnzs4ZHDR1SVlJ5ywild0lITo+PefO21hx+4VzQ8aQdfyHWgMdFv/p/+Nhn6gSYYyVqwTeSC+JOhPAuw1K4PDTwetSg4IKh65+a4UZ1lYLCPoB2YMJePGj6UpQU2CqCDjB01MtLpTIyO/OeNf3v55VfunD0nISH+7N+cxVSgT36+QzyOsa+ahg8flJqYkN814+9//surb77x/sefDh40+PyzTg00BsYMGzJ33bJnnn2225V/zEhOGjtuzAufvdu/Vy++C/h9SQkJaSnJTz3xVCDKefNf/+SymJPjYnr37I76cMapJ+Nl9vRTz0Q6HVdeeiESCrRgBnLSicc/+dwLc7/7ftLECZdfdD7rJMP797vmyt99+NFHjQ3148eO7d4j75orLv3w+dcLN2044qzjx44aJXvJ/IEhAwccf/RRR06aJJAJhgb175uRlh5hNlx5+aVPvP5cyeoNN191XX6P7IS46B69eydERnL+vOgtndevFwKijINXwmmCGB2hFE6a5h9NRvv3G7YERoA/+Cufe+bpuV27ih8GLvYWMwTUK6/HuWefKR75BsOgfn3jElNQW/K6ZU+9/dYvvvyqvqZ2YP/eUZFOmyX5iCMOQ8difpCZnnbNVb93mExD+vS59eabvpz/HQrc3/58bXpSHI4ZF553dkxKHM6U1BhpN2dnZnRJT0mMiRHNzusdMWzIsGEDN6xdd/MNfx0+qK+Xmf0RRzjZlBMM/uF3l8z6enbB5s0ZKSkRkVHSMdGnjZdfcM7CHjmb1m10uezJiQlp6E1//8u82fO8Ht91f7wWKyhi4JLzz+uek+PzBUwW0xWXXpCWnBDwunK6dSkuLRkzasTEsaOY75xz2ilZKUkOk2XbloSiouLM1OQpkw5j9nP2ySdlxMWVl1QgSa685MKBfXuvWr3GYbF0zcygVWeecooDh1QMTcHgiccfi90VMjzn9NNiUuM3bN2UkpwUGeGipxnpaV0yMvr2ymcWg4mbogTE2m1dSLfzaoIAG1JETooPPwqK+ATxJEIBJ0vBYZVN0hTWqt8Dw8/vDbHfg/BaLCPbWfoymrFoIq+ZzAJ/tTmA3QKy6YO9kcwTGusaIqMiZc8AqC/7GMUNmUb4aILXz7SU/9wGwrP4o9mE5UYzCvns5jqIzOOLt9iZLz4/7b15q5f+69Ybo/y2tUVll9zyt7cfeTiZHSUOKzKMpWEayi4EcUkOBm0Wc2O9G23dYTVjbGVDFzUj5DxeD3NFMJ5m4IJNreRB8ff6vLi3st/AYzJGuP3BRl8wxuEO+aMCIebj7N7x4b9MseI5Qb/FnRaKJX9tsMEZMMU4HYXl1Rf/6Zq/3fT34T16OrCZKsuSAmTnz68NAhAImCZx4sSuyExa2A30I9QkexX3JRmV32/Bm41YELw0G/BSI5KjqP9YLQ3WgNmC62TA68FTPmi2YW4UDKMg7I8KeJTg9njZKuAL+VH2zaJ3mYKQTDAAFUEeaGTkYR0adZtS2amFaYYUPJMKikqv++uNSIih/fOsJsuTr7yydM2mh+/6J+WQAZOs2GmVPuhF6ZYtzmaWoIVNUKAUwQQkhLGfQDM0GEHiCfmwR1nNTloIFZBF9ngaTBAdfFoSKcaud5phjyVsJeDC/BXEH51cqFamAPLPxAqEiCX+ZN+XEVs/5QNKuAfLhKRTFM3wUJzRgOQw+uBeeObK1iLW5f3sOLPapZHUyHoeUvC2Owf06vmbM05hZg8DEUFAD8Q41gRG6UznJboI9j4BDcAQBONGSwLRSRSAZPeIvNV4oPYOqPR9f9jsZ5ZRo4yQsiGh2jOQMsYysCEQjrktCBryeZgBWCJdVETB1MjSKnYnJg0qnKGRkTR5fHzA93abBbqwiJeSP+j2RJut7O7dsG3L9wsWffzJ9D9d90eX1MWOYo/f2+jzuC0Ou9vbIKtu7FtW8ZFMYrcEL0KRxMZQ6gOVIx1swsZ9kSAhOONrwN4ongRgoNfDzjWr3SHrYZgdfT52rJic9jqvh13BsjktFMBmyWeCaAEcJ/iIO8gQUgvGmWz8O2/J0tffeKt/Tt6wnDzA6sOUpGC4L9A6n38tEEAbCHcFkpGVKFyoBfNFeT7QRaq8gArc+NiDxvyRV/wRxFkCJRoyAQOhDpgli3UUKHtoERhqhzB7YiEZkuQ7bFFBI96i5GfDetDjN7IcRdEhv8Um//CdMcie29Ar096e/tU3g4eyj6WnSBxTyGU2R1hQq4QcKIcNxsiaADMCNChkkR+PbvYA+60S2UItg2O4YltyoNFgiWiUbZosVOB+AXOGwbIMIb2yClWz4w2lTqgcQmYNwxjwWM1Qaq3P6ICKbUgTqoE4iKYhW/rFdwpFjFU7JCkPlITvBt/jmAH9IjTFwASohI9jKvPajRYxPZHCVjplw/YFPXTEHLJs2rDl6Vdfd4dCJ51wLJwM9kLvVBZF5E0zMYrrvAQCTEllLgrjbWm2IBGsaMZeOB9C+iDILN8KH2S8/CgSBqQ4z1pyCALDzPEEwO+A9SJzk2Ykyg1/4CZZrSazeOOAc7jiQD8wVnBIjFEgtEy00dLl14KpCof/AD6gdfW1ReXFf7z298P79QkFG0CzhNjoy847N9rlxLVHlrEpwAICe8wOG6KF9STmzeLsKpEhaJzsj4T3o2qBTvyBgygfMjMRJw2VWYAie+XJjT6Fk5xMT+gobTZL9D28JkT1E2UFD2xBXGiM8tHFQNay6sqBAweeetQxTqYO7HFE7kidTRDlc0FvfoXuecXsCX2F0iSDUJDcNecmqcWlBkuedVnhx3AKNySqEiSPuqdI7nWafMtFV/VN0z86rSkXObla5pdcOpWbFp/sSQzn1m915nDO8LdS8IFKaMrZ/BlQJZdOhMU1aSL6473qb0r6Bfyjmi5NhpRoovAxwcQwCPZuokoWfCMZPos/htlsxYAOF7PYhbuBezKpENwQXBGSkmHkExAHVMbkg77MHBMWKQoL70XnRalWj+SSz5igh3x8YIG7KlW9X78+Pfr26dMHVxxKZi3ae8Skw4aPGIN+b5bIp+LESTXob1KFrAeSJEZd8sJPFRGQAd9xH7oZE2rpFp9IdTRE2Dl0Qn7mASSJ55DQGtvUpIEBXyOtZkWDcshAx2lViFk4rJsZCxMCWYUWCpUMcAhR3GSmgzsRVjbkn9BjECbDdmIwmkm2QEdoSDXbDgth/hEIpSYmnXT8cV3yciNdTiQAnzShkB4bNRYKx7iTIeD7pgx7MJq7JiTkrUBXLjXK6k49qeK4E1jLK/5VBUqZXJSgxkzdygte026pT14rmpSPVAPoiSQ2Vcu/whzUs/5XlyYV8Hlz5qb3qiL1StUonzVdUra6lbI0i5FqpZmqelUFrecVlzzJK/XQ6h8Q08PSFkqQIIFVh67Cr4sCAY8wc8ZvT/lAVKKa8EeVZoObqSTqtccH9jQJJnQiMBuTCxm0PBL8ktglLLsJajLeSBmQRiiB8gSExHwX+4zUqYsRtKYKhaKSh8LYrk4aTFzcPCUgBKZ+L+50phBzYjIIfMVbiWmsmocyxyEjH2k0kGaTT5clt1RFLVInX1EdTB8ZZLWKWiQdhAKAihlVR9GVkf3U7LQTWKDXMe3wB5ESPjQ76Rz1WLATBD04beMjJW0XnsIlNYrIwaWaG+mWrHErpENPomLAwUxFOgDJiGoqKTwqoQrQpL9wDdUmiqBNTdyKfyASqYmeU7B8hNFOxZahGgVbyvYDXY2WNEKhs3SdiiB+aSCNlUusBAISg1uYAL2hcUz1VHAOXYMMuNQtTaVDZtZNGAylIPhDTAmlc1axsVA4OYlFwzDhby7qhWJMfEMTmurTtf4CfgVzaLEaLSF9dTX/u3/7yC5gE7rQ5hGZJYNBKBmwT1AcIYCyztgKR0bZJrciB4Ea4yg4KfqOYCpwEcxR/s1wTNyqIRvRRgRRhJsaZZqu0UE0MxwsUbwEawR90ZTZDw9eMGVVGAWdiQDi0u2jLqhMhAAr0m53owwFYwQKU4DJKhYa3U8iTzDo5oAZNq2Agd4HW2fd20KvQhapmrm5eNUZMTdpAw4YgvpIAC4mJEzfzSar3+ehfDrEgh89xByEaEK/AmlxNUIUUJumCkiSVooZmtmJag0oI1+SHdQBICrahni44OoiEx5QHVwjL0BBWDRBQaoLBtFHJRvFAzxqpPFISbpIaVilCFpDP0UyQbFeolbIYDNWISxY4oOIHZkQbMagF+ca9EaX3QlZQftev1tiOWFREGXGIhHQ2C3BgMrkS6Av0k0kF9XIIPEnhE4nMTYTxoM1dj4hAA+jIOMNGET/VK5SNByIgDZ8wVCLqcJoJLAG00LZ5yvjovLjw4KvGbsxQhJPja1hYrbjtcmGX6fkA87yr1QQ/kc/tO6XDporKqvKK6qgfWQ0GgmgVl3DhIJVB+bms3Kj/hg4ou0ICQjbMTT4vK+//WZpaQkRUpavWFnb2MhoiJ8FOE8OQWCAQr9QtUEwiXsd4jgP2BXARVcQhsOIg3Ayp2Z5AsgqKFABDFx+GS0u+iR8KoBA4QU4SXNMbHwGPJINAMuERMbdZLFsKyjAJQ4SsZqCMGdqJdCeWHOZqgpPlNLoAIMI0RK0CMSh16RAVxs2bHzjzXfY5MLQS8ni0hbatbtw5ZrVZFiwZMmqtWstNsvatetLSsvNVjvChlmMMl/RVUXfmgYFQvLHpVgGVA7+UhNdknha/EkkcBlxRTrUB55DLFABNwbT7u27tm/ehmEYpVBEBGybhiMmxWgN7+ErQIE1i1qkreA/k3uKxHTbXDOFIV2pGkSDVhgYXQ436LEma5D+U5oIGSUQKYdm+IAbPsBq16EPase6BxUCJvGfEdSVH7CA9ZW6isrnX321prFBKFMsw9QN6QVqamura2qxk5OgKI4vBewa9AKUX8wF6KQtarT0kOlRO3gD+UJIhPHEyr9o0ZJGt0dcQ2E/aPm8QyKKsis2baxD2FQlhX38JhvfMPoMtCjbisqE5QmumVjTgjMJ9IRmRNtBXsDYaRkJAjcA6fOEGt0S9Jp8lIBpCQuUjLZQLiPMBlKhJDXmkBopzItrauteePE/sjPApBYjmJ4ro47gj+AVjRH7uxAP9yExBtNyUIX99uy4+WLW7EeefC6EQiCyXhw0aMvsufO+mvOtL2T6591Tt+zYTdmvvP56UWkFuOkV2cF2BPGi0lF+aTzcECbOJVAFUUXsiLkIPqkt0DAJHiURdhjw+FmxVG/BTRCbP3r86fQZK1auQloIUYDrCghUJnITPy3BM+kTIhjLgfqjsbBLGU9YCPwHBoNAgt7phtCdEAuAFD5ktDpmz/v+pWlv0gYGE1RHJ7Yzz+G1UA3AoUaTGL+EPGUChh4ksBZuSFlQpIV1EqkF+la0CTnTFForvQLAPpkeeXw0XdpOoxh6GV+YI0WgcxL9k74IldFoU1A1DpnmwF6tdEUULxZbthbtKqmpkrGSWjrkMhoqGxr//s/bdxUWw8TglEErPZFW0jFgBZtW9j+1D1lQJuQ3w1CZxApfggymvf1mRU01TqWPP/nkrqJivqXL5EMfgOmK+RNtUAZC0n3cWc1scSwsq5LRYfABMYASnRJURAfX3I5bXgNGwRMaIkMnq2VirOQd443cXrJ0bSPYAvMUspIREU3ZZJr+xcy1GzZRP6HiZdBklOmQUAewFfSRiQk1aIksot5kYs+Zg9I3Fex+5f33auD9grdWnEcY2sUrlz/z0gvI/yUrV65Yu4ZhevWNN5YtW4HgWLZxYwXWWPQyWWBgTYN1aIUW+44Odcif0DQinbYIx5VWABkBkeLA0j+5E+jP/HzWJx98BvawzsIgGDDJmlgPF1GqhwccF1JBuBkMu0tKtxbupqngkuqbyDYpmbKlI1KkoDLZeYRlCUhEMpGJPyqFE1ErjAgyWbZ8uYQYE+WR5lK3wU4Zul7kDkShbG4MRE1Z5StvvNHI8GB7w37MH/9abNu37fjXw4+w65CCNa5reNCO//FLuAewVQqe8e0PPpj2ztuMPEiGfo33Mzhjxg9CTJFgK2AiahwjiLKMXkkS2p6o5JA/mdkEw4IBgwhGY8FX/M0WMtoMZieFhGSFWUtegRkyFV5ttTsJ9oAFifoojFmp0Wr1Ynw3mipr61+e9iY8Bk4EqaClix4kRGeuqK4XLUuaja1XWkjtaDk0RiYvKKGCgOI4TRWSwUgfbAGmxyHj7orKzTuLmR+g8mMWEjXREGJLEJFjQLbtOwtr2fUTMhSVVfJL/Ltp733cCHu0OIImVn/BP/aECULzI1slNEULTbLEjGsu3AbEkzU74EHrBP3NdlyU6BdIKtYzrSybzJwKV11bS5GwHmLByj45xIvgN82nyTYjUUn5il+zffpXXxOyDJVdpgjCMLQCaQOqwrKpmqaYzZu2bfvks88tgNpgKaxsXL+zRChS9Eq7lQ0eLC+GWNKmQLmElTMkGAAIqcx4KNMzRI2C72XbCH3kOxqDiqtGmtEXeKJdWqxMCQjiRCHs7oB/MhOkRiLuiCziM2SIlaMtZFghMMoX6cgwWRwwMCYqdsAlw2qFP6/cvu3Jaa9CqNA0fLNjro+/+ioqLq5v7z71PlRbM0v91RXVyXGxtAoviNLikuTEeLvNgT4jGGs2E5gaHSQ1PkF4Gg48jgjSIyIjb7z5lti4OFQah8lYWVdXU1cTHx/vsklcrfKammq3Oz4+gc6gI7zw+nv5fXokHzaWYuFI9Y3esvLKpKR4lx1gASKRAbAs/H8AdEllDYMcHxvNYJaVVwG8pKRYdKgdhSX3Pf7UP/55U1ZSHAPCJ4xfcWkFXkynnnE6+yRpKyPm9jSWV1YmJSdbTNYGn5vRYdOmTN6NFo/HY8b3yReorqlHIKXEx6B2B8gREdkQCtZWVEeYrInRLnwnxk6c2HfoEMEOlu9QxE3GK676fZTLVVpb/+CTT1555ZXmtExZr2OYkWtKzfF7vTZ8NgT7hdU3ejzlFTX0IspJhG04hqGh0VNUWh4dF4OybTdbomy2BrenrLaGIIvJ0dF05sTTTwXn1MYdshjKK6o9AR8BjV2AA4oKGYiAxJyaCJfQzusffABFXXzOmWwcBYGAlbArla20uMLusEdEOtkASEtKymt4kRwfDYcADAwqNjSCKjFHS0tKwBK8u6T2noeeuOPWW1KTEyE2CAmwlJWWJrui7VH2mkBgd0WZ0+lIj4xhpATpCTPA9ll2C/r8RaXyKtbh6p3fKy4hefW6dcMGDJBFTmkLFySsb9TT/+YPMBS+YjA1BPzz5n9/4QUXYTsj5NCSpSu3biU6dRLWxT598skRHRU5f/4C8GHC+InAaeY33xL3sF/fvj3zckCBzZu3sMkrOTERExIbbuEVUTExW7cXxMclrly5rLahdvSYkXERTnioTA2MQlNEdUyMT5w9e86AgQNyumZv2V343YIFcfEJo0eORM58/Nnnn342IzkllQUEpLglMqKitGzjunVjR4468qhjzHYHMtntD3wzex5MfMzI4cnxcavWr09PSYmJjGBgYPe47XfP7rZ+3fp127Y4IlzjR46Mtzn9OHfY7Ws2b1+3csWAfn2653RFEvTu3dcbMjQQiNdsh+ThYZOPPDomIXH6zNkvv/leRGxc14y0GJcjJysTXyCwZMeOHdAykRthXnD9hkb3slWrCoqLiM7do0sXdOeq+rpv5y+sqauNT4pny8LAAQMibc4Vy1cQobJLly4DBgyABNiIEBURRdya4rJyi836/cKFINPIEcPoCJi4Zuv2dStWOkzmwcOGNoYCr7z+5sjBQ0MN3sF9811R7IuFpYjys3D5GsLODxrQv39eDhrPzG9mL1myJC4uhgCUKHNBe9RXK1cWbNwwqt/AXrm5MucwGVau37Ji3Zr0tOTRQ4ay92jLrt22yMji4sK68vJxQ0eKNsaM2WreWlA0b+Eih8MuR1XY7bmZmTBGBCm7l/r375+b03XDlu2LliyNio8eNWqUy+moqa/fXbAzr1uOgzivDZ7NO3bk5Hbz+jy7i4sI3EDMWrZGDerXmzBVjV7vt3O/I71LavrgAQMG9h/0xFPPbTji2N7du3WMGAA0y9aum3zkkTaLiRggH8/8Ctvu7g1bbr3xxoJg4Mln/1NVUhjhsF56yUX983u8N+PLhUuW1vtqK0tKc7OyL7/ityaXA9Fgdzrc3sC/Hnvyd1f+NiM1+aW33ps1a2Yg4D3m6GPOOekU/II+/uJzv8WckdGFDTLffPP9J9NnrNq4tnzn5ksuuXTFirX/eXWax+1OT0353aUXJ8XFOMAqk6Wqpu6JZ57L7tb1s+mfjh4x4rxzz3nipRfZe4k1cdCAQWecedqzb7y5dNOGOx96dMKgAd1zc5avWAVyr1ix/I/XXbdh48bc3K5D+vRevnLdy6+9XltXm5CcfMVlFxPha8OG9X+54nJE9rfz5n024/Obb7nlzbffn79ggbvRO6hv30t/dxHCvs7d8Mxzzxds2hqo97ArbcrhY7+eN/f7hYtvYGcNurEog4ZXXp02euSo3dUVi5eufOzxJwf3yA9U1/XN63nSyccA0lffeNNTV3vpxReKtdds27Zr97+fehZER/xcfcXv+vXsvnbj5mdfeJFydhTtske5rrzk8rT4hMeferKKXQ/uhlOPO+GEw4/89JvZVVU1v7/g7Dfe/2j9ho0V1RUlFeXpGWnXX3kV/lBPPPvsjoIdDW53z169jjnumPe+mOEyO7Zs2XzlpZf2yumKsknhW7fveOa5F2ExkdHRf7n+TzExUc+8+MqKlSvQ64YPG3z+GaejCD73wotsI9qydWt1fe2gwYMvOv+C5974YNmmXXc9/NSQgf379em1cOEiTAHLliy/7srfJqUnTX38sYqGWnTYScNHXXbm2W6AgbXJaNq8c9fzL75YVF4Kb7/ozLMmjBjZq0+fhYuXjxg4uDksDxIAtfd/XwzgO8B+YauFPYyRMTH5xKO1WV5+/c3PZ88dMnTwoy+81D0n++89rv1+EXE/FxNWoSdxf0aPfu3laRs2bo6NjXlr+vRTTz35uKOOfOrFlwcP6HPBGaeB7QsXLTaabUcfdcRTzxHVxR8ZEVFWVfbBJx/dcfutieKc7UcTX1uw4977H+iW1cXn8Xbplf/tihUPP/lkXl5e5YLFBbuLjp4y5fuFS4pKyuZ+t4DIpl/MnLl68yZ04JS4hO7detx3z/0PP3J/TEzkEy/+Z1vRbpvD+t6Mz/7xt7/MmPttt6zME46YArY8+/rrTD1OmDLludenJWalb96+7cs53069+Saz3bp+46ZXp73ltBgJvXXN1VePGNj/409neIym315ygdgzzBbix937wENXX3XlshWrqqvr5i9a6ne716xYdslF52WkpXl9/tvvuee8M8+QABWoNmbjoqXLP57xWXRCPDvjbvzTnwYOGnjXg/9Cs+nStetjTz01dMiAgX37z/t+/owZX6Snp73/0ccXXnjBURPGg8zDhxN57NjHX3qptr4OBai0tPTV99578N67163d8OQLLwzp17+qqDRgsbEgUFCwOzF6a4TJlt89xxUdLZZoo/G1aW9+M+f7rtndpk//8sTjDz/hhBPWb9oCgRDWPiEl2e5wLViyPGSuswT8b7713l+uvoYt5Z9+Pe/5117r2Sd/xtxZhaXFZx53/Jufzli/bTPOk2MGDx7DhiRILRQqrah58NEnuvXoVlCwa86cb675/e927t5NkPCkpERUn9ScrqWrVj7wwMN9+vSqXlz23kcf3vqPf5RVVNx2591PP/aYM9a2s2j3rffd/9hTj+0sKbrtrtt75Oehr/7nvQ/PPO2MC04+9o2PP1uwfGlaQtLSBUsTI6MH9OuVn9l10cJFvTpKDDSwQzjky8/vycTf7/G8/8H7t915+8gLL3AYbdfddO+kSaOOPfzKuV/PmXr/w08++q/dpZXlNY233vxX/DefeOKpRx597Nq//llm/aJ1hjZt3U5o6A+nfz5nzty7b7sNVoUKw4LS8FGjBo0ZZXQ47r77nm/nzh8/hi1cfcZNHn/a5PHbdu26Y+p9f/773+nPtDfe+tcTT9x+49/ASLG7mk1ffTPnSLvzrrvviouMePfDD0orq2697VZg+s9b73J99uWRR05euGLpn6+/Nis6YuGCxW++Me3e++6++KLzIiJdM7+emZaesrOy9s5/PXLFFZcP6NebA2Yf+PfTV19z5XMvvnLs5MN79sgl1u6ECRPQoQ6fcsRRxx7pdvv++Kcbho4bye6VqqKSw8eN7XHe+UXFpU8/9dywUUNq6xp27iyAwYkEQLcxGDZv3cZxCEOGD81Oz7z+91flpaXOn7fw40+nTznx6OLyyvfe//j2m/8Kw8PEggNdQmLS1X/8A/rLux9+9NzLL999+61PPP/SgEEDzz/t5KfffHPhokX9+vQhkMYfrv2TOcKxfP1atpKOHTdhVzkqeCmTx4Ki4h27dt11561YBqfeN5V99j169Fi8fMV999wTHx/HegzusD3z8vv16Hn+qSc77E7t8V1VV3/Trbced+JJfz3iMLQJGNC/n36W7T93330743XHPfe89O6755115qqNmyaMH3/BhRcQGvaKa//Qa+CgyVOO/Pb7RVdf+4cuyXELliyjzXfecdv55/0myuV88rlnJk+ePHTY8PqG+ocefHCawzVq+EhcuCKstk+++rq0vOKBqfcQOIRwxygHGdldP/5sRr3X62D+BENS/P9/XggwpqyZMtE3GEuKSrpldyXIvsfrm/HZF2ddftnJY4ZX1MrkNS87e/qsmelZ6Vf97kpyfrdo8cxvvn766aei7bZl69bdctttg4YNCVmUzURNicSqLVZNQ2VdfV6XLtf94XeNgQB08dEnH1169jnom7AabKqoz9f+4dpBffphOz/v0stPPfvMCRMnNFRU3jt16uTDDzv22KPwvb7xhutdJsMHH9bV19X86557YyNdu3eWscnGZLXOXrhkzvz5t0+9LcLp/NfDDz/z8svnnXP2O2+/c/jhRxKk69sF39996209unX969//6jUbt+7ccduN/ygiSKXREBVh+fOfrkp2Oj747PNH/v3Ec08/jpYstj4R7OhF2BJZwQ44nfZTTj5h07adf7vuDzFWw7+2bWI75zWXX/7599/XuD0jRoxQFn5ZQBoxakSPfn05laairmH2d99n9+heWVt75913p9ksleUV0U47WlHM+PFDhw1jVSWjS/bWHQUABwgoMyMz2pLTzzxj1PBhsJ3fX30163lbtm/DwHX8icf36JJJuxqDgQ8+/JBNpsdPGseSiphPjMYNmzZ//NHH9997b25G6potBX/++18HjRo3cuzI+Pjov117DeOwZsvu3Ozsm/7051iD4enXp33w1Vdd+/Z58Olnrrrqd0zvSkp3v/DMc4dNnuIhcmVt3aP33RXvdLFZg5HBSrBo8ZLYhLi/XHZhidd//vlrR48aQaDZjVs33XDjX7plZzO+99z3wJXXXDl66GCil999/wOvvfHW0cccx4wKwwY/TPfYH8isGVeZkqqyf5zzz7ysrotWr//HP28/bMLY7bsL8nr3POeU0+MwKhHyOxAaPmTQitVrYQ4dMxtgaRd7F+MHFLBZJqcm5vfORxmp2Fm2ccN6p8u+dulib11lfRVaHv315OVkp0axMGsYM2LUsy+/zEZ6NEHQAXsxVjkwAjk/dsyY1MREpsYYSrnWb9z82TezsRusX7/h8DHjIiKcTC8infYYl2vLho0cJ/Deux9/YDSW7N5pCbpBLrVeKYtCUbHxRx1/fEJiAo2rqKzYuHHLAw8+htWleNeuhqpKQk3A8JIjIzgrhh0D+fk9OLpAPBbE8iBuAwTC275j66effjLj8xnEZMeQlRYfT7yg1avW2OyuisrasRMmMEeev2jhd99/j52nqKbczXGDPm9OStqQ/F7iCdAtOzY1ibhgmCDpPZJSWdr5Fy8IzIVi/8P2FO+IiHY4R40cPnfJwuKGuq+//S4lJTU/v7cfG5S4dhgqq2o+mP5FQVHhzoIdCTGxeOCmxCfUVdYReKtgS0H3lMwYk3lnaSXnlpQ0Yj1rcDd61Nk9LEOJXd4aCuXndEuLjIR952V1q6qoHNizZ79eff9xy20Txo099pijE6LtbHzDrBQbJXsyET78T6CY6vrqyUccFhsZEeUnBn1w7bJlZ5x9ZmJUFLx49Ijhc79f8Jszz4yKih3Qu0+shBtzdknKqKuo7pLW1WUOpsRHQY0Og79PbtaIgX1Bv9qGxrLisrNPOyvBFYmNafzgEWvXbBg+dATG3aDbd+Rhk5atXHHTHXeccvJJowYOxo+AgxxqsPfJCSoEvRRUB27qFzH6P3xhXAAadKC2pgbzLXTIzGDIkCEzPv6krrxs+cIll19yIV11mcy98ntjgcakU7hte0psDGZDPLB7dAW85orSUj2LAi4MhxIDeEBjqrT2yu+BpZTPu+d2LSkqFMHJgpkEnzOkxSf3y+vFGlF1VX11ScXcWd8uXbQk5HM7XQ6RTkE8SmTxXor0+/vk9ySSIzZPHHcw7LMusYkVm+Ldzz/xJIynpqy8x5ChmUlpm9dvLdhZtHjJ0vzcHn26dZ09a867H3wQlRwv5Ox2s4Rq8jVmpMQTZoPmoZvX1NTgSwali16PwQpyQ4YRssvPjjn26vhNAY+ZlhisY0aOeOTfj51P/OovZ44//Ainw4mFgGUAgPP2u+/PXbg4ISUVdSolPs7hcJqsDjSzzOSkVctWXnP5pWxLeOnl1xYtX4ZFqLCoeOiQQdIrjP4I4KAhNS6uZ9dsgihDmHFOV3111TmnnuwNBW679db0+KTfX3VFUnoqTufwaIDHh8zeCJa3Y9u2uJhIzibx+AI5WemO6NjdpSVuL4uYtEgmC7idpMRGx8leKDNhoFgOBTS7C3d/Of3zzz/8MGRsjIuM5oRF6h3Qq3eiy2WUvROekKzpWHJ75H70xVevTf9q4/r1fEt8pJU+L/auLimpuO+5G+pD7sae3briXAXbyO+et3TtOgQbSyVENqOF4vAn2i+DFQAghFoAtr27do23ONzlleeecuoDjz36uxmfTxk36YKzzmaNwJUQi4YNk+wYMQCYxLmN8miT3+sktjcMDr8oI/FV7Ccec1hilNNhDETarbFOG55WXrYdIoK4wb0SbV/wgB8ACVKISmO3i1GH4mTRw2yeM3vuXf969LKrrsjt2aOxth4dHwIK+NwuK2XIgn16UsqZJ59E/3lOjo1kjGkNxABQZAlMTr6UvcqIqwmjx4wZNZpW4l3VLTtze8EOG56aHFFpcGLkcblcsgDLcrMsKkN6TNoakpNimX+zckfTEhMSnIbQUZPGrli7ed78hfm9e2elJH761TczvvjivLPPiEtN3bq7mDMCXXRGUJkNOGbQ3e0HcVm1kqUfgRFdFCKjcfxgSIfPs5IqiBQd4cjKzkTbmjP3ew7PgSQQJCghHKh5w8235PXqfe6pJy1asnzVilUswJ11+ml33/PAPQU7IyJcv7/4QhwL3n79bVdkxHmnnlra0PDQtocD7MLz+dh2L74djA7u29QO5L0BTu2Ictj//ufrVq1c+f57H//jrzfdf+89EWa7mGaU2UXccXEAZ6GFruB2jYeHotsIHD98QYlWRvvZyApJiYswy78scLM11MgHBKU0szIYcLNWiNrCZqRYooX4Gk0GIozI2qWNtR9YG/LQGzR4cCGDePBE93fLSr/rjtvf/fTj+x544OwTT7rgjDPgCBpK6IBqNgD8NItSPfmf/WHNEhUYc30kMRAxEKEfGI1jRo9e/syLeD6gV44eNkiEN55U2EthzwFjSmy8p6aO9SuWNetr2a7ujrY5QGlsjGL6R6I01DvBFZEHhqrqaj4HWJUVlURnUSiHMwyRr8Q5n40JTpsx2mZLjoq56KyzsWBz1h4IT8DnFQT5UQvTlMMSrOwFohloMzzgdOL3c8AyYYNv/vP1slIppChrwdkpGTO/mEW8h2OPOZyFwbdee/2Ms88aMW50RW3t5uUroS+7xVhTVaZ4ggkZYLM7wG1Bb6Wj0FA4AE0Xz2lZ+QEycGdx+Rg6aGBEVNSX874rKi656soreIF2Ah2tXbvu1WlvTn3gvtxu2fc88jgusHgVd8vptmnTZpvJet2f/jhuUN9vvpn3zocfPvTIv7BZzfluAdZOoCTGMYG+wBZMZrca7hIwYVbVo23WS845+8wTT3zrtWmvvfHGn677E0KRSKtAQFRRoelgXFxsQ0O9zFocdg6EqKtviIyJFc9HIWpgRnAODwiPlOUeJ1P8JhLj41MSk6757W9T4qL9RrfNYo9ghZfsATBfuVVI2cA51AXjV2rapo1bU5NSrrj3N+i4LNA5DEYXigLxUxHULCPX1lliYhjciopKm5VtTsKr0Aaou54Ybuw85xmfMWIyu30EgS2vrGExJ85qT0tNfeiOO1dvWD/17qmR0ZFnnXxyRWM9qxHwTGlr+y/WAO1eE0YP5CaTHas3EI1yYzTGJMckJyUU7NzZt3+vnn16Z3bpJjzFbFm7s7AmFOLv60Xfd+veDX5kdLuV4zyDjAroz+uZO/PrWZU1dUwT8LxftnxN1+65x0ye2CUtrbK8EkdMyAYZWClnyBh69+vr8bnxzx0ygDMie8QnJIi7FWHfmV/g6IOTopkoiHjiG1Lik7ftKOiRlzuwf6/evfIiozD62Wq8nrpAEHEKOYlLnvJ5E57JyAYN3XJykCRVtXXMEnr37cfiKjjKGld5ffVXc2YfffQUBnLbhi15OXljBw/G87u4YCdLtfgOr9+1e9nGTfDKnTuLGquqExIScAWi+wpTlKc0NQZ9dk6rNJtK/LXF7mo8BOl/t8zs9z74pKamfuSIUeTWzhglFRWFZaWnnnV6Xl7O1p1bar21UPimHdtze3T/wx+u+P3vr3DExVb5AyU+93Fnnd6nZ4+a8qrKshq1PgKghJS9RA2HmWh0w0MWsOJwF/ANGtjvuhuuq2qoKygqMjvsxTUNbhlEbBaCTQlxibFRibNnzfWEgjU+N2HAeg7s//k3c4BYVaNv1jffD+7VF5EgJ4yGkLUAXCSchEWz2Brdvuq6RlAiaHZ4YTJWB/NqlPq4xPg5SxbXmQzFDe7Pv5tLXHjhiMrdvbLRDVZffMopJ0w5dvac7/iWE63jY6PsEqKV+IMAmL7gOSS8oP14+wMl7FU6w3bAv+bv98rcnPgj/yJvxbPKkJ6WXlpS4mU7icGwYs1aFucHDRoAIIvKayiW7btoiegPcL7+gwax02TusqWFNTUvvPJaampGzy5dMmJjv/3uu1XlJfMLtn70+WcUCeuEs733+VfLtu+cu2LtwvnLxg4fi0MX3s6YDhhz2R8MQzf4o6IdY8cMe2PatJLi8oaqus2rNwDX2KjY3TuLN2/eQax1lnPk4EHlsMAeS06a5PjHySOGmxs8i1etqWps3LJtS/HuXSh2U6ZM/PDTT0oqyyaOGM5khYPFQ04HFkLOsdlSsJO9cB6DaeW6Td/MX4jd5tPPvhgxdBhBfzhVGaUYXIPIkRBIGgnjgv5ks20tK9m2bUdtaQXGwPFHHPXQM88TnqhHcjJnmPAdPEKi/4pfkHPl5h3fzl1EIwHX1s1benXPYyaEu31ZQwOb8XA+sdidOB189uknrDTA7yw4Gnlxm5dDbXG+gXejd0KbRMPfsG0HNh/kUH6/3oT6Qywx+VixenVhVVVDI5M2JJMB06vZ5npn+lfbq2ufev295Ni4numpkXb7Vk65YsrP8qZsm/DiNQXKiBD1eHJSEiHJp198vrS+rqq+cXtxSQPEx65UvEvQnEBrZhBYdggM0uirKavu26t3j9yeOwuKMEwxTuIDDDPy+wnOkZKZwWGOu2rqFq3b/NmXs4YOGRLtdHpqa7+fu6C4uvr7+YvqEf8YhYz2zUU1b834mjNjnnvtteTslLj0pJUbNpSVlmWnZ6SkpeFuA7g2bNhAQCcQRmuHP4KxP/oa2sYfauO69fkZqchkZj0yZ0NDtFquu/qyex5+eN2qheyG792t+yUXXYAbE6uOd9wz1dvQUF5a+s+/30hwKz6RqZrREOPimLvQ0UdMXrN06Q033pgUGz9yyJDDj5z8zV133XrnPTItDfiddhzcDBPGjnnrjTc8FaXnnHfeH6658qGHHszL64k55JjJhx09+TBhRvwFgpg1bSjaXCHDcVOOnLts5fXX/y0zKbG8sPCCiy7IRxrk9bj9jtuPGDM2ITaW0whEGUGLN5kiHU67xZIUGfW7yy/7z4svzp71dUNd/fiRw0874fjoSDHnsVVkQO98VLlxI4c9/ORTN069j3k9KXxFEPb8XnnvvvcehyKsWbPmiMMPi3cQF8MinZM4vTai7KGgiSuA0ZAU4Ro+ZMi9999z+IChF59zYb++/bZuvvOUKcdlJMXIJFOpP8SXHzF0yL333EdEMPQOnCWEvRpCs+bMXr56JSphbvfcP139u8wuWVPvuSc1JRltpVdeN9wyGQIqZcjZTolHIGDhjwY47A4WG//99FPpWVnFxcW9eiEcu0wYN+rRZ56vqS4797STMdxB/jERzt9fcfnDjz2+evH3HAV+1VVXn3naqXffe//fb/mn1xck3OOpJxwPqhLljLmSTJcIA86ZgYZgWlJcfl7e1LvvGTd6VJesNIcTzy+qR6MPHjZp/NQHH1q4fBn1pqWlEDASNIiKZmpn+vzzL76dO5cwZaxmn3nmGWBnUcGuXnl97BYbcWcl8KFABBDqfvwoYnZMBlHzgODegkfM8O0oXs1spIDIyIiioqLC3bvTMtLq62pJn/HFzOqK8tqaqgem3t61azYR/8lG1sSEmOuuu/bxZ5/1+3wZKak333gDTOSCM05+4OmnHrx7alx0xMlTjs3JyCIns8tefXs+958Xy8vLzv/thd0H9qpGCDALMxldUZHDBg8khCe6KJsMfnvpRc+9PO3eqffbDIGBA/r3yssdNngQ/j8P3n//5b+9rHevfAwMUARQdzntI4cNIRhMdETE3/94zb9fegnqcpmMF593bjAzY9jQwf365Xfv3gPSYC335JOOf+XV15wREfCp884+hxI4A+TKK367cP78N155Nbdrt4vOP4d25nbN4gxanO7Qq6IJBhcKDejbO8LpyMxIO+HYo++5++6TJk449dxzRo0c8eATT4wfPRKjKpYTWUYwGPv0zj/+2KPunXpPVnbXC39zusNqIsYw5zRAbjt37qyurHi+ofaf/7hl0oRxU++6KzEullNJMtPTwb+e3XMy04gfbOzbp5cdZ1E0RZ+/b++eSQnx27dvf+Odt8Ux02I5+6yzcYa96Pzznn322QeLdv/pysuIXY8CGulw3HTDX1mE/3but1GR0bfccD3Bj0Hy5UsW33LLPy+5+KKu2Vmc+cBCFq1M57Sf3GyY1XXX/A43k9tvv5V9YOPHTuh+clZet26c8AxgZaeSGZdf2RC4edfOGo97zvwFGDYKtmw84rDxvXr07N+/L7MxYMi4n3TCcY+/+NI/b7/VabSeesqJkyeMYWyu+8M1b7391swvPsvJyT3umKNkjuXz5HTthmfl3266PSkm+o9/ugbtatniJbPnfWe2WTmq4eTjji9vcK9ZvvKkq64S7ALt2oHM6lOhTOPnX89588237pl6F4tdtdW1cZy6h6qDmDUaUegKS3YzMcSGyNkU/3r2Pzi7nHbMYXW1dQRWdOJt6fXW1zXERUezPlNcVcZR1LBHtNXt27b6PL7MjMyYmBi8fQt27krCxI85g9MuI7BThzglkuhUKSlpVqdzd1l5aVkZLC87LVX5XYs1g8M6qurqYmOVjMGL1GisDQR37drtqa2Jj4lCtcd6Xufzb92+LSE6JiEu3u1xx8XGwkPBs/qGBma9OF1hUWUfGT5nWLmzUpOIkQXQqlFifP4YVwSTSmbTu0pKiyrLMOJhwcFZF7h4sTsZTZRMd7K7ZAENJpBMSJ1OF+cq8w2oVl9bF2OLCDhM1QZ/0a4CvD5zunXbUVZ2wdW/f/j2qf26d0NFRosDd/Ep8QRDHITJY1aXTI4731VUMvW++zn0PD4xAfvN32+46fDDJp1z2gnrN26hm127ZLLUgdtZg9fHtoyk+PiG+nomA7FR0RA10Ia4We4rLEaHqMIUlp2ZqTTu0E5O0qyu7JaejLsaggajbshiZ17JYbUul5PDHfBSB/LbCwrA3sy0dKZZAtXaWgbFyikO+t7hIOYHO1zQzmKiIpgR+twN8bGxfplhwaVsJVXVu4qKEEXZGel4+2J6w3qQEJ8IYyooKMDOkZwM34itr2+894HHzj337N65XQED8aUEY/nhH/Wr8O+n/mG0abT+C9clLUFJDz+39kb8XwVPpISXXn9j3YZNo0aP4fDeRx97JMZi2F1Zc/llV/zj5r8P6NMLNVc4hTgQB9kTzrEq6MtxUdEwmqDXDyNjsbSuoQ5/QafVAa40WIx/uunmKZPGHnXYJAL9YDH3M39iAgDgcVxnux9N97vhKjLVtNo9QVN1bR3b3yEHilbGD0zQRNDCnkcfmQzLbA2KwGEfAcP0Gm/7aiLCu92xTofWsZhlyn577Fdo62wOIfB7Y2Mj7t1xcUwgwAp6gN0GVa+mqipOIptKj7AkI5qoRFgIBmFhRQJnZvNu3tfVcKaCPTLmzc8+f3/69KfunRoB1DEuiluZ1MWUvbqq2hUVRat4/OSzGbuLiq+++AKPwVBYWvG7314+9a47Wduorm9ABXNxECHVyb5gwX2gCoehEmicQ2xAYhlk8cn2QbkYpaEd8cwwmqEjwBLtYqOAKANMXWDwUFxtXR0iHKTnc7rAsll9fX1EZARFstRhZ2EDDQ6gBdgeTnhABz2FTGh1dGRM0M9kW4Jx0Arsrij7fnOA2AG//+Pfjj7xpFMOG49x4u0PP/7y88+effBhtbcK+IeYKJnsbDW1YIyCxUVH4v0lS9d0H1hDO9GRUQJGo3HpqtUPPPzYI488iKE1MTqKiT3cChlaUVVpsTuioiNBhs+/mrV0ydIbrr0aRtQRswFFkRPHjt65bds2DgMY2N+ZGMfsUqBsZB9UIM5pic3Jhj8G2BbCpfY19sjMogPcA342RLjiEZlAL5genyBgZcJnCPXMzQEpGRcsQMnxMQlRkYBboSnfsUfC2D07i03woD7sMik2OisxAbgybcSKTbfBX3bg4MjPuLExnUysFsB0cmHKEttBviKQQ4Td2jcvD7SCNaP/EytRmUEDmOnBLVm3wtE+JjopJgYEDfpYf4YYzHF2tjKYvA2NsjUvGEhPik9JSRAKUevA5MQKxbD1x4+YkCmU4/dHOZ1UAba5bKzvAh9/fHQ0s/iGgCfabonr2oPNJytXrp728Yejhw/PRwawp01roRiqTEGs6726d/U2ukHi6Lg4Dq1119ctXbI4u2v2+s1bOQZk2KD+bJPhoAVBPwJpSSwwtq+4DCz5BoI4SkFwcHDIAFd00ApUyM3OMhmy4M6CPBL1wZydmmxKTZaZhvxJxDM+wQE3LS6aTjHLwWTHnIx1KgqHjKkLEmIrA3MF+YSz4eKiCTDACxwK+/XuKSsemMAdfA5+yvig1aTGx8Hmha6oGqcgmyUdYUa8P4etZ25XiFxlDG0v2tU7r1t+bhcIWESAKOUioalF+IbiUoJRP+VFVeyWpQZupEp1SRuabtouCWg+ZQDlU085ZcmypWyIYa/11Lumsv60etWKEUMH9c7PA3HwQ6NumClCB0MrrmJYEXHIg91YOGQJcRIKxjpRpc2EZGD7BWQkbgcefyQWE3Z7NfrttFEYpcCeHfbs3WRbFWyBYoisheEkMSaKOsBR5nSCuuw+4NwYCFaFsRMDPj5bSCPZlcVwQ2GeSJsNhyVogdqhPxqD0KAv5IFg2EPuEqWAlVi8LikbBhxi4svLJKFH6JstmfwnPeOS0Ag0Gy6r+CnjzJkHtgiXv6Hm0Ycf+Gz2d3/9+43MMolLRwckq6CxsI74qAgILkD8O6uNM8tmfjXzvseepEHr1q45bML4vNwcCo+VE6jQ2oADHZDYGFIAXEi27sC8ZKOk/APVcGSWw0Fu6ZLHYxE3RTZTWghwzadq+QSLC5CQYJf2aCcQC4miJHKFhQ4n25IgCZYTmLJQGH+IUBP75EWWIuUQ3tJ0Gk+4VSLiCUBZ6xFzWKOn0emImjxp/IyPP961Y0fh7l0F27ddftEFEukV2Cv7BJvNKJ/VgPgoF1NwbFw0HxsDvYty2oi0CvDomN1sdbElkJ3CwWBcbLScVyHUzWj64mNj0Gy9HlbkjLmZWWOHD6d99BZIKiGokbrdv1qggzQi/7moHi9Q0Aj2h/eOrFCav128DKXmsOGDBeOUAkATdTMAMX/ARUaHi27p5gFGbngHBNUbKVumXTzI4DG10nyBZLBc+AS1CfzIxA/JQnPIdr3jmlaB0KSC3lIuOgHD1UTcUkFz4dzIUqjgPvoLfBltQNgZ7RZdSYpXxKzqkNvm1knz9aWKkuZL/n0ukIUS2G0nYkPMpO998NG23TvPOe+cpIhILFRUgBRRUBLVjYYK9oObCgprNm7+fOZMLLBJyYkTR49jig36Cs7J5jNhvtSGF4Bgqa5XEtTFs2Jfoq6wNOv1IQBAShC9vKqGOXVJ0S7qTUlJYf4Yl5AkOqDXLYtRslcUfwlZNOPD5h7p4sOPAr1m0w2AUZN4GSZJl6GB7GSXsuoDHZfFMaFKDT0+BS9R09hVQKqKQwadQ6u6X+QSmBMLWbVBQKI1A9WxDvuh+zSAntJcWIjgiDRA/lQ/+RHuAVaLiAJ2bPXdf3wP3hz5Xl16TPiYlPKq2tVr1zBvY1qZ16O7DBsQ45K61HKIgI//gYC0iAI8BmKwg9bY4WQJn3hvbkNow5YtrCenJyYq4kPuKo0Cnitba2k/Si55ZQTolWq2wgaKU9Sg/pUG6iq44QpThL6HkBlatY+Vf/kMckOGMBZoA9JeUfYUQGB8YCNIwIfyv+4AvypFeqMT9SP3YIOQEwSJl1ztkmXL4xMzu/foLuw75DWwS18wj6r5jopVwbJSKCsoW3fuYuc/uMwxA3j6Ux0ZGDcZM2mG/MqPNEKDXKWppKYfwmA0P7bsPmJMeIp0QZdDGQJAWe5rGqLmAtSQKRTmG9iZrE3yLoDKyflUBg+r7eyQVwJTUJ9ZA1DD3gW+s6N5+ao1RJ2Jjorq1bOH7GjDm0a60dROOs4jLWQk9xoRBVsZB8Y1ZKyprd+0ZStWPnQIlZ1Wyr8am/iXRw1vXUgHigENXypDSaQp6A40FNz0sQDBpIsWEnmG4aNbeGVxLIwwfHXxbxgHJDE8NCpVZ4LgmsC81z+85E94tIKDeofuhgWBUEagqhluIqFaYOAoLE00zEhCVMTmUJTA5/xByC1HXVciDYEFKDHAmEs+GThhW3hP7KlR5/7BX0W5++YQLCYUsNlWVV23cPHS3cUVrMLhnY3cToyJRZL3yu+Z0zULjkTrlTBgykIzwWrZZiGdb2409/BK7FFKRmiwyLArcm3GIQVnknjW38m0VCl9QGrT1q1Mq5lkuBy4cxFyhhNDsUmiZtm7ZncZM3IoBzD4UB4VqKQnekSa+Mi+XeM1Re9zqXYgeRUg9wyzii8hibqdQKXpojfIXgrBtCUKADoO/FZoGp7A+AooeEumfSpq/yMKJoF20AQlLhvu2B5vPRsL3eK8G+Fysi9EtDTaBOf1EvefjKgFrbj2AQ6P9EQn0hkwDCGk2QeF8kpxvX3LBwfcBs4bkLBOjK04pBEzRJRsM8f4inFDtE0BGUVjncAkR3AD0Rnl0tDmhmo1AMMp6v0P/jTjM2MnboogJIyJf4j/pkpmdJBcuhJ4mFL39tTYXLRUeKBKhYegHsFHPBZiugVlWZfNuVKX0J3+RkOLX7nBJCAEKtgob/lTqSj+wgfUpXOSrekTuEHzq/C/pITzhxPlRnslKDApGdxcg8JyxWmbhw+UpA58W+g0VAojLt22Y9WSJRW7d4sXjDmAoxeREaKTkrJ65kWxyAcL4JAulvYFSFBXc7uYVTHzFnsNraJm3VryaNI/SDuZnwldMPlDb9EfkZ8/PcSU1nxDSbpsckFw6qmdPxTCCHFJHUCBlSUKlymmzIICOBUwUOwIiHJFgKk4ojC9be4uGZW0ls/5mKKaOizqV/gSnNq3qTwrMSmsr+kl/6phFhNFKEQUA0DA8ZE11Q01dQ31dfXQRGxsrDPCSlwEUTklCLYYu5lrhqsK30jzZXbHbIA/SWZskQSqMhmfQ7+ayWavL4CBJ+hfsnT5+299ZLFGpHbtiklS1sGC2AGxvTZWVVXk9+wx5ciJLpdDW7EkvjutgGkyo1FzGcUE1ZDTY8EkZruCAPraa3ib4SxAEszA3iw4g0vP/GUrP//6m/jEpMToWCWehfuK2VTsO35WGmOiHaedcoJ4AuOdIbDSMoah3qM9Nde5179Us/dFlQcYLxlB2qMupUNgvyL4RAkb7tZv3lJSUcPu0MiISJbK+/TKS09OYiERWU52EJh27l1FBzwhBhgdbOeFxWVrN2xGx9xdWEJvEQNYxlOS4ocPHZSdjh9/FJZFcIxFpFbVStOBsKZIlAHBdFCEUaHzciOTIaoSKmlxtXyQkRZdUsWfktBmghR4vZWuXV+xeWt5TXUVOxKM5qSklJjUlLiMtLisdJvTKXYdKVBEgS5N/9K1FvUcwq36XtGa3AktK1IMGMUXXILE06c9FdBSrqYq5IFbMEhItSlRZWj6IYnYeJgLAxIhlzA4DmHDUIUF4Y9Ma1FO+DOsM0xz0NiFFWBJU8wH7Mcfq7lyVR0f8Cz1Csq1qFySyL43wMPF40Ko71W/mziptFNSm0oJF0a1rCeg+Qbc7vWLFi798otklzM7KcmFTzVBNWRNx1Pv9WyrKM8eNLj3qFGO2DjCLzEBRWYAFUZI5phCuhzTiTWdRGGlzTXRElpxYJxXRGHCDgTzZFak+QKAUV2mAFq+hzmoIlWxHSUGgL8GLqUyUYKAaPfKVWu+/vLrDVu3WCOcnvqGCPbLG0M53XOnHH90dkamrlqNhaCDviiFS8aDX4ZJBkcKlv8OdAnuyf+SUwrkFiajbklrrG94461p3333PcvI0bEJrMjbJGahv7R0t9vXePxxx0wcNx5iRxjQNApRdezBDilMGKvQGH9SjQBSiQHW7UgA5VSivNpzp5L2/sFGQ4LKriY88qmRfbn3PfpITVVdXtd8hzMa27xEQAKJ0d2UtRGTQ10dgZRqDp80bmDfnvh3woekGP4HqUEoceTgEuyh0+pBJASXtFv+0c0SIPFfcxt5EiUOmzGGwqn3Peoz2bJ69EBbZJERSkITo35kADKGarBherxI0ZrDD5uQ37O7TEikBukT5jK5PfjVVH0L8OhG6F8ZOgFKM4XKg0zctm/f+cJ/XsaJICMt0xERaSRwN0HK8Ot1N+KHVF1emp2dfPppp+CpDIjIr/qqGnXwlhziG4WWwohLSkruf+Bhtq51696LvX0sYGnVEhJln20w0FBbWZqRloxXTCTL6bCbpose/3hLmvBcf6LEgJhoZYykfvxGkDe0QU1zJVO4UIGbQiOdSGbtlcece+G8b19/7uk+sQm945OSoyIjrNhPzLVBf0XAu7OqfFN50THnnN13+AQ+VCv1wiJUQymDQn9kHKURzZd8xeDzjx47odSmi+VoSm2KxKhLV7/ys3fLdd3N3+37b5BYAujHJlnHtmJMFjGAbRn1UdFdMzpRpCqVSTtiGypAvEmb+F+sviAyDlIt621qBxlUs/at9iCpUouIAUpSgNLIK4+aGlX6nrIAKEO3feP61/79WN/0tF4pKZEES/f7oGu1YQYLAwuMxjpDaHtVdbHPf8xvzo3OymJQaDZal6ymcIc1X9ECENBoQIvVH7XRmLAZfE+9NBAsQoFgvQfKFboQYPCnmTM59UjtKwmElbQspm33lIFHN4PPOi/rFVixt20rePjfT2zasqNnj4GZmZkODA1OBzSMjrJtxzb8/o89euKRhx8u5hpZehLJRg/DTTnwEP1w4yRIC/uopQyx85tCbPG95Z93bN9ZMnzU2MyuXYl2xzurkbUTYiYaduGXu3v78CH9UHIlmJsgtQYZ0xXZsaWbFK6zRdv0bSvayAeMA3SmmC92BKa4OO6HHnnsiW27KkeMGIUVCN5CNmwwMt9Ay9OhgrGD4Jfqri8r3HnumSfndMtWbaTqVtQe7oK+oRY+FnyhTUbDPfc8sLusatio8Y1Sv8FBfFUxVfFDizUbEi6htHVfQ33FeeeeyfE+aK3yMRZ7sYn/0CWFtrgO0m7BILQh0V6MtiWLlv31pn/2GzxswKAhzMbg/kLX/DGoMssGW4IL/o+9v4Cz40oPvOF76zJT921mUqsltZgZTAMeD1OSnWQy2STvZrOUvJvdL8luNptfGDeZzGQykHjIM4YBsy0LLWZWq5m5L/P9/k9Vd6sld8uSLUOyb7l9Vbdu1TmnznnOw3D6ED6C//03/xMhnepY+VWg6F4PFf6nB6URAOIfYcEIT/nlX/11i9O3eccDelIiMkB1EiR9pqAYNUOuPnPh3CnO/9v/+/+6HA66hv8TXMRQ4RJUiHozKnnbeLXZWmCSbrtXBQU0PwTIEJL39OPfuvSTF3HbD5YWmgnj0CV90bwZLyCrEjcaHDrzwOT4qaGeZbsfaN26NZTK2s0OYqYEEIx4yGNpYU/cVb/q671hKDMX7ukFtJtnHr35rzYO9VfpbXZYrMEb0bfcMf3ozVOW8g07+A033exQzm4+fOv1275x29xjmiCoI4CDF+YJi7fBMDY29pu/9G8eWbZkfX2jLkIIp0mXjItdBhosJXuEU0Z1g9pxIBIfTCe2/+K/URxeHIjQ6UltTlE4ShZ5UbTNvN/Ma9LGzYtzB6OdayB9c9bUqwK+c2bytqfeZA/fdvdCX+mSISJvikZPMQ0Pjv75X/5NKJr6yEc/tWLl6oLCYrSpMCaktw2WlK7bsHH1mvVHjp764VM/Io+K4JPpFxViSDtz3nahDue7zhjkTyAbOjgxFf3zv/4bh9v3qc98vrq6noSCGlYTn0XUpulMeXnlmrUb+/pHnvnRs1hhoZw8zofsYnUMtw1DG5h6UTudbwxvfk0aYElwxnjllVfb2jrXrdmYJLeLFNITlA8R547pFRM0K0Zsi8VWGCx69rmX4nF84XhBdRRv3tf8d0j7oCvq6hiU8+cuvLx339Jly1KphIhvYFkYECZBvE1kJhkqg2FVUFaoozMePvS6NkNCRpgy9cv8PalXaXXu30J3gu1ZAPwtBvoH/viP/2z9+k0rVqyGPyaCiV7UlZV9w59qedKt27DJbLV95atfU+eEVlU4X6j1N7ku7yBzoh6c87JE8JWUVT3yoUexDYjOEBqA2CG6adGG0xm7HcfW1uWrjGbHt/75O5zP6eTNJmXOrbeealN167WFv0GcKI2Cd9CrP/pR+/FTP7vroXqn35bKGbEuAeayjJI1AZdSYzxZanNsWNTceepk95VLdlJ5qwpE2gbqZzRFC/c05xeGeIfjnl5gLmDMPdfaV6+w8jePN9IAfptzw81TgZi5v9xsY4GrC90950HtVGt39nPu7yr7zQWM4/nv//lfLba4N1bUWUJxh4L7u15nsRORjKs4/rPIfYAIGwz1TZHDUWS1Xj1y1IL1VKoPaDZoATmV4Z9+k5vvJl3e+m3uIPhN3S+3XuOBW2bytl8Zyf05JHGCiDDK5GT42997sql5+fZdDwGBoF1VTJEdRk/gDvxygdCGxqVToeSTT/1Yuy7bWCQd/t7iFlJpoGxgcAXE5ftP/jRYUrt46UpcHJOJlNRxyKipFPLUFkjjAMZGIUqypLSS2Oe9+w5DpfgqOG/eWXx7k6QBzWwbaJxj0Tgp5BYtbsGWzfqgzWf8U1NT/f19WCKn4QlRUOVAmSKnyx2NJ85fuDzbyNs54RXpghaeff7FbTt2Gk1EBYEXqDWoTE5NnD5z+vy582Qz5jatF2BIpBIy/Nhdly5fpbiQOs+IkqIyuz8HwWdI2HrjsWNnVq/ZtKSllYXT1nS2fZaMc0gBnWaSqaamZq83MDQ8KgqZe+S6Z9ucORHmEVLEVzTyzPPIZHTl+s14Y4sLFQ6CuizZF2DTKBKMSCe1dIStIE5Q19i4FPeVkeExpgvwFhCSA+p1592q3fb2PilzYjRdP3X69IuvPrp2UyBvlFS9CSzDZofRmdGb8SgEKSEukHsd5Ygrn19cUDh49kJyYhwyj+5AaD6Z9WXyp9f67Q3o/76n56yyBgBIq9cuXAxdbfvcngfNmLYVJZxPj+tzXcno5cmxSWrUmOwk1lY3t1TTNOZSQYsl1zc83tXLfkdCk8BtmhXO4y2jw3tbiPtGBoSRJ/Q5nf/xj18wmB0ubyEhcqgM1PfAgAbeUXXZMJViwWTfkpDV19k1tO/A6yoAsnv4U0WDe3sFHtH+hKGjEYzABw6+PjQWtbsKYbLRdaPnJhSAUvG4v2CxxvkE+xN8r6g+8OA1Wk+cOn/p0lW0sfSs6krk35m/exvNHe+exhComM+evxBNZgLBIjzxQcigoBs32k6cPH716uV9+18jqJQ5mtmXUCgjyQbcHt/FS1dm0cwdO7qrH0OhCLl/g8WluBLzAD7So8MD+/fvh0IwqgvnL0B+gGwOGR9OX+LVhmuM0tvTp5INUaDPjvKuulzwJnC71Kga6B+9er27tKKGhChi+BVvJYkMYiSMCpUR57JAQBYuFxmd1e6+er0DxlxYK9k0/L2FQzYcOFwwogQTZV8/csITwIWDFA6yDoRFZxIxwmImx8ZQAeE3r/YFNcJ/Bys6qai8YxSxAPiAKNVwrQ5iernfwoDu8hFBQZns01/7xpKicie1ECnTruRiZv1kPjswQioZfcxhTWp8FS5zhJJkUgEGNTxy6rX9aNtIh0bUgBxiX77LPv+/226ZAaZN++Oq7BdASafv6uioqS9PKzhZ51IO84mOtm/+9Mf7L5x94djrT776ShiGWGJMxaaD5RnLpAf381Ck48QprQy6gJe2r2Z4ilu6fAe+yP6/TwfauPze1/YT6VpdvwgZGQFfdXgyhMMRtrDNZlOxhuxkdOBEMFCRpri8kqzRLc1NFWVFqvvmHUWX+QeqrQK7VThcDhINfetb316+aQ/iFb5+iO/4840MkbUzSmSgr8SLDRR0hisBRBcigd3Y4y945ZXX8NSme8E+03tCwyn3i1KCIbQGgRP92fOXPD4/nUswQj5HNoGLly7u3r2bghjDI+OSWU+NeebdUEAgszOZqNT6+3tjpLKivoeoPmfIxPzTcseryG1GpaevP5ZIO1wexCCmgqCg08ePUa68ob6RSC7cJelVcK62bELFQb64PFsGB0f1reJHJGz5/ToEv+v2HzikEFXOy5EKEz86fe4CbtR9A2ywysrq5uZmTWAG+0GNsKNQxranb/jy9U4yZ0DAMa3dq1gg0IgzirqXgRNe8EZnZ2gqXlBdITpKLPYmdvWNi+fOUQYE3xsy1jQ1LdKmhQHLdlX1VG03OurrqogIlTHQmvj3YoQHLO8X/Mwz0TR95uiR8Rtdyzds0ifDSWN2wqL77sG9bp0tMxoOZxNbd26t9RWYUoQX5FKwXtgAMpkKj/flM2eXbNnmLClDvAIFaIrQeTr4/y692QxMYx/1NvaLtiPIBVJOvL1gRCGxowMjlYGinRu2ZNOx7zz9k66e/hU1NRkyLeIuSfo5tlE85TObzt5oS0fjRq8TuGHXCSCJsP02tvmbDX729/sGoyA53Buee/ElfzBI8lJBtbkUhd4PHty3b99e/o4dO0avzIyIPWA2dGREGJKY2O46c/acvK1Y1e51PNoqyGSrXJhgrLYb7fFY3EuALqsC3JuV7p7OU6dP9g30nzx15vLla1IcVcFfCA2ACF+ELNvs9vFxcjKG0ACAFwQ1MP5pMs/5fTk0GkBTsq4DQ8Meb4GqOMiT16StjRI3tW63k8SoxPSD6LlJpkmlbWBFBkvMKBovKtZKE7yYDPJtHHkdyS2I9BS9t+pcF56ahBKQ1pzQeRoHy/MJrqUPTpheCR4Th0lLMoGLpGpHmJ6itzEM7VFehdCgFDh9kMyAJDQFetDFXLp0ntwSpF+m0BK+qmjxZnvCuUYqQytmPLH7B0cFrmSYb0GpKA/OWW5dT/eA2WwTSRGXLZGQhs6dQU+1Zuv2HXseeLi4sETtBC0KsyHMn6yrQR8Jk9Qrgm1Z9dijQa3N2UWfHfh9Prl48vSSunqzeIzl4D2HE+Ge8bHGRYs//thHi2urXjpxWFxtyIiA0hnrJTeB93P6Eqe3r60TAsB8qR4J93lU/5c0py3zLPTIxlYPZpgU285EzpHSmyjcORrdWL+kMKUrM9gIESeFDOWYpTwnYjf56tjeVK+1UeIwRuEsPBBUBxVREcxE4M20+479e9+kATQuJD4dGBtptJJyGLQlbk9XL1+0WEy7d++kogCZfNjG8JLMmhwSzo5FWedyu9Np8TGFMuDrRI1P7fe7/gSw5REIK/uSEzIRFVCTj2rHcK9KNhyaOn7i6J7dD3rc/kgklszEstjQTBZ6U1cRBIJzHjyotb9/IFDgQ1iT3T19zJ7MXHj7/6qDJAOIxWEXgzpmR44cuV/E8RzMgseYMMMMQv4gSwgEYBvAAm5DYmfuw6GOwWw2E7oi5k7hXXUYBtIkx4jH3B5/OBpFU2SyWHBgEq6f+2HWxXMUXx1MkqqPvDQyPflve0jMM9H7SVR0MNCqk3Q2HJ5sb7+6bcsuv7+AGfJ4fIxAsKt2CNmXjULGkAQJZtMZkr6ImMd6qm83c99d/isDAIK4G08hQknkfXl3k679+rWSomBRSXES25LF5je7xHGJFVH7kQ6FYLOTyZsgSF9EvlmXJZVG3OUI3tpteLWuCPjhwQATqPloz2BTsKy1sNqUSjQ01Fzuu0FUoTFD7hQEHjUehXErRrfJTrJxbZjwnmrMzL3uu7c23n9tT92yAQAFFTAwDwBC7Czc2iZiiclcxlYciCokLY1OTo2vb2w2we7BubLjBGdhkzMkcY21knVW2Ao8hdkEUGgMUGz4d2Fh7hsZYOBirNPpqYIrIbwGHd6hnR3Xd+x6iIh8s9lKoTt2KLuUe/jgHnYPJ+TwkBAktpZsJS695ffGholAoacYKhVviH8lAgvv1d7eLn/A6/G6qeRHwii7gbIVFI+BNxLGTS3WLRtaSJeEY6Hgy0jYhaCStz6UNwK7IHW5Sg+y1uRpGJkgw556QZerrq45e/Y0eBnTMXop1A6xWIx4JYhjSVk5zrY8LIGzBiNMsdrKPMjutuHe9vWWIQkey5cUk2eRlGIpZCNUZxQqaGhs2vvagUCgIBKNr1692m+1wXOPjQy33bixdtUa1bijS6VSbo84R0KkBOfeFyjl7UTyYBnUdlX7DeXK6cLtkWxFQJBorqRXIUVoYwAXvjGbjAMCyUX111t2pVy524PWtD9JyANtFhQpBjt1hRgXYhHiEZ4deFgpZNQHSKRXYImNq04n0V4yBmi1Cjk8yKGuuXr2Dn1Aq8xOCzppxkCw2MD17sbyejf2C6Op82J7qb/ARg1z3gTuAionmbZk5Eya5pBMDgron7qKvPGbjFZ7pdveavbiG6+/SXPv0Iy8u81q78gnfyyBbHL+yCKXzzjsDjxxrw30dufDL147WVrsPXvyCEWbqQaTiaU7+roGs7GiyvIyh48MjhQKMJKcmSy7gBAMDjm3WTJVZUd7bzzoi55mf5KdOP1l9tr0QzM/TV+Xx97QnLp53nD13i8wKvTJqGoR1El1RJEScfVLkA9LNAko3IFTicHC2Q57JMhX3fZgH7Jo2KhtJP52XMRpXeDyng6ZN/WPtxOYpAgaqA1Il/cXFZuSTsTB8WReIgc4mcaRQchGZVBSBoX8XDBDwlSy4cUcKFOr0QBOaVYu3NNo5r8ZpIXsLRFo0DvRbBABMDwwSG4rlGO8eU1dU+uKNe0dPSTLLS0vQ3E/GQ4zFLL9XDp/EXqlQKKScQ8F7NUSbyqVuqUrZo2Xlz0988dXmY75Drkzpwv4fZVlwamRAbMRXIYvs6l+2aq1G7YFAmXr123zegoRmwiz7u9uT0VDKI4ANJKfJzOhsnKSXUv2UW3e5uvhXq+xWDCpJMm0kJAR3Q7ymNflV3KG9s62tC7V298zOj4CdoZVHxkeCIdHpZyE1GTDJpqVIh6ydjDlanD6vXROmyrOh+uiBJwgf0qo5fIRSesonmOGsoqavv7hi+cv9na0nzxyKJYI5/Rpk0Ufikx1dnXiBUeGGICHtTJbpYiDAJ2IA+iTMLrfNzZroXeq9ARJMQhyZy2iSWV8NNHfN3p+rPflvivXrl7f1bTanDMPxOMXu3r7BsdzRuqL6WL6bJgYRasJhtOexTYARApDMv8hHsuIOaIJm/njXGQxfFJJpy8RCjmSaKr3qbKlQNcMNE43u2Dr8/f5L+WqttIstoZGmSmWn8HnTQaqQHPK9h7q7FnXtKjQZL569kpd1aIHNq6z6LLRdHo8kTXYPIdfPyFR6Mxw0mBxuIxO7DiwXhI3JKKB7OB55g5si9YUllVmmk7xkaDER4bCkmn+BAuzHFkyEYBXSUJPCQT85dmu8Cw8KPKq2qiGKmSytfHfh2lnHH6fx+vx0DNJlOFGzBaH11tI7QgS0l65cmV8fByMmkwmBgb6SIRNxnPRBJGQOZkgkbd8hU4IZ3evaJf7px9RmUld8+JFsPnMDCoUkF15RVUskTpz7tyFixcPHjyozgA0QDcw0N/T0wXlMuMvl9NbLZbCwgAtIQrMNHivI7mraeT9WIl1a1dbzWbyRfAMRJIAwurqum07dmzcvMXp9kAPi4tLGxoagwWFUsIFOqrkkqn4ihVL4Enn7Uabhds+571Tuwgax0T/6Ic/MNDXhakEhxzRdeiM3oC7flEtyTZka+syl66cc7osXj/SFcAJtKWXty6pKC8B9kRmmpn5O3R0lz9Be80mc1FBAcoY6DUAa7U5167fjCDyyiuvnD9/QTgtvZ7MkSMjQxcvne/q7FQxOG+cKy8vYdEQHVQSO8+2ebMxyLSp+5cTXVV1BTPDPkL2Yi+VllWs27BhbGJS8pwHgxSAYh7CkdCx40fa26+j+STdJnuusqqCvK0a8KqUYHYp3qzzt/d7desScrCbU6TkNI6mYmGbUrKo6tS5431Xr3902+5q7E+ZdN/E8FQmcfLy+etdnSaDiQKI5LUpq6vWkMEsuM87kNnZBGzFy077g05nYxQzg50Spg/jPJ953I4QOfgThg/eSmZTO26ezVz51/Kvtsy8jfC18EkqJaipq+0aHY5YDONKtntqtLS4cHN98y9t2POxymVusH8mhfGvoWWxncxxTkvYlI1YdOROWrRqJaIu8yi7CmiHMbrjLKkCgfRMt2wNusYmyz6gmiFyNZw3SV+kthjwypLLcmjrwqd23Gz+PnEr6h6isnnA7yW9vcUGqCHiW1esXH/k8OHr129QMCAYLAaLUO+G+SJVd1V9Y0VFOZ4YqWTc5cZvXZiLBVDcHSdjzo/oc0Bo1L6AHiSo7IMDqGJwON3bd+6+euUaVLdpcYvRJJFiw8NDp06eLCkpratuJJMb6niPx029YtE+i7755gTNaf5tnGoTD5JQcRlkoLDA9/BDuy9fbg/Yi1SKhflOpBfSZoN6uJ0qB2Mj49TQWLakJUeeeVJtBz2trYsXGgRNL/DTzKLP+VlADJNELk/p49Nnz+OnVBgsAfmKsw7hE6JlBqR1F8+fi0Qnw/EsFR4kj3Q+7/U6N2/eIHG8wlWo5FLDfHMaf4unJIyh/MjixS/sPWAyWlnHeCodKCzetesB+BzJyWhgDJmWJUssJqW3r3eYCnyKOZ6JUS2kvLyQOeSNph013tIIhAVRj+LikvLKyr6hiElSDkMMs1gGiopLpQu5gXiTFN69xRR6Fbd95pJLuSpKTUBKIV/MirBz80y72vz9/KCbli0bzjz3Uxg+k8U0ODZsLXDUNFasrCyxJklJQmr3BCEBpTXlMSo9U8aKAGnFnEolA2Vl3pIi2E5GKRLzwsyXhovAIKy+vBKvJoAGo4tkj2YbBEKOKZWU8Dt4QN59FtFI83I7Zjv5/Nd/gDrYxs2trSCXrvGxAqcbH6Fyh8eZSpsS2OnzaUsaLz38VgYHBg+dOrpyzUqLRQqQBBvqA83NSUVqxTJ9KgYSiFxg3uSXGSZMfDxQsLBD2NWUIhgeHh/oG8LKV1pSUllVRjUReGwEDsE+qtingbrIKjPrcp/IAICPlGm1bdqwobt3BK0pw4cddzp8O3fuwdYmilSZoNzSpS0Wi+X0mTOx8BSl4OWuXLKyshwAYXCCCGd241sAGboAj1NkpqVl8VR40l+AaREKmbc5PSvXroNaqwaJDERoeHS4urraja9kCqu1aTI01bK8UbIKv70B3GnM0+spi4cIwvzs2rGZuLaB0TG7wwmSRdPMqkCGUI5QZmigv7u3qxvnRJ/PQyoqi82wfet6653s5+p2u2UEvA2zelN6mH07TsQIkkubjbbt27fu238oFokQGob3FIIlZJLBYCnxoVm22W7cuFJbE8TkDvbfsG4VlcvY58L0CBDdbPyWnt/KFyFkpFMtLiiYDCdQqyBOpilIS1iHleAnccpmXKwR+aBwnC0rr6V6FlruyvIiGxWUSWEidBT4eSt9a88Ai5o9fO3qNd958lmTarRnBtVkL8JxsW8wKx07djTg8xFagS0HOQCdSjoZxb+LRoDAtwPA9zp0lsDgdKx6cM/xffs2LV1qTGSbPUEvpYbJWwAjAVeh5EwO+8jIwJETJ/1Oz+rVG5PYy4zmuqUt4qaCOUG0z+BoWpp/4gSCQB/C56mEn9sE6ZC0hvsNoWhidIICjUmr2er1gO3skvkQHiEnOXHlEWkWqBY3s3t9u39Z90/jVlUmoObSBz//uae//LXyCs/HVm41kMMgmxunIg/pXSk/lsmPDPX6g0WLaxsSI5PmwjKqcQQWNeO4wiwz38yals5GXHwXWBduET28TLaKOhX92ET48NEzP35pb3//sNftQyWDBQvnw63bNi1vbWquLJD5VImB8HDqctCRtoHvExlgJCoZXLd21eVL37VbHBAn7QpyCUp3kUvwHTLBLqXPnT8bj0VXLW5GXx+NhMh8W14SFF5DGND5YVFe4M0PwV8qO6/fs2fn17/9REAtcEFQEopL0dmxLQxYOJPHjh8t8Bf0dPe2NC9GyM1SvDkeXr58iXTP/+/owRYCEmSudDazsnXTmse/92QknXR7fSyRWC2QA0zW/r6+a5fPe1wuUJ7P6zCbdStalwUCXnaXmn9pepCC9gA7debFrUW8XQUXgs6YTDrinGZ5KblPpXCCTMXcKTwLk3Lk6OuptO6BPTuefPInVEb1UvMnh5QvW12fN1aW1QExBf4gqdMg2zu3bykuDMAfC9yp5n1mVRbt7R/qMFUbuFJVVvzc176xecduqBTdCF4X0MCDiFrf8bPnLuCIvGRJi9nkAAUPD/SWPLiF/jXuSQPu2eEwD9o578oJ88Chnczec9sJBnAoDfXXwpOYxoewmWsKX3W6VJVHLldeUQ5KO3XyxPp1azGp9Pd0bli3jtTTd275to7uy1deBitc89ZNP7p+7lpoorV6sQN6GUnG81nK8LLMaBfbO25YA4EH9zy477lX0vEMFekKG+oC5eUJFRfIdKiTP696WH5h3uSDMzYxOen4InEsVzr7X3ntcDSR8waKESEtBKLj1mLI1lX4ljbX8gUlIjZ11RMNLhXdwH154/d1I7PQxSQFKqo2fOCDl3/6kqOk3Gu1ErnB/kSh70kZiA4bHx49ffai21/Q1NJC9HnTilZFKqgLnIOEVCQhb8pEz/vCzCXJGGRJBG1SMtNw9NS1YycvOPxF2z7weaoN4YePehsjQSQcThvyP91/5ag59JGHtzusFtSnot/F/VBoO+3LwmiYYt6+7u1ilhpqrLtifP3wCfIF+QqL8FUQjlE8QQVrYBZOJGJnz51xu11NTU3sbypXD/T1fP5nPlVVXiYESn1nbSrvum+1g2niIVPGm4HpcN9+5cBBCjQXl1YxTTC4qkwrDDflU0YGhqLhaMeNjg3r1lCOBx/FDRvW1NZW3tqphkH4vE+UUuZi5lCREUISGzUcjj/+7e92dvfWNzRJHh8DoryITYYctqI8Ze1Ajh8mIWtlCXYe3o9oaJlN9RAgmEHxYCuuq7qcmV7Uf1GkaEwu3zjhERheOdFlL1+5TKjExg2b+TYxMfnTn75wra2trLrOandKBnQ1mACSEk+gCZ+CYm3csAYaALMCIQFy5IWEBk0P5pZe7/0LnA3DQHHHHHzla18/de7i6o1b8LsTZCX2GlxacocPHxgbH66uLPW4XZVlVcgEO3ZsWb1mOV5hKgjIqOZyEhoZmIUovjJawENj+W8bIzODxyphESjihoaG16xd+yd//tfJVKa5ZYnIZ7ytxqHls2yyRDx29cql5saG8bGRJc2LHnhgj5Ddd/8gIR/F1425cGjw8PeeqgoZGhz+vJKIGeMxA/ScQgPG65091/r7cH0tCRQ31tQ5qqoqNq7J2E3EE6AGNCDjsIzCAM0zelYYJZfYA1SrMGHb5EZIZpXXXj996FRbUWlVWWVNHgs5wALU6nWpeGRypCufmPrAnrXVJQGRCsRKRwEvTEvztP+v9RLzFkslcVsY7Gw/9tyL5kh0UWGxM5OzpXM2nRJVdCGrIYJ3byw+EI+ufeSBsqZGMqeiCmK2eZbVAJSFT1e/zDdLlIpKycaQNLSGb//w+f7RZGX9UpPNG5PQKzJ8gtiZcUEP7FQssKGBq3Yl8dFHtkChSaQpyAA8o8I07d83MoA9mp1MZgZehnCw0xcupQnPtdhy+OMgdapk4MSJo+MTY1hifV5PdUV1QYGveVFtZWUpYxWmUiTU2T0737vPc03eR+ZNDgl5FeooeIlXzx47cfLa9a5EKo+SFzIAKUYZignGarTGIjHKvNVSgbe0gDEUFxcy7aoYq7YkH+8kGZD2QTvqiEg3nUgdO3EikaTesCkaRzbJYKKkGgJur1CCmurqsrJiMfJIXjMQmVr1TFqQg1b4VFdb190/eOjw4Y6OTvyLcNG1WW011ZUU8sZmo86M3KmdUGaZxEE+f2DpkhYYNggShBOXgnPnz4VBhWZrMoY+Ju92um02SY9eFCwoQ5WMAgS9GlHZYkUXvy4BoxmaJKN5qwcjE68c0n2LhgK3iey+A4epVxwhy3MGQ5eFQQIfuJCKR4U+g3RCeamK8tK6+mpYHtR6LJ/wGzNC7uxAmB+OubRKIwOzN8w9geheuHABpWVLSwtTS5WZAwcPx1MZsjmlBVOit1UhDCDL59xOW1V5aXlJcakqy6LundvUu3QOWKdwKknpLbnEVHjwbFv0Ro8pGbaaUJGlLXhCkC/CZhvLZUIYDa3W2kWLi2obKPqREsc1iSNTKQBuczJ3b0TUrAs6L5UM0BN8m76jb+SffvCct6imtn4lEY1JKuMJAIjkDwnH3VGWLx6KjnQsrS9cvbyRbQR9EGbq/yY6AMhFqTdOOAa1JmOxy8dPWkMxXzpnpjBQIkrlzLzfnbfZ4ll9RW2DEvBGlbyHyuBMN0+KkV2WQrChbNn5FgbNQYpqWsj15r/7x28dPHHl0c/8UkqRBKVqgRWekfgeWiEgCe4FbgE3lFxswpoL7d681E16LOQ6kfJAlkJuFiQDCw5AxnXLod0JfgFNwCICEfBulAp59dAhcuRZjB7hUsGxSj4Wj2YyUloL16C1K1bU1VWh9pXU+mwx/hN3PSFjAJw6D0zJHcmC/MxIpsmA7E3BiXJJJhP7lWLu7O4jbVySSBlcCqn/CTGQmm4ANjoZS11N6eqVLeIzSniNQfzx5xwaGaBBTXs255c7ncoA1N65SUZy8wDpy0wJqhKSpI1chGbQFxdBpywU9Shybe1dpWXFlLeymMy8Id2jK+I+dU6wvEPqpw9el76Y2dGxiW9+5/v7D75eWFhI5RjKs3APBcEnx8d0mcTmjRseefghhDA0HszsyMjIiZMnm1taqqqqcUNiNCInSgVKPMoRJ3BgBSWrY2RMLIgYcMDMLCzLK17n6vKINCDDW9gncnoxZkZ7h3+5E083WZgc6RflZQ1GRyIT6xsca7sxiMmLWSJHIfZYu82EYFRXV+Nxkl+auHWWE56WLUMud5kYdX5n50cgKRFP9A8M9Pb1UxAcbX5tbU15WZlab11uU2FGwAzL78uvvEJSdGgAyreshFMgjRCblpqcCnf29PX0DQiuV1VqxcGC+ppKt9PB/LAhBbyJNHv3DzZPXJISA7zRbNpidkTGRkcuXdKPDlmJjIlnFFSAdjsF7G1VFR1jwyXVVaRNxSGXmRIvZDHlwlKS1+YOZACCIdGsTO1kJP7rv/UHLau31zUtzyRxhyVegSBYqb0uPyNgAqqiDs7acpG+a2d2bV7WWFfO5kJUpeTWuz8972GPOBTAFgPLuJ+I444RAwFhwrHzp44v37hOAfHpUaGZdAkRTvNUSQZLMov8QVB5TMgAW0F9A4HrWw+mGqgzGc9cuPRrv/E7v/QffldxlCZ1Tqm1no1IrTmcwaRcAU2wq/iXqoHwSunISGdNgWnnpmWY+gQNCSoUuJ2fDNC7hgVnBjB9gWHRrDY2dd2nB8cVYQn4piFi9bGpULiru2dwLDQyOo4vCvFQlF4rLi4iIIhS0VUlBTNvNtOe2oBUBUCQJKNNNmO32VGSjI9NXLl6tWdwGPQAlqooL2uoq/V63Jo6hK40KyitwHfIW8sghDYIadHpSZkwMDA0PjlO/s5QKEQEmduFa5+zpLgEIUC7Xx2JOuiZMb3pv5o9X25jK6pJDkDH8lWmR8Yi5zMnwAKoFqQqF7Spkt/VydJmTB4DI+E5lbl0+Upz8yIKqkgDtx8aMRFCrt1sNtlOnz77h3/0557iihWr1lDUQX4QnlXV1yhKNBrq7+mOhyb/3S9/saqqsq+vlwR2q9asgZGlbW5TZ0A9VTnD2aHPeQW5xjDnzNXssBacNA1+uE+7g0/6UieEf2UWpDl8k9SW1MmQBVMP7Zp2Tq1X4crPnD3jcKDPqKIQptUKdWQGtLulNXVg0w8LM0LtJQpzW22JePJHP3n2pZdeQcCyOb2IO2iPbFazx+VYvWrFpo3rEUyxFaHQQBd0/sw5dKr1DQ1wt+l8GpFRXplRzAwLk7r2nU/2tTa+mQHP/Trnl3f+lE0hYxGAEPZJo4K5VAoNVzoJTQUzmBWbBVVgOMJ+7F7cvBjgAFZ5SIR0joXHTpNZqXbMOpkR2P/hiWdeOHzyM5/71VzGjjVdl7erjiBj2P5yaRdF2qkwiyqTfWDCYp8K65MjD+1Y4qK6PLrEW6XYd35i3kc9yIZUvQ86u0lx0L5t2w70APBaoiaZuwQzm2Hm34VfQWytRBsrf/fPT3SPZlZv+mAygY++LhwaMesT0dC42WpxFxQn9ba8HjcGNMw4f1tRJhj1yWy455HNTT4nxB0jBXQAMrAAKycgMoPCZOsKcbp5zICNdhFIkj05fVH9F1wGnFH+aNmSxU05HbJ2e3vn8RMnNm1aW1lRSdYENXh9tsGZ9tQL6hfhNK02x9jk1NGjPHeKcAjMKXg+MZ8dXT2nz5yDHUOhUV9fy0NQ3emJmx4F3zTkIESOyrF1ddX1umqYQcIXWpcvxwYoKIgnp++/ZQCzw3qzk5k5YSaFYxdHQgV1iUqLZp/lJnghrgEKGlmas+u0d53unV8ZLVM3OTEuHpnzH9ys7nfZ8hiTzQRD/NVf/vWmTVtcwTIqkID/CZDmUd4PBp724GfrG5tiU5NPPPHUzh3bR0cHN2/eEggEtOa1aVDPaU9GMj0a7Wf55IJ6ffqHN/x+885bzm65TxCqHIJJReTlUPnLac9CQUbTSyFt3PIo6Ar/r1g0vGL5MigBU8TB8zOdSVsz59q/AgwUyuzo6H7+hRf7hsZWrd9ittj1uDWichKvHmq2Z663d12/0f7JT3wUhRtk4MyZc9FQeGPrUnEmEIsH8397w7D8t3Y0++22AcxefzdOAC2tm+n1U78oFGUjcN8lXxBCmX2A0+Fw9vX2+X0k/Qqyj2TGF3ohrUX5lI0lXIWiGxkPnTx78QOPfjxL5B6+3UoqTn6r5GQk0VVcVGHRe7gvlQnrzXbWh7JKVqtraLjn4pWOdSvqEScZxe0TerOXf+VnvL0GUQN9A35fgWw0FBCsnMY4zr79DBzN/Dv7wxtOIL/w8+AUg23J8pYE+cSRwnTxp5/4J7fPX+hz3Lh2ceX6DUvWbEuoYrxqLWCx0YlbbE7fZCjhQ5hWV1fb9UjT8x90pP5wpyGpv3GbhoRvYi7WG+rH42KtNRg9DvvK1pbJ0WGf0+G2WwAqyZIzzbnf3jsxEAAMCecuXr763AuvKEZzef1itEyiKEB6yma9Pj/ulaOh2LefeHrlitYHd2+HkUbhM9OQ9KsCuGBVgTxV3uccTUhFRQWaX0bFdaQ2Gpx56l7/FX5cFKcqzqJx/iVNHmiOcjdkLhobH2PjIXmgpMGHxIpLo7rkYJlpFkzt8LbJhXYSZMfY7jAwHtFWjn/Ribzw0stLl6/yB4KUrsFOStCG5vYqQ+LA2TFHPdSM0+WJhyNPPf3Mf/4vv+50OYgulLQ57+Qx99VgP2UhBDsz4dNzjoundnFG8pxdwVuGxT2XL1+BbgkToL0UD6jG3lvum/nCTyDAru6+f/ruD6h31NBcnkLBI5tGYiuFqGCEz+uKy8mgl3z+5X0P7No2SVrBycnNmzeyMdHm4mbHAtDXzPBmmv4X9a/wHbyDyvgjIXHC5C9ZsgTjx86dO/l6l2/DfTA3APux02cMFo/LXRqLK1aT8dK5104cOxcs8o9Ottmtno988JfNRgchHXFhGdH0Siy401Nw+XrXqtZaduiCiOYux/Ev+zZmUVKHwRCjcuSEt7n7JZj31eGjIokk+8CgOHR50Et+arxfp4ts3/PZypLA9bNFL7/6SuvKdbgJSNIeyVEG40WQMZUzlJ7+oeoypyzUjAPXQqvDuHlKRiuDEMnljbuUn1RWQ27Q7ry598FoKgiCLRHrRVxorK/pbL9OQB3yqHCFC2RJw8hETb1vfPOfX9p3aOfuByXVhJSWRHLJJtOEEUm1ZVS2Rou5orrx7MW2cCj2iY9/iHGgDCCpy8wIIDLyAtpcgxrOnz8PKmloaGAN2A9cf9ubnDZkImVyeNmc7tkXXnj5ldf8/hK8tdR64uTJMMfjFK3NeH3uT3z8I8GAG7yMNMggF+LEcIkn1E7m/E4HDTD5ypFjR8kUXVdTR0y/KKnIUyjeWUjlquMsl/ARhw4rZpodGhiuq6mamAiRO4i2mZwZcnKnnt7Gb0KPVcjQI6C0t3eg2Wtvb8fdHt3O8mWtBA86hSVRoWjhbtAlosrbtGmTrKW6aiwc0LXQE0ajiaoMf/f3/7hkxRqz3YWpGzMa+B11q0qJMEIwMEOWeBHxcTR869tPtDQ1PPbhh0VwFyItRS7xMJ6hVgv18y/gOhPFwUD5ZMaA/JKSkt7e3qtXr5Kym9e9i3cAkNhFEr7X1tlXWl6f19lpCrGqv+98VWnpA3sei2d6v/nPf9vecWpx4xbyesBfsSegPogbVqenbygRjie9dpG/76K7W27Rlnvupduu3Pnr3Aff23NZBmxUiQTyKDnBmHm+3gGG72K08J2iOSCA3WJ0YI22mPOjQ91lpV5fMEheSNL0Wqn7nsGMbMnpTaoNALxH9hUFd68r19o2rqrDWKDa4wR5z08G4BWkXq6gUb3RQvRmdmBo8Mzpc4NDg7wAwNSIer6mCkpECI9QFJZ9jgnoVmZWfBIAxbKyUhRjJJMgIkn8GucFC/pTjHtffeXr33z8E5/5GYPJDisHgya4G3abnsRqQjIbcAfzaCgqq+zu6z9x+uLKlS28jdioOBA0BEMLomfq+RwaGpqYmNi6datGA2Q06iE3v9WDteQQpkp60//N3375xz99dsuW7aSggZsHlWDSZN5TySSddXV1/cVf//1jH3lk+fIWbI8kpVe5MXmZ28grPot1dXV3GJRGeni5ZDr9k2dfqK1dhLcGpiBZKl3+WtvVkeERs8UMwcNQrIog+tBUZGBwtGXJciDn3MVrZeUlgOA9b8o7jOkNPzEYNPRgZEZ29NixJ5744Y2ODhepRrw+s9nUP3R63/7DBX7/8tale/bs8pBemyxPt0KiOrfCwGLQrqmpQcThyq1w9YZe1QsYlf74T/7MHSixODzJNM2ahQAIDZCDHI8sCk1hyMS0OjY+NTIWOnP+0tatm/0eB9ZvlSwp5nd4fuYf+n29ysvOtjf3fOXKlSTnqKysVGnw7C0LnRDjgtIZypmZnJoM1mM8J4ESpofJyYnuBzZ/0JB2ue3lRUUFgJiwOCmz0RwnZo0SOPSPKA9kkl1Y71BT0i7UyXzXWSMO0ePNHDg48JV2QX/addgLTcGr3cL9sxDCPuIivwLqGrWY3fsz7b31f2mKYWjN0srsubzzfId2HUHf7XbjpMAts8/Od/vdXFO5OHH0Aa1TwRs8kh0bGSopDjIDBov1Rm+/3eGCDZWss2BKbkckxrYqAyS4zQK2REIUCqD+f3OW53bOuCHmZPri4qFDR154+dUz5y6gWPD6fGhIj50+PzTQv3H9uo899qHa6ipUNIJWaHOBSdBaZkOSzBmPbEAQF4y53d08F21S9oXnX9rzwIO+QGFSElZlTcSqi35dphvJXjS3MODC3skKFARLKOHS1FzrsE27aqDq5QVZKt4dOIhEIogCJKzncWb/Zl9v54yGhMowxeLh8+3vP3Xm/JXP/+wvEPlF0DLTnVLzx6nWApnhhsbGaDTyo5++QOKnVa1LIFEwTLJ2tx4QLWBd4xeY0lt/nP4mHaszMDDYz5SaLPY8bsKQx1zmyOuHE8kEmVwnxidGR4ZxrocajoxM9PcNLVq81EBpgXRiKhSh6Jjfp6qN5+3gPl1kmNFY/Mtf+Ydjx0+2rly1ZPV6tDEApHDiAIz4mEf3Hjj8+tHj//ZLX6yurrwNIAQC1aOvrw9VBoPiyt0M7eLFyxQC2/HQR0ghCMZHnZFPJzC3RBIp1Eoenw8g0OYWz4X+vsFlK1Z1tl3Zu3//px77EP5K0o/4vPyrPdgRxM9fvnx53bp1b/qSzDjeROxtYaqIWDFZkZdIxRuaGEwn05gZwNKhSHxkNNy6HKaQqCEdeQviWagBG4EYAxCxBe5MdOELcJzzjkG2l7pMp06d+t73vgdme+ihhzZu3IhS5bvf/e7Zs2dLS0t/5md+pqysbP/+/WxwrpBw+7Of/SzGv0OHDn384x9H8QXAcP6DH/wAPfAeAkp37py3r7dwEfhheDwIhM7u07lEaN42KVI/K+jfJTDP2456EQxosJrMXrc9n40poJ1spm9g0JMw93S0QSdPnb2wYcvmlGKJROLx8AhbzhvwwdGjMmAxmxsb1b2kyYOcirZnnkNdA+PkZOiP/+wvn/nJC/5gyWOf+MyDH6Dc6dbV67ds2fEAX+1u30uvHHh134F4nLAmYe/naUiYKyEQ6p+uoqoab0j4fcjYvDdzcXRkjJDamrr6dJq6rzq7xRSfHD1+aO8rLzx34exp7LAA5U0cimiAeslo7hsYEsARDQMHYxESqH4ogAjsJBohvqq/3o8P5g2xV3gCw+HXT95o69q1+2EKyZHZSSRtovToClQnBhbZQqS0IHlmXUMjtX/buzpV9zyGeju2UdVfmPYJELvTUFF/0cnwyKjPRy48QjQxnViIhkPy2LNrd1VF5fLW1trqGvDd1StXqLm2vHU5FF1i6Ej+iocfvIywAHfq4m3OEeMnI9Bv/4//NRGKPfLhx4rLqjKKKZrOx7JKlHyheSN/OpMdcKppXPzagSNTkdjcHllJqDgcH2IcdFHjW2V57+LA079x8dI8qUYx8wpBjh9+7aX+jqt2s4HKAUP9PWhRkSevXb0cCU2uXLncYLTUNTThS5ZIkebvHZyTuxj7O34Lcwi2qq+vByUNDAzcTX/k0ICS4oNIFuQQdZn0KB5zo4N94Un9idNHLrQd/d4Pv1Jf11peuiKSiF3rPnpg37OhsRHJsoRzcTqJjy/BBCKovwHa79A78MPB6v/n//yfYeCgWGpSSN3Jkye5+OlPf5rB//Zv/zb7BcnmL//yL5ctW8b5F7/4RZ6CDfqf//N/QjAoWPT1r3+din67du3667/+65/85Cd36PFef6IjiBMgChngnE++LtQIUwE8I9fiKrnQPfd2HVYwrXNbzblUNJWe0ivJsamxaDy7Zs2WqZH+wf7uhz/8WGldS0JvlVKk2dTEwI1De1+wKCRtzBHUVhIMIBaotBmVjxyz6FjWSdtpfPJiqFD/+m+/AqlvaV2OcwHKoWgCgRuxWpdIkUVU53b7bS438Vn/9Pj3w5E4bc1s01uWXPuifXp9gWQqPRmJinp2gePK1WuY+EClzB3QE52aeO2Vl7wu58rlyyIhfN7C4owjaBZdEwgW25OJyKqRkXGxgmnbWGWz2dAsEppQLqMh0fxn2APaOy7Q+b1cRntsQNSIHz12sryyGsyMVhmUD/JBPYpCOxYLi1VGtPRY64WnQhVRWFh84NBhFaHNzNacPjEkuFyIcqIAmXP5llOWBhqBvUXEXhX8QOtMxPjEhNfrgTylicxAo5dNnzl7FiG0rrYOTAqYMg51StmTMsRbGr3fXxj/n/zZXylm26LFS1AVE6nCBEj1NBHgpMCveDnqTemM3un0WuyuCxevq/q16XEwX9rZ9evXCThXX1l97TcbJ5vt4uUrhYVF3E0jqBC6Om8Qdr9p3ZrSYMH6tavLSktgIwYH+ixmY31NDXF2jAimBLMwxedmtoMQ8ff2EAiZB0BuHdSce4DquwRsbmNfLF26lLm9m0dkKsSYZXLa7D0d7eh7KBM30Ne/es0uu9t0pfO1JSur1m/Yqcs7k5l4LNlbVGA/8Oor8WiUDAfpdFyXTzqsZi2X8K2jf/NvQBHbFgc/LEO/9Vu/xZpCDyhFh6EL4aCzs5MbQL4IAR/84Ac/+clPFhcXf+pTn/qFX/gFLmJSeuqpp7DH8uyaNWugHF/72tc0JPDmHc93BysyO12MJJlOtXVe3//6a68efHXvoVdPnD0xODqI3WvOo3PWT58Lh0NQJqfTNeeGt3XKkrCT7Vb99WvnrFalv6/X4Qr4CypWLF2xYf0mjPN5xZbOGipIsFxabMpnKaGK/wM16Gxmnd+Djo4dKNhfA3Wp9y2WXhV1sovwM1ZHp8fRwmBz1peVwT+CVIxUSpS0LqSEUPEtwiIsblZxuArwx//y1779b3/xc256QluDq7HoZWgfNC7Ekg60ztCJV1WUDvb1+Ckqu8AxPDFBQXvcjkH2JmP+9JXzZZVVS5evJ5w0ECwDWaSSqHeR9kUuw0hAxArxv1g+AVbwMq/H+8kK6HXoWFApUD5FUoCpqJcNsEC393xZFFOK/vkXXiH4lyQeqgoKCTGfjEweO/o6ybYYlr8guGrVGlw2BV4loI/4zWDbtfPnL15Z1rIIF1MRsecofzBgIMDK2O+EpuHyRdQhBI+XYgKgR4Q7ORxustHhBY83LH4vbW0ddXVNHqc7GQ5ZbLgGkXAcyx6iA+Y+0QsistzzO8/3gDqv/CDoVN0qrIJy7Ni53sGJLdt3oNXDW1+mHd82NbQbMUqDIBUscDgnnDHf3jFod11ZvrSZ+0R2Egxuau/oBC7Z29psAEnz9X/LtWQy1T8y0rJqbSodJ6Adazxfy+sa4zpjSgIbsZbrcdqLhSOLW1qI5iELBTl4JG1+TjcVjuG+oO5deScA+Zam360vIhHKxpHZVJWOSjgaOnX6dE9XRzQWxu/L5/cVFRQtXdxaXFjKbRLrJ24Y4jDIvpBdJ7YxFUm8YczMJActo06BoeZgevnKxtd01m94gl2FGhZXWeWB7TuufvkJc2osrTP0DUY3bltR31TXmF7DZFE6D4xvM7hXNu4x5AauXO6D6YRPDqeGmxcXYarUZSTs7u5nlCHB5TC2v/qrv3ruuef+1//6X+yL3/zN3/zGN74B9t+8eTNWbnC69jrayAEPwIwHua4yPahDR0ZHR1944QWa4s5HH32Uzze+4F1eSYEEs2miF0dGxl545ZWeka6sORYo9zi9fiY+OZK8OHgmFc7Y8o4Htz9c6CsgRBv2gjWERQMtjE0MlpQUkc5dqPtbH8XMYMlEbIiRNnDdqtaTJ7+XnBi2Wy3Ny9Yk8l7ZaaSHBIXgqcW8K5n+3s6hidCy7buTjN8QXVxX6CTeQ6Q8iWXSRjOLFplA0dxwMJtHjpw6c+7i2nXrSTPJDge8BOULuPGVknWkg+cEZMTWVlxub1//wNFjpx7as1WCIgQPM9yZ15URTY+eZQD+cFnLNjbS5sw73fIvFWMIEFIlejpB4dW3Zf2WJGHsMBeqjUgIC7yepEjDesE2pkOJGYLw0A+9Sd+8okFBsejz4SCLO8qdEestA7jLL7w8tq9nnvkxqW94YwbBsIh6O3Rgf0lpSWNTM9aMwaERmQ3RXDEiBoyeVAn4g5cuXV62ZBEDvw0gQN9EsUIzFhqDbDn+F6Sb505IM1sYlRB4E655eKjvpRdfsliNEL8PfegjuAgdO3accEPIQHPrSggnCT8KvMgbzptLslBPd399Zp15goUgQTQU8ciR44tbljJSnAvYpcTkQqjOnzkzOjZiMVuXt65wudxCN5CPhBwR7pi93tbe0txAliy+iq9OOoWf6Op1a+9+INyJdG6TtKBwM2BGCg0RnamEohExFOfADnpmBn0IxgbkWQaWIsZCnKno0GCjCt7MAaRr22Pmwrv3LztTAlBFF89rMJOv/+lf/JnFaVrS2hyETOnz45mRjis3nnjuezVlNT/3uS8UF5QhDXKzBvjT/wgludPB9lm8ePGNGzeogqfunwVBjgWFVJO5qLG+eO2q+p7Oy8VVjRVl1cFgUyxKjhZZdFwPYDD4S6aSBw++Vt/Y7Hbac+lwQ2XhiuYg2xIqO8t43mlYc35DJsZDjOVDDkARBL+Pxv/cuXOPPfYYCP3AgQNPPvkkO0XFA8IfcILUq10B7/NS8H+vv/76Jz7xCdaXdcczhZM5PdzbKejKYjYcP3foy1/7clPL4tYNS80OArgo2y016fQ6D3g1n9Cdff3cb/72f/71X/33y5YsBarFIgJQ6ojzHy4vr7m3Lhe+W8Uc4E99eXHh5z/1wWdfPrpk1baM4qKEGaV3pV6SsAFghfSJE0cmR/o2bdxot1J9Jd1YFSj0U+0R8NYgfJq1ujkv4FYGzB9BSMeOn6itq4P1gFlFE0ReKDZPd3fP2OiYy+FYsWIFi8TdjFPDWdVVleIZSedcWwAAWRgINUgI4YgFBkHP+5oWMyqRmGj/hZrquH9weChYWEb7KpYRHRznrCgACuskvH+OZCZYa292zMt1dnYhTq5du5Z73s7yzztIweA6XSgcxSSLRVfFGtB8Qzgcgydpam4m9g1zb3V1FeQSTI2/gkYqWBlCmGGMSKrBcoGpZ9tnfpCr0GzOfZHZX2dPeEloPQ4Tfh+qMjt+R6AzGY6i37Vrd1sbWTzaHnjgYY/bG4kmmhc3O22WfQcPRMJTksQ0myYBjpFcwMztQus029M9n7AUQg5J2yf0rLFY0BnoH5jM5dmQjLy1dXlPd8/A4KDL7WFe5E1lIoWNRY6ZmJiyBTHhCL/R0dGBPY36aPdEsYhMrK+tnprAJcNHlqJUMoXb1cnjR+E8qMTU19+Pkxsawra2NsYJs0kdbDAJki3Z4jwe1z2/8f1+gFEJCQD4jflIMvLP3/+ns5dPf+TzH6xtrMkqyTTZg3ACxNlJMS6PLzl74tzv/Z/f/U9f+s2ywgpYASZZ2DJ1Pu8wLhArvfBJLB7LpAkE2sLNC3gaG0OsBWv50MNbvvH4T5Rc1a7tmyJS6ComGa7YbPlUVh8eG+84f+5kQVFVA7J+esrvzCyrKTHlgXOyUt+b7KmNBGz+x3/8x2xeUPyWLVtQZH3uc5/76le/ih0YrxsQCGADk8cNvBGf2P80WOIEkvDII49ALT7zmc+w0IODg//1v/7Xt6Oah4c4dHzv//6r//WJn3+ssro2jRIACBadIjiVoAwYIJ1iVTY9sK66ofwn+56qrC9zWv3yi8T3pImE93i8gr6nEe8dluhufkIpLh4xZmM+4LNRQD001uH0VVsUBwYu0U+zi0zG0+fOHjt1pqmuqr2zuwFvmkJ3ZWGpSbX28KwIK0LA5LiJhvgibLZeIXlDKBQurW/gFeHsEMeGB4eOHj2KraZl8aIERd0z1E+f9n7lKfhT4qSgB6jGWG5U9yqpkdZvOwA+VgsrP/7j6PhYvDdCHnlarrf1Op2F/AQLvXr1mmNHTg72vwKTtHhxMyxwd3c3ykEoChgzWES+M/Lk5EkPQAYyrTs2AgB0+cqVzZs20R0AAbi8saPbxnZvX/FjEe4jQcs2u00iI1TCwCSYrXYS6oH7UGvIlhOGBd2G8HcCE5LHXxIusmPBkhyzTlPQNhSabE5GwnVafuOQQJuyQhDJfA4lHRVpXtl7oKTECgQidOCbT187tu/CJYsbyEzX0d5+qqOdxMg+jxP9gc2MrrBY8L8oPzQAeGMnb/0Kk8BuAbMQsEopsTjSm1pc9fLFS6zCA3v2IN5R3Z00B4J8wR8zOIsHmaZ4PKrXB/AwAeNcuXx567ad4MS7SUnG49r6Mj+rVrQ+/9LeqspqZh4lYTBY+MADD46PjrW1tSMw4SwAF4KvGhQX2oDiWIo/4MRsQfeNRUoGBNjL2N6jQ2bGkE+k4k8//0NnkfWTaz+GKSeRi4BtUpKPi1ShZLhL49O6duuaxPLklfYLEL9CXxAYUIcvI2fjLLS6zBUHkMltzAZOdGDJO7wrsC1QJz5U2QKvbduWFc89f7hp6RqL15nJ4XeADyI5JJRwKLT/wCs2q34EL7XR/mKnbVFlqY+Sl4gSDF1Evjt0Ms9PLGhBQcH/+T//5/Tp06Ag9PsMe9euXagTUPVAEjSlEGZhfmVa4P3/5m/+hveCPPzDP/wD7CON/v7v/z5WZbDBokWLQDvzdHPXl3q6u7/+z9/47C99wlPuDuUiBC7pEwarAcWsVPxKszhoTYyGqdSov9LtKmi52nV5ScNqsx5HPrTTSRShwq7d8zTMPz5BAiJn5EYGey+cOfrZjz/UMxh+ce8Ju7PMXVhnszlBxcDRikWrVjQsJYv+xEivOTVBbn+EYlQ6s/Axs/+ErWR9tL9pzNDV1aniILT/4g7F7FMyrKyktKiwAN0rVboYA3hqZuMhfrOlRREfiyWoTkU38rKy6jPNqu/C/cgQtIajAqo9WoCcaAhaA0rtjRc1NRw5clZliiQ+wuPx7dixa2oiCsknUybrDakn/gjyDrohkxo11lCPUsyLzogLFZd9xXDo9SMEDCMGLoRPtb7ezifIGBoDVha9mWj4ZaLslPgYG79y7Tpbq79vIOD3B/wB+KhINOIUIQCqwF0Ag2BzHmepeCPO+QkJCf5Xm6K5EzJ3kDwLiuQKJ4Dd0iXN+17bPzU+UlAQvHDpCoSnqakZZtyE9AAArTNJREFUXTxYTNWQ6mpqa5x2Cy4TyOWxSNhuM5ORjTlhjVCazW35fpzzFgIUWDhU2icQIi+rV3Dp83q9s0ifrvkJFM8ngMIM8KDAQ1oiIHC/6G6/AYF3U9BUJa5vOjaelZZ4bUXZsG7t008/E4tGTBYnLZvMVF8R4MRLUlO4ARXolwEeBAWmE6+uvv7eXds2q/yq4CytO3bI9Nmbdn//blDnAxBWvvX4N4Yj/dsf3hpPx5g3QmcMaZNNsWVyaRaeYaOPzsYzNosVgLrYdm7dsvWUChWTHMyGapdbaPizoMXSgBmZClRDSEi8hACGeszeIxdF2wnE0if0OY6G4bEPbTlxum1iMF9c2mQ2w14QsWj2GGs+9cFfy+YiU9HxicH+rctWFRc60JvqyH0KWVMJ7D3Np8YGsSO2b9+ujUqFJT26LO2r9jmrVGA3gQ20e8iWMXsPhoTZx2cv3uUJrWl3si7PvfRiy/LWopKymE5U1PmUkknkQ+Epp8+mmMUelgGQEdfMxB4mFZsyEZ8YmRwu8lQQmT4yNEzZFViiu+x3odvUTT0tzAEClO+9fPHi5k0b3A7rojp3WUnhawfOnT7xotdX6JK9I7GQZNQs9du3tC6vq3DL/IueSlsHtv+0KMDlW6QBrXsELuHuoeGS3VL2J4xqgd8HuicyDaSn7luV6WCeVHFM4BKqodID7r8zigHTwfPySpBHjRLMee18cVERDCBKDIfbC4ODZRjBM1AYpNiIdjOPaxRejYSEsmVLg8HqijK6px1owLmLl+x2R9OiRdAS1k875nRxf05hWjGJe9zuWDxmd5rZq0wFxRrXrd9w6dJFatrgpVNZXgEncvT11+0Ox7qNGzNqtmLmB/4FCzPSgDpRQhq4iNpNA1/aYYgMe4GBiq0BjTYCDuEhy5Y2P/uTF8aGhxx2N04BoqfFkJ/TExxw9epl8miPDg8hr5DYyGkzbdq0GnudNi0LNH4fLtvsDgamvYLARz4P5b506QJuXsgGQ2PDRUUl6ttpPwqrAXjytiTH5hOUfe3qtbXrqRY3vQnfdEy0xs0a8goW+ElkdeP61ZZlq5BlITZk3obE1NSA9AFhSI4Cq4iyWFWmsWyZtauWr1rZCn1VtwVDuO8E8k3fYOYGnI4t5iMnDx47eeQzv/hJPL7whMDka9IZUpEsWZFIjEo+AKGtErkOcQcJC0U/fvLI1tU7CVtXN7lM3ELQM9OT/Mts4Gp55MgR6IEmhs79lXPaIRRAJlH0dg4W0W6xLGtGB1xy/PS5SHzY5TAmkhFFsTIGmwWekiKY9vJla6uKfWgH8ORQG7yJbm5r/56+LrwjbjZzh3vu8NPN528904CKz8nQZCQbWbVhOUnHMcxGhsNP/+AHFUUlkYlxiid+5BMfcfkcbGFBAUJJkbVxG8x2DXYWe8vheXp6ektKKMGL3wtTseDGvrXzeb7xCiyZaJLz+aGR/mvXrjQtWuzxUK4cS1IGjP/QntU7NjYODY0SLYBF2+GwlxQXuojdI3Q4RyJYgEKFDUHscswCyS1kgJ0A2Ydd4gbURrwWG4L1LyzwIxNRGYAnpyKJgkLR2IDH+YQI8+7oK0EvfBVeZIHXlClS8TINwh5ix4c74/y2+9mlD+ze8cTTLzVABlRQZ1BiaTTCd8s25hHkCThc7Vm7zbpy+RKsyllKvJmM3T09qJURHoXhVOuraNhBfev7+UHvBG/t2rWzb2TShYZHJULoZIpZcJYcdMMcZnOhyUmUM4WFBYhWyNcmxZRMJCorl6paEcH1AjaqTgONEAKvNksLjZn7sTuJBYelyusuXjwDvdyxbXNv35DZ4cUQS6eEsLHUgUBw0SLdwGAvIpQEfFeWrVi+FBuXCggikqCVum3m3/7sqOur8BZgK9kV6gHxhhOnktfevXtRX3g9PsgAQSFUmcc+WV5WydQxHjgPwruAncH+gYqqCkIicM2Y1ZjdeWwCJ9MoXn/44P6HHtgzFUle6+wjWuLGjXYC1xcvaqYX3pcgI00pxPAAYKaeZLfr1q7A2QW1FLtU6+hu6c+dh/UWfhVraua73/tey9IWh8MWTYeJuDvwyoFkJBkLp4fGh3c+uK2uuZ4dzkYTl2lQtD5jQQt87KLPWrB65UZhR7iq7vDZTT7vQJgN5g3dem1t7YkTJ3bs2MEUvQHwACUqEEkq6lQiPdI3sXrdSrShNnN69+Yl4Lt4Jp9Ii1sXwUAY6ww6l1EHciDMOKEiEKl/og4DDQGjvfOI5h3me3wROIFj6xvoNXtheBNgIofJfqWzTZfKbtmx0e9zfueb3zl54syO3VuVPNHpws0YRMKEqREhOByPmqxum8VWytaGpQZ9vQ0zNXMB2kH10NHZcenK2c0bt7idXlG+CEUnoAphOuFyGtzOEjh40UTjZAAsZHGUYmRm8dWeZnEEztXFYJPKufyvHlzkEfkJjaGEv4oxgyWkmkQWTRz50V57bd/eV1/t6emEpEUj4UOHDiJZM03sQIZmt9sAXJUWqO3PLrn2Te2DO2VaVf0dbstc4wqH+qP2wWZMrVy1fNPGdR0dbXhDAj2CTxTiFUiWaers6iRZLlMBVbWYTNQhIWU1sezML2OFv4Y8bNy4QVMX0hewzuec9u/bqbSc1W3btpmM0HiSyc5jBiVzCzmExC6s7aiCwgDeL06Xi+HhjwKyIr9QXV2tLAk7lqeADpWZhQuGS+Wcdm6dk+lZ0i4ClPL2ev25c2f583o8j3zg4V/+5V/cunlDUWGA9QpNTUxNjmEmJaNy69KWXTu3UWOSwmHQS54VSq2Saq2j+zAdc9ZXBa08tlZ/wIerD7MhjKTqKLV23brNm7bgPrtq9Wqo9cBA/9gYblSQRrCM1Kol/x6iFe+YSFGbuiojNfZkIu5mhDzFAUfS29MDV0slaspDtjQ3tV29mohHlxGELHAngIreANH25MkTFRVlLrezobFu5cplVH6WQUpfwrdqf3fT7ztxz/jY+I3OtpblixLpGLtJyZuuXbzmdXseenTP6g0rf/zTn0DG2Jl55GQDbiGkwcNNIkv5m46uDlnZ6fHfsioLjROcwpzAVPE5NDjIV23LzL0fVKKyLPq2tk5s9mjqIKCSyDFt1mWMNr3RTZUDk8FOKmoJnMGhO5HPJHBeEyaMPcKYBMvc1TrO7Xfec5qf9/o7dJEtDBMDQIyMDjkCRp0xifeWOZcf6OzZsHpFQcCpN2Xq6xsmxsaFlSFVA1U48ygGBKniKc0fLiS42ODPT858QUSasvzehgvJR8/Bq8tcsoOvt127fOX81u1brQ5bEtkQDww8xcj5gfoJPiYHF4i10pzJElAFQ2/XKx6d3iVxpiKmaH/aCG4ixmmpTV0sbdPlCwr9VouiI4M14Tdi2gSjGSlViMM+Q5HQrhy1hCcMSs7rdgE6aQDSoEM/hbsOg4W1BU2r0Dj9ofUJ3mGjauco7tFIojDRhAl5RkV/PIAHITj9kQe3kHvj0NGTgUIqDvqlAip5kfT68qoqgj8jkcmykgJSKZWXlTTV13A/88swu7t6wHM+rEO5LLoj+prTstbzffsUrgtvk6CfGuqXr7cXl1ayGFzCeU7bguLKbVSiiXgoFnX5fBI/Zsx2drZ97NFHqAjKJOLFzmgEMIi5RYjLiAsjV2ZnSRsrrwDfCkTOvEs+m8ofP34MU3lBoGhJy1KmDuS+qKGivraUDHJoe2PRGCYSMvOLQrys5OY7w66hNrs52zd/ectn0+sm/ARbVACL0mmlZYU3TlwoKasAPGVlJYIu7/H6OcVFCqRcVBKEw3JiuKY8k7Dh2aqyIBZsFJLIc2wt4EoeXPhgUzEnGtrSzjEXXbh4CcIMiU0l4hPD/ZvXrjh06HDv9SuFwWKd2a6Z6Ztbmgjum5gcLa/0tbY22Mh+oB7QEQ1eZzDpwn2/k79Ewom6phprIJvMxYw5b2/nhNHqXLV1BXaBpsUNp45cyo4rDpslmk/AhJMKNZc1J/N6X1Fgonsym0tgsBW+W3iLNx8lMwyw8Ylt4NTps7t3FSIiss3N1AfnUBkUXGAkW4xBNxEeXrF8OVyZUcqg0omqWGC7zu2H1QaGaVYuCsch/8gHd93FgOTO6QNo54xNwSqDZNkagmrhOKe91adV03zlNq7P/Um7CFRwkce1Gxizdj7dwZv9ozYp3XEjjrBmuwnhEaeDeCo5NjGwacdaeJxsOn/jRkfjokp8j415nD4suGsRKU8iFfFmJw5WdjpP4csn4jczeqdueeO5v8tXsknHuUhFKNALjs09vV0Xr5zdDg2weuDwyZoI+hBNEzfnQc3qTKvNTIM159wypybSnE5kZrTj5s0zV3RI9M3NTacvtBUGS7R5FFcXxmAx0SBmIoyw3kCBydJjtdtUeMiHQlPGCnQ4sN4ye7e+zWzDN0/A0UijWPzf4KWA0gPdNgxIbvfOrRVVlfsOHrnedhH3G5/PQ1VF/COpVoA7ndNubW1pXLdujTC4DELsw2mqyJJ4REMKWmfaTzc7vn9n8PXqayq7d23uHRgcHx/2eANcQTYUCsHBmiv6WAwrdwHZnhARKOq7auWypsZ6hAUBLm3N1YWHIgKjGhmQX249NPDlvQBK+Of9+/djSCgpKcXbCpcttgo/sZ+RNiorSvm7+fR83NM7NieyzdirtA/8fOWb38al1WZTY9zkVfFvkf0KVwI0kWuATU0EHAQbZobVbKyrYyXx3iHu8TZCePN15pxpc0JfHJxjlz527BhRppiF4Jf37t3X1LSosbFh+bKlZ8+eGxoei8SmmGS7wxYI+OqqF1VBH0ul0sBsk7Sjnd+8NPvbu3hCCn+X15kTKR4uwjTQMxjwBVCYwRKODPbjj2J3eSVBnpE9T5Y3c0qHexVsEOJygrgQA1hYhb43HbKG5riNJUM929HRNTA4UFZaql6nvVnwRPo0UrsJxabdQQgOLL6G5BfoYY5H5JyZnHO6wHO3XdaGBySgPf7Sl76Emvff/bt/xwqyTDgK/tqv/dru3bt/5Vd+Rdvs2tbgRXBIQxNAU2j/fv3Xf/0LX/jCAw88wK9cmV3f2zq681ftKYvFasiZ1MhLM7WrRkanjh8729K6GO9nIBoPRnblwNBwX184GCwtKfdRBBpDgElvZG+G0ehJGD+R81Lm5U7I8bZJkq9446gqFlyI9frwRKz7xtCOTQ/aTDa2EqZ77lBJizCTM4cg95vfZq7O/jvvT7NkQG7jnZl9sMrq1asGhsdT6QT6b3YKfJdGz5lOvcGaMyijIwOkKjNZTOlUAqqAJLCoqQkawGsvhMtmx6Gd4O2DEl/Ths9dIWgo4hX0DVLQUF9dVll69dqNjra2qVA0FQ9VVZRDUyvLS6srK9jS1FcCKCEA9Hv8+HHcwtAJaAjitu7u91eJ7kFYRlCz2a0/97Of+O4TT4dDY3a7U1Fs6prIPiKpn9vhbVnkYe+UlAQjUWNzUyP6CYQ3EZTVgwnn9eFkQehz52F2wNqi8FJMLMwy/nNYEQiAwkKuLhZ+QbJw3AC4a5tn7te7Qamzfb3NE+1daKQw4P/iz37m+RdfbmhqljzPgvo5hBvS68xkNQlFJlAi2kxm2HaX1bp+1Qq320mUEK9w5wSrsyOkL2lRxd1gCnzJyZ2JsaG7u5MaR62traA2TEo4jD708G6M83hbAifMBopss3g64O2h6qxQfc42+j44sTrMyUwK/YoqveuH+vvGxyd6OwZMiuXFZ/euWLPC5DElc4nhwTECpSvLq/PWMOIdK0/cHLpZpoSXmEPd7vRK2nppDO/y5a04HJeWlohEOwdZgUJhZs+fR41WrrY1Dbd3avc+/cbicqBhf+211y5evEgcAEgDOCeW+Nlnn0WjwDkHVB+DPy5DeCKwutpTjz/+OOjlpZde2rlzJ1e4bRY473J0PMWdGgkpKynva283Uisyb+rvG66qqi8Mlr34wj727MOPPKhSx1RHR4/d6tu/b+/GzatrasswrKIjctlcFy5dxL9GOCCkUVXPfpcD0G6jngz4BNEY62xne19DU63bgT9ohitog7hHVbgxVPaWRmLQKMjI7+m4hQxMk02djuSUD+7Z8d3vPwmHq+b2ZBuz7YjO5wBKDETeO8QzV2gEZVY++PDuoqJCbZvd5XSjpSXKF/ftWYW4Nm7BoWh45D9BalajsmpJ8+JFta+9vG/1qlWkzcEgJnMKPynqYxhrwY/IAXjmoOWEX+ApVv2eZuEt3AznwTyIIxU1YC3mz336Y23tnRfOX0pTTViEGRGgRFVHiIfJXN9Qs3RZU09vz2Bff01FqUon1MXiLVUyoBnM7zAMgBItEDOGILV+/XrwHZRPGp/Jk0GP2gbQIJ5m+VXb4Xdo9v7+NDvznOzYuqmsuJDK8mgpgGIxEIjjoAhvmawUu3fW1xN0bVb0y1qaqiuKwWRnzpzBceUu106bN8aPHEBsGlIgEMU0Xr58lQAXgArGkLniNtga0CMhpKg6ZYrIcUQyIxkJ/6tuovd3Ft5eayRuHoQcUh2EHBfJBKliYTaPHj4ZGg+vXr1i+cblcd1E33Bf17VuQ8LWe7170wda8ukMFu6AFyQo6TwFpmQMb4ILmIlZUOHc7XZNTIxRo5SgirnPAkKQZyIx1etv793u8WlZKxURE/CBehPs/3u/93sY/0DuxAazssA8cQ//7b/9N/YCalVyjpJQiIvIyt/5znf+9//+3//4j/8I20RIARc57hK05g6TMfBgWWmZ4Tp1sjNkO+7ovOELeNatX9WyrJkdbiJjlhQGzm9cv8lktEGQUvFUPoXKPB/wYKtThoaHm5vqgH96195obvvzns/CtiA6A8Yz/ZWrV3q7uzdu2kKOgJwuRkOogNA78Ttn6jyx5hoZmLfJN7l4CxlgybWBMg4MfdhpydeoN1ldXtQaRkRRaI9wwZkMXAOxqNjxigq8wYKai5cu+P1wdcLPQgw0uezOPbNFcVMjET87f+6dwCaLz7uxytgPwffhWPjw64eb6puKCv0siWhj8I9gDxuwiYjw3E/F8YEBpD/auZuu53b3ls+ZEHX2VeldlG/GpU31i+prJiZD8OyQN16AweD4DJ9C/gaSJBDQ2xOPi5/f7A6FoguCzGEfJlhm7mCQcAFuDXTgcaBzGNW5iLmeYEhOaJ85nPvI7PldQtvs/ffrZJbqCJTmUo2N9ZCrC1euX23rwqfA5vSIfRtTFflWlZzFaS0K+NkhosXS69jevCwus3e5V5k0hg2wEdjIPmf22PyQSWgADIE2OWpTKkqUHSJslUZ70Z4JAzO7Cvfr/e9DOwRCuqrKq4Z6xtBZhcKTyVRszbrVeKOj77A49FH9FNq1stJgY0XDWNf4wf378tlF5PqKx9M1pWXIEDIEeS8mR9U73nFIwMksqDBj7ESQJksG3GrTi5GPGzDjtbZCnt/WfAGxNMWK0NFsp3cYHffMvRMU/6d/+qdsE2gAoQO4scDs0xq04WMf+xh55RAX0AKRNYC3ePHFF9l0yAEvv/wyearRnfI6DGCh/bLQMBgDB7/aKWKayo4PDpVWlukNydrG2lhqXFJ5wQWL/4/eanECil0dV3HGq6uugwyYqYbiK4I4EWjlUgunk5xVVeAs1NvN69rGBwHCSlKDq+16W09v39Zt22E3Vb5RDMWC/FVbNLM0o1mQFrQB32zr7s5u4ZrnNsG8rVqx7LHHPgS2HxnoHh/uzSanDOS2TidSsVAqOpVNRCvLi7ZuXb92zYq6ump0MoyeuaZfGdmbHdzDWhIzxVNvvFcYNSE4zJzh9IlTxQVF9fV1mKoEvUgXYqES2of3fTZ3+vQp8lu8sZF39Apdq4odlakU5wDBSqQqKwx4FjXUrFvdunZ16/JlzbVVZT63nbSwLGrA645FQtRJn7Of5BTgZuYhonMHDOCCVTXwhU2GDPCVWErc+7Tb7hJdzm3z3TxnfkD3MBPr16zatnmd2ZALjQ2M9nf191xLxScCPvuGdSt27NhQUVEKvQNk2tpuaKLA3QAPL8KMMSHogjBvolrUZAK0gtCAmfnhX0CFP5YGYNHWi0+VAPAvx5yVUL+/9x9GxbBj064j+05aDc50MkmlBoNNSeRSil2fyEcVJQ33adM7etr79h3cu2Xnej0FmTImu9ldVVEvtI0/eV/VHH4vb8PmwmpitZrb2q6rqA/uVZ4nKJiUdsFgQNze1ZZn5vNeWmf21eY06nJvT6qGYo37oXgAlRLIKQRCBwBgtkA4SAaogiEAH/jAByAGNP7DH/7wox/9KDc8+OCDe/fuxU4wy6Dca9ez969cvGbf8/vx93jooQerasqx3OYNyWw+ms7GcWtGzid8p7+3d+umjSZSYuosNoPD7/RPjk8SOKm+u6BEUdsseMiqaXOrKuXAcxIo2Nc72tkxtH3rboNELCPdovPB+GFGFyTJpnFIwcAliy5Lf5dk5o1DuEUamPszacvIwF5RXvpvf/ELA4NDV69chykYHugxGszoW+vqq5ctbQkEvBgICFvGA5K8b6jwNm/arHp/39X2wkqM5I4aB253tmtB7tNfsEqberr73E5f67LlkCXWVfREACn2Fu4RjZly4cIptMCYmmlqbjuzDb4zJzLjjONm4ypl5iviCpSBn2Q7ilVUXkdoRj5DcmOLyUxkuc0uViwhl2Jwk4hf+K/bIBXqyOtwQABgfAB07J/IFjzFnUA/sMXz6o5VG3t/fajzg31L8K+uuqL8S7/ws2T4AYQmJsapIA3NE6MZ4l42TbTg4FA/zkTodpkK3ovjTd9GowFIAJhJ2Of79+8nXhQwYE7mTAu9ywDUqZ6VnGY2owpnb97Tmw7lvt6Ae8TGlZt/8twLJ14/uWZ1c0lZIJyN6uy2RC4DM2lIK+SwvHau90ZHz+4HHnF6CQbUZ1OGloZWi8mhvStApRK/m3vqrgYIUkmnYafwCyefmFmynQNpugsXzjU01ELTMfxJAgM5mFLmbtoOcVeNqzexf+/+5rl3shfQ8n3kIx/5+7//e3jHHTt2EEDKDSADQEXTi9I4hwYVICL4Sz7BCeStOXDgAKSC2+a2eY/n+obq5R/94Oef/METH/rMB0S7ye7T5SQTIpk9RCQ9MTQwkM/Fujra66uXToxOLd+wiWx7oamwx+uWoar0+S47FagF35stMDeDXdd3b11lNpBVnmJNgkum/8j8KrufrywKf7joaj+9FYheEFZgJxAJxVdMp6ssK64sK92xbSPOZMCYzYEvKgMliRAp/YR7w+MPSQ0nxeMnj9XU1FZVVs15cwY3zwFfAI6DVJLnC4PebXeo2F5/o60DWzymP3UOoQGStQNRSGN1EBeGx7Fjp/Fj05Cm9tRtTb1jX5mYmRln3WYOsThKKC8wyc8i2PKjaLpkkRQIVjIR0ekEMrgsHh463eRUCChXl159TAMCixnj2NUrV/v7+kpLSkF2QjUl4onGROkPs6BqtkFqwgzcHIycvw8OPa5BDEylgOIYhA1TjMNE3GhJQCXYgpcxKIlM7tzFy82LgB8jbisyI7ILeFJ9rQVeBa4QSwBJgch4SGKRrVu3wZ1o1JFWOYDPmRZAi0KWZlvS5kt6n730vjkhQpJY9H/3S7924OgrpLCVIGGjPg7fBw7OG3A+uXGx57kf76+qqT9z9sLKDYsseWtoIh4PpJN2UslbQM5StFVFOwJ4028oO4a1UCcBTQZX2cDy/hrJZK4kGRg2TRIjNpadP3sQnoMicb19g+HQQEXpknwmLHhPTNC0AUEFpUoaOwpJqJ3Q4JvPJcvBAuHgACTPLI2s9Oz5vIvADTyFdgUG/8///M9JDkh8K5gXGRrJD37i7/7u78g/ipGAcOgPf/jD6I4QAjAS8Ai/4knx5S9/GUejN+aVU7fmgmB2y8BAcYn8ts0PWVyu0cigzUwOD1z7NJgSMH70w48SfaLPRYXdS2TqK+s9Di9uphPjY5VV5erk84uokFAlqa+pzr/2wjOroH5j6WCOIizc8VNnz546sWFlbWjkiNlK3hq2hsy2IBTy8pocyQTigmKxOt2+Ar2CLgHmkqWZLepFR3Sp9ssTd1yf+ckAC0NRUQ101MFJz/Dm/IFw8Y/B/REuNm8QRx2D3g6nQlX0uob6QJEfe104Gl/UtChN7nt8O6WS5TxDoAsmmvRwEG2qQ/CVg14gDxycI/FdvnJpy5bN8M6EztI32E8GQ5orNYCZ/GTHjxxpXbYMVhpA0R6XG96V4xZRYPb9JN4tji09L6vCgqLFyupNlngq3nX94vVLZzqunSXAsnXFmqr61vK6xSabC6lyMhojXTg5zjBiQRiYdmw0I+Pjp46fyMSTddU1S1csl02bY5ciC3PGLZKOGQ2ZxCqIBoDVnn+e35XJmKcTxoUBXV158iBPh6dGUZX6Xex7CRUw4I4dxRV4aHTM4SsoL5eEyWqickleAzYTy7LQvGlXHqAF2OMT7gEfUBilzZu3jo6M9Pb0b9y0Di0B2wPqyFA03MfjMlFyyEUoNl9mF4ort32VG98Hh8HCaHM1JZVlH/78masnx2PDOVOC3C5pzIHUcMlni+oLvvDvP6bDVYQyhFl7c21zIpAZbB8euTFJpS+P31lSUeQAz5rZSgIZ2WySWQHRa7FxqhseoipR/4RsSMBNVjeVTEzm4sSEjidSZK5PvfD6415HW6HXN9DePdBxfbTbAUDr83G1UFDe5CjSKQUOVy1u8VR3A/UoOvyhUY0wu6AFZnp6mtnIrBeTyglLA15Ga4/xBjKgbVj2rOx2ChyrpZb4ysH92lPaJ3IwcgB8Eid/+Id/iNKfG7AGYTTm5l/+5V/+kz/5k//yX/4LGJ9SZdixoQ3/4T/8h9nUQyiLfvd3fxcfUx7UGtcWeRZj0It2AGlc1EaLMEGPmpyhARUUGJy6ec3mRD7WNdzVO9wVS0QJxoIhFrqbSzrNdp+92G1zlwYrDQppcsixFkukw26PQyCNkuq0LWdJdX4w5oP+xR9S4rRpOxeKR0PR8Gg6PZbToSqPHz340rq1m8rLJYZLsI1iN9ER68BS5kHCUTKAJyPx2ERHZgrZwWZ11ticVdyWy1OGnuVN0iVF/vQS0aaiq+ll0Sbglk/BxbdceJMvcHUkfDbh0gZjzqrjpiiMLjbrLKIDpQYzqWxq/2uHS0oqmptbuEGsTDNgMds2nbL2zDsnkA0EPcR5HqchVgKYQFP0/PPPY9uZ9Shl888+zj2sDRHwNKKlkubr3DWevfPdPmEyWSA4dcn3aNQlWN3klcuvf/fxr0xdPd1cEygrsuNGGk1kJ2OmnDmwaueDK3Y+1tE95PMFPS6PbFxm02DsHRw6dfIU+K+qrHTZsiVGMzgAZZOsFNka1TBFdUkFqmbwmeC699OhgZWEkWq4QF7s0OEjNfVVATwOJKSa3R5N58Jnzp8gu3iR34OdzWzzE3NNohzZezwBCiehjgo/gArrDsxgDyBE7oEHHkQXdOrUaWoMWm2aku399PpvcSzMGmiCtYRpVOLZ+MhE36UbZ3KUigARq6wd3J3d5LAYHQ6Lu6K02mZzG/RGkg5l4qlELNp243rfYLfFZi4sKSwuLvc6gzBJgjfw0zICX5QdUjeKJZTLDE5NDo6NDY1MDLrdVITK221unc7qdATGxsLjY2OVFeXxRLq4tEFHLk19OJsgvykqzWwyTYL1cbhQjzsY8Nfqdd5chmhVrP84FWLl4nxaShYEr7J0rCYu0X/xF38Bb/75z3+epWQdOZgkbsC7hqLBIAFEEG3auIET7ZPhciK4RRWpZ7c5FzU8wJ2gC02tqmmG+YlmwRhaC1ovaofTA+MKDeJpCqJXc+fIMGiNizzFaPfu3cvn9u3baYEDDCZEALwt9mBKOOEOmJ6Ij4ep7B2dyqTSFcVVLpvTbfOYFQsuLBrGu3r5+mQktGb9KuHa8qAEjCvokIRdExYdCpqP6nVRZIaOjguhaC+EStGbnA5XOp2z23x2W7HFHszA44uYbBLeXhVwBbUIfU+AB5KxuMUQjUXbw9HBSCTp9dcEgot1erdkPRXMzEjwWQJ7qA8tTAZu4lam5m4O6lhB9c0mu5LV4xwZipLp8/L4xIDV6CyvrPf6y6w2766tu/YfOnTdeJn8bgt1ra0un/h4ACL4J2hgwTKwGKh6UYOg9GcNtOtzx8YK4cRGtvQtW7ZwA181aHvjnXOfelfOgRRxq83qIkbSgaWSx378/ejQ6x9Znqnf3OqilEImDjmnLDCh3qPhzPD1Zw73nvI07SivrCGpmD4VMym5zt6BU5dwCsovXdJCrXYgD9wPQ61lCEEVJpQAUBBrhCrxaQiXz4Xm+l1589s70aQW4X8AXxG94fEjYTYerptCAvo6rp8/sy+dGrzSdnRpa024j6mzhMLmguCiZat3WMx+qVPAhqEOrsohap94YVFYdM+e3QDAkSNHt23bOh3yenv3/3K/a6sonyadqSxQ7rTYJibHRkcmeXfMTV6Pt9RbFvQXQQCABFRqxD9kdCmDNeO0GVsLEMNrqNY6MTbVeaM7Guow6a3VVaUFQZ/VRlQTEUykCab+WncsPkJUE0me6qqWks7J6kL+cukyfrwcvAEleuLc0aPXtu7aYrUXJHMhqiUpVoiNwyE8LLzqSCTSQcQ34dhFxQ0Wa5C1I2MWVERbJm32Z9E0Tm6o9RHgCPtit2rkHJ0ndi/cH1D0w63j/gCPz688SyPsaLaz4OA56F5rHPwwu+W1LqABPAUN0Prltrk30I72Vft19pPq9ngYLl++HPszsgXcp9Y7XaOf0NJWY3DicYxY0rXIpdAu3tNERSNc3Spc5sGxketXr9csqZO4V4iFag2TxVNypP4tq6xmB/AwryTgLIye6t6oi+Zzk5HJAdwlEqFxv9NaULzM5oAT8t24QaRIZs3qLbqMiYIveVOCBGw3rp+bmOxLpcK04bS7vZ5gWc1Sj6fcYHcRfmm3+h2B8UR4eHJweDLX5isqM+idis5NNnr8mMSCzGZUN9Hsu992cq9kAHW0C8ZBlwufOP5a24WT5JorKWXtpzJWXcfV07l0scFYUrt45aaNK/bufy2RCq9Ytva2LvnKnGoLzIyj7CORtXaRTyYdrzVog5oHWPSG3Dn7qTWFdIljErKCqgoQRCOL9D458BkSNG3JJCf+6c9+Z2lhakUDfFKMIMJUjFqeBC7B55GGKx30mwr9jtGpkbNnn29cuQnnAnRovR03Ll/tRLJbu2YlIXKk5idbiGx0zK0yDyJuIDIRza1ODBzzDDFAC/D+ogPCvmhrAhOKSo/izPEYwWLObDry1JN/N9xzdv2qxmi4f9vqosZGP8UqxYKgM4xNXLlyZrioam1pxcpMlqCDWzg46k+hVRgaGj579uzOnTvwdtfwxftk8e/HMKYhGc0zxgDczd1mv6800FABptPmU7zSyFiDpg3EZBYemR9Em6iiGTyZ3SSxCbr1uUqwTrq/v3N46GJn9xARJplcyOuN8ZvB6HK565y2OkUpIE8+yBTVgaDdHPnjOsf6O+KjXaH2k72F41O+Al/RIndhE5kL0rk4miSqeyiKx+srcthGJsY7+vqOkW7A5W7QZcvIeYDhQNuLbFu2JxOCUzhlA/ikIDA+4mxeqALLhzQPRQd341RCxA/BAbNkYwZshBJwPru7NTwwO8mzWFswtUo2tBOua+iFT+0Kv8Jfzj7ICbSEYWh0CHUTSiT0CtADok+4E+UE+iXEFzIdfOhDHxI8wxyr3Ay+i0IQsjrSS4PdvWZ3eDScjEu5b1LAqshIliOdm4zpJ7xFS9inYlURj0JmA1GvLx4fmwqNxuNhfc7otAWCFY0Wa4D4P8VowvW9r19Zv25nKk1IEARw4vy+7186d4AqVvX1AU8hSmEDNrXweObYjees3rrlKx71+BuyOY8h57RaS/2BkbHh8+O6KX9xUz5NSWCKAunRNNP/zV00dxZmzgXJzpwv+K82p7IYUJaMrr393PEjX3fbxptqit0um0FSm5BQMJHPktDYMzgU7ejl/Zeu3fjJU6c70daQwAsXF21JtD5m15WvrBDmPuID0e6xflj24RrwWOAeZnbumDAJcANwQ9oA1gnXSeAGsW5ua3Pvfy/OkS0JJzGnE8m//YP/VKqc/fB6vyU+oSRxswAMqE/KG2HTR1RDTicnGCliTRPGmhHPysrWB/q6Bno6u5wFZQ3LVhV5pagZFRJZQoFA9Y8VoHU1QQgScRKSAD1BvQhcmg1a9PJ78dLz9AlQiZpVo0wo/VlKfISOHj2+fePmxx//A5fjxtZNFUZd+vV9xzauW4d90pAHwhM5Q5ipiWc8wxMFoXjVytUftkjJZfCbGIRYdwIL2MBsTgLdwWSqmCw8o9rX9IcGrvT4fgKMuQO8wzmvqtUixhVCsPv0wTZVpv2qBQdxnT/oBfeo/yAyagCCEl57kOqsaGqj8e5cblCnjyeiiamxxGQ4HYtHvQGvw1MQixuc9tJCfyG5o+mUhPgH9/706umnDPFri8rtfqveohhSCUQ4d++wLueur12zvW7l6nTOlMpacRcEOvW6lM44FQ1dG53oKi5pMpnqslkP4ht7mplnq6Jhx2z7ox/9CNENjQc2Xq6QLwSvEE5g9cD+EAaQLzRA9ZK4ifFn3nyef1nfu1zZ2TtnT2abA4rQLmJYRhWBVorYTGzO0ACGhOYK5QQjJCiB+3FgofJlY2OD2K5g8dB+iTQ7TZTxcHv5pedXrloVKMROyypw8KH0h8YvXrq0de1GM2RAFMETychEHkqaGKMgAXkxLQ6P2eI3GAOZnAu2HX7n6vWLI+MDS5Yto3QgfHZf7+l9rz5Rbbc01hTZHElFP0kdXtxySGthMFiJku0by/SN2woqttY0bDYZ3VIsPpfMJNrGRk+5/SVO1wZd1pE3Y2fDiE1lKo06y/jeeNwtGQDhsq+gaaeOnPr+d37vsY+WNVZjq01gEoA0COzmeREAN260UbjUcP5SJpnasOeRX+3t7zlz/vTGDRuBg9sI8uxoYPGwGoHWWQ+Vy9vJnRokzd7DCnDOEAgRgqHARQR6wD0axzF723t9AqhEDXr3T7775Mvf/h+//UuNruw1Az4FuYK8Pi5qOoUthytehgQl+lyMJOAZnTVjK70RcnWnimwlrTmjZ/1G8kbBmlGyAxpggh+EXsjUq9xMNk6SeQqYKdQs4GXZp+qmUCTZ+3v98rf2DxnQkBT2HrIC5nt6Owf6e06+9pPqqsTqdaivxy+cvqZk/SQERRJQ8uiUQ4ppVIeXvN6dzFa/+tpwPF35c7/8GxBOkkW/+uqr4Au6YMeiREa80JhHdrhGbAQpyhzItHCRQ7brv7RDVH+CS6YZIMHpcqBa1jQeXJ++ItgW4NBsReKbq7ILWVK9sCHRVw9OTvb0D15xuoweT9DlqLJZqtAfsFdjlGfSJSfDY4MDIyN9Q26rraG2bP9zT184+tQj24OLyzP2TMiYzCtpJwXrsO2mjbm+ifCZrkTxso9s/MAXIzgjmBUHtkHSZ0kSe2yhN0KxoWApkT1OXZ5sLmxfI/YbXHTwdWb0LARLwxYG1cK3gfRRtoBe4Q7R5/ATi8Wv7+Z6CUaf0SdjWkAuOXjwIDoGDE5QCLAKnkXYeIExTV75hS/+/NYt23gKRg+SKVp3De6U9JlTJ212O6n/81KjTRYLrfn5G6MkwVy3okGfHetsO5WOjZiNOZfb73Y0E4+rs2HuJT8bD5CrTmao/cLVUHh44+a1pFvD2HP1wvFvf/P3HnlocX2VN52eUhPrhUG4CICQFUNWsaZNOb05nLWeuRaqbNrTtGw7hUAZEzUmQ+HDUxMj5SWP6HUFeUsco61O58RfQ4MbdYS3f9wiKN3+48x35otTmPORkcGv/v1/+6VfXFddHsnFR4xZOFBrFp9WRQKgdBm8HKz5aNJgjC9tDrZ39F0+++yStR8wW81oANH9serqRpVdOvfE5/P39/cVFBSePHlqE/EXqiyp3TAzhOlHACMWBnUeK8cNrMnsDe+PE6bamM9GRrtf/+SDLY7cBB5ULFlWoVyCIZ7RdfVNKiY79RuSsVGvEw9KM4QhHx+qsaHudKQ8jupVu9no2fiUweESXxumHdHRnE1Ghi6cPXzpzInx4amxyXGX11VQVFTTuKR1xSaXuxi5Xo/G8g7r/B7MzjQKZq054PZT6ejZsweKC8Y3rKnI60fSEWNiyr5kyTKKceQskVwuxZ4w5+yU9QDczEpo97bSJ545dOH88SXL1h8+fBjtLUuP/nDbtm3a26jIhc0oJHL2/XiW7mAX+Jy9+C/oRDX8yMjFJsSnDB2co9r4hCRMeylKMnlBN/wOuYUAIGzKoQBliaFouG8y2oWapLqqzuEoMVkCVMYj+TBoB4RsUXJmA1mKyyqLqnJN8fHh7m/9ze/50zf+/ceWOA1hJTxMVUnSqOF9gtSa04fNunht0FBcEDx549CJl6yrP/BpdngCh32dywB911vsbv94qDebThhNTjJfsy7odUnkQGQ47J22SflE+QNbDS1H3wvkM1rWSKPltMKhvsG79EF3DAmMD6ighyAIkQOXJEJbiEEDzCBj3MBtoCP4zm9+41swK7t27RTBS7h+RiuLwz8uv39qclJdGnIFaOTBGB3oqSz3Z+PdY4OXY+FeMhA73QVme1CfC5L6n8dRq8ja5dJ2Y/bkieOpqHHLlp3kD4fwE6T293/6F5/5xPqGahLNdlD4PkOOKdqmlg1KBKZYfJNQ+FDMILp0qfty28FAcam/qDFHktGMweYomAoPpNIRi7WAaAM0VAz0zgzRHcgAz8rCqMImn0Jq/umbf7t2raW8FLEmYswTGeogTaRixCeSXRfHpqFkfAZdQU4Xxq2qvCI8Hj40MbG4uKgmFom8fvggFl0y76tt8gEvI8o1zsrKiq9fvXLk8P7161t9VCsUnwNyLIoPEuSCG8TCwT86glkuAEzo8iADfH032Qe1/zf9YEOassmxInekphBDfkqXcept9qTR3D2Zf/anh5qXNCE3vXr2TD6Z/OSHN2VQg+RiNgXRbTxgKbRUlStmE+EoSH1q3mOVscuHD+774fmTr1QW2+orTZ5m/LsL8bcMxUbjsUOnDl9d2rqnINik0wWYj+k1UwFUsIQ2a2866nfiBtkjcjAITcE7Mjw4Odr+2EerDPkRoqpHR/R+7xK9UnLy9AGTKz46NLFh1VKj3kGpc1JaAnV6w8DuPVXXui7E4zqggkW/fPnKho1ryWeOxQ6Dm+ihdfmhgeuDQ12iMNQb8LgvLCgpKKzQkYJREKUoU6YB6L2cC20m7v6TITN9QscQtafnkG+40Wqvo1I9fkD1ot4onwTU6/ShZKxnfOgKuYjcBaVef62iL9XlMRVimUJMiOUNMQDLoLdlMzYTxqQMNpvMSM+rjWV9uxcVmVLd+liKwrqY83VmUtmAI1FmJ8wIajFKICdbG+2XR/feOBuoWfGRFMSb0WAYzkfz+VAqJQWvXEYvbD16Xc0gjBMOWJ4DpM8na0S4H6+j8Xlc4Vwj2O8yzaY7DrpmAJyA69ULeTQWmKk5IAMUNEaUUcmVZIUhx+pXv/oP5K352Mc/Sgp3qf4EQhR0DnD6JiancJMSDzdaxAs0Gx7rfq2uqHmkO4TkUL9oicFcnNMVpnWOrBHvz7SSp1o4DKMRf4lrV64yhdu2bgTdpzIxzPg/fOJvG+oTDQ1hfabdRmJvkzs8pUvFlOGxkQCJXgsKM7lw3IouJEaZavKyVFdUXj1zZsPu5fAQQEU8mYnEwkV+DXKgGUAOEy0jW+hYiAxoeh5kHwhiJpmDmzUO9F4f6937+X+7QYljJnLnjKN56/j4ZOLc6RFSYScnJvbsLNMbw+IIA6Slmau0Md97+dzejetr6qrrzabEgf0vrF67saCwAH0V3j3oyNOh8Yn+i3i5Hd93jJQzXuVK1N9asGi50enG8xa7TpQKntm8KZ42OM09fb1EDO3YsYOXYXneZfZhoRm89TraGn0sQjmKsMM6akwiAxTkccnO6X96+lplRdnO5vKk3RSNjqcn7G6DO50OWZSYKWvF3TqRi/e2nV9StjirACgevPOAUn0m+uKzfzs5fPTDuxrcVlL+op5MQCKRyNEe4LuWzkbHe19Fvq9s3q6Y3QiGOJNYiS4QVtIixHuhFb513Pf/mwAh4S1GMrll8jGzztt/ra+hyGS3ouFBwaVQS33x4i39YwPf//7eVStXNNRVm/RwFWG0Pekc7BJKzazHZ4pdvZBPV3n99ZcvXt66fRvVO7LZBBxYXp/s62k/e3hvJPq6vzjqcbnNemd2KtU/qHTnG/xlqyqaq/QGWzblMpOjlymV+JU7boX7PwVvpcUZnph/BaXILAopxcGEOFLh+oWc6dM5fYzzfBbOnpciuIzyJmiB2mLRSbejuKhstWIp1oKJoCS0hd9aXmfHn0e0RzQAUhe7pT4zObLvh9/45M5SfW4MJY9Rb0FVkbdCpg2hCWQQo6HYlo+kHAYPbKpJN17nSZ/b/1RZ+U57URGCAgV49XpnPl2ky1zT51AJSFE55ACCflRZTUgwJyBZPsH7aPYQ7FAMkC4UD1ENF2tY+LbtDJ/OI+hqQNaaQQhFExe5jcbRK8DC42uEGplfQd+obmiNLrhOp/DvmmKZR2Dq8UQingCDIjdwG43QOLfNHlp3/MqduKqjIMKdlAdRP6jDgzRK5fBnnvpRT2f3z3/h3wSChbBjeHHr0imHnsSFejwcmNZMLmExjSWT7aOh69FUdVHZIqsjqDc4dXpEMXrM6DJYs5n6jPDoRmNHV+fI+MimHdvYqSKYmHyTE+OXzr32S19oyeSGjXofXNRY0vq3X3/Fbw6YrPFrvWcf++DDG5ZB9NO6rA92KadPeq3ZaxN9ofFeT0E5vEIyFYnF47JcLDW4QuI4hVzdgRG6I5JQYRC/FKaMNzh57FBFqcNuU5LhtBhDBAKsE5PpF57fv3n7ivqGoGKO5Al8EFkLjGVjTdwO++XOs8l0BGfw8qpKxei4cqmjIDi+qGlZ97VrR/d9z5vrsGRHnXbTB1Za7QZbsuvUwJX9HdcrbYUt9VXrC+taeYMsagJzbmpqgkL2WPM157DbgGZ2Od/rE8gn1Q2F80EyUuVD4UaTqezwUOKRPa1MTipjmYrkGyso1zeqoExjFlklJZFNTw51d7ci9ZNlAZChxFu077lnvlJWEF2/a5kuNYkuMZOK5y1IeTZdBsMpPiHweJHCgGkqfOWnT17d9tCn7Z4ykf9Ee8DiCSssG/G9OCSPMzCoghC6VIbQ19G+qB7LVgwpfHRs1GQx+AKuy5evL2pc8tGPPWwyjudSsXwOhQRK4gheMNjBwuFUONSvlOb6ewa2bFmPyYSqDWyh8bHeA/u/YTf01VdYigqCRNJJTDUu1nprUp+MpaY6B350+uJUw6KHFjc/IrtOn8Tz+r2YhrfTpyzd9PKJAcApa2qIwRqw9EreIcyWTG9yKnQ9mRlMJWMGvbe4aInFTlYJfsCzSMV0MxCg0hXagaKIloCm4TMOvvKSCcMuCgeMAejnDZm0tah9JPPS0cOoHSwKlaVy1Zb8ppWF1PJRcmmfNVtoHzt35DvrH/1/aEDaxEJABG0mqobr5cC2/+YLX6AkH9gWRMznbVPAFXA0CJfr4D1tI79xO6OX/6u/+itUNCgDuZ8bfv/3f/8rX/kK6mV8C6k9sG/fvieeeII0BDiOU3vgU5/61GuvvYZmnxMw0B/8wR9gQCI9yR/90R8RQox6Corymc98BrcffuWYOyq+MhI+kWNwQ8A4TBJfbkBq4RX4FPwspwrFbtFDjE4Me/xuYSzYuyaLBNZaxnW5KZJex6NjVIyITEbXrnuwsmmNLJOOrSr2D3WfI8KyjKKNYgauXLmEgZq+ZKuwqhL/aLx86YLbpXg9ki1NhzVeZ56aSiVSqU98bltBkeG5fV0H9h5bv2i1YiahEG5AKIsiOtMo5dBvtJ9dFcQ5DGeUHGUvJbyUvrQgyhkYmPvWc88XJAOqvCR3woHA4EM+h/raK8qKSO+sN6bwC5KWc17KrG3dsn7PQ4uUXDiXmTAgUcJBYMaQQDed2WpJZfqTqUGLvQrQCwarggVVZ08feuJbX33uO9/YvjK3fVMBdih9NqIoIQNBxw5vumBiQpkaGBx+9rmXV3/w55t27A5h/6ZO94EDLc2LWXVW6zZKPvd93ttzgIu4YRhwq8UhwSJ5jMDMkoGpUIj2wP3Y6h5PeXvHEhtXBnXKCHSa2s94POMGYDVYS52FUtwJodJA+J/+mR/+H5vlavOihnxyAtfwdCZpMOVSMA1wV+RO1tuzGYo9wCSOkbPObkg988SXf+5L/7+0VJ6DBxcGQHUgmdYav5szw9ZnUeFhoUJgK8AbsWVicsTpwlYybjSUXLt8rm5RBQ6yAz1jdbWVimkomx+mMi7FKHGJUYwJnT4FH3b86KW6mhbiRxuaNugs1FfBImqdnGz/7//9/9m01LbroSKbfjKbdGfTdj1yIzsgYctZk1ZbeHGDUjylf/KpL9uUsoaW5QTqMGfvGUm8H1MP8oYAUKdOkG/OQVwm/FleN97TdSaRGvX6nMFCwnrLdYoP9gvVMbfN061sWsENgCY/K9nUwZf3PthcbIK1zDCH6YwlHM0V/OSVzpL6mg0bypR49Ysv7zf4sjqkfAKZsybCWCsCzqM9R5PxT1nsRdB6cRBRQhYbvJoFdhgN+zz9znfpNlx82y0//vGPDxw4gGs/HkRf/epXiS8DF8OqE0v0W7/1W/j780luCfTMePtQXgaTAwIBggLt0DIxFmBztFKw8x//+Mfx/IGJ/B//439gWEKrDEaf2x0YWhsMjSOjCMaeOebcxn5mMzNtpHQgszRJRBHK8gkJh8clMxuZ6ujvfd3jcrrtzXry71S69Dp/Xm8F9GS6RTFDoyB3irPiTqXgLwslYDxia4dTyQreBNl2d14oCFA0HJUA+cMxVvv6OoZrKksLg9jPwjUVhecPI1CwwFqUmpgSdMaY3eMZ6L+hrmwyEppw2D2CJOlYTcHJYrMl+bbQMR+saPcibPLWYhkQOwbX4rGY22mH7aLSEeGCQuVz9sHeqZoKxM8JUBKhxfk8cr2dxBJq5XR4GJ3DSnXyJOmAcAlnIdmNlaXWoy9//VceK/n0lhJ/csKWjFmoOJ/KGXNhnKCMiclC3Vizb2L3Ss8rj//RlSNPOnTp8+euOZ0eIopZLeZyoZd5P1yHybHYzXZXQYpYbtI/oeHQGSwmpbzQdWD/gcudV5977XgiY/C6bfjbsbP0OTYPqD1DKGx97SJ2ugibir6vvSc8dn3TuqAuM4RDBrhcgQjoHLl8MJ6yx5JkN8SHDJ47SxYKJRdbt7IoGWm7fPEYCyY+ykC2oAkW7nZ27F2ZJcAePwhhBfkDT1BffnJyxGLNG8y5ocFJXc4WLAwQCD/UP1Ac9CnKFBWEYKrYH1ILIO/Jpr1XLvcWBotGRvtqautgxKhym8WxKp398z/9w9qazKMPLTGnLfq4T581K6Zo3jCGs6nBwuaEq40bkqGgNfPZjyzvbX8ukegTyjzHjPyuzMD97ARGLKckpSRZ3q3LeqEBOE+l0leHR/fpjf2lZdXBwtVmc5W4EuvjcLYZ0aottE24zp+U3IyGJieH28oKXNADPHxIEKM3hsaGu61x5cHlNWW5C5W63qJiR0GpKa/vzedJMGw1ZArsOY+kk0xFwTEwGzAxeX3E4SS6zC76cRCaGo6raWNgcd94sIv5lesghIWmCW9OAoM090Jc+LVdz/0gdNhzXE75Cd0AjRDqxQ3YdblHw++ccF2zQHAbMgFXKFPDrzijc67JIrNdczMXOVA10wUHP3GR2xgqJ8K/E9XCB9E/cokUOVa+cxc6YIuS6O8++9JPf1hcUFFSttRXUpcgBMAWIB8C/qVsQmEFVc2AnMie0OGSRHdbt24FWbNX6YguYe7QGqXTEbeTYWSy1JIzKGl9/kbXaLAYVQqigr23f8jqInwN/QEaF3cexS/YIxd1OFkKCV2LRQajk+FgYTWKYxHVJBydgNaFgGF6Dm6hirPzMvdEMIqKeWWKEQLFBwP9Ijgmn0mlRocGW1cu1aeH8xmXxehOpyBsWRMly/Qot1N4yxtwJc468WtHwuGNc+nx4y999VN7vCt8MWMYn0hQXiyFJ6yplvotWUPYaHCbc2jVRwIOy0d2lZ098HiRpzjaa9i8ZwPDYAzaYOaO8H11zlpa7LapuCVk8trtEZAxO86sZD6wo3G4H4NwxGT2lhT47QazjhB9mF8EZ709bQokTX6vvxjZMK1PW0y2Hz/1ZFmx3WFO52NqAgBEPKPtzLn+F1493dREBWZ3Q11QpQEwwaR2Qbqe3LSucaDv6uIl22YwP2AllPg9OgBsEfy1HQbkgJgisQT1sq9fa1+6tJWUesPD/elUrKzEnUm2G41FZ85S/+pKNmurr12OmB6a0jl88eqqBvYa+wMYhLx1dV53mMd+5lPrc9mpvM6FJ23WFE3prelsbT6ZIJu1WcwmTiWFIK9z2UPlleGRkeMV1R9W+bj3aCbedrfsG+Rv0j3oxY0qozdPkNy+f/A826GsslVRioU2SFIpeA6mhG2FBmxBDDs7HLJ+gc5h1JCjYKExH4DixAfPaDHbHFQ1iSfTA/3jm5eU5nMTegVfRDhUYh+zmEhz2SSIUTajHu6T2DSvYnASBSN6ItUYcId9yk+gWkGvCx/4a+IaPvu7IB9wqEo/OAG5w/hTDAdPf5T4nBOFgAlaa1Nw68zBRfriG88iLmA54OttZGB2MJxoPXL/bNdywnvpVMpKS8IZyx/slz6ftJnGU7HxIl/KlKkNlu7O5i2xXCpk6C0z6VAngfRpCQ1QOkU9ZyR4VDXKuXPnaZ8oSD5nEVoObYHsW4PD4Y2GcQhi0Mj0+slMtH1woH8yXOxNk8j69PXJT3x8d9aUmIoYBzv7F9XhPkeUoT2dMjvsPsY0PtqDUsViCUIA2HQw31CMW95lvi8LkwFxBNB0i5wJ1Q8Wl0yM9yi6ILwGk4GuMJIaiSZ7HdZWAyZOvfn6tZ7z528QE7ViVUN1jc9oIFrKZMh5jUafTAep0nWpaxcOezK9y6oVwySeReY8QbVYt6yuwUlHOKQUFlZlMhZvLukwhnWZiYA931qS6z/z8uot/5HCgYz/9uWZ75Xe02uYI7H8WLL2qgPnDn9ou5/0EUrKYsyEA9Z0cW0wY/Sdvnq9uhAGDJ88M+ottncsZ5/Se/S+Fr2nmLVDdGB/9nRe2LqpOJMYMGcdaCnzSiKVN1+8Olror6VKs8M2nM2MQI/zOqROqpDD4YSDAZ/O4SJXiuwZbDfQ2PdsvlBJQrjERiEMkC4biqSWrdysM7b3dI+TIsnttZJ/kdoMn/3cw04nEg0Je22FBcH6xuqJSd03v/7chrVbXL6CxS2ll7qIPSNlTp56qDjvjQ237d7cYNVFSfGYEQhP6q220ycjhw8O28yhD36gtMxZmE/DLJG8LJMzDXl92XC8LZcIGayB9xQw3mbn2Ivg91A/oIfoG588MzU54PHUet1LFF0hvBWFZyAPaKLFJCCJfUJ6nWvaNjB/z+JDoKDHsTnJj5NRqG8fY1OnM6R1L+lPtD13vr+iorrtdGR0xGjKBdHZwghLKklsCEYTEcdoiQFhfBbzmeFENBosXipUGYdUomtnGMf5e565emfYJGkoFWNIF0r2yW9+85uqx45w6Bqip7AE1Sl+53d+5+d+7ueefvppdMWYnQk/IkEZkadELO/fL4nHoQeojHicLMUUIUDhg2RA/3RNO3MHMPd8ZoC3/cvbCvYHV2dx4MwmLXjvGxKR8c7x8Z5goV+nwxrqgvefTMQHhvqW1bRwo8qF8wwEA8HfCI/c1taF2YOwJ8iSRq6kGwBZDLrsFEN5WePB8z9NpqD35AIyjIyF0vnkI7v2XDi432FxfPrTD1VWG9OhxHPPHk2Gzc1NVQb80XPOqXFjYTCYTg4nUqGi4lo8U7IZRAqaJiOnqp69IzFYkFAwdnkH8VEgQEzoQW394r6BUVBMFsdQHZRHsbniH/nYBmLbMH/n9VOFRfYHHt7TsmzFqbNX2bSEriViit0WsDmcMBmk3kwlJi4e2reo0KdMjoq6yoLDA0xM1bnLkceffuHlIwd+uvfwt3/66sD4FGHQuLXBJ/vtqUBB3ObFPVRG9D4/eCdy7CEQrNv5aH/M3TlFNn1HHhN7LmVMRQwpk24qvXpRYHmdPRcZxx82bYzkjbGE3pV1tVorN/ElnIDHGe+4dl0xjDjdRI7BgmFlguSH0vrk8Gh4zdoGiyWawbEM7i+H7weRB4TrTcEIwCqxZUVXKDHrmGchMnCFKip+dyeOLg28njixIDcSHpPpGxpuaFlLYb5rV8cXNTegIjJJ+bZscQlqwhDjVHRx6pjabEXdPSPF5QEcSuvqSmKRtMVaY3N5xVMbB8ZkcmzgcpE7h0Bu4MX1UZwg9TrH+XPdfp/r0596pDRYJKY2QyxjnErjV6KXEARjNpKc6vsXAT8LrhIvDw3Qx9MphIBzsUgyWLgy4F1l0MOnI2db2aHYgRC4aYGchuDiBddd5Tm5je3kwaW0YenIeESxmhHExaATdwas1gd2VnbduHr8aG8iqxQUWMmaoMtYQTd4heAEnTYYTY5is80nHkq68cmpG2ajA60nSQTwWhHcdz8ObAxk9cGCCnJH+YNCn1axpoL9OYGp/+M//mOsx1/72tegEBgJMAOA66EZOHpiKP6P//E/QipAuFzBwsxFzklHCuZln9wF0n/jO4hmRYQfCChaRyU20nNhvOvi1NhUsLjFWbBkIibx39hHQgNjpbZywjNoAhlY9gJKSXwpY+nLl66SLmLFipVvGAPef3j54WykNC5aHY5ao1GQH55+luGeOHqRxU22X/z8nk8/urOmjDjkTmvWU1WxqLK2KKdMUe8gn3Xn0u6a2opw9LrFYUB9nknbUcDQN3gZyMH4f2f4n18aAER4V2H5xbFAPECTmfzSFWvOnSjpYpcGK6hDwCvioGF2YmLnfYllmHR5/USTXb18qb62ChdatH8Go7N19Xr2L9Yto87Y39tOyBB40ZgDA8YVhYIzJR29ngP7zj7wyJryCtuN7slnXjzrdDbls3FJl4pfqC6SiXRNTlwudBYtCNlvXLJ3/gpTJPhVaNNcNazw58yct6jwZ//D7+x96jd8VbZCLsGpIa9nrSRybKkmgVdMiSWRlLIm7AOmqN4zEHaELw2m8sesJpRr+tBIPJUes9ikZhnSISsNvosn7LF4uqLMos+P06s+71KMmGrwnAmTjFKX8cCdgU0lYAT2WgiAEG91lPLvO3TIBEx3op3O9sP08AdSAD704UiktKxmfIjk6ddtDhDKGNcJicxmIxAwwDWrRHGG2ffK+WvtlyvqXCvWltns5vFR1/IVj4DjcvoEnpKUwYqE+10OvCRRSlpEFECflvJMjA99+LFKr78vH0saFUvaEM2awiRa0WXNEGUjOccSI3YZhjohswN8P54wh8yYKkchVnKwBQXOGHw8k+4eHLpITFChf7Ne8cuvstzY3dAuulER5/VRHjDkXcIfyE9yy+0HF8E5srt16by+sKq0s+t8S6lHwpHxzKbcZWp4a5NxVX2jQSl58aWTmUDcYA6It2relpHwdctk3Bso3U6YbS4fa+88puhGKou3oZlU+XQYz/tzYAT+9re/TbgZzWHa/dKXvsQJ5QQwKqDSoS8kgN/4jd9A54MNAKzKCSieK9ygaZC4/5lnnuHXX/3VX+UGfuWKpu6fVcVw5a4O1oJtJe8m0j7xwJ1tJ/qunV3auqakeoViCGQy5mTy+o22E7pUuvtyX1P9kkw6hrGEB8S4ADbOpQ8dPFwYCBKKzLpoZEBTRglNEuoCYywRYhabo7Zh9ZFjx3bvqoMuG9PG9csbHKbxbCRmzHuVzIgll3YY/LHwjYJivJWSktI4a65vWqqYMA53l5Qu0xtcSp500zQpIKDB0fzAMPPy85MB9VmSSGnGcV4iAWuHdW/dzp8/te9v95QYs7kRsl0b0p5c3s4uJUo2b/Qno55zR68V2itWNjdnMhFK4zkKG1yFSzAWQgViaVNPT3elb8qiC2cMDn0ec1M/TN2Z9t6auqpV1bZkbtBgtQSKij0Omy47kLIklIzJmsj5TNlYcmJmwO+Lf2WzIr8xyyyxmNAz7FxQL5KTKWuBh08pyUB1y65P/sno2Zf1qfM+gwM2gZqCekPUSOkeFGoWa1pxTiVsUVtz4eItBcFaxegz2nzkCALfxUNTpw49DgbD7JnXO3DOsireoa4pj8HpN1pJXp3M2xNp5+h4X1Fx2oLXQMqTyeO4ljGATAksMIJnMaoyV6wg0C/w+04c9EAH4BRSoAEgIBj5IhIkjg98ES0G8goYKRsZsxX5wxPWFWs/Hopd9nqsumwUu5AOpolifgYCXuxPfvelge7JJcsL1m+otngMQyOuQMlDTncNcyz6LUmnNsbO0hMalosZMkQDmPIG68DQFH4bhX4vCliTkagElEh+6rYrpoQp5yBtUd5E+oOFkOI7MStvp01SRk3q8l5JCgA9kKkUDa9OiWSj/cOjp01OZP9Gfc4bmhgNjQ9TwsgZsDgcFU6HH7dFFPY5lDY5alfYSFo1e4A0OfgqnwApiQ5NSu/w2JnrNypWbLgxcCycsbpNmASobR+G51ByVqyQ4uemm6qrpNRNPBfFjGzXW2LjSm5Q8S1q3KlLG9tuHInluhobFxtRuKH/ICZqJiB0tuu3fIL2H+05mhxsFV/84hfJR6Qheg3F8yIaRw+W55xNo/mR0x03YAPQ9C0kCIKQaDhX++Q6x72PSjBqMsfSuEykTkinDLnOZZtbXO6V2GmHOq/s/cF3jr32gs2mt9mcFn32kjFhKAk0ND+0ettHfOWVkYzhwOF9pUWOlUuXkVhMGzyfGj3jRTBFY//N5OLt3Zd7O4Y3bn/s4knT6ES0wB9asdafVZypaBSX1SRG/EyxEYe4fGisf2RJ8yocB3PZgNNbavF6+4fa3YGgyVRD7WT23ZyXBIDeBAnMTwbkOdFMTU8XZSWASOxHTY2bw739F84fW9ZqV/JTuK/hxJJDCaW3nD4zvG/v82VFvsXLKnPmgVjG1tXlcHmLdV2XM9GpcCoXV2x950+vt4IEJQKd1tjaaSU9MjVW4C9N4V9kKLvW3V9c4LViB87QI6ZvfARJ3SeGh3cOl907TMgTrJ1gJ3WPUmEAjpbXYbJhxsSQlicg0OwrWhbYXNFx4ieHjj9jMU6VFBXZYYBzGUSreNYcyVhN3vLGNR+2+0p0elve5IJr0Hg/NLaJZGl3d6q+zq2kiMV0KMkRn2fsgx+t0FkjyUw8rbc898rBUHjoU5/agDcfxD9vyJGYZ3IqXVaDEklrZmb93tob3u1TwiSpHIf8K7ifDxLmkSWI7yr4ZSQAMdk5eKWkobaqecnrr04dP3J++5Z6uxmcHc3rIoS4DE+ExiITi1qb65vLjYprZFjnC6wsKFoMkQWGERcwPWJtkioO6CZQshlMabIrK8b29hsFfq/HQRqDsZzeEk/bRiYjngonHqdYo0gFkMlZ0uJEPwcv3u2rvfv38XYegAidu4q0bbKShqlU5vLIxAWzsbLAs/L8yeNXr/1RMtldVEAyYcU46EmnnC5PUzC4qLy6EaMI8gFReKQaluVQDzCgavwEdWItMsRi+bZz5zpHQ0vWrqsMeIvz41cOPt5a6zFI4iLoj4ioEKG8Ob4LMww83iRhQrG8qT9pyo2lyr3VO5y+0lS2PRrvqapdajbU6PJ+dS+w8nC29+cAm1M5kkMbPEiTE+2cDjQaoPU091y7AqLnIkiWJHH4EXEiCGeGcry18eEdZNZj44wphrHB4fOuQKXb3QTxO/Hy493nny0y9PzyZ01eH6gLn1pzJmGejFoGhg8d/s7r9WsfDuUbSwPFK5aTcwl7LrGd05PEwFgX3IFSqXRXV09bxyWzQ792/Xq3jWBh++HX/i6ZSZcXOvXZpLjQ5fAXByWiVcnEM5MldWF/Efp5dyxh9RVXDQ9MmK0lXk8TJorbQV16e5NlmZ8MAILiE8DOkfh1pAGiVxEGMO7nVmx67MTrqaMnX1vS7HfbMX2gk0ww7R5fYPP2jSYjec+TQ5H4+KSloemD2VwVadAdFqrnWfVmW4dzPHLyori8ZyIYmLCzIO7X1hUeOnS+JGiK6SyHj/d/9uEmPaUWMx7illEDsB/Ql1kIw3uzN3lrq/vWnoJJw9yN2i1LehAmwIAsrWFeaQ9HLQLAieLIop4xuSrXf8LbsKbnxvH+oZ6Oq5d9fnewpIJ4v4aaxZ6CEhAlLC31l9QsnII24dcUk2HZ2nXd/T9qqnXjFmlMu1LZyfJSL1ATTYyY7LZMMutwuR0uGxmKED9lk5vz/Z3RpL7JZChGBwf5lBmTihM3B/bWXvYOTwFcGKOFBkx7JosakYMPAQzORBPDeqKmN49FU0tWtqSz1hUbPhWK6L/1vR9t3lRcWY7ux4UN6cDhl1pWLt+4Zsf4aGR41FLRsKqgcEkaHQN5EKQDpAq9zerHrSCXFtGAbQ4YEYzmsGXWr26kNhYJTaYmoz/68XlqYXz8k+vIrQAGyysULPQ4DO47vMX76CfmDEUWMWK6cSKlxc6vhOOJa/0jZyB0NkP1y8+8EIscXNISLij0YlEUsohKIB+ZCh1ru3b06pXazds/Y7YSYo1odnPhQYsqAoV85ts7e66eO1dbHnxw+7aU2U4tmdrlD4329Fzoe72pLGg3JZRkBGFLwSM8p3fBOMfRLpryprGIeWo4Wegq/0BZy7a0MjA4eqCoLOB0EDCEn5IkSlN5AfCJCgFve04BadQ7jBz0jUAA1z+LPe/c9iy617h+UK2Gbbk++9OdW3jjr8AfbvIW3M/0saHR5+MkzCl4WJfyfv9v/nuR+dq2RWTBhfsOkWozn6IQiwXU5jWEaitsUX38yumvWap+ZsXazycoV2OEct/kRxgYfQ0MjJw+fcbvLVqzco23wI5qD7nW5S/ZsONzB/d9Z2R4sr7W4yRoJh+RCDc9faTJ9rR2y1ZYpVg427i0aWRqwmT3+oPN+XSBXgTfez4WJANYGnHrQlVFrmO8Z9H6oHoQc4TRvG7Xx69dKjtz4WBpoSHgReE9SThFYXlRUZkvl7GPT8aHhmPLVu/2+pvSpC7By0PECjGwlFdXnTxliyFeZqfYpVk0KPHYysaaXDRx7sIlo7fc73KUu8WNlpR4eMySZEN0Cxav01lyz2/2zj4Ad0EgN2RAgJNo3q7uyzcunUgmSH5B8lhyJWTwea9pbDFa3SmdyVmxqKW8+uql87aqtZTTsTt4STN61gQGELGqQQlE3OFk+shltj+y8fDB/bjAWfJJg37KQjxJ0pZVLEo+kqPuLM5XyXh5ZdCgw9EjajCYY1klkrCuWb8nj7bEBPqMq16D7OF39lB1QfD/6ubXXoBPGFIjAYaYiRmAGWtw743+TVu2Oi2OZDZG6veNez6X0OOePtw5HFVS5otnO7MEVteuvNE36XFUkiXJAOJWcA1CJ8348S6UxIpmi6+ictHk5LVgwInXdj5H/oDkmlXNyKP53JRen47HyNoSCBQReYFnJf52hgRWK1OB2xt8Z2fh/rUukyfEk43J6xNu0Tc60uGz142PpF46/OVF1ca6VXa2RjpqQwok87zNBBMSC3rTXg9qhK4rF5+orHnE66kTwjyDkEF/YNXR0ZGu3r6JaHLthrXBgAPO1MK+lRRCzhUPfeH8fueVwbMVzkSBfdySm1JhG7HSQUKOVD6eNLuTtpaEqTWRXlRsDI+E9ptthkLP4nw6iFlUIBcRX+R3wFjQ0FtGuLMTyb4Cj2uIksELClffYvaGhU54kDtn5Qae5VyjBLJX39IhLvn6THhiODVyxuBNF5UsxaXqW3/x546x06u3OW3ZcZ24WZXoFadeko+S5mEKf+h8LOq0ZlZWuUfiZyavBFyt60J5l+xzdos6SAzgeLuSEqN1WWtZaalauTBOImtsujjPODz1uz/47w++9sMTF67UlttK/IEkQG8wJbGUKfbJfk9oKlHeWBzNTlncRn9BGckF1Piit/KGt5ABbdJliDrB95JZG3DA9Q/XAzh3GDPkRoMuljfXLN5V3bAxFuqfmrhBDJvZlMYabdD7nbbaonKf2ebC2iHpM8wYRoUhRUGg1yVcnoCzfNlo4kKFLqKLG4xWAhdxeA7tWlkZW1N1bTA+MdhV7CgRcGL1IB2KJUJK1kCDwVEgpOQtLuJbmZc3e0afIf0uPinmzOBg21NPfe3SmUMN1YHmxhK/tyCTTEenosfOD7/ynLJi/cOrt3wsnrUdPLi/tDi4cv0OWiaoDLASO52BWEBJBc5F/udvRqeSt7ucGzZ+fKRtLxUnsqbBdH4ymw/kUl6IfT6HS2guG5ss8lcZ4Npks+SmYralqx5weoqZPLUp5gts8k4famfq+OlJ3AnwW5QKpaBhfMXy8cTUUHfHlTNnrxx7aUWte8Q85a8oMVuCR85frlu8uaWhaCLU862vfeeRh3+lfnFlTkdGdfh8L3nSaU2tYIxMg91sGqPhGBksazp/9ESgsCyfnSKVHOm4c+kY8RM4JxNvU1FZZTw7VVRmMxlwnyDlPi701PVo0AuV/ZdwsHKSN4/584sLUHZoaOSS2xEcGbD/zd/87y/9Un2htzPDxklSqsGumJgcwAEHE0eeikZKtNAbCsd7Th/7wYaNXzKYfJQ2Zxtx4CRDSQ8Ca1evWbPU7zWJ1SFuxNVAYv5RNBgUR8HKh35+sv/c6NUXh6LXXEYyAjDZBPu5sm5r3mx0FS8pLNldYi97+qmv6K5cLyh2FgZX63NlOqxQ6LjzU0gP6M3JNk9SSAABHAIvDwv/liddQ9m3WQLusjWe1R7U7tfOtQbvsgXtNl5t5ilEL0pFHihyGoNB7PNVz77w/JXj3//tn1tmygxSCkOi9g2wPqm8QVw2MNGlSYuSxg0ugaOaT+noOPfP+UKLvbyUSQdQSW5B4gr8nVBbYesWwUWMK+hJCBEVzMCakNqJfCpb93whnRzRZ0MkScmRdNFkwyvTaDaXFnnyuZH+kbN2t8/radTpwJAOYR7e0nH7c9NUVJJYMQglFholQcbExGgkFrE6nIGiIpvTabW4WWqiV63+gDdQwo4XW7KBQEQwGZocVLmqKkkPYQQVEe4iPCHWgKzeXta6p+/1kaASN8EFG6hdkTIkw9gYbDazJTW6sspsN0jSJdFCUaBL743o/TZ3o2Lx3BRx39J73veHkK/xUDiy/0dPPfHldWuKP/zrD1hh1nUUG+iA/Bny7rXLGyZChqefffLYoXOr13+ouqKoqaGRvG9wsdQWFRKgzhfEVXgpxsf/miJFxsqPHr93U77EMTTwqtszYbOZKDYL/6unzBtRQvlUaUnA6XYkUbcohelsSbB0ib+wGTWCaqsDmYq/GoKVqp6X03fqkKFrg6dzdGUZM9lwjfrR/qFnvv/PZ4++UuCI+2zZRlf6zHO/89xIVGddtm77o0Z38bKlO6cSyX98/AdLVy5vXNbEfCioNRkwzlZAH/9JOgpaB0T51EzyuqLyxv2vuftGLMX+APUJSN6EdRpiKrXRFSUcjk6N9ZcwD1DGvDtpcqGTc3oacxgJ2FyqH/U7NQ/3p10E4YgRto7QHF0K5Xs6FXUHW/70f/7FupXWQv8kjC7cgMXuT+tG9JZJNh4a43zGp896TEg++RGPNVZeamy79uSytZ81GvFUyYNuiLAlxT/p2WGLFcJgJQWEtvFl6hC9oZckvncVL/cGW9LhyVxoKJeZFM9jvLGcDqOt0Kiv0ukd7V37M9bzBtPaIu8DuZyHBKRwjEST5bJxyYMvVmXhA+iUydB0MvdlVmZw8d02dtv9t32921Zm7hOmVK+/dvmY1Z0sqF2E53wu5ei9ePCjD5RbckOAK0RbJ1mwlITBkchYhrpR4GT91QWJjM9pyLj0eC6Eg/ZY3/m9i4t2TYTjFy+eg0ZSboGQNzrRhBWxf0F0hK6rsgI8lbimgjatRnOpog/qsPoLqREHQ+JC4snrA0PX/QXYA5bks0HV7Y69LohY3S8zo7+7f28lAyoXzhoigWdSg6/vf6Hz6tGgB5BJETBhMLkHnH6FOC5zsHnJNrcPeZCwdXAfESugLcwYeC5j8QM6KLmDV580niMqGkMWL8gG19kDFSs7zh8b7LtRX2COIkhiUuF3EgikDbUlgUVFBZLP1EAxuziBUZG47cKAsmUrOWGItb1/5qe7m5o73AWsm6zZV1/43uEXv/FvP726tAjEPEL+uAwGbV3cSLafbCadn3C5bZ/79Jqzp0fDA4c3bv8VlVsG6UMkWHSWGLdrtgzrLttGlQPUC6pYzVyy4v6S1RRY+MFTJxtrLS2LAmZLVkkzz2QBzresas0YjOMJXSTiaWh4xOWvQTjJGSAVwAEbEmoBDWCPI1ZpqFoafycOGT0HugzU9pINI9tx9uje7/ydNR/64o7CyiDpsaKUTKBebjpj7Os2dl7+TtnyDflQ2fMvnWhtWr1j2yaxvJH/UogfM8N0wOdiEcA5D7GACcN/hpYRLA0mc2DN1o/9/d/80ac/3FJT6yEGj4gUZlDYF4tlcjhUVewLekw5StcYi8+29S1ZvTmfL5bSZqLhfL8fKgyw8owWn5++sfHrwcLSs6eO63UdezYuU+ITSeJ1jK4LPT2Uue7qPu/zm1YuX4GwbiO9QzqB/iuVTJd4sx19V8NTo2ZbkET2SAMkriElJ6gHuEWjgKUuLciC/3ETJXsVGmfILHUTIZX2vN1ucJRKTSOFJKZIGfjB4X5qnZy4FE9dWbN27UAX2aW9YCkjnAbmeqlJjtuu3mxSTNgvRZMD4/fOgty7s5Czb0GVQKfRYHEVZax+MmrEx0a86dHFpU4l0yvSqihGyWHiCsdtL712vriwlMDsS4c6bnSd/fhj6+2mtDmZdxgsXn3s+KGnp1KeRc0tpaUlNA6vwieH7H6MeVSrR40KxOMiLpy1KEL5F+0Y9lFu5a3JIabox8eHzk9MdgeKarzeZjyF5E6scqLEeYtsH9iV/SYdsKDABZgdu3BX+6nTpx63m0K7trjd1rAZRT6ZssExuSnsAyNDof0/7V69+bHy+kU4qOl0EcFsVLOQCAIgCggTIFLHRNNclNfUYWI2WibimUD1isMv/HOuSe9vKMolY06IRQrPGhKKkTobkUgSaOHwFYnrr/bEF+38gsVbQRCBMNDvzaFhuVt6Z+XOXzz2wgv/+Gs/t63AHNMnR4iBQA8uGWVzLqKsRRVmSKRzIRiBFSvssWhqcqyDqFgcSiH66pwzRby5qFHU11LtoNKJ1hGsawR5I6uzBCvWPfqxP3rtxe+Hp3qKggmHwWyyYkPPx1P6sXDaGShd3voxi7kpS8w8KbnzUZXnBZ/SvkZs3tFZAzfTEa8gArRCBgd96sbpQxf2/uBDq+IFVhuJjHEMhTPXJVHLWIyKvrlosrnaPJg9ffa5ngr/jk1bd6F9AIwl9wlygAh9tIaiGfhR4x8FIoEoMo2kqT3Z1dEbjUUe+/QvXTn3jA3vyQKHBCszhwYsiulAwLdz6xZdti8c0V9q63FWLvYFmhXqpGtz/I7OxP1pnP2MOyza2OjI6GW2v8NRcOT1x3furrErxlTSa7DCeCeeeOZFr725onjFy8/uu9Fx4VMf25jNDRjN5BYm3ZkdA6MumTx96kha562rrautqYVGpvFxRPOAFhKoA/kDZfzxlTOJOxBLfBb2S0+ZekE8Jiw64nVqx9UXCW9y8tLU1PmKsmqHrfH8yaMTsT6fr/Dq1baO9t6pyVQI8pTXezz2YJG3sNhXW1tld9im4VoWR1OtaIB9f6bpnWxFpcV0IOyIOmZIcjbsoCqYtyKRdaLtnhq5VOXO2sU3FydGvOFxzrEYDL6L59rLq4pWrixCD647a+rrinqdaI9JxEkyBYMhGSsszq9auh1VD7AKeaYTdXI4Ae/BFALzmvYSlI9gzS7gXyHWiqBgViuZy/SPD57JZ0IlpSvtzjpd1sVD4ArqEKpcFMwf+JYG1ZED9vKvBv2EF2Iimn8VEODTBpFA0TtFyOtozDvOHnxmtO3pDSt8TntJLhfCJpkiPbD0l9QZxwnvrShxVAZ8w8PPdhuGCyvXKDkbOZVN8HzAAqUUJOZVowfCv+NGY+OF0pmRsXECZHv6+n0u+9p/84cHf/rlpj5jfcCS0A1aLehSoHz2DLXVdOak3hkzF42bCpof2xqoXUcIhUZY3sm1X6htyCt0jhUTDTVEiimFRrOvDrz0wmMfLnE7u9PRAr3RmTKGzKTWSlrF5YdCWqmkEYpOHCBKVn3EaM2MjR4wWlw2T11aHwMtGeBzWWAaVom8tCsLNPM/8pMBRlg1qej0ZRU1n/uF3wyHKNzSOTVFAo+Y2WL2B/21vkBBQTGmYJZP8KY0J96icj7dkmjjFnq3t38d+EIPpGDIFd8UM1rEaxeev/ry13bUeezmTl20gRRyOtMwFq1cmiJFOdXGRNSI2280uxzD53qfHxld5QwuwlwOo6ESRYYLvKJa1NgIPvODvQOJRIx4IsgJlUrLystxM1u+pPrMqZdG2/tLikxWU4yARByHeHO40kx28WAs1bhmZVXDOvYnLcBeLwD/b38O7m8L1KTKmHLGydHu8MhkeU09LuKRyXhxiS+Vj+FARj2e6IQIQI99dnF5kW1Z88f+6nvf3TORqrBQu8iQNOAuO6HXOb0e/5WrFz/02C+6XRWihORgdwIYVAxTAUL+AVCEQ1NRhogDKKahvPgj4OCGMZKSM+ZMMpFL91DDIByNFBQ1EAekKIEVy9ccePn1zs6hqUkUUHUed7DYBzFGqk8P94barlx4fd/pNeuWL2ltzlJ0F79qxDoZgaQ8ob/39wH2Susp7AXQKBmUFYzZkCd70TGzW7FbilI6L8bRqfCk2zqozydSit+WTerTAZ1xMp0ZIhXohl2LTLl+ouN7J8bqfFZPzBKxFSd1A7bYhAOfeGrFGsHU04V3OJmhkex01gPFpobqmChSGIF52GFkk5dEc+SlT6S6JyZ7jXpnYUkr+YhxnkH1hiyXSChmVNLZuMgKwoXzpx40I7RM1E7JtKpeUMMszKT2FbQjy68CgDCk07SC56EG3/rm39048uRv/dqWvDJOHjSBGaxV0hY+YYjqop8iQMmoTBX4LOduHMqbPeUlrToGKWvMn4bWJDICywx8HIleu7tvkBGQLOHUna8oKzOhVjIZ6xc3TfWcyUxenxq/MhUb4WUIOU5ljWmT01FcW1izvMRRmjMXZlBGoQtD6tJK8aoDf9c+BM2pU6D1KIwT+Eox9PRcs5pGGmpducSo0eBFZFMMxTeuh8xKZDTc3bSohnQewhFgWBJLiWSQVbIg8as1nmpZGGHBZK74kH/kmPlX+yZf1Y2rfmUZacDlLnS4CmbRWU6q3DMxMNFoEWdGKvE7c49bv8395X6dywTJG9Hx5Njgk9/6+0+u9jnzSeq9AGWKYSJHWiXYT3PMYAnhwJqn/hVW8UzarZB0MP3tr/7hF3/jr+F+RWmj6m1UN0HECgUPisHBgevX28gyVl9XX1JSRgEybdRMiMVes2HbF1KpiWioB0BV9RuC57xUL1KcJXVevZ60+5K9VZWvGeU7SxHv03SiWCXPePLGjTPlFX6zozQ0ESYrmZ20McoocXMAzODgeEEB3oHY1noxDzop8UpFFAkh9uYURzI9jlOZ3YpHuVT9FYlcNAbAorpOAg6czEAbDK9AoEBkFtueuAXyJAwdyT1io6Pnr1875rUXlZTXVlTX6hVLFt+ivPXEsctP/WDv1s17mlfVii8piytqXLhXk8tudTowHKZee+VIe0fHQ4/spOQ0rwMGkuxZM93ep7l6J5oRA/cMEhVneaAnn4nGQgMFwaVkisEznhkjWA1DiAEFOEozA3Z4nP5g7zFP6eJpq85YmkgVdw337qwLGExTqXTWrHeD35AH0GnMcOvTg1fRMWsCGXDJTyITkJOCNaELm0qncSoMXbv+ak4Zt9mdPm+t21mT15LJq6icraNkDKhcSdpIVtGhwYmrl9uY/Fg0WVlRuXrNCp/fyyrkcwks9pK9WNDF7WhBcI0wBEg3OltX29kXfvid//7vd+qNUxnqo7Brja5MmtTVeH1OJlNRk2KHXWEqyPqry0+UFnr6Oo+UFVQqSuHcNWYPQ/3J6Uo9TwR5di/JQAgLZCo4+DWTyhgNgUDdVl1meSY2kgqPYh7A6xEpwhQoMjh96N7JpAY0Gs3kM0mpuI3VuX307wQg3Nom6wEe1/plkWCZEJmzFy++Xl2N8y4qejj6iE6xxaLB7373tUKfvmlxIZm31EB9tHlY5EQSB5zclniE+KZk1GwUxk0WHh5eTRn8pu/FsoEcmTcGwgTKNEBiBPeytbgKny3/3Dryd+Ob1qUISByK7sCLPzLGx0s9RUo4k80VZk2RaG7caiyKR73R1AAyi81E7hGmNGfFBSAx1RAsPzc0ce3MwRVrd4OKtANgpcYcdeeBHxAZKcNIByZvjaooLYGH7EVuRqjGjmqyFnksBQA3cygOFswJHgkyPQaqThFnDC8nVin50ziVd2Na3nIf7H+E/3C432ALOwsaqG8IdiW1PbiZYtYEExkN/rZrV32+UspU8cLXbnT4zBan6CSILIddIp+jDa8SEwnp8WKe1jgyHxrfp+0fEA0HFyXnE2sHCMGuJEULgXeyKOdQDcQiA30dJ8pLHcWla8yWgGQmEK2v8ZWXj7700smPffTnrSZnKik0BrQpbg4SmMa8Y9UHGkyNDct7+67vf+3Irj1bjCb2tarfeMvz8m49CLcrfywDL8yEgDIxfibC2Gos1hJSGSEtofAo8Jf1p8FmUQvR3ThPGvEOIjDGWVRVeexMWzjhHeid6u1LuTb48sYBa9avz7jQu2WoCRCkEWboDQeQK5sZLwmAmD8OAWX+mQq3d/cemghfr6ltLi5abqQGk8jKzDwNcYOcSF0cvant+tAPnnjm9KkzSGao7KwW6+ULPdQEKy4pXLd+xeata9VmcW/UKJ2gDp7VDpF6pDchQIaXf/LMRx9paagxZpKjOjM1rNOpVO7GldFYLOT0DrcurU5EMPZC1tEzoqiK+BywbLnBvqtl5T5o1kybwHGYdOFI8eSHIpceZEDkCTpRD07AaJLfKU85e0veFbDiI87gBLHBNoh8j2mEjY/yBbcp7p8bGT/by7t1IoCtLh7vAGYnu3Y8lRpvqnflExOGHIEj2LQNg0Mps6ngI5/YUBhAgRbH4RaeCD9ZMD6ICLpppLwi7ti5lEGHXx36WeE6aFBw6LyQMef1mDbIKhfUaZR0wJwTUqtOqhAGbphz+7t7KmALCYBaZ6+dObSyoYB0quTVhaincHE2FZ89b7x8/kqwCgyh7+8Z39DqaqilJg+KbHKvTq6uKum+cTqzcjPVzBk3ScEoAIs/NZ4txH/iUccrq28t7w7YcAAQ9EcKP1WpQYYJ5pBrkpWXCaEek8wtl6bnlZlhdm5C/Ls7O/famwgven2YaBODmZxaHkqSkJ8gmciYLC4QUDLsGejQ2xymp39wiAI+EwPxT31wt8Oautpz5djxUb1SsHHdkooiYygR9rrrzBaHrIxsIPlfhWE+talQ8Z0Anrr9RbngNOmSBmUqb5gaG2oPhSZLKhYVFFcbDIVwYyqlN3X2DH39G0985AOfNBopBi4uH5IekCknmYravqCY6WRWSmV5U1f71Y727rqGiuni0m8G52oj758P3iUjhR2SUbgwHKVQyuV0Icozewsqzie80eyYFRaQchdizsVnLbFxbfPljm6qb9mslrJiV8Bnyqci1qyLbU5WyazN4Zf4p9mtyqKoa6Ox58AoOBX1JWIrUkA+osuHMpmJoZGTNrvS1PxRs7kin/VCA3LCd5K+wcs2yJOWns+c/oUX9p0+cd1qCn780Z/xuL2qSKNkYYXy1PkYO/H6lfa29oc/sLusrATN7QxbMDsSyUMggVzwU8l4LDLa94FHqzOpXtLapkhXaba3XR/+zvf2bdu2qqjcivMLVdjAXcJqSYxxirxv1C0aGe4qr9mEJyT+sGxjPJTZq+h/8IiCm+OchZ3FUzP+vMxMUk2LSD17ERexqArLj3gp9EDylsPK5bEZy8OwvXzcHPS7Bil0iWvdTMeCj8AtqUyCqbBAwFI28f80R8n909ndUVLmKaAma3KS8eawuMhriQ8lnBZiAWWEZa5x1ZjemSqquus30SZwZhqnxVYN+avretcN3e8bGYO6RkoyHklMDjas8eqSw+gHSKANDR8Yt766v/2B3a21zRFSPJy/0IYdxQS6Ru9qIh1qstiamMyFUf0Pdg1SB5wShigPkQA0sGGwvLL21ky+diIXQUvAhEhUWTRuyNH4p6nXEM+gmUIBhCIAqQI2pJ1RSdX9fvd3oj24eDylUedIBlkiguyOVNY0Oq5zlzlz+XgkHImHY48+urF/5ByZaR/cuqzMSTmeIbPDsGbT6oHe5IGDBz7ziW0jU9FgEciXJOTMg8zUNMYRgER/JlPIf6ItJjpULDZgeoT/kcjU5Uiok8kqqyfysTGj81CQQBJUCouse/KpZ5oXNzudTkpMAd5gf9J9I6zINpfEwzRlzqeR1XA8EabO7yk6/vrZmtpKkzAxM9vonZi1d6BNmTLRmcncIYACiCnRB8OGpBWHN2Et6wz1NAcDBuJ2gDLE8Tx1gYZbGzxms/PI4ctV3qxbsSkJE/s9YYzHsRiMxxM3xv//7d0HlF9HdTj+bd/9bq/aImnVm1VsuWODmwADNqamAUn+BFJIchLSy8lJziGk9+SXQnICJHBIAUKH0IIptsG922Bjy+pt1Vbby3f3/5l3pecv0kqWOwsay2/nO+/OnTt37tx7p7yZ5vlNxbqa7AQk0+DJiBrV0SLGrZoqG+uzryNTU3sHBh4dHO53vH9b89qZ6fnpWH6tUO0UcSQ1ZJ9Pphua5H/X/3v3zETjBRsoYeeSOkiRx5V6hwUG9qm9dX5X54KBgZ03fOnWH37jq5Ne5bklP9vFUI4eS8rEnjw19LtqZPhIZ1tNa7PR9qRWpoKrppt27dy2/pzVL7v2hZUz26tL1h+cMTiBZF90ppzTU631jdsPmvjZe3hgxK1v7gVdsWLFggULDO2T4xb+6rFGyrtxSrADyqSTFeTsiLYsJbEh3XSWqpaWTw1Q2AN2wshHhZ5vOcI6DjgKyUL6ALO6qJvFXsaqx7Z/e9WSFW6JmKwYLdY2l9KdYjqK/ew8LDMkJnXc+WpwidupFyaH9vmuTyLjaQcioXVUaWx8ZHzoUH11YzpVXfdJDlRh89ZDzZ0dS1aSyW/W1C9q6xns6OianrT5xBRO0bRmszXJIwduufnGuqb5F1xwYVfXvNwAHEfadwgP3lmqw8W0//2oTUhUpFX89FV2mqbQOeNf2pI7Z7jt8wmzM7WVRQvFbuwr1FatO/cFd9z3laWL26trBvftf6y+YWzR4iN9S1urajpmJg6nj5Gmp+Z1Ly7ULNu1+dbOdhcV1c7U9i1dvjZX/tloCVdSoNETw5JvZtIDryZ1upkSl3fv0MDuianh5o7lTW3zZ6rax7KDmKgB3ZKS2rV337333PPaa3/EriNTAqWZ0euv/2KhWKPL02VXXPHi9rZ5qQkyi0tB2AzZ3NS2fefePTv3Llq+ICt8Lj1UgdLlndbUNo7oqtMDLvi0r98olofRetYFX/3c55d2LW/2vZjjN8z8pkMkDqQb7EuVy3qmVy1tdVJf5XSzeeHR6sJw44KexVfu6B/YeeibE6WJYrHQ2tpUX19kCXDf0n8d1ducLhYen+gfGtlvZ5cJ8QXzL6ipXlgxo1zjbWpx0OJBVfpMzynrUxOlyWJt47+9+wNbN/e/5PIX2GmfXHQzRmZP0hHraSuLHkILTU1Ot9T3TpaGb73pvvMvWVdsqOEBpC2FaV4hMwPp62AfgKTdPaOdnb7fGbLfYrLSR88Katm3e3T5mq6KqgPTYxO+B6hyBbFPBSp9yO4EF4fmTDQW6x5+5KGhqjvXrztv4YKFdrBEd2XWyt23E9qfFqhjBhCSqZHUoZGM7+wtq5gNVZMjg7+aILkuz39gP1mmqsbG9s6O3ompg06HS55UVe3Q8OjhQ7sWvfACU4mk5rGte2+/c/PYWMWFF5y3fMm8mcojvnpwX25tvXMmUvslnXT0OPKnUK/MihzPjYxJxyc++7+TbknTdqnFHPpcxzfwiXTiia9GCjXTQ2OHauqa6pyqONU0M1zR1DTRXGerwaizeidqGmucZTA51m7Me/EFza1LCEz66suAKZsBOxX1qkuRpVIzAUkyIvgU08RRimVvDDrSTC/KjoGmV9/NQdM6ebFQbJzeV1Uadt2cbnToZde97t3/9PDuvRO9C2ca6itffvWGmso9U6VD086WTcxnTVtKE203f+W2vdseuu41V5QqWlesvax93rJMEai+/zACS1JgBdIMtMnJqolS6dDgwO6KqcHx0cHqqtrWzgXtjnM3+1HpC2OjXRmZeB3esZiF2265s1hd31hsnhl3uFjVoYG9O3Zuf8U1L+vu7rnpxptvufn2V77ydZNOT7fnnR9ACKx22ztRWWNeaK6YgeRc+5dJM1PpyEg8KDS0ju8vjY1ur2la4vx2s45TM9MXvPiaI4PfvPfR289dVEjneZiYTCI5Xqyqt2y6qMvuwYLxMHSl+sJE88rODW+s61i/gNfi4AGW283itkCT/+qZycnxhx58cP+Ohx6497a+pb3nnH92c0vbuIs26jqKtV21hcaGdCAlQtz5Q2WbNXKOGWVpD07trbfc9dEPfe5tb3m7O7qTqUgD4LQNyYoxBZyp4qzl0zJ+XW1V7a5tuyZLd1z5iktZa+Ynd7mSM0VOZGa/UagbOW22prp5emZwerywe2f/C69YPl1xyBxlobLVXbCVtaPmoksWyH1HMO2joMply5dffOXlxZpmwqZgfU8QETLBm+WhOFfeqVU2QM28STsOuRgc7kzl68tZZrQZLgiSs/49C7JnN+k76+BXmpFfsmz1wK672lo4+T7XaqytaHjVNS/u6+2YntzrCwfXKF504dUTpeLnPvvJt/x/r64v1E+6wLSytbnFQXI+KeAv+fSXhGG+yn1nCU9cmyRws4Uni2c2HE82LVnqtLEBTfUNDR3zeiyb2GliYcDVN3ar9/Q0333rI48+2D6vZemtdz7W2dGTTqy0XFZR56KFqjSxP0aw7C9xirwKkBmaj3SeQngyGs0ejmeRtHUjyUbiSiY1xmGZuGSqP0xmPJ8P/mQknv4DiRPT43W1TbXV7SOHhltqhysK4/h67Wt+ass975/XPW/xop6C674nt9WmbX0tpezTzNGhii997WaTuK++7tpqx0kVe1s717sM3R4VE4aCuUxS5j/tZCLb9Iad5nv2PrLl0ft8GbpscV9717xiw/LKGseVOyAoOybRWA5LyTqHOC3HVGx5ZHNbU6fNlHZ9edG/b9/ixUsW9S21HWTN6vU33XjbhN2hJgiy3e4qQlUptVhbNzZqS/tcCQbpRwOmUb2sZlVVfVPHwpGRvW2NFrQaKqacJF85NDPzktf8+IMfn9gz8sCCtqZaN9SbJ8fgKYawpYIpGJ9gQLg5/aWqhoUXVrVdODzt2LcjBXPDVdXWvKjAzFmveOTRbdOlg+vWt6xceVVtXW9P7+rJyWL/gYH9/UNbtz10eOBwQ2ONY7m6uvo6WxdZdahrHKuu8XlkdcGHhPd++6rLr2ms65x0fL3Z5/ERvejgwQNNTQbhXQ4uTTo4TejxX7miFU2NnYf29x/uP9LW1WSUkNRw1i1qKgqDRi6mKiwo7RzYMV7lC+kCXTVWV791yy63ffc4Bnlqy+j00E23bB4Zap+u23vJCza6FJXTXirUDYzOFBsdEhl6O+fhMV6e5K+iM+XuNVHJCEnLA8Zgj4cstQzw8TfPaewocVmZ2Sy8Os4s6Ftz961V7a299TUHnblXnGk8e7Fto4NOOqutql6yYMUNt23bfaj/3Beuqqg95OuT2um20emu2nqrbWkurVQ54vBU12aly8rKCzitms3K5CeN5bSKemIg67QOP2hMOxeKDbXdS+7b/sD8szoqhg9M1U86DXrjos6aTdPu5Bs40jQ8NraopbPGvWD2kJYaG9PO9Mk9pcLBxq6+qhYrvGb1ORBPPBTIqEpHqiYPJim4ZEpzBiSVlYVkSY4K1DERO/bqu/UvV7QhnZza0Ni2on/PnsJksb6+0wT8ytXrGmuuu+veT6xZUd3asD9NQ7vRtWR+1/Jj3aE9jzXVTvSt7Xt0z/72nrNXLL5wulSYMoEZ63gcxPTpDUY5jWZ8ZnL/6MiugcGtAyP7Fi/v6uo8q1Cz2EVk2M95c4Qm/iVeBuc4Z8mGWgjU20eb0/eAxHnSkVY792zpW5Q+hXWMwJ49+1vbWhyY70h4xywnYOv/xoQmj9KKX9oLkULeRtmv78oHSclOS00OCX8+UWwTckvLJXt2fv7IgVvb28+fnm7wFaSvqHwtufG1b9p537duvOETfQ1VS9tH66uGzRk5WM7Z5vAcGqveOdyw5JJXtq94iRPITOuUphsLnJx0/lJ/aXrfyODuwwcPz4xOLl/U2zpvQ0OT/ZYcGsq4ur2nL/gzOjoyMuo8n8PDw6Nbd2xmU0slUzLTvtMqVDWPDU6tXLJmYmzSdefDY0c+/cVPLOhd0jmv5a77b1i4oO/cja7sRqxpRkuVg+nu5LRO1nj40GhbV3NS2WkIkMrhkDIUqZka6pv27JvYf7ByaVvjzNhgxcxYb2/9m998VWOdb3h5EKWFSzu6Oy922/CuzcPnrC9OTByZqW4bmSg2NqPYNxFPrpHLoB+PPh4LHnyXPh1i3zw61nn9jXe85tqlU6O73Uyd9tqlXWbVk1ZQChNnr180dNe2yvThWMP0eNXYeHVt+3yTQmnEaQEu+Ve6VuZvqOOTq/aTg36WWUiWmtPmHAJUVXHepZtu/dR9I1UuR7F/qqNYcATmkY3r6yerWmeqWh7euaumrq5iqqF6+lBV1W7jzqHplkcP16246IpsNjDV64kGAXltAJ9Et39XsSen9zQjlsLsPausbmztGBrbsav/joXFtXV1i5z7s3Dx5aPDdXff/Rm3lyxa1FKoPWLXSmXlYT5318rGjuUv2bFr5vBA/cr1ryxUr+HkpTMqrb1ZLEnfcI3MzLi8bODIoX0jR8aMD+qbFi7vPju7SNLEE8Wt9dJAXDjKv/hDS4QGr5heML/n9sc2V7mIeNon/6N79+yVoafbuaUDDz74zauvfim9Pzo8tG/XfuefNTQ1dXa0TZZmJiYnGxrjeKvTZMHzDpa7FLmAYVhzZ9eKPbsfHhnf3929oqams9q9j+Pt09WdvRsWF7tW3Hn9R7/1rXu7Wppri5PjjukuFqfr17cvPWvNWRc1tveVKAyHcc+MzkyPlKYPDQ0eGBnePzo6VFtd31BvJXVpdQ0zrCF0I90iKeQ81NW5XrOhs6M7UvhJLtUZHx8bnxjbvmXP6MiY6zktp1LRA0cOWoO84PwXNDS68Kt4ww03bTznMp8lW59MW4rSbItPWx3fU9y2deeSlT1pnZIZyIIOXJeamvGqaVi7ZtM9d969YNOy6sKgbXfNxXG3OVncrPFpQ2XjvPaK22790siBxWsvWz4xudnBohOl9o55q+f1vcA2/5xneQW+VyNpsm2m8tWvf9u7/uEX735g68Z1TZPjY65qM36urm6874HHDh7cv2Ht+ZOHRoudzsfvHpw0hGtZ1rOuotrl60ljpu2V5sRwPdOec5hRZhsyJcLTtMaz7pwXHdhy3/bBby5pKDaMOAVvtKZiqDQx4169wZGxJYtq57U3Ogqtaqa/onbvSKHzscHW0vxL5y9/oeHp46PxOcyOp0s6ceB8EyRfwPXOX3Po8Nbdex5paz3S1NxTKrWsXLfadSQ3fOkLj2zeuXrN8qbO8Zqim+htTmvp31tfXbPkyk2XN7e1TJbG0tnjpUF7WswUlMZHxyYG3QY4Pj5QbKjrmL/Qhc9V1d2+Vk67DdNZXU4PGHG0+7EhwHfWgqJIuqJ03vkXfunzd0+mWdDCwMBIoVDs7l5w443f8KXCZS+6vLdnwcTk2M5d24ePHHEJFTPU0dltbVKNuro7sm0RRxF9J/a58Yt+q60+t7d74cGBe3ftvauhwQxIX6Fy6fRU3XjFnpae2pe96SeO9A8f2bd3avKIvf71rcX6zvm1je2uCxud3J2GBxMD0yMHpiaHx3iAjgmtb5/Xtaq6prOqoiXNJtAEGtJEQXYsdvkZiMx4GnJlS60xUDb1LzT5EqnQ/I3GBy2oZX1n+sDBfd3dXQ31rY67b6xvsx0/9c10e1iSKY/0SSt74c6JzVtedNV5Ssm9LtrecNzHHdY4al7z2p/+z/f80Y7+qYW9xQomzcqeT6VLddOTbdW1dTNpP2zN0KRn70y1LwpsClzYs/DSQn23xj5qVuZGsz4tKtVUfWvrW3727X/wwL0f2j9wqLEwZROkraQWfRavWnfwtoduu/XmxQsWr199zshw7WRV94KV5xfq5xu/G+AbJmvubIvL9wTP8MLxlCZLK1yZ2XLZNT/1yB3/0T9yy/z6R80yVJXqqx2+MlzXXFl9xYZltvE4hdqthqXpjoHS4saVL1tz7htKVtXGfZBiLDzrfNfTaqw5lll/twORKM201VS2tLf0NNTtPnT44UMDj1DExUJH5XT7S1915ejQBLvb4NieqvHSjLXG4vxFPhxrnJjYc+NNn2hoHSvWTlZOTLU2zSvN1DU1d9c1tLfU9qVzuROT+YB1vrxLjqByZny3mrZBn4z1FhLDLq1YueqstSv3H9w9v3vFvkcP1Nc1b9hw7llrzvUJh02uboZxOdzAkYGFzsVvn19TaLNSbPmwo7N18TJfPLkZqWHuKok0QJgp1lb3ze9um5rZPT6+a3Bg7/jYI5ZdBg8cqDFR19Q3M9Py0KMPb1h3Vl1FYeTQ7sN7v9rS2TnuS9JiEyNSW1usa25qcqtzTRdWVFZaHKD9rUJbf2fLbcys1whpueA7g8GBhPyZrEVS6h6m49KNm/G9pG3G+/r3tbe3GhbU1RVvu/0h830SHQxSa71fE5asWVp6TWcTtbe3MQACbKGD2AdVNH1IQTnFuvXaV731G1//l67eecXC8MT0UIG02MJTqr7t9jsXLOs8a92ig3vvLhW6nT/rqJumpktq65Y6NMTtuenwlpPKkkK+l0KSCpvi6xp7Lr70pwb6Hxs+uHV4fKeLPaqLI1WNM5dsOrc63b9QN13V3NTQ19azsaKmy51+VL9LKVKrmv6LkBbf5rIxQLuqcCcrkjtZcLJNzcK1F71hy13Tu/ePttW6QtnNsCXS7RMBy5/qPVJfPzFp71lf6/Kreje8dLTSvalJ8uY2H54x6daX2FTLv2knAmfDiuD8nubJif7R4f7RkSNDQ9sOH/r2rt27uru762zLqy2OTYyOTY4dPjAwPDyyYtmqQtWRnpY+9xoV6psdAWC3SYV5bCdXpy850n4Wc/yp/1vKTV9spkJsLkyfVmSTwyfWI9tWntrYHYwvveaKj37w891d853ut3jx8rRJMk032Bvsi4GCPY4wbtux/c6777vsRS9tampzVfLGc1fW1dsdRzxSfeZqcDJS7VBmDGprZpbUNCxsbBzjFk+MHJlXZ+fPxETFyFTV2LqLewuVB62GuHq+MLWywkH6xcaK2qZ0e3ZVGiGpvmkDvExc90M0bdZ3wddR7R/qvpxLGisBHptsORbB9emGhrp589rd8OpmNlvs9vfvHxwabKy7f3JydMfOzZs2Xe0739HxkYceecS29ZaGXssGrjW0xWjVqmVp38BRc5JKs7SNPPcpOoS2znbHyorxI0cevO2WD85vH53fXTS7bZtwVWX9lq27Dg8dLtT6yKG4YvX6rbtG2jsvXb32ddNOt6i2cmDB8ySDyvI6fa/EtUHJYcimwnwW5seUWwC/PXTkoS1b79m7f69voC/cePH8vjU1xZ7K2t6p6aa0iTKb80v9KbE/rRFkG9PMqhwzCXOSOUkJVCYb4CIw24ibnG7pjI3S2N5bPvPJrfffsGJhRd+8ifqqoaK1x6mqkcmZHeO+slm+8ZK3FhaeO91o2cAFhrazYUIs9s5JLjyDRKfvDQ3dk5Rk84xW7yy/09/6qVtNqg77eNjeblddT/tQy+YUB/rVOFXDcTU6r82jnRWTjWmtLn2sSIMYpZoVTv4gFH76IIhn6351W7kyxWxWiLuYMs8WIhe1lfSQmYWPf+TTh/ZNdXUsg8PHBOlUWxN66b/S0PCgxeTOrpYvfuGLK1et6e7p6ls6b8NGqsfOQ7ZnTruJttQfTtxM3/QV02QLXgoWdOn2tIfeUs2QgwOqib8j+WaKdgamd4lrSeWn9kzZ5fARAmYyvWk3akpNTZN9VZr9OL2HD/98kFD42Ae/UBqtbG/p3rf/wP99+YtXXnXlzu17jBT7+nrnzeuht++88053lM6fP69Q0bmgd/F4adytOJe/5JyWTsdDEYTs/nSEOd4g0ZFEw/FIJMcmSBej7brnzk/v3HbfhrULO9ssG0wU7B5K56k2Hj4y+uDD/V0LLjj7vJc3NpvosPINg40LhC+Zu++HYIg1OT2axrkYZmRls17NpPWfsdERW4GZ3GKtDmnRpd6tMElQ0qcfxgE2v+rThhKpW5TSPJK1Ah0wpGHucY74pqFiUllEmU1MouTLb3uPzSbu3vbNXVu+PnrggbHh3eCMNRtaupp7Fy1YcVFz7zrzrRYw8cKGEl8IYtJc5cIz2G44SUAISrqlI02s17iHxI0eaZexxDSMTB+BOgiO98D+MhlJRaPAtdf0sY+RkmbSGN57pIx0kTSxtJ0Lw2XwRk57C+kj6NKm25PMyCnWOeCFTJ/RDEN0x8c/8uV9uyaX9a2xESv7CMnmE58Uzezfv+/e+++GsrGp7uxzVnf2tJx/4dpivUNP0uai2gIzMIdbOAm4ymf2NFUj+8VQT6YZdVy1BIuFBYfD+8tGWHYBYs5dO2XPNPEmo0k8T3xPPzWn5knjg0x3JrynGXwBPG4e6TMf+8LNX7vn6k3X3Xb7XVt3bL/m2uvSV2/p253sK9fKuv/97KcbGitbWms3rr80XSdcOdHWWfOiK9c7J5CIpFNtMp3NDJjPSYOUVKXSZKqmj5zNKlWOP3T/HXff+bXGusMdHao9XXTX+URjXWt7z6L1Pb1rnaLtW1puIBTVBCW7Yew0KzH3wVwC5eYF7amHYai9j46YMuLSD7HXCkBy1JJg6LxpzoRg2JWrt4nIUpM6JQOSLvOe02YgqZN0GFlWramakSlXw7txyMgyHTylxjMzY2PpUxkrXTWFYrG+eqY4UeWWmYliZSntjZgqVNRImRvnfz7rcpuUC1a6GybtWE+8JT8uhHXvCb8heROkxWwx5eHcAAvAxgh8uEyhmEdKTqesJEtvFrE7mdeZGQqYBSJXNabj2kCShgmhd5J58A/kiXqIGUCPT5YUU5qcHEh3gkw1fO36e7557/bueQ4LqDMxlS1g+pqpamJyfGR0pFhX2d3bcMEl64s+rPIJmU7gI6x0ZMUcDum7rDwkdpi7SwYgNYUBVnLwsgo6OsAmrfQj6dcEkJiqIyQdm1nj9FeDpS8F8TZl9xb4kxoNO7NsuLqi6FrIP/zdv7z04qtGxyz91vT2LDRNYSdXEhHbxKoKg4OHa4tTt95xw4KepatWrbYSdN5FK3oXNTtzQo+srvQxZ6oVuSBsKZI99Vr0Hd06lqU4OX8Q3gSRNh87I9reUFzI/kuZEnuIZkr8/go4pe7BN302yXrGjKNpiS8pJJc//UnqPg/HGJfeliXn7+dO5CgLsj/J5Uz/wsfEE+Hxzh8MSX5G1ns8ktDoBpnfNLe58Ey21+MchTVjC5ambqljZk9pWJj+JbHK/g9/MnsrITifMmSqJ3sZ7xK61EDp3+Mpj8eyLMc9vM2oSCadafE2tenB/uEdO3Yd2D9gUsg8HzterK3n+3d21/T2zmtuaeBJBiKkH7U3xyGe2z+xJf4FM4/xM5rIq5xtwT3NlYej0cguNX6XAeSQJ484DcJLX6Jt27Lzwx/61PKlZ1en46zdzuUNu1zl9N3+/XuODO5f2Nd5+503L+hz/9CiCy5e19XT4CSIDLEGOto905TGycs68+YMB85w4AwHZuFA+ibN5J8lMlfU8H59LmajqKnQtNjPXiStcuKC5yyIziQ9JQ5kanvafZ/VNQWW4P57t4yNVNc6oMVKhesPqhzN65C5kXvuvW2qNNTW2bxi9cpVq5es27DEgCCtVSTLzAYctT1nzMBTaoQzmc5w4PuaA/S8TQFcXM6nL55sgE6jYal2o+ee5Rkz8OzJiEmcbAIwDTqsF4+MTGx+ZO8NX7u1YqJmwfxF6a43XyK6UX5iaGz8SO+CznUbV81f0G6bgC8TmID07ViaLTwazpiBY5w48/cMB85w4BgHkoo/1VROOmUaLDBbFdNKYpp5ilmg5GOGJXiWzEAgz8l7IlKPVemZ+/vcl3gi7VQ/NkcLOKvOMoAF5/7+A7d8/e7t2/bW1tbjj81jfYt7165d0d7RYguxk+zcReoooaxdzpiBE5l6JuUMB+Y+B2jkvBK6Om0Vz0jMtWeuQE0h5/E8Aixb8k15ZYxnjjaPZPtEs2WdbPdMmho3wZCWAcIY5ICzRKIILxQqnEiYV+qSvxVBkkTERCReeeaQ8dYTtkgXPy5EQYHhNI+uyjHk5UoRD7YoCM4IUiISpZ+MbzmecmDxJ0tP2dpDNHqwPZvkKTn13fXxM7VOpLNtJQUGA1hmNtKqEuD4d3RS6PFxQQZ95nGGA2c4MFc5oOfTQeUKKFKoHulCRPLqeSuAl16uhiIxB5s1kjY/pkAPUi/80tPR/5ElPRUaRktZCJNyHA2RmGeIn4CliAt5POiPlBxbnvG4yHEcOO7tKX7mJSoiSvTMaYi3nqfAEK8QIHLs9q2UJtfpZIzsjz8fL4pCT2w5puXtvq6o81Hm0RCrwQGQ8hw7b1Fc4lEsR7l5LM+Zv2c4cIYDc5UDoZWo11zLnFoteksrUWezqiGvAJSbh3K+2I9iSSDTqpKTIovSZ0VVnlE8qAr8OXxCUaZGgzDPyItIkfh5HGSUG89QzeV4yosOGEgABMLyt6eIR0a58FZGGIIt0qOsAPD0NsYxJ8MfVfAWcMDnkVMQcNqv2MnHR4Rpou7ov+MQSA/GsgTJYCTmPs2gGrNiOFn6rMBPmBjMesZxKjdw5pjzyClIymHyyKzAJ749LuW4n7MiefqJSnlSBZ0MuDw9YTxJu5cTfDow5fDiGeLHJerpYzgO//fGz5wteUS9QiXRUFu3bnUfeNSU3tm1a9f999/vdtj+/v68+tIPHTrk544dO/bu3ZuniwwMDHz729+enPQJ1MncxJn9B/bs2bvN3nlrxZNTY3v27Mi2lSfLUY7qZPFt27YNDQ3FW2QcPHhQxvK8FCXChoeHwUhHoQtuI45yV5657fzBBx8EE4oVqSJusUb2yQqN8QfOHFffgC8vvTxFOuQHDhyQS8RRm966aREBgquzHfmZMwo9IyM+qj+qVQJP+ROkvFpEBD3bt28fHXWYxFGvvBzyieJHDWQCe5zlJudM//uXfYtAxafvEiQeswdHI/LkY4iE4GmZAdXAI9yPpwYQ/MSXeIVlzpzy9FPw1jNSUuGnHeDPEKRHtPqsWQM/gAALSHlnBc5xAhMQ6RkYZoXPE/OMQZJc+avjIl4FJeVZFKSUKM5bryLI62cwJwD8DGB48iAxghRvxXEbfHnRgRCASGCILCfCAABWnh4cyIsLVLJHWZ5RXPyUXp63PB7kBVh5eiCMZ3k6VIqWyzOoimekh0TFWziFHEYkxyMeb4PgiMezHCyHn0MRNVJ9tRCJunhGJGoRbykUqvA3fuM3Pve5z0UK1v3d3/3d+973vk9/+tPvfOc7//u//zuk5aMf/ejtt98O25//+Z//6Z/+KTDYopR/+7d/g8HVsrCFPEQ6hMA80fLe97z/z//srw0JqtNHpFXvfe/77r/vwWz/SfLZoynL2ZvlSk0s8Ytf/OIf//Ef33LLLeFTf+ITn2CflJVV6Ojjvvvue9e73qXtwDMJf/M3f/MP//APYMSFz3zmM8j2/Nu//du/+Iu/oNm9Yue+8pWveKssGT2FYFf8BAPb2NgYVJs3b1ZSwAQYSAGkZ54ugmaQ2PX3f//3MBhpKeIb3/iGcj/72c++5z3v+e3f/u2HHnpI4r333utn5M2Qpd6RUyIdHun0/r/8y79s2bLFQaFf+9rXvvzlL0sH+SRDmeouMyKqmP1DaZZ6TP8fRV4GmSn/o3NHZbieJBXAlaRu8cQFQbuqpxQRP7FMXAjgvITgSP7zCSMw4FQITWCbNUsqKSuLWNCn4sH3kwFLDxj0IBVwZJ8VPhKjwUIUAAuRMmsWHAAQr3LiIwvZEoiIt5GChpxjeRYpwSsw4gI8QvwMgsWFcgKCJE8BcBQBTzlMHoczj0cENongI4sGzcqsjtYUj1eRURHHZY+fqhZUnQiQyP1OgmWBU6Ky9A0/A8BToVKc0JiXi2/RWIH5RPzROhCiUIBEmJXIOZQYVSDVXPXdu3ffdddd/FCs4Lbfc889vEt8iPb66le/unTpUgB6geqrI71z3XXX/fqv//rP//zPU500IN/2pptuOvvsszG8rq6OA0uFKQKTIQcwf/788FIBPProo5Qy65IzEzzhJVDf+tZD2spBERs3nvuRj3wUPcgQQMrO3+fOewp+eqsIT+QtX75cCtrUoqOjY+3atQry01N2T7bhkksuaW1thYqSBazuFD0Mgp8bN2785V/+5d/93d9V9y996UtyUcrQqoJxAwoffvhh8CIhWiEPwHp7e1X84x//uDhUChVRCvMQNhKGPDFe8d9FEPDII494JZcaveAFL/jVX/1VBGzYsIHRJfAf/OAHg2aFAkC2pjHQiSwwCOJ4u2zZMhVUzSuvvNLAiHGV/pyHxzvF8SrgyZISzUbgbr31VvHgtQhp+8AHPvCxj33MaDRgsODGG2+MYal4NMDpFwc+mjNYOWtGbQlGcf/0T/8UYi1OKcwKLBGqIFj8X//1X0lkSMAp4JEhqK8BqbynICbwA0YVmfjkJz9pACguBUn66s0330y7+RlIvNL99N7IQvI+9alPyRj0k3ty8x//8R8f/vCHyZZ66U5eYa9x6HFkBE51oTK4G1HH46oGRggiy+uLDDrif//3f5UIAGY0aMf/+q//4hCRbCnS4eTTwR9IyjFEHP0UTbhC5eTl8HkkaIifOjDnSO0CCSWiuyr6f/7nf9QUbUyCt4o2k6BGQUxeOiQChmAvYP2WyxnYpOdgczSiChj+B3/wB5QOpfze976XT21egm7izmsaDKF0sP3nfu7nKFBdMmoqY0ga/Sud0rnhhhsWLVpEHwUDr776asIWTgneXnjhhcDkpRn5vJ///Oe1y7vf/W5Ng/+w6Vy08Cte8QrdHAbCcNVVV5moYZOwXUaoOLy/+Iu/+Cu/8iuUtYgzzorFYkjC6tWrISG9ZEzj9vT0GJdoa4nywhCTLS95yUukaD7S+PKXv/zFL34xIgODvCEMbW1tDlslovqOqTDmBABSkY0YXQy7QhEjUoDQU33BM3hRokIl6lCsnSrDEEG6CK7qcS0tLQjQBSQKCBBEGNH169eDUUEceOELXygRo/7sz/6MPtm5cycCwnqlbMfCtddeq9UoQ02gObTdsTfPz98nMAO4cGq6NAYW3HHHHUZnuADYT8ZQA2Cc5tEkWloi6cREV+mQyPD4To35uLeGtJoTnpCz8rc5kV5pGyJIajUnGD/zt+VZIq6fwCkXIVi4cCExlZ5Lxonw+StV+L//+z/DOvQAm7UIOAWv0MNbMSoPE6g4rf6FL3yByEIYeSXSa//4j/8oIhFa6vsv//IvUSguRYf5yEc+okuA/+d//mf2QLn4TOAIYpCRE1xOJ4Woz+cF5dRqLAE2ROYZRaDSJ7WmLhHERNGaTGsaF3N5wDBpjDo+lOctjzc1NfHOWLVQHPkrJSI75hYkRvWRJyiODnr/+98fbectZccMaBc64q//+q+pAzAG/opWawBBYY48ELqPCR5mQ+vjPPhygDkaD0ZFY732ta+lR2hhrfCyl73sB37gB9rb20OVMHvhbG7atMnEBdnDWHkpVpLwR3/0R/T7mjVr/GQGsAKAQJHhMLeUEGqyF73oRRpI45IEKT/7sz+rFMVxRKTDRuVBctlll+lolK8iGhsbOfXGDd76ifM/8RM/Yd6GofqTP/kTkfPOOy9GA95Gudru+uuvX7FihdknepbZIGkhyfDw2REAGFU05qWXXsrRNjvP95ddKWRDBXlvKh4+NSaEpUEzg/eqV73qzW9+M0qiEykuml5eTCAkdLQURXAXdD2WQ1kk//d///cpbvA44y2eYx12qa8syg0CAMNMp/F3V61axXnq7Oyk3+DnJhoivPKVr9RMDOQDDzwQBUEILYR4RYDD75RFJGh7vp4n9ZTRqj7I0iRIjwa44oorpFPx9JHqqXYw67bbbsMmQqPaANTWqMc41Ntzzz3XvJ4m4VMw6RoAK8kchDCfk4XgtebkZWCQUrCGMdeE3B+Q/AXah9riR2hgDcBhITGEEgel8D21PTycdEYbi4kUFSA7n4JBUhfSxlxBctFFF61cuZI8IUl/gFPK4sWLm5ubowm1LmANuW7dOjh1NvVFtiDxrLPOIm3qTpUbZUe7qjUYcRjE8Q15oQGlECBaVaK4oBe94Q1v6OrqihRFiDCT0J5//vmy6zBo+KEf+qGvf/3ruIEPciFPb9Q90MD7e/3rX4/hEnV7BkZNlU7gVF+fFIdTRBYDCB0b8hBrqCDkx+k22gsPQQYNnpoGWsjx6uKLL1YLiXwuWkBZBt3//u///oM/+IMaiwDQ9VqHeYAEJ8FouKiUJoCcsbzgggsUjTnSBYSxcBgOp0TEwB/UajKNrrfrhIr2CivU93Wvex0YDcQkKBTbdW9Vi+U4TwSopkqBx3NNwykz/CJcL33pS40k8F+3DwLm6BMHMASf582bh59qQUtq+pANbwWJTD5hsBigR2AjNQ0eexnFEGmNIiOOabtgBbRaVofi1uhN+tqCBS6KSVNqxGnJkiVYqqV0GYnEw5MG0I9gYAPICRioiBP1hwwAfhJ4pSNPXIq20E+judVCiwBmcn7hF35B9xRnooh9yA/kIoDlhV+/I3XiIirFY1MKJaDLEIN3vOMdZAMqbFE1Qo5gnVR2pYeClleQErSRavJA3iJRTRkSCoTMiCgaE+KVJwG7++67cYCJpQoUhO2yqDtJVpbuoJNydjEk+gtKkCSvoMtQayLR++KJfh1EraWLByUJ+nkKJx0NBLlqxZli0nGf3vy93/s97cFZo8G5BiogiOMg00cENTAGRWNHK9L7UphKfjePQDXN2UHIwmM3x9YIVyncN4Zd25AeXiS0/FwOoEEASkLFE0HiS1hxXCJU5Aaw7NQH0ecvM+zyKpqKp6E0G2cEu6VojHAl+CZo4JNSnTDIgmxOqPaGljtATWukD33oQ5oc8ZrfyE41yRAPnaVRNG8ITyCJakKF2ne+850g1U7cq6AEeRQTppEk2IiUWlBz3sIT2T1V3GyjCBgShipmQMXVV4q8OXD0EClMBYmUHtX/z//8T5gDocQILJlOpWqQoD+eXonDABgZEiPFk77ALpqXaGKOtwEQaLFFRF1QZTQDXnXwhE8UqKTkgY9p3E0rBfHwgImGwxPxnADESIEKfoaQsx/YECYXMuDUx/RqgqSPsdxScIwTJ5f0IFJilAKJKiua9lEi9ZFTNUcjGCKEVo0q4JiIxHiKaBecJDZML3XGXSC0wPDEAMKqL+eDxgdJeYGUEXP8FCeQ/Fw6V8Zgu1wUNDEGBgmBVDrmc42ZBN4GcaUBqULlQgIVLQxYRgGw1gQviHglMZc0dprQbtq0iR6XkadCk4a7BgPy4ATvyZaoEfKEV7/61WoEgOSoo9UO01/GE1IUpxbIiKBHyy4d2UGVooNjIiCFnJmKM8dFZpDKCzHyCBcNBmCkkS/LxUGAgZf6SvSKK4OAX/qlX8IxP5WirBB1NQq++akfwY8SAEoEiQzpQhAWKQCexzD7aAArERdkqTOP7Cd/8ifVx/CQKscR6sZsj6pS3PjC4pE5DjvNqOOpLaUv7skfxD55KS/aEE7Itdxb3vIWcYads4b1xvsam2DBb/5Ee+v82tLcov6PvzxBQwo2licSLR3kRfOLU/G/+Zu/yeMATHp4N29605vw+rd+67c0JG+RzCFDC/Fx6Pcf+ZEfsVIkHU7wIFHIeMjO2mkqbpQ5cdKGFTwd2EgJdc8qqKzRD7PkJw7Iq3fpY5AHVSiETTzYiIeGR2SLYuJx6JN8Z0VQ4rKDQQBnDYyMcjGo1DcLSpMa+dJ6JAlDsJS/ZpRD8qRQsqwveKhg/rVf+zXVRwwkefPBz7ljDiHBK7VTR10LKtWRizQrBXwEHpYuoUOqfq7r1RS8Rtea/Dh4VFwHVrRO5S3xICTRLoHHK/Rz+SHRyqiSnSMvoxS10AfUgkJRL/RjnXrhNsZyEagkzME3uah72t/byy+/XAtCG/1HcX6+7W1vU8fgG54oCAEkjeqBBM/FlcgrRNKcDlitR2BUqBVNiT8SVSqqT7FqZRPfOCAR8zkK2lcuwFJCNjQfkSA8colDKB3b2VQIqTwtQiAFytp4lGegj2h6TQySGdAXdFUItZfuYEpHF+Z1UaASBQQYgEY8f0Iu+Ilmbp8IJFLYAGRoXPGoDqkwwYA8rU/J0BXRmygHm3yIkLyAo9aecmllQoJyzJFCKakIOWfYDDik0EWkJcZSqszBCpcfKtSqFxrQDFKcQEoXEEDhMDb6o59MhQ7CoYyKeEZGGGDzCmZ4MBznaQnp3Fm5pHAfYaP6QKJTLfSLKCLcmlTe8xRmNwOIUYFoFU9a3s/gdfT26LpYRnnRUwTOrDEvkm9OiUgnQ1wwTfLGN75RJbFSQ2KQVxBqjEBO8YGkwY1haUb9X9G5KWZR2ACMpmiUjpVBmHi0E2yBEPcRSXEAlkJNxEAVmCJIvIzslqcOoDFCj8gVATF0orwUB30hlxRkqAIA6ZAjXna1IEwIoChpn7D5foqwZF4pXXYVf+tb3wqnn8G6H//xH8cfe+Be85rXUKBWqNTX6hb+wK8UtVNZqKhsIyQK3SqTLCgBIN2TX4/VzCEJU6JWoOxQJbuOSrwgCTolRvBTD4HHT8QgjIFEp8B06XveShcUgSrdDypzPiwEtucLGH4i22wM/misIClwYo6iIUSMyka5EIr4ic5IxDpFy0izGLOrCA6jPPhD1+uxhEH/Uc2Y+fFKjXRgLGVpFM2Eh0aIouHxE4v8RD/4CH5qYj3NT2+hlTLXA5ZqAppXRcRJMjUaDcHT6uvrwysN6lUwhDJl/yQyt9pUokAePIEZfGtfP7nhBI9gmEBjYhWhKa+66ioSroHe/va30276uNlaFld/Zxv0ceUiQ3YCTLq0HWz8KqXjuVdKiUiwPX5K0ZTwCz/6oz/qZ+AhiqRCoUQIhvBLeB7SmRaJssOjQ7Fq1D2lTK6iLE+BJADjXhASdSGTRIig/vRP/7TOQqL0KdU0U61Q055qFzyRF2aJBIZTpSCClKFMHdmYEn/gJKh+KgLZ5Ip6wRBgMkbV8MT8gY4jgof0pHkU5SodBjoHWlnMWJBkvUy/g0QtoMW34NLz9ZzdDKibEDTlceRKiZprfnHsVnNN8jM/8zN4oarmrGlSbyk4KwF5rTQnKQnjIZFoYooIqYKEr60zm8RnKrktoT1DvgODFvITpJ8ywiOCMHjwUbpEhQap6NRmucvASaF9yDENQrEqSBsACAxyBSWRgk5zjlEK4dbqgTwnBnCUQrMoGqkyStTwtDMm4E8wCs0hTCFYIHU8NolHA79yDTXCC4BQFgWFy8anA2PVQXbITfUwDPonDLwVCld2wU9FoEE8+hKc8MiVvU8zZiJSOD7aKCcsfBASSRHTFMgD5okME306OefLT03zV3/1V54yqp0uqhcF5mBCFCSXiIAY1KJBLfANBom4FK0mXV7V9zSu1680OhgBBokGQAykSQZ10QTspfUJr4DRYpBH0aqjyoFN3qAkmkBGKSAT0mx9BYvQQBI0emSfu89oWd2KIotacBSEiOeJ5RXkphhV48AP//APR3o0lielyWfSETZt2mSiA4xwzTXXBBh2GStHXEsJZCnaUQeJRoy3crFA4u94xzsoO28j3TOaNf8ZLRU/4TfrmMcpVnGoPCMXeUOM2QXbMaO4HBuCQdLykd0TZzwJs2EQ18psrdYn2GwkyHir+1DxMUdPEVkYA6xG0a/Rlhcd8DlaBqlcR4M0Gva2PCgFeewKjWcSWxbswmGcgZ+s+ukpnegCpif1fWyXeP3115Nw5JUjfO7jqf6nDniqm4HxVB/VEFdtER4iG86zwAI1IViGlpxi/I1+CAYjwDN9uE+xyoiVfAfz70y3jbpcforJCM5ckNEA80tfB35IoiwIsUwWOl0p+MjSym6mCA3AlELjlDenoSt96kmHEgjZATBRxmhGZ9HYyIPTyFFE7eDhEBkBqIIBCieIZUJJVEEEMQxP/AQmhTKVKDsK2XYiS0BFBCVKj1ye5jToI2pOKVwGeHROgyTYvKU3sYjvBpuFAeXip2DYBCFKpGO+XCIR6DilY4ifKp43jTgfxFSbp2qSPCzlmwQlx3JXkGbGJkr3SgCP8zigM2tQ5VI6BteQ8N3Ki6Zq85EQvnkVNeW22zVh2K5eEMqlskQiL1REifqV7JEITF610EX1TJZGrXUYEYZB9mgXwOIg0YaNMEvxU9EwiGsvq1aK1i44QEJUnFwROfAc5yhuTj+jpk+2CuW58jhmmtjRviIS8/STISfe8Sq6WA4WGfUsSwXhSuevjos8YRGJiKwpIyN1rxdoxOhu5djKwfJ0wsCDIedEQhahnFRx/SVECE7Ukn8wcuUYsvKTLJWHciTSZy06T0SzwQpu5JIpe3BYXu4Rl85gDoWsIKUkoqMxn0FYebnPcXz20UBeYfXhsRICEerADCDVI07HmS5QMUM2o56M7clbt2xgKKdWcuEOSMFP6oy5pv0Zeene8kToXzYTR2Q3Z0L5miXU7TUnRSDk3FH0j/3Yj7HhspiXsERhptLaIMsfwqf5UQKzoET+IxvLMqPQtmXi7icFQUnRbmb6cB8YA6ZEDi/fR6tQPeiE3PiReaDKIccKEaNj8JDzMkQk0pgoDDcTnUJIjLcCyGhIccTzx2lGVQjbQBAVoaYIkAuMp7GkIQsB5eJx3mUPnOJWMiAxFMi1ZyAncyolrvroh0S5pNDEKxPoLbJjZK3WkSV/KgXbg12e8hJKfDMgy2G452ytn1dddRXVH/Xy1KMgZMyUS9WSbBgENcITzad2yDanDBL3coQRMaCBJEJk1KyKgE2iWqusDS10Pa+QS4W2yMjUaThkW+E338rA4KdXACTqTp4MPwWHY+oOIZHTRrnXfBwl34c/8SqYzDQKOIYJ0QpPmRvaK3QZbE8TVU4DOvUFMpmnnDpCbPQO4361s/amxaOa8CBJXgCRQhHR11K8IlHx9tTIT+ct5PoILwpCPSI0jMRgiK7howfqjppSKF8WTm+Jdw5zOqU8SzBJac6KOuQDp7AvBzjup7wCIchhAkCi/hks9tZPvDDnS9H/zu/8jgGEbkzbamOWQ0EgRSKLsiAJeYq8skeiUiIdu7FV4ER7KwI/4KDTTxgEESkwi3gLhlWIvN4GTq8Cf4CJh77jTpIqFFJkCkVbXkd4gJm1IKaUXaRHomfQkD+9VRY8tDN9FEQCgx9ashJZwHDG//AP/5CRo0m9Ahl5oVKcECmBIfAzYDZNGVMzcoADlQh+moLkTLFqNlBRxJbfc2yBUBEisMEcKZ64gQ/wRCKYiKu+SBQRr1gXM0gW8PHHKxkDUnbet0Eey6poQm8RHuY8RLl+BjxsgrggUdwTTDBciZojKM8A01tSpHRFY5pc3kpUEVPGFhj85KzYDMaE62NmKfU9egHMmYAD+IOTWBd8w8D4Gfx/aiyKNoUQEg331JAcl4sMa1OYkSo8IXngYQgwNMRPGKREIq+C48g3AhYCJgIzsoEdV/pT+6mbQxhoFRqs9hM9JiEEAinR09AfgHJpJBEAzxQNT43yk5oBwhHsy/GWp+TxEyPgT0yMFL2XlbY+w1e1gppjLs8SiTmGcpgnfHUi8GmmnKI4GE58S+uZX+JMxSuMOo5XJyv3RFTlkOZGWIuYRTlNnKbRYyKlvKsQNd+XMQOGblZ9Ofi0s4JCRvMSc2LyiFcRL3/m8HkEfsEMHnebbxXpsoiwTAZhBgRGCSajzJLNKuJ5iXkkR35iJIeJInQ2lcIlY4UARgx22Qgg3Rwgw2N4YcRJF5hhYwXREBw4Efn3c0rO2KfPhGia0+wFT7+4U2CISp1IjxRykmvbADsFnmfwlbJMALBDRFecc8a5+W7gVV7Hk5qBHOIZjGgGXLBIwAYasz+DmJ97VCoiKNezXAU/HUrwRyAfcJLXJxSU8JU8QebyjQApFlpoQ7tH/MxxHmcGnhqpaAtnKqpfTiS9b3RiXEz7m8SPV0qnhZ9aWcflgiqqkyNEg1Koe2Mp4xI7QxBgg6CBF2Ahqhz8PA7bmZ9nOPDccCCkNC8rfh6XmL99XiLPTP88TdJj6oCC4MCeZpbvWjCtiDbPZ8oGRE1hw6XTsQEBH5rOs1yq/GQAQhWGqSh/+zRZSu0KiEQqtOXmx/hAcUZ7puYthmtlAMLTLLE8e2ALFomHlkeDaUa711kCiwocf2TIJR0rhNxslKM6Ez/DgeeMA8f1Aj91oues9Ccs6DkdDRgEhO7Agrk+SA9NFPx9plqUcESAMBTcqdsvVDwYmg58Tob0SPGkCuEMmGeK5xDCHLSVmwFjXsG8ljFv2AAwp1mXwHbqJ68/rya0yAhxQoy4USZ1b7nIKymeEQlOxvPU+M+8PcOBZ4MDuTQ+G8ifEZz/P75qPj3lvO/aAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=514x640>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1_path = \"F1.large.jpg\"\n",
+    "fig1_url = \"https://www.biorxiv.org/content/biorxiv/early/2024/04/02/2024.04.01.587631/F1.large.jpg\"\n",
+    "\n",
+    "if not Path(fig1_path).is_file():\n",
+    "    image = Image.open(BytesIO(requests.get(fig1_url).content))\n",
+    "    image.save(fig1_path)\n",
+    "\n",
+    "image = Image.open(fig1_path).resize((514, 640))\n",
+    "display(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download data\n",
+    "\n",
+    "Download a single plate of profiles from the dataset \"cpg0004\" (aka LINCS), which contains Cell Painting images of 1,327 small-molecule perturbations of A549 human cells. The wells on each plate were perturbed with 56 different compounds in six different doses.\n",
+    "\n",
+    "> Way, G. P. et al. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Syst 13, 911–923.e9 (2022)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A01</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.3544</td>\n",
+       "      <td>-1.07770</td>\n",
+       "      <td>2.26020</td>\n",
+       "      <td>-0.377010</td>\n",
+       "      <td>-0.065840</td>\n",
+       "      <td>2.12360</td>\n",
+       "      <td>2.8740</td>\n",
+       "      <td>2.87500</td>\n",
+       "      <td>2.3047</td>\n",
+       "      <td>-0.92358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A02</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.3840</td>\n",
+       "      <td>-0.73440</td>\n",
+       "      <td>1.12090</td>\n",
+       "      <td>-0.182500</td>\n",
+       "      <td>-0.061450</td>\n",
+       "      <td>0.66985</td>\n",
+       "      <td>2.3919</td>\n",
+       "      <td>2.35230</td>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>-0.11820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A03</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.9493</td>\n",
+       "      <td>-0.36148</td>\n",
+       "      <td>0.44050</td>\n",
+       "      <td>0.326660</td>\n",
+       "      <td>0.547200</td>\n",
+       "      <td>0.25015</td>\n",
+       "      <td>1.2271</td>\n",
+       "      <td>0.77847</td>\n",
+       "      <td>1.0651</td>\n",
+       "      <td>-0.44810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A04</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.2909</td>\n",
+       "      <td>-0.46380</td>\n",
+       "      <td>0.96434</td>\n",
+       "      <td>1.132200</td>\n",
+       "      <td>0.753500</td>\n",
+       "      <td>0.31403</td>\n",
+       "      <td>1.4384</td>\n",
+       "      <td>1.48110</td>\n",
+       "      <td>1.2943</td>\n",
+       "      <td>-0.83810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A05</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.8955</td>\n",
+       "      <td>-1.05350</td>\n",
+       "      <td>1.64840</td>\n",
+       "      <td>0.057781</td>\n",
+       "      <td>0.070229</td>\n",
+       "      <td>1.60990</td>\n",
+       "      <td>1.1296</td>\n",
+       "      <td>0.90213</td>\n",
+       "      <td>1.1016</td>\n",
+       "      <td>0.53225</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",
+       "      <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>379</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.1522</td>\n",
+       "      <td>1.81410</td>\n",
+       "      <td>1.54220</td>\n",
+       "      <td>-1.874700</td>\n",
+       "      <td>-1.133900</td>\n",
+       "      <td>1.57540</td>\n",
+       "      <td>-3.0962</td>\n",
+       "      <td>-3.25160</td>\n",
+       "      <td>-2.7683</td>\n",
+       "      <td>1.40170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.1586</td>\n",
+       "      <td>1.50580</td>\n",
+       "      <td>1.68420</td>\n",
+       "      <td>-1.126400</td>\n",
+       "      <td>-1.066600</td>\n",
+       "      <td>1.24740</td>\n",
+       "      <td>-1.5305</td>\n",
+       "      <td>-1.79020</td>\n",
+       "      <td>-1.2474</td>\n",
+       "      <td>1.17600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.9475</td>\n",
+       "      <td>1.42100</td>\n",
+       "      <td>1.51020</td>\n",
+       "      <td>-1.103600</td>\n",
+       "      <td>-1.666500</td>\n",
+       "      <td>1.19840</td>\n",
+       "      <td>-2.6086</td>\n",
+       "      <td>-2.97620</td>\n",
+       "      <td>-2.0026</td>\n",
+       "      <td>0.91557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8.4408</td>\n",
+       "      <td>2.99620</td>\n",
+       "      <td>2.55230</td>\n",
+       "      <td>-2.275200</td>\n",
+       "      <td>-1.783500</td>\n",
+       "      <td>2.49200</td>\n",
+       "      <td>-4.3964</td>\n",
+       "      <td>-4.19030</td>\n",
+       "      <td>-3.8360</td>\n",
+       "      <td>1.02240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.9510</td>\n",
+       "      <td>2.55730</td>\n",
+       "      <td>3.05790</td>\n",
+       "      <td>-1.466300</td>\n",
+       "      <td>-1.673800</td>\n",
+       "      <td>1.99540</td>\n",
+       "      <td>-4.2176</td>\n",
+       "      <td>-4.49940</td>\n",
+       "      <td>-3.4922</td>\n",
+       "      <td>1.01170</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>384 rows × 507 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "0                      DMSO            0.000000                   0.000000   \n",
+       "1                      DMSO            0.000000                   0.000000   \n",
+       "2                      DMSO            0.000000                   0.000000   \n",
+       "3                      DMSO            0.000000                   0.000000   \n",
+       "4                      DMSO            0.000000                   0.000000   \n",
+       "..                      ...                 ...                        ...   \n",
+       "379  BRD-K82746043-001-15-1            3.248700                   3.333300   \n",
+       "380  BRD-K82746043-001-15-1            1.082900                   1.111100   \n",
+       "381  BRD-K82746043-001-15-1            0.360970                   0.370370   \n",
+       "382  BRD-K82746043-001-15-1            0.120320                   0.123460   \n",
+       "383  BRD-K82746043-001-15-1            0.040108                   0.041152   \n",
+       "\n",
+       "    Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "0                NaN                     NaN                A01   \n",
+       "1                NaN                     NaN                A02   \n",
+       "2                NaN                     NaN                A03   \n",
+       "3                NaN                     NaN                A04   \n",
+       "4                NaN                     NaN                A05   \n",
+       "..               ...                     ...                ...   \n",
+       "379    BRD-K82746043  BRD-K82746043-001-15-1                P20   \n",
+       "380    BRD-K82746043  BRD-K82746043-001-15-1                P21   \n",
+       "381    BRD-K82746043  BRD-K82746043-001-15-1                P22   \n",
+       "382    BRD-K82746043  BRD-K82746043-001-15-1                P23   \n",
+       "383    BRD-K82746043  BRD-K82746043-001-15-1                P24   \n",
+       "\n",
+       "    Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "0                      control            control               NaN   \n",
+       "1                      control            control               NaN   \n",
+       "2                      control            control               NaN   \n",
+       "3                      control            control               NaN   \n",
+       "4                      control            control               NaN   \n",
+       "..                         ...                ...               ...   \n",
+       "379                        trt                trt     BRD-K82746043   \n",
+       "380                        trt                trt     BRD-K82746043   \n",
+       "381                        trt                trt     BRD-K82746043   \n",
+       "382                        trt                trt     BRD-K82746043   \n",
+       "383                        trt                trt     BRD-K82746043   \n",
+       "\n",
+       "    Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                   NaN  ...                                        -1.3544   \n",
+       "1                   NaN  ...                                        -2.3840   \n",
+       "2                   NaN  ...                                        -1.9493   \n",
+       "3                   NaN  ...                                        -2.2909   \n",
+       "4                   NaN  ...                                        -1.8955   \n",
+       "..                  ...  ...                                            ...   \n",
+       "379      JLYAXFNOILIKPP  ...                                        -6.1522   \n",
+       "380      JLYAXFNOILIKPP  ...                                        -5.1586   \n",
+       "381      JLYAXFNOILIKPP  ...                                        -5.9475   \n",
+       "382      JLYAXFNOILIKPP  ...                                        -8.4408   \n",
+       "383      JLYAXFNOILIKPP  ...                                        -7.9510   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                          -1.07770   \n",
+       "1                                          -0.73440   \n",
+       "2                                          -0.36148   \n",
+       "3                                          -0.46380   \n",
+       "4                                          -1.05350   \n",
+       "..                                              ...   \n",
+       "379                                         1.81410   \n",
