From 0ab5241cf22d4c337908da8f6e4edfd9ace74abf Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Wed, 18 Dec 2024 16:27:28 -0800
Subject: [PATCH 1/6] added example notebooks w/o streaming

---
 001259/README.md                    |   8 +
 001259/environment.yml              |  11 +
 001259/ephys_example_notebook.ipynb | 781 ++++++++++++++++++++++++++++
 001259/opto_example_notebook.ipynb  | 558 ++++++++++++++++++++
 001259/stream_nwbfile.py            |  36 ++
 5 files changed, 1394 insertions(+)
 create mode 100644 001259/README.md
 create mode 100644 001259/environment.yml
 create mode 100644 001259/ephys_example_notebook.ipynb
 create mode 100644 001259/opto_example_notebook.ipynb
 create mode 100644 001259/stream_nwbfile.py

diff --git a/001259/README.md b/001259/README.md
new file mode 100644
index 0000000..9b11b72
--- /dev/null
+++ b/001259/README.md
@@ -0,0 +1,8 @@
+# Example Sessions for Dandiset 001259
+
+This submission provides 2 notebooks showcasing example sessions for the Dandiset 001259.
+
+Each notebook provides an example of how to access the critical data and metadata from the 2 types of experiments in the dataset:
+
+- `ephys_example_notebook.ipynb` showcases one example session from the 001259 dataset containing operant behavior and concurrent OpenEphys recordings in primary auditory cortex (A1).
+- `optogenetics_example_notebook.ipynb` showcases one example session from the 001259 dataset containing operant behavior and concurrent optogenetic stimulation.
\ No newline at end of file
diff --git a/001259/environment.yml b/001259/environment.yml
new file mode 100644
index 0000000..9674997
--- /dev/null
+++ b/001259/environment.yml
@@ -0,0 +1,11 @@
+# run: conda env create --file environment.yml
+name: schneider_notebook_env
+channels:
+    - conda-forge
+dependencies:
+    - python==3.12
+    - ipywidgets
+    - pip
+    - pip:
+      - matplotlib
+      - lerner-lab-to-nwb @ git+https://github.com/catalystneuro/lerner-lab-to-nwb.git@main
\ No newline at end of file
diff --git a/001259/ephys_example_notebook.ipynb b/001259/ephys_example_notebook.ipynb
new file mode 100644
index 0000000..f7ad8e1
--- /dev/null
+++ b/001259/ephys_example_notebook.ipynb
@@ -0,0 +1,781 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">1fb4f618-1681-43b1-b541-cedcce1466cc</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:06:51.025092-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time\">starting_time: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].rate\">rate: </span><span class=\"field-value\">30000.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].description\">description: </span><span class=\"field-value\">Recording of AC neural responses in mice performing this behavioral task utilized dense 128-channel recording probes (Masmanidis Lab). These recording probes span a depth ~1mm, allowing for sampling of all layers of cortex. Electrophysiology data was recorded using OpenEphys Acquisition Board v2.4 and associated OpenEphys GUI software.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].conversion\">conversion: </span><span class=\"field-value\">1.9499999999999999e-07</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].data\"><b>data</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>rel_y</th>\n",
+       "      <th>rel_x</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Primary Auditory Cortex (A1)</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>CH16</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>valued_events_table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\">description: </span><span class=\"field-value\">Metadata about valued events.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "      <th>event_times</th>\n",
+       "      <th>label</th>\n",
+       "      <th>event_description</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, 24.0, 8.0, 12.0, 4.0, 20.0, 2.0, 16.0, ...]</td>\n",
+       "      <td>[2017.8980716115984, 2018.9518499695782, 2020.003974437991, 2021.0560366067878, 2022.1107962585747, 2023.1668696021022, 2024.2185851730928, 2025.2744759177485, 2026.3273306815604, 2027.3805422431042, 2028.433056109059, 2029.4864062697368, 2030.5382417399826, 2031.5908030056212, 2032.6432292721304, 2033.6949235432678, 2034.7478345067166, 2035.8035136527246, 2036.858632502247, 2037.911757564347, 2038.9679882068874, 2040.0206993715876, 2041.073034538676, 2042.125070507642, 2043.177316575283, 2044.231457230973, 2045.2869840779456, 2046.3397792421156, 2047.3954766880022, 2048.4485546503893, 2049.5003617208013, 2050.562680124963, 2051.614361396171, 2052.6670118612574, 2053.720565420659, 2054.7738006820596, 2055.8256480522045, 2056.877617321607, 2057.929639990643, 2058.98234325542, 2060.034222725374, 2061.0869674898563, 2062.1391423579566, 2063.191559724513, 2064.2458221794604, 2065.2981518465704, 2066.3499962167407, 2067.4024316831747, 2068.454694650721, 2069.5074332152535, 2070.559872881664, 2071.6122939482248, 2072.6642931174047, 2073.717558778641, 2074.769599947556, 2075.8216661163287, 2076.8742220820186, 2077.9281686389386, 2078.979919009713, 2080.032674574151, 2081.085022241139, 2082.140088191, 2083.195803236793, 2084.252941573597, 2085.3072098284943, 2086.363447670986, 2087.4153593407204, 2088.4670414119287, 2089.5229857562476, 2090.5753192233337, 2091.63422514901, 2092.6878982076373, 2093.7414865668234, 2094.7941654317433, 2095.846430199263, 2096.8987763662785, 2097.951348931874, 2099.0051972893925, 2100.0587607487496, 2101.1111402155466, 2102.1648806737676, 2103.216960242471, 2104.269369109074, 2105.3220012742813, 2106.373825644571, 2107.426654208552, 2108.4840629436444, 2109.5373144049495, 2110.592115056499, 2111.646091113225, 2112.6982058817125, 2113.750644748122, 2114.8060345959625, 2115.858254063772, 2116.9107193300124, 2117.96300249744, 2119.015053166309, 2120.0683294274427, 2121.1231206790385, 2122.175454446154, ...]</td>\n",
+       "      <td>tuningTones</td>\n",
+       "      <td>Times at which tuning tones are played to an animal after a behavioral experiment during ephys recording sessions. Integer values correspond to the frequency of the tuning tone played i.e. 2 for 2kHz, 4 for 4kHz, etc.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>group</th>\n",
+       "      <th>group_name</th>\n",
+       "      <th>channel_name</th>\n",
+       "      <th>rel_y</th>\n",
+       "      <th>rel_x</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Primary Auditory Cortex (A1)</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup</td>\n",
+       "      <td>CH16</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrode_groups\"><b>electrode_groups</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup']\"><b>ElectrodeGroup</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].description\">description: </span><span class=\"field-value\">ElectrodeGroup for all channels in the recording probe.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (A1)</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN']\"><b>MasmanidisSiliconMicroprobe128AxN</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1993.435631</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017.898072</td>\n",
+       "      <td>2164.295733</td>\n",
+       "      <td>[Passive Listening]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['trials']\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1192.054760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1192.917609</td>\n",
+       "      <td>1193.742364</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1194.216813</td>\n",
+       "      <td>1194.700726</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1196.710184</td>\n",
+       "      <td>1197.109255</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 483 more rows.</p></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".subject\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age\">age: </span><span class=\"field-value\">P12W/</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.description\">description: </span><span class=\"field-value\">12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.genotype\">genotype: </span><span class=\"field-value\">C57BL/6 or VGATChR2-EYFP</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.sex\">sex: </span><span class=\"field-value\">U</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.species\">species: </span><span class=\"field-value\">Mus musculus</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.subject_id\">subject_id: </span><span class=\"field-value\">m53</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.strain\">strain: </span><span class=\"field-value\">C57BL/6</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epochs\"><b>epochs</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".epochs\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".epochs\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1993.435631</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017.898072</td>\n",
+       "      <td>2164.295733</td>\n",
+       "      <td>[Passive Listening]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".trials\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".trials\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".trials\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1191.866913</td>\n",
+       "      <td>1192.054760</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1192.917609</td>\n",
+       "      <td>1193.742364</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1194.216813</td>\n",
+       "      <td>1194.700726</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1196.710184</td>\n",
+       "      <td>1197.109255</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 483 more rows.</p></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".units\"><b>units</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".units\">description: </span><span class=\"field-value\">Neural spikes were sorted offline using Kilosort 2.5 and Phy2 software and manually curated to ensure precise spike time acquisition.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".units\">waveform_unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".units\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spike_times</th>\n",
+       "      <th>unit_name</th>\n",
+       "      <th>sh</th>\n",
+       "      <th>n_spikes</th>\n",
+       "      <th>amp</th>\n",
+       "      <th>KSLabel</th>\n",
+       "      <th>original_cluster_id</th>\n",
+       "      <th>ContamPct</th>\n",
+       "      <th>fr</th>\n",
+       "      <th>ch</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>Amplitude</th>\n",
+       "      <th>depth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[3.5251333333333332, 3.5982666666666665, 3.615466666666667, 3.6355, 3.667133333333333, 3.673233333333333, 3.6804, 3.6820666666666666, 3.719533333333333, 3.7214, 3.7286333333333332, 3.7361, 3.7454666666666667, 3.7516666666666665, 3.7582333333333335, 3.7706333333333335, 3.7723333333333335, 3.7761666666666667, 3.7828, 3.789266666666667, 3.7908, 3.7944333333333335, 3.7975, 3.801433333333333, 3.8028333333333335, 3.8101, 3.8163, 3.8306666666666667, 3.8476, 3.8533333333333335, 3.8613333333333335, 3.8677333333333332, 3.8763, 3.8783333333333334, 3.8937333333333335, 3.8977333333333335, 3.9047666666666667, 3.9154666666666667, 3.9199, 3.9278, 3.9289666666666667, 3.9656, 3.9732666666666665, 3.9785, 3.9968333333333335, 4.253466666666666, 7.058, 7.0735, 7.076233333333334, 7.093833333333333, 7.0957333333333334, 7.117, 7.1205, 7.1216333333333335, 7.122833333333333, 7.1245666666666665, 7.1294, 7.131433333333334, 7.1349, 7.138366666666666, 7.144266666666667, 7.145533333333334, 7.151666666666666, 7.1612, 7.1795333333333335, 7.1817, 7.194966666666667, 7.198966666666666, 7.227566666666666, 7.2441, 7.253466666666666, 7.256, 7.270533333333334, 7.2833, 7.3402666666666665, 7.348166666666667, 7.385433333333333, 7.3868, 7.429133333333334, 7.468233333333333, 7.493733333333333, 7.500633333333333, 7.533966666666666, 7.572033333333334, 12.2529, 12.287133333333333, 12.332033333333333, 12.345333333333333, 12.362333333333334, 12.431366666666667, 12.447833333333334, 12.468133333333334, 12.476366666666667, 12.4821, 12.4863, 12.489433333333332, 12.491666666666667, 12.497033333333333, 12.509133333333333, 12.5132, ...]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25481</td>\n",
+       "      <td>115.930557</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.7</td>\n",
+       "      <td>9.435566</td>\n",
+       "      <td>127</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[3.5561333333333334, 3.5773, 3.5833666666666666, 3.5907, 3.6018333333333334, 3.607, 3.6252333333333335, 3.6276, 3.630333333333333, 3.636433333333333, 3.672, 3.6748, 3.689233333333333, 3.6984, 3.7062, 3.712666666666667, 3.7167666666666666, 3.722366666666667, 3.7267333333333332, 3.7298, 3.7329333333333334, 3.7546666666666666, 3.759533333333333, 3.761833333333333, 3.7788666666666666, 3.7809333333333335, 3.784, 3.8009, 3.804866666666667, 3.807, 3.8131, 3.8219333333333334, 3.8369, 3.8399, 3.8489333333333335, 3.872733333333333, 3.875, 3.8805666666666667, 3.9079333333333333, 3.9105666666666665, 3.9217333333333335, 3.923566666666667, 3.9467333333333334, 3.9625666666666666, 3.9875333333333334, 4.0126333333333335, 4.046733333333333, 7.04, 7.048466666666667, 7.0652333333333335, 7.066766666666667, 7.071133333333333, 7.075466666666666, 7.080466666666666, 7.083566666666667, 7.084633333333334, 7.0874, 7.0924, 7.099066666666666, 7.102333333333333, 7.105033333333333, 7.108833333333333, 7.1101, 7.111666666666666, 7.1146, 7.1165666666666665, 7.118266666666667, 7.124033333333333, 7.129833333333333, 7.1328, 7.147766666666667, 7.1531666666666665, 7.157033333333334, 7.1607666666666665, 7.163333333333333, 7.165933333333333, 7.1679, 7.170233333333333, 7.172533333333333, 7.173666666666667, 7.174633333333333, 7.177266666666666, 7.189033333333334, 7.202366666666666, 7.203533333333334, 7.2062, 7.208933333333333, 7.2103, 7.215233333333333, 7.221533333333333, 7.2261, 7.233466666666667, 7.2365, 7.240233333333333, 7.259133333333334, 7.263366666666666, 7.2657, 7.2673, 7.289833333333333, 7.308666666666666, ...]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>35443</td>\n",
+       "      <td>126.206665</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>1</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>13.124476</td>\n",
+       "      <td>127</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.12136666666666666, 0.8584333333333334, 1.2345333333333333, 3.9112, 5.9219333333333335, 9.9444, 12.450633333333334, 14.325033333333334, 14.3353, 14.5326, 18.6813, 23.658733333333334, 28.391166666666667, 37.837133333333334, 40.08743333333334, 41.8005, 41.903533333333336, 49.185966666666666, 56.691766666666666, 60.7654, 65.25846666666666, 66.39336666666667, 66.88976666666667, 70.34863333333334, 71.04976666666667, 73.94253333333333, 74.4071, 74.6132, 81.01176666666667, 82.65003333333334, 84.92176666666667, 85.42363333333333, 93.15026666666667, 97.1867, 97.19306666666667, 97.2139, 97.38656666666667, 97.42413333333333, 97.6594, 97.6653, 97.6856, 97.86983333333333, 98.4364, 98.4886, 98.82413333333334, 99.05023333333334, 99.52346666666666, 99.52543333333334, 100.2741, 100.2827, 101.2859, 102.92483333333334, 103.9318, 104.3477, 105.0469, 105.2011, 106.22393333333333, 106.25263333333334, 106.41993333333333, 108.4289, 109.03933333333333, 109.5766, 109.80056666666667, 109.87266666666666, 110.75076666666666, 110.92793333333333, 112.9565, 112.98053333333333, 113.53836666666666, 114.05953333333333, 114.5931, 115.62396666666666, 116.19866666666667, 116.86976666666666, 116.8932, 116.94683333333333, 118.0651, 118.55736666666667, 118.57543333333334, 118.8569, 118.9409, 119.22193333333334, 119.87903333333334, 119.9101, 119.91233333333334, 120.007, 120.09633333333333, 120.14376666666666, 120.9085, 120.93486666666666, 121.11313333333334, 121.12623333333333, 121.22286666666666, 121.22786666666667, 122.9742, 123.09116666666667, 123.1092, 123.1423, 123.53393333333334, 125.6168, ...]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12217</td>\n",
+       "      <td>116.071793</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>2</td>\n",
+       "      <td>49.8</td>\n",
+       "      <td>4.523932</td>\n",
+       "      <td>63</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[3.7224333333333335, 9.597633333333333, 12.7993, 12.8781, 16.0953, 27.6601, 27.8507, 28.9355, 29.759833333333333, 33.2042, 40.22743333333333, 41.89406666666667, 42.1827, 42.194966666666666, 42.94793333333333, 54.178066666666666, 56.0236, 59.0662, 60.64053333333333, 60.64763333333333, 60.76893333333334, 60.851, 61.1273, 63.092866666666666, 70.4433, 71.1334, 81.05643333333333, 81.0611, 81.08413333333333, 81.24146666666667, 81.48196666666666, 88.2934, 91.73243333333333, 96.785, 97.18263333333333, 97.19186666666667, 97.27646666666666, 97.50363333333334, 98.0959, 98.18673333333334, 98.71266666666666, 99.0543, 99.05646666666667, 99.5565, 99.56853333333333, 99.57726666666667, 102.64806666666667, 102.65586666666667, 102.89906666666667, 103.50246666666666, 103.6804, 104.14823333333334, 104.19613333333334, 104.62423333333334, 105.57153333333333, 105.73293333333334, 105.88566666666667, 105.99263333333333, 106.2862, 106.4737, 106.8489, 107.3072, 107.99803333333334, 108.38136666666666, 108.45516666666667, 109.1129, 109.27656666666667, 109.31046666666667, 109.3976, 109.81196666666666, 110.6496, 110.84356666666666, 110.96526666666666, 111.6309, 111.7842, 112.01633333333334, 112.08223333333333, 112.9762, 113.05623333333334, 113.51366666666667, 113.78466666666667, 114.62746666666666, 114.64606666666667, 114.65686666666667, 115.90943333333334, 116.88066666666667, 117.24156666666667, 118.57846666666667, 120.12076666666667, 120.17023333333333, 120.411, 121.76593333333334, 122.1256, 122.51696666666666, 123.53856666666667, 123.59466666666667, 125.3487, 126.73206666666667, 126.78536666666666, 127.2204, ...]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11622</td>\n",
+       "      <td>116.476280</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>3</td>\n",
+       "      <td>50.2</td>\n",
+       "      <td>4.303605</td>\n",
+       "      <td>63</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 231 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231029</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
+      ],
+      "text/plain": [
+       "root pynwb.file.NWBFile at 0x4945540784\n",
+       "Fields:\n",
+       "  acquisition: {\n",
+       "    ElectricalSeries <class 'pynwb.ecephys.ElectricalSeries'>,\n",
+       "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
+       "    video_camera_2 <class 'pynwb.image.ImageSeries'>\n",
+       "  }\n",
+       "  devices: {\n",
+       "    MasmanidisSiliconMicroprobe128AxN <class 'pynwb.device.Device'>,\n",
+       "    intrinsic_signal_optical_imaging_laser <class 'pynwb.device.Device'>,\n",
+       "    lickometer <class 'pynwb.device.Device'>,\n",
+       "    rotary_encoder <class 'pynwb.device.Device'>,\n",
+       "    two_photon_microscope <class 'pynwb.device.Device'>\n",
+       "  }\n",
+       "  electrode_groups: {\n",
+       "    ElectrodeGroup <class 'pynwb.ecephys.ElectrodeGroup'>\n",
+       "  }\n",
+       "  electrodes: electrodes <class 'hdmf.common.table.DynamicTable'>\n",
+       "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
+       "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 6, 51, 25092, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: 1fb4f618-1681-43b1-b541-cedcce1466cc\n",
+       "  institution: New York University\n",
+       "  intervals: {\n",
+       "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
+       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  }\n",
+       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (3,), type \"|O\">\n",
+       "  lab: Schneider\n",
+       "  processing: {\n",
+       "    behavior <class 'pynwb.base.ProcessingModule'>,\n",
+       "    intrinsic_signal_optical_imaging <class 'pynwb.base.ProcessingModule'>\n",
+       "  }\n",
+       "  session_description: Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).\n",
+       "  session_id: 231029\n",
+       "  session_start_time: 2023-10-29 16:56:01-04:00\n",
+       "  source_script: Created using NeuroConv v0.6.5\n",
