{ "cells": [ { "cell_type": "markdown", "id": "de21d523-849f-439f-bf04-12ca746c94c4", "metadata": {}, "source": [ "# JUMP UMAP analysis with coSMicQC\n", "\n", "This notebook analyzes [JUMP](https://jump-cellpainting.broadinstitute.org/) data (`cpg0000-jump-pilot`) by leveraging [UMAP](https://arxiv.org/abs/1802.03426) and [coSMicQC](https://github.com/WayScience/coSMicQC).\n", "\n", "## Outline\n", "\n", "We focus on a single file from the JUMP dataset: [`BR00117012.sqlite`](https://open.quiltdata.com/b/cellpainting-gallery/tree/cpg0000-jump-pilot/source_4/workspace/backend/2020_11_04_CPJUMP1/BR00117012/BR00117012.sqlite).\n", "This file is downloaded and prepared by [CytoTable](https://github.com/cytomining/CytoTable) to form a single-cell [Parquet](https://parquet.apache.org/) file which includes all compartment feature data at the single-cell level.\n", "We use coSMicQC to find and remove erroneous outlier data in order to prepare for UMAP analysis.\n", "Afterwards, we use UMAP to demonstrate patterns within the data." ] }, { "cell_type": "code", "execution_count": 1, "id": "bc8482c5-b7f4-48c8-aa5e-718aae9ee8be", "metadata": { "editable": true, "lines_to_end_of_cell_marker": 2, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "%opts magic unavailable (pyparsing cannot be imported)\n", "%compositor magic unavailable (pyparsing cannot be imported)\n" ] }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.4.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " var reloading = false;\n", " var Bokeh = root.Bokeh;\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " if (!reloading) {\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", " window._bokeh_on_load = on_load\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " var skip = [];\n", " if (window.requirejs) {\n", " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", " root._bokeh_is_loading = css_urls.length + 0;\n", " } else {\n", " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", " }\n", "\n", " var existing_stylesheets = []\n", " var links = document.getElementsByTagName('link')\n", " for (var i = 0; i < links.length; i++) {\n", " var link = links[i]\n", " if (link.href != null) {\n", "\texisting_stylesheets.push(link.href)\n", " }\n", " }\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " if (existing_stylesheets.indexOf(url) !== -1) {\n", "\ton_load()\n", "\tcontinue;\n", " }\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " } var existing_scripts = []\n", " var scripts = document.getElementsByTagName('script')\n", " for (var i = 0; i < scripts.length; i++) {\n", " var script = scripts[i]\n", " if (script.src != null) {\n", "\texisting_scripts.push(script.src)\n", " }\n", " }\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (var i = 0; i < js_modules.length; i++) {\n", " var url = js_modules[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " var url = js_exports[name];\n", " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.3.min.js\", \"https://cdn.holoviz.org/panel/1.4.5/dist/panel.min.js\"];\n", " var js_modules = [];\n", " var js_exports = {};\n", " var css_urls = [];\n", " var inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", "\ttry {\n", " inline_js[i].call(root, root.Bokeh);\n", "\t} catch(e) {\n", "\t if (!reloading) {\n", "\t throw e;\n", "\t }\n", "\t}\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", "\tvar NewBokeh = root.Bokeh;\n", "\tif (Bokeh.versions === undefined) {\n", "\t Bokeh.versions = new Map();\n", "\t}\n", "\tif (NewBokeh.version !== Bokeh.version) {\n", "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", "\t}\n", "\troot.Bokeh = Bokeh;\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", "\trun_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.3.min.js\", \"https://cdn.holoviz.org/panel/1.4.5/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", "}\n", "\n", "\n", " function JupyterCommManager() {\n", " }\n", "\n", " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", " comm_manager.register_target(comm_id, function(comm) {\n", " comm.on_msg(msg_handler);\n", " });\n", " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", " comm.onMsg = msg_handler;\n", " });\n", " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", " var messages = comm.messages[Symbol.asyncIterator]();\n", " function processIteratorResult(result) {\n", " var message = result.value;\n", " console.log(message)\n", " var content = {data: message.data, comm_id};\n", " var buffers = []\n", " for (var buffer of message.buffers || []) {\n", " buffers.push(new DataView(buffer))\n", " }\n", " var metadata = message.metadata || {};\n", " var msg = {content, buffers, metadata}\n", " msg_handler(msg);\n", " return messages.next().then(processIteratorResult);\n", " }\n", " return messages.next().then(processIteratorResult);\n", " })\n", " }\n", " }\n", "\n", " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", " if (comm_id in window.PyViz.comms) {\n", " return window.PyViz.comms[comm_id];\n", " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", " if (msg_handler) {\n", " comm.on_msg(msg_handler);\n", " }\n", " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", " comm.open();\n", " if (msg_handler) {\n", " comm.onMsg = msg_handler;\n", " }\n", " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", " comm_promise.then((comm) => {\n", " window.PyViz.comms[comm_id] = comm;\n", " if (msg_handler) {\n", " var messages = comm.messages[Symbol.asyncIterator]();\n", " function processIteratorResult(result) {\n", " var message = result.value;\n", " var content = {data: message.data};\n", " var metadata = message.metadata || {comm_id};\n", " var msg = {content, metadata}\n", " msg_handler(msg);\n", " return messages.next().then(processIteratorResult);\n", " }\n", " return messages.next().then(processIteratorResult);\n", " }\n", " }) \n", " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", " return comm_promise.then((comm) => {\n", " comm.send(data, metadata, buffers, disposeOnDone);\n", " });\n", " };\n", " var comm = {\n", " send: sendClosure\n", " };\n", " }\n", " window.PyViz.comms[comm_id] = comm;\n", " return comm;\n", " }\n", " window.PyViz.comm_manager = new JupyterCommManager();\n", " \n", "\n", "\n", "var JS_MIME_TYPE = 'application/javascript';\n", "var HTML_MIME_TYPE = 'text/html';\n", "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", "var CLASS_NAME = 'output';\n", "\n", "/**\n", " * Render data to the DOM node\n", " */\n", "function render(props, node) {\n", " var div = document.createElement(\"div\");\n", " var script = document.createElement(\"script\");\n", " node.appendChild(div);\n", " node.appendChild(script);\n", "}\n", "\n", "/**\n", " * Handle when a new output is added\n", " */\n", "function handle_add_output(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", " if (id !== undefined) {\n", " var nchildren = toinsert.length;\n", " var html_node = toinsert[nchildren-1].children[0];\n", " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", " var scripts = [];\n", " var nodelist = html_node.querySelectorAll(\"script\");\n", " for (var i in nodelist) {\n", " if (nodelist.hasOwnProperty(i)) {\n", " scripts.push(nodelist[i])\n", " }\n", " }\n", "\n", " scripts.forEach( function (oldScript) {\n", " var newScript = document.createElement(\"script\");\n", " var attrs = [];\n", " var nodemap = oldScript.attributes;\n", " for (var j in nodemap) {\n", " if (nodemap.hasOwnProperty(j)) {\n", " attrs.push(nodemap[j])\n", " }\n", " }\n", " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", " oldScript.parentNode.replaceChild(newScript, oldScript);\n", " });\n", " if (JS_MIME_TYPE in output.data) {\n", " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", " }\n", " output_area._hv_plot_id = id;\n", " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", " window.PyViz.plot_index[id] = Bokeh.index[id];\n", " } else {\n", " window.PyViz.plot_index[id] = null;\n", " }\n", " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", "}\n", "\n", "/**\n", " * Handle when an output is cleared or removed\n", " */\n", "function handle_clear_output(event, handle) {\n", " var id = handle.cell.output_area._hv_plot_id;\n", " var server_id = handle.cell.output_area._bokeh_server_id;\n", " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", " if (server_id !== null) {\n", " comm.send({event_type: 'server_delete', 'id': server_id});\n", " return;\n", " } else if (comm !== null) {\n", " comm.send({event_type: 'delete', 'id': id});\n", " }\n", " delete PyViz.plot_index[id];\n", " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", " var doc = window.Bokeh.index[id].model.document\n", " doc.clear();\n", " const i = window.Bokeh.documents.indexOf(doc);\n", " if (i > -1) {\n", " window.Bokeh.documents.splice(i, 1);\n", " }\n", " }\n", "}\n", "\n", "/**\n", " * Handle kernel restart event\n", " */\n", "function handle_kernel_cleanup(event, handle) {\n", " delete PyViz.comms[\"hv-extension-comm\"];\n", " window.PyViz.plot_index = {}\n", "}\n", "\n", "/**\n", " * Handle update_display_data messages\n", " */\n", "function handle_update_output(event, handle) {\n", " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", " handle_add_output(event, handle)\n", "}\n", "\n", "function register_renderer(events, OutputArea) {\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[0]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " events.on('output_added.OutputArea', handle_add_output);\n", " events.on('output_updated.OutputArea', handle_update_output);\n", " events.on('clear_output.CodeCell', handle_clear_output);\n", " events.on('delete.Cell', handle_clear_output);\n", " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", "\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " safe: true,\n", " index: 0\n", " });\n", "}\n", "\n", "if (window.Jupyter !== undefined) {\n", " try {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " } catch(err) {\n", " }\n", "}\n" ], "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1002" } }, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.4.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " var reloading = true;\n", " var Bokeh = root.Bokeh;\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " if (!reloading) {\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", " window._bokeh_on_load = on_load\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " var skip = [];\n", " if (window.requirejs) {\n", " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", " root._bokeh_is_loading = css_urls.length + 0;\n", " } else {\n", " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", " }\n", "\n", " var existing_stylesheets = []\n", " var links = document.getElementsByTagName('link')\n", " for (var i = 0; i < links.length; i++) {\n", " var link = links[i]\n", " if (link.href != null) {\n", "\texisting_stylesheets.push(link.href)\n", " }\n", " }\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " if (existing_stylesheets.indexOf(url) !== -1) {\n", "\ton_load()\n", "\tcontinue;\n", " }\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " } var existing_scripts = []\n", " var scripts = document.getElementsByTagName('script')\n", " for (var i = 0; i < scripts.length; i++) {\n", " var script = scripts[i]\n", " if (script.src != null) {\n", "\texisting_scripts.push(script.src)\n", " }\n", " }\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (var i = 0; i < js_modules.length; i++) {\n", " var url = js_modules[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " var url = js_exports[name];\n", " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " var js_urls = [];\n", " var js_modules = [];\n", " var js_exports = {};\n", " var css_urls = [];\n", " var inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", "\ttry {\n", " inline_js[i].call(root, root.Bokeh);\n", "\t} catch(e) {\n", "\t if (!reloading) {\n", "\t throw e;\n", "\t }\n", "\t}\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", "\tvar NewBokeh = root.Bokeh;\n", "\tif (Bokeh.versions === undefined) {\n", "\t Bokeh.versions = new Map();\n", "\t}\n", "\tif (NewBokeh.version !== Bokeh.version) {\n", "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", "\t}\n", "\troot.Bokeh = Bokeh;\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", "\trun_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = true;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", "}\n", "\n", "\n", " function JupyterCommManager() {\n", " }\n", "\n", " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", " comm_manager.register_target(comm_id, function(comm) {\n", " comm.on_msg(msg_handler);\n", " });\n", " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", " comm.onMsg = msg_handler;\n", " });\n", " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", " var messages = comm.messages[Symbol.asyncIterator]();\n", " function processIteratorResult(result) {\n", " var message = result.value;\n", " console.log(message)\n", " var content = {data: message.data, comm_id};\n", " var buffers = []\n", " for (var buffer of message.buffers || []) {\n", " buffers.push(new DataView(buffer))\n", " }\n", " var metadata = message.metadata || {};\n", " var msg = {content, buffers, metadata}\n", " msg_handler(msg);\n", " return messages.next().then(processIteratorResult);\n", " }\n", " return messages.next().then(processIteratorResult);\n", " })\n", " }\n", " }\n", "\n", " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", " if (comm_id in window.PyViz.comms) {\n", " return window.PyViz.comms[comm_id];\n", " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", " if (msg_handler) {\n", " comm.on_msg(msg_handler);\n", " }\n", " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", " comm.open();\n", " if (msg_handler) {\n", " comm.onMsg = msg_handler;\n", " }\n", " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", " comm_promise.then((comm) => {\n", " window.PyViz.comms[comm_id] = comm;\n", " if (msg_handler) {\n", " var messages = comm.messages[Symbol.asyncIterator]();\n", " function processIteratorResult(result) {\n", " var message = result.value;\n", " var content = {data: message.data};\n", " var metadata = message.metadata || {comm_id};\n", " var msg = {content, metadata}\n", " msg_handler(msg);\n", " return messages.next().then(processIteratorResult);\n", " }\n", " return messages.next().then(processIteratorResult);\n", " }\n", " }) \n", " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", " return comm_promise.then((comm) => {\n", " comm.send(data, metadata, buffers, disposeOnDone);\n", " });\n", " };\n", " var comm = {\n", " send: sendClosure\n", " };\n", " }\n", " window.PyViz.comms[comm_id] = comm;\n", " return comm;\n", " }\n", " window.PyViz.comm_manager = new JupyterCommManager();\n", " \n", "\n", "\n", "var JS_MIME_TYPE = 'application/javascript';\n", "var HTML_MIME_TYPE = 'text/html';\n", "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", "var CLASS_NAME = 'output';\n", "\n", "/**\n", " * Render data to the DOM node\n", " */\n", "function render(props, node) {\n", " var div = document.createElement(\"div\");\n", " var script = document.createElement(\"script\");\n", " node.appendChild(div);\n", " node.appendChild(script);\n", "}\n", "\n", "/**\n", " * Handle when a new output is added\n", " */\n", "function handle_add_output(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", " if (id !== undefined) {\n", " var nchildren = toinsert.length;\n", " var html_node = toinsert[nchildren-1].children[0];\n", " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", " var scripts = [];\n", " var nodelist = html_node.querySelectorAll(\"script\");\n", " for (var i in nodelist) {\n", " if (nodelist.hasOwnProperty(i)) {\n", " scripts.push(nodelist[i])\n", " }\n", " }\n", "\n", " scripts.forEach( function (oldScript) {\n", " var newScript = document.createElement(\"script\");\n", " var attrs = [];\n", " var nodemap = oldScript.attributes;\n", " for (var j in nodemap) {\n", " if (nodemap.hasOwnProperty(j)) {\n", " attrs.push(nodemap[j])\n", " }\n", " }\n", " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", " oldScript.parentNode.replaceChild(newScript, oldScript);\n", " });\n", " if (JS_MIME_TYPE in output.data) {\n", " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", " }\n", " output_area._hv_plot_id = id;\n", " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", " window.PyViz.plot_index[id] = Bokeh.index[id];\n", " } else {\n", " window.PyViz.plot_index[id] = null;\n", " }\n", " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", "}\n", "\n", "/**\n", " * Handle when an output is cleared or removed\n", " */\n", "function handle_clear_output(event, handle) {\n", " var id = handle.cell.output_area._hv_plot_id;\n", " var server_id = handle.cell.output_area._bokeh_server_id;\n", " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", " if (server_id !== null) {\n", " comm.send({event_type: 'server_delete', 'id': server_id});\n", " return;\n", " } else if (comm !== null) {\n", " comm.send({event_type: 'delete', 'id': id});\n", " }\n", " delete PyViz.plot_index[id];\n", " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", " var doc = window.Bokeh.index[id].model.document\n", " doc.clear();\n", " const i = window.Bokeh.documents.indexOf(doc);\n", " if (i > -1) {\n", " window.Bokeh.documents.splice(i, 1);\n", " }\n", " }\n", "}\n", "\n", "/**\n", " * Handle kernel restart event\n", " */\n", "function handle_kernel_cleanup(event, handle) {\n", " delete PyViz.comms[\"hv-extension-comm\"];\n", " window.PyViz.plot_index = {}\n", "}\n", "\n", "/**\n", " * Handle update_display_data messages\n", " */\n", "function handle_update_output(event, handle) {\n", " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", " handle_add_output(event, handle)\n", "}\n", "\n", "function register_renderer(events, OutputArea) {\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[0]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " events.on('output_added.OutputArea', handle_add_output);\n", " events.on('output_updated.OutputArea', handle_update_output);\n", " events.on('clear_output.CodeCell', handle_clear_output);\n", " events.on('delete.Cell', handle_clear_output);\n", " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", "\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " safe: true,\n", " index: 0\n", " });\n", "}\n", "\n", "if (window.Jupyter !== undefined) {\n", " try {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " } catch(err) {\n", " }\n", "}\n" ], "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import logging\n", "import pathlib\n", "from typing import List, Optional\n", "\n", "import holoviews\n", "import hvplot.pandas\n", "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px\n", "import pycytominer\n", "import umap\n", "from cytotable.convert import convert\n", "from IPython.display import HTML, Image\n", "from parsl.config import Config\n", "from parsl.executors import ThreadPoolExecutor\n", "from pyarrow import parquet\n", "\n", "import cosmicqc\n", "\n", "# set bokeh for visualizations with hvplot\n", "hvplot.extension(\"bokeh\")\n", "\n", "# create a dir for images\n", "pathlib.Path(\"./images\").mkdir(exist_ok=True)\n", "\n", "# avoid displaying plot export warnings\n", "logging.getLogger(\"bokeh.io.export\").setLevel(logging.ERROR)\n", "\n", "# set the plate name for use throughout the notebook\n", "example_plate = \"BR00117012\"" ] }, { "cell_type": "markdown", "id": "cab14cb7-a2d5-45bb-a6f9-8952e5ba5114", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## Define utility functions for use within this notebook" ] }, { "cell_type": "code", "execution_count": 2, "id": "f8d31435-17c2-46b4-bc50-7f818a30ceaf", "metadata": {}, "outputs": [], "source": [ "def generate_umap_embeddings(\n", " df_input: pd.DataFrame,\n", " cols_metadata_to_exclude: List[str],\n", " umap_n_components: int = 2,\n", " random_state: Optional[int] = None,\n", ") -> np.ndarray:\n", " \"\"\"\n", " Generates UMAP (Uniform Manifold Approximation and Projection)\n", " embeddings for a given input dataframe,\n", " excluding specified metadata columns.\n", "\n", " Args:\n", " df_input (pd.DataFrame]):\n", " A dataframe which is expected to contain\n", " numeric columns to be used for UMAP fitting.\n", " cols_metadata_to_exclude (List[str]):\n", " A list of column names representing\n", " metadata columns that should be excluded\n", " from the UMAP transformation.\n", " umap_n_components: (int):\n", " Number of components to use for UMAP.\n", " Default = 2.\n", " random_state (int):\n", " Number to use for random state and\n", " optional determinism.\n", " Default = None (random each time)\n", " Note: values besides None will turn\n", " off parallelism for umap-learn, likely\n", " meaning increased processing time.\n", "\n", " Returns:\n", " np.ndarray:\n", " A dataframe containing the UMAP embeddings\n", " with 2 components for each row in the input.\n", " \"\"\"\n", "\n", " # Make sure to reinitialize UMAP instance per plate\n", " umap_fit = umap.UMAP(\n", " n_components=umap_n_components,\n", " random_state=random_state,\n", " # set the default value if we didn't set a random_state\n", " # otherwise set to 1 (umap-learn will override anyways).\n", " # this is set to avoid warnings from umap-learn during\n", " # processing.\n", " n_jobs=-1 if random_state is None else 1,\n", " )\n", "\n", " # Fit UMAP and convert to pandas DataFrame\n", " embeddings = umap_fit.fit_transform(\n", " X=df_input[\n", " [\n", " col\n", " for col in df_input.columns.tolist()\n", " if col not in cols_metadata_to_exclude and \"cqc.\" not in col\n", " ]\n", " # select only numeric types from the dataframe\n", " ].select_dtypes(include=[np.number])\n", " )\n", "\n", " return embeddings" ] }, { "cell_type": "code", "execution_count": 3, "id": "1d4a960b-47ed-4948-8c0f-8d5dd1aedb87", "metadata": {}, "outputs": [], "source": [ "def plot_hvplot_scatter(\n", " embeddings: np.ndarray,\n", " title: str,\n", " filename: str,\n", " color_dataframe: Optional[pd.DataFrame] = None,\n", " color_column: Optional[str] = None,\n", " bgcolor: str = \"black\",\n", " cmap: str = \"plasma\",\n", " clabel: Optional[str] = None,\n", ") -> holoviews.core.spaces.DynamicMap:\n", " \"\"\"\n", " Creates an outlier-focused scatter hvplot for viewing\n", " UMAP embedding data with cosmicqc outliers coloration.\n", "\n", " Args:\n", " embeddings (np.ndarray]):\n", " A numpy ndarray which includes\n", " embedding data to display.\n", " title (str):\n", " Title for the UMAP scatter plot.\n", " filename (str):\n", " Filename which indicates where to export the\n", " plot.\n", " color_dataframe (pd.DataFrame):\n", " A dataframe which includes data used for\n", " color mapping within the plot. For example,\n", " coSMicQC .is_outlier columns.\n", " color_column (str):\n", " Column name from color_dataframe to use\n", " for coloring the scatter plot.\n", " bgcolor (str):\n", " Sets the background color of the plot.\n", " cmap (str):\n", " Sets the colormap used for the plot.\n", " See here for more:\n", " https://holoviews.org/user_guide/Colormaps.html\n", " clabel (str):\n", " Sets a label on the color map key displayed\n", " horizontally. Defaults to None (no label).\n", "\n", " Returns:\n", " holoviews.core.spaces.DynamicMap:\n", " A dynamic holoviews scatter plot which may be\n", " displayed in a Jupyter notebook.\n", " \"\"\"\n", "\n", " # build a scatter plot through hvplot\n", " plot = pd.DataFrame(embeddings).hvplot.scatter(\n", " title=title,\n", " x=\"0\",\n", " y=\"1\",\n", " alpha=0.1,\n", " rasterize=True,\n", " c=(\n", " color_dataframe[color_column].astype(int).values\n", " if color_dataframe is not None\n", " else None\n", " ),\n", " cnorm=\"linear\",\n", " cmap=cmap,\n", " bgcolor=bgcolor,\n", " height=700,\n", " width=800,\n", " clabel=clabel,\n", " )\n", "\n", " # export the plot\n", " hvplot.save(obj=plot, filename=filename, center=False)\n", "\n", " return plot" ] }, { "cell_type": "markdown", "id": "324a8e01-6544-4d3a-a68b-fd8c8bb3a56e", "metadata": {}, "source": [ "## Merge single-cell compartment data into one table" ] }, { "cell_type": "code", "execution_count": 4, "id": "26f49501-2154-4600-a718-e0d66559f4e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", " created_by: parquet-cpp-arrow version 16.1.0\n", " num_columns: 5804\n", " num_rows: 279789\n", " num_row_groups: 14\n", " format_version: 2.6\n", " serialized_size: 12762532" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if we already have prepared data\n", "if not pathlib.Path(f\"./{example_plate}.parquet\").is_file():\n", " # process BR00117012.sqlite using CytoTable to prepare data\n", " merged_single_cells = convert(\n", " source_path=(\n", " \"s3://cellpainting-gallery/cpg0000-jump-pilot/source_4/workspace\"\n", " \"/backend/2020_11_04_CPJUMP1/BR00117012/BR00117012.sqlite\"\n", " ),\n", " dest_path=f\"./{example_plate}.parquet\",\n", " dest_datatype=\"parquet\",\n", " source_datatype=\"sqlite\",\n", " chunk_size=8000,\n", " preset=\"cellprofiler_sqlite_cpg0016_jump\",\n", " # allows AWS S3 requests without login\n", " no_sign_request=True,\n", " # use explicit cache to avoid temp cache removal\n", " local_cache_dir=\"./sqlite_s3_cache/\",\n", " parsl_config=Config(\n", " executors=[ThreadPoolExecutor(label=\"tpe_for_jump_processing\")]\n", " ),\n", " sort_output=False,\n", " )\n", "else:\n", " merged_single_cells = f\"./{example_plate}.parquet\"\n", "\n", "# read only the metadata from parquet file\n", "parquet.ParquetFile(merged_single_cells).metadata" ] }, { "cell_type": "markdown", "id": "6958a5b1-b566-45d8-a5b6-0ebd8537eb7c", "metadata": {}, "source": [ "## Process merged single-cell data using coSMicQC" ] }, { "cell_type": "code", "execution_count": 5, "id": "64e7830f-7b75-45e5-aa60-213e341beac1", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['Metadata_ImageNumber',\n", " 'Image_Metadata_Row',\n", " 'Image_Metadata_Site',\n", " 'Metadata_ObjectNumber',\n", " 'Metadata_ObjectNumber_1',\n", " 'Metadata_ObjectNumber_2',\n", " 'Metadata_Plate',\n", " 'Metadata_Well',\n", " 'Image_TableNumber',\n", " 'Cytoplasm_AreaShape_Area',\n", " 'Cytoplasm_AreaShape_BoundingBoxArea',\n", " 'Cytoplasm_AreaShape_BoundingBoxMaximum_X']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show the first few columns for metadata column names\n", "schema_names = parquet.read_schema(merged_single_cells).names\n", "schema_names[:12]" ] }, { "cell_type": "code", "execution_count": 6, "id": "ba8f2255-e7e4-4118-a700-9727b23e4c2d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metadata_ImageNumberImage_Metadata_RowImage_Metadata_SiteMetadata_ObjectNumberMetadata_PlateMetadata_WellImage_TableNumberNuclei_AreaShape_AreaNuclei_AreaShape_FormFactorNuclei_AreaShape_Eccentricity
02122BR00117012A013519678168080910122386703159622985615610910.8953930.694154
12123BR00117012A013519678168080910122386703159622985615610070.8376310.819062
22124BR00117012A013519678168080910122386703159622985615613680.8331970.820257
32125BR00117012A01351967816808091012238670315962298561568470.9029950.345575
42126BR00117012A01351967816808091012238670315962298561569830.8630050.742060
\n", "
" ], "text/plain": [ " Metadata_ImageNumber Image_Metadata_Row Image_Metadata_Site \\\n", "0 2 1 2 \n", "1 2 1 2 \n", "2 2 1 2 \n", "3 2 1 2 \n", "4 2 1 2 \n", "\n", " Metadata_ObjectNumber Metadata_Plate Metadata_Well \\\n", "0 2 BR00117012 A01 \n", "1 3 BR00117012 A01 \n", "2 4 BR00117012 A01 \n", "3 5 BR00117012 A01 \n", "4 6 BR00117012 A01 \n", "\n", " Image_TableNumber Nuclei_AreaShape_Area \\\n", "0 35196781680809101223867031596229856156 1091 \n", "1 35196781680809101223867031596229856156 1007 \n", "2 35196781680809101223867031596229856156 1368 \n", "3 35196781680809101223867031596229856156 847 \n", "4 35196781680809101223867031596229856156 983 \n", "\n", " Nuclei_AreaShape_FormFactor Nuclei_AreaShape_Eccentricity \n", "0 0.895393 0.694154 \n", "1 0.837631 0.819062 \n", "2 0.833197 0.820257 \n", "3 0.902995 0.345575 \n", "4 0.863005 0.742060 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# set a list of metadata columns for use throughout\n", "metadata_cols = [\n", " \"Metadata_ImageNumber\",\n", " \"Image_Metadata_Row\",\n", " \"Image_Metadata_Site\",\n", " \"Metadata_ObjectNumber\",\n", " \"Metadata_Plate\",\n", " \"Metadata_Well\",\n", " \"Image_TableNumber\",\n", "]\n", "\n", "# read only metadata columns with feature columns used for outlier detection\n", "df_merged_single_cells = pd.read_parquet(\n", " path=merged_single_cells,\n", " columns=[\n", " *metadata_cols,\n", " \"Nuclei_AreaShape_Area\",\n", " \"Nuclei_AreaShape_FormFactor\",\n", " \"Nuclei_AreaShape_Eccentricity\",\n", " ],\n", ")\n", "df_merged_single_cells.head()" ] }, { "cell_type": "code", "execution_count": 7, "id": "87ac8cf2-798a-4275-bb63-bead487a5ef0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Large nuclei outliers:\n", "Number of outliers: 1355 (0.48%)\n", "Outliers Range:\n", "Nuclei_AreaShape_Area Min: 1754\n", "Nuclei_AreaShape_Area Max: 4414\n", "Nuclei_AreaShape_FormFactor Min: 0.3367261940249281\n", "Nuclei_AreaShape_FormFactor Max: 0.7140072671383899\n" ] } ], "source": [ "# label outliers within the dataset\n", "print(\"Large nuclei outliers:\")\n", "df_labeled_outliers = cosmicqc.analyze.find_outliers(\n", " df=df_merged_single_cells,\n", " metadata_columns=metadata_cols,\n", " feature_thresholds=\"large_nuclei\",\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "66f4f702-587d-49e4-9aef-6164c1044583", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elongated nuclei outliers:\n", "Number of outliers: 15 (0.01%)\n", "Outliers Range:\n", "Nuclei_AreaShape_Eccentricity Min: 0.9868108584805481\n", "Nuclei_AreaShape_Eccentricity Max: 0.9995098494433163\n" ] } ], "source": [ "# label outliers within the dataset\n", "print(\"Elongated nuclei outliers:\")\n", "df_labeled_outliers = cosmicqc.analyze.find_outliers(\n", " df=df_merged_single_cells,\n", " metadata_columns=metadata_cols,\n", " feature_thresholds=\"elongated_nuclei\",\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "id": "9316b38a-9a1e-412b-a256-18d61fafafc1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Small and low formfactor nuclei outliers:\n", "Number of outliers: 6548 (2.34%)\n", "Outliers Range:\n", "Nuclei_AreaShape_Area Min: 79\n", "Nuclei_AreaShape_Area Max: 744\n", "Nuclei_AreaShape_FormFactor Min: 0.0945907341645769\n", "Nuclei_AreaShape_FormFactor Max: 0.7781815132858318\n" ] } ], "source": [ "# label outliers within the dataset\n", "print(\"Small and low formfactor nuclei outliers:\")\n", "df_labeled_outliers = cosmicqc.analyze.find_outliers(\n", " df=df_merged_single_cells,\n", " metadata_columns=metadata_cols,\n", " feature_thresholds=\"small_and_low_formfactor_nuclei\",\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "3a8cd288-5899-4931-a8b2-e8096c26a4ce", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Areacqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactorcqc.small_and_low_formfactor_nuclei.is_outliercqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricitycqc.elongated_nuclei.is_outliercqc.large_nuclei.Z_Score.Nuclei_AreaShape_Areacqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactorcqc.large_nuclei.is_outlier
00.0301210.826248False-0.154094False0.0301210.826248False
1-0.219592-0.073800False0.765830False-0.219592-0.073800False
20.853580-0.142903False0.774634False0.853580-0.142903False
3-0.6952360.944704False-2.721308False-0.6952360.944704False
4-0.2909380.321578False0.198723False-0.2909380.321578False
\n", "
" ], "text/plain": [ " cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Area \\\n", "0 0.030121 \n", "1 -0.219592 \n", "2 0.853580 \n", "3 -0.695236 \n", "4 -0.290938 \n", "\n", " cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactor \\\n", "0 0.826248 \n", "1 -0.073800 \n", "2 -0.142903 \n", "3 0.944704 \n", "4 0.321578 \n", "\n", " cqc.small_and_low_formfactor_nuclei.is_outlier \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "\n", " cqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricity \\\n", "0 -0.154094 \n", "1 0.765830 \n", "2 0.774634 \n", "3 -2.721308 \n", "4 0.198723 \n", "\n", " cqc.elongated_nuclei.is_outlier \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "\n", " cqc.large_nuclei.Z_Score.Nuclei_AreaShape_Area \\\n", "0 0.030121 \n", "1 -0.219592 \n", "2 0.853580 \n", "3 -0.695236 \n", "4 -0.290938 \n", "\n", " cqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactor \\\n", "0 0.826248 \n", "1 -0.073800 \n", "2 -0.142903 \n", "3 0.944704 \n", "4 0.321578 \n", "\n", " cqc.large_nuclei.is_outlier \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# label outliers within the dataset\n", "df_labeled_outliers = cosmicqc.analyze.label_outliers(\n", " df=df_merged_single_cells,\n", " include_threshold_scores=True,\n", ")\n", "# show added columns\n", "df_labeled_outliers[\n", " [col for col in df_labeled_outliers.columns.tolist() if \"cqc.\" in col]\n", "].head()" ] }, { "cell_type": "code", "execution_count": 11, "id": "5c083891-5eb8-480b-9040-b177a9972108", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "analysis.included_at_least_one_outlier\n", "False 271883\n", "True 7906\n", "Name: count, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create a column which indicates whether an erroneous outlier was detected\n", "# from all cosmicqc outlier threshold sets. For ex. True for is_outlier in\n", "# one threshold set out of three would show True for this column. False for\n", "# is_outlier in all threshold sets would show False for this column.\n", "df_labeled_outliers[\"analysis.included_at_least_one_outlier\"] = df_labeled_outliers[\n", " [col for col in df_labeled_outliers.columns.tolist() if \".is_outlier\" in col]\n", "].any(axis=1)\n", "\n", "# show value counts for all outliers\n", "outliers_counts = df_labeled_outliers[\n", " \"analysis.included_at_least_one_outlier\"\n", "].value_counts()\n", "outliers_counts" ] }, { "cell_type": "code", "execution_count": 12, "id": "1f5b040f-b229-4ef2-a08a-de31b4154265", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.9078684581235312 % of 271883 include erroneous outliers of some kind.\n" ] } ], "source": [ "# show the percentage of total dataset\n", "print(\n", " (outliers_counts.iloc[1] / outliers_counts.iloc[0]) * 100,\n", " \"%\",\n", " \"of\",\n", " outliers_counts.iloc[0],\n", " \"include erroneous outliers of some kind.\",\n", ")" ] }, { "cell_type": "markdown", "id": "6bb31625-e0a7-46e2-807c-b715bb59169d", "metadata": {}, "source": [ "## Prepare data for analysis with pycytominer" ] }, { "cell_type": "code", "execution_count": 14, "id": "3fa421ce-a2eb-4616-8e88-c44b666ff2e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metadata_ImageNumberImage_Metadata_RowImage_Metadata_SiteMetadata_ObjectNumberMetadata_ObjectNumber_1Metadata_ObjectNumber_2Metadata_PlateMetadata_WellImage_TableNumberCytoplasm_AreaShape_Area...Nuclei_Texture_Variance_RNA_5_03_256cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Areacqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactorcqc.small_and_low_formfactor_nuclei.is_outliercqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricitycqc.elongated_nuclei.is_outliercqc.large_nuclei.Z_Score.Nuclei_AreaShape_Areacqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactorcqc.large_nuclei.is_outlieranalysis.included_at_least_one_outlier
0919363636BR00117012A011234277823015101247267054164817976898553816...17.533683-0.4157950.971457False-0.130095False-0.4157950.971457FalseFalse
1818333BR00117012A011959702122324537759917826803215282063903042...7.773093-0.166082-2.590543False1.539330False-0.166082-2.590543FalseFalse
2515474747BR00117012A013272563011551011521931470885712653073814343...5.1352721.5967730.052141False0.483920False1.5967730.052141FalseFalse
3717505050BR00117012A01404648534312356676579271304652685804534114...6.9229580.1847050.997999False-0.488523False0.1847050.997999FalseFalse
4717171717BR00117012A01404648534312356676579271304652685804532239...3.321053-0.5317331.294974False-0.612494False-0.5317331.294974FalseFalse
..................................................................
1175083454167686868BR00117012P24533766286550352169093785878112837204565259...53.4363831.3678700.176541False0.095166False1.3678700.176541FalseFalse
1175093448161656565BR00117012P241150660260698387431892113808479207336162777...6.429991-0.290938-1.220927False1.124422False-0.290938-1.220927FalseFalse
1175103451164878787BR00117012P242028302135531844952968577046605143255543542...7.046681-0.3474210.684254False0.167217False-0.3474210.684254FalseFalse
1175113454167767676BR00117012P24533766286550352169093785878112837204562646...20.230426-0.671454-0.050317False-1.363158False-0.671454-0.050317FalseFalse
1175123453166272727BR00117012P241913716237722554003312681160248553195125172...7.0191601.4124610.669994False-0.154804False1.4124610.669994FalseFalse
\n", "

117513 rows × 5813 columns

\n", "
" ], "text/plain": [ " Metadata_ImageNumber Image_Metadata_Row Image_Metadata_Site \\\n", "0 9 1 9 \n", "1 8 1 8 \n", "2 5 1 5 \n", "3 7 1 7 \n", "4 7 1 7 \n", "... ... ... ... \n", "117508 3454 16 7 \n", "117509 3448 16 1 \n", "117510 3451 16 4 \n", "117511 3454 16 7 \n", "117512 3453 16 6 \n", "\n", " Metadata_ObjectNumber Metadata_ObjectNumber_1 \\\n", "0 36 36 \n", "1 3 3 \n", "2 47 47 \n", "3 50 50 \n", "4 17 17 \n", "... ... ... \n", "117508 68 68 \n", "117509 65 65 \n", "117510 87 87 \n", "117511 76 76 \n", "117512 27 27 \n", "\n", " Metadata_ObjectNumber_2 Metadata_Plate Metadata_Well \\\n", "0 36 BR00117012 A01 \n", "1 3 BR00117012 A01 \n", "2 47 BR00117012 A01 \n", "3 50 BR00117012 A01 \n", "4 17 BR00117012 A01 \n", "... ... ... ... \n", "117508 68 BR00117012 P24 \n", "117509 65 BR00117012 P24 \n", "117510 87 BR00117012 P24 \n", "117511 76 BR00117012 P24 \n", "117512 27 BR00117012 P24 \n", "\n", " Image_TableNumber Cytoplasm_AreaShape_Area \\\n", "0 123427782301510124726705416481797689855 3816 \n", "1 195970212232453775991782680321528206390 3042 \n", "2 327256301155101152193147088571265307381 4343 \n", "3 40464853431235667657927130465268580453 4114 \n", "4 40464853431235667657927130465268580453 2239 \n", "... ... ... \n", "117508 53376628655035216909378587811283720456 5259 \n", "117509 115066026069838743189211380847920733616 2777 \n", "117510 202830213553184495296857704660514325554 3542 \n", "117511 53376628655035216909378587811283720456 2646 \n", "117512 191371623772255400331268116024855319512 5172 \n", "\n", " ... Nuclei_Texture_Variance_RNA_5_03_256 \\\n", "0 ... 17.533683 \n", "1 ... 7.773093 \n", "2 ... 5.135272 \n", "3 ... 6.922958 \n", "4 ... 3.321053 \n", "... ... ... \n", "117508 ... 53.436383 \n", "117509 ... 6.429991 \n", "117510 ... 7.046681 \n", "117511 ... 20.230426 \n", "117512 ... 7.019160 \n", "\n", " cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Area \\\n", "0 -0.415795 \n", "1 -0.166082 \n", "2 1.596773 \n", "3 0.184705 \n", "4 -0.531733 \n", "... ... \n", "117508 1.367870 \n", "117509 -0.290938 \n", "117510 -0.347421 \n", "117511 -0.671454 \n", "117512 1.412461 \n", "\n", " cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactor \\\n", "0 0.971457 \n", "1 -2.590543 \n", "2 0.052141 \n", "3 0.997999 \n", "4 1.294974 \n", "... ... \n", "117508 0.176541 \n", "117509 -1.220927 \n", "117510 0.684254 \n", "117511 -0.050317 \n", "117512 0.669994 \n", "\n", " cqc.small_and_low_formfactor_nuclei.is_outlier \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "117508 False \n", "117509 False \n", "117510 False \n", "117511 False \n", "117512 False \n", "\n", " cqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricity \\\n", "0 -0.130095 \n", "1 1.539330 \n", "2 0.483920 \n", "3 -0.488523 \n", "4 -0.612494 \n", "... ... \n", "117508 0.095166 \n", "117509 1.124422 \n", "117510 0.167217 \n", "117511 -1.363158 \n", "117512 -0.154804 \n", "\n", " cqc.elongated_nuclei.is_outlier \\\n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "117508 False \n", "117509 False \n", "117510 False \n", "117511 False \n", "117512 False \n", "\n", " cqc.large_nuclei.Z_Score.Nuclei_AreaShape_Area \\\n", "0 -0.415795 \n", "1 -0.166082 \n", "2 1.596773 \n", "3 0.184705 \n", "4 -0.531733 \n", "... ... \n", "117508 1.367870 \n", "117509 -0.290938 \n", "117510 -0.347421 \n", "117511 -0.671454 \n", "117512 1.412461 \n", "\n", " cqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactor \\\n", "0 0.971457 \n", "1 -2.590543 \n", "2 0.052141 \n", "3 0.997999 \n", "4 1.294974 \n", "... ... \n", "117508 0.176541 \n", "117509 -1.220927 \n", "117510 0.684254 \n", "117511 -0.050317 \n", "117512 0.669994 \n", "\n", " cqc.large_nuclei.is_outlier analysis.included_at_least_one_outlier \n", "0 False False \n", "1 False False \n", "2 False False \n", "3 False False \n", "4 False False \n", "... ... ... \n", "117508 False False \n", "117509 False False \n", "117510 False False \n", "117511 False False \n", "117512 False False \n", "\n", "[117513 rows x 5813 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parquet_sampled_with_outliers = f\"./{example_plate}_sampled_with_outliers.parquet\"\n", "\n", "# check if we already have normalized data\n", "if not pathlib.Path(parquet_sampled_with_outliers).is_file():\n", " # set a fraction for sampling to limit the amount\n", " # of data processed based on system memory constraints.\n", " # note: data was processed on system with 16 CPU, 64 GB ram\n", " sample_fraction = 0.44\n", "\n", " # read the dataset\n", " df_features = pd.read_parquet(f\"./{example_plate}.parquet\")\n", "\n", " # group by metadata_well for all features then sample\n", " # the dataset by a fraction.\n", " df_features = (\n", " # note: we add the column selection here to avoid a pandas\n", " # DeprecationWarning. See the following link for more details:\n", " # https://stackoverflow.com/questions/77969964/deprecation-warning-with-groupby-apply\n", " df_features.groupby([\"Metadata_Well\"])[df_features.columns]\n", " .apply(lambda x: x.sample(frac=sample_fraction))\n", " .reset_index(drop=True)\n", " )\n", "\n", " # join the sampled feature data with the cosmicqc outlier data\n", " df_features_with_cqc_outlier_data = df_features.merge(\n", " # select metadata columns plus those which don't exist in\n", " # df_features (cosmicqc or analysis-specific columns)\n", " df_labeled_outliers[\n", " [\n", " *metadata_cols,\n", " *[\n", " col\n", " for col in df_labeled_outliers.columns\n", " if col not in df_features.columns\n", " ],\n", " ]\n", " ],\n", " how=\"inner\",\n", " left_on=metadata_cols,\n", " right_on=metadata_cols,\n", " )\n", "\n", " df_features_with_cqc_outlier_data.to_parquet(parquet_sampled_with_outliers)\n", "\n", "else:\n", " df_features_with_cqc_outlier_data = pd.read_parquet(parquet_sampled_with_outliers)\n", "\n", "df_features_with_cqc_outlier_data" ] }, { "cell_type": "code", "execution_count": 15, "id": "ad1b282a-9b55-42a2-a987-7429e9262f56", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "analysis.included_at_least_one_outlier\n", "False 114138\n", "True 3375\n", "Name: count, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show our data value counts regarding outliers vs inliers\n", "df_features_with_cqc_outlier_data[\n", " \"analysis.included_at_least_one_outlier\"\n", "].value_counts()" ] }, { "cell_type": "code", "execution_count": 16, "id": "9627a1f6-8ac0-440e-a44b-941e80d4a87b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5804" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prepare data for normalization and feature selection\n", "# by removing cosmicqc and analaysis focused columns.\n", "df_for_normalize_and_feature_select = df_features_with_cqc_outlier_data[\n", " # read feature names from cytotable output, which excludes\n", " # cosmicqc-added columns.\n", " parquet.read_schema(merged_single_cells).names\n", "]\n", "# show the modified column count\n", "len(df_for_normalize_and_feature_select.columns)" ] }, { "cell_type": "code", "execution_count": 17, "id": "d03a9261-a02c-476f-bf9f-3ad94225a437", "metadata": {}, "outputs": [], "source": [ "# join JUMP metadata with platemap data to prepare for annotation\n", "df_platemap_and_metadata = pd.read_csv(\n", " filepath_or_buffer=(\n", " \"s3://cellpainting-gallery/cpg0000-jump-pilot/source_4\"\n", " \"/workspace/metadata/platemaps/2020_11_04_CPJUMP1/\"\n", " \"platemap/JUMP-Target-1_compound_platemap.txt\"\n", " ),\n", " sep=\"\\t\",\n", ").merge(\n", " right=pd.read_csv(\n", " filepath_or_buffer=(\n", " \"s3://cellpainting-gallery/cpg0000-jump-pilot/source_4\"\n", " \"/workspace/metadata/external_metadata/\"\n", " \"JUMP-Target-1_compound_metadata.tsv\"\n", " ),\n", " sep=\"\\t\",\n", " ),\n", " left_on=\"broad_sample\",\n", " right_on=\"broad_sample\",\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "id": "422ba39f-8e2d-4efa-bea1-f6fd2fd2cf0e", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_annotated = f\"./{example_plate}_annotated.parquet\"\n", "\n", "# check if we already have annotated data\n", "if not pathlib.Path(parquet_pycytominer_annotated).is_file():\n", " # annotate the data using pycytominer\n", " pycytominer.annotate(\n", " profiles=df_for_normalize_and_feature_select,\n", " # read the platemap directly from AWS S3 related location\n", " platemap=df_platemap_and_metadata,\n", " join_on=[\"Metadata_well_position\", \"Metadata_Well\"],\n", " output_file=parquet_pycytominer_annotated,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 19, "id": "63716bff-ed90-4d85-abf9-53682d46061a", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_normalized = f\"./{example_plate}_normalized.parquet\"\n", "\n", "# check if we already have normalized data\n", "if not pathlib.Path(parquet_pycytominer_normalized).is_file():\n", " # normalize the data using pcytominer\n", " df_pycytominer_normalized = pycytominer.normalize(\n", " profiles=parquet_pycytominer_annotated,\n", " features=\"infer\",\n", " image_features=False,\n", " meta_features=\"infer\",\n", " method=\"standardize\",\n", " samples=\"Metadata_control_type == 'negcon'\",\n", " output_file=parquet_pycytominer_normalized,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 20, "id": "49443650-e9b4-4968-bd4a-f3ca00990f26", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_feature_selected = f\"./{example_plate}_feature_select.parquet\"\n", "\n", "# check if we already have feature selected data\n", "if not pathlib.Path(parquet_pycytominer_feature_selected).is_file():\n", " # feature select normalized data using pycytominer\n", " df_pycytominer_feature_selected = pycytominer.feature_select(\n", " profiles=parquet_pycytominer_normalized,\n", " operation=[\n", " \"variance_threshold\",\n", " \"correlation_threshold\",\n", " \"blocklist\",\n", " \"drop_na_columns\",\n", " ],\n", " na_cutoff=0,\n", " output_file=parquet_pycytominer_feature_selected,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 21, "id": "c7a1b96a-306c-4530-8906-db3e8f1772b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Metadata_broad_sample',\n", " 'Metadata_solvent',\n", " 'Metadata_InChIKey',\n", " 'Metadata_pert_iname',\n", " 'Metadata_pubchem_cid',\n", " 'Metadata_gene',\n", " 'Metadata_pert_type',\n", " 'Metadata_control_type',\n", " 'Metadata_smiles',\n", " 'Metadata_ImageNumber',\n", " 'Metadata_ObjectNumber',\n", " 'Metadata_ObjectNumber_1',\n", " 'Metadata_ObjectNumber_2',\n", " 'Metadata_Plate',\n", " 'Metadata_Well']" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# regather metadata columns to account for new additions\n", "all_metadata_cols = [\n", " col\n", " for col in parquet.read_schema(parquet_pycytominer_feature_selected).names\n", " if col.startswith(\"Metadata_\")\n", "]\n", "all_metadata_cols" ] }, { "cell_type": "code", "execution_count": 22, "id": "9df221a5-474b-4404-bcb4-1988146e5450", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(117513, 2)\n" ] }, { "data": { "text/plain": [ "array([[-0.39003304, 4.0597053 ],\n", " [ 0.430353 , 5.700904 ],\n", " [-0.33348054, 4.008859 ]], dtype=float32)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculate UMAP embeddings from the data\n", "# which was prepared by pycytominer.