+       "380                                         1.50580   \n",
+       "381                                         1.42100   \n",
+       "382                                         2.99620   \n",
+       "383                                         2.55730   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                         2.26020   \n",
+       "1                                         1.12090   \n",
+       "2                                         0.44050   \n",
+       "3                                         0.96434   \n",
+       "4                                         1.64840   \n",
+       "..                                            ...   \n",
+       "379                                       1.54220   \n",
+       "380                                       1.68420   \n",
+       "381                                       1.51020   \n",
+       "382                                       2.55230   \n",
+       "383                                       3.05790   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          -0.377010   \n",
+       "1                                          -0.182500   \n",
+       "2                                           0.326660   \n",
+       "3                                           1.132200   \n",
+       "4                                           0.057781   \n",
+       "..                                               ...   \n",
+       "379                                        -1.874700   \n",
+       "380                                        -1.126400   \n",
+       "381                                        -1.103600   \n",
+       "382                                        -2.275200   \n",
+       "383                                        -1.466300   \n",
+       "\n",
+       "     Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                          -0.065840   \n",
+       "1                                          -0.061450   \n",
+       "2                                           0.547200   \n",
+       "3                                           0.753500   \n",
+       "4                                           0.070229   \n",
+       "..                                               ...   \n",
+       "379                                        -1.133900   \n",
+       "380                                        -1.066600   \n",
+       "381                                        -1.666500   \n",
+       "382                                        -1.783500   \n",
+       "383                                        -1.673800   \n",
+       "\n",
+       "     Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                              2.12360                              2.8740   \n",
+       "1                              0.66985                              2.3919   \n",
+       "2                              0.25015                              1.2271   \n",
+       "3                              0.31403                              1.4384   \n",
+       "4                              1.60990                              1.1296   \n",
+       "..                                 ...                                 ...   \n",
+       "379                            1.57540                             -3.0962   \n",
+       "380                            1.24740                             -1.5305   \n",
+       "381                            1.19840                             -2.6086   \n",
+       "382                            2.49200                             -4.3964   \n",
+       "383                            1.99540                             -4.2176   \n",
+       "\n",
+       "     Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                               2.87500                             2.3047   \n",
+       "1                               2.35230                             1.8672   \n",
+       "2                               0.77847                             1.0651   \n",
+       "3                               1.48110                             1.2943   \n",
+       "4                               0.90213                             1.1016   \n",
+       "..                                  ...                                ...   \n",
+       "379                            -3.25160                            -2.7683   \n",
+       "380                            -1.79020                            -1.2474   \n",
+       "381                            -2.97620                            -2.0026   \n",
+       "382                            -4.19030                            -3.8360   \n",
+       "383                            -4.49940                            -3.4922   \n",
+       "\n",
+       "     Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                            -0.92358  \n",
+       "1                            -0.11820  \n",
+       "2                            -0.44810  \n",
+       "3                            -0.83810  \n",
+       "4                             0.53225  \n",
+       "..                                ...  \n",
+       "379                           1.40170  \n",
+       "380                           1.17600  \n",
+       "381                           0.91557  \n",
+       "382                           1.02240  \n",
+       "383                           1.01170  \n",
+       "\n",
+       "[384 rows x 507 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "commit = \"da8ae6a3bc103346095d61b4ee02f08fc85a5d98\"\n",
+    "plate = \"SQ00014812\"\n",
+    "url = f\"https://media.githubusercontent.com/media/broadinstitute/lincs-cell-painting/{commit}/profiles/2016_04_01_a549_48hr_batch1/{plate}/{plate}_normalized_feature_select.csv.gz\"\n",
+    "\n",
+    "df = pd.read_csv(url)\n",
+    "df = df.loc[:, df.nunique() > 1]  # remove constant columns\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that in this dataset, pertubations can target multiple genes. We can list these targets from the `Metadata_target` column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'CHRM1|CHRM2|CHRM3|CHRM4|CHRM5', 'HMGCR',\n",
+       "       'HDAC1|HDAC2|HDAC3|HDAC9', 'ERBB2', 'DNMT1|DNMT3A',\n",
+       "       'GABRA1|GABRA2|GABRA3|GABRA4|GABRA5|GABRA6', 'TUBB', 'KIF11',\n",
+       "       'PSMA1|PSMA2|PSMA3|PSMA4|PSMA5|PSMA6|PSMA7|PSMA8|PSMB1|PSMB10|PSMB11|PSMB2|PSMB3|PSMB4|PSMB5|PSMB6|PSMB7|PSMB8|PSMB9|PSMD1|PSMD2|RELA',\n",
+       "       'SQLE', 'GABRA1', 'KCNT2|TRPV4', 'AURKA|AURKB',\n",
+       "       'DRD2|GRIN2A|GRIN2B|GRIN2C|GRIN2D|GRIN3A', 'CFTR',\n",
+       "       'CACNA1C|CACNA1S|CACNA2D1|CACNG1|HTR3A|KCNA5',\n",
+       "       'ADRA1A|ADRA1B|ADRA2A|ADRA2B|ADRA2C|CHRM1|CHRM2|CHRM3|CHRM4|CHRM5|DRD1|DRD2|DRD3|DRD4|DRD5|HRH1|HTR1A|HTR1B|HTR1D|HTR1E|HTR2A|HTR2C|HTR3A|HTR6|HTR7',\n",
+       "       'EGFR|NR1I2', 'ADRA1A|ADRA2A|HRH1|HTR1A|HTR2A|HTR2B|HTR2C|SLC6A4',\n",
+       "       'EGFR|ERBB2', 'HIF1A', 'ESR1|ESR2|MAP1A|MAP2', 'SCN4A|SCN9A',\n",
+       "       'BIRC2|XIAP', 'AKT1|AKT2|AKT3|PRKG1', 'ACE',\n",
+       "       'HTR1A|HTR1B|HTR1D|HTR1E|HTR1F|HTR2A|HTR2B|HTR2C|HTR5A|HTR6|HTR7',\n",
+       "       'CYSLTR1|CYSLTR2', 'GAST', 'HTR1A', 'PSMB1', 'MET', 'NAE1|UBA3',\n",
+       "       'VDR', 'HRH1', 'HTR1A|HTR2A', 'AURKA|FLT3|KDR|PDGFRA|PTK2|SRC',\n",
+       "       'BIRC2|BIRC3|BIRC7|XIAP', 'ABCB1|ABCB4', 'KCNH2',\n",
+       "       'ABCB11|CAMLG|FPR1|PPIA|PPIF|PPP3CA|PPP3R2|SLC10A1|SLCO1B1|SLCO1B3',\n",
+       "       'FGFR3|KIT|PDGFRA|PDGFRB', 'FLT3|PIM1|PIM2|PIM3', 'PSEN1',\n",
+       "       'HSPA1A', 'ATP1A1', 'RELA', 'AVPR1A|AVPR2', 'DPP4',\n",
+       "       'BCL2|BCL2L1|BCL2L2'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Metadata_target\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assessing phenotypic activity of compounds with mAP\n",
+    "\n",
+    "Phenotypic activity of a perturbation reflects the average extent to which its replicate profiles are more similar to each other compared to control profiles (Figure 1E)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we treat different doses of each compound as replicates and assess how well we can retrieve them by similarity against the group of negative controls (DMSO).\n",
+    "\n",
+    "To ensure correct grouping of profiles, we can add a dummy column that is equal to row index for all compound replicates and to -1 for all DMSO replicates. "
+   ]
+  },
+  {
+   "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>Metadata_treatment_index</th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A01</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.3544</td>\n",
+       "      <td>-1.07770</td>\n",
+       "      <td>2.26020</td>\n",
+       "      <td>-0.377010</td>\n",
+       "      <td>-0.065840</td>\n",
+       "      <td>2.12360</td>\n",
+       "      <td>2.8740</td>\n",
+       "      <td>2.87500</td>\n",
+       "      <td>2.3047</td>\n",
+       "      <td>-0.92358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A02</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.3840</td>\n",
+       "      <td>-0.73440</td>\n",
+       "      <td>1.12090</td>\n",
+       "      <td>-0.182500</td>\n",
+       "      <td>-0.061450</td>\n",
+       "      <td>0.66985</td>\n",
+       "      <td>2.3919</td>\n",
+       "      <td>2.35230</td>\n",
+       "      <td>1.8672</td>\n",
+       "      <td>-0.11820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A03</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.9493</td>\n",
+       "      <td>-0.36148</td>\n",
+       "      <td>0.44050</td>\n",
+       "      <td>0.326660</td>\n",
+       "      <td>0.547200</td>\n",
+       "      <td>0.25015</td>\n",
+       "      <td>1.2271</td>\n",
+       "      <td>0.77847</td>\n",
+       "      <td>1.0651</td>\n",
+       "      <td>-0.44810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A04</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.2909</td>\n",
+       "      <td>-0.46380</td>\n",
+       "      <td>0.96434</td>\n",
+       "      <td>1.132200</td>\n",
+       "      <td>0.753500</td>\n",
+       "      <td>0.31403</td>\n",
+       "      <td>1.4384</td>\n",
+       "      <td>1.48110</td>\n",
+       "      <td>1.2943</td>\n",
+       "      <td>-0.83810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1</td>\n",
+       "      <td>DMSO</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A05</td>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-1.8955</td>\n",
+       "      <td>-1.05350</td>\n",
+       "      <td>1.64840</td>\n",
+       "      <td>0.057781</td>\n",
+       "      <td>0.070229</td>\n",
+       "      <td>1.60990</td>\n",
+       "      <td>1.1296</td>\n",
+       "      <td>0.90213</td>\n",
+       "      <td>1.1016</td>\n",
+       "      <td>0.53225</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",
+       "      <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>379</th>\n",
+       "      <td>379</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.1522</td>\n",
+       "      <td>1.81410</td>\n",
+       "      <td>1.54220</td>\n",
+       "      <td>-1.874700</td>\n",
+       "      <td>-1.133900</td>\n",
+       "      <td>1.57540</td>\n",
+       "      <td>-3.0962</td>\n",
+       "      <td>-3.25160</td>\n",
+       "      <td>-2.7683</td>\n",
+       "      <td>1.40170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>380</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.1586</td>\n",
+       "      <td>1.50580</td>\n",
+       "      <td>1.68420</td>\n",
+       "      <td>-1.126400</td>\n",
+       "      <td>-1.066600</td>\n",
+       "      <td>1.24740</td>\n",
+       "      <td>-1.5305</td>\n",
+       "      <td>-1.79020</td>\n",
+       "      <td>-1.2474</td>\n",
+       "      <td>1.17600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>381</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.9475</td>\n",
+       "      <td>1.42100</td>\n",
+       "      <td>1.51020</td>\n",
+       "      <td>-1.103600</td>\n",
+       "      <td>-1.666500</td>\n",
+       "      <td>1.19840</td>\n",
+       "      <td>-2.6086</td>\n",
+       "      <td>-2.97620</td>\n",
+       "      <td>-2.0026</td>\n",
+       "      <td>0.91557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>382</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-8.4408</td>\n",
+       "      <td>2.99620</td>\n",
+       "      <td>2.55230</td>\n",
+       "      <td>-2.275200</td>\n",
+       "      <td>-1.783500</td>\n",
+       "      <td>2.49200</td>\n",
+       "      <td>-4.3964</td>\n",
+       "      <td>-4.19030</td>\n",
+       "      <td>-3.8360</td>\n",
+       "      <td>1.02240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>383</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.9510</td>\n",
+       "      <td>2.55730</td>\n",
+       "      <td>3.05790</td>\n",
+       "      <td>-1.466300</td>\n",
+       "      <td>-1.673800</td>\n",
+       "      <td>1.99540</td>\n",
+       "      <td>-4.2176</td>\n",
+       "      <td>-4.49940</td>\n",
+       "      <td>-3.4922</td>\n",
+       "      <td>1.01170</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>384 rows × 508 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_treatment_index   Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
+       "0                          -1                    DMSO            0.000000   \n",
+       "1                          -1                    DMSO            0.000000   \n",
+       "2                          -1                    DMSO            0.000000   \n",
+       "3                          -1                    DMSO            0.000000   \n",
+       "4                          -1                    DMSO            0.000000   \n",
+       "..                        ...                     ...                 ...   \n",
+       "379                       379  BRD-K82746043-001-15-1            3.248700   \n",
+       "380                       380  BRD-K82746043-001-15-1            1.082900   \n",
+       "381                       381  BRD-K82746043-001-15-1            0.360970   \n",
+       "382                       382  BRD-K82746043-001-15-1            0.120320   \n",
+       "383                       383  BRD-K82746043-001-15-1            0.040108   \n",
+       "\n",
+       "     Metadata_mmoles_per_liter Metadata_pert_id    Metadata_pert_mfc_id  \\\n",
+       "0                     0.000000              NaN                     NaN   \n",
+       "1                     0.000000              NaN                     NaN   \n",
+       "2                     0.000000              NaN                     NaN   \n",
+       "3                     0.000000              NaN                     NaN   \n",
+       "4                     0.000000              NaN                     NaN   \n",
+       "..                         ...              ...                     ...   \n",
+       "379                   3.333300    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "380                   1.111100    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "381                   0.370370    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "382                   0.123460    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "383                   0.041152    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "\n",
+       "    Metadata_pert_well Metadata_broad_sample_type Metadata_pert_type  \\\n",
+       "0                  A01                    control            control   \n",
+       "1                  A02                    control            control   \n",
+       "2                  A03                    control            control   \n",
+       "3                  A04                    control            control   \n",
+       "4                  A05                    control            control   \n",
+       "..                 ...                        ...                ...   \n",
+       "379                P20                        trt                trt   \n",
+       "380                P21                        trt                trt   \n",
+       "381                P22                        trt                trt   \n",
+       "382                P23                        trt                trt   \n",
+       "383                P24                        trt                trt   \n",
+       "\n",
+       "    Metadata_broad_id  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                 NaN  ...                                        -1.3544   \n",
+       "1                 NaN  ...                                        -2.3840   \n",
+       "2                 NaN  ...                                        -1.9493   \n",
+       "3                 NaN  ...                                        -2.2909   \n",
+       "4                 NaN  ...                                        -1.8955   \n",
+       "..                ...  ...                                            ...   \n",
+       "379     BRD-K82746043  ...                                        -6.1522   \n",
+       "380     BRD-K82746043  ...                                        -5.1586   \n",
+       "381     BRD-K82746043  ...                                        -5.9475   \n",
+       "382     BRD-K82746043  ...                                        -8.4408   \n",
+       "383     BRD-K82746043  ...                                        -7.9510   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                          -1.07770   \n",
+       "1                                          -0.73440   \n",
+       "2                                          -0.36148   \n",
+       "3                                          -0.46380   \n",
+       "4                                          -1.05350   \n",
+       "..                                              ...   \n",
+       "379                                         1.81410   \n",
+       "380                                         1.50580   \n",
+       "381                                         1.42100   \n",
+       "382                                         2.99620   \n",
+       "383                                         2.55730   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                         2.26020   \n",
+       "1                                         1.12090   \n",
+       "2                                         0.44050   \n",
+       "3                                         0.96434   \n",
+       "4                                         1.64840   \n",
+       "..                                            ...   \n",
+       "379                                       1.54220   \n",
+       "380                                       1.68420   \n",
+       "381                                       1.51020   \n",
+       "382                                       2.55230   \n",
+       "383                                       3.05790   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          -0.377010   \n",
+       "1                                          -0.182500   \n",
+       "2                                           0.326660   \n",
+       "3                                           1.132200   \n",
+       "4                                           0.057781   \n",
+       "..                                               ...   \n",
+       "379                                        -1.874700   \n",
+       "380                                        -1.126400   \n",
+       "381                                        -1.103600   \n",
+       "382                                        -2.275200   \n",
+       "383                                        -1.466300   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                         -0.065840   \n",
+       "1                                         -0.061450   \n",
+       "2                                          0.547200   \n",
+       "3                                          0.753500   \n",
+       "4                                          0.070229   \n",
+       "..                                              ...   \n",
+       "379                                       -1.133900   \n",
+       "380                                       -1.066600   \n",
+       "381                                       -1.666500   \n",
+       "382                                       -1.783500   \n",
+       "383                                       -1.673800   \n",
+       "\n",
+       "     Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                              2.12360                              2.8740   \n",
+       "1                              0.66985                              2.3919   \n",
+       "2                              0.25015                              1.2271   \n",
+       "3                              0.31403                              1.4384   \n",
+       "4                              1.60990                              1.1296   \n",
+       "..                                 ...                                 ...   \n",
+       "379                            1.57540                             -3.0962   \n",
+       "380                            1.24740                             -1.5305   \n",
+       "381                            1.19840                             -2.6086   \n",
+       "382                            2.49200                             -4.3964   \n",
+       "383                            1.99540                             -4.2176   \n",
+       "\n",
+       "     Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                               2.87500                             2.3047   \n",
+       "1                               2.35230                             1.8672   \n",
+       "2                               0.77847                             1.0651   \n",
+       "3                               1.48110                             1.2943   \n",
+       "4                               0.90213                             1.1016   \n",
+       "..                                  ...                                ...   \n",
+       "379                            -3.25160                            -2.7683   \n",
+       "380                            -1.79020                            -1.2474   \n",
+       "381                            -2.97620                            -2.0026   \n",
+       "382                            -4.19030                            -3.8360   \n",
+       "383                            -4.49940                            -3.4922   \n",
+       "\n",
+       "     Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                            -0.92358  \n",
+       "1                            -0.11820  \n",
+       "2                            -0.44810  \n",
+       "3                            -0.83810  \n",
+       "4                             0.53225  \n",
+       "..                                ...  \n",
+       "379                           1.40170  \n",
+       "380                           1.17600  \n",
+       "381                           0.91557  \n",
+       "382                           1.02240  \n",
+       "383                           1.01170  \n",
+       "\n",
+       "[384 rows x 508 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_activity = df.copy()\n",
+    "# make deafult value equal to row index\n",
+    "df_activity[\"Metadata_treatment_index\"] = df_activity.index\n",
+    "# make index equal to -1 for all DMSO treatment replicates\n",
+    "df_activity.loc[df[\"Metadata_broad_sample\"] == \"DMSO\", \"Metadata_treatment_index\"] = -1\n",
+    "# now all treatment replicates differ in the index column, except for DMSO replicates\n",
+    "df_activity.insert(\n",
+    "    0, \"Metadata_treatment_index\", df_activity.pop(\"Metadata_treatment_index\")\n",
+    ")\n",
+    "df_activity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we define the rules by which profiles are grouped based on metadata:\n",
+    "\n",
+    "* Two profiles are a positive pair if they belong to the same group that is not a control group. In this case, any two replicate profiles of the same compound are a positive pair. To define that using metadata columns, positive pairs should share the same value in the metadata column that identifies compounds (`Metadata_broad_sample`). We add this column to a list names `pos_sameby`.\n",
+    "\n",
+    "* In this case, profiles that form a positive pair do not need to be different in any of the metatada columns, so we keep `pos_diffby` empty. Although one could define them as being from different batches, for instance, to account for batch effects.\n",
+    "\n",
+    "* Two profiles are a negative pair when one of them belongs to a group of compound replicates and another to a group of DMSO controls. That means they should be different both in the metadata column that identifies the specific compound and the treatment index columns that we created. The latter is needed to ensure that replicates of compounds are retrieved against only DMSO controls at this stage (and not against replicates of other compounds). We list these columns in `neg_diffby`.\n",
+    "\n",
+    "* Profiles that form a negative pair do not need to be same in any of the metatada columns, so we keep `neg_sameby` empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# positive pairs are replicates of the same treatment\n",
+    "pos_sameby = [\"Metadata_broad_sample\"]\n",
+    "pos_diffby = []\n",
+    "\n",
+    "neg_sameby = []\n",
+    "# negative pairs are replicates of different treatments\n",
+    "neg_diffby = [\"Metadata_broad_sample\", \"Metadata_treatment_index\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `average_precision` function to calculate the average precision score for each replicate of each compound.\n",
+    "\n",
+    "It returns metadata with 3 new columns: number of positive and negative pairs for each replicate profile and the average precision score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d583875de81417fa8f66f87e2bfb80b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7687333b8544cc6a2e377ac848e3457",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>Metadata_treatment_index</th>\n",
+       "      <th>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>Metadata_moa</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "      <th>Metadata_broad_date</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>n_pos_pairs</th>\n",
+       "      <th>n_total_pairs</th>\n",
+       "      <th>average_precision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>5.655600</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.050922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>1.885200</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.308904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.628400</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.412513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.209470</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.377730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.069823</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>acetylcholine receptor antagonist</td>\n",
+       "      <td>CHRM1|CHRM2|CHRM3|CHRM4|CHRM5</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.715591</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>379</th>\n",
+       "      <td>379</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>3.248700</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.726786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>380</th>\n",
+       "      <td>380</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>1.082900</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.658824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>381</th>\n",
+       "      <td>381</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.360970</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P22</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.517619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>382</th>\n",
+       "      <td>382</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.120320</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P23</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.543290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>383</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>0.040108</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>BRD-K82746043-001-15-1</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K82746043</td>\n",
+       "      <td>JLYAXFNOILIKPP</td>\n",
+       "      <td>BCL inhibitor</td>\n",
+       "      <td>BCL2|BCL2L1|BCL2L2</td>\n",
+       "      <td>broad_id_20170327</td>\n",
+       "      <td>P24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>383</td>\n",
+       "      <td>0.535714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>360 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_treatment_index   Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
+       "6                           6  BRD-K74363950-004-01-0            5.655600   \n",
+       "7                           7  BRD-K74363950-004-01-0            1.885200   \n",
+       "8                           8  BRD-K74363950-004-01-0            0.628400   \n",
+       "9                           9  BRD-K74363950-004-01-0            0.209470   \n",
+       "10                         10  BRD-K74363950-004-01-0            0.069823   \n",
+       "..                        ...                     ...                 ...   \n",
+       "379                       379  BRD-K82746043-001-15-1            3.248700   \n",
+       "380                       380  BRD-K82746043-001-15-1            1.082900   \n",
+       "381                       381  BRD-K82746043-001-15-1            0.360970   \n",
+       "382                       382  BRD-K82746043-001-15-1            0.120320   \n",
+       "383                       383  BRD-K82746043-001-15-1            0.040108   \n",
+       "\n",
+       "     Metadata_mmoles_per_liter Metadata_pert_id    Metadata_pert_mfc_id  \\\n",
+       "6                    10.000000    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "7                     3.333300    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "8                     1.111100    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "9                     0.370370    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "10                    0.123460    BRD-K74363950  BRD-K74363950-004-01-0   \n",
+       "..                         ...              ...                     ...   \n",
+       "379                   3.333300    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "380                   1.111100    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "381                   0.370370    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "382                   0.123460    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "383                   0.041152    BRD-K82746043  BRD-K82746043-001-15-1   \n",
+       "\n",
+       "    Metadata_pert_well Metadata_broad_sample_type Metadata_pert_type  \\\n",
+       "6                  A07                        trt                trt   \n",
+       "7                  A08                        trt                trt   \n",
+       "8                  A09                        trt                trt   \n",
+       "9                  A10                        trt                trt   \n",
+       "10                 A11                        trt                trt   \n",
+       "..                 ...                        ...                ...   \n",
+       "379                P20                        trt                trt   \n",
+       "380                P21                        trt                trt   \n",
+       "381                P22                        trt                trt   \n",
+       "382                P23                        trt                trt   \n",
+       "383                P24                        trt                trt   \n",
+       "\n",
+       "    Metadata_broad_id Metadata_InChIKey14                       Metadata_moa  \\\n",
+       "6       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "7       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "8       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "9       BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "10      BRD-K74363950      ASMXXROZKSBQIH  acetylcholine receptor antagonist   \n",
+       "..                ...                 ...                                ...   \n",
+       "379     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "380     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "381     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "382     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "383     BRD-K82746043      JLYAXFNOILIKPP                      BCL inhibitor   \n",
+       "\n",
+       "                   Metadata_target Metadata_broad_date Metadata_Well  \\\n",
+       "6    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A07   \n",
+       "7    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A08   \n",
+       "8    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A09   \n",
+       "9    CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A10   \n",
+       "10   CHRM1|CHRM2|CHRM3|CHRM4|CHRM5   broad_id_20170327           A11   \n",
+       "..                             ...                 ...           ...   \n",
+       "379             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P20   \n",
+       "380             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P21   \n",
+       "381             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P22   \n",
+       "382             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P23   \n",
+       "383             BCL2|BCL2L1|BCL2L2   broad_id_20170327           P24   \n",
+       "\n",
+       "     n_pos_pairs  n_total_pairs  average_precision  \n",
+       "6              5            383           0.050922  \n",
+       "7              5            383           0.308904  \n",
+       "8              5            383           0.412513  \n",
+       "9              5            383           0.377730  \n",
+       "10             5            383           0.715591  \n",
+       "..           ...            ...                ...  \n",
+       "379            5            383           0.726786  \n",
+       "380            5            383           0.658824  \n",
+       "381            5            383           0.517619  \n",
+       "382            5            383           0.543290  \n",
+       "383            5            383           0.535714  \n",
+       "\n",
+       "[360 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metadata = df_activity.filter(regex=\"^Metadata\")\n",
+    "profiles = df_activity.filter(regex=\"^(?!Metadata)\").values\n",
+    "\n",
+    "replicate_aps = map.average_precision(\n",
+    "    metadata, profiles, pos_sameby, pos_diffby, neg_sameby, neg_diffby\n",
+    ")\n",
+    "replicate_aps = replicate_aps.query(\"Metadata_broad_sample != 'DMSO'\")  # remove DMSO\n",
+    "replicate_aps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the next step, we average replicate AP scores at the per-compound level to obtain mAP values using `mean_average_precision`.\n",
+    "\n",
+    "It also calculates p-values using permutation testing, and performs FDR correction to compare across compounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b70f4682ff947ca875777958c499f94",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eed952af896e48ffaa790e3c997714fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/58 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>Metadata_broad_sample</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>p_value</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>below_p</th>\n",
+       "      <th>below_corrected_p</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BRD-A69275535-001-01-5</td>\n",
+       "      <td>0.203576</td>\n",
+       "      <td>0.012899</td>\n",
+       "      <td>0.016390</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.785430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>0.269093</td>\n",
+       "      <td>0.000800</td>\n",
+       "      <td>0.001365</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.865004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRD-A69815203-001-07-6</td>\n",
+       "      <td>0.862226</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRD-A70858459-001-01-7</td>\n",
+       "      <td>0.351816</td>\n",
+       "      <td>0.000200</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.397983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.263986</td>\n",
+       "      <td>0.000900</td>\n",
+       "      <td>0.001491</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.826441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BRD-A72390365-001-15-2</td>\n",
+       "      <td>0.554667</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-A73368467-003-17-6</td>\n",
+       "      <td>0.788666</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>BRD-A74980173-001-11-9</td>\n",
+       "      <td>0.500600</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000276</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3.558835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-A81233518-004-16-1</td>\n",
+       "      <td>0.140208</td>\n",
+       "      <td>0.015598</td>\n",
+       "      <td>0.018700</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1.728154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-A82035391-001-02-7</td>\n",
+       "      <td>0.052362</td>\n",
+       "      <td>0.077692</td>\n",
+       "      <td>0.078692</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.104069</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Metadata_broad_sample  mean_average_precision   p_value  \\\n",
+       "0  BRD-A69275535-001-01-5                0.203576  0.012899   \n",
+       "1  BRD-A69636825-003-04-7                0.269093  0.000800   \n",
+       "2  BRD-A69815203-001-07-6                0.862226  0.000100   \n",
+       "3  BRD-A70858459-001-01-7                0.351816  0.000200   \n",
+       "4  BRD-A72309220-001-04-1                0.263986  0.000900   \n",
+       "5  BRD-A72390365-001-15-2                0.554667  0.000100   \n",
+       "6  BRD-A73368467-003-17-6                0.788666  0.000100   \n",