+       "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
+       "  subject: subject pynwb.file.Subject at 0x4946217632\n",
+       "Fields:\n",
+       "  age: P12W/\n",
+       "  age__reference: birth\n",
+       "  description: 12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.\n",
+       "  genotype: C57BL/6 or VGATChR2-EYFP\n",
+       "  sex: U\n",
+       "  species: Mus musculus\n",
+       "  strain: C57BL/6\n",
+       "  subject_id: m53\n",
+       "\n",
+       "  timestamps_reference_time: 2023-10-29 16:56:01-04:00\n",
+       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  units: units <class 'pynwb.misc.Units'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231029.nwb'\n",
+    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
+    "nwbfile = io.read()\n",
+    "display(nwbfile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve Ephys and Behavioral Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ephys\n",
+    "electrical_series = np.asarray(nwbfile.acquisition[\"ElectricalSeries\"].data[:, 0])\n",
+    "fs = nwbfile.acquisition[\"ElectricalSeries\"].rate\n",
+    "raw_to_uV = nwbfile.acquisition[\"ElectricalSeries\"].conversion * 1e6\n",
+    "electrical_series_in_uV = electrical_series * raw_to_uV\n",
+    "electrical_series_timestamps = np.arange(0, len(electrical_series)) / fs\n",
+    "\n",
+    "# Sorted Units\n",
+    "units = nwbfile.units.to_dataframe()\n",
+    "good_units = units[units.quality==\"good\"]\n",
+    "\n",
+    "# Behavioral Events\n",
+    "tone_in = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneIN\"].timestamps)\n",
+    "tone_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneOUT\"].timestamps)\n",
+    "target_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"targetOUT\"].timestamps)\n",
+    "valve = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"valve\"].timestamps)\n",
+    "\n",
+    "# Behavioral Time Series\n",
+    "encoder_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].data)\n",
+    "encoder_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].timestamps)\n",
+    "lick_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].data)\n",
+    "lick_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].timestamps)\n",
+    "\n",
+    "# Behavioral Trials\n",
+    "trials = nwbfile.trials.to_dataframe()\n",
+    "example_rewarded_trial = trials[trials[\"rewarded\"] == True].iloc[0]\n",
+    "example_unrewarded_trial = trials[trials[\"rewarded\"] == False].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot an Example Rewarded Trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return math.isfinite(val)\n",
+      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  return np.asarray(x, float)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "example_trial = example_rewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 0\n",
+    "linelengths = 750\n",
+    "ylim = [-500, 500]\n",
+    "grace_time = 0.100\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "electrical_slice = slice(int(plot_start_time * fs), int(plot_end_time * fs))\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_electrical_series_timestamps = electrical_series_timestamps[electrical_slice] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "unit_masks, normalized_spike_times = [], []\n",
+    "for _, unit in good_units.iterrows():\n",
+    "    unit_mask = np.logical_and(unit.spike_times >= plot_start_time, unit.spike_times <= plot_end_time)\n",
+    "    spike_times = unit[\"spike_times\"][unit_mask] - trial_start_time\n",
+    "    unit_masks.append(unit_mask)\n",
+    "    normalized_spike_times.append(spike_times)\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Rewarded Trial\")\n",
+    "axs[0].plot(normalized_electrical_series_timestamps, electrical_series_in_uV[electrical_slice], color=\"k\", label=\"Electrical Series\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].eventplot(normalized_spike_times, colors=\"k\")\n",
+    "axs[1].set_yticks([])\n",
+    "axs[1].set_ylabel(\"'Good' Unit Spikes\")\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot an Example Unrewarded Trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "example_trial = example_unrewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 0\n",
+    "linelengths = 400\n",
+    "ylim = [-250, 250]\n",
+    "grace_time = 0.100\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "electrical_slice = slice(int(plot_start_time * fs), int(plot_end_time * fs))\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_electrical_series_timestamps = electrical_series_timestamps[electrical_slice] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "unit_masks, normalized_spike_times = [], []\n",
+    "for _, unit in good_units.iterrows():\n",
+    "    unit_mask = np.logical_and(unit.spike_times >= plot_start_time, unit.spike_times <= plot_end_time)\n",
+    "    spike_times = unit[\"spike_times\"][unit_mask] - trial_start_time\n",
+    "    unit_masks.append(unit_mask)\n",
+    "    normalized_spike_times.append(spike_times)\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Unrewarded Trial\")\n",
+    "axs[0].plot(normalized_electrical_series_timestamps, electrical_series_in_uV[electrical_slice], color=\"k\", label=\"Electrical Series\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].eventplot(normalized_spike_times, colors=\"k\")\n",
+    "axs[1].set_yticks([])\n",
+    "axs[1].set_ylabel(\"'Good' Unit Spikes\")\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "schneider_notebook_env",
+   "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/001259/opto_example_notebook.ipynb b/001259/opto_example_notebook.ipynb
new file mode 100644
index 0000000..c5b2c43
--- /dev/null
+++ b/001259/opto_example_notebook.ipynb
@@ -0,0 +1,558 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <style>\n",
+       "            .container-fields {\n",
+       "                font-family: \"Open Sans\", Arial, sans-serif;\n",
+       "            }\n",
+       "            .container-fields .field-value {\n",
+       "                color: #00788E;\n",
+       "            }\n",
+       "            .container-fields details > summary {\n",
+       "                cursor: pointer;\n",
+       "                display: list-item;\n",
+       "            }\n",
+       "            .container-fields details > summary:hover {\n",
+       "                color: #0A6EAA;\n",
+       "            }\n",
+       "        </style>\n",
+       "        \n",
+       "        <script>\n",
+       "            function copyToClipboard(text) {\n",
+       "                navigator.clipboard.writeText(text).then(function() {\n",
+       "                    console.log('Copied to clipboard: ' + text);\n",
+       "                }, function(err) {\n",
+       "                    console.error('Could not copy text: ', err);\n",
+       "                });\n",
+       "            }\n",
+       "\n",
+       "            document.addEventListener('DOMContentLoaded', function() {\n",
+       "                let fieldKeys = document.querySelectorAll('.container-fields .field-key');\n",
+       "                fieldKeys.forEach(function(fieldKey) {\n",
+       "                    fieldKey.addEventListener('click', function() {\n",
+       "                        let accessCode = fieldKey.getAttribute('title').replace('Access code: ', '');\n",
+       "                        copyToClipboard(accessCode);\n",
+       "                    });\n",
+       "                });\n",
+       "            });\n",
+       "        </script>\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">b6a37a08-f2ab-45af-b49b-225aa17c8012</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:07:21.778849-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".stimulus\"><b>stimulus</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series']\"><b>optogenetic_series</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].description\">description: </span><span class=\"field-value\">In optogenetic perturbation trials (~33% of trials), during each lever press, continuous wave stimulation of 473nm light (15-20mW) was delivered bilaterally over primary auditory cortex A1 (or secondary motor cortex, M2, as necessary using similar protocol - see Aim 2) to activate the terminals of ChR2 expressing neurons.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].unit\">unit: </span><span class=\"field-value\">watts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site\"><b>site</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site.device\"><b>device</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['optogenetic_stimulation_laser']\"><b>optogenetic_stimulation_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".ogen_sites\"><b>ogen_sites</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site']\"><b>optogenetic_stimulus_site</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>786.229029</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>trials</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['trials']\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['trials']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>126.368786</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>126.976659</td>\n",
+       "      <td>127.150010</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142823.197278</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>128.341690</td>\n",
+       "      <td>128.464307</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>128.784895</td>\n",
+       "      <td>129.004811</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142825.050398</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 433 more rows.</p></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".subject\"><b>subject</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age\">age: </span><span class=\"field-value\">P12W/</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.age__reference\">age__reference: </span><span class=\"field-value\">birth</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.description\">description: </span><span class=\"field-value\">12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.genotype\">genotype: </span><span class=\"field-value\">C57BL/6 or VGATChR2-EYFP</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.sex\">sex: </span><span class=\"field-value\">U</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.species\">species: </span><span class=\"field-value\">Mus musculus</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.subject_id\">subject_id: </span><span class=\"field-value\">m53</span></div><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".subject.strain\">strain: </span><span class=\"field-value\">C57BL/6</span></div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epochs\"><b>epochs</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".epochs\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".epochs\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>tags</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>786.229029</td>\n",
+       "      <td>[Active Behavior]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".trials\"><b>trials</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".trials\">description: </span><span class=\"field-value\">experimental trials</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".trials\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start_time</th>\n",
+       "      <th>stop_time</th>\n",
+       "      <th>rewarded</th>\n",
+       "      <th>time_reward_s</th>\n",
+       "      <th>opto_trial</th>\n",
+       "      <th>opto_time</th>\n",
+       "      <th>opto_time_end</th>\n",
+       "      <th>ITI_respect</th>\n",
+       "      <th>ThresholdVector</th>\n",
+       "      <th>endZone_ThresholdVector</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126.256702</td>\n",
+       "      <td>126.368786</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>126.976659</td>\n",
+       "      <td>127.150010</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142823.197278</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>128.341690</td>\n",
+       "      <td>128.464307</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>128.784895</td>\n",
+       "      <td>129.004811</td>\n",
+       "      <td>True</td>\n",
+       "      <td>142825.050398</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>... and 433 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231013</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
+      ],
+      "text/plain": [
+       "root pynwb.file.NWBFile at 0x4858229584\n",
+       "Fields:\n",
+       "  acquisition: {\n",
+       "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
+       "    video_camera_2 <class 'pynwb.image.ImageSeries'>\n",
+       "  }\n",
+       "  devices: {\n",
+       "    intrinsic_signal_optical_imaging_laser <class 'pynwb.device.Device'>,\n",
+       "    lickometer <class 'pynwb.device.Device'>,\n",
+       "    optogenetic_stimulation_laser <class 'pynwb.device.Device'>,\n",
+       "    rotary_encoder <class 'pynwb.device.Device'>,\n",
+       "    two_photon_microscope <class 'pynwb.device.Device'>\n",
+       "  }\n",
+       "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
+       "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 7, 21, 778849, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: b6a37a08-f2ab-45af-b49b-225aa17c8012\n",
+       "  institution: New York University\n",
+       "  intervals: {\n",
+       "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
+       "    trials <class 'pynwb.epoch.TimeIntervals'>\n",
+       "  }\n",
+       "  keywords: <StrDataset for HDF5 dataset \"keywords\": shape (3,), type \"|O\">\n",
+       "  lab: Schneider\n",
+       "  ogen_sites: {\n",
+       "    optogenetic_stimulus_site <class 'pynwb.ogen.OptogeneticStimulusSite'>\n",
+       "  }\n",
+       "  processing: {\n",
+       "    behavior <class 'pynwb.base.ProcessingModule'>,\n",
+       "    intrinsic_signal_optical_imaging <class 'pynwb.base.ProcessingModule'>\n",
+       "  }\n",
+       "  session_description: Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).\n",
+       "  session_id: 231013\n",
+       "  session_start_time: 2023-10-13 00:00:00-04:00\n",
+       "  source_script: Created using NeuroConv v0.6.5\n",
+       "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
+       "  stimulus: {\n",
+       "    optogenetic_series <class 'pynwb.ogen.OptogeneticSeries'>\n",
+       "  }\n",
+       "  subject: subject pynwb.file.Subject at 0x5028608960\n",
+       "Fields:\n",
+       "  age: P12W/\n",
+       "  age__reference: birth\n",
+       "  description: 12-week-old C57BL/6 or VGATChR2-EYFP mice were used for all behavioral, electrophysiology, and optogenetic experiments. In the VGAT-ChR2-EYFP mouse line, channelrhodopsin (ChR2) was coupled to the vesicular GABA transporter, inducing expression in GABAergic inhibitory neurons ubiquitously across cortex and allowing for real time optogenetic inhibition of brain regions of interest.\n",
+       "  genotype: C57BL/6 or VGATChR2-EYFP\n",
+       "  sex: U\n",
+       "  species: Mus musculus\n",
+       "  strain: C57BL/6\n",
+       "  subject_id: m53\n",
+       "\n",
+       "  timestamps_reference_time: 2023-10-13 00:00:00-04:00\n",
+       "  trials: trials <class 'pynwb.epoch.TimeIntervals'>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231013.nwb'\n",
+    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
+    "nwbfile = io.read()\n",
+    "display(nwbfile)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve Opto and Behavioral data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Opto\n",
+    "opto_stim_timestamps = np.asarray(nwbfile.stimulus[\"optogenetic_series\"].timestamps)\n",
+    "opto_stim_data = np.asarray(nwbfile.stimulus[\"optogenetic_series\"].data)\n",
+    "stim_power = 0.020\n",
+    "opto_onset_times = opto_stim_timestamps[opto_stim_data == stim_power]\n",
+    "opto_offset_times = opto_stim_timestamps[opto_stim_data == 0]\n",
+    "\n",
+    "# Behavioral Events\n",
+    "tone_in = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneIN\"].timestamps)\n",
+    "tone_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"toneOUT\"].timestamps)\n",
+    "target_out = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"targetOUT\"].timestamps)\n",
+    "valve = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"valve\"].timestamps)\n",
+    "\n",
+    "# Behavioral Time Series\n",
+    "encoder_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].data)\n",
+    "encoder_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"encoder\"].timestamps)\n",
+    "lick_data = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].data)\n",
+    "lick_timestamps = np.asarray(nwbfile.processing[\"behavior\"].data_interfaces[\"behavioral_time_series\"][\"lick\"].timestamps)\n",
+    "\n",
+    "# Behavioral Trials\n",
+    "trials = nwbfile.trials.to_dataframe()\n",
+    "trials = trials[trials.opto_trial]\n",
+    "example_rewarded_trial = trials[trials[\"rewarded\"] == True].iloc[0]\n",
+    "example_unrewarded_trial = trials[trials[\"rewarded\"] == False].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "example_trial = example_rewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 1\n",
+    "linelengths = 2\n",
+    "ylim = [-2, 2]\n",
+    "grace_time = 0.100\n",
+    "y = np.arange(-1, 1, 0.1)\n",
+    "alpha = 0.3\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "opto_onset_mask = (opto_onset_times >= plot_start_time) & (opto_onset_times <= plot_end_time)\n",
+    "opto_offset_mask = (opto_offset_times >= plot_start_time) & (opto_offset_times <= plot_end_time)\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_opto_onset_times = opto_onset_times[opto_onset_mask] - trial_start_time\n",
+    "normalized_opto_offset_times = opto_offset_times[opto_offset_mask] - trial_start_time\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Rewarded Trial\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "for i, (onset_time, offset_time) in enumerate(zip(normalized_opto_onset_times, normalized_opto_offset_times)):\n",
+    "    x1 = onset_time * np.ones(len(y))\n",
+    "    x2 = offset_time * np.ones(len(y))\n",
+    "    if i == 0:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha, label=\"Optogenetic Stimulation\")\n",
+    "    else:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha)\n",
+    "\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[2].legend()\n",
+    "\n",
+    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.0063527])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_opto_onset_times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.0845106])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalized_opto_offset_times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "start_time                    198.942435\n",
+       "stop_time                     200.023562\n",
+       "rewarded                            True\n",
+       "time_reward_s              142896.074106\n",
+       "opto_trial                          True\n",
+       "opto_time                  142894.990439\n",
+       "opto_time_end              142896.068597\n",
+       "ITI_respect                         True\n",
+       "ThresholdVector                     27.0\n",
+       "endZone_ThresholdVector             33.0\n",
+       "Name: 60, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "schneider_lab_to_nwb_env",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/001259/stream_nwbfile.py b/001259/stream_nwbfile.py
new file mode 100644
index 0000000..5208c0d
--- /dev/null
+++ b/001259/stream_nwbfile.py
@@ -0,0 +1,36 @@
+from pynwb import NWBHDF5IO
+from fsspec import filesystem
+from h5py import File
+from dandi.dandiapi import DandiAPIClient
+
+def stream_nwbfile(DANDISET_ID, file_path):
+    '''Stream NWB file from DANDI archive.
+    
+    Parameters
+    ----------
+    DANDISET_ID : str
+        Dandiset ID
+    file_path : str
+        Path to NWB file in DANDI archive
+    
+    Returns
+    -------
+    nwbfile : NWBFile
+        NWB file
+    io : NWBHDF5IO
+        NWB IO object (for closing)
+        
+    Notes
+    -----
+    The io object must be closed after use.