\n", "embeddings_with_outliers = generate_umap_embeddings(\n", " df_input=pd.read_parquet(parquet_pycytominer_feature_selected),\n", " cols_metadata_to_exclude=all_metadata_cols,\n", " random_state=0,\n", ")\n", "# show the shape and top values from the embeddings array\n", "print(embeddings_with_outliers.shape)\n", "embeddings_with_outliers[:3]" ] }, { "cell_type": "code", "execution_count": 23, "id": "67de5343-26cd-4dfc-b780-95926d2d8fd0", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_hvplot_scatter(\n", " embeddings=embeddings_with_outliers,\n", " title=f\"UMAP of JUMP embeddings from {example_plate} (with erroneous outliers)\",\n", " filename=(\n", " image_with_all_outliers\n", " := f\"./images/umap_with_all_outliers_{example_plate}.png\"\n", " ),\n", " bgcolor=\"white\",\n", " cmap=px.colors.sequential.Greens[4:],\n", " clabel=\"density of single cells\",\n", ")\n", "# conserve filespace by displaying export instead of dynamic plot\n", "Image(image_with_all_outliers)" ] }, { "cell_type": "code", "execution_count": 24, "id": "b7741b0e-ab6a-43f0-a9bf-954e1913944b", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Image [0,1] (0_1 _color)" ] }, "execution_count": 24, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1136" } }, "output_type": "execute_result" } ], "source": [ "# show a UMAP for all outliers within the data\n", "plot_hvplot_scatter(\n", " embeddings=embeddings_with_outliers,\n", " title=f\"UMAP of JUMP all coSMicQC erroneous outliers within {example_plate}\",\n", " filename=f\"./images/umap_erroneous_outliers_{example_plate}.png\",\n", " color_dataframe=df_features_with_cqc_outlier_data,\n", " color_column=\"analysis.included_at_least_one_outlier\",\n", " clabel=\"density of single cells classified as outliers\",\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "id": "72ca8d38-3be3-4c64-ae55-063f9b013b79", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzQAAALGCAYAAACTX9TRAAEAAElEQVR4nOydeZgcVbn/304mmQkZmARZAiGEgbDIIkEIKCAmgjdKcgVlF8WwBK+AQpBLuPGnIYhBRAmCiBCMy1VBBC6IW9wSXFiMKHBVRJYIAQ0gTAYGMhMyqd8fuady+p33fc+p7q7prp7v53n66e6qs7znVFX3+633nFOlJEkSAgAAAAAAAIACMqzeBgAAAAAAAABApUDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAAoLBA0AAAAAAACgsEDQAAAAAAAAAApLUwuaiy66iEqlEh166KED9t13331UKpWoVCrRv/71LyIi2mqrrdJtd911V1n69evX05gxY9L9P//5zweUeffdd6f73/zmN4s2+XW416hRo2j//fenb3zjGzVoNdHKlSvpsMMOo80224ze8IY30OrVq9W0Dz744AB77rjjDiIanP770pe+NKD+4cOH03bbbUcf+tCH6Nlnn61Jn+TB17/+dSqVSjR58uR020477VTWh4NNb29v2o9//etfxTR///vf0zRr1qwZXAMp2/k5GCxdupT22GMPamtro7e85S11taUIVHr+1PvaIJJtnzVrFpVKJTrvvPPqZldWYo+B9BslUcs+kP7jNt98c9pzzz3p4osvpldeeSVNK/3+l0olGjNmDB144IH03//935QkSVn5V155JU2aNIna2tpo9913p69//esDbIhJc++999LOO+9MpVKJjj766Ez7zzvvPNHuUqlE7e3tabq//e1vNHPmTBo7dix1dHTQcccdJ/7effWrX6X29nYqlUp01VVXDdj/m9/8hqZOnUpjxoyhbbbZht71rnfRAw88MCAdAEOZphY01fDDH/6w7Ps999xD3d3dZp5bbrkl/fzHP/6RnnjiCTXtNttsQ7vsskv6g/mHP/yBZs2aRVdffXV1hhPRpZdeSr/+9a9pu+22o5NPPpna2trUtCNHjqSJEyfSxIkTq67XJ2v/tbS00C677EK77LILbbvttrR69Wr65je/SYcccgi9/PLLNbUN1Jcs5+dgcNZZZ9Gjjz5KU6ZMoSOPPLKmZZ9++uk0ZsyYmpZZVA4//HCaMWMGjRs3btDqjOn/yZMn04wZM2ivvfYaHKNqwGabbUYzZsygGTNm0IgRI4iIaNWqVTRs2DDRIQ6RRx/svPPOdNBBB9FBBx1E22+/PT3yyCO0YMECeu973zsgrf/7v8suu9D69etpxYoVdMopp9AnP/nJNN2XvvQl+vjHP05PP/00dXZ20uOPP06nnnpq2Q20UJr+/n5asGABve1tb6OVK1cOsCW0n4hoyy23TP833Wv8+PFERDR8+HAiInrttdfoiCOOoB/+8Ic0evRoam1tpVtvvZX+/d//PRVpL730Eh1zzDF0xhln0KuvvirW9b//+790xBFH0N13301tbW20fv16Wrp0Kb3jHe+gp59+OuZQADA0SJqYuXPnJkSUHHLIIQP23XvvvQkRJUSUvPDCC0mSJMkb3vCGhIiSbbbZJtlhhx3K0l900UUJESVbb711QkTJz372s7L9/f39ybhx4xIiSvbZZ5+EiJLLLrtsQL2ujq997WvptrVr1ybHHHNMWveGDRuqavcRRxyREFFy+eWXZ8o3fPjwhIiS//mf/0mSZHD675prrkmIKJk4cWJZ+t/+9rfJiBEjEiJKrr322kztGCy+9rWvJUSU7Lvvvum2iRMnlvXhYLN27dr0uDzyyCNimpUrV6Zpurq6BtfApPLzU+L111+vuoyWlpaEiJL777+/6rJ81q1bl4wdOzbp6OioSXm1aGstqPf5E4vU/3naXu/jc8UVVyRElCxatCjdJv1G5Y30H5ckSXL11Venff/UU08lSaL//q9bty459dRTEyJK2trakrVr1yZJkiTjx49PiCi54447kiRJkquuumrAf1QozSOPPJIQUTJ9+vS0jqOOOirNH9qv8ZnPfCYhouTcc89NkiRJbrjhhoSIkilTpiSvv/560tPTk9rm/v+uu+66ZNiwYcn555+fHHDAAQOOX5IkyWmnnZYQUfKe97wn6e/vT1577bVkt912S4go+fznPx+0C4ChAiI0AgceeCA988wz9PDDD6fbfvzjH9OYMWNo1113FfP86le/otWrV9P2229Pn/jEJ4ioPGJj0dbWRueccw4RET3//PP0/PPPq2n/+7//m/bff38aNWoUbb755vT2t7+dli5dWlaWG841d+5cKpVK9Mwzz0TZUSsq6T/OwQcfTAcffDARbbxDpfHd736XDjzwQOro6KAxY8bQYYcdRj/72c/S/ddeey2VSiV673vfSz/4wQ9o0qRJNHr0aDr66KOpp6eHlixZQhMmTKAtt9ySPvaxj1F/f3+a9/HHH6fjjjuOxo0bR5tvvjkdeOCB9OMf/zhrdwwgVO6f//xnKpVKNHbsWHr22Wfp3e9+N40ePZomTZpEN910U1lZt99+O+22227U1tZGBx10ED300EMV23XffffRu971Luro6KC2tjbae++96eqrr6YkSShJEtp6662pVCrRqlWriIjohRdeoGHDhlGpVKJPfepTaTknnXQSlUol+va3vz2gDuv8tOp3uOEsv/zlL+ltb3sbjR49moiIjjvuOCqVSvTFL36RFi5cSFtttRVtueWW9P/+3/+j/v5+Ovfcc2nMmDE0YcIEWrJkSVpfqVSi9evXExHRQQcdRJMmTYo6RkREL7/8Mn30ox+l8ePH06hRo2ivvfair3zlK0RE9IMf/IBGjhxJXV1d1N3dTaVSiS644AIiIurr66NPfvKTNGnSJBo5ciRttdVWdNxxx9Gjjz6alv2nP/2JSqUSbbnllrRixQrq7Oykd7/73eJx89t+3XXX0Y477kgdHR10zDHHpMNBiSgdEvPggw+m29yw0hNPPDGqXRa33347HXDAATRq1Cjaaqut6MwzzywbXhQ75Cz0G7d8+fJ0WJLPW97yFiqVSvSVr3zF7H+ONNzqxRdfpNmzZ9O4ceOotbWVJk+eTD/60Y/S/dbxWb16Nc2ePZt23HFHamtro4kTJ9KcOXPUO/DHHHMMlUol+u///u9021577UWlUokOO+ywdNv1119PpVKJZs+ePWDI2QEHHED/+Z//SUREc+bMoVKpRD09PWnelpYWeuCBB+jAAw+k0aNH00EHHUR//OMf1T7I8hsUyxFHHJF+fu2118y0I0aMSP8Te3t76e9//zs98cQT9Oyzz1JrayvNmDGDiCgdCnbffffRa6+9FpWmra2NvvzlL9NPfvIT2n777QfUHdov8dRTT9Gll15KY8eOTX8Lf/WrXxER0Xve8x5qaWmh0aNH0zvf+U4iIvrFL35BREQ77rgjLVu2jL7whS9Qa2urWPaMGTPouuuuo8suu4yGDRtGo0aNoj333JOIaND/2wFoZCBoBNycETds6h//+Ac99NBD9Pa3v502bNgg5nHi5b3vfS/NmDGD2tragsPOfJxTRUTqEJzPfe5zdMopp9DDDz9MM2bMoEMPPZR+9atf0bvf/W668847iYjoox/9KE2YMIGIiN72trfRueeeS5tvvnmUDbWikv6TcH2i9cdPf/pTOvHEE+npp5+mD37wg3TCCSfQgw8+SDNmzEidtlGjRhER0aOPPkoXXHABHXTQQdTb20t33nknnXLKKbRw4UI69NBDac2aNXTNNdfQzTffTEQb/3Df8Y530K233kqHHHIIvf/976ff//73dNRRR9Gf/vSnivoltlxn8yuvvELHHnssbbHFFrTPPvvQE088QR/84Afp8ccfT9t0/PHH02OPPUaTJ0+mSZMm0UknnVSRXb/5zW9Sx3Hy5Mn03ve+l5588kk699xz6YILLiibS/X73/+eiDY6lkmS0MiRI+nuu+9Oy/rd735HRERvf/vbB9SjnZ+h+h3uXJg3bx6tXLmS9tlnn7I++9a3vkV33HEH7bffftTV1UWf+cxn6IQTTqDf//73tN9++9EzzzxDH/7wh+mpp56i7bbbjs4991wqlUpEtFGInXrqqVHHaMOGDTRjxgz60pe+RK2trXTCCSfQc889Rx/5yEfosssuo5133pmOO+44Ito4rPPcc89NndP3ve99dOmll1J3dzedcMIJtMMOO9Ctt95Kb33rW+mpp54qa+crr7xCH/nIR2izzTajnXbaSTx2ru3f/e53adGiRfSOd7yD+vv76fbbb1edeI1QuzRuvfVWOuaYY+hPf/oTnX322XTwwQfT4sWL6eSTT85Uf8xvXAxW/4d4/fXX6Z3vfCfdeOONtPPOO9N//ud/0rPPPktHHXUU3X///URkH59jjjmGbrzxRtpnn33o7LPPpgkTJtBVV11Fs2bNEutzdrnr6oUXXqBHHnmERo4cSffffz/19vYSkX1dvf/970+vq8MOO4zOPfdcGjlyZLq/r6+Pjj76aBo2bBgNGzaMfve739Fxxx1X9r/jE/sblAXnxI8fP5522WWXYHpfAI4ZMyatc9y4cdTS0kJERDvssAOVSiXq7++nlStXRqXZaaed6CMf+Yhab2i/xIUXXkhr166liy66iLbccksiotSWHXbYIU234447lu078sgjg+fl+973PvqP//iPVMQ8//zztGzZMiIi2nvvvTPZCUBTU98AUb5UOmTq/vvvT0qlUnLwwQcnSZIkN954Y0JEyXXXXZccdNBBA4ac9ff3J9tuu21CRMkvf/nLJEmS5D3veY847EwKx7/22mvJjBkzEiJK3vjGN4pt6erqSjbbbLOEiJLFixen288+++yEiJK99tor3Xb44YcnRJRcccUVmfqrVkPOsvSfNuTgl7/8ZWrPd7/7XdHeCy64ICGi5OKLL063/eQnP0k+85nPJH/84x+TJNk05IKIkoceeihJkiSZNWtWQkTJsGHDkscffzxJkiQ58cQTEyJKTjnllCRJkuSPf/xjcswxxyTvf//7k/7+/iRJkuSwww5LiChZuHBhWdlZhpzFlOsPi7n00kuTJNk4nMUNV/jSl76UJEmSnHvuuQkRJW9605uS9evXJ0lSPqwjy5Czgw8+OCGi5OSTT07Tfe9730uIKGlpaUn++c9/JldeeWVCRMm8efOSJEmSs846Kxk2bFhy7LHHJq2trcnatWuTf/3rXwkRJTvvvLNYd5LI52dM/X7/HnDAAUlvb2+a9kMf+lBCRMn48eOTtWvXJv39/cnOO++cEFEyadKk5PXXX0/Wr1+f9uGSJUvSvO48W7FiRfQx+sEPfpAQUTJy5MjUtl/84hdJa2trMm7cuGTdunXJr3/964SIyoY8/fSnP03Pvb/+9a9JkmwcXjN58uSEiJKzzz57wDH66Ec/qval3/atttoqWbNmTZIkSfLlL3853eZw5blrI0k2XeMnnHBCdLuk88cNs3Xnq7/N1Re6NmJ/45YtWzagX5MkSX9brrvuuiRJErH/Jdtd/7mhQrfddluar6enJ0mSJLn11lvLhh5px+eVV15J+6+vry9Jko3X7ic+8Ynky1/+stjuP/zhDwkRpb+Xt9xyS3pMiChZtmxZkiRJsvfeeydElDz99NNiO9x1JQ05I6Lk1ltvLesXIkr+9Kc/iX0Q+xsk4f4Hdt555+Sggw5KDjrooGT33XdPr8Xf/va3aVrt9//pp59Opk2blhBRMnny5LJ+2WOPPcrStra2JkSU/Pa3v41K4/OJT3zCHFIW2p8kSXL//fcnRJSMHTs2efnll9Pte+65Z0JEyc0335xuu+yyyxIiSt75zncOKOeQQw4Rh5z5vPzyy+lv5R577JGeYwAADDkT2WabbWjKlCl033330YsvvpgONXEhbM7dd99Nzz33HG299dbp3ZZjjz2WiPRhZ3PnzqVJkybRpEmT6A1veAP98Ic/pFKpRJ/5zGfE9C5cTkRld+GPP/54Ito4ROCll16qoLW1J2v/ERE9++yzaX+MHz8+vct8wAEHiJNIiYg6OzuJiGjhwoV09NFH09VXX0077LADzZs3b8CqPjvuuCO96U1vIiKifffdl4iIdt111/ROodvmVqCZPHky3XrrrfTtb3+bNmzYQL29vemdtn/84x+VdEtF5bqhQC0tLenKeS6dixZMnz49nYjqzrssvPbaa3TfffcR0cY7vY73ve991NLSQuvXr6d77713wJ3kZcuW0Z577klHHHEE9fX10f3332/eRa62fp9TTjlFHKJx+OGHU1tbGw0bNiyN3hx++OHU0tJCw4cPT+9oWiurxRwjN5xk//33Tye5v+Md76De3l765z//mU7U5ri71Pvvvz/tvvvuRLRxeI07x3/9618PyDN79mzVVh83XI9o4/A5IqJ//etf9Prrr0flJ6qsXa+88ko6LHS33XajZ555hp555pn0fP3lL38ZVXej/Mbdc889RET0xje+kbq6uuiZZ55Jh8pKbfGPz2abbUbjxo2jdevW0d57703nn38+/ehHP6KLLrpIveu/7777UkdHBz344IPU39+f3n13Q67uvvtuevXVV+mRRx6hzs7ONBKThW222YaOOeYYItoYQd9ss82IKO63zPoNsnjyySfp/vvvp/vvvz8dTtnf308//elPB5yTTz31VNlKYW4oVkdHB91www1ERGl030VUHf73mDS15rOf/SwREZ199tlloyEkW6qx46WXXqLDDz+c7rnnHtp6663pe9/7XlkUDoChTlMLGufkrV27dsA+f3yx9KMwc+ZM2rBhA/3iF7+gX/ziF7TvvvuqfyROtKxdu5b2339/mjx5cipMtGFnzz//PD3xxBP0xBNPUKlUore85S30/e9/X3XeX3zxRSLaOBTAzRsg2jinwNHV1SXmrZTB6j+ijcPLXH/885//pO23357OOuss+vnPf646h2eeeSadc845VCqV6M4776Rzzz2X9t57b3rb2942oC/Gjh2bfnbDRfwx+G6bm0Ozbt06mjNnDm233XY0cuRIGjVqFH3nO98hIhqwjGgWspb7hje8If3snBBn4wsvvDCgbX76WLq6utI/X/98GjZsWFr2Sy+9RJMnT6YtttiCHnjgAXr++efpkUceocMOO4ze9ra3EdFGx6sSQRNbv482tj3rcZaIOUbuevTri8Hl89vpf5cc9thx/NK5QmS3VbMvS7v81QuPP/54mjBhAk2YMCFdht4No4utezB/4yTcMsj33Xdf2hZ3w+OVV14ZYIN/fIYNG0a33347vfGNb6THHnuMFi1aREcddRSNGzdOXDrY5Tn00EPptddeo0ceeYSWL19Oe+65Jx166KG0zTbb0N13302///3vqb+/P9N15cNXlnOOd8y5Yf0GWXzta19L59+5Gx5JktCCBQtSEeDwVznbZpttiGjjdfvXv/6VpkyZkn4nKh+K1t/fT319fUS08ZyNSVNL/vGPf9D3v/99IqIBwyslW9znrHZ0dXXRtGnTaMWKFbTjjjvS8uXLMdwMAEZTCxr3I/7EE0+k45Adf/jDH4iIqKOjg7bYYosBeV004eqrr6Y1a9ao0QU3Vp1oo5P/0EMP0UMPPVQ2wfd73/vegHz+j/2rr75K9957L82cOVNtixuXu3bt2rIJlc6pJYp3Zl966SX6whe+QKeffno6aXjdunWpU+l+iAej/xwTJ05M+2PDhg307LPP0rXXXpvecZZoaWmha665hl588UX62c9+RhdffDHtsMMO9Jvf/IYuuuiiiJ7QueSSS+iqq66iUaNG0fe//32699570wmdjVKuc/R8B8taUEJjzJgxNGzYxp8CfxL5+vXr07K32morGj58OB1yyCH04osvphOYp06dSnvuuSdtvfXW9Ktf/YpWrFhBRNkETWz9Pk5s50HMMXLXCBcgq1evptWrV6tzE9x17LeTaNN1zNtJVLu2uj72b1BwOypply8YP/e5z9Fdd91V9jrjjDOi7Iv9jdNutPC2VIpzNidPnjygLXfddVeZWCQaeHze+ta30l/+8hd69NFH6Wtf+xodc8wx9Oqrr9Lpp5+uTuJ20c+f/OQn9Mgjj9DUqVPT7ffddx/99re/JaJs15VPnhGKGEaOHEkHHnhgGm3jC2yMHz+eHn/8cXr88cfp4YcfpjFjxtCaNWvKns3momSrV69OIzxPPfUUJUlCI0aMoJ133jkqTS35n//5H+rv76fdd9+d9thjj7J9zhZ/aeW///3vRERpdDaG/v5+Ovroo+nhhx+mPffck+699950Pg0AYBNNLWje+c53UqlUou7ubjr//PPTuzT33HMPXXHFFURE4gO1iIje/OY30/jx49M/Ek1s3H333fT8889Ta2srvfzyy6lTniRJehcqdrUzi7e+9a1lk38d7s7x/vvvH/28i1GjRtEnP/lJWrJkSbp60f/8z/9QkiRUKpXojW98IxENTv9Vw7e+9a109aAjjjiC5s+fT4sWLSIiqnp9fjcE6qijjqKZM2fSm970plSkas7qYJfrjtPSpUvTO6b+uRGLW/WI5//e975H69evp9bWVjrkkEOIaJPjdf3115d9f9vb3ka///3v0zuI2gT2ausfDGKOkVsg4YEHHqB//vOfab7tttuOJk6cSGvXrk2dyN7e3vT4HH744US08YaAmxi8bt06uu2228r254G7QeGiaD09PQOeFxXTLk57e3s6vK+lpYVmzpxJM2fOpGHDhtG6deui70bH/sa5dqxbty5d1W/FihUDIuFS/8faQUT0z3/+k4444giaOXMmvfnNb6Y1a9bQqFGj1NWoiDbe/Ln00kvpq1/9Ku222240a9YsuvXWW2mvvfZKb9RIuOvIDa9ywuWwww6jtWvXpo69JWhce/3oeSOxdu3adEiltVDNtttumy5AcfHFF9Njjz1GRBsXeth5551p3bp16TNl3HkydepUam1tjUpTS9zwQOmmlNt255130vr166m7uztdre9d73pXdB1f+MIX6Fe/+hVtscUWtHTp0uiILQBDjsGftjO4fOxjH0snN44YMSIZO3Zs+n3ChAnJs88+m6Z1kxlXrlyZJEmSzJ49OyHa+OwUNzmYT2r/8Ic/nND/rRHPeeKJJ9K63ORzbY3+GBYuXJgQUdLa2pq8//3vT975znemk6Z/8YtfpOliFgU477zzUtve8IY3JKVSKSGi5IwzzhjU/tMmhcZw4YUXJkQbF1I4//zzk/POOy/ZaaedEiJKrrnmmiRJ5In71113XUJEyUEHHZRuW7RoUUJEyeGHH54kycYJ70SUbLvttsm8efOSN7/5zcnb3/72tN3f+MY3KloUIKZc7VkZbpLw3LlzkyRJkgcffDA9bm9961uTk046KZ18S0TJX/7yF9EGqfy77747fe7P9OnTk5NOOimdSHvJJZekeX/729+mef0FLL74xS+m2z/4wQ+ax006P2Pr1/qXT2qW+itJkuSoo45KiCj59Kc/nW7jiwLEHKPXX389OfDAA9PJz6effnq6MIir78knn0z7ZPr06cl1112XbNiwIZk+fXpCRMl2222XnHrqqenk+e222y5ZvXq1eow0pLa7Z2kQUfoMjzPOOCMhomSzzTZLTj/99GTPPfdMpkyZkhBRcvzxxydJkkS1S7Ltu9/9bkL/Nxl+1qxZyXHHHZeUSqVk7NixyT/+8Q/z2PnE/Mb19/en5/nEiROTM844I9lxxx2TvfbaKyGidPK91P8xiwKsW7cu2XfffdNr+6yzzkp22WWXhIiSiy66yDw+q1evTrbYYotk+PDhyQc+8IFk7ty5ybHHHpsQUbLDDjukx4Lz+uuvJ6NHj07LdOfBQw89VPZ767DasfXWWyennXZa8s9//lN9Do07pj/+8Y/FPoj9DZKQFgXYf//9k46OjoRo44IYd911V5Ik+u9/f39/+l9x2GGHpc9mc+0ZMWJEssceeySlUikZPnx4cvfdd6d5Q2n+/Oc/JzNmzEhmzJiRTJo0Kb3W3bYf//jH5v7HHnssrcudh9IiCX19fenzYsaPH59stdVWCbEFAW6++ea0XPffuueeeyYzZsxIjj322CRJkmT77bdPiDY+j2f8+PFlr/e+973qcQBgqNH0giZJNq6ydeCBByajR49ORo4cmey8887Jueeemzz//PNl6bhDfueddyZEm1a+SpJyh3z9+vXJNttskxBR8s1vflOse7/99kuINq2OVI2gSZIkWbJkSTJ58uSktbU12XzzzZMjjjgi+fWvf12WJkbQ9PX1JfPnz08mTZqUtLa2Jtttt11y3nnnJa+99tqAtHn1X5JUJ2jWr1+fXHLJJckee+yRbLbZZskWW2yRHHDAAclXv/rVNE2lguaFF15IjjrqqKS9vT0ZN25ccvHFFyevvPJK8ta3vjUZMWJEcsopp1QkaGLKzeJMLFmyJNlhhx2Stra25NBDD03+/Oc/J6NGjUqIKHnggQdEG7Tyf/Ob3yRHHHFE0t7enrS2tiaTJ08uWw0sSTY6e24lqv/4j/9Itz/44INpmTfeeKNYr0M7P2PqHwxBE3OMkiRJXnrppeTDH/5wsu222yYjRoxIdtttt+Tzn/98Kt6TJEk+/vGPJ+3t7cnmm2+efPKTn0ySZOPDT//rv/4r2WmnnZIRI0YkW2+9dfKBD3wgfdhgkuQjaP71r38lxx57bLLFFlsk22+/fXLFFVekq6H5N2RC7dJs++53v5vst99+yciRI5Mtt9wyOeqoo8pEdexDZ2N+4x588MHkoIMOSkaNGpXstddeyU9+8pPk+OOPT4goufLKK9X+jxE0SZIkzz//fDJr1qxk6623TlpbW5Pdd989+fznP5861tbxeeCBB5KZM2cmW2+9dTJy5Mhkhx12SGbNmpU8+eSTZrvdA2f9Fbo2bNiQbLnllgkRJR/4wAfS7VL9//u//5vsueeeyciRI5POzs7k2Wefraug8V/Dhg1Lxo8fn8ycOTP51a9+laa1fv8ffPDB9KG3/gpx11xzTdLZ2ZmMHDky2XvvvZPbb799QF4rjb/Km/RavHixud/9Tvht/fa3vy32xcqVK5OZM2cmo0ePTsaMGZPMmjWrrD8//elPq/WMHj06SZIkFYLSS1qBFIChSilJqpjhDAAAAAAAAAB1pKnn0AAAAAAAAACaGwgaAAAAAAAAQGGBoAEAAAAAAAAUFggaAAAAAAAAQGGBoAEAAAAAAAAUFggaAAAAAAAAQGFpqbcBeXHuuefS3Llz620GAAAUgn/84x94CjkAAESSJAmNHz++3maA/6Npn0NTKpXqbQIAAAAwqLS3LiQiop6+edTeupB6+ubV2SIAyunq7aSxbSvrbUbVdHd30xZbbFFvM8D/gSFnAAAAQMFpb12Yihn33YkZfzsAADQjTTvkDAAAABgq+OLFj8pAzIBGoau3s94mgCYGERoAAACgoFiCxYmb0LAziB6QF+2tC2nF9Ln0Afo2jbh6N2r98i5NMdwMNB4QNAAAAEDB4EPMHIvGjCubR8Pz+O8OTfBA6IBY+LmyYvqmRZmmLe+gc6c/TK9/7G/07AO7IFIDcgGLAgAAAAAFgs+P8RcA8B3LVd2LaWzbygHD0Nyk7NhFA5plEjcYXKTztJnOJSwK0FggQgMAAAAUCCdcuIjhAkVyHNtbF2YSM1o5AFi0ty6kv895gBZvu4SINoprd+6B5mDp0qX07//+7/SnP/2p3qYQURNFaBYsWEAXX3xxvc0AAAAAckGKwvj09M2jxdsuodnPnSbm9XHRGwBAZQzlCM3PfvYz+tvf/kZPPPEEnXbaabT33nvX26TmETQcDDkDAADQrFiixk9Tq+fRSMOHYvYB0KwMZUHz2muv0WabbUYXXnghnXLKKQ0haDDkDAAAAGgS2lsX0qeG30RE5QsEhFZD4/v5Nl+kcMFi7ZPqAgBUxrRp06hUKuX66uwML9qw2WabDUJrs4Hn0AAAAAAFgq9ixoXHla0Lqb1lIc1Zs7psuxY90bbVWny0ty78v6Fum74jmtPc4BjXluXLl+e+SlxRh6IiQgMAAAAUCEnIuO1c7FTjTGqiRormSEjRIecswdEdGuAYg8ECggYAAAAoADHDx4hIXfksa9Slq7ezouiNu4PMRZW/Otuq7sXRdgAANlHaMCzXV1EpruUAAADAEIJHYPh3vgpaT988Wja1e0A5MRGW9taFNKFjtprOmjfjD1nhc3GckHFlZxk+EyOiAAD5c8MNN9AZZ5xBjzzyCF1++eV0xhlnUE9PT11twipnAAAAQAHg4oVoo0CY0DE73ea+O7TVyLTyK9nPh7lJ27gYqvYBi5ItfpnN9ABH0JjUY5WzUqlEa17dNdc6xox+jIooDRChAQAAABoYaRUyoo3CwIkX5+CPbVs5wNFvb11IK6bPNct35VlYc3f4Pn8bT7di+lxTbPjlS5EX11beJr9MiBkAhhaI0AAAAAANjvasl5j5NFIZMfW5/DxfNdEPl1ezJaZsRF9AI4AITWOBCA0AAABQALS5L9pzYbTFAXgZLo0fDfEjLHwRgLFtK9O0Xb2dZftCAssJEU1YSUKlq7ezzDaIGTCUKW0o5foqKojQAAAAAA0On/BPJK82JkVxNPxIB09fi2WVeSTFKjN22BsAjUK9IjTdr+yWax0dm/+tkBEaCBoAAACgAbFETMwSzHzyvbQKmtvuf5dYMX0uTVl6uSmgpO1+vUQYLgaah3oJmpe78xU0W3RA0DQUEDQAAACKjiRC/G3aKmJSGX46N6l+ytLL03Sa2PDzxK4kJkVj8DBN0ExA0DQWEDQAAABAgxETCbGiNNbwMU3gaBEcns6yN4tgyTosDoBGom6CZk3OgmZMMQUNFgUAAAAAGgy+7LGbvK9N1Hfp+QM3rQdOrpg+Ny1v8bZLBjwsU1pQwC+PLyedNfoSkx5iBgAQAwQNAAAA0GBIYsVt96Mpbrt7Jgtfccw9l8aJD1+cTFvekX6fs2Y1EQ1c6cx/J9ooMFZMn1tWvxTxaW9daIopqa2hOUEAAKCBIWcAAABAAyMNM1s0ZhzNfu60Afs1NOHhl5PVBqnc2PZoq7FpQ8xCz68BYLCp15CzV17Kd8jZ5lsWc8hZwwqav/3tb/SVr3yFWlpaqK+vjz74wQ/SAQccEJ0fggYAAEBR0VYyi1kMQJvEz9Nb5XJbeBpJiEzomE2ruheXraom1SO1L8ty0wA0AhA0jUVLvQ3QuO666+iUU06h/fbbj5566in6xCc+Qd/61rfqbRYAAACQK9bEfym6sXjbJURENGfNwAdvTuggIho432bF9Lk06c5bBkRDQs+jcZ9XdS+mCR2bxIgvZvhwOCsi45fp4EtNS2kAGLJsqLcBjUnDzqHp6Oigl19+mYiIenp6aOzYsXW2CAAAABg8tGgKn4x/7FOfTufA+PNeVnUvLsvni4JJd95CEzpmp3NdpLq0SJCbm+PK7OrtTEUNEdGyqd1lZUrza1y+Vd2Ly9Ium9pNjx91PLW3LkyFWqg/AACgYYecrV69mubMmUNbbrklvfDCC3TJJZfQHnvsEZ0fQ84AAAAUBT6USxuuFbtsM8+n1SWl47a44WRS5CZktzWEbdnUbpqy9PK0/GVTu2na8g76+5wHaKdF+4tD2zAkDTQKdRty9q+ch5xtVcwhZw0bofniF79IZ599Nl177bV06aWX0uWXX04bNgyMsy1YsIBKpdKAFwAAAFA0+PAu7VkwfP5JT988On/9wBXKlk3tLouMuNXQpDr9yE9760Ja1b2YzqKb6PGjjiciSlc343ZKSz77Nro8ft2ruhfTtOUdtHjbJXTrxE/Ssqnd6fadFu0v2sb7AYChSGlDvq+i0rARmne/+9101113UUvLxmk+J554Il177bX0hje8ISo/RA0AAICikiViE3rA5qeG30SX9J9UVjbPz+uU6uD2uQiLX+b56zvpypaNw9H8BQJWTJ9L05Z3pFGYZVO702FvbmiciwJJESHeLwDUm3pFaHqezzdC074NIjQ1ZYcddqC//vWvRET0/PPPU39/P+bRAAAAaHr4AzQdftTFpfPfeTrHlS0rxefQaOJAG1omPeuGz3NxwskXM2fRTen+SXfeUiZgXDo35IyI6PGjjldFiyVmELkBQ4FSku+rqDRshObRRx+lr3zlKzRixAgs2wwAAKCp0ZZD5mmI5OWTXcRk2vIOs54s814qEQgbVz+bXfZZE0i8PgCKRL0iNK8+l2+EZvS2xYzQNKygqRYIGgAAAEXDGgIWGlrm8lSCVLYvTlyaFdPn0pSll6dDyPx8XMBYkSM3R0d6iCYARaBuguafOQua7YopaBp2yBkAAAAwlLDEij8ETXuOixNAi8aMM+tZNrWb/j7ngQF5/fK4OOFLQE9Zejmdv75TfE6NPzxNGjrnkJ6BAwAAlYAIDQAAANAgaMsUa8+E8Vk0ZhzNfu40MTJiiSUpAmQ9FFNa6SxWjMQuwewerolFAECjgghNY4EIDQAAAFBnJCHBH0TJoyg8WuPEjLQimV+2ts96kCZfgGBV9+KybYvGjCtbtEBazICXawkV/8GdAIBNlDYkub6KCgQNAAAAUEckEcKHamV5oKY0DIyXIcGHl0mrrbn3sW0ry0TNnDWr03KmLL1cjBJJw+UAAKAWQNAAAAAAORIrJHjarIsAaGmlqIg078WldWJFWwnNfbfmwEC8AJATSc6vgoI5NAAAAECdCc0V0ebSWPNtsizHzPdZS0jH5Pe3u5XRMB8GNBP1mkPz2jO75lrHZjs8Vsg5NBA0AAAAQIMgzaXxkYSGNIFeW+pZeg6Nll4SR6Hy/XoAaGYgaBoLDDkDAAAA6og0JMxNxOfLNfPJ+URU9qwYvi80DM2vh4uTs+imNE1oqWii8qWdXbmxq59hyWYA4ihtyPdVVBChAQAAAOqMNFwsZogZJ7RggJTeleU/6FJbjcx6WCZvi2QzAM1CvSI0a5/ON0IzakdEaAAAAABQBVIkhEdriMIrmbmHY7rP/iR9f1llP4oyoWP2gLp41MUvj2/384aGzvkgOgNABjYk+b4KSku9DQAAAABAeP6MxPnrO+mS/pPESMjGeTWbyj5/fSddSStp2vKONM2q7sU0oWO2OqfGzc1x9PTNS+fs8Dzawzj9fRKxD9sEAAANRGgAAACAGsPFiDafhEc9NGdei9xc2VIuOKSHYDqubFlZlpeIyoaYuRXJHH50xrfBFzkh8eX2d/V2ivtD7QQAlFNK8n0VFQgaAAAAoMZIEQppmJY00V8SOVoaq25NbHAB5NfhPxSTr5wm0dXbGUzT0zdvQKQHAABqCRYFAAAAAAaR0HwYa8iWtFhAKI9Uh79dImZ550raatkCQJGo16IAvSsn5VpHW+fjWBQAAAAAADKaGNCEhjasy1rtjEd+tOFoPO2q7sUDFgJwZazqXizOo/GHp3E7rLZBzABQBRtyfhUUCBoAAABgEJCEiXt3r67ezgFDxqxypOgJ/+zn0yItboUz99mlccPFuE1j21aWDU+z7LXswjA0AEAtwCpnAAAAwCCiTfAncmJi3oB9XIzwOTlW1EOaM8P3xy6vLA13C2HZiWgNANko8sMv86RpIjQLFiygUqmUvgAAAIBGxBoG5pCiObV+tosUCXLDzyTRwp9JkyW6gkgMACBPmkbQzJ8/n5IkSV8AAABAI+OvYiZFTfx00j4XLfGXRNaWbeaT/KW07uUPMfNxSzz79lriKmY4GgAA1AKscgYAAAAMMlwAaMJAG+LFxYlfhr9fq5fnleyQygu9AzBUqNcqZ32P5rvKWevuWOUMAAAAAAxtZTEenZGeWSPNb7GWbvYXCpBWNfOfOSMNObOGhvmRICniE9N+AADIAwgaAAAAIGekoWC+ALGWYublaILEf5eWiJY+x5Thr3bG7ZDK5iByA0DtKCX5vooKhpwBAAAAg4glXqxhXFnm1VTykM3Qfm01NAgWMBSp15CzdX/Nd8jZyD0w5AwAAAAA/0eWoVh8iJk1nItvt5Zl9oeYSXXxh2P6+7mtUjnS0DYAQI7gwZoiiNAAAAAAOSFFTiqJmlRSj1a2VteK6XNpytLL0+9dvZ00tm1lVMQmyz4AmoG6RWj+knOEZk9EaAAAAADAkCbfxxIb/dBWJ5PqlYaNtbcuLBMzRBsf8ikt9ezX45aNdgsGIFIDAKgHEDQAAABATkhDsvhzZ7S5KTELBXCBEYO0CIE0vM1aRMCVc/76jVEcHslBdAaAnEhyfhUUCBoAAACghljCwE8jPRNGGpIWs/pZ1rSSTVIkRrJ3xfS5af4rW1YOEGFaGQAAkBeYQwMAAABUifXwS22/tF1a4Sw0V4UoHBGpZB5Mljp4mq7eTprQMRuRGtC01GsOzev/m+8cmhH7FHMODQQNAAAAUAe0BQO0JZetif9a+dbyzlp6afECKeqkbYOIAUMBCJrGAoIGAAAAqAG1iKRYuFXHYuqyokUhNFHlPp/S10nfbF2ZbgdgKFI3QfNQzoJm32IKGsyhAQAAAHJCWqVMeoaMS2fNO3FihsgWErERFcnOkJjp6ZtHX6aTxLYAAEC9QIQGAAAAqDFZ5sDkFeUIlW09D0cbbhbznBsAhgKI0DQWiNAAAAAAFRJadSxmGJr/PesKYVr0p6u3M7jiGd9vLbssLfHsylzVvThoFwCgRmDZZhFEaAAAAIAaI0U63Hb3PcvKZ46Y7ZINq7oXD3hWjLVogFWmDyIzYKhStwjNgzlHaCYXM0IDQQMAAADkgCVYHFkFQWjlMmsYWUzZjlXdi2lCx2yzLAw1A0OZegma9X/YNdc6Wt78WCEFTUu9DQAAAAAanUqc9ywT92PL19KElkyOicb4+8e2ERHZwgViBgDQKGAODQAAABDAEgOh7Xz1Mmmeii9wtDJCtvG5LTG2cyGjzbXx59iEVmMDAIDBpmkEzYIFC6hUKqUvAAAAIC+sSIm0TRIN/gR+KW9INEgCyNVliRpt4j/fZg2NQ3QGgDqxIedXQWkaQTN//nxKkiR9AQAAAINJbNSCCwo3V0VLxz9L6SRxo0V8pDRWudoSzgAA0ChgUQAAAACgRvjDsqyJ+dLkfV6OIxQN0aIxeUzar2ZBAwCaibotCrAi50UBphRzUYCmidAAAAAA9YbPZ+HbifQHWvJ9MfNb/PJ5emnhAZ9Koy3asDYAAKgXiNAAAAAANSYmUlPLCIoV8alVPViyGYBN1C1Cc/9uudbRctDfChmhgaABAAAAciA0rIynrcUzafLMl1c5ABQRCJrGAkPOAAAAAIGYIVXWMC5pyJg2UT9mLk2W7db+WokQiBkAQKOAB2sCAAAAApU86FIb8mXl0+bUcEHE9/HvIbGCiAoATUCBl1bOE0RoAAAAAMr+QMvY8kLLL0vCxZp0r4koP4/0TBuIGQBAswJBAwAAAFA+Q6gsgeG/18IeP1JTbVkAgMaklOT7KioQNAAAAIBC1rkt/n5tyWV/nzakzBI8oQdj9vTNo67ezsw2AwBAUYGgAQAAMCSxHHxtsn9oeWSH9fwYf+njUF5tiJq1n4hobNtKMx8AADQTWLYZAAAACFDtssoxSzhrdcTUjTkyAAwu9Vq2uf83u+dax/BDH8WyzQAAAECRiR2W5Q8Li0mrrVjml6XtD4kglyZmGBwAADQjEDQAAADA/xErKiSBIs17sSbq+2krja7wsmu5yAAAoAFJSvm+CgoEDQAAgKYlyzyZauHLJ0tzXWIfqCkRYy9ECwBgKAJBAwAAoGmxHPxqnH8ejQk9M8YaVhZrRyh6JEVruno7MRQNgGZiQ86vggJBAwAAoKnIMreFp4/Nqz0IUxtCNliRE9+Onr55NLZtZbBuRHUAAEUHggYAAEBTEbMimJ8uZhlknp+X0dXbST1982hV9+IoGysRUTHlhZabBgCAZgTLNgMAAGgqsky0r8WSyP7E/2qWdq4V2jNtAAC1o27LNi9/Y651DJ/6SCGXbYagAQAAMKRolme2xDy3pppn2wAAdCBoGgsMOQMAADBksBz8wbShFumsRQKqebYNAKCBwaIAIk0jaBYsWEClUil9AQAAAA7pAZTS0KzQ82Ji99XieTCVig4eoQEAgGanaQTN/PnzKUmS9AUAAAA4Yif+1yqiEfMwzVqgLQLgbEAkBgAwFGgaQQMAAABkIdbZr2ToV0hoVEKlS0oDAJqIDaV8XwUFiwIAAABoOmJXJqtH3UWrBwAwkLotCvDzPXOtY/gRfynkSCdEaAAAABQSN/FdilxwR5+nqTY6U4s5MpXUy+vR5u9g7gwATUpSyvdVUBChAQAAUAhCK5QVPeICACgOdYvQ/GyvXOsY/s4/I0IDAAAA5IU1Yd/fZ81fqbTeRol4hOyoddsBAA0Glm0WgaABAABQd7I43pLTLi3F7L5bYie2jkaJ0ITs4OKrUewGAIA8gaABAABQdyqZ0+IPNdOWZY5x8GPm4NQTTaxpNFJECQAABgMIGgAAAIXCiQ1JdMRus/ZnFQN5igcXYcoyj8fPAwBoMrAogAgEDQAAgEIQ49SHViWLXT0s1p4s6SvBEm9aW6w8AADQjEDQAAAAaHhiIxTWwgHuPetSx7FLQ1eDNi8IURYAQBl4sKYIBA0AAICGRhIzlUzuDz07JmYVtbxWEeMLF7g6LdGECAwAAGwEggYAAEDhiHXmpcUCKhEC2vCymLKyip5qhAoiOgCAoQgEDQAAgLoTip5UmlcbtlWNyMgapRnMSAqiNgA0OUnOr4ICQQMAAKDuVCNatOFgVrmVPJtGKxMiAgAA6gsEDQAAgIankgUBpGFiMaIHw7YAAA0LFgUQgaABAADQ8FSyCIBb0SzrgzUrjbhACAEAQH2AoAEAANC0hFYKc2mIsgmSvJdxBgAAETxYUwSCBgAAwKATEg98ueVKVjWrdG5MLR++CQAAIH8gaAAAAAw6WaIm2sMw3X5pm5YPYgUAAJoPCBoAAACDQiVzTKxnx2iRG+nZM9r3Su2qFMyzAQBUBRYFEGkaQbNgwQIqlUrpCwAAQGMRM6QryzNlrEhKlihLnhEZCBgAAMifUpIkBX6Mjg5EDQAANA6heTBZ5snkZQMAAMTS3d1NW2yxxaDWWSqVqP9/9su1juHv/SMVURo0TYQGAABAYxC7Alge0YuYB3ACAABoLhChAQAAADKCaA8AQ5t6RWjW35ZvhKblGERoAAAADGGaYb6Ia0OoLRAzAADQOEDQAABAE9AIYiIPJz/Lcs3S9kqfRQPBAgAAxQGCBgAAmoBKHXA/IlHp6mN5oQ3r4tu1lc+sdNXY1AjiEQAwRElK+b4KCubQAADAEMM55JoIil0aWcsbmyfPeShdvZ00tm1lzcvF3BkAAFEd59Dc+uZc62g59g+YQwMAAKB2xEQCpDT+Nq0M7pRzEeNeWewMzT/J8hwZq5wYO8a2rcwlkgIxAwCoK3iwpggiNAAAUHD8qIEffbGGYvnbmiHqYLWh2qhRXtEeAEBxqVuE5pb9c62j5fgHEKEBAACQH1okxHfApciKJWa0/dbnPAhFmkL5LMGSRaxJaSFmAACgsYGgAQCAAiBNcPcjLM6xXzF9bpmT7yI17qUNJdMehunni7Uza7tcXVL9IWrxwM5KxBoWBgAA1AUsCiCCIWcAAFAALEEjfQ+VRWSvCGaVWekQtSzDwvIeBtcMw+wAAPWjbkPOvntArnW0nPD74JCzNWvW0Oc+9zl68cUXqaWlhS688EKaOHFiWZqXX36ZrrjiCurt7aW+vj5629veRscdd1xudiNCAwAAg0jWYVzWpH5/6BmP1vhRm5jJ+FxMSPNytLpi2jBYYiZkjzR/qJJyAACgLjTAogA33HADHXzwwXT99dfTrFmz6Nprrx2Q5vbbb6dddtmFrrjiCrryyivptttuo+eee67WvZECQQMAAIOINneFKJvA0SIqRETLpnYPqDO0aAAvQxMzPE3Mc15C6WKfFRPTPyEhFCuUEL0BAACZ3/3ud3TEEUcQEdGUKVPoySefpN7e3rI0HR0d9PLLLxMRUV9fHw0fPpw222yz3GyCoAEAgAahmnkq/vyYacs7yubPaHVIokWKlLhyQs+t8efc8HJjl2u2lovWRJhkSywQLgCAQpHU7nXJrc9Sy0kryl4h1q9fT/39/dTW1pZuGzNmDL344otl6d7znvfQk08+SbNnz6ZZs2bRiSeeSJtvvnmVjdeBoAEAgAbAGh7miwn+7j5bE/j5EDS+jwsRXqcWFQo94yYknmKiMZYgs+qOqQMAAIYynzpmPK3/zpSyVwx8jk2SJDRsWLmk+N73vke77rorLV68mJYsWUK33347PfvsszWzndOSW8kAAACi8aMPMVEDzdGXlmzmaUMrpvn5Yh6wmcV2a8gdJ+tcGl+c+f0RGx0CAABg09LSQiNHjqS1a9fSqFGjaMOGDdTV1UVbbrllWbo//vGPdMIJJxAR0eabb0677bYbPfroozR+/Phc7GrYCE1/fz994QtfoNNOO43OPPNMevjhh+ttEgAA5Aqf66KtRsbT+tuld+7c8wdqhp5jw+3x04aWg5Zskvbxz5VEWixRFyPMAACg0Uk2lHJ9xfCWt7yFfv7znxMR0T333EN77rkntba20ksvvZQOPZswYQI98sgjRLTRp3/iiSdoxx13zKdTqIEFzY9//GPq7++nJUuW0Ny5c+mnP/1pvU0CAICKyDLsKeTUa846zyOJGGlYmzbUjefxy+L1S22U2hw7p0cqO69VzwAAAGTjQx/6EN199910+umn0y233EJnnXUWERH94Ac/oFtvvZWIiD7wgQ/QX//6V7rgggtozpw5NH36dJo0aVJuNjXsc2guuOACOvPMM2m33XarKD+eQwMAKDLWc2FiJugTyYJAEyhWRETbJ4mlrAsbVDLsTIoyxeYN5clKLcsCABSHej2H5vX/PijXOkZ88P7gc2gakYaN0KxevZoeeeQR+n//7//RBRdcQH/5y1/EdAsWLKBSqTTgBQAAjYSLgHT1dkal5REVrTz/sz/0y3rOir9fGubGh5BpdoSGkEnbYoaocVv9/F29ncHFBvg+bkctBQjEDAAA1J+GjdAcf/zxdMopp9DMmTPpb3/7G33qU5+i73znOwNWUdCAqAEANBpZow3aqmKhNNbyxtowspDzz6NDoWhRKHKjzcfhdUp5sxAT1bLyAQCABCI0jUXDRmi22mormjx5MhER7bbbbjR8+HDq6uqqr1EAAFAlPNphOc3WPj/Swct2eaX5NP4+Ps/GighJ27X6NTEjiRj3ipkflCVSxLdroksDYgYA0JAkpXxfBaVhBc3BBx9My5YtIyKiVatWUX9/P40dO7bOVgEAQHVoIoKjrf7lvkvDwXxxYJVllW3ZIgkOPkxNsjG2baGFCjS0iFRoWBtECwAANAcNK2iOPfZYeuaZZ+jss8+mz3zmM3ThhRdGDzcDAIBGRIumcEGgCRarTO7UcxHCozNatCSGkE1+fdwWTej40Rppfo+UP0sEBqudAQCagUZYtrkRadg5NNWCOTQAgEYndoWukHCITevS+3XwMrRV0KR0oVXO2lsX0rKp3TRteYc6FyfU1pj5RSFhAwAAtaZec2jWff2tudYxcta9mEMDAABARxteFRp2pjnpWlTG7fPzLZvaLebnq6nxKIlWP8+n2e3EDBdPflpNEIXax1dMCw1xQ5QGAFB4MIdGBBEaAADICWuFLcmR15x+LRIiRVWsPP4+Z0MIq3y/LaG80mcXvZH2+2SZnyOBFcsAALWmbhGarx2cax0jT70HERoAAACb4PNX+D4/uuCjOeBahCcmOsEFgbaqmNUOXq4ldKy2u+3+UDTNdsvO2IgLxAwAADQ3EDQAAJAD0jAnLVLif+fDwKRoDRcLflpNPEn7tdXTrDJC9ltl+581oSINJfPz8PZyajG8DEPTAAANy4acXwUFggYAAHIgFBWwliiWJsxLjr1WnhVR0SbQa2Ikdo6PFDWx6uLCxSrfimRxOyWxZ4nK0HA6AAAAjQ8EDQAA5Ii2Spc1t4Vvs0SMJCa4cy8JC61+SxBJk+8loSTl06Iq2sR/3m9S5KWnbx6tmD5XnaMkzb3R+qOWIMIDAMgNLAog0lJvAwAAoNnRxAWR7lBrAkia7B8TZbCiG7xOzeF3+1dMn0vTlst2+Xm0IWIh0cTbrNnc3rqQpiyNWwGOtyVPEOEBAIDBBREaAACokmoiFA7JAY+JasQsIGDZaYkMKTrU3rowncwviRleN48ISWKMR4b8qI82/0fCr8OyS+tnRFYAAKCYYNlmAACoATET1bUoiyRmeFlcJHAB0tXbSRM6Zqtl8TKtoV0hu7gd/vLL0n6pbm1ImWSfVoZUT2zES9pv5QcAAJ96Ldvcd8OhudbReuZvCrlsMwQNAADkSMzwKilK4bYT6U48T+djiaQYwaOVm6WsGDSRY7GqezFN6Jhdlt9HEidaHwMAQCVA0DQWGHIGAAA5Ys0n4Y43H+KlRVMc0sR3f19oZTDJ1tBQNm5rrDiwIifSZ75amf95bNvK4OIEUl9qgrEaMEwNADCoYFEAEQgaAADIgDVJvZq8vjjRIhXaMCwfS8BwEaB9lurj9cbUbw2Z8+2RolV+fZoo9Mt2ZUl9p81xkgRRVhDtAQCA+gNBAwAAGVjVvXhAZMSPDkiOMXe4NaES2uYjDafy82n1WFGV0NA3yYaQCPPTWsPLtGFiPH0WceOn533PBQ+ECQCgCCRJKddXUcEcGgAAiCB2PoufXoswxEyO98vWtsXOo6lkrosmUPhQOWtej7TIgI/Vp277WXQTfbN1pZjOyhfajvk0AIBqqNccmt6vHJZrHW3/8SvMoQEAgGZFG67F52pYEZLQcCgrEqEhRV2yzJuRsObOWAJNa48WFZE+87q/TCeJZUv2WWJTEmL8GGE+DAAAFBMIGgAAiCRL9MR99gVPyHkORQ1C20NzYkLp+RA6P40k1KxhXA5t2BuvU7LRpV02tVttQ0z7LHHp14NoDQCg4dlQyvdVUJpG0CxYsIBKpVL6AgCAWhOa5O5/90VMSGj09M1L5+Zo+7U6uC0+UgQlNAxtVffiAW3Q7A5FTKTPUn7eFm7blKWXi+VJArHSif5ZxAwiOQAA0FhgDg0AAFSANXfEbeNpsn7n9WiE5uHwOmLmx8TUo9nnyjl/fSdd2SLPf7Fsi+1DLV8ILepkiRppvhMAYOhStzk0107NtY62s5cXcg5NS70NAACAImLNUwmJCq0MXp40NEyqT/puodkaY3NIdPhc0n8SUX9YxPh1W3NrNFERWnzAsl0r3+oDiBkAAGgsEKEBAIAKkRzgkENNZK8O5qfjhCbma/NepDI02zQ0IWAJGylytKp7MU3omC2WmSXCEmuvZqtvFwAAZAURmsYCggYAADKiOcKaA+2QRAjP77bHREI0xz7WtthhYNUQE92x2hET+ZJEUiVD0AAAIJZ6CZq110zLtY5RH11WSEHTNIsCAABAXsQICUsw+MJBEkNdvZ2i882Fj7ZggJbPX5hA2+/KsIZaWcOt/P2SnZoNbp9UtmaPJiR9MRPCle2/QosqAAAAaGwgaAAAIIDkoFuT+K05KZID7Tvki8aMS8uPGYpmDV3T7LciQ1JbfHusYVwx81gk+6V5NFwEceFjiSdpeBwXL1rZMf0JAAB1I8n5VVAgaAAAQ5JK775rzjTfL0U2rKiDc7bnrFmt2hdysCUhoQ3B0ubjSG3jeaWIT6wQ4HX09M2jZVO71UgNz8fr0ob18f7WomO8jNAwPAAAAI0HBA0AYEiS5933LA4yjyZow7d8R54LEGuoW2iOimSzZi+vT3qPEQI8EjNl6eXqvKJQNEiKNoUiPFoUh+8HAIBGI0lKub6KCgQNAABkIBQhsBx6bc4KHwoVGmoWcrglR96vUxripUWQrHkt2vCz2OiSFbWSPrs80n4twmP1q2Zrlv0AAADqD1Y5AwCAGmEJjax3/Xk0wn3292VBGnKmRW9itsVGY6yhbqG6tLbz7ZKwCrVBKg9RGQBALPVa5ey1L74j1zo2O/eXWOUMAACGMtqkc4e0Txsi5e+3Iif8uzRvZ1X3YlV8aMOuJLThX5Zdkgji83Ykm6RIDO8/LkSkNFpbuR3W8DutPQAAMOhsKOX7KigQNAAAkBHLqdXu9vtO96ruxQPK0cQCHz6lRR8sweJWUeNlat+1yEbM3CCeRptfExJq1pwjqQxenrXIgBYdCg0nlPIAAACoPxA0AACQEW0uhjWMym3v6ZtHY9tWqqInJFSkYVNSlIHntebPWMPb+DZLKPB2WgJMEn7a8DSpDgttng23T0prtasSWwAAoKYkpXxfBaWl3gYAAEARsSIC/LslXqSIhLRNG27FbdL28Xr59xgxJtVnzUuxHP+YKJdfvjZ/J0sfWeKKf7aiPwAAABoLCBoAAKiAkIPL52dI4sUSM1ad0tAoaZ82t4RvtyIRkkiw0vpCRhMhPpa4kLAiVtq2mDlIfJvUnpBdED0AgLwp8tLKeYIhZwA0INxhBYNLpX0fijpo37nTLE2clyIpfn5pkruUXhpC5tJZ80li2hwbbdIcfyvqouWzxEzs/Bg+/E4Tb1mOLwAAgMEDggaABqTIzlEzCLFK+98agqY5+drwLre/p28eLZvarQ5b88v2owqWU645/ZIdoflCvH1++bFzYriYk9oYGkYXG4XRIiladEmKbAEAAGgs8BwaABqY0DAfUDmVOKixeUKiQkvniIlEhNDmymjvPE/osyRCpGiMZZ+U1ppbZLVDarPUF9acH+0zxAwAgFOv59C8esW/5VrH6P/8KZ5DAwCoPdbda1A5WYYTaXm0/H5UwYrG+On89LFDoCw7pQhKSIz4UQqeLjRMjLdRGlLH28v7TIvsaPVx27jNfHlsrZ+1/NxeywYAAAD1A4IGgAYnZu4BqJ5q+zY0NCs0uTwUaZBEUqh+qx5rwrs0F0dKI5WtiYPQ/BxJCFnCgZcniST/+TuOrt7OMnulerShgH6/AQBAPUiSUq6vooIhZwA0KHCcmg9rOJQ1XErKz7drc2X4d0toSFjzYPz80jCtRWPG0Zw1q8V2xg5Ps+bjhKI1Vlqt/7Jcd7hGARi61GvIWc/npudaR/uFSzHkrJ4sWLCASqVS+gKg6MBRKqdZhvlIkRY+3IsP0dIiPTySoE1g1yIQkkMu2aWhzZ1x5fpiRrIlFH0MzYmRbNfaq9UhpfPz+xGdGJsBAAAMPk0jaObPn09JkqQvAIpOFge+WZx9i2ZwHjWh0N66kJZN7U4/+9ulfKGhbCHB45Ml6uHnCUVZ+JA2SVSs6l6s2i7N6dHqkOzShtJJ7bDaOrZtpdhmAACoC0nOr4LSNIIGgGZjMIe9FEUQhZzzvOrKu9yevnk0Zenlano+5EqarK5FJXg91nepDEl8ubyWGPD3a1Ekf44LT+vXrUWC+OfQMDypv/hiBFrbeH5eNwAAgPoBQQNAwYmdi1BpGdoE6XogrUClUa3NWQRlqD4eVeHlS848p6u3U41WWFEdnk5rFx/mZkVGJAGg2RGKgITmtnBbtKFpUp2WqOFRLK1fpP6MHcIGAAC1JtlQyvVVVCBoACggsU50KA1HcxZDzl4e5CVIam2zFC1x9fAhULwvNceYl9nTN4/Gtq0UnW/JGY8RNZYw0gSYFYGx6tTslUSUNGTMisxpkSJfsFiRGr8vJMGZt3AGAABQPRA0ADQ41jAga56An6aWkRfr7ncl5fDPDs3uLOVK1OKuutaf/rskYEK2WHNOQuJViqxUEkmQIi9SGt5Of5+U3iEJIekcD0VjuL3SXBotvWSndKw0+7JECgEAoKYkpXxfBQWCBoACIDlhmuPL00l30V36apyxrHeuK3EKK7Ev61CxStLEONpWOZJDHxqaJYkdP50kqNy7FsXR5oyERI22T9oeU45WhhWNCQ1d87dLgkrrNz8fF6WxAhtRGwAAGFzwHBoAGgjuaFnDZqS8RLbQiak/lCc05KnayE01xA7HqsS+SttZzXwLKdombeefre9SWZJQ4GktJ12zy8qntUkrM4asUb2Ya62e5zMAoHGp13NoXln47lzr2Hzejwu5WjAEDQBNQsgh1O54x5RrOcFSBKBWomGw81dTX4wAteyT+tBHc9alPNo2S7RIaULpNSRBownL2HZq5Ybs8PvAKq9SIHgAGJrUS9C8/Jkjc61ji0/8CIKmkYCgAUWhlg6VJViyRnykdJWUAaqP0sQMr7IETzVRDqvuWAEk5YtpRyXiKSaaZNnM25pFqOOaAGDoAEHTWEDQADAI5O3oxN6tDkUO4JDJVCNI/DJCznBMHaGhZ9J3i1CEx9/O81gRKsuW2PkvWSM4VhlWH2mRR04lkTcAQHNSN0Fz6Yxc69ji//2wkIIGiwIAMAjk5ey0t4afGO9v1+yw9mvOo7Q9i6MZS0yZ1dbr8luOtTVULMvwJ2s/FwT+u3vFHiN+7K26eH6rH/w8krjQbNSG07n0/jkoCTYpLz9veRm+fZJ44WX726V+CV0/AAAA6gMiNAA0ATHzL2pdR73LKSLaECYtMlBp1IUTmksipQuJCB/JzpgoEi+j0r6whp1pESgtahNqj9T2oXo+AzCUQYSmsUCEBoAmIEbMVBPFqKXTVkTnr9K+i53TxN+5M86d6lDUxd/v8kplavh5pAiMFsXgAkE6b0KRKKkuSZxY5fAytG08usnbEjMMs4jnMwCguCQbSrm+igoEDQBNBB++47YRyc5mLEPdacvS/lA0Q9uXdd6JJIIkx14bjhZy9GMED7dl8bZLBtjW3rqQVnUvHpBPslUSdJZY5zZp6aTyrGFxXATWgjyGYwIAANgIBA0ATQCfXxE7pyOUDk5YfeDRGO5ca3NYpDkz0jZrzo1fJ98fyj9nzWpx/9i2leJwL153lqFuoXk+fD4NF2tc/PvRIEnQafVp4kmzCwAAqiFJSrm+igoEDQAFJjYa4KfJkiemzEYQPbW2oZryYh1XSTBI0bTQcCspv2WXNtTNGkYmlSUN+eL1xNjlkMqQhIiflm+T+k2qQ4pgWREbCy1yBgAAYPCAoAGgQalkSJg1vCirg5m1bk61c3aypKu1E1ltebHzNqz5Ttq8Gr88ba6NX54UdZDSaPNFLBHgRzmsiI6VxhoKKbXZipxo50PMkDO/LT1986irt3OArZZYAgCAQSHJ+VVQIGgAyIFaODyV3h2OcfJDTuFg2V9t3ka5Gx6a7+JvswRKKL80VEsqSyvDEloaMYKVCxvNLisS5PbzMvjQsRCSQNIEFRdyvvib0DF7QPs1MQQAAKC+QNAAkAP1cHiku+58uyZWpGFCIJ5K+ivLkDEpAqPNOfGd95BY0YZsSbZJjj3PJ+XV2mMJrmVTu8V9seenVL42h2bxtktM0aT1I6I0AADQOEDQAFBQrKgAFzehu9zSkJwsdVeStpEdwmqGFsWICIckDLLYJR3zkGiNsVebr+KXw+fRuG1+lEMrVyvLMWXp5arA0oaP+fby8n2hx22d/dxpal9Jx2TF9LllabhdVvsBAKBasGyzTNMImgULFlCpVEpfAAw2jeDAWI4u/+xjDV2y0leTtpGjQJUMLbLmqUj7/e1aXVnnh/A2WIJJG7omzTPh7dKifqG2aNGPWNHL28MFkkRP3zxaNGacOsRNqsftk9oxbXmHKRgloQcAACBfmkbQzJ8/n5IkSV8ADDaN4MBo4/0tR1vLX0uqiXgUAS0C4CMJDMkhjx0mJg0x1JxpawgWT6cdq1Akx+X3t/Ohb1xU8MhKSJxoaaSoj2/z7OdOE/uft0vbxtvYCNc6AGCIkpTyfRWUphE0ADQzWYYQWU6122Y5q3mIjkoiHo1GSAhWMrROOz5aub5IsOqQohF8qBYXvDxtKOLAh4RxUeELNm1omWZjSNSE2irt1wSZs0t6558te5pVrAMAQBGAoAGgAFQqBGIctqx1ZXHcBsPJGyxHMtQvUjTCymdFzLiI0fZxwSANB7Ps16JDPFIjzUuR2q1FaKx2SNEWKwoiDYnTzmtJuEkikRMTnfLbqZUDAABgcICgAaCO5OWMx8wv8B1VLV2WyJDEYDh5jeBI8uiH+xyTx0/LnX9pWJlfjzUUKnZ4lWRrjCiTbJQEkb9PKs+KFnGBJNXvf+fnM7cnNHRMsz8mGgoAAINBkpRyfRUVCBoA6kg1DlHornHIsdaGHrW3LlRXcoq1px7DcOo55McaRhXKkzUipkUUtOhMTAQiZJMmhDSnPySEubjQBJcUYZQiRprw4+WEBJc0bM5qEwfDzgAAoD5A0ABQUKq5a2w5tD1982jK0sursqcew3Dqcbe82ggWL8saMuWXLQ0702yQxIMVceHl8/3+UDItsqKV62/zRYY1jMylkcRNV2/ngHbF2KG1z233y9XaUcmxh+ABAFQLIjQyEDQANCGhOQDWXfss8wckx69aau305elExoqX2OF8oTKkyIM2T8Wyzc/HxYWfV4uiSPks8WXNsfGRhopZomlCx2wz6sLLjRFLPX3zaGzbyqBA4n0SA4aoAQBAPkDQAFAgYoe9hOYbhO7Ax84fcI5fqOwsznutnb7BcCIt0RcLFwDSfssxl45xKHLjb7dsjY36WUPGrEhTSBhI4kuyjQugZVO71fJ5H0qCzWprqO8BACAXNpTyfRWUUtKkD23BwzVBkQg5UINRrpbWEjdSHulOfFZbhho8YhKzX4rKSE6/FsGx0kgRHGt+iVSOv43XqZXJ26yRJZ+VlohoxfS54hDLrt5OGtu2Mi0z67lr5cG1AEDx6e7upi222GJQ6yyVSvTif70v1zrecNnthXyeIyI0AESQ9x3Xap0bLSpSiZiRHEJpeJDbZ22LHQI1VLAiEZo4JNIFgDZki3/XRKckjkL5pTq1SJCWVivTH54WKpu3JxRZ4UPk3OdpyzvE8pyY0eoMYeXBtQAAALUFggaACOrhgFQ7TCuLzTHDgSplqA6/CQ3r89NZgpEP4+KOOX/PMqwsZrigJHRDkRpt6JkvOvhnvz5f+Pg2aHZJwyW5eIqJgkm2Sp+JaMCCBFlob900/6xW1xkAYGiARQFkMOQMgAoIDQ1qFiwH19/fTG2uhGqPe+zwpJh5JJpI4GVK+S0BpEXwrDq0YWrW8LkYuyVhovVTJcMfs6SV8mTJF0Oz/a4A0AzUa8jZvy46Jtc6tvrsbRhyBkCzwx14y7FsBkJDmhyDcXe51nXUsryQsx+qK8t540d0+FAtXpaUJlaMS+c6r1uLOEltkyI8vt28bZJ9Wl1a/y4aM860KWSzlFZC6lNLoPp09XamL41m+10BAFRBUsr3VVAgaADIQF53YIsGdy4th7ZW1Lqva1FerKANRQOs7VqEgZetRWmk4VwWofkyrnxtSJYVVQnVrwlmP78WReLD2IiIZj93mmlXrSKMPX3zBggSSWx19XYOSDu2bWX6AgAAUBkQNABUyFARM9r8hFD6Zp8TEDNsKWZ+RMycF/89JB5XTJ9r2soFBk8n1c/t5FEaLbJj9ZEmlni7tcgOt0XCj/hYx0CqIwY/rRMkTqxI7XFpfPHC62v26wYAUB1Jku+rqGAODQAZiHXWmg1pTgKonND8DyI94pJlCJSWz5rX4tDskcr284e2SfVKfcLbY9ml9ac1RHTZ1G6asvTyYN9afRiTT/tOtGlpaFxXABSPes2heeHCY3OtY+vP3VrIOTQQNACATBTV+Qo5zFK0otbtzFqmJga0tD6xw9y0YWwhIaLl1eqOaYNWVki8OFZ1Ly4TCFqdlsDT9secH1nPMZ+u3k6a0DG7kNcWAEMRCJrGAkPOAMjIUB8SUlSHK+Rkx6QP5edDl2JFhjU0TRoixuew8LIloREa/sbL1+y0hIC/PTSMiw/Jcy9rDo6037ebRzv8IXFZxI3URmsIoRZV04YKOvhcmqJeWwCAwSPZUMr1VVQgaADIyFB1OiQHvej4jm81+aXv2twSrRzf+fUdXck2a15KSNjw/ZrwkESB1D5fiEht5gLDbZc+S8PEJEEi9WXoWIaEEscSoz1988pWUOvpm0crps9Nj53fH9aQNDeXptmuKwAAGGwgaACIxHdW3PehhBQhKDohJ7mWhESTLwj4pHFN1MQ649K+kEjwoyY8ShE7DM7fJw0d423kbdUiTau6F4t28m1SuzUb+TA7yQ7f7jlrVpeln7a8Iz12oTZofQQAAEGwbLMIBA0YslTiwPpj3GMclTxsyIOsdtTLAau1kJIcz0aCRzw4PNLB90nb+X4tqqRFSLhzr0WheATH384jOpJg0Ox3osEa7sZtlsrS8vn72lsXDniWjXb9axEgvuqZVr/L0yi/CQAAUCSaRtAsWLCASqVS+gIgRFYHVnMuq3GEB8OJjnGQYu2IKSvPCFbIwY8lq215Opm1KpsP4ZKGYFnzSPx3K3ojlaE5++4zFz+WMy8dY7fNiQvJRl5HJeehJJ7cs2y0qBKPXPr7iDbeBPHzhR6g2YjCGgAAGp2mETTz58+nJEnSFwAWlTg5Rb1zWksHSRviJNXXyI5ZJWI2RLXnRxax6NAEthaRkCIhvBxp+FYokqX1j+TgS0PDeFSF53Pf/QdlxogZSyBIkSMejfFt4/lcndJ8Jy4ufbQHaGrH3xJAAIChR5KUcn0VlaYRNADEkvUurjS0plZ2FBHNKR7qZDk/YuefSOmtuSFahCU0LEsbNsWR9ksOtzY8zd/GIzaSCOHiLEbQtbdunGPj9mlDvog2PovGZ/Zzpw1Iq/VJV28nLZvaTRM6Zqf1+n0hXRt8GJq/XatHE0AAAAA2gefQgCFHrPPmp7fuuAKbRumzau3gYoHfta/lUDgrqhCqJ0sZWlTDEg5WZIeXy98tG620Vpt5FCdWIGpiSCpD6hupre45OO6BmVaZAIBiU6/n0Kyec2KudYxbdHMhRzohQgOGHP6daYcWaQjdES76cLSik6XfazFPSBtOlyXKEio/1tG2IgB+5CVLhMNvnzQ/RBt+5dfp77OGH2oRPi26JKXt6u0cEE3RhrRZ7ebD3vztfn/wepyIcbjFCvx5M9bxxO8GAADUBggaMGTRHCHJ+dEcj0rmizSKE2M5xLWklpGLPMqOLTNrXZoTXYnNlURNuCDxt/F8krjXBH9MlEgSZpL40YZ2aUPkeB/4UZBV3YsHRInc0DM+tE6yl7eT16/Z4YacSf0sRXP8774YsuoHAIAULNssAkEDhhSWU+Pwx95zxysmf4hqHeNaod01D1EPp0uLjA0WlRz3Womt0J1+bTgX/6yJBGm/Fb2UojpSlMaPjvDoh3ZNxV6bPH1P37x0SXW/DH/+iSWyJPv8ND1988p+D7gQIaKyB2rydmjChs+PwbA0AACoDAga0DTEOJyhO7E+ztngDlsthi5lodGcnEawJzSnIo/68mp3rBMfs0+bk8Lrk8QMP8e5mJeGakqRFr9uKSrFxYxjxfS5qr2aiAr1jV+WExxu2+JtlwSjZn4bfPHhPzzT1eMiNTG2NMI1BAAAzQQEDSgk1UZK+N1h7Y6qdac6tg5QOfw4WMdAihJI5dQjwqTZkyW/NSTSyieJB2kIlhQx8YWDNVzML1+LxGj2uu9Tll4utkGLHGnptIiRf4OCiMqeL+O3W9vG26oJRifMrHaE2gIAABrJhlKur6KCVc7AkEZz3LJEYfJIC8oJHRtp2FXefV1JHbH2V1JXpW2WBEtsnrypRT1coFTSPqlv+EpmoTpWTJ9L05Z3DPp5CgDIh3qtcvbPj70/1zq2u/o7WOUMgDzh0ZJa3OHkw2K4cxeK0GRxRuC4xCFFI2KGBknpY+uphEqOp2V/TPRBw4pgSdEp3natbisqVE3UKSatJiqs34Es12iov90QNamc9taF6Ypm/m+GdRymLL08GK0CAIAQSZLvq6hA0IDCwMf819oZaG9dSOev3/QQvkruXNebZhjGkvfQHN8JjRUKg0FI1DgkB96f98LTSpEtPkdG64vQHBmeVrNTKldKExJWUj2WDdJ+abiYX74TMv6cGO3Y8BsiUl/GDJMEAABQHRA0oBBYdz6z5A9xZUv5XVeibAsJ1JtGF12hPuTOuu94x5RhpZXmeISopj+riVhY0SNJZIfqCkW4eLqQgIjpF8u2UNv868+tLiaJCm27FWF1SzlrYo+vjOZwy0Jr7eB1xtwIafTrFQAAigLm0ICGJ/YOcS3LxNj2+iI5g7HzSHwa+RhWco7lcd5XWja/XhzaMav0+tLSVlqGf25xUcMjN346nscvm4ho2dTudGED/H4A0PzUaw7Ns+ecnGsd47/0bcyhAaDWWHdyq8Fy6mrhjMRGFIY62vwOovK79ZKjKZXj8vnvWW3R7KsllZ5jsfNBLLutPrfgUSMeTfGjJZIg1cRMbHRJql9ql1UG0aYIjd8GN2dGG4bH2yGdq9OWdxDRpogSn4Pj6OrtNNsLAAAgOxA0oOGJnVuQBUso1eLOquTIgYHwO94xw3Q4WcVMzFAuqbwsQ6di7awWLv7cNq0fpDkfsTZJ14a2LVROlvRatC5UrgZ/mKUrz03y98t3706ESEMi/fr9cvhzaVxeqX4AAIgmKeX7KigQNKChqfRuO8caihRzpzrkuCIKUx3+/AZ/myYwuTiIOT80sRR717/SuSSVpLWQhItffpboh5+3knOY53FRCe2Yha4vKbKptdGPdGSNeqyYPjetwxJ1rl+cOHH7/SiPjz/Pxj8vs0alAAAAZAOCBjQstfzTlxxV7jBxx1gbRy/ZiSjMQGJEIHf+/Od68DKqjXBoeWLODcmGSs/Pas/rrOItdliZNB8mlI/bMrZtpdnP1j4t4qHhR0D4eROy38110USqJqKcEPKjOX5aLnx4GZVEIAEAwCdJSrm+ikrDC5pVq1bRkUceSX/605/qbQoYZPL809ecFusucqxDXGTyFJG8Di4e+d19a3hUJfZmcdS1/LUYSljN+RIr6iRhIIl1beiWVk/sXJusaDcFLFGTNWLmzjH/+4rpc8W+cft4/ilLLxfFottPVB5l9LcjKgMAAPnR0IJmw4YN9MUvfpF23HHHepsCGhBLfFSStxKHtRKnuJHJW5y5Ppb6nz91nQ8FlBzPaoZ8adELTTwNtnCNmScSG5HQIgZZRXrM3KQs/cSPa62uD0mQuQigv8897JKfBz1982jSnbdEDUH1xU9P37w0SuMLHGv4JAAAgOppaEHz3e9+lw4++GDadttt620KaDAqcRBCTqvm5GUZchOqeyg7NKHognRMpehCqOxKqPdxCYkQTbxLEQxpW2xkJbYfY0RTTBlWpNRPkxUtstPV25lGUPj8Fve+bGq3Wg7/7ObS+MLI/x1pb11oDsMDAICsJBtKub6KSsMKmpUrV9KDDz5I733ve+ttCmhAajmHwkdy6mrhjAxlh4aLOcs5l9Lz/dL3Svo3NFE7Zg5QHmh9E5p/wrdZ3zViIyUxkZrYuqQ80s2F2GiUVf7YtpUD5ts4AeP60c2vmdAxm9pbF9Kyqd1l/b9ozLj0O4/G8GPBh58BAADIh4YUNOvXr6err76azjvvvOADMhcsWEClUmnACzQXg+085l13ow89qyX8Tjh3VkN36UNzcbLCBVWsgOLkJVIrEQu1EBix5eWZl9saWr0sq1DjdPV2ps+PkfJIAm/2c6elc2+IaMBcG1/UYIlmAEDNwbLNIg0paB577DF66aWXaOHChfTRj36UHn74YVq0aJG4MMD8+fMpSZIBL1BsnNOZx1Ata+hT1onJlTIUIjaSMxob/eLzKkLznTT43Aj/fArNI8mLakR0LYZfxUQ6YoeoxWyLKUc7FnkKAjenxrKrq7czjdj4OBHU07fpgZrWMLlK+xMAAEAcDSlo3vjGN9I3vvENuuaaa+iaa66hN73pTTRnzhzae++9620aGEQG6w64NDG8lrbUyvHLUn4jwJ1FJyJiJoFrUR1pf5bj5oYL1fNp7bWeWG+VI9UZM+E/ZuiZJv5Dtmi2xQ5zC4mzWKQFCfjnCR2zacX0uek5Yw3v6+mbl6atZNjqULjJAQAAedGQggaAwbx7nnddsY5fNeVrUYxGwncgrYnWPn7amLkl2nAy93n2c6cREQ14inteZD0GvL2VHsOs55c2V6UW0UluizSvJMvk/2rmTknRWWmO16Ix49J005Z3UHvrQrp14ifFOTW+7VI0h9cLAADVgOfQyJSSJh2fhXk0zYHl6GhDO7I4OVYZWll5DIPTbGkmNMdx9nOnqXfKpe9a2aEJ5L5zXvR+ruW5UtTzzhraJZ0LMXOzVkyfmz5rxv8N8OtyaWphr7UdANDYdHd30xZbbDGodZZKJXr6zFm51rHjDV8PTt1Ys2YNfe5zn6MXX3yRWlpa6MILL6SJEycOSHfHHXfQHXfcQcOHD6eZM2fmutAXIjSgoanmTnNedUtDoCQquTvfbPB5BVxUuIiJNQwwJkoQSuM7pdoQp3rdRa/lpP3Ysqz5HrWg2qFg/mctUiUdRylix7dZ168/N4YPH3PvLmLj4A/rlD779mrtAACAGJIk31cMN9xwAx188MF0/fXX06xZs+jaa68dkObpp5+mu+66i77yla/QtddeS/fffz/19fXVuDc2AUEDGgrNmZGcmlrMa4kZDlbN0J+hjj+cz7p7zvPwu+KhPH4a7dhy51SblxOqp9bEziHJUlZsusEcZpklLz+G1rGx6so6ZM5P7yI1oXTawgK1GK4HAACNyO9+9zs64ogjiIhoypQp9OSTT1Jvb29Zmrvvvpve9a53UVtbG7W1tdFnP/tZam1tzc0mCBrQUEh3UrlTU6u7v7Fp+B3drt7OqHIqceoaJWpQLZIwle52W9GSSu9o80iQVF8tJmlXcmxCUaS8qPV5JeXPGh2yCM2TydJXlUZ5XeRFO2fcOeZHaKzIWVGvZQBAY1HvOTTr16+n/v5+amtrS7eNGTOGXnzxxbJ0q1evptdee40WLFhAH/vYx+hHP/pRzfvCB4IGNCyhYUdW9EYjJvoScoDyfPJ3KGpQNPiQH0nUuO0+1bbbml+RpQyLSmysRbsqQRqGVY2DXY3IyPuctoZ9WWl42v6L3yzOpeMRJGmBCV/Eu3RFv5YBAM3HVX/4I+1049fKXjHwOTZJktCwYeWSYt26dfSPf/yDPvnJT9JnPvMZuvnmm2nlyvyW4oegAQ2LNjGcOwnSuHn/nSM5KFZ6i2a565pXO/y+dXezteOVBSviIDnsldQ5GA5ojPPtY9lk5ZfEYy3al/f5X838oqxRPn6u7rRo/wFluGiMW6lNqwMCBgBQBM5783709zNOLXuFaGlpoZEjR9LatWuJiGjDhg3U1dVFW265ZVm6rbbait70pjfRsGHDaPPNN6e9994bggYA56D6EZXQ3BrJoQjdba3UrlgqGfI2GOTlfPG+ndAxWxQfUh4LK6KjHffYeReDScjuSsvKsq9WdeZx3obOjWqH8FlzuJZN7R6Qzp2/Y9tWDjh3tQgyAADUkmRDKddXDG95y1vo5z//ORER3XPPPbTnnntSa2srvfTSS+nQs4MPPph+/etfU39/P/X19dFf//pX2mmnnfLqFgga0Jhojoa/v5K7oJIA0YazSTaF7IqpvxZpigYXE9XMkQmVL32v5cT7SogZ+lR0qjlvY/un1kMTreiuW5pZE8K+4HH7+Hyaaoc9AgBAI/KhD32I7r77bjr99NPplltuobPOOouIiH7wgx/QrbfeSkRE++yzD+277750zjnn0DnnnENHHnkk7bzzzrnZhOfQgIbFEh7SkKLYMmOGpEjpNfskpyUmOhS7r9HhQ7wk8dDTN6/s+R21ipZId8m1qFlR+7fW5H2uVVv+YOXXooOV/Ca4bV29nWWrnnX1dtKEjtk49wBoQur1HJqVp56eax2dX/tq8Dk0jQgEDWhotDuakiip1BHiTkcthEeRBYpFrMDLOmysmmF/kg1aXb7gKerxKbLtlTAY7Q3VYf3OcBEDABgaQNA0FhhyBhoGyxmVJv9rZfChYdK8Gx9/1bLQnI7QnIFaOeaNCo+++O/u+FjRF/8YSnl9YucgSJP//fKkeSpaBK0eZJ2DVbRzK2u/hoaZVVsHT+sWqvD3WTZwe9x8Gl6mVCfm1QAAQD4gQgMaGu2OetYoSpbJw5VGYXiEohEdz0qH2fhpiWo/J0UTlFmOO08jRYxqeUwqsS1LumalESIuEi7SYg0ZJbKfNTOUjysAQ416RWienHVGrnXs/PUbCxmhgaABhUWbu+Hvs767PP7cDi29lK+RojH1dqasuSsObUGGvG1phOMTS57Hsd7nSLXUYj5KlqFlsXkAAEMTCJrGAkPOQMOjDe3SIiF8vDt3cHmeKUsvNx1tf5gIH8rUKI5OaCjcYODqXdW9OGpIV0ykRXrPYos77oPdJ7H1DYbAG4yya9m/2vVeyQNt+dCvUH73fBnLDgAAqCdJUsr1VVQgaEDDE5qEHrNPEjUxeWKHWDWSo1Mrx7XSNvFnzRCF56xIdfH5LjGLDfjzcXj+wRQ2sQKz1vNDYqhVedZcqWpskMqq9JzOOlnfpedt6+rtjIpCAgAAqA8QNKCQcCdXGmKkiRqeV5qUzrdJQ82qceiKgOaUuz7zXzx9aA5TlrknUj9rTqVWjnQsB4tKBHlM/kqp1YII1YiYWME3mMfKeoZMzFA3Hg3iQAgBAGoBIjQymEMDCgUfTkYkT8KPcR60ieih+vy8VvpK9jcqMRPgJdGiRV40kRJzTKqxuxHIOum8EdvQCGjDTq3+RT8CAGpFvebQPH7KmbnWMembNxRyDk3TCJoFCxbQxRdfXG8zQB3QHBopqsKxnHJNIA1Vp8haECEkZiQRysvOc4GAZqXZ21cPqj1HcUwAGBpA0DQWTTPkbP78+ZQkSfoCQwfr7mzsvAv/fdGYceJ+LU/WycNFGHrCh5MRyQsiWHNf/O/W3KNKHUBp6F9o7k4R+j4LcJxrhy9kQvN4+LmXZVgkAABURZLzq6A0TYSGgyFnQwdtsq4UJYiJLEjbY8qt9XCpRiHrHBfpDrc0PFCb+5Sl7mrsBsUhj2MZKtM9l4Z/rqU9OEcBKC51i9B8MOcIzX8XM0IDQQOaitg7pZUMScs6Zr/ZsYbhSaJFG4bGyxuKfUk0dM8ji7zmpGnX8mDAxREAoJjUS9A8dvKHc61j129fX0hB0zRDzsDQhA83csNFQtESa5gSTxfaH7NqVLMNP5GiMn5fWQs0SCvK+ccuVF8W26opZ7CBmBlIqE8qHd7lD53MgvW7EWuDJmZCq6QBAADQgaABhUUb5sWd62qdWCnS4JcbM44+dshWPYnpr5jFEfxyfMfRuhvOj52237JHSmvNqWmUfh9K5NXngyUG+fy8WtqAqA0AIAYs2ywDQQMahkrvsvIJ+jxKE8rPP2vl821+PZqzHuNAN9qd+dBQPYck8nh/ZBliJonFWFulcrO0pUgUWYg1Wp/zqEiob7ngLvKxAACAZgKCBjQMlTo7kngJDRVzafzPy6Z2ByMKUpmxjnijOXMc645zaCUxSVSG4KvJaQKzUqEb2lYEmkmI1Qvr/OFRkZibIKEbIQAAAAYfLAoACo8lKKwJ/zGs6l6cOj0x5TSTg2Ot3GYN93OEht1px6rWq50BAAAAtaZeiwI8etJHcq1j95uuw6IAANQbfvc05Ai7fcumdpd9d+8TOmYTke6Ux04uLvLQFKkPtbvUfBiev12bzxIzPyaLmClyX8cyFNoIAAAAxAJBAwoPX2VrVfdiIrIn6zvcvmnLO9Q8/nwZLVIhbec2ajSac8rFW+zwL6vvrCiOtHgA/5zF9mpWrhosarFQxVDCn+sSM5y0UrLOj6nVymR+XVjtDABggUUBZDDkDDQ81vCmGBGRZYK5VH5oSJVmq/S9iGiiLSaP/91CO6ZWmQBkoVKxi3MOACBRryFnfz3xrFzr2OPmL2PIGQB5UM0qWCumz42uw5rYHhu1CM0bsWi0SI0jdlgdkdxuqX/8oWmxk6zhWDYe2iIZtS67FmVmOX+z5MlSHgCDBf8/A6DZgaABhUGLiPAfbH94mBtKpuXX6sgqnKQ/j6wOWdEcdt6mUHuzzJnR+hFsohH6Jc8V5eqxmlgloqfa8gAYDBrh9wLUBgw5k4GgAYVCuoMas2RzzKR9HlXwow1aHf78Gj+tNYm+0Yn546t0aJ0kgvicGalPa2l7I1GNvUU6p2IoyrHjNzuKYjcYevDfUgCaGcyhAYXCGgrmPp/S10nfbA0/ddufF6I5JdbqXNbkdj70qtnH4kvt09qspSXS5zEBHfQRAAAMPvWaQ/PI8efkWscbb/lSIefQQNCAQmLdFdWcaC2tJmqkpYdDNmVJUwRHNDRJn0hemYxvy1KflCdr3wIAANgIfhvzAYKmsYCgAYXDWhxAEiH8++JtlxAR0Zw1q6PFRZYIQiWrgg02WW2LWX0Mq0gBAEBjg9/c2lEvQfOX4z6aax17fu+aQgoazKEBhcTNteAPxHRogqe9dSHNWbOaZj93Wtk2Xq604hlfCc3VIy1K4Dv5ea4EVSlZ/9BihuVlWaa5Ujvq3W9gaIBnwYBmxfpfAqDIQNCAQsEd5ylLLx+w33/naBP2LYfdL4/nD93t0vYV8Q5ZqJ2h4Xm1mERdxH4DxWNsW3gOHgAAgMYBggYUAs0JtpxkF71x+M6wez6Nn196Nor/LtWlRSVqueRso91Jq9QebSGAUJmh+hqtf8DggOMOQGXgxlCxSTbk+yoqmEMDGh5rToy/nafl+3h51gpcsXNqeD0x+Zt5DHMztw0AB85zAEC95tD8+Zh859DsdRvm0NSVBQsWUKlUSl+geMQOE9OECB8WFrMqVmheSGg1NSmioy1aYK3gVW8awQYi+UGpsfkAGCwgZgAA9QIP1pRpGkEzf/58SpIkfYHiEVoe2MrDBYU2nImLCl948IUAJJukRQb4dk18Ndp8mixLUsdiCTn+WfoeK0a1epsZiDYAAABApmkEDWheJPERu9yyNoFfcqR5+tAwNj7/phKHs55Oap4iIFbUVWrDUHTuh4JoAwAAACoBggYUEmkpZYd2h98XLX4aKz8XTNLnap45k3WeTpGodFWzmPRDybkv+nkAAACgdmDImQwEDWh4+JAlLmSkCI6PFWmRhl1lGYolRXa08rNSdKe9UqFX9HbXGvQHACBPcNMENANY5Qw0DZqQcftioiFZ8mhpajU3pUgrKRXJVgAAAKBa6rXK2cNHn5drHW+646pCzkVHhAYUBj5pn0/kJ5IjJtpT7LnwiH24po810d+KDMWgPeOm0QgtUS19ji23knwAAAAAGFpA0IDCIK1oxj/HOMExK5dJTrolMKyoTC0iF40c/YhdlKHSchu57aB5kH4zuno762AJAADoYA6NDAQNaEik6Iv/2XpYpvbcGmtImtuvRXis+rUIRVEjC1kjI1mjWoPBYNVb1GPcLNSi//k17YuYsW0rqy4fAABA/mAODSgslczbsARRNauVDTWy9pM21yjrPKdaHhscawAAAJVSrzk0Dx01J9c69r1zEebQANCI8AdtStEXvgRz1vKHAtUsdmAtox1bZq3FRxHFzFA51wAAACgkpXxfBQWCBhQWSXxw8eLS8eiLFqmRtoWcyGZ+loxPJQJAOh6gcooowgAAAIC8gaABhSM0IV+bz+JEjbXUsjZ3x6rfYqg5oLEPOc2jLgAAAKDZwaIAMhA0oHDwCf8xDrM22d96aKe1QECzT3avlMFaahrzXwAAAADggKBRaHTHcShRzbHIMuQp5rkx0ipqsXbUgiI58Xmu/FakfgAAAABAvgwpQeM/jDH0fAE4TI1DrGMsPWxTEh/Sks4xVOOMF2FZ58GwB9dV/Wm08w4AAEA8yYZSrq+iMmSWbdb+xOFgFZdqhh3xZZqzruCFIU8AAADA0KVeyzb/ccYFudax3w8/j2WbGxnp6fIOrMRUTEKCQnogprRMcx51Nxu4PkAe4LwCAIBsYFEAmSEjaIiIlk3tJiKiTw2/Kd3mO7j4c20urCWZpdW4tDzNSpZV3OrRL/VegAHkz1C63gAAAOTHkBA0ziGasvRyIiK6smVluk9zcOFEFQfrWEnPm5GeUB8zhMyK+BQRrb0xiyMMBkNRZAIAAAAWSZLvq6g0taCRHFXJSVs0ZlzUAxdB41DpU+sl4cKHIWriVlpMIOuSzvUWCT7VPDA0Txqpjyqh6PYDAAAARaOpBY0vUrp6O0UnuL11Ic1+7rSybW47aFxine3QhH9JhKzqXly2P0tdIdsaSSg3gi1ZhrcV5ZpshH4FAAAAhhJNI2gWLFhApVIpfTmcczG2bWX6PYvj2WhOVKPZk4VGsF2aO8NFiztX3P5Gptpn9NSbLP3b6MeikWiEYwsAAKD2YFEAmdyWbe7t7aW2trY8io5CW7ZZcmD9P/9lU7tp2vKONC0vA05V7Yidt1KrPvfLCpU7lI91M7e9mdsGGouu3s6ymyMAgOaiXss2//5dF+ZaxwE/+dzQWrb5Bz/4AT366KO0bt06cf+xxx5bsVF5YEVm/O+SmMHdznyIcSyrec5MbFmNsqpXLan1Q0CrpVGuobyOa6O0DzQOEDMAgDxAhEampdKMN954I73++uu0fv162nHHHWnXXXelSZMm0aRJk2iXXXahDRs21NLOmhCa8O/Pt8CqZ7VlsO+MhyIx0gR/n6LfyW802/MUEo3Q1kawAQAAABiqVByhOe200+jd7343feMb36APfehDtM0229Af/vAHWrhwIR199NH0+uuv19LOzOy///7i9kqc10ZZPrbIoqped/2tqIwlWut9rEEctT5ORb7GAAAADB4vv/zyoNc5derU3OvYf+yE3OvIg4rn0GzYsIHmzJlDZ599Nu22225l+9asWUMnnXQS/fjHP66JkZXA59D4OAHDhYw/5lmbPO5/htPb3MQeY5wLg480Dw7HAGQB5wwAoBrqNYfmd/92Ua51HPjTzw6tOTTDhg2jq666isaPHz9g35gxY0xB0QjwCeLtrQtpbNtK6urtJKJNc27cy/rzk+7qFuVZJECmkuWaweBhzYNrNvB7kQ/NfM4AAMBQo6plm0ulEo0ePVrcd/TRR1dTdK5YcyrGtq0c8ER4KaKTZY6GtA9OykBCfTIYfVakB6viHBoaFOFcBAAAMDhgUQCZ3J5Dc+aZZ+ZVdE3QBIkvXpzDuKp7sTjfgkd5stafNU8zObCVrCyWxbGrtK+K5DwWydZYqj3H+c2IPKnH9dhMvwEAAABArWiaB2tWirZIgM+Ejtll+ySnqRLnMquoibG1KOTtjBdh6eGiHrvBoBaCVHqOlPReqV31EJTNKGIBAADEgwiNzJAXNA5fqGhDyPizbPzvfjkxdfnlZrFRsqvINIpTX82KaZVS9GOXB1lWFKwkKurnrSS6imMGAAAANB4QNB7cweGLAvBhZhqhBQFi8/vldPV2DhgK1wxUOmQvLztAftT6GEsPv40VpqHnEMWWBQAAAID6A0Hzf1gOLXds3Epo2nNMQg6S5HxZURu3Apvb14zOd9Y25elswpHdRNaIo0We520lKxFK++s9pAwAAACwSDaUcn0VFQgahj/kpb11IZ1FN5VtJ6JUXDj4cDVJuEgrpEn1Wvj1aJGaoeKM5+0cg43ERBMHq79C57YU7Yud4yZdm0PlWgIAAACKDgSNgO/cfJlOGjB5nwsLH77Esxsq5m+LjeTwPNJQGS5sYoZwafua3YFrhvY1QxsqIXb1Mi6wrGFpoTKzLtoxVI8NAACAwQOLAshA0Aho4oUoPOSLR2jcCmmWE+XPz+F2SM+98b9rtoRs5PXw7c3onDVD5KWWbSjKMdYm8IciMbx9/JrhNxdCAic0f61Zjk1RzgsAAADAUUqSJKm3EXlQKtVGZXJHSXOAJEdKEg6n9HXSl+mk6LH60pAeKYIT25ZmcOpBsanFeSiJHG0VM3+bli7WpsEeYjfUr1f0AQCgUenu7qYttthiUOsslUr026mfzLWOQ5Z/moooDRChEZAiMv48GMkB4lEW7Y7vl+mksrqsZ2VoThlfProRJmXHgDu/gKiy8zAUGfHFjeYEx0ZW/XJiy5AWCqn2fM9j6epGJkt/AwAAAD4QNAIhEaHNn9HK4XeTeXkrps+t2DZtW15UMjfH0UgrmdWz3qI4oYOx6ERsHXwop7aEOR8SGjPHjN+EkIZ+hmz0VyH0bcxCJX3bTA5/M7UFAADA4IIhZ5FoDkrshHxrvD7PX4kNtXAGuno7B6zgViswdKT2VNunjXRMrKGd7nslAkHLbw1Jc/Vq3xulzwAAANSPeg05+83bP5VrHYfefUkhh5xB0GREm6CsrWKm3XUNCRNtbD+vwyovth15pAfxWHOuhnqfVyNKiOSVAkP9agmXWgitWlLv+gEAYKgCQdNYNM2QswULFlCpVEpfeaEtlcyHw7jPfhppxTI+94Y7TJoD58rjQ2z8tNrQuEqHDWXNV5ShVfXCP9bScMZKHdXYfq/F8anlMbailtowM2nOGb8mpGtJqleaJyfZw8uOHYJqtbNSIGYAAGBogWWbZZpG0MyfP5+SJElfeSHdvZUEg3TnWBu375DG/2t1WLbxsrgtfKGDmLItJ8/K08jUW3BZ86Gs86sapCGS1dRT7TEOzYvh0U5+HnKBL5XH6+Fla+0JLRQg2RDqQ0RUAAAAgNqDIWc1Qht+5u/ThqXxMjRHM3SHWZt/AIY2tZp7FZM+pq6YoWOhdNbQS020WUPOQkM7Y/dpbchSZ6NTFDsBACBP6jXk7NeHzc+1jrf9agGGnA01JMeJ3xXW7ghrDoF0l9pKL9kkRXqyRnuypMsrfzNi9Uk1Q/qsSJt1rtWaUF2SEOHRQn/ol0MaFuby8mtPO/+t6KcmhKwbDtJQQa3dVjRO+96o1w/EDAAA1A8MOZOBoKmCmLvKmmMkOaPaMDVp6Au3I+RkxN5R5nmk+ixiRNtQJibCIX2OKTfrkMCs1PJ4cuHORYI0XIzvk0SRg0dGtGgpr9tPwz/73yu94WCVwcH1AwAAAMQBQVNjuKMUcl54Ws1Rssb2S+n98kMOWy0dKzhhOlI/WwJQu2NvCVNNZGe1KyZfVjSxHyO0raFZXPBY57x0HWnXnDVMTrv5YH3meXGtAAAAyEqS5PsqKhA0NYTfEdbuAPPP/jY+hIZIvqMr7bPSWTZniRqAyrEECD9XrGOpOeI8vVVvzP4Yhzs0nFEbQsbrkPJJwt1qn9YfXJjwtFY9/HqW+p7flODXsNWP1YpJXJsAAAAABE3Nke6yh4anSA6ZFZGxxvprESFpeI9PV2/ngHSV4MqphsGKGNQL7a59jBD1HfTYCE2MPZWml+wItUMbMmnZwaMwfB+PyljXkgSPpmp9q/V9rPiT2qPZEwOiPAAAMLTAHBoZCJoaE3Lm/Du8fP+q7sVmpEUbwiY5dFmd3bFtK1VHTmqfdkd7bNtKcbu2LTTkJ8b+2EhFHlRSTxYnVnKcq3ViQ/0dEhaWSAm1IetwR5dfspGf76EojzQ0zbeDCyIr8iPVIUWVtAiVdpMiRtQ1i7AHAAAAagWWbc4RbViLNkbfTxe6s62N7Q/ZI9mRpT0xgsdqg5bOanOed6Gzll+pPVI+TfxW2s9WPu3Y8zyxx62W6a3zsqu3c4BIturi+WPrjRXyrvxKz5ssxzb2OwAAgMGnXss2Lz9kQa51TP3t/EIu2wxBU0M0hypWuGiChX/X7iCHnCCp3rwc+kpFU63LqAW1dCBDgkLaFpOmljbXKm+MiIsVOFbk0+ov7aYCrz9GDGrbrWvNSgcAAKC41EvQLHvrJbnWMe3eTxVS0GDIWQ2xhgP5w2v40JaYcq3hKVq9oaFYWe9M+7b47ZGGDmUZGqUNpanF8KpYrOMQ27eh8l1/S856rJi1bPLrqqbf/Pq0Y6N9twSMJjyk85n3Fc/nv7SbBVJ73HdJzPDjYLXfL5e3WTrGGlo/Zj2/AAAAgKEMBE2OSALGbfedMUkQxIgfaRsva8X0uWXppbvGUr0x+LZZ4ixUpuYYZi3HShdrQ2wdlnANpZec1kqiNFrdkgC22mUJyhBadFDLHyt8pG1+Humc0SIifl4pPS+fCyUurEPniCZW+TkmHdPYayFU91AQREOhjQAAwMGiADIQNIMAd6z8l4M7MtKda7ddcrAk57inbx5NW94xwB7pDjXPqwmlmO3aHffQHe8QWp9I6ULbYoSHRKj+kOPLowSx9sYIAH4+SXVZ7bDanqVfNJHh2yKJDC4gJGHG+00Sbpp41G4ehGyKOee0iJEU7ZL6iZcfujkQOv9qdXMgRD1FRTVRSAAAAM0F5tAMArF34rV02t15La/LH7rLL93BlsrS7OH2at9jnCvtjnpMvkr3V0qWckPRCivSIznGsWVUU7Z2PmjH1DrPrKiP5fxnaW/seRB7vUgRlVC0SYvCZG2f1d5KywAAAFB76jWH5pdv+XSudbzjvk9iDg3Qke44u+3+nV3/DrTkMPEhLNodf+6cSc6cFQWSbJS+S5Em6Q69BHf6Qk4nJyTCslBJfusueig6Yt29d/mkz7FIfcjrsPqaH2/p3OVlSPm1iIEmTKTz0Y9+aHVIERCpHkmQSed7jHCwzvXQviznW0zaekZKAAAADB4YciYDQTMIWM5M1s++c8lFTsydaukuL3dq+XatDOsuut9u376QrTGOn+aYW6IgRCVpQ46u5YBrojGrY1qN3VpfcwFQibMsnVMakvjRxAG3k4tHXr/fhpBw0doR0wbtutYEWkiMSzcQQiKsGYAwAwAAUAkQNDnT1dtZ9l2LlGjOlRZN8fPw/FZ6H8tp5OVITpVvo+bASZEf6c68VKa2v1pnLuQ0xTjfWdKF0oQEUgxZhIfloIec9pgonxXBs6JELr1msyQa/HOfR3Kk/ZpY4yJUE0r+Pk2s+HlihbbUh35+qX2STdXQCGKiWYQZAADkBSI0MhA0OTOhY3b6mTtloTvEWhQkJCr8uqQyNMfIEiZS/ZZDrt3xl9ogRZyku/Wx0ZoQIafJ2h/jcPl9n9W+UIRNK0MSDqEoWOjc0/pbiy5ZaXh9WuSDCwpNQErCw2qTdiOBl8HPO+mc9PeH8vCbCX67pGvQEkDWTY/QeVmL6wIAAABoVBp2UYBnn32WrrzySurp6aFSqURnn3027bPPPtH567koQIyDwdM7tDvJHC2dJF4051SKvkjpNUdU+6whlaNFa0IRoVo4X1nKGaw6pb6ppB+yts0RqlMq3zpmsbaHyvDtk87DkG0cK9Kk2WURssVqbyitZKfVZum7ZG81+wEAYKhTr0UBfjYl32j6O1fMw6IAteSLX/wizZgxg66//nr68Ic/TFdffXW9TYrGcjKlu9La8BTJ6ZHuxPM6/M8x0RHJButuP7dByiNFhyTbpDvslgMYEnsxd6KlfsiSNqYOy5G28vPjwT+H6go54DytH0GQnGQtKsIFQEiASnXyOixBbZ2zvC5JlEkRFetakUSUFlWRbNSEPu8z3gcxQkK6brQ+qJRGEzNZruuiMhTaCAAAedGwguaiiy6iqVOnEhHRtttuS2vXrq2vQTXAcvyyOIRSPu5AWcIpxk7f3lC5VuSGO9i+A621QbJVcmI1x7ISx8DKI/V5CCuNdvc91h6rDk2USA6wFsXQnG4uOCThEeP4h7AiLP55FeojTWhLQk47v/i1wPtEeue2cluk4yCJLS5yrBsb0udmcpAbTWDlQZY28rmZAAAw1GlYQbPlllvSsGEbzbvpppto5syZYroFCxZQqVQa8GpktD+uLM6UhnZnW3NGpZdkFxcm0p1nq13WnX7f1pCT6G+z7opnpRoBEhJDVlu0uiuJHoUiB9r5w/s91KfaMdKEuuTEh/qCR3K0dkoCwCqf2xAS/9o1EbpRELp54ZcR01bt+rHIck6D+pJVoIxtW5mTJQCARgeLAsg0rKAhItqwYQMtWrSINmzYQMcff7yYZv78+ZQkyYBXI5DVwXLprCgEj8Zo75ITrkUvNMEUE90JRU6k/VY0ysrH98U6drW4Ux2KHmkOtXb3PYuNIXGniQ7/pYlKrZ8lcanBj6clerX8mmjmbbHOC16PJgb4+W5FZ6Q+1eqTzgPpvOffNcFsCVD/PXScmilS06xUI1AQrQEAgAYWNBs2bKBPf/rTtPnmm9N//ud/ptGaIqENCSHShYoUSbGQnDUp2qKJHe6E+e/LpnaLdWhOquaccaeb2yn1RwyaoOO2aULOgqcPHQe/X/1+0ARkzDZeVkw+/zySHPaQcJKcbn6stPPaOpbSecfzaeVojj0vn3/W8knnsCUWudiyoinScdcElnZsQ2JNsle7ERLzG8Lz1AOIscpBtAaAoQUiNDINqxJuvvlm2mqrreiMM86otyk1RXIK3XftDrd2x1dyiLRtvoNjOWp+PVOWXj6gPM2p5iKMf9buhEt5q3VuQo53DCEB6rZJzmUW55SXFbIjZJMmcPz9mvPrPkuiUzvveN0hUcudfq19XCD65Wtla2JCutZ4Ov87P2/5+aSJcl6+VLYlzqTzwbeF9412Tvo2SmVbeeqFdR5o10ajUQQbLYpuPwBgaNOwyza/733vo80335xGjBiRbrv66qtps802i8rf6PNoONyRtLbHOlCaEyrl18SEdSddqlsSX1LdfJ/m6Ga5w8zRnOu8HbeYOmJFQiX7rf6ThCwvxydr/2mRDOn8Ch13bod2jfD0VlrNDq2NofOTt1nqD2t77Pkfs5+3x+qnWlwDg3Et1ZtKruV62FDk+gAoKvVatnnp/p/NtY7pD1zUMFM3stCwgqZaiiBoYhz3GOdJEwwhMRRyei27uQ1W+yxhEXLOfFtr6aBV+qedVXhox8uRpd+l8qzzhpdvbY/ZFzqHfDQxYdkeK4Ys4R0SzLHXmVSvVJ9EjCCRyrDab9VpibMYG61teQGnuRz0BwDFo16C5idvvjzXOt71h7mFFDQNO+RsKKDdcXUOiu+oWE4LL0farznWVt3cDp7ex+3zX9wZlNrgl+Vv45+1eit1AjRHVWsXry8mr0uvOaZZxYxUniQItPOA18nboeVx+3z7tfPMTyu1W3L0LQGr2c9t4HWE+tq98/qlcz5Up0/sNr/tlgjj7ZbKldqo1WuVl+Va4n0Wez3E1pW1vEagUputfP6Ef0z+BwAAHQiaBoA79ZKTr32XxI+033egfMcsdKffL4c7cpJDKDnpIUfLcigl2zSBlYcTZAmDmDv3ksPqf+biTxOA2jYrjyUmrXRWvZLg9dPwaIhUn99+nleyR+tTK3Lh12mJJi09vz5CAjh0rKS28etP6iPrupSOoyaQsmJdo1LdtY4uxESjGo3YmyRSPq29/oT/CR2zxTQQOgAMLZINpVxfRQWCpgGIceIlB01yjtpbF6Z/cNofpZ/Xcjql/drdYEmUaXfgtQiSVD53jiVnWesjiZDDrqWLES9SmfwYSMeYRwC08qU795ZNlkNr1SPZatWlnbf8uGrngmWzdax5nbHntHS++baErjWpb7Tz0E9vCQ3tPAmd0/x6ka750I0KiZgbKZJ4BZUTe71xrFXOYsQOjh0AoBnAHJpBwLrLauWR8J2IFdPn0rTlHWoEJMbhsO6ka86k3ybtbryWNyRu/LKzUEkerQwtKpClfKssno7Ijljw7zHHN9QOzS7rrruUV9vG4emlekP5rShBSPxp56hUv9QfmgjR6ouxn5erXcdWWdZ1Jp0vobbG/JZI5Vd77Q1VQsdOYsX0uTRlqTyGvpa/gwAAm3rNofnRvlfkWseRD/1nIefQQNAUAOsPRhMPIefTpdUEjLTNcgqzOM+xDlWMKLDy+P1RCTF/7LF//pZQsMqy2hHbPy5/7DHSBFMWB9kqI+sdYescCtnllyHZFaqXty+LgOLE9JPVfq0dMfVUQtbrsRbX3FAg6/H51PCb6MqWjVGYmJskRBsjM1meT1PNOQMBBIYqEDSNBYac1Zn21vAQKO5U+HncPv6HIv3B+GmlfO6PyX+F6uG2+vb5eXgbY5xaywnnaaw2arh8liMZY2MI3q8xZfn9Huo/t5+/rPy+Xf67b6vfP5oN2j6trdwu3m6ej5ftt42/+86+VI9fJn+Xtkl9lkVw8jTWZ98Obo90DUiizRJ0kj2h818q13JeY665rFi2ZRXGldZTq/LaWzcOB9aui67ezgFDxNpbF9Il/Seleaz8PlkftlnNcYOYAQA0AhA0dUZzAiTHSXLS+HYukHg6f9+K6XPLyrPSuu1+mSG7YxwnS5zEEitctHyxf8gxtkl9FnIwLds0h9kXHZIQ5cfScjZ5P2gi27LH3y8db+tc0s7fGMETY5d1/knXjNYurQy+PdTPfFtItHEhIfWRJiwtO2IcY6s9WvpaEnsDIM96alVeT988dWJ/T988Gtu2ksa2rUzFjduuXTtZFwPI+1gBAAaHJCnl+ioqEDQNhuV8cyfWbXf5LMdVcmKmLe9Q6+dOpHvXnFTuWFkOr3QH2nLcpfxZ7zLXAqtNWhrNaYy9e87bysUm72spDbfRfZeElhW94Pjno+ZMaxGZUITBOsZW9MXfHoooSGLQT2+1288r7eN28rZKfRS6hrXrRBI9Uj7rfNDaGdpWqRDI63qNFaaDVSffF3MjgEhf0WxCx+y0jLFtK6mrtzMoZkM3IUJgFTUAQBGAoGkwQndDLcEhpY25c+znkYRJlsiK5iSHIkBZ/2y5XSEHme/TiLkDz7dLokt6D4kEzZmWjoFWpxbNkBxg6bPlHEuiOiSG/HfuuHO0aIWFJswkQSjZYtVhCU+pbOuz9d2KlGjnA08n9YN1vll1Wt9DfWKR9bcoa7mWeI2ttxKsMi2h4eAi1roO+PaxbStpVfdiIqL0XSpbIiRUXD1Zh6/lJVYBABtBhEYGiwI0AJKDGfPnzMsgsv9cu3o70zt8koPM69bK5H+uPG3MZ6sdkrMdS6X5NWFSzR3omLwhZ9vfLu1z+0PHiqeLqc/KG6pbOkf8fTHnmdVerX28Dm5LqH6rPuvaDPWrJiC1/pH6Q8vr1xG6sWCJCu04hajkt6qWwqLS38zBotrfIWlVs9B5FPNb6+ALCDRqPwLQaNRrUYAf7POFXOuY+b8fL+SiABA0DYT2R2I5Gpbj6Kd3aaRyQyIg5DBoDqPVjhgBYf2xxrRJ2l8LYkVTzLEJlW+VqdWhOcg+sUIi5vwItUO7YxvjzIdEhCXSYsvneXkdvq2W/dWIRSmtVJdF1t8Oa1sl5Qwm9a5fInTd+vt8EeHvk1YnC/W9dKOqGoHpIjduPk+j9TMAjUK9BM33974y1zre86fzCyloMOSsjmgOEsf6Q+npCz/N3E/T3qqvQiY5iNYdcMeyqd1mG3l9loPr54ttt2Sjv9+3g9smfefvUt28TSEbNWeT93XsMfDz+umtNnJbnH1au7lI8vdrfSPVLx2TWOdbKlMq17dXS2+Jk1CdUn1aWbxt0vXntknnEi9faqdlu9UW6VjGkNWpjS23UmL7IOv+agj9FsTsc3Nn+PnqthENHFrmi5nQjQxpHy/PX5zAT5v3MQUAgGpAhKbASCJBu0snOTX8u+Vkxt7x1vJJNltlSE61lF/6049xvqz2SrZkKTN0p9aRpQ6rn9320Dmg1aMJa6lPQ8c11K++TZYDptWttdVKw8vUyrXanuU6CAlh7V2y1To2sddwiCzXatYy6kElNlRjd0xeK82nht9El/SfpOYj2njTyA07a29dSKu6Fw+IoLjoDj9/V3UvToXSojHjaPLkR4mIyoaxxf7+x7StEc4BAAYDRGgaCwiaAhLjuGr5JEfNLyvktIUcPak+ywHzbdccWr7NaptWl9WGagn9oVdbT0jMOLQ+tY6Zlp9v12ziea2+zSo2sooEqd7Q+RmqW7PP32ad1yFBop2rVv2aDTHXVKWOaZGd1Dwc7lgRQxTX7y4d3ybld3NqQja0ty5Mo+duRcssNyEc/hyeRhKuANSbegmaO/dalGsdR/15TlDQrFmzhj73uc/Riy++SC0tLXThhRfSxIkTxbTr1q2jj3zkI/SOd7yDTj755DxMJiIMOSsk/h9JT9/AFYzaW/VVpEKf/e/ujyvGsZTq5uX426Q/RNcWbbtUt1SXVreUx3222uXvs/rVtV37Hio71Datbr/PNIdFE6D+y6/POqdihAA/d/z6/D6x2sbr0LZJZfC+57b69vjbua18n1Y/tyFWFGm2ae3j+6XPkkgKiWGtXdq+0O9ArQi11f8eew5ldcRjRb6VRjvG7kGZ/vXS3roxqsKvF7fPiQtXppvz4ud3YmbK0stp2vIO8zc0dA1OW95B7a0LacX0uWXXk1sy2sobA5aFBqB43HDDDXTwwQfT9ddfT7NmzaJrr71WTfu1r32NRo8enbtNEDR1xPoTDv1BSH+oMY6aVg53SCXnVhMh3HEMCR+pTs1Zib3jreX133l7JJs4vM2SvVLZUl/FiCE/HS+LOz38u1aXZJN0fkhOOa+f9wMvS2snRxOn0nmoiUKpP/h+XnaMGJDq5TZZIiPUPq0v+DXE65WEq3Q8te1We7R2aG3Rfn+sPLVEug6t38QQIUHkO92h8kI2+dv95834Nxf4wgBO+Ej2urR+/mVTu1MhwsUHFyLutWjMOPU87umbl5bnyhjbtlI8n7KSdVloAIY6SZLvK4bf/e53dMQRRxAR0ZQpU+jJJ5+k3t7eAen+9Kc/0erVq2nq1Kk17AGZphE0CxYsoFKplL6KiuZcxuTTHDjpLp+UVxM+2vcYJ9r/rjlnli1+WdyRlOrSnGvNZl4ed8o0xz90h9YSTZJjrQlLl0/rDykNdzK4INHOgViHlItYvs/aL9msHT/ejhixrNVnlaudE/y60eq2REZIcPjppGvUuh789klt1uyUBKAmdCTbNazrMIR27lll8vNBE2YxtoSuad/p1tKF+osfFxfx0K5H/6GZEzpmU1dvJ/19zgMD6lgxfe6Az9JDk10+LkQcc9asTr+7cqTfmvbWhWWLFwAAisvNLyylo/8yp+wVYv369dTf309tbW3ptjFjxtCLL75Ylq63t5duuOEG+tjHPlZzuyWaRtDMnz+fkiRJX42M5Rhbzoufl3/m6XnZIcfAz6Pl1WwNOXxSHsuJlOqyHF+ezn/nTrPkDGr7Y4SEVq9WBi+H9x+3N+Qkac4wb4/m5GnnIj8nuKMoOb68Hkko8/6X2q6d15YYihU60vEPObNS/pAIcmliBJtfnnS8tX6WPvs2WCLVur6tayUvKhFDod/RmLJC7Yppt3WN+seBnyN86Jifx0369/OMbVtJW332trI6u3o70/k0RJuEjL/yZFdvJ9068ZNl9mjtc/unLe+gFdPnpnbzldD4b07sQzoBAI3FiVtPpzv2XFT2ioH72UmS0LBh5ZJi8eLFdMwxx9DYsWNrZq9F0wiaImE5xv7+LPus7dwRdYT+3Nw2/mdlORC8HO5YWc6q9OfKy9f6zLorqzm7MU6PtE/qw1BZmpMrCQ/JwQm123JCJZGq1SW1TXN6LPz6LJus80IqUxLQkoi32irZwI+PVrdUTsj+rELL7xepTTHXRaxD7/LGHE8/fb2wxLIjdA36hPqnUkEUuvakdO77sqndNKFjdvDa6OmblwoVfi34iwBM6JhNc9asJqKNq5y5NMumdlNP3zxaNGZcWbnLpnanQ9ZO6dsYJXJLQ7sy/eiSq0Nqo/vvsG5WAACykWwo5foK0dLSQiNHjqS1a9cSEdGGDRuoq6uLttxyy7J09913H91666300Y9+lG677Tb6wQ9+QHfccUceXUJEEDR1JfZOJFF2J4L/wXPHx7orzgWQ/wA4f7/mEFt3Wy3njKPZLAkk7Q65X5aUTxNRmriTbNTupmtOaYzTLdXDnThNMFhCge+X8vO2SbZLef00IedRE9m8vZbt2jlrHVNN2PD2Wm2QjqvUB7zd3OnktmrXjSacNLultmtt4HXFkDW9RRZB69ctiVn/3UUX+G9HNTZq9mh9rh23xdsuMW8cTFl6uSpw/XOvvXUhzVmzmtpbF6ZzbNpbF6blS+11wmbF9LnpnJjZz51W1n/TlnfQpDtvISKib7auLCtn0Zhx1NXbSdOWd5TVqf2+8qF62nUFwFDnN7/5DX3xi18kIqL+/n76yU9+Qr/85S8bdsTRW97yFvr5z39ORET33HMP7bnnntTa2kovvfRSOvTs29/+Nl1zzTV0zTXX0DHHHEMzZ86ko48+OjebsGxzHQn9yVoOn1aOdOfaqkdzkmPqcemtMqxyJedOst3Pr7VF2m45bTF9Lzmgobxav2cpJ0Zs+MTYydsj9TGvUxI/Gloe67NWd+y5yNNzW3gf8G1a/1i2xvRHbL/ytvC0ob7R9kv1h/qkVsT+Zml5K82nEfotbG/dNLwrtp4sv6tZcLYQDVwsIEsZHPccGu2cWbztknT+jLPbCZ7Yc6m9dWHZs3IAGArUctnm008/nS688ELafffd6dprr6WHHnqIWlpaaMqUKXTqqaem6UqlEt2++xdrUqfG+x49NyikXnzxRbrsssuoq6uLRo8eTRdddBFtv/329M1vfpPWrl1LH/7wh8vS33777bR27dpcl22GoCkQsQLAEh1WeVYd/ndXZsjGUFmx7ZTqlv5gY8q3hIqW3q+jUmLFGkdqa4ywsZxdS8z46aX9Ut/F2me1S6pTE8vasQ99z3JdaH2g9XFW4RgjqLg9vP3auZFVvGvtlsrIUwzxuirJU+vrNcYuy173DJeu3k56/KjjU4d/8bZLaPLkRwcIAOv69Z8/43BDw/w01oIA1u8PUfnDOWNxgsl/5w/49NsGQLNQS0Fzxhln0I033kivvfYavf/976evf/3rNGrUKDrrrLPoq1/9apquUQRNI4IhZw2A9eeR5e64/9n9eVgOqLXNr9d9zvqHZIkS99m9+D4pDW+P9ces1enSWO2UyuQ2avkseB7JXmsbd9j8PpDS+OmkNLHOn1Q2P3ZS3TxvbNnaeevq5d+lbVo9ITHHzy3fHn+/VqcmMrRrkJ//fj0xbZP6V8sX+p3h7bbqiTnnszjFUl2hdvB+y1JfqH+061CyyfrdcQJkbNvKsodTzlmzuuy73w5/iWb/WvCFi3v5z5ghorKoitbm9tbyYWn+uTa2bSV9avhN6aICq7oXl8274W3u6ds0f8ZFlfxlpP26IWYA0EmShPr6+ujuu++m/fbbj8aMGUOtra3izfkkKeX6KioQNHUi5KhIDnXo7rSfV3O6JMdKcqQkGyynwRJJ3HmWHG7fZunPUxJtVn28L7Q0lhiU6pccyBghpaXljrx2fLW+18Sg5BhrTpvUXl62dGwssRI6rpITz50rSXiFzgOtj6TzWtsv9RtPHxISmkCTbNf6LtS/3CYpL0+vCRvp2PDPvJ4QoTQhgSXZpZ1/Vt9qx10rNzadZnNI8PBzlV8nThjw69N911YUC7XTL9PNvZH2TdhqbSqM3IIC7a2bHvbJbVjVvVit0196GgCgc8ghh9Ds2bPp+uuvp2OPPZaINg7rWr9+fZ0tKw4QNHVCc5il/dJ3Cc3Z4HWFnHr+4mVZDpbk1IWcLG5XbDs1wSU56prIib3jqjmCvGxrW4wDrvWtVofmULt8ft9oIoM7zpKAsJw1rTy/TOtdKtMSjNrx1c5b65yV9lniXPvOy+FiPlYYWMJLS6f9NkjXR8y5HRJbftmx16xVlrZPEyr8N4y3018QwLoZ4JerCWvJXu28sI6D9PuT5TfPCQMneKTnxLjP0qplfjncTheBcbbMfu60sryp0PHm9Tx+1PFlZfhl+g/u9OcmWUIZgKFMkiT0b//2b3TeeefR1VdfTXvttRcREV155ZV01FFH1dm64oA5NA1IzJ1A7c5l1rth3BnV8sfUmdXBCtkSsiFUpmWzVp5kCy8/tv+1dFaZVtsdUr9L/WI5TNYxDzlp0jnD7bLq4tu1NJYw1eri5Uo2hNqpXRPaMZC2xRxDrS3c1pAzXqkdWr1Sn/D8WX5jshDz2+eQzh/3WcLND/HniVTyW5k1fcw5yo/F+es7ac6r8wYsVsDb2NXbKS4goB1Th5vvQkQDJvO7/uFt6OnbOEdn0p23lC3T3N66MXrj5ghZ16/0HYCiUss5NLNmzaKvf/3rZdteffVVGj16dNm2UqlEt+56TU3q1Dj2sY9iDg2IJ8ZJ0fLx/fxudOjuqb9fctisu5O8HH5nU2tfjIMmlaOl9+uOTW851CEHWLrLqtWhla+1z7LRL4uXKdkh7eM28XTaHWk/nXY8NVu4vf73ShxpP62Wnp8Lkg28ndwe6zjx7ZJN2rUUEhFSX0j5eBut61S6LqRrRrqG6oXfH9p14NsrtUO7Vp2TLs3xkOBlSNeOdS36x9IN19L6mh/rS/pPSkWXG94lRWXGtq1MV0ZbNrU7Te/XI82BcYKkp29eunyz3wb37qI7ri3TlneUPfDT7fPnCPX0zUvz+REhfn0BADYxY8YM+tnPfla2jYsZYIMITRNRyZ8F/6PW7qRJZYfuiFr2cMcv5s5iVqHH7eTlcmdbs09yIK1+kuqxiBF7fjmSELXaYuWR6rTaraWRBIZ0jKVjYZ1jvH5L8EllhcSz1D+WrTHEnjf+di2flLeSPtP2x+aNPZfzwuoXIr3PQ9dFTF0x+a3jHXsuWue1i6JIUSY/nY8fhdF+9xZvu4SIyoeZdfV2Uv/Fb6adFu1P56/vpCtbVorXg9VX1u+qnx+AIlPLCM306dNp+PDhtOOOO9L+++9Pu+22G02aNIm23377Mn+2VCrRLZO+VJM6NY5//BxEaEDt4H9OljMVcvak9NIdUOmONL/r7N/BC/0phRwQ9y7V63+W0mh3cLU00l3pmPI026S2aPZrtrl36a4y7x+p/yVbpe2WY+7qD/Uz35+lr3hdUpoYh9USuZK4kvLzNoYEdOyx1a6dmDp8W/zjzM8J7fj72/wXP17ScdfOJ6m/a+WESraH4P3Ez0neDv98kK4vXr+1z2o3/3307e3pm5dGVWL7sadvXtnke5fPrXDG56RI0ReHi6QsGjNOPL7trRsfqjlnzeoB8162+uxt1NM3b4CYySqeefulawoAsPE5LVdccQUdeeSR9PLLL9PNN99MZ5xxBubQZACCpiCE/gS1dJITFHJgpfK5w6k5C5oDprXBcvo027izppXtT4bV6uX5Jfu1dvB2W45ajBjxP0vOrd9mS1hYNvPv2vHTziOtHzSnMiQoLUdTEx6SIxsjLrTjyeHp/fql8vl+Xk7IFil9SDhJNkjXtVa+2yf1sbY/ps0xabIKfslm7dzw04SuWSJKh2v5x4X/3lnXrm+HK8tPO2Xp5WXDtlwaP8+K6XPTSEl768IBQ+Jcfr9ul8aJEW6Tb/uxT326bD/vz0VjxpXNmfFf/nA2brskGjluTg1EDAA2o0ePpr322ove85730Mc//nG67rrr6K677qJFixbV27TCAEHToMT8AcTe3eRlxoqO2PIlR1sqjxPzh8jTxtrknImQvS6tv487SNadeqnd1h84d5SyYgkxzdkLHRPuCPr1aHdUNedREi6hc1nrX0t8SE6s3w88rSTgNeElXQ+aQ8zPkZBQk9ou1asJG+v80dqtiQrtOMWIRG6zZosl/kOi0rfLaiv//fGFQOhY+hEP64YB73vHiulzU0efLzTg8vjPm/GHgPX0bVwWedryDjr2qU+nK4pxm/25Ka7fVkyfm5YhLRzg/x5Yc4aWTe2myZMfHXCN9PRtnAfj7J105y1i+/ixkL5DzAAQ5tlnn6Xzzz+fZs6cSaedtnEI6Nq1a2mXXXYZkBbPoZFpqbcBII6Yu6WSY0oU/kPx/6y1u5q8DM355eWG6pXKk+qy8km2SnWH7uRKf9QhYWLd0Q05gVo9ltPgOzVaXqmvQn2YRSxqjrV2HvE0mvMu2WedmyGxJokU7XhazlnovIg5h7S+lY5jFtGbRQhJ5yxPq4nH0PmpHQfteoz9LePpuYjSfit8IWBdi1peKY3Ud1OWziNq27QyGO/Xnr55/7dv4DXjlleePPnRASuV8TkynGnLO2jF9Lllq4otm9otPlhTKuNTw2+iS/pOoilL/++ZMl79Lv205ZvS+7Zo58jGOTv2+Rq6DgEYqlx11VV04IEH0rnnnkv/9V//RURECxcupHe961309re/vc7WFQNEaOqI9QfBkRwc7qi4/bECxv/M73Byx9kqmzvymjNrtZc7KrxOyxG1nD9/P3emQwJK226Jr1B7NDukO7sub+gY8Duyfj1Se/xjG7q7ysuVhAavxzoOMQ669NmyQbOdp+Fl83Nd2i4dR+3YSmJTskODiw6/f0P5eV18u+aYh8rTxEzovJHySva5dH6/SXl4+7Tr3uqrkFDkvzPcLul30u23xMeEjtllZbh5JXPWrKY5a1aLERi/Pu233Rczfhu0/pSOJRHRrRM/mX7/1PCbiIjK5vH4afm14aJKlqCV8nNi/gMBaGZeeOEFOvHEE2nixInpIgDnnHMOfec73xmQFhEaGQiaOhJyih2as2Klc9+1P3dJDPn5Qs6F5Cxrf1ZSOyVnWnJUY/4oub3+tpDDq/UNt9NyGGMdN6k8SaxIDpr/3RJU2jkQcmK58xQ6N3n/a8dJs4un0Zwuvs8vj9vsb5fqlfJb27n9koDy+5Wfg5rdUj9Ix9m3x+o/TQRa+6zzIeRwatd/qBypHZajq/0Oab8blhNviRl/Qjwvl18T1m+pS8fnmvA5NE7A+EPM+LF8/KjjBwgcrU5L9PjXiVscYFX34nSyP9FGYeXKvKT/JOrpmycKNOmcdCuvEdGA5ZylvJzY32QAhgIvv/xy2ffx48fT2rVr62RN8YCgKSiSI2A5v5KTH+skhJxVnj8k0PgfsOTwhcrgZYX+SCXHxu0P/YHG3FX0nWNJlGjl8H2SMOPtkGywzgFul5RHapvmbGhCgqeTHFQtT+i4h/JJ6dx3yTHl/S21UyuTb9PK4WJSs09qh3ZctX7g5WhiRRJI1rGT3i0BJbXBT8vtD127Ut9KbdXKcvNorN8wPgdFwjnt0rXNr60JbPiWGwbW3rowXXKZaOO8FD+df1z8+X+ruhen4sd99+t2L78e3sb21oWpcHHPoPH7TYMff/4sHNd/0jnkxJN2bvP+43UCMJQ48sgj6ROf+AT95S9/Sbc99dRTNGLEiAFpEaGRwXNoGgTN2eN/mG5bTFn8rqT055HVJm6P5rxLdfN2xdbL88U4y6GytT99zU7NwbUcX6utsXbwOkLlxZQfslGqT3P8pf6yjn1IQFrXQGx6yQ5OyEnm2ySxaeXX0lgihKeJ+T3Ick1LtlkOup9POp7Sfqtuqz+ltFl+q7R2Wna6eSqxZcbYxMv0bfHnvFjXx+Jtl9Ds505Lv59FN9E3W1fSojHjaM6a1WXPhiEisVzJZun3zT2rxj3nRjtn/Xz+821WdS82oznclkqoJi8AeVLL59AQEX3729+mm2++mfr6+miHHXagl156iebMmVM2h6ZUKtFNndfVrE6Jk1Z+pJDPoYGgaUBinAYrTazDkLVeycm3nFXrz1UqL0v7/e1+fZrzqW0LtcP6LNml7Y91tkNi0bJF6hM/r7QvRmzx9DG2hWyJOUe1skICJ2SfZnOWfBLaeSvlixVJoTJ52aF+0+zk6bI6kV29nemdf16mdZx5uhBaf0r2+KtyWYIkZFusvaHfFyc8eBmWYPT7NXTOn7++k+a8OnDVM982J0Bi2h/zW+bX76flDwDl5Yb+ywAoArUQNHfffTcdcsgh1NKycY2uvr4+evzxx2nt2rW0884705ZbblmWHoJGp2mGnC1YsIBKpVL6KjLSjzvf5v5ELOemp6984ipP675Lf1zSn5wrz6XR/tz8eiWbtDqltsbazR0D31Zrm/ZZazfvU61/+X6pXN4mKZ3/bjm/vE98NDHj16vlq8TR8G2RyrAEgiYmJGdRO56S/bxcybnlfaydU1p/8TbGiBHr/PfbbtWp2eTbIF23/Hz167bOGb9shxSV4OVZ9Wp1aO3lNvC+cvb481e08rXzz7LFH0rlPwfG6j8XAeF9IP3+uP0uEiKdh/w5W5f0n1Q23E2yzX/ezIrpcwdcV9p1ol3TWntdPTx6o/3OhK4nAJqV73znO9TT00OnnXYaXXbZZfT973+f1q1bR7vvvvsAMePYsCHfV1FBhKbB0ZyimG2hconsO7vadu1uZ4wNsXZa6SxbpbuFWnnSnVFLEPB00t1Vl976467E7krrlYRQlj7y91nlSu3g6Xk5lpCR6rbawuu1nKSQsx7q7xh7LbukdJYNUpn+Nq1uqTypbCl/XlTym5G1fCJbpFi/XVIUhaOd+/x7zDngcJEYN5yL27hi+tx0joz/2RICoWvZr+tTw2+ifz/iQXFInN9mf4iZJda13xg/bei/B4BGp5ZDzu699156/PHH09fzzz9P22yzDU2aNIkWLFiQpiuVSvTtiflGaE5+qpgRGgiaBiTkWFoOjZSeKG4IStY/FksQZP1z0hzGkAiIKTMkFBwhoWA5QiGHNyQYs6TVHFaeRrIvxhHRbNPsDKWJcXws58ZyDrOk1RyrEDHHn5cpCQhenm9TlnNUIiTeJHstUROqyxJFobKyXkuhsv3yQ0LNOney4Jx7/vwYrR5/rgp/Vox2TLRzRLLfH9oVc95Ife1EldQeP59UntQWyZbY4wRAEaiFoLn66qtp9913p912240mTpxIw4ZtHDj1yiuvpOLmuOOOS9OXSiX61o5fqarOEB94+j8KKWiaZshZM2H9oMeKhvZWe+llP53/x+X/4cQ4e9yuUF3aNp5PcsilNvEyeZpYp8Cy39+uOSKa/X46qW/97VLbY9os2eN/15wsyw6tL3i6kP1aO6S2+P3k5+XlSnVr55Z23KT9fnn+O7fFepeuJ0u8hMSrdO74aSQHndseK4h8+zVhy/tM6wtuE2+3VHYlYibmnPXT8eOs2e8P1dLwh9lx210dbnUyl9Z/2Ce3zS9Haq9ftlvpzB2zCR2z0xXI/PTa7xkfruY/GJSofCU1y17rN53T0zevbLU4P62/elqW/x0Ais7w4cPphz/8IZ1zzjn0nve8h+bMmUPXXXcdrVixgrbeems69thj621iYUCEpgDEChMrjXYX0EpXjS3aH6kjdBdVc/D4/pBDqN2ZDH2X6ovpYx8tr/WHr7WXl6nVob1r9WWxL9bmUB2hfFr/+2mldrr91jkU218x14pULm9fyLZQnkrs1s4XrY2htvm28W0x/VwNVvmx9kp5sthm/Z6FfsecLf4x8Yd6ufQucuPbrp2Hfvk8nx9lka5l6/fDiQ1pEQLrmszaH1obASgatRxy1t/fT3//+9/pb3/7W/pauXIljRw5ku644440HSI0Oi31NgCECd21yyJQ/HIsYWA5YtwW/kel1RcSElqbLAeT2+Pvl+rj/cTr8t/bW8MPWbQEgLU/5EhbxIoO3hb+2RKOfn6+3d8Xcnosu0MCkR8T6dhL9vnt8+u1zrdQXqsdfjnSNSWdn9L1ph1/SwzyckP9YokPqfyQ4xlz3lq2h/KGytWuX1629junlcnTuu18jg1Pt1FQlG/bOOdlU50TOgb+nk1ZOtAObo+/3S3tTFQelZnQUd5v/NhujMSU/wZsFFgb825MX16GD+9PLqDK61mZ1ufnt35/eB9A7IChwlVXXUUf//jHaZdddqF3v/vdRES0fv16evLJJwekLfKzYvIEQ84KgvXnEpMv5EhYzitPr9kiiQBJRGh2+mVodvj2WM6JJdSktDF1+jbyfCFHVBKHvD1West+Xg5Pp33W+kgTK1I90nljCQTeXvdZEyTSPq09vq2aQ+fvD9lutV0SxlJ9/nZuF683JIik/pLODy2fJWakPpHaKwm9kCDVvmvOOk+jiTLtnPTr0469lL6rt5N6+jY+B8Y5+rwsX8xI5fvOvavHRVB8+6W+cxGS9taF6VA1fk4sm9pNy6Z205w1q6m9dSFNuvMW8di5IVx+Pcumdg+YI9PTNy8daqb1p3ac21sXDsjrjpcbYvf4UceLwjD0OybVB0Az8uMf/5guu+wy+t3vfkcPP/wwvfrqq+m+lpYWWr58ef2MKxgQNAUh5GhL6SRHUHK8uNMglW/9CYUcF94OyUHy92vOH7c7iy0hZ8lytqQyQk4rdxQl+yv9w9bawm3wkcSr5ixKTqvkxPvHkp9Xlrjh8POL97t0HmjCmdfv28cdNE1487Ike6XzVruOJJt4Xo7V79px4Pu0dkvtlWyxyvTbJbWXf5euIU0k+S/p+uHt1s4963hL7XVO+Jw1q8scfb/ukFjU+rKnb2Okxr1Ldrj6V3UvHhAFcmmmLe8oG2LmL4vsBBnRxrk6/nciKlsZjR87a84QT8vb7osn3v4pSy8v28bn5vhpY0QOAM3GEUccQfvvvz+98sortHDhQjr66KPpQx/6EF166aV07bXX0l133TUgT5KUcn0VFcyhaWL4HdjY9LHfqy1PSxty+GPu7GoCRbpbGFu2b5/mGEjftXJi26b1C2+3Zp/UfskWqY+sfVY/WO0PpYnpf3+bdhw4Wdqu2anVzcuwzk8tn2SHdH5Ktlh28m2VCOnQORUqv7114YCnyme1IyZfTL/xNknf+QMhQ8I29Hti9Z//Was39DuuPdhUsz/mdy/L9RGyzw3Vy/IfUMl5CsBgUss5ND/84Q9pxowZtGbNmnR1s2eeeYbe+MY30owZM9J0pVKJvrnDDTWpU+OUZ84s5BwaRGgKhHXHkf/RxIoZ665lyCnkd9X4Z8kBte6WSvmkO66aw2bZ699B5XdTJSfVvUJ3hP18vv3upTnZUrv8Y8b387Zbjht3oEKCz2+X1P9SX1qOmv/u55H6XmqfVLfU/75NvA6/D6W+kvrRP3Z+eVL7/b7mdmjtks4J6dyWyrCOIbeXt0PKI9ms2cWdWek6l/qCn9fOUY91uiXbQ+eH1G5/u1WHExRENODp9v7x4+eRdE7z1bx4+5xN/uperl6XzpXhP8RT+91w0SR/f1dv54DzjyPZ6SJJ3Fb37kdZLAHit9eJGX91tRiRBcBQYcSIEfTkk09Se3s7HXDAAXTiiSfSBRdcUCZmHBuSUq6vooIITYHQxEmMaJHETexdMEvgaGVJd9tiRZZWp+X0adu5k6U545yQIyuVxbfxtvv7Q3VJ+7UypP1aes1RscqV+iHkzIdst/o95nhYbeJlhfqjkuMf6nPLbi1dSKyF+lw753j+GDu0Mi1CxyWmrFBeK7/mVGvH22oPf66Lnz7m9zTUDum5MX4+JxqkiJb1Wya1O3QdOyzb/e3+vn9ddAxt9dnbBqTT2snzc2L/kwBoBGoZoTnvvPPoiSeeoP7+fpo4cSJNmjSJdtllF+rs7KR99903TVcqlejr4xcbJVXPrGdnI0ID8iWLmNHukkrlSXc7efmhPxnNQdLqtep0aWIcZg3eflee1B9+XSHHlPeHJWB4HVJ+XlfIFs154U6Kn0dzlLlD49vGz43Q+aHB+99H6n9LEEj1VSNI+WepT7XzWdvnt0e6vvzzQmt3SKhJ567UJ9p1J9nq18ffJaw+l9oVU1bot4Y7+9wO7XqR9oV+z3iExq9P6l83X8Wfi+LXyeeoTOiYnUZQpPNpbNtKcfUwv2yXl5fd3rowjepovxN+OVK/LRozTmw//x3d6rO3Dajfr5MvQuDe/QUQLKFTyW8/AEXkqquuou9///t0ww030AknnEClUoluvPFGuuqqq+ptWmGAoGlANEdJQvtj9v/wQoKE/6HF3EmT0vM/e8s5lQQGf/F8mqNttU9yZHjdflrN2eftDokM3l6tD7iNmuNq3XGVHNoQluDSnHntPOFOouRcS+cWb59mp2+XtJ3ntxx5bmesEx86rzVBqTn+vI+lvrXq4bZI5WkiSTrfpLL9vJIYktqnHV8rrdTnUt+4d8lJ9vOE2i71G0+rndsOX1QRbYqkOKHCj6cTSF29nemQLmvOi18nt0Wqm+dzfSSd9/yhnG6fPxxszprVoj3asfcFir9fuxZ55Em7/mN+ywBoFkqlEu2www40depUOv/88+myyy6jww8/fEC6JMn3VVQw5KyBkP4AqilHK08SHtKdsVhbqrFbctrdZ4d1x1ZyvPzvFpaTytP55fE+C/WztJ3bYNUXY0uW/VKbLQdWQqpHamfIHl5WqF6pbqvdof7hNknXg3S9WLbF9muWPo+5TrXrWhJNsddcTL9wrHZaaa3rXLPJQvs9CF23ofrdNv8Bma6sFdPn0qQ7b6EJHbNp2dTudLUyf1K8wzo3Q/vcZ/dgTbfggm+v1VY/L0/jLzDAFynIE+1ci1lQAIB6UMshZ2vXrqVRo0YN2H7BBRfQ5z//+fR7qVSir22f75CzU/9RzCFnEDRNSoxoqabcrOkkh9ZyqiwhYDmNUtqQfSGhYtUVussYcjAtJ9VyrHmdWYWDlF8rSyvDEo8h0Rfbp9I267PWbxKhskLlSjZLdfB0IeESShc6X7TzNtSOLNdUSMxohM5t6bNmj1V+jC2xNsYKOYmND9XcJDisuTmaLb4w0mzk20Ll+jibYvsudH1KDx/NYl8tjiEAg0EtBc073/lOGj9+PO26666066670sSJE6m/v5+uueYauummm9J0pVKJlmx3Y03q1Djtn2dA0DQSQ0XQWE5Klj+OWtSZVWRkqT/mbmVWR0tzwjmh/uVpYhzLUJ2aDTH7QmWGhI3bFyMc/XJiCPWx1h6/LklwhY655aRaDhov3xJuMX0ulW+Vze0LObBWWsnmmHL8NNZn3r7YNNrvlNVHMc526JyuxlF2ZblohsNFbXwb/EiOtCwzj65INkqLCGjCwe8HLoakY+GXze3z69BEC4+gxEZ1Ym4UWHYDUG9qKWief/55euyxx+ixxx6jxx9/nJ566ilat24dfeADH6B///d/T9NB0OhA0BSA0B3CWv5Ba9+l7aE7wZXaEONca/tjHCffTr8cl5aXzeuMccRiHXet3ZaAC6WX7LcEplaGhNXnlmOttT+L4xxjp5XXzx8ryGPPB+27hVamVH6MSM9yDfv1a/sswSiVHWunhiViNTu0cmJ+u7KWp4lFKY1vb2x+X9hYtmsrpPnftaiLVL92LWf5DQ/1R+g3FmIFFJFaChrH66+/TiNGjFD3Q9DoYFGAAhDjtMQ6UlqarH/s/E9Qs8Glddu1+v12ubJ4nZadkkOhOfn+Pr8ey3n182rttPJI+fw6eR7/XepvyQnQjoNfhl+3ld5qt99Wvs06VjHH0bfP7yP/nNBslOyzjpOfXjovQw4wPw78/PVt5p/9MkP9IR0/qT3+NunYSGXycqR0IWJEn5Q+VDfPH/qN89st7ePHzhKUriw3wZ+3cVX34gGre3F7l03tFs8Pt8/V4z/7ZvG2S8q2+WU6myZ0zDbPBa3NfjnStr/PeaAsn39tWNcSt8/6Lfe3+c/AiWkPAM3Mk08+SWeeeSYdeeSR9N73vpcuuugievDBB8W0SVLK9VVUEKEpGCEnqNq7XdJdPOsun2+LJCr4Pqke3i7eNqnNmrCzbOTbNNGk2S61N8tnyzbLcZbaqG3PYkesCI4RkVZZUp3W+cPb4X/n6UN1ZSlL6xvtXOR2hPpC2h+6Jqy+064rrUyrjVIaq14f7XdCSx/Tj9UScx1Z54+WJ1RnT588FKy9dWME5hNtC+mbrZuiMKFzZdGYcTT7udPSuTghMWz9ZvtprevDkXVBgGr/e6z6rPMOgHpRywjNueeeS9OmTaMpU6bQSy+9RA8++CDddddd9LGPfYwOPfTQNF2pVKKvjvtqTerUOH316YjQgNoj/fFrdxe1P7Es+GVLd0a1u5x8n1SuhrObt43ntZwN/92q23L4Q3+OlmNrpeX1Sk6MdgdUapu2zToXeL2xx80vO8YW7dzh6Xyb+HZuO+87qR1Wv/J28HMtJMj89mt9FRI92n7et/y48Hqla9Ivk/e3v00qj9sn2WldX5JtMWJGut61Yyghtd1H+410+6Rzyio3tN/fNm15h3hcJ3TMpm+2rhT72i2lvGjMuLLtbvlkaR6L1h+uPOkaka4Pt9+PCrW3Lhww16fS/xSHe+4Or8thiSfpPIKYAc1ET08PHX300TR+/HjaZ5996IMf/CBdddVV9K1vfWtA2g1JKddXUYGgaXCkPy7N+bXySVh/UpoD68rWnDDL6QsJD81xlhxcySHTRIpmk3YH03J2+X7JAZXq4G3k+2IcZb8vNLv8z1zIhPqf16s5o74dkkPo22XVxQVGqO1+ebw+P490vEMOoOa8S8669D3GdimNJSR4WfwY8vySiJHs0cSQ/1k6r7K0U3P2eXus/ZI97rN2XXGn3Do+od8EyY6Q4JKEJ9HGifOruhen+bngcIsGzH7uNLG9RETnr+8csM2xaMy4MpvHtq00j7nb5z/Txs25kdou/Y74+/3jpPWPv3iC/znEiulzo9MCUFQ2bNhAa9euLdu2/fbbU19fX50sKh4YctagaHfi3D7pj1dz4LX0Wn2Ssxyyk+e17NHssMSQlM5yVmIdEJdGaotVl7Y/pj5up9TnMXn9+rn9PJ2Gdc7E2Blqg+Woa/m5fVYfaE68dP5bx0jKG3PMQ2n5viz1+FjXRky/xVzDoeMrtUVrYxb7tGPvsFbz8suP/f2Q8krDnTQbtZW/eD/5SzU7wfKvi46hnRbtP8AO6dxyw81cH0xb3jHg2TE8j2SL1N6YfguJOumYulXVrP8gjUryAFBPajnkbMmSJfTXv/6VPvKRj1Bn58abF4888ggtWrSIbrjhhjRdqVSiG7ZeUpM6Nc584TQMOasnCxYsoFKplL6KjnVnlN954595vpCY4Wn8d35XTnKUQnc7pXwa0p1Jbpf0hyrZENNPUhnc4Q2JQK1+Xia3I8sfOLdZOiYhp5W3ief3t/k2a86qb5NvO3+X7OOfuVMm3enNck3wvNL5wsvkfSult4QdP5a8HD8fL4+fJ1L7/TL5seLHU7KHf7bOGyu/lIe3kZfP22MdS16Xc5BDvyFSedIT7N13/9hIw5203wOtXpeORxT8sndatH9apouOaALQDTcj2jTkzD3TRjr+/Hpzdmi2a9epb4PVZr9vnYCbtryjzKbY36YV0+eav98ANDuzZs2iXXbZhc4++2x63/veRyeffDLNnTuXTj/99HqbVhiaRtDMnz+fkiRJX0OBrCJBclAs5ybkJEr4f2TW3VzJyeH5pfSWcJJEl+RoS84zf7fq0RwBSRBwx1YSMbHHkbctq4izHHDNGbfqkezWRJNkh9XH/LvkdHFbNFEU4xhJ/SqJAK0fYuriTqd2vvK2h8Si5tjGtFM6N0Oi0t9nHQ++TbPZf+efpXPQso/jPxNGKi/L9efKcALLxy9nytLLqadv3oAHaDqnn2jgBHiX//z1nalgWTa1mxZvuyRN73D5fPulFdWcuPDrkMRTe+vGoXrONuk3yl+RTLKjp2/TsDm/DKk+aQW3SXfeQhJSP0vbASg6w4YNow9/+MN022230cUXX0znnXceffOb36SDDjqo3qYVhqYRNM2O9ENu3eHM6iRLzi9Hu2srCQhNDEnOmdQu7lRyZ1C6I5zFQQ7ZI/ULr0+rU2oTr5O31SqLt1Fy6DQHUxIn/vaYPtPapDn9vCzu9GvpeJu0umOFtnWsJbstAaMJYH6+S4685CBKDrxUvtQeqwwpvyTQfPx5FFJ91m+BdA5YfSL9PoSON99mCXOtDT5+BCXWFun48/0h0UZUHq3xHXuX5sqWlakwmLL08nTImT8nxpXrxM6q7sVlQ+D48eTnoCRQxratTIfHcfvd0DnrHJL6Q9vOl51ub11YNn9HKscSwAA0E6NGjaI3velNNGXKFBozZoyYBss2y2AOTYOhOZlWeiL5bq6VVnPKJefLKl9yMKw2SZ/5u1a3VF+ozhgHRdquvUt9IW3X0MRJyCG26pX6IeQYxCA5QpodoWOinXuajdoxjznXuO3aeaSlk+ypBqlvJNus68QqT8vP22C1J4tQl9COibXd+k2QyrfOCSuNVY60j6eRoimuTVodRFQ2n4RokzBw389f30lXtqw0+5qX7+bSuPIWjV5IV7bIy0DH/C5qbeBtkcrTjkFMubxt7a2b5gxpv79Zl5EGIG/yeLBmiFKpRNdv9bVc6/jwv04t5EgnCJomwPrjjnEQpD8tK5+fTton7df+SHnarM5sSJiFhI3U5pBo8+2Pcar551hCAiXkJPO2WXl5W6X2aPlDtvtlhJxArZxYcRtrpyUKpP0xYtLqU2m7j3Zea86nJkq0+ixHVjrfrXJjRVPovOL7QtdXVmfct9GJiZAtkl3Sd952t1CA1AduUQCH5Ljzsvy5KA5JWPnt8vPxvDEipxaLL8QKUh+/Xe5zVsEbWxcAtaRegua6N3w91zo+8uIsCJpGopkETaXiIlYo+ITugvKyQqIkJGSsu3zcLutOoJZO6pOsTq5URoydMfVJ5Vrts2y2RAS3VxOJmvDRxGRMX1i2hhz0mGMV05ZKiBEXUlscoeOvtUcqM8s+a7tVp1SGRoxz66fzv1ttsH4LYoSLVK51jVRjNxENWHVM+z1z23wB4pfjr4jmHPpV3YvLhJDUT34a6zfPF1Vaf3PBxAUO7xN/9bYsfRp73WjfAWgUailo1q5dS0888QTtvffeZjoIGh0ImoIREhNZ8kvbLec4lM9yQkJ39SQ0hzLktFXiRFpCzqojRqBJ5ca0J0tf8Db4aaVy+H5NuFr9pjknfprQZ44mKC3HXmuPv90SXlL7NYeUt0Ej1M+hPCFbtXQxIkPLF5u+WlHAsQQAr09LF3t9WNdlJYTOZTfsbPG2S+jYpz5dNuQstARzSJz5ZTgb+JLSWa5Dq27NvlDfWrj5OtJy2VL9vuCCyAGNQC0FzaWXXkpbbbUV/cd//Af9+c9/ps985jM0cuRImjdvHu22225pulKpRF/e8us1qVPjrJeKKWiwKEDB0MSM/yfX3mo/UM5Px/P6+PtDfx78D8i9tHolZ8rP5/4geTstocH3c5u5PdKfNe8L609fKldK45drOW6anb4dWh1+OqkdUjul48TPCb9feRptOz+e3G63zzpnpXNA6gPNXt5O/7Nkm3SstLby80bqR6ufpLzcZr89Utl+eq1u3iapjVbdGlpfWW2W2qqJAH6MtOMunZvSZ+t3SLNDaxNfBpqXxyf6u9W7jn3q0/T4UceX7Xvwwd3TMvlSz9K1zfvCXwzA7XPixokp6Vhq4iNLP4R+S0O0ty5MFyKwfqt8/GFpADQbf/vb3+jMM88kIqKrrrqKTj75ZDr11FPLnkEDbCBoCorvHFjOLYc7M5qD6aeXPvvfQw6u5HRqDrJkIy9D+iMOOfIh4aAJBmm/9Fn6E5aOibbfr1NzUv18kmPO8/F9HO6YcgcyRrhZgoK3i7ebCw0uZnkb/PKld82Bls4ZTUBZdkt95Led59X2+XZL260+9I/n4m2XiKJP6jN+Xki2S/slm7XzWnNqpevOOo5S34UEnvb7wMVPSLSFBN3YtpVlz0zh1yxfwevxo45PnXeefvZzp1FP3zx6/KjjxTkzvk2+E79sandqiyuvq7eTevrm0fnrO1Mx45aPtgSd1i++wOLXqN/f/Phpv/m8Hf4S0doqe65cfjz8Fd8AaBba2tpo2LBh9MQTT9C6detoxowZ9Pa3v51eeeWVeptWGCBoGpzYu6VEtgNt3V3TBIKUnpcn/clJDgx3nvl+f5/kcMU4WVp7+XfNydccZ+vPk7dNcnildsQISM1GySnjotZqsyYcJCHJP/vb+EtriyWSJFt5W7jjJbXJclC1Pg+dM9p1oTnuLo2UT6pHEiMxzrbb75b09euQbJTqkEQFF5n8mPC+jPmd4Ntjfs8sYcVtc5/9h1Ty89gSd1Ld0vHzBYX/zBStzU4QuAdhus9Em5ZbdnW6SI2zu6u3U3TiHU6oOOFCtOl5O3NenZfWJR1DH/7cmZ6+ebRsajf19M0ri/xYQoaXax27nr55qQjz5/NYK5dl+f8DoMiMHj2afvSjH9FXv/pV+rd/+7d0e29v74C0WLZZBoKmgZB+nKU7lvwPhP9haw4Mr8sSFtofmZSW160JEsnpkpzomLo0gaTdMdS2a7bzPuTlS3l432v1ag5XjEMofZccf76P2+yn5/VL7ZDKCjne2j5N9PG2aPmkY8vPXcke6TwLleWn1/LyOnhZMX0hoR0HTYxq57CfXrJJK1/6PdCOD2+X9q61NQS3n5+zvlMs9an222p993EO+Irpc8uemSL1Y0/fvFRQ9PTNS6M0TkA4IerST578aJndEzpm06Ix49I00rEmKl8W2tnuD2vz7XLCxy/HfzCm26etcmYdb+03le8LXRcx/288vXV9ZWkDAPXm7LPPpp/+9Ke0xRZb0LHHHktERLfccguNGzeuzpYVBwiaOqM5yz7c0bR+4P081t1IDSstd7C0u5kxjrDmJHIbNKdOqlMTBpKjxwkdB004+vmlP2zJIbHSZLVR6lvpPLCcTMnZ19op2SQ51rx9oXZIbYlxSiUbePrQ+a/VExK3lr1cDIT6kOeVrnWpbdL5Eyv0QnZagk777rZxEaOdp9L5yuvn+fg+V74f2eDtiPkN5GX45bj8fuTCf5ceZsnbzefQOFwUx69rzprVAyIo/FhzMePK4uX7292QNN5uP4/028DbK13XUh9r/xXSMfHPYe1YSvXxftbm2FQqpgEYDCZNmkRXXXUVXXTRRdTa2kpERJttthl9/OMfH5B2Q5Lvq6hA0NSZGHHib9P+NCSnRvvz9z+vmD53QFrJAZYcLJ4vJDx8Zze0nTt1knMmOfVWuyVRIfULr5+Xy8vS+kVqj5ZGcxqkbVLbpHpiHXmpbMkhs/qNn4P+d55POr+kcqV+lJwxy8mSytYcOI51/mht4v2miRnJ0Y8Vd5pgkdJbooCntwSUlddt523h/ST1ofZ7INWvnR/uuz+3QmsHP+/8uSK8jFC7/T52+VZ1Lx6wcIATE+7d7ffr8Zc/dvlcmVIfOgFkXW+SrZf0n6T2a+jakc4x3p9cEFq/QU5ASn3i90fM8chyrmq2AFBPXn31Vbr55pvppptuonvvvZdefPFFmjlzJt122231Nq0wQNA0IJIoIbLvlmsOuZTPd1z4sqG8fs02v04uOkJ/YpJTo6WTnD7JgecOJS+Lf7YcHi7OtP6R6pCcTK1M6bhYDqjkFEptkkQpd4o055kLBMkGSTRKIkwqXzuvpHZpZca0PySEtHNXg59bvP3aNaOVbx1H3tdSWr+dfDtvLz+m0rkntVdriyUqed0cq/+ka95qv2W71A5+Hnb1dopDrKS6LaffvyYmdMwumx+yqnvxgN9it7CAm8hPRHRK38DV0/gx8CfU8yF2UtutYxLjwEvnOy+DX6v8uTRc3PF8rq/85+1IvyGSbRp8KF0MWdICkAef/exn6Z577qGnn36aFi5cSB/4wAfo6KOPpp/97Gf1Nq0wQNA0INz5lPYRDfzz04hxRCxB4Ncl5ZecKWlfqGzNSeaOqtZGKY3lqEp5rD82SVyF9mtCT7JTs1Fy3LmTJwkN7hjyurnjYIkGS4hIwoznlfJI+zVRIp27miDgfSc5Xlp/SOeB1Eat3/x6pfqk48Hz8DI1B087r6TrjhNz7knngyTseFutcznm3OB1+eliHXGeVvocmoyuiZnQ76gTS07gcLuINt5IcvUvm9pN32zdOC/n/PWbBMCyqd20aMy4tN9cel8wuQiTJmidPW6yP7eXp5X6T7pW+DWh/X764kKqR/p9cZ/9oWPSb3/ovwCAIvHYY4/RokWLaO7cubTTTjvRrbfeSm9/+9tp/vz5A9JiUQAZCJoGR7qDaTmP/jaH5NRZIkcSJL7zJDmb2p+fX59vj5/PEm28TOlP1BJi3D7LLgnNwbJEQchZltootdfqX8vZt5D60ipHqs/l1Y6bdi7ydljnILcv5KBzmySHK6a91nHh7bD6gLeb1y3VpfWVf81p55ZWntYHPL2/T3PYrd8Yvl06ZzXxqzm6kj3c/lB5kugLHWPpevajKdrvmW+vP3SN2+eWfXYOux8lv7JlkwCYtrwjXdrZn9Tv188foqn9PvrpuE2833g9PX2bIk1EGwWYP0RM+g2XjolrsxS14f1ORAOWuo75vXbl4Fk1oGgMHz6c+vv708+jR4+mc845h26//fY6W1YcIGganJADUqmT4Zcvlac5UVK5Unl+GukPT0rD2+vbpTm9mpOo5dOcWWm71Pchpz9GaEiOup9Pao90nKU+42X65fCyJOfGchqsY8C3S44V3+/3g9R+6TyQnEmt//00fhl+f/CX5vzH9InWP1J5ko2aYJTO8ZADycsOnbdSv/j5eRu0Y6U5uC4NF068PknASH0l2W456TGOsHTs3Wc+jInb64uenr5NcxN7+jY9G8Y52f4wNy6UeFtdOXwxAqmvXHl+P/nPzPHrkH4z+Epofn1j21aW2e8/EDP0H+SLIfcwTd4G65zVfv+t/xReBwCNzgEHHEDz5s2jvr4+KpVK1N3dTSNGjKCXXnppQNoNSSnXV1GBoGlQpD927khYf/6WeOF1aM6W5kxrf4puf0y7JAdcEzMSkiPkv0t/2Fr5Wnslx1tLL/WVZK/URyFH3RIZWpt5+bH9ap0zmiOptV9zRrVzV7MvVjRIokTKEyrXcuClPFY50r7QsQ1dV7HOOXfKeR2a6PA/a8eII/WZ/11qi1SeZpdmo1SeJMascrVrw+XR5oH4aXzRQ1QuWtxEfBe18cWNGz7mtjkB4srnc3wkIejbN7ZtZTq8rL11YboQAT+W0jmhLdnsly3ll84Rv2w/0uLj7PcFD1+kQfs98leV4w8B5XaHzicAGoGzzz6b9ttvPxo5cmQqbq666irxOTRABoKmgbEcWf6Hookb7rT7eTUHXRJDkh3aHzxvQ8iB9b9btvA2aOVpTri03y+H//lJDqnkFEjbeV3WcQz1rYZkq9av/ndNfMU4uJrtMeLEcmhjHGftOErnmORwxrZBEy28Pu36CdmrtY2LAb9eqa28TKsPLcdOE01S2Za400SuVh+3j5fJy+bHlH+OEa6aiNLOc9+R135jNFZ1L04n/7uyVkyfmz5jZkLH7DLHfELH7LLn14Ta4PrEn1NDtGk1Namd0gprUjper8vrP8CUl+W++wKDlyv9V/mCh9uu2eny8IUdYn7TLBsBqBcjR46kk08+mUqlEp1wwgn0lre8hdauXUsXXXTRgLRJku+rqEDQ1BnLmdXu4nJnwXesJCfK2ub/wXCngdcpOfxWWzRHQ8on1cXbGuPsSOVx+7gzyNsmObia48jbZd0htPpTcyaltmgOoXbMeB7LedQEkOWcSn0fEpy873neECGhyu3x94fqkM5xS9xKfW+VFxJdoXZK5411TWm/DX5avwy/Pfw8tc5XSdxrDmaMIPDzhH4DrDJjhLrV/1Ie9729tXzpYf/Yjm1bSZPuvKVMcEy68xaaPPnRARP8/Un7Vhv4MZLapJXjix8pn/S74Jc1oWN2WZSGR17cMDRflGjXqPabqNHTN69MhLg+94e+aUjHzT3DB4BGpKWlhT74wQ/Sf/3Xf9Euu+xSb3MKAwRNneF/mNYfNk+nOTq8TO7YSo665mRKhPJLZWh3/bjNlhMmiSq/bK393FYpr0tjOQWSMyb9YVt96bcri5Mp2RL7R66JPt4uyzmW2sbz8+/cyeX2uM8hh5PXYYlBCU3YaeVL1wpvj1WWtC/GIdeOkXa++ceDn/vSeS2JKW6bduwtuzSxKtmswa8X6ZzR+jB0THl52jnH7ed38Xn9ToT44sRvR1dvZ5kA4HNoXFoXZfDnu7S3LkzntPj2aELMb48/f4fb7vrXF1+SeF02tbtMrPltkrZL/UdEZQ8H1dJLfczz+P1GRAOWreaLAGj2+f3inuUjneMAgOIBQdMAcIfRffa3SYIhdPfQ/xwSDJYTZzlWVr2hNvL9oTuEvD2SIxbj7EiOThZnXUuvOexWu/19oTS8D3i5Wt9zZ8ZqKxd1vqNjCVTtXNQEKD/3Qs6xJcj5eaD1IW+b1AbpOuP49lvXQejYaALD2s/PXb8uTbDysiwhxsvSjgGvo1LHULvmtevQ+t3Tfgf49cX7UOsP50BzZ9n1jzTnZPG2S9Ky/chMe+tCdS6J2z5l6eVlCwvw+S9uNbR/XXSMKLxcPZPuvIV6+uQFAfx33ib/fcrSy6mnbx5N6JhdZq+bBzShY7a4khgXXS4dP2Y8rz80j9vE+yzm/NKW5PbLHNu2smz+jnVdxILV1cBggGWbZUpJUuQRc5tYsGABXXzxxfU2oyosx9DBnUzN4bOEgJTeL99Pqzm3EqH0kgDhbdXar5WttcuyQWuHZqtUJ69Ls1HqI6kPtLZKWLaH8mq2xGyX6teOY4ydErHHJNS/fjrps9VWzW6rjVK9MXXGHGsOL18656TjEjo/pd8WrQ95uXwbb2dMe6TPMbbGYp1H2raY3wM/EmOdC0Tlk9mJNg2ZCtmRhdBvFn8wZ3vrxmgQXwKal+nn09rJo1KafaH6iEiMcPGyY//rtPbw5wRpxz2GLH0AmoPu7m7aYostqirjoosuot12241233132m233Wjrrbc205dKJbqy/VtV1Rni/J4PUBGlQdMIGk7p/7f352F2VlW+OL5OCKlAShJEGeQiREIi4m2btmlp2vYXvAhOCEHQFloMYIQWG0ijhBtliGKAMINMCSAOGBsRkEYaGtpgt49XyfWCDCIYEhUQGWISDCEFlTq/P/LdxapVnzW8p86pU6eyPvW8T52zhzXttfde651OrfOyTGsTs4J5DivgsAIND1rig2SUPKLJgRUUWgGnFSBGA/1oomOVId2sZC6SxEQTPK0N52kF2pFEyErmkExWoOolQqiPpZcs1+S3bObJj/hr9LW2Q0mqNN01HhLa+mIlaN48tXSp4peItwVLV2+MLXqon/Zdfi5BuhXs7zRxFi2Zvoam/OBGIhr4cLtMbrg8Mmnicmr6Ru2J7M//l0TGSryiiYyUW7OtliQ2muiV9ksPmDPgytrSA+bQlB/cqL7BrRHw53sSoxvNSGhuvfVWevjhh+nXv/41PfvsszRp0iSaNm1af4IzZcoU2mabbfrb12o1On/L1iY0n1+XCc2IQqckNNFAT9tsrKBf46PJwft5AatVpsmryWIF3JqcUsdIciF5W4GAxsuzvZfIaMGz1kbSisiPZJY8NRmi9owkIJ4vI9kjNrXk18YPQbO7prfGx+Ol2d0Kxj0aHq9IUhixLw+0eblHW6NXBZG55iVDXkCOeKIrGCip8BJH/l3KqNm1kbHT/FeTwUqOUHvNNo0mF4hP4SUTDo12SQ4jfCVNT3bNVlHkFZpNB81IaApuv/12evDBB+l//s//Sc8//zzdf//99Otf/5qIiO6+++7+dpnQ6MiEZoSikYBY9tX6e5tgJMCL8EPfpewafUuGaFKH+HnBeDT4j9rR0tcLdCMBrEcT1UXtIb9HEgtZXlBl/C15LT0sXp6vczRid6SXF6BG7RYJdCNjGqGpJQjeXEA6W4E9+i5102yG5EH6abqics0PrHXWoi91RklS5BYuSYfrbNkvEvBbMhf65bYsNAb8eRGUIA010dHGxxsri6eXbEibaklsJi0JouYmNEcffTRdc801NGbMa4+233bbbTRmzBj68Ic/3F+WCY2OfCnACIIWWMrFutRZAWb5z+uqBpV8Y9A2V28DKfLLMi6j1Ee2k22sZEbSkHyQLEguxFv2iQQ8Uhakq5RN2gzJ4PFDfKTckWBDyoJ8EI1nKee2kXpq9tWCUcvfCy/UVtoioncjNpfg8qO5ZiUzct5rZVqQJ+VHPitlkzQRH2vt0XxCtpPjL/XX1kFUh9Y4zU8ifGQbrY5DC7A5nWUHfWxQv/LAPXpoXn4vD69znUsZmqP8Bzt5uZwn8lXT0jYyKZJ6bj1+xQBe2hrHZeSJkDZG1p5ApL+BTvM5Lq9VL31Svk0NlScSzUBPTw+9+uqrA8o+9KEP0U033TSobf4ODUYmNG0E2oj5hkM0OAi2giBZhoKcwksLVGQ7rY2UXwtiI7pr9FA7LXlBm7IWDHJIWnJTl+Oh6aXpqtnXSxSkztpYcl1lH/7fklHWeX0sW8r+vD2v0xIexMuTUxtzJL/0CTTOvAyNG/8eSbTk/EW2QzojP5e0ke9GEyg0T1FCxW3F+1rBq+YbaA3i/bwAF8lp2d9aO70xiMwDbe6VZKHU8d89kT5Rbj3jbyvTdC3gD7Rzv+c+qY0JrytvLeN+z9+OZq2DJaj/l97BCQr/bR7Ek+uGki6OYkeun7yFDL1dzBpfDqnbqvWT+w8JXoZ+nDPfcpZoFH/9139Np512Gj344IPU29tLRETPP/88vfjii22WrHOQCU0bIBdwIv1qCG8nN28tCdISChRoyD5o45I0UXCAAlMtOOGycT20vkieCI9I0oSCDPQdBRkyEJDtpI7RJAMFJZGNH42LJrump0fPGyfEx0q4rABXyil9xdIP+TXXN6qzlNNKuqxgUpMV8Za+FeGv2QP1R7ZHNuVrC+eFEh2ko+ynzRNtDmvzRVufrLHxgHxPvpEsmhCVfvxWpe6ugb8rw4P0Av7Wr3KUALlcKSnty/dF2103aC9AsnjBPfePfe+dCMdzbc9cumjS9gPGZdX6yTT7pcHzvCRb/KUHyI+Q70r/KTQsHUpSWOzl+RTRa79DI+fs1uNXDBo7otdeyV30eeCBaf1JTb4IIDFUfO5zn6Np06bR6aefTh/5yEfoH/7hH+hTn/oU7b///oPa9tVrLT06FfkMzQiBtuFr33kZ6l/qvaQC8Y3Q0gIaLQhGsnv0NFkiNK1kzrKpZieJKnbW5JH10QSlUXh2jPiY7GPJWVV+GcRzvlpZJGnW6Hg+GwlgEY2Iv0bnWQSReWzRteZNkbnq2hShixChiWTzyiJ1Wn30dcEebYvnRZO2p1nPHj2gjGigHeTLDSRfrRzJGJE/qo/2PEmV50wasV1knC3ZtGeMrKQb2afQKuCvpE6MbjTzGZqCvr4+WrFiBa1cuZK22WYb2nXXXQfU12o1Onf8DU3lKTFn/RH5DE0iBn7WB23YcpGNnNm0NgN+ho3/5xsCKrPoSh20dlbiwc+MybN3sg/iX2hZARY6Kxi1FZdNHlIX/t0642iNGZIL+YNs642Xlbhxm2gJprQ1siPSGY2R/Mz78HHnhxwD3l6jJ/WTPCRfNA+kvMgHkA20YFG2Q/J4CRo/ZJ2kYyWaWuBa2pczz1XooDG29NLWQY0mohXxd6+95yPouQuk35Lpa9xkVF6pKLSsZAZ9RmuvNoclXQm09qL+Wh/0fBARvlohr6B4Ca8ns+ZX5WqY9WOmll+X/ryMy7hq/eT+q18XTXhtDP6ldzJN3nMZlDmRsLB+/Xr6xje+Qaeddhr94Ac/oL/5m78ZlMwU5DM0GJnQDCO8YAUFdOW7FiRr9CU9SQcFaTJg5bTkZy2IRAGaDDylnsgWcpO2AhGkl9RR9tMSLUt2pLu2oSIZZb3Uyxs/WWclg5qPSHiBt5WMaUmNFaRoY450lAmCZqtIgKslDlZCEbGfTLgQ0DhVgZyX0r5acIhsqtGX/sR/T0Wbv5K35a+yjSaTlaBo807zJ9neSqA4Tc1O6OF9jr3uOtcNzEsgXcrQ+sDlkc9kSL0j9uNtrXWyfC63saHbt/hzPqUcXY3QxkTePmbZ09KHr8lczjJG/JklTkc+G6P5Er/9Dz0rs+ygj/VfLbtw7GuJ2+yX5tKkv/h9PkuTqIxLL72UfvOb39C73vUu+vnPf05ERAsWLKAnnniizZJ1DvKWszZDBtfRQFsLRGWgqQXJHJI/CrRRsKrR5jSiQZyXoGnyR4PoCH9k84gezayPjFFU3miZBYsvGmcvwbXoyj4ab0sHzUc8mppcFp1IwoDso83tqv5u0dJoWjSsBM7yF8sP0Fol6UVlQPpUsYe2jqF6jbcmKy9besAc2vfeiVCOqJ5e21LHX/0sf5yT31bl6Sb5RW4jk20sHqhO4xGRdThg3ZZWZJevrubP0uSrnUc/mnnL2cyZM+naa6+lzTbbjI444gi64YYb6Gc/+xnddNNNdP755/e3q9VqdE5Xa285O7UnbzlLBBA5syjL0Zk8+Zmf1bOCOu0snZQJySBpWTpamyiXQdNToyvpSJrITrwtP7MnaUjZPH7lvxZ4cJkQHURPgstiyVPqtTLpd3J80H9uT2QzjXb5fvpmi6G9raBH4y3bIVuggBbJjAJbZDMpO6ertUHzU+unteNtuexSPpQ8SP5oXCOBMuKn2RR9lgmcPGNtJRLW2iN14f01X0E8pZySnsV/yfQ10J9KWblSI31HtpM80JplrQtre+YOCJj5W8+6u+YPuBqE1iF5BYbzkz/6WT5ryUzpL1FusUN1WrDPZdX2AnnrHrqVD/WVV1u43aWPcvtpMvC2q9ZP7r/9Tr7lLa/YJDxs2LBhUNnee+9Nzz333KDyPqq19OhUZEIzzNCCcP5dC7ojZ628jV0GRFbAXdojWlpwHwl4tOCb00B6a8GpJTu3ibSDhGyH7I0CVxQoWd9lgIjGRPZHwQ2yGbKJlsxo8iE5JR0uu5ShtL1w7IoBCaYlv0wyUPCH5gnXB40p5y/lRkG61g/Nk0jSovWTfJGttTpOQ36W9JHevI1HR7ML0kvagX/WnmVAiZA2fzRd5Fyy1knUR6OtteVvAbPWWVmuJTJWu1LPn2nSEFlvefDOE6Cq9ErCY8mD+iJoa7HVVyYbXvJRwK+cFfryrXQRlDeh7TRxFi076GP9Ccu+904cQKd8zis1CQ9/+Zd/SV/72tfolVde6S976aWXOvJKSbuQt5wNE6oELNomITdfK8iRG74VPGq8rMAI6aDRQP81XS1eGv+I3kgmKYNlM89Wng003bwkgUNLQrgdlkxf079pN5K4eH5q8bfaa/pKeshPrTFEfuX1s3hq9kE6a/JEeXq+a/GxaGiw/MpKnix6npyeb3g+qa1bnjyRej5fovp7iUmETqmTv0JPNPgNWVXtZ/lVxDfK79to85so9vYypGPEBkMZ34g8kXr+Wd7G58G6JS8x+tDMW87WrVtHZ555Jj322GPU29tL+++/Pz388MO0zz770FFHHdXfrlar0VfHfacpPDV88ZXDOzKRyis0wwQtyONn9NAZQS2otRZX7cwaOmtn0UL8y+aI5Ebt+Oaq/Zdn4/h/LQCOnBnkskh6RW5OxwpWrLGJ2ACNg8YPjZOXCJZ6fgYSBVmW/pJeRC4LyH8QLYu/l5BqMllBPrIH92vNt70xjkDSjySPnI+s4/Jbvmv5j/RNa74i2TUf15Iv3l7zR5TcIZk020X9E52xR9BunUL218bJWm9Lf0sebS2WcnB6aJ4UfRB/9PYvaVf57Ii1Vli3lcl6bw5p6210rIk2Xumy5rGUo8gnf5OI8+aQz9EkEh5Wr15NRERbbrklLViwgM466yw64ogjqLu7mz71qU8NSGYSNjKhaRMiSUv5LDd/ueFYgZdVx89CSfCgIBL0S5qSj6Uj4qlt1ihQ8uq0AFLK7MmN7K4lfShYk/20PlJ/SU/aK5o0WLJy3aW9tCBSllkBuQakN5Id6YWCdSsALGUeHy2gk3oif0B6SZn4Ycki+UTGOZrsoASO8+R0kU3ROEt/18YV+TKv13xHS9g0Ga02BU+uWWSOY4F2yxwqQ3a7aNL2kL98pkMbY8smxb6r1k8eQM+ag+gZGem/1pqFEpHonB8qyphx/nIOaHuafBOdtzcUoN8hKry9tomEhcMOO4xWrVpFl1xyCd1xxx3U1dVFhx56KB1zzDH07ne/u93idRQyoRkmyI3dOkuEFmjeRqOJ+lgBfGnDfxRM2yi0gMNLVFDQFJUVBWJSBi1A8vrz/4g/Cpa1AFP2t84YogTJCuIkPTQmyLZWkIwCGY0+SmYsn0AJGeclbclpe/awfE/r59HkdZq/aEE7kkv6Ixpvjb6kgfryAFzzN00WzU9Q4sFpWvNDQsou6SH/QOOP5JcyoO9Id80XiXCgurZn7oBnVqy1VOqo/Z+9+o+Qd7ki4yUDWkLKsdPEWQNuV9NsU4L9kgSVz5ZtvTUH8ZO8tTGQn60H6Lu75rsJA/8BUk2+Ik+58lJ+d0bjLR/ul7rmQ/+JRnHFFVfQxIkTqauri370ox/RnDlz6MADD6TPfOYztGDBArr55psH9cnfocHIhGaYYQUCaPPVgixJEwVVWjDlnY3SghzZRqNlbYTWxomCXCsAlN+1/1Z/K3FEemj0tbZoE+W2RXJpyQjqp40jahcJmmSQwX1JC5BKXy0ZsMab99XoWX7s8dLmFJIZ8df00miiOWPJK+XhNJBcO02cNSjoR7JJ/SU8Hy7fF213ndpXJkQo2LWCSk5Dq/fKtKSIaPDvxsj+/C1f3J7dXfPVxMBb65A9+VvRpEya/zUSIGvJhvyuJQSNrptR2SL7hJSvu2vg1ZYIX/lbN5ZMpW0Zb+15H+01zPL2skxsElUxd+5cGjNmDG3YsIHOP/98uuWWW+gb3/gGHXnkkbTtttvS//t//6/dInYMRmxCs3r1apo7dy4de+yxdPzxx9Pvfve7dos0JFgBPW+jBWVawCCDQRSUyj7lO9pQtKTHSrZk4CtpavyRLFpAzetk8IEg5UA0tORA1llJBJIXBXHIltomX/hZAXA0oOC0vKDCS3ik/Ehe5I/8P9dL82ctoEfJgvXZmkuyDH23gnFtvskyy6ZWUM75IHvK9lxfpI81d6wAlv+CPZfH0gXNPctnrTGJzAWpT0G58qzZgz/szQNntJ5G/IbrzJ+54MmRdXWB25BfNZdBfSnTklquj9Vu6QFzBrytTM4Z61XbqL4qiowWHZ50IGh9+a13qE1k/ZRXY+SzQ6UMfc/EJhHF1ltvTeeffz799Kc/pWeffZaIiLbffnt697vfTTNnzqSzzjprUJ++eq2lR6dixCY0CxcupH322YeuvvpqmjlzJl1++eXtFmnIQMGV/G8F2ryNpCEDSRTE8n5lw9USCVmG2qA+sj/a9DW5rGTISmokUJ0XEGl1KEjm37VkhX/XxlPWaYmAFQjyNkg22R4liVbCpemgBUpIJuQDvE4G45ZsXB7NHlI+K6nQAnRE1+PJ9eLf0WcriZBt+Xdt/ltyofFFPo4SKy2pi46JV6/NFy2Jk3JYvsW/S39D/m49oI701fyeSH/mQvtv8ZOyeXbmfTgdGWTve+/E/itG6ASADOC9YF6TRUORkSdvVaE928RfrhC1nUbbKtMSl3yOJhHFF7/4RRo7diy98MILdOSRR9KMGTPo85//PF111VV0zz330G9/+9t2i9gxGLGvbT700EPp29/+No0fPx5+9zDSXtssYQVwsg3/ToQDXyuAi2zGKHiRvLQ6FEBoQYkWQKJ2lhxasGXposmh2RUF257eSHarPko3MgZcL88HInJ4/DxZraS0EZ+xdEM2lHyR3hodS2etrSY7r0f8NT1Rf8vnuX4ROtpcsGhp+nlJljfvtO+afp4NpPyyb6P+FEHV9tqrfqu+MrgRueRtVM3g1d01v/85FgvaLVyR1x63ekwSiQia+drmr371q/SFL3yBVqxYQcuWLaMnnniCli1bRsuXL6fbb7+9v12tVqN5Yxc3haeGM3o/ka9tbhZ6e3tpw4YNA5KXSZMm0cqVKwe1nTdvHtVqtUHHSIe2Ucozd/JzIyhnJhEtLwBGMloBotQLBVRFHtmPn0W1oMmDznp6QQ6yM7eHN0aeLSw5IsE/Aqen+QmSSx6IrtZfC8a9sZK+p9muin8W/bnPaPaSfWVb6afyM6LjzQfZVmuvBela/yK7pBmxo5dwcPreHJS8eR8uI9LfkhvpaiWfaPzl64Q9vZG8Uj5rDUO0LbkR0A8x8s9re+aatzCh8fLmN+fB+1cN+jmfcpvX2h58m1i5BY7zlnrxqzXowf6CiJxD3TcjyFvLEkPFpZdeSuvXr6fPfvazNG7cOJo2bRp96EMfohNOOIEuvfRSuu2229otYsdgRF6h6e3tpUMPPZRuvfXW/rJjjjmGzjrrLNphhx1CNEZCUtPI2Uj+2QrUvLOL2qaqnbHldLV2Gn2rTH5GPBFvTfZIoBMJLqwAFUFra5VHx7qqbpq9pUxRfVHgp8mOZNbg+bbUScpk6S7pI1qaraR8CFaCgXSy6Fn+aOmjyarZX/vuzU/JI7J+RPVGMsh6qbOlh6SP6q11E8nXCDR+jf6YIr8aI39cU/IlwvNB69fdNfCKSWR/QfyGAmt8hjIOzUD+AGaiUTTjCs0VV1xBn/rUp+jggw+mbbbZhqZMmTLg2H777Qe0zys0OkbkFZqxY8fSuHHj6OWXXyYior6+Plq1ahW9/vWvb7Nk1SAX6WhyogWn3tlK/p2feURnGnmZPMOnlUmZpQzobCc648lpIB6anqXc22TRZhlJOLg88rOUT8qOZND6ecmUtbl7iQtqZ8ki9ZU6e/JU4YnaS/+2fAL5kjZnurvm00WTtneTRM4TjTeaO5ymliBwmnIeevpLHZHduJyIFv/O/xc50FxFNC2bofmD6i0ZNZ7oP2+3aLvrwn5pzRPUBn3Wkr8C+bYrtD5YZ/P5rWXyxzUlX2mTUrbXXedC2df2zB2UzHAdLN2R31aFNne176i//B65MmI9/xPtl0i0Gp/97GdpwoQJdOutt9LcuXPpr/7qr+i5556jb37zm/2JjkRfvbVHp2JEJjRERHvvvTfdc889RET005/+lN72trdRV1dXm6VqHDIg4mXoDGMkIEAbhRUgob7WmTOUGHgbuxUgynbIJhpdLVGykiQtiKy6gUo+qL+W5Gi6y7pIMCjrLT1QoK8ljJYtNUjfQXWePFImza7ITxB9WVZ++wPNN8RTS3wiZXL8JB+UrFjzTQarcl5pY4USFn54c437lZeMIbuhBM+bLygZ1eZgKZ+9+o9wDUP8PL+PyMvlQ3Kh51DKi1dKH/nWNW8eamu39hpjzQ4yoZV0iu78ldIoYa0CS0avvQQaT35rWuTBfHR7m4bI1ZpMehLNxoQJE2iXXXahQw45hE455RRauHAh3X777bRgwYJ2i9YxGJG3nBERrVy5ks4++2xatWoVTZgwgU499VR605veFO4/Em45K0AbZWTjR4FRgdZPC3YifBBtCR5AWPJU2QStDW+otJDeMhDS/nN5NDqILqqL0NJsi/pY44N01+wj6Ufk1cY9KkNUBy5HFbtaukj5LP2lDp6PeDp4sOa0rB8KLc3nUD2iFdFX0tLqLDtWHRMNWn1kDDV7cZ2Rr2k+GJE3CpSgoGB86QFz+m9F6+6aP+CHJ6vKgfpUvWUrwrdZNtIw1NvM8ja1BFFzXwrwzW9+k37/+9/Tl770JfrTn/5EixYtonHjxtExxxwzgEetVqPTN2vtLWdf3tCZt5yN2IRmqBhJCY2GqkEXqouc/dLooQA6GvRH+KNAKBKoeWcGvcDf62vxtqDJzmEFh5o9o/JZAV5U/kgyKnXxgj0r4Yj4l5TBkjcyfs3wHy4jh5eUavwkGkkWI7bT5qaUL5LwSDk9ua01BumAbBRZZzQ5m5m0RPhGA3Oun6YjR7PfbubJF01GG03IWqlHlPZw2DKx6aGZCc2RRx5JV1xxBXV3d9P//t//m7beemvaYost6MUXX6QvfvGL/e0yodExtt0CbAqIBKHWBu0FAbw9CjJKGeKJZJWfrcBVyuOVa4mA1CkacEpdEQ9k/2jgF5VVQtoO9a0STPByL1DXZLQSTik35x9Jcko5GutoMoOCbP4f+bHnwxLIf5AeWhsrOdH4DSWxknrLceHjKulYCaKmC+dTRTZt7qE6JIsVcFp+IemidpE1BNUhv/fq5WcriNbq+NvNOC1+pQWhSltNBqRnd1e5quPrhLDxZQXx9pHkoyR9vB26aoL08WgmEu3A+PHjqbu7m55//nn6zW9+Q4sXL6axY8fSpz/96XaL1jEYsc/QjCZoAX0koJB0eJvyPxq4NHqGSguUULCsyWjJEElAZPtiP2lbuYFpwTXiL/WQ+vFDttfkj+qL2iB5pO5IVy6PJh/6j3wS2dYLoFHyyINvLQFFMnjJkdRNo+Px8gJPqYMcY8RLs7+Elqhp/sXrEU/0X/JBvqzJhmy39IA5g+rQd0SHf9fWDZSsIdrSF5EPWz7tQfN12V/TBSG6jkvsdde5g57d0PwDvSCg9NV4aevu2h78w56chiWX9ZIDBCvhLECJB/qBzip7XvRHMxOJVmDMmDH09NNP0y233ELvec97aPPNN1fvNMqXAmDkFZphgjzjaW26XpBl0ZHtkBwROTU6KIBA8mvBjZcAyP6evFYgJuXS2sqx0eo4HSvojgAFQ5oc2llUlOxwelYyoI2jlQDJQF328+wn5dL4IzqyPRpfLcFCn635owWqSA7Z3kKULuqDfMLzcc020bVDG3+ijUGqJkPEthpvtA6iNpb/aUG5lihFEPEZbT2zUNrJVy7L9a98964gaHJu7DuQZ2lfyjyZ0VWRgmUHfYyIXpMfjWN03+KokpBUaRtBXq1JDCc+/vGP0zHHHEPbbLMNXXTRRURE9OCDD9LYsRmmR5HP0AwDrEACBafRYMAK+mQfC1ZAZAXzKHhFslqBWDTpsvSwzo5Gg/UqtLWkwwt8EW8tgI0gaktrzDQduXxWkuHVe3J7dVZypyWWmny8DfruJXyeD0o5ookp+mzx0NYM1CYqU2RsrXlfpcwLcDXbewkDsoume5VkrvRbMn1NfyKnyR9Zu6ske5aPaO2GeutUFVqN8rKSmqUHzKEpP7iRdpo4K5SkNCpj3mKWGCqa+QwNEVFPTw9tvvnmNGbMxpunLrjgAnrve99Le+65Z3+bWq1GXxzz3abxRPhq3z905DM0mdC0ECggtAIkL0D1Ns9IQIfaRYPRSAAt23M5qyYAXuKk2ZXrGQkqkTyR4NKjXyWRjULb4CO29PTTZOfyarw0Ho3S8fSSsOaUrI8kqZH5KvVsJKnUZPLmgEXXWnO4vTRbavaKrCVIZs/XNFrWeCE5Pbre3LfGIkLXCtI9O1ZFlWSqEbpV65oFLcloxG7DkbBkUrRpo5kJze9//3v65S9/SQceeCARET3zzDM0duxYeuMb3zigXSY0OvIZmhaiLLxooyzfURuNDqfB6fD/2qZdPlfZEIp8Uk4ui9YH8ZfySZpIXkSX6y9R6ryNWbZBgZoXcEv6SD40HihoQnZB/OR4S9m1/5KelUhpPmMFr0hmK6DVdJd8JW2LlhxDKTPSw/psJUhSVjSeUlbNn705L9sj/kh31I7Li/xVjjfyJ5SYaIkG8gPNLrzesg2SWfJCtpZzesn0Nap+nO+Taxapvu8lW5qdLD+0dOX0osmMRVvC8pvyuzrNBpdPSw48f0BAz/0QbbwC1CxkMpNoFs4//3zabLPNiIjojjvuoFmzZtHRRx9NP/rRjwa1zWdoMDKhGQZoG5vcqLRgVAsItM9WomBtWBEdogmHhWjw7kELrnidDN4svrytxV8Gf7IcBbQoUJIBJQqQrESB80QyaoEuGkukD7INAgqq5OdIAqX5j5bYyKTHCvK0wM+zt5QB6R0JZDWgxA35FZIRJSayj9RP6m8ljdb4aeMZnb+aHEhniw/6LOenHI9Sxh+clzaWQTZarxFPTUZvnmptlh4wZ9C6QEQDfgRT8kL6IEQfei/9teC9SsJk0bdoWzwiCQrnEXn7WyIx3Fi7di198IMfpA0bNtD1119PF198MS1atIhuvPHGdovWMciEpsVAAbV1Vo/30wIyKwBEgUxUJg7vbKf8rAUOKKi3Ag7JW8IK4JAeUmfNNlLXSJCL7CD1shJWOb5a4CXtKNtL3lZCqyVVPAlDvqoFdMhusj8KtqQfoXJJA9lV1qGEzwra5WdNZmkLqQ+XHSWq0l4ab8SzSmKB9Je2thIkRNdLTJGPaL6syamh9HtyzSIop2wn5bLmLlrXIvPegwywkc9wHmjeFfAfxOT13pUBSY/LVHQsv3VjQUsoVq2f7K65vG2j0NZHjmKjfCtZopNRHpO4//776U1vehNNmTKFtt9+e+rr62uzZJ2DTGhaDBT8yHItWUFnGxFNTtvaOBE/K0Hh/zkPJJPUTwaXKMHhsmhBoBacSv0Qf8kXBWnSRl5A7AV3Fg9E0/qvBZtSbimLFfR6gWS0LUqiIrLy/5aPyXZaIib7ozmgJSCSlxXQy7ko6UcDdCu50dqgMfGSMC1AlnPL8leenKAEVOpgrV2lfMn0NWryYdlhp4mz4JrB7WElmnJeWXog/dGapK3J3m/FWHNVtkW2QPXlMw/qeT1/dXKVRE1LKOTbziw9mnVblid3JEHT0Gi/RKJZmDx5Mp199tl02WWX0Yc//GEiInr11Vfp5ZdfHtS23uKjU5EvBRgGoABNC/Aj7bTAMRJUaQmF5G/9SJkWBDYKlGxYuqHARZPLo1W1XEuQrLaynsvttdOSQOkbEXtpbSx7avJKekgWz26ejFFf0+wTgZWEaQFbxP5IB60sOp6eLBwROqitNe5IjohvWfS9eRXxJw9aH/TZWmP4q5Utf+FrZ2m38Ycp/cDemy+SN5ezVQ+oe3ZuF9/urvkD7Brxh6jPJBIRNPOlAH/+85/ppptuou23354+8IEPENFrz9XMnj27v12tVqNTa619KcA59XwpQAIgEhzw+6R5W23zlxscOjsp61CbQktuzt1d8/vPiPID0deAzoKWh3AtuQt/2R9t6loAawWzXpCn2QoFOKgvGhukqxZYafQ1P4gE78VWmk0jfa0AzgqGeTvEE42/l6AgWxa+qK3kg/p68qIyaz6i/ki+0gb5BvJDKxGSc0KCt0Pl2pyOJDdIf0nX0gXpjuo4XUSH69fdNR9esdASBK+8PHOD1msurwzu1/YM/P0Y7daoEqAXWvwhfP5Z84vIb9RU+V5k9eaL9lay0r9RRBKPwhvJ2SjNRKIdeN3rXkdHHXVUfzJDRPShD32Ijj/++EFt+1p8dCryCs0wwDqDpQUS1plI7XvkjBanqX1GQWEk+JXQgkspj9Y2EtRH2snPWuCifdf6arppAbIls1VvjT+SIUJH+oyXBGp2QjRQglPFxyxf1sZKyuslTlbA49H0xszr69namt9aW88POCxZvGQ3OpZcbqmDxsNKJEu/Vesn9/8ifMQmni2kHMhW8uoK8k1v7Y3AkhFB7ivSRpqu1l6CypFcSw+YA38ItJ3gMuTrlBPDgWZeoXniiSfommuuoXq9TrvssgtNnTqVdt55Z7rgggvoiiuu6G9Xq9XolBZfoVmQV2gSBXKTLRtwqeMH35Tlxlrqytk5eXZMbjRa4MJ5le+cPu+HggQtwEPftcBeC1a04EbqKXVBsqJ2XoAWSZqQnpoOvD+ykTVWvF72Qz5jBdhIN1mOxgf9l3pxeTkt6VPIflayIPWXc6VqMMzLrfaSb+SzZTv5Wepp0ZY6Wv5k6RFJoKy5j/xAlsmx5zQ8H7D8XcrO+ZRAXa5dmu9FkwTkJ4UWejZDo4/WI9RXXrmQ/SJXNvi+wmWQz7dwWlqSIuVEewWXb997J5qJ4FAhbaY9G6RB3vKXSIx0nH/++bTLLrvQ9OnTacmSJXTLLbfQcccdB18KUK+39uhU5BWaYYAXiKHgwzo7JjcS7WyaFzwiOppsFj8tYEa8rAArQlO2q6KrFZBzul69ZreonoifxVfTJcJX8w9EG8nm+SOyL7K1ZYOIP2oBPxpbDuSLSKYo/0XbXUezV/9RHY+q46Lp5smv6YDqLBtrfBE0fSUNL4mIrDu83ZNrFsErDtb4R3WSMhV6S6avGfQgvedL3jpTxcYIkasOVWzhjZN2BajZMjfSJ2LzZiCv9CQsNPMKzRFHHEE33HADERH98z//M1122WV055130nbbbUd77rlnf7tarUZfoNZeoTmP8gpNQkALHtf2zO2/VzoaYKIzaPzsJF/gI8G0F2TyNpwPkq98RjKX/2iD4WUWTWuD8oKFKgmftLGUEelSvnM+KFjln63gQn6WcqNxQ32tQNPqr8mM+GjJoCYLb4vk08o1/tzORR5pJ5T8VAl4pEyzV/9xkCz8u+YPmo24TJqOWltJU9Odfy7yIJ9AtuT8rHVF8xlEj8uB1hg5RiWZ8eYIspvls0iH0nevu85V/RaNCy/nrzReMn2Nyk+TQcPW41e4V26sBBG1tfiVK0CFp0UHobvLf66H0y+I9LHmVaPJDLJtJjOJ4cLYsWPp+eef7/9MRPT+97+fFi9e3E6xOgqZ0LQQWgJA9NpCKQMMIhwccTpaoCcDBimLthlrMnP60aA4GmxJurLMSgqss3ORoNHSVcpiJRBakoT4oDG0Ej5t/FAQgjZ3LVHWIMdKjpdMPDi0REMGp0g2XiaDXY0HKtNsg+YQSjYQXzlGCF5CJucm8gVuB2kLaUcO6f/afJB9reRL0tf8TcplzRXNPlJvWY7muec7VRJX5OP8vyazRqeA3/JVXiRQJcju7sIvMyi0PTmeXLNoUHCu/fikleQVmeVVmgiq6Fzl7W+tBHqrZyIxXPjIRz5Cxx13HK1fv566urpo+fLlVK/XafXq1YPa5ksBMEZNQjNv3jyq1Wr9x0gDSmqIXttQFm13nblZy6BFbkRy45W8UPtSXv6jgAFt5tbZUcnTSzYQX4uWllxpCZMVCMv/kTN9VpLK26BAzdLRCnjluMrgn/PlNKxgEfFHelvBbNUgzQvoLd/W5NF4yf8o6EXycbmsIBrxk3S0ZMjyO4s+WgNQ0oXmHUqe1vbMhWelNRm8wF+TW9NX2g35rZZEo/kn7SFtY62P0i81+SxoSakslzLzsjIePLj2fI73K31lcC4f4Oe0q8zjSILotSuw3vam0av6LI2GSN8qdkkkhoqPfvSj9OUvf5nGjx9P73//+2n27Nn06U9/mrbeeut2i9YxGDUJzRlnnEH1er3/GKmQQRbRxoVz1rNHqwuoduYU0UXtLTkQPS34k4kVp6MFIiiIigTaqB+SvQq0QJrTtb7zMktvmYRGgiHJQ373aMgx04JzTXcpP+/Hy3k/rQ75ouSN9NV0tOSV+muBqheweuMuaaLvVj/EXwuwUTIi6Wtjx2El0aWOvzFMo6UlsJrNtPGXfaU/lGAV+S4vR2UeLL/X7OaNqzePkL8/uWbRAF+VdpUP9BdYD/aXcYwmG4imBc+HpCxRaFdmih78ZTioz1ASjqp9h/L66UTCwqWXXkp33XUXrVixgnbffXciIpo+fTp95StfoY9+9KN0+umnD+qTP6yJkS8FaBGiQawWNBPpt1qUOt7f6oOCTC8A1eq1MiRbRD4riUFBUoR3tF7THfHzgngOK7mM6Cjl5e2s5M9qr8ns2dUaf09uzWct+TSdLJ9BOmvzQNLX/CFia6RrFV/XAmbPtpZ8EVhrk7YeoTH3bKzR1PpE1ytE10uopDwcaHws3aWsnp4ePN6afkMJ6FuNqO6JRCejGS8FuPzyy+mxxx6jJ554gmq1Gu222240depUmjZtGk2dOpV23HHHAfFsrVaj2y2oAgAAdjxJREFUf2nxSwEu7NCXAmRCM4yoEkh4we1QyrX6aPJiJVoaHytI8wLdqvASPE0mKxnQaEt9tKDDCuAjQZOVCEkZkH4aPaQfsoclM+JpBatIftRGIprganJYQXh0/JFOlm28ZDc6jtp3r1zWDTWx0PpptpS6e/aI1EUC/WiyY/kx0jn61qvI3LX8hOi1N2xpPC2bl7ryhjhpQ2kXT8dGkjVtDBpF1TEo8NoPtT6x6aKZbznbsGED/fa3v6XHH3+8/1ixYgWNGzeObr311v52mdDoyIRmmBE524naarCCFd6Gl2nBiEffqrf+S5kiwVckSEG6WXbQ2lv8OZoRAEcCM0TTSx6iekmZJK9owlZQNvtIghKV10s+NFrIpqiPJUs0uNVs6vlb1aDWSygi9LSxsXwA8ecyeHWenbT6iOyWbSxdLNm8pCxiQ09eJEPVgH+oiQFPCvjb47y9o4pcaD+y2kfpenXNSJqqypDYtNHMhIaI6JFHHqHdd9+dxozZ+DRIb28vLV++nKZOndrfplar0UktTmgu7tCEZtQ8QzOSgTYILwBFm/LanrmDjlJfPpdDSyTQdy4bPxB4nbfII5m8BKnUlT6ID+8vbWvprfVBcksZvPaRZIbT5N+lXGjMkX5W8CfHkdsfyYvk9+zX3TUf/mq7tJsW2Fq2lfSkLprtvKRD9uc+yss0uRAdzicyP7S5remGxlGzAaKn0db8DY2LXGNkeaEj/Rv5o5bcaXaT42PZBulirRfWuPP2aL1G44DWH2u9KyjPiyCgdSjSBn0vMvMf5bT2AUQP6YXkl74g+0SeS7H2mOg8tRAZG2+vTiSageuuu46+9KUv0Z///GciInr22Wfp29/+Nu26665tlqxzkAnNMMDbKL1gAiUo3lkwSd9KnNB378yYBi3wkP0imw+S3QoCOV8pP0oGkC7eJsxliPDyghEriEdyyIAWBR1aMqAFJNL/LPklfdkGjbGXxErayD5VgkPeN5KoaDZE8mn9kL9oY+iNM+KrBfLSF7SxQHJKO8jvKBny5JJ0kDwokZG0uK9rY8H7ldcSI3nLg/hcJslf+igfY/67MlqCbCXGkYBYe2WwtJGWMC2ZvsYN8FFyiXhqZWid4PJbPsp1Kf+t27iGmjxE+1dJgoovJBKtwJ133knXXXcdTZy48Qd9u7q6aNmyZXT11VcPapsvBcDIW87aALQxeQGU3Ay0fhYtK2HSyhBvFEyjjc8rQ0A6ojZIX82umi6aroiXZj8kk4TFH+mtyYMCCSmb1sfjhdp7Y6bZQwsOES1tfHhba25ockXaWTogepb/S14R2/H6iB9a42rN0yqI+HhkjdH01/zL8iWLPuKhyavND3TbpDWXqs4/aTNNfwtV+Eb3BCKiRdtdR7OePdrkHYUmI0eVBMKir/lCIjGcaOYtZx/72Mfou9/9bv/tZkRE69evp5kzZ9J3v/vaLWa1Wo1ObPEtZ5d06C1nmdAMA6wEwNokeR9rY/ACYNmvSoCL6r2EIpIgSESCoEiwj2DZNhI4It0k3Ui9pB8JdiR9+Vnj7clv8UZ90XeNjtZG04XbpUpC5yUDkeSskTmjJWGRRDAS8EUTFc2/JH1vbZFyePNF8x0vyYkgskZ6CUFVP7JktYJmby55c9qjFbFLRJbId60sgtKvXL3wXiFdhWazkQlQoploZkLzla98hXbccUc68sgjaezYsUREtG7dOjrqqKPoX//1X/vb1Wo1+ucWJzSXZUIzstDOhKa7a37/22WGmgREgqPomTrOX9LxkgErIInIrCVNViAYTRDKdy/osD5XSVYiQU80UdJ4yHZeYlilfyRAH0pdJFC2EikN2phEAkfPjyyeFi/Uripdr33VIF9bD3g9EQ1YoySiSZPm99ba5MlWZa205PDWJSsRscq8ZKORNpzXkulrBvwQpoZmJDGSTpWA37JVAX/5gCdHVXg+Ze1/Hv9MfBIRNDOhWbNmDZ1xxhn09NNP09ve9jZ63eteR4888gjtueeedMIJJ/S3y4RGRyY0w4xIgIf6eAFsJIir2lbbJCJ0o0GVtvl7wQtq78mn8YuUe0EWhxXkoyDA0yvSzuKN2mntI/aK6OX5eXQMLFpIFy34RagasHrtPH0i36Uu3vxD/Xi7oawxkqYmI5JZtvfkQvpoCZvE6ZstpgvHrlB5oX5Vgl5LXks/K4GIJGZeUhXhoelbJfiXeiH6mvxR2khHD9bYNpLY5OuZE1XR7Lec1et1euihh+ihhx6il156id7ylrfQe9/73gG3oWVCoyMTmhYhEiBU2RAWbXcdzV79R3cDLYgEJ7y/7BfdbLzkBvWLBLMoiIsEwla/aHKjwQqYvH5V+lhBYqTOCyyrBjpWohVpL+W2ZPQCQytpsQK9SNKijUvEbzQf92wUTVAseD5l+QWiUcU+keTDGj9OO7rWWEmVLLdsi1DaL5m+hva9d6Lph4gmksdLqhCNcpVGo6cF/tEExyu32mlzC+kW8YnhgKV/9IpYIiHR7IQmglqtRp9rcULztUBCs3r1alqwYAGtXLmSxo4dS6eccgrtvPPOA9r09PTQhRdeSMuXL6cNGzbQjBkz6MADD2yZ3PmWsxbBC5BKfWkj25YFuCzC/MFN3hYFS5I34iEXd77RyL4oOJDtkFwoCJQbG5IXycW/c7vIOiuwkzaV3xFdXofoaW20QEwbA05Djpc1lpHgVJNbK9OCQBmc8CBBGxNNp2hih/ohGSy7STk1/S0dkGyWb2h9pC6cr2VfjbeUXcqE5i1qi8bcmyseLJ+SNDQ9kUxyHSnf0Zpk+S5vv/SAOf2fn1yziPa661w1aF+1fvIg2yG/LjTLG8jQmij1LHx4MuMlZdLf0Zu40PpuBfnWvENrW+HJ9wuks8UX6VS1Tmuztkd/Q1kmM4lEdSxcuJD22Wcfuvrqq2nmzJl0+eWXD2pz00030fjx42nRokV08cUX09e//nVavXp1y2TKhKaF0JIEXo8CEA6UYEQCer6ZyLN5aJOUG60mjxaEaLp4m5pmG6m/FphJ3bXAEOmttbPksIJMi4+1gSMbW0GMlhRZ+qJAA+moBdfShzT6iIbWtuCiSdsP0kvzCxk8aoGiN4+stlrAiGRB88yymaSPxk/aStPF0lGbb1oAy2VBQa/lM1JmS0fZD8ku7Yh8WPJe2zNX/S0UtB7J7/veO7G/rNx2JNsVlHrk35xfSYr2uuvc/tdKS1gJhZW4yDWRj1P5jRnL3yPrP/IlLcnkb4mT41cSOjQf5JyXfCIyIJQ2JYnRbifT9EwkRjL6qN7SI4L77ruP9ttvPyIi2muvvWj58uW0fv36AW0OPvhgOu6444iIaKuttqLx48dTT09Pc43BMLZllBMwwNYCGFkm662F39ok+PeNizq+RQfJrfGSMkv9vI0B0ZblKMiyZPPkRwka0kXq6eltJQZSF8Qf6YLGxxpzy26y3tIJBUeyH9IjIgPiWT7PXv1H2F7aQ+PnyYT6WgmDBhm0onlpBYtaoKgF/964WX7uwRpjlFBoiR8ab29dQb5ryY3qkO03rm0bPz+5ZhFtPd4+0cBlQt+1+Y3aawlGgbwKYK39smzjyxvwbWaarXU6s9SxRPpotpLtyt4i5SKi/mQR0dHWVtSuEZQkhiepmh+v7ZlLSw+Yk1dsEgkHvb29tGHDBho/fnx/2aRJk2jlypW044479pdNmDCh//M999xDO++8M2233XYtkysTmhbCWjitoKd81xZ7XhdJPsr3rccPrEMbFJJfbtJeQoXKtaABtbeSM2kr7eyaFbh4bZH+mv01eTw5eXDrBfJW0IUCeivIl7p6NrH6a4F5JEmrOg8ktKAoEoCiPtHAEMmGkhpLZwQtMdLsryVXlv6avJyeFcwjGbU1DY0f8g/N/6wkQwOXo1wxQLZCdDlfb92K+mSED+on7bkxGPdPFHHaSK6N67+eNFg0Tt9sMX255xNqX371w1qzOW253kv7IrnKFZdGH97X/Gfj1b3JtPX4TGYS7cFPfvIT+sUvfkEnnngibdiwge6++24aN24c7bvvvi19JnwpfZ+W0k2V+8lnbOr1+oCXF3DceeeddPvtt9P8+a29CpovBWgDUFDlBTrWBor+8z5awI3a8HaSlhX4a8mRFeBZgRayj9bO0kPKgGyo0bB0tnTX5LLG27O3Nq5WIIn6Ipm99khnzj8CFAxq462Nu6SFeEj5LH08G0dkjdKPzj3NDgiW/Bp9bey0NQTJ68FaS6rYNSKv5It8Oiq3188aJ62N1q60LbfJyQDdW9Or6uMlYF5bWaeNobW2e/7K9bNoeEA/lGqhyjqWSHA086UAxxxzDJ1yyik0bdo0uvzyy+mXv/wljR07lvbaay866qij+tvVajU6jhY3haeGq+gT7ksBPvaxj9E3vvEN2mKLLaivr48OPfRQWrx4MXV1dQ1od/PNN9PPfvYzmjdvHm2xxRatFDufoRlO8E2Jn5HzFlO0ccize/J7KUN9LLoWf+07p8/l4J95X6uNFhygIIf/L7Qse0o9UXtZxutKueSLgIJqWSdtwOsjSVs0+JPQ7GDpYemMdChlmh9wnmjMuA8juaUPyQDIC5rReGh90LxF9pGf0ThxH5Jy8rbI7pbsUk5kM16G1gy0hkieckw1HpounJcXUCO7yDZIby+pK8+z8IfEpR9JfpIemsO83UWTtoc+LPXcevyKQc/sSL0kb1kXXb81ObRy+V/SkWOI5qs2lla9lEO28fRFzzgVoGeZMplJjATUajWaNm0arVu3ju6++25asGABXXTRRfSTn/yk3aJB7L333nTPPfcQEdFPf/pTetvb3kZdXV30pz/9iVauXElERA8//DAtWbKEzjrrrJYnM0R5hWbYYAUba3sGv5ZZtuHQ2siAwQuMeX90BgzJjPrwNlrygqCdgYsG+KiNFZwi+2gyW5unJqOmixXcSlkLrLIq9onKYumgyaCNmfShiG9ZuqE+3vh6NpP6o3HR9Nbk02TxkivNzoiPpY/VXtMHfUZ6yX7euEo5orDksvxM8o/MJ61OG0PJN+rTko/mF9b80z5rsOwU6W/J7+kb8YHursE/Pk2Ef+w1qq+3ZiUSzUazr9BcccUV9KMf/Yjuu+8+OuOMM4iI6NOf/jRdc801/e1qtRod2+IrNFcHrtCsXLmSzj77bFq1ahVNmDCBTj31VHrTm95E3/zmN+nll1+mY489lubNm0e/+tWv6HWve11/v+OPP5723HPPlsidCU0bEdmcrUCnSmJSJbDltLUNvTw86QVWlm4ab62/FeB4G21ET9TG+45oWDJbuslyK7CJ6MTtEekTCbarymn1scqs7xGfQbLJOq6TNz9QW48n171K8B/xGW0dsPxY1nNUTQot+2l9NKCxtehVneuN0NT6W3y1dUCzucZL46H11b5rbaOJF7KRZkck+9qeuQPeLib7orpmoRVJTCZGCYlmJjTXXXcd3XvvvfTiiy/SV7/6Vdpjjz1o5cqVdPLJJ9P111/f326kJDQjEXnL2TCBb3J8AyiHVt/dhV9JG1lYowFgWailPHwB5zy7u+YPSGa4LFI2zhvpUv6XV3tKGijw8nThfLlu/LNsL+XhNDR7yzJpI00u2dcKCBANzk8r8wLJiD6cL6eJZEf+G5FZjo3sZ/lCxO+sZMayEyq3AvsqfmUlFVoCI/sjfsjHkF24j2hzRLMBklu25T4hx08bSyk3t7Vch7R1xBrngifXLBpEU+rh+YyUX9JC64W0ubzdDY2ltZ5UAUpQrLm+ZPoadb2K+CKv32niLFq1fjIcQ/566ZLgaPNOK9dQJfGI2jaTmUQrUH6X5aijjqKTTjqJLr30Utpjjz2IiOjCCy+kgw46aFCfvhYfnYq8QtMmoE3GqrOCqSiP0kcLBqzEB8kn6chyT26tvdbOklPS4TpXQSSo0wJXjQZvqyUp0lZIH9le0pT9JV/NZkhXz9eiuqD2kaBZohGamu9rvov4afyrtEf+bfl91A4RXTTfkFdYo/4egbdGWTyiY6zxidg3QsMK+JFeWrlsg8Z64yumBz/EHl1Dlh4wh/a9d6KZoHkyEW18pqgkGd58kzR4H+8zp6XxLOXW2jTUBCP6imZ+JUn7PZvEpolmXKF53/veR9tuuy1NnTqVpk2bRtOmTaPddtuNuru76aWXXhrw6mOijbHtrBZfoVnUoVdoRk1CM2/ePDrzzDPbLYYLFHRagatW7gUK1ibiJUyefNGAHwV9lpxaOyvQiOjI23rJAZLJC3QiQaCVlFUZE09XK6CIjqcXREQCe22cLEQCV8teXvuh8NT4W/NG0okmcRF/tPTnZV7Qb7WNJm1aGxQMRwNRbz4gnSzfjCQrlgxWeWRd0nSK8rR09Pwvuh42oqN8Diayh2n8eFvpV0umrxn0ezaSdmRsPTtZbYhoUKIV5ZsYnWhGQvPiiy/S448/To899hg9/vjj9Pjjj9MLL7xAO+64Y3+S89GPfrS/fSY0OkZNQiMx0q/QFESDvyobKNr4NRpaPy0ILDT4WUErWLQCMSRLhH8kOJVtOU9+VlCTLZK0IdqSP/ou21n0LF28QDQScHp9LV6Sriazx0faApV7SZaU0ZtTli6aPlobay5JeAEoktlLcDy5PNpa4GmNiaabpCHbe+uBRgPRitZFkh1vzYj4mTzj7/k0quOIBM9IJ22cIlcXLPtXWe80n62axCF63tq0tmfwFZ4qPNGVG94+Yse8krNpoBkJzerVq2nSpEmDykqC89hjj9FZZ53VX1er1ejTLU5orsmEZmRhJCQ0Vc7cRDYBtHDL/pFACfGLBnAo+PGCEmsT1mS3eFvBiSW3FsxoNDybRAI3LVHT9KmiS0QPy55IbqmvlhTxfhZ9zWcjyRni78mjIZKMeQlD1XHS9PL81/KZAm8NkHpoNLSxiSQZXvDrrX0RHT0/5XIURO2EZLBk5fRlwGr5s0bfWksjPhGxS3QPqNKvSvuqtLR+2vcyDugtodGxHaouWvIiZUyMPrT6ljOETGh05EsBWggv0Oruwg9ar+3RfweF0yr9+UKLNsPyXfLj32W91AF9R/01/TRe1qbD7SChBV/WBs35oXZaXzlGpb8XQKPAStsUPd0lTxRAST/gdtWCKTk+lg9wOkgf5IMoYJZyoDH0AhApi5aAaP4r/3t+r+ls8ZO+jWynyYVkQvyWHjBn0LxCvmMFiJps1jpilSOfQvUySOf05JzV1ilOi/u8LC8PnFsJg0af27T0lb8dE7W55CltL8dkbc/GF6ZY9tJ4WAkU56WtKRLauHtrIQJ/KQKi7e07BeVKO09mLFroxQOWzTjKOEj5rWSmu2vgyw8SCYnvf//7NHv2bJoyZQo9+uijtGDBApoxYwbNnDmT5s+fT9///vcH9emjekuPTkVeoWkjogupbK8F84im3Ljkpmhthl4gKmWRusjN15JDs4NGA9VFZZfyIrlRnbWBW4FsI/S0tlH+VeHJj3zOCpKRDPI3JjT6SDbN3zz7RnxO0o2OnRdAeYF7xDe8754uVXhF+Ba63hyX+nN4wStCFV+z2kV1LO2XTF/T//IEbx301ktNLklH62vJYK3riJ9WFoV85TKS3dNZg7XGWG01WawrJFXsYsmC6uRt2eU5oKhuiZGPZr62mcO75exo+k7TeXJcR4fnFZqEDXkmjCh2Nrr0KW27u+zX8qIF2gtu+EbA+/PFlx+INpcPyVPaegE5l4frLsuR3FqApsmP7MRtID9LSHt5m5S2mUke/L+ls8dHttHKpW20QFbjj/SWvPjDw5yWFVTKz4g3H3vejvOK+pyVfCA7eMGPNq7SZigoQzI2IwBCc0Xj4c0ra77JfmiuWmsV8kU5ntLOUf01e3A+a3vm0l53nUur1k9WbYXocBqcFpLBWosQDWkL1K9K4oJoaK9P5n34M4hyfZc8I+uV1s6zO68v4yTpyWSGX13R1nROw5NfGxv5jGl5qQFvt2i766i7az4tPWCOqmNi08EDDzxAN954I9133330rne9iz75yU8OSGYSNjKhGUZENjUtYfCCDC3IlH20tpKfFnBreqEF3dqMZGCPgk25KaLABdlQS468ABPZRwuqrIDEGjOkh+SH/iPZrTFHgaWU3UocEF2ZMGi2kX204Bf5gNVe2lfa3IIWYHuBpUXfC4a4rDIwkv7gBU3e3EW+IccLzVM0H7Tx1NYLz7eQDtImsp22rllJkLYWVF3PpKz8lco8eJbtpEyclrw1EPVBvq/NXWkTzTaSLvJFCfS6ZG094TxQuXZrmTYvpF2kzmjNKfUlcdHWL5ngSP6cJv/tHGu9Q+jumt9/e9rSA+aoc5Vo461yRBR6fXRidOPGG2+kc845hx577DFauHAhERHdeuut9OKLLw5qW2/x0akY224BNkVYQbm1+FkLKQpU0GagBdIIKEjQNh2vv5QxqiviawUjWjCMNk4UUMpyzhMFGxosO2lJkJRb1mn1XEYELTDyEhEkq9VPtov203SS8ngJjxZ8ITt4gTTXgQexO03EfTQf0ujLfpp/W/JY88eyqdVPlnnrSGSuSF1QQmLZT2uP+Fu05dsNkU7WmsjbbD1eT46QPfa9d+L/93szA2lqa5Gcrx4frZ3nb4gekkGzr4aBSYae8HrrFqKL1kfv9dEaP16+8fZCGiS35O353Nqeuf109roL6+jtvYlND3fccQctXLiQttpqKzriiCOIiGjt2rV05ZVX0pw5eQUvgrxC02RYwaoX3GpBJK/nm4xVr20G6D9aXCUda+NEMnHaSEctuSnt0WbK+3lBvZZ8yP6RgNQKnK2kwNIj8p3zRJsw/8z1WDJ9jbppS1tp/oQSQKmTtRmXtp4fogAEBR9yvsigS/MxJJdshz5z+QuPrcevgHNM8vcCMc3nrKTOmpuejp6/WmNl6cL7aL6FdIgkWigoRTw0mZE+JeDV5pknY9WgvrQttMqVHo23xRfx1NZQ1FdLfmRbXuf5A7eztcdZ0GSWssv1SM6HMjctHtJn5F5gXSVB66zU09t3ZVkmMwmOer0+6Hmcww8/nB555JFBbftafHQq8gpNk2EtUlogGqWBNi20WEc2bRRUaUmAF0igBEfbYCNnplBAq/XVNhO+WVl1SBZpw0iiJOWX4+EFcdoYILqWjYg23qvN+Wp0kP7Whi9tZOksaXmbPqIn9bPoI15a8mL1RfWernIue0EPL4vOdS95ssbSs5/1H8kbCY5l8Inmn+YzHIgf8ks5DtHAmvcrD49r9pJ6IX0Rfy/Q7e4qVxh0nTVoPqjJK/VFv9Vi+ai13mltkS6R+aH1jZRzenJcNRm9vUnbWz37INkiY5vYtLDDDjvQPffcQ/vtt9+A8r6+Tk4xhhd5haYF4PcNa4mBhNw0rbZEA8/wyLZWf21DkjStflrAyMv5JofkQRsa74+CU7T5IJ1QECL7WpubFvBrQSnSDen45JpFJg8r6JHfkb20xKR81wIPrQ+ql76CgpaIDyG5LX+Q0PTw9LKCZ0lf6ovaRL6juW3NQ1SO5jqSD/m9ZUstGeOyonHS1hjpHzKxkTTQ2uMF0fLhb9mXjx3iKfmXsp0mzlLnrsYLyYOA1k9ex5//sBKp8tl7sF1bq6S+aLxlf1QvbaHJwcu1RELSstb2KryK7Oj3giIyRnl6kGORyUwC4fjjj6eFCxfSqaeeSuvWraN7772XLrjgAnrrW986qG29xX+dikxoWgC+gFqLuAa5UWiBdflsBV5yE9ISDE43EvQgnbRN20qUUEArAxHUFsktYcmNggZNPi254XJqmzDvywMmTV5JDwWXmo4oALISIulPXiDBAzgv8JEBq6Uz8k8NKGlAvC0dNL/ykoJosqSNg8VDC7SRj6LgnfOQ7fh3bX3Qxl6zJfdTKY/UrwTf2lzS5NDkLM/BcBk8v0H0pG7lVk1rfZU2KAEzb4eCdG4H+ZnT0/Qun0s774ca0TyX9r5o0vamDFbCKuueXLNoQHl0Dmt2RfaxfEtrq61/CK1KNDKBSUSw00470TXXXENvf/vbaerUqXTzzTfTlltuSSeeeGK7ResY5O/QjACg4NJaBK1Ax0ssZH8rqNfOmFnfZRni4yUl0WBO449sgeziJTReohKR1ZMT6WXpJOXn0Pyou+u130BAMnt2joxDtAzxljpIPprfIL+K6KXpMhQ5EC0tcLfsJPtp7bU+mrzeGmONRWQOe/PLsrE1VyPzQeMdkQPp7fVDMkX8WbO/ZgdPziptEV8EJJvlh9oYaLB+E8aSaTiSAimbxXe4ZEqMbDTzd2jWrVtHt956Kx122GG0+eabq+1qtRodSTc0haeGb9IR+Ts0iWrwXv2plfGFtJwFlBuuF2jytugslhUsoP5SButMHj/TZskkeVt6SPnLf95WnuGLbPBSrmifSDAq7ezJVdpYdCX90pY/8IrsK/9LPsgWjZZxGaXOUn5Jy9JXS2aQP3D7S7rSp9FYSl+QtNAYcblkX2kv5LvIZ3idpOcli5I2Wje8uc911dYqz25cX23Oe7aSPKW8a3vmDrqKoLVF9bwO+ZFGK6KH5mMS3V2DbzXT5g+aK8h3kG5yLCzbeGuxhExmkJzaOqi1awa6u+YPks1a46usi4lEBOeeey794he/oJ6eHiIiWrZsGc2ePZv+9Kc/DWqbLwXAyCs0bYB3didy9kfbWLSzgLKPRGRDtQIklABZZ/s0uihg8Da20qb8qreUUeMpeSBeFg2kA0oWNH2LzOXKiaavhPQPyxcsvS2e1vhrdpO0I5u+1RfZEtkgwlP2i9jM8xGtPOIrET+Pyq/xKO0jZVE+0fZW4qHpifwajb8VVFedM5rvWwlaZP1G+mr9NXqSTrmCoM1fbz4iHYk2/lbKXned27D/ofVO08ebnx6itkskhgPNvEJz2GGH0eLFi2ns2I3v6urr66PFixfTo48+OuDHNWu1Gv1ji6/QfLtDr9BkQjNMsBbeqpuEFsxEgj+trJEECckf6eslSyiIlHxkv0iShHSVfaoEs14/WW8lhBqs5Mjj5QUZlm0tWb2x9gI5z5e98Y4kabLcC+a9oFPqpsmH6FoJTNS3ouMkdUEyobYefW/uWXJ6QbznF9YaFAnYkc6af0eCeUt+JC+XTerF3zRmzdfouq7JHPEbq5+si9CIyMlv88rkJNFpaGZCc/jhh9OiRYtowoQJ/WX1ep0+9rGP0fe+973+slqtRoe3OKH5TocmNHnL2TDBWqhL3dqe2C+Ilzayf2nb3YXfEsbLC6LBIWpvBWe8DddJ6iDboqBG6mNt7JyXpKf1L/20z1qAKIMPNI6yP5IL2SISmPF2kpcWJEv+yCc4bz7GiAe3kUZTC/Clbpq9tDrOW9qP95W6oHopp9RDyo380Qq0EQ9ZhnyI12s0kTzSp1BfaTdtHsn2lk5ecofWJjSu5Y2AyJ+0tUCul5KPB8t/eL2WeGhzmMsj25TXCaP1Xs4ha/w9naxxtaCN91BpFGgv0CHyx23pAfljg4nRg//1v/4XfeUrX6Hly5f3l/3ud7+jcePGtVGqzkImNCMIaANDwYLXTm4MVmAkN1otAEZ0JS2vDZIVycfLtWARJT0yUNaSGyvY5jpbgaaVfHl2ayRw520QpN7SJlZQIWXjfZFtrLaa3FrSh/xN+qQ1ZrIvr9eCOU5H+oUWQCOZPJtqyY429lI/5I8aTznOXmIh5dT8VwvELT5a4qjNZyQfP2vP+2pJAtLHkgXpzr8vPWBOP13+Vj/ZN5JocJ29RJfbtTzzU2RANrPmdiTRidYjWtE1pQqsJE7KgH4I02rfiAyJxHDhqKOOot12241OOOEEmjFjBn3yk5+kz372s3TEEUcMaluv1Vt6dCrylrMOgXZWUNvQZYCGgtwC68wgghUcyXZaUO3xt2TU+nG6WhDs6anx1fhbulhtPVtoukXpS1qov2X3qP0i/ar4IwqStH4RGyBY8yjqE1w2VGbNyYismk0s/7VsZ5VZfiHl0fxTo6nJbclk+R+npcHrayUAmp8h2Sw+lm6SnpTZsyNPtPgrozV4c2vjj3rOGqSjhapttPZRX9Dae3KU54MSiVagmbecFfT09NBvfvMbeumll2jnnXem7bcf+Hr1Wq1Gn6h9u6k8JRbX/7EjbznLhGaEQdtAtXpehvpFAyYrESjfy8aHeGm8qwQmXhCIZPL0ithCgxX0efwsWpwegpVISJ6oHeKltbMC+EgyovW1PlfhhfpGg1uPlpXQeXpxG0o5NL4cEb4R36qa2FQNCLVgXPNHbc5Yvv4vvZPpyxs+4QaykteTaxbR1uNX9D+Hgmyg+W0kobPGz7MV+q6VWeXoVcJSx4gsRHpwr+ndiM5aG6LXxqtROkNpn0i0Aq1IaDxkQqMjbzkbAfCCMq+sfI8GkF47bVMu93tL2SMbNOdbDsnPs4NlCy3oKt+5PJwWklHqwenxdlbAiehweaSNLZsU2a2khfP2aHJwXSxIH0O24IGQ9RkFqpqcyNc1vTRf4HKiem2uLJm+BtZL/tIWSCdNRzSW3tha/SPJHpoTSC/JB8mG5hSad9Im3K8vHDv4KoM2hvx/SWL41QXpX5ZPWDp4sOhEkiBka0RXe6uZ5SNaMqglM3KORpMULRGXKPRl0inbcDraD2by9lF48iUSnYZ8bTNGXqEZofDOakpYZxXRxqMFDOiMrIbI5qfRsDbCSFJh0dACi0hbTjeS+MkgAJ3tRLw12o3IbtVHAtSIP1l2QrJYvKvoJ3lbQWPEnyJ21mhGxjfqo1I2zx6arS0dImPFaVhBsjYGVuKh2VLzlfIKc2/ds5JBRJdo4NXlSDCs+T6ycSP/kU7eehBZZ7U12RvHgqhv8DL0Y5mRpC6KyDgnEu1Au67QfLzFV2j+Na/QtBfz5s2jWq3Wf3Q65KannX2TbTm0s4CovQyAOB/tvxa4WmcUOW9UbgW4/Gwib8+/S72LfJHgWquX9uB0pR1QGyuR5P20QMZLShBfFNBa+vEylKAhObyEF8njJSJofKX/c3tpZcjfOA/Uh8uDZPKSjGgSgWTnbaSfa3OM6yBtac0Vzafk/Lf6aD6JxkvKo6EkM7Jv5LP8L21fri5z+bT1UQuS5Rha7dF3NKfQ+MjPiB6yhTYGHj1ZJ1+C4Pm+dkscL5Plkoe1lmh7QiYzidGGl156iR577DH6wx/+YLart/ivU5FXaDoQkc1GtpUbvBfQaEDJjMbPkqERPlo9+q7RRIkEsqeWXMg2yBZWgqQlGR4Nj1dEbk8eREcL3LRx9sZN9uF9OX/0GbWVvFGd7CdpS3tw3ZceMGdQoI3GH9Gx4Nnf09+zodbOGm9JQ7OlZ380Vkgua6yk/BH6RDTgx3W1ueHNbY93FR3lZ8uOlq9LPTQaVeH5iOQ1lOdgqiYgsj3/zZ5W8Btp6HT5NwU08wrN0qVL6ayzzqLNN9+cNmzYQLfccgs98sgjtPvuu9OYMa9de6jVavSx2reawlPDjfVP5hWaRGuBzrh5i55c/HkffkYP9akij9ZXnoUrfDXeiJ4WhEk9tM2d6410iATY6GyilxxJelJeRE+OKdcNtfP04Dbgn6VO0kb8v+QtfSkCFIRJfVCgieyF7Cf9msut2Z77IrIv0eCrBlxuT18kO6dh2V8LuuU4cHm84M6CFsRG1wJtLZJ2QDqXcjTP5WckD6+r8kYrK6nSxsLTEdFD/uqNM+qL5r22dkXH2/J92X5tz2vPwZT2/FmXRtZyS05pd/kMZ4Sfheja1S5kMrNp4ZprrqFzzjmHbrrpJtpyyy2JiOgHP/gBffvbrb29bDQhE5oOgraZ880JBQhawG8FxDKAkIGftUlr/LW2nL7cwK2AQ+qAbIOSHy1AtYIayRfJKHlI20mZteDO+895abJoCY8VGEYDSq6HZ0stqJdJiBXEWWMskz/Lt7zAX6Mj55c2x2QAZtkD+Yklm+YrqA8KGq3gCI0RHxctmZU2KChBLkqGLV7SflrwayU/2rpn+Zpc09B3yUeTi+vP68vBf9+Gy8Tl1CDnrpaUaDaWtJCttPVBK9eenZG6ozqrj8c3Cm9/SCRGCnp6emj33XcfUDZ79mz60Y9+NKhtvhQAI285G2HwzpDxz94GiDbwAhk0ljJJE23+VkCrbbao3grQLb6arlI/K0CP8EN9NZtGNkfLTpIvCsBQUG4FXpoNrbHREiWpa4S21V+TF/kY6qe1l/UR/RGvCLx+WuCu6eXJr/FCOvFbryxYtrASTdQXyaqNi7X+ePJUWfOsOR+BNh8sn9dsVsrl6+9LGf9NmWgSZSXAjdrJolElAfNkbDUaGe92oFPkTAxGM285+6d/+ic65ZRTaPLkyXTEEUfQDTfcQEREn/zkJ+lb33rtFrNarUaHtviWs5vylrNEM9DIBsHPgMmzYVXO2iF+2kaH6CM5I4EOoqvpJ9uiPog2l4X/t26ZKDazAhWpN5IR0YzQK21QezkGVqITCR4lrPGV8vK2RT8kE6IvdZM6Wb7u2ceSVQtIOT85/pKeVoZsoAWqXHbUHtnIkqv048kMoo101tYe5D/RgFmTT7MDam8hMve5PJqf8atKGm3EQ0va0NiWvugZlFLGx4L7EdJFlqF2nv28uRmxh0SxZZU9J0pb64v6e/7ZiL1aAetkRWLTwcyZM+kLX/gCLV68mHp7e2nVqlV055130utf//pBbfMKDUZeoRmFiGzEpR0KLlC5RgcFiFZdpEx+1oACCYsm76NtgFqA5OmpySLl1NpEZLN01eyFxgPVReRGyUN0nGRfi7dsp8ll+adlGzSGUidJH+kt+1ltJS3+Y5Ca3yGbIJ6er3myaONavls+78mGaFWBtqZEZLdoStk4L4snou+tOVLWyNhptkLzQ/5gZhUf9XTXEPHJZqGRMY7QipQnEhaa/drmBx98kL71rW/Rww8/TL29vTR58mQ69dRT6S1veUt/m1qtRoe0+ArNzR16hSYTmg5Do5uGFURGPkcDKC0o1Pos2u46mr36jwPaRINzKwhD7T0ekcTLoqnp6QWJVoLkBZKRwJyXIZtqY2t9jwTGkbZeoIXkjwTtkTLEtxH/QzQj80dLnLREykssLNpVgmVrLZC0rL6WjJ4cGp+oTJGkShsbi4c1VxFfDRZfWR8dS28tiCSlVRObiAxW+yp1VdoMRbaCTHBaj05PJJuR0Fx66aU0bdo0mjp1Ku288840ZswYqtfr1NvbS5tvvvmg9rVajWbUvjkknh5uqR+ZCc1IwmhNaCxowW40QPA2P14nPxdEN/to4Ct5I1qSvxYsarw1+bSEx+Lp8ZYycDksnSx6EftZ/hCRMxrsIpm4nqgN+sxRxT4RnTz+Un4v8JYyaHysuenJqcELgD35IwG9xg/xsGhKutHxjiZwVjKFdIrQsPpodrHml5fYlastkeQxIlOkv1aHfjATtR2JSUDVQNna39qlU6cH+xZGi27NSGguv/xyeuyxx+iJJ56gWq1Gu+22G02dOrU/ydlxxx0HxLOZ0OjIZ2hGOKJn/LRgjJfxtt4mu2T6mgHt1vYMfk00/8zryyG/S3mt4EHKJBc/TTfZl9dLGaXdpHylPdJdyil5akGG1EULgLV2sk77X/pYSZocA0QD2QTRQ314Pbcnaoc+Sz/SbCNllDJV4SnHWvq71IfzlfRlQOvNOdkP+YDlr0+uWTSgnbS/JQuyqTZ3eb029zT9teRi6QFzBtDg/2V/Th/J5q2Z2trA5efyoLFF9DT7yTJtzevumt+fzKD1VPJBz6qgdpqN+PODKKBHyQx65tCaj549OG+5NiH5ovDWWNQerReajyWGBss3OeQbA0cjjj/+eLr00kvptttuo0suuYT2339/euWVV+j73/8+feYzn6EZM2a0W8SOQV6hGSXQAmO5YZZy+V1LiFBgZ/FDdLXA3PuMUKW+8C23tWmBCJLfs5mmcxUZUWCDAngps7UZoMAC6aDp5+ni6evR0RJATXeLt0XL0tPyZW/OSFh+G9XRo+n5QCTArrIOWDaw+HE9tXJr7DT6UgevjeSn+Ye0i8YL9YnoEBlrDZ5c2mdr7D2/4LoVeOtEFZ0ageXrUd+P0m9UtuGww2hExG6aj1s/8DqcaOYzNPfffz/ttttu1N3d3V/W29tLy5cvp6lTp/aX1Wo1OmhMa6/Q/KCvM6/QjG23AInGoQUfcpFAtw5EzjppZ844lkxfQ/veO3FAXdWzWhZ9Lbiy6uT38owOCm4kLS4HP2unJQWoPBIkITms8bQSIM2WiH9EP0tGZIsCa3OKJhFIRtm3lFmJkQekr5TT860qwZX3mcMLkNFcQDbQfMOaS7yc6+QF11rSpQX8qB0CsquVqHljEk2aNq5rg+XU+nC5kC0tvhG5OS1LZ9TPoiXl0nRsV+Ji6ebpGhnnqEwIaLwzsYkjsl9sTFxea1/KS9lowkUXXUTPPPMMbbfddjRlyhTaddddadddd6UpU6a0W7SOQV6hGSXQAiotMJbBjNUGBaJeGdHG20hKsuMF6IhOVH6kB6rzgistcZD1Gl0Eq7+lm5asIVoWXaS7lhh5ga4mo8YT0UJ2kXpZ9tNkRqiSUERtiOyiwfIna65KHaVMqJ82V5CeFk8pP5I5MoeRDSwZvIBQm/eeHaN0NHoenfI5IntVula5/F7emufZ1puvpSxyBnzRdtfRrGePbkjPZqIRvloCFU0uE81HlX10pKCZV2h++MMf0gMPPEDveMc7aPXq1fR//s//oSeeeII2bNhA22+/PX3wgx+kj3/847TZZpvRR8Z8oyk8NdzW96mOvEKTCc0ogBWIeEFRdLOrErgiWbw2VjBptasShGq0vYDQ0isSKFvtCvjDwF6wgeQeSkISDTC9oNIbm8i4EA1+850lV8TOko8VgHpJikdHm3MRX5b8NTpesij1qboeWLohOTR4yVaVpMjjofG0ZLDoVKHFbXLRpO1p1rNHV6Jn6VHAX/PN+UkbVdFN9pVtkUy8T7nyHwlEm5HsRWkOByLjm2gcnWLLZiY0xxxzDC1atIjGjHnt0fabb76Zxo8fT9tuuy19+9vfpre//e00a9asTGgU5EsBRgHW9gx8eJ2X8wWBb4LdXYNvjeL9rcCQf48EgIUXkpvTWDJ9jRqoNhJ8St0RfxlgcPtwe2iLqmbzKjIS0YA3G6Eg1rKh7KfpiuzhBbiFLtpgrMAa+Z0XdBVoyUwpk+UyANc2Qi+AtsbKC7otHlbion3n7aWOmmzSf7k8vB1aKzTI+cA/o3WBy4BsoSVpsiwqI/JBJDuXD82piH21ZIajJDOoXluHEX/ZZuvxK2htz1y6aNL25hxGviDtUdohH/USD75G899RQu2l3giNBqtDCXItXxkqRnoAjiB9pVm2GIosxYblRSebAl588UXq7e0dUHbIIYfQHXfcQX/9139NX/3qV+nHP/5xm6TrDGRCM4qBNnS0+aH2MghB/2U/FIBYiYms4z8OV8pRsoFksIJ4ZAvEX0IGQFxHGSRZmwAK8KRulhwo6UQyITvJ/jLo1YJvK1HTEigpk2V7KR8K7K0gTRtPK/Hj7bittMDbChgjQYsWlCNZpC5SH5S8ITmQb1pjodkR6S75WcmYpovsZ/m+F3ii/hH7lP/IxzV+0i+tNU+zA7K1Z0/plzxhkvpIPiUY1GyD5r63B5Q1WvqVhMaTQ0u6vIC60YQBrXdVaVr+qu1/7UwQIojMg1ZA24s5/5I0bwpvO9t7771p7ty59NBDD9Grr75KRERPP/00vfDCC0REtOWWW/YnPH0tPjoVmdCMImiB7JNrFpmbtbX5aJsA/87PoqCFJ7LxIX6aPjIxsJKkoZ4d9AItjY9FWwtyrHIUBPK2UiaUeGkBtsab9+N0pd4y6Jb9kJ08P9OSIG08uG5WoI30l3pL39b0QjZGvot09ZI4TT5eX16tLqEF2TJ50xIXVCcTQKkL5yPpWfpaZ2Aj44T8KTIn5fiU1zSjeYTGVPMja0211lf5uXyXdvT04OC3qEV4ovmt0bYSKQltHBEPa34OBZz2UBIMyycjcxGN/6YKLZlHc/mFY/YfXuHagBNOOIGmTp1Kp512Gn3wgx+kj370o3T00UfTRz7yEarX63TZZZfRjjvu2G4xRzTyGZpRAi/QQBsS38j4/dBaOyKif+mdTBeOXaEmHHLTlYsTWqxQGy2QsPhY+qNyFGgg2rxfNPjUdNboIjsg2RF9WR/xA8/2Gm8tCOGftbHw5JO25W/QkzprNC16lv9JuSOJlOQn+2lyRmXWdLD4Rm2LaKI+ml2sckseZBtk0wgtTf8q88jTHfWROsgySyZtffPWKgRvTlrjHKUZ7ReVudE+0TZE8bnabBkjMg1FhpGMZurl/aDrSEIzn6Ep6O3tpT/84Q/03HPP0bbbbktvfvObiYjo+uuvpw9/+MP0xje+kT445vqm8pS4o29mRz5DkwnNJgC5qJYH0EsdCiAjQSD6ztt5CYPkI+laATLqK+uRPJEgSPbj7a0AUfuO4AVgVtAl9bECcaS71i8aeFcJpqsGV5rPebp6vCMJldRH2gHJotka9UW8eVkjQbqVVGhtIgFwlL6mn5eY8c+WD1pySlRZW5DsHqxxqjLPLR2r0td4RhMkzzd4udeWB6KRdXwkQcreKjnRuHdSAN9qSNuXOz7kG/xGCpqZ0Nx999303e9+l8aPH0+77LILTZkypf/1zVtuuWV/u1qtlgmNgrzlbBPA2p6Bl9r5A+i8zV53nTuoLV9gUDAxlEBLW6A4P06vHFIWXsfryyHlsWTSyvimjDZrK6DhNirfZR/0HdkA6Y7srAX4kpYs4+PP6UoaUi9um0hgLvsjlNuRpA+iMuSXkh+vQ+PCdeHgvmS1k314X6S/NkbeHPNklf1QAI30j8wFzaYaH16HfNoK9C35tDkn573sL3nIcdIOzyacD/9vrXFIRvTdSuJkPyvpi9hU8wvPV7q75g9KCOR4ewGppqO3TmjzmGhjQBxZa3hCMZTAOTJOcg6iOyM2VaztmTvgtvWtx6+gZQd9bEQmM83G17/+dfr4xz9OBx98ME2aNIl+9rOf0bx58+iggw4a1Lbe4r9ORSY0oxxW4IvacaBgDAUEaNPzzshFNjeZPKC+a3s2vvknog8vjwSt1kaOki3eZsn0NQNoa8mYlEsmBZYdPL20gFlLDjg/jY60l1ZmbdBaACiDwfJmJ4umFWBriaP8r/WTsHwHtUVjbgV52hy16CO9UZnlN1pyoCWtmj9EAlJpj+6u+QPmiqTlBYjWOEqaso/0dS/R5fXysGT3ZJN8kE7WeonWLG3ONDKvZTmavxoPBO0B70aDVksnvoYMZ9Kg8eLy8Wdbh1u+kYxN9WrVLrvsQvvvvz+9733vo1mzZtG5555LN910E33nO99pt2gdg1GT0MybN49qtVr/sakDbaZW4CY3dY1eaSuDdHT2CdGWMvF6Dq+M0+c/7qZttnLjkEmYTCSQ7po+COWWPhQEWuOh6YxsLhNKxAMlajL54vpLeWS/aMAdqUOySr0kLzR2nJfU25JDS161oFCzlZUUSJmlzpEkyQqMIwkqp4H6yMBcmwdSLmTjyFyWvsOvGGtJE1pv0DoiEw3ZT9ogoqOWFMi5Je2GdJDjavkW0jGSGKE1GPXVkjJep/mcBFpLZdvyeaeJsyoF72iMIvtUO6CNPSqTV2bkeqPpKDGS3gDmjUlEn/KCjgL0HOVoxFve8hZatmzZoPI3vvGNbZCmQ1EfpSCiPNjR3TV/wOdyyHr5H/X3aFo0NDq8jycboqXxlXRRmcYPyePJYvGxbGfJ69VZ8kXooDGJ+IxGU6uv0r/qeGn0rfGrYl9LDs/2Vr3lu54NLbtUmcdVxjIin0fLmkPaEeVntUGfNRoRuaPjGh0Xb52IHNq60sjYN2L3qvIOte9Q+DVCv1n8vHk0HLqNtMPSd9X6yW2XDx1r1qxpWsx6/PHH1w866KD61VdfXV+6dGl91apVamx7wJivt/Qg6szUYNRcoUnY0M4Ad4MzxN3izF70FgF51jdyhpKDy6JdBZHydncNPkOLzghHrxR0d+lndTW55ZUSjXd3l37mVdZzfbgspS+6WiPpamOryW7pzNtKWaVsnJcmgzyivmKd+dTsgtqWz0h2RIfLwWXTxtsaW22MZJm0Fb8iIOki/TRZkExyHsl2lpxSPimPNQd4O3RFTfqcNw+k7SQtyU9eyZI0EE2pkzYepVw+C2atR5F1Csko6XNa1tzR5q+0C5qr2tzQaHs6aHSbBUsWDdb6UZVvlI7mV9H9SCtrpE3V/lVpRttvCrehHX744XTYYYfRM888Q5dccgkddthh9A//8A/0pS99qd2idQwyoRmlsBY/ubGj32Dg4IsJCkYLHS9Al2Vy4Y4kIpKPl0QhulwOGdjwPl7wyMu1QA0hkvh5n7UgxQpKtDLJGwXU/LMWzHG5UJn8PSQtoOR9pOyRBEDKhQJiSRMlMHKcNPtoMnAdkQ4Skp8XMKCgv7RBPFHAixIRy4byM+ffaDCD/ErKzfnKoFoLvq25oSUpyD9QkiDlt1Ce4+A0pd5VYQX/jQTdHNrcK/+lHaU/ybU1si4jXrJcm+dR+2lrVisR2Q8ikHNU1mlllm2GKld0r43SsPbp0Ywf//jH1NvbS/vssw8dccQRdMYZZ9C3vvUtuvXWW2nu3Ln0V3/1V4P69NXqLT06FZnQjFJoiwPanPmrmnlbja4VoKEFCgWBWrDCectgk/eVbVFwhGRHgTNqrwU2clPUgk4UKKJABtkP2QrpivRDsnCesj8KFjlfKwiwAnc0TvJNQlqCJOW0gldUr0EbMy0BRAG2ZgvNDhY/RAvp5tFEiRiyIQoukU08mVBfa/7INUfaEc0t1B595/wkPaSL1k/6muafyC7eeCKeQ01EtORLrmeRhACNh5z/yAbIN9f2zIUvefD0kPsB0gmNk0e/3aiScFmoSgPZqdVo5FkeT6+RPLbNwne+8x1au3YtHX300XT22WfT9773Pbr//vupr6+P/uIv/oIOOeSQdovYOWj3PW+tAo2A+ytHytGt3FfdHbw/2GrXrdz/L8u6wb3ZqJ/kg3hr5YiPPDTalrwan6humg4aTdRH0z2ig8ZbkzFqi8h4aDZA/TX6lu7W+EZ8yeJRxbaWLSVNjc+i7a4zbRuxoSd3dN6hcm+O8vvcrTnh8anqB5pva22KnNZaoflNVdt4eng+WoW2VV/V9t6a1oh/NEOvKn1XrZ88JHpD5T/cdIdT13YdI03HZj5D89Of/rT+zW9+s3766afXDz/88Pp+++1XP/zww+unn376oNj2fZtd19KDqDNTg7xCswlAO5uFzs52K2fJeDutX+RsduSMqcZXyoVk4rJEzjTz/t3sLCC6EiN1RHS1s8zSrlqZ1IPrzs+IyjrZTsrKZdF0sPojm0mZkE2kj2h8Nb0tOTXZ0BjLcUT6SftKepZtIlcNJA/ZZ/bqP0J9Nb/lsmlXcLyrCNb4WOPA9Ss80BU4qTvyCU0GOe/R2iBpoKs0UoedJs5SdfKgrX3oc/SqjrW2SJrlOxpDyz5af+lHsj/SV/N19Bn5uTXG3hUNy18kH36rn+fLQ31TmDV+Q0WUbsR2EVhra7PQKK3RfMXmb//2b+mTn/wkzZs3j2644Qa6+eab6fOf/zy9/e1vH9S21Zlax6LdGVWrQCMge++EQzvj0R08S4k+W2dRNLqSB6Lp9ZX80X9Ll6g+UdtInpaMnh6NjFNkjC3ZkZ01PpqO2rhZdomMl9UnYk9rXBFfi57mL1H6lvze2HhzKurHkfkamR/WfPX6RHh4Onjjb/m85f+WP3jzKTJHLb7e2Hhzosp4e/5l+ZmnS4RuI3Sq+G8erT1G6tvIWnU08wrNf//3f9cvvvjier1er/f29tb//d//vf6f//mf9b6+vkGx7X6bXdfSg6gzU4O8QrMJo9u5d5l/9s5Ayz6lXtKOXCXhfeT93Lyc95Wyc91kv4smbQ/PCKK2UkfOT8ol+yN7emdWJaS+vFz2R2el+X/0mX/XzmZqV3pQW01vrVy7uoP4Ij7lzCqye6G9ZPqaQWMsdUAyWWcDvbG26hHQVQw0t5DPyD7oqhBvZ8mP5h/n5+kkbYhsa607aH6hMeP1sh/6jmSUOktovqLNN01n3k+7YqGNq9VWs4WUl5fx8dXWAW3MNL+I+IQ2P6VO2nepL9I/gu6u+eGrMVGaraDVTN7DiWa+jaxTbdAovv71r9P73/9+IiK66qqr6Oabb6abbrqJrr/++kFt+6je0qNTMbbdAiTaBxQkEA0OFLyNBLVF/VBQJDe16G0XUh4U/GjgP8TJZfDoWBsyaoM+a4mCpIeCNcTfC+QiQbUMLmUfLShCssv20p7RgDgatO000R+nve7Ciammp5fIomAOBbaorUTEX7XAXvMLVObprrXlZVX8z0vqkE9x3jLxQvQRTSsZRgmdNodRciDlQ2OPdLBsjdpIWeX8kfJKuVHipPmOZwtkA69ekwv5BC+vIiPir+1Vsly+tdOiaSEyd6O0htpeoopsjfZphEcVtJL2SEStVqNp06bRunXr6O6776brr7+etthiC/rsZz9LRx11VLvF6whkQpMYBC8IQ221z1qQoQWAKAiyAksrENECPmtDjiQ3vGzV+sm008TB5VJui7cWBKJAGQV00WBXS6BkH01/LahHenAamh5aQCwTHy8YRNB8xkpatMTISk6kjIiGDMQiwZ/moyhotoI5C3x8ZJmmh+YLq9ZPHnR2FtGKJIqyr+X7WhLByyLrWSRRQO0tG0v/9xJIbQ4iPhpfZDNtPlnjgmihdtb6a+mq6WT5s7RfVX+3+lQNzjf6uz0GVWDp3QgaodHMpKvVyc5oRL1ep56eHvrxj39Me+65J02aNImINiY6Ep18FaWVyFvOEhDRzckKaNFG7gWAkn55BSg6k8m/awGNDCZ5ndRXBndeHyL9EjuiFUkIZBDiyctpeMEit6EVkMsgzEpAuGwycNLKtKBYJnOIhyyTeki7abZEtmok+UP8tHH2/EvyQ2OsyaIlgEgeL6iPJAeSTpkHGn20Nmj+ifSs6ttPrlmk0tMSZS2B1Wwr+2i+xz9r81bS0uyl8Ud9tbUWjRFaa6T+kWQGyYn6oPnPy621RhtH1F7bGyLriwXr1qqhJBOafYcLQ+FprZ9DRTtsMdz4u7/7O5o1axZdffXVdOihhxIR0cqVK6m3t7fNknUO8gpNAkILStCGvfEqxaz+Mt5fO4OHNm3Jv7trPu11l56M8GBV27h4O20zlWWRjQ8FBEg/pGMkwdJoa8G4F6QSbfxhy63H6+MogxYrIUPjbAWyjQCNkzWGvI1mJ60tamP11fS27KiNkZYA8fYb55h9ggDNAy+wsGTS6EheaH55fLyk05PRC1LLVVOkT+mvJZiaT1jjqQXfiC73D8uG2pha89/zNWveWP2k/nwtisxxpJeVtCBZtL5aEhSZv7y+yryp0q5Z8Ma2GYjSjuxBmv9yXpYN5X7aDAz3mFXBUUcdRX/5l39Jb3jDG+jNb34zERFdeOGFdNBBB7VZsg5Cu99K0CrQCHgDxmg5uiu8KabbectOt/K2G1SO2mn1Wh+vf+mL2iC6llyo3rPHUGTW5EXyID0RneiYa3w12Sz9PLm9Mffspo231ybii5ae5f/SA+aYvjqUcdR01coiPhfxN+9YesAcd/54c6bKeHu2iegc8TPruzWvrDGrMo6NjH/Exz0/4ePq2dWT1eIToa/5iudv3rhV8dNGD41PlP9Q3yLG+VSl1d01+DecqujZqI2Ha2waOZr5ljOEtWvXwtj2/7fZNS09iDozNchbzjZxdBtnuQuqnNHQrkR0G2d0tDP75UBXdTSeskzjg/SL6Ky14fJKObq78Ble1E/WWXR4O64jkkvTCV1RQjIgGQtf7ey6d8WG99fqeTupi9XPOpvL/8s23JbaWGhzBsnT3TWf9rrrXJN26SvnCDoD7tlZu/KBZI6WcVt7Z+VL+V53nav6ouyLxlX6GpJB0uC+yO2t+YbUR44PGg+pp3eWGvVH7bz1iNvA8unIFRbeVpvD0g7l8773Thwkt7S1lFNbu5C8kr5EZL/y6jy5LDpoPYzKYfHx9pvyf6hvEeN85MsR0B4k+/JbSz0+1rzj5fI/mvuoD5LVs3+nYcKECe0WoaNQq9fr9XYL0QqgB6kS1YA23qHQIvJvUdJ4Lz1gTn9gWCAXSC2A9hIWLciRssp6pA+Sz5JLCyB5Ow+WPBqdtT0DH+S2giyuh6W3po+lm2UzxMvSF/GpYucqY4zqlx4wpz8g03wI8dN8z+pj6cCh0dB01vpa89Xiq82p0obrYc3ViH0kf0Q7sqYhOfj3J9csop0mzlLbSBm8sYzYXRsLz07eZ0t/NKcicsg+mj08GXh9JLjV+EXk4WWWTpYfR/ZK+fKM4k9WolLVdzRZmrWfIx2aFSdIetH5yhGd54hfVaxZs4a22mqrhvoW/OEPf6A3velN4fa1Wo3+frNFQ+Lp4b83zKJOTA3yCk1ChVxErTMjqI1GC32X/eWmws/c8b7lM5I1kjTxz5Inl2XpAXPgRof4rO3Br+3V+PP2SDZPX9lP6lK+SxtpyYzsw+3By2Q52liQ7JZeWiCMgiGkr2VLb1yk72nyanLve+/EQTaTPJBckQ1cBjFWoIjoWkAyW+D0ZR8viETlkaCMy6jNdaST9N+IXtL/pHzWi0C4TNqYWuMTXT95ubQNmgvIfp6Pav5ura3IXkhXuT7JMdToyD7WvOe20dZh1AfRtqDNRwTkO95VF0571frJplyW3SJrgSV7qeP7BppbWrnGx+LpxQlynmprhIVmJmON4MQTTyQiossvv7ytcowKtPmWt5aBRsD9lXn49zWXNrI96tNt3ANt8fXaWXLKeuseb42vJYvXhusakRW11fogPlHZUFutjaSNxi86pqit1tfTSZPN40VEA+4lR209eaz6RscwMvescdT8QRuryDyw2nm2rjLmUdt49vDmR6RdxM+sMfJoezw1u0XbRfwx6uMRPaM+XMXXrHHW6EV8NjJOkTpv3D2fa+SosnY0i0531/z6ou2uc8fPKvf+a7QidL354R3NeIZm1qxZ9fPOO69++OGH1//4xz+GYtu/32xRSw+izkwNRuwVmqeffppOPvlkOvbYY+m4446jhx56qN0iJRR0g7O03V36GU4J7Ux4dxe+4iPPukm+3caZaUSP0+3u2vhL0pwOp4fu8UY0u5UzV+iMsewvz5ZrZ59QP00GdFZb0x/RiJ75kjpK2bSxQWfUkQ6aX8krR6W95UtIFjQ+8kx3d9d8eC+59lnOByS/tIFsy/svPWDOIPtJW0k5tDGP9NVkRfpJ22tnWDX9JNCYW9+R/JqMlgyeH0tbafXobLr0aWtuyrku5ZY6IKC5otnMkkHzV29uaWMr7e2th5YNZTttnLwrSHKdteYMl1sbp8JT4+XpbV1dQrJYKHRWrZ/c/91bFyLw5Jv17NH9dWXtsuxT6tE6KP+jOavJZfk8oj9c+OIXv0hjx46lF154gY488kiaMWMGff7zn6errrqK7rnnHvrtb387qE8f1Vt6dCpGbEJzySWX0Ic+9CG6+uqr6dhjj6VLL7203SIlFFjBDgIKLMp3bXHjGwzqjzYySRNtPHKzXNsz8MFHtEh6NLkNrAAVBbDW5q8tzNomijZDbcPXEgkkvxWsWJuM11aWa0FSpN4LLGUwLPkf2TN5AA1JSwbFKNCUvGU5ghYgcchnyaygm5dpwZoWeHuySn6ershXpZ9p44LWGOnvETmt5EHKw+1kzXGkp6YHsjWat1Zw6yUgCNoYaGOvrR3Sz7UxR4j4VmS95eWyD++7aLvr+sueXLNokC4o+JVtSj+pv+U7REQXTdoe2sDi69mx0UBbyslvcRvKfJftvcRmbc9c2uuucyE/bb5bsngJjCa3lSxF5lKzsfPOO9NJJ51E73nPe+jf/u3f6JxzzqF9992XXnnlFbrtttvoc5/73LDK09Fo9yUiDStXrqxv2LChXq/X608//XT9iCOOqNSfmnCJNo/mH93scu6q9ZPDt3GgNryd/C/7oXqLDuqv0UN1Vpmlp+Rt8Yva2rKxrJPtIq/fRTp57S2eyH5VbK7xiMjpyYb4o3HSPkd83RujqB9UaeONp+efVX28aj+vf5Xx47eNRsetCk/riM4jS64Ib6vcK7P0iawZiI732eNp0bDGXVsfrPH0dKpqI49WRJ6I/7TiqOIHjfT3bGnZ3xr76FyJ6qEdrXht8yuvvDLge4mDeWy7z9iFLT2IRmxqYKIjpD7//PPrixcvrtRnOCd9Ho0f1kbkLXqNBEKSlvbfoqW1szbRyIIZCQA0vS2eskyTq3zmv5ui6R3dNKzNvYq+mnzW+Hl298ZLs1HEhp4fegFPxEetjTwydpb+1vyK2FGzFe8j/SzSr+q8jvpsZG5WqY+0j+pVRUZPf4t+VTtofbTx88bE09+aa43YTv6mSkR//tstQxmTQsuyUWR8IrpG5IzYMeIP0bHx1karTaSdZTPNBz2fREczE5onnniiPmvWrPp+++1XP/jgg+tz5syp33///TC2zYQGo62vbf6v//ov+t73vjegrLu7m84++2wiIurr66NLLrmEent76eSTT6YxYwbfITdv3jw688wzh0PcRAPoNm7PQpf5rdt85Hf+X+sr6UhofSI0JX/ZHpUjGVAbTz/ZRpNZlmk2QHSQbJJ/FR5SbsTfKpe20GSwdER9kV4aPe12C1SnQeMtaSI5pW7e7R8Wb2TbyO1j1ny2fFWTEfGqslZ49vTg8YrYgcvj6RRZN6x1zbOXxc8bZ29Mtc9RORAi81x+tuxmyYLQiP+sWj+5/9XdER8p36XMVp/yemQ0blFYPqSVR+WTtCS9oc5LjzfaK6QMVftKePI347XNBSeeeCLtu+++tNdee9Gf/vQneuCBB+jf/u3f6IQTTqB3v/vd/e1qtRrtPfbqpvDU8LPeYzvytc0jNg3bsGFD/cwzz6wvWrSoof7UwBmJPFp3RM+iWJ/Ld/SGKdkueoZFk43TKYd2i1xUB01Gi95Q2mjyof7okLQ1XTSbReXVxjnKy7MBkkUbJ2QTT65Gxtkqs8bS4h/xKctXLf09f7LmgmXDKjprdDz/98ZJ888qdCy7R3S35Ij6QlQHzwcsn4qsR1E/sXhp/qGtU54eEZki8kXGRbNXZPwtOeStkt58qOoz3qFdUa3iV1ZZxK5DmePamHhrp3U08wrN0UcfPajs6aefrh977LGDYtu9x17d0oNoxKYGJkas1DfccEP9a1/7WsP9ow6ZR3uOoQQNjdKz+lqBXKROtpN0h9oWLbbaJmsFIJ5ekcAiMkaRjS0SSFUdB22zr7rpW+Ni2ciTw7NdRF7E4/TNFsNbaBrRuco4VuGn2dKzBS8vJxWq+pOld/SIBlQR+1Ydh2bQjejg1Xnj26h/RedrxKcja1jVcdT8VKuL2MKiqbWvesubJ3O0n1xbGvGdRmRAY675XnStiqzN0aOZCc3MmTPr69atg+Uyts2EBmPESj1jxoz6kUceWT/mmGP6j5deeincvxHnzKM9RzMCDm1Rim7QWqCF2nt80EJpBcVaXSPtvYW8ynhomwjiVyXItDYoK5Dx6ESCrYhMlp09WSLjYPWJfEY+Yekc9QPLz6rOq6HMpUb8shlrSFU9LRsOha7nQx4tb/3xbGnRrerjnpxV1lKPR2R91tbjyHh649GID2oJ+lBoVunDE6QIvSr2aLatouNp+U5kf6myNjUzobn22mvrX/jCF+rLly/vL/vVr35VnzVr1qDY9m/GXtXSg2jEpgYmOlPqAIYyWfIYmUdkEfP6yPLy2dvwrc04Eux6NKsGqFbAbcmLFmtLPu+zRtsLppGu0f7aeDUatHg29cYvEqh5do9uoJpe0WDL8vUqcnlzRpPHK4/4fmS8I7ZrZttm9anqs1XoWD5IZN/K26j+VX3Vm3Pos0c36u+WPzbiXxE+EXto9vHWc89W1ty27FFFD88eEftG9owIjSp7StT/m5nQbNiwoX7VVVfVP/CBD9RnzJhRP/zww+sHHnhg/Wc/+9mg2DYTGozOlDqAiDPm0d6j0U0T0fEWI2uD0oLYKkEg6m/xj24MEbmtIFDj5+kckUvTU9PV25CqBBqN6lTFRsjeVQIdbxy9+RANOqrYKeo7kcMLUCwbevwi8kSDMUveKvyqyFNVtqHo5805y6e8MfTmZBUbRsY9YtPIemzNmyprltamqu9E9IisV5bNq8jhzf/o/IyuT5F2VXwuSl/TG9FrxD9b8drmdevW1X/5y1/W77vvvvqqVatgbPvXY69q6UHUmalBZ0odQBWnzGP4j2YEEI3S8jblSFAWlSMaYER18TZarcyi1Sh/bSPQ+GvBVBW5vf4R3aTcVQK4yDhE/AbZwgrUIr4VqYts7JF5oflxI3PDksE7onJF/XukHFXXAW9svfKh+lmjekRk8/zCWnsi619UHm+uevpqc17TMyK/NY4eL893outBZC2Mro1Vxthaz63x0ewe8VN5tCKhicS2mdBgdKbUATTinHmMzKORIIf/t+hUCUy99lUCNG1BtRbaRoLGiF21jSEihxdMVrGTp4sXAETsF+Hv+Ul313zzt3CiY2HZW/OVRoKRKvMg6kNV23r2r0oj0raZOrTraFRGa16g+W75T2QchiKn99njb31Gz4g0ul5bc8Sri7aJfLfGMmK7yHhExlnTJ2rX8t37PSDtexV/1eaEpUPEXu1KaN459sqWHkSdmRoM/mGXRKLN6Aa/NaC9Q162Le35/9JO0kG0y/dCt7tr/qB31mu/4YDeWa+9Ex/912T3aHIZPdvwOs0eGs9u8J5/7TcruG0kL+v3ajgPpFf09xgivy2AbFbqOX9EZ6+7zu2vk36GbIH0LHy033WRZVJ2yd8aO00Oq7xg1frJpo0sHaVcET+NzmnET5NHo+n5UVWbSb0821r243LK9qvWTzbpSj/h9uNzSH7n/a3fKJHzM2JLidJ36QFzzPoq5VKH8jsu1hqL9EDrjrcmy75WO8lX08PzH0mb09DqrXXN2g80H5G+IGXgvobGYevxKwZ8b/S3gyz90Xe5J2l+Mlx4+umnh5XfqES7M6pWgSqeKcpjZB/d4MyQ1i5KT7bXPnv9I7J4Z4qi+lo05VUDi6dnN8nbqo/I7uljtdF4Rsagiv0t/pGx1Gwh20b4e34Y0TkyPlGf9eiiK1bauEbHyNMtYqeh9I2OxVBli46RZztPD2+OR/W2/FyTIzKXtTlRdVzk76U0e2xRX22dlO2rzkdvHCNrkSVXo2tnI/5WyuSrp6OyRdaRRsaxyjonj2ZeofngBz9Yv+yyy+qrV692Y9u8QoPRmVIHEHXqPEbX0YyNYShtIxswai83douvF+wgmhH7RHmh8sgGinT2gpihBAmozgtGOD/01qfIplplrKJ2iPg08p1I4OGVW+McpVm1jWfTqvQb6VNFpmYezQzWiAa/Gthac6zXCDfiS+XgPwopeWoyWTy89cmTXfPxyDzU1gvLXz1+aM3zbGTxqzp+Vf1PW6OHOh+i4+LZHvlYpH+UdjmamdA89dRT9fnz59cPPvjg+uLFi+s9PT1qbLvn2CtaehB1ZmrQmVIHMJRJlcfoOpoRzFTdRIayyXqbqxa4ItreZmMt3lWDDEvHyGao0W00II/S0OyJ5NbG2fs9CYufNr6N+GsVP4wGStEx4XS8++KbfVQNwhqx5VBlqdo3GlRF+VYd70Z1sQLxZvmaPOSzMpJP5KqAxk/OWc+OVrm2NkXnvyd3FbtV8a/oPlfFp6z9SFs/LDs1wzba+qz1acUzNMuXL6+ffvrp9cMPP7x+99131/v6+gbFtpnQYHSm1AFEJmkeo++ILkS8XG56jQSm0SBaW5CrBBtVF3Wr3tpgtQ2v0WAkYiOkjyZPlWALnSG2dLI2W8uGzQgivIDGs2+UXpWgrio/y2erjl+jYz4S6Fax61DlqRLQoXGpuoY1ytOj0YgcEZs1Ole8NVKri9oFJVtV57m3jko+Ed9pdP1vxvgguzey3kT5ovGMrNWtfCnAo48+Wj/xxBPrxx13XP2Xv/zlgNj2HWMvb+lB1JmpQa1er9dpFKJWq7VbhEQL0A0eNNbaaW1KndamWzwY6LXTHuBED9J3dw1+WB7xlTpymRE9rd6zh9XfomXZBull8dB4oXGSYyPlicAaA8s+nK9nZ0sf/p1De2hWto/4hcbPkt3Sf6jQZLHaVR3XRuSI1jWbL/LtVesn9z8gXUUey07I1y0fGyrQXLfW0KHwGEr/gug+Yq0D3rh68st1gMPjae0Lln7WvuTVW+tys9aKgip7Fu9TZV/S+Ef20DVr1tBWW20V1MzGr3/9a1qxYkX/8bvf/Y7WrVtHb3nLW+jFF1+kPfbYg0444QQaP348vWPs5U3hqeGXvcdTR6YGbU6oWgYKnEHIY/Qe8kyLVmb1i9D2eMo6Xq/JiGT25EZySVqazBZNS37Nvla/iG2RHlXkrSJTVF/rf5SnZzM0VtoYVvWRZo9T5Bhq/2YcUf9upm7e2lLF56rUN0PeVtlrqDYcCb7k2ayROVT6VXnOiZctPWCO6SvaWuGtEc2yfRWfrbJXWXut50fauqnZz+LTzCs0Bx98cP3kk0+uX3nllfV77rmn/tvf/ra+YcOGer1er7/yyiv1888/vz5//kYZ8goNRmdKHUC7F7w8hu9AC412eb3RYKOqHFUD3KhuzQw4vEAm8rYgRA9totFExNIxuolZdqsaKEWSGa2t1y4qlxcsRXVC9tNk9W5PqRqcRPxN69/InG1WENbuI+J3VelVoRFpW1Wm6Liil3I0y57NkD9S18h6481rK8mJ8NXmfnSeVZmXSL7o+t7d9Vqip+nYyFhE1+SIrs1MaG6//Xazvre3t/7Rj360TkT1/zn2ay09iDozNehMqQNoxuKXx8g/IsGb1t7q6wWR6LPFMxLEElH/GbeqcnsLvuQZDZA1O0R0sfo0On4Re3s6eGPj6RYJxCPyWZt9Ff0Rvao+UmX8onXNOCK2jvib5+PNlLNZNqnid+0Ym+E6GtG7Sh/thQKt0CFCu7SRz9Zo+kXWdNk3sjZFbRJZw9E+FOXj7T+ebkiOKjKi+tK/GQnN//2//7f+wx/+sP7JT36y/uqrrw6qv/feewd8JsqERkNnSh1AsxekPEbPEd2wIoFl5O1W0c0sElwiml4/L7i3NoBG7agF3Br9SKCN+vEzeJZ9NFsMxT+q9vH01vwt0i/qs1awX0VPK0ho9tFMP2zmeLbj0IKsTeWIBuDRMRyKb/HDuqJZpb/Fz0q8GpkDVWwZoWslF4ivltyU//LknrePSdrov9de00/j14yE5qmnnqovWLCgvt9++9Xf//7314877rj6BRdcUL/tttvq//3f/12fMWPGoNh2j82/1tKDqDNTg86UOoChLC55jP4juphXDfZ4e+3+Zq2PFsQ2snF5mwui7/H0bBOxnZW4VAkuqiZHjfhFNDGIJE3euGnjEhl7r481fhE/iMreqiNiQ2/cI5/bpV8ejflDo+Ml33gY5TnUpKVRXaskI9r3qolHlXW6iiy8HCVxHt/IOo/Kvdd6a2uel9Q085az8847r/7UU0/V77333vo111xTP/XUU+v/+I//WD/nnHMGxbaZ0GB0ptQBDPfCk8fIP7TgrtFbDqygVquPJiS8vxdYV91k0KId4TeUjdDqFw1YPT7aOFjlEXtV4R/hVcWGXptG+Ub8r9GxaeVhBVaeHTTf83x2JB1V/bQd8nQi73YkK1X0qKprIz5s7QkWzWgSIv+jhEY+N2XtRdb819poddqa6+2FzUhojj766PqLL75YX7JkSf2pp54a9LszKLbdffPLWnoQdWZq0JlSB9CuxSmP9h3eoiTbeQt+JAlBvLWyKomDl8RE7IESNW0Rt3SK2CF6ts0aq4hdqwSkzQjGLR2GUh7x30Y+ewG/5gND1bMRWw7X0agfjARdo3Oo3XJ26tEJ+jfTb6Nz3wv8tb2hqr9q6/mi7a6Dt5xZ61LVtdGT2WrXzITmJz/5SX39+vX14447rv6BD3ygfuCBB9ZPOumk+te+9rX6nXfeWV+2bNmg2DYTGozOlDqAdi9CebT36O4a+CrMaKDNvw91s6uSjAw1+Pf0iwSsXruqgXBkY2zUZtHALpq0DaXPcPhJs5K0RuaBRqdZekdoWfPY+5/HpnW0Ihlvpy5V1qNI26rzGiUCaJ1H61MjCUlVOoi3ldBU8RVP/mb/sOarr75aX7ZsWf3OO++sf+1rX6ufdNJJ9QMPPHBQbJsJDUZnSg1w5plntn3xyWPkHpEgH7Uv/xt9hbEnA2rjJViRhbsR+eQhH7iPbmJV+VVJdlqZyETtErW1FRw0QybP9p5/abYcjsSgythZ/ufx0Pp0YnCbR+cfnv9Ffm4gMm+jgXsVuausvVXWk+6u+QOuxvA9FyVQnu6WLJ7M6JB0OL1mJjTr1q2rP/TQQ4PK5S1oRFSftvmlLT2IOjM16EypA2j3wpVH+w/vTI5V3mhAHJFJ9rcWY+97I3I0mjAMJThHm0REPmuTrqpnMzb1obaPJBSa3awxQu2GomsrjkaSxYgvDGVeRH0yj9FxtGqcG6UbebuZxsNK/Ksm/xbP6DqKkjG0PkX2htM3W6zytGhYNousH9o6qq2rzUxovvKVr9SvvPLKer1erz/88MP1T3ziE/VPfepT9ccee2xQbJsJDcYYSiRGKdb2zKW1PXOpu2s+dXfNH1BGRAPKyn9ZZtHmNORniVXrJ1N31/wBvDitUsdpSBl4Padl8ea6a0C2kTpyfvw7t6/HB9nZA++DbC6/e/ZD/VFfSxbUjo8L4u+ND/I3bmNOk8sh/Yn7kuRr+Wp0PCxYNKL+SmTPQ9RP+qUss/gVe3rzvdlohr0T1dGqca5Cl/vk1uNXhPtr6xlqp+0l0bUPfedrFZ9r5f/W41cM6ifXb7l+aWvplzd8Qt1XtL0gur5InVCMgGwR3esbxeOPP06f+cxniIjo4osvpiOOOIKOOuooWrhw4aC2fVRv6dGpqNXr9c6V3kCtVmu3CIkRApksoKCv6kYX6cPbrFo/uX/zqkqLy71ou+to1rNHD+ob1UG2j/ZHi70WOJd2KIiNyjqUvlEgWhb9ofD2EmUt4Pd8VLOLldiNJFh6SaAgzZrHVca3Ff6VGD3YFP0C7ZteW1lWoM1dTlfuz7KtNT+1vV3TQ9uvPEjea9asoa222srtF8FnPvMZWrhwIT3xxBP05S9/mb7xjW8QEdGxxx5LV199dX+7Wq1Gu21+SVN4avjNqydSJ6YGeYUmMaphnZnl9daZXHSmKHpGjZ+9stohedFGgpIZTR50li16lcQ6K4bkRnJo7asmTkh+C95ZeU0+VFZlzDXfKX2t/t4mK88iSh5aMjRSgjB0Rpdo8FVHOf7alafSXkuoNV/R/D7im4lNF9GAt5UYKv+q/eXVFYuutA/aa9CcjsiG9mi5J6N1m98VsWr95EGy8SO6zmtrcDMwYcIEuuOOO+jaa6+l/fffv798/fr1LeE3GpEJTWJUgy/G6HK3tchKGo0EO9YZea89D2Kt4Dsa5HoBNaclA01Nfv6/SlAoNz9Lz0Izav9mBqdVaHgJkmVHrZ9MatCYy7EaqUE5CjrkmVcvSUaBEu9rnYWV/VFdIuGhXX4y1HldpX9URzl/S78n1ywawHMoVz/l/i33Y+1k404TZ/Xz5ScU+bqB9h1EazjW1OOPP57+4z/+g7baais69NBDiYjoxhtvpO23335Q27zlDCNvOUuMeni3mUT6tipQrEo3EpCh5MRrG+GHzoijYBNdaagKLyGTgetwBfHeVQLPPgXNGnNeHx2fKrRbgSpyoIAJ2TvKz5s/IzUZTGw6aJcftmMdtdYCa10j0m/H9fYftHegPqhO0mrmLWcIt99+O/3N3/wNbbvttv1ltVqNdt384pbxJCJ64tWTOvKWs0xoEpsM5MKHnmupemWj0xE5O+X1kXWo73AHkq0ctyqJsPxeRS5tsycavJl3ip9aianVTtaVPt4VmaoJTyLRKWgkuW+1PET+c3+yvQRa24g2XvXZaeKs/jZaEsJpIDoaD68/QjMTmieeeIJ++MMf0vjx42mXXXahKVOm0M4770ybbbbZgHa1Wo0mb35RU3hqWPHq7ExoRhIyoUlEEDlT1E5UeZnAUM5EN5JwRINp7wxbVI5OgJWEcETPTlp0Ee9CezjQyFhFr6w1U5dO96lE56HRuTEcftrO+RA9IWOdqLCumlgnkiy+VnJlrVnNTGhmzpxJe+yxB9VqNfrtb39LK1asoHq9TrvssgtdccUV/e0yodGRCU1ik0MGOK+hauDonYVrtm1bfWWnXYHHUAN2L8kc6YgkNo3qMtJPUiRGP2TAPdwnGzoN3i2l6LN3+5mVrFgnnbTb3NDYNTOhOfnkk+mCCy7o/97X10e///3vadmyZbTffvv1l9dqNdp58wubwlPD7179l45MaPKlAIlNDpEz6J0IrkdEp7JoW2eoJEp7/j1ym1pUJsSvkbpm0Lf6DMVnuN2r9JEyoPqRGjBZQYUmM7qqZ9Ht7hr8og9ZryVOiUQzUeY390V54ieyXvN2I9VPi1z8TWKoXivj+ll7i+zPbYrWAURT0pDjgGgN15q67bbb0osvvtj/fcyYMbTLLrsMSGYSNvIKTWKTRDQIT1Rf1KPtW311pxmoeqvXcOkUvd3P6z/aoN1iwusSiZGGKr4ZXWM6zd+tqzTaCRvtd9kQbQTttjNZrtmxmVdoLrzwQnr88cfpqKOOone84x00fvx42C6v0OjIhCaxSaPTFv1OQ6sD+lbRG2m3ulm3+mmfRxssPUf6FarEpouhzEmv72ia75E1t0ryEUmQOMpLByz6Es1MaK688kpasWIFLVu2jF566SX6H//jf9CUKVNoypQpdNhhh/W3q9VqtNO4CwxKQ8eTr5ycCc1IQiY0iUT7MJo22iiGQ+fovd2bIqpctdoU/TPRWeiEK9hEzZMrclU1cjXcu0qj3a570aTtadazR1N31/xBb1TT0KrXNj/33HO0bNkyeuKJJ2jZsmU0b968/rpMaHRkQpNIJEYsGtksW9kHtRvqht6OKz6bMtJWiZGMVt3iOxS08kpRI1fDvWRPJj4F6GQQej6x0Ft6wBza665zVdmbndA88MAD9Pjjj9OkSZNo//33h21qtRrtOO78pvFEePqVz3dkQpMvBUgkEiMWjT60H0EjD9ki2kMNJoaaDDUTI/XB4yqwdMhkJjHS4SUOVdoPBdHnTJuZzGjJBarT6MkrMfwFDavWT4YvJODtOL99753YkF6N4MYbb6RzzjmHHnvsMVq4cCEREd16660DXhSQsJEJTSKR2CQh36jTieCbdtmItbcNVaHXidDOuo6GJC2x6QH5sHUlQ/apykN+j64F0Svbkb7WCaMqV3HKGiDtwm8jk8fpmy2GvBu1bVXccccdtHDhQjrttNOoq6trI/+1a+nKK69sKd/RhExoEolEYhSgbOraD7GOdkTejCRvOUkkRjKiz8l5Qb/XT3t+pVnwEjH5vZGrrKVce+hfu124HBeOHbxucnlafbKnXq8Pun3t8MMPp0ceeWRQ2w1Ub+nRqciEJpFIJBIdD+uBYa1NJjaJkYoSnFtAVyEa5RUpawasq0DyirN1i52WtPC5j95yhmTQ9OdHq9eKHXbYge65555B5X19fS3lO5qQCU0ikUgkOh7WlRjtak0n32KX2LSArmZYt822I1nXkgwO6wqKbBe5sqPNe+2lAehKrrwSMxwJjMTxxx9PCxcupFNPPZXWrVtH9957L11wwQX01re+dVDbvEKDkW85SyQSiUTTMJIfvEdBUyKxKWAorzNv5PXJVWXyXrus9Y0+XyTbVuWH0Iy3nPX19dGYMRuvLbz44ot022230UMPPUQvv/wyTZs2jY488kh63ete19++VqvRG8ctGBJPD8+/ckpHvuVs1CQ08+bNozPPPLPdYiQSiURiBCL6PEIiMdoQDf61Pt7tm6if/Ky1iZRb8mkyF9mknOgWtHYmNB/+8Idp1113pWnTptHUqVNp6tSptNNOO6kn5Wu1Gm0zTn+VdDOw8pU5mdCMJOQVmkQikUgURJ6xSSRGMxq5ShNJhiJJzFDqpBzeq55RuUQz5n8zEpoVK1bQww8/TI8++ijde++99Oqrr9KWW25Ju+22G02dOpWmTJlC733ve/vbZ0KjI5+hSSQSiREMbWNuxj3em9JD8fze+ExmEpsS5MPyvEy205IF+WyJ9RwLamM9N8P/e28ok8+38HLvystIeM5IYvLkyXTggQfS2LFj6dOf/jRde+21dPrpp9Nb3/pWuuOOO+iWW25pt4gdg0xoEolEYgRDC76bEZRvioF9Ox74TSSGG9orh1FyU4ASAOu3WFDCIpMNnlxYa5m1Flm3vKFbyHgblAjJFwSMBDz44IN0yCGH0Jvf/GZ65zvfSZ/+9KfprLPOot13331Q2w21vpYenYq85SyRSCQSmxzymZrEaIf1imStfdUH/IfyPIx1q1rVK6nyljSUWDX7Cm0zbjkrOPLII+n888+nbbfddkD5Jz7xCVq8+LUf/azVajSp6+ym8NSwuud/u7ecrV69mhYsWEArV66ksWPH0imnnEI777xzS+XykFdoEolEYgSi0asIefUhhnxtc2I0oxnrgHWLWvSZGkse61Xr8pXKiE6p15IXJM9InfOHHHIIfe5zn6Nvfetb9NBDD9HTTz9Nd999N0wseqne0iOChQsX0j777ENXX301zZw5ky6//PJmm6QyMqFJJBKJEYjoGVX5PfpQbCKRGL3gCXs0iI/e9qU9a4PeLobKSrmWrKBkCSU66Ldl5K1oIzWBkTj44IPplFNOoccee4xOO+20/iTh2GOPbbdoEPfddx/tt99+RES011570fLly2n9+vXtFao+SkFEeeSRRx555JFHHnlUPLq75jfUjn/v7prf/13+9/rKct5fOyy+rTjWrFnTshh2/fr1sHz69OktH/saTTJle/XVV+sHH3zwgLJjjjmm/tRTTzVN/0YwlhKJRCLRkah6j3wikUhE0OhVHXQbmYR2hcd69bP2fIzs16geIw1dXV2wfMmSJcMsCUZd3ApXr9f7fyC0XchbzhKJRKJDgDbyyG0V1n3oiUQi0UzI1zBrz7BYz9nIvt6a5b1NLde85mHs2LE0btw4evnll4mIqK+vj1atWkWvf/3r2ypXJjSJRCLRIdA2au/BWe/3GBKJRKJZkMmLtf6gVyvL53+ivyujvUgAtU0MDXvvvTfdc889RET005/+lN72trepV5WGC/na5kQikehgoFefeq8mbcUrTBOJxKaF6PoRuTrivZ0M3V6r3XpWRbahoJmvbe40rFy5ks4++2xatWoVTZgwgU499VR605ve1FaZMqFJJBKJUYD8XZVEItEONPr7NQXaK5u1W82852mG60TNppzQjERkQpNIJBKJRCKRaAm8h/61VztrZbLOeplAK5EJzchCJjSJRCKRSCQSiZYAJTQFWpl2W6z1o53DfRttJjQjC/lSgEQikUgkEolES8Af8kc/eCnfUCaTGXSFRntlff6w8KaLvEKTSCQSiUQikRh2aFdgtP8jCXmFZmRh1CY0e++9N/385z9vtxiJRCLREXjnO99Jv/jFL9otRiKRSHQELrnkEjrhhBPaLUbi/8OoTWg01Gq1Qb9wuinT7kSZk3bSbifdpJ202027E2VO2km7nXRbTTvRfuQzNIlEIpFIJBKJRKJjkQlNIpFIJBKJRCKR6FhscgnNmWee2W4RGkKr5G6lPTqVdivRqTbpVNqtQqfao1NptxKdaJNOlLnV6FSbdCrtViHtkWgUm9wzNK1E3p85OpDjODqQ4zh6kGM5OpDjOHqQY5kYadjkrtC0Epn9jw7kOI4O5DiOHuRYjg7kOI4e5FgmRhryCk0ikUgkEolEIpHoWOQVmkQikUgkEolEItGxyIQmkUgkEolEIpFIdCwyoUkkEolEIpFIJBIdi0xoEolEIpFIJBKJRMdibLsFGK34j//4D7rgggvorrvuarcoiQbQ09NDF154IS1fvpw2bNhAM2bMoAMPPLDdYiUC2LBhA11xxRX0yCOP0CuvvEJHHXUU/f3f/327xUo0gPvuu4+uvfZaqtfrNHHiRDr11FNpm222abdYiQbx5JNP0rHHHksLFiygt7/97e0WJ9EAHnzwQbrsssuIiGi33Xajk08+mTbbbLM2S5VI5BWaluD555+nf//3f6ctt9yy3aIkGsRNN91E48ePp0WLFtHFF19MX//612n16tXtFisRwD333EN//vOf6aqrrqLzzjuPrrrqKlq/fn27xUpUxCuvvEILFiygM888kxYuXEh77LEHLV68uN1iJRpEX18fXXLJJfTmN7+53aIkGkRfXx9dcMEFdNppp9GiRYto3LhxtGzZsnaLlUgQUSY0LcEll1xC//RP/0S1Wq3doiQaxMEHH0zHHXccERFttdVWNH78eOrp6WmzVIkIfv7zn9N+++1HRETbbLMNTZ06lR588ME2S5WoirFjx9LVV19NO+ywAxERbbfddvTyyy+3WapEo/jXf/1X2meffWi77bZrtyiJBvHwww/TTjvt1J+UnnTSSTRt2rQ2S5VIbEQmNE3G7bffTlOmTKGpU6e2W5TEEDBhwgTaYostiGjjGf+dd945N+IOwcqVKwfclvT617+eXnjhhTZKlGgEY8aM6R/H9evX0w9+8AP64Ac/2GapEo1gxYoV9MADD9CMGTPaLUpiCPjjH/9IW221FZ133nk0e/ZsuvLKK6m3t7fdYiUSRJTP0DSE//qv/6Lvfe97A8q6u7vpxBNPpP/8z/+k8847r02SJargmWeeofnz5w8qnzt3bv9Z4TvvvJNuv/122C4xMiGvjNbrdRozJs/ddCpWr15NZ5xxBn3kIx+hPfbYo93iJCqit7eXLr30UjrllFPyroUOxyuvvEK//vWv6bLLLqMtttiCvvKVr9APf/hDOuigg9otWiKRCU0jeM973kPvec97BpXffPPN9Oc//5lmz55NREQvvfQS/fM//zOdf/751NXVNdxiJhzssMMO/Q83Itx88830s5/9jM4777z+qzWJkY83vOEN9MILL9Cuu+5KRBufadtnn33aLFWiEaxevZpOOeUU+tSnPkV/93d/125xEg3gN7/5Df3pT3/qPyn01FNP0VNPPUWzZ8/OFwN0GN7whjfQlClTaMKECUREtPfee9OvfvWrNkuVSGxEJjRNxCGHHEKHHHLIgO9WwJwYuXj44YdpyZIldMEFF9C4cePaLU6iAv72b/+W7r77bnrXu95Fzz33HC1fvpze8Y53tFusRAM455xz6Mgjj8xkpoOx++670ze+8Y3+72eccQYddthhmcx0IN7xjnfQVVddRatXr6ZJkybRL3/5y7y9PjFikAlNIgHw/e9/n5577jn67Gc/2192/PHH05577tlGqRIRTJ8+nR544AGaOXMmbbbZZnTCCSfQ5ptv3m6xEhWxYsUKuv/+++mFF16g66+/noiIdtppJzrjjDPaK1gisYliiy22oOOPP56++MUvUm9vL+266670oQ99qN1iJRJERFSr1+v1dguRSCQSiUQikUgkEo0gn5RNJBKJRCKRSCQSHYtMaBKJRCKRSCQSiUTHIhOaRCKRSCQSiUQi0bHIhCaRSCQSiUQikUh0LDKhSSQSiUQikUgkEh2LTGgSiUQikUgkEolExyITmkQikUgkEolEItGxyB/WTCQSiU0cv/jFL+jaa6+lZ555hrbddls6+eST8xfAE4lEItExyCs0iUQisQnj0Ucfpa9+9at03HHH0S233EIHHXQQnXPOOe0WK5FIJBKJMDKhSSQSiU0Y1113HX3iE5+gv/iLvyAiove973305JNP0p///Oc2S5ZIJBKJRAyZ0CQSicQmilWrVtGDDz5I++23X39ZX18fERHV6/V2iZVIJBKJRCVkQpNIJBKbKB577DHaYYcdaOutt+4v+8Mf/kDjxo2j7u7uNkqWSCQSiUQcmdAkEonEJornn3+edthhhwFlDz30EL3tbW+jMWNye0gkEolEZyB3rEQikdhE0dXVRbVabUDZ3XffPeAWtEQikUgkRjoyoUkkEolNFHvssQc9+uij9Mwzz9Crr75K119/PW3YsIHe+973tlu0RCKRSCTCqNXzyc9EIpHYZHH77bfTt7/9bdqwYQO9853vpOOOO44mTZrUbrESiUQikQgjE5pEIpFIJBKJRCLRschbzhKJRCKRSCQSiUTHIhOaRCKRSCQSiUQi0bHIhCaRSCQSiUQikUh0LDKhSSQSiUQikUgkEh2LTGgSiUQikUgkEolExyITmkQikUgkEolEItGx+P8DO0P3x4fyA5YAAAAASUVORK5CYII=", "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show small and low formfactor nuclei outliers within the data\n", "plot_hvplot_scatter(\n", " embeddings=embeddings_with_outliers,\n", " title=f\"UMAP of JUMP small and low formfactor nuclei outliers within {example_plate}\",\n", " filename=(\n", " plot_image\n", " := f\"./images/umap_small_and_low_formfactor_nuclei_outliers_{example_plate}.png\"\n", " ),\n", " color_dataframe=df_features_with_cqc_outlier_data,\n", " color_column=\"cqc.small_and_low_formfactor_nuclei.is_outlier\",\n", " clabel=\"density of single cells classified as outliers\",\n", ")\n", "# conserve filespace by displaying export instead of dynamic plot\n", "Image(plot_image)" ] }, { "cell_type": "code", "execution_count": 26, "id": "8fe1c34f-453a-4178-95e3-c303b1b132a2", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzQAAALGCAYAAACTX9TRAAEAAElEQVR4nOy9ebgcVbX+vzqcDCQhCfMQIAwhKIOigEBA4SIKEkTAoCgKEVAZBbwIKD8NRxkUFAKIIioqTlxBROWCCnpBvQh6ZboKCAScohBuCHMGck79/si32n1W1lTVXae7+ryf5znP6a7ae+21d+2qWm/tvasbWZZlBAAAAAAAAAA1ZFSnHQAAAAAAAACAskDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGoLBA0AAAAAAACgtkDQAAAAAAAAAGpLTwuaM888kxqNBu2xxx6r7Lvzzjup0WhQo9Gg//u//yMionXWWae57cc//vGQ9CtWrKApU6Y09996662r2Lz99tub+1/72teKPqVl5H+rr7467bjjjvSNb3yjDbUmevzxx+kNb3gDjR8/ntZee2164okn1LT33nvvKv7ccMMNRDQ87ff5z39+lfJXW2012nDDDenII4+kBQsWtNQWX//616nRaNAOO+zQkp268ZOf/IQajQZtsMEGnXZlCJttttmQPhZhzpw51Gg06JRTTqnMrwjc9zr2rT//+c/N8+yZZ55pOV0720C6Nq6xxhq0zTbb0Nlnn03PP/98M+0f/vCHVdLm19Jtt92W5s6dSy+99NIQ+z/5yU9o5513pvHjx9OGG25IZ5xxBr388stD0lx00UU0ffp0GjduHG299db09a9/fRU/FyxYQPvssw81Gg2aMmVKof033HCD6Hf+9z//8z9ERPTCCy/QscceSxtuuCGNHz+eZs6cSXfdddcqZf3mN7+hLbbYghqNBh100EGr7P/1r39Ne+21F02ZMoXWW2892m+//ej3v/+9cgQAAKC+9LSgaYX//M//HPL9jjvuoGeffdbM873vfa/5+Z577qH58+eraddbbz3acsstmzeju+++m+bMmUOXXnppa44T0TnnnEO/+tWvaMMNN6TDDz+cxo0bp6YdM2YMTZs2jaZNm9ZyuSlF26+vr4+23HJL2nLLLWn99denJ554gq6++mrafffd6bnnnmurb93K3/72Nxo1ahTNmzev0650FTvssAPNmjWLtt1222Er85vf/CY1Gg2699571TSbbLIJzZo1i97whjcMm1+tMn78eJo1axbNmjWLRo8e3dw+ffp0MSD2qKINtthiC9pll11ol112oY022ogefPBB6u/vp4MPPlhMP23atOa1Y+LEifTAAw/QJz/5Sdp3330pyzIiIrr77rvpwAMPpP/5n/+hTTbZhJ577jm64IIL6Mwzz2za+fznP0///u//Tn/9619p8803p0cffZTe9773DXk4c/3119OrXvUq+vnPfy764u0fP35883qb/q222mpERM3/c+bMoS996Uv00ksv0QYbbEC/+c1v6M1vfjP9/e9/JyKigYEB6u/vp9e//vX0+OOPi2X97//+L+2zzz50++2307hx42jFihX005/+lPbee2/661//ah0CAACoH1kPc8YZZ2RElO2+++6r7PvNb36TEVFGRNlTTz2VZVmWrb322hkRZeutt1628cYbD0l/5plnZkSUrbvuuhkRZbfccsuQ/QMDA9kGG2yQEVG2/fbbZ0SUnX/++auUm5fxta99rbltyZIl2dvf/vZm2YODgy3Ve5999smIKPvMZz5TKN9qq62WEVH2gx/8IMuy4Wm/yy67LCOibNq0aUPS//d//3c2evTojIiyyy+/vFA9Ur72ta9lRJS9+tWvLm1juLjwwgszIsouvvjilm3dfPPNGRFl66+/fuuOtZFp06YN6WPdyqxZszIiyu65557mtqp8f/nll9tqryi/+93vMiLK3va2tzW3Pf74483ze/HixcPih3RtzLIsu/TSS5u+/OUvf8myLMv+93//t7nt8ccfH5L+K1/5SnPfbbfdlmVZlr373e/OiCg75ZRTsizLsnvuuScjomzcuHHZc889l2VZlk2dOjUjouyGG27IsizL5s2bt8r1b/3118+22mqr7FOf+lRGRNnkyZOHlO3tl/j1r3+dNRqN7DWveU02MDCQ/elPf8qIKBs/fnz2j3/8I8uyLDvooIMyIsrOOuusLMuy7MEHH8yIKNt3332z973vfascvyzLsqOOOiojouzAAw/MBgYGspdeeimbMWNGRkTZZz/7WdcvAACoExihEXjd615Hf//73+n+++9vbrv55ptpypQptNVWW4l5fvnLX9ITTzxBG220EZ111llENHTExmLcuHF04oknEhHRwoULaeHChWrab37zm7TjjjvS6quvTmussQbtueee9NOf/nSIrXw61xlnnEGNRqP5VG+4KNN+nJkzZ9LMmTOJaOWTRo3777+fZs2aRZMnT6YJEybQfvvtRw8//LBpe9myZfTxj3+cpk+fTmPGjKF11lmHDj30UPrTn/7UTHP55ZdTo9Gggw8+mH75y1/SDjvsQBMmTKBddtmF7rnnniH2rrvuOpoxYwaNGzeOXve619E999xDa6+9NjUajSFtcPnll9N2221H48ePp0022YSOO+645qjVTjvtRB/5yEeIiOjUU0+lRqNBL7zwQqiOWZbR2WefTeuvvz5NnDiRZs+eTYsXL3bbOFrHvfbaixqNxpCRI2lKW5ZlNG/ePHrFK15B48aNo80224xOO+20IVOFJLz6Raec3XnnnbTffvvR5MmTady4cbTddtvRpZde2nxKT0TNqT3pyEs+tfKwww6jF154gRqNRnOE8TWveQ3ttNNOYnnSdKuBgQE677zzaPr06TR27FjabLPN6MILL2zuz6dKrbXWWvS73/2ONt98c3rLW95CRERPPPEEvf/976dNN92Uxo0bR9OmTaNTTz2VXnzxRbH8yy67jBqNBh199NHNbSeccAI1Gg0aO3YsLVmyhIiI/vSnP1Gj0aCtttpqlalkp512Gu28885ERPTDH/6QGo0G3XjjjUPKeeaZZ+iggw6iiRMn0vTp0+m73/2u2gZ//OMfqdFo0JprrkkLFiygt7zlLTRhwoRV8hVhn332aX7m08gkjj76aFpjjTWIiOjBBx8kopXXZyKit7/97US0ctRvs802o6VLl9Idd9xB8+fPpwULFtDYsWNp1qxZRETNEas777yzWe5xxx1H9957L+26665i2d5+zsDAAB1//PGUZRlddNFFNGrUKPrVr35FRES77bYbbbjhhkN8yUd+xo0bR1/4whfoJz/5CW200Uai7VmzZtEXv/hFOv/882nUqFG0+uqr0zbbbENENOz3BAAAqBoIGoF8zUge1PzjH/+g++67j/bcc08aHBwU8+Ti5eCDD6ZZs2bRuHHj3GlnKStWrGh+1qaIXXDBBXTEEUc0A8A99tiDfvnLX9Jb3vIW+uEPf0hERCeddBJtsskmRET0+te/nk4++eTmzX24KNN+EnmbaO3x+OOP0+tf/3q66aabaNasWXTUUUfRrbfeSnvvvbc5Te2QQw6hc845h5599ll65zvfSRtvvDFdd911tNtuu9Ff/vIXIiJaffXViYjo4YcfpiOPPJJ22GEHWnfddem3v/0tHXrooU3fHnroITrssMPokUceoW233ZY233xzmj17dlOojBkzhohWBp8nnngiLVq0iE4++WSaNGkSXXHFFXTccccREdG73/3u5nF7wxveQCeffDKNGTMmVMerrrqK+vv7aeHChfTmN7+ZnnvuuaY4sojWMcpZZ51Fp556Kv3zn/+kQw89lMaPH0+f+9zn6NBDD1XzlD2GnF//+tdNcb/DDjvQwQcfTI899hidfPLJdNppp4XtjBkzhk4++WRqNBpEtPK4vPvd7w7nP+WUU+iss86i5cuX0+mnn06TJ0+m008/nS666CIi+ldffv755+m4446j8ePH02abbUZEK4Ptr3zlK7T99tvTCSecQJtssgnNmzeP5syZI5aVT/PK110QEd122200ZswYWr58Od15551ERPTb3/6WiIj23HNP0cZuu+1GRCunep188sm0xRZbDElzzDHH0J///GeaPHkyzZ8/n9773vfSo48+KvqU96nnn3+eZs+eTZMmTaLtt9/ezWeRB/FTp06lLbfc0k3/8ssv0/Lly4mIaMqUKbRs2bJmAL/xxhs302266aZERPToo482/dpggw2or6+vmbbRaNDAwEBzWtfcuXNp/Pjxatnefs6VV15J999/P73lLW+hvfbaq+mP5SvRyvVc+bVD45BDDqFjjz22KWIWLlxI//Vf/0VERNttt13YRwAAqAWdHSCqlrJTpu66666s0WhkM2fOzLLsX1MYvvjFL2a77LLLKlPOBgYGsvXXXz8jouwXv/hFlmVZduCBB4rTzqRpFS+99FJzissrX/lKsS6LFy/Oxo8fnxFR9uUvf7m5/YQTTsiIKNt2222b2974xjdmRJRdeOGFhdqrXVPOirSfNuXsF7/4RdOf//iP/xD9PemkkzIiyvbZZ5/mthNPPHHItC0+5exnP/tZRkTZqFGjsoceeijLsixbvnx5tsMOO2RElJ1wwglD8hFR9utf/zrLsix74IEHmtv+8Ic/ZFmWZSeffHJGRNk222yTLV++PMuyLLvoooua6R588MEsy7Lsox/9aPb2t789u/7665v1o/835WVgYCDLsn8dt3TKWaSOr371q4f4nmVZdsghh7hTzqJ13HPPPVfxi09pW7RoUTZmzJiMiJp1fOaZZ7K11lorGzt2bPb73/8+y7JVp21F6nfkkUdmRJSdfPLJal1mzpyZEVF2+OGHN7dde+21GRFlfX192T//+c8sy7Jm3dKpZHk/f+c739nclvc9a8oZ71tPPvlk1tfXN6Q9//nPf2ajR4/O1l577ezll18eMpXrpJNOatp+/vnnMyLKxowZky1btizLspVT0c4666zsC1/4gljngYGBbM0118z6+vqyl156KXvyySeb9SCibO7cuUPa8+qrrxankuXTo7QpZ3m/yo8nEWWf//znxTZI851zzjnNeuTTufJ8Evn1Y4sttsh22WWXbJdddsm23nrrjIiy6dOnZ//93//dTKtNOXvmmWeyD3/4wxkRZWPHjs2eeOKJbOHChc20TzzxRDPtvvvumxFRdu6552bf+973MiLKXvGKVwzxaezYsRkRDSk7y7LslltuMaeUefuzLMteeOGFbL311hvSX7Isy44//viMiLJjjz22uS2/3o4ePXoVO2eddZY45Szlueeea54jr3jFK5p9DAAAegWM0Aist956tPPOO9Odd95JixYtoptvvpmIqDkVgXP77bfTk08+Seuuu27zqens2bOJSJ92dsYZZ9D06dNp+vTptPbaa9N//ud/UqPRoHPPPVdMn057eNe73tXc/o53vIOIVk71ePrpp0vUtv0UbT+ilW8Gyttj6tSptPfee9PAwADttNNO6mLgO+64g4iItt9+e/r73/9Of//735tPHn/xi1+IefKnvTvuuCNtvfXWREQ0evToZhn5dI+cjTfemHbffXciInrlK1/ZHO36xz/+QUQrpxAREb3lLW9pLrJ+5zvfuUq55513Hl133XV08MEH07Jly5pTtZYuXWpOD/PqmGUZPfDAA0REtP/++zfz5f0vglfHCHfddVfzqXh+nCdPnkyLFi2ipUuXqm/9K3MMOS+99FJzNCIdTTnkkEOor6+PVqxYQb/5zW/CdSnLb3/7W1qxYgWNGjWKNt54Y/r73/9OK1asoM0335wWLVo0ZPohEdH73//+5ufx48fTBhtsQMuXL6ftttuOPvzhD9NNN91EZ555pvokftSoUbT77rvTihUr6N5776XbbruNiIgOPfRQ2mCDDej2229v+kUkj9BEOOGEE4ho5fHMp1JF+sZhhx1GRCtf+JEf/0i+xx57jO666y666667mtNABwYG6Gc/+9kqbyUjItp8882b0+imTJlCF110ETUaDbrkkkto/fXXHzIqnI+88c95mnSb9L2dfPWrX6WFCxfSHnvs0Tz/NF9a8ePpp5+mN77xjXTHHXfQuuuuS9dee21z5BgAAHqFnhY0+Rtj8rnkKfn6BCISL+4HHHAADQ4O0s9//nP6+c9/Tq9+9aubU4I4uWhZsmQJ7bjjjrTDDjs0hYk27WzhwoU0f/58mj9/PjUaDdp1113pRz/6kRq8L1q0iIhWTumYMGFCc/s666zT/BxZN1GE4Wo/opXTy/L2+Oc//0kbbbQRHX/88XTrrbcOeRtTSv462Ysvvpg22WQT2mSTTejYY48lImpOHePk7Zi2W/qdi8K11157yPd8OsnAwAARET311FOrpOO2iVbO4Z85cyaNHz+exo0b15wGQkRD1ngUreMzzzzTDPLWXHNN1W8Lr44R8nYdP358oWCpzDHkLF68uBkEpm0/atSoZpsMh9jP6zI4OEibbbZZsz75eiBen3Ttw6hRo+j666+nV77ylfTII4/QxRdfTG9729togw02EF8dnJOLlN///vdNQfP617+eXv/619Odd95Jzz//PN1333202WabNactFSVdJ5WL3UjfSPtVkT71ta99jbIsoyzLaNmyZXTXXXdRlmXU399Pn/70p1dJn7/lLH1T480330wf/OAHiWilEMtJ1yPln9dcc83m65XT/QMDA7Rs2bJmmnZzxRVXEBHR4YcfPmS75EvqaxEWL15M//Zv/0a/+93vaNNNN6XbbrsN080AAD1JX6cdqJL8Rjx//nxaunTpkLUYd999NxGtvNlNmjRplbyzZs2iT3ziE3TppZfSM888Q8cff7xYxsDAAF1//fVEtDLIv++++1ZJc+211w55PSjRypu2NjdeYq211iKileLipZdeagYIeUBNFA9in376afra175GDzzwAH3mM5+hddZZh5YvX94MCvMb6nC0X860adPoz3/+c8j/nPzm/t73vrc5UpUzceJEMU/ejvlv5+Tk7SiJEYu8zVMxmR6TfN8BBxxAzz//PH3kIx+hAw88kP7xj3+IIzkcr46TJ09ujkKkPlgvliiKJGx5++V95qWXXqJly5bR2LFjiWhlX1u+fDlNmjRJXFtQ5hhypkyZQqNGjaLBwcEhfqVtkh/XPJ1Vl7Lkdenr66Mf/OAHq+x/7Wtf2xzFIvpXu+bstttu9MADD9DDDz9Md9xxB9144430/e9/n44++mjaZ599hqypyEnX0fz+97+nbbbZhtZbbz3ac8896dprr6WvfvWrtGzZstKjM0TVjlJ4jBkzhl73utfRO97xDrrgggvo5ptvpo9//OND0tx222202WabUZZltMcee9Add9xB8+bNo3333ZeIVq5b2mSTTehvf/tb85XMRNS83my99dbNdUxPPPEEvfzyyzR69Gj6y1/+QlmW0ejRo1dZV9Qqf/zjH+nBBx+kRqNBBx544JB9+YtT0lcrp75GGRgYoIMOOojuv/9+2mabbeiWW25RXyAAAAB1p6dHaN70pjdRo9GgZ599lj784Q83n7bdcccdzTcPab+98NrXvpamTp1K//3f/01EK0ccJG6//XZauHAhjR07lp577rnmk8Usy5pPE6NvO7PYbbfdmgtu/+M//qO5/Tvf+Q4RrZxCJf3Im8Tqq69OH//4x+mqq65qPiX8wQ9+QFmWUaPRoFe+8pVENDzt1wr5YuZnn32WDjjgADrggANoww03pBdffFF9kvnGN76RiFYKsnyB7fLly+n73//+kP1R8ra66aabmovo+duc/vjHPzbf9HXWWWfRHnvsMURw5PnywDEd/fLqOGrUqGaQk/72Tzv6XE4ubPOpS0RE11xzzZA0u+yyS3MkLX9L1osvvkgzZsygDTfccJWpfDlljiEnfzMb0dBz49prr6UVK1bQ2LFjm1N6eF1eeOGFVX4ziUg+Fh4777xzU1xOnTqVDjjgANpvv/3omWeeoSzLzPNz/vz5dM4559BXv/pVmjFjBs2ZM4euu+462nbbbWlwcFD9gdnXvva1NHHiRPrVr35FDzzwQFO45ELni1/8IhHZ083K1HU4WbJkSbP/WC84aTQadMUVV1BfXx/95Cc/oW9+85vNfW9605uIiJrn+Z133kl/+9vfaI011qCZM2fSFltsQVtssQUtX768+bszeV/aa6+9mgK9XeSL87fddttVRMYb3/hGajQadOedd9KCBQsoyzK69tpriYhov/32C5fxuc99jn75y1/SpEmT6Kc//SnEDACgt+nIyp1h5EMf+lBzQejo0aOzNddcs/l9k002yRYsWNBMmy9KzReZvv/978+IVv52Sr5wmy9q/+AHP9h81z9n/vz5zbIeffTRIWXw31qIcN555zUXu7773e/O3vSmNzUXPf/85z9vpou8FOCUU05p+rb22mtnjUYjI6LsmGOOGdb2014KEGH+/PnZxIkTMyLK9t9//+yYY47JJk+enI0aNSq76aabsixbddHy4OBgczHwhhtumL3vfe9r/m7Qhhtu2Fw0rP1+Tf7yh5tvvjnLsiy77777mm332te+Njv44IOz6dOnD3kpwIIFC5q/qXPooYdmH/zgB7PNNtus+ZsQRxxxRPb44483F7+vu+662VFHHZX985//DNXxkksuaZb39re/PXvjG9+YbbHFFhnRyt8E0ojW8Vvf+tYQ+/vss0+28847r2I/X4w9adKkbM6cOdmrXvWqjIiy3XbbrXn8+cL6SP0iLwW4/fbbm2287777Zu9617uaC7o/+clPNtMdc8wxGdHK3/g4+uijs2222aZZl3e84x3NdLmf22+/fXbqqaeKvkvtd9xxx2VElG2wwQbZ8ccfn+2+++4ZEWUzZ87MBgcH1d93eeKJJ7JJkyZlq622Wvae97wnO+OMM7LZs2dnRJRtvPHG2ZIlS9S659cBon+9QGNwcLB5PhJRNn/+/CzL5N+XyevR19eXvec978l+8YtfqH7mLxw444wzxDaI5pOQXgqw4447ZpMnT86IVr7I48c//nGWZfbv0HzkIx9pXteefPLJLMtWvuxi3LhxGRFlM2bMyFZfffXmCwFy8rqMHj06e8UrXpE1Go1stdVWy26//fZmmgMPPDCbNWtW8zrW19eXzZo1K5s1a1Z2/fXXu/tz8vvG7NmzxbbI+/zkyZOzzTbbLCNa+QKORYsWZVmWZX/84x+bdvPrzfrrr9/c9sgjj2QbbbRRRrTyxSNTp04d8nfwwQerxwEAAOpIzwuaLFv5lq3Xve512YQJE7IxY8ZkW2yxRXbyySdnCxcuHJKOB+Q//OEPmwFnThqQr1ixovmWmquvvlos+zWveU1GRNl55503pIwygibLsuyqq67Kdthhh2zs2LHZGmuske2zzz7Zr371qyFpIoJm2bJl2dy5c7Pp06dnY8eOzTbccMPslFNOyV566aVV0lbVflnWmqDJsiy7++67sze/+c3ZxIkTs4kTJ2a77bZbduONNzb3S0HnkiVLso9+9KPZZpttlo0ePTpbd911s/e85z3NH+3T8mXZqsF+nnaTTTbJxo0bl+2xxx7Z//zP/6wiZL/5zW9mm2++eTPN/fffn33rW9/KJk2alK211lrZ3Xffnf3v//5vts0222RjxozJNt9886ZY9Oq4YsWK7JRTTskmT56cTZo0KTv88MObb3NbY4011LaL1nFgYCD76Ec/mq2//vrZpEmTsqOOOqpZx0mTJjXzDQwMZJ/5zGey6dOnZ6NHj8422GCD7IQTThgS2Eo/TunVLyJosmzlDxTus88+2cSJE7OxY8dmO+ywQ3bVVVcNSfN///d/2ezZs7NJkyZlG220UXbhhRdmX/jCF1Z5KHHNNddk66+/fjZu3LjsjW98o+i71H4rVqzI+vv7s8033zwbPXp0NnXq1Oz444/Pnn322SzL7B+s/P3vf58dcMAB2brrrpuNGTMm23jjjbM5c+Zkjz32mFnvc845p2kzf5tblv3rxxjTH7mVyn/hhReyN73pTdnYsWOz9dZbL/vxj3/cUUGT/o0aNSqbOnVqdsABB2S//OUvm2ktQfPiiy82j1UqUn/+859nO+64YzZ27Nhs4403zj71qU+t8iPGl112Wbb55ptnY8aMybbbbrshIiTL/vX2O+nvwgsvdPfn5D+k/P73v19si5deeik78cQTs7XXXjtbffXVs7333rv51sEsy7Jf/epXajlElP3ud79rCkHpT3pzJQAA1JlGlhkrkgEAJosWLaKHHnqIlixZ0vwBwLvuuot23XVXGjduHC1evFj9HR0AAAAAANA6Pf1SAACq5s9//jPtueeeNDAwQG9729to2rRpzfnuxx13HMQMAAAAAEDFYIQGgBb5yU9+Queffz7dd999NDAwQFtuuSW9//3vp2OPPXaVN1kBAAAAAID2AkEDAAAAAAAAqC09/dpmAAAAAAAAQG8DQQMAAAAAAACoLRA0AAAAAAAAgNrSs285O/nkk+mMM87otBsAAFAL/vGPf+DX5AEAIEiWZTR16tROuwH+Hz37UoBGo9FpFwAAAIBhZeLY84iI6IVlH6OJY8+jF5Z9rMMeATCUXumXzz77LE2aNKnTboD/B6acAQAAADVn4tjzmmIm/54Hjel2AADoRXp2yhkAAAAwUkjFS/r0G2IGdAvoi6BKIGgAAACAmmJN34lO7emVKUCg++AiZv3B8TT/5VM64wzoaTDlDAAAAKgZfIoZ30dEq4iUfDvPZwkiACLwvqL1nSdHvYR+BSoBggYAAACoEfmIijW1LBU8nsDRwBocEIX3rfSlFPm+F5Z9jP727JcxGggqAYIGAAAAqBF5sJgHjKlwSYNFKXBM80QDSwSgoCjSaODEsefRmuMe75RLoM389Kc/pbe+9a30hz/8odOuEFEPvba5v7+fzj777E67AQAAAFQCFzAcS6hIeSBUACjPSH5t8y233EIPP/wwzZ8/n4466ijabrvtOu1S7wgaDn6HBgAAQK9iiZo0Tbt+j4a/BloqR9oHQK8ykgXNSy+9ROPHj6fTTz+djjjiiK4QNJhyBgAAAPQI0u/RpP8jeaRt1lQ2b5obtwsAKMe//du/UaPRqPRv8803d/0YP378MNS2GHhtMwAAAFAj+CJ/LjwkgWKN1Gjb2i0+JNGE0ZzeBse4vdx22220eKkvOFqhruucMEIDAAAA1Ajt7WOpCEnfLNVKOZKoib6iVxodwtS0kQWOMRguIGgAAACAGhCZPkY0VIikwqHoqIs1ouNNYcvT8f/W7+cAAHwag6Mq/asr9fUcAAAAGEHwERj+nb8FLfqDmZFRGL4tuqbGEzBFBVY77AAAWuPKK6+kY445hh588EH6zGc+Q8cccwy98MILHfUJbzkDAAAAagAXLynaqIn2NjLNfpn9koCKrPNpZTqSlB9vWwPDSSfectZoNOiZF7eqtIwpEx6hOkoDjNAAAAAAXYw2yiGJlfSX2b386X5uT8Jau8P3SX6ko0qecJI+p9s0USd9BgD0PhA0AAAAQBeTrkHxfjxTe8OZFeBbtvLPRX3Vtns/DsrrVtRvAMDIBIIGAAAAqAHa2hdtDYv2cgBuQ3sbmfYyAW7XG1HRfLP2az5aeQEYCTQGG5X+1RWsoQEAAAC6HGlkw5p2FVlDYq05accalCI2o9PeAOgWOrWG5tnnZ1RaxuQ1Hq7lGhoIGgAAAKALsURM5BXMfHqa9ha0iJjw1q1Y61mwWB/0Ip0SNM89W62gmTS5noIGU84AAACALiSygN5afJ/a8D5761qiZUq2IGYAAFWDERoAAACgy4iMhFgixJrqxUdMUlva79hEf9umiGApOi0OgG6iYyM0z1Q8QjMFIzQAAAAAaAN8FCRfGG8t1JdGSLzXNWujJ9oLBfgLAFpZrB9JDzEDAIgAQQMAAAB0Gdobx9JRFOn3X7jgSAUOF0VpGq3c9H/+ObUjrdPJ/0df91zm9dAAAJCCKWcAAABAF6NNM9MW5EtowiO1U9aHotPCyrxdjQs5ADpNp6acPf90tVPO1lirnlPO+jrtgMbDDz9MV1xxBfX19dGyZcvove99L+20006ddgsAAACoHO9NZpYw0cSGZs8TC9KLBNLyrJcASH5rb1+z8H6/BgAwsunaKWdf/OIX6b3vfS9dcMEF9OEPf5jmzZvXaZcAAACAyrFGT7zRDe1HLtN9fAqZNuUsLUMSFNK0MuntaelnTeRo9S8ydQ2AEcFgxX81pWsFzeTJk+m5554jIqIXXniB1lxzzQ57BAAAAAwf1muUvZEbaSRDG33R7FkvA+CjM5Io0abG8dEnqXxtGwAASHTtGponnniCTj31VFprrbXoqaeeok9+8pP0ile8Ipwfa2gAAADUBT7tSxvJiL62mefTypLSab5Ia108v60pbF4dJLGENTSgW+jYGpr/q3gNzTr1XEPTtSM0l1xyCZ1wwgl0+eWX0znnnEOf+cxnaHBw1bGw/v5+ajQaq/wBAAAAdSMyHYxP39J+6JLn00ZiUiHD3zgWmbYmvfKZv12N2+K+aWgjRACMVBqD1f7Vla4VNPfeey/tuuuuREQ0Y8YMWrJkCS1evHiVdHPnzqUsy1b5AwAAAOqC9YOV2nQub+0Ltyu9/pmXrwmjVKRwHyThIu2X/Iy2g+QrAADkdK2g2Xjjjemhhx4iIqKFCxfSwMAA1tEAAADoefgalRxt8Xx0mhcXQtr0LW1qmTZqo+WNvlY6ut7H2h4pB4BeoJFV+1dXunYNzZ/+9Ce64ooraPTo0aVe24xpZwAAAOpC5HdWpNcxe6935hRZ99IugaAJJGk/AHWhU2toXnyy2jU0E9av5xqarhU0rQJBAwAAoG5IIyja78CktCpCIqJIe1FAVBBp64EAqCMdEzT/rFjQbFhPQdO1U84AAACAkYQlVvhaFr4/z19UIEjiI7JexVqLI708IDKtDFPGAABlgaABAAAAugBJqETWrXg/PhkVOVykpCMyfHQm3c59kb5zH71XPEdfIgAAAESYcgYAAAB0HO+tZdLaF5626O/YWPa99JLNyOf8OwB1p1NTzl5asFWlZYyf+gimnAEAAACgGNJUMT5Vq8gPamqvZC76tjHpbWuWX5Z//LXPAADQTiBoAAAAgAopIiSiwqXID05KoyPSupc8bZpGeyuZ9vs4aRqIFwAqIKv4r6ZgyhkAAADQYbwF/dJ0M2nqWdnXMfN91iukI/nT7ZFXUgNQNzo25ezvFU8527ieU84gaAAAAIAugb8dzBIIaR5J7KTppfxeem1djWU/LQeAXgaCprvAlDMAAACgg0hTwtI3ieXbtbeeWfa8aWj8jWVSXmuNjzWK4719TfMZAKDTGKz2r65ghAYAAADoMNJ0scgUM07kzWQ8fWrL+10a61XLvC6SzwD0Cp0aoVny12pHaFbfFCM0AAAAAGgB7bdotGlmPB8XJvlnvkifjwSldr21M9yWNNrjTZ2T7AIAAgxm1f7VlL5OOwAAAAAAf/2MhrbgPjpFTbIRWYPD80ijMpLAksrOwYgOAKAMGKEBAAAA2gwXENp6Ej7qoQXzUbGSluP9iGZq1/pBTu5DkRGYMuIMAKDTyKr9qysQNAAAAECbkUYopGlakjCRRI6WxipbExNcAPHRlsirm9P8XhrphQMAANBO8FIAAAAAYBjx1sNYU7a033TxpnkV+U2ZPK/3eucydbV8AaBOdOqlAEsfn15pGeM2fxQvBQAAAACAjCYGrNcep/u11yhLr1pOXyYg2ZT8iU5rk2zzNFbdIGYAaIHBiv9qCgQNAAAAMAxIwiT/n/5pAkOyI42e8M9pPm8UJyWdjsZ9kqaRaf5afmEaGgCgHeAtZwAAAMAwYo2ESGlTYUFEq3yXbKZIa2b4/ujrlSPraqQ6aH5itAaAYtT5xy+rpGdGaPr7+6nRaDT/AAAAgG7EmgaWI43mtPu3XaSRoOgPYmp+t+IPAACUBS8FAAAAAIaRyA9X8tEQ7c1jnsixbPE06fei9cDifzDS6NRLAZY9Uu1LAcZuVc+XAkDQAAAAAMOMJCq07ZZY0IRIVDRxNIEiiSLtTWgAjAQ6Jmj+VLGg2bqegqZnppwBAAAA3Yj2ZjG+4F76zRppNMZ6dXP6ogDprWb8N2fy8iRfrXpYIz5WPgAAqAKM0AAAAAAVoo2EeNPD0u2pHet3aKQpakX8tGx62wAYSXRqhGb5Q9WO0Ix5RT1HaCBoAAAAgGHE+h0ZaxqXtc4lusZGKt/zT9peZs0NAL0EBE13gSlnAAAAQAUUmYqljYBERnes1zJbvxfjvWGN+yrZ0X5kEwBQEfhhTRGM0AAAAAAVob25LN+WpmtlpCMyHc0rKzLlzEof3QdAL9CxEZoHKh6h2QYjNAAAAABgSIvvo0RHP6z1LNp37wc30+3pCwn49tRHjNQAADoBBA0AAABQEdKUrPQtZkT62hRrDQ3fVmREJH0TGveT+84/S/54b04DALSRrOK/mgJBAwAAALQRSxikaaTfhJGmpEVeq1w0reSTNBIj+WsJLYzWAAA6AdbQAAAAAC1i/filtl/aXvS1yNHXM5dZB1OkDJ6mzGujAagTnVpD8/L/VruGZvT29VxDA0EDAAAAdADthQGaKLAW/mv2rdc7a+mllxdov5kjbYOIASMBCJruAoIGAAAAaAPtGEmJ2i/6hrGia2w0EcWBeAEjlY4JmvsqFjSvrqegwRoaAAAAoCKkt5RJvyHD3xYmIS3S19JFRlQkPz0xI73QAAAAOg1GaAAAAIA208poShU+aPuJ5N/D0UZmIr9zA8BIACM03QVGaAAAAICSeG8di0xDS78XfUOYNvoTeeUz32+9dll6xbM34gMAqAC8tlkEIzQAAABAm5FGOvLt+fcibz7LiWy3fPBGjiI2UzAyA0YqHRuhubfiEZod6jlCA0EDAAAAVIAlWHKKCgLvzWXWNLKIbQnrR0AhaMBIpVOCZsXdW1VaRt9rH6mloOnrtAMAAABAt1MmeC+ycD9qX0vjvTI5MhpTdJ0PxAwAoFvAGhoAAADAwRID3na+3kRap5IKHM2G55u2tqWIkNHW2qRrbLz1MwAAMNz0jKDp7++nRqPR/AMAAACqwhopkbZJooH/j4omzU5aliVqtIX/fJs1NQ6jMwB0iMGK/2pKzwiauXPnUpZlzT8AAABgOImOWliCQkrnpdVGd7QRHymNZdf7cU0AAOg0eCkAAAAA0CbSaVnWwnxtVIbvt9JIadP0VSzab+WFBgD0Eh17KcDvKn4pwM71fClAz4zQAAAAAJ2Gr2fh24n0H7Tk+yLrW1L7PL304oGUsqMt2rQ2AADoFBihAQAAANpMZKSmnSMo1ohPu8rBK5sB+BcdG6G5a0alZfTt8nAtR2ggaAAAAIAK8KaV8bTt+E2aKvNVZQeAOgJB011gyhkAAAAgEJlSZU3jkqaMaQv1I2tpimy39rdLhEDMAAC6BfywJgAAACBQ5ocutSlfVj5tTY32Y5faNDZPrGBEBYAeoMavVq4SjNAAAAAAVPwHLaP2vNcvS8LFWnSviag0j/SbNhAzAIBeBYIGAAAAoGqmUHk/mum9uaxoWZoQgpgBoDdoZNX+1RUIGgAAAECh6NqWdL/2yuV0nzalzBI83g9jaqIGr1kGAPQqEDQAAABGJFaAry32916PnGP9fkz66mMvrzZFzdofyQcAAL0EXtsMAAAAOLT6WuXIK5y1MiJlY40MAMNLp17bPPDrrSstY7U9/oTXNgMAAAB1JjotK50WFkmrvbEstVVktCXNm6eJTIMDAIBeBIIGAAAA+H9ERYUkUKR1L9ZC/TRt2dEVbrudLxkAAHQhWaPav5oCQQMAAKBnKbJOplX465OltS7RH9SUiPgL0QIAGIlgDQ0AAABQEP5jltqPXabfiaoXHNK6Ha9crL8BoDgdW0Nz+ysqLWO1PR/CGhoAAACg0xRZ28LTR/NqP4SpCZfhEgypH/m0OK9siBkAQN2BoAEAANBTRN4IlqaLvAaZ5+c2io5ylBFREXve66YBAKAXwZQzAAAAPUWR6V3teCWyNu0s6mu7R0iiU80AAOXp2JSz215ZaRmr7fVgLaecQdAAAAAYUfTKmpHI79a08ts2AAAdCJruAlPOAAAAjBisAH84fWhHOusV0638tg0AoIsZrPivpvSMoOnv76dGo9H8AwAAAHKkH6CUpmZ5vxcT3deO34MpKzq0N50BAECv0jOCZu7cuZRlWfMPAAAAyIku/G/XiEbkxzTbgfYSgNwHjMQAAEYCPSNoAAAAgCJEg/0yU788oVGGsq+UBgD0EIONav9qCl4KAAAAoOeIvpmsE2XXrRwAwKp07KUAt25TaRmr7fNALWc6YYQGAABALckXvksjFzzQ52laHZ1pxxqZMuXycrT1O1g7A0CPkjWq/aspGKEBAABQC7w3lNV9xAUAUB86NkJzy7aVlrHam/6IERoAAACgKqwF++k+a/1K2XK7ZcTD86PddQcAdBl4bbMIBA0AAICOUyTwloJ26VXM+XdL7ETL6JYRGs8PLr66xW8AAKgSCBoAAAAdp8yalnSqmfZa5kiAH1mD00k0sabRTSNKAAAwHEDQAAAAqBW52JBER3Sbtb+oGKhSPOQjTEXW8aR5AAA9Bl4KIAJBAwAAoBZEgnrvrWTRt4dF/SmSvgyWeNPqYuUBAIBeBIIGAABA1xMdobBeHJD/L/qq4+iroVtBWxeEURYAwBDww5oiEDQAAAC6GknMlFnc7/12TOQtalW9RYy/uCAv0xJNGIEBAICVQNAAAACoHdFgXnpZQBkhoE0vi9gqKnpaESoY0QEAjEQgaAAAAHQcb/SkbF5t2lYrIqPoKM1wjqRg1AaAHier+K+mQNAAAADoOK2IFm06mGW3zG/TaDYhIgAAoLNA0AAAAOh6yrwQQJomFhE9mLYFAOha8FIAEQgaAAAAXU+ZlwDkbzQr+sOaZUdcIIQAAKAzQNAAAADoWbw3heVpiIoJkqpf4wwAACL4YU0RCBoAAADDjice+OuWy7zVrOzamHb++CYAAIDqgaABAAAw7BQZNdF+DDPfL23T8kGsAABA7wFBAwAAYFgos8bE+u0YbeRG+u0Z7XtZv8qCdTYAgJbASwFEekbQ9Pf3U6PRaP4BAADoLiJTuor8pow1klJklKXKERkIGAAAqJ5GlmU1/hkdHYgaAADoHrx1MEXWyVTlAwAARHn22Wdp0qRJw1pmo9GggR+8ptIyVjv4HqqjNOiZERoAAADdQfQNYFWMXkR+gBMAAEBvgREaAAAAoCAY7QFgZNOpEZoV3692hKbv7RihAQAAMILphfUieR28ukDMAABA9wBBAwAAPUA3iIkqgvwir2uWtpf9LRoIFgAAqA8QNAAA0AOUDcDTEYmybx+rCm1aF9+uvfnMSteKT90gHgEAI5SsUe1fTcEaGgAAGGHkAbkmgqKvRtbyRvNUuQ6lKttYOwMAIOrgGprrXltpGX2z78YaGgAAAO0jMhIgpUm3aTZ4UM5FTP5XxE9v/UmR35Gx7ET8eGHZxyoZSYGYAQB0FPywpghGaAAAoOakowbp6Is1FSvd1gujDlYdWh016oX2AQC0l46N0Hxvx0rL6HvH7zFCAwAAoDq0kZA02JZGViwxo+23PleBN9Lk5bMERxExIqWFmAEAgO4GggYAAGqAtMA9HWGRFvfn4ibflueJ/shlmrfo9LMi9crLksr3aMcPdpYRa3gxAACgI+ClACKYcgYAADXAEjTSd88Wkf1GMMtm2SlYRaaFVT3NC9PIAACt0LEpZ/+xU6Vl9L3zf9wpZ8888wxdcMEFtGjRIurr66PTTz+dpk2bNiTNc889RxdeeCEtXbqUli1bRq9//evp0EMPrcxvjNAAAMAwUnQal7Wony+Czz+nf+m2NK9kLy1TWpejlRWpw3CJGc8faf1QGTsAANARuuClAFdeeSXNnDmTvvSlL9GcOXPo8ssvXyXN9ddfT1tuuSVdeOGFdNFFF9H3v/99evLJJ9vdGk0gaAAAYBjR1q4QFRM42oiKVqb30gBuQxMzPE3kd168dNHfiom0jyeEokIJozcAACDz29/+lvbZZx8iItp5553pscceo6VLlw5JM3nyZHruueeIiGjZsmW02mqr0fjx4yvzCYIGAAC6hFbWqfD1Men6Ga0MSbRIIyW5He93a9I1N9xu9HXN1uuiI69iLjqyAuECAKgVWfv+PnndAup71++G/HmsWLGCBgYGaNy4cc1tU6ZMoUWLFg1Jd+CBB9Jjjz1G73//+2nOnDl02GGH0RprrNFi5XUgaAAAoAuwpoelYoL/zz9bC/j5FDS+jwsRXqY2KuT9xo0nniKjMZYgs8qOlAEAACOZT7x9Kq34zs5D/iLwNTZZltGoUUMlxbXXXktbbbUVffnLX6arrrqKrr/+elqwYEHbfOf0VWYZAABAmHT0ITJqoAX60iubeVrvjWlpvsgPbBbx3Zpyxym6liYVZ2l7REeHAAAA2PT19dGYMWNoyZIltPrqq9Pg4CAtXryY1lprrSHp7rnnHnrnO99JRERrrLEGzZgxg/70pz/R1KlTK/Gra0doBgYG6HOf+xwdddRR9IEPfIDuv//+TrsEAACVwte6aG8j42nT7dJ/HtzzH9T0fseG+5Om9V4HLfkk7eOfy4y0WKIuIswAAKDbyQYblf5F2HXXXenWW28lIqI77riDttlmGxo7diw9/fTTzalnm2yyCT344INEtDKmnz9/Pm266abVNAp1saC5+eabaWBggK666io644wz6Gc/+1mnXQIAgFIUmfbkBfVasM7zSCJGmtamTXXjeVJbvHypjlKdo2t6JNtVvfUMAABAMY488ki6/fbb6eijj6bvfe97dPzxxxMR0Y033kjXXXcdERG95z3voYceeohOO+00OvXUU2nfffel6dOnV+ZT1/4OzWmnnUYf+MAHaMaMGaXy43doAAB1xvpdmMgCfSJZEGgCxRoR0fZJYqnoiw3KTDuTRpmieb08RWmnLQBAfejU79C8/M1dKi1j9Hvvcn+Hphvp2jU0TzzxBD344IN09dVX09KlS+moo46ibbbZZpV0/f39dPbZZw+/gwAAUIAi6zi4cLFe0eytEdFGSqTPmhjS/LBeMqBtKyp8tGlykp/aCA/3o50CBGIGAAA6T9eO0LzjHe+gI444gg444AB6+OGH6ROf+AR95zvfWeUtChoYoQEAdBtFRxs0AeGlsV5vrE0jswQTt+uJLckPT2hJaYuKH4nIqJaVDwAAJDBC01107RqaddZZh3bYYQciIpoxYwatttpqtHjx4s46BQAALcLXrlhBs7VPekUzFyrSepp0H19nYy3el7Zr5WtiRhIx+V9kfZDmX8RfTXRpQMwAALqSrFHtX03pWkEzc+ZM+q//+i8iIvrb3/5GAwMDtOaaa3bYKwAAaA1NRHC0t3/l37lISKdTWQG/tPjfQxNfaR280aVI3bwXFWhoI1Le9DKIFgAA6A26VtDMnj2b/v73v9MJJ5xA5557Lp1++unh6WYAANCNaKMpXBBogsWyyYN6LkL46Iw2WhLB8yktj/uiCZ10tIb7mqaxXnCg+en5CgAAdaEbXtvcjXTtGppWwRoaAEC3E31Dlyccomnz9GkZ3Ib2FjQpnfeWM+0lA9ZCf0lo8TRWHoy6AACGg06toVn+9d0qLWPMnN9gDQ0AAAAdbXqVN+1MC9K1UZl8nxfc89GadNqa9iY0qWxtips0eiT5rAkir37pyJZV38iIEgAA1AKsoRHBCA0AAFSE9YYtKZDXgn5tJEQaVbHypPtyHzws+2ldvLxW/aT9KUXW50jgjWUAgHbTsRGar82stIwx77sDIzQAAAD+BV+/wvelowspWgCujfBERie4INDeKmbVg9u1hI5V98j+iJ/REReIGQAA6G0gaAAAoAKkaU7aSEn6nU8Dk0ZruBhI02riSdqvvT3NsuH5b9lOP2tCRZpKlubh9eW0Y3oZpqYBALqWwYr/agoEDQAAVIA3KmC9olhaMC8F9po9a0RFW0CviZHoGh9p1MQqiwsXy741ksX9lMSeJSq96XQAAAC6HwgaAACoEO0tXdbaFr7NEjGSmODBvSQsrHUsmiCSFt9LQknKp42qaAv/ebtJIy+WKJK2azbaDUZ4AACVgZcCiPR12gEAAOh1NHFBpAfUmgCSFvtHRhms0Q1ephbwa/s10aJNEfNEE7et+WyN7kQFYxVghAcAAIYXjNAAAECLtDJCkSMF4JFRjcgLBCw/LZEhjQ7xaWaSyLFGhCQxxkeG0lEfbf2PRFqG5ZfWzhhZAQCAeoLXNgMAQBuILFTXRlkkMcNtaaMiWhka0osE8u9Rv7gf0f2SePJGdywbUjnRES9pv5UfAABSOvXa5mVX7lFpGWM/8OtavrYZggYAACokMr1KGqXItxPpQTxPl2KJpIjg0ewWsRVBEzlF86dI4kRrYwAAKAMETXeBKWcAAFAh1noSHnjzKV7aaEqOtiA+3+e9GUzy1ZvKxn2NigNr5ET6zN9Wxrd5LyeQ2lITjK2AaWoAgGEFLwUQgaABAIACWIvUW8mbihNtpCIyrcwSMFwEaJ+l8ni5kfKtKXOpP9JoVVqeJgpT21zk8LaOTOkrI04w2gMAAJ0HggYAAArCR0bS0QEpMOYBtyZUvG0p0nSqNJ9WjjWq4k19k3zwRFia1ppepk0T4+mLiJs0PW97LnggTAAAdSDLGpX+1RWsoQEAgADR9Sxpem2EIbI4PrWtbYuuoymz1kUTKHyqnLWuR3rJQIrVppb9SD5vO9bTAABaoVNraJZe8YZKyxh37C+xhgYAAHoVbboWX6thjZB406GskQgNadSlyLoZCWvtjCXQtPpooyLSZ152dFqYNkokTZfThB7WwwAAQD3BD2sCAEAQSThYoyeWKPFGVCS0kRYtaNeQAno+fS7fpi2u53mi9bRGXCJ15XmtfVK7SGkibQYAAF3BIGYgSfTMCE1/fz81Go3mHwAAtBtvkXv6PV1P4y2+l0Y/+H6tDO5LihSoR6ehSUJGsm+NmEifpfy8LtJUM8meNLpSdqF/ETGDkRwAAOgusIYGAABKYK0dybfxNEW/83I0vHU4vIzomhWvHM2/6HQ7zbdoG2r5PKQpc956Gmm9EwBg5NKxNTSX71VpGeNOuK2Wa2gw5QwAAEpgrVPxRIVmg9vjU6e08qTvFpqvEZ890SHl80RMmsZaW6OJCu/lA5bvmn2rDSBmAACgu8AIDQAAlEQKgL2Amsh+O1iajuMtzNfWtkg2NN80NCFgCRtv5CgqjCxfvDSar6lfAABQFIzQdBcQNAAAUBAtENYC6BxJhPD8+fbISIj3kgDPtyIjKGWJjO5Y9YiOfPE0ZaagAQBAlE4JmiWX/VulZax+0n/VUtD0zEsBAACgKiJCwhIMqXDQRnWk4JsLH+uFAlK+9MUE2v7chjXVyppule6X/NR8yPdJtjV/vBGViDDJbad/mvDB4n8AAKgHEDQAAOAgBejWIn5rTUp0zYkU1Fuih/tp+W+NDEl1Sf2xpnFF1rFI/kvraLgI4sLHEk/S9DguXjTbkfYEAICOkVX8V1MgaAAAI5KyT9+1YJrvl0Y2rFEHLnSKrH/htngeT4BpC+25Dc03S3xYPmptqYmV3A/LF22URxsd4za8aXgAAAC6DwgaAMCIpMqn70UCZD6aoE3fSgN5aySHCwZvjYrks+YvL0/6HxECfCRGGlGxfEzrpU3fs0SWVyZeFgAA6FayrFHpX12BoAEAgAJ4IwRWQK+tWeFTobypZl7ALQXyaZnSFC9tBMla16JNP4uOLlmjVtLnPI+0XxvhsdpV87XIfgAAAJ0HbzkDAIA2YQmNok/9+WhE/jndVwRpypk2ehPZFh2Nsaa6eWVpdefbJWHl1UGyh1EZAECUTr3l7KVL9q60jPEn/wJvOQMAgJGMtug8R9qnTZFK91sjJ/y7tm5HEx/atCsJbfqX5Zckgvi6HcknaSSGtx8XIlIara7cD2v6nVYfAAAYdgYb1f7VFAgaAAAoiBXUak/7pQDeWuORb+PTp7TRB0+wpOVooictR6pTZG0QT6Otr/GEmrXmSLLB7VkvGdBGh7zphFIeAAAAnQeCBgAACqKtxbCmUeXb8z9N9HhCRZo2JY0y8LzW+hlrehvfZgkFXk9LgEltoE1Pk8qw0NbZcP+ktFa9yvgCAABtJWtU+1dT+jrtAAAA1BFrRIB/t8SLNCJhjeZYoskTVNI+PvpiiTGpPGtdihX4R0a5Uvva+p0ibWSJK/7ZGv0BAADQXUDQAABACbwAl6/PkMSLJWasMqWpUdI+bW0J326NREgiwUqrrdvRhJIlLiSsESttW2QNEt8m1cfzC6IHAFA1dX61cpVgyhkAXQgPWMHwUrbtvVEH7TsPmqWF89JISppfWuQupZemkOXprPUkkTpHR5u0wN8addHyWWImuj6GT7/TxFuR4wsAAGD4gKABoAupc3DUC0KsbPtbU9C0IF+b3pXvt6Y9aYvcpWlgqR9a0C/54a0X4vVL7UfXxHAxJ9XRm0YXHYXRRlK00SVpZAsAAEB3gd+hAaCL8ab5gPKUCVCjeTxRoaXLiYxEeGhrZbT/PI/3WRIh0miM5Z+U1lpbZNVDqrPUFtaaH+0zxAwAgNOp36F58cI3V1rGhI/8DL9DAwBoP9bTa1CeItOJtDxa/nRUwRqNSdOl6aNToCw/pREUT4ykoxQ8nTdNjNdRmlLH68vbTBvZ0crjvlmjL2k63s5afu6v5QMAAIDOAUEDQJcTWXsAWqfVtvWmZnmLy72RBkkkeeVb5VgL3qW1OFIaybYmDrz1OZIQsoQDtxcdHfLW92j+pdtxHgIAOkWWNSr9qyuYcgZAl4LAqfewpkNZ06Wk/Hy7tlaGf7eEhoS1DibNL03TsuoZFSDWehxvtMZKq7VfkfMO5ygAI5dOTTl74YJ9Ky1j4uk/xZSzTtLf30+NRqP5B0DdQaA0lF6Z5iONtPDpXnyKljbSw0cStAXs2giEFJBLfmloa2es6WqSvzxNus0TgXw7tyv56dVH8zHiMwAAgOGnZwTN3LlzKcuy5h8AdadIAN8rwb5FLwSPmlDwPvN83lQ2T/CkFBn1SPN4oyx8SpsmKrTv0poerQzJL2stDU8f8QfTzQAAXUFW8V9N6RlBA0CvMZzTXuoiiLzgvKqyqrZrra9JA2lJ4Gh2I8LEW+wuTfWKrPPh+7VRJKlsPsrDxZtmj5dvjeDwevARnyLCDgAAQOfBGhoAeoR2CRtvjURdGK6n6draFJ4mRVvvEl33YQX41nZuVxJJnu9pvqLrYbR6efuj63M8e5Yd65h465MAACOPTq2hef78/SotY42P/qSWM50wQgNADdGechdNw9HWMRQJdttFq7aHy2dptCQvh0+B4m3pTcPi622kaU9FRag1jSvtM3w6Fvedj8BYZWr+8jbh08y4X/wz90Py1Rup4W3L00TX3QAAAOgcEDQAdDnWNCBrnQB/ii6lKxOM8SlBZfGmj3lP8iN2JdrxZN1ai8KnMlnlSYF2dBSGl62JH6kcC25LS8Prme6T0udIQkjq41r/0kZLpLU0WnrJT+lYaf5xQQQAAMNG1qj2r6ZA0ABQA6QgTAt8eTrpKXqevpVgrOiT6zJBYRn/onkiYsmaxlSk3MhIiDctTRI7aTpJUOX/vWlTXDx6okbbJ22P2NFsWKMxmlDhSP1emianPTjQ2t0CozYAADC8YA0NAF0ED7SsaTNSXiJb6ETK9/J4U546ub4gOh2rjH9l61nmOEh5pSCcB/jaSAi3IfUtqf94o3ypbc1fSxRKddJsRig6qhc51zrZnwEA3UvH1tCc95ZKy1jjYzfXcg0NBA0APYIXEGpPvCN2rSBYGgFol2gY7vytlBcRoJZ/UhumaMG6lEfbZokWKY2XXkMSNJqwjNZTs+v5kbaBZa8sEDwAjEw6JWieO3f/SsuYdNZNEDTdBAQNqAvtDKgswVJ0xEdKV8YGaH2UJjK9yhI8rYxyWGVHBZCUL1KPMuIpMppk+czrWkSo45wAYOQAQdNdQNAAMAxUHehEn1Z7IwcIyGRaESSpDS8YjpThTT2Tvlt4Izzpdp7HGqGyfImufyk6gmPZsNpIG3nklBl5AwD0Jh0TNOfMqrSMSf/ff9ZS0OClAAAMA1UFOxPH+r8Yn27X/LD2a8GjtL1IoBklYrPVcvP8VmBtTRUrMv3J2s8FQfo//4seI37srbJ4fqsd0jySuNB81KbT5enTPigJNikv77fcRuqfJF647XS71C7e+QMAAKAzYIQGgB4gsv6i3WV02k4d0aYwaSMDZUddON5aEimdJyJSJD8jo0jcRtm2sKadaSNQ2qiNVx+p7iO1PwMwksEITXeBERoAeoCImGllFKOdQVsdg7+ybRdd08T/82CcB9XeqEu6P88r2dRI80gjMNooBhcIUr/xRqKksiRxYtnhNrRtfHST1yUyDbOO/RkAUF+ywUalf3UFggaAHoJP38m3EcnBZpSRHrQVqb83mqHtK7ruRBJBUmCvTUfzAv2I4OG+aEJMyif5KtmxxDr3SUsn2bOmxXER2A6qmI4JAABgJRA0APQAfH1FdE2Hlw5BWGfgozE8uNbWsEhrZqRt1pqbtEy+38uv7edTwLhg0kZDvClqEtp6Gi7WuPhPR4MkQaeVp4knzS8AAGiFLGtU+ldXIGgAqDHR0YA0TZE8EZvdIHra7UMr9qKBayToT9Np062k/JZf2lQ3axqZZEua8sXLifiVI9mQhEialm+T2k0qQxrBskZsLLSRMwAAAMMHBA0AXUqZKWHW9KKiAWbRsjmtrtkpkq7dQWSr9qLrNqz1Ttq6mtRedIqXNYKXjlZYIxOSCEhHOawRHSuNNRVSqrM1cqL1h8iUs7Qumq+WWAIAgGEhq/ivpkDQAFAB7Qh4yj4djgT5XlA4XP63mrdbnoZ7613SbZZA8fJLU7UkW5oNS2hpRAQrFzaaX9ZIUL6f2+BTxzwkgaSJFC7kUvEn1V8TQwAAADoLBA0AFdCJgEd66s63a2JFmiYE4pRpryJTxqQRGG3NSRq8e2JFm7Il+SYF9jyflFerT1Rwpfui/VOyr62h4eJJyldGCAIAABg+IGgAqCnWqAAXN95TbmlKTpGyy6Tt5oCwlalFERGRIwmDIn5Jx9wTrRF/tfUqqR2+jibfJo1ycLuaLW5HE208T/pZEj6p0JPK0NrKmuJmCaZ2jXICAAAHr22W6RlB09/fT41Go/kHwHDTDQGMFejyzynW1CUrfStpu3kUqMzUImudirQ/3a6VVXR9CK+DJZi0qWvSOhNeL23Uz6uLNvoRFb28PlwgSWh+831SviIi0BJ6AAAAqqVnBM3cuXMpy7LmHwDDTTcEMNp8fyvQ1vK3k1ZGPOqANgKQIgkMKSCPThOTphhqwbQ1BYun046VN5KT50+386lv0voUacpbkbpzv63RHG1Ey9vG69gN5zoAYISSNar9qyk9I2gA6GWKTCGygup8mxWsViE6yox4dBueECwztc6a7iTZTUWCVYY0GqEF95o9b8SBTwnjoiIVbNrUMs1HT9R4dZX2a4Is90v6zz9b/vSqWAcAgDoAQQNADSgrBCIBW9GyigRuwxHkDVcg6bWLNBph5bNGzLiI0fZxwSBNB7P810aH+EiNtC5Fqrc2QmPVQxptsUZBpClxWr+WhJskEjmR0am0npodAAAAwwMEDQAdpKpgPLK+IA1UtXRFRoYkhiPI64ZAko9+5J8jedK0PPiXppWl5VhToaLTqyRfI6JM8lESROk+yZ41WsQFklR++p33Z+6PN3VM8z8yGgoAAMNBljUq/asrEDQAdJBWAiLvqbEXWGtTj6yn8VF/OjENp5NTfqxpVF6eoiNi2oiCNjoTGYHwfNKEkBb0e0KYiwtNcEkjjFIf1YQft+MJLmnanFUnDqadAQBAZ4CgAaCmtPLU2Aporek+UX86MQ2nE0/LWx3B4rasKVOpbWnameaDJB6sERdun+9Pp5JpIyua3XRbKjKsaWR5Gk3c8HpF/NDqZwlxS+RpaTgQPACAVsEIjQwEDQA9iLcGwHpqX2T9QBUBWrttVhlERsVLdDqfZ0MaedDWqVi+pfmkQF4TFVq5XJSkefnURi19noeLF080WaMu3G5ELHHBZh2noiORmKIGAADVAEEDQI2ITnvx1ht4T+Cj6we0qTtl/PbKK8twBJGW6IvCBYC03wrMpWPsjdyk2y1fo6N+1pQxa6TJEwaWwNDWxXhT2ng6SbBZdfXaHgAAKmGwUe1fTWlkPfqjLfhxTVAnvABqOOxqaS1xI+WRnsQX9WWkwUdMIvulURkp6NdGcKw00giOtb5EspNu42VqNnmdNYrks9Lm6b3tZfqulQfnAgD159lnn6VJkyYNa5mNRoMWffSQSstY+/zra/l7jhihASBA1U9cWw1utFGRMmJGCgil6UH5PmtbdArUSMEaidDEIZEuALQpW/y7JjolceTll8rURoK0tJrNdHqaZ5vXxxtZ4VPkvKlkrfZdKw/OBQAAaC8QNAAE6EQA0uo0rSI+R6YDlWWkTr/xpvWl6SzByKdx8cCc/y8yrSwyXVASut5IjTb1LBUd/HNaHp/OaPVvPgWPiyFttEnyWfJV+qy1RxTPHgAAaOClADKYcgZACSLTVHoBK8BN9/dSncvQ6nGPTk+KrCPRRAK3KeW3BJA2gmeVoU1Ts6bPRfyWhInWTmWmP5aZbmb50yq9dl0BoBfo1JSz/zvz7ZWWsc6nv48pZwD0OjyAtwLLXsCb0pQzHE+X211GO+15wb5XVpF+k47o8Kla3JaUJirGpb7Oy9ZGnKS6SSMafOqXNVol1S86uuGdp9b+MmLGyieNinlT4HrtugIAaIGsUe1fTYGgAaAAVT2BrRs8uLQC2nbR7rZuh72ooPVGA6zt2ggDt62N0kjTuSy89TK5fW1KljWq4pWvCeY0vzaKxKexSfu5X+0aYbSm80mCTZtiBwAAoBwQNACUZKQEIJHgUErf62sCItOWIiMIkTUv6X9PPGrbpOld1kiFNf2Lj9JoIztWG2liiddbG9nhvkikIz7WMSi7HsaakmedN9Yx7PXzBgDQGllW7V9dwRoaAAoQDdZ6DWlNAiiPt/6DSB9xKTIFSstnrWvJ0fyRbKf5vW1SuVKb8PpYfmntGZki6rWt1YaRfNr3iA0AQPfSqTU0T50+u9Iy1r3gulquoYGgAQAUoq7Blxcwa8HmcPlgpddGSnjalOg0N20amydEtLxa2ZE6aLY88SKlt9rNEnja/kj/KNrH+H7uJwCge4Gg6S4w5QyAgoz0KSF1Dbi8IDuS3svPpy5FRYY1NU2aIpbbtqaESf5oZUj2NT8tIZBu96Zx8Sl5+Z82DU4byUj95mnSKXFFxI1UR+6v5qNWv4jIq+u5BQAYPrLBRqV/dQUjNACAEHiC3BrRJ/Q53nSoIsdDm3rFbRcZRfGmyFl1Sbd5+yNTzMrg2Y74o40Gpe0RmZJW11FPAEYynRqhWXjaoZWWsd5nr63lCE1fpx0AoC5oU3JGCpEn83VjOINKz3bRqUqW35H1KDwIt0abeHppJMSrlyZmUn8lEcF9sfz0RJbmpyZStDpI6bV2iPSvup9HAIBhpMavVq4STDkDI5ayU8d4ENfKFLRumb5W1I9OBWDSNK5WkILWbsKbhqSNrqTfreljeRlSeXzaFk/vrWdJ82kPA9K/dJ/lv+YPbxPPliUCLSGlpdFG0LT92qhNt1wTAACgTvSMoOnv76dGo9H8A8CjaACrBZetBMLDEURHAqSoHxFbXiDdCu1aZ1DUtyqDzHbZ5iMB0uiJtY4k/W+N3kRGRCSR5I3o5MG8dIy1bdaoaZl+aIknbVQp9U1bM2ONMHn1BAAA4NMzgmbu3LmUZVnzDwCLMkFOXZ+ctjNAktYKaOV1c2BWRsx6tNo/iojFHE1gayMS0kgIt8NHCbTpV54fafnaSFJeFh9V4fkkoRURM5ZAkEaOJL8lcZL6Jdmwppl5bRXdDgAYmWRZo9K/utIzggaAKEWf4kpTa9rlRx3RguKRTpH+YU198tJba0i0ERZvWpa1BiRFG1nRtkl9RQr2+X5tClpE0EnlROrG/bXKSO2m372RGO16Y11fuvnBAAAAdAt4yxkYcRQJcPL01hNXYNMtbdaqH1ws8Kf27ZwKZwXRXjlFbGijGpZwsEY1uF3+3/LRSmvVWRrF8dBsajaktpHqmqYpI6gAAPWhU285e+LUwyotY4OLr6nlTCeM0IARR/pkOic63UMLdDBS0RmKtHsRAevZiE4jkuyWWUOhCQArXTqdK9/n9ee0ftL6EG36VVpmus+afqiN8GmjS1p9+WiKNqXNqjef9pZuT9uDlyPZ5cfZOp64bgAAQHuAoAEjFi0QsoIUzUbZ6UadxAqI20k7Ry6qsB21WbQsLYgu43OZURMuSNJtPJ8k7jXBHxklkoSZJH60qV3aFDmtDVKfpZERTYBodjjcvibEeP2LTn/rlmsDAKCLyRrV/tUUCBoworCCmhxt2kw0v0ergXG70J6ae3Qi6NJGxoaLMse9XWLLe9KvTefinzWRIO23Ri+lUR1plCYdHeGjH9o5VeTcTP+nIk6rsyWyJP/SNHz6nERqh6eJChtMSwMAgHJA0ICeIRJwek9ipbQ8YGvH1KUidFuQ0w3+eGsqqiivqnpHg/jIPi5KtODamqqVjuzwaWOSYJBGbLhAkPzidbf81USU1zbW9DNvSlxaZ56Gb48cQ0tcAgAAaA0IGlBLWh0p4U+HtSeq1pPqaBmgPPw4WMdAGiWQ7HRihEnzp0h+a0qklU8SD9IULGnERBqZsERMvl8aidH8jYgKXoaVThsx4uVoo6/aNl7XIgJMqh+mlwEAypANNir9qyt4yxkY0WiBW5FRmCrSgqF4x0aadlV1W5cpI+p/mbLK1lkSLNE8VdOOcrhAKVM/qW00u9YxtmwAAOpFp95y9s8PvbvSMja89Dt4yxkAVSJNH2kVaeoIXwcglZ/mL1IW8JFGI6JP8YsG5e0Y5WtHHm/aUkTMpP+1fdpIlVa2NSrUyqhTJK0mKqzrQJFz1GtvbQQr9Y2P1FjHQZq2iGsCAKAoWVbtX12BoAG1QZvD3i6kKTlFn1x3ml6YxlL11Jw0CI0KheEgshaDSBbZ6boXnlYa2eJrZLS28NbI8LSan5JdKY0nrKRyLB+k/dJ0Me9hiXZs+AMRqS2tduiF8xUAALoBCBpQC6wnn0XyR9OmAZsWfHUj3S66vDbkgWUaeEdsWGmlNR4erbRnKyMW1uiRJLK9srwRLp7OExCRdrF88+omTeGSRIW23RthtaaYap8lvyX72uiiVl8AAACtgzU0oOuJPiFup03Mbe8sZdcbSAFvt1Kmj1XR78va5udLTivrTIr4VdZG2re4qOEjN2k6nie1zeuO6wcAvU+n1tAsOPHwSsuY+vlvYw0NAO3GepLbClZQ145gJDqiMNLR1ncQDX1aLwWakp08X/q/qC+af+2kbB+Lrgex/Lba3IKPGvHRlHS0RBKkWrAfHV2SypfqZdng3y1Rw6eT8XqnZUoiThtlw/UAAADaDwQN6HqiawuKYAmldjxZtaatgH/Bn3hHpulwioqZyFQuyV6rwak3jasMXPzl27R2kNZ8RH2Szg1tm2enSHpNNHh2o+Xn9iShov1P86R2LeFdpm8DAMAqZI1q/2oKBA3oaso+bedYU5EiT6q9wBVPXVtDCwA1gcnFQaR/aAFl9Kl/2bUkZdJaSMIltV9k9CPNW6YPSyND1hQx7/ySRja1OrYyCuq1Yfpd6wPeaFnaL4uOSgEAACgGBA3oWtp505cCVR4w8cBYm0cv+YmnrqsSEYE8+OMjNVrQ2s7pWpG+IflQtn+22q+LirfotDJpPYyXLzJiE92njXhE0HzXtlkPSrSRq9Qf/lmzr/mH6wUAoCxZ1qj0r650vaD529/+Rvvvvz/94Q9/6LQrYJip8qavBS3WU+RoQFxnqhSRvAxplIxPl9LEZhl/iwTqWv52TCVspb9ERZ02FYrb0YJ9rZzoWpuiaA8FLFFTdMSMn9tSn+PbJR8lUZPv18q19gMAAGidrhY0g4ODdMkll9Cmm27aaVdAF2KJjzJ5ywSsZae6dCtVi7M0KNSmAUpptMCzlSlf2uiFJp6GW7hG1okUHZHg34uKdE8weGmkPOlxbNf5IQmy/LhKx5r3A88fSRil+aQ00amRAAAAitPVguY//uM/aObMmbT++ut32hXQZZQJELygVQvyiky58coeyQGNN7ogHVNpdMGzXYZOHxdPhGjiXRrBkLZFR1ai7RgRTREb1khpmqYo2siO1AdTAe2toZE+p9Ml+QgjhAwAoN1kg41K/+pK1wqaxx9/nO699146+OCDO+0K6ELauYYixRo5aIWRHNBIT72tgFgTf9b3Mu2rTcPSvkv5q0BrG2/9Cd9mfdeIjpRERmqiZUl5pIcL0dEoy37ajpoQ9PJJ37VpaAAAAIaHrhQ0K1asoEsvvZROOeUU9wcy+/v7qdForPIHeovhDh6rLnskBT38STgPVr2n9N5anKJIaymkcjwRUJVILSMW2iEwovaqzFtUVBYVal55PI8kfqTpkDwfRmQBAJWB1zaLdKWgeeSRR+jpp5+m8847j0466SS6//776eKLLxZfDDB37lzKsmyVP1Bv+MLcdgYG1tSnoguTyzISAp3IE3Ur0LRGdKJTkXjQKT1xr8vaGCldBGvalLYtOkUtsi1iRzsWVR4bbyqYtG5LspGmz/+XeSgykh5yAABAu+lKQfPKV76SvvGNb9Bll11Gl112Gb3qVa+iU089lbbbbrtOuwaGkeF6Aq49aW2XL+0K/IrY7wak4JRP6/ECd20Ngjd9TfMh39bJNouMprTa9y3haE1d45/LtK3ni+ZbdDTGE2dRuGDxPqf/Jb8iaSxGwkMOAACoiq4UNAAM59PzqsuKBn6t2NdGMbqJNID0FlrnpGkja0u06WT5524YjbHg9S17DIvWU1ur0g7xFx1JKzN9rGg9pdFZaY2Xlk9bi+NdQ7rtXAQA1Bf8Do1MI+vR+VlYR9MbWIGONrWjSJBj2dBsVTU/vhMB93CiBY7SqA0PsL120RZzp/TS2oZ29pW69jtrape2YF9Lw21q/7Vyy/rbij0AQGd59tlnadKkScNaZqPRoL9+YE6lZWx65dfdpRvPPPMMXXDBBbRo0SLq6+uj008/naZNm7ZKuhtuuIFuuOEGWm211eiAAw6o9EVfGKEBXU0rT5qrKtube59T5ul8r8HXFXBRoT3Z5vki05G86VF8+lq3jGiVKbfVkQBrvUc7aHUqWPpZG6mSjqM0Yse3Rc5fafpYZFTRG23UygIAgChZVu1fhCuvvJJmzpxJX/rSl2jOnDl0+eWXr5Lmr3/9K/34xz+mK664gi6//HK66667aNmyZW1ujX8BQQO6Ci2YkYKadqxriUwHa2Xqz0iHry9ItxHZ08i0qTyRdTc8D/dBCuQ1UVU10TUkRWxF0w3nNMsiefkxtI6NVVbRKXO8bKuveaN97ZiuBwAA3chvf/tb2meffYiIaOedd6bHHnuMli5dOiTN7bffTvvttx+NGzeOxo0bR5/+9Kdp7NixlfkEQQO6CulJKg9q2vX0N5rGW6ehUXZaivW9LkjCVHrabY2WlH2izUeCpPIixyYyza0o3ihSVbS7X1nrTMrk5XjrZIq0VdlRXq/P5H0sIq7wUAQA0C46vYZmxYoVNDAwQOPGjWtumzJlCi1atGhIuieeeIJeeukl6u/vpw996EN00003tb0tUiBoQNfiTTuyRm80IqMv0YC5CrxRg7rBp/xoU3jaXW9rfUURGxZlfGxHvcogTcNqJcBuRWRU3aetaV9WGmvdFRdY3uhMKuLzdHU/lwEAvce8u++hzb7ytSF/EfgamyzLaNSooZJi+fLl9I9//IM+/vGP07nnnkvXXHMNPf74423znQNBA7oWKQhLt6cjONKUJCuYkZ4At/uJe52oqh5p26ZBXmQKmYU14iAF7GXKHI4ANBJ8p1g+Wfkl8diO+lXd/1tZX1R0lI/3VclGdCQRAgYAUAdOee1r6M/HvG/In0dfXx+NGTOGlixZQkREg4ODtHjxYlprrbWGpFtnnXXoVa96FY0aNYrWWGMN2m677SBoAEind6TBhjXCIgUUZZ62RvyKUmbK23BQVfDlrZspO5Jijehoxz267mI48fwua6vIvnaVWUW/9fpGq1P4rDVcWrpUMHtip1ceeAAAuodssFHpX4Rdd92Vbr31ViIiuuOOO2ibbbahsWPH0tNPP92cejZz5kz61a9+RQMDA7Rs2TJ66KGHaLPNNquqWSBoQHeiBRrp/jJPQSUBok1nk3zy/IqU3440dYOLiVbWyHj2pe/tWH/VCpGpT3WnlX4bbZ92T020RnelPuOJRmtUkNsCAIC6cuSRR9Ltt99ORx99NH3ve9+j448/noiIbrzxRrruuuuIiGj77benV7/61XTiiSfSiSeeSPvvvz9tscUWlfmE36EBXYslPKTgIWozMiVFSq/5JwUtkdGh6L5uh0/x0gLBMkFitGzNl5S6tm+7qbqvtWp/uPJro4NlrgneqA36HgC9R6d+h+bx9x1daRmbf+2r7u/QdCMQNKCr0Z5oSqKkbCAkBeKtCo86CxSLqMArOm2sXYFftL/U+fjU2fcyDEd9vTKs68xIOx4AgJVA0HQXmHIGugYrGJUW/2s2+NQwad2NZD/fb00T8dYM9PoTWT7CwqfoeCNnvK153pToGgRpmk9qT1qnoo2gdYKia7Dq1reKtqs3zazVMqyF/bxfSz5Ifdqb8mhdfwAAALQORmhAV2NN5SgyilJk8XDZURg+QtGNgWfZaTZpWqL2r0mJPv0uMiVIGjFq5zEp+6S+W/vGcNENIy5WHmvKKJH9quaRfFwBGGl0aoTmsTnHVFrGFl//Si1HaCBoQG3R1m6k+6zveZ4iQX7VQXJZOh1MWWtXcqT9Vfhs9Ytup8rj2Ok+0irtOJ5FppZF8wAARiYQNN0FppyBrkeb2hURI/kUjzTA5Xm8QDudJsKnMnVLoONNhRsOrKlmfL+WRkqvTQOK5M2P7XC3SbS84RB4w2G7ne1rne9lRl1SiuS3ppQCAECnyLJGpX91BYIGdD3eIvTIPknURPJEp1h1U6DTrsC1lTpFAkm+zonD17tEXjaQBr48/3AKm6jAbPf6kAjtsucJ2LI+SLbK9umi+aTzPBXFkT4IAABg+IGgAbWEB7nSFCNN1PC80tNfvk2aatZKQFcHtKA8b7P0j6f31jAVWXsitbMWVGp2pGM5XJQR5JH8ZWnXCxFaETFRwTecx4r7Jy3+j+Yvsx8AACJghEYGa2hAreDTyYjkdTCR4EFbiO6Vl+a10pfZ361EFsBLokUbedFESuSYtOJ3N1B00Xk31qEb0KadWu2LdgQAtItOraF59IgPVFrG9KuvrOUamp4RNP39/XT22Wd32g3QAbSARhpV4VhBuSaQRmpQZL0QwRMzkgjltqt8QUCv0uv16wSt9lEcEwBGBhA03UXPTDmbO3cuZVnW/AMjB+vpbHTdRfpf26/lKbp4uA5TT/h0MiL5hQjW2pf0u9W2ZQNA6bh5a3fq0PZFQODcPlIh463j4X2vyLRIAABoiaziv5rSMyM0HEw5Gzloi3WlUYLIyIK0PWK33dOluoWia1ykJ9zS9EBt7VORslvxG9SHKo5lkemh0Wlt7fYBANC9dGyE5r0Vj9B8s54jNBA0oKeIPiktMyWt6Jz9XseahieJFm0aGrc3EtuSaOT2I4uq1qRp5/JwgOMMQG/QKUHzyOEfrLSMrb79pVoKmp6ZcgZGJny6UT5dxBstsaYp8XTe/shbo3pt+ok0KpO2lfWCBj6lJ5121uobwby03XwcEOSuitcmZad3pVMni2BdN6I+tKOPAwAAGAoEDagt2pQQHly3GihIIw2p3cg8+uiUrU4Saa/IyxFSO2ngaD2Z5sdO22/5I6W11tR0S7uPJKpq8+ESg3x9Xjt9gKAFAETAa5tlIGhA11D2KStfoM9Habz8/LNmn29Ly9GC9UgA3W2BjDdVL0cSebw9ikwxk8Ri1FfJbpG61Ik6C7Fua/OioyxccNf5WAAAQC8BQQO6hrLBjiRevKlieRr+2RtRkGxGA/FuC+Y41hNnK4hLhUsqKlspv5WRFE9Y1oleEmKdosg5GXkI4j0IAQAAMPzgpQCg9liCwlrwH6XIqEEvBTjRtzxp7e9Nu9OOVbvfdgYAAAC0m069FOBP7zqu0jK2/u4X8VIAADoNf3rqBcLaE9roG7iii4vrPDVFakPtKTWfhpdu19azRNbHFBEzdW7rKCOhjgAAAEAUCBpQe6S3bBHZi/V5GitPul5GG6mQtnMfNbotOJWm3/F9Vj6p7axRHOnlAfxzEd9beXPVcNGuUcORAu9TVR2zoutj2uUH1uUAAKLgpQAymHIGuh5relNERBRZYC7Z96ZUab5K3+uIJtoiedLvFtoxtWwCUISyYhd9DgAg0akpZw8ddnylZbzimi9gyhkAVdDKW7CKipk8jxaQe6MW3rqRdvg63ESn1RHJ9ZbaJ52aFl1kjcCy+9BektFu2+2wWaT/FslTxB4AwwW/nwHQ60DQgNqgjYjwC7a0hkPLr5VRVjilvhQNyOoWsFuvvLXezhVZM6O1I/gX3dAuVb5RrhNvEysjelq1B8Bw0A3XC9AeMOVMBoIG1ApJpERe2RxZtM9HFdLRBq2MdH1Nmrbo62C7iejagTJT6yQRxNfMSG3aTt+7iVb8rVOfilCXY8cfdtTFbzDy4NdSAHoZrKEBtcKaClY0sEjXhWh5rZEea3E7n3rV63PxpfppddbSEunrmIAO2ggAAIafTq2hefAdJ1Zaxiu/9/larqGBoAG1xBIvWhCtpdVEjfTqYc+nImnqEIh6i/SJ5DeT8W1FypPyFG1bAAAAK8G1sRogaLoLCBpQO6yXA0gixPvObWplpvmtfGXeCjbcFPUt8vYxvEUKAAC6G1xz20enBM0Dh55UaRnbXHtZLQUN1tCAWiK9CCBFEzzeW7j4Swb4mg4+bz5d95GWnQb5Vb4JqixFb2iRaXlFXtNc1o9OtxsYGaCfgV7Fui8BUGcgaECt4IGzthbDCr4l8eOtwdFeEOA97dL21fEJmVdPb3peOxZR17HdQP1APwMAgHoBQQNqgRYEFwmSpRGE9L/02yjpf6ksbVSina+c7bYnaWX90cSnZ9Mrr9vaBwwPOO4AlAOCvd5kg9X+1RWsoQFdj7UGJt3O0/J93J71Bq7omhpeTiR/L89h7uW6AZCDfg4A6NQamj++vdo1NNt+H2toOkp/fz81Go3mH6gf0WlimhDh08Iib8Xy1oV4b1OTRnS0lxZYb/DqNN3gA5H8Q6nRfAAMFxAzAIBOgR/WlOkZQTN37lzKsqz5B+qH93pgKw8XFNp0Ji4qUuHBXwQg+aS9ZCAy3azb1tMUeSV1FEvI8c/S96gY1crtZSDaAAAAAJmeETSgd5HER/R1y9oCfimQ5um9aWx8/U2ZgLOTQWqVIiAq6sr6MBKD+5Eg2gAAAIAyQNCAWiK9SjlHe8KfipY0