{ "cells": [ { "cell_type": "markdown", "id": "ce5e74ca", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "f556d558afa992735daacfe80ab0daca", "grade": false, "grade_id": "cell-e12df818732a12f6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "\n", "# Exercise Sheet No. 10\n", "\n", "---\n", "\n", "> Machine Learning for Natural Sciences, Summer 2023, TT.-Prof. Pascal Friederich, pascal.friederich@kit.edu\n", "> \n", "> Deadline: July 2nd 2024, 8:00 am\n", ">\n", "> Tutor: jonas.teufel@kit.edu\n", ">\n", "> **Please ask questions in the forum/discussion board and only contact the Tutor when there are issues with the grading**\n", "---\n", "\n", "\n", "**Topic**: This exercise sheet will introduce you to machine learning on graphs. Specifically, we'll be looking at graph neural networks (GNN) and apply them to the prediction of blood brain barrier penetration." ] }, { "cell_type": "markdown", "id": "bc0a7217", "metadata": {}, "source": [ "Please add here your group members' names and student IDs. \n", "\n", "Names: \n", "\n", "IDs:" ] }, { "cell_type": "markdown", "id": "20f770c9", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "940809d8b3b079377e3a2af3ea69b596", "grade": false, "grade_id": "cell-56d4d2e446991fe5", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**Installing RDKIT Library.** For this exercise we are going to need an additional library called [RDKit](https://www.rdkit.org/docs/) that is not installed in Google Colab by default. The following code cell will install the library into your current jupyter environment" ] }, { "cell_type": "code", "execution_count": 1, "id": "f62eb097", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "8d84272d93d463818a4cb026e40df090", "grade": false, "grade_id": "cell-9a3e55d18caa7709", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;31merror\u001b[0m: \u001b[1mexternally-managed-environment\u001b[0m\n", "\n", "\u001b[31m×\u001b[0m This environment is externally managed\n", "\u001b[31m╰─>\u001b[0m To install Python packages system-wide, try apt install\n", "\u001b[31m \u001b[0m python3-xyz, where xyz is the package you are trying to\n", "\u001b[31m \u001b[0m install.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m If you wish to install a non-Debian-packaged Python package,\n", "\u001b[31m \u001b[0m create a virtual environment using python3 -m venv path/to/venv.\n", "\u001b[31m \u001b[0m Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make\n", "\u001b[31m \u001b[0m sure you have python3-full installed.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m If you wish to install a non-Debian packaged Python application,\n", "\u001b[31m \u001b[0m it may be easiest to use pipx install xyz, which will manage a\n", "\u001b[31m \u001b[0m virtual environment for you. Make sure you have pipx installed.\n", "\u001b[31m \u001b[0m \n", "\u001b[31m \u001b[0m See /usr/share/doc/python3.11/README.venv for more information.\n", "\n", "\u001b[1;35mnote\u001b[0m: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.\n", "\u001b[1;36mhint\u001b[0m: See PEP 668 for the detailed specification.\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "!pip install rdkit\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 2, "id": "6df97a70", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "182d91a51c050631ed6540a574da32ba", "grade": false, "grade_id": "cell-d81e3d7913409a61", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "import os\n", "import io\n", "import csv\n", "import random\n", "import itertools\n", "import textwrap\n", "import importlib\n", "import importlib.util\n", "import hashlib\n", "import tempfile\n", "from collections import defaultdict\n", "from copy import deepcopy\n", "\n", "import requests\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import networkx as nx\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import rdkit\n", "from rdkit import Chem\n", "from rdkit.Chem import Draw\n", "from sklearn.metrics import accuracy_score\n", "\n", "\n", "def hashcheck(value: str) -> str:\n", " \"\"\"\n", " Computes the hash of a string.\n", " \"\"\"\n", " return hashlib.sha256(value.encode()).hexdigest()[:16]\n", "\n", "\n", "def nextcloud_download(url: str, raw: bool = False) -> str:\n", " \"\"\"\n", " Downloads the content of a file from a nextcloud server and returns \n", " it eithers as a string or a bytes object if the ``raw`` flag is set.\n", " \"\"\"\n", " response = requests.get(f'{url}/download')\n", " content = response.content\n", " if not raw:\n", " content = content.decode('utf-8')\n", " \n", " return content\n", "\n", "\n", "def nextcloud_import(url: str, name: str = 'module') -> 'Module':\n", " \"\"\"\n", " Downloads the content of a python module file from a nextcloud server \n", " and returns the imported module instance.\n", " \"\"\"\n", " with tempfile.TemporaryDirectory() as path:\n", " file_path = os.path.join(path, f'{name}.py')\n", " with open(file_path, 'w') as file:\n", " content = nextcloud_download(url)\n", " file.write(content)\n", " \n", " spec = importlib.util.spec_from_file_location(name, file_path)\n", " module = importlib.util.module_from_spec(spec)\n", " spec.loader.exec_module(module)\n", " \n", " return module\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "df953d36", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "0c1dd68fa14b3ce88223996f6ebc890a", "grade": false, "grade_id": "cell-a24430a8ffa75cf0", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# 10 Machine Learning for Graphs\n", "\n", "\n", "## 10.1 Graph Theory\n", "\n", "From [wikipedia](https://en.wikipedia.org/wiki/Graph_theory): \"In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices (also called nodes or points) which are connected by edges (also called links or lines). A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges link two vertices asymmetrically. Graphs are one of the principal objects of study in discrete mathematics.\"\n", "\n", "In one restricted but very common sense of the term, a graph is an ordered pair $G = ( V , E )$ comprising:\n", "\n", "* The vertex set $V$ of vertices (also called nodes or points);\n", "* The edge set $E\\subseteq \\{ \\{x, y\\} \\mid x, y \\in V \\}$ edges (also called links or lines), which are unordered pairs of vertices (that is, an edge is associated with two distinct vertices).\n", "\n", "To avoid ambiguity, this type of object may be called precisely an undirected simple graph. " ] }, { "cell_type": "code", "execution_count": 3, "id": "760abe09", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "6061b58820acd4e1fc52af9f25d57c4e", "grade": false, "grade_id": "cell-0268a3c2d823ea3c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "# Example of a graph\n", "G = nx.krackhardt_kite_graph()\n", "nx.draw(G, pos=nx.kamada_kawai_layout(G))\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "fac5b973", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "34705192a97eee0bc6c6db477ef01b40", "grade": false, "grade_id": "cell-ae513ca0cdb0800f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## 10.2 Graph convolutional neural networks\n", "\n", "Graph convolutional neural networks are a natural extension of CNNs for graph-structured data. A simple but efficient Graph Neural Network was introduced in [\"Semi-Supervised Classification with Graph Convolutional Networks\"](https://arxiv.org/abs/1609.02907) by Kipf et al. (2016). Below is a description of the model and its applications taken are from https://tkipf.github.io/graph-convolutional-networks/. We will implement this model and test it on some graph data.\n", "\n", "**Definitions.** Currently, most graph neural network models have a somewhat universal architecture in common. We will refer to these models as Graph Convolutional Networks (GCNs); convolutional, because filter parameters are typically shared over all locations in the graph (or a subset thereof as in [Duvenaud et al., NIPS 2015](https://proceedings.neurips.cc/paper/2015/hash/f9be311e65d81a9ad8150a60844bb94c-Abstract.html)).\n", "\n", "For these models, the goal is then to learn a function of signals/features on a graph $G = (V, E)$ which takes as input:\n", "\n", "* A feature description $x_i$ for every node $i$; summarized in a $N\\times D$ feature matrix $X$ ($N$: number of nodes, $D$: number of input features)\n", "* A representative description of the graph structure in matrix form; typically in the form of an [adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix) $A$ (or some function thereof)\n", "\n", "and produces a node-level output $Z$ (an $N\\times F$ feature matrix, where $F$ is the number of output features per node). Graph-level outputs can be modeled by introducing some form of pooling operation (see, e.g. [Duvenaud et al., NIPS 2015](https://proceedings.neurips.cc/paper/2015/hash/f9be311e65d81a9ad8150a60844bb94c-Abstract.html)).\n", "\n", "Every neural network layer can then be written as a non-linear function\n", "\n", "$$H^{(l+1)}=f(H^{(l)}, A),$$\n", "\n", "with $H^{(0)}=X$ and $H^{(L)}=Z$ (or $z$ for graph-level outputs), $L$ being the number of layers. The specific models then differ only in how $f(⋅,⋅)$ is chosen and parameterized.\n", "\n", "**The GCN Layer.** As an example, let's consider the following very simple form of a layer-wise propagation rule:\n", "\n", "$$f(H^{(l)},A)= g(AH^{(l)}W^{(l)}),$$\n", "\n", "where $W^{(l)}$ is a weight matrix for the $l$-th neural network layer and $g(⋅)$ is a non-linear activation function like the ReLU. Despite its simplicity this model is already quite powerful.\n", "\n", "But first, let us address two limitations of this simple model: multiplication with $A$ means that, for every node, we sum up all the feature vectors of all neighboring nodes but not the node itself (unless there are self-loops in the graph). We can \"fix\" this by enforcing self-loops in the graph: we simply add the identity matrix to $A$.\n", "\n", "The second major limitation is that $A$ is typically not normalized and therefore the multiplication with $A$ will completely change the scale of the feature vectors proportional to a node's degree. Normalizing $A$ such that all rows sum to one, i.e. $D^{−1}A$, where $D$ is the diagonal node degree matrix, gets rid of this problem. Multiplying with $D^{−1}A$ now corresponds to taking the average of neighboring node features. In practice, dynamics get more interesting when we use a symmetric normalization, i.e. $D^{−\\frac{1}{2}} A D^{−\\frac{1}{2}}$ (as this no longer amounts to mere averaging of neighboring nodes). Combining these two tricks, we essentially arrive at the propagation rule introduced in [Kipf & Welling](https://arxiv.org/abs/1609.02907) (ICLR 2017):\n", "\n", "$$f(H^{(l)},A)=g \\, ( \\hat{D}^{−\\frac{1}{2}} \\hat{A} \\hat{D}^{−\\frac{1}{2}} H^{(l)}W^{(l)}),$$\n", "\n", "with $\\hat{A}=A+I$, where $I$ is the identity matrix and $\\hat{D}$ is the diagonal node degree matrix of $\\hat{A}$." ] }, { "cell_type": "markdown", "id": "c89b9978", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "cb5ae5e95139e463f0d8ba50ad285aaa", "grade": false, "grade_id": "cell-9691d7e3b67b0bbe", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**A Simple Example.** To gain a better understanding of how the GCN update rule can be implemented, we are first going to look at how all the different elements of the update function evaluate for the following simple example graph ``g``. This graph consists of only 3 nodes which are connected in a circular pattern and can be visualized as a kind of triangle pattern. Each node of the graph is associated with a two-element feature vector (e.g. [0, 1] or [1, 1]) which we'll consider as the input features $X$. Additionally, the graph has *weighted* edges where each edge is associated with a single edge weight value." ] }, { "cell_type": "code", "execution_count": 4, "id": "7a8e2056", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "cac6af275c5604bc700606d5b454da3f", "grade": false, "grade_id": "cell-a5ed375c8d932c69", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "visualization of the example graph:\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "g = nx.Graph()\n", "\n", "# We want the graph to have 3 nodes\n", "g.add_node(0, features=[0, 0])\n", "g.add_node(1, features=[0, 1])\n", "g.add_node(2, features=[1, 0])\n", "\n", "# and those nodes should be connected in a triangle pattern\n", "g.add_edge(0, 1, weight=2)\n", "g.add_edge(1, 2, weight=1)\n", "g.add_edge(2, 0, weight=2)\n", "\n", "print('visualization of the example graph:')\n", "pos = nx.circular_layout(g)\n", "nx.draw_networkx_nodes(g, pos, node_color='lightblue')\n", "nx.draw_networkx_edges(g, pos)\n", "\n", "node_labels = {index: f'{index}: {features}' for index, features in nx.get_node_attributes(g, 'features').items()}\n", "nx.draw_networkx_labels(g, pos, labels=node_labels, font_size=10)\n", "\n", "edge_labels = nx.get_edge_attributes(g, 'weight')\n", "nx.draw_networkx_edge_labels(g, pos, edge_labels=edge_labels)\n", "\n", "plt.show()\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "85b74491", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "6e48a4fbe6ab67ceb1664e41af237109", "grade": false, "grade_id": "cell-af595a3a53294b4e", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.1 (2 points)** Manually create numpy arrays corresponding to the node feature matrix $X$ and the node adjacency matrix $A$ for the previously introduced example graph into the corresponding variables ``node_attributes`` and ``node_adjacency``. For this purpose, use the integer node indices shown in the illustration above as the corresponding row/column indices of these matrices." ] }, { "cell_type": "code", "execution_count": 5, "id": "b1421c08", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "962159ac8924f4597b02092b7d12fece", "grade": false, "grade_id": "ans-10-1", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Fill in the node features matrix and adjacency matrix for the previously \n", "# introduced graph.\n", "\n", "# HINT: Recall how edge weights are included into an adjacency matrix!\n", "\n", "node_attributes: np.ndarray = None\n", "node_adjacency: np.ndarray = None\n", "\n", "node_attributes: np.ndarray = np.array([[0,0],[0,1],[1,0]])\n", "node_adjacency: np.ndarray = np.array([\n", "[0,2,2],\n", "[2,0,1],\n", "[2,1,0]\n", "])" ] }, { "cell_type": "code", "execution_count": 6, "id": "a82ff33f", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "86a629b919fa021f46f52a18ab9d1ddc", "grade": true, "grade_id": "test-10-1-matrices", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-1-matrices - possible points: 2\n", "\n", "assert isinstance(node_attributes, np.ndarray)\n", "assert len(node_attributes.shape) == 2, 'node attributes need to be 2d matrix'\n", "assert np.isclose(np.sum(node_attributes), 2), 'node attributes likely incorrect'\n", "\n", "assert isinstance(node_adjacency, np.ndarray)\n", "assert len(node_adjacency.shape) == 2, 'adjacency needs to be 2d matrix'\n", "assert np.isclose(np.sum(node_adjacency), 10), 'node adjacency likely incorrect'\n", "assert np.isclose(node_adjacency, node_adjacency.T).all(), 'adjacency matrix needs to be symmetrical'\n", "\n", "# NOTE: The hidden tests will check for the exact values of the numpy arrays\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "c6fdaca5", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "766fe54652b1583d79d2f92421c372bb", "grade": false, "grade_id": "cell-b4f67cf48f66c85d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**Node Degree Matrix.** Another important part of the GCN update function is the diagonal node degree matrix $D$, which is used to normalize the adjacency matrix. At each diagonal position, this matrix should have the cumulative node degree of the node with that corresponding index." ] }, { "cell_type": "markdown", "id": "fa9e5b3d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "60b7e38964499042557ecf572bc7a1a0", "grade": false, "grade_id": "cell-151eb92bd23fa394", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.2 (2 points)** Manually fill in the diagonal node degree matrix $D$ for the example graph into the corresponding variable ``node_degrees``. Additionally compute the squared node degrees matrix $D^2$ to populate the ``node_degrees_squared`` variable." ] }, { "cell_type": "code", "execution_count": 7, "id": "0eae1d1d", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "769df0ee9f27137bb50a867e7c350c3c", "grade": false, "grade_id": "ans-10-2", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Create the following numpy arrays to represent the diagonal node degree and squared \n", "# node degree matrix of the givne example graph.\n", "\n", "# HINT: Recall how applying a power to a matrix can be simplified for diagonal matrices!\n", "\n", "node_degrees: np.ndarray = np.diag([2,2,2])\n", "node_degrees_squared: np.ndarray = np.diag([4,4,4])" ] }, { "cell_type": "code", "execution_count": 8, "id": "b8c0a601", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "b8460d58a1b05e9ce04b238fc776d48d", "grade": true, "grade_id": "test-10-2-node-degrees", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-2-node-degrees - possible points: 2\n", "\n", "assert isinstance(node_degrees, np.ndarray)\n", "assert len(node_degrees.shape) == 2, 'node degrees should be 2d matrix'\n", "assert np.isclose(node_degrees, node_degrees.T).all(), 'node degrees matrix should be symmetrical'\n", "\n", "assert isinstance(node_degrees_squared, np.ndarray)\n", "assert len(node_degrees_squared.shape) == 2, 'node degrees squared should be 2d matrix'\n", "assert np.isclose(node_degrees_squared, node_degrees_squared.T).all(), 'node degrees squared should be symmetrical'\n", "\n", "# NOTE: The hidden tests will check for the exact values of the numpy arrays\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "841df698", "metadata": {}, "source": [ "**GCN Implementation.** As previously introduced, the *Graph Convolutional Network (GCN)* layers can be implemented with the following update rule\n", "\n", "$$H^{(l+1)} = f(H^{(l)},A)=g \\, ( \\hat{D}^{−\\frac{1}{2}} \\hat{A} \\hat{D}^{−\\frac{1}{2}} H^{(l)}W^{(l)}),$$\n", "\n", "which calculates the updated node feature matrix $H^{(l+1)}$ based on the previous layers's node features $H^{(l)}$ and the adjacency information $A$ of the graph. Specifically, the formula uses the *modified adjacency matrix* \n", "\n", "$$\n", "\\hat{A} = A + I\n", "$$\n", "\n", "to which we append the identity matrix $I$ to introduce self-loops to the graph structure. This adjacency matrix is normalized by using the diagonal node degree matrix $\\hat{D}$ of the modified adjacency matrix $\\hat{A}$. Finally, the normalized adjacency matrix is multiplied by the previous node feature matrix $H^{(l)}$. This operation represents the convolution aspect of the layer, whereby node feature information is spread across all directly neighboring nodes. Finally, a learnable weight matrix $W^{(l)}$ is then applied to possibly transform the output shape into the output feature dimension." ] }, { "cell_type": "markdown", "id": "b5d1a159", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "5a522fc6bacef31ad33904433c86e9d8", "grade": false, "grade_id": "task-10-3", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.3 (5 points)** For this task you should implement the ``gcn_conv`` function according to the previously described GCN update rule. The function accepts three arguments: ``node_attributes`` is the previous layers node feature matrix, ``node_adjacency`` is the graphs node adjacency matrix and ``weights`` is the weight matrix to apply the feature transformation. The function should return a numpy array that represents the updated node feature matrix." ] }, { "cell_type": "code", "execution_count": 9, "id": "1d1278cb", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "3498a11e523fd584573007705238f14b", "grade": false, "grade_id": "ans-10-3", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Use numpy operations to implement the GCN update rule into the function below.\n", "\n", "# HINT: If at some point you encounter \"inf\" (infinity) values in one of the \n", "# arrays simply replace them with 0, using the np.where function, for example.\n", "\n", "# HINT: Use the RELU activation function as the final non-linear transformation \n", "# of your implementation. Numpy doesn't directly implement a relu function \n", "# but can easily recreated with the np.maximum function.\n", "\n", "def gcn_conv(node_attributes: np.ndarray, \n", " node_adjacency: np.ndarray,\n", " weights: np.ndarray,\n", " ) -> np.ndarray:\n", " \"\"\"\n", " Given the ``node_attributes`` matrix of shape (num_nodes, num_features_in), \n", " the ``node_adjacency`` matrix of shape (num_nodes, num_nodes) and the ``weights`` \n", " matrix of shape (num_features_in, num_features_out), this function should return \n", " the updated node feature matrix of the shape (num_nodes, num_features_out)S\n", " \"\"\"\n", " d12 = np.sqrt(np.diag(1/ (np.sum(node_adjacency, axis=0) + 1)))\n", "\n", " t = np.dot(node_attributes, weights)\n", " t = np.dot(d12,t)\n", " t = np.dot(node_adjacency + np.identity(node_adjacency.shape[0]), t)\n", " t = np.dot(d12, t)\n", " t = np.maximum(t, 0) \n", " return t" ] }, { "cell_type": "code", "execution_count": 10, "id": "654c605a", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "d75058e662dc05ba7444d9c491a3ad4b", "grade": true, "grade_id": "test-10-3-gcn-conv", "locked": true, "points": 5, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-3-gcn-conv - possible points: 5\n", "\n", "# We'll check the correctnes of the implementation for one concrete example:\n", "_node_attributes = np.array([\n", " [1, 0],\n", " [0, 1],\n", " [1, 0],\n", " [1, 0],\n", "])\n", "_node_adjacency = np.array([\n", " [0, 1, 2, 0],\n", " [1, 0, 1, 1],\n", " [2, 1, 0, 0],\n", " [0, 1, 0, 0],\n", "])\n", "_weights = np.array([\n", " [1, -1, 1],\n", " [-1, 0, 1],\n", "])\n", "\n", "_output = gcn_conv(\n", " _node_attributes,\n", " _node_adjacency,\n", " _weights,\n", ")\n", "\n", "assert isinstance(_output, np.ndarray)\n", "assert _output.shape == (4, 3), 'output shape incorrect'\n", "assert np.isclose(np.sum(_output), 5.707, atol=0.1), 'output likely incorrect'\n", "\n", "# NOTE: The hidden tests will randomly generate some configurations of node feature and \n", "# adjacency matrices and compare the results of the given implementation of \"gcn_conv\"\n", "# against the expected results of a reference implementation.\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "f34839ae", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "591669c060c189959da942d0f7ea038e", "grade": false, "grade_id": "cell-638339e897dcda49", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**Applying Graph Convolutions.** After implementing the graph convolutional layer, we want to see how this layer actually works now. As it is already indicated in its name, the GCN graph network layers apply a convolution-like operation on the node attributes of the graph. In practice, this means that the feature information of any node is shared with its immediately neighboring nodes in each convolutional step. The most simple kind of (linear) operation we can imagine for this process of information sharing is the *average* operation. In this case we can imagine that the updated node features are simply computed as the average features of all of its neighbors and itself. By applying multiple of these operations in sequence it is therefore possible to propagate information throughout the (majority of) the graph.\n", "\n", "By introducing the learnable weight matrices we can furthermore control *how* exactly this process of information sharing works and can therefore (hopefully) learn a function that aggregates and processes useful information from different areas of the graph that we can then ultimately use to solve some downstream prediction task." ] }, { "cell_type": "markdown", "id": "0198d814", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "943ae7152e7de9f70c747b74311f10ab", "grade": false, "grade_id": "task-10-4", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.4 (2 points)** In this task we want to look at a concrete visual example of how these graph convolutions can work. The given example graph ``g_color`` is a small graph with 8 nodes and some undirected, unweighted edges. Each node is associated with 3-element feature vectors, containing values in the interval $[0, 1]$, representing RGB color values [R, G, B] (e.g [1, 0, 0] is red, [0, 1, 0] is green and [1, 1, 1] is white). Your task is to apply **3** gcn operations on this graph using the same pre-defined weight matrix ``weights`` that is given below. After the 3 convolutions, report the new color code values of the node with *node index 2* in the variable ``color_2`` as a list of float values." ] }, { "cell_type": "code", "execution_count": 11, "id": "76f6b393", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "75c73b181a34dc5f606003108fa12c7e", "grade": false, "grade_id": "cell-1726da12b56116fe", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "visualization of the color graph:\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "g_color = nx.Graph()\n", "\n", "# Graph consists of mostly \"white\" (1, 1, 1) nodes and some colored nodes\n", "g_color.add_node(0, color=[1, 1, 1])\n", "g_color.add_node(1, color=[1, 1, 1])\n", "g_color.add_node(2, color=[1, 1, 1])\n", "g_color.add_node(3, color=[0, 0, 1])\n", "g_color.add_node(4, color=[0, 1, 0])\n", "g_color.add_node(5, color=[1, 1, 1])\n", "g_color.add_node(6, color=[1, 0, 0])\n", "g_color.add_node(7, color=[1, 1, 1])\n", "\n", "# The graph has a white triangle in the middle with some branches as the sides\n", "g_color.add_edge(0, 1)\n", "g_color.add_edge(1, 2)\n", "g_color.add_edge(2, 0)\n", "g_color.add_edge(0, 3)\n", "g_color.add_edge(2, 4)\n", "g_color.add_edge(1, 5)\n", "g_color.add_edge(5, 6)\n", "g_color.add_edge(5, 7)\n", "\n", "# We can use this function to visualize the graph after different stages of convolution\n", "def draw_graph(graph: nx.Graph):\n", " node_colors = []\n", " for value in nx.get_node_attributes(graph, 'color').values():\n", " value = np.array(value)\n", " value[value > 1] = 1\n", " node_colors.append(value.tolist())\n", " \n", " return nx.draw_kamada_kawai(graph, with_labels=True, node_color=node_colors, edgecolors='black')\n", "\n", "print('visualization of the color graph:')\n", "draw_graph(g_color)\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 12, "id": "a8f0f452", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "66a17af3e959531311018f491e2d948d", "grade": false, "grade_id": "ans-10-4", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "visualization of the color graph after the convolutions:\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weights = 1.0 * np.array([\n", " [1, 0, 0],\n", " [0, 1, 0],\n", " [0, 0, 1],\n", "])\n", "\n", "# TASK: Use the previous implementation of the GCN update rule and the given weight \n", "# matrix to determine the new node features after subjecting the given g_color \n", "# to 3 gcn update steps.\n", "\n", "# You may use this graph instance (copy of the original color graph) to update after \n", "# the individual convolutional steps.\n", "g_current: nx.Graph = deepcopy(g_color)\n", "color_2: list[float] = None\n", "\n", "# HINT: Look into the networkx documentation how to extract the adjacenycy matrix \n", "# and the node feature matrix from a \"Graph\" object and also how to update \n", "# the features of the Graph object after each convolutional step.\n", "\n", "for i in range(3):\n", " new_attributes = gcn_conv(\n", " np.array(list(nx.get_node_attributes(g_current, \"color\").values())),\n", " nx.to_numpy_array(g_current),\n", " weights,\n", " )\n", " nx.set_node_attributes(g_current, {i: att for i,att in enumerate(new_attributes)}, name=\"color\")\n", "\n", "color_2 = list(nx.get_node_attributes(g_current, \"color\")[2])\n", "\n", "# HINT: Dont forget to update the \"color\" attributes of the g_current graph object\n", "# at the end of the convolutions so that the final results will be visible in\n", "# the visualization of the graph.\n", "\n", "print('visualization of the color graph after the convolutions:')\n", "draw_graph(g_current)" ] }, { "cell_type": "markdown", "id": "ad7c3703", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "b53664831cfd3300d6cc7dddae0e0e9c", "grade": false, "grade_id": "cell-be674b6f876edeb4", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "For this example, we can easily observe the basic working principle of the GCN update rule! The original graph consisted mostly of white nodes with 3 distinctly colored nodes. After 3 convolutions, all the previously white nodes have gained some slight coloration, while the originally colored nodes have slightly faded in their intensity." ] }, { "cell_type": "code", "execution_count": 13, "id": "179b977e", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "f86dfd177b64cb1d79fb25d9fd45f3c9", "grade": true, "grade_id": "test-10-4-conv", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-4-conv - possible points: 2\n", "\n", "assert isinstance(g_current, nx.Graph)\n", "\n", "assert isinstance(color_2, list), 'solution needs to be a list and not a numpy array'\n", "assert all([isinstance(c, float) for c in color_2]), 'solution elements need to be floats'\n", "assert len(color_2) == 3, 'solution needs to be a valid 3 element color code'\n", "assert hashcheck(','.join([f'{v:.2f}' for v in color_2])) == '2e0401c6a66a6b15', 'color_2 likely incorrect'\n", "\n", "# NOTE: The hidden tests will test for the exact values of the color code of node \"2\"\n", "# (with 3 decimal points accuracy)\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "f6b99174", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "f89e665950b8cd69ac616bd6e0731d6a", "grade": false, "grade_id": "cell-53cb7b55d6253a1e", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## 10.2 Molecular Graphs\n", "\n", "**Molecules as Graphs.** Another important application domain for graph neural networks is chemistry and material science. This is because [molecules](https://en.wikipedia.org/wiki/Molecule) can also be represented as graph structures. Molecules can be defined as a set of atoms that is held in a close ordered configuration by set of attractive forces also called chemical bonds. Therefore, we could define all the atoms in a molecule as the nodes of a molecular graph and the chemical bonds between individual atoms as the graph edges.\n", "\n", "**Molecular Representations.** A full graph structure, consisting of nodes and edges, is somewhat unwieldy to represent molecules - especially in an easily human-readable manner. Consequently, different methods of representing and communicating such molecular graph structures have been established. One important representation is the [Simplified molecular-input line-entry system (SMILES)](https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system) method which represents each molecule as a single string consisting of the abbreviated atom names and a special syntax to indicate which atoms are connected via bonds. The following image shows a rough illustration of how such a SMILES string is constructed from the molecular graph structure.\n", "\n", "\n", "\n", "In the end, every molecular graph can be reduced into one (mostly) human-readable string representation. Due to the relative efficiency und understandability of this representation, the SMILES format is one of the most common formats in which machine learning datasets for molecular property prediction are distributed. A common dataset format, for example, is to simply provide a list of the molecule's SMILES representations along with the annotated target properties like this:\n", "\n", "| smiles | target |\n", "|:-------------|:--------|\n", "| CCO | -0.2 |\n", "| C1=CC=CC=C1 | 3.2 |\n", "| CC(CO)C=N | -1.2 |\n", "| ... | |\n", "\n", "**Chem-Informatics with RDKit.** To then apply graph machine learning methods such as GNNs to property prediction datasets such as this, we need to somehow reconstruct a molecule's full graph structure given the SMILES representation. We generally don't have to implement the SMILES parsing and processing ourselves but can rather rely on large cheminformatics libraries such as [RDKit](https://www.rdkit.org/docs/). These libraries provide tools to parse the SMILES strings and construct the corresponding molecular graph structures, which can then be further converted into a format suitable to be fed to a specific machine learning model.\n", "\n", "In terms of the actual implementation, the rdkit library provides the ``MolFromSmiles`` function that can be used to convert a SMILES string into an rdkit ``Mol`` object. Such a mol object is populated with a list of atoms and bonds that have been derived from the SMILES representation." ] }, { "cell_type": "code", "execution_count": 14, "id": "08905c1d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e2493ed523b41383e690abd1758cd73c", "grade": false, "grade_id": "cell-ac127d63b58eb57b", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Molecule Object: \n", "\n", "Iterating over Molecule Atoms\n", " * 0 - type: \n", " * 1 - type: \n", " * 2 - type: \n", " * 3 - type: \n", " * 4 - type: \n", " * 5 - type: \n", " * 6 - type: \n", " * 7 - type: \n", " * 8 - type: \n", " * 9 - type: \n", " * 10 - type: \n", " * 11 - type: \n", "\n", "Iterating over Molecule Bonds\n", " * 0 - type: \n", " * 1 - type: \n", " * 2 - type: \n", " * 3 - type: \n", " * 4 - type: \n", " * 5 - type: \n", " * 6 - type: \n", " * 7 - type: \n", " * 8 - type: \n", " * 9 - type: \n", " * 10 - type: \n", " * 11 - type: \n", "Visualization of the molecule:\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "example_smiles: str = 'C1=CC=CC=C1CC(CO)C=N'\n", "mol = Chem.MolFromSmiles(example_smiles)\n", "print(f'\\nMolecule Object: {type(mol)}')\n", "\n", "print(f'\\nIterating over Molecule Atoms')\n", "for atom_index, atom in enumerate(mol.GetAtoms()):\n", " print(f' * {atom_index:2} - type: {type(atom)}')\n", "\n", "print(f'\\nIterating over Molecule Bonds')\n", "for bond_index, bond in enumerate(mol.GetBonds()):\n", " print(f' * {bond_index:2} - type: {type(bond)}')\n", " \n", "print('Visualization of the molecule:')\n", "molecule = Chem.MolFromSmiles(example_smiles)\n", "img = Draw.MolToImage(molecule)\n", "plt.imshow(np.array(img))\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "4b20a0df", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "3ad5bb609cc961a0505d027a9444b29f", "grade": false, "grade_id": "cell-dba09c51305d9712", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.5 (3 points)** In this exercise, the goal is to convert the ``Mol`` molecular graph structure obtained from RDKit into a format that can be directly processed using the previously introduced GCN neural network layers. For this purpose, your task is to implement the following ``graph_from_smiles`` function. This function receives the ``smiles`` string as the single argument and is supposed to return the following tuple of values:\n", "\n", "- ``node_attributes``: The node feature matrix of the shape (number of atoms, 8). Spefically, we want the corresponding node feature vector of a node to be a [One-Hot Encoding](https://en.wikipedia.org/wiki/One-hot) of the atom type (carbon, oxygen, nitrogen, ...). In total, we want to differentiate between the *7 most common* atoms in organic chemistry with the following one-hot indices:\n", " - 0 - carbon (C)\n", " - 1 - nitrogen (N)\n", " - 2 - oxygen (O)\n", " - 3 - fluorine (F)\n", " - 4 - chlorine (Cl)\n", " - 5 - bromine (Br)\n", " - 6 - iodine (I)\n", " - 7 - *default case* (all others that are none of the above)\n", " \n", " as an example, a *bromine* atom would therefore be encoded as the array ``[0, 0, 0, 0, 0, 1, 0, 0]``\n", "\n", "- ``node_adjacency``: The node adjacency matrix of the shape (number of atoms, number of atoms) that determines the connectivitey of the molecule - meaning whether or not two atoms are connected by a bond or not." ] }, { "cell_type": "code", "execution_count": 27, "id": "f3b595b4", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "11ecf020eadff33ac47d2688ea40df88", "grade": false, "grade_id": "ans-10-5", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Implement the following function to create a node feature / adjacency \n", "# graph representation from a molecule's SMILES string.\n", "\n", "# HINT: How to determine the type of an atom within the molecule you can find in \n", "# the RDKIT documentation.\n", "\n", "# HINT: For the sake simplicity we'll disregard the different bond types here, \n", "# resulting in graphs with unweighted edges. This simplifies the adjacency \n", "# matrix that now only conists of binary values that are either 0 or 1.\n", "\n", "def graph_from_smiles(smiles: str) -> tuple[np.ndarray, np.ndarray]:\n", " \"\"\"\n", " Given the ``smiles`` string, this function returns a tuple consisting of the \n", " node feature matrix of shape (num_atoms, 8) and the adjacency matrix of the \n", " shape (num_atoms, num_atoms) based on the molecular bonds.\n", " \"\"\"\n", " mol = Chem.MolFromSmiles(smiles, sanitize=False)\n", " adj = Chem.GetAdjacencyMatrix(mol)\n", " #print(adj)\n", "\n", " elems = [\"C\",\"N\",\"O\",\"F\",\"Cl\",\"Br\",\"I\"]\n", "\n", " attrbs = np.array([[int((elems.index(atom.GetSymbol()) if atom.GetSymbol() in elems else 7) == i) for i in range(8)] for atom in mol.GetAtoms()])\n", " #print(attrbs)\n", " \n", " return attrbs, adj\n", " " ] }, { "cell_type": "code", "execution_count": 16, "id": "24cee397", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "0f751598383007546f4d8208bdff152d", "grade": true, "grade_id": "test-10-5-molecule-process", "locked": true, "points": 3, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-5-molecule-process - possible points: 3\n", "\n", "_test_list = [\n", " ('CCO', 3),\n", " ('C(CN)CCl', 5),\n", "]\n", "\n", "for _smiles, _num_atoms in _test_list:\n", " \n", " node_features, node_adjacency = graph_from_smiles(_smiles)\n", " \n", " assert isinstance(node_features, np.ndarray)\n", " assert isinstance(node_adjacency, np.ndarray)\n", " \n", " assert node_features.shape == (_num_atoms, 8), f'node feature shape {node_features.shape} incorrect'\n", " assert node_adjacency.shape == (_num_atoms, _num_atoms), f'adjacency matrix shape {node_adjacency.shape} incorrect'\n", " \n", " # more specific checks for the node freatures\n", " assert np.isclose(np.max(node_features), 1), f'one-hot encoding cant have values larger than 1'\n", " assert np.isclose(np.min(node_features), 0), f'one-hot encoding cant have values smaller than 0'\n", " assert np.isclose(np.sum(node_features), _num_atoms), f'each node can only have a single one-hot value'\n", " \n", " # more specific checks for the adjacency matrix, such as symmetry\n", " assert np.isclose(np.max(node_adjacency), 1), f'adjacency matrix cant have values larger than 1'\n", " assert np.isclose(np.min(node_adjacency), 0), f'adjacency matrix cant have values smaller than 0'\n", " assert np.array_equal(node_adjacency, node_adjacency.T), f'adjacency matrix needs to be diagonally symmetrical'\n", "\n", "# NOTE: The hidden tests will evaluate the exact values of the node feature and adjacency arrays\n", "# for a number of sample molecules.\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "a925fa76", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "135aff13f0e831e04b3830e9d96ddb28", "grade": false, "grade_id": "cell-e40a081dfb3a270d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**Graph Batching.** The previously implemented function ``graph_from_smiles`` directly converts the SMILES string representation of a molecule into a node feature matrix and an adjacency matrix. Together, both of these elements can be used as inputs to the previously implemented ``GCN`` network layer. As is usual in neural network training, we would like to process these graph representations in *batches* where multiple graphs are passed through the network at once. However, similarly as with Recurrent Neural Networks (RNNs) we run into the problem that these graphs usually have *different sizes* and can therefore not directly be stacked into a single tensor.\n", "\n", "**Input Padding and Masking.** One common solution to this problem is to use *padding* and *masking* to fit the differently sized graph structures into a common tensor shape. For this purpose we choose some maximum number of nodes $N_{\\mathrm{max}}$ such that *all* the graphs in the target dataset have less nodes than this number. Then we simply embedd all graph feature and adjacency matrices into larger matrices of shapes $(N_{\\mathrm{max}}, F)$ and $(N_{\\mathrm{max}}, N_{\\mathrm{max}})$ and set all the unused entries to zero. In addition to this we also need some way to then recover the actual node features from these larger arrays. We'll do this by additionally maintining a binary mask matrix $M$ which has 1 entries only for those matrix elements where the feature matrix has actually meaningful entries.\n", "\n", "Consider the following constructed example to illustrate this idea. We'll consider the case in which we want to batch two graphs which are given by the features matrices $X_1, X_2$ and adjacency matrices $A_1, A_2$. Graph 1 consists of 2 nodes and Graph 2 consists of 3 nodes\n", "\n", "$$\n", "X_1 = \\begin{bmatrix}\n", "1 & 0 & 0 \\\\\n", "0 & 1 & 0 \\\\\n", "\\end{bmatrix} \\quad\n", "A_1 = \\begin{bmatrix}\n", "0 & 1 \\\\\n", "1 & 0 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "$$\n", "X_2 = \\begin{bmatrix}\n", "1 & 0 & 0 \\\\\n", "0 & 0 & 1 \\\\\n", "0 & 0 & 1 \\\\\n", "\\end{bmatrix} \\quad\n", "A_2 = \\begin{bmatrix}\n", "0 & 1 & 1 \\\\\n", "1 & 0 & 0 \\\\\n", "1 & 0 & 0 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "We can now choose $N_{\\mathbf{max}} = 4$, which is guaranteed larger then the number of nodes of both of our graphs. We get the following padded arrays as well as the feature masks that contain the information about which parts of the feature arrays now actually contain the relevant information:\n", "\n", "$$\n", "\\hat{X}_1 = \\begin{bmatrix}\n", "1 & 0 & 0 \\\\\n", "0 & 1 & 0 \\\\\n", "0 & 0 & 0 \\\\\n", "0 & 0 & 0 \\\\\n", "\\end{bmatrix} \\quad\n", "\\hat{A}_1 = \\begin{bmatrix}\n", "0 & 1 & 0 & 0\\\\\n", "1 & 0 & 0 & 0\\\\\n", "0 & 0 & 0 & 0\\\\\n", "0 & 0 & 0 & 0\\\\\n", "\\end{bmatrix} \\quad\n", "M_2 = \\begin{bmatrix}\n", "1 \\\\\n", "1 \\\\\n", "0 \\\\\n", "0 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "$$\n", "\\hat{X}_2 = \\begin{bmatrix}\n", "1 & 0 & 0 \\\\\n", "0 & 0 & 1 \\\\\n", "0 & 0 & 1 \\\\\n", "0 & 0 & 0 \\\\\n", "\\end{bmatrix} \\quad\n", "\\hat{A}_2 = \\begin{bmatrix}\n", "0 & 1 & 1 & 0\\\\\n", "1 & 0 & 0 & 0\\\\\n", "1 & 0 & 0 & 0\\\\\n", "0 & 0 & 0 & 0\\\\\n", "\\end{bmatrix} \\quad\n", "M_2 = \\begin{bmatrix}\n", "1 \\\\\n", "1 \\\\\n", "1 \\\\\n", "0 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "Since these arrays now have the same shape we can easily stack them along an additional tensor to create the batched feature tensor $X$ with shape $(2, N_{\\mathrm{max}}, 3)$ and the batched adjacency tensor $A$ with shape $(2, N_{\\mathrm{max}}, N_{\\mathrm{max}})$, such as like this:\n", "\n", "```python\n", "X_batch = np.stack([X_hat_1, X_hat_2], axis=0) # shape: (2, 4, 3)\n", "A_batch = np.stack([A_hat_1, A_hat_2], axis=0) # shape: (2, 4, 4)\n", "```" ] }, { "cell_type": "markdown", "id": "0ebfb9a7", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "06286fd8681ae382d85de3e293b1ae57", "grade": false, "grade_id": "cell-95058df52e815d2e", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.6 (2 points)** In this exercise you need to implement the ``batch_graphs`` function. The function takes a list of individual tuples consisting of the ``node_features`` and ``node_adjacency`` numpy arrays as the input and is supposed to return a tuple of 3 ``torch.Tensor`` instances:\n", "- batched feature tensor $X$ with shape ``(batch_size, n_max, num_features)``\n", "- batched adjacency tensor $A$ with shape ``(batch_size, n_max, n_max)``\n", "- batched feature mask tensor $M$ with shape ``(batch_size, n_max, 1)``" ] }, { "cell_type": "code", "execution_count": 17, "id": "869fadfc", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "956a30cf96c07186854f010884b6e4d7", "grade": false, "grade_id": "ans-10-6", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "\n", "def batch_graphs(graph_tuples: list[tuple[np.ndarray, np.ndarray]],\n", " N_max: int\n", " ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " Given the list of ``graph_tuples`` (node_attributes, node_adjacency) of differently \n", " sized graphs and an integer number ``N_max`` that determines the maximum number of \n", " of nodes across the given graphs, this fucntion should return a tuple of 3 torch \n", " tensors:\n", " - the batched node attributes tensor with shape (num_graphs, N_max, num_features)\n", " - the batched node mask matrix (num_graphs, N_max, 1) which consists of \n", " binary values 0 and 1 that determine which columns of the node feature tensor \n", " contain actual features for every element in the batch.\n", " - the batched node adjacency matrix (num_graphs, N_max, N_max)\n", " \"\"\"\n", " num_graphs = len(graph_tuples)\n", " num_features = len(graph_tuples[0][0][0])\n", " att = np.zeros((num_graphs, N_max, num_features))\n", " mask = np.zeros((num_graphs, N_max, 1))\n", " adj = np.zeros((num_graphs, N_max, N_max))\n", " \n", " for i, graph_tuple in enumerate(graph_tuples):\n", " for j, a in enumerate(graph_tuple[0]):\n", " att[i,j,:] = a\n", " mask[i,j,:] = 1\n", " adj[i,:graph_tuple[1].shape[0],:graph_tuple[1].shape[1]] = graph_tuple[1]\n", " \n", " return torch.Tensor(att), torch.Tensor(mask), torch.Tensor(adj)" ] }, { "cell_type": "code", "execution_count": 18, "id": "55f22b56", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "e017c7c85988d2d34598211fe12b842d", "grade": true, "grade_id": "test-10-6-batch-graphs", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-6-batch-graphs - possible points: 2\n", "\n", "# We are going to test the batching functionality with the following two simple example \n", "# graphs that we want to combine into batched tensors using N_max = 4\n", "_graph_1 = (\n", " np.array([\n", " [1, 0, 0],\n", " [0, 1, 0]\n", " ]),\n", " np.array([\n", " [0, 1],\n", " [1, 0]\n", " ])\n", ")\n", "\n", "_graph_2 = (\n", " np.array([\n", " [1, 0, 0],\n", " [0, 0, 1],\n", " [0, 0, 1],\n", " ]),\n", " np.array([\n", " [0, 1, 1],\n", " [1, 0, 0],\n", " [1, 0, 0],\n", " ])\n", ")\n", "\n", "_graph_tuples = [_graph_1, _graph_2]\n", "_N_max = 4\n", "\n", "node_feature_batch, node_feature_mask_batch, node_adjacency_batch = batch_graphs(\n", " _graph_tuples,\n", " _N_max,\n", ")\n", "\n", "# node features\n", "assert isinstance(node_feature_batch, torch.Tensor)\n", "assert len(node_feature_batch.shape) == 3, 'batched node features need to be 3 dimensional'\n", "assert node_feature_batch.shape == (len(_graph_tuples), _N_max, 3), f'node feature shape incorrect: {node_feature_batch.shape}'\n", "\n", "# node feature mask\n", "assert isinstance(node_feature_mask_batch, torch.Tensor)\n", "assert len(node_feature_mask_batch.shape) == 3\n", "assert node_feature_mask_batch.shape == (len(_graph_tuples), _N_max, 1), f'node feature mask shape incorrect: {node_feature_mask_batch.shape}'\n", "\n", "# adjacency matrix\n", "assert isinstance(node_adjacency_batch, torch.Tensor)\n", "assert len(node_adjacency_batch.shape) == 3\n", "\n", "# NOTE: The hidden tests will generate some random graph lists of graphs, apply the batch_graphs function\n", "# and similarly check the shapes of the arrays.\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "ae191edf", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "8474d90c964dcff29309bbc7c56d927c", "grade": false, "grade_id": "cell-e59361d931911bf2", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**BBBP Dataset.** As a practical application we'll be looking at the [Blood-Brain Barrier Penetration (BBBP)](https://paperswithcode.com/dataset/bbbp-scaffold) dataset. This dataset consists of a number of molecules that have been annotated with a binary classification label that indicates whether or not they are able to pass the [blood-brain barrier](https://en.wikipedia.org/wiki/Blood%E2%80%93brain_barrier). In the following section we'll train a graph neural network that will predict this classification label given the molecular structure of molecule. Being able to accurately predict this property for unknown molecules would be an important step to speed up the development of new brain-related treatments and drugs for example.\n", "\n", "In the first step we'll download the dataset and explore some example elements:" ] }, { "cell_type": "code", "execution_count": 19, "id": "f1fff5cc", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "b86357e1608c4c6b1646128fd60e40b2", "grade": false, "grade_id": "cell-4985b683bbe36094", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datasets consists of 2050 elements\n", "Column names: num, name, p_np, smiles\n", "Dataframe head:\n", " num name p_np \\\n", "0 1 Propanolol 1 \n", "1 2 Terbutylchlorambucil 1 \n", "2 3 40730 1 \n", "3 4 24 1 \n", "4 5 cloxacillin 1 \n", "\n", " smiles \n", "0 [Cl].CC(C)NCC(O)COc1cccc2ccccc12 \n", "1 C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl \n", "2 c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO... \n", "3 C1CCN(CC1)Cc1cccc(c1)OCCCNC(=O)C \n", "4 Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)... \n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAlFCAYAAACusXjnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXgUV9sG8HvjShxCPEAgwa24h6IBWghSihS3UKAUaN8ihUIFihUrGqRQ3GmxFi+lWCkegZCgcSGe3fP9Md8uWXYTEghZkr1/17UXZGZ25pnVs8+c8xyZEEKAiIiIiIiIiIj0hoGuAyAiIiIiIiIiouLFhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBEVm5MnT0Imk+HkyZOFvu8nn3wCKyurIo2nVatWaNWqVZHuk4iIiIhe+OSTT+Dl5fVa95XJZAgKCiqyWCIiIiCTybB+/foi2ydRScaEEFEJ8vjxY/Tr1w9VqlSBtbU1bG1t0aBBA2zYsAFCCI3tt27dirp168LMzAxOTk4YMmQIYmNjte577dq18PPzg5mZGXx8fLBkyZK3fTqlAh83IiIieh1z5sxB165dUa5cOchkMnz99ddat9uzZw/at28PFxcXmJqaws3NDYGBgbhx44bGtl5eXpDJZBq3kSNHamybmJiI4cOHw8nJCZaWlmjdujWuXLlS1KdZqjx58gRffPEFWrduDWtr69e+0En0rjDSdQBEVHCxsbF4+PAhAgMD4eHhgezsbBw7dgyffPIJ7t69i2+//Va17YoVKzB69Gj4+/tjwYIFePjwIRYvXoxLly7hwoULMDMzU227cuVKjBw5Ej169MBnn32GM2fO4NNPP0VaWhqmTJmii1MtEfi4ERER0euaOnUqnJ2dUadOHRw5ciTP7a5fvw47OzuMGzcOjo6OePr0KdatW4cGDRrg/PnzqFWrltr2tWvXxsSJE9WWVa5cWe1vhUKBzp0749q1a5g0aRIcHR2xfPlytGrVCpcvX4aPj0/RnWgpcvfuXfzwww/w8fFBjRo1cP78eV2HRPRmBBGVeAEBAcLS0lLk5OQIIYTIzMwUtra2okWLFkKhUKi2O3DggAAgfvrpJ9WytLQ04eDgIDp37qy2z48//lhYWlqK+Pj4IovzxIkTAoA4ceJEoe87cOBAYWlpWWSxCCFEy5YtRcuWLV/rvsX5uBEREVHpc//+fSGEEDExMQKAmDFjRoHv+/TpU2FkZCRGjBihttzT01OjbaLNtm3bBACxY8cO1bLo6Ghha2srPvroowLHURADBw4Unp6er3VfAGLMmDFFFsv9+/cFABEcHPxa909OThZxcXFCCCF27Njx2u1aoncFh4xRqfD1119DJpMhLCwMn3zyCWxtbWFjY4NBgwYhLS1Nbdvg4GC0adMGZcuWhampKapWrYoVK1Zo7NPLywsBAQE4efIk6tevD3Nzc9SoUUPVLXT37t2oUaMGzMzMUK9ePVy9elVjH3fu3EFgYCDs7e1hZmaG+vXrY//+/RrbhYeHIzw8/LXP38vLC2lpacjKygIA3LhxA4mJiejduzdkMplqu4CAAFhZWWHr1q2qZSdOnEBcXBxGjx6tts8xY8YgNTUVhw4deu24CuLMmTPo2bMnPDw8YGpqCnd3d0yYMAHp6elat7937x7at28PS0tLuLi4YNasWRrD5RQKBRYtWoRq1arBzMwM5cqVw4gRI5CQkPDKeCIjI3Hnzp1Xbqfrx42IiKg00qc23evW1QGAsmXLwsLCAomJiVrXZ2VlITU1Nc/779y5E+XKlUP37t1Vy5ycnNCrVy/s27cPmZmZrx1bQfz4449o0qQJHBwcYG5ujnr16mHnzp15br9582ZUqVJF9RydPn1aY5tHjx5h8ODBKFeuHExNTVGtWjWsW7fulbFkZ2fjzp07ePLkySu3tba2hr29/Su3IyopmBCiUqVXr15ISUnBd999h169emH9+vWYOXOm2jYrVqyAp6cn/ve//2H+/Plwd3fH6NGjsWzZMo39hYWFoW/fvujSpQu+++47JCQkoEuXLti8eTMmTJiAfv36YebMmQgPD0evXr2gUChU97158yYaNWqE27dv44svvsD8+fNhaWmJDz74AHv27FE7jr+/P/z9/Qt8nunp6YiNjUVERAQ2bNiA4OBgNG7cGObm5gCg+hJX/p2bubk5rl69qopV2eipX7++2nb16tWDgYGB1kZRUdqxYwfS0tIwatQoLFmyBO3bt8eSJUswYMAAjW3lcjk6dOiAcuXKYe7cuahXrx5mzJiBGTNmqG03YsQITJo0CU2bNsXixYsxaNAgbN68Ge3bt0d2dna+8QwYMAB+fn6vjFvXjxsREVFppi9tusJITExETEwMrl+/jqFDhyI5OVnrsf78809YWFjAysoKXl5eWLx4scY2V69eRd26dWFgoP5zsEGDBkhLS0NISMhbOQelxYsXo06dOpg1axa+/fZbGBkZoWfPnlovqJ06dQrjx49Hv379MGvWLMTFxaFDhw5qNZSePXuGRo0a4fjx4wgKCsLixYtRqVIlDBkyBIsWLco3lkePHsHPzw9ffvllUZ8m0btP112UiIrCjBkzBAAxePBgteUffvihcHBwUFuWlpamcf/27duLChUqqC3z9PQUAMRff/2lWnbkyBEBQJibm4sHDx6olq9cuVKjy6i/v7+oUaOGyMjIUC1TKBSiSZMmwsfHR+NYhelK+9133wkAqpu/v7+IjIxUrY+JiREymUwMGTJE7X537txR3Sc2NlYIIcSYMWOEoaGh1uM4OTmJPn36FDiuV9E2ZEzb8/Hdd98JmUym9hgPHDhQABBjx45VLVMoFKJz587CxMRExMTECCGEOHPmjAAgNm/erLbPw4cPayzXNmSsZcuWoiAfjcX5uBEREekLfWvTCVHwIWNVqlRRteOsrKzE1KlThVwuV9umS5cu4ocffhB79+4Va9euFc2bNxcAxOTJk9W2s7S01HiMhRDi0KFDAoA4fPhwoc4hP9qGjL383GVlZYnq1auLNm3aqC1Xnu+lS5dUyx48eCDMzMzEhx9+qFo2ZMgQUb58eVX7VqlPnz7CxsZGdTxtQ8aUywYOHFio8+KQMSoN2EOISpWXZ1Bo3rw54uLikJycrFqWu9dMUlISYmNj0bJlS9y7dw9JSUlq969atSoaN26s+rthw4YAgDZt2sDDw0Nj+b179wAA8fHx+PPPP1VXt2JjYxEbG4u4uDi0b98eoaGhePToker+ERERiIiIKPB5fvTRRzh27Bi2bNmCvn37AoDaECtHR0f06tULGzZswPz583Hv3j2cOXMGvXv3hrGxsdr26enpMDEx0XocMzOzPIduFZXcz0dqaipiY2PRpEkTCCG09rLJPfWocirSrKwsHD9+HIDU48jGxgbvv/++6nGPjY1FvXr1YGVlhRMnTuQbz8mTJ7XO2PYyXT9uREREpZm+tOkKIzg4GIcPH8by5cvh5+eH9PR0yOVytW3279+PyZMno1u3bhg8eDBOnTqF9u3bqyYYUUpPT4epqanGMZSTjhRn+y8hIQFJSUlo3ry51lnOGjdujHr16qn+9vDwQLdu3XDkyBHI5XIIIbBr1y506dIFQgi19l/79u2RlJSU7+xpXl5eEEJwKnrSS5xljEqV3F/oAGBnZwdA+qIpU6YMAODcuXOYMWMGzp8/rzEWPSkpCTY2NnnuT7nO3d1d63JljZqwsDAIITBt2jRMmzZNa6zR0dFwdXUt1PkpeXp6wtPTE4CUHBo+fDjatm2Lu3fvqr5gV65cifT0dHz++ef4/PPPAQD9+vVDxYoVsXv3blhZWQGQvpCVtYdelpGRoXXYmVJWVhbi4+PVljk5OcHQ0LDA5xIZGYnp06dj//79GjV+Xm7MGRgYoEKFCmrLlLNmKBtfoaGhSEpKQtmyZbUeLzo6usCx5edNHjciIiLKn7606Qojd0KrT58+qiHuP/74Y573kclkmDBhAo4cOYKTJ0+iX79+AKR2jLY6QRkZGar1eUlKSlJLGJmYmBS6rs7Bgwcxe/Zs/Pvvv2px5K59qaRtxrPKlSsjLS0NMTExMDAwQGJiIlatWoVVq1ZpPV5Rtf+IShsmhKhUySsRoezxER4eDn9/f/j6+mLBggVwd3eHiYkJfvvtNyxcuFBtvHh++3vVcZT7+fzzz9G+fXut21aqVOnVJ1RAgYGBWL16NU6fPq06no2NDfbt24fIyEhERESokkhNmjSBk5MTbG1tAQDly5eHXC5HdHS0WhIlKysLcXFxcHFxyfO4f/31F1q3bq227P79+wUukiiXy/H+++8jPj4eU6ZMga+vLywtLfHo0SN88sknGs9HQSgUCpQtWxabN2/Wut7JyanQ+9TmTR43IiIiyp++tukKys7ODm3atMHmzZvzTQgBL5JeuS/ilS9fXmsRZeWy/Nox48aNw4YNG1R/t2zZUlWguyDOnDmDrl27okWLFli+fDnKly8PY2NjBAcHY8uWLQXej5LyOerXrx8GDhyodZuaNWsWer9E+oAJIdIrBw4cQGZmJvbv3692pehVw4gKS9mLxdjYGG3bti3SfWujvErzco8aQLoipjzXxMREXL58GT169FCtr127NgDg0qVL6NSpk2r5pUuXoFAoVOu1qVWrFo4dO6a2zNnZucBxX79+HSEhIdiwYYNaEemX96mkUChw7949Va8gAKqih8okVMWKFXH8+HE0bdr0rfbSeZPHjYiIiN5MaW3TFUZ6errWtt/LlMPfcl8Uq127Ns6cOQOFQqFWWPrChQuwsLBQa2u9bPLkyaqeRsCL3lsFtWvXLpiZmeHIkSNqw9aCg4O1bh8aGqqxLCQkBBYWFqpzsra2hlwuf+eeI6J3HWsIkV5RXgXKXSMmKSkpzy+g11W2bFm0atUKK1eu1Hr1JSYmRu3vgk5R+vL9lNauXQuZTIa6devme/8vv/wSOTk5mDBhgmpZmzZtYG9vrzFN64oVK2BhYYHOnTvnuT87Ozu0bdtW7aYce14Q2p4PIYTW2TCUli5dqrbt0qVLYWxsrJplo1evXpDL5fjmm2807puTk5Pn9KxKBZ12/k0eNyIiInozJb1NVxjahjtFRETgjz/+UJvtND4+XqOmUHZ2Nr7//nuYmJio9eoODAzEs2fPsHv3btWy2NhY7NixA126dNFaX0ipatWqam2/3PV9CsLQ0BAymUwt1oiICOzdu1fr9ufPn1erARQVFYV9+/ahXbt2MDQ0hKGhIXr06IFdu3apzTymlFf7Wakw084TlTbsIUR6pV27djAxMUGXLl0wYsQIPH/+HKtXr0bZsmWL/Etg2bJlaNasGWrUqIFhw4ahQoUKePbsGc6fP4+HDx/i2rVrqm2VyYxXFSGcM2cOzp07hw4dOsDDwwPx8fHYtWsXLl68iLFjx6p1Wf7+++9x48YNNGzYEEZGRti7dy+OHj2K2bNn47333lNtZ25ujm+++QZjxoxBz5490b59e5w5cwa//PIL5syZU+gx4YXh6+uLihUr4vPPP8ejR49QpkwZ7Nq1S6OWkJKZmRkOHz6MgQMHomHDhvj9999x6NAh/O9//1NdIWrZsiVGjBiB7777Dv/++y/atWsHY2NjhIaGYseOHVi8eDECAwPzjGnAgAE4derUKwtL6/JxIyIi0nclvU0HAJs2bcKDBw9U9Y9Onz6N2bNnAwD69++vqhdZo0YN+Pv7o3bt2rCzs0NoaCjWrl2rSvYo7d+/H7Nnz0ZgYCC8vb0RHx+PLVu24MaNG/j222/VenEHBgaiUaNGGDRoEG7dugVHR0csX74ccrkcM2fOfOPHLD+dO3fGggUL0KFDB/Tt2xfR0dFYtmwZKlWqhP/++09j++rVq6N9+/b49NNPYWpqiuXLlwOAWpzff/89Tpw4gYYNG2LYsGGoWrUq4uPjceXKFRw/flyj5mVuymnnBw4cWKDC0srn6ObNmwCk5/Hs2bMAgKlTpxb4cSB6JxT7vGZEb4FyilLl1ONKwcHBAoC4f/++atn+/ftFzZo1hZmZmfDy8hI//PCDWLduncZ2np6eonPnzhrHAiDGjBmjtkw5XeW8efPUloeHh4sBAwYIZ2dnYWxsLFxdXUVAQIDYuXOn2nYFnaL06NGjIiAgQLi4uAhjY2NhbW0tmjZtKoKDg4VCoVDb9uDBg6JBgwbC2tpaWFhYiEaNGont27fnue9Vq1aJKlWqCBMTE1GxYkWxcOFCjX2+KW3Tzt+6dUu0bdtWWFlZCUdHRzFs2DBx7do1jSlBBw4cKCwtLUV4eLho166dsLCwEOXKlRMzZszQmHJVeT716tUT5ubmwtraWtSoUUNMnjxZPH78WLXNm0w7n/s4b/txIyIi0hf60qYT4kWbQ9std1tpxowZon79+sLOzk4YGRkJFxcX0adPH/Hff/+p7e/SpUuiS5cuwtXVVZiYmAgrKyvRrFmzPNt/8fHxYsiQIcLBwUFYWFiIli1biosXLxYo9sLQNu382rVrhY+PjzA1NRW+vr4iODhY9dznpnyOfvnlF9X2derU0TrV+7Nnz8SYMWOEu7u7MDY2Fs7OzsLf31+sWrVKtU1RTDuf13PGn9ZUEsmEKMD8ykREREREREREVGqwhhARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQu+w9evXQyaTISIiQrWsVatWaNWqlc5iKmk6deqEYcOGvdE+FAoFqlevjjlz5rxxPM+fP0fZsmWxefPmN94X0fbt22Fvb4/nz5+/1eM0atQIkydPLpJ9FeX7iYiouL3LbTNlbMpbbGysrkMqVosWLSrW8/fy8lIdKygo6K0eq6iNHz9eFbuVlZXG+qioKJiZmeHcuXOqZaX5fAvik08+Ue2jevXqRRxh4dSuXVsVS0BAgMb6l9uHERERau+NnTt3FnfI9A5jQojeqm3btqFfv37w8fGBTCbLs8F08eJFBAUFoVq1arC0tISHhwd69eqFkJCQ1z72uXPncPToUUyZMkVj3bNnz/D555/D19cXFhYWsLS0RL169TB79mwkJiaqbfvrr78iKipK7ctP2egyMzPDo0ePNPbfqlUrrV8WixcvhrW1Nfr06aOx7t9//0W/fv3g7u4OU1NT2Nvbo23btggODoZcLi/UuX/77bdo1KgRnJycYGZmBh8fH4wfPx4xMTGF2k9u//zzD0aPHo169erB2NgYMpnstfdV1G7evImePXuiQoUKsLCwgKOjI1q0aIEDBw681v7u3r2LCRMmoEmTJjAzM9No/L8L5HI5ZsyYgbFjx75W4+b58+eYMWMGOnToAHt7e8hkMqxfv17rtlOmTMGyZcvw9OlTrevf9P2kFB4ejhEjRqBChQowMzNDmTJl0LRpUyxevBjp6emFPkciIn20cOFCbNq0CdbW1hrrTp48ie7du8PZ2RkmJiYoW7YsunTpgt27dxdo36mpqYVuk+QmhCjwRQwvLy+tP3YB6Txe/mHboUMHbNq0CR9++KHatmlpaVi2bBnatWuH8uXLw9raGnXq1MGKFSs0zuXx48fo168fqlSpAmtra9ja2qJBgwbYsGEDhBAacTRv3hybNm3CwIEDNdYlJydj5syZqFWrFqysrGBubo7q1atjypQpePz48SvPX6FQIDU19ZXbAcg3SaNss166dEm1rH///ti0aROaN2+u9T6zZs1Cw4YN0bRpU7XlpfF8C/pbBQAcHR2xadMmfP/99xrr5HI5goOD0apVK9jb28PU1BReXl4YNGiQWiz5SUlJ0fo6e9m3336LTZs2wdHRUWscebUPhw8fjk2bNqFBgwYa93vTNlhWVhYyMjJeuV1+nj9/DoVC8crtPvnkk3zbviUxaalTgt5ZOTk5Ij09XSgUCtWyli1bipYtW+ouqEJq2bKlsLKyEq1btxZ2dnZ5xt6jRw/h7Owsxo4dK1avXi2++eYbUa5cOWFpaSmuX7/+Wsfu1q2baNeuncbyf/75Rzg6OgozMzMxdOhQsWLFCrFixQoxZMgQYWlpKd5//3217WvVqiWGDx+utiw4OFgAEABEUFCQ1vOuVq2a2rKsrCzh5OQkvv32W43tV69eLQwNDYWLi4uYMmWKWLNmjVi4cKEICAgQMplMzJkzp1Dn3r17dzFixAixcOFCsWbNGjFx4kRRpkwZUalSJfH8+fNC7UtpxowZwtjYWNSrV09UrlxZvEsfH4cOHRLt27cXX3/9tVi1apVYtGiRaN68uQAgVq5cWej9BQcHCwMDA1G9enVRu3ZtAUDcv3+/6AN/A3v27BEymUw8fPjwte5///59AUB4eHiIVq1aCQAiODhY67ZyuVw4OzuLadOmaawriveTEEIcPHhQmJubC1tbW/Hpp5+KVatWiaVLl4o+ffoIY2NjMWzYsNc6TyKiovQut82UbZO8vq+mT58uAAgfHx8xffp0sXbtWjF37lzVd8DmzZu13u/o0aOie/fuwtbWVgAQhoaGolKlSuKLL74QT548eWVcqamp4scffxTvvfeeMDY2FgCEhYWFaNmypVi/fr2Qy+Va7+fp6Sk6d+6sdd2JEycEALFjxw6NdTNmzBAARExMjBBCiOvXrwuZTCbatm0r5s6dK37++Wfx4YcfCgBiwIABave9du2aaNmypfjf//4nfv75Z7FkyRLRtWtXAUB8+eWXGvENHDhQa3zh4eHC29tbGBoaij59+oilS5eKVatWiaCgIOHg4CB8fHy03i8+Pl7MmDFDVK9eXRgaGgoAwtraWnTq1Ens2bNH632EEAKAGDNmjNZ1ytfFxYsXNdYNHDhQWFpaqi2Ljo4WxsbGYsuWLXpxvgX9rTJw4EDh6empdV1aWpro0KGDACBatGgh5s2bJ9auXSumTZsmqlSpImQymYiKitK4X3Z2tli9erVo2bKlMDU1FQCEqampeO+998TixYtFRkZGno+BENrfI9rah8o2X17tvNdtg925c0eMGDFCuLm5qX4XlS1bVgwaNEhcvnw539iFEEKhUIjt27eLjh07CktLSwFAGBsbi+rVq4vZs2eLpKQkrffT9jzmlt/rgzS9O7/oqEDelUZHQUVGRqq+6KtVq5Zn7OfOnROZmZlqy0JCQoSpqan4+OOPC33cZ8+eCSMjI7FmzRq15QkJCcLV1VWUK1dO3L59W+N+T58+Fd98843q7ytXrggA4vjx42rbKb9sateuLUxNTcWjR4/U1mtLCO3evVsAEGFhYWrLz58/LwwNDUWzZs1EcnKyRkwXL17M8wO8MHbu3CkAiF9//fW17v/06VORlpYmhBBizJgx71RCSJucnBxRq1YtUaVKlULfNy4uTvVczJs3751MCHXt2lU0a9bste+fkZGhashfvHgx34aCEEIEBQUJT09PtR9BRfV+unfvnrCyshK+vr7i8ePHGvsJDQ0VixYtKuwpEhEVi3elbZZfQmjHjh0CgAgMDBRZWVka6w8fPiwOHDigtuz58+eiR48eQiaTiY4dO4olS5aIgwcPiu3bt4vp06cLHx8fYWtrK3bu3JlnTBcvXhRubm7C3t5ejB49Wvzyyy/it99+E+vXr1f9qGvUqJFGO0qIoksIxcTEiBs3bmhsN2jQIAFAhIaG5hm/UkBAgLC0tBQ5OTlq8WlLkGRnZ4tatWoJCwsLcebMGY31SUlJ4n//+5/G8t9++03Y2dkJFxcXMXHiRLFt2zZx8OBBsXr1ahEYGCiMjY1Fp06dtLYVizJBsmDBAmFubi5SUlLUlpfW8y3ob5X8EkLKdvHChQs11uXk5Ih58+ZpJITCwsJEtWrVhKWlpRg4cKAIDg4Wv/32m9i0aZMYPXq0sLe3F76+vlpfu0ra3iPa2of5JYRetw02e/ZsYWRkJGrXri1mz54t9u7dK/bt2ycWLFggmjVrJgwNDbU+70rR0dGiefPmwtjYWPTs2VOsXLlSHDp0SGzZskVMmjRJuLq6CldXV3Hq1CmN+zIhVLTe7V9077Dk5GQxbtw44enpKUxMTISTk5No27atWjZUmRS4du2aaNGihTA3NxcVK1ZUfXmdPHlSNGjQQJiZmYnKlSuLY8eOqR1D2xe7tkZHRkaGmD59uqhYsaIwMTERbm5uYtKkSRpZ5aNHj4qmTZsKGxsbYWlpKSpXrqxxtePBgwdaf9hpk56eLmbMmCF8fHyEqampcHZ2Fh9++KFGwkMpvw/ZvNStW1fUrVtXY/lvv/0mWrRoIaysrIS1tbWoX7++2pWtdevWCQAiIiJC7X7ff/99vlfBXjZ9+nRhYmKi0XhSPjfbt28XRkZGYuzYsWrrtSWEBgwYILy8vDSO0aFDB2FkZCQePHhQoJjkcrlYtGiRqF69ujA1NRWOjo6iffv2Wr/4crt06ZIAIFasWKFxHmfPnhUTJkwQjo6OwsLCQnzwwQciOjo6z329KiH0qteG8otp3rx5YuXKlaJChQrCxMRE1K9fX/zzzz8a+7t9+7bo2bOnqidK5cqV8/2SUQoICBDlypXTWP6q109u+SWECnsef/zxh2jWrJmwsLAQNjY2omvXruLWrVsa2z18+FAMHjxYlC9fXpiYmAgvLy8xcuRIVdI0PT1dmJiYiK+//lrjvsovwT179ohq1aoJExMTUbVqVfH777/n+TgVJCG0b98+AUBcuXJFtayo3k8jR44UAMS5c+cKtB8iIm30vW2WX0LI19dX2Nvba/1xrU12drZo1aqV8PDw0Pp9ptzmhx9+ECYmJuLgwYMa6//9919haWkp+vTpIxITE7XuIyoqSvj7+ws/Pz8RHx+vtq6oEkJ52b9/vwAg9u/fn+92QkgXRWQymerCmDI+bQmSrVu3CgCF6t19+PBhYWhoKCZMmCDS09O1bnPr1i1Ru3Zt0aJFC42LqEWZIGnRooVo1aqVxral9Xxze52EUFRUlDAyMtLoEZ2fhw8finLlyok2bdpoTcIIIfWe+uijj0T58uXzvCj58nskr/Zhfgmh12mDffXVV8LMzExs2LAhz20OHTok7OzsxBdffKGxLjk5Wfj5+YmaNWuKkJAQrfdPS0sT48ePF1ZWVhrPJRNCRcvodYaZETBy5Ejs3LkTQUFBqFq1KuLi4nD27Fncvn0bdevWVW2XkJCAgIAA9OnTBz179sSKFSvQp08fbN68GePHj8fIkSPRt29fzJs3D4GBgYiKitI65jsvCoUCXbt2xdmzZzF8+HD4+fnh+vXrWLhwIUJCQrB3714AUo2VgIAA1KxZE7NmzYKpqSnCwsLUisUBwIABA3Dq1KlXjl+Vy+UICAjAH3/8gT59+mDcuHFISUnBsWPHcOPGDVSsWLHgD2YehBB49uwZqlWrprZ8/fr1GDx4MKpVq4Yvv/wStra2uHr1Kg4fPoy+ffsCAP766y84ODjA09NT7b779++Hubk5AgMDCxTDX3/9herVq8PY2Fjrem9vbwwYMACrV6/GF198ARcXl3z3lfu1AUjj2v/44w+0aNECHh4eBYppyJAhWL9+PTp27IihQ4ciJycHZ86cwd9//4369eurthNCIC4uDjk5OQgNDcUXX3wBQ0NDrWOjx44dCzs7O8yYMQMRERFYtGgRgoKCsG3btgLFlFthXhtbtmxBSkoKRowYAZlMhrlz56J79+64d++e6jH/77//0Lx5cxgbG2P48OHw8vJCeHg4Dhw4oFGYODU1Fenp6UhKSsL+/fvx+++/o3fv3mrbFOT1U1gFOY/jx4+jY8eOqFChAr7++mukp6djyZIlaNq0Ka5cuQIvLy8AUv2CBg0aIDExEcOHD4evry8ePXqEnTt3Ii0tDSYmJrh8+TKysrI0Xk9KZ8+exe7duzF69GhYW1vjp59+Qo8ePRAZGQkHB4fXOsd69eoBkGpz1alTB0DRvZ8OHDiAChUqoEmTJq8VGxERwLZZXkJDQ3Hnzh0MHjy4wOfx3Xff4e7du7h8+TLKly+vOq/09HRYWlpCoVAgMTERkydPhrW1NQYPHoywsDDV/nNyctC7d2/07NkT69atU9UdzMjIgKGhIYyNjZGWlgZbW1scOnQI77//Pv73v/9hxYoVanFkZ2drLQ6dlJT0Wo9Fbsq6eNrqsKSnpyM1NRXPnz/HqVOnEBwcjMaNG8Pc3PyV+92/fz8AqW5NQSQmJuLjjz/G1KlT8fXXX6uWp6WlwdTUFIaGhnj+/Dk8PT3x559/onHjxli4cKFGjcyMjAytj1VhJp7Izs7GxYsXMWrUqALfpySfb1H4/fffkZOTU+DzB6T3dM2aNXHo0CFVmyg7OxtyuRxmZmbIzMyEgYEBNm/ejI8++gijRo3C77///sr9vqp9qE1h22CnT5/G999/jyNHjsDf31+1/Pnz57C0tIRMJkNCQgLef/99/PHHH2jatCm6deuGRo0aqbYdP348jIyMcPbsWdVnhlwuR2ZmJiwsLJCdnY2MjAwsXLgQJiYmGDhwIK5fvw4DA/Xyx/pWOP+t0W0+quSysbF5ZeaxZcuWAoDaGNw7d+4IAMLAwED8/fffquVHjhzRyNwW5CrUpk2bhIGBgUYXzZ9//lkt27tw4cICXS1Rxvwqyh44CxYs0FiXe0hJboXtIbRp0yYBQKxdu1a1LDExUVhbW4uGDRtqXFHIfdxmzZqJevXqaezTzs5O1KpVq8AxuLm5iR49emgsz331ITw8XBgZGYlPP/1Utf7lHkLZ2dlCJpOJiRMnqu3n2rVrAoAYN25cgeL5888/BQC1Yym9/Lg/efJENZ4XgHBzcxPbtm3Teh5t27ZVu/+ECROEoaFhnlf08ushVJDXhvJKhYODg9oVQWUvlNzd1lu0aCGsra01elBpe52NGDFCdb4GBgYiMDBQbf8Fff3kVpAeQgU5j9q1a4uyZcuKuLg41bJr164JAwMDtRoGAwYMEAYGBlqvbCljXLNmjQCgtb4WAGFiYqLWU0/5OluyZInWcyxIDyEhhDAxMRGjRo1S/V0U76ekpCQBQHTr1q3A+yEi0kbf22Z59RBSfidpG86iTVJSkihTpozYu3evatmqVauEnZ2dACCqVasmdu3apRZT3bp1xapVq1R/r1+/Xri5uamGHaWkpIiePXsKQ0NDYWRkJPr37y+mTJmi6nFy7do1YWZmptaDydPTU60do+32uj2EMjMzRdWqVYW3t7fIzs7WWP/dd9+pHcff319ERkaqbZNXj5k6deoIGxubPI/9sq+//lrUq1dPNRzt6dOnwt/fXwAQZmZm4rPPPhMDBgwQM2bMEEJIz6erq6vaPl71OCnbrC97uadFWFhYnu2F0ni+L3udHkITJkwQAMTVq1cLdP4nT54UlpaWqmGS2dnZYvTo0cLExETIZDLRuXNn8eOPP6riiI6OFmZmZlp70rzcQyiv9mFePYRepw3WqlUrMX78eNXff/31l/Dx8REAhJOTk9i4caPw9PQUJ06cEEJIj0/fvn1V24eFhQkjIyO1x2vmzJmqGkJNmjQR69atUz3WmZmZwtnZWRw9elS1/cCBA1/5/LOHUMFxlrHXZGtriwsXLryyar6VlZXajFJVqlSBra0t/Pz80LBhQ9Vy5f/v3btXqDh27NgBPz8/+Pr6IjY2VnVr06YNAODEiROqeAFg3759+VZvP3nyZIGuQO3atQuOjo4YO3asxrqimH3qzp07GDNmDBo3bqw2k8GxY8eQkpKCL774AmZmZnkeNy4uDnZ2dhr7TU5OLtRVvrz2k1uFChXQv39/rFq1Ck+ePNG6TXx8PIQQGvtKTk4GgALHtGvXLshkMsyYMUNj3cuPu729PY4dO4YDBw5g1qxZcHR0zPOqyfDhw9Xu37x5c8jlcjx48KBAcb0cY0FfG71791Z7TJSzPyjfBzExMTh9+jQGDx6s0YNK2+ts/PjxOHbsGDZs2ICOHTtCLpcjKytLtb6gr5/CetV5PHnyBP/++y8++eQT2Nvbq7arWbMm3n//ffz2228ApKuve/fuRZcuXdR6e70cY1xcHADk+dps27atWk+smjVrokyZMoX+fHmZnZ2d2tWYong/FfY9QESUF31vm+WlsJ+zR48ehb29Pbp27QoAuHLlCkaMGIEePXpgz5496N27N4YNG6Z2n27duuHkyZOqv3fs2IHBgwerZgL66quv8Mcff2D+/PnYtm0bkpKSsGTJEtX2NWvWRPny5fH333+r7bdhw4Y4duyYxu3HH38s9OOQW1BQEG7duoWlS5fCyEhzwMRHH32EY8eOYcuWLarewwWd7bKw3407duzAmDFjYGhoCEBqk4WEhGD16tXYuHEjLly4oDGbWmxsLEJDQ9X2061bN62P1aRJkwocy6vaF9qU5PMtCoV9f+3YsQM9evRQjSpYsmQJgoODMX36dOzevRvlypXD9OnTVds7OTmhcePGau+vvBT2+Sts7Mp2ubKNn5qaih49esDZ2Rnbt2/HnDlzMG3aNLVZaT/44AO12Pfs2YMmTZqgdu3aqr9nzpyJ0aNHY+/evWjcuDE+/fRT1fYmJibo2LGjxvmbmZlpff6PHTtWoHOhFzhk7DXNnTsXAwcOhLu7O+rVq4dOnTphwIABqFChgtp2bm5uGj80bWxs4O7urrEMkLoxF0ZoaChu374NJycnreujo6MBSD9Y16xZg6FDh+KLL76Av78/unfvjsDAQI3udwURHh6OKlWqaP0SfVNPnz5F586dYWNjg507d6q+MJTHBaB1SveXaWs8lSlTBikpKYWKpyCNsKlTp6qmoVy8eHGB91WmTBkAKHBM4eHhcHFxUUsq5MXExARt27YFAAQEBMDf3x9NmzZF2bJlNaZxfTnZovwiKezrURljQV8brzqushFekOcbAHx9feHr6wtA6o7brl07dOnSBRcuXIBMJivU66cwXnUeysRalSpVNO7r5+eHI0eOqLqmJycnFzi+vF6b2oYf2tnZvdbz+fLxcn+eFcX7qbDvASKivOh72ywvhf2cvXz5Mlq2bKl6jNasWYNWrVph9erVAKQfeHK5HDNnzlTdp1y5cjh79qzaPj7//HMA0uf+mjVrsGLFCgwYMAAA0LVrV9X3de59xMTEqC1zdHRUtWVye5P257x587B69Wp888036NSpk9ZtPD09VWUHPvroIwwfPhxt27bF3bt3XzlsrDAXYDIzM3Hz5k20bt0agPTa2L9/P06dOoUWLVoAANq1a6f2vW5iYgI7OzvExMTAx8dHtdzNzU3rY/Xw4cMCxZJbYRKQpeF838TrvL9GjBih+ltZduKrr74CIL2/lO1VJW3vjfwU9PkrbOxXrlyBu7u76jP10KFDSEtLw8GDB1X78vHxUT2/2mK/fPmy2vrVq1dj4MCBmDt3LgAp0RcbG6uWANJ2/oaGhlqffyo89hB6Tb169cK9e/ewZMkSuLi4YN68eahWrZrG+M7cyYyCLC/sFSCFQoEaNWrkmSEdPXo0AMDc3BynT5/G8ePH0b9/f/z333/o3bs33n//fcjl8kId821KSkpCx44dkZiYiMOHD+dbkyc/Dg4OWhtwvr6+CAkJUes18jr7eVmFChXQr1+/PHsJ2dvbq8bU5lapUiUYGRnh+vXrBYrnTTRp0gTly5fH5s2bNdYV1euxsN72cQMDA3Hx4kWEhIQUyf7yUtyPn7IOUF6vzbcVT2JiolqthaJ4P5UpUwYuLi64cePGG8VGRMS2mXbKxEtB2xpxcXFq7a+IiAi89957ats0aNBA7e+oqCi1GnW59xETE4O0tDS1fRgZGWnUOXl5H2/D+vXrMWXKFIwcORJTp04t8P2UtaROnz79ym19fX2RlJSEqKioV26r7NGhfKwiIiIAQO2xsrGxUbuglJmZiejo6LfyWL2qfaFNST7foqCL91deCvv8FbYNpi32KlWqqJJBQOE+G5T7KKrzp9fDhNAbKF++vKp72/379+Hg4KBR5PZtq1ixIuLj4+Hv74+2bdtq3HJ/oBoYGMDf3x8LFizArVu3MGfOHPz555+qrsuFPe7du3eRnZ1dZOeSkZGBLl26ICQkBAcPHkTVqlW1HhfAKz+4fH19cf/+fY3lXbp0QXp6Onbt2lWgmPLajzZTp05FTk4OfvjhB411RkZGqFixosa+LCws0KZNG5w+fbpAX6QVK1bE48ePER8fX6CYXpaRkVEkhRjzU5SvDeUViNdNFii7dyvPuaCvn6KmvMp49+5djXV37tyBo6MjLC0t4eTkhDJlyhTo9Q2gwK/NovDo0SNkZWXBz89Ptayo3k8BAQEIDw/H+fPniyxeItJP+tw2y0vlypVRpUoV7Nu3r0AFd8uUKaPWVnB2dtbosZC7R0hGRgY2bdqkdrU+9z4cHBxgbGyc7z5+//13JCQkoHHjxoU7uULYt28fhg4diu7du2PZsmWFuu/L7Yn8dOnSBQDwyy+/vHJb5Q9p5X6dnZ0BQO2xysnJQWRkpOrvdevWwdXVFZUrVy5g9AXn4eEBc3PzQrUvSvL5FoWOHTvC0NCwQOcPFP79dfPmTVy4cEE15DQ/r9M+LEwbTFvskZGRyMnJ0Ro7IPUAyuuzQbmP/M4/Ojoa+/btY2+gt4gJodcgl8s1vhDKli0LFxcXZGZmFmssvXr1wqNHj1TdeHNTzpAAQGsCQTl2M3fMkZGRuHPnziuP26NHD8TGxmLp0qUa616nF4JcLkfv3r1x/vx57NixI88GQbt27WBtbY3vvvsOGRkZeR63cePGSEhI0PhQGjlyJMqXL4+JEydq7TUSHR2N2bNnq+3nxo0bBXpeK1asiH79+mHlypVqY2dz7+vSpUsay2fMmAEhBPr376+1oXb58mVs2LABgPS4CyHUumkrKc8/NTUVaWlpGut37dqFhIQErbVpilJRvjacnJzQokULrFu3Tq1x8PK+lN3vc8vOzsbGjRthbm6uSi4W9PVT1MqXL4/atWtjw4YNSExMVC2/ceMGjh49quqybmBggA8++AAHDhzQ+lpRxlivXj2YmJho3eZtuXz5MgCozUJRVO+nyZMnw9LSEkOHDsWzZ8809hMeHq42FFPb51RsbCzu3Lmj9tpPS0vDnTt3OAsFkR5g2yx/M2fORFxcnGp20pcdPXoUBw8eBCANZb5w4YJq3Ycffog9e/Zg2bJlePDgAX777Td8++23AIAzZ86gXbt2sLOzQ79+/VT3yb0PQ0NDdOnSBRMnTsTp06dx//59zJgxA1euXEFKSgqCg4Px0UcfYdq0aWo9DYrS6dOn0adPH7Ro0QKbN2/Oc0heXsNy1q5dC5lMVqDZmwIDA1GjRg3MmTNH64/slJQU1fAgKysruLm5qR4r5XDHYcOG4dKlSwgNDcXo0aMRHR2N2NhYLFiwAOPHj8fcuXOLpGbny4yNjVG/fv1CtS9K8vkWBXd3dwwbNgxHjx5Vq4ulpFAoMH/+fNVQNm3vr9mzZ+PQoUN48OABli9fjn379iEzMxO7du1C+/btMWTIELXhcnl5nfZhYdpgfn5+CAkJUbVl27Vrh5SUFIwaNQqhoaG4cuUKhg0bBplMhpCQEIwYMQK///47pk2bptqftvP/+eefsWXLFjx48AC//vorVq1aBblcjiNHjqB169Zo1qyZ2oxmhZVXe/DOnTsavy+K4vO2pGENodeQkpICNzc3BAYGolatWrCyssLx48dx8eJFzJ8/v1hj6d+/P7Zv346RI0fixIkTaNq0KeRyOe7cuYPt27fjyJEjqF+/PmbNmoXTp0+jc+fO8PT0RHR0NJYvXw43Nzc0a9ZMtb+CTm06YMAAbNy4EZ999hn++ecfNG/eHKmpqTh+/DhGjx6Nbt26AZC+gJXda2NiYpCamqr6gdiiRQvVeOGJEydi//796NKlC+Lj4zWy7MpGRpkyZbBw4UIMHToU7733Hvr27Qs7Oztcu3YNaWlpqsRJ586dYWRkhOPHj2P48OGq/djZ2WHPnj3o1KkTateujX79+qmm075y5Qp+/fVXtWRUt27d8M033+DUqVNo167dK5+Pr776Cps2bcLdu3dRrVo1tXXdunXDpk2bEBISonaVo0mTJli2bBlGjx4NX19f9O/fHz4+PkhJScHJkyexf/9+1WPWunVr9O/fHz/99BNCQ0PRoUMHKBQKnDlzBq1bt0ZQUBBCQ0PRtm1b9O7dG76+vjAwMMClS5fwyy+/wMvLC+PGjXvleWjz4MEDbNq0CQBUXzTKuDw9PVXTbRb0tVFQP/30E5o1a4a6deti+PDh8Pb2RkREBA4dOoR///0XADBixAgkJyejRYsWcHV1xdOnT7F582bcuXMH8+fPVxW1LOjrJ3exS+X0v0uXLoWtrS1sbW0RFBRU6Mdv3rx56NixIxo3bowhQ4aopp23sbFRm3b122+/xdGjR9GyZUvVdMVPnjzBjh07cPbsWdja2sLMzAzt2rXD8ePHMWvWrELHorR06VIkJiaqCrAeOHBA1WAZO3asqn4GIBXk9vDwUE05DxTd+6lixYrYsmULevfuDT8/PwwYMADVq1dHVlYW/vrrL+zYsQOffPKJanttn1NLly7FzJkzceLECbRq1QoA8M8//6B169aYMWOG2mNMRKUP22b56927N65fv445c+bg6tWr+Oijj+Dp6Ym4uDgcPnwYf/zxB7Zs2QJAKuI7cuRIXL16FXXq1EGXLl0wYsQIBAUFISgoCBYWFpg5cyYmTZqEVq1aITAwELt374apqanqeAEBAVi7di3GjBkDmUyGhQsXol27dmjZsiUAqYj08OHDsXLlSpw+fRqzZs1SKyRblB48eICuXbtCJpMhMDAQO3bsUFtfs2ZN1KxZEwAwZ84cnDt3Dh06dICHhwfi4+Oxa9cuXLx4EWPHjkWlSpVeeTxjY2Ps3r0bbdu2RYsWLdCrVy80bdoUxsbGuHnzJrZs2QI7OztVz7WAgACsWbMGAQEBkMlkWLNmDTp16qQaRtO6dWv06NEDy5Ytg7u7OzZs2KBWFL2odevWDV999RWSk5MLlKAr6edb0N8q+Zk/fz7Cw8Px6aefYvfu3QgICICdnR0iIyOxY8cO3LlzR3UOAQEBmDBhAr755huYm5tj+vTpOH/+vKq+p6enJz777DPMnTsXn3zyCcaPH691MhltXqd9WJg2WMWKFVGpUiWsX78e48ePh7OzM5YvX44RI0ZgzZo1kMlk+Pzzz/HkyROMGDECDRo0wKlTp9R+9wQEBGD+/Pl48uQJypcvj5EjR+L48eP4+OOPAUg9CidNmoTp06eja9euGDJkyBsXkc+rPejn54eWLVuq1Ssqis/bEqcYZjIrdTIzM8WkSZNErVq1hLW1tbC0tBS1atUSy5cvV9vu5anHlV6eIlAJL02RV5CpTYUQIisrS/zwww+iWrVqwtTUVNjZ2Yl69eqJmTNniqSkJCGEEH/88Yfo1q2bcHFxESYmJsLFxUV89NFHGlMYFnRqUyGESEtLE1999ZXw9vYWxsbGwtnZWQQGBorw8HDVNsqpP7XdlNNJ5j5uXreX7d+/XzRp0kSYm5uLMmXKiAYNGohff/1VbZuuXbsKf39/rbE/fvxYTJgwQVSuXFmYmZkJCwsLUa9ePTFnzhzVY6ZUs2ZNMWTIELVluaedf5lyKsSXn/vMzEzh6OgovvnmG60xXb58WfTt21e4uLgIY2NjYWdnJ/z9/cWGDRuEXC5XbZeTkyPmzZsnfH19hYmJiXBychIdO3YUly9fFkIIERMTI4YPHy58fX2FpaWlMDExET4+PmL8+PEaU7DmdR4nTpwQAFRTRuZepu328mvyVa8N5fSX8+bN03gcXn5tCCHEjRs3xIcffihsbW2FmZmZqFKlipg2bZpq/a+//iratm0rypUrJ4yMjISdnZ1o27at2Ldvn9bH+lWvH2V82m65pxwt7HkcP35cNG3aVHXcLl26iFu3bmnc98GDB2LAgAHCyclJmJqaigoVKogxY8aIzMxM1Ta7d+8WMplMYxrclz9HlLRNF5vflL65P3fkcrkoX768mDp1qraH843fT0ohISFi2LBhwsvLS5iYmAhra2vRtGlTsWTJEpGRkaHaTtvnlPKzRttr9uXngYhKH7bN8p52PjflMcuWLSuMjIyEk5OT6NKli8b35cCBA0XDhg3VvnfCw8PFmTNnREJCgkhPTxfnz58XiYmJWo+TkJAgbGxsxKJFi1TLsrOzxYULF8Tly5eFXC4XERER4r///lNNP/6yvJ4TIV58vhdk2vn82i8vf0ccPXpUBAQEqNpiyu+h4OBgoVAoNOLTNg177sdg+vTpokaNGsLCwkKYmZmJ6tWriy+//FI8efJEtV1oaKgwMjISe/bsUS1LT08X586dU00ffufOHXH79m2NGJTy+u4X4tVt1penYX/27JkwMjISmzZt0ovzLehvlbymnVfKyckRa9asEc2bNxc2NjbC2NhYeHp6ikGDBqlNsZ6dnS0qVqyoNnW7QqEQV69eFX///bfIysoST548EZcvX1Z7/2mj7T2irX2Y17TzuRW0DRYcHCzs7e3FvXv3VMtiY2PF6dOnVce8ePGiiIqKyvNYLVu2FB9++KHa83vr1i1x7tw5kZqaKhISEsQ///wjUlNTtd5f2/OY28uvj7zag9p+wxTmt3BpoV9nS3rl9OnTwsDAQKNhVVgbN24U1tbWIiEh4Y1jmjVrlvD29s6z8UNUUDk5OaJy5cp5JmmK0p49e4S5ubl4/PjxG++rKN9PRET0gvKH8JUrV0RMTEyeP6YLIiYmRnh4eIhOnTppJPaVcnJytCZklLZt2yaMjIzEihUr8tzmwYMH4ty5c68dZ27p6ekiJiZGTJo0SS0h9LZ4enqKPn36iJiYGPH8+fM32tfcuXOFpaWl2Lt3b57bXL9+Xdy4ceONjqP0/PlzERMTI/r06aP1h/XgwYNFs2bN1JaV5vMtiIEDBwp3d3cRExPzxm2Ys2fPCmNjYzFt2jS1i765xcXFiSNHjmhdl5CQIGJiYoS7u7tGQkhb+1CZEFqyZImIiYl5ZaIpPwqFQnTu3FlUqlRJ3L59O8/tDhw4IFJSUrSuCwkJETY2NmLw4MF5xpKWlpbnhV0qWkwIUanWoUMHMXTo0Dfah1wuF9WqVROzZ89+43hSUlKEk5OT+OWXX954X0Rbt24VdnZ2eX7hFpVGjRqJSZMmFcm+ivL9RERELygTQsrbmyZE7t69KypVqiTKli0rZs2aJc6fPy8ePHggrl+/LlasWCGqVasmHBwcxIMHD/Lcx6pVq4SRkZFo1qyZWL9+vbh586Z48OCBOH36tJg4caKwsrISPXv2fKM4lRYuXFik5/8quXvZ5tVbpTCmT58uZDKZCAgIENu2bRN37twRERER4tixY2L48OHCxMSkyL6Lx40bp4pdW4LkwYMHwtTUVJw9e1a1rDSfb0EoRwBoGwXwOvbv3y8sLS1FjRo1xIoVK8S1a9dEZGSk+Pvvv8XXX38tnJycRMOGDbUmjGrVqqWKRVsvupfbhy/3fM8vkVsQKSkponPnzsLU1FSMGjVKHD16VNy7d0+EhoaK7du3iw4dOggjI6N8Ezp///23KFu2rPD29hbz588Xly5dEpGRkeLKlSvixx9/FB4eHsLb2/utt3FJCJkQ+jRAjoiIiIio9Hny5Alu3ryp+rtly5YwNjZ+o32mpKRg3rx5WLNmDZ48eaJabm1tjY8//hjTp09H+fLl893H9evXMW3aNBw+fFitWHblypUxceJEDB06NM8iz4URFRWlNptnUZx/fs6dO6eafczd3V1t9rjXdebMGXz99dc4deoU5HK5anmdOnXw5ZdfomfPnm98DAAICQlRFdM1MjJS1d7Lj76d78tu3bqlqrloZWWFRo0avXFcygLre/bsUZtYxs3NDUFBQRg3bhzMzMw07nfhwgWkpKQAkCZgqVWrVr7HycjIwNmzZ1V/16xZE2XLln2j2BUKBTZu3Igff/xR7XPHyMgI7du3x8yZM1V1JfMSExODWbNmYfPmzUhISFAtd3R0xNChQ/HFF1+o1bOkt4MJISIiIiIiypMQAmFhYXj69CnKlCkDPz8/mJiYFGofqampCAkJwfPnz+Hm5gZvb++3FG3Jl5iYiLCwMGRmZsLb2xsuLi66Dumt0rfzfVlmZibu3r2LxMRElCtXrkiSbcXp0aNHiIyMhKGhIapUqVLoJI5cLsfdu3cRGxsLBwcH+Pr6wtDQ8C1FSy9jQoiIiIiIiIiISM+8ef9MIiIiIiIiIiIqUZgQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBERERERERGRnmFCiIiIiIiIiIhIzzAhRERERERERESkZ5gQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBERERERERGRnmFCiIiIiIiIiIhIzzAhRERERERERESkZ5gQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBERERERERGRnmFCiIiIiIiIiIhIzzAhRERERERERESkZ5gQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBERERERERGRnmFCiIiIiIiIiIhIzzAhRERERERERESkZ5gQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkmhIiIiIiIiIiI9AwTQkREREREREREeoYJISIiIiIiIiIiPcOEEBERERERERGRnmFCiIiIiIiIiIhIzzAhRERERERERESkZ5gQIiIiIiIiIiLSM0wIERERERERERHpGSaEiIiIiIiIiIj0DBNCRERERERERER6hgkhIiIiIiIiIiI9w4QQEREREREREZGeYUKIiIiIiIiIiEjPMCFERERERERERKRnmBAiIiIiIiIiItIzTAgREREREREREekZJoSIiIiIiIiIiPQME0JERERERERERHqGCSEiIiIiIiIiIj3DhBARERERERERkZ5hQoiIiIiIiIiISM8wIUREREREREREpGeYECIiIiIiIiIi0jNMCBERERERERER6RkjXQdAREREREREOiAEoFAAOTmAXC79LZMBRkbSzcBAc3vltspttFEogKwsaV8mJtK/RPTOYUKIiIiIiIhI3ygUQHo6EBEBXLwIhIQAGRmAjQ1QtSpQvz5QvjxgavoioZOWBly9Cty+DdSpI22jzePHwMGDgJ0d8OGHUlKIiN45TAgRERERERHpE4VCSgStXg1s3AgkJADGxlLiR6EAsrMBT09g9GhgwAApSSSTAcnJwKFD0n0+/TTvhNC9e8DUqYCfH9CxIxNCRO8o1hAiIiIiIiLSF0IAkZHAvHnA4sVSL6DvvgPOngWuXweOHAHGjZN6C337LbBwofR/Iip12EOIiIiIiIhIX8jlwL59wM6dQLNmwLRpQPPmL9a7uwP16klDwr76Stq2Zk2ge3fdxUxEbwV7CBEREREREemL0FDg77+lgtBdukhJoZeZmABt2gB9+wJ37wLHj0vDyIioVGEPISIiIiIiIn1x7x4QHg74+ko9f/KaAczJCahRA7C1lYaYRUYClpbSutyzk2mjnLGMiN5pTAgRERERERHpi9hYICYG8PICypXLf1t7e8DNDUhMlGYO8/GRlmdlSYmlEye03+/69byTRUT0zmBCiIiIiIiISF9kZEg3MzPA3Dz/bU1NAQsLabr51NQXy+PigOBgabYxbYTgEDOiEoAJISIiIiIiIn1haCjdFAppaFd+5HLpZmgoTUuvZGMDtG0LdOig/X4hIcCKFUUXMxG9FUwIERERERER6QtLS+mWkgIkJ+e9nRBSz6CEBKmeUJky6vt47z1g6FDt9z19Gli3rmjjJqIix1nGiIiIiIiI9IWLC+DqCjx6JBWKzqv4s1wOPHkCPHgAODoC3t7FGycRvXVMCBEREREREekLX1+gWjXgzh3gwgWpF9DLSSEhgLAw4OxZqYZQzZpSLyEiKlWYECIiIiIiItIX5coBLVtKPX527AA2b5aKTCvrBeXkAM+eSet27gTq1gW6ddN11ET0FrCGEBERERERkT7p2FGaRv6HH4AvvgAOHADatAHs7ICHD4EjR4B//gGqVweGDwdq1dJ1xET0FjAhREREREREpE8sLaVET8WKwOLFwIkTwOHD0lAxmQywtQX69AHGjgXq11e/r6EhYGQEGOQz2EQmk7YxNHyrp0FEb0YmRF5VxIiIiIiIiKhUyP2zTyZ78X+5HHj6VKoZlJYGODgAFSpIhaRfJpcDqalAerqUVLKy0n6szExpdjIjI2l/uY9HRO8MJoSIiIiIiIhKMyFezCjm7v6idw8TNUR6jUWliYiIiIiISrOcHODgQSAwEBg3DvjvP6m3jxB5TztPRKUeewgRERERERGVZsqffFFRwKZNwN69QJUqwOjRwHvvSUO7APYYItIzTAgRERERERGVVkIACoX0f+VQsYgI4JdfgP37gcqVgaAgoEEDFoEm0jNMCBEREREREZVWDx8CP/0EJCUBI0YANWu+SPw8eiQlhWxtgd69mRAi0jNMCBHRW5d7eLpMptkbWble27rilNfHoXK5TCaDjF2piYiIqKSJiAB+/VUaKubtLQ0Va9QIMDaW1rN9Q6SXmBAiorfu8mVg0iQgJESqY/j55+rtjr//Bvr1Azp2BJYseTsxvOqjTrk+OTkZkZGRePjwIR4+fIioqCicOnUK5cqVw8KFC+Hm5vZ2AiQiIiIqai+3f6KigK1bgV27AA8PYMwYoGnTF4khItIrRroOgIj0x6NHwI4dQNu2QO3aRXcxSnuuR7y0jUBGRgYePXqEx48f48mTJ7h//77q//fu3cPjx4/x7NmzPI9z7949XLp0ib2EiIiIqGQIDQX+/ReoWBGoVUuacn7SJOlK3K5d0tW6evWYECLSU0wIEVGxMDAA7OyAtDSpF9DPPwMmJgW776v6MQoh7ffRI2mYfGQk8PixAikpofjvv88QFRWFR48eISEhId/9yGQyWFpawtPTE25ubnBzc4O7uzuSkpLw008/4datW3j48CHc3d0LeNZEREREOmRhAYSFAT/8APj6Sj2C3nsPKF9eKiTNi1xEeo0JISIqNlWrAu3bAxs3Atu2SRentNUTeplcDsTGAvfuSUPgHz+Wbrn///Ch+n0NDQ3g4WGG+/d/Vy0zNTWFo6MjXFxcUKFCBXh7e6N8+fKqv8uXLw9nZ2eNHkA5OTl4/PgxduzYgaVLl+L7779nLyEiIiJ6twkBuLoCX34JDBokddMePx5wcpKSQa1bA2Zmuo6SiHSINYSI6K27fBmYMgWwtga++goYOxYwNQU2bZJ6LueuITR5sjQRRkiIlOSJipKSQa/6pDIzk9o8bm6Apyfg6irg6fkcrq6n4ObmBldXVzg5Ob1W/AqFAhcvXkSjRo1gZ2eH+/fvw8bG5rX2RURERFQsnj6V/nV0fDF72LNnwM6dQHY20LOn1HAiIr3FHkJEVKz8/KQZT7/8Eli6FPj+e/X18fHAvn3SkHclQ0OpLePlJfVwdnVV/3+FCtK/6jOlygBYAwh445gNDAxQr149tGrVCqdOncLatWsxYcIE9hIiItJXr7pKwe8HehecPw8sXgw4O0s9gho0AMqVk4aN8TVKRGBCiIiKmaWlNJlF27bAb79JQ8gsLF6sd3cHvvtOaqe4u0s3R0fA6C19WuXXSTJ3wsfAwAATJkzAyZMnsXjxYgQFBcGkoEWQiIiodFB+Z4SEAAcPAmfOSOOWlV9abdoAnTpJXVUB/ugm3REC+OADoHFjqXj0pElSg2rMGKBlS8DcXNcREtE7gEPGiOityz1kbM8eqSbQyZPA0KFA3brSELKhQ9/OtPOv+ojLzMzE48eP8fDhQzx48ACPHj3C06dP0aNHDzRv3lxtPzk5OahduzZu376NjRs34uOPP2YvISIifSEEkJkJrF4NLFggFbKztpa6q8rl0vCc1FSpcO///gf07i3N3MTvCSpuL7d9ZDIgOhrYvVv698MPgRo1dBMbEb1T2EOIiIqdoSFQs6aUBFqzBti69e0cR6FQID4+Hvfu3cOTJ0/w+PFjjanmIyMjoVAo1O5nZGQEd3d3tYSQTCaDoaEhxo8fj+HDh2P+/Pn4+OOP307gRET0blq9GvjxR+n/P/wADBwIlC0r/R0VBSxfDgQHA7NnS8v69dNNnKTfFApg3jzg9m1gyBCgYUOpkPSIEbqOjIjeMUwIEZFOODoCAQHA0aPA5s3SRdaCEgLIyZEuxj58+GKq+ago6f+PH8ciObkX7tw5Dblcnu++jI2N4ebmBg8PD7i7u6ummm/ZsqXGtjKZDH379sWMGTNw7do1HD9+HO+//35hT12vPXnyBD/99BO2bNmC+Ph4ODs7qwp0W1hYwNTUNN/7m5qawszMDAYGBnluY2RkBHNzcxjlM87QwMAAZmZm+R4vJSUF9+7dg42NDTp16oSPP/4YhuqFqohIn1y+DBw4AKSlAd9+KyWDcvcAcncHZswArKykH+MHDgD16klTbBIVJwMD6arb3r1SF20bG6k7dps2HCpGRGqYECIinZDJgCpVpPZJ//5S+1opJweIiwOePAEePVKfXv7+/RfTzKena9+3kZEhKlYsD4VCAQcHB1SoUAHOzs5wdXXVmGre3d29wD/yZTIZzM3NMWbMGEydOhULFixgQqiQ5s6di7Vr1yIlJQUAEBYWpuOICubw4cO4ePEilhT1mEYiKjnOnJF6XLRvLxXDe3k4mEwmTXnZrp1UzPfqVeDSJSaEqPjJZICDg9Q76IMPpMTQyZNSYqhZMx0HR0TvEiaEiEhnTE2B996TEkLbt0vLMjKkYtM9euR/X0NDqWyDm9uLqeaV/3d3LwN39yVwdl6fby8RpcLUAZLJZBgxYgS+++47HDlyBP/++y9q165d4Pvrsxs3buDatWtISUnBBx98gA4dOsDHxwdlypQBAKSlpSEzMzPffWRmZiIjI0NjmF9uOTk5SE9PR05OTp7bKBQKpKenIysrK89tkpOTcenSJdy9exeRkZE4deoUbty4gerVq7/iTImo1FEogLAwqWtqtWrSVJfavjuUVzsqVwb++EO6oqFQSD02iIpDaipw/DhQpoxUUFqZGCIi0oIJISJ667y9gWnTNGcKU07K8vXXLyZlMTEBXFykGVJdXQEPD+lfKdEj/e3mJq3Pe7SPAQC7/z9G0RbzlMlksLe3x+DBg7FkyRIsWrQI69evL9JjlEYKhQK//vorrl27hvfeew/jx49HixYtdB1WgVy6dAk9evTAkydPsH37diaEiPRRSgqQlCR9kTk65j/spkwZaRuFAkhMlLqzWloWW6ik5+RyIDYWmDNHqm+lnFUs95SuRET/j7OMEdFbp/yUSU4G7t2T2sUVKkjt6jf5BNLVxC1CCNy/fx+VK1eGoaEhwsLC4O7urptgSog//vgDX375JcLCwrBkyRL06tULxsbGug6rQDIzM3H48GH07t0blSpVwubNm1GrVi1dh0VExenZM+DTT4EjR4CFC4FBg/Lfft48YOZMqc7QN98A9vbFEyeRsmGVkADs3w/89Zc0bKxTJ52GRUTvJvZfJaK3TiaTbmFhUns6MBD480/1da9z0yUvLy8EBgYiKyuLdWVeIS0tDb/88guuX7+Ovn37okmTJgUayveuMDExQe3atdG7d288ePAAP/30E3gthUjPmJpKNYOysqRCd6+SnS3dTEykG1ExSElJQUxsLNLS0wE7OykhuXIl0LGjrkMjondUyWmRE1GJl5gIhIQAPj5SiYWSSjkM7bPPPsO2bduwatUqfPXVV6rZskjdtm3bcO7cOXh4eKBr167w8vJSG8qXkJCAJ0+eID2vKuGQkjLm5uavnF3MzMws355HBgYGqpnKCkomk8HNzQ2DBw/GoUOHcOrUKRw9ehTt27cv8D6IqISzspJuWVlSz4uMDKmAtDYpKUB8vNRTw8aGQ3Wo2ISGhmLOnDlIT0/H6NGj0apVK1hZWek6LCJ6hzEhRETFIjNT6nEfFyfVOHRz03VEb65+/fpo1aoVTp48ibVr1+GzzyboOqR3TlRUFHbs2IF79+7h22+/xXvvvaexzfHjxzFnzhz8999/OohQk5eXF7788ksMGzZMtczAwAC+vr4YMWIEfvzxRyxatAgtW7YsVGKJiEowQ0NprLOTE3D3LhAVJV3d0ObePemmLISn6y6tpDfq1KmDtWvX4sCBA9i0aRPS0tLQq1cvXYdFRO8wJoSIqFgkJQGhoVKtzUqVSv6EK8oeLpMmTYVCEYA1a4Zg7FhpRAG9EBwcjCtXrsDf3x9t2rSBra2tRqFvW1tb+Pr6wtDQMM/9FOXsYpmZmfnOZqZtOJhMJkPZsmXRq1cv7N69G//99x82b96MIZy5hUg/yGTS1YzffpNmD3v/faBiRc0vM7lcmp7+r7+ARo2AunWZEKJiI5PJYGNjg379+uHjjz/WdThEVAIwIURExSIxUUoI2dhIs/GWlvZxu3ZtMGlSa9y5I8P27QDbXy+cP38ehw4dQmpqKvr164fq1atrnfWtbdu28Pf3z3dfxV2zJ6+had7e3vj0008RFBSE1atXo2vXrnBycirW2IhIRxo3Btq2BX76CfjxR6k2UKdOL4aOpaUBW7cCy5dLsya0bw/UrKnbmElvbNmyBefOnUO7du3Qpk0bDhUjogJhQoiIikViolRU2sYm7172JY8MhobAhAkyDBsmTSrTt2/pSXa9iczMTKxduxY3btxA37590bhxY5iammrdViaTaU0UvWtkMhmsra3h7++P5s2b48aNG1i2bBm+/vprXYdGRMXB0BAYNUqqEbRmDdCvH+DrC/j5ST2DbtyQvuhsbYEJE/iFQMUqICAAMpkMK1euREJCAgYMGJBv3T2i/GRkZCAtLQ3W1tYlZlZYej2cdp6IisXRo8BHHwHVqwNbtkilFUoDIYD0dGkY3NOn0nm2bavrqHTv119/xaxZs5CamoqVK1eiffv2paZhmpGRgSNHjqBXr16oUKECdu3ahapVq+o6LCIqDkJIhaVPngR++QU4dUr68JfJpOJ4/v7AgAFAw4ZSLyEmhKiYCCEghIBcLoeBgQEMDAzeqYstQghkZWUhNTUVpqamsLS01LqdXC5HSkoKhBAoU6ZMvsPJqWAyMjIQHR2Nx48fIz4+HomJiUhISEBCQoLW/9+7dw+pqamQyWRo3rw5fvrpJ1SvXl3Xp0FvCXsIEdFbl5EBPHkCJCdL9TjLl9d1REXL3BwYPRqYNg1YsIAJoZiYGGzZsgWhoaGYMWMG6tWr9041St+Uqakp6tati549e2Lv3r2YO3cugoODS9U5ElEeZDJpqNj770sf9kJIvYMAqQeRTPairhA/E6iYHD9+HIaGhqhbty7KlCkDAO/cd1JaWhr27NmDoKAg9OjRA2vXrtW63YMHD/DBBx8gLS0Nhw8fRqVKlYo50ndTZmYmEhISEB8fr/o3r1vu9c+fP1fVX3y5H8ir/gaAEydOYN26dfjss8/gVhpmhCENTAgR0VuXkPCifpCPT+lqI8tk0u+BkSOB778HDh8G/v0XqF1b15Hpztq1a3H58mU0btwY7du3h5OT0zvXMH0TMpkM5cuXx6hRo/D777/j1KlT+PPPP19ZB4mISritW4Ft24AmTaSCcS4u0nJlD4ZS9DlHJYdCoUBqaiqWLFmC7t27Y9CgQTA3N9d1WFoJIZCTkwO5MomaB7lcDrlcXuz1A4tDRkZGgXrp5P5/YmIinj59CoVCofaYFOT/AGBiYgJbW1s4OzvD2dkZ9vb2sLOzg62trdq/yv+bm5vD1dUVU6ZMwa+//orVq1ejXbt2cHZ2hpER0welDZ9RUhMdHY1ly5bB1NQUAwcOhGtpGddDOqWsH2RrW/oSQkoODsCgQcDSpcDixUBwsK4j0o1r165h3759iI+PxzfffINq1aqVqmSQkqGhIXx8fDBs2DAsXLgQ8+fPR/PmzWFiYqLr0Ijobbl/Hzh/HvD2Vl8ukwHZ2cCKFcCRI8CwYcAHH+gkRNI/MpkMXbp0Qfv27WFoaMgf7DqUk5ODGTNm4PTp00hMTETlypWRlJSk1rMnLS1NlbApTHLHxMQEDg4OqmSOtn+VNzs7Ozg4OMDOzg7W1tZqQ/aVbbLcbbO8/j9//nzExcXh4MGDWL16Nby8vODr61tUDxe9I/iJQWoSEhKwZs0aWFhYoE+fProOh0qJhITSWFD6BWUvofHjpd8Dv/4KzJoFuLvrOrLiJZfLsXLlSty6dQuBgYFo2rQpLCwsdB3WWyGTyeDo6Ih+/fph165d+Pfff7FlyxZ88sknug6NiN6WmBggKQlwdJS+0HKLi5OKSv/zjzTzGFExiYmJgZmZGaytrQG8e0PF9Em7du1w6tQpVT2nmzdvqtblTvIoe+vk10vn5X+dnZ1haGioNXlT0GWFZWFhgdmzZ+PRo0fYv38/GjRoAEdHRzg6Or7W/ujdxIQQqeTk5ODp06d4+vQpateuDU9PT12HRKWEsodQvXqlMyGk5O0NdO8O7NgBLFsmDSHTJwcPHsTJkydhYWGBvn37olKlSqW6YSqTyeDh4YGJEydizJgxWLZsGQICAthQIiqN0tKA+HhAoQDs7YGXk91xcdJ6OzvpRlQM4uLiMH36dFSoUAFDhgyBg4ODrkPSW6GhoTh16hQUCgXq1q2LKlWq4P3334erq6taTx5lMe2XZ1h9OZFTkB48b5tMJkPlypUxYsQIPH36FIsXL0bt2rXh7+/PnmilCJ9JUklLS8PNmzdhZmaG6tWrl5oZgUi30tKAhw+lmbjKlZNupZHy+3niRCkhtGoV8OWXQJkypXOI3MuSk5OxYcMGhIeHY9KkSahTp06pnxlEOQ39+++/jyZNmuDWrVv4+eefMXXqVF2HRkRFLTo6795BgJQQSkyUxg8zIUTFxN7eHvPnz4ehoSFMTU11HY5emzdvHhQKBZycnPDXX39pzPSmq6TOm5LJZPjoo49w/vx5bN26FUuXLoWrqytnHStF+IufVNLT01UJodJa94OK38vDxUr7y+q994AWLaTz1qc6QsHBwbh06RKqVq2Kzp07w9nZWS8+Q2QyGVxdXTFx4kSkpKRg06ZNCA0N1XVYRFTUYmJeTJVpY6P5ZRYfL33w29tLN6JiIJPJYGFhAVNT0xLznZuWloYdO3bA3d1d661p06YICQnRdZiFkpSUhF27dgEAVq5cCRMTExgbG8PQ0FAtMfRyr6CSwtjYGJMnT0adOnVw+PBh7N27F9HR0boOi4oIE0KkokwImZubM+tLRSYxEQgPf1FQujSTyaTbhAnS34sXS3VGS+EkGWrCwsKwfft2PH36FKNGjUL16tVLZIPndZmamqJ+/fro3r07Hj16hB9++EHXIRFRUYuOlhJCZctq7yEUG/siIcQeQlQMLl26hP379+Phw4cl6jvXwMAANjY2qFKlitZbhQoVStwEDcuWLUN8fDxat26Nrl276jqcIieTyeDl5YXhw4fD29sbS5Yswd9//42srCxdh0ZFgEPGSCU9PR23bt1CmTJlUK1aNV2HQ6WEsoeQrS1QqZKuoykenTsDfn7AnTvArl1Aaa7PLoTAzz//jNu3b6NLly5o3rw5rKysdB1WsZLJZHB2dsbYsWNx+PBhHDt2DKdOnULLli11HRoRFRVlQsjHJ+8hYwkJ0pAx9hCiYlCjRg34+fmVuKFiZmZmaNu2LVatWqV1fUREBLp374709PRijqzwhBBIS0vDkiVLAAD/+9//1IaJlSYGBgYIDAzEP//8g40bN2LZsmXw8PBA7dq1dR0avSH2ECIAQFZWFh4+fIj4+Hg4OjrCzc1N1yFRKSCE1D6+d0+/EkJGRsC4cdL5z58vLSutvYT++OMPHDlyBEIIDBgwABUrViyVDaFXMTQ0ROXKlTFs2DA8ffoU8+bNQ3Z2tq7DIqKi8vKQsdzkcmnIWEqK1DtIW8KIqAjEx8fj2rVruHbtGtLT02FhYVEi6/UZGBjAzMxM683ExKREtSNWrlyJp0+fomHDhmjdurWuw3mrjIyMMGHCBNSvXx8nT57Ezp078eTJE12HRW+IPYQIwIveQcr6QSwoTUUhNTUVKSl/oGbNQ2jatAscHAJ0HdJbp5yC/uOPgRkzgKtXgZMngVatdB1Z0UtLS8PKlSsRFhaGUaNGoW7dujA2NtZ1WDohk8ng4OCAQYMGYffu3bh06RK2bduGfv366To0IioKyh5C2hJCCQnSzdpa6h3E2XfoFTIzM5GUlITExMQ8b9HR0Xj69Cni4uKQmJiI+Ph4pKWlQS6XQyaToWnTppg5cyYaN26s69PRS0IIZGZmYvHixQCAqVOnltreQUoymQxubm4YMWIEoqKi8PPPP6NmzZro1q1bieupRi/wG4sASD/sbt26paofVJo/zKj4JCTE4/Ll04iI+A1GRl6QyUp/QkjJ0hIYORKYORNYsKB0JoQ2b96MixcvwsvLC4GBgXB1ddXrzw5lQ+nTTz/FxIkT8dNPPyEgIAC2tra6Do2I3kRWltQDKCtLGhL28rBY5QxjyoLSevw5qG9SUlKQkJCAhIQExMfHIzExUfV/5XJtt8zMTCgUCoj/7z4shCjQDZB61wgh8Oeff6JevXrw9PSEi4uLLh8GvbVu3TpERUWhVq1aaN++va7DKRYGBgbo0qULLl68iLVr12Lp0qXw9PREw4YNdR0avSYmhAjAiynnzc3NWT+Iikx8fDzCwkJRpowVKlTw0nU4xUbZS2jUKKl+UlCQriMqeo8ePcIvv/yCR48eYd68efDz89PrZBDwYhr6rl27YuvWrbh79y5+/vlnfPHFF7oOjYjehHLKeQcHafzzy591yoLSyvVU4qSnp+fbY+fZs2d48uSJ2rL4+HhkZGS8VmLH2NgYNjY2sLGxga2tbZ63smXLwtnZGfb29rC3t4eFhQUMDAzwv//9Dxs2bMDGjRtRr149dO/enb37i5EQAtnZ2Vi4cCGEEJg6dSqM9KhnoJGREUaPHo3//vsPx48fx+7du+Hm5gZXV1ddh0avQX9euZQnZUG027dvw9HRkTOMUZFJSEhAeHg4bG1t4VPapxjTwsYG+N//NC8mKyUlSb8hXFwAY+OSdVF52bJluHnzJvz9/dG2bVvY2trqfUIIkJJC5cuXx5QpU9CzZ0+sW7cOgYGBqKQvBbSISqOXh4u9/Fmn7CHk4MAZxt5RMTExCAoKQkhICDIzM+Hi4oKkpCRVj52cnJxXJnGUiZ/cN0tLS9ja2qoSNnZ2drCzs4Otra3q/9pupqamGlOR53fLPRRJJpPhyy+/VE11vnv3bvj6+rL9Xsw2bdqE8PBwVK9eXTWzmL60g2QyGVxdXTFy5EhERUVhzZo1qFmzJnr06AEzMzNdh0eFxIQQISsrC5GRkUhOTka1atXg7Oys65CoFBBCICEhAffv30eLFi1QsWJFXYdU7O7eBZo3l8pJnDgB1Kyp/jti/Xpg+nTgzBmgRg2dhVlo58+fx8GDB5GRkYEhQ4bobSHpvJiamqJBgwbo1q0bDh8+jAULFmD58uW6DouIXlfuhFCZMprrlTOMVa7MhNA7qmnTpoiIiIBcLocQAiEhIWqJHVNTU629dXIvc3Z2Rrly5VQJH3t7e5iZmb0ysaNtPfBmyYPy5ctjwoQJePz4Mfbu3YuaNWvC1dUVdu/w68/ExAT16tXD7NmzUaVKlTy3s7e3x8SJE5GdnQ0nJ6dijLBghBDIycnBjz/+CCEEpkyZAmNjY71rBxkYGKBDhw64ePEiVq1aheXLl8Pb2xtNmjTRdWhUSEwIkap3EAtKU1FKSUnBgwcPAABubm56WUdFLpcmnQGkWcf+/FP6v7LNkJUl/caQy3UT3+vIzs7G8uXLce/ePfTr1w8NGjTg1aCXyGQyODk5Yfz48Thy5AgOHDiAjz/+GE2bNtV1aET0OmJipA9zL6+8E0K5awjRO2X16tW4f/8+cnJy0Lt3b3h6eqJVq1ZqPXqMjIxemczR9n9AN71CDAwMUKtWLfTu3RtRUVFYu3YtKleujA8++OCdascLISCXy2FgYAAjIyNUqVIFFStWzDdGGxsbfPzxxwCkJNK7aPv27QgJCUHlypURGBio63B0xtjYGCNHjsSNGzfw+++/Y8eOHXB1dYWnp6euQ6NCYEKIVDOMWVhYoGrVqroOh0qJ+Ph4hIeHw8bGRu97kLRuDZw+DWzYAHzyia6jeTM7duzAuXPn4ODggL59++p9Iem8GBkZwdfXF4MHD8bPP/+M77//Hnv27NGrGgNEpcazZ1L2vmxZzRnGFAopIZS7xhC9UzZu3Ai5XI6mTZti+fLlMDMzg7GxsVqCByh5w30MDQ3Rp08f3Lx5Exs2bMCWLVvg7e2NOnXq6Do0lZCQEPzwww94/vw5+vbtiy5durxyNiqZTPbOzlilTHDNmzcPCoUCn3/+OUxNTUvca6coOTs7Y/jw4YiIiMCGDRtQo0YN9OnTBxYWFroOjQro3Ukhk87knmGMBaWpqCQkJODevXuws7PT+/opXboADRoAU6dKIw/+v/ZkiRMbG4u1a9ciKioKI0aMQNWqVWFoaKjrsN5JMpkMdnZ2GD58OJydnfHPP/9g9+7dug6LiF5HTEzeQ8aSk6XhYubm0nCxd7RHg766cuUKLl68CEtLS6xatQq2trawsLCAsbExDA0NNZJCJY21tTWGDh2Kxo0b49ChQ9i7dy9iYmJ0HRaAF6UDzp49i3379mHMmDHo3r07NmzYgKysLF2H99r27t2LGzduwMvLC3379tV1ODpnYGAAf39/9OjRA+bm5vj555/xzz//qAqt07uPCSE9J4TA8+fPcfv2bZibm+t1D6GEBOCjj6Tbs2eaP9r/+gv4+GPg/HkgJ0c3MZYkyoLSdnZ2elk/KDcTE2nq+ZgYYNo0XUfz+lauXIkbN26gUaNGCAgIgL29fYltRBcHAwMDuLu7Y+zYsYiLi8OCBQuQohxDSEQlQ06O9OGdlgY4OgLW1urrY2NfDBezsytZswPkISMjA2fOnIFCodB1KG9s3rx5yMrKwpAhQ1CpUqVS950lk8ng5+eH3r17w9vbGxs3bsTx48eR8w40VOPi4nD69GlERUUhKysLT548we+//47Jkyeja9euWLlyJTIyMnQdZoEp6039+OOPkMvl+Oyzz2BhYVHqXlOvw9jYGEOHDkXLli1x8+ZNbN++Hffv39d1WFRATAjpuczMTERERCAjIwOurq4oW7asrkPSmaws4OBBYPduYPZsIDtbff2jR8ChQ8DjxyW3h0dxUSgUiIuLQ2RkJGxtbfU+IQQA9esD/fsDmzYBp06VvNfQzZs3sXPnTiQnJ2PUqFGvrAFAL6ah79GjB2rWrIm7d+9i9erVug6LiAojLk7qBWRrK91e7hWZe8r5d7igb0EIIRAfH4+pU6fi22+/RUJCgq5DeiM3btzAgQMHYGZmhqCgoFJb+NfIyAjdu3dHx44dkZiYiK1bt+Lq1au6DguPHj1STUABvJiqPTo6Gn/++SemT5+OoKAgHUdZOIcOHcKlS5fg4uKCAQMGlMrX0+tycnLCiBEjULNmTWzZsgWHDx/mRbASgq15PZe7oLSfn59e/8ATQkoKWVsD69ZJPYFyF/tVKKT1peCC2VuXnJyMBw8ewNDQEB4eHrDKa951PWJoCHz9tfT6+vxz6bWU2/HjgJ8f0LWrtH7VKilxFBEhveZ0mUCSy+VYuHAhwsLC0KNHDzRq1IhjwwtIJpPB2dkZX3zxBVJSUrBy5UpVsXUiKgFenmEsvynnS3D9IIVCgaioKHz66ad4/PgxVqxY8U7PWFUQ8+fPR1paGgYMGAAPDw9dh/NWWVtbY/DgwWjevDmOHTuGvXv3Ijo6Wmfx5OTkIDo6Gvfu3dO6Pjs7G4aGhiVuZML8+fMhl8sxfvx4lNFWYF6PGRgYoHHjxvjggw9gbW2NtWvXcuhYCaG/v/4JAJCamqoqKM36QZKePaVe4V9+KfUQ5+dY4SkLSit7B+lzojE3V1dgxgzg33+BNWvU1924AYSGAocPA8uXA5MmAd27S7WHqlYFOnQAhg0D5s0D9u0Dbt+WkkrF8fo8dOgQTp48CQsLCwwaNAju7u68KlYIZmZmaN68OTp27IiHDx9i4cKFug6JiApKWVDayUmzoDQg9RBKSnoxZKwEysnJwfXr1zFixAg4Oztj8eLFcHd3R2JiIpYsWYLnz5/rOsRCCw0Nxc6dO2FiYoJPP/201PYOUpLJZKhSpQr69OmDSpUqYfPmzTh69KjOho49evQI+/fvx9OnT7WuNzc3R7NmzTBkyJBijuz1CCFw/Phx/PXXXyhbtiwGDx5cql9Pr8vExASDBg1C69atcffuXWzduhWhoaG6DotegdOd6DllDyF9rx+Um40N8NVXwIQJwOrVQFAQa0QWVnx8vKqgNIeLvWBgIM0y9ssvwHffAf36vVg3aJCU/AkNBcLCgPDwF/8PDQXu3ZN6GRkbA0ZG0r/m5oC3N1CpElCxIuDjI/1boUI2rK2NiqSxkpycjJ9//hlRUVGYMmUKatSowZmyCkkmk8HR0RGTJ0/GiRMnsHPnTnz00Udo2LChrkMjoleQx8VBlpoKAzc3zfpBgNRDqAQPGcvIyMDJkycxa9Ys9OjRA0OHDoWVlRUePHiAyZMnw8HBoURe1FmwYAFSU1MxcOBAvZnYwsjICF26dMGNGzewatUq/Prrr6hYsSIaN25c7LFER0fjr7/+gjx3V/v/Z2hoCD8/P4waNapE9bKZP38+srOzMWbMGNjb2+s6nHeWo6Mjhg0bhnv37mHHjh2oWbMmnJ2dS9RzrW/YqtdjyoLSISEh8PDwgJ+fn65DeifIZECfPsCvvwI//gh07iz90KaCUyaEXFxctCaEsrKykJ6eDhttV1tLOQsLqZePvz+wceOL5TY2QMOGQN26Ug3T7Gzp35wc6bdGZCQQEiIlh0JCpNvDh1Jtq7//Vk8UVazYBykpofD29kLlypXh4+ODypUrw9PTE+7u7jAyKniyKDg4GFevXoWfnx+6d+8OR0dHXhV7DUZGRqhWrRr69++PdevW4YcffsDOnTtL5A8tIn2y9ulTbMvIQCdXV/SzsUG5lzfIPeV8CUsIKRQKREZGYv78+ZgyZQr8/f1hZmaGq1evYtKkSWjQoAEmT54Mc3NzXYdaKA8ePMDWrVthaGiIcePGlfreQblZWVlh4MCBCA0NxW+//QY/Pz94enrCxcWl2GJIT09HREREnsOjy5Qpg1atWqFp06Yl4nkRQuDMmTM4ceIE7O3tMXz48BIRt64YGBigYcOG6NGjBx49eoQ1a9agYsWK6NChA9s87ygmhPRYRkYG7t+/j6ysLLi7u8PBwUHXIb0zzM2BOXOkYTozZwJr1+o6opJDLpcjJiYGUVFRqF69Ory9vdXWJyQkYOvWrfjll18wZMgQ9OnTp1TVoxFCqvmT13eeTCb1BBo0SOqBlpuRkXR7mbOzlJRs0UJKFClvKSlSzyFlbyLp/wIhIf8hMfE+QkLu4s8//4SxsTGMjY1hZGQEOzs7VKhQAZUqVULFihVRqVIlVKpUCe7u7jAxMVFr5ERERGDz5s2Ii4vDrFmzOPzvDchkMtjY2GDUqFHYt28fzp49iwMHDqBbt266Do2I8hHx7Bmuxsaiha0tjF66wv38+XP8mJyMq46OGOvpibYlLHEik8ng5eWFdevWwcnJCQBw8OBBzJ07F0OGDEGPHj1gbW2N0NBQzJs3D9999x0cHR11HPWrLVq0CElJSejTp4/eXeyUyWSoWLEievXqhbCwMGzbtg1+fn4YMGAAjI2NiyWGBw8eYPv27YiPj9dYZ2pqioYNG2LcuHEwKSHd75Uzi2VlZWHEiBF6PQFPQZmYmKBfv364desWtm3bhm3btsHLy4ujUd5RTAjpMWX9IDMzM1StWpU/9F5Sv740vGf9euCPPzjVfEElJSUhMjISJiYm8PDwgKWlpdr6+Ph4XLhwAf/88w/Cw8Nx8OBBBAUFoVWrViX+NSiE1GNn8GCpBlVeHaBMTIDp06VZ7R49evV+DQyk+7zcdlIopCFjLyeKMjOPIyrqAcLDwxEeHo579+4hNDQU4eHhuHv3LsLCwnDixAlVosjY2Bjm5ubw8PBAxYoVVYmiPXv24M6dO+jcuTNat26t8VxS4RgYGMDb2xtBQUGYNm0apk6diiZNmqh+iBHRu0WhUCAmJgYpz5/DwckJ1i8NGYuNjUVIXBz+MzDAMxOTEjflvEwmg7GxMdzc3JCamopNmzZhy5YtmD59Opo3bw4TExNcuHABkydPRvv27UtEkelHjx5h06ZNkMlkGD9+vMaFDn1gZGSETp064fbt21i2bBm2bt0KHx8ftGjR4q0fWzlT3fXr1zWKCctkMri4uKB79+5wc3N767EUBSEELl++jKNHj6JMmTIYPXp0iW+rFhcHBwcMGTIEYWFh2Lt3L6pVq4by5cuXiM8RfcOEkB5LS0vDnTt3YGFhoXdXUArCxASYPFmaan72bKlHB71a7vpBFSpU0GiIeXh4YNq0aahVqxZWrlyJ3377DVevXkWbNm0QFBSEOnXq6CjyNyMEcPeuVBfov/8AU1Npivl9+4AaNTS3d3YGdu2S6pVWqPB6x9SeKJJBCA+4ubmgfv36yM7ORnZ2NrKyspCdnY3ExEQ8fPgQYWFhCAsLQ2hoKEJDQxEVFYXIyEhcuHBBlSRSPnfDhw+Hu7u7RiPo6tWrsLS0hIeHB8zMzF7vJEopIQSEEGqPmUwmg6WlJXr27Ik5c+YgLCwMkydPRnBwsA4jJaK8JCQkIDExERYWFrC1tdXoYREbG4uEhASUsbODVQmtj6H8nDcwMICPjw8WLlyIGjVqQAiB3bt3Y9GiRQgKCkLnzp1hYGCAtLS0d7pX79KlS5GQkIBu3bqhZs2aug5HZywsLNC3b1/cvXsX+/btw86dO+Hl5fXWZ1tLSEjAxYsX8fDhQ411lpaWaN68Ofr06VNikipCCMybNw+ZmZkYM2ZMsQ69K+kMDAxQt25dBAYGIioqChs2bECVKlXQpUuXEvP86wsmhPQYZxh7NRcXqcD0uHHA9u1S7wtAqukyezbQvz9Qp472YT76RgiBhw8fYvv27Th69CjkcjmSkpKQnp6uVn/A2NgY3t7eGDp0KPz9/bFt2zasXbsWW7duxfnz59GrVy+MGDEC5cuX1+HZFI4Q0sxhAwZIs3/Vry/VnypTBmjfXvvrw8AAqFcPkMuluj9FSXnVV1v3cDc3N/j6+qJly5bIyspSJYqSk5Px4MGLXkV3797FhQsXkJGRgezsbI0rffv378e3334LNzc3zJ49G76+vkV7EiWYEEJVhLtdu3bo27cvTE1NAUjPjYODA1q0aIFjx44hJCREx9ESUV6ePXuGlJQUlC1bFjY2NhoXOOLi4pCUlAQHB4cSf9XbzMwMTZs2hbGxMTIzMxEcHIzt27dj1qxZquV//PEH5s6diy1btryTQ8eio6Oxbt06AMDnn38OU1NTvesdpKQcDtirVy/VjGu+vr4YMmSI6vvobXjw4AEOHDiAtLQ0teVGRkaoWbMmRo0aBSsrq7d2/KIkhMCNGzdw6NAhWFtbY+zYsUxkFJKJiQn69OmD27dvY/Pmzdi2bRu8vb31Oln7LuKrWk8pFAokJycjLCwMlpaWqFKliq5DeicZGAC9egHNmgEXLrwYNrZ2LbBlC9CjB/D558D9+9IPe32Uk5ODW7du4euvv8YHH3yAH3/8EU+ePEFcXBxWrFiBoUOH4syZM1AoFKr7GBgYwNraGtWqVcPEiROxc+dO9O7dG5GRkVi8eDE+/PBDrFq1qkRMdSsEcOKE9Dq5dQto3RrYvFma+cvAQOopZGiofSSBkZG03sCg+EYaGBoawtTUFFZWVrC3t4ezszPc3d3h5+cHf39/fPLJJ5g+fTrWrVuHQYMGwcrKCqtXr8aTJ0/U9lOrVi0YGhri8OHDOHXqFJKSkornBEqApKQk/Prrrzh06BB27typsT4tLQ0XLlyAgYEBypXTKFFLRO+I6OhopKSkwEnLcDFA6iGUmJgIBwcH2NraFn+ARcjAwADm5uYQQuC///7DqVOnsHjxYrRo0QIGBgbYvHkzpk+fjpEjR76z57pixQrExsaiY8eOqFu3rq7D0TkjIyP4+/ujW7duEEJgx44dOHfu3Fs7nlwuR2xsLEJDQzUuItnZ2cHf3x9169YtMUk6IQTmz5+PtLQ0DBgwAO7u7roOqUSyt7fHgAED8N577+HgwYPYt28f4uLidB0W5cKEkJ7KyMjAvXv3kJOTA09Pz3f2y704HDwIxMbmvd7KCvj6a/VhOb17S3VikpKAdeuAjh2lItQxMVJdF32QkZGBv//+G5999hl69OiBxYsXIzQ0FDVq1MCcOXPw5ZdfwtLSEnv27MHQoUMxbtw43LhxQ20fhoaGsLOzQ8OGDTF79mxs3LgR9evXx9WrVzF9+nT07NkT+/fv1zpt6btACGlI2KBBUmHnbt2kZKG3t5QEKkkMDAxgamoKa2tr2Nvbo3z58hgzZgy8vb1x4sQJnDhxAikpKartXV1d0atXL9ja2mL16tUICwvTaADqI7lcjvDwcCxbtgzm5ub45JNP1ApnpqWlYefOnUhKSoKzszN++OEHHUZLRPlRJoTKli2rdcpkZULI0dGx1LSjjIyMUKtWLSxatAg1atRAdnY2fvrpJ2zYsAHfffcdOnbsCAC4c+eOjiNVFxsbi1WrVkEIgYkTJ+p176DcLC0t0adPH7z//vu4ePEidu7ciYiIiLdyrMePH+PIkSN4/Pix2nJzc3M0a9YMI0eOLFGFpG/fvo3du3fDwsICY8eOhWFJa9i9I2QyGWrXro2ePXuifPny2LJlC06ePPnOtu31ERNCeio1NVVVP8jX11dvu0CuXAmMGgUMGSIlctq1AypX1uytUacOMGWKNAW9szPg5ib9feiQtCwyEli4EOjaVSpC/fy5lCwojVJSUnDs2DGMGjUKH3/8MdavX4/o6Gi0aNECK1euxKZNmzB8+HCMHTsWmzdvxvDhw5GQkID169ejf//+mD17Nh7lqqSsHN5Uvnx5dOzYEcuXL8eCBQvg6OiIP//8E59++ikGDRqEixcv6vCsNQkh1QgaMwaIigKGDgWWLgXc3fOeYayk8fT0RP/+/WFpaYlly5YhMjJSlfQxMjJC3759Ubt2bdy9exe//fYboqOjdRyx7sXFxWHlypWIi4tDy5Yt0alTJ9WPErlcruoFZ2VlhdmzZ6PC6xaQIqK37tmzZ0hOTs43IaQcMlZaEkIymQzm5uZwcXHB8+fPMWnSJFy9ehU//fQTGjduDJlMhlWrViEoKAix+V1NK2Zr1qzB06dP0aZNGzRq1EjX4bwzZDIZPD090bt3b9SoUQN79+7F/v37kZGRUeTHevLkCc6cOYOcXLOwGBgYwMvLCz179oSzs3ORH/NtEULgp59+wvPnz/HRRx+hYsWKug6pRDMxMUH37t3RsWNHPHv2DFu3bsW1a9d0HRb9P1Y+0VPPnz/H7du3YWFhoZdTAAoBzJ8v1XmJjZWmAXdwAH7+WeoR9PJFABMTYMQIqWCwvb1U88XBAWjYEKhYEejZE1iwALh8WZpdau9eYPx4oGlTaUhQafDs2TMcP34cO3bswJUrVxAfHw9bW1v07t0b3bt3R82aNWFnZwdzc3PVD+C6devC29sbnTp1QnBwMA4ePIiFCxfi2LFjGDBgAHr16qXqhq9shFasWBEDBgxAs2bNsHv3bqxevRq7du3CxYsX0bVrV4waNQpeXl46exyU08ovXy71CouOloYNfv454ORU4iaZyZehoSH69euH3377DX/99Rf27duHcuXKqWpHODg4YMCAAbh79y7Wr1+PZs2awdHRUW+vomVkZODSpUvYvn07ypYti88//1xtZraUlBQsX74cT58+Rdu2bdG9e3e9fayISoL8egilp6cjPj4eOTk5sLe3LzF1UQpC+R1uamqKgIAAVK9eXTUT2dy5c3Hx4kXMmjXrnUmCJSYmYsWKFVAoFPjss89gZmbG3kG5GBkZoVWrVrh9+zYWLVqEHTt2oEqVKmjfvn2RHSMjIwORkZEavY+srKzQvHnzElVIWAiB8PBwbNu2Daamphg/fjyMWCz0jSmHjoWGhuLo0aOoWrUq3N3dS99Mqzk5QFgYcPas1GMgPV360VizplSD5F2cgECQXrp+/bqoXr26qFy5srhw4YKuwylW2dlCTJsmhJOTEMbGQsydK0RMjBAKxevvMy1NiPv3hVi2TAgfHyEsLIRwdxfik0+EuH5dOmZJpFAoREREhFi8eLFo1aqVcHZ2FhYWFqJSpUpi8uTJ4uzZs+Lp06ciPT09331kZGSIiIgIsWXLFtGmTRthamoqXF1dxYcffigOHDggsrU8QDk5OSI2NlacOXNGDB06VFhZWQlbW1vx3nvviUWLFomEhIS3eOZ5nYsQWVkvXj9GRkLMni29fkorhUIhtm/fLjw8PIS3t7f466+/RE5Ojmp9cnKyGDx4sLC1tRWjR48WEREROoxWd5TvlbZt2wobGxvx5ZdfiszMTNX6zMxMceLECWFnZyecnZ3FuXPndBgtEb2KQqEQw4cPFyYmJmLx4sUiLS1NbX1UVJTo27evKFeunFi7dq2Oony75HK5SE1NFXK5XERFRYmBAweKgQMHips3b4qsrCyRkZEhdu7cKTIyMnQa56JFi4ShoaFo3ry5eP78uVC8SYOulFIoFOL+/fuq9tSQIUNEaGhoke3/9u3bol+/fsLQ0FAAEACEiYmJ8Pf3F//++2+RHac4yOVy8emnnwqZTCY+/vhjkZWVpeuQSo3MzEyxfv16UbVqVeHj4yO2bNmi9TdAiaRQCPHsmRATJghRqZIQZcsKYWcnhI2NEA4O0g/D5s2F2LtX15FqYEJID8nlcnH27FlhYWEh6tSpIxITE3UdUrFJSxNi9Ggh7O2FMDcXYvlyIeLi3iwZpKRQCJGUJMR//wnxv/9JCQNrayF8fYX46ishHj4UQi5/8+MUh5ycHHHr1i0xc+ZM0bBhQ+Hk5CSsrKxE/fr1xQ8//CAuXbokoqOjC/UlKZfLxfPnz8WtW7fEwoULRfXq1YWZmZnw8fERw4cP15qYVCgUIisrSzx8+FDs379fdO7cWZiYmIhy5cqJdu3aie3btxfbF7VCIUR6uhDjx0uf77lfP6VdSkqK6NevnyhTpowICgoSDx8+VK1TKBTi9OnTombNmsLJyUns2rUr3wRhaZWUlCSWLFkiLC0tRd26ddUSYwqFQjx79kwEBAQIS0tLMWHCBJ3/gCKi/CUlJYmePXsKS0tLsWnTJiF/6Qv86tWronPnzqJatWpi165dOoqyeGRkZIhdu3aJKVOmiIiICJGTkyPi4+PFuHHjRK9evXTajkxJSRE+Pj4CgNi5cyeTQfnIzs4Whw8fFs2bNxflypUTP/74o0hNTS2SfZ8+fVpUrVpVlQwCIFxdXcX3339fohIqysSZg4ODMDExERcvXtR1SKVOXFycmDRpknB0dBQffPBB6eiYoFAIER0tRL9+UvKnZk2pl8D580JcvSrEgQNCjBwphJWVlCx6x74z2P9ND6WnpyM8PBwKhQLe3t5aZ84ojZKTgbFjpeFcMplUP6hrV6nnXu6exdevS0O/+vUr3HTyMpm0r2rVgAkTpOnGg4OBXbuk4UXHjknTkvftC9javptDi9LS0nDp0iVs27YNZ86cwaNHj6BQKFC/fn306dMHTZs2Rbly5VCmTJlCD3UxMDCApaUlKleuDBcXF9WQsI0bN2LLli24cOECPvjgAwwePBgeHh4AXtQXcnFxgYODA/z8/HDmzBksX74cJ0+exN27d7F3716MHDkSzZs3fxsPCQBpmFhSEjBunFREGpBeP926vZs9P4ualZUVRo4ciX/++Qdbt25Fhw4d4ODgoOqW/95776F9+/ZYv349tmzZAj8/P/j5+ek67GKjUCjw4MEDLF++HBYWFhg1ahTc3NxU69PS0nDw4EGcPHkS7u7uCAoKeqvT/hLRm4uOjkZycjIcHR1RpkwZjeEuMTExqhnGSvqU869ibGyM1q1bo2nTpnByckJkZCS+/PJLmJqaYubMmTodLvfLL7/g3r17aNCgAdq1a6ezOEoCIyMjNGvWDIGBgViwYAG2b98OHx8fdO3a9Y32m5iYiP/++w8PHz5ULbO0tESLFi0wcOBAGBsbv2noxUahUGDFihWIj49Hjx49OD36W2BnZ4d+/fohNDQUf/75J3x9feHh4fFu1piSy6XCsMnJQGKi9GMgKUn6Oz0d8PIC2rQB0tKAPXuAAwcAHx9g9WqgQgXA0lIqLOrnB9Su/eJH4syZ0vCxsmV1fIISJoTekFwux9WrV/HHH3/A3NwcrVq1QpUqVd7pxv7z58/1rqB0TAwwejRw+DBgbQ2sWAH4+0vv09yJmb/+kn70P34MuLpK7/HClvgwMAAcHYHGjaX6QgEBUrHhf/4BZs+WClGPGiUd38KiaM/zdSUmJuLs2bPYvn07Lly4gKdPn8LU1BStW7dGYGAg3nvvPZQtWxaWlpZv/HoxNDSEjY0NateuDQ8PD/j7+2Pz5s3YvXs3li5dij/++AO9e/dG//79YWNjA0BKDJmZmaFChQpwdnZGvXr1cPDgQaxevRp79+7F1atX0alTJ4waNarIC/8JIdUJGjMGOHJEes6WL5cSfqWoZMQr1atXD507d8aGDRsQHBwMX19f1WNtZmaGQYMG4fz58zh+/Djef/99uLi4qJ6/0i42NhYbN25EZGSk6j2jTJgqFAo8efIEixYtgqGhIYKCguDp6anjiInoVZ49e4bnz5+jbNmyWi+cxcXFISkpCZUrV35naum8LQYGBrCzs4NCocDTp08xcuRINGrUCIMGDYKbmxsSEhKwYsUKBAUFFWtyLD09HUuWLIFcLsf48eNhZWXF2kGvYGFhgQ8++AB3797FL7/8gt27d8PX1xeVK1d+7X1GRETg0KFDqplIDQ0N4evri48++gjlypUrqtDfOiEEnv4fe+cdFsW59uF7d+m9Skd6E0UFxIKIvcbeu0ZTTDPlJGpychJjYjwm+RJNNUYTNbFrYu8K2HtBQYqiYEXpnd2d7485uxEliQVcFua+rrl2d2bZfXaZnXnf3zzP77l5k19++QWFQsFrr72mN13R9AmZTEZQUBCDBw8mPT2dVatWERQUxMiRI2tPPFSpRAGnoAAKC6veau7n54sTxrt3q24rLxf/XqmsemtkJE7yOnUSn/fLL+Ik8LXXoGnTqhNIY2NxYjlokNjeOiEB1q2DF16onc/7iEiC0BOyfv16vv32WxITE5HL5Xz77bc4Ojri7+9P8+bNadKkCUFBQTg5OdUZQ7Li4mKSkpIwNzdvEFfxMzJEQ+iEBLFD2A8/iKKsiUlVMWj7dtEYOCkJunaF0NAn6xZlaCj+9nv1grAw2LJFFKLi4uDGjWy2b/+ZZ5/tSpMmITo74Vy7do0dO3bwxx9/cPbsWbKzs3FwcGDUqFFaI0l7e3vMzMxqfJBlYGCAo6Mj7du3x8/Pj549e/Lzzz+zZ88eLl26xLZt2xg/fjx9+/bVfj9yuRwLCwuaNGmCm5sbsbGxLF++nF9//ZWffvqJuLg4hgwZwqRJk7Czs3viGAVB4MqVCl54wZj4eHBxEY3Ho6PB1PSJX16vMDEx4bnnnmP//v3s3LmTXr16VZko+fn5MWDAADIyMli6dCnh4eGEh4fX+8F5RUUFiYmJLF26FHt7e1599dUqQlhBQQE///wz6enptGnThhEjRkhG0hISesCtW7coLCzEw8OjWkEoOzu73nUY+ydkMhm2tra89tprhIeH4+DgwKVLl3j77bdxdXXF9CmfGFeuXElKSgpNmzald+/eT/W99RWZTIabmxuDBg0iKSmJbdu2ERwczMsvv1ylCcLDolaruX37NsnJydoupNbW1sTExNCxY0e9GgOo1WoWLFhAdnY2vXv3JiIiQtch1VuMjIzo1asXSUlJ/Pjjj6xevRp/f3/atm37t3+nVCopKSmhsLCQgoKCB27z8/PJzs4mJydHuy5MqWQGYJyVVVXQqe5+ZaW4aNYJgngl2NpaLAmwt696v0UL8Xm3b8OpU+JEs3fv6rMJZDKxM1H//rB7N+zaJQlC9YFDhw6xePFiDh8+jJGREXK5nCtXrnDp0iVOnz7N1q1bMTc3x8LCAldXVwIDA7WlFAEBATrrxnNvhlB9F4QuXBB/a0eOiBl8330ndhQzMqoqBq1ZA++9B6mpMHCgmMlTU1l8pqZiptDYsdC+PaxcCUeO/MaaNT8QF7eEgQMHMmHCBDw8PJ7K/iD8r3vCH3/8wZYtW0hJSSE3NxdPT09ee+01unfvjq+vL3Z2dpiYmNRqLDKZDCMjIzw9PXF0dCQ0NJS9e/fy888/s2vXLpKTk9m0aRMTJ06kffv22oGFQqHAzs6OVq1a0bhxY3r27MmiRYvYtGkTmZmZbN++nfHjxzN06NDHztZTq9Wkpqby8svTyMlZgI+Po3b/qcMJgLWKn5+fNtX8p59+IjIyktDQUG1p37Bhw9izZw/x8fFs374dT09PGtWRdNja4tatW3z33XeUlZUxZMgQ2rVrp91PKysruXjxIosXL8bCwoLXX3+93peWSEjUF/6uwxiImYF5eXk4ODg0KEHIxMSEjh07YmBgwNGjR/n3v/9Nhw4dGDNmDMbGxqhUqqcylikvL2fevHkolUpee+01LC0t9Up80CUKhYKoqCgGDRrE5cuXWbNmDYGBgfTv3/+RX+vmzZskJCRw48YNQOxMFxUVxaRJk/Sq854gCNy5c4dFixYhl8t57bXXan0M3NDRdCpOTk5m586dfP7555w4cQKZTEZBQcEDS35+PiUlJahUKpRKZZVbzX2lUkllZSWVlZXadYKlJWX29hjfuiUKOdbWophjZfXnohF57l9naSle4Tcw+HNRKP68b2YmCklXroi3jRuLviB/hYGBWDqmUokZCHUESRB6TBITE5k3bx7x8fF069ZNO6FXKpVkZ2eTmprKxYsXSU5OJjk5mYsXL3Lw4EHMzc21IlGjRo3w9fUlKChIu7i6utbqiVSlUpGbm8uVK1cIDQ3F39+/1t5L1xw/LpaJnToltof/8ksxU8fAoKoYtGwZ/Oc/cPmymEn01lvg7S0+p6bGFjKZeHwICwNXV8jM7MqiRSmsXLmCb7/9lp07dzJy5EiGDRuGg4NDrQxqlEolFy5cYPXq1ezevZtLly5RWlpKaGgoU6dOJSYmhsaNG2Nra/vU671lMhlmZmYEBgbi6upKq1at+OOPP1iyZAlr1qzh5MmT9OzZk0mTJmn3WZlMhoGBAa6urjg4OBAQEMAzzzzDjz/+SHx8POnp6WzYsIHnn3+ezp07P9J3qlarOXbsGK+99hqnT5+lc+fP+fDDTwkLE88LDRUDAwNGjx7Nzp07OXLkCFu3bsXFxUXbht7FxYXhw4eTkpLCsmXLaN26NbGxsfU2I6awsJDdu3ezc+dOGjduzJQpUzC7pw40NzeXefPmkZ+fz/Dhw4mNjZUmLBISesLflYxVVFSQk5NDWVkZdnZ2DcaLEf4UhcrLyzl9+jQjRoygT58+ODg4cOHCBWbPns1XX32Fvb19rcaxbt06EhMTCQwMZODAgbX6XvURc3Nz+vbty8WLF7VjrYCAAEJCQh7pda5evcrevXspKytDJpPh7u5Onz599G5+oVarWbx4MdevX6dLly60adNG1yHVe2QyGYGBgQwdOpRLly6xfft2Dh06BKAVd+4VepRKJTKZDFNTUywtLbWLlZWV9r6NjQ0ODg7Y2tpq13va2WFiaytm+1Qn6lT3+N71/zRuKymB3FyxrMTW9u+fL5OJviKCAHfu1OC3+WRIgtBjkJGRwfz589m2bRtRUVFMmTKF9u3bY2pqiiAIVFZW0rZtW4qLiykuLqagoIBbt25x8eJFzp49y4ULF0hPTycxMZFDhw5pBSJLS0tcXFwIDg6mSZMmBAYG4ufnV6MCQUlJCenp6QB4e3vrlXr/KMTFwdSpokF0ly4wdy6EhIi/Vc1XqVLBF1+IfjBXr8K//iX6xHh41J7hs1wOTk5gbx/AjBnT6d69GwsXLiQ+Pp7Zs2ezdetWJk6cSNeuXau9Ivk4FBcXc+jQIdauXcuRI0fIzMxErVbTunVrBg0aRKtWrbR+L7qeuMvl8ir+Qp06dWLVqlWsWLGChQsXsn//fgYOHMi4ceO0IoRMJsPY2FjrL9SiRQu2bt3KggUL2Lx5M+fPn6dTp068+OKLNGnS5B9jUKlUxMXF8eabb5KYmEhMTAyffjqGkJBH95Oqjzg7OzN27FhSU1NZvHgx7dq1w9bWFoVCgVwup3fv3uzatYs//viDDRs24OfnVy89c9RqNZmZmXz//fcYGRkxduzYKgPg0tJS9u3bx+bNm3F2duaVV155rHR8CQkJ3aApGasuQygnJ4fc3FysrKy0x7+GhqGhIf369cPMzAwLCwsSEhKYOXMmvXv3rnWBTKlUMm/ePCorK3nllVcaTIZWTePq6qotHduxYweBgYG4u7s/9PizoqKCrKwsLl26BIgiU/v27Rk8eLBeGUkLgkBubi4LFixAJpPxyiuvVLm4I1F7GBkZ0bVrVxITE/nmm28oLS0lKiqKsLAwrKysHlhMTU0xMDDQLgqFospjAwMDjIyMMDQ0fGBbrXbz0XiM/K9s8m9RqcTbOvQbkQShR+T27dvMnz+f9evX4+vry4svvki7du20ddOaEhgjIyNtaYBGJGrXrh0FBQUUFRWRn59PVlYWKSkpXLx4kYsXL2pFooMHD2JhYYGFhQVWVla4urri7+9PYGAggYGB+Pv74+jo+FgikaZczNzcvN4aSq9dC//+NyQni95dH30EAQFVM34qKsT1P/0kCrQffgjPPiuWfj6NC/gGBga4u7vj4OBAkyZN2L17Nz/88APx8fEkJSXRvn17Jk+eTHh4+GPX5Ofk5BAXF8fatWs5ceIE169fx8zMjC5dutC/f39atGiBi4tLjRhF1zQaf6Ho6Gh8fX3p3r07S5YsYevWrVy+fJndu3czatQoBg0apE3p1fgLhYaG4ubmRvv27Vm5ciVLly5l6dKlHD16lBdeeIGJEyf+5fuqVCo2bdrEjBkzSElJYcCAAXz44YcEBgY+kZ9UfUIul9OnTx+2bt3Kli1bWLt2LY0bN9Z21bKxsWH06NGcPn2atWvX0qFDB5ydneu00f7jkJOTw+rVq7lw4QJRUVGMGjVK6xMnCAK3bt1i3rx5AEyYMOGRr7pKSEjojuLiYnJycgBwcHB44Dx8586dKv5BDTHzTy6X4+Ligkql4vTp03z00UdMmDCBHj16YGhoyO3bt2utZHjjxo2cOnUKb29vhg4dCtAg/wdPikKhICIigqFDh5KRkcG6desIDAxkyJAhD/V9Xrlyhe3bt5OdnY2BgQHNmjVj9OjRODo6PoXoaw61Ws2yZcu4evUq7du3JzY2VtchNShsbGwYPny4tnMwQMeOHYmIiNCKOYaGhhgYGCCXy+veb12hEL2BVKp/zvpRq0W/IblcLFurI0iC0CNQUFDAd999x6pVq7Czs+Pll1+mS5cu/3jVtzqRSK1WU15eTlFRkXbJz8/n2rVrpKamaoWi9PR0Tp8+rc0isrCwwMbGBhcXF3x9fbUikZ+f30M5+d8rCNVH/6ClS9czb14/kpNlPP+8jLfeErv+wZ9CT0mJ6Be0bJloOP/FFzB0KDg6Pv1W8CYmJvj5+eHo6EirVq3YsGEDP//8M3/88QcnT56kV69ejBs3Dn9//4e+2nL16lV27NjB5s2bSUxM5ObNmzg7OzNx4kS6detGYGAgTk5OtWIUXZNofGk8PDxo1KgRQUFB9O7dm8WLF7Nnzx5SUlLYtm0b48aNo1OnTlpRSy6XY2dnR1RUFJ6envTs2ZMlS5awffv2vxUllEolq1at4oMPPuDSpUuMGjWK999/Hx8fnzr9PekCGxsbJk6cyKlTp1i5ciWdOnXC0dERY2NjZDIZbdq0oXPnzixdupTVq1cTFBRUrwSRyspKkpKSWLx4MTY2Nrz44otVjr8FBQWsXr2aM2fO0LRpU8aPH69XV0slJBo62dnZFBQUYGdnV23LeY0g1JD8g/4KuVyOj48P06ZNIyoqCjMzM7Zu3cp3333H4sWLtdm8NYVKpeKrr76ivLycKVOm1PjrNzTMzc3p1asXFy9e5KeffmLt2rUEBwfTtGnTf/zb69evc+zYMZRKJY0aNaJjx460bt1ar8ZMgiBQUFDA999/D8Arr7xSb6sn6ioymQw/Pz9iYmI4ePAgR48e5ezZs3Tq1Ek/urwZGort542MICtLFHz+SgxXKuHkSVFEeojf2NNCEoQekvLychYuXMiyZcuQyWS89NJLPPPMM49d1iOXyzE1NcXU1FSrpGtEouLiYgoLCykqKiIvL4/MzEySkpI4f/48KSkpnD17lhMnTlQpNbO2tsbd3Z2goCBtuZmPj88Dabv1URASBAG1Ws2XX37J119/j6HhWaZNe58XXxRwd5dVEXlyckSPoN9/F8WfefNEs3cbm6cvBt2LpkxK0zlr5cqVrF27lp9++omEhAQGDRrEsGHDcHV1rTabR61Wk5KSwu+//86uXbtITU3l7t27+Pr68uabb9KxY0et8KQXB9d70PgVBAQE4ObmRosWLdi+fTuLFi1i3bp1nD17lq5duzJp0iSCgoK0f6NQKHBzc6NRo0b4+fkxcuRIoqKiHnh9TQbfd999x1dffcW1a9d44403ePHFF/H29n7aH1cv0Ig+Xbt25ddff2X58uUEBQXh5+cHiAPMMWPGcOzYMXbs2EHHjh0fKQ29rnPz5k0WL15Mbm4ugwYNomvXrtoBsFKpJC0tjR9++AFTU1NeeuklXFxcdByxhITEo3Cvf5AgCOzYsQNLS0uaNm2KhYWFtsNYRUUF+fn5ug5X59jY2NChQwcAli1bxm+//cbEiRNrpXRs+/btHDlyBE9PT8aMGQNI2UFPirOzM4MGDSI5OZndu3cTEBCAh4fH34qdhYWFpKSkcPXqVUxNTWnfvj3jxo3Tu9JotVrNihUrSE9Pp1WrVnTt2lXXITVIjIyM6NOnD8eOHeOXX37h6tWr5OfnP91sM0EQs3zub0lfXat6za2RkehRYm8PbdvCsWPiJPO556p//eJiWLVKFJH69Hl6n+0fkAShh0ClUvHrr7/y888/k5+fz2uvvcagQYOws7Or0ZPQvSKR5oqHWq2mrKyMTp06advq5ebmcvXqVVJSUrTm1WfPnuXkyZPs3btXa6Jla2uLu7s7AQEB+Pv74+fnx7Vr18jKyqJFixb4+vrWWOy6QhAElEoln3zyibZV5McfN2L06AfLv27eFH+zW7aAhQX83/9Bz56igXxdGEvIZDIaNWqEnZ0d3t7edO3alaVLl7Jv3z6++OIL9u7dy4gRI+jZs6e2pbpSqeTs2bOsXbuWhIQEUlNTKSkpoXnz5kydOpXWrVvj4+ODnZ2d3mcoyOVyLC0tCQsLw9PTk7Zt2/L777/z66+/snjxYg4dOkSfPn149tlntWnqmuw8X19fPDw8HhDDBEGgrKyML774gu+//567d+/y7rvvMmnSJFxdXXXxMfUGc3NzJk6cyOHDh9m2bRvdunXDyclJOwEIDg6mT58+XL58meXLl9O8eXMiIyP1fuBeUlLC4cOH2bBhA+7u7jz//PNVJj25ubn89NNP3Lx5kz59+tC7d+86V5IpISHx92j8g/Ly8vjvf/9LQUEBUVFRTJ06lWbNmnHz5k1ycnIIDg5+qOzs+ozmmG5gYEBlZSW3b9/mzTffpF27dhgaGnLo0CFatWpVIz5LKpWKL7/8krKyMiZPnlzvu1g+LRQKBWFhYQwaNIiUlBT++OMPgoKCGDly5F+esy9dusTWrVspKioiICCAvn374qNJydcTBEGgsLCQb775BkEQeOmllxqUQXxdIy0tjUuXLqFQKLC2tn7si4iCIFBRUUFhYSGFhYWUFRZiXVSEa15eVUGnuvvFxWLbeU0L+ntb0d+/TqkUy0uGDoXISNF75OBBsaV1QADcW3ooCJCfD59/DmfOiJ3GunWria+tRpAEoX9AEATWr1/PggULyMzM5LnnnmPkyJE4Ozs/lYmNXC7HzMwMMzMznJ2dAfGEWFpaqt3RCwsLuXPnDpcuXdKWmqWmppKcnIxMJtMKRBYWFtp2fGVlZezZs4fAwEAaN26sl60VNZP5999/nyVLllBcXMxnn33G8OGD/lf+9ef/Jz39Ev/+tycbNypwd5fxxRfi79TMrG6IQfdiYGCgbdfdpEkT4uPj+eWXX9i/f7/W+G/IkCFUVFSwY8cOTp48yeXLlwFo164d/fr1o2XLlnh4eNQJo+iaRqFQYG9vT7t27fDx8SE2NpYVK1awYcMGLl26xP79+xkxYgQDBw7UmgJqjKfvRZMm/NFHH/Hrr79SXFzMrFmzGD16tDTIfEhCQkLo378/8+fP5+effyYsLIywsDDt9z106FD2799PfHw8u3fvxtvbW++8Be5FEAQyMzP54YcfkMlkDBkyhObNm2u3l5WVcezYMdasWUOjRo0ks1MJCT2jsLCQo0ePsmzZMi5fvkxpaSk3btzA398ff39/rKysqKysJCMjg+zsbAYPHoyPjw+JiYkolUoCAgIarBmtpvPnsGHDcHZ2RhAE5s+fT3x8PAsXLtRaJjwJ+/bt48CBA7i4uDB+/Hjt+0o8OWZmZnTv3p3k5GQWLFjA2rVrCQkJoUWLFg88V61Wc+PGDc6dO4eZmRkxMTH06tVL78abarWadevWkZKSQvPmzendu7euQ2qwqNVqzp49S1JSEkFBQYSFhWFkZIRSqdTOeTX2Knfv3iUvL0+7rqioSHu/sLBQm0BRUVFBZWUl9kZGjDA2Zsy1a/8s9oCYKWBlJd5qlr967OgIgYFgbAwdO8LLL8PXX8Prr0OnTmJ7aRMTuH5d7Hh06JDYvWjmTLEjWR1BEoT+gZ07d/Ltt9+SlJTEmDFjGD9+PB4eHjq94qtQKLR+QppSBKVSSevWrbU/hIKCAvLy8sjKyuLChQtcuHCBlJQUMjMzkclkpKSk8M4772BlZYWNjQ2NGzcmJCREO+hp3LhxnTaB1aj606dPZ8WKFQiCwJdffsmgQYMeMHhMSkpi6tTXycyMpWnTN/nkEwPatZNhZFT3xKB70ZRJOTs707JlS7Zs2cKyZcvYsGED586do7y8nMzMTGxtbenXrx89evSgSZMmuLu7Y2FhUa8HSZqBp7u7O40aNSIwMJAuXbrw888/a0vmtm/f/oC/kAZBELhz5w7vv/8+q1evBmDu3LkMHjy41lvl1ieMjIwYOXIkcXFxHDlyhF27dmnN0gE8PDwYMGAAycnJrFy5klatWtGxY0e9zZjJzc1l06ZNHDt2jGbNmjFu3DjtcVJjJP31119TUVHB4MGDCQ8P13HEEhISD8OVK1fYtWsXO3bsICkpiatXr6JQKOjUqRM9e/YkPDwcLy8vHB0d2b17N8ePH0cmk+Hq6kphYSErV67Ew8ND71pt1zRyuRxPT0+USiXr1q3jwIEDvPTSS09cRiT8r3PPV199RUlJCVOnTpWyeGsBJycnBg4cSFJSEvHx8axatQovL68HxLzs7GyOHTvG3bt3iYyMZNSoUXo3dhIEgeLiYr755hvUajVTpkyRLuDokNTUVE6cOEFBQQHm5ub8+OOP/PLLL5SXl6NSqaisrESpVFJRUUF5eTkVFRXaJIf7bysrK7VVBZaWlpg4OFBiaQmNG1cVde5frKzA3Fws59IsBgZVH1e3zsRENIl2cIApU8DVFZYvh19/FcvHFAooKxMnnT16wLhx0KZNnZqESoLQ33Ds2DG+/vprjh07xoABA5g0aRJ+fn7aTjJ1CQMDA6ytrbG2ttauU6vVlJSUkJeXR15eHomJicyePZvMzExat25NRUUFqampnDx5EhMTE2xsbLRt/ezt7fH19dWWmwUEBODu7l4nPrsgCNy+fZt//etfbNiwARMTE7744gv69OmDpaVlFSHk8OHDvP322xw+fJgOHcyZOVNJRIQBtd19sCaxsrKiRYsWuLu7Ex0dzeeff05CQgLm5ubajh5BQUG4uLhoTX0bCppMlMDAQNzc3GjWrBm7d+/m559/Zt26dSQmJtKpUyeeffZZramxWq3m+vXrzJgxg40bN2Jpacmnn35K7969q/x+JB4ODw8Phg0bRkpKCkuXLqV169a0adMGhUKBQqGgd+/e7Nu3j40bN7J582a9bUOvVCpJSUlh0aJFWFlZMXHiRDw9PbXbCwsL2bx5MwcOHMDPz4/JkyfrZealhERDobS0lDNnzrBlyxYOHTpEeno6t27dwsnJiaFDh9K1a1dCQkJwc3PD0tIShULBqVOn2LhxI126dGH69Om4uLiwfv16iouLiYyMxMjIiNzcXORyeYM+n8jlcsLDw/Hx8aF58+YolUp+/vlnBg8e/NiGvQcOHGDv3r04OjoyefJkZDJZgxrvPA0UCgVNmzZlyJAhpKWlsWHDBkJCQhg1alSVCzmXL19mz549WFhY0LlzZ1q2bKl3/wu1Ws2GDRtITEzUZjtL6Aa1Ws3+/fs5dOgQlZWV3Lhxg5s3b1L5v4wdU1NTbSKEpaUljo6O2NnZYWNjo11/73bNYmxsjKGhIabGxjQyMhJFm/vFnerEnsdFoRCzf8aOhehouHFD9CupqBCzgdzcxKVx4yd7n1qgbkVTh0hOTubLL78kLi6Ozp0789xzzxEcHKxXPiyaNtwWFha4u7tTXl5OYWEhTk5OvPnmmzg4OGhT765evUpaWpp2OXnyJPv379eKTNbW1tjZ2eHh4YGfnx9+fn74+/vj6+v7VEUiQRC4evUqb775Jtu2bcPBwYHPP/+cHj16PNA1a/fu3bz77rscO3aMTp068fHH7xAeboRcrj9ikIZ7/YV+//13Dhw4QJ8+fZg0aRKBgYF6ZxRd08hkMiwtLQkPD8fb25uoqCj++OMPli9fzuLFizl27Ji2Y1tJSQnTp09n+/btODk5MWfOHLp166Z3Roh1BYVCQZ8+fdixYwfbt29n48aNeHl5advQOzo6Mnz4cM6ePcv69etp27atXrahv3XrFitWrCArK4sePXrQt29fbXq8SqXiypUrLFiwAGNjYyZNmqR3XgoSEg2Fa9euERcXx44dO7hw4QIZGRlUVlYSFhbGCy+8oBUynJ2dMTEx0Y4rbty4oe0yO3z4cDw9PVm+fDlXr16lb9++hISEcOrUKfbu3UtkZCSdOnXS8SfVHTKZDG9vb7y9vSkoKODDDz+koKCAIUOGPPZrzp8/n6KiIp577rkqYrxEzWJmZkaXLl1ISUnhu+++Y82aNQQHBxMREQGIF0euXbvG9evXiY6OZujQoZiamuo46kdDEARKS0v55ptvUKlUPP/881pvTomnz+XLl0lISCAnJ4eBAwfSqVMnrd2JsbExCoUCQ0PDKouJiQlGRkbadvT3bzc0NNSNSCmXi+JPy5Zii/nSUvHWyEgsK6ujE1BJEKqOq1fJWLWKc/HxtGzZkilTptCyZUu9m8Dci1Kp5O7du9y4cYOIiAhat26Nubk5giCgUqm0dZmaUrM7d+5w5coVkpKSuHDhApcuXeLw4cOYmZlhZWWlFYkcHR3x9fUlODgYPz8/fH19cXNzq5WSEEEQSElJ4a233mLXrl34+fnx3//+l44dOz5wJf6PP/7ggw8+4MyZMwwcOJB///vfNG3aVO+vKMnlcm7fvk1JSQkRERH4+Pg0eDHoXuRyOfb29sTExODr60tsbCy//fYbmzZtIi0tjd27dyMIAkeOHMHf35/Zs2fToUMHKZPjCXF0dGTs2LEkJiaycuVKoqOjq7Shj46OpmPHjixdulR7xVGf2tCXlpZy/PhxVq1ahbOzM5MnT66SHp+Tk8Nvv/1Geno6sbGxDB48WO+8FCQk6jNlZWWcOnWKbdu2cfToUdLT07l+/TrOzs4888wzdO7cmaCgIBo3boyNjU21v1+1Wk3z5s3x8fHBy8uLPXv2cPToUdq0aUObNm24du0av//+O4aGhvj4+GjLnPR5zPG4aMZagiBgaGiIvb09Y8aMeaxzrSAIHDt2jO3bt2NjY8Pzzz+v92O5uo6TkxP9+/cnKSmJvXv3snz5cry8vHBwcODq1askJCRgZ2fHgAED6nzGr6aT7L0eM/n5+ezbt4/Tp0/j5+f3REKlxJMhCAL79+/n4MGDNGvWjAkTJtC+fXut2KO3YymZTMwYesyMyKeNJAjdz507MH8+zdev5/XAQLxffZWItm313iSwuLiYtLQ0FAoFPj4+Vcx2DQwMsLGx0dbO3isS5eTkkJOTQ15eHrdv3+bSpUukp6drlyNHjmBlZYWtrS02NjZYW1vj5OSEl5eXViDy9fXF3d39iU7egiBw/Phxpk+fTkJCAi1atGD27NlER0dXydoSBIFffvmFzz77jAsXLjB+/HjeeustgoOD9WrwsHTpUvLz8+nVqxeNGzfWHhBv377NzZs3MTMzw83NTe+uyjwNNC3nPTw8cHJywt/fn+7du/Prr7+yb98+HBwcCAoK4rPPPqNt27Z6lfVXV5HJZLRv357Y2FiWL1/O2rVrCQoK0vppWFpaMnToUI4fP86OHTuIiYnB09PzsUsHniaCIJCVlcUvv/xCRUUFzzzzDG3atNFur6io4Ny5c/z666/Y2dnxwgsv6LVxtoREfSIrK4u4uDh2797NhQsXuHz5MuXl5bRo0YKJEyfSsmVLfHx8cHNzq5INVB329vZ069YNExMTkpOT2bp1K76+vnTt2hVBEPj9998pKSlh1KhRuLm5sXPnTnJycujSpYvWV60hYmpqyrhx43B3d+f27dvMnz+fN95445EyMr799lvy8/OZMmVKveiQW9eRy+WEhIQwePBgkpOT2bx5M02aNGHcuHFkZGRw/vx52rRpQ6dOnZ7qhF2tVmvnJkVFRRQXF2tNhat7rFlycnKoqKjQmgxXVFRw48YNzMzMtBewJHTDlStXiI+P586dOwwbNoy2bds+dncxicdHEoTupbgYfvgBVq7E2cyMQRMnYhIbi5EeTFr+icLCQpKTk7GwsCAoKOhvBz33i0SaK12VlZXk5+eTl5dHfn4+ubm53Lp1i4yMDK1AlJKSwv79+7VZRJrXcHZ2pnHjxlqBSNMG/GEQBIF9+/bx3nvvceTIEWJiYpg1axZRUVFVTkQqlYpvv/2WefPmkZGRwRtvvMELL7yAr6+vXolBpaWlrFy5kuTkZFq2bFnl6kt6ejp5eXl4eXlhZ2ent+a8TwONv1BISAiNGzcmMDCQV199lYyMDIYNG0Z0dLT+Xnmog1haWjJmzBiOHTvG1q1b6dy5My4uLlrRJywsjB49enDp0iVWr15NWFgYkZGROo76n8nPz2fnzp3ExcURGBjIhAkTqpQX3r59m4ULF5Kfn8/o0aPp0KGDDqOVkJCorKzk9OnTbN26lSNHjpCWlkZWVhaurq4888wzxMbGEhwcTOPGjbG1tX3o84CJiYk2w8XMzIyuXbvi5+eHnZ0dq1evJj09nV69etG0aVPt+wcFBen9BcUnQZPJ4+HhgUqlYtGiRahUqof+TgRB4OzZs1q/vxdffBG5XK5XYzp9xdTUlI4dO5KSksL8+fNZs2YNXl5epKWlYW5u/thG0mq1mvLycm22zl91iqruViPqlJWVac2DNQLPvY/vX6dWq7X+Mvf6zdy9e5fExEQKCgoatOeXLtF4B4WEhNC+fXvJ2FtHSIKQhooKWLwYli4FQYApU7Dq1k10Ha8HFBYWcvHiRa0g9KjIZDKMjIxwdHTUKun3i0S5ubnk5eVx/fp10tPTSU5OJj09nRMnTlBSUqLtaKbJJnJ1dcXHx4egoCB8fX3x8/N74EAgCAKbN29m5syZHD9+nP79+zNjxgxatmypFUMEQUCpVPLFF1/w3XffcfPmTWbMmMGkSZOeODNJF2RkZHD16lWUSiWGhoZVRJ/U1FTy8/OJiIiQDpoPiUwmw8LCgrCwMIKCgrh48SIODg6SGFQLhIWF0atXL77//nt+++03mjZtqm1Db2pqyqBBgzh8+DAHDhxgz549eHt71+kr52q1mvT0dJYsWYKJiQnDhw8nICBAu72oqIg9e/awY8cOvLy8mDx5sl5kPUlI1Edu3brFoUOH2LFjB2fPniUtLY3i4mKaNGnC8OHDad26Nf7+/g+VDfRPuLm54ezsjJGREQcPHuTo0aNEREQQHR3NzZs32bRpE+bm5nTp0gWAEydOYGhoSLNmzWrq4+odMpmMrl27YmNjg7GxMaWlpQ+V5fzdd9+Rk5PDhAkTHmv8KvH4ODo60q9fP5KSkti+fTtz587F29ub6OhoQkJCKC4u1i73Zuj83W1hYSG5ubla0ebe5Z/WCYKAhYWF1kzY3Nwca2trzM3NtY+ru7Wzs8PY2BgjIyNtKVJxcTH/+te/OHv2LPPmzePf//63rr/uBkdmZiZxcXHcuHGD/v3766U5eX1BEoQAVCqxPdzChZCTA6++CoMGgZ1dnTV/elSKioq4ePEiNjY2NXZC/SuRqKKigtzcXO7cuUNubi45OTlkZWVVKTfTOMnb2tpib2+Pra0tdnZ2uLu74+Pjg5+fH97e3pw7d47PPvuMs2fPMnr0aP71r38RGhqqPWAIgkBJSQmffPIJv/zyC/n5+XzwwQeMGzcOZ2dnvTywJCcnU1RURGBgIFZWVlU+Q0pKCgUFBfj7+0tXMx4RuVyOg4MDgiCQn5+v63DqJRrRRDNB2rt3L56entrSAF9fX/r06UNSUhLr1q0jIiKiTrehv3XrFuvXryc5OZmYmBgGDx6sLTFUq9VkZmaycOFCZDIZI0aMoEmTJjqOWEKiYaFUKklMTGTv3r3s37+f1NRULl++jI2NDbGxsXTu3JmQkBD8/Pywt7evsSYY93rhODo60r9/fwIDA7WlY7m5uYwcORJPT0/27t1LQkJCgzaZBnHMGB4eDkBaWhpz5szhv//971+Wjml8I9etW4eZmRkvvfQSCoVCL8d1+opcLicgIIDBgwdz/vx54uPjSU9Px9PTk+PHj2szde4Xb/5uXWVlJTKZTCvWaBZ7e/sqnaLu7x5lYWGhFXXuF3fuv3//unsFYKVSyc6dO+nUqRPvvvsu48eP58cff6Rjx45ER0fr+BtvWBw8eJDDhw8TGBhI+/btJWNvHSIJQoIAGzbAd9/BlSswaRKMHAnOzqJTeD2gsrKSW7ducfv2bXx8fGrVAE5TpuPs7IyzszPwp2iTk5OjFYhycnK4desWly9f5tKlS1y6dInTp08jCIJWHLK1taWgoIDExERefvllXnrpJQIDA7XvpZnYf/DBB/z666+oVCo+/vhjRo4cib29vd4OGpKSkiguLiYoKKhKtoFKpSItLY3CwkJ8fX0lQegRkclk2NjYIAgCeXl5ug6n3uLr60v//v1JSUlhxYoVtGrVijZt2iCXyzEwMKB3794cPHiQjRs3sn37dgICAh66fPRpUl5ezpkzZ1i+fDmOjo5MmDABFxcX7fbc3FzWr1/PmTNniIyMZNSoUZIflYTEU+L27dscPHiQ3bt3k5iYSHp6Orm5uYSGhjJ16lQiIiLw9/encePGD3QgrWk0ZfCGhoZs2rSJy5cv06VLF8LCwjh37hzbt2/HxcWFJk2akJ+fT0VFBba2tk+1Q2td4N4LeaampoSEhPxjhtAPP/xAdnY2I0aMoGnTpk8jTIn7MDU1JTw8nA4dOpCUlERmZiZXrlxBpVJhZGSEubl5lUUj5ty/XiMAWVpaarPENKLN3y2a52m6ST0pcrkcNzc3DA0N6datGxMmTOCbb75h5syZLF++/LHK4CQenevXr7Nv3z6ysrJ47rnnCA8Pr7MXBxsCDetsVB1798I338CFCzBiBEyYAJ6eojN4PaG4uJj09HQUCgXe3t5P3YhYcyXA3NxcO/HTtHzUiEO5ubncvn2bjIwMrUh09uxZ7ty5Q+PGjXnjjTfw8vLSvqYgCOTk5DB9+nRWr16Nubk5H374IYMGDcLa2lpvxSAQBaGioqIHBKFbt25x8+ZNLCwstCnvEg+PXC7H2toatVotCUK1iIGBAX379iUuLo6dO3eyZcsWvLy8cHNzA8DV1ZV+/fpx+vRp/vjjD1q3bo2zs3OdE1OysrJYvnw5ubm5DB8+nE6dOmmPK5WVlSQlJfHLL79gaWnJhAkTcHd313HEEhL1G6VSyblz59i3bx+HDh0iJSWF9PR0bGxsaNeuHbGxsTRp0gR/f38cHByemuBybwdaHx8fHB0dCQ4OJjs7m82bN2NkZETfvn2xsLBg48aN3Lhxg+HDh2svmjVEnJycGDt2LCYmJqSnp+Pj41Nl3CYIAhkZGaxYsQJTU1NeeeUV3bWRliAvL48bN25ojdXbtm2Lubm5tvV3dcu9WTrViTy6+l/KZDKCg4ORy+VYWlryyiuvcOjQIeLj45k3bx4ffPCBtJ89BQ4cOMDBgwfx8/MjOjq6TtsHNAQatiB08iR89RUcOQK9e8Nzz4GfH9SzqzYFBQVa/6C60m1LJpNhZmaGmZkZ7u7u2vasBQUF3L17l5ycHNLS0nj22WfJy8vTbr8XQ0NDzp8/j52dHbNmzaJfv36YmprWic/3uBQXF5ORkYFarcbX17eK8WJaWhr5+fl4e3tja2srKemPiJQh9PRwdXVl2LBhJCYmsmbNGtq0aYOjoyNGRkbIZDI6dOhAx44dWbZsGZs2bSIkJKROeUMUFhayf/9+tm3bho+PD+PGjauSkXf79m2WLVvGzZs36devHz179tTr446ERF0mLy+PAwcOsHPnTs6dO0dqaio5OTk0bdqUV155hYiICAIDA7UXvHT5W9Qcx2QyGVu3biU/P59Bgwbh7e1NXFwcCQkJREREYGFhgVqtRhCEBudnJ5PJtK3oVSoVP/30E2+99dYD5SILFy7kxo0bDBo0SPIW0SGlpaWcPHmS/fv3ExoayksvvURYWJjetQRXqVQkJiZy/vx5Ro4cCYj7op+fH++99x5jx47lp59+omPHjsTGxuo22HrO7du32bdvH1evXmXy5MlERkZKcxodU7+Uj0chNRW+/FLMEGrXDl58EZo0ASMjXUdW49xrKH1vyVVdQnOit7a2xtraGm9vb5o2bcpvv/3Gxo0bWb58OTNmzKjyNxYWFrz77ruUlZXRq1cvnV5xqCkyMjLIycnBxcUFR0fHKlc4Nf5Bbdq0kcrFHgNJEHp6yGQyYmNjiYmJYeXKlWzYsIGgoCD8/PwAsLOzY9CgQZw8eZIdO3bQpk0b3N3d64Qhs8ZIeunSpcjlcvr370/z5s2120tKSjh8+DB//PEH7u7uTJ48Wap7l5CoYdRqNampqcTHxxMfH09SUhIpKSmYmZnRqlUrOnfuTFhYGIGBgQ+cK3WJJg61Wk3Tpk0JCAggJCSECxcusG3bNtzc3OjRowdlZWXs3LkTIyMjevfureOodYdcLqdLly5VMp4FQeDq1assXboUIyMjXn755SpZWBJPl/T0dHbv3o1cLqdDhw6EhYXpZYa6TCbTdkC+f32XLl2YMGEC8+fP5+OPPyY0NFTKWKlFNN5BGoNyjQ+thO6oG2fQp83166IYtHkzNG8Or70GERGghwe4f0IQBAoLC0lJScHR0bFOXYX/OzSG1VOmTGHLli38/PPPPP/889raXo3w07VrV4A6V27yuFy4cEHbFcXCwkIylK5B5HK5JAg9RWxtbRkxYgQnT55ky5YtREdH4+Liom3Z3rJlS7p27UpaWhpbt24lPDycli1b6jhqyM7OZsuWLZw8eZLWrVszYsQI7WREEASysrL46aefqKiooH///kRGRuo4YgmJ+kN+fj5Hjx5lz549nD17lpSUFLKysggMDGTcuHG0a9eOgIAA/P39MTc3r7NXleVyOSEhIQBUVFRw5swZTE1N6d+/P9bW1mzcuJGEhAQGDBig40h1h6YlfadOnRAEgVOnTtGkSRMMDQ1ZsmQJmZmZ9O7dm6ioKF2H2mApKyvj2LFj7Nu3j5CQEHr06KGXYhCI+5uHh8cDF3A0thYvv/wyBw8eJCEhgfnz50ulY7XEnTt32LNnD5cuXWLChAlEREToVaZZfaXhCUK5uaJn0Nq14OoKr7wC0dHwv0lKfaOyspKbN29y9+5dgoKC9M7nol27drRq1YojR47wxx9/MHHiRO02TdpxfSI5OZni4mICAwOrZEsolUrS09MpLi7Gz88PKysrHUapn0gZQk+f8PBwunXrxoIFC1i3bh1hYWGEhYUBYGZmRr9+/cjNzcXJyalOGTkaGBhoW1V7e3tr1+fl5bF161YOHTpEkyZNGDNmjN4OjiUk6gqabKCEhAQOHDhAcnIyycnJGBkZ0apVK1544QVCQ0MJDAzE3d29zmQD/RMasUqhUBAZGUl4eDh+fn4cOHCA/fv3ExYWRkREBJmZmWRmZuLn50ejRo10HLXuUCgUlJWVcefOHRYvXoyBgQGvvPLKU/e9lPiTS5cusXfvXlQqFdHR0XrZSVPTgObrr79m8ODB1V4Yl8lk+Pr6MmPGDMaPH89PP/1EbGwsHTt21EHE9ZtDhw5x6NAhPDw8iI6OxsnJSdchSdDQBKGSEliwAJYtAzMzUQzq0gXqQJlCbVFUVER6ejoGBgb4+Pjo1YlVJpNhYWHBCy+8wKFDh/j+++8ZPnx4FV+d+oamw1hwcHAVQejmzZvcvHkTKysrXF1dpfTpx0AShJ4+ZmZmDB48mMOHD3Pw4EH27duHp6cntra2AAQGBvLCCy9gbm5eZwYFtra2DBgwgMjISEJDQ7WTOqVSSUpKCr/88gtmZmaMGjUKf39/HUcrIaG/FBQUcOTIEeLi4jhz5gzJyclkZmYSFBTEqFGjaN26NUFBQdoLJPp6td7Q0JDg4GBAzIC6evUqnp6e9O7dm4qKCrZs2cLNmzfx8fHRcaS6xdfXl9zcXL777jttpzapDbjuqKio4MSJE+zdu5fg4GC6d++ut+NvAwMDTE1NKSkp+cvnyGQyunXrxuTJk/n888+ZNWsWISEhdWZsUh/Izc1l9+7dpKWlMXbsWCIjI6XsoDpCwxGEBAF++w0WLQKlEl59FQYMABsb0NNBxsNQUFBASkoKlpaWelMudj+9evUiKCiI06dPs3v3bp555hldh1QrFBYWkpGRAfCAeJeamkpBQQE+Pj7Y2NjU2TT5uoxGYFQoFBQVFaFUKvXmSrM+ExwcTJ8+fUhJSWHNmjWEh4fTtm1b5HI5RkZGdU5UMTIyws/PDz8/vyoT0OzsbNasWUN6ejrdu3dnwIAB0kBGQuIxyMzMZN++fcTHx3P+/HmSkpIwMDAgKiqKSZMm0axZM4KDg3F3d0ehUOitEFQdRkZGtG7dGoVCga2tLVu3buXkyZN0794dW1tbcnJyKCkp0bts7sdBEATUajWXLl3i3LlzJCUlcf78eRISEjAxMeGZZ57RlhhLPH0uX77M3r17qaiooG3btjRt2lTXIT0ymoY0ZmZmjBw58m8zejXNbqZMmUJ8fLy2dGzmzJnSmLuGOHLkCIcPH8bV1ZX27dvj4uKi65Ak/kfDmg0ZGorZQKNGiS3m7e3rtRgEfxpKW1pa1llD6b9DJpNhb2/PuHHjmD59Ot988w09e/aslxP5S5cukZubi5ubGw4ODlUmmxcvXqSgoIAWLVpI/kGPiabE0NzcnMrKSgoLC7WZKhK1h6GhIX379uXAgQPs2rWLHTt24OPjg6urq65D+0vun4CWlpZy/PhxVq1ahZOTE+PHj2/QLaMlJB6HGzdusGjRIs6cOcO5c+e4dOkSgYGBDB8+nKioKJo0aUJQUJBeZwP9E6ampgQEBACiyFxcXExERASdO3fm5s2bbNu2DUtLS0aMGFEvvwNBEMjNzeX8+fMkJiaSlJTE1atXuXLlCllZWRQXF2Nra4tKpeLYsWNUVFRgVA+bvdR1KisrOX78OHv37iUwMJDu3bvXiaYPj0pFRQWLFi3C2dmZbt26/aPAKJPJ8PLy4t1332X8+PEsWrSIDh06aP1KJR6f/Px8du3aRUpKCiNGjCAiIqJezuX0lbr1n1Cr/xRoqjsR/tN2pRKKisT75uZi+/h7n9+zpygCtWgBTk5QzxVfQRAoKCggNTUVV1dXvRSENIwYMYKvv/6a/fv3c/ToUdq2bavrkGqcpKQkSkpKCAsLq9ZQurCwEH9/f8k/6AnQlI3l5uaSn58vCUJPCU9PT/r3709iYiLr168nKioKR0dHvfAAEwSB69ev89tvv1FYWEi/fv1o3769rsOSkNA75HI5v/zyC7dv36Zly5YMGzaM8PBwQkND8fDwqHfZQP+EmZkZ0dHRGBsbo1Kp2LlzJ6dPn2bMmDGo1WrUajUGBgZ6/Z1osoCysrI4d+6cNiMsIyODq1evkpWVhbW1NaGhoVqPGjMzM9577z02bdrE2rVrGTFihK4/RoMjIyODuLg4ioqKaNu2bZVOm/qEJjP82LFjxMTEPFTGmaZ07LnnnmPu3Ll88sknhIaGStksT8ixY8c4dOgQjo6OxMTE4ObmpuuQJO6hbglCb78N/v4wfjzc75EiCPDee+DpCWPGVDWBvnYNdu6Ec+dE02gAa2sIDYVu3UTzaIUCGjUCTXtPPT7BPiwVFRXcvHmT3NxcwsLC9PbHJ5PJ8PT0ZPDgwXz11Vd8//33tGnTRq8HSdWh6TAWHBxc5aRVWQmlpS1wdLxFaGhrSRB6AjSCUE5ODrm5uXh5eek6pAaBXC6nc+fOxMXFsXr1atavX4+dnZ32mKRQKDA0NPzbtGxNhtffXVGSyWQYGBjUqNBUUFDAnj172L17N4GBgYwfPx5LS8sae30JiYaCvb09zz77LHZ2dgQFBdG0aVOsra3r3bn8YTE3N9ee67OysjAxMaFLly6Eh4eTnJzMyZMnadu2Lb6+vjqO9NHQdLdNTU0lMTGRc+fOkZmZyeXLl8nIyCA/Px9vb29atmzJ+PHjady4Md7e3nh7e2szR7Ozs3nnnXf473//S0RERJ0rLa7PKJVKjh8/zp49ewgICKBr1656N+4UBAFBEDA0NKRfv37aroQPg0wmw8TEhBdffFFbOvbVV1/x8ccfS2Xij0lhYSE7duwgOTmZIUOGEBERoRcXBBsSdUsQWrAAYmNh9OgHBSEQ/X/Cw2Ho0D8FodRUmDcPDh0SBR8XF1HsSU6G+Hg4elRsKx8QUDVjqAFQWFhIWloaRkZG+Pj46H03nIkTJ/LLL7+wZcsWkpOTtSaN9YW8vCAcHLoRFtamSmrujRuQldUTE5NmODl5SIbST4DUel53NGrUiEGDBnHq1Cl27drFjRs3tOWPcrn8H6+Ey+VyFArF3w7IZDLZQz/nn8oQNM8pLCxk//79KBQKBg4cqJc+ChISdQEDAwMmT56MtbU1crm8wQpB1WFjY0PHjh2xsLDg9u3bbN68mbt379KpUyddh/aPCIKASqXi2rVrpKSkaLOAMjMzuXTpEhkZGVhYWBAYGEi/fv20GWHe3t74+fk9ILALgsDYsWPZu3cvmzdvZu7cucybNw9jY2Npn3kKXL16lfj4ePLz8xkwYADh4eG6DumRUSqV7N27l4yMDDp37kzr1q0f6e9lMhmNGzdm2rRpTJgwgcWLF9OpUye6detWSxHXb06cOMHhw4extbUlJiYGDw8PXYckcR91SxB6VMrL4euv4fffxayh7t3BzU0Ufa5fhy1b4NdfxY5i06aJZWINiIKCAr32D7qfJk2a0KNHD1asWMHPP//MnDlzdB1SjZGfD2lp3VAqQ/DycsXE5E9D6ZQUSE93wNbWATOzBqVp1jhSpzHdEhAQgL29PWfPnqWkpEQrbqpUKpRKJSqV6i//VhAEKisr//E5SqUSpVL5t3EoFIp/rF3XmF5rJjteXl7ExsZKV7UkJJ4AOzs7XYdQJ7GwsNBeCLpz5w5OTk60adMGR0dHkpOTuXnzJjExMXXG3FaTBZSWlkZKSgqJiYlcunSJK1eucPnyZe7evYu3tzchISEMHToULy8vPD098fHxwdPT8x8zPR0cHJg+fTpnz55l5cqVdOjQgZEjRz7FT9gwUalUWu8gf39/unbtio2Nja7DemQ0BtGXLl3i7t27vPPOO4/12+nevTuTJk2qUjpWl/0P6yLFxcVs376dCxcu0K9fPyIjIyVfsDqIfgtCp0/DH39Amzbw3HNiOZnmB+/rKy7JyeJzBg4EOzvRWLqBoPEPqg+CkEwmQy6X88ILL/D777+zYsUKXnnllXrTiSM9XRR9DAwcsLISk9k0XLwIhYUQFQV6lrVb59AIQmq1WhKEdEBKSgrZ2dl4e3szePBg/Pz8AFCr1SiVStRq9V/+rUbseZjn/JNopFKpqKys/NtYVSoV5eXlFBQUcPDgQXJycjh9+jQRERHSVWoJCYlaw9nZmZ49e2JjY8Ply5dZs2YNLi4uxMTE6Cyme7OAUlNTuXDhAhcvXuTKlStaEcjU1BR/f3969uxJcHAwjRs3xtfXFz8/v8cqOWrTpg2vvfYa06ZNY+7cuYSHhxMYGCgdf2uRzMxMEhISyM7OplevXkREROg6pEdG01ksKioKKysrrly58lj7jEwmw9jYmClTprB//37279/PN998w4cffiiZIT8Cp06d4tChQ1haWhIbGytlB9VR9HuP3rNH9Azq27d6k2hXV+jSRSwnO30amjUTvYUaAIIgkJ+fT1paGp6entquFvpOVFQU0dHR7Nq1izVr1jB16lRdh1QjJCVBSQlERFS1x4I/BaGAAEkQelKkDCHdUVlZyd69e7l8+TJDhw5lwoQJeHt7P/U4NBObv8si0hihVlZWkpeXx6pVq/jkk09YtWoVnTp10jtPDwkJCf3hXm8hAHd3d6KioigrKyMnJwdLS8un0m1UrVZTVFREWloaaWlpJCUlkZqaypUrV0hPTycnJ4fGjRvj7+9Pjx49CAgI0IpAjRs3fqJJs2YCP27cOOLi4ti4cSNz585l/vz5mJqaSqJQLaBWqzlx4gR79+7Fz8+Pzp07611Gn1qt5sqVK6xYsYLY2FjCw8Np1qzZY7+epnRsxowZjB8/noULF9K+fXt69OhRg1HXX0pLS7XZQb169SIyMlLv7UvqK3VPELpyBRYvrt5UurS06rqUFNFxNzDwrzN/AgPBxATS0sS/byCCUHl5OTdu3CAvL4+IiIh64Y6vUetffPFF9uzZw6JFixg3bly96BR14QIUF0NwcFVBqKJCzB4qKxMT3iQv2ydDLpdjbW0tCUI6ID09nZMnT2JgYEDr1q11dkzSGE8/7GTFysqKnj17snPnTs6ePcuKFSuYNm2aZC4pISFR6zRu3BgnJyesrKw4e/YsO3fupG/fvrUmCKlUKm7cuMHFixe1IpDGB+jy5cuYmJhoxYLg4GB8fX3x8vLCz88Pe3v7Go1FJpNhb2/PtGnTOHv2LKtWrSI2NpbRo0fX6PtIiFy7do2EhARu3LjB6NGjiYqK0nVIj4VcLqegoIC1a9dy48YNBg4c+MSv2a1bNyZPnsycOXOYPXs2oaGh9aZCoTY5c+YMBw8exNTUlI4dO9K4cWNdhyTxF9Q9QejWLdH7p7rBdnl51ceaFvPW1tU/X7PNwEB87t+UEdQ3NLXdJiYm+Pr61isj4i5duhAWFsaFCxdIio+nbb9+ug7piRCEPzOE7heErl+H7GywtRX90qWy2ydDyhDSHXFxcaSmphIeHk6TJk305iqRXC7Hy8uLMWPG8NZbb7FhwwZ69Oihl0abEhIS+oWJiQkmJiao1WqMjY1xcnLC2tpa65X2pMdRQRAoKSkhPT2dixcvkpKSQnp6OmlpaWRkZHD79m3c3NwIDAwkNjaW4OBgbUcwb2/vp+IF0rp1a6ZOnco777zD3LlzadmyJSEhIVKWUA1yb3aQt7c3nTp1qnGBr7bRZP96eHjw0ksvceTIEUxNTf/5D/8BTYfTKVOmkJCQwP79+5k/fz6zZs2SPAX/hvLycrZt28b58+fp2rUrkZGRNfL/kKgd6p4g5OEhGkRXJ2AkJFR9rHlOaSmo1dWLQpptRkYPlpTVY/Lz80lNTcXKyqrelIuBeGC2tLTktcmTSZs2DdvvvoNu3cQsMD0dHOTlwdWrYpKbt7f4UTQkJ4vlYv7+orappx+xziCTybC1tZUEoadMbm4uBw4c4M6dO7Rv3x4vLy9dh/RIWFhYEB0dTbdu3di8eTNLliwhODgYMzMzXYcmISHRAJDL5fj5+eHl5YWhoSHJyckkJiYyZMiQR85WVKlUZGdnawUgTUlYamoqGRkZ2vdq3749/v7++Pj4EBAQgJeXF87OzrX0Cavn3tKxffv28ccff/DZZ58xf/58zM3NJVGohrhx4wb79+8nKyuLkSNH0qZNG736bgVBoLi4mAULFhAYGEjbtm0ZOHCg1k/oSZHJZLi7uzN9+nQmTJjAokWLiI2NpWfPnjXy+vWRxMREDhw4gIGBAZ06ddK7cV9Do+4JQi4uoifQ/UYqggBTplRd5+kpikBXr0JQUPWC0NWrYmaRh0f1IlM9pT4ZSlfHgAEDMPj6a8z27oWDB0EPWrP+FWlpYpcxT0/R9/ze3VjjHxQdLfkH1QQymUwqGdMBJ0+e5MKFC7i5udGyZUu9K/OUyWS4uroyfPhwEhIS2LFjB71795Za0EpISDw1DA0NMTQ0RKlUaktfHwZNFtDVq1dJSUkhJSWFy5cvc/HiRVJTU7l9+zZOTk74+/sTHR2Nn58ffn5+BAQE4O3trfMMc01m7zvvvMPp06dZtWoVHTt2ZMyYMTqNq75wb3aQp6cnsbGxODo66jqsx6KsrIzdu3dz/Phxhg8fXuPzH03p2Keffsrs2bNp0qQJnp6eNfoe9YGKigq2bt1KYmIiHTp0ICIiQrqAVsepe4LQoxAdDQsXwu7d0K6dmAV0r6JdXg7794tiUosWD4pM9RTNZDc9PR1fX1/8/f11HVKNIpPJsHJyEjvHzZoF334LsbFiBpgeXdHQcOGCWC7WvDncf7y811Ba8g96cuRyubZkLDc3V9fhNAgqKyvZvXs3GRkZDB06FH9//zrTOvlRMDExoWXLlgwcOJCffvqJX375hYiICL0z3ZSQkNBvDAwMCA4OJjAwEKVSyfXr11EqlVVM+pVKJXfu3OHSpUukpKSQmprKpUuXuHjxIunp6chkMry8vIiKitIKP4GBgQQEBNCoUaM6mR0SFRXFq6++yvTp0/nss88IDw+XSsdqgNu3b5OQkEBGRgbDhw+nbdu2evWdCoJARUUF5ubmvPzyyxw6dIgTJ05Qfr/NyBOiEWHvLR375ptvpNKxakhKSiLhf1U9Xbp0wcfHR8cRSfwT+i0ItW4NrVrBtm3ibLp3b7C3F4WBnBzYulUUi9q0gZCQBmPAUlZWxo0bNygoKMDJyempp/g+NcaMgQULYOdOOHtW3Af0EI0gdL9/UFkZXLokGkv7+YGFhe5irC/c6yGUn5+v63AaBJcvX+bEiRMoFAratGmj1wb3jo6ODBo0iJ07d5KQkMCWLVskg1MJCYmnjlwuRy6Xo1KptEtxcTHnzp3j5MmT5OTkcOvWLVJTU7l48SK3bt3C1dUVHx8fWrVqhY+PD76+vgQGBuLn51fnPd1kMhkymYwJEyYQHx/PH3/8weeff868efOk0rEnQBAETp48SVxcHG5ubnTo0EHv5gzl5eVs3ryZsrIy2rRpQ7du3ejQoUOt7BOabOFp06Yxfvx4belYjx49pH3wf1RWVrJlyxYSExNp27YtkZGRVbomStRN9FsQsrOD116DuXPh669FUcDLS8wSuXpVFIN8fODFF6FRI73MHnkcCgoKSEtLw8zMDF9f36di+vfUkclEY52+fcUssR9/hG++EbPB9Oj/rDGULi19UBC6dg1u3xZ3c2fnBqNn1ir3CkJShtDTYe/evaSlpemdmXR1GBoaEhwczOjRo5k9ezbLli2jQ4cOeHh46Do0CQmJBoixsTGNGzemsrKSkydP8vHHH3Pw4EHy8vIwNzfHx8eH8PBw/P39CQgI0ApAjo6OejmBtbW1Zdq0aZw7d46VK1fSvn17xo8fr+uw9Jbs7GwSEhJIT09n8ODBepcdpKGiooKjR4+SmJhIkyZN6N+/Pxa1eBW1W7duTJo0iTlz5vDJJ59IpWP3kJKSQnx8PCqVim7duknZQXpC3RKEuneHpk3/umNYly5iqsS9qXmdO4sCwNatYn1NUpI4yzYwgJgY6NcPoqKgATmb11dD6WqZPBlWroR16+Ctt0RXZj0iNxcyM0Wxx8urqs1VcrLYHC8gQPQP0sNzdJ1DJpNhYWGBQqGgqKiIyspKKdW3FsnLy2P//v3cuXOH559/vl60HLWxsaFXr15s3ryZU6dOsWrVKl5//XW9LIOTkJDQf2QyGUVFRRw8eJCtW7dibW3NwIEDadasGcHBwdr28Posxt+LpnRs2rRpfPbZZ7Rq1UoqHXsMBEHg1KlTxMXF4ezsTExMDK6urroO66ERBIHKykoUCgWDBw8mICCAffv2cf78eZ555plae1+ZTIZCoeCll15i//797N+/n2+//ZaZM2diaGjYoPdDpVKp9Q6KioqiVatWWEp+F3pB3RKE3n1XNEqpboImk8Hbb4smK/eKO3K5KBRFRoruvHfviuvt7MQMEkvLBtVdDBqYIBQeLvoHbdwIv/0m7kN6REoKFBSIYpCtbVUtVCMIBQZK/kE1haYG3MLCAqVSSWFhoeQBU4ucOHFCayYdHh6ud2bS1aFQKPDy8mL8+PG88847rF69mh49etCkSRNdhyYhIdFAKS0tJTMzE0EQ8PPzY+bMmQQGBtZLoVomkzF+/Hji4uJYv349c+fOZd68eVhaWjboyfijkpOTQ0JCAikpKQwYMIDo6Gi92l+USiWJiYkcPnyYVq1a0bRpU8LCwsjIyKh1EUJTOjZjxgzGjx/Pjz/+SPv27enVq1etvm9dJy0tjX379lFWVkb37t3x9fXVdUgSD0nd+uU3bw6+vn+dIdSsmZghVF1nBWtrURzo1k1cIiLEdXp0cKsJ1Go1ubm5XLp0CWtr63pnKF0FmUz8/774ophis3SpWGNVQ20mnwYa/6CQkAcNpe/NEJIEoZrjXmNpqdNY7aFUKtm1axdXrlyhY8eO+Pj46NVg8++wtLSkU6dOxMTEcPHiRX755ZcaN7CUkJCQeFhkMhmGhoYYGRnh5eVFcHBwvTneVoe1tTXTpk3Dz8+PlStXsnr1agRBqLE24w2B06dPEx8fj729Pe3bt9fL0ufy8nIuXLjAqlWrWLhwIadPn8bPz++p7ftdu3Zl0qRJ5OfnM3v2bK5cudJg90GVSsW2bds4d+4cERERtGrVCiupPbLeUH/PFg2UsrIyrl+/TmFhIU5OTjRq1EjXIdU+HTuKYmBKipgppEecP1+9f1BpqWgoXVkpaqCSH1vNca+PkCQI1R6XL1/m2LFjyOVy2rZtq9dm0vcjk8lwdnZm7NixmJmZsWnTJo4eParrsCQkJBoohoaGWFhYYGJioldlP09Cq1atmDp1KnK5nLlz53L+/Hldh6Q35ObmkpCQQFJSEm3bttW77CC1Wk15eTmtWrXinXfeITg4mIsXL3Lo0KGnFoNMJkMul/Pyyy/Trl07Dhw4wNdff015eXmDFIUyMjLYs2cPRUVF9OrVq34nJNRD9OfXL/FQFBQUkJ6ejoWFBX5+fvXfH0UmE413XnhBzBb68UcxrUZPDsY5OeJHuD9DKDNTrH50cAAnp+qrKCUeD0kQejrs2bOHS5cuaVsD1xf/Cg2mpqZERUXRt29fsrKy+OmnnygoKNB1WBISEg0QQRBQq9XakuiGwtixY+nZsycXL15k7ty55OfnN8jJ+KNy5swZ4uPjsba2pn379nrl76dWq7lz5w4//fQTcXFxmJubM27cOGbMmEFkZORTjUUmk+Hk5MSMGTNwcXFh4cKF7Ny586nGUBdQq9Xa7KDmzZsTFRUlZQfpGXXLQ0jiicnLyyM1NbX+l4vdzzPPiLVVx4/Dvn3Qp4+uI3oounUTO4i1bFm9obTGP0gqi685JEGo9snLyyMuLo67d+/SqVOnett9w97enpGjRrL/9H4SDRPZk7OH/lb9dR2WhIREA0OtVlNRUYFcLq+fnWX/AktLS6ZNm8aZM2dYuXIlMTExTJw4EUDyE/oL8vPziY+PJzExkR49ehAdHY3ir6w66igVFRVcuXKFy5cvc/z4cVq2bEmLFi1o3bq1TuLp2rUrzz77LLNnz9Z2HfP29q6T+6BSqaSiooLy8vJ/XEpKSigpKUEQBLp27fqXmd6ZmZns2rWL/Px8+vTpg7+/f5387BJ/jSQI1TMalKG0BplMdGSeOFE0Hv/uO1FpMTKq80rKiBEwfLh4/95QbWzEj+DvL/qjS9QcMpkMW1tbqfV8LaIxk3Zxcak3ZtLVYWRkRJPQJoydNpYlNktYpFxEVGUULob1pzxOQkKi7qNSqSgvL0cul2N879WlBkBERMQDXceaNm2q67DqLGfPniU+Ph5zc3Pat2+vV23B1Wo1RUVFuLq68sEHHxAXF8fevXvZsmULMpmMzp07P/WYZDIZMpmMl156ifj4eOLj4/n666/55JNPMDY2rjVh5K+EnfvXlZaWUlxcTHl5OWVlZVWEnnuX4uLiB9YVFBSQn5+PTCZj/fr11QpCgiCwfft2zp49S9OmTWndujU2Nja18pklag9JEKpHqNVqcnJyyMjIoGnTpg3P3X3ECPjqK9izB06eBB1dKXhUsrKguBhcXP5sihcdLWYHqVRQWAhqtVg+1oAu/NUaMpkMa2trBEEgPz9f1+HUO5RKJTt37iQrK4thw4bVKzPp6rC2tqZ7t+6surKKwyWHWZu7limOU5DL6u9nlpCQqFvcmyHU0AQhgPHjx5OQkMDq1au1XcdsbGykLIX7KCwsJD4+nrNnz9K1a1eio6MxqK5RTx2lpKSE33//HSsrK5o3b07v3r3p2LEjR48e1XlmnLOzM++99x7jxo1j0aJFdOzYkT4PUa2gVCqrFXLufXyvqFNRUaEVdqoTce5fl5+fT35+PsXFxRQXF6NWqzE0NMTY2BhjY2OMjIyqvd+oUSMaN26MmZkZZmZmODo6Vhv/tWvX2LFjB7m5ubz44osEBARIvzs9RH+OAhL/SGlpKdevX6e4uBhnZ+e//PHWW9zdYcgQ+PJL+P57aNVKVFfq+IFp9WrRXLpDB+jbV8wOkstF76DcXNEWycUF+veHhuARXttIXcZqlytXrmgNlqOjo3F2dtZxRLWLQqbAy8iLZ+2fZfr16SzLWUYPqx74mfjpOjQJCYkGgsZkt7ZKxiorKzl//jx5eXk0a9YMu2pSlwVB4Pbt2yQmJuLs7EyTJk1qPI6/wtLSkhkzZnDq1ClWrVqlLR2Ty+XS5PQeEhMT2b9/PyYmJrRv3x4/P/06T6nVam7dusWhQ4c4f/48QUFBhIaG0qFDhzpR9ta5c2eeffZZ5syZw8cff0xoaCheXl5/uQ9evXqVrVu3asWa+wUdzf38/Hzy8vK0j4uKihAEASMjI62I81e37u7uBAQEaIUeY2NjzMzMMDc314o9mvv3r7OyssLGxgYLC4tqjyuCILBz507OnDlDUFAQbdu2rfbYIFH3kQShekR+fj5paWlYWVnh7++vV6p/jTFxIixZAhs2iF3HgoJ0HdE/snu3mNR07Bh4eYmJTZrjbmkpbNkifozOnSVBqCa410NIKhmreXbt2sXly5eJjIysl2bS1WEpt6S7VXc25G/gUPEhfsn5hfdd3sdQJrnBS0hI1D617SFUVlbGokWLOHXqFJ999hlRUVHVxnD27FmmT59Ojx49mDVrVo3H8Xc0b96cqVOnMm3aNObMmUNkZCRhYWFPNYa6THFxMXFxcZw+fZrY2Fiio6P1pvGMWq2mpKQEExMTpk6dyoULF9i8eTMbN27kzJkzPPvss3XCGFsul/Pqq69y4MAB9u7dy/z585k1axampqbVikJJSUlMnToVhUKBiYlJtZk6xsbGWFlZ4eDgUGWdRtipTtC599ba2hobGxvMzc0xNzfHyMioxkTSmzdvsm3bNu7evcuzzz4rZQfpMQ1QMai/5OTkkJSU1PAMpe+laVPo3h2WL/9TSdEDHBzE0rFVq8DbW0x2ko6ptYNkKl175Ofns2/fPnJycujSpQseHh66DumpIJPJcDJ04ln7Zzlecpy1eWvpZ92PCPMIXYcmISHRAKjtkjGVSkV6ejpnz579y26Kmossp0+fJkhHY68JEyawf/9+Vq5cydy5c5k/fz62trbSJBVRfNi/fz8KhYKYmBi98hktLy/n2LFjXLt2jbCwMIKCgggJCeH8+fPs27evTpX/Ozo68u6773Lx4kV+/PFHYmJi6NevX7XP9fb2Zvz48ZiammJhYYGFhQXm5uba23vv379OoVDodL/WeAedOnUKPz8/oqOjsbe311k8Ek+GJAjVI7Kzszl9+jQ2Nja4ubnpOhzd8fzzUFAgqix6QuPG4OEBO3ZA27Zi6VgD6hz7VJEEodpDYybt7OxMREREgzIWNJOb0c6iHb2serE2by0L7iwgyCQIC4X0Q5aQkKhdartkTF8wMzPj3Xff5eTJk6xatYr27dszadIknU+edU1JSQl79+7l5MmTtGvXjnbt2unVfqIRG3fv3s2FCxcIDAwkNDSU4OBgmjdvruvwHqBTp04899xzfPLJJ3z00Uc0a9as2q5jAQEBfPfddzqK8slIT09n3bp1XLt2jTFjxkjZQXqO5Hqp5wiCQHFxMRcvXuTw4cNkZmZibm6Oq6urrkPTHe3awTffwOjRcP06nDsnmkwnJsLNmyAI4lKHkMlgzBhwc4OlSyE1FZRKXUdVP5EEodpBqVSyfft2rl27RteuXfHy8qrXZtLVYaewY6L9ROwUdmwt2EpCUYKuQ5KQkGgANHRT6XsJDQ3l9ddfx8zMjDlz5nD27Fldh6RzkpOTSUgQz0f6lB0kCAIVFRVUVlbSr18/Zs6cSePGjdm1axdLly5l/fr1lJeX6zrMB5DJZLzyyitER0dz8uRJ5s+fr23fri9UVlZSVFRETk4ON2/e5OrVq6SmpnL06FF27tzJ/PnzOXr0KNbW1kRGRuKgRxfhJR5EyhDSQzQi0PXr17U/0CNHjpCQkICpqSnBwcENt2QMwMBAbNe1cSMcPCjWYgmC6NTs7S0KRu3aic95imp2ZaXoCVRcLN4aGYnG0Rr8/MQW9J9+KlogNeQkr9pE8hCqHa5evao1k46JicHp3p27gWAkNyLUNJRRdqOYlz2PH+78QKR5JA4G0kBJQkKi9lCpVLXqIfQkCIKAIAio1eqn5m2p6Tq2fPly/vvf//LNN99gZ2fXIDMYysrKtNlBrVu3pl27dnrj7adSqbhy5Qq7d+/WlopNnjyZvn37snnzZo4dO0a/fv3qpAhqZ2fHe++9R0pKCgsWLCA6OpoBAwYAPNX9UHNsqKyspKKiospSVlZGaWmp9rHmOZoOZoWFhRQVFVFcXExRURFFRUVcvnyZzMxMrl+/TllZGWPGjKn33WQbApIgpCdoDNU0Km1KSgpHjx7l6NGjXL58GQsLC9zd3YmOjmbo0KEN01BaQ3Ex/Pyz2ILe0xP8/UXxJy9PrMlasQLeeQfGjhVVmRo4MKtUUFYmCj1lZWIIGuFH8zg/H+7ehTt3xFsfH5gwoerrDB4sGkyvXg1RUaAnF3H0Ck2XMRA9bwRBaJCDxJpm586dXL58mYiICIKCgvRmwFnTWCusGWE3go35GzlQdIA/8v5gosNEZEj7mISERO1wb8lYbU6O1Wo1GRkZnDt37oFtKpWKzMzMB9YLgsCFCxdITU3F0dERT09P3N3da3UCaWJiwvTp0zl+/Dhr166lY8eOTJw4EQMDgwZ3vk9NTSUhIYGKigpiYmIIDg7WdUgPjVqtpqCggJMnT3L+/HlCQkIIDQ0lICCAZ599lvLy8jo91ujYsSPPPfccs2bNYubMmTRv3hwfH59Hfh1BEKoVdB520XQx0wg7mvt5eXnk5uZSWFhY5TmFhYWo1WptFzMjIyMMDQ0xMjJCrVajVCpxdHQkMDCQiRMnPtZnkqhbNGDVoO5zrwiUlZXFxYsXOXbsGIcPH64iAvXo0YNmzZrRunVrmjVrhouLi65D1y3x8fD++9CxI3zwAbRsKa5XqyEhAd59F2bMgGbNxNb0/4RKJSo7mqWkRKv05MntOHzHl/wiA3Jz0S63b8OtW6Lwo3lcWgrGxmBqKi7R0aJYdC/29mKjtH/9SzSYnjixxr8dCdAa8hUVFVFRUVEnry7pEwUFBezdu5ecnBy6deuGu7u7rkPSGQqZgsZGjZlkP4l/3/g3i+4uoptVNzyMGobBtoSExNPnaZWMlZWVsWTJErZu3frANkEQuH79erWxLVq0iF9//RVPT0/at29Ply5dsLGxwdPTE1dX11oRh0JDQ3njjTd4++23mT17NpGRkTRv3rxBCULl5eXs2bOnSnaQqamprsN6KJRKJYWFhbRo0YLPP/+cuLg4Nm3axJEjR2jatCmxsbGEh4frOsx/5OWXX2bv3r3s3buXefPm8dFHH2FoaKgVeCorKykrK6OsrKxKlk5lZWWVjJ3CwkJtxs79i2Z9ddsBDA0NtYLOvfdNTEwwMTHB0NAQBwcHXFxctAKQubk5lpaWWrNrCwsLLC0ttfGYmZnRuXNn/Pz8GnYSQj1B+g/WMTTlYPeKQEePHuXw4cNkZGQ8IAK1a9eOli1bYmdnp+vQ6waCAP/3f6BQiLVXISF/ZgApFBAbCx9+CM88Az/8ABERollPUVEVoUd7v7RU3Hb37p/pPZrl7l2uOXVi0oF/c6PAAmNjMDERF3PzP++7uorZQDY2ouDj6Cje+vqCtfWDH6FTJ+jVS8wSCg4Ws4skag6ZTIZCocDQ0BClUsmtW7fw9PTUdVh6zYkTJzh//jxOTk5ERkY2KDPp6rCUW9LPph/r8tdxpvQMS3OW8rbT2xjIpFOuhIREzfM0TaXLysooKSl5YL0gCNX6uajVak6fPs3t27e5ffs2J06cYMWKFbi5udGpUyc6deqEjY0NPj4+ODg41KhgM378eOLj41mxYgWzZ8/m22+/rfH3qMukp6eTkJBAaWkpHTp0ICQkRNchPRSCIJCfn8/atWvx9fUlMDCQnj17Ehsby759+9i6dSuVlZV6IQjZ2trywQcfkJSUxMKFC/H398fU1LRKtk5ubi45OTkPlGdp7hcUFCAIwt8KO5p29Y0aNcLd3V27zdjY+IFuZZr7tra22NraYmlpWeU5VlZWNdqeXqLuI41O6wJqNWUlJdy6dYuMrCxSUlI4fPgwhw8f5urVq1haWuLq6qoVgaKjo2nRooUkAlVHYaGYBRQeXlUMupeOHcHFBXbuFMWgxETYtUtM47lX9MnOFh8XFlZN7TE1FZUeU1MaOctp21aG0hjs7MDWVrx1chJv77+vUPzzRzA0FDODjh2D338XfbH14JynVwiCgPJ/rt1Hjx6VBKEnQKlUsnXrVm7cuMHw4cPx9PRs8LXkMpmMRoaNeNHhRaZkTmFpzlL6Wvcl1DRU16FJSEjUQ55WhpCpqSnTpk0jJibmgW0qlYrNmzfz/PPPa9dpLnLeW0omCAI3btzgxo0bnD59Wps51K9fP1q1aoWlpSVeXl41YlJrbGzMv//9b06ePMn69evp2LEjkyZNwtDQsN5PdisrK9mzZw8nTpygVatWtGvXDnNzc12H9dCUl5eTlpZGXFwcYWFhNG/enICAAHr27Em3bt3Izs7WdYgPTUxMDB06dGD16tVMnTpVm5Vzv7Bz7+N7M3Y02+8XdTSP7ezsHhB2LCwssLKyahD7usSTIwlCukKtFlM/bt2C69cpzMjg96NH+W7XLjIzM7G0tMTFxYXu3bsTGRlJu3btaNq0KdbVpZRI/Elmppjh4+f3189RKMSUnT17xOyfgwfhs89EJeYesUeb2mNhIao5Dg5ieo+Dg3axdnTjJwcTzC1Fz+qaIiDgT4NpqRFWzSOXy3F3dyczM5OlS5cSFBREUFCQlPb6GGRlZXHo0CHUajWxsbEN0ky6OkzlpnS07Eg3q25syt/ED3d+4L9u/8VUrh/p+hISEvqDJkPI3Ny8VjOEZDIZVlZWODo6PrBNqVRiaWlZZZ0gCJw6dYpbt25V+3pKpZJr165x7do1Tp06hZOTEx4eHvTt25fWrVtjbW2Nj48PFhYWjx1zUFAQb731Fm+99RazZ8+mVatWtGzZst5PkjMyMoiPj6egoIAOHTrQpEkTXYf0UGhKxezt7fnoo484e/Ysv//+Oz/88AOhoaG0a9eOZs2a4aZHnVcqKytRKpXacZK/v7+2HMvS0lJ738rKSrvu/m0ymaze77MSukOa/TxNNCLQ7dti2kdyMhw6BAcPYlZejszWFjMzM7p160bz5s2JiYmhefPm2Nra6jpy/aG0VLz9J+FMU9JSViam34wfL9ZxacSeRo3+vG9r+5fG00b/W54EhUJsjHb/WwwZItoh3b0rik3SeaDmMDY25vnnn2fhwoVs27YNQRB46aWXiIyMlDLvHpEdO3Zw9epVIiMjG7SZ9P3IkGGrsOV5h+fZV7iP9XnrGWIzhBjLB6+sS0hISDwJmk5ClpaWdcoTT5M1VFhY+I/PLSsr48qVK1y5coUTJ07g5uaGn58fw4YNw9/fHxsbG3x9fTE2Nn7kifHYsWOJi4vjt99+05aONWrUqN5OsJVKJbt37+bEiRNERETQrl27B8S6ukpJSQl79+5FoVAQEhJC06ZNCQsL48KFC6xZs4aff/6ZcePG4ezsrOtQH5oLFy6wfft27O3tmT9/Pk2aNKm3+56EflL/BaHSUigoELtMmZg8mMZRWiqWBJmbi1kh924XBNFLJj8fysvFbRYWYGUlZpM8DPeLQElJYkbKwYNw7ZoYl7MzZs2a0a1VKyLatiU0NBQrK6ua+w4aEpoTXk7O3z/v7l1RhTE3hzZtoG3b2o/tL2jdWtwN778AZmMDzz4rfqTgYDAz00l49RJjY2Nee+01nJ2d+fnnn4mLi+PUqVO88cYb9OzZk4CAgAZf9vQwFBUVsWvXLnJzc+nRoweurq66DqlOYSQ3Isw0jOG2w1l8dzFr89bS3rK91HFMQkKiRnlaJWOPiiAInD59+pH/rrS0lLS0NNLS0jhw4ABOTk4EBQUxfvx43NzcsLe3x9PT86F9TgwNDXn//fc5ceIEv//+OzExMTz//POPJS7pA1evXiU+Pp7c3FxiY2MJDdWfcmW1Ws3t27fZvXs3/v7+2otNAQEB/Oc//yEzMxO1Wq3rMB8apVLJ/PnzKSoqYuTIkYSEhNTLfU5Cv6n/gtDFi7BtG3TpAk2bil4w95KWBtu3i4JAixaiKASiAJSeDidPwoULYt2OgYHYxjwsTOxQ5eBQvSmMRgTKzoYbN0QR6MABcbl2TZz5OzuLMYWHQ4cOyJo3J0gqB3tyXF1FwS41VewOplA8mFpTUgIZGeDhIQpCOmbGjL/eFhsrLhI1i0wmw8TEhDFjxtC2bVvmzp3Lnj17eOeddzh48CCvvvoqzZs315srarri+PHjnD9/HkdHR1q1atXgzaSrw1phzWSHyVypuEJT06YICH8rCKkFNbeVtylSF+Fn/DelrxISEhL/42maSj8sgiCgUqnIzs7G1NRUWzbzqGi6K6WlpXHw4EFcXFwICwtj+PDhuLq60qhRI1xcXFAoFH870fbz8+Ott97ijTfe4NNPP6V169ZERkbWu8m5Jjvo+PHjtGjRgnbt2umV3YSNjQ2TJ0/mmWee4Y8//mDZsmW4u7sTHR1N06ZN8fLy0qtM5LS0NNasWYO1tTVTpkypd/ubRP2g/gtC58/D/PmieBMY+KAglJQE330nZvwEB4uCUGWlWMr15ZeQkiL6x9jYQEUFxMXBkiWiwcuYMeDmJmYOqdWiH82dO3Dzpvi+GhHoxg1RBHJy+lMEio0VhSU9OkjrBZaW4ne7cyecPSuKfILwpyikVov/w6wsePFFqRargSOXy/Hz82PevHmsXr2ar7/+mi1btnD69GmmTZtGly5d8PT0lE7g1aBUKtm0aRM3b95kxIgReHh4SFlV1aCQKQg2CWaJ1xIsFZaoBTU5qhxuK29TrC5GQMBMZoaToRP2BvZUCpWszVvLseJj/Oz1s67Dl5CQ0AOeRoaQqakpFhYWf+u1Z2hoiIWFhXbCrlQqad26NU2bNuXu3bvcvHmTgoICiouLqaioQKVSaVtrP0zWR05ODjk5OZw/f55du3bh5uZGdHQ0zzzzDHZ2djRq1Ejbxr668/bo0aPZu3cvy5cv55NPPuGHH37AycmpXp3jr1+/TlxcHNnZ2YwdO5amTZvqOqRHRqFQ4ObmxgsvvMCAAQNYt24dy5cv58SJE0yZMkVvGoGoVCq+/vpr8vPzGTVqFE2bNq1X+5pE/aH+C0KPQ3q66OZ77Ro89xwMHix2paqsFFs/zZsHCxeKYs7o0WKnqkuX4PRpUQDav180i7a0FE2IO3WCyMg/RSCpHKx2mTIFjhyB998XW8y7uICRkegXlJkprnN3hwkTatYJWkIv0WQLjRo1imbNmvHpp5+SkJDAyy+/zPjx43n22Wdp0qQJpqaSGfC9XL9+nYMHD6JSqejUqRONGjXSdUh1FrlMjoXCgkqhkqSyJHYW7ORg8UHyVfkICFgrrGlr3pa+1n1xNXTlSPERNuRt0HXYEhISekJtZwgZGRnRoUMHXF1d/9K7RSaT4eHhwbBhw2jVqhUymQxLS0t++OEHVCoVt27dIjMzk6SkJK5cuUJeXh6lpaXk5ORw/fp1cnJyKCwspKysjMrKSm1GUWVlZbXvp2ljf/r0aVatWoWzszMxMTEMHDgQOzs7nJyccHR0rGLGa2hoyH/+8x9OnjzJpk2bWLlypd6UjqnVapRKpfY7qe62oqKCbdu2cezYMW1XYn32IZXL5bi4uPDiiy8ydOhQ4uLi9Kr5R0ZGBitWrMDS0pLXXnutzu9jEg0X/flVPS0EAdasEQ2fX3sNxo4VM4RALD9q314UF955BzZuhIgIsZzs44/hxAkxE6hRI+jaVTSHiY0V258/QYcEiUekWzd45RX45Rd4+WVo1040hr5zR8wOqqiA994TxTnp4CzxP+RyOc2aNWPhwoUsWLCAxYsXs3jxYg4ePMh7771HdHQ0Li4uug6zzrBt2zaysrKIioqSzKQfApWgIrksmWnXpnGi5AThZuFEmEWgQMHF8ov8eOdHsiqy+Mj1I12HKiEhoUdoSrOUSiUKhQLDh/W4fATMzMx45ZVX/vY5CoWCiIgIIiIiqqyXyWQYGBjg5uaGm5sbrVu3rhJ7cXEx165d49KlS6SkpHDz5k3y8/PJz8/n7t273Lhxg8LCQkpKSigvL68igqjVagRB4NatW9y6dYuzZ8+ydu1aPD096d69O127dsXS0hJXV1fs7e0B8PX15Z133mHq1KnMnj2bqKgoIiMjUVRnAVEL3Cvs3Cvm/NO6kpISbflcQUFBlVvN/by8PNLT07l+/Tr9+vUjJCTkqXym2kYul+Po6MjgwYN1HcpDo1ar+fbbb7l79y5DhgwhPDxcEoQk6iwNRxBSqcQMn4qKB9ffS0UFHD0qGlBHRv4pBt1LeDg0aQKbNsHVq2Jr8sBAMWOoVSvo3Fn0K5L8R3SDTAbTp4v/o99/F8v/lEqxXDAyEkaMgA4dJDFI4gFkMhnm5ua89tprtG7dmo8//pijR48yceJEpkyZwtixY/H3968zHg26ori4mJ07d5Kbm0vPnj31qtuHLhAEgQJVAd9mf8vB4oO87Pgy7zi9g6XCEgGBMnUZq3JXAUiG0xISEo+EplxMJpNhaGioV6W7MpkMCwsLAgMDCQwMpGfPntptgiBw9+5d0tPTSUlJITMzk+zsbPLz88nLy+PmzZvcuXOH4uJiysvLqaioQKlUkp2dzfXr1zl+/Dg//fQTbm5uDBo0iNjYWCwsLHBxcWHEiBHs3buXZcuWMWvWLH788UecnZ0facKuEXbuF2/uz9q5f315eblWxLl3KSoqqna9ZlteXh4qlQoDAwMMDQ0xMDDQLvc+NjIywsLCgvz8fAoLC7VZUhJPl8zMTJYuXYq5uTmvvvqq9D+QqNM0HEEoMVH0Abq/7OP0adFkWEN2ttihytn5rw2HDQxE8+KKCvG5PXvCJ5+IbaDqgEmxBGIpWP/+4lJeLvo7WVqKXlHSQVniH5DL5bRu3Zply5bx5Zdfsnz5cv7v//6P/fv3895779GqVSvt1caGyLFjx0hMTMTW1lYyk34IBARuVd5ibd5ago2DtWIQiAKQqdyUcfbjAChWFesyVAkJCT2jrnYYe1JkMhkODg44ODgQFRVVZVtFRQXXrl0jKSmJtLQ0bty4QU5OjlY4ycrKIj8/n7KyMi5evMicOXNYvHgx7u7uPPPMM7Rr147Ro0dz8OBBtm7dyrJly5g4cSIGBgbVZulUl7FTWlr6QLbO3y2a52hK++4VdKoTdjSPbWxscHBwwNjYGHNzc6ysrB5YLC0ttbcpKSksXbqUzZs306RJE8aPHy91Ln7KqNVqfvzxR7Kzs+nduzdtddjJWELiYWg4gtCaNWI3sfuvnBQWii3INZSWillDdnZiadhfYWn5py+NXC56BUnUTYyNHzQTl5D4B2QyGTY2Nrz//vu0a9eOjz/+mFOnTjF69Gjeeecdhg8fh5ubU7WNBuszKpWKjRs3cuvWLQICAjAwMEAQBF2HVaepECpILk+mRF1CtEW0VgySkJCQeFLqYoex2sbIyAhvb2+8vb0f2FZQUEBaWhrnzp3j+vXrZGdnc/fuXQoLC7l16xbfffcdq1atwtfXlzZt2nDp0iX++9//YmxsjEKh0Ao3+fn5fyvwVFRUoFAoUCgUVUSc6h4bGhpqDa/NzMy0As5fLdbW1g+sMzMz+9uytoqKCnJycggPD6eyspIvv/yS1atX4+/vT5cuXWqllFCiem7evMnixYsxNTXljTfeqOJjJSFRF2k4gtC4cdCx44MZQvv2wQ8//PnY2Fj0CiopEcuM/grNdiOj6lvPS0hI1AvkcjldunShSZMmzJo1i61bt/L++/8hNdWLCROGEhrasHziU1NT2bdvH4WFhZw5c4bNmzfj7OyMm5vbU/Ng0DeUgpKsiiwUKPAw8tB1OBISEvUIlUpVLzOEHhcrKytatmxJy5Ytq6wvKyvTmlpfuHCBa9eukZqaipGREfn5+bz99tsYGRlVm7mjWTSd1jw9PTExMcHS0lK7aDJ0/m6dlZUVdnZ2GBgY1LhAkJ+fz8mTJ/Hy8mLw4MEkJyezcuVK1qxZg6+vL/7+/pIo8RQQBIFFixZx/fp1OnfuTIcOHXQdkoTEP9JwBCE/P2jb9kFfn5s3Rb8gDXZ24uzu2jUoLq7aslyDUil2EZPLRbPiBnJFRkKioSKTyXBxceHrr79m/fr1fPvtL2zZ0pktW+CDD8SqUVfX+t20Tq1WU1BQwFdffUVaWhouLi6UlpYyb948zMzMGDt2LE5OTnrlX/G0EBCoECqQy+SYy6WyYgkJiZqjvpaM1TQmJib4+/vj7+9P3759UavV7Nmzh2PHjmFkZER0dDTOzs5YW1trM3TuzdS5P2vH1NS0zggsgiDg4OCg9WCSyWQMGTKElJQUtmzZQnBwMJMnT8ba2lrHkdZ/srOz+fHHHzE2NubNN99ELpfXmf1EQuKvaDiC0MNiYQGhoXDuHKSmip2o7vUFEgS4cgVSUsDLC9zcJE8aCYkGgCbld+DAgTRv/gzvv29EXBy8+CKMGSM2JQwIEK3E6gtKpZLCwkLy8/O5c+cOBw4cYMuWLRQXFzNmzBhMTEz4/fff+eKLL7CwsGDYsGHY29tLg5/7kCPHWmGNSlCRrczWdTgSEhL1iIZYMlYTpKWl8dZbb1FaWsqECRP47LPPMDEx0bvzl0ql4tq1axgaGuLg4KAtDevYsSMXL17k888/Z+3atQQGBtKjRw+pdKwWEQSBJUuWcPXqVdq1a0fXrl11HZKExEMhCULV0acPHDwIy5eDkxO0bCnO8lQqscX8okWQlgajR4szQAkJiQaDTCbDx8eIn3+GZcvgs8/EQ8X+/TBrFsTGQqNG+qsTl5WVaf0Tbty4wfHjx4mPj+fgwYMUFRVhaGhISEgIY8eOpWnTplhbW/Pjjz/y6aefYmJiwsCBA7GxsdG7QXVtYigzxMfIB4DU8lRUggqFTCqvk5CQeHKkDKFH586dO8yYMYMLFy7QoUMHPvjgA70UgwDKy8tZtmwZjo6ODBo0CLv/dUc2NDRk4MCBJCcns2TJElatWoWfnx9BQUF6+Tn1gby8PL7//nsMDQ156623UCgU0nctoRdIglB1REfDyJHw448wcybExIC/v+gbtH+/2JmsdWuxg5WTk66jlZCQeMrIZGLDuvHjITwc3n0XDh+GsWPhhRfErCFvb/3wMler1ZSUlGhFoOTkZA4cOEB8fDyJiYnIZDJtlxV/f39iYmKYOHEizZo1w8TEhNdff52ioiKWLFnCJ598gqmpKc888wwWFhbSQOh/GMoM8Tfxx8PIg/iieFLLUwkwDkAuE8vrBEFAjRqVoNJxpBISEvrGvR5CUobQP1NcXMz8+fP5448/8PHx4csvv9TrzFYzMzMmTJiAQqHA8j5bDDc3NwYNGkRycjLbt28nODiYKVOmSJ1Ba4nffvuN9PR0IiIi6NWrl67DkZB4aOq/IGRjA0FBojdQdd4W1tYQGAj29n+aQ8vl8PzzYknYb7/Btm2wbp24vlEjcRY4YgQ0bvwUP4iEhERdQyaDpk1h9Wr48ktYsAC+/Rbi4uDTTyEyUjy01DWUSiVFRUUUFhaSk5PD6dOnSUhIYM+ePVy/fh1jY2MsLS0JCAggJCSEu3fvcvz4cdRqNb1796ZZs2baK9EWFhZMmzaNkpISVqxYwZw5czAzM6Nbt251ymNBl8hkMmwVtky2n8zsW7OZdm0a/3H5Dw4GDihQUCaUkV2ZjUwmo4lJE12HKyEhoUdoSsYUCoWUIfQPKJVKtm3bxueff461tTWffPIJgYGBeul9JwgCt2/fRqVSYWtr+5cZTjExMaSkpHD58mXWr19PUFAQffr0kcTDGqagoIBvvvkGAwMD3nzzTQwNDaXxj4TeUP8FoV69oEcPUcyp7ofZrRt06fLgdoVC/NuePSE3F3JyxMv9jo5VTaglJCQaNDKZeEh45x1o3x5mzIAzZ2DwYHjrLZgwQTScNjDQbRlZWZmaoqJcCgsLuHnzJocOHSIuLo4DBw5QUlKibYXbokULWrVqRYcOHWjfvj329vYUFhby9ttvs3r1at577z1MTEzo0KGDdgBqa2vLe++9R2lpKWvXrmX27NmYmZkRExODiXS8BMBcbs5Y+7FkVGSwPm89wy8PJ8w0DCuFFdcrr5Nanko/63586PohJnITzBWS+bSEhMQ/c2/JmDTJ/2sEQeDChQu8+eabCILA1KlT6du3LwYG+jkVUqvV/Pbbb1y7do2RI0cSFhZWbadPAwMD+vbty4ULF1i0aBGrVq0iICCAJk2aSIJFDbJmzRqSkpJo2rQpAwYM0HU4EhKPhH4eBR8Fmezv28I/zHY7O3GRkJCQ+AtkMrGR4caNopfQb7/BRx/Bnj3wySeiP72FxdMThQQBSkuhsFBckpPL2b//a3bv3sS5c+dQKBRYWFhga2tLu3btaN++PbGxsYSGhmJsbFxloGhtbc3MmTMpKipi06ZNvP/++8yZM4e2bdtqr0g7OTnx7rvvUlxczKZNm/j8888xMTGhdevW0iQFMUvIzsCOOW5z6GXdi20F27hcfpm7yrv4GvvS36Y//az7a2kwkAABAABJREFUYYAB7czbSd3IJCQkHgrJVPrhuHXrFq+88grXr1/nmWee4fXXX9dbMQhAoVAwdOhQduzYgUql+ltxx9XVlSFDhnDx4kV2795NSEgI7u7uUulYDVFUVMT8+fNRKBS8+eabGBkZSWKbhF4hEwRB0HUQEhISEvUJQYDt20VvoeRkMYNo5kwYMEC0HZPLoaIClErRi8jQ8EGhqLRUvDUxeXgRSamE4mIoKoKCAjh7FnbsgN274fZtsLfviSBcwNragubNm9OhQwe6dOmCp6fnQw2Mr1y5wuuvv8727dtp27Yts2bNomXLllW6liQlJTFjxgy2b99Ojx49mDZtGi1atJA6m0hISEjUAllZWXzxxResWLGC119/nX/961+6DqnOUVRUxL/+9S9+/PFHQkND2bhxI+7u7no7ac/JyUGlUmFlZfXQ4oNSqeTnn39m9uzZWFtbM336dPr16yeJiE+IIAisWLGCkSNH4u/vz+nTp6VyeQm9Q3+lcQkJCYk6ikwG3btDs2YwfTps3QqvvCKKM7NmiR71Z87AhQvg6yv62Gv+TsP27aJw1KfP3wtClZWiAFRcDNeuif5F+/aJ/vfl5WKDRAsLaN4c+vSZRni4itatw7G0tHxk34TGjRsza9YsiouLiY+PZ+7cubz77ruEhoZqBZ/g4GCmT59OSUkJW7duxczMjLfeeosmTZpIopCEhIREDSN1Gft7KioqWL58OQsXLsTR0ZFvv/0WNzc3vZ6wr1u3jitXrtCxY0ciIiKwsrL6x7/RlI4lJyfzww8/sHLlSgIDAwkNDdVLD6W6gCAIlJWV8cUXXyCXy3njjTckMUhCL5EEIQkJCYlaQCYDFxdYvBh++QVmzxZFntOn4fffxZKyefNEb/oDB0SfoXt5/33Rd6hnz6p++IIAZWVi08OiIrh8WRSA9uyBo0fF55qZiVWuAQHQoQN07AgREWBo2OGJS9ZCQkKYNWsW//rXv9i4cSNmZma8/fbbBAUFabOMWrVqxfTp0ykrK2PNmjWYmZnx+uuvExAQUK3HgYSEhITE4yGVjP01arWao0eP8vbbb2NsbMzMmTOJiorSewGkW7durF27lqVLl2JoaEj79u0f6u8aNWrEoEGDSEpKYs+ePQQFBeHh4YGtrW0tR1x/2bVrF8ePH8fb25vRo0frOhwJicdCEoQkJCQkagmZTFwmTBANp998E5KSxOaHGnJzYe5cmDMHjIyqzwZSqcQSsuJisRTs9GnYu1fMOLpyRSwrMzMTGyOGh0NsrOiX7+7+9xZpj0tkZCQffvgh77zzDitXrsTGxoZXX30Vb29vreATGxvLG2+8wccff8yvv/6Kj709k154AYfaCkpCQkKiASJlCP01WVlZTJkyhZKSEsaMGcP48eP1WgwqLCxEpVLh7OzM1KlTqaysfOTPExUVxbBhw0hLS2PDhg0EBwczZMgQSUx8RARBoLy8nDlz5iCTyXjttdcwNzeXsoMk9BJJEJKQkJB4Cvj5wZo1YpmYl9ef60ePhh9+ELuStWtX/d+ePy+WgO3ZA7t2ieKQqakoAjVtKopNnTtDTAxYWz8d4+oOHTrwzjvvMHPmTBYsWIC5uTnPP/88Hh4eWlGob9++FBQUsOirr3DbsgVTJycYMkRMndLjQbmEhIREXUGlUkldxqohPz+fN998kwsXLtCmTRvmzp2LgYGBXk/Yd+zYQXp6Oq1atSI0NBQbG5tHNsaWy+X07t2bixcvMm/ePFatWkVwcDDNmzfXa7FMF+zbt48DBw7g7u7O+PHjdR2OhMRjIwlCEhISEk8JQ0Ox29i9vPkmbNggeg1t2fJgJzJBgGHDxEwgU1OwtBS7mXXsKIpAzZqJCTe6GOMOGDCAoqIiPv30U7766itMTU2ZNGkSzs7OyOVyZDIZo0aNoreBAaZz5mA6c6bopj1+PDg4SKKQhISExBMiZQg9SHl5OV999RXr1q3Dw8ODBQsWYGNjo9diEEBERARpaWnMmjWLDh06MG7cODw9PR/5dezt7enfvz/nz59n7969BAQE0LhxY+zt7Wsh6vqHIAhUVFQwd+5cZDIZL7/8MlZWVnq/f0k0XCRBSEJCQkKHODnBtGnw+uui19ALL1StqJLJYOhQuHpVFIE6dQI3N90IQNUxevRoioqK+OKLL/jiiy+wsrJi1KhR2NvbI5fLkcvl2A0ZAjdvwtdfw+efizVuo0eDrW3d+SASEhISeojkIVQVlUrFzp07+eSTT7CwsOCrr74iMDBQryfrpaWlKJVKXF1defvttxk1ahR5eXlYWlo+9muGh4czdOhQkpOT2bx5M6GhoYwcOVJq/vCQHD58mD179uDk5MSkSZN0HY6ExBMhCUISEhISOmbyZNFk+sMPoVcv8Pauuv3DD3UT18Mgk8mYPHkyeXl5fP/998yaNQsrc3OGDBmCpaZ+zcBAVLqKisT6uP/+VxSFhg0DKytJFJKQkJB4TDQZQgqFosFnCAmCwMWLF3nuuecQBIE333yTPn366H0p1OHDhzl//jwtWrQgMDCQRo0aPXGnNLlcTs+ePUlJSeHzzz9n1apVNGnShJYtW+r991WbCIKAUqlkzpw5ALz44ovY2dnpteAoISH94iUkJCR0jKGh2IWspAQ0VVX6hIGBAW+88QajR4/G3NiYPd98w7U9e0QXbEEQn2RqClOnwpgxoFTCZ5/Bpk3ih9Y8R0JCQkLikVCpVFKG0P/Izc1lypQp3L59m65du/L222/XC3HDy8uLvLw8ZsyYwX/+8x/OnTtHZWXlE7+ura0t/fr1o0ePHiQkJPDrr7+Sm5tbAxHXb06ePMm2bduws7Pj+eef13U4EhJPjP4fJSUkJCTqATExMGqUmCl05Ij+aSTGxsZMnz6d2S++yAdlZQT++99iG7Sysj8/jKUlvP02jBwJd+/Cp5/C9u1QXq7b4CUkJCT0FMlDSKS0tJT33nuP+Ph4/P39WbBgAcbGxnqduVFRUUFJSQkeHh68++67LFu2DC8vL9LS0igqKqqR92jevDnDhg3D09OTzZs3s27duhoRm+ojgiCgUqmYM2cOgiDw3HPP4ezsrNf7mIQESIKQhISERJ3hww+hUSN4/33Qx/GYhYUFI196Cb8OHZBdvy6mO+3bJ6Y8aUQhW1t46y0YOFB0yv7ssz+fIyEhISHxSNwrCDXUDCGlUsnq1av54YcfsLW15aeffsLFxUXvJ+oXLlxgxYoVnDx5kvz8fJydnXnrrbcYNmwYdnZ2NfIeMpmMrl27MnLkSG7evMmqVas4fvw4arW6Rl6/vpGYmMiGDRuwsrJiypQpug5HQqJGkAQhCQkJiTqCiwv8+99w6BBcvqzraB4TW1tR2XrmGUhPh48+gv37qypcrq4wYwb07w9nzohG0wcP6qcKJiEhIaFDGnrJmFqt5uTJk7zyyisYGRkxe/ZsWrdurfdiEICNjQ1XrlzhjTfe4P333+fYsWOUlJQg1HAKsbW1Nc888wx9+vTh0KFDLFmyhDt37tToe+g792YHqVQqJkyYgIeHh67DkpCoESRBSEJCQqIO8eyz0Lq1nmsjzs7wwQfQvTucPg2ffALHjoneQRq8vcXyse7dIT5eFIWOH6/6HAkJCQmJv0WtVlNWVtZgBaFbt24xbtw4iouLGT16NJMmTdJ73yCVSkVZWRkeHh588MEHLFu2DCcnJ6ZNm8auXbtQqVQ1/p6hoaEMHz4cPz8/tm3bxsqVK6XSsftITU1l7dq1mJub8+qrr+o6HAmJGkO/j5gSEhISeoq5OdjbP9hgy8AA5s4VS8esrXUTW43g4yOWjHXqJGYI/d//wdmzcO9ANjQUpk+HLl1EL6Evv3zwORISEhISf0lpaSk3btxAqVTWmK+MvlBSUsLUqVO5ePEiLVu25PPPP68XmUFZWVmsX7+es2fPUlhYiLu7OzNmzOD333+nW7duGBjUfJNomUxGp06dGDNmDLm5uaxfv57Dhw9LpWOI2UFqtZo5c+ZQWVnJ6NGj8fHx0XVYEhI1hiQISUhISOiA996DtDQwNhYbbZWX/2mz06oVpKTAhg2iQKS3hISI5WMxMeKH+eorSE6uKvhERsKbb0K7dvD77/DNN5CaCtIgVEJCQuIf0ZSMqdVqSktLKSkpaRCT+MrKSv7v//6PVatW4eTkxJIlS7C0tKwXgpBcLuf8+fO8+OKLvP/++5w6dYrCwkKsra0xNTWttfe1sLCgV69e9O/fn8OHD7N48WKys7Nr7f30iYyMDFasWIGJiQlTp07VdTgSEjWKJAhJSEhI6AAzM7Cxgdu34Z13YNmyqtutrMSmXHo/tg0PF7OAIiPFFmrffAOXLlUVfDp1gjfegJYt/3zOlSuSKCQhISHxDxgYGGBqakpSUhKjR49m5syZZGVlUVJSQmVlZY37zdQF1Go1+/btY+bMmZiamvLdd98RGBio92KQIAhUVFTg6urKRx99xK+//oqVlRUvv/wy//nPf7j8FMwFg4ODGTp0KEFBQezatYvffvsNZQMu5dZkB82dO5fy8nKGDRtGUFCQrsOSkKhRJEFIQkJCQoecOwfffgtLluhfq/mHplMnsbNYWBgsWgQLF0JWVlXBp08fePVVCAwUv4ylSyEvT2chS0hISOgDPj4+PPfcc5iamnLjxg2++OILoqKieO2119i1axe5ubmUlpaiVCrrhTgkCAKXL19m7NixqNVq3n77bfr166f3YhBAYWEhmzdvJjk5mZKSEry8vPjwww9ZvXo1nTt3xtDQ8KnE0aFDB8aPH09hYSHr1q0jISGhQWSd/RXXr19n6dKlGBkZ8a9//UvX4UhI1DiSICQhISGhQy5eFIWgen/BacAAMQsoIAC+/hoWL4Zbt/5UwWQyGDYMXn9ddNVu1AgaoEGqhISExKPg4eHBJ598ws6dOxk3bhxRUVGUlJSwdOlS+vXrR3h4ODNmzODQoUMUFRVpy8v0laKiIsaOHcutW7fo1q0b7777rq5DqjFKSko4cuQI48eP5z//+Q/nzp2juLgYNzc3evfuTePGjZ9KHObm5vw/e/cdH0XxuHH8ufRGQgKhQwJIF5AivUkHAUGkShVpomJBiiLNBtJEflIEBUQQQYoFFQGpigVsoCAIhCYtlFDS7+b3x5n7EpJAAoFA9vP2dS+T3dnZ2UvIzT03O9OiRQt16NBB27dv1/vvv6+TJ09mi0AxI5JGB02ZMkXR0dFq166dypUrl9XNAjKdzVjtXzcA3EEGDXKOEJo8WXrmGekuXxzl2hwOafZs5wTTJ086byV7/PHks2snJEixsc4wyNs7a9sLAHeZ+Ph47d27VytXrtSqVau0Z88eJSQkyN3dXffee6/at2+vNm3aKCwsTJ6envL09LxrRtckJCTomWee0YwZM1SsWDFt3bpV+fLlu2van5akJc3d3Nxks9kUERGhBQsW6IsvvlD58uU1YMAA3XffffK+za+J69ev10svvaTDhw/rqaee0pAhQ27bKKU7gTFGx48fV6lSpRQXF6cffvhBlStXzupmAZmOQAgAslCjRtK330qrV0stWmSDOYOuJzFRevtt5yM6Who9WurePZtMmAQAd46YmBj98ssvWrJkiT777DOdPn1aCQkJ8vDwULVq1dSpUye1bt1auXPnlqenp9zd3e/YcMXhcOjjjz/Wo48+Kn9/f61Zs0Y1a9a8Y9ubEXa7XevWrVOxYsVUqFAheXt7y2az6dChQ1qwYIESEhLUo0cPlSxZ8ra2KyYmRh988IFefPFFlShRQuPGjVPjxo3llq0/uXIyxsgYoxdffFETJkzQQw89pFWrVmV1s4BbgkAIALKIMVKhQtK//zpXHCtWzCKZSFycNHGic2iUm5v0yitS587S1aunGONckcxu/998Q25uzqXX3Nws8mQBwM0xxujSpUvaunWrlixZoi+//FIXL16U3W5XQECAGjdurE6dOqlx48by8fGRp6ena7TKncAYo99//1316tVTTEyM3nnnHfXt2/eOad/NOn/+vF5//XWtX79ejRs3Vo8ePVS0aFF5e3tn+c/h0KFDmjJlimbNmqWHHnpIU6ZMUcGCBbPNc58WY4xOnz6tUqVK6dKlS9q8ebNq1qyZ1c0CbgkCIQDIIlFRUkiI886oqChnzpHN+1j/c/Gi9Oqr0vz5zgmlX3gh+URKdrt09qy0ZYu0caMzNXN3l+65R2rWzLl6ma9vNr/HDgAyl8Ph0JkzZ7R69WotXbpUW7ZsUVxcnIwxCg0NVcuWLdWpUyfVqlVLnp6e8vDwyNIRIcYYnT17VnXq1NHff/+tnj176v333882gUTSrWKSdPDgQb3//vtavXq1atSoob59+6ps2bKuEUNZZdOmTXrxxRe1b98+DRw4UCNHjszWt44ljQ4aO3asxo0bp+bNm+urr77K6mYBtwyBEABkkR9/lGrWdC6+tWOHBbONM2ecT0KxYsnDIIdD+vln6bXXpA0bpIIFnWXi4qR9+6RLl5y3mT3/vFS4sIVSNADIPImJiTp48KBWrFihTz75RLt27XItMV60aFF17NhRXbp0UfHixeXu7i4PD4/bGkwYY5SQkKDOnTtr5cqVqlKlijZv3ixfX99sEQgZY/TDDz+ocOHCCg0Ndc3ndGUw1L9/f3Xq1EnBwcFZ1s7Y2FgtXrxYL7zwgooWLaoxY8aoZcuW2fbWMWOMzp07p5IlS+r8+fP69ttvVa9evaxuFnDLEAgBQBaZP1/q3du5uNbixRYMhNKyb580cqQzLHr2WalnTylnTue+AwekSZOk5cudE1IPH+6cfwgAcMPi4+P1xx9/aPny5frkk090+PBh1+iVqlWrqkuXLmrXrp1CQ0Pl7u5+y+cbSlrh6c0339SLL76okJAQff/99ypZsmS2CIMk53P+4osvat26dXrwwQfVo0cPhYeHu4KhI0eOyM/PTzlz5pSHh0eWtvXIkSOaNm2a3n77bbVq1UrTpk1ToUKFss3PIknS6KDx48frpZdeUt26dbV58+asbhZwS/H2AwCyyN9/O/+f7ZeczwhjpK++kn76yRkE9er1vzBIco4UeuIJqUED561k33+fNe0EgGzEy8tLVatW1euvv64//vhDq1evVs+ePRUYGKjt27frmWeeUenSpdWpUyctXrxY58+fV3x8vOx2e6YvR55U35YtWzRq1Ch5enrqvffey1ZhkOR8zidNmqTFixfr8uXLat++vZ577jnt3LlTcXFxKlSokHLlypXlYZAkFS5cWA8//LBq1aqlLVu2aNasWa7RZNnNhQsXNH36dLm7u2vkyJFZ3RzgliMQAoAssmeP8/9lynDXk0tMjLR7t3N+oAoVpKCglGXKlnXOIXTkiPTXX84QCQBw02w2m3x9fdW4cWPNnTtX+/fv17x589SiRQtJ0urVq9W7d2+VLFlS/fr10/r16xUXF6fExEQ5kib/vwlJYdCRI0fUpUsX2e12jRgxQg899FC2CoOuVLZsWb311ltauXKl/P391b17dw0ZMkR79+7NlOc0s1StWlV9+/aVm5ubvvjiC33xxReZHgZmpaRRaU8//bROnDihypUrq0mTJlndLOCWIxACgCxgjDP3kKRSpbK2LXeUkyedcwvlypV8ZNCV3Nycs3E7HNKpU7e1eQBgFTabTUFBQXr00Uf12Wefae/evXrrrbdUq1YtRUVF6cMPP1SLFi1UpkwZvfzyy9q9e7fi4+OVmJh4U0FBTEyMunbtqhMnTqhJkyYaNWpUJl7VnatEiRJ688039dlnnyk8PFxxcXF3VAjm5eWlhg0bqm/fvvrrr780Z84cHTx48I4NhZJu/0oKehwOh+x2u+x2uxITE5WYmKiEhAQlJCQoPj5e8fHxOnPmjBYvXiybzaZWrVrdUc8/cKswhxAAZIH4eCkgwLmYVlSU5O/PKCFJzpRsxAjnEzRqlFSjRurlPvpIGj3auULZ+PGSl9ftbScAWJTdbteff/6pZcuWacmSJYqIiHCNZClbtqy6deumbt26KU+ePHJzc0v30ulXjtCYMWOGihYtqh9//FGhoaG3+pJuGYfDocTERNlstmtOyp2QkCBjjKvMnRxE/PTTT3rppZe0fft29e7dW+PHj5fXLXwNvvKtampfX+v/MTExOnfunM6fP5/s/+fOndOJEyd0/PhxnTt3TidPntTOnTuVkJAgScqRI4fOnz+fbSfOBq6U9TelAoAF7d8vJSRIRYtKfn6EQS7e3pKnp3PJ+ZiYtMtFRzv3e3lJd8D8CgBgFe7u7qpQoYLKly+vl19+Wd9//70WL16s5cuXa/fu3RoxYoRefvllNWjQQF27dlXbtm0VEBDgCoZSCzuSRnJ89NFHmjFjhnx8fLRo0aK7OgySnEu29+/fX6VLl9bbb7+t8PDwVMt17NhR33zzjVasWKEmTZrc0YFQ5cqVNWDAAD311FNas2aNqlWrpo4dO143PElvsHP1NmOMoqOjUw12jh8/rhMnTqQIe86fP6/IyEjZ7XZXnUnP6ZXP7ZXbkgI5Ly8vDRw48I7+GQCZiV40AGSBpNvFypTJ2nbccZJuFduzxzl0ypiUaZkxzn2SlDs3y7MBQBaw2Wzy8vJSgwYNVL9+fU2dOlVr167V4sWL9fnnn2v9+vVat26dnn32WbVu3VqPPvqoHnjgAbm7uycbNZT05v+vv/5yvRF/++23VbNmzay8vEzhcDhctyNd66aMhIQExcXFJQsw7lQeHh6qV6+eBg4cqDFjxmjSpEkKDQ1V/fr1Jf0vXEmS9LXD4dDly5eThTpnzpzRiRMndOLEiRSBTtLXZ8+edT0vqYU5qX1vs9nk5uYmPz8/BQcHK2fOnMn+HxwcrPz58yt//vzKmTOngoKCdPHiRYWFhSl37tyqX7++Bg8erPz58xMMIdsjEAKALJA0oXTp0owOSiYoSCpRQlqzRtq7V4qLc44aSnqSjJEiIqQ//5Ty5ZNKlszS5gIAnG/A/f391bZtWz300EOKjIzU8uXLtWjRIm3btk2LFi3Shx9+qIIFC6pdu3bq2bOnKlas6HrjfujQIbVr106XLl1S9+7d1bdv36y+JFxDaGioatSoofDwcP3yyy/q1auXypcvL09PT+XNm1dnz551hTlJj0uXLiWrI73hjru7u0JDQxUcHKxcuXIpODhYISEhCgkJSfXrK7fdSJjjcDjUs2dPNWrUSL/99pu8vb0zXAdwNyEQAoAskBQIMaF0Kho3ljZvds4TVLy41KaN5O7u3HfpkvTxx9LXX0sdOki1a2dtWwEAydhsNoWGhmrAgAHq16+f9uzZo48//lgfffSR9u/fr//7v//T9OnTVaZMGZUuXVojR45Uq1atdPz4cZUsWVKzZs3K6ktAOtSrV0+jRo1Sv379dPToUR09ejTVWwKTvvf09JSfn1+yUTo5c+ZUrly5lD9/fuXNmzfFKJ6kEMjjNt4abrPZNGTIEM2bN08DBw7U3LlzmUsI2RqBEABkgb//dg5/LlMmqZPBMCGXypWlPn2k116TBg6UFi+W7rvPOVpo61bpl1+k5s2lxx5z3mIGALgjubm5qWzZshozZoxefvllbd26VR9++KFWrFihPXv2aPfu3Vq5cqUk50iQd999V35+flncaqSHt7e32rZtK29vb82dO1cXL17UPffco7p16ypXrlwpRu4EBgZmdZPTJWkC8KlTp6p79+4qX768nnnmGW4dQ7ZFIAQAt5lz5Yuaql69kEqXXiyJ4cgptGvnHD710UfSl186Rwx5eEhly0qTJztHDeXNm9WtBACkQ9Kb7KT5hqZPn66ZM2dqypQpOn78uDw8PDRixAjXPDTZUdLS56m5Wxd9DgoKUufOndW5c+esbkqmstlsatGihapVq6ZJkyapWrVqqlWrFqEQsiWWnQeA2+zo0aMqXLiwcuXKpVOnTjEUGQBgScYYRUREKE+ePPL398/q5mS69evXq0+fPjp79qwKFSqU5vLsBw4c0MWLF7V69Wo1b96cfsEdwBijf/75R40aNVKOHDm0ceNG5c6dm1AI2Q5/bQDgNtvz3wRCZcuWzeKWAACQdWw2m4oWLZotw6ArGWOUmJiY5oPP5+88NptNxYsXV/PmzbV792516tTprlgFDsgoAiEAuM2SAqFSpUrxSRMAANlc7dq1tWbNGu3atSvVR/369eWetHgC7hg2m00TJ05UqVKltGXLFj399NOEd8h2CIQA4DZLCoRKly6dxS0BAABAamw2mwIDA9WrVy+5ublpyZIlmjt3LqEQshUCIQC4zZICoTJlymRxSwAAAHAtQ4YMUYUKFXTu3Dm98sor+v333wmFkG0QCAHAbWSM0e7duyUxQggAAOBOZrPZ5Obmptdff13BwcE6cuSIevfurQsXLhAKIVsgEAKA2+jChQs6fvy4fH19FRYWltXNAQAAwDXYbDY1btxYVatWlST99ttv6tixo4wxhEK46xEIAcBttGfPHhljVLJkSbm5uTGpNAAAwF1gxowZKlSokCRp48aNGjp0aBa3CLh5BEIAcBsxoTQAANYQHh6uJ554Qp06dVJQUFCa5Tp06KBhw4apePHifFB0h0pahr5Fixay2WyKj4/XggULtHTpUkYJ4a5mM/wGA8BtM2LECI0fP16jR4/W6NGj6fgBAADcBYwxunTpku6//379/fffkpyh35o1a1SiRAn6dLgrMUIIAG6jpAmlWWEMAADg7mGz2RQQEKC+ffvK29tbkhQREaGuXbsqOjqakUK4KxEIAcBtxC1jAAAAd69nnnlG5cuXd33/yy+/qEuXLlnYIuDGEQgBwG2SkJCgAwcOyM3NTSVKlMjq5gAAACADkpahf+ONN5QzZ05JzlvJ1q1bp7FjxzJKCHcdAiEAuE327dunhIQEhYeHy9fXl3vNAQAA7jI2m02NGjVStWrVXNtiYmI0a9Ysffnll4RCuKsQCAHAbcLtYgAAANnD7NmzVbhwYdf3J0+e1LPPPqsjR44QCuGuQSAEALcJE0oDAADc/Ww2m8LCwtS8efNkI7737dunTp06KS4ujlAIdwUCIQC4TU6fPi2bzcYIIQAAgGxg8uTJKlWqVLJtP/30kx577LEsahGQMTZDdAkAt4UxRpcvX5bNZpOfnx9zCAEAANzFjDGaOnWqXnrpJcXGxrq258iRQyNHjtQLL7xAfw93NAIhALiNkv7k0jkAAAC4+9ntdtWoUUPbt29Ptr1AgQJavHix6tWrR78PdyxuGQOA28hms9EpAAAAyCbc3Nw0fvx4hYSEJNv+77//auDAgTp58iTzCeGORSAEAAAAAMANsNlsatiwYbJl6JPs3r1bnTp1UkJCAqEQ7kgEQgAAAAAA3CCbzaaZM2cmW4Y+yXfffaeBAwcSCOGORCAEAAAAAMBNCAsLU4sWLVJMDWC327V8+XL93//9nxwORxa1Dkgdk0oDAAAAAHCTLl68qBo1auivv/5Ksa9gwYL6+OOPVatWLeaTxB2DEUIAAAAAANykgIAA9enTRz4+Pin2HTt2TH379tXp06e5fQx3DEYIAQAAAACQCRITE1WrVi39/PPPqe5v3LixVq9eLU9PT0YKIcsxQggAAAAAgEzg7u6uN954Q7lz5051/6ZNm/Tcc88xnxDuCARCAAAAAABkApvNpgceeED3339/qvsTEhK0ZMkSzZs3T3a7/Ta3DkiOW8YAAAAAAMhEBw4cUKNGjRQREZHq/vDwcC1fvlz33Xef3NwYp4GswW8eAAAAAACZqGjRomrWrFma8wRFRESof//+On/+PJNMI8swQggAAAAAgEx24cIF1axZM9Vl6CXn7WUtWrTQypUrmWQaWYIRQgAAAAAAZLIcOXKkuQy9JBlj9O2332rUqFFKTEy8za0DGCEEAAAAAMAtER8fr7p16+qnn35Ks0zevHk1bdo0PfLII3J3d7+NrYPVMUIIAAAAAIBbwNPTU6+++qpy5cqVZpmTJ09q1KhR+vvvv1mOHrcVgRAAAAAAALdA0jL01atXv2a5S5cu6aeffiIQwm3FLWMAAAAAANxCe/fuVbNmzVIsQ+/m5qZy5crpnXfeUbVq1eTl5cXk0rhtGCEEAAAAAMAtdM8996hZs2Zyc/vfW/B8+fLp8ccf14YNG1SzZk3CINx2jBACAAAAAOAWO3v2rOrWrau///5bZcuW1eTJk1WrVi35+flJEmEQbjsCIQAAAAAAbjFjjGbNmqXff/9d48aNU3BwsDw8PAiCkGUIhAAAAAAAuA1iY2Nlt9sZFYQ7AoEQAAAAAACAxTCpNAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRCA2yYiIkI2m03z58/P8LFjxoyRzWZTZGRkprWnV69eCg8Pz7T6AAAAkNzN9LdsNpuefPLJTGvLzfRFgeyIQAi4y7z22mtq06aN8ubNK5vNpjFjxqRZdsmSJapcubJ8fHwUGhqqPn36pAhUjhw5orFjx6patWoKDg5W7ty51aBBA61bt+4WX8nd7+OPP1a3bt1UokQJ2Ww2NWjQIKubBAAA7hLp7dOtXLlSzZo1U4ECBeTt7a1ChQrpkUce0a5du65Z//79++Xj4yObzabt27en2L9jxw61atVK+fLlU0BAgCpUqKC3335bdrs9My4vWzp+/LiGDx+uBx54QDly5JDNZtPGjRuzulnADSMQAu4yI0eO1M8//6xKlSpds9zMmTPVpUsXhYSEaMqUKerbt6+WLFmiRo0aKTY21lXu008/1YQJE3TPPffo1Vdf1csvv6yLFy+qSZMmmjdv3q2+nLvazJkz9emnn6pw4cIKDg7O6uYAAIC7SHr7dDt37lRwcLAGDx6sGTNmaODAgfr1119VrVo1/f7772ke9+yzz8rDwyPVfTt27FCtWrUUERGhYcOGafLkySpWrJgGDx6s55577qauKzv7+++/NWHCBB07dkzly5fP6uYANy31vxAA7lgHDx5UeHi4IiMjFRoammqZ+Ph4vfjii6pXr57Wrl0rm80mSapVq5Zat26tOXPm6KmnnpIkPfDAAzp8+LBy587tOn7AgAG67777NGrUKPXu3fvWX9RdauHChSpYsKDc3Nx07733ZnVzAADAXSQ9fTpJGjVqVIptjz/+uAoVKqSZM2dq1qxZKfavWbNGa9as0dChQ/Xqq6+m2D979mxJ0ubNmxUSEiJJ6t+/v+rXr6/58+dr2rRpN3pZ2VqVKlV05swZhYSE6JNPPlGHDh2yuknATWGEELKFpPll/vnnH/Xq1Us5c+ZUUFCQevfurejo6GRl582bp4YNGypPnjzy9vZW2bJlNXPmzBR1hoeHq1WrVtq4caOqVq0qX19flS9f3jUsdMWKFSpfvrx8fHxUpUoV/frrrynq2LNnjx555BGFhITIx8dHVatW1WeffZai3P79+7V///50XWt67sHetWuXzp8/r06dOrnCIElq1aqVAgICtGTJEte2cuXKJQuDJMnb21stW7bU0aNHdfHixXS160b98ccf6tWrl4oVKyYfHx/ly5dPjz32mM6cOZNq+cjISHXs2FGBgYHKlSuXBg8enGzEU5IPP/xQVapUka+vr0JCQtS5c2cdOXLkuu05fvy49uzZo4SEhOuWLVy4sNzc+DMKAEBmoU+XPnny5JGfn5/Onz+fYl9CQoIGDx6swYMHq3jx4qkef+HCBfn4+ChnzpzJtufPn1++vr433K70mjRpkmrVqqVcuXLJ19dXVapU0SeffJJm+UWLFqlUqVKun9HmzZtTlDl27Jgee+wx5c2bV97e3ipXrpzef//967YlISFBe/bs0fHjx69bNkeOHK4ADcgOeCeDbKVjx466ePGi3njjDXXs2FHz58/X2LFjk5WZOXOmwsLC9OKLL2ry5MkqXLiwnnjiCb3zzjsp6vvnn3/UtWtXtW7dWm+88YbOnTun1q1ba9GiRXr22WfVrVs3jR07Vvv371fHjh3lcDhcx/7555+qUaOGdu/ereHDh2vy5Mny9/dX27ZttXLlymTnadSokRo1apRpz0NcXJwkpfqC7uvrq19//TVZW1Nz4sQJ+fn5yc/PL9PalZq1a9fqwIED6t27t6ZPn67OnTtryZIlatmypYwxKcp37NhRsbGxeuONN9SyZUu9/fbb6tevX7Iyr732mnr06KESJUpoypQpeuaZZ7R+/XrVq1cv1Y7TlUaMGKEyZcro2LFjmXmZAAAgA+jTpXT+/HmdPn1aO3fu1OOPP64LFy6keq633npL586d08iRI9Osq0GDBrpw4YL69++v3bt369ChQ5o1a5ZWrFihESNG3JL2X2natGmqVKmSxo0bp9dff10eHh7q0KGDVq9enaLspk2b9Mwzz6hbt24aN26czpw5o+bNmyebQ+nkyZOqUaOG1q1bpyeffFLTpk3TPffcoz59+uitt966ZluOHTumMmXK3JbrBu44BsgGRo8ebSSZxx57LNn2du3amVy5ciXbFh0dneL4Zs2amWLFiiXbFhYWZiSZ77//3rVtzZo1RpLx9fU1hw4dcm2fPXu2kWQ2bNjg2taoUSNTvnx5Exsb69rmcDhMrVq1TIkSJVKcKywsLN3Xa4wxp0+fNpLM6NGjU91ns9lMnz59km3fs2ePkWQkmcjIyDTr3rdvn/Hx8THdu3fPUJuu5+DBg0aSmTdvnmtbaj+Pjz76yEgymzdvdm1L+hm3adMmWdknnnjCSDK///67McaYiIgI4+7ubl577bVk5Xbu3Gk8PDySbe/Zs2eK571nz55Gkjl48GCGrq1cuXKmfv36GToGAAAkR58ubaVKlXL14wICAszIkSON3W5PVub48eMmR44cZvbs2cYYY+bNm2ckmZ9//jlZucTERPPkk08aT09PV53u7u5m5syZGWp7eqTW37r6ZxcfH2/uvfde07Bhw2Tbk9q2fft217ZDhw4ZHx8f065dO9e2Pn36mPz586fo33bu3NkEBQW5zpdaXzRpW8+ePTN0XcuWLUvxuwLcbRghhGxlwIAByb6vW7euzpw5owsXLri2XTlqJioqSpGRkapfv74OHDigqKioZMeXLVtWNWvWdH1fvXp1SVLDhg1VpEiRFNsPHDggSTp79qy+/fZb16dbkZGRioyM1JkzZ9SsWTPt27cv2QiUiIgIRURE3OTV/0/u3LnVsWNHLViwQJMnT9aBAwe0ZcsWderUSZ6enpKkmJiYVI+Njo5Whw4d5Ovrq/Hjx2dam9Jy5c8jNjZWkZGRqlGjhiTpl19+SVF+0KBByb5Pmgvpyy+/lOQc9u1wONSxY0fX8x4ZGal8+fKpRIkS2rBhwzXbM3/+fBljWI4eAIAsRJ8upXnz5unrr7/WjBkzVKZMGcXExKRYEWzYsGEqVqyYHn/88WvW5e7uruLFi6tZs2ZasGCBPv74Y7Vu3VpPPfWUVq1adUvaf6Urf3bnzp1TVFSU6tatm2rfr2bNmqpSpYrr+yJFiuihhx7SmjVrZLfbZYzR8uXL1bp1axljkvX/mjVrpqioqFTrTRIeHi5jDEvRw5KYVBrZypUv6JJcKz+dO3dOgYGBkqTvvvtOo0eP1rZt21Lcix4VFaWgoKA060vaV7hw4VS3nzt3TpJzWLIxRi+//LJefvnlVNt66tQpFSxYMEPXlxGzZ89WTEyMhgwZoiFDhkiSunXrpuLFi2vFihUKCAhIcYzdblfnzp31119/6auvvlKBAgWueQ673a7Tp08n2xYSEiIvL690t/Ps2bMaO3aslixZolOnTiXbd3VnTpJKlCiR7PvixYvLzc3N1fnat2+fjDEpyiVJCsQAAMCdiz5dSlcGWp07d1aZMmUkOefjkaQffvhBCxcu1Pr16687x+H48eM1bdo07du3z9Un7Nixox544AENGjRIrVq1SnOFsqioqGQfLHp5eWV4Xp0vvvhCr776qn777TfXVAeSks19mSS1Pl3JkiUVHR2t06dPy83NTefPn9e7776rd999N9XzXd3HBOBEIIRsxd3dPdXt5r+5aPbv369GjRqpdOnSmjJligoXLiwvLy99+eWXmjp1aop5ddKq73rnSapnyJAhatasWapl77nnnutf0E0ICgrSp59+qsOHDysiIkJhYWEKCwtTrVq1FBoammISQUnq27evvvjiCy1atEgNGza87jmOHDmiokWLJtu2YcMGNWjQIN3t7Nixo77//nu98MILuu+++xQQECCHw6HmzZtfd54jKWXHweFwyGaz6auvvkr155RaEAYAAO4s9OmuLTg4WA0bNtSiRYtcgdDQoUNVt25dFS1a1PVBWWRkpCTnohmHDx92BWMzZsxQw4YNU/SL2rRpo+eee04RERFpXtfgwYO1YMEC1/f169d3TdCdHlu2bFGbNm1Ur149zZgxQ/nz55enp6fmzZunxYsXp7ueJEk/o27duqlnz56plqlQoUKG6wWsgEAIlvL5558rLi5On332WbJPiq53G1FGFStWTJJzNErjxo0zte6MKlKkiOtaz58/rx07dqh9+/Ypyr3wwguaN2+e3nrrLXXp0iVddefLl09r165Ntq1ixYrpbtu5c+e0fv16jR07NtmSqvv27UvzmH379iULof755x85HA7XLV7FixeXMUZFixZVyZIl090WAABw97Bin+5qMTExyUZTHz58WIcOHUrxYZ3kDHqCgoJci2ucPHkyxe1mklyrrCYmJqZ53qFDh6pbt26u75NGb6XX8uXL5ePjozVr1sjb29u1fd68eamWT61fuHfvXvn5+Sk0NFSSc/Uvu91+x/2MgDsdcwjBUpI+BTJXrF4VFRWV5gvQjcqTJ48aNGig2bNnp7qE5dW3WWVkidKbMWLECCUmJurZZ59Ntn3ixImaNGmSXnzxRQ0ePDjd9fn4+Khx48bJHhnpFKT285B0zdUgrl45ZPr06ZKkFi1aSJIefvhhubu7a+zYsSnqNcakuZx9kowsOw8AALKGlfp0qd3uFBERofXr16tq1aqube+++65WrlyZ7JE01+KkSZO0aNEiV9mSJUtq7dq1yfpFdrtdS5cuVY4cOdJcrl5yzsd0Zd/vyvl90sPd3V02my1ZIBUREZHm3EXbtm1LNgfQkSNH9Omnn6pp06Zyd3eXu7u72rdvr+XLlydbeSzJ1T+jq2Vk2Xkgu2GEECyladOm8vLyUuvWrdW/f39dunRJc+bMUZ48eTL9ReCdd95RnTp1VL58efXt21fFihXTyZMntW3bNh09elS///67q2zSkqHpmYRw4cKFOnTokOte+c2bN+vVV1+VJHXv3l1hYWGSnPeG79q1S9WrV5eHh4dWrVqlb775Rq+++qruv/9+V30rV67U0KFDVaJECZUpU0YffvhhsvM1adJEefPmvannIi2BgYGqV6+e3nzzTSUkJKhgwYL65ptvdPDgwTSPOXjwoNq0aaPmzZtr27Zt+vDDD9W1a1fXyKTixYvr1Vdf1YgRIxQREaG2bdsqR44cOnjwoFauXKl+/fq55lRKzYgRI7RgwQIdPHjwuhNLb968WZs3b5bk7GxcvnzZ9bOoV6+e6tWrl8FnBAAApIeV+nTly5dXo0aNdN999yk4OFj79u3Te++9p4SEhGQLgDRt2jTFOZJGBNWvXz9ZeDR8+HB169ZN1atXV79+/eTr66uPPvpIO3bs0KuvvnpL51x88MEHNWXKFDVv3lxdu3bVqVOn9M477+iee+7RH3/8kaL8vffeq2bNmunpp5+Wt7e3ZsyYIUkaO3asq8z48eO1YcMGVa9eXX379lXZsmV19uxZ/fLLL1q3bp3Onj2bZnuSlp3v2bNnuiaWTvoZ/fnnn5KcP8etW7dKkkaOHJnu5wG4ExAIwVJKlSqlTz75RCNHjtSQIUOUL18+DRw4UKGhoXrssccy9Vxly5bV9u3bNXbsWM2fP19nzpxRnjx5VKlSpWS3R2XUe++9p02bNrm+37Bhg2t4dJ06dZJ1HlauXKnPPvtMdrtdFSpU0NKlS9WhQ4dk9SV1Yvbt26fu3bunON+GDRtuWSAkSYsXL9ZTTz2ld955R8YYNW3a9JoTWn/88ccaNWqUhg8fLg8PDz355JOaOHFisjLDhw9XyZIlNXXqVFdnoXDhwmratKnatGmTaW3/9ttvk3VGJLkmnBw9ejSBEAAAt4iV+nQDBw7U6tWr9fXXX+vixYvKkyePmjZtqhdffFHly5e/oXM/+uijyp07t9544w1NnDhRFy5cUKlSpTRr1iz179//hq8pPRo2bKj33ntP48eP1zPPPKOiRYtqwoQJioiISDUQql+/vmrWrKmxY8fq8OHDKlu2rObPn59sXqC8efPqp59+0rhx47RixQrNmDFDuXLlUrly5TRhwoRMbf/Vk4u///77rq8JhHC3sZmr76kAAAAAAABAtsYcQgAAAAAAABZDIAQAAAAAAGAxBEIAAAAAAAAWQyAEAAAAAABgMQRCAAAAAAAAFkMgdAebP3++bDabIiIiXNsaNGigBg0aZFmbgKz2008/ycvLS4cOHbpt55w1a5aKFCmiuLi4TKvzzTffVOnSpeVwODKtzus5cuSIfHx89N1332XouL/++kseHh7atWtXmmVatmypvn37ZrhNNWrU0NChQzN8HADg1qEPmnE3+jp4s5YuXaqQkBBdunTppuq52X7JmDFjZLPZXN8nJCSocOHCmjFjxh11zvQKDw9Xr169XN9//fXXCggI0OnTp2+6bon+D+4cBEK4IREREbLZbKk+atSokaG6jh8/ruHDh+uBBx5Qjhw5ZLPZtHHjxlvT8Czy2muvqU2bNsqbN69sNpvGjBlzzfIbN27Uww8/rHz58snLy0t58uRR69attWLFCleZI0eOaOzYsapWrZqCg4OVO3duNWjQQOvWrcu0dvfq1Us2m00VKlSQMSbFfpvNpieffDLF9gsXLmjs2LGqWLGiAgIC5Ovrq3vvvVfDhg3Tv//+e0PXm+Sll15Sly5dFBYWdtPXFx8fr2nTpqlSpUoKDAxUzpw5Va5cOfXr10979uxxlevVq5fi4+M1e/bsFHWEh4fLZrPpqaeeSvW6bDabPvnkk2TbL1y4oAkTJmjYsGFyc0v+Zzg2NlZTp05V9erVFRQUJB8fH5UsWVJPPvmk9u7dm+Icv/32m7p166bChQvL29tbISEhaty4sebNmye73Z6s7Lhx41S9enXVrl072bWl9W/566+/liSVLVtWDz74oEaNGpXq8/jdd9/pm2++0bBhw1Jce2qPzp07u8oNGzZM77zzjk6cOJHqc3e94wEA1pTevsbt6Mtc73Vwx44dKerv1auXAgICUr22lStXqkWLFsqdO7e8vLxUoEABdezYUd9++22ycna7XaNHj9ZTTz2Voi673a558+apQYMGCgkJkbe3t8LDw9W7d29t3749xbVmVr8kiaenp5577jm99tprio2NTbE/K84pSfv371f//v1VrFgx+fj4KDAwULVr19a0adMUExOTZt3NmzfXPffcozfeeCPV/cYYLVy4UPXq1VPOnDnl5+en8uXLa9y4cbp8+XKK8mn1f27Wn3/+qW7duqlgwYLy9vZWgQIF9Oijj+rPP//M1PMg+/DI6gYgbd27d1fnzp3l7e2d1U1JU5cuXdSyZctk20JDQzNUx99//60JEyaoRIkSKl++vLZt25aZTbwjjBw5Uvny5VOlSpW0Zs2aa5YdPXq0xo0bpxIlSqh///4KCwvTmTNn9OWXX6p9+/ZatGiRunbtqk8//VQTJkxQ27Zt1bNnTyUmJuqDDz5QkyZN9P7776t3796Z1v6dO3dqxYoVat++/XXLHjhwQI0bN9bhw4fVoUMH9evXT15eXvrjjz/03nvvaeXKlclezNN7vZIz/Fi3bp2+//77TLmu9u3b66uvvlKXLl3Ut29fJSQkaM+ePfriiy9Uq1YtlS5dWpLk4+Ojnj17asqUKXrqqaeSfRqVZM6cORoxYoQKFChw3fO+//77SkxMVJcuXZJtj4yMVPPmzbVjxw61atVKXbt2VUBAgP7++28tWbJE7777ruLj413l586dqwEDBihv3rzq3r27SpQooYsXL2r9+vXq06ePjh8/rhdffFGSdPr0aS1YsEALFixI0R5vb2/NnTs3xfaKFSu6vh4wYIBatmyp/fv3q3jx4snKTZw4UY0aNdI999yToo6nn35a999/f7Jt4eHhrq8feughBQYGasaMGRo3blyGjwcAZL47vQ+a0b6GdGv7Mtd6HZScI1k+//zz657XGKPHHntM8+fPV6VKlfTcc88pX758On78uFauXKlGjRrpu+++U61atSRJn3/+uf7++2/169cvWT0xMTF6+OGH9fXXX6tevXp68cUXFRISooiICC1dulQLFizQ4cOHVahQIUmZ1y+5Wu/evTV8+HAtXrxYjz32WLJ9WXHO1atXq0OHDvL29laPHj107733Kj4+Xlu3btULL7ygP//8U++++26adffv319DhgzR2LFjlSNHDtd2u92url27aunSpapbt67GjBkjPz8/bdmyRWPHjtWyZcu0bt065c2b13XM9fo/N2LFihXq0qWLQkJC1KdPHxUtWlQRERF677339Mknn2jJkiVq165dppwL2YjBXaV+/fqmfv36Wd0Mc/DgQSPJTJw48abrunDhgjlz5owxxphly5YZSWbDhg03Xe+d5ODBg8YYY06fPm0kmdGjR6daLun6H3nkERMfH59i/9dff20+//xzY4wxu3btMqdPn062PzY21pQuXdoUKlQoU9rds2dP4+vra0qWLGkqVKhgHA5Hsv2SzKBBg1zfJyQkmIoVKxo/Pz+zZcuWFPVFRUWZF1980fV9Rq7XGGOefvppU6RIkRTtuBE//fSTkWRee+21FPsSExNNZGRksm3bt283ksz69euTbQ8LCzPlypUzHh4e5qmnnkq2b8OGDUaSWbZsWbLtFSpUMN26dUtx3gcffNC4ubmZTz75JMW+2NhY8/zzz7u+37Ztm3F3dzd16tQxFy5cSFH+559/NvPmzXN9P2XKFOPr62suXryYrFzPnj2Nv79/iuOvFh8fb4KDg83LL7+cbPvJkyeNh4eHmTt3brLtaV17ap588kkTFhaW7OeakeMBALfendIHzWhf41b3Za73OnjfffcZSWbHjh3J9qf2+jtx4kQjyTzzzDOp9nU++OAD8+OPP7q+b9OmjalTp06KcoMGDTKSzNSpU1PsS0xMNBMnTjRHjhxxbcuMfsno0aNNam8tW7VqZerWrZti++0+54EDB0xAQIApXbq0+ffff1Mcs2/fPvPWW2+5vg8LCzM9e/ZMVubkyZPG3d3dvPfee8m2v/7660aSGTJkSIp6P/vsM+Pm5maaN2+eYl9q/Z8b9c8//xg/Pz9TunRpc+rUqWT7Tp8+bUqXLm38/f3N/v37b/pcyF4IhG7QhQsXzODBg01YWJjx8vIyoaGhpnHjxsn+2NevX9+UK1fO/P7776ZevXrG19fXFC9e3PUGZ+PGjaZatWrGx8fHlCxZ0qxduzbZOebNm2ckucKEpDqvfjGOjY01o0aNMsWLFzdeXl6mUKFC5oUXXjCxsbHJyn3zzTemdu3aJigoyPj7+5uSJUuaESNGJCtz6NAhs3v37utef0YCoR9++MG0aNHC5MyZ0/j5+Zny5csn+4N7pfQEQgsXLjT333+/8fX1NTlz5jR169Y1a9asSVbmyy+/NPXq1TMBAQEmR44cpmrVqmbRokUZbtfu3btNhw4dTO7cuV0/pys7AcYYc/ToUfPYY4+Z/PnzGy8vLxMeHm4GDBhg4uLiUrT9eoFQ6dKlTUhISKpv8NPrueeeM5Juqo4kSZ2VDz74wEgyy5cvT7b/6k7UkiVL0gxZUpPR6y1SpIjp1atX+i/gGj766CMjyWzcuDHdx4SEhJinn3462bawsDDz4IMPmscee8z4+PiYY8eOufalFmocOHDASDLz589PVs8PP/xgJJm+ffumqy3Nmzc3Hh4e5tChQ+kqX69ePdOgQYMU29MbCBljTLt27UyFChWSbXv//feNJBMREZFse0YCnU8//dRIMr/88ssNHQ8AVmL1PmhG+xq3ui9zvdfB9957zwQHB5vWrVun2q4k0dHRJiQkxJQuXdokJiZe97wxMTHGy8vLjBkzJtn2I0eOGA8PD9OkSZN0tT+z+iVphTPTpk0zNpvN9eFvVp1zwIABRpL57rvv0lV3aoGQMcZUqlTJtGnTxvV9dHS0CQ4ONiVLljQJCQmp1tW7d28jyWzbti3Z9tT6Pzeqf//+RpLZvHlzqvs3bdpkJJn+/fvf9LmQvTCH0A0aMGCAZs6cqfbt22vGjBkaMmSIfH19tXv37mTlzp07p1atWql69ep688035e3trc6dO+vjjz9W586d1bJlS40fP16XL1/WI488oosXL2aoHQ6HQ23atNGkSZPUunVrTZ8+XW3bttXUqVPVqVMnV7k///xTrVq1UlxcnMaNG6fJkyerTZs2KSaX7dGjh8qUKZPu80dHRysyMjLZIyEhwbV/7dq1qlevnv766y8NHjxYkydP1gMPPKAvvvgiQ9eZZOzYserevbs8PT01btw4jR07VoULF052T/X8+fP14IMP6uzZsxoxYoTGjx+v++67zzUfSnrb9ccff6h69er69ttv1bdvX02bNk1t27ZNNuT333//VbVq1bRkyRJ16tRJb7/9trp3765NmzYpOjo6Q9e2b98+7dmzR23btk02DDWjTpw4IT8/P/n5+bm2xcXFpfg5pfVITdeuXVWiRAmNGzcu1fvvk3z22WeSnEPNryej13vs2DEdPnxYlStXTrHv0qVL6bq2qKgo1zFJcxAtWrRIiYmJ1z2/JFWuXDnNCZlfeuklJSYmavz48desI+l2t6uvIyPPXXR0tNavX6969eqpSJEi1y2fkJCgn3/+OdXnLsm1nqskVapU0a5du3ThwoVk15MrV64053S6ePFiirqvnjyySpUqkpTqc5ue4wHASqzeB83I6+WVbkVfRrr+62BgYKCeffZZff755/rll1/SrGfr1q06e/asunbtKnd39+ued8eOHYqPj0/x2v7VV18pMTExQ+2Xbq5fci1VqlSRMSbZ7f5Zcc7PP/9cxYoVc91udzN1X1nv1q1bde7cOXXt2lUeHqnPxtKjRw9JSvH+J7X+j8PhSHef/cr3XJ9//rnCw8NVt27dVNtQr149hYeHa/Xq1Td24ci+sjaPunsFBQUl+zQhNfXr1zeSzOLFi13b9uzZYyQZNzc388MPP7i2r1mzxkhKdotHej6dWbhwoXFzc0sxpHXWrFnJUvCpU6caSSluMUqrzdeTNEIotUfS6J7ExERTtGhRExYWZs6dO5fs+LSGRl5rhNC+ffuMm5ubadeunbHb7anWd/78eZMjRw5TvXp1ExMTk2qZ9LarXr16JkeOHClGYFxZpkePHsbNzc38/PPPKdqb2jVea4RQ0qcEqQ3vTa99+/YZHx8f071792Tbk36X0vO40pWfXi1YsMBIMitWrHDt11WfqlWqVMkEBQWlq60Zvd5169YZScluIbuynem5tiv/7TgcDtfve968eU2XLl3MO++8c80RN/369TO+vr7JtiWNEDLG+QmQj4+PayhyaqNcRo4caSSluHWrXbt2RlKK38nU/P7770aSGTx48HXLGuMcRizJTJ8+PcW+tJ671G4LWLx4sZGUbLh6nTp1TJUqVVKUTbr21B5X/k1L4uXlZQYOHHjDxwOAVVi9D5qRvoYxt7YvY8z1XweXLVtmzp8/b4KDg5ONLLl6hNC0adOMJLNy5cp0nXfu3LlGktm5c2ey7c8++6yRZH799dd01ZMZ/RJj0h6t8++//xpJZsKECVl2zqioKCPJPPTQQ+mq15i0Rwgl3R528uRJY4wxb7311nV/bmfPnjWSzMMPP5xi39X9n2u9x0rrPdf58+fTdX1t2rQxUubcRYDsg0mlb1DOnDn1448/6t9//73mJLIBAQHJVsQpVaqUcubMqYIFC6p69equ7UlfHzhwIEPtWLZsmcqUKaPSpUsnG93RsGFDSdKGDRtUq1Yt5cyZU5L06aefqnfv3ilm80+S0dW9+vXrpw4dOiTbljQR7a+//qqDBw9q6tSprvMnSW1S3utZtWqVHA6HRo0alaL9SfWtXbtWFy9e1PDhw+Xj45NqmfS06/Tp09q8ebMGDx6cYgRGUhmHw6FVq1apdevWqlq1aor2ZvQak0Zd3OjooOjoaHXo0EG+vr4pRqk0a9ZMa9euvaF6kzz66KN69dVXNW7cOLVt2zbV67tw4UK625/R6z1z5owkKTg4OMW+oUOHqlu3btet48pjbTab1qxZo0mTJunDDz/URx99pI8++kiDBg1Sx44dNXv27BS/H8HBwYqJiVF0dHSyEVhJRo4cqYULF2r8+PGaNm1amtfh4eGRYjWQjDwfmfncSc5Js6+e7DK1sknbrvxbc+bMGRUsWDDNc48aNSrFp1X58uVLte7URqil93gAsAqr90Ez0te4Wmb3ZaTrvw5KUlBQkJ555hmNHj1av/76qypVqpTqeaWbf22/kXputl9yLWn1HW7nOTOr3qvrzpMnj2tk3bXqTtp35QjrK+u78rnJly9fuvvsSe+50tOGq9uRGc8FsgcCoRv05ptvqmfPnipcuLCqVKmili1bqkePHipWrFiycoUKFUrxYhMUFKTChQun2CY5h/dmxL59+7R79+40V/Y6deqUJKlTp06aO3euHn/8cQ0fPlyNGjXSww8/rEceeSTNF+b0KFGihBo3bpzqvv3790uS7r333huu/+r63NzcVLZs2WuWud4501MmqVN0rTKnT5/WhQsXMu36AgMDJSnDQ7Yl5+oGnTt31l9//aWvvvoqRQcxf/78yp8//021z93dXSNHjlTPnj21atWqVFcpCAwMTHeH8kav16QyzLts2bLX/L1Ii7e3t1566SW99NJLOn78uDZt2qRp06Zp6dKl8vT01IcffpjqudMK+4oVK6bu3bvr3Xff1fDhwzPUliufj6uDqGuVzYjUnjvJ+bNN699xasdfff1p1StJ5cuXT3fdqT2v6T0eAKzC6n3QjPQ1rpbZfZkk13odTDJ48GBNnTpVY8aM0aeffprqeaWbf22/mf5kWvVcr19yLdfrO92Oc2bWc5Ja3UnByrXqvlZgc3X/x8fHJ8P9nvS04XrtgHUxh9AN6tixow4cOKDp06erQIECmjhxosqVK6evvvoqWbm07gFOa3t6XlCu5HA4VL58ea1duzbVxxNPPCFJ8vX11ebNm7Vu3Tp1795df/zxhzp16qQmTZrIbrdn6Jy4NZKWON+5c2eGj+3bt6+++OILzZ8/3/XJ3JViYmJ04sSJdD2u5dFHH9U999yT5v33pUuXVlRUlI4cOXLdNmf0enPlyiUp9Q5rVFRUuq7t7NmzadafP39+de7cWZs3b1aJEiW0dOnSFHMLnTt3Tn5+fvL19U2znqS5hCZMmJDmdSQmJqZ40c7I83HPPffIw8MjU567jEg6Pnfu3Mnqvtl6Jen8+fPJ6gUApM7qfdCM9DVSk5l9GSn9r4NJo4Q+++wz/frrr6meV7r5ftGN1HOz/ZJrSavvcDvPGRgYqAIFCmjXrl03VW9qdSfNe/XHH3+keUzSvtQ+vLy6/2O329PdZ4+Pj5fk/N3Knz//NduQ1I6CBQu6AjJAIhC6Kfnz59cTTzyhVatW6eDBg8qVK5dee+2129qG4sWL6+zZs2rUqJEaN26c4lGqVClXWTc3NzVq1EhTpkzRX3/9pddee03ffvutNmzYcMvaJilT/vgm1edwOPTXX3/d1DnTUybpU7ZrlQkNDVVgYGCmXV/JkiVVqlQpffrpp7p06VK6j3vhhRc0b948TZ06VV26dEm1zMcff+waJXS9x7UkfbL222+/pfrpVuvWrSUpxcia1GT0epM6CQcPHkyxb/Dgwem6tocffvi65/H09FSFChWUkJCQ4hamgwcPXnfCy+LFi6tbt26aPXu2jh8/nu7ryMhz5+fnp4YNG2rz5s3p6rAWKVJEvr6+qT53GXHw4EG5ubmpZMmSrm2lS5e+6XqPHTum+Pj4DE1oDwBWZuU+aEZeL1OTmX0ZKWOvg88884xy5sypsWPHpthXp04dBQcH66OPPkpXUJZWf6JFixZyd3fPUPtTq+dmn+ckSfVe+RqfFeds1aqV9u/fr23btt103blz53aNjKtTp45y5sypxYsXp/lz++CDD1xtuFJq/Z8jR46ku89+5eTWrVq10sGDB7V169ZU27BlyxZFRESkaANAIHQD7HZ7ihV48uTJowIFCiguLu62tqVjx446duyY5syZk2JfTEyMLl++LEmpjoy47777JClZmw8fPqw9e/ZkStsqV66sokWL6q233tL58+eT7cvop1CS1LZtW7m5uWncuHEpVhlKqq9p06bKkSOH3njjDcXGxqZaJj3tCg0NVb169fT+++/r8OHDqZZxc3NzrTq2ffv2FO29kWscO3aszpw5o8cffzzVla+++eabZCsUTJw4UZMmTdKLL76owYMHp1lv0hxC6XlcT7du3XTPPfek2pl55JFHVL58eb322mupvuBevHhRL7300g1db8GCBVW4cOFUn+uhQ4em69omT57sOmbfvn0pfraS85Oabdu2KTg4OMUw+F9++SVdq1OMHDlSCQkJevPNN1Psq1mzpiSluI6aNWuqefPmmjt3rlatWpXiuPj4eA0ZMsT1/ejRo2WMUffu3VMN1Hbs2KEFCxZIcoZcVatWTfW5y4gdO3aoXLlyrtsLktp97ty5Gx6+n1SvpHSv/BEdHa09e/akCOz27NmT4meamX/TACCr0QfNeF8jNZnZl8nI62DSKKFPP/1Uv/32W7J9fn5+GjZsmHbv3q1hw4al2o/88MMP9dNPP0lyrlDl5eWV4rW9cOHC6tu3r7755htNnz49RR0Oh0OTJ0/W0aNHXe2Xbr5fkpYdO3bIZrO5zpNV5xw6dKj8/f31+OOP6+TJkymO2b9/f5rzP15d95X1+vn5aciQIfr7779T/b1bvXq15s+fr2bNmqlGjRop6pKS93+S5hBKzyNpDiHJ+QGxr6+v+vfv75pfKsnZs2c1YMAA+fn56YUXXkh2zUlTaSQ5fvy49uzZk2wFs6ioKO3ZsyfVFWhx92MOoRtw8eJFFSpUSI888ogqVqyogIAArVu3Tj///HOyN5y3Q/fu3bV06VINGDBAGzZsUO3atWW327Vnzx4tXbpUa9asUdWqVTVu3Dht3rxZDz74oMLCwnTq1CnNmDFDhQoVUp06dVz19ejRQ5s2bbqhMONqbm5umjlzplq3bq377rtPvXv3Vv78+bVnzx79+eefWrNmjavsq6++Ksm5NKkkLVy40JVwjxw5UpLzNpmXXnpJr7zyiurWrauHH35Y3t7e+vnnn1WgQAG98cYbCgwM1NSpU/X444/r/vvvV9euXRUcHKzff/9d0dHRWrBgQbrb9fbbb6tOnTqqXLmy+vXrp6JFiyoiIkKrV692vYi//vrr+uabb1S/fn3169dPZcqU0fHjx7Vs2TJt3brVdf/zwoULdejQIddS9Js3b3Zdc/fu3V1LlXbq1Ek7d+7Ua6+9pl9//VVdunRRWFiYzpw5o6+//lrr16/X4sWLJUkrV67U0KFDVaJECZUpUybFpylNmjRR3rx5JWXOHEJJ3N3d9dJLL6l3794p9nl6emrFihVq3Lix6tWrp44dO6p27dry9PTUn3/+qcWLFys4ONj1KWZGrleSHnroIa1cuTLF/dY3MofQ77//rq5du6pFixaqW7euQkJCdOzYMS1YsED//vuv3nrrrWTD6nfs2KGzZ8/qoYceum7dSaOEkgKZKxUrVkz33nuv1q1bp8ceeyzZvg8++EBNmzbVww8/rNatW6tRo0by9/fXvn37tGTJEh0/flyTJk2S5Ow8vPPOO3riiSdUunRpde/eXSVKlNDFixe1ceNGffbZZ67fsaTn7qWXXtKFCxduaKhwQkKCNm3a5LoFIMmDDz4oDw8PrVu3Tv369ctwvZJzMvgiRYqkOslman766Sc98MADGj16tMaMGePaXqZMGdWvXz/ZxKSZ+TcNALIafdCM9zVSk5l9mYy+DibNJfT777/L398/2b4XXnhBf/75pyZPnqwNGzbokUceUb58+XTixAmtWrVKP/30k2tUiI+Pj5o2bap169Zp3LhxyeqZPHmy9u/fr6efflorVqxQq1atFBwcrMOHD2vZsmXas2ePa8LxzOqXpGXt2rWqXbu26xa3rDpn8eLFtXjxYnXq1EllypRRjx49dO+99yo+Pl7ff/+9li1bpl69el2z3lOnTumPP/7QoEGDkm0fPny4fv31V02YMEHbtm1T+/bt5evrq61bt+rDDz9UmTJlUu0Tptb/uZE5hCTnvK4LFizQo48+qvLly6tPnz6u9y7vvfeeIiMj9dFHH7nulJCkRo0aSZIiIiJc20aMGKEFCxbo4MGDCg8Pl+R8z9G7d2/Nmzfvus8R7kK3ZzGz7CUuLs688MILpmLFiiZHjhzG39/fVKxY0cyYMSNZufr165ty5cqlOP7KZaqvpKuWvUzPkp/GGBMfH28mTJhgypUrZ7y9vU1wcLCpUqWKGTt2rImKijLGGLN+/Xrz0EMPmQIFChgvLy9ToEAB06VLF7N3794UbU7Pr0XSkogTJ068btmtW7eaJk2auJ6rChUqpFj+WulcBt0YY95//31TqVIl17XWr1/frF27NlmZzz77zNSqVcv4+vqawMBAU61aNfPRRx9luF27du0y7dq1Mzlz5jQ+Pj6mVKlS5uWXX05W5tChQ6ZHjx4mNDTUeHt7m2LFiplBgwaZuLg4V5mk5zW1R9KSkVdK+nnlyZPHeHh4mNDQUNO6dWvz6aefusokLbWZkXoz6uolUZMkJCSY4sWLp/idTXLu3DkzatQoU758eePn52d8fHzMvffea0aMGGGOHz9+Q9drjDG//PKLkZRiidsbcfLkSTN+/HhTv359kz9/fuPh4WGCg4NNw4YNzSeffJKi/LBhw0yRIkWMw+FItj2tf8/79u0z7u7uriVnrzRlyhQTEBBgoqOjUxwXHR1tJk2aZO6//34TEBBgvLy8TIkSJcxTTz1l/vnnnxTld+zYYbp27WoKFChgPD09TXBwsGnUqJFZsGCBsdvtya7Xw8PDLFy4MNnxaf2Mr/bVV18ZSWbfvn0p9rVp08Y0atQo2bYrl9u9FrvdbvLnz29GjhyZ7uOT9o0ePTrZdkkp/j6m928aANwN6IP+T3r7GrejL5PR18GkPlxar7+ffPKJadq0qQkJCTEeHh4mf/78plOnTmbjxo3Jyq1YscLYbDZz+PDhFHUkJiaauXPnmrp165qgoCDj6elpwsLCTO/evVMsSZ8Z/ZLUloA/f/688fLyMnPnzk1Rb1ac0xhj9u7da/r27WvCw8ONl5eXyZEjh6ldu7aZPn26iY2NdZVLbdn5mTNnGj8/v1SXbbfb7WbevHmmdu3aJjAw0Pj4+Jhy5cqZsWPHmkuXLqVaPrX+z836448/TJcuXUz+/PmNp6enyZcvn+nSpYvZuXNnirJhYWEmLCws2baePXum+Lef9Pdg3rx5mdpW3BlsxvCxKYC7R6NGjVSgQAEtXLjwtp0zLi5O4eHhGj58+DVvzUuvqKgoFStWTG+++ab69OmTCS1Mnz59+mjv3r3asmVLho9NWp535cqVKfZt2bJFDRo00J49e1SiRIkM1btq1Sp17dpV+/fvz7RRbAAA3E438zp4M+x2u8qWLauOHTvqlVdeueF6blW/5K233tKbb76p/fv3p1iQIyvOebMqVaqkBg0aaOrUqTddF/0f3CkIhADcVX788UfVrVtX+/btc91qd6vNmjVLr7/+uvbt2ydvb+9MqXPChAmaN2+e/vrrrxtadvdGHD58WCVLltT69etVu3btdB+3e/dulS9fXr/99pvuvffeVMu0aNFChQoVSnUuiWupWbOm6tatm+p8SwAA3C1u9HXwZn388ccaOHCgDh8+rICAgBuuJ7P7JQkJCSpevLiGDx+e4nbzrDznjfr666/1yCOP6MCBA8qTJ89N10f/B3cKAiEAAAAAAACLYZUxAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACLIRACAAAAAACwGAIhAAAAAAAAiyEQAgAAAAAAsBgCIQAAAAAAAIshEAIAAAAAALAYAiEAAAAAAACL8cjqBgAAMi7R4dCPFy/Kbsw1y+Xx8tI9vr7ysNluU8sAAAAA3A0IhADgLhTrcGj6sWOKcziuWa5uUJAGFCggD3f329QyAAAAAHcDAiEAuAs5JB2Ni1Osw6GagYHyTyPwKeDtLTdGBwEAAAC4CoEQANzl+hUooCLe3qlOCudhs8mbQAgAAADAVQiEAOAu5+/mphzu7owEAgAAAJBurDIGAAAAAABgMQRCAAAAAAAAFsMtYwBwl/v54kUdio2V7apbxgLc3VXR31+ebmT/AAAAAJIjEAKAu9z0Y8eU2uxBpfz8NLV4cQIhAAAAACkQCAHAXe7+HDlSXXa+gJeXPJhoGgAAAEAqCIQA4C73VMGCCvfxYZUxAAAAAOnGfQQAAAAAAAAWQyAEAAAAAABgMQRCAAAAAAAAFkMgBAAAAAAAYDEEQgAAAAAAABbDKmMAcBdyk5TXy0txDgdLywMAAADIMAIhALgLebm5qVe+fEo0RsEeHrpeJGT++z/REQAAAABJshljzPWLAQDuRonG6GR8vOKNUWFvb0YTAQAAAJDEHEIAkK1dtNv17r//atKRIzoVHy8HnwEAAAAAEIEQAGRrNkkRsbH6+cIFbY6KUpwxIhICAAAAQCAEANlYTg8PdQwNVaCHh5acOqXjcXHiTmEAAAAABEIAkM01CQlROT8/nYiP15dnzuiy3U4oBAAAAFgcgRAAZHNebm7qmjevQjw8tCoyUgdjY2XP6kYBAAAAyFIEQgBgAdUCA1UjMFDRDoc+OX1aUYmJjBICAAAALIxACAAswCapW968yuvlpXXnzunPy5eVQCAEAAAAWBaBEABYRDFfXzUNDpaHzaYlp07pTEICo4QAAAAAiyIQAgAL6RAaqsI+Ptpx8aJ+vHBBsQ4Hy9ADAAAAFkQgBAAWktvLS+1z51YODw8tPnVKJxglBAAAAFgSgRAAWEzLkBCV9PXV4bg4rT17lmXoAQAAAAsiEAIAi/Fxd1fn/5ahX3b6tA7FxbEMPQAAAGAxBEIAYEF1goJUJUcORdvt+jQykmXoAQAAAIshEAIAC3KT9GjevAr18tJXZ8/q7+holqEHAAAALIRACAAsqrSfnx7ImVPukpayDD0AAABgKQRCAGBhXfLkUUFvb227cEHbL15UrMOR1U0CAAAAcBsQCAGAheXx8lLrXLmUw8NDS06d0smEBDkYJQQAAABkewRCAGBxbXLnVnEfH+2PjdXGc+dYhh4AAACwAAIhALA4f3d3dcyTR8EeHvr49GkdiYsTN44BAAAA2RuBEK7ByBiHHI5Y2e1RSkw8+9/jvOz2yzLGntUNBJBJGuTMqQr+/roUHa3P9u/XuZgYRgkBAAAA2ZhHVjcAdybnG0G7EhKO68KFtbp0abPi449Kkjw988nHp7yCgprK1/de2WyeWdtYADfN3WZT19BQnf78c82bP1/Vp05V7dq15eXlldVNAwAAAHALEAghDXbFxPyhQ4cGKjp6u9zcfGWzeUmySXLo/PmVOn36bZUo8Y18fctldWMBZIKKgYEKjIjQiYMHNX36dBUrVkxFihSRzWbL6qYBAAAAyGQ2wz0BuIoxRgkJx3To0EBduPCVvL2LKTT0KeXIUUdubgGKjz+qy5d/VFzcXoWFzZHN5p7VTc6WjDGKiYlRfHy8PDw85O3tLQ8PD96c45aKiIhQmzZttGfPHs2bN09t27aVv79/VjcLAAAAQCYjEEIKxtgVHb1de/bUlodHqMLCZilnzoeyulmWYrfbFRkZqdmzZ2vdunUqW7asHnroIVWoUEH+/v7y9PSUh4eHPD09ZbPZCImQqd58801NnDhRxYsX1/z581WyZEm5uTHlHAAAAJCdcMsYUjAm/r/5guxyc/ORn1/lrG6SpSQmJuqvv/5S//799fPPP0uSfvjhB82ZM0eBgYEqU6aM7r//flWrVk3333+/8uTJI09PT1dIRECEm9W3b1998cUX2rZtm1avXq28efMqZ86c/F4BAAAA2QgjhJCCwxGnixc36J9/WsrDI6/Cw+cqMLCpbDYPOecQwq0SHx+v7du3a9CgQdq5c6d8fX1VoUIFFShQQAcOHNDhw4cVExOjxMREJSYmysvLS2XKlFH16tVVo0YNVa1aVfny5XPdXubh4SE3NzfeyCPDFi9erGHDhsnd3V3Lly/XfffdJ3d3bg8FAAAAsgsCIaRgjFFc3N/au7eJEhL+lZdXURUq9LoCA5vJZvOWzebJvEGZzPmcx2n58uUaNWqUjh49qnz58mny5Mlq0aKFaw6XEydO6Ndff9WPP/6oH374Qbt27VJUVJQrIDLGqHDhwqpSpYqqV6+u2rVr65577pGfn588PDzk7u4ud3d3bv/BdSUkJKhTp0766quvNHDgQA0dOlR58+YlXAQAAACyCQIhpMpuv6izZz/UkSMvyJhYSZK3dzEFB3dSSEhHeXsXl83mI5stebDg/HVySGJUSno5HA5duHBBEyZM0Jw5c3Tp0iW1a9dO48aNU/Hixa8Z3pw/f167d+/WTz/9pB9++EE///yzTp06pYSEBNntdiUmJiokJEQVK1Z0jSKqWLGicuXKlSwg4jYzpGbTpk16/PHHderUKS1fvlz16tVjGXoAAIBMZoyRkfNtue2/OzLom+N2IBBCmhyOGF28uFGnT8/QpUtb5HDEypgE2WxeCgxsqgIFXpWvbzlXKOT8VUpUdPQf8vIKk4dHzv9uM0NaEhMTdejQIQ0ZMkRr166Vt7e3XnrpJfXs2VO5cuXKcH2xsbHau3evfvjhB9coomPHjikuLk52u112u10BAQEqU6aMatSo8d9tZrWUL18hubu7yd1dcneX3NykzHwNMkay26WEBGe9Hh7OR2rljJHi4pxt8PLK3HYg455++mktWLBALVq00Pjx4xUWFkYHBQAAIBMdTziui/aLMjLK55FPge6B9LdwWxAI4bqMsSs+/ojOn1+h8+dX6vLl7TImXj4+ZVSixFfy8ir8XzmH4uOPaO/eunJzC1DhwlMVENBANpsXf9CuYoxRYmKiNm7cqOeee0579+5VeHi4Jk2apMaNG8vX1zdTzmO323XkyBFt375dP/zwg7777jvt3btX0dHRstvtMsYoIGC0wsKeV8WKvqpRQ6pZUypaVPL2/l84dLMB0eXL0tat0scfSyEhUvPmUqNGKes0Rtq3Txo/XipUSBo8WLqBXAyZaO/evXrkkUe0Z88eLVy4UK1bt5afn19WNwsAAOCOY4xRohLlMA6Z//6z/fefu81d7nJOu3H1e6OOBzpq+fnlcsih94u8r+65usuDD9ZxG/Bbhuuy2dzl7R2uvHmfU2joE4qK+kIREX0UHx+hyMh3VaDAK/+VNLLbz8vDI49iYv7QP/88pNy5+yp//pfl4RHCaKH/JM0X9P777+uVV17RmTNn1KBBA02aNElly5aVR2pDZ26Qu7u7wsPDFR4erkceeUQOh8M1D9G2bdu0e/d+7dx5r/7+21e7d0uLFjnDn0KFpGrVnOFQjRpSiRKSr+//AiJ394wFRPHx0q5d0rx5zmN37pTKlpUKFLj6uZFOnnSWK19e6tuXQCirlSxZUp06ddK0adP0zjvvqHz58ipTpgwhLwAAwH8cxqF4E6/IxEhtvLRRv0f/rkh7pKLsUQpxD1EejzyqHVBbdfzryM/dTx7GQ7L97/YwIKvwDh0Z4ubmoxw5GiskpLPOnJmny5d3uPbZbO7y9a2gkiU36tSpaTpx4k2dPj1TUVFfqFChSQoKamX50UIOh0Nnz57VyJEjtXjxYiUkJGjAgAEaPny48ufPf8ufGzc3NxUoUEAFChTQgw8+KEm6cEHavVvats352LFD+vdfaeVKaflyyeFwjuqpUMEZDtWqJVWs6Axqrh5BlJ7m2+3S779Ls2dLo0Y568CdbeDAgfrmm2/03Xff6euvv1b+/PlZhh4AAEBSoknUrphdeuXEK1p1fpUccqRe8KSUyz2XRuYfqcdzPy5/+TtHENGfQhYiEEIaku4kvPoPlJHN5iZ392AZY+RwXE6212azyd09QPnyDVdgYHMdOTJY0dE/6cCBzgoOfkSFCk2Sp2deS44Wstvt2rVrlwYNGqTt27crKChIb7/9ttq3b68cOXJkWbsCA6Xq1Z2PZ55xzt8TESH9+OP/QqL9+523fG3e7AyIfHyco4Zq1nQ+qlWTwsKuHxB5eko5czpHAn36qdSkiVS7NvME3elCQkL02GOP6eDBg5oxY4bq1aunypUr04EBAACWlmgS9V7ke3ryyJNKVKIkyV3ucre5y01usskmIyOHHLIbu87Yz+jZo8/q86jPtbToUoW4h2TxFcDqWHsaKRhjZIxDDkecjEmUMY7/tjkfdvsFXbq0WTabh7y8iqZah83mLn//yipZcp0KF35L7u45dO7cMu3ZU0Pnzi2XwxEvq0xflTRf0KpVq9SuXTv9+OOPKlWqlD755BN17do1S8Og1Hh7S6VKST16SDNnOkcM/f23c7TQ0KFSnTrOMrt3S3PnSr17S5UqSZUrO2/xmjvXOQLo8mXnrWJ2+//qdnOTypSRHn1U2rtXmj5dio7OumtF+nXu3FmVK1fW0aNHtWzZMp08edIy/4YBAACu5jAOzTg9QwOODHCFQb5uvnos92P6tNin2nfvPp2scFL/3PuPVhRboS4hXeRj85Ekbbi4QYOPDJZd9mudArjlrDdMA9dlTLwuXdqqo0dfUEhIFwUGNpWXV7hsNjfFxPypU6em6PLlbfLwCFVISCcZk9ZQR5vc3HyUO/cABQQ8oCNHntbFixt18OCjCgp6UIULT5OXV6FsPVrIGKPY2FhNnTpVEydO1MWLF9W2bVu9+eabCg8Pv+aS8ncKd3fnXD8FCkitWjlHCEVGSr/+Kn3/vfPxxx/SgQPSP/9IH37oDH7y5XPeYvbQQ1LTpv+rr2BB6eGHpS++kH74QVq4UOrXz3kM7lze3t4aNGiQdu/erTlz5qhJkybKnTu3PD09s7ppAAAAt5UxRr/F/Kahx4a6toV5hWlVsVWq6Fsx2XujHO45VDCooB4MelA9Q3qqy8EuKuBVQC/nf9k1yTSQVbLvO3HcMIcjWpcv/6DY2F06dmyEjh0bJkmy2TxkTKIkN7m7hyhPnsHKkaO+jImW5Otafv5qNpubfH1Lq0SJ1YqMfE///vuyoqK+0KVL36lgwVeVO3cfSR7Z7vYTh8OhkydP6umnn9bq1atls9n00ksv6ZlnnlFwcHBWN++GublJefJIzZo5H5JzlM++fc5waNs26aefpIMHpa++knLkkFq2/N/xNptUrpw0aJD0/PPOCaQfeEAqWTJrrgfp17hxYzVq1EiLFi3SggULVLx4cYWHh2e7f7sAAADXYmQ07NgwxZk4SVIOtxz6+p6vVcq71DX7RY0DG+vrEl+ruFdxBbkH0YdCliMQQgoeHsHKm/cFBQW10LlzK3X58jbFxx+RMfHy9Mwjf/8aCgnpJh+fUvr331G6dGmrChV6S/7+VSW5pzlayGbzUu7c/RUU1FJHjjyj8+e/0OHDgxQV9bkKF37bNQop5bxFdx+73a4ff/xRAwYM0O7du5UvXz793//9n5o2bZppS8rfSfz8nBNNV6woDRwoxcY6A6Fdu6TcuVPOERQcLDVsKNWv77wlbdo05+1jvCbe2Ww2m5588kl9//33Wrp0qVq3bq18+fJly99pAACA1BhjtDdur9ZdXOfaNjLfyOuGQUmq+FW5lc0DMoRACKlyc/OSn19l+flVTnW/MUYJCccVFbVGsbF/ad++JsqT52nlyzdMbm4B1xwt5OUVpmLFPtG5c8t05MhgRUV9rUuXqip//lHKk+cJSXfvSmTOOZbsWrx4sV544QVFRkaqRo0amjlzZqYvKX8n8/FxzhVUpozz+3PnUpYpWVJ66innXEXr1kmrVztvScOdrWzZsmrXrp3+/fdfzZo1S+XKlVPZsmXv2n+zAAAAGbX83HLX1z42Hz2e+/EsbA1w45i1AzfEZrPJ0zO/SpbcqLx5h8gYu06cmKC//66tS5c2yhj7NSectdncFRzcSWXK/KLg4Pay2y/q6NHntXdvE8XG7r7u8XciY4yio6P13HPPaeDAgTpz5ox69+6tFStWqHz58pYJg9LL29s5EXWPHs7RRNOnS+fPZ3WrkB4DBw5UmTJltHnzZq1bt05nz55VYmJisofdbr8tD4fDcdseAAAAkvRT9E+ur+vnqK9g92A+HMNdiXeouGHOUCiXChZ8VTlzttHhw08rJuY37dv3oHLn7qsCBcbK3T3oGqOFbPLyKqiiRZcoJORzHT78lC5d2qo9e+5XvnwvK2/eZ3W3jBZyOBw6cOCA+vTpo23btsnHx0czZszQo48+Kn9//6xu3h2rUCGpWzfpm2+kv/6S5s+XqlbN6lbhevLkyaPu3bvr4MGDevXVV7Vp0yblypUrWRlfX195eXmlOXG6zWaTn5/fNSel9vDwkI+PzzXLeHp6ytfXV+7uaU/K6O3tfc3b2tzc3OTj4yNvb2/XNrvdrqioKJ06dUpeXl4qVqyYqlates3zAAAAa/gt+jfX15XTuKMCuBsQCOGm2Wye8vevpVKlNujkyak6cWKCTp9+R1FRX6hw4bcUFPSgJLc0gx2bzU1BQW1UtmwdHTv2oiIj5+rff19UVNSnCgt7Vz4+Za95fFYyxsgYo7Vr12rAgAE6fPiwihUrpvfee081a9ZkBabrSFqG/plnpKeflhYvlkJDs7pVSI+GDRvq/fff119//aXVq1crMTExw3XcKaMA09OOnDlzqnfv3po8efId+bcIAADcPqcTT7u+LuZVLAtbAtwcAiFkCpvNJnf3QOXP/5ICA1voyJEnFR29Xfv3t1dISCcVKjRFHh6h1wiFbPLwyKUiRWYqKKiNjhx5Upcv/6jdu6soT55nVaDAWEned9QbMWOMHA6H/u///k8vv/yyLl26pGbNmmnmzJkqUqTIXbGk/J0gKEhq1Mi5Ytm2bdLMmVndIlyPMUZz587V3r17VaRIEXXo0EH58uVLViY6Olrx8fFphi1Jt1jGx8eneZ7ExETFxsYqISEhzTLx8fGKjY1N85YuY4zi4+MVHR2dZh0Oh0NxcXGKjY11bUsaIXTy5EnZ7XadP39eq1evVqtWrdSwYcM06wKA9Lryz2NGuzc3cyyAG2eMUbyJV4yJcW0LdA/MwhYBN4dACJnKZvNQQMD9KlVqkyIj5+jYsZE6e3axLl3arEKFpipnznZyrjh2rdFCLRUQ8KuOHRumyMj3dPLkRB06dEDe3sNUuXJl2WxpH3+7GGMUFRWlQYMG6ZNPPpHD4dDzzz+vl19+WTly5Mjy9t1tihZ1TjD93XfOUAh3LmOMtm/frs8++0yXL1/W6NGj9eCDD8rHxyerm3bL7NixQ126dNGBAwc0YcIEPfDAA/wbB3BTHA7nQ3IGOm5u6Q92jHEea0zGjwVw8xJM8g+qPHhLjbsYQxhwC9jk5uar0NBBKl16mwIC6is+/qgOHOikAwceUULCsetMOG2Th0dOFSkySyVLbpC3d2UNGvSdatasqREjRigmJiZLbzUxxuiPP/5Q/fr19fHHHytHjhxasmSJXn31VQUGBvJG8QZ4eTmXrO/XL6tbguux2+166623dPDgQfXs2VP3339/tg6DJOfKaq+//rrc3d0VERGhrVu33jG3uwG4O82eLfn5SZ6eUvv2zsUV0mvTJikszHlspUrS1q23rp0AkrPZbPJ385eH7X8h0EXHxSxsEXBzCIRwy9hs7vL1LasSJb5W4cJvy93dX+fPr9Lu3ZV15sx7MsZx3WAoIKCOihbdpIYNO0uS3nzzTVWtWlXff/+9a/6e2yXpfEuXLlXjxo21c+dO3XvvvVq/fr3atm2bbEJapHS9nCxfPunRR6V777097UHGGWO0bNkyfffddwoODlaPHj1UqFChrG7WLefr66tatWppwIABOnTokKZMmXLN29gAAED2FuQe5Po6MjEyC1sC3BwCIdxiNrm5eSs09AmVKfO7goIeVGLiaR061F///POQ4uMP/xfqpB7s2Gw2+fv7a/Lkydq8ebPKlSun3bt3q27duurfv78uXrx4W0IhY4wSExM1ZswY9ezZU5GRkerQoYPWrl2rChUqsPLQdXh5SSVLOj8FrV499TI2m1S8uPT661KHDlLTptI1FoZCFjh79qxmzZqlo0ePavDgwSpdurRl5srKnz+/unXrpoIFC+qXX37R8uXLGSUEAIBFlfAu4fp6Z8zOLGwJcHOs0ZNHlrPZ3OTtHa7ixVcqPPxDeXiE6MKFL7R7dyWdPv1/6RotVKNGDf3yyy8aP368PD09NWfOHFWsWFHr1q27paOFjDE6fvy4WrRooddee02S9NZbb2nBggUKDU17omz8j7+/1KqVtGiRczWx65X7+GNp4kQpd+7b10ZcmzFGM2bM0O7du1WlShW1bNlSISEhWd2s28Zms6lYsWJ64okndOzYMc2cOVNRUVFZ3SwAAJAF7ve73/X11ktbZf77D7jbEAjhtrLZPBQS0lVlyvyqnDnby24/pyNHBmvv3kaKi9t7zWDHZrPJy8tLQ4cO1U8//aSqVasqIiJCzZo1U48ePXT27NlMD4WMMfr+++9Vp04dffvtt8qbN6++/PJLDRo0KNvPm5JVIiOlOXOk115LvooKso4xRrt379bHH3+s8+fP6/nnn1fRokWvWf5uf6QmJCRELVu2VNWqVfX3339rzpw5jBICAMCCHsjxgOvr/fH7te3ytrRueADuaEyJjtvOGewUUrFiHysq6jMdOvSkLl3apN27qyh//lHKk+c5Se7XXKK+QoUK+v777zVlyhSNGTNGH374odatW6d33nlH7dq1c5W7UUlv8ubNm6fBgwfr0qVLqlOnjj744AOFhYVZ5jaZ283hkP7+Wxo2TMqbV2rRQqpSJatbBck5f1dERIQ6d+6s6tWry8/PL82yvXv31urVq5WYmJhsu5eXl3x8fK55i6WPj498fHyu+e8/qUxa3N3d5ePjIy8vrzTLeHh4yNfXVx4eyV8GQ0JC1KRJEzVq1CjV48LCwvTss8+qW7du+uCDD9SuXTvdc889aZ4HAABkP60CW6mgZ0EdSzgmSRpxbIQ2ltwoSbKJuwdw9yAQugtl5BPpO/l2JpvNXUFBbVW2bG0dPfqczp5dpGPHhuvcuS8lvaPSpcv+Vy7lNdhsNnl6emro0KFq3769Hn/8cW3atEnt27fXI488ohkzZih37tw3dP3GGMXGxurpp5/W/PnzZbfb9eyzz+qVV16Rn5/fHf2c3i2SfoWvfird3aXwcKlTJ+mjj6R586TKlVlONysZY/TZZ59p48aN8vHx0eOPP66CBQte85hLly7p3Llzstvtt6mVmadIkSIqVKhQmoGQn5+fqlWrptatW2v9+vV6++23NW3aNP4uAABgETabTR7y0OA8gzX02FBJ0pbLW/Tyvy/r1QKvysik2S+48n0cfQfcCQiE7kLrzp3Ta4cOySjtPyQ5PTzUNndu9cqX7/Y2LoOcwU4ehYd/oJCQLjp06An9+KPUv39lPffccxozZoy8vLyuOVqgePHi2rBhg+bMmaPnn39en3zyiTZt2qSJEyeqR48ernLpYYzR/v371blzZ/3yyy/y9/fXzJkz1alTJ3l6embadVuZMc6RQAkJkrd3yrAnXz6pSxfpww+l9eul3bulsmWzpq2QoqOj9fbbb+vff//Vyy+/rLJly6YYVXO1+fPnp7oKV3x8vGJiYuRwONI8NjY2VrGxsWkG30mBbWxsbJp12O12xcbGKj4+Ps0yiYmJiomJSTGKKSAgQBUqVEjzOEkqVKiQBg0apLVr12r16tXq3LmzatasSccOAAALeSb0Ga04v0I/XP5BkvT6ydd1MP6g3iz4pgp6pv7h2dGEo3rvzHs6lXhK0wtNl7st7VHTV/eF6GfgViAQugslGKNLDoc8bDb52GxyS+WPg5vt7hqsaLO5KTCwpUqX/k3vvvuiEhI2a/z48VqzZo3mzp2rSpUq/Vcu9dFCktSvXz81adJE/fr107p169SrVy8tW7ZMM2fOdC2Nfb20/quvvlKvXr10+vRplShRQkuWLFHFihVZRSwT7d0rjR0rnTkjvf++dPVgEw8PqWhRqV076YsvpPnzpQkTGCWUFYwxmjVrlnbu3KkyZcqoffv2ypUr13WPCwgIuA2tyzqenp4qV66cHnvsMc2ePVsTJ07UsmXLrhuUAcC1MCUZcPdIGiW0rNgyNd/XXH/G/ilJ+ujcR1p2bpnqBNRRdf/qyuuRVw45dDjhsH68/KN+vvyzHHLIS14ak3+MQt3TXqDmcMJhzYmco8dyPaZwr3BuRcMtwUQod7GqOXJocZky+rZixRSPFeXKqecdPjroas65hYI1ffoMbdy4USVLltSvv/6q6tWra8SIEYqLi7vu7XJFixbVN998owULFigkJESrV69W+fLl9e6776Y5WWzStkmTJqlt27Y6ffq0WrVqpa1bt6pSpUqEQZksPl46e1b6809p6dLUO8D580vdu0sxMdKXX0qHDt3+dkI6ePCg5s+frzNnzuj5559XWFgYn079J0+ePOrVq5fy5Mmjn3/+WStWrGCCaQA3JCFBOn/e+UFJeh4XLjhH2gLIWjabTYU8C2lLqS3qENzBFdgkKlEbL23UhJMT9Nyx5zTk2BC9fept/Xj5Rznk/Mfr7+6vf2L/uebKZGP+HaMJJybo3ch3FW/SHvUM3AwCIdxxbDab6tatq19++UVDhw6Vw+HQhAkTVL58eW3atClddXTv3l27du1Su3btFBUVpQEDBqhFixaKiIhI8abt7NmzatWqlYYNGya73a5XXnlFy5cvZ0n5W6RYMalzZ+nkSenzz52d26t5ekolS0oPPiidOOEcJYTbb+LEiTp06JDatWun+vXry9/fP6ubdMdwc3NTeHi4nnrqKZ04cULTpk3TpUuXsrpZAO5CX37pXEAhNDR9j4ceko4fz+pWA0iS0y2nPg7/WD+W/lF9c/dVmFdYquVC3EPUIrCFZhWepQPlDqimf0252ZxvxwPcApTTPaeC3ILk7eYtSXo89+PytHlq3pl5uuy4zAdPuCUIhHBHstls8vPz0/jx47VlyxaVKVNG//zzjxo2bKj+/ftf842XzWaTzWZTvnz5tHz5ci1dulShoaH65ptvVK5cOY0fP16JiYkyxui3335TjRo19OWXXyokJERffPGFRowYIU9PT8KgW8TPT7rvPqlWLWn/fumTT1Ivly+f1KuXFBUlrVhB5/d2W79+vb788ktJ0tNPP60CBQpkcYvuPMHBwWrbtq0qVqyovXv3as6cOVndJAAAcJslvfeo6ltVswvP1sFyB3WmwhntLrtb20tv184yO/Xvvf8qskKkVhdfrX65+ynIPSjZe433wt5TZIVInat4Tl2Cu8jD5qFa/rVU0beiIhMj9VnUZ0owKednBG6WzRA13nW+PHNGoyIiVCMwUC8WKaIC3t5Z3aRbyhijhIQETZkyRaNGjVJCQoLCw8M1Z84cNW7cOF3Hnz59WkOGDNHChQslSbVq1VLr1q312muv6dKlS6pUqZKWLl2q4sWLEwTdBhcuOFcRGzRIeuABZygUFJS8jDFSRIQ0cKC0Y4c0dKj0wgtZ0lzLiY+PV9u2bbV+/Xo999xzeu6552541b7sLjo6Wp9//rm6du2qUqVK6csvv1R4eHhWNwvAHW7mTGnwYOftYsWKSXXrSoGB6Tv22DFpzRrp8mWpfHnpnXecxwO4c6T2FvtG+lFzI+fqqSNPqYpfFX19z9cKcM/e8zTi9mMGzLtYnMOhk6msomOT5OvmppzZZFWspCXmhw0bppYtW6pPnz7avn27mjZtqm7duumtt95ScHDwNVciCw0N1YIFC9SuXTsNGjRI33//vb7//ntJUp8+ffT222/L19eXN7y3SY4c0v33S5UqOUcJffqp9N+CcC42m5Q3r9S7t7R2rbRkifT441JwcNa02Urmzp2rHTt2qHDhwnr00UcJg67B19dXNWvWVKtWrbRx40ZNnTpV06ZNy+pmAbiLlC8vjRrlXFAhPTZtkn780RkIAbgzZVa/qXNIZ405PkbfXf5Of8T8oer+1a+5MhmQUdwydhf79dIl9d27V2127Ur26PDXX5p27FhWNy9TJQ3FLF++vL7//nu98cYb8vLy0sKFC1WuXDmtWrXqmvfVJh1fuHBh10pAXl5emjVrlmbPni1fX9/bdSmQM+wJD3cuL3/kiDPsiY5OWc7XV6paVapdWzp61DmqCLfW8ePHNXv2bJ0+fVojRoxQWFjq98HDyWazKX/+/Hr22WeVkJCgVatW6eeff87qZgG4C9ls6XsAsA5/m786B3eWl81L887MU5yJy+omIZshELqL5XB3V1k/P93n75/sUdHfX2E+PlndvFvCZrPJw8NDw4YN065du1S/fn2dOHFCDz/8sDp16qRTp06lGQxNnz5dNWrU0JEjRxQcHKwNGzaob9++cnNzcwVGuH2Cg6V69aRSpZxL0f83XU0yNptzLqG+fZ2TTy9cyCeit9qbb76piIgINWvWTE2aNFFAQAD/Nq7Dw8NDZcuWVe/evXXy5Em9/vrrcrAEEAAAuEk2m02P535cXjYvLTm3RGcSzzC5NDIVgdBdrJy/v8YXK6Y5pUole/xfiRLqmTdvVjfvlkkKb4oXL65vv/1Ws2bNkr+/v5YtW6by5ctr4cKFKf5QHjx4UM8//7zsdruCg4O1ZcsW1axZkyAoC9lsUljY/0YJLVrknEvhan5+Us2aUoUKzjmF0pqEGjdvx44dWrVqleLj4/Xcc88pX758/PtIB5vNpty5c6t///4KCQnRjz/+qE8//TSrmwUAALKBUt6lVC9HPcU4YrT03FJGCSFTEQhlA0mhxtWP7C7pOvv166edO3eqUaNGOnXqlHr27KnWrVvryJEjrmBo/fr1SkxMlKenp44ePaqyZcu66kDWyZ1batxYKlhQ+vNP51xBV7PZpDx5pD59pMhIad48KZWps3CT7Ha7xo4dq+PHj6tv376qWLGiPLPJPGS3g81mU5EiRTR48GCdPn1akyZN0mWGswEAgJtks9nUJ6SPvN28NSdyjmJMjIwYJYTMQSCEu1pSKBQeHq41a9Zo/vz5CgkJ0erVq1WhQgXNmTNHDodDZcqUkSTdf//98v5vVTbCoDtDWJjUtatzjqCFC6XU7rTJkUNq2lQqUcJ5e9lnn93+dmZ3ixYt0o8//qjQ0FD169ePiaQzyGazKSgoSB07dlT58uW1Z88ezZ07N6ubBQDIRk6elL74QnrvPef8i4cOpf/YS5ekzZudx86fL/3++y1rJm6B1kGtVcSziPbG7dX3l75XoknM6iYhmyAQQrZgs9nk5uamHj16aOfOnWrbtq3Onz+vAQMGqFGjRjp8+LAkyc3NzVUeWS9pJbEHH5RCQqRff3V2VlIrFxrqXHEsMlJ6913Jbr/97c2uzp07p7fffltnzpzRsGHDVKRIEf6N3KC8efNq6NChOn/+vGbNmqVj2WyCfwBA1vnnH2n8+P9n776jorrTBo5/7xR6772jqAgq2Bv2HnsvURM1anpikk3ZlE3bbHrUFI29995FVBQUa+yKiiIgIErvM/P+cRffZMGIBWaA3+ecObs53sFnEGbufe5T5NmKM2bAqVOVf+6dOzBvnvzcadNg69YqC1OoAipJxVj7sRhLxsy/M59CbaG+QxJqCZEQEmqNsmohV1dX1q5dy4oVK3BwcCAqKornnnsOnU53PyEkGBZvbxg2DJKS5CqhilhbQ9++4Or64PYy4fF88803xMfH06JFC/r164elpaVICD0GSZIwNTWlffv29OjRg6SkJL799lt9hyUIgiDUIjrd/z+e5PlCzSJJEuPtx2OuMGdT9iZuFt9EoxN3R4UnJ66OhVqnrFpo2LBhnD59mmHDhlFQUABAaakorzQ0ZZvEBg4EY2OIjYUHbe12dobx4+WNY7/8Ik5onoZz586xZMkS8vPz+eCDD8Qg6SckSRJOTk688847lJSUsHLlSo4fP67vsARBEARBqOFcVa70su4FwLJ7y8RwaeGpEAkhodaSJAkXFxeWLVvGRx99BHB/fpBgWCQJfHxgyBC5SmjRooqPsbGRj7GyguPHITq6uiOtXXQ6HZ9++impqamMHTuWsLAwjIyM9B1WjadSqWjQoAHPPvssd+7c4YsvvhArYgVBEARBeCKSJPGc/XOYSCYsylhEriZXnF8IT0wkhGqgJhYW/MvHh2ednbFVqfQdjkGTJAmlUkmbNm3uVw4JhkeS5FawoUPl2UCRkXDmzIOPGzdOrhL67bfqj7U22bBhA5GRkZibm/Piiy9ib28vqoOeAkmSsLOzY/r06VhZWXHw4EG2bNmi77AEQTAgXbrIWzMXLYJXX5W3aVZWgwbwww/ycz//HOrXr7IwBUEwMB0tOhJsEkxKSQo7sndQrBOrd4UnI66Oa5ASrZZCrRYXIyN62NkRbmmJiUhwVEpZ9lwkhAyXUgn+/jBgAKSkwJIl5Y+RJLC1hTFj5Pay/fvhxIlqD7VWyM3N5d///jcZGRnMmDEDPz8/8fvxFCkUCry8vHj11VfJyMjg888/v9+6KgiCEBgII0fC6NHQoQOYm1f+uU5Ocpv16NHQu7e8dEEQhLpBkiR5uLTCmIV3F1KgFecWwpMxqPKSqZcuUVjRzuk/CbGwYJKbGxZKZTVFZThO5eayKDUVXxMTxjg74yRaOypN+9+fK4VCISogDJQkgZubfIK8ejXs2AFTpoCf31+PUyjA3V3Hc88VcPz4WVasiKFZs1f0E3QNNnPmTC5evEjjxo0ZOXIkVlZW4nfjKZIkCSsrK0aPHs2yZcu4ePEi8+bNY/r06foOTRAEA5CaCmfPQmam/N+entC0KTzs1E6rheRked4eyMmgdu3kmyqCINQNI21H8tntz9ifs5+LRRcJV4ajkgzqsl6oQQzqdvD5/HzOPORxvbAQTR3slbxXUsKJ3FyO5eRwr6QEM3En/5H8OSEkGC61GoKC5G1iyck6liwp/7suVwlpGTr0PEePdmPVqu+5dOmSHqKtuRISEpg7dy45OTm89957OD1Kr4LwSBwdHXnnnXfIysrip59+Ii0tTd8hCYJgAE6ehHfegREj5Edl14BrNBAT8//P+9e/oKSk6uMVBMFwWCutGWwzGLWkZnHGYlElJDwRg7o6Lrv0+8bfn22NG7Ozgsenvr5Y1sHbIBfy89l77x5exsZ0sLHBvA5+D56ESAjVDGVVQi+8kEdAwEYOHhxLSkpKueOUSgU+Pp4MHNiPjIw7/CaGCT2Sjz/+mOTkZIYMGUL79u0xNjYW1UFVoGwNfZcuXejSpQu3bt0Sa+gFQQDkLZkazf8/Tp+GFSvk6p9Hfa4gCHWLJElMdJiIicKEZfeWcbf0rhguLTw2g6wts1KpsFerDStbpUdpxcUczcnhVlERPezsaGNtLS7eHpFICNUcxsbg45OKre3vHD8ex9KlS3nzzTf/ckzZ0N7nn3+eVatWsX79el5++WW8vb31FHXNsWfPHrZt24ZarWbGjBlikHQVkyQJBwcH3nvvPXr06MHixYsZPXo0jRs31ndogiAYEI0GDhyAhQvlyiHxtiw8TG4ubNwIV69W7vh79+DcuaqNSag+jU0a09KsJZE5kWzM2sjzDs9jJpnpOyyhBjLIhFAZcZEiV01dyM9nf2YmfiYmdLKxEe1ij0EkhGoOuUrIhXHjRrN79w7WrFnDxIkTsbOz+8txKpWKoKAgevfuzb59+5g3bx4ff/yxnqKuGYqLi/n000+5e/cub7/9NoGBgShFtWGVU6lUNG7cmDFjxrB06VI+++wzVqxYoe+wBEEwEJIkP27fhm3b5JlA7dvrOyrB0GVlyQnEyp7a6nTyDCqhdlBICp61f5bDeYeZlzGPUXajMFOIhJDw6MTVsYFLKSoiNjub9JISGltY0EIMfn0sIiFUs5iamtK0aVNat27NjRs3WLlyZbljJEnC1taWyZMnk5eXx4oVK8R8loeYM2cOp06dwsfHh4kTJ2JpaSneT6qBJEnY2Njw6quvYmZmxt69e9m1a5e+wxIEwUAEBcmbxgCOHZPXyWdn6zcmoWbQaqG0tHIPjUZOCgm1R3+b/rir3TlbcJa4vDiKtWIFvaHS6XTkaHL4POVzPk/5nG9Sv9F3SPeJq2MDptPpOJ+Xx/7MTAJMTYmwscFYXLw9FpEQqlkkScLNzY3x48eTnp7O0qVLyc3NLXeckZERTZo0ISIigtTUVBYtWqSHaGuGlJQUfvzxR7Kzs3nvvfdwcXERyaBqpFAo8Pb25uWXXyYzM5NPPvmEoqIifYclCIIBcHKCiAgIDYXCQrl1bPVqfUclGDpHR/jqK3njXGUe69dDnz76jlp4mkwlU4bbDsdYYcziu4vJ1+brO6Q6R6fTUaQtqtT3PkeTw4cpH/Jhyod8cfuLaoiucgyyZeylK1dQVHCh4mZkxM+Bgdiq1XqIqvrdLCricHY2uRoNXc3NaWphIS7gHpNICNU8FhYWtGnThtDQUK5fv8769esZO3bsX44pqxJ64YUXGD58OAsXLmTy5MlYWVnpKWrD9cUXX3Dr1i169OhBr169MDU1Fe8n1UiSJCwtLZk4cSLLli3j7NmzLFq0iEmTJuk7NEEQ9EySoHlzUKnkVfRXrsCaNXLrWP36+o5OMFRGRhAQAOHhlTv+xg2wt6/amITqJUkS4+3H8/Odn9mYuZH3XN7DUmmJUhLjAKpKgbaAw3mH2ZK1hUO5h7hRfINcbS4SElYKKwJMAuhp2ZPBtoOpb/LXN3AdOkopBaBUV6qP8CtkkFfHRpKESQUPI4WizlzAaHQ6zublEZ2VRX0zMzrZ2qKuI6+9KoiEUM0jSRKurq48++yzpKens3DhQgoLC8sdZ2JiQuvWrWnevDm3bt1i2bJleojWsMXFxbFq1Sq0Wi3vvvsudnZ2dea91NA4OjrywQcfkJOTw3fffcfdu3f1HZIgCAbAxga6dIHOneW2niNHYN48udVHEB5EoQClsnIPhUIMK6+NvIy86GLZBQ0aVmeuFivoq0iRtojo3Gj6X+1P3/i+zEybybH8Y6SVppGvzSdPm0dKaQqHcg/x8e2PaX+pPR8mf0ieNk/foT+UQV4df+7nx7rgYDb8z+PXwECs68gA1OuFhRzOyqJUp6OppSWNzc3FBdwTEAmhmsnKyoouXbrg7+/PpUuX2LZtW7ljyqqEpkyZQk5ODnPnzq0wcVRXaTQa/vnPf3L37l0mT55M48aNUakMsji01pMkCRMTE7p3707btm25efMm33//vb7DEgTBAEgSNGkCI0aAtbW8EWr3bti+Xd+RCYJgyBSSgrF2YzFVmLLk7hJytDnoEMOinqY8bR6bsjYx9PpQInMiKdQVUkopRpIRTU2b0tuqN32t+tLMrBkqSUWxrph0TTrfpH3DhIQJBp+kM8irY2OFAlOFAjOl8i8PU6WyTiRFSnU6/sjNJSY7myAzMzpZW6OsA6+7KomEUM1UViU0btw40tPTmTdvHqUV3C41NTWla9euNGrUiGvXrrF27Vo9RGuYli1bRmxsLA4ODkyfPh0rMZheryRJws7Ojg8++IDi4mJ+//13Ll26pO+wBEEwAMbG0LYtDBwo//fZs7B4MYh9CYIg/J2uVl2pb1yfm8U3icqJokgrZhQ+LcXaYmLzYpmWOI3bJbfRoMFeZc87Lu9wruE5DtY7yGq/1az0W0lUYBRnGpxhhvMMTCQT8rR5HMw9SGxerL5fxt8SV8cG6Ep+PoezslBKEk0tLalnZoa4fHsyZQkhcSFc89jY2NC3b19cXV05c+YMe/fuLXdMWZXQ888/T3Z2NrNnz6akpEQP0RqWzMxMvvrqK7Kzs3n//ffx8PAQvwMGQKVSERYWxrBhw7hz5w6ffPKJvkMSBMEASJI8E2bwYAgMhJISOHwYFizQd2SCIBgylaRiuN1wTCQTltxdQq62/CIW4fEkliTyz+R/cqf0DgCeak/mes3lQ5cP8TXyxVxpjpnCDDOFGRYKCwKMA/jA5QOW+SwjxDSEJT5LaG/RXs+v4u/V2IRQenExi2/f5m5JCbpatEOxWKvlj7w84nJzaWhuTicbmwoHbAuPRlQI1VxlVUKjR4++XyVU0e+8ubk5zzzzDD4+Pg9sL6trvv76a65du0bLli0ZPHiwGCRtICRJwtramrfeegtTU1N27NhBVFSUvsMSBMEAKBTQqhU8+6w89yU5GTZvhqNH9R2ZIAiGSkJilO0obFW27MnZQ3xRvEENLa6pcjW5HMo9xJG8IwA4qBx43fl1+lj3wURhgkL663WlJEkoJAUWCgv62vRlf+B+Olp2RCUZ9qiGGnt1/HNyMvNu3+bl+HiO5eRQ8t8L/pruYn4+h7KyMFcoaGFpia+JiagOegpEQqjmKqv+GTRoEFZWVsTFxXHo0KEKj7Wzs2PixIlkZ2cza9as+//uddG5c+eYN28excXF/POf/8TW1lYkgwyIQqHA19eXadOmkZ2dzSeffCKq2gRBQJLAzg66doVOneQB06dOyQOmi0QXiCAID+CocqSPVR+UKFl5b6WoEnoK7pTeYW3mWjRoUKEiyDiI5+2fRy39/cZzSZJQocJaaW3wySCowQmhYU5O+Juacq2ggLeuXePX5GQya3i1UIFGw8ncXE78tzqonZgd9NSU/VyIC+KaSaFQ4OHhwYgRI0hNTWXOnDnljpEkCXNzc4YNG4aDgwMnT56ss1UXOp2OTz75hIyMDMaMGUPLli1Rq//+w0uoXmU/r5MmTcLDw4Pjx4+zYsUKfYclCIIBUCggNBTGjpW3j+XmQnS0XCkkCIJQEYWkYIzdGMwV5qy4u4KMkowafV1sCHK0OcTkxQBgpbSil3UvzBXmlXquJEk15rqzxiaEAk1N+Y+fH2NdXECnY3laGjOuXeNMXl6NrRa6kJ9PTFYWdioVrSwt8TA21ndItYaJSRitWy/H2XkUiJqrGqdsEO/IkSMxMjIiOjqaU6dOVXick5MTzz77LNnZ2cycObP6gzUAmzZtYvfu3VhaWvLmm2+KQdIGSpIkXFxcePvtt8nNzeXf//43mZmZ+g5LEAQDUDZgevhw+b8vX4blyyE9Xb9xCYJguFqYt6CJWRPuau6yM2cn+dp8fYdUYxVpi7hZfPP+7CALpQWtzVvXyvNpg0oIjXJ2ZpyzM85q9UMv2ZWShI1KxXhnZ/7l54eniQnn8vJ48+pVFqemkl1aWqMW7uVqNJzIzeVMXh7B5ua0FdVBT1VeXjDHjg3g3r0O+g5FeExKpRJvb2+GDBlCWloa8+bNK3dMWdXF2LFjMTc359ChQxytY4MX8vLy+Oyzz8jKyuKNN97Ax8dHtEoaqLI19IMGDaJVq1Zcv36d2bNn6zssQRCesoICucJn+XKIj6/ccyQJfHxg0CBo0EAeMH38uPw1hLpLoQC1GoyM5KTho3y8SxKoVP//XKWy6uIU9EMlqRhlO4pwTTiH1h+iIM+w150bsiKdnBDS/TejYCKZUN+kvp6jqhoGdZXwrLMzk1xdcTYyqlQNhyRJmCqVtLSy4vuAAAY7OFCk1TLv9m3+ef06l/PzKa0hpXJn8/I4lJWFi5ERrayscDYy0ndItYpGo6KkxBgwQlQI1UySJGFvb8+YMWPQ6XTs2LGDixcvljtOoVDg5ubG6NGjycrKqnMX2LNnz+b8+fM0aNCAcePGYWZmVivvZtQWZTOy3n33XYqKivjll1+4fv26vsMSBOEpSE2FJUtg6FA5sfOvf8GjdDIrldC8OTz3nHwhn5gIGzfK1UJC3RQeDuvXyz8LR45A9+6Vf66HB3z7rfzcK1fgxRerLk5BfwaaDyTn5RzWz1jPscPHKBLDxx5Lqa6ULE3W/f9WS2oclA56jKjqGFRCyFSpxFSpRPmIPXdqScJZreYFd3fe8/bGSa3maE4Ob169yvr0dHI1miqM+slll5ZyIieHS/n5NLawoLWVldgs9pSVdRGKQomaTaVSERAQQL9+/UhNTWXRokXljpEkCUtLSyZOnIhKpWLnzp2cO3dOD9FWv5s3bzJz5kwKCgr4+OOPcXR01HdIQiWoVCratGnDgAEDSE1N5csvv9R3SIIgPKaySp4ZM+TB0NOnw+7dYGkJffpAixaP9vWsreXh0j16yOcy586JKqG6TK0GW1twcgIHB7nSp7KUSrCykp/r6AjmlRuFItQw1ibW9OnYB6VWycqVK8nJydF3SDWSBg152jxA3uJmrDB+6DDpmqrWXB5LkoS5UkmEjQ3fBQTQxdaWrNJSfkpK4tMbN7hWWIjGQKuFTufmcjg7G08TE1pZWWEvhr8+dSIhVDtIkoSjoyMTJ06kqKiIDRs2kJiYWO44hUKBj48PQ4YMITMzk99++00P0Va/Tz75hNu3bzNgwAA6deqEWq0W1UE1gCRJWFlZ8e6772JkZMSaNWvYv3+/vsMSBOER3L0rV26MHg0DBsDs2ZCQIA+H/uEH2L4d/vlPaNjw0b6uQgGNGsG4cXIC4N49uHSpKl6BUBMUFcEvv8DkyfLjzTflBGRlLnHi4+H77+XnffQRHDtW1dEK+iBJEmPHjsXKyooNGzaQkpKCxsCLIwyRhIQSua9Shw6NTlNrz6kNfw/aIzJSKPA0NmaGpydNLS1ZkJLCwcxMrhcU8JyrK+2srTEzoKbZ26mpnD9/njyNhjb16tHS0lJUB1UBkRCqPdRqNQ0aNKBbt25ER0ezePFi3n333b8cU1YlNGXKFFasWMGyZctwcnLCxsam3NcyMzP727/P1NQUo79p4SybW6RS/f3bqbW19d/+uVKpxMLC4m+PMTY2xsTEpMI/O3LkCOvXr8fIyIi3334ba2vrWvvBVRspFAoCAwMZM2YMv/76Ky+88AKnT5/+2589QRD0S6uF69dhwwZYs0ZO1OTnyxUcAwfKA6GbN5erMkxMHv8cxMgIWraEUaPgxx+homs7nU5OFl25Ilchibf/2qm0FA4ckJOPIP9MFRfLP3N+fn//3NRUuVotMlJOMjZuLLegCbVPvXr1aNOmDVu2bGHz5s14e3tjZWWl77BqFCVKzJX/X0ZXqiulSFuEsaL2LX2qdQkhAIUkYalS0dvOjoZmZsxJTiYuJ4cvb96kk40N411ccDc2NojES3R0NL98+SXmjo4Evfkm1p6e+g6pVhIJodpDkiQcHBx46aWXsLe3p0ePHhUep1Qq8ff3JygoiDt37vDll1+WG65cmZWQlTmmMkObn8YxfxdLUVER9+7d44033qBhw4Z/+7VOnTpF/fr1MTExEUkjAyFJEmZmZkyfPp05c+Zw/fp1PvjgA/7973/rOzRBEP5Hbi4cPiy3bh08CGlp8kV5/fowciT06gVeXmBmJidznvRtVpLk+S8DB8L+/XD6dPljUlJg1ixYvRrGjIHXX4eH3GMQaqjiYigs/P//XrECmjYFNzc58fggWu3/P7ewsOLEolA7qFQqRowYQVRUFEuXLmXMmDFYWlqKc75HoJJUWCv//2Zusa6YlJIUfIx99BdUFamVCSGQxwabKBQEmprynrc32zIyWJaWxs5794gvKOB5V1fCLS0x1WO1UFJSElH79nH5/HkmTZpE26ZNDSJJVRuJhFDtYmxsTMeOHQkPD8fS0rLCY7RaLenp6Vy9ehWdTkffvn2xs7O7/+c6nY7S0lLy8x+8klOn01FYWEhxcfHfHpOXl/fQctysrKy//fPS0lLy8vL+9piioiIK/3wW+CcFBQXodDpMTExQKpUP/ND/8ssvmTNnDm+++Sbjxo3DXAwRqBZarZbU1FTWr1/PuHHjKqwGUygUeHt7M3DgQNauXcvmzZtFQkgQDMitW7B1K6xcKc/yycmR57B06QIjRshVPPb2ciLoaZ9eKpXyRf+kSXKy538/ltRqOQGUkgI//SS3B338Mfj6Pt04BMNz5478MxkUBK1b6zsawVD06NEDb29vzp49S2xsLI6Ojpiamuo7LIOUp8njSP4Rtmdtx0ntxAznGZgoTPAz+v+yu0JtIZeLLouEUE2klCRsVSoGOToSZGbGnJQUzufn8+mNG/S2t2eEkxNOepqzceDAAXbv3k1ISAjdu3fH5iEtJcLjEwmh2kWSJIyNjTH+m2mK+fn5zJ07l5KSEgYPHsyPP/5YrmpGp9Ohe0jjfWWO0Zb9gD3BMU8ay5EjR3j22Wf5/fffGTduHP7+/hVWCfn6+nLv3j2+/vprunTpQkBAgFhLXw2ys7P59ttvWbJkCdeuXePrr78ud4wkSeh0Om7evAnwt4lIQRCqR2EhxMXBqlWwbx8kJcltYb6+8vavZ54Bf395aLSRUdWeZ1haQocO8nDqspahMnZ28PzzcmXSxx/D2rVw7Rq8/z707Fl1MQn6o1DIScDiYnl7XcuW8s+ik5O+IxMMgbm5OYMHDyY+Pp6VK1fSoUMHkRD6E41OQ3JJMusz17Mucx3nC89ToC2gmVkzJjlMwlphjbPaGQ+1B7dKbpGrzSU6N5ruVo+w2q+GqPUJIfhvKb5SSaiFBR/7+LApI4O16emsTU/nYn4+E1xcCLWwwLgaL4quXr3Knj17SE5O5plnnqFt27bioqwKlV1Di29x3aDRaEhMTGTx4sWYmZndby+rzRwdHenatSvbt2/np59+4osvvqiw+qdPnz60atWKffv2sWDBAt56661ys5WEp6ukpISzZ8/y+++/o1KpeOaZZx54XExMDKdOncLY2Jg33nijmiMVBKFMWpo8a2XFCjhxQh4arVTKF90jRkDbtuDsLF+QK5WP3hbWrp38tQsK5K/h6vrw5ygUclvajz/KA6pBfm5ZIsrBQR5o7eUF//437NwJr7wiVxW9+OLftxMJNY9CISf7rl6VB0svXw4hIXJroTjfFSRJYsSIEfz222/s2rWL69ev4+Dg8NCZl7VdgbaA0wWnWXl3JTtzdpJSkkKhthAXtQsT7CcwwnYElgq5vc5GaUMHiw4su7eMbE02u3J28Vrpa9iqbPX9Mp6qOvV2oVYocDYyYpSzM+97e+NnasrZvDw+uXGDhbdvk15SQnXsIdPpdBw4cIDIyEiaNWtGjx49xKCvKqbVykkh8QFZN+Tl5TF//nxyc3Pp3r07zZo103dIVc7ExIR//vOfWFpasnDhQk6fPk1paWm548zNzXnzzTexsrJizpw5XLp0qcLjhKfn3r17fPPNNxQVFTFs2DBatmxZ4XFFRUX8+uuvlJaW0rRpU8aOHVvNkQpC3VZaKl9Yv/++XIXz0kuwY4d8/jB6tFx1s2CBnBAKDAQbG1CpHm9GkKWl/DUaN5aHAT9kv8F9ajW4u8vPK3tu2d8vSfLXadkSvv5ajv/WLfjmGzkxlJz86HEKhq1TJzm5aGEhV4StWwfnz+s7KsFQeHl50blzZ7RaLRs3biQ7O1vfIemFDh13Su+w8t5KRl4fyeBrg5mbMZdbxbdoZtqM7z2+Z3fAbv7h/A8amzZGKck9vw4qBwbaDESBAg0arhReYWb6TD2/mqevzl0eS5KEhVJJCysrPvLxoY+9PQUaDcvT0vhi7VpOnT9f5WX6Fy9eZM+ePWRmZhIREUGrVq3EkK8qJlrG6g6tVktycjKLFy/G3NycqVOn1plNTSEhIYwcOZKioiL+/e9/VzgfSZIk2rZtS58+fcjPz2fWrFncu3dPD9HWDfn5+ezcuZNdu3bh7u7Oa6+9VmGrY3FxMbt27WLnzp24uLjw22+/PXTrnCAIT0dmJmzeLK92HzFCnsFz4YI8k+Wrr2DLFvjsM7ldy9VVTro86fmEJMlfQ6mU/7eyp4GSJD+UyoqfK0lyxVBgIMyYIa+8NzKSq0fGjZOHYAu1h7k5DB0KzZrJQ6K3bYM9e+T5VoKgVCoZMWIEVlZWrFq1ioyMjIeOJ6hNirRFnM4/zYfJH9IzvicvJb7ErpxdSEiMtRvLBr8NLPZdzGi70fgb+2OjskEl/X8FlYlkQjOzZvS17gtApiaT3zN+55f0X9Dy96MgtDotGaUZZGsMPwlXZy+PjRUKvIyNecHNjTc9PWl89y6bZs1izLBhzJ07lzt37lTJ36vRaIiKiiIqKorw8HC6desmhrpWA5EQqjtyc3NZtGgRWVlZdO7cmVatWuk7pGqjVqt5/fXXcXFxYc+ePezZs6fCBHdZO5KzszMbNmwgJibmgcOqhcen0+lITU3l66+/Rq1WM3XqVHx8fCo8Lisriy+//BKtVstLL71EgwYNqj9gQahDdDq5kuLf/4a+fWHKFHl9fG6uPBdo+XJYsgQmTIDgYLkd62lsDKsuSqXc0jZsGMyZA6GhcjJo2jT4+We5GkqoHcLDoUcPOVmZmSm3IsbE6DsqwVC0a9eOhg0bkpKSQlRU1EMXmNQGWZostmdtZ2riVAZdG8SP6T9yqfAS/kb+fOr6KTsCdvCJ2ye0s2iHm9oNC6VFhcUZkiThofbgNafX8DbyRouWxOJEPrn9CeMTxhOVE0We5v+/n1qdlkxNJruzd/N60uv0iO/BhswN1ffCH1OdbiJUSBI2KhURNjYEBAVRGBzMutWr+fDDDzlw4ABvvPEGTZo0Qa1WP7W/8+zZs0RGRlJUVERERATNmjUT1UHVQCSE6oay6qCFCxdiamrK1KlT/3bwdG3k7e3NCy+8wCeffMIXX3xB27ZtcXJyKvc+U79+fUaPHs2PP/7IDz/8QFhYGG5ubuL96CnKyspi0aJFXLlyhbCwMJ599lmUFaweKiwsZP78+fzxxx80bNiQSZMmVXicIAhPLj8fYmPlrUzR0fJWroICaNgQXnhBvrD28QFrazA2rjkJoIooFHJrWseOcrLg119h7lz4/HO4fFlujavl4/XqBGNjOfF3/Lic1Dx5Ut6GV78+eHvrOzpB30xNTRk0aBCnT59m1apV9OrVq1ZWIJfoSrhWdI2NmRvZmr2V+KJ4sjRZmEqm9LDswVDbobQwb4GN0gZzhfn9trCHUUtqmps150ePH5mRNIPLRZe5XXKbtffWsi9nH9ZKa5xUTlgprcjSZJFSkkKuNpdsTTZFuiJW3VvFOPtxVfzqn0ydTgiVMVUq8XN25l8ffUTzZs34/vvv2bp1K5cvX+bll1+mf//+2No++fCo0tJS9u3bx8GDB2nVqhXdunUT096rSVlCqCaf2AkPl5uby7Jly7h79y49e/akTZs2+g6p2imVSp5//nlWr17N2bNnWbVqFc8//3y59xqVSsWUKVPYsmULsbGxbNu2jZEjR9bKkwR9KC0t5cqVK/z6669YWFgwY8aMCj9HNBoNN27c4Mcff0SlUvH+++9jZ2enh4gFoXZLSLjB5s32bNhgTny8REYGmJpCt25yy01ICDg6ygmU2jRzVZLkhEGDBvD221CvHnzxhTwL6coV+OQTud1IqNl8fOTKtgsX5Mf69dC0qTz76ine1xZqqEGDBvHzzz9z+PBhzp07h6OjY625YZqjyeF4/nHW3ltLZG4kt0tuU6QrIsA4gCkOU+hh1QMvIy9slDaYKEyQeLSLQUmSMFOY0dWqKwtUC5hzZw6rMleRp80jvySfpJIkLnEJpaREo9NQilx+qUCBr5EvXSy7oEX7yH9vdapFH3lPRqlU4uTkxIgRI2jcuDE//PADu3bt4h//+AexsbH3S/ifZBPYyZMn2bt3LwCdO3cmJCRE3I2vJmLLWO2n1WpJTU1l4cKFmJmZMWXKFEzq6EoVe3t7Xn/9dV544QV++OEHevfuja+vb7n3Lzc3N6ZMmcJ7773HDz/8QMeOHcUa+qfk7t27/PTTT2RnZzNkyBC6du1a4ft9fn4+3377LXfu3KFfv3706NFDfC4IwlNSUlLC8ePH2bhxI7t27Uap/JyrVzvh7Kxm+HB5cHRAgLyy3di4dp8jqFTyMOqxY+Xkwccfw969coXUa6/BqFG1+/XXdiqV3Pp47BjcvAlJSfKA6QYN5CHjQt3m7OxM9+7dmTt3Lhs3bqRp06Y4OTnpO6zHptFpSCxOZHv2drZmbeVMwRnuae6hklS0Nm/NMNthtDFvg73KHiul1V/mAj0OSZIwxZRw83C8jb2Z6DCRw3mHOZZ3jJTSFDJLM9HoNFgqLXFRuxBkEkRzs+aEmobioHK4nwwyV5gz3n48AKaS4RSFiITQn0iShKWlJWFhYXz11Ve0aNGC2bNns3z5cs6dO8fLL7/82BvBioqKiIyMJCYmhg4dOtClS5dak5mtCUTLWO2Xm5vLihUrSEtLo1u3bnTo0EHfIemNJEk888wzLF26lMjISObMmcP7779frvpHoVAwfPhw1q1bx6FDh1ixYgUvvfTSU6mIrMsKCwuJjY1l48aNODk58corr2BWwQqhkpIS4uLiWLVqFdbW1rz99ttippwgPAWpqals376d9evXc+HCBdLT09FqtfTrd45XXgkjJMQeF5cn2xJWEykU8mvu1g3c3GDmTFi2DD78EC5dgrfekiukhJrJ1hYGD4bTp+V5UVFREBYmJz1Fa2DdplAoGDlyJGvWrGHDhg1MmTIFBweHGncDMF+bz9mCs6zPXM+enD0kFieSq83FXe3OFNsp9LLuRYBxAHZKO8wV5k/1BpskSahR46pyxUnlRLBJMKNsR1GiK0GDBp1Oh1JSopbUmChMsFBY/KUiSYcOS6Uln7t9Ln89A6oYEgmhCqjVajw8PJgwYQKNGjXi+++/5/Dhw7z55pvExMTwwgsvEBgY+Eg/ZMeOHSMyMhITExM6d+5MUFCQuAtcjURCqHbTarXcvn2befPm3a8OqugCvC6xtLTknXfe4ciRI8ybN49BgwbRrFkzVP/TC2FjY8Nrr73G2bNn+e233+jevTvh4eHljhMqp2yQ9DfffIMkSTz77LMEBwdXeFx2djafffYZxcXFTJ06lZCQED1ELAi1g1ar5dy5c2zatIlt27Zx/fp17t69i6urKyNGjKB///40bNgEe3trTE3r7vmAJIGJCTRpAv/8pzxn5uuv4Zdf4OJFuYVMzLSvmRQKuRqoTx+4ehWSk+XWsSZN5HYyoW4LCQmhWbNm7Nq1iz179uDt7Y2NjY2+w6qU1JJU9ubsZUPmBk4WnOROqbz8qZlZM4bYDKGdRTtcVC5YK60xUlTtZmFJklChwkZlgw02j/Q8JUpc1a5VF9xjEmf8DyBJEjY2NnTo0AFfX19WrlzJvHnzWLBgAefOneOll16iU6dOlbqbm5+fz549ezh69Cg9e/akc+fOojqomomEUO2Wm5vLmjVrSE1NJSIigoiICH2HZBBatGjBgAEDWLZsGT/++CMzZ84s9+EvSRIdO3akS5curF+/nnnz5uHr64uzs7N+gq7hsrOzWbt2LcePHyc4OJjnn3++wsUEhYWFrFmzhtjYWPz8/Jg+fTpGRlV7EiMItVFeXh779u1j5cqVnDx5ktTUVHJzcwkNDeXVV1+lU6dOeHh4YGdnh5GRkbgZh5wUUqnk1rHnnwc/P/joI3lleXKyvK5+wAA9Byk8FlNTGDQITpyANWvkeUI7d0LjxvqOTNA3Y2NjBg8eTGxsLCtXrmTgwIEGnRAq1hZzsegimzI3sTtnN9eKrnFPcw9HlSPDbIfR26o3jU0b46BywEJhgUISF3mPSySEHsLExAR/f3+mTp1KcHAwP/zwA7GxsSQkJDBmzBjGjRuH90NG+MfGxhIVFYWdnR1du3YlICCgmqIXyoiEUO2l1WpJS0tjwYIFmJmZMXnyZNF2818mJia8/vrr7N69m82bNzNixAi6detWLiFtZmbGyy+/zKFDh1i7di0DBgygc+fOdXYG0+PSaDQkJCTwyy+/YGZmxosvvoira/k7QVqtlpSUFL7//nsUCgWvvfYabm5ueohYEGquq1evsm3bNjZu3Mi1a9dITU3FysqKnj17MnDgQBo0aICLiwtWVlZia98DKBTg4CDPnvH0hG+/hY0b4Z135BayV16Rq4kE/Sopkf+tKvtj7OMjJ/QuXIAzZ+SNY+Hh4OtblVEKNUGvXr348ccfOXfuHCdPnsTV1dXgFhyll6QTnRfNxsyNHMs/RmpJKsW6YoJMgpjmOI1Olp3wMvLCVmn7WEOihfJEQqgSFAoF9vb2dO/eHX9/fxYsWMDKlSv56aefOHnyJNOnT6dt27YVXjxlZ2ezZ88eTpw4wcCBA+nYsaO4C6wHIiFUe+Xm5rJ+/Xpu3bpF27Zt6dq1q75DMij169dn/PjxfPPNN3z99dc0b968wjX0ISEhDB48mDlz5jBz5kyCg4Px9PQUd9MfQUZGBnPnziU5OZnu3bvzzDPPVNifn5eXx88//0xCQgIdOnRg8ODBNa6PXxD0obi4mEOHDrFq1SqOHj1KcnIyd+/epV69erz00kv06tULX19fHB0dMTExEe9flSBJYGYGzZvDp59CUBD88IP8uHhRni/k46PvKOum1FR58PemTTByJHTvXrnnqdXysXFxcP26PGB6xw6ow6MVhf+ys7Ojd+/exMfHs2HDBlq3bm0QCaHS0lIuX77MgbsH2GOzh2McI6M0AxulDb2te9PHug+NTRrjpHbCWmn9xEOihb8S381HYGZmRsOGDZkxYwbNmjVj1qxZ7N27l6tXrzJhwgSGDx9e7m7woUOHiIqKwtXVlS5dujy0mkioGiIhVDuVVQeVzQ6aNGmSWJv+P8rWy2/atIm4uDg2bdrE6NGjy81YMjY2ZurUqURGRhIdHU1kZCRDhgwR389KKioq4sSJE6xYsQJHR0def/31ChcQlJSU8Mcff7Bw4UIsLCx45513DLpkWxAMQVJSErt27WLDhg1cvHiRlJQUlEolERERDBw4kNDQUFxcXLCzs0OlUolE0COSJDmJEBgI06fLLWT/+hesXStvrPrgAxCd2NWjtBROnZKTQPv3Q0IC3LkjV3A1ayZvxKsMOzt5wPSZM7BnD0RGyl+7oKAqoxcMnUKhYNiwYSxdupQdO3Ywbdo0nJyc9DY3Mjc3l2PHjrF27VoOHz5MiUcJecPysGpqxWin0XS17EqAcQD2KntMJVPx3l5FRELoESmVSlxcXHjmmWeoV68e8+bNY8OGDXz11VecOHGCF154gebNm6NWq7l37x579uzh7NmzjB49mg4dOlQ4S0KoeiIhVDvl5uayadMmbty4QZs2bejZs6e+QzJIrq6uTJs2jRkzZvDjjz/SuXPnCtfQe3t7M3bsWD777DNmz55N69atCQwMFNUrD6HT6UhLS2P27NmUlJQwZMgQwsPDKzwuOzub7777jtzcXMaPH0+rVq30ELEgGD6tVsuxY8dYt24d+/fvJykpibS0NNzd3Rk7dizPPPMMAQEBODs7Y2ZmJt6nngKlEpydYcgQ8PaGL7+UkxKvvALPPQdTp8qJI+Hpy8yEw4flIdDHjsGtW3LyJjRUTtL16gWOjqDRVO7rSZKcQOrdW27/u3VL3jxW2ecLtVdgYCCtW7dm/fr17Ny5k3r16mFfjWvodDod169fZ+fOnezatYsLFy5w+/ZtTExM6BTcib5efWnk0whXtSt2KjvUknjTqWoiIfSYLC0tadq0Ke+++y7NmjXjl19+YdOmTVy+fJmJEycycOBADh8+zP79+/H29qZz5864u7vrO+w6SySEah+dTkd6ejrz58/HzMyMiRMnVliRIch3hIYOHcratWs5ePAgS5Ys4bXXXiv3/VIqlYwaNYrNmzdz9OhR1q9fz+TJk7Gr7C3JOionJ4dt27axf/9+/Pz8mDx5coUtxIWFhezYsYPdu3fj7u7Oyy+/bBCl2oJgSMpupq1fv56zZ8+SlJREYWEhbdu25Z133iE8PBx3d3ccHR1Rq9XijvFTplDIq+fbt5cTEAsWwM8/y5vILl2Sq4VcXPQdZe2g08lteTt3yo+rVyElBSwsoGtX6N8fgoPBzQ2srORB4Hl5lf/6pqby1zh5EpYvh4yMqnstQs1hZGTE4MGD2bdvH2vWrGHEiBHY2dlV+XtpYWEhp06dYsOGDURHR5OQkMC9e/dwd3dnzJgxdO/eneDgYBydHDE3NRdDoquRSAg9AZVKhYeHB0OHDiUgIIA5c+awe/duPvvsMw4dOkRycjKXL19mwoQJtGnTRqxx1qMBA+Qe+A4d5LsmQs2Xm5vLtm3buHbtGs2bN6dXr176Dsmg2dra8uqrr3L8+HHmzp1Lnz59aNKkSbn3JUdHR6ZOncrFixeZO3cunTp1IiwsTLx/PYBWq+XGjRv88ssvGBsb89xzz+FbweROrVZLamoq3333HTqdjhdeeEEsGBCEPymbabFt2zZu3rxJcnIydnZ29O3blwEDBhAUFISbmxuWlpaiGqiKSRIYGUGjRvDqq/Iw4s8/h2XLIDFRXldfQRGkUEn5+XD0KGzYALGx8vf03j15ftNLL0GnTuDvLyfeTE2f7LzVy0teOX/2rJwYqsj163JVkpsbtG4tbp7WBZ06daJevXocPXqU2NhYPDw8qmxEwO3btzlw4ABbt27l1KlTJCUlodPpCAsLo1+/foSHh+Pp6YmDg4NYZqIn4gz/CZWtp2/Tpg0eHh40adKE+fPns379ekpLS/Hx8aFNmzZihbOetWwJTZrIH6xCzVfWolM2O2j8+PFiDksldOjQgZ49e7Ju3Tp+++03vvzyy3LVP5Ik0b17d9q3b8/WrVtZtmwZ3t7euIhbwhXKyMhg2bJlxMfH06FDB0aMGFFh8iw3N5clS5Zw4cIFwsPDGTt2rEiyCXVefn4++/fvZ8OGDZw8eZJbt25x584dQkNDmTBhAh06dMDT0xMXFxeMjY1FNVA1UyrBwwPGjJGTQp99Brt2yVUs06bBuHGV33wlwI0bsHs3bN8uV1slJcnJl7Zt5RuXoaHg7g729k+vNU+lgs6d5WTQ9etya9qfFRfLMX3/vdwu+PzzcixiWWvtZmVlRe/evTlz5gzr16+nU6dOTzUhVFpaytmzZ9myZQv79+/n2rVrpKWlYW9vT9++fenVqxchISG4urpiaWkpNkHqmTgbfUqMjY0JCAhgwoQJmJiYMGvWLC5fvkxKSgr79+8nJCSEoKAgcTJTDd5+W+6VNjeH776Tt2eUPf7s55/h0CF53erQoeKkpibJzc1lx44dXLly5f4dBvG79XDm5ua8+uqrREdHs27dOgYMGECXLl3KraG3srJi6tSpxMXFsXLlSnr37o2trW254+q64uJizp07x5IlS7C1tWXq1KkV9uGXlpZy5coV5s6di6mpKa+88gqOjo56iFgQDENiYiLbtm1j8+bNXLt2jVu3bqFWq4mIiKB///73txza2dmhUCjE+7seKRRgYyO3MDk5wY8/wqpVWmbPvk1y8lJefHEy1tbW+g7TYJWUyEOid+yQBztfvy5vD/PxgbFjoVs3eZi3u7t83loV1Tm2tnKS59Qp2Lbtr3+mUsmzhpo2lRNVn30G167Bs8/K1UVC7SRJEoMGDWLhwoUcOHCAy5cv4+rq+sSbsDMyMoiJiWHLli2cOHGCmzdvUlBQQEhICJMmTaJly5b4+Pjg7OyMqakYEm0oRELoKZIkCSMjI1JSUkhPT8fLy4uSkhJWr17NzZs3GT9+PD169MDS0lLfodZqe/fCuXPyh1yDBnK5c0WOH5eH9/n5VWt4whMqmx20YMECTExMGDt2rJhx8whCQkIYNmwYP//8MzNnzqRp06a4uLiU+1Bu0aIF/fr1Y+HChcyZM4egoCCxJfF/pKamMmfOHHJychg5ciQREREVntxkZWUxe/Zs0tPTGTRoEN26dRMnQUKdU1JSwpEjR9i6dSsHDx4kMTGR5ORkAgICmDZtGp07d8bHxwd3d3fMzMzE74gBkSQwNoawMLldrFGjQtas+YZZs1Zw9uwJPvzwQ4KCgvQdpkFJS5PPR7dtg/Pn5RuV+fly8uXll+XKdS8vOclmZFT5tjCFAho2hPR0+Ty3Mg0IkiTPIho1Sq48undPrvhydJS/XnAwvPuunKRaulS+YXrtGkyeDK1aiVELtZWXlxft27e/v3EsJCTksTpatFrtX4ZEX7p0ieTkZMzNzWnfvv39EQUeHh7Y2NiI6mgDJP5FniKdTsfWrVvZtm0bTk5OTJ06FScnJxYtWsThw4dJSEi4v3EsMDBQ3+HWWkVFUFgo///vv4cePeTE0P9+oJWUyBscSkrkwX5CzZCbm8uePXu4cOECTZo0oX///uLC4REYGRkxefJkduzYwaFDh9i1axdDhw4tt4be1NSUSZMmceDAASIjIzl48CD29vZiDf1/5ebmEhUVxY4dO/D29mbKlCkVfm8KCws5cOAAGzZswNHRkZdfflncFBDqpKSkJN58803i4+MpKCigXbt2vPXWWzRr1gxvb2+cnJxQKpXi/dxASZJcSR0QAM89pyY4uCvvvbebTZs2kZyczMsvv8ygQYPq9L+fVgsXLshtdbt2yUOik5LkxEufPtCzJ9SrJyeCrK0frzLd2Fje+DZkiPxv4uFRueeZmsqbypo1k897TUzkZBTI/z8oSG4D9PGBOXPkG6bJyTBpklxJL0a71D4qlYqhQ4eyY8cONmzYwJgxY3B0dKz0jLacnBzi4uLYunUrx48f5/r162RkZODv78/06dNp3749/v7+uLq6YmFhUaffGwydSAg9RXFxcaxYsYJbt24xZcoUhg4dirW1NfXr12fVqlWsWrWKX375hQsXLvDcc8/RoUMHMTyrCoWFyVVAn38ub8lQKsVdjppOp9Nx584dFi5ciImJCaNGjcLBwUHfYdU4vr6+TJw4kY8//piZM2fStm1b/Pz8yp0E1K9fn2HDhvHNN98wZ84cwsPDqV+/fp3/UNfpdNy6dYvffvsNhULByJEjadSoUYXH3blzh5kzZ1JSUsLYsWMJDQ3VQ8SCoH8ODg64u7vTsmVLunTpgq+vL15eXlhZWdX595SaRKEAR0cVXbp0xNp6JrNmzWLt2rWkp6dz8eJFXnnllTp34yA7G/bvh61b4Y8/4OZNeaNXcDCMHg3t2skVOW5uckLnSX7cFQp43GJdOzv5URGlUo5v6FC5fW3uXNi3T25vu3ZNbm9zdX38uAXD1Lx5c4KDg4mMjCQ6OhpfX9+HzuRMSkpi7969bN++nfPnz5OYmIhKpaJFixb06tWLpk2b4uvri4ODA+qnNQxLqFIiIfSUpKens2LFCqKjo+nYsSMDBw7E2dkZpVJJaGgoTk5ONGrUiHnz5rF7926uX7/OsGHDGD58OJ6envoOv1YaN06+M7N5s/wYMEDfEQlPKi8vj8jISM6cOUOjRo3q/N3Ix6VUKhkxYgSbNm3i6NGjrF27lhdeeKHcHAiVSsXo0aPZuXMnJ06cuF/9WNdb9O7evcv69ev5448/CAsLY/To0RWe9OTm5rJ27Vri4uIICgriueeee+L+fEGoqSwsLPjXv/6FhYUFrq6uqFQq8f5dQ0mShKmpKW3atMHBwQE/Pz9mzpSTQ9evX+f999/Hx8dH32FWuevXYc8eeT7QpUtyIsjMTN5o26uX3Nrl4yMPia4JXTKSJM+LioiQN5z5+sLKlTBzJiQkyAOnmzXTc5DCU2VhYUGfPn2Ii4tj7dq19OzZs8KEUFFRESdOnGDHjh0cOXKE+Ph4UlJS8PDwYPTo0fe3lnl4eGBlZSU2QdYwNeDtyfCVlpayfv16tmzZgqenJyNGjCA4OPj+xHSlUomHhwf9+/fHz8+PJUuWsGnTJn744QfOnDnD+PHjadeunciiPmV+fvL6zvfek4fktWsHDg6iSqimKpsdtHDhQoyMjBg5cqTY3vcEnJ2dmTp1KmfPnmXu3Ll069aNkJCQcr3d7u7ujBs3jitXrjBv3jzatm1LeHh4nd0IUVJSwoULF5g3bx5WVlZMnDgRd3f3csdpNBquX7/OL7/8glqtZsqUKXiJCZ1CHdewYUN9hyA8JZIkoVKpCAoK4sUXXyQwMJAvvviCVatWcevWLd544w26deum7zCfuuJiOHJEvtF47JicKElOltfET5okb/Ty9wdPTzk5VBPPOc3MICRETmT5+MC8ebBihXyTddIk6N5dnnsk1A59+vTh999/5+TJk5w7dw4PDw9M/7uWOS0tjf3797Njxw7OnDlDQkICJSUlNGnShClTphAWFoa/vz8uLi4YGRmJJH8NJRJCT8GhQ4dYvXo1WVlZPPvss3Tq1On+L9KfWVlZ0aJFC1xdXQkNDWXx4sX3N2yMHDmSQYMGidXOT5FCARMmwNq1cPo0zJolD0MU71U1U15eHvv37+fUqVM0bNiQwYMHiw+eJ1C2Xj4iIoKtW7eyaNEiPvjgg3JbshQKBf369WP79u3s3LmTtWvX1uk19KmpqSxevJi0tDT69+9P7969K7wTlpmZyfz580lMTKR79+4MGDBA3DETBKHWUSgUuLm5MXToUFxdXfn222/Zu3cv6enpXLhwgSlTptSKDZUpKRAVJVcDnT8vt1FpNPLK+Ndfh8aN5RuRrq61Y0SBSiXPOho1Sm4h+/13uS3u9m35tY8aJd9kFWo+V1dXOnXqRHx8PNu3b6dp06bcu3ePyMjI+xvIbt68iYODA3379qVLly4EBQXh4+ODjY1Nnb1BWJuIhNATSkpKYvny5cTFxdGvXz+eeeaZv51polar8fX1ZcSIEQQEBLB48WJ27tzJN998w7lz5xg3blydvvv+tLm4yGvoR4+Wh+T16gUtWtT8D+q6pqw6aMmSJRgZGd0/8RSejLW1NS+++CJHjx5l1apV9O7dm44dO5Y7eXdwcGDSpEmcOnWKlStX0rVr1zq5hj4/P5+jR4+yceNG3N3dmTx5coXtc2Wl1StWrMDOzo6XXnqpwnX0giAItYEkSVhYWNC1a1ccHR1ZsGABc+fO5dtvv+X69eu8/fbbNfImQmlpKSdOnOD48RK2bGnO1atG3LghJ0pGjpRbqwID5dYqC4uqWRmvT5IkVwn16CEnupYuhTVr4IcfIDFRHm4tlsvVfEqlkkGDBrFx40a2bNnC7du3uXfvHtevXycrK4uQkBBGjBhB8+bNCQwMxN3dHRMTE3FTthYRCaEnUFRUxOrVq9mxYwf169dn+PDhBAYGPvQusCRJ2NjY0L59ezw8PGjcuDFLlixhxYoVxMfHM3LkSPr16ycuIJ6Snj3hmWdg3Tr44gu57LWOXcfWePn5+URHR3Ps2DHq1avH0KFDRbXFU1K2Xn7BggXMmTOH4OBgXF1dy33Qt2nThq5du7J8+XKWLl1KvXr16sSMiDI6nY7ExETmzp1LaWkpAwcOJCwsrMLj7ty5w+zZs8nLy2PixIm0atVKDxELgiBUH0mSUKvVNGvWDDs7O3x8fPjmm29YuHAhN2/eZMaMGTXmvTAzM5ODBw+yfft2Tp8+TXq6kuzs9TRoYMezz0o0by5vW3Nzk9e41/brYnNzCA+XK4I8PGDhQvlx8yZMnAhduz7exjRB/3Q6HVeuXCEmJoacnBzu3bvHzp07sbGxoU2bNvTo0YPQ0FD8/Pyws7MTK+NrKfGv+gQiIyNZs2YNxcXFDB8+nDZt2jzS1jBjY2Pq16+Pvb09gYGBLFmyhKioKG7evMn58+cZNWoUoaGhIgP7hKys4B//gOhoefjf+vUwfLi+oxIqq6w6aOnSpffvYnhUds+q8FAmJiZMnjyZqKgoIiMj2b9/P/379y+3ht7c3JwJEyYQExPDjh076NGjBw4ODnVmm0xmZiY7duwgJiaGRo0aMW7cuApbg/Py8tixYwdRUVH4+voyefLkCo8TBEGojRQKBb6+vkyYMAFvb2+++eYbNm/eTGpqKs8//zxjxowxyItKnU7HhQsX2L17NwcPHiQ+Pp6rV69ibW1N167d6d5dS0CA3BZmZ1f7qoEeRq2WZyNNmCBXSM2bJ7fPpaTIiaFhw+B/9lIIBiwvL48jR46we/duTpw4QXx8PHfv3qW0tBRzc3Pee+89OnbsiJ+fH+bm5uJatJYzvHfkGuLq1assW7aMs2fPMnz4cPr27Yutre0jfx1JknB0dKR79+74+PjQuHFjVq5cyYIFC7h06RIjR46kd+/eWFlZVcGrqDuaNIEpU+DTT+Grr6BTJ31HJFRWfn4+MTExHDlyhICAAIYPHy6qg56yBg0aMGLECL7++mt++eUXmjdvXuEa+tDQUPr378/s2bNZsGABTZo0oUGDBrX+REGj0XDlyhUWLFiAubk5Y8eOJSAgoMLjEhMT+fXXX1EoFEyYMIH69evrIWJBEAT9kSQJOzs7+vfvj4uLC3PmzGH58uWkp6eTkJDAK6+88ljnzFWhoKCA2NhYduzYwbFjx4iPj+f27duEhIQwbdo02rVrR7169fD2tnrilfE1nUIBzs7Qr59cHbV4MWzaBN9+C7duwfjxcvucYLgSExM5cOAAe/fu5dy5c8THx6PVamnZsiXTp09nyZIlXL58GVtbW/z9/TE3N9d3yEI1EAmhx5Cfn8+qVavYt28fTZo0YdiwYfj4+DzRRZGpqSmNGzfGycmJevXqsXTpUqKjo7lx4wYXLlxgxIgRNGjQ4Cm+itpBp5O3O+zYIQ+4e1CBlkolb0bYsgX++EMuddVqqzdW4dGVVQctW7YMpVJJ//798fb21ndYtY5KpWLMmDHs3LmTkydPsnXrVsaPH19uDb2RkRFjxoxh//79xMXFsXfvXlxdXQ3mxL6qpKamsnr1aq5fv07Xrl0ZOHBghXPesrKyWLlyJRcuXKBNmzYMHz5czIMTBKFOKmsha9u2LQ4ODvj6+vLTTz/x888/k5CQwJtvvklwcLDe4ktOTiYyMpLdu3dz6dIlLl++jE6no3Xr1rz++us0adKEgIAAXFxcUCgUtf7Gx6OwtITWrcHRUW4hW7JEntN544ZcQdShQ91OnBma0tJSzpw5Q1RUFAcOHODSpUskJCTg4eHBgAED6NixIw0bNiQgIIDc3FxmzpzJ9u3badu2Lb4iw1cniITQI9LpdOzYsYO1a9eiUqkYNWoU4eHhGD2F/YsKhQJXV1f69euHr68vq1evZt26dfz2229cvnyZsWPHPnCDWV2j08kfPNu3y48LF+TBd/37P/g5np7ygOlx4+DXX+WtCYJhKygo4MiRIxw6dAhfX19RHVSFvLy8mDhxIm+//Ta///47HTp0ICQkpFxCw8/Pj+HDhxMfH8+iRYto2bIlYWFhtTbxUVhYyPHjx1m1ahXOzs5MmDABJyencscVFxdz5swZFi1ahJWVFVOmTBGDzwVBqNMkSUKSJIKCgpg2bRo+Pj58++23rFy5kuTkZF544YVq3cBYNiR63759HD58mMuXL3P16lW8vb0ZOXIk7du3JzAwkHr16mFhYSGSQH/D2FgeKD15Mnh7w/z58kiGpCT5PHvQIHn2kKA/d+7cITo6msjISM6ePUt8fDz37t2jadOmjBo1iqZNm1KvXj28vLzur4wfOHAg69atIzIykvj4eDw8PFCr1fp+KUIVEwmhR3T27FmWLl3K1atXGT9+PN26dXvq7Vzm5uaEhYXh4uJCw4YNWbFiBbt27eLGjRucP3+eIUOG1Klhrn+m08Hly7B1K0RGyomgxERo3lyeFfQwvXvLSaM1a+DuXfnrCYYrPT2d5cuXI0kS/fr1w8/PT98h1VqSJNG3b182btzInj17WLVqFZ6enuW2JioUCvr378+uXbuIjIxk8+bNeHl51cgNMg+j0+m4desWS5cuJScnh4EDB9KhQ4cKj8vIyGDBggWkp6czfPhwunTpooeIBUEQDI8kSTg5OTFixAhcXV355Zdf2Lx5M2lpaSQkJDBlypQqbU25d+8e+/fvZ/fu3Zw7d44rV65w7949mjdvzujRo2natCkNGjTAy8sLpVIpEkGVpFTKrWODB8v/u2iRXLGfliYnhsaOFTdfq5tWq+XKlSscOHCAqKgozp8/T3x8PDY2NrRt25aIiAiCg4MJCgrC1ta23M28+vXr06JFC1auXElUVBQhISE4Ozvr6dUI1UUkhB5BVlYWy5Yt4+DBg7Rq1YohQ4bg6elZJR8cKpUKb29vhg4dSkBAACtWrGDz5s38+OOPnD9/npEjRxIREVEnsrZlSZtTp2DzZoiJgbNn4c4daN8eXn1VnhEUGvrwr2VpCW+9BYcOya1mf3bypJxgGjKkbmyNMHT5+fnExcVx8OBBvL29GTFiRK2tQjEUDg4OTJ48mZMnT7J8+XK6detG27Zty62Xd3V1Zdy4cZw9e5YVK1bQrl077OzsnkqlpCHJysoiMjKSvXv3Uq9ePcaPH4+lpWW54/Lz89m/fz9bt27Fy8uLKVOmlGu3EwRBqMskScLExITu3bvj7OyMn58fv/32G9999x3Xr1/n1Vdfxd/f/6n9fVqtlgsXLrB//36io6O5cOECly5dwtHRkS5dutCxY0eCgoJo0KABtra2Ign0mCRJHiYdEQEuLnIL2YoVMGuWXMk/fjy0bKnvKGu/nJwcYmNj2bdvH6dPn+bSpUskJSVRv359Jk+eTOvWrQkKCsLf3/9vV8YbGRnRr1+/+zf8Bg8ejKOjo6jOr+VEQqiStFotmzZtYvPmzdjZ2TF69GhCQ0OrfFOCpaUlrVu3xs3NjQYNGrB8+XLWrVvH1atXuXLlCgMHDqy1bQk6HWg0cOSI3BZ25AicPg2FhfJQ6F695ERQSAj8z0Kkv9W0KbzwAnz44f8nm4qK4PPP5YRQQgJMmyZ/wInzA/1JT09n1apVAPTu3bvCIb7C09ehQwe6d+/OihUrWLhwIfXr18fNza3cyUOnTp3o2LEja9asYe3atdSvX79WVS5qtVquXr3KokWLUKvVDBkyhEaNGlV43K1bt/jtt98oLS1l2LBhhFYmOy0IglDHlLWQNWnSBHt7e7y9vfnxxx+ZP38+SUlJTJ06lW7duj3R31F2M2n37t0cP378/oVx48aNeeGFF2jfvv39C2O1Wi0SQU+JiQkEB8P06fIWskWLYPlyuVLo2Wehb1+5zUx4uhITE4mOjmbfvn2cOXOGixcvolaradmyJVOnTiU4OJiGDRvi6upa6WvWtm3bEhQUxIEDB4iLi8Pf31/c5KrlREKoko4fP86yZctISUlh2rRpdOrUqdrWLavVavz9/RkzZgwBAQGsXLmSHTt28O2333Lx4kVGjBhBy5Yta031hE4HpaVw9Oj/VwT98Ydcmtq1K3TvLieCGjWq+MOla1eoV0++U1ERpRImToRr1yAnR/4AU6nkCqPISJg5U/4Ae+01eb2oSIpXv4KCAo4fP86+ffvw8vJi5MiRBrmmtjYyNzdn8uTJxMbGsn37dvr27UufPn3KraG3trZm3LhxHDt2jK1bt9KlSxccHR1rzUaKtLQ0Nm3axNmzZ2nfvj3Dhg2rsCIzKyuLzZs3c/z4cZo0acK4ceNqXaWUIAjC0yRJEp6enjz33HN4eHgwe/ZsNm3aREpKCjdu3GDs2LHlKlMf5saNGxw4cIADBw5w8eJFzp49C0C7du145ZVXaNSoEUFBQbi7u4skUBVRqeRk0OjRcqXQokXyeXVKijzeYfRoeRC18GRKSko4efIke/bs4cSJE1y8eJH4+Hi8vLwYOnQo7du3p2HDho89C8vOzo5u3brdXzLSqVMnkRCq5cQVViWkp6ezZMkS4uLiiIiIYODAgdU+L0OSJGxtbencuTNeXl40aNCA1atXs3TpUuLj4xk2bBh9+/YtN++jJtHpoKAADh+WK4JOnJAfFhYwYAB07iwngoKC5JauB5kyRa4i+rtxM+7u8oDpwkJwcgIjI3kLmZ0dfP+9/CGWng6vvy7PJ6olubYaIy0tjdWrV6PRaOjRo4dY3V3NQkNDGTBgADNnzmT+/Pk0bdoUf3//cicV4eHh9OrVizlz5rBs2TIaN25cK9bQFxcXc/r0aVasWIGDgwOjR4/G09Oz3HElJSVcvnz5/jr6CRMm1KoqKUEQhKoiSRLm5ub0798fZ2dnZs2axcqVK8nIyOD69etMnz4dNze3v/0aJSUl/PHHH0RGRhIbG8uFCxeIj4/H39+fIUOG0K5dOxo2bEijRo0wNTWt8Z9NNYEkga0t9Ogh35h1c4O1a+HHH+XV9OPGyZX9wqNLS0sjNjaW/fv388cff/DHH39QWFhIeHg4Q4cOJTQ0lODgYHx8fJ54Flbv3r1ZsWIFsbGxXLx4ES8vL0wetMpZqPFEQughNDodR2/f5vC5c3h4ejJ69GiCgoL0Vo1jbGxMw4YNcXZ2JjAwkJUrVxIZGUlCQgIXL15k2LBhNG3atEZ96Ol0kJ0N+/fDrl3yLJ9Tp8DBAQYOhJ495TavwMDKVesEBVXu7/3f45yd5bJWZ2f44QfYuFEePD15spyQEnOFqkdBQQEnT54kMjISLy8vRo0aJaqDqplarWbs2LFERkYSExPD7t27cXR0LHeHyNTUlFGjRhETE8OhQ4eIiorCzc0NGxsb/QT+lNy6dYtVq1aRnp7O0KFD6d69e4XvqRkZGSxdupSbN2/Sp08f+vXrV6PeewVBEPRJkiRUKhVt27blzp07rFixgoSEBGbPns2tW7d46aWXCA8PL/e8tLS0+585Fy5c4MyZM2RlZdGyZUtGjRpFaGgoDRo0wM/P736bmlC9TE0hLEw+l/f0hMWL5U1kSUlannuuhIgIpTi3qwStVsv58+eJioriyJEjXLhwgQsXLmBnZ0dERAQRERE0btyYRo0aYW1t/dRm/fj6+tK6dWsuXbrE3r17adasGR4eHk/lawuGR/wmPsQfubns1Giw7t+fQU5OtGvXrlzrRHWTJAkHBwd69eqFj48PgYGBrFu3jrlz5xIfH8/IkSPp3r17hcNPDYlOJw+G3rcP9u6Vq4HOnJHLTMeOlSuCQkKgfv3qS8SYm8vJH3t7+P13WLVKjjE5GSZMkDeZifOKqpWWlsbatWspKSmhW7duNGjQQN8h1Un+/v6MHj2ajz/+mPnz59OqVasK19A3aNCA/v37Ex8fz/Lly2nevDlhYWE1dgBhTk4O0dHRbN++HT8/P8aMGYOdnV254woKCoiLi2PdunW4urry/PPPY29vr4eIBUEQaj7df4c6NmnSBI1Gw/Lly7l9+zZjxoy537J7+fJloqKiOHToEOfPn+fs2bMUFxfTt29fevbsSePGjQkNDcXKykokgQyAWi1X60+cKJ/bL1miIzIyFaVyNdevmzJ06NAafwOpqmRnZxMXF8eBAwc4efIkf/zxB2lpaYSEhDB9+nSaNGlyvw2ybGX806RSqejXrx87duxgx44dDBs2DFdX11oznkT4K5EQ+htpxcVsycjgDNC5d2/6ODvjoOdk0J+ZmprSpEkTXFxcCAoKYuXKlezbt4/r169z/vx5Bg0aVOEQVH3T6eSy0chIORl04gRcvCgnfl56CVq1gmbNwNdXP/EplfK2BCcncHWFuXPhu+/kHujp0+UPtRp6rWvwCgsLOXPmDLt378bT05ORI0fWiU16hkihUDBw4EC2bdtGVFQUmzZtqnANvVKpZNCgQURFRbF//3527NiBl5dXjVxTWjZIetmyZWi1Wvr161fh3WmdTkdKSgrz588nLy+PkSNH0qZNGz1ELAiCUDtcu3YNgBYtWtC3b19+//131q9fz82bNzl69CgKhYIbN25w+vRpkpOTadq0KSUlJSgUCt577z3CwsJQqVQiEWRgFAp5btDAgeDpqWHFigT27p1HXFwBt27dYty4cU91u1xNd+3aNQ4cOEBsbCxnz57lzJkzGBsb06JFCzp27EiTJk0ICQmpls1fYWFhBAcHs2PHDmJjY2nQoEGFN8iEmk8khB6gRKtl9717HMnJwdPYmM4ODriYmRncXW+FQoGbmxuDBg3C39+f1atXs2HDBmbNmsXly5cZOXIknTt3Noi+T61Wy7VriezZ48nBgwqOH4fr1+WS0rfeghYt5Ec1j2d6oIYN4ZVX5Bay2bPht98gLU1OCjVpIg/PE56utLQ01q9fT1FREV26dCE4OFjfIdVpLi4ujB8/njNnzrBs2TIiIiJo3bp1uaHJnp6eDBs2jPPnz7Nq1SratGmDnZ1djUvmZWRksHPnTuLi4mjevDkjRozA1NS03HHZ2dns2rWL/fv3ExQUxPjx4ys8ThAEQaic69evA+Dn50ePHj1wcXHBwcGB3377jStXrqDVajEzM6NTp05MnToVNzc3xo0bh52dHc2bN0ehUIhkkAEzM4PWrSWcnFxxchrA8uXL+eWXX0hMTGT06NFERETU2eqTwsJCTp48SXR0NMePH+fkyZMkJCRQv379+4uDGjVqROPGjat1FpaVlRXdu3fn6NGjbN26le7du2Nrayt+z2ohcUn7AMdycth97x4anY6+9vYEm5tjbGDJoD8zMzOjRYsWuLu7U79+fdatW8eWLVu4du0aFy9eZNCgQXobdlp21z0yMpLIyEOkpMwgLq4xLVvCqFHQujWEh8tD6AyNu7s8Q8jJSR6It2KFnBSaPFleey/mCj09ZdVB27dvx8vLixEjRohtTQaga9euREREsG7dOpYvX46/v3+5PnJJkujevTuxsbGkp6c/1lYLQ5CdnU18fDwODg4MGzaMwMDAcsdoNBri4+OZP38+arWakSNHirZGQRCEJ6DT6UhISADk2SWSJBEaGspbb72FnZ0dp0+fRpIkevXqRfPmzQkODubkyZPodDp8fHxEMqiGUKuV1KvnxbRp0/Dy8mLx4sWsXr2amzdvkpKSwoABA6ptg7MhSE1NJSYmhujoaP744w9OnjxJcXExzZo1Y8SIEYSFhdG4cWO8vb31NgurW7duLF26lFOnTnHmzBn8/f31PjpFePpEQqgCyUVFbMnI4EZhIV1tbWljZYVVDchaK5VKvLy8GD16NPXr12f58uVs3bqV7777jitXrjBs2DDatWtXLXftdTodpaWlnD59mqioKOLi4oiLi+POnXuMGNGH3r3lhFB4OBj4qCMsLWHECHkD2Zw58ga0O3fkLWTDh8tb0MR5yJMrW/NdUFBAx44dCQ0N1XdIAvJ6+YkTJ3L8+HE2b95Mjx49sLe3L1cR4+DgwHPPPQfI84dq4p0+e3t7Bg8eTOvWrenTp0+FFaEZGRmsX7+eixcv0qlTJwYPHmxwlaOCIAg1TVmFkO9/5wVIkoSPjw9vvvkmly9fRpIkmjdvfv/P/lxRJNQcCoUCR0dHhg0bhru7OwsXLmT79u2kpqZy69YtRo4cibe3t77DrDKlpaWcP3+eI0eOEBsby+nTpzlz5gyurq50796dli1bEhISQtOmTQ1iFpanpydt27bl3Llz7N27l1atWonfuVpIJIT+R4FWy/a7dzmem0uAqSk97exwroJhXVXJwsKCtm3b4uHhQVBQEGvWrGHZsmVcuXKFoUOH8swzz+Dq6lolf7dOp6O4uJhTp06xd+9eDh06RFxcHMXFxbRq1Yrp07vRoUMjgoPlDQQ1hVoNvXvL7WOurrBsGXz1Fdy+LQ/Lc3ERc4WeRFFREefOnWPbtm14enoyYsQIjI2N9R2W8F8tW7akV69ezJ07lyVLltC4ceMK19DX9CSejY0N3bp1o7S0tMKfv7Ky7uXLl+Po6MiECRNwd3fXQ6SCIAi1Q9kNxMTERJRK5V+SAZIkYWtrS8uWLcs9738TSELNIUkSFhYWdOrUCVdXVzw9PVm5ciU//PADN2/eZMyYMbRu3bpGXXs9TFZWFkePHuXQoUOcOnWK06dPc/v2bRo3bsy0adMICwsjLCyMwMDAJ14Z/zQpFAr69evHtm3b2Lt3L8OHD8fT07PGjQQQ/p5ICP2JDjianc2ee/dQAn3t7alvZoa6Bl7pq9Vq/P39mThx4v319Lt37+b69etcvnyZIUOG0KJFi6d2F1+n05Gfn8+xY8eIiooiNjaWo0ePolQqadOmDRERETRv3pzw8PAae6EvSXJFk729nACaOxd++glSU2HKFHmNvZgr9HjS0tLYsmULOTk5PPPMMzRr1kzfIQl/YmxszNixYzl48CD79+9n//79ODk5YWVlpe/QnjqlUlnh+6JOp+P27dssW7aMjIyM+/PZBEEQhCeTnp5Obm4uDg4OWFtbV+o5ZQkhfY1DEJ6ckZERwcHBvPLKK3h4eLB06VIWL15MYmIiEyZMoHfv3gYxA/VxybNTrxEXF0dsbCwnTpzgxIkT2NjY0Lx5cyZPnkxoaCjh4eE4OjoaTBLof4WEhNCkSRPWrFlDTEwMoaGhNXJxiPBg4vL1T64XFLApI4PbxcX0s7enpZUV5jWw7aGMJElYW1vTvXt3/P39CQoKYv369fz+++/3B0736tUL2ycY3qPT6cjKyiI2Nvb+VPxjx45hbW1Nt27d6NixIy1atCA0NBRVLcmW+PrK29CcneHXX2H+fDkpNHEidOoENTTfpTfFxcVcuHCBLVu24OHhwfDhw2v0CUBt1ahRIwYPHszXX3/NwoULadasWYVr6Gur3Nzc+1vUAgICmDBhApaG3u8qCIJQAyQkJPxlHlBllG0lExVCNZtCocDV1ZXx48fj6enJggULiIyMJCUlhVu3bjFs2DBcDGXbTCUVFBRw9uzZ+10SJ0+eJD4+nnr16jFs2DDatm1Ls2bNaNSoUZWsjH/aTE1N6dmzJwcPHmTbtm307t0bJycng49bqLzacYX+FORpNGy7e5c/cnNpaGZGdzs7HNVqasOPupGREUFBQUybNo169eqxcuVKoqKi7lcL9e/fnyZNmjzSL7ZWq+XOnTscPnyY6OhoYmJiOHXqFC4uLgwcOJD27dvTqlUrGjZsWCvna9jZwYQJ8rDpOXNg0ya5fSw9HQYMEHOFHkVaWhrbtm0jKyuLnj17VrjmW9A/hULB0KFD2bdvH9HR0ezYsaPCNfS1kUaj4dq1ayxcuBCdTsfAgQNp2rSpvsMSBEGoFR6n/atsCLWYZ1LzSZKEpaUlvXr1ws3NDQ8PD9atW8d//vOf+3OFasJnbnJyMseOHePo0aOcOnWKI0eOoNFoCAsLY8CAATRt2pRmzZrh5+dX45IpHTp0oH79+kRHR3P69GmCgoLETbFaRCSEAK1Ox6GsLKLu3cNcqeQZBwf8TExQ1rBf1r9T1of9zDPPEBAQgL+/P5s2beLHH3/kwoULjBo1ii5dujx0ur9GoyE9PZ0jR46wb98+Dh48yPnz5/Hz82PMmDG0adOGNm3aEBAQUOPe7B6VsTEMHCgnheztYeNG+PJLOSk0erQWR0cJhaJ2fw+eVHFxMefPn2fTpk33q4PE9gLD5e3tzciRIzl37hzLli0jNDSUTp061fqV6+np6axatYoTJ07QqlUrRo4cKfrnBUEQngKdTvfI7V/5+fncvn0bIyMj3NzcqjA6oToZGxsTFhaGi4sLHh4e91fT37hxg4kTJ9K5c2eD+uzV6XRoNBouX758f1TGsWPHOHPmDO7u7nTs2JE2bdrQvHlzmjVrhpmZWY29NnJxcaF9+/acOHGCPXv20K5dO4KCgvQdlvCUiIQQcKWggI137pBRWsowR0fCLC0xq6VtEMbGxoSGhuLm5kZQUBCrV69m165dXL169X61UL169co9r7S0lKSkJGJjYzl48CAxMTGcO3eOgIAAJk6cSIcOHWjXrl2dG7AqSdCunVwx5OoKS5bA/PnpZGZuYsyYDvj5+daaVrmqkJaWxo4dO8jIyKBLly4VDo4UDEuvXr2YP38+Bw8eZO7cuQQGBla4nr22yM/P59ChQyxatAgrKyuGDx8u7kgLgiA8RY9aIZScnExhYSGenp41+iJbKE+pVOLp6cnUqVPx8vJi/vz5bN26leTkZG7dusXAgQOxs7PTa4w6nY579+5x8uRJjh07xsmTJzl8+DBpaWmEhIQwZcoUmjVrRnh4OA0bNtTbyvinrVevXmzcuJHo6GguX76Mn58fRkZG+g5LeArq/JVqVmkpG+/c4UJ+Pk0tLOhia4ttLb+AVygUODs7M3LkSOrXr8+yZcvYsmUL//nPf7h48SJ9+vSha9euWFlZUVJSQkJCAjExMURHR3P48GGuXbtGgwYNmDJlCq1btyYiIqLG9fc+bQ0bwptvgqMj7Nixk19//ZykpMM899xzhIeHizfMCpSUlHDx4kU2b96Mu7s7Q4YMwdzcXN9hCQ9hb2/PkCFDuHz5Mjt27MDJyQkvL68Kj1WpVA/dlqFWq/92DpEkSahUqr9NrJYd87Cvo1ar/7aFVaFQoFarkSSJwsJC0tPTycrKYs+ePaSmptKxY0d69+79wOcLgiAIj+769etIklTpZLtYOV+7lc1AHTRoEO7u7veTQp999hlJSUmMGjWKgICAao1Jp9Oh0+m4ceMGcXFxHD16lLi4OOLi4rC2tqZJkyaMHz+e5s2b07ZtW2xtbWtFEujPgoKCCAsL48KFCxw6dIiwsLA6VwhQW9XuzMdDaHQ6ojIzic7Kwl6tZoCDA161rFXs75iYmNCyZUu8vLyoV68ea9euZfny5fd7X4OCgsjMzOTIkSMcOnSItLQ0mjVrRt++fWndujXt27fXe5bekDg7w/Tp4ONjwW+/+bFixQpSU1N59tln6dWrF+bm5rXuw+FRlZaWkp2dTXJyMvHx8WzcuJHU1FQGDBhAmzZt9B2eUEnDhg3j+PHjrF27ll9//fWBx5Ulcv7u574yx6jV6kolhP7uGIVC8dCkUVlCSKFQUFBQwO3btyktLSUrKwsvLy9GjRolNmsIgiA8ZY9aISRWztcNJiYmtG3bFg8PDzw9PVmxYgXfffcdiYmJjBo1ig4dOlT5YgudTkdeXh5nzpzhxIkTnDx5kkOHDhEfH0/9+vUZPnw4zZo1o0WLFjRr1uyh5zM1mZGRET179iQyMpKdO3fSr18/XF1da+Ws2LqmTieEzuflsTkjg3yNhsEODjQ2N8ekjv1QK5VKPDw8eP7556lfvz6zZ89m+/btfPXVV3h4eJCbm0tRURFNmzZl1KhRRERE0KJFi4fOGqqrzM1hyJBncHCwYvbs2ezYsYOkpCTS09MZMmQIDg4OdeaNU6fTUVRURHp6OklJSSQnJ5OSksLNmze5ceMGV69e5dKlSxgZGeHg4ICxWM9WY9jb2zN58mQCAgK4e/cuOp2uwuM0Gg2lpaUP/HOQk4QajeaBf67T6R56jFarRaPRPPSY0tJStFrt3x5TUlKCTqfDxMQEjUaDJEkEBwfTr18/Bg8e/MDnCoIgCI9Gp9PdT74/yjwgkRCqO1QqFX5+frz22mt4eHiwbNkylixZwrVr15g0aRJ9+vSpkuHGOp2O1NRUTp8+zZEjR4iNjSUmJgZJkmjcuDE9e/akZcuWtG/fHjc3t1qbBPpfrVq1okGDBuzevZsTJ04QHByMjY2NvsMSnlCdTQjdKSlh3Z07xBcU0NrKighbW2xqeavY3zEzMyMiIgI7Ozs8PDzYvn07eXl5eHl58cwzz9C+fXtatGhR64fHPg0KhYLOnTtja2uLk5MTa9as4d///je3b99m7Nix+PrWzrlCJSUl3Lt3j5SUlL88kpKSuH79Ojdu3OD27duUlJTg7OyMi4sLISEhXL58mZiYGPbs2UOPHj3qzBrzmq5Fixa0aNHib48pKSl5aEKo7JgH0el0lJSUVCrZ83dfpyzZ87CEUHFxMVqt9v5FiiRJODo60rp1a9HSKAiC8JQlJydTVFSEl5dXpd9jRUKobpEkCTs7O5599ln8/PyYO3cuO3fuJCkpiaSkJIYNG4anp+cT/z06nY7i4mIuXrzIqVOnOHnyJLGxsZw4cQJXV9f7N8VbtmxJq1at6uQSFHt7ezp16sSxY8fYuXMnERERIiFUC9S+q9JKKNFq2XPvHrHZ2bgZGdHfwQFXI6M6k919EGNjY8LDw/Hz8yM0NJS8vDz8/Pzo3bu3QU31rymaNm3KP/7xD5ydnVmyZAkzZ84kPT2d5557jpCQkBo9V0in05Gfn09aWhopKSmkpaWRnJx8P/Fz48YNbt68SWZmJtbW1ri4uODu7k54eDg+Pj54e3vj5eVFcXExK1asYOvWrXz33XeYmZnRoUOHOlNFVdup1Wrx3iEIgiA80OMkd8QMobrJxMSETp064eHhgaurK+vWrePLL7/k1q1bjBgxghYtWjzWtVzZkOhz585x7Nix+3NTs7KyaNiwIc8++yzNmjWjffv2NGrUqM5fL3bt2pW1a9cSFxfH+fPnqVevHiYmJvoOS3gCdTIhdDovjy0ZGWh0OgY4ONDQzAwjcQEKyFn4snYQ4cl5enry+uuv4+Liwvz581myZAnp6elMmDCBiIiIGrMdo7i4mMzMTFJTU++3fiUnJ3Pjxg0SEhJITEy8X/3j5OSEm5sb7du3x9XVFS8vL3x8fPDw8MDLywsnJ6f7VUAlJSW4urpSUFDAnj17+PrrrzE2NqZVq1Y14vsiCIIgCMLje9TkjlarJSEhAaj8mnqh9lCpVNSvX593330XLy8vVqxYwc8//0x8fDyTJ0+ma9eulepmKFsZf+3aNc6cOcPx48c5cuQIMTExWFpa0qRJE8LDw2nVqhXt2rXD1ta2Gl5dzRAQEEDLli35448/OHDgAC1bthTVejVcnUsI3S4qYk16OomFhXS2taWdtTWWokVFqEKWlpY899xzODs7s2DBAnbu3ElKSgrp6en069cPW1tbg6qI0Wq196t/UlNTuX37NsnJySQmJt6f/ZOQkEBOTg42NjY4Ozvj6elJy5Yt7w/+8/f3x9fXFzc3t7+9a6BWq2nYsCGvvfYaeXl5REdH8/333/P+++/TuHHjanzVgiAIgiBUJ51Odz+5U9kLyuzsbDIyMjA3N8fR0bEKoxMMlUKhwNHRkWnTpuHv78+cOXOIiooiMTGRlJQUBgwYgJOTU4XPLRsSffHiRU6ePElMTAyHDh0iISGBwMBABgwYQJMmTYiIiCAsLEyMMaiAUqmkR48e7Nq1i8jISAYOHIinp2etHIdRV9S5f7mU4mJuFhbib2pKfwcHHEWrmFANVCoVAwYMwNPTE0dHRzZs2MDnn39Oamoqw4cPx8PDQ29vpMXFxdy9e/d+61dqaipJSUkkJCSQkJDA9evXSUlJQalU4uzsjLOzMx07dvxL9U/Zw97e/pF/n4yNjWnWrBlvvvkmn376Kbt378bKyooZM2ZQr169KnrVgiAIgiDoW9nK+comhG7evIlGo8HLywu1Wi3O4esoSZIwMTGhT58+eHt7M2vWLDZv3syHH35IYmIiQ4YMITQ09P7xWq2WlJQU/vjjD06dOkVMTAwHDx5EkiQaNWpEjx49aN26NR07dqz0cPO6LCwsjODgYNavX09cXBxNmjQRCdoarM4lhNyMjellb4+3sTGBpqaoxQeJUI3CwsJ47733cHBwYPny5XzzzTekpqYyduxYGjZsWOWbtjQazf27a2XVPykpKSQmJhIfH09CQgI3b96koKAAa2trnJyc8Pf3p23btnh6euLr64uvry8+Pj64uro+tXhNTExo1aoVb7zxBp988gnr1q3D3NycV199VZSEC4IgCEItde3aNUCsnBcej0qlIiQkhI8//hgPDw/WrFnDf/7zH+Lj45kyZQrh4eEkJCRw9uxZjh49yr59+zh37hxeXl60a9eOsLAwOnbsSOvWrcUcnEdgaWlJ586diYmJYffu3XTt2hUHBweRoK2hakVC6FZREQmFhZT+zSYbgAZmZjio1YxwckIJKMQPraAHPj4+zJgxA0dHRxYvXsycOXNISUnh+eefv7+14Gm9oRYUFHDnzh3S09O5c+fO/bXvN2/e5Nq1a8THx5ORkYGpqSkODg44ODjQsWPH+/N+fH19CQgIwMvLCysrqyp9oy8bKP3666/z+eefs3TpUiwsLHjxxRdxcXGpsr9XEARBEITqpdPp0Gq13LhxA6j8PKCyBJK4WSSUUSgUuLq68tZbbxEYGMivv/7Khg0buHnzJgMGDODEiRPs37+f3NxcGjRowNixY2nVqhXt27enfv36IonxmDp27MjqNas5e+ssV+5dIbg0GDN13du8VhvUioTQyZwclqelkfs3a4kB3vT0xMbKCmMDmtci1E12dnZMnz4dV1dX5s+fz5YtW0hPT+f555+nR48e2NjYPPIHVGlpKTk5OaSnp5OamvqXzV/Xrl27v/2ruLgYOzs7nJycaNiwIW5ubnh7e9/f/uXn54eLi0uVbYcqW0Fe0euztLSkZ8+e5Ofn8/nnnzNv3jysra2ZOHEi9vb2VRKPIAiCIAjVLycn5/48oAfNfPlfjzpzSKgbJEnC1NT0/gr6n376iU2bNnH69GksLS1p1KgRzZo1o1OnTrRt2xYrKyt9h1zjeXt703pAa25l3OKw9WGalzSnnlqMeqiJakVCKFerJaW4GLUk0djcHJMHJHxsVCpEKkgwFMbGxowYMQIPDw9++eUXduzYwWeffUZaWhoDBw7Ezc3tgcPsyobi3b17l4yMjPvr32/dusW1a9e4cuUKV69eJSsrC3Nzc+zt7XF0dKRr1664ubnh4+NDYGAgvr6+eHp6YmFhUS13SHQ6HXfu3OHy5ct4eHjg7e1d7hhra2ueeeYZMjMz+eGHH/jpp58wNTVl7NixWFtbV3mMgiAIgiBUvRs3bvxlHlBllFUIiZXzQkVUKhVt27bFycmJXbt2oVAomDZtGr1796ZJkyZiSPRTJEkS3Yd3Z9utbWwr3Ub34u74mvqilqrmhrJQdWpFQqiMr4kJr3h4YP+ADxVjSUIpygIFAyJJEu3bt8fJyQknJyfWrVvHV199RWpqKqNGjSIwMBAjIyNKSkrIysq63/qVlpZGYmLi/cHPZZu/JEnC3t4eBwcHQkND/1L94+/vT2BgIPb29nr7QCwoKCA6OpqffvqJDh06MHXqVJydncsdZ2dnx+jRo8nNzeWXX37hu+++w9LSkqFDh2JmJspRBUEQBKGme5x5QGKGkPAwkiRhbW1NYWEhFhYWvPHGG+LcsYo0NmtMqFkoZ+6e4UjeEZqbN8dNLYZy1zS1KiGkkCRMFQrMRfZXqGHq16/Pe++9h4uLC4sXL2bWrFncvn2bnj17YmJiQkZGBjdu3ODKlStcu3aNq1evkpeXh7W1NQ4ODvfbvzw8PPD398fPz4+AgADc3d0xNTXV98u7r6SkhOzsbG7evMn8+fOxtrZm7NixODg4/OU4SZJwdHRk0qRJ5ObmMn/+fL766ivMzc3p169flQ/fFgRBEASh6uh0uvvJncrOAyotLSUxMRGFQoGXl1cVRifUZFqtluTkZIqLi3F2dhbJoCpkojChs2VnDuQeIDInkp5WPXFRuaCQRE9OTVKrEkKCUJM5ODjw8ssv4+joyPz581m9ejVRUVEUFxdz584dTExMsLW1xcHBgfDwcNzd3fH39ycgIICAgAB8fX2xsLBAYcAzsqytrenZsyepqanMmjWLWbNmYWpqyogRI7CxsfnLsZIk4eTkxPTp08nOzmbVqlV89tlnWFhY0LVrV1Qq8fYlCIIgCDVVWWVzZat9MjIyyM7Oxt7eHltb2yqOTqipyoaVKxQKMXy8GrS1aEuQSRC7s3dzuuA0TcyaYKUUM5pqEnFFJQgGxNTUlPHjxwPw6aefcvfuXRo2bEjbtm3vb/2qV68eQUFBODk5Vdng56rk7OzM6NGjyc/P57fffuP777/H3NycQYMGYW5u/pdjFQoFHh4ezJgxg/z8fDZt2sSnn36KmZkZbdu2Fb3ggiAIglAD6XS6R54H9OcNY5Ikie1QQoVEQqh6uapdaWvelqN5R4nKjaKjZUdCTEP0HZbwCGpVQuhuaSnRWVlYVVA54G9qiruREWoDrp4QBJCTIMbGxmi1Who2bMhHH31EmzZtnuo6en1zc3Nj4sSJ5OTksHjxYr755pv7G8