+    '''
+    with DandiAPIClient() as client:
+        client.dandi_authenticate()
+        asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path)
+        s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)
+    fs = filesystem("http")
+    file_system = fs.open(s3_url, "rb")
+    file = File(file_system, mode="r")
+    io = NWBHDF5IO(file=file, load_namespaces=True)
+    nwbfile = io.read()
+    return nwbfile, io
\ No newline at end of file

From f3b8b78def1d69e3694c54981896af92807848d6 Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Thu, 16 Jan 2025 11:23:14 -0800
Subject: [PATCH 2/6] fix stream_nwbfile

---
 001259/stream_nwbfile.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/001259/stream_nwbfile.py b/001259/stream_nwbfile.py
index 5208c0d..02bff64 100644
--- a/001259/stream_nwbfile.py
+++ b/001259/stream_nwbfile.py
@@ -1,6 +1,6 @@
 from pynwb import NWBHDF5IO
-from fsspec import filesystem
-from h5py import File
+import remfile
+import h5py
 from dandi.dandiapi import DandiAPIClient
 
 def stream_nwbfile(DANDISET_ID, file_path):
@@ -27,10 +27,9 @@ def stream_nwbfile(DANDISET_ID, file_path):
     with DandiAPIClient() as client:
         client.dandi_authenticate()
         asset = client.get_dandiset(DANDISET_ID, 'draft').get_asset_by_path(file_path)
-        s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)
-    fs = filesystem("http")
-    file_system = fs.open(s3_url, "rb")
-    file = File(file_system, mode="r")
+        s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
+    file_system = remfile.File(s3_url)
+    file = h5py.File(file_system, mode="r")
     io = NWBHDF5IO(file=file, load_namespaces=True)
     nwbfile = io.read()
     return nwbfile, io
\ No newline at end of file

From a415e622e408560a23145e9b6059bd70861b1db8 Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Thu, 16 Jan 2025 11:29:04 -0800
Subject: [PATCH 3/6] update environment

---
 001259/environment.yml | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/001259/environment.yml b/001259/environment.yml
index 9674997..c7ba931 100644
--- a/001259/environment.yml
+++ b/001259/environment.yml
@@ -4,8 +4,10 @@ channels:
     - conda-forge
 dependencies:
     - python==3.12
-    - ipywidgets
+    - ipykernel
+    - matplotlib
+    - dandi
     - pip
     - pip:
-      - matplotlib
-      - lerner-lab-to-nwb @ git+https://github.com/catalystneuro/lerner-lab-to-nwb.git@main
\ No newline at end of file
+      - remfile
+      - schneider-lab-to-nwb @ git+https://github.com/catalystneuro/schneider-lab-to-nwb.git@main
\ No newline at end of file

From f4c01bb875c0eb94e0f90e655d9a26b3546b8b04 Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Thu, 16 Jan 2025 11:39:18 -0800
Subject: [PATCH 4/6] updated ephys notebook for streaming

---
 001259/ephys_example_notebook.ipynb | 156 +++++++++++++++++-----------
 1 file changed, 94 insertions(+), 62 deletions(-)

diff --git a/001259/ephys_example_notebook.ipynb b/001259/ephys_example_notebook.ipynb
index f7ad8e1..e799906 100644
--- a/001259/ephys_example_notebook.ipynb
+++ b/001259/ephys_example_notebook.ipynb
@@ -1,19 +1,33 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ephys Example Session"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pynwb import NWBHDF5IO\n",
+    "from stream_nwbfile import stream_nwbfile\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook showcases one example session from the 001259 dataset containing operant behavior and concurrent electrophysiology recording."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +69,7 @@
        "                });\n",
        "            });\n",
        "        </script>\n",
-       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">1fb4f618-1681-43b1-b541-cedcce1466cc</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:06:51.025092-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time\">starting_time: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].rate\">rate: </span><span class=\"field-value\">30000.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].description\">description: </span><span class=\"field-value\">Recording of AC neural responses in mice performing this behavioral task utilized dense 128-channel recording probes (Masmanidis Lab). These recording probes span a depth ~1mm, allowing for sampling of all layers of cortex. Electrophysiology data was recorded using OpenEphys Acquisition Board v2.4 and associated OpenEphys GUI software.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].conversion\">conversion: </span><span class=\"field-value\">1.9499999999999999e-07</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].data\"><b>data</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">b661cbac-4f29-4f2d-a115-a681eea4b6b2</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-29 16:56:01-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-19 10:42:13.233766-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries']\"><b>ElectricalSeries</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time\">starting_time: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].rate\">rate: </span><span class=\"field-value\">30000.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].description\">description: </span><span class=\"field-value\">Recording of AC neural responses in mice performing this behavioral task utilized dense 128-channel recording probes (Masmanidis Lab). These recording probes span a depth ~1mm, allowing for sampling of all layers of cortex. Electrophysiology data was recorded using OpenEphys Acquisition Board v2.4 and associated OpenEphys GUI software.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].conversion\">conversion: </span><span class=\"field-value\">1.9499999999999999e-07</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].unit\">unit: </span><span class=\"field-value\">volts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">int16</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(81015808, 1)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">154.53 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(5000000, 1)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.398557570953712</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].starting_time_unit\">starting_time_unit: </span><span class=\"field-value\">seconds</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.description\">description: </span><span class=\"field-value\">electrode_table_region</span></div><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".acquisition['ElectricalSeries'].electrodes.table\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
@@ -63,8 +77,8 @@
        "      <th>group</th>\n",
        "      <th>group_name</th>\n",
        "      <th>channel_name</th>\n",
-       "      <th>rel_y</th>\n",
        "      <th>rel_x</th>\n",
+       "      <th>rel_y</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
@@ -80,14 +94,14 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Primary Auditory Cortex (A1)</td>\n",
-       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4995524336\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4995525104\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
        "      <td>ElectrodeGroup</td>\n",
        "      <td>CH16</td>\n",
-       "      <td>100.0</td>\n",
        "      <td>200.0</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>valued_events_table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\">description: </span><span class=\"field-value\">Metadata about valued events.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">uint8</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(0, 0, 0)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">0.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">undefined</td></tr></tbody></table><br>[]</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(24536,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">191.69 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(24536,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.3248648393258502</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'sub-m53_ses-231029_behavior+ecephys+image/a97f87e6-5455-4e63-94ad-b62509df125d_external_file_0.mp4']</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">NumPy array<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">int64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr></tbody></table><br>[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">uint8</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(0, 0, 0)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">0.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">undefined</td></tr></tbody></table><br>[]</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(24536,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">191.69 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(24536,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.3240515892288598</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'sub-m53_ses-231029_behavior+ecephys+image/df8e411c-a9dd-48d4-9372-c7882c331396_external_file_0.mp4']</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">NumPy array<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">int64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr></tbody></table><br>[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(3,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">24.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'auditory cortex' b'predictive coding' b'optogenetics']</div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">complex128</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1407445,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">21.48 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(625000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">159.71120362555763</td></tr></tbody></table></div></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1407445,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.74 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.4941158595449668</td></tr></tbody></table></div></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1407445,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.74 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">173.16272703504913</td></tr></tbody></table></div></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1407445,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.74 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.4941158595449668</td></tr></tbody></table></div></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(65,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">520.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(65,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.9792843691148776</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(264,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">2.06 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(264,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.051269288203086</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(65,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">520.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(65,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.9792843691148776</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(199,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">1.55 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(199,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.0425671250818598</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>valued_events_table</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\">description: </span><span class=\"field-value\">Metadata about valued events.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valued_events_table']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