jZXfequaNs2sCEXX6dSJsm81K/qCh16n7v0AAABA+8CUMxkIGtD18ClLXMhIIzgp1kiLNO2qyFQsaWRHs1+UugftZYVe3evdbtAeAIAqwUMT0AvgLWegZ9CETL4vMhpSJI+Wpl1rU+r0JqU6+QoAAAC0Sqfecnb/QadUWsarbphXy7XoGKEBtYEv2ucL+YnkERPtV+y58Ij+uGaKtdDfGhmKoP3GTbfhvaJa+hy1WyYfAAAAAEYWEDSgNkhvNOOfI0Fw5M1lUpBuCQxrVKYdIxfdPPoRfSlDWbvdXHfQO3hTUgEAoBvAGhoZCBrQlUijL+ln68cytd+tsaak5fu1ER6rfG2Eoq7BUNGRkaKjWsPBcJVb12PcK7Sj/fk5XcXrzAEAAFQL1tCA2lJm3YYliFp5W9lIo2g7aWuNiq5zauexwbEGAABQlk6tobnvbadWWsarf3gx1tAA0I3wH9qURl/4K5iL2h8JtPLk2nqNdtRmu8VHHcXMSOlrAAAAFLJGtX81BYIG1BZJfHDxkqfjoy/aSI20zQsie/m3ZFLKCADpeIDy1FGEAQAAAFUDQQNqh7cgX1vPkosa61XL2todq3yLkRaARn/ktIqyAAAAgF4HLwWQgaABtYMv+I8EzNpif+tHO60XBPT6YveyDNerprH+BQAAAAA5EDQK3R44jiRaORZFpjxFfjdGeota1I92UKcgvso3v9WpHQAAAABQLSNK0Eg/yKiBgKl7iAbG0o9tSuJDeqVzhFaC8Tq81nk4/MF51Xm6rd8BAACIkw02Kv2rKyPmtc3aTRwBVn1pZdoRf01z0Td4YcoTAAAAMHLp1Gub75l1WqVlvOY/P4vXNncz0q/L5+BNTPXEExTSD2JKr2muouxeA+cHqAL0KwAAKAZeCiAzYgRNivbL77i59hbWK5mlt3FpeXqVIm9x60S7dPoFDKB6RtL5BgAAoDpGhKCJ/OaI9Spg0N1Yx0o69tIv1EemkFkjPnVEq2/k5QjDwUgUmQAAAIBFllX7V1d6WtBIgaq3lsYSP6B7KPur9ZJw4dMQNXErvUyg6CudOy0SUrr1xRjd1EZlqLv/AAAAQN3oaUGTihS+TibdZwWnoDuJBtvegv/Ij2gWKcvzrZuEcjf4UmR6W13OyW5oVwAAAGAk0TOCpr+/nxqNRvMvhweZ6dP4SODZbUFUt/lThG7wXVo7w0VL2dc6d4JWf6On0xRp324/Ft1ENxxbAAAA7QcvBZCp7LXNS5cupXHjxlVhOoT22mYpgI2+0hmv6m0v0XUr7Wrz1JZndyQf616uey/XDXQX6GsA9Dadem3z/+x3eqVl7PSTC0bWa5tvvPFG+tOf/kTLly8X98+ePbu0U1VgjcxYLwsgwtPOqojc7Fv5nZmorW55q1c7afePgLZKt5xDVR3Xbqkf6B7qfg0BAHQnGKGR6Sub8Stf+Qq9/PLLtGLFCtp0001pq622ounTp9P06dNpyy23pMHBwXb62Ra8Bf/pegu89ay9DPfTSm8kxptWVvenq93me5VCohvq2g0+AAAAACOV0iM0Rx11FL3lLW+hb3zjG3TkkUfSeuutR3fffTedd955dNBBB9HLL7/cTj8Ls+OOO4rbywSv3fL62DqLqk499bdGZSzR2uljDWK0+zjV+RwDAAAwfDz33HPDXuZee+1VeRk7rrlJ5WVUQek1NIODg3TqqafSCSecQDNmzBiy75lnnqF3vetddPPNN7fFyTLwNTQpuYCR1tJYP7xorcMBvUf0GKMvDD/WuQtABPQZAEArdGoNzW/ffGalZbzuZ58eWWtoRo0aRfPmzaOpU6eusm/KlCmmoOgGuHhJRQ7Rv9bc5H/WzU96qluX3yIBMmVe1wyGj8g6uF4B14tq6OU+AwAAI42WXtvcaDRowoQJ4r6DDjqoFdOVYq2p4L9bwsUOn34WWaMh7UOQsipemwxHm9Xph1XRh0YGdeiLAAAAhge8FECmst+h+cAHPlCV6bagCZJUvHBhk5IKHWl/pPyieXopgC3zZrEigV3ZtqpT8FgnX6O02setc7bddOJ87KVrAAAAANAueuaHNcuivSRAQxM6ZYLLoqKmqK/dTNXBeB1ePVzXYzcctEOQSr8jJf0v61cnBGUvilgAAABxMEIjM+IFTU4qVLQpZPy3bNLvqZ1IWandIj5KftWZbgnqW3ljWlnqfuyqoMgbBcuMiqZ5y4yu4pgBAAAA3QcETQIPcPhLAfg0Mw1vhCean48ESVPh6k7ZKXtV+QGqo93HWPrx26gw9X6HKGoLAAAAAJ0Hgub/YQW02u+VaNu9AEkTLJov/GUEvRh8F61TlcEmAtl/UXTE0aLKflvmTYTS/k5PKQMAAAAsssFGpX91BYKGwd92JokUSXDw36jJt6f7rek00Sk23KaUZiRQdXAMVhIZTRyu9vL6tjTaF13jJp2bI+VcAgAAAOoOBI2AtE5GepVz/jmFv+I5/8+3peVwpB/wTO2kfnJhE5nCpe3r9QCuF+rXC3UoQ/TtZVxgWdPSPJtFX9oxUo8NAACA4QMvBZCBoBHQxAuRP+VLGqFJP2vbpBEXaVSHiyHNF89HXg7f3ovBWS+MvLSzDnU5xtoCfm8khtePnzP84YIncLz1a71ybOrSLwAAAICcRpZlWaedqIJGoz0qkwdKWgAkBVKWcIjO1Zem9EgjONG69EJQD+pNO/qhJHK0t5il27R0UZ+Ge4rdSD9f0QYAgG7l2WefpUmTJg1rmY1Gg/57r49XWsbut32K6igNMEIjII3IpOtgpACIj7JoT3z5zdn6rQwtKOOvj+6GRdkR8OQXEJXrh97ISCputCA4OrKa2onaiE5jK0IVr67uZoq0NwAAAJACQSPgiQht/Yxmhz9N1uyV8U3bVhVl1ubkdNObzDpZbl2C0OF46US0DD6VU3uFOZ8SGlljxh9CSFM/PR+lqaHD0d97KeDvpboAAAAYXjDlLIgWoEQX5Fvz9Xn+Mj60IxiocnoHpo60n1bbtJuOiTW1M/9eRiBo+a0paXm52vduaTMAAACdo1NTzn695ycqLWOP2z9ZyylnEDQF0RYoa28x0566esJEm9vPy7DsRetRRXoQx1pzNdLbvBVRQiS/KdBrV0u4tENotZNOlw8AACMVCJruomemnPX391Oj0Wj+VYU0LUWaDpN/TtNI01L42hseMGkBXG6PT7FJ02pT48pOGyqary5TqzpFeqyl6YxlA9Vou7fj+LTzGFujlto0M2nNGT8npHNJKpfb0PzhtqNTUK16lgViBgAARhZ4bbNMzwiauXPnUpZlzb+qkJ7eSoJBenKszdvPkeb/a2VYvnFb0osI0uAwYtsK8qw83UynBZe1HsrqX60gTZFspZxWj7G3LoaPdvJ+yAW+ZI+Xw21r9fFeFCD54LUhRlQAAACA9oMpZ21Cm36W7tOmpXEbWqDpPWHW1h+AkU271l5F0kfKikwd89JZUy810WZNOfOmdkb3aXUoUma3Uxc/AQCgSjo15exXb5hbaRmv/2U/ppyNNKTAiT8V1p4IawGB9JTaSi/5JI30FB3tKZKuqvy9iNUmrUzps0barL7WbryyJCHCRwvTqV850rSwPC8/97T+b41+akLIeuAgTRXU6m2Nxmnfu/X8gZgBAIDOgSlnMhA0LRB5qqwFRlIwqk1Tk6a+cD+8ICP6RJnnkcqziIi2kUxkhEP6HLFbdEpgUdp5PLlw5yJBmi7G90miKIePjGijpbzsNA3/nH4v+8DBssHB+QMAAADEgKBpMzxQ8oIXnlYLlKy5/VL61L4XsLUzsEIQpiO1syUAtSf2ljDVRHZRvyL5iqKJ/YjQtqZmccFj9XnpPNLOOWuanPbwwfrM8+JcAQAAUJQsq/avrkDQtBH+RFh7Asw/p9v4FBoi+YmutM9KZ/lcZNQAlMcSILyvWMdSC8R5eqvcyP5IwO1NZ9SmkPEypHyScLfqp7UHFyY8rVUOP5+ltucPJfg5bLVjq2IS5yYAAAAAQdN2pKfs3vQUKSCzRmSsuf7aiJA0vYfv5+nK0I4Aa7hGDDqF9tQ+IkTTAD06QhPxp2x6yQ+vHtqUScsPPgrD9/FRGetckuCjqVrbam0fFX9SfTR/ImCUBwAARhZYQyMDQdNmvGAufcKrBXvaU2RtCpsU0BUNdvlUHa9+2hNtK7iVtnlTfiL+R0cqqqBMOUWCWClwbjWI9drbExaWSPHqUHS6Y55f8pH3d2+UR5qalvrBBZE18iOVIY0qaSNU2kOKiKjrFWEPAAAAtAu8trlCtGkt2hz9NJ33ZFub2+/5I/lRpD4RwRMRRzydVecqn0IXtV/WHymfJn7LtrOVTzv2PE/0uLUzvdUvi/Zvnj9ablTI5/bL9psixzb6HQAAwPDTqdc237Z7f6Vl7PXfc2v52mYImjZijVJEhIsmWPh37QmyFwRJ5VYV0JcVTe220Q7aGUB6gkLaFknTTp/blTci4qICRxN/Ujna6IpkJyI0PLFtnWtWOgAAAPWlU4Lmv3b7ZKVl/NtvPlFLQYMpZ23Emg6UTq/hU1sidq3pKVq5km1up8iT6dSXtD7S1KEiU6O0qTTtmF4VxToO0bb17OftLQXrUTFr+ZSW1Uq7peVpx0b7bgkYTXhI/Zm3Fc+X/mkPC6T65N8lMcOPg1X/1C6vs3SMNbR2LNq/AAAAgJEMBE2FSAIm354GY5IgiIgfaZsUlKXppafGUrkRUt8scebZ1ALDonasdFEfomVYwtVLLwWtZUZptLIlAWzVyxKUHtrooJY/KnykbWkeqc9oIyJpXik9t8+FEhfWXh/RxCrvY9IxjZ4LXtkjQRCNhDoCAAAHLwWQgaAZBnhglf7l8EBGenKdb5cCLCk41oIi6Qk1z6sJpch27Ym798TbQ2sTKZ23LSI8JLzyvcCXjxJE/Y0IAN6fpLKselh1L9IumshIfZFEBhcQkjDj7SYJN008ag8PPJ8ifU4bMZJGu6R24va9hwNe/2vXwwGPToqKVkYhAQAA9BZYQzMMRJ/Ea+m0p/Na3jy/95RfeoIt2dL84f5q3yPBlfZEPZKv7P6yFLHrjVZYIz1SYBy10YptrT9ox9TqZ9aojxX8F6lvtB9EzxdpRMUbbdJGYYrWz6pvWRsAAADaT6fW0Pxi109VWsbed34ca2iAjvTEOd+ePtlNn0BLAROfwqI98efBmRTMWaNAko/Sd2mkSXpCL8GDPi/o5HgirAhl8ltP0b3REevpfZ5P+hxFakNehtXW/HhLfZfbkPJrIwaaMJH6Yzr6oZUhjYBI5UiCTOrvEeFg9XVvX5H+FknbyZESAAAAwwemnMlA0AwDVjBT9HMaXHKRE3lSLT3l5UEt367ZsJ6ip/VO/fN8jQR+WmBuiQKPMmm9QNcKwDXRWDQwbcVvra25ACgTLEt9SkMSP5o44H5y8cjLT+vgCRetHpE6aOe1JtA8MS49QPBEWC8AYQYAAKAMEDQVE3kqro3M8DSS4OB/OVr6FCto5HakoCr1UQvgpJEf6cm8ZFPb32ow5wVNkeC7SDovjSeQIhQRHlaA7gXtkVE+awTPOh/y9JrPkmhI+z4fyZH2a2KNi1BNKKX7NLGS5okKbakN0/xS/SSfWqEbxESvCDMAAKgKjNDIQNAMIzwo854Qa6MgnqhIy5JsaIGRJUyk8q2AXHviL9VBGnGSntZHR2s8vKDJ2h8JuNK2L+qfN8Km2ZCEgzcK5vU9rb210SUrDS9PG/nggkITkJLwsOqkPUjgNni/k/pkut/Lwx8mpPWSzkFLAFkPPbx+2Y7zAgAAAOhWuvalAAsWLKCLLrqIXnjhBWo0GnTCCSfQ9ttvH87fyZcCRAIMnj5He5LM0dJJ4kULTqXRFym9FohqnzUkO9pojTci1I7gq4id4SpTapsy7VC0bjlemZJ965hFffdspP5J/dDzjWONNGl+WXi+WPX10kp+WnWWvkv+trIfAABGOp16KcAtO1c7mv6m330MLwVoJ5dccgnNmjWLvvSlL9EHP/hBuvTSSzvtUhgryJSeSmvTU6SgR3oSz8tIP0dGRyQfrKf93AcpjzQ6JPkmPWG3AkBP7EWeREvtUCRtpAwrkLby8+PBP3tleQE4T5uOIEhBsjYqwgWAJ0ClMnkZlqC2+iwvSxJl0oiKda5IIkobVZF81IQ+bzPeBhEhIZ03WhuUpdvETJHzuq6MhDoCAEBVdK2gOfPMM2mvvfYiIqL111+flixZ0lmH2oAV+BUJCKV8PICyhFPEz9Rfz641csMD7DSA1uog+SoFsVpgWSYwsPJIbe5hpdGevkf9scrQRIkUAGujGFrQzQWHJDwigb+HNcKS9iuvjTShLQk5rX/xc4G3ifSf+8p9kY6DJLa4yLEebEifeylA7jaBVQVF6thLxxYAANpB1wqatdZai0aNWuned7/7XTrggAPEdP39/dRoNFb562a0G1eRYEpDe7KtBaPSn+QXFybSk2erXtaT/tRXL0hMt1lPxYvSigDxxJBVF63sMqNH3siB1n94u3ttqh0jTahLQbzXFnwkR6unJAAs+9wHT/xr54T3oMB7eJHaiNRVO38sivRp0FmKChQcPwBGLngpgEzXChoiosHBQbr44otpcHCQ3vGOd4hp5s6dS1mWrfLXDRQNsPJ01igEH43R/ktBuDZ6oQmmyOiON3Ii7bdGo6x8fF80sGvH00xv9EgLqLWn70V89MSdJjrSP01Uau0siUsNfjwt0avl10Qzr4vVL3g5mhjg/d0anZHaVCtP6gdSv+ffNcFsCdD0v3ec8DS/+2lFoOD4AgBAFwuawcFB+tSnPkVrrLEGfeQjH2mO1tQJbUoIkS5UpJEUCylYk0ZbNLHDgzDriXDqp1W+VC8e9GnlFkETdNw3TchZ8PTecUjbNW0HTUBGtnFbkXxpP5ICdk84SUE3P1Zav7aOpdTveD7NjhbYc/v8s5ZP6sOWWORiyxpNkY67dm5px9YTa5K/2oOQyDWE5+kEEGPlwWgNACMLjNDIdK1KuOaaa2idddahY445ptOutBUpKMy/a0+4tSe+UkCkbUsDHCtQ4/5we1pQzUUY/6w9CZfythrceIF3BE+A5tuk4LJIcMpteX54PmkCJ92vBb/5Z0l0av2Ol+2JWh70a/XjAjG1r9nWxIR0rvF06Xfeb3l/0kQ5ty/ZtsSZ1B9SX3jbaH0y9VGybeXpFFY/0M6NbqMOPlrU3X8AwMima1/bfMghh9Aaa6xBo0ePbm679NJLafz48aH83b6OhsMDSWt7NIDSglApvyYmrCfpUtmS+JLK5vu0QLfIE2aOFlxXHbhFyoiKhDL7rfaThCy3k1K0/bSRDKl/eced+6GdIzy9lVbzQ6uj1z95naX2sLZH+39kP6+P1U7tOAeG41zqNGXO5U74UOfyAKgrnXpt8093/HSlZez7+zO7ZulGEbpW0LRKHQRNJHCPBE+aYPDEkBf0Wn5zH6z6WcLCC85SX9sZoJW9aRcVHtrxyinS7pI9q99w+9b2yD6vD6VoYsLyPSqGLOHtCeboeSaVK5UnEREkkg2r/laZljiL+GhtqwoEzUNBewBQPzolaH7y2s9UWsZ+d59RS0HTtVPORgLaE9c8QEkDFSto4Xak/VpgbZXN/eDpU/J96R8PBqU6pLbSbfyzVm7ZIEALVLV68fIiefP0WmBaVMxI9iRBoPUDXiavh5Yn35f6r/WzNK1UbynQtwSs5j/3gZfhtXX+n5cv9XmvzJTotrTulgjj9ZbsSnXUyrXsFTmXeJtFz4doWUXtdQNlfbbyaddPAAAAQ4Gg6QJ4UC8F+dp3SfxI+9MAKg3MvCf9qR0eyEkBoRSke4GWFVBKvmkCq4obviUMIk/upYA1/czFnxfASG2n5bHEpJXOKlcSvGkaPhoilZfWn+eV/NHa1Bq5SMu0RJOWnp8fngD2jpVUN37+SW1knZfScdQEUlGsc1Qqu92jC5HRqG4j+pBEymcdY49ubhMAQPvJBhuV/tUVCJouIBLESwGaFBzxgMYaTUnL1G7GfL/2NFgSZdoTeCkvFyfa02opWNbaSMIL2LV0EfEi2eTHQDrGfARAsy89ubd8sgJaqxzJV6ssrd/y46r1Bctn61jzMqN9WupvqS/euSa1jdYP0/SW0ND6iden+fkinfPegwqJyIMUSbyC8kTPt+j2iM1oGgAA6HawhmYYsJ6yWnkktCfgUt5IwGE9SffKsJ7Ga3k9cZPaLkKZPJoNbVSgiH3LFk9HZI9Y8O+R4+vVQ/PLeuou5dW2cXh6qVwvvzVK4Ik/rY9K5UvtoYkQrbyI/9yudh5btqzzTOovXl0j1xLJfqvn3kjFO3ZaHushRLuugwAAm06tobnp1RdWWsb+932klmtoIGhqQOQGpgUtWvCZp9UEjLTNCgqLBM/RgCoiCqw8aXuUIXJjj978LaFg2bLqEW2fPH/0GGmCqUiAbNko+kTY6kOeX6kNyS+vXF6/IgKKE2knq/5aPSLllKHo+diOc24kUObhSE7kIUnZMsoeNwggMFKBoOkuMOWsw0wc60+B4kFFmiffx28o0g0mTSvly29M6Z9XDvc19S/Nw+sYCWqtIJynseqokeezAsmIjx68XSO20nb32i/fz/+s/Klf6f/U17R9NB+0fVpduV+83jwft53Wjf9Pg32pnNQm/y9tk9qsiODkaazPqR/cH+kckESbJegkf7z+L9m1gtfIOVcUy7eiwrhsOe2yx6+L0j7pHEvb1ROTOUWPQyvHDWIGANANQNB0GC0IkAInKUjj2/lNkafTAiDJphZEW+JGCsqtwMkSJ1GiwkXLF70hR3zTApJofu6bFjCnokMSovxYWsEmbwdNZFv+pPul4231Ja3/RgRPxC+r/0nnjFYvzQbf7rUz3+aJNi4kpDbShKXlRyQwtuqjpW8n0QcAVZbTLnteXfg5nW/Xzp2ibV/1sQIADA9Z1qj0r65A0HQZVvCt3fDyfFbgGg3epcBQe2qtCScreOb7tfpwuzx/0afM7cCqk5bGehpr2ZICmlRoaiMQUhruY/5dElrW6AUn7Y9aMC35yQWXllY7xlrduV/eiIIkBtP0Vr21p+2S30VGTKzzRztPJNEj5bP6g1ZPb1tZIVDV+RoVpsNVJt8XeRDgIV07LTHrPYTwgBACANQBCJouw3saagkOKW3kyXGaRxImVqCa27WCN23UoIhvHO6XFyDzfRqRJ/B8uyS6pP+eSNCCaekYaGVqoxlSACx9toJjSVR7Yij9zwN3jjZaYaEJM+0Jt5VWsm2Vb/nKP1vfrZESrT/wdFI7WP3NKtP6XiTwtuxHH6wUsWuJ12i5ZbBsWkIjh4tY6zzQ+rNnW9vn5Y2UUdQuAKA1MEIjg5cCdAFSgBm5OXMbRLHpIVqAzMvWbHI7PG3ks+WjFGxHKZtfEyatPNWM5PWC7XS7tC/f7x0rni5SnpXXK1vqI+m+SD+z6qvVj5fBffHKt8qzzk2vXTUBqbWP1B5a3rQM78GCFQxrx8mjzLWqncKi7DVzuKjiOuT1oyJipOxxB2Ck06mXAty4/ecqLeOA//33Wr4UAIKmi9BuJNYNxwoc0/R5GsmuJwK8gEELGK16RG7ckaeLlq/S/nYQFU2RY+PZt2xqZWgBckpUSET6h1cP7YltJJj3RIQl0qL2eV5eRuqr5X8rYlFKK5VlUfTaYW0rY2c46XT5Et55a13DW7kuFn1oFPEttddt7QxAt9ApQfOj7S6qtIwD//DhWgoaTDnrIFqAxLFuKC8s83/NPE0zcaz+FjIpQLSegEeQyrMC3DRftN6Sj+n+1A/um/Sd/5fK5nXyfNSCTd7W0WOQ5k3TW3XkvuT+afXmIindr7WNVL50TKLBt2RTspv6q6W3xIlXplSeZovXTTr/8m1SX+L2pXpavlt1kY5lhKJBbdRuWaJtUHR/K3jXgiL7eH/Nt1k2Ig8yIvu86wIAAHQjGKGpMZJI0J7SSUEN/24FmdEn3lo+yWfLhvaUMPLEPRJ8WfWVfCli03tSm1OkDKud8+2RJ7VW+6d+eU+QtePqtWvqkxWAaWVrdbXScJuaXavuRc4DTwhr/yVfrWMTPYc9ipyrRW10gjI+tOJ3JK93TShyvfCujVrf5kSOdfSaVrbfAFBnMELTXUDQ1JBI4Krlk25+qS0vaPMCPak8KwBLfbdu3l4dvbKsOrSKd0NvtRxPzORobWodMy0/3675xPNabVtUbBQVCVK5Xv/0ytb8S7dZ/doTJFpftcrXfIicU2UD0zoHqVUE3FERQxRr9zwd3yblj4pH71wuYqdo2QCMBDolaH647cWVlvG2P57qCppnnnmGLrjgAlq0aBH19fXR6aefTtOmTRPTLl++nI477jjae++96fDDD6/CZSLClLNakt5IXli26huMJo7V3yLlfU6/5zeuSGAplc3tpNukG2JeF227VLZUlla2lCf/bNUr3We1a1537btn26ubVnbaZlrAognQ9C8tz+pTESHA+05aXtomVt14Gdo2yQZve+5r6k+6nfvK92nlcx+iokjzTasf3y99lkSSJ4a1emn7vOtAu/Dqmn6P9qGigXhU5FtptGPMzwt+/eTnrHXupPml66J2DfXOwdS2dm9opT8MV18CALSPK6+8kmbOnElf+tKXaM6cOXT55Zerab/2ta/RhAkTKvcJgqaDWDdh7yIv3VAjgZpmhwekUnCriRB+o/OEj1SmFqxIQbJUvpY3/c/rI/nE4XWW/JVsS20VEUNpOm6LBz1SEGS1gxXg83Ra+bwduC2tnhxNnEr9UBOFUnvw/dx2RAxI5XKfLJHh1U9rC34O8XIl4SodT227VR+tHlpdtOuPlaedSOehdU308ARRkbp5Pmnb+QMGblMTRbzfaA8a+PFOP2v3CO3awK8J3jXfoqjABGCkk2XV/kX47W9/S/vssw8REe2888702GOP0dKlS1dJ94c//IGeeOIJ2muvvdrYAjI9I2j6+/up0Wg0/+qKdgOJ5NMCOOkpn5TXu6nx75EgOv2uBWeWL6ktHkhKZWnBteazdKPmvnBbmu+SXa0srRyt/lp7SGmkp6hcbEnHLhq0cRHL91n7JZ+148frERHLWnmWXa1P8PNGK9sSGZ7gSNNpwad2/NP6SXXW/JQEoCZ0JN81rPPQQ+t7lk3eHzRhFvHFO6cjdfPay7ruaudj5JpXRGxx0aP1S+s+NJzCFQBQLdc89VM66IFTh/x5rFixggYGBmjcuHHNbVOmTKFFixYNSbd06VK68sor6UMf+lDb/ZbAGpoOoAV7kf1S8GvlkYLyVp+IpTc7KeDz7Es3y0ieiN+SH17bRNs7sq1I+hzLX+34aW0oBVVeWdwOzy/Z4NtS29Y27bsl0qQ6WmJD649ecBgpi7eRd2wl+9E8lniy0msCqMg5ymnHdaPdSOdGTtTXVq7FkTSWCNG+R4Skdp5YwjulyIMB67y2xDb3t9v6DwCt0qk1NDdsU+0amoMesNfQrFixgmbPnk033HBDc9vRRx9N55xzDm244YbNbZdddhm96lWvoj333JOuv/56WrJkCdbQ9BpaUML3F9lnbU+fAPKbqPe0TXp6KD3NtJ4Ypja0IE17Ssnta21mPZX1gsOi7S21oWcrrb+U3hJfUttEyk+PuXTMpLKkuknHJRIwRXyy+oVk0xIaWpm8rpIP/PhoZWuiyPI/WjdellanqLjkdi3/IsczTd8p0uOk9Z+IuPDSRvdb52WeXzr3pHTcnnduSH3GOp+1fB7cR+v8s/JG+hkAwCcbbFT659HX10djxoyhJUuWEBHR4OAgLV68mNZaa60h6e6880667rrr6KSTTqLvf//7dOONNw4RQe0GgqaDFHliVfRGwG/w/IaZ34ikGw0XQFJeHtxJZeXf+f70v1UvzWdJIPGbrdQGUj5NRGniTvJRCva0NowG3VI5PIjTBIMlFLynqlK/kXyX8qZpvOBRE9m8vpbvWp+1jqkmbHh9rTpIx1VqA15vKei0RLiURkrP/ZbqrtWBlxWhaHqLIoI2LVsLptNjy/tEWb+9a5TW5tpxk/qAdr3Iv0vXO6lfp5+t+kr3gdyuVEcrb+S6nNYLogaAVfn1r39Nl1xyCRERDQwM0E9+8hP6xS9+0bWvT951113p1ltvJSKiO+64g7bZZhsaO3YsPf30082pZ9/+9rfpsssuo8suu4ze/va30wEHHEAHHXRQZT5hylkH8W6yVsCn2ZGenFnlaEFypJw8vWXDsivd2CXf0/xaXaTtVtAWaXvtqWekPa3Pnp2I2EiJ+CkFH5qolPJ4aHmsz1rZ0b7I03NfeBvwbVr7WL5G2iParrwuPK3XNtp+qXyvTdpF9Jql5S2bT8O7FhbxN3ot8vZ59jlF7ETaQttmXcu9vtTKcQegrrRzytnRRx9Np59+Om299dZ0+eWX03333Ud9fX2088470/ve975mukajQddvfUlbytQ45E8nu0Jq0aJFdP7559PixYtpwoQJdOaZZ9JGG21EV199NS1ZsoQ++MEPDkk/HFPOIGhqRFQAWKLDsmeVkX7PbXo+erai9ZTKlm6wEfuWUNHSp2WUJSrWOFJdI8LGCnYtMZOml/ZLbRf1z6qXVKYWYGnH3vte5LzQ2kBr46LCMSKouD+8/lrfKCretXpLNqoUQ7ysMnnafb5G/IoIG69/aulzrHObp9Hwrj9W+UXRrhMQPKCXaKegOeaYY+grX/kKvfTSS/Tud7+bvv71r9Pqq69Oxx9/PH31q19tpusWQdONYMpZF2DdPKLBIf+c3zysANTalpabfy56Q7JESf45/+P7pDS8Pl5wIJWZp7HqKdnkPmr5LHgeyV9rGw+M0jaQ0qTppDTR4E+yzY+dVDbPG7Wt9du8XP5d2qaV44k53rdSf9L9WpmayNDOQd7/03IidZPaV8vnXWd4va1yIn2+bFDM24DblM7FokG41z7aeSj5FBEz1vVKqkfa3to5zdNE/EnrmtpOt1nnD/8u9T+tT0LMAKCTZRktW7aMbr/9dnrNa15DU6ZMobFjx4oP57OsUelfXYGg6RBeoCLd9KynwjyvFnRJgZUUSEk+WEGDJZJ48CzdnKWbOU/D622Vx9tCS2OJQal86wautQmvC/9utU3qjxaAS2VJgbEWtEn15balY2OJFe+4SkG8FlxxoSO1pSdSpH6t7Zfajaf3hIQm0CTftbbz2pf7JOXl6TVhIx0b/pmX4+Gl8QSW5JfW/6y21Y67ZjeaTvPZEzySsPGu75Igipav+RL1k/sq5bV8gpgBwGf33Xen97///fSlL32JZs+eTUQrp3WtWLGiw57VB0w5qxnSzcEKGqXv/ObZzpuNJsS8Okh5vfyebemzdmOOBl+a31aAWabO2rYcLQhK92v+ee3nlc3LsOxJSP1QaydPEFn92AtUvTpaAp6n8YJNL7+13fKj7Hnm9beojaqD1Uj5OdG2lOwWaRPPP+86q12XtL7sHXutPTSkvqWdh5JfUptLZVvXB4gc0Au0a8pZlmW0YMECWrhwIa2zzjq06aabEhHRWWedRTvttBMdfPDBzbSNRoO+P+PSlsu0ePvDH6rllDMImi4k8iRQuxkXvVFYwWXRMr0gILK/iA+ezajA83zh9qPtbwXnmk1P+KT5IsLOCoY8scB91HzVRKJWFt+updHa1CqL25V88OqpnRPaMZC2RY6hVpciIqkVP7RypTbh+asKRiPXvhwt0I5eX8peK4umj/RR69zhNiPpI2JYKycibDQfyohHAOpKO9fQzJkzh77+9a8P2fbiiy/ShAkThmxrNBp03VaXtaVMjdmPnFRLQYMpZx0iEqRo+fj+9Ht6Q9HKSPdLN6uJY2PzxPN0+T7rqWIkQJPsaOnTsqPprYDaC4B5m1hlaPa1+lk+pra4TckPaR/3iafLbWp10sqxfOH+pt/LBNJpWi097wuSD7ye3B/rOPHtkk/aueSJCKktpHy8jtZ5Kp0X0jkjnUOdIm0P7TxI/ZXqoZ2rPI9XZ25DOnesc1Hqp1pb82Mt1cUTCqkd7Vzg/vE6eddDnk+qlyWOIGYAkJk1axbdcsstQ7ZxMQNsMELTQ5S5WUhP4yL78m1E5Z7k8sAv8mSxqNDjfnK7PNjW/JMCSKudpHIsImIvtSMJUasuVh6pTKveWhpJYEjHWDoWVh/j5VuCT7LliWepfSxfI0T7TbpdyyflLdNm2v5o3mhfrgqrXYj0NvfOi0hZkfzW8Y72RatfS7as/lrETupDxA/uc5E273Q/AqCdtHOEZt9996XVVluNNt10U9pxxx1pxowZNH36dNpoo42GxLONRoO+N/3zbSlT4x2PnogRGtA++E3FullFb2ppeukJKL/JpE8IpaeF3k3JC0Dy/1K56WcpjfYEV0vjPbHU7Gm+SXXR/Nd8y//ztk3zeU9rebnSdiswT5/mWu3M9xdpK16WlCYSsFoiVxJXUn5eR09AR4+tdu5Eykh9SY8z7xPa8U+3pX/8eEnHXetPUnu3KwiVfPfg7cT7JK9H2h+k84uXb+2z6s2vj6m/Wvta9lKfJb8iIpn7oNnh5wy/VvL2s8ryBLt2HQYArOT666+nCy+8kPbff3967rnn6JprrqFjjjmG3va2t3XatdrQ12kHQIzITVBKJz2V827QWnAm3di0J3+8bK0O/Ime5af19K/oTZLb4OIt9Z+XwQMkKVDVvmuiQ2tH66moF6Rxn6268HItcWH5I323ytcCWn58PL+kY6l913yy0nv2ogJT84Xv8z5Lvlvnl5RXa2Ptu2W7SBrresXz8eNrCV9+DluixhKg1nVMu25ovmvHUfKf14+n1/ZJPkm2JTtS20Wx+lbqS6SfADDSmTBhAm277ba07bbbNretWLGC/vKXv3TQq3qBEZouJXIDKHLzSW3ym6QWeBZ5euoFzhLWzVtLG/XJuonywFgLVlPfLEGQ/vfKtgRTBE1kpYGc54slLqRAxwqepaCNB1deX9baN6+TFQRyf6X0abtIx90Sp/z4aucLtx8NDq3gNd2fptH6jxVYS+Vqx0krW7KRtqHki9Qe1nHl5eV+eQJTO1e9Y6m1QUTMSPY9ASmVxett1S1tO6lP5+VI14PItVizqdWP10v6DjEDgM+CBQvowx/+MB1wwAF01FFHERHRkiVLaMstt1wlLX6HRgYjNDWhyNNS64mdhPQ0Mc2n3citACharmTPChqkfJKvUtlaWknAcJ8kW7zdJHEh1dl76mwFDWlQo+WV2sprQy3wldACNa0f8TRa8C75Z/VNT6xJIkU7nlZwFgmEvT6kta10HIuI3iJCSOqzPK0mHr3+qR0H7XyMXst4ei6itGsFF2jauajlldJYbZfWU+rrlriR/Iqck1oa7/qZ5pX2a+ejJl7TbV5/9c5DAEYq8+bNo9e97nV08skn00c/+lEiIjrvvPNov/32oz333LPD3tUDjNB0kOhNgEgOuHigku+PCpj0cxowpDdiHixZgYH25JXXs8gN0xIoqb+WXV43Lfjk/mrbLfHl1Ufzg9tM83rHIG13KRiT6usdK25bCmD5sfGOb0Q0acGk5YPmO0/DbfO+Lm2XjqN2bPk5I+2z4KIjbd9IcJuWxbdrgblnTxMzkSC4iJBI203Kw+unnfdWW3mCgl9nuF/SdZIfMw3tfE7rK/UfS5hpYkZrT+lYch8j/Yi3v3R91q4J2rU8cg8EoJd56qmn6LDDDqNp06Y1XwJw4okn0ne+851V0mKERgaCpoN4QXGOdpOx0uXftRutJIbSfF5wIQXL2s1Kqmfkxi6VL90oub/pNi/g1dqG+2nd6KOBm2RPEitSgJZ+twSV1ge8IJYHT17f5O2vHSfNL55GC7r4vtQe9zndLpUr5be2c/8lAZW2K++Dmt9SO0jHOfXHaj9NBFr7rP7gBZza+e/ZkephBbradUi7blhBvCVmrPbl54R1LfWu49rx0s5nLnC0Mi3Rw89P617CxYrXzlK/19rAu4542wAYCTz33HNDvk+dOpWWLFnSIW/qBwRNTZFuIlbwKwX50SDBC1Z5/siNXQpCeL0iWEIqtSMFNvl+7wYaeaqYBseSKNHs8H2SMOP1kHyw+gD3S8oj1U0LNjQhwdNJAaqWxzvuXj4pXf5dCky1oE2y6wXrmh0uJjX/pHpox1VrB25HC7AlgWQdO+m/JaCkOqRpuf/euesF1mmdrPM0KvS8ukjntnRuafmsvqJdmzU/+XWd++dd37nYkeDHX7It1Sn1SWuTNG/R4wFAr7H//vvTWWedRQ888EBz21/+8hcaPXr0KmkxQiOD36HpErRgT7oRehd87amkdPMo6hP3RwvepbJ5vaLl8nyRYNmzrd30NT+1ANcKfK26Rv3gZXj2IvY9H7XgR0JqL+vYewLSOgei6SU/OF6QzLdJYtPKr6WxRAhPE7keFDmntaDXazfteEr7rbKt9pTSFrlWafW0/CxzLfTyRK/j3vnhCaC0fppvVj+RbFp9tkw+q12K0EpeAKqknb9DQ0T07W9/m6655hpatmwZbbzxxvT000/TqaeeOmQNTaPRoO9u/sW2lSnxrsePq+Xv0EDQdCGRoMFKEw0YipYrBflWsGrdXCV7Reqfbk/L04JPbZtXD+uz5Je2Pxpse2LR8kVqkzSvtC8itnj6iG+eL5E+qtnygjzPP83nIvkktH4r5YuKJM8mt+21m+YnT1c0iIyIE2k/Txctx7LFy/XEn+db1F/v+uIJDslukT6ffpf81fZHz0HtumBdF8veywCoA+0QNLfffjvtvvvu1Ne38h1dy5Yto0cffZSWLFlCW2yxBa211lpD0kPQ6PTMlLP+/n5qNBrNvzojXdz5tvxmYQU3LywbunCVp82/Szcj6SaX28vTaDe3tFzJJ61Mqa5Rv/nNM/XV2qZ91urN21RrX75fssvrJKVL/1vBDW+TFE3MpOVq+coEGqkvkg1LIGhiQgoWteMp+c/tSsEWb2OtT2ntxesYESNW/0/rbpWp+ZT6IJ23vL+mZVt9JrXNfdbqorWd1Y5WfbkPvK2k66VmX/Pd80U656z2064V0vVHOpbSdSxaR56P+yEdG+mY8f7kXdf5tUq7znjnEwC9yne+8x164YUX6KijjqLzzz+ffvSjH9Hy5ctp6623XkXM5AwOVvtXVzBC0+VEnnBp2zy7RPaTXW2798SxaH2KprN8lZ4WavbSbZpA0+xIbWAJjnR7Gb/LlisJoSJtlO6z7Er14Om5HS8I5vusuvByrSDJC9a99o74a/klpbN8kGym27SyJXuSbSl/VZS5ZhS1T2SLFOvaZR1nnj8txzoXrD4Q9dE6BzTKnsvRci2xrl1j0rTevQeAbqedU85+85vf0KOPPtr8W7hwIa233no0ffp06u/vb6ZrNBr07WnVjtAc/pd6jtBA0HQhXmBpBTRSeqLYFJSiNxZLEBS9OWmBhCcCIjY9oZDj3czLBkJWu0SEgtYmPB9PI/kXCUQ03zQ/vTSRwMcKbqzgsEjaSCDp1SMSkEv9zusPRfqohCfeJH8tUeOVZYkiz1bRc8mzndr3hJrVd4rgidDIucF9kz6nabUyNRtFRLdk37quRe5Pki/R4wRAHWiHoLn00ktp6623phkzZtC0adNo1KiVE6eef/75prg59NBDm+kbjQZ9a9MrWirT4z1/PbaWgqZnppz1EtYFPSoaJo6135iTpktvXOkNJxLscb+8srRtPJ90c5bqxG3yNNGgwPI/3a4FIpr/aTqpbdPtUt0jdZb8Sb9rQZblh9YWPJ3nv1YPqS5pO6V5uV2pbK1vacdN2p/aS/9zX6z/0vlkiRdPvEp9J00jBaHc96ggSv3XhC1vM60tuE+83pLtMmIm0mfTdPw4e8fUgp8HUn+SPnP7kh2pvlpflnyWjmO6T7PvnctS+RH7vG5a+0fEIAC9xGqrrUb/+Z//SSeeeCIdeOCBdOqpp9IXv/hF+t3vfkfrrrsuzZ49u9Mu1gaM0NSAqDCx0mhPAa10rfji3Ugt4SDl5zc8K0jytltBkPREUXvKaNXPymvd8LX6cptaGdp/rbwi/kV99srw8mntn6aV6pnvt/pQtL0i54pkl9fP883LU8Zvrb9odfTqlvrGt0XauRUs+1F/pTxFfLOuZ951LPclIvSk7V4/4vmsa4WVJnqd4XnKtIdWRwDqRjunnA0MDNCf//xnevjhh5t/jz/+OI0ZM4ZuuOGGZjqM0Oj0ddoB4OM9tSsiUKSneOn2SCDGfeE3Kq08T0hodbICTO5Pul8qj7cTLyv9P3Gs/yOLlgCw9nuBtEVUdPC68M9aUMLz8+3pPi/osfz2BCI/JtKxl/xL65eWa/U3L69Vj9SOdE5J/VM637Tjb4lBbtdrF0t8SPa9wDPSby3fvbyeXe385ba165xmk6e1zlHt3OLbItczbjNyneT70rK06yP30/I3RWtPnl5rL+/6w9sAYgeMFObNm0f//u//TltuuSW95S1vISKiFStW0GOPPbZK2jr/VkyVYMpZTbBuLpF8XiBhBa88veaLJAIkEaH5mdrQ/Ej9idx4pWBCShspM/WR5/MCUUkc8vpY6S3/uR2ezgt6eJlesCLl8bZp9c0/a4JE2qfVJ/VVC+jS/Z7vVt0lYSyVl27nfvFyPUEktZfUP7R8lpiR2kSqbzTwlbZ77ahddzRRpvXJtDzt2Evp0/bQbGnBOcdry8i5IvUJqb5Wf/b6Nf+T7Gs2pH4jCXNNGHpAzICRwM0330znn38+/fa3v6X777+fXnzxxea+vr4+uu222zrnXM2AoKkJXqAtpZMCQSnwkm7g3L5387a+83pIAVK6Xwv+uN9FfPGCJSvYkmx4QSsPFCX/y96wtbpwH1K4D3ke6+kt7yNSG/E68oDQC1hTn7UAXOoHmnDm5af+8QBNE97cluSv1G+180jyieflWO2uHQe+T6u3VF/JF8tmWi+pvvy7dA5pIin9k84fXm+t71nHW6qvVE/t3PX6qmTbCuYlsWT5mvoo9RVNCGl+eNdUq19ofVU6flr9vHsQAL3KPvvsQzvuuCM9//zzdN5559FBBx1ERx55JJ1zzjl0+eWX049//ONV8mRZo9K/uoIpZzUhGvxaTwV5Gi1gT78XDbqlG1q+3QrOpMDTKtsLlD1RJQUgnn3uqxQYWAELL9vz1QsYpaBe8o/7qgVxRdqR27SEo9cmfBsPgrhY09pbq5dUphZ8eWKRiwded6n/aqLRajNeDy34t/zkdrgAsMrjnzW/pfPACsaldF4gL9nw0kr+R9Lw80XKb32WBJ91rkiBveW3Jg6kvi8Jz8g5ol2r0/xF+qxUL75fEk1F7z0A1JHRo0fTm9/8Znr55Zdp1qxZ9MwzzzTfbvb3v/+djj322E67WBswQlMjrCeOUnBj3Zi0fOk27ykhD461G5KURrsh8nxpGV7A5gVSPJ3kXxr4ScGFJoYkYaYFGqkt3ia8fJ5WCqql9tMEjOWLFHBJx84L1KRAKm0Tqy20sqX2T33iZaRtKLWVJU6kvsLrn7Y190Orl9QnpL4t2bCOIfeX10PKI/ms+cUFS1Sg8X7tCRErSE7rl/rF80v15kLFsq8dh/T48X6k9WltW+qTdH2yru/WdYP3aan/cTQ/pe98Oz8eUrunaa3+xn2Q9gPQy4wePZoee+wxmjhxIu2000502GGH0WmnnUazZs1aJe1g1qj0r67gLWc1QhMnEdGiPXWMPAWzBI5miwc4WvBbpEwr6NO28yBLC8Y5XiAr2eLbeN3T/V5Z0n7NhrRfS68FKpZdqR28YN7z3Wr3yPGw6sRtee1R5vh7bW75raXzxJrX5lqf4/kjfmg2LbzjErFVJMDWyvd8Kno9046/d82LtkH0eESuZVK9vfM4LUvznfvmCRmvbtbxj9yTAOgG2vmWs1NOOYXmz59PAwMDNG3aNJo+fTptueWWtPnmm9OrX/3qZrpGo0Ffn/rltpSpMWfB+2v5ljOM0NSIImJGe4on2Uuf5Gn2vZuMdkPWyrXKzNNEAmYNXv/0CSb3Ky3LC0x5e1gChpch5edleb5owQsPUtI8WqDMA5rUN943vP6hwds/RWp/SxBI5ZUVJHyflEeqNz++Vl2l8yvtF1q9PaEm9V2pTbTzTvI1LY//l7DaXKpXxJZ3rZHKtAJyqS4Rf6z9EQEh+aP5rvUnfjwl/6Qy0zpa14nUTpHrLL+OSuVrbcevmZJPlogCoJeZN28e/ehHP6Irr7yS3vnOd1Kj0aCvfOUrNG/evE67VhsgaLoQ7QYlYd08pMDJssGDHcu+lJ7f7K0bnSQw+B/PpwXaVv2kQIaXnaa1btRpvT2RweurtQH3UQtctUCKt4tWrlYHzQct+MnTSP+lQJgHa1b9ND9Tv6TtPL8VyHM/o0G81681QakF/ryNpba1yuG+SPY0kST1N8l2mlcSQ1L9tONrpZXaXGob73ziAkGruxVQe31bQ2s/6fhK551mU/LFagP+3RML0frlabRj753f3Eb0WhvxC4BeodFo0MYbb0x77bUXffjDH6bzzz+f3vjGN66SLsuq/asrmHLWRUg3gFbsaPYk4SHd7KK+tOK3FLTnn3O8p6XWdwsrSOXpUnu8zbx2lrZzH6zyIr4U2S/V2QpgJbSAjdfT84fb8sqVyrbq7bUP90k6H6TzxfIt2q5F2jxynmrntRRUR8+5SLtwrHpaaa3zXPPJQrseeOetV366Ld+uHT+rb1t909vHfeBprHxeGuuYV4l3fR1OXwCI0M4pZ0uWLKHVV199le2nnXYaffazn21+bzQa9LWNqp1y9r5/1HPKGd5y1kW062KtBTX8qWD0yWeK9tTNSycFtOnTUv7k1BIC2tNQK3iJ2LJupLyuUvCgBaPaDTkSpEp10nyX9hURLZFAkbeNF1xJ27TA3epX1vHS6hQVk2m+iA3LbkTsSMcm9V+qi9TfuU0JLzj1rjla3/TEjGab5/dsSMchIpAidfGucdZ5qNmS2kvzQ+tj1nliBflS39DqatWnyHVY2lZW6GqizepPAPQKBx54IE2dOpW22mor2mqrrWjatGk0MDBACxYsWCVtnV+tXCUYoak5VoARCerL3CS0MrXgWfOnSPmRp5XRQEuymdvVAhAvvyUivaeyVhAcSVfUpiZ++D5NzHEi/nu+WUJFC74i/U3rH17QbAWSkt/RNpfse6IwKrilz1pbFbGTprE+8/pF02jXKauNogJOqkNEuHtEj7nV7lJ9NB+LXOtSu6kNbkfaJvlXpB96fknlRtKXvVcBUCXtHKFZuHAhPfLII/TII4/Qo48+Sn/5y19o+fLl9J73vIfe+ta3NtM1Gg26asOvtKVMjaP+eUwtR2ggaGqAdZPJvxO15watfZe2WwF7q0Ipt6WVo+2PBE6pn6mdPC23zcuMBGLRwF2rtyXgvPSS/5bA1GxIWG1uBdZa/YsEzhE/rbxpfiu45Hak71J9Iv5pvlppIudi5Hu6PS1f22cJRi8wjmzXyublecI1Wp4nYj17WkAvpUn9jea3/IkKSktcRAWJZDfaRlabaXWFWAF1pZ2CJufll1+m0aNHq/shaHTwUoAaEAlaooGUlqbojZ3fBDUf8rT5dq38tF65LV6m5acUUGhBfrovLccKXtO8Wj2tPFK+tEyeJ/0vtbcUBGjHIbWRlm2lt+qd1pVvs45V5Dim/qVtlPYJzUfJP+s4pemlfukFwPw48P6b+sw/pza99pCOn1SfdJt0bCSb3I6UziMi+qT0Xtk8v3eNS+st7ePHzhKUab/wAnPNX16u5hc/d7TrgncdldrVuwZJ26RzwzqXJN+1a3m6TbpmWPUBoJd57LHH6AMf+ADtv//+dPDBB9OZZ55J9957r5g2yxqV/tUVjNDUDC8IavVpl/QUz3rKl/oiiQq+TyqH14vXTaqzJuwsH/k2TTRpvkv1LfLZ8s0KnKU6atuL+BEVwRERadmSyrT6D69H+p2n98oqYktrGyuIldq5SF/yzgmr7bTzSrNp1VFKY5Wbol0ntPSRdmyVyHlk9R8tj1em1UYSXl+RPnN71vHS0lrnR5E6t5K+SH6r3wHQKdo5QnPyySfTv/3bv9HOO+9MTz/9NN1777304x//mD70oQ/RHnvs0UzXaDToqxt8tS1lahz9xNEYoQHtR7rxa08XtZtYEVLb6X/pppem5fskuxq537xuPK91s0v/W2VbAb93c7QCWystL1cKYrQnoFLdtG1WX+DlRo9bajvii9Z3eLrUJ76d+87bTqqH1a68HryveYIsrb/WVp7o0fbztuXHhZcrnZOpTd7e6TbJHvdP8tM6vyTfImJGOt+1Yygh1T1Fu0bm+6Q+Zdn19vNt3nHl27h/2jZLfGi+aH0mT+PdS9L6tIJ2Dckpev+AmAG9xAsvvEAHHXQQTZ06lbbffnt673vfS/PmzaNvfetbq6QdzBqV/tUVCJouR7pxacGvlU/CuklZNx8eNElPCDWbkj1ephb8pmmkgEwTKZpP2hNMK9jl+6UAVCqD15HviwY3vE7cr/QzFzJe+/NytWA09UMKCFO/rLK4wPDqntrj5aV5pOPtBYBa8C4F69L3iO9SGktIcFv8GPL8koiR/NHEUPpZ6ldF6hkJsLVjYImq/HPkumhd37w+JwX0+X6rT0vCU8sv9bGiwolvk2xJfmt1l66v2nUk3c+FXDtptz0AupHBwUFasmTJkG0bbbQRLVu2rEMe1Q9MOetStCdx+T7pxqsF8Fp6rTwpWPb85HktfzQ/LDEkpbOClWgAkqeR6mKVpe2PlMf9lNo8kjctn/vP02lYfSbip1cHK1DX8nP/rDbQgnip/1vHSMobOeZeWr6vSDkp1rkRabfIOewdX6kuWh2L+Kcdeyu/tD96/Yjm9c6BNI23nX/mWNcT7ku6XctjXecj12ee1qprZH/k2JTJA0AnaeeUs6uuuooeeughOu6442jzzTcnIqIHH3yQLr74Yrryyiub6RqNBl257lVtKVPjA08dhSlnnaS/v58ajUbzr+5YT0b5kzf+mefzxAxPk/7nT+WkG6vmqxfMSmhPJtPypRum5EOknSQbPPDwRKBWPrfJ/ShyA+c+S8fEC/p4nXj+dFvqsxaspj6lvvP/kn/8Mw/KuL/cJ619Unu8/l4f4G0rpbeEHT+W3E6aj9vj/USqf2qTHyt+PCV/+Ger31j5pTy8jtw+r491LDWx6V1DrHNUa0vtmpn6aAmRNK/mp9RW2rmQ+2Zdy1L/03K1a7gnoqTz3BMTkXOV+6ghnW9eHgB6jTlz5tCWW25JJ5xwAh1yyCF0+OGH0xlnnEFHH310p12rDT0jaObOnUtZljX/RgJFRYIUoFjBjRckSqQ3Mi0QToPBSPDiPUnUbuRpXp5OCrx4QKiVowUCkiDgga0kYqLHkdetqIizAnAtGLfKkfzWRJPkh9XG/LsUdHFfNFEUCYykdpVEgNYOkbJ40Kn1V153TyxqgW2knlLf9ALVdJ91PPg2zef0P/8s9UHLPw2el5+fRWxI9ZPOl+h1SaqLJUo0AeUJNKlvSMfR8k06TmldJaGutRO3IyG1s7QdgLozatQo+uAHP0jf//736eyzz6ZTTjmFrr76atpll1067Vpt6BlB0+tYNzYpbdEgWQp+OdpTW+lmqIkh7aYrBZRaPm7DegqoBciRIEAKAHgw0+rNWAoGpDrwoDW3wz9rgRZvIysALxoQa/VN65f6oaXjddLKjgpt61hbwaNUliaAeX+XgjUpQJQCQyuI5fXWbEj5JYEWweqX0jmal2m1iXR98I4332YJc60Omt2oL9Lx5/s90cY/W9cRbs8TSFr/S333BKUlZov2Ietaxv1Mj6nW5yV/AehFVl99dXrVq15FO++8M02ZMkVMg9c2y0DQdBlecOY98dIEhZaW29aCcOl7eqOUAmRJgHBbUsAn+cdtpcGxJcZ4IC0FGVagzf2xgmIrsOVtkn+2bKZppLySbc0Hrw9Y/YJv53XWAnZPLFnHxcovtREPuCS/IoGv1UekNuB5pH08UJQCTF4XqXyvr6Ttzusg9U+tLlK7WO1oCQwvQLXKl/qydP5oZUXOQZ7eugZGg+gi11DuV1RgaWVq11xpuyfMJBFnnRNclHj15Pn4Z+mcAQAADbwUoAeQBEFELHiBrycytH3Sfn5T0gJ1LTiW8ltlavkkJLGk2UrzRNpI+hzFCwwtcSnVzcrL6yrVR8vv+Z7a0NrNCz6tvmLlLbLPOl5SH7HsacfOCqZ5OZpPWr0i57Nmq4gIsb5r1w/tXJF84/5p6axzUKqLd23T/JK+a3WX2kA7VmWvabxumkCx0kr1jLSlly5yv7H2We3o2YmUBUA7aedLAaI0Gg364tpfr7SM4xbNqeXSDQiaGlBWXESFQoolCiRbnijxhIx2A+NoaYoKjTJBrmQj4mekPMmuVT/LZ0tEcH+1gEoTPlrgFWkLy9dI0Bept1eXMkTEhVSXHO/4a/WRbBbZZ223ypRsaBQNgtPvVh2sa0FEuGiBsXWtLOt3ShFho4lH75rh9e/oNS9yDdbaICqSJCwBV6RcALqFdgqaJUuW0Pz582m77bYz00HQ6EDQ1AxPTBTJL223guPo0z0r+NJ8ktACSi9oKxNEWkLOKiMaHHC7kfoUaQtehzStZIfv1wIaq9204CRN433mRAJFqS5SfdLtlvCS6u8FpF7/8trZy+P5qqWLiAwtXzR9q6KAU1QAeOKB29TEkuZnESJ9uYgQ8QSR13+i/dYSFtp5YPlSpN9J7cO3W+eh5BcAnaCdguacc86hddZZh4499lj64x//SOeeey6NGTOGPvaxj9GMGTOa6RqNBn1hra+3pUyN45+up6DBGpqaoYmZ9CY3caz9g3JpOp43Jd3v3Tz4jSb/08qVgqk0X36D5PW0hAbfz33m/kg3a94W1k1fsiulSe1aAYfmZ+qHVkaaTqqHVE/pOPE+kbYrT6Nt58eT+53vs/qs1AekNtD85fVMP0u+ScdKqyvvN1I7Wu0k5eU+p/WRbKfptbJ5naQ6WmVraG1l1VmqqyYC+DHSjrvUN6XP1nVI80OrU6S/WfXT/JDOV+3aJ9mRzifuO7dtXR8lrP5cRsxox1ezo10LAegFHn74YfrABz5ARETz5s2jww8/nN73vvcN+Q0aYANBU1PS4MAKbjk8mNECzDS99Dn97gW4UhCgBciSj9yGdCP2AnnvxqkJBmm/9Fm6CUvHRNuflqkFqWk+KTDn+fg+Dg9MeYAREW5WgMfrxevNhQYXs7wOqX3pvxZAS31GE1CW31oQ5rWtROTYSr5w4SqJPqnNeL+QfJf2Sz5r/VoLaqXzzjqOUtt5Ak+7PnDx44k2T9Dx/sTPWam+VvqIqNB84ucRL19qK+14SaKXly9dT/jx0675Wj08caJdt4uIJwDqwLhx42jUqFE0f/58Wr58Oc2aNYv23HNPev755zvtWm3o67QDwMZ78sUDEivokgKWSKBtBSnSDVILYLV8PCjz0nNfJZ8tQcKDC8lP7UYtla8FFVL5lsCSypFu5lZdNB+4eJD80o651kcivnsiiaeV6iIF7rzulk9am3vCRAo2vXpI/UUTShFhxPNp/vF2knzR9kntrtVHOi+iQoin1+po2bOOn1RHSzBYwkOyGT0PLFu83Ojxl87HaH/RrkmSr9I1UUqrHVPr2EXODa0O1n6IG9ALTJgwgW666Sb69a9/TW9+85ub25cuXbpK2jq/WrlKMELTRVhP4dL9/IZgiQMpiE2387LyffzpnpWWl60FzFJwzYVMtCxeP542+iRR8523Ibcv5bGCbm5XaxcJKyjjbaHt4z6n6aVgROof3vd0u7VPCualumj5pGNrBW1WP/Nspem1vLwMbivSFhLacdBEotaH0/QRISW1M89nBZra9aqV4JP7bwXQUpta4lP7LpG2Mz82kf7F9/PyNQEhidL0uycSJR+la1NEhHEsgSVdT7XzInJ/4+mt86tIHQDoNCeccAL97Gc/o0mTJtHs2bOJiOh73/sebbDBBh32rD5A0HSYyNMqHmhGbjrpjb9IEGGl5QGWZNu6UaXftSCR+6AFdVKZmjCQAj2Odxw04Zjml27YUkBipSnqo9S2Uj+wgkwp2NfqKfkkBda8fl49pLpEglLJB57e6/9aOVIw5gkB/jkSdEnng3SuS3WT+k9U6Hl+WoJO+55v4yJG66dSf+Xl83x8nyQweD2iYsUTKZaQSvHErJRP669cRGkC1rPPy9HKla4Nkvjxzldumx9b6zqtHUupPK3faukB6EamT59O8+bNozPPPJPGjh1LRETjx4+nf//3f18l7WBW7V9dgaDpMEWeiGk3L+1Gp938089SICAFwNLNk+fzhEca7HrbeVAnBWdSUG/VWwtO+GdePrfLbWntItVHS6MFDdI2qW5SOdFAXrItBWRWu/E+mH6XgsAifdMKcnkbRoIZLYDjWP1HqxNvNy2olQL9qLjTBIuU3hIFPL0loKy8+XZeF95OUhtq1wOpfK1/aOVJ+Xi/s3z26u1dj7x+pdXPqhM/d6zzTbIt7ffOA6uPSe2p9WmpHaT7jOSzlFfy0UpjUSQtAFXw4osv0jXXXEPf/e536Te/+Q0tWrSIDjjgAPr+97/faddqAwRNFyKJEiL7abkWkEv5vMBFCrakvNINXbuhpvm1G7aUTgr6pACeB5TcFv9sBTxcnGntI5WhBTaSTem4WAGoFBRKdeLtxo8ZbyOtb2jtZgUiVtuk+TlSvTSbkfp7Qkjruxpe0KqdM5p96zjytpbSpvXk23l9+TGV+p5UX60ulqjkZXOs9pPOeav+lu9SPaR+aAXCadlW0C9dq1NfNVHiXUe4TU0oaG1vHZNIAC/1d26jqIDk+SS8/bwMbV+kr0TsATAcfPrTn6Y77riD/vrXv9J5551H73nPe+iggw6iW265pdOu1Qb8Dk2NkQIV7SbJgyMprRU4SjdKLyCwAlLpuxYQaHalfWneSCAs1c2ypQWWUrqIn1K50fbWAuJIXaxyrW2Wb1KdUl+19pDqp7WFhJXG8j+aT6tDVHikea2A2BI51nG3tnvnmpbWEimR4yoROX+1/qX5qNnjPnO8gDr1RerjWpt6aa16eGjniXbOFDm2lo/Wue1d77Q6e/uj54HXtgBUSTt/h+awww6jb3/727TaaqvRSSedRJ/+9KfpyiuvpL333pte/epXN9M1Gg26dPLVbSlT40PPHoHfoQHtJ33Clf7x/dbTOH7D0YJTXo70ZDF9cpjebLQncWl5qT9pPn7z0QI1biv9n/oqtWHELwleHved+89tasctGlBx/zVfokhtadmRysvzasdN64u8HlYf5P7xsiWk/sr99+obCYSk84Pn5/WWgj6rn6dtlZ5zWt/S7GltwNOn+6Tjweuq9Tl+fNNt/JhI9UzTSf5w/z176V9qw0I6n6XrJ0/P02p9Qer31rkUEaRpmZFrhXbdl8qxzmtNzFh11nywziXpWGtE0wHQTay22mo0MDDQ/DxhwgQ68cQT6frrr++wZ/UBIzQ1wBMGPB3f5wmcPI301M2zp91cOdqTNCmddLO0RIjWDlpaaX+RtB5eWi2w5vW1joflZ2ozTVPUlue7dWwidrg/Xh+T6ub1ByuIl9DKtepo1dvKm/rHy9f6hrTPKsuyI6Xz2sYKYLVj6rUhtyNtj/gSaXfLtuWv5UeK195aGs0PzWevn/LtVn21cr1jY/UDqT9I5aeU6VMRiqQFoAjtHKG5+OKLacGCBXTuuefSmWeeSWeffTZNnjyZTjrpJLrsssua6RqNBs2b9M22lKlxynPvxQgNaB/8iZn0tExKk94AIjcM/vQsTac9mcvLiYoZrUzuM78hWzei1Ie0XC1o4v7yukj1TffzevP0UltJ/kptJPlWRBx46Yq0q9VnUju8L0r1T7dLddUCNkvccP+k/s/7gtZ2ml0tiNLyWHakfd6x9c4r7xzjvkvHWzo2UjkRIcD9tPpJWoZkT/NL81Gyx/ugZ1c7N7T+xMvk1wXJB34NkupgXYt5OumaJt0feD6pnpLPUhtp56jUR6S20K57Whrt3JB8kz5LfgPQrZxwwgn0mte8hsaMGUM77bQTfexjH6N58+aJv0MDZCBouhgrkOU3FE3c8KA9zasF6NoNV/KBfy4TwKbfLV94HTR7WhAu7U/t8JtfNCiQtvOyrOPota2G5KsXpFjiq0ywIAV7WuBjBbSRwFk7jlIfkwLOaB000cLL084fz1+tbjyYTcvVguxoG1qBnSaaJNuWuJPayCqP+8dtctv8mPLP1jlmla0Jz/yzFaR78P5VRIx6dSgiOPP9lpiU7KTpNbHB21+rY3qORq+RkT5rHZfotQSATjNmzBg6/PDDqdFo0Dvf+U7addddacmSJXTmmWeukjbLqv2rKxA0HcYKZq2bifYEUAqirG3pDYYHDdINzLp5aAGcF9hJNzgpeIkEO5I97h+/EfO6SQGuFjjyekkBs/X00QsmpbpoAWEk6JDqZ9nmQbtXZ6kvcbs8IJX6oocnVLk/6X6vDKmPW+JWanvLnie6vHpK/cY6p7RrQ5o2tZHWh/dTq79yUSn5pYk/i8g1wLJpCSjuv5VXOg6aYJFEiSeIIiJDC+RT/4oG8dL1TvNJqoN2z5DKtPqRV39JdFr9WvPBO18B6DR9fX303ve+lz760Y/Slltu2Wl3agMETYfhN0zrhs3TaRdqbpMHttJNVwsyJbz8kg3rqZ8UsEhBmCSqUtta/bmvUt48jRUUSMGYdMO22jKtV5EgU/IleiPXRB+vlxUcS3Xj+fl3HuRGAz/uHy/DEoMSmrDT7GvBEu+TkQCUf7fOb+0Yaf0tPR6870v9WhJT3Dft2Ft+aWJV8lmDny9Sn9Ha0Dum3J7W57j/WjnSMdDqofUrbs/q07weqT3pnJTqqLUv95X7IF1/LdGqCUMrfYp3bdU+W0JKuh+m36U+DgCoHxA0XQAPGPPP6Tbpxmld/KUbn1QWv5hrIiL1KU3vPZGz6sj3W8EWT8sDjyLBjhToFAnWrUBCq4tW73Sfl4a3Abertb0UZGl1tQKdIkGCti/1WQpWtT4u9U+tH2htyOsm1UELUDX/rfPAOzaawLD2S8EmFyuSPe085nBb2jHgZZQNDLVzXjsPreueJQikcywibqT6SGLFq0/kGsXPz9SmdE5a54bko3SOafu0vuv1aX6dyLdp9bG2WcfZI3IdlercKhBFYDjIskalf3WlZ95y1t/fT2effXan3WgJKzDM4UGmdlOzhICUPrWfptWCWwkvvSRAeF21+ls3ayuQKVIPzVepTF6W5qPURlIbaHWVsHz38mq+RLZL5WvHMeKnRPSYeO2bppM+W3XV/LbqKJUbKTNyrDncvhbQanmsOkq2vfPWO5elums2Itco7msUqx9p2yLXA6uNotdMz48iRK5Z1rboMfX6fMQ/rTyrDCuNtN2qT4p13CO0csxAPWnHW87OPPNMmjFjBm299dY0Y8YMWnfddc30jUaDLpr4rZbK9PjwC++p5VvOekbQcOr42mbrgmgF8ynWBd8KNDw04SP5yMuIigPrBmkFnFaAGA30o0LH2ibVzbp5R0RMRPRYadIyrUA7IoQsMSf5ZAWqnhCS8lj14ts1/72ALnKeWeeedj5Ggvwi/cE7lhLa9UU7X9I83Aa3q/kXSWNdFzSsukaCdi2PlE/7XvTaVuR4eseE59X8LiowND/aIbyioijyuaho8Pq+18eLUEYMgXrSDkFzww030B/+8Ad66KGH6Mknn6QpU6bQ1ltv3RQ406dPp7XXXruZvtFo0GfHVytoTnsJgqarqIugiQZ62s3Guglq5Wh+pPm8gNXapvmr+WIF3JqfvI4RccHL9m5mRcSBF2RoAYKXhtuK+C/5zMvUfIi2Z0SAeH1Z8j3Sppb/2vGT0Npdq7dWjleWF6CmRHywsASJd+y19o0eb2k/t1eEyLnmiSEvIJfK5L5agbzld8QHT7hEjp3Wfz0ftPpa7ZBuKysupHIk39IytHyRcrV+UaTPFKFVQQTqQzt/h+bGG2+k+++/n7bffnt66qmn6J577qGHHnqIiIhuueWWZjoIGp2+Tjsw0kkvfNGbR0p68UzTWjcq6ybolSXZlOqj3YCl72n6iH/pdl7/NL30uWwwzu1p8HK0oCStnxdscNvadl4/LzjWfOBppXpp9dbKjG6X+oFVR61u/LvX1/O0lk/WZ6ts7odV/9SP1D8v0JWOqWbX6v/RYE5rB57Pu6Zp9bV80Pqsdv5KPknXmReWyeuqNJu83aw683ySfasOKfy81eql+RS5zkltbF23+feoGLCuN9FjwX3WbKbppHbh/nMi9wgtLwAe119/PX3lK1+hUaP+tbT9Rz/60ZDvwAYt1UVogYZ209UCsEiAbwWF3A7/LAXmVp2km17qo3VzyfdHghHrZha5IUs3LB7kpHmkINBCqyv3jbeZ5INXnhVcScGbBvdF6oPS8dQCIt5ekh9asGj197wsKS1vi0i9y7Q5RwoM+X7NviROpG1WwG0de8k3SSBookE6Flqf4On48ef1jwiuNL91Dmto5fA02r4ULcC2bKfprOA9taWdQ9I5qn3m54l2/DX/y4oFqRz+WbJrXXu99Nb5Ze3X6mu1AwCtsmzZMnr55ZeHbJs1axZdd911q6TF79DIQNB0EOlGnN5wiFYNgr2LdLpNCnLysrRAhafT0nD/tSA2UnfNnpROEy/STVkLBlO4LSlQkAQGR6ur1r6eUOB11o5lWleeJ/1v+cj3eXmstuT50/TpPk3wSGV5fmrHXPKf9wnpOKfbpOOWfo8ILX7+Sm0n1Vnq59y21HejAko6TyVBlbZVmtcKXrW+IV2D0nxegCv5abW/de2MCA0rrXbOStcoqd+lPlrXTU+gpTbSPqkdE94uaV5+3IteB/k5qd3HUhuS6LLa07reSe3mwesm1VUqJ3KOARBlp512oo9//ON0//3304oVK4iI6KmnnqLnnnuuw57VBwiaDiBdFL2nP1KAoYkg7cYoBRrSDUi7qeQ2peBAunFZF3epHlpeyZ9IGd4NRwsypO9SkMEDAZ6O1zEqMqSgJHLjl46L5rtWT8+ed5ykcqwAxApwuZ+8r1j10wIrLQjV6sz9tII4K5jUfJXK5n0rUr7WHlJ+qe2lNuWBdp5WEjpSHXk+7TzRzmHtfLGCXs2GR0TsFBFE6X5JCHjtpgkIrS0ke2V8lY6dtk/yzxJjUj+S+i4vx+r7aT7tGpui9RtJ5HG/peuCd88FIMqJJ55IW2+9NX3iE5+gAw88kA477DA68sgj6c1vfvMqaQezRqV/dQUvBegStBu+9j3dJuXP93uiQio3YksLaLQgWPLds6f5ErFpiTmrTbV24hRpZ80fvj8qUMritWOkj/E8lp9F/edBfFquts3r35Ydr88WCXKLngPR8yxC5Dz2gl0rICtzbYrYlYjYlHzztkX2FbFXtFwLS+RZ/YaXq22XfIz4H62PJSaLHPeibRc5zp7Qje7TtlnXPtD7tPOlADmDg4P0+OOP06JFi2jttdemLbfccsj+RqNBnxn37baWyTlj6eG1fCkARmg6gPUkSXvKZV1k+XYOf8IlBbHSNssur4OWzhIe6ZMx6+mXVn5uywqwuF3vxsmf0qVP/tI/Xpf0O28/zR/pOEv+aNu9z3yb1J5pm2gCk7e11I5SnaVjxD+nedLjnv7xY5Cm1+zx+vEyeLnSecD9lfqA1AZasMjTSf54Ai394/u4HSvY0gJXfu4VsSMdY6te2nVQsynZivR3L32kj/Cytfp5YpRfA3JbXhnWuaL1p8gx4Nuj9xSvnlp+qX9ZZXk+a/2K+6C1eVSMaMfIygNAEZYuXUrf+MY36OMf/zj98Ic/pNe97nWriJkcrKGRgaAZRrxgRQro8u9akKzZ5/a4HSlI4wFraot/1oJIKUDjgSevp9QW/CZtBSJSvXgdeT5NaFm+S3X3bqiSCNKOq3f8+D5LDGp9hOMF3pYY00SNFaRox9wK8nj/4ekjAa4mHCxBEWk/LrgkrOApAj8veftqwWE06JL6k/Rfsiudy1q5WjDv+eidd1p/4uktAZXatIJrL6j1AnNPIHN/rP4pXRc9363rZP7ZaitJ+Hn3IslfT6h69UmvybwPSuebdx2T6iel5z54gguAKJdeeik98sgjtMsuu9Bdd91FREQXXHABzZ8/v8Oe1QcImmEkvYhbwZJ3k5JuJNI2yY53A+XbuG3rBivVM01nXfw9gaSJGi9YtG7w0o1NC2ilG7x1HCI+pPWKBKk8wOfBrRTwceGhpZOOqxewWMGEZktrEy4aywTnXt+0AkAv4ORlSm3H66Cdn6ldnkfzjZeh1U27xmjnn2TLExCaPe+apvXh1KaHdB5K/VAqn/tYNuhMjx3fnu6X+o50LL37QVoHrUxePi+rSN0s0eHlTX3V7Erp0zxFfdb6vHW/kPLzz9b1j8PrZ92TAJB44IEHqL+/nw488EDq61v5iypveMMb6Itf/GKHPasPEDTDjHcD157KSU/NeLCfBq/SzUIKejT/rODQu1inPkRvKryeml1uh9uU2ilNawWd3DevvPy/duPTgk+pbO2mKQVCUh4pyEq38X4nCR3+P21Pqc0021K/1IKOFClo1wIhqS24L5rPqU3tXLOCTX4uSX1G85G3ZURoeMdUssXrGemvXntobSp9lgQcr19ahiU0vPPHut5YQbbUXyLl87TS+SH1HZ6OlyFds6zrgrZfu9Zr9ZLKk85rXm9LGPB6aX5qddPuQ9yu9l3Lq/VvaZ/V/jy91l5ePQDIGRgYWGXbrrvuSgsXLlxl+yA1Kv2rKxA0w4wWhKfftaDburimNq0bOw+IrIA7Ty/Z0oL7SMCjBd+pDaneWnBq+Z62CW8HDk9n3YStdN53HiBKx4Tnl26uUpt5N3bpmFsCwwo0pb7L0+b+WH2S11sKEqX68j6sBTB8Hy9Pa9s0nxYQeqLFCiT5cdPOOy9Qt+xrQZd0Hkh2tHaR6sXbIf2siQVJCGnnj1YXfi5FglDpWsJta2klO3x7pC0j6YoExpHrrWTHEhaaPeucs+xIaNdizzetjxXhhWUfo789++WQn1b5WnsWuXeDkc0OO+xAn//852n58uXNbS+++GItF+d3ir5OOzBSsAIWKa32XbuRW/l4HivQT32z7KX+SGWn9qX/FtrNQQtKrZuJF8zyOmv1jwRwUvBhtTe3KaXX2iXdb4kv70ab5pcCfS3A4mk0vzjaNp5HO75SHqlczS+tP/A2kXz2AmWpD3giRetTWv+M9FPNjnb+Su2fptP6TfpdC8w1YSaJOm7PutZZbaSl1/qyVK7VF6Q28PqIJgiiga52nKx+YvkXvbakNq06cSLC0uofZWxa/lnHdc1xj5v91DpWWjt69wsAOMcddxydffbZdOihh9KKFSvokksuoT/84Q+09957r5IWGkcGIzTDhCc6tCeC1gXTK8v6LJUn2dFu3pLfUjot0JfqIv3XbgpFbhaSvdxvSVBIZVnHJtIGVtCm+cv91IIxzRb3WTpuRdpX6z8aVmDoBSa8j6T5pMCV+xQJ8vlx5W0i9Q+pHkUCLW7fEjxSOXyfJgx4eZFgXitHag8p6POEUtrGeXqtP2oiQesXvKxo/4xsS21a5xg/dvw4Wddb7fh5YtK6lqY2IwIm/dPEniSYrGuFd/5b9dPySOdl9Fh71+qiPlnnaRHxBUYuzzzzDBERjR8/ni644AI655xz6PDDD6eJEyfSkUceSe973/s662CNgKDpEBHRkn/mNxnphqLdYKx9WvCU5osG/ZZNr45SmdrNWgqUvH1aAMl99vyW2l0TfVKwxvNpeXj9pSAjtRsVDZavad15e2lBJN9mBeQaUr0l36V6ScG6F4xYgoin0exI52S+XaoX94kHjlbQqdmR2oP7YIkdScClZaZ2pTaVjjPv79pxlfpyul/rO1L9I0GoF2AWCdC9cyBNK7V5NK+XLk3P29rLk+ZL0/L+a12zpM/Rc74dSOdUjiZapOu1d2+QykvzefcTADwOPfRQWrx4MV1yySV000030dixY2n27Nl09NFH0x577NFp92oFBM0wwW/s1lMi7aKbp9FsSnmsC651484/S8GNF+xJ9bZu3JqvUiDGfdACJC9/+l8qXwqWtQCT59duqLwMHoxopPakYyK1rRUkS4GMZl8SM1afkARZWhZvy9S21x5W39PyeTbTfVp/0YJ2yS/eH6XjrdnnNrS8Uv+zhIwmSLmw0mxa5weH+87tSf1Dq5d0bnsiQaq71he1/FJ/ltJyH3h9tHbX2tNCqnfk+i2Vy9s7/2y1rXfNkcrjZWvtYH2W7EQEg3YtsPyxyrbOBc9nACy+8IUv0OTJk2ns2LH0i1/8gs444wx661vfSh/4wAfoggsuoOuvv36VPPgdGhkImmHGCgSkm68WZHGb0s1JC4giT6OkIIen0WxZN0LrxikFuZGbTjSQkPJrQYXUflp5fL+VLz1WXqCjBTFWgMfL4nkteJo0yJeOW6SfWv1DC7y5Pasfe2Vp55Tks1S+Vi/NpnTORANbT6BI9bTOTZ5WKtsTC9b5xwWRFOxaQWVqQ9vvbbOCdOs6p6XjdeHle9c675pg2Y4G9Rqa2PCOAU9X9LoZ9S1yn+Cfo9c7ya6XVrrGWNcw7fqS5oOwAUX52Mc+RqNGjaKBgQH67Gc/Sz/4wQ/oG9/4Bh1xxBG03nrr0d13391pF2tD1wqaZ555hj72sY/RBz/4QTrhhBPoL3/5S6ddagkroE/TaBdNLWDgwaAUlPI8+Xfthivtt8QWD3y5Ta18yRctoE738eBDgvsh2dBulnyfdVOV/JUCCKkttZt8Xp4VAEcDitSWF1REb8ZSAMjLiATrWn/WAnpJLFifrXOJb5O+W4Ggdr7xbVabWkF5Wo7Unjx9Wl+pPta5YwWwUvt5AkM696w+ax2TyLnA6yPZl8rjbSYd08j5qLWF9N06by1BYl2/vWuIlo7XnZ8znhBqNXjX2pWXabVZ5JhLaSLXz0gf0441hA2Isuaaa9JnP/tZuuOOO+jJJ58kIqINNtiA9thjD5ozZw6dc845q+QZzBqV/tWVrhU0V155Jc2cOZO+9KUv0Zw5c+jyyy/vtEsto90QuIjwAmgvqMm38yDWC0Q0v/j21L62T0rD62A9HdPqrAXKUp0igkp7Ipfvk4Lk9LsmVtLv2vHk+zQhYAWCaRrJN56et70kdKQ00vG22jn1SQsotMDS8i31R2sP7p8VXGgBumTXKzOtV/pd+uwFYlagVMROnlfqk3yfJKy0AC56TLz92vmiiTjuh9W30u+8v1n9XSrTE3bedUm61kv5pPIkfzX4cfXypXXndeLt6AXzmi8anliJEDnHo21n5dO2RUUtABpnnXUW9fX10f/93//REUccQQcffDCddtppdMUVV9Ctt95Kf/7znzvtYm1oZF36kuvZs2fTt771LRo3bpz43aPR6G6VaQVwPE36nUgOfIs+WZPKiQQH0j4pgNCCEi2AlNJZfmjBllUXzQ+tXaVg26u35Lu1P2o3cgzSekWCD88PrzzPV60vl+0zVt2kNuTlSvXW7Fh11tJqvqf7pfK1ekr5rT6f1i9iRzsXLFta/byg0TvvtO9a/bw24P7zvGX7U4RW0mvnSTsCZO1csO45Zcshigkeyx/Ll6qPCQARnn32WZo0aVJbbJ177rn0kY98hB5//HF69NFHaf78+fToo4/SY489RjfeeGMzXaPRoP6+77alTI25K95Vy9+/6coRmhUrVtDAwMAQ8TJlyhRatGjRKmn7+/up0Wis8tftaDcq/uSOfy7DxLHy62zz71YALPloBYi8XlJAlfvD8+V5vfpq/vA65jY5WqAnBaveMfLawvIjEvxLpPa0fiL5xf8ku1p+LRj3jhXve1rbFemfef3TPqO1F8/L0/J+yj9LdrzzgafV0mtBupY/953bjLSjJzhS+945yMtO86Q+SvW3/JbqaolP6fhbx1qyJfnL/bOuYZJty2+tLtZnz550vLzzm/saFSFS2fyzdd0t0k7e8S/iW1UMRxmgt7n00ktp6dKldPzxx9OYMWNo6623plmzZtGHPvQhuvTSS+lHP/pRp12sDV05QrNixQqaPXs23XDDDc1tRx99NJ1zzjm04YYbhmx0g6gp8zQy/Rx5QmU9rbRualrwq/lp2be28c9SmVLZmu+RQCdy07QCVAktrbU9eqyL1k1rb+5TtL5S4Kf5Lvms4fVtXifuk1V3bl+ypbUV90/CEhhSnSx7Vn+06qP5qrW/9t07P3kZketHtN6SD3w/r7NVD25f2m9dNyX/yqCVV9amdT7zdPl+6Th511VeludrWaEj2dHaqJXj0A46XT6oL+0YofnCF75ARx55JB100EG09tpr0/Tp04f8bbDBBkPSY4RGpytHaPr6+mjMmDG0ZMkSIiIaHBykxYsX01prrdVhz4rBL5JRcaIFp1agy5/spU8epSeN/OmfFEBYN0or4Extc7+4DakMrZ75du8mK90sI4Ij9Yd/5v5x3yUftHxe4GLdXD3hIqWzfOH15XX2/ClSppSe92+rT0h9STtn0npYwistUzre0rmT2tQEQmqTn4de/XkdpXZL/ZRspd95W2jnqmTTajPp/JH2Wz5qZUr/eVtF+6V1nkhppM+a+OPpeD+J+hEVGPyY8f4l+Z7m4dd+r+5Svy2Kdu5q36X8/Hsrx7RIPgCq5vjjj6cJEybQDTfcQB/72Mfota99LS1cuJCuvvrqptDhDGbV/tWVrhQ0RES77ror3XrrrUREdMcdd9A222xDY8eO7bBX5ZFudNKNKU0TCYC0IJKXlX/nea0nZ5IwiNzYreBY8l26QUl1l4SSJZK0ILLoDZSXI+XXRI5Wd74vEgzy/VY9pEBfE4xWW2pYAZjUTzRxo/W9NJ/UT4oEOdL5JpWpCZ/INn78eDmSWLHONx6s8vPKCqilQJfbSeHl8vbRziOp3SSB550vkhjVzkHvGiaV5/X7iL9pmZJf0rVSy+OJpIjQkOqgtUNanuRX2i+kc6NMgG/56KXneAJIyxs5tyPlSUD0gHYzYcIE2myzzeiQQw6h008/na688kq68cYb6YILLui0a7WhK6ecEREtWrSIzj//fFq8eDFNmDCBzjzzTNpoo43C+bthylmOdKOM3PilwChHy6cFO5FyJNucNICw/ClywbdueK3akurNAyHtf+qPZkeyK+2L2NLaVsrj3cQtO9yf1GbEX+24R32I1iH1o0i7WnXh/ln153Xw+ohXBw/rnOb7W7Gl9Tlpv2QrUl9uS9tntWPRY6Kh7Y8cQ6290jpLfU3rgxF/o2j2I/Us64d1PFqxUSZNK7Rqv2r/QD1o50sBrr76avrrX/9K/9//9//R008/TV/+8pdpzJgxdPTRRw8po9Fo0CdWq3bK2ScH6jnlrGsFTat0k6DRKBp0SfsiT780e1IAHQ36I+VLgVAkUPOeDHqBv5fXKttC8z3FCg619oz6ZwV4Uf8jYpTXxQv2LMER6V/cB8vfyPFrR/9JfUzRjotXHqeMWIy0nXZucv8igof76fltXWOkOkhtFLnOaH62U7REyo0G5mn9tDpKZQxH0Bxpt1b9qrIeUdsQIKAK2ilojjjiCPrCF75AEydOpI9+9KO05ppr0uqrr07PPfccnXXWWc10EDQ6fZ12YCQQCUKtG7QXBKTppSAj3yaVKfnKP1uBK/fH264JAV6naMDJ6yqVIbV/NPCL+srhbSflLRJMpNu9QF3z0RKc3O+0/IjIybdLxzoqZqQgO/0v9WOvD3Ok/iPVQ0tjiROtvFaEFa83Py7pceV2LIGo1SUtp4hv2rkn7ZN8sQJOq19wu1K6yDVE2if1e28//2wF0d51pUgbWT5YaKLFErWdEllSGk/USulaKReAqhg3bhxNnDiRnnrqKXrkkUfou9/9LvX19dExxxzTaddqQ9euoekltIA+ElBwO2ma/H80cCl7sdYCJSlY1ny0fIgIEJ4+bz/etvwGpgXXUvm8Hrx+6R9Pr/kfra+URvKH112qa+qP5p/0X+qTUtt6AbQkHtPgWxOgkg+eOOJ10+x4ZXmBJ68DP8ZSWVr7czShpvWvdL9UpvSflyP1Zc03qe14G2jfJTvpd+26IYk1yTbvi1Iftvq0h9bXeX6tLhLR67iUz/NBS2tdt9I0ku/eOck/S9+LYAlOKU3UVtG0rdQBgKKMGjWKFixYQD/4wQ/oDW94A40ePVqdaYSXAshghGaYSG+40o1DC4gkLDs8neRHxE/NjhRASP5rwY0nAHj+yJM6a3v05sgDLK0ukr9FAiTJF6lvWIGwFAxKQsMSA9pxtAQQD9R5Pq/9uF9a+ZIdnl46vprAkj5b548WqEp+8PQWUbtSHqlPeH1ca5votUM7/p4PkbbVypaug1Iaq/9pQbkmlCJE+ox2PbPQ6suvf0Wuh5KfVjn5tqjtSN2k8qL3Lc1OO9N2wh4AFu985zvp6KOPprXXXpsuvvhiIiK6//77qa8PYXoUrKEZBqxAQgpOo8FA5OlVJNiyAiIrmJeCV8lXKxCLii6rHlpgZfnrtaFlWxMdXuArla0FsBGibWkdM62OqX+WyPD2e357+yxxpwlLzb80jfTdE3xeH+R+RIWp9NkqQ7tmSGmiPkWOrXXeF9nmBbha23uCQWoXre5FxJzVZkV8k9pAS2P5IpVTVlBJFLFVtiwrn3XOlPEhIkIBKEM719AQES1btoxGjx5No0atnDz1uc99jvbee296zWte00zTaDTorFHXtK1MiXMHD6vlGhoImgqRAkIrQPICVO/mGQnopHTRYDQSQPP0qZ9FBYAnnLR2TesZCSolfyLBpWffCmKKigDNf16W5CP/nNqJ+J76q5WllVHWjlcvjnVO8f2WX1YwXbQvSPt5nTSfvHPAsmtdc9L20tpSa6/ItUTy2etrmi3reEl+ena9c986FhG7XpButWNRioipMnaL7msXEbEa9aGT/oKRQTsFzV//+le677776K1vfSsREf3zn/+kvr4+WnfddYekg6DRwRqaCskvdNKNMv8updHspDZSO+l/7aadfy5yAc79436mvmh5pPK5f9ym5K9kN60/J9/n3Zh5GilQ8wJubl/yTzoeUtAktYtUHj/e3HftP7dnCSmtz1jBq+SzFdBqdeflctuWLX4Muc9SPazPlkDivkrHk/uq9WfvnOfppfKlukvpUn+l/sqPt9SfJGGiCQ2pH2jtku632kbymZcltbV0Tmv1s66jkl2tDbxyLPtSXVN7UTFj2eZEhGa78YRuur2IeIjcV1oFYgb8/+29e7ydVXUuPFYISYAth3oBgSOghETEr5ZjbSk99VMP+imKEBB7DIIERVJRLodKU1uBFAXkJhcRCKB4jSIiWkRaqGD157F4/IlcpMEQ6BGkR4mEO4Fkr+8Pz9qdGft5xhjzXWvttdbe48nv/e213nfOcZtjzvmM911rpVc4++yzZbPNNhMRkeuvv16OPPJIOeKII+S73/3upLb5HRqMLGimAGxj0xsVI6OMELDXVqHQdMNiRLrUjcgmQpS8e2DkqrymyZult2xr6dfkT59HhBYRJU0oEUGyCoVSJ7KREV00lsgfFBsERKr060gBxfKHFTa66LFIHiN+Xry1DcjvCJFlQIUbyitkIypMdB/tn/bfKhqt8WPjGZ2/zA7ks6UHvdbzU48Hio2OcbQgQu2RXd48ZW3QulCeR7qQP0x3BJHishtE11qvTVRHFiGJYcQTTzwh++67r2zcuFGuvPJKOe+88+Syyy6Tq666atCmjQzy20Z9BiKCiGSgfuV1RFoYkbHkapusTVxv+lonIjzoTqdFrhC8a1pnaY8VT+ZTGQdkZ3kd2anbRMkesknHD/mtr2vihPy0/EM5iq57cY3GjxUVNXkeiYs+z/KPxRrpQrFgxBHJ1z5Yua79s8abybOu6wKO9bPIfHmdxRH5yPJOw1t7dDtEXCNFCCsSmwAVa0heRAfz2euL4oN89MYArU2d8x09tcVrDdB+wtp0oyeRGDQ6X5P46U9/KjvssIPMnz9fRETGx8cHadZIIZ/Q9Bm1Gy8jYNE7bmzBZ/rKa+VrRsBKOxDJiRJnVACVMhkRtkgtstsjWzpGiBCizZvZEiHGOobsLxoP5Dsq7iybLAIdbYsIW8TW8q+VY7qdRegsAq/blvK0LhZDNBe1fC+unt1WGzQmLH/Z+1Iey2EWJ7ROIB+stavsh+ZOhPCjNaOMB7Nf2+L5gfxHaxJbk70CwZqryF8mE8mJyK4h/awotYrhbvRFbLGuR+YgQtN+iUSv8NKXvlROP/10ufDCC+Vtb3ubiIg899xz8vTTT09q2+7zMarIHwWYAqANnRH8SDtGHCMLOisoIqSJFSndAm2Wlm81dnmyas+zAslqq6+XdnvtWBGocyMSL9bGiiezV8tDtnhx82yM5hqLTwRWEcYIWyT+yAd2Ljqeni0lInJQW2vckR2R3LLke/Mqkk8eWB/02lpjyvNWvnjreq2t3hwp+/aqeNA2eUXaoPSK1D2h6ZetiZmJXv4owOOPPy5XX321vPjFL5a3vOUtIvIf36s5/vjjJ9q1Wi1Z1urvjwKc0c4fBUgARMhBp42+68U2f72Q637oGmrTkaU3Z30HErWJ3C1D7Uo7LJt0f7R5MQLr3S20fGGx8giOlq3t1zIZsWLyWR5EyHsnViymkb4WgbPIcNkO6UTj7xUoKJblHEJzTI+T7uvZi85Z8xH1R/Z12qDcQHloFUJ6TmiU7dB5NqcjxQ3yX8u1fEG+o2ulXCSn9E/7ysar5rzONS+/Ou+tOYD8s8DyIkLma94judG9QMeuCSKFB1tDmd4sZhLDiuc973myZMmSiWJGROStb32rHH300ZPajvf5GFVkQdNnoE09clet3Dg1KULyS1kWgfDIPOrHyJy1WSGyjci/JttMjrURofiU5JH1twim7ufdLUXFgvYNESyreNLjqvVYRF/HFJFWRFQQEbEIpibVDChOjHAivdo3bX/pg0XAI2SRFSxaDoovax+ZLx4prSWHbF6U19CY67mJiijkq1WooVxGbXX70mY0/toPr5jwcoCNKVsbdTtLnnXO6tskb5E9aL1g6zPKhdIWrxiy4tUrsLFKJEYR9957r/z1X/+1LFu2TC655BL57ne/K3PmzJHjjjtu0KaNDLKg6QMixAUVLIh8acKACIRVwJT9NNllJDdy546RcItMIrsRgdHvNYFChIa1Y/KRLZ33Vh9EqnTBYhWG1lhZhRfKGUTsrfzQ5y3SiAoPrUfnrs4pFD+LaGn/9VyxxsbLO6s9Ir3eayt2+rX205KtfbTyyfKDzZES1txHeWAVRSxfWA5Y+c7INCLV2j62pkYKgrJ96Ze1JqL1Q/uJ+kbXuxqwAsiaDzrXUDsmj+VyL+DFx4O37icSw4azzz5bdtllF3nd614nN998s3zjG9+QpUuXwh8FaLf7e4wq8js0UwCPiCHywTYeLUNv7B6RYYQTtbfOIQKm7UO6LIIVkanb1fhqEfJSrnedxS3qJ9Jn6WW+RPSy/ECykW1ePqL4olhbMYjkIyP8aGxLoFxENtXoR3Y3HRfmm2c/8wFds2LM9CIwf7UMr4iIrDuef8gedC2K6FpSvrZyImIjs8OKmyejJhaRcUK+9NrmJn0iMe8FeikrMf3Qy+/QHHLIIfKlL31JREQ+9KEPyYUXXig33HCDbLfddrLnnntOtGu1WvJh6e93aM6S/A5NQoFtjOWdtCjBRHfQkJzOeY9MeySzbFPqQfahO3b6PNoUynOWTGtT8chCTcGnY6xtRL503pd6EFktX1vkQr/WdqNxQ30tomn1ZzYjPawYZLaUbZF97DzTX8a5Y4+OEyp+akiKtgnZUr5n+YDi0DmH7EYx0G21TOZ7+bpjj+VTGctSn7WusJxB8ko70BqDxoitD0wf889b98oxY3mLbNOFnrf+WjZ49nn9vTWsPB8tsjw5rF+NfE+ebsPmVdMChO3FicRUYPbs2fKb3/xm4rWIyJvf/GZZuXLlIM0aKWRB00dYhQsjGCKYHJVyGNHThEHrY5sxs6uUHyXFUbKl5epzVlFg3Z2LkEbLV22LVUCwIgnpQWNoFXxs/CyihWyyZLJriGDrwqMEKzQ0OUW2lec02WU60DkWGzSHULGB9OoxQvAKMj03US6UcdCxsEiyzn82H3Rfq/jS8lm+abusucLio/3W59E893KnpnBFOV7+ZTYzOagPi60FNl6lPM8O3cbq4xWpaB33UONzk6KnH4isOYlEv/D2t79dli5dKs8884zMnTtX1qxZI+12W9atWzepbf4oAMa0KWiWL18urVZr4hg2sI2IEQK9WesNErVHG7zWg+R3/iLCgDZzVrQg37xiA+m1ZLHiihVMFhHWf72iojxn2cyIteWjRXj1uGryX+otZVhkEelHfltktglJswi9ldvMHqZL/0WkF9lX2mWRaKRPy2HFkJV3lny0BqCiC807VDyxHGY2eMSf2c381XFDeYtINbNdx0PHxlofdV4y+yywotQqCFjxEs0J9J4VodG2FiIFotcOtUfnrf3K6h9BpG9NXBKJbnHQQQfJ3/3d38m8efPkzW9+sxx//PHyvve9T37v935v0KaNDKZNQXPyySdLu92eOIYVmmSJ8DuhHbA7p0guam/ZgeQx8qcLq1IOIyKIREWINuqHbK8BI9KlXOt9ec7yWxehETKkdej3ngw9ZoycM9+1/WW/8nzZj11Duah1I3+Zj5a92n9GVD3C6o27loneW/2QfkawUTGi5bOxK4Hiwa55BDhCkq24sjUMEVSUu/o6KxYZrLxncfPG1ZtHVr7rOan7MV2lPqswtvpb56J9O+etItdDdL+z5kZT1PbtpnhKJCxccMEF8g//8A9y3333ye677y4iIq973evk1FNPlYMOOkhOOumkSX3yP9bEyB8F6BOiJJaRZhH+UYvOtbK/1QeRTI+AsuvsHLItYp9VxCCSFNEdvc58R/o8El/CIooRH7W9ZTur+LPaM5u9uFrj79nNctayj/lk5Qzymc0DLZ/lQyTWyNeaXGeE2YutZV8E1trE1iM05l6MmUzWJ7peIbleQaXtKYHGx/Jd2+r56cHTzfzrhtD3G1HfE4lRRi9+FOCiiy6SVatWyb333iutVkt22203WbBggSxcuFAWLFggO+644yZ8ttVqyf/o848CnDuiPwqQBc0UooZIeOS2m/PserR4sQotpsciaR7RrYVX4DGbrGKAydb+MNJhEfgIabIKIW0D8o/JQ/6heFg2I50WWUX2ozYa0QKX2WGR8Oj4I5+s2HjFbnQc2XvvvL7WbWHB+rFYat+9eESuRYh+tNix8pj5HC1SorJqiymmI7p/aPkRfU2KNTYGTVE7BlFbu72emLno5a+cbdy4Ue6//3655557Jo777rtP5syZI9dee+1EuyxoOLKgmWKgRdkitB65t8hK2aY8x8iIJ9+6bv3VNkXIV4SkIN+sOLD2lv4SvSDAEWKGZHrFQ9QvbZPWZRFc3Q/1ZfFibS1y7tlkzRlLv2VLlNyymHr51oTUIlneGuKRySiZZf5417w4sesR263YWL5Ytlk2MDui5xiaEP5uyXXU1w6a+uL17YW/TYqsJsiCJsHQy4JGROSuu+6S3XffXWbN+t23QTZs2CBr1qyRBQsWTLRptVpyXJ8LmvNGtKCZNt+hGWagDcIjoGhTfmL9RyYdneud152DFRLofWlbeSCU17xFHtnkFUida50+SE/ZX8fW8pv1QXZrG7z2kWKmlFm+13ahMUf+WeRPj2MZf2Qvst+LHyPzOm6M2Fqx1fK0L7p/tOjQ/cscLc8xu5CcUk9kfrC5zXxD48higOQx2Szf0LjoNUaf78jR+Y3ykRV3LG56fKzYIF+s9cIa97I9Wq/ROKD1x1rvIkDrUKQNes/GV79HewDLH88mFv9IXKw9JjpPPRujNnQ7jomEhc985jPyt3/7t/L444+LiMj/+T//R774xS/KrrvuOmDLRgf5hGZAsDZQi6h23ovYRL0DRoQ61xgpQ0RI97FkM2gdXlskn8mw/ClRYy+z29JdswFGYshIOht/axyR3fqaZ3/TsUZjyHxF9lp90ByxfEW6LJnRnI/mtedHJB+07SxW1nhaJNDKnVo/rTHStkXXGIucR8Zf+++tq1aR6Pmq+6H3qK23D6BYNUXEZq9tCWvd9WyNtOln/6mWmxhN9PIJzTvf+U659NJLJ37VbN26dXL22WfLDjvsIB/4wAcm2rVaLTm2z09ozh/RJzRZ0AwA0Y0tSlAt2fp8hIBZxQPbQKPEIUqCoiSsdsO3iFktQWI2aXiFWMQ2FBvkZ5T0RfKhaWHDiCSSZRFBi6hZeWTpi/qA5HkEsibfI4TYstPKDS9XPV8jNntrDPM/WqxonUw+0sHsteYHW1t7Mf90zKwCiaFGb3RPiOqOokmR01R+TXGUSPQLvS5ovvKVr0x83ExE5JlnnpHDDz9cvvKV/yhgsqDhyIJmCmAVANYmWfaxNgaPAOt+NQQXXfcKikiBoBEhQRGyj2DFNkIckW9abuS6lh8hO1q+fs10e/ZbulFf9J7JYW2YL2Vcago6rxiIFGdN5gwrwiKFYITwRQsVll9avre2aDu8+cJyxytyIoiskV5BUJtHlq0WafbmkjenPVmRuERsibxn5yLw1r4m6FehkgVQopfoZUFz6qmnyo477iiHHXaYzJ49W0REnnrqKVmyZIl89atfnWjXarXkQ30uaC7Mgma4MMiCxtsQa4qACDmK3qlD9iGdTAdDxGZWNFlEMFogdN57pMN6XVOsREhPtFBiOnQ7rzCs6R8h6N1cixBlq5BiYGMSIY5eHlk6LV2oXa1cr30tyWfrQXmdyUd6vHOReWn9ZTHR8r2Czpqz2iZkI7KX+cDi17RNTfFTymF+RN4jOTWE34oVs9GyoxZeTln7n6c/C59EBL0saB599FE5+eST5cEHH5RXvOIV8rznPU/uuusu2XPPPeWYY46ZaJcFDUcWNFOMCMFDfTwCGyFxtW3ZJhGRGyVVbPP3yAtq79nH9EXOeySrhEXyEQnw/Iq0s3Sjdqx9lABHiCuzr2YMLFnIF0Z+EWoJq9fO8yfyXvvizT/Ur2zXzRqjZTIbkc26vWcX8ocVbMw+pgv1qyG9lr2Wf1YBESnMvKIqosOKUZT8a7+QfGZ/VDby0YM1tk0KmyxiErXo9a+ctdttueOOO+SOO+6QJ598Ul72spfJG97whk0+hpYFDUcWNH1ChCDUbAjeph0lflHCGN1svOIG9YuQWUTiIkTY6hctbhiscfH61fSxSGLkmkcsa4mOVWhF2mu7LRs9YmgVLRbRixQtbFwiecNy3ItRtECx4OWUlRdIRk18IsWHNX6l7OhaEyGh1jhbiOS6bufZ4xVVSIbWzXxj15BfNeetdpFCwNqvavT2Cpb/pV2JRA16XdBE0Gq15IN9Lmg+FSho1q1bJ2eeeaasXbtWZs+eLSeeeKLsvPPOm7RZv369nHvuubJmzRrZuHGjLFq0SPbbb7++2Z0/29wneASpc73TRrftLMCdhZaRHESWtG6kQy/g5Uaj+yJyoNshuxAJ1BsIshfZVb4v46KvWcROxxTFmMUlshmWbSyCgtqUMvR4WWMZIafMbnaOkUBNTkqSwMaE+RQt7FA/ZIMVN20n89/yAdlm5Qbro30p9VrxZbq17domNG9RWzTm3lzxYOWUlsH8RDbpdaTzHq1JVu7q9VfPTUbaUT6hvGZ2MttK2bqQsuISXRO0TRbJt+YdWtu0z2istI8WvHXcA4pZdI9JJBI+VqxYIXvvvbdceumlcvjhh8tFF100qc3VV18t8+bNk8suu0zOO+88+exnPyvr1q3rm01Z0PQRrEgoryMCUgIVGBFCX24m5eao25VyLbu0T+g18sXb1FhstP+MmGnfGTFEfrN2lh0WybT0WJsmirFFYlhRZPmLiAbykZFrnUNMPpLB2iL95WvWprSJEUVvHlltGWFEtqB5ZsVMy0fjp2PFfLF8ZPONEdjSFkR6rZzRNls+6n7Idh1HlMNaN5tfet3T89Saq0w+Wht1HpT9asm0Nb76Ohon3V7ri67/KJdQLusCzJqTrCBieiI2IKD+3rodKZQSiWHAuLT7ekRw6623yj777CMiIq95zWtkzZo18swzz2zS5oADDpClS5eKiMjWW28t8+bNk/Xr1/c2GAVm901yAhJsRmD0OX3dWvitTaJ8rzdBRrgQadB2oA0eFR4ISLY+j0iWZZtnPyIhyBftp+e3VRhoX5B+5AsaH2vMrbjp65ZPjBwxYoTeMxuQTjb+GpZ+j2wheAUDgyataF5aZJERRUb+vXGz8tyDNcaooEBFAyrgyvNsXUG5a9ntEVEt28pzNDZsTrL5jdqzAoP5YK396ByKXWkji5GW0zlvzd/ovqD9jqyNWg5bW1G7JkBrJMtjK5aJROI/sGHDBtm4caPMmzdv4tw222wja9eulR133HHi3FZbbTXx+qabbpKdd95Ztttuu77ZlQVNH+EtnCUQuWSLvd68PeKj+7O+1iaHbNI+MV+Z/aw924hRrDQ5ZP2sQohtvCw2iLxoezw7S3LrEXmLdKExtkg+KyK8fET9GTFHMnUsaueBBiNFEQKK+kSJIbINEWvLZwRGzi0ijIory39mbynPIvPIRramofGzyCXLSyYDgRUXLP5N161oTkb0oH5s7WfrlIZVGFg218qw/EV6tGy93uv4shgj+6KI5E8iMQj84Ac/kJ/85Cdy7LHHysaNG+XGG2+UOXPmyOtf//q+fif8x/J1+bFcXd1Pf8em3W5v8uMFJW644Qa57rrr5LTT+vsUNH8UYABApMojOtYGam3kTF8HVjGESAGC3uSt11Z7ZBvq45E2dC5CJCPxivjO7LLG24s3G1eLSKK+yGavPfK51B8BIoNsvNm4a1lIh7bP8seLccTWqPzo3GNxQLDsZ/LZ2LE1BNnrwVpLauIasVfrRTldS1KtOYps9cg8K4KQn+U1a02v9ccrwLy2+hobQ2tt9/K19M+SEfW3tn0iUYte/ijAe9/7XjnxxBNl4cKFctFFF8nPfvYzmT17trzmNa+RJUuWTLRrtVqyVFb2RCfDJfIu90cB3vnOd8rnPvc52WKLLWR8fFze8Y53yMqVK2Xu3LmbtLvmmmvkRz/6kSxfvly22GKLfpqd36GZSpSbUnlny1tM0cah7+7p951zqI8l19LP3pfySzvK12Vfqw0jB2jzL/92ZFnx1H6i9vpcea1zXutFQKRaX9MxKK9HirYo+dNgcbD8sHxGPnTOsTwodaIxK3MY2a1zSBMgjzSj8WB90LxF8dGv0TiVOaTtLNuiuFu2aztRzMpzaM1Aa4jWqceU6WC+lLo8Qo3iotsgv72ijuWjXkPY+qVtQzEp42aNgY6tbqPloWvR9ZvZwc7rv1qOHkM0X9lYWte1HbpN1F9WxLH2icQg0Wq1ZOHChfLUU0/JjTfeKGeeeaZ88pOflB/84AeDNg1ir732kptuuklERH74wx/KK17xCpk7d6789re/lbVr14qIyJ133ik333yzfOxjH+t7MSOST2imDBbZ0IQH9SvB2mjC4BHjsj+6o8Vs0n3KNqx4QdAbG7PZk6N9L2VqP5CvyGZr82Q2Ml8scqtt7cA6VxOfqC2WD8wGNmY6hyK5ZfmG+njj68VM+2/Nwah9zBavuGJxRnosf6z2zB/0Gvml+3njqu2IwrLLyjOtPzKf2DU2hlpvNKe1HpYX1vxjrxmsOEX6W/Z7/kZywNsnLL2WrdG1NJHoBXr9hObTn/60fPe735Vbb71VTj75ZBERed/73ieXX375RLtWqyVH9fkJzaWBJzRr166V008/XR555BHZaqutZNmyZbLDDjvI5z//eXn66aflqKOOkuXLl8vPf/5zed7znjfR7+ijj5Y999yzL3ZnQTNARDZni+jUFCY1xLaUzTb0KLGyfGO6WX+L4HgbbcRP1MZ7j2RYNlu+6fMWsYn4VMYj0idCtmvttPpY56z3NXkXLZS8+YHaejpL32vIfyRn2Dpg5bG+XqK2KLTix/owoLG15NXO9SYyWX9LL1sHWMyZLqaD9WXvWdto4YVixOKIbPfiXVuk1aAfRUwWRgmNXhY0n/nMZ+SWW26Rxx57TD7+8Y/LHnvsIWvXrpUTTjhBrrzyyol2w1LQDCPyRwGmCGjx9jYRtNHVLKhIjmdbeQ7pHZs7+QvdyE5GJpDMmk3OI0ieTdq+8lzEnhIWabQIkI4Hsjniu1cYWLkSLWo8vfqv9RrpYAQY+eAVObqfV8x4cwKdYzagsUJ5VV7TsiJFks4tFH9rrbByWdvNCBwj5vo1k+WNi7dmWGuTlteJF7I1UmBo29DY6Th5hQ2TaeV+N2S6pnDQPrEcrskRawxLGWxcrPPMlyb7pIcsZhL9wLp162SbbbaRJUuWyB/8wR/IC1/4Qtlpp51EROTcc8+V/ffff1Kf8ak2ckSQT2gGBGuT8e5qdeAtsOwuWoRQaiD7GLHzSCXT6bWz7LTIbQ28IgbFwpNRtrU2azbWFjmwckXrZTGLELCmvqD2XnwRmsiMFjmR4s7Li5r5YL2uiUPEF2sdic6ZWiLnrVGWjugYMz2R+EZkMJ9rSbduw8bay2/vHNMdKWCs+R8pqmry21rHo3nJ1tdaRGXUFISJmYVePKF54xvfKNtuu60sWLBAFi5cKAsXLpTddttNxsbG5Mknn9zkp49Ffsdtj+zzE5rLRvQJzbQpaJYvXy6nnHLKoM1wgUinRVzZeY8oeJtIhOhYBDFCgBDps+xk7azNJOJj2dYrDpBN3mYWIYFWUVYzJp6vFqGIjqdHIiLEno2ThQhxteLlte9GJ9NvzRstx5ozzCZkv+d/ec4j/VbbaNHG2iAy3A2J9HyycrMJIbXme6TwKPUyn6I6LR+9/Iuuh0197PgZyX0GL68s1IytFyerTWmnF5vEzEAvCprHHntM7rnnHlm1apXcc889cs8998jDDz8sO+6440SRc9BBB020z4KGY9oUNBrD/oSmgyj5q9lA0cbPZLB+bFPxyJZu62162paI/gg51W09smqRRM+OSJFltYvY6hU0TI5HOL2+li4tl9ns6dGxQOe9Ikvb6M0pjzRFihsWG4u4ewQU2Vwz55BdnmxGPK0xYb5pGbp9lARHiXbkWqTYiawZXp5ZhL6U563vur+nw5ortcTbin/NesdytraIQ/K8tclamyM6veKvpnBKTG/0oqDpfORMn+sUOKtWrZKPfexjE9darZa8r88FzeVZ0AwXhqGgqVnUIpsAWrh1/whRQvqiBA6RH4+URDb+mkLGIieW3YzMMBmRIqiEN4bMT8935kvEDyueyG7tLyuKyn6WfJazkeIM6ffsYYgUY17BUDtOzC8vf62c6cBbA7QfFgGPzFetyzqPZCBEfIwQcpYPzMZIcWLpiIxHZD221tJITkTiEt0DavrVtG9K7JvuGVabfvninc/iZvqi3x85Q8iChiMLmimAtxHXbh4I0c1Vny/7RwoJZkOU7HmbdY397FqNHxHiadnZDeFi/lhtooSf+Wj1RfBIrtaJNnHLT0tPk2LAkhmxUfezZHs+eHq0P5Y9kWKkhtTV5Ko11t75iJ9eEcbssXxm5yxbUP9oQYTgzQGrvSXHIsk19kRlMtmROETP1xYdIrGcssa2Vnd03LUN2tbE9MGgPnJ2hHy5W9NNfEYWZ0EzTBimgoYBkahIe2tRjZBbi+Tovsw2rzjS+th5RIy0nmgxE7Vd24vsRtc8ooXs8ggek8faRvXXwrPfIkK6n2eDRTwisSrbRQg08yliU1Q2k6Xbe+OIzlvvPV9qdEX0duR6c1z7X8LKXYaaXPNId8RHbb9FzLWcJgQ+0tcrDjwiHzkXRaRYa1q0NCkcyvfalmiR57VvIqdjizePEqOJXv5scwnvI2dZ0GDMGrQBMwmdRU0vbjWLfGdxjBDGsq9HbsrFt+xfLr7lgWSX9iF7Om09Ql7aU/quzyO7GUFj9qM4lTHQrzV0vLxNim1mWkf51/LZ06PbsPM6NmwDZvqR30iXzl9GMtlrpLsc+7JdqSuac1bxgeLgkR82rjpmiJQhG3tBgNBcYTq8eWXNN90PzVVrrUK5qMdTxznqP4tHqae0n8UKySlllLKQDdZahGToWKB+NYULk2H56q3vWmdkvWLtvLijnLPkafvZml629ey3xsZav0v5Xm4lZgZuu+02ueqqq+TWW2+VP/7jP5ZDDz10k2ImYSMLmilEZFNjBYNHMhjJ1H1YW62PEW7mF1rQrc1IE3u0SepNEREXFEO2gXkEE8WHkSqLkFhjhvzQ+tBfZLs15ohYatutwgHJ1QUDi43uw8gvygGrvY6vjrkFRrA9YmnJ98hQaasmRowss3nmzV2UG3q80DxF84GNJ1svvNxCPlhEzlrXrCKIrQW165m2FY2TtsvKRz1fUBvtN1ormA9WbLRclIue//ovW4fZ2KD3bF7oPqxI8XJY+8b0WOuYnjOoHYJlv7bLOpeYWbjqqqvkjDPOkFWrVsmKFStEROTaa6+Vxx57bFLbdp+PUUX+x5oDgEXKrcXPWkgRUUGbASPSCIgksE3H669tjPqK9FqLPyPDaENDREWfL3UissFgxYkVQdpufY1dL21EYMTIK0SQrVY/3S7aj/mk7fEKHka+UBw8Il36YI2bl0NMvu7H8tuyx5o/VkxrCJa3jkTmivYFkV4rfqw90m/JRuPBbLCKN684ssZTg61Fer56elg7L9+QPGQDi2/EL2RvJD6ROarbefnKxs7KDSTbyjlvnEp9WcgkOrj++utlxYoVsvXWW8shhxwiIiJPPPGEXHzxxfJXf/VXA7ZuNJBPaHoMi6x65JYtruX1cpOxrrPNAP1Fi6uWY22cyKZSNvJRE1DdHm2mZT+P1LPiQ/ePEFKLOFtFgeVH5H2pkxGoCLFDeVXaxsiNHmftk7UZd9p6eWgRRDSeaNzQ2OkYlHbpduh1aX/pL5pjWr9HxFjOWUWdNTc9H718tcbK8qXsw3IL+RAptBApRTqYzVbusXnm2VhL6jttWZ4gPVGbtTxWGLA5ZvmHxhT1K+Ns7XEWmM3adr0eoflg7Ysde9B4RIoLts6Wfnr7rj6XxUyiRLvdnvR9nMWLF8tdd901qe14n49RRT6h6TEii2L52lrQNdCmhRbryKaNSBUrAjwigQoctsHWbB7WxoHOISJnFSvMFh3DSKGk7dfj4ZE4NgZIrhUj7QMidpb/1oav5Vs+a1nepo/kaf8s+UgXK16svui656ueyx7pKc9F57pXPFlj6cXP+ovsjZBjTT7R/GM5UwLpQ3mpxyFKrFF8WLy0X8hfpN8julZ+eWA5yOxlaxmyS7dHbUsbWFvkS2R+sL6R8yjHUYxKG729ie2tXnyQbZGxTcwsbL/99nLTTTfJPvvss8n58fFRLjGmFvmEpg9gG623sZavrbYim97h0W2t/mxD0jKtfowwlufLTQ7Zgza0sj8ip2jzQT4hEqL7WpsbI/yMlCLfkI+eDov06PcoXqww6bxnxIP1Qdd1riDSEskhZLeVDxrMD88vizxr+YwElW0i79HctuYhOo/mOrIP5b0VS91fz+PyYLnL5OlYa3u1TEaAPZKr+5Zjh3Rq/ahgscZIxzpCUNH6iWR6dpU+sviU7VFxYq3bOk/YdW03s6M8zwoJLcvzLaoLFQ/evmiNY9MiRI9FFjMJhKOPPlpWrFghy5Ytk6eeekpuueUWOeecc+TlL3/5pLbtPv8bVWRB0wewRT5S2HTaoUXYIi1ssdSbECswSrkR0oN8YhuHVSghB41hSgAAVUVJREFUQquJCGqL7Naw7EabO7OPFTelnWwTRvFnQPIQuWQ+Ij2M8OsiT9tmxTlCfBCZYD5bZI3ZUL5Hui0fWF55RUG0WGLjYOlgRBvlKCLvpQ7drnzP1gc29iyWiDCywonlMJonbOyZnWXMvbms5bH1zFpfdQz0WOl8YGsispXlK5pr0QKK+emtmZ6N1jhZ1/U1b055sj072PqH0K9CIwuYRAQveclL5PLLL5dXvvKVsmDBArnmmmtkyy23lGOPPXbQpo0M8v+hGQIgcmktghbR8QoL3d8i9eyOmfVen0N6vKIkSuaYfhQLFBevoPEKlYitnp3IL8snbX8JlkdWnkTiHBmH6DmkW/uAbNX+I9uifjFfurEDyWLE3YqT7sfasz7MXm+NscYiMoe9+WXF2JqrkfnAdEfsQH57/ZBNkXxm8Wdx8OysaYv0IiDbrDxkY2DJryX4Tfo0gZVHg7IpMdzo5f9D89RTT8m1114rBx98sGy++ea0XavVksPkSz3RyfB5OST/H5pEHdjGhtqUKBfSzt06vRB7RLNsi+5iWWQB9dc2RHzSdmqZWrclU9vf+Yvu5um/0Y2ptNfrEyGjOs6eXZ02llwtvxyfUo62T//VelAsmp4rbdQ+a/u1LMtfVsygfCjjr+XqnEZjqXNBy0JjVNql++p4odxFOVNe0/Iscq3Pozgw28r3pa9srfLiVvrL5rwXK61T28v8QG1Zv841lEdMVsQPlmMaaHzY/EFzBeUO8k2PhRUbby3WiMxptg6ydr0A2wcZatbFRCKCT3ziE/KTn/xE1q9fLyIiq1evluOPP15++9vfTmqbPwqAkU9oBgDv7k7k7g/bWNBdP9RHI7KhWgQJFUDIJrQJMrLt+Wb5z+SxPowEWzKQD6hYYP6yGHtFk84PKxcsvy2d1vizuGnZkU3f6otiiWIQ0an7RWLm5Qg7H8mVSJ5H7Wc6Ou0j56J6ou2twsObq0hmJN8j9iGboutU2d4bI+Qv68/k1ayfaB5a46fnTzf5h9Y75o83Pz1EY5dITAV6+YTm4IMPlpUrV8rs2b/7ra7x8XFZuXKl3H333Zv855qtVkve3ecnNF8c0Sc0WdBMEayFt3aTYGQmQv7YuSYFErI/0tcrlhCJ1Hp0v0iRhHzVfWrIrNdPX7cKQgarOIqSGxQDfc4jNRESj2RG7EI+I5s8n5Ecj1x6/ukYMMKN7NdtrJyKEugI+fTmF2rryffmnmWnR+K9vLDWoAhhRz6z/I6Qect+ZG9pm7XOWvM1uq4zm2uKFtRPX4vIiNgZKYQSiWFFLwuaxYsXy2WXXSZbbbXVxLl2uy3vfOc75Wtf+9rEuVarJYv7XNB8eUQLmvzI2RTBWqg7155YH/sfxDttdP9O27G5+FfCyvMdRMkham+Rs7JN6ZP2QbdFpEb7Y23spS4tj/Xv9GOvGUHUGzEaR90f2YViEd3kkd9lPzTepX6UE6XucoyRjjJGTCYj+No3Fi92rdSt41f21b6g69pO7Ye2G+WjRbSRDn0O5VB5nclE9uicQn113Ng80u0tn6yYoth12lhzTI8dWwtQgcB8R7Dyp7zOSDibw6U9qA1b71nxo2H5p3OIzSOvvz7XrQx0zYu/RlR/IjEK+G//7b/JqaeeKmvWrJk492//9m8yZ86cAVo1WsiCZoiANjBEFrx2emOwiJHeaBkBRnK1LK8NshXZV55nZBEVPZoos+LGItulzxbRtIovL25NiHvZBkH7rWNikQptW9kXxcZqy+xmRR/KN52T1pjpvuV1RuZKOTovGIFGNnkxZcUOG3vtH8pHplOPs1dYaDtZ/lpEnOlhhSObz8g+LR/FnOU2m+ue7+V7bYNVEHqFRukza2ut0aUu3cea25FCJ3qdFZy9hlXE1ew1qH0TGxKJqcKSJUtkt912k2OOOUYWLVokhx56qHzgAx+QQw45ZFLbdqvd12NUkR85GxGwu4JsQ9cEDZHcDqw7gwgWOdLtGKn29Fs2sn6lXEaCPT+ZXqbf8sVq68WC+RaVr2Wh/lbco/GL9KvJR0ZSPMLOYoBgzaNoTpS2oXPWnIzYymJi5a8VO+uclRfaHpafTCaz27LJyr9SFoPX1yoAWJ4h2yw9lm9anrbZiyOzhcGbW9p+T16TNqx9NBdY+4jv/Si8EgmR3n7krIP169fLL37xC3nyySdl5513lhe/+MWbXG+1WvKu1hd7qlNjZfvdI/mRsyxohgxsA2XXy3OoX5QwWYWAJcOyuZaYeCQwalNNIWDBIn2ePktWKQ/BKiS0TtQO6WLtLAIfKUZYX+t1jS7UN0puPVlWQef5VcZQ28H0lojojeRWbWHTDSGMrBFszni5jnIrUshZ48VsiBZ01vgxX7zY1hB7JlP7GLHF09GRFy2SPJlIvmVzbeGRhUpiGNCPgsZDFjQc+ZGzIYBHyrxznfdRAum1Y5typ6+2PbIRl3o7h9bnxcGKBSNdnffMD2Sj9qOUV7azCCeSU9qjY2zFpGO7VbSUuj2ZJUpfLOgcQ7EoiZD1GhE0ZifKdeYXy4XSTnSdzRWts7RZvy91IJ+Yj2gsvbG1+jPyi2QhsmzpQbahOYXmnY5JmdeWH9Y4e3agPlbxE5kHHXhFFLOz7I9kRAtWK0esYhD10XM0WqSwQlwjur6gOWW1j8KTlUiMGvJnmzHyCc2QInJXs4TekDrnSlleEaE3s8im4m1+TIa1EUaKCksGIxaRtqXcSOGnSQC624l0M9lNbLeuRwhqJJ+sOCFbLN01/jHSy/QiRPQyYm35y+yJ5Ki2zYsHi7XlQ2SsShkWSWZjYBUeEWJtrU01tjB/vHnDwHIfxbjJX+STtx5E1lm2JnvjaMWGybNsixR1UUTGOZEYBAb1hObP+/yE5qv5hGawWL58ubRarYlj1KE3PXb3Tbctwe4CovboDpm+u6n/MtJg3VEsdaPzFsEt7yaW7fUdwNLvjn0Rcs2u63iUcnUcUBurkCz7MSLjFSVIr0cSWQ5om0od3jgzlPZ4hQgaX53/ZbzYOZRvpQ7Up7QH2eQVGdEiAtlettF5zuZY6YOOpTVXWE7p+W/1YTmJxkvbY0HnHNKHXuu/aGy0L2x9ZCRZj6HVHr1HcwqNj36N5KFYsDHw5FnrZykP+YNigWxk+Wv107YiGVnMJKYTnnzySVm1apX86le/Mtu1+/xvVJFPaEYQkc1Gt9UbkEdoGFAxw/RZNjTRw66j90wmKiRQPFlxodugWFgFEisyPBmerojdnj1IDiNubJy9cdN9yr6lfvQatdW60TXdT8vW8bDiq2OE5OpzCF78Pf+9GLJ21nhrGSyWXvwZwWW5YZH16HqjfUK+ROe2p7vGR/1a+2fZgGRH1tsaeDlSo8srMGoLEM/XXusbNoy6/TMBvXxC8+Mf/1g+9rGPyeabby4bN26Ub3zjG3LXXXfJ7rvvLrNm/cezh1arJe9sfaEnOhmuah+aT2gS/QW64+YtenrxL/uUd/RQnxp7WF99F66jl+lG8hgJ036wDbf0G/kQIdjobqJXHGl52l4kT49p6Rtq5/lRxqB8rX3SMSr/at06lyJAxET7g4gmiheKn87r0m4W+zIXUXx1vLT9nr/I9lKGFX9GuvU4lPZ45M4CI7HRtYCtRToOyOfOeTTP9Wtkj5UrEZuRXDYWno9IHspXb5xRXzTv2doVHW8v97Vu3d+zpQRaLyw7UdxrctO7Hl27BoUsZmYWLr/8cjnjjDPk6quvli233FJERL75zW/KF7/Y34+XTSdkQTNCYJt5uTkhgsAIv0WINYHQxM/apJl+1raUrzdNi3BoH1BsUPHDCKpFarReZKPWoWOnbWbkzvtb6mK2sILHIoZRQln64cWSkUxdhFgkzhpjXfxZueURfyZHzy82xzQBs+KB8sSyjeUK6oNIo0WO0BiV48KKWR0DrR8Vw5YuHT9PrvZBX0N5inJNr2novdbD7NLtdRxQvmo7GfTcZUUJi7GWhWLF1gfvPNOD5pGGVeRY61UE3v6QSAwL1q9fL7vvvvsm544//nj57ne/O6lt/igARn7kbMgQuUOmN2hPFlrUNWnsnGN34tB1i4wyYs027xq9zFftn0XQI/pQXxbTyOZoxUnrRQQMkXKLeLEYWmPDCiXta0S21Z/Zi3IM9WPt9fWI/0hXBF4/RtyZX579TJeXIxasWFiFJuqLbGXjYq0/nj01a5415yNg88HKeRYzL7+sogvZxa4xu5kPlg4vhyNr8qAKhybjPQiMip2JyejlR87+4i/+Qk488UR56UtfKocccoh86UtfEhGRQw89VL7whf/4iFmr1ZJ39PkjZ1fnR84SvUCTDaK8A8buFkbu2iF9bKND8pGdEaKD5DL/dFvUB8kubUHxQv06MbOIivYb2YhkRuR12qD2egysQidCHjWs8dX2lm07/iGbkHztm/bJynUvPpatjJCW+vT4a3nsHIoBI6ql7ag9ipFlVzkGZSysfGG5h3xD9iGg6xZh9+aspYfZiuxhecbWGASrSCrjr8fW81X/LfMI+aLPoXaRIkqDrd+eLNQ/uudEZbO+qL+Xn03i1Q+g9Ssx83D44YfLhz/8YVm5cqVs2LBBHnnkEbnhhhvk+c9//qS2+YQGI5/QTENENuJOO0Qu0HkmBxFE61rknH7NgIiEJbPswzZARpA8P5kt2k7WJmKb5SuLFxoPdC1iNyoeouOk+1q6dTtml5WfVmzQGGqftHzkt+5ntUX62XvmA7JV97cIvaWb+cdsiNqGZNWArSkR2y2Z2rZSl6UTyffWHG1rZOxYrKz5oe1t6kNkjCI52Ss0GeOIrMj5RMJCr3+2+fbbb5cvfOELcuedd8qGDRvkpS99qSxbtkxe9rKXTbRptVpyYJ+f0Fwzok9osqAZMTTdNCwSGXkdJVCMFFobt5YbJecWCWN6LB2RwsuSyfz0SKJVIHlEMkLMdZyRz7XvI8Q40tYjWsj+CGmPnEN6m+QfkhmZP6xwYoWUV1hYsmvIsrUWaFlWX8tGzw6mJ2pTpKhiY2PpsOYq0stg6dXXo2PprQXeWsLOefBssNrXXKtp041tHWSB03+MeiHZi4LmggsukIULF8qCBQtk5513llmzZkm73ZYNGzbI5ptvPql9q9WSRa3Pd6XTwzfah2VBM0yYrgWNBUZ2owTB2/zKa/p1B9HNPkp8tW4kS+tnZJHpZvaxgsfS6enWNpR2WD5Z8iLxs/IhYmeU7CKbSj9RG/S6RE18Ij55+rX9HvHWNjA91tz07GTwCLBnf4TQM31IhyVTy42Od7SAs4op5FNEhtWHxcWaX15hF1mza2yK9GfXPLLpjfUgUUuUrf1tUD6NOtm3MF1860VBc9FFF8mqVavk3nvvlVarJbvttpssWLBgosjZcccdN+GzWdBw5HdohhzRO36MjJXnyrbeJqvbPbF+8s9El6/L651Dv9f2WuRB26QXP+ab7lte1zbquGn7Ou2R79pOrZORDO0LI8Csnb7G/nb6WEWaHgMkA8UEyUN9yutlPFE79FrnEYuNtlHbVKNTj7XOd+1PqVfL14Q2Muf02Frjqf3W7XT8LVtQTNncLa+zucf894qLMh6sfykf2eatmZYuvT4geVYxo8+jdY+tecw/PQ46LjrWVkHFbLMKMuRred2aj148Spkoh5HeCLw1FrVH6wXLsUR3sHKzxEyI/dFHHy0XXHCBfOtb35Lzzz9f3vSmN8mzzz4rX//61+X973+/LFq0aNAmjgzyCc00ASPGesPsnNfvWUGEiJ2lD8llxNx7jVBznfnNfEBkyyMH6HzURkRsEIHXNlubgUVCIv55vnj+enJYAch8t3Rbsiw/PTLtxaeElbdRHz2ZXg5ECHbNOmDFwNJX+snOW2PH5GsfvDZaH8sPHRemC/WJ+BAZawbPLvbaGnsvL0rfOvDWiRqfmsDK9WjuR+U3tW0q4jAdEYlbNMcHhV5+h+anP/2p7LbbbjI2NjZxbsOGDbJmzRpZsGDBxLlWqyX7z+rvE5pvjo/mE5rZgzYg0RyMfFgEqDwXka/bWv2iRQnrh+QzcmVdY4QKkRstS7e1ZLPiKUKSkB3WeFoFkJbBEPXPshHFogNrg4kWEchG3bdzziqMPCB/tZ1ebtWQK+91CY8go/xGMWC5Yc2l8nzpk0eu2ZrDCD9qh4DiahVq3phEi6YyDkxWea20C8XS0huxu5Rl+Yz6WbK0XczHQRUulm+er9FxjtiEgMZ7GEj2qKBmv+i0n87F4yc/+Ul56KGHZLvttpP58+fLrrvuKrvuuqvMnz9/0KaNDPIJzTSBV8hYZNprg4iod648b+lA7Rmh9Ig2K2Q61zxyxQoHfZ3JRbD6W76xYg3JsuQi31lh5BFdZiPTiWShuGi/rPgxmxFqCopoDFFcGKx8suaq9lHbhPqxuYL8tHRq+5HNkTmMYmDZ4BFCNu+9OEblMHmenM7riO21cq3zkbyK5EHNel5r01ShiV5WQA2LTzMRNfvosKCXT2i+/e1vy2233SavetWrZN26dfI//+f/lHvvvVc2btwoL37xi2XfffeVP//zP5fNNttM3j7rcz3RyfCt8ffkE5rEYIDuBrLr+s7X2NzJ/ys221B1v877zkZpFRnITkYWkV5mi0Uqy+uIjFrEFsm2CC+zC8XZI31oQ9X6LMIbId5aDyPD1jWreLHsQPllFZHab0umRdAQdAyQnhoZZYxQbjCZLIaoD8sFLYuRWYvMa/tR7lnFpFcgeTazfnrdQvFA5/VfqxBj59hfFBMUH1YIonXXWoeteY36sZxAslAcWNHE+kT2DXS9STGCfKoByz0tq8ZutoYnYmDrKMut6YZrrrlGLrvsMpk163dfbX/3u98t11xzjcybN0+23XZb+eIXvyhPPvnkgK0cbuSPAkwDdCa8nvhoYy0JBSIV1kbdgUfUNLxixyJ21qZVQ/4Y+UIkruzrbZos5jU26n6s+GIxZAUGaqf9YcRI24g2a2/8tI4oWfNkIRJY+s5IhUVmmQ0sLpGiALVl+c3GrpzXls6ODJQLaC5FSQIqQHRhW7YtbUCxQPmmc5utZ57vlu2lfWhOReLLihndluUTW4eRft0GrUVIHsoFHY9OO5SjEQIfIf3emhK5ZqEbgmvlSrcYxWJG50qvYtGNLaMWw17gsccekw0bNmxy7sADD5Trr79e/vAP/1A+/vGPy/e+970BWTcayIJmGgNt6GjzQ+3RXSu2oWkyya7pvl7RwooN1lYDEQe0cFubOIoZKgytTQARPO2bZQcqOpFNKE66vya9jHxbhRoroLRNVuy1fYjYWySNjadV+JXtylgx4m0RxsiGy0g5skX7ov1BxRuyA+WmNRYsjsh3rc8qxpgvup+V+x7xRP0j8en8RTnO9Om8tNY8FgcUay+eKC9Rfyv2LDZo7kf3AJ1XGkxnCVZ0eYS6Kdm1yHJUppWvbP8bZIEQQWQe9ANsL46uBdMNe+21l3zkIx+RO+64Q5577jkREXnwwQfl4YcfFhGRLbfccqLgGe/zMarIgmYawSKy1iJhbT5sE7CKAI3Ixof0eRszIiU1xDNKSjU8QuHJZiTHOo9IYNlW24QKL0awme6yXylX+61Jt+6H4uTlGSuC2HiUvllEm22YrL32BcXDKiCYr14Rx+xDsjQYydbFGytc0LVynFlBVsaO5Z7210JknFA+ReYkK4zQPEJj6hEvK666vbUG6Dh6fkTAdKL5zWRHCind1juv86SXKGV3Q46tnIzMxcj8nSlA6wbay2YKjjnmGFmwYIF89KMflX333VcOOuggOeKII+Ttb3+7tNttufDCC2XHHXcctJlDjfxRgGkCj2igDQkRGKudd17L1HoRKWBtojYxAs3uxCHSzEgHkhcln8xnJhfFAdmO5OvrkTzwYs90R8g+GwvPvmhsUW6gPPDy27I7UkhpfbofszNqM/PB0lsTW2usvLhY5y17UGxQTCOymP8188jzHfXRPuhzlk1sffPWKgRvTlrjHJUZ7Re1uWmfaBuR+FzttY0Rm7qxYZjRS79GKUa9/FGADjZs2CC/+tWv5Ne//rVsu+22stNOO4mIyJVXXilve9vb5EUvepHsO+vKnurUuH788JH8UYAsaGYAPAJlFQ1eEWIRQK9g0Hq0XIsgo776OvOHyUGoJd8RQuX11z54vltEHPnO+kWJdw2ZriVXLOc8Xz3dkYJK+6PjgGxhsUZ9ke7yXBOSbhUVrE2EAEflM/+8wqx8beWgZadGzdqCbPdgjVPNPLd8rJXPdEYLJC83yvPRPOq8Ln2tKaoGgUgceqVHxF+DZirYGigynE9relnQ3HjjjfKVr3xF5s2bJ7vssovMnz9/4uebt9xyy4l2rVYrCxqCLGhmEDxSq9t2YJFDdF2fL9+Xsq0Ci9nCZKPrur9HJpFsq/DzCBmyp2ZDqyW4EZ8Yya7xF/WNFmYWwWd6vXYWcY2Q1miRhs5b8YjY6xV+EVu1DvQ+kkse6bTWANQ/Igu99mSh9p0+ns1enBiipNPSZxUxqG83sYzkiLXWWrY3LYw81BRiUd1TTYi9cSrtiM67mYYm83NQ6GVBs3jxYjn88MOl1WrJ/fffL6tXr5Z7771XHn30Ubnxxhsn2rVaLXnLrM/2RCfDd8aXjGRBk9+hmeboLJqMBOh2JZ5YP/mL0XpzixQ5CBEC0Tl0e/ba86c8r/t1fLFIZemPXnBRnEvZ2hdtX9m/c5T9ImRIX0ebAvKhc67U58W4zA10ziokEOHQMSzlWTLLWKKYaju172wsGLHS9qP3pT+MtLA8qiHqzG90ziPDng7kg5bvzbnytZ4TTJaVRzqekZzTMUbvkb36uj4s2z3btB7kk7VeWgWHlQfRea3Ps4LBkluCxaIpabV8Quv6VCDqYzR/ZxKGtXjpN3bZZRd505veJG984xvlyCOPlE984hNy9dVXy5e//OVBmzYymDYFzfLly6XVak0cMx1oM7WIm97UmbxOW01I2J09fU7bVF4v4Z1jxYV3d06TdlZIIN+ZPwioENTyWFHD5KDCqJSPdKANHRVfaNxRjKKEO3IN2ar90rrQ2JW6tN+WHax4ZaSQxcoqCrTN2udIkWQR40iBWspAfTQxZ/NA24ViHJnLbC0p7UHx0DaidUQXGqxwssZM+8SKAj23dNwYuS5tsHIL+RgpjNAajPqyoqy8xnJOA62lum2NH1q2RmSfGgTY2KNzbB9EcbQwaJ9LeGMyav5MJV72spfJ6tWrJ51/0YteNABrRhTtaQoRyaM4xuaetsnrzqGv67+ovyfTksHklH0825AsplfLReeYPmSPZ4ulx4qdZa93zbIvIgeNSSRnmEx2vaZ/7Xgx+db41cTXssOLvXXdyl0vhlZcauZxzVhG7PNkWXOIHVF9Vhv0msmI2B0d1+i4eOtE5GDrSpOxbxL3Wnu77duNvibye6XPm0dT4duwHZGYDNvx6KOP9oyzHn300e3999+/femll7Z//OMftx955BHKbf+/WZ/t6yEymqXBtHlCk7DB7gCPgTvEY86dPU+mdZfVu2Oo21lt9HWmF51DGHPu6jK79ZMSpntsLr/zqq+X/pS2dPqipzVaLhtbZrvlc9lW26ptK3UxG/QRzRXrzieLC2rbeY1sR3JKO0rb2HhbY8vGSJ/TsSqfCGi5yD9mC7JJzyPdzrJT26ftseZA2Q49UdM5580DHTstS+vTT4i0DCRT+8TGw8oNth5F1ilko5ZfyrLmDpu/Oi5orrK5wWR7PjC5vYJlC4O1ftTqjcpheRXdj9i5Jm1q+9fKjLZvEvdRw+LFi+Xggw+Whx56SM4//3w5+OCD5b//9/8uf/u3fzto00YGWdBMU1iLn0XqEDwyquVoGxBZQIQgUoiwx/QWUWAbeecaIooR8lieZ0QNIVL4ea8ZSbFICTundSNCXb5mZK60i51jMY7kjiZrERJbvkakQBeCVm6x+DAbSh+RDxpaXzSWKE+RTkR4USFixVC/LvU3JTMor7TdpV5Nqhn5tuYGK1JQfqAiQdtvgRVreg2tgUX+uyV/bO51/uo46nzSa2tkXWZrBJtX1r7DwNasfiKyH0Sgc0hfY+es2HRrV3Svjcqw9unpjO9973uyYcMG2XvvveWQQw6Rk08+Wb7whS/ItddeKx/5yEfkv/yX/zKpz3ir3ddjVJEFzTQFWxzY5qzfW6TLImhogUIkkJGVUrcmm2Vf3RaRI2Q7Is6oPSM2elNkpBMRRURkUPxQrJCvyD9kS6lT90dksdRrkQCLuKNxYmRVF0jaTou8ousMbMxYAYhsZrFgcbD0IVnIN08mKsRQDBG5RDHxbEJ9rfmj1xwdRzS3UHv0vtSn5SFfWD+dayw/UVy88UQ6uy1EWPGl17NIQYDGQ89/FAOUm7UkV6+Dlk9s75rKAqUWNQWXhVoZbI/vJ5r4GbkZMN3x5S9/WZ544gk54ogj5PTTT5evfe1r8tOf/lTGx8fl93//9+XAAw8ctImjg0F/5q1fkCH4fOWwHGPkc9Vjwc8HW+3GyOf/9bkx8Nls1E/rQbrZeaRHH0y2ZS/TE/WN+cBkoj7M94gPTDezMRqLyHiwGKD+TL7luzW+kVyydNTE1oqllsn0eLkeiaFnd3TeofPeHLVy2opZRL+VB8yW2vyvWSeisfH88HK0RrZ1vTb23prWJD964Vetz93I61b/VMudSl8HdQybj738Ds0Pf/jD9uc///n2SSed1F68eHF7n332aS9evLh90kknTeK2b9zsM309REazNMgnNDMA7G4Wujs7Ru6Sle1Yv8jd7MgdU6ZX24VsKm2J3Gku+48VdwHRkxjtI5LL7jLruLJz2o/S9/KOqL6m22lbS1usu6isP4qZtgnFROcI08v8tuxktqEx1uOI/NPx1fKs2ESeGmgdkbuP2h7tJ8r/zvXOeTY3y3b6tfYdoRx/pIPNCWu9Qa/R/Gf5gp7SWD7Ugq196HX0qY61tmiZnfdoDK34sP46j3R/5C/LdfQa5bk1xt4TDStf2HxG/Sz7m8Aav24RlRuJXQTW2torNJU1nZ/Y/Mmf/Ikceuihsnz5cvnSl74k11xzjfzlX/6lvPKVr5zUtt+V2shi0BVVvyBDUL2PwsHueIwF71Ki19ZdFCZX60Ayvb5aP/pr+RL1JxobrdOy0fOjyThFxtiyHcWZ6WE+snGz4hIZL6tPJJ7WuCK9ljyWL1H5lv3e2HhzKprHkfkamR/WfPX6RHR4Pnjjb+W8lf9WPnjzKTJHLb3e2Hhzoma8vfyy8szzJSK3iZya/M2jv8dMi3cvn9B8//vfb5933nntdrvd3rBhQ/s73/lO+5/+6Z/a4+Pjk7jtPpt9pq+HyGiWBvmEZgZjzPnscvnauwOt+3Sua9mRpyRlH/157vJ82VfbXvpm9dN349GTFeuOtn5iwu6SsScRzAcUG31e90d3pcu/6HX5nt3NZE96UFvmNzvPnu4gvUgPGsdSDooHu/uMnqIweGNtXUdATzHQ3EI5o/ugp0JlO8t+NP9KfZ5POoYotta6g+YXGrPyuu6H3iMbtc8aLFfYfGM+l/3YEws2rlZbFgttb3muHF+2DrAxY3kRyQk2P7VP7L32F/kfgbde6ba9Qq2sXuqeSvTyCcqoxqApPvvZz8qb3/xmERG55JJL5JprrpGrr75arrzyykltx6Xd12NUMXvQBiQGB0QSRCYTBW8jQW1RP0SK9KYW/diFtgeRH6s9eu/JsTZk1Aa9ZoWClofIGtLvEbkIqdbkUvdhpAjZrtvreEYJcZS0RcdJt0NETPvPfEJkDhFb1FYjkq+M2LO8YDGwfGdty3M1+ecVdSinSt268ELykUyrGEYFHZvDqDjQ9qGxRz5YsUZttK16/mh7td2ocGK548UCxcC7zuxCOVGer7ER6Wd7lTWe1vyLzssIaol+t4VBjW1N+zTRUYN+yh5GtFotWbhwoTz11FNy4403ypVXXilbbLGFfOADH5AlS5YM2ryRQBY0iUnwSBhqy14zksEIICJBFrG0iAgjfNaGHCluGFlDpFPDIndaLiLKiNBFyS4roHQf5j8j9ciPUgbzgxFiXfh4ZBCB5YxVtLDCyCpOtI1IhiZiEfLHchSRZovMWSjHR59jfkTy3jofKRR1Xyv3WRFRnousZ5FCAbW3Yqzz3ysgWVyRHqYXxYzNJ4/Ys6LNmltN94vSXysHrRhECDDr00sy300x0asioYmMXhZd/S52piPa7basX79evve978mee+4p22yzjYj8rtDRGOWnKP1EfuQsARHdnCxCizZyjwBq+Ui2Rab0NU0mkexOP03uvD6W/UhWpCDQJMSzt5ThkcUyhhYh1yTMKkBK2zRxYuc8Uoxix2Kv/dBxY7FEsWpS/CF9bJy9/NL60BgzWxBhZfZ4pD5SHLD8ZPLR2sDyE/nZTW5reaxQZgUsi63uw3KvfM3mrZbF4sX0o75srUVjxMaylBkpZpCd0XW8PG+tNWwcUXu2N0TWFwvRfatWHovvVKEbndb62S0GEYupxp/+6Z/KkUceKZdeeqm84x3vEBGRtWvXyoYNGwZs2eggn9AkIBgpQRu2V9Sg/mjT1vpZUaVJglXUIDsZeWKEEOlGhAD5h3ysKYy0bEbGPZJawhpHL476tR4LpKsp0DhZY1i2YXFibVEbqy/z24ojGyNWAKFCic035LOVF8hHbZNliyaekfnM3pe5HbUxQlKZP53+rMBkOWGNJyPfSK4eSxZDNqbW/PdyzZo3Vj/tf7kWReY48ssqWpAtrC8rgiLzt7xeM29q2vUK3tj2AlHZkT2I5W+py4qh3k97gakesxosWbJE/uAP/kBe+MIXyk477SQiIueee67sv//+A7ZshDDoXyXoF2QIfgFjuhxjFb9cMub8ys4Y+bUbdB61Y9dZH69/py9qg+RadqHrXjy6sZnZi+xBfiI50TFnepltln+e3d6Ye3Fj4+21ieSi5ac3dpF4euPIfGXnIjkXyTfviMwfb87UjLcXm4jPkTyz3lvzyhqzmnFsMv6RHPfyxJuPNbZG8tEbN88nz/ZInvTj8OLatH8T/d3MTa9vZPy6jdkwHL38lTOEJ554AnLb/3ezy/t6iIxmaZAfOZvhGDPucndQc0eDPYkYM+7osDv7nQM91WE69TmmB/kX8Zm1Ke3VdozNxXd4UT99zZJTtit9RHYxn9ATJWQDsrGjl91d957YlP3Z9bKd9sXqZ93NLf/qNmUs2ViwOYPs0bazp1/obiW6A+7FmT35QDZHz5Wx9u7K6/GJzBE0rjrXkA1aRqmzjDfLDe2PHh80HshPLVvHTr9G7bz1qIyBldORJyxlWzaHdRyQPeV1NOcsWyP2IkT2K++aZ5clB62HUTssPd5+U2NnVD/bJ6z1LfrExJt35Xn91/PZWou9+I8attpqq0GbMFJotdvt9qCN6AfQF6kSdUAbbzeyRPyPKDHdaEFE7y05VjGCSI62VV9H/iD7LLsYgSzbebDsYXKYj9pG3dbym/lj+WbFDOmy/EV6auJcM8aeDV58LVn6mhcvfb0Ek8F8Zn2t+WrpZXMqEi99zYuP1o9kR9Y0ZIe1hnjEyxvLSNzZWHhx8l5b/tesGRYJZvHwbCivR8gt0xexpzxn+WTlcbQoqu1bmztMXq/2c2tN6QWi607ZvkR0niN9tXj00Udl6623btS3g1/96leyww47hNu3Wi35s80u60qnh+9vPFJGsTTIJzQJCr1oWXdGUBsmC73X/b1NRb9GtnoLlfZH67QKHK1L26TtYvHS7ZFtnr+6n7a1894jtkhGST70OX0ebSzIdssvRoQRGUL+WrH0xkXnHrOX2V3q8OZKeT6ygWsSYxFFJNcCstlCKV/38eYrOh8hZaWNbK4jn3T+RvzS+RdZu8r+aA6i8bIIqYYVQx0bNBdQ/LwcZflura0oXshXvT7pMWRydB9r3pexYesw6oNkW2DzkbWNnGPXPbusuEXWAst2tDejucXOMz218ULy0TyIyGc6phLHHnusiIhcdNFFA7VjWmDAH3nrG2QIPl+Zh/+55k4b3R71GTM+A23p9dpZdjKdTCZqY/Xz2pR6I7YyO1EfpCdqG2rL2mjZaPyiY8rGgtlv+cRsi4w7iw0aAxYjdr3pGEbmnjWOLB/YWEXmgdXOi3XNmEdj48XDmx+RdpE8s8bIk+3pZHGLtovkYzTHI35Gc7gm16xxZvIiORsZp8g1b9y9nGty1KwdvZITHT/rvPeXyYrI9eaHd/TiOzRHHnlk+6yzzmovXry4/e///u8hbvtnm13W10NkNEuDoX1C8+CDD8oJJ5wgRx11lCxdulTuuOOOQZuUIBgDd2nH5vI7nBrsDujYXPzER99103rHjDvTSF4pt5TRkWPJYzLHyJ0rdMdY99d3y9ndJ9SP2YDuajP/kYzonS/to7aNxRLdUUc+sLwq+5ZjZ+USsgWNj77TXfZDd531az0fkP06BrqtHgcElE/eXIr09Z5SlLJ17NkdVuafBhpz6z2yn9lo2eDlsY4Vu47yROe0NTf1XNd2ax8Q0Fyx8onZwPLVm1tevmqdbD20YqjbsXGychKts9acKe1m49TRyXR5fus1zLPFgpbDxq4Wnn3enmeNR3ndWhd1H2SXt4Z686hf+Ju/+RuZPXu2PPzww3LYYYfJokWL5C//8i/lkksukZtuuknuv//+SX3Gpd3XY1QxtAXN+eefL29961vl0ksvlaOOOkouuOCCQZuUILDIDgIiFp33bHErNxjUH21kWibaeBBBZeQVnWMbN5OnZSB9LGZsYWabBNoM2YaPNgdmv0VWrE3Ga6vPM5IUue4RS02Gtf7yL9KhSTEimlq3Po/ACJK2D42Vtl+fY2SNEW/PVq3P8xXlqs4zNi5ojdH5HrHTKh60PWWcrDmO/GR+oFijeWuRW68AQWBjwMaerR06z9mYI0RyK7Lelud1Hz2+7Bwjv7qNto3tXag9gqXXi2NTom3NpW7mu24fKWys/R3psGzxChhmt1UsReZSr7HzzjvLcccdJ6997Wvl7//+7+WMM86Q17/+9fLss8/Kt771LfngBz84pfaMNAb9iIhh7dq17Y0bN7bb7Xb7wQcfbB9yyCFV/aUHj2jz6P0x1vBjHKhN2U7/1f3QdUsO6s/koWvWOctPFhtPjucTi7G+huzz9LE+VvuoTtQuEl/UN2KnZxvSj8aJvY7keiT+0XGpHQtmg5eftTle28/rXzt+uq03bjU6rSM6jyy7Irqt8945y5/ImoHkeK89nZYMa9zZ+mCNp+dTbYw8WRF7IvnTj6MmD5r092Jpxd8a++hcifrBjn78bPOzzz67yfsODy657d6zV/T1EBna0sDE0D6hef7zny+zZv3OvJUrV8rb3va2AVuU6AVq7g6NOXerx8gdurHijhG7sztG7sJZtjJ57I64tpP1Rz5pey255euOb8iv8hqS2YmjvubdrUX26zteeqzKcWBPXLynKFYs0R1dNk4oF1iOsHypvbvK4qfjhvK6PG/J9MZOy9H9WZ5o2VZ+ap2ReYH6afnobmspD73W+aafWDE/tR7WpoSXJyzfrfaRJylsXmsZul3Zl80VrRONKbLDih3yg9mn27P+aN6w9btsz/IYIRIfLdNbk3XeonbefmGtybVg60zHPssWy49SRilLr7fMbm+82L6BcgzlHFub+401a9bI+9//ftl3331l0aJFsmzZMrntttsmeHDCx0B/tvmf//mf5Wtf+9om58bGxuT0008XEZHx8XE5//zzZcOGDXLCCSfAgV2+fLmccsopU2FuogH0JsoIAlsAWdtyo7L6ajkarE9EprewWuQCARUWzD/dhtmMCgykl5Ed5Fepv0aHthvpt84jomqRWeQj6ov8YvIiG7QHiyggX1hOWfZYeW3lGZufVn8tS+v35jojatG1wounB09XJA6lPZ5PkXXDI7c152rG2RtT9jpqB0JknuvXVtwsWxCa5E/NWoL6RGyxxq2JndHzUfu0LC2v23np6UZ7hbahtq+GZ38vfra5g2OPPVZe//rXy2te8xr57W9/K7fddpv8/d//vRxzzDHyX//rf51o12q1ZK/Zl/ZEJ8OPNhw1kj/bPLTPlTZu3Ng+5ZRT2pdddlmj/jKFj2XziD8qRufHyKN2/XpsLv4YAZLH9EVtK+Wgo4kPzEZLXjdtmH2oP/PRijUbo4g+Zrc1dkyXFwNkCxsnFBPPribjbJ2zxtLSH8kpK1ct/718suaCN1+jPjM5Xv5748Tys0aOFfeI75Yd0VyI+uDlgJVTkfUomieWLpYfbJ3y/IjYFLEvMi4sXpHx92z3xqUmX2riYeV2TV5Z5yJx7WaOszHx1k7r6OVHzo444ohJ5x588MH2UUcdNYnb7jX70r4eIkNbGpgYWqu/9KUvtT/1qU817h9NyDwGc3RDGprKs/paRC5yTbfTcrttixZbtslaBMTzK0IsImMU2dgiRKp2HNhmX7vpW+NixShKOljsIvZGN+SmPteMY40+FksvFpG88fLJ8jt6WDqi/WvbNVknu/HBu+aNb9P8is7XSE5H1rDacWR5yq5FYmHJjPjUJJ+a5lXE1tprNfZH105PNlsvm87rXhY0hx9+ePupp56C5zW3zYIGY2itXrRoUfuwww5rv/e97504nnzyyXD/JsmZx2COXhAOi+hE+jCihdp7etBCaZFidq1Je28hrxkPtokw8lwzJhHZtfIjZCtikxVnz5bIOFh9Iq9RTlg+R/PAyrPaedXNXGqSl71YQ2r9tGLYjdwIebRkeeuPF0tLbm2Oe3bWrKWejsj6zNbjyHh649EkB7uZZ73oU5urNfHodayi42nlTmR/qVmbelnQXHHFFe0Pf/jD7TVr1kyc+/nPf94+8sgjJ3HbP5p9SV8PkaEtDUyMptUBdDNZ8hjOI7KIeX30+c5rb8O3NuMI2fVk1hJUtDBHyYi3qEdItWW7R2J1H88+K0ZMNopXJFesjS0yjlY+WXGPbqDMryjZsnK9xi5vzjB7vPOR3I+MdyR2vWzbqz61OVsjx8rB6PjV+l+bq96cQ689udF8t/KxSX5F9ETiweLjrederKy5bcWjxg8vHpH4RvaMiIyaPSWa/70saDZu3Ni+5JJL2m95y1vaixYtai9evLi93377tX/0ox9N4rZZ0GCMptUBRJIxj8EeTTdNJMdbjKwNipHYGhKI+lv6oxtDxG6LBDJ9ns8Ru5ifzFdvQ6ohGk19qokRincN0fHG0ZsPUdJRE6do7kQOj6BYMfT0ReyJkjHL3hp9NfbU2taNf96cs3LKG0NvTtbEMDLukZhG1mNr3tSsWaxNbe5E/IisV1bMa+zw5n90fkbXp0i7mpyLymd+I3lN8rMfP9v81FNPtX/2s5+1b7311vYjjzwCue0fzr6kr4fIaJYGo2l1ADVJmcfUH70gEE1leZtyhJRF7YgSjKgv3kbLzlmymupnGwHTz8hUjd1e/4hv2u4aAhcZh0jeoFhYRC2SW5FrkY09Mi9YHjeZG5YN3hG1K5rfw3LUrgPe2Hrnu82zpn5EbPPywlp7Iutf1B5vrnr+sjnP/IzYb42jp8vLneh6EFkLo2tjzRhb67k1PizukTzVRz8Kmgi3zYIGYzStDqBJcuYxnEcTklP+teTUEFOvfQ1BYwuqtdA2IY2RuLKNIWKHRyZr4uT54hGASPwi+r088XRGx8KKN8uVJmSkZh5Ec6i2rRf/WhmRtr30YVBHUxutHEXz3cqfyDh0Y6f32tNvva7xwetjzRHvWrRN5L01lpHYRcYjMs7Mn2hcma5oLGrGms0Jy4dIvAZV0Lx69sV9PURGszTI/7EnMXQYC/yHWKxtp335t9NOy0GyO+87csfm4v+ssJSp9SJbkH/6L7Pdk1na6MWmvMbiwXSOgd/5Z/9nRRkbrcv6/2pKHciv6P/HEPm/BVDMOtdL/SzHOtd0nqFYID87etj/66LPadu1fmvsmB3Wee2TlhnxUdsVydPonLbsjMr08qg2ZtqvSGwteR07a9qXfVCOlHNIvy/7W/9HiZ6fkVhqeL40Oc/WcmuNRX6gdcdbk3Vfq53Wy/zwxlnLLmWw6966ZslE8UUxLm0oc80ah8gcZ7YgW5Etuj/L56gdvcSDDz44pfqmJQZdUfULUnmnKI/hPsbAnSHWLipPt2evvf4RW/RrTxfzt0am9d6Lm9ZtXY/Y7vljtWE6I2NQE39LfyTuLBa6bUS/l4cRnyPjE83ZaL5YPtfIjM7jpu2jMe6FjprctMbIi53nhzfHo35bec7siMxlNidqx6XJvKgZW9SXrZO6fe189MYxshZZdjVdO5vkGxvnqG2RdaTJONasc/ro5ROafffdt33hhRe2161b53LbfEKDMZpWBxBN6jym19GLjaGbtpENGLXXG7ul1yM7SGYkPlFd6Hxkk0I+eySmG5KArnlkxBoTNmZe3kRt8Gz3chrZHyEe3nlrnKMya9t4Ma2V36RPjU29PHpJ1iL5xfKf5VgT26LrnEeGkbxozlm56623SFZ0fllrgLXmeTHyYlwzfrX5x/Kr2/kQHRcv9ijHIv2jsjtHLwuaBx54oH3aaae1DzjggPbKlSvb69evp9x2z9mf7ushMpqlwWhaHUA3kyqP6XX0gszUbiLdbLLe5sqIK5LtbTbW4l1LMiwfI5shk1tLoqz4WfZZMbbibPkR0cfGt0m+1uRhlChFxwTFoldEJxLXbmyujXMTW2r7RnMqqrfJmDTxxSLivco1z05vLa2dQ9Z66ulGepk93vz37K6JW01+Rfe5mrGK7EfW3xp/o23Z+sz69OM7NGvWrGmfdNJJ7cWLF7dvvPHG9vj4+CRumwUNxmhaHUBkkuYx/Y7oQlSeb7rRW5sck8MW5Bobahd167q1wbINrykZicQI+cPsaUL2utmYvY0uQhC6bWPFINonEr8IoaohXdH50VRnk2Mq5NbEtVt7aggdGpfaNaypTk9GEzsiMWs6V7w1kl2LxiWy9jaJRU18rPWuJs96NT4o7k3Wm6heNJ6RtbqfPwpw9913t4899tj20qVL2z/72c824bavmn1RXw+R0SwN8kcBEiOFMeeLttaXD0sZuo9uV+pB+iI/JMC+LIl0Rr9gjfzS/iL/OueiX4KOtrPGAtnGbCxjhr4kj75M6325VttR2qO/dMu+YMriXur08qc8h/zwcq3sj2xl/cr2LD8Z2JeS0Q8UsL6eXM8PZGe3X9Rl64L3Ze4aWH6xvEL5F7XHGk8kv8w5NL+8eFvXS7+sL2lbMjw7vDnLYM3psg36Ijxaz5F93rxitlprWGRfYNeZTm/NZPlq6bbGGL1n9mlZ6EccmH5rr0Xrcec6+lEHlAv9xL/+67/Kd77zHfn0pz8tH/7wh+Wkk06S1atXy+abby7nnnuunHXWWbJ+/fq+2jDyGHRF1S9I4A5CHtP30Hda2DmrX0S2p1NfK68zG5HNnt3ILi2L2WzJtOxn8bX6RWKL/Kixt8amqL/W36hOL2ZorNgY1uZIr8cpcnTbvxdHNL976Zu3ttTkXM31Xtjbr3h1G8NhyCUvZk3mkDXH9V+2b1i5wtYKb43oVexrcrZmr7L2Wi+P2LrJ4mfp6eUTmgMOOKB9wgkntC+++OL2TTfd1L7//vvbGzdubLfb7fazzz7bPvvss9unnfY7G/IJDcZoWh3AoBe8PKbuqFmEm5KNWjtqCW7Ut14SDo/INCVNNRuv1b/2eiRutT4yIuDZF83NiF0RshPxCcWP2erlZC05ieQb699kzvaKhA36iORdrbwaGZG23awTUZ/7seZ1a3/kWpP1xpvXaF2t0cvmfnSe1cxLZF90fUcHi1W3dnt7N9PTy4LmuuuuM69v2LChfdBBB7VFpP3/zP5UXw+R0SwNRtPqAHqx+OUx/Ed0U0Ttrb4eiUSvLZ0REmtdi5BkS6+WGyXILA4RX6w+TccvEm/PB29sasbCGj/PPmuzr/EfyavNkZrxi17rxRGJdSTfvBzvpZ29iklN3g1ibKbqaOJ3TZ9u41yjIyK7Zq/x2nvzvWadqB0fvfdo/VE93v7j+YbsqLERXe/070VB87/+1/9qf/vb324feuih7eeee27S9VtuuWWT1yJZ0DCMptUB9HpBymP6HNENqwmxtPRF2kU3Y2vxtfz1FvVaeyOxY7H0Nqroxh3dqDydtflR26eGqEQJS1SGF+daPy2S0Oujl3nYy/EcxMFI1kw5ogQ8Oobd5FaTdl7/msIhspbVxqcmp7y11dPLiht0ncn34hjZvyOxYPp6UdA88MAD7TPPPLO9zz77tN/85je3ly5d2j7nnHPa3/rWt9rf//7324sWLZrEbffY/FN9PURGszQYTasD6GZxyWP6H9HFvJbsRRZd1oeR2CYbl7e5IPmeTi82kdhZhUsNubA2rG7H0PObxbdWhtc+OvZeH2v8InlQQ7L6cURi6I175PWg/MujWT40Ha9oPuk+g8iF2mKEva8tPGrW6RpbvLXR0xtZ5611jrVlax573Tl6+ZGzs846q/3AAw+0b7nllvbll1/eXrZsWfvd7353+4wzzpjEbbOgwRhNqwOY6oUnj+E/vOKBvbfkIdnW9WhBUvb3iHXtJoMW7Yi+bjZCq19TgmH55sXWk+fZE4lNNB+7iXNTvZH8azo2/TwsYuXFgeVehAANy1Gbp4OwZxR1D7sftfY1yWFrT7BkRouQyD7j7VER/6y9gF1ja663F/aioDniiCPajz32WPvmm29uP/DAA5P+3xnEbXff/MK+HiKjWRqMptUBDGpxymNwh7co6Xbegh8pQpBudq6mcLBsjG5UkeIi0j8SB2tzYuPD7Kr1wdpQa+IV1R8d/1rdVnyirz3Cz3KgWz+bxHKqjqZ5MAy+RufQoO0c1WMU/O9l3kbnvkf8awoPyy62nrOiwlqXatdGz2arXS8Lmh/84AftZ555pr106dL2W97ylvZ+++3XPu6449qf+tSn2jfccEN79erVk7htFjQYo2l1AINehPIY7BFZjLz33W52NcVIt+Tf8y9CWL12tUQ4sjE2jVmU2HljWDNG0fzpR570qkhrMg+YnF75HZFlzWPvbx4z6+hHMT5IX2rWo0jb2nltFRnoemlHk4KkVo5X+Fj6vVzx7O/1f6z53HPPtVevXt2+4YYb2p/61Kfaxx13XHu//fabxG2zoMEYTasBTjnllIEvPnkM7xEh+ah9U3IUJamojVdgRRbuJvYxWxiZrCHZNZtoZNPrRp+no2Z8m5KDXtjkxd7LLxbLqSgMasbOyj9PB+sziuQ2j9E/vPyLnI/M2yhxr7G7Zu2tWU9YscQKKM93yxbPZs8WraOXBc1TTz3VvuOOOyad1x9BE5H2ws0v6OshMpqlwWhaHcCgF648Bn94d3Ks800JccQm3d9ajL33TexoWjB0Q869zYn1tTbpWj97sal32z5SULC4WWOE2nXjaz+OJsViJBe6mRfRnMxjehz9Gude7gdRHVbhX1v8Wzqj66hVdFjrPysgmGxLhmVTZP1gdjC7elnQnHrqqe2LL7643W6323feeWf7Xe96V/s973lPe9WqVZO4bRY0GLMkkZimeGL9R+SJ9R+Rsbmnydjc0zY5JyKbnOv81ecs2aUM/VqjY0Opq5TVuVbK0DaU10tZlu7Sd8u2jnxtF7K9fF/G19OD4uyh7INirt978UP9UV/LFtSuHBek3xsflG9ljEuZpR06n8pc0nqtXI2OhwUv/yPxELHnIeqn81Kfs/R14unN916jF/FO1KNf41wjF63hkf5sPUPt2F4SXfvQ+3KtKudaOed0P71+6/WLraXWvsL2guj6on1CHAHFIrrXN8U999wj73//+0VE5LzzzpNDDjlElixZIitWrJjUdlzafT1GFa12uz261htotVqDNiExJNDFAiJ9tRtdpE9UhyeL2V32jfqg20f7o8WeEede2NqtnxEgWd2Mk6dLhJMQRvi9/GFxsQq7YYLllwYiadYcqxnffuRXYvpgJuYF2je9tvpcB2zulnJZYRTZp6J7JJLPziFo3Y8++qhsvfXWbr8I3v/+98uKFSvk3nvvlb/7u7+Tz33ucyIictRRR8mll1460a7Vaslum5/fE50Mv3juWBnF0iCf0CSmNaw7s+V1604uulMUvaMW2QwYEUV9GWGLkELd3rujrm207hAiO1j72sIJ2W/BuyvP7EPnasac5U6nr5cD1iar7yJqHawYGhYShu7oikx+6qjHnz156rRnBTXLFZb3NXfKEzMPUcLbT3Srv7a/frpiydXxQXsNmtMR29AerfdktG7rJzDatvKIrvNsDe4FttpqK7n++uvliiuukDe96U0T55955pm+6JuOyIImMa1RLsb6UbW3yGoZTciOdUfea1+SWIt8R0lupKhCd7Esol7+rSGFeoOx/OzIjMa/l+S0RoZXIFlxZP10UYPGXI/VsJJyRjpKwuMVyYgolX2tu7C6P7qWSHgYVJ50O69r+kd91PMX3ZTp/G26Nun9W+/H1s1GpLdcN9C+g2RNxZp69NFHyz/+4z/K1ltvLe94xztEROSqq66SF7/4xZPa5kfOMPIjZ4lpD+sxdeSuezeLcVPbmrS3HuF7bSP60B1xRDbZplYDryDTxHWqSLz3lMCLTwe9GvPyenR8amT3AzV2IMKE4h3V582fYS0GEzMHg8rDQayj1lpgrWsi/OO43v6D9g7UB13Tsnr5kTOE6667Tv7oj/5Itt1224lzrVZLdt38vL7pFBG597njRvIjZ1nQJGYMIk8tap9sjDoid6e8Pvoa6jvVRLKf41ZTCOv3NXax/BSZvJmPSp5ahanVTl/r9PGeyNQWPInEqKBJcd9ve0T87/3p9hpobUNtWBFSymBy2PoZvRHUQS8LmnvvvVe+/e1vy7x582SXXXaR+fPny8477yybbbbZJu1arZa8dPNP9kQnw33PHZ8FzTAhC5pEBJE7RYNETSHQzZ3oJgVHlEx7d9iidowCrCKkRPTupCUX6e7Ingo0Gavok7Ve+jLqOZUYPTSdG1ORp4OcD9EbMtaNCuupiXUjydJrFVfWmtXLgubwww+XPfbYQ1qtltx///1y3333Sbvdll122UU+/elPT7TLgoYjC5rEjEMSnP9ALXH07sL1Orb9frIzKOLRLWH3isxhR6SwaerLsN+kSEx/aMI91TcbRg3eR0rRa+/jZ94THq2/vIZuwqGx62VBc8IJJ8g555wz8X58fFz+9//+37J69WrZZ599Js63Wi3ZefNze6KT4d+e+x8jWdDkjwIkZhwid9BHEaUfEZ86i7Z1h0qj0758H/mYWtQmpK/JtV7It/p0kzNl3Gv6aBvQ9WElTBapYDajp3qW3LG5k3/oQ19nhVMi0Ut05neZi/rGT2S9LtsNa5569qHzyHc9P611Vq+haB1AMrUMPQ5I1lStqdtuu6089thjE+9nzZolu+yyyybFTMJGPqFJzEhESXiiflGPtu/3051eoPajXlPlU/Tjfl7/6Qb2EZPyWiIxbKjJzegaM2r5bj2lYTdsamKBwD52ps+zOPbyCc25554r99xzjyxZskRe9apXybx582C7fELDkQVNYkZj1Bb9UUO/CX2/5A3bR92sj/qx19MNlp/D/oQqMXPRzZz0+k6n+R5Zc2uKj0iBhGDJ1+hlQXPxxRfLfffdJ6tXr5Ynn3xS/vN//s8yf/58mT9/vhx88MET7VqtlrxkzjmGpO7xy2dPyIJmmJAFTSIxOEynjTaKqfA5+tnumYiap1YzMT8To4VReIIt0ju7Ik9VI0/Dvac0ke/PlLB869fPNv/617+W1atXy7333iurV6+W5cuXT1zLgoYjC5pEIjG0aLJZ9rMPatfthj6IJz4zGRmrxDCjXx/x7Qb9fFLU5Gm4V+zpwqcDdDMIFTDRmyO9Lmhuu+02ueeee2SbbbaRN73pTbBNq9WSHeec3TOdCA8++5cjWdDkjwIkEomhRdMv7UfQ5Eu2SHa3ZKLbYqiXGNYvHtfA8iGLmcSwwyscatp3g+j3THtZzLDiAl1j8vSTmPIHGvSPAGgfa/X1EldddZWcccYZsmrVKlmxYoWIiFx77bWb/FBAwkYWNIlEYkZC/6LOKKLctHuxEY9qHET4XdfpUKQlZh5QDltPMnSfWh36fXQtiD7ZjvS1bhjVPMXRxQsqlPTBisWpKnCuv/56WbFihXz0ox+VuXPn/k7/E0/IxRdf3Fe90wlZ0CQSicQ0gLfxT3dEfhlJf+QkkRhmRL8n13TuW9/H6+Uc8Qox/b7JU9bOeVacsI8L66c2zJ5+r6vtdnvSx9cWL14sd91116S2G6Xd12NUkQVNIpFIJEYe1heGWZssbBLDig45t2A9hajVFTnXC1hPgfQTZ+sjdqxoKec++pUzZAPz33uC00tsv/32ctNNN006Pz4+3le90wlZ0CQSiURi5GE9iWFPa2bq06zE6AE9zbA+NjuIYp0VGSWsJyi6XeTJDpv37EcD0JNc/SRmKgoYjaOPPlpWrFghy5Ytk6eeekpuueUWOeecc+TlL3/5pLb5hAYjf+UskUgkEj3DMH/xvuYnWROJ6YTIvKz5VTGR7v8zYf30JPLLYrpv9PtFum2tPoRe/MrZ+Pi4zJr1u2cLjz32mHzrW9+SO+64Q55++mlZuHChHHbYYfK85z1von2r1ZIXzTmzK50efvPsiSP5K2fTpqBZvny5nHLKKYM2I5FIJBJDiOj3ERKJ6YYo+Wd9vI9von76NWsTOW/Zx2zu2KbtRB9BG2RB87a3vU123XVXWbhwoSxYsEAWLFggL3nJS+hN+VarJS+Y84mudHpY++xfZUEzTMgnNIlEIpHoIPIdm0RiOqPJU5pIMRQpYrq5pu3wfuoZndfoxfzvRUFz3333yZ133il333233HLLLfLcc8/JlltuKbvttpssWLBA5s+fL294wxsm2mdBw5HfoUkkEokhhvcLPP2QPR1RfjY+i5nETIL+snx5TrdjxYL+bon1PRbUxvreTPnX+4Uy/f2W8rz35GUYvmek8dKXvlT2228/mT17trzvfe+TK664Qk466SR5+ctfLtdff7184xvfGLSJI4MsaBKJRGKIwch3L0j5TCT2g/jCbyIx1WA/OYyKmw5QAWD9XyyoYNHFRllcWGuZtRZZH3lDHyEr26BCSP9AwDDg9ttvlwMPPFB22mknefWrXy3ve9/75GMf+5jsvvvuk9pubI339RhV5EfOEolEIjHjkN+pSUx3WD+RzNrXfsG/m+/DWB9Vq32Sqj+ShgqrXj+h7cVHzjo47LDD5Oyzz5Ztt912k/Pvete7ZOXKlRPvW62WbDP39J7oZFi3/q/dj5ytW7dOzjzzTFm7dq3Mnj1bTjzxRNl55537apeHfEKTSCQSQ4imTxHy6UMM+bPNiemMXqwD1kfUot+pseyxfmpd/6QyktO5zooXZM+wzvkDDzxQPvjBD8oXvvAFueOOO+TBBx+UG2+8ERYWG6Td1yOCFStWyN577y2XXnqpHH744XLRRRf1OiTVyIImkUgkhhDRO6r6ffRLsYlEYvqiLNijJD76sS/2XRv062LoXOc8K1ZQsYQKHfR/y+iPog1rAaNxwAEHyIknniirVq2Sj370oxNFwlFHHTVo0yBuvfVW2WeffURE5DWveY2sWbNGnnnmmcEa1Z6mEJE88sgjjzzyyCOPPCqPsbmnNWpXvh+be9rEe/3X66vPl/3ZYentx/Hoo4/2jcM+88wz8PzrXve6vo99S7YxbXvuuefaBxxwwCbn3vve97YfeOCBnvnfBLMlkUgkEiOJ2s/IJxKJRARNn+qgj5FpsCc81k8/s+/H6H5N/Rg2zJ07F56/+eabp9gSjLb6KFy73Z74D0IHhfzIWSKRSIwI0EYe+ViF9Tn0RCKR6CX0zzCz77BY37PRfb01y/s1tVzzeofZs2fLnDlz5OmnnxYRkfHxcXnkkUfk+c9//kDtyoImkUgkRgRso/a+OOv9fwyJRCLRK+jixVp/0E8r6+//RP9fGfZDAqhtojvstddectNNN4mIyA9/+EN5xSteQZ8qTRXyZ5sTiURihIF++tT7adJ+/IRpIpGYWYiuH5GnI96vk6GP17KPntXY1g16+bPNo4a1a9fK6aefLo888ohstdVWsmzZMtlhhx0GalMWNIlEIjENkP+vSiKRGASa/v81HbCfbGYfNfO+TzNVN2pmckEzjMiCJpFIJBKJRCLRF3hf+mc/7czO6WvWjwn0E1nQDBeyoEkkEolEIpFI9AWooOmAnWMfi7X+086p/hhtFjTDhfxRgEQikUgkEolEX1B+yR/9h5f6F8p0MYOe0LCfrM//WHjmIp/QJBKJRCKRSCSmHOwJDPs7TMgnNMOFaVvQ7LXXXvIv//IvgzYjkUgkRgKvfvWr5Sc/+cmgzUgkEomRwPnnny/HHHPMoM1I/F9M24KGodVqTfofTmey7FG0OWWn7EHKTdkpe9CyR9HmlJ2yBym337ITg0d+hyaRSCQSiUQikUiMLLKgSSQSiUQikUgkEiOLGVfQnHLKKYM2oRH6ZXc/4zGqsvuJUY3JqMruF0Y1HqMqu58YxZiMos39xqjGZFRl9wsZj0RTzLjv0PQT+fnM6YEcx+mBHMfpgxzL6YEcx+mDHMvEsGHGPaHpJ7L6nx7IcZweyHGcPsixnB7IcZw+yLFMDBvyCU0ikUgkEolEIpEYWeQTmkQikUgkEolEIjGyyIImkUgkEolEIpFIjCyyoEkkEolEIpFIJBIjiyxoEolEIpFIJBKJxMhi9qANmK74x3/8RznnnHPkH/7hHwZtSqIB1q9fL+eee66sWbNGNm7cKIsWLZL99ttv0GYlAti4caN8+tOflrvuukueffZZWbJkifzZn/3ZoM1KNMCtt94qV1xxhbTbbflP/+k/ybJly+QFL3jBoM1KNMQvf/lLOeqoo+TMM8+UV77ylYM2J9EAt99+u1x44YUiIrLbbrvJCSecIJttttmArUok8glNX/Cb3/xGvvOd78iWW245aFMSDXH11VfLvHnz5LLLLpPzzjtPPvvZz8q6desGbVYigJtuukkef/xxueSSS+Sss86SSy65RJ555plBm5WoxLPPPitnnnmmnHLKKbJixQrZY489ZOXKlYM2K9EQ4+Pjcv7558tOO+00aFMSDTE+Pi7nnHOOfPSjH5XLLrtM5syZI6tXrx60WYmEiGRB0xecf/758hd/8RfSarUGbUqiIQ444ABZunSpiIhsvfXWMm/ePFm/fv2ArUpE8C//8i+yzz77iIjIC17wAlmwYIHcfvvtA7YqUYvZs2fLpZdeKttvv72IiGy33Xby9NNPD9iqRFN89atflb333lu22267QZuSaIg777xTXvKSl0wUpccdd5wsXLhwwFYlEr9DFjQ9xnXXXSfz58+XBQsWDNqURBfYaqutZIstthCR393x33nnnXMjHhGsXbt2k48lPf/5z5eHH354gBYlmmDWrFkT4/jMM8/IN7/5Tdl3330HbFWiCe677z657bbbZNGiRYM2JdEF/v3f/1223nprOeuss+T444+Xiy++WDZs2DBosxIJEcnv0DTCP//zP8vXvva1Tc6NjY3JscceK//0T/8kZ5111oAsS9TgoYcektNOO23S+Y985CMTd4VvuOEGue6662C7xHBCPxltt9sya1beuxlVrFu3Tk4++WR5+9vfLnvsscegzUlUYsOGDXLBBRfIiSeemJ9aGHE8++yz8q//+q9y4YUXyhZbbCGnnnqqfPvb35b9999/0KYlElnQNMFrX/taee1rXzvp/DXXXCOPP/64HH/88SIi8uSTT8qHPvQhOfvss2Xu3LlTbWbCwfbbbz/x5UaEa665Rn70ox/JWWedNfG0JjH8eOELXygPP/yw7LrrriLyu++07b333gO2KtEE69atkxNPPFHe8573yJ/+6Z8O2pxEA/ziF7+Q3/72txM3hR544AF54IEH5Pjjj88fBhgxvPCFL5T58+fLVlttJSIie+21l/z85z8fsFWJxO+QBU0PceCBB8qBBx64yXuLMCeGF3feeafcfPPNcs4558icOXMGbU6iAn/yJ38iN954o/zxH/+x/PrXv5Y1a9bIq171qkGblWiAM844Qw477LAsZkYYu+++u3zuc5+beH/yySfLwQcfnMXMCOJVr3qVXHLJJbJu3TrZZptt5Gc/+1l+vD4xNMiCJpEA+PrXvy6//vWv5QMf+MDEuaOPPlr23HPPAVqViOB1r3ud3HbbbXL44YfLZpttJsccc4xsvvnmgzYrUYn77rtPfvrTn8rDDz8sV155pYiIvOQlL5GTTz55sIYlEjMUW2yxhRx99NHyN3/zN7JhwwbZdddd5a1vfeugzUokRESk1W6324M2IpFIJBKJRCKRSCSaIL8pm0gkEolEIpFIJEYWWdAkEolEIpFIJBKJkUUWNIlEIpFIJBKJRGJkkQVNIpFIJBKJRCKRGFlkQZNIJBKJRCKRSCRGFlnQJBKJRCKRSCQSiZFFFjSJRCKRSCQSiURiZJH/sWYikUjMcPzkJz+RK664Qh566CHZdttt5YQTTsj/ATyRSCQSI4N8QpNIJBIzGHfffbd8/OMfl6VLl8o3vvEN2X///eWMM84YtFmJRCKRSISRBU0ikUjMYHzmM5+Rd73rXfL7v//7IiLyxje+UX75y1/K448/PmDLEolEIpGIIQuaRCKRmKF45JFH5Pbbb5d99tln4tz4+LiIiLTb7UGZlUgkEolEFbKgSSQSiRmKVatWyfbbby+/93u/N3HuV7/6lcyZM0fGxsYGaFkikUgkEnFkQZNIJBIzFL/5zW9k++233+TcHXfcIa94xStk1qzcHhKJRCIxGsgdK5FIJGYo5s6dK61Wa5NzN9544yYfQUskEolEYtiRBU0ikUjMUOyxxx5y9913y0MPPSTPPfecXHnllbJx40Z5wxveMGjTEolEIpEIo9XOb34mEonEjMV1110nX/ziF2Xjxo3y6le/WpYuXSrbbLPNoM1KJBKJRCKMLGgSiUQikUgkEonEyCI/cpZIJBKJRCKRSCRGFlnQJBKJRCKRSCQSiZFFFjSJRCKRSCQSiURiZJEFTSKRSCQSiUQikRhZZEGTSCQSiUQikUgkRhZZ0CQSiUQikUgkEomRxf8PajrToCDQWBYAAAAASUVORK5CYII=", "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show elongated nuclei outliers within the data\n", "plot_hvplot_scatter(\n", " embeddings=embeddings_with_outliers,\n", " title=f\"UMAP of JUMP elongated nuclei outliers within {example_plate}\",\n", " filename=(\n", " plot_image := f\"./images/umap_elongated_nuclei_outliers_{example_plate}.png\"\n", " ),\n", " color_dataframe=df_features_with_cqc_outlier_data,\n", " color_column=\"cqc.elongated_nuclei.is_outlier\",\n", " clabel=\"density of single cells classified as outliers\",\n", ")\n", "# conserve filespace by displaying export instead of dynamic plot\n", "Image(plot_image)" ] }, { "cell_type": "code", "execution_count": 27, "id": "31bc5998-0aba-4d34-9ced-7a7e949ddc61", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzQAAALGCAYAAACTX9TRAAEAAElEQVR4nOydebxVZfX/14ELFwQFFEdURBF/aSWlqKk5ZWJi5jxkKYo4z5n41QxvGaZWomYq+FWzLDMzLctMS21wotLMMkcqwwFDwEguCHf//uC7T89dd631rL3P2fecfe7n/Xrd1z1n72dYz7D3Xp/9DKeSJElCAAAAAAAAAFBC+jXaAAAAAAAAAADICwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLRA0AAAAAAAAABKCwQNAAAAAAAAoLS0tKA577zzqFKp0M4779zj3GOPPUaVSoUqlQr961//IiKikSNHVo/9+Mc/7hZ+xYoVNHz48Or5Bx54oEeaDz/8cPX8Bz/4QdGmMI/0b/DgwbTNNtvQN7/5zTqUmmju3Lm0yy670GqrrUZrrbUWvf7662rYp556qoc9d911FxH1Tv19/etf75F///79af3116ejjz6a5s2bp9p+8803U6VSofHjx2esodbnb3/7W7U+Fy1a5I63ySabdOsDjUCyffLkyVSpVOjMM89smF1Z8fZPb9nq1TbPPPNMj2uuUqnQyJEjafvtt6ebb76ZkiSphj/44IPF8Ouvvz7tvffe9Oijj3ZLf8mSJXTiiSfS+uuvT6utthrtuOOO9Pjjj2cOQ0T06KOP0qabbkqVSoX233//TOfHjx8v2l2pVGjfffethnv++edp3333pREjRtCwYcPokEMOEe+Z//u//0tDhw6lSqVCM2fO7HH+xhtvpPe///00ZMgQGj16NE2ZMsW89wIAAKgfLS1oauEnP/lJt++PPPIILV682Ixz++23Vz8/+eST9NJLL6lh11lnHdpss82qD+M//OEPNHnyZLrqqqtqM5yILr74Yvr1r39N66+/Ph155JE0aNAgNezAgQNp9OjRNHr06JrzDclaf21tbbTZZpvRZpttRuuuuy69/vrrdMstt9BOO+1Eb7/9dl1tAzof+chHaNKkSbTeeuv1Wp5Tpkyh4cOHm2HGjx9PkyZNoq222qp3jKoDG220EU2aNIl22WWX6rFXXnmF+vXrJzrEMYpom/Hjx9P2229P2223HQ0cOJCeeOIJOuaYY+hLX/pSj7Crr7569RrdZJNNaMGCBXTffffRzjvvTD//+c+r4SZPnkzXX389vfPOO7TeeuvRo48+SnvttRf985//dIdZuXIldXR00Ic//GGaO3duD1ti54mINthgg+q9Lf1ba621iIiof//+RET0zjvv0J577kk/+clPaMiQIdTe3k533HEHffzjH6+KurfeeosOOuggOu644+g///mPmNesWbNoypQp9Kc//YnWWWcdmj9/Pt1444308Y9/nFasWOFpCgAAALWQtDDTpk1LiCjZaaedepx79NFHEyJKiCh58803kyRJkrXWWishomSdddZJNtxww27hzzvvvISIkrXXXjshouT+++/vdn7lypXJeuutlxBR8r73vS8houSSSy7pkW+ax0033VQ9tnTp0uSggw6q5t3V1VVTuffcc8+EiJJLL700U7z+/fsnRJT88Ic/TJKkd+rv6quvTogoGT16dLfwv/3tb5MBAwYkRJRcc801or033XRTQkTJ1ltvnamcnHfffbem+M3I3Llzq+2zcOHCRpujsnz58mTEiBHJsGHDqseKtH3FihV1TS8rl19+eUJEyRVXXFE9dvTRRydElJxxxhm9YsOf/vSnav3OnTu3enzlypXJIYcckhBRMmbMmOrx9N509NFHd0vnzTffTMaPH58QUbLbbrslSZIkzz33XEJEyWqrrZa8+uqrSZIkyf77758QUXLBBRe4wzz77LMJESUTJ05MjjnmmISIkk984hPVvGPnNT760Y92u8fNmjUrIaJkwoQJybvvvpssWbIkGTVqVLd71LXXXpv069cvOfvss5Ntt922R/slSZJsuummCREll19+eZIkSfL8888n/fr1S4go+d3vfhe1CwAAQG1ghEZgu+22o3/+85/09NNPV4/de++9NHz4cNp8883FOL/61a/o9ddfpw022IAuuOACIuo+YmMxaNAgOvXUU4mIaP78+TR//nw17Le+9S3aZpttaPDgwbT66qvTrrvuSvfdd1+3tNLpXNOmTaNKpdLtzWhvkKf+ODvuuCPtuOOORET0pz/9yZ33kiVL6Oyzz6ZNNtmEBg8eTOPGjaOvfvWr1bet6XSbNddck+bMmUNjxoyhj33sY0REtGjRIjriiCNo6NChNHLkSDrvvPNo5syZVKlU6MADD6zmsWDBApo6dSqtt9561N7eTuPHj6ef/vSnpl2HHHIIVSoVuvLKK+naa6+ljTfemIYNG0YHHXRQdcoeEVWnxDz11FPVY+nUv8MPP7x67O2336bTTjuNRo0aRYMHD6atttqKrrvuOtOGlStX0owZM2js2LHU3t5Om2yyCV1++eXdwnimNS1btowuvPBCGjt2LA0cOJBGjhxJhxxyCD333HPVMA899BBVKpUeIy877LADVSoVuu666+iee+6hgQMH0sKFC2nx4sVUqVTonHPOEfOUpmXF2iGdznjggQfSrFmzaMSIEXTJJZcQEdHvfvc7mjRpEq2zzjq02mqr0Xvf+176+te/LuadJAmtvfbaVKlU6JVXXiEiojfffJP69etHlUqFPv/5z1fDHnHEEVSpVOjWW2/tMeVs2223pc9+9rNERHTWWWdRpVKhJUuWVOO2tbXRrbfeSmPGjBH7Bm+ba665hiqVCh1wwAH0q1/9isaPH09Dhgyh7bffnp588kmxLBb9+vWj3XffnYhWjVzEGDlyJH36058mIqJnn32WiIh+/etfExHRhz70IVp//fWJiKpTwX7xi1+4wwwaNIi+8Y1v0M9+9jPaYIMNeuQdOy/x/e9/n+6//37addddq/n96le/IiKi/fbbj9ra2mjIkCH00Y9+tJstG2+8MT344IP01a9+ldrb23uku2LFCjr33HPp2muvpeOOO46IiDbffHNaY401iIh6/f4LAAB9EQgagXTNSDpt6tVXX6U//vGPtOuuu1JXV5cYJxUvBxxwAE2aNIkGDRoUnXYWEk5L0KaIXXbZZXTUUUfR008/TZMmTaKdd96ZfvWrX9HHPvYxuvvuu4mI6LTTTqONNtqIiIg+/OEP0xlnnEGrr766y4Z6kaf+JNI6sabMcY4++mi64ooraO2116bTTz+d5s+fT+eccw7Nnj27W1r//ve/6aSTTqLVVluNNtlkEyIiOvXUU+m2226jJElor732orvvvpuuvvpqIlo1NY+I6N1336WPfvSjdMMNN9Cmm25Kn/3sZ2nevHn0iU98QlwDkDJ48GAiIvre975HV1xxBe2xxx60cuVKuvPOO1UnXqOrq4smTZpEX//616m9vZ0OO+wweuONN+ikk06qOuwSZ555Jl1wwQW0fPlyOvfcc2nYsGF07rnn0te+9rVM+R944IF08cUX0+LFi+mwww6jDTfckO644w760Ic+RH//+9/d6Wy66aZ0yCGHENGq+j3jjDO6Tc+y8LRD2tYvv/wynXPOObTpppvSyJEj6c0336SPfvSjdP/999OkSZPo+OOPp3feeYdOO+00uuaaa3rkFa4j+93vfkdEqwRbkiQ0cOBAevjhh6thn3jiCSIi2nXXXXuk88lPfrJ6be6yyy50xhlnVPsV0appqqeddhqtu+66tGTJErrzzjvpM5/5jFoHaZ96/vnn6eijj6bx48fT2muvTU888QQdcsghmac6dXV10UMPPaTaL5FOwUqF64svvkhERBtuuGE1zMYbb9ztnCfMJptsQieddJKab+w8p7Ozs3qdXXbZZdXjHlv22Wcfs1+2tbXRCSecQCeeeGK1Hn7+859X13+VaZokAACUFQgagd13350qlQrdc889RLRqdIGIaO+99+62WDalq6uL7rzzTiIiOuigg2jo0KG01157EdGqt4Ixli5dWp1T/573vIeGDRvWI8yiRYuoo6ODiIiuvfZauuOOO+jee++lU045hZIkqY4KXX755TRu3DgiWvXWcebMmWJ6RZK1/iQefPBBeuyxx4ho1ZtcD8uWLaO2tjY66KCD6NZbb6VLL72UzjjjDCIi+sEPfkBEq5wPolViaccdd6Q///nPNHv2bHrzzTfpe9/7HhGtmg//ne98h+bMmUOdnZ3d8vjxj39MTz75JA0bNozuv/9+uvjii+m6666jFStWmGKiX79Vl9oLL7xAc+bMoZtvvrk6OsLXG8W499576Te/+Q0NHDiQHnnkEbr55pvp9ttvp/b2drrqqqvo3Xff7RFn/vz51RGc7373u/TFL36R7rvvPhowYADNmDHD7fzef//99NOf/pT69etHv/nNb+hb3/oWzZkzh8aPH08LFy7sMeJjseWWW9Lpp59ORKuc85kzZ9J+++3niutph7St//jHP9LNN99Mv//97+nEE0+kJ554ghYtWkQ77bQT3XTTTTRz5kz6+c9/ThdeeKG6lix1aENB069fP9pvv/3o8ccfp87OTlqwYAG9/PLLtOmmm3ZzkFPOPvvs6rV5wAEH0MyZM7sJmieeeIKefPJJeuyxx6rXujXyl/apv/zlL/Ttb3+bbr755uq19tJLL3UbMdM44IADaIcddqAddtiBNtpoI7r99ttp9913j67l6+rqojlz5lT7VDrKma53S8VW+DldQ+cJU2+uvvpq+sc//kF77rknbbfddtXjRdjy7LPPVkeujjnmGBo7dmxeswEAADiBoBFYZ511aMKECfTYY4/RggULqk7CpEmTxPAPP/wwvfHGG7T22mtXHZ+DDz6YiPRpZ9OmTaOxY8fS2LFjaa211qKf/OQnVKlUxMW4RKt2FUungRxxxBHV44ceeigREf35z3+mt956K0dp60/W+iMimjdvXrU+Ro0aVR3B2HbbbemAAw5w5dve3k7f+9736I477qCxY8dSZ2dndTrKq6++2iP81KlTq5+fe+65qlP/iU98goiIhg4d2sPmRx55hIhWCc+FCxfSP//5z+o0ul/+8pdRG/fee++qwNx+++2JiOhf//qXKEI00mky22yzTXWB+B577EGdnZ302muv0YABA3rEeeKJJ2jFihXUr18/2nDDDemf//wnrVixgsaMGUMLFizoNj3QIp2Gs80229AWW2xBREQDBgyotlE6nahosrTDWmut1a0PpSNyDz30EO288870hS98gV577TWaPn16t92vQrigefDBB2nLLbekPffck5YtW0aPP/64OTrjYZ999qkKqokTJxKRr29suOGGtNNOOxHRqvpIR2SlPs956qmn6PHHH6fHH3+cXn31VapUKrR48WJ68MEHe4T95je/2W0nwu22245effVVet/73kef+9zniIiqI7CVSqUaL/zsDVNPli9fTl/96leJaNX0zSJtefLJJ2mXXXah+fPn0/bbb59r8wcAAADZaWlBk+5ks3Tp0h7nwrnr4VvSlH333Ze6urroF7/4Bf3iF7+grbfeujpdhJOKlqVLl9I222xD48ePrwoTbdrZ/Pnz6aWXXqKXXnqJKpUK7bDDDvSjH/1Idd4XLFhARKveHg4ZMqR6fOTIkdXPCxcuFOPmpbfqj2jViElaH6+99hptsMEGdPLJJ9MDDzwgOugas2bNoi222ILa29tp8ODBdOKJJxIRiSND4dz7N998s1qWoUOHVo+H9UtE1Wkkjz32GG200Ua00UYb0dZbb01Eq6axxdog3WWJiGi11Varfl65cqWneET0374wYsQId5zU7q6uLtpkk02qtj///PNERO6pYmnevF7S770lqrO0w/rrr9/NSd1qq61o9uzZNHLkSPrtb39L06dPp1122YU22WQTde3J+PHjaY011qDf//73NH/+fHr22Wdpl112oQ9/+MNEtOqlRq2CJty9LJwmGusbYZ8i+m+/8vSpuXPnUpIklCQJ/fvf/6bbbruNnnnmGTriiCOqwjm0Kd3lLLVvl112od/97ndVG9IpV+FuYOnntL96wtSTu+66i9544w1af/31aY899uh2rp62/P73v6fdd9+d/vWvf9FHPvIR+vnPf15dRwMAAKBY2hptQJGkDsJLL71EnZ2d3dZi/OEPfyAiomHDhokPnUmTJtHnP/95uuqqq2jRokV08skni3mk6yCIVjn5f/zjH3uE+f73v9/jzeBNN91EkydPdpdlzTXXJKJV4uKdd96pOi2pI07U07HReOutt+imm26iv/zlL3TppZfSyJEjafny5dW3lelDvjfqL2X06NH0t7/9zWW/xgMPPEAnnHACtbe30/XXX0/vec976O6776ZLL71UDJ8KNqL/1t3y5cvV+iX6r5Mzfvx4+uIXv9gjzVCk5KFfv37U1dXVTUSGC8OJ/ts+XDykv3nBxUZod1tbG/3whz/scV773SRO2g+5TWk9pXlrYpjHy0uWdgjbOeW4446jY445hn7/+9/To48+SrfeeivNmTOHDj/8cHGqVv/+/WmnnXaie++9l771rW8REdFuu+1GW265Ja299tr0q1/9qnp95BU0RY5SeBg6dCgdeuih9NWvfpWeeOIJ+tnPftZt7ciBBx5IN998MxGtWiMyceJE+s1vfkO///3vq9NC01Gyf/zjH9V46XWdjuh5wtSTdNrvfvvt16OON998c3rsscdqtmXevHm0zz770OLFi+mQQw6hW2+9NdOLGAAAALXR0iM0H/3oR6tTKM4++2xatmwZEa2arpLO9Zd+rI1olYM3atQo+u1vf0tEpE5Fefjhh2n+/PnU3t5Ob7/9dvVtZ5Ik9OUvf5mI/LudWXzoQx/qtrA85Tvf+Q4RrZoCFPstj5TBgwfThRdeSDfeeGN1DvwPf/hDSpKEKpUKvec97yGi3qm/epKuuXnPe95Dxx57LH3oQx+qjo7F1oiMGzeu6vimGyz8+9//7rG+JXXcXnvtNdpzzz1p3333pQ9+8IO0aNEiGjx4sLgLUhZSEZm+7V+yZEkPG9IF6r///e/ptddeI6JVZV9//fVp9OjR4ojahAkTqK2tjVasWEGjRo2ifffdl/bee29atGgRJUni7jsf+chHiGiVoE0XTS9fvry6Rik9n5Zj+fLlVZE/Z86cHqOVqYPZ2dmZaZSqlnb41a9+Reeffz799re/pe22247OOOOM6lqV0LHlpM799ddf3+37hz/8Yfrd735Hc+bMoY033rg6pU0iLW84wtlM/O1vf6sKOmszkb322osOO+ww6urqoilTptDy5cuJaFX7VyoVeuyxx2jevHmUJElVUOy9997uMPUknT6X7l4Wkh67++67acWKFbR48eLqrpFZbJk6dSrNnz+ftttuO/rud78LMQMAAL1Nr28U3cucfvrp1d9cGDBgQDJixIjq94022iiZN29eNWz6OyrpbzNMnTq1+tspK1euTJIkSbbffvtuv1FwwgknJESU7Lfffj3yfumll6p5vfjii93yCH+HxsuMGTMSIkra29uTT37yk9XfVGhra0t+8YtfVMN95CMf6fabCBJnnnlm1ba11lorqVQqCRElxx13XK/Wn/Y7NB7479DcfvvtCREl/fv3T84+++xkv/32S3bZZZekUqkk/fv3T84//3zzN07S39sYMmRIcuihhybjxo1LNtlkk4SIksMOOyxJklW/m7L11ltX8z355JOTzTbbLCGi5LzzzlNtlX5rJP0tDSJKli5dmiRJkhx33HHV3+iYMmVKsuWWWyYTJkxIiCg59NBDkyRZ9bs52223XUJEyaabbppMmTIlWXfddRMiSqZNm5YkifxbLieddFJCRMl6662XnHzyyclOO+2UEFGy4447Vn/7aPTo0d1+p4PT1dWVTJw4MSGiZP3110+OOeaY6u8urb/++snrr7+eJMmq3zTZYostqm173HHHJRtvvHGy1VZbJUSUfOMb30iSJElefvnlqp0TJ05Mrr32WtF2Xn+edtB+p+gnP/lJtd+ffPLJybnnnpt86EMfSogoOeigg9Q2/O1vf1u16z3veU/1+JVXXlk9/ulPf7p6XMo/Lcfaa6+dHHvssclrr73m7hu8bbTypX3h3nvvFcsR/g7N+PHjk+233z7Zfvvtk/e///3JwIEDEyJK1lxzzeSVV15JkkT/HZpXX301GTZsWEJEyec+97keZRw2bFj1+ll33XWTBQsWuMP8+c9/TiZNmpRMmjQpGTt2bPV8euzee+81z7/wwgtJkiTJa6+9Vi3rM88806Muli1blowbNy4homTUqFHJyJEjEyJKPvrRj1bD3HbbbdV00/vflltumUyaNCk5+OCDk+eff76ax7Bhw5JRo0Z1+7vsssvEdgAAAFA/Wl7QJEmS3HDDDcl2222XDBkyJBk4cGCy6aabJmeccUYyf/78buG4Q3733XcnRJQcddRR1TChQ75ixYpknXXWSYgoueWWW8S8P/CBDyRElMyYMaNbHnkETZIkyY033piMHz8+aW9vT1ZfffVkzz33TH796193C+MRNMuWLUumT5+ejB07Nmlvb0/WX3/95Mwzz0zeeeedHmGLqr8kqa+g6erqSj7zmc8kI0eOTFZfffXkk5/8ZPLWW28lp5xyStLe3p5ssskmpqB54403ko9//OPJoEGDkvXWWy+ZPn16cu655yZElHzqU5+qhps/f34yefLkZO21107a29uTLbbYIvnKV75i/iCq12n917/+lRx88MHJGmuskWywwQbJ5ZdfnnzjG9/oIZrfeuut5IQTTkjWXXfdZMCAAcm4ceOSr3zlK1XhKJVzxYoVSUdHRzJmzJhkwIAByahRo5KTTz45Wbx4cTXdmKBJklU/BPs///M/ySabbJIMGDAgWXvttZNPfepTyd///vdu4Z566qlk++23TwYPHpxstdVWyc9+9rPk0EMPTYgo+drXvlYN95nPfCYZOnRosvrqqycXXnihS9B42sH64dXvfe97yQ477JAMHz48GTx4cDJu3Ljkc5/7XLJkyRK13MuXL09WW221hIiSE088sVs5U3tvuOGG6nEp/z/96U/JlltumQwcODAZM2ZMMm/evIYKmvAvvbanTp2avPzyy9XwmqBJkiT5+te/Xn3Z8cc//jFJkiR55513klNPPTVZa621ksGDByd77LFHDzERC/PrX/9atDH9mz17tnl+zpw5PcoavnwJmTt3brLvvvsmQ4YMSYYPH55Mnjy5273hi1/8oprPkCFDkieffNK0Jf2xUAAAAMVRSRLnProAtDh/+tOf6NVXX6Utt9yyuoHBxz72MfrZz35GF110EU2fPr3BFgIAAAAAAA4EDQD/x/7770933303bbzxxrTffvvR3/72N7rnnnto2LBh9Kc//cncpQ0AAAAAADQGCBoA/o8lS5bQBRdcQHfffTe9/vrrtPbaa9OHPvQhuvjii6s/iAgAAAAAAJoLCBoAAAAAAABAaWnpbZsBAAAAAAAArQ0EDQAAAAAAAKC0QNAAAAAAAAAASktbow0oijPOOIOmTZvWaDMAAKAUvPrqq7TBBhs02gwAACgFSZLQqFGjGm0G+D9adlOASqXSaBMAAACAXmVo+wwiIlqy7Hwa2j6Dliw7v8EWAdCdVumXixcvpjXWWKPRZoD/A1POAAAAgJIztH1GVcyk31OnMTwOAACtSMtOOQMAAAD6CqF4Cd9+Q8yAZgF9ERQJBA0AAABQUqzpO96pPa0yBQg0H5KIQV8DRYApZwAAAEDJ4FPM+Dmino5jepzHswQRAB54X7H6DvoVKAIIGgAAAKBEpCMq1tSyUPDEBI4G1uAAL7xvhZtSpOf4fwDqCQQNAAAAUCJSZzF1GEPhEjqLkuMYxvE6lnBAQVak0UBMbWwt7rvvPvr4xz9OzzzzTKNNIaIW2ra5o6ODLrrookabAQAAABQCFzAcS6hgLQMA9aUvb9t8//330/PPP08vvfQSHXvssfTe97630Sa1jqDh4HdoAAAAtCqWqAnD1Ov3aPg20FI+0jkAWpW+LGjeeecdWm211ejcc8+lo446qikEDaacAQAAAC2C9Hs04X9PHOmYNZUtNs2NpwsAyMfuu+9OlUql0L8xY8ZE7VhttdV6obTZwLbNAAAAQIngi/y58JAEijVSox2rt/iQRBNGc1obtHF9eeihh2hhZ1xw1MKIQXMLTb8oMEIDAAAAlAht97FQhNRjRylN1Hi36JVGhzA1rW+BNga9BQQNAAAAUAI808eIuguRUDhkHXWxRnQ8vzMibddr/X4OACBOpatfoX9lpbyWAwAAAH0IPgLDv/Nd0Lw/mOkZheHHvGtqYgImq8CqRzoAgNqYNWsWHXfccfTss8/SpZdeSscddxwtWbKkoTZhlzMAAACgBHDxEqKNmmi7kWnp5zkvCSjPOp9apiNJ8bHbGuhNGrHLWaVSoUX/2bzQPIYPeYHKKA0wQgMAAAA0MdoohyRWwl9mj8UPz/P0JKy1O/ycZEc4qhQTTtLn8Jgm6qTPAIDWB4IGAAAAaGLCNSixH8/UdjizHHwrrfRzVlu147EfB+Vly2o3AKBvAkEDAAAAlABt7Yu2hkXbHICnoe1Gpm0mwNONjahotlnnNRutuAD0BSpdlUL/ygrW0AAAAABNjjSyYU278qwhsdac1GMNSpY0vdPeAGgWGrWGZvG/xxWax7DVny/lGhoIGgAAAKAJsUSMZwtmPj1N2wXNIyZi61as9SxYrA9akUYJmrcXFyto1hhWTkGDKWcAAABAE+JZQG8tvg/TiH2OrWvx5imlBTEDACgajNAAAAAATYZnJMQSIdZULz5iEqal/Y6N97dtsgiWrNPiAGgmGjZCs6jgEZrhGKEBAAAAQB3goyDpwnhp5OXB3RarIySx7Zq10RNtQwG+AUAti/U94SFmAAAeIGgAAACAJkPbcSwcRUmP7/7QMFHAhGHCLZ1jozdhvuH/9HOYjrROJ/3v3e45z/bQAAAQgilnAAAAQBOjTTPTFuRLaMIjTCevDVmnheXZXY0LOQAaTaOmnP37rWKnnK2+ZjmnnLU12gCN559/nq677jpqa2ujZcuW0ac//WnadtttG20WAAAAUDixncwsYaKJDS29mFiQNhII87M2AZDs1nZfs4j9fg0AoG/TtFPOrr32Wvr0pz9Nl112GZ199tk0c+bMRpsEAAAAFI41ehIb3dB+5DI8x6eQaVPOwjwkQSFNK5N2Tws/ayJHK3+WqWsA9Am6Cv4rKU0raIYNG0Zvv/02EREtWbKERowY0WCLAAAAgN7D2kY5NnIjjWRooy9aetZmAHx0RhIl2tQ4Pvok5a8dAwAAiaZdQ/P666/TWWedRWuuuSa9+eab9IUvfIH+3//7f+74WEMDAACgLPBpX9pIhnfbZh5Py0sKp9kirXWJ2W1NYYuVQRJLWEMDmoWGraH5V8FraEaWcw1N047QXHnllXTKKafQNddcQxdffDFdeuml1NXVcyyso6ODKpVKjz8AAACgbHimg/HpW9oPXfJ42khMKGT4jmOeaWvSls98dzWeFrdNQxshAqCvUukq9q+sNK2geeqpp2iHHXYgIqJx48bR0qVLaeHChT3CTZ8+nZIk6fEHAAAAlAXrByu16VyxtS88XWn7Z56/JoxCkcJtkISLdF6y01sPkq0AAJDStIJmww03pL/+9a9ERDR//nxauXIl1tEAAABoefgalRRt8bx3mhcXQtr0LW1qmTZqo8X1bivtXe9jHffkA0ArUEmK/SsrTbuG5rnnnqPrrruOBgwYkGvbZkw7AwAAUBY8v7Mibccc296Zk2XdS70EgiaQpPMAlIVGraH5zxvFrqEZsm4519A0raCpFQgaAAAAZUMaQdF+ByakVhHiEUXaRgFeQaStBwKgjDRM0LxWsKBZv5yCpmmnnAEAAAB9CUus8LUs/HwaP6tAkMSHZ72KtRZH2jzAM60MU8YAAHmBoAEAAACaAEmoeNatxH580ityuEgJR2T46Ex4nNsifec2xrZ49m4iAAAARJhyBgAAADSc2K5l0toXHjbr79hY6cfCS2l6PqffASg7jZpy9s68zQvNY7VRL2DKGQAAAACyIU0V41O1svygprYlc9bdxqTd1iy7LPv4ts8AAFBPIGgAAACAAskiJLzCJcsPTkqjI9K6lzRsGEbblUz7fZwwDMQLAAWQFPxXUjDlDAAAAGgwsQX90nQzaepZ3u2Y+TlrC2lP/PC4Z0tqAMpGw6ac/bPgKWcblnPKGQQNAAAA0CTw3cEsgRDGkcROGF6KHwuvraux0g/zAaCVgaBpLjDlDAAAAGgg0pSwcCex9Li265mVXmwaGt+xTIprrfGxRnFiu69pNgMAdCpdxf6VFYzQAAAAAA1Gmi7mmWLG8exMxsOHacV+l8baapmXRbIZgFahUSM0S/9R7AjN4I0xQgMAAACAGtB+i0abZsbjcWGSfuaL9PlIUJhubO0MT0sa7YlNnZPSBQA46EqK/SspbY02AAAAAADx9TMa2oJ77xQ1KQ3PGhweRxqVkQSWlHcKRnQAAHnACA0AAABQZ7iA0NaT8FEPzZn3ipUwn9iPaIbpWj/IyW3IMgKTR5wBAHQqSbF/ZQWCBgAAAKgz0giFNE1LEiaSyNHCWHlrYoILID7a4tm6OYwfCyNtOAAAAPUEmwIAAAAAvUhsPYw1ZUv7TZfYNK8svymTxo1t75ynrJYtAJSJRm0K0Dl3bKF5DBrzIjYFAAAAAICMJgasbY/D89o2ytJWy+FmAlKakj3eaW1S2jyMVTaIGQBqoKvgv5ICQQMAAAD0ApIwSf+Hf5rAkNKRRk/45zBebBQnJJyOxm2SppFp9lp2YRoaAKAeYJczAAAAoBexRkKksKGwIKIe36U0Q6Q1M/y8d3tlz7oaqQyanRitASAbZf7xyyJpmRGajo4OqlQq1T8AAACgGbGmgaWkjv7CzjE9wtXrt12kkSDvD2JqdtdiDwAA5AWbAgAAAAC9iOeHK/loiLbzWEzkWGnxMOH3rOXA4n/Q12jUpgDLXih2U4D2zcu5KQAEDQAAANDLSKJCO26JBU2IeEUTRxMokijSdkIDoC/QMEHzXMGCZotyCpqWmXIGAAAANCPazmJ8wb30mzXSaIy1dXO4UYC0qxn/zZk0P8lWqxzWiI8VDwAAigAjNAAAAECBaCMhselh4fEwHet3aKQpalnstNKMHQOgL9GoEZrlfy12hGbg/yvnCA0EDQAAANCLWL8jY03jsta5eNfYSPnH7JOO51lzA0ArAUHTXGDKGQAAAFAAWaZiaSMgntEda1tm6/diYjuscVuldLQf2QQAFAR+WFMEIzQAAABAQWg7l6XHwnC1jHR4pqPF8vJMObPCe88B0Ao0bITmLwWP0GyJERoAAAAAMKTF9168ox/Wehbte+wHN8Pj4YYE/HhoI0ZqAACNAIIGAAAAKAhpSla4ixmRvjbFWkPDj2UZEQl3QuN2ctv5Z8me2M5pAIA6khT8V1IgaAAAAIA6YgmDMIz0mzDSlDTPtspZw0o2SSMxkr2W0MJoDQCgEWANDQAAAFAj1o9fauel41m3RfZuz5xnHUyWPHiYPNtGA1AmGrWG5t0/FbuGZsD7yrmGBoIGAAAAaADahgGaKLAW/mvpW9s7a+GlzQu038yRjkHEgL4ABE1zAUEDAAAA1IF6jKR408+6w1jWNTaaiOJAvIC+SsMEzR8LFjRbl1PQYA0NAAAAUBDSLmXSb8jw3cIkpEX6WjjPiIpkZ0zMSBsaAABAo8EIDQAAAFBnahlNKcIG7TyR/Hs42siM53duAOgLYISmucAIDQAAAJCT2K5jnmlo4fesO4Rpoz+eLZ/5eWvbZWmL59iIDwCgALBtswhGaAAAAIA6I410pMfT71l2PkvxHLdsiI0cedIMwcgM6Ks0bITmqYJHaMaXc4QGggYAAAAoAEuwpGQVBLGdy6xpZJ60JawfAYWgAX2VRgmaFX/YvNA82j74QikFTVujDQAAAACanTzOe5aF+970tTCxLZM9ozFZ1/lAzAAAmgWsoQEAAAAiWGIgdpyvN5HWqYQCR0sjZpu2tiWLkNHW2oRrbGLrZwAAoLdpGUHT0dFBlUql+gcAAAAUhTVSIh2TRAP/7xVNWjphXpao0Rb+82PW1DiMzgDQILoK/ispLSNopk+fTkmSVP8AAACA3sQ7amEJCilcLKw2uqON+EhhrHRjP64JAACNBpsCAAAAAHUinJZlLczXRmX4eSuMFDYMX8Si/Vo2NACglWjYpgBzCt4UYEI5NwVomREaAAAAoNHw9Sz8OJH+g5b8nGd9S5g+Dy9tPBCSd7RFm9YGAACNAiM0AAAAQJ3xjNTUcwTFGvGpVz7YshmA/9KwEZrHxxWaR9v2z5dyhAaCBgAAACiA2LQyHrYev0lTZLyi0gGgjEDQNBeYcgYAAAAIeKZUWdO4pClj2kJ9z1qaLMet8/USIRAzAIBmAT+sCQAAAAjk+aFLbcqXFU9bU6P92KU2jS0mVjCiAkALUOKtlYsEIzQAAAAAZf9BS296se2XJeFiLbrXRFQYR/pNG4gZAECrAkEDAAAAUDFTqGI/mhnbuSxrXpoQgpgBoDWoJMX+lRUIGgAAAEAh69qW8Ly25XJ4TptSZgme2A9jaqIG2ywDAFoVCBoAAAB9EsvB1xb7x7ZHTrF+Pybc+jgWV5uiZp33xAMAgFYC2zYDAAAAEWrdVtmzhbOWhydvrJEBoHdp1LbNK3+zRaF59N/5OWzbDAAAAJQZ77SscFqYJ6y2Y1mYVpbRljBuGsYzDQ4AAFoRCBoAAADg//CKCkmgSOterIX6Ydi8oys87XpuMgAAaEKSSrF/JQWCBgAAQMuSZZ1MrfDtk6W1Lt4f1JTw2AvRAgDoi2ANDQAAAJAR/mOW2o9dht+Jihcc0rqdWL5YfwNAdhq2hubh/1doHv13/SvW0AAAAACNJsvaFh7eG1f7IUxNuPSWYAjtSKfFxfKGmAEAlB0IGgAAAC2FZ0ewMJxnG2Qen6eRdZQjj4jypBfbbhoAAFoRTDkDAADQUmSZ3lWPLZG1aWdeW+s9QuKdagYAyE/Dppw99J5C8+i/27OlnHIGQQMAAKBP0SprRjy/W1PLb9sAAHQgaJoLTDkDAADQZ7Ac/N60oR7hrC2ma/ltGwBAE9NV8F9JaRlB09HRQZVKpfoHAAAApEg/QClNzYr9Xoz3XD1+Dyav6NB2OgMAgFalZQTN9OnTKUmS6h8AAACQ4l34X68RDc+PadYDbROA1AaMxAAA+gItI2gAAACALHid/TxTv2JCIw95t5QGALQQXZVi/0oKNgUAAADQcnh3JmtE3mXLBwDQk4ZtCvDAloXm0X/Pv5RyphNGaAAAAJSSdOG7NHLBHX0eptbRmXqskcmTL89HW7+DtTMAtChJpdi/koIRGgAAAKUgtkNZ2UdcAADloWEjNPdvVWge/T/6Z4zQAAAAAEVhLdgPz1nrV/Lm2ywjHjE76l12AECTgW2bRSBoAAAANJwsjrfktEtbMaffLbHjzaNZRmhidnDx1Sx2AwBAkUDQAAAAaDh51rSEU820bZk9Dr5nDU4j0cSaRjONKAEAQG8AQQMAAKBUpGJDEh3eY9b5rGKgSPGQjjBlWccTxgEAtBjYFEAEggYAAEAp8Dj1sV3JvLuHee3JEj4PlnjTymLFAQCAVgSCBgAAQNPjHaGwNg5I/2fd6ti7NXQtaOuCMMoCAOgGflhTBIIGAABAUyOJmTyL+2O/HePZRa2oXcT4xgVpnpZowggMAACsAoIGAABA6fA689JmAXmEgDa9zJNWVtFTi1DBiA4AoC8CQQMAAKDhxEZP8sbVpm3VIjKyjtL05kgKRm0AaHGSgv9KCgQNAACAhlOLaNGmg1np5vltGi1NiAgAAGgsEDQAAACanjwbAkjTxDyiB9O2AABNCzYFEIGgAQAA0PTk2QQg3dEs6w9r5h1xgRACAIDGAEEDAACgZYntFJaGIcomSIrexhkAAETww5oiEDQAAAB6nZh44Nst59nVLO/amHr++CYAAIDigaABAADQ62QZNdF+DDM9Lx3T4kGsAABA6wFBAwAAoFfIs8bE+u0YbeRG+u0Z7Xteu/KCdTYAgJrApgAiLSNoOjo6qFKpVP8AAAA0F54pXVl+U8YaSckyylLkiAwEDAAAFE8lSZIS/4yODkQNAAA0D7F1MFnWyRRlAwAAeFm8eDGtscYavZpnpVKhlT/8QKF59D/gSSqjNGiZERoAAADNgXcHsCJGLzw/wAkAAKC1wAgNAAAAkBGM9gDQt2nUCM2KHxQ7QtN2EEZoAAAA9GFaYb1IWoZYWSBmAACgeYCgAQCAFqAZxEQRTn6W7Zql43l/iwaCBQAAygMEDQAAtAB5HfBwRCLv7mNFoU3r4se1nc+scLXY1AziEQDQR0kqxf6VFKyhAQCAPkbqkGsiyLs1shbXG6fIdShFpY21MwAAogauobnjg4Xm0XbwH7CGBgAAQP3wjARIYcJjWhrcKeciJv3LYmds/UmW35Gx0vHYsWTZ+YWMpEDMAAAaCn5YUwQjNAAAUHLCUYNw9MWaihUea4VRB6sMtY4atUL9AADqS8NGaG7fptA82g79PUZoAAAAFIc2EhI629LIiiVmtPPW5yKIjTTF4lmCI4sYkcJCzAAAQHMDQQMAACVAWuAejrBIi/tTcZMeS+N4f+QyjJt1+lmWcqV5SfnHqMcPduYRa9gYAADQELApgAimnAEAQAmwBI303WJh5xjaaNjUaHzvLmNeskwLK3qaF6aRAQBqoWFTzr63baF5tB32u+iUs0WLFtFll11GCxYsoLa2Njr33HNp9OjR3cK8/fbbdPnll1NnZyctW7aMPvzhD9MhhxxSmN0YoQEAgF4k6zQua1E/XwSffg7/wmMpIwbNFdML85TW5Wh5ecrQW2ImZo+0fihPOgAA0BCaYFOAWbNm0Y477kjXX389TZ48ma655poeYe68807abLPN6PLLL6evfe1r9IMf/IDeeOONetdGFQgaAADoRbS1K0TZBI42oqLlGds0gKehiRkexvM7L7Fw3t+K8dRPTAh5hRJGbwAAQOaJJ56gPffck4iIJkyYQC+//DJ1dnZ2CzNs2DB6++23iYho2bJl1L9/f1pttdUKswmCBgAAmoRa1qnw9THh+hktD0m0SCMlaTqx360J19zwdL3bNVvbRXu2Ys46sgLhAgAoFUn9/r5wxzxqO2JOt78YK1asoJUrV9KgQYOqx4YPH04LFizoFm6//fajl19+maZOnUqTJ0+mww8/nFZfffUaC68DQQMAAE2ANj2Miwn+P/1sLeDnU9D4OS5EeJ7aqFDsN25i4skzGmMJMitvTx4AANCX+fxBo2jFdyZ0+/PA19gkSUL9+nWXFN///vdp8803p9mzZ9ONN95Id955J82bN69utnPaCksZAACAm3D0wTNqoDn60pbNPGxsx7QwnucHNrPYbk2542RdSxOKs7A+vKNDAAAAbNra2mjgwIG0dOlSGjx4MHV1ddHChQtpzTXX7BbuySefpMMOO4yIiFZffXUaN24cPffcczRq1KhC7GraEZqVK1fSV7/6VTr22GPp+OOPp6effrrRJgEAQKHwtS6SSIitX5H+c+ee/6Bm7HdsuD1h2Nh20JJN0jn+Oc9IiyXqPMIMAACanaSrUuifhx122IEeeOABIiJ65JFHaMstt6T29nZ66623qlPPNtpoI3r22WeJaJVP/9JLL9HGG29cTKVQEwuae++9l1auXEk33ngjTZs2jX7+85832iQAAMhFlmlPMadec9Z5HEnESNPatKluPE6YFs9fKqNUZu+aHintonY9AwAAkI2jjz6aHn74YZoyZQrdfvvtdPLJJxMR0T333EN33HEHERF96lOfor/+9a90zjnn0FlnnUUTJ06ksWPHFmZT0/4OzTnnnEPHH388jRs3Lld8/A4NAKDMWL8L41mgTyQLAk2gWCMi2jlJLGXd2CDPtDNplMkbNxYnK/VMCwBQHhr1OzTvfmv7QvMY8OnHo79D04w07Rqa119/nZ599lm65ZZbqLOzk4499ljacsste4Tr6Oigiy66qPcNBACADGRZx8GFi7VFc2yNCB+heGXx7G6/Q6Mt8udT3yQ7rE0GtGNZhY82TU6yUxvh4XbUU4BAzAAAQONp2hGaQw89lI466ijad9996fnnn6fPf/7z9J3vfKfHLgoaGKEBADQbWUcbNAERC2Ntb6xNI7MEE083JrYkO2JCSwqbVfxIeEa1rHgAACCBEZrmomnX0IwcOZLGjx9PRETjxo2j/v3708KFCxtrFAAA1Ahfu2I5zdY5aYtmLlSk9TThOb7Oxlq8Lx3X8tfEjCRi0j/P+iDNPo+9mujSgJgBADQlSaXYv5LStIJmxx13pAcffJCIiF555RVauXIljRgxosFWAQBAbWgigqPt/pV+5yIhnE5lOfzS4v8YmvgKyxAbXfKULbZRgYY2IhWbXgbRAgAArUHTCpqDDz6Y/vnPf9Ipp5xCX/rSl+jcc891TzcDAIBmRBtN4YJAEyxWmtyp5yKEj85ooyUeYjaF+XFbNKETjtZwW8Mw1gYHmp0xWwEAoCw0w7bNzUjTrqGpFayhAQA0O94dumLCwRs2DR/mwdPQdkGTwsV2OdM2GbAW+ktCi4ex4mDUBQDQGzRqDc3ymz9UaB4DJz+KNTQAAAB0tOlVsWlnmpOujcqk52LOPR+tCaetSTuJaXlrU9yk0SPJZk0QxcoXjmxZ5fWMKAEAQCnAGhoRjNAAAEBBWDtsSY685vRrIyHSqIoVJzyX2hDDSj8sSyyuVT7pfEiW9TkS2LEMAFBvGjZCc9OOheYx8JhHMEIDAADgv/D1K/xcOLoQojng2giPZ3SCCwJtVzGrHDxdS+hYZfec99jpHXGBmAEAgNYGggYAAApAmuakjZSE3/k0MGm0houBMKwmnqTz2u5pVhox+620w8+aUJGmkoVxeHk59ZhehqlpAICmpavgv5ICQQMAAAUQGxWwtiiWFsxLjr2WnjWioi2g18SId42PNGpi5cWFi5W+NZLF7ZTEniUqY9PpAAAAND8QNAAAUCDaLl3W2hZ+zBIxkpjgzr0kLKx1LJogkhbfS0JJiqeNqmgL/3m9SSMvliiSjmtp1BuM8AAACgObAoi0NdoAAABodTRxQaQ71JoAkhb7e0YZrNENnqfm8GvnNdGiTRGLiSaetmazNbrjFYxFgBEeAADoXTBCAwAANZJ3hOKK4etVP0sOuGdUw7OBgGWnJTKk0SE+zUwSOdaIkCTG+MhQOOqjrf+RCPOw7NLqGSMrAABQTrBtMwAA1AHPQvUly86nhZ1jaMSguepoS4i2rkWaGhbmYTnm0kYC6XdrFEhLK8t5STzFRnesNKR8vCNe0nkrPgAAhDRq2+Zls3YuNI/2439Tym2bIWgAAKBAPNOrpFGK9DhRTyf+wd0W04T7Lu0RLsQSSR7Bo6Wb8sri2VVhVguayMkaP0QSJ1odAwBAHiBomgtMOQMAgAKx1pNwx5tP8ZJGK15ZPJt2f2hY9XjooIdT2MJF/CHeERXN8U+PhaNMWeqB45mqJh2LbU4g1aUmGGsB09QAAL0KNgUQgaABAIAMWIvUa4kbihNtpGJo+wzaaNhUWrLsfJozcRq9snh2t/NnLXrdFDBh2lcMX08UC1JcLrgk26R0rClzoT3SaFWYnyYKw7S5yOF17ZnSl0ecYLQHAAAaDwQNAABkhI+MhKMDkmPMHW5tUXrsWMiE+y6ljYZN7ZZ+GC8VPZrdZy16Xcwzy0hGGDY2XYyHsUaueL5c5EjxeBl4eF73XPBAmAAAykCSVAr9KytYQwMAAA60aWDhMR5eG2HwLI4P09aOSc7/K4tnV4WOd3G/hCZQ+FQ5rR60TQZCrDq10vfEix3HehoAQC00ag1N53W7FJrHoBN/hTU0AADQqvA1G9q6F83ZD+No06GskQgNPlqUTknjYbJgrZ2xBJpWHm1URPrM8/ZOC9NGiaTpcprQw3oYAAAoJxA0AADgJMvoSfo5FDzceeZrYGKjBrHjsTUxsfB8Cl0YRhJq1jSusJz8O4+v2RgTMN7yWeIyzAejNQCApqerUuxfSWkZQdPR0UGVSqX6BwAA9Sa2yD38vrBzjOgoh5/DqWFpmtroiJRHbIG+NILinYbGBYU2rc4jODShENaPNqqTxpfSk0ZX8i70zyJmMJIDAADNBdbQAABADqy1I+kxHibrd56PRmwdDs/Du2Yllo9mn3e6nWabtw61eDG0USdL1EjrnQAAfZeGraG5ZrdC8xh0ykOlXEPT1mgDAACgjFjrVGKiQkuDpydNDZPyk74Tdd8gwGOrx+aY6JDixURMGMZaW6OJitjmA5btWvpWHUDMAABAc4ERGgAAyInkAMccaiJ7d7AwHCe2MF9b9yKlodmmoQkBS9jERo68wsiyJRZGszW0CwAAsoIRmuYCggYAADKiOcKaA50iiRAePz3uGQnRHHuvbVlGUPLiGd2xyuEd+eJh8kxBAwAAL40SNEuv3r3QPAaf9mApBU3LbAoAAABF4RESlmAIhYM2qiM531z4aBsGaPHSdLXpaWH61lQra7pVeF6yU7MhPSelrdkTG1HxCJM07fBPEz5Y/A8AAOUAggYAACJIDrq1iN9ak+JdcyI59Zbo4XZa9lsjQ1JZQnusaVyedSyS/dI6Gi6CuPCxxJM0PY6LFy1tT30CAEDDSAr+KykQNACAPknet++aM83PSyMb1qgDFzpZ1r/wtHicmADTFtrzNDTbLPFh2ajVpSZWUjssW7RRHm10jKcRm4YHAACg+YCgAQD0SYp8+57FQeajCdr0rdCRt0ZyuGCIrVGRbNbs5flJ/z1CgI/ESCMqlo1hubTpe5bIiuWJzQIAAM1KklQK/SsrEDQAAJCB2AiB5dBra1b4VKjYVLOYwy058mGe0hQvbQTJWteiTT/zji5Zo1bS5zSOdF4b4bHqVbM1y3kAAACNB7ucAQBAnbCERta3/nw0Iv0cnsuCNOWMj1RcMXw9OmvR6+JIixY/T75a+Szb+KiUNFqToo1KxdLDqAwAwEujdjl758o9Cs1jtTN+iV3OAACgL6MtOk+RzmlTpMLz1sgJ/66t29HER+jgn7Xodb1wLG3PFDGeb7gGxrJLEiTS5gFciEhhtLJyO6zpd1p5AACg1+mqFPtXUiBoAAAgI5ZTq73tlxx4a41HeoxPn9JGH2KCJcxHEz1hPlKZPGuDeBhtfU1MqFlrjqQ0eHrWJgPa6FBsOqEUBwAAQOOBoAEAgIxoazGsaVTp8fRPEz0xoSJNm5JGGXhca/2MNb2NH7OEAi+nJcCkOtCmp0l5WGjrbLh9UlirXHlsAQCAupJUiv0rKW2NNgAAAMqINSLAv1viRRqRsEZzLNEUE1TSOT76YokxKT9rXYrl+HtGucL0tfU7WerIElf8szX6AwAAoLmAoAEAgBzEHFy+PkMSL5aYsfKUpkZJ57S1Jfy4NRIhiQQrrLZuRxNKlriQsEastGOeNUj8mFSemF0QPQCAoinz1spFgilnADQh3GEFvUveuo+NOmjfudMsLZyXRlLC+GGcVxbPVsNLU8jScNZ6Ek+ZvaNNmuNvjbpo8Swx410fw6ffaeItS/sCAADoPSBoAGhCyuwctYIQy1v/1hQ0zcnXpnel561pT9oi9xGD5ppOueb0S3bE1gvx8oXpe9fEcDEnlTE2jc47CqONpGijS9LIFgAAgOYCv0MDQBMTm+YD8pPHQfXG0daWxMKleEYiYmhrZbT/PE7ssyRCpNEYyz4prLW2yCqHVGapLqw1P9pniBkAAKdRv0Pzn8v3KjSPIZ/9OX6HBgBQf6y31yA/WaYTaXG0+OGogjUaE4YLw3unQFl2SiMoMTESjlLwcLFpYryM0pQ6Xl5eZ9rIjpYft80afQnD8XrW4nN7LRsAAAA0DggaAJocz9oDUDu11m1salZscXlspEESSbH8rXysBe+hY6+JC01YaOIgtj5HEkKWcODpeUeHYut7NPvC47gOAQCNIkkqhf6VFUw5A6BJgePUeljToazpUlJ8flxbK8O/W0KDiOiVxbNpo2FTe9hHRHTF8PVo6hvHimWSpmlZ5fQKEGs9Tmy0xgqr1V+W6w7XKAB9l0ZNOVty2cRC8xh67n2YctZIOjo6qFKpVP8AKDtwlLrTKtN8pJEWPt2LT9HSRnr4SIK2gF0bgZAc8hGD5qqjM2cter1HOmGZtPytkRopTHgsJgL5ca28Wh5SOM1Gj80AAAB6n5YRNNOnT6ckSap/AJSdLA58qzj7Fq3gPGpCIfaZx4tNZYsJnpAsox5hnNgoC5/SpokK7bu0pkfLQ7LLWkvDw3vswXQzAEBTkBT8V1JaRtAA0Gr05rSXsgiimHNeVF5Fp2utrwkdaUngaOl6hElssbs01cuzzoef10aRpLz5KA8Xb1p6PP/wunhl8WzxfHqMj/hkEXYAAAAaD9bQANAi1EvYaE5b2d5M99bbdG1tCg8Toq13kUZLwjUt2miHlr41XUv67LE9jJd1PQzP03veuz4nlp6VjtUmsfVJAIC+R6PW0Pz7kr0LzWP1//lZKWc6YYQGgBKiveXOGoajrWPI4uzWi1rT7i2bpdGSNB8+BYrXZWwa1pJl59NGw6Z2G0WQplllEaHWNK6wz/DpWNx2PgJj5anZy+uETzPjdvHP3A7JVmutTRhWKq9WnhCM0gAAQOOBoAGgybGmAVnrBPhbdClcHmeMTwnKS2z6WOxNviddiXq8WbfWovCpTFZ+kqNtjcKkU6ekvLlIiIkMq2wxZ56nbU1948ckIST1ca1/aaMl0loaLbxkp9RWmn1cEAEAQK+RVIr9KykQNACUAMkJ0xxfHk56i56Gr8UZy/rmOo9TmMc+bxyPWLKmMWXJ1zMSEpuWtmTZ+TRi0Nwe6fF+wPuDZ9oUF48xUaOdk4570tHSsEZjNKHCkfq9NE1Oe3EgicwYGLUBAIDeBWtoAGgiuKNlTZuR4hLZQseTfyxObMpTI9cXeKdj5bEvbznztIMUV3LCuYOvjYTwNKS+JfWf2ChfmLZmryUKpTJpaXrIOqrnudYa2Z8BAM1Lw9bQzPhYoXmsfv69pVxDA0EDQIsQcwi1N96edC0nWBoBqJdo6O34teTnEaCWfVIdhmjOuhRHO2aJFilMLLyGJGg0Yektp5ZuzI6wDqz08gLBA0DfpFGC5u0v7VNoHmtc8FMImmYCggaUhXo6VJZgyTriI4XLkwaofZTGM73KEjy1jHJYeXsFkBTPU4484skzmmTZzMuaRajjmgCg7wBB01xA0ADQCxTt6Axt7769r4T1NlobaQGrqEWQhGnEnGFPHrGpZ9J3i9gIT3icx7FGqCxbvOtfso7gWGlYdaSNPHLyjLwBAFqThgmaiycVmscan/tJKQUNNgUAoBcoytkZ2v7fBc8jBs3tkc+SZT23xbXs05w1LW9v2FrwpFlrvml8y7G2poplmf5kneeCIPyf/nnbiLe9lRePz7/zH6YMbZHianlK6afhwz4oCTYpLu+3PI3QPkm88LTD41K9xK4fAAAAjQEjNAC0AJ71F/XOo9HplBFtCpM2MpB31IUTW0sihYuJiBDJTs8oEk8jb11Y0860ESht1CZWHqnsfbU/A9CXwQhNc4ERGgBaAI+YqWUUo55OWxmdv7x1513TxP9zZ5w71bFRl/B8GldKUyOMI43AaKMYXCBI/SY2EiXlJYkTKx2ehnYsTFcSN55pmGXszwCA8pJ0VQr9KysQNAC0EHz6TnqMSHY2vfR1py1L+WOjGdq5rOtOJBEkOfbadLSYo+8RPNwWTYhJ8SRbpXQssc5t0sJJ6VnT4rgIrAdFTMcEAACwCggaAFoAvr7Cu6YjFg5OWGPgozHcudbWsEhrZqRj1pqbME9+PhZfO8+ngHHBpI2GxKaoSWjrabhY4+I/HA2SBJ2WnyaeNLsAAKAWkqRS6F9ZgaABoMR4RwPCMFnieNJsBtFTbxtqSc/ruHqc/jCcNt1Kim/ZpU11s6aRSWlJU754Ph67UqQ0JCEShuXHpHqT8pBGsKwRGwtt5AwAAEDvAUEDQJOSZ0qYNb0oq4OZNW9OrWt2soSrtxNZa3redRvWeidtXU2YnneKlzWCF45WWCMTkggIRzmsER0rjDUVUiqzNXKi9QfPlLOwLJqtllgCAIBeISn4r6RA0ABQAPVwePK+HfY4+ZZTuLBzTK/ZX2vcZnkbHlvvEh6zBEosvjRVS0pLS8MSWhoewcqFjWaXNRKUnudp8KljMSSBpIkULuRC8SeVXxNDAAAAGgsEDQAF0AiHR3rrzo9LTmQaPj2/0bCpcNgykqe+skwZk0ZgtDUnofMeEyvalC3JNsmx5/GkuFp5vIIrPOcdkZPS19bQcPEkxcsjBAEAAPQeEDQAlBRrVICLm9hbbmlKTpa884RtZoewlqlFHhGRIgmDLHZJbR4TrR57tfUqYTp8HU16TBrl4OlqafF0NNHG44SfJeETCj0pD62urClulmDyiEkAAMgDtm2WaRlB09HRQZVKpfoHQG/TDA6M5ejyzyHW1CUrfC1hm3kUKM/UImudinQ+PK7llXV9CC+DJZi0qWvSOhNeLm3UL1YWbfTDK3p5ebhAktDs5uekeFlEoCX0AAAAFEvLCJrp06dTkiTVPwB6m2ZwYLT5/pajrcWvJ7WMeJQBbQQgRBIYkkPunSYmTTHUnGlrChYPp7VVbCQnjR8e51PfpPUp0pS3LGXndlujOdqIVuwYL2MzXOsAgD5KUin2r6S0jKABoJXJMoXIcqrTY5azWoToyDPi0WzEhGCeqXXWdCcp3VAkWHlIoxGac6+lFxtx4FPCuKgIBZs2tUyzMSZqYmWVzmuCLLVL+s8/W/a0qlgHAIAyAEEDQAnIKwQ8DlvWvLI4br3h5PWWIxmrF2k0woqXHl/YOabHOS5iiIge3G1xj3NcMEjTwSz7tdEhPlIjrUuRyq2N0EjxNCEQm7IlTYnT+rUk3CSRyPGMToXl1NIBAADQO0DQANBAinLGPesLQkdVC5dlZEiiN5y8ZnAk+ehH+tkTJ9xVjjv/Yf1PuO9ScwqVNeXNml4l2eoRZZKNkiAKz0npWaNFXCBJ+YffeX/m9sSmjmn2e0ZDAQCgN0iSSqF/ZQWCBoAGUotDFHtrHHOstalH1tt4rz2NmIbTyCk/1jSqWJysI2LaiII2OuMZgYjZpAkhzemPCWEuLjTBJY0wDm2fQSfTd03xoaUTE1zStDmrTBxMOwMAgMYAQQNASanlrbHl0FrTfbz2NGIaTiPeltc6gsXTsqZMhWlL0840GyTxYI248PT5+XAqmTayoqUbHgtFhjWNLA0TCo1b2ueKgsNrh1Y+S4hbIs8qaywOAABkASM0MhA0ALQgsTUA1lv7LOsHinDQ6p1mkU6kV7x4p/PF0pBGHrR1KpZtYTzJkddEhZYvFyVhXD61UQufxuHiJbTl8/2/K5YljCshCbAwvjSVLyaQeJ14wBQ1AAAoBggaAEqEd9pLbL1B7A28d/2ANnUnj92x/PLSG06kJfq8cAEgnbccc6mNYyM34XHLVu+onzVlzBppigmD1L4vrDzCFOfaVEspfR5OEmxWWWN1DwAAhdBVKfavpFSSFv3RFvy4JigTMQeqN9LVwlriRoojvYnPaktfI62bhZ1jaMSguep5Xp9E8vQyTax4BA0fnZBGKzQBJB2X8tTS5GXWyBLPCpuGjx3P03etOLgWACg/ixcvpjXWWKNX86xUKrTgfw4sNI+1LrmzlL/niBEaABwU/ca1VudGGxXJI2Ykh1CaHpSes455p0D1FayRiKHtM3qImZgA0KZs8e+a6JTEUSy+lKc2EqSF1dIMp6fF0ubliY2s8ClysalktfZdKw6uBQAAqC8QNAA4aIQDUus0rSw2e6YD5aWvTr+JTesLw1mCMUxnYeeYHo45/59lWplnuqAkdGMjNdrUs1B08M9hfnw6o9W/+RQ8Loa00SbJZslW6bNWH15i6QEAgAY2BZDBlDMAcuCZptIKzJk4jXZ/aFi3Y9oUor5Mre3unZ4kCQAi37QznqYU3xJA2gielYc2Tc2aPuexWxImWj3lmf6YZ7qZZU+ttNp9BYBWoFFTzv513kGF5jHyyz/AlDMAWh3uwFuOZSsw4b5Lu333vJEvinrnUc/0Ys5+LK8s/SYc0eFTtXhaUhivGJf6Os9bG3GSyiaNaPCpX9ZolVQ+7+hG7Dq1zucRM1Y8aVQsNgWu1e4rAIAaSCrF/pUUCBoAMlDUG9iywZ1Ly6GtF/Wu63qk5xW0sdEA67g2wsDT1kZppOlcFrH1Mmn62pQsa1Qllr8mmMP42igSn8Ymned21WuE0ZrOJwk2bYodAACAfEDQAJCTvuKAeJxDKXyrrwnwTFvyjCB41ryE/2PiUTsmTe+yRiqs6V98lEYb2bHqSBNLvNzayA63RSIc8bHaIO96GGtKnnXdWG3Y6tcNAKA2kqTYv7KCNTQAZMDrrLUa0poEkJ/Y+g8ifcQlyxQoLZ61riVFs0dKO4wfOyblK9UJL49ll1afnimisbq16tATT/vuSQMA0Lw0ag3Nm+ceXGgea192RynX0EDQAAAyUVbnK+Ywa85mb9lghddGSnjYEO80N20aW0yIaHG1vD1l0NKKiRcpvFVvlsDTznv6R9Y+xs9zOwEAzQsETXOBKWcAZKSvTwkpq8MVc7I94WPx+dQlr8iwpqZJU8TStK0pYZI9Wh5S+pqdlhAIj8emcfEpeemfNg1OG8kI7eZhwilxWcSNVEZur2ajVj6PyCvrtQUA6D2Srkqhf2UFIzQAABd4g1wb3jf0KbHpUFnaQ5t6xdPOMooSmyJnlSU8FjvvmWKWh1jaHnu00aCwPjxT0so66glAX6ZRIzTzzzmk0DzW+cr3SzlC09ZoAwAoC9qUnL6C58182ehNpzKWdtapSpbdnvUo3Am3Rpt4eGkkJFYuTcyE9koigtti2RkTWZqdmkjRyiCF1+rB07/Kfh0BAHqREm+tXCSYcgb6LHmnjnEnrpYpaM0yfS2rHY1ywKRpXLUgOa3NRGwakja6En63po+leUj58WlbPHxsPUsYT3sZEP6F5yz7NXt4ncTSskSgJaS0MNoImnZeG7VplnsCAACUiZYRNB0dHVSpVKp/AMTI6sBqzmUtjnBvONEeB8lrhyetmCNdC/VaZ5DVtiKdzHqlzUcCpNETax1J+N8avfGMiEgiKTaikzrzUhtrx6xR0zz90BJP2qhSaJu2ZsYaYYqVEwAAQJyWETTTp0+nJEmqfwBY5HFyyvrmtJ4OkrRWQMuvmR2zPGI2Rq39I4tYTNEEtjYiIY2E8HT4KIE2/SpmR5i/NpKU5sVHVXg8SWh5xIwlEKSRI8luSZyEdklpWNPMYnXlPQ4A6JskSaXQv7LSMoIGAC9Z3+JKU2vqZUcZ0Zzivk6W/mFNfYqFt9aQaCMssWlZ1hqQEG1kRTsm9RXJ2efntSloHkEn5eMpG7fXyiNMN/weG4nR7jfW/aWZXwwAAECzgF3OQJ8ji4OThrfeuAKbZqmzWu3gYoG/ta/nVDjLiY7lkyUNbVTDEg7WqAZPl/+3bLTCWmWWRnFiaGlqaUh1I5U1DJNHUAEAykOjdjl7/azDC81jvStuK+VMJ4zQgD5H+GY6xTvdQ3N0MFLRGLLUexYBG0vDO41ISjfPGgpNAFjhwulc6blYfw7LJ60P0aZfhXmG56zph9oInza6pJWXj6ZoU9qscvNpb+HxsD54PlK6vJ2t9sR9AwAA6gMEDeizaI6Q5aRoaeSdbtRILIe4ntRz5KKItL1pZs1Lc6Lz2Jxn1IQLkvAYjyeJe03we0aJJGEmiR9tapc2RU6rg9BmaWREEyBaOhyevibEePmzTn9rlnsDAKCJSSrF/pUUCBrQp7CcmhRt2ow3foxaHeN6ob01j9EIp0sbGest8rR7vcRW7E2/Np2Lf9ZEgnTeGr2URnWkUZpwdISPfmjXVJZrM/wfijitzJbIkuwLw/DpcxJhOjyMV9hgWhoAAOQDgga0DB6HM/YmVgrLHbZ6TF3KQrM5Oc1gT2xNRRH5FVVurxPvOcdFieZcW1O1wpEdPm1MEgzSiA0XCJJdvOyWvZqIitWNNf0sNiUuLDMPw4972tASlwAAAGoDggaUklpHSvjb4TkTp/VIP/zPP2fJA+SHt4PVBtIogZROI0aYNHuyxLemRFrxJPEgTcGSRkykkQlLxKTnpZEYzV6PqOB5WOG0ESOejzb6qh3jZc0iwKTyYXoZACAPSVel0L+ygl3OQJ9Gc9yyjMIUERZ0J9Y20rSrous6Tx5e+/PklbfMkmDxximaeuTDBUqe8kl1o6VrtbGVBgCgXDRql7PXTv9koXmsf9V3sMsZAEUiTR+pFWnqCF8HIOUfxs+SF4gjjUZ43+JndcrrMcpXjzixaUseMRP+185pI1XWuhDpvDUK5sETVhMV1n0gyzUaq29tBCu0jY/UWO0gTVvEPQEAkJUkKfavrEDQgNKgzWGvF9KUnKxvrhtNK0xjKXpqTuiEeoVCb+BZi0Eki+xw3QsPK41s8TUyWl3E1sjwsJqdUrpSmJiwkvKxbJDOS9PFYi9LtLbhL0SkurTqoRWuVwAAaAYgaEApsN58ZonvDRs6bJrz1Yw0u+iK1SF3LEPH25OGFVZa4xGjlvqsZcTCGj2SRHYsr9gIFw8XExCeerFsi5VNmsIliQrteGyE1Zpiqn2W7JbS10YXtfICAACoHayhAU2P9w1xPdPE3PbGkne9geTwNit5+lgR/T5v2vx6SallnUkWu/KmEfYtLmr4yE0YjscJ0+Zlx/0DgNanUWto5p16ZKF5jPr6rVhDA0C9sd7k1oLl1NXDGfGOKPR1tPUdRN3f1kuOppROGi/8n9UWzb56krePedeDWHZbdW7BR434aEo4WiIJUs3Z944uSfmH8J0KpTT4d0vU8OlkvNyh7ZKI00bZcD8AAID6A0EDmh7v2oIsWEKpHm9WrWkr4L/wN96eaTqcrGLGM5VLSq9W5zQ2jSsPXPylx7R6kNZ8eG2Srg3tWCydLOE10cCZcN+lZjpWflwwx/6HccJ0LeGdp28DAEAPkkqxfyUFggY0NXnftnOsqUieN9UxxxVvXWtDcwA1gcnFgad/aA6lJTTqsZYkT1gLSbiE6WcZ/Qjj5unD0siQNUUsdn1JI5taGWsZBY3VYfhd6wOx0bKwX2YdlQIAAJANCBrQtNTzoS85qtxh4o6xNo9eshNvXXviEYHc+eMjNZrTWs/pWp6+IdmQt3/W2q+zijfvtDJpPUwsnmfExntOG/HwoNmuHbNelGgjV6E9/LOWvmYf7hcAgLwkSaXQv7LS9ILmlVdeoX322YeeeeaZRpsCepkiH/qa02K9RfY6xGWmSBHJ85BGyfh0KU1s5rE3i6Ouxa/HVMJa+otX1GlToXg6mrOv5eNda5MV7aWAJWqyjpjxa1vqc/y4ZKMkatLzWr7WeQAAALXT1IKmq6uLrrzyStp4440bbQpoQizxkSduHoc171SXZqVocRY6hdo0QCmM5njWMuVLG73QxFNvC1drZMEK44kXm8qZR7znGX3g7Vqv60MSZGm7Sm3N+0HMHkkYhfGkMN6pkQAAALLT1ILme9/7Hu2444607rrrNtoU0GTkcRBiTqvm5GWZchPLuy87NLHRBalNpdGFWNp5aHS7xESIJt6lEQzpmHdkxVuPHtHkScMaKQ3DZEUb2ZH6YCigY2topM/hdEk+wgghAwCoN0lXpdC/stK0gmbu3Ln01FNP0QEHHNBoU0ATUs81FCHWyEEt9GWHRnrrbTnEmvizvuepX20alvZdil8EWt3E1p/wY9Z3De9IiWekxpuXFEd6ueAdjbLSD+tRE4KxeNJ3bRoaAACA3qEpBc2KFSvoqquuojPPPDP6A5kdHR1UqVR6/IHWoredx6Lz7ktOD38Tzp3V2Fv62FqcrEhrKaR8YiKgKJGaRyzUQ2B40ysyblZRmVWoxfLjcSTxI02H5PEwIgsAKAxs2yzSlILmhRdeoLfeeotmzJhBp512Gj399NN0xRVXiBsDTJ8+nZIk6fEHyg1fmFtPx8Ca+pR1YXJe+oKj43mjbjma1oiOdyoSdzqlN+5lWRsjhfNgTZvSjnmnqHmOedLR2qLItolNBZPWbUlphOHT/3leivSllxwAAFBvmlLQvOc976FvfvObdPXVV9PVV19N73//++mss86i9773vY02DfQivfUGXHvTWi9b6uX4ZUm/GZCcUz6tJ+a4a2sQYtPXNBvSY42sM89oSq193xKO1tQ1/jlP3cZs0WzzjsbExJkXLlhin8P/kl2eMBZ94SUHAAAURVMKGgB68+150Xl5Hb9a0tdGMZqJ0IGMLbROCcN61pZo08nSz80wGmPBy5u3DbOWU1urUg/x5x1JyzN9LGs5pdFZaY2XFk9bixO7hzTbtQgAKC/4HRqZStKi87OwjqY1sBwdbWpHFifHSkNLq6j58Y1wuHsTzXGURm24gx2rF20xd0grrW2oZ18pa7+zpnZpC/a1MDxN7b+Wb157a0kPANBYFi9eTGussUav5lmpVOgfx08uNI+NZ90cXbqxaNEiuuyyy2jBggXU1tZG5557Lo0ePbpHuLvuuovuuusu6t+/P+27776FbvSFERrQ1NTyprmovGNz71PyvJ1vNfi6Ai4qtDfbPJ5nOlJsehSfvtYsI1p58q11JMBa71EPap0KFn7WRqqkdpRG7Pgxz/UrTR/zjCrGRhu1vAAAwEuSFPvnYdasWbTjjjvS9ddfT5MnT6ZrrrmmR5h//OMf9OMf/5iuu+46uuaaa+jxxx+nZcuW1bk2/gsEDWgqNGdGcmrqsa7FMx2slqk/fR2+viA8RmRPI9Om8njW3fA43AbJkddEVdF415BkScsbrjenWWaJy9vQahsrr6xT5njeVl+LjfbVY7oeAAA0I0888QTtueeeREQ0YcIEevnll6mzs7NbmIcffpj23ntvGjRoEA0aNIi+/OUvU3t7e2E2QdCApkJ6k8qdmnq9/fWGia3T0Mg7LcX6XhYkYSq97bZGS/K+0eYjQVJ+nrbxTHPLSmwUqSjq3a+sdSZ54nJi62Sy1FXeUd5Yn0n7mEdc4aUIAKBeNHoNzYoVK2jlypU0aNCg6rHhw4fTggULuoV7/fXX6Z133qGOjg46/fTT6ac//Wnd6yIEggY0LbFpR9bojYZn9MXrMBdBbNSgbPApP9oUnnqX21pfkSUNizw21qNceZCmYdXiYNciMoru09a0LyuMte6KC6zY6Ewo4tNwZb+WAQCtx8w/PEmb3HBTtz8PfI1NkiTUr193SbF8+XJ69dVX6cILL6QvfelLdNttt9HcuXPrZjsHggY0LZITFh4PR3CkKUmWMyO9Aa73G/cyUVQ5wroNnTzPFDILa8RBctjz5NkbDqjH+Q6xbLLiS+KxHuUruv/Xsr4o6ygf76tSGt6RRAgYAEAZOPODH6C/HXdMt78YbW1tNHDgQFq6dCkREXV1ddHChQtpzTXX7BZu5MiR9P73v5/69etHq6++Or33ve+FoAEgnN4ROhvWCIvkUOR52+qxy0ueKW+9QVHOV2zdTN6RFGtER2t377qL3iRmd960spyrV55F9NtY36h1Cp+1hksLFwrmmNhplRceAIDmIemqFPrnYYcddqAHHniAiIgeeeQR2nLLLam9vZ3eeuut6tSzHXfckX7961/TypUradmyZfTXv/6VNtlkk6KqBYIGNCeaoxGez/MWVBIg2nQ2yaaYXZ786xGmbHAxUcsamVj60vd6rL+qBc/Up7JTS7/11k+9pyZao7tSn4mJRmtUkKcFAABl5eijj6aHH36YpkyZQrfffjudfPLJRER0zz330B133EFERO973/to6623plNPPZVOPfVU2meffWjTTTctzCb8Dg1oWizhITkP3jQ9U1Kk8Jp9ktPiGR3ynmt2+BQvzRHM4yR689ZsCSlr/dabovtaren3VnxtdDDPPSE2aoO+B0Dr0ajfoZl7zJRC8xhz0/9Gf4emGYGgAU2N9kZTEiV5HSHJEa9VeJRZoFh4BV7WaWP1cvy8/aXM7VNm2/PQG+WN5WHdZ/paewAAVgFB01xgyhloGixnVFr8r6XBp4ZJ626k9NPz1jSR2JqBVn8jy0dY+BSd2MgZr2seN8S7BkGa5hOmJ61T0UbQGkHWNVhl61tZ6zU2zazWPKyF/bxfSzZIfTo25dG6/wAAAKgdjNCApsaaypFlFCXL4uG8ozB8hKIZHc+802zCsET1X5PiffudZUqQNGJUzzbJ+6a+WftGb9EMIy5WHGvKKJG9VXNfblcA+hqNGqF5efJxheax6c03lHKEBoIGlBZt7UZ4zvqexsni5BftJOel0c6UtXYlRTpfhM1Wv2h2imzHRveRWqlHe2aZWuaNAwDom0DQNBeYcgaaHm1ql0eMpFM8QgeXx4k52uE0ET6VqVkcndhUuN7AmmrGz2thpPDaNCBP3LRte7tOvPn1hsDrjbTrWb/W9Z5n1CUkS3xrSikAADSKJKkU+ldWIGhA0xNbhO45J4kaTxzvFKtmcnTq5bjWUiaPI8nXOXH4ehfPZgOh48vj96aw8QrMeq8P8VCv9GICNq8NUlp5+3TWeNJ1HopiTx8EAADQ+0DQgFLCnVxpipEmanhc6e0vPyZNNavFoSsDmlOe1ln4x8PH1jBlWXsi1bPmVGrpSG3ZW+QR5J74eanXhgi1iBiv4OvNtuL2SYv/vfHznAcAAA8YoZHBGhpQKvh0MiJ5HYzHedAWosfyC+Na4fOcb1Y8C+Al0aKNvGgixdMmtdjdDGRddN6MZWgGtGmnVv2iHgEA9aJRa2hePOr4QvMYe8usUq6haRlB09HRQRdddFGjzQANQHNopFEVjuWUawKprzpF1oYIMTEjiVCedpEbBLQqrV6+RlBrH0WbANA3gKBpLlpmytn06dMpSZLqH+g7WG9nU8f67BVj1PixRefWehttXUazTKPJC59ORiRviGCtfQm/W3Wb1wGU2i22dqcMdZ8FOM71IxQysXU8vO9lmRYJAAA1kRT8V1JaZoSGgylnfQdtsa40SuAZWZCOe9Kt93SpZiHrGhfpDbc0PVBb+5Ql71rsBuWhiLbMMj3UO62t3jYAAJqXho3QfLrgEZpvlXOEBoIGtBTeN6V5pqRlnbPf6ljT8CTRok1D4+n1xbok6rv9yKKoNWnatdwboJ0BaA0aJWheOPKEQvPY/NbrSyloWmbKGeib8OlG6XSR2GiJNU2Jh4ud9+wa1WrTT6RRmbCurA0a+JSecNpZrTuCxcI2czvAye1JrE7yTu8Kp05mwbpveG2oRx8HAADQHQgaUFq0KSHcua7VUZBGGsJ0PfPovVO2GomnvjybI4TphI6j9Waat5123rJHCmutqWmWeu9LFFXnvSUG+fq8etoAQQsA8IBtm2UgaEDTkPctK1+gz0dprhi+nhmff9bS58fCfDRn3eNAN5sjE5uqlyKJPF4fWaaYSWLRa6uUbpaylIkyC7Fmq/OsoyxccJe5LQAAoJWAoAFNQ15nR5qqFDoeZy16XYwnOSaxEQXJgfE64s3mzHGsN86WExcKl1BUxuBCUxOYeYVu7FgZaCUh1iiyXJOeKW6xFyEAAAB6H2wKAEqPJSisBf9esowatJKD493lSav/2LQ7ra3qvdsZAAAAUG8atSnAc0ecVGgeW3z3WmwKAECj4W9PY46w9obWuwOXd3FxmaemSHWovaXm0/DC49p6Fs/6mCxipsx17aUvlBEAAADwAkEDSo+0yxaRvVifh7HihOtltJEK6Ti3UaPZnFNp+h0/Z8WT6s4axZE2D+Cfs9hey85VvUW9Rg37CrxPFdVmWdfH1MsOrMsBAHjBpgAymHIGmh5repNHRGRZYC6lH5tSpdkqfS8jmmjzxAm/W2htaqUJQBbyil30OQCARKOmnP318JMLzeP/3fYNTDkDoAhq2QUrq5hJ42gOeWzUIrZupB629jbeaXVEcrml+gmnpnkXWcOxbD60TTLqnXY90szSf7PEyZIeAL0Ff54B0OpA0IDSoI2I8Bu2tIZDi6/lkVc4hbZkdcjK5rBbW95au3N51sxo9Qj+SzPUS5E7yjViN7E8oqfW9ADoDZrhfgHqA6acyUDQgFIhiRRry+YwXCxdPqoQjjZoeYTra8KwWbeDbSa8awfyTK2TRBBfMyPVaT1tbyZqsbdMfcpDWdqOv+woi92g78HvpQC0MlhDA0qFNRUsq2MRrgvR4lojPdbidj71qtXn4kvl08qshSXS1zEBHdQRAAD0Po1aQ/PsoacWmsd7bv96KdfQQNCAUmKJF82J1sJqokbaejhmU5YwZXBEY4v0ieSdyfixLPlJcbLWLQAAgFXg3lgMEDTNBQQNKB3W5gCSCIl952lqeYbxrXh5dgXrbbLa5tl9DLtIAQBAc4N7bv1olKD5yyGnFZrHlt+/upSCBmtoQCmRNgII0QRPbBcuvskAX9PB582H6z7CvEMnv8idoPKS9YHmmZaXZZvmvHY0ut5A3wD9DLQq1nMJgDIDQQNKBXectbUYlvOdxnlwt8Xdjlk3eG2DgNjbLu1cGd+QxcoZm55Xj0XUZaw3UD7QzwAAoFxA0IBSoDnBWZxkLkR2f2hYt/jSb6OE/6W8tFGJem4522xv0vLao4nPWJqx/JqtfkDvgHYHIB8Q7OUm6Sr2r6xgDQ1oeqw1MOFxHpaf4+lZO3B519TwfDzxW3kOcyuXDYAU9HMAQKPW0Pz5oGLX0Gz1A6yhaSgdHR1UqVSqf6B8eKaJpd+luHxamGdXrNi6kNhuatKIjrZpgbWDV6NpBhuI5B9K9cYDoLeAmAEANAr8sKZMywia6dOnU5Ik1T9QPmLbA1txuKDQpjNxUREKD74RgGSTtsmAZ7pZs62nybIltRdLyPHP0nevGNXybWUg2gAAAACZlhE0oHWRxId3u2VtAb/kSPPwsWlsfP1NHoezkU5qkSLAK+ry2tAXnfu+INoAAACAPEDQgFIibaWcor3hD0VLGMaKb+2qpk0zy0LWdTplIu+uZlk3eGh1yt4PAAAA1A9MOZOBoAFND5+yxIWMNIITYo20SNOuskzFkkZ2tPSzUnanPa/QK3u56w3qAwBQJHhpAloB7HIGWgZNyKTnPKMhWeJoYeq1NqVMOymVyVYAAACgVhq1y9nT+59ZaB7vv2tmKdeiY4QGlAa+aJ8v5CeSR0y0X7HnwsP745oh1kJ/a2TIg/YbN81GbItq6bM33TzxAAAAANC3gKABpUHa0Yx/9jjBnp3LJCfdEhjWqEw9Ri6aefTDuylD3nSbueygdYhNSQUAgGYAa2hkIGhAUyKNvoSfrR/L1H63xpqSlp7XRnis/LURirI6Q1lHRrKOavUGvZVvWdu4VahH/fNruojtzAEAABQL1tCA0pJn3YYliGrZrayvkbWetLVGWdc51bNt0NYAAADy0qg1NH/8xFmF5rH13VdgDQ0AzQj/oU1p9IVvwZw1/b5ALW+urW20vWnWW3yUUcz0lb4GAABAIakU+1dSIGhAaZHEBxcvaTg++qKN1EjHYk5kK/+WTEgeASC1B8hPGUUYAAAAUDQQNKB0xBbka+tZUlFjbbWsrd2x8rfoaw6o90dOi8gLAAAAaHWwKYAMBA0oHXzBv8dh1hb7h07xws4x4mhObMvn3qTZnfje2moa618AAAAAkAJBo9DsjmNfopa2yDLlacSgudH8pF3UvHbUgzI58UXu/FamegAAAABAsbQ12oDeJLZtr/cc6F2sKWT8WBhHe4svbensoZZRAW8ZGsnCzjFVUVcUzVTevkqz9TsAAAB+kq7yTgsrkj6zbbP2ZhgP9vJSi2PGt2nOuoMXnEIAAACg79KobZufnHROoXl84CdfwbbNzYz06/Ip2ImpnMQEhfSDmNI2zUXk3Wrg+gBFgH4FAADZwKYAMn1G0IRov/yOh2trYU0306Yf9iWhkmUXt0bUS6M3YADF05euNwAAAMXRJwSN5zdHrK2AQXNjtZXU9tIv1HumkFkjPmVEK692TfQ2fVFkAgAAABZJUuxfWWlpQSM5qrG1NJb4Ac1D3l+tl4QLn4aoiVtpM4GsWzo3WiSE1PKDoUXSTHWUh7LbDwAAAJSNlhY0oUjh62TCc5ZzCpoTr7MdW/Dv+RHNLHnFbGsmodwMtmSZ3laWa7IZ6hUAAADoS7SMoOno6KBKpVL9S+FOZvg23uN4NpsT1Wz2ZKEZbJfWznDRkndb50ZQ62/0NJos9dvsbdFMNEPbAgAAqD/YFECmsG2bOzs7adCgQUUk7ULbtllyYL1bOmOr3vriXbdSrzoP04ql25fbupXL3splA80F+hoArU2jtm3+3d7nFprHtj+7rG9t23zPPffQc889R8uXLxfPH3zwwbmNKgJrZCb244t421kMnod9Lb8z402rWXb1qie19Nkiyt4s11BR7dos5QPNQ9nvIQCA5gQjNDJteSPecMMN9O6779KKFSto4403ps0335zGjh1LY8eOpc0224y6urrqaWddiC34D9dbYNez+tLbbytjIzGxaWVlf7vabLYXKSSaoazNYAMAAADQV8k9QnPsscfSxz72MfrmN79JRx99NK2zzjr0hz/8gWbMmEH7778/vfvuu/W0MzPbbLONeDyP89os28eWWVQ16q2/NSpjidZGtzXwUe92KvM1BgAAoPd4++23ez3P3XbbrfA8thmxUeF5FEHuNTRdXV101lln0SmnnELjxo3rdm7RokV0xBFH0L333lsXI/PA19CEpAJGWktj/fCitQ4HtB7eNkZf6H2saxcAD+gzAIBaaNQamif2Oq/QPLb7+Zf71hqafv360cyZM2nUqFE9zg0fPtwUFM0AFy+hyCH675qb9M96+ElvdcvyWyRAJs92zaD38KyDaxVwvyiGVu4zAADQ16hp2+ZKpUJDhgwRz+2///61JF0o1poK/rslXOzw6WeeNRrSOTgpPYnVSW/UWZl+WBV9qG9Qhr4IAACgd8CmADKF/Q7N8ccfX1TSdUETJLPXvbEqOLiwCQmFjnTek3/WOK3kwObZWSyLY5e3rsrkPJbJVi+19nHrmq03jbgeW+keAAAAANSLlvlhzbxwp/CsRa9Hp4tJTlMe5zKrqNE2NCgjRTvjZdh6uKxt1xvUQ5BKvyMl/c9rVyMEZSuKWAAAAH4wQiPT5wVNSihUtClk/Ldswu9hOp68wnSz2CjZVWaaxamvZce0vJS97Yogy46CeUZFw7h5RlfRZgAAAEDzAUETwB0cvikAn2amERvh8cbnI0HSVLiyk3fKXlF2gOKodxtLP37rFaax3yHypgUAAACAxgNB839YDq32eyXa8ZiDpAkWzRa+GUErOt9Zy1SkswlH9r9kHXG0KLLf5tmJUDrf6CllAAAAgEXSVSn0r6xA0DD4bmeSSJEEB/+NmvR4eN6aTuOdYsPTlML0BYp2jsEqPKOJvVVfsb4tjfZ517hJ12ZfuZYAAACAsgNBIyCtk5G2ck4/h/AtntP//FiYD0f6Ac8wndBOLmw8U7i0c63uwLVC+VqhDHnw7l7GBZY1LS2WZtZNO/pq2wAAAOg9sCmADASNgCZeiOJTvqQRmvCzdkwacZFGdbgY0myJ2cjz4cdb0TlrhZGXepahLG2sLeCPjcTw8vFrhr9ciAmc2Pq1VmmbsvQLAAAAIKWSJEnSaCOKoFKpj8rkjpLmAEmOlCUcvHP1pSk90giOtyyt4NSDclOPfiiJHG0Xs/CYFs5rU29Psevr1yvqAADQrCxevJjWWGONXs2zUqnQb3e7sNA8dnroi1RGaYARGgFpRCZcByM5QHyURXvjyx/O1m9laE4Z3z66GRZle8CbX0CUrx/GRkZCcaM5wd6R1TAdbxreaWxZKGLr6mYmS30DAAAAIRA0AjERoa2f0dLhb5O19PLYph0rijxrc1KaaSezRuZbFie0Nzad8ObBp3JqW5jzKaGeNWb8JYQ09TNmozQ1tDf6eys5/K1UFgAAAL0Lppw50RwU74J8a74+j5/Hhno4A0VO78DUkfpTa502U5tYUzvT73kEghbfmpKW5qt9b5Y6AwAA0DgaNeXsN7t+vtA8dn74C6WccgZBkxFtgbK2i5n21jUmTLS5/TwPKz1vOYoID/xYa676ep3XIkqI5J0CY/VqCZd6CK160uj8AQCgrwJB01y0zJSzjo4OqlQq1b+ikKalSNNh0s9hGGlaCl97wx0mzYFL0+NTbMKw2tS4vNOGssYry9SqRhG2tTSdMa+j6q33erRPPdvYGrXUpplJa874NSFdS1K+PA3NHp62dwqqVc68QMwAAEDfAts2y7SMoJk+fTolSVL9Kwrp7a0kGKQ3x9q8/RRp/r+Wh2UbT0vaiCB0Dj1pW06eFaeZabTgstZDhecWdo6pW57SFEnpnJda2zi2LoaPdvJ+yAW+lB7Ph6etlSe2UYBkQ6wOMaICAAAA1B9MOasT2vSz8Jw2LY2noTmasTfM2voD0Lep19orT3hPXp6pY7Fw1tRLTbRZU85iUzu957QyZMmz2SmLnQAAUCSNmnL2612mF5rHh3/VgSlnfQ3JceJvhbU3wppDIL2ltsJLNkkjPVlHe7KEKyp+K2LVSS1T+qyRNquv1ZtYXpIQ4aOF4dSvFGlaWBqXX3ta/7dGPzUhZL1wkKYKauW2RuO07816/UDMAABA48CUMxkImhrwvFXWHCPJGdWmqUlTX7gdMSfD+0aZx5Hys/CItr6MVSevLJ7dbXpZ1ul9S5adT3MmTqvJvlge9UwrvA64SJCmi/FzkihK4SMj2mgpzzsMwz+H3/O+cLDS4OD6AQAAAHxA0NQZ7ijFnBceVnOUrLn9Uvgw/ZjDVk/HCk6YjlTP4bERg+Z2O6e9sbeE6YT7LnXl6zlf7xECTex7hLY1NYsLHqvPS9eRds1Z0+S0lw/WZx4X1woAAICsJEmxf2UFgqaO8DfC2htg/jk8xqfQEMlvdKVzVjjL5liYZp36UjasaUdpHaeiJh2p0aZBLewcExVIVr6e8x6HOzadUZtCxvOQ4knC3SqfVh9cmPCwVj78epZEEH8pwa9hqx5rFZO4NgEAAAAImrojzYuPTU+RHDJrRMaa66+NCEnTe/h5Hi4P9XCwemvEoFFob+2t0Roed8Sgua51GV578oaXHPaYoNamTFp28FEYfo6PyljXkgQfTdXqlguWWLpSGXh5NHs8YJQHAAD6FlhDIwNBU2dizlz4hldz9rS3yNoUNsmhy+rs8qk6sfJpb7Qt51Y6Fpvy47HfO1JRBHnyyeLESo5zrU5srL5jwsISKbEyZJ3umMaXbOT9PTbKI01NC+3ggsga+ZHykEaVtBEq7SWFR9S1irAHAAAA6gW2bS4QbVqLNkc/DBd7s63N7Y/ZI9mRpTweweMRRzycVeYi30JnTT+vPVI8TfzmrWcrntb2PI633eoZ3uqXWfs3j+/N1yvk0/Tz9pssbev9DgAAoPdp1LbND+3UUWgeu/12eim3bYagqSPWKIVHuGiChX/X3iDHnCAp36Ic+ryiqd5p1IN6OpALO8fQRsOmmvWfRQQUYXO94npEnFfgWCOfVn1pLxV4/h4xqB23rjUrHAAAgPLSKEHz4Ie+UGgeuz/6+VIKGkw5qyPWdKBweg2f2uJJ15qeouUbm4qV9c10aEtYHmnqUJapUdpUmnpMr/JitYNkQ7i9soc5E6fR0Pb/rn0J244v8LfErGVTSq2Oc5if1jbad0vAaMJD6s/pOU0whH/aywKpPOl3SczwfmyVP0yXlzlMXzqv1U/43XNfAAAAAMAqIGgKRBIw6fHQGZMEgUf8SMckpywML701lvL1ENpmibNYmppjmDUdK5zXBq9QsRbtS0y471J6ZfHsbmHS/+GITZhOnmloRLIAtspvCcoY2uigFt8rfKRjYRypz2gjImFcKTxPnwslLqxjfYm3gyZMpTb1XguxvPuCIOoLZQQAAA42BZCBoOkFuGMV/qVwR0Z6c50elxws6Y205hRJb6h5XE0oeY5rb9xjb7xjaHUihYsd0+JrQiUlFTya8LEc3xc/cag4quax1yMAeH+S8uJxvKM+XjGlCQ9tdCQMwwWEJMx4vUnCTWoD6+VBzCZPn9NGjKTRLqmeePqxlwPa+Xq/HIjRSFHRW6O3AAAAmh+soekFLKeROz1SOO3tvBY3jR97yy+9wZbS0uzh9mrfPc6V9kbdEy/v+bxkSTc2WqGloznG3jRqSVvrD1qbWv3MGvWxnP8s5fX2A+/1Io2oxEabtFGYrOWzyps3DQAAAPWnUWtofrnDFwvNY4/HLsQaGqAjvXFOj4dvdsM30JLDxKewaG/8uXMmOXPWKJBko/RdGmmS3tBLcKcv5nRyYiIsneLlIeuaGC1PolXrZdL0tNER6+19Gk/67EWqQ56HVde8vaW+y9OQ4msjBpowkfpjOPqh5SGNgEj5SIJM6u8e4WD19di5LCMbnrCNHCkBAADQe2DKmQwETS9gOTNZP4fOJRc5njfV0lte7tTy41oa1lv0sNyhfTFbPY6f5pjz47HpY3nDxqb1TLjvUhoxaK7pgGuiMatjmkXscLu1uuYCII+zLPUpDUn8aOKA28kFPs8/LENMuGjl8JRBu641gRYT49ILhJgIawUgzAAAAOQBgqZgPG/FtZEZHkYSHPwvRQsfYjmNPB3JqQpt1Bw4aeRHejMvpamdr9WZizlN1vlwJMca1ZkzcZrbjphA8pBFeFgOesxp94zyWSN41vWQhtdslkRD2Pf5SI50XhNrfBRKE0rhOU2shHEkoW2NaGnxpfJJNtVCM4iJVhFmAABQFBihkYGg6UW4UxZ7Q6yNgsRERZiXlIbmGFnCRMrfcsi1N/5SGaQRJ+ltvXe0JkbMabLOhyM5Gw2bKoYZ2j6DJtx3qejMxuyz8k4FlKeveEbBYn1Pq29tdMkKw/PTRj64oNCErSQ8rDJpLxJ4GrzfSX0yPB+Lw18mhOWSrkFLAFkvPWJ9uh7XBQAAANCsNO2mAPPmzaOvfe1rtGTJEqpUKnTKKafQ+973Pnf8Rm4K4HEwePgU7U0yRwsniRfNOZVGX6TwmiOqfdaQ0tFGa2IjQvVwvrKk01t5SnWTpx6yli0llqeUvtVmWjpzJk6jCfdd2sOGWP+X+qjW53ncEGukScrbKxis6y+Wbi3Xhbfe63UeAAD6Oo3aFOD+CcWOpn90zvnYFKCeXHnllTRp0iS6/vrr6YQTTqCrrrqq0Sa5sZxM6a20Nj1FcnqkN/E8j/CzZ3REssF6289tkOJIo0OSbdIbdssBjIk9z5toqR6yhPXkYTnSVnzeHvxzLK+YA87DhiMIkpOsjYpwARAToDzNVMzw0RFNUFt9lucliTJpRMW6ViQRpY2qSDZqQp/XGa8Dj5CQrhutDvLSbGImy3VdVvpCGQEAoCiaVtCcd955tNtuuxER0brrrktLly5trEF1wHL8sjiEUjzuQFnCyWNnaG8sXWvkhjvYoQOtlUGyVXJiNccyj2NgxZHqPIYVRnv77rXHykMTJZIDrI1iaE43FxyS8PA4/jGsEZawX8XqSBPakpDT+he/FnidSP+5rdwWqR0kscVFjvViQ/rcSg5yswmsIshSxlZqWwAAqAdNK2jWXHNN6tdvlXnf/e53ad999xXDdXR0UKVS6fHXzGgPrizOlIb2ZltzRqU/yS4uTKQ3z1a5rDf9oa0xJzE8Zr0Vz0otAiQmhqyyaHnnGT2KjRxo/YfXe6xOtTbShLrkxMfqIhQdVjklAWClz22Iif85E6dFBadWp9bLizANT1m168ciS58GjSWrQEH7AdB3waYAMk0raIiIurq66IorrqCuri469NBDxTDTp0+nJEl6/DUDWR2sNJw1CsFHY7T/khOujV5ogskzuhMbOZHOW6NRVjx+zuvY1eNtZrijmeSgag619vY9i40xcaeJjvBPE5VaPUviUoO3pyV6tfiaaOZlsfoFz0cTA7y/W6MzfJ1PTGDzMFK/5981wWwJ0PB/rJ3wNr/5qUWgoH0BAKCJBU1XVxd98YtfpNVXX50++9nPVkdryoQ2JYRIFyrSSIqF5KxJoy2a2OFOmPVGOLTTyl8qF3f6tHyzoAk6bpsm5Cx4+Njv1IT1GtaDJiA9x3hannhhP5Ic9phwkpxu3lZav7baUup3PJ6WjubY8/T5Zy2e1IctscjFljWaIrW7dm1pbRsTa5K92osQzz2Ex2kEEGP5wWgNAH0LjNDINK1KuO2222jkyJF03HHHNdqUuiI5hel37Q239sZXcoi0Y6GDYzlq3B6enuZUcxHGP2tvwqW4tTo3McfbQ0yApseGts/o8Xs0WZxTnlbMjphNmsAJz2vOb/pZEp1av+N5x0Qtd/q18nGBGKavpa2JCela4+HC77zf8v6kiXKevpS2Jc6k/hDawutG65OhjVLaVpxGYfUD7dpoNspgo0XZ7QcA9G2adtvmAw88kFZffXUaMGBA9dhVV11Fq622mit+s6+j4XBH0jrudaA0J1SKr4kJ6026lLckvqS8+TnN0c3yhpmjOddFO26ePLwiIc95q/4kIcvTCclaf9pIhtS/Yu3O7dCuER7eCqvZoZUx1j95maX6sI57+7/nPC+PVU/1uAZ641pqNHmu5UbYUOb8ACgrjdq2+b5tvlxoHhN/f17TLN3IQtMKmlopg6DxOO4e50kTDDExFHN6Lbu5DVb5LGERc85CW+vpoOV9aGcVHlp7pWSpdyk9Lc7CzjG00bCpmerL65hrfShEExOW7V4xZAnvmGD2XmdSvlJ+Eh5BIqVhld/K0xJnHhutY0UBp7k7qA8AykejBM3PPnhpPGAN7P2HaaUUNE075awvoL1xTR2U0FGxnBaejnRec6ytvLkdPHxIei78486gVIYwrfAY/6zlm9cJ0BxVqVzShgBa3FcWz+6Rj+aYZhUzUnqSIBjaPoNGDJrrqi/eJpoNvA9p/SwMK5VbcvQtAavZz23gecTqOv3P85f6fCzPEO+xsOyWCOPlltKVyqjla6WX5Vridea5lrLklTW9ZiCvzVY87f4JAACgOxA0TQB36iUnX/suiR/pfOhAhY5Z7E1/mA535CSHUHLSY46W5VBKtmkCq4gH/pJl54sbAixZdn6PtTNEPTcPkBzW8DMXfzEHRqq78NicidPEsLyNtHOac6qJWx6Gj4ZI+YXl53Ele6SwWr1KZbREkxaeXx8xARxrK6ls/PqT6si6LqV21ARSVqxrVMq73qMLntGoZsP7kkSKZ7VxjGauEwBA/Um6KoX+lRUImibA48RLDprkHHGHxhpNCfPUHsb8vPY2WBJl2ht4bQRJSp87x5KzrNWRRMxh18JJcWI7n0mCTmpjPgIQlkdKz3Jgwm2GLYfWykeyVSImQHi7an1BE3phGpbNUh/hcXmevL+FtsSuNalutH4YhreEhtZPYn2aXy/SNR97USHheZEiiVeQH+/15j3uSdMbBgAAmh2soekFrLesVhwJ7kS8sni2OCoghbXS45+lc1KZtLfxWtyYuAnTzkKeOFoa2qhAlvSttHg4InvEgn/3tG+sHJpd1lt3Ka52jMPDS/nG4lujBDHxp/VRKX+pPjQRouXnsZ+nK5UhlpZ1nUn9JVZWzQat3a30QJxY22lxrJcQ9boPAgBsGrWG5qdbX15oHvv88bOlXEMDQVMCPA8w/mBc2DmGRgyaqzqfaVhNwEjHLKcwi/Psdag8osCKE9ZHHjwPdu/D3xIKVlpWObz1k8b3tpEmmLI4yFYaWd8IW30oZleYhmRXLF9eviwCiuOpJ6v8Wjk8+eQh6/VYj2uuL5Dn5UiK5yVJ3jzythsEEOirQNA0F5hy1mCGtsenQHGnIoyTnuMPFG3dR/pfipc+mMK/WD7c1tC+MA4vo8eptZxwHsYqo0Yaz3IkPTbG4PXqSSus91j9pef5nxU/tCv8H9oa1o+UxsLOMeo5razcLl5uHo+nHZaN/w+dfSmfME3+Xzom1VkWwcnDWJ9DO7g90jUgiTZL0En2xPq/lK7lvHquuaxYtmUVxnnzqVd6/L4onZOu87BeY2IyJWs71NJuEDMAgGYAgqbBaE6A5DhJTho/zh+KPJzmAElpak60JW4kp9xynCxx4sUrXLR43geyxzbNIfHG57ZpDnMoOiQhytvScjZ5PWgim9uz0bCp4nmpva2+pPVfj+CR0tPOaQLFKq8nDX48Vs/8WEy0cSEh1ZEmLC07PI6xVR4tfD3xvgAoMp96pRcrC7+m0+PatZO17otuKwBA75AklUL/ygoETZNhOd/aAy+NZzmuXuddcgy1t9aacLKcZ35eKw9Pl8fP+pa5Hlhl0sJYb2Ml0p3TJIcmFJraCIQUhtuYfpeEljV6wQn7o+ZMS3ZywaWF1dpYKzu3KzaiIInBMLxVbu1tu2R3lhET6/rRrhNJ9EjxrP6glTN2LK8QKOp69QrT3sqTn4v1RQ/SvdMSs5bY9wAhBAAoAxA0TUbsbaglOKSwnjfHYRxJmFiOapqu5bxpowZZbONwu2IOMj+n4XkDz49Lokv6HxMJQ9tnVKcK8vaNOUZav5Ccc6md08+WcyyJ6pgYCv9zx52jjVZYaMJMe8NthZXStvK3bOWfre/WSInUfrwt0/+8bFZ/s/K0vmdxvK30vS9WsqRriVdvvnmw0rSERgoXsdZ1oPXnWNrauVhcTx5Z0wUA1AZGaGSwKUATkC7gJ4q/bdXwPHy0h2J4THrjbjnPUljPZ8tGydn2kjd+2AZSWlnxxo052+Fx6Vx6PtZWPJyWX7hrnhU3lrfUR8Jznn5mlVcrH8+D2xLL38rPujZj9aoJSK1+pPrQ4oZ5xF4sWM6w1k4x8tyr6iks8t4ze4ssdml9y9sXtD6fJc9mrUcAmo1GbQpwz/u+Wmge+/7pM6XcFACCponQHiTWA8dyHMPwaRgp3ZgIiDkMmsNolcPz4Pa8XbRslc7XA69o8rRNLH0rTS0PzUEO8QoJT/+IlUN7Y+tx5mMiwhJp3vR5XJ5HaKtlfxZx6gkr5WWR9d5hHcuTTm/S6PwlYtetdQ+v5b6Y9aWRx7YwvWarZwCahUYJmh+992uF5rHfM2eXUtBgylkD0RwkjvVAWbIs/mvmYZih7fouZJKDaL0B9yDlZzm4YTxvuSUbw/OhHdw26Tv/L+XNyxSzUXM2eV172yCMG4a3yshtSe3Tys1FUnheqxspf6lNvM63lKaUbmivFt4SJ7E8pfy0tHjZpOsvPSb1JZ6+VE7LdqssUlt6yOrUetPNi7cOOOkatSKI3QuynOP9NT1mpeF5keE5F7svAABAM4IRmhIjiQTtLZ3k1PDvlpPpfeOtxZNsttLQ3hJ63rh7nC+rvJItWdKMvalNyZKHVc/pcc+b2s/3/y59YeURqk1hfOsNstausXoNbbIcMC1vraxWGJ6mlq5V9izXQUwIa/8lW6228V7DMbJcq1nTaAR5bKjFbk/c2D0hy/0idm/U+jbH09bee1refgNAmcEITXMBQVNCPI6rFk96+IVpxZy2mKMn5Rf+f2XxbNpo2FTRduvhHSuj5SCG8aT1SrUSe6DXmk9MzKRodWq1mRafH9ds4nEtYZlVbGQVCVK+sf4Zy1uzLzwmCQurnJ6+auWv2eC5pvI6pmV2UotwuL0ihshX72k4fkyK7xWPsWs5SzpZ8wagL9AoQXP3VlcUmscn/nxWVNAsWrSILrvsMlqwYAG1tbXRueeeS6NHjxbDLl++nE466STaY4896MgjjyzCZCLClLNSEj5IlizruYPR0HZ9F6nY5/B7+uDyOJZS3jwdIjLFRFoW7biUt5SXljcR0UbDporlscoVnrPqNS279j2WdqxsWt5hnWkOiyZAw78wP6tPeYQA7zthfmGdWGXjeWjHpDR43XNbQ3vC49xWfk7Ln9vgFUWabVr5+HnpsySSYmJYK5d2LnYfqBexsobfvX0oqyPuFflWGK2N+XXB75/8mrWunTC+dF/U7qGxazBMW3s21NIfeqsvAQDqx6xZs2jHHXek66+/niZPnkzXXHONGvamm26iIUOGFG4TBE0DsR7Csbne0gPV46hp6XCHVHJuNRHCH3Qx4SPlqTkrkpMs5a/FDf/z8kg2cXiZlyw7n2ave6MpLqR43DaeryUYNKdHcoKserAcfB5Oyz9MUxI2Wjk5mjiV+qEmCqX64Od52h4xIOXLbbJERqx8Wl3wa4jnKwlXqT2141Z5tHJoZdHuP1acesLvGw/utti8J8aICaIsZbPEr3Wcv2DgaWqiiPcb7UUDb+/ws/aM0O4N/J4Qu+dbZBWYAPR1kqTYPw9PPPEE7bnnnkRENGHCBHr55Zeps7OzR7hnnnmGXn/9ddptt93qWAMyLSNoOjo6qFKpVP/KSvpQkLbOjcXTHDjpLZ8UN/ZQ4989TnT4XXPOLFvCtLgjKeWlOdeazdKDOmXOxGmi4z/1jWOjD2FLNEmONS/jnInTusXT6kMKI71F5WJLajuv08ZFLD9nnZds1tqPl8MjlrX8rHS1PsGvGy1vS2TEBEcYTnM+tfYPyyeVWbNTEoCa0JFs17Cuwxha37PSDOtt94eGVcvFXzh4bNHuRx47uN2x8+F3657M28nTB2LXBhc9Wr/UhEzW/AAAzc1tb95H+//lrG5/MVasWEErV66kQYMGVY8NHz6cFixY0C1cZ2cnzZo1i04//fS62y2BNTQNQHP2POcl59eKIwmdWt+IhQ87yeGLpS89LD1xPHZLdsTqxlvfKdZv1kjhtWN8PZGnXbmDy8NJTpUlIjTHRROGUlietnVM+26JNKmMltjQ+mPMOfTkxetIa1teB1KasTiWeLLCWwLYe41y6nHfqDf8OswqYtI4ee/FnjCWCNG+e4Skdp1Ywjsky4sB67q2xDa3t9n6DwC10qg1NHdtWewamv3/Yq+hWbFiBR188MF01113VY9NmTKFLr74Ylp//fWrx66++mp6//vfT7vuuivdeeedtHTpUqyhaTU0p4Sfz3LOOh6+AeQP0djbNuntofQ203pjyN84x2yU0onVGY/P36hazmHW+uZOVPiwjjk2YfhUzPDySc5urNxS/mGbS20m5SX1D6ldvG+CYzZZ/UJK0xIaWp68rJINvH20vDVRZNnvLRvPSyuTV1zydC37PO0Zhm8UYTuNGDRX7D8ecREL6z1vXZdpfOnak8Lx9GLXhtRnrOtZixeD22hdf1ZcTz8DAMRJuiqF/sVoa2ujgQMH0tKlS4mIqKurixYuXEhrrrlmt3CPPfYY3XHHHXTaaafRD37wA7rnnnu6iaB6A0HTQLK8scr6IOAPeP7ATB9E0oOGCyApLnfupLzS7/x8+N8ql2azJJD4w1aqAymeJqI0cSfZKDl7Wh16ne6QVxbPFt+IaoLBEgqxt6pSv5Fsl+KGYWLOoyayw7Ax27U+a7WpJmx4ea0ySO0q1QEvt+R0WiJcCiOF53ZLZdfKwPPykDW8RRZBG+atOdNh2/I+kdfu2D1Kq3Ot3aQ+oN0v0u/S/U7q1+Fnq7zScyBNVyqjFddzXw7LBVEDQE9+85vf0JVXXklERCtXrqSf/exn9Mtf/rJpt0/eYYcd6IEHHiAiokceeYS23HJLam9vp7feeqs69ezWW2+lq6++mq6++mo66KCDaN9996X999+/MJsw5ayBxB6ylsOnpSO9ObPy0ZxkTz5peCsNK13pwS7ZHsbXyiIdt5w2T91rbz099Wl9jqXjERshHjsl50MTlVKcGFoc67OWt7cv8vDcFl4H/JhWP5atnvrw1isvCw8bqxvtvJR/rE7qhfeepcXNG08jdi/MYq/3XhQ7F0ufkyUdT11ox6x7eawv1dLuAJSVek45mzJlCp177rm0xRZb0DXXXEN//OMfqa2tjSZMmEDHHHNMNVylUqE7t7iyLnlqHPjcGVEhtWDBArrkkkto4cKFNGTIEDrvvPNogw02oFtuuYWWLl1KJ5xwQrfwvTHlDIKmRHgFgCU6rPSsPMLvaZoxG2Npecsp5S09YD3pW0JFCx/mkRevWONIZQ2/p+twwnMxZ9cSM2F46bxUd5Z9GpYQiTlYWtvHvme5LrQ60Oo4q3D0CCpuDy+/1jeyinet3FIaRYohnleeOPW+Xj12eYRNrH9q4VOsa5uH0Yjdf6z8s6LdJyB4QCtRT0Fz3HHH0Q033EDvvPMOffKTn6Sbb76ZBg8eTCeffDL97//+bzVcswiaZgRTzpoA6+HhdQ755/ThYTmg1rEw3/Rz1geSJUrSz+kfPyeF4eWJOQdSnmkYq5xSmtxGLZ4FjyPZax3jjtGSZedX1/NIYcJwUhiv8yelzdtOypvH9aat9ds0X/5dOqblExNzvG+F9oTntTw1kaFdg7z/h/l4yibVrxYvdp/h5bby8fT5vE4xrwOeZnos3do+jxMeqx/tOpRs8ogZ637F2z/sB+G1EObHr2vpGaCVWbqfhses64d/l/qf1ichZgDQSZKEli1bRg8//DB94AMfoOHDh1N7e7v4cj5JKoX+lRUImgYRc1Skh571VpjH1ZwuybGSHCnJBstpsEQSd56lh7P0MOdheLmt/HhdaGEsMSjlbz3AtTrhZSFaNbKSfrfqJrRHc8ClvCTHWHLaHtxtsVhenrbUNpZYibWr5MRrzhUXOjx/yWZeR1K/1s5L9cbDx4SEJtAk27W6i9Uvt0mKy8NrwkZqG/6Z5xMjFiYmsCS7wu+hmLfqVmt3LV1vOM3mmOCRhE3s/i4JIm/+mi1eO7mtUlzLJogZAOLstNNONHXqVLr++uvp4IMPJqJV07pWrFjRYMvKA6aclQzp4WA5jdJ3/vCs58NGE2KxMkhxY/FjaUuftQez1/nS7LYczDxl1o6laE5QeF6zL1Z/sbx5HlZ6ElI/1OopJoisfhxzVGNltAQ8DxNzNmPxreOWHXmuszkTp9GE+y7tFi5My5NG1jbPiyf/FG9dSula/SurfbH7rHZf0vpyrO21+tCQ+pZ2HUp2SXUu5W3dHyByQCtQrylnSZLQvHnzaP78+TRy5EjaeOONiYjoggsuoG233ZYOOOCAathKpUI/GHdVzXlaHPT86aWccgZB04R43gRqD+OsDwrLucyaZ8wJ8JzPYkMsTa/Ai9nC0/fWv+Wca2nGhE8YzyPsLGcoJha4jZqtmkjU8uLHtTBanVp58XQlG2Ll1K4JrQ2kY5421MqSRSTVYoeWr1QnPH5Rzqjn3peiOdre+0vee2XW8J4+al07PE1PeI8Y1vLxCBvNhjziEYCyUs81NJMnT6abb76527H//Oc/NGTIkG7HKpUK3bH51XXJU+PgF04rpaDBlLMG4XFStHj8fPg9fKBoeYTnpYfV0HbfPPE0XHrOeqvocdCkdLTwYd7e8JZDHXOAeZ1YeWjpa+WzbAzT4mlKdkjnuE08XJqmViYtH8sWbm/4PY8jHYbVwvO+INnAy8ntsdqJH5ds0q6lmIiQ6kKKx8toXafSdSFdM9I11CjC+tCug9BeqRzatcrjxMrM05CuHetalPqpVte8raWyxIRCmI52LXD7eJli90MeTyqXJY4gZgCQmTRpEt1///3djnExA2wwQtNC5HlYSG/jPOfSY0T53uRyx8/zZjGr0ON28nS5s63ZJzmQVj1J+Vh4xF6YjiRErbJYcaQ8rXJrYSSBIbWx1BZWH+P5W4JPSismnqX6sWz14O034XEtnhQ3T51p571xvX25KKx6IdLrPHZdePLyxLfa29sXrX4tpWX11yzphDZ47OA2Z6nzRvcjAOpJPUdoJk6cSP3796eNN96YttlmGxo3bhyNHTuWNthgg27+bKVSodvHfr0ueWoc+uKpGKEB9YM/VKyHlfehFoaX3oDyh0z4hlB6Wxh7KMUckPS/lG/4WQqjvcHVwsTeWGrpabZJZdHs12xL//O6DePF3tbyfKXjlmMevs216pmfz1JXPC8pjMdhtUSuJK6k+LyMMQHtbVvt2vHkEdoStjPvE1r7h8fCP95eUrtr/Umq73o5oZLtMXg98T7JyxH2B+n64vlb56xy8/tjaK9Wv1Z6oc2SXR6RzG3Q0uHXDL9X8vqz8ooJdu0+DABYxZ133kmXX3457bPPPvT222/TbbfdRscddxx94hOfaLRppaGt0QYAH56HoBROeisXe0Brzpn0YEuPvbJ4No0YNFd0LPlDWsovtE2z03r7l/UhydPg4i20n+fBHSTJUdW+a6KD163mJIW2x5w0brNVFp6vJS4se6TvVv6aQ8vbJ2aX1Jbad80mK3wsPa/A1Gzh52KfJdut60uKq9Wx9t1KO0sY637F4/H2tYQvv4YtUWMJUKmepOM8nmS71o6S/bx8PLx2TrJJSltKR6o7L1bfCm3x9BMA+jpDhgyhrbbairbaaqvqsRUrVtDf//73BlpVLjBC06R4HgBZHj5hmvwhqTmeWd6e8t9DkdLjWA9vLazXJushyh1jzVkNbbMEQfg/lrclmDwMbZ9BVwxfr4cdoSMXs8USF5KjYznPktPGnatYX9bqNy2T5QRye6XwYb1I7W6JU96+2vXC0/c6h5bzGp4Pw2j9x3KspXy1dtLyltII61CyRaoPq115fqldMYGpXauxttTqwCNmpPRjAlLKi5fbKltYd1KfTvOR7geee7GWplY+Xi7pO8QMAHHmzZtHZ599Nu2777507LHHEhHR0qVLabPNNusRFr9DIwNBUxJizpHkDKV4HErJ2QidCetBrjknnnzDNLQHr1Uenrf2BlYLKzltVrl5XP6WVHOWwjy48685omGYMP5Zi14X8w0dHl6msF2t/DxYzrfl4Gpvg7ltmhM6tH0GzZk4zeWoS0Ig5kBnaYtY+lkcyTAfqQ96HP8wPe16kfpszBmVjlkiQcozLE943rq2OJLw02yNXV88TSmuZKdUdzwP3maWCLDsstootFcKw68BrZ60NtDqQbteuS0Wsf4MQF9l5syZtN1229E111xDnZ2dREQ0Y8YMevjhhxtsWXmAoGkg3ocAkfw2LHx4xhxpnpb1wNWcKctJT89bTpJVXs1RsR68ob1WupKTGNqtCSjtuCW+YuXR7OBpas6K1AZhvUsCTSpvrK142ry8oc08H6sdYn1dsj8s9+4PDRPrycp3aPsMenC3xaJDLbWFdi4m3CShxs9ZhHXNHeZYfElkcJu5LVmEVnhcE35SGtb9IgzHRUCsfNp1b9UVv09KdvGy8vbUBKe3fTSxkP6X8tPu7VKe0rWtxdeudW5XGJZfG1K98Tyl+Fr9ANBXefPNN+nwww+n0aNHVzcBOPXUU+k73/lOj7AYoZGBoGkgMac4RXvIWOHS79qDVhJDYbyYcyE5y9rDSiqn58Eu5S89KLm94bGYw6vVDbfTetB7HTcpPUmsSA5a+N0SVFofiDmx3HmK9U1e/1o7aXbxMJrTxc+F6XGbw+M8390fGibG19KVyisJqLBeeR/U7JbqQWrn0B6r/jQRaJ2z+kPM4dSu/1g6UjksR1e7D2n3DcuJt8SMVb/8mrDupbH7uNZe2vXMBY6WpyV6+PVpPUu4WInVs9TvtTqI3UdixwDoC7z99tvdvo8aNYqWLl3aIGvKBwRNSZEeIpbzKzn5Xich5qzy+J4Hu+SE8HJ5sIRUmI7k2KTnYw9Qz1vF0DmWRImWDj8nCTNeDskGqw9wu6Q4Utk0Z0MTEjyc5KBqcWLtHosnhUu/S46p5rRJ6cacdS0dLiY1+6RyaO2q1QNPR3OwJYFktZ303xJQUhnCsNz+2LUbc6zDMlnXqVfoxcoiXdvStaXFs/qKdm/W7OT3dW5f7P7OxY4Eb38pbalMoU1anYRxs7YHAK3GPvvsQxdccAH95S9/qR77+9//TgMGDOgRFiM0MvgdmiZBc/akB2Hshq+9lZQeHllt4vZozruUNy+XN18eb87EaTThvkujcay0tYe+Zqfm4A5tXzWdKZwKJdmc1w6edyw9T/oxGzXnR0KqL6vtYwLSuga84SU7ODEnmR+TxKYVXwtjiRAexnM/yHJNa05vrN609pTOW3lb9SmFzXKv0spp2ZnnXhiL472Px66PmAAKy6fZZvUTKU2rz+aJZ9VLFmqJC0CR1PN3aIiIbr31Vrrtttto2bJltOGGG9Jbb71FZ511Fu26667VMJVKhb475tq65SlxxNyTSvk7NBA0TYjHabDCeB2GrPlKTr7lrFoPVym9LOUPj4f5ac6ndixWDuuzZJd23utsx8SiZYtUJ2Fc6ZxHbPHwHttitnj6qJZWzMmL2afZnCWehNZvpXhekRRLk6cdqzfNTh4uqxPpESfSeR7Om4+VFs83Jv5itnntjd1fYoJDSjdLnw+/S/Zq573XoHZfsO6LeZ9lAJSBegiahx9+mHbaaSdqa1v1KyrLli2jF198kZYuXUqbbroprbnmmt3CQ9DotMyUs46ODqpUKtW/MiPd3Pmx9GFhOTdLlnVfuMrDpt+lh5H0kEvTS8NoD7cwX8kmLU+prF67+cMztNU6pn3Wys3rVKtffl5Kl5dJChf+t5wbXichmpgJ89Xi5XE0QlukNCyBoIkJyVnU2lOyn6crOVu8jrU+pdUXL6NHjFj9Pyy7ladmU2iDdN3y/hrmbfWZMG1us1YWre6serTKy23gdSXdL7X0NdtjtkjXnFV/2r1Cuv9IbSndx7xl5PG4HVLbSG3G+1Psvs7vVdp9JnY9AdCqfOc736ElS5bQscceS5dccgn96Ec/ouXLl9MWW2zRQ8ykdHUV+1dWMELT5HjecGnHYukS2W92teOxN45Zy5M1nGWr9LZQSy88Jgm0VxbPpo2GTTXLztNI87Ue3HnszpuvJISy1FF4zkpXq8MwPE8n5gTzc1ZZeL6WkxRz1mP17bHXsksKZ9kgpRke0/KW0pPSluIXRZ57Rtb0iWyRYt27rHbm8cN8rGvB6gNeG61rQCPvtezN1xLr2j0mDBt79gDQ7NRzytmjjz5KL774YvVv/vz5tM4669DYsWOpo6OjGq5SqdCto4sdoTny7+UcoYGgaUJijqXl0EjhiXxTULI+WCxBkPXhpDkSMRHgSTMmFFJiD/O8jpBVLx6hoNUJj8fDSPZ5HBHNNs3OWBiP42M5N5ZzmCWsx5GMlcPjkEv9bmh797VWMYdYSkcrg1UO67qxRE0sL0sUxdLKei3F0g7Tjwk1q+9kISZCPdcGt036HIbV8tTSyCK6pfSt+5rn+STZ4m0nAMpAPQTNVVddRVtssQWNGzeORo8eTf36rZo49e9//7sqbg455JBq+EqlQt/e+Lqa8ozxqX+cWEpB0zJTzloJ64buFQ1D2+0dc8Jw4YMrfOB4nD1uVywv7RiPJz2cpTLxNHkYr1Ng2R8e1xwRzf4wnFS34XGp7J4yS/aE3zUny7JDqwseLma/Vg6pLGE9hXF5ulLeWt/S2k06H6YX/ue2WP+l6yk9n25kYfVPzUatviQnlNvuFUSh/Zqw5XWm1QW3iZdbSjuPmPH02TAcb+dYm1rw60DqT9Jnnr6UjlRerS9LNkvtGJ7T0o9dy1L+nvR52bT694hBAFqJ/v37009+8hM69dRTab/99qOzzjqLrr32WpozZw6tvfbadPDBBzfaxNKAEZoS4BUmVhjtLaAVrhZbYg9SSzhI8cN8F3aOoRGD5pr2WMctJ0h6o6i9ZbTKZ8W1HviS/ZLTp+Wh/dfyy2Kf1+ZYHrF4Wv2HYaVypuetPuStL8+1IqXLyxezLRYnj91af9HKGCtbaBs/5qnnWrDS99orxclim3U/i93HUls8Qk86HutHPJ51r7DCeO8zPE6e+tDKCEDZqOeUs5UrV9Lf/vY3ev7556t/c+fOpYEDB9Jdd91VDYcRGp22RhsA4sTe2mURKNJbvPC4xxHjtvAHlZZfTEhoZeKfRwzS32iGYaX8pDeQUpz0uKfeNQFgnY850hZe0cHLwj9bwjGMz4+H52JOj2V3TCDyNuH2Wv2apx/rb7G4VjnCdKRrSuqf0vWmtb8lBnm6sXqxxIeUfszx9PRby/ZY3Fi62vXL09buc1qaPKx1jWrXFj8WEypSmp77JD8X5qXdH7mdlr0hWn3y8Fp9xe4/vA4gdkBfYebMmfSZz3yGNttsM/rYxz5GREQrVqygl19+uUfYMv9WTJFgyllJsB4unngxR8JyXnl4zRZJBEgiQrMzTEOzI7TH8+CVnAkprCfP0EYeL+aISuKQl8cKb9nP0+HhYk4PzzPmrEhxYse08qafNUEindPKE9qqOXTh+ZjtVtklYSzlFx7ndvF8Y4JIqi+pf2jxLDEj1YlUXq/jKx2P1aN239FEmdYnw/y0tpfCh/WhpaU555xYXXquFalPSOW1+nOsX/M/KX0tDanfSMJcE4YxIGZAX+Dee++lSy65hJ544gl6+umn6T//+U/1XFtbGz300EONM65kQNCUhJijLYWTHEHJ8RraPoMWdo4x0489vK3vvBySgxSe15w/bncWW2LOkuVsSWnEnFbuKEr2531ga2XhNoQsWXY+XTF8vR7lsd7e8j4i1REvI3cIYw5raLPmgEv9QBPOPP/QPu6gacKbpyXZK/VbyRGW6kSKy7HqXWsHfk4rt1ReyRYrzbBcUnn5d+ka0kRS+CddP7zcWt+z2lsqr1RO7dqN9VUpbcuZl8SSZWtoo9RXNCGk2RG7p1r9QuurUvtp5dOuHQBanT333JO22WYb+ve//00zZsyg/fffn44++mi6+OKL6ZprrqEf//jHPeIkSaXQv7KCKWclwev8Wm8FeZjw/4hBREPbu5/X3gx68/c+YMOwXoc/5ijHRJXkgMTS57ZKjoHlsPC8Y7bGHEbJqZfsSz9PfeNYNU5oYxbbeLt6BYAWVnKCuFjT6lsrl5Sn5nzFxCIXD7zsUv/VRKNVZ7wcmvNv2cnT4QLAyo9/1uzm/eCVxbOr69titknXYOy61eLFymKJaV5G6V6lOf9S20r3PKv+eBktuzVxIPV9SXh6rhHtXh3Gz9JnpXLx85JoyvrsAaCMDBgwgPbaay969913adKkSbRo0aLq7mb//Oc/6cQTT2y0iaUBIzQlwnrjKDk31oNJixcei70l5M6x9kCSwmgPRB5PeuOqOWyWveFDVnMSw7Ql548fkxyx0KnRHI0wLV4nPH8eVnKqpfrjaXG0ckn1L9Wl5ahJjlRYJ1ZdaHlL9R/axPMI61CqK0ucSH2Flz+sa26HVi6pT0h9W0rDakNuLy+HFEeyWbOLCzVLoG00bKrar2NCxHKSw/KFdvH4Urm5ULHS19ohbD/ej7Q+rR0LbZLuT9b93bpv8D4t9T+OZqf0nR/n7SHVexjW6m/cBuk8AK3MgAED6OWXX6ahQ4fStttuS4cffjidc845NGnSpB5hu5JKoX9lBbuclQhNnHhEi/bW0fMWzBI4Wlrh93RXMs0Ob56W06cd5w6n5oxzYo6slBY/FtrAz8fyks5raUjntfCao2KlK9VDzJmP2W7Vu6c9rDLxtGL1kaf9Y3Vu2a2Fi4m1WJ1rfY7H99ihpWkRaxdPWlkcbC3/mE1Z72da+1t2ZakDb3t47mVSuWPXcZiXZju3LSZkYmWz2t/zTAKgGajnLmdnnnkmvfTSS7Ry5UoaPXo0jR07ljbbbDMaM2YMbb311tVwlUqFbh41uy55akyeN7WUu5xhhKZEZBEz2ls8Kb3wTZ6Wfuwhoz2Qh7bP6DEFhb9J1NLzOMwavPzhG8z0eJp+mFfMMeX1YQkYnocUn+dl2fLK4tmq88KdlDBNzVHmDk1oG+8bsf6hwes/RKr/OROniWG0/PIKEn5OiiOVm7evVVbp+gr7hdbHY0JN6rtSnUj1rV0XYX78v4RV51K5PGnF7jVSnpZDLpXFY4913iMgJHs027X+xNtTsk/KMyyjdZ8I08lyn+X3USl/re74PVOyyRJRALQyM2fOpB/96Ec0a9YsOuyww6hSqdANN9xAM2fObLRppQGCpgnRHlAS1sNDcpysNLizY6UvhecPe+tBJwkM/sfjaY62VT7JkeF5h2GtB3VYbm6fJRitOuA2ao5rKgy1fGIOkFYGzQbN+UnDSP8lR5g7a1b5iIh2f2hYDzu1upH+S+XXHE3LmefE+rUmKDXHn9exVLdWPtwWKT1NJEn9TUo7jCuJIal8WvtaYaU6l+omdj1xgaCV3XKoY31bQ6s/qX2l605LU7LFqgP+PSYWvOVLw2htH7u+eRree63HLgBahUqlQhtuuCHttttudPbZZ9Mll1xCH/nIR3qES5Ji/8oKppw1EdIDoJZ0tPQk4SE97Ly21GI3f/hbb1lj+cYcKI7lpPJwYXq8zmL1LB3nNlj5eWzJcl4qs+XASmgOGy9nzB6eVixfKW+r3LH64TZJ14N0vVi2ees1S517rlPtupacau8156kXjlVOK6x1nWs2WWj3g9h1G8s/PJYe19rP6ttW34yd4zbwMFa8WBirzYskdn/tTVsA8FDPKWdLly6lwYMH9zh+zjnn0Fe+8pXq90qlQjdtUOyUs2NeLeeUM+xy1kTU62atOTX8raD3zWeI9tYtFk5yaNO3pXMmTqMJ910adYKkNLS3nTGnR7NHe5DyskrOg+aMag9kj5MqlUmzXTqXRbR4HEVeNzHnSjqmOe5Wv7LaSyuTV0yG8TxpWOl6xI7UNqH9Ulmk/s7TlIg5p7F7jtY3Y2JGS5vHj6UhtYNHIHnKErvHWdehlpZUX5odWh+zrhPLyZf6hlZWqzxZ7sPSsbxCVxNtVn8CoFXYb7/9aNSoUbT55pvT5ptvTqNHj6aVK1fSvHnzeoQt89bKRYIRmpJjORhzJk6j3R8alkmc1JKn5jyH58NtXbPk73lb6XW0pDTTdDUHJBbfEpGxt7KWE+wJlzVNTfzwc5qY43jsj9lmCRXN+fL0N61/xJxmy5GU7PbWuZR+TBR6Bbf0WaurLOmEYazPvHzeMJqDa9WRV8BJZfAI9xjeNrfqXSqPZmOWe12YbpgGT0c6JtmXpR/G7JLy9YTP+6wCoEjqOUIzf/58euGFF+iFF16gF198kf7+97/T8uXL6VOf+hR9/OMfr4arVCp04/o31CVPjWNfO66UIzQQNCXAesik34nq84DWvkvHLYe9VqGUpqXlo533OE6hnWE6aVieNs/T44h5HXet3JaAi4WX7NfqRbLLwqpzy7HWyp/FcfbYacUN41vOJU9H+i6Vx2OfZqsVxnMter6Hx8P8tXOWYIw5xp7jWt48v5hw9eYXE7Gx9DSHXgoT2uuNb9njFZSWuPAKEildbx1ZdaaVFWIFlJV6CpqUd999lwYMGKCeh6DRwaYAJcDjtHgdKS1M1gc7fwhqNqRh0+Na/mG50rR4npadkkOhOfnhuTAfy3kN42rltOJI8cI8eZzwv1TfkhOgtUOYRpi3Fd4qd1hWfsxqK087hvaFdRT2Cc1GyT6rncLwUr+MOcC8HXj/DW3mn8M0Y/UhtZ9UnvCY1DZSmjwdKVwMj+iTwsfy5vFj97iw3NI53naWoAz7Rcwx1+zl+Wp28WtHuy/E7qNSvcbuQdIx6dqwriXJdu1eHh6T7hlWeQBoZV5++WU6/vjjaZ999qEDDjiAzjvvPHrqqafEsElSKfSvrGCEpmTEnKBa33ZJb/Gst3yhLZKo4OekfHi5eNmkMmvCzrKRH9NEk2a7VN4sny3bLMdZKqN2PIsdXhHsEZFWWlKeVv/h5Qi/8/CxvLKkpdWN5cRK9ZylL8WuCavutOtKS9MqoxTGyjdEu09o4T31WCue68jqP1qcWJ5WHUnE+or0madntZcW1ro+spS5lvBZ4lv9DoBGUc8RmjPOOIN23313mjBhAr311lv01FNP0Y9//GM6/fTTaeedd66Gq1Qq9L/r/W9d8tSY8voUjNCA+iM9+LW3i9pDLAth2uF/6aEXhuXnpHQ1Urt52Xhc62EX/rfythz+2MPRcmytsDxfyYnR3oBKZdOOWX2B5+tttzBtjy1a3+HhQpv4cW47rzupHFa98nLwvhYTZGH5tbqKiR7tPK9b3i48X+maDNPk9R0ek9Lj9kl2WteXZJtHzEjXu9aGElLZQ7R7ZHpO6lNWurHz/FisXfkxbp92zBIfmi1an0nDxJ4lYXlqQbuHpGR9fkDMgFZiyZIltP/++9OoUaPofe97H33605+mmTNn0re//e0eYbuSSqF/ZQWCpsmRHlya82vFk7AeUtbDhztN0htCLU0pPZ6n5vyGYSSHTBMpmk3aG0zL2eXnJQdUyoOXkZ/zOje8TNyu8DMXMrH65/lqzmhoh+QQhnZZeXGBESt7mB7PL4wjtXfMAdScd8lZl757bJfCWEKCp8XbkMeXRIxkjyaGws9Sv8pSTo+DrbWBJarSz577onV/i/U5yaFPz1t9WhKeWnypj2UVTvyYlJZkt1Z26f6q3UfC81zI1ZN6pwdAM9LV1UVLly7tdmyDDTagZcuWNcii8oEpZ02K9iYuPSc9eDUHXguv5Sc5yzE7eVzLHs0OSwxJ4SxnxeuApGGkslh5aec9+XE7pTr3xA3z5/bzcBpWn/HYGSuD5ahr8bl9Vh1oTrzU/602kuJ62jwWlp/Lkk+IdW146s1zDcfaVyqLVsYs9mltb8WXznvvH964sWsgDBM7zj9zrPsJtyU8rsWx7vOe+zMPa5XVc97TNnniANBI6jnl7MYbb6S//vWvdNJJJ9GYMWOIiOjZZ5+lK664gmbNmlUNV6lUaNbaN9YlT43j3zwWU84aSUdHB1Uqlepf2bHejPI3b/wzj5d+X9g5Jprmws5VFxJ/OPG3itxO7Y1fitdR195MhvlLD0zJBk89SWlwxyMmArX8eZrcjiwPcG6z1CYxp4+XiccPj4U2a85qaFNoO/8v2cc/c6eM28tt0uonTI+XP9YHeN1K4S1hx9uSpxPG4+nxfiKVP0yTtxVvT8ke/tnqN1Z8KQ4vI0+fl8dqS01sxu4h1jWq1aV2zwxttIRIGFezU6or7VpIbbPuZaH9Yb78etPKLtnNw8XEhOda5TZqSNdbLA4ArcbkyZNps802o1NOOYUOPPBAOvLII2natGk0ZcqURptWGlpG0EyfPp2SJKn+9QWyioT091+IegoXKUzMSZQIH2SaIxw6gx7nJfYmUXuQh3F5OMnx4g6hlo/mCEiCgDu2kojxtiMvW1YRZzngmjNu5SPZrYkmyQ6rjvl3yenitmiiyOMYSfUqiQCtHjx5cadT66+87DGxqDm2nnJKfTPmqIbnrPbgxzSbw//8s9QHLfs0eFx+fWZJQxPMPG3vfUkqiyVKNAEVE2hS35Da0bJNaqewrJJQ1+qJpyMh1bN0HICy069fPzrhhBPoBz/4AV100UV05pln0i233ELbb799o00rDS0jaFod68EmhY3d8DcaNrVbeMn55WhvbaWHoSaGtIeu5FBq8Xga1ltAzUH2OAHaW0NPG/Ay8Tx5Wa20eBklh05zMCVxEh731JlWJs3p52lxp18Lx8uk5e0V2lZbW86jlJcmgHl/l5w1yUGUHEPLieXl1tKQ4ksCzYPVL6VrNM3TqhPp/hBrb37MEuZaGbR0vbZI7c/Px0Qb/2zdR3h6MYGk9b/Q9pigtMRs1j5k3cu4nWGban1esheAVmTw4MH0/ve/nyZMmEDDhw8Xw2DbZhkImiYj5pzF3nhpgkILy9PWnHDpe/iglBxkSYDwtCSHT7KPpxU6x5YY44605GRYjja3x3KKLceW10n62UozDCPFldLWbIj1Aatf8OO8zJrDHhNLVrtY8aU64g6XZJfH8bX6iFQHPI50jjuKkoPJyyLlH+srYb3zMkj9UyuLVC9WPVoCI+agWvlLfVm6frS8PNcgD2/dA71OdJZ7KLfLK7C0PLV7rnQ8JswkEWddE1yUxMrJ4/HP0jUDAAAa2BSgBZAEgUcsxBxfKd7CzjG00bCppgAJ05HsSc9rD0npuxTfylOLJyGJJS2tMI5HiEmfvcQcQ0tcSmWz4vKySuXR4sdsD9PQ6i3mfFp9xYqb5ZzVXlIfsdLT2s5ypnk+mk1auTzXs5ZWFhFifdfuH9q1ItnG7dPCWdegVJbYvU2zS/qulV2qA62t8t7TeNk0gWKFlcrpqctYOM/zxjpn1WMsHU9eANSTem4K4KVSqdC1a91caB4nLZhcyqUbEDQlwHpIvLJ4drd1L1ocj5NOZIsCKa2YKIkJGe0BxtHCZBUaeZxcKQ2PnZ78pHSt8lk2WyKC26s5VJrw0RwvT11YtnqcPk+5Y2XJg0dcSGVJibW/Vh4pzSznrONWnlIaGlmd4PC7VQbrXuARLppjbDn0ee0OySJsNPEYu2fE+rf3nue5B2t14BVJEpaAy5IvAM1CPQXN0qVL6aWXXqL3vve9ZjgIGh0ImpIRExNZ4kvH50ycRrs/NMx05KXjMSck9oZOQnMoY05bHifSEnJWHl7ngKfrKU+WuuBlCMNK6fDzmkNj1ZvmnIRhYp85HkdRKotUnvC4Jbyk8scc0lj/itVzLE7MVi2cR2Ro8bzhaxUFnKwCICYeeJqaWNLszIKnL2cRIjFBFOs/3n5rCQvtOrBsydLvpPrhx63rULILgEZQT0Fz8cUX08iRI+nEE0+kP//5z/SlL32JBg4cSOeffz6NGzeuGq5SqdA31ry5LnlqnPxWOQUN1tCUDE3MhA+5oe32D8qF4Xjc3R8aJp6PPTz4gyb90/KVnKkwXvqA5OW0hAY/z23m9kgPa15v1kNfSlcKE6ZrORyanaEdWh5hOKkcUjmlduJ9IqxXHkY7ztuT252es/qs1AekOtDs5eUMP0u2SW2llZX3G6kerXqS4nKbw/JIaYfhtbx5maQyWnlraHVllVkqqyYCeBtp7S71TemzdR/S7NDK5OlvVvk0O6TrVbv3SelI1xO3nadt3R8lrP6cR8xo7aulo90LAWgFnn/+eTr++OOJiGjmzJl05JFH0jHHHNPtN2iADQRNSQmdA8u55XBnRnMww/DS5/B7zMGVnADNQZZs5GlID+KYIx97cGqCQTovfZYewlKbaOfDPDUnNYwnOeY8Hj/H4Y4pdzA8ws1y8Hi5eLm50OBilpchTF/6rznQUp/RBJRlt+aExepWwtO2ki1cuEqiT6oz3i8k26Xzks1av9acWum6s9pRqruYwNPuD1z8xERbTNDx/sSvWam8VniPqNBs4tcRz1+qK629JNHL85fuJ7z9tHu+Vo6YONHu21nEEwBlYNCgQdSvXz966aWXaPny5TRp0iTadddd6d///nejTSsNbY02ANjE3nxxh8RyuiSHxeNoW06K9IDUHFgtHnfKYuG5rZLNliDhzoVkp/aglvLXnAopf0tgSflID3OrLJoNXDxIdmltrvURj+0xkcTDSmWRHHdedssmrc5jwkRyNmPlkPqLJpQ8wojH0+zj9STZop2T6l0rj3RdeIUQD6+V0UrPaj+pjJZgsISHlKb3OrDS4vl621+6Hr39RbsnSbZK90QprNamVtt5rg2tDNZ5iBvQCgwZMoR++tOf0m9+8xvaa6+9qsc7Ozt7hC3z1spFghGaJsJ6Cxee5w8ESxxITmx4nOeVnuNv96ywPG/NYZacay5kvHnx8vGw3jeJmu28Dnn6UhzL6ebpavUiYTllvC60c9zmMLzkjEj9I/Y9PG6dk5x5qSxaPKltLafN6mextMLwWlyeB0/LUxcSWjtoIlHrw2F4j5CS6pnHsxxN7X5Vi/PJ7bccaKlOLfGpfZcI65m3jad/8fM8f01ASKI0/B4TiZKN0r3JI8I4lsCS7qfadeF5vvHw1vWVpQwANJpTTjmFfv7zn9Maa6xBBx98MBER3X777bTeeus12LLyAEHTYDxvq7ij6XnohA/+LE6EFZY7WFLa1oMq/K45idwGzamT8tSEgeTocWLtoAnHML70wJYcEitMVhulupX6geVkSs6+Vk7JJsmx5uWLlUMqi8cplWzg4WP9X8tHcsZiQoB/9jhd0vUgXetS2aT+4xV6MTstQad9T49xEaP1U6m/8vx5PH5OEhi8HF6xEhMplpAKiYlZKZ7WX7mI0gRsLH2ej5avdG+QxE/seuVp87a17tNaW0r5af1WCw9AMzJ27FiaOXMmnXfeedTe3k5ERKutthp95jOf6RG2Kyn2r6xA0DSYLG/EtIeX9qDTHv7hZ8kRkBxg6eHJ48WER+jsxo5zp05yziSn3iq35pzwzzx/ni5PS6sXqTxaGM1pkI5JZZPy8TryUtqSQ2bVG++D4XfJCczSNy0nl9ehx5nRHDiO1X+0MvF605xaydH3ijtNsEjhLVHAw1sCyoqbHudl4fUk1aF2P5Dy1/qHlp8Uj/c7y+ZYuWP3o1i/0spnlYlfO9b1JqUtnY9dB1Yfk+pT69NSPUjPGclmKa5koxXGIktYAIrgP//5D91222303e9+lx599FFasGAB7bvvvvSDH/yg0aaVBgiaJkQSJUT223LNIZfiWY7Lg7stFp0tKa70QNceqGF87YEthZOcPsmB5w4lT4t/thweLs6k9CwnTHJspDSldrEcUMkplMrE6423Ga8jrW9o9WY5IlbdhPE5Urm0ND3ljwkhre9qxJxW7ZrR0rfakde1FDYsJz/Oy8vbVOp7Unm1sliikufNsepPuuat8lu2S+WQ+qHlCId5W06/dK8ObdVESew+wtPUhIJW91abeBx4qb/zNLIKSB5PInae56Gd8/QVT3oA9AZf/vKX6ZFHHqF//OMfNGPGDPrUpz5F+++/P91///2NNq004HdoSozkqGgPSe4cSWEtx1F6UMYcAsshlb5rDoGWrnQujOtxhKWyWWlpjqUUzmOnlK+3vjWH2FMWK1/rmGWbVKbQVq0+pPJpdSFhhbHs98bTyuAVHmFcyyG2RI7V7tbx2LWmhbVEiqddJTzXr9a/NBu19LjNnJhDHdoi9XGtTmNhrXLE0K4T7ZrJ0raWjda1HbvfaWWOnfdeB7G6BaBI6vk7NIcffjjdeuut1L9/fzrttNPoy1/+Ms2aNYv22GMP2nrrravhKpUKXTXslrrkqXH64qPwOzSg/oRvuMI/ft56G8cfOJpzyvOR3iymcRZ2jun2sNHexIX5hfaE8fjDR3PUeFrh/9BWqQ49dknw/Ljt3H6eptZuXoeK26/Z4kWqSysdKb80rtZuWl/k5bD6ILeP5y0h9Vduf6y8HkdIuj54fF5uyemz+nlYV2lcqR/zcDw9rQ54+PCc1B68rFqf4+0bHuNtIpUzDCfZw+2PpRf+hWlYSNezdP/k4XlYrS9I/d66ljyCNMzTc6/Q7vtSPtZ1rYkZq8yaDda1JLW1hjccAM1E//79aeXKldXPQ4YMoVNPPZXuvPPOBltWHjBCUwJiwoCH4+diAicNI711i6WnPVw52ps0KZz0sLREiFYPWljpfJawMWJhNceal9dqD8vOMM0wTNa0YrZbbeNJh9sT62NS2WL9wXLiJbR8rTJa5bbihvbx/LW+IZ2z8rLSkcLF6sZyYLU2jdUhT0c67rHFU+9W2pa9lh0hsfrWwmh2aDbH+ik/bpVXyzfWNlY/kPqDlH9Inj7lIUtYALJQzxGaK664gubNm0df+tKX6LzzzqOLLrqIhg0bRqeddhpdffXV1XCVSoVmrvGtuuSpcebbn8YIDagf/I2Z9LZMChM+ADwPDP72LAynvZlL8/GKGS1PbjN/IFsPotCGMF/NaUr/5kycJpZFKm94npebh5fqSrJXqiNel5L9Vr3GwmWpV6vPhOnwviiVPzwulVVz2Cxxw+2T+j/vC1rdaelqTpQWx0pHOhdr29h1FbvGuO1Se0ttI+XjEQLcTqufhHlI6Wl2aTZK6fE+GEtXuza0/sTz5PcFyQZ+z5TKYN2LeTjpHiw9H3g8qZySzVIdadeo1EekutDue1oY7dqQbJM+S3YD0Kyccsop9IEPfIAGDhxI2267LZ1//vk0c+ZM8XdogAwETRNjObL8gaKJG+60h3E1B1174Eo28M95HNjwu2VLGGdh5xg1Pc0JJyLa/aFhPepCcgikuJpTIB3ntljtGKtbDcnWmJNiia88zoLk7GmOj+XQehxnzbmR+pjkcHrLoIkWnp92/cTs1crGndkwX83J9tah5dhpoklK2xJ3Uh1Z+XH7eJo8bd6m/LNHuGoiSuvnUv/V0tHyDsuWRYzGypBFcKbnLTEppROG18QGr3+tjOE16r1Hevqs1S7eewkAjWbgwIF05JFHUqVSocMOO4x22GEHWrp0KZ133nk9wiZJsX9lBYKmwVjOrPUw0d4ASk6UdSx8wHCnQXqAWQ8PzYGLOXbSA05yXsIwGw2b2sN+LT1uH38Q87JJDq7mOPJySQ6z9fYx5kxKZdEcQo/TIZXPSps77bEyS32Jp8sdUqkvxtD6NYf3aX5MS5t/tsStVPdWejHRFSun1G+sa0q7N4RhwzTC8vB+avVXLioluzTxZxETLTEn3RJQ3H4rrtQOmmCRRElMEHlEhubIh/ZldeKl+51mk1QG7Zkh5Wn1o1j5JdFp9WvNhtj1CkCjaWtro09/+tP0P//zP7TZZps12pzSAEHTYPgD03pg83BD22fQg7st7nacP5j5zZs7VNKbztjNPhZfSsN66yc5LJITJomqMG2t/NxWKW4axnIKJGdMemBbdRmWK4uTKdnifZBroo+Xy3KOpbLx+Pw7d3K9jh+3j+dhiUEJTdhp6WvOEu+THgeUf7eub62NtP4Wtgfv+1K/lsQUt01re8suTaxKNmvw60XqM1odxtqUp6f1OW6/lo/UBlo5tH7F07P6NC9HmJ50TUpl1OqX28ptkO6/lmjVhKEVPiR2b9U+W0JKeh6G36U+DgAoHxA0TQB3GNPP4THpwblk2fm0+0PDuqXB00w/xwSD5MS9sni26sBpDwHLQX9wt8XmA89ytnhY7nhkcXYkRyeLsz5n4jTVkdDKopU7PBcLw+uAp6vVveRkaWW1HJ0sToJ2LrRZclY150jqn1o/0OqQl00qg+agavZb10GsbTSBYZ2XnE0uVqT0tOuYw9PS2oDnkdcx1K557TrUrrnQdi093r4ecSOVRxIrsfJ47lH8+gzTlK5J69qQbJSuMe2c1ndjfZrfJ9JjWnmsY1Y7x/DcR6Uy1wpEEegNkqRS6F9ZaZldzjo6Ouiiiy5qtBk1YTmGKdzJ1B5qlhAgWrUGZaNhU3s4Qtw51JxbiVh4SYDwsmrltx7WliOTpRyarVq9xuJr7SfVgVZWCcv2WFzNFs9xKX+tHT12SnjbJFa/YTjps1VWzW6rjFK+njw9bc3h6WsOrRbHKqOUduy6jV3LUtm1NKx7mmarF6sfacc89wOrjrz3zJgdWfDcs6xj3jaN9XmPfVp+Vh5WGOm4VZ4Qq9091NJmoJzUY5ez8847j8aNG0dbbLEFjRs3jtZee20zfKVSoa8N/XZNecY4e8mnSrnLWcsIGk4Zt222boiWMx9i3fAtRyOGJnwkG3keXnFgPSAth9NyEL2O/pJl59PCzjE0YtBc06m2jkllsx7eHhHjET1WmDBPy9H2CCFLzEk2WY5qTAhJcaxy8eOa/TGHznOdWdeedj16nPws/SHWlhLa/cUSaF6nUrPPE8a6L2hYZfU47VocKZ72Peu9LUt7xtqEx9XsziowNDvqIby8osjzOatoiPX9WB/PQh4xBMpJPQTNXXfdRc888wz99a9/pTfeeIOGDx9OW2yxRVXgjB07ltZaa61q+EqlQl9ZrVhBc847EDRNRVkEjdfR0x421kNQy0ezI4wXc1itY5q9mi2Ww63ZycvoERc879jDLIs4iDkZmoOghbli+Ho09Y1je6TlsV+ymeep2eCtT48AifVlyXZPnVr2a+0nobWNVm4tn1heYfxXFs+uimbLGbfSiJUp5nRbZdLS8rS3dJ6nlwXPtRYTQzGHXMqT22o58pbdHhtiwsXTdlr/jdmgldeqh/BYXnEh5SPZFuahxfPkq/WLLH0mC7UKIlAe6vk7NPfccw89/fTT9L73vY/efPNNevLJJ+mvf/0rERHdf//91XAQNDptjTagrxPe+LwPj5Dw5hmGtR5U1kMwlpeUplQe7QEsfQ/De+wLj/Pyh+Glz3mdcZ6eBs9Hc0rC8kkP0Klv9MwvLLNmF68bCW6b1V9ioihmm2SjdVzqB1YZtbLx77G+noa1bLI+W3mHn0cMmmuWP7QjtC/m6EptqqVr9X+vM6fVA48Xu6dp5bVs0Pqsdv1KNkn3mSXL5HVVWpq83qwy83hS+lYZQvh1q5VLs8lzn5Pq2Lpv8+9eMWDdb7xtwW3W0gzDSfXC7ed4nhFaXABi3HnnnXTDDTdQv37/Xdr+ox/9qNt3YIOaaiI0R0N76GoOmMfBt5xCng7/LDnmVpmkh15oo/VwSc97nBHrYeZ5IEsPLO7khHEkJ9BCKyu3jdeZZEMsP8u5kpw3DW6L1Ael9tQcIl5fkh2as2j19zQvKSyvC0+589Q5R3IM+XktfUmcSMcsh9tqe8k2SSBookFqC61P8HC8/Xn5PYIrjG9dwxpaPjyMdi5Ec7CttMNwlvMepqVdQ9I1qn3m14nW/pr9ecWClA//LKVr3Xtj4a3ryzqvldeqBwBqZdmyZfTuu+92OzZp0iS64447eoTF79DIQNA0EOlBHD5wiHo6wbGbdHhMcnLSvDRHhYfTwnD7NSfWU3YtPSmcJl6kh7LmDIbwtCRHQRIYHK2sWv3GhAIvs9aWYVl5nPC/ZSM/F4tj1SWPH4YPz2mCR8orZqfW5pL9vE9I7Rwek9ot/O4RWvz6lepOKrPUz3naUt/1CijpOpUEVVhXYVzLedX6hnQPCuPFHFzJTqv+rXunR2hYYbVrVrpHSf0utNG6b8YEWphG2Ce1NuH1Esbl7Z71PsivSe05FqYhiS6rPq37nVRvMXjZpLJK+XiuMQC8bLvttnThhRfS008/TStWrCAiojfffJPefvvtBltWHiBoGoB0U4y9/ZEcDE0EaQ9GydGQHkDSzXxh55hqmpJzID24rJu7VA4trmSPJ4/YA0dzMqTvkpPBHQEejpfRKzIkp8Tz4JfaRbNdK2csvVg7SflYDojl4HI7eV+xyqc5VpoTqpWZ22k5cZYzqdkq5c37lid/rT6k+FLdS3XKHe00rCR0pDLyeNp1ol3D2vViOb1aGjE8YieLIArPS0IgVm+agNDqQkovj61S22nnJPssMSb1I6nv8nysvh/G0+6xIVq/kUQet1u6L8SeuQB4OfXUU2mLLbagz3/+87TffvvR4YcfTkcffTTttddePcJ2JZVC/8oKNgVoErQHvvY9PCbFT88PbV+1wPysRa/3yFNzyK20eN5afJ6PZHssPc0WT5qWmLPqVCLm3FiOhJaGR4x40s1CrB49fYzHsezMaj934sN8tWNW+rF0Yn02i5Ob9RrwXmcePNdxzNm1HLI89yZPuhI8fLq9fJimZFvsWEq6k6GVf948rHwtLJFn9Ruer3ZcstFjv7c8lpjM0u5Z686KY5UzPU8Uv6Zjx6x7H2h96rkpQEpXVxfNnTuXFixYQGuttRZtttlm3c5XKhW6dNCtdc2TM63zyFJuCoARmgZgvUnS3nJZN1l+nJPuliW9abOOWenyMmjhLOERvhmz3n5p+adpWQ6W9FYw9qYvTDd88xf+8bKE33n9afZI7SzZox2PfebHpPoM60QTmLyupXqUyiy1Ef8cxknDz5k4rVtd8zYIw2vp8fLxPHi+0nXA7ZX6gFQHmrPIw0n2xARa+MfP8XQsZ0tzXPm1lyUdqY2tcmn3wTBuKj6y3Pe0soTpaXZofYTnrZUvJkb5PSBNK5aHdq3wvmvVjWabdO+V4nvSCrHEAv9vPWes41q/4jZode4VI1obWXEAyEJnZyd985vfpAsvvJDuvvtu2m677XqImRSsoZGBoOlFYs5KeJO2BIPm5GqOtvbw43lxhzVMi3/WnEjJQeOOJy+nVBf8IW05q1K5eBl5PE1oWbZLZY89UCURpLVrrP34OUsMan2EE3O8LTGmiRrLSdHaPDy2+0PDeuRtORQeB1cTDpag8NQfF1wSlvPkgV+XvH4159DrdEn9SfovpStdy1q+mjMfszF23Wn9iYe3BFSYpuVcx5zamGMeE8jcHqt/SvfFmO3WfTL9bNWVJPxizyLJXskOr7hIj/N2k55jPB3tPiaVTwrPbYgJLgC8XHXVVfTCCy/Q9ttvT48//jgREV122WX00ksvNdiy8gBB04uEN3HLWYo9pKQHiXRMSif2ACVa9VsZkgNjOYY8n7NXjOlhn3XzjwkkTdTEnEXrAS892DSHVnrAW+3gsSEsl8dJ5Q4+d24lh48LDy2c1K4xh8VyJrS0tDrhojGPcx7rm5YDGHM4eZ5S3fEyaNdnmC6Po9nG89DKpt1jtOtPSismILT0Yvc0rQ+HacaQrkOpH0r5cxvzOp1h2/Hj4Xmp70htGXsehGXQ8uT587yylM0SHbG4oa1aulL4ME5Wm7U+bz0vpPj8s3X/4/DyWc8kACT+8pe/UEdHB+23337U1rbqF1V22WUXuvbaaxtsWXmAoOllYg9w7a2c9NaMO/uh8yo9LCSn58HdFvfIa6NhU03nULtZv7J4djXsF1YeERUhWtm9b734A0QSbPyhZDmd3LZYful/7cGnOZ9S3tpDU3KEpDiSkxUe4/1OEjr8f1ifUp1paUv9UnM6QiSnXXOEpLrgtmg2h2lq15rlbPJrSeozmo28Lj1CI9amUlq8nJ7+GqsPrU6lz5KA4+UL87CERuz6kfqzFFdrR34+lj8PK10fUt/h4Xge0j3Lui9o57V7vVYuKT/puubltoQBL5dmp1Y2bpuWrvZdi6v1b+mcVf88vFZfsXIAkLJy5coex3bYYQeaP39+j+NdVCn0r6xA0PQymhMeftecbuvmGqZpPdi5QzThvkszORvSsfTziEFzXQ6P5nyHdSCVW3NOLdvDOuH1wOHhrIewFS72nTuIvE2k+NLDVaqz2INdEiGWwLAcTanv8rCpPVaf5OWWnESpvGE8TRiE+XO7JSddi6c5hDHRYjmSvN206y7mqFvpa06XdB1I6Wj1IpWL10P4WRMLkhDSrh+tLPxa8jih0r2Ep62FldLhxz116QmXxTH23G+ldCxhoaVnXXNWOhLavThmm9bHsqCV0RNPuzdKaeaxDfQtxo8fT1//+tdp+fLl1WP/+c9/Srk4v1G0NdqAvoLlsEhhte/ag9yKx+NYjn5om5VeaI+Ud5i+9N9CezhoTqn1MIk5s7zMWvk9DpzkfFj1zdOUwmv1Ep63xFfsQRvGlxx9zcHiYTS7ONoxHkdrXymOlK9ml9YfeJ1INsccZakPxESK1qe0/unpp1o62vUr1X8YznL6rHuPZi+3RbufWPc6q4608FpflvK1+oJUB7E+ogkCr6OrtZPVTyz7vPeWME2rTByPsLT6R540Lfusdo31U6uttHqMPS8A4Jx00kl00UUX0SGHHEIrVqygK6+8kp555hnaY489eoSFxpHBCE0vERMd2htB64YZy8v6LOUnpaM9vCW7pXCaoy+VRfqvPRSyPCyk9FK7JUEh5WW1jacOLKdNs5fbqTljWlrcZqndstSv1n80LMcw5pjwPhLGkxxXbpPHyeftyutE6h9SObI4Wjx9S/BI+fBzmjDg+XmceS0fqT4kpy8mlMI6TsNr/VETCVq/4Hl5+6fnWJimdY3xtuPtZN1vLWdbs01qK35/yiJgwj9N7EmCybpXxK5/q3xaHOm69LZ17F6d1SbrOs0ivkDfZdGiRUREtNpqq9Fll11GF198MR155JE0dOhQOvroo+mYY45prIElAoKmQXhES/qZP2SkB4r2gLHOac5TGM/r9Ftpxsoo5ak9rCVHKXZOcyC5zTG7pXrXRJ/krPF4WhxefsnJCNP1igbL1rDsvL40J5IfsxxyDancku1SuSRnPeaMWIKIh9HSka7J9LhULm4Tdxwtp1NLR6oPboMldiQBF+YZpivVqdTOvL9r7Sr15fC81nek8nuc0JiDmcVBj10DYVipzr1xY+HC8LyuY3HCeGFY3n+te5b02XvN1wPpmkrRRIt0v449G6T8wnix5wkAMQ455BBauHAhXXnllfTTn/6U2tvb6eCDD6YpU6bQzjvv3GjzSgUETS/BH+zWWyLtppuG0dKU4lg3XOvBnX6WnBvJniuGryemKTlNXlslR4zboDlIsfjhfyl/yVnWHEweX3ug8jy4M6IRpie1iVS3lpMsOTJa+pKYsfqEJMjCvHhdhmnH6sMSGlq8WJrhOa2/aE67ZBfvj1J7a+nzNLS4Uv+zhIwmSLmw0tK0rg8Ot52nJ/UPrVzStR0TCVLZtb6oxZf6sxSW28DLo9W7Vp8WUrk9928pX17f6WerbmP3HCk/nrdWD9ZnKR2PYNDuBZY9Vt7WtRCzGQCLb3zjGzRs2DBqb2+nX/7ylzRt2jT6+Mc/TscffzxddtlldOedd/aIg9+hkYGg6WUsR0B6+GpOFk9TejhpDpHnbZTk5PAwYVpnLXq9x7nwM3dSrLTSz+F/Xl7pe8yRkOJrToVUf1p+/LwVL2yrmKOjOTGWg8fz4nEteJjQyZfazdNPpf4jCRmr31v9OJaXdk1JNkv5a+XS0pSuGa9jGxMoUjmta5OHlfKOiQXr+uOCSHJ2LacyTEM7HzuWpr+wc0wPm637nBaOl4XnH3P6Y/cEK22vU6+hiY1YG/BwWe+bXts8gol/9t7vpHRjYaV7jHUP0+4vYTwIG5CV888/n/r160crV66kr3zlK/TDH/6QvvnNb9JRRx1F66yzDv3hD39otImloWkFzaJFi+j888+nE044gU455RT6+9//3miTasJy6MMw2k1Tcxi4Myg5pTxO+l174ErnLbHFHV+eppa/ZIvmUIfnuPMhwe2Q0tAelvyc9VCV7JUcCKkutYd8mp/lAHsdijCtmFPhfRhLDiDPw+Osa/1Zc+glsWB9tq4lfkz6bjmC2vXGj1l1Kl1zUj5SffLwYXml8ljXjuXASvUXExjStWf1WatNPNcC0aodFj2OeJgmr7MwzTkTp1U/x2zQ6kL6bl23liDJImqla1IKx8vOr5mYEKrVedfqledp1ZmnzaUwnvun53mjtTWEDfAyYsQI+spXvkKPPPIIvfHGG0REtN5669HOO+9MkydPposvvrhHnK6kUuhfWWlaQTNr1izacccd6frrr6fJkyfTNddc02iTakZ7IHAREXOglyw7v9tvvqRhuJPCndiYI6LZxY+H6WvnpDC8DNbbManMvLyxMnkElfZGLj0nOcnhd02shN+19uTnNCFgOYJhGMk2Hp7XvSR0pDBSe1v1HNqkORSaY2nZFtqj1Qe3z3IuNAddSjeWZ1iu8Lv0OeaIWY5SlnTSuFKf5OckYaU5cN42iZ3XrhdNxHE7rL4Vfuf9LUxzYeeYHscm3HepmJ9ldxjOc6+X4kn5Se2nwds1Fi+sD14mXo8xZ16zRSMmVjx4rnFv3VnxtGNeUQuAxgUXXEBtbW30r3/9i4466ig64IAD6JxzzqHrrruOHnjgAfrb3/7WaBNLQyVp0k2uDz74YPr2t79NgwYNEr/HqFSaW2VaDhwPE34nkh3frG/WpHw8zoF0TnIgNKdEcyClcJYdmrNllUWzQ6tXydmOlVuy3TrvTdfTBmG5PM5HzI5YfjFbtb6ct89YZZPqkOcrlVtLxyqzFlazPTwv5a+VU4pv9fmwfJ50tGvBSksrX8xpjF132netfLE64PbzuHn7k4dawmvXST0cZO1asJ45efMh8gkeyx7LlqLbBAAPixcvpjXWWKMuaX3pS1+iz372szR37lx68cUX6aWXXqIXX3yRXn75Zbrnnnuq4SqVCnW0fbcueWpMX3FEKX//pilHaFasWEErV67sJl6GDx9OCxYs6BG2o6ODKpVKj79mR3tQ8Td3/HMehrbL29mm3y0HWLLRchB5uSSHKrWHx0vjxsqr2cPLmKbJ0Rw9yVmNtVGsLiw7PM6/RJie1k8ku/iflK4WX3PGY23F+55Wd1n6Z1r+sM9o9cXj8rC8n/LPUjqx64GH1cJrTroWP7Wdp+mpx5jgCNOPXYM87zBOaKNUfstuqayW+JTa32prKS3JXm6fdQ+T0rbs1spifY6lJ7VX7PrmtnpFiJQ3/2zdd7PUU6z9s9hWFL2RB2htrrrqKurs7KSTTz6ZBg4cSFtssQVNmjSJTj/9dLrqqqvoRz/6UaNNLA1NOUKzYsUKOvjgg+muu+6qHpsyZQpdfPHFtP7667vSaAZRk+dtZPjZ84bKeltpPdQ051ez00rfOsY/S3lKeWu2exwdz0PTclAltLDWcW9bZy2bVt/cJm95JcdPs12yWSPWt3mZuE1W2Xn6UlpaXXH7JCyBIZXJSs/qj1Z5NFu1+te+x65Pnofn/uEtt2QDP8/LbJWDpy+dt+6bkn150PLLm6Z1PfNw6XmpnWL3VZ5XzNa8QkdKR6ujWtqhHjQ6f1Be6jFC841vfIOOPvpo2n///WmttdaisWPHdvtbb731uoXHCI1OU47QtLW10cCBA2np0qVERNTV1UULFy6kNddcs8GWZYPfJL3iRHNOLUeXv9kL3zxKbxr52z/JgbAelJbDGabN7eJpSHlo5UyPxx6y0sPSIzhCe/hnbh+3XbJBixdzXKyHa0y4SOEsW3h5eZlj9mTJUwrP+7fVJ6S+pF0zYTks4RXmKbW3dO2EaWoCIUyTX4ex8vMySvUW2imlFX7ndaFdq1KaVp1J14903rJRy1P6z+vK2y+t60QKI33WxB8Px/uJ1w6vwOBtxvuXZHsYh9/7Y2WX+m1WtGtX+y7F599radMs8QAompNPPpmGDBlCd911F51//vn0wQ9+kObPn0+33HJLVehwupJi/8pKUwoaIqIddtiBHnjgASIieuSRR2jLLbek9vb2BluVH+lBJz2YwjDSjX/2ujdWv0sPCstBkh5W1pszSRiEaVwxfD1VpAxtn1HdKUh6QMxe90bz4S+VXRJKlkjSnMisD1CejxRfEzlSWpKT5HEG+XmrHJKjrwlGqy41LAdMEhCauNH6XhhP6idZnBzpepPy1ISP5xhvP56PJFas6407q1x4WQ615OjydEJ4vrx+tOtIqjdJ4MWuF0mMatdg7B4m5Rfr9x57wzwlu6R7pRYnJpI8QkMqg1YPYX6SXWG/kK6NPA6+ZWMsPCcmgLS4nmvbk58ERA+oN0OGDKFNNtmEDjzwQDr33HNp1qxZdM8999Bll13WaNNKQ1NOOSMiWrBgAV1yySW0cOFCGjJkCJ133nm0wQYbuOM3w5SzFOlB6XnwS45RihZPc3Y8+Uhpc0IHwrInyw3feuDVmpZUbu4Iaf9De7R0pHSlc560tLqV4sQe4lY63J4wTY+9Wrt7bfCWIbQjS71aZeH2WeXnZYj1kVgZYljXND9fS1pan5POS2l5ysvT0s5Z9Zi1TTS085421OorLLPU17Q+6LHXi5a+p5x57bDao5Y08oSphVrTL9o+UA7quSnALbfcQv/4xz/oc5/7HL311ls0e/ZsGjhwIE2ZMqVbHpVKhT7fv9gpZ19YWc4pZ00raGqlmQSNRlanSzrnefulpSc50F6n35O/5Ah5HLXYm8GY4x+La+VtodkeYjmHWn167bMcPK/9HjHKyxJz9izB4elf3AbLXk/71aP/hDaGaO0Sy4+TRyx66k67Nrl9HsHD7YzZbd1jpDJIdeS5z2h21lO0ePL1OuZh+bQySnn0htPsqbda7SqyHN60IUBAEdRT0Bx11FH0jW98g4YOHUr/8z//QyNGjKDBgwfT22+/TRdccEE1HASNTlujDegLeJxQ6wEdcwLC8JKTkR6T8pRs5Z8tx5XbEzuuCQFeJq/Dycsq5SHVv9fx89rK4XUnxc3iTITHY466ZqMlOLndYf4ekZMel9raK2YkJzv8L/XjWB/mSP1HKocWxhInWn61CCtebt4uYbvydCyBqJUlzCeLbdq1J52TbLEcTqtf8HSlcJ57iHRO6vex8/yz5UTH7itZ6siywUITLZaobZTIksLERK0UrpZ8ASiKQYMG0dChQ+nNN9+kF154gb773e9SW1sbHXfccY02rTQ07RqaVkJz6D0OBU8nDJP+9zoueW/WmqMkOcuajZYNHgHCw6f1x+uWP8A051rKn5eDly/84+E1+73llcJI9vCyS2UN7dHsk/5LfVKq25gDLYnH0PnWBKhkQ0wc8bJp6cTyijmevAy8jaW8tPrnaEJN61/heSlP6T/PR+rLmm1S3fE60L5L6YTftfuGJNaktHlflPqw1adjaH2dx9fKIuG9j0vxYjZoYa37VhhGsj12TfLP0vcsWIJTCuNNK2vYWsoAQFb69etH8+bNox/+8Ie0yy670IABA9SZRtgUQAYjNL1E+MCVHhyaQyQxZ+I0mnCfb+64ZIfHTi0dyYGQ7Necm//f3rtH2VVV+cLzhJAKUPLwwXOEEIFExK9prk03Td/2Ay+KoggB0TYIEiCSFgVyUUhj88hFw/v9TgBRwShgQBsRbmiJrcNrw2XIUzp0SOgGxFZiEgixApXa3x98u1w16zcfa5996pxTNX8Ze9Q5e681X2uutX5z73NOrAKA9/fcqdPOezdHTrAkX5C9OQQJ2YJyQyPCiAyiQkMrBqRx1AogTtR5Pyt+3C5JP5LD26PxlQos9FqahygG0jzKHXevXNQH5YSV41JsNN9TOdL4WzZ4YivpRusgaqPln0TKpULJA0/OSOuZBslfvv7lrIfITk1Pec4r2+Mb0mfF0JJTZ9t2yAsENHz605+m448/nt7xjnfQ5ZdfTkRETzzxBI0fHzTdi/gOzQhAIxKInHrJgOfulYdsaYRII/OIvCJbNSLmLbo0PyRipdlrxVCTLRUdFvFFuiUC64E3ltqYST6m9mlFhnXdstu6phV3UmEp2Ze2Qe+tgs/KQW6HtzBFrzUd0pqB2nht8oytNu9zzlkEV4q9VTCguEi+5xRzWsxybEMxkNpotiA9VQsqhBxZVXVp/bQ5U8UGTxEaCFRBnd+hISLasGEDbbrppjRu3Fsfnrr00kvpgx/8IO29996DbRqNBn113Hdr04nw9YG/68rv0ERB00IgQqgRJIugWpunh9Chdl4y6iHQvH1qZ24BYBVOUlxTPz2kEtnjIZeWfI3E5BYBkv1cF7KRv07leGxP7ZV0STqqyrH84tDmFL+u2aWR6dxcQNe5T5JN1hzQ5GprThovKZZSvDxrCbLZyjVJljZeyE5LrjX3tbHwyLVIuhbHXOQUU1Xk5l6rC55i1WtDO+0NjA3UWdD853/+Jz3++ON0yCGHEBHRyy+/TOPHj6d3vetdQ9pFQSMjvkPTQpQLXboQp/83y7oN+D9Jk+SU11f3TRncTPlfadMuX+cswKV93M7UFqkP0s/t4zKRvUhu6QdCec3amHkbRNQsws3lI/vQeCDShOKC9PHx5rZLf7k8rZCSckYjr8hmjdBKvnO9XLYmi48htxn5ob3WCiRuKxpPbquUz9ac5+2RfuQ7apfai/KVjzfKJ1SYSIUGygMpLul1LTbIZq4LxRrNack/bR1FcqUYWHo0+cjXVJ63mNFkc3gKzbphFbrp+ZziwbOvNIsoZgJ14ZJLLqFNNtmEiIjuu+8+mjVrFh133HH0k5/8ZFjb+A4NRhQ0I4B0Ad3ngQuJiOAGK5FRvmFuM3HlsD5afyRDss/ywVtwaPCSdwsSuUqvcfKm6U3bavo5+ePnEaFFRIkTSkSQtEIh1YlslIguGkvkD4oNAiJV/LWngJLyRypseNGjkTyJ+Fnx5jYgvz1EVgIq3FBeIRtRYcL7cP+4/1rRqI2fNJ7e+SvZgXzW9KDXfH7y8UCx4TH2FkSoPbLLmqdSG7QupOeRLuSPpNsDT3HZDLxrrdXGqyOKkEAnYt26dXTwwQfTxo0b6dZbb6UrrriCFi5cSHfccUe7TesaxLeNWgxEBBHJQP3S64i0SERGk8tt0jZxvulznYjwoDudGrlCsK5xnak9Wjwln9I4IDvT68hO3sZL9pBNPH7Ib36dEyfkp+YfylF03YqrN35SUZGT55648PNS/kmxRrpQLCTiiORzH7Rc5/5p4y3J067zAk7qp5H59LoUR+SjlHcc1trD2yHi6ilCpCKxClCxhuR5dEg+W31RfJCP1higtak8X+rJLV5zgPYTqU0zegKBdqP8msSvfvUr2nHHHWm33XYjIqKBgYF2mtVViCc0LUbuxosI2EP7r3XfcZMWfElfei19LRGw1A5EcrzEGRVAqUyJCGukFtltkS0eI0QI0eYt2eIhxjyG0l80Hsh3VNxpNmkE2tsWETaPrelfLcd4O43QaQSet03lcV1SDHleIHJuxdWyW2uDxkTKX+l9Kk/KYSlOaJ1APkjFozSeks+aPel5Tsol+7ktlh/If7QmSWuyVSBocxX5K8lEcjyyc0i/VJRqxXAz+jy2aNc9cxChar9AoC5MmTKFzj//fLr66qvp4x//OBERvfnmm/THP/5xWNuixUe3In4UYASANnSJ4HvaScTRs6BLBYWHNElFSrNAm6XmW45dlqzc81KBpLXl11O7rXZSEchzwxMvqY0WT8leLg/ZYsXNstGba1J8PNCKMImweeKPfJDOecfTsiWFRw5qq407ssOTW5p8a1558smC1Ae91taY9LyWL9a6nmurNUfSvnUVD9wmq0hrl16ivCc0rbI1MDZR548CvPbaa3TXXXfR9ttvTx/96EeJ6E/fq5kzZ85gu0ajQXMbrf1RgAuK+FGAAICHHJRt+F0vafPnCznvh66hNqUsvjnzO5CojeduGWqX2qHZxPujzUsisNbdQs0XKVYWweGyuf1cpkSsJPlSHnjIexkrKaaevhqB08hw2g7pRONvFSgolukcQnOMjxPva9mLzmnzEfVH9pVtUG6gPNQKIT4nONJ26Lw0pz3FDfKfy9V8Qb6ja6lcJCf1j/sqjVfOeZ5rVn6V77U5gPzTIOWFh8znvEdyvXsBj10VeAoPaQ2V9EYxE+hUvO1tb6OZM2cOFjNERB/72MfopJNOGtZ2oMVHtyIKmhYDbeqeu2rpxslJ0UP7rx0mP5WlEQiLzKN+EpnTNitEthH552RbkqNtRDw+6Wutv0YweT/rbikqFrhviGBpxRMfV65HI/o8poi0IqKCiIhGMDmploDiJBFOpJf7xu1PfdAIuIcsSgULl4PiK7X3zBeLlOaSQ2lepNfQmPO5iYoo5KtWqKFcRm15+9RmNP7cD6uYsHJAGlNpbeTtNHnaOa1vlbxF9qD1QlqfUS6ktljFkBavuiCNVSDQjXjuuefoH/7hH2ju3Ll0ww030E9+8hOaMGECnXrqqe02rWsQBU0L4CEuqGBB5IsTht6e+XTA0q2GtNcKmLQfJ7sSyfXcuZNIuEYm+XmJwPD3nEAhQiO1k+QjW8r3Wh9EqnjBohWG2lhphRfKGUTsEcHU8oL7hf5KhQLPXZ5TKH4a0eL+87mijY2Vd1p7RHqt11rs+Gvupyab+6jlk+aHNEdSaHMf5YFWFEn5IuWAlu8SmUakmtsnramegiBtn/qlrYlo/eB+or7e9S4HUgGkzQeea6idJE/K5TpgxceCte4HAp2GSy65hHbZZRfaf//96aGHHqK7776bZs+eDX8UoChae3Qr4js0IwCLiCHyIW08XAbf2C0iIxFO1F47hwgYtw/p0giWRyZvl+OrRshTudZ1KW5eP5E+Ta/ki0evlB9INrLNykcUXxRrLQaefJQIPxrbFCgXkU05+pHdVcdF8s2yX/IBXdNiLOlFkPzlMqwiwrPuWP4he9A1L7xrSfpaywmPjZIdWtwsGTmx8IwT8qVum6v08cS8DtQpKzD6UOd3aI466ii6/fbbiYjoS1/6El199dV0//3303bbbUd77733YLtGo0FfodZ+h+Ziiu/QBBikjTG9k+YlmOgOGpJTnrfItEUy0zapHmQfumPHz6NNIT2nydQ2FYss5BR8PMbcRuRL+T7Vg8hq+lojF/w1txuNG+qrEU2tv2Qz0iMVg5ItaVtkn3Re0p/GubSHxwkVPzkkhduEbEnfS/mA4lCeQ3ajGPC2XKbke/q6tEfzKY1lqk9bV6ScQfJSO9Aag8ZIWh8kfZJ/1rqXjpmUt8g2XuhZ669mg2Wf1d9aw9Lz3iLLkiP1y5FvyeNtpHlVtQCR9uJAYCQwfvx4+v3vfz/4mojoIx/5CC1atKidZnUVoqBpIbTCRSIYRJgcpXIkoscJA9cnbcaSXal8Lyn2ki0ul5/TigLt7pyHNGq+clu0AkIqkpAeNIZawSeNn0a0kE2aTOkaIti88EghFRqcnCLb0nOc7Eo60DkpNmgOoWID6eVjhGAVZHxuolxI48BjoZFknv/SfOB9teKLy5fyjdulzRUpPtxvfh7Ncyt3cgpXlOPpX8lmSQ7qI8VWgzReqTzLDt5G62MVqWgdt5Djc5WipxXwrDmBQKvwiU98gmbPnk19fX3U09NDK1asoKIoaM2aNcPaxo8CYIyagmbevHnUaDQGj06DtBFJhIBv1nyDRO3RBs/1IPnlX0QY0GYuFS3IN6vYQHo1WVJxJRVMGhHmf62iIj2n2SwRa81HifA+ctAZw8aVk/9UbypDI4tIP/JbI7NVSJpG6LXcluyRdPG/iPQi+1K7NBKN9HE5UjGk5Z0mH60BqOhC8w4VT1IOSzZYxF+yW/KXxw3lLSLVyPYX1i4cFg8eG2195Hkp2adBKkq1gkAqXrw5gd5LRai3rQZPgWi1Q+3ReW2/0vp74OmbE5dAoFkcccQR9L/+1/+iiRMn0kc+8hGaM2cOnXDCCbTNNtu027SuwagpaM455xwqimLw6FRwkkUk3wktId05RXJRe80OJE8if7ywSuVIRASRKA/RRv2Q7TmQiHQqV3ufntP85kWohwxxHSUOWLrVMHlSHym3eDGhFTZpwYLkpP2kaygXuW7kr+SjZi/3XyKqFmG1xp3LRO+1fki/RLBRMcLlS2OXAsVDumYRYA9J1uIqrWGIoKLc5df5uUlbzYJ+pvqkPJLiZo2rNY+0fOdzkveTdKX6tMJY66+d8/Ytz2tFrgXvfqfNjarI7dtM8RQIaLjqqqvogQceoJUrV9Iee+xBRET7778/nXfeeXTEEUfQ2WefPaxP/MeaGPGjAC2Cl8RKpJlI/qhFeS3tr/VBJNMioNJ16RyyzWOfVsQgkuTR7b0u+Y70WSQ+hUYUPT5ye9N2WvGntZdstuKqjb9lt5Szmn2ST1rOIJ+lecDlS/ngiTXyNSfXJcJsxVazzwNtbZLWIzTmVowlmVIf73qF5FoFFbcnBRofzXduq+WnBUu35F8zhL7V8PoeCHQz6vhRgGuvvZaWLVtGzz33HDUaDdp9991p6tSpNG3aNJo6dSrttNNOQ/hso9Gg/9niHwW4rEt/FCAKmhFEDpHo7ZlPL6xdOOTOo0Rycs9L173Fi1ZoSXo0kmYR3VxYBZ5kk1YMSLK5PxLp0Ai8hzRphRC3AfknyUP+oXhoNiOdGllF9qM2HN4CV7JDI+He8Uc+abGxil3vOErvrfP8WrOFhdRPiiX33YqH55qH6HuLHS2PJZ+9RYpXVm4xJenQ8ijVz+V79FUp1qQxqIrcMfDa2uz1wNhFnb9ytnHjRnr++efp2WefHTxWrlxJEyZMoHvuuWewXRQ0MqKgGWGgRVkjtBa518hK2iY9V/a5fOvtadZ/HeeWr13X/nKbPOTLQ1KQb1ocpPaa/hR1EGAPMUMyreLB6xe3ievSCC7vh/pK8ZLaauTcskmbM5p+zRYvuZViauVbFVKLZFlriEUmvWRW8se6ZsVJuu6xXYuN5otmm2aDZIf3nIQqhL9Zcu31tURVX6y+dfhbpciqgihoAhLqLGiIiJ5++mnaY489aNy4t74N0t/fTytWrKCpU6cOtmk0GnRqiwuaK7q0oBk136HpZKANwiKgaFNet+HMYUd5vXxdHlIhkb6f9V/HDdHJZSOk16xFHtlkFUjltbIP0pP257HV/Jb6ILu5DVZ7TzGTykzfc7vQmCP/NPLHxzGNP7IX2W/FTyLzPG4SsdViy+VxX3h/b9HB+6c5mp6T7EJyUj2e+SHNbck3NI5SDJA8SbaUb2hc+BrDz5dyeH6jfJSKOylufHy02CBftPVCG/e0PVqv0Tig9Udb7zxA65CnDXovjS9/j/YAKX8sm6T4e+Ki7THeeWrZ6LWh2XEMBDTccsst9I//+I/02muvERHRf/3Xf9Ftt91Gu+66a5st6x7EE5o2QdtANaJavifSiXoJiQiV1yRShogQ76PJlsB1WG2RfEmG5k+KHHsluzXdORugJ4YSSZfGXxtHZDe/ZtlfdazRGEq+Inu1PmiOaL4iXZpMb85789ryw5MP3HYpVtp4aiRQy51cP7Ux4rZ51xiNnHvGn/tvratakWj5yvuh96ittQ+gWFWFx2bt/CMHnUEHLN1q8L227lq2etq0sv9Iyw10J+p8QvOpT32KbrzxxsFfNVuzZg1dcskltOOOO9IXvvCFwXaNRoNOafETmiu79AlNFDRtgHdjswjq6r4ptM3Elapsft5DwLTiofz7wtqFQ3R7iYOXBHlJWO6GrxGzXIIk2cRhFWIe21BskJ9e0ufJh6qFjUQkkSyNCGpETcsjTZ/XByTPIpA5+e4hxJqdWm5YuWr56rHZWmMk/73FCtcpyUc6JHu1+SEVD3XMPx4zrUCSkKPXW5R4dXsh2ZiiriIlpzgKBFqFugua7373u4MfNyMi6uvro2OPPZa++90/FTBR0MiIgmYEgDYaz928tI+2MVgEmPfLIbjoulVQeAoEDg8J8pB9BC22HuKIfONyPde5fA/Z4fL5a0m3Zb+mG/VF7yU5UhvJlzQuOQWdVQx4irMqc0YqwjyFoIfweQsVKb+4fGtt4XZY80XKHavI8cCzRloFQW4eabZqpNmaS9actmR54uKxxfNeOueBtfZVQasKlSiAAnWizoLmvPPOo5122omOOeYYGj9+PBERrV+/nmbOnEnf+973Bts1Gg36UosLmqujoOkstLOgsTbEnCLAQ468d+qQfalO/qtqln7eX+sjFU0aEfQWCGkMJF8t0ptTrHhIj7dQknTwdlZhmNPfQ9CbueYhylohJUEaEw9xtPJI06npQu1y5Vrtc0m+tB6k1yX5SI91zjMvtb9STLh8q6DT5iy3CdmI7JV8kOJXtU1O8ZPKkfzwvEdycgi/FivJRs2OXGg5xT9BgPp5ZAcCGuosaNauXUvnnHMOvfTSS/Te976X3va2t9HTTz9Ne++9N5188smD7aKgkREFzQjDQ/BQH4vAekhcblupSPLI9ZIqafO3yAtqb9kn6fOct0hWCo3kIxJg+eVpp+lG7aT2XgLsIa6SfTljoMlCvkjkFyGXsFrtLH8877kv1vxD/dJ2zawxXKZkI7KZt7fsQv5IBZtkn6QL9bMKBw9Jt/zTCghPYWYVVR4dWoysPUHyC8mX7PfKRj5a0MbW61srCq3A2EHdv3JWFAU9+eST9OSTT9Lrr79O7373u+mDH/zgkI+hRUEjIwqaFsFDEHI2BLT4pz+77CV+XsLo3Wys4gb185BZROI8RFjr5y1uJGjjYvXL6aORRM81i1jmEh2t0PK053ZrNlrEUCtaNKLnKVqkcfHkjZTjVoy8BYoGK6e0vEAycuLjKT608Utle9caDwnVxlmDJ9d5O8seq6hCMrhuyTfpGvIr57zWzlMIoBzg70eyeND8T+0KBHJQd0HjQaPRoC+2uKC5xlHQrFmzhi666CJatWoVjR8/nk4//XSaPHnykDYbNmygyy67jFasWEEbN26k6dOn0yGHHNIyu+Nnm1sEiyCV11f3TSGi4RtpuQCXCy3a8Oas+S0kS1x3+VoreNKNhvdF5IC34/L59bI/30CQvciu9H0aF35NI3Y8pijGUlw8m2HaRiMoqE0qg4+XNpYecirZLZ2TSCAnJylJkMZE8slb2KF+yAYtbtxOyX/NB2SblhtSH+5LqleLr6Sb285tQvMWtUVjbs0VC1pOcRmSn8gmvo6U79GapOVu2h6tVxJpR/mE8lqyU7Itlc0LKS0u3jWB26SRfG3eobWN+4zGivuowVrHLaCYefeYQCBgY8GCBbTffvvRjTfeSMceeyxde+21w9rcddddNHHiRFq4cCFdccUV9I1vfIPWrFnTMpuioGkhpCIhvV5+zlciCqjA8BD6dDNJN0feLpWbypHskUiIRKasTU2KDfdfImbcd4kYIr+ldpodGsnU9GibJoqxRmKkokjzFxEN5KNErnkOSfKRDKkt0p++ltqkNklE0ZpHWluJMCJb0DzTYsblo/HjsZJ80XyU5ptEYFNbEOnVcobbrPnI+yHbeRxRDnPd0vzi6x6fp9pcleSjtZHnQdovl0xr48uvo3Hi7bk+7/qPcgnlMi/A0li8sHbhMJmSjd612VOAoP7Wuu0plAKBTsAAFS09PHj44YfpwAMPJCKiffbZh1asWEF9fX1D2hx22GE0e/ZsIiLacsstaeLEibRhw4Z6g5FgfMskByDBlggMP8evawu/tkmk7/kmKBEuRBq4HWiDL/ut7ptCk7YiEUg2P49IlmabZT8iIcgX7qflt1YYcF+QfuQLGh9tzLW48euaTxI5kogRei/ZgHRK48+h6bfIFoJVMEjgpBXNS40sSkRRIv/WuGl5bkEbY1RQoKIBFXDpeWldQbmr2W0RUS5by3M0NtKclOY3ai8VGJIP2tqPzqHYpTZKMeJyyvPa/PXuC9xvZBf/kRkuR1pbUbsqQGuklMdaLAOBwJ/Q399PGzdupIkTJw6e23rrrWnVqlW00047DZ7bYostBl8/+OCDNHnyZNpuu+1aZlcUNC2EtXAS0f//ayzyo31pk+PkqjzPdRERPbT/WtrngQuHXUMbFLJfKoi4TyUmbTVLJC6aLkScpIKB+yiRSKTTaov8l+Iv2WPZmZJbi8hrpAsReo3kS0WERsKl/hIxRzJ5LDzkH/metkOkyENAUR8vMUS2IWKt+YwgkXONCKPiSvNfsjeVp5F5ZCNa05DO9By6JuWlJANBKi6k+FvrkGSHNyc9elA/ae2X1ikOrTDQbM6VofmL9HDZfL3n8ZVijOzzwpM/gUA78POf/5weffRROuWUU2jjxo20ZMkSmjBhAh1wwAEt/U74I/R9eoTuyu7Hv2NTFMWQHy9Icf/999O9995L8+e39ilo/ChAG4BIlUV0tA1U28glfUQ05KctpSJF6puCb/Laa609IjWoj0Xa0DkPkbTi5fVdsksbbyve0rhqRBL1RTZb7ZHPqX4PEBmUxlsady4L6eD2af5YMfbY6pUv5bc3Dgia/ZJ8aeykNQTZa0FbS3Li6rGX60U5nUtStTmKbLXIvFQEIT/Ta9qanuuPVYBZbfm19D911tYWLa+sOSHJ8Pqb2z4QyEWdPwpw/PHH0+mnn07Tpk2ja6+9lh5//HEaP3487bPPPjRz5szBdo1Gg2bTolp0SriBPmP+KMCnPvUp+uY3v0mbbbYZDQwM0Cc/+UlatGgR9fT0DGm3ePFi+uUvf0nz5s2jzTbbrJVmx3doRhLpppTe2bIWU7T587t75fuzN1k0KLO3Z/j3b1KgDUnTL71P5ad2pa/TvlobiRygzT/9W8rS4sn9RO35ufRaeZ7rRUCkml/jMUive4o2L/njkOKg+aH5jHwoz0l5kOpEY5bmMLKb5xAnQBZpRuMh9UHzFsWHv0bjlOYQtzNti+Ku2c7tRDFLz/F5iN4jnXxMJR2SL6kui1CjuPA2yG+rqJPyka8h0vrFbUMxSeOmjQGPLW/D5aFr3vVbskM6z/+mciZtNWvYGKL5Ko2ldj3F6r4pw9qUP6Rj+SsVcVL7QKCdaDQaNG3aNFq/fj0tWbKELrroIrr88svp5z//ebtNg9h3333pwQcfJCKiX/ziF/Te976Xenp66A9/+AOtWrWKiIieeuopeuihh+hrX/tay4sZonhCM2LQyAYnPKhfCqkNJwwWMU77oztakk28T9pGKl4Q+MYm2WzJ4b6nMrkfyFdks0YCJBslXzRyy20toZ3LiY/XFs0HyQZpzHgOeXJL8w31scbXihn3X5uDXvskW6ziSooz0qP5o7WX/EGvkV+8nzWu3A4vNLu0POP6PfNJuiaNIdfrzWmuR8oLbf5JryVocfL01+yX/C2f2nhyAK255X/szP9TTK+/1poVCNSNup/QXHfddfSTn/yEHn74YTrnnHOIiOiEE06gm266abBdo9GgE1v8hOZGxxOaVatW0fnnn0+rV6+mLbbYgubOnUs77rgjfetb36I//vGPdOKJJ9K8efPo17/+Nb3tbW8b7HfSSSfR3nvv3RK7o6BpIzybs0Z0cgqTHGKbypY2dC+x0nyTdEv9NYJjEQuPn6iN9R7J0GzWfOPnNWLj8SmNh6ePh2zn2qn10c5p73PyzlsoWfMDtbV0pr7nkH9PzkjrgJbH/HqK3KJQi5/URwIaW01e7lyvIlPqr+mV1gEp5pIuSYfUV3ovtfUWXihGUhyR7Va8c4u0HLSiiInCKMBRZ0Fzyy230NKlS+nVV1+lr3/967TnnnvSqlWr6LTTTqNbb711sF2nFDSdiPhRgBECWrytTQRtdDkLKpKD8NadNZngpH97e4Z/oRvZKZEJJDNnk7MIkmUTty8957EnhUYaNQLE44Fs9vhuFQZarniLGksv/6u9RjokAox8sIoc3s8qZrRckuIg2YDGCuVVeo3L8hRJPLdQ/LW1QstlbrdE4CRizl9LsqxxsdYMbW3i8sp4IVs9BQa3DY0dj5NV2EgytdxvhkznFA7cJymHc3JEG8NUhjQu2nnJlyr7pIUoZgKtwJo1a2jrrbemmTNn0p//+Z/TO9/5Ttp5552JiOiyyy6jQw89dFifgZE2sksQT2jaBG2Tse5qlbAWWOkumodQciD7JGJnkUpJp9VOs1MjtzmwihgUC0tG2lbbrKWx1siBlitcrxQzDwGr6gtqb8UXoYpMb5HjKe6svMiZD9rrnDh4fNHWEe+cySVy1hql6fCOsaTHE1+PDMnnXNLN20hjbeW3dU7S7SlgtPnvKapy8ltbx715Ka2vufDKyCkIA2MLdTyh+dCHPkTbbrstTZ06laZNm0bTpk2j3XffnXp7e+n1118f8tPHRG9x21ktfkKzsEuf0IyagmbevHl07rnnttsME4h0asRVOm8RBWsT8RAdjSB6CBAifZqdUjttM/H4mLa1igNkk7WZeUigVpTljInlq0YovONpkQgPsZfGSYOHuGrxsto3o1PSr80bLkebM5JNyH7L//ScRfq1tt6iTWqDyHAzJNLyScvNKoRUm++ewiPVK/nk1an5aOWfdz2s6mPppyf3JVh5pSFnbK04aW3Ka1W+4xMYnaijoHn11Vfp2WefpWXLltGzzz5Lzz77LL3yyiu00047DRY5RxxxxGD7KGhkjJqChqPTn9CU8JK/tM3C7W6hWf91HOwjbfxchkaqpf6pDIls8bbWpsdt8ej3kFPe1iKrGkm07PAUWVo7j61WQSPJsQin1VfTxeVKNlt6eCzQeavI4jZac8oiTZ7iRoqNRtwtAopszplzyC5LtkQ8tTGRfOMyeHsvCfYSbc81T7HjWTOsPNMIfSrPWt95f0uHNlesosSj07qm6c2ZE5pNWuyl+Sf5Yem0ir+cwikwulFHQVN+5IyfKwucZcuW0de+9rXBa41Gg05ocUFzUxQ0nYVOKGhyFjXPJoAWbt7fQ5SQPi+BQ+THIiWejT+nkNHIiWa3RGYkGZ4iKIU1hpKflu+SLx4/tHgiu7m/UlGU9tPkSznrKc6QfsseCZ5izCoYcsdJ8svKXy1nSlhrAPdDI+Ce+cp1aeeRDASPjx5CLuWDZKOnONF0eMbDsx5ra6knJzxx8e4BOf1y2lcl9lX3DNQm/T9zmvFFkmPFMYqb0YtWf+QMIQoaGVHQjACsjTh380Dwbq78fNrfU0hINnjJnrVZ59gvXcvxw0M8NTubIVySP1obL+GXfNT6Ilgkl+tEm7jmp6anSjGgyfTYyPtpsj2FXpXiBdnjKUas+VE1V7Wxts57/LSKMMkezWfpnGYL6u8tiBCsOaC11+RoJDnHHq9MSbYnDt7zOaSfj4+WU9rY5ur2jju3gdsaGD1o10fOjqPvNGu6iltoRhQ0nYROKmgkIBLlaa8tqh5yq5Ec3leybeF2t9CcNb81CaFWSEjEiPvjLWa8tnN7kd3omkW0kF0WwZPkSW29+nNh2a8RId7PskEjHp5Ype08BFryyWOTV7Yki7e3xhGd195bvuTo8ugt5VpznPufQstdCTm5ZpFuj4/cfo2YczlVCLynr1UcWETec84LT7FWtWipUjik77kt3iLPal9FTmmLNY8C3Yk6f7Y5hfWRsyhoMMa124CxhHJR44tbziJfLo4ewpj2tchNuvim/dPFNz3mrPntMNmpfciesq1FyFN7Ut/5eWS3RNC4/QipfaU+/pqDx8vapKTNjOtI/2o+W3p4G+k8j420AUv6kd9IF89fiWRKr5HudOzTdqkub85pxQeKg0V+pHHlMUOkDNlYBwFCc0XSYc0rbb7xfmiuamsVykU+njzOXv+leKR6UvulWCE5qYxUFrJBW4uQDB4L1C+ncJFkaL5a6zvX6VmvpHZW3FHOafK4/dKanrZd3TdFtf+Rg84YZufC7W6B9r+wdiHMZyu3AmMDjz32GN1xxx308MMP01/91V/R0UcfPaSYCeiIgmYE4dnU+OLW2zN/cMFM+2lkIe3L+0htuX6JcEt+oc1W24w4sUebJN8UEXFBMZQ2MItgovhIpEojJFL8Up+kjReRakTYeT8uCxFLbrtWOCC5vGCQYsP7SOQX5YDWnseXx1yDRLAtYqnJt8hQamtqM5+/kp9IrtQO5QYfLzRP0XyQxlNaL6zcQj5oRE5b17QiSFoLctczbisaJ26Xlo98vqA23G+0Vkg+aLHhclEuWv7zv9I6LI0Nep/awQuGRw46Y0h7bX2T4sB9Qz7zWKB9eN2GM2mbiSvFdkREByzdapiP6Q2/1I5JW81yrRuBsYc77riDLrjgAlq2bBktWLCAiIjuueceevXVV4e1LVp8dCviP9ZsAzRSjq7t8wANXtdkok1UIqWpPGkxRSQBbeCSXWgDRmQyvc5lIr3a4i+RYbShIaLCz6c6EdmQoMVJKoK43fyadD21EUEiRlYhgmzV+vF23n6ST9weq+CRyBeKg0WkUx+0cbNySJLP+0n5rdmjzR8tplo/fs5aRzxzhfuCSK8WP6k90q/JRuMh2aAVb1ZxpI0nh7QW8flq6ZHaWfmG5CEbpPh6FOINEAAAWnVJREFU/EL2pr7xL9qXBYJnjvJ2Vr5KY6flBpKt5Zw1Tqm+KGQCJe677z5asGABbbnllnTUUUcREdG6devo+uuvpzPOOMPoHSCKJzS1QyOrFrmVFtf0errJaNelzQD9RYsrl6NtnMimVDbykRNQ3h5tpmk/i9RLxQfv7yGkGnHWigLND8/7VKdEoDzEDuVVaptEbvg4c5+0zbhsa+WhRhDReKJxQ2PHY5Daxduh16n9qb9ojnH9FhGTck4r6rS5aflo5as2VpovaR8pt5APnkILkVKkQ7JZyz1pnlk25pL6sq2UJ0iP12YuTypSpDmm+YfGFPVL41yeL5+2SP0lnehcajtfj9B80PbF0h40Hp7iQlpnUz+tfZefi2ImkKIoimHfx5kxYwY9/fTTw9oOtPjoVsQTmprhWRTT19qCzoE2LbRYezZtRKqkIsAiEqjAkTbYnM1D2zjQOUTktGJFsoXH0FMocfv5eFgkThoDJFeLEfcBETvNf23D5/I1n7ksa9NH8rh/mnykSypetL7ouuUrn8sW6UnPeee6VTxpY2nFT/uL7PWQY04+0fyTciYF0ofyko8DihECio8UL+4X8hfpt4iull8WpByU7JXWMmQXb4/aludW902hSVvNp20m6jcmJLnWWszhWfdKeXxckT+ewkbaW634INs8YxsYW9hhhx3owQcfpAMPPHDI+YGBbi4xRhbxhKYFkDZaa2NNX2ttiYbe4eFttf7ShsRlav0kwpieT0kHsgdtaGl/RE7R5oN8QiSE99U2N4nwS6QU+YZ8tHRopIe/R/GSCpPyfapTygMt7zgpQOQP2Sz5oo0FygGtr6SD+6WRZy5fIkFpG897NLe1eYjOo7mO7EN5r8WS9+fzOD2k3JXk8Vhze7lMiQBbJJf3TccO6eT6UcGijRGPtYegovUTybTsSn2U4pO2R8WJtm7zPJGuc7ul74akNkiFBJdl+eY5n+afFnvNRq9OC3wsopgJIJx00km0YMECmjt3Lq1fv56WLl1Kl156Kb3nPe8Z1rZo8b9uRRQ0LYC0GXsKm7IdWoQ10iItlnwTkgqMVK6H9CCfpI1DK5QQoeVEBLVFdnNodqPNXbJPKm5SO6VNGMVfApKHyKXkI9IjEX5e5HHbtDh7iA8iE5LPGlmTbEjfI92aD1JeWUWBt1iSxkHTIRFtlKOIvKc6eLv0vbQ+SGMvxRIRRqlwknIYzRNp7CU705hbc5nLk9YzbX3lMeBjxfNBWhORrVK+ornmLaAkP60107JRGyftOr9mzSlLtmWHtP4htKrQiAIm4MGkSZPopptuove97300depUWrx4MW2++eZ0yimntNu0rkH8PzQdAEQutUVQIzpWYcH7a6ReumOmvefnkB6rKPGSOUk/igWKi1XQWIWKx1bLTuSX5hO3P4WUR1qeeOLsGQfvOaSb+4Bs5f4j27x+Sb40YweSJRF3LU68n9Re6iPZa60x2lh45rA1v7QYa3PVMx8k3R47kN9WP2STJ5+l+EtxsOzMaYv0IiDbtDyUxkCTn0vwq/SpAi2P2mVToLNR5/9Ds379errnnnvoyCOPpE033VRs12g06Bi6vRadEr5FR8X/QxPIg7SxoTYp0oW0vFvHF2KLaKZt0V0sjSyg/twGj0/cTi6T69ZkcvvLv+huHv/r3ZhSe60+HjLK42zZVbbR5HL56fikcrh9/C/Xg2JR9VxqI/eZ289laf5KxQzKhzT+XC7PaTSWPBe4LDRGqV28L48Xyl2UM+k1Lk8j1/w8ioNkW/o+9VVaq6y4pf5Kc96KFdfJ7ZX8QG2lfuU1lEeSLI8fUo5xoPGR5g+aKyh3kG98LLTYWGsxh2dOS+ug1K4OSPughJx1MRDw4MILL6RHH32UNmzYQEREy5cvpzlz5tAf/vCHYW3jRwEw4glNG2Dd3fHc/ZE2FnTXD/XhsDbUF9YuHPbzmtadOmQT2gQlsm35pvkvyZP6SCRYk4F8QMWC5K9EfKyiieeHlgua35pOjZRJceOyPZu+1hfFEsXAo5P388TMyhHpvCdXPHnutV/SUbb3nPPq8bbXCg9rriKZnnz32Ids8q5TaXtrjJC/Un9JXs76ieahNn58/jSTf2i9k/yx5qcFb+wCgZFAnU9ojjzySFq0aBGNH//Wb3UNDAzQokWL6Jlnnhnyn2s2Gg36bIuf0NzWpU9ooqAZIWgLb+4mkba7fOvtB/8TLw/5k85VKZCQ/Z6+Glnm7bWNV5OpbXoSGcwls1Y/fh2NgQWtOPKSGxQDfs4iNR4Sj2R67EI+I5ssn5Eci1xa/vEYSIQb2c/baDnlJdAe8mnNL9TWkm/NPc1Oi8RbeaGtQR7CjnyW8ttD5jX7uQ0vrF1Ik7aaNXheW2e1+epd1yWbc4oW1I9f88jw2OkphAKBTkWdBc2MGTNo4cKFtMUWWwyeK4qCPvWpT9Gdd945eK7RaNCMFhc03+nSgiY+cjZC0Bbq8tq6Db7/QbxsQ0Q0Z81vh234vT34V8LS8yU0cvg/+6cQR9peI2dpm9Qn7gNvi0gN90fb2FNdXJ7Uv+wnvZYIIt+I0Tjy/sguFAvvJo/8Tvuh8U71o5xIdadjjHSkMZJkSgSf+ybFS7qW6ubxS/tyX9B1bif3g9uN8lEj2kgHP4dyKL0uyUT28JxCfXncpHnE22s+aTFFsSvbaHOMj520FqACQfIdQcuf9LpEwtEc5sUMaiOt91Lxw6H5x3NImkdWf36uWRnomhV/Dq/+QKAb8D/+x/+g8847j1asWDF47j/+4z9owoQJbbSquxAFTQch/U/JpOIg/Su14xuDRoz4Rpteu2z8ymGklkMjMLwNshXZl56XyCIqejhRloobjWyXfyWiwmUiO1IZml85xD1tg8D95jHRSAW3Le2LYqO1leyWij5UZPCc1MaM902vS2QulcPzQiLQyCYrplKxI4099w/lo6STj7NVWHA7pfzViLikRyocpfmM7OPyUcyl3JbmuuV7+p7boBWEVqGR+mwVulJcpXVTm9ueQsd7XSo464ZWxOXsNah9FRsCgZHCzJkzaffdd6eTTz6Zpk+fTkcffTR94QtfoKOOOmpY26JRtPToVsRHzroE0l1BaUPnBA2R3BLanUEEjRzxdhKptvRrNkr9UrkSCbb8lPRK+jVftLZWLCTfvPK5LNRfi7s3fp5+OfkokRSLsEsxQNDmkTcnUtvQOW1OemyVYqLlrxY77ZyWF9weKT8lmZLdmk1a/qWyJFh9tQJAyjNkm6ZH843L4zZbcZRskWDNLW6/Ja9KG6m9Nxek9h7fW1F4BQJE9X7krMSGDRvo3//93+n111+nyZMn0/bbbz/keqPRoM80bqtVJ8ei4rNd+ZGzKGg6DNIGKl1Pz6F+XsKkFQKaDM3mXGJikUCvTTmFQPoZd8k/ry0ess3lIWiFBNeJ2iFdUjuNwHuKEamv9jpHF+rrJbeWLK2gs/xKY8jtkPSm8Oj15FZuYdMMIfSsEdKcsXId5ZankNPGS7LBW9Bp4yf5YsU2h9hLMrmPHlssHaU8b5FkyUTyNZtzC48oVAKdgFYUNBaioJERHznrAFikzDpXvvcSSNTu8q23HyavtC2VX3WDTvWWB9dnxUGLhUS6yveSH+kvt0n+pfLSdhrhRHJSe3iMtZiUtmtFS6rbkpki9UUDzzEUi5QIaa8RQZPsRLku+SXlQmonui7NFa4ztZm/T3UgnyQf0VhaY6v1l8gvkoXIsqYH2YbmFJp3PCZpXmt+aONs2YH6aMWPZx6UsIooyc60P5LhLVi1HNGKQdSHz1FvkSIV4hze9QXNKa29F5asQKDbED/bjBFPaDoUnruaKfiGVJ5LZVlFBN/MPJuKtflJMrSN0FNUaDIkYuFpm8q1CkREAtDdTqRbkl3Fdu26h6B68kmLE7JF053jn0R6Jb0IHr0Ssdb8lezx5Ci3zYqHFGvNB89YpTI0kiyNgVZ4eIi1tjbl2CL5Y80bCVLuoxhX+Yt8stYDzzorrcnWOGqxkeRptnmKOi884xwItAPtekLz6RY/oflePKFpL+bNm0eNRmPw6HbwTU+6+8bbEhEds2HKsH5a+/Icv0PG727yvxJp0O4oprrReY3gpncT0/b8DmDqd2mfh1xL13k8Urk8DqiNVkim/SQiYxUlSK9FEqUc4DalOqxxlpDaYxUiaHx5/qfxks6hfEt1oD6pPcgmq8jwFhHI9rQNz3NpjqU+8Fhqc0XKKT7/tT5STqLx4vZo4DmH9KHX/C8aG+6LtD5KJJmPodYevUdzCo0Pf43koVhIY2DJ09bPVB7yB8UC2Sjlr9aP24pkRDETGE14/fXXadmyZfSb3/xGbVe0+F+3Ip7QdCE8mw1vyzcgi9BIQMWMpE+zoYoe6Tp6L8lEhQSKp1Rc8DYoFlqBJBUZlgxLl8duyx4kRyJu0jhb48b7pH1T/eg1ast1o2u8H5fN46HFl8cIyeXnEKz4W/5bMZTaaePNZUixtOIvEVwpNzSy7l1vuE/IF+/ctnR7fHzkoDNonwcuHCaL+6fZgGR71tscWDmSo8sqMHILEMvXuvV1Grrd/rGAOp/QPPLII/S1r32NNt10U9q4cSPdfffd9PTTT9Mee+xB48b96dlDo9GgTzW+XYtOCXcUR8cTmkBrge64WYseX/zTPukdPdQnxx6pL78LV+qVdCN5Egnjfkgbbuo38sFDsNHdRK2YeGHtwmHyuL1IHh/T1DfUzvIjjUH6mvvEY5T+5bp5LnmAiAn3BxFNFC8UP57Xqd1S7NNcRPHl8eL2W/4i21MZWvwl0s3HIbXHIncaJBLrXQuktYjHAflcnkfznL9G9mi54rEZyZXGwvKxbFcWM0h26qtWXPG+aN5La5d3vK3c57p5f8uWFGi90OxEcc/JTeu6d+1qF6KYGVu46aab6IILLqC77rqLNt98cyIi+sEPfkC33dbaj5eNJkRB00WQNvN0c0IEQSL8GiHmBIITP22TRvrTHx2QyAvfsJAP3D5rQ0TFj0RQNVLD9XIbH9p/7RAd20xcOSx23GaJ3Fl/y75eopS214ihl1CmflixlEgmL0I0EqeNMS/+tNyyiL8kh88vaY5xAqbFgx/Id+4n90fqg0ijRo7QGKXjIhWzPAZcPyqGNV08fpZc7gO/hvIU5Rpf09B7rkeyi7fncUD5yu2UwOeuVJRIMeayUKyk9cE6L+lB84hDK3K09coDa38IBDoFGzZsoD322GPIuTlz5tBPfvKTYW3jRwEw4iNnHQbPHTK+QVuy0KKO7rYhmWjz1wittNmi6xpB1/RKvnL/NILu0Yf6SjH1bI5anLheRMAQKdeIlxRDbWykQon76pGt9ZfsRTmG+knt+XWP/0iXB1Y/ibhLfln2S7qsHNGgxUIrNFFfZKs0Ltr6Y9mTs+Zpc94DaT5oOS/FzMovrehCdknXJLslHzQdVg571uR2FQ5Vxrsd6BY7A8NR50fO/v7v/55OP/10mjJlCh111FF0++23ExHR0UcfTd/+9p8+YtZoNOiTLf7I2V3xkbNAHaiyQaR3wKS7hZ67dkiftNEh+chOD9FBciX/eFvUB8lObUHxQv3KmGlEhfuNbEQyPfLKNqg9HwOt0PGQRw5tfLm9advSP2QTks994z5puW7FR7NVIqSpPj7+XJ50DsVAIqqp7ag9ipFmVzoGaSy0fJFyD/mG7ENA1zXCbs1ZTY9kK7JHyjNpjUHQiqQ0/nxsLV/53zSPkC/8HGrnKaI4pPXbkoX6e/ccr2ypL+pv5WeVeLUCaP0KjD0ce+yx9JWvfIUWLVpE/f39tHr1arr//vvp7W9/+7C28YQGI57QjEJ4NuKyHSIX6LwkBxFE7ZrnHH8tAREJTWbaR9oAJYJk+SnZwu2U2nhs03yV4oXGA13z2I2KB+848b6abt5OskvLTy02aAy5T1w+8pv309oi/dJ7yQdkK++vEXpNt+SfZIPXNiQrB9Ka4rFdk8ltS3VpOpF8a83htnrGToqVNj+4vVV98IyRJyfrQpUx9sjynA8ENNT9s81PPPEEffvb36annnqK+vv7acqUKTR37lx697vfPdim0WjQ4S1+QrO4S5/QREHTZai6aWgk0vPaS6AkUqht3Fyul5xrJEzSo+nwFF6aTMlPiyRqBZJFJD3EnMcZ+Zz73kOMPW0tooXs95B2zzmkt0r+IZme+SMVTlIhZRUWmuwcsqytBVyW1lez0bJD0uO1yVNUSWOj6dDmKtIrQdPLr3vH0loLrLVEOmfBskFrn3Mtp00ztpWIAqf16PZCso6C5qqrrqJp06bR1KlTafLkyTRu3DgqioL6+/tp0003Hda+0WjQ9Ma3mtJp4e7imChoOgmjtaDRIJFdL0GwNr/0Gn9dwrvZW8T3of3XDvnZUyQbQSOLkm7JPqng0XRaurkNqR2aT5o8rXDg8jWiZOn2kF1kU+onaoNep8iJj8cnSz+33yLe3AZJjzY3LTslWATYst9D6CV9SIcmk8v1jre3gNOKKeSTR4bWR4qLNr+sws6zZufY5OkvXbPIpjXW7UQuUdb2t3b51O1kX8Pqvim0zcSV7TajadRR0Fx77bW0bNkyeu6556jRaNDuu+9OU6dOHSxydtpppyF8NgoaGfEdmg6H946fRMbSc2lba5Pl7dZtGP4z0enr9Hp58PfcXo087PPAhcN0p5B84/6k17mNPG7cvrI98j39izZ2iWRwX7j+h/ZfO0Qnkpdek/6WfbQijY8BkoFiguShPun1NJ6oHXrN8wj5j2zkNuXo5GPN8537k+rl8jmh9cw5PrbaeHK/eTsef80WFFNp7qbXpbkn+W8VF2k8pP6pfGSbtWZquvj68MLahXBskTwpfvyctOZJ/vFx4HHhsdYKKsk2rSBDvqbXtfloxSOViXIY6fUgteeRg85wtUfrhZRjgeZQFjOr+6ao7cZC7E866SS66qqr6Ic//CFdeeWV9OEPf5jeeOMN+v73v0+f//znafr06e02sWsQT2hGCdDER4SsPM/fSwURInaaPiRXIubWa4Sc65Lfkg+IbFnkAJ332oiIDSLw3GaNqGgkxOOf5YvlryVHKgAl3zXdmizNT4tMW/FJoeWt10dLppUDHoKdsw5oMdD0pX5K57Wxk+RzH6w2XJ+UHzwuki7Ux+ODZ6wlWHZJr7Wxt/Ii9a2EtU7k+FQFWq57c19DM08Kyr4jEYfRCE/s0/+gtmy/um8KTdpqVkfEvM7v0PzqV7+i3XffnXp7ewfP9ff304oVK2jq1KmD5xqNBh06rrVPaH4w0J1PaMa324BAdUjkQyNA6TmPfN5W6+ctSqR+SL5ErrRrEqFC5IbL4m012VLx5CFJyA5tPLUCiMuQ4PVPsxHFooS2wXiLiPT8W5sZftqYxlkrwDR7uL/cTiu3csiV9TqFRZBRfqMYSLmhzaX0fOqTRa6lNUci/KgdAoqrVqhZY+ItmtI4SLLSa6ldKJaaXo/dqSzNZ9RPk8XtknxsV+Gi+Wb5qo2Zh0xbhUrZv2rROtahxb+M/QFLtxrS/k/jNvrifPnll9PLL79M2223He22226066670q677kq77bZbu03rGsQTmlECq5DRyLTVBhFR61x6XtOB2kuE0iLaUiFTXrPIlVQ48OuSXAStv+abVshxWZpc5LtUGFlEV7JR0olkobhwv7T4STYj5BQU3hiiuEjQ8kmbq9xHbhPqJ80V5Kemk9uPbPbMYRQDzQaLEErz3oqjV44kz5JTvvbYnitXO+/JK08e5KznuTaNFKroTQsa6XVV2YFqyNlHOwV1PqH50Y9+RI899hjttddetGbNGvo//+f/0HPPPUcbN26k7bffng4++GD69Kc/TZtssgl9Ytw3a9Ep4YcDn4snNIH2AN0NlK7zO1+9PcP/V2zrjh5/X26UWpGB7JTIItIr2aKRyvQ6IqMasUWyNcIr2YXibJE+RBK4Po3weog31yORYe2aVrxodqD80opI7rcmUyNoCDwGSE+OjDRGKDckmVIMUR8pF7gsicz29synh/ZfSwcsHe4Htx/lnlZMWgWSZbPUj69bKB7oPP+rFWLSOekvigmKj1QIonVXW4e1eY36STmBZKE4SEWT1Mezb6DruQSVy6xCcNOiRXqtyfbkSyAPKKfKAnMsxHPx4sW0cOFCGjfura+2f/azn6XFixfTxIkTadttt6XbbruNXn/99TZb2dmIHwUYBSg3E76poI01JRSIVGgbdQmLqHFYxY5G7LRNK4f8SeQLkbi0r7VpSjHPsZH3k4ovKYZSgYHacX8kYsRtRJu1NX5ch5esWbIQCUx9l0iFRmYlG6S4SG2RDk9+S2OXzmtNZykD5QKaS/s8cKGLJKACBBW9qX6t8EH5xnNbWs8s3zXbU/vQnPLEVypmeFspn6R1GOnnbdBahOShXODxKNuhHPUQeJSjqL123XNNQzMEV8uVZtGNxUxvz/whP5xQVyyqYHXflCExnLTVrLbZMtJ49dVXqb+/f8i5ww8/nO677z76i7/4C/r6179OP/3pT9tkXXcgCppRDLSho80Ptdfu7kn9EAHRChOraJGKDaktByIOqNjQNnEUM1QYapsAInjcN80OVHQim1CceH9OeiXyrRVqUgHFbdJiz+1DxF4jadJ4aoVf2i6NlUS8NcLoIS0SKUe2cF+4P6h4Q3ag3NTGQooj8p3r04oxyRfeT8t9i3ii/p74lH9Rjkv6eF5qa54UBxRrK56eglDTydtI41y+9+4BPK84JJ0ppKLLItRVC4bVfVNodd8Uca/wQMtXaf9rZ4HgwboNZw5+8b58PxJAc0N6GtPpMawD++67L5155pn05JNP0ptvvklERC+99BK98sorRES0+eabDxY8Ay0+uhVR0IwiaERWWyS0zQeRXkkeskGSK8lBJIy35YVBTpHk8cFqYxEKS7ZEcrTziASmbblNqPCSCLakO+2XyuV+c9LN+6E4WXkmFUHSeKS+aUQb+c/9RvFBfqEYo9xFvlpFnGQfksUhkWxevEmFC7rGC0AUmzR2Uu5xfzV4xgnlk2dOSoURmkdoTKU80tZUbX3lr4lokHyjokrywwNJJ5rfkmxPIcXbWud5ntSJ3p75tM3ElYO/SlYVWk6inyCW9ifp/VgCWjfKJzSozWjHySefTFOnTqWzzjqLDj74YDriiCPouOOOo0984hNUFAVdffXVtNNOO7XbzI5G/CjAKIFFNNCGhAiM1s46z2VyvYgUSG28NkkEGhVh/DwiGkh22s9LPiWfJbkoDsh2JJ9f9+SBFXtJt4fsS2Nh2eeNLcoNlAdWfmt2ewopro/3k+z02iz5oOnNia02VlZctPOaPSg2KKYeWZL/OfPI8h314T7wc5pN0vpmrVUI1px85KAz6IClW6k6LJnefl6bq/bxtiHyz9W6bfTY1IwNnYw6/eqmGNX5owAl+vv76Te/+Q397ne/o2233ZZ23nlnIiK69dZb6eMf/zi9613vooPH3VqrTo77Bo7tyh8FiIJmDMAiUFrRUP69fOvtac6a35oEhvfXXnM9XK5GkFFffl3yR5KDkEu+PYTK6s99sHzXiDjyXernJd45ZFqSa5EOrTDwxEgijlJbbgPXLdkixRr1RbrTc1VIulZUSG2k6zlFEbeR22YVZulrLQc1Ozly1hZkuwVtnHLmueZjrnxJp7dAsnIjPe/No/J16mtOUdUOeOJQB9IiE+ke65DWQKLOfFpTZ0GzZMkS+u53v0sTJ06kXXbZhXbbbbfBn2/efPPNB9s1Go0oaAREQTOGYJFa3pborf8pO/3PwzyEk59P36eytQIL2ZJCIzapbKRPsgHJ1go/i5Ahe7ykGp33FkGaTxLJzvEX9fUWZhrBl/Ra7TTi6iGt3iINndfi4bHXKvw8tnId6L0nlyzSqa0BqL9HFnptyULtyz6WzVacJHhJp6ZPK2JQ32Zi6ckRba3VbK9aGFnIKcS8ukeaEGu28v8Q0jvvxhqqzM92oc6CZsaMGXTsscdSo9Gg559/npYvX07PPfccrV27lpYsWTLYrtFo0EfHfaMWnRJ+PDCzKwua+A7NKEe5aEokgLdLsW7DmcP+8zC+uXmKHAQPgSgP3l56bfmTnuf9Sl80Upn6wxdcFOdUNveF25f2L4+0n4cM8etoU0A+lOdSfVaMy/bSOa2QQISDxzCVp8lMY4liyu3kvktjIRErbj96n/ojkRYpj3KIuuQ3OmeRYUsH8oHLt+Zc+prPCUmWlkc8np6cS78Uzn9RiY8Zsl06NNst27ge5JO2XmoFh5YH3nnNz0sFgyY3hRSLqqRV8wmt6yMBpAv97/be/B1L6NTipdXYZZdd6MMf/jB96EMfolmzZtGFF15Id911F33nO99pt2ldg1FT0MybN48ajcbgMdaBNlONuPFNXZJXtuWERLqzx89xm9LrKaxzUnFh3Z3jpF0qJJDvkj8IXJ9E4pEeSQ4qjFL5SAfa0KUiV8oPqT23UYuDBhRfKW/R2KW6uN+aHVLxKpFCKVZaUcBt5j57iiSNGHsK1FQG6sOJuTQPuF0oxp65LK0lqT0oHtxGtI7wQoP3K38GtrdnvviLStwnqSjgc4vHTSLXaRy03EI+egojtAajvlJRll6Tco4DraW8bY4fXDaHZ59qB6SxR+ekfRDFUUO7fU6Bfhghd9w7yZ+RxLvf/W5avnz5sPPvete72mBNl6IYpSCiOJKjt2f+kNflwa/zv6i/JVOTIclJ+1i2IVmSXi4XnZP0IXssWzQ9Wuw0e61rmn0eOWhMPDkjyZSu5/TPHS9JvjZ+OfHV7LBir13XcteKoRaXnHmcM5Ye+yxZ2hySDq8+rQ16Lcnw2O0dV++4WOuE55DWlSpjXyXuufY227cZfVXk16XPmkcj4VunHZ6YdNqxdu3a2jjrSSedVBx66KHFjTfeWDzyyCPF6tWrRW570LhvtPQg6s7SYNQ8oQnokO4A94I7xL3GnT1LpnaX1bpjyNtpbfh1SS86h9Ar3NVF7ZBflu7eHvnOK7+e+pPaUvZFT2u4XGlsJds1n9O23FZuW6pLsoEf3lzR7nxKcUFty9fIdiQntSO1TRpvbWylMeLneKzSJwJcLvJPsgXZxOcRb6fZye3j9mhzIG2HnqjxnLPmAY8dl8X18SdEXAaSyX2SxkPLDWk98qxTyEYuP5WlzR1p/vK4oLkqzQ1JtuWDJLcuaLZI0NaPXL1eOVJeefcj6VyVNrn9c2V621eJe7dhxowZdOSRR9LLL79MV155JR155JH0d3/3d/SP//iP7TataxAFzSiFtvhppA7BIqNcDrcBkQVECDyFiPSYXiMK0kZeXkNE0UMe0/MSUUPwFH7Wa4mkaKREOsd1I0KdvpbIXGqXdE6KsSd3OFnzkNj0NSIFvBDUckuKj2RD6iPygYPr88YS5SnSiQgvKkS0GPLXqf6qZAblFbc71ctJtUS+tbkhFSkoP1CRwO3XIBVrfA3NgUb+myV/0twr//I48nzia6tnXZbWCGleafuOBGnNaiU8+4EHPIf4NemcFptm7fLutV4Z2j49mvHTn/6U+vv7ab/99qOjjjqKzjnnHPr2t79N99xzD5155pn03/7bfxvWZ6BRtPToVkRBM0ohLQ7S5szfa6RLI2hogUIkUCIrqW5ONtO+vC0iR8h2RJxRe4nY8E1RIp2IKCIig+KHYoV8Rf4hW1KdvD8ii6lejQRoxB2Nk0RWeYHE7dTIK7ouQRozqQBENkuxkOKg6UOykG+WTFSIoRgicoliYtmE+mrzh685PI5obqH26H2qj8tDvkj9eK5J+YniYo0n0tlsISIVX3w98xQEaDz4/EcxQLmZS3L5Oqj5JO1dI1mg5CKn4NKQK0Pa41uJKn56bgaMdnznO9+hdevW0XHHHUfnn38+3XnnnfSrX/2KBgYG6M/+7M/o8MMPb7eJ3YN2f+atVaAO+Hxlpxy9wueqe52fD9ba9Qqf/+fnesFns1E/rgfpls4jPfyQZGv2Snq8vkk+SDJRH8l3jw+SbslGbyw84yHFAPWX5Gu+a+PrySVNR05stVhymZIeK9c9MbTs9sy7Rw46A5635qiW01rMpDH35oFkS27+56wT3thYflg5miNbu54be2tNq5IfdfiV63Mz8prVP9JyR9LXdh2d5mOd36H5xS9+UXzrW98qzj777GLGjBnFgQceWMyYMaM4++yzh3HbD21yS0sPou4sDeIJzRiAdDcL3Z3tFe6Spe2kfp672Z47ppJebheyKbXFc6c57d+b3AVET2K4j0iudJeZx1U6x/1IfU/viPJrvB23NbVFu4sq9Ucx4zahmPAckfRKfmt2SrahMebjiPzj8eXytNh4nhpwHZ67j9we7ifK//J6eV6am2m79PUBS7ca5jtCOv5IhzQntPUGvUbzX8oX9JRG8yEX0tqHXnuf6mhrC5dZvkdjqMVH6s/ziPdH/kq5jl6jPNfG2HqioeWLNJ9RP83+KtDGr1l45Xpi54G2ttaFqrJG8xObv/7rv6ajjz6a5s2bR7fffjstXryYvvzlL9P73ve+YW1bXal1LdpdUbUK1AHVezcc0h2PXuddSvRau4siyeU6kEyrL9eP/mq+eP3xxobr1Gy0/KgyTp4x1mxHcZb0SD5K46bFxTNeWh9PPLVxRXo1eVK+eOVr9ltjY80pbx575qtnfmjz1erj0WH5YI2/lvNa/mv5YM0nzxzV9FpjY82JnPG28kvLM8sXj9wqcnLyN47WHmMt3nU+ofnZz35WXHHFFUVRFEV/f3/x4x//uPjnf/7nYmBgYBi3PXCTW1p6EHVnaRBPaMYweo3PLqevrTvQvE95ncv2PCVJ+/DPc6fn077c9tQ3rR+/G4+erGh3tPkTE+kumfQkQvIBxYaf5/3RXen0L3qdvpfuZkpPelBbyW/pvPR0B+lFetA4pnJQPKS7z+gpigRrrLXrCOgpBppbKGd4H/RUKG2n2Y/mX6rP8onHEMVWW3fQ/EJjll7n/dB7ZCP3mUPKFWm+ST6n/aQnFtK4am2lWHB703Pp+ErrgDRmUl54ckKan9wn6T33F/nvgbVe8bZ1IVdWnbpHEnU+QenWGFTFN77xDfrIRz5CREQ33HADLV68mO666y669dZbh7UdoKKlR7difLsNCLQPiCQQDScK1kaC2qJ+iBTxTc37sQtuDyI/Wnv03pKjbcioDXotFQpcHiJrSL9F5DykmpNL3kciRch23p7H00uIvaTNO068HSJi3H/JJ0TmELFFbTk8+SoReykvpBhovktt03M5+WcVdSinUt288ELykUytGEYFnTSHUXHA7UNjj3zQYo3acFv5/OH2crtR4VSee2HtQtpm4krorybbKh60HOZzn/uWrileG5F+aa/SxlObf9556UEu0W+2MMixrWqfKjpy0ErZnYhGo0HTpk2j9evX05IlS+jWW2+lzTbbjL7whS/QzJkz221eVyAKmsAwWCQMtZVeSyRDIoCIBGnEUiMiEuHTNmRPcSORNUQ6OTRyx+UioowInZfsSgUU7yP5L5F65EcqQ/JDIsS88LHIIIKUM1rRIhVGWnHCbUQyOBHzkD8pRxFp1sichnR8+DnJD0/ea+c9hSLvq+W+VESk5zzrmadQQO21GPP8twpIKa5Ij6QXxSydT5O2IurtGS7DM+aonbb+ar5KPvX2zKdtJubdUJLeW/rT93WS+WaKibqKhCoy6iy6Wl3sjEYURUEbNmygn/70p7T33nvT1ltvTURvFToc3fwUpZWIj5wFIFb3TXETohIa2eJtpI2ay0eyNTLFr3EyiWSX/Ti5s/po9iNZnoKAkxDL3lSGRRbTGGqEnJMwrQBJbeOFiHTOIsUodlLsuR88blIsUayqFH9InzTOVn5xfWiMJVtSm3is0XhIOrnN0jUpPyX5aG2Q8hP52Uxuc3lSoSwVsKX8F9YuVPtIuZe+luYtlyXFS9KP+kprLRojaSxTmZ5iBtnpXcfT89paI42jVYyifQjZ6IF338qVJ8V3pNCMTm39bBbtiMVI42/+5m9o1qxZdOONN9InP/lJIiJatWoV9ff3t9my7kE8oQlAbDNx5bBzaEPzFDWoP9q0U2h3qzhJ0IoaZKdEniRCiHQjQoD8Qz7mFEZctkTGLZKaQhtHK478NR8LpKsq0DhpY5i2keIktUVttL6S31ocpTGSCiBUKEnzDfms5QXykduk2cKJp2c+S+/T3Pba6CGpkj9lf6nAlHLirTVRf1Kk2Y3mmRZDaUy1+W/lmjZvtH7pNb4WeeY48ksrWpAtUl+pCPLM3/R6zrzJaVcXrLGtA17Znj1Iyt9UlxZDvp/WgZEesxzMnDmT/vzP/5ze+c530s4770xERJdddhkdeuihbbasi9DuXyVoFagDfgFjtBy9Gb9c0mv8yk6v8Gs36DxqJ12X+lj9y76oDZKr2YWuW/FoxmbJXmQP8hPJ8Y65pFeyTfPPstsacytu0nhbbTy5qPlpjZ0nntY4Sr5K5zw558k36/DMH2vO5Iy3FRuPz548095r80obs5xxrDL+nhy38sSajzm2evLRGjfLJ8t2T5604rDiWrV/Ff3NzE2rr2f8mo1ZJxx1/soZwrp16yC3/X83uamlB1F3lgbxkbMxjl7lLneJnDsa0pOIXuWOjnRnvzzQUx1JJz8n6UH+eXyW2qT2cjt62Z21tA3vx69pctJ2qY/ILskn9EQJ2YBsLPVKd9etJzZpf+l62o77ovXT7uamf3mbNJbSWEhzBtnDbZeefqG7legOuBVn6ckHstl7Lo21dVeej49njqBx5bmGbOAyUp1pvKXc4P7w8Snfv7B24WBb5CeXzWPHX6N21nrEYyrlNJf70P5rRVv4HJFsRWtTajePNbdTWrukdUODZ7+yrll2aXLQeui1Q9Nj7Tc5dnr1S/uEtr55n5hY8y49z/9aPmtrsRX/bsMWW2zRbhO6Co2iKIp2G9EKoC9SBfKANt5mZBHZH1GSdKMFEb3X5GjFCCI53FZ+HfmD7NPskghk2s6CZo8kR/KR28jban5L/mi+aTFDujR/kZ6cOOeMsWWDFV9NFr9mxYtfTyHJkHyW+mrzVdMrzSlPvPg1Kz5cP5LtWdOQHdoaYhEvayw9cZfGwoqT9VrzP2fN0EiwFA/LhvS6h9xK+jz2pOc0n7Q89hZFuX1zc0eSV9d+rq0pdcC77qTtU3jnOdKXi7Vr19KWW25ZqW+J3/zmN7Tjjju62zcaDfrbTRY2pdPCzzbOom4sDeIJTUAEX7S0OyOojSQLvef9rU2Fv0a2WgsV94fr1AocrovbxO2S4sXbI9ssf3k/bmv53iK2SEZKPvg5fh5tLMh2zS+JCCMyhPzVYmmNC889yV7J7lSHNVfS854NnJMYjSgiuRqQzRpS+byPNV/ReQ8pS22U5jryieevxy+ef561K+2P5iAaL42Qcmgx5LFBcwHFz8pRKd+1tRXFC/nK1yc+hpIc3keb92lspHUY9UGyNUjzUWrrOSddt+zS4uZZCzTb0d6M5pZ0XtKTGy8kH80Dj3xJx0jilFNOISKia6+9tq12jAq0+SNvLQN1wOcr47A/11y24e1Rn17lM9CaXqudZqekU5KJ2mj9rDapXo+tkp2oD9LjtQ21ldpw2Wj8vGMqjYVkv+aTZJtn3KXYoDGQYiRdrzqGnrmnjaOUD9JYeeaB1s6Kdc6Ye2NjxcOaH552njzTxsiSbemU4uZt58lHb457/PTmcE6uaeMsyfPkrGecPNescbdyrsqRs3bUJcc7ftp5668kyyPXmh/WUcd3aGbNmlVcfPHFxYwZM4rf/va3Lm77t5ssbOlB1J2lQcc+oXnppZfotNNOoxNPPJFmz55NTz75ZLtNCgjoBXdpe3vkO5wc0h3Q3h78xIffdeN6e5U700heKjeVUcrR5Ekye4U7V+iOMe/P75ZLd59QP8kGdFdb8h/J8N754j5y26RYojvqyAcpr9K+6dhpuYRsQePD73Sn/dBdZ/6azwdkP48Bb8vHAQHlkzWXPH2tpxSpbB576Q6r5B8HGnPtPbJfslGzwcpjHivpOsoTntPa3ORzndvNfUBAc0XLJ8kGKV+tuWXlK9cprYdaDHk7aZy0nETrrDZnUrulcSp1Srosv/kaZtmigcuRxi4Xln3WnqeNR3pdWxd5H2SXtYZa86hV+OpXv0rjx4+nV155hY455hiaPn06ffnLX6YbbriBHnzwQXr++eeH9RmgoqVHt6JjC5orr7ySPvaxj9GNN95IJ554Il111VXtNikgQCM7CIhYlO+lxS3dYFD/dJMov8TLZaKNBxFUibyic9LGLcnjMpA+KWbSwixtEmgzlDZ8tDlI9mtkRdtkrLb8vESSPNctYsnJMNef/kU6OClGRJPr5ucRJILE7UNjxe3n5ySyJhFvy1auz/IV5SrPM2lc0BrD891jp1Y8cHtKf9L/k8siqmkcpGIBzSduo0ZurQIEQRoDaeyltYPnuTTmCJ7c8qy36Xneh4+vdE4iv7wNt03au1B7BE2vFceqRFubS83Md97eU9ho+zvSodliFTCS3Vqx5JlLdWPy5Ml06qmn0gc+8AH6p3/6J7rgggvogAMOoDfeeIN++MMf0he/+MURtaer0e5HRBJWrVpVbNy4sSiKonjppZeKo446Kqs/1fCINo76j96KH+NAbdJ2/C/vh65rclB/SR66pp3T/JRiY8mxfJJizK8h+yx9Uh+tvVcnaueJL+rrsdOyDelH4yS99uS6J/7ecckdC8kGKz9zczy3n9U/d/x4W2vccnRqh3ceaXZ5dGvnrXOaP541A8mxXls6NRnauEvrgzaelk+5MbJkeezx5E8rjpw8qNLfiqUWf23svXPF64d0tOJnm994440h70senHLb/cYvaOlB1LGlgYqOfULz9re/ncaNe8u8RYsW0cc//vE2WxSoAzl3h3qNu9W9wh263uSOkXRnt1e4C6fZKsmT7ohzO6X+yCduryY3fV36hvxKryGZZRz5NetuLbKf3/HiY5WOg/TExXqKosUS3dGVxgnlgpQjUr7k3l2V4sfjhvI6Pa/JtMaOy+H9pTzhsrX85Do98wL14/LR3dZUHnrN840/sZL85HqkNimsPJHyXWvveZIizWsug7dL+0pzhetEY4rs0GKH/JDs4+2l/mjeSOt32l7KYwRPfLhMa03meYvaWfuFtibnQlpnSvs0WzQ/UhmpLL7eSnZb4yXtGyjHUM5Ja3OrsWLFCvr85z9PBx98ME2fPp3mzp1Ljz322CAPDtho6882/8u//AvdeeedQ8719vbS+eefT0REAwMDdOWVV1J/fz+ddtppcGDnzZtH55577kiYG6gAvolKBEFaAKW26Ual9eVyOKQ+HpnWwqqRCwRUWEj+8TaSzajAQHolsoP8SvXn6OB2I/3aeURUNTKLfER9kV+SPM8GbUEjCsgXKac0e7S81vJMmp9afy6L67fmukTUvGuFFU8Lli5PHFJ7LJ8864ZFbnPO5YyzNabSa68dCJ55zl9rcdNsQaiSPzlrCerjsUUbtyp2es977eOyuLxm56WlG+0V3IbcvhyW/XX8bHOJU045hQ444ADaZ5996A9/+AM99thj9E//9E908skn03//7/99sF2j0aB9x99Yi04Jv+w/sSt/trljnytt3LixOPfcc4uFCxdW6k8j+Fg2Dv+jYnS+V3jUzl/39uCPESB5kj6vbakcdFTxQbJRk9dMG8k+1F/yUYu1NEYefZLd2thJuqwYIFukcUIxseyqMs7aOW0sNf2enNJyVfPfyidtLljz1euzJMfKf2ucpPzMkaPF3eO7Zoc3F7w+WDmg5ZRnPfLmiaZLyg9pnbL88Njksc8zLlK8PONv2W6NS06+5MRDy+2cvNLOeeLazByXxsRaO7Wjzo+cHXfcccPOvfTSS8WJJ544jNvuO/7Glh5EHVsaqOhYq2+//fbimmuuqdzfm5BxtOdohjRUlaf11Yic5xpvx+U22xYtttImqxEQyy8PsfCMkWdj8xCp3HGQNvvcTV8bFy1GXtIhxc5jr3dDrupzzjjm6JNiacXCkzdWPml+e4/envnF6r4pTfXPbVdlnbTaVl1HPeNbNb+889WT0541LDcnpDyVrnliocn0+FQln6rmlcfW3Gs59nvXTku2tF5WXSPqLGiOPfbYYv369fA857ZR0GB0rNXTp08vjjnmmOL4448fPF5//XV3/yrJGUd7jjoIh0Z0PH0kooXaW3rQQqmRYulalfbWQp4zHtImIpHnnDHxyM6V7yFbHpu0OFu2eMZB6+N5jXJC89mbB1qe5c6rZuZSlbysYw3J9VOLYTNyPeRRk2WtP1YsNbm5OW7ZmbOWWjo867O0HnvG0xqPKjnYzDyro09urubEo+5YecdTyx3P/pKzNtVZ0Nx8883FV77ylWLFihWD5379618Xs2bNGsZt/3L8DS09iDq2NFDRnVY70MxkiaMzD88iZvXh58vX1oavbcYesmvJzCWoaGH2khFrUfeQas12i8TyPpZ9Wowk2ShenlzRNjbPOGr5pMXdu4FKfnnJlpbrOXZZc0ayxzrvyX3PeHtiV2fbuvrk5myOHC0HveOX639urlpzDr225HrzXcvHKvnl0eOJhxQfaz23YqXNbS0eOX5Y8fDE17NneGTk7Cne/K+zoNm4cWNxww03FB/96EeL6dOnFzNmzCgOOeSQ4pe//OUwbhsFDUZ3Wu2AJxnjaO9RddNEcqzFSNugJBKbQwJRf02/d2Pw2K2RQEmf5bPHLslPyVdrQ8ohGlV9yokRincO0bHG0ZoPXtKREydv7ngOi6BoMbT0eezxkjHN3hx9Ofbk2taMf9ac03LKGkNrTubE0DPunph61mNt3uSsWVKb3Nzx+OFZr7SY59hhzX/v/PSuT552OTnnlS/5jeRVyc9W/Gzz+vXri8cff7x4+OGHi9WrV0Nu+xfjb2jpQdSdpUF3Wu1ATlLGMfJHHQSiqixrU/aQMq8dXoLh9cXaaKVzmqyq+qWNQNIvkakcu63+Ht+43TkEzjMOnrxBsdCImie3PNc8G7tnXkh5XGVuaDZYh9cub353ypG7Dlhja51vNs+q+uGxzcoLbe3xrH9ee6y5avkrzXnJT4/92jhauqzc8a4HnrXQuzbmjLG2nmvjI8Xdk6f8aEVB4+G2UdBgdKfVDlRJzjg686hCctK/mpwcYmq1zyFo0oKqLbRVSKMnrtLG4LHDIpM5cbJ8sQiAJ34e/VaeWDq9Y6HFW8qVKmQkZx54c2h13xR32zrIQ07fHLuqEpmROqraqOUomu9a/njGoRk7rdeWfu11jg9WH22OWNe8bTzvtbH0xM4zHp5xlvzxxlXS5Y1FzlhLc0LzwROvdhU07x9/fUsPou4sDeJ/7Al0HHod/yGW1LZsn/4t23E5SHb5vpTb24P/s8JUJteLbEH+8b+S7ZbM1EYrNuk1KR6Szl7wO//S/1mRxobr0v6/mlQH8sv7/zF4/m8BFLPyeqpfyrHyGs8zFAvkZ6lH+n9d+DluO9evjZ1kh3ae+5Rim4krXT5yuzx56p3Tmp1emVYeSbFZ3TfFbC/lDmqvtbPip/VBOZLOIf4+7a/9HyV8fnpiyWH5UuW8tJZrayzyA6071prM+2rtuF7JD2ucuexUhnTdWtc0mSi+KMapDWmuaePgmeOSLchWZAvvL+Wz14468dJLL42ovlGJdldUrQJl3imKo7OPXnBnSGrnlcfbS6+t/h5b0J0hTZfkb45M7b0VN65bu+6x3fJHayPp9IxBTvw1/Z64S7HgbT36rTz0+OwZH2/OevNF8zlHpnceV22fHtJPMXOZVX+y2ernHSMrdtaYWnPcM7aavZodnrkszYmc3PPmQpU+Wl9pneTtc+ejNY6etUizq+raWSXfpHH22uZZR6qMY846x486n9AcfPDBxdVXX12sWbPG5LbxhAajO612wJvUcYyuo46NoZm2ng0Ytecbu6bXIjtIpic+Xl3ovGeTQj5bJKYZkoCuWWREGxNpzKy88dpg2W7lNLLfQzys89o4e2XmtrFimiu/Sp8cm+o86iRrnvyS8l/KsSq2edc5iwwjed6c03LXWm+RLO/80tYAbc2zYmTFOGf8cvNPyq9m54N3XKzYoxzz9PfKLo86C5oXX3yxmD9/fnHYYYcVixYtKjZs2CBy273HX9fSg6g7S4PutNqBZiZVHKPrqIPM5G4izWyy1uYqEVck29pstMU7l2RoPno2Q0luLonS4qfZp8VYi7Pmh0efNL5V8jUnD71EyTsmKBZ1ER1PXJuxGR1Vn8g046tnblTVW2VMqviiEfG6cs2y01pLc+eQtp5aupFeyR5r/lt258QtJ7+8+1zOWHn2I+1vjr/ettL6LPVpxXdoVqxYUZx99tnFjBkziiVLlhQDAwPDuG0UNBjdabUDnkkax+g7vAtRer7qRq9tcpIcaUHOsSF3UdeuaxustOFVJSOeGCF/JHuqkL1mNmZro/MQhGbbaDHw9vHEz0OockiXd35U1VnlGAm5OXFt1p4cQofGJXcNq6rTklHFDk/Mqs4Va42Urnnj4ll7q8QiJz7aepeTZ3WND4p7lfXGqxeNp2etbuWPAjzzzDPFKaecUsyePbt4/PHHh3DbvcZf29KDqDtLg/hRgEBXYXXfFNeXLnuVL/fx/tKPCPT2yF9a9fyQgPRlSaTT+wVr5Bf3F/lXnvN+CdrbThsLZJtkYxoz9CV59GVa68u13I7UHv6lW+kLplLcU51W/qTnkB9WrqX9ka1Sv7S9lJ8SpC8lox8okPpaci0/kJ3NflFXWhesL3PnQPNLyiuUf157tPFE8tOcQ/PLird2PfVL+5K2JsOyw5qzErQ5nbZBX4RH6zmyz5pXkq3aGubZF6Trkk5rzZTyVdOtjTF6L9nHZaEfcZD0a3stWo/L6+hHHVAutBL/9m//Rj/+8Y/puuuuo6985St09tln0/Lly2nTTTelyy67jC6++GLasGFDS23oerS7omoVyHEHIY7Re/A7LdI5rZ9HtqWTX0uvSzYimy27kV1clmSzJlOzX4qv1s8TW+RHjr05Nnn91f56dVoxQ2MljWFujtQ9Tp6j2f51HN78rtM3a23Jybmc63XY26p4NRvDTsglK2ZV5pA2x/lfad/QckVaK6w1oq7Y5+Rszl6l7bVWHknrphQ/TU+dT2gOO+yw4rTTTiuuv/764sEHHyyef/75YuPGjUVRFMUbb7xRXHLJJcX8+W/ZEE9oMLrTagfaveDFMXJHziJclWzk2pFLcL2+1Uk4LCJTlTTlbLxa/9zrnrjl+igRAcs+b2567PKQHY9PKH6SrVZO5pIToqHfSentma9+R8UzP3LmTZU87oTDk3e58nJkeNo2s054fW7Fmtes/Z5rVdYba16jdTVHrzT3vfMsZ14i+7zrOzqkWDVrt7V3S3rqLGjuvfde9Xp/f39xxBFHFERU/D/jr2npQdSdpUF3Wu1AHYtfHJ1/eDdF1F7ra5FI9FrT6SGx2jUPSdb0crlegizFweOL1qfq+HnibflgjU3OWGjjZ9mnbfY5/iN5uTmSM37ea3Ucnlh78q0snspraTFV9cv/uTndrO+5cls9NiN1VPE7p0+zcc7R4ZGds9dY7a35nrNO5I4P33u4fq8ea/+xfEN25NiIrpf96yho/u///b/Fj370o+Loo48u3nzzzWHXly5dOuQ1URQ0ErrTagfqXpDiGD2Hd8OqQiw1fZ523s1YW3w1f61FPddeT+ykWFoblXfj9m5Uls7c/Mjtk0NUvITFK8OKc66fGkmo+2gmD1f3TWnZeLbjkEjWWDm8BNw7hs3kVpV2Vv+cwsGzluXGJyenrLXV0isVN+i6JN+Ko2f/9sRC0ldHQfPiiy8WF110UXHggQcWH/nIR4rZs2cXl156afHDH/6w+NnPflZMnz59GLfdc9NrWnoQdWdp0J1WO9DM4hLH6D+8i3ku2fMsulIficRW2biszQXJt3RasfHETitccsiFtmE1O4aW31J8c2VY7b1jb/XRxs+TBzkkqxWHJ4ZEw5+0SLHjH33LkRlH+49m89GbT7zPSOS6V693bfW89ux72jqdY4u1Nlp6Peu8ts5JbaU1zypq6vzI2cUXX1y8+OKLxdKlS4ubbrqpmDt3bvHZz362uOCCC4Zx2yhoMLrTagdGeuGJo/MPq3iQ3mvykGzturcgSftbxDp3k0GLtkdfMxuh1q8qwdB8s2JrybPs8cTGm4/NxLmqXk/+VR2bVh7lExfN19V9U4pHDjoDni8PrW3VpzojceTmaTvs6Ubdne5Hrn1VCjBtT9BkeosQzz5j7VEe/7S9QLomrbnWXlhHQXPccccVr776avHQQw8VL7744rD/dwZx2z02vbqlB1F3lgbdabUD7Vqc4mjfYS1KvJ214HuKEKRbOpdTOFhFTG48uFyv/944aJuTND6SXbk+aBtqTry8+r3jn6tbi4/3tZb3Wg4062eVWI7UUTUPOsFX7xxqt53denSD/3XmrXfuW8Q/p/DQ7JLWc6mo0Nal3LXRsllrV2dB8/Of/7zo6+srZs+eXXz0ox8tDjnkkOLUU08trrnmmuL+++8vli9fPozbRkGD0Z1WO9DuRSiO9h6exch63+xml1OMNEv+Lf88hNVql0uEPRtj1Zh5iZ01hjlj5M2fVuRJXUValXkgyanLb48sbR5LX/jvBrIaR/1HK4rxdvqSsx552ubOa63IQNdTO6oUJLlyrMJH02/limV/3f+x5ptvvlksX768uP/++4trrrmmOPXUU4tDDjlkGLeNggajO60GOPfcc9u++MTRuYeH5KP2VQmkl6SiNlaB5Vm4q9gn2SKRyRySnbOJeja9ZvRZOnLGtyo5qMMmK/ZWfkmxbDb3c+LnuVZ+JCz9aNgjB51h2sU/XlZHQRhHHM0cVv55znvmrZe459ids/bmrCdSsSQVUJbvmi2WzZYtXEedBc369euLJ598cth5/hE0IiqmbXpVSw+i7iwNutNqB9q9cMXR/sO6k6Odr0qIPTbx/tpibL2vYkfVgqEZcm5tTlJfbZPO9bOOTb3Z9p6CQoqbNkaoXTO+tuLwfsFe8vXsTRbB8+jHANB3aCSbqs6jOLrvaNU417kfeHVI76V9r6p93nVUKzq09V8qICTZmgzNJmsN5bag8eE66ixozjvvvOL6668viqIonnrqqeIzn/lM8bnPfa5YtmzZMG4bBQ1Gd1rtQCsWrTi689AWUv66GdKq9bU2Fa9+70Kr6c0l+tYG6SkIvfbmxCB3DKw23rG3Nm5r3KQiJDe3rBh7X+f4XiWu6JpV3PT2DP+PN8tCZeF2t8D+ZZ+0mCn1dtqvlUUBNXaPVt10yF0PvH2lgiNHvqRTOtD1nFhqtlm6vP4T1VvQHH300cXGjRuLoiiKE044obj33nuLpUuXFqeddtowbrv7ple29CDqztKgO612oN2LVhydc9S5COf0ySl2LDk5C7c3Fjn9PQWhtOhXsbVZP6uOYTPjlBs/y98cIiL173TiLBUa5cfFHjnojCG/OvbIQWcUr8w9AvrNZUlFj2ZHp8crjvYcYzEv0L6ZEx9P0YD2EKmtNg7efcdb1GiFVnrUWdDMmjWrKIqiWL58eXHMMccMnv/85z8/jNtGQYMxjgKBUYzenvnw/LoNZw65zv9yGeXB+2tYt+HMwT5a+/RaqgP15W3L90g+94W3l2KD+q7bcOYwHcguyVapjaRX8scTd20skT3auZwxl3Kn7GvlAIp52p/nYKoD5ZClcySR2p76sM3ElUPOl6+3mbiSiIj2eeBCumvyWbRuw5n0/AkzaZ8HLqRdLn8/ERGt7psybB6XWN03ZVBGinUbzqTVfVOGnS/bdkq8Ap0Fa70cCTSrP7d/ul5bazaPD9pr+Pov7XuS3XydSNcLtG6Xh2RbenjXeWkNrgNbbLEF3XfffXTzzTfThz/84cHzfX19LdE3GtEoiqJotxGtQKPRaLcJgQ4GJ4ojTWRyyblnQ9HOSW247LRduhlI8rU2ll+IjI5mQmmNkxTDnPhXHY9WIy0wytePHHQG7faDO2ibiSvFAoTLIPpTEZQWJ1w2f43s0GwMBDg6dW61Ajn7k3f9sdaxEtJ7z007vqdwm5B86ZzUP9Wzdu1a2nLLLYXo5GH58uV0zTXX0Pbbb09z5syhnp4euuOOO+jRRx+lCy+8cLBdo9GgXTe9ohadEp5781TqxtIgCprAqEcOUZTatGoTa6YQQNeI8KJutfXoQ0UIKoakTSYHVkHWroJUs8sTnxJ1jXl63Ts+ObJbgZxCozzP/2p9OFIftT5jgagGOh/tysN2rKO5N8vQGirtDbwdvy4VK9o1LqvOggbh3nvvpb/8y7+kbbfddvBcFDQyoqAJjBl4n1pYbUYTtM1AK5y8RZW3T90xbuW45RTC/H2OXZ6nat1215jbyZ+wlHjkoDNonwcuJIS0z+q+KbT80E/RPg9cqH6ULMemQKAb4Hm6O9L2EMmFhtSeA61tqI32lMW60Satn94bQSXqLGiee+45+tGPfkQTJ06kXXbZhXbbbTeaPHkybbLJJkPaNRoNmrLp5bXolLDyzTlR0HQSoqAJeJD7iHykkVMIeDe4XF+tJ1zeJwjl6xJVn5p1MrQiJIX37qQmF+kuZY8EqozV6r4pNGmrWWbeSIVOFWjFUSDQClSZGyO19rVzjfXekMn5eJj1JB8VKlIhw4E+qpbKr7OgOfbYY2nPPfekRqNBzz//PK1cuZKKoqBddtmFrrvuusF2UdDIiIImMObQ7aS5TuSSYOsuXN2xbfWTnXYRj2aLD6vI7HT09synF9YuVL8/U/U7Lejjac3KDARywAn3SN9s6DZIHy/TXlsfP7Oe8HD96TXr47sl6ixoTjvtNLr00ksH3w8MDNB//ud/0vLly+nAAw8cPN9oNGjyppfVolPCf7z5P7uyoIlfOQuMOXjuoHcjUj88PpWLtnaHiqNsn773fEzNaxPSV+VaHfK1Ps3kTBr3nD7cBnS9UwmT9nEwqcBIvysj4ZGDzhiio7dn/hC5aV+tcAoE6kQ5v8v5yOd7b89813qdtuvUfcqyD51HvvObMdo6y9dQLk+SyWXwcUCyRmpN3XbbbenVV18dfD9u3DjaZZddhhQzAR3xhCYwJuEl4YH8Rd3bvtVPd+pA7ke9Rson78f9rP6jDenTGaKhxVI8nQl0KnLmo3eN6bY5rj2lkW7Y5MQCQfrYGT8/Et+hueyyy+jZZ5+lmTNn0l577UUTJ06E7eIJjYx4QhMYk8i9Qz6WkRsn9Floj9xmnlg0C+kunWdz5Hdi+TWPPo992ndxcuV2Y+6nvvGnKqv7pgwpWLaZuHJY8RLFTKAd8MzJnHXCe8OkG+c4kfx/maVrnecJF39Kg9Zo9HSJ62n2ibwXm222GW255ZZ04YUX0qGHHkrHH388nX/++XTnnXcOazvQKFp6dCviCU0gEKgd3XZ3sA6MhM/ez3aPRWjfnZHaBgKdim54gk1Un11oPUMxKNtI8bGe0ni+P5NC861VP9v8u9/9jpYvX07PPfccLV++nObNmzd4rdFo0KQJlyq9m8cLb5zWlU9ooqAJBAIdi5H60n7Vj8lV1Vdn/7pkjBVEMRPoZLTqI77NwNLRjA3W91w8H6ezCp8S6GYQKmC8H+mtu6B57LHH6Nlnn6Wtt96aPvzhD8M2jUaDdppwSW06EV5648tdWdDER84CgUDHouqX9j2o60cKmiUTzRZDdaJTv3icA+1L/lHMBDoduT+s0qpiJufHXuoqZqTiAl2T5PEnMekPNGgfVUvbePXViTvuuIMuuOACWrZsGS1YsICIiO65554hPxQQ0BEFTSAQGJOQPqvdTUg37To24m6NA9Hw/3CTnw8EugmIeFvftany3TzpvXct8D7Z9vTVbhjlPMXhxYv0vUde7CDdI1Xg3HfffbRgwQI666yzqKen5y3969bR9ddf31K9owlR0AQCgcAogLXxj3akP/O8zcSVgwQkfSLj+SnoQKBT4P2eXNW5r30fr04C7/3RA0/xYH0MTSpOpI8LSz+4wu1p9bpaFMWwj6/NmDGDnn766WFtN1LR0qNbEQVNIBAIBLoe6RMaiYDwpzhR2AQ6FZ5f4tSeQuTq8pyrA9pTIP7EWfuInVS0pHOffw9G+lUzyX/rCU6d2GGHHejBBx8cdn5gYKClekcToqAJBAKBQNcjfRJTEpSyYEH/Lw36aedAoFOBnmZoH5ttx/fhtJ9PLqE9QeHtPE92UAy0Hw3gbXhROFIFDMdJJ51ECxYsoLlz59L69etp6dKldOmll9J73vOeYW3jCQ1GFDSBQCAQqA2d9MMC6f9LQ/Sn/68mns4Eug3a0wxPe++X6j3npSdD3p89tp6yWODFCi9Ycn/EIH0SI9ndqidW5ROYSZMm0U033UTve9/7aOrUqbR48WLafPPN6ZRTTmmJ3tGIUfOzzfPmzaNzzz233WYEAoFAoAOBntIEAmMB6dMIIv8X+av+vy7SkxLUxnNes0+yubSN24k+gla1WKnjZ5s//vGP06677krTpk2jqVOn0tSpU2nSpEnifz3SaDToHRMubEqnhVVvnNGVP9s8agoajvh/aAKBQCBQgn9/JgqbwFiDp2CQPq6lkX9PEdPMNW6H9VPP6DxHHU9c6ihoVq5cSU899RQ988wztHTpUnrzzTdp8803p913352mTp1Ku+22G33wgx8cbB8FjYz4yFkgEAh0MLwfA6lT9mhE+Z2ZKGYCYw38Y1npOd5OKha0/6eFf1QLtdE+Xpb+tX6hjH+sDX3UTCq+OuF7RhxTpkyhQw45hMaPH08nnHAC3XzzzXT22WfTe97zHrrvvvvo7rvvbreJXYMoaAKBQKCD4f2MfJ2yRzP4/1MTCIxGSD85jIqbEtJ3S6RfUkMFi/SdGu27Ldb3XrSPvKGPkKVtUCHUiT9x/8QTT9Dhhx9OO++8M73//e+nE044gb72ta/RHnvsMaztxsZAS49uRXzkLBAIBAJjDvGdmsBoh/YTyVJ76dfBvH2q9q2iG8niH5EjomHn6ypk6vjIWYljjjmGLrnkEtp2222HnP/MZz5DixYtGnzfaDRo657za9EpYc2GfzA/crZmzRq66KKLaNWqVTR+/Hg6/fTTafLkyS21y0I8oQkEAoEORNWPQ3TCxyi6AfGzzYHRjDrWAe0jat7v1Gj2SD+5XL6XPhbHbZKKF2RPJz2VSXH44YfTF7/4Rfr2t79NTz75JL300ku0ZMkSWFj0U9HSw4MFCxbQfvvtRzfeeCMde+yxdO2119YdkmxEQRMIBAIdCO8dVf7e+6XYQCAwepF+jMtL4r0f+5K+a4N+XQydK89LxQoqllChg/5vGfR/ynQDDjvsMDr99NNp2bJldNZZZw0WCSeeeGK7TYN4+OGH6cADDyQion322YdWrFhBfX197TWqGKUgojjiiCOOOOKII444Mo/envmV2qXve3vmD77nf62+/HzaXzo0va041q5d2zIO29fXB8/vv//+LR/7Bm2t2vbmm28Whx122JBzxx9/fPHiiy/W5n8VjKdAIBAIdCVyPyMfCAQCHlR9qqP955QlpCc82k8/S9+P4f2q+tFp6OnpgecfeuihEbYEo2AfhSuKgsaNa++HvuIjZ4FAINAlQBu552MV2ufQA4FAoE7wn2GWvsOifc+G97XWLOvX1GLNqw/jx4+nCRMm0B//+EciIhoYGKDVq1fT29/+9rbaFQVNIBAIdAmkjdr64qz1/zEEAoFAXeDFi7b+oJ9W5t//8f6/MtIPCaC2geaw77770oMPPkhERL/4xS/ove99r/hUaaQQP9scCAQCXQz006fWT5O24idMA4HA2IJ3/fA8HbF+nQx9vFb66FmObc2gzp9t7jasWrWKzj//fFq9ejVtscUWNHfuXNpxxx3balMUNIFAIDAKoP2MaiAQCLQKVf//mhLSTzZLHzWzvk8zUjdqxnJB04mIgiYQCAQCgUAg0BJYX/qXftpZOsevaT8m0EpEQdNZiIImEAgEAoFAINASoIKmhHRO+lis9p92jvTHaKOg6SzEjwIEAoFAIBAIBFqC9Ev+6D+85L9QxosZ9IRG+sn6+I+Fxy7iCU0gEAgEAoFAYMQhPYGR/nYS4glNZ2HUFjT77rsv/eu//mu7zQgEAoGuwPvf/3569NFH221GIBAIdAWuvPJKOvnkk9ttRuD/x6gtaCQ0Go1h/8PpWJbdjTaH7JDdTrkhO2S3W3Y32hyyQ3Y75bZadqD9iO/QBAKBQCAQCAQCga5FFDSBQCAQCAQCgUCgazHmCppzzz233SZUQqvsbmU8ulV2K9GtMelW2a1Ct8ajW2W3Et0Yk260udXo1ph0q+xWIeIRqIox9x2aViI+nzk6EOM4OhDjOHoQYzk6EOM4ehBjGeg0jLknNK1EVP+jAzGOowMxjqMHMZajAzGOowcxloFOQzyhCQQCgUAgEAgEAl2LeEITCAQCgUAgEAgEuhZR0AQCgUAgEAgEAoGuRRQ0gUAgEAgEAoFAoGsRBU0gEAgEAoFAIBDoWoxvtwGjFf/7f/9vuvTSS+mBBx5otymBCtiwYQNddtlltGLFCtq4cSNNnz6dDjnkkHabFXBg48aNdN1119HTTz9Nb7zxBs2cOZP+9m//tt1mBSrg4YcfpptvvpmKoqCtttqK5s6dS+94xzvabVagIl544QU68cQT6aKLLqL3ve997TYnUAFPPPEEXX311UREtPvuu9Npp51Gm2yySZutCgTiCU1L8Pvf/55+/OMf0+abb95uUwIVcdddd9HEiRNp4cKFdMUVV9A3vvENWrNmTbvNCjjw4IMP0muvvUY33HADXXzxxXTDDTdQX19fu80KZOKNN96giy66iM4991xasGAB7bnnnrRo0aJ2mxWoiIGBAbryyitp5513brcpgYoYGBigSy+9lM466yxauHAhTZgwgZYvX95uswIBIoqCpiW48sor6e///u+p0Wi025RARRx22GE0e/ZsIiLacsstaeLEibRhw4Y2WxXw4F//9V/pwAMPJCKid7zjHTR16lR64okn2mxVIBfjx4+nG2+8kXbYYQciItpuu+3oj3/8Y5utClTF9773Pdpvv/1ou+22a7cpgYp46qmnaNKkSYNF6amnnkrTpk1rs1WBwFuIgqZm3HvvvbTbbrvR1KlT221KoAlsscUWtNlmmxHRW3f8J0+eHBtxl2DVqlVDPpb09re/nV555ZU2WhSognHjxg2OY19fH/3gBz+ggw8+uM1WBapg5cqV9Nhjj9H06dPbbUqgCfz2t7+lLbfcki6++GKaM2cOXX/99dTf399uswIBIorv0FTCv/zLv9Cdd9455Fxvby+dcsop9M///M908cUXt8myQA5efvllmj9//rDzZ5555uBd4fvvv5/uvfde2C7QmeBPRouioHHj4t5Nt2LNmjV0zjnn0Cc+8Qnac889221OIBP9/f101VVX0emnnx6fWuhyvPHGG/Rv//ZvdPXVV9Nmm21G5513Hv3oRz+iQw89tN2mBQJR0FTBBz7wAfrABz4w7PzixYvptddeozlz5hAR0euvv05f+tKX6JJLLqGenp6RNjNgYIcddhj8ciPC4sWL6Ze//CVdfPHFg09rAp2Pd77znfTKK6/QrrvuSkRvfadtv/32a7NVgSpYs2YNnX766fS5z32O/uZv/qbd5gQq4N///d/pD3/4w+BNoRdffJFefPFFmjNnTvwwQJfhne98J+222260xRZbEBHRvvvuS7/+9a/bbFUg8BaioKkRhx9+OB1++OFD3muEOdC5eOqpp+ihhx6iSy+9lCZMmNBucwIZ+Ou//mtasmQJ/dVf/RX97ne/oxUrVtBee+3VbrMCFXDBBRfQMcccE8VMF2OPPfagb37zm4PvzznnHDryyCOjmOlC7LXXXnTDDTfQmjVraOutt6bHH388Pl4f6BhEQRMIAHz/+9+n3/3ud/SFL3xh8NxJJ51Ee++9dxutCniw//7702OPPUbHHnssbbLJJnTyySfTpptu2m6zAplYuXIl/epXv6JXXnmFbr31ViIimjRpEp1zzjntNSwQGKPYbLPN6KSTTqKvfvWr1N/fT7vuuit97GMfa7dZgQARETWKoijabUQgEAgEAoFAIBAIVEF8UzYQCAQCgUAgEAh0LaKgCQQCgUAgEAgEAl2LKGgCgUAgEAgEAoFA1yIKmkAgEAgEAoFAINC1iIImEAgEAoFAIBAIdC2ioAkEAoFAIBAIBAJdiyhoAoFAIBAIBAKBQNci/mPNQCAQGON49NFH6eabb6aXX36Ztt12WzrttNPifwAPBAKBQNcgntAEAoHAGMYzzzxDX//612n27Nl0991306GHHkoXXHBBu80KBAKBQMCNKGgCgUBgDOOWW26hz3zmM/Rnf/ZnRET0oQ99iF544QV67bXX2mxZIBAIBAI+REETCAQCYxSrV6+mJ554gg488MDBcwMDA0REVBRFu8wKBAKBQCALUdAEAoHAGMWyZctohx12oG222Wbw3G9+8xuaMGEC9fb2ttGyQCAQCAT8iIImEAgExih+//vf0w477DDk3JNPPknvfe97ady42B4CgUAg0B2IHSsQCATGKHp6eqjRaAw5t2TJkiEfQQsEAoFAoNMRBU0gEAiMUey55570zDPP0Msvv0xvvvkm3XrrrbRx40b64Ac/2G7TAoFAIBBwo1HENz8DgUBgzOLee++l2267jTZu3Ejvf//7afbs2bT11lu326xAIBAIBNyIgiYQCAQCgUAgEAh0LeIjZ4FAIBAIBAKBQKBrEQVNIBAIBAKBQCAQ6FpEQRMIBAKBQCAQCAS6FlHQBAKBQCAQCAQCga5FFDSBQCAQCAQCgUCgaxEFTSAQCAQCgUAgEOha/H/1IoMnN1A0tAAAAABJRU5ErkJggg==", "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show small and large nuclei outliers within the data\n", "plot_hvplot_scatter(\n", " embeddings=embeddings_with_outliers,\n", " title=f\"UMAP of JUMP large nuclei outliers within {example_plate}\",\n", " filename=(plot_image := f\"./images/umap_large_nuclei_outliers_{example_plate}.png\"),\n", " color_dataframe=df_features_with_cqc_outlier_data,\n", " color_column=\"cqc.large_nuclei.is_outlier\",\n", " clabel=\"density of single cells classified as outliers\",\n", ")\n", "# conserve filespace by displaying export instead of dynamic plot\n", "Image(plot_image)" ] }, { "cell_type": "code", "execution_count": 28, "id": "060fe0e2-acab-41dd-9990-7db763496843", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Metadata_ImageNumberImage_Metadata_RowImage_Metadata_SiteMetadata_ObjectNumberMetadata_ObjectNumber_1Metadata_ObjectNumber_2Metadata_PlateMetadata_WellImage_TableNumberCytoplasm_AreaShape_Area...Nuclei_Texture_Variance_RNA_10_02_256Nuclei_Texture_Variance_RNA_10_03_256Nuclei_Texture_Variance_RNA_3_00_256Nuclei_Texture_Variance_RNA_3_01_256Nuclei_Texture_Variance_RNA_3_02_256Nuclei_Texture_Variance_RNA_3_03_256Nuclei_Texture_Variance_RNA_5_00_256Nuclei_Texture_Variance_RNA_5_01_256Nuclei_Texture_Variance_RNA_5_02_256Nuclei_Texture_Variance_RNA_5_03_256
0919363636BR00117012A011234277823015101247267054164817976898553816...17.71055718.99189018.44750618.36788718.70132317.70235617.61566217.58745617.95397317.533683
1818333BR00117012A011959702122324537759917826803215282063903042...8.1793836.6716377.1894787.5360117.1047087.2795667.3021987.5801787.3766247.773093
2515474747BR00117012A013272563011551011521931470885712653073814343...5.1631185.6407615.0208674.9921945.1845115.0084455.0557474.9641135.1629845.135272
3717505050BR00117012A01404648534312356676579271304652685804534114...7.2587917.6103466.6376046.8179556.7118806.8383806.8086157.0530986.8684366.922958
4717171717BR00117012A01404648534312356676579271304652685804532239...3.5511653.7465883.3516213.3061943.1728123.1659313.3548273.3333333.1707663.321053
..................................................................
1175083454167686868BR00117012P24533766286550352169093785878112837204565259...41.94483056.98397646.35211039.91075548.54859952.98399041.88931438.85550345.22118253.436383
1175093448161656565BR00117012P241150660260698387431892113808479207336162777...7.3266427.8535966.9978976.9805286.3500926.1862326.9054166.8967256.2231406.429991
1175103451164878787BR00117012P242028302135531844952968577046605143255543542...6.7819336.5324447.1272146.9053566.8955366.9373267.1912956.8936066.9804517.046681
1175113454167767676BR00117012P24533766286550352169093785878112837204562646...19.97367922.50648320.69011820.08129422.25942220.40896520.77429019.25376920.83872520.230426
1175123453166272727BR00117012P241913716237722554003312681160248553195125172...6.7971737.0685337.5073987.2803717.0283567.1287167.4015077.0718186.7565977.019160
\n", "

114138 rows × 5804 columns

\n", "
" ], "text/plain": [ " Metadata_ImageNumber Image_Metadata_Row Image_Metadata_Site \\\n", "0 9 1 9 \n", "1 8 1 8 \n", "2 5 1 5 \n", "3 7 1 7 \n", "4 7 1 7 \n", "... ... ... ... \n", "117508 3454 16 7 \n", "117509 3448 16 1 \n", "117510 3451 16 4 \n", "117511 3454 16 7 \n", "117512 3453 16 6 \n", "\n", " Metadata_ObjectNumber Metadata_ObjectNumber_1 \\\n", "0 36 36 \n", "1 3 3 \n", "2 47 47 \n", "3 50 50 \n", "4 17 17 \n", "... ... ... \n", "117508 68 68 \n", "117509 65 65 \n", "117510 87 87 \n", "117511 76 76 \n", "117512 27 27 \n", "\n", " Metadata_ObjectNumber_2 Metadata_Plate Metadata_Well \\\n", "0 36 BR00117012 A01 \n", "1 3 BR00117012 A01 \n", "2 47 BR00117012 A01 \n", "3 50 BR00117012 A01 \n", "4 17 BR00117012 A01 \n", "... ... ... ... \n", "117508 68 BR00117012 P24 \n", "117509 65 BR00117012 P24 \n", "117510 87 BR00117012 P24 \n", "117511 76 BR00117012 P24 \n", "117512 27 BR00117012 P24 \n", "\n", " Image_TableNumber Cytoplasm_AreaShape_Area \\\n", "0 123427782301510124726705416481797689855 3816 \n", "1 195970212232453775991782680321528206390 3042 \n", "2 327256301155101152193147088571265307381 4343 \n", "3 40464853431235667657927130465268580453 4114 \n", "4 40464853431235667657927130465268580453 2239 \n", "... ... ... \n", "117508 53376628655035216909378587811283720456 5259 \n", "117509 115066026069838743189211380847920733616 2777 \n", "117510 202830213553184495296857704660514325554 3542 \n", "117511 53376628655035216909378587811283720456 2646 \n", "117512 191371623772255400331268116024855319512 5172 \n", "\n", " ... Nuclei_Texture_Variance_RNA_10_02_256 \\\n", "0 ... 17.710557 \n", "1 ... 8.179383 \n", "2 ... 5.163118 \n", "3 ... 7.258791 \n", "4 ... 3.551165 \n", "... ... ... \n", "117508 ... 41.944830 \n", "117509 ... 7.326642 \n", "117510 ... 6.781933 \n", "117511 ... 19.973679 \n", "117512 ... 6.797173 \n", "\n", " Nuclei_Texture_Variance_RNA_10_03_256 \\\n", "0 18.991890 \n", "1 6.671637 \n", "2 5.640761 \n", "3 7.610346 \n", "4 3.746588 \n", "... ... \n", "117508 56.983976 \n", "117509 7.853596 \n", "117510 6.532444 \n", "117511 22.506483 \n", "117512 7.068533 \n", "\n", " Nuclei_Texture_Variance_RNA_3_00_256 \\\n", "0 18.447506 \n", "1 7.189478 \n", "2 5.020867 \n", "3 6.637604 \n", "4 3.351621 \n", "... ... \n", "117508 46.352110 \n", "117509 6.997897 \n", "117510 7.127214 \n", "117511 20.690118 \n", "117512 7.507398 \n", "\n", " Nuclei_Texture_Variance_RNA_3_01_256 \\\n", "0 18.367887 \n", "1 7.536011 \n", "2 4.992194 \n", "3 6.817955 \n", "4 3.306194 \n", "... ... \n", "117508 39.910755 \n", "117509 6.980528 \n", "117510 6.905356 \n", "117511 20.081294 \n", "117512 7.280371 \n", "\n", " Nuclei_Texture_Variance_RNA_3_02_256 \\\n", "0 18.701323 \n", "1 7.104708 \n", "2 5.184511 \n", "3 6.711880 \n", "4 3.172812 \n", "... ... \n", "117508 48.548599 \n", "117509 6.350092 \n", "117510 6.895536 \n", "117511 22.259422 \n", "117512 7.028356 \n", "\n", " Nuclei_Texture_Variance_RNA_3_03_256 \\\n", "0 17.702356 \n", "1 7.279566 \n", "2 5.008445 \n", "3 6.838380 \n", "4 3.165931 \n", "... ... \n", "117508 52.983990 \n", "117509 6.186232 \n", "117510 6.937326 \n", "117511 20.408965 \n", "117512 7.128716 \n", "\n", " Nuclei_Texture_Variance_RNA_5_00_256 \\\n", "0 17.615662 \n", "1 7.302198 \n", "2 5.055747 \n", "3 6.808615 \n", "4 3.354827 \n", "... ... \n", "117508 41.889314 \n", "117509 6.905416 \n", "117510 7.191295 \n", "117511 20.774290 \n", "117512 7.401507 \n", "\n", " Nuclei_Texture_Variance_RNA_5_01_256 \\\n", "0 17.587456 \n", "1 7.580178 \n", "2 4.964113 \n", "3 7.053098 \n", "4 3.333333 \n", "... ... \n", "117508 38.855503 \n", "117509 6.896725 \n", "117510 6.893606 \n", "117511 19.253769 \n", "117512 7.071818 \n", "\n", " Nuclei_Texture_Variance_RNA_5_02_256 \\\n", "0 17.953973 \n", "1 7.376624 \n", "2 5.162984 \n", "3 6.868436 \n", "4 3.170766 \n", "... ... \n", "117508 45.221182 \n", "117509 6.223140 \n", "117510 6.980451 \n", "117511 20.838725 \n", "117512 6.756597 \n", "\n", " Nuclei_Texture_Variance_RNA_5_03_256 \n", "0 17.533683 \n", "1 7.773093 \n", "2 5.135272 \n", "3 6.922958 \n", "4 3.321053 \n", "... ... \n", "117508 53.436383 \n", "117509 6.429991 \n", "117510 7.046681 \n", "117511 20.230426 \n", "117512 7.019160 \n", "\n", "[114138 rows x 5804 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# prepare data for normalization and feature selection\n", "# by removing cosmicqc and analaysis focused columns.\n", "df_for_normalize_and_feature_select_without_outliers = (\n", " df_features_with_cqc_outlier_data[\n", " # seek values which are false (not considered an outlier)\n", " ~df_features_with_cqc_outlier_data[\"analysis.included_at_least_one_outlier\"]\n", " ][\n", " # read feature names from cytotable output, which excludes\n", " # cosmicqc-added columns.\n", " parquet.read_schema(merged_single_cells).names\n", " ]\n", ")\n", "# show the modified column count\n", "len(df_for_normalize_and_feature_select_without_outliers.columns)\n", "\n", "df_for_normalize_and_feature_select_without_outliers" ] }, { "cell_type": "code", "execution_count": 29, "id": "777d6867-092e-49fa-9d9f-d8adc529a326", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Length of dataset with outliers: 117513\n", "Length of dataset without outliers: 114138\n" ] } ], "source": [ "print(\"Length of dataset with outliers: \", len(df_for_normalize_and_feature_select))\n", "print(\n", " \"Length of dataset without outliers: \",\n", " len(df_for_normalize_and_feature_select_without_outliers),\n", ")" ] }, { "cell_type": "code", "execution_count": 30, "id": "38c6d104-c18d-4a56-89e1-955cff414a08", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_annotated_wo_outliers = (\n", " f\"./{example_plate}_annotated_wo_outliers.parquet\"\n", ")\n", "\n", "# check if we already have annotated data\n", "if not pathlib.Path(parquet_pycytominer_annotated_wo_outliers).is_file():\n", " # annotate the data using pycytominer\n", " pycytominer.annotate(\n", " profiles=df_for_normalize_and_feature_select_without_outliers,\n", " # read the platemap directly from AWS S3 related location\n", " platemap=df_platemap_and_metadata,\n", " join_on=[\"Metadata_well_position\", \"Metadata_Well\"],\n", " output_file=parquet_pycytominer_annotated_wo_outliers,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 31, "id": "0ae2b201-6396-44fd-9059-d15dc9f93d7f", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_normalized_wo_outliers = (\n", " f\"./{example_plate}_normalized_wo_outliers.parquet\"\n", ")\n", "\n", "# check if we already have normalized data\n", "if not pathlib.Path(parquet_pycytominer_normalized_wo_outliers).is_file():\n", " # normalize the data using pcytominer\n", " df_pycytominer_normalized = pycytominer.normalize(\n", " profiles=parquet_pycytominer_annotated_wo_outliers,\n", " features=\"infer\",\n", " image_features=False,\n", " meta_features=\"infer\",\n", " method=\"standardize\",\n", " samples=\"Metadata_control_type == 'negcon'\",\n", " output_file=parquet_pycytominer_normalized_wo_outliers,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 32, "id": "a090b427-5550-46cd-85fd-7a6753a9e851", "metadata": {}, "outputs": [], "source": [ "parquet_pycytominer_feature_selected_wo_outliers = (\n", " f\"./{example_plate}_feature_select_wo_outliers.parquet\"\n", ")\n", "\n", "# check if we already have feature selected data\n", "if not pathlib.Path(parquet_pycytominer_feature_selected_wo_outliers).is_file():\n", " # feature select normalized data using pycytominer\n", " df_pycytominer_feature_selected = pycytominer.feature_select(\n", " profiles=parquet_pycytominer_normalized_wo_outliers,\n", " operation=[\n", " \"variance_threshold\",\n", " \"correlation_threshold\",\n", " \"blocklist\",\n", " \"drop_na_columns\",\n", " ],\n", " na_cutoff=0,\n", " output_file=parquet_pycytominer_feature_selected_wo_outliers,\n", " output_type=\"parquet\",\n", " )" ] }, { "cell_type": "code", "execution_count": 33, "id": "78d9b892-5b9e-4765-9381-a5dcf8ead08b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(114138, 2)\n" ] }, { "data": { "text/plain": [ "array([[-0.5193578 , 3.6437721 ],\n", " [ 0.13389692, 5.410578 ],\n", " [-0.40673473, 3.684243 ]], dtype=float32)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculate UMAP embeddings from data without coSMicQC-detected outliers\n", "embeddings_without_outliers = generate_umap_embeddings(\n", " df_input=pd.read_parquet(parquet_pycytominer_feature_selected_wo_outliers),\n", " cols_metadata_to_exclude=all_metadata_cols,\n", " random_state=0,\n", ")\n", "# show the shape and top values from the embeddings array\n", "print(embeddings_without_outliers.shape)\n", "embeddings_without_outliers[:3]" ] }, { "cell_type": "code", "execution_count": 34, "id": "c704ace2-c2eb-4caf-9955-8196d8f5c91f", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# plot UMAP for embeddings without outliers\n", "plot_hvplot_scatter(\n", " embeddings=embeddings_without_outliers,\n", " title=f\"UMAP of JUMP embeddings from {example_plate} (without erroneous outliers)\",\n", " filename=(\n", " image_without_all_outliers\n", " := f\"./images/umap_without_outliers_{example_plate}.png\"\n", " ),\n", " bgcolor=\"white\",\n", " cmap=px.colors.sequential.Greens[4:],\n", " clabel=\"density of single cells\",\n", ")\n", "# conserve filespace by displaying export instead of dynamic plot\n", "Image(image_without_all_outliers)" ] }, { "cell_type": "code", "execution_count": 35, "id": "8daad307-ee1c-47f8-a1f3-8a5e55d5b63f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \"UMAP\n", " \"UMAP\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# compare the UMAP images with and without outliers side by side\n", "HTML(\n", " f\"\"\"\n", "
\n", " \"UMAP\n", " \"UMAP\n", "
\n", " \"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": 36, "id": "73ac51c1-9e69-47fd-90d8-cb67e117fe8d", "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Image [0,1] (0_1 _color)" ] }, "execution_count": 36, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1536" } }, "output_type": "execute_result" } ], "source": [ "# concatenate embeddings together\n", "combined_embeddings = np.vstack((embeddings_with_outliers, embeddings_without_outliers))\n", "\n", "# Step 2: Create the labels array\n", "combined_labels = np.concatenate(\n", " [np.zeros(len(embeddings_with_outliers)), np.ones(len(embeddings_without_outliers))]\n", ")\n", "\n", "# visualize UMAP embeddings both with and without outliers together for comparison\n", "plot_hvplot_scatter(\n", " embeddings=combined_embeddings,\n", " title=f\"UMAP comparing JUMP embeddings from {example_plate} with and without erroneous outliers\",\n", " filename=f\"./images/umap_comparison_with_and_without_erroneous_outliers_{example_plate}.png\",\n", " color_dataframe=pd.DataFrame(\n", " combined_labels, columns=[\"combined_embedding_color_label\"]\n", " ),\n", " color_column=\"combined_embedding_color_label\",\n", " bgcolor=\"white\",\n", " cmap=[\n", " \"#e76f51\", # Darkest Orange\n", " \"#f4a261\", # Darker Orange\n", " \"#ffbb78\", # Light Orange\n", " \"#aec7e8\", # Light Blue\n", " \"#6baed6\", # Darker Blue\n", " \"#1f77b4\", # Darkest Blue\n", " ],\n", " clabel=\"density of single cells with (orange) and without outliers (blue)\",\n", ")" ] } ], "metadata": { "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 }