ZMTEz+cqxCocDb25t//OMf5OXlsWvXLj755BO+/PJLwsLCas33RBAEQRDqkkfdGFbWYiYGSgt/R6PR3E8IidbC6tHFsgtbsrcQmxfLhYIL1Deuj7FCjHeoKWpVQuh6QQFfJyZS0eXhZDc3Bjg4iISQYPC0Wi3R0dHcu3ePiRMnEhoaWq5ypqaTJAlPT0+mTp1KTk4O69at44svvsDc3JyIiIhylU9KpRJ/f3/ee+89cnJyiImJ4fPPP+df//oXjRo10tOrEARBEAThUel0uvvzgJRKZaUv2sVAaaEyRIVQ9Wtg2oCmpk05mX+SmLwYWlm0wtuo/CZhwTDVqoSQnUpFEwuLCtfO+5qYoBaVBEINkJ6ezunTpwEICwsrN1untpAkCT8/P15//XXy8/PZvn07n332GaamprRu3bpcO5haraZRo0Z89NFHvPXWW+zevRtzc3M+/vhjcbdQEARBEGqQsnlADg4OlZ4HJBJCQmX8OSHk7S2SEtVBLanpatWVqNwo9uXuo7d1bzzUHiglMdqhJqhVCSFfU1Pe8PTE0chI36EIwmOLiYkhNTWV4OBgfHx8MKrFP88KhYLAwEDeeustcnNz2b9/P5999hmfffYZTZs2LdcOZmRkRNOmTfnnP//JO++8w8aNG7GxsWHGjBmiLFgQBEEQaog/J3cq2/pdNkNIJISEB/nf6jOREKo+Lc1a0sikEesy13Ei/wThZuHYqez0HZZQCaJ/ShAMiE6n48CBA9y9e5cOHTpgb2+v75CqnFKppEGDBvzjH/+gefPmHDlyhC+++IILFy6g0+nKHW9iYkK7du348MMP8fLyYtGiRcyaNYvU1FQ9RC8IgiAIwqP488r5yiZ3Huc5Qt2j0+nIyckhIyMDc3NzHB0d9R1SnWGrsqWdRTuc1c5E5URxtegqOsqfxwuGRySEBMGAZGVlcezYMUpKSmjVqlWF7WJFRUVkZWVRXFxcYcKkJlKr1TRp0oQPPviABg0asGfPHr766qv7dwP/l6mpKV26dOG9997D3t6eOXPmsHDhQu7evVvNkQuCIAiC8KiuX79e6ZXzOp2OoqIiUlJSUKvVuLu7V0OEQk2k1WpJTEwEwNPTE1UFi4aEqtPRoiP1jetzLP8YZwvPUqQt0ndIQiWIhJAgGJBjx46RnJyMv78/fn5+5TZuAcTHx7N27VpiYmLIzc3VQ5RVw9jYmPDwcN5//318fX3ZtGkT3377LUlJSRUeb25uTt++fXn99dcxNzfnP//5D8uXLycnJ6eaIxcEQRAE4VEoU1MJtrUlqJIzAJOSkigqKsLFxaVWbV0Vni6tVktCQoKYH6QnAcYBNDdrjkJScCj3EInFifoOSagEkRASBAOyf/9+MjMziYiIeGC72J49e/joo4+YP38+t2/fruYIq5apqSkdOnTg/fffx8nJiVWrVvHLL7+QlpZW4fGWlpaMHTuWadOmoVQq+fzzz9m0aRP5+fnVHLkgCIIgCJUh6XQMzMjgP+7utPL3r9RzRLuYUBllK+fF/CD9UEgKOll2ItA4kOjcaM4XnqdUV6rvsISHEAkhQTAQeXl5HDlyhIKCAtq0aVNhu1hhYSFxcXHcvXuXpk2b1sreaHNzc7p27co777yDpaUlv/32GwsWLHhgO5iVlRUvvPACEydOpLCwkPfff59du3ZRVCTKVAVBEATBoOh0SDodDWJj6XHuHEGVXAihTUggwhgj+d4AAQAASURBVMyM9oGBVRygUJOJDWP6F24eTohpCLdKbhGXH8fdUjHOwdDVioSQuUKBi5ER9mo1ClFCKtRQp0+fJiEhAXd3d4KCgjA1NS13zNWrV4mPj8fKyopGjRphZWWlh0irnpWVFf379+eNN95ApVLx/fffs3LlSrKzs8sdK0kSNjY2vPbaawwbNox79+7x9ttvc+DAAUpLxV0JQRAEQTAoRUWQkgJqNVRyHlCnmzfZbm/PKw0aVHFwQk2m0Wi4efMmCoUCHx8ffYdTJ5krzOlg0QFvI28O5B7gUtGlWjPztLaqFQmhxubmPOvsTB87O8yVSn2HIwiPZd++fWRmZtKxY0fs7Owq7I8/dOgQqamptGrVCjc3NxSKWvErXCFbW1uGDx/OtGnT0Gq1fPXVV2zevLnCdjBJknBwcOCf//wn/fr1Izk5mbfeeou4uDg0Go0eohcEQRAEoUJJSXJSyMUFzMweergEqC9fxiQxEUcnJ8StX+FBxAwhw9DOoh0NTRryR8EfnC44Tb5OjHIwZLXiatLX1JSe9va0trbGpBZfIAu1V2FhIYcPHyY3N5d27dpha2tb7piSkhJiY2PJyMigVatWODs76yHS6uXg4MCkSZOYOHEi2dnZfPTRR+zevZvCwsJyx0qShLOzM//617/o1KkTly9f5o033uDs2bPizoQgCIIgGAKdDhIS5P//KBUc/50hhJghJDyATqejuLiYpKQkVCoVHh4e+g6pzvI08qSleUssFBYcyj3EtaKKtwYLhkFkTwTBAFy+fJmrV69iZ2dH48aNMTc3L3fMrVu3uHjxIkZGRoSGhlY4Y6g2cnR05MUXX2TYsGHcuXOHd999l4MHD1JSUlLuWIVCgaenJ19//TWtW7fm9OnTvP3221y5ckUkhQRBEATBEFy/DpIEldkwptM9fhJJqFN0Oh0ZGRnk5eVha2tbZ86TDVVHi44EmQQRkxvD2YKzlOjKn7cLhkEkhATBAJS1i7Vv3x57e/sHtoulpaURHh6Om5sbyjrSHilJEq6urvzjH/+gT58+JCYm8u6773L8+PEKZwQplUr8/Pz47rvvCAkJYf/+/bz33nvcunVLD9ELgiAIgnDfn5M7la32yc2F9HS5vawOVEcLj6dsw1hZu1hF59JC9Qk1C6WJaRPuau5yNP8oqSWp+g5JeACREBIEPSspKeHAgQPk5OTQsWPHCtvFNBoNhw8f5s6dO7Rp06ZOtIv9mSRJeHh48OGHHxIREcHFixeZMWMG586dQ6vVljtepVIRFBTEt99+S2BgIFu3buWzzz7j9u3beoheEARBEIT7HrX9KyFBTiR5eYFKJVcXCcL/+N+EkKBfRpIRHSw63F9Bf67wHFpd+XN2Qf9EQkgQ9OzmzZtcuHABMzMzmjZtioWFRblj0tLS7s/Cadq0KXZ2dnqIVL8UCgV+fn588cUXtGjRglOnTvGPf/yD+Pj4CtvB1Go1YWFhfPvtt7i7u7Nw4UK+//57MjIy9BC9IAiCIAjodP/fMlbZ9q+yBFJlWsyEOqts5bxSqRQJIQPR2qI1wabBXCi8wMn8k+Rqc/UdklABkRASBD2LjIwkMzOTNm3a4OTkVOHmsMOHD5OamkqTJk3w9PREpVLpIVL9UyqV1K9fn6+//prg4GAOHjzIe++9R2JiYoXHGxkZ0b59e7744gscHR356aef+PXXX8nMzKzewAVBEAShrtPpQKuVK34kqfIVQmKgtFAJf145LxJChsFR5Ugr81Y4qZyIzo3mUuEldIiZnoZGJIQEQY80Gg379+8nOzubiIiICgfgabVaDh06RHp6+v2kUV2mUqlo1KgRX331Fb6+vuzcuZNPP/2UtLS0Co83Njamb9++fPzxx1hYWPD555+zdOlScnPFXQpBEARBqFZl84BMTSs/D0gkhIRKKKsQEgkhw9Leoj2NTBoRlx/HHwV/iOHSBkgkhARBj1JTUzl9+jRKpZIWLVpgZWVV7pjMzExOnTpFUVER4eHh2Nvb6yFSw2JkZESLFi34+uuvcXZ2ZsWKFXzzzTcPbAczMTFhxIgRvPnmm5iYmPDBBx+wadMmCgoKqjlyQRAEQajDbtz46zygyrj235XVIiEkPIBOp7s/Q0i0jBmWBiYNCDMLo1RXypG8IyQWV1zVL+iPSAgJgh5FRUVx9+5dmjdvjqura4Wbw44ePUpKSgoNGjTA19cXY2NjPURqeIyNjWnfvj2ff/451tbW/PLLL/z8888PbAczNTXlxRdfZPr06Wg0Gl599VX27NlDcXFx9QYuCIIgCHXR42wYAzFDSKiUwsJCbt++jZGREe7u7voOR/gvpaSkvWV7Gpg04HDeYf4o+AONTqPvsIQ/EQkhQdATrVZLVFTU/XYxa2vrcsfodDqio6O5c+cObdu2xdHRUQ+RGi5TU1N69+7NJ598grGxMd988w2LFi16YDuYiYkJb775JmPGjKGgoICXXnqJI0eOVLi+XhAEQRCEp+jPA6UrkxDS6UCjgZs3QaEAUfUhPIBWqyU5ORmNRoOLiwsmJib6Dkn4k+ZmzQkxDeFa0TWO5x8nU5Op75CEPxEJIUHQk3v37nHs2DE0Gg2tW7eusF0sLy+PuLg48vLyaNWqFQ4ODnqI1LCZm5szZMgQ3nrrLSRJ4l//+hcbNmyosB1MkqT7c4T69+9PWloazz//PKdPn0ajEXcrBEEQBKFKlVX7VHbD2J07kJMDNjbyQ6ycFyogVs4bNiulFa3NW+Nj7MPhvMOcKzxX4YZgQT9EQkgQ9KRsUHRoaCje3t6o1epyxxw/fpykpCR8fX0JCAjA1NRUD5EaPktLSyZNmsS0adMoLi7mrbfeYvfu3RW2g0mShJWVFd9++y2dOnXixo0bPPfcc1y6dAmtVquH6AVBEAShDihrGZOkyrd/iXYxoRLE/CDD19aiLY1NGnMy/ySn809TpCvSd0jCf4mEkCDogU6nY+/evWRnZ9OpU6cHtosdPHiQO3fu0K5dO9Eu9hBWVla88cYbPPvss+Tk5PDKK68QExNDSUn5bQaSJOHg4MCvv/5Kq1atuHDhAlOnTuXmzZvijoUgCIIgVJVHrRASA6WFShAr5w2fn7Efzc2bYyKZEJsXy9Wiq/oOSfgvkRASBD3Iy8sjJiaGoqIi2rdvX2G7WHFxMUeOHCE7O5vWrVuLdrGHkCQJGxsbPvzwQwYPHkxaWhovvPACp06dqrAdTKFQ4Obmxm+//UajRo2IiYnhxRdfJDU1VQ/RC4IgCEIt9ud5QEpl5RNCYuW8UAmiQqhmaGPehmDTYOLy40TbmAERCSFB0IPY2Fhu375NvXr1CAgIqHBz2JkzZ0hISMDJyYkGDRpgbm6uh0hrFkmSsLOz44svvqBLly7cuHGDSZMmcfHixQrbwRQKBQEBAcyfPx9/f3927drF22+/zd27d/UQvSAIgiDUYmXzgKytwda2cs8RCSGhErRaragQqgGamjWltXlr7FX2aHVaShFLXQyBSAgJQjXT6XRERkaSk5ND586dK2wXAzhw4AAZGRm0b98eR0dHJDFIsVIkScLZ2ZmZM2fSqlUrLl26xNSpU7lx40aFdyIUCgXBwcHMmzcPNzc3li1bxkcffSQqhQRBEAThaSnbMAaPNg9IJISEh9DpdJSWlnLz5k2USiVeXl76Dkl4AFOFKS87vcx2/+0MtxuOWio/P7WyirRF3Cm9Q0pJChmlGRRry88NFSpHJIQEoZoVFxcTHR1NQUEBHTt2rLBdrKSkhMOHD5OVlUXbtm1Fu9gjUigUuLu7M3v2bBo1asTRo0eZNm0at2/frjAppFQqadGiBbNmzcLMzIxZs2YxceJEUSkkCIIgCE/Lo66cL3sOiISQ8GA6HQW5uZTk5GBjaYmTk5O+IxL+hoPKARuVDRL/f6Nbp9Oh0Wko0hZRoC2gQFtAkbYIjU6Djorbys4UnOEfSf9g8LXBvJf8HucLz1fXS6h1REJIEKrZyZMnuXnzJm5ubjRs2LDCdrH4+HiuXLmChYUFjRs3xtLSUg+R1mxKpZLAwEB+//13/Pz82LdvH2+++Sb37t174PHdunXj+eefR61Ws3PnTj744INqjloQBEEQaqE/VwhVdn5QcTEkJckzhzw9qyw0oWbTabVIycm8GhDA4OBglEqlvkMSKqlEV0KWJovkkmROF5xmT84e1meuZ13mOiJzIjlXeI7UklTyNHlodX8d/ZCpyeSPgj+IyYvhTMEZsjRZenoVNZ9K3wEIQl2zZ88esrOzGTx4MNbW1hW2gu3fv/9+u5iTk5NoF3tMSqWSRo0aMW/ePEaNGsXatWuxtLTkP//5T4VJNiMjIz7//HNOnjzJvn372LZtmx6iFgRBEIRa5s8r5ytb7XPrFpSUyMkgExP5uYLwPxQaDZ7x8Xx0+TI0bqzvcIRK0Oq05GpzOV1wmvWZ69mevZ1rRdco1v217ctMYUaIaQjDbYfTz7ofHmoPjBXlb6QLT0ZUCAlCNSotLWX//v3k5+fTqVOnCtvFNBoNBw8eJDMzk3bt2ol2sSekUqkICwtj9uzZ2NjYsGjRIubMmfPA48tW0oPceiYIgiAIwhN6nITQ48wcEuqeP2+vEwOlDZ5GpyGpJIn/pP6H4deH813ad1wsvIgOHeYKc6wV1lgrrDFXmFOiKyE2L5YZt2Yw+cZk9uXso0hbpO+XUOuICiFBqEYXL17kypUr2NjY0KRJE0xNTcsdc+vWLc6fP49KpaJZs2YPHDotVJ5araZz5858//33vPvuuxUm4uD/BxNGRkaiVCrp06dPNUcqCIIgCLWMTvd4LWPXrsn/K+YHCX+nLCGkUIAYKG3QdDodN4tv8mXqlyy7u4xcbS5GkhH2KntCTUMJNwvH08gTBQpuFd/iaP5RTuafJEOTQWRuJBo0mChMiLCM0PdLqVVEQkgQqtGePXvIzc2le/fu2NraVtgKFhUVxd27d2nTpg3Ozs6iSuUpMTIyYujQobRs2RLfvzm5vHv3Ljk5OZiamjJ9+vRqjFAQBEEQaqmSEnkekEpV+XlAokJIqAyNhv9j777jm6oe/4+/k+7BaMveQ/amDEUEZCjIEIUvoqKgouIWFXGCoKiICC7QjwMExQECDhQXS2UKiKAggoDs2dJN2+T8/sgvoaEptKVtWu7r6SMPm5uTe09yL+3JO2dozx56CJUAKSZF7x9/X3Pj5irJmaQQW4i6luqqhyo8pMsiL8s2HOyU85SWJi7V5COTtTZ5rSLsEV6TUaNgEAgBRcThcOinn35SUlKSunXr5nMOG6fTqRUrViguLk6dOnVSTEyMH2p64QoICDhrGORwOLR48WJlZmaqe/fuatCgQRHWDgCAC5R7PqBatSQfvaN9oocQcoNAqEQwxui7hO+06OQixTniFKAA9SvTT49Xelytwlv5fE6IPUQ9y/RUdGC0/kr7SxdHXKyGoQ2LuOYXPgIhoIj8999/+vPPPxUaGqp27dopPDw8W5ljx45p06ZNcjgcateuncqWLVv0FbUo93Cxr776Sna7Xf369fN3lQAAKPncw8Vstrz19tm92/V/AiHkxJjTvc9CQxkyVoylmTStSFyhrWlbJUmNQxvrxugbcwyDsmoX0U7tItoVdhUti7EoQBH54YcflJiYqC5duig6OtrnULAVK1bo2LFjatOmjapUqaLAQDLbopSQkKAlS5YoODhYvXr18nd1AAAo+dwTSku5C3fOnHOIQAg5cTqlo0ellBQpOlrKYY5I+N/m1M36M+1PpZk02WTTFaWv0MURF/u7WhCBEFAknE6nfvzxRyUnJ+c4XMwYo+XLl3uGi0VHR/uhptblcDi0ZMkSJScnKzY2VjX4lgkAgPOXtYdQbieUTk52fdAPC5MqVSrU6qEEy7rCWG6vLfjFjlM7dDDjoCSpQmAFNQ1rqopBFf1cK0gMGQOKxLFjx7RhwwbZbDZ17NhRERER2cokJiZq3bp1SktLU4cOHRguVoSyDhcLCAhguBgAAAUpvyuM1arlWj0K8CXr/EF8kVes7UnfoyOZRyRJNYNrqnJQZT/XCG78hgWKwI8//qiEhARdcsklKl++vAICArKV+fnnn3XkyBE1b95cNWvWVHBwsB9qal1paWn64YcfFBQUpD59+vi7OgAAXBjcQ8byMoeQOxBihTGcDRNKlxjxjnglOZIkSZWCKik6gJEQxQWBEFDIjDH64YcflJycrO7du+c4XGzZsmWKj4/3zDGEouNwOLRy5UodO3ZMDRo0UMOGrGAAAMB5M8Y1z4s7EMrth3aWnEduuIeM2e30ECrGjDFKcaYo3aRLksLsYQq1h/q5VnAjEAIKWUJCglatWqWMjAx17tzZ53CxtLQ0rVy5UomJierYsaOioqL8UFPryszM1JdffslwMQAAClpysnTsmGs+oMq5HCaS1yFmsKbMzNM9hLhWii0jowyTIYcckqRgW7ACmbmm2CAQAgrZsmXLFBcXp9jYWFWrVs3nymFr1qzRoUOHVL9+fV100UUKCQnxQ02tyRij9PR0ffvttwwXAwCgIGVdYaxmzdzPB8QKYzgXY7wnlWbIWLFlt9kVZAtSgFxTZmSYDGUq08+1ghuBEFCIjDH6/vvvlZycrB49eigyMtJnmaVLl+rkyZPq0qULvYOKmNPp1O+//669e/eqWrVqio2N9XeVAAC4cMTHSxUqSA0a5P45zCGE3EhLkw4elIKDc9/7DH4RZgtTsM01P2qKM0WnzCk/1whu9NUCClFaWpqWL1+utLQ0de3a1Wcg5HA49MsvvyghIUGdO3cmECpiWVcX69u3r+ysZgIAQMGw26WuXV0f2lNTz13eGNf/Dx6UAgMZBoScORzSvn2un6tWlehdX6yVDSyrCHuEUh2pOpp5VHGZcf6uEv4/AiGgEP366686duyYGjdurLp16yooKChbmY0bN+q///5TlSpV1KhRI4WFhfmhptbkHi729ddfKzAwUH379vV3lQAAuPDYbFJ4eO7KpqdL99wjHTkilSlTuPVCyZV1uBgTShd7NYNqqnxQeR1zHNPe9L06lHFIxhjZbDZ/V83yCISAQnLmcDFfk0lL0pIlS3Ty5En16dOH3kFFzOl06p9//tG2bdtUrVo1dejQwd9VAgDgwuPu+eN0un5237fZXDe7/fTPISHSc8/5r64oGbJOKM38QcVe7ZDaqhRYSVu1Vfsy9umvtL8U54hTdCArK/sbgRBQSBwOh5YsWaK0tLQcAyGn06nly5d75g8qW7Zs0VfUwtzDxQIDA9WrVy8FBwf7u0oAAFw43MvOZ2ZKx49Lq1e7ev4cPy4FBUnR0a7eHRdfLIWGuoaJMXQbuUEPoRKlZXhLNQxrqF+Sf1GGydDypOW6vNTl6lGqB72E/IxACCgkv/32m/bv36/q1aurcePGPlcO27p1q3bs2KEyZcqoRYsWOfYiQsEzxigjI4PhYgAAFDR3D6BTp6SvvpJeeUVau9YVDvkSGSn17Ck98YTUrJnrQz4fEnE2rDBWooTbw9UxoqN+TvxZW9K2aHXyas2Pn6+LQi5SreBasttyDoKNMTJy/U45WznkD+8oUEgWL16s5ORkXXHFFeccLnb55ZcrKiqKhLwIGWN04MABbdiwQaVLl1bXrl39XSUAAEo+95Cw/fulXr2kQYNcPYOMcX14Dwo6fXP3CEpKkubNk9q2lR5/XMrIOB0qAb5kZtJDqITpW6avrih9hSLsETIyev/4+3r20LPamrZVmSZTTuN0hT///+YwDmWaTB3NPKpfkn7RptRNMvxeKHD0EAIKgTFGP/74o1JTU3MMhIwxWrJkiRISEnT55ZczXKyIZWRk6KuvvpLdble3bt3onQUAQEHZu9cVBm3d6rofGCg1bSpdd53Urp1rGfr0dFdo9NNP0scfS0ePunp9vPyydOiQNGOG63nAmYxxXSt79riCRXoIlQilAkppeLnh2pu+VwvjFyrDZGjm8ZlakbhCvcv0Vo9SPVQvpJ4i7BFKdCZqW9o2LU1aqsUnF+tAxgHdX+F+NQ1rqiBlX6QH+cdvWaAQbNmyRTt27FB0dLRat27tc7jYnj179Ndffyk4OFht27b1uSQ9CgfDxQAAKATGSGlp0ogRp8OgsDBp0iTXtoAA7/KtW0t9+khPPeVaWWzuXNc+PvzQ1VvovvsYOobsjJHi46WEBFcYVK6cv2uEXGoU2kgTqkxQpD1Sn8V/phRniv5N/1evH31drx99Pcfn2WXX32l/a3vadjUJa1KENb7wMWQMKARTpkxRQkKCLrvsMkVERPgcCrZq1SqlpqaqU6dOiomJkZ1JFIuMMUbHjx/Xr7/+qtDQUF1xxRX+rhIAACWfw+Hq7bN4set+UJD0zjuusOfMMMjNZpNiYqSPPpKuvfb09scec/UgAs6UdbhYzZqEhiVMvdB6er3665pZc6a6RHZRhD1CAQqQ3cd/gQpUzaCaGlFuhB6r9JgahTaSJNlsNtltdgX8//9s4hrIL3oIAQXMGKMVK1YoNTVV9erVU1CQ726NgwcPVosWLeRwOFSxYsUirqW1ZWRkaPHixXI6nbrssssUExPj7yoBAFCyGeOa+2faNNd9u1265hrpxhvP/VybzfXhfvp0acUK1/Cx1FTprbdYgh7ZMaF0iRcREKGBUQN1Tdlr9F/6f1qful7/nvpXxzKPyWEcKhtQVjWCa6hpaFPVD62vUgGlvJ7fIqyFXq76suId8YoKiFLD0IZ+eiUlH4EQUMBWrlyphIQESdKQIUNUunRpn+VsNpsaN25clFWDvIeLBQUFMVwMAICC4HRKmzdL69e77gcGSg8+mPvn22yuoT+33ipNnOjaNmOG9Oyz9ACBt8xM1/xBTChd4gXYAlQ7pLZqh9TO0/PKBZZTuUiGChYExqgABaxUqVKe4V+ff/65EhMTmRG/mElISNCSJUsUHBysXr16+bs6AACUfE6ntHLl6fvVqkkXX5z3/QwefPrnAweknTtZcQzeHA7XxOUEQsB5IxACCljTpk3VuHFjBQUFaezYsZo/f75SUlIIhYqJjIwMLVmyRKdOnVKbNm1UrVo1f1cJAICSz+GQ1q51/Wy3S61a5a9nT5Mmromo3dasKZj64cKRtYcQQ8aA80IgBBQwu92uxYsXq3Xr1goMDNRjjz2mDRs2KDMzk1DIz9zDxRYtWqSgoCD16dPH31UCAODCYIx08KDrZ5tNqlcv7/uw2VxDzerWPb1t376CqR8uDO4l5//7z3Wt0EMIOC8EQkAhCA4O1rJlyzR48GAlJiZqyJAh2rRpkxwOB6GQn6Wmpur7779XYGCgevfu7e/qAABwYTBGiotz/WyzSaVKnb382WSdf9G9T8AtPd21Al1AgFS9ur9rA5RoBEJAIQkNDdW0adPUtWtXHT58WLfccov+/vtvOZ1OQiE/yczM1C+//KL4+Hg1atRI9evX93eVAAC4cKSlnf4567CvvAoPP/1zamr+94MLj8MhHTrkWtGuYkUpIsLfNQJKNAIhoBCVKlVKH3zwgS699FL9/fffuu2227Rr1y4ZYwiFipgxRunp6Vq0aJGCg4PVr18/f1cJAIALS9ZeQUlJ+d9PYuLpn8uUyf9+cOHJuuQ8vYOA80YgBBSymJgYzZgxQy1bttRvv/2mESNG6L///vN3tSzp1KlT+uabb5g/CACAgmazSWXLun42Rjp5Mv/7yjpMzL1PQHJNKO0OhJhQGjhvBEJAEahRo4Zmz56tJk2aaOnSpRo9erQOHjxIL6EilJmZqd9++00HDx5U9erV1aJFC39XCQCAC4fdfvoDujHS1q15Xy7eGNews507T2+rU6fg6oiSzx0IBQYSCAEFgEAIKCINGjTQzJkzVa9ePc2dO1fPPfecjhw5QihURNLT0/XNN98oODhYffv2lS0/S+ECAADf7HapXTvXz06ntHGja3hPXq1f7/28iy8umPrhwhAWJt1wgzRlinTFFf6uDVDiEQgBRahVq1aaNm2aatasqenTp+uVV17RsWPHCIUKmXv+IHcgxHAxAAAKWECA1LGja+iYJB07Jv34Y957CX388emfGzWSKlU6vU8gOFhq21YaPlxq2dLftQFKPAIhoIh17dpVr776qqpVq6ZJkyZpxowZio+P93e1LmgOh0Nbt27Vjh07VL58eV3Mt40AABQsu901vOvyy133MzOlV17J/fONcS0lPmvW6W133lmwdUTJYkzub3nd1/nWA7hABPq7AoAV9evXT0eOHNHTTz+tJ598UpGRkbrxxhtVhpU0CsWpU6c8q4v16tVLgYH86gMAoMAFBUn33y8tWeL60LxkifTmm9I997gez6mnjzGuZcRvv/306mTlyknDhhVJtVHMuAOXhARp+XLXdfTnn65eZ4mJUkyMVKGCFBsrde/uGlYYEJDz9eV0uuYdGj/edT8oSHr77dzXJTVVuvde132bTXr5ZSkq6vxeI1BM2AxjVQC/MMZo8uTJeumll5SYmKi33npLAwYMUGRkpL+rdsE5ceKEunbtqn///VefffaZevbs6e8qAQBwYTp1SrrtNumjj1z3AwNdH8QfecT1QfxMxkiHDrmGAH3zzent8+ZJ117LcDGrMcYVBM2cKU2eLO3de/byAQFSixbS2LFSv36+yzgc0oYNp+e4CglxTV6e2/rEx0vR0ae37d0rVauWu+cDxRyBEOBHxhiNHTtWb775ppxOp95//3317NlTYWFh/q7aBcPhcGjLli1q1aqVqlWrpr///pv3FwCAwmKMqydH377SmjWnt9erJ113ndShg1S5sqvXxf790g8/uOYNSkw8Xfbxx6UJEwiDrMYYV9jy4IPSggWnt9tsUqlSrhAmIkKKi3OVO3XqdJnQUFfvtBdecA1fzIpACMgR4yYAP3v66ad14sQJzZ49WyNGjNAHH3ygyy+/XCEhIf6u2gXBPVwsKChI3bt3V2hoqL+rBADAhctmcw33+u476dZbpfnzXdv/+Ud67rmzPzcsTHrpJdcQM8Ig69m/3zU066uvXPdDQqTOnaVHH5UuvdR1Xzod0nz5pfTii9Lff7sCnldfdQ0PmzTJby8BKGmYVBrwI5vNpsDAQL388su69tprlZKSonvuuUdr1qxRRkaGv6t3QTh16pRndbHevXuz3DwAAEWhdGlp7lzXSmPdu7tWh8pJuXKu+YP+/pswyKri4lzDDN1hUNmy0pNPSt9+K3Xt6gqDbLbTt6goaehQ6ZdfpGuucT3n1CnprbekxYv99jKAkoYhY0AxYIxRUlKShg8frq+//lq1a9fW+++/r9jYWAUEBPi7eiWW0+nU9u3b1axZM8XExOjvv/9m4m4AAIpK1o8ZSUnS6tXSgQOuIWWBga6JgevVk1q1Oj3MhzDIeoyRtmyRunWTjh6VIiOlm25yTUgunX0yckk6flzq1Uv67TfXddS6tbR27ennMWQMyBFDxoBiwGazKTIyUm+88YYSExO1ZMkSjRw5UtOmTVOzZs1kP3MsNHIlLS1N3333nex2uzp37qzSpUv7u0oAAFiH+wO5Ma4P+d27n7ssrOfkSdcQw6NHXferVJGefvrc14TN5rq2ypZ1rfzVpYtryNjWra6eaT16FHbNgRKPT5lAMWGz2VSuXDm9/fbbuvTSS7Vu3TqNGjVKW7duFR358ifrcLGrrrqK4WIAAPhD1qE+Od1gXSdPuiYXl1zB4eWXS5Uq5e65NtvplcbatnVty8hwzS8E4JwIhIBixGazqVq1anr99dfVunVrLVmyRE8//bT++ecff1etxHE6nTpx4oRWrFihsLAwXXnllf6uEgAAAM6UnCxt2uT6OTJS6tgxbyGhzeaao6pbN9f9zEzXkDEA50QgBBQzNptNjRo10htvvKHmzZvriy++0OTJk7V7925/V61EOXXqlJYuXSqHw6F27dqpYsWK/q4SAAAAssrMdA0VO3zYdT8kRGrUKO/7CQyUmjd3/ex0Stu3536eIMDCmEMIKIZsNptiY2M1ZcoU3XfffXr33XdVqlQpjRw5UlWrVvV39UqEtLQ0ffvttwoJCWG4GAAAQHGUmematNktKMg12Xhe2e1S5cqn7zscrv36GnqWkXF6ZbLc1hG4QBEIAcWUzWZTp06dNG7cOI0ePVpTp05VZGSkRowYoUq5HVd9gXA6nTp+/LjWr1+vNWvW6I8//lCNGjU0ZcoUn+WNMUpOTtaSJUsUEhKiXr16FXGNAQAAcE4Oh5SYePq+3S5FROR9PzablHXxEPfqYL7azE6ntHBh3o8BXIAIhIBizGazqX///oqLi9Ozzz6rF198UWXKlNHQoUMVnXX5ywtMenq6du/erZUrV2rVqlVav369tmzZooyMDBljZLPZ1KxZM2VmZiowMPuvsVOnTunnn39WUlKSOnbsqFq1ahX9iwAAAMC5ndmLO7+9us9chOVs+wkPz9u+U1LyXh+gBCAQAoo5m82mYcOGKTk5WZMmTdLTTz+t8PBwDR48WGXKlPF39c5bZmamjh07pt9//13r16/Xr7/+qrVr1youLk6SPCushYaGqkWLFmrTpo06dOigiy++WAEBAT736R4uFhoaynAxAACA4iogQCpV6vR9p1NKSpKiovK2H2O8exrZbK7l6H0JCXGtbJZb8fFS+fJ5qw9QQhAIASWA3W7XXXfdpbi4OE2bNk2PP/64wsLCdO211yoyMtLf1cuTlJQUbd26VWvWrNFvv/2mNWvWaMeOHXI4HDLGyBijwMBA1apVS61bt/YEQC1btlRYWJhsNpvn5osxRikpKfr+++8ZLgYAAFCcBQZ6D/VyTzJdvXre9uN0SocOnb5vt0tn++LURw9zn4xxhVbABYpACCghAgICNHr0aKWkpOi9997TE088oYiICPXu3VuhoaH+rp5PGRkZOnz4sDZs2KBVq1Zp5cqV2rx5sxISEjzhjyRFRkaqcePGnp4/LVu2VJ06dSTJKwDKTU+f9PR0rVu3TseOHVOrVq3UpEmTQn2NAAAAyKeAAFfvm/LlXUFQWpr0999S69Z5209mprRli+tnu12qV8/VEwjAWREIASWEzWZTcHCwnnzyScXHx+uTTz7R6NGjFRISoiuuuELBwcF+rZ/T6VR8fLz++usvbdiwQb/99pvWrl2rXbt2efX+CQ4OVt26ddWqVStP75/mzZsrJCRENptNdrs91+GPL6mpqVq8eDHDxQAAAIo7m801iXSzZtKSJa65etaska6/Pm/7yciQfvnF9XNgoNS2bf7nIgIshEAIKEFsNpsiIyP1/PPP69SpU5o3b56eeuophYeHq3PnzjnOqVMY0tPT9d9//2nt2rWe4V9//fWXkpKSvHr/REVFqWXLlmrfvr3atGmjNm3aqGLFil7Bz/kEQGdKS0vzBEIMFwMAACjmSpeWunRxBUKJidJPP7nm7clpDqAzORzSzp2nA6GgIIk2IJArBEJACWOz2RQVFaXnn39eKSkp+vrrr/Xkk09q0qRJ6tChg+x2e4Ef073s+59//ql169Zp1apV2rBhgw4ePCin0ymn0yljjEJDQ1WvXj21bdtWl1xyidq3b6969eopODjYEwC5/1+QjDFKS0vT5s2b9dlnn2nv3r1q2LChYmNjC/Q4AAAAKGBly0q9e0uvvOIKgvbtk159VRo7NnfPT0qSxo1zBUN2u1S7ttSzZ2HWGLhgEAgBJZDNZlOlSpX0wgsvKCUlRT/88IOee+45TZgwoUBCkKSkJP3zzz/asGGD1q1bp9WrV2vnzp06deqUJ/wJCAhQpUqV1KJFC7Vt29Yz9KtMmTKy2+3ZegAVJIfDoePHj3uFU+vXr1dcXJxsNpuqVKmilJQU/ffff6pbt26BHhsAAAAFyGZzTSJ9223S5MmuUOh//5MaNZIGDTr7c5OTpSlTpK+/dt0PDZUefzz3k0YDFmcz7nEdAEocp9OprVu3auTIkVq2bJmuueYaPfXUU2rWrFmu9+FwOHT06FH9/vvvWrVqldatW6fNmzfryJEjMsZ4egCFh4erfv36uuSSS9S2bVu1bt1adevWVVBQkKfnT2H1/klOTtYff/yhtWvXau3atVq/fr327NnjCaecTqfsdrvq1Kmjxo0ba+vWrdq5c6fatWun+fPnq2LFigVaJwAAABQgp9M17OvGG6V161zbypWT7rhDeuABqUIF7/IOh7Rtm/Tss9Lnn7smlQ4OdgVIM2d6rwzmcEgbNkjt2rnuh4S4Jq/ODWNcAVV09Olte/dK1arl95UCxQqBEFDCOZ1OrVmzRqNGjdKaNWs0ZMgQPfbYY2rQoME5n/v1119r0qRJWr9+vTIyMjzhT1BQkGrVqqW2bduqbdu2ateunerVq6eIiAhP8HO+kz/nJD09Xfv27dP69eu1ceNGbdiwQRs2bNDJkyc99ZOkMmXKqHnz5mrVqpViY2N18cUXq9r//+O8d+9eDR48WJs3b1anTp00b948lc3tOHQAAAAULWNcwc0ff7hCoPXrXdsDA6XISCk2VqpbVypVSjp2TNq61VU2Pd0VJgUFSdde6+pZlHUZe4lACDgLAiHgAuBwOLRs2TI9/vjj2rhxo0aMGKGRI0d6lm7PyXfffafx48dr69atatasmdq0aaO2bduqTZs2qlKligICAmS32xUQEFAo4Y/T6VRSUpL++usvrV69WmvWrNGmTZu0e/duORwOTwBkt9tVo0YNz7xErVq1UrNmzRQSEuIVUNntdhlj5HA4tHXrVl1zzTXat2+frrzySn3yyScKCwsr0PoDAACggLhDoT17pBdekD78UDp1yvWY3e4aWmazuco5na7/22yu3kMjR0r33SeFhWVfXYxACMgRgRBwgXA4HFq0aJHGjh2rrVu36qGHHtI999yjqlWr5vicxMREJScnKyQkRKGhoZ4AqLB6/6SlpWnPnj2enj+rV6/W5s2blZaW5gmAJCk6OlrNmjVT69at1aFDB7Vt21bR0dFe9Tvb8DRjjDIzM7Vx40Zde+21OnHihAYPHqz//e9/CmRMOQAAQPFkjOuWmir9/bc0b560bJn0559SQoKrjDsEio2VevSQBgyQKlVy9Sby1TZ0OqV//pHuvNN1PzhY+v773NcnOVnq0+f0trlzpfLlz+tlAsUFgRBwAcnMzNSHH36oF198Ufv27dPjjz+u22+/XRXOHHf9/2X9518YvX9OnDihTZs2eeb92bBhgw4fPuwJfxwOh4KDg1W/fn21a9dOrVu3Vrt27VS/fn0FBgZ6eie5eyjlhTFGGRkZWrVqlQYMGKC0tDQNHz5cU6ZMKfDXCgAAgALkDoYyMlw9fBwOV2+h1FTXELKgINc8QQEBrp/P1rZz78vdK8hmc/Ukym09JNdx3Xz1QgJKKAIh4AKTnp6ud955R6+88ori4uI0ZswYDRs2rNDn0ElJSdGuXbs8PX/Wrl2rHTt26NSpU3I4HHI4HLLb7SpXrpyaN2/uGf7lXpnMHfwU5OTUxhidOnVK3333nW644QYFBAToiSee0GOPPVYArxgAAABF4syPrAQyQIEgEAIuMO4Q5PXXX9frr7+u9PR0jRs3TjfeeKMiIyML5BgZGRk6ePCgNm3a5DX0Kz4+3hP+OJ1ORUZGqmHDhmrdurUuvvhitW7dWtWrV1dgYKACAwPz3fsnL4wxSk1N1fz583XbbbcpPDxcEydO1B133FFoxwQAAACA4o5ACLgAuZdqnzhxot555x0FBwdr/Pjxuu666/I1sXJSUpK2b9+uNWvWaN26dfr999+1a9cupaeny+FwKDMzUwEBAapSpYpiY2M9y9I3bdpUpUqVUkBAgCcAKoy5ic7F/X68//77GjlypKKiojRt2jQNGjSoSOsBAAAAAMUFs6sCFyCbzaaIiAg99NBDSk1N1YwZMzRx4kSFh4frmmuuUVBQUI7PdS/7/scff2j9+vVatWqVtm7dqsTERGVmZiozM1NOp1NlypRRbGys2rdvrzZt2qhZs2aqWrWqJ/jJGgD5m/v9GDZsmOLj4zV27Fjdd999Kl26tHr27Onv6gEAAABAkaOHEHABM8bo0KFDGjdunD7++GOFhobqueee09ChQxUcHCxJOnHihLZs2eKZ9HnDhg06cOCAJ/zJyMhQaGioateurRYtWig2Nlbt2rXTRRddpLCwMM/wr8DAQL/0/skLY4zi4+P15JNP6q233lL16tU1b948tW3b1t9VAwAAAIAiRSAEXOCcTqd27NihRx99VF9++aXKly+vevXqKSMjQ0ePHlVKSopSU1OVkZGhzMxMSVKlSpXUsmVLxcbGqk2bNmrUqJGio6O9wp/i0vsnr4wxOnbsmO6//3599tlnqlu3rhYuXKjGjRv7u2oAAAAAUGQIhAALcDgcWrFihZ555hn9/PPPstvtklxhUUREhOrWrevp+RMbG6saNWooNDRUgYGBCgoK8qz+dSEwxsgYo8OHD+vWW2/VDz/8oObNm+uLL75Q9erV/V09AAAAACgSBEKARWRkZOiXX37RrFmztGXLFtWoUUNXXXWVLrvsMpUrV05BQUGeW0Et+15cuUOhffv26cYbb9Tq1avVvn17LViwQOXLl/d39QAAAACg0BEIARaSkZHhGR4WEBCg0NBQBQcHXzC9f/LCGOMZTjdgwAD9888/6tGjhz755BNFRkb6u3oAAAAAUKgIhABYljFGDodDW7Zs0YABA3TgwAFdffXV+uCDDxQSEuLv6gEAAABAobFetwAA+P9sNpsCAgLUpEkTzZw5U5GRkfpq0Vd6aclLcsrp7+oBAAAAQKGhhxAAyzPGKD09XUtXLNXzO5/XH7F/6P6q92t8lfH+rhoAAAAAFAoCIQCQKxRKy0jTDyd/0HX7rlOIPUTjKo/TAxUe8HfVAAAAAKDAMWQMAOQaPhYaFKruMd31avVXleRI0viD4zXz+Ex/Vw0AAAAAChyBEAD8fzabTWG2MF0fdb1eqPqC4hxxemz/Y1p0cpG/qwYAAAAABYpACACysNlsirRH6vaY2zWywkgdzjysu/67Sz8n/ezvqgEAAABAgSEQAoAz2Gw2lQkoo8cqPaZhMcO0L2OfbttzmzambPR31QAAAACgQDCpNADkwBijQ5mHdM9/9+iLk1+oWVgzza8zX3VC6vi7agAAAABwXgiEACAHRkYy0p70Pbp5z81ambRS7SPaa27tuaoSXMXf1QMAAACAfGPIGADkwCabJKlGcA29U+MdNQ9rrnXJ6zR8z3DFZ8b7t3IAAAAAcB4IhADgLGw2m2yy6aKQi/R+zfdVO6S2liQt0Z1771SKM8Xf1QMAAACAfCEQAoBzsNlsssuupmFN9V7N9xQTEKOvT36th/c9rAyT4e/qAQAAAECeEQgBQC7YbDYFKEDtwtvpvVrvKdQeqjkn5mjMgTFyyunv6gEAAABAnhAIAUAu2Ww2BdmC1CWyi96s/qYylam3j72tlw695O+qAQAAAECeEAgBQB7YbDaF2ELUt3RfTa02VUmOJL1y5BXNPD7T31UDAAAAgFwjEAKAPLLZbAq3h2tQ1CC9UPUFHc88rqcPPK0v4r/wd9UAAAAAIFcIhAAgH2w2m0rbS+uWmFs0quIo7cvYp4f3P6wliUv8XTUAAAAAOCcCIQDIJ5vNpqiAKD1Y4UHdGnOrdp7aqZ8SfvJ3tQAAAADgnGzGGOPvSgBASWaM0d6Mvfok7hMNLDtQdULq5HtfEw5N0Ork1bo95nb1KdNHdhu5PQAAAICCF+jvCgBASWez2VQ9qLpui7lN0YHR2R5PdaZq8cnFWpOyRvvT9yvVpCrCHqH6ofV1ZakrFRseK5vNJknamLJR3yd8rytLXVnULwMAAACAhRAIAUABsNlsigmM8dpmjNGG1A0ae3CstqVuU5wjTmnONDnkUIAtQJH2SH1y4hMNjBqoxys9riAFKdNkKsNkyCGHn14JAAAAACsgEAKAQmCM0V9pf+nOPXfqj7Q/VCGwgu4qf5fahbdT6YDSOpF5Qj8n/awZJ2Zo2tFpahPeRleVvsrf1QYAAABgEQRCAFDAjDFyyqnHDzyuDakbVCu4lt6u8baahzVXdEC0Am2BSjfpah/RXu0j22t9ynq1Cmvl72oDAAAAsBACIQAoBGuT1+rHhB9lZDSm8hh1KdVFgQr0zBUUYgtR1eCq6hfYT5dFXKbKQZU9jwEAAABAYSMQAoBCsDhhsVJNqmoF19I1Za/xCoOyCreHKzw43A81BAAAAGBlrGcMAIVgTfIaSXLNGWQvTe8fAAAAAMUKgRAAFIK9GXslSReFXuTnmgAAAABAdgRCAFAIkhxJkqTS9tJ+rgkAAAAAZEcgBACFIMQeIklKN+l+rgkAAAAAZEcgBACFoHxgeUnS/oz9fq4JAAAAAGRHIAQAhaBJaBNJ0m/Jv0mSjIw/qwMAAAAAXgiEAKAQXFn6SknS1rSt+i3lN5EHAQAAAChOCIQAoBB0LtVZDUMaKsWk6LlDz+lw5mEZ4zsVSnem5/gYAAAAABQGAiEAKGA2m00xATF6qvJTKmUvpR8SftADex/Q5/Gfa+epnYrLjNOe9D1ambRSLx9+WQ/vf1hOORlWBgAAAKDIBPq7AgBwoepXpp+OVzmuqUemasHJBdp6aquqBlVVuD1cp5yndNJxUrvSd8kpp56q9JQqBFbwd5UBAAAAWASBEAAUApvNpkh7pIbFDFPdkLpanrhcm1M360jGEZ0ypxRkC1KFwAq6puw1ahfeTuH2cElSq/BWSjNpqhlc08+vAAAAAMCFzGaYuAIACpWR0YH0A9qfsV+JjkRlmkwF2AJUKqCUKgZWVLXgagpQgGw2m7anbVe8I141gmuoYmBF2Ww2f1cfAAAAwAWIQAgAAAAAAMBimFQaAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACIDfdenSRV26dMnz83bv3i2bzaaXX365wOqybNky2Ww2LVu2rMD2CQAAcKGpVauWhg0blufnudta8+bNK7C6zJw5UzabTbt37y6wfQJWQCAEFGMTJkxQv379VLFiRdlsNj3zzDM+yz3zzDOy2WzZbqGhoT7LHz58WHfeeaeqVq2q0NBQ1apVS7fddttZ69KjRw/ZbDbde++95/uyLmh///23Ro4cqQ4dOig0NJTGCQAAOG8HDx7UHXfcodq1ayssLEx169bVQw89pOPHj2cru3XrVvXs2VORkZGKjo7WTTfdpKNHj/qh1iXH2rVrdffddys2NlZBQUGy2Wz+rhJQJAL9XQEAOXvqqadUqVIltWrVSt999905y0+fPl2RkZGe+wEBAdnK7N27V5deeqkkacSIEapataoOHDigtWvX5rjf+fPna9WqVfl4BdazatUqvfbaa2rcuLEaNWqk33//3d9VAgAAJVhSUpIuueQSJScn6+6771b16tW1adMmvfHGG1q6dKnWr18vu931Pf++ffvUqVMnlSlTRs8//7ySkpL08ssva/PmzVq7dq2Cg4P9/GqKp2+++Ubvvvuumjdvrjp16mj79u3+rhJQJAiEgGJs165dqlWrlo4dO6by5cufs/zAgQNVrly5s5a58847FRgYqHXr1ikmJuac+0xLS9PDDz+s0aNHa8yYMbmuu1X169dP8fHxKlWqlF5++WUCIQAAcF6+/PJL7dmzR19//bV69+7t2R4dHa3x48dr06ZNatWqlSTp+eefV3JystavX68aNWpIktq1a6cePXpo5syZuuOOO/zyGoq7u+66S6NHj1ZYWJjuvfdeAiFYBkPGUOK4h0ft2LFDw4YNU9myZVWmTBndcsstSklJ8So7Y8YMde3aVRUqVFBISIgaN26s6dOnZ9tnrVq11KdPHy1btkxt2rRRWFiYmjVr5plHZv78+WrWrJlCQ0MVGxurjRs3ZtvHtm3bNHDgQEVHRys0NFRt2rTRl19+ma3czp07tXPnzly91lq1auWqnJsxRgkJCTLG+Hx827Zt+vbbbzVq1CjFxMQoLS1NGRkZZ93nSy+9JKfTqUceeSRPdTkf6enpGjNmjGJjY1WmTBlFRETosssu09KlS3N8zpQpU1SzZk2FhYWpc+fO2rJlS7YyuT1HZ0pJSdG2bdt07Nixc5aNjo5WqVKlzlkOAACcH6u0CRMSEiRJFStW9NpeuXJlSVJYWJhn2+eff64+ffp4wiBJ6t69u+rXr6/PPvvsnMc6HydOnNAjjzyiZs2aKTIyUqVLl1avXr20adMmn+UdDoeeeOIJVapUSREREerXr5/27t2brdyaNWvUs2dPlSlTRuHh4ercubN+/fXXc9bn5MmT2rZtm06ePHnOshUrVvR6HwGrIBBCiTVo0CAlJibqhRde0KBBgzRz5kyNGzfOq8z06dNVs2ZNPfHEE5o8ebKqV6+uu+++W2+++Wa2/e3YsUM33HCD+vbtqxdeeEFxcXHq27evPvroI40cOVJDhgzRuHHjtHPnTg0aNEhOp9Pz3D///FMXX3yxtm7dqscee0yTJ09WRESE+vfvrwULFngdp1u3burWrVuhvCd16tRRmTJlVKpUKQ0ZMkSHDx/2evzHH3+U5Pqj161bN4WFhSksLEy9evXyOc/Nf//9pxdffFETJ04s0j+SCQkJevfdd9WlSxdNnDhRzzzzjI4ePaorr7zSZ4+bWbNm6bXXXtM999yjxx9/XFu2bFHXrl29Xn9eztGZ1q5dq0aNGumNN94o6JcKAADO04XeJuzUqZPsdrseeOABrV69Wvv27dM333yjCRMmqH///mrYsKEkaf/+/Tpy5IjatGmTbR/t2rXzGV4VpH///VcLFy5Unz599Morr2jUqFHavHmzOnfurAMHDmQrP2HCBC1atEijR4/W/fffrx9++EHdu3dXamqqp8ySJUvUqVMnJSQkaOzYsXr++ecVHx+vrl27nnW6A0lasGCBGjVqdM52HmBpBihhxo4daySZW2+91Wv7NddcY2JiYry2paSkZHv+lVdeaerUqeO1rWbNmkaSWblypWfbd999ZySZsLAws2fPHs/2t99+20gyS5cu9Wzr1q2badasmUlLS/NsczqdpkOHDqZevXrZjlWzZs1cv15jjDl69KiRZMaOHevz8alTp5p7773XfPTRR2bevHnmgQceMIGBgaZevXrm5MmTnnL333+/kWRiYmJMz549zaeffmomTZpkIiMjTd26dU1ycrLXfgcOHGg6dOjguS/J3HPPPXmqe2507tzZdO7c2XM/MzPTnDp1yqtMXFycqVixotd537Vrl+cc7du3z7N9zZo1RpIZOXKkZ1tuz9HSpUuznV/3tpze/5xMmjTJSDK7du3K0/MAAMC5WalN+O6775qyZcsaSZ7b0KFDTUZGhqfMunXrjCQza9asbM8fNWqUkeRVr/NVs2ZNM3ToUM/9tLQ043A4vMrs2rXLhISEmPHjx3u2udtVVatWNQkJCZ7tn332mZFkXn31VWOM632rV6+eufLKK43T6fSUS0lJMbVr1zY9evTwbJsxY0a2Npd724wZM/L0uu655x7Dx2RYBT2EUGKNGDHC6/5ll12m48ePe7rVSt5daE+ePKljx46pc+fO+vfff7N1H23cuLEuueQSz/327dtLkrp27erV7da9/d9//5Xk6h67ZMkSz7dTx44d07Fjx3T8+HFdeeWV+ueff7R//37P83fv3l3gq0498MADev3113XDDTdowIABmjp1qj744AP9888/mjZtmqdcUlKSJKlSpUpatGiRBg0apEceeUTvvPOOdu7cqTlz5njKLl26VJ9//rmmTp1aoHXNjYCAAM+kh06nUydOnFBmZqbatGmjDRs2ZCvfv39/Va1a1XO/Xbt2at++vb755htJeT9HZ+rSpYuMMTmu8gYAAPzHCm3CqlWrql27dpo6daoWLFighx56SB999JEee+wxTxl3z5qQkJBsz3evPJu1901BCwkJ8Uxu7XA4dPz4cUVGRqpBgwY+228333yz1zD7gQMHqnLlyp722++//65//vlHN9xwg44fP+55P5OTk9WtWzetWLHCq3fWmYYNGyZjjIYNG1awLxS4gDCpNEqsrH+QJSkqKkqSFBcXp9KlS0uSfv31V40dO1arVq3KNpb85MmTKlOmTI77cz9WvXp1n9vj4uIkuboVG2P09NNP6+mnn/ZZ1yNHjngFFkXhhhtu0MMPP6wff/zR01hwN4YGDRrk+YMtSf/3f/+nm266SStXrtTw4cOVmZmp+++/XzfddJPatm2b52OfOHFC6enpnvthYWFe73VufPDBB5o8ebK2bdvmNc9R7dq1s5WtV69etm1Zx8oX13MEAADO34XeJvz111/Vp08frV692jMcrH///ipdurTGjRunW2+9VY0bN/a0806dOpVtH2lpaZJ01ikAjh49KofD4bkfGRnptXrtuTidTr366quaNm2adu3a5bUvXwuZnNl+s9lsuuiiizwh2T///CNJGjp0aI7HPHnypOd8A8g7AiGUWL6WVJfkmVB5586d6tatmxo2bKhXXnlF1atXV3BwsL755htNmTIl2zcKOe3vXMdx7+eRRx7RlVde6bPsRRdddO4XVAiqV6+uEydOeO5XqVJFUvZJCQMCAhQTE+Np0MyaNUt///233n777WzfXCUmJmr37t2qUKGCwsPDfR732muv1fLlyz33hw4dqpkzZ+a63h9++KGGDRum/v37a9SoUapQoYICAgL0wgsv5HpC7qyK8zkCAADn50JvE7799tuqWLFitrmB+vXrp2eeeUYrV65U48aNPZNMHzx4MNs+Dh48qOjoaJ+9h9zatm2rPXv2eO6PHTs2T72jn3/+eT399NO69dZb9eyzzyo6Olp2u10PPvjgWXvy5MT9nEmTJqlly5Y+y+QlsAKQHYEQLlhfffWVTp06pS+//NLrm56zrVSVH3Xq1JEkBQUFqXv37gW67/NhjNHu3bs9y5BKUmxsrCRlGx6Vnp7utbT9f//9p4yMDF166aXZ9jtr1izNmjVLCxYsUP/+/X0ee/LkyZ5wSTodROXWvHnzVKdOHc2fP182m82zfezYsT7Lu79Bymr79u2eVdqK6zkCAACFr6S3CQ8fPuzV28bN3YM6MzNTkmtYWfny5fXbb79lK7t27docQxW3jz76yGtImfv15Na8efN0+eWX67333vPaHh8fr3LlymUrf2b7zRijHTt2qHnz5pKkunXrSpJKly5N+w0oJMwhhAuW+1sck2UJ9pMnT2rGjBkFepwKFSqoS5cuevvtt31+I3P06FGv+3lZdj63zjyG5FpN4+jRo+rZs6dnW5cuXVShQgV99NFHnq7DkjRz5kw5HA716NFDkjR48GAtWLAg202SrrrqKi1YsMAzbt6X2NhYde/e3XNr3Lhxnl6Pr3O3Zs0arVq1ymf5hQsXeoVca9eu1Zo1a9SrVy9JeT9HZ8rLsvMAAKB4Keltwvr16+vw4cNatmyZ1/aPP/5Ykry+/BswYIC+/vprr+Xbf/rpJ23fvl3/93//d9bjXHrppV7tt7wGQgEBAV7vsSTNnTs3x3kaZ82apcTERM/9efPm6eDBg572W2xsrOrWrauXX37ZMw9mVudqv+Vl2XnAqughhAvWFVdcoeDgYPXt21d33nmnkpKS9M4776hChQo+/0ifjzfffFMdO3ZUs2bNdPvtt6tOnTo6fPiwVq1apX379mnTpk2esu7lRXMzieDs2bO1Z88ez1j3FStW6LnnnpMk3XTTTapZs6YkqWbNmrruuuvUrFkzhYaG6pdfftEnn3yili1b6s477/TsLyQkRJMmTdLQoUPVqVMn3XTTTfrvv//06quv6rLLLtO1114rSWrYsKFnCdMz1a5dO8eeQQWlT58+mj9/vq655hr17t1bu3bt0ltvvaXGjRv7bBBcdNFF6tixo+666y6dOnVKU6dOVUxMjB599FFPmbycozOtXbtWl19+ea66Tp88eVKvv/66JNeYf0l64403VLZsWZUtW1b33ntvPt4RAACQXyW9TXjvvfdqxowZ6tu3r+677z7VrFlTy5cv18cff6wePXp4fUn3xBNPaO7cubr88sv1wAMPKCkpSZMmTVKzZs10yy23FOhrPVOfPn00fvx43XLLLerQoYM2b96sjz76KMdgKTo6Wh07dtQtt9yiw4cPa+rUqbrooot0++23S5Lsdrveffdd9erVS02aNNEtt9yiqlWrav/+/Vq6dKlKly6tr776Ksf6LFiwQLfccotmzJhxzoml9+zZo9mzZ0uSp4eVu81ds2ZN3XTTTXl9O4ASgUAIF6wGDRpo3rx5euqpp/TII4+oUqVKuuuuu1S+fHndeuutBXqsxo0b67ffftO4ceM0c+ZMHT9+XBUqVFCrVq00ZsyYfO/3vffe85qLZ+nSpZ7uzR07dvQEQjfeeKNWrlypzz//XGlpaapZs6YeffRRPfnkk9nm+bn55psVHBysF198UaNGjVLZsmV155136vnnn89xbHxRGzZsmA4dOqS3335b3333nRo3bqwPP/xQc+fOzfbtmOR6TXa7XVOnTtWRI0fUrl07vfHGG56x9FLhnaMzxcXFZZtIcvLkyZJcDQoCIQAAilZJbxM2aNBA69ev11NPPaUPP/xQhw4dUpUqVfTII49o3LhxXmWrV6+u5cuX66GHHtJjjz2m4OBg9e7dW5MnTz7r/EEF4YknnlBycrLmzJmjTz/9VK1bt9aiRYu8VkI7s/wff/yhF154QYmJierWrZumTZvm1Xbt0qWLVq1apWeffVZvvPGGkpKSVKlSJbVv397rS8/ztWvXrmztN/f9zp07EwjhgmUzZ/brAwAAAAAAwAWNOYQAAAAAAAAshkAIAAAAAADAYgiEAAAAAAAALIZACAAAAAAAwGIIhAAAAAAAACyGQAgAAAAAAMBiCIRKsJkzZ8pms2n37t2ebV26dFGXLl38VqfzddVVV+n222/33H/mmWdks9lks9kUGRmZr30uXLjQsw+bzabffvvN63Gn06mmTZtqwoQJ51X3pKQkVahQQR999FG+nn/8+HFFRETom2++ybHM3XffrR49euS3iho8eLAGDRqUbbv7WnLfjh07lud9//777177mDdvXrYyZ57fovTWW2+pRo0aOnXqlM/Hz/f8na+czk1h+uuvvxQYGKgtW7bkWMaf5wwALlQXYhuuqBXV36fCaJvmR8uWLT3H7dOnT7724Y+2RlZr165VcHCw9uzZU2THXLx4sSIjI3X06NEiO+aZMjIyVL16dU2bNq3Qj+WP9xglG4EQClVCQoLGjRunFi1aKDIyUmFhYWratKlGjx6tAwcOeJX99ddf9f3332v06NHZ9jN79my99957XtucTqdmzZql9u3bKzo6WqVKlVL9+vV18803a/Xq1Z5ybdq00ezZs3XHHXf4rOPHH3+svXv36t5778322M6dO3XnnXeqTp06Cg0NVenSpXXppZfq1VdfVWpqqlfZV199VaVKldLgwYOz7ef333/XkCFDVL16dYWEhCg6Olrdu3fXjBkz5HA4JEkxMTEaPny4nn76aZ/13LVrl95991098cQT2R7L7fs8evRoff7559q0aZPPY0yZMkWzZ89WqVKlvLZ/9dVX6ty5sypUqKDw8HDVqVNHgwYN0uLFiz1latasqdmzZ/usn3T285tff/75p4YMGaKqVasqJCREVapU0Y033qg///wzW9lhw4YpPT1db7/9ts99ne385dfXX3+tnj17KiYmRqGhoapfv74eeeQRHT9+PFvZc52bvPxbkqRly5bp2muvVaVKlRQcHKwKFSqob9++mj9/vqdM48aN1bt3b40ZM8bnMf19zgAAxdenn36qIUOGqF69erLZbDmGWevWrdO9996rJk2aKCIiQjVq1NCgQYO0ffv2fB+7MP4+5fU4vtqmubVs2bIcvzyTXG2WM8Om559/XrNnz1a5cuWylZ8wYYL69eunihUrymaz6ZlnnvG535zaGrt37/aETZ9//nm257lDMF9fGOamveH25JNP6vrrr1fNmjW9thtjNHv2bHXq1Elly5ZVeHi4mjVrpvHjxys5Odnna5GkBQsWqFevXipXrpyCg4NVpUoVDRo0SEuWLPGU6dmzpy666CK98MILOe4nPzIyMvTaa6+pbdu2KlWqlCIjI9W2bVu99tprysjI8CobFBSkhx56SBMmTFBaWlqB1uNMvt7j3H5mcjt8+LAeeeQRNWzYUOHh4YqIiFBsbKyee+45xcfHZyufm/OQ332jCBiUWJmZmSY1NdU4nU7Pts6dO5vOnTv7r1JZ7Ny509SuXdsEBASYwYMHmzfeeMP873//M/fee6+JiYkx9erV8yp/9dVXmyuuuMJr29ixY01Ol+k999xjJJmrr77avPrqq+bNN980999/v6lXr54ZO3ZstvIzZswwksy6deu8trdo0cLccccd2cp//fXXJiwszJQtW9bcf//95n//+5954403zODBg01QUJC5/fbbPWXT09NN+fLlzfPPP59tP++8844JCAgwVapUMaNHjzbvvvuumTJliunTp4+x2WxmwoQJnrJ//fWXkWR++umnbPt54IEHTP369bNtz+v73K5dO3PTTTf5fG927dqVbf+TJk0ykkznzp3NK6+8Yt566y3zyCOPmJYtW5qhQ4dmK7906VIjycydO9dru6/zez4+//xzExwcbCpVqmSefPJJ8+6775qnnnrKVK5c2QQHB5v58+dne86jjz5qatas6fVvxpizn7/8evjhh40k06JFCzNx4kTzzjvvmLvuusuEhISYqlWrmm3btmV7jq9zY0zez/GYMWOMJFOvXj0zZswY895775mXXnrJdOnSxUgyH330kafsN998YySZHTt2ZDtucThnAHAhKu5tuNzo3LmziYyMNJdffrmJiorKse4DBgwwlSpVMvfdd5955513zLPPPmsqVqxoIiIizObNm/N17IL++5SX45ytbZpbObWV3IYOHWoiIiJ8PlazZk3Tu3dvr22STKVKlcyVV15pJPlsB7v5amvs2rXLSDKSTPPmzbO1k9yv+ejRo17b89Le2Lhxo5FkVq5c6bWPzMxMM2jQICPJXHbZZWbKlCnm7bffNkOGDDF2u900bdrUHDp0yOs5TqfTDBs2zEgyrVq1MhMmTDDvvfeeee6550xsbKyRZH799VdP+WnTppnw8HCTkJCQ4/uSF0lJSaZz585GkunTp4954403zLRp00y/fv08beakpCSv58TFxZng4GDz3nvvFUgdfMnpPc7LZ6a1a9eacuXKmdDQUDN8+HAzffp0M336dHPbbbeZiIgI06NHD0/ZvJ6HvOwbRYdA6AJTXBoTGRkZpkWLFiY8PNz8/PPP2R4/efKkeeKJJzz3Dx8+bAIDA827777rVS6nP7qHDh0yNpvNK5Rxczqd5vDhw9m2+wqENmzYYCSZH3/80avsv//+ayIjI03Dhg3NgQMHsu3rn3/+MVOnTvXcnz9/vs8P1atWrTIBAQGmY8eOPv8IrVu3zsyYMcNrW9OmTbP9oU5PTzflypUzTz31lNf2vL7Pxhjz8ssvm4iICJOYmOjZllMglJGRYUqXLp3jL2hf77OvRk5O5ze/duzYYcLDw03Dhg3NkSNHvB47evSoadiwoYmIiDA7d+70euy3337zGbjldP7ya86cOUaSue6660xmZqbXY2vWrDHh4eGmWbNmJiMjw+sxX+cmr+d47ty5RpIZOHCgSU9Pz1Z+8eLF5quvvvLcT09PN1FRUebpp5/2KldczhkAWEVxacPl1n///WccDocxxpgmTZrkWPdff/3VnDp1ymvb9u3bTUhIiLnxxhvzfNyC/vuU1+MUx0DI3X47evToOQMhX20NdyDUsmVLI8l8/vnnXs/xFQjltb1x//33mxo1amQLm55//nkjyTzyyCPZ9vHll18au91uevbs6bXd/WXlgw8+mG1/xhgza9Yss2bNGs/9w4cPm4CAgAILY+644w4jybz++uvZHnvjjTeMJDNixIhsj/Xp08dcdtllBVIHX3y9x3n5zBQXF2eqVq1qKlasaLZu3Zqt/KFDh8yzzz7ruZ+X85DXfaPoEAgVkoSEBPPAAw+YmjVrmuDgYFO+fHnTvXt3s379ek+Zzp07myZNmphNmzaZTp06mbCwMFO3bl3PH4dly5aZdu3amdDQUFO/fn3zww8/eB3D14d4X42JtLQ0M2bMGFO3bl0THBxsqlWrZkaNGmXS0tK8yn3//ffm0ksvNWXKlDERERGmfv365vHHH/cqs2fPHp//iM/0ySefGElevV/O5v333zeSzO7du7225/RHd9WqVUaSmTlzZq72b4zvQGjMmDEmODg42x+yESNGZEu1z+bmm282tWrVyra9Z8+eJjAw0OzZsyfX9Rw5cqQpW7as1y/WJUuWGElm2bJlXmXz+j4bY8ymTZuMJK/eGDkFQgcPHjSSzDPPPJPr/ftq5OR0fvPrzjvvNJLMihUrfD6+fPlyI8nceeed2R6Ljo42999/v9e2nM5ffjVo0MBERUWZkydP+nx83LhxRpL5+OOPvbb7Ojd5PccNGzY00dHRefoW7JprrjHNmzf32laczhkAFCWrt+GMMSY1NdWMHTvW1KtXz4SEhJhKlSqZa665JscvTs4WCOWkdevWpnXr1tm2f/PNN6ZTp04mMjLSlCpVyrRp08arp0lOf5/c7+kvv/xiRo4cacqVK2fCw8NN//79s30RcT7HOVsg5A5rfv75Z9O2bVsTEhJiateubT744AOvcgUdCLnlJhDy1dZwB0IvvviiqV+/vmnRooVXO9RXIJTX9kaNGjXMsGHDvLalpKSYqKgoU79+/WxfkrndcsstRpJZtWqV5znR0dGmYcOG2b50O5tWrVqZfv365bp8Tvbu3WsCAgJM165dcyxz+eWXm8DAQLN3716v7a+++qqx2Wzm+PHjXtv37dtnbr31Vk+P6Vq1apkRI0Z4BalxcXHmwQcf9Pxeqlq1qrnpppu8zomv9zgvn5lefPHFbD27cpLX85CXfaNoMYdQIRkxYoSmT5+uAQMGaNq0aXrkkUcUFhamrVu3epWLi4tTnz591L59e7300ksKCQnR4MGD9emnn2rw4MG66qqr9OKLLyo5OVkDBw5UYmJinurhdDrVr18/vfzyy+rbt69ef/119e/fX1OmTNF1113nKffnn3+qT58+OnXqlMaPH6/JkyerX79++vXXX732d/PNN6tRo0bnPO6XX34pSbrppptyVc+VK1cqJiYm25jinLjLzZ07VykpKbl6Tk7Hbdq0qYKCgry2f/XVV6pTp446dOiQ6/20bt3aa1tKSop++uknderUSTVq1Mh1nWJjYxUfH+81p8rKlStls9nUqlUrr7J5fZ8l17wxYWFh2c6tLxUqVFBYWJi++uornThxItfHOFNO59fpdOrYsWO5umUdj/3VV1+pVq1auuyyy3wer1OnTqpVq5YWLVqU7bHWrVtne+2+zp8knTp1Ktf1c/vnn3/0999/6+qrr1bp0qV91u/mm2+W5JpjKCtf5yYv5/iff/7Rtm3b1L9//2zzQJ1NbGystmzZooSEBM+24nTOAKAoWb0N53A41KdPH40bN06xsbGaPHmyHnjgAZ08efKsixDkhTFGhw8fzjYfzsyZM9W7d2+dOHFCjz/+uF588UW1bNnSa87Cc7UZ77vvPm3atEljx47VXXfdpa+++irbPJEFcZyc7NixQwMHDlSPHj00efJkRUVFadiwYT7nyktMTPT59zOnBTAKwtnagQEBAXrqqae0adMmLViwIMd95LW9sX//fv3333/Z2lq//PKL4uLidMMNNygwMNDnc89sM/3yyy86ceKEbrjhBgUEBJzz2G6xsbFauXKl17akpKRctWdOnjzpec63334rh8PhqVdOdc7MzPS6ntx1MMZ41ePAgQNq166dPvnkE1133XV67bXXdNNNN2n58uWezzhJSUm67LLL9Prrr+uKK67Qq6++qhEjRmjbtm3at2+fpJzf47x8Zvryyy8VFhamgQMHnrWclPfzkJd9o4j5O5G6UJUpU8bcc889Zy3jHns6Z84cz7Zt27YZScZut5vVq1d7tn/33XdGktfwotx8uzR79mxjt9uzDTV56623vHrATJkyxefY4JzqfC6tWrUyZcqUOWc5t44dO5rY2Nhs28/2LczNN99sJJmoqChzzTXXmJdffvms33z56iFUrVo1M2DAAK9yJ0+e9IyzzY2MjAxjs9nMww8/7LXd/Q3MAw88kKv9uK1cudJIMp9++qln25AhQ0xMTEy2snl9n93q169vevXq5bl/tjmE3OPDIyIiTK9evcyECRO8viU9k69vvXI6v1nHrJ/rtnTpUmOMMfHx8bk6P+5x3Gd+c3XHHXeYsLAwz/2czl/W9yU3N7eFCxcaSWbKlClnrV/p0qV9fjN65rnJyzn+4osvcnXsM7mHuGXtXl2czhkAFCWrt+HcPWNeeeWVbI/5GhZiTN57CM2ePdtI8hrCEx8fb0qVKmXat29vUlNTczxuTn+f3O9p9+7dvcqPHDnSBAQEmPj4+AI5zrl6COmM3rBHjhwxISEhXu0Md1vpbLfC6iFkTPa2hvtv+6RJk0xmZqapV6+eVy+hM3sI5bW98eOPPxpJXkPIjDFm6tSpRpJZsGBBjs89ceKEkWSuvfZaY4yrl825nuOLe2ha1iFSQ4cOzVV7Juu1/eCDDxpJZuPGjTkeyz0lxUMPPeS1/cCBA0aSmThxomfbzTffbOx2e7Y5To05fT262+K+5lp0l8npPXYfIzefmaKiokyLFi1yfF1Z5fU85GXfKFq+o1ict7Jly2rNmjU6cOCAqlSpkmO5yMhIr1WNGjRooLJly6pq1apq3769Z7v753///TdP9Zg7d64aNWqkhg0bevVi6Nq1qyRp6dKl6tChg8qWLStJ+uKLL3TLLbfIbvfdeWzZsmW5Om5CQkKeeigcP35cVatWzXV5SZoxY4batWun999/XwsWLNCCBQv0yCOPqGvXrpo1a1au9nf8+HFFRUVlq7ukXNf/xIkTMsac937c3PvJer581dN9jLzu332M3C4tP27cODVs2FDTpk3Td999p2+//VZPPvmkWrVqpY8++ihX3zbmdH4rVaqkH374IVf1aNGihSR5vmE91+t2P37mexQVFaXU1FSlpKQoPDw8x/MnSVdeeWWu6+eWl/pl7ZGTtX5Zz01eznFBX3PF5ZwBQFGyehvu888/V7ly5XTfffdle8xms+XpNfiybds23XPPPbrkkks0dOhQz/YffvhBiYmJeuyxxxQaGprjcc/VZrzjjju8yl922WWaMmWK9uzZo+bNmxfYcXLSuHFjr96w5cuXV4MGDXye/zFjxvjsOTtp0qRc9eTOr7O1A929hIYOHaqFCxfqmmuuyVYmr+0N9+qqZ7a1ctM+yNo2yM+x3bK2dSpUqCBJevTRRzVkyJBcPze/dfZVB8nVC3DhwoXq27ev2rRpk20/7uvx888/V4sWLXyeC3eZnN5jKfefmQqzzUnbrvgiECokL730koYOHarq1asrNjZWV111lW6++WbVqVPHq1y1atWy/XEtU6aMqlevnm2b5OqenBf//POPtm7dqvLly/t8/MiRI5Kk6667Tu+++66GDx+uxx57TN26ddO1116rgQMH5tiwOJvSpUvnueFjjMlTebvdrnvuuUf33HOPjh8/rl9//VVvvfWWvv32Ww0ePFg///xzvo7rHuaT167dBb2fM68LX+9Pft5n977y0qi7/vrrdf311yshIUFr1qzRzJkzNWfOHPXt21dbtmzJ1qDK6ZhnCg0NVffu3fNUd/cfk3O9rzn9wc7L+1u5cmVVrly50OrnbpCcWY+sdcvLOS6Ka84f5wwAipLV23A7d+5UgwYNchzCcz4OHTqk3r17q0yZMpo3b57XUJOdO3dKkpo2bXrO/ZytzXjmMH33B2T3+19Qx8nt8d118HX+mzVr5vNv6ocffpjn4+bFudqBN954o5599lmNHz9e/fv3z/Z4QbWVc9M+OLNtUJBtncaNG6tx48Z52k9+6pxTHY4ePaqEhIRzXos7d+7UgAEDclU/X9dsbj8zlS5dOtfva17PQ172jaJFIFRIBg0apMsuu0wLFizQ999/r0mTJmnixImaP3++evXq5SmX05jLnLbn9Q+T0+lUs2bN9Morr/h83N1oCQsL04oVK7R06VItWrRIixcv1qeffqquXbvq+++/z9MYXUlq2LChNm7cqL1792ZrGPkSExOT54bSmc/v16+f+vXrpy5dumj58uXas2fPOcd9+zpu6dKlVaVKlVyPk4+OjpbNZsu2n4suukiBgYHavHlznl6Lez9Zx9Xn9P7k9X3Oeox69erlqV6S673p0aOHevTooaCgIH3wwQdas2aNOnfufNbn5VR/h8Oho0eP5urY0dHRCg4OVpkyZVS5cmX98ccfZy3/xx9/qGrVqtnm8YmLi1N4eLjCwsI8+/V1/iQpNTXVa9z42VSqVEmSPD2mzla/PXv2KCEhwWcj5Mxzk5dz3LBhQ0kq1GvOH+cMAIqS1dtwheXkyZPq1auX4uPj9fPPP5+199XZnKvNWFDvf37bpgV1/MJ0rnagu5fQsGHD9MUXX2R7PK/tjZiYGM9xs8raZvIVPLkfk+RpM2U9dk7P8cVXW+fkyZNKTU0953ODg4MVHR2drc4tW7bMVZ3PVoeCktN77KtcTp+ZGjZsqN9//13p6ekKDg4+637yeh7ysm8ULSaVLkSVK1fW3XffrYULF2rXrl2KiYnRhAkTirQOdevW1YkTJ9StWzd17949261Bgwaesna7Xd26ddMrr7yiv/76SxMmTNCSJUu0dOnSPB+3b9++knL/DUfDhg21a9euPB/HF3eXy4MHD+b7uH369NHOnTu1atWqc+4jMDBQdevWzbaf8PBwde3aVStWrNDevXtzWXt59pN1KFbDhg0VFxeXLZzI6/ssSZmZmdq7d2+uhnqdTUG8z3v37vX0wjnXLesEfH369NGuXbv0yy+/+Dzezz//rN27d6tPnz7ZHtu1a5fXa8/p/EnSp59+muv6udWvX1/169fXwoULc/wmZNasWZ7XkZWvc5OXc1y/fn01aNBAX3zxhZKSks5Z3m3Xrl2y2+2qX7++Z1txOmcAUNSs3IarW7eu/v77b6+FAc5XWlqa+vbtq+3bt+vrr7/2+YVI3bp1JemcX8idb5uxqI5TXOW2HThkyBBddNFFGjduXLYwK6/tDXd4cOb72bFjR5UtW1Zz5syRw+Hw+dwz20wdO3ZUVFSUPv744xyf48uuXbtUrlw5rx53DzzwQK7aM9dee63nOb169VJAQIBmz56d47FmzZqlwMBA9ezZM1sdpNNt/PLly6t06dLnvBbr1q2bq+s16zFy48y2fN++fZWamqrPP//8nM/N63nIy75RtAiECoHD4cj2wb1ChQqqUqVKoa4a4MugQYO0f/9+vfPOO9keS01NVXJysiT5XEHKnXpnrfN///2nbdu2nfO4AwcOVLNmzTRhwgSfoUpiYqKefPJJz/1LLrlEcXFxuR4ac+jQIf3111/Ztqenp+unn36S3W7XRRdddM79XHLJJdqyZUu28/Loo48qIiJCw4cP1+HDh7M9b+fOnXr11Ve99vPbb79lKzd27FgZY3TTTTf5/IO5fv16ffDBB9m2lSlTRk2aNPHavzFG69ev9yqb1/dZkv766y+lpaXlagW1lJSUHEOxb7/9VpK8GqQ5yen8uuejyc3NPR+NJI0aNUphYWG68847PWOm3U6cOKERI0YoPDxco0aNylaXDRs2ZHvtOZ0/9xxCubllNWbMGMXFxWnEiBHZ/kiuX79eEydOVNOmTbN1//V1bvJ6jseNG6fjx49r+PDhyszMzFb++++/z7a62fr169WkSRPPsAb3e+Lvc7Zz505P1363gwcPatu2bV4fVE6ePKlt27blujcXAOSENpw0YMAAHTt2TG+88Ua2x/LTy8XhcOi6667TqlWrNHfuXF1yySU+y11xxRUqVaqUXnjhBaWlpeV43Ly2Gf11nOIqt+1Ady+h33//3bPiaVZ5aW9UrVpV1atXz9bWCg8P1yOPPKK///47W3tVkhYtWqSZM2fqyiuv1MUXX+x5zujRo7V161aNHj3a5zX54Ycfau3atV7b1q9fn+3ae/TRR3PVnpk8ebLnOdWrV9ctt9yiH3/8UdOnT8927LfeektLlizRbbfdpmrVqmWrg81m89TDbrerf//++uqrr3y2Q92vbcCAATmu/OYuk9N7nJfPTCNGjFDlypX18MMPa/v27dmec+TIET333HOS8n4e8rJvKfftvYyMDG3bti1XX1DDN4aMFYLExERVq1ZNAwcOVIsWLRQZGakff/xR69at8/qFUhRuuukmffbZZxoxYoSWLl2qSy+9VA6HQ9u2bdNnn32m7777Tm3atNH48eO1YsUK9e7dWzVr1tSRI0c0bdo0VatWTR07dvTs7+abb9by5cvP2SAICgrS/Pnz1b17d3Xq1EmDBg3SpZdeqqCgIP3555+aM2eOoqKiPN+29e7dW4GBgfrxxx91xx13nPN17du3T+3atVPXrl3VrVs3VapUSUeOHNHHH3+sTZs26cEHH8xVd8yrr75azz77rJYvX64rrrjCs71u3bqaM2eOrrvuOjVq1Eg333yzmjZtqvT0dK1cuVJz587VsGHDvPYze/Zsbd++3auXRYcOHfTmm2/q7rvvVsOGDXXTTTepXr16SkxM1LJly/Tll196/fKTXJMq9u3b12uMc8eOHRUTE6Mff/zRM5lkft5n9/7Dw8PVo0ePc74/KSkp6tChgy6++GL17NlT1atXV3x8vBYuXKiff/5Z/fv3V6tWrc65n5zOb37mo5GkevXq6YMPPtCNN96oZs2a6bbbblPt2rW1e/duvffeezp27Jg+/vhjz7eAbuvXr9eJEyd09dVXe23P6fzlZw4hyTX2ft26dXr11Vf1119/6cYbb1RUVJQ2bNig999/XzExMZo3b56CgoK8nufr3OT1HF933XXavHmzJkyYoI0bN+r6669XzZo1dfz4cS1evFg//fST5syZ49l/RkaGli9frrvvvturLsXhnHXr1k2StHv3bs+2xx9/XB988IF27dqlWrVqSZIWLFigW265RTNmzPD6dwkAeUUbzlVu1qxZeuihh7R27VpddtllSk5O1o8//qi7777b8zd0xYoVWrFihSTXXCjJycmeNk2nTp3UqVMnSdLDDz+sL7/8Un379tWJEyey9Xh1T+pbunRpTZkyRcOHD1fbtm11ww03KCoqSps2bVJKSornC7S8thnPVFTHKUqzZ8/Wnj17PMuKr1ixwnMubrrpJq8pFPLSDnTPJfT7779neyyv7Y2rr75aCxYsyDZ/0WOPPaaNGzdq4sSJWrVqlQYMGKCwsDD98ssv+vDDD9WoUaNsX56OGjVKf/75pyZPnqylS5dq4MCBqlSpkg4dOqSFCxdq7dq1Xr2Ujxw5oj/++EP33HOP137yM4eQJE2ZMkXbtm3T3XffrcWLF3t6An333Xf64osv1LlzZ5+/L3744QddeumlnuFdkvT888/r+++/V+fOnXXHHXeoUaNGOnjwoObOnatffvlFZcuW1ahRozRv3jz93//9n2699VbFxsbqxIkT+vLLL/XWW295voDz9R7n5TNTVFSUFixYoKuuukotW7bUkCFDFBsbK8n1herHH3/sFarl5Tzkdd+5be/t379fjRo10tChQzVz5sw8n0uIZecLw6lTp8yoUaNMixYtTKlSpUxERIRp0aKFmTZtmle5zp07myZNmmR7fk7LSUryWgY1N0uWGmNMenq6mThxomnSpIkJCQkxUVFRJjY21owbN86cPHnSGGPMTz/9ZK6++mpTpUoVExwcbKpUqWKuv/56s3379mx1zstlExcXZ8aMGWOaNWtmwsPDTWhoqGnatKl5/PHHzcGDB73K9uvXz3Tr1s1rW05LeyYkJJhXX33VXHnllaZatWomKCjIlCpVylxyySXmnXfe8bksqq9l540xpnnz5ua2227zWf/t27eb22+/3dSqVcsEBwebUqVKmUsvvdS8/vrrJi0tzVPu1KlTply5cubZZ5/1uZ/169ebG264wVSpUsUEBQWZqKgo061bN/PBBx8Yh8PhKbd161Yjyfz444/Z9nH//febiy66yOf+8/I+t2/f3gwZMsTne3PmsvMZGRnmnXfeMf379zc1a9Y0ISEhJjw83LRq1cpMmjTJnDp1KltdfC07b4zv83u+/vjjD3P99debypUrm6CgIFOpUiVz/fXXm82bN/ssP3r0aFOjRo1s18e5zl9+LVy40PTo0cNERUWZkJAQc9FFF5mHH344x6WBfZ0bt7ycY2NO/5uuUKGCCQwMNOXLlzd9+/Y1X3zxhVe5b7/91kgy//zzT7Z9+Puc1axZ09SsWdNrm3uJ2KzXqvv6zbqkMwDkB204l5SUFPPkk0+a2rVre35XDxw40OzcudNTxt1G83XLuuy5+7g53c705Zdfmg4dOpiwsDBTunRp065dO/Pxxx97lfH19ymndp67XbJ06dICOc65lp33df7PPLc5tZXchg4dmqdl58/2Hp/5un21NbIuO38m9/uqLMvOZ5Xb9oZ7Kfaff/452z4cDoeZMWOGufTSS03p0qVNaGioadKkiRk3bpxJSkry+T4YY8y8efPMFVdcYaKjo01gYKCpXLmyue6668yyZcu8yk2fPt2Eh4ebhISEHPeVV6dOnTJTpkwxsbGxJiIiwoSHh5vWrVubqVOnmvT09Gzl4+PjTXBwsHn33XezPbZnzx5z8803m/Lly5uQkBBTp04dc88993i1s48fP27uvfdeU7VqVRMcHGyqVatmhg4dao4dO+Yp4+s9zs9npgMHDpiRI0ea+vXrm9DQUBMeHm5iY2PNhAkTPL93ssrtecjLvnPb3nNfu0OHDs12LOQOgRCKjRUrVhi73e7VgHH/0T169KjXL7y8OHXqlDl69Kh5/fXXfTYUZs2aZUqVKmXi4uLOp/pm/Pjxpnbt2iYzMzPf+3jggQdMq1atfP5y3rlzpwkKCvIZFuXWxo0bjc1mMxs3bvTa7v4Fu2HDBnP06FGfxz+XzMxMc/ToUbNw4UKfjRxf57copaWlmUqVKpmpU6f6fLwgzt/5yOncFLarr77a9O/f3+dj/j5nAAD4UlR/nwqrbZofcXFx5ujRo6Z69eo+Q6fc8Fdbw61r1645fvFVmFq2bGkefPDBIj9uVlOmTDGVK1c2KSkphXocf73HKLkIhFCs9OzZ0wwfPtxzP+u3Tzl9U3IuCxYs8Pqm5MxAyOFwmCZNmpjnnnvuvOqemJhoypcvbz788MN8Pf/YsWMmIiLCLFq0KMcyI0aMMN27d89vFc11111n/u///i/b9qzf/uT0DdC5bNy40Wsfvr71OvP8FqXp06eb6tWre/Xsyup8z9/5yuncFKa//vrLBAQE5Nijyhj/njMAAHJSVH+fCqNtmh8tWrTwHDe/gZA/2hpZrV692gQFBZndu3cX2TG//fZbExERYQ4fPlxkxzxTenq6qV69unnzzTcL/Vj+eI9RstmMKUZrIAJn+Pfffz2T+QUGBqpLly553sfRo0e1adMmz/327durVKlSBVXFC8LBgwf1559/eu537tw52/w255KUlKTVq1d77jdv3lwVKlQosDoCAAD4W0G0TfNjzZo1ntVLy5cv77VwAwDkF4EQAAAAAACAxbDsPAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxRAIAQAAAAAAWAyBEAAAAAAAgMUQCAEAAAAAAFgMgRAAAAAAAIDFEAgBAAAAAABYDIEQAAAAAACAxQT6uwLAORlz+mebzX/1AAoS1zUAAAAAPyIQQvHi/pDscLhuTqdrW3Kya3tEhOvDs90uBQS4bnyYRnFnTN6uaYnrGgAAAEChIhBC8ZA1CIqLkxYulH76SVq5UjpyRDp1yvV4SIhUsaLUoYPUvbt09dVS2bIEQyie3EFQfLzrmv7xR2nVKunwYe9rukIF72s6KopgCAAAAEChshmTddwC4AfuSzA5WZo+XZo0STp61LuM+0PxmZdr+fLSY49JI0ZIYWF8eEbxYYyUmuq6pidOzNs1/eijrms6IsK7LAAAAAAUEAIh+Jf78jtxQhoyRFq8+PRj7uEz5ctL5cq5th05Ih075hp243CcLnvVVdKcOVLp0nx4hn+5r+mTJ6UbbpC+/fb0YwEBrqFhFSp4X9PHj58eUubWq5f04Yeu3kIS1zUAAACAAkUgBP9yz6XSq5f0yy+ubTab6wPzQw9JAwdKdep4l//nH2nuXOmVV1zDy9yX8GWXSd9/7xqCw4dn+IsxruFgV1wh/fyza5vN5go2s17TWa/Rf/6RPvtMmjLFFY66r+mOHV2BknueIQAAAAAoIARC8B9jpMxM6ZlnpOefd22z2VzBzrx5rg/QZ3vugQNSv37Shg2ntz/9tDRu3Ol9AUXJ/et07Fjp2WdPb+/YUZo/39Ur6GzX5f79Uv/+0vr1p/f1xBOua5p5sgAAAAAUIAIh+I/TKW3bJjVvfnqoTIsWrp5CkZHnfr4xrh5CbdtK//7r2hYQ4Npn3bp8eEbRM0basUNq1Oj0Nd20qWsi6dz28jlxQmrf3rUfyXVNb94sNWzINQ0AAACgwNj9XQFYlHv1pbffPv3BOTBQmjYtd2GQ5PpwHBXlmrTXzeE4fZ+sE0XJfb1Nn+49F9Bbb+VtyFd0tOvfgXuVsaz/TrimAQAAABQQegjBP4yRUlKk6tVdvXxsNunSS0/PuZLXfbVuLf3+u+t+VJRrWe/AQHpUoOi4h0BWrOi6piXpkkukX3/N33XYpo1r6Jjkuqb37WMlPQAAAAAFhh5C8A9jpD//PP3B2W6Xrrsu//u78cbTP8fFSX/9dX71A/Jjy5bT17QkDR6c/33dcIPr34V0+pomvwcAAABQQAiE4B9Op7Rmzen7NpvUrl3+93fxxd73V6/O/76A/DrzumvfPv/7at/euzfQmjWufzcAAAAAUAAIhOAfxpyeCFpyffBt0iT/+2ve3Pv+zp353xeQX2ded02b5n9fzZp5B0L//ksPIQAAAAAFhkAI/uFeIcwtJMQ18W5+2GxS6dJScPDpbSdOnF/9LMoY47khH7Jed+5rOr9z/px5TWf99wIAAAAA54lACP5z8uTpn0uXPv/9Zd0HH57z5cCBA/rtt9904MABf1elZCrsa5qgDgAAAEABIRCC/wQGnv456zLd+WGMlJFx+n5Q0Pntz6LeeusttWvXTm+//ba/q1IyFfQ1nZl5+j7XNAAAAIACRCAE/ylb9vTPCQn57/1gjOuWlHR6W1TUeVUNyJeCuqYl13MTE0/fj4piyXkAAAAABYZACP5hs3l/eM7MPL95f44d8+6RQSAEf8h63WVmSseP5z8UOn7c+5rO+u8FAAAAAM4TgRD8w2aTGjU6fd8YafPm/O9v40bv+82a5X9fQH41bux9/48/8r+v33/3DpOaNpXs/MoGAAAAUDD4dAH/sNuldu1O33c6pTVr8r+/Vau877dvn/99Afl18cXe91evzt9+jHE9N2sg1LYtQ8YAAAAAFBgCIfiHzSZddJFUtarrvtMpzZmT9+E17vmDPvro9LbatV03PjyjqGW9piXXNZ0fxrie63S67teq5bqm6SEEAAAAoIDw6QL+YbO5VmS67bbT2/78U/r227yHQvPnSzt2nL5/550FU0cgL2w21+2OO05v+/NPadGivF3TxkgLF0rbt5/edvvtUkBAgVUVAAAAAAiE4D8BAdKtt0qhoa77Dod0//1SXFzunm+MdOSI9OCDp7dFRnqHTEBRu/3209e0JN13n2vC9NyGQkePuq5pd++gyEjpllsIhAAAAAAUKAIh+I/NJlWrJj3//OltO3dK3bpJ+/ef/bnGSP/9J3Xt6l32lVekmBiGi8E/bDapUiVp4sTT23bvPn2dnisU+u8/1/W/d+/pbZMmSRUqcE0DAAAAKFAEQvAvu1265x7pqqtOb/v9d9cKZM88I23b5l3eGOmvv6QxY6QmTVw/uw0YIA0fzgdn+N+990q9e5++/8cfp6/prVuzB0Nbt56+prdsOb392mtd1zS9gwAAAAAUsEB/VwAWZ7NJQUHS3Lmu4WOffuranpgojRvnukVHu3pdSNKhQ67hN2caNkz63/+KrNpAjtyB5Oefu67LTz5x3U9KksaPd91yc00PHSq9/TZhEAAAAIBCUWx6CGVmZuq9997TiRMnZPI6qTBKvrAw6cMPXaFO1lWaJNeH5b/+ct3O/OBcs6Y0a5b03nuuSarpHYTiwGaTgoNd1/S77+btmq5RQ5o503VNBwdzTVuMMUYnTpzQ+++/r8zMTH9XBwAAABewYhMI3XXXXbrrrrs0YcIEpaWl+bs6KEruD7wBAa7hMX//7VpGfuhQqU4dV1jkFh7uWtr7lltcPS+2bpWGDDm9whNQXNhsriGRt97qWjFszpzT13R4+OlyYWFS3bqua/rjj13X9M03s8S8RaWmpuq5557TiBEjdPfdd/u7OgAAALiAFZshY3fffbc++eQTvfHGG7rhhhvUsmVLBTBUwjrcYY4xrg/L11/vurklJ7v+HxGR83OB4sZ9bYaFSYMHu25uZ7umsz4XluFwOPT3339r2rRpCgkJ0R133OHvKgEAAOACVmy+gm7ZsqVuuOEGGWM0btw4paam+rtK8Ad3T58zbxERrpuvx4DiLq/XNNe1JaWkpGjMmDFyOp0aMmSIYmNj/V0lAAAAXMCKTSBks9k0fvx4lS9fXl9//bW+++47ZWRk+LtaKC74kIwLDdc0skhPT9dPP/2kRYsWqVy5cnr66adl4/oAAABAISo2gZAkVahQQSNHjlRISIieeuopJSQkMMG0VWRmupbpjo6Wfv01d88xRvrlF6lMGalPn+xLeQP+ZIzruixTxnWd5vb6/PVX17+D3r1d/y5wwTPGKDExUU899ZRCQ0P18MMPq3Llyv6uFgAAAC5wxSoQstlsuu+++9SoUSNt375dM2bMYIJpqzDGtdR8QoKUl55hGRmu5yQlFV7dgPxKSnJdn3kJdjIypJMnuaYtJC0tTe+99562bt2qBg0a6O6776Z3EAAAAApdsQqEJCk4OFhjx45VWFiYXnjhBe3fv19Op9Pf1UJxR+8gFEfu6zI/1yfXtCU4nU4dOHBAL730ksLDwzV+/HiFhob6u1oAAACwgGIXCNlsNvXt21c9evRQYmKiXn75ZaWkpPi7WgAAFLjk5GS99NJLSkhI0OWXX67evXvTOwgAAABFotgsO5+V3W7Xc889pxUrVuj999/XjTfeqEsuuUSBgcWyugAA5FlmZqb++OMPzZw5U6VKldKECRNktxe772kAAPApKSlJS5Ys0ezZsxUUFKQmTZqoRYsWKlu2rKKiolS2bFmVLVtWERER/q4qgBwU24SlcePGGjp0qKZPn66nn35aCxYsUFRUlL+rBQBAgUhMTNQzzzwju92uW265RU2bNvV3lQAAyJWUlBTNmTNH9913n2dlaJvN5tXL1f2z3W5XeHi4Jyhyh0W+/n/mz+XKlVNAQIBfXiNgBcU2ELLZbHriiSf05Zdf6ueff9aiRYv0f//3fwoJCfF31QAAOC9paWn6+uuvtWTJEtWqVUujR49mqBgAoMRYvny5xo8fr8zMTMXExKhOnToqVaqUQkNDFR8fr7i4OMXFxenkyZNKSUlRenq64uPjtXv3bp+hUU4/BwUFnTU08vV/989lypQpujcEKKGKbSAkSTExMXrooYf06KOPauzYseratasqV65MoxkAUGIZY3T8+HE9++yzCg8P16hRo1SuXDl/VwsAgFw5cOCAVqxYoQMHDujiiy/WK6+8otjY2GzljDEyxig9PV1xcXGeoOhc/z958qTnflJSklJTU3Xw4EHPZ0BfwVFOPZN8BUllypTRvn37ZLfb1bp1az3xxBNMTQLLKtZXvs1m02233aY5c+ZozZo1euedd/Twww8rMjLS31UDACBfkpOT9c4772jXrl1q166dhg0bxhcdAIASIT09XStWrNB7772npk2b6tFHH1W7du1ynAPPGKPg4GBFRkaqWrVqXtt9/f/MbcYYpaSk5DpMyvr/o0eP6tChQzp06FC24Mh9jEWLFumbb77R6tWrC/idAkqGYh0ISa5l6MeNG6eBAwdqypQpuuaaa9SkSRPGkgIAShyHw6Hdu3fr1VdfVWRkpMaOHctQaABAibFhwwa99dZbcjqduuqqq9S7d++zLoiQ05Cw3DLGKCQkRFFRUapVq5ZnW27+n5mZqfj4eM/NHRSdOHFCP/30k3bt2qXNmzdrw4YN+umnn9StW7c81w8o6Yp9IGSz2XT55ZerV69emj9/vl5++WW99tprKlu2rL+rBgBAniQkJGjixIlKTU3VgAED1K1bN3oHAQBKhP3792vBggVauXKlevXqpXvuuafQh1rlN1By90wKDw9X5cqVPdvc/x8xYoTS09PVunVr7dixQ9OmTSMQgiWViPVt7Xa7xo0bp+joaH3yySdavXq10tPT/V0tAAByLT09XevWrdOnn36qmJgYPf300/R2BQCUCOnp6Vq6dKnef/99NWnSRMOHD1e1atW8Qhr38K79+/crOTnZj7U9veKZzWaT3W6X3W5XQECAAgICFBgY6AmL5syZo8DAQH377bfasWOHX+sM+EOx7yHkVrduXd12222aMmWKxo8fr9atW6t8+fJ8swoAKBHi4+P17LPPKjg4WMOHD1f9+vX9XSUAAHJl7dq1+t///idJ6t27t3r27Jntc1h6eroWLVqkm2++2RPEZF1u3r0CWNaVwHzdj4qKUnR0dJH0PmrevLkGDRqkjz/+WFOmTNGbb75ZqMcEipsSEwjZ7XY99NBDWrBggdatW6eFCxdqyJAhCg8P93fVAAA4q+TkZM2fP1+rV69W/fr1df/99591zgUAAIqLvXv3auHChVq9erX69OmjO++802dY43A45HQ6FR0drfj4eCUnJys5OVnHjh2T5N1r51z3AwMDzxoeuVcL87U9t1OL2Gw2BQUFafTo0frkk080a9YsjRo1yjNXEWAFJSYQkqSyZcvq4Ycf1siRIzVhwgR17dpVderUoVENACi2nE6nDh8+rIkTJyoyMlKjRo1SdHS0v6sFAMA5paWl6YcfftDMmTPVvHlz3XbbbapatarPURphYWEaMGCA+vTp41lu/swJnXN7S0hIUEJCgvbu3ZtjaJRTsGS3230GSeXLl9dzzz2nsLAwr3o3bNhQAwcO1GeffaapU6dq6tSphfumAsVIiQqEbDabbrjhBn388cdavny53nnnHT3xxBMqU6aMv6sGAIBPCQkJeuutt3TgwAF16tRJgwcPZrgzAKBEWLt2rd5//33Z7Xb17t1bV1xxRY5fxrt79gQGBsoYo4iICJUtWzbbMvJn3nw95nQ6lZqa6jMsOle4dPz4cSUlJWn//v1eYVFkZKQmTpzos86PPfaY5s6dq/fee08PPfSQatSoUbhvLFBMlKhASHItQz9mzBhde+21mj59uq699lrFxsYW+hhTAADyKjMzU9u3b9f//vc/lSlTRk899RTLzAMASoT//vvPM11Hv379NHz48Fx/5sraeyc/jDEqXbq0KlSokOsQKWuY5CsoSktLy3Exh8aNG+uaa67R/Pnz9dprr+nll1/OV72BkqbEpSg2m00XX3yxrr76as2ZM0eTJ0/Wm2++qfLly/u7agAAeDlx4oReeuklZWRkaNCgQbr00kvpHQQAKPbS0tL03Xffafbs2WrZsqVuvfXWHIeKFYbzCZSMMSpVqpSqVauWLUTytT/3XEKPPfaYFixYoHfffVcPPfSQqlSpcn4vAigBSuTkO4GBgXriiSdUsWJFffHFF1qxYoXS0tL8XS0AADzS0tK0cuVKffXVV6pYsaJGjx5Nb1YAQImwatUqzZw5U0FBQerXr5+6d+9eYuZtda9w5l5iPigoSMHBwQoODj7r85o3b64+ffooISFBr7/+ehHVFvCvkvGv2oeaNWtq+PDhCg0N1QsvvKBjx455kl8AAPzJGKOjR4/qhRdeUFhYmO644w7Vrl3b39UCAOCc9uzZowULFmj9+vXq0qWLbr755gv+Cw13L6EnnnhCNptNb7/9tg4ePOjvagGFrsQGQna7XXfffbcaNGigzZs3a+7cuUpKSvJ3tQAAUGJioj777DP9/vvvql+/vu64444S880qAMC6UlNT9e233+rjjz9Wq1atNGzYsCIdKuZvrVq1Us+ePRUfH6/p06f7uzpAoSvRrdMyZcpo5MiRKl26tF555RXt2bNHDofD39UCAFiY0+nU3r179dprr6l06dJ6+OGHFRUV5e9qAQBwTr/88os++OADBQUF6eqrr9bll19umS803L2EHn/8cUnS9OnTdfToUT/XCihcJfpft81m0zXXXKOOHTvq+PHjeueddxQfH+/vagEALOzEiRN6++23dfToUXXo0EFXX321Zb5ZBQCUXDt37tTnn3+u33//XZdffrluvPFGBQUF+btaRa5t27bq3r27jh8/rrfeesvf1QEKVYkOhCQpJCREjz/+uKKjozVjxgz98ccfSk9P93e1AAAWlJ6eri1btuiDDz5QTEyMHnvsMZaZBwAUe6mpqVq8eLHmzZun2NhYyw0Vc7PZbAoODvb0Enr99dd17NgxP9cKKDwlPhCy2Wxq1aqVrr32WhljNHXqVP7RAgD84ujRo3rllVdkjNGAAQPUpk0byzWmAQAlz4oVK/Thhx8qNDRU/fr1U6dOnSwzVMyXDh06qHPnzv+PvfuOb6re/zj+TtK06Z60BcreS2RvKCBDEREQRIYsARHcV0Wv815/7gHqFReCypApiIIIyAZBZW8E2XuV7pXz++PY0tICBQtpm9fz8cijzTknp9+k3zbJO5/v96vTp0/r888/d3VzgBumSPyV2+12PfHEEypdurR+/vlnLV68WPHx8a5uFgDAjcTHx2vp0qVauHChoqKi9Oijj7plqT0AoHDZs2ePZsyYoc2bN6t9+/bq06fPVZdoL8qyVgkZhqEPP/yQaUlQZBWJQEiSSpUqpcGDB8vHx0fvvvuujh07xjL0AICbwjAMHTt2TO+99558fX01ZMgQlS5d2tXNAgDgijJWFZs9e7bq1aunPn36qHjx4lS3SmrZsqWaN2+uEydOaNy4ca5uDnBDFJlAyGazadCgQapVq5Z2796tadOmkeQCAG6K8+fP69tvv9WOHTtUo0YNDRgwQDabzdXNAgDgipYuXarJkyfL4XCoS5cuat68uVsPFctgsVgy56p1Op0aPXq0YmJiXN0sIN8Vqb/2wMBAPfLIIwoMDNTHH3+sP//8U2lpaa5uFgCgCEtPT9e+ffv06aefKigoSI899piCgoJc3SwAAK5o165dmj59urZu3aqOHTvq3nvvdeuhYrlp3bq1GjdurKNHj2rChAmubg6Q74pUIGSxWHT77berTZs2On/+vL744gudOXPG1c1CHhg2iw6+6KfdE4MVXy1v3dKQFF/dqt2Tg3XoBT8xQBAFiSHp0At+2j05WPHVrXnun/HVrNo9MVgHX/STUaT+QxddJ0+e1Keffqpz586pZcuWuv322ym1BwAUaPHx8Zo7d67mzJmjBg0aqHfv3gwVu4TFYpHD4dBTTz0lp9OpMWPGKC4uztXNAvJVkXu74e3trccff1wRERH69ttv9ccffygpKcnVzcLVWAwlRSUorkKMnN55j3ac3obiKsYoMSrhBjYOuD6JUQmKq3g9ffqCkqISJF6TFXjJycnasGGDpk6dqoiICD3xxBNyOByubhYAAFe0dOlSTZ8+Xb6+vurSpYuaNGnCUOfL6Nixo+rXr68DBw7o66+/dnVzgHxV5AIhSapdu7Z69Oghq9WqMWPG6Pjx465uEvLAsDhlWJ2SJe9vng0ZMqxOGRbnDWwZcH0y+rRxLfVrFkOGNZ0+XUgcO3ZMH374oaxWq3r27Klbb73V1U0CAOCKdu7cqWnTpmnbtm264447dM8998jLy8vVzSqQLBaLvL299eSTT8rpdOq9995jNWsUKUUyELLb7XrooYdUoUIFrVy5UgsXLlRsbKyrmwUAKEIuXLign3/+WcuXL1e5cuX04IMPMvcCAKBAi4+P1/fff68ff/xRjRo1Uq9evRgqlgedO3dW3bp1tX//fk2ePNnVzQHyTZEMhCSpZMmSGjRokPz8/PTBBx/o4MGDcjr5xB0A8M85nU4dPHhQ//vf/+Tv768hQ4YoKirK1c0CAOCKFi9erBkzZsjf31/dunVT48aNGSp2FRaLRT4+PnrssceUnp6u9957T4mJia5uFpAvimwgZLPZdN9996lu3br666+/NHXqVCaYBgDkizNnzujbb7/V3r17deutt+ree+/lBTUAoEDbvn27pk+frh07duj2229Xly5dmPfuGnTt2lU1a9bUnj17NHXqVFc3B8gXRTYQkqSgoCA9/PDDCg0N1bhx47Rz506lpqa6ulkAgEIsNTVVO3fu1Pjx4xUaGqoRI0YoODjY1c0CAOCy4uLiNGfOHM2fP1+NGzfOHCqGvLFYLPL19c2sEnr33XeVlJQkw2CdYxRuRToQslgsio6O1m233aa4uDh9+eWXOnHihKubBQAoxI4fP65x48YpPj5e7dq1U9u2bZl7AQBQoC1evFizZs1SQECAunXrpgYNGlDZeh169OihGjVqaOfOnZo5c6armwP8Y0U6EJIkHx8fjRw5UiVLltTs2bP166+/KiGBJcoBANcuMTFR69at0+zZs1WyZEkNHz5cPj4+rm4WAACXtW3bNk2bNk27du1Sp06d1LlzZ3l7e7u6WYWOxWKRv7+/Ro4cqbS0NL333ntKTU2lSgiFWpEPhCSpRo0a6tGjh2w2m8aOHavDhw/zhwsAuCaGYejQoUP65JNPZLfb1aNHD9WqVcvVzQLgjtLTpXPnpH37pIMHpbg4V7cIBVRcXJxmz56tn3/+WU2aNFHPnj0ZKvYP9erVS5UqVdKWLVs0e/ZsVzcH+EfcIhDy9PTU4MGDVbVqVa1bt04//fSTYmJiXN0sAEAhcv78ec2fP19r165V5cqVNXDgQJaZB3BzxcRI338v9e0rtW4t3XmndPvtUvv20hNPSL//7uoWooBZsGBB5lCxrl27qn79+gwV+4cCAwM1cuRIpaam6p133lFaWhrFBii03CIQksxl6Pv376+AgAB9+umn2rt3r9LT013dLABAIeB0OrVv3z598cUXCggI0MCBA1lmHsDNdeKE9MUXZvCzfr1Up450331S165SsWLStGnS449Ls2a5uqUoILZu3aoZM2bozz//1F133cVQsXyQMWdgnz59VKFCBW3cuFE//PCDi1sFXD+3CYRsNpu6d++uRo0a6dChQ5o2bZpOnTrl6mYBAAqBEydOaOrUqTpw4IAaNGigbt268QkrgJsnMVFavlz6+GOpeHHpww+l//5XGj5ceuQR6a23pGeflfbvlz76SNq2zdUthovFxsbqu+++06JFi9SkSRN1795dkZGRrm5WkRESEqIHH3xQqampevfdd5Wenk6VEAoltwmEJCk4OFgPPvigIiIiNHHiRG3evFkpKSmubhYAoABLSUnR1q1bNWnSJEVERGjo0KEsMw/g5jp0SPrhB8lmkwYOlFq1kqKipLAwKTxcqlRJ6tJF6tNH2rLFrBaC2zIMQz/99JO+++47BQYGqnv37qpXrx4fZOSTjCqh+++/X6VLl9bvv/+un376ycWtAq6PWwVCFotFzZo1U/v27ZWQkKCvv/5aR44ccXWzAAAF2OHDhzVhwgQlJiaqQ4cOatmyJcvMA7i5jh2Tfv3VDIE6dJC8vLLvt1qlyEipXTvJMKTVq6XkZNe0FS63ceNGTZ06Vfv27VOXLl10xx13MFTsBihWrJiGDRum5ORkvfPOO1QJoVDycHUDbjZfX18NHTpUa9as0bx589ShQwcVK1ZMfn5+rm4aAKCAiYuL0+rVqzV//nyVLVtWgwcPlq+vr6ubBcCdGIY5mfTx41L9+mbwkxubzZxLqFQp6fRpc86h0qVvblsLgNRUKTZWcjgkb28pt/w+IUFKSpL8/SUPD/Mhjo83v/r7536bxEQpLU3y8THzN1d/LpCYmKhTp07p6NGjmZeDBw9q3759OnLkiPbu3aumTZuqW7duDBW7ASwWiwzD0KBBgzR27FitXbtWixYtUocOHVzdNOCauF0gJEnVqlVT9+7d9cEHH2jcuHGqU6eOatSowSe+AIBMhmFo//79+vLLL+Xp6al77rlHNWrUcHWzALib9HQzrXA6zbTicsN+LBbJ01MKCpJOnTJDJDf0++/Sk0+a822PGJF7cPPxx9J330ljxphzc588aS7clpBgTsV0++2S3Z79Nq+9Ji1bJn3zjZm53Yy3DQkJCTpx4oQOHz6ceTly5IgOHz6sU6dOKT4+XklJSZmXxMRExcfHyzAMpaenq1ixYgoLC2Oo2A1isVgUERGhwYMH65VXXtH777+v9u3bZ+4DCgO3DIQ8PT3Vr18/LV26VOvWrdO8efMUGRmpsLAwVzcNAFBAnD59Wj/++KP++OMPNWjQQH379mWZeQA3n8VihkCGYYZCV5L1GDcNAc6fl9aulZo1Mx+O3OzfL61bJ124YF5PTjavx8VJr75qFmIVL5499NmzxzxvYmL+tjchQTp16oIOHdqhw4f/yhb8nDp1SnFxcUpMTMy8ZAQ/FotFYWFhioqKUtWqVRUVFaWoqCiVLFlSx44d08SJE/XHH39o27Ztqly5MgHFDWKxWPTAAw/os88+04oVK7RkyRK1adPG1c0C8swtAyFJioqKUt++fbVnzx5NmDBBzZo1U3BwMAk6AEDp6enavXu3vv76a4WEhOj+++9nmXkArmG1muOUPD2ls2fNcUsel3kJn5xslrv4+JgTTuOalCwpbd4sff659PTT5pCz/JCaav5aDh6Ujhwxp4TKuBw+LAUGHtDhw2/p9OlNSkxMVEJCghITE+V0OhUSEqLSpUurcuXKKl68eOalZMmSCg4Olo+Pj7y9vTMvPj4+iouL0/79+/X1119rxYoVatiwIc9hN1CJEiU0YMAAvf7663r33XfVunVrSYWvSig19ZRSU48oLe2srFYfeXmVl4dHiCwWt40M3ILb/natVqs6d+6sBQsWaN68eZo+fbrKlCnDP0sAgI4eParp06fryJEjuv3223XnnXfKanWrdRgAFBQWixQcLJUpY6YK+/ZJlSvnPC4lRTp61JxrqFUrKSTk5re1kKtXz5x+6csvzbm769e/fPZ2qaQk86E/ciTn5dQpc9RfQoJZYZT1YhhS1aqBCggoptq1a6tkyZKZl/DwcPn5+eUIfby9veVwOGSz2XINHby8vNS6dWutWrVKixYtUosWLVSyZMlCF1AUFlarVcOGDdOXX36pZcuWacWKFWrZsqWrm5Vn6elxOn36c8XEzFda2mkZRoosFptstkAFBd2tkJDestuZh6qocttASJJCQkI0aNAgbdq0STNmzFCbNm0UFhYmh8Ph6qYBAFwkKSlJ69ev14wZM1SiRAkNHDiQZeYBuFZUlNSihTRzpjn5zZNPZk8qDMMMg6ZPN6uDbrst70kGMgUGSgMHSoMGSW++aQZDQUE55ws6eFDasMH8evSoWeVz8uTF0CfrJSP0KVbMrECqUEEqUcL8vkQJKSJCCg6OlJfXv+TtnZ4Z/vj4+MjhcFzXhxFWq1XNmzdXs2bN9M0332jFihVq3LixSpQokT8PFHIoVaqU+vXrp3feeUejR49WixYtJBWOKqHTp7/QqVOfyGbzU2BgB3l6llZ6epySkrbLYvGUVPDvA66f2z9TNGnSRB07dtRXX32liRMnqmrVqqqc26cuAAC3cODAAU2cOFHJycm644471KxZs0Lxgg5AEVa8uHTHHeZy8hMnmsPHunc3ZzdOTpa2bpW++kpatEhq2FDq1s3VLXa5/fuln382R9xd6uDBy0/H1Ly5dPfd0uzZ5uW++8wVy7LauNGcZPr48YvBT1qaWcgVGSmVLWsu8Fa6tBn6lCghBQSYWd2lF4dDslo9JVXMx3svhYaGKjo6WitXrtSSJUsUHR2tLl265OvPcCcZE3W/9tpr6tSpk+rVq5dtv9Vq1YMPPqgvvvhCixcv1oMPPphZ4ZVx8ff3v+z3DofDJa810tMv6Pz57+R0xqlEiVfk59dSNpufDCNN6ennZLF4y2bjQ7GizO0DIV9fX/Xv319r1qzR4sWL1a5dO0VERCgwMNDVTQMA3GQXLlzQihUr9Msvv6hKlSrq168fy8wDcD1PT6lxY+lf/zKXyBozxkwrAgLMZOPkSenMGbOK6PHHzQDJzS1dKu3cmftqYEePXj4Q8vaWnnhCWr5cev99qWlTqeIlWU14uFS9utSggflQZ1T6+Pqat/fxkfz8zIuPj7ntZi9Vb7Va1axZMzVp0kSTJk3KnEuoOH3juk2fPl0ff/yx1q1bp++++072S5aii4yMlMPhUHx8vKZMmSKHwyG73Z7rxcPDI9t1T0/PbOHRpcFRq1ahCgqyy8PDT1arn2w286t58bnuMCkt7ZzS0k7LZguWt3ct2e3FM8/l4cGwU3fg9oGQZC5D361bN73//vv66quvVLduXdWpU4f5IgDAjRiGoT179ujrr7+Wt7e3unfvrmrVqrm6WQBgCgw0q4RKlzbXVt+61Zxk2sNDqlJFqlvXnPSmYsWbmzwUUPXqmUVUuT0UkyebS8hfTrVq0rBh0n/+I332mfTKK9n316wp/fvfZk7n63sx9LFYXPPQHz9+XL/++qsqVKigmjVrZr6hL1asmKKjo7Vq1SotWbJELVu21F133XXzG1jIGYahgwcP6rXXXtOJEyfUunXrXN8nfvHFFzp37pxKlSqlIUOGKDExUXFxcXm6JCcn5wiJsl4qVqyi+PgjslptsljsmRfJLqvV85KQyDdbYJTxffZtvvLwKCabzV8Wi6dSUvYrLm61PD2jZLNRGOFOCIQk2e129ezZU0uXLtXKlSs1b948RUVFKSIiwtVNAwDcJCdOnNCPP/6oLVu2qFmzZurRowfLzAMoGAzDnDQ6PV2qU8csTzlzxpzJ2Go1S1FCQ81kApKkqlWlXr1yHzL2++9mBdDl2GxSv37S/PnS1KnmELK0tIv7/fxyn9fbFZxOp2bOnKkJEyaoU6dOCgsLy6wCslqtatGihZo1a6bJkydr+fLlatSoEe9xrkHGULFXX31V27dv1x133KF+/frlWJl68+bNGj16tNLT0/Xvf/9bnTt3VlpamlJTU3O9pKSkZLuenJycGQ7Fx8crPj4+W2Dk4+Mtuz1NTmesnM44pafHyemMk9MZL6czURaLx98hkeff33tmC44uvVitDpUu/Yk8PUsrIKCjTp/+TCdOvKv4+NXy94+Wv39r2e0lWGHMDfAb/ltUVJR69+6tXbt2afLkyWratKlCQkJylAICAIqetLQ0bdu2TVOmTFGxYsXUp08fVp0EUHCkpUmrVknvvmsOCxs1ypztOMORI9L48eZMxXfeyYTSkux2Mx+75H27JPPhuVolT2SkOXd3//7mCL3Y2BvTzn/KYrEoLCxMycnJmjNnjho2bKjIyMjMKqHw8HC1atVKK1eu1PLly9W6dWt16tTJxa0uXKZPn64ZM2YoLCxML774osLDw7PtT0pK0ssvv6z9+/erX79+6tq1qwICAq5pGJfT6bxiYFS8uFM2W5IMI0WGkfr3xfze6UzJDIeyB0UZwVH279PT42SxWDJXEytWbKg8PIJ1/vwPiomZp7i4VTpzZqL8/VspJKSfPD1LyWJh5ExRxbPF36xWqzp06KBFixZp9uzZmjVrlipUqKCyZcu6umkAgBvs0KFDmjlzpk6cOKEuXbqoQ4cODBsGUHCkpprLzf/yixQWln2fYZizJL/7rjmpTefOrmljERQdLXXtKs2ZYwZMWauECgqLxaLo6GgtWrRIM2bM0MqVK1W3bl1FRprLhGedS2jq1Klavny5GjRokCPUQE6GYWj//v16/fXXFRMTo9dff11169bNcdwXX3yhBQsWqFy5cnrmmWeuOQySzN+Tl5eXvLy8rqOdzhwhUfbvM4Kji99Lkt0eJckiL69yCg0dIH//aCUl7VZ8/K+KiZmvpKSdcjrjVazYCHl68iFZUcWr3SxCQ0PVr18/lSlTRnPmzNG6deuUkJDg6mYBAG6gxMRErVu3Tt9//73KlCmjvn37KiSEiRQBFCCpqeaa5na7OV4pq/R06dw5MxQ6dy73MVK4Lr6+0iOPmKuHnTx5+YmoXa1YsWJq06aNoqKitHDhQm3cuFGGYWTuj4yMVKtWrRQVFaVly5bp999/d2FrC4eMoWJvvPGGtm3bpo4dO2rAgAHZRo8YhqEtW7bo/fffV0pKip5//nmXzD1osVhltXrJZvOTh0eI7PYIeXpGycurvByOKvL2riUfn7ry82skf/8WCghoo4CANrLZLv4vsdvD5evbUMHB9ygi4l+KinpTNluQzp6dopSUA9n6E4oWnjEu0bBhQ3Xs2FGpqamaMmWK/vrrL1c3CQBwA+3bt0/Tpk1TWlqa7rjjDjVu3NjVTQKA7FJTpbi43AOhtDRzPJPNZu5jQul8Vb26NHx4zqXnCxKr1apWrVqpYcOG2rt3r1auXKmTJ09m7rfZbJlzCe3evVvLli3TqVOnXNjiwmHGjBmaPn26wsLC9Pzzz2ebe8kwDCUmJuqVV17Rvn371KtXL3Xv3l0Wi8Uly8fnF6vVIS+vsgoMvFPe3jWVlnZaaWmnJBXA8jjkC4aMXcLX11e9e/fWr7/+qhUrVmjJkiUqUaKEgoODXd20Is6m8PBHFBzcQ15eFa9++N8cjkoqVWqM7PaSN7BtwPUx+3R3ORyV8nwbL6+KKlVqtOz24iKzv/HOnj2rX375RcuXL1fNmjXVq1cvlpkHUPCkpFw5EMrYFxDgmvYVIGXLSiNHSs2bXz4bi4425xGKijKPCQgwQ59bbjEfxqxsNum++6Tz581LcHDBzNwiIyPVpk0b/frrr1q4cKFatGih9u3bZ4YTxYsXV6tWrbRixQqtWLFCrVu3VseOHV3c6oIpY6jYG2+8kTlUrEGDBjmOmzBhgubPn6+yZcvq6aefvq6hYgWBYTglXRpkOeV0Jkgy/l7NrPDdL+QNgVAuqlatqrvvvlv79u3T5MmTVa9ePTVq1Ij5JG4oiwIDO8kw0mW1euf5VnZ7cYWGDpLFksuMgYCLBQbecZ19euDffZon3xvJ6XRq586d+vbbb+Xr66uuXbuqatWqrm4WAOR0pSFjqalmhZCHh+Tv75r2FSBly0pPPGGGPJd7b96mjTndUkTExUDo4YfNpeNjY81Rd4GBF0ffRUSYy9CnpkoFdUSx1WpVdHS0Fi9erNmzZ2vlypWqU6dO5lxBNptNzZo1U7NmzTRjxgwtW7ZMDRo0UGhoqItbXrAYhqG0tDS9/fbb2rJli9q3b6/+/fvnGCq2bdu2zKFizz33nGrUqOHCVl+/1NQTOn36c9ntEXI4asrDI1Tp6RcUG7tQiYmb5HDU+PtDSt5rFVUkHLmw2+3q2rWrGjRooJ07d2revHk6fvy4q5tVpFksFlmt3rLZ/PIc7pglmTbZbH6yWr0LZSKPouvSPp3X/kmfvnmOHTumH3/8UTt27FD9+vV19913s7IkgIIpL0PGCIQkmaFOmTJXruQJCTGPcTjMYzw8pFKlzPm658yRHnpI+vFHszArQ/HiUunS5q+goD49Fy9eXNHR0YqMjNTixYu1adOmbPtLlCihli1bKiIiQitWrNAff/zhopYWXIZhaPbs2Zo6darCwsL03HPP5TpU7NVXX9Wff/6pHj16qEePHoV2qFha2lklJPyhkyc/1JEjz+jQoUd1+PBTOnXqE3l4hCs8/GF5epYvlPcNeUOF0GWUKlVK9957r7Zv367p06ercePGCgsLk6enp6ub5hbMidzOKj5+rZKT9yot7ZSczgQZhiGbzVceHpHy8iovb+9a8vRkuBgKh7S0M3no0zVlt5fkifcGS0lJ0caNGzVz5kxFRESoZ8+eLDMPoODKy5AxAqF8sWiRNHOm1L69uYBbYZJRJbRkyRLNmTNHK1asUJ06dRT298p0Hh4eat68uVq0aKEZM2ZoyZIlql+/Pgsp/M0wDB04cECvv/66zp8/r1dffTXXeQW//vpr/fDDD6pQoYJGjRqlwMDAQvu6zW6PVHj440pK2q20tKNKSzsri8WhoKAu8vauIR+f+rLZAl3dTNxABEKXYbFY1LZtW/3yyy+aNm2aZs+erSpVqqhixbzPb4NrZxiGDCNJZ89+qwsXflZy8p9KSzul9PRYGUaKzHGsXrLZAuXhUUyeniXl7V1LxYo9KA+PyEL7zxhFm9OZ0acX/B0GnbxKn66psLBhsttL0KdvkAMHDmj27Nk6c+aMevToobZt2zIsGEDBxZCxm2bHDnPhtipVzPmDCpuSJUsqOjpaa9as0S+//KKWLVvqtttuy9wfFRWlFi1aaNmyZVq5cqXatm2bbb+7MgxDqampeuedd7Rp0ya1a9dOAwcOzDFUbPv27Xr//feVnJysp556SjVr1izUr9U8PILl799Svr6N5XTGKj09XhaLh2y2wL+r1a1KTt4nmy1INlsg03QUQQRCVxASEqL77rtPGzZs0Lx589SiRQtFRkbK79InYuQLwzDkdCbq+PHXde7cNCUn75GU20cz8UpPP6uUlL+UkCDFxa1UsWLDbnZzgasyA85kHT/+ms6enabk5N3KW59eobCwITe7uW4jPj5ea9as0bx581SxYkX16tWLORQAFGwMGbspzp+Xjh69OOysMAZCNptN0dHR+uWXXzR37tzMuYQynuc8PDzUrFkzNW/eXLNnz9bSpUtVv359BQUFubbhLmYYhubMmaMpU6YoLCxMzz77rCIjI7PtT0xM1Guvvabdu3erV69e6tmzZ5H5MMlq9ZTVGioPj+yvh2JiFujs2cny9W2k4OB7ZLeHu6iFuFEIhK6ifv366tChgz777DNNnz5dt9xyi2rXrl2ok+CCy9DZsxN16tQnSk8/LUmy2YIVENBOXl4VZLMFy2KxKz39rFJTTyoxcZsSEzfIwyNUNhtv5lAwnTkzUadOjVVa2tX69CklJW1VQsJGeXiE5XhCRv4wDEN79uzRzJkzZRiGOnbsqHr16rm6WQBwZVkrhC5dCTHrkDE+tPxH9u6VEhPNial9fQvuXEFXExUVpVatWunXX3/V0qVL1bJlS7Vp0yZzf6lSpdSyZUutWLFCK1euVJs2bbLtdzcZQ8XeeustxcTE6OWXX1bTpk1zHDdx4kTNmTNH5cuX19NPP+0Wq1AbRrLi49cqMXGLfHxulYdHKFVCRQyB0FX4+PioZ8+eWrdunX799VctWrRIUVFRmWNxkT/MGf1P6sSJ9zPDIE/PcoqMfFZ+fo3l4REhq9VPFovH3+WMMUpJOaTk5L2yWDxksXgS0qFAMfv0GZ048W5mGOTpWUaRkc/J17ex7PbIS/r0BaWkHMzSpx306RvgzJkzWrhwoVavXq169erpnnvuYZl5AAVfxhxCoaFUCN1AGcPFqlYtnNVBGTKqhJYuXap58+ZpxYoVuvXWWzPnCrLb7ZlVQt9//71++eUX1atXT4GB7jdXjGEYSklJ0XvvvacNGzbotttu0wMPPJBjqNiWLVv07rvvKjk5WU8++aRq167twlbfPL6+jeXtXUuxsQsVG7tMXl6VZLcXc3WzkI+KRo3bDValShXddddd8vf31/Tp07Vlyxalp6e7ullFjKG4uF+VnLzz7+sWhYePUGjo/fL2riW7PVw2m4+sVk95eITKy6u8/PxaKjR0gIKCuvHGGQVSfPyaLH1aKlZspEJD+1+mT5eTv3+rzD7NkvP5Lz09XZs3b9aMGTMUEBCgLl26qHLlyq5uFgBc3ZWGjDGHUL7JCISqVSvcgZAklS5dWq1atVJwcLCWLVumLVu25NjfsmVLBQUFadWqVdqwYYOLWupahmFo7ty5mjx5ssLDw/Xss8+qePHi2fYnJibqrbfe0u7du9W1a1f16tWryAwVuxq7PVxBQXfJZgvV+fOzlJy8V4bhdHWzkI/coyf/Qx4eHurcubOaNGmiPXv2aP78+Tp69Kirm1XEGEpI+D3LdatCQvrIavW67C3M5R2tstkoj0bBFB//W5ZrFoWG9r1qNVtGnybkzH9HjhzRvHnztGfPHjVp0kSdO3dmmXkAhcOVJpVmlbF8kzUQ8ijk4yg8PDzUqlUrNW7cWFu2bNHy5ct17ty5zP2enp5q2rSpmjVrpq1bt2rJkiWKjY11YYtvvkuHig0fPlzNmzfPcdzkyZM1Z84clStXTqNGjXK7VdkCAtrJx6e2kpP3KC5uudLTz7q6SchHBEJ5VLJkSd1zzz0qVaqU5syZo3Xr1ikpKcnVzSpCjMxhNRlstgAXtQXIH9n7tCGbjRfqrpKcnKzffvtN33//vaKiotStWzeVLFnS1c0CgKszDHPI2JXmEKJC6B9zOqVdu4rGkLEMZcuWVcuWLRUYGJhrlVCZMmXUqlUr+fv7a+XKldq4caNrGuoChmEoOTlZH3zwgdavX682bdpo8ODB8siSBBqGoW3btundd99VSkqKnnjiCd16662ua7SL2O2RCgy8UzZbiM6fn6WkpD+pEipCCITyyGKxKDo6Wq1bt9aZM2c0ZcoU7dq1y9XNKkIsslqzfuJlKCFhvctaA+SHS6vXEhLcsxy7INixY4emTJmic+fOqW3btoqOjqYKC0DhkJ4uJSWZiYW3t+TpmX0/gVC+OHtWOn5cCgiQSpaUisKIoIwqoUaNGmnTpk1avny5YmJiMvd7eXmpadOmatGihbZs2aJFixYpLi7OhS2+eZxOp3788Ud98803KlasmEaNGqUSJUpk7s8YKvb2229r586duuuuu9SnTx+3GSp2qYCADvLxqa2kpJ2Ki1up9PRzV78RCgX37NHXKTg4WN27d1elSpW0ePFizZ07V8ePH3d1s4oML68KWa45deLEaCUn73NZe4B/Knuflk6ceE/JyftkGLktPY8b5fjx45o7d64WLVqkypUrq1u3bm5X7g2gEMuYP8jDI+fSV4Zh7k9IuLgf12X3bik5WSpf3szdispnBuXKlVPLli3l7++v5cuX56gSytjv4+OjVatWadOmTS5q6c1jGIYOHjyot99+W+fPn9fQoUPVokWLHMd9++23mjVrlsqWLatnnnnGLVYVuxxPzxIKCLhDNluwYmJmM5dQEUIgdI3q1aununXrKj09Xd9++602bNggp5M/hn/OKj+/ptmGicXE/KgjR17Q2bOTlZS0m386KHT8/JrJZru4YkdMzDwdPfqCzp2boqSkXTIMJ+HQDeZ0OrVx40ZNmzZNTqdTdevWVd26dV3dLADIu4zhYp6eOecPSk83w6D09Nyrh5BnRWlC6azsdnvmXEIZVUIXLlzI3O/l5aUmTZqoefPm2rJlixYvXqz4+HgXtvjGyhgq9tFHH+n3339X69atNWTIkByrim3fvl3vvPOOUlJS9Nhjj6levXoubHVBYFFgYEd5e9+ixMRtiotbpfT0865uFPJBIZ8u7ebz8fHRPffco/Xr12vdunWaOnWqbDabKlSoIIfD4ermZWN+UGSRp2fBfMNpsdhlt4dnXnc4qiosbIhOnhwjw0iTYSTp3LkpSkzcKG/vWvL2vkU+PrXl7X2LPD1LXfHcqaknZBhpN/ouXJfkZIvi4gwVxBwxYzLB2NhYHTlyxMWtyclqlfz9C3Kf9pDdHpF53eGoorCwoTp58v2/+3Rylj59i7y9a8nH51Z5e98iuz3qikOYUlNPyjBSb8bduGYpKWafLmiLLyYmJmrfvn369ttvtWPHDjVs2FDdu3dnmXkAhcuVVhjLmFDaZjOHixWVshYX2LHDHJVX1AIhSSpfvrxatmypNWvWaPny5YqOjlbTpk2z7W/VqpVWrFihVatWqX379mrcuLELW3zjOJ1OzZ8/X19//bXCw8P1zDPPKCoqKnO/YRiKj4/XO++8ox07dqhbt27q168fw8wleXpGKTCwoxITN+v8+dl/f/DZgMemkCMQug7NmjVThw4dtG/fPv3www/au3evSpcuXeACoVKlvNW1a6ACAwvmsDa7vYRKlvyvJP39j8RT4eGPyOlM1Llz0/6ekNdQUtJ2JSVtV0zMj/L2ri6Ho7p8fOrIz6+5fHxuVW6FbidOjFZa2smbeXfybOfOKP300wnFxha8N/d//PGHJGnp0qXZPj0qKEJC7OrdO0LBwYdd3ZRceXiEKyrqdUlmnzYMjyx9eqrS0k5JkpKSdigpaYdiYn6Qt3eNv/v0rfLzay5v7zp//z1kf3I9efIDpaYeu9l3KU/27i2un38+r9OnE13dlGySkpJ04MAB7dixQ6GhoerQoUO2F8AAUChknVA6t0CI+YPyRUaFUFGZUDorT09PtWjRQkuXLtWSJUu0bNky1apVS/5/9xmHw5FZJbR48WItXLhQt9xyi3x8fFzc8vzldDr1119/Za4q9uyzzyo6OjrbMYZhaPr06Zo+fbrKlCnjlquKXZ5FgYF3KCbmJ8XFLVdc3Gp5eVWSh4f7DqUrCgiEroOnp6e6d++u48ePa86cOVq7dq22bdtW4AKhGjUC1KJFpCyW3a5uSi4s8vKqlH2LxSK7vZQiI0fJ4aim+Ph1SkzcpKSkHTKMVDmdcYqPX6f4+HWKiflRvr5NFBR0t4KDe8pq9cmWTsfFLVNKyv6bfJ/yZv/+Glq8eK/OnCl4q9RlVAj9+eefOnHihItbk1OJEg61b19BNts2VzclV56eZbNdt1gs8vSMUmTkM3I4qig+/jclJm7M0qfjs/TpYvL1bZylT/te0qdXKDn5T0kFrzrq4MHKWrbsmA4eLFjL1SYlJSkuLk6hoaHq2rWrunXrJk+GUwAoZOLS0rRfUnxUlKJCQ5V1fcT0tDSlpqbKEhkpa2io7Jc5B64sPV3as8eckqkoBkKSVLFiRbVs2VKrV6/W8uXL1bp162xVQBUqVFB0dLSWL1+ulStXqkOHDmrYsKELW5y/MoaKffLJJ/rtt98UHR2tYcOG5VhVbOfOnXr33XeVmpqqhx9+WPXr13dhqwseT8/SCghor8TETYqJ+V5+fs1ls9WjSqgQIxC6TrVq1dJDDz2k2rVr68yZM/Lz8ytwgVBIiF0VKvjI1zfm6ge7gM2WM002Q6EoFSv2kAICOioxcZMSEzcpIWGzEhO3KiVln8wl6k8pJuZ7JSZukdOZqGLFhskwrJn/jMLDH1Z6esG8340bB8vhuKCkpAI2vkbS999/r/nz56tly5bq3Lmzq5uTg6+vTRUqBMjfv2CubJB1vqCsPD0z+vRfSkzceEmf/kuS8+8+PVeJiVvldCb83ac9Mvt0sWIPFdgVHerXD5SnZ0KBq3rLGgg1b95ctWrVcnWTAOCanUlN1ey4OO2229XLzy9bIBSfmqrVcXFa7+GhBv7+aueyVhZuJ0+al+BgKTKyaI688/LyUsuWLbV06VKtWLFCy5YtU82aNeX3d9WZt7e3GjVqpKZNm2r58uVauHChatWqJW9vbxe3PH+kp6drwYIFmUPFnn766RyrisXHx+u9997Ttm3bdPfdd2vAgAEEHTlYFBh4uy5cmK+4uDWKj/9VXl4V5eER5OqG4ToRCP0Dt9xyi2655RZXN6PIyRgu43BUlJdXBQUG3qmkpO1KTNyixMQtio9fq7i41ZLSlZLyl06ceEs+PrXk69s88xwhIfe5rP1XU6yYdOutrm5F7o4cOaL58+erXr16evDBB13dnCLFYrHK4agghyP3Ph0fv0aGkfZ3n35H3t615OfXQhlDx0JC7nXtHbiCYsUkshYAuDHik5O19cQJbTx3Th3t2WuA4lJTtfT0aU2IidHDnp4EQtdp505zqqZq1SQvr6IZCElSpUqVss0l1Lp162xVQBUrVlTr1q21cuVKrVixQh07diwSkyk7nU4dOHBAb7/9tmJiYvTUU0+pTZs22Y4xDEOzZs3S1KlTVa5cOY0aNUqhoaEuanHB5ulZVv7+7ZSYuEXnz8+Rr29jqoQKMVYZQ4FmsVhktXrJx6eOQkL6qXjxl1S8+IsKCemVeUxKygGdOvWJC1sJXJuMPh0aer+KF39ZxYu/qODgXsoIf1JSDuj06U9UEIeHAQBurtTUVMXHx8tut2dWc2RIS0tTbGysPDw8MueDwbXbubPoTiidlZeXl1q0aKFGjRppw4YNWrZsmRISEjL3+/j4qEmTJmrWrJk2b96sn3/+WUlJBW+Kg2thGIaSkpL02Wefae3atWrevLmGDx+eY6jYrl279M477yg1NVUPPfRQkRoul98sFqsCA++Qw1FLCQnrFR//q9LTC97co8gbAiEUGhaLRTabn/z926p48Rfl5VU1c9+FCwslsYQ3Ch+bzVcBAbepePEX5HBUy9x+4cIiGUYqfRoA3FxKSori4uJkt9tzrJKYmpqquLg4AqF/KOuS89Yi/u6oSpUqatWqlSwWi5YvX64dO3Zk21+pUqXM6pnc9hc26enpWrhwoSZMmKDw8HA99dRTKlny4sBLwzAUFxen0aNHa+vWrerQoYMGDhxItctVeHlVUEBAW1mtvoqJ+VEpKXvFB5mFUxH/l4eiyGIxJ6QODGyfuS0t7XSBnTMIyAuHo7ICAtopo0ooLe0MfRoAkBn6XKlCyG63Ewj9A7t2TVRAwHeqXj1GNlvRflPrcDjUvHlzNWrUSOvXr9fSpUtzVAllzCW0ZcsW/fzzz0pOTnZhi69f1qFi58+fV//+/dW2bdscx8yZM0eTJ0/OXFWMoWJXl1El5O1dQ2fObNehQ5uUkBDn6mbhOhAIodCy20tnuWYoPb1grXAEXCtPz+x92unkiRUA3F1KSori4+Pl6enJkLEbICUlRbt3vywPj5dVpUpckR4ylqFq1apq1aqVDMPQ8uXLtWvXrmz7K1eurLZt2yotLS3X/YWBYRhKSEjQ559/rrVr16pZs2YaMWKE7Fnm4cpYVeytt95Samqqhg8fnm3lNVyZl1clpae308KF/vrii3nau3cvle2FEIEQCq3U1CNZrllls/ld9ligMEhNPZrlmlVWq+9ljwUAuIcrVQgxZOyfO378uE6dOi7phMLCfIvshNJZeXt7Z6sSWrJkSba5gnx9fdWoUSM1adJEmzdv1oIFC5SSkuLCFl+7tLQ0LV68WOPGjVNERISeeuopRUVFZe7PGCr2wQcfaOvWrWrXrp0GDx7MULFrYLFY5eNzp37/vay++mqZVq9eq7g4PswsbAiEUCAYhqH09AtKSzubp2NTUw8rNnZx5jZPzzKXXfIbcAWzT8fmqU9LUkrKYV24sEgZ4689PUvLZgu6cQ0EABQKGRVCTCp9Y+zYsUNOp1NVq1aVh4eH2wQC1apVU6tWrZSamnrZKqHbbrtNKSkpWrZsmXbv3u2ill47wzB09uxZvffee4qNjdX999+vdu2yr8HndDo1d+7czKFizz77LEPFrkNUVEU1a9ZSnp6emjePKqHCiEAIBYSh2NgVOnlytM6dm6WEhM1KT8+eMBuGIcNIUWLiJp04MVqJiVsy9wUHd5dkc5sncRQOcXErdOLE+zp3bqYSEjYpPT0ux5OkYaQoIWGTTp7M2actFjt9GgDcmGEY2QIhJpXOfzt27FB6erqqVq0qmzuMF/ubj4+PmjVrpoYNG+ZaJeTn56dGjRqpUaNGmVVCqampLmxx3pkfyqWrRIkSuu222/TQQw9lW1XM6XRmDhVLS0vTsGHD1KRJExe2uPCyWq264447VKNGDf36669at24dVUKFjMfVDwFuNENSus6cGa/z52fJy6u8fHzqytv7VtntEbJa/WS1esvpTFRa2nHFxq5QTMwPyqikcDhqKDR0gCvvAHAJs2+ePj1B589Pl5dXeXl715WPT52/+7SvrFafzD4dF7dS58/PleSUlNGn+4vMHgDcm9PpVHJystLS0uRwOOTp6Zltf0aFUIkSJXJUDyFvtm/fLqfTqerVq7tVICRJ1atXV3R0tNauXatly5apbdu2qlWrVub+KlWqqH379vr111+1dOlS3XHHHapWrdoVzlgwWK1WRUZG6o033tDRo0dzHSr24YcfavPmzerUqZOGDh3KB3D/QEa12ZYtWzR//nw1bNhQtWvX5jEtJAiE4HKGIRlGqlJTj8pi8VBy8l4lJ+/VuXPTZbX6yGYLks0WoPT0WKWmHpeU/vctrfLxqaOIiCfkcFTnnw4KDLMIKFWpqUdksdiVnLxPycn7dP78jKv0aYu8vesoIuJxORw16NMA4OZSU1MVHx8vDw8P+fr6ypplTXRzCP3F/QRC1ydjyFi1atWyPb7uwNfXV82aNVODBg20e/dubd68WTVr1sx8/eHv769GjRqpQYMG2rx5s3766SdVrFgx28TMBZXValWZMmVUpkyZbNudTqfmz5+vyZMnq3Tp0ho1apRCQkJc1MqiwWazqVOnTlq2bJnWrFmj3377TRUrVuR/UiFBIIQCIyxssBISblVS0m4lJ+9TWtpJOZ3xcjoTsk22a7MFytOztLy9ayo4uJeCgu50YauB3BmG8+8+XfvvPv2X0tJOXLFPOxw1FRJyrwID7yQMAlDkxMTEyN/fXxaLhf9xeXSl+YOcTqeSkpIyq4e8vLxc1MrCKykpSXv37pXNZlPFihXdLhCSzCqhXr166Y8//shWSZOhSpUq6tChg9auXasff/xRVapUUcmSJeXl5ZV58fT0zPxqt9sL7OOYMVTsjTfeUFpamoYOHaqmTZu6ullFQo0aNdSyZcvM4YWNGjXSLbfc4upmIQ8IhOBy5gtDH4WGDlJISF8lJm5WfPzvSknZq9TUE3I6k2QYKbJYvGS1esvTM0re3rfI3z9adnuEq5sP5GD2aYdCQwdm9umEhN+UnHxpn/aU1eqTpU+3kodHBG+UABQ5CQkJ+vrrr1WmTBk1b95cwcHB/K/Lg6wrjF06f1BaWpri4uJks9nk5+fH43kdDh8+rJiYGEVERCg0NNQtH0N/f3/dfffduuuuuxQQEJDr/goVKigiIkJ//PGHXnrpJZUrV04+Pj7y9fXN9tXHx0fe3t6y2+05gqK8fPXy8rphYZJhGIqNjdVHH32kzZs3q2PHjho2bJhb/s5vBA8PD91+++1aunSpVq1apd9++00VKlTI8X8LBQ+BEAoM8020l3x9G8jXt8Hfk0gnKT09Vk5nwt9DbQJltfIJGAoHs097yte3vnx960uSnM7EHH3aYvHkBQmAIu3IkSN655135OnpqUGDBunOO+9UtWrVZLOxIMSVZFQIeXp6suT8DZAxf5C798XLDe1xOp06fPiwfvvtN8XGxsrhcCgxMVHbt29XSkqKUlJSlJycnO379PT0zAAzt9DoSl/9/Pwy58rKCIry8n3G1yuFSWlpaZo0aZK++eYblSpVSs899xyriuWzWrVqqUWLFtqyZYt+/vlnNWzYMNucVCiYCIRQYJlvpr1ltXq7uilAvrFa6dMA3FPnzp31yy+/6NVXX9WWLVvUq1cvNW/eXEFBQQV2iImrZcwRlNuQsdTUVJac/4fceULpq3E6nTp48KC++eYbffXVV/L09NQ999yjhg0bSpLi4+MVHx+vhISEzO/j4+OVmJiYLSTKejl16pSOHDmSbVvWYzOGP14uMLr0+0uPyQiTsl6sVqtiY2MVFxenF154QcnJyerbty9DxW4Au92uO+64QytWrNCKFSsyq4R8fHxc3TRcAYEQCgTDcCo+fo3S0s7Kz6+pPDyuntibS0qeUVzcanl4hMnXt4nbfrKDgscwjL/79Gn5+TWVzZa3UvS0tIw+HfJ3n+ZNEoDCr1KlSnrzzTc1a9YsTZw4UXPnztVvv/2mwYMH64477sh8Q87zeHYpKSlXHTKWW1iEvHHnCaWvxOl06sCBA5owYYImTJggh8OhQYMGaeDAgQoPD7/ibdPT05WQkJAtKMr4/tLw6NJtCQkJuQZJKSkpOnnyZK4hUtaLp6dnjqDIarXq1KlTiouL07lz51S9enUNGTKE/zU3SO3atdW8eXNt3rxZixYtUqNGjVSjRg1XNwtXQCCEAsKpEyfeU3z8bypffqr8/Jrk6VZJSXt08OBw+fo2Ufny029wG4FrY/bpNSpffoZ8ffNWlpyUtFsHD46Qr28DlS8/VSw9D6Co8PX1Vb9+/XTLLbfo008/1YIFC/TKK69ow4YN6t+/v5o2baqAgADemGdxpUmlqRD65zICISqELsoIg8aPH68JEybIx8dHQ4cOVf/+/fM0xMpms8nf3/+6+qRhGEpOTs4RGuXlEhcXp6SkpBwhUWxsrFJSUmS1WtW0aVMNGjQox8pjyD92u10dO3bU8uXLM6uEypcvL29vquMLKgIhFBCG0tJOKTX1qAwjOc+3cjqTlJp6VGlpp25g24Drk9Gnnc6kPN/GMJKVmnpEaWllb1zDAMCFateurTfffFM1a9bU9OnT9eOPP2rLli0aMmSIOnTooMqVK1Mt9Lesk0qnpqZq5cqVOnbsmIKCghQcHMwcQv9AQkKC/vrrL9ntdpUvX54gUmZ1z19//aUvv/xSEydOlL+/v4YMGaL777//pizNbrFY5HA45HA4rnl+H8MwlJSUlC0gio+P1/Hjx7Vr1y4lJiaqdevWatGixQ1qPTLUqVNHzZo105YtW7R48WI1bNhQ1atXd3WzcBkEQgAAALip/P399dBDD6lBgwb69NNP9csvv+iFF17Qhg0b1KdPHzVq1IhqIV0cMhYXF6fFixdr7NixWr9+vcqVK6fbb79dhw4dkoeHB0PGrsNff/2l+Ph4lShRQoGBgbkGkOfOnVNgYKBb9MP09HTt27dPn3/+uSZNmqSQkBA9+OCD6tu3rwIDA13dvKuyWCzy9vaWt7e3wsLCXN0ct+bp6anbb79dK1eu1PLly9W2bVuVK1eOKqECquj/dwMAAECB1KBBA7333nt6/PHHVa9ePc2aNUtPPvmkvvnmG+3atUupqakyDMPVzbzp0tPTdebMGe3YsUNHjx7VwYMHNWvWLB07dky33HKLkpKSNG7cOK1bt04Wi4XhTtchY0LpGjVq5Pr4nT17Vl9//bV++eUXHT9+XE6n0wWtvDnS0tK0Z88effrpp5o4caLCwsL00EMPqV+/foUiDELBU7duXTVr1kzx8fH65ZdftH//flc3CZdBhRAAAABcJiAgQA8//LAaN26ssWPHasmSJXr22Wf122+/qW/fvmrYsKHbVAulp6fr/Pnz2rVrl1atWqXvv/9ehw8fVrly5XTLLbeoVatWqlWrljZs2KAZM2Zo69atSk1N1fr169WkSROVL19eHh68vM+LrCuM5da3tmzZoldffVVBQUG699571a5dO9WsWVPBwcFFqi+mpaVp9+7d+uSTTzRt2jQVL15cI0aMUK9evag8w3Xz8vJSx44dM6uEmjRpovDwcAUFBRFgFzA8YwAAAMDlGjRooCpVqmjChAmaPn26Zs2apQ0bNujBBx9U27ZtVaFCBdntdlc384ZIT0/XuXPntGvXLq1evVpz587Vxo0bFRYWpg4dOqhjx47q1KmTSpYsKUlq2rSpGjZsqM8++0wrV67UpEmTdOHCBfXv31+33nqrgoKCXHuHCoGrrTDmcDjUqVMnrV+/Xu+8847mzZunXr16qXnz5qpevXqRCCkzwqCxY8dq2rRpKlmypEaOHKl77703x6p2wLWqV6+e6tatqz/++EOTJk1SbGysypcvL4fDIQ8PD9nt9qtePD09s33v4eHB/HL5jEAIAAAABUJAQIAeeeQRNW7cWB9//LGWLFmiUaNGqXv37np58GCVrl1bVj8/qZC/Ec+QlpamM2fOaPfu3Vq1apVmz56tbdu2KSIiQu3atcsMgkqUKJHtdt7e3mrRooUqVaqk6dOna9KkSZozZ442btyooUOH6rbbblP58uX5JP4yEhMTtW3bNjmdTlWpUiXXYKdRo0aqXbu2fv75Z02ZMkUbNmzQCy+8oNq1a6tPnz5q1qyZqlSpkrm0eWGTmpqqXbt26eOPP9bMmTNVunRpjRgxQj179pSPj4+rm4ciwOFwqEWLFlq7dq1+//13rVq1SpK5Ep23t7f8/Pzk6+srPz+/HJfctgcGBsrf3z9bSJRxyUvAxP/D3BEIAQAAoEBp2LChKleurC+//FLTp0/X2p9/1rH9+1WyTx9ZW7eWypSRCnG1UEYQtGvXLi1dulTff/+9du3apYiICHXo0EGdOnVSx44dVbx48cuew2KxqHjx4ho6dKjq1q2rTz/9VMuXL9fzzz+vdevWaeDAgapduzZzwGSRmJio/fv3a/369Tp48KAkae/evQoLC1O5cuVyrG7ncDh01113qXXr1po7d66mTZumzZs365lnnlGTJk3Uu3dvNWjQQBUrVpSvr2+hqVxITU3V9u3b9b///U/fffedypcvr0ceeUTdunVj4l/kqzZt2ujChQsqU6aMzp07J8MwZBiG0tLSlJqamvk1Pj5e58+fV2pqarbtWS/p6emy2Wx5CpD8/f1z7Pf09JTNZstTkOTh4SGr1Sqr1Spvb285HI5C8/d9rQiEAAAAUOAEBQXpiSeeUKNGjXT4559Vc9Ik2Z98Urr3Xql3b6luXcnfv1BVC2UEQTt37tTSpUs1Z84c7dmzR5GRkerYsaPuvPNOdezYUREREXk+p8PhULNmzVSpUiV9++23mjhxombPnq0NGzZo+PDhmSv8uPOn4xlLzG/cuFEzZ87U/PnzM5dRHzlypO666y717t1bNWvWVPHixWW1WrO9+fP391fv3r3VoUMHzZo1S7NmzdL27dv12GOPqVmzZrrvvvtUv359VahQQd7e3gX6jWNKSoq2bdumDz/8UHPnzlWlSpX08MMPq2vXrnI4HK5uHoqYgIAA3X///br//vszt6WlpSkhISFzBcVLL7Gxsbluj4mJUXx8vNLT07MFRadOndLRo0dzDZOyfp+eni4vL69cA6VLQ6SsAVLx4sVVtWpV1a5du0gOWyYQAgAAQIHVrFkzqUYNKShImj5dmjJFWrdOGj5catNGKltW8vR0dTOvLDVV58+c0dZdu7RoyRLNmTNH+/btU/HixXX77bdnBkHh4eHXdXqLxaKIiAgNGzZM9evX18cff6wVK1Zo1KhRuvvuuzV48GDVqlXL7aqFMiqC/vjjD3377bdauHChgoKCVKdOHTVv3lyxsbH6448/NGfOHC1atEi9e/fWnXfeqcqVKysyMjLHULDQ0FANGTJEnTp10qxZszR79mxt375dDz/8sKKjo9W3b1/VqVNHZcqUkZeXV4ELhlJSpB07EvTxxz/ohx9+UtWqVfXYY4+pc+fO8izof0MoMjw8PBQQEKCAgIBrvm16eroSExNzhEZZr+f2/YULF3ThwgWlpKRkC4ouXLigM2fO5KhYSk1NVVJSkpKTk2W329WoUSONGTNGdevWvQGPiGsRCAEAAKBgCwqSHn9catZM+vBDadky6emnpW7dpPvvl+rVK5jVQikp0unT0vbt+mvNGo3+4Qf9vGOHSpQooc6dO6tr165q06aNgoOD8+XHORwONW3aNLNa6KuvvtJ3332n9evXa8SIEbrttttUpkyZIl8tlJiYqAMHDmj9+vWaMmWKFixYoKCgINWrV0/t27fX/fffr7Jly8pqtWrjxo367LPPtGbNGn3yySeaPn26evXqpbvuuktVqlRRSEhIjmCoRIkSGjlypLp06aLp06dr9uzZWrt2rVasWKGOHTvqvvvuU926DVW8ePjfk+C66IHIIjlZ2rxZ+uwzP61YUVMNGnTRAw+0V6dOnQiDUGhkHTJ2LZxOp1JSUi4bGF267cKFCzp58qSOHTumP//8U+vXr9fo0aM1YcKEQjln2JUQCAEAAKBwaNjQDIS+/NKsFpoxQ1q/XhoxwqwWKlOmYFQLpaRIp05J27dLCxdK33+vMunpalW2rLy7dFHXrl3VunXrfAuCsrJYLAoPD8+sFvrggw+0atUqPf300+ratauGDh2qGjVqXNen8wVdfHy8Dh48qI0bN2rKlCn66aefFBISorp166pDhw4aOHCgypQpk61y59Zbb9VHH32k1atX6/PPP9fvv/+uDz74QHPmzFH//v112223qVKlSrmuKlaqVCk98cQT6tixoyZOnKglS5Zo4cKFWrZsme6//z21b99FVat6KCJC8vCQy4Kh5GRpwwbp/felhQs91LhxJz3+eEdFR5tzpQBFndVqlcPhkMPhUFhYWJ5vd+bMGX300Ud6/fXX9dtvv+nIkSMqVarUDWzpzcd/AAAAABQeQUHSE09IzZtLH3xgVgs99ZTUtas0cKBUp47rqoUyKoK2bZPmzZO+/146eVIqWVJBLVpoQM+e6t+06U0JY7y8vNSkSRNVqlRJU6ZM0fjx4zVr1ixt2LBJzz33hho0aK/SpaWiUCwUHx+vQ4cOae3atZo8ebJ++eUXhYSEqH79+rrjjjvUr18/lS5d+rJDuKxWq5o3b65GjRpp8eLF+vLLL7Vx40a9/PLLmjp1qgYNGqSWLVuqQoUK8vPzy3Ge6tWr67XXXtPWrVs1YcIELVu2WnPmlNC0aR7q0kXq0kWqXl0KCzODoZspKcnMTN99V/rlF6l+fWnkSE9FR9/8tgCFTUhIiPr06aP58+dr7969mjZtmh5//PEiVSXEvwEAAAAUPg0bSh99ZFYLTZsmzZwpbdwoPfSQ1LatVLr0zasWyloRtGCBGQQdPy6VLCndfbd0zz2yRkfL/ybP4WOxWFSsWLHMaqH33x+t9ev36f33T6hKFbOwqlo16RpHXxQYGRVBv/32myZPnqzFixcrLCxMDRs2VMeOHdW/f3+VKlUqz3P52O12dezYUdHR0Zo7d66++uorbd26Vf/617/UoEEDDRo0SI0aNVLZsmVzXVWsZs2aeuutt/T773v05ZdltHq1l8aNk378UerVS+rYUapcWQoJuTlhTFKS9Pvv0nvvSUuXSg0amFnqbbcRBgF5YbFYFBkZqeHDh2vkyJGaNm2aevfufcUVIAsb/hUAAACgcMpaLTRmzMW5he6+Wxo82KwW8vW9cdVCyckXK4J+/FGaO1c6c0aKipLuuUfq2dNsm4sTl4xqoYoVK2nixF81aVJ7ffedtGmT+fC1aCGVKlV4qoXi4uJ0+PBhrV27Vl999ZWWLVumYsWKqUGDBurSpYsGDBig8PDw657U2eFwqEePHurYsaNmzJihSZMmadeuXXrooYfUtGlTDRgwQA0aNFDZsmVzrCpmtVrVsGEV1a0rLV8uff21OQf6++9LU6dKffpI7dtLVaqY3fdGPeaJiebPffttaeVKc/qtJ56QWrcueFNtAQWZr6+vWrVqpTp16mj79u2aNm2aHn744SJTJUQgBAAAgMKtYUPpf/+Txo2Tvv3WnFto27aLcwuVKCHl53LBSUmZk0Xrhx/MIOjsWTMIuvde89K4sRlGFRDm3ELFNGJEZzVpYg4hWrPGfIi6dZNGjpSqVnV5dnVFsbHSqVMJWrlyVmYQFB4eroYNG6pLly4aNGiQihUrlm+re/n7+2vgwIHq3Lmzpk+frm+//VY7duzQ0KFDFR0drQceeED16tVXyZKl5OmZffJoDw+z6zVrJi1ZYgZDf/whvfmmWdA2YIBZyFahghQQkL8hTWKi9Ouv0jvvSKtWmZnkv/4ltWxJGARcK4vForCwMA0YMECPPPKIpkyZol69eikiIsLVTcsXBEIAAAAo/IKCpCeflJo0kUaPllasMFcm691b6ttXuuUWM6CxWCTDkC5cMKt5YmOltDRzu7e3FBpqniu34WZJSebQsM2bpTlzzHmCYmPNIKh9e3NcUKNGko/PTb7zeeflZWZVn30mTZxofp050xxa9PTTZkhRvHjBCg4uXJCOHTMrbubPP6H58x9RYKBDjRs3Vs+ePdWnTx+FhITcsGXew8LCNHz4cN1111365ptvNGfOHG3YsEEDBw5Unz6D1bXrS6pWLUTFi5u5Y9ZmeHmZQ8VatZIWL5YmTDDn9HnpJTMY6t/f3FeuXP4UsyUkmCHQm2+av9NWrcw/i+bNC9bvFChMfH191bp1a9WuXVu7d+/WjBkz9NBDD92w/zk3E4EQAAAAio6mTc2JWj7/3HzHPWGCtGWLOZFK3brmu/XDh825fn76Sdq7V4qPN0s6ihUzyyg6dZJq1boYIGUEQZs2SbNnm7eLizODoE6dpPvuM6uUCnAQlJXFYs5j8+CDZn71xhtmiDBypNSvnzRsmFSxomvvjmGYWdvRo2a29/XXZkVTiRLl1KFDPzVvXkYDBgxQaGjoTXtTVrJkSY0aNUr33XefJk6cqFmzZmnHDru++066/XbzsateXQoPN4eCZW2Wt7d0551mQPPjj9I335i54tNPm3P7DBhgdt1SpczH/XruUkKC+Vi99ZYZBrVrZ4ZBjRu7boUzoCjIWiX0+OOPa9KkSbr33nuvacWygopACAAAAEVLWJg0apT5Dvujj6T0dMnhMEskDh6U/vtf6eefzXfuTZqYQ8oSEszAZ9w4cwbeUaPMchm7XVq71py8esECc96gqChzVbPevc2QycvL1ff4unh6mjnWZ5+Z+dlXX5mXVaukZ5815xaKjLy5lSWGYWZtR4+aq2J98YU5V3jGr6pPH+m++8YoIMDisk/ny5Qpo3//+9/q2rWrpk4NUmxsgGbPlubPN4vEevaUKlUyi80uDYb8/c1j2rUzs8UpU6StW6WHHzYf7/79zZCuZMlr61bx8WYF1Ztvmo9Xx47mnEENGxIGAfnBz89Pt912m6pXr67du3dr5syZGjp0aKGvEiIQAgAAQNFjsZjlGDVqmBU+YWFSaqqZfPz0k7nvmWfM/Rkz+8bESB9/LH36qXlc8eJS+fLmvEQ//yxFRJhLNPXubVYQ3axVzG4gi8UsjMqYY+b1183JiIcMMStehg83H4IbXS2UURF07JgZSH3yiTm0qlgxMyDp1cusovH3lyyWgjH2qXr16nrlFTMb/OwzM0f88ktp1ixzlGLXruZjFxycc1Wv0FBz3vOOHc3u9d135hxDa9aYU1A9/bRZpZUX8fHmcLS33jKL4e6806wMqls33+8y4LYyqoQGDRqkJ598Ul9//bV69OihkJAQVzftHyEQAgAAQNGVtaR/xw5p0SLzHfrw4VLNmtnLXwIDze1//mnOEbR+vRkYdeliHtu8uTkmKD8nqC4gPD3NCZC//trMw8aPN0fbrVwpPf+8Wb0SHp7/1UIZFUHHjpkVQR9/bM7VHRZ2sSKod++MICh/f3Z+ufVWc07z1avNYOjXX6UPPzSDnl69pO7dzXAnICDnqmIlS5rhTbdu0qRJ0vffm4+xYeTtZ8fFSQsXmmHQjh1mV33iCbNNAPKXn5+fOnTooM8//1y7d+/Wd999p0GDBhXqKiECIQAAALiHtWvNuYDuuMOcrCW3dCMoyCxJWbbMHMtz8qRZxuEGLBazcuWpp8xqoVdfNeeiGTjQrM4ZMUIqW9acD8cwpPPnzfm4fXxyLqhmGObouthYc1/WCqOMsCM2VjpxwlyF68MPzUAjNNSc86Z3b7NCyd//Jt35f8hiMQO1xo3NrvP552al1Zgx5tCwgQPNblSunLmS26Vdr1w5M3jr3t18rIoXv/rPjIsz5zV/800zw+ze3QyData8IXcRcHtZq4SeeuopffXVV7rnnnsUGBjo6qZdt4JRbwkAAADcaAcOmO+iy5c3yzUup2xZs4royBEz9XAzdrsZbkyZYi7UFh5uDoXq1cucJ+fkSTPsmTjRDCPmzzenacrKMMw87Y03zKFQGdsyhob9+adZjXT33dJDD5k5XePG0osvmiP6Hnqo8IRBWdls5nLzX39tVjt17GguA//ii2bA9fXX0q5dZjd0OrPfNj1dKlPGrI66XCVWcrI5RCwlxayoeustc170nj3NSiPCIODG8vPz0+23366KFStq+/btmjNnjoy8lvQVQARCAAAAcA/x8WZJi6/vlYd9+fqaM/omJprvvN2QxWJmYk8/ba6I1ayZuTjb/febIVB8vDR9url42xNPSH/9lf32hmEO/RozRtqwwbx+4YK0Z48ZimQMbTpzxgyCXn3VHM03YoRZQVPY2e1Shw7S5MnmY9S6tXlfn3zSrBaaPt0MchISLgZD586Zw7+mTZP27TOnvLrUxo3msLJ9+8xVyhISzN/JU0+ZoxsB3FgWi0XFihXT4MGDlZCQoHHjxik2NtbVzbpuBEIAAABwD3a7WXqRlpazPCOr1FSzXMPTM+ekL27GbjcXa5s2zQxwIiLMyp+MiqCMOYDeeMPMzy4nLs5cwO32283znDtnhkxvvWUGQQ88YC4EV9R4e5tDuaZPl157zQy/9u0zp6p68EEz3Dl0yJz3/OBBMxgbNEh6/31z+6WFB99+a4ZKmzaZVVSffmo+npUru+b+Ae7Iz89Pd911l8qVK6dt27bphx9+KLRVQgRCAAAAcA9hYWblz4kTV04vTp0yE4zg4JyT47ihjGqhZ56Rpk41Q5yMOYE8Pc1hX1OmmEOYcntPlFEt9MorZkVLixbSBx9ICxaY1S1FMQi6lL+/uaT8zJnSSy9JdeqY1T6DBplLzq9da4ZCkvmYTp0qrVhhPl6XExJiPpZly96MewAgg8ViUWhoqAYPHqz4+Hh98cUXio+Pd3WzrguBEAAAANxD9epmsrF5sxn65JZepKaa6cWZM1KFCtlXKXNzHh5S/fpS7doXt3l6mhUrfn7Syy+blT+XsljM2z34oPTRR+YcQT16mLd1N2Fh5rC4GTPMYV5Vq5oruS1eLMXEmMdUr25WFk2alL0aC0DB4e/vr7vvvlulSpXS5s2bNW/evEJZJUQgBAAAAPfQqJG5/vfq1ebyWRcuZB86lppqhkXLl5slHXXqmAESLstqlSpVModAbd0qjR1rjsi7lM1mzj3UvfuVp29yFyVLSqNGmcHQ88+bq6qFh5v7atUyJ6PescOcsPty2SUA18mYS2jIkCGKj4/XJ598osQrVZ4WUARCKBgMyX7WIq/jFllSLHm+mTXFIs9jFtnP0pVR8NjPWuV5zCLrNfRpS4pFXsetZp/mxR8A5K+gIKlvXykqyhz3NHWqObPvsWPmimKrV5vjmrZulfr0kerWNctbcEV2uzmfTfny5tw3O3e6ukWFR/ny5hxAVatmX1msXz+z+02aZA4ny22CaQCu5efnp+7du6tkyZLatGmTFixY4OomXTPeRaNgcFpU7mVP1ejtLd/teX/h5bvdopq9vFXuFT5qQsFT7hW7ava69j5d4z6Hyr3sKV1hvlMAwHXq2lV69FGzAui558zrfftK3bqZZRrr15vLQPXrJ0VGurq1hYLFYs5n88or5tRML7xw5SmacHUlS5rD6jw8zAm9d+++8jzoAG4+i8WisLCwzLmExo4dq6SMycAKCQIhFAgWw5AlMVmW+ARZ0vP2bGeRZEl3mrdJTBaf36EgsUjZ+nRe++fFPp1EnwaAG8FqNWcynjxZ+u9/pQYNzImjy5Qxg6Bp08xEo1w5V7e0UPHwkDp3NpdanzfPvOCf6dZNatnSXIp+6VJznnMABYufn5969OihyMhI/fHHH1q4cKGrm3RNCIQAAADgfipUMCe+GT/eXPt72jRzze+mTVlZ7DrZ7dJ//mMWX/33vwQY/5SPj7kyWeXK5ipuGzdSJQQUNFarVeHh4ZlVQh999JGSk5Nd3aw8IxACAAAA8I9ZrVKVKtIjj0i7dpkhBv6ZJk2kTp2k/fuln3+WTp50dYsAXMrPz099+vRReHi4fvvtNy1ZssTVTcozAiEAAAAA+cLDw5yiqVo1czl1/DMWiznCsXFjafp0c4orVhwDChar1apixYpp4MCBSkhI0P/+9z+lpKS4ull5QiAEAAAAIF9YLJKfnzlkzMvL1a0pGkqWlHr1Mh/PP/+U4uNd3SIAl/L19VXfvn0VHBysVatWafny5a5uUp4QCAEAAAC4Jhkri4WH59xns0m33y517y5FRJhz4eCfuesuqU0bc34mKoSAgsdqtSoyMlKDBw9WQkKCPvroI6Wmprq6WVdFIAQAAADgmvj4SN98Yw5hyo2Hh/TVV9L27VKfPje3bYWVzSYFBJhzmlsveZfm5SX17WvOeR4cLHl6uqaNAC7P19dXAwYMUGBgoJYtW6aVhWDcLIEQAAAAgGuSmCiNHCk99VTu+w3DnFi6f3/p669vbtsKq0qVpLFjpWeflaKicu6vX1/65BNp3jypdeub3z4AV2a1WhUREaFBgwYpMTFR//vf/wp8lRCBEAAAAIBrkpoq/fSTtHDh5Y85fVqaO1fatOnmtasw8/Y2l5gvXfry8y+VLi3VrCkFBd3UpgHIo4wqIT8/Py1evFjr1q1zdZOuiEAIAAAAAADgH7JarSpRooQGDhyoxMREffDBB0pLS3N1sy6LQAgAAAAAACAf+Pr6asiQIfLx8dGCBQv0+++/u7pJl0UgBAAAAAAAkA+sVquKFy+u/v37Z1YJpaenu7pZuSIQAgAAAHDNDENyOqULF3K/JCS4uoUA4Bq+vr4aOnSoHA6H5s+fr40bN7q6SbkiEAIAAABwzQxDOnhQKlMm56VsWalnT1e3EABcw2q1KioqSvfff78SEhI0evToAlkl5OHqBgAAAAAofCwWKSBAGjIk9/2HD0tTp97cNgFAQeHj46OHHnpIEyZM0Ny5c7Vlyxbdeuutrm5WNgRCAAAAAK6ZxSKFhEhvvZVzn9MprVhBIATAfdlsNpUqVUp9+/bVl19+qTFjxmjcuHGyWgvOQK2C0xIAAAAAAIAiwtvbWyNGjJCHh4dmz56tHTt2uLpJ2RAIAQAAAAAA5DObzaayZcuqd+/eSkhI0Pvvvy+n0+nqZmUiEAIAAAAAALgBvL299cgjj8hqtWrGjBnavXu3q5uUiUAIAAAAwDWzWMzLlfZbrVc+BgCKOpvNpvLly6tXr16ZK44ZhuHqZkkiEAIAAABwjTw8pKZNpcaNL39MUJDUsqVUqdJNaxYAFEgOh0OPPvqoLBaLpk2bpr1797q6SZIIhAAAAABcI19fadYsaeLE3PdbrdItt0iLF0uPPXZTmwYABY7NZlOlSpXUo0cPJSQk6KOPPnJ1kySx7DwAAAAAAMAN5XA49PTTT+vUqVPq3Lmzq5sjiUAIAAAAAADghrLZbLrlllu0YMECVzclE0PGAAAAAAAA3AyBEAAAAAAAgJshEELRwHqmKIgy+uX19E/6NAAAAIAbiEAIBYPFIvn7S4GBkt2e99vZ7eZt/PxuXNuA6+XnZ/ZPj2uYro0+DQAAAOAmYFJpFAweHtKPP17bbSwWqXlz6fz5G9Ik4B+xWKQffrj22zVrJp09m//tAQAAAIAsCIRQ8BnGxe8ZRoOign4NAAAAwIUIhFCwZH2TnPV6fLz51dfX/Jr1DTRvplHQZe3XufVpw8jZj+nXAAAAAG4gAiEUDFnfMMfGSrNmSQsXSitXSidOSMnJ5j6HQwoPN4eKdeggde16ca4V3kCjoMno13FxZp/++WezT588KSUlmfu8vKSICLNPt29v9ml//4u3pV8DAAAAuAGYVBqul/HGNyVFGj1aqlhRGjhQmjxZOnjwYhgkmW+iDx409/XvL1WoIH34oZSWlrO6CHAlwzD75QcfmP10wICLfTojDJLM/p3RpwcMMPv/mDHm30PGeQAAAAAgn1EhBNfKeLMbGyvdd580b17OY4oVMy+SWS105szFfadOSY8+alYTffut5ONDRQVczzCkhATp3ntznyz9an368celRYvMkCijWoh+DQAAACAfUSEE10tKku68M3sYFBIivfaa9Oef5vCabdvMy6lT0s6d0ssvm2+UM/zwg3THHVQKwfUyKoPuuCN7GBQcbPbpPXvMECijT58+Le3aJb3yihQQcPH4H380/y6yVhMBAAAAQD4hEIJrpaebb5JXrLi4rWlTM/R59llzqE1WFotUpYr04ovS9u3SLbdc3Ld8ufR//3dz2g1cyf/9n9kfMzRubPbpUaPMIWGXVvtUrnyxT99668XtK1ZIr78uOZ03pdkAAAAA3AeBEFzHMMwKoNdfv7itZk1pwYKLw2kux2KRSpaUfvlFKlPm4vZXX5X++osqIbiGYUj79pn9MEO1auZk0sWKXX3YV8mS0uLFUrlyF7dlVMrRpwEAAADkIwIhuE5amvTJJ2aVkCTZbOb1jFXDrsZiMYeWffLJxW3p6dLHH+d/W4G8+vjji31akj77zOzTeZ0DKCREGjvW/HuQzHN9+mn2cwIAAADAP0QgBNcwDCk1Vfr6a/O6xWIOq2na9NrOY7GYy8/Xrn1x25dfmm+eqajAzWQYZr8bP/7itsaNpWbNrn1C6Pbts/fpCRPMVcfo0wAAAADyCYEQXMMwpK1bpXPnzOtWq7ki0/WupNSnz8Xvz50z52IBbrasfVoyV867HhaL1Lu3+XchmefcsYNACAAAAEC+IRCCazid0tq1F69bLFLDhtd/vsaNs19fs+b6zwVcr0v73T/p040aZQ9If/2VyaUBAAAA5BsCIbhGxuS7GSwWc0Lp65V1tTFJ2rv3+s8FXK9L+90/7dNZA6F9+6gQAgAAAJBvCITgGoaRfWiNl5fk43N957JYpIAAydPz4razZ/9Z+4DrkbXfeXlJvr7XPwzS3z97n8769wIAAAAA/xCBEFwnJubi9wEB1//GWTJv6+9/8Tpvnq9L8eLFVadOHZUoUcLVTSmc8rtPBwRcvH7uHBVCAAAAAPKNh6sbADfmkaX75ceS2lnPYbf/8/O5oeHDh2v48OGubkbhld99Oi3t4vf0aQAAAAD5iEAIrhMYePH7Cxeu/zwZVROxsRe3BQdf//ncmOWfVLRACgq6+P0/6dOS2a8v7dP8fgAAAADkE4aMwTUsluyhTVqadObM9Z/v9OnsFRkEQnCF3Pr09Q7zurRPZw2bAAAAAOAfIhCCa1gsUtWqF68bhrRly/Wfb8OG7Ndr1br+cwHXq3r17Nc3b77+c23cmD1MqllTsvIvGwAAAED+4N0FXMNqlRo1unjd6ZTWrr3+861Zk/161nMDN8ul/e7XX6//XL/+mj0QatCAIWMAAAAA8g2BEFzDYpEqVJAyVrNyOqUpU679PIZhXiZPvritbFmpXDnePOPmq1RJKlny4vWs/fJaGIb59+B0mtfLlKFPAwAAAMhXBEJwHbtdGjz44vWtW6X586/9PLNnS7t3X7w+bNg/bhpwXSwWaciQi9cz+vS1ziN0aZ8eMkSy2QiEAAAAAOQbi2Fc74ynwD9kGNKBA1K1alJSkrmtUiVzqExISN5uf+qUVL++dOiQuc3PT9q3TwoL480zbj7DkI4elSpWvNiny5eXfvst76uEnTxpDj3bv9+87ucn7dolFS9OnwYAAACQb6gQgmtFRUmvvnrx+p9/Su3bS0eOXPl2hmGGQLfddjEMkqR33jHDIMBVSpSQXn/94vV9+8x+evTo1SuFDh40+/+BAxe3vfmmFBFxY9oKAAAAwG1RIQTXMgwpOVnq2lX66SdzW8aS9I89JvXsKVWpkv34nTulqVOl0aOlmJiL+7p1k6ZNM4fWAK5iGOZy8V26SPPmXdweFCQ9/vjFPp212ufSPp3xb/nuu83tdjvVQQAAAADyFYEQXCuj+8XGSg88IE2ffnGf1WqGOyEhFyskTpyQzpwxJ9vNmHBXku6/X/r0U8nLizfOcK2MPp2QIA0aZIaUGS7Xp8+eNUOkrH26Xz9p7FjJx4c+DQAAACDfEQjB9TK6YFKSNGGCOYTs6NHsx2S8Ib60u5YuLb38stSnD1UUKFgMQ0pJMfv0f/+bcxjk5fp0qVLSSy9JfftKnp7ZjwUAAACAfEIghIIhoxump5vVQnPnSgsXSqtWScePS4mJ5n5vb3OOlubNpXbtpDvvNCfdtVp504yCxzDMqp9L+/SxY9n7dPHi2fu0v7/ZpyX6NQAAAIAbgkAIBUtGd3Q6Lw6hMQwpPt7c7utrvkHOGHpDEITCICMYymuflujXAAAAAG4oAiEUfFm7KG+SUVTQrwEAAAC4EIEQAAAAAACAm7G6ugEAAAAAAAC4uQiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAgAAAAAAcDMEQgAAAAAAAG6GQAgAAAAAAMDNEAgBAAAAAAC4GQIhAAAAAAAAN0MgBAAAAAAA4GYIhAAAAAAAANwMgRAAAAAAAICbIRACAAAAAABwMwRCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyBEAAAAAAAgJshEAIAAAAAAHAzBEIAAAAAAABuhkAIAAAAAADAzRAIAQAAAAAAuBkCIQAAAAAAADdDIAQAAAAAAOBmCIQAAAAAAADcDIEQAAAAAACAmyEQAuByAwYMUNmyZa/rthaLRSNHjsy3tuzfv18Wi0UTJkzIt3MCAAAUdUuXLpXFYtHSpUuv+bYDBgyQn59fvrYnOjpa0dHR+XpOoKghEAIKsP/7v//TXXfdpYiICFksFr388su5Hvfdd9+pQ4cOKlGihLy8vBQVFaV77rlHW7duzfX42NhYPf300ypXrpy8vLxUsmRJ3XPPPUpISMhx7KJFi9SmTRsFBgbK399f9erV09SpU/PzbhYpx44d06hRo9S6dWv5+/tf9wsjAADgfvL62u/ll1+WxWLJcXE4HLkef+LECQ0bNkwlS5aUw+FQ2bJlNXjw4Bt4T4qGcePGqVq1anI4HKpUqZI+/PBDVzcJyFcerm4AgMt7/vnnFRkZqTp16mjBggWXPW7Lli0KDg7Wo48+qrCwMB0/flxffvmlGjZsqDVr1qh27dqZx8bExKhVq1Y6fPiwhg4dqooVK+rUqVNasWKFkpOT5ePjk3ns+PHjNXjwYLVr106vvfaabDabdu3apUOHDt3Q+12Y7dq1S2+++aYqVaqkWrVqac2aNa5uEgAAKCTy+tovw9ixY7NV1thsthzHHDp0SM2aNZMkPfjggypZsqSOHj2qdevW5V/Di6BPP/1UDz74oLp3764nnnhCK1as0COPPKKEhAQ988wzrm4ekC8IhIAC7K+//lLZsmV1+vRpFStW7LLHvfjiizm2PfDAA4qKitLYsWP1ySefZG5/9tlndeDAAa1fv17lypXL3H7pE9v+/fs1YsQIPfzwwxozZkw+3Bv3UK9ePZ05c0YhISGaMWOGevTo4eomAQCAQiKvr/0y3HPPPQoLC7viMcOGDZOHh4d+++03hYaG5ldTi7TExET9+9//VqdOnTRjxgxJ0pAhQ+R0OvXf//5XQ4cOVXBwsItbCfxzDBlDoZNRIvvnn39qwIABCgoKUmBgoAYOHJhjyNP48ePVpk0bhYeHy8vLS9WrV9fYsWNznLNs2bK68847tXTpUtWvX1/e3t6qVatW5lCfWbNmqVatWnI4HKpXr542bNiQ4xw7d+7UPffco5CQEDkcDtWvX1/ff/99juP27t2rvXv35um+Xu+8OpIUHh4uHx8fnT9/PnPb+fPnNX78eA0dOlTlypVTSkqKkpOTc739J598ovT0dP3nP/+RJMXFxckwjOtuz7V655131LRpU4WGhsrb21v16tXLfELOzaRJk1SlSpXM39Hy5ctzHHPkyBENGjRIERER8vLyUo0aNfTll19etS2pqanauXOnjh07dtVj/f39FRISctXjAABA3vDa7/IMw9CFCxcu+xpt586dmj9/vp566imFhoYqKSlJqamp1/Qz/okVK1aoR48eKl26tLy8vFSqVCk9/vjjSkxMzPX4ffv2qUOHDvL19VWJEiX0n//8J8d9czqdGj16tGrUqCGHw6GIiAgNGzZM586du2p7Dh48qJ07d171uCVLlujMmTN66KGHsm0fMWKE4uPj9eOPP171HEBhQCCEQqtnz56KjY3V66+/rp49e2rChAl65ZVXsh0zduxYlSlTRs8995zeffddlSpVSg899JD+97//5Tjfn3/+qd69e6tz5856/fXXde7cOXXu3FmTJk3S448/rr59++qVV17R3r171bNnTzmdzszbbtu2TY0bN9aOHTs0atQovfvuu/L19dXdd9+t7777LtvPadu2rdq2bXtDHpPz58/r1KlT2rJlix544AFduHAh289auXKlkpKSVLFiRd1zzz3y8fGRt7e3mjVrpo0bN2Y716JFi1S1alXNmzdPUVFR8vf3V2hoqF544YVs9/1GGTNmjOrUqaP//Oc/eu211+Th4aEePXrk+gS8bNkyPfbYY+rbt6/+85//6MyZM+rYsWO2OZROnDihxo0ba9GiRRo5cqTGjBmjihUravDgwRo9evQV23LkyBFVq1ZNzz77bH7fTQAAkEe89supfPnymfM89u3bVydOnMi2f9GiRZKkiIgItW3bVt7e3vL29tbtt9+u/fv335A2ZTV9+nQlJCRo+PDh+vDDD9WhQwd9+OGHuv/++3Mcm56ero4dOyoiIkJvvfWW6tWrp5deekkvvfRStuOGDRump556Ss2aNdOYMWM0cOBATZo0SR06dLhq2HX//ferWrVqV213RgBYv379bNvr1asnq9Waa0AIFEoGUMi89NJLhiRj0KBB2bZ37drVCA0NzbYtISEhx+07dOhglC9fPtu2MmXKGJKM1atXZ25bsGCBIcnw9vY2Dhw4kLn9008/NSQZS5YsydzWtm1bo1atWkZSUlLmNqfTaTRt2tSoVKlSjp9VpkyZPN9fwzCMU6dOGZKMl1566YrHValSxZBkSDL8/PyM559/3khPT8/c/9577xmSjNDQUKNhw4bGpEmTjI8//tiIiIgwgoODjaNHj2YeGxAQYAQHBxteXl7GCy+8YMyYMcPo3bu3IckYNWrUNbX/avr375/jMbn0d5eSkmLUrFnTaNOmTbbtGff3999/z9x24MABw+FwGF27ds3cNnjwYKN48eLG6dOns92+V69eRmBgYObP++uvvwxJxvjx4zOPydjWv3//a7pf06dPz9FXAADAteG1X06jR482Ro4caUyaNMmYMWOG8eijjxoeHh5GpUqVjJiYmMzjHnnkkczXfh07djSmTp1qvP3224afn59RoUIFIz4+/pradSVLlizJ8Tjl9vt4/fXXDYvFku0x7t+/vyHJePjhhzO3OZ1Oo1OnToanp6dx6tQpwzAMY8WKFYYkY9KkSdnO+dNPP+XY3qpVK6NVq1bZjmvVqpWRl7fAI0aMMGw2W677ihUrZvTq1euq5wAKAyqEUGg9+OCD2a63aNFCZ86c0YULFzK3eXt7Z34/x7wBAAEAAElEQVQfExOj06dPq1WrVtq3b59iYmKy3b569epq0qRJ5vVGjRpJktq0aaPSpUvn2L5v3z5J0tmzZ/XLL79kfmp1+vRpnT59WmfOnFGHDh20Z88eHTlyJPP2+/fvv2GfyIwfP14//fSTPv74Y1WrVk2JiYlKT0/P3B8XFyfJXKp98eLF6t27t4YPH67Zs2fr3Llz2T49i4uL07lz5/TKK6/oP//5j7p3765JkyapY8eOGjNmjGJjY2/IfciQ9Xd37tw5xcTEqEWLFlq/fn2OY5s0aaJ69eplXi9durS6dOmiBQsWKD09XYZhaObMmercubMMw8j8HZ0+fVodOnRQTExMrufNULZsWRmGwVL0AAC4EK/9Lnr00Uf14Ycfqnfv3urevbtGjx6tr776Snv27NHHH3+ceVzGa7/IyEj9+OOP6tmzp/71r3/p888/1969ezV58uR8bdelsv4+4uPjdfr0aTVt2lSGYeRaZTNy5MjM7y0Wi0aOHKmUlJTMSqfp06crMDBQ7dq1y/Z6rl69evLz89OSJUuu2J6lS5fmaQqExMREeXp65rrP4XBcdsgbUNgwqTQKraxP1JIyJ3Y7d+6cAgICJEmrVq3SSy+9pDVr1uQYYx4TE6PAwMDLni9jX6lSpXLdnjFO+c8//5RhGHrhhRf0wgsv5NrWkydPqmTJktd0/65H1hc1vXr1yiyJfeeddyRdfFLu3LlzthUpGjdurHLlymn16tWZ27y9vRUfH6/77rsv28+477779NNPP2nDhg1q2bJlru2IiYnJ9kTp6el5zfPq/PDDD3r11Ve1cePGbPMcWSyWHMdWqlQpx7bKlSsrISFBp06dktVq1fnz5/XZZ5/ps88+y/XnnTx58praBwAAbi5e+11Z79699eSTT2rRokUaNWqUpIuv/Xr27Cmr9WItQI8ePdSvXz+tXr1aDzzwQK7nS0lJ0dmzZ7NtK1asWK4rmV3OwYMH9eKLL+r777/PMcfPpQGd1WpV+fLls22rXLmyJGUGanv27FFMTIzCw8Nz/Xn59XrO29tbKSkpue5LSkrKFnQBhRmBEAqtyz0ZZaT+e/fuVdu2bVW1alW99957KlWqlDw9PTVv3jy9//77OebBudz5rvZzMs7zr3/9Sx06dMj12IoVK179DuWz4OBgtWnTRpMmTcoMhEqUKCHJHEd+qfDw8GxP1CVKlNCePXtyHJvxBHylifseffRRffXVV5nXW7VqlTlJY16sWLFCd911l1q2bKmPP/5YxYsXl91u1/jx46/rk6yM31Hfvn3Vv3//XI+55ZZbrvm8AADg5uG139WVKlUqW4hzudd+NptNoaGhV3w9t3r1arVu3TrbtoxV0PIiPT1d7dq109mzZ/XMM8+oatWq8vX11ZEjRzRgwIDrmpPS6XQqPDxckyZNynV/XlZmy4vixYsrPT1dJ0+ezBY+paSk6MyZM5mPK1DYEQihyJo7d66Sk5P1/fffZ/sE6GqlpNcq45MMu92u2267LV/P/U8lJiZm+/QlY1hV1jLmDEePHlXVqlWzHZtR8pz105qjR49KuvIT7tNPP62+fftmXr/WZTlnzpwph8OhBQsWyMvLK3P7+PHjcz1+z549Obbt3r1bPj4+me309/dXenp6gfsdAQCA/OHur/0Mw9D+/ftVp06dzG2Xe+2XkpJy1aXta9eurYULF2bbFhkZmef2bNmyRbt379ZXX32VbRLpS8+Zwel0at++fZlVQZL5ek66uPpahQoVtGjRIjVr1uyGVunceuutkqTff/9dd9xxR+b233//XU6nM3M/UNgxhxCKrIxPd7KOE46JiblsqHC9wsPDFR0drU8//TTXZclPnTqV7fq1LD2aV7mVx+7fv1+LFy/OtjpClSpVVLt2bc2ZM0enT5/O3P7zzz/r0KFDateuXea2e++9V5I0bty4zG1Op1Pjx49XSEhItjl7LlW9enXddtttmZcrHZsbm80mi8WSbf6j/fv3a/bs2bkev2bNmmxzAB06dEhz5sxR+/btZbPZZLPZ1L17d82cOTPbymMZLv0dXepalp0HAACu4U6v/XJ77TJ27FidOnVKHTt2zNwWHR2dWVGTlJSUuX3ChAmZFTyXExwcnO313G233SaHw5HnNub2+zAMQ2PGjLnsbT766KNsx3700Uey2+2Zq7T17NlT6enp+u9//5vjtmlpaTp//vwV25TXZefbtGmjkJAQjR07Ntv2sWPHysfHR506dbrqOYDCgAohFFnt27eXp6enOnfurGHDhikuLk6ff/65wsPD8/2N/f/+9z81b95ctWrV0pAhQ1S+fHmdOHFCa9as0eHDh7Vp06bMYzOe0PIyueA333yjAwcOZI6BX758uV599VVJUr9+/VSmTBlJUq1atdS2bVvdeuutCg4O1p49ezRu3DilpqbqjTfeyHbO999/X+3atVPz5s01bNgwxcTE6L333lPlypU1fPjwzOO6dOmitm3b6vXXX9fp06dVu3ZtzZ49WytXrtSnn36arXInv3Xq1EnvvfeeOnbsqN69e+vkyZP63//+p4oVK2rz5s05jq9Zs6Y6dOigRx55RF5eXpmTKWZdivaNN97QkiVL1KhRIw0ZMkTVq1fX2bNntX79ei1atCjHGPmsMpad79+/f54mls74HW3btk2S+XtcuXKlJOn555/P8+MAAADyzp1e+5UpU0b33nuvatWqJYfDoZUrV+rbb7/VrbfeqmHDhmWez8vLS2+//bb69++vli1bql+/fjp48KDGjBmjFi1aqFu3bvn1kORQtWpVVahQQf/617905MgRBQQEaObMmZcdpuZwOPTTTz+pf//+atSokebPn68ff/xRzz33XGYlU6tWrTRs2DC9/vrr2rhxo9q3by+73a49e/Zo+vTpGjNmjO65557Ltun+++/XsmXLrjqxtLe3t/773/9qxIgR6tGjhzp06KAVK1Zo4sSJ+r//+79rnhsTKLBu9rJmwD+VsfRoxvKTGcaPH29IMv7666/Mbd9//71xyy23GA6Hwyhbtqzx5ptvGl9++WWO48qUKWN06tQpx8+SZIwYMSLbtowlyN9+++1s2/fu3Wvcf//9RmRkpGG3242SJUsad955pzFjxoxsx13L0qMZS2Pmdsm6pOdLL71k1K9f3wgODjY8PDyMEiVKGL169TI2b96c63kXLlxoNG7c2HA4HEZISIjRr18/49ixYzmOi42NNR599FEjMjLS8PT0NGrVqmVMnDgxT22/FrktOz9u3DijUqVKhpeXl1G1alVj/Pjxmb/7rDJ+RxMnTsw8vk6dOrku9X7ixAljxIgRRqlSpQy73W5ERkYabdu2NT777LPMY/Jj2fnL/c74lwsAwLXjtV/O134PPPCAUb16dcPf39+w2+1GxYoVjWeeeca4cOFCruedMmWKUbt2bcPLy8uIiIgwRo4cedljr1duy85v377duO222ww/Pz8jLCzMGDJkiLFp06Ycr7X69+9v+Pr6Gnv37jXat29v+Pj4GBEREcZLL71kpKen5/hZn332mVGvXj3D29vb8Pf3N2rVqmU8/fTTxtGjRzOP+SfLzmf9OVWqVDE8PT2NChUqGO+//77hdDrzfHugoLMYRh7W3QMAAAAAAECRwRxCAAAAAAAAboZACAAAAAAAwM0QCAEAAAAAALgZAiEAAAAAAAA3QyAEAAAAAADgZgiEAAAAAAAA3AyB0A0wYcIEWSwW7d+/P3NbdHS0oqOjXdamDBlty7icPn3a1U36R4KCgjLvy8iRI6/7PHFxcQoPD9ekSZMyt0VHR2ee+84778yP5rrM7Nmzs/3ef//99+s+17p16+Tp6akDBw5kbst67nfeeSc/mnzT3H333Zltr1mzZo797nZ/86Io/W0AQEFUkF9LXqsBAwaobNmyrm6G23nooYfUrl07VzejwBs1apQaNWp02f2HDh2Sw+HQqlWrbmKripZevXqpZ8+erm4GLoNAyE29//77+uabb+Tv759j39KlS9WtWzdFRkbK09NT4eHh6ty5s2bNmpWnc8fHxys9Pf2622YYhuLi4vJ07GeffaZvvvnmqsf93//93xXfAI8ZM0b+/v7q1atXtu1Vq1bVN998o3/96185bpOUlKT3339fjRo1UmBgoBwOhypXrqyRI0dq9+7dV23TtdzPy0lJSVFSUtJVj6tfv76++eYbDR06NMe+zz//XK1atVJERIS8vLxUrlw5DRw4MNuL0Kz+/e9/67777lOZMmWybe/atau++eYbderUKcdtTpw4oX/961+qWrWqfHx85Ovrq3r16unVV1/V+fPnr9r+tLQ0JSQkXPW4/fv3XzGkefnll3MEoY8//ri++eYbVa1aNdfbuMv9PXTokF555RU1bNhQwcHBCgsLU3R0tBYtWpTruYvK3wYA4PpcuHBBr7zyimrXri0/Pz95e3urZs2aeuaZZ3T06NF/fP6ff/5ZgwcPVs2aNWWz2YpcqLRr1y49/vjjatq0qRwOR44A8FLX8tz6119/6YsvvtBzzz13XW2bNWuW7r33XpUvX14+Pj6qUqWKnnzyyTy9hikIruU1zWOPPaZNmzbp+++/z/Vc//nPf9SoUSM1a9bsmtqwfPly3XXXXSpVqpQcDociIyPVsWPHIhMsHT16VH379lWVKlXk7++voKAgNWzYUF999ZUMw8h27DPPPKOZM2dq06ZNLmotrshAvktLSzMSExMNp9OZua1Vq1ZGq1atXNeov40fP96QZPz111+57n/xxRcNSUalSpWMF1980Rg3bpzx1ltvGdHR0YYkY9KkSbne7ueffza6detmBAUFGZIMm81mVKxY0Rg1apRx7Nixq7YrPj7eeOedd4wGDRoYdrvdkGT4+PgYrVq1MiZMmGCkp6df8faSjBEjRuS679ChQ4aPj4/h6+tr1KhRI8f+lJQUo1ixYsZrr72WbfuVfmenTp0y6tWrZ0gy7rzzTmP06NHGF198YTz11FNGqVKlDLvdfkPup2EYxs6dO41hw4YZUVFRhiRDkhEeHm4MHDjQ+OOPP65424zf/2+//Za5bfjw4Ub//v2Nd955xxg3bpzx/PPPGxEREUZYWJhx5MiRbLffsGGDIclYvXp1tu2SjJdeeinXn7lu3TojLCzMcDgcxgMPPGCMHTvWGDt2rDF48GDD19fXaNeuXa63O3z4sPHEE08YFSpUMCwWiyHJCA4ONnr27GksWbIk19v89ddfhiTj7bffznX/Sy+9ZEgyTp06lWNfq1atcvQPd7q/H374oeHt7W3cd999xkcffWSMHj3aqFu3riHJ+PLLL3Pctqj9bQBAQVKQX0sahmHs3bvXKFeunGGz2YxevXoZH330kfHZZ58ZI0eONEJDQ41KlSplHtu/f3+jTJky1/wz+vfvbzgcDqNp06ZGVFTUdZ2jIBs/frxhtVqNmjVrGrfeeusVX59f63Pro48+alSuXPm62xYaGmrUqlXLeOGFF4zPP//ceOSRRwxPT0+jatWqRkJCwnWf92a5ltc0hmEYPXv2NFq0aJFj+8mTJw273W5Mnjz5mtvw+eefG126dDFeffVV44svvjDefvtto3bt2obVajXmz59/XferINm0aZPRqlUr47nnnjM++eQT48MPPzTuuusuQ5Lx7LPP5ji+YcOGRr9+/VzQUlwNgdBNUlCexK8UCE2fPt2QZNxzzz1GSkpKjv0//fSTMXfu3Gzb4uLijO7duxsWi8W4/fbbjQ8//ND44YcfjGnTphkvvviiUalSJSMoKOj/2bvvuKau/g/gnyTsvYeiiCiggkz33lXrqlq1rbZ1tNZa9151jxb3aq2ttn063dra1tG6xQEIKogogluG7J3k/P7IL6kIKlQgSD7v58XrqcnNzbk3N8m9n5zzPWLnzp3PbNOFCxeEi4uLsLGxEWPGjBH/+9//xMGDB8X27dvFu+++K0xNTUXz5s2LhRNPel4gNGjQINGxY8cSL/iFEGL37t0CgLhx40aR25/3mvXs2VNIpdIStysvL09Mnjy5QrZz8eLFQk9PT/j5+YnFixeLvXv3in379olVq1aJ1q1bC5lMJmbNmvXMx5cUCJXk4sWLAoBYtmxZkdvHjRsnateuXeQEVYhnBySpqamiZs2awtHRUURHRxe7/+HDh2LRokUlttPY2FjUq1dPzJkzR+zcuVMcOHBAbNy4UXTv3l1IpVLx/vvvFztOyzsQ0qXtvXLlSrHl8vLyhJeXl3BxcSn22Or23iAiquqqyrlkYWGh8PX1FSYmJuLkyZPF7k9PTy/yeftfA6F79+5pvvd69uxZ7QKhlJQUkZGRIYQQ4vPPP39uIFSW79aCggJhZ2cn5syZ85/bVtIPUd9++60AIL766qv/vN7KUpZzGiGE2Llzp5BIJOLmzZtFbl+1apUwNjYWmZmZ5dKu7Oxs4ejoKLp161Yu66uKXn/9dWFqairkcnmR24ODg4WpqWm57UsqPzoXCGVkZIjx48cLV1dXYWBgIOzt7UXnzp2L/HqsvlCKiIgQbdu2FcbGxsLd3V3s2LFDCCHEsWPHRNOmTYWRkZHw8PAQhw8fLvIcJYUuJX2J5+XliXnz5gl3d3dhYGAgXFxcxNSpU0VeXl6R5Q4dOiRatWolLC0thampqfDw8CiWvCYkJJR4Afq05wVCXl5ewsbGRvPl9CKFhYWiffv2onbt2uL8+fPPXGbFihXCwMBA/Pbbb8Xuv3TpkjA1NRWDBw8WaWlpJa7jzp07olOnTqJBgwbi8ePHJS7zrEDo+PHjQiaTicjIyGcGQsOGDRN16tQpdvuzTrxCQkIEADFq1KgS21KS8tjO2bNnCyMjI/Htt98+83l+//13YW1tLWbMmFHi/aUNhJKTkwUAMX369CK3165dW7z33nvFln9WQLJ8+fLn9iwryVdffSWkUqlYsWJFsS8TtbNnzwpXV1cxePDgIreXdyCka9tbkkmTJgkART4XquN7g4iotHT9XPLnn38WAMSSJUtKs7tKDIQUCoVYs2aN8Pb2FoaGhsLOzk5069btmecnLwqEUlNTxYQJEzSvSc2aNcXQoUOLfP/l5uaKTz/9VNSvX18YGhoKJycn0a9fvyI/CJa2Xd9//71o0qSJMDY2FlZWVqJNmzbir7/+KrLMwYMHRdu2bYWZmZkwNzcXQUFBzzw/eF4gVNbv1r///lsAEMeOHSty+z///CMAiF9++UUsXrxY1KxZUxgaGoqOHTuK2NjYF643IyNDABCTJk0qcnt57TNXV1fRs2dPcfLkSdGkSRNhaGgo3NzcSvxuL83rXZKSzmmEECItLU1IJBKxatWqIre3bdtWtG/fvsR1hYSEiO7duwsrKythYmIifHx8xJo1a577/EII4e3tLZo1a1bs9vI6pkrTrujoaDFw4EBNj3YPD49iP5rdvXtXDB8+XDg7OwsDAwNRp04dMXr0aJGfn//c7Rs7dqyQSCTFepJFREQIAGL37t0v3EdUuXSuhtDo0aOxefNm9O/fH5s2bcKUKVNgbGyM6OjoIsulpqbi9ddfR7NmzfDZZ5/B0NAQgwcPxi+//ILBgwejR48eWL58ObKzszFgwABkZmaWqR1KpRK9e/dGcHAwevXqhfXr16Nv375YvXo1Bg0apFnu6tWreP3115Gfn4+FCxdi5cqV6N27d7Hxp8OGDUODBg3+836JjY3FtWvX0Ldv3xLrCpVk2bJliImJQUhICJo0aaLZruzsbM1/p6WlYdq0aVizZg2GDx9eZD/J5XIMGjQIAwcOxI8//ghLS0sAqjHShYWFAICcnBxYWVnh999/h52dXZnGQisUCnzyyScYOXIkfHx8nrncmTNnEBAQUOr1qscYDx06tFTLl8d2njhxAsuXL8dvv/2GYcOGaW7PysrSjNNNTU1Fly5dcPToUaxduxYhISGl3iYASElJQWJiIi5evIj3338fANCpUyfN/ffu3cPt27fLvK+MjY0xYMCAUi1/48YNjB07Fl9//TWmTZsGmUwGQLWdSqUSAJCeng5/f3+cOHECR44cwS+//FJsPTk5OUhOTi72V5q6PGq6tr3P8vDhQ5iYmMDExOSFy1bX9wYR0ZN0/VyyrJ/1JRkxYgQmTJiAWrVqYcWKFZgxYwaMjIz+0+dzVlYW2rRpg/Xr16Nr165Yu3YtRo8ejWvXruHu3bsAVOeEr7/+OhYsWIDAwECsXLkS48ePR3p6Oq5cuVKmdi1YsABDhw6Fvr4+Fi5ciAULFqBWrVr4+++/Ncts374dPXv2xOPHjzFz5kwsX74cfn5++PPPP8u8fWXd32fOnIFEIoG/v3+J9y9fvhx79uzBlClTMHPmTISEhODtt99+4XofPnwIALCzsytye3ntM0B1XjRgwAB06dIFK1euhLW1Nd577z1cvXpVs0xpXu/nbUNJ5zSWlpZwd3cv8p4oLCzEhQsXSjwPPHz4MNq2bYuoqCiMHz8eK1euRIcOHfDbb78VWzYjIwPJycm4du0aZs2ahStXrhQ5vy7t/inNMVWadkVGRqJZs2b4+++/MWrUKKxduxZ9+/bFgQMHNMvcv38fTZs2xc8//4xBgwZh3bp1GDp0KI4fP17s3DI3NxfJycmIj4/Ht99+i23btqFFixYwNjYuslzDhg1hbGxcbWooVSvaTqQqm6Wl5TOHFqm1a9dOACgyXvTatWsCgJBKpSIkJERz+19//SUAiG3btmluK82vOt9//72QSqXFutp+8cUXAoA4ffq0EEKI1atXP/MX/pLa/CLP6iG0b98+AUCsXr36hesQQtUd2MLCQuzdu1dz25YtW4S1tbUAIBo1aiR27dpVpE0BAQFiy5Ytmn9v375duLi4aLoOZmZmioEDBwqZTCb09PTE0KFDxfTp08W7774rhFAly0ZGRiX2YEIJPYQ2bNggLC0tRWJiohCi5B4RhYWFQiKRlDiM5Vm9IPr16ycAiNTU1OfvpHLczvbt24sJEyZo/n3mzBlRv359AUDY29uL7777Tri6umq6+E6cOFG89dZbxdryvB5ChoaGmrortra2Yt26dUXuP3LkiABQbNigEM/uMWNtbS18fX1ftIs03nvvPdG3b1/Nv69du6YZM29hYSE+++wz0a5dO837be3ataJly5aa5dU9Zl70V5oeM7q2vSWJjY0VRkZGxcZ8V8f3BhFRaen6uaS/v7+wtLR84XJqT/cQUvdgGTduXLFlnx6irfa8HkLq+pcl9TxQr++bb74RAIr1AHlymdK0KzY2VkilUtGvX79ite3Uy6SlpQlzc3PRrFkzkZubW6rte14PobJ+t77zzjvC1ta22O3qHkINGjQo0stj7dq1AoC4fPnyc9c7YsQIIZPJxPXr1zW3ldc+E0LVQwiAOHHihOa2xMREYWhoWOQ8vTSvd0medU6j1rVrV9GgQQPNv2/cuCEAiPXr1xdZTi6XCzc3N+Hq6lrsNSnp+bt166Y5HzMwMBAffvhhkeOivI6p0rarbdu2wtzcXCQkJDxzmWHDhgmpVFri9cLT27hs2bIi55ydOnUSt2/fLvY4IYTw8PAQ3bt3L/E+0h6d6yFkZWWFc+fOvXD2AzMzsyIzTnl6esLKygoNGjQoMjWh+r/j4uLK1I4dO3agQYMG8PLyKvKLfseOHQEA//zzj6a9ALBv3z5Nj4GSHDt2rFhF97LIyMgAgFL3Djp06BBsbGzQu3dvAEBYWBg+/PBD9O/fH3v27MGgQYMwatSoIo/p06cPjh07pvn3jh07MHz4cJiZmQFQzeZ09OhRrFy5Er/88gvS09Oxfv16zfKNGzeGs7NzqX49SklJwbx58zB37lzY29s/c7nHjx9DCAFra+tSbTdQ9n31stuZlJSEEydO4JNPPgGgmsWtf//+cHJywq+//oolS5Zg7ty5ml9uANXU4k/u69L4448/cPDgQaxcuRK1a9fW9PRSS0lJAYAy76vS7ieFQoG9e/di3LhxAFS/fA4ePBj5+fn43//+h40bN2L79u24cOGC5jF9+/bFuXPnis0m9cEHH+Dw4cPF/srya6aube/TcnJyMHDgQBgbG2P58uWlekx1fW8QET1J188ly/JdV5Jdu3ZBIpHg008/LXafRCL5T+vz9fVFv379nrm+Xbt2wc7OTvN98axlXtSuvXv3QqlUYt68eZBKpSUuc/jwYWRmZmp6yrzs9pX1uzUlJeW55y7vv/8+DAwMNP9u06YNgOcffz/++CO+/vprTJ48GfXr19fcXl77TK1hw4aa9gCAvb09PD09i7StNK/300pzTmNtbV1kVtZnnQeGh4fj1q1bmDBhgua99bznX758OQ4dOoSvv/4azZs3R0FBAeRyueb+8jqmStMu9XnT8OHDUbt27RKXUSqV2Lt3L3r16oWgoKBi2/P0Ng4ZMgSHDx/Gjz/+iLfeeguAqtdQSZ7ex1Q16Gm7AZXts88+w7vvvotatWohMDAQPXr0wLBhw1C3bt0iy7m4uBQ74C0tLVGrVq1itwGqbsFlERsbi+jo6GeGFYmJiQCAQYMGYevWrRg5ciRmzJiBTp064Y033sCAAQOKfWi8DAsLCwAodXfl0NBQtGvXTrOPtm7divbt2+Orr74CoLroUigUWLBggeYxjo6OOHXqVJF1qKesFkJg69at2Lx5s2bYR+/evYtNje3o6IikpKQXtm/OnDmwsbEp8Yu/JGUJ057cV09/4JbkZbczLCwMtWrV0hyjv//+O3JycvDbb79p2lK/fn106NChxMeXlvrx3bt3R58+feDt7Q0zMzOMHTu2yHJl3VelPaZu3LiBzMxMtG3bFgBw8eJFRERE4NatW5op31u1agV3d3fNYxwdHaFQKPD48WPUqFFDc3v9+vXRuXPnYs/x5PFXWrq2vYAqrBo8eDCioqLwxx9/FHmu56mu7w0ioifp+rmkhYVFmcOrJ928eRM1atSAjY3Nf17H0+vr37//C5fx9PSEnt6zL31K066bN29CKpWiYcOGz10GALy9vV/Q8tIp63cr8Pxzl6eDAHXg8azj7+TJkxgxYgS6deuGJUuWFLmvvPbZs9qmbt+TbSvN6/2k0p7TCCFKDHSe3pdlfX39/Pw0//3OO+8gICAA7733Hnbu3KlZX3kcU6VZRv2+fd4ySUlJyMjIKPX2ubq6as5bhwwZgg8++ACdO3dGTExMsWFjz9rHpF0610PozTffRFxcHNavX48aNWrg888/R6NGjfDHH38UWU5dy+Npz7q9rL1zlEolfHx8SvxV//DhwxgzZgwAwNjYWFM7ZOjQoYiMjMSgQYPQpUsXKBSKMj3n86gvui5fvlyq5VNSUop8oMbHx2vqCKk1bdq0yL/v3LkDW1vbEteRlJSEnJycIuvQ09MrNm736XWUJDY2Flu2bMG4ceNw//59xMfHIz4+XlOXJD4+Ho8fPwYA2NjYQCKRlOkk7GX21X/ZzpL2taenp+YEAXjxvi4rd3d3+Pv744cfftDcpl5fWffV9evXUVBQ8MJlU1JS4ODgoHmPxcfHw97eXvMlAwBubm5Fxq7fuXMHUqm01CdIZaFr2/ukUaNG4bfffsP27ds1vzSXhi68N4iIdP1c0svLC+np6bhz506ZH0tlV9bvVltb2+eeu5Tl+IuIiEDv3r3h7e2NnTt3PjdQKw/l9d54UmnPaVJTU4ucc/2X88AXMTAwQO/evbF79+5n9qJ51Q0YMAB37tzBiRMnit339D6mqkHnAiEAcHZ2xpgxY7B3717cunULtra2xRLviubu7o7Hjx+jU6dO6Ny5c7E/T09PzbJSqRSdOnXCqlWrEBUVhSVLluDvv//WdAUuDx4eHvD09MS+ffuQlZX1wuUtLCyQnp6u+beTk5MmmVZ78tejvLw8fP/990V6MTy5DltbW+jr6z93HX/88QdSU1PRokWL57bt3r17UCqVGDduHNzc3DR/586dw/Xr1+Hm5oaFCxcCUF1wuru749atWy/cZrVevXoBAP73v/+VavmX3c6S9vXt27eLdDd9+pe6r776qsQeI2WRm5tb5HnVJyRl3Ve5ubnYtWvXC5e1sLDQdIsGVNuZkpKCtLQ0zW1paWmaMA9QbWfLli1LVfC4rHRte9WmTp2Kbdu2YfXq1RgyZEiZHqsr7w0iIl0+lyzrZ31J7b5//36R77eX4e7uXqQw9LOWiYmJ0UxY8F/b5e7uDqVSiaioqOcuA+CFbSqtsu5vLy8vpKamFvl+/C9u3ryJ1157DQ4ODjh48KBmePeTymuflUVpXm+1spzT3Lp1q0hR9dq1a8PY2LjYeeDLvr65ubkQQmh6lJfXMVWaZdS9GJ+3jL29PSwsLF5q+wAUO/7kcjnu3LnzUpMgUcXQqUBIoVAUOzgdHBxQo0YN5OfnV2pb3nzzTdy7d08zxOpJubm5mvotJX3AqrsePtnm27dv49q1ay/VpgULFiAlJQUjR44sckGldujQIU2V+gYNGuDcuXOa+/r164c9e/Zg48aNSEhIwMGDB7F06VIAqq6mXbt2hbW1Nd555x3NY55ch0wmQ69evTB58mScOHECt27dwqeffoqwsDBkZmZi27ZtGDJkCObOnVvk1/+SeHt7Y8+ePcX+GjVqhNq1a2PPnj0YMWKEZvkWLVrg4sWLpd5PLVq0wGuvvYatW7di7969xe4vKCjQDIMpj+1s0KABrl+/rgkKunbtiszMTHz00UeIjY1FWFgYRo0aBYlEguvXr+PDDz/EH3/8gblz575wW+RyeYm/fJw/fx6XL18uMna4Zs2aqFWrVpn21ejRo+Hs7IzJkyfj+vXrxe5PTEzE4sWLAai+pORyueYLqEmTJnBycsKwYcNw9epVREVFYdiwYVAqlbh79y7mzJmDNWvWYNmyZaVuT1no2vYCwOeff47g4GDMmjUL48ePL/PjX8X3RmFhIa5du4YHDx4UaevNmzeLhVMPHjzAtWvXnntBQUTVG88lVT0AfHx8sGTJEpw9e7bY/ZmZmZg9e/YzH9+/f38IIYqUFVD7Lz1B+vfvj4iICOzZs+eZ6+vfvz+Sk5OxYcOG5y7zonb17dsXUqkUCxcuLFaPSb1M165dYW5ujmXLlhWr+fdftq+s360tWrSAEAKhoaFlfi61hw8fomvXrpBKpfjrr7+eOSyxvPZZWZTm9QbKdk6Tnp6OmzdvomXLlprb9PX1ERQUVOw8MCAgAG5ublizZk2RH/Gefn71kM0npaWlYdeuXahVqxYcHBwAlN8xVZp22dvbo23btvjmm29w+/btEpeRSqWaWcdKOgdWL/es4fdff/01JBJJsR7eUVFRyMvLK7KPAeDatWvF2lLSZ5F6prbymEGXnlKhJaurmNTUVGFqaireffddsWrVKrFlyxbx5ptvCgBi5cqVmuWeNfuOq6ur6NmzZ7Hb8dQMV6WZGUKhUIgePXoIiUQiBg8eLNavXy/WrFkjRo8eLWxsbDRV3cePHy/8/f3FnDlzxFdffSWWLFkiatasKVxcXERaWlqR9Zfm5XzWLGNqs2fPFgCEh4eH+PTTT8U333wjPv/8c9GpU6cis2XcvXtX6OnpibCwMM1jP/roI02FeRMTE82MCVKpVLz55pvFZrdYvny58PPz01SrT0hIEJ6enpp1NG7cWHz44YcCgLCzsxNr16595nY9/RqU5Fmv686dOwUAERMTU2z5kmZSEkI164Gfn5+QSCSid+/eYu3atWLr1q1i+vTpwtXVVRgYGJTrdnp5eRWZAe7bb78VRkZGAoCQSCRi6tSpmtkZmjZtWuR1edLTs4yp3xPDhw8XK1euFF988YX4+OOPhYmJibCxsSkyk4QQQowdO1bUrFmz2AwDeMasW0IIERISImxsbISxsbEYNWqU+OKLL8QXX3whPvjgA2Fubi66du2qWfa1114T48eP1/z7r7/+EpaWlpp9NWTIENG6dWvNLBlHjhwp8lzqWbc+//zzEtvy6aeflmnWLV3a3t27dwsAon79+uL7778v9vfw4cMij60u7w31PlTPZKbm6upabEabd99997mfn0RU/fFcUiU2Nla4uroKPT098dZbb4mNGzeKLVu2iPHjxwt7e3vh4eGhWfbpWcaEEGLo0KECgOjevbtYu3atWL16tXjjjTeKzOgUEREhFi1aJBYtWiQ8PT2FlZWV5t/79+/XLJeZmSkaNmwoZDKZ5nt36dKlonnz5uLSpUtCCNUMTO3btxcAxODBg8XGjRvFZ599Jrp27VpkxtzStGvu3LkCgGjZsqUIDg4W69evF8OGDRMzZszQLLN161YBQHh7e4ulS5eKzZs3i9GjR4thw4ZplklLS9Nsz2uvvSYAiMmTJ4tFixYVm9mqLN+t+fn5wtbWVsycObPIOtSzjO3YsaPI7ervwSdnufP19RUAxLRp04qdDxw6dKjMr2Vp9tmz3htPH/eleb3Lck4jxL/XAjdu3Chye3BwsDA0NBTp6elFbv/zzz+Fvr6+cHV1FfPnzxdffvmlmDhxYpFzvICAANG7d2+xZMkS8dVXX4m5c+cKFxcXIZVKi70G5XVMlaZdly5dEmZmZppjZMuWLWLWrFlFZsm9e/eucHJyEiYmJmLChAniyy+/FPPnzxeNGjXSzGA2fvx4ERQUJObMmSO2bNkili9fLpo0aSIAiE8++aTY6xgcHCxMTEyKzRYNoNg5ZUmfRepzWvWssVR+dCoQys/PF1OnThW+vr7C3NxcmJqaCl9fX7Fp06Yiy1XGl7gQQhQUFIgVK1aIRo0aCUNDQ2FtbS0CAwPFggULNB88R48eFX369BE1atQQBgYGokaNGmLIkCHFLtTLKxB68jkdHByEnp6esLe3F7169RL79u0rsty7774rmjVrVmTqyps3b4qTJ0+K1NRUkZubK86ePVvkZONJqampwtLSUqxZs0ZzW2FhoTh37pwIDQ0VCoVCxMfHi8jISCGXy5+7XS8TCOXn5ws7OzuxaNGiYss/66JXCCFycnJEcHCwaNKkiTAzMxMGBgaifv364pNPPinyhVIe27lt2zZhY2Mj4uLiNLclJyeLEydOaKZ2vHDhgrhz585z98HTgVB+fr4YP368aNy4sbCwsNB8iYwYMaLEYyQsLEwAKDbF7fMCEiGEuH//vpg4caLw8PAQRkZGwsTERAQGBoolS5YU+ZL9559/hIGBgTh37pzmtoyMDHHy5EnNMR8RESFu3rxZ4vOUdyCkS9urXvZZf09+AVen9wYDISIqC55L/is1NVXMmzdP+Pj4CBMTE2FkZCS8vb3FzJkzxYMHDzTLlRQIyeVy8fnnnwsvLy9hYGAg7O3tRffu3UVoaGixfVDS39Of2SkpKZofcQwMDISLi4t49913RXJysmaZnJwcMXv2bOHm5ib09fWFk5OTGDBgQJHv2NK0SwjVNPb+/v6afd6uXTtx+PDhIsvs379ftGzZUhgbGwsLCwvRtGlT8dNPP2nuV3//lPT39P5St780361CCDFu3DhRr169IreVJRB63vnA08dgee2z0gZCQrz49S7LOY0QQgwaNEi0bt262HM/evRI6Onpie+//77YfadOnRJdunTRfA40bty4SAi2YcMG0bp1a2FnZ1fkeurEiRPF1lWa/SPEi4+p0rRLCCGuXLki+vXrJ6ysrISRkZHw9PQUc+fOLbJMQkKCGDZsmLC3txeGhoaibt264uOPP9Zc9x06dEi8/vrrokaNGkJfX1+Ym5uLVq1aiW3bthX7IVUIIZo1aybeeeedYrczENI+nQqE6N8v17CwMJGUlFTiG7a0kpKSRO3atUWPHj2KJedqcrm82BfPk3755Rehp6cnNm/e/MxlEhISxOnTp0u8LyUlRSQlJZUqEHqehQsXCjc3tyIXne3atRMtW7YUSUlJz9y+0nrZ7VQqlaJnz56iXr16Ijo6+pnrOHDggMjMzCx2e35+vkhKShLr168vEgj9Fx07diz2gQ5ATJ06VSQlJYmcnJz/vG4hhBgzZoxwcHB45r4QQogTJ06Ie/fuvdTzqGVkZIikpCTRsmXLEk/edW17S6M6vTeIiIiqm5s3bwp9ff1iPYupuAcPHggjI6MiPcWeNHz48BLDIiq98PBwIZFIRHh4uLabQiVgIKRjnv61paSeA2URExMj6tWrJxwcHMTChQvF2bNnRUJCgrh8+bLYvHmzaNSokbC1tRUJCQnPXMeWLVuEnp6eaN26tdi+fbu4evWqSEhIECdOnBCTJ08WZmZmYuDAgSU+9snhNS8TCGVmZgp7e3vxv//9T3ObOp0GUOIvFmX1MtupbmPPnj2FoaGh+Oijj8ShQ4dEXFyciI2NFb/++qt47bXXhJ6eXrGeXEIIsWfPniKv+8sEQiEhIUJfX1/Ex8drbnty3c/qrVJahYWFYsSIEUIqlYq3335b7N+/X8TGxoq4uDhx4MABMWjQICGTycSGDRte6nnU+vTpo2l7SQGJrm1vaVSn9wYREVF1NHr0aNG5c2dtN6PKmz59umjSpMkz709ISBCGhobi1KlTldiq6mXQoEHPPY8j7ZII8RLz+NEr58GDB7h69arm3+3atYO+vv5LrTMzMxOff/45tm7dWqQwq7m5Od5++23MmzcPzs7Oz13H5cuXMXfuXPz5559FChx6eHhg8uTJGDlyJKTS4jXQjx8/rinwWqtWrSIzarys0NBQTcFle3t7+Pr6vvQ6/+t2qimVSnz33XcIDg4u8jrq6emhW7duWLBgAQIDA4s9LikpCREREZp/N2vWDObm5i+9PWpHjhzR/LeHhwdq16790uvct28fli5digsXLmgK2EkkErRp0wbz5s1Dp06dXvo5ACAyMlJT+M/MzAzNmzd/4WN0bXufVp3eG0REREREuoqBEJUbIQRu3LiBhw8fwsLCAg0aNICBgUGZ1pGdnY3r168jKysLLi4ucHNzq6DWald5bOe9e/dw+/ZtyGQyeHp6wtLSsgJaqn1JSUmIi4uDUqlEvXr1njnTRXWha9v7NL43iIiIiIgqBwMhIiIiIiIiIiId8+z+90REREREREREVC0xECIiIiIiIiIi0jEMhIiIiIiIiIiIdAwDISIiIiIiIiIiHcNAiIiIiIiIiIhIxzAQIiIiIiIiIiLSMQyEiIiIiIiIiIh0DAMhIiIiIiIiIiIdw0CIiIiIiIiIiEjHMBAiIiIiIiIiItIxDISIiIiIiIiIiHQMAyEiIiIiIiIiIh3DQIiIiIiIiIiISMcwECIiIiIiIiIi0jEMhIiIiIiIiIiIdAwDISIiIiIiIiIiHcNAiIiIiIiIiIhIxzAQIiIiIiIiIiLSMQyEiIiIiIiIiIh0DAMhIiIiIiIiIiIdw0CIiIiIiIiIiEjHMBAiIiIiIiIiItIxDISIiIiIiIiIiHQMAyEiIiIiIiIiIh3DQIiIiIiIiIiISMcwECIiIiIiIiIi0jEMhIiIiIiIiIiIdAwDISIiIiIiIiIiHcNAiIiIiIiIiIhIxzAQIiIiIiIiIiLSMQyEiIiIiIiIiIh0DAMhIiIiIiIiIiIdw0CIiIiIiIiIiEjHMBAiIiIiIiIiItIxDISIiIiIiIiIiHQMAyEiIiIiIiIiIh3DQIiIiIiIiIiISMcwECIiIiIiIiIi0jEMhIiIiIiIiIiIdIyethtARERERERE9DQhBNLT07Fr1y7ExsaiY8eO6Nq1q7abRVRtSIQQQtuNICIiIiIiIhJCICcnB0ePHsXatWtx8+ZNZGRkoLCwENbW1hg6dCiWLFmi7WYSVQsMhIiIiIiIiEhrhBDIzs7G2bNn8ccff+Cff/7B3bt3kZ6ejsLCQujr68PU1BTp6emwtrZG37598dVXX0EqZQUUopfBIWNERERERERUqYQQyMrKwoULF/DHH3/g77//xr1795CdnY2CggLUqFEDzZs3R9euXdGjRw9kZmZi3rx5OHDgAPbs2QMHBwcsWrQIenq8pCX6r9hDiIiIiIiIiCqcEAKZmZlFQqAHDx5oQqA6deqgU6dO6Nq1K/z8/GBkZARTU1OYmppCqVQiPj4eK1euxPbt22FiYoI333wTn332GczMzLS9aUSvJAZCREREREREVCGEEMjIyNCEQP/88w8ePnyIrKwsFBYWok6dOujYsSO6dOkCf39/mJqawszMDIaGhpBIJEXWpVQqkZiYiKNHj2LixIkoKCjAa6+9hnXr1sHBwUFLW0j06mIgREREREREROVGPTvY+fPn8eeff+LYsWN49OgRMjMzoVAo4Onpifbt26Ndu3bw9fWFmZkZzMzMYGRkVKp1Z2Vl4Z9//sEnn3yClJQUtGvXDps2bYKrq2slbB1R9cFAiIiIiIiIiF6KUqlEUlISTpw4gcOHD+P8+fNITk5GZmYm5HI5GjRogG7duqFDhw6oV69emUKgpwkhkJubi/Pnz2PcuHGIjY1FkyZNsH79evj6+lbA1hFVTwyEiIiIiIiIqMwUCgUSExNx8uRJHDp0SBMCZWRkAAAaNmyoCYHq1q0LCwsLmJmZwcDA4KWfWwiBgoICREREYOLEibhw4QJ8fHywdu1atG7d+qXXT6QLGAgRERERERFRqSgUCjx8+BCnTp3C4cOHcfHiRSQlJSEjIwNCCDRq1Ahdu3ZF+/bt4ebmBktLS5ibm5dLCPQ0IQTkcjmuXbuGOXPm4Pfff4enpycWL16Mfv36lfvzEVU3DISIiIiIiIjomRQKBR48eIBTp07hyJEjuHjxIpKTk5Geng4AmhCoXbt2qFOnDiwtLWFhYVEhIdDThBBQKpWIi4vDypUr8dVXX6FOnTqYMmUKPvroowp/fqJXGQMhIiIiIiIiKkIul+P+/fs4deoUjh49itDQUE0IJJFI4O3tja5du6Jt27aoXbs2rK2tK6wnUGkolUrcvXsXmzdvRnBwMJycnDB69GhMnz4denp6WmkTUVXHQIiIiIiIiIggl8tx7949nD59GkeOHEF4eDiSk5ORlpYGfX19+Pj4oGPHjmjdurUmBLK0tIS+vr62mw5AFQo9fPgQ3333HRYsWABLS0sMGzYMCxcu/E/Fq4mqOwZCREREREREOqqgoAA3b97EyZMncfLkSVy5ckUTAhkZGaFJkybo1KkTmjRpgho1asDKyqpCQyClUonHjx/j9OnTSEhIwNixYyGVSsv0+JSUFOzduxfTpk2DTCZDnz59EBwcDGtr6wppM9GrioEQERERERGRDsnPz8eNGzdw/PhxnDhxAlFRUXj8+DHS0tJgYmKCoKAgTQhUs2ZN2NjYwNzcvMKGXimVSqSnp+PcuXM4evQoTp06hUePHsHMzAw//fQTGjVqVKb1CSGQnp6Ow4cPY+LEicjMzESXLl2wZs0auLi4VMg2EL2KOJiSiIiIiIiomsvLy0NsbCxOnDiBEydO4Nq1a5qeQGZmZpqeQIGBgahRowZsbW0rNAR6csr6Y8eOISwsDMnJyXj8+DHy8/Ph6+uL1157DVZWVmVet0QigaWlJXr06AFTU1NMmDABBw8eRFZWFtatWwcPD4/y3yCiVxB7CBEREREREVVDeXl5uH79Ok6cOIGTJ08iJiYGycnJSE1Nhbm5OZo0aYKOHTsiMDAQzs7OsLOzq/AQ6NGjRzh58iSOHz+OsLAwJCYmIiUlBXK5HI0bN0aHDh3Qpk0buLq6wtbWFjY2Nv95eJoQAvn5+Th37hymTp2KyMhINGnSBMHBwWjWrFk5bx3Rq4eBEBERERERUTWRm5uL69ev4+TJkzhx4gSuX7+uCYEsLCw0PYH8/f01IZCFhQVkMlmFtEcul+Phw4c4ffo0jh07hvDwcE0IpFAoNCGQulC1nZ0drK2ty222MiEE5HI5IiIiMG/ePBw+fBg+Pj5YtGgRevbsWS7PQfSqYiBERERERET0CsvJydGEQCdPnkRsbCySkpKQlpYGW1tbBAYGol27dvDz84OzszPs7e0rPAR68OABTp8+jRMnThQLgXx8fNChQwe0atVKEwLZ2NhU2JT1QggolUpcu3YNK1aswA8//ABPT09Mnz4d7777boU8J9GrgIEQERERERHRKyYjIwORkZE4ffo0zp8/j7i4OCQlJSE9PR2Ojo5o0aIF2rZti4YNG8LBwaHCewIVFhbi/v37OHPmDE6cOIFLly5pQiClUqkJgVq2bIlatWrB3t4etra2lTZlvRACQgjExcVh9erV+OKLL1CnTh1MnDgRY8aMKdNMZkTVBQMhIiIiIiKiV0BGRgYiIiJw4sQJnD9/HvHx8UhKSkJGRgacnJyKhEBOTk6wt7eHmZlZhYUdhYWFuHv3LkJCQnDy5ElNCJScnAwhBHx8fNCuXTtNCOTg4FCpIVBJhBBISEjAtm3bsGLFCjg6OuL999/HzJkzYWhoqLV2EWkDAyEiIiIiIqIqKi0tDZGRkThx4gQuXLiA+Ph4PHr0CJmZmahZsyZatGiBNm3aoEGDBnBycoKDgwNMTU0rLATKy8tDQkICzp07h7NnzyIiIkITAunr66NJkyZo06YNAgIC4OTkBEdHR62HQE8TQuDRo0f45ZdfMG/ePBgZGeHNN9/EkiVLYGFhoe3mEVUaBkJERERERERVSFpaGiIiInDq1CmcP38eCQkJePToETIyMoqEQF5eXprQpaJDoJs3b+LUqVM4e/YsYmJikJSUhJSUFBgYGCAwMFATArm4uMDBwQHW1tYVNltZeRBCIDU1FXv27MHMmTNRWFiIfv36Yfny5XBwcNB284gqBQMhIiIiIiIiLYuLi8Mff/yBqKgoxMfH4969e5oQqFatWmjevDlat25daSFQbm4ubty4gdOnTyMkJATXrl3Dw4cPkZKSAhMTEwQFBaF169YICAhAzZo14eTkBCsrqyodAj1NCIHMzEz89ddfmD59OpKSktCtWzd89tlnqFu3rrabR1ThGAgRERERERFpyYULF/Ddd98hMjISCQkJSE1NhVwuh4eHB1q0aIFmzZqhbt26cHZ2hpOTE0xMTCosBMrJycGNGzdw5swZnDt3rsQQqFWrVvD390fNmjXh7OwMa2vrCitUXRmEEMjLy8Pff/+NGTNm4MaNG+jQoQNWrFgBHx8fbTePqEIxECIiIiIiItKC3NxcjB07Fjt37kRmZiasrKzQpEkT9OvXD76+vnB2doaDgwOMjY0hkUgqpA3Z2dmIjY3F2bNnce7cOVy/fh0PHjxASkoKTE1NNSGQn59ftQmBniaEQGFhIc6cOYN58+YhJCQELVq0wKJFi9C2bVttN4+owjAQIiIiIiIi0oKQkBB8+OGHuHr1KmbOnIkWLVqgRo0aqFu3LszNzSssBMrKykJsbCxCQkJw/vx5xMTEaEIgc3NzBAQEoFWrVvD19UXNmjVRo0aNahcCPU0IAaVSidDQUCxbtgz79+9HYGAgZs6ciX79+mm7eUQV4tUZ4ElERERERFSNhISEICUlBdbW1ujYsSPatWtXYcPBsrKyEBMTg/Pnz+PcuXOIjY3F/fv3kZqaCltbWzRt2hRNmzZFw4YNdSYEepJEIoFUKkWTJk2wcOFC2NjYYNu2bZg7dy5SUlIwfPjwCnttiLSFgRAREREREZEWREVFIT09HV5eXrC0tCz3wCEtLQ1XrlzRTA8fHx+P+/fv4/Hjx7Czs9PUKFKHQOrC0LoafKh7ZHl7e2PGjBkwNTXF5s2bsWLFCmRnZ2PMmDHQ19fXciuJyg8DISIiIiIiIi3IycmBQqFA69aty22q89TUVFy5cgUhISG4dOkS4uLicOfOHWRkZMDJyQnNmzdH06ZN0aBBA9SqVQs1atSAhYWFzoZAJZFIJKhXrx4mTZoEa2trfP7551i9ejVSU1Mxbdo0mJiYaLuJROWCgRAREREREVEly8vLQ3JyMpRKJZo3bw47O7v/vC51CKTuCaQOgdLT0+Hs7Ix27dppQiAXFxfUrFkT5ubmDIGeQyKRwNXVFR999BGsrKywaNEibN68Genp6Zg7dy5sbGy03USil8ZAiIiIiIiIqJLduXMHycnJsLe3R82aNWFoaFimxz9+/FjTEygyMhJxcXG4ffs2MjIyUKNGDbRr1w5NmjSBl5cXateujZo1a8LCwqLCClVXRxKJBI6OjnjvvfdgamqK+fPnY/v27cjOzsb8+fPh7OzM/UmvNAZCRERERERElezatWvIzMyEh4cHrKysShUsPH78GJcvX0ZISAguX76MuLg4JCQkIDMzEzVr1tT0BPL09ETt2rXh4uJSobOV6QKJRAIrKyu89dZbMDMzw4IFC/Djjz8iKysLn376KTw8PLh/6ZXFQIiIiIiIiKiSqQMhT09PmJubv3D5qKgofPvttzh16hTi4+ORk5MDNzc39OzZE76+vqhbty5q166NWrVqMQQqZxKJBKampujfvz/MzMywaNEi7Nq1C1lZWZg7dy6CgoK4v+mVxECIiIiIiIiokj0ZCFlYWLxweQMDA9y8eRMFBQXo1asX/Pz8UL9+fbi6uqJGjRowNjZmKFGBJBIJDAwM0KNHD5iammLx4sU4ePAgsrOzMWfOHLRv3577n145DISIiIiIiIgqUXZ2NuLj4yGXy+Hu7g4zM7MXPqZWrVoYP348CgoKUKdOHdSoUQNGRkYMISqRRCKBTCZDhw4dYGRkhFWrVmHPnj3Iy8vDpEmT8MYbb2iWI3oVMBAiIiIiIiKqRPHx8UhJSYGTkxPs7e2hp/fiyzJDQ0O0adOmElpHzyORSCCRSNCyZUuYmprCwsIC33//PRYtWoSMjAwMHToUMpmMoRC9EhgIERERERERVaJr164hKysLXl5enPnrFSWRSODn54fp06fDysoKmzdvxooVK5CWlobRo0ez9xa9EhgIERERERERVaLo6GhkZmZqAiF6dXl5eWHSpEkwNTXFunXrsGrVKmRnZ2PcuHEs7k1VnlTbDSAiIiIiItIl6h5CpZ1hjKq2WrVq4ZNPPsHs2bORn5+P9evXY8mSJUhKSoIQQtvNI3omBkJERERERESVJD09HQkJCZBIJHBzc4OJiYm2m0TlwNHREaNHj8a8efNgaGiIL774AgsWLMCdO3cYClGVxSFjRERERERElSQuLg6pqamoWbMm7OzsSlVQml4NlpaWeP/992FqaorPPvsM27dvR05ODqZOnYoGDRpw+BhVOfz0ISIiIiIiqiTR0dHIysqCn58fh4tVQ6ampnjnnXdgbm6O4OBg/PDDD8jMzMTkyZPRvHlzhkJUpTAQIiIiIiIiqiTqQMjLy4uBUDWlr6+PN954A2ZmZli5ciX27NmDrKwsTJw4EV27dmUoRFUGAyEiIiIiIqJKIITAtWvXkJ2dzUCompNKpejWrRvMzMxgamqKAwcOIDMzEzk5OejduzekUimDIdI6BkJERERERESV4PHjx7h79y4MDAzg6uoKY2NjbTeJKpBEIkHr1q1hZGQECwsL/Pzzz1i8eDEyMjIwePBgGBgYMBQirWIgREREREREVAliY2ORlpYGV1dX2NjYQCaTabtJVAmCgoIwe/ZsWFhYYPv27Vi6dCkyMjLw3nvvwczMjKEQaQ2nnSciIiIiIqoEUVFRyM7ORoMGDWBmZqbt5lAl8vT0xIwZMzBmzBgkJiZixYoV2LRpE1JTUzktPWkNAyEiIiIiIqJKwILSus3FxQVTpkzBxIkToVQqsXLlSqxevRr3799nKERawSFjREREREREFUyhUCAmJgY5OTnw8vJiDyEd5eDggIkTJ8LCwgIbN27E2rVrkZqaiiFDhqBFixaQStlngyoPjzYiIiIiIqIKlpycjPv378PY2Bi1a9eGkZGRtptEWmJubo6PPvoIM2bMgLW1Nb744gssWrQIhw4dgkKh0HbzSIewhxAREREREVEFi4mJQUZGBtzd3WFlZcWeIDrO0NAQ7777LgoKCrB48WL89ddfyMjIgFKpRLdu3VhwnCoFP4WIiIiIiIgqGAtK09P09PTwwQcfYNq0aXBzc0NISAiCg4NRWFio7aaRjmAgREREREREVMEYCFFJZDIZxo8fj3bt2kEqlSIiIgJKpVLbzSIdwUCIiIiIiIioAhUWFuL69evIy8uDp6cnTE1Ntd0kqmLUQwgtLS0hkUi03BrSFQyEiIiIiIiIKtCjR4/w8OFDmJubw8XFBYaGhtpuElUxYWFhUCqV8PX1ZX0pqjQsKk1ERERERFSBoqKikJWVBQ8PD1hYWLAHiA64ceMGkpOTUa9ePdja2j73NRdC4NGjRxBC4LXXXmNBaao0jB6JiIiIiIgqEOsH6Z5ffvkFCxYsQERExAuXTU9PR15eHoyNjdG1a1cGQlRpGAgRERERERFVIHUg1LBhQwZCOuKPP/7An3/+WaoZw6Kjo5GdnY26devC2dmZPcio0jAQIiIiIiIiqiD5+fmIjY1FQUEBPD09YWJiou0mUQXLyclBVFQU9PX10bhx4xcuHxoaCqVSiaCgINYPokrFo42IiIiIiKiC3Lt3D4mJibC2tkaNGjVgYGCg7SZRBbtx4wZSU1NRq1YtODo6vnB5dSAUGBjIQIgqFY82IiIiIiKiCqIeLubl5QUzMzMOB9IBFy5cAAAEBQVBIpE89zVXKpUICwuDEIKBEFU6Hm1EREREREQV5OrVq8jJyUHDhg1hamqq7eZQJbh48SIAoEmTJi8MAFNTU3Hr1i2YmZnBw8ODgSFVKgZCREREREREFSQqKoqBkA4RQmgCoaCgoBcuHxkZiby8PHh6esLc3JyBEFUqBkJEREREREQVIDc3Fzdv3oRcLoeHhwcLSuuArKwsxMTEwMjICN7e3i9cnvWDSJt4xBEREREREVWAhIQEJCcnw97eHo6OjtDT09N2k6iCRUdHIzMzE25ubrCxsXlhj5/Q0FBN/SD2DqLKxkCIiIiIiIioAqjrBzVq1Aimpqa84NcBTxeUfh6FQoHw8HAIIRAQEMAeQlTpeMQRERERERFVgKcDIar+niwo/SLJyclISEiApaUl6tWrx8CQKh0DISIiIiIionImhOAMYzpGCIHQ0FAApQuEwsPDIZfL4e3tDSMjIwZCVOkYCBEREREREZWz7OxsxMXFQQiBevXqwdjYWNtNogqWnp6O2NhYmJiYoEGDBi9c/uLFi5r6QRwuRtrAo46IiIiIiKic3bx5E2lpaXBxcYG9vT0LSuuAJ6eQt7CwKFVBac4wRtrEo46IiIiIiKicsX6Q7ilL/aDCwkJcunQJEokE/v7+HC5GWsFAiIiIiIiIqJxduXIFubm5aNiwIUxMTLTdHKoE6hnGShMIPXz4EPfv34eNjQ3c3NwYCJFWMBAiIiIiIiIqR0qlElFRUZqC0gyEqj+FQoGwsDAAQGBg4AuXDw0NhUKhgJ+fH/T19RkIkVYwECIiIiIiIipHmZmZiI+Ph0wmg7u7O4yMjLTdJKpgjx8/RlxcHMzNzeHp6fnC5S9cuMCC0qR1PPKIiIiIiIjKUUxMDDIyMuDm5gYbGxvIZDJtN4kq2JNTyBsbG5eqoLQQAkFBQewdRFrDQIiIiIiIiKgcqesHNWrUiMPFdERZCkoXFBQgMjISMpkMfn5+7CFEWsMjj4iIiIiIqBxdvXqVgZCOKUtB6du3byMxMREODg6oVatWRTeN6JkYCBEREREREZUTpVKJq1evIi8vjwWldYRcLkdYWBgkEgkCAgJeuLy6flBAQACkUimHjJHWMBAiIiIiIiIqJ2lpabhz5w709fVRt25dGBoaartJVMEePXqEO3fuwMrKCvXq1Xvh8urhZSwoTdrGo4+IiIiIiKicXL16FVlZWfDw8ICFhQUv+HWAukC0v7//C6eQF0KwoDRVGfx0IiIiIiIiKieXL19m/SAdo+7xExQU9MJl8/PzcfnyZchkMvj6+jIQIq1iIERERERERFROnpxhzNjYWNvNoUpQloLSsbGxSE9Ph4uLC5ycnBgIkVYxECIiIiIiIioHCoUC0dHRKCgoYEFpHVFYWIjw8HBIpdJSFZS+ePEih4tRlcFAiIiIiIiIqBwkJSXh/v37MDY2Rp06dWBgYKDtJlEFu3fvHh49egQ7OzvUqVPnhcurA6HAwEAGQqR1DISIiIiIiIjKweXLl5GTk4OGDRvC1NSUF/w64Pz58wCg6fHDgtL0KmEgREREREREVA7U9YO8vb05XExHlKWgdG5uLqKioqCvr4/GjRszECKtYyBERERERERUDi5fvoy8vDx4e3uzoLSOUPcQKk1B6atXryI7Oxvu7u6wsbFhIERax0CIiIiIiIjoJcnlck1B6QYNGjAQ0gGFhYWIjIyETCaDv7//C5d/sjcRwyCqChgIERERERERvaS7d+/i0aNHsLKyQq1ataCvr6/tJlEFEkLgxo0bSE1NhbOzM2rUqPHCkIczjFFVw0CIiIiIiIjoJV25cgV5eXlo1KgRTExMeMGvAy5cuAAAaNq06QuXfbqgNFFVwECIiIiIiIjoJT1ZP8jIyEjbzaFKoA6ESlM/KDMzEzExMTA0NESjRo0YGFKVwECIiIiIiIjoJQghWFBahwghAPwbCJWmx8+lS5dQUFCAhg0bwszMjIEQVQkMhIiIiIiIiF6CXC7HtWvXUFhYiIYNGzIQ0gH5+fm4cuUKZDIZ/Pz8Xri8un5QkyZNGAZRlcFAiIiIiIiI6CXExcXh8ePHcHR0hJOTE/T09LTdJKpgUVFRminkbW1tS1VQGgACAwMZCFGVoROBUGFhIbKzs6FQKLTdFCIiIiIiqmbUw8V8fHxK3TuosLAQSqWygltGFaUsw8WUSiVCQ0M1yzMQoqqi2gdCQgiEh4dj8+bNuH//vma8JxERERERUXm4dOlSmQpK5+fn4+LFi7h37x7kcjmvUV5BZSkonZqairi4OBgbG8PLy6uim0ZUajoRCH366aeYNWsWjh07hpycHG03iYiIiIiIqgkhBE6ePIns7Gx4enqWKhA6ceIERowYgY8++ghhYWEoLCxkKPSKUL9O6iFgpekhFBoaCoVCAT8/PxgaGrKHEFUZ1T4QkkqlaNu2LSwtLXHixAmkpaVpu0lERERERFRNZGRk4M6dO5DL5XBzc4OBgcELH2NgYABra2scPnwYkydPxtmzZ5Gfn89Q6BWRk5OD6Oho6Ovrw9fX94XLPxkeMQyiqqTaB0IANIHQqVOnkJqayg9aIiIiIiIqF0eOHEF2djYMDAzg4uJSqkCoXbt22Lx5Mzp27IjQ0FCMHTsWhw8fRk5ODq9VXgEREREoKChAgwYNSjWF/IULFyCEKFVvIqLKpBOBUFBQEBwdHXHjxg3Ex8ejoKBA200iIiIiIqJqICQkBLm5ubCysoJCoSh1oejGjRvjiy++QO/evXH79m2MHTsWu3fvRnp6OotNV3Hnz58HULr6QeqC0hKJhDOMUZWjE4GQoaEhWrRoATMzM5w8eZLDxoiIiIiIqFzUq1cPhoaGSEpKwp49e5CYmFjqQMfV1RUbN27E22+/jezsbEycOBHfffcdkpOTGQpVYWUpKP3w4UPcvXsXlpaWqFevXkU3jahMdCIQAjhsjIiIiIiIyl///v3h5eUFfX19zJ07F8uXL8fDhw9LHejY2tris88+w4gRI2BgYIA5c+Zgy5YtePDgAUOhKkYIASFEkSnkX0QdHgUGBkImk7GHEFUpOhMItW7dGra2tggNDcXDhw+hUCi03SQiIiIiInrF2dnZ4c8//0Tnzp1hbGyM9evXY+vWrXj06FGpAx0zMzMsXrwYkyZNgq2tLRYtWoTg4GDExcXxuqWKycjIQGxsLIyNjeHt7f3C5VlQmqoynQmEbGxs4OvrCz09PZw7dw4ZGRnabhIREREREVUDpqam2L9/P9577z3Y2NhgwYIFWLRoEe7cuQOlUlmq0Qn6+vqYOHEiZs2ahbp162LDhg1YsmQJrl27BrlczhEOVcTFixehVCrRuHFjGBgYlKmgNAMhqmp0JhACgDZt2sDKyorDxoiIiIiIqFzJZDJs3LgR69evR506dfDll19iypQpiI2NLXUoJJPJMGrUKKxcuRJ+fn74/vvvMWnSJFy4cAEKhYLXL1WAeghYaYaLKZVKhIeHawpKE1U1OhUItW3bFtbW1jh9+jSSk5P5gUpEREREROVq8ODB+Oqrr9CoUSPs2bMHH374ISIiIsrUy6dHjx4IDg5Gq1at8M8//2Dq1Kk4ceIECgsLeQ2jZeohYKUpKH3r1i0kJSXBwcEBtWvXZg8hqnJ0KhByd3dHvXr1kJOTg8jISGRlZWm7SUREREREVM107NgR27ZtQ1BQEM6cOYORI0fi3LlzKCgoKHWg065dO2zYsAHdunVDaGgoxo0bh99++w15eXkMhbRAXVD6yZpAL6Kenp7Dxaiq0qlACCg+bIyIiIiIiKi8BQYG4ocffkCHDh0QHR2NYcOG4ciRI8jNzS11oOPj44O1a9eiX79+iI+Px+TJk7Fjxw5kZWUxFNKCxMRE3L59G+bm5vDy8nrh8hcvXoQQolS9iYi0QScDIWtra5w6dQqPHz/mBykREREREVUId3d3bN++HT169EBiYiJGjBiB/fv3lynQqVu3LtasWYNhw4YhPT0dU6ZMwddff81rGS0oyxTyT/cmYg8hqop0LhDy9/dHzZo1cfv2bcTGxiIvL0/bTSIiIiIiomrK2dkZ27ZtwzvvvIP8/Hx88MEH+O6778oU6Dg4OGDFihX4+OOPIZPJMHfuXKxduxb3798v9dT29PLUgVBpevwoFApcunQJEokEAQEBFd00ov9E5wIhPT09NG/eHJaWljh9+jSHjRERERERUYWysLDAunXrMHbsWBgbG2PSpElYt24dHjx4UOpQyNzcHHPmzMGkSZNga2uLzz77DJ9//jni4+MZClWSstQPio6ORmZmJlxdXeHo6MgeQlQl6VwgBPxbR+j06dNISUlhV0siIiIiIqpQBgYGmD9/PqZPnw4HBwcsWbIEy5cvR0JCQqmnpTc0NMTkyZOxYMECuLu7Y8OGDZg3bx6uXLnCUKgCqQtKh4aGAgCaNm36wsdcuHCB9YOoytPJQKhFixaws7NDZGQk7t27B7lcru0mERERERFRNSeTyTBp0iSsXr0a9erVw4YNGzBhwgRERUVpQocXkUqlGDZsGJYuXQp/f3/89NNPmDlzJs6dO1fqYInKLiEhAYmJibC1tYWrq+sLl2f9IHoV6GQgZGFhgcDAQBgbG+PcuXMcNkZERERERJVmwIABWLduHRo3bowDBw5gwoQJCAsLK3WgI5FI0KdPH6xZswYdOnTAX3/9hUmTJuHQoUNQKBQMhSqAun5Q06ZNXxjwCCHKVG+ISFt0MhACgNatW8PGxobDxoiIiIiIqNJ17doVX3/9NVq2bInjx49j1KhROH78OAoLC0t9bdKqVSusXLkSvXr1QlhYGKZOnYrdu3ejoKCA1zflrCwBT0FBAS5fvgypVAp/f/+KbhrRf6azgVCbNm1gY2ODkJAQJCUl8QOTiIiIiIgqVWBgILZu3YouXbogOjoaH374If766y/k5eWV+vrE19cXq1atwttvv424uDhMnToV27dvR3Z2Nq9xyoF6H5YlEIqIiEB+fj48PT1hYWHBIWNUZelsIOTi4gJPT0/I5XKEh4cjPT1d200iIiIiIiId4+npiW+++Qb9+/fHw4cPMWrUKPzyyy/IzMwsdaDj5uaGFStW4KOPPkJmZibmzJmD9evXl2lqe3o2pVKJS5cuAVCFeC/C4WL0qtDZQAj4d9jYmTNnkJKSou3mEBERERGRDnJycsLmzZvx/vvvo6CgAOPGjcPWrVvLVNrC3t4e8+bNw4QJEyCTybBs2TKsXLkSDx8+ZCj0kmJiYpCRkYHatWvD2dn5hcs/GQixdxBVZXraboA2tWrVCjY2Njh16hSSk5Ph7u7ONywREREREVU6CwsLfPbZZ7C0tMRXX32FWbNmIS0tDaNHj4azs3OprlMsLCwwffp02NraYtWqVVi5ciWSk5Mxbdo0Xuu8BIVCgb59+8LJyemFywohNDOMsYcQVXUSocNxsUKhQO/evXH48GFs2bIFAwcOhKmpqbabRUREREREOkqhUGDz5s1YuXIlbt++jQ8++ABTpkxB3bp1Sx3oyOVy/PTTT1i+fDliYmIwcOBAzJw5Ez4+PgyFKlhmZiasra0hlUqRlpYGY2Nj7nOqsnR6yJhMJkOLFi1gbW2Ns2fPIjk5WdtNIiIiIqIqTAgBpVKpmR5ch39bpQoik8nw8ccfY+nSpfD09MSXX36JGTNm4MqVK6U+5vT09PDOO+9g9erVaNGiBX755ReMHz8ex44d43FbwUJDQ6FQKNC4cWMYGRkxDKIqTacDIUA1bMzW1lYTCPHDkYiIiIhKIoTA5cuX8emnn2LatGk4evQoMjMzi4RDvNim8iCRSDBkyBCsX78eQUFB2LVrF8aNG4czZ86U+hiTSCTo2rUrli9fji5duuD48eOYOXMmfv/9dx6nFYj1g+hVovOBULNmzeDk5ITo6Gjcvn0bhYWF2m4SEREREVUxQgj88MMPGDJkCBYvXoyVK1eiS5cusLGxQWBgIKZOnYodO3bg5s2bKCwsLBYQ8eKb/otOnTphw4YNaN++PU6ePInx48fjyJEjkMvlpT6mWrVqhVWrVmHw4MEIDQ3F1KlT8b///a9M66DSY/0gepXodA0htSlTpmDbtm0YNWoUxo0bhxo1ami7SURERERURQgh8Pvvv2PhwoW4cOECzM3Noaenh+zsbBQUFBRZViqVwtnZGX5+fmjSpAmCgoLg5+cHJycnSKXFf4tlDwIqjdjYWEyfPh0HDx6Ei4sLFi9ejD59+pRpSNKtW7ewfPlyfPfdd3B0dMTkyZMxcuRIDmsqR0IIuLu749atW4iMjIS3tzf3LVVpDIQA7NmzB9OnT4ezszPWrVuHxo0b841LRERERBBCICoqCtOmTcPBgwdRt25drFixAgMGDEBhYSHi4uIQHR2N6OhohIWF4eLFi7hz5w4UCoVmHVKpFI6OjggKCkLTpk0RGBiIxo0blzhzFM9B6VkePnyIOXPm4JdffoGJiQnmz5+Pt956CxYWFqU+bhITE7FmzRps2rQJ+vr6GDt2LMaNGwcrKysee+UgKSkJDg4OMDExQVpaGvT09LhfqUpjIATVB2O/fv0QHh6O3bt3o0uXLpDJZNpuFpHuUn8s8QuUiIi0SAiBpKQkzJ49Gz/88APMzMwQHByMoUOHPvMir6CgALdv30Z4eDhCQ0MRFhaGyMhIPHr0qMhy+vr6qFmzJnx9fREQEICmTZsiICAA9vb2xdbJC0pSS01NxaJFi/Dtt99CoVBgxowZGDlyJGxtbUt9nGRmZmLLli0IDg5GVlYW3n//fUyfPh01atTgsfYShBD4448/0LNnT7Rp0wbHjx/n/qQqT0/bDagKHBwc0KhRI0RGRiI8PBxBQUGws7PTdrOIdMfTubQQgFIJPBnMqr9Qn1726S/aF91PRERUCkIIZGVlYdmyZfj111/h6uqKpUuXonfv3s+9yDMwMEC9evVQr149DBw4EACQn5+PO3fuICIiAufPn8fp06dx+fJlxMfHIz4+Hvv27QMAGBsbo27duggICEBgYCCCgoLg4+MDc3PzYs/DC03dZG1tjaVLl8Le3h4bNmzA/PnzkZKSgnHjxsHFxaVUx4W5uTnGjBkDCwsLLFu2DFu2bEFmZibmzJlTpqntCUVqMKWmpmLr1q0AgPr162vu5/6kqow9hP7ftm3bsHDhQjRu3BjLly+Hl5cX37xElUEIQKEArl8HNm0CfvsNuH0b0NMD3NyAvn2Bjz4CXF1V4U5uLvDnn8DcucDs2cCQIUXXV1AAHD8OjBkDzJoFvP++VjaLiIheXUIIFBQUYOnSpdi8eTNMTEywfPly9O/fH/r6+uXyHNnZ2bhx44amF9GFCxcQFRWFrKysIssZGRnBzc0NTZo0QatWrdC8eXN4eHjA0NCw2Dp57qo75HI5tmzZgpUrVyIhIQHvv/8+pk6divr165f6OJDL5di3bx8WLlyIq1evomfPnpg/fz78/Px4LD3H0yHQgQMHsGPHDhw+fBgFBQVwdnZGUlIS3n//fcybNw81a9YEwPcnVU0MhP5fTEwM3nnnHSQkJGDHjh1o27Yt37RElaGgADh5Ehg1CsjIAF5/HWjaVBUSnTkDHDigCoa++QYIClIFQnv3AhMmAMuWASNGFF1ffj5w6BDw9tvA0qXA2LHa2CoiInpFqWcEW7NmDdasWYO8vDwsWbIEb7/9NkxMTCr0uXNycnDz5k1cuHABp0+fRkhICG7cuFGscLW5uTkaNWqEgIAABAQEICgoCA0aNCgxrOL5bPUlhNAEOuHh4ejRowc+/fRTzexWpXnthRA4duwYFixYgOPHj6NNmzb49NNP0bFjx1KvQxc8ecmcnJyMvXv3YufOnfjnn380s1Tr6emhbdu20NPTw99//w25XA4jIyOMHTsW06ZN04xA4T6lqoRDxv6fh4cH6tati8uXLyMqKgr+/v6wsLDQdrOIqr/YWGDJEsDAAPjxR6Br13/vGzUKOHwYGD4cmDIFOHiQQ8CIiKjCbd26FZs2bUJqaioWLlyIN998s8LDIAAwMTGBj48PfHx8MHz4cCiVSqSnp+Pq1asICwtDaGgoQkNDERMTg5CQEISEhABQXWCamZmhYcOGaN26NVq0aAE/Pz+4ubkVew5ejFYfEokEffv2hZmZGebPn4+DBw8iOzsb8+fPR7t27Uq9jg4dOsDCwgJLlizB3r17MWnSJMyaNUsz5FFXj5knQ6CUlBT8/vvv2LFjB/766y/I5XIAgEwmQ6tWrTBgwAAMHjwYjo6OkEgkiImJwZIlS/DDDz8gODgYmzdvxpgxYzBz5kxYWVkB0N39SlULA6EnNG/eHKdPn8bZs2fRuXNnBkJEFS0vD7hyBYiMVPX06dKl6P1GRkDz5sDIkcB336mGk/XqpZ22EhGRTvj111+xfv163LlzBzNnzsTbb78NS0tLrbRFKpXCysoKrVq1QqtWrQCoLlLT0tIQFhaGs2fPIiwsDJcuXUJCQgLOnTuHc+fOaR5rZ2eHxo0bIzAwEIGBgfD390e9evW0si1UcTp16gQbGxt8+umn+OOPPzB+/HjMnz8fvXr1gp5e6S73AgMDsWTJEpibm+N///sf5s6di6ysLLz77rulXkd1IoR4YQg0cOBADB48GA4ODprHqUMeDw8PfPvtt5g2bRoWLlyIHTt24PPPP8fXX3+NKVOmYPz48TA2NmYoRFqne+/uZ5BIJGjRogXs7e0REhKCxMRETTEwIqogqanApUuAlRXQrFnJvX8sLIC2bYHNm4Fz5xgIERFRhTly5AiCg4Nx9epVTJgwAcOHDy9x1q/KVNIFo7W1NTp16oROnToBABQKBZKSkhAREYHQ0FBcvHgR58+fx/3793HkyBEcOXIEgGpIS61atRAYGKgZahYQEABbW9tK3SYqXxKJBP7+/li5ciXMzc2xa9cuTJ06FRkZGRg0aBCMjIxKtR4vLy8sWrQIdnZ22LRpE+bNm4fExERMmDABxsbGFbwV2qeeVfCPP/7Ajh078Oeff0KhUAAADA0N0b59e7z++usYMmRIkc+Fkt6j6tsaNWqEX375BRMmTMDs2bNx7NgxzJo1Cxs3bsTs2bMxfPhwGBgYMBgirWEg9ISAgAC4uLjgzz//RFxcHAICAnTiw49Ia3JzgYcPVT2BHB1LXsbAQHWfQgHcv//v7RkZwNdfqwpIP0mhAB48qLg2ExFRtXTx4kUsXrwYYWFhGDlyJMaMGYNatWpVuQu1ktojk8ng5OQEJycndOvWDQBQWFiIhISEIkPNwsLCcOvWLdy6dQs7d+7UPNbFxUUzq1mrVq0QFBRUKUPkqPxIJBLUr18fK1euhK2tLbZv347p06fj8ePHGDFiRKlGPkgkEtSqVQuzZs2Cvb09VqxYgc8//xwpKSmYPXs2rK2tK2FLKpcQAnfu3MGuXbvw+++/4/jx45qeQEZGRujevTsGDhyI3r17F+kpWNrPBfVyLVq0wN9//42jR49ixowZCA0NxZgxY7BmzRrMmDED77zzDvT09Krc5w1VfwyEnqCvr4+aNWvC2NgY58+fR9u2beHq6qrtZhFVX0IAhYWq6eWfd+Kprw8YGqoKUKspFEBSEiCVFl1WqQTS0yumvUREVC3FxsZi3rx5OHXqFPr3749PPvkE7u7upb44y8/Ph0QigYGBQQW3tGQltVNfXx/16tVDvXr18OabbwJQzSp1+/ZtTS+iM2fOIDQ0FAkJCUhISMDu3bsBqHpDeHl5ISAgQDPczM/Pr9Q9TUg7JBIJnJycsHjxYtjY2GDTpk1FpqV/cmjT89ZhY2ODMWPGwNzcHIsWLcKXX36JjIwMLFiwAM7OzpWwJRVLCKE53nfu3ImQkBBNvSAjIyO89tprGDhwIPr06VMkSHuZsEb92E6dOuHcuXPYsWMHPv30U1y/fh0jRozAXtu9GNl2JHpY9oBMInu5DSQqAwZCT/j9999x8uRJZGZm4syZM3j06BEDIaKKpKcHmJurZgZLSip5GaVS1RsoLw94soaDtTUwY4aq4PSTCgr+nWWMiIjoBR48eIBZs2bh6NGjaN++PaZOnQpvb+8yXfxt2LABGzZsgK2trWY4VtOmTeHp6am1njYltV9PTw9169ZF3bp1NQWD8/LyEBUVpelJFBYWhoiICM3ftm3bAKgulD08PBAYGIhWrVqhZcuW8PT0hPTpH2ZIqyQSCSwsLDB9+nRYWlpi9erVWLVqFTIyMjB58uRSXdtIJBKYm5tjxIgRsLW1xdy5c/Htt9/i8ePHWLJkCTw9PSthS8qXUqlEfHy8ZnawJ0MgExMTdOrUSRMCmZubax5X3j12JBIJpFIpBg0ahDfeeAPffvst9l3Yh9+cf8P+uP1obdoaC2ssRFuztpUeDCmEAuezzyNDmQFzqTl8jH1gLjN/8QPplcZA6P+dOHECc+bMQXR0NAAgPDwcN27cgK+vLwwNDbXcOqJqyswMqFtXNXvY7dslL5OXB9y6pRpW5uVV/H52rSUiov/o8ePHmD59Og4ePAgfHx/MmTMHgYGBZboILCwsRGpqKh48eID4+HiEhoZq7rO1tYWPjw+CgoLQpEkTBAQEoHbt2lWmJ5EQAkZGRprp60eOHAkhBPLy8nDt2jWcPn0aZ86cQVhYGGJjYxEZGYnIyEhs27YNEokElpaWaNy4saYnUUBAABo0aMBhL1omkUhgbGyMTz75BDY2NlixYgU2bdqElJQUzJo1C40aNSrVOgwNDdG/f3+YmZlhzpw52Lt3LzIzM7Fs2TIEBARUwpa8HKVSiVu3bmlCoHPnzmklBHqaev36+voYOXIkho0Yhu8ff4/5D+bjVPYpdIztiA5mHbCkxhI0NW1aacFQrjIX4+6Ow8Wci2hs3BjfuX4HXxPfSnlu0h4GQgAuXLiAyZMnIzIyEsOHD8eFCxdw9epVbNq0Ca6urmjevDlkMnbdIyp31tZAUJCqV88ffwDvvKMKfp6UnAz8+quq8PSTU9ITERG9hJycHMybNw/79+9HjRo1sHDhQrRu3brMF4N6enpYtGgRpk6diqtXr+LMmTM4d+4cwsLCcPv2bRw7dgzHjh0DoJr5y9bWFo0aNdKEMEFBQahbty709fUrYCuf71nbamxsDH9/f/j7+2Ps2LGamc3URavDwsIQFhaGmJgYnDhxAidOnNCsz8rKCs7OznByckK7du0waNCgV7JHyatOIpFAT08Pw4YNg6WlJRYvXoyffvoJGRkZmDt3Lpo2bVrqdfTo0QPW1taYO3cuDh8+jPT0dCxatAhdnp4dtgpQKBS4efMm9u/fj127dhUJgczMzNCxY0dNCGRmZqZ5nDZCTPVzGsAAI2xHYJD1IHyZ/CWWP1yOf7L+QavrrdDXqi8WOC9AQ6OGZQqGruddx+8Zv+PvjL9xq+AWkuRJkEIKOz07+Jv4o7dlb3S16ApzmTkkYICr04SOi4yMFM2bNxcymUwMHjxYXL9+XcyfP184OjoKAEImkwl3d3fx1ltviU2bNokrV66I3NxcbTebqHpQKoW4e1eIyZOFsLERYvx4IR4//ve+O3eE+PhjISwtVfcplUJkZwvxww9C2NsLsXVr8XXm5Qmxf78Q5uZCrF9fiRtDRESvisLCQjF79mxhb28vnJycxK+//ipyc3OFUqn8z+tUKpVF/uRyuXjw4IE4fPiwWL58uXjzzTeFu7u7kMlkAoDmTyqVCkdHR9GjRw+xYMECceDAAXH79m2hUCjKcYtfztPbpt6+27dvi927d4uZM2eKLl26CBsbmyLbpt6+Hj16VKnt0SXq1+vUqVOia9euQiKRiKZNm4rffvut1K+Jeh3h4eGib9++AoDw8fERu3btquDWl45cLhfXrl0Tn332mWjWrFmR48/c3Fz06dNHfP/99yIzM7PIMVzVqNuVVJAkZt+dLczDzQVCIfTC9MTguMEiKjdKyJXy567jYcFDMTphtOaxz/trEt1EHM04KnIVqmvbTHmmCIoOEgiFaBzVWFzKvlQZm01aJhHi/yNTHRQbG4sRI0bg7Nmz6Ny5M4KDg9GwYUPI5XIsXrwY+/fvx40bN5CVlaV5jEQiQY0aNYqMn/bx8SlSdZ6IykAI1XCxxYuBb79V1RTy9FQVjb5+XXX/228DK1aohpjl5AB79wITJgDLlgEjRhRdX37+vzWEli4Fxo7VxlYREVEVJYTAqlWrsHr1auTk5GDNmjXo378/TExMyrWXQEmn2AUFBbh37x7Cw8M1vWwiIiLw4KnZMQ0MDODk5ITGjRtrhmL5+fmhdu3a5da+l/X09gkhIITA9evX8cUXX+DkyZO4efMmMjIyIJPJsG3bNgwdOlRLrSUhBC5duoTFixdj79698Pb2xqxZszBgwIBSj4QQQiAuLg7BwcGIiIjA0qVL0b59+4pt+DMUFhbi8uXL+O2333Dw4EGcP38eQghIJBLY2tqiV69e6N+/Pzp16lSk/MerMJxR/d66V3gPyx8txzcp3yBXmQsDiQHesn4L85znwdXAFVJJ0fpdN/JuYEj8EFzKuQQ5VDOlWcos0cykGVwNXSEXciQUJOB89nlkKVXXtzYyG2x33Y7OFp2hEAp0iO3AIWM6RmcDodu3b2PEiBE4fvw4WrRogXXr1sHHx6fIh0RCQgLGjRuHP//8Ey4uLqhbty7i4+Nx+/ZtFBYWapa1sLBAgwYN0KJFC7Rs2RJNmzZFzZo1OcyMqLSEUE1BHxmpqicUH6+aPax+faBbNyAgQFUrSCJRzUoWEwP8+SfQqRPg7190XQoFEBcH7Nqlur9JE61sEhERVU1fffUVli1bhocPH2LFihUYOnQoLC0tK+VCsaQQpaCgAPfv30dERATCwsJw/vx5hIeHI+mJyRYkEgmMjIxQp04dTUAUGBiIxo0bw8rKqsLbXVrq7VP/f0FBAZo1a4bLly+jZcuWOHXqlDabp/PUgc6KFSvw3XffwdnZGVOnTsXw4cNLPYOcEAJZWVnIzs6Go6NjpQYsBQUFiIyMxM6dO7F3715cv35dEwLZ29vj9ddfR//+/dGxY0cYGBho2vYqhEAlEUJAQOBGwQ0sfrAYv6T+ggJRAAuZBcbZj8NMp5kwkaqK1mcrs9E1tivOZ5+HHHJYyiwxy2kWRtiOgLXMush6HxY+xKKHi/Dj4x+Ro8zBwhoL8YHdBzCUGDIQ0kE6GQg9evQIw4cPx9GjR9G4cWNs3rwZ/v7+kEgkmg+MxMREjB8/Hvv27UOjRo2wcuVKtGzZEkII3Lp1C+fPn9cU2ouNjUVubu6/40ANDODu7o7mzZuje5s2aNeiBezr1FFNm01EJVN/FD39kaQOgkpa7un7SnM/ERHprAMHDmDGjBmIiYnB/PnzMWrUKDg4OGj1gvHJU3H1f+fk5CAuLk7Ti+jixYu4cuUKMjMzAfx7gWtoaIg6depoahG1bNkSjRs3hrGxceVvSAkUCgU2bNiACRMmwMjICNnZ2ZyVTMuEEHj06BHWrFmDzZs3w8jICGPHjsW4ceNKPeJBfZxWxvumoKAAly5dws6dO7Fv3z7ExsZqntvBwQE9e/ZE//790aFDh2oRApVEHQxF5UVhxaMV+DX1VwyxHoL1tdZrZgGbe38uVieuRrYyGxZSC/xS9xd0MOsAA4mqgP2T+0MIASWUWJW4Cl6GXmhv3h5mUjNkK7MZCOkgnQuEHj9+jJEjR+LPP/+Eu7s7vvrqKwQFBUEmk2neKGlpaZgwYQJ27twJNzc3rF27Fm3bti3W40fdNTYtLQ2RkZE4c+YMzpw5g4sXL2p+1Rns5oZZhYXwzs0FfH2Bli1VfwEBgIPDy2wIcOwY8NdfwN27gEwGeHgAr70GtGsHaKEwIVGZZGYCv/0G/PADMGiQqifQy7wniIiInuPYsWOYOHEiIiIiMGHCBEycOBEuLi5V8sLx6Z424v9n/rp16xYuXryIM2fOICQkBNevX0d+fj4AaH7YNDMzQ4MGDTRFqwMCAuDt7a2Vmc2EEMjJyYGVlRWEEDh58iRatGhR6e2gooQQSE1NxRdffIHVq1dDLpdj5MiRmDp1KhyqwLlYfn4+wsPDsWvXLuzZswe3bt3S9ARycnJCjx49NCGQvr5+tQyBSqIOhkKyQ1DToCZc9F0gk8iQociAX7Qf4gviISCwvOZyjLEbAzOp2TP3iYDqOlZdUFoikSBLkcVASAfpVCCUlZWFUaNG4cCBA6hRowa2bt2KFi1aQE9PT/NmycrKwuTJk/Hzzz/D0dERa9euRefOnYss87Qnv6zV3SivXbuGM2fOoG5kJNqHhMAyNlY1lEXdY8HSEvD2/jcgCgoCnJxUw2SeRwggNBSYPx84ehRwcQEaNlRNzR0dDaSnA2+8ASxfDjg6lufuIypfQvwbbO7aBZw7BzRuDAwYAHTvDtjYaLuFRERUTURERGD06NE4f/48hg8fjhkzZqBu3bqv1AXk0+ebQghkZmYiKipK05MoLCwM0dHRmtIGT4ZEXl5eaNWqFZo3bw5/f3/UrVu3wnvrCCGgUCjwySefYMuWLZg1axYWLVpUoc9JpaMO677//nssX74ciYmJGDx4MObMmYO6detWenvy8vIQHh6u6QmkDoGkUimcnZ3Ro0cP9OvXD+3bt4e+vr7m2H2V3sPl5cnLd4lEgp8e/4SPbn+EdGU6HPUccd7zPGoZ1CrzvmEgpJt0JhDKzc3F2LFjsWPHDlhZWeGbb75BmzZtinQtzMvLw4wZM/Ddd9/B3Nwc69evR7du3YosUxpFftURAhKFApJ794ALF4AzZ4BTp1ThTU7OvwGRgYEq3AkKUgVErVoBDRoAT3f5jY5WFdI9cUJVLHf06H+XuXsX2LAB2LYNePNNYN06QE+vXPYfUbl7cmiXUgkkJqqmnt+zRzUN/Z49gKmpdttIRESvvFu3buGDDz7A8ePH0a1bNyxfvhwNGzZ85S8knx5qJoSAUqlEVlYWoqKicObMGZw9exaXLl1CfHy8poeFRCKBTCaDjY0NGjdurOlF5OfnBw8Pj3Jvp1KpxNGjR9G1a1d4eHggJiam3J+D/ht1Datdu3Zh8eLFuHHjBvr27Yt58+bB29u7wp8/NzcXYWFhmppAt2/f1oRANWvWRPfu3fHGG2+gXbt2mh/nnyzxQSqT707G5qTNyBW5GG47HME1g2Els2IgRKWiE4FQYWEhJkyYgB9++AFGRkb4+uuvNRXn1W+UwsJCzJ07F19//TX09PSwbt06vP766zAyMnr5D50nL3yfvPgND1cFRGfPqorpPn78b0Akk6l6DKkDorZtgUaNVLMrTZum6gUUHKwKktTtEwK4dk3VeygmBli9GujQ4eXaTlQRhABu3lQFQJ07A15e/96uUAByOWBkxPo/RET0UpKSkjBq1Cj89ddfaNq0KVavXg0/P79qW8empF5ECoUCycnJiIyMRFhYGMLDwxEeHo64uLgiF9hSqRT29vYIDAxEixYtNIWr7ezsXrpNeXl5qFu3LpKTk3Hx4kX4+vIis6pQB4mHDh3CwoULcf78eXTs2BGffvopWrduXe7PlZWVhfPnz2P//v3Yv38/7ty5AyEEZDIZ6tWrhz59+qB79+5o2rSpZjgYQ6Dn6xLbBccyj0EOOda4rMFI25EwlZX9R1UGQrqp2ncfUSgUmD17Nn766SdIpVJs2rQJHTp0KBIGKRQKLFu2DNu3b4dSqURwcDC6d+9ePmEQ8O9Frfr/pVKgRg3A2Vk1NEYIICsLuHFDNWzm7FkgJEQ109LevcC+faphNDNnAleuANbWqh5ET4ZB6vW7uqpqsZw8qVoXAyGqipRK1Wxh0dHA5s2q4WFvvgm8/jrg5qbq2cYvfiIiegnqMgBHjx6Fh4cHli1bBl9f32p9YVlSLRWZTIYaNWrA2dkZXbt2hRAChYWFuHPnTpGhZuHh4bh//z4ePHiA33//XdOTyMXFBf7+/pqeRM2bNy/zzGb6+vro1asXvv76a+zevZuBUBWiDgO7desGa2trLF68GAcPHkRqairmzJmDvn37vtT6hRBIT0/H8ePHsXv3bhw+fBiJiYkQQkBPTw9eXl7o27cvevfuDT8/P01dV4ZApZciT4ESSgBAHYM60JNU+0t8KkfV+2gRAsply2B04gQs9fWx4v+HgBkbG2s+YIQQWLVqFbZs2YLs7GwEBwfj9ddfh6mpacV9CD0dEAGqmkIBAaoptD/8EMjPB27fVoU6Fy8Cnp6qAOj+fcDMTFUfqKT2GRurhp4BQEJCxbSf6GVJpapeQevWARkZqgB0507g+++BoUOBceO03UIiInqF5efnY/r06di/fz/s7Ozw+eefIygoCFKpVOcuMp8VEtWvXx/16tXDgAEDNCHR9evXERoaiosXLyIsLAyXL19GXFwcbt26hb1790IikcDQ0BANGjRAYGAgmjRpgi5duqB27drPfX6pVIo33ngDX331FXbu3IkFCxZU+HZT6anDl6ZNm2LZsmWwsrLCzz//jFmzZiE1NRXDhg0rNrnO86iLVqtDoCNHjiA5ORlKpRL6+vpo2LAh+vTpg169eml67Knfm7r2/iwPaYo0CKh6B5rLzCGVVM8ekFQxqncg9Nln0Fu3DnOFwJA1a1C7e3eYmJgU+aDZtGkTNmzYgJSUFKxYsQIDBw6EhYVF5X8YPT09tkymumD28ADeeUd1X1SUqni0sbEqQHrWevT0VOFRdnbltJ2orLKyVMMma9VS9Xh77TWgSxdVr6HCQm23joiIXmFKpRKLFi3Cjh07oK+vj5UrV6JNmzZFZiPSdU+GROrhc3p6emjcuDF8fHzw7rvvQqlUIj8/H9euXdP0IAoLC0NkZCRCQ0MRFhaGH374AVu2bHluIKR+nnbt2sHe3h4xMTG4cuVKpdSoobKRSqVo1KgR5s+fD0tLS2zduhWLFi1CRkYGRo8eDUNDw2c+VgiBlJQUTQh09OhRpKSkaEIgb29v9O7dG7169YKPjw9kMhlDoHKihFITCEnBMIjKpvoGQhs3AmvWQJKWBr1Vq+DZsyckZkWn3tu6dSuCg4Px4MEDLFy4EEOGDIG1tXXV+FBSt+HJNF4mUwU9+fmqKbtLIsS/9xsZVXw7if6LW7eAGTNUveB69wb69gUCA1Vh59OF1OmVlZ+fj9u3b8Pe3r7MwwuIiP6r9evXY9u2bcjJycGGDRvQuXPn8isDUI09fWEuhIC+vj4CAwMREBAApVKpKVodERGB0NBQREdHIygoqFTrNzAwQK9evbBt2zbs3r2bgVAVJZVK4e7ujtmzZ8PGxgZr167F8uXLkZycjGnTpsHc3FyzrBACycnJOHbsGHbv3o1//vlHEwIZGhrC19cXr7/+Ol5//XWGQBXIXGYOKaRQQolsRTaUQglw91IpVc9A6McfVTNxJSUBS5dCMmQIJJaWRXrg7Ny5EytWrMCdO3cwe/ZsvPfee7Czs6vaH04WFqqhYpGRQHJyycvk56vu09f/d+gYUVXj7Q388ouqCPrevcDHHwN2dqp6QnXqaLt19B8UFhbi3r17iIyMRHh4OI4ePYorV64gOzsb1tbW2LhxI3r37g19fX1tN5WIqrHvvvsOq1evRnJyMlauXIk+ffrA3Ny8ap/fVVFPDzWTyWQQQsDGxgbt27dH27ZtNcWAS7MuiUSCN954A9988w127NiBefPmVWj76b+TSCRwdnbGxIkTYWtri2XLlmHDhg14/Pgx5s6dC4lEguPHj2PXrl04duwYUlNToVAoYGRkhICAAPTs2RM9e/aEt7c3Q6BKYCW1guT/E6AH8gdQQKHlFtGrpPoFQvv2qYov378PzJ0LvP++qmDtEx9A1w8dwtJ58xAXF4fp06fjww8/hKOjY9X/kHJwUE1F/+OPqrpC/fqparE86cED4PRpwNZW1eOCqKoRQvV+NDNTHaN+fqqZ81JTgZo1td06KoWMjAxER0cjIiICkZGRiIiIQExMDDIzMzW/ICsUCs1sN48ePcKECRNgbGyM7t27V/3PWiJ6JR06dAhLlizBnTt38Omnn2LgwIGwsir71Mv0bCUNNSvLY9u3bw9bW1tER0cjOjoaDRo0qIhmUjmQSCSwtrbG8OHDYWZmhkWLFuGbb77Bzp07IZfLkZmZCYVCAVNTU7Rp0wb9+vVD165dUbt2bchkMk0QRBWvoVFDXMy5CIVQICovCoWC5Reo9KpXIHT0KDBpEnDnDjBxIvDRR6peB0+eCPz9N+pNmoT1xsa4PGsWen7wAZydnV+NkwV9fcDXV1V4+uBB1ZT0b7zx7/alpwOHDqlCsY4dOcMYVU3R0cDq1aqp5fv3B9q1U/V+s7DQdsuoBElJSYiNjdUEQOHh4YiKikJ2drYm/BFCQCKRoGbNmvDx8YG/vz88PDzQoEEDJCUlYcGCBTh//jy2bduGWrVqwcfHR9ubRUTVzMWLFzFt2jTcuHEDH3/8MYYPHw4HB4dX4/xOhxgZGeH111/Hd999h927d2P27NnabhI9h0Qigbm5Od5++20YGxtj0aJFSE5OhlwuR9u2bfHGG2+gS5cuqFWrFvT09DTFoaly+Zv446fUn5An8nAk4wimOU6DpewZ9WaJniIR6p9wX3Vnz6p6A8XGAqNHA3PmAE5ORcOg8+dV90VGQjF8OJSzZkFWu/ar9cFVWKgKfWbPBh49Alq2VM1Olpen2r7z54FmzYCVK4GGDTl1N1U9hYWq2kF//AH89puqN9/w4cCECdpumc5LTEzElStXEB4ejkuXLiEsLAx3795FQUGBJvxR1wVwdXWFp6cnGjduDD8/P/j7+8Pe3r7ICaFUKoUQAhcvXsSUKVMQEhKCESNGYMqUKahXr562N5eIqolr167h3XffRVhYGAYPHowFCxagTp06r9b5nY5QKpX47bff0KdPH/j7+yMsLEzbTaJSEELg2LFjmD59OvLz8zFr1iz06dOHPYGqiIT8BDSLaYZH8keQQIJddXehu0V3GEnLVk82S5GFDrEdcDHnIhobN8Z3rt/B18S3glpNVUX16CEUEQF88AFw4wYwaJBqyNjTYdDly8Ann6j+v29fyKZOhax27eJDrqo6PT2ga1dVnZWffgKOHFGFQDIZUK8esGAB8OabquFlDIOoKtLTA9zcgA8/BN57T1XzKidH263SKXK5HLdu3UJERAQuXbqk6f2TmJiIwsJCzZAvpVIJExMTeHl5wdfXF35+fvDz80ODBg1gZmamOQl80XSxQUFBmD17NubMmYPt27fD3t4eY8aMgbOzsxa2noiqk3v37mHs2LG4dOkSOnTogNmzZzMMqsIkEgk6duwIKysrXL58GTExMfD09NR2s+gFJBIJUlNTcfv2bQQEBKBZs2Yw4uQ1VUYtg1roatEVv6b+inyRj3n358HbyBvuhu6cgp5e6NUPhGJiVL0Lrl0DevQAli4FatQoGobcuAGMGQOEhwOdOwMLFwLu7q9eGAT8O618gwaqGkkzZ6pqsgCq7dHTUw0tYxhEVVFKCnDqlOr/u3ZV1QwyMdF2q6q1goICxMfHa8If9V9GRgbkcrkm+FEqlbC1tUVgYCAaNmwIT09P+Pr6omHDhjAxMdGEP0/+EljaoRgymQydO3fGw4cPsXz5cqxduxbW1tZ4//33YW1tXZGbT0TVWFpaGsaOHYszZ87Ax8cHy5cvR/369TlMrIozNjZGz5498eOPP2Lv3r2YPn26tptEL5Cfn49Hjx7h8ePHsLe3hwsnrqlSpBIp5jrPxamsU4gviMfVvKsYGj8U62qtg7+JP/QlJU/oEZkbicTCRDQ1bQoLGUs36KpXOxBKSFD1MLh8GWjbFli1Cqhdu2gYcueOapjY+fNAixbAZ58Bnp6vdmAikaj+DA1VfwCQmwtcuAD8/jvg5aUaPkdU1RgaqqaV//tv1bDGpk2BKVOARo203bJq4cliz+rg5/r168jLy9MEPwqFAgqFAnZ2dvDx8dH0+gkMDETNmjVhYGCg6fHzX8KfZ9HT08Nbb72F1NRUrF27Fp999hksLCwwZMgQmJqalsfmE5EOycvLw6RJk3D06FHUrl0b69evh4+Pj6a3IlVN6p6k/fv3xw8//IBdu3YxEHoFJCcnIy4uDlZWVnBzcyvVzHJUudwN3PGZy2f4+PbHSJQn4kLOBXSN7YrXLF5DJ4tOcDdwh6nMFI/lj3E59zIOZRxCeG44aurXxI66OxgI6bBXNxB6+FAVBoWFqWrobNigGoaiDksAVY2d0aNVs275+qoCowYNVD1pqtvJghDAzZuq/dCjB/D224CBgbZbRVSUqamq2HmrVqqhYgkJqhnxqMwiIiJw5swZ5OfnIyYmBhEREbh27RoKCgo0oY9CoYBEIoGbm5um1o+fnx98fHxgZWUFfX19zfh/mUz2n6eETUlJQXR0NC5duoSIiAiEhYVh8uTJ6NOnT5GwR19fH6NGjUJSUhK2bt2Kzz//HDY2NujVqxenoyeiUlMoFJgzZw727t0Lc3NzrFu3Dv7+/tDT02MY9AqQSCTo0qULrKysEB4ejtjYWNSvX1/bzaLnSE5Oxq1bt2Bra4u6devyfVYFSSVS9LHsAzNXM0y8NxHX864jXZmOXWm7sC99H6QSKSSQQEBAIRSQCzkUUCBPmYe7hXdR17CutjeBtOTVDIQeP1aFQWfPqgKeLVuA+vWLBj2pqaphYseOqWrrrFsHNG6sqrVTHT/EjI1VvaPs7VW9oq5eVc1GRlRFyOVyPHr0CMnJyfD29oasVi3VkLFXceimFsXFxWHAgAG4ceMGCgtV04qqwx9jY2M0bNgQPj4+aNy4MXx8fIoM+XrZ8KewsBC3bt3C5cuXERkZicuXL+PKlStITk4uFkRFRUWhQ4cORQIhiUQCU1NTTJo0CWlpafjxxx+xbNkymJqaokuXLqz5QUSlEhwcjO+//x5CCKxbtw6tWrWCoaEhL1JfIcbGxujevTt+/vln7Nu3D1OmTNF2k+g5kpKScOvWLdjZ2cHd3V3bzaFn0Jfoo7NFZxw3OY4fHv+Avel7cSHnAnKVucAT00jpQQ9eRl7oat4Vg60Hw8fEB3rQQwEKIFP/TyLjZ6qOePUCocxMVc2gEydUPYK++UYVCj0Z9GRlAWPHqmbjqlFD1WvG319VX6e6HtgSCeDsDDRpAly8qBoix0CIqpC8vDycPXsWwcHBMDQ0xNChQ/H+++9DxhCgTGbPno0rV66gsLAQVlZWGDx4MAICAuDj4wN3d3cYGhpCJpNBT09PE/4AZR/ylZOTg/j4eMTExODy5cua3j+PHz9GYWEhFAqFpgaRlZUVfH19NTWHPD094efnB9sSen9JJBLY2tpixowZyMjIwJ49e7By5UqYmZmhVatW5bKPiKj6+vLLL7F+/XpkZGRg/fr16Ny5M0xMTHjh8gpR/yDRr18//PTTT9i1axcDoSpO3UOobdu2qFuXPUmqMj2JHuz17PGR/UcYaTcSGcoMPCx4iGR5MqQSKSxkFqilXwumMlPoS/RhKDGEFKqhtiZSExyqfwhyIYeeRA8mUtb51AWvXiBkYABYW6t6w2zdCnh7Fw168vKAiRNV01lbWwObNqmmYTcwqL5hkJqTk6omy8GDQEiIaua16r7N9MowNTVFr1690LJlS1y7dg05OTnsEfIfHDt2DIWFhRg3bhxmzpwJc3Pzlw5/MjMzERsbi8jISERERCAiIgIxMTHIycmBXC4vUnzawcEBPj4+8PHxga+vLwICAlCrVi0YGRkVacfzhm5IJBK4uLhg6tSpSE1NxdGjR7F582ZYWlrC29v7pfcREVVPe/fuxbJly/Dw4UMsXboUffv2hYWFBcOgV5BEIkHXrl1hbm6O0NBQxMXFMWioorKzs3H//n3k5OTA0dERDg4O2m4SvYBEIoGRxAhGMIKZ1AxOek5QQgkAkP7//9TLPf041hLSPa9mILR6tar+SK1aRWfUKiwEpk8Hdu1SDaHauFFVq8TQUDeCESsrwMcHMDJSzawWH6/qRUVUBSiVSkgkEjg7O8Pe3h5CCK2cxAsh8ODBA7z++usYMGAAZs2aVez+9PR0tGvXDn369MHChQsrvY3PcuPGDaSlpcHExAQTJkyAg4NDmUO1xMREXLlyRRP6XLt2DTExMcjNzdWEP3K5XBPaeHt7a3r/+Pj4wM7ODvr6+tDT09P8/ZcirlKpFN7e3pg+fTqys7Oxa9cu2NraYsKECXDj5xYRPeXUqVOYMWMG7ty5g0mTJmHYsGGwsbFhGPQKMzMzw2uvvYYdO3Zg//79mDBhgrabRCVITk5GfHw8rK2t4ebmxh/zXjESiUQzDIyoJK9eICSRAJaWgIVF0QLSCgUwbx7www+qf2/YoCpea2ysG2EQoNpOJydVke3bt1XDxnhhVTYXLgD/+x8QHq6auc3MDAgKUs3a5umpGppIZaZQKHDkyBEsWLAAnTt3xpAhQ9CgQQOttEUIgYKCAkRFReH+/fslLiOXyxEVFYWgoKBKbt3z7dq1C3K5HAMHDoSzs/MLL4QSExM19X4iIiIQHh6OBw8eoKCgoEjPHwMDA9SvXx9eXl7w8PDQzD5mbW2tCX/U//9fC0+XRCaToUWLFhg7diwyMjKwbds2WFlZYcyYMXB0dCyX5yCiV9/Vq1cxbtw43LhxA8OHD8e4ceP+UyBOVYdEIoFUKsUbb7yBHTt2YPfu3QyEqij1cDE7OzsWlCaqhl69QAgoGgQBqhm2li1T1RPKz1cNE+vaVTWjkS59aEkkgKOjatjYxYuqYWODBmm7Va+O1auBpUtVIWL79qohh0lJqoDo++9V06S/+aaqVxqViVQqRatWrbBq1SrExMQgOztb202CUqmEUql87v1CiGferw07d+6EQqHAwIEDoa+vX+Sk7MGDBzh+/DgiIyNx5coVREVFISMjo0j4o6475O3trRn25e3tDXd3dxgbG2uGfFVE+PMs+vr66NWrF9LT0xEcHIxNmzbB0tISI0aMgKWlZYU+NxFVfXfu3MFHH32EK1euoGfPnpg5cyZq1qzJMKiaeO2112BmZoaQkBAkJCTA1dVV202ipyQlJSE+Ph52dnbswUtUDVXNQCgnB9i3TzVdfHKy6gLc0xPo10/1/3pPNfviRWDbNlUx6bVrgV69AHNz3QqD1OztVT1aCgqAy5eBlBRO610ahw4BCxcCvr6qWescHFS9gRQK1dToQ4cCkyYBXl6Anx97CpWRenapoKAg+Pr6Qu/p9zC9UGRkJK5evQo7Ozu0a9eu2MXQxYsXMXHiRGRkZKCwsBByuRy2trbw9/dH48aNNb1/vLy8NOHPk0O/gLLXHiovhoaGGDx4MJKSkrBx40Zs2LAB1tbWeOutt2BoaKiVNhGR9qWmpuKjjz7ChQsX0LRpUyxduhSurq4Mg6oRc3NzdOnSBXv27MGBAwcwduxYbTeJniCE0AwZ8/T0ZCBEVA1Vvauya9eAkSOB6GjVcKfatYH0dODoUVVNoIkTgY8/VvX+UfP1BaZNUw3v6d5dNaRMF8MgQBVU1KwJNGwIPHyoCsu6ddN2q6q+4GBViLZxI+DurpoKXX0MmZur7u/XD/jyS2DNGsCEVffLIioqCnv37kXjxo3RqVMn6LOXVZnt3r0bBQUF6NWrV5Gp3NW8vLzQsWNHuLm5wdvbG97e3rC1tYWBgQH09fWLhD9Vsbu3iYkJPvjgA6Snp2Pr1q1YvXo1LCws0LdvX02xbCLSHdnZ2RgzZgyOHTuGunXrYs2aNfDw8GAYVI08OWxsz5492LlzJwOhKiYjIwN37tyBUqlEjRo1YGNjo+0mEVE5q1qBUEYGMGIEEBMDrFoFdO6sKpAshCrcmDQJWLFCFXi88YZqaA+gKjQ9dKjqIl4XZhN7HvWwsWbNgB07VMPGGAg9X2oqcOaMap89HQYBqpCtfXtVEfPDh1XFy6lMbGxs4OTkhFu3bmm7KRoKhQKnT58usWZBXl5e5TfoOYQQ2L17N5RKJQYOHFhiqOPm5oYNGzZoev7o6+v/p2LP2iKRSGBtbY2xY8ciLS0NP/74I9asWaP59fhV2Q4ienmFhYWYNm0a/vjjD1hbW2PDhg3w8fGpsoE2vZzu3bvDxMQEISEhuHv3LlxcXLTdJPp/6t5BNjY2LChNVE1VrUDop5+AiAhg9mygb99/C0cDqnouGzeqagOtXg106fJvIASwx8aTHBxU4caWLarC0rm5RfcVFRUXp6o95e6uCn9KOtk0MFD1Vjt+HMjOLnps0gvZ29tj0KBBUCqVMDIy0nZzAKhClsTERISGhha7Ty6Xa6FFz3b+/HnExsbC2dkZLVq0KPGETE9PD9bW1lpoXflRz0I3btw4pKWlYf/+/di8eTOsrKzQtGlTbTePiCqBEAKLFy/GL7/8AqlUio0bN6JZs2YwMDBgGFRNWVlZoVOnTjhw4AB+++03jB49WttNov+XmJiI+Ph42Nvbc7gYUTVVtQKh339X1WwZMqR4DSCZDKhbF+jYEfj1V9VFvI1N8XpCpOpVVbcuUKcOcP8+EBmpCoioZOoCx7a2zw957O0BpVJV40oIBkKllJqailu3bsHW1rZKFYvU09PDa6+9hmXLlhW77/Hjx/Dx8dFCq0q2a9cuFBYWom/fvjCp5uG3VCpF/fr1MWHCBGRmZuLQoUOwsbGBubm51mamI6LKs379enz55ZfIy8vDF198gQ4dOsDY2JhhUDX15LCxAwcOYNeuXQyEqhB1QWlXV1cGQkTVVNVKU65cUV10W1uXfLEtkwH166v+OzZWVTuIgVBxEomql1DTpqoeLSEhDISeR32BnZKiCnqeJSXl3+V5YlpqmZmZCA0NhbW1dZUKhADA2NgYTk5ORW4TQlSpotdyuRx79+6FQqHAm2++CZlMVu0vjPT09BAQEIBx48YhMzMTv/76KywsLDBx4kTUrl1b2817eUol8OCBaghqbCyQl6f6zG7eHGjTRjVslUgH7dy5E59//jkeP36M4OBg9OzZE2ZmZtX+M4+Anj17wtjYGKdOncL9+/dRo0YNbTdJ5ymVSiQmJuLOnTsIDAxEnTp1tN0kIqoAVeusMz1dFQg9XcPlSU5OqmAoPV11Uk0lUw8bS04Gzp7lvnoed3dVsBgf/+xASAjVbGM1a3J4Yhk5Oztj0KBBeO2117TdlFfSmTNncPv2bbi5uSEgIEBnLoz09fXRtm1bfPjhh3B3d8f333+Pbdu2ITk5WdtNezm5ucD336t6u86YAfz1F3DhgmqI71tvAYMGqXp2EumYEydOYNasWbh//z5mzJiBIUOGwNLSUmc+83SdjY0NOnTogLy8PBw8eFDbzSEAaWlpuHPnDvT09ODi4gJzc3NtN4mIKkDVCoSMjIDMzOf30lAHQUZG7KXxPObmQKNGgJUVcOuW6ldoKpmlpeqX+fPngbt3Sz7+Tp0C7t1TXcTpeuHyMpDL5SgsLISxsTHMzMy03ZxX0s6dO1FYWIg33ngDRkZGOnVxZGRkhD59+mDkyJGwsrLC119/jR9//BHZ6mGer5r8fFWvoJkzVT9+/PwzsGePqn7ewYPABx8AR44AH36o6jVEpCPCwsIwduxYxMXF4aOPPsLo0aNha2vLArY64slhY4BqmDRpX2JiIhISEmBra4s6derw/UhUTVWtd7arK/DokWoWp2eFQvfuqeoM1a7N4WLPI5WqZhsLCACSkoBz57TdoqpLKlXNYJeXB4wfr5rR7snj78YN1QWcVKq6UDM01F5bXzGPHj3CqVOncPv2bW035ZWUn5+P/fv3a2YX08Xp101MTPDWW2/h/fffh0KhwObNmzUhWZWnVKpmz0xPV/07I0M1g6aeHrBhA9CqlWr2who1VMOhR48G3n0XOHkS2LtXq00nqixxcXH4+OOPER0djd69e2PKlClwcnLixacO6tWrF4yMjHDixAk8evRI283Reer6QSwoTVS9Va1v23btVL+gnjoFlDTLT16eqiaOjQ3g6Qno61d+G18hCgcHPOzeHXtr1MC26GhtN6dqOHkSmDu3eOjYuTOwYIHq/i5dVBdmc+YA778P9OwJREcDK1cCPj6s71EGDg4OaNmyJWrVqqXtpmhIJBKYm5tjzJgxaN++fYnLGBkZ4eOPP0anTp0qt3FPOXbsGB48eIBGjRqhUaNGOtU7SE09Hf3w4cPxzjvvICkpCRs3bsSBAwegrEpDYRUKVfh++rRq+Nf48UC3bkCHDsCOHarvtPh4IDwcaN8e8Pb+9ztMIlF9rtjZASNGqL7r9u3T5tYQVYrExER89NFHCAsLQ/PmzbF06VLUqlWLYZCOsrW1Rdu2bZGTk4M//vhD283ReeoeQnZ2dqwfRFSNVa0uNsOGAdu3A4sWAV5eql9M1ScFcjkQHAxcuwZ8/LGq8DQ9l8TaGjfq18eUmBjU0dND94cPixXQ1SlhYcDIkapeaH5+QL9+/w79MjZWhUCBgcDu3apeQVevAqamQPfuwMCBQOPGquV08KK8LAoLC3H79m2cP38eBgYG6NChQ5UbLmZlZYU5c+bAyMioxPtNTEwwb948GGq5N9jOnTshl8vRr18/nRsu9iSJRAJHR0eMHDkSqamp+Pnnn/Hll1/CxsbmmaFehcrPV9UUi47+9+/6dVUgVFCgCnTy8lTLWVn92/M1JkYVRHt5ldzDVSpV1SmztgYiIip9s4gqU1ZWFsaNG4dTp06hYcOGWLt2LerVq8cwSEeph43169cPhw4dwq5du/Dee+9pu1k6Sy6XIzExEQ8ePEDbtm2r1A97RFS+qlYgVK8esGwZMG0a0L8/0KOHquBvTo6qZ9Dp06peRKNGAWZmvDB/AYlMBjNHRzh6eODOgwe4cOECevXqpe1maUdMjKq3z40bwOuvq36hf/r4sbJSHV++vqrCr0qlqoC5iYmqzpCeHo+5pxQWFiIxMRGxsbG4ceMGrl+/jitXriA2NhaZmZmQSqVo06YN5syZUyWmcc/KysKUKVNgYmKCvn37olWrVsWWkUgkkEgksLOz00IL/5WdnY3ff/8dADBgwACdv0iSSqVwc3PDmDFjkJ6ejt9//x2bN2+GhYUFAgICKuZJhVAFO3fuqOqwXb2q+ouOVhXsV4c+eXmqwMfcHPDwABo2VH131a+v+m9HR9XnSWqq6jPF2bnk55NIVJ8zjo4sLE3VWmFhIaZNm4aDBw/C0dER69evh7e3t07MokjP17t3b4wfPx7Hjh1DUlIS7O3ttd0knfT48WPcvn0bxsbGqFWrFkw4oQpRtVW1AiF9fWDAAMDFBfjf/1Qh0L59qtv/j72zDo/q3N72PRO3SSY6cffgkqAJrqVQKDXKoXbqLRSoUKRogZZSqFAvlXOqlBYJBA0EgksgToS4TNx19vfHfDOn/AohWHTf17WvQLL3zEpmZr/v+7xrPcveXt2RZepUtX+QOGG4KRKJBBsbG0JCQvjuu++Ijo7unoJQRgbMmaNeyIWFwYcfqssOr4e+vrpDm8g/qK2tJTs7m8TERBITE0lISODKlSsUFhZSV1dHfX09dXV11NXV0dTUhFQqRRAEwsPDMTMzY+PGje3aoUIQBKqrqzl8+DD5+fns2rULHx8f3N3deeihhxgyZEiHWogcOHAApVJJnz598Pb27lCxtRe6uroEBATw4osvUllZSXh4OObm5ixcuBBvb+87euzm5mbKy8tJTk6mKDUVn/R0fM6cUZd5VVerhZ/aWvXR3Ky+TwQFqbN9/P3VZcwODuqGB4aGaq8xzVepVP0Yenpqkam29saBaEQoff07+n1ERDoqKpWKZcuW8csvv2BoaMhHH31E//790dPTE+9zItjZ2TF06FAOHTpEREQEs2bNau+QuiWacjEbGxvRUFpEpIvTsQQhUGf+DBum3lWtqlLvukql6om1XA4ymejhcgtYWVkREhLCJ598wtmzZ6mursbExKS9w2o7CgrUmUFnz8KAAfDpp2rzcvifqJiXp16oWVmJQuP/p7q6moyMDOLj40lOTiYtLY0rV66Ql5dHbW0tdXV11NbW0tDQgImJCR4eHgQEBODv74+/vz8uLi5IJBL279/PqlWr2Lt3Lx4eHsyfP7/dyrCampo4f/482dnZ1NTUUFlZSWZmJgYGBhw+fJj+/fuzdevWdontevz+++80Nzczbdo0DAwMxIXS/0dfX5+BAwfy73//m5KSErZt24a5uTnz5s3DwcGhVY/R1NREUVERSUlJJCYmkpaWRnJyMqmpqVRVVWGrq8tLJib4JCSoBRqFQi3++PurxyZfX3WXMCMj9aERgVoScXR11Rsbzc3qe871EAT1z/Pz1UKTiEgX5Omnn2bHjh3U19fz+eefExoaKt7jRID/lY1NnTqVQ4cO8ccff4iCUDvxfwUhERGRrkvHE4RAvaPayom9SMsYGBjg5uaGp6cneXl5XLx48bplMl2SkhJ48km1SXlAgNro1dtbLfpoJp7FxWpfIZUKvvxS7d/RjSalKpWK8vJyMjMzSU5OJiEhgdjYWNLT0ykvL6e2tlYrAKlUKiwtLfH19cXHxwdPT098fHxwdXXFxMQEIyMjjI2NMTIyQv//L4ydnJzQ19dn3bp1fPfdd8jlcp599tl22WlqaGggPDyc2v+fnSEIAvX19dTX13PlyhUGDhzY5jHdiPLycvbt26f1UxB35q7F0NCQ0aNHU1FRwfvvv89///tf5HI5L774Iubm5tecW1dXR05OjjazLSkpiStXrlBQUKB9f2uy2/T09PD09CQgMBDzHj3U4rG7u7pkVCP+aI5b7XKpp6cWlAwN4cQJtfDzf7vGNTXBmTPqbKTBg+/wryQi0vGYP38+f/zxB+Xl5axatYpJkyZhYmIiikEi1zB16lQWLFjAwYMHKSkpwfJGWd0i94yCggIyMjLo37+/KAiJiHRxOqYgJHLX0JSNBQcHs2fPHqKjo7uHIFRZqTaJPngQ3Nzgq6/UotDfxaCqKrVB+aFDav+qLt7Su7m5maKiIlJSUkhISCAhIYGkpCRyc3Oprq6mtraWmpoaampqEAQBW1tbfH198ff3x8/PD19fXxQKhVb0MTQ0xMjIqMWdXWtra2bOnEl5eTkffPAB33zzDXZ2djzwwANt/Nurs0Li4+MR/t5djv8ZFr/44ottHtONiIiIoLi4mODgYLHV6w0wNTVlypQplJaW8uGHH/Ltt99iYGDAmDFjSEtLIyEhgeTkZNLT068Rf2pqaqivr8fIyAh3d3dCQkK073EPDw9kMhnGRkbIjI3Vwo+Bwa3fGzRlX2lp6kYIV6+qs4wmTID771eXQn//vTp7UYNKpc5oXLdOnSkr7oqLdDE2bNjA999/T3l5OQ8++CBz5sxBJpOJYpDIP7C3tyckJISjR4+yf/9+HnroofYOqVtRX19Pfn4+RUVF2Nra4ujo2N4hiYiI3ENEQagbYGVlxeDBg/nvf/9LdHQ0TU1N6N7q7nZnorYWXnkFdu9W+3x8/TX06aMuNdRMPOvq1Ofs2qUu/fjoI3XL5y5CfX092dnZpKamasthEhISyMvLo6qq6hrxR09PDxcXFwYMGICfnx8+Pj64ublhbW2tzfrRiEC6urq3NHmXSqUoFApmzZpFeXk5X3zxBZs3b8bKyorQ0NB7+Be4FkEQaGhoIDs7+7ox9unTp0OYXmv45ZdfUKlUTJ8+HX19fXHBdAPMzc15+OGHKS8vZ8uWLbz33nt8+eWX2vd3bW0tzc3NWFhY4OPjg7+/vza7zcnJCVNTU+3729jYGAMDg1vPxtJ4Al29qjabTkxUf01JgbIy9c+amqB/f3joIXj5Zbh8GZYvV3c+HDpULQClpKg7HMbFwaJF6hI1EZEuxH/+8x9KSkoICQlh5cqV2Nvbi9mPIv9AIpGgo6PD1KlTOXr0KNu2bRMFoTamuLiYrKwsTE1NcXZ2vmFHVhERka5BF1YFRDSYmJjg5+eHtbU1GRkZJCUlERgY2N5h3RsaG9Vd6n7/Xb3I+vJLCA5W7/BrFtV/P8fEBD7/HAYN6rRdxGpqasjJySE5OVlbFnPlyhWKi4upqamhrq5OuziWyWS4ubnh7u6Ot7c3AQEBuLm5YW5ujomJiXZhbGhoeNdEQx0dHVxdXXniiScoKSnht99+Y8OGDcjlcnr27HlXnuNmNDU1ERcXR951vFtsbGx48cUXtWVu7U1RURGRkZHo6ekxZcoUccHUApoMyNmzZ1NeXs7nn39OWVkZQUFBhIaGasUfGxuba4Sfm2W23ZCmJnUpalKS+khOVncfy85Wd8Osrv7f16YmdYOEwEC1H9DAger7kL8/fPKJOkPo2DG1KC2Vqu8/Xl6weTOMHy+aSot0OZRKJSqVCl9fX5ydncV7m0iLTJ06lddff50DBw5QWlqKhYWFuDnSRmjKxTT+QeLfXUSkayMKQt0AqVSKra0t/fv35/z585w4caJrCkLNzbB0Kfzwg3px9fnn6q5ienr/E3qam2HZsv+ds2WL+hx9/U4hBimVSg4dOsSFCxeoq6sjNzeX7OxsKisrqampobq6murqapqamrCxscHb2xsfHx98fX3x8vJCoVBgamqKoaEhxsbGmJqaYmBggM49LpfT09PDz8+PZ555hoKCAiIjI1m3bh2rV69uk9r0hoYG9uzZQ1VV1TXf19HRITAwkKFDh97zGFpLeHg4ZWVlhIaG4uzs3N7hdHikUinOzs48+eSTFBcX89///peysjKcnZ155JFHMDIyQk9P75Yft6mhgca8PIw0wk9SkjqLJz//f8JPTY06A0gqVXsN9eunbjvv46NuOy+Xg7GxWng2MVHfY/T0oHdvtU9ecfH/OpiZmqq9iuzt1eeKiHQx+vbtS15eHqdPn6apqanV12nKfMVFaffC2dmZgQMHEh0dzcGDB5k+fXp7h9RtKCgoIDMzE1tbW1w1jVhERES6LKIg1E3QlI0dOHCAY8eO8fTTT7d3SHcPjSfMunXqjKCmJvjsMxg79lqhRxBg/Xr1OY2NajFo3Di1yWsnmGjm5uayfPlyDh48SGlpKSqVirq6OiQSCfb29tpsCHd3d3x9fbG3t8fMzAxjY2Nt9k97lh/p6+vTr18/Xn31VUpKSti7dy+WlpYsX778nhtGNjU1ERMT8w//IJlMxpw5czA2Nr6nz38raMrFZsyYIbZhbiU6Ojp4e3szY8YMEhMTuXDhArGxsTQ0NCCTyW56fX19Pbm5uSQlJWk7j+VnZDCwupq38vLUok11tVr80dVVm00PGKDuNubnp87sMTf/n/BjYqL2H7qe0CqRqB/DwUEt/oDaP6iLe5iJiDz//PPs37+flJQUSktLMTU1bTFLqKamhi1btnDx4kXee+897OzsxPthN+HvZWPR0dFs375dFITaEE2HsdDQUNFQWkSkGyAKQt0Ec3Nz+vbti66uLgkJCeTk5HQtk7hPPoFNm9RG0Zs3w9Sp6gXZ3yePmnMqK9Vfp05V7953kgnmH3/8QUREBNnZ2bi4uDBq1ChCQkLw8PBALpdfU/JlYmLSIcUEQ0NDhg0bxoIFC1i6dCl//PEH5ubmLFq06J6JMoIg0NjYSFZW1jXfl0ql+Pr6Mnny5HvyvLdDTk4Ox48fx9jYmMmTJ3e4168jo6+vz9ChQxk7diynTp2isLAQpVKJ9d+8wVQqFRUVFaSnp5OcnExycjIpKSlkZGRQXl5OdXU1VVVV6iy7xkZ0ra1p9vFBx81N3aHQy0st5GhEH1NT9VdjY3WW0K2ieX1FMUikGzBs2DBcXV1JSkri7NmzODg4tCgIGRgYsH//fqKjo3n00UcZOXIkBgYGbRixSHszbdo0Fi1axL59+6isrMTMzEwcF+8xtbW15OXlUVlZiZ2dHQqFor1DErlLqFQqiouLkcvlXdtLVuSWEd8N3QQdHR0cHBzo2bMn2dnZnD59mmnTprV3WHeMIAjw3Xewdi2S0lJ47z21caumPEPDN9+oM4iKi2HtWvU5pqadRgwCOHv2LEqlkqFDh/Lqq6/Sr18/5HI5xsbG97zk624hkUgwMTFhzJgxVFRU8M477/DTTz8hl8t59dVX78kA1dzczJUrV/7hH2RsbMwDDzyAhYXFXX/O22X37t1UVlYyduxYcRJ2G5SUlFBQUIBUKsXa2hqZTMbFixe1mT/JycnXFX+am5uxtrbG3d1dm2Hn4+ODv4sLUpns2rIvA4NOdd8QEekomJiY8OCDD7Ju3TrCw8MZP358i+WcOjo6BAUFcezYMQ4cOEBISIgoCHUzXF1d6du3L2fOnOHw4cNMmTKlvUPq8iiVSjIzMzE3N8fZ2fm2Sq5FOh4JCQmsWrWK1NRU9PX1MTc3x93dnfHjx9OvXz+sra07zVpC5O4jCkLdBIlEgqWlJYMHD+bzzz/n2LFjnV4QEgSB7du3k/ef//BCWRnCkiVIZs8GmezaBdsff8CKFZCbq/YYmjNHXd7RiRZ1RUVFZGZmolKpePTRRxk9ejRmZmbtHdZtIZFIsLCw4L777qO4uJi1a9eydetW7OzsmHUPWm3X19cTERFBeXm59ntSqRQvL6978nx3gqZcbObMmbfc0U0EMjMzSUxMRFdXl6NHjzJz5kyqqqquOTSeQ0FBQXh7e+Pl5YWbmxtWVlba7DpTU1Otv5b4GoiI3D1mzJjBxo0b2bt3L5WVlRgbG7f4GZs4cSLff/89e/bsYe7cuaKxcDdCIpGgq6vL/fffz+nTp/njjz9EQagN+Lt/kGgo3TXIy8tj4cKFHD58mJqaGkDt7WlsbExERASWlpa4urri4+ODn58fvr6+eHt737SsV6TrIApC3Qi5XM6gQYPYvHkz586do7y8HHNz8/YO67YQBIGIiAjeeustavPy6D9/PgOffRYsLK4VevbtU7dwzsiABQvguefA0rJTiUEAJ0+eJC8vj6CgIHr37o1JJzedlUgkWFtb89BDD1FWVsbHH3/Mp59+io2NDePGjburz9XY2MjZs2dRqVTa7+nr63e4LJy0tDTOnDmDTCZj3Lhx4iTsFmlubiYlJYX4+HgaGxvJyMhAqVTi5uZGnz598PLywsPDQyv+aEQfjc+WmD4tInLv8fX1JSAggPPnz3P69GkmTJjQYgbCgAEDcHZ2JiEhgfj4eGxtbcUsoW7GtGnTWLJkCXv37qWqqgpTU1NxfLyHiIbSXYvKykpee+01Dh8+TGNjIx988AEA0dHR5OTkUFVVRVxcHDExMdo5kZmZGebm5jg4OODt7X1NgxqZTCaKRF0QcQbcjdDX18fV1RUvLy/y8vK4ePEioaGh7R3WLSMIAtHR0SxcuJDk5GSee+45vJ5/HmxsrhV6jh1Ti0BJSWohaO5csLXtdGIQwOHDhykoKGDy5Mk4Ojp2iZuxjo4OTk5OzJ49m9LSUn744QdtO/qBAwfelecQBIHm5mYyMzO135NKpXh4ePD00093qEnlzp07qaqqYurUqdja2rZ3OJ2OvLw8Ll26RENDA1OmTGHmzJk4ODggk8kwMzPTCkBGRkai+CMi0k4YGBgwfvx4YmJi2L17N6NGjWpREDI1NSU0NJTk5GQOHjzIwIEDRUGom+Hp6Unv3r25cOECR48eZeLEie0dUpdFEAQKCgrIzs4mKChIFIQ6OSqVik8++YR9+/ZRX1/PypUreeyxx9DT02Py5MnU1tbS2NhIZWUl2dnZXLlyhStXrhAfH8/Zs2cRBAETExPtPMrKygp3d3etQOTt7Y27u/tNMz1FOj7irLgbIZFIkMvl9OvXj99++40NGzZQXl5OWFhYqzrxdAQEQeDixYvMmzePuLg4Hn/8cd544w2s/m+mx4ULMG8exMbC7NnwxhtqM9hOeMPKy8vjwoULNDU1MWjQoGtMcjs7urq6eHp68tRTT5Gfn8+BAwdYv349a9aswcfH544fX6VSkZmZSX5+/jXPOWjQIDw8PO748e8mv//+O4Ig8OCDDyKVSsXB9RZJTk7m4sWLuLu7M3PmTKZNm4ahoaH4dxQR6WDcf//92rKxlStXtriYkEgkjB8/nv/85z/s2bOHl156SSwb60ZIJBL09PSYMmUK58+fZ/v27aIgdA+pqqoiNzeXuro6FApFl5pvdke+//57vv76a5qamnjzzTd5/PHHsba2RiqVIpfLtecJgkBtbS2VlZVUVFRQXl5OUVER6enppKSkaIWikydPcvr0aczMzJDJZMhkMqysrHB2dsbT05OAgACCgoJwcHAQhftOhigIdTPMzc3p168fP/zwA4cOHSIlJQUnJyfc3d0JDAwkMDAQPz+/DtmBTBAEEhMTeeWVVzh37hz33Xcfy5Yt+0dLzIT4eDxXrULv/HkkU6fCkiXqNtGddAIZHR1NVlYWPXv2xMPDA319/fYO6a6ir69Pjx49eOWVVygpKeHAgQNYWFiwevVq7Ozs7uix6+vrOXDgAKWlpYB6cuns7Mxzzz3XoczzEhISuHDhApaWlowcOVJc7NwijY2NJCYmEhsby5gxYxg8eDBGRkbtHZaIiMh1CAgIwNfXl5iYGM6cOcPYsWNbzBIKDg7G0dGRxMREkpKSUCgUXW4cFGmZ6dOns2LFCvbs2UN1dTUmJibiOHkPKCwsJDMzE7lcjouLi2go3Yn5448/+OCDD8jJyeHNN99kzpw52NvbX7fCQCKRaLsUa+bdzc3NVFdXU1FRQUVFBZWVlVo/08TEROLi4khKSuLSpUvo6elhZmaGhYUFFhYWWv8pTbmZn5+feN/u4IiCUDfD1NSUsWPH8uqrr/Lnn3+SmZlJamoqJ06cQC6XI5fLsbKywsPDg8DAQAICAvD398fJyaldy5QEQeDq1au8+uqrnDhxgrCwMFavXo2np+c156WlpfHKK6/glpDAB/ffj+mqVUg8PTutGAQQGRmJUqlk5syZKBSKLjkJMjQ0JDg4mPnz57NkyRJ27tyJpaUlS5YsuSPz7IaGBk6ePElTUxOgzg7q06cPPXv2vFuh3xV27NhBTU0NkydPFnfkboOcnBwuX76Mrq6udndKRESkY2JoaMjYsWO5fPky4eHhhIWFtbjwlMlkDBs2jOTkZA4dOkT//v3FhUU3w9vbmx49enD58mWio6MZM2ZMe4fUJSkoKCArKws7OztcXFzaOxyR2yQyMpK1a9eSlJTE888/z+zZs3F0dLyljVAdHR1tFpCGpqYmampqKCsro7S0lPLycgoLC7WZRCkpKSQlJXHx4kUMDAyQyWSYm5tjYWGBtbU1rq6u+Pn5ERAQgI+PD1ZWVmIJfwdBfBW6Gbq6uri7uzN37lymTp1KTU0N1dXV2ppRTd3o6dOntQKRpaUldnZ22nTAgIAA/Pz8MDY2bpOYBUEgNzeXuXPnEhkZyYABA1i/fj2BgYHXnKc958gRwgYPRnf5cvD3b/Gx4+Pj8ff377AiS2ZmJjExMUgkEoKDg7G0tGzvkO4ZxsbGjBgxgoULF7J06VJ+/vlnLCwsWLBgwW1P/pubm0lPTwfUOyB2dnY888wzHWoxoVKptOViDz/8sFgudhskJSVx4cIF3N3d6du3b4d6fUVERP7J/fffz8cff8yePXtYunRpq8rGfv75Z/bu3cvzzz8vlo11IyQSCfr6+tx3333ExMSwfft2URC6R+Tn55OZmYmbm5voH9RJuXjxIsuXL+fixYs89NBDPPfcczg5Od2VrHhdXV2tSKQRDBsaGqiqqqK8vJzy8nLKyspQKpWkpqYSHx9PcnIy8fHx2q6SFhYW2vWlQqHA3d0dLy8vvLy88Pb2xtraukNl8HcXREGoG6Knp4ezszPOzs5a093y8nJKSkooKSmhsLCQjIwMbVePpKQkTpw4gUwmw9LSEktLS206oCaDKCAg4J5kNgiCQHFxMQsWLCAiIoKAgAA2bNhAnz59tJNBzTnz589n3759BAYG8s6aNRhoBKMbTBr//PNPfv31V7788ssOa4h27NgxcnJy6NOnD+7u7l06fVcikWBmZsaECRMoKSlh1apVfP/999jZ2fHEE0/ccoZac3MzeXl55OXlAerdjqCgIIYMGXIvwr9tLl++TFxcHAqFgmHDhrV3OJ2OhoYGbSnJ5MmT6dWrV3uHJCIichOCgoLw8vIiNjaWCxcu3NRcOiQkBEdHR5KTk0lOThbLD7ohDzzwAGvWrGH37t3U1tZiZGTUIedtnRWNoXROTg4DBw4UM4Q6IWlpaSxbtowTJ04wYcIEXnvtNTw9Pe9pFo6+vr52baihoaGBiooKSkpKtNlEeXl5pKenk5qaSmpqKpcuXeLEiROYmppibm6Oubk5lpaWODo6ak2rPT09cXNzEzcA2gBREOrmSCQSdHV1sbKywsrKClBnLFRVVWkFIk3NaHJyMomJiSQkJHDu3DmMjIywsrLC0tISa2trnJyc8Pf3x9/fHz8/P9zd3e/oAywIAuXl5bzxxhvs2LEDV1dXNmzYQHBwsFYcEASBiooK3njjDf766y/tOSEhIUgkkhs+/+HDh/noo494+umnO+ykUqVScfjwYZRKJXPmzMHOzq7L3xAlEgmWlpbMmDGDkpISNm7cyJYtW7CxsWHKlCm39FgNDQ1ERUVRXFwMgFwuZ9asWZiYmNyL0G+bP//8k9raWmbOnHmNyZ9I68jMzOTSpUsYGBjQo0cPFP/XYF5ERKTDYWxszOjRo4mLiyM8PJyhQ4e2KAhZWFgwePBgkpOTOXLkCP369euwY7fI3UcikWjnlwkJCZw4cYKRI0e2d1hdivLycnJyclCpVDg6OmJhYdHeIYncAoWFhSxbtkzbjfHNN98kKCioXTaS9fX1sba21iYKCIJAXV0dFRUVlJWVUV5ejlKp1NqWaI7Tp0/T0NCAubk5crkcCwsLrKyscHBwwN3dHU9PT3x8fHB3d+80zZA6C6IgJPIPpFKpNiXQzc0NlUpFXV3dNQJRXl4eKSkpJCYmkpiYyLFjx1CpVFphycrKSqvy+vn54efnh6+vb6sncIIgUFNTw9KlS/ntt9+wsbFhw4YNhIaGalMJNa74S5Ys4bfffsPW1lZ7TktlNydPnuTdd9/lkUce4b777kMikbBx40YmTZqEt7d3h2npnpaWRmxsLAYGBgwYMKDbiAVSqRSFQsHjjz9OaWkpX3/9NRs3bsTS0pKhQ4e2+nHq6+s5evQojY2N6Ojo4Ovry/jx4+9h5LdOU1MTf/zxBwAPPfRQiyKmyPVJTEzk4sWLeHp60qdPH7EeXUSkk3D//fezZcsW9uzZw+LFi1s0CpZIJIwdO5bff/+dvXv38vTTT3ebMVFEjaGhIZMnTyYuLo4///xTFITuMoWFhWRlZWm7RoljaeehoqKClStXsnPnTjw9PVmyZAn9+vXrMFUFEokEIyMjjIyMtKbVKpWK6upqysrKtEdxcTHZ2dlcvXqV1NRUrly5QmxsLCqVSutFJJfLtR5Xnp6eeHh44OXlhbOzs9hM5A4QP+0iN0UqlWrd552cnBAEgcbGRsrKyigqKqKoqEhrKpaUlERiYiKXLl0iKipKmz1kbW2NnZ0dHh4e+Pr64ufnR2Bg4HUNgwVBoL6+nlWrVvHDDz9gYmLC+vXrGTt2rHaAEgSBhoaG656jo6Nzw0nlpUuXWLFiBZMnT+bBBx/ExMSELVu2cOzYMR555JEOtRiPiooiNzeXfv364erq2q0GZx0dHVxdXXnqqacoKChg586dvPfee8jl8n94R92IpqYmUlNTAbUp6fTp07VZcB2FCxcukJSUhIuLCyEhIe0dTqejrq6OhIQEUlNTmT59Oj169GjvkERERFpJr1698PDwIDExkZiYmJuaSw8ZMgSFQkFiYiKpqanY29uLWULdjOnTp7N+/Xp2797N+vXrMTQ07FDzts5Mfn6+1lDa2dm5vcMRaSX19fVs2LCBX375BUtLS5YuXXrTjMuOgFQqxczMDDMzM+37rbm5+RrT6pKSEq2vVVpaGmlpaaSmpnLx4kV0dXW1WUSataajo6NWIPL19cXJyanD/x06Ct1nhSly19AY/Nna2mJra4sgCKhUKioqKiguLkapVFJUVERWVpZWIEpOTubYsWOYm5tjZWWFjY0Ntra2ODs7awUiX19fHB0daWpq4v333+err75CR0eHtWvXct9992knfhrfo/fee48vv/wSqVTKu+++y3333Yeenl6Lk4OffvqJkSNHMmvWLGQyGT/88AN79+7llVdewdbWltLSUmpra3FwcGjXSUZzczOHDx+muLiYkSNHYmtr2+0mPXp6evj5+fHyyy9TVFTE4cOHWb9+PWvWrMHR0bHFa1UqFaWlpeTn56Ojo4OXlxcPPvhgG0Xeev744w/q6+uZMGEC5ubm3e41vlOuXr3KpUuXMDExISgoCBsbm/YOSUREpJWYmJgwatQokpKS2Lt3L4MGDWpx8m5paUlISAgpKSkcPXqUPn36iIJQNyMoKAgfHx9SUlI4c+aM6Lt3F8nLyyMrKwt/f3/RP6iT0NTUxMcff8w333yDRCJh8eLFjBs3rtVCaU5ODjY2Nh3mPqqjo/MPkaixsZGqqipKS0u1IpFSqSQ7O5uUlBStnUlxcTEGBgbabtnW1tYoFApcXFzw8PDA3d0dDw8PHBwcutUGe2sR/yIid4xEIkFHR0frGu/l5aUt+SoqKkKpVKJUKsnNzdW2JExKSuLs2bPaOlMbGxtsbGxwcXFBIpFofVXWrFnD9OnTtWmAGvHp448/5tNPP6W+vp61a9cyY8aMVt0AJ0+ejK+vL3K5nN9++41ff/2VZ599ltDQUGpqali+fDmDBw9m+vTp7XrD0HR9MzMzo2/fvpibm7dbLO2JgYEB/fr107aj37VrF1ZWVixdurTF+vb6+npOnjyJUqnEyMiIsWPHdrhW5PX19fz5558APPjgg6IYdBskJCQQExODt7c3ffr0ETtTiIh0MqZMmcJXX31FeHg4r7/++k3LxsaNG8eff/5JREQETzzxhFg21s0wMjJi4sSJbNy4kb/++ksUhO4Szc3N5Ofnk5eXR1hYmJgh1AkQBIHvv/+eLVu2UFFRwbJly5g2bVqL99C/s3//fr777jvuu+8+Jk6ceN2KjY6Anp6edn2poampicrKSoqLi7XrzIKCAjIzM0lPTyc9PZ24uDiOHj2KqakplpaWWqFIU27m4+ODt7c3rq6u2NjYdPs5uCgIidwTJBIJJiYmmJiY4Orqqi0zKy4uprCwEKVSqfUhSk5OJikpiaNHj9LU1IRUKqWmpgY9PT0iIiLIzMzk7bff1goA33zzDRs3bqSsrIwVK1bw2GOPYWpq2qq4NB2m8vLy+PHHH5k9ezZjx46lqamJNWvWUFFRQVBQULv7CEVGRpKfn6/t9NCd1WxDQ0OGDx/OggULrmlHv3DhwhvWC9fX13Po0CEaGxvx9vZm1qxZHeZmX1VVxdWrVzl06BApKSm4u7vTv3//9g6r01FdXU1cXBwZGRkMGTKEoKCg9g5JRETkFunbty9ubm4kJSURGxvL8OHDW8wSGjp0KAqFgri4ONLT08WysW7IjBkz2LhxIzt27GD16tUYGBh0mPG9s1JWVkZOTg46Ojo4Ojp2WHFA5H9s376dDz74gJycHBYsWMBjjz3W6kzz8+fPs2HDBqKioujRowdNTU1tEPHdQ1Mu9vckhIaGhmtKzYqLi8nNzSUtLY2UlBTS09M5deoUlZWVyGQybSKClZUVCoUCV1dXnJ2dsbGx0XoTdSe67ypTpE3RlJnZ29tjb2+vzfTRCEQakSgrK0srEJ0+fZrw8HCOHTtGU1MTGzdu5Oeff2b9+vXk5+ezaNEinnjiidvKnjExMeHf//43o0aNQiKRsGHDBtLT01mwYAH+/v6kp6fz119/8dJLL920DO1u09jYSGRkJCUlJYwYMaLbl8FoxMXx48dTWlrKihUr+O6777C1teWZZ565blZIY2MjSUlJ6OnpMWTIELy9vds8bkEQKCsr4+rVq+Tk5JCTk8PVq1dJT0+nuLiY1NRUZDIZBgYG4oLmNkhPT+fy5cuYm5vTo0ePa1qeioiIdA7MzMwICwsjKSmJffv2ERwc3KIgZG1tzcCBA0lJSSEqKopevXqJ989uRq9evfDy8iI9PZ3z588zaNCg9g6p06PxD7KxscHZ2VnMtu3gREVFsW7dOq5cucJzzz3H008/jY2NTas2sxMTE1m7di1RUVGMGjWKESNGdLjuu7eKRCLBwMAAOzs7rWm1xo9WU2KmySbKycnR+hGlp6dz4cIFGhsbsbS0xMLCAlNTU63vrbOzM9OnT6dnz57t/Bvee0RBSKRd0JSZaXyIQP3hrayspLCwkIKCAq5evapNafz+++8JDQ1l9erVpKenM3/+fJ577rnbNgmWyWRMmjQJgG3bthETE8Orr75K3759yc3NZfny5e2WcZCQkEBiYiJWVlb06dNH3KlB/X4xNzdn+vTpFBcX88EHH7BlyxYsLS2ZOXPmNeeqVCrt+8jFxYV//etf93RyIwgCgiBQWFhIRkbGNUdhYSHFxcXXdFCoq6vD2dkZZ2dnGhoayM7O5uOPP+a1115rsTueyLXEx8dz6dIlfH196d27d7tn9YmIiNwe999/P1u3biU8PJz58+e3qtvYzp07iYiI4PHHHxfLxroZJiYmTJgwgY8++ogdO3aIgtBdIC8vj+zsbK3nikjHRdMc5/z58zz66KO8+OKLODk5tWoOlJ2dzfr169m7dy8hISG89tpr9OzZs0uK6hKJBENDQxwcHLSWESqVSmtn8vdys5ycHNLT00lOTubs2bNUVlYilUqRy+VcvXqVdevWoVAo2vk3ureIgpBIh0EikWjb3Xt5eTF48GD69u3Lb7/9RllZGStXriQhIYFnn32Wl156SasC38nzAbi4uPDCCy8waNAgioqKWLFiBZaWlsyYMQNdXV3i4+Px8fFBV1e3TRbrhw8fprCwkKFDh+Lk5NSty8X+jlQqxdramtmzZ1NaWsrnn3/Opk2bsLOzIzQ0VHteQ0MD58+fp6SkhAkTJtC3b9+7FoMmsy0/P5+rV6+SkZFBbm4uGRkZ5OXlUVxcfM0hlUpxdHTE1dWVPn364OrqiouLC1ZWVlhYWHD+/HkWL17MRx99hLW1NXPmzLlrsXZlKisriY+PJzc3lzFjxuDn59feIYmIiNwm/fv3x9nZmZSUFOLj4xkyZMhNy8ZsbW25fPkyGRkZODo6ip1kuhnTp0/XCkLLly9HX19f3Ey5AzSG0prPokjH5OrVq6xYsYIjR44wYcIEXnvtNTw9PVu16VlcXMyGDRv4888/8fT05LXXXiM4OBhDQ8M2iLxjIJVKMTU1xdTUFDc3N0Dtn1VVVUVRURH5+flkZGRQWlrKyZMn+fXXXzlw4ADh4eE8+eST7Rv8PUZcaYp0WCQSCb6+voSGhrJnzx4uXLjAnDlzWLBgwV0dsPr27YtUKkUQBNauXYuuri7PP/88bm5unDp1is8//5y1a9fesQDVGurr64mMjKS0tJSRI0dibW19z5+zM6Gpb3/mmWdQKpX8/vvvrF+/HplMRp8+fQB1K/LDhw8jk8mYNWvWbe98CIJAU1PTPzJ/rl69qu2kV1RUpM38MTQ0xNXVFUdHRwYMGICrqytubm7I5XKsra21h4WFBYIgEBERwZw5cygpKWHNmjWsW7cOR0dHxowZI05sb0JqaiqXLl3CysqKHj16tGgwLiIi0rGRyWSEhYWRmprK/v376d+/f4sCj52dHf379yc1NZXjx4/Ts2dPMUuom9GvXz/c3d25cuUKMTExDBgwoL1D6rQ0NjaSl5eHUqlEoVB0uAYcImry8vJYvnw54eHhDBgwgDfffJPAwMBWiUFVVVV89NFH/PTTT9ja2rJw4UJCQ0O7lRh0I3R0dDA3N8fc3BxPT0+GDBlCQ0MDPXv2JDk5mUuXLrFnzx5REBIRaU8kEgnTp09nz549WFpa8sYbb9x1oy9NBo5KpcLPz0/rORMbG8sHH3zAwIEDMTIyQhAEbUz3iri4OK5cuYJCoaBnz56tNsvuTujq6uLp6clLL72EUqkkMjKS999/nzVr1uDq6qr1D+rRowfDhw9v1WNquuLl5OSQmZlJZmamNuuntLRUm1aqEYFMTU21rSxdXFxwc3NDoVBgY2OjNbqztrZGLpdf9/0iCAI2NjaYmZnx7LPPUlhYyGeffcby5cuxsrKib9++oijUArGxsVy+fBk/Pz969eollouJiHRyJk+ezI8//kh4eDivvPLKTcvGxowZQ3h4OPv27eOxxx4TBaFuhqmpKePGjeOzzz5jx44doiB0B5SUlJCTk4OBgQGOjo7ivLMDUl5ezrp16/j999/x9PRk8eLFDBgwoFUVBA0NDXz11Vd8++236Onp8dprrzFp0qRWdyPrjujr6xMUFMRDDz3EmTNnSE1NpaysrEtvPoqCkEiHZ/To0RgbG9PQ0HBPjWOlUimPPvooJiYmZGRksG7dOnr16sWcOXMwMzPjp59+wsvLi379+t2zMq4DBw6gVCq1bdJFY7/ro6+vT69evZg/fz5lZWXadvRLliyhurqa/Px83n777esa5alUKsrKysjMzCQ/P5/c3FytCPT3mmKlUklVVRU2Nja4uLgQEBCAi4sLTk5OWFpaYmNjg7W1NTY2Ntja2mJsbNzq+KVSqTajyd7envnz55Obm8uOHTtYtmwZH374IZ6enuJgfR1KS0uJjY2lsLCQ+++/H19f3/YOSURE5A4JDg7G0dGRpKQkkpKSsLS0bDFLaNiwYdjY2BATE0NGRgYODg5i2Vg3Y8aMGXz++ef89ddfLF26tM0bgHQVNP5Btra2rfaiEWk76urq2LhxIz/88ANWVla8/fbbjBw5slXrkObmZn744Qe2bNlCbW0tCxcuZPr06chkMvGzchNkMhlDhw7F0dGR8vJyYmNjGTp0aHuHdc8QBSGRDo+TkxO9e/cmOjqa6Ohopk6des+eS7MzoqurS48ePZgzZw7W1tbs3LmTX3/9lSVLltyzm2hNTQ1Hjx6lsrKSESNG3LZhdnfB0NCQIUOGMH/+fJYuXcpPP/2EqakpwcHBGBsbM27cuGvEn6ysLLKyssjIyCA/Px+lUqltT6lUKqmtrcXW1haFQkGvXr1wdnbGzc0Na2trbG1ttcKPtbX1HS08NJ1RHnjgAe17SbPjU1JSQkREBCtWrGD9+vXY2dmJg/b/ISUlhdjYWGxtbenRowcymay9QxIREblD5HI5w4cPJy0tjQMHDtC3b98W77P29vb07duXtLQ0Tpw4QY8ePcQsoW7GgAEDcHZ2JikpidjYWO0mi8it8XdDadE/qGPR1NTEF198wRdffIG+vj5vvfUWU6ZMaZVnliAI/Pnnn2zevJn8/HxeeuklbTalOK+8OTo6Ojg4ODBy5Eh2795NZGSkKAiJiLQXEokEqVTKxIkTiY6OZteuXfdUENKgUCh45plnsLS05MCBA3z//fdMnTqVgIAA6uvrycnJwcPD465m8MTExJCSkoKjoyM9evTo9G0g2wIjIyNtO/p33nmHr7/+mkuXLiGXy/nmm2/Izc3VCj6FhYUUFhaiVCrR1dXFyckJJycn/P39cXJywt7eHktLSywtLbXd72xsbO56NphEIqGpqekf3+/fvz/Lli3j5Zdf5rfffsPW1pZly5ZhamoqDt7/H0EQiI2NJS4ujp49e9KrVy/xbyMi0kWYNGkSP/30E7t37+bFF19ssaRBKpUyevRoIiIi2L9/Pw899JAoCHUzZDIZY8aM4euvv2bnzp2iIHSbaAShYcOGiYJQB0IQBH7++Wc2bdpETU0Nixcv5pFHHsHIyKhV854DBw6wYcMGUlJSeOKJJ3j66aexs7MTM8BuAblczrhx4/j111+JioqipqbmlqoBOhOiICTSKZg4cSJLly7lwIEDbfKB1NXV1WbolJaWMm7cOB588EF0dHT49NNPMTAw4Mknn7yrgtD+/fspLi7m/vvvR6FQiDftViCRSDA1NWX69OkolUreffddoqOjMTc3JyYmhtLSUoyNjXF2dsbJyYkePXqgUChwc3PTCj6azB9zc/M2ERccHBwYNWrUdX8WFhbG0qVLWbhwId9++y22tra8+uqrYgeV/09xcTGxsbGUlpYSGBiIp6dne4ckIiJylxg0aBAODg4kJiaSkpJy07Kx4cOHY2Njw4ULF8jMzBS7jXUBBEEgOTm51aXAM2bM4Ouvv+bPP//krbfeEsvGbpH6+nry8vIoKyvD3t6+y7fW7kzs3LmT9957j7y8PObOnau1r2jN+/vs2bO89957XLhwgZkzZ/LCCy/g7Ows2lDcIkZGRgQEBODo6EhaWhqXL18mODi4vcO6J4iCkEinwN/fHy8vL1JSUoiJiWHQoEFt9txDhw7FwsICfX19vvnmG06ePMm///1vdHR0UKlUSCSSO56AVFRUcOzYMWpqahg5cuQ99UrqakgkEuRyOQ888AAffvgh9fX1uLu7ExAQgIODAzY2NtjZ2WFra4uVlRVWVlbtljIbFRVFU1MTYWFh131+HR0dHnjgAYqLi1myZAmbN2/GysqKOXPmIJVKu/1EV1Ma4ODgQFBQkGh+KSLShbCysmLIkCGkp6dz8OBBevbs2aLAoyknT01N5dSpU2LZWCdHEASioqLYvHkzDz/8MDNmzLjpNSEhITg6OhIfH09CQgI9e/Zsg0i7DkVFReTk5GBsbIyjo2OXzX7obBw5coQ1a9aQkJDA008/zQsvvIClpWWr5oBJSUmsW7eOqKgoxo0bx9y5c/H29r5n3qddGalUio2NDcOHD2fHjh0cPHiwywpCYgqCSIdHIpFgYGDAmDFjUKlUhIeHt+nzOzg4YGxsTHNzM2lpacyePZvhw4dTUlLCli1bqKio0HYgu10uXLhAWloarq6uBAQEYGRkdJeivzXq6urIysoiOzub+vr6f/y8vr5ea8JcW1vbDhFen+bmZgoLC6mrq0OhULBs2TLeeOMNXnvtNZ555hmmTJlCSEgI3t7erR5U7wXFxcXExMS0eI6+vj6zZ8/m5Zdfpri4mPfee4+IiIg7fo91dlQqFbGxscTHxxMYGEjPnj27vUAmItLVmDRpEoaGhoSHh1NTU9PifU8qlTJy5EhkMhkHDx6kvLy8DSMVuZsIgkBcXBwrVqxg27ZtpKSktOo6CwsLRo0aRUNDA3/99dc9jrLrkZube41/kDimtj8xMTGsXr2aM2fO8MADDzB37lwcHBxaVTWQnZ3N+++/T3h4OIMGDeK1114jMDBQzJy8DoIgoFKprmvh8HcsLCwYOXKk1ue1I6197iaiICTSabjvvvsA2L17N83NzW3+/Do6Ojz00EOMHTuWyspKNm3axJUrV+7KQn3fvn2UlJQwYsQIbG1t261cLD8/n59++olffvmFwsLCf/y8qKiIbdu28f3335Obm9sOEV6fmpoaDhw4QHNzM6GhoQwfPhxnZ2cMDQ3bO7RrCA4OZvjw4Tc9z8zMjBdeeIEnnniC1NRU7eRApVK1QZQdE6VSSWxsLFVVVQQFBeHu7t7eIYmIiNxlhgwZgkKhIDY2ltTU1JtO1sPCwrCxseHcuXNkZ2ff9HyRjocgCKSlpbFixQoOHTrExIkTmTx5cquv13Rc+u2333jnnXf49NNPCQ8PJz4+nsrKym6/mdISeXl55OTkYG9vj5OTU3uH0+1JS0tj9erVHDp0iHHjxvHGG2/g5eXVqjWBUqlk48aN/Prrr/j6+rJw4UKCg4PR19dvg8g7FxUVFRw4cIBPPvmEixcvtniukZGR1m4iJSWF+Pj4tgmyjRHzx0Q6DQMHDsTW1pb4+HjS0tLw9vZu0+fX1dXVmhZmZ2dTWlrK008/jampKSkpKSgUitsyAC4rKyM6Opr6+nrCwsLaNeW9rKyMEydOoKury/jx4//x86qqKs6ePUtBQQETJkxohwj/iSAIVFZWsmfPHoyNjZkyZUqH3OWKiIjAy8uL3r17t+p8Ozs75s+frxXhVq1axfr16/H19e2W/lIJCQnExcXh7OxMUFCQaLouItIFsbGxISQkhKtXrxIZGUmPHj1a3N12dnamR48epKamcubMGYKCgrCwsGi7gEXuCEEQKCgoYO3atWzfvp2BAweyZMkSgoKCWnV9ZWUlp06dQhAEUlJSWLduHdbW1lrfQI3Q4eTkhKOjo/argYFBh5wntCWCIGgzhIKCgkRD6XamoKCAdevW8ddffxEcHMyiRYvo2bNnq+Z7lZWVfPrpp2zduhU7OzsWLlxIWFgYBgYGbRB556OmpoYTJ06wdetWysvL6d+//w3PlUqlWFtbM3ToUMLDwzl48CD9+vVrw2jbBlEQEukUSCQSLCwsGDZsGNu2bWP//v1tLgj9HUtLS55//nkCAgJIS0vj448/5vnnn8fHx+eWTdvOnj1LRkYGnp6e+Pn5dbislo5OY2MjiYmJJCQk4OPjw+DBg9s7pH/Q3NzM6dOniY6OZsmSJa1+j3h6evLWW29RUFDAnj17sLS0ZPXq1Tg6OnaryWxzczOxsbEkJCQQFhZGYGBge4ckIiJyj5g0aRJ//vknu3fv5sknn2xxo0VHR4dRo0Zx6NAhDhw4wNSpU0VBqJMgCALl5eVs2LCBH3/8EW9vb5YuXUpISEirrq+vr+fTTz/lp59+QqFQ8Oyzz1JVVUVubi65ublcvnyZ3bt3o6+vj729Pfb29jg4OKBQKFAoFLi4uODo6IiDg4PWkLw7jau1tbXk5uZSVVWFvb09tra27R1St6W8vJyNGzfy448/4uPjw+LFixk0aFCr5or19fV88803fP755xgZGTFv3jymTJkiriVawMzMDBcXF5RKJadPn6asrKzFccPc3JxRo0bx22+/ERkZySuvvNLl/r6iICTSqZg8eTLbtm1j586dvPDCC+0Wh2bHCdTdwYyNjbVmfLdqNL13715KS0uZOnUqNjY23WpCcjeorq5m3759SCQShg4dqu0O19EYNWoUqampt/z69unTh8WLFzN//nx++eUXbG1tWbRoERYWFt3mvVJYWEhsbCz19fUEBQXh6ura3iGJiIjcI4YOHYqtrS2XLl3i6tWrWFtbt2iIGhoairW1NWfPniUnJwdnZ2fRQLWDIwgCdXV1fPbZZ3zxxRcoFArefvvt62YmX4/m5mZ++OEHPvzwQ6RSKQsXLuS5556joaGBvLw88vLytH6HWVlZ2v9HRUWRm5uLqakpTk5OODg4YG9vj6OjI3Z2djg4OODg4ICLiwsKhQIdHZ0uO84qlUpycnIwMzPDwcGhyy1wOws1NTV8+umnfPHFF9jY2PDmm28yZsyYVmUGNTc388svv7B582aampp47bXXeOSRRzA2Nu6y79u7gbGxMb6+vjg5OXH16lUuX77MsGHDWjy/Z8+e2NrakpycTGJiYquz/TsL4ogp0qkYMWIEJiYmnDp1isLCwg6xo+Ht7c2UKVNwcHAgKioKQRAYNGhQqwbXoqIiTp48SVNTE6GhoR1mZ1MQBJqbm//hx9Dc3Nyh6vE1O4wRERGYmJjcku9AWyAIAqWlpWRkZNCnTx8GDRp0W4P06NGjefPNN3n77bf56quvsLGx4aWXXsLIyKhbDPqxsbHExcXh7u5OUFBQu5mui4iI3HsUCgUDBw7k999/58iRIwQGBmJmZnbD893c3AgKCuKvv/7i3LlzYtlYB0cQBJqamvj+++/58MMPMTQ0ZOHChcycObNVi2BBEPjrr79Yt24dFRUVzJ8/n6eeegp9fX0MDAwwMzPDx8cHUG/QFRUVkZubqxWFsrOzKSgo0ApHsbGxFBYWYmFhoc0icnFxwcXFRZtZpDlsbGy6TMdPTSaV6B/UfjQ2NvLdd9+xadMmdHV1mT9/PjNmzGiVECkIAuHh4WzYsIGioiJefPFFnn76aczNzbvE+/NeIpFIUCgUDB48mN27d3P06NEWBSGpVIqtrS1Dhw5l3759HDp0SBSERETaE2dnZ3r37s3x48c5fvw406ZNa++QGDt2LKBO+fz5558ZM2ZMq0WTkydPkpmZiY+PD76+vh2m3jchIYHPPvsMGxuba75fXFzMpUuXUCgU7RTZtTQ0NBAfH8+VK1cICgpi4MCB7R3SP1Aqlfz6668YGRkxd+5czMzMbnmwlkgkPPjgg5SUlLBixQo+/PBDrKysePzxx7t8mntTUxOXLl0iOTmZCRMmiOViIiLdgIkTJ7J7927Cw8OZM2fOTcvGRowYQWRkJAcPHuT+++8XBaEOiqazz7Zt21i/fj21tbW8/vrrPPHEE63uhHTw4EFWrlxJZmYmzz33HK+++iomJibXfX9oFnK2trbaBdzfRSJNFlFmZqZWIMrLy+PChQuUl5djY2OjzRpycHDAyckJOzs7bVcuZ2dnZDJZp/T1y83NJScnBycnJ9E/qB3QfA7ee+89amtrmT9/Pk888QT6+vqtmtNFRkby7rvvkpKSwr/+9a9bak0vovarGzx4ML/++iunT5+msrKyxY0Hc3NzRo8ezR9//MGhQ4d48cUXO8ya7W4gCkIinQbNTW7SpEkcP36cnTt3dghBSIMgCAwZMoQRI0ago6NDYmIiHh4eN3T4FwSBiIgIysvLeeyxx7CysuowN/LS0lLi4+P/cXOsqqqiuLi4wwhCVVVVREREoKOjQ2hoaLsact8ICwsLevToQXx8/B1lV+np6fHEE09QWFjIxo0bWb9+PXZ2dowfP75Ll0fk5eURFxdHU1MTgYGB4k6miEg3YPjw4VhbW3Px4kUyMjKwsrJq8T4XFhaGtbU1Z86cITc3Fycnpy59X+ysCILA/v37Wb16Nbm5ubzwwgu8+OKLrc76PH/+PCtWrODSpUvMmjWL119//ZYXwdcTiTSlZhqRJDc3l6ysLAoLC8nOziY+Pp6DBw/S2NiItbU1Dg4OuLq64ubmhr29vdaXSKFQ4OjoiKmpaYcWiTSG0rm5uQwYMABHR8f2DqnbsX//ftasWUN+fj7PP/88L7744g2Fzf/L2bNneffddzl//jzTpk1j3rx52Nvbd+j3XEfD2NgYf39/7O3tSUtLIzY2lkGDBt3wfCMjI/r06YO1tTWJiYnajeiugjhainQ6JkyYwNKlSzl06BBVVVWYmpq2d0iAeuH/+OOPA+rMn+PHj2vTmK9HQUEBp0+fBtSTX5lM1max3ozAwED+/e9/4+Hhcc33MzIy+Pbbb2lsbGynyP6Hphxr3759mJqaMmnSpPYO6RoEQSA7OxtDQ0MeffRRGhoa7jibx8TEhJdffhmlUsm3337LmjVrkMvlhISE3LKZeUegsbGRsrIyiouLsbGxua7/0+XLl4mPj8fb25ugoCDR50BEpBvg4ODAgAED+OOPP4iKisLf37/F3VsPDw/8/f3ZtWsX58+fJzAwEHNz8zaMWORmqFQqTpw4wYoVK0hMTOTxxx9nwYIFrc7munLlCqtWrSIqKor77ruPt99+GwcHh7uykaavr4+rq+s1/nR1dXUUFBSQmZnJ1atXyc7OJi8vj/z8fAoKCoiJiSE8PFxbfuLo6IijoyMuLi7Y2dlpu5pphCJDQ8MOs+lXXV1Nbm4utbW1ODg4YG1t3d4hdSuio6NZuXIliYmJzJ49mwULFrRa2ExKSmLdunVERkYyevRo3njjDdzd3TvlHLA9kUqlKBQKhgwZwt69ezly5EiLgpCOjo42q+jgwYMcPHhQFIRERNqTgIAAPD09SU5O5tKlSx2yq1RTUxNubm7o6enR3Nx83Zrz48ePk5OTQ0BAAF5eXjcUjtoDCwsLAgIC/lGeY2pqiqWlJQUFBe0U2f+or68nNjaWq1ev0qdPnw7ZBvL06dPExsbi4uLCpEmT/lGCdzvY2Njw+uuvU1xczPbt21m7di3vvvsu/v7+HXpCoBF/CgsLKSgo0Ho45ObmUlhYyEMPPcS4ceOu2dVvaGjg0qVLpKamMnXqVAICAtrxNxAREWkrJBIJEyZMYM+ePYSHh/PYY4+1WDamq6tLWFgYUVFRHDp0iEmTJomCUAdCEATi4+NZvXo1p06dYurUqSxatAh7e/tWXZudnc26dev4888/CQ0NZenSpXh7e99TgcXQ0FArEmn8RSorK8nJySEnJ4fs7GwyMzMpKCjQZhYdO3aMgoIC9PT0cHJyws3NTVuSpVAosLOz0x7tKRLl5+eTm5uLpaUljo6OHWr+2dWJj49n1apVnDhxgqlTp/Lmm2+iUCha9T7Izs7mgw8+YOfOnQQHB/PWW28RGBgoZkPeJjY2NgwZMoTffvuNkydP3jTJwNzcnDFjxrBjxw4OHTrE888/32U+O+I7SKRTIZFI0NPTY+zYsSQlJbF79+4OKQj9PaY//vhD2zVFk86pUqnYu3cv5eXlPPPMM2Ld721QWVnJnj170NfXZ9SoUS3uHrcXCoWChIQELl26RGho6F0RhADc3d15++23KSoq0rajf+edd3B1de0QKcPV1dUUFxdTWFioPZRKJQUFBddMpgsLC9HT08PGxoawsDAaGxuvmdjk5OQQGxsLQFBQUKsWDyIiIl0DzT3z3LlzZGVl3bTbWFhYGFu2bOHUqVPk5ubi4uLSoUXy7oIgCFy9epV169YRERHByJEjWbp0KV5eXq26tqSkhI0bN/Ldd9/Rs2dPli1bRr9+/dplzmRmZoafnx9+fn7a71VWVpKZmUl6ejrZ2dlkZWVpNz0KCgqIj4+nsLAQExMTrQ+Rk5MTLi4uWm8ijUhkbW3dJr6AGgFLE49I25Cens7atWvZu3cvI0aMYMmSJXh6erbq9S4uLuajjz7ixx9/xN/fn0WLFhESEtJq7y2Rf2JiYkJgYCAKhYLU1FTi4uIIDg6+4flGRkb069cPuVxOXFwcqamp+Pv7t2HE9w5REBLplEyePJmPPvqIPXv28M4773S4G+LfhZ+CggIaGhqu8Y/Jy8vj7Nmz6OrqMmzYsA4pZnRkNKaQhw4dQiaTMXHixPYO6RoEQSAnJ4eQkBAGDx5Menr6XRczevXqxVtvvUVpaam2Hf3rr7+OtbV1m02UVSoVNTU1lJaWaoUfzW5pdna2VvzJycmhrKwMY2NjbG1tsba2pkePHigUCu3keMCAAf9YvMXExJCYmIifnx+BgYFdZidGRETk5jg5OdGnTx/+/PNPjh8/jp+fX4tjpbe3N35+fuzevZuYmBiCgoLELKF2RhAECgoK2LRpEz///DP9+/dnyZIl9OrVq1XXVldX89lnn/HZZ5/h4uLCokWLCAsL61AbaGZmZgQGBmozqlUqFcXFxWRnZ2uPtLQ0cnNzKSgoIDs7m/Pnz1NaWoqFhQWurq64uLhoM4ns7Oy0Hkf29vZYWlre9TmuZlz29fUV/YPaiIKCAj744AP++9//aj8HPXv2bNV7uaqqiq+//prPP/8cBwcHXn/9dUaNGtXh1j6dDalUip2dHUOGDGH//v0cPXq0RUFIR0cHW1tbBg8ezJEjRzh48KAoCImItCcDBw7Ezs6O2NhY0tLS8PHx6VATBA1SqZQnn3wSAwMDSktLAXU51pEjR8jPz6dXr164u7uLC91bpL6+npiYGDIzMxk8eHCrJpdthSAINDc385///Efb/rZ37973JDV89OjRvP766yxdupQvvvgCGxsbnnvuudvqZHYzVCoVFRUVFBUVXZP5o9kF1Ux8MzMzqaysxNTUFGtra22rToVCod2NdHBw0Hou3KhDS01NDRcvXiQ9PZ1HHnnkmh1ZERGRro9EImH8+PHs27ePPXv28PDDD7dYNqanp0doaCjHjh3j8OHDTJgwQRSE2hGNz9+WLVv44osv8Pb2ZvHixQwfPrxV1zY0NPD999+zYcMG5HI5CxcuZMaMGW0Q+Z0hlUqxsbHBxsaGPn36AOry54KCArKysrRZRJpMIqVSSWJiIocOHaK2thZLS0utOOTm5oarqysKhUIrEikUCszMzG67TEilUpGbm0t+fj5hYWGiINQGlJeX88knn/Dll1/i5eXFkiVLGD58eKsyuuvq6vjxxx/54IMPMDU1Ze7cuUybNk0Ug+4S1tbWDB06lG3bthEdHU11dTUmJiY3PF8mkzF27Fh2797NgQMHePbZZ7vEayEKQiKdDolEgrm5OcOGDeP3339n//79+Pj4tHdYN0TTPaO+vp6qqiqMjY2JiIigoqKCcePGdaj2uJaWlowaNQqpVHrduDR/94qKimsMgJuamigpKaGhoaFN0o8rKirYu3cvRkZGjB49GmNj43v+nLeCIAgYGhoSFxdHeno6lpaW1zVMvlMkEgkzZ86kqKiId999l40bN2JlZcWjjz56RwJUY2MjpaWllJSUUFJSglKpJD8/n/z8fO1EVnM0Nzcjl8u1E2AvLy+tP4LGYNPZ2Rl7e3uMjIxaHdPly5c5efIkzc3NeHt7d5jOdiIiIm1HWFgYVlZWnD17lpycnFaVjX3++eecOnWK/Px8sWysndBk92zdupUPP/wQGxsbFixY0KrmD5rW9Nu3b2fNmjUAvPLKKzz11FNIJJIOufl3M/T19bVt6jXU19dTWFhIVlYW6enppKWlaUuplUolly9f5sCBAzQ3N2Nra6sdSzWdzWxtbbGzs9P+28TEpFUCQ1VVFbm5uTQ2NuLg4IClpeW9/NW7PbW1tWzdupVNmzZhZ2fH66+/zqRJk1r1Pm5qamL79u28++67NDY28uKLL/Kvf/0LAwODTvk56IiYmpoSFBSEjY0NKSkpJCYmtuhJamxszIABA5DJZFofU29v7zaM+N4gCkIinZb77ruP33//nV27dvHiiy8iCEKHvkEqFAry8vI4efIkp06dQldXl8GDB3eYLmkAjo6OPPnkkwAYGBj84+c2NjY89thjqFQqbbenhoYG4uLiOH78OO7u7vdcEFKpVBQWFhIZGYm5uTnjx4+/p893K2h2NZuamnjllVcoKyvj/PnzyOXye/acOjo6PPPMMyiVSj766CPeffddrKysmDhxYqsyzxoaGigtLaWoqAilUqkVfzStd/8uAuno6CCXy5HL5Tg6OtK3b1/s7Oy0fgjOzs44OjpiY2NzW1lvtbW1FBYWkpaWxs8//8y5c+e0nVpE00QRke6Hi4sLvXr1YteuXURHR+Pj49Ni2Zivry8+Pj7s2bOHS5cuERgY2KE6eHYHBEGgvr6eX3/9lfXr12NgYMDLL7/M448/flPBQhAEBEFg7969vPPOO5SVlfHCCy/w8ssvo6ur26HneLeKgYGBViTS+E5qxJqsrCxtd7O8vDytB19UVBTbtm0DwM7ODnd3dzw9PXF1dcXBwUG7MaM5ricSafyDbGxscHR0FMfWe0hjYyO//fYb7777LgYGBsydO5fZs2e3StgUBIF9+/axcuVKSkpKeOaZZ3jppZda3ZpepHX8vWzs0KFDHDlypEVBSEdHBzs7OwYNGsSxY8c4ePCgKAiJiLQnI0aMwNTUlJMnT6JUKu+aYe+9oKSkhOTkZM6dO8dff/1FXl4ehoaGbWIeeCvo6Oi0mG2jo6OjzXgCdVnPhQsX2LFjByqVipCQkHseY21tLRcuXCAvL4/hw4f/oxNae5OZmUl4eDgeHh54eXkxZMiQe94q3dDQkFdffZWioiK+++47rSjUkuFgYmIiMTEx2m5fmZmZZGZmkpGRoTXAtLKywtraGm9vb0JCQnBwcMDe3h57e3ucnZ1xd3dHLpff8oRSs3tcVlZGaWmp1oA6NzeX1NRULly4QFxcHNXV1UyYMKHL1GiLiIjcGlKplPHjx3P48GH27t3LjBkzWhSE9PT0GDZsGNHR0URGRjJ27FhREGpDBEGgqamJPXv2sGrVKurr63nxxRd54YUXWl1WER0dzdKlS0lLS2P27Nm8+eabHS4L+F5hamqKj4/PNVnvmhbx6enppKSkaEUizSbOvn37KCoqQk9PT5uV6+Lick0mkabkzNLSkpycHPLy8nBwcBDLxe4hzc3NhIeHs3z5cmpqanjllVd47rnn0NHRadW8/8SJE6xYsYKrV6/yyCOPsHDhQiwsLDrUmqGrYG1tzbBhw7R+dc8991yL9xwzMzPGjh1LREQE+/fv5+mnn+70wmrnjl6k2yKRSLSGk1FRUURFRfHAAw+0d1jX0NTUREZGBvHx8Vy4cIHjx49z8uRJZDIZtra25Obm8ssvv+Dg4ICzs3OnS2uvqKjgxIkT7N27F5lMxiOPPIKXlxd5eXnU1tbi7u5+TwauiooKwsPDMTU1Zdy4cfdcbLlVGhsbSUlJISUlhXPnzjFmzBgGDx58zwdxKysr3nrrLUpKSrQpxqtXr6ZHjx7XHaiio6NZsWIFeXl5yGQyrK2tsbS01Jo9Ozs7ayeWzs7OODk5YWRkdMtdzJqbm6msrKS4uJji4mKKioooLi4mLy9P23Y+Ozub9PR0KioqMDY2Rk9PD2tra4YPH87MmTO7xO6LiIjI7TFixAgsLS05deoUeXl52Nratjhejhgxgq+//poTJ06Ql5cnlo21ISqViqNHj7J8+XIKCgp46qmnWLhwYasEHUEQiImJ4Z133uHixYtMmzaNZcuWdfuSJhMTE7y9vfH29mbs2LGoVCrKysrIzs4mIyODzMzMa8rNsrKyOH/+POXl5RgYGODi4oKnpyfu7u64urpqRSV/f3+MjIxoaGgQfSzvMoIgcOTIERYvXkxubi5PPvkkCxcubHUp/8WLF1mxYgXnz5/n/vvvZ8mSJdjZ2Yli0D3C1NSUnj17YmVlRXJyMsnJyfTu3fuG5xsbGxMcHIypqSkxMTFkZGTg6enZdgHfA0RBSKRTM2nSJKKioti1a1eHEYSqqqpISUkhLi6OqKgoDh8+TE5ODs7OzowePZpBgwYhlUrZunUrP/zwA1ZWVjzzzDPY2dl1iJbhraGyspKoqCj27NmDq6srDz/8MLa2tsTGxnLkyBEcHR1xd3e/68/b3NxMQUEBx44dw8LCgrFjx97157hdNOVi/v7+fPDBB1y+fJlTp05RXl7eZjE4OzuzePFiioqK2L9/P9bW1ixZsgQPD49/LIhcXV0ZMWIEBgYGODo64urqiqOjI/b29ri5ud2S34+GpqYmysvLtd5DZWVl2gmqJgMpMzOT7Oxs6urqtEKUlZUVwcHB2NnZ4ejoiJGREebm5gwcOBA/P78uYdgnIiJye7i7u9OjRw/Cw8M5efIkPj4+LZZa+/v74+Xlxd69e4mNjSUoKEjMEmoDmpubOXPmDCtXriQhIYGZM2eyaNGiVhl7C4JAWloaq1ev5uDBg4wZM4YVK1Zc47kjokYqlWJpaYmlpSU9e/YE1GOvUqnk6tWr2iMzM5PCwkJKSkpISUnhxIkTVFVV0dzcTGNjI6ampmzfvp2EhARty3tNVrCFhYU47t4B586dY9myZSQmJjJjxgwWL16MTCZr1ZwqLS2Nd999lwMHDjBixAiWLVuGi4tLp1kfdEY0ZWOa7mGRkZEtCkI6OjrY29sTEhLCiRMnOHz4sCgIiYi0J+PHj2fx4sUcOnSIqqqqFjuQ3Es0rVWvXLnC+fPnOXLkCEePHkUQBDw8PBg+fDjDhg27pqODiYkJGzZsYMuWLZibm/P4448jl8s7xQ5ATU0NJSUl9OrVi+nTp2NoaMjZs2fZsWMHTU1NDBky5J48b21tLWfPnkWpVDJmzJgOV0oUGRlJRUUFHh4euLu707dv3zaPISgoiLfffpvy8nJ++eUXbGxsmD9/PgqF4poJRXBwMD179kQul7c6hfnvNDY2UlFRoc380ZR9aXYtNW1tNcbTFhYWyOVyLC0t8fHxwdbWFicnJ20XFTc3N2xtba/rXSUiItJ9kUqljBs3jiNHjrB3716mTZvWoiCkr6/PsGHDOHnyJEeOHGHMmDGiIHSPaW5uJj4+nlWrVnHs2DEmTpzIO++806pmAIIgkJeXx4YNG9i2bRvBwcGsXr2agICANoi8a6Crq6st5x40aBCgHqOLioq0XkRpaWlcvnyZEydOkJOTQ35+Plu3bqWhoQFra2vtOPz3zmbW1tZaocjMzEzMtGsFSUlJLF++nOjoaMaPH8/KlStRKBStmmPl5eWxceNG/vjjD/r168fy5cvx9/cXxaA2wMrKitDQUHbu3MmxY8d47rnnWqxAMDMzY9y4cRw4cICIiAieeOKJTv35EAUhkU5NQEAAnp6eJCUlERMTc8+EiBvR2NhIZmYmiYmJnDhxgiNHjnD+/HksLS3p3bs3/fv3JzQ0lCFDhvyjFfi//vUviouL+eSTT9i8eTNWVlbcf//9LfojdBSsra2ZPn06oN6Z0mQLyWQynnrqKTw8PMjLy6Oqqgpvb++7JnKVl5cTHh6OmZkZ48eP73A7WFlZWURHR2u7bA0cOFA7OWtLRowYwcKFC1m2bBlffPEFtra2PPPMM9cIjqampq02NK+rq9N6/ZSUlGgFoNzcXO1k8+rVq+Tm5iKVSpHL5VhZWWFjY4O/vz8ODg7a0jMHBwfc3d2xsbHpcK+fiIhIx2TEiBHI5XJOnjxJQUHBTcvGwsLC+Oabbzhx4gQFBQW4urqKi6p7hEql4urVq2zYsIE9e/YwdOhQ3nnnnVbtmGta03/22Wd8+eWXBAYGsnz5cgYMGNAGkXdt9PT0tCJRcHAwoM5cWb58OU1NTYwaNQp7e3tyc3O1Y/qJEye0G3v29vbazS13d3ccHR214pCmc6pMJhM/V38jKyuL9evXs2vXLoYOHcqaNWvw9PRs1Ry4uLhY+znw9fVl6dKlBAcHd2qRoTNhZmZGr169sLCwICkpiZSUFIKCgm54vrGxMYMGDcLY2JgLFy6QnZ2Nq6trG0Z8dxEFIZFOi0QiQVdXl/Hjx5OUlMTu3bvbTBCqrKzkypUrxMbGcvz4cfbv309hYSGurq5MnDiR4OBgxowZQ0BAwA0XvYaGhjz//PMUFxfz/fff8+GHHyKXyxkxYkSHN1D8u/l0fHw80dHRODg4MGvWLKytrYmPj2f//v1YWlri5eV1VwShpqYmcnNziY6OxsrKitGjR9/xY94tNG1yn3rqKcaMGcPJkyc5efIkenp6hISEtEvW14wZMygqKmLdunVs2LABuVzOI4880mIWnUqloqqqitLSUsrKyigvL6e4uJj8/HwyMzNJT0/Xij9lZWUYGxtrU9c9PDzo378/CoUCFxcX3N3dtbuNMplMnNSIiIjcNl5eXgQGBhIREcHp06fx8vJqUdAODAzEy8uLiIgI4uLiCAwM7BSbLZ0NlUpFfn4+H3/8Mf/5z3/o27cvy5Yta1V2rCAIVFVV8d1337FhwwacnJx4++23GTduXBtE3j1RKpUUFRXh6enJ7NmzGTlyJHV1deTn55Oenk5aWhrJyclkZmZSVFREUVERycnJlJSUIAgC9vb22rHd3d0dZ2dnrKystBtAVlZW3bYLVlFRER999BFbt26ld+/erFq1ip49e7bqb1FZWan9HNjb27No0SLGjRsnzpvakL+XjR07dozDhw+3KAjp6Ojg4ODAwIEDOXPmDIcOHeKJJ55ow4jvLqIgJNLpmTx5Mps3b2bv3r2sWLHinrUm1aQ1X7lyhYsXL3LgwAEiIyMxMDDAy8uLUaNGMWLECEaPHo21tXWrdk3kcjnz5s2jrKyM33//nU2bNiGTyRgwYECHM0u+EUZGRowePZoePXpgYGDA+fPn+euvv6iurmbIkCF3bfeopqZG68kzePDgDmM0LAgCzc3N/PHHH1ovnhkzZjBz5kyqqqraLS6pVMrTTz+NUqnk448/1nYemzRpEgYGBtrJeGlpKSUlJdrW8zk5OVy9epXs7Gyys7NJTU2lpqYGExMTzM3Nkcvl9OrV65qSL83E0NnZGWNjY3HHUERE5K4ilUoZO3Ysx44dY+/evdx3330tCkIGBgYMHjyYkydPcvToUUaOHCkKQncZTXbPt99+y2effYa3tzdvv/02I0eObNW19fX1/Pbbb6xYsQIzMzPmzp3LzJkz2yDy7kt2dja5ubkMGTJEa19gaGio3bwZMWIEgiBQWVmpbfagEYqysrIoKSkhNzeXy5cvU1ZWhkQiwdHREU9PT3x8fPDy8sLR0VFbHi6Xy5HL5V1aJNLMpb755hs2b96Mu7s7S5cuZfjw4a36nevr69m2bRurV6/G1NSUV155hQcffFAUg9oBTdlYeHg4UVFR/Pvf/27RxkDT3CYyMpKIiAhmz57daV83URAS6fQEBwdrDY3T09PvulBQV1fH1atXuXLlCtHR0URERBAfH6+t1e7fvz/jxo0jODgYAwODWx70nJycWLBgASUlJezbt4+PP/6YN954g6CgoE7R+UGTTtzY2Mj58+f59ddfMTIy4sUXX8TT05OcnBzKy8vx8/O7baFAEATKysq0ZWkTJkzoUDfdpqYmwsPDkclkWpHE29ubPn36tGtc+vr6zJ07l8LCQn744QdWr15NU1MTZmZmVFRUkJeXp/UW0GT/AFrhRy6XM2jQIK3Zs5OTE66urnh4eODk5HRb73cRERGR22HUqFFs3LiR48ePt7ps7IcffuD48ePk5+eLZWN3EY1o8PPPP/Pee++hUCiYP38+U6dObdW1mjFzyZIlAPz73//mhRdeEF+fe0hTUxM5OTkUFRXh4OBwQ38niUSCTCYjICBA6+OkUqmorq4mMzOTpKQkrly5oi0TLy0tJTMzk5iYGCoqKtDT08PV1VU7N3R3d8fFxQVLS0usra21c4vOML+9GYIgUFdXx88//8yqVauwsrLi9ddfZ+rUqa2aGzU1NbFnzx6WLFmCIAg888wzPP/88/dsY1ukZczMzOjTpw8ymYyEhARSU1Nb9DIzMjJi6NChGBoacu7cOXJzczutEb4oCIl0aiQSCaampoSGhvLrr7+yd+/euyYIlZaWkp6ezuXLlzl06BD79u2jqqoKFxcX7rvvPkJDQxk/fjzu7u53LE74+/szb948ysvL2bVrF5aWlsybNw8PD4/rtgzviGgMDDWlY3K5nPj4ePbt24exsTE+Pj63Pdlrbm4mOzubU6dOoVAoGDFixF2O/vaRSCQYGBjw0UcfcfLkSY4fP8727dvp06dPuwtCoBZ3Fi1ahFKpZPfu3SxcuBBBECgsLERfXx8LCwvMzc0xMzMjLCwMZ2dnbdaP5qulpWWHEuBERES6Hz4+Pvj7+7Nv3z7OnTt307KxHj164O7uzv79+0lISKBHjx6t9k0TuTGCIFBbW8uOHTtYtWqVtvx9zpw5rbpWpVIRFRXFokWLKC8vZ86cObzxxhuip9w9prS0lNzcXHR1dXFwcLiljDmpVIqZmRmBgYEEBgYCajGjrKxMu6mk2VjKzs6mpKSE2NhYoqKiqKqqwsTEBC8vL7y9vfH09MTd3R1bW1vkcjkWFhbabKLO9B7QCJs7duzg7bffRk9Pj+eff56nnnqq1dcfO3aMt956i9LSUp544gneeOMNcaOtHZFKpSgUCgYNGsTJkyeJjIxsURDSfJb69+/PhQsXOHz4MLNnz27DiO8enWOlKSJyEyZPnsyvv/7Krl27eOmllwBu64ba3NxMfn4+qampnDlzhgMHDnDkyBFMTU1xd3enT58+jBw5kjFjxmBhYXFXb9rDhw/nlVdeYfXq1fz0009YW1vz73//G0dHx06xa2ZsbMy4ceMYNWoUKpWKCxcuaEvH5syZc0fCVlVVFcePH6empoaePXvi4eFxFyO/fQRBIDs7m5SUFJycnBg6dCijR4+mtLSUrKys9g5Pi6OjI3PmzOHYsWPU1NTg7+9P//79cXBwwMPDAxcXF5ycnPDx8cHCwkIUf0RERDocUqmUMWPGaDN1J06c2KLAY2RkxODBgzl9+jRRUVGMGDFCFITuEEEQaGhoYP/+/SxZsoT6+nqeeeYZXnnllVaPGxcuXOCtt97i6tWrPPjgg6xcuRITE5N7HLlIdnY2eXl5ODg43JV5pa6urrYLWf/+/QG1SKRpc5+UlERqairp6ekolUrKyso4efIke/fupaqqCgsLCzw8PPD09MTT0xNvb29sbW214pCFhUWH9R8UBAFBEDh8+DBvvvkmNTU1PPPMMyxYsACpVHrTtYEgCJw7d4633nqL9PR0pk+fzrJly7p0aV1nwdLSkrCwMCIiIjh69ChPP/10i9lspqamjB8/nmPHjhEREcGsWbM6xZrt/yIKQiJdghEjRmBiYsLJkydRKpXY2tre0vWasrDk5GSOHz/O7t27uXLlCo6OjgwdOpSBAwcyceJEBg4ceFstulvLlClTKCkpYcOGDXz++edYWFjw+OOPY2Nj0ykGCV1dXXR1dcnOzubEiRMAvPrqq7i6upKdnU1ZWRn+/v63NMBrysX27duHubk5EyZM6FA32/T0dL777jvc3d3x8PDAzc0NJycnevbs2d6hXUNycjJVVVUMHjyY1atXExgYKO5EiYiIdCpGjhzJpk2bOH78OIWFhTctGwsNDeXHH38kOjpaLBu7QzQZEdHR0SxZsoSioiIeeeQRFi1a1CrPQ0EQiI+PZ8mSJZw7d44JEybw7rvvIpfL2yB6EY0g5OjoiIODwz15Dl1dXWxtbbG1tWXw4MGAOnu8sLCQ1NRUUlNTSUlJ4cqVKxQWFlJWVsbRo0fZsWMHtbW1KBQKfH19tSKRh4eHVhzSZBLJZLJ2nbcIggDAmTNneP3118nPz2fmzJm88847rZ5TJSQksGjRIs6ePcvYsWO1Ho/ifKz9MTMzo1+/fpiYmBAbG8vVq1fx8fG54fnGxsYMHz4cfX19Tp8+TX5+/j37fN1LREFIpNMjkUhwcnKib9++REVFcfToUWbMmNGqa0tKSkhPTycuLo6IiAjCw8MRBEFbFjZq1CimTJmCvb19m0wipVIps2bNuqYdvYWFBdOnT8fc3LzTDBZGRkaMGjUKOzs75HI5SUlJ7Nq1C1Cn/d+KINTU1ERmZiZnz57F2dmZ0NDQexX2bTFkyBBkMhlHjx4lPDwcIyMjAgMDmTdvXod5vRoaGtizZw/19fU8/PDDBAQEdBrTchEREREN/v7++Pj4cOjQIc6fP4+np2eLWT+9evXC3d2dAwcOkJSUJJaN3QHNzc1cuHCBd955hytXrjB58mSWL1+Oubn5Ta8VBIHMzExWrlxJREQEQ4cOZf369Tg5ObVB5CLwP0EoMDAQe3v7NntePT09HB0dcXR0ZPjw4VrfnaysLK1ApMkkKi0tJT8/n/j4eMrKymhqatKaVnt5eeHv74+np6e21F3zta0za+Li4njzzTeJi4tj8uTJrF+/vtVCVUZGBitXruTgwYMMGjSI9evX4+zs3GHmi90dHR0dbdnY6dOnOXz4cIuCkK6uLk5OTvTp04fLly8TGRnJo48+2oYR3x1EQUikyzB58mSioqLYtWtXi4KQpn351atXOXXqFDt37uTkyZNYWVnh4+NDcHAw48aNY8SIERgZGbX5TVpfX59nn32W4uJitm7dyubNm7G0tGTMmDGdZiKraUMqCAKpqan88ssvFBcX8+STT6Knp4cgCK3+u1ZUVHD06FEaGxvp06cPLi4u9zj61iEIAnFxcQC4uLjw8ssv89xzz3H+/Hni4uI61C50SkoKMTExyOVyhgwZ0mLXBBEREZGOio6ODmPGjOH06dPs37+fcePGtTguGhsbExISwpkzZ4iKiiIsLKzTjKMdiebmZpKSkli1ahUnTpxg1KhRrFmzplXCgiAI5Ofns2nTJn799Vd69erFunXr8Pf3b4PIRUC9KZSTk0NpaSmOjo7Y2dm1WywSiQQjIyN8fHy0C22NSJSdnc2VK1dISEggMTGRrKwsysrKyMrKIjY2lh9++AFQNzPx8vLSCkVubm6YmZld40d0L+Y5giCQkZHB4sWLOXLkCGFhYWzYsAFbW9tWzWkLCgrYsGEDv/zyC7169WL9+vX4+/uLYlAHw9LSkhEjRmi7ST/xxBMtlo2ZmJgwYcIETp06xZ49e3j44Yc71BqgNYiCkEiXYfz48SxevJhDhw5RXV39jx2D6upqsrOzSU5O5sCBA+zcuZPc3FwcHBwIDQ0lLCyM8ePH06dPn3b/IMtkMubOnUtZWRm//vormzZtwszMjCFDhmBkZNSusd0KKpWKkpISDA0NmT9/Pi4uLuTk5FBSUkJAQMBNfYU0bW015WITJ05so8hvjiAI7N+/n/z8fO3ERKFQ4OPjQ0hISHuHdw07d+6kqqqKKVOmYG1t3e7vbxEREZHbZdSoUXz66accO3YMpVKJQqFo8Z4WFhbGzz//rC0bc3FxEe+Bt0BzczMZGRls3LiRPXv2EBISwrvvvtuqBh6CIFBSUsKXX37J5s2b8fb2ZuXKlQwaNKgNIhfRUFRURG5uLoaGhjg4OHQ4UVQjEnl7e+Pt7c3EiRNRqVRUVVVps4g0R0ZGBhUVFVrT6oqKCgwMDHBzc8PLy4vAwEACAgJQKBTIZDLMzc2RyWRYWFjcUWczQRDIy8vjvffe46+//qJv3768//77rfa0LCsr48svv2TLli14eXnx7rvvEhISIt6LOiBmZmYMGDAAIyMjYmNjyczMxMvL64bnGxsbExYWhq6urta6pD1F19tBFIREugyBgYF4enqSmJjIxYsXGTJkCIIgUFxcTEZGBjExMURERLBnzx4kEgkuLi7cf//9jBkzhilTpnQ4nx6FQsGCBQu0ncc+/vhjTE1N6dOnT6fJ8NDR0aF///7069eP5uZmkpOT+euvv6itreXNN9+8qSDU2NjI1atXuXjxIj4+PgwbNqyNIr85EomERx99lOjoaA4dOsTu3bvx8PDAx8eHJ598ssO0VK2vryc8PJzGxkbuu+++TiUoioiIiPxfAgMD8fb25tChQ1y8eBEvL68WTYl79+6Nq6srBw8e1JaNiSbGrUOlUpGfn8/nn3/O999/T8+ePVm5ciW9e/e+6bWCIFBVVcVPP/3E2rVrsbe3Z+nSpUyePPneBy5yDVlZWeTl5eHk5NSm5WJ3glQqRSaTXdOxVaVSUVFRwdWrV4mLiyM+Pl7rR1ReXk5MTAxRUVGUl5djbm6u7Wzm7e2Nn58f+4BZewAAgFRJREFUNjY2WFhYIJfLkclkrTat1gibn376KVu2bMHPz4/169fTt2/fVv0u1dXV/Pzzz6xZswaFQsGyZcsYO3asKAZ1UDRlYyEhIZw/f57IyMgWBSFdXV1cXFzo06cPcXFxHDlyhJkzZ7ZhxHeOKAiJdBl0dHQYP348iYmJ7NixA2dnZzIyMjhx4gR//vknZ86cwcbGhoCAAPr27cvUqVMZMWIEurq6HUoI+jteXl7MmzeP0tJS9u/fj5WVFfPmzcPPz6/TtKPXDHgVFRWcO3eOjIwMXnjhBYyMjFCpVC0OiOXl5URGRgIwYMCADmHUpmmbGxcXh42NDffddx9Tp04lPT2dyMhIoqKiePTRRzuMIJSUlER8fDzW1taEhIR0mLhEREREbgddXV1GjhzJ6dOnOXDgAGPHjm1R4DExMSE4OJgzZ85w/PhxQkNDRUGoFahUKoqLi/nxxx/56KOP8PDwYPHixYSFhd30Wk0J0K5du1iyZAlmZma8/PLLndJboyuQnZ1Nfn4+Tk5OHWIedbtIpVIsLCzo3bu3VpRUqVQolUquXLmiPRISErSm1YcPH2bHjh1UVVUhl8sJDAzUisqazqoacUhz/H1eqhE2f/zxR9avX4+joyMrV65k1KhRrYq5vr6eXbt28dZbb2FiYsK8efN45JFHRDGogyOXyxkxYgSHDx/m8OHDN+2WrCkbO3PmDHv27GHGjBmd6jXuHCtKEZFWMnnyZDZt2kR4eDhVVVVs376d8vJynJycGDFiBKNGjWLGjBl4enp2WBHo/zJw4EBeeeUVqqqq+O2337CysuL555/HxcWlQ7bjvBHGxsaMGDGC4cOH4+joSE5ODkqlkqCgIPT09P5xvia7a//+/VhYWDB+/Ph2iPr6NDQ08Pnnn+Ph4UFAQABOTk7Y2dnxr3/9iyeffLK9w7uG7du3U1NTw5QpU7C0tOxUA5SIiIjI9Rg1ahSfffYZUVFR2vT8lu5toaGh/Prrrxw/fpy8vDyxbOwmCIJARUUFv/32G6tXr8bW1pZ58+Yxbdq0Vl3b2NjI4cOHWbBgAQBPPfUU8+fP7zTzrq6EIAjaDKGOsrF2N5FKpdjZ2WFnZ8fQoUMBtUiUm5tLbGwsV65cITk5mZSUFEpLS1EqlezYsYPKykpqampwcHDAx8dHKxAFBAQgl8sxMzNDJpNhYGDA7t27Wbx4MZaWlixcuLDVjWuampo4dOgQc+fORaVS8dRTT/Hyyy+Ln4NOgEwmIzg4GH19fWJiYsjOzsbNze2G5xsbGzNy5EhWr17NsWPHKC4uxsbGpu0CvkNEQUikU6DJyqivr6ehoYHq6mrq6uqor6+nubmZxsZGamtrEQQBIyMjEhISyMzMxNnZmZEjRzJ16lTGjx+Pqalpp7wRT5gwgdLSUt577z2+/vprLCwsmDNnzk29EzoSBgYG2o4i+fn5bNu2jYyMDN55553rCkINDQ2kp6cTGxtLUFAQQ4YMaeuQb4iOjg7Tpk3j0KFDbNq0CVdXV4KCgggMDGTgwIEdpj6/traWPXv20NTUxOTJk8XOYiIiIl2CHj164OHhwZEjR7h06RKenp4tZv307dtXWzZ25coVevXqhbGxcRtG3HkQBIHq6mrCw8NZvnw5JiYmPPPMMzz99NOtura5uZnTp08zf/58ysvLeeyxx1iyZEmn2sDqSmgMpSsqKnB0dOxUi9TbRSqV4uTkhJOTE+PHj0cQBJqamsjKyiI5OVl7aLqZpaenc+nSJSorK1GpVHh5eWlNr62trVm3bh26uro899xzvPTSS62KQaVScebMGV599VXKy8t59NFHWbp0aYeuShD5Hzo6Otjb2xMcHExMTAyRkZHMmTPnhufr6uri6upKz549SUxMJCoqigceeKDtAr5DREFIpH0RBGhshIYGhLo66ioryWpspKmpiebmZu3R1NRETU0N+fn5KJVKrl69Sl5eHuXl5QDa8+RyOU5OTmRmZjJ69Ghefvllhg8fjlQq7dQ3YIlEwkMPPURJSQkfffQRn3zyCRYWFjz88MPI5fJO97tVVlYC8OSTT2JmZkZzc/M/JotlZWUcOnQIPT09Bg8ejLW1dXuE+g8kEgn6+vqMHj2aUaNGUVlZydGjRzl48CC7du3ik08+abHWuC2JjY0lKSkJhULBgAEDxHIxERGRLoGenh6jRo3iwoULHDhwgJEjR7YoCJmamhIcHExycjJXr16loqJCFISug6bUKzIykrfffpvm5mZmzZrFggULbiroaDbuLl26xLx580hPT2fKlCmsXbtW9K5rR/Lz88nLy0Mmk+Hg4NAtXwuJRIKenh4eHh54eHhoRaLa2lptBpHmSElJobKyUusdU15eTnNzM//6179YtGhRqzZhBUEgPj6eV199lYyMDCZPnsx7773XLp2LRW4fuVzOqFGjiIqK4uDBg8yaNavFsjFjY2MmTpzIhQsX2L17N9OmTes0r7coCIncmOZmKCuDoiKorVWLN/r6IJeDrS3c6EMhCOqjoUF91Nerr6+rA5VK/biao6kJSkpAqYScHAqTklhcUkLx/0/lrK2tpba2lpqaGhoaGqirq6OpqQl9fX3kcjn+/v64uLjg7u7OxIkTCQgI4LHHHiM5OZnp06cTFhbWaT6MN0NXV5cnn3yS4uJivvrqKz766CPkcjmTJ0/GzMysvcO7Jby9vXn11Vdpbm6moKCA7OxsevbsqRUsBEFAqVRy8OBB5HI548aNa+eIr49EIkEmkzF58mQmTZpEcXExFhYW7R2Wlu3bt1NfX8+kSZOQyWRd5rMgIiIiMnr0aL766iuOHj1KUVHRTTNmx4wZg4GBgZgddAM0pV5nzpzh7bffpqSkhIceeoh33nmnVY0sBEHgypUrLFq0iIsXLxIWFsYHH3yAXC5vg+hFbkRnNJRuCyQSCcbGxvTq1YtevXoB6vdweXk5iYmJJCYmkpCQwPbt20lJSWHSpEmt3lRLT09nwYIFnD9/nrCwMD766CMsLCzEOVgnw8zMjEGDBqGnp8eFCxfIy8vD2dn5hucbGRkxcuRI3n33XaKioigtLcXS0rINI759REFI5J9oxJzERNi7F6KjobxcLeCYmkJAAEydCn37gomJ+vymJrh0SZ3t09ys/lpWBgUF6iMzE/Lz1aJQTY1aIKqpUR8NDeqv1dVYGhhwob6eFEHQhqOrq4uBgQGGhoa4ubnh4+ODk5MTAwcOZNy4cf/4sNna2gJQWFjYhn+0tsHExISXX36ZsrIy/vOf//Dhhx8ik8kYOXJkp9z1aWxsJCYmhr/++osVK1Zos4Dq6+tJSUkhPj6eAQMGdIoWtRKJpMNkMYG6q0VERAQqlUosFxMREely9OrVC3d3d44cOUJsbCxeXl4tCj0hISGEhIS0YYSdi6amJmJiYli6dKl2Abxq1apWbTipVCqysrJYvnw5Bw4cYMCAAWzatElbJi7SfmRlZZGfn4+3t7coCN0EiUSChYWF9l6hUqmQyWQsXryY3bt3M2PGjFaJOlu3bmX//v307duXTz75BHt7e1EM6oTo6upib2/PwIEDiY2N5fDhw8yePfuG5+vp6eHp6UlgYCApKSlERUVx//33t2HEt48oCIn8k6YmiI2Ft9+G+HgYMABGjVJnBKWlqUWinTth/Xq47z6QSqGiAh59FHJz1WIPqM9vbFRnBWl27a53Q9SIPxIJRvX1jNDTIxMwNDTE1NQUPz8/bS3whAkTCA4Ovq7njAZNfbRSqbyLf5SOg5WVFa+99hplZWVs376dTz75BJlMxsCBAztNO3oNBgYGDB48GF9fXywtLWlsbERPT4+SkhIOHjyIsbExQ4YMwdzcvL1D7XScO3eO1NRUnJyc6NevX4ufGREREZHOhp6eHmFhYZw/f56DBw8SFhYmZv7cJk1NTSQmJrJy5UpOnjxJWFgYa9eu1W6wtYQgCBQWFrJp0yZ++eUXgoKC2LRpEwEBAW0QuUhLaAylCwoKCAsLEwWhW0QikTB58mRWrFjB3r17qaqqapVAOnDgQAYNGsSGDRvw8fERxaBOjKZs7Pjx4xw6dIjHHnusxfJZTdnYunXrCA8PZ8qUKZ3i9RcFIZFrEQQoLoYtW9QZP2+9Bc8+qy4VA3U2T1QUvPwyLF4MvXuDm5ta8DExgaqq/z1Wc7P6q1QKenrqr5oPhUSiPjTlZf/f40cqCEyyt6fA3x9nZ2cCAgKYNm3aLQ1iGkGoK2YIaXBxcWHevHmUlZVpu3CZmJjQo0ePTrXwl0gkmJmZYWZmRnV1NTk5OTg7O6NUKjl8+DCWlpaMHTu2vcPsdAiCoC0XmzJlCiYmJp1iQBIRERG5FUaPHs0333zDkSNHWlU2JvJPmpubSU9P5/3332fv3r0MGDCAtWvX4uHhcdNrBUGgtLSUb7/9ls2bN+Pp6cm6desYOHBgG0QucjNqa2vJycmhqqoKR0fHDpXF3FkICgrC39+fmJgYjh8/3qqOt5MnT2bQoEFYWlqKc69OjpmZGYMHD0ZHR4ezZ8+Sn5+Po6PjDc83NDRk9OjRrF+/XutB1ZGsJG6EOGqKXItKpS7v2rkTBg6E5577nxgE6n+HhMBLL6nP27ZN/X2pFOzsbvyYGh8hTZlYdbX6e6AWkvz8YMgQJNOn03vpUn755Rc+/vhjXnjhhVve0dDsaHXVDCENvXr14pVXXiE4OJgdO3awdetW0tPTadYIcZ2Mqqoq4uPjyc3NJTk5mStXruDs7CxOLG+D6upq9u3bhyAITJ48udNljomIiIi0hj59+uDq6kpmZiYJCQnU1dW1d0idCpVKRU5ODp9//jk//fQTQUFBrFy5kt69e9/0WkEQqKys5Pfff2fFihUoFAqWLVvGhAkT7n3gIq0iNzeX/Px8bGxscHBwEBtL3CISiQSpVMrUqVMB+P333xH+ZmnRElZWVqIY1AXQ1dXF0dGR/v37U1JSQmRkZIvn6+np4ePjQ0BAAEqlkujo6LYJ9A4RBSGRa6mthbg4dXbPsGHqzJ7/i5ERjB6tLi07dkz9PR0duFlnJalUfa1Mps4qGjkSZs6EefPg++8hPBzJ1q24zpp1R34n3UUQAhg5ciQvvfQSgYGBfP/99/z444/k5uaiUqnaO7RWo1KpqKurQ1dXl379+pGfn8/OnTsxMzNjyJAhHaaFe2ciOjqarKwsPD096dmzZ4tdEUREREQ6K/r6+oSFhWFmZkZERASlpaXtHVKnQaVSoVQq+fHHH/nkk0/w9vZm2bJljBw58qbXCoJATU0Nu3fvZuHChZiZmfHqq68ya9asNohcpLVkZmaSn58vGkrfIVOmTEFPT489e/ZQXV3d3uGItDEWFhaMHj2aiooKDhw4cNONdyMjIyZMmEBtbS27du1qtYjYnoirBJFraWiA7Gy1/8+NBg+pFMzM1MJOZqb6e/r6agHp88/VvkEadHXVPzMyAmdn8PYGGxsYPhwmTlQ/zl2mK5tKX4+pU6dSUlLCxo0b+eyzzzA3N2f27NlYW1t3qN0JQRBobm7Wdo7TdI+rqqoiOzubhIQEEhMTiY2NJT4+HisrKxwcHK7bkl7kxvy9XGzq1Klim1MREZEuzciRI9m6dSu7d+/mueeeQ6FQiGPGTRAEQetDuHbtWhwcHJg/f36rDFAFQaChoYGoqChee+01pFIp//rXv3jttdfaIHKRW0FjKN2rVy9RELoDevXqhbe3N/Hx8Zw6dYpRo0a1d0gibYiZmRnDhg1DIpFw5swZlEolCoXihucbGhoyduxY3n//fQ4dOkRlZSUymawNI751REFI5Fo0HcY0nkA3QiJR/1xT9qWrq+46Zm2tbiNvYADm5uDurs4GcnOD++9Xew7d4/p+jRCiVCq1qmxXXhBLpVJmzZpFUVERn332GZ988glyuZzp06e3W6txjfhTV1enFX5qampQKpUkJyeTlJREUlISiYmJZGVlIQgCRkZGGBoaYmBggEKhoLS0lG3btjFixAh8fX1FX4hWUllZyYEDB5BIJEycOFEsFxMREenS9OzZE5lMRnp6OseOHcPHx6dTeDa0F5pSr71797J06VJkMhn//ve/mTNnTquubWpq4uzZs7z88stUVVXx4IMPsnLlSlGE62CoVCoyMzMpLCzEycmpxQWsyI3RlI1NmzaN+Ph4fv/9d1EQ6mZoysb69etHcnIykZGRPPzwwzc8X09PDz8/P3x8fMjMzOTEiROMGzeuDSO+dURBSORadHTUQk5zM9yo5EoQ1FlAJSUQFKT+nlSqzvzp0UOdDeTiAkOGwNixIJe3XfyoU/sMDAyoqKigrq6uU7Zjv1UMDQ15/vnnKSsrY+vWrWzatAlzc3MmTJhwz7uuqFQqmpqa/pH5U1JSQlpaGvHx8SQkJJCQkEBmZiYSiQQjIyPt4evri7OzM35+fnh6euLh4UFzczPr1q3j7NmzLFmyhA8++ABnZ+cuLezdLSIjI8nPz8fPz4+AgACxXExERKRLI5PJ8PDwICkpiV9++YXg4GACAwMxMzND+v8bVoio0ZR6HT58mDfeeAOARx99VJvpc7NrVSoV8fHxzJs3j5ycHCZNmsSHH354R2X+IveGmpoacnNzqaurw9HREUtLy/YOqVMzZcoU1q1bx65du/jggw+6xdpC5H+Ym5szZswYzp07x4EDB5g5c2aL90xDQ0MmTpzIhx9+yM6dOxk7dmyHHovElYLItRgYgKenWhBKTb22ZbyG5mZ1WVldHfy9raipKWzdqhaG2nERKpFIsLa2Jjs7G6VSiYuLS7vF0paYm5szd+5cysvL+emnn/jkk08wNzdn2LBhdy1LRKVSUV9fT319PXV1ddTX11NZWUlubi5JSUnEx8cTHx9PUlISxcXF6OnpaYUfQ0ND/P39cXJyIjAwkICAAAICAvDx8flHW/nGxkZkMhkvvPACBw4cYPXq1axZswYrK6u78nt0VTTlYg0NDUyfPl3MDhIREekWTJgwgZMnT3Lu3DlWrlzJmDFjGDx4MDY2NteMQYaGht0221RT6nXq1CkWLVpEZWUlM2bMYOnSpa3qTqpSqUhNTeWNN94gJiaGYcOGsXnz5g5fCtFd0ZSLKRQK7O3txc2hO6Rfv354eHiQnJzM6dOnCQ0Nbe+QRNoQmUxGaGgoa9as4eTJk5SUlLTYtc/Q0JBx48axceNGDhw4QE1NDSYtVd60M+LdQeRaDA3VIo+tLRw6BLm54Oj4v3bxKhUUFcEvv6j9f/6eAqejc2PfoTbGxsaG7OxsCgsLu40gBKBQKJg7dy5lZWXs2LGDjz/+GBMTE/r373/L7eibm5tpaGigrq5OW/pVVlZGeno6ycnJpKSkkJycTHJyMiUlJejr62sn3GZmZri5ueHl5aUVfgICAnBxcWnVTqKenh59+/Zl9erVvPzyy/zxxx84OTkxd+5czO6B71RXoaysjIMHDyKVSsVyMRERkW7Dgw8+yM6dO4mKiuLQoUPs27cPiUSCl5cXQUFB2jHIz88PuVx+jUikp6fXoXdu7xZNTU1cvHiRxYsXk5mZycSJE3n33Xdb1bhBpVKRnZ3NkiVLOHjwIH379uXTTz8VfWk6MJmZmRQUFODs7CyWi90hfy8bW7duHdu2bRMFoW6Grq4uTk5O9OnTh9TUVCIjI5kxY8YNz9fT0yMwMBBvb2+ysrI4efJkhy41FAUhkWuRSNTt4598Ej74AJYvh4UL1QbSEglUVsL+/fDf/8KgQdBB24t2p05j/xdfX1/mzZtHRUUF+/btw9raGplMhp+f33Vr/DV+P3/P+qmvr6esrExb8hUXF0dcXBzp6ek0NDRgYGCgFX/kcjl+fn64u7vj4+NDYGAggYGBuLq63rII9XeMjIwIDQ1l+fLlLFy4kE8//RSFQsGsWbPEVN0bsH//foqLi+nduzdeXl6ip4OIiEi3wMLCgg8//JAvvviCs2fPUlhYSE1NDbW1tURFRbF//37q6uowNTXF29ubHj16EBQURFBQEA4ODpiYmGBoaKgVinR1dbuUSNTU1ERCQgIrV67k4sWLjBw5kvfffx8bG5ubXqtSqSgoKOD999/n999/x8/Pj82bN+Pj49MGkYvcLhpBKCQkRBSE7hL3338/77//Pjt37uS9994TN926GTKZjLFjx7J+/Xr279/PAw880Kqysc2bN7Nz505GjhzZYccVURAS+Sfm5vD445CVBdu3w5kz0KuXugV9WhokJUH//vD++2q/oA5IdxaEAAYOHMiLL75IRUUFv/zyC+bm5rz00ku4uLhoM380AlBtbS0FBQUkJSURFxenNXzOyclBpVJpjZ719fVxdnbG0dERX19frfDj5+eHjY3NPUnDNzMz47777kOpVLJixQpWrVqFjY0NEydORF9f/64/X2dGpVKxfft2GhsbeeCBB8SJioiISLfCw8ODtWvXav9fUFBAamoqCQkJxMXFcenSJbKzs8nNzSUtLY2ff/5ZKxJpsln79OlDnz59cHBwwNjYWLvxYWBg0KlFopKSEn788UcOHjxIcHAw69evx9nZ+abXCYJASUkJX331FVu2bMHNzY333nuP4ODgNoha5HZRqVRkZWWhVCrFDKG7yIABA3B1dSUtLY1z584xePDg9g5JpA0xMzMjLCyMtWvXcvz4cUpLS1u0stCUjX344Yfs27evQ/vaioKQyD/RZAmtXatuDR8eDikpau8gNzf417/ggQfuScv4u4Vm16u7tJ6/HhMmTKCsrIz33nuPb7/9FjMzMyZPnkxFRcU1k+S4uDiUSiU6OjoYGBhoDy8vL9zd3QkMDMTf3x8vLy88PT2xsbFp00mxXC5n1qxZZGVl8dVXX/HWW29hbW3NoEGDxAyYv1FSUkJkZCS6urpMnDjxjrKzRERERDo7dnZ22NnZXbNoKy0tJTk5mYsXL3Lu3DkuX75MYWGhViTatm0bjY2NWFlZ4efnp9346NGjB/b29toMIs042VlMq01NTRk2bBiJiYm88cYbBPzd//EGCIJARUUFv/32G6tWrcLe3p6VK1cyceLENohY5E6oqKggNzeX5uZmHBwcxK57dwGJRIKOjg5Tp07lgw8+4PfffxcFoW6Gnp4eLi4u9O7dm/T0dI4ePcq0adNueL6uri49evTA3d2d3NzcDu09JQpCItenqQmqqqBPHxgxAjpZNoZGEOquGUKgHrwefPBBCgsL2bhxI+vXr2ft2rU0Nzejp6enndAaGhrSu3dvfHx8rvH7cXZ27jCdQ2xtbZk/fz4FBQXs3LmTBQsW8O2334rt6P/G7t27KS8vJzg4GGdnZ1EsExEREfk/yOVygoODtRkuKpWKvLw87QZJbGysViRKSEjgwoUL1NfX09zcjKWlJUFBQfTr14+ePXvi5+eHtbX1NRspBgYGHfLea2xszMSJExk1alSrOo9qupHt3buXN954A7lczoIFC3j00UfbIFqROyUjQ0lRkQ5ubn1xcHAT50l3kWnTpvHhhx/y559/snbtWjFbvZuhKRvbsGED+/bt4/7777/h50vTVXnSpEl8+umn7Nixg+HDh3fITQRREBL5J4IAhYXwxhvqfy9dCp2sVlxTMtadM4RArU6HhoayZ88elEolBgYGuLm50bt3b61/QmBgIPb29h06o0QikeDo6MiKFStQKpWcOHGCefPm8eWXX+Lk5NQhb65tiVguJiIiInLrSKVSHB0dcXR0ZPTo0YC6oUJ+/v9r777jqq7bP46/DnuLigtFzZGCiAhuzZ1bG476pZlZd9rQ7srW3bLS6rbbzFVpS1umOSv3yr0BNw6cICAKiiD7nN8f3zhpObCQc4D38/E4D9c5eB0Uzjnvc32uK4F9+/axc+dO9uzZQ3R0NMnJyezZs4cdO3aQnZ0NQJUqVaxdRPnDqytWrHhVQOTs7GwXL8gdHR0LHAZlZWWxceNGnn32WZycnBg6dCgjRowogiqlMMTG1iY3dxJhYSlUr24fb+yVFC1atCAgIIDjx48TFRVFs2bNbF2SFCFvb286derEf//7XzZs2EBqauoNO/BcXV3p3r07kydPZtmyZYwdO9Zu3my/kgIh+SuzGeLj4eefjaNjxTD91pExQ2ZmJgsXLmT37t3Url0bf39/hgwZwn333WeX35BuxMHBgVq1ajF+/HgeffRRNm7cyCuvvMLkyZMpV66crcuzqcTERDZs2ICLiwtdu3a163BPRMSeOTo6WkOirr9vUs3MzOT06dPWLqJ9+/Zx4MABzp07x+bNm/ntt9/IysrC0dGRgIAAQkNDCQ0NJSQkhNq1a1OmTBlcXFxwcXGxq5DozywWCzk5OezYsYPhw4dz+fJlHnzwQUaPHm2X9cq1HT/uwN69rnTqVJmyZW1dTcmRf2zsnnvuYdKkScydO1eBUCmTf2ysYcOGnD59mvXr19OnT5/rXt/JyYnQ0FBq1KhBbGwsO3fupE2bNkVYccEoEJK/SkuD336D7GwICzPmBhUzpX2oNBhdI+vXr2f58uWYTCaee+45YmJimDt3Ln5+frRv377YBQeOjo4EBgby0Ucf8eijj/Lrr78SEBDA66+/XqDVuSXVzz//zOXLl+nQoQP+/v564i4iUojc3NyoW7cudevW5d577wXg0qVLxMTEsHfvXvbu3cvu3bs5fvw4qamprFq1iiVLlpCVlWXtzA0ODqZhw4bWI9o+Pj7WkMjFxcXmQ6vzN47u37+fESNGkJiYSI8ePRg/fryOxRQjeXnGTphz56B6deN9XSlc9913H1OmTGHRokWMHTu22D2Xln/G29vbOix6+fLl9O7d+7rfu00mE25ubvTs2ZNp06bxyy+/0Lp1a7s72aBASK5mscDFi7B0qbFqvpgOD7zyyJjFYrG7L7yikJKSwuzZs9m/fz+PPvooHTp0IDw8nPfee49vvvkGX19fGjdujJNT8fo24OLiQvPmzRk/fjzDhw9n+vTpVKlShWHDhhW7rqfCkJeXx4IFC8jNzeW+++7TE3cRkSLg7e1t7QTKd+nSJY4dO8bevXutg6uPHz/O2bNnWbZsGT///DO5ubl4e3tz55130rBhQ4KDgwkJCaFmzZp4eHjg7Oxs7SRydHQssucvZrOZmJgYnn/+eaKjo7nrrruYMmUK3na8QET+6vx5OHMGHB3B39+u978UW61bt6ZKlSocPnyYPXv2EBYWVipfZ5RW+cfGPvzwQ9atW8elS5fw8fG57vVdXV3p0aMHU6dOZcmSJbzzzjt2N9qheL0SlNsvL894JNmyxXhr4e67bV3R35K/CSspKQmLxVLqQqHc3Fx++ukn1q9fT+3atenZsyc1a9bEZDLRoUMHPvvsM7777jvKlClDnTp1il1Hibu7O126dGHMmDG88MILjB07lsqVK5fKQOTMmTNs3brV+jkpbgGfiEhJ4e3tTaNGjWjUqBGDBg3CbDaTnJxMdHQ0e/bssXYTnTx5kqNHj3LgwAG+/fZbcnNz8fLysoZEjRs3JiwsjBo1aliPmOVfbkdIZDabOXXqFG+88QabNm2iSZMmTJ8+XevKi6FTpyAx8Y/uoFL01LdImEwmnJycuOeee/jkk0+YP38+YWFhti5LipCzs7N1C/OZM2fYsGEDPXv2vO71nZycCAsLo2rVqpw8eZLIyEhatGhRhBXfnF45yNUuXYJVq4yfN28OVarYtp6/ycvLCw8PD9LT00lPTy9V73BZLBYOHTrEwoULiY+P56233qJZs2bWJ5C9evViz549LFq0iHLlyjFs2DAqVapU7AIzLy8v+vfvz8mTJ5kwYQIvvvgiFStWpG3btna55eV2mTdvHpmZmfTs2RM/P79iF+6JiJRUDg4O+Pn50aZNG+vciPyQ6NChQ0RFRbFjxw52795NfHw8R44c4cCBA3z33XdYLBbKly9vXXuf300UEBBg7SJycXH5x/OI8jetffDBB8yfP58GDRrwySefcMcddxTWp0GK0MmTxl6Y6tVBed7tc//99/Ppp5+ycOFCRo8erWNjpUz+sbH8YdE9evS46bGxXr168cUXX7Bo0SIFQmLHLBZISYFly4r1cbF8FSpUID09nbNnz5aqQCgrK4tvv/2WXbt20bVrV+6++27KXjFVsFq1arRq1Yp169Yxffp0vL29GTp0KGXKlCl2oZCvry///ve/OXPmDD/++CMjR45k1qxZBAUFlYpg5M/HxfSERETEvuWHRH5+frRu3RqAnJwcEhIS2L9/P3v27GHPnj3s37+fuLg4duzYwebNm8nJyQHAz8+P4OBgmjZtSpMmTQgKCqJcuXJXdRE5OTkV6DHQbDaTmJjIF198wRdffEGtWrWYMGHCVcfgpPiwWP4IhFq00Pyg26lt27ZUrlyZAwcOsH//fho1alTsnkPL35cfCP3vf//jt99+4/Lly3h6el73+q6urtY5QosXL+add96xq+fsJS4QysnJwWw22+0GB7uWm2tMotuxw1gz36mTrSv6RypWrMiJEydISkqidu3ati6nSJjNZlavXs3KlStxcnKif//+BAcH/+V6vXv35uDBg3z66adMnjwZLy8vHn74Ydzd3W1Q9d9nMpnw8/NjzJgxJCYmsnbtWoYNG8aPP/5YKtbRHz9+nF27duHt7U3nzp3t6sFFREQKxtnZmYCAAAICAujWrRsA2dnZxMfHExkZyc6dO9m9ezfR0dGkpKSwbds2Nm3aRE5ODg4ODlStWpWQkBBCQkIIDQ2lYcOGlC1b1hoO5f945WNifmfQuHHj+PTTT6latSpjxoyhY8eOtvo0yD9kNhtP45OTISAAfh+nKYUs/9hY7969mT59OvPnz6dRo0a2LkuKUP6xsaCgIBISEtiwYYP1e/e1ODk50axZM/z9/Tlx4gRRUVE0bdq0CCu+sRKVmOTm5rJ+/Xp+/vlnLly4gMVisXVJxUtqKqxcaayZb9MGype3dUX/yJWDpUuL5ORkfvjhB6Kjo3nooYdo06bNNUOCMmXK0KxZM+rWrcvp06et2xKys7NtUPU/YzKZqFKlCpMmTaJhw4ZERETw9NNPc+HCBVuXdtvNnTuXnJwcevToUSw7vERE5NpcXFyoUaMG9957L2PGjOGXX35h7969bN68mS+++ILnn3+erl27UqVKFVJSUli1ahXjxo2zvhHUtm1bRowYwWeffca6deuIi4vj3LlzJCcnk5qaSnx8PNOnT2fy5Mn4+Pjw3HPP8cADD9j6bss/kJAA8fHg6WkMlPbwsHVFJdv999+PyWRiwYIF5OXl6XVnKePl5UXXrl1JT09n2bJlN/z3N5lM1i6h7Oxsfv755yKs9OZKVIfQsWPHGDlyJKdOneLTTz/lnnvuwcvLSy+SCiL/uNjy5VCmTLE/LgalLxDKy8tj1qxZbN68mcDAQHr16kVAQMB1r9+tWzeio6M5fvw40dHRTJ8+HV9fXzp37lzsBhM7ODhQs2ZNPvnkEx544AFWr17Nf/7zH8aPH49HCX1GlJuby/z588nLy+P+++8vdv9mIiJya9zc3Ljzzju588476d+/PwAZGRkcOXKEHTt2sGfPHnbv3s3hw4c5c+YM8+fPZ/bs2eTl5eHg4ICvry8VKlQgODgYd3d3Zs6ciaurK61bt+bZZ5+18b2Tf+rECeO4WI0aOi5WFDp06EDFihXZt28fhw4dIigoyNYlSRHKD4QmTJjA6tWrycjIuOFrDldXV3r16sXnn3/Or7/+yltvvWU3z91LVIdQ3bp16devH25ubjz//POsX7+ezMxMJbYFkZMDx4/D7t1QoQK0bWvriv6xChUqAKUjELJYLERHR7NgwQLOnj3L4MGDCQ8Pv+FtXF1dadGiBaGhoVgsFrZt28bXX3/Nrl27yMvLK6LKC4+TkxMhISFMmzYNT09PvvvuOyZOnEhWVpatS7stoqOj2bdvH+XKlaN9+/Z286AiIiJFx93dnZCQEB577DEmTpzI2rVrOXz4MKtWrWLSpEk88cQTNG3aFC8vLy5dusThw4eZM2eONQxq1aoVs2fP1piFEiB/fpACodvPZDLh7Oxs3S41b948G1ckRc3FxYW6detSr149zp49y6ZNm254fScnJ5o3b07FihU5evQoe/fuLaJKb65Effc3mUy8/vrr9OvXj6ysLEaMGMGuXbvIyclRKHQzFy4Y3UHu7tC+vdElVMzlB0Lnzp2zcSW3X3Z2Nl9//TW7d++mZ8+edOjQAR8fn5ve7q677qJ3795UqlSJy5cvs2TJEmbMmEFMTAxms7kIKi9cLi4utGnThokTJ2KxWPjvf//Ljz/+aB3GWZL89NNP5Obm0qdPHzw8PNQJKSIimEwmvLy8CA8P59FHH2XSpEls2LCBqKgoxowZQ8+ePalfvz7Vq1dnwIABzJkzBzc3N1uXLf/QlQOl81fOy+13//33A7BgwQLMZrNeb5Yynp6edOvWjcuXL7N06dKbHhtzc3OjZ8+e5OTksHDhwqIr9CZKVCAExpCnDz74gI4dO5KQkMCoUaOIjo4uli9ui4zFYkygW74cfH2he3dbV1Qo8o+MJSUllehv0BaLhWXLlrFy5UpcXV154IEHCAwMLNBtHRwc6NChA+3bt8fNzY20tDRmz57NzJkzi+3nzc3Njd69e/PGG2+QkZHBqFGjWLNmTbHserqe3NxcFi5ciNlspm/fvhomLSIi1+Xo6EhAQADPP/88CxYs4MCBA5w4cYKZM2dSvpjPixRDbi6cOmW8v1u9utHsL7df586dKVeuHFFRURw5csTW5UgR8/LyokePHuTm5rJp06abvtZwcXGhe/fuZGdn89VXX9nNG9YlLhACY2DulClTaNGiBZGRkbz22mscO3ZModD1ZGfDkSNw8CBUrmwMlC4BSssMoeTkZL7//nsOHz7MI488QosWLW7p+FBwcDAdO3bE398fgJSUFL788ktmzJjBpUuXimUo5OXlxZNPPsm//vUvLl68yPDhw9m3b1+J+R6wc+dODh06RIUKFWjVqhWOjo62LklERERsJDYWEhONfTBVqoCrq60rKvlMJhMuLi706tULgPnz59u4IilqLi4uBAcH8/jjj/PMM8+Qm5t7w+s7OTnRqlUrHBwcOH/+PL/88ksRVXpjJTIQAqhatSpTpkyhYcOGLF26lAkTJhAXF1diXhAWqpQUWLYMvL2NVfMlZAhvaZghlJeXx8yZM9m6dSvBwcH07NmTqlWr3vLH6datG3369MHLywuAxMREpk6dysyZM4vl5jEAb29v3nnnHXr16kVcXByDBw8mNja2WAZcV7JYLHz44Yfk5uZy77334ubmpuNiIiIipdiVA6W1br5o9e3bFzACIYvFUuyfZ8qtqVChApMnT+bhhx++6fHb/GNj9erVIzs7m+nTpxdRlTdWYgMhgKCgIMaNG0edOnWYNm0an3/+ebE9BnO7WCwWLMnJWFauNI6Ldetm65IKzZ87hErav7vFYmH//v0sWLCAc+fOMXToUBo1avS3Ppa/vz/NmzenRo0a1t+LjY3l008/Zfbs2TdNvO2RyWSibNmyTJ06laZNm3Lw4EEeeeQRLly4UGz+L1gsFvLy8sjJySEzM5O0tDSSkpJYsWIFeXl5hISEaBCoiIhIKXfiBCQlKRCyhS5duuDr68uuXbs4duyYrcsRO2axWKxdZXl5eURFRdm6JKCEB0IAHTt25PXXXycgIIBx48axYMECLl68WGxeEN5uuZmZJB46xNmzZ8kJCIAWLWxdUqGpUKECDg4OJCUllcjOsJycHKZNm8bevXu57777aN++Pd7e3n/74/Xq1YsHH3yQsmXLAsY3rSNHjvDFF1+wbNmyYjmDx2QyUbFiRb788ktq1KjB5s2bGTFihF1uHrNYLOTm5pKdnU1mZibp6elcuHCBiIgIZsyYwQsvvEDDhg2pXLkyaWlpODs7M3DgQG0XExERKcUsFmNRsAKhonfloGDQtjG5uZSUFObMmYODgwOVK1e2dTlAKQiEAAYNGsQzzzxDuXLleOWVV1i+fDmXL19WKAScTU7mjSVLaOntzbx+/cDFxdYlFRpXV1d8fHzIzc3lwoULti6nUFksFn755RfWrl2Lh4cHDz74IHXr1v1HHzN/K0n9+vWtR5Byc3PZuXMnX331Fdu3by+WwZqjoyN16tTh22+/pUyZMsydO5d33nnHpoPcruz8ycrK4vLly5w5c4ZVq1YxYcIEhg4dSrNmzahYsSLNmzfnySef5PPPPyc+Ph43NzfKly9PcHCwtdtJ38tERERKp6wsY4ZQWpoxUNrPz9YVlT75x8YWLFig52VyTRaLheTkZO655x5OnDiBl5cXo0aNsnVZAJSat5afe+45zp49y5dffsnzzz+Pr68v7du3x8XFpdTO37BYLJw/f57ly5fj6uZG7ZYtbV1SoatYsSIXLlwgKSmpRG3SOHfuHN988w0xMTG8/PLLNG3atFA6RTp37szhw4c5duwYiYmJAGRkZLBixQrKli1L2bJlqVevXrH7mnFyciI8PJyvv/6aBx54gI8//piqVasyfPjw2z6Q2Ww2/+WSkpLCwYMHiYyMtF4OHz6MxWLBwcHBenF3d6dOnTo0btyYwMBA7rjjDho3bkzNmjX54Ycf6N27N7/88gvVqlUDKHb/LiIiIvLP5K+br1LFWDevxuGi17VrV3x8fNi2bRsnTpzgjjvusHVJYkcsFgtpaWl8/PHH7Ny5E09PT1577TUGDRpk69KAUhQIOTo6Mnr0aOLj41m4cCEvvvgi06dPp0mTJjg6OpbKF1Lp6elERkYSHx9Pu3btaNy4sa1LKnQVK1bk8OHDnD17lvr169u6nEJhNpv58ssv2blzJ02aNKFnz56F1nLo7OxM06ZNadKkyVXHxNLT05k/fz5ly5blpZdeokKFCsXua8bZ2ZnOnTvz4YcfMnLkSF555RWqVq1Knz59/vEcnvx3giwWC2az2fqj2WzmzJkzVwU/kZGRJCQk4ODggMlksoY/np6e1vAn/xIaGoqHh8c1P9eDBg1i6dKl9OzZkyVLlliHiRe3fxcRERH5+/KPi9WsqeNituLh4UH37t2ZPXs2CxYs4Pnnn7d1SWInLBYLmZmZzJ07l/fff9+6Cfmll16ydWlWpSYQAuOL9aOPPuLixYusXLmSV199lUmTJtGgQYNS9yIqv20tv/Oja9euJXIWSf6msaSkJBtXUjgsFgt79+5l4cKFpKSkMHbsWIKDgwv172jZsiX33nsve/bs4fTp09bfv3DhAt988w3e3t48//zzeHl5FbuvG1dXV4YMGcKpU6f48MMPGT58ONWqVSM8PLzA9+V64U92djaHDx8mOjqaAwcOEBkZyfbt2zl//vxVwY+DgwMVK1akQYMG1uAnKCiIBg0a3HLH4ldffUW7du247777mD9/vjqFRERESpn8gdJt2igQsqW+ffsye/Zs5s2bx3PPPQfo+VhpZ7FYyM7OZs2aNTzzzDO4u7szZswYnn32WVuXdpWSlwDcRIUKFZg4cSJDhgxh48aNfPDBB7z99tvUrl271H3Rnj9/njVr1lCuXDm6laDtYlf686ax4i4nJ4epU6dy4MAB+vfvT+vWrfH09CzUv8NkMtG+fXu2b9/Od999R0ZGhvXPkpKSmD59Op6enowcORKXYjhzys3Njddee43Tp0/z448/8uCDD7Jy5Upq1qz5l+8BNwp/jhw5QkREhLXrJyoqioyMjKs6f0wmE5UqVSIwMJCwsDAaN25MWFiYdU7TP/2e4+rqyrfffkufPn3o27cv8+bNUygkIiJSSuQPlD53Th1CttazZ0+8vLzYunUrcXFx1udjUjrlL4vZuXMngwcPBmD48OF2FwZBKQyEAO644w7GjRvH8OHDmTVrFn5+frzwwgsEBASUmhdRly5dYtu2bSQnJxMeHl7oXSb2oiQFQhaLhQULFrBu3TrKlCnDwIEDqVWr1m35u+rUqUO7du1Yu3YtR48everP4uPj+fzzzylTpgxDhw697TN4CpvJZMLT05PJkycTHx/PunXr6Nu3LwsWLCAgIMA6DDA//Ll48SJ79+4lMjKSiIgIoqOj2bdvH7m5udZQJz8AqlGjhjX0CQsLIzw8nEqVKt3W7yt169bl5Zdf5uWXX6Zfv37MnTtXoZCIiEgpkJYGcXGQnQ0BAfD7olixAQ8PD7p27cq8efOYP38+I0eOtHVJYiP5y2P279/Pgw8+SHp6OgMGDOD999+3dWnXVCoDIYDmzZszZswYnn/+eT755BP8/f157LHHKF++fIl/EZU/THrlypWUK1eOrl27ltj77Pf7qoWkpCQsFkuxvp/nzp1jxowZHD9+nDfeeIPQ0NDbGsZ06dKFgwcPMnnyZFJTU62/b7FYOHbsGF999RV+fn7cc889/3gGT1EzmUyUKVOGGTNm0LlzZyIjI2ndujVz584lMjKS9PR0a/dP/rDnK8MfFxcXgoKCCAsLIygoiMDAQFq0aGGzweVDhgxhw4YNfP/99/Tt25e5c+cSEBBgva8iIiJS8uTPDwoIMLqDitnTsRInv1t73rx5jBgxAtDzsNIm/03l48ePM2jQIBITE+ncuTOfffaZ3Y5nsc+qikjPnj2JjY3l/fff5+2336ZcuXIMGDAAHx+fEv3Fa7FYOHv2LL/99huVK1ema9euti7ptikpHUJms5lPP/2UyMhIWrRoQa9evaz37XapUKECTZs25Y477mD37t1X/Vlubi5RUVF8/fXXVKpUiVatWt3WWm4HBwcHqlSpwsSJE+nTpw9xcXG0/H3TXv5xL5PJhI+PDw0bNiQ0NJRGjRrRuHFjGjRogKurq43vwdWmTp1KTEwMGzdutIZC1atXB/RkREREpCS6cqD072MzxYZ69eqFh4cHGzdu5MyZM9aFH1I65I+aiI+PZ+jQoRw6dIimTZvy7bff4uHhYePqrq/U58hPPPEE//rXvyhTpgyvvvoqK1asIDMz0/oPWhJdvHiRzZs3c/nyZYKCgrjzzjttXdJtk/+ifd++fTau5O+zWCxERUWxaNEiUlNTGTZsWJFtTOvcuTODBg2iXLlyf/kzR0dH0tPTOXfuXJHUcjs4OTnRqVMnJk6cSIUKFXBxcaFixYqMGDGCGTNmsH37dhITE1m/fj2TJk3iscceIywszO7CIDBmI02ZMoXAwEAiIyPp168fJ0+etL5TISIiIiVL/vygO+7Q/CB74O3tTZcuXbBYLCxatMjW5YgNnDt3jpEjR7JlyxaCgoKYPXu2zU4QFFSpD4RMJhMvvvgi/fr1Iy8vj1dffZVNmzaRm5tbIl9EXXlcrHz58iW6Owj+6IyIiYkhNzeX3Nxc6zrw4vJCOScnhwkTJnDo0CEGDhxIq1atcHd3L5K/29PTk7CwMBo2bHhVl0n58uUZOHAgU6ZMoU+fPkVSy+3i7OzM8OHDSUhIICsri8TERD7++GMGDRpE48aNi9Xg7ODgYIYNG0b58uXZtWsX999/P8eOHbP+fxcREZGSIX+g9Pnz6hCyJ/369QNg7ty5xea1hvxzFouFlJQU/vOf/7BgwQJq1qzJ999/bx3hYM9KfSAE4OLiwtixY+nZsycJCQm8+uqr7Ny5s0R+AVssFhISEli/fj3lypWjS5cuti7ptqpfvz4ODg7k5OTg5uZGp06d+OCDD1i/fj2XL1/+Szhkb//mFouFOXPmsGnTJsqWLcvAgQOpUaNGkdZw11138dBDD1GpUiVcXV1p2LAh7777LhMnTiQwMLBIa7ld8odClwTPPPMM3bt3x8XFhaioKPr166dQSEREpIRJToYzZ4yfV6sGPj62rUcMffr0wd3dnfXr1xf7kRVSMBaLhdTUVKZOncoXX3xB5cqV+eqrr4rN0qaS8QqoEPj4+PDhhx/SsWNH9uzZw9tvv83evXtL3Auo5ORkNmzYAEDjxo2tM0ZKqvr16/PSSy/h7OyMyWRi48aNvP7663To0IGKFSty99138+abb7J06VJSUlKsAZG9dBAlJiby5ZdfcurUKZ599llCQkKKPLhwdnYmPDyc7t278+CDDzJjxgyefPJJXF1db/g5steQrTSYOnUqLVq0wMnJid27d9O/f39iYmIUComIiJQQV66br1ABNC7QPnh7e9O5c2fMZjMLFiywdTlym1ksFtLS0vjmm28YPXo0fn5+TJo0ibZt29q6tAIzWfTq4Cr79u3jySefZPPmzQwZMoRXX32V2rVrl4ihrBaLhUOHDvHUU09x5MgR3nrrLR577LEScd8KYufOnZw4cYJNmzaxadMmIiMjycvLA4zPjYODA3feeSetW7emVatWtGnThlq1alkDmCs/T0XxObNYLLz++utMnz6dkJAQJk6caLOk2WKxkJOTA2A9QpWdnU16ejpOTk54eXn95XOSk5NDeno6Dg4OeHt7l5r/Z/YiKiqKQYMGcfDgQcxmMw0aNGDevHnUqVPHOjRbREREiqc5c+Ddd41A6K23oEkTW1ck+WbOnMmQIUPo1KkTK1assC4qkZLFYrGQkZHBokWLGDJkCB4eHvz3v//liSeesHVpt0QdQn8SHBzM2LFjCQ0NZcaMGXz55ZfEx8eXiHfVzWYzcXFxbNq0iQsXLrBp0yZycnJKxH0riCZNmtCvXz8mTJjA9u3brbOURo8eTZcuXXB3d+fQoUN89dVXPPbYY9SrV49q1arRr18/PvroI7Zt20Z2dvZVnS+3qwPGYrGwfft2Fi1axOXLl3n66aepU6dOof89BZW/av3KeTpr166lU6dO/Pvf/77m52Dnzp307NmTwYMHk5ubW5TlChAaGsrQoUOtA8H3799P3759OXLkiDqFREREirmEBLh0yQiE/PxsXY1c6Z577sHV1ZW1a9eSlJRk63LkNrBYLGRnZ7NhwwaGDx+Oq6srTz/9dLELg0CB0DW1bduWl19+mbp16/LRRx/x/fffk5ycXKxfQOUPk163bh05OTlkZ2fz888/M3r06GJ9v/4JHx8fOnXqxJtvvsny5ctJSUlh586dTJw4kQcffJBKlSqRlJTEwoULefHFF2nZsiV+fn506tSJ119/naVLl3Lx4sW/zCAqjM9nTk4OH330EceOHWPIkCE0bdoUNze3QrjXUpo8//zzdOvWzboVbf/+/fTr149Dhw4pFBIRESnGRo6EHTuM7iBtN7cvvr6+dOrUCbPZrG1jJZDFYiE3N5eIiAgeffRR8vLyGDx4MO+8846tS/tbFAhdx4ABAxg5ciT+/v68++67zJ8/n7S0tGLxAupaHSx5eXkcO3aM2bNn4+/vz8CBA8nMzGTGjBl8/vnnxeJ+3W7Ozs6EhYUxYsQIZs2aRUJCAkePHuXbb7/lySefJCQkhIyMDH777Tfee+89evbsiZ+fH2FhYTz11FN8//33xMbGkpeX94+6iCwWC9999x1btmyhcuXKDBo0iKp6pJe/acqUKTRp0gQnJyfACIUGDBhAdHS0QiERERE7Y7H89XI9FSoY3UFOTgW/jRSNvn37AjBv3jzN1CxBLBYLZrOZ6OhoHn30UVJSUujVqxfjx48vtgtqNEPoBvLy8hg7diyffPIJTk5OTJw4kV69euHi4mI350Cv9c9nNptJTU3l4MGD7Nq1i61bt7Jp0yZOnjwJQHh4ON9++y0rVqzghRdeoFq1akyePJlevXrZzf2yVwkJCURERLB582Y2bdrE9u3bycjIuOrfoXr16jRv3pw2bdrQqlUrgoODrR0aV7re5zo+Pp5+/fqxbds2PvroIx5++GHKli172+7T37V8+XJeffVVGjduzOeff/6Xb4Jbtmxh1KhRVKhQgZ9++glnZ2cbVSpbtmzhscces3YGgXE89scff7Ru4tPXvoiIiO0tXw7790N2thH4dOlibBG70cP0+fOwdi0cPQr+/nDXXXDHHUVXs/xVSkoKVapUIS8vj/j4eMqXL6/nWsVcfrB39OhRHnroIfbu3Uu7du346aefKFOmjK3L+9ucbF2APXN0dGTUqFGcPXuW7777jjfeeAMvLy86d+5skxdQ1wp/cnJyiIuLIyoqisjISKKiotizZw+nT5+2vvDLZzKZqFu3Lq+88gqBgYGUL1+es2fP8v777/Piiy9SoUIFmjdvrm9WN1C5cmV69OhBjx49AEhPT2fv3r3WgGjjxo3ExsZy6tQpfvrpJwDKly9P48aNrcOqmzVrhs81doOaTCYsFgv/+9//OHjwIHfffTddu3a1yzDoz671zoeyZvvRsmVLHn74YcaPH8/58+cBY4D+Aw88wA8//ECDBg0UComIiNiBBQvg++8hLc0Igd5/Hx5/HMqVu34olJQEM2fCr79Cs2ZQubICIVsrW7Ys7dq1Y8WKFSxatIihQ4fauiQpBLGxsYwYMYLdu3cTHh7OjBkzinUYBAqEbsrDw4N33nmH1NRU5s6dyzvvvIOXlxetWrW6rX/vtV5MZ2dnExcXx969e9m1axcRERHs3r2b2NjYq65nMpnw8fGhdu3aODg4EBERQfXq1Xnrrbd45JFHrC/6KlasyPDhwzlx4gQ//vgjTz31FLNnz6ZOnTp6YVhAnp6etGjRghYtWvD888+Tl5fH0aNH2bJlC5s2bWLDhg3ExMSwatUqVq1aBYCbmxtBQUG0atXKGhL5+/vj6OjIzJkzmTNnDtnZ2TzzzDPUrFnTtnewABYtWkRUVNRffj8tLY24uDg6d+5c9EXJX7zyyivs3r2bhQsXkpWVBRjHxx566CF++OEHgoKCcHR01Ne+iIiInbBY4NNPoXFj6NxZq+WLm379+rFixQrmzZvHo48+qudYxZjFYiEhIYHXXnuNVatWERgYyIwZM/D397d1af+YAqECKFeuHGPGjOHixYssXbqUjz76CG9vbxo2bPiPv7Cv10WRnp7O0aNHiYiIsIY/Bw8e5OLFi1ddz8HBgbJlyxIYGEhYWBjh4eGEhYVRr149XF1dSUhIYOrUqYwdO5bx48dTu3Zt2rRpY627WrVqvP3228THx7Nx40aGDx/O7Nmz1db4Nzk6OlKvXj3q1avHkCFDADhx4gTbtm1j06ZNbN68mX379hEREUFERARTpkzBwcGBSpUqUaZMGU6fPk16ejp9+vShfv361zxqZm/8/PwIDw//y/+XxMTEv/x/FdsxmUx89tlnnDlzhi1btlg3v+WHQt999x3BwcEKhUREROzIyZPw449Gx0+dOgqFipP77ruPp59+mpUrV3L+/Hn8/Pz0HKsYslgsJCUl8d///pfvv/+eOnXq8PXXX1O/fn1bl1YoFAgVUH6HTWpqKgsXLsTPz4+XXnqJWrVqFfgL+3rzfs6dO8e+ffvYvXs3Bw4cICoqioMHD3L58uWrruvs7EyNGjUICQmxhj+NGjWiatWqODo6XvPvrFy5MkOHDuXUqVN89913jBo1iq+++oqgoCBMJhMmk4latWoxceJEBg0axJYtWxg+fDjffPMN7u7u+qZVCGrWrEnNmjV54IEHAKNzJioqynrMbNOmTSQkJBAfHw+Aq6sro0aN4o5i0uvbunVrPvvss+vOEBL74evry/vvv88TTzxhHSoNRig0cOBAvv/+e4VCIiIidqJiRePo2KxZ0Lq1sU3Mw8PWVUlB+fn50b59e1auXMmvv/5qfbNYig+LxcKFCxf4+uuvmTRpknX2btOmTW1dWqFRIHQLwsLCeP3113nllVf48ssvKVeuHCNGjKBKlSp/efF0vfDn7Nmz7Nu3z9r1s2vXLk6dOmV9tz6fh4cHQUFBBAUFWQOg/PDnVl+o1axZk1deeYXExETWrFnDc889xzfffEOlSpUwmUw4ODgQGBjItGnTGDBgAMuXL+eFF15gypQp1w2a5O/z8vKiTZs2tGnTBoCsrCx++eUX5s6dS05ODsOGDSM8PLzYTqoX+9a6dWsefPBBJk2axLlz56y/f+DAAR588EG++eYbwsLCrFvJRERExDYGDoR16yAiAr74AgIDoWVLdQkVJ3379mXlypXMnz//qtEdUjykpaUxb948Xn/9dSpVqsTYsWPp2rWrrcsqVHrGf4s6derEs88+y9ixY/n4449xdnbmqaeeolKlSlddLzMzk5MnTxIZGUlERAR79uxh//79xMfH/2XYs6enJ8HBwdbjXuHh4QQGBl5z8PDfYTKZqFevHu+//z7/+te/2LhxIyNHjuTLL7/Ey8sLk8mEk5MToaGhfPLJJwwcOJA5c+ZQo0YNXn75ZX3jus1cXV3p168f/fr1s3UpUkq89tpr7Nmzh19++cU6Twjg0KFDDB48mBkzZhAeHq7NcCIiIjbUrp3xY0wMbN0KP/8MNWoYnUJSPPTt25cRI0awYsUKLly4QNmyZfXaqphIT09n8eLFjBw5El9fX15++WUefvhhW5dV6BQI/Q0DBw4kJSWF8ePHM3bsWJKTkwkKCiIjI4O4uDgiIiLYu3cvycnJV93OwcGBChUqWMOfkJAQQkJCqFWrFh63uf/TwcGBhg0bMmHCBIYMGcLy5ct58cUXmTx5svVFn5ubGx06dGDKlCk89thjTJgwAX9/fwYPHnxbaxORomUymfj0009JSkpi8+bN5OTkWP/s0KFDPPzww8yYMYNmzZopFBIREbERBwf417+MDqF16+DLLyEsDO6/H9TIWzyUL1+eu+66izVr1vDrr78yaNAgBULFQFZWFuvXr+eZZ57B1dWVoUOH8uyzz9q6rNtC30r+pkcffZQdO3Ywa9Yspk6d+pc/d3FxoU6dOoSGhhIaGkrjxo0JCQmhSpUq1zwKVBTfGJycnGjatCkff/wxQ4YMYd68eVStWpU33njDeh1PT0/69OljXUX/2muvUaVKFe6+++7bXp+IFA2TyUT58uV54403GDly5FXzhACOHj3KI488wowZM2jevLlCIRERERupXx/69IFjx+D0aWMl/Z13QmiorSuTgjCZTPTt25e1a9cyb948Bg4caOuS5CZycnLYsWMHTz75JFlZWTzwwAOMGTPG1mXdNgqE/gaTyYSnpycffPABnp6ezJo1i9zcXFq0aEHXrl1p3rw5QUFBlCtX7ppBjy1TYVdXVzp06MCECRMYNmwY06ZNo2rVqgwdOtR6HR8fHwYPHszx48f55JNPeOaZZ5g/bx4NgoNtVrfYn9DQUP73v/9RpkyZa/6frlevHu+99x4uLi6aR2OHTCYTHTt2ZMCAAUyePJnz589f9ecxMTEMGTKEr776ihYtWuDi4mKjSkVEREovkwkGD4ZNm+DMGVi8GNq0gZo1wdfX1tVJQfTt25dnn32W5cuXk5qaiq+vr7qE7JTZbGbfvn08/fTTJCUl0atXLyZNmoSTk1OJ/TfT1Nq/yWQyUaVKFT755BNSUlK4dOkSK1euZNSoUdx1113Wte3Xuti6bk9PT3r37s17771HfHw87777LosXL77qOn5+fvznP/9h8IAB3O/sTL1//xtiY21XuNidihUr0qFDB8LCwq75/7ps2bK0bduWFi1a2KA6KQiTycQbb7xBp06dcHNz+8ufx8TEMHToULZs2UJ2drYNKhQREZHy5WHQIAgKgrw8Y8D0li1wjR02YocqVqxI69atycrKYsmSJddcPiS2Z7FYOHToEM899xzR0dG0bduWqVOnlvjN2wqE/oHrBT72Ev5cj8lkwtfXl//7v/9jxIgRnDx5klGjRrF9+/arrlelShWmv/8+Y8qWxXHDBhg6FC5dslHVYm9u9v+8OHwtiPHvNGXKFFq3bn3No2ExMTE8/vjjbNq0SaGQiIiIDZhM0KsXdOwIPj5w+DDMmwdHj9q6MimI/GNjJpOJefPmKRCyU8ePH+eNN95g06ZNNG3alE8//dTa5FGSKRAqpUwmE5UqVeL5559nwIABHDlyhBEjRnDixAnrnwM4V6+Ow7RpmGrWhA0b4PHHQS8KRUqM/I7AF154gbp16+Lo6PiX6xw9epQnnniCDRs2XLWVTERERIqGk5PxNDw83Pj17NnGU3M9LBcP/fr1w9HRkWXLlnHp0iWFQnbm1KlTjBkzhgULFhAcHMzUqVOpUaNGiQ+DQIFQqWYymQgICODdd9+lTZs2REVF8cQTT1hniZhMJkwODpjq1jXWGnh5GQeXX3vNxpWLSGEymUx069aNe++9lzJlylzzOkePHmX48OGsX79eoZCIiEgRM5mgQQO47z5j9XxamhEKRUXZujIpiMqVK9OyZUsyMjJYunSpAiE7Eh8fz9SpU5kxYwZ33nknEyZMICQkpFSEQaBASIBatWoxYcIE6tWtS8a+feT8979/vN1gMhlvSTRpAl9/DWYzfP45TJ5s26JFpFCZTCZGjx593XlCYIRCw4YN4+DBg3oiIyIiUsRMJmOW0F13gbMzrFplXC5csHVlcjMmk4n7778fk8nE/PnzC/w8avv27dSpU4e+ffsyfvx4fvvtN3UYFaLz58/z7bffMn78eGrWrMm7775Lu3btSk0YBAqESj2TyYSDgwMNGjRg2eefs8Tfn0rTpsGrr/4xqc5kAldX4+DylCnGHKHRo43DyyJSYjg5OTFhwgRatmx5za1i5cuXp3///lSqVMkG1YmIiJQseXmwdq3RgP+nZZ/X5esLDz0EjRoZ79POmQM7dtzWMqWQ9O3bFwcHB5YsWUJaWlqBQp2IiAhiY2OZP38+o0aNomPHjpQvX56goCAeeughxo8fz9q1awv88eQPqampzJ8/n9GjR+Pv789rr71mnfVUmmgXtGAymXB2dqZK48bw5puYBg2Cb76BatXg+efzrwTu7tCvHxw/DmPGwMiRULkytG5t2zsgIoXCZDLh7+/Pv//9bxITEzl06BB5eXkA+Pv785///If/+7//o2zZsqXuwVJERKSwXL4My5cb77Nu3Wo8nf7+eyhX7ua3NZmgSxfYuBGOHYM9e2DFCmMlvdi3atWq0bx5czZv3syyZcvo37//TZ9PPfbYY9x1111EREQQERHBrl272LlzJ9HR0URHR/Pjjz8C4OzsTFBQEC1btiQ8PJywsDACAwNxdXXVc7ZruHz5MkuXLuXll1+mTJkyPPXUUwwdOrRUfq4UCAnw+xDp/C6gSZPgiSdg3DjjEeqhh/KvBN7e8Nxzxgr6b74xNo8tXgx16tj2DohIoTCZTPTu3ZutW7cyffp0zp8/zx133MFHH33E3XffjYeHR6l8sBQREfmnzp6FH36AadMgJgZyciAgAB58EPz9C/5xHBzgkUcgMhKWLoWVK41uI7Fv+dvGtmzZwoIFC6wdQzfi5OREUFAQQUFBDBw4EID09HT27NljDYkiIiI4cOAAUVFRREVFWZ+neXt7U79+fcLCwggLC6NVq1bUrVv3ml3gpUl2dja//fYbo0aNwmQyMWjQIF544YVS+/zWZFFvmVzJYoGUFJg6Fd58E6pXN2YHdex49XWSkuDhh2H1aggLgyVLwM/PdnWLSKHKzMzkkUce4fjx40yYMIGmTZvi7Oxcah8sRURE/q4jR4z3UWfONDp58vKgYUMYMsQIgypVMkIegCefNLqF0tLg55+hd+9rf0yLxdj5Mm6c8fHzNWsG775rdBGJ/Tl16hS1a9fG1dWVuLg4fHx8bvm5Vf7L9yt/vHTpEgcOHLAGRJs3byYmJobc3FzrxzeZTFSoUIGQkBBrSNS8eXOqVq2Kk1Pp6BPJzc1l/fr1PPnkkyQkJDBgwAAmTpyIu7t7qX2Oq0BI/spiMd7CePNN+OILqFcP5s6FoKA//txigdOnjVUH+/YZgdH8+eDhYdvaRaRQWCwWUlJScHJywtPTEwcHh1L7QCkiIvJ3REYa77EuWGC83wrQqhWMGAFdu4KPj9GAD3/8OHx4wQIhgNRUePFFI2jK3wejQMi+WSwWWrVqxbZt25gzZw7333//TbuECvpx83/M/3liYiK7d++2hkTbt28nPj6evLw8Y5v07xc/Pz8aNmxIWFgYLVu2pGXLlvj5+eHo6PiP67InZrOZ7du3M3LkSPbv30+vXr345JNPKFeuXKl+jls6okC5NSYTVKwIb7wB8fFGL+rQocajWZUqfzxiVatmdA/17Anr1sGzzxo9sIXwTU1EbMtkMlG2bNmrfi0iIiI3lpNjNM5PmgTbthnzgtzcjE6gZ56B0FDj1ybTH0+p/y5vb+Pj7tsHmzcXSvlym+VvG9u+fTuzZs3i3nvvLZRA6MouoHxVqlShcuXKdPk9HbRYLJw9e5Y9e/awdetWNm3aREREBElJSaxevZrVq1djMplwcnKievXqNGrUyNpJFB4eTvny5QulVluwWCzs3buXN998k927d9OpUyc+/PDDUh8GgTqE5Hryu4COHTP2W0ZGQocO8NNPxqNP/nVyc41HoD59jP7Xl182giQRERERkVIiLc1YwDtxIhw4ANnZxpDoBx6Ap54yxm3mj2650evPW+kQAuPp9wcfGAHU2bPqECoODhw4QEhICBaLhYSEBPz8/IoslMjvIMq/5ObmcuLEias6iSIiIrhw4QIWi8XaReTk5ES1atVo2rQpLVu2pEmTJjRo0OBvHXmzhYMHD/LWW2+xYMEC2rRpw6effkq9evWKRe23mwIhuT6LxXiU2bsX+vaFxEQYMAA+/xzyz5laLEaP6q+/GsOn3dzg44+NjiIRERERkRLs9Gn46itjnk9CgtEhdOedMHiwcalc+Y+nzQV57bl/vzFnKDcXwsONpv0bsViMlfVHjxrH0nx9oW5dY7TnmTPGavqKFf8Io8T2LBYLZcqU4dKlS/znP//h7bffttkMnz8fNbNYLOTk5HD06FG2bt3Krl27iIyM5MCBA6SnpwNYxwi4u7tTp04daydR48aNadiwIWXKlLHJfbmeEydO8OGHHzJt2jSaNGnCxIkTadasmcKg3ykQkhuzWIxHts2bjZXz2dnw9NPw/vtXXycjAz77DF56Cdq3h2XL/nj0ExEREREpQfbtM94j/eEHI4jJyzOOgz39tDFi09f3jykKt/K6M79JP/92BbntlbfJv11MDIwZA1FRRgP/gAFQwkbCFGs9evRg2bJlVKtWjYMHD9rVFtf8eMBsNltDovT0dI4cOUJUVBSRkZFERESwf/9+0tPTr5pH5OnpSb169WjWrBlNmjShSZMm1KpVC3d3d5vcl/j4eD799FPef/99goKCGDduHHfffXexPfp2OygQkpuzWCAz05glNHCgMTj6/feN1fRXXufyZWPOUM+exqOgnXxTExERERH5p8xmWLPGOJ61dq3x1NfREbp3h5EjjeNaHh5/LwgqbOfOwXvvGaFV3brw1lvGhAc9PbcPK1eupHv37uTl5eHi4oKXlxf169fniSeeIDw8nMDAQLta6HFlB1F+UJScnGzdbBYZGcnOnTs5efIk2dnZmEwma/2+vr7Uq1ePxo0b07hxY5o3b06tWrVwc3O7rTWfO3eOL7/8ktGjR1OzZk1Gjx5N//79FQb9iQIhKRiLxTjMPGMG/PvfRh/q9Olwzz1XX8dsNh4F7eSbl4iIiIjIP5GZaUxHGD8edu82piV4esL99xs7VerXB1fXwhkUXVgsFoiNNbqEvv0WwsKMMZ9du9q6MgGj+2b06NGMGzeOrN9XxJlMJhwdHXFwcKBcuXLWYc75F39/f7sKM/4cEJnNZlJSUjh48CCRkZFs376drVu3EhcXR15enjUgcnR0pHLlygQHB1tDosaNGxMQEFBoR+dSU1P54YcfeOWVV/D19WXUqFE8+eSTJW5zWmFQICQFZ7HAhQvG2w3jx0ONGsY6+vBwW1cmIiIiIlKokpKMle6ffWaEK9nZxpLdoUPh0UeN+UDOzvYVBF3JYoHDh40h03PmQKdORijUqpWtKxOAnJwccnNzOXnyJIsWLeL48eMkJSWxZcsWEhISruqycXBwoHLlyjRq1OiqTpuKFSvabUhkNpvJzc0lLi6O3bt3s2vXLiIiIti9ezdJSUnAH/OInJ2dqVatGo0bNyY8PJywsDAaNmyIn5/fLd+/jIwM5s2bx4svvgjA8OHDefXVV3HRIK1rUiAkt8ZiMR4dn33W2DjWpImxct7V9errmc1w8iTs2gWpqcZmsrAwqF3bNnWLiIiIiBRATIwxKPqrr4yBzbm50KABPP64sUPF1/fWBkXbktlszDt65x1YtMho7n/tNWjc2NaVST6z2UxeXt5VYUp8fLx1Xk9+t825c+es4VD+pVKlSoSEhNCyZUtatmxJ48aN8fb2tpuQ6M9HzfJDooSEBPbv38+OHTvYsmULu3fvJjk5GTBCImdnZ0aNGsWbb755S11DOTk5LFmyhBdeeIGLFy/y6KOPMnr0aNzd3e3m+J29USAkt85shvh4ePNN4y2SVq3+eDQ0myEuDp57DpYsMSbs5XN2hrvvhilToGpV29QuIiIiInINmzYdZuLE2ixf7sjly8b7oF27wjPPQOvW4O5efIKgK5nNsGWL0Sm0ejU88IARCgUG2royuZ788OTKS0JCAlFRUWzbto3Nmzeze/du0tLSrjpq5uzsTK1ataydRKGhoYSHh+Pt7W03gciVIVFeXh5ms5mMjAyOHz9OREQEu3btIi4ujn79+vHwww8XuO68vDzWrl3LSy+9RExMDA8++CAffPABvr6+dnPf7ZECIbl1+f9lsrONR8X8s5j53UM9e8KhQ/DCC8YQ6kqVjJX1338P48ZBnTpGV5Gvr83ugoiIiIhIdnY2y5YtY9KkSezYEUGZModISfHjnntMPP00hISAm1vxH5GZlwerVhkzhXbsMI68vfKKMQFCiocrw6G8vDxycnKIiYmxdhJFRUURERFBRkaGtZPI0dERFxcXatSoQbNmzaxdRIGBgbi5udlNUHJlF1F+t5STk1OBu4MsFgsbN27kP//5DxEREdx77718+OGHVKlSxW7uo71SICSF5/JlmDDB2ED27rvG3k0nJ+PRM39TWf5Q6hdfhLFjbV2xiIiIiJRCFy5c4JtvvuGzzz7jxIkTZGVlUaFCBZ57bg79+7eiShUnXFyKfxB0pdxcWLzYeAp+4IBxBO7ll6FKFVtXJn/Hn7ts8vLyyM7OJjo6mm3bthEREUFUVBSHDh0iJyfHGhA5ODjg7e3NnXfeeVUnUVBQkM3Ww/9Tu3bt4u2332bFihV06dKF//3vf9StW1dhUAEoEJLCk5ICHToYg6f37QMvr6v/3GIx5gmFhhq/Pnas5DzCioiIiIjdO3nyJDNnzuSrr74iISGB7Oxs6tevz9ChQxk4cCDlylXA2dkRk8lUIp+m5uQYA6bHjoWEBOP92+efh7JlbV2ZFIYru2zyf7x06RLR0dHs3r3beuQsJiaG3Nxca0Dk6OhIhQoVaNCgAaGhoYSGhtKkSROqVauGs7Ozre/WDe3fv5/333+fOXPm0LZtW/773//SuHFju5mjZO8UCEnhyN9teeed0KYNrFjx17DHYjH2dA4YACtXwsGDULOmTcoVERERkdJj9+7dTJs2jTlz5pCWlkZ2djZNmjRhxIgR9OrVCw8PD+sWopLcVZD/dPy77+CDD+DSJRgxwmjg//N7uVIyXBkS5V8SExPZt28fUVFRREVFsWvXLhISEjCbzTg6Olov5cuXJygoiNDQUJo1a0azZs2oWLFioa2H/6eOHz/OhAkTmDZtGk2bNmXs2LG0adNG6+VvgX38S0rxZzYbHUJms7GP81oPpCaT0XcbEGBcLylJgZCIiIiI3BZms5kVK1Ywffp0Vq1aRWZmJgD33HMPI0aMIDQ0FHd3d+uL25IcBOUzmYzlwA8+CGlpxnjPzz83FgIPH/7XxcFS/OUPnb4yJPHw8KB69ep069aNvLw8cnNzSUpKYv/+/ezevZvt27ezfft2YmNjiY2NZfXq1Tg5OVnXwzds2JBGjRpZu4kqVKhQ5CFMfHw8X331FV9++SXBwcG8+OKLtG7dWmHQLVIgJIXH0dF42yEn5/rXsViMA8zwx5oGEREREZFCkpWVxcKFC/n444/Zt28fmZmZuLm58cgjj/DMM89Qu3Zt3NzccHR0LBUh0J+ZTODpCY88AhkZ8NFHxhJgNzd47DE9RS8N8tfWX8nLy4vq1avTpUsXcnNzyc7O5tSpU+zdu5fdu3dbj5xFR0dz+PBhFi5ciKOjI66urvj7+9OgQQOaNm1Ks2bNaNiwIWXKlLltX1/Jycl8//33TJkyhZo1a/Lvf/+bHj162E3nUnGiI2NSOCwWOHMG6taFli2NnZbXkpkJ/fsbR8aOHQN//6KtU0RERERKpKSkJL7++mu++OILzpw5Q2ZmJlWrVuVf//oXgwcPpnz58ri5ueHg4FAqg6A/s1jg7FmYPNm41KxpDJl+8EGjqV9KtyuPmuXm5pKXl0dWVhaxsbFERkZaB1cfOnSItLQ0HB0drZvBPD09qVmzJiEhIYSEhNCoUSMaNGhA2UIYVnXp0iVmzpzJu+++i4eHB88++yzDhw/Hzc2tEO516aNASAqHxWIMk+7SBeLjjdUFPj5/vc6lSxAYaPzZ/v16tBERERGRfyQmJoYvv/ySmTNnkpKSQmZmJg0bNuRf//oXAwYMwNvbG1dX198HRSsIupLZDHFx8PHHMG0aNGgAr74K995r68rEHuWHRLm5udZLeno6MTExV3USHTx4kPT09KtCIi8vL2rXrk14eDhhYWE0btyYWrVq4eHhUeC/Pz09nR9//JExY8aQmZnJ8OHDeeGFF/D09NTX9t+kniopHCaT0Wf60EPw2mvG+vm33rr6Ojk58OmncO6cMb1OYZCIiIiI/E3btm1j0qRJLF++nPT0dLKzs+nUqRMjRoygdevWeHh44Pr7UBy9WLw2BweoWhWGDTNmCs2cCZMmGe/dduxo6+rE3lw5jyj/a8vHx4eKFSvStGlTa0iUnJxs3Wy2Z88eIiMjOXnyJImJiezYscMaEvn6+lKnTh1rF1FYWBh16tS5ZrdPZmYmM2fO5M0338RisTBkyBCeeeYZhUH/kDqEpPBYLMag6P/7P9i1CwYPNgKiSpUgMRF+/BG++spYO//rr3/tIBIRERERuYGcnByWLl3KJ598wrZt27h8+TImk4nevXvz7LPPEhwcjIeHB87OznqReAvy8owFwP/9r7GWvnNnePNNaN7c1pVJcZR/1CwnJ8caEqWkpHD48GH27NnDnj172LVrFydPniQ3N9caELm4uFCpUiUCAwOpWbMmVatWpWXLljRq1IjZs2fz2muvkZiYyH333ce4ceO444479HX+DykQksJjsRiX06eNR5OffjIeXRwdjX5UR0fo1w9efx2qVLn2JjIRERERkT9JS0tj1qxZfPbZZ8TExJCeno6vry+DBw/m8ccfx9/fH09Pz1I7KLow5OYa7+l+8AEsWQK9ehkN/yEhtq5MSoIrj5rl5OSQnZ1NfHw8+/btIyoqiqioKPbt20dSUhIODg44Ojri4OCAi4sLLi4uZGZmkpqaSufOnRkzZgzh4eHaKFYIFAhJ4coPhdLSjJlCBw/CxYtGN1BQEPj6Gnst9UAtIiIiIjdgsViIjY1l5syZfPPNNyQkJJCenk7NmjUZMmQIgwcPply5ctYjIwqC/rmcHNi4Ed57DzZvhvvvh7ffhlq1bF2ZlDRXziPKyckhJyeHrKwszp49S3R0NIsXL2b58uWcO3eOvLw8TCYTbdq04bXXXqNjx444Ozvb+i6UCAqE5PbI/2+Vk2P83GQCZ2cFQSIiIiJyQxaLhXnz5vHZZ59x8OBBUlNTSU9Pp02bNgwfPpyOHTvi5eWFp6cnoPlAhS07G9asgTFjYM8eYxrEm28as4ZEbqcrQ6KMjAwyMzO5dOkS+/fvJzs7m+bNm1O5cmXr/CL55xQIiYiIiIiIXbBYLOzdu5cOHTqQmpoKQNeuXXnyySdp3rw5Xl5eGhRdBLKyjJGf77wDsbHGaNDXX4fy5W1dmZQ2ZrOZnJwcLBYLLi4uOGgxUaHSljEREREREbEbX331FcnJybi5ubFw4ULCw8Px9vbGxcVFIVARcXGBbt0gM9MIhWbNAg8PeOUVY/qDSFFxcHBQR9BtpHhNRERERETsRv369QGoXbs2HTp0wM/PD1dXV4VBRchkMgKg3r3h+eeNgOjbb2HKFKN7SERKBnUIiYiIiIiI3WjZsiVgrJh3cXGxcTWll8lkdAP162d0Ck2ffpq1axfh7e3AsGH/0lBfkRJAgZCIiIiIiNiNmjVrAhAXF0f+uFN1B9mGyQRly8IDD+Rw+fIWJkz4kISEMnh7ezJ48GD9u4gUczoyJiIiIiIidsPb2xsfHx8uX77MhQsXbF1OqefgABUrOjFwYCueeOJhTpw4weTJk/npp59sXZqI/EMKhERERERExC6YTCZMJhP+/v5YLBbi4uJsXZIADg4mqlatwiOPPMKQIUM4cOAAU6ZMYeXKlbYuTUT+AQVCIiIiIiJiV6pVqwZQoEAoIyODgwcPsm/fPusRMyl8jo6O3HHHHTzxxBMMHDiQHTt28OGHH7J582ZblyYif5MCIRERERERsSv5gVBsbOxNr7tv3z569uzJm2++qUDoNnNycqJevXoMGzaM3r17s2HDBj744AOioqJsXZqI/A0aKi0iIiIiInalatWqQME6hMqUKcOJEydwcHBQIFQEnJ2dCQkJ4ZlnniE1NZU1a9bg4eHBmDFjqFOnjq3LE5FboEBIRERERETsyq10CFWtWhUHBwfOnDlDXl4eDg4O2n51m7m4uNCsWTNGjRpFRkYGS5cuxd3dnXfffdf6byci9k9HxkRERERExK7kdwgVJBByd3enbNmyZGZmkpycfLtLk9+5ubnRqlUrXnjhBWrVqsUvv/zCBx98wPnz521dmogUkAIhERERERGxK7fSIeTg4EDVqlWxWCycOXPmdpcmV3B3d6dDhw688sorVKhQgXnz5jFu3DhSU1NtXZqIFIACIRERERERsSvVq1cH4PTp0wW6/pUdRZojVHRMJhNeXl507dqVF198ERcXF2bNmsXEiRPJzMy0dXkichMKhERERERExK74+vri6elJamoqaWlpNw15bmVNvRQuk8lEmTJl6NOnDy+99BI5OTnMnDmTTz/9lNzcXFuXJyI3oEBIRERERETshslkwsHBgSpVqmA2mwt0DKxq1aqYTCYFQjZiMpkoV64c999/P//+979JTk7miy++YObMmZjNZluXJyLXoUBIRERERETszq2snr/yujoyZhsODg5UrFiR//u//2PEiBHExsYydepU5syZY+vSROQ6tHZeRERERETszq0Mlr6V68rt4+joSNWqVXn44YdJTU1l+vTpTJ48mfLly3P33XfbujwR+RMFQiIiIiIiYnduJeTRkTH74ejoSI0aNXj88ce5fPky3377LePGjcPT05NWrVrZujwRuYKOjImIiIiIiN25lSNjAQEBmEwmYmNjMZvNOjZmY87OztStW5dhw4bRp08fNm3axNixY4mKirJ1aSJyBQVCIiIiIiJid65cJX8z3t7eeHt7k5aWxsWLF293aVIALi4uNGjQgGeeeYa77rqLdevW8d5773H06FFblyYiv1MgJCIiIiIidudWVsnnbyWzWCzEx8ff7tKkgFxdXQkPD+ell14iPDycFStW8O6772rWk4idUCAkIiIiIiJ251YHRV95fR0Zsx/u7u60bNmSF198kTvuuIPFixfzwQcfcP78eVuXJlLqKRASERERERG7U6FCBdzc3Dh//jwZGRk3DXk0WNp+ubu7065dO15++WXKlSvHvHnz+N///kd6erqtSxMp1RQIiYiIiIiIXTGZTDg5OVGxYkVyc3NJTEy86W1uZQi1FC2TyYSXlxddu3bllVdewdnZmW+++Ya3336bS5cu2bo8kVJLgZCIiIiIiNilWxksfeV1dWTM/phMJnx9fenTpw/PPfccKSkpzJo1i88++8zWpYmUWgqERERERETELt3KYOlbua7Yhslkoly5crRr1w5/f3/Onz/Pjh07bF2WSKmlQEhEREREROzSrQyWzp8hpA6hopOdnU1ERASzZs0iOTm5QLfJyckhMTGRpKQkPD09CQ0Nvb1Fish1Odm6ABERERERkWu5lblAAQEBBb6uFI4TJ04wffp09u7di8Vi4aGHHrrpbWJjY5kyZQrp6ekEBgYycODAIqhURK5FHUIiIiIiImKXbqVDqGzZsnh4eHDx4kXS0tLUJXSbpaWlsWXLFhYuXIjZbKZWrVo3vU1mZiYHDhxg3bp1VK5cmWHDhlGjRo0iqFZErkWBkIiIiIiI2KVb6RBycnKicuXK5OXlkZCQcLtLK/WOHDnCnDlzcHR0pEePHjRq1Oimt4mPj+f777/H2dmZ+++/nz59+hRBpSJyPQqERERERETELuV3CJ0+ffqWrq85QrfXuXPnWLVqFVu3biUkJIT+/fvj7u5+w9tcunSJ9evXs2rVKmrXrs3jjz9O+fLli6hiEbkWzRASERERERG7VKlSJerV+ze5uZXJyQFnZzCZrn/9atWqYTKZNEfoNjKbzezfv585c+bg6+vLfffdR506dW54G4vFwqlTp5gxYwaOjo7079+fwMDAIqpYRK5HHUIiIiIiImKX3NzccHJ6jnPnHuPs2Ztf39/fHzCOmKlD6PaIi4tjyZIlxMTE0KJFC3r16oWT0437DJKTk1myZAlRUVE0bNiQBx54AGdn5yKqWESuR4GQiIiIiIjYHZPJBJhwc6vOxYt+xMXdoDXod7cyc0huXf6a+QULFhAQEED//v2pUqXKDW+Tm5vLoUOH+Oabb/D29mbQoEFUr169iCoWkRtRICQiIiIiInbr923yFGDRGNWrt6V8+Q9ISOiLxXLzAEluzfHjx1m0aBEXLlygQ4cOtGvX7vfg7vrOnj3LvHnzOH36NG3atKFnz544OOhlqIg90AwhERERERGxW7/PiS5QIFS1aiDZ2bWIjfUAFAgVprS0NDZv3syyZcuoW7cu/fv3p2zZsje8TWZmJpGRkcydO5cqVarwyCOPUKFChSKqWERuRtGsiIiIiIjYrd9PgVGQU2D+/q6kp/tw6pSTOoQK2aFDh5g/fz4mk4lu3brRuHHjm94mLi6OWbNmcenSJbp27Urr1q2LoFIRKSgFQiIiIiIiYrduJRDy8wM3Nzh/HjIzQXOlC8e5c+dYvXo1W7dupXHjxvTt2xcPD48b3iY1NZUNGzawcuVK6taty+DBg/Hy8iqiikWkIBQIiYiIiIiI3bqVI2OurkYolJNDgbaSyc2ZzWb27NnD/PnzKVOmDL1796Zu3bo3vI3FYuHEiRN8//33mEwm7r33Xho2bFhEFYtIQSkQEhERERERu3UrgRBc3VGkDqF/Li4ujmXLlnHkyBFatWpFjx49broy/vz58yxfvpxdu3YREhLCgAEDtGZexA4pEBIREREREbtVpQo4OkJCAuTm3jzkqVYNTKaCHTGTG8vOzmbHjh388ssvBAQEcO+991ItP6G7jtzcXA4ePMgPP/yAj48PAwYM4I477iiiikXkVigQEhERERERu2QygacnlC0Lly9DSsrNb5PfIRQbqw6hf+r48eP8+uuvnD9/no4dO9K+ffubrplPSEjg559/5sSJE7Ru3ZrevXtrzbyIndJXpoiIiIiI2C2TCfz9jZ8XpOunalXjNmfO3N66Srr09HQ2btzIypUrqVevHvfeey/lypW74W0yMzOJiIhg3rx5VK1alYceeohKlSoVUcUicqsUCImIiIiIiF27lTlCmiFUOA4ePMiiRYswm8107dqV8PDwm97m9OnT/PTTT1y8eJHOnTvTtm3bIqhURP4uBUIiIiIiImLXbmX1/JVHxuTvOXfuHKtWrWLbtm00adKEe+65B09PzxveJjU1lY0bN7Jq1Srq1avHQw89hLe3dxFVLCJ/hwIhERERERGxa7fSIZQ/VFozhP4es9lMVFQUP//8Mz4+PvTo0YP69evf8DYWi4WYmBhmz56NyWSiV69ehIaGFk3BIvK3KRASERERERG7disdQpUqgbMzJCVBdrZCoVsVFxfHihUrOHz4MK1bt6Zbt24FWjO/atUqduzYQaNGjejXrx8uLi5FVLGI/F0KhERERERExK7dSoeQhweUKweZmXDu3O2tq6TJyclh27ZtLF68mICAAHr37k316tVveJvc3Fz279/P7Nmz8fHx4b777qNOnTpFVLGI/BMKhERERERExK7dSofQldc/c0YdQrciJiaGJUuWcPbsWTp27Ei7du1uumY+Pj6exYsXExMTQ+vWrenVq5fWzIsUE/pKFRERERERu3blKnmz+eYhT/4coYIGSGKsmV+3bh2rV68mKCiI3r174+fnd8PbZGZmsmvXLhYsWEBAQAADBgzA39+/iCoWkX9KgZCIiIiIiNi1MmXA2xtSU43LzVy5aUwdQgVz8OBBFi9eTE5ODl26dKFJkyY3vc2pU6dYuHAhycnJdOzYkfbt29/+QkWk0CgQEhERERERu+bgAP7+Rrhz5szNr39lR5ECoZs7f/48K1euZNu2bTRr1oxevXrh5eV1w9tcunSJjRs3snLlSgIDA3nggQfw8fEpoopFpDAoEBIREREREbuX3/Vz+nTBr6sjYzdnNpvZtWsXixcvxsfHh+7duxMUFHTD21gsFg4fPsy8efOwWCx069aN8PDwIqpYRAqLAiEREREREbF7+ZvGChLy5HcIxcWpQ+hmzpw5w/Lly4mOjqZt27Z06dKlQGvm16xZw7Zt22jcuDF9+/bVmnmRYkiBkIiIiIiI2L1bWT2vGUIFk5OTw6ZNm1i+fDkBAQH06NGDmjVr3vA2ubm57Nmzh7lz5+Lj40Pv3r2pV69e0RQsIoVKgZCIiIiIiNi9W+kQqlIFnJwgMRFycxUKXc+xY8dYvnw5iYmJdO7cmbZt2950zXxCQgLLly/n8OHDWjMvUszpK1dEREREROzercwF8vY2NpOlpcHFi7e3ruLq8uXLrFmzhjVr1hAcHEyPHj2oUKHCDW+TmZnJtm3bWLRoETVq1KBv375Uy0/qRKTYUSAkIiIiIiJ271aOjJlMxlYy0Byh6zlw4ADLli0jKyuLLl260LRp05ve5tSpU/z6668kJSXRvn17OnToUASVisjtokBIRERERETs3p8HRd8s5Lny+nK15ORkli9fzrZt22jVqhXdu3cv0Jr5DRs2sHLlSoKDg+nXrx9lypQpoopF5HZQICQiIiIiInavXDlwd4eUFEhPv/n1rzxipg6hP5jNZnbs2MHSpUvx9PSka9euNGjQ4Ia3sVgsHDp0iIULF2I2m7n77rsL1FEkIvZNgZCIiIiIiNg9JyeoXBnMZoiPv/n1tXr+2uLj41m2bBnR0dF07NiRzp0733TNfHJyMmvWrGHr1q00adKE++67D1dX1yKqWERuFwVCIiIiIiJSLNzKprH69SE83BgufZPFWaVGTk4OGzZsYNWqVQQEBNCtW7cCrZmPjIxkwYIF+Pj40L17dwIDA4umYBG5rRQIiYiIiIhIsVC/PlSvXrAjYy1awOjR0Ls3aCu64fjx4yxbtoz4+Hi6dOnCXXfdddOV8QkJCaxcuZLo6GitmRcpYZxsXYCIiIiIiEhB3HMPBAVB7do3v2716sZFDJcvX2blypWsW7eOkJAQunXrRsWKFW94m6ysLLZu3crPP/9MzZo1uffeewkICCiiikXkdlMgJCIiIiIixUKPHrauoPg6cOAAy5cvJyMjg65du9KkSZOb3ubEiRMsXryYxMREHnnkETp27FgElYpIUVEgJCIiIiIidsNigYQEiIj44/dCQ8Hf/+azgLZuhfPnjZ+3bw8eHpofBHDhwgWWLl3K9u3badOmDV27dsXb2/uGt0lLS2P9+vWsWrWKkJAQ7rvvPnx9fYumYBEpEgqERERERETEruzaBY899sevn3oKXnsNHB1vHPB8+CFs2mSESps3Q61at79We2c2m9m2bRvLly/Hw8OjwGvmDx48yC+//EJubi6dO3emefPmRVSxiBQVTQMTERERERG7kpkJZ8/+cZk8+eqOoeu5cAESE43b5Obe9jKLhcTERJYsWcLBgwe5++676dix403XzKekpLBmzRq2bNlC8+bNueeee7RmXqQEUiAkIiIiIiJ27fx5eO89SE01un+kYHJycli7di1r1qyxrpmvdZO2qby8PHbt2sWiRYvw8fEpUEeRiBRPCoRERERERMRuVa5s/PjLL7BwoREIKRQqmBMnTrB06VLi4uLo3r07rVu3xnSToUr5a+YPHDjAXXfdRc+ePbVmXqSE0le2iIiIiIjYrS5doGpVMJth7Fg4ccLWFRUPGRkZLFu2jI0bNxIaGkqXLl0KtGZ+06ZNLF68mFq1atG7d2+qV69eRBWLSFFTICQiIiIiInbrjjuMAdMODnDkCEyaBFlZ6hK6mfw18+np6XTv3r3Aa+aXLl3KmTNn6NChg9bMi5RwCoRERERERMSuPfkk3Hmn8fMvvjDWy8v1paam8uuvv7Jz507atWtH586db7pmPj09nbVr17J69WoaN25Mnz59KFu2bBFVLCK2oEBIRERERETsWqVKMGoUuLlBejq88w4kJ6tL6FrMZjObN29mxYoVuLm50b17d4KDg294G4vFwv79+1m8eDE5OTl07tyZFi1aFFHFImIrCoRERERERMTuDRoErVoZP1+7FmbNMuYKKRS6WlJSEr/++isHDx6ke/futGvX7qZr5i9cuMDq1avZsmULLVu2pFevXlozL1IKKBASERERERG75+oKr78O5coZv/7gA2OmkPwhNzeXVatW8dtvv93Smvlt27bx66+/UqZMGbp06XLTjiIRKRkUCImIiIiISLHQoQP83/+ByQRxcTBuHFy+rC6hfCdPnmTx4sXExsbSu3dvWrZsedM184mJiaxcuZJ9+/bRvn17unfvrjXzIqWEvtJFRERERKTYeOklY/MYwHffwfr1N75+WhocPQrx8cbPS+oxs6ysLH799Ve2bNlCWFgYd999903XzGdnZ7Nu3TqWLl1KnTp16NWrFzVq1CiiikXE1hQIiYiIiIhIsVG9+h8DpnNy4N13ISXl+iFPRIQRIn38McydCxs3wp49cOwYnD1rDKm2WIp/SBQbG8vGjRu5dOkSvXr1Iiws7Ka3OXHiBMuXLycuLo6OHTvSvn3721+oiNgNJ1sXICIiIiIiciuGDoWffjKGS2/ZAj/+CMOGXfu6J07A7t2wYYPRIZSbC7VqQcOGUL8+1KsHdeqAjw94ef1xcXExjqYVF1lZWVStWpWHHnqIjh073nTNfEZGBitXrmT16tU0bdqUXr16ac28SCmjQEhERERERIoVV1cYPRqioozuoHHjoHfva3f5tG0LY8fCgQPG5eRJoyto505Ytw4uXQJnZyMYCgoyLoGBUK2aEQx5eICnp/FzZ2f7DYmCgoJ49913ycvLw9PT84bXtVgs7Nmzh6VLl5KdnU2XLl20Zl6kFFIgJCIiIiIixU7btvDQQ/Dpp0bI88knRvfPn9WsaVzypacbM4Wio43L7t1GF1FqKqxeDb/8YlzH2xvuvNMIh+rXh+BgqFzZCIfyAyIvL3B0tJ+Q6GZdQflSU1NZuXIlW7dupUOHDnTv3l1r5kVKIQVCIiIiIiJSLL36KixdaswDmjYNbjJDGTDCnEaNjEu+c+eMcOjgQePHvXshMRGSkoyw6KefjG1mvr5/hESNGkFICPj5GR/Tw+OPiz2FRH+Wl5fH5s2bWbJkCT4+Ptx99900aNDA1mWJiA0oEBIRERERkWKpalV45RUYOdI4OpaS8vc+jp8ftGljXPKdOweHDhkdRFFRRlh0/jycOWN0GM2eDVlZ4O9vdBAFBhqXBg3+HBJZcHMzAqKbrYAvCmfPnmX58uXs27ePBx98kK5du2rNvEgppUBIRERERESKrSFDYM4cWLWqcD+un59xad3a+HVentEtdOAA7NsH+/fD4cPGDKLoaNi1CzIyjLX2NWsas4gaNIDQUAs1a8bg7W3B3d0dd3d3PDw8cHNzK/IgJicnhzVr1rBixQrq1q1Ljx49tGZepBRTICQiIiIiIsWWs7Oxen7nTrhw4fb9PY6OULu2cend2/i93FyIjTXW2EdGGiHRkSPGPKItW2DNGvDzy+T8+cFUrnyBwMBAgoKCCAoKon79+vj6+l4VEjk7O9/WLqITJ06wbNkyYmNjeeqpp2jXrt1t+7tExP4pEBIREREREbvi4GAEPWAEMTfTogU88gh89pnRoQNFM8PHyemPodV9+hi/l5ZmdA7t3290EqWnX2DbtvIkJcWzefNmVq9eTUZGBp6entSpU4fg4GAaNGhAcHAwVatWxcvLCzc3N2tQ5OLiUighUWZmJkuXLuW3336jRYsW9OjRQ2vmRUo5BUIiIiIiImJXKlSA9u2Nn1+5IexGXn0VYmKMuT5gzO+xBS8vCAszLgZ/UlK+5dChQ+zfv58DBw6wZ88e4uLiOHv2LEuWLGH+/PlkZGTg4eFBnTp1CAoKokGDBoSGhlK9enVrSJR/cXJyuqWQyGw2ExUVxbJly8jKyqJr1640b978ttx/ESk+TBaLxWLrIkREREREREqTlJQUDh8+bA2JIiMjiY2NJT09nczMTDIzM8nOzsbPz4969erRoEEDgoKCCA4OpnLlyri7u1s7idzc3HB0dLxuSJSUlMRHH33EtGnT6Ny5M2+88QYNGzYs4nssIvZGgZCIiIiIiIiNmc1mzpw5Q3R0NPv372ffvn3s37+fs2fPkpGRQUZGBpmZmeTk5FwVEoWGhhISEkLFihWtHUSurq64urri4OBAXl4en3/+Oe+//z7Z2dm88cYbPPnkk9osJiIKhEREREREROxRXl4eCQkJREdHExUVRUREBAcOHCA5OdnaRZT1+xk5f39/awdRgwYNCAwMxM/Pj6SkJMaPH89PP/1E7969ef3112nSpImN75mI2AMFQiIiIiIiIsVEZmYmx48fZ+/evezZs4e9e/cSExPDhQsXyMrKsl6cnZ0pV64cOTk5nD9/nvLlyzN69GiGDRtm67sgInZCgZCIiIiIiEgxlpGRwYkTJ4iMjGT37t3s3buXY8eOkZycTEZGBi4uLjzyyCM899xzBAQE2LpcEbETCoRERERERERKmAsXLrBu3TrWrVtH/fr16d27N1WqVLF1WSJiRxQIiYiIiIiIiIiUMhotLyIiIiIiIiJSyigQEhEREREREREpZRQIiYiIiIiIiIiUMgqERERERERERERKGQVCIiIiIiIiIiKljAIhEREREREREZFSRoGQiIiIiIiIiEgpo0BIRERERERERKSUUSAkIiIiIiIiIlLKKBASERERERERESllFAiJiIiIiIiIiJQyCoREREREREREREoZBUIiIiIiIiIiIqWMAiERERERERERkVJGgZCIiIiIiIiISCmjQEhEREREREREpJRRICQiIiIiIiIiUsooEBIRERERERERKWUUCImIiIiIiIiIlDIKhEREREREREREShkFQiIiIiIiIiIipYwCIRERERERERGRUkaBkIiIiIiIiIhIKaNASERERERERESklFEgJCIiIiIiIiJSyigQEhEREREREREpZRQIiYiIiIiIiIiUMgqERERERERERERKGQVCIiIiIiIiIiKljAIhEREREREREZFSRoGQiIiIiIiIiEgpo0BIRERERERERKSUUSAkIiIiIiIiIlLKKBASERERERERESllFAiJiIiIiIiIiJQyCoREREREREREREoZBUIiIiIiIiIiIqWMAiERERERERERkVJGgZCIiIiIiIiISCmjQEhEREREREREpJRRICQiIiIiIiIiUsooEBIRERERERERKWUUCImIiIiIiIiIlDIKhEREREREREREShkFQiIiIiIiIiIipYwCIRERERERERGRUub/AfFfj8Vf+2T3AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "# ~ loading the dataset\n", "\n", "content = nextcloud_download('https://bwsyncandshare.kit.edu/s/yBssSfzoN2NT5oz')\n", "df: pd.DataFrame = pd.read_csv(io.StringIO(content))\n", "\n", "print(f'Datasets consists of {len(df)} elements')\n", "print(f'Column names: {\", \".join(df.columns)}')\n", "print(f'Dataframe head:')\n", "print(df.head())\n", "\n", "# ~ visualizing some examples\n", "\n", "num_examples = 8\n", "indices = random.sample(list(df.index), k=num_examples)\n", "fig, rows = plt.subplots(\n", " ncols=2,\n", " nrows=num_examples // 2,\n", " figsize=(15, 30),\n", " squeeze=False,\n", ")\n", "\n", "for ax, index in zip(itertools.chain(*rows), indices):\n", " \n", " ds = df.iloc[index]\n", " ax.set_title(f'name: {ds.name} - label: {ds.p_np}\\n'\n", " f'smiles: {ds.smiles:.30}...')\n", " mol = Chem.MolFromSmiles(ds.smiles)\n", " mol_img = Draw.MolToImage(mol, size=(500, 500))\n", " mol_arr = np.array(mol_img)\n", " ax.imshow(mol_arr)\n", " ax.axis('off')\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 26, "id": "a1d164c5", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "9e24c04fd6be4b27c701c735903185cc", "grade": false, "grade_id": "cell-780b27a30cd91191", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Cl].CC(C)NCC(O)COc1cccc2ccccc12\n", "\n", "C(=O)(OC(C)(C)C)CCCc1ccc(cc1)N(CCCl)CCCl\n", "\n", "c12c3c(N4CCN(C)CC4)c(F)cc1c(c(C(O)=O)cn2C(C)CO3)=O\n", "\n", "C1CCN(CC1)Cc1cccc(c1)OCCCNC(=O)C\n", "\n", "Cc1onc(c2ccccc2Cl)c1C(=O)N[C@H]3[C@H]4SC(C)(C)[C@@H](N4C3=O)C(O)=O\n", "\n", "CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SCC(=C(N3C2=O)C(O)=O)CSc4nnnn4C)c5ccc(O)cc5)C(=O)C1=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCN5CCCC5)C1=O\n", "\n", "Cn1c2CCC(Cn3ccnc3C)C(=O)c2c4ccccc14\n", "\n", "COc1ccc(cc1)[C@@H]2Sc3ccccc3N(CCN(C)C)C(=O)[C@@H]2OC(C)=O\n", "\n", "NC(N)=NC(=O)c1nc(Cl)c(N)nc1N\n", "\n", "OCC(C)(O)c1onc(c2ncn3c2CN(C)C(c4c3cccc4Cl)=O)n1\n", "\n", "CC1=CN([C@H]2C[C@H](F)[C@@H](CO)O2)C(=O)NC1=O\n", "\n", "C(Cl)Cl\n", "\n", "C1N(C(CC2CCCCC12)C(NC(C)(C)C)=O)CC(C(Cc1ccccc1)NC(C(NC(c1nc2c(cccc2)cc1)=O)CC(N)=O)=O)O\n", "\n", "CCC(=O)C(CC(C)N(C)C)(c1ccccc1)c2ccccc2\n", "\n", "CCN1N=NN(CCN2CCC(CC2)(COC)N(C(=O)CC)c3ccccc3)C1=O\n", "\n", "CN(C)C(=O)C(CCN1CCC(O)(CC1)c1ccc(Cl)cc1)(c1ccccc1)c1ccccc1\n", "\n", "CN1C2CCC1CC(C2)OC(=O)[C@H](CO)c3ccccc3\n", "\n", "COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)[S](=O)(=O)NC(=O)NC3CCCCC3\n", "\n", "Nc1nnc(c(N)n1)c2cccc(Cl)c2Cl\n", "\n", "CCCC(C)C\n", "\n", "C[C@H](N)Cc1ccccc1\n", "\n", "c1cc2c(cc(CC3=CNC(=NC3=O)NCCSCc3oc(cc3)CN(C)C)cc2)cc1\n", "\n", "CC(C)(C)NCC(O)COc1cccc2C[C@@H](O)[C@@H](O)Cc12\n", "\n", "CCC(=O)N(c1ccccc1)C2(CCN(CCc3sccc3)CC2)COC\n", "\n", "CCNC(=NC#N)NCCSCc1c(cccn1)Br\n", "\n", "CN(C)C(=O)Cc1n2cc(C)ccc2nc1c3ccc(C)cc3\n", "\n", "CN1CCN(CC1)C2=C3C=CC=CC3=Nc4ccc(Cl)cc4N2\n", "\n", "FC(F)(F)c1c(Cl)nc(N2CCNCC2)cc1\n", "\n", "O.CCN1CCN(C(=O)N[C@@H](C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4ccccc4)C(=O)C1=O\n", "\n", "CCCC(C)C1(CC)C(=O)NC(=O)NC1=O\n", "\n", "C[C@H]1COc2c(N3CCN(C)CC3)c(F)cc4C(=O)C(=CN1c24)C(O)=O\n", "\n", "c1ccc(C(NCCCOc2cc(CN3CCCCC3)ccc2)=O)cc1\n", "\n", "CC(C)(C)OC(=O)C1=C2CN(C(=O)C3=C(N2C=N1)C=CS3)C\n", "\n", "CCC(=O)N(C1CCN(CC1)CCc2ccccc2)c3ccccc3\n", "\n", "CCOC(=O)c1cncn1C(C)c2ccccc2\n", "\n", "CN(C)c1cc(C2=NC(N)=NN2)ccn1\n", "\n", "CN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1\n", "\n", "IC1=C(N([H])[H])C=CC(C2=NC3=CC=C(O[H])C=C3S2)=C1\n", "\n", "O=C(C)Nc1cc(c2csc(N=C(N)N)n2)ccc1\n", "\n", "CN1[C@H]2CCC[C@@H]1CC(C2)NC(=O)c3nn(C)c4ccccc34\n", "\n", "C[S](=O)(=O)c1ccc(cc1)[C@@H](O)[C@@H](CO)NC(=O)C(Cl)Cl\n", "\n", "c1ccc(cc1)c1csc(n1)N=C(N)N\n", "\n", "CC(N)COc1c(C)cccc1C\n", "\n", "CCC(=O)O[C@@](Cc1ccccc1)([C@H](C)CN(C)C)c2ccccc2\n", "\n", "ClC1=CC=C(C2=C1)SC3=C(N2CCCNC)C=CC=C3\n", "\n", "CN(C)Cc1ccc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)o1\n", "\n", "Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)[C@H](O)c4ccccc4)C3=O)C(O)=O\n", "\n", "n(ccc1)c(c1)CCN(C)C\n", "\n", "O=C1N(CCC1)CCCCN2CCN(CC2)c3cc(C(F)(F)F)ccn3\n", "\n", "COc1ccc2nccc([C@H](O)[C@H]3C[C@@H]4CCN3C[C@@H]4C=C)c2c1\n", "\n", "c1(ccccc1)CC\n", "\n", "c1ccc(cc1N)c1csc(n1)N=C(N)N\n", "\n", "CC[C@]1(O)C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](OC(C)=O)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1\n", "\n", "c1(c(cc(NC(CCC)=O)cc1)C(C)=O)OCC(CNC(C)C)O\n", "\n", "ClC1=CC=C(SC(C=CC=C2)=C2N3CCCN)C3=C1\n", "\n", "CN(C)Cc1ccc(CSCCNC2=C([N+]([O-])=O)C(Cc3ccccc3)=CN2)o1\n", "\n", "CNC(=NC#N)Nc1cccc(c1)c1csc(n1)N=C(N)N\n", "\n", "n(ccc1)c(c1)CCNC\n", "\n", "O=N([O-])C1=C(CN=C1NCCSCc2ncccc2)Cc3ccccc3\n", "\n", "[Cl-].CN[C@H]1CC[C@@H](c2ccc(Cl)c(Cl)c2)c3ccccc13.