@@ -113,7 +127,7 @@
        "      <td>Times at which tuning tones are played to an animal after a behavioral experiment during ephys recording sessions. Integer values correspond to the frequency of the tuning tone played i.e. 2 for 2kHz, 4 for 4kHz, etc.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "</table></details></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>electrodes</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrodes\">description: </span><span class=\"field-value\">metadata about extracellular electrodes</span></div><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrodes\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
@@ -121,8 +135,8 @@
        "      <th>group</th>\n",
        "      <th>group_name</th>\n",
        "      <th>channel_name</th>\n",
-       "      <th>rel_y</th>\n",
        "      <th>rel_x</th>\n",
+       "      <th>rel_y</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
@@ -138,11 +152,11 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Primary Auditory Cortex (A1)</td>\n",
-       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x5118772528\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x5118623520\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4995524336\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4995525104\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
        "      <td>ElectrodeGroup</td>\n",
        "      <td>CH16</td>\n",
-       "      <td>100.0</td>\n",
        "      <td>200.0</td>\n",
+       "      <td>100.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".electrode_groups\"><b>electrode_groups</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup']\"><b>ElectrodeGroup</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].description\">description: </span><span class=\"field-value\">ElectrodeGroup for all channels in the recording probe.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (A1)</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".electrode_groups['ElectrodeGroup'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".electrode_groups['ElectrodeGroup'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN']\"><b>MasmanidisSiliconMicroprobe128AxN</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].description\">description: </span><span class=\"field-value\">Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['MasmanidisSiliconMicroprobe128AxN'].manufacturer\">manufacturer: </span><span class=\"field-value\">Masmanidis Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
@@ -375,17 +389,17 @@
        "      <th></th>\n",
        "      <th>spike_times</th>\n",
        "      <th>unit_name</th>\n",
-       "      <th>sh</th>\n",
-       "      <th>n_spikes</th>\n",
        "      <th>amp</th>\n",
-       "      <th>KSLabel</th>\n",
-       "      <th>original_cluster_id</th>\n",
-       "      <th>ContamPct</th>\n",
+       "      <th>sh</th>\n",
+       "      <th>Amplitude</th>\n",
        "      <th>fr</th>\n",
-       "      <th>ch</th>\n",
+       "      <th>ContamPct</th>\n",
        "      <th>quality</th>\n",
-       "      <th>Amplitude</th>\n",
+       "      <th>KSLabel</th>\n",
+       "      <th>original_cluster_id</th>\n",
        "      <th>depth</th>\n",
+       "      <th>n_spikes</th>\n",
+       "      <th>ch</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
@@ -409,71 +423,71 @@
        "      <th>0</th>\n",
        "      <td>[3.5251333333333332, 3.5982666666666665, 3.615466666666667, 3.6355, 3.667133333333333, 3.673233333333333, 3.6804, 3.6820666666666666, 3.719533333333333, 3.7214, 3.7286333333333332, 3.7361, 3.7454666666666667, 3.7516666666666665, 3.7582333333333335, 3.7706333333333335, 3.7723333333333335, 3.7761666666666667, 3.7828, 3.789266666666667, 3.7908, 3.7944333333333335, 3.7975, 3.801433333333333, 3.8028333333333335, 3.8101, 3.8163, 3.8306666666666667, 3.8476, 3.8533333333333335, 3.8613333333333335, 3.8677333333333332, 3.8763, 3.8783333333333334, 3.8937333333333335, 3.8977333333333335, 3.9047666666666667, 3.9154666666666667, 3.9199, 3.9278, 3.9289666666666667, 3.9656, 3.9732666666666665, 3.9785, 3.9968333333333335, 4.253466666666666, 7.058, 7.0735, 7.076233333333334, 7.093833333333333, 7.0957333333333334, 7.117, 7.1205, 7.1216333333333335, 7.122833333333333, 7.1245666666666665, 7.1294, 7.131433333333334, 7.1349, 7.138366666666666, 7.144266666666667, 7.145533333333334, 7.151666666666666, 7.1612, 7.1795333333333335, 7.1817, 7.194966666666667, 7.198966666666666, 7.227566666666666, 7.2441, 7.253466666666666, 7.256, 7.270533333333334, 7.2833, 7.3402666666666665, 7.348166666666667, 7.385433333333333, 7.3868, 7.429133333333334, 7.468233333333333, 7.493733333333333, 7.500633333333333, 7.533966666666666, 7.572033333333334, 12.2529, 12.287133333333333, 12.332033333333333, 12.345333333333333, 12.362333333333334, 12.431366666666667, 12.447833333333334, 12.468133333333334, 12.476366666666667, 12.4821, 12.4863, 12.489433333333332, 12.491666666666667, 12.497033333333333, 12.509133333333333, 12.5132, ...]</td>\n",
        "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>25481</td>\n",
        "      <td>115.930557</td>\n",
-       "      <td>mua</td>\n",
-       "      <td>0</td>\n",
-       "      <td>80.7</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.4</td>\n",
        "      <td>9.435566</td>\n",
-       "      <td>127</td>\n",
+       "      <td>80.7</td>\n",
        "      <td>noise</td>\n",
-       "      <td>12.4</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>0</td>\n",
        "      <td>50.0</td>\n",
+       "      <td>25481</td>\n",
+       "      <td>127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>[3.5561333333333334, 3.5773, 3.5833666666666666, 3.5907, 3.6018333333333334, 3.607, 3.6252333333333335, 3.6276, 3.630333333333333, 3.636433333333333, 3.672, 3.6748, 3.689233333333333, 3.6984, 3.7062, 3.712666666666667, 3.7167666666666666, 3.722366666666667, 3.7267333333333332, 3.7298, 3.7329333333333334, 3.7546666666666666, 3.759533333333333, 3.761833333333333, 3.7788666666666666, 3.7809333333333335, 3.784, 3.8009, 3.804866666666667, 3.807, 3.8131, 3.8219333333333334, 3.8369, 3.8399, 3.8489333333333335, 3.872733333333333, 3.875, 3.8805666666666667, 3.9079333333333333, 3.9105666666666665, 3.9217333333333335, 3.923566666666667, 3.9467333333333334, 3.9625666666666666, 3.9875333333333334, 4.0126333333333335, 4.046733333333333, 7.04, 7.048466666666667, 7.0652333333333335, 7.066766666666667, 7.071133333333333, 7.075466666666666, 7.080466666666666, 7.083566666666667, 7.084633333333334, 7.0874, 7.0924, 7.099066666666666, 7.102333333333333, 7.105033333333333, 7.108833333333333, 7.1101, 7.111666666666666, 7.1146, 7.1165666666666665, 7.118266666666667, 7.124033333333333, 7.129833333333333, 7.1328, 7.147766666666667, 7.1531666666666665, 7.157033333333334, 7.1607666666666665, 7.163333333333333, 7.165933333333333, 7.1679, 7.170233333333333, 7.172533333333333, 7.173666666666667, 7.174633333333333, 7.177266666666666, 7.189033333333334, 7.202366666666666, 7.203533333333334, 7.2062, 7.208933333333333, 7.2103, 7.215233333333333, 7.221533333333333, 7.2261, 7.233466666666667, 7.2365, 7.240233333333333, 7.259133333333334, 7.263366666666666, 7.2657, 7.2673, 7.289833333333333, 7.308666666666666, ...]</td>\n",
        "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>35443</td>\n",
        "      <td>126.206665</td>\n",
-       "      <td>mua</td>\n",
-       "      <td>1</td>\n",
-       "      <td>62.4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13.2</td>\n",
        "      <td>13.124476</td>\n",
-       "      <td>127</td>\n",
+       "      <td>62.4</td>\n",
        "      <td>noise</td>\n",
-       "      <td>13.2</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>1</td>\n",
        "      <td>50.0</td>\n",
+       "      <td>35443</td>\n",
+       "      <td>127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>[0.12136666666666666, 0.8584333333333334, 1.2345333333333333, 3.9112, 5.9219333333333335, 9.9444, 12.450633333333334, 14.325033333333334, 14.3353, 14.5326, 18.6813, 23.658733333333334, 28.391166666666667, 37.837133333333334, 40.08743333333334, 41.8005, 41.903533333333336, 49.185966666666666, 56.691766666666666, 60.7654, 65.25846666666666, 66.39336666666667, 66.88976666666667, 70.34863333333334, 71.04976666666667, 73.94253333333333, 74.4071, 74.6132, 81.01176666666667, 82.65003333333334, 84.92176666666667, 85.42363333333333, 93.15026666666667, 97.1867, 97.19306666666667, 97.2139, 97.38656666666667, 97.42413333333333, 97.6594, 97.6653, 97.6856, 97.86983333333333, 98.4364, 98.4886, 98.82413333333334, 99.05023333333334, 99.52346666666666, 99.52543333333334, 100.2741, 100.2827, 101.2859, 102.92483333333334, 103.9318, 104.3477, 105.0469, 105.2011, 106.22393333333333, 106.25263333333334, 106.41993333333333, 108.4289, 109.03933333333333, 109.5766, 109.80056666666667, 109.87266666666666, 110.75076666666666, 110.92793333333333, 112.9565, 112.98053333333333, 113.53836666666666, 114.05953333333333, 114.5931, 115.62396666666666, 116.19866666666667, 116.86976666666666, 116.8932, 116.94683333333333, 118.0651, 118.55736666666667, 118.57543333333334, 118.8569, 118.9409, 119.22193333333334, 119.87903333333334, 119.9101, 119.91233333333334, 120.007, 120.09633333333333, 120.14376666666666, 120.9085, 120.93486666666666, 121.11313333333334, 121.12623333333333, 121.22286666666666, 121.22786666666667, 122.9742, 123.09116666666667, 123.1092, 123.1423, 123.53393333333334, 125.6168, ...]</td>\n",
        "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>12217</td>\n",
        "      <td>116.071793</td>\n",
-       "      <td>mua</td>\n",
-       "      <td>2</td>\n",
-       "      <td>49.8</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.3</td>\n",
        "      <td>4.523932</td>\n",
-       "      <td>63</td>\n",
+       "      <td>49.8</td>\n",
        "      <td>noise</td>\n",
-       "      <td>9.3</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>2</td>\n",
        "      <td>50.0</td>\n",
+       "      <td>12217</td>\n",
+       "      <td>63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>[3.7224333333333335, 9.597633333333333, 12.7993, 12.8781, 16.0953, 27.6601, 27.8507, 28.9355, 29.759833333333333, 33.2042, 40.22743333333333, 41.89406666666667, 42.1827, 42.194966666666666, 42.94793333333333, 54.178066666666666, 56.0236, 59.0662, 60.64053333333333, 60.64763333333333, 60.76893333333334, 60.851, 61.1273, 63.092866666666666, 70.4433, 71.1334, 81.05643333333333, 81.0611, 81.08413333333333, 81.24146666666667, 81.48196666666666, 88.2934, 91.73243333333333, 96.785, 97.18263333333333, 97.19186666666667, 97.27646666666666, 97.50363333333334, 98.0959, 98.18673333333334, 98.71266666666666, 99.0543, 99.05646666666667, 99.5565, 99.56853333333333, 99.57726666666667, 102.64806666666667, 102.65586666666667, 102.89906666666667, 103.50246666666666, 103.6804, 104.14823333333334, 104.19613333333334, 104.62423333333334, 105.57153333333333, 105.73293333333334, 105.88566666666667, 105.99263333333333, 106.2862, 106.4737, 106.8489, 107.3072, 107.99803333333334, 108.38136666666666, 108.45516666666667, 109.1129, 109.27656666666667, 109.31046666666667, 109.3976, 109.81196666666666, 110.6496, 110.84356666666666, 110.96526666666666, 111.6309, 111.7842, 112.01633333333334, 112.08223333333333, 112.9762, 113.05623333333334, 113.51366666666667, 113.78466666666667, 114.62746666666666, 114.64606666666667, 114.65686666666667, 115.90943333333334, 116.88066666666667, 117.24156666666667, 118.57846666666667, 120.12076666666667, 120.17023333333333, 120.411, 121.76593333333334, 122.1256, 122.51696666666666, 123.53856666666667, 123.59466666666667, 125.3487, 126.73206666666667, 126.78536666666666, 127.2204, ...]</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11622</td>\n",