+       "7  BRD-A74980173-001-11-9                0.500600  0.000100   \n",
+       "8  BRD-A81233518-004-16-1                0.140208  0.015598   \n",
+       "9  BRD-A82035391-001-02-7                0.052362  0.077692   \n",
+       "\n",
+       "   corrected_p_value  below_p  below_corrected_p  -log10(p-value)  \n",
+       "0           0.016390     True               True         1.785430  \n",
+       "1           0.001365     True               True         2.865004  \n",
+       "2           0.000276     True               True         3.558835  \n",
+       "3           0.000400     True               True         3.397983  \n",
+       "4           0.001491     True               True         2.826441  \n",
+       "5           0.000276     True               True         3.558835  \n",
+       "6           0.000276     True               True         3.558835  \n",
+       "7           0.000276     True               True         3.558835  \n",
+       "8           0.018700     True               True         1.728154  \n",
+       "9           0.078692    False              False         1.104069  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "replicate_maps = map.mean_average_precision(\n",
+    "    replicate_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0\n",
+    ")\n",
+    "replicate_maps[\"-log10(p-value)\"] = -replicate_maps[\"corrected_p_value\"].apply(np.log10)\n",
+    "replicate_maps.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can plot the results and filter out phenotypicall inactive compounds with corrected p-value >0.05."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "active_ratio = replicate_maps.below_corrected_p.mean()\n",
+    "\n",
+    "plt.scatter(\n",
+    "    data=replicate_maps,\n",
+    "    x=\"mean_average_precision\",\n",
+    "    y=\"-log10(p-value)\",\n",
+    "    c=\"below_corrected_p\",\n",
+    "    cmap=\"tab10\",\n",
+    "    s=10,\n",
+    ")\n",
+    "# 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r',\n",
+    "plt.xlabel(\"mAP\")\n",
+    "plt.ylabel(\"-log10(p-value)\")\n",
+    "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
+    "plt.text(\n",
+    "    0.5, 1.5, f\"Phenotypically active = {100*active_ratio:.2f}%\", va=\"center\", ha=\"left\"\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assessing phenotypic consistency of compounds grouped by targets\n",
+    "\n",
+    "Phenotypic consitency of a group of perturbations reflects the average extent to which members of this group are more similar to each other compared to other groups (see Figure 1F).\n",
+    "\n",
+    "First, we are going to filter out compounds that were not phenotypically active using mAP p-values from the previous section.\n",
+    "\n",
+    "Next, we will aggregate each compound’s replicate profiles into a \"consensus\" profile by taking the median of each feature to reduce profile noise and improve computational efficiency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_mg_per_ml</th>\n",
+       "      <th>Metadata_mmoles_per_liter</th>\n",
+       "      <th>Metadata_pert_id</th>\n",
+       "      <th>Metadata_pert_mfc_id</th>\n",
+       "      <th>Metadata_pert_well</th>\n",
+       "      <th>Metadata_broad_sample_type</th>\n",
+       "      <th>Metadata_pert_type</th>\n",
+       "      <th>Metadata_broad_id</th>\n",
+       "      <th>Metadata_InChIKey14</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>5.655600</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A07</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.51038</td>\n",
+       "      <td>-0.76402</td>\n",
+       "      <td>1.616400</td>\n",
+       "      <td>-0.49600</td>\n",
+       "      <td>-0.481360</td>\n",
+       "      <td>2.421100</td>\n",
+       "      <td>1.10790</td>\n",
+       "      <td>1.13820</td>\n",
+       "      <td>1.14320</td>\n",
+       "      <td>0.329230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>1.885200</td>\n",
+       "      <td>3.333300</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A08</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.23602</td>\n",
+       "      <td>-0.41129</td>\n",
+       "      <td>0.304960</td>\n",
+       "      <td>0.47884</td>\n",
+       "      <td>0.005852</td>\n",
+       "      <td>-0.710330</td>\n",
+       "      <td>0.41986</td>\n",
+       "      <td>-0.23888</td>\n",
+       "      <td>0.54949</td>\n",
+       "      <td>-0.092826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.628400</td>\n",
+       "      <td>1.111100</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A09</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.52939</td>\n",
+       "      <td>-0.54727</td>\n",
+       "      <td>0.722570</td>\n",
+       "      <td>0.73399</td>\n",
+       "      <td>0.223850</td>\n",
+       "      <td>0.035842</td>\n",
+       "      <td>0.33318</td>\n",
+       "      <td>0.39064</td>\n",
+       "      <td>0.42969</td>\n",
+       "      <td>-0.811390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.209470</td>\n",
+       "      <td>0.370370</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A10</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.58515</td>\n",
+       "      <td>-0.41533</td>\n",
+       "      <td>0.044874</td>\n",
+       "      <td>0.76374</td>\n",
+       "      <td>0.062913</td>\n",
+       "      <td>-0.656850</td>\n",
+       "      <td>0.18149</td>\n",
+       "      <td>-0.10960</td>\n",
+       "      <td>0.48699</td>\n",
+       "      <td>-0.345260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.069823</td>\n",
+       "      <td>0.123460</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A11</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.52686</td>\n",
+       "      <td>-0.57823</td>\n",
+       "      <td>0.591610</td>\n",
+       "      <td>0.85184</td>\n",
+       "      <td>0.560370</td>\n",
+       "      <td>0.039184</td>\n",
+       "      <td>0.59864</td>\n",
+       "      <td>0.44123</td>\n",
+       "      <td>0.75783</td>\n",
+       "      <td>-0.018031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.023274</td>\n",
+       "      <td>0.041152</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>A12</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K74363950</td>\n",
+       "      <td>ASMXXROZKSBQIH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.48060</td>\n",
+       "      <td>-1.47220</td>\n",
+       "      <td>0.814150</td>\n",
+       "      <td>0.79463</td>\n",
+       "      <td>0.089249</td>\n",
+       "      <td>0.072240</td>\n",
+       "      <td>0.91828</td>\n",
+       "      <td>0.39626</td>\n",
+       "      <td>1.09120</td>\n",
+       "      <td>-0.243750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>4.615400</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>BRD-K75958547</td>\n",
+       "      <td>BRD-K75958547-238-01-0</td>\n",
+       "      <td>A13</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>trt</td>\n",
+       "      <td>BRD-K75958547</td>\n",
+       "      <td>VGYFMXBACGZSIL</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-5.89680</td>\n",
+       "      <td>-0.97404</td>\n",
+       "      <td>-5.025000</td>\n",
+       "      <td>-10.41400</td>\n",
+       "      <td>-6.067500</td>\n",
+       "      <td>7.625700</td>\n",
+       "      <td>3.31830</td>\n",
+       "      <td>3.27410</td>\n",
+       "      <td>-2.12240</td>\n",
+       "      <td>2.299300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 507 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample  Metadata_mg_per_ml  Metadata_mmoles_per_liter  \\\n",
+       "6   BRD-K74363950-004-01-0            5.655600                  10.000000   \n",
+       "7   BRD-K74363950-004-01-0            1.885200                   3.333300   \n",
+       "8   BRD-K74363950-004-01-0            0.628400                   1.111100   \n",
+       "9   BRD-K74363950-004-01-0            0.209470                   0.370370   \n",
+       "10  BRD-K74363950-004-01-0            0.069823                   0.123460   \n",
+       "11  BRD-K74363950-004-01-0            0.023274                   0.041152   \n",
+       "12  BRD-K75958547-238-01-0            4.615400                  10.000000   \n",
+       "\n",
+       "   Metadata_pert_id    Metadata_pert_mfc_id Metadata_pert_well  \\\n",
+       "6     BRD-K74363950  BRD-K74363950-004-01-0                A07   \n",
+       "7     BRD-K74363950  BRD-K74363950-004-01-0                A08   \n",
+       "8     BRD-K74363950  BRD-K74363950-004-01-0                A09   \n",
+       "9     BRD-K74363950  BRD-K74363950-004-01-0                A10   \n",
+       "10    BRD-K74363950  BRD-K74363950-004-01-0                A11   \n",
+       "11    BRD-K74363950  BRD-K74363950-004-01-0                A12   \n",
+       "12    BRD-K75958547  BRD-K75958547-238-01-0                A13   \n",
+       "\n",
+       "   Metadata_broad_sample_type Metadata_pert_type Metadata_broad_id  \\\n",
+       "6                         trt                trt     BRD-K74363950   \n",
+       "7                         trt                trt     BRD-K74363950   \n",
+       "8                         trt                trt     BRD-K74363950   \n",
+       "9                         trt                trt     BRD-K74363950   \n",
+       "10                        trt                trt     BRD-K74363950   \n",
+       "11                        trt                trt     BRD-K74363950   \n",
+       "12                        trt                trt     BRD-K75958547   \n",
+       "\n",
+       "   Metadata_InChIKey14  ... Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "6       ASMXXROZKSBQIH  ...                                       -0.51038   \n",
+       "7       ASMXXROZKSBQIH  ...                                       -0.23602   \n",
+       "8       ASMXXROZKSBQIH  ...                                       -0.52939   \n",
+       "9       ASMXXROZKSBQIH  ...                                       -0.58515   \n",
+       "10      ASMXXROZKSBQIH  ...                                       -0.52686   \n",
+       "11      ASMXXROZKSBQIH  ...                                       -0.48060   \n",
+       "12      VGYFMXBACGZSIL  ...                                       -5.89680   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "6                                         -0.76402   \n",
+       "7                                         -0.41129   \n",
+       "8                                         -0.54727   \n",
+       "9                                         -0.41533   \n",
+       "10                                        -0.57823   \n",
+       "11                                        -1.47220   \n",
+       "12                                        -0.97404   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "6                                       1.616400   \n",
+       "7                                       0.304960   \n",
+       "8                                       0.722570   \n",
+       "9                                       0.044874   \n",
+       "10                                      0.591610   \n",
+       "11                                      0.814150   \n",
+       "12                                     -5.025000   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "6                                          -0.49600   \n",
+       "7                                           0.47884   \n",
+       "8                                           0.73399   \n",
+       "9                                           0.76374   \n",
+       "10                                          0.85184   \n",
+       "11                                          0.79463   \n",
+       "12                                        -10.41400   \n",
+       "\n",
+       "    Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "6                                         -0.481360   \n",
+       "7                                          0.005852   \n",
+       "8                                          0.223850   \n",
+       "9                                          0.062913   \n",
+       "10                                         0.560370   \n",
+       "11                                         0.089249   \n",
+       "12                                        -6.067500   \n",
+       "\n",
+       "    Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "6                            2.421100                             1.10790   \n",
+       "7                           -0.710330                             0.41986   \n",
+       "8                            0.035842                             0.33318   \n",
+       "9                           -0.656850                             0.18149   \n",
+       "10                           0.039184                             0.59864   \n",
+       "11                           0.072240                             0.91828   \n",
+       "12                           7.625700                             3.31830   \n",
+       "\n",
+       "    Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "6                              1.13820                            1.14320   \n",
+       "7                             -0.23888                            0.54949   \n",
+       "8                              0.39064                            0.42969   \n",
+       "9                             -0.10960                            0.48699   \n",
+       "10                             0.44123                            0.75783   \n",
+       "11                             0.39626                            1.09120   \n",
+       "12                             3.27410                           -2.12240   \n",
+       "\n",
+       "    Nuclei_Texture_Variance_RNA_10_0  \n",
+       "6                           0.329230  \n",
+       "7                          -0.092826  \n",
+       "8                          -0.811390  \n",
+       "9                          -0.345260  \n",
+       "10                         -0.018031  \n",
+       "11                         -0.243750  \n",
+       "12                          2.299300  \n",
+       "\n",
+       "[7 rows x 507 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only keep active compounds, i.e. those with corrected p-value < 0.05\n",
+    "active_compounds = replicate_maps.query(\"below_corrected_p\")[\"Metadata_broad_sample\"]\n",
+    "df_consistent = df.query(\"Metadata_broad_sample in @active_compounds\")\n",
+    "df_consistent.head(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>Metadata_broad_sample</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "      <th>Cells_AreaShape_Eccentricity</th>\n",
+       "      <th>Cells_AreaShape_Extent</th>\n",
+       "      <th>Cells_AreaShape_FormFactor</th>\n",
+       "      <th>Cells_AreaShape_Orientation</th>\n",
+       "      <th>Cells_AreaShape_Solidity</th>\n",
+       "      <th>Cells_AreaShape_Zernike_0_0</th>\n",
+       "      <th>Cells_AreaShape_Zernike_1_1</th>\n",
+       "      <th>Cells_AreaShape_Zernike_2_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_AGP_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_ER_5_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_10_0</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_Mito_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumAverage_RNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_10_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_20_0</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DNA_5_0</th>\n",
+       "      <th>Nuclei_Texture_Variance_RNA_10_0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>[CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...</td>\n",
+       "      <td>-0.326365</td>\n",
+       "      <td>0.651610</td>\n",
+       "      <td>0.211280</td>\n",
+       "      <td>0.092412</td>\n",
+       "      <td>0.456915</td>\n",
+       "      <td>0.486515</td>\n",
+       "      <td>0.435545</td>\n",
+       "      <td>0.863160</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.175200</td>\n",
+       "      <td>0.557360</td>\n",
+       "      <td>-0.859465</td>\n",
+       "      <td>0.409045</td>\n",
+       "      <td>0.201909</td>\n",
+       "      <td>-1.003185</td>\n",
+       "      <td>-1.405850</td>\n",
+       "      <td>-1.495100</td>\n",
+       "      <td>-0.867225</td>\n",
+       "      <td>-0.066115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BRD-A69815203-001-07-6</td>\n",
+       "      <td>[ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...</td>\n",
+       "      <td>2.487450</td>\n",
+       "      <td>-2.872750</td>\n",
+       "      <td>0.616635</td>\n",
+       "      <td>-0.451942</td>\n",
+       "      <td>-2.260100</td>\n",
+       "      <td>-3.300900</td>\n",
+       "      <td>0.316320</td>\n",
+       "      <td>-1.825400</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.681800</td>\n",
+       "      <td>-0.197230</td>\n",
+       "      <td>-4.717350</td>\n",
+       "      <td>0.644170</td>\n",
+       "      <td>1.324100</td>\n",
+       "      <td>0.103070</td>\n",
+       "      <td>0.986025</td>\n",
+       "      <td>1.346200</td>\n",
+       "      <td>0.773450</td>\n",
+       "      <td>-2.749350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BRD-A70858459-001-01-7</td>\n",
+       "      <td>[ESR1, ESR2, MAP1A, MAP2]</td>\n",
+       "      <td>-0.920210</td>\n",
+       "      <td>1.461550</td>\n",
+       "      <td>0.445630</td>\n",
+       "      <td>-0.394235</td>\n",
+       "      <td>1.528450</td>\n",
+       "      <td>1.116100</td>\n",
+       "      <td>-0.054990</td>\n",
+       "      <td>1.061270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.238875</td>\n",
+       "      <td>0.326475</td>\n",
+       "      <td>0.064563</td>\n",
+       "      <td>0.187646</td>\n",
+       "      <td>0.200447</td>\n",
+       "      <td>-0.695660</td>\n",
+       "      <td>0.100225</td>\n",
+       "      <td>0.401885</td>\n",
+       "      <td>0.114583</td>\n",
+       "      <td>-0.245753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>[HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...</td>\n",
+       "      <td>0.045435</td>\n",
+       "      <td>0.099755</td>\n",
+       "      <td>0.103628</td>\n",
+       "      <td>0.592620</td>\n",
+       "      <td>-0.352200</td>\n",
+       "      <td>0.202930</td>\n",
+       "      <td>-0.059855</td>\n",
+       "      <td>-0.353755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.069575</td>\n",
+       "      <td>-0.475915</td>\n",
+       "      <td>-0.174002</td>\n",
+       "      <td>0.217965</td>\n",
+       "      <td>0.090715</td>\n",
+       "      <td>-0.154695</td>\n",
+       "      <td>0.165235</td>\n",
+       "      <td>-0.160191</td>\n",
+       "      <td>0.242195</td>\n",
+       "      <td>-0.126886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRD-A73368467-003-17-6</td>\n",
+       "      <td>[HRH1]</td>\n",
+       "      <td>-0.062074</td>\n",
+       "      <td>-0.314820</td>\n",
+       "      <td>0.526190</td>\n",
+       "      <td>-0.502485</td>\n",
+       "      <td>-0.444675</td>\n",
+       "      <td>-0.191225</td>\n",
+       "      <td>0.145019</td>\n",
+       "      <td>0.018870</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.527805</td>\n",
+       "      <td>-1.204250</td>\n",
+       "      <td>0.615420</td>\n",
+       "      <td>-0.187645</td>\n",
+       "      <td>0.321880</td>\n",
+       "      <td>1.013235</td>\n",
+       "      <td>0.793675</td>\n",
+       "      <td>0.682925</td>\n",
+       "      <td>1.075500</td>\n",
+       "      <td>0.844115</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 495 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Metadata_broad_sample                                    Metadata_target  \\\n",
+       "0  BRD-A69636825-003-04-7  [CACNA1C, CACNA1S, CACNA2D1, CACNG1, HTR3A, KC...   \n",
+       "1  BRD-A69815203-001-07-6  [ABCB11, CAMLG, FPR1, PPIA, PPIF, PPP3CA, PPP3...   \n",
+       "2  BRD-A70858459-001-01-7                          [ESR1, ESR2, MAP1A, MAP2]   \n",
+       "3  BRD-A72309220-001-04-1  [HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A, HTR...   \n",
+       "4  BRD-A73368467-003-17-6                                             [HRH1]   \n",
+       "\n",
+       "   Cells_AreaShape_Eccentricity  Cells_AreaShape_Extent  \\\n",
+       "0                     -0.326365                0.651610   \n",
+       "1                      2.487450               -2.872750   \n",
+       "2                     -0.920210                1.461550   \n",
+       "3                      0.045435                0.099755   \n",
+       "4                     -0.062074               -0.314820   \n",
+       "\n",
+       "   Cells_AreaShape_FormFactor  Cells_AreaShape_Orientation  \\\n",
+       "0                    0.211280                     0.092412   \n",
+       "1                    0.616635                    -0.451942   \n",
+       "2                    0.445630                    -0.394235   \n",
+       "3                    0.103628                     0.592620   \n",
+       "4                    0.526190                    -0.502485   \n",
+       "\n",
+       "   Cells_AreaShape_Solidity  Cells_AreaShape_Zernike_0_0  \\\n",
+       "0                  0.456915                     0.486515   \n",
+       "1                 -2.260100                    -3.300900   \n",
+       "2                  1.528450                     1.116100   \n",
+       "3                 -0.352200                     0.202930   \n",
+       "4                 -0.444675                    -0.191225   \n",
+       "\n",
+       "   Cells_AreaShape_Zernike_1_1  Cells_AreaShape_Zernike_2_0  ...  \\\n",
+       "0                     0.435545                     0.863160  ...   \n",
+       "1                     0.316320                    -1.825400  ...   \n",
+       "2                    -0.054990                     1.061270  ...   \n",
+       "3                    -0.059855                    -0.353755  ...   \n",
+       "4                     0.145019                     0.018870  ...   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_AGP_5_0  \\\n",
+       "0                                        0.175200   \n",
+       "1                                       -2.681800   \n",
+       "2                                        0.238875   \n",
+       "3                                        1.069575   \n",
+       "4                                        0.527805   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DNA_20_0  \\\n",
+       "0                                         0.557360   \n",
+       "1                                        -0.197230   \n",
+       "2                                         0.326475   \n",
+       "3                                        -0.475915   \n",
+       "4                                        -1.204250   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_ER_5_0  \\\n",
+       "0                                      -0.859465   \n",
+       "1                                      -4.717350   \n",
+       "2                                       0.064563   \n",
+       "3                                      -0.174002   \n",
+       "4                                       0.615420   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_10_0  \\\n",
+       "0                                          0.409045   \n",
+       "1                                          0.644170   \n",
+       "2                                          0.187646   \n",
+       "3                                          0.217965   \n",
+       "4                                         -0.187645   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_Mito_5_0  \\\n",
+       "0                                         0.201909   \n",
+       "1                                         1.324100   \n",
+       "2                                         0.200447   \n",
+       "3                                         0.090715   \n",
+       "4                                         0.321880   \n",
+       "\n",
+       "   Nuclei_Texture_SumAverage_RNA_5_0  Nuclei_Texture_SumEntropy_DNA_10_0  \\\n",
+       "0                          -1.003185                           -1.405850   \n",
+       "1                           0.103070                            0.986025   \n",
+       "2                          -0.695660                            0.100225   \n",
+       "3                          -0.154695                            0.165235   \n",
+       "4                           1.013235                            0.793675   \n",
+       "\n",
+       "   Nuclei_Texture_SumEntropy_DNA_20_0  Nuclei_Texture_SumEntropy_DNA_5_0  \\\n",
+       "0                           -1.495100                          -0.867225   \n",
+       "1                            1.346200                           0.773450   \n",
+       "2                            0.401885                           0.114583   \n",
+       "3                           -0.160191                           0.242195   \n",
+       "4                            0.682925                           1.075500   \n",
+       "\n",
+       "   Nuclei_Texture_Variance_RNA_10_0  \n",
+       "0                         -0.066115  \n",
+       "1                         -2.749350  \n",
+       "2                         -0.245753  \n",
+       "3                         -0.126886  \n",
+       "4                          0.844115  \n",
+       "\n",
+       "[5 rows x 495 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# aggregate replicates by taking the median of each feature\n",
+    "feature_cols = [c for c in df_consistent.columns if not c.startswith(\"Metadata\")]\n",
+    "df_consistent = df_consistent.groupby(\n",
+    "    [\"Metadata_broad_sample\", \"Metadata_target\"], as_index=False\n",
+    ")[feature_cols].median()\n",
+    "df_consistent[\"Metadata_target\"] = df_consistent[\"Metadata_target\"].str.split(\"|\")\n",
+    "df_consistent.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we again use metadata columns to define grouping of profiles. Here, we'd like to group those compounds that share a target and assess their similarity against compounds that do not have the same target:\n",
+    "\n",
+    "* Two compound profiles are a positive pair if they share the same target. To define that using metadata columns, positive pairs should share the same value in the metadata column that identifies targets (`Metadata_target`). We add this column to a list names `pos_sameby`.\n",
+    "\n",
+    "* In this case, profiles that form a positive pair do not need to be different in any of the metatada columns, so we keep `pos_diffby` empty. Although one could define them as being structurally different, for example.\n",
+    "\n",
+    "* Two profiles are a negative pair when do not share a common target. That means they should be different in the metadata column that identifies targets (`Metadata_target`).\n",
+    "\n",
+    "* Profiles that form a negative pair do not need to be same in any of the metatada columns, so we keep `neg_sameby` empty.\n",
+    "\n",
+    "We use `map.multilabel.average_precision` because each compound can have more than one target. If that's not the case, the standard `map.average_precision` should be used instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1dfaedfbdea44ec4ba89eb7d49a4d9ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7e2c7e2ab214da09395dab1d71b9221",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>Metadata_broad_sample</th>\n",
+       "      <th>average_precision</th>\n",
+       "      <th>n_pos_pairs</th>\n",
+       "      <th>n_total_pairs</th>\n",
+       "      <th>Metadata_target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>BRD-A69636825-003-04-7</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>46</td>\n",
+       "      <td>HTR3A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.396412</td>\n",
+       "      <td>4</td>\n",
+       "      <td>46</td>\n",
+       "      <td>HTR1A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "      <td>HTR1B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "      <td>HTR1D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>BRD-A72309220-001-04-1</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43</td>\n",
+       "      <td>HTR1E</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.094538</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46</td>\n",
+       "      <td>CHRM3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.094538</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46</td>\n",
+       "      <td>CHRM4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>BRD-K74363950-004-01-0</td>\n",
+       "      <td>0.094538</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46</td>\n",
+       "      <td>CHRM5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>BRD-K76908866-001-07-6</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>46</td>\n",
+       "      <td>ERBB2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>BRD-K81258678-001-01-0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>46</td>\n",
+       "      <td>RELA</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>66 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Metadata_broad_sample  average_precision  n_pos_pairs  n_total_pairs  \\\n",
+       "54  BRD-A69636825-003-04-7           0.500000            1             46   \n",
+       "34  BRD-A72309220-001-04-1           0.396412            4             46   \n",
+       "39  BRD-A72309220-001-04-1           0.142857            1             43   \n",
+       "41  BRD-A72309220-001-04-1           0.142857            1             43   \n",
+       "43  BRD-A72309220-001-04-1           0.142857            1             43   \n",
+       "..                     ...                ...          ...            ...   \n",
+       "16  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "19  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "22  BRD-K74363950-004-01-0           0.094538            2             46   \n",
+       "28  BRD-K76908866-001-07-6           0.500000            1             46   \n",
+       "63  BRD-K81258678-001-01-0           0.100000            1             46   \n",
+       "\n",
+       "   Metadata_target  \n",
+       "54           HTR3A  \n",
+       "34           HTR1A  \n",
+       "39           HTR1B  \n",
+       "41           HTR1D  \n",
+       "43           HTR1E  \n",
+       "..             ...  \n",
+       "16           CHRM3  \n",
+       "19           CHRM4  \n",
+       "22           CHRM5  \n",
+       "28           ERBB2  \n",
+       "63            RELA  \n",
+       "\n",
+       "[66 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# positive pairs are compounds that share a target\n",
+    "pos_sameby = [\"Metadata_target\"]\n",
+    "pos_diffby = []\n",
+    "\n",
+    "neg_sameby = []\n",
+    "# negative pairs are compounds that do not share a target\n",
+    "neg_diffby = [\"Metadata_target\"]\n",
+    "\n",
+    "metadata = df_consistent.filter(regex=\"^Metadata\")\n",
+    "profiles = df_consistent.filter(regex=\"^(?!Metadata)\").values\n",
+    "\n",
+    "target_aps = map.multilabel.average_precision(\n",
+    "    metadata,\n",
+    "    profiles,\n",
+    "    pos_sameby=pos_sameby,\n",
+    "    pos_diffby=pos_diffby,\n",
+    "    neg_sameby=neg_sameby,\n",
+    "    neg_diffby=neg_diffby,\n",
+    "    multilabel_col=\"Metadata_target\",\n",
+    ")\n",
+    "target_aps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can compute mAP scores and p-values for each target group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4cf611dd48f421a92039c551475d6e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36db5ef9e7e24a55899d35ab9b7d746a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/27 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>Metadata_target</th>\n",
+       "      <th>mean_average_precision</th>\n",
+       "      <th>p_value</th>\n",
+       "      <th>corrected_p_value</th>\n",
+       "      <th>below_p</th>\n",
+       "      <th>below_corrected_p</th>\n",
+       "      <th>-log10(p-value)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ADRA1A</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.104890</td>\n",
+       "      <td>0.167542</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.775876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ADRA2A</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.104890</td>\n",
+       "      <td>0.167542</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.775876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AURKA</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>0.022298</td>\n",
+       "      <td>0.100340</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.998526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BIRC2</td>\n",
+       "      <td>0.051316</td>\n",
+       "      <td>0.413459</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CHRM1</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CHRM2</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CHRM3</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CHRM4</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>CHRM5</td>\n",
+       "      <td>0.091024</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>0.483152</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.315917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DRD2</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.000900</td>\n",
+       "      <td>0.006074</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.216497</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_target  mean_average_precision   p_value  corrected_p_value  \\\n",
+       "0          ADRA1A                0.238095  0.104890           0.167542   \n",
+       "1          ADRA2A                0.238095  0.104890           0.167542   \n",
+       "2           AURKA                0.625000  0.022298           0.100340   \n",
+       "3           BIRC2                0.051316  0.413459           0.483152   \n",
+       "4           CHRM1                0.091024  0.483152           0.483152   \n",
+       "5           CHRM2                0.091024  0.483152           0.483152   \n",
+       "6           CHRM3                0.091024  0.483152           0.483152   \n",
+       "7           CHRM4                0.091024  0.483152           0.483152   \n",
+       "8           CHRM5                0.091024  0.483152           0.483152   \n",
+       "9            DRD2                0.750000  0.000900           0.006074   \n",
+       "\n",
+       "   below_p  below_corrected_p  -log10(p-value)  \n",
+       "0    False              False         0.775876  \n",
+       "1    False              False         0.775876  \n",
+       "2     True              False         0.998526  \n",
+       "3    False              False         0.315917  \n",
+       "4    False              False         0.315917  \n",
+       "5    False              False         0.315917  \n",
+       "6    False              False         0.315917  \n",
+       "7    False              False         0.315917  \n",
+       "8    False              False         0.315917  \n",
+       "9     True               True         2.216497  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_maps = map.mean_average_precision(\n",
+    "    target_aps, pos_sameby, null_size=10000, threshold=0.05, seed=0\n",
+    ")\n",
+    "target_maps[\"-log10(p-value)\"] = -target_maps[\"corrected_p_value\"].apply(np.log10)\n",
+    "target_maps.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, we can plot the results, where groups of compounds targeting the same gene are called consistent if their corrected p-value < 0.05."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "consistent_ratio = target_maps.below_corrected_p.mean()\n",
+    "\n",
+    "plt.scatter(\n",
+    "    data=target_maps,\n",
+    "    x=\"mean_average_precision\",\n",
+    "    y=\"-log10(p-value)\",\n",
+    "    c=\"below_corrected_p\",\n",
+    "    cmap=\"tab10\",\n",
+    "    s=10,\n",
+    ")\n",
+    "plt.xlabel(\"mAP\")\n",
+    "plt.ylabel(\"-log10(p-value)\")\n",
+    "plt.axhline(-np.log10(0.05), color=\"black\", linestyle=\"--\")\n",
+    "plt.text(\n",
+    "    0.5,\n",
+    "    1.5,\n",
+    "    f\"Phenotypically consistent = {100*consistent_ratio:.2f}%\",\n",
+    "    va=\"center\",\n",
+    "    ha=\"left\",\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can list compounds that are phenotypically active and consistent.\n",
+    "\n",
+    "Note that in multi-label scenario, when each compound can have multiple targets, the same compound can have \"consistent\" response in respect to one target, but not another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Phenotypically consistent targets: DRD2, EGFR, HTR3A, PSMB1\n",
+      "Phenotypically consistent compounds: BRD-A69636825-003-04-7, BRD-K50691590-001-02-2, BRD-K60230970-001-10-0, BRD-K70330367-003-07-9, BRD-K70358946-001-15-7, BRD-K70401845-003-09-6, BRD-K70914287-300-02-8\n"
+     ]
+    }
+   ],
+   "source": [
+    "consistent_targets = target_maps.query(\"below_corrected_p\")[\"Metadata_target\"]\n",
+    "consistent_compounds = df_consistent[\n",
+    "    df_consistent[\"Metadata_target\"].apply(\n",
+    "        lambda x: any(t in x for t in consistent_targets)\n",
+    "    )\n",
+    "][\"Metadata_broad_sample\"]\n",
+    "\n",
+    "print(f\"Phenotypically consistent targets: {consistent_targets.str.cat(sep=', ')}\")\n",
+    "print(f\"Phenotypically consistent compounds: {consistent_compounds.str.cat(sep=', ')}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "copairs",
+   "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": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 5489728..2133055 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,14 @@
 [project]
 name = "copairs"
-version = "0.4.1"
+version = "0.4.2"
 description = "Find pairs and compute metrics between them"
 readme = "README.md"
 requires-python = ">=3.8"
 license = {file = "LICENSE.txt"}
 keywords = ["pairwise", "replication"]
 authors = [
-  {name = "John Arevalo", email = "johnarevalo@gmail.com" }
+  { name = "John Arevalo", email = "johnarevalo@gmail.com" },
+  { name = "Alexandr Kalinin", email = "akalinin@broadinstitute.org" }
 ]
 dependencies = [
   "pandas",
@@ -17,6 +18,8 @@ dependencies = [
 
 [project.optional-dependencies]
 plot = ["plotly"]
+test = ["scikit-learn", "pytest"]
+demo = ["notebook", "matplotlib"]
 
 [project.urls]
 "Homepage" = "https://github.com/cytomining/copairs"
diff --git a/src/copairs/__init__.py b/src/copairs/__init__.py
index d07a328..ee93afd 100644
--- a/src/copairs/__init__.py
+++ b/src/copairs/__init__.py
@@ -1,4 +1,7 @@
 """
 Package to create pairwise lists based on sameby and diffby criteria
 """
+
 from .matching import Matcher, MatcherMultilabel
+
+__all__ = ["Matcher", "MatcherMultilabel"]
diff --git a/src/copairs/compute.py b/src/copairs/compute.py
index d290af8..954bf01 100644
--- a/src/copairs/compute.py
+++ b/src/copairs/compute.py
@@ -41,7 +41,6 @@ def par_func(i):
     return batched_fn
 
 
-@batch_processing
 def pairwise_corr(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     """
     Compute pearson correlation between two matrices in a paired row-wise
@@ -62,7 +61,6 @@ def pairwise_corr(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return corrs
 
 
-@batch_processing
 def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     x_norm = x_sample / np.linalg.norm(x_sample, axis=1)[:, np.newaxis]
     y_norm = y_sample / np.linalg.norm(y_sample, axis=1)[:, np.newaxis]
@@ -70,6 +68,49 @@ def pairwise_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
     return c_sim
 