[H+]\n", "\n", "c1(nc(NC(N)=[NH2])sc1)CSCCNC(=[NH]C#N)NC\n", "\n", "CC[C@]1(O)C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C=O)[C@H]7[C@](O)([C@H](OC(C)=O)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1\n", "\n", "CCC(NC(=O)c1c(c(nc2c1cccc2)c1ccccc1)C)c1ccccc1\n", "\n", "ClC1=CC=CC(OC2CCN(CCC3CCCN3S(C4=CC(N([H])C=C5)=C5C=C4)(=O)=O)CC2)=C1\n", "\n", "CN(C)Cc1ccnc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)c1\n", "\n", "CNCCCC12CCC(c3ccccc13)c4ccccc24\n", "\n", "N1(Cc2cc(OCCCNc3nccs3)ccc2)CCCCC1\n", "\n", "OC(C)(C)c1onc(c2ncn3c2CN(C)C(c4c3cccc4Cl)=O)n1\n", "\n", "[Na+].CO\\N=C(C(=O)N[C@@H]1[C@@H]2SCC(=C(N2C1=O)C([O-])=O)COC(C)=O)\\c3csc(N)n3\n", "\n", "C1=CC=C2C(=C1)C3C(O3)C4=CC=CC=C4N2C(=O)N\n", "\n", "C1CCCCC1\n", "\n", "Cc1csc(n1)N=C(N)N\n", "\n", "CCCN(CCc1c2c(c(cc1)O)NC(=O)C2)CCC\n", "\n", "Clc1cc2c(Oc3ccccc3C3CN(CC32)C)cc1\n", "\n", "CN(C)Cc1oc(CSCCNC2C([N+]([O-])=O)=CC=N2)cc1\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3occc3\n", "\n", "N1(Cc2cc(OCCCNc3oc4ccccc4n3)ccc2)CCCCC1\n", "\n", "OC12c3c(Oc4c(C)cccc4C2CN(CC1)C)cccc3\n", "\n", "[O-][N+](C1=CC=NC1NCCSCc2ccccn2)=O\n", "\n", "c12c(nc([nH]1)NC(OC)=O)cc(SCCC)cc2\n", "\n", "c1cccn2c1nc(c2)CCN\n", "\n", "Cc1ncc(n1CC(O)CCl)[N+]([O-])=O\n", "\n", "CCCN(CCc1c2c(ccc1)NC(=O)C2)CCC\n", "\n", "Clc1cc2c(Oc3ccccc3C3CNCC32)cc1\n", "\n", "CN(CCC=C1c2ccccc2CCc2ccccc12)C\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSC3=NC(=O)C(=O)NN3C)/c4csc(N)n4\n", "\n", "N1(Cc2cccc(OCCCNc3ccccn3)c2)CCCCC1\n", "\n", "OC12c3c(Oc4c(C)cccc4C2CNCC1)cccc3\n", "\n", "[O-][N+](C1=CC=NC1NCCSCc2ncccc2Br)=O\n", "\n", "c12c3c(C(NC(C)=O)CCc2cc(c(c1OC)OC)OC)cc(=O)c(cc3)OC\n", "\n", "C1CCN(CC1)Cc1cc(OCCCO)ccc1\n", "\n", "Cc1ncsc1CCCl\n", "\n", "CCN(CC)C(=O)Nc1ccc(OCC(O)CNC(C)(C)C)c(c1)C(C)=O\n", "\n", "ClCC(F)(F)F\n", "\n", "CN1C(=O)c2c(cccc2Cl)n2cnc(c2C1)c1noc(n1)C(C)C\n", "\n", "CO[C@]1(NC(=O)Cc2sccc2)[C@H]3SCC(=C(N3C1=O)C(O)=O)COC(N)=O\n", "\n", "NC(CC=C)c1ccccc1c2noc3c2cccc3\n", "\n", "OC1C(N2CCC1)=NC(C)=C(CCN3CCC(CC3)c4c5ccc(F)cc5on4)C2=O\n", "\n", "CC(=O)Nc1ccc(O)cc1\n", "\n", "CC(=O)Oc1ccccc1C(O)=O\n", "\n", "O=C1N=CN=C2NNC=C12\n", "\n", "CCCCC[C@H](O)/C=C/[C@H]1[C@H](O)CC(=O)[C@@H]1CCCCCCC(O)=O\n", "\n", "CN1C(=O)N(C)c2nc[nH]c2C1=O.CN3C(=O)N(C)c4nc[nH]c4C3=O.NCCN\n", "\n", "CCCCc1oc2ccccc2c1C(=O)c3cc(I)c(OCCN(CC)CC)c(I)c3\n", "\n", "O.O.O.CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O\n", "\n", "C[C@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C\n", "\n", "OC(=O)CCC(O)=O.FC(F)(F)c1ccc2Sc3ccccc3N(CCCN4CCN(CCC5OCCCO5)CC4)c2c1\n", "\n", "OC1N=C(c2ccccc2)c3cc(Cl)ccc3NC1=O\n", "\n", "O=C1Nc2ccccc2N1C3CCN(CCOc4ccccc4)CC3\n", "\n", "COc1ccc2C[C@H]3N(C)CC[C@@]45[C@@H](Oc1c24)C(=O)CC[C@@]35O\n", "\n", "CN1CC[C@]23[C@H]4Oc5c(O)ccc(C[C@@H]1[C@]2(O)CCC4=O)c35\n", "\n", "CN1CCCC(C1)CN2c3ccccc3Sc4ccccc24\n", "\n", "NC1=NC(=O)C(O1)c2ccccc2\n", "\n", "C[C@@H]1[C@H]2Cc3ccc(O)cc3[C@]1(C)CCN2CC=C(C)C\n", "\n", "CCc1cc(ccn1)C(N)=S\n", "\n", "COc1cc(cc(OC)c1O)[C@H]2[C@@H]3C(COC3=O)[C@H](O[C@@H]4O[C@@H]5CO[C@@H](C)O[C@H]5[C@H](O)[C@H]4O)c6cc7OCOc7cc26\n", "\n", "NC(N)=Nc1scc(CSCCC(N)=N[S](N)(=O)=O)n1\n", "\n", "OC(Cn1cncn1)(Cn2cncn2)c3ccc(F)cc3F\n", "\n", "[Na+].[Na+].[Na+].[O-]C(=O)[P]([O-])([O-])=O\n", "\n", "NCC1(CCCCC1)CC(O)=O\n", "\n", "[Na+].CCOc1ccc2ccccc2c1C(=O)N[C@H]3[C@H]4SC(C)(C)[C@@H](N4C3=O)C([O-])=O\n", "\n", "CCN1C=C(C(O)=O)C(=O)c2ccc(C)nc12\n", "\n", "Cc1c(O)cccc1C(=O)N[C@@H](CSc2ccccc2)[C@H](O)CN3C[C@H]4CCCC[C@H]4C[C@H]3C(=O)NC(C)(C)C\n", "\n", "COc1ccc(CCN2CCC(CC2)Nc3nc4ccccc4n3Cc5ccc(F)cc5)cc1\n", "\n", "CN1C2CCC1CC(C2)OC(=O)C(CO)c3ccccc3\n", "\n", "[Cl-].CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O.[H+]\n", "\n", "C[C@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(C(O)=O)c3ccccc3)C(=O)N2[C@H]1C(O)=O\n", "\n", "C[C@@](Cc1ccc(O)c(O)c1)(NN)C(O)=O\n", "\n", "CC(C)(C)NCC(O)COc1cccc2NC(=O)CCc12\n", "\n", "[Na+].Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cn4cnnn4)C3=O)C([O-])=O)nn1\n", "\n", "CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)CSc3ccncc3)C2=O)C(O)=O\n", "\n", "OC(=O)CCCc1ccc(cc1)N(CCCl)CCCl\n", "\n", "CCCN1C[C@H](CSC)C[C@H]2[C@H]1Cc3c[nH]c4cccc2c34\n", "\n", "CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24\n", "\n", "OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1\n", "\n", "CCOC(=O)C1(CCN(C)CC1)c2ccccc2\n", "\n", "C1CCN(CC1)C2(CCCCC2)c3ccccc3\n", "\n", "NNCCc1ccccc1.O[S](O)(=O)=O\n", "\n", "CCC1(C(=O)NC(=O)NC1=O)c2ccccc2\n", "\n", "NC(=O)OCCCc1ccccc1\n", "\n", "CNC(=O)Oc1ccc2N(C)[C@H]3N(C)CC[C@@]3(C)c2c1\n", "\n", "NCC(O)c1ccc(O)c(O)c1\n", "\n", "COc1ccc2nc([nH]c2c1)[S](=O)Cc3ncc(C)c(OC)c3C\n", "\n", "Cc1onc(c2ccccc2)c1C(=O)N[C@H]3[C@H]4SC(C)(C)[C@@H](N4C3=O)C(O)=O\n", "\n", "OC[C@H]1O[C@H](C[C@@H]1O)n2cnc3[C@H](O)CN=CNc23\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)COc3ccccc3)C(=O)N2[C@H]1C([O-])=O\n", "\n", "OCC(NC(=O)C(Cl)Cl)C(O)c1ccc(cc1)[N+]([O-])=O\n", "\n", "N[S](=O)(=O)c1cc2c(NC=N[S]2(=O)=O)cc1Cl\n", "\n", "CCCNC(=O)N[S](=O)(=O)c1ccc(Cl)cc1\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O\n", "\n", "N[S](=O)(=O)c1cc(ccc1Cl)C2(O)NC(=O)c3ccccc23\n", "\n", "CCOC(=O)C(C)(C)Oc1ccc(Cl)cc1\n", "\n", "CC(C)(Oc1ccc(Cl)cc1)C(O)=O\n", "\n", "Clc1ccccc1C(n2ccnc2)(c3ccccc3)c4ccccc4\n", "\n", "C[C@]12C[C@H](O)[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2C(=O)CO\n", "\n", "C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=O)CO)[C@@]4(C)CC(=O)[C@H]23\n", "\n", "OCC[C@@H]1CCN(CC\\C=C2/c3ccc(F)cc3Sc4ccc(cc24)C(F)(F)F)CC1\n", "\n", "Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4ccccc34)c5ccc(F)cc5\n", "\n", "OCCN1CCN(CC\\C=C/2c3ccccc3COc4ccc(Cl)cc24)CC1\n", "\n", "NC(=O)C1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)N3CCCCC3\n", "\n", "Clc1ccc2N(CC3CC3)C(=O)CN=C(c4ccccc4)c2c1\n", "\n", "CC(=O)[C@H]1CC[C@H]2[C@@H]3CC[C@@H]4C[C@H](O)CC[C@]4(C)[C@H]3CC[C@]12C\n", "\n", "CCC1(C(=O)NCNC1=O)c2ccccc2\n", "\n", "CCN(CC)CCOC(=O)c1ccc(N)cc1\n", "\n", "OC(CCN1CCCC1)(C2CCCCC2)c3ccccc3\n", "\n", "CN(C)CCCN1c2ccccc2Sc3ccccc13\n", "\n", "CC(C)NCC(O)COc1cccc2[nH]ccc12\n", "\n", "CO[C@@H]([C@@H]1Cc2cc3cc(O[C@H]4C[C@@H](O[C@H]5C[C@@H](O)[C@H](O)[C@@H](C)O5)[C@H](O)[C@@H](C)O4)c(C)c(O)c3c(O)c2C(=O)[C@H]1O[C@H]6C[C@@H](O[C@H]7C[C@@H](O[C@H]8C[C@](C)(O)[C@H](O)[C@@H](C)O8)[C@H](O)[C@@H](C)O7)[C@H](O)[C@@H](C)O6)C(=O)[C@@H](O)[C@@H](C)O\n", "\n", "CC[C@H](C)C(=O)O[C@H]1C[C@H](O)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(O)=O)[C@@H]12\n", "\n", "O=C1CN(CC2N1CCc3ccccc23)C(=O)C4CCCCC4\n", "\n", "COc1cc(Cc2cnc(N)nc2N)cc(OC)c1OC\n", "\n", "COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC\n", "\n", "NC(CO)(CO)CO\n", "\n", "NC1[C@H]2CN(C[C@@H]12)c3nc4N(C=C(C(O)=O)C(=O)c4cc3F)c5ccc(F)cc5F\n", "\n", "NC(N)=O\n", "\n", "O=C1Oc2ccccc2C=C1\n", "\n", "Nc1ccc(cc1)[S](=O)(=O)c2ccc(N)cc2\n", "\n", "C[C@H](CCC(O)=O)[C@H]1CC[C@H]2[C@H]3[C@H](CC(=O)[C@]12C)[C@@]4(C)CCC(=O)C[C@H]4CC3=O\n", "\n", "OC1=C(CC2=C(O)Oc3ccccc3C2=O)C(=O)c4ccccc4O1\n", "\n", "CCN(CC)CCOC(=O)C1(CCCCC1)C2CCCCC2\n", "\n", "CC\\C(c1ccc(O)cc1)=C(\\CC)c2ccc(O)cc2\n", "\n", "C[C@H]1O[C@H](C[C@H](O)[C@@H]1O)O[C@H]2[C@@H](O)C[C@@H](O[C@@H]2C)O[C@H]3[C@@H](O)C[C@@H](O[C@@H]3C)O[C@H]4CC[C@@]5(C)[C@H](CC[C@@H]6[C@@H]5CC[C@]7(C)[C@@H](CC[C@]67O)C8=CC(=O)OC8)C4\n", "\n", "C[C@H]1O[C@H](C[C@H](O)[C@@H]1O)O[C@H]2[C@@H](O)C[C@@H](O[C@@H]2C)O[C@H]3[C@@H](O)C[C@@H](O[C@@H]3C)O[C@H]4CC[C@@]5(C)[C@H](CC[C@@H]6[C@@H]5C[C@@H](O)[C@]7(C)[C@H](CC[C@]67O)C8=CC(=O)OC8)C4\n", "\n", "Clc1ccc2N(C3CCN(CCCN4C(=O)Nc5ccccc45)CC3)C(=O)Nc2c1\n", "\n", "CC(CN1c2ccccc2Sc3ccccc13)N(C)C\n", "\n", "CCC(=O)c1ccc2Sc3ccccc3N(CC(C)N(C)C)c2c1\n", "\n", "CC(C)c1cccc(C(C)C)c1O\n", "\n", "CNCCCC1c2ccccc2C=Cc3ccccc13\n", "\n", "O=C1C=CNN=C1\n", "\n", "Fc1ccccc1C2=NCC(=S)N(CC(F)(F)F)c3ccc(Cl)cc23\n", "\n", "CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)\\C=C\\c6cc(OC)c(OC)c(OC)c6\n", "\n", "CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6\n", "\n", "CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\n", "\n", "[H+].[Cl-].Clc1ccc2Sc3ccccc3N(CCCN4CCC5(CC4)NC(=O)CS5)c2c1\n", "\n", "OC1=C(C2CCC(CC2)c3ccc(Cl)cc3)C(=O)C(=O)c4ccccc14\n", "\n", "OC(=O)CCCCCCCC(O)=O\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)C(C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@](C)(O)C[C@@H](C)CN(C)[C@H](C)[C@@H](O)[C@]1(C)O\n", "\n", "CCC(C)C(N)C1=NC(CS1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@H](CCC(O)=O)C(=O)N[C@@H]([C@@H](C)CC)C(=O)NCCCC[C@@H]2NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](CC(O)=O)NC(=O)[C@H](Cc3[nH]cnc3)NC(=O)[C@@H](Cc4ccccc4)NC(=O)[C@@H](NC(=O)[C@@H](CCCN)NC2=O)[C@@H](C)CC\n", "\n", "[Cl-].CC(C[N+](C)(C)C)OC(N)=O\n", "\n", "CC[N+](C)(C)Cc1ccccc1Br.Cc2ccc(cc2)[S]([O-])(=O)=O\n", "\n", "CC(C)C[C@@H]1N2C(=O)[C@](NC(=O)[C@H]3CN(C)[C@@H]4Cc5c(Br)[nH]c6cccc(C4=C3)c56)(O[C@@]2(O)[C@@H]7CCCN7C1=O)C(C)C\n", "\n", "c12[C@]34[C@@]56[C@H]([N@@](CC7CC7)CC4)Cc2ccc(c1O[C@H]3[C@](OC)([C@H](C5)[C@](C(C)(C)C)(C)O)CC6)O\n", "\n", "NCCc1ccc(O)c(O)c1\n", "\n", "COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO\n", "\n", "O.C[C@@H]1[C@H]2[C@H](O)[C@H]3[C@H](N(C)C)C(=O)C(=C(N)/O)/C(=O)[C@@]3(O)C(=O)C2=C(O)c4c(O)cccc14\n", "\n", "[Cl].CCCCOc1ccc(cc1)C(=O)CCN2CCCCC2\n", "\n", "Clc1ccc(COC(Cn2ccnc2)c3ccc(Cl)cc3Cl)cc1\n", "\n", "CC(C)[C@H](NC(=O)[C@H](Cc1ccccc1)NC(=O)CNC(=O)[C@@H](C)NC(=O)[C@@H](N)Cc2ccc(O)cc2)C(N)=O\n", "\n", "CN[C@@H](C)[C@H](O)c1ccccc1\n", "\n", "CNC[C@H](O)c1ccc(O)c(O)c1\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@](C)(O)C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCc4cc(O)ccc34)[C@@H]1CC[C@@H]2O\n", "\n", "Nc1c2CCCCc2nc3ccccc13\n", "\n", "CN1C(=O)C(O)N=C(c2ccccc2)c3cc(Cl)ccc13\n", "\n", "COc1cc2CCN3CC(CC(C)C)C(=O)CC3c2cc1OC\n", "\n", "CCCCCc1cc(O)c2[C@@H]3C=C(C)CC[C@H]3C(C)(C)Oc2c1\n", "\n", "COC1=CC=C2[C@H]3Cc4ccc(OC)c5O[C@@H]1[C@]2(CCN3C)c45\n", "\n", "CCSc1ccc2Sc3ccccc3N(CCCN4CCN(C)CC4)c2c1\n", "\n", "CSc1ccc2Sc3ccccc3N(CCC4CCCCN4C)c2c1\n", "\n", "CN1CCN(CC/C=C/2c3ccccc3Sc4ccc(cc24)[S](=O)(=O)N(C)C)CC1\n", "\n", "[Cl-].N[C@@H]1C[C@H]1c2ccccc2.[H+]\n", "\n", "Clc1cccc(c1)N2CCN(CCCN3N=C4C=CC=CN4C3=O)CC2\n", "\n", "C[S](=O)(=O)OCCCCO[S](C)(=O)=O\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)C3=CCC=CC3)C2=O)C(O)=O\n", "\n", "CCN(CC)CCCC(C)Nc1ccnc2cc(Cl)ccc12\n", "\n", "O.COC1CN(CCCOc2ccc(F)cc2)CCC1NC(=O)c3cc(Cl)c(N)cc3OC\n", "\n", "Nc1nc(Cl)nc2n(cnc12)[C@H]3C[C@H](O)[C@@H](CO)O3\n", "\n", "[Br-].C[N+]12CCC(CC1)C(C2)OC(=O)C(O)(c3ccccc3)c4ccccc4\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCc4cc(O)ccc34)[C@@H]1CCC2=O\n", "\n", "CCC(=C)C(=O)c1ccc(OCC(O)=O)c(Cl)c1Cl\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCc4cc(O)ccc34)[C@@H]1CC[C@@]2(O)C#C\n", "\n", "CC(Cc1ccc(O)cc1)NCC(O)c2cc(O)cc(O)c2\n", "\n", "OC[C@H]1O[C@H](C[C@@H]1O)N2C=C(F)C(=O)NC2=O\n", "\n", "Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(F)cccc4Cl\n", "\n", "FC1=CNC(=O)NC1=O\n", "\n", "N[S](=O)(=O)c1cc(C(O)=O)c(NCc2occc2)cc1Cl\n", "\n", "NC1=NC(=O)c2ncn(COC(CO)CO)c2N1\n", "\n", "[Br-].C[N+]1(C)CCC(C1)OC(=O)C(O)(C2CCCC2)c3ccccc3\n", "\n", "OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3cccc(c3)C(F)(F)F\n", "\n", "CN(C)CCCN1c2ccccc2Sc3ccc(cc13)C(F)(F)F\n", "\n", "CN1CCN(CCCN2c3ccccc3Sc4ccc(cc24)C(F)(F)F)CC1\n", "\n", "OC(CCN1CCCCC1)(C2CCCCC2)c3ccccc3\n", "\n", "COc1cc(cc(OC)c1OC)C(=O)N2CCOCC2\n", "\n", "CC(CN(C)C)CN1c2ccccc2CCc3ccccc13\n", "\n", "CCCCNC(=O)OCC(C)(CCC)COC(N)=O\n", "\n", "OCC1OC(C(O)C1O)N2C=CC(=O)NC2=O\n", "\n", "CCCC(CCC)C(O)=O\n", "\n", "CN(C)C/C=C(/c1ccc(Br)cc1)c2cccnc2\n", "\n", "CC(C)N=C1C=C2N(c3ccc(Cl)cc3)c4ccccc4N=C2C=C1Nc5ccc(Cl)cc5\n", "\n", "COC1=CC=C2C(=CC1=O)[C@H](CCc3cc(OC)c(OC)c(OC)c23)NC(C)=O\n", "\n", "OC(COc1ccc2OC(=CC(=O)c2c1)C(O)=O)COc3cccc4OC(=CC(=O)c34)C(O)=O\n", "\n", "NC1CONC1=O\n", "\n", "NC(=O)NO\n", "\n", "[Br-].CC(C)[N+]1(C)C2CCC1CC(C2)OC(=O)C(CO)c3ccccc3\n", "\n", "NC(N)=N\\N=C\\c1c(Cl)cccc1Cl\n", "\n", "NC(N)=NCCN1CCCCCCC1\n", "\n", "CN1C2CCC1CC(C2)OC(=O)C(O)c3ccccc3\n", "\n", "NNc1nncc2ccccc12\n", "\n", "N[S](=O)(=O)c1cc2c(NCN[S]2(=O)=O)cc1Cl\n", "\n", "C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@](O)(C(=O)CO)[C@@]4(C)C[C@H](O)[C@H]23\n", "\n", "N[S](=O)(=O)c1cc2c(NCN[S]2(=O)=O)cc1C(F)(F)F\n", "\n", "CC(C)Cc1ccc(cc1)C(C)C(O)=O\n", "\n", "COc1ccc2n(c(C)c(CC(O)=O)c2c1)C(=O)c3ccc(Cl)cc3\n", "\n", "c12c(c(ccc1)Cl)C(N(Cc1n2cnc1c1nc(on1)[C@](CO)(C)O)C)=O\n", "\n", "NCCc1cn2c(n1)cccc2\n", "\n", "CCCN(CCC)CCc1ccc(c2c1CC(N2)=C)O\n", "\n", "c1(c2c(cc(F)cc2)on1)C1CCN(CCc2c(n3c([C@@H](CCC3)O)nc2C)=O)CC1\n", "\n", "Cc1nnc2CN=C(c3ccccc3)c4cc(Cl)ccc4n12\n", "\n", "ClCCNC(=O)N(CCCl)N=O\n", "\n", "CCC(C)N1N=CN(C1=O)c2ccc(cc2)N3CCN(CC3)c4ccc(OC[C@H]5CO[C@@](Cn6cncn6)(O5)c7ccc(Cl)cc7Cl)cc4\n", "\n", "NCC1OC(OC2C(N)CC(N)C(OC3OC(CO)C(O)C(N)C3O)C2O)C(O)C(O)C1O\n", "\n", "CC(C(O)=O)c1cccc(c1)C(=O)c2ccccc2\n", "\n", "OC(=O)C1CCn2c1ccc2C(=O)c3ccccc3\n", "\n", "C[C@H](N)[C@H](O)c1cccc(O)c1\n", "\n", "NNC(=O)c1ccncc1\n", "\n", "CC(C)NCC(O)c1ccc(O)c(O)c1\n", "\n", "OC[C@H]1O[C@@H](OC2=C(Oc3cc(O)cc(O)c3C2=O)c4ccc(O)c(O)c4)[C@H](O)[C@@H](O)[C@@H]1O\n", "\n", "CC(=O)N1CCN(CC1)c2ccc(OC[C@H]3CO[C@@](Cn4ccnc4)(O3)c5ccc(Cl)cc5Cl)cc2\n", "\n", "CC(CCc1ccccc1)NCC(O)c2ccc(O)c(c2)C(N)=O\n", "\n", "N[C@@H](Cc1ccc(O)c(O)c1)C(O)=O\n", "\n", "OCC(O)C(O)C(O)C(O)CO\n", "\n", "CN(C)C1C2C(O)C3C(=C)c4c(Cl)ccc(O)c4C(=C3C(=O)C2(O)C(=O)\\C(=C(N)/O)C1=O)O\n", "\n", "Cc1ccc(Cl)c(Nc2ccccc2C(O)=O)c1Cl\n", "\n", "Cc1cccc(Nc2ccccc2C(O)=O)c1C\n", "\n", "CN=C(NCCSCc1nc[nH]c1C)NC#N\n", "\n", "Clc1cccc(Cl)c1NC2=NCCN2\n", "\n", "OC[C@@H]1CC[C@@H](O1)n2cnc3C(=O)N=CNc23\n", "\n", "COCCc1ccc(OCC(O)CNC(C)C)cc1\n", "\n", "Cc1ncc(n1CCO)[N+]([O-])=O\n", "\n", "CC#C[C@]1(O)CC[C@H]2[C@@H]3CCC4=CC(=O)CCC4=C3[C@H](C[C@]12C)c5ccc(cc5)N(C)C\n", "\n", "C1[C@@H]2[C@](C(=C3C(c4c(ccc(c4C[C@@H]13)N(C)C)O)=O)O)(C(C(C(N)=O)=C([C@H]2N(C)C)O)=O)O\n", "\n", "Clc1ccc(cc1)C(=O)NCCN2CCOCC2\n", "\n", "[Br-].CC(C)[N+](C)(CCOC(=O)C1c2ccccc2Oc3ccccc13)C(C)C\n", "\n", "NC(=O)c1cnccn1\n", "\n", "[Br-].CN(C)C(=O)Oc1ccc[n+](C)c1\n", "\n", "CCc1nc(N)nc(N)c1c2ccc(Cl)cc2\n", "\n", "COc1ccc2nccc([C@@H](O)[C@@H]3C[C@H]4CCN3C[C@@H]4C=C)c2c1\n", "\n", "NC(Cc1ccc(cc1)N(CCCl)CCCl)C(O)=O\n", "\n", "[Br-].C[N+]1(C)CCCC(C1)OC(=O)C(O)(c2ccccc2)c3ccccc3\n", "\n", "COc1ccc(CN(CCN(C)C)c2ccccn2)cc1\n", "\n", "C1CN2CCC1C(C2)CN3c4ccccc4Sc5ccccc35\n", "\n", "COc1ccc2[C@H]3CC[C@@]4(C)[C@@H](CC[C@@]4(O)C#C)[C@@H]3CCc2c1\n", "\n", "CC[N+](C)(CC)CCOC(=O)C1c2ccccc2Oc3ccccc13\n", "\n", "CN(Cc1cnc2nc(N)nc(N)c2n1)c3ccc(cc3)C(=O)N[C@@H](CCC(O)=O)C(O)=O\n", "\n", "CN1C(CCl)Nc2cc(Cl)c(cc2[S]1(=O)=O)[S](N)(=O)=O\n", "\n", "C[C@](N)(Cc1ccc(O)c(O)c1)C(O)=O\n", "\n", "[Cl-].[Cl-].[Cl-].COc1cccnc1CCCCNC2=NC=C(Cc3ccc(C)nc3)C(=O)N2.[H+].[H+].[H+]\n", "\n", "CC(C)c1onc(n1)c2ncn3c2CN(C)C(=O)c4c(Cl)cccc34\n", "\n", "Cc1ccc(CC2=CN=C(NCCSCc3oc(cc3)C(C)(C)N)NC2=O)cn1\n", "\n", "CN1CCN2C(C1)c3ccccc3Cc4ccccc24\n", "\n", "Cc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12\n", "\n", "CC(C)[C@H](NC(=O)N(C)Cc1csc(n1)C(C)C)C(=O)N[C@H](C[C@H](O)[C@H](Cc2ccccc2)NC(=O)OCc3scnc3)Cc4ccccc4\n", "\n", "CC(C)(C)NC(=O)[C@@H]1C[C@@H]2CCCC[C@@H]2CN1C[C@@H](O)[C@H](Cc3ccccc3)NC(=O)[C@H](CC(N)=O)NC(=O)c4ccc5ccccc5n4\n", "\n", "FCOC(C(F)(F)F)C(F)(F)F\n", "\n", "[Na+].CC(=O)Nc1c(I)c(NC(C)=O)c(I)c(C([O-])=O)c1I\n", "\n", "CC(C)NCC(O)c1ccc(N[S](C)(=O)=O)cc1\n", "\n", "CC1=CN([C@@H]2O[C@H](CO)C=C2)C(=O)NC1=O\n", "\n", "CN[C@H]1[C@H](O)[C@@H](O)[C@H](CO)O[C@H]1O[C@H]2[C@@H](O[C@@H](C)[C@]2(O)C=O)O[C@H]3[C@H](O)[C@@H](O)[C@H](N=C(N)N)[C@@H](O)[C@@H]3N=C(N)N\n", "\n", "Nc1ccc(cc1)[S](=O)(=O)Nc2ncccn2\n", "\n", "Cc1noc(N[S](=O)(=O)c2ccc(N)cc2)c1C\n", "\n", "C[C@H]1C[C@H]2[C@@H]3CC[C@](O)(C(=O)CO)[C@@]3(C)C[C@H](O)[C@@H]2[C@@]4(C)C=CC(=O)C=C14\n", "\n", "Clc1ccc(COC(Cn2ccnc2)c3ccc(Cl)cc3Cl)c(Cl)c1\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@H]2O\n", "\n", "CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O\n", "\n", "OCc1cccnc1\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C\n", "\n", "NCC(O)c1cccc(O)c1\n", "\n", "CC(CCc1ccccc1)NC(C)C(O)c2ccc(O)cc2\n", "\n", "C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4CC(=O)OC[C@]4(C)[C@H]3CC[C@]12C\n", "\n", "OC(=O)CCc1oc(c2ccccc2)c(n1)c3ccccc3\n", "\n", "CN1CCN2C(C1)c3ccccc3Cc4cccnc24\n", "\n", "Cc1ccnc2N(C3CC3)c4ncccc4C(=O)Nc12\n", "\n", "C1NCCN(C1)c1ccc(c(n1)Cl)C(F)(F)F\n", "\n", "c1c2c(cc(c1)Cl)[C@@H]1[C@H](c3ccccc3O2)CNC1\n", "\n", "c1c2c(ccc1)[C@@]1([C@@H](c3cccc(c3O2)C)CNCC1)O\n", "\n", "c1ccc(c(c1)[C@H](CC=C)N)c1c2c(on1)cccc2\n", "\n", "c1c2c(ccc1)[C@@]1([C@@H](c3cccc(c3O2)C)C[N@](CC1)C)O\n", "\n", "c1c2c(cc(c1)Cl)[C@@H]1[C@H](c3ccccc3O2)CN(C1)C\n", "\n", "CNC(/NCCSCc1oc(CN(C)C)cc1)=C\\[N+]([O-])=O\n", "\n", "Cc1onc(N[S](=O)(=O)c2ccc(N)cc2)c1\n", "\n", "CC/C(c1ccccc1)=C(c2ccccc2)/c3ccc(OCCN(C)C)cc3\n", "\n", "[Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](C([O-])=O)c3cscc3)C(=O)N2[C@H]1C([O-])=O\n", "\n", "CC[C@@]1(O)C(=O)OCC2=C1C=C3N(Cc4cc5c(CN(C)C)c(O)ccc5nc34)C2=O\n", "\n", "NC1=NC(=O)N(C=C1)[C@H]2CC[C@@H](CO)O2\n", "\n", "N[S](=O)(=O)Cc1noc2ccccc12\n", "\n", "C[C@H]1O[C@@H](O[C@@H]\\2C[C@@H]3O[C@](O)(C[C@@H](O)C[C@@H](O)[C@H](O)CC[C@@H](O)C[C@@H](O)CC(=O)O[C@@H](C)[C@H](C)[C@H](O)[C@@H](C)\\C=C/C=C\\C=C/C=C\\C=C/C=C\\C=C2)C[C@H](O)[C@H]3C(O)=O)[C@@H](O)[C@@H](N)[C@@H]1O\n", "\n", "[Ca++].NC1=NC(=O)C2=C(NCC(CNc3ccc(cc3)C(=O)N[C@@H](CCC([O-])=O)C([O-])=O)N2C=O)N1\n", "\n", "O.O.CN(C)[C@H]1[C@@H]2[C@@H](O)[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O\n", "\n", "COc1ccc(Cc2nccc3cc(OC)c(OC)cc23)cc1OC\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)Cc3ccccc3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CCOc1ccc(NC(C)=O)cc1\n", "\n", "Nc1ccc(N=Nc2ccccc2)c(N)n1\n", "\n", "CN(C)CCC(c1ccccc1)c2ccccn2\n", "\n", "Oc1ccc(cc1)C2(OC(=O)c3ccccc23)c4ccc(O)cc4\n", "\n", "CC(COc1ccccc1)N(CCCl)Cc2ccccc2\n", "\n", "CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3\n", "\n", "CC(C)(C)NCC(O)c1ccc(O)c(CO)n1\n", "\n", "CC1=C(CCN2CCC(CC2)c3noc4cc(F)ccc34)C(=O)N5CCCCC5=N1\n", "\n", "CN1CC[C@]2(C)[C@H]1N(C)c3ccc(OC(=O)Nc4ccccc4)cc23\n", "\n", "Cc1ccc(CC2=CN=C(NCCCCc3ncc(Br)cc3C)NC2=O)cn1\n", "\n", "C[C@@H]1CC2=C(CCC(=O)C2)[C@H]3CC[C@@]4(C)[C@@H](CC[C@@]4(O)C#C)[C@H]13\n", "\n", "CC(C)(O/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C([O-])=O)C[n+]3ccccc3)/c4csc(N)n4)C(O)=O\n", "\n", "NC1=C(F)C=NC(=O)N1\n", "\n", "NC(N)=NCC1COC2(CCCCC2)O1\n", "\n", "ClCCN(N=O)C(=O)NC1CCCCC1\n", "\n", "[Na+].[O-][S](=O)(=O)CCS\n", "\n", "CN(N=O)C(=O)N[C@H]1[C@@H](O)O[C@H](CO)[C@@H](O)[C@@H]1O\n", "\n", "COc1cc(cc(OC)c1O)[C@H]2[C@@H]3[C@H](COC3=O)[C@H](OC4OC5COC(OC5C(O)C4O)c6sccc6)c7cc8OCOc8cc27\n", "\n", "S=[P](N1CC1)(N2CC2)N3CC3\n", "\n", "CN[C@H](CC(C)C)C(=O)NC1[C@H](O)c2ccc(Oc3cc4cc(Oc5ccc(cc5Cl)[C@@H](O)[C@@H]6NC(=O)[C@H](NC(=O)[C@@H]4NC(=O)[C@H](CC(N)=O)NC1=O)c7ccc(O)c(c7)c8c(O)cc(O)cc8[C@@H](NC6=O)C(O)=O)c3O[C@@H]9O[C@H](CO)[C@@H](O)[C@H](O)[C@H]9O[C@H]%10C[C@](C)(N)[C@H](O)[C@H](C)O%10)c(Cl)c2\n", "\n", "CN1C(CSCC(F)(F)F)Nc2cc(Cl)c(cc2[S]1(=O)=O)[S](N)(=O)=O\n", "\n", "C[C@]12C[C@H](O)[C@H]3[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1CC[C@]2(O)C(=O)CO\n", "\n", "C[C@]12CC(=O)[C@H]3[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1CC[C@]2(O)C(=O)CO\n", "\n", "CCCN(CCC)[S](=O)(=O)c1ccc(cc1)C(O)=O\n", "\n", "CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C\n", "\n", "CC(C)NCC(O)COc1cccc2ccccc12\n", "\n", "CC1OC(O[C@H]2CC[C@]3(C)[C@H]4CC[C@]5(C)[C@H](CC[C@]5(O)[C@@H]4CCC3=C2)C6=COC(=O)C=C6)C(O)C(O)C1O\n", "\n", "COc1ccc(C[C@H](N)C(=O)NC2[C@@H](O)[C@@H](O[C@@H]2CO)n3cnc4c(ncnc34)N(C)C)cc1\n", "\n", "NC(=O)c1ncn(n1)[C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O\n", "\n", "CN=C(NCCSCc1csc(N=C(N)N)n1)NC#N\n", "\n", "OC(=O)\\C=C/C(O)=O.OC(=O)\\C=C/C(O)=O.C1CCN(CC1)Cc2cccc(OCCCNc3sc4ccccc4n3)c2\n", "\n", "OCc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12\n", "\n", "CC(Cl)(Cl)Cl\n", "\n", "C(CCl)(F)(F)F\n", "\n", "CC(C)CO\n", "\n", "CCC(C)(C)C\n", "\n", "Nc1ncnc2n(cnc12)C3OC(CO)C(O)C3O\n", "\n", "c12c(C(N(Cc3n1cnc3c1noc(n1)C(CO)(CO)C)C)=O)c(ccc2)Cl\n", "\n", "Cc1nc(sc1)\\[NH]=C(\\N)N\n", "\n", "c12c(C(N(Cc3n1cnc3c1noc(n1)C(O)(C)C)C)=O)c(ccc2)Cl\n", "\n", "S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3\n", "\n", "CC1C=CC=C(C(=O)NC2=C(C3=C(C(=C4C(=C3C(=O)C2=CNN5CCN(CC5)C)C(=O)C(O4)(OC=CC(C(C(C(C(C(C1O)C)O)C)OC(=O)C)C)OC)C)C)O)O)C\n", "\n", "OC(=O)c1ccccc1O\n", "\n", "CN1[C@@H]2CC(C[C@H]1[C@@H]3O[C@H]23)OC(=O)[C@H](CO)c4ccccc4\n", "\n", "CC(=O)S[C@@H]1CC2=CC(=O)CC[C@]2(C)[C@H]3CC[C@@]4(C)[C@@H](CC[C@@]45CCC(=O)O5)[C@H]13\n", "\n", "C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4Cc5[nH]ncc5C[C@]4(C)[C@H]3CC[C@]12C\n", "\n", "OC(=O)C1=C\\C(C=CC1=O)=N/Nc2ccc(cc2)[S](=O)(=O)Nc3ccccn3\n", "\n", "CC(C)N1CCN(CC1)c2ccc(OC[C@H]3CO[C@@](Cn4cncn4)(O3)c5ccc(Cl)cc5Cl)cc2\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1CCC(=O)O2\n", "\n", "C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O\n", "\n", "CCCC(C)CC\n", "\n", "CCC(C)CC\n", "\n", "Cc1ncc2C(O)N=C(c3ccccc3F)c4cc(Cl)ccc4n12\n", "\n", "CN(C)C1=C(C)N(C)N(c2ccccc2)C1=O\n", "\n", "CC(C)NCC(O)COc1ccc(CC(N)=O)cc1\n", "\n", "c1ccccc1\n", "\n", "CC(C)(C)OC(=O)c1ncn2c3cccc(Br)c3C(=O)N4CCC[C@H]4c12\n", "\n", "OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Br)cc3\n", "\n", "OC(C1CCCCN1)c2cc(nc3c2cccc3C(F)(F)F)C(F)(F)F\n", "\n", "CC1COc2c(N3CCN(C)CC3)c(F)cc4C(=O)C(=CN1c24)C(O)=O\n", "\n", "CN1C(CNC(=O)c2cscc2)CN=C(c3ccccc3F)c4ccccc14\n", "\n", "C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@H](C(=O)CO)[C@]4(C[C@H](O)[C@H]23)C=O\n", "\n", "c1(cc(c(cc1)Cl)Cl)CC(N1CCCC[C@H]1CN1CCCC1)=O\n", "\n", "c1(cc(c(cc1)Cl)Cl)CC(N1[C@@H](c2c(CC1)cccc2)CN1CCCC1)=O\n", "\n", "c1(ccc(cc1)C(F)(F)F)CC(N1[C@@H](CCCC1)CN1CCCC1)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](c2c(CC1)scc2)CN1CCCC1)=O\n", "\n", "CN1C(=O)N(C)c2nc[nH]c2C1=O\n", "\n", "NC1=NC(=S)c2[nH]cnc2N1\n", "\n", "Cc1ccc(cc1)[S](=O)(=O)NC(=O)NN2CCCCCC2\n", "\n", "CCCCNC(=O)N[S](=O)(=O)c1ccc(C)cc1\n", "\n", "C[C@]12C[C@H](O)[C@@]3(F)[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1C[C@@H](O)[C@]2(O)C(=O)CO\n", "\n", "Nc1nc(N)c2nc(c3ccccc3)c(N)nc2n1\n", "\n", "N[S](=O)(=O)c1cc2c(NC(N[S]2(=O)=O)C(Cl)Cl)cc1Cl\n", "\n", "CC(C)[C@@H]1NC(=O)[C@H](C)OC(=O)C(NC(=O)[C@H](OC(=O)[C@@H](NC(=O)[C@H](C)OC(=O)[C@H](NC(=O)[C@H](OC(=O)[C@@H](NC(=O)[C@H](C)OC(=O)[C@H](NC(=O)[C@H](OC1=O)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C)C(C)C\n", "\n", "COc1ccc(CCN(C)CCCC(C#N)(C(C)C)c2ccc(OC)c(OC)c2)cc1OC\n", "\n", "CC(=O)CC(c1ccccc1)C2=C(O)Oc3ccccc3C2=O\n", "\n", "CCC(C)=O\n", "\n", "C1CC1\n", "\n", "N1(c2c(CCc3c1cccc3)cccc2)CCCN\n", "\n", "ClCCl\n", "\n", "CCOCC\n", "\n", "C=COC=C\n", "\n", "FC(F)OC(F)(F)C(F)Cl\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](c2c(CC1)[nH]cn2)CN1CCCC1)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](CN2CCCC2)CC(CC1)(C)C)=O\n", "\n", "CC(C)(C(O)=O)c1ccc(cc1)C(=O)CCCN2CCC(CC2)OC(c3ccccc3)c4ccccc4\n", "\n", "OC(=O)COCCN1CCN(CC1)C(c2ccccc2)c3ccc(Cl)cc3\n", "\n", "OC(=O)C1=CN(C2CC2)c3cc(N4CCNCC4)c(F)cc3C1=O\n", "\n", "c1cn(CCO)c(CC)c(O)c1=O\n", "\n", "c1cn(CCCCO)c(CC)c(O)c1=O\n", "\n", "COC(=O)[C@H]1[C@@H](O)CC[C@H]2CN3CCc4c([nH]c5ccccc45)[C@@H]3C[C@H]12\n", "\n", "CC1=CN([C@H]2C[C@H](N=[N+]=[N-])[C@@H](CO)O2)C(=O)NC1=O\n", "\n", "CN(C)CCCN1c2ccccc2Sc3ccc(cc13)C(C)=O\n", "\n", "CC(=O)c1ccc2Sc3ccccc3N(CCCN4CCN(CCO)CC4)c2c1\n", "\n", "CC(=O)Nc1sc(nn1)[S](N)(=O)=O\n", "\n", "C=CCC1(CC=C)C(=O)NC(=O)NC1=O\n", "\n", "[Cl-].NC12CC3CC(CC(C3)C1)C2.[H+]\n", "\n", "CN(C)CCC=C1c2ccccc2CCc3ccccc13\n", "\n", "CCC1(CCC(C)C)C(=O)NC(=O)NC1=O\n", "\n", "CC(N)Cc1ccccc1\n", "\n", "CCO\n", "\n", "CCc1ccccc1\n", "\n", "CCOC(=O)c1ncn2c1CN(C)C(=O)c3cc(F)ccc23\n", "\n", "CN1C(=O)CN=C(c2ccccc2F)c3cc(ccc13)[N+]([O-])=O\n", "\n", "FC(F)(F)COC=C\n", "\n", "FC(F)(F)C(Cl)Br\n", "\n", "CCCCCCC\n", "\n", "CCCCCC\n", "\n", "CN1C=CC(=O)C(=C1C)O\n", "\n", "CCN1C=CC(=O)C(=C1C)O\n", "\n", "CCCCN1C=CC(=O)C(=C1C)O\n", "\n", "CCCCCN1C=CC(=O)C(=C1C)O\n", "\n", "c1cn(CCCCC)c(C)c(O)c1=O\n", "\n", "c1cn(CCCO)c(C)c(O)c1=O\n", "\n", "CCN1C=CC(=O)C(=C1CC)O\n", "\n", "CN1CCN(CC1)c2cc3N(C=C(C(O)=O)C(=O)c3cc2F)c4ccc(F)cc4\n", "\n", "CN1N(C(=O)C=C1C)c2ccccc2\n", "\n", "CN1CCc2cccc3c2[C@H]1Cc4ccc(O)c(O)c34\n", "\n", "COC(=O)C1=CCCN(C)C1\n", "\n", "Fc1ccc(cc1)C(=O)CCCN2CCN(CC2)c3ccccn3\n", "\n", "[H+].[Cl-].CCN(CC)CCOC(=O)C(O)(c1ccccc1)c2ccccc2\n", "\n", "Fc1ccc(cc1)C(=O)CCCN2CCC(CC2)N3C(=O)Nc4ccccc34\n", "\n", "CCOC(=O)c1ccc(N)cc1\n", "\n", "CCN(CC)C(=O)C1CN2CCc3cc(OC)c(OC)cc3C2CC1OC(C)=O\n", "\n", "CN1C2CCC1CC(C2)OC(c3ccccc3)c4ccccc4.O[S](O)(=O)=O\n", "\n", "OC(CCN1CCCCC1)(C2CC3CC2C=C3)c4ccccc4\n", "\n", "CN1C(=O)NC(=O)C(C)(C1=O)C2=CCCCC2\n", "\n", "CC(C)(C)NC(=O)[C@@H]1CN(CCN1C[C@@H](O)C[C@@H](Cc2ccccc2)C(=O)N[C@@H]3[C@H](O)Cc4ccccc34)Cc5cccnc5\n", "\n", "FC(F)OC(Cl)C(F)(F)F\n", "\n", "CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\n", "\n", "COC(F)(F)C(Cl)Cl\n", "\n", "CC1CCCC1\n", "\n", "N1(c2c(Sc3c1cccc3)ccc(c2)Cl)CCCNC\n", "\n", "N1(c2c(Sc3c1cccc3)ccc(c2)Cl)CCCN\n", "\n", "CN(C)CCOC(C)(c1ccccc1)c2ccccn2\n", "\n", "c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1\n", "\n", "CCN1C=C(C(O)=O)C(=O)c2cc(F)c(nc12)N3CCNCC3\n", "\n", "CN1CCN(CC1)c2c(F)cc3C(=O)C(=CN(CCF)c3c2F)C(O)=O\n", "\n", "c1(cc(c(cc1)Cl)Cl)CC(N1[C@@H](CN2CCCC2)CN(CC1)C(=O)C)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](c2c(CC1)occ2)CN1CCCC1)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN(CC1)C(=O)OC)CN1CCCC1)=O\n", "\n", "Brc1ccc2NC(=O)CN=C(c3ccccn3)c2c1\n", "\n", "CC(C)(C)c1ccc(CN2CCN(CC2)C(c3ccccc3)c4ccc(Cl)cc4)cc1\n", "\n", "CN(C)CCc1c[nH]c2ccc(O)cc12\n", "\n", "CC(NC(C)(C)C)C(=O)c1cccc(Cl)c1\n", "\n", "OCCOC(=O)NCc1ccccc1\n", "\n", "CCC(C)C1(CC)C(=O)NC(=O)NC1=O\n", "\n", "Cn1cnc2N(C)C(=O)N(C)C(=O)c12\n", "\n", "CCCCCc1cc(O)c(C2C=C(C)CC[C@H]2C(C)=C)c(O)c1\n", "\n", "CCC(C)C(CC)C(=O)NC(N)=O\n", "\n", "NC(=O)N1c2ccccc2C=Cc3ccccc13\n", "\n", "CSc1ccc2Sc3ccccc3N(CCC4CCCNC4)c2c1\n", "\n", "C1N(CCN(C1)c1cc(ccn1)C(F)(F)F)CCCCN1C(CCC1)=O\n", "\n", "Cn1cnc2NC(=O)N(C)C(=O)c12\n", "\n", "CCCCC\n", "\n", "CCCO\n", "\n", "CC(C)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN(CC1)C(=O)OCC)CN1CCCC1)=O\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)OCCC)CN2CCCC2)=O)cc(c(cc1)Cl)Cl\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](c2c(CC1)occ2)C[N@@]1C[C@@H](CC1)O)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(=O)N1[C@H](CN(C(=O)C)CC1)C[N@@]1C[C@H](O)CC1\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](C2)O)=O)ccc(C(F)(F)F)cc1\n", "\n", "c1(ccc(cc1)SC)CC(N1[C@H](CN(CC1)C(=O)C)C[N@]1CC[C@H](C1)O)=O\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](C2)O)=O)cc(cc(c1)F)F\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](C2)O)=O)cc(ccc1)OC\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](O)C2)=O)ccc(N(=O)=O)cc1\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](C2)O)=O)ccc(OC)cc1\n", "\n", "CCC(=O)c1ccc2Sc3ccccc3N(CCCN4CCN(CCO)CC4)c2c1\n", "\n", "CCCCNc1c(cnc2n(CC)ncc12)C(=O)OCC\n", "\n", "NN1C=Nc2cc3ccccc3cc2C1=O\n", "\n", "OC(O)C(Cl)(Cl)Cl\n", "\n", "CN=C1CN(O)C(=C2C=C(Cl)C=CC2=N1)c3ccccc3\n", "\n", "CN(C)CCCN1c2ccccc2Sc3ccc(Cl)cc13\n", "\n", "CN(C)CC\\C=C/1c2ccccc2Sc3ccc(Cl)cc13\n", "\n", "Clc1ccc2OC(=O)Nc2c1\n", "\n", "C[N+](C)(C)CCO\n", "\n", "CN1C(=O)CC(=O)N(c2ccccc2)c3cc(Cl)ccc13\n", "\n", "CN1Cc2n(cnc2C(=O)OC(C)(C)C)c3ccsc3C1=O\n", "\n", "C1C(Nc2c1c(ccc2)CCN(CCC)CCC)=O\n", "\n", "C1C(Nc2c1c(ccc2O)CCN(CCC)CCC)=O\n", "\n", "OC(=O)CNC(=O)c1ccccc1O\n", "\n", "CN1CCCCC1CCN2c3ccccc3Sc4ccc(cc24)[S](C)(=O)=O\n", "\n", "FC(Br)C(F)(F)F\n", "\n", "Cn1cnc2N(C)C(=O)NC(=O)c12\n", "\n", "CCCC(C)C1(CC)C(=O)NC(=S)NC1=O\n", "\n", "Cc1ccccc1\n", "\n", "Cc1nnc2CN=C(c3ccccc3Cl)c4cc(Cl)ccc4n12\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](O)C2)=O)cc(ccc1)N(=O)=O\n", "\n", "c1(ccccc1)CC(N1[C@H](CN(CC1)C(=O)C)C[N@]1CC[C@H](C1)O)=O\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](O)C2)=O)ccc([S@](=O)C)cc1\n", "\n", "c1(CC(N2[C@H](CN(CC2)C(=O)C)C[N@]2CC[C@H](O)C2)=O)ccc(S(=O)(=O)C)cc1\n", "\n", "CC(C)[C@@H](CN1CCCC1)N(C)C(=O)Cc2ccc(Cl)c(Cl)c2\n", "\n", "c1(ccccc1)[C@H](CN1CCCC1)N(C(=O)Cc1ccc(c(c1)Cl)Cl)C\n", "\n", "CN1C(=C(\\O)Nc2cc(C)on2)/C(=O)c3ccccc3[S]1(=O)=O\n", "\n", "Oc1ccc2C[C@@H]3[C@@H]4CCCC[C@]4(CCN3CC=C)c2c1\n", "\n", "CCN1C=C(C(O)=O)C(=O)c2cc(F)c(N3CCNC(C)C3)c(F)c12\n", "\n", "CN(C)C(=O)C(CCN1CCC(O)(CC1)c2ccc(Cl)cc2)(c3ccccc3)c4ccccc4\n", "\n", "CN(C)CCCN1c2ccccc2CCc3ccc(Cl)cc13\n", "\n", "[O-][N+](=O)c1ccc2NC(=O)CN=C(c3ccccc3Cl)c2c1\n", "\n", "OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1\n", "\n", "Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4cc(Cl)ccc34)c5ccc(F)cc5\n", "\n", "COC(=O)[C@H]1[C@H](CC2CCC1N2C)OC(=O)c3ccccc3\n", "\n", "COc1ccc2C[C@@H]3[C@@H]4C=C[C@H](O)[C@@H]5Oc1c2[C@]45CCN3C\n", "\n", "CC1C2Cc3ccc(O)cc3C1(C)CCN2CC4CC4\n", "\n", "FC(F)(F)c1ccc2Sc3ccccc3N(CCCN4CCN(CC4)C5CC5)c2c1\n", "\n", "CN(C)CCO\n", "\n", "[H+].[Cl-].CCOC(=O)C1(CCN(C)CC1)c2ccccc2\n", "\n", "ClC=C(Cl)Cl\n", "\n", "ClC(Cl)Cl\n", "\n", "c1c(nccc1)CCNC\n", "\n", "c1c(nccc1)CCN(C)C\n", "\n", "n1c(scc1)CCN\n", "\n", "n1c(scc1c1ccccc1)CCN\n", "\n", "c1c(nc2n1cccc2)CCN\n", "\n", "Cc1cccc(C)c1\n", "\n", "CCOC(=O)N1CCC(CC1)=C2c3ccc(Cl)cc3CCc4cccnc24\n", "\n", "CN1C(=C(/O)Nc2sc(C)cn2)/C(=O)c3ccccc3[S]1(=O)=O\n", "\n", "O[C@H]1C=C[C@H]2[C@H]3Cc4ccc(O)c5O[C@@H]1[C@]2(CCN3CC=C)c45\n", "\n", "Oc1ccc2C[C@H]3N(CC[C@@]45[C@@H](Oc1c24)C(=O)CC[C@@]35O)CC6CC6\n", "\n", "CCN1C=C(C(O)=O)C(=O)c2cc(F)c(cc12)N3CCNCC3\n", "\n", "CCN1C=C(C(O)=O)C(=O)c2cc(F)c(cc12)N3CCN(C)CC3\n", "\n", "CN1C(=C(/O)Nc2ccccn2)/C(=O)c3ccccc3[S]1(=O)=O\n", "\n", "C[S](O)(=O)=O.Oc1ccc2[nH]cc(CCCCN3CCC(=CC3)c4ccccc4)c2c1\n", "\n", "CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5ccccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6\n", "\n", "CNCCCN1c2ccccc2CCc3ccccc13\n", "\n", "CN1C(=O)CN=C(c2ccccc2)c3cc(Cl)ccc13\n", "\n", "COc1ccc2C[C@@H]3[C@@H]4CC[C@H](O)[C@@H]5Oc1c2[C@]45CCN3C\n", "\n", "CN1CCC23C4CCC(O)C2Oc5c(O)ccc(CC14)c35\n", "\n", "CN(C)CCOC(c1ccccc1)c2ccccc2\n", "\n", "O=C1NC(=O)C(N1)(c2ccccc2)c3ccccc3\n", "\n", "CC(CN1CCN(CCOCCO)CC1)CN2c3ccccc3Sc4ccccc24\n", "\n", "COC(=O)c1ccc(cc1)C(=O)OC\n", "\n", "Cc1ccccc1C\n", "\n", "Cc1ccc(C)cc1\n", "\n", "CC(C)O\n", "\n", "NC(=O)N1c2ccccc2C3OC3c4ccccc14\n", "\n", "c12c(nc(c(c1C(N[C@@H](CC)c1ccccc1)=O)C)c1ccccc1)cccc2\n", "\n", "c1cc(ccc1CCCC(OC(C)(C)C)=O)N(CCCl)CCCl\n", "\n", "CC(C)Nc1cccnc1N2CCN(CC2)C(=O)c3[nH]c4ccc(N[S](C)(=O)=O)cc4c3\n", "\n", "CC(C)(C)c1ccc(cc1)C(=O)CCCN2CCC(CC2)OC(c3ccccc3)c4ccccc4\n", "\n", "CN1CCN(CC1)CC(=O)N2c3ccccc3C(=O)Nc4cccnc24\n", "\n", "c1ccc2N([C@H](CN3CCCC3)C)c3c(ccc(C(=O)NCCC)c3)Sc2c1\n", "\n", "CN1CCN(CC1)c2c(F)cc3C(=O)C(=CN4CCSc2c34)C(O)=O\n", "\n", "CC(C)(C)NCC(O)c1ccc(O)c(CO)c1\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(=O)N1[C@H](C[N@@](C)CC1)CN1CCCC1\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN2CCCC2)c2n(CC1)ccn2)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](C[C@]2(CC1)NC(NC2=O)=O)CN1CCCC1)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](C[N@@]2C[C@@H](CC2)O)c2n(CC1)ccn2)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN2CCCC2)c2n(CC1)ncn2)=O\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@@H](C[N@@]2C[C@@H](CC2)O)C[N@](CC1)CCO)=O\n", "\n", "[H+].[Cl-].CN(C)CC\\C=C/1c2ccccc2COc3ccccc13\n", "\n", "CN(C)CCOC(C)(c1ccccc1)c2ccccn2.OC(=O)CCC(O)=O\n", "\n", "Fc1ccc(cc1)C(=O)CCCN2CCC(=CC2)N3C(=O)Nc4ccccc34\n", "\n", "CC\\C(=C/C)C(=O)NC(N)=O\n", "\n", "CCC(C)(CC)OC(N)=O\n", "\n", "Clc1ccc2n3cnnc3CN=C(c4ccccc4)c2c1\n", "\n", "CCc1ccccc1N2C(=Nc3ccccc3C2=O)C\n", "\n", "CCOC(=O)c1cnc2n(CC)ncc2c1NN=C(C)C\n", "\n", "CCC(O)(\\C=C\\Cl)C#C\n", "\n", "NC(=O)OC1(CCCCC1)C#C\n", "\n", "s1cc(CSCCN\\C(NC)=[NH]\\C#N)nc1\\[NH]=C(\\N)N\n", "\n", "c1cc(CCNC)ncc1\n", "\n", "c1cc(CCN(C)C)ncc1\n", "\n", "s1c(ncc1)CCN\n", "\n", "c1nc(C2CCN(CC2)C(NC2CCCCC2)=S)c[nH]1\n", "\n", "c1ccc2Oc3c(cc(cc3)Cl)[C@@H]3[C@@H](c2c1)C[N@](CC3)C\n", "\n", "c1c(Cl)nc(N2CCN(CCCCN3C(CCC3)=O)CC2)cc1C(F)(F)F\n", "\n", "c1ccc2Oc3c(cc(cc3)Cl)[C@@H]3[C@@H](c2c1)CNCC3\n", "\n", "c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](C[N@@]2C[C@@H](CC2)O)c2c(CC1)[nH]cn2)=O\n", "\n", "c1ccc2nc(c(c(C(N[C@@H](CC)c3ccccc3)=O)c2c1)O)c1ccccc1\n", "\n", "c1cc2c(C(N[C@@H](CC)c3ccccc3)=O)c(c(nc2cc1)c1ccccc1)OCCCN(C)C\n", "\n", "c1cc2c(C(N[C@@H](CC)c3ccccc3)=O)c(c(nc2cc1)c1ccccc1)OCC(O)=O\n", "\n", "c1cc2c(C(N[C@@H](CC)c3ccccc3)=O)c(c(c3ccccc3)nc2cc1)OCCCC(O)=O\n", "\n", "c1ccc2c(c(C(N[C@@H](CC)c3ccccc3)=O)c(OCCNC(Cc3c(cccc3)C(=O)O)=O)c(c3ccccc3)n2)c1\n", "\n", "c1cc2c(c(OCCNC(Cc3ncccc3)=O)c(c3ccccc3)nc2cc1)C(N[C@@H](CC)c1ccccc1)=O\n", "\n", "c1cc2c(c(c(c3ccccc3)nc2cc1)OCCNC([C@@H]1CCCN1)=O)C(=O)N[C@@H](CC)c1ccccc1\n", "\n", "c1cc2c(C(N[C@@H](CC)c3ccccc3)=O)c(c(nc2cc1)c1ccccc1)Cn1cncc1\n", "\n", "C[C@H]1CN(C[C@@H](C)N1)c2c(F)c(N)c3C(=O)C(=CN(C4CC4)c3c2F)C(O)=O\n", "\n", "CCC1(C)CC(=O)NC1=O\n", "\n", "CCc1ccc2Sc3ccccc3N(CC(C)CN(C)C)c2c1\n", "\n", "OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(cc24)C(F)(F)F)CC1\n", "\n", "OCCN1CCN(CCCN2c3ccccc3Sc4ccc(cc24)C(F)(F)F)CC1\n", "\n", "CCN(CC)CCN1C(=O)CN=C(c2ccccc2F)c3cc(Cl)ccc13\n", "\n", "COc1cc2CCN(C)C3CC4(C=CC(=O)C=C4)c(c1O)c23\n", "\n", "CCC1(CCC(=O)NC1=O)c2ccccc2\n", "\n", "OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3\n", "\n", "CC1=C2NC3=CC(=O)C=CC3=C2C=CN1\n", "\n", "Cc1nccc2c1[nH]c3ccccc23\n", "\n", "c1c(c(ncc1)CSCCN\\C(=[NH]\\C#N)NCC)Br\n", "\n", "c1cc(ncc1)CSCCNc1c(cc[nH]1)[N+](=O)[O-]\n", "\n", "c1(cc(nc(c1C)C)C(SC(CNc1c(cc[nH]1)[N+](=O)[O-])(C)C)(C)C)C\n", "\n", "n1c(csc1\\[NH]=C(\\N)N)c1ccccc1\n", "\n", "n1c(csc1\\[NH]=C(\\N)N)c1cccc(c1)N\n", "\n", "n1c(csc1\\[NH]=C(\\N)N)c1cccc(c1)NC(C)=O\n", "\n", "n1c(csc1\\[NH]=C(\\N)N)c1cccc(c1)N\\C(NC)=[NH]\\C#N\n", "\n", "s1cc(nc1\\[NH]=C(\\N)N)C\n", "\n", "c1(c(oc(c1C)C(N(C(C)(N)C)C(N)(C)C)(C)C)C)C.C[NH+]O.[OH-]\n", "\n", "[NH]C(CC(C)C([N@@](C(C)(C)C)C(N)(C)N)(C)C)c1c(c(c[nH+][o+]1)C)[O-]\n", "\n", "CCNCc1c(O)c(C)ncc1CSC\n", "\n", "CN1C(=C(\\O)Nc2ccccn2)/C(=O)c3sccc3[S]1(=O)=O\n", "\n", "CC(C)(C)c1ccc(cc1)C(O)CCCN2CCC(CC2)C(O)(c3ccccc3)c4ccccc4\n", "\n", "CN(C1CCCC[C@H]1N2CCCC2)C(=O)Cc3ccc(Cl)c(Cl)c3\n", "\n", "CN1CC[C@]23[C@H]4Oc5c(OC(C)=O)ccc(C[C@@H]1[C@@H]2C=C[C@@H]4OC(C)=O)c35\n", "\n", "NC(=O)OC1(CCCCC1)CC#C\n", "\n", "[H+].[H+].[Cl-].[Cl-].OCCN1CCCN(CCCN2c3ccccc3Sc4cc(ccc24)C(F)(F)F)CC1\n", "\n", "COc1ccc2C[C@@H]3[C@@H]4CCC(=O)[C@@H]5Oc1c2[C@]45CCN3C\n", "\n", "CCC(O)(COC(N)=O)c1ccccc1\n", "\n", "OCCOCCN1CCN(CC1)C(c2ccccc2)c3ccc(Cl)cc3\n", "\n", "CN(C)CCCN1c2ccccc2CCc3ccccc13\n", "\n", "CNC1(CCCCC1=O)c2ccccc2Cl\n", "\n", "OC(=O)C1=CC(=O)c2ccccc2N1\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)c1ccccc1\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCO\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCNc1ncccc1\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCNc1nccs1\n", "\n", "C1CN(CCC1)Cc1cccc(c1)OCCCNc1nc2c(o1)cccc2\n", "\n", "c1c(oc(c1)CN(C)C)CSCCNc1nc(c(c[nH]1)Cc1cc2c(cc1)cccc2)=O\n", "\n", "CN(C)Cc1oc(cc1)CSCCNC=1NC=C(CN1)Cc1cnc(cc1)C\n", "\n", "c1(nccc(c1)c1nc([nH]n1)N)N(C)C\n", "\n", "n1(c(c2nc[nH]c2n(c1=O)C)=O)C\n", "\n", "Fc1ccc(cc1)C(=O)CCCN2CCC(CC2)C(=O)c3ccc(F)cc3\n", "\n", "CN1CC[C@]23CCCC[C@H]2[C@H]1Cc4ccc(O)cc34\n", "\n", "CCN(CC)CC(=O)Nc1c(C)cccc1C\n", "\n", "OC1N=C(c2ccccc2Cl)c3cc(Cl)ccc3NC1=O\n", "\n", "CC(C)N1CCC(CC1)N(C(=O)Cc2ccccc2)c3ccc(Cl)cc3\n", "\n", "CN(C)CCOC(=O)COc1ccc(Cl)cc1\n", "\n", "CC1=Nc2ccccc2C(=O)N1c3ccccc3Cl\n", "\n", "CN1CCN=C(c2ccccc2)c3cc(Cl)ccc13\n", "\n", "CCCC(C)(COC(N)=O)COC(N)=O\n", "\n", "c1c2CCOc2c(cc1OC)CN[C@@H]1CCCN[C@H]1c1ccccc1\n", "\n", "c1(cc(N\\C(=[NH]\\c2cccc(c2)CC)C)ccc1)CC\n", "\n", "c1(c(cc2n(ncc2c1)CCN)Cl)Cl\n", "\n", "Nc1nc(NC2CC2)c3ncn([C@@H]4C[C@H](CO)C=C4)c3n1\n", "\n", "CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(C)=O\n", "\n", "NC1=NC(=O)c2ncn(COCCO)c2N1\n", "\n", "CCCSc1ccc2nc(NC(=O)OC)[nH]c2c1\n", "\n", "NCCCNCCS[P](O)(O)=O\n", "\n", "COc1cc(CCN)cc(OC)c1OC\n", "\n", "CN1CCCCC1CCN2c3ccccc3Sc4ccc(cc24)[S](C)=O\n", "\n", "CN[C@@H](C)Cc1ccccc1\n", "\n", "Cc1ccccc1N2C(=Nc3ccccc3C2=O)C\n", "\n", "COc1ccccc1OCC(O)COC(N)=O\n", "\n", "COc1ccc2Sc3ccccc3N(CCCN(C)C)c2c1\n", "\n", "COc1ccc2Sc3ccccc3N(C[C@H](C)CN(C)C)c2c1\n", "\n", "CCC(C)(O)C#C\n", "\n", "COC(=O)C(C1CCCCN1)c2ccccc2\n", "\n", "CCC1(CC)C(=O)NCC(C)C1=O\n", "\n", "NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)[C@@H](O)[C@H](O)[C@H]2O)[C@H](O)[C@H]1O[C@H]3O[C@H](CO)[C@@H](O)[C@H](N)[C@H]3O\n", "\n", "Nc1ccc(C(O)=O)c(O)c1\n", "\n", "NC1=NC(=O)N(C=C1)[C@@H]2O[C@H](CO)[C@@H](O)[C@@H]2O\n", "\n", "CN(C)N\\N=C/1N=CN=C1C(N)=O\n", "\n", "COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)C(C)=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4c(Cl)ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\n", "\n", "Clc1ccc2NC(=O)CC(=O)N(c3ccccc3)c2c1\n", "\n", "CNCCCN1c2ccccc2Sc3ccccc13\n", "\n", "C[C@@H](CN1CC(=O)NC(=O)C1)N2CC(=O)NC(=O)C2\n", "\n", "Cc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12.OC(=O)\\C=C/C(O)=O\n", "\n", "Cc1ccc(cc1)C2(O)CCN(CCCC(=O)c3ccc(F)cc3)CC2\n", "\n", "CN1CC[C@]23[C@H]4Oc5c(O)ccc(C[C@@H]1[C@@H]2C=CC4O)c35\n", "\n", "CN1CCC(=CC1)c2ccccc2\n", "\n", "Oc1ccc2C[C@H]3N(CC[C@@]45[C@@H](Oc1c24)C(=O)CC[C@@]35O)CC=C\n", "\n", "CN1CCC[C@H]1c2cccnc2\n", "\n", "[O-][N+](=O)c1ccc2NC(=O)CN=C(c3ccccc3)c2c1\n", "\n", "Clc1ccc2NC(=O)CN=C(c3ccccc3)c2c1\n", "\n", "CNCCC=C1c2ccccc2CCc3ccccc13\n", "\n", "CN(C)CCOC(c1ccccc1)c2ccccc2C\n", "\n", "COc1ccc2C[C@H]3[C@H]4CCCC[C@@]4(CCN3C)c2c1\n", "\n", "CN1C[C@@H](C[C@H]2[C@H]1Cc3c[nH]c4cccc2c34)C(=O)N[C@]5(C)O[C@@]6(O)[C@@H]7CCCN7C(=O)[C@H](Cc8ccccc8)N6C5=O\n", "\n", "CCN(CC)C(=S)SSC(=S)N(CC)CC\n", "\n", "FC(F)(F)[C@]1(OC(=O)Nc2ccc(Cl)cc12)C#CC3CC3\n", "\n", "CCOC(=O)[C@H](CCc1ccccc1)N[C@@H](C)C(=O)N2CCC[C@H]2C(O)=O\n", "\n", "CC[C@@H](CO)NCCN[C@@H](CC)CO\n", "\n", "CC[C@@]1(O)C[C@H](OC2CC(C(OC3CC(O)C(OC4CCC(=O)C(C)O4)C(C)O3)C(C)O2)N(C)C)c5c(O)c6C(=O)c7c(O)cccc7C(=O)c6cc5[C@H]1C(=O)OC\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)CCC[C@@H](N)C(O)=O)C(=O)N2[C@H]1C(O)=O\n", "\n", "CNCC(O)c1ccc(O)c(O)c1\n", "\n", "COc1cccc2C(=O)c3c(O)c4CC(O)(CC(O)c4c(O)c3C(=O)c12)C(=O)CO\n", "\n", "[Na+].CC1(C)SC2C(NC(=O)CSCC=C)C(=O)N2C1C([O-])=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C34C[C@H]5C[C@H](CC(N)(C5)C3)C4)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC(C)(C)C(=O)OCOC(=O)C1N2[C@H](SC1(C)C)[C@H](N=CN3CCCCCC3)C2=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@@H]3Cc4ccc(N)c(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\n", "\n", "[Cl-].CC(=O)[C@@]1(N)C[C@H](O[C@H]2C[C@H](O)[C@H](O)CO2)c3c(O)c4C(=O)c5ccccc5C(=O)c4c(O)c3C1.[H+]\n", "\n", "CC1(C)S[C@@H]2C(NC(=O)[C@H](NC(=O)C3=CNc4cccnc4C3=O)c5ccccc5)C(=O)N2[C@H]1C(O)=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NC(N5CCN(CCO)CC5)C(O)=O)C1=O\n", "\n", "CN[C@H]1[C@@H](O)[C@H]2O[C@@H](O[C@@H]3[C@@H](N)C[C@@H](N)[C@H](O)[C@H]3O)[C@H](N)C[C@@H]2O[C@@H]1O[C@H]4O[C@H](CO)[C@@H](N)[C@H](O)[C@H]4O\n", "\n", "NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)CC[C@H]2N)[C@H](O)[C@H]1O[C@H]3O[C@H](CO)[C@@H](O)[C@H](N)[C@H]3O\n", "\n", "CNC(=O)C[C@@H](N)C(=O)N[C@@H](C(=O)N[C@H]1[C@H]2SC(C)(C)[C@@H](N2C1=O)C(O)=O)c3ccc(O)cc3\n", "\n", "CO[C@H]1[C@@H](O)[C@H](N)[C@@H](O[C@H]2O[C@@H](CC[C@H]2N)C(C)N)[C@H](O)[C@@H]1N(C)C(=O)CN\n", "\n", "C[C@H]1[C@H](NC(=O)C(=N/OC(C)(C)C(O)=O)\\c2csc([NH3+])n2)C(=O)N1[S]([O-])(=O)=O\n", "\n", "CCOC(=O)OC(C)OC(=O)[C@@H]1N2[C@H](SC1(C)C)[C@H](N=CN3CCCCCC3)C2=O\n", "\n", "NCC1OC(OC2C(N)CC(N)C(OC3OC(CO)C(O)C(N)C3O)C2O)C(N)C(O)C1O\n", "\n", "NC(CO)C(=O)NNCc1ccc(O)c(O)c1O\n", "\n", "CC(C)COCC(CN(Cc1ccccc1)c2ccccc2)N3CCCC3\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](OC2CC(C)(OC)C(O)C(C)O2)C(C)[C@@H](OC3OC(C)CC(C3O)N(C)C)[C@](C)(O)C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@H]1C\n", "\n", "NCCC(O)CNC1CC(N)C(OC2OC(CN)C(O)C(O)C2O)C(O)C1OC3OC(CO)C(O)C(N)C3O\n", "\n", "CC(C)(C)NCC(O)c1ccc(O)c(NC(N)=O)c1\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(C(=O)Oc3ccccc3)c4ccccc4)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(C(=O)Oc3ccc4CCCc4c3)c5ccccc5)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC1OC(CC(N)C1O)OC2C[C@@](O)(Cc3c(O)c4C(=O)c5cccc(O)c5C(=O)c4c(O)c23)C(C)=O\n", "\n", "NC(=O)OCC1[C@H](NC(=O)C(=N/OCC(O)=O)\\c2csc(N)n2)C(=O)N1[S](O)(=O)=O\n", "\n", "N[C@@H](C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)Cl)c3ccccc3\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C2=O)C(O)=O\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O\n", "\n", "CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)Cc3ccccc3)C2=O)C(O)=O\n", "\n", "Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)C(N)c4ccc(O)cc4)C3=O)C(O)=O)nn1\n", "\n", "NC(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3cn[nH]n3)c4ccc(O)cc4\n", "\n", "[Na+].Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CSC(F)(F)F)C3=O)C([O-])=O\n", "\n", "Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CN4C=C(Cl)C(=O)C(=C4)Cl)C3=O)C(O)=O)nn1\n", "\n", "Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)[C@H](O)c4ccccc4)C3=O)C(O)=O)nn1\n", "\n", "CC(N)C(=O)OC(C(=O)NC1C2SCC(=C(N2C1=O)C(=O)OCC3=C(C)OC(=O)O3)CSc4sc(C)nn4)c5ccccc5\n", "\n", "CC/C=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3csc(N)n3\n", "\n", "COCC1=C(N2[C@H](SC1)[C@H](NC(=O)C(=N\\O)/c3csc(N)n3)C2=O)C(O)=O\n", "\n", "Nc1scc(n1)\\C(=N\\O)C(=O)N[C@H]2[C@H]3SCC(=C(N3C2=O)C(O)=O)C=C\n", "\n", "CO\\N=C(C(=O)NC1[C@H]2SCC(=C(N2C1=O)C(O)=O)\\C=C/c3scnc3C)\\c4csc(N)n4\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccc(O)c(Cl)c3)C2=O)C(O)=O\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)C)/c3csc(N)n3\n", "\n", "OC(=O)C1=C(CS[C@@H]2[C@H](NC(=O)Cc3sccc3)C(=O)N12)CSc4[nH]ncn4\n", "\n", "Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CS\\C=C/C#N)C3=O)C(O)=O\n", "\n", "Nc1scc(n1)\\C(=N\\OCC(O)=O)C(=O)N[C@H]2[C@H]3SCC(=C(N3C2=O)C(O)=O)C=C\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C)/c4csc(N)n4\n", "\n", "CO[C@]1(NC(=O)CSCC#N)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C\n", "\n", "CO[C@]1(NC(=O)CSC[C@@H](N)C(O)=O)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C\n", "\n", "CO\\N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3sc(CC(O)=O)c(C)n3)/c4csc(N)n4\n", "\n", "OC(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C[S](O)(=O)=O)c4ccccc4\n", "\n", "NCc1ccccc1CC(=O)N[C@H]2[C@H]3SCC(=C(N3C2=O)C(O)=O)CSc4nnnn4CC(O)=O\n", "\n", "CO[C@]1(NC(=O)C2S\\C(S2)=C(\\C(N)=O)C(O)=O)[C@H]3SCC(=C(N3C1=O)C(O)=O)CSc4nnnn4C\n", "\n", "CN(C)CCn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cc4csc(N)n4)C3=O)C(O)=O\n", "\n", "CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)c3c(C)onc3c4ccccc4Cl)C2=O)C(O)=O\n", "\n", "Cn1nnnc1SCC2=C(N3[C@H](SC2)C(NC(=O)[C@H](NC(=O)C4=CNC(=CC4=O)C)c5ccc(O)cc5)C3=O)C(O)=O\n", "\n", "[Na+].COCC1=C(N2[C@H](SC1)[C@H](NC(=O)C(=N/OC)\\c3csc(N)n3)C2=O)C([O-])=O\n", "\n", "O.C/C=C/C1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C2=O)C(O)=O\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)Cc3ccc(cc3)C4=NCCCN4)C2=O)C(O)=O\n", "\n", "COC1=C(N3C(SC1)C(NC(=O)C(N)C2C=CCC=C2)C3=O)C(O)=O\n", "\n", "CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3cccc(N[S](C)(=O)=O)c3)C2=O)C(O)=O\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)Cn3nnc(C)n3)/c4csc(N)n4\n", "\n", "OC(=O)C1=C(CS[C@@H]2[C@H](NC(=O)Cn3cnnn3)C(=O)N12)CSc4scnn4\n", "\n", "O.O.Nc1scc(n1)\\C(=C\\CC(O)=O)C(=O)N[C@H]2[C@H]3SCC=C(N3C2=O)C(O)=O\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSC(=O)c3occc3)/c4csc(N)n4\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)\\C=C\\SC3=NNC(=O)C(=O)N3CC=O)/c4csc(N)n4\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@@H]2N(C1=O)C(=C(COC(C)=O)C[S]2=O)C(O)=O)/c3csc(N)n3\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC=C(N2C1=O)C(O)=O)/c3csc(N)n3\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(C)=O)/c3occc3\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(=O)OC(C)OC(C)=O)COC(N)=O)/c3occc3\n", "\n", "COCCCC(=O)OC(C)OC(=O)C1=C(COC(N)=O)CS[C@@H]2[C@H](NC(=O)C(=N/OC)\\c3occc3)C(=O)N12\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3snnc3)/c4csc(N)n4\n", "\n", "[Na+].CC(=O)OCC1=C(N2[C@@H](SC1)[C@@H](NC(=O)CC#N)C2=O)C([O-])=O\n", "\n", "O.O.CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O\n", "\n", "CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)CCC[C@@H](N)C(O)=O)C2=O)C(O)=O\n", "\n", "CC(=O)OCC1=C(N2[C@H](SC1)[C@H](NC(=O)Cc3sccc3)C2=O)C(O)=O\n", "\n", "CC(=O)C1=C(O)[C@]2(O)[C@@H](Cc3c(C)c4ccc(C)c(O)c4c(O)c3C2=O)[C@@H](N)C1=O\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@](C)(C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O)OC\n", "\n", "CCC[C@@H]1C[C@H](N(C)C1)C(=O)NC(C(C)Cl)[C@H]2O[C@H](SC)[C@H](O)[C@@H](O)[C@H]2O\n", "\n", "CCC[C@@H]1C[C@H](N(C)C1)C(=O)NC(C(C)Cl)[C@H]2O[C@H](SC)[C@H](O[P](O)(O)=O)[C@@H](O)[C@H]2O\n", "\n", "COC(C(=O)N[C@H]1[C@H]2SC(C)(C)[C@@H](N2C1=O)C(O)=O)c3ccc(Cl)c(Cl)c3\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C3(N)CCCCC3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C\n", "\n", "CC(C)C1NC(=O)C(NC(=O)c2ccc(C)c3OC4=C(C)C(=O)C(=C(C(=O)NC5C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C6CCCN6C(=O)C(NC5=O)C(C)C)C4=Nc23)N)C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C7CCCN7C1=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4cccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\n", "\n", "CCOC(OCC)C(=O)OCC(=O)C1(O)CC(OC2CC(N)C(O)C(C)O2)c3c(O)c4C(=O)c5c(OC)cccc5C(=O)c4c(O)c3C1\n", "\n", "NCC1CCC(N)C(O1)OC2C(N)CC(N)C(OC3OC(CO)C(O)C(N)C3O)C2O\n", "\n", "Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl\n", "\n", "CN[C@H]1[C@H](O)[C@@H](O)[C@H](CO)O[C@H]1O[C@H]2[C@@H](O[C@@H](C)[C@]2(O)CO)O[C@@H]3[C@@H](O)[C@H](O)[C@@H](N=C(N)N)[C@H](O)[C@H]3N=C(N)N\n", "\n", "COc1ccc(cc1O)C2=CC(=O)c3c(O)cc(O[C@@H]4O[C@H](CO[C@@H]5O[C@@H](C)[C@H](O)[C@@H](O)[C@H]5O)[C@@H](O)[C@H](O)[C@H]4O)cc3O2\n", "\n", "CNCC(O)c1ccc(OC(=O)C(C)(C)C)c(OC(=O)C(C)(C)C)c1\n", "\n", "CCC(=O)O[C@H]1[C@@H](C)[C@@H](C)OC(=O)C(C)[C@@H](O[C@H]2C[C@H](OC)[C@@H](OC(=O)CC)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)C(C)C[C@@]4(CO4)C(=O)[C@@H]1C\n", "\n", "OCCN(CCO)c1nc(N2CCCCC2)c3nc(nc(N4CCCCC4)c3n1)N(CCO)CCO\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@](C)(O)C[C@@H](C)[C@@H]4N[C@@H](COCCOC)O[C@H]([C@H]4C)[C@]1(C)O\n", "\n", "CC(=O)O[C@@]12CO[C@@H]1C[C@H](O)[C@]3(C)[C@@H]2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)OC(C)(C)C)c6ccccc6)C(=C([C@@H](O)C3=O)C5(C)C)C\n", "\n", "CCC(C(=O)NC\\C=C\\C=C(/C)C(OC)C(C)C1OC(\\C=C\\C=C\\C=C(/C)C(=O)C2=C(O)N(C)C=CC2=O)C(O)C1O)C3(O)OC(\\C=C\\C=C\\C)C(C)(C)C(OC4OC(C)C(OC5OC(C)C(OC)C(O)C5OC)C(OC)C4O)C3O\n", "\n", "[Cl].CC[C@H]1CN2CCc3cc(OC)c(OC)cc3[C@@H]2C[C@@H]1C[C@H]4NCCc5cc(OC)c(OC)cc45\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)C3=CCC=CC3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CCC(C)[C@H]1O[C@]2(CC[C@@H]1C)CC3C[C@@H](C\\C=C(C)\\[C@@H](O[C@H]4C[C@H](OC)[C@@H](OC5C[C@H](OC)[C@@H](O)[C@H](C)O5)[C@H](C)O4)[C@@H](C)/C=C/C=C/6CO[C@@H]7[C@H](O)C(=C[C@@H](C(=O)O3)[C@]67O)C)O2.CO[C@H]8CC(O[C@@H](C)[C@@H]8O)O[C@H]9[C@H](C)O[C@H](C[C@@H]9OC)O[C@H]/%10[C@@H](C)/C=C/C=C/%11CO[C@@H]%12[C@H](O)C(=C[C@@H](C(=O)OC%13C[C@@H](C\\C=C%10C)O[C@@]%14(CC[C@H](C)[C@H](O%14)C(C)C)C%13)[C@]%11%12O)C\n", "\n", "COc1cccc2C(=O)c3c(O)c4C[C@@](O)(C[C@@H](OC5CC(N)CC(C)O5)c4c(O)c3C(=O)c12)C(=O)CO\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCN(C)CCN(C)CNC(/O)=C/5C(=O)[C@H]([C@@H]6C[C@H]7C(=C(O)c8c(O)cccc8[C@@]7(C)O)C(=O)[C@]6(O)C5=O)N(C)C)C1=O\n", "\n", "CCNCC(O)c1cccc(O)c1\n", "\n", "CCOC(=O)C1=C(C)NC(=C(C1c2cccc(Cl)c2Cl)C(=O)OC)C\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(Oc3ccccc3)c4ccccc4)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC(C)c1ccccc1OCC2=NCCN2\n", "\n", "CC(C)(C(O)=O)c1ccc(cc1)C(O)CCCN2CCC(CC2)C(O)(c3ccccc3)c4ccccc4\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)C(C)(C)Oc3ccc(Cl)cc3)c4ccccc4)C(=O)N2[C@H]1C(O)=O\n", "\n", "[Na].CO[C@]1(NC(=O)CSC(F)(F)F)[C@H]2OCC(=C(N2C1=O)C(O)=O)CSc3nnnn3CCO\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@](C)(O)C[C@](C)(F)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O\n", "\n", "C[C@@H](O)[C@@H]1[C@H]2SC(=C(N2C1=O)C(O)=O)[C@H]3CCCO3\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@@H](N=Cc3occc3)c4ccc(O)cc4)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC(C)CC(OC(=O)c1occc1)C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O\n", "\n", "C[C@@H]1[C@H](O)CC[C@@]2(C)[C@H]1CC[C@@]3(C)[C@H]2[C@H](O)C[C@H]4\\C([C@H](C[C@]34C)OC(C)=O)=C(/CCC=C(C)C)C(O)=O\n", "\n", "CNC(C)C1CCC(N)C(O1)OC2C(N)CC(N)C(OC3OCC(C)(O)C(NC)C3O)C2O\n", "\n", "CO/N=C(C(=O)N[C@H]1[C@H](C)N(OCC(=O)OCC(=O)OC(C)(C)C)C1=O)/c2csc(N)n2\n", "\n", "CC(C)CC1C(=O)NC(C(=O)N2CCCC2C(=O)NC(C(=O)NC(C(=O)NC(C(=O)NC(C(=O)N3CCCC3C(=O)NC(C(=O)NC(C(=O)N1)CCCN)C(C)C)CC4=CC=CC=C4)CC(C)C)CCCN)C(C)C)CC5=CC=CC=C5\n", "\n", "CC1CN(CCN1)c2cc3N(C=C(C(O)=O)C(=O)c3c(C)c2F)C4CC4\n", "\n", "CN(C)C1C2CC3C(=C(O)c4c(O)cccc4C3(C)O)C(=O)C2(O)C(=O)\\C(=C(/O)NCN5CCN(CC5)C(=N)N=C(N)N)C1=O\n", "\n", "CC1(C)N[C@@H](C(=O)N1[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4ccccc4\n", "\n", "CC(Cc1ccccc1)NO\n", "\n", "CNC(C)C(O)c1ccc(O)cc1\n", "\n", "CC1=CC(=O)Oc2cc(O)ccc12\n", "\n", "C[C@@H]1O[C@H](C[C@H](N)[C@@H]1O)O[C@H]2C[C@@](O)(Cc3c(O)c4C(=O)c5ccccc5C(=O)c4c(O)c23)C(C)=O\n", "\n", "O.C[C@H](O)[C@@H]1[C@H]2CC(=C(N2C1=O)C(O)=O)SCCN=CN\n", "\n", "CNC1C(O)C(OCC1(C)O)OC2C(O)C(OC3OC(CN)C(O)C(O)C3O)C(N)CC2NC(=O)C(O)CN\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(C)(C)Oc3ccccc3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CO[C@H]1[C@@H](CC(=O)O[C@H](C)C/C=C/C=C/[C@H](O)[C@H](C)C[C@H](CC=O)[C@@H]1O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](OC(=O)CC(C)C)[C@H](C)O3)[C@@H]([C@H]2O)N(C)C)OC(C)=O\n", "\n", "CC1=C(COC(=O)[C@@H]2N3[C@H](SC2(C)C)[C@H](NC(=O)[C@H](N)c4ccccc4)C3=O)OC(=O)O1\n", "\n", "CNCC[C@@H](O)[C@@H]1C[C@@H](CN1)SC2=C(N3[C@H]([C@H]2C)[C@@H]([C@@H](C)O)C3=O)C(O)=O\n", "\n", "COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](NC(=O)[C@@H](N)CC(C)C)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](OC2CC(C)(OC)C(O)C(C)O2)C(C)[C@@H](OC3OC(C)CC(C3O)N(C)C)[C@](C)(O)C[C@@H](C)\\C(=N/OC)[C@H](C)[C@@H](O)[C@]1(C)O\n", "\n", "CCCC1CC(N(C)C1)C(=O)NC(C(C)O)C2OC(SC)C(O)C(O)C2O\n", "\n", "O.N[C@@H](C(=O)NC1C2CCC(=C(N2C1=O)C(O)=O)Cl)c3ccccc3\n", "\n", "CCCCc1nc(Cl)c(CO)n1Cc2ccc(cc2)c3ccccc3c4n[nH]nn4\n", "\n", "[Cl-].CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(N)\\O)C1=O.[H+]\n", "\n", "CC1(C)S[C@@H]2[C@H](N=CN3CCCCCC3)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC(CN(C)C)OC(C)(c1ccccc1)c2ccc(Cl)cc2\n", "\n", "CC1OC(CC(N)C1O)OC2CC(O)(Cc3c(O)c4C(=O)c5ccccc5C(=O)c4c(O)c23)C(=O)CO\n", "\n", "O.O.O.C[C@H](O)C1C2[C@@H](C)C(=C(N2C1=O)C(O)=O)S[C@@H]3CN[C@@H](C3)C(=O)N(C)C\n", "\n", "CC1(C)SC2C(NC(=O)C(N=C)c3ccccc3)C(=O)N2C1C(O)=O\n", "\n", "CN(C)[C@H]1[C@@H]2[C@@H](O)[C@@H]3C(=C)c4cccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\n", "\n", "CNC(C)C(=O)c1ccc(OC)cc1\n", "\n", "COc1cccc(OC)c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)N3CCN(C3=O)[S](C)(=O)=O)c4ccccc4)C(=O)N2[C@H]1C(O)=O\n", "\n", "COCC(=O)O[C@]1(CCN(C)CCCc2[nH]c3ccccc3n2)CCc4cc(F)ccc4[C@@H]1C(C)C\n", "\n", "CCC(=O)OC1CC(=O)OC(C)C\\C=C\\C=C/C(O)C(C)CC(CC=O)C(O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@H](OC(=O)CC)[C@@H](C)O3)[C@@H]([C@H]2O)N(C)C)C1OC\n", "\n", "CCC1NC(=O)C(NC(=O)c2ncccc2O)C(C)OC(=O)C(NC(=O)C3CC(=O)CCN3C(=O)C(Cc4ccc(cc4)N(C)C)N(C)C(=O)C5CCCN5C1=O)c6ccccc6\n", "\n", "CCCCCC1CNC(C1)C(=O)NC(C(C)Cl)C2OC(SC)C(O)C(O)C2O\n", "\n", "CO[C@@]12[C@H](COC(N)=O)C3=C(N1C[C@@H]4N[C@H]24)C(=O)C(=C(N)C3=O)C\n", "\n", "CO[C@]1(NC(=O)C(C(O)=O)c2ccc(O)cc2)[C@H]3OCC(=C(N3C1=O)C(O)=O)CSc4nnnn4C\n", "\n", "NC[C@H]1O[C@H](O[C@@H]2[C@@H](N)C[C@@H](N)[C@H](O)[C@H]2O)[C@H](N)[C@@H](O)[C@@H]1O\n", "\n", "CCN[C@@H]1C[C@H](N)[C@@H](O[C@H]2OC(=CC[C@H]2N)CN)[C@H](O)[C@H]1O[C@H]3OC[C@](C)(O)[C@H](NC)[C@H]3O\n", "\n", "COC(=O)C1=C(C)NC(=C(C1c2cccc(c2)[N+]([O-])=O)C(=O)OCCN(C)Cc3ccccc3)C\n", "\n", "COC(=O)C1=C(C)NC(=C(C1c2ccccc2[N+]([O-])=O)C(=O)OC)C\n", "\n", "CCOC(=O)C1=C(C)NC(=C(C1c2cccc(c2)[N+]([O-])=O)C(=O)OC)C\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@@H]3Cc4c(ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O)[N+]([O-])=O\n", "\n", "CO[C@@H]1[C@@H](OC(N)=O)[C@@H](O)[C@H](Oc2ccc3C(=O)C(=C(O)Oc3c2C)NC(=O)c4ccc(O)c(CC=C(C)C)c4)OC1(C)C\n", "\n", "COC1CC(OC(C)C1O)O[C@H]2[C@H](C)[C@@H](OC3OC(C)CC(C3O)N(C)C)[C@@H](C)C[C@]4(CO4)C(=O)[C@H](C)[C@@H](O)[C@@H](C)[C@@H](C)OC(=O)[C@@H]2C\n", "\n", "CC1(C)NC(C(=O)[NH+]1[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4ccc(O)cc4\n", "\n", "CO\\N=C(C(=O)NC1[C@H](C)N(OCC(O)=O)C1=O)\\c2csc(N)n2\n", "\n", "CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(C)=O)C2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7)C(=C1C5(C)C)C\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCN5CCCC(C5)C(O)=O)C1=O\n", "\n", "CC(=O)OCOC(=O)C1N2C(SC1(C)C)C(NC(=O)Cc3ccccc3)C2=O\n", "\n", "[Na].CC1(C)SC2C(NC(=O)Cc3ccccc3)C(=O)N2C1C(O)=O\n", "\n", "CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)cccc4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCNC(C(=O)NC5C6SC(C)(C)C(N6C5=O)C(O)=O)c7ccccc7)C1=O\n", "\n", "CC(Oc1ccccc1)C(=O)NC2C3SC(C)(C)C(N3C2=O)C(O)=O\n", "\n", "CN(C)C1C2CC3C(=C(O)c4c(O)cccc4C3(C)O)C(=O)C2(O)C(=O)\\C(=C(/O)NCN5CCN(CCO)CC5)C1=O\n", "\n", "COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O[C@H]6CCCCO6)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO\n", "\n", "CC1(C)SC2C(N([C@H](C(=O)NC(=O)CN)c3ccccc3)C(=N)c4ccncc4)C(=O)N2C1C(O)=O\n", "\n", "[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)C3=CC=C(NC3=O)c4ccc(cc4)[S](=O)(=O)N(CCO)CCO)c5ccc(O)cc5)C(=O)N2[C@H]1C([O-])=O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)NC3=CN=C(NC3=O)Nc4ccc(cc4)[S](N)(=O)=O)c5ccc(O)cc5)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC(C)(C)C(=O)OCOC(=O)[C@@H]1N2[C@H](SC1(C)C)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O\n", "\n", "CNCC(O)c1cccc(OC(=O)C(C)(C)C)c1\n", "\n", "Cc1cnn(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl\n", "\n", "CC1OC(=O)C(NC(=O)C2CC(=O)CCN2C(=O)C(Cc3ccc(cc3)N(C)C)N(C)C(=O)C4CCCN4C(=O)C(C)NC(=O)C1NC(=O)c5ncccc5O)c6ccccc6\n", "\n", "CCCNCC(O)COc1ccccc1C(=O)CCc2ccccc2\n", "\n", "CCC(Oc1ccccc1)C(=O)NC2C3SC(C)(C)C(N3C2=O)C(O)=O\n", "\n", "NCC1OC(OC2C(N)CC(NC(CO)CO)C(OC3OC(CO)C(O)C(N)C3O)C2O)C(N)C(O)C1O\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)c3nc4ccccc4nc3C(O)=O)C(=O)N2[C@H]1C(O)=O\n", "\n", "CO[C@H]1/C=C/O[C@@]2(C)Oc3c(C)c(O)c4C(=O)C(=C5NC6(CCN(CC6)CC(C)C)N=C5c4c3C2=O)NC(=O)C(=C\\C=C\\[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@@H]1C)/C\n", "\n", "CCN(CC)C(C)=NN=Cc1c(O)c2c3C(=O)C4(C)OC=CC(OC)C(C)C(OC(C)=O)C(C)C(O)C(C)C(O)C(C)C=CC=C(C)C(=O)Nc1c(O)c2c(O)c(C)c3O4\n", "\n", "CCN(CC)c1sc2c3NC(=O)C(=C\\C=C\\[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@H](C)[C@@H](OC)/C=C/O[C@@]4(C)Oc5c(C)c(O)c(c3O)c(c2n1)c5C4=O)/C\n", "\n", "CCN(CC)C(=O)COc1cc2NC(=O)C(=C\\C=C\\[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@H](C)[C@@H](OC)/C=C/O[C@@]3(C)Oc4c(C)c(O)c(c2O)c1c4C3=O)/C\n", "\n", "COC1C=COC5(C)Oc4c(C)c(O)c3c(O)c(NC(=O)C(=CC=CC(C)C(O)C(C)C(O)C(C)C(OC(C)=O)C1C)C)c(C=NN2CCN(C)CC2)c(O)c3c4C5=O\n", "\n", "CO[C@H]1/C=C/O[C@@]2(C)Oc3c(C)c(O)c4c(O)c(NC(=O)C(=C\\C=C\\[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@@H]1C)/C)cc(O)c4c3C2=O\n", "\n", "CO[C@H]1\\C=C\\O[C@@]2(C)Oc3c(C)c(O)c4C(=C(NC(=O)C(=C/C=C/[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@@H]1C)\\C)C(=C/NN5CCN(CC5)C6CCCC6)\\C(=O)c4c3C2=O)O\n", "\n", "CO[C@H]1/C=C/O[C@@]2(C)Oc3c(C)c(O)c4c(O)c(NC(=O)C(=C\\C=C\\[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@@H]1C)/C)c5n6ccc(C)cc6nc5c4c3C2=O\n", "\n", "[Na+].C[C@@H](O)[C@@H]1[C@H]2SC(=C(N2C1=O)C([O-])=O)COC(N)=O\n", "\n", "CC[C@@]1(O)C[C@H](O[C@H]2C[C@@H]([C@H](O[C@H]3C[C@@H]4O[C@H]5CC(=O)[C@H](C)O[C@H]5O[C@@H]4[C@H](C)O3)[C@H](C)O2)N(C)C)c6c(O)c7C(=O)c8c(O)cccc8C(=O)c7c(O)c6[C@H]1O[C@H]9C[C@@H]([C@H](O)[C@H](C)O9)N(C)C\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)Cc3ccc(cc3)C4=NCCCN4)c5ccccc5)C(=O)N2[C@H]1C(O)=O\n", "\n", "CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@](C)(O)C[C@@H](C)\\C(=N\\OCOCCOC)[C@H](C)[C@@H](O)[C@]1(C)O\n", "\n", "COC1CCCC2C3C(C(C)O)C(=O)N3C(=C12)C([O-])=O\n", "\n", "COCOC(=O)C1N2C(SC1(C)C)C(N3C(=O)C(NC3(C)C)c4ccc(O)cc4)C2=O\n", "\n", "COCOC(=O)[C@@H]1N2[C@H](SC1(C)C)[C@H](N3C(=O)C(NC3(C)C)c4ccccc4)C2=O\n", "\n", "CC1C2CC(OC(C)=O)\\C=C\\C(=C\\CC(O)/C=C/C(=C/C(NC(=O)C(C)=O)C(C)(C(=O)O2)C1=O)C)C\n", "\n", "CN[C@@H]1[C@@H](O)[C@H](OC[C@]1(C)O)O[C@H]2[C@H](N)C[C@H](N)[C@@H](O[C@H]3OC(=CC[C@H]3N)CN)[C@@H]2O\n", "\n", "COC1C(O)CC(=O)OC(C)C\\C=C\\C=C\\C(OC2CCC(C(C)O2)N(C)C)C(C)CC(CC=O)C1OC3OC(C)C(OC4CC(C)(O)C(O)C(C)O4)C(C3O)N(C)C\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)C(c3ccccc3)[S](O)(=O)=O)C(=O)N2[C@H]1C(O)=O\n", "\n", "C[C@@H](O)[C@@H]1[C@H]2SC(=C(N2C1=O)C(O)=O)SC3CC[S](=O)C3\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O.CC4(C)[C@@H](N5[C@@H](CC5=O)[S]4(=O)=O)C(O)=O\n", "\n", "[Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N[S]([O-])(=O)=O)c3ccccc3)C(=O)N2[C@H]1C([O-])=O\n", "\n", "CO[C@@H]1C[C@@H](CC[C@H]1O)/C=C(C)/[C@H]2OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@]4(O)O[C@H]([C@H](C[C@@H](C)CC(=C/[C@@H](CC=C)C(=O)C[C@H](O)[C@H]2C)/C)OC)[C@H](C[C@H]4C)OC\n", "\n", "CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(=O)OC4OC(=O)c5ccccc45\n", "\n", "C[C@]1(Cn2ccnn2)[C@@H](N3[C@@H](CC3=O)[S]1(=O)=O)C(O)=O\n", "\n", "CCCCCCCCCC(=O)N[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1Oc2c3Oc4ccc(C[C@H]5NC(=O)[C@H](N)c6ccc(O)c(Oc7cc(O)cc(c7)[C@H](NC5=O)C(=O)N[C@H]8C(=O)N[C@H]9C(=O)N[C@@H]([C@H](O[C@@H]%10O[C@H](CO)[C@@H](O)[C@H](O)[C@H]%10NC(C)=O)c%11ccc(Oc2cc8c3)c(Cl)c%11)C(=O)N[C@@H](C(O)=O)c%12cc(O)cc(O[C@H]%13O[C@H](CO)[C@@H](O)[C@H](O)[C@@H]%13O)c%12c%14cc9ccc%14O)c6)cc4Cl\n", "\n", "CO[C@]1(NC(=O)C(C(O)=O)c2cscc2)[C@H]3SC(C)(C)[C@@H](N3C1=O)C(O)=O\n", "\n", "CN1C(=O)N(C)c2ncn(CCNCC(O)c3ccc(O)c(O)c3)c2C1=O\n", "\n", "CC1(C)SC2C(NC(=O)CSc3ccccc3)C(=O)N2C1C(O)=O\n", "\n", "CC1(C)[C@H](NC(=O)C(=N\\OCC(O)=O)/c2csc(N)n2)C(=O)N1O[S](O)(=O)=O\n", "\n", "CC(C)C(=O)OCc1cccc(OC(=O)[C@@H]2N3[C@H](SC2(C)C)[C@H](NC(=O)Cc4ccccc4)C3=O)c1\n", "\n", "NC[C@H]1O[C@H](O[C@@H]2[C@@H](N)C[C@@H](N)[C@H](O[C@H]3O[C@H](CO)[C@@H](O)[C@H](N)[C@H]3O)[C@H]2O)[C@H](N)C[C@@H]1O\n", "\n", "Cc1ccc(cc1)C(=O)c2cc(O)c(O)c(c2)[N+]([O-])=O\n", "\n", "CO[C@H]1C[C@@H](O[C@@H](C)[C@@H]1OC(C)=O)O[C@H]2[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3OC(C)=O)N(C)C)[C@@H](C)C[C@@]4(CO4)C(=O)[C@H](C)[C@@H](OC(C)=O)[C@@H](C)[C@@H](C)OC(=O)[C@@H]2C\n", "\n", "CCCC[C@@H]1CC(=O)[C@]2(O)O[C@@H]3[C@@H](NC)[C@@H](O)[C@@H](NC)[C@H](O)[C@H]3O[C@@H]2O1\n", "\n", "CC(C)c1ccc(C)cc1OCC2=NCCN2\n", "\n", "CC[C@]1(O)C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C)[C@H]7[C@](O)([C@H](O)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(N)=O)C1\n", "\n", "CC(C)C1OC(=O)C2=CCCN2C(=O)c3coc(CC(=O)CC(O)\\C=C(C)/C=C\\CNC(=O)\\C=C/C1C)n3\n", "\n", "Oc1ccc(cc1)/C=C([N+]#[C-])/C(=C/c2ccc(O)cc2)[N+]#[C-]\n", "\n", "[Cl-].COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)/C(C)=N/NC(=O)c6ccccc6.[H+]\n", "\n", "ClC(C(F)(F)Cl)(F)F\n", "\n", "[C@@]3(C1=CC=CS1)(N2CCCCC2)[C@H](CCCC3)C\n", "\n", "[H+].C1=NC3=C([N]1CCNC(CC2=CC=CC=C2)C)C(=O)N(C(=O)N3C)C.[Cl-]\n", "\n", "C1=C(Cl)C=CC(=C1N2CCN(CCCNC(C)=O)CC2)Cl\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C([C@@H](C(CC1)OC(C)=O)Br)[C@@H](C2)Br)C)CC3)CC[C@@H]4[C@@H](CCCC(C)C)C)C\n", "\n", "C(C(C(NC(NC(C)=O)=O)=O)(CC)Br)C\n", "\n", "C2=C(CN(C(=C(SC(=O)C1=CC=CO1)\\CCOC(=O)COC(=O)C)/C)C=O)C(=NC(=N2)C)N\n", "\n", "C(C(NC(C)=O)C(O)=O)CC(N)=O\n", "\n", "C1=CC=CC=C1C3(CCN(CCCC(C2=CC=C(F)C=C2)=O)CC3)CNC(C)=O\n", "\n", "C2=C(C(OC1=CC=C(C=C1)NC(C)=O)=O)C(=CC=C2)O\n", "\n", "CC(NC1=NN=C(S1)[S](N)(=O)=O)=O\n", "\n", "C1=NC(=NC(=C1CN(C(=C(\\CCOC(C)=O)SC(C)=O)/C)C=O)N)C\n", "\n", "C2=C(CC(C(NCC(OCC1=CC=CC=C1)=O)=O)CSC(C)=O)C=CC=C2\n", "\n", "[C@H]23OC1=C(OC(=O)C)C=CC6=C1C25C4(C[C@@H](C3(OC)C=C4)[C@](O)(CCC)C)C(N(CC5)C)C6\n", "\n", "C2=C(C(C1=CC=CC=C1)(C(OC(=O)C)CC)CC(N(C)C)C)C=CC=C2\n", "\n", "[C@]12(OC1)[C@@H]3C(=C[C@@H]2OC(=O)CC(OC(=O)C)(C)C)C(=CO[C@H]3OC(=O)CC(C)C)COC(=O)C\n", "\n", "[C@]12(OC(O[C@@H]1CC3C2(CC(O)C4(F)C3CCC5=CC(=O)C=CC45C)C)C=C)C(=O)CO\n", "\n", "C1=NC2=C([N]1COCCO)NC(=NC2=O)N\n", "\n", "C1=CC(=CC=C1OCC(OCCNC23CC4CC(C2)CC(C3)C4)=O)Cl\n", "\n", "C1=NC(=NC=C1)N5CCN(CCNC(C23CC4CC(C2)CC(C3)C4)=O)CC5\n", "\n", "C1=CC(=CC=C1CC(OC(C(C)N)=O)(C)C)Cl\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)C[C@H]2Cl)C)[C@H](C3)O)C[C@H]4C)C)(C(COC(CC)=O)=O)OC(CC)=O\n", "\n", "C1=C3C2=C(C=C1O)C=CC=C2CC(C3)N(CCC)CCC\n", "\n", "[C@@H]12C4([C@@H](CCC1[C@H]3C(CC2=O)(C(CC3)C(=O)CO)C)C[C@H](O)CC4)C\n", "\n", "[C@@H]23[C@H]([C@H]1[C@]([C@@H](C(C)=O)CC1)(C)CC2=O)CC[C@@H]4[C@@]3(CC[C@H](C4)O)C\n", "\n", "CC14C5(C(CC1C3C(C2(C(=CC(=O)CC2)CC3)C)CC4)OC(O5)(C)C)C(C)=O\n", "\n", "COC1(C(F)(F)C1(F)Cl)F\n", "\n", "[C@@](O)(C5C6(OC)C2OC1=C(O)C=CC4=C1C23C(C(N(CC3)CC=C)C4)(C5)C=C6)(CCC)C\n", "\n", "C(N1C(C(C)OC1=O)=O)C=C\n", "\n", "[C@H]3(CN(C2=CC=C(OCC1=CC=CC(=C1)Cl)C=C2)C(O3)=O)CNC\n", "\n", "[H+].C4=C3OCC(N(CCCCN1C(=O)CC2(CC1=O)CCCC2)CCC)CC3=C(OC)C=C4.[Cl-]\n", "\n", "C1=CC=CC3=C1C2(C(NC(=O)CC2)=O)CCC3=O\n", "\n", "C1=C([N](C(=C1C=O)C)CCNC(C)=O)C\n", "\n", "C1=C(C(=CC=C1Cl)N(C(CN(C(CC#N)C)C)=O)C)C(C2=C(C=CC=C2)F)=O\n", "\n", "[C@]2(C1=CC=CC=C1)([C@H](CN(C)CC2)CC)OC(CC)=O\n", "\n", "[C@H](C(C1=CC=CC=C1)(C2=CC=CC=C2)C[C@H](N(C)C)C)(CC)O\n", "\n", "C1=C(C(=CC(=C1[S](NC)(=O)=O)N)OC)C(NCC2N(CCC2)CC=C)=O\n", "\n", "C1=CC=C3C2=C1C(N(CC(O)=O)C(C2=CC=C3)=O)=O\n", "\n", "[C@H]23C([C@@]1(OC(O[C@@H]1C2)(CC)CC)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5=CC(=O)C=CC45C)C\n", "\n", "[C@H]24C([C@@]1(O[C@@](O[C@@H]1C2)(C3=CC=CC=C3)C)C(=O)CO)(C[C@H](O)[C@@]5(F)C4CCC6=CC(=O)C=CC56C)C\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC6(O5)CCCC6)C)C(COC(C)=O)=O\n", "\n", "[C@H]12[C@@]([C@](OC(=O)CC)([C@H](C1)C)C(=O)COC)(C[C@H](O)[C@@]3(F)[C@H]2CCC4=CC(=O)C=C[C@]34C)C\n", "\n", "C1=C(C(=C(C=C1)C)NC(C2=CC=C(N)C=C2)=O)C\n", "\n", "[C@H]4(CC3C2=C1C(=C[N](C1=CC=C2)C(C)C)CC3N(C4)C)C(NC5CCCCC5)=O\n", "\n", "C2=C(C(NC(CC1=CC=CC=C1)C)C#N)C=CC=C2\n", "\n", "C3=C2C([NH2+]CCCCCCC(O)=O)C1=CC=CC=C1CCC2=CC=C3.[Cl-]\n", "\n", "C1=C([S](CC)(=O)=O)C(=CC(=C1C(NCC2N(CCC2)CC)=O)OC)N\n", "\n", "C1=CC=CC2=C1C(C3=C(CC2)C=CC=C3)=CCC[N+](C)(C)[O-]\n", "\n", "C1=CC=CC=C1C(CN2CCCC2)OCCC(C)C\n", "\n", "C3=C(C(C1=CC=C(C=C1)F)CCCN2CCN(C(NCC)=O)CC2)C=CC(=C3)F\n", "\n", "C1=C(C=CC=C1N2C(C=CC=C2)=O)N\n", "\n", "CN(C)c1cnccn1\n", "\n", "C(C(C)(C)O)C\n", "\n", "C3=C(CCN1CCC(CC1)(C2=CC=CC=C2)C(OCC)=O)C=CC(=C3)N\n", "\n", "C1=CC(=CC=C1C(N2C(CCC2)=O)=O)OC\n", "\n", "COc2cc1oc(=O)c(C)c(C)c1cc2OCCCN3CCN(CC3)c4ccccc4OC\n", "\n", "[C@H](N1C3=C(SC2=C1C=CC=C2)C=CC(=C3)C(=O)NCCC)(CN4CCCC4)C\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)C(C)C)C=C\n", "\n", "C1=C4C(=CC=C1OCCCCN3CCN(C2=CC=CC(=C2Cl)Cl)CC3)CCC(N4)=O\n", "\n", "C(O)C(O)C1OC(=C(O)C1=O)O\n", "\n", "C1=CC=CC(=C1C(O)=O)OC(C)=O\n", "\n", "C1=CC=CC(=C1C(NC2=C(C=C(N(CC)CC)C=C2)C)=O)N\n", "\n", "C1=CC=CC=C1C(C(OC2CC3N(C)C(C2)CC3)=O)(CO)C\n", "\n", "C1=CC=CC=C1C(C(OC2CC3[N+](C(C2)CC3)(C)[O-])=O)CO\n", "\n", "[C@@H](C(NCC(N(C1=CC=C(C=C1C(C2=CC=CC=C2)=O)Cl)C)=O)=O)(CCCCN)N\n", "\n", "C1=CC(=CC5=C1N(C4CCN(CC3OC2=C(C=CC=C2)OC3)CC4)C(N5)=O)Cl\n", "\n", "C1=CC(=CC=C1[S](N2CC3CCC(C2)CC3)(=O)=O)N\n", "\n", "C1=CC(=CC=C1C(CCCN2CC3N(CC2)CCC3)=O)F\n", "\n", "C3=C(C(O)(C1=CC=CC=C1)C2CCNCC2)C=CC=C3\n", "\n", "O=C1NCCN1C4CCN(CCC3COc2ccccc2O3)CC4\n", "\n", "C1=CC=CC2=C1C3N(CC2)CCNC3\n", "\n", "C1=CC=CC2=C1[N]3C(=C2)CNCCC3\n", "\n", "CN(CCc2cc3cccc4CCc1ccccc1n2c34)Cc5ccccc5\n", "\n", "[C@H]13[C@H](C[C@@H]1C2=CC=C(F)C=C2)CN(C3)CCN4C(=O)C5=C(NC4=O)C=CC=C5\n", "\n", "C1=CC=CC=C1CC2=NC3=C([N]2CCN(CCO)CC)C(N(C)C(N3C)=O)=O\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CC)C\n", "\n", "C1=CC=CC3=C1N=C([N]2C(=NC(=N2)C)C3)N4CCN(C)CC4\n", "\n", "C1=CC=CC=C1CNC(CCCl)=O\n", "\n", "[C@]23([C@H]([C@H]1[C@]([C@](C(CO)=O)(O)[C@H](C1)C)(C)C[C@@H]2O)CCC4=CC(=O)C=C[C@]34C)Cl\n", "\n", "[C@@H]2(CN(C1=CC=C(C=C1)OCC[C@H](C(F)(F)F)O)C(O2)=O)COC\n", "\n", "C(C1(CC(NC(C1)=O)=O)C)C\n", "\n", "C2=C(CN(C(=C(SC(=O)C1=CC=CC=C1)/CCO[P](=O)(O)O)/C)C=O)C(=NC(=N2)C)N\n", "\n", "C1=CC=CC(=C1C(OC2=CC=C(NC(C)=O)C=C2)=O)OC(C)=O\n", "\n", "C4=C(C2=C1C3=C(SC1=NC(=O)CN2)CCCC3)C=CC=C4\n", "\n", "C3=C(C(SC(/CCOC(C1=CC=CC=C1)=O)=C(N(CC2=C(N=C(N=C2)C)N)C=O)/C)=O)C=CC=C3\n", "\n", "C1=CC=C(Cl)C3=C1\\C(C2=CC=CC=C2C=C3)=C/CC[N+]([O-])(C)C\n", "\n", "[Cl].C1=CC=CC2=C1[N](C=C2CCC3=CC=NC=C3)CC4=CC=CC=C4\n", "\n", "C(C1=CC=CC=C1)O\n", "\n", "[C@@H]4(C1=C2C(=CC(=C1)Br)CCO2)C3=C(C=C(Cl)C(=C3)O)CCN(C4)C\n", "\n", "[C@H](O)(/C=C/C1=C(C3=C(OC12CCCC2)C=CC=C3)C4=CC=C(F)C=C4)C[C@H](O)CC(OCC)=O\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@@H]4C)C)(OC(C5=CC=CC=C5)=O)C(CO)=O\n", "\n", "[C@]14([C@@]([C@@H](C)C[C@H]1[C@H]3[C@]([C@@]2(C(=CC(=O)C=C2)CC3)C)(F)[C@H](C4)O)(OC(CCCC)=O)C(CO)=O)C\n", "\n", "O=C2CC1(CCCC1)CC(=O)N2CCNCC4COc3ccccc3O4\n", "\n", "C(C2C1C(NC(N1)=O)CS2)CCCC(O)=O\n", "\n", "S1[C@H]4N(CC1(C2CN3[C@H](S2)CC3=O)O)C(C4)=O\n", "\n", "C1=CC=CC2=C1C4=C([NH]2)CN3CCN(CC3C4)CCCC(C5=CC=C(C=C5)F)=O\n", "\n", "C1=NC(=NC(=C1CN(C(=C(SSC(=C(\\C)N(C=O)CC2=C(N)N=C(C)N=C2)/CCOC(=O)C3=CC=CC=C3)/CCOC(=O)C4=CC=CC=C4)\\C)C=O)N)C\n", "\n", "[C@H](CCCNC(C)=O)(C(O)=O)NC(C)=O\n", "\n", "C1=C(C3=C(N=C1N2CCN(CC)CC2)CCCCCC3)C4=CC=C(F)C=C4\n", "\n", "[C@@]23(C([C@H](N(CC1(CC1)O)CC2)CC4=C3C=C(C=C4)O)(C)C)CC\n", "\n", "C1=CC(=CC2=C1NC(=O)OC2(C)C)Br\n", "\n", "CC(C(C(NC(N)=O)=O)Br)C\n", "\n", "C(Br)(Br)Br\n", "\n", "C1=C(C(=C(C=C1Br)C)O)C(N)=O\n", "\n", "C2=C(C1C(NC(C1)=O)=O)C=CC=C2Br\n", "\n", "C1=C(Br)SC2=C1C(=NCC3=NN=C([N]23)C)C4=C(C=CC=C4)Cl\n", "\n", "[C@@H](CC[N]2C1=C(C(N=C(N1)N)=O)N=C2)(CO)O\n", "\n", "C1=C(C=CC(=C1)NC(C)=O)OC(C)(C)C\n", "\n", "[C@]1(O)(C[C@@H]4N(CC1)C[C@@H]2C5=C(CCC3=CC=CC=C23)C=CC=C45)C(C)(C)C.[H+].[Cl-]\n", "\n", "C1=C(C(CCC)=O)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(C)CC4\n", "\n", "C(C(CCCC)CC)NC(CC(C)O)=O\n", "\n", "[C@]235[C@]([C@H](N(CC1CCC1)CC2)CC4=C3C=C(O)C=C4)(CCCC5)O\n", "\n", "[C@@H]34C1=CC=CC2=C1C(=C[NH]2)C[C@H]3N(C[C@@H](C4)C(N(C(NCC)=O)CCCN(C)C)=O)CC=C\n", "\n", "C1=NC3=C([N]1CCNC(C(C2=CC=CC=C2)O)C)C(N(C)C(N3C)=O)=O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(OC(N(C)C)=O)C(N2C)=O)C3=CC=CC=C3\n", "\n", "CC12C(C(CC1=O)CC2)(C)C\n", "\n", "C1=C(C(=CC=C1C(/CCCCC)=N\\OCCN)C)N\n", "\n", "C1=CC(=CC=C1C(C2=CC=CC=C2)SCCN(C)C)SCCCC\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CCCC)COC(N)=O\n", "\n", "C1=C(Cl)C=CC2=C1C(N(CC(=O)N2C)C(N)=O)C3=CC=CC=C3\n", "\n", "C1=C(C(OC(N)=O)C#C)C=CC=C1\n", "\n", "O=C(C4N(C12CC3CC(C1)CC(C2)C3)CC4)O\n", "\n", "C1=CC=CC2=C1CN(CC(N)=O)C(O2)=O\n", "\n", "C2=C(C(CCCN1CCC(OC(NC(C)C)=O)CC1)=O)C=CC(=C2)F\n", "\n", "C1=C(C=CC3=C1N(CCCN2CCN(CC2)CCO)C4=C(S3)C=CC=C4)C(CC)=O\n", "\n", "C1=C(C=CC(=C1)[S](N)(=O)=O)C(O)=O\n", "\n", "[C@@H](C(C1=CC=CC=C1)=O)(C)N\n", "\n", "C1=CC(=CC=C1C3CN(CC(N2CC(NCC2)=O)=O)C(C3)=O)Cl\n", "\n", "OC1OC(COCCOC(O)C(Cl)(Cl)Cl)C(OC2OC(COCCOCCOC(O)C(Cl)(Cl)Cl)CC(OC(O)C(Cl)(Cl)Cl)C2O)C(OCCOCCOC(O)C(Cl)(Cl)Cl)C1O\n", "\n", "[C@@H](/C=C/C1=C(N=C(C(=C1C2=CC=C(C=C2)F)COC)C(C)C)C(C)C)(C[C@H](CC(O)=O)O)O\n", "\n", "CC1=CC(=NNC1=O)C\n", "\n", "C1=C(C(O)C(CO)NC(C(Cl)Cl)=O)C=CC(=C1)C(=O)C\n", "\n", "C1=C(CN(C(=C(SC(OCC)=O)/CCOC(OCC)=O)/C)C=O)C(=NC(=N1)C)N\n", "\n", "[C@@H]2(OC1O[C@@H](O[C@@H]1[C@H]2O)C(Cl)(Cl)Cl)[C@H](O)CO\n", "\n", "C(C(OC(C(Cl)(Cl)Cl)O)C)C(C)(C)O\n", "\n", "C1=CC(=CC=C1C2[S](CCC(N2C)=O)(=O)=O)Cl\n", "\n", "C(Cl)(Cl)Cl\n", "\n", "C1=C2C(=CC=C1)C(C=C(O2)C(O)=O)=O\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)C[C@H]4O[C@H](O5)C6CCCCC6)C)C(COC(C(C)C)=O)=O\n", "\n", "C1=C(SC2=C1C(=NCC3=NN=C([N]23)C4CCCCC4)C5=CC=CC=C5Cl)Br\n", "\n", "[C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)COC(=O)C)(CC(O)[C@@]4(F)C3CC(=C5C4(CCC(=C5)OCCCl)C)C#N)C\n", "\n", "[C@@]1([C@H](C2CCC1CC2)NC(C)C)(C3=CC(=C(C=C3)Cl)Cl)O\n", "\n", "C(C1CCCCC1)NN\n", "\n", "CC(C(C1CCCCC1)O)C\n", "\n", "C1=CC(=CC=C1N2CC(COC)OC2=O)OCC3=CC(=CC=C3)C#N\n", "\n", "C1=C(C(=C(C=C1\\C=C\\C(N2CCCCCCC2)=O)OC)OC)OC\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(O)C(N2CCC#N)=O)C3=CC=CC=C3F\n", "\n", "C1=C(Cl)C=CC(=C1C(C2=C(C=CC=C2)Cl)=O)N(C(CNC3CC3)=O)C\n", "\n", "[C@]34([C@H](C2[C@@](F)([C@@]1(C(=CC(=O)C=C1)[C@@H](F)C2)C)[C@@H](O)C3)C[C@H]5OC(O[C@@]45C(=O)COC(=O)C6CC6)(C)C)C\n", "\n", "C1=C(C#N)C=CC3=C1C4=C(C2=C(C=CC=C2)S3)CCN(CC4)C\n", "\n", "C1=C(C(=CC(=C1Cl)N)OC)C(NC3CCN(CC2=CC=CC=C2)CC3)=O\n", "\n", "C1=C(C(=C(C=C1)Cl)NC2=NCCO2)Cl\n", "\n", "[C@@]23(F)[C@H]([C@H]1[C@@]([C@@]([C@H](C1)C)(O)C(=O)CCl)(CC2=O)C)CCC4=CC(=O)C=C[C@]34C\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)C(C3)=O)C[C@@H]4C)C)(OC(CCC)=O)C(CCl)=O\n", "\n", "C1=C(Cl)C=CC4=C1N(CCCN3CCC(N2CCCCC2)(CC3)C(N)=O)C5=C(CC4)C=CC=C5\n", "\n", "[C@@]24(Cl)C1(C(=CC(=O)C=C1)[C@@H](F)CC2C3C(C([C@@H](C3)C)C(=O)CO)(CC4O)C)C\n", "\n", "[C@H]2(C1=CC(C=C[C@@]1(C4([C@@H](C2)[C@@H]3C[C@H]([C@H](C(COC(=O)C)=O)[C@@]3(C)C[C@@H]4O)C)Cl)C)=O)F\n", "\n", "[C@@]24(Cl)C1(C(=CC(=O)C=C1)[C@@H](F)CC2C3C(C([C@@H](C3)C)C(=O)COC(=O)C(C)(C)C)(CC4O)C)C\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=CC=CC=C2)C(=O)N3CCCN(C)C\n", "\n", "C2=C(C1(OCCN(CC)CC)CCCCC1)C=CC(=C2)Cl\n", "\n", "C1=C(C(=CC=C1C3(CCN(CCCC(C2=CC=C(F)C=C2)=O)CC3)O)Cl)C(F)(F)F\n", "\n", "C1=C(Cl)C=CC3=C1\\C(C2=C(C=CC=C2)S3)=C/CCN4CCN(CCO)CC4\n", "\n", "C1=CC=CC4=C1C(N(CCCN3CCN(C2=CC(=CC=C2)Cl)CC3)C(N4)=O)=O\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(CO)=O)(O)CC1)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)C(=C4)Cl)C)O)C\n", "\n", "C(C(Cl)(Cl)Cl)OC(OCC(Cl)(Cl)Cl)=O\n", "\n", "C1=C(C(=CC(=C1)Cl)Cl)OCCCN(CC#C)C\n", "\n", "C3=C(C(C2CCN(CCCC(C1=CC=C(F)C=C1)=O)CC2)=O)C=CC(=C3)Cl\n", "\n", "C1=C(Cl)C=CC4=C1C(N2CCN(C)CC2)CC3=C(C=CC=C3)S4\n", "\n", "C1=C(CC)SC2=C1C(=NCC(N2C)=O)C3=CC=CC=C3Cl\n", "\n", "[C@]34([C@H](C2[C@@](F)([C@@]1(C(=CC(=O)C=C1)[C@@H](F)C2)C)[C@@H](O)C3)C[C@H]([C@]4(OC(=O)CC)C(SCCl)=O)C)C\n", "\n", "CNC(=O)CCN1CCN(CCC=C2c3ccccc3Sc3ccc(cc23)Cl)CC1\n", "\n", "C1=C(O)C=CC4=C1C3(C(C(N(CC2CCC2)CC3)C4)(C)C)CC\n", "\n", "[C@H]12C5CC4=C3[C@]1([C@H](C(C[C@@H]2CC)=O)OC3=C(C=C4)OC)CCN5CC6CC6\n", "\n", "CC1CC2C3CC(F)(F)C4=CC(=O)C=C[C@]4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO\n", "\n", "C(O)C(=O)C4(O)C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)CC3)CC4)C\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)C(C3)=O)CC[C@@]4(C(COC(C)=O)=O)O)C\n", "\n", "[C@H]37[C@H]2[C@@]([C@](C(COC(C1=CC(=CC=C1)[S](O)(=O)=O)=O)=O)(O)[C@@H](C2)C)(C[C@@H]([C@@H]3[C@@]4(C(=CC5=C(C4)C=N[N]5C6=CC=CC=C6)C(=C7)C)C)O)C\n", "\n", "[C@H]26[C@H]1[C@@]([C@](C(COC(C)=O)=O)(O)[C@@H](C1)C)(C[C@@H]([C@@H]2[C@@]3(C(=CC4=C(C3)C=N[N]4C5=CC=CC=C5)C(=C6)C)C)O)C\n", "\n", "C1=CC=NC=C1C2N(C(=O)CC2)C\n", "\n", "C1=CC=CC2=C1C(C3=C(CC2)C=CC=C3)=CC(CN(C)C)=O\n", "\n", "CC2(CC(OC(C1NC(=O)CC1)=O)CC(C2)C)C\n", "\n", "C(C(N(C(\\C=C\\C)=O)CC)C(N(C)C)=O)C\n", "\n", "C1=CC=CC=C1NC(OCC3(COC(NC2=CC=CC=C2)=O)CCCC3)=O\n", "\n", "C1=CC=CC2=C1C3=C([NH]2)CCC(C3)N(C)C\n", "\n", "C(C2(C1=CCCCC1)C(NC(=O)NC2=O)=O)C\n", "\n", "C1=CC=CC2=C1C(C3=C(C=C2)C=CC=C3)=CCCN(C)C\n", "\n", "[C@]23(C1(C(C1)CC2)[C@H](O)C[C@@H]4[C@@H]3CC[C@]5([C@H]4CC[C@@H]5C(=O)C)C)C\n", "\n", "C1=NC(=NC(=C1CN(C(/C)=C2\\CCOC(=O)S2)C=O)N)C\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)CC3)C(N)=O\n", "\n", "[C@@]135C67[C@@H](N(CC1)CC2CC2)CC4=CC=C(O)C(=C34)O[C@@H]5C(OC)(C(C6)C(O)(C)C)C=C7\n", "\n", "C(C1CCCCC1)CC(OCCN(C)C)=O\n", "\n", "C4=C(C(C2C(C1=CC=NC=C1)C2)(C3=CC=CC=C3)O)C=CC=C4\n", "\n", "C1=CC(=CC=C1C23C(C(=O)NC2=O)C3)Cl\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)C(CN(C)C)=O\n", "\n", "C1=CC(=CC=C1C(C2=NCCN2)(C3=NC=CC=C3)O)Cl\n", "\n", "C1=C(C(N)=O)C=C(C(=C1OC)OCCCCCCCCCC)OC\n", "\n", "C1=C(C(=CC=C1C)CC2=CC=CC=C2)N3CCNCC3\n", "\n", "C3=C(C1=C2C(=NC(=O)CN1O)C=CC(=C2)Cl)C=CC=C3\n", "\n", "C1=NC2=C([N]1CC(C)=O)C(N(CCCC)C(N2CCCC)=O)=O\n", "\n", "CC(=O)C3(O)CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C\n", "\n", "C1=NC(=NC2=C1N=C[N]2COCCO)N\n", "\n", "[C@H]34[C@H]2[C@@](F)([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@@H](O)C[C@@]3([C@](O)([C@H](O)C4)C(=O)C)C\n", "\n", "[C@]12(OC(O[C@@H]1C[C@@H]3C2(CC(O)[C@H]4C3CCC5=CC(=O)C=CC45C)C)(C)C)C(=O)CO\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3C[C@@H](C)[C@@H]4C(CO)=O)C)O)F\n", "\n", "C2=C(CC1=CN=C[NH]1)C(=C(C=C2)C)C\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4C)C)(OC(C5=CC=CO5)=O)C(COC(C)=O)=O\n", "\n", "[C@@H]3(C1=CC=CC=C1)CN2CCSC2=N3\n", "\n", "C3=C(C1N2C(=NC1)NCC2)C=CC=C3\n", "\n", "[C@@H]1(NCCCC1)[C@@H]2OC(OC2)(C3=CC=CC=C3)C4=CC=CC=C4\n", "\n", "[C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)CO)(CC2Cl)C)CCC4=CC(=O)C=CC34C\n", "\n", "[C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)COC(=O)C)(CC2Cl)C)CCC4=CC(=O)C=CC34C\n", "\n", "C1=C(Cl)C=C(C(=C1C(NCCN(CC)CC)=O)OC)Cl\n", "\n", "[C@]23([C@@H]1[C@@H](C(=CO[C@H]1OC(=O)CC(C)C)COC(=O)CC(C)C)C[C@@H]2OC(=O)C)OC3\n", "\n", "C2=C(C1(C(N(C(=O)N(C1=O)CC(COCCCC)OC(N)=O)CC(COCCCC)OC(N)=O)=O)CC)C=CC=C2\n", "\n", "C4=C(C(C1=CC=C(C=C1)F)CCCN3CCN(CCNC2=CC=CC=C2)CC3)C=CC(=C4)F\n", "\n", "[C@@]24(F)C1(C(=CC(=O)C=C1)[C@@H](F)CC2C3C(C([C@@H](C3)C)C(=O)CO)(CC4O)C)C\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)([C@H](C[C@]4([C@H]3C[C@@H](C)[C@@H]4C(COC(C(C)(C)C)=O)=O)C)O)F\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC4(CN(C)CC4)C\n", "\n", "CC1(C(NC(O1)=O)=O)C\n", "\n", "C1=NC2=C([N]1CCN(C)C)C(N(C)C(N2C)=O)=O\n", "\n", "CN(C(C)CC1=CC=CC=C1)C\n", "\n", "O=C2N1C(NC(=O)C1)CC2\n", "\n", "C(C1(OC(COC(N)=O)CO1)C)CCCCCCCC\n", "\n", "C2=C(C(C1=CC=CC=C1)(CC(NNC(C)=O)=O)O)C=CC=C2\n", "\n", "[C@]235[C@@]67[C@H](N(CC1CC1)CC2)CC4=C3C(=C(C=C4)O)O[C@H]5[C@](OC)([C@H](C6)C(C)(C)O)CC7\n", "\n", "C1=C(N=C2[N]1C(=C(CC)C(=N2)OC)C)C(C3=CC=CC=C3)=O\n", "\n", "C1=CC=CC2=C1C3(C4=C(CC2N3)C=CC=C4)C\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)CCC4)C)(OC(CCC)=O)C(C)=O\n", "\n", "[H+].C1=C(OC)C(=CC2=C1C(=O)C(C2)CC3CCN(CC3)CC4=CC=CC=C4)OC.