        "      <td>116.476280</td>\n",
-       "      <td>mua</td>\n",
-       "      <td>3</td>\n",
-       "      <td>50.2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.3</td>\n",
        "      <td>4.303605</td>\n",
-       "      <td>63</td>\n",
+       "      <td>50.2</td>\n",
        "      <td>noise</td>\n",
-       "      <td>9.3</td>\n",
+       "      <td>mua</td>\n",
+       "      <td>3</td>\n",
        "      <td>50.0</td>\n",
+       "      <td>11622</td>\n",
+       "      <td>63</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table><p>... and 231 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231029</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
       ],
       "text/plain": [
-       "root pynwb.file.NWBFile at 0x4945540784\n",
+       "root pynwb.file.NWBFile at 0x4987780608\n",
        "Fields:\n",
        "  acquisition: {\n",
        "    ElectricalSeries <class 'pynwb.ecephys.ElectricalSeries'>,\n",
@@ -494,8 +508,8 @@
        "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
        "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
        "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
-       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 6, 51, 25092, tzinfo=tzoffset(None, -28800))]\n",
-       "  identifier: 1fb4f618-1681-43b1-b541-cedcce1466cc\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 19, 10, 42, 13, 233766, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: b661cbac-4f29-4f2d-a115-a681eea4b6b2\n",
        "  institution: New York University\n",
        "  intervals: {\n",
        "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
@@ -512,7 +526,7 @@
        "  session_start_time: 2023-10-29 16:56:01-04:00\n",
        "  source_script: Created using NeuroConv v0.6.5\n",
        "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
-       "  subject: subject pynwb.file.Subject at 0x4946217632\n",
+       "  subject: subject pynwb.file.Subject at 0x4993512896\n",
        "Fields:\n",
        "  age: P12W/\n",
        "  age__reference: birth\n",
@@ -533,9 +547,9 @@
     }
    ],
    "source": [
-    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231029.nwb'\n",
-    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
-    "nwbfile = io.read()\n",
+    "DANDISET_ID = '001259'\n",
+    "file_path = 'sub-m53/sub-m53_ses-231029_behavior+ecephys+image.nwb'\n",
+    "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)\n",
     "display(nwbfile)"
    ]
   },
@@ -548,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,22 +604,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1762: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "/opt/anaconda3/envs/schneider_notebook_env/lib/python3.12/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n",
       "  return math.isfinite(val)\n",
-      "/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/matplotlib/cbook.py:1398: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "/opt/anaconda3/envs/schneider_notebook_env/lib/python3.12/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n",
       "  return np.asarray(x, float)\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 2000x1000 with 4 Axes>"
       ]
@@ -688,9 +702,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmUAAANVCAYAAACJW/q8AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXVYFNv/x9+LNCgWio0KiootdmB3YXd3d1wDO/GqX/ParZjXbsXA7hYVsbAVpKTm9we/mXtmdmZ3dllC/byeZx7YM2dmztSZcz6p4TiOA0EQBEEQBEEQBEEQBEEQBEEQBJGkmKV0AwiCIAiCIAiCIAiCIAiCIAiCIP4ESClDEARBEARBEARBEARBEARBEASRDJBShiAIgiAIgiAIgiAIgiAIgiAIIhkgpQxBEARBEARBEARBEARBEARBEEQyQEoZgiAIgiAIgiAIgiAIgiAIgiCIZICUMgRBEARBEARBEARBEARBEARBEMkAKWUIgiAIgiAIgiAIgiAIgiAIgiCSAVLKEARBEARBEARBEARBEARBEARBJAOklCEIgiAIgiAIgiAIgiAIgiAIgkgGSClDEARBEARBEKmI9evXQ6PRKC5nz55N6SaaBGdnZ3Tt2tVk+/P09IS7u7vsus+fP0Oj0cDb29tkx/vV8fT0hKenp0n3qe8ae3p66ny2+UVpH97e3tBoNEa1LTHbEgRBEARBEIQpMU/pBhAEQRAEQRAEoc26devg5uamVV64cOEUaA1BJJ5ly5YhNDRU+H3o0CFMnz5d61nPmTOn7PY9e/ZEvXr1krydBEEQBEEQBJGUkFKGIAiCIAiCIFIh7u7uKFOmTEo3g5CB4zhERUXBxsYmpZuiSGRkJKytrVOVd4hUofj48WMA+p/1iIgI2NraImfOnIoKG4IgCIIgCIL4VaDwZQRBEARBEATxC7J9+3ZoNBosWbJEVD558mSkSZMGJ06cEMqmTJmCcuXKIWPGjEiXLh1KlSqFNWvWgOM40bbOzs5o1KgRDh48iJIlS8LGxgaFChXCwYMHASSEVitUqBDs7OxQtmxZXL9+XbR9165dYW9vjwcPHqBmzZqws7ODo6MjBg4ciIiICL3nFBoaipEjRyJv3rywtLREjhw5MHToUISHhxt7mRThw1k9ePAA7dq1g4ODA7JmzYru3bsjJCREVFej0WDgwIFYsWIFChUqBCsrK2zYsAEAEBAQgPbt2yNLliywsrJCoUKFsHTpUmFbjuOQNWtWDBgwQCiLi4tDhgwZYGZmhg8fPgjlCxYsgLm5Ob5//w4AuH79Otq2bQtnZ2fY2NjA2dkZ7dq1Q1BQkKh9fMi748ePo3v37nB0dIStrS1+/vwJjuMwd+5c5MmTB9bW1ihVqhSOHDkie03UXv/Q0FD06tULmTJlgr29PerVq4enT58afhNk4O/LzZs30bJlS2TIkAH58+cXrWPZsWMH6tSpg2zZsgnP69ixY5PkmSEIgiAIgiAIU0CeMgRBEARBEASRComLi0NsbKyoTKPRIE2aNACAtm3bws/PDyNGjED58uVRpkwZnD59GtOnT8f48eNRu3ZtYbuXL1+iT58+yJ07NwDg8uXLGDRoEN6+fYtJkyaJjnHnzh2MGzcOf/31FxwcHDBlyhR4eXlh3LhxOHXqFGbOnAmNRoMxY8agUaNGCAwMFHmMxMTEoEGDBujTpw/Gjh0Lf39/TJ8+HUFBQThw4IDi+UZERKBatWp48+YNxo8fj2LFiuHBgweYNGkS7t27h5MnTyaJ10eLFi3Qpk0b9OjRA/fu3cO4ceMAAGvXrhXV27dvH86fP49JkybByckJWbJkwcOHD1GxYkXkzp0bPj4+cHJywrFjxzB48GB8/vwZkydPhkajQY0aNXDy5ElhX9evX8f3799hY2ODU6dOoX379gCAkydPonTp0kifPj2AhPtWsGBBtG3bFhkzZkRwcDCWL18ODw8PPHz4EJkzZxa1sXv37mjYsCE2bdqE8PBwWFhYYMqUKZgyZQp69OiBli1b4vXr1+jVqxfi4uJQsGBBg68/x3Fo1qwZ/P39MWnSJHh4eODixYuoX7++Se+Ll5cX2rZti759++pUsAQEBKBBgwYYOnQo7Ozs8PjxY8yZMwdXr17F6dOnTdomgiAIgiAIgjAJHEEQBEEQBEEQqYZ169ZxAGSXNGnSiOpGRUVxJUuW5PLmzcs9fPiQy5o1K1etWjUuNjZWcf9xcXFcTEwMN3XqVC5TpkxcfHy8sC5PnjycjY0N9+bNG6Hs9u3bHAAuW7ZsXHh4uFC+b98+DgC3f/9+oaxLly4cAG7RokWiY86YMYMDwF24cEF0rC5dugi/Z82axZmZmXHXrl0Tbbtr1y4OAHf48GGd161atWpckSJFZNd9+vSJA8BNnjxZKJs8eTIHgJs7d66obv/+/Tlra2vRdQHAOTg4cF+/fhXVrVu3LpczZ04uJCREVD5w4EDO2tpaqL969WoOAPfq1SuO4zhu+vTpnJubG9ekSROuW7duHMdxXHR0NGdnZ8eNHz9e8RxjY2O5sLAwzs7OTnSN+Wemc+fOovrfvn3jrK2tuebNm4vKL168yAHgqlWrJpSpvf5HjhzReY/Za6wPvt3sMfn7MmnSJK36/Dol4uPjuZiYGM7Pz48DwN25c0f1tgRBEARBEASRXFD4MoIgCIIgCIJIhWzcuBHXrl0TLVeuXBHVsbKygq+vL758+YJSpUqB4zhs27ZN8KbhOX36NGrVqgUHBwekSZMGFhYWmDRpEr58+YKPHz+K6pYoUQI5cuQQfhcqVAgA4OnpCVtbW61yaSgtAOjQoYPoN+8JcubMGcXzPXjwINzd3VGiRAnExsYKS926daHRaHD27FnFbRNDkyZNRL+LFSuGqKgoretSo0YNZMiQQfgdFRWFU6dOoXnz5rC1tRW1uUGDBoiKisLly5cBALVq1QIAwVvmxIkTqF27NmrVqiWEmbt06RLCw8OFugAQFhaGMWPGwMXFBebm5jA3N4e9vT3Cw8Px6NEjrXNp0aKF6PelS5cQFRWldT8qVqyIPHnyiMrUXn/+HirdY1MhPRclXrx4gfbt28PJyUl4tqtVqwYAsteIIAiCIAiCIFIaCl9GEARBEARBEKmQQoUK6Ux+zuPi4oIqVarg0KFD6NevH7JlyyZaf/XqVdSpUweenp5YtWoVcubMCUtLS+zbtw8zZsxAZGSkqH7GjBlFvy0tLXWWR0VFicrNzc2RKVMmUZmTkxMA4MuXL4rn8eHDBzx79gwWFhay6z9//qy4LX/cuLg42XV8GDi5fUvbamVlBQBa10V6Xb98+YLY2Fj873//w//+9z+dbc6TJw/y58+PkydPok2bNrh06RJGjBgBFxcXDB48GE+ePMHJkydhY2ODihUrCtu3b98ep06dwsSJE+Hh4YF06dJBo9GgQYMGWu1TaiPw3/VnkZapvf5fvnzReY9NhfRc5AgLC0OVKlVgbW2N6dOno0CBArC1tcXr16/h5eUle40IgiAIgiAIIqVJlFLm58+fwqSFIAiCIAiCIIjkZ/Xq1Th06BDKli2LJUuWoE2bNihXrpywfvv27bCwsMDBgwdhbW0tlO/bty9J2hMbG4svX76IhPbv378HoK0AYcmcOTNsbGy0crmw63WRNWtWXLt2DRzHaeWeefv2rVDHWKT7zJAhA9KkSYNOnTphwIABstvkzZtX+L9mzZr4999/4efnh/j4eHh6eiJt2rTInj07Tpw4gZMnT6JKlSrC/CokJAQHDx7E5MmTMXbsWGE/P3/+xNevX1W1kb/e/PVnef/+PZydnYXfaq9/pkyZdN5jU6Emf9Dp06fx7t07nD17VvCOAYDv37+btC0EQRAEQRAEYUoMCl927NgxdO3aFfnz54eFhQVsbW2RNm1aVKtWDTNmzMC7d++Sqp0EQRAEQRAEQUi4d+8eBg8ejM6dO+P8+fMoVqwY2rRpg2/fvgl1NBoNzM3NRSHNIiMjsWnTpiRr15YtW0S/t27dCiAhBJoSjRo1wvPnz5EpUyaUKVNGa2EVCHLUqlULoaGhOHr0qNY6X19fmJmZoUaNGgafixK2traoXr06bt26hWLFism2mVVa1KpVCx8+fMDChQtRvnx5pE2bFkCCsmbv3r24du2aKHSZRqMBx3FaRnCrV69W9AiSUr58eVhbW2vdD39/f62wc2qvf/Xq1QEo3+PkhFfcSK/RypUrk70tBEEQBEEQBKEWVZ4y+/btw5gxYxASEoIGDRpg1KhRyJEjB2xsbPD161fcv38fJ0+exLRp09C1a1dMmzYNjo6OSd12giAIgiAIgvhtuX//vhB2iyV//vxwdHREeHg4Wrdujbx582LZsmWwtLSEr68vSpUqhW7dugmeMA0bNsSCBQvQvn179O7dG1++fMH8+fOTzOPd0tISPj4+CAsLg4eHB/z9/TF9+nTUr18flStXVtxu6NCh2L17N6pWrYphw4ahWLFiiI+Px6tXr3D8+HGMGDFC5AEkpUOHDli2bBlat26NsWPHwsPDA5GRkTh8+DBWrVqFQYMGIV++fCY910WLFqFy5cqoUqUK+vXrB2dnZ/z48QPPnj3DgQMHcPr0aaFujRo1oNFocPz4cUyZMkUor1WrFrp06SL8z5MuXTpUrVoV8+bNQ+bMmeHs7Aw/Pz+sWbMG6dOnV9W+DBkyYOTIkZg+fTp69uyJVq1a4fXr1/D29tYKN6b2+tepUwdVq1bF6NGjER4ejjJlyuDixYtJquRTomLFisiQIQP69u2LyZMnw8LCAlu2bMGdO3eSvS0EQRAEQRAEoRZVSpmZM2di/vz5aNiwIczMtJ1rWrduDSAhLMCiRYuwceNGjBgxwrQtJQiCIAiCIIg/iG7dusmWr1q1Cj179kTfvn3x6tUrXLt2DXZ2dgCAfPnyYfXq1WjVqhUWLlyIoUOHokaNGli7di3mzJmDxo0bI0eOHOjVqxeyZMmCHj16mLzdfKi0wYMHY/r06bCxsUGvXr0wb948ndvZ2dnh/PnzmD17Nv755x8EBgbCxsYGuXPnRq1atfR6ylhaWuL06dOYPn06NmzYgKlTp8Lc3ByFCxfG8uXL0bt3bxOeZQKFCxfGzZs3MW3aNEyYMAEfP35E+vTp4erqigYNGojqZsqUCSVKlMCtW7dEyhf+f349y9atWzFkyBCMHj0asbGxqFSpEk6cOIGGDRuqbuPUqVNhZ2eHZcuWYdOmTXBzc8OKFSswf/58UT2119/MzAz79+/H8OHDMXfuXERHR6NSpUo4fPgw3NzcDLh6iSdTpkw4dOgQRowYgY4dO8LOzg5NmzbFjh07UKpUqWRtC0EQBEEQBEGoRcNxHJfSjSAIgiAIgiAI4tena9eu2LVrF8LCwlK6KQRBEARBEARBEKkS1Tllhg4divv37ydlWwiCIAiCIAiCIAiCIAiCIAiCIH5bVCtljh49iuLFi6Ns2bL4559/EBoampTtIgiCIAiCIAiCIAiCIAiCIAiC+K0wKHzZxYsXsXbtWuzcuRPx8fHw8vJCz549UbVq1aRsI0EQBEEQBEEQBEEQBEEQBEEQxC+PUTllIiIisGPHDqxbtw4XLlxA/vz50aNHD3Tu3BnZs2dPinYSBEEQBEEQBEEQBEEQBEEQBEH80hillGF5/vw51q5di+XLlyMsLAzR0dGmahtBEARBEARBEARBEARBEARBEMRvg+qcMnKEh4fDz88Pfn5++P79O/Lnz2+qdhEEQRAEQRAEQRAEQRAEQRAEQfxWGOUpc+7cOaxbtw67du0CALRq1Qo9evRApUqVTN7A1Eh8fDzevXuHtGnTQqPRpHRzCIIgCIIgCIIgCIIgCIIgCIJIQTiOw48fP5A9e3aYmSn7w6hWyrx58wYbNmzA+vXr8fz5c5QrVw49evRA27ZtYW9vb7KG/wq8efMGuXLlSulmEARBEARBEARBEARBEARBEASRinj9+jVy5sypuN5c7Y6cnZ2RKVMmdOrUCT169EChQoVM0sBfkbRp0wJIuLjp0qVL4dYQBEEQhDpiYmLg6+sLAGjdujUsLCxSuEUEQRAEQRAEkQCNVQmCIIhfndDQUOTKlUvQHyih2lNmz549aNKkCczNVetxfltCQ0Ph4OCAkJAQUsoQBEEQBEEQBEEQBEEQBEEQxB+OWr2Bag2Ll5cXAODVq1c66+XOnVvtLgmCIAiCIAiCIAiCIAiCIAiCIP4YDHZ7cXZ21pncPi4uLlENIgiCIAgiaYiNjcWxY8cAAHXr1iXvV4IgCIIgCCLVQGNVgiAI4k/B4C/crVu3RL9jYmJw69YtLFiwADNmzDBZwwiCIAiCMC0/f/5Eo0aNAABhYWE00SUIgiAIgiBSDTRWJQiCIP4UDP7CFS9eXKusTJkyyJ49O+bNmyeEOSMIgiAIgiAIgiAIgiAIgiAIUxMXF4eYmJiUbgbxh5EmTRqYm5vrjCSmBpOZHRQoUADXrl0z1e4IgiAIgiAIgiAIgiAIgiAIQkRYWBjevHkDjuNSuinEH4itrS2yZcsGS0tLo/dhsFImNDRU9JvjOAQHB8Pb2xuurq5GN4QgCIIgCIIgCIIgCIIgCIIglIiLi8ObN29ga2sLR0fHRHssEIRaOI5DdHQ0Pn36hMDAQLi6usLMzMyofRmslEmfPr3Ww85xHHLlyoXt27cb1QiCIAiCIAiCIAiCIAiCIAiC0EVMTAw4joOjoyNsbGxSujnEH4aNjQ0sLCwQFBSE6OhoWFtbG7Ufg5UyZ86cEf02MzODo6MjXFxcKAkbQRAEQRAEQRAEQRAEQRAEkaSQhwyRUhjrHcNisBalWrVqiT4oQRAEQRAEQRAEQRAEQRAEQRDEn4YqpcylS5dQoUIFVTsMDw/Hy5cvUaRIkUQ1jCAIgiAIZXbu3IkfP36ge/fuqrextLTEkiVLhP8JgiAIgiAIIrVAY1WCIAjiT0GVr03nzp1Ru3Zt+Pr6IiwsTLbOw4cPMX78eLi4uODmzZsmbSQAzJo1Cx4eHkibNi2yZMmCZs2a4cmTJ6I6HMfB29sb2bNnh42NDTw9PfHgwQNRnZ8/f2LQoEHInDkz7Ozs0KRJE7x588bk7SUIgiCIpILjOLRu3Ro9evTA//73P/z999+qtrOwsMCAAQMwYMAAWFhYJHErCYIgCIIgCEI9NFYlCOJPR6PRYN++fSlybE9PTwwdOtRk++vatSuaNWtmsv0Zwvr165E+ffoUObZaVCllHj58iKZNm2LSpEnIkCEDihQpgtq1a6Nx48aoXLkyMmfOjNKlSyMoKAgnTpxAp06dTN5QPz8/DBgwAJcvX8aJEycQGxuLOnXqIDw8XKgzd+5cLFiwAEuWLMG1a9fg5OSE2rVr48ePH0KdoUOHYu/evdi+fTsuXLiAsLAwNGrUCHFxcSZvM0EQBEEkBbGxscL/gwcPxvDhw/Hw4cMUbBFBEARBEARBEARBEEp07doVGo1Ga6lXr16SHdMQJc+ePXswbdq0JGuLHCtXrkTx4sVhZ2eH9OnTo2TJkpgzZ06i99umTRs8ffrUBC1MOlSFL7OwsMDAgQMxcOBA3Lx5E+fPn8fLly8RGRmJ4sWLY9iwYahevToyZsyYZA09evSo6Pe6deuQJUsW3LhxA1WrVgXHcVi4cCH++usveHl5AQA2bNiArFmzYuvWrejTpw9CQkKwZs0abNq0CbVq1QIAbN68Gbly5cLJkydRt25d2WP//PkTP3/+FH6HhoYm0VkSBEEQhH5iYmK0yiIjI/VuFxcXh/PnzwMAqlSpgjRp0pi8bQRBEARBEARhDDRWJQjid6