 
+def pairwise_abs_cosine(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    return np.abs(pairwise_cosine(x_sample, y_sample))
+
+
+def pairwise_euclidean(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    e_dist = np.sqrt(np.sum((x_sample - y_sample) ** 2, axis=1))
+    return 1 / (1 + e_dist)
+
+
+def pairwise_manhattan(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    m_dist = np.sum(np.abs(x_sample - y_sample), axis=1)
+    return 1 / (1 + m_dist)
+
+
+def pairwise_chebyshev(x_sample: np.ndarray, y_sample: np.ndarray) -> np.ndarray:
+    c_dist = np.max(np.abs(x_sample - y_sample), axis=1)
+    return 1 / (1 + c_dist)
+
+
+def get_distance_fn(distance):
+    distance_metrics = {
+        "abs_cosine": pairwise_abs_cosine,
+        "cosine": pairwise_cosine,
+        "correlation": pairwise_corr,
+        "euclidean": pairwise_euclidean,
+        "manhattan": pairwise_manhattan,
+        "chebyshev": pairwise_chebyshev,
+    }
+
+    if isinstance(distance, str):
+        if distance not in distance_metrics:
+            raise ValueError(
+                f"Unsupported distance metric: {distance}. Supported metrics are: {list(distance_metrics.keys())}"
+            )
+        distance_fn = distance_metrics[distance]
+    elif callable(distance):
+        distance_fn = distance
+    else:
+        raise ValueError("Distance must be either a string or a callable object.")
+
+    return batch_processing(distance_fn)
+
+
 def random_binary_matrix(n, m, k, rng):
     """Generate a random binary matrix of n*m with exactly k values in 1 per row.
     Args:
diff --git a/src/copairs/map/__init__.py b/src/copairs/map/__init__.py
index c646fd6..0e1998c 100644
--- a/src/copairs/map/__init__.py
+++ b/src/copairs/map/__init__.py
@@ -1,3 +1,5 @@
-from .map import mean_average_precision
 from . import multilabel
 from .average_precision import average_precision
+from .map import mean_average_precision
+
+__all__ = ["mean_average_precision", "multilabel", "average_precision"]
diff --git a/src/copairs/map/average_precision.py b/src/copairs/map/average_precision.py
index c335bfa..10b481a 100644
--- a/src/copairs/map/average_precision.py
+++ b/src/copairs/map/average_precision.py
@@ -28,15 +28,24 @@ def build_rank_lists(pos_pairs, neg_pairs, pos_sims, neg_sims):
 