[Cl-]\n", "\n", "C4=C(CCNC(C12CC3CC(C1)CC(C2)C3)=O)C=CC(=C4O)O\n", "\n", "C1=CC=CC=C1C2(C(CN(C2=O)CC)CCN3CCOCC3)C4=CC=CC=C4\n", "\n", "[C@@]123C4=C(C[C@H]([C@@]1(CC[C@H](C2)O)O)N(CC3)C)C=CC(=C4OC)OC\n", "\n", "C1=CC=C3C(=C1OC(C2=CC=CS2)CCNC)C=CC=C3\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN5CCC(C(C4=CC=C(F)C=C4)=O)CC5\n", "\n", "C(N1C(CCC1)=O)C(NNC(CN2C(CCC2)=O)=O)=O\n", "\n", "[C@H]2(N(C1=CC=C(C(=C1)Cl)Cl)C(CC)=O)[C@@H](CCC2)N(C)C\n", "\n", "C2=C(N1N=C(C)CC1=O)C=CC=C2\n", "\n", "C1=CC=CC=C1C(C)[N]2C=CN=C2C\n", "\n", "C1=CC(=CC3=C1\\C(C2=CC=CC=C2N3)=C/CN(CC)CC)Cl\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CC[S](CC)(=O)=O)=O)C3=CC=CC=C3F\n", "\n", "C1=C(C2=C(C=C1)C=CO2)N5CCN(CC3=CC=C([NH]3)C4=CC=C(C=C4)F)CC5\n", "\n", "C1=C(C2=C(C=C1)OCCO2)N3CCNCC3\n", "\n", "C1=C(C=CC=C1C(CNC(CCCO)=O)(CC)CC)OC\n", "\n", "C3=C(N2CCN(CC(COC1=CC(=C(OC)C(=C1)OC)OC)O)CC2)C(=CC=C3)OC\n", "\n", "C1=CC=CC=C1CN(C2CC2)C(OCC)=O\n", "\n", "C3=C(C2=CC=C(N1CCC(O)CC1)N=N2)C(=CC(=C3)Cl)Cl\n", "\n", "[C@@H]1([C@@H]([C@H]2C=C[C@@H]1C2)C(N(CC)CC)=O)C(N(CC)CC)=O\n", "\n", "CC3CC2C1CCC(C(C)=O)C1(C)CC(O)C2C4(C)C=CC(=O)C=C34\n", "\n", "C3=C(OC2C1(NC(=O)C(O1)C)CCCC2)C=CC=C3Cl\n", "\n", "[C@@]12(C3=C(N(C)[C@H]1N(C)CC2)C=CC(=C3)OC(NCCCCCCC)=O)C\n", "\n", "C1=CC=CC3=C1C4=C(C2=C(C=CC=C2)N3C)CCN(CC4)C\n", "\n", "C1=C(C(C)=O)C=CC(=C1C(NCC2N(CCC2)CC)=O)OC\n", "\n", "C1=NC2=C([N]1CCN(CC)CC)C(N(C)C(N2C)=O)=O\n", "\n", "C1=C(Cl)C=CC3=C1C(C2=CC=CC=C2)SC(=N3)NCC\n", "\n", "C1=CC=CC2=C1C(C3=C(N(C)C2=O)C=CC=C3)OCC\n", "\n", "[H+].C2=C1OCC(OC1=C(C=C2)OCC)CNCCCC.[Cl-]\n", "\n", "C(C)Cl\n", "\n", "C=C\n", "\n", "C(C1(C(C(=CNC1=O)C)=O)CC)C\n", "\n", "C2=C(C1(CCCCC1)NCC)C=CC=C2\n", "\n", "[H+].C2=C1C(OC(=NC1=CC=C2Cl)NCC)(C3=CC=CC=C3)C.[Cl-]\n", "\n", "C(C(N1C(CCC1)=O)C(N)=O)C\n", "\n", "[C@@H]34C1=CC=CC2=C1C(=C[NH]2)C[C@H]3N(C[C@H](C4)N[S](N(CC)CC)(=O)=O)C\n", "\n", "C3=C(C(N1CCN(CC1)CCOCCOCCO)C2=CC=CC=C2)C=CC(=C3)Cl.O=C(O)\\C=C\\C(O)=O\n", "\n", "C1=C(C=CC=C1N3CCN(CCCN2C(N(C(=N2)CC)CC)=O)CC3)Cl\n", "\n", "C2=CC(=C3OC5C14C6(C(N(CC1)C)CC2=C34)CC(C5(OC)C=C6)C(O)(CCC)C)O\n", "\n", "C3=C(C2(OC(C1CCCCN1)CO2)CC)C=CC=C3\n", "\n", "C1=NC2=C([N]1CCC(COC(C)=O)COC(C)=O)N=C(N)N=C2\n", "\n", "C1=CC=CC2=C1C3=C(C(=O)N2CCCN(C)C)C=CC=C3\n", "\n", "C1=C(N=C2[N]1C(=C(CC)C(=N2)OC)C)C3=NOC(=N3)C\n", "\n", "[C@H]1(NC(=O)CC1)C(=O)N2CCCCC2\n", "\n", "C2=C(C1(C(N(CC(COCCCC)OC(N)=O)C(=O)NC1=O)=O)CC)C=CC=C2\n", "\n", "C1=C(C(COC(N)=O)COC(N)=O)C=CC=C1\n", "\n", "C3=C(C2C(COC1=CC=C(OC)C=C1)CN(C)CC2)C=CC=C3\n", "\n", "C1=CC(=CC=C1C(CCCN3CCN(C(OC2CCCCC2)=O)CC3)=O)F\n", "\n", "C3=C(C1C(N(CCO1)CCOC(C(C2=CC=CC=C2)CC)=O)C)C=CC=C3\n", "\n", "C3=C(C1C(NCC)C2CC1CC2)C=CC=C3\n", "\n", "C2=C(\\C(NCCCC)=C\\1C=C(Cl)C=CC1=O)C(=CC=C2)Cl\n", "\n", "C4=C3[NH]C1=C(CCNC1CC2=CC=CC=C2)C3=CC=C4\n", "\n", "C2=C(C1(C(C(=O)NC1=O)CC)C)C=CC=C2\n", "\n", "C1=C(C(=CC=C1OCC2=NCCN2)Cl)Cl\n", "\n", "C1=CC=CC=C1C2C(NC(=O)CO2)C\n", "\n", "C1=C(C=CC=C1NC(NC2=NC(=O)CN2C)=O)Cl\n", "\n", "C1=CC=CC=C1C2C(N=C(O2)NCC)=O\n", "\n", "C1=CC(=CC=C1C(CC(C)(C)O)(C)O)Cl\n", "\n", "C3=C(C1=NC(=N[N]1C)C2=CC=CN=C2)C(=CC=C3)CO\n", "\n", "C1=C4C(=CC(=C1)CN3CCN(C(COC2=CC=C(Cl)C=C2)=O)CC3)OCO4\n", "\n", "C1=C(C(CNC(C)(C)C)O)C(=CC=C1)F\n", "\n", "[C@@H](C1=CC=C(C=C1)[S](C)(=O)=O)([C@H](NC(C(Cl)Cl)=O)CF)O\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)C(CCN(CC)CC)=O\n", "\n", "[C@@]34(F)C(C2C(C1(N=C(OC1C2)C)C(=O)COC(=O)C)(CC3O)C)CCC5=CC(=O)C=CC45C\n", "\n", "CCOC(=O)N(CCN(C)C)c1cccc(c1)C(F)(F)F\n", "\n", "C1=C(C(=CC=C1[S](N)(=O)=O)OC)C(NCC3N(CC2=CC=C(F)C=C2)CCC3)=O\n", "\n", "[C@@]23(Cl)C1(C(=CC(=O)C=C1)[C@@H](F)CC2C5C(CC3Cl)([C@]4(C(=O)CO)OC(O[C@@H]4C5)(C)C)C)C\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(CO)=O)O)C)O)F\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F\n", "\n", "C1=C(F)C=CC2=C1NC(=C3C(=N2)SC(=C3)C)N4CCN(CC4)C\n", "\n", "[C@]12(OC(O[C@@H]1CC3C2(CC(O)[C@@]4(F)C3C[C@H](F)C5=CC(=O)C=CC45C)C)(C)C)C(=O)C(OC)OC\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C)=O)=O\n", "\n", "CCCCOC(=O)C(=O)C3C(C)CC4C2CC(F)C1=CC(=O)C=CC1(C)C2C(O)CC34C\n", "\n", "CCCCCC(=O)OCC(=O)C3C(C)CC4C2CC(F)C1=CC(=O)C=CC1(C)C2C(O)CC34C\n", "\n", "C1=CC(=CC=C1[S](CC)(=O)=O)F\n", "\n", "[C@]23([C@H]([C@H]1[C@]([C@@](OC(C)=O)(C(C)=O)CC1)(C)C[C@@H]2O)C[C@H](C)C4=CC(=O)C=C[C@]34C)F\n", "\n", "[C@@H]1(C3=C([C@H](C2=C1C=CC=C2)C)C=CC(=C3)C(F)(F)F)CCCN(C)C\n", "\n", "C2=C(C(OC1=CC=C(C=C1)C(F)(F)F)CCNC)C=CC=C2\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(CCOC(CCCCCC)=O)CC4\n", "\n", "C1=CC=CC3=C1N(CCCN2CCC(OCCO)CC2)C4=C(S3)C=CC(=C4)C(F)(F)F\n", "\n", "C1=C(C=CC=C1N2CCN(CCNC(N)=O)CC2)C(F)(F)F\n", "\n", "C1=CC=CC=C1N2C5(C(NC2)=O)CCN(CCCC(C3=CC=C(C=C3)F)C4=CC=C(C=C4)F)CC5\n", "\n", "C1=C(Cl)C=CC2=C1C3(N(CC(=O)N2CCO)CCO3)C4=CC=CC=C4F\n", "\n", "CN2C(=O)C(O)N=C(c1ccccc1F)c3cc(Cl)ccc23\n", "\n", "CC4CC3C2CC(F)C1=CC(=O)C=CC1(C)C2(F)C(O)CC3(C)C4(O)C(=O)SCF\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4C)C)(OC(CC)=O)C(SCF)=O\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(SC=C2)S3)CCCN4CCN(CCO)CC4\n", "\n", "C1=C(C(OCC)=O)[N](C=N1)C(C)C2=CC=C(C=C2)F\n", "\n", "C1=CC(=CC2=C1[N](C4=C2CN(CCCC(C3=CC=C(C=C3)F)O)CC4)C5=CC=C(F)C=C5)F\n", "\n", "[C@@H](O)(/C=C/C1=C(C2=C([N]1C(C)C)C=CC=C2)C3=CC=C(F)C=C3)C[C@H](O)CC(=O)O\n", "\n", "C1=C(C(F)(F)F)C=CC(=C1)C(=N/OCCN)/CCCCOC.O=C(O)\\C=C/C(=O)O\n", "\n", "C2=C(OC1CN(C(NC)=O)C1)C=CC=C2C(F)(F)F\n", "\n", "[C@H](NC(C3=CC=C(NCC1=NC2=C(N=C1)NC(=NC2=O)N)C=C3)=O)(CCC(O)=O)C(O)=O\n", "\n", "[C@]12(OC(O[C@@H]1CC3C2(CC(O)[C@@]4(F)C3CC(=C5C4(CCC(=C5)OCCCl)C)C=O)C)(C)C)C(=O)COC(=O)C\n", "\n", "C1=CC(=CC2=C1N(C(CN=C2C3=CC=CC=C3)=O)C[P](C)(C)=O)Cl\n", "\n", "C1=CC=CC=C1[P](C2=CC=CC=C2)(CC(NN)=O)=O\n", "\n", "[C@@H]1([P](O)(O)=O)[C@H](C)O1\n", "\n", "C1=CC=CC=C1C3(C2=CC=CC=C2)C(N(CO[P](O)(O)=O)C(N3)=O)=O\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)C(CCN4CCN(C)CC4)=O\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)C(CCN4CCN(CCO)CC4)=O\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN5CCC(C4C(OCC4)=O)(O)CC5\n", "\n", "C1=NC(=NC(=C1CN(C(=C(SSCC2CCCO2)\\CCO)/C)C=O)N)C\n", "\n", "O=C2C1=C(CNCC1)ON2\n", "\n", "[C@H]13[C@@H]([C@@]2([C@@H](CC1)C[C@](O)(CC2)C)C)CC[C@]4([C@H]3CC[C@@H]4C(=O)C)C\n", "\n", "C1=NC(=C(C2=C1[NH]C3=C2C(=CC=C3)OC4=CC=C(C=C4)Cl)COC)C(OC(C)C)=O\n", "\n", "C1=C(O)C=CC4=C1C3(C(C(N(CC2CC2)CC3)C4)(C)C)CC\n", "\n", "[H+].C1=CC=NC(=N1)N2CCN(CC2)CCCCN3C(=O)CC(CC3=O)(C)C.[Cl-]\n", "\n", "C1=C(F)C=CC2=C1C3=C([NH]2)CCN(C3)CCCC4=CN=CC=C4\n", "\n", "C1=C(OC)C(=CC2=C1C(=NN=C(C2)C)C3=CC=CC(=C3)Cl)OC\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)CC1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(CCl)=O\n", "\n", "[C@H]23C(Cl)([C@@]1(C(=CC(=O)C=C1)[C@@H](F)C2)C)[C@@H](F)C[C@]4([C@H]3C[C@H]([C@@H]4C(=O)CO)C)C\n", "\n", "C1=CC(=CC4=C1N(C3CCN(CCNC(C2=CC=C(F)C=C2)=O)CC3)C(N4)=O)Cl\n", "\n", "C1=CC(=CC=C1C3(CCN(CCCC(C2=CC=C(C=C2)F)=O)CC3)OC(CCCCCCCCC)=O)Cl\n", "\n", "[C@]34([C@H]([C@H]2C([C@@]1(C(=CC(C(=C1)Br)=O)[C@@H](C2)F)C)([C@H](C3)O)F)CCC4(C(=O)CO)O)C\n", "\n", "C1=C(Br)C=CC3=C1C2(N(CCO2)CC(=O)N3)C4=C(C=CC=C4)F\n", "\n", "C2=C(C(C1=CC=CC=C1)(C(CCCCC)N)O)C=CC=C2\n", "\n", "C3=C2N(C1=CC=CC=C1C=CC2=CC=C3)CCCN4C=CN(CC=C4)CCO\n", "\n", "C1=CC(=C5C2=C1CC6C34C2(C(C(OC)(C(C3)C(CC)(C)O)C=C4)O5)CCN6CC7CC7)OC\n", "\n", "[C@H](C(NCCCC(O)=O)=O)(C(CO)(C)C)O\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(CN(CC)CC)=O)=O)O)C\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C3)CC[C@]4(O)C(=O)COC(=O)CCC(O)=O)C.O\n", "\n", "[C@@]4(OC(=O)CCCC)(C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)C(O)C3)CC4)C)C(=O)CO\n", "\n", "CC14CCC(=O)C=C1CCC5C3CCC(O)(C(=O)COC(=O)CCC2CCCC2)C3(C)CC(O)C45\n", "\n", "[C@@]125C3=C4C[C@H]([C@@]1(CC[C@@H]([C@@H]2OC3=C(C=C4)O)O)O)N(C)CC5\n", "\n", "C1=C(O)C=CC2=C1C(=C(C)[N]2CC3=CC=C(OC)C=C3)CCN\n", "\n", "C(C(C(C)C)(C(N)=O)Br)C\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CCOCC3CC3)=O)C4=CC=CC=C4\n", "\n", "[C@@H]2(OC1=C(C(=CC=C1)C)C)[C@@H](CNCC2)O\n", "\n", "C1=C3C(=CC=C1\\C=C\\C(N2CCCCC2)=O)OCO3\n", "\n", "C1=CC(=CC2=C1C(=NO2)C4CCN(CCCOC3=C(C=C(C(C)=O)C=C3)OC)CC4)F\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN5CCN(CCN4C(N(C)CC4)=O)CC5\n", "\n", "C2=C(N1C(N(CCN(C)C)CC1)=O)C=CC=C2Cl\n", "\n", "C1=CC=CC2=C1N(C3=C(CC2)C=CC=C3)CCC[N+](C)(C)[O-]\n", "\n", "[C@@H]2(C1=CC=CC=C1[C@H](NC)C2)C3=CC=C(Cl)C(=C3)Cl.[H+].[Cl-]\n", "\n", "C2=C(C1=C(C=CC1)C=C2)OCC3CNCCO3\n", "\n", "C1=CC(=CC2=C1[N](C(=C2CC(O)=O)C)C(C3=CC=C(Cl)C=C3)=O)OC\n", "\n", "C1=CC=CC3=C1C(C2=CC=CC=C2)(C=C3)CCN(C)C\n", "\n", "C1=CC(=CC=C1OCC(NNC(C)C)=O)Cl\n", "\n", "CSC1=C(N=C(N=C1N2CCN(C)CC2)NC(C)C)Cl\n", "\n", "[H+].C1=CC=CC2=C1[S](=O)(=O)N(C2=O)CCCCN4CCN(C3=NC=CC=N3)CC4.[Cl-]\n", "\n", "C1=C(C)ON=C1C(NNCC2=CC=CC=C2)=O\n", "\n", "C(C(C1(CCC2C4C(C(CC12C)O)(F)C3(C=CC(C=C3CC4)=O)C)O)=O)O\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F\n", "\n", "[C@]34([C@H]([C@H]2C(F)([C@@]1(C(=CC(=O)C=C1)[C@@H](F)C2)C)[C@@H](O)C3)C[C@H]5O[C@H](O[C@@]45C(O[C@@H](OC(OCC)=O)C)=O)CCC)C\n", "\n", "[C@H]3(CCCC1=C2C(=NC=C1)C=CC(=C2)OC)[C@@H](CNCC3)C=C\n", "\n", "[C@H]12N(CC[C@](C1C)(C3=C(C2=O)C=CC(=C3)O)C)CC4CC4\n", "\n", "C1=C(Cl)C=CC2=C1C3(N(CC(=O)N2C)C(C=C(O3)C)=O)C4=CC=CC=C4\n", "\n", "C1=C(C=CC=C1C2(CCN(C)CC2)C(CC)=O)O\n", "\n", "C1=CC=NC(=N1)N3CCN(CCCC[N]2C=C(Cl)C=N2)CC3\n", "\n", "C5=C(C(CN1C2C4C(CC1)(C3=C(C2)C=CC(=C3)O)CCCC4)=O)C=CC=C5\n", "\n", "C1=CC=CC2=C1C(C(N2C3=CC=CC=C3)=O)(CC4=CC=NC=C4)CC5=CC=NC=C5\n", "\n", "[C@@H]14C(=C[C@H](NC(N(CC)CC)=O)CN1C)C2=CC=CC3=C2C(=C[NH]3)C4\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)C3(C)C)=CCCNC\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=CC=CC=C2)C(CCN3)=O\n", "\n", "[C@]3(N(C1=CC=CC=C1)C(=O)CC)(C(=O)OC)[C@H](CN(CCC2=CC=CC=C2)CC3)C\n", "\n", "[H+].C1=CC=CC2=C1N(C3=C(CC2)C=CC=C3)CCCN(CC(=O)C4=CC=C(Cl)C=C4)C.[Cl-]\n", "\n", "C1=C(C2=C(C(=C1)OC)CCCC2N(C)C)Cl\n", "\n", "C3=C(C1=NC(C(=O)NC2=C1N=C(Cl)C=C2)O)C(=CC=C3)Cl\n", "\n", "C1=CC(=CC4=C1N2C(=NC(/C2=O)=C\\N3CCN(C)CC3)CN=C4C5=C(C=CC=C5)Cl)[N+](=O)[O-]\n", "\n", "C1=CC(=CC2=C1C(C3=C(CC2)C=CC=N3)=C4CCN(C(OCC)=O)CC4)Cl\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(O)C(N2C)=O)C3=CC=CC=C3Cl\n", "\n", "[C@H]23C1=NN=C([N]1C[C@H]2CCC3)CCN4CCN(CC4)C5=CC=CC(=C5)C(F)(F)F\n", "\n", "[C@@]123OC4=C(C(C1CN(CC2)C)CC(=O)N3)C=C(Cl)C=C4\n", "\n", "C1=CC(=CC(=C1N(C(=O)CNC(=O)CN)C)C(=O)C2=CC=CC=C2Cl)Cl.O\n", "\n", "C2=C(C(C1C(=CC(O1)=O)OC)O)C(=CC=C2)Cl\n", "\n", "CCOC(=O)OC3(CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C)C(=O)OCCl\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCN(CC4)C\n", "\n", "C1=CC(=CC2=C1C(=NCC(N2C)CNC(C3=COC=C3)=O)C4=CC=CC=C4)F\n", "\n", "C1=CC=CC2=C1C(N3C2=NCC3)(C4=CC=C(C=C4)Cl)O\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)CC[C@@]4(C(CN5CCN(C)CC5)=O)O)C\n", "\n", "C1=CC=CC=C1C(NN)C\n", "\n", "C(C(C(CC)C)(COC(N)=O)C)OC(N)=O\n", "\n", "[C@H]2(N=C(C1=C(C=CC(=C1)[N+](=O)[O-])NC2=O)C3=CC=CC=C3Cl)C\n", "\n", "CNC(NC(C(Cl)(Cl)Cl)O)=O\n", "\n", "[C@@]23(Cl)C(C1C([C@](O)([C@@H](C1)C)C(=O)CO)(CC2Cl)C)CCC4=CC(=O)C=CC34C\n", "\n", "[C@@H]23[C@H]([C@H]1[C@]([C@@H](C(C)=O)CC1)(C)C[C@@H]2O)C[C@H](C)C4=CC(=O)CC[C@]34C\n", "\n", "[H+].C1=C(OCC(=O)NCCN(CC)CC)C=CC(=C1)OC.[Cl-]\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)C3(C)C)=CCCN(C)C\n", "\n", "C1=CC(=CC=C1C(CCCN2CCC(C)CC2)=O)F\n", "\n", "CC12CC3(CC(C1)(CC(C2)C3)N)C\n", "\n", "C1=C(C(=CC=C1)OC)OCC2CNC(O2)=O\n", "\n", "C1=C(OC)C=CC3=C1C(C2=C(C=CC=C2)S3)(CCCN(C)C)O\n", "\n", "C1=CC=CC2=C1SC5=C(N2CC3C4CCN(C3)CC4)C=CC=C5\n", "\n", "[C@@H]34C1=CC=CC2=C1C(=C[N]2C)C[C@H]3N(C[C@H](C4)N[S](N(C)C)(=O)=O)C\n", "\n", "C1=C(C=CC=C1O)NC(C)=O\n", "\n", "C1=C(C(C(C)(C)O)(C)O)C=CC=C1Cl\n", "\n", "C2=C(N(CC1=CC=CS1)CCN(C)C)N=CC=C2\n", "\n", "C(C1(C(NC(N(C1=O)C)=O)=O)CC)C\n", "\n", "C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)CSC\n", "\n", "C5=C3C1=C(OC4C12C(C(N(CC2)C)C3)CC=C4C)C(=C5)O\n", "\n", "[C@@H]25CC1=CC=C(C4=C1[C@@]3([C@H]2CC[C@@H]([C@@H]3O4)OC)CCN5C)O\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O\n", "\n", "C1=C(C)C=CC3=C1C(=NC2=C(C=CC=C2)S3)N4CCN(C)CC4\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN5CCN(CCOC(C4=CC(=C(OC)C(=C4)OC)OC)=O)CC5\n", "\n", "C1=C([S](C)(=O)=O)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCC(C(N)=O)CC4\n", "\n", "[C@@]125C3=C4C[C@H]([C@@H]1CCC([C@@]2(OC3=C(C=C4)O)C)=O)N(C)CC5\n", "\n", "C5=C4C3=C(C2N(CC1CC(OC)C(C(C1C2)C(OC)=O)OC)CC3)[NH]C4=CC(=C5)OC\n", "\n", "C1=C(OC)C=CC3=C1C(=CC2=C(C=CC=C2)O3)N4CCN(C)CC4\n", "\n", "C1=C(OC)C=CC2=C1C4=C3[N]2CCN(C3=NCC4)C\n", "\n", "[C@@H]23[C@@H](CC[C@@H]1C[C@H](CC(O1)=O)O)[C@H](C=CC2=CCC[C@@H]3OC([C@H](CC)C)=O)C\n", "\n", "C1=C(Cl)C=CC3=C1C2(N(C(C)CO2)CC(=O)N3)C4=CC=CC=C4Cl\n", "\n", "CSC2=C(N1CCN(C)CC1)N=C(NC)N=C2Cl\n", "\n", "C1=C(C=CC(=C1Cl)COC(C[N]2C=NC=C2)C3=C(C=C(C=C3)Cl)Cl)Cl\n", "\n", "C1(=NC(NC1(C(F)(F)F)C(F)(F)F)(C(F)(F)F)C(F)(F)F)N\n", "\n", "C(NCC(N)=O)CCCC\n", "\n", "CN1CC(=CCC1)\\C=N\\OC\n", "\n", "C1=C(C(=NN=C1C2=CC=CC=C2)NCCN3CCOCC3)C\n", "\n", "[C@@H]23[C@H]([C@H]1[C@]([C@@H](C(C)=O)CC1)(C)C[C@H]2N(C)C)CC[C@@H]4[C@@]3(C[C@H](OCC)[C@H](C4)O)C\n", "\n", "[C@@H]45N([C@@H](CC(NC(=O)C1=CN(C2=C(C1=O)C=CC=C2)C3CCCCC3)C4)CC5)C\n", "\n", "C2=C(C(NCCN1CCOCC1)=O)C=CC(=C2)Cl\n", "\n", "C2=C(C(C1=CC=CC=C1)[S](CC(N)=O)=O)C=CC=C2\n", "\n", "[C@H]34[C@H]2[C@@](Cl)([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@@H](O)C[C@@]3([C@](O)([C@@H](C4)C)C(=O)CCl)C\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)C3(C)C)CCCNC\n", "\n", "C3=C(C2(C(N(CN1CCOCC1)C(C2)=O)=O)C)C=CC=C3\n", "\n", "C1=C(Cl)C=CC5=C1N(CCCN4CCC3(N2C(CCCC2)NC3=O)CC4)C6=C(CC5)C=CC=C6\n", "\n", "C1=C([N+](=O)[O-])C=CC2=C1C(=NCC(N2COC)=O)C3=CC=CC=C3\n", "\n", "[C@@H]13C=C[C@@H]([C@@]2([C@H]1[C@H](OC2=O)OC(NC)=O)C)C3\n", "\n", "C(N1CCOCC1)CNC2=NNC(C=C2C)=C3C=CC(=O)C=C3\n", "\n", "[C@@]126C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OCC5=CC=CC=C5)OC(CCCCCCCCCCCCC)=O)N(CC6)C\n", "\n", "C1=C(C(C(CCCCC)C)C)C=C(C2=C1OC(C3=C2CC(C)CC3)(C)C)OC(CCCN4CCCCCC4)=O\n", "\n", "[C@H]23C1=C(C=C(C(CCCCCC)(C)C)C=C1O)OC([C@@H]2CCC(C3)=O)(C)C\n", "\n", "C2=C(C(C[N]1C=CN=C1)=O)C=CC3=CC=CC=C23\n", "\n", "[C@]14([C@H]([C@H]3[C@](F)([C@@H](O)C1)[C@@]2(C(=CC(=O)C=C2)CC3)C)C[C@@H]6[C@]4(CC5=CC=CC=C5C6)C(=O)CO)C.O\n", "\n", "OC1CCC2(O)C3Cc5ccc(O)c6OC1C2(CCN3CC4CCC4)c56\n", "\n", "[C@@]126C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=C)O)N(CC5CC5)CC6\n", "\n", "[C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CC=C(C)C)CC5\n", "\n", "C1=C(C=CC2=C1C=CC=C2)CC([NH2+]C)=NC.[Cl-]\n", "\n", "C1=C2C(=CC=C1)CCC(=C2)CC3=NCNC3\n", "\n", "C2=C1C(=CC=CC1=CC=C2)OC(\\C(=N\\O)N)C\n", "\n", "[H+].C4=C(N3CCN(CCCN1N=C(N(C1=O)CCOC2=CC=CC=C2)CC)CC3)C=CC=C4Cl.[Cl-]\n", "\n", "C1=CC=C(C(=C1C)NC(CN2C(CCC2)=O)=O)C\n", "\n", "Fc5ccc4c(CCCN1CCC(CC1)n2c(=O)[nH]c3ccccc23)noc4c5\n", "\n", "C1=CN=CC=C1C(NNCCC(NCC2=CC=CC=C2)=O)=O\n", "\n", "C1=CC(=CC=C1N3CCN(CCC(NC(C2=CC=CN=C2)=O)C)CC3)F\n", "\n", "[C@]125OC(O[C@@]([C@]1(C[C@H](O)[C@H]3[C@H]2CCC4=CC(=O)CC[C@]34C)C)(CC5)C(=O)COC(=O)C6=CC=NC=C6)\\C=C/C\n", "\n", "[C@@]127C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OC(C5=CN=CC=C5)=O)OC(C6=CN=CC=C6)=O)N(C)CC7\n", "\n", "C1=CC=NC=C1C(N2CCOCC2)=O\n", "\n", "C(C1=CN=CC=C1)(=O)N\n", "\n", "C1=CC=NC=C1C(N(CC)CC)=O\n", "\n", "C1=C([N+]([O-])=O)C=CC2=C1C(=NCC(=O)N2C)C3=CC=CC=C3\n", "\n", "C(C(COC(N)=O)(C(CC)C)C)OC(NC(C)C)=O\n", "\n", "C1=CC=CC=C1C(OC2=CC=CC=C2OC)CCNC\n", "\n", "NC(=N)c1ccc(o1)c2ccccc2N(=O)=O\n", "\n", "[C@H]12C([C@@](O)(CC1)C#C)(CC[C@H]3C2CCC4=CC5=C(CC34C)C=N[N]5C6=CC=C(F)C=C6)C\n", "\n", "C1=CC(=CC=C1\\C(C2=CC=CN=C2)=C\\CNC)Br\n", "\n", "C1=CC=C(C3=C1C(C2=CC=CC=C2)CN(C)C3)N\n", "\n", "C1=C[NH]C2=C1C(=NC=N2)NCCCCCCCCC\n", "\n", "C(C(F)(F)F)F\n", "\n", "C1=C(O)C=CC3=C1C24C(C(NCC2)C3)CCCC4\n", "\n", "C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=O)N2)C3=CCCCC3\n", "\n", "[C@@H]3(C1=C(C=C2C(=C1OC)OCO2)CCN3C)[C@@H]5C4=C(C(=C(OC)C=C4)OC)C(O5)=O\n", "\n", "C1=CC=CC2=C1C(C3=C(CC2)C=CC=C3)=NOCCN(C)C\n", "\n", "C1=C(Cl)C=CC3=C1\\C(C2=C(C=CC=C2)S3)=C\\C4C5CCN(C4)CC5\n", "\n", "C1=CC(=CC2=C1C(=NO2)C5CCN(CCC3=C(N=C4N(C3=O)C=CC=C4C)C)CC5)F\n", "\n", "C1=N[N]3C(=C1C(C2=NC=CC=C2)=O)N=CC=C3C4=CC=NC=C4\n", "\n", "C(C(NN)C)CCCCC\n", "\n", "CNCCC=C3c1ccccc1C2CC2c4ccccc34\n", "\n", "[C@H]2([C@H](CN(CC(C1=CC=C(F)C=C1)=O)CC2)COC4=CC3=C(OCO3)C=C4)C5=CC=C(F)C=C5\n", "\n", "C2=C(C1CN(CCO1)C(C)C)C=CC(=C2)C(F)(F)F\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)CC3=O)C(N)=O\n", "\n", "C1=CC=CC=C1C[N+](C(CC2=CC=CC=C2)C)(C)[O-]\n", "\n", "C(N1CC(O)CC1=O)C(N)=O\n", "\n", "[C@H]34C1[C@@H](C2(C(C(=O)CC1)C(=O)C=C2)C)C(O)CC3([C@](O)(CC4)C(OC)=O)C\n", "\n", "C1=C(O)C=CC2=C1C(=C[NH]2)CC(C(O)=O)N\n", "\n", "CN(C)C(=O)COC2c1ccccc1CCc3ccccc23\n", "\n", "C4=C3C(N(C1=CC=C2C(=N1)N=C(Cl)C=C2)C(=O)C3=CC=C4)CC(CCC(C)C)=O\n", "\n", "C1=NC(=C3[N]1C2=C(C=CC=C2)N(C3=O)C(C)C)C4=NOC(=N4)C5CC5\n", "\n", "[C@H]5(CN3CCC(C2=CC1=C(OCO1)C=C2)(CC3)O)CN(C4=CC=C(OC)C=C4)C(O5)=O\n", "\n", "C3=C(C(NC1C2CCN(C1)CC2)=O)C(=CC(=C3Cl)N)OCC4CC4\n", "\n", "C1=C4C(=CC=C1CN3CCC(NC(NC(C2=CC=CC=C2)=O)=O)CC3)C=CC=C4\n", "\n", "CC1OC(C)OC(O1)C\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)[C@H](C3)O)C[C@H]([C@@]4(C(COC(C)=O)=O)O)C)C\n", "\n", "C1=CC=CC=C1CN(CC#C)C\n", "\n", "[C@@H]3([C@H](COC1=CC=C2C(=C1)OCO2)CNCC3)C4=CC=C(C=C4)F\n", "\n", "C1=CC=CC2=C1C(N(C2=O)C3=NC4=C(C=C3)C=CC(=N4)Cl)CC(=O)N6CCC5(OCCO5)CC6\n", "\n", "C4=C(C(CCCN1CCC(CC1)(C2=CC=C(C(=C2)C(F)(F)F)Cl)O)C3=CC=C(C=C3)F)C=CC(=C4)F\n", "\n", "CC(C(C(OC(N)=O)C)C)OC(N)=O\n", "\n", "[C@]135[C@@]4([C@@H](CC2=C1C(=C(C=C2)O)O[C@H]3C(=O)C=C4)N(C)CC5)NCCCCC\n", "\n", "C(C1(COC(C(Cl)(Cl)Cl)OC1)CO)O\n", "\n", "CN1CCN(CC1)C3=Nc2ccccc2Sc4nccn34\n", "\n", "C2=C1C(=NC(=CC1=CC=C2)N3CCNCC3)C4=CC=CC=C4\n", "\n", "[C@@H]45C(N(CCCCN3CCN(C1=NSC2=C1C=CC=C2)CC3)C([C@@H]4CCCC5)=O)=O\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(CCO)CC4\n", "\n", "C(C(COC(C(Cl)(Cl)Cl)O)(COC(C(Cl)(Cl)Cl)O)COC(C(Cl)(Cl)Cl)O)OC(C(Cl)(Cl)Cl)O\n", "\n", "C1=CC=CC=C1CC(NC(N)=O)=O\n", "\n", "C1=C(C=CC2=C1C3(C)C(C)C(C2)N(CC3)CCC4=CC=CC=C4)O\n", "\n", "C1=CC=CC=C1C(C(NC(N)=O)=O)CC\n", "\n", "C2=C(C1C(C)NCCO1)C=CC=C2\n", "\n", "C1=C(OCC(NN)C)C=CC=C1\n", "\n", "C1=CC=CC=C1C2C(N(C)C(C2)=O)=O\n", "\n", "C1(=CC=CC=C1)OC2=CC=CC=C2C(=O)[O-]\n", "\n", "C1=CC=CC=C1C3(C2=CC=CC=C2)NC(=O)NC3=O\n", "\n", "C1=CC=CC=C1N2C(C(C(NC2=O)=O)(CC)CC)=O\n", "\n", "C3=C(C(N2CCN(CC1=CC=CC=C1)CC2)=O)N=CC=C3\n", "\n", "[C@]2(C1=CC(=CC=C1)O)([C@H](CN(C)CC2)C)CCC\n", "\n", "[C@H]2(C(OC1=CC=C(NC(C)=O)C=C1)=O)NC(=O)CC2\n", "\n", "C2=C(C1=CC=CC=C1)N=C4C(=C2CCC3CCNCC3)C=CC=C4\n", "\n", "C1=C(C(C)=O)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCC(CCO)CC4\n", "\n", "C3=C(C(C1=CC=CC=C1)(C(OCCN2CCCCC2)=O)O)C=CC=C3\n", "\n", "C2=C(N1CCN(C)CC1)N=NC3=C2N(C4=C(O3)C=CC=C4)C\n", "\n", "C1=C([S](N(C)C)(=O)=O)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCC(CCOC(CCCCCCCCCCCCCCC)=O)CC4\n", "\n", "[C@H]12C(C3=C(C[C@@H]1CCN(C2)C)[NH]C(=C3CC)C)=O\n", "\n", "C(N1C(CCC1)=O)C(N)=O\n", "\n", "C1=CC=CC2=C1C3=C(C2)CCOC3(CCN(C)C)C\n", "\n", "C1=CC=CC(=C1N2C(=NC3=C(C2=O)C=CC=C3)\\C=C\\C4=NC=CC=C4)C\n", "\n", "C1=CC=CC=C1C(C2=CC=CC=C2)(CCN4CCC(N3CCCCC3)(C(N)=O)CC4)C#N\n", "\n", "[N]14C3=C(C2=C1C=CC(=C2)C)CCCC3NCC4.[H+].[Cl-]\n", "\n", "C(NC(C(C)(C)C)=O)CCC(O)=O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(C(=O)N2)OC(C(C)(C)C)=O)C3=CC=CC=C3\n", "\n", "C(N1C(CCC1)=O)C(NCCN(C(C)C)C(C)C)=O\n", "\n", "C1=CC=CC=C1C3(CC2CCCCN2)C(NC(=O)NC3=O)=O\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)CC4)C)(C(COC(CC)=O)=O)OC(OCC)=O\n", "\n", "C(C(C4(C3(C(C2C(C1(C(=CC(=O)C=C1)CC2)C)C(C3)O)CC4)C)O)=O)OC(CN(CC)CC)=O\n", "\n", "[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@@H](O)C[C@@]3([C@](C(COC(COC(CCCCCCCCCCCCCCCCC)=O)=O)=O)(O)CC4)C\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(CC(C)(C)C)=O)=O)O)C\n", "\n", "C(C(C3(C(CC4C2CCC1=CC(C=CC1(C)C2C(CC34C)O)=O)=C)O)=O)O\n", "\n", "[C@@H](CC(=O)O)(CC(C)C)CN\n", "\n", "C(C(OC4CC3=CCC2C1C(C(CC1)C(C)=O)(CCC2C3(CC4)C)C)=O)CC(O)=O\n", "\n", "[H+].C3=C(C(=C1CCN(C1)CC)C2=CC=CC=C2)C=CC=C3.[Cl-]\n", "\n", "C1=CC(=CC=C1C(NC(C)C)=O)CNNC\n", "\n", "C(C(C1CCCCC1)OC(N)=O)C\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CC(CO)O)=O)C3=CC=CC=C3F\n", "\n", "C2=C(\\C(NCCCC(=O)N)=C/1C=C(F)C=CC1=O)C=CC(=C2)Cl\n", "\n", "C1=CC=CC=C1CC(N2CCCC2)CCC\n", "\n", "C(C1OC(C(C)C)(C(C)C)OC1)O\n", "\n", "C1=CC(=CC=C1OC(CN(CC)CC)=O)NC(C)=O\n", "\n", "[CH2-][CH-]C\n", "\n", "C1=CC=CC2=C1N(C(C3=C(N2)N=CC=C3)=O)CC(N(C)C)C\n", "\n", "C1=CC=C(C(=C1C(C)C)O)C(C)C\n", "\n", "C1=CC(=CC=C1C(CCCN3CCC(N2CCCCC2)(CC3)C(CC)=O)=O)F\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=CCCN(C)C\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)O\n", "\n", "Oc5ccc4CC2C1CCOCC1(CCN2CC3CC3)c4c5\n", "\n", "C1=NC2=C([N]1CC(C)O)C(N(C)C(N2C)=O)=O\n", "\n", "C1=CC=C(C2=C1[NH]C=C2CCN(C)C)O[P](O)(O)=O\n", "\n", "C2=C(C1=CC=NC=C1)OC3=C2C=CC=C3\n", "\n", "C1=C(C(=C(C(=N1)C)O)CO)CSSCC2=CN=C(C(=C2CO)O)C\n", "\n", "C1=CC=CC3=C1C(=NC2=CC=CC=C2S3)N4CCN(CCOCCO)CC4\n", "\n", "[C@@]45(C1=C(C=CC(=C1)OC(N3CC2=CC=CC=C2CC3)=O)N([C@H]4N(C)CC5)C)C\n", "\n", "C1=CC=C(C2=C1CC3C(C2)CC(N[S](N(CC)CC)(=O)=O)CN3CCC)O\n", "\n", "C1=CC=CC=C1CC2(C3(C(C)(C)C(C2)CC3)C)OCCN(C)C\n", "\n", "[C@@H]1(C2=C(CC1)C=CC=C2)NCC#C\n", "\n", "C1=CC(=CC3=C1N(C2=NC(=O)CO2)CCN=C3C4=CC=CC=C4Cl)Cl\n", "\n", "C1=CC=CC=C1C(CC2=CC=CC=C2)(NC(CN)=O)C\n", "\n", "C1=CC=CC=C1N(C2(CCN(CCC(OC)=O)CC2)C(OC)=O)C(CC)=O\n", "\n", "CCC(=O)C1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3C(O)CC21C\n", "\n", "C1=CC(=CC(=C1NC(OCC)=O)N)NCC2=CC=C(C=C2)F\n", "\n", "C3(NC(N1C2=C(C(C)(C)C1)C=CC=C2)=O)C[C@@H]4N(C)[C@H](C3)CC4\n", "\n", "C2=C([N]1C(=NC(=N1)C(N(C)C)=O)CNC(CN)=O)C(=CC(=C2)Cl)C(C3=C(C=CC=C3)Cl)=O\n", "\n", "CC4CN(CCCn2c1ccccc1c3ccccc23)CC(C)N4\n", "\n", "[C@H]12[C@@]([C@](C(CC)=O)(C)[C@@H](C1)C)(C[C@H](O)[C@H]3[C@H]2CCC4=CC(=O)C=C[C@]34C)C\n", "\n", "C1=CC=CC=C1C2=NCC(NC3=C2[N](N=C3C)CC)=O\n", "\n", "[C@H](C1=CC(=CC=C1)OC(N(CC)C)=O)(N(C)C)C\n", "\n", "[C@@H]3(N(C1CCC1)C2CCC2)CC4=C(OC3)C(=CC=C4C(=O)N)F\n", "\n", "C1=NC2=C([N]1COC(COC(C)C)COC(C)C)N=C(N)N=C2\n", "\n", "C3=C(C2CN1C(CCN=C1C2)=O)C=CC=C3\n", "\n", "[C@H]23[C@@]([C@@]1(O[C@@H](O[C@@H]1C2)CCC)C(=O)CO)(C[C@H](O)[C@@]4(F)[C@H]3C[C@H](F)C5=CC(=O)CC[C@]45C)C\n", "\n", "C3=C(C2C(NC(=O)C1NC(CC1)=O)C2)C=CC=C3\n", "\n", "C1=C(C(=CC=C1C2CNC(C2)=O)OC)OC3CCCC3\n", "\n", "[C@@H]25C(=C[C@@H](CN1CC(NC(C1)=O)=O)CN2C)C3=CC=CC4=C3C(=C[NH]4)C5\n", "\n", "C1=C(C(=C(C=C1)Br)NC2=NCCN2)F\n", "\n", "C2=C(C1=C(NC(C1)=O)C=C2)CCN(CCC)CCC\n", "\n", "C1=CC(=CC=C1C(CCCN3CCC2(C(N(C)C(C2)=O)=O)CC3)=O)F\n", "\n", "CON=C(C#N)C1CN2CCC1CC2\n", "\n", "[C@@H]2(OC1=C(C=CC=C1)CO)O[C@@H]([C@@H](O)[C@@H]([C@H]2O)O)CO\n", "\n", "C(C1=CC=CC=C1O)(=O)N\n", "\n", "C1=CC=CC(=C1OC(C2=C(C=CC=C2)O)=O)C(O)=O\n", "\n", "C1=CC=CC=C1C2(C(N(CC)C)=O)CCN(CCCCCC)CC2\n", "\n", "C3=C(C1=C([N]2C(=N1)C=CC=C2)CN(C(CCC)=O)C)C=CC(=C3)Cl\n", "\n", "N#Cc5ccc4Oc1ccccc1C2=C(CCN(CC2)CC3CCCC3)c4c5\n", "\n", "[C@@H](CC1=CC=CC=C1)(N(CC#C)C)C\n", "\n", "[C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CCOC)CC5\n", "\n", "C2=C(C1=C(C=CC(=C1)Cl)[N]2C3=CC=C(C=C3)F)C5CCN(CCN4C(NCC4)=O)CC5\n", "\n", "C1=CC=CC3=C1C4=C(C2=C(C=CC=C2)C3)CN(C)CC4\n", "\n", "C1=CC=NC2=C1C(C3=C2N=CC=C3)(CC4=CC=NC=C4)CC5=CN=CC=C5.O\n", "\n", "C1=CC(=CC=C1C2(C(CC(C)C)N(C)C)CCC2)Cl\n", "\n", "C1=CC=CC(=C1N2CCN(C)CC2)CC3=CC=CC=C3\n", "\n", "[C@H]12[C@@H](OC(C(CC)(C)C)=O)C[C@@H](C)C=C1C=C[C@@H]([C@@H]2CC[C@@H]3C[C@H](CC(O3)=O)O)C\n", "\n", "C1=CC=C(C(=C1C)C(NC2=NOC(=C2)C)=O)C\n", "\n", "C1=CC=CC=C1N2C4(C(NC2)=O)CCN(CCCC(C3=CC=C(F)C=C3)=O)CC4\n", "\n", "[C@]24(C[C@@H]([C@@H](N(C(CC1=CC(=C(Cl)C=C1)Cl)=O)C)CC2)N3CCCC3)CCCO4\n", "\n", "C4=C(N1C3(C(NC1)=O)CCN(CCCOC2=CC=C(C=C2)F)CC3)C=CC=C4\n", "\n", "C1=CC=CC=C1N2C4(C(NC2)=O)CCN(CC\\C=C(C3=CC=C(F)C=C3)\\C)CC4\n", "\n", "C1=CC=CC2=C1C4(C3=C(OC2)C=CC=C3)OC(CN(C)C)CO4\n", "\n", "C(C1=NC2=C([N]1C)C(N(C)C(N2C)=O)=O)CCN3CCN(C(N(CC)CC)=O)CC3\n", "\n", "C1=C2C(=CC=C1\\C=C\\C(C(C)(C)C)O)OCO2\n", "\n", "C3=C(N2C(C(C1=CC=CC=C1)CC2=O)=O)C(=CC(=C3)[S](N)(=O)=O)Cl\n", "\n", "C1=CC(=CC=C1[S](NC2=NOC(=C2)C)(=O)=O)N\n", "\n", "C([S](C(C)(C)[S](=O)(=O)CC)(=O)=O)C\n", "\n", "C1=C(C(=CC=C1[S](N)(=O)=O)OC)C(NCC2N(CCC2)C)=O\n", "\n", "C1=CC(=CC=C1N2[S](CCCC2)(=O)=O)[S](N)(=O)=O\n", "\n", "C1=C([S](CC)(=O)=O)C=CC(=C1C(NCC2N(CCC2)CC)=O)OC\n", "\n", "[C@H](C(=O)OC1=CC=C(C=C1)NC(=O)C)(NC(=O)C)CCSC\n", "\n", "O=C1CCC(=O)N1CC1CCC2CN(CCN2C1)c1ncccn1\n", "\n", "C4=C(N2C(C1=C(SCCS1)C2=O)OC(N3CCN(C(CC)=O)CC3)=O)N=C5C(=C4)C=CC(=N5)Cl\n", "\n", "C4=C(N2C(C1=C(SCCS1)C2=O)OC(N3CCN(C)CC3)=O)N=C5C(=C4)C=CC(=N5)Cl\n", "\n", "C1=C(C=CC=C1C2=NN(C)C(N2C)=S)F\n", "\n", "O=C3C1C(C2CCC1C2)C(=O)N3C4C(NC(=O)CC4)=O\n", "\n", "C1=CC=CC3=C1C(C2=CC=CC=C2)(OC3(C)C)CCCNC\n", "\n", "C1=CC=CC3=C1C(C2=CC=CC=C2)(SC3(C)C)CCCNC\n", "\n", "C1=CC=CC2=C1C(N(CC[S](NC(C)C)(=O)=O)C2=O)=O\n", "\n", "C1=NC5=C([N]1CCN4CCC(C2=C[NH]C3=C2C=CC=C3)CC4)C(N(C)C(N5C)=O)=O\n", "\n", "CNC2CCC(c1ccccc1)c3ccccc23\n", "\n", "C1=C(C(=C(O)C(=N1)C)CNCC)CSC\n", "\n", "C4=C(C(N2CCN(CC(C1=CC=C(C(=C1)OC)OC)O)CC2)C3=CC=CC=C3)C=CC=C4\n", "\n", "CN(C)CCCN2c1ccccc1C(=Nc3cccnc23)c4ccccc4\n", "\n", "C1=CC=CC2=C1C3=C([N]2CC)C(SCC3)(CCN(C)C)C\n", "\n", "[C@H]13[C@H]([C@H]2C[C@@H]1CC2)C(=O)N(C3=O)CCCCN4CCN(CC4)C5=NC=CC=N5.C(C(O)(C(=O)O)CC(=O)O)C(=O)O\n", "\n", "C2=C(C1=NOC(=N1)C)N=C3[N]2C4=C(C(=N3)OC)CCCC4\n", "\n", "C3=C(CN2C1=NC(=NC(=C1CCC2=O)C)C)C=CC(=C3)C4=CC=CC=C4C5=N[NH]N=N5\n", "\n", "C(SC1=NSN=C1C2=CCCN(C2)C)CCCCC\n", "\n", "[C@@H]2(C3=C([C@H](C1=CC=C(C=C1)F)C2)C=CC(=C3)C(F)(F)F)N4CCN(CCO)CC4\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1C(C2=C(C=C(F)C=C2)S3)CCCN4CCN(CCO)CC4\n", "\n", "C2=C(C1C(NCCN1)=O)SC=C2\n", "\n", "C3=C(C1=CCN(CCC)CC1)C2=CC(=CC=C2[NH]3)Cl\n", "\n", "[C@@H]34C1=CC=CC2=C1C(=C[NH]2)C[C@H]3N(C[C@H](C4)NC(N(CC)CC)=O)C\n", "\n", "C(C1(C(CCC)CC)C(NC(=O)NC1=O)=O)C\n", "\n", "C1=C(OC)C(=CC3=C1C2N(CC(CC(C)C)C(C2)=O)CC3)OC\n", "\n", "C1=CC=CC2=C1CC3(CC2)NC(=O)NC3=O\n", "\n", "C1=CC=CC3=C1C(N(C2C(NC(=O)CC2)=O)C3=O)=O\n", "\n", "C1=CC(=C4C2=C1CC5C3C2(C(C(=CC3)OC(C)=O)O4)CCN5C)OC\n", "\n", "C(C2(C1CCCC=C1)C(NC(=S)NC2=O)=O)C=C\n", "\n", "C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)C=C\n", "\n", "C1=CC=CC2=C1N(C(CC(S2)C3=CC=CC=C3)=O)CCN(C)C\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(CCOC(C)=O)CC4\n", "\n", "C1=CC=CC2=C1N(C3=C(S2)C=CC(=C3)[S](N(C)C)(=O)=O)CCCN4CCN(CC4)C\n", "\n", "C1=CC=CC=C1C2OC(=NC2=O)N(C)C\n", "\n", "[C@@H]3(CN(CCC=C(C1=C(C=CS1)C)C2=C(C=CS2)C)CCC3)C(O)=O\n", "\n", "C1=CC(=CC2=C1C(C3=C(N(C)[S]2(=O)=O)C=CC=C3)NCCCCCCC(O)=O)Cl\n", "\n", "C1=C4C(=C2C(=C1)SC=C2C)C3=C(CCN(C3)C)[NH]4\n", "\n", "C2=C(OCC1OC(NCC1)=S)C=CC=C2\n", "\n", "[H+].C2=C(C1(C(N(C)C)C=CCC1)C(OCC)=O)C=CC=C2.[Cl-]\n", "\n", "C1=C(F)C=CC2=C1N4C(CN=C2C3=CC=CS3)CN(C)CC4\n", "\n", "C1=CC=CC4=C1N(C3CCN(CCCC(C2=CC=C(F)C=C2)=O)CC3)C(N4)=S\n", "\n", "C2=C(N1C(SCC1)=N)C(=CC(=C2)Cl)N\n", "\n", "[C@H]12[C@@](C(OC(=O)C)([C@H](C1)C)C(SC)=O)(C[C@H](O)C3(F)[C@H]2CCC4=CC(=O)C=C[C@]34C)C\n", "\n", "C1=C([N](CC[S](CC)(=O)=O)C(=N1)C)[N+](=O)[O-]\n", "\n", "C5=C2C1=C([NH]C=C1CC4C2=CC(CSC3=NC=CC=C3)CN4C)C=C5\n", "\n", "C4=C(N3CCN(CCCCN2C(C1=C(C=CC=C1)NC2=O)=O)CC3)C(=CC=C4)SCCC\n", "\n", "C2=C1C(=NSC1=CC=C2)N5CCN(CCCCN3C(CC4(CC3=O)CCCC4)=O)CC5\n", "\n", "[C@]23([C@H]([C@H]1[C@]([C@@](SCC)(SC)CC1)(C)C[C@@H]2O)CCC4=CC(=O)C=C[C@]34C)F\n", "\n", "C1=C(OC)C(=CC2=C1[S](OC(C2)C(NC(CC(N(C)C)(C)C)C)=O)(=O)=O)OC\n", "\n", "C1=CC=CC=C1CC(C)NCCC3C2=C(C=CC=C2)SC4=C3C=CC=C4\n", "\n", "[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C\n", "\n", "[C@@H]23[C@H]([C@H]1[C@]([C@](C(CSC(C(C)(C)C)=O)=O)(O)CC1)(C)C[C@@H]2O)CCC4=CC(=O)CC[C@]34C\n", "\n", "C2=C(C(C1=CC=CC=C1)OCCNC)C(=CC=C2)C\n", "\n", "C1=C(OC)C(=CC2=C1C(=NN=C(C2CC)C)C3=CC=C(C(=C3)OC)OC)OC\n", "\n", "C1=CC=CC(=C1OCC(COC(C(Cl)(Cl)Cl)O)OC(C(Cl)(Cl)Cl)O)C\n", "\n", "C1=C(C=CC=C1N3CCN(CCC2=N[NH]C(=C2)C)CC3)C\n", "\n", "C1=C(C)C=CC2=C1C3N(CC2)CCC(C3)(CC)O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CC[S](C3=CC=C(C)C=C3)(=O)=O)=O)C4=CC=CC=C4Cl\n", "\n", "[C@]1([C@]2(C3=C(C[C@@H]1N(CC2)C)C=CC(=C3)O)C)(CCC(=O)CCCCC)C\n", "\n", "[C@@]23([C@H]([C@H]1[C@H](OC(O1)(C)C)CO2)OC(O3)(C)C)CO[S](N)(=O)=O\n", "\n", "C1=N[N](C2=C1C(=C(C(=N2)C)C(OCC)=O)NCCCC)CC\n", "\n", "[C@]12(OC(O[C@@H]1CC3C2(C[C@H](Cl)[C@@]4(Cl)C3C[C@H](F)C5=CC(=O)C=CC45C)C)(C)C)C(=O)CF\n", "\n", "[C@H]1([C@](O)(CCCC1)C2=CC(=CC=C2)OC)CN(C)C.[H+].[Cl-]\n", "\n", "C1=CC=CC2=C1CC(N(C)C)C(O2)C\n", "\n", "[H+].C4=C(C2(N(C1=C(F)C=CC=C1)C(=O)CC)CCN(CC2)CCN3N=NN(C3=O)CC)C=CC=C4.[Cl-]\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C(CNC(C6=CC=CC=C6)=O)C)=O)=O\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C6=CC7=C(O6)C=CC=C7)=O)=O\n", "\n", "BrCC(O)(Br)Br\n", "\n", "C1=C(C(=C(C=C1C(NCC(N(CC)CC)=O)=O)OC)OC)OC\n", "\n", "C3=C(C2(C1=CC=CC=C1)C(N(C(C(Cl)(Cl)Cl)O)CN2)=O)C=CC=C3\n", "\n", "O=[P](O)(OCC(Cl)(Cl)Cl)O\n", "\n", "[C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CCl)(C[C@H](Cl)[C@@]4(Cl)C3C[C@H](F)C5=CC(=O)C=CC45C)C\n", "\n", "C1=C3C(=C(C(=C1)CNC2=CC(=C(C(=C2)OC)OC)OC)C)C(=NC(=N3)N)N\n", "\n", "C3=C2N(CC1=CC=CC=C1)C(=O)\\C(NNC2=CC=C3)=C(\\N)N=O\n", "\n", "C2=C(C(N1CCCCCCC1)=O)C=C(OC)C(=C2OC)OC\n", "\n", "N[C@@H](CC1=C[NH]C2=CC=CC=C12)C(=O)O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=N2)NOCC=C)C3=CC=CC=C3Cl\n", "\n", "C4=C(N3CCN(CCCCN1C(C2C(C(C1=O)C(=O)N(C2=O)CCCC)(C)C)=O)CC3)C(=CC=C4)OC\n", "\n", "NC(=O)OCC\n", "\n", "[C@@H](N)(C(OCCOC[N]1C2=C(N=C1)C(=O)N=C(N2)N)=O)C(C)C\n", "\n", "C(C(C(N)=O)(CCC)CCC)CC\n", "\n", "[C@@H](N)(C(OCC(OC[N]1C2=C(N=C1)C(=O)N=C(N2)N)CO)=O)C(C)C\n", "\n", "C(C(C(OCOC(C(C)(C)C)=O)=O)CCC)CC\n", "\n", "[C@@]13([C@@H]2C(=C[C@@H]1OC(CC(C)C)=O)C(=CO[C@H]2OC(CC(C)C)=O)COC(C)=O)CO3\n", "\n", "C1=CC=CC2=C1C(=C3C(=N2)CCCC3O)N\n", "\n", "C1=CC(=CC=C1C(C2(CCCCC2)O)CN(C)C)OC\n", "\n", "C1=C([S](N)(=O)=O)C=C(C(=C1C(NCC2N(CCC2)CC=C)=O)OC)OC\n", "\n", "C(C(C=C)N)CC(O)=O\n", "\n", "C1=CC=CC(=C1OCC2CNCCO2)OCC\n", "\n", "C(=C)OC=C\n", "\n", "[C@]2(OC1=C(C(=C(C(=C1CC2)C)O)C)C)(CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)C\n", "\n", "C1=C(C(F)(F)F)C=CC=C1C2=CCN(CC2)CCC4=CC3=CC=CC=C3C=C4\n", "\n", "OCCCN4CCN(CCCC2c1ccccc1Sc3ccc(Cl)cc23)CC4\n", "\n", "[C@]135[C@H]([C@H](N(CC1)CC2CCC2)CC4=C3C=C(O)C=C4)[C@H](CC(C5)=C)C\n", "\n", "C1=C(C(=C(C=C1)C)NC2=NCCCS2)C\n", "\n", "C1=CC(=CC=C1CNC(C(C)C)CCCCCC(O)=O)F\n", "\n", "[C@H]14[C@H]([C@@H]2[C@@H]3[C@H]([C@H]1C=C2)C=C3)C(=O)N(C4=O)CCCCN6CCN(C5=NC=CC=N5)CC6\n", "\n", "C1=C(C=CC=C1N3C(N(CCN2CC(C)(C)C2)CC3)=O)Cl\n", "\n", "C1=CC=CC2=C1C(=NS2)N5CCN(CCC4=CC3=C(NC(C3)=O)C=C4Cl)CC5\n", "\n", "C1=CC=CC(=C1C2=NCC(N(C3=C2C(=N[N]3C)C)C)=O)F\n", "\n", "C4=C(C3=C(CCN2CCN(C1=C(C=CC=C1)OC)CC2)OC(N3)=O)C=CC(=C4)F\n", "\n", "C1=C(Cl)C=CC2=C1C(=NO2)C([N]3C=CN=C3)C\n", "\n", "C1=CC(=CN=C1N2C(C3=C(C2=O)N=CC=N3)OC(N4CCN(C)CC4)=O)Cl\n", "\n", "C1=C(Cl)C=CC2=C1C(=CC3=C(S2)C=CC=C3)OCCN(C)C\n", "\n", "[C@@H]1(CCCO1)[C@@H](CC2=CC=CC=C2)NCC\n", "\n", "C1=CC2=C(C=C1)SC3=C(N2CC(C)N(C)C)C=C(C=C3)C(=O)C\n", "\n", "C1=CC(=CC3=C1[N]2C(=NN=C2CN(C)C)CN=C3C4=CC=CC=C4)Cl\n", "\n", "C(N1C(C(CC(C)C)NC1=S)=O)C=C\n", "\n", "[C@H]24[C@H]1[C@@]([C@](O)([C@@H](C1)C)C(=O)CO)(C[C@H](O)[C@@H]2[C@@]3(C(=CC(=O)C=C3)C[C@H]4Cl)C)C\n", "\n", "C1=C(CC(CC=C)N)C=CC=C1\n", "\n", "[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)CC[C@@]3([C@](O)([C@H](O)C4)C(=O)C)C\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC(CN(C)C)C\n", "\n", "C1=C(OC)C(=CC2=C1C(=C(C(OCC)=O)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC\n", "\n", "C2=C(C1(C(CN(C)CC1)C)OC(CC)=O)C=CC=C2\n", "\n", "C3=C(C1=C([N]2C(=N1)C=CC(=C2)Cl)CC(N(CCC)CCC)=O)C=CC(=C3)Cl\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O\n", "\n", "CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23\n", "\n", "C1=CC(=NC2=C1C(C(=CN2CC)C(O)=O)=O)CC3=CC=CC=C3\n", "\n", "[C@@H](O)([C@@H](O)C(=O)O)C(=O)O.[C@@H](N)(CC1=CC=C(N(C)C)C=C1C)C\n", "\n", "C1=CC(=CC=C1C2(C(NC(=O)CC2)=O)CC)N\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC(CN(C)C)N(C)C\n", "\n", "C1=CC(=CC=C1C3(CCN(CCCC(C2=CC=C(F)C=C2)=O)CC3)C(N(C)C)=O)Cl\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCNCC4\n", "\n", "C3=C(N2CCN(CCCC(C1=CC=C(F)C=C1)O)CC2)C(=CC=C3)OC\n", "\n", "[N]12C(=CC=C1)C4N(C3=C(C2)C=CC=C3)CCN(C4)C.O=C(O)\\C=C/C(=O)O\n", "\n", "C1=C(Cl)C=CC3=C1N(C2=CC=CC=C2)C(CCN3C(OCC)=O)=O\n", "\n", "[C@H](N(C(C(C1=CC=CC=C1)C2=CC=CC=C2)=O)C)(C3=CC=CC=C3)CN4CC[C@@H](C4)O\n", "\n", "C2=C(OCC1=NN=C(S1)C(C)C)C=CC3=C2OC(=O)C(=C3C)C\n", "\n", "C1=CC=CC=C1C(C(OC2CC3N(C)C(C2)CC3)=O)CO\n", "\n", "C1=C(C(=NN=C1C2=CC=CC=C2)NCCN3CCOCC3)C#N\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(Cl)[C@H](C3)O)C[C@@H]4C)C)(C(COC(CC)=O)=O)OC(CC)=O\n", "\n", "C3=C(C(N2CCN(CC1=CC=CC=C1)CC2)=O)OC4=C3C=CC=C4\n", "\n", "[C@@H]24[C@@](C1=CC=CC=C1OC3=C(C=CC=C23)C)(CCN(C4)C)O\n", "\n", "C2=C(C(NNC(C1=CC=CC=C1)=O)C)C=CC=C2\n", "\n", "C1=CC=CC=C1C3(C(N(C(C2=CC=CC=C2)=O)C(=O)NC3=O)=O)CC\n", "\n", "C3=C(C(OC1C2CCN(C1)CC2)=O)C=CC=C3\n", "\n", "C1=CC=C2C(=C1)C3C4=C(C2(CNC)CC3)C=CC=C4\n", "\n", "C1=CC(=CC=C1)CC(N(C)CC2=CC=CC=C2)C\n", "\n", "[H+].C1=CC3=C([N]1N(C2=CC=NC=C2)CCC)C=CC=C3.[Cl-]\n", "\n", "C2=C(C(CC(N(C)C)C)(C(CC)O)C1=CC=CC=C1)C=CC=C2\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@@H]4C)C)(C(COC(C)=O)=O)OC(C(C)C)=O\n", "\n", "[C@H]34[C@H]2[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@@H](O)C[C@@]3([C@](C(COC(C)=O)=O)(O)[C@H](C4)C)C)F\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@@H]4C)C)(C(COC(CC)=O)=O)OC(CC)=O\n", "\n", "[H+].C2=C(CC1=CC=CC=C1)C(=CC=C2)OCCCCNC.[Cl-]\n", "\n", "C2=C(C1=C(C=CC=C1)[N]2N(CCN(C)C)C)C3=CC=CC=C3\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CC=C)C(=C)Br\n", "\n", "[C@@H]3(N(C1=C(C=CC=C1)F)C(COC)=O)[C@@H](CN(CCN2C(N(CC)N=N2)=O)CC3)C\n", "\n", "C2=C(C1CCNCC1)OC3=C2C=C(OC)C=C3Br\n", "\n", "C1=CC(=CC=C1NCCC(N(C)C)=O)Br\n", "\n", "C2=C(C(OCCN(C)C)C1=CC=CC=C1)C=CC(=C2)Br\n", "\n", "C1=C(Br)C(=CC(=C1C(NCCN(CC)CC)=O)OC)N\n", "\n", "C1=CC(=CC=C1C3(CCN(CCCC(C2=CC=C(C=C2)F)=O)CC3)OC(CCCCCCCCC)=O)Br\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)C[C@H]4OC(O5)CCC)C)C(CO)=O\n", "\n", "C1=CC=CC=C1C3(C2=CC=CC=C2)CCN(C(C)(C)C)CC3\n", "\n", "C2=C(C(C1=CC=CC=C1)OCCN(C)C)C(=CC=C2)C(C)(C)C\n", "\n", "C1=NC(=NC=C1)N4CCN(CCCCN2C(CC3(CC2=O)CCCC3)=O)CC4\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)C\n", "\n", "C1=CC=CC2=C1OC(CNCCCC)CO2\n", "\n", "C1=CC=CC3=C1C(C2=CC=CC=C2CC3)CC(CN(C)C)C\n", "\n", "C1=CC=CC=C1C(NNC(OCC)=O)C\n", "\n", "C(OC(NC(C(Cl)(Cl)Cl)O)=O)C\n", "\n", "C(C(C(NC(N)=O)=O)(CC)Br)C\n", "\n", "C1=CC=CC=C1N(C3(CCN(CCC2=CC=CC=C2)CC3)C(OC)=O)C(CC)=O\n", "\n", "C1=CC=CC4=C1N(CCCN3CCC(N2CCCCC2)(CC3)C(N)=O)C5=C(CC4)C=CC=C5\n", "\n", "C1=C(F)C=CC2=C1C3=C([NH]2)CCN(C3)CCC4=CC=NC=C4\n", "\n", "[H+].C1=C(CC(N(C)C)(CO)C)C=CC(=C1Cl)Cl.[Cl-]\n", "\n", "C3=C(C(N1CCN(C)CC1)C2=CC=CC=C2)C=CC(=C3)Cl\n", "\n", "ClC(CCC)O\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(COC(C)=O)=O)(O)CC1)(CC([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)Cl)C)=O)C\n", "\n", "[C@@H]36C2=C(C1=CC(=CC=C1[NH]2)Cl)CCN3C[C@H]5C[C@@H](OC(C4=CC(=C(C(=C4)OC)OC)OC)=O)[C@@H]([C@H]([C@H]5C6)C(OC)=O)OC\n", "\n", "C1=CC(=CC=C1OCC(COC(N)=O)O)Cl\n", "\n", "C2=C(C(C1=CC=CC=C1)(OCCN(C)C)C)C=CC(=C2)Cl\n", "\n", "C1=C2C(=CC=C1)OC(CCCl)NC2=O\n", "\n", "C1=C(C#N)C=CC3=C1N(C2=C(C=CC=C2)CC3)CCCN(C)C\n", "\n", "C1=CC=CC2=C1C(C3=NCCCN23)(C4=CC=CC(=C4)Cl)O\n", "\n", "C4=C2C1=C(CCC3=C(N1CCC2NC)C=CC=C3)C=C4\n", "\n", "CN(C)CCC=C2c1ccccc1COc3ccccc23\n", "\n", "[C@@H](CN2CCN(C1=CC=CC=CC1=O)CC2)(C3=CC(=C(OC)C=C3)OC)O\n", "\n", "C1=CC=CC=C1C(C2=CC=CC=C2)OCCN(C\\C=C\\C3=CC=CC=C3)C\n", "\n", "C1=C(C(=CC(=C1[N+](=O)[O-])N)OCC)C(NC3CCN(CC2CCC=CC2)CC3)=O\n", "\n", "C1=CC=CC=C1C4(C3CCN(C\\C=C\\C2=CC=CC=C2)CC3)C(NC(=O)CC4)=O\n", "\n", "C1=C(C=CC=C1Br)\\C=C\\C(NCC)=O\n", "\n", "C1=C(C(=C(C=C1\\C=C\\C(N)=O)OC)OC)OC\n", "\n", "C3=C(N2CCN(CCCC(C1=CC=C(F)C=C1)=O)CC2)N=CC4=CC=CC=C34\n", "\n", "C1=CC(=CC3=C1C(C2=CC=C(F)C=C2)(OC3)CCCN(C)C)C#N\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)C=C3)C(N)=O\n", "\n", "C1=C(Cl)C=CC4=C1C3C2=CC=CC=C2CCN3CC(=O)N4C\n", "\n", "C1=CC=CC(=C1)C(C(CN(C)C)O)C2=CC=C(C=C2)Cl\n", "\n", "C1=CC(=CC3=C1[N]2C(=CN=C2C)CN=C3C4=CC=CC=C4Cl)Cl\n", "\n", "[C@@]23(F)C(C1C([C@](O)(C(C1)C)C(=O)CCl)(CC2O)C)CCC4=CC(=O)C=CC34C\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@@H]4C)C)(OC(CC)=O)C(CCl)=O\n", "\n", "C1=CC(=CC=C1OCC(NCCN(CC)CC)=O)Cl\n", "\n", "C1=C(Cl)C=CC3=C1C(C2=C(C=CC=C2)N3)CCCN(C)C\n", "\n", "CN1CCC(CC1)=C3c2ccccc2Oc4ccc(Cl)cc34\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC3=C(S2)C=CC=C3)N4CCN(CC4)C\n", "\n", "C1=C(C(=N/OCCN)/CCCCOC)C=CC(=C1)Cl\n", "\n", "C1=C(Cl)C=CC3=C1C2(OCCN2CC(=O)N3)C4=C(Cl)C=CC=C4\n", "\n", "C1=C(Cl)C=NC2=C1SC4=C(N2CCCN3CCN(CCO)CC3)C=CC=C4\n", "\n", "CC4CC3C2CC(F)(F)C1=CC(=O)C=CC1(C)C2(F)C(O)CC3(C)C4(O)C(=O)COC(C)=O\n", "\n", "C(C(N(C(\\C=C\\C)=O)CCC)C(N(C)C)=O)C\n", "\n", "C1=C(C#N)C=CC3=C1N(C2=C(C=CC=C2)S3)CC(CN(C)C)C\n", "\n", "C3=C(C1OC(=NC1=O)NC2CC2)C=CC=C3\n", "\n", "C2=C(C1C(CCC1)N)C=CC=C2\n", "\n", "C4=C(C2=C3C(=NC(=NCC1CC1)CN2O)C=CC(=C3)Cl)C=CC=C4\n", "\n", "C1=CC=C3C(=C1OCC2CNCCO2)N(CCC3)C\n", "\n", "C1=CC=CC3=C1N(C2=CC=CC=C2)CC3(CCCNC)C\n", "\n", "C1=CC=CC3=C1SC2=C(C=CC=C2)C=C3CN(C)C\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)C3O)=C4CCN(C)CC4\n", "\n", "[C@H](CCOC1=CC=CC2=CC=CC=C12)(C3=CC=CC=C3)N(C)C\n", "\n", "[C@](OC(=O)CC)([C@@H](CN(C)C)C)(CC1=CC=CC=C1)C2=CC=CC=C2.[H+].[Cl-]\n", "\n", "CN2C(Cc1ccccc1N=C2C)c3ccccc3\n", "\n", "C1=CC=CC2=C1N(C(=O)N2)CCCN4CCC(C(C3=CC=C(F)C=C3)=O)CC4\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)C[C@H]4OC(=N5)C)C)C(COC(C)=O)=O\n", "\n", "[C@@H]34CC1=C[NH]C2=CC=CC(=C12)C3C[C@@H](CN4C)CC#N\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2)=O)C3=CC=CC=C3Cl\n", "\n", "C1=CC=CC2=C1C(C3=C(C=C2)C=CC=C3)=NOCCNC\n", "\n", "C1=CC(=CC=C1C(C[N]2C=CN=C2)O)CCC3=CC=CC=C3\n", "\n", "C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C\n", "\n", "O(C(C(F)(F)F)F)C(F)F\n", "\n", "C1=CC(=C3C2=C1CC5C4C2(C(O3)CCC4)CCN5C)O\n", "\n", "[C@H]15C3=C(CCC2=C1C=CC=C2)C=CC=C3[C@H]4C[C@](C(C)C)(O)CCN4C5\n", "\n", "C4=C(C(C(=O)N1CCCC1)(C2=CC=CC=C2)C(CN3CCOCC3)C)C=CC=C4\n", "\n", "[C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4\n", "\n", "C1=C(C=CC=C1OC2CN(C(N)=O)C2)C(F)(F)F\n", "\n", "CC13CCCCCC(Cc2ccc(O)cc12)C3N\n", "\n", "C1=CC(=C3C2=C1CC5C4C2(C(O3)C(C=C4)OC(C)=O)CCN5CC=C)OC(C)=O\n", "\n", "C1=CC=CC2=C1N(C(C3=C(N2C)C=CC=C3)=O)CCN(C)C\n", "\n", "C1=C(C)C=CC3=C1C2C(CCN(C2)C)N3\n", "\n", "C2=C(C1[S](CCC(N1C)=O)(=O)=O)C=CC(=C2Cl)Cl\n", "\n", "C1=CC(=CC3=C1C(C2=CC(=C(Cl)C=C2)Cl)CN(C)C3)OC\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CCN(CC)CC\n", "\n", "Clc1ccc(cc1)C(OCCN2CCOCC2)c3ccccc3\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@@H]4C)C)(C(COC(C)=O)=O)OC(C)=O\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O\n", "\n", "[C@]134[C@@H]([C@H](CC2=C1C=C(C)C=C2)N(C)CC3)CCCC4\n", "\n", "C3=CC1=C(C(/C2=C(O1)C=CC=C2)=C/CCN(C)C)C=C3OC\n", "\n", "[C@]23([C@H]([C@H]1[C@]([C@](C(CO)=O)(C)[C@@H](C1)C)(C)C[C@@H]2O)CCC4=CC(=O)C=C[C@]34C)F\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)C3(C)C)CCCN(C)C\n", "\n", "C1=C(C(=CC=C1Cl)N(C(CNCC(C)=C)=O)C)C(C2=CC=CC=C2)=O\n", "\n", "C1=CC=CC2=C1C(C(N2CC(N)=O)=O)C3=CC=CC=C3\n", "\n", "[H+].C3=C2\\C(C1=CC=CC=C1SCC2=CC=C3)=C\\CCN(C)C.[Cl-]\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(O)C(N2CCO)=O)C3=CC=CC=C3F\n", "\n", "C3=C(C2(C1=CC=CC=C1)C(NCN2)=O)C=CC=C3\n", "\n", "[C@H]34[C@H]2[C@@](F)([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@@H](O)C[C@@]3([C@](O)([C@H](C4)C)C(=O)C)C\n", "\n", "[C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5C4(CCC(=O)C5)C)C\n", "\n", "C1=C(C(=CC=C1Cl)N(C(CN(CCO)CCO)=O)C)C(C2=C(C=CC=C2)Cl)=O\n", "\n", "C1=CC=CC(=C1N3CCN(CCC2=C[N](C)N=C2)CC3)Cl\n", "\n", "C1=CC=CC2=C1N(CC3=C(C2=C)C=CC=C3)CCCN(C)C\n", "\n", "C1=CC(=CC=C1C(NCCCN2CCOCC2)=O)Cl\n", "\n", "C1=C(O)C=CC3=C1C2(CC(CN(CC2)C)C3)C\n", "\n", "C2=C(C1(C(N(C(=O)N(C1=O)COC)COC)=O)CC)C=CC=C2\n", "\n", "C1=CC=CC=C1C2C(N(CC)C(N2)=O)=O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NC(C(=O)N2)C(OCC)=O)C3=CC=CC=C3F\n", "\n", "C1=C(SC2=C1C(=NCC3=NN=C([N]23)C)C4=CC=CC=C4Cl)CC\n", "\n", "C2=NC1=C(CC(NC)CC1)S2\n", "\n", "C1=CC=CC2=C1C(=C[NH]2)CC(CC)N\n", "\n", "C1=CC=CC=C1CCNC(CCCl)=O\n", "\n", "O=C2\\C(=C1\\OC=NN1)C=CC=C2\n", "\n", "C1=C(C(=N[N]1CCCN(C)C)C2=CC=CC=C2)C3=CC=CC=C3\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCCN2CC(F)(F)F)C3=CC=CC=C3F\n", "\n", "C1=CC=CC(=C1N3CCN(CCCC(C2=CC=C(F)C=C2)=O)CC3)OC\n", "\n", "CN(C)C3CCc2[nH]c1c(F)cc(F)cc1c2C3\n", "\n", "C1=CC(=CC=C1N3CCN(CC(O)COC2(CCCCC2)C#C)CC3)F\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=O)N2C)C3=CC=CC=C3F\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)([C@H](C[C@]4([C@H]3C[C@@H](C)[C@@]4(C(CO)=O)O)C)O)F\n", "\n", "[C@]12(F)[C@H](CC4([C@H](C1C[C@@H](C3=CC(=O)C=CC23C)F)C[C@H]([C@]4(O)C(=O)COC(=O)C(C)(C)C)C)C)O\n", "\n", "[C@]14(C([C@H]3[C@H]([C@@H](O)C1)C2(C(=CC(=O)C=C2)[C@@H](F)C3)C)C[C@H]5OC(O[C@@]45C(=O)CO)(C)C)C.[C@]69(C([C@H]8[C@H]([C@@H](O)C6)C7(C(=CC(=O)C=C7)[C@@H](F)C8)C)C[C@H]%10OC(O[C@@]9%10C(=O)CO)(C)C)C.O\n", "\n", "[C@]12(OC(O[C@@H]1CC3C2(CC(O)[C@H]4C3C[C@H](F)C5=CC(=O)C=CC45C)C)(C)C)C(=O)COC(=O)C\n", "\n", "[C@H]2(C1=CC(=O)C=C[C@@]1([C@@]4([C@@H](C2)[C@@H]3C[C@@H](O)[C@]([C@@]3(C)C[C@@H]4O)(O)C(=O)CO)F)C)F\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(CO)=O\n", "\n", "[C@H]24[C@H]1[C@@]([C@@H](C(C(OCCCC)=O)=O)[C@@H](C1)C)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)F)C)O)C\n", "\n", "[C@H]24[C@H]1[C@@]([C@@H](C(CO)=O)[C@@H](C1)C)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)F)C)O)C\n", "\n", "[C@]23([C@H]([C@H]1[C@]([C@](C(C)=O)(O)CC1)(C)C[C@@H]2O)C[C@H](C)C4=CC(=O)C=C[C@]34C)F\n", "\n", "C1=CC=CC3=C1C(=NC2=C(C=CC(=C2)F)C3)N4CCN(C)CC4\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C([C@@H](OC(C)=O)C)=O)O)C)O)F\n", "\n", "[C@]34([C@H]([C@H]2[C@@]([C@@]1(C(=CC(C=C1)=O)CC2)C)(C(C3)O)F)CC([C@@]4(C(=O)CO)O)=C)C\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(CO)=O)(O)CC1)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)F)C)O)C\n", "\n", "[C@@]4(OC(=O)CCCC)(C3(C(C2C(C1(C(=CC(=O)C=C1)C(F)C2)C)C(O)C3)CC4)C)C(=O)CO\n", "\n", "C1=C(F)C=CC3=C1C=C(SCCNC)C2=CC=CC=C2O3\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)F)C)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(CO)=O\n", "\n", "C1=CC(=CC=C1N2C4(C(NC2)=O)CCN(CCCC(C3=CC=C(F)C=C3)=O)CC4)F\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CC3CC3)=O)C4=CC=CC=C4F\n", "\n", "C1=CC=CC=C1CC(N(CC2=CC=CO2)C)C\n", "\n", "[C@]234C1=C(C=CC(=C1O[C@H]2C[C@@H](O)C=C3)OC)CN(CC4)C\n", "\n", "C2=C(C(C1CCCCC1)CCN(C)C)C=CC=C2\n", "\n", "NC(=N)NCCCOc1ccccc1\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=O)N2CC(F)(F)F)C3=CC=CC=C3\n", "\n", "[C@H]23[C@@]([C@@]1(C(=CC(C(=C1)Cl)=O)[C@H](C2)F)C)([C@H](C[C@]4(C3C[C@H]([C@@]4(C(CO)=O)O)C)C)O)F\n", "\n", "C(C2(C1=CCCCCC1)C(NC(=O)NC2=O)=O)C\n", "\n", "C1=CC=CC2=C1C(C3=C(CC2)C=CC=C3)OC4CCN(C)CC4\n", "\n", "C1=CC(=CC=C1C(N2CCN(C)CCC2)C3=CC=CC=C3)Cl\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(CCO)CCC4\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC4)C)(C(COC(CC)=O)=O)OC(CCC)=O\n", "\n", "[C@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC4)C)(OC(CCC)=O)C(CO)=O\n", "\n", "[C@@]125C3=C4C[C@H]([C@@H]1CCC([C@@H]2OC3=C(C=C4)O)=O)N(C)CC5\n", "\n", "C2=C(C1(C(OCC)=O)CCN(C)CC1)C=CC=C2O\n", "\n", "[C@@]4([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(Cl)[C@H](C3)O)C[C@H]4C)C)(C(COC(C)=O)=O)OC(CCC)=O\n", "\n", "C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2\n", "\n", "C1=CC=CC2=C1C(=C[NH]2)CCC3CCNCC3\n", "\n", "CN(C)CC#CC=C2c1ccccc1C=Cc3ccccc23\n", "\n", "C1=CC=CC3=C1C2=C(CCCCCC2)[N]3CCCN(C)C\n", "\n", "C1=CN=CC=C1C(NNC(C)C)=O\n", "\n", "C1=CC=CC=C1C(C2=CC=CC=C2)(C(CN(C)C)C)C(CC)=O\n", "\n", "[C@@]4(O)(C3(C(C2C(C1(C(=CC(=O)CC1)C=C2)C)C(O)C3)CC4=C)C)C(=O)CO\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC(N(CC)CC)C\n", "\n", "C1=CC=CC2=C1N(C3=C(CC2=O)C=CC=C3)CCCN(C)C\n", "\n", "[C@]235[C@H]([C@H](N(CC1CC1)CC2)CC4=C3C(=CC=C4)O)CCC(C5)=O\n", "\n", "[C@H](CC1=CC(=C(C=C1)O)O)(C(O)=O)N\n", "\n", "[C@H](CN1C3=C(SC2=C1C=CC=C2)C=CC(=C3)OC)(CN(C)C)C\n", "\n", "C13(C4=C(C(C2=C1C=CC=C2)CC3)C=CC=C4)C[C@H](CNC)O\n", "\n", "[C@H]5(CN(C(C3=C2C1=CC(=CC=C1CCN2C(=O)C(=C3)C4=CC=CC=C4)Cl)=O)CC5)OCC\n", "\n", "C2=C(COC1CCNCC1)C=CC3=C2C=CC=C3\n", "\n", "C3=C(C(CC2N(C(CC(C1=CC=CC=C1)=O)CCC2)C)O)C=CC=C3\n", "\n", "C1=CC(=NN=C1C2=C(Cl)C=CC=C2)N3CCC(O)CC3\n", "\n", "C3=C(N2CCN(C\\C=C\\C1=CC=CC=C1)CC2)N=NC(=C3)Cl\n", "\n", "C1=CC(=CC(=C1\\C(=C\\[N]2C=NC=N2)Cl)Cl)Cl\n", "\n", "[C@@H]1([C@H]3[C@@H](CC2=C1C=C(Cl)C=C2)CN(C3)C)C4=CC=CC=C4\n", "\n", "C1=CC=CC3=C1C4=C(C2=C(C=CC=C2)O3)CCN(CC4)C\n", "\n", "C3=C(CN2CCN(C1=C(OC(C)C)C=CC=C1)CC2)C=CC=C3C(=O)N4CCCCC4.C(C(=O)O)CC(=O)O\n", "\n", "C2=C(OC(OC1=CC=CC=C1)CN(C)C)C=CC=C2\n", "\n", "C1=CC(=CC(=C1OC)OC)CCN2CCN(CC2)C3=CC(=CC=C3)Cl\n", "\n", "C1=C([N+](=O)[O-])C=CC2=C1C(=NCC(N2C)=O)C3=CCCCC3\n", "\n", "C2=C(C1(C(N(C)C(N1)=O)=O)CC)C=CC=C2\n", "\n", "C2=C(C1(C(N(C(NC1=O)=O)C)=O)CC)C=CC=C2\n", "\n", "C1=C(C=CC=C1N3CCN(CCC2=N[NH]C(=C2)C)CC3)Cl\n", "\n", "[C@H]12[C@@]([C@](C(CO)=O)(O)[C@H](C1)C)(CC(=O)[C@H]3[C@H]2CCC4=CC(=O)C=C[C@]34C)C\n", "\n", "C1=C(Br)C=CC2=C1C(=NCC(N2C)COC)C3=CC=CC=C3Cl\n", "\n", "C1=CC=CC(=C1)C(=O)C(C)N(C)C\n", "\n", "C1=CC=CC2=C1C(CC3=C(N2C)C=CC=C3)NC\n", "\n", "C1=C(C=C(C=C1OCC2CNC(O2)=O)C)C\n", "\n", "C1=CC(=CC2=C1CC3C(C2(CCN3C)C)C)O\n", "\n", "C1=CC=CC=C1C2(N(C(=O)NC2=O)C)CC\n", "\n", "C1=CC=CC=C1C(C2=CC=CC=C2)(C(CC)=O)CC(N(C)C)C\n", "\n", "C1=CC=CC=C1/C=C/C2C(NC(O2)(C)C)=O\n", "\n", "C1=C(SC)C=CC3=C1N(C2=C(C=CC=C2)S3)CC(CN(C)C)C\n", "\n", "C2=C(C1(C(N(C)C(C1)=O)=O)C)C=CC=C2\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(CO)=O)(O)CC1)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)C)C)O)C\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(COC(C)=O)=O)(O)CC1)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)C)C)O)C\n", "\n", "[C@H]24[C@H]1[C@@]([C@](C(COC(CCC(O)=O)=O)=O)(O)CC1)(C[C@@H]([C@@H]2[C@@]3(C(=CC(=O)C=C3)[C@H](C4)C)C)O)C\n", "\n", "C1=C(SC)C=CC4=C1C(N2CCN(C)CC2)CC3=C(C=CC=C3)S4\n", "\n", "C1=NC=C([N]1C(C2=CC=CC=C2)C)C(=O)OC\n", "\n", "C1=CC=CC2=C1N(CC3=C(C2)C=CC=C3)CCCNC\n", "\n", "C1=CC(=CC3=C1[N]2C(=CN=C2C)CN=C3C4=CC=CC=C4F)Cl\n", "\n", "C1=CC(=CC2=C1N(C(=O)N2)CCCN4CCC(C(C3=CC=C(F)C=C3)=O)CC4)Cl\n", "\n", "C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3OC)CC4)OC\n", "\n", "[C@]1([C@H](C1)CN)(C(=O)N(CC)CC)C2=CC=CC=C2.[H+].[Cl-]\n", "\n", "C1=CC(=CC2=C1C(=C([NH]2)C)C4CCN(CCCC(C3=CC=C(F)C=C3)=O)CC4)OC\n", "\n", "C1=NC=CN=C1N(C3CCN(CCC2=CC=CC=C2)CC3)C(C4=CC=CO4)=O\n", "\n", "C1=CN=C(C(=N1)N2CCCCC2)C\n", "\n", "C1=C(CC\\C(CN)=C/F)C=CC(=C1)F\n", "\n", "C(C1=C(C)[NH]C3=C1C(C(CN2CCOCC2)CC3)=O)C\n", "\n", "[C@]3(C1=CC=CC=C1)(C(C2=C(C=CC=C2)CC3)=O)CCN(C)C\n", "\n", "C5CC(CN1CCC(C1)c2ccccc2)c4ccc3OCOc3c4C5\n", "\n", "C1=C4C(=C2C(=C1)C=CC=C2)CC3CN(C)CC(C3(O4)O)C\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)(C)C\n", "\n", "C1=CC=CC=C1CN2C(CC(C2)CN)=O\n", "\n", "C1=CC=CC3=C1C(C2=CC=CC=C2)OCCN(C3)C\n", "\n", "[C@H]2(NC(=O)C1=C(OC)C=C(NC)C(=C1)Cl)[C@H](N(CC2)CC3=CC=CC=C3)C\n", "\n", "C2=C1C(=NN=C(CC1=CC(=C2OC)OC)C)C3=CC=C(N)C=C3\n", "\n", "C1=CC(=C3C2=C1CC6C4C2(C(O3)C(CC4)OC(C5=CN=CC=C5)=O)CCN6C)OC\n", "\n", "C1=CC(=CC=C1C(CCCN2CC3CCC(C2)CC3)=O)F\n", "\n", "C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)OC\n", "\n", "C2=C(C(C1=CC=CC=C1)(CCN(C)C)C(CC)=O)C=CC=C2\n", "\n", "C3=C(N(C2CCN(CCC1=CC=CC=C1)CC2)C(COC)=O)C(=CC=C3)F\n", "\n", "[C@@H]4(C1=CC=CC2=C1OCC2)C3=CC(=C(Cl)C=C3CCN(C4)C)O.CC(=O)O\n", "\n", "C1=C3C(=CC=C1)N=C2SC(=CC2=C(N3)N4CCN(CC4)C)C\n", "\n", "C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN4CCN(CCO)CC4\n", "\n", "O1CCCCC1C(=O)N\n", "\n", "[C@@]12(C3=C(NC(CN1C[C@H](O2)C)=O)C=CC(=C3)Cl)C4=CC=CC=C4\n", "\n", "[C@]235[C@]([C@H](N(CC1CC1)CC2)CC4=C3C=C(O)C=C4)(CCCC5)O\n", "\n", "C1=CC=CC3=C1N(C2=C(C=CC=C2)[S]3(=O)=O)CC(CN(C)C)C\n", "\n", "C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC\n", "\n", "C1=C(OC)C=CC3=C1N(C2=C(C=CC=C2)S3)CCC4N(CCCC4)C\n", "\n", "N(C(=O)C)C1=CC=C(O)C=C1\n", "\n", "C(C)C1(C)C(=O)N(C(O1)=O)C\n", "\n", "[C@]12(C(=CC(C=C1)=O)[C@H](C[C@H]3[C@H]4[C@](C[C@@H]([C@H]23)O)([C@](C(=O)CO)(O)[C@@H](C4)C)C)F)C\n", "\n", "C1=C(NC(CC)=O)C=CC(=C1)O\n", "\n", "C1=CC=CC2=C1N(C(CO2)=O)CC(N)=O\n", "\n", "C1=NC2=C([N]1CCC(CO)CO)NC(=NC2=O)N\n", "\n", "CC(C)CCNCC2COc1ccccc1O2\n", "\n", "C2C1=NN=N[N]1CCCC2\n", "\n", "C1=C(C=CC3=C1N(CCCN2CCC(CC2)O)C4=C(S3)C=CC=C4)C#N\n", "\n", "C1=C(OC)C=CC3=C1N(C2=C(C=CC=C2)S3)CC(CN4CCC(O)CC4)C\n", "\n", "C2=C(C1=CC=CC=C1)ON=C2C(CN3CCCCC3)O\n", "\n", "CC(=O)OC(C1CCCCN1)c2ccccc2\n", "\n", "C1=C(C(C(C)(C)O)(C)O)C=CC(=C1)Cl\n", "\n", "[C@H]2(C1=CC=CC=C1)[C@@H](N(CCO2)C)C\n", "\n", "[Cl].C1=CC=CC=C1CCNN\n", "\n", "C1=CC=CC=C1CC(NN)C\n", "\n", "C1=C(O)C=CC4=C1C35C(C(N(CCC2=CC=CC=C2)CC3)C4)CCCC5\n", "\n", "C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=C4CCN(C)CC4\n", "\n", "C3=C(C1(C(OCC)=O)CCN(CC1)CCCNC2=CC=CC=C2)C=CC=C3\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(N2CC#C)=O)C3=CC=CC=C3\n", "\n", "C1=C([S](N(C)C)(=O)=O)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCC(CCO)CC4\n", "\n", "C1=C(C=CC=C1)C(C2=CC=CC=C2)(C3NCCCC3)O\n", "\n", "C4Oc3ccc(CN1CCN(CC1)c2ncccn2)cc3O4\n", "\n", "C1=CSC2=C1C(C3=C(CC2)C=CC=C3)=C4CCN(CC4)C\n", "\n", "C1=CC=CC2=C1N(C3=C(CC2)C=CC=C3)CCCN(C)C\n", "\n", "[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C\n", "\n", "[C@]14([C@@](C(=O)COC(CCC(=O)[O-])=O)(CC[C@H]1[C@@H]3CCC2=CC(C=C[C@@]2([C@H]3[C@H](C4)O)C)=O)O)C.[Na+]\n", "\n", "C(C)CCC(=O)OC3(CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C)C(=O)CO\n", "\n", "C1=C2C(=C([N]1C)C)C(=NCC(N2)=O)C3=CC=CC=C3\n", "\n", "C1=C(C(=CC=C1C#N)OC)C(NCCN3CCC(C(C2=CC=C(F)C=C2)=O)CC3)=O\n", "\n", "[C@@]23(F)C1(C(=CC(=O)C=C1)[C@@H](F)CC2C5C(CC3O)([C@]4(C(=O)COC(=O)CC)OC(O[C@@H]4C5)(C)C)C)C\n", "\n", "C1=CC=CC=C1C2CN(C(NCCC)=O)C(O2)=O\n", "\n", "C1=CC=CC2=C1N(C3=C(S2)C=CC=C3)CC(C)N(C)C\n", "\n", "C1=NC=C([N]1C(C2=CC=CC=C2)C)C(OCCC)=O\n", "\n", "C1=C([S](N)(=O)=O)C=CC(=C1C(NCC2N(CCC2)CCC)=O)OC\n", "\n", "C1=CC=CC2=C1N(C3=C(S2)C=CC=N3)CCCN(C)C\n", "\n", "C(C1(C(C=CNC1=O)=O)CC)C\n", "\n", "C2=C(C(C(N1CCCC1)CCC)=O)C=CC(=C2)C\n", "\n", "C1=CC=CC4=C1N(C2C3CCN(C2)CC3)C5=C(CC4)C=CC=C5\n", "\n", "C1=C(Cl)C(=C(C=C1)C)NC(=O)/C=C/2SCC(=O)N2C\n", "\n", "C3=C(N2C1=C([NH]N=C1N(C(=O)CC2=O)C)C)C=CC=C3\n", "\n", "[C@H](C1=CC=CC=C1)([C@@H]2CNCCO2)OC3=CC=CC=C3OCC\n", "\n", "[C@H]2(CNC(C1=C(C=CC(=C1OC)Br)OC)=O)N(CCC2)CC\n", "\n", "C1=CC=CC4=C1C(N(CCCN3CCN(C2=NC=CC=N2)CC3)[S]4(=O)=O)=O\n", "\n", "C1=C(Cl)C=CC\\3=C1C=C(N2CCN(C)CC2)C4=C(C3=C/C#N)C=CC=C4\n", "\n", "[H+].C1=C(OC(F)(F)F)C=CC2=C1SC(=N2)N.[Cl-]\n", "\n", "COC(C(F)Br)(F)F\n", "\n", "C1=C(C(=C(C=C1C(\\C=C\\N2CC=CC2)=O)OC)OC)OC\n", "\n", "O=C1N2C(CC1)CCC2=O\n", "\n", "C4=C(C(N2CCN(\\N=C\\C1=NC(=CC=C1)C)CC2)C3=CC=CC=C3)C=CC=C4\n", "\n", "C1=C(C(N)=O)N=N[N]1CC2=C(C=CC=C2F)F\n", "\n", "C1=CC=CC2=C1N=C(S2)N(C4CCN(CC(COC3=CC=C(C=C3)F)O)CC4)C\n", "\n", "NCCC(Oc1ccc(cc1)C(F)(F)F)c2ccccc2\n", "\n", "C4=C(C(C3CCN(CCC1=C(N=C2N(C1=O)CCS2)C)CC3)=O)C=CC(=C4)F\n", "\n", "C3=C(C2=C(N=C(N1CCN(C)CC1)N=C2)N)C(=C(C=C3Cl)Cl)Cl\n", "\n", "[C@@H]3(C1=CC=C(Cl)C=C1)[C@H]2CC[C@@H](C2)C34CCC(=N4)N5CCOCC5\n", "\n", "C1=CC=CC=C1N2C5(C(NC2)=O)CCN(CC4OC3=C(C=CC=C3)OC4)CC5\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=S)N2C)C3=CC=CC=C3\n", "\n", "C(C([S](CC)(=O)=O)([S](CC)(=O)=O)C)C\n", "\n", "C1=C([S](=O)(=O)N)C=CC(=C1C(NCC2N(CC)CCC2)=O)OC\n", "\n", "C1=CC=CC3=C1C(N(C2C(NCCC2)=O)[S]3(=O)=O)=O\n", "\n", "C5=C2C1=C(CCC4=C(C1CN3CCCCC23)C=CC=C4)C=C5\n", "\n", "C(C(C)C1(CC=C)C(=O)NC(NC1=O)=O)C\n", "\n", "C3=C2C(=NC1=CSC=C1C(/C2=CS3)=C/C#N)N4CCN(C)CC4\n", "\n", "C1=CC=CC(=C1CC2=CC=CS2)OCC3OCCNC3.O=C(O)\\C=C/C(=O)O\n", "\n", "C1=C(Cl)C=CC2=C1C(=NCC(=O)N2C)C3=CCCCC3\n", "\n", "C(C1(C(CCC)CC)C(NC(=S)NC1=O)=O)C\n", "\n", "C1=C([S](C)(=O)=O)C=CC(=C1C(NCCN(CC)CC)=O)OC\n", "\n", "[C@]14([C@](OC(=O)CC)([C@@H](CC1C3[C@@](F)(C2(C(=CC(=O)C=C2)[C@@H](F)C3)C)[C@@H](O)C4)C)C(SC)=O)C\n", "\n", "C1=CSC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C\n", "\n", "C1=C4C(=C2C(=C1F)SC=C2C)C3=C(CCN(C3)CC)[NH]4\n", "\n", "C1=C(SC=C1)C2(C(CCCC2)=O)NCC\n", "\n", "CSC(=O)C3(O)C(C)CC4C2CCC1=CC(=O)C=CC1(C)C2(F)C(O)CC34C\n", "\n", "C1=CC(=CC=C1B2OCC(CCC)(C)CO2)C\n", "\n", "C2=C(N1CC(CO)OC1=O)C=CC=C2C\n", "\n", "[C@H](OC1=C(C=CC=C1)C)(C2=CC=CC=C2)CCNC\n", "\n", "C1=CC=CC(=C1N3CCN(CCCOC2=CC=CC=N2)CC3)C\n", "\n", "[C@H]2(C1=CC(=C(OC)C=C1CCN(C2)C)OC)C3=CC=CC=C3\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(CO)=O\n", "\n", "[C@]34([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]([C@@]4(C(COC(C)=O)=O)O)OC(C)=O)C\n", "\n", "[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(CC(C)(C)C)=O)=O\n", "\n", "C1=C(NC(C(Cl)(Cl)Cl)=O)C=CC(=C1)O\n", "\n", "C1=C(C(F)(F)F)C=CC3=C1N(C2=CC=CC=C2)C(CC(N3C)=O)=O\n", "\n", "C1=CC=C2C(=C1)SC3=C(N2CC(C)CN(C)C)C=C(C=C3)C(F)(F)F\n", "\n", "C1=C(C=CC3=C1N(CCCN2CCN(CC2)C)C4=C(S3)C=CC=C4)C(F)(F)F\n", "\n", "CC1(C(N(C(O1)=O)C)=O)C\n", "\n", "[C@H]13N([C@H](CC(C1)NC(C2=CC=CC(=C2OC)OC)=O)CC3)CC4=CC=CC=C4\n", "\n", "C1=C2C(=CC=C1)N(C(Cl)(CN=C2C3=CC=CC=C3Cl)CO)C\n", "\n", "CC4CC3C2CC(F)C1=CC(=O)C=CC1(C)C2(F)C(O)CC3(C)C4(O)C(=O)CCl\n", "\n", "C(C(C(N)=O)(CC)CC)C=C\n", "\n", "C(C(C(CC)C)C(N)=O)C\n", "\n", "[C@@H]1(C[C@H]3[C@H]2[C@@]1(O[C@H](O[C@H]2OC)[C@@H]3C)CN4CCCCC4)O\n", "\n", "C(C(CCC)C(N)=O)CC\n", "\n", "C(C1(C(NC(=O)NC1=O)=O)C(=C/CC)/C)C\n", "\n", "C(C(C1(C(NC(=O)NC1=O)=O)C=C)C)CC\n", "\n", "C1=C(OC)C=CC2=NC=CC(=C12)CCCC3C(CNCC3)C=C\n", "\n", "C(OC1=NSN=C1C2=CCCN(C2)C)CCCCC\n", "\n", "C2=N[N]1C(=CC=NC1=C2C#N)C3=CC(=CC=C3)N(C(C)=O)CC\n", "\n", "C2=C(\\C(C1=CC=CN=C1)=C\\CN(C)C)C=CC(=C2)Br\n", "\n", "C3=C(N2C1=C([N](C)N=C1C)N(C(=O)CC2=O)C)C=CC=C3\n", "\n", "C3=C(C1=NCCN=C2N(NC(=C12)C)C)C=CC=C3Cl\n", "\n", "[N+](=[N-])=O\n", "\n", "C1=C(Cl)C(=C(C2=C1NC(=O)C(N2)=O)[N+](=O)[O-])Cl\n", "\n", "[C@H]3([N]2C1=C(C(=NC=N1)N)N=C2)[C@@H]([C@@H]([C@H](O3)C[S+](CC[C@@H](C([O-])=O)N)C)O)O\n", "\n", "[O+]1=N[N](C=C1[N-]C(NC2=CC=CC=C2)=O)C(CC3=CC=CC=C3)C\n", "\n", "C1=C(OC)C(=CC2=C1C(=[N+](C(=C2CC)C)[NH-])C3=CC=C(C(=C3)OC)OC)OC\n", "\n", "[N+](=NCC(=O)N[C@@H]([C@H](O)C1=CC=C([N+]([O-])=O)C=C1)CO)=[N-]\n", "\n", "dataset graphs: 2050\n", "dataset labels: 2050\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# ~ Converting to graph dataset\n", "# The dataframe that we loaded only contains the SMILES representations of the graphs, \n", "# which cannot be used to train the graph network directly. We first have to convert \n", "# the SMILES representations for all of those molecules into numeric graph representations \n", "# first (node attributes and adjacency matrices).\n", "\n", "ys = []\n", "graphs = []\n", "for _, ds in df.iterrows():\n", " try:\n", " node_attributes, node_adjacency = graph_from_smiles(ds.smiles)\n", " graphs.append((node_attributes, node_adjacency))\n", " ys.append(ds.p_np)\n", " except AttributeError:\n", " continue\n", " \n", "print(f'dataset graphs: {len(graphs)}')\n", "print(f'dataset labels: {len(ys)}')\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "dcf6b982", "metadata": {}, "source": [ "**Graph Classification.** Our target dataset BBBP is a *graph classification* dataset which means that we want to predict some class label for each individual graph. When we think about our previous GCN implementation, we notice that the update rule only describes how to update the node-level feature matrix $H$. Even after subjecting a graph to multiple graph convolutional steps, the information is still distributed across all its different nodes. To solve a graph classification task, however, we would need the graph-level features. This can done by adding a *global graph pooling* operation. In this step, all the individual node features of a graph are aggregated into single *graph feature vector* $h$ which can then be further processed to solve graph-level prediction tasks.\n", "\n", "**Global Sum Pooling.** The most simple pooling operation that we are going to look at here is the *global sum pooling* where the graph feature vector is simply calculated as a sum \n", "\n", "$$\n", "h = \\sum_{i \\in \\mathcal{V}} H_{i,:}\n", "$$\n", "\n", "of the individual node feature vectors.\n", "\n", "**GCN Implementation.** The following section introduces the ``GCNModel`` class which contains the majority of the implementation that is required to train a GCN-based graph neural network model to solve the given graph classification task. In the first part of the network, multiple GCN layers are applied to refine the node features of the graph. Then a global sum pooling operation is applied to distill the node features into a single feature vector per graph. This graph feature vector is then used as the input to a multi-layer dense neural network which outputs a single value. This single value then acts as the classification output to solve the given binary classification problem." ] }, { "cell_type": "markdown", "id": "f0c14a9d", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "870404f1cae14ed432ea6d5f2061b1e7", "grade": false, "grade_id": "task-10-7", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.7 (2 points)** The following code cell defines the ``GCNModel`` class used to solve the BBBP binary classifcation problem. The code is largely complete except for the implementation of the *global sum pooling* operation. Your task is to implement the ``pool_nodes`` function which accepts batched node feature tensor and the batched node feature mask tensor and is supposed to output the batched graph feature tensor!" ] }, { "cell_type": "code", "execution_count": 28, "id": "93a6d125", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "21842c6b64bd40017c32869d3851e642", "grade": false, "grade_id": "ans-10-7", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "utils = nextcloud_import('https://bwsyncandshare.kit.edu/s/ZBkPiGd9HnDqC4g')\n", "\n", "\n", "class GCNModel(nn.Module):\n", " \"\"\"\n", " Implementation of the graph classification model.\n", " \n", " Is constructed with the integer number ``in_dim`` of input node features, \n", " the integer number ``hidden_dim`` of hidden units and the desired ``out_dim`` \n", " output dimension.\n", " \"\"\"\n", " def __init__(self,\n", " in_dim: int, \n", " hidden_dim: int,\n", " out_dim: int\n", " ):\n", " super().__init__()\n", " self.in_dim = in_dim\n", " self.hidden_dim = hidden_dim\n", " self.out_dim = out_dim\n", " \n", " # These are the actual graph convolutional layers which are applied to the \n", " # input graph. Throughout multiple steps, the localized information in each \n", " # node is spread and processed throughout the graph, resulting in a final \n", " # updated node embedding.\n", " self.conv_layers = nn.ModuleList([\n", " utils.GCNConv(in_dim, hidden_dim),\n", " utils.GCNConv(hidden_dim, hidden_dim),\n", " utils.GCNConv(hidden_dim, hidden_dim),\n", " ])\n", " # The non-linear activation that we'll use after each GCN convolution\n", " self.lay_act = nn.LeakyReLU()\n", " \n", " # A dense network / multi-layer perceptron which then performs the final \n", " # classification. Projects the graph embedding of shape (hidden_dim, )\n", " # into a single float value which will then be sigmoid activated to \n", " # approximate the classification probability.\n", " self.lay_out = nn.Sequential(\n", " nn.Linear(hidden_dim, hidden_dim),\n", " nn.ReLU(),\n", " nn.Linear(hidden_dim, hidden_dim),\n", " nn.ReLU(),\n", " nn.Linear(hidden_dim, out_dim)\n", " )\n", " self.lay_final_act = nn.Sigmoid()\n", " \n", " def forward(self, \n", " node_attributes: torch.Tensor, \n", " node_adjacency: torch.Tensor, \n", " node_mask: torch.Tensor,\n", " ) -> torch.Tensor:\n", " \"\"\"\n", " Given the ``node_attributes`` tensor of shape (batch_size, N_max, num_features), the \n", " ``node_adjacency`` tensor of shape (batch_size, N_max, N_max) and the ``node_mask`` \n", " tensor of the shape (batch_size, N_max, 1), this method performs the forward pass \n", " of the model and ultimately predicts the classification logits of the shape \n", " (batch_size, 1).\n", " \"\"\"\n", " \n", " node_embedding = node_attributes\n", " for lay in self.conv_layers:\n", " node_embedding = lay(node_embedding, node_adjacency, node_mask)\n", " node_embedding = self.lay_act(node_embedding)\n", " \n", " graph_embedding = self.pool_nodes(node_embedding, node_mask)\n", " output = self.lay_out(graph_embedding)\n", " output = self.lay_final_act(output)\n", " \n", " return output\n", " \n", " # TASK: Implement a global sum pooling operation in the method below.\n", " \n", " # HINT: Remember that the node_mask tensor contains a 0/1 binary mask that determines \n", " # which columns of the feature matrix actually contain the feature information.\n", " \n", " def pool_nodes(self, \n", " node_embedding: torch.Tensor, \n", " node_mask: torch.Tensor,\n", " ) -> torch.Tensor:\n", " \"\"\"\n", " Given the ``node_embedding`` tensor of shape (batch_size, N_max, num_features) and the \n", " node mask tensor with the shape (batch_size, N_max, 1), this method applies the \n", " global sum pooling operation to return the graph feature vector of shape (batch_size, num_features).\n", " \"\"\"\n", " pool = torch.sum(node_embedding*node_mask, axis=1)\n", " return pool" ] }, { "cell_type": "code", "execution_count": 29, "id": "0bbfadce", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "21e46f3c427fd384d8a508d1acba0064", "grade": true, "grade_id": "test-10-7-pooling", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-7-pooling - possible points: 2\n", "\n", "# ~ testing the pooling implementation\n", "\n", "_node_attributes = torch.tensor([\n", " [\n", " [1, 1, 1, 1],\n", " [0, 2, 1, 0],\n", " [0, 1, 3, 1],\n", " ],\n", " [\n", " [0, 2, 3, 5],\n", " [4, 2, 1, 1],\n", " [0, 0, 1, 1],\n", " ]\n", "])\n", "_node_mask = torch.tensor([\n", " [\n", " [1],\n", " [1],\n", " [1],\n", " ],\n", " [\n", " [1],\n", " [1],\n", " [0],\n", " ]\n", "])\n", "\n", "model = GCNModel(4, 4, 2)\n", "_graph_embedding = model.pool_nodes(_node_attributes, _node_mask)\n", "\n", "assert isinstance(_graph_embedding, torch.Tensor), 'pooling output needs to be tensor'\n", "assert _graph_embedding.shape == (2, 4), 'node pooling needs to reduce the node dimension'\n", "assert np.isclose(_graph_embedding.numpy(), np.array([\n", " [1, 4, 5, 2],\n", " [4, 4, 4, 6],\n", "])).all(), 'pooling implementation is likely incorrect'\n", "\n", "# NOTE: There are no additional hidden tests\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 30, "id": "221ff18a", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "d50a10a9b73e171b75cca2d212022e8b", "grade": false, "grade_id": "cell-e2435476174e549c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ~ train test split\n", "\n", "indices = list(range(len(ys)))\n", "indices_train = random.sample(indices, k=1800)\n", "indices_test = list(set(indices).difference(set(indices_train)))\n", "\n", "graphs_train = [graphs[index] for index in indices_train]\n", "graphs_test = [graphs[index] for index in indices_test]\n", "\n", "ys_train = [ys[index] for index in indices_train]\n", "ys_test = [ys[index] for index in indices_test]\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "3b2e5764", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "b99a1834926fdaacbd6350bcff3851ec", "grade": false, "grade_id": "task-10-8", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.8 (1 points)** In the following code cell your task is to initialize the model for the subsequent training on the BBBP data. Choose the input and output dimension according to the dataset properties and the required output characteristics. Use a size of ``64`` hidden units." ] }, { "cell_type": "code", "execution_count": 35, "id": "ea25c05c", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "911e682d4d7c3313ddeb9165d415de0c", "grade": false, "grade_id": "ans-10-8", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Initialize the model with the correct arguments to be used for the BBBP\n", "# binary classification problem.\n", "\n", "model: GCNModel = None\n", "\n", "model = GCNModel(8, \n", " 64,\n", " 1)" ] }, { "cell_type": "code", "execution_count": 36, "id": "51a7ddc8", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "2f03971722066f5cbf5bf8259b9db875", "grade": true, "grade_id": "test-10-8-model", "locked": true, "points": 2, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-8-model - possible points: 2\n", "\n", "assert model is not None\n", "assert isinstance(model, GCNModel)\n", "\n", "assert isinstance(model.in_dim, int)\n", "assert hashcheck(str(model.in_dim)) == '2c624232cdd22177'\n", "\n", "assert isinstance(model.hidden_dim, int)\n", "assert hashcheck(str(model.hidden_dim)) == 'a68b412c4282555f'\n", "\n", "assert isinstance(model.out_dim, int)\n", "assert hashcheck(str(model.out_dim)) == '6b86b273ff34fce1'\n", "\n", "# NOTE: The hidden tests will check for the exact values of the model's\n", "# dimension properties.\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "b9c0c22c", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "593fc95c3ae5107298cd1a267c191aa2", "grade": false, "grade_id": "task-10-9", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "**🛠️ Task 10.9 (1 points)** In addition to the model initialization we also need to choose an appropriate value for the ``N_max`` parameter that will be used for the graph batching. Based on the given BBBP dataset, select an appropriate value such that all dataset elements are eligible for batching." ] }, { "cell_type": "code", "execution_count": 39, "id": "9fd1c965", "metadata": { "deletable": false, "nbgrader": { "cell_type": "code", "checksum": "57ef645c2dbaf38da56d43bbe38a4dba", "grade": false, "grade_id": "ans-10-9", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# TASK: Select a proper N_max value for the given dataset.\n", "\n", "N_max: int = None\n", " \n", "N_max = max([len(g[0]) for g in graphs_train])" ] }, { "cell_type": "code", "execution_count": 40, "id": "30d9c03f", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "d9bc4448014ddd0a33f98ba51e0769b2", "grade": true, "grade_id": "test-10-9-n-max", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "##### DO NOT CHANGE #####\n", "# ID: test-10-9-n-max - possible points: 1\n", "\n", "assert isinstance(N_max, int)\n", "\n", "possible_values = [\n", " 'dbb1ded63bc70732', 'd2f483672c0239f6', '5d389f5e2e34c6b0', \n", " '13671077b66a2987', '36ebe205bcdfc499', 'd80eae6e96d148b3', \n", " 'd6a4031733610bb0', '8d27ba37c5d81010', 'dbae772db29058a8'\n", "]\n", "assert hashcheck(str(N_max)) in possible_values\n", "\n", "# NOTE: The hidden tests will check for the exact range of values, which is \n", "# the same that is also covered by the hashchecks\n", "\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 41, "id": "03a1233e", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "62b9fb306254fe420d0d19cd59f928bf", "grade": false, "grade_id": "ans-10-10", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * epoch 00/10 - loss: 0.633 - acc: 0.736\n", " * epoch 01/10 - loss: 0.625 - acc: 0.736\n", " * epoch 02/10 - loss: 0.604 - acc: 0.768\n", " * epoch 03/10 - loss: 0.573 - acc: 0.836\n", " * epoch 04/10 - loss: 0.497 - acc: 0.852\n", " * epoch 05/10 - loss: 0.458 - acc: 0.840\n", " * epoch 06/10 - loss: 0.450 - acc: 0.844\n", " * epoch 07/10 - loss: 0.441 - acc: 0.836\n", " * epoch 08/10 - loss: 0.448 - acc: 0.864\n", " * epoch 09/10 - loss: 0.438 - acc: 0.824\n" ] } ], "source": [ "##### DO NOT CHANGE #####\n", "# ~ training configuration\n", "batch_size = 64\n", "epochs = 10\n", "learning_rate = 1e-3\n", "\n", "criterion = nn.BCELoss()\n", "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n", "\n", "# ~ model training\n", "\n", "history: list[dict] = []\n", "\n", "for epoch in range(epochs):\n", " \n", " indices_epoch = list(range(len(indices_train)))\n", " random.shuffle(indices_epoch)\n", "\n", " losses_epoch = []\n", " while indices_epoch:\n", " \n", " num = min(batch_size, len(indices_epoch))\n", " indices_batch, indices_epoch = indices_epoch[:num], indices_epoch[num:]\n", " \n", " y_batch = torch.tensor([ys[index] for index in indices_batch], dtype=torch.float32).unsqueeze(-1)\n", " graphs_batch = [graphs[index] for index in indices_batch]\n", " \n", " model.zero_grad()\n", " \n", " node_attributes_batch, node_mask_batch, node_adjacency_batch = batch_graphs(graphs_batch, N_max)\n", " y_pred = model(node_attributes_batch, node_adjacency_batch, node_mask_batch)\n", " loss = criterion(y_pred, y_batch)\n", " \n", " loss.backward()\n", " optimizer.step()\n", " \n", " losses_epoch.append(loss.detach().numpy())\n", " \n", " # ~ evaluating the model\n", " \n", " node_attributes_test, node_mask_test, node_adjacency_test = batch_graphs(graphs_test, N_max)\n", " y_pred = model(node_attributes_test, node_adjacency_test, node_mask_test)\n", " \n", " acc_epoch = accuracy_score(np.round(y_pred.squeeze().detach().numpy()), ys_test)\n", " loss_epoch = np.mean(losses_epoch)\n", " history.append({\n", " 'accuracy': acc_epoch,\n", " 'loss': loss_epoch,\n", " })\n", " \n", " print(f' * epoch {epoch:02d}/{epochs} - loss: {loss_epoch:.3f} - acc: {acc_epoch:.3f}')\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "code", "execution_count": 42, "id": "f76f8e6a", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "code", "checksum": "8193c3cf0f2f2e101832f0e47ffa2e0a", "grade": false, "grade_id": "cell-b60521fce6271508", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'epochs')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### DO NOT CHANGE #####\n", "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(5, 5))\n", "fig.suptitle('Training Results')\n", "\n", "# ~ plotting the training loss\n", "ax.plot(\n", " np.arange(epochs),\n", " [data['loss'] for data in history],\n", " color='lightgray',\n", " label='training loss'\n", ")\n", "\n", "# ~ plotting the test accuracy\n", "ax.plot(\n", " np.arange(epochs),\n", " [data['accuracy'] for data in history],\n", " color='orange',\n", " label='test accuracy'\n", ")\n", "\n", "ax.legend()\n", "ax.set_xlabel('epochs')\n", "\n", "##### DO NOT CHANGE #####" ] }, { "cell_type": "markdown", "id": "4c4ca2d4", "metadata": { "deletable": false, "editable": false, "nbgrader": { "cell_type": "markdown", "checksum": "4aa1c791bbfe5e378721ffdf94ec6c95", "grade": false, "grade_id": "cell-1ed508301b8aab61", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "👋 This concludes the exercise about *graph neural networks*. Hopefully you've gained some additional understanding about the ideas behind the practical implementations of GNNs and how they can be used for molecular property predictions in chemistry and material science. Ultimately, this exercise sheet has only scratched the surface of vast research domain that has been largely initiated by Kipf & Welling's GCN implementation. If you are interested in this topic, feel free to also check out the survey paper published by our research group: https://www.nature.com/articles/s43246-022-00315-6" ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 5 }