 
 def average_precision(
-    meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby, batch_size=20000
+    meta,
+    feats,
+    pos_sameby,
+    pos_diffby,
+    neg_sameby,
+    neg_diffby,
+    batch_size=20000,
+    distance="cosine",
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
+    distance_fn = compute.get_distance_fn(distance)
 
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
     logger.info("Indexing metadata...")
-    matcher = Matcher(*evaluate_and_filter(meta, columns), seed=0)
+    matcher = Matcher(meta, columns, seed=0)
 
     logger.info("Finding positive pairs...")
     pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
@@ -61,10 +70,10 @@ def average_precision(
     )
 
     logger.info("Computing positive similarities...")
-    pos_sims = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+    pos_sims = distance_fn(feats, pos_pairs, batch_size)
 
     logger.info("Computing negative similarities...")
-    neg_sims = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+    neg_sims = distance_fn(feats, neg_pairs, batch_size)
 
     logger.info("Building rank lists...")
     paired_ix, rel_k_list, counts = build_rank_lists(
diff --git a/src/copairs/map/filter.py b/src/copairs/map/filter.py
index ff94328..c2956da 100644
--- a/src/copairs/map/filter.py
+++ b/src/copairs/map/filter.py
@@ -2,8 +2,8 @@
 import re
 from typing import List, Tuple
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 
 def validate_pipeline_input(meta, feats, columns):
@@ -30,22 +30,49 @@ def flatten_str_list(*args):
 
 
 def evaluate_and_filter(df, columns) -> Tuple[pd.DataFrame, List[str]]:
-    """Evaluate the query and filter the dataframe"""
+    """Evaluate queries and filter the dataframe"""
+    query_list, columns = extract_filters(columns, df.columns)
+    df = apply_filters(df, query_list)
+    return df, columns
+
+
+def extract_filters(columns, df_columns) -> Tuple[List[str], List[str]]:
+    """Extract and validate filters from columns"""
     parsed_cols = []
+    queries_to_eval = []
+
     for col in columns:
-        if col in df.columns:
+        if col in df_columns:
             parsed_cols.append(col)
             continue
-
         column_names = re.findall(r"(\w+)\s*[=<>!]+", col)
-        valid_column_names = [col for col in column_names if col in df.columns]
+
+        valid_column_names = [col for col in column_names if col in df_columns]
         if not valid_column_names:
             raise ValueError(f"Invalid query or column name: {col}")
 
-        try:
-            df = df.query(col)
-            parsed_cols.extend(valid_column_names)
-        except:
-            raise ValueError(f"Invalid query expression: {col}")
+        queries_to_eval.append(col)
+        parsed_cols.extend(valid_column_names)
+
+        if len(parsed_cols) != len(set(parsed_cols)):
+            raise ValueError(f"Duplicate queries for column: {col}")
+
+    return queries_to_eval, parsed_cols
+
+
+def apply_filters(df, query_list):
+    """Combine and apply filters to dataframe"""
+    if not query_list:
+        return df
+
+    combined_query = " & ".join(f"({query})" for query in query_list)
+    try:
+        df_filtered = df.query(combined_query)
+        if df_filtered.empty:
+            raise ValueError(f"No data matched the query: {combined_query}")
+    except Exception as e:
+        raise ValueError(
+            f"Invalid combined query expression: {combined_query}. Error: {e}"
+        )
 
-    return df, parsed_cols
+    return df_filtered
diff --git a/src/copairs/map/multilabel.py b/src/copairs/map/multilabel.py
index 2961b42..ff124a3 100644
--- a/src/copairs/map/multilabel.py
+++ b/src/copairs/map/multilabel.py
@@ -74,16 +74,17 @@ def average_precision(
     neg_diffby,
     multilabel_col,
     batch_size=20000,
+    distance="cosine",
 ) -> pd.DataFrame:
     columns = flatten_str_list(pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    meta, columns = evaluate_and_filter(meta, columns)
     validate_pipeline_input(meta, feats, columns)
+    distance_fn = compute.get_distance_fn(distance)
     # Critical!, otherwise the indexing wont work
     meta = meta.reset_index(drop=True).copy()
 
     logger.info("Indexing metadata...")
-    matcher = MatcherMultilabel(
-        *evaluate_and_filter(meta, columns), multilabel_col=multilabel_col, seed=0
-    )
+    matcher = MatcherMultilabel(meta, columns, multilabel_col=multilabel_col, seed=0)
 
     logger.info("Finding positive pairs...")
     pos_pairs = matcher.get_all_pairs(sameby=pos_sameby, diffby=pos_diffby)
@@ -113,10 +114,10 @@ def average_precision(
     neg_pairs = np.unique(neg_pairs, axis=0)
 
     logger.info("Computing positive similarities...")
-    pos_sims = compute.pairwise_cosine(feats, pos_pairs, batch_size)
+    pos_sims = distance_fn(feats, pos_pairs, batch_size)
 
     logger.info("Computing negative similarities...")
-    neg_sims = compute.pairwise_cosine(feats, neg_pairs, batch_size)
+    neg_sims = distance_fn(feats, neg_pairs, batch_size)
 
     logger.info("Computing AP per label...")
     negs_for = create_neg_query_solver(neg_pairs, neg_sims)
diff --git a/src/copairs/matching.py b/src/copairs/matching.py
index f7ebd68..f840b1c 100644
--- a/src/copairs/matching.py
+++ b/src/copairs/matching.py
@@ -1,11 +1,12 @@
 """
 Sample pairs with given column restrictions
 """
-from collections import namedtuple
+
 import itertools
 import logging
-from math import comb
 import re
+from collections import namedtuple
+from math import comb
 from typing import Dict, Sequence, Set, Union
 
 import numpy as np
@@ -442,5 +443,8 @@ def _only_diffby_multi(self):
         pairs = itertools.chain.from_iterable(pairs.values())
         pairs = set(map(frozenset, pairs))
         all_pairs = itertools.combinations(range(self.size), 2)
-        filter_fn = lambda x: set(x) not in pairs
+
+        def filter_fn(x):
+            return set(x) not in pairs
+
         return {None: list(filter(filter_fn, all_pairs))}
diff --git a/src/copairs/plot.py b/src/copairs/plot.py
index cbe7704..eea7010 100644
--- a/src/copairs/plot.py
+++ b/src/copairs/plot.py
@@ -1,4 +1,5 @@
 from typing import Optional
+
 from plotly import graph_objects as go
 from plotly.subplots import make_subplots
 
diff --git a/src/copairs/replicating.py b/src/copairs/replicating.py
index c1311e0..3674f42 100644
--- a/src/copairs/replicating.py
+++ b/src/copairs/replicating.py
@@ -1,17 +1,20 @@
 """Class for getting Percent replicating metric"""
+
 from typing import List, Literal
 
 import numpy as np
 import pandas as pd
 
-from copairs.compute import pairwise_corr
+from copairs.compute import get_distance_fn
+
 from .matching import Matcher
 
 
 def corr_from_null_pairs(X: np.ndarray, null_pairs, n_replicates):
     """Correlation from a given list of unnamed pairs."""
     null_pairs = np.asarray(null_pairs, int)
-    corrs = pairwise_corr(X, null_pairs, batch_size=20000)
+    corr_fn = get_distance_fn("correlation")
+    corrs = corr_fn(X, null_pairs, batch_size=20000)
     corrs = corrs.reshape(-1, n_replicates)
     null_dist = np.nanmedian(corrs, axis=1)
     return pd.Series(null_dist)
@@ -56,7 +59,8 @@ def corr_from_pairs(X: np.ndarray, pairs: dict, sameby: List[str]):
     list-like of correlation values and median of number of replicates
     """
     pair_ix = np.vstack(list(pairs.values()))
-    corrs = pairwise_corr(X, pair_ix, batch_size=20000)
+    corr_fn = get_distance_fn("correlation")
+    corrs = corr_fn(X, pair_ix, batch_size=20000)
     counts = [len(v) for v in pairs.values()]
 
     if len(sameby) == 1:


Test patch:
diff --git a/tests/helpers.py b/tests/helpers.py
index 49a11e3..a7a4c25 100644
--- a/tests/helpers.py
+++ b/tests/helpers.py
@@ -1,8 +1,8 @@
 from itertools import product
 from typing import Dict
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 from copairs.matching import ColumnList
 
diff --git a/tests/test_compute.py b/tests/test_compute.py
index 696d965..63444c7 100644
--- a/tests/test_compute.py
+++ b/tests/test_compute.py
@@ -1,4 +1,3 @@
-import pytest
 import numpy as np
 
 from copairs import compute
@@ -24,6 +23,18 @@ def cosine_naive(feats, pairs):
     return cosine
 
 
+def euclidean_naive(feats, pairs):
+    euclidean_sim = np.empty((len(pairs),))
+    for pos, (i, j) in enumerate(pairs):
+        dist = np.linalg.norm(feats[i] - feats[j])
+        euclidean_sim[pos] = 1 / (1 + dist)
+    return euclidean_sim
+
+
+def abs_cosine_naive(feats, pairs):
+    return np.abs(cosine_naive(feats, pairs))
+
+
 def test_corrcoef():
     n_samples = 10
     n_pairs = 20
@@ -33,7 +44,8 @@ def test_corrcoef():
     pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
 
     corr_gt = corrcoef_naive(feats, pairs)
-    corr = compute.pairwise_corr(feats, pairs, batch_size)
+    corr_fn = compute.get_distance_fn("correlation")
+    corr = corr_fn(feats, pairs, batch_size)
     assert np.allclose(corr_gt, corr)
 
 
@@ -46,5 +58,34 @@ def test_cosine():
     pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
 
     cosine_gt = cosine_naive(feats, pairs)
-    cosine = compute.pairwise_cosine(feats, pairs, batch_size)
+    cosine_fn = compute.get_distance_fn("cosine")
+    cosine = cosine_fn(feats, pairs, batch_size)
     assert np.allclose(cosine_gt, cosine)
+
+
+def test_euclidean():
+    n_samples = 10
+    n_pairs = 20
+    n_feats = 5
+    batch_size = 4
+    feats = rng.uniform(0, 1, [n_samples, n_feats])
+    pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
+
+    euclidean_gt = euclidean_naive(feats, pairs)
+    euclidean_fn = compute.get_distance_fn("euclidean")
+    euclidean = euclidean_fn(feats, pairs, batch_size)
+    assert np.allclose(euclidean_gt, euclidean)
+
+
+def test_abs_cosine():
+    n_samples = 10
+    n_pairs = 20
+    n_feats = 5
+    batch_size = 4
+    feats = rng.uniform(0, 1, [n_samples, n_feats])
+    pairs = rng.integers(0, n_samples - 1, [n_pairs, 2])
+
+    abs_cosine_gt = abs_cosine_naive(feats, pairs)
+    abs_cosine_fn = compute.get_distance_fn("abs_cosine")
+    abs_cosine = abs_cosine_fn(feats, pairs, batch_size)
+    assert np.allclose(abs_cosine_gt, abs_cosine)
diff --git a/tests/test_map.py b/tests/test_map.py
index 907ba31..816d7d8 100644
--- a/tests/test_map.py
+++ b/tests/test_map.py
@@ -1,7 +1,7 @@
+import numpy as np
 import pandas as pd
 import pytest
 from sklearn.metrics import average_precision_score
-import numpy as np
 
 from copairs import compute
 from copairs.map import average_precision
@@ -140,8 +140,8 @@ def test_raise_no_pairs():
         average_precision(meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
     with pytest.raises(UnpairedException, match="Unable to find negative pairs."):
         average_precision(meta, feats, pos_diffby, [], pos_sameby, [])
-        
-        
+
+
 def test_raise_nan_error():
     length = 10
     vocab_size = {"p": 5, "w": 3, "l": 4}
@@ -154,14 +154,20 @@ def test_raise_nan_error():
     meta = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
     length = len(meta)
     feats = rng.uniform(size=(length, n_feats))
-    
+
     # add null values
     feats_nan = feats.copy()
-    feats_nan[2,2] = None
+    feats_nan[2, 2] = None
     meta_nan = meta.copy()
-    meta_nan.loc[1,"p"] = None
-    
+    meta_nan.loc[1, "p"] = None
+
     with pytest.raises(ValueError, match="features should not have null values."):
-        average_precision(meta, feats_nan, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
-    with pytest.raises(ValueError, match="metadata columns should not have null values."):
-        average_precision(meta_nan, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+        average_precision(
+            meta, feats_nan, pos_sameby, pos_diffby, neg_sameby, neg_diffby
+        )
+    with pytest.raises(
+        ValueError, match="metadata columns should not have null values."
+    ):
+        average_precision(
+            meta_nan, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby
+        )
diff --git a/tests/test_map_filter.py b/tests/test_map_filter.py
new file mode 100644
index 0000000..9b1b311
--- /dev/null
+++ b/tests/test_map_filter.py
@@ -0,0 +1,45 @@
+import numpy as np
+import pytest
+
+from copairs.map.filter import evaluate_and_filter
+from tests.helpers import simulate_random_dframe
+
+SEED = 0
+
+
+@pytest.fixture
+def mock_dataframe():
+    length = 10
+    vocab_size = {"p": 3, "w": 3, "l": 10}
+    pos_sameby = ["l"]
+    pos_diffby = ["p"]
+    rng = np.random.default_rng(SEED)
+    df = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
+    df.drop_duplicates(subset=pos_sameby, inplace=True)
+    return df
+
+
+def test_correct(mock_dataframe):
+    df, parsed_cols = evaluate_and_filter(mock_dataframe, ["p == 'p1'", "w > 'w2'"])
+    assert not df.empty
+    assert "p" in parsed_cols and "w" in parsed_cols
+    assert all(df["w"].str.extract(r"(\d+)")[0].astype(int) > 2)
+
+
+def test_invalid_query(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['l == "lHello"'])
+    assert "Invalid combined query expression" in str(excinfo.value)
+    assert "No data matched the query" in str(excinfo.value)
+
+
+def test_empty_result(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['p == "p1"', 'p == "p2"'])
+    assert "Duplicate queries for column" in str(excinfo.value)
+
+
+def test_empty_result_from_valid_query(mock_dataframe):
+    with pytest.raises(ValueError) as excinfo:
+        evaluate_and_filter(mock_dataframe, ['p == "p4"'])
+    assert "No data matched the query" in str(excinfo.value)
diff --git a/tests/test_matching.py b/tests/test_matching.py
index ad95625..5bc1132 100644
--- a/tests/test_matching.py
+++ b/tests/test_matching.py
@@ -1,4 +1,5 @@
 """Test functions for Matcher"""
+
 from string import ascii_letters
 
 import numpy as np
diff --git a/tests/test_matching_multilabel.py b/tests/test_matching_multilabel.py
index 856d29f..50f978e 100644
--- a/tests/test_matching_multilabel.py
+++ b/tests/test_matching_multilabel.py
@@ -1,4 +1,5 @@
 import pandas as pd
+
 from copairs.matching import MatcherMultilabel
 from tests.helpers import simulate_random_plates
 
diff --git a/tests/test_replicating.py b/tests/test_replicating.py
index 2e1b016..a273bbe 100644
--- a/tests/test_replicating.py
+++ b/tests/test_replicating.py
@@ -3,10 +3,9 @@
 from copairs import Matcher
 from copairs.replicating import (
     corr_between_replicates,
-    correlation_test,
     corr_from_pairs,
+    correlation_test,
 )
-
 from tests.helpers import create_dframe
 
 SEED = 0