devXpYt26dqMzKyiqFWpNATEwMLCwsklSuL8eaNWswfPhwLF68GNWqVcPPnz9x9+7dRBucxsTEwMbGBjY2NiZqadKgylOGpVSpUhgyZAj+/vtvrFixAtOnT0eLFi2S/caFhIQAgHDcwMBAvH//HnXq1BHqWFlZoVq1avD39wcA3LhxAzExMaI62bNnh7u7u1BHjlmzZsHBwUFYcuXKlRSnRBCEAXz9+hUNGzbEzp07U7opBJHsyCll1Hh8RkVFoXr16qhevTqioqKSomkEQRCGEx4OaDQJC+MFTyQv4dHh0EzRQDNFg/Boug/E7w11O6kTGqsSBGEMHMchPDw8RRaO4wxqq5WVFZycnERLhgwZFOu/ffsWbdq0QYYMGZApUyY0bdoUL1++FNVZu3YtihQpAisrK2TLlg0DBw4EADg7OwMAmjdvDo1GI/z29vZGiRIlsHbtWuTLlw9WVlbgOE4rfNnPnz8xevRo5MqVC1ZWVnB1dcWaNWsAJMgfevTogbx588LGxgYFCxbEokWLDLoWBw4cEMKyu7i4oEiRImjXrp2Wt866detQqFAhWFtbw83NDcuWLRPWvXz5EhqNBr6+vvD09IS1tTU2b94sG77swIEDKF26NKytrZEvXz5MmTJFFIXE29sbuXPnhpWVFbJnz47BgwcbdD6GospTJrXBcRyGDx+OypUrw93dHQDw/v17AEDWrFlFdbNmzYqgoCChjqWlpdbDnjVrVmF7OcaNG4fhw4cLv0NDQ0kxQxApyPnz51G1alUAwOHDhw3+CBLEr46cUoYmrgRBEARBEARBEMSfRkREBOzt7VPk2GFhYbCzs0uSfUdERKB69eqoUqUKzp07B3Nzc0yfPh316tXD3bt3YWlpieXLl2P48OGYPXs26tevj5CQEFy8eBEAcO3aNWTJkgXr1q1DvXr1RN6Hz549g6+vL3bv3q3oldi5c2dcunQJixcvRvHixREYGIjPnz8DAOLj45EzZ074+voic+bM8Pf3R+/evZEtWza0bt1a1fk5OTnBz88PQUFByJMnj2ydVatWYfLkyViyZAlKliyJW7duoVevXrCzs0OXLl2EemPGjIGPjw/WrVsHKysrHD9+XLSfY8eOoWPHjli8eDGqVKmC58+fo3fv3gCAyZMnY9euXfj777+xfft2FClSBO/fv8edO3dUnYex/JJKmYEDB+Lu3bu4cOGC1jqNRiP6zXGcVpkUfXWsrKxS3JWMIIj/aNmyZUo3gSBSFKXwZS9fvsT+/fvRo0ePJBsYEgRBEARBEARBEARhOAcPHtRSII0ZMwYTJ07Uqrt9+3aYmZlh9erVgtx63bp1SJ8+Pc6ePYs6depg+vTpGDFiBIYMGSJs5+HhAQBwdHQEAKRPnx5OTk6ifUdHR2PTpk1CHSlPnz6Fr68vTpw4IaQAyZcvn7DewsICU6ZMEX7nzZsX/v7+8PX1Va2UmTx5Mry8vODs7IwCBQqgQoUKaNCgAVq2bAkzs4TgXtOmTYOPj4+QqiRv3rx4+PAhVq5cKVLKDB06VKgjx4wZMzB27Fhhm3z58mHatGkYPXo0Jk+ejFevXsHJyQm1atWChYUFcufOjbJly6o6D2P55ZQygwYNwv79+3Hu3DnkzJlTKOcfrvfv3yNbtmxC+cePHwXvGScnJ0RHR+Pbt28ib5mPHz+iYsWKyXQGBEEklo8fP6Z0EwgiRZFTyixatAhHjhwBAAQFBcHHxye5m0UQBEEQBEEQBEEQyYqtrS3CwsJS7NiGUL16dSxfvlxUppQS5MaNG3j27BnSpk0rKo+KisLz58/x8eNHvHv3DjVr1jSs0QDy5MmjqJABgNu3byNNmjSoVq2aYp0VK1Zg9erVCAoKQmRkJKKjo1GiRAnVbciWLRsuXbqE+/fvw8/PD/7+/ujSpQtWr16No0eP4suXL3j9+jV69OiBXr16CdvFxsbCwcFBtK8yZcroPNaNGzdw7do1zJgxQyiLi4tDVFQUIiIi0KpVKyxcuBD58uVDvXr10KBBAzRu3Bjm5kmnOvlllDIcx2HQoEHYu3cvzp49i7x584rW582bF05OTjhx4gRKliwJIEHr5+fnhzlz5gAASpcuDQsLC5w4cULQ2gUHB+P+/fuYO3du8p4QQRAEQRiJXKgyXiEDAFu2bCGlDEEQBEEQBEEQBPHbo9FofplIEXZ2dnBxcVFVNz4+HqVLl8aWLVu01jk6OgreJMa2Qxc2NjY61/v6+mLYsGHw8fFBhQoVkDZtWsybNw9XrlwxuC3u7u5wd3fHgAEDcOHCBVSpUgV+fn4oXLgwgIQQZuXKlRNtIw25pu984uPjMWXKFFlvGmtra+TKlQtPnjzBiRMncPLkSfTv3x/z5s2Dn58fLCwsDD4nNSRKKRMVFQVra2tTtUUnAwYMwNatW/Hvv/8ibdq0Qg4YBwcH2NjYQKPRYOjQoZg5cyZcXV3h6uqKmTNnwtbWFu3btxfq9ujRAyNGjECmTJmQMWNGjBw5EkWLFhVcsQiCIAgitRMREaFz/YcPH5KpJQRBEARBEARBEARBmJpSpUphx44dyJIlC9KlSydbx9nZGadOnUL16tVl11tYWCAuLs7gYxctWhTx8fHw8/OTlZmfP38eFStWRP/+/YWy58+fG3wcKbwiJjw8HFmzZkWOHDnw4sULdOjQIVH7LVWqFJ48eaJTIWZjY4MmTZqgSZMmGDBgANzc3HDv3j2UKlUqUcdWwmClTHx8PGbMmIEVK1bgw4cPePr0KfLly4eJEyfC2dkZPXr0SIp2Cq5dnp6eovJ169aha9euAIDRo0cjMjIS/fv3x7dv31CuXDkcP35c5Ob1999/w9zcHK1bt0ZkZCRq1qyJ9evXKyY1IgiCIIjUhj6lTL9+/ZKpJQRBEARBEARBEARBqOHnz5+CowGPubk5MmfOrFW3Q4cOmDdvHpo2bYqpU6ciZ86cePXqFfbs2YNRo0YhZ86c8Pb2Rt++fZElSxbUr18fP378wMWLFzFo0CAA/yltKlWqBCsrK1E6D104OzujS5cu6N69OxYvXozixYsjKCgIHz9+ROvWreHi4oKNGzfi2LFjyJs3LzZt2oRr165pRbbSRb9+/ZA9e3bUqFEDOXPmRHBwMKZPnw5HR0dUqFABAODt7Y3BgwcjXbp0qF+/Pn7+/Inr16/j27dvGD58uOpjTZo0CY0aNUKuXLnQqlUrmJmZ4e7du7h37x6mT5+O9evXIy4uDuXKlYOtrS02bdoEGxsb5MmTR/UxDMVgPye+oXPnzoWlpaVQXrRoUaxevdqkjWPhOE524RUyQIK7mre3N4KDgxEVFQU/Pz+4u7uL9mNtbY3//e9/+PLlCyIiInDgwAHkypUrydpNEARBEKZGn1KGzbnGYmFhgblz52Lu3LlJ5oJLEARBEARBEMZAY1WCIH53jh49imzZsomWypUry9a1tbXFuXPnkDt3bnh5eaFQoULo3r07IiMjBc+ZLl26YOHChVi2bBmKFCmCRo0aISAgQNiHj48PTpw4gVy5cgnpPtSyfPlytGzZEv3794ebmxt69eqF8PBwAEDfvn3h5eWFNm3aoFy5cvjy5YvIa0YNtWrVwuXLl9GqVSsUKFAALVq0gLW1NU6dOoVMmTIBAHr27InVq1dj/fr1KFq0KKpVq4b169cbpPwBgLp16+LgwYM4ceIEPDw8UL58eSxYsEBQuqRPnx6rVq1CpUqVUKxYMZw6dQoHDhwQ2pEUaDiO4wzZwMXFBStXrkTNmjWRNm1a3LlzB/ny5cPjx49RoUIFfPv2LanammoIDQ2Fg4MDQkJCFN3HCIJIOjQajfC/jY2NXgE1YRxhYWGwt7dP6WaYlCtXrqBnz55YsGABateundLNMZo9e/agRYsWABIS2l2/fl20fsqUKZg0aVJKNI0gCMJwwsMB/nsTFgb8IjHBfzfCo8NhPyvhPoSNC4OdJd0H4veFuh2CIIhfl6ioKAQGBiJv3rzJllaDIFh0PYNq9QYGe8q8fftWNv5afHw8YmJiDN0dQRBEojAmNiahn+nTpyNt2rQ4dOhQSjfFpDRq1Aj3799HnTp1UropiWLXrl3C//PmzdNaHxsbm5zNIQiCIAiCIAiCIAiCIFRisFKmSJEiOH/+vFb5zp07DXaDIgiCSCwxMTEw0OGPUMHEiRMBAH369EnhlpiWz58/p3QTTELGjBkBAJaWlrC1tdVar5RgLy4uDteuXYO/vz+ePn2apG0kCIIgCIIgCEPgx6rXrl0j4zuCIAjit8bc0A0mT56MTp064e3bt4iPj8eePXvw5MkTbNy4EQcPHkyKNhIEQQhIFTAcxyEuLg7m5gZ3Z4QKyOMidcIrZXr37i0bb3vr1q1wc3ND//79RTFQo6KiULZsWeH3nj170Lx586RvMEEQBEEQBEHogR2rhoWFwY7iyhEEQRC/KQZ7yjRu3Bg7duzA4cOHodFoMGnSJDx69AgHDhz4pePzEwTxa/DkyROtMmnoxI8fP2LEiBEIDg5Ormb9tiS3hdrly5dx586dZDnWhw8fkuU4ieHMmTPIly8f/P39ReW8skyXMnLSpEnInDkzOnXqpKhc8/Lywl9//WW6BhMEQRAEQRAEQRAEQRA6MVgpAwB169aFn58fwsLCEBERgQsXLvzy8fkJgvg16NWrl1aZVCnj7u6OBQsWIHv27LL7WLhwIRYuXJgUzfvtSE5Pmc+fP6NChQooUaJEkh2jcuXKwv9btmxJsuOYiho1aiAwMBCVKlUSlfP3JU2aNMiQIYPOfWzevBnLli1TXD9z5szEN5QgCIIgCIIgCIIgCIJQhVFKGYIgiJTiwoULWmVSpcynT58Utw8JCcGwYcMwbNgwhISEmLx9vxvSa5uUvHnzRvg/qTx02OcnTZo0SXIMYwgMDMTDhw9V12c9ZZydnbFgwQKsXbsW7969k60/ZMgQk7STIAiCIAiCIAiCIAiCSBwGJ2HIkCEDNBqNVrlGo4G1tTVcXFzQtWtXdOvWzSQNJAiC4Ll9+7bot0ajAcdxiImJwf3799G2bVuMGjVK5z6CgoKE/ylfijyBgYHC/+Hh4SnShp8/f8omsE8s6dKlQ2hoqHCM1EBkZCTy5csHMzMzfPnyBenTp9e7Da+04sOXDRs2DADw7ds3xW1iYmLQo0ePxDeYIAiCIAiCIAiCIAiCMBqDPWUmTZoEMzMzNGzYEFOmTIG3tzcaNmwIMzMzDBgwAAUKFEC/fv2watWqpGgvQRB/MCdOnBD95hOcx8TEYObMmXjw4AG6du0qqsMqXuLj41G8eHHhN8dxSdfYVEp0dDS+fPmis87379+TvB2RkZFYtWqVyDuGvR+XLl1KkuOy3jE/fvxIkmMYytevXwEkPJ+fP39WtQ0bvozF2tpacZtjx45hx44dRrby9+Xnz5+4fv064uPjU7opBEEQBEEQBEEQBEH8ARislLlw4QKmT5+OTZs2YdCgQRg8eDA2bdqE6dOn48aNG1i1ahXmzZuHxYsXJ0V7CYIgBHilTHR0NO7fvy9bJywsTPhfGooruZPYpwaqVauG7Nmzi7xhpEiVVR8/fjTqWGvWrEHWrFlx9OhRrXWTJk1C7969Ua5cOaGMFYpLFXCmIiIiQvif95hJaaKiooT/b9y4oViPvS9r164FAC1Fgi6ljK7zTc4wdamNjh07wsPDA0uWLEnpphAEQSSKK1euoESJEjh9+nRKN4UgCIIgCIIgCB0YrJQ5duwYatWqpVVes2ZNHDt2DADQoEEDvHjxIvGtIwiCYOBDNfGwnjJKochYpYxUCaMr98zvyuXLlxEdHY3jx48r1mGVBADg5uZm1LGmT5+Ojx8/ankvAYCvry8AiHKg8IoGAHB0dDTqmLqIj48XhSxLjUqZtm3bCv9Lw/Wxnj38Ntu2bRPVkQsvyqMrXJ+lpeUf6TkGALt27QIAzJ8/P4VbQhAEYThr165FtmzZcPPmTdSqVQt37txBzZo1U7pZBEEQRmFhYYHJkydj8uTJwlyPIAiCIH5HDFbKZMyYEQcOHNAqP3DgADJmzAggIQdB2rRpE986giAIHVhaWgJQr5SR1pk5c2bSNS6VY2am3P1LQ4fpylOii5cvXwIAPnz4oLXu1atXWmXLli0T/s+XL59Rx9RFZGSk6DerEEpJpEowHqn3F3/NWMUS6/mjD30TW0P29TuSWpR0BGEKtm7digoVKohCRBK/Jz169MD79+/Rtm1b0ZiHIAjiV8TS0hLe3t7w9vYW5noEQRC/AxqNRuciZ8ya1Jw9exYajSZZQtgT2pjrryJm4sSJ6NevH86cOYOyZctCo9Hg6tWrOHz4MFasWAEgIexMtWrVTN5YgiD+bNgQSwEBAfD09BTKAwICZLfRpZT5k4WwupQyI0eO1Cp7+fIlnJ2dk7BFYpQUFYlBqnQwNiybqVE6V+lENDg4GO7u7qIQZY0bN1Z9HH1Klx8/fsDOzk71/n4HWO+gkJCQFGwJoYvw8PA/7tlMLB06dAAADBkyBLt3707h1hDJgdI4iCAIgiAIgkh5goODhf937NiBSZMm4cmTJ0KZjY1NSjSLSEEM9pTp1asX/Pz8YGdnhz179mDXrl2wtbWFn58fevToAQAYMWIEJRMmCMLk8B4CPXr0gIuLi2D53759e8VtdCllzp8/b/I2chyXasNAsflHpAni9TF48GBTN0cnrDeIqZB6yiSF4scYpO3ikSpl5K7JxIkTVR9HnwU1Hx5t1qxZ8PDw+C2VltL3kw0XR6RO9u7dC3t7eyxdujSlm5JquHv3LpYvX66VU0qOz58/J0OLCIIgCMI0xMfH48GDB3jw4IGq7xxBEISI8PDkXQzAyclJWBwcHKDRaERlW7duRf78+WFpaYmCBQti06ZNou01Gg1Wr16N5s2bw9bWFq6urti/f7+ozsOHD9GgQQPY29sja9as6NSpk0HzgfXr1yN9+vQ4duwYChUqBHt7e9SrV0+kUCJMh8FKGQCoVKkStm3bhps3b+LWrVvYtm0bKlasaOq2EQTxB/L27VtFYTkfBsvBwQHAf+GYWOsCKWyIqqT2lOE4DrVq1ULVqlVT5SSC9TTS5Skjh9SL4MiRI5gzZ47RCihWkXbr1i2t9boUJl+/ftW7f09PT2g0GgQFBQllUk+RpFD8GIPSubL3C5BXqvBhQ9UQLhk0SsOM8u/D+PHjcf36dcH79Xfh8uXLMDMzg5mZGTQaDXbu3CnkNvqT+fHjR6oeZHt5eQEABg4cmMItST0UL14c/fv3x+bNm/XW/RNzp/0p9O/fH6NGjUrpZhAEQZiUyMhIuLu7w93dXdFwSRevXr1CdHR0ErSMIIhfAnv75F1MxN69ezFkyBCMGDEC9+/fR58+fdCtWzecOXNGVG/KlClo3bo17t69iwYNGqBDhw6CfCQ4OBjVqlVDiRIlcP36dRw9ehQfPnxA69atDWpLREQE5s+fj02bNuHcuXN49eqVbDQVIvEYpZThiYyMRGhoqGghCIIwlsePHyNnzpywsbHBoUOHtNavWbMGAGBunhB5UU3yxw4dOggDemni9BYtWhjVTqUwUGFhYTh9+jQuXLiAFy9eGLXvpIRVShmqlDl37pzod4MGDTB27FicPn3aqLawCelfv34NAEiXLp1QpqSo2Lx5MzJlyoTp06cr7pvjOPj5+QEAihQpIpSnRqXMvHnzFEOQSZUyw4YNg7+/v6jMEBdnqVJHGg5Kum92QstxHObPn48TJ06oPl5qo1u3bqLfPXv2TKGWpAwrVqzAtGnTtMrLly+P7Nmz61RuE6kH9tt4/fp17N27F1u3blWs/+jRo+RoFpHMBAQEYPny5Zg/f77Oem3atEHNmjVTpaEIQRCEqfH390eePHlQu3Zto7a/fPkyNBqNznkGQRBEUjB//nx07doV/fv3R4ECBTB8+HB4eXlpjfW6du2Kdu3awcXFBTNnzkR4eDiuXr0KAFi+fDlKlSqFmTNnws3NDSVLlsTatWtx5swZPH36VHVbYmJisGLFCpQpUwalSpXCwIEDcerUKZOeL5GAwUqZiIgIDBw4EFmyZIG9vT0yZMggWgiCIIxlz549wv+NGjXSWl+8eHEA/wmi1ShlAMDW1hafP3/G3bt3ReXGCOW3bdsGe3t7rF27VmvdlStXhP/5UFCpCVbILxe+LCoqCjly5DBon2/fvjWqLey1X7JkCQAgT548orbIwQvS+bBdp06dwq5du0R1WAOB8PBwYV+pUSkzevRorbJv374BgJaV3/v371GpUiVRGavc0sesWbNEv6UKncGDBysq7nbt2oVRo0ahTp06OgXAvxKGGpKk1rCEaunXrx8mTZqklXfi4cOHAP5TehOpG/bbGBUVBS8vL3To0IHClP1hqLEej4+Ph6+vL06fPq01/iEIgviV+fr1q6wH+cqVKwFoG5OppUKFCgAMCw9MEEQqIywseRcT8ejRI625fqVKlbQMrIoVKyb8b2dnh7Rp0wq5cm/cuIEzZ87A3t5eWNzc3AAAz58/V90WW1tb5M+fX/idLVu2VJOP93fDYKXMqFGjcPr0aSxbtgxWVlZYvXo1pkyZguzZs2Pjxo1J0UaCIP4Q9AmYeeE6P2BWq5QBEhQ+efPmFZXpS3wuR58+fcBxnJBDi4W1yoqLizN430kNq5TZu3cvgISYoRqNBgsXLoSXl5co3JtSvg12P8+fP0dgYKDBbWHDbvEeGux+pV4iPFZWVsL/fLi4Vq1aCd42gDhkHQCsXr0awH9CLPv/dzNODUoZOfiQa0rXQB9Xr15VpVzLmjWrVhl7HdnJLpt/iU8g/qsQFRWFOXPmGDQQlTJp0iTkzp071Yb5+vDhg5anEwvbH0nD2PH86kqnPxGpApr4c1DzvrLfOFbZf+rUKcEIJigoSCu0K0EQRGomKioKmTJlQtq0abXmIOw4liCIPxQ7u+RdTIhUHsZxnFaZVAam0WgEj+j4+Hg0btwYt2/fFi0BAQGoWrWq6nbIHYPmikmDwUqZAwcOYNmyZWjZsiXMzc1RpUoVTJgwATNnzsSWLVuSoo0EQRAAgDt37gD4LxeGNBE6i9TKgOM4LYsqOaXMjx8/8OrVK8X9qlUEpUaBPyt42blzJ4D/QjoNGzYMR44cEdVXEkCz+WWmTp2KfPny4ezZswa1hVU4DB06FIDYM0RJIcEKIdn79/37d+F/1uMKSEh0HRMTI4Ra4706deWtSUn4gZex8bA9PDwE7yNdWFhYYNiwYaKyRYsWCf+zidVTo5JRLfXr18fYsWNVKbnevHmD5cuXa3m6TZs2DW/evMGcOXOSqpmJIkeOHKhUqRIuXLggu559lvjwj1Lmz5+vs+9LDfzKz2FSwCoa+clYs2bNtCZvGzZswF9//UWTqd8INfeSfe/r1q2L79+/o2jRoqhVqxZatGiBWbNmwdnZGc2bN0/KphIEQSSKx48fo2fPnoIC5v3798I6Nk/CixcvtHIvGIIxxnoEQRCmolChQlpzOX9/fxQqVEj1PkqVKoUHDx7A2dkZLi4uokUaupxIHRislPn69atgbZ4uXTohoVDlypWNdhMlCII4f/68zpwGrILA2toagGGeMnJKGbnwH+7u7siTJw9evnwpux/WU0MXT58+FcICpRb0CaWl1hNKSgu5sE/bt283qC2ssIj3mlGjlGHhPUoAsYJOrt1DhgzB7NmzAfynlImNjU2Vcfb5sGH6rgF/PnLo8prgsbGxwYIFC2BrayuUsUoZ9j6zdYCEWLa/CoYoDMuXL4/+/fujVq1aQhmroEmtQm1eWaF0rkqhC6VJ4NkQgqkRPoQlkQAfjgBI6PeioqLw77//atXr2rUrZs6cqapfIFIPQUFB8Pb21npPAXVKe7bviomJQYYMGUTxxMePHw8AOHjwoAlaSxAEYXrevXuHqlWrYs2aNWjSpInW+sePHwv/b9u2zejjVK5cmQSWBEGkKKNGjcL69euxYsUKBAQEYMGCBdizZw9Gjhypeh8DBgzA169f0a5dO1y9ehUvXrzA8ePH0b17dzJuS6UYrJTJly+fIKwsXLgwfH19ASR40KRPn96UbSMI4jfn+PHjOH/+PB49eoSqVatiw4YNinVZ4QIvVNSllGE9J4AEK2IlT5kjR44IieF5S/HECim6d++OIkWKyApTUgp9Qn4+rBePVBDPC6SfPXumta3UM0if8Jq9LiNGjAAgFjLdv39f5/YAcPjwYeF/9tyk916j0WD58uXCbzb/GdvuwMBAHD16VO9xkxpeUaTvfg0cOFBxXdmyZfUeh5986lLu8JQpU0b0e8OGDUaHV0su4uLidPYpcvA5kq5evYolS5bgypUrSJcunbCezbOTGlESKLDvFquU+dU8nB88eIAdO3Yk6THi4+NT9bPNe4oC4v7r58+fsla+7Nj8y5cvSdo2wrTUqlULU6ZMQadOnbTWqfH0zJUrl+pjpUbvXoIgiAIFCghzBn5uwBsFAwlzOY7jcOvWLa0+z5Bv3sWLF03QWoIgCONp1qwZFi1ahHnz5qFIkSJYuXIl1q1bB09PT9X7yJ49Oy5evIi4uDjUrVsX7u7uGDJkCBwcHFL9PPZPxeC70q1bNyGE0Lhx44TcMsOGDcOoUaNM3kCCIH5PPn36hLp166Jq1aq4ceOGbJ27d++iVq1aOH/+vMirhU9upkspIxVYvHjxQhR2C0iIwX/u3Dk0aNAAnp6eorjE0rrGkpg8FqYkICBAKzSRVHEiFehJlTJ8eLPNmzdr7V/qcaLPileqrHrz5o1IEKpGKcZ+c9jjSZUyUpSUMvny5UP9+vVF+VNSAv5c2EmnHDY2Norr2PxGcrljAG0lnBz8MyInpE7NE9gjR47A3Nw8UR49gwcPxuTJk0Vl+vJepQSswlrpnqoJB8iTmqyo5JLXK+W6MhXVqlWDq6trigipo6Oj8c8//yAwMBARERE4f/68Vr4P1luTvX9RUVGyeWWkebiIXwfeAOLYsWNa6+Q8fROD3HedIAgiJbCwsNBpGT5p0iTh/yZNmmDhwoUoVaoUunTpIqrHej3rIjV6zRME8fvTtWtXLblFv3798Pz5c0RHR+PJkydahjkcx6FZs2aisu/fv4vmvK6urtizZw++ffuGiIgIPHr0CH///bfiPNbT0xMcxwmGXHLtatasGc0jkgiDlTLDhg3D4MGDAQDVq1fH48ePsW3bNty8eRNDhgwxeQNZzp07h8aNGyN79uzQaDTYt2+faD3HcfD29kb27NlhY2MDT09PPHjwQFTn58+fGDRoEDJnzgw7Ozs0adIEb968SdJ2EwShDSuUVxIYFi9eHKdOnULVqlUFL5f8+fMLHxRDlDI+Pj6YN2+eqCw0NBTVqlUTft++fVv4f9WqVfj48aPWflnhv3QQzyav51HK35CcfPz4EQUKFECNGjVE5dLrLg0f17dvX9Hvhg0bYv369ShRooTWMaQKBH2eM9L1z549ExIR64K18ODD2PH7u3LlCr59+6alUJMKmVkhp5zgVUlJmFzwz9i6det01tNl7cIKYtesWYP8+fNr1eE9QHQJufnrI/eOGpvzJjmYMGGC4jpWKacLc3NzLcVkamTq1KnC/0qeMuxzzr4PcoNraU6mlOLevXtwdHRM1mPGx8fjwoULCAoKws2bN5P12AAwb9489OnTB66urrCzs0PVqlW1nmVWEcP2mVeuXJHNA8b2dzSZ+n2QhqI7cuQIevToYfT+2BBABEEQKYmlpaXWnI2F7a+yZ8+u6PHNzut00b17d8V127ZtMzhEM0EQBEGoxWClzMaNG0WT+9y5c8PLywuFChXCxo0bTdo4KeHh4ShevLhiAuO5c+diwYIFWLJkCa5duwYnJyfUrl1bZEU6dOhQ7N27F9u3b8eFCxcQFhaGRo0apSrLUIL40xg0aJDeOryChM0dokspwwrspfB5E6ThzFghc1BQkGwIKNbrQLq9XDiR1CAEUwoFJk2GKRXo5cuXT2ubbt26aVluA9BKQCcV2Ds6OmLx4sXCb6ky5MOHD7JtlOLi4iL836FDB+H/PXv2oHz58qhQoYKsRxTLqVOnBKUF345Lly4J69nwQEmJklKDL9cVembu3Lk6980qZSpVqiQrYOYF+LoE37z3lJxSRs56O7Wgy9ji1KlTqvaRJk0aLSXH33//nah2JQWPHj0S/lfKecX2TawyWU6Js3r1ahO2TtyG7t2743//+5+s8kDKzJkzk6QdumD7JTXhoUzN8ePHAYgVZ3PmzIFGo4FGo0GFChVE/cbVq1eF/4cOHYp79+5p7dPUHhWE6fn58yfWrVsnhE+UQ/qNZJWxQMK7nylTJqPbkJhcDARB/D6kZoMbICGKAmuUdP/+fYO8mL99+4ZOnTqhZcuWQr+qFOo2KCgI7du3R7t27bTmfLrgw6np83gnCIIgCKPCl8mF9fnx4we6detmkkYpUb9+fUyfPh1eXl5a6ziOw8KFC/HXX3/By8sL7u7u2LBhAyIiIrB161YACeGI1qxZAx8fH9SqVQslS5bE5s2bce/ePZw8eTJJ204QhDJyIVek8O89O/CWKmWKFi0q/M+HOJNj2LBhqtoRFBQEV1dXkTUya2XPCrs4jpMV4qWEYE+K0gRLn0W8kpfP2rVrtcqkEyLpMb98+SLyppQqZaRtKVy4sOyxWc8F9hjz588HkODtIw3DJg3RtGLFCkF4zd8ffnsg+ZQySs89f168Aor15gISvED0hQvVaDR4+vQp7t27h/Tp08veSzb8WcGCBWX3M2PGDBQrVkzW82TBggU625AScBwHjuMUlRNAgrKRndBLPWrZff0KSV9z5Mgh/C/13uM4DvHx8YqeMnKKbV4xYGoWLVqEdevWYfDgwciePbuorZGRkZg7d65IwZQSlqlsn54Sygw2hKYcly9f1rm+Z8+eWmVsf9izZ09ERUXhypUriI+Px549e6DRaHD27Fmj2kuYhmnTpqF79+4oXbq0Yh0nJye8fv1acb25uXmi7iOvEFLyFP38+fNvHeJHzoAmICAAs2fPFhn3EcTvzKFDh2BlZYWVK1emaDvi4+Px8uVLVK5cWWvd/v370bhxY+F3cHCwoud4u3bttMpGjBiBzZs3Y/fu3bLrWdzd3YX/5XK2KbXdysoKpUqVgqOj42/dbxIEQRCJx2ClDMdxstYIb968gYODg0kaZQyBgYF4//496tSpI5RZWVmhWrVqgov/jRs3EBMTI6qTPXt2uLu7a4UBYPn58ydCQ0NFC0EkByEhIQgKCkrpZqgiLi7OoBj8xoYNfPfunfA/6zUDAL6+vgASYl4uX74cQ4YMQefOnbX2kSFDBtl8HIsWLdIqe/bsmSgsGasI4IV23t7eyJcvn+zAOzUoZfi49FJ05V5xcHBQtDyThjkDtM9TX6Js6bMizYXx8OFD2e1YLx2lCZK0LdJcOp07d9bylGGFLkllJfjx40dcvHhREP4oKWUePnwIf39/QXguVQw0b95c1fFcXV2FCaWc8J0Nzzd+/HjZfSxYsAD37t3TygGUGomJiUGJEiVQvXp1nUoZBwcH0fOmpHz8+fOnyIOKJzV4v7Gw4QSloclq1KgBDw8P0buyd+9e4beS4iEpzvHly5ei3+x75u3tjTFjxqBw4cK4ffs2du7cqbgfJycnk7eNh+07du/enWTHkSMgIECn0N0UfPnyBQ0bNkT58uWxbNkytGjRAkBCOOItW7bo7beJpIEPx8x6w8hZWPMGZHLfPnNzc0WvWLX06NFDywgAAK5fvw5HR0e0bt06UftPjcTFxaF8+fKoXr26VtSEAgUKYNy4cZg+fXoKtY4gkhf+myANX5zc7Nq1C3nz5sWFCxdk17Pf6oCAAEXv2zNnzuD69et4+vSpUHbt2jXRel2w3jFyUQLkePTokfAtjY+Px/v371VtRxAEQfyZqFbKlCxZEqVKlYJGo0HNmjVRqlQpYSlevDiqVKmiOplaUsB/8KQJjbNmzSqse//+PSwtLbXiybN15Jg1axYcHByERVdIGYIwJZkzZ4azs/MvkfeoZs2asLa2Vm09P3ToUKOO06ZNG+F/VtBcr149uLm54dOnT/D19UXWrFmxcOFCFC9eXGsf9vb2suHN7t69q/f4rDKBD980ZcoULYEjT2pQyiiFh5MqK1ji4uKQJUsW1ceQKlmUFBsdOnRAaGioVn05K7crV65olbFCQyWhKS+Y4hPescnC+ZBn/P3n21GhQgWhTqdOnXDkyBHZfSeGhg0bonLlyjhx4gQA7fB3PMOHD0elSpWESaQ0r4musH1KyCke2MTOupQYvwIcx2HChAm4e/cu/Pz8FOvxzzyr6NKV90muT8ifPz/Gjh2biNaaFraPYYWKP3/+xNmzZ3Hz5k1R/HVvb294e3sDSLCKBRIS5bIWoWfOnIGnp6dezw1DkAo02Hf5/Pnzwv8lS5aUFf7y3o8lS5Y0WZuksEqq5LaOP3z4cLIc5/Tp0wCAyZMni8o7duwIHx+fZGkDIYb11tuxYwc0Go1sKDL+/ZYzDrO3t9fpacOiNNaR84L9/v274Ema3IrK5CA4OBhXrlyBn5+frMEJkJDTlCD+BNQqHpIadq4n5f79+7JRW+R4//49PDw8ULBgQWFOYazyWq3BltRAj59npPawcARBEETKoFop06xZMzRt2hQcx6Fu3bpo2rSpsLRt2xYrV64UCXhSCqllt5JnjyF1xo0bh5CQEGFJaktGguDhB8f6QpakBnhB6IgRI1TVZ8PUGIJSThne8yVz5syicjlvkLRp06pKKs/CCz5ZAagaS7LUHM9f13MVGxsLBwcHXLp0CdWrV9e7L6nlrpLF9datW9GpUyctZRVvFcwqzaUTp58/fyoKTeTInDkzgP88QtKmTSt8p6SeMs7OzqJtGzRoACDhvEylFL1+/TqA/wThvGeXErzgTOopU6RIEYOPLfeNY3MSGKPoSU3s27dPlGdHej4lS5YEx3GCUQX7fJqbm2sJqHURGBiIOXPmqK6/ZcsWNGjQQLUQwVBYBScrUGH//+uvv0TbzJs3DyNHjhSsUC0tLUXeQzVr1oSfnx88PDyMbpebmxs0Go2Qz0gq7GEFFGry+vXp0weAtgJYjpMnT6J///4GxYAHxP27LmXd74DcNV+xYkUKtIRgadu2reK658+f482bN1p9SZs2beDu7q7o8SilaNGiWL9+vd56Xbp0QYYMGbBjxw5R+YcPH36bkHfs2KV3797C/6y34OXLl38Jb1GCSCxqvsVyhlXJyY4dO4waT+3atStRx1VrZCf1NI6KisKdO3eQNm1aTJw4MVFtIAiCIH4/VCtlJk+ejMmTJ2PdunWYNm2a8Hvy5MkYN24c2rVrpxVKKDnhw1lIPV4+fvwoeM84OTkhOjpaSxjL1pHDysoK6dKlEy0EkZRs2LBBEOACqV84JB2Avn//XrAU4jhOVVJntbDCVjmljBQ55Ys0VJYa+vfvD0CdQJCFH8QvXrwY+fLlw4sXLww+dkrAC1DLly+P8uXL663/5s0bHDx4UBBw6LII279/v7C+YsWKAP6zSmfDYEqfe0NDAEiVMqzHiVQpo2QdWKhQIeTKlQvPnz836Ni64BUk+hQB/HslVagoxc42FDZvz6+ulJHmQZFO2KXPEus9ZW5ujgIFCiRZ2zp27IgjR46oFpgaCisoeP/+vfAss4onuT6Y9YpghZEsX758MbpdvAK1dOnSWLNmjZbhjlLCeiX491eNYKR27dpYvnw51qxZY0iTRUp0tfHjDeH8+fPw8fHR+maeOHECy5YtM2hfiR0XyJ1fajYiIIDZs2cjV65cGDx4sFAWHh6O7du3Q6PRKObAGjBggPA/7znbqVMnvcdT8ozx8PBA9erVBQMDnsePH8PZ2TnF81EYAvseXLx4Ufhf6inHjwEJwtRcvXoVo0aNUpVfM6UJDQ2Fg4MDXF1dTb7vt2/fYtu2baq8dQ4ePKhzvdx4a8mSJaLcloaidv4np5QZNWoUoqOjKRQiQRAEoYXBkp0uXbrIhv1JafLmzQsnJychLAyQMOH38/MThH6lS5eGhYWFqE5wcDDu378v1CGIlGb9+vXo2rWryEI5TZo0Kdgi/UiFO9myZRMm/JMmTUL27NmxYcMGAAmWnmyYHEPR5ykjRS4+ujFKmXXr1gHQFgjqG6Tz9YcMGYLAwMBfxkqKnRSpef7OnDmDxo0bo1evXgDUu+nzShjeol1XbjJDhaSOjo6ituhSysiFvPr69asQ7krfBFAficnRwebXaN++faLawcIK7VnryMWLF4tyr/0KSD3v2NxTgDiGuBQLC4tkGdcYKnRXC9snjR07Fl5eXgD053VicXV1xbhx47TKy5Ytm/gGIiHBvFTQ0qdPH4MUrfz7y/a5oaGhqFy5spZwmCcgIAARERGq+qPv37+LlBL//vuvbE6hxFC1alWMHDlSK2dOnTp1RDHv1ZBYQyi556Nx48Y6w1oSqQNWCc1+18qXLw93d3c0atQIPXr0EMpHjRqFGjVqIFu2bEKIWTMzM1UGF1K+fPkiRAzYuHGjaN3gwYMRFBSU4vkoDEFpXCE16Ll9+3YytIb4EylXrhzmz5+PadOmpXRTFOnTpw/c3d2FsXJio4b07t1b8H7lyZkzJ9q3b4/Fixfr3FYa0ldK+vTptbzfgYSxkty+1YZlVDII4b+lSmOuqKioVG9cSRAEQaQcqpQyGTJkQMaMGVUtSUlYWBhu374tDIwDAwNx+/ZtvHr1ChqNBkOHDsXMmTOxd+9e3L9/H127doWtra0gxHJwcECPHj0wYsQInDp1Crdu3ULHjh1RtGjRFM2HQxAs3bp10ypTyleSEvj6+mLJkiWiMrn45lu3bgUAwSqIPy8PDw9R/HRDUfKUURqkyw2EjVHK8EofqRJGn5Jl7dq1orwMakIDpDTm5uY4cOCA8NvNzU31tvx9VysQTp8+PYD/PBfSpk0rrJPuw1Arbuk3iVXc8UJ8Xqi0ZcsWre3ZmP6JDdXATuYWLVpk0LasQof3/jEF+fLlky0fOHCgVu41JXbu3Ily5cqZNPeIMejKI6MPc3NzNGrUCHXr1jVoO17J8O3bN5HnTXIjDaXGv7uGKGUsLCzQpEkT4X1ky5OKAwcOoFGjRqqt6nkvAPZdcnJywsWLF9GoUSOhbPbs2cL/7969g52dneyzznEczp49i0ePHmHTpk3IkCGDVk4VtQY73759w8qVK2WTs8vBWuSr4e+//9Yq05UHylil6po1a5AnTx7BiIJIGnx8fKDRaFTlsTMECwsL3L17F/v378fq1asRGxuLb9++IU+ePDh16hTevXsnGhPt3LnTYAUKG+IuKioKUVFROHHiBKKiorS8Tn6FHApK3gnbt28X/TY0FCJBGMqdO3dSugki2Fx0//zzDx48eIAmTZoIZfPmzdMyOGrbti00Gg1OnTqFDx8+YNeuXYiNjUVUVBSCgoJw8eJFlC9fHqtWrcI///yDRo0aoVatWqK+Q18YbH0GWt+/f1flCcjTsmVL4f+CBQsqRk9p1qwZateuLcoZM3/+fNja2sLJyQmWlpaybY+KivrlvdEJgiCIJIRTwfr161UvScmZM2c4AFpLly5dOI7juPj4eG7y5Mmck5MTZ2VlxVWtWpW7d++eaB+RkZHcwIEDuYwZM3I2NjZco0aNuFevXhnUjpCQEA4AFxISYqpTIwgBuWdc5avKLV68mOvVqxcXHx+f5O179OgRx3Ect3btWs7CwkKxzbp+Ky1WVlaK66ZNmya0ZcKECUK5t7e3bHv37NmjtY/o6GhV7ZAu8fHxnLW1tVHb8ku3bt2S7N4owR7/3r17XP369XW2MTo6WrS9oefNcRx3+vRpVXW9vb1Fv+vVqyf8f+jQIaENx44dM/hanz9/XvS7TJkystdE+ltumTFjRqLuwadPn0T7i42NVX0eDg4Owv9jx441ug3S/YaFhQnrIiMjuRYtWnCzZ8/mOI7jevbsqdievn37apXVrl07Udcnsei7hiVLllSsHxoaqno/7DJ79mwuLi5O+B0REaG3babm3Llziu9gYGCg6nP59u0bx3Ec5+npKSqvWLGi0W0z9H3Vt7B9itwx9B1XOtY7cuSIquM+ePBA5/X39PTknJ2dOQBcrVq1FOvGx8fLtldaLl3i4+O5iIgIUdn48eN1bhMSEsLt3btXZ53MmTMrrnNycjLmlieOsDCOAxIWpm/63YiJidF6DooWLWrUOzFr1iyTtEm0Xwtw8P7/xUL7mMOHDxf+L1u2LNelSxcOANezZ08uT548WnVTO/v27RO1OTIykrt586bWedvb26d0U4kkIDV0O/wz1qBBg5RpgExb+MXHx0fnnOncuXPCth8/fhSt4/uDBQsWcKVLlzbZWEBuke5f7lzULGfPntX7Te7RowcXHh6ueAxp/3H48GHOy8tL1DaCIExDZGQk9/DhQy4yMjKlm0L8oeh6BtXqDejLYASklCGSEqVBoCHbHj9+PEnaxg5UN23apLO9coNiffX5JXv27Irr5syZI7Rn6tSpQvny5ctl2yynlFHbDuny48cPTqPRJGri0L9//yS5N7qQnvuHDx/03jcp4eHhqs+R4zju6NGjeutZWFhwK1euFJU1btyYc3Jy4oAEIWx8fLzQ5xq6BAcHi35XrVpV8Zro2xf73BnDy5cvRftbvny56vNgBV1Tp041ug38PlxdXbn+/ftzUVFRinUHDRqk2J779+/Llr9//97othlCXFycSJHCcRxXsWJF2TZt27aN69KlCxcUFCSqP2bMGKEOq0ypXLmywX0C/39gYKBWW58+far33UoMbB/ILmFhYVrHVvPON2/eXFReoUIF2ePu2rWL2759u2K79Ak1jFkuX74s/D906FBFJYfS9o0aNRK1cciQIaqO6+7uzr18+ZJbuHAh9+PHD9E+dF1LjuO4Hz9+cLGxsVx8fDxXrVo1Ub02bdpwHMdx9+7d03tv2HMtUqSI3v5Y37XQt2TKlEnXY5c0pAbpaDLw/ft3rXvl4uJi1H3at2+fSdok2q8epUy3bt0MaqOU7du3c82bN9fqw1OKLVu2iNorHTfoOhc13Lx5k3N1deX2799v4pYTpiClup3o6Ghu5MiRXP78+YXnq2HDhsL6mJgY7t9//+U+ffqUpO2QGvHJPfdLlixRfCfYuVdQUJDR35zELtK2K52L2v5KaUzJL2ZmZorHuHr1quj37t27uSJFiiSqHyEIQp5fUSmjrx/iHQ6Sm9jYWG7BggVc0aJFOSsrK87BwYGrV68ed+HCBYP3Va1aNW7IkCGmb2QqxBRKGaOyBcfFxWH37t2YPn06ZsyYgb179/4SIXkI4k9BLrm9KWATn3p7e+utz7p486gJi6QrXrBS+LIsWbLo3W9i+fr1a6JygwBIFTm5jAmBZWtrqyrpt5OTEwB1OWWsrKy0ngdzc3MhF0xMTAzKli2rM8+MEnnz5kWWLFlEIX6U8g6pIbF5ndj3JUOGDAblF+FzAADqc/XowsPDA0uXLtUZ/kjXtVJ6B5IrN1vt2rWRLl06vHnzRihTSnBdpkwZrF+/Hrlz5xaVt23bVvifzc1x9uxZg/pPNpSWXHgKQ3KmGINSGI/w8HDVzwqb30Ta98ol3P3+/TtatmyJtm3b4tOnTwa1KzGwoQ0XLlyo9bwFBwfr3F6aF4rPE6aP+/fvw9nZGUOHDsXIkSOFcqX48mx70qZNi2rVqmHfvn1aIfZ27NiBS5cuoWjRoor74PtHjUaDnj17okqVKrh586bemPpyuLi4qK7L33dd37vXr1+jQoUK2LZtm8Ft+ZOR5rsCDA/NyZOYb5qxPHv2zKD6e/fuxZUrV4Tfbdu2xd69ezF//nytuvreqaRAGr5MV79pTL9WqlQpBAQEiMI+EX82jx8/hqWlJebPn4/nz58L5Ww/sHjxYjRt2jRJx1WBgYHIli0bZsyYAUA5vPLAgQMV99GvXz9MmTIF69evlx0vJDW3bt0SrmGhQoVMtl99IUbj4+Oxd+9e2XXS6xAVFSUb5psgiD+T4OBgYVm4cCHSpUsnKjM0zLkp4DgObdu2xdSpUzF48GA8evQIfn5+yJUrFzw9PbFv375kb9OfhMFKmWfPnqFQoULo3Lkz9uzZg127dqFjx44oUqSIaGBBEIRxKAkX9cEKT5JKSbp27Vrh/+fPn+tVULBKHHt7e/zzzz+qhNtDhgxRXMcKUFkhqFIMYCXBsyFCLY1GAyAhaTSQcI/4SYyhvHv3Dj179tSZeNzU8IqgI0eOAEhI8CulVKlSeveTMWNGUexlOfLnzw9AXQ6W6OhoLaVMdHS00L67d+/i+vXrevczZswYrTJLS0uYmZkhR44cQhl7z5cvXw4gIX40oF9ZmBilTIMGDbB+/Xrhd7Zs2VC9enWtekrPZIECBYT/1ebf0IWa2Na6hH1yzw8AvHjxAkBCnp4yZcokOhGsEqdPnwaQINTmUbo/Su9/8eLFMXPmTOzatUu0bZo0abTyquiCFaLIna/0nuoSbhiDnOIbSHiP1OaUYd9pafL4sLAwfPjwQVTGChe+f/+utb+4uDhRknFTIc0Lc/nyZdHvU6dOGbQ/Y4QkJ0+eBABcuHBBr0C8QYMGABKEO15eXrJ19AncNm/eLPy/atUqnDt3Tuse6WLevHnC/0uXLkWZMmWQM2dOUZ1ChQrh7NmzorK4uDgMHDgQuXLlwuXLl2WfpcGDB+Py5ctC3kaW+Pj4RBsw/K786koZQ9vq5eWF8uXLa5VLjTxGjRqFtGnTmjzPji6OHj2K3r17i8r27NmjWN/Ozg5BQUFJ3SziN2XJkiUoW7YsPD09Zdffvn0bR48exYMHD4Q5Bj/vABLGWGrfvx8/fuDVq1c66wwdOhQfPnzAhAkTcPDgQaPzJnl7e6Nbt24oVqyYUdsnhhIlSghjg8OHD6No0aJYvXo1AGDq1KmK28l9t6TI5SRlUfquS5UyHTp0EM1FCIJIesLDk3cxBCcnJ2FxcHCARqMRlW3duhX58+eHpaUlChYsiE2bNom212g0WL16NZo3bw5bW1u4urpi//79ojoPHz5EgwYNYG9vj6xZs6JTp04685/6+vpi165d2LhxI3r27Im8efOiePHi+Oeff9CkSRP07NlTMGLp2rUrmjVrJtp+6NChwreta9eu8PPzw6JFi6DRaKDRaFJVfuzUiMFKmcGDByN//vx4/fo1bt68iVu3buHVq1fImzcvBg8enBRtJIg/CqWko/pgB4FJJQyRdub6rBrZSXdYWBj69OmjKiF2//79FdcZ6imjlPC4T58+etsBJFhTp0uXDsB/55MuXTqRoNwQtm/fjjVr1qBs2bJGbW8M/H3KkyeP7HoPDw94eHio2hcrBPL19cWlS5dE6/nnV43Fa3R0tJYHw9GjRw1WgMg9L/yzwQrFeS8eIEEoz7czJiZG8I5o3bq17DH0tSk2NhZ3794Fx3F4//69IDAOCgoSlGE8SpPqTJkyyZaz3lUNGzbU2Q5dTJo0CRkzZsTAgQPx6dMnnf0Ee595zyUeXdaI4eHh6NKlC27cuKE3WasxsM8Vq/gzVCmj0Wgwbtw4tGjRIlHtYZVt7dq1AwD4+/sLXhhSpcnSpUu1ksknBiWlTExMjGqlDEtgYKDo96NHj+Dk5IQBAwYASPi2sNdd+p7zChlWYSalRo0aetsxYcIErTJ9XobGCraNQdc3CkhQ0N2+fdvo/adNmxbW1taoV6+eYh22P1OCFSplyZIF165dw/3790V1SpYsqbWvNGnSYOnSpXj79i0qVKiAVq1aae1byUsqLi4OpUqVgqenZ6pRzMTGxuLevXupoj1Sg4zChQvj69evivV5Qwc5UsLz1hTemoC2wHP+/PmIjY3FpEmTTLJ/NdSvX1+rbNiwYTq3cXZ2xqFDh0RlcXFxWL16NUaPHo2WLVsaLdwmfl9+/PiBQYMG4dq1a1qGDjxfv35F/fr14e7uLuoT1q9fj6pVqyJ//vwoU6aMzuNERUXBz88POXLkQJ48eXQKwthjNG7cGLNmzTLspCQYO381Fc7Ozrh7965gFDJx4kSEhoZi1apVWnXVzDP0KWWUqFq1qlYZ++2ZPHmyUftNLXAch9DQUHAch3v37ukda/r6+goGLQSRXNjbJ+9iKvbu3YshQ4ZgxIgRuH//Pvr06YNu3brhzJkzonpTpkxB69atcffuXTRo0AAdOnQQ+vTg4GBUq1YNJUqUwPXr13H06FF8+PBBUcYBAFu3bkWBAgXQuHFjrXUjRozAly9fcOLECVXnsGjRIlSoUAG9evUSvH9y5cplwFX48zBYKePn54e5c+ciY8aMQlmmTJkwe/ZsrdAMBEEYjtxgDtC2KpSiRkCZWNgBatu2bfWGcjA0dI+DgwN69+4NMzMz+Pr6AoDWR4i1EmbD1Uitf9k2s5Ns3lJ93rx5greC0ofixYsX6Nq1qyCg5j92tra2Or0NJkyYgOnTpytaUSUnrKWckgC3adOmmDt3LsaNG4c7d+7o3B8rrM+QIYOWBawhShkAWl4JlpaWgoJEaeKqq008vDCeDUXFhj/ilUFBQUG4evWqUN6rVy/ZYwwePBgNGjSQtXLmOA4WFhYoXrw45s2bh/Lly6NChQq4fPmyrKdPZGSk7PVRCivHKlz79u0rW0cNU6ZMwcuXL+Hh4YEsWbLofH/Za8pew6NHj0Kj0Sgq+OyZkamcF0ViiI6OFl0jPiTUly9fFL13dIVoMwWsAJEXflSqVAndu3fH1atX8fDhQ61t2BBYiUUuBBBgmKcMi9RjgmfZsmWIj49H7dq1UaFCBaFcqgiZMGECNmzYoPMYx48fx4oVK3TWUbK4zZs3r+I2agRCakIw6oL3CNclrBk5cqSWstkQsmTJgk+fPiE8PFzxuQbE31fW24lVurB9G/9OSz1tHBwctM5H6un477//ah1fySM3ICAAd+7cwblz54x6BpOCnj17olixYvj7779TuikYN26c6PejR4901p8/fz46duwouy4lPGVM5cmiNE411ls8OWnUqBHSpk0reINv3rwZvXr1wrx587B7925BCKzv3hK/Nl++fMGuXbtUKSp1eW3oo1u3bjh//jwAyI4pWLp27QpPT0/h2fTx8VFsn1R5OGfOHKPbmFpJmzat7DhQ7tu6cOHCJGsHG8Jx6tSpokgSycmrV6/w9u3bRO2jT58+cHBwQPfu3VGsWDHF7xOQYOjTpk0b1K5dO1HHJIg/hfnz56Nr167o378/ChQogOHDh8PLy0trvte1a1e0a9cOLi4umDlzJsLDw4Ux//Lly1GqVCnMnDkTbm5uKFmyJNauXYszZ87g6dOnssd9+vSpotElX660rRQHBwdYWlrC1tZW8P5JKtnk74LBShkrKyvZD0lYWJhBIRUIgpBHSdijy/IYEAvBk6rjYwXM8fHxepUy+gT8Ul69eiWEZ2rVqhU4jkOlSpVEdVhlCHt8XcKJGTNmYN26dbh69SoWL14MIOEanThxAvv378eNGzdkt+P7NKlSxsbGRkvY1LhxYzRp0gT79+/HtGnT8Ndff2H27NmqzjspYYVrbIiux48fC//nzp0b6dKlw8yZM/WGH2CvMz/ROXbsmFDGC0blwpft3LkThQsXFpVJlVuRkZGCtb6SwJnF3d1d9p3h7x07+WDrsYIfVhGaIUMGrTBJPEeOHEGxYsUQHx+PW7duYcyYMQgNDRWFahozZowQ3kRJwK2klFHy9oqNjcXMmTPRu3fvRHtY6RLysrD3mVUK1a1bV2u9EkpKQGN59uyZSPB+7do1eHp6InPmzKL7yYeNAkxnSf7582ctV205njx5IvwfHByMKVOmyNYbO3asqrxcSuzatUvn+6HWU2bo0KGqj2llZYVTp06JFBvS91xNn5cmTRpFLw9HR0d4e3ujZcuWWLp0qZaXgC5Psblz5+o9Nv/NSGxoPV1KmcR6QmXMmBFWVlZ631U+rCaQIEzjOA4cx6FIkSJCOfv88/21tM/NkiWLloJFrn+SethIt/nx4wfGjBkjhIaUq5NS8IrCxAhGk4NGjRoBSAhzxOPq6qr4vCWHUsZUwrROnTph6dKlwm+l51tX+DBTERsbqzUWMZSwsDBs3boVALRCi/DzZGmIRZakyLtFJC+1a9dGq1atFL/zLMkV4l06V1yyZAk6d+4MIGFMdvnyZaF/T4w3Z0pgrJGN3HdIbp4sDZ2dlEYF+t7/devWwcPDQ9YQzFiioqKQJ08e5MyZU++5hYaG4tKlS+jWrRtKly4t8rjilc68pzhvRClHYhVABGEsYWHJu5iKR48eacm+KlWqpGXkwcpr7OzskDZtWnz8+BEAcOPGDZw5cwb29vbC4ubmBiBx3yJ23kGYFoOVMo0aNULv3r1x5coVYQJ4+fJl9O3bl5IYEoQJUBJi6PM8YIVjphaG8rAhG2JjY/WGi9EX4kWK3CDZwsIC3bt3F36zyl9DBMxdu3aFh4eH6IOSJk0aNG7cGI6OjiLvP+mxeMHH8ePHASQoZ3jhNM/+/fvx77//itw+dVl1K7F//36thNSJgRViZ8+eXfi/YMGCGDNmDKpXr67TnVUKGw6MnyDVqVNHED58/vwZHMfJPq9169bVsoJNrDLf1tZWdpDAt429r0qh79j3pXTp0jqF5V++fMGWLVtQqlQpzJ07Fw4ODqKE4ew1jo+PF+Vh4mGVMmxscSVBtbOzM8aNG4eVK1cm24CID9mnhBplh6n7IV39DX89p0yZIgrvYSpjkUyZMqk6Z37QCyRYaHbr1k223pw5czBlyhSjwn1wHIdWrVph1KhRinXUespIhb1sDhMpckl8DRG6BwcHC4IIpb7748ePmDx5MjQaDfr376+V62r69Ok696+PM2fOYPbs2YnyZAHU5cwyFrX5ylhvNSVFMttf899X6Xf24cOHWs8B61XIU7RoUVEIFjYM14gRIzBx4kTMnTtXMHwA5J+Z5OLYsWNCPGuekJCQFGuPPrJnz44DBw4gNjZW5PmUKVOmJFfKTJw4UbZcY6bR2c8YwubNm1Xl1IqKihLlQkoKtm3bZhIvFt64RZrPiu/D2LErkKC0vn37NubOnQs7OzuTjvWI5OfWrVsAtJVyciTlN0MfvKKGDylTuXJlnd9SfdSqVctUTVNNtWrVBG8hnm3btqnaVs6YSc38MSk9K/UZhnTv3h3Xr18XGRmdPHkSCxcuNDoMJxuuTp+njoeHBypWrIj169fj5s2bgsGAobBjxKSSTRCEHHZ2ybuYEulcn+M4rTKpgZVGoxHesfj4eDRu3Bi3b98WLQEBAYoReQoUKKDoicmPl1xdXQEk9J/Sfii1eMb/qhislFm8eDHy58+PChUqwNraGtbW1qhUqRJcXFywaNGipGgj8Yvy5csXNGrUCLt3707ppqQoJ0+eVJWsnEdJiKFPuMgKwQ0RhHTq1AkVKlQwWDh44cIFk1v6KXn4sHG/2eswZswYuLm5YfTo0Yk+touLi1YZfyxeGMuHaHz37p2s0EqKubm5zhw6vXr1Et2rnz9/omnTpmjcuDEOHDiAcuXKCUnNDeXHjx/w8vJCzZo1AcjHpZ89ezZOnz5tkPWZnKcMkJCXQKPRIDo6Gp8/f5ZVytjY2Ii2qVOnjqqk87qwtbWVFUzx927ZsmVCGSvY0mXp3qlTJy2lGwtr7SslW7Zswv9xcXE4fPiwVp3o6GjhfatcubJQrqSUsTdlsFqVsF5VfD6JggULCmVqnhlTT8CkCZlZ3rx5AyDBc4rtR0ypxDLUStPc3BwODg466xgjqFEjTGjSpIkQskSXMkn6HrBKRTUYco+zZs0qvKtqFerSQb+u68krxPk6/v7+WLlyJV6/fi3ka+rSpYtW+ChjSExoPmneHim6+h4WNUp/VkHJh/iUvhPDhw9Hvnz5MHr0aFSrVg2AssAmR44cePbsmVb5ggULRN4dPCmllPn69Svq1atnkCdYcsB6lUrhvx1p0qSBo6MjKleujCpVqiBLliyK30lW6ZYYpkyZIvut4uI51KpVC2fOnFGlUDEEtg+Q9iOmGNPpgv9eqGHw4MFaufN4DA13NG7cOJQsWRJjxowBAC2lDfFrosbz0lS5mADj84bySoYbN24oKmL1kdh8KFJjPT5kWtGiRRW3OXbsGM6ePQsPDw8MGjRIKFc7jihcuLBorC23LW9hzsIaTZmaBQsWqKp3584dfPz4ES9evEDt2rUxbNgwrbDeamHHe/oEqNIwRQ8ePFB9HH9/fxQoUADLli0THcfYdhPEn0ShQoVw4cIFUZm/v7/OfK5SSpUqhQcPHsDZ2RkuLi6iRSlEbNu2bREQEIADBw5orfPx8UGmTJkEz2lHR0ctQzip56WlpWWq8Zb/FVCtlGnWrBkOHjyIdOnS4d9//8XTp0+xa9cu7Ny5E0+ePMHevXv1Ch+IP4sJEybg0KFDIou/P42XL1+idu3a8PDwUK1BVurA9A3o2UmjIYKQzZs34/Lly9i4caPqbYCEAawuhYMxKA2wWfdt9phOTk549OiRSeIgy4W+kXrKGEOmTJkUrcpWr14tUhqwAtomTZrg6tWrglLFUGbMmIG9e/cKllGm8hiQ85Th9581a1YACYN3NpcNj7m5uUgguHHjxkQrZaysrGBlZYXHjx+LrFX5trHtVfKUkYP1eJCia5DBhsLTVY+/L6zChb9+LCmVl4hVOnbv3h3nzp0ThWJh42Mrkdik2hzHYenSpUIYuJs3byrWDQ0NBZBwPdVO1A3FUKVMYGCg3r5Ybe4lHiXluYuLiyjM0Nu3b4Vvji6lnlQRbqgSS59S5sGDByhXrhxGjBgh2rcapTYg/wzt379ftm5YWBg4jhO8IbJmzYrevXsjZ86cwvMhh6HJjaOiomT7N7U4OzvrXK82ubCaep06dcL06dNx7tw5xXvL5wWbM2eOXoFbcHCwIFCWKvDk+ruUUsq0aNEiRY6rD10hENkxopmZGc6dOwc/Pz+YmZmJ7jXrfSfNyWYsGo1G8Zun0Wjg6empmEcsMcfkMbQfTCyGWHR6eXlp5c4zFUn1rSKSny1btuhcb0orYn1RCpRQG9q6atWqioa2iVHKLF68WKS8z5kzJ0aPHo1v377hzp07QlgsQOzlzobyYY0qdClypJw/f15kUJAmTRrR++fo6Ki1jSH7z5UrF27cuCFqX65cuVCuXDnZ+rr6FNb7HkgYy7CGdQ8fPgTHcVi5cqXWeemC/UYb0+fy4cXlKFiwIDQaDT59+oRKlSohICAAAwYMEH2vUsLDiiB+NUaNGoX169djxYoVCAgIwIIFC7Bnzx6D8pEOGDAAX79+Rbt27XD16lW8ePECx48fR/fu3RVlE23btkXz5s3RpUsXrFmzBi9fvsTdu3fRp08f7N+/H6tXrxYUOjVq1MD169exceNGBAQEYPLkyVohjp2dnXHlyhW8fPkSnz9/Jk85PageDUZGRqJZs2bImTMnxo8fD+C/HApyFuYEQXFExdZ4uoRCLEpCDH0W1ayVj9rBP3ssY7xe2ETDQEL8bDWxlZVQmqCyfYwxIcHUUK1aNQQHB4smIryFeWJDhOzdu1fRKmrIkCHCoNWUHyxpPh9TWdSy91w6weMFrU+fPpW1OgMgmjxkzJhRVlAoF/KL5Z9//hH+5wcXBQsWRPHixbXaxt47VrClTxiiS4ml1vMtLi5OMQEmr0RlBeZsTpkWLVogODgYO3fuVHUsU8Na0tjb26NKlSoGCwCNfZ4/fvyII0eOCNbZ1atX17sNb9lvbW2dZGFCDFXKdO3aVW9fbOjEWEnBkiFDBiG8Ig9/bF2Js6WCVtazQg284FiJwoUL4/Lly1r5b6RWq0rIKWXYEJEsZ86cEbWf9fZSug9NmjTB2LFj8eLFC1GuHF2cPHlSVT05eGGLNHTS27dvMWrUKEyaNEn1cybn/SglTZo0+Ouvv1ClShVV+zTkW6fmHia3UubBgwc4efKkYj4vADqfV0Pw9/eXDVs0btw4eHp6aoVKO3TokM73/e7du6LfGo1G+D6y36727dvjw4cPJrW8lx5DDlPnKmS//cYKmY3FEAE5f130KVONgQ0nRPzadOzYUet79fnzZ/j4+ODTp0+KISZ5DAnZFyZJYBAdHY3379/rjXig9h3euHEjevXqJbtOo9HoNbhhv72sx4WlpaXovefndunTpxfC/R0+fBhv375FQECA7L6zZcuGJ0+e4Ny5cwZZjgNiYywzMzPBoE+aS0aObdu2YcmSJahYsaKovFOnTjhy5AhevXolJNbmsbGxQb9+/UT1+XNWOuaPHz/0etANGjQIK1asQN++fVG1alW4urpCo9Fg2rRpOrdjv8fG9Ll9+/ZVNO7g77M0N6Yur8QZM2agePHiqaYf/PLlC6ZMmaLXm5kgkpJmzZph0aJFmDdvHooUKYKVK1di3bp1BnnuZc+eHRcvXkRcXBzq1q0Ld3d3DBkyBA4ODoryD41GA19fX/z111/4+++/4ebmhipVqiAoKAhnzpwRGRXVrVsXEydOxOjRo+Hh4YEfP34Iuct4Ro4ciTRp0qBw4cJwdHRMlDHbHwFnAK9fv+amTp3K5c+fnzMzM+OqVKnCbdiwgYuIiDBkN788ISEhHAAuJCQkpZuSqmnQoAEHgDPwMfutOHnypHANgoKCVG1TokQJYRt26dChA3fixAnF7aZOnSrUXbFihapjhYaGCtsMGzaMa9Cggc5j2Nvbi9ok/f39+3eO4zjZ9qtZ4uPjZY8bFxfHzZ8/n1u4cKFiHVPh5+en9dxmyZJF1M5Zs2ZxHMdxPXr04ABwDRo00Lvfa9eu6Tx3juO4z58/K67TR1BQEFe2bFmudevWXLly5Tg3NzfRPiwtLY24Gtr89ddfwj5fvXolWteoUSMOALd48WKuZs2aHADOxcVFdB5y58WWnThxgvv33391XqtHjx4J/5crV07YT3h4uFBes2ZNjuM47uzZs0LZokWLRO3Vda3Hjx9v9HPMLyNHjtRbZ/369cL/vr6+3NOnT7kVK1ZwkZGRJrlfUsLCwoTjhYWFKdZ79uyZUO/WrVta66Xn0a9fP62yihUrGtXGrFmzcgA4a2trYV/se6lruXLlCnf+/HmhfzKWjRs3aj0Xw4YNM/gZmD59us71Dx480NuW2NhYLjY2VrF/AMCVLVuW4zjxfalatapi/X///ZcbPHgwFxMTo3U8tl5ERISq89y9ezfXvn17xfdJjlu3bnFz5szRWd/Ly0t23cGDB/W2Semc2GXIkCE666VPn16rbOXKlUb1BwC4hw8fCscaMWIEB4A7deqUzuukxNevX7m2bdtyhw8fNnhbpet069YtVefBcRzXsmVLvfWk3widhIVxHJCw6OiblIiLi1N9H0wBv6+LFy8KZfv37xfK27RpI5Sz3yZ+yZQpk+p2jR49Wqhz4cIFk7Rfyrt37xKOYQEO3v+/WPzXpm/fvqm6tjlz5lRVb9y4ccK+PTw8kuQeKbFo0SKt4zVp0kS2nVeuXOE4Trn/j4qKMro/SOrzJPSTmG5H7n76+/sL6wsWLCiUT548WfEZ8Pb25jiO444fP67qmXn27JmoHcWLF9e7zapVqzhXV1e99XLkyCHsNzAwkHv16pXW88qP79UsQr+ChHEce910jRHVjlUNITg4WNjnoEGDuPj4eO7p06dcXFyc4jbXrl3jtm3bJirr27evzveXX1egQAHO19dX8dqsWbOG69mzJ/f8+XNh21WrVqm6rvz805D+5MWLF0I9uXG93DmYeuFh72+PHj10tiW5aNasGQeAc3JySummEAYQGRnJPXz4MMnmzQShD13PoFq9gdGjwdOnT3MdO3bk7OzsuHTp0nG9e/fmLl++bOzufilIKaOOunXr/vGTjn379gnXQI3wjeM4zt3dXeeA5saNG7Lb8QIeANySJUtUHevBgwcGDerMzc11ti06OprjOOMHc6mB+Ph4btOmTdzjx4+FMmk7J06cKKyLiopStV99wi6O47gPHz4YfV2GDh2qc/+DBw824CooM2XKFMV2dezYkQMgErTWrl1bVF/uvHhhR8uWLTmO47hDhw7pPBepoJhHKpTjOI67cOGC8Hvp0qWi9kr3u2zZMmGdt7e3Qc/u2LFjtcqkigobGxutOrt37xb+9/X1Nck90kVUVBTXpUsXrkuXLjqfXXby+uTJE631FhYWWtdaem7ly5c3qo3G9h8AuDt37nAcx3H+/v7chw8fjDo+T8WKFUXnx/ax9+/fV9UeJSU7vyj15zxxcXFcsWLFuAIFCnB16tRR3M+ECRMMuna6WLFiBQeA27BhA8dxHPf3338bfT/UoKt+8+bNFdfZ2dmpPrZSnZ49e8rW69y5M9e+fXtZBXGtWrWMvh4BAQHCseLj47mvX7+qukamRuk6PX78WPW5SBX/Ssv+/fvVNSqRSpkfP36Y9LnkOI6LiIhQNATh98V+N6TX5MiRI1xkZCT3/v17rTbMmzdP9NvV1VWxHTNmzBDqXb9+Xf1FMYCPHz8mHINVyliKr5WlpaXea6tvDMsvvFImPj4+UffIGPbu3at1PCXDGb6PVlLKBAYGGt0fJPV5EvoxtVImR44c3JMnT7TKy5cvr/f+SxUgSsvt27eFbWJjY1U/a5kzZ5Yt79Chg85nUrqON8BiFznDHCBBVnLhwgVu1apVwv4WLVrE2dra6lQwqx2rGgJr2DJw4ECj9/P06VMuXbp03JgxY2TXs326GgMSXjllyL00pj8JCAgQ6rHKQ13nYOiSMWNGnevDw8M5juO4T58+pbp+kG078etAShkipTGFUsboYLbVq1fHpk2bEBwcjLlz52LXrl2imJ8EQeHLIApfIXU3V0JfOBulvArs9VYbMkRtSDUgIRSTvv0q5ekoW7Ystm7dKiqzsrLS686fEmg0GnTs2FGU1Fzqrs3G41QbakZfaBCO4xIVvkwpXBiPqcKX6Qp/wIf8YnO7jBo1CgULFhQS98q57M+ZMwfnz58XYnLryvfSrl07xRA7ci65uvLISL9ZpUqVUqyrDz5BNos0Mabc+8Yn304urKyssH79eqxfv17ns8uGvJK752ryWaREPgk+5GCFChW0wigYijTurjTsRpEiRfTug098mDVrVrRq1Qpt2rQRrZfr7zmOE74XP378wN27d/H06VOt8GQsiQkbKaVPnz4IDQ0VXNGluUPUMnz48ES3RdczpC9UizH737BhA9q0aYOVK1diy5YtaNSoERo2bCgKm5mY8GXse6PRaERhXlIDcn2rr6+vbN3Hjx+r2meTJk0S1Sa1HDp0SHXdlStXIjo6Wud4KyAgALa2tujZs6fOfbHhEqVhL+vXr49+/frJhnqThpTTFTqQDR1paBhFtcj16dKxj5qQaWx/oes7yocQ05UrTArHcSb5rsjFVGdDQ7LJxPnrwo4JWaQhOwzl5s2bGD9+PPbu3au1juM4ITRnYomMjETNmjV15jUiEk9UVJTss8Lm5AMS8pWcPn1aVKb23WZDT+kK1ShFLg/orVu3ZMMw6oINezp58mQsXrxYlB8TALZu3YqtW7ciXbp0qFSpkqgfHTx4MEJCQnTKjdSOVQ2B7Z+5ROQ8dHV1xZcvXzB79myd9TiO0xk+lsff3x+A+nxyutA1j1QTvuz169f4/v270cfXF4ps4sSJAMTfEjb0dEqSVN9WgiAIfSQqw+CLFy8wb948zJgxAyEhIZTAixCIjIzUSvj0J2KMUkafoEkqeD5z5gwqVKggynOhNl62rkTkUqQ5SnSxZ88e0e/4+Hjkzp1bVLZ7927FmMGpjXTp0ol+GyMU0BfLOS4uzqD7IUVfjhRTCDAB3ZMGfkDLCrpy5cqFx48fC7GbFy5ciIcPH4raY2FhgcqVKwsTJl2CHF65179/fwDKE9LSpUtrtVe637///lv0mxVI6sopI0fdunW1ytjrYGFhAXNzc63rxwplDU2ynpSwE8kcOXJorWevpZLQVilPFcdxCAoKkl2X2HwPic3/xCJ9z9n7Y2lpqaV000WHDh3g6+urlcNF7r3s2LEjMmTIgGfPnon6BKWEscB/77+c8lVNvHQpfH4oAGjUqJHB2wPGK3NYTJUfSMkAoHnz5qLfnTt3xvbt2wXlnpmZGQ4ePIj//e9/JmmHKYQuSYnc+2NoniHAdEYAhtC2bVvVdfv27QsrKyvY2NgI46Xo6GiEhobi7NmziI+Ph4+PDwD5HGesQO/9+/eYMGECXrx4ISuMXb9+vWwiZisrK9SoUQNAwvs2atQoxfaywiL23TQlcmMU6biCfaefPHkiu588efJgwoQJmDx5sk4lDt+3KQkHV69ejevXr4uE2R07dkTu3LkNMijiCQsLQ40aNbBkyRKhb3dychLWZ86cWRASz5kzB1myZIGZmRkKFCgAAOjRowcmTJig1RecP3/e4LawlC5dGrNmzYKXlxfevn2L+Ph4hISE4Pv377CwsEC6dOmEHHSJ4cqVKzh9+jT+/fdfxXtHKLNjxw4cPXpUVObq6mrUvgoXLowzZ85o5cvLkiWLzuTy/Lj05cuX4DgODx48UMyxBugeM/A4ODjoHXv26NEDwH8Kdg8PD2Gdt7e3SIkJAJkyZUK7du3Qrl07xX2mxLeQHbcmRikDqG8/+y3U1cebik+fPimuY8e0cgYJT58+Re7cuUUGaqaGz6/KHj8pj2cI/LiPIAgiuTFYKRMZGYmNGzeievXqcHV1xaZNm9CzZ08EBgZqDVaIP5fg4OCUbkKqgJ04SgWUT548kVV06LMykSq7atSogcuXL4sm/WqVBoYoFwxRoDRo0ED0+/r161rWQmnSpNGrSEgtSJUyuiZNSug7158/fyZKKaNvUiW1yDOWsmXLKq7jJ4zsZCdTpkxa9QoVKqRTaKfGS2Xp0qXgOE7WQwUAWrRoAUA8cZJOotiJJSAWSBrqKaPRaLSeC1bYtH37dgDagiz2OiSHUobjOISHhyM8PFznpNTMzAzfv3/H58+fZScqrOV2zZo1ASRYn7NILXx5C77BgwfD2dkZK1as0Fp/4cIFxTZlzpwZQEIS2oiICFkLRFNOqqTvIytg1HWvpEncgf8U5VJl38OHD0W/f/z4ga1btyI2Nlaw5ue5cuWK3jZLk4UDiVdUGWM92KBBA0FxmhhMlcz82rVrsuW6BFoshiQG14Wpk6Uby5gxYwCIvRoB7X6vd+/eqix9pSgpZE3Nz58/FRW8avH29sa7d++QJUsWODg4oHr16lixYoXO/pF9LufMmYMZM2agatWq2Llzp+rjWlpa4sSJE/j27RtCQ0NRsmRJxbpsfyMdj5gKOSGjdNzCJ7EeOHAgChQogN27d2ttkyZNGkybNg3e3t4AlAWRfP+qZDDSq1cveHh4oEKFCvj27RuABKOM4OBgODg4aHke6GPJkiU4c+YMBg0aJGvMYWFhgQsXLoDjONjY2ODVq1f48eOH8E0xNzfHtGnTMHDgQMVnQ9f3Sw3v379H/fr1kT59emTIkEG4Rvy1TAxsH7Z48eJE7+9P4vnz52jbti3q168vKpdTkKrxbHrw4IHs2BhI+IZzHIclS5ZojWd444LRo0cja9ascHd3V1Rq9uzZU9b7SooaA6TFixdj165d2Lx5M4CE5PaLFy/GjRs3RPWWLVuGXLlyCUnfE4PasaohmFIpowaO40Qe2x4eHorGKomJlMDi5OSE2NhYWVkC29dKn5vQ0FCUL18eAJI80f2dO3cwdOhQxbakFHLzhydPnuDw4cMp0BqCIP4kVEtE/f390atXLzg5OaFPnz5wcnLCsWPHEBgYiEmTJiFXrlxJ2U7iF+PLly8p3YQUJS4uDhzHYdeuXUIZO+i4e/cu3NzcUKJECWGyCQDfv3/Xaxn8999/63VX55Utz549w9ChQ/H69Wud9dTAezoowQ5a5IR40sFOahFOqYENawFAKwSRGvQJ+X/+/GnwoDwiIgL+/v54+/at3nfOVEKy6tWrY/fu3VrCZOC/yR1rAeXo6GjwMQxViMjBW7zqCl8GABkzZhT+Z59RY6z4pMLLyMhIwROmUKFCABIm1bxAFDCtZ4caIiIiYG9vD3t7e73PhIODg6LggD0H/hpK67L9zu3bt5ExY0ZoNBosWbIEAISQdgDw7t07ODk5YcKECYrt4UNvZM6cGTY2NggLC9PywDOlUkYqiFdrnS0nNOX3JX0Ghw4diqVLlwq/ect5IOH658+fX3V7AcjWN9TrS4oxffWhQ4dM8mxnzZpVcd26detU7ydjxoyCtS+LWkWoUj1eSM1Trlw5PHr0CK9fv0bTpk1x+vRpNGzYUHU7k4vZs2cjNDRU9LwBCc8cS58+fQxWynTo0EGvBfmzZ89QpEgRg8PmSKlYsSKcnZ1x7Ngxo/cxc+ZMIZwOz6ZNm3Qq4uQ8XwwN2WtpaQkzMzORglsJ9puaVJ4yasKXdenSBYGBgVi4cCEAwMvLC1xCflKhjnS8pBRakR9/yo0lpPj7+2t5i1SoUEH0++fPn5g1a5aiQJj1VucNAt6/fy+USQ1FrKysDPb4khtj7NmzR2+YI57Y2FjZMJWJDcUpJTHhif5E2FCN7LMu10eYypBgwIABWgqeEiVKAEgYW+nyiACAVatWIVu2bHqPI31m5Z5hW1tbtGjRQuh7zMzMMGjQIC0Ph379+uHVq1eicbWxGDJWVQs7lkkupQwbojg+Ph716tWTrbtx40aTHdfCwgKZMmXC8+fPReXsN46VSfz48QMODg4ieQQPP143FOlYgqVEiRI4cOCA8FvNNyA5kJNduLm5oWHDhgYbARAEQRiCaqVM5cqVcf36dcyYMQPv3r3Dtm3bKFwZoYiSNf2pU6cwatQo2cFQeHi4bLzbX42oqCi4uLigatWqQj4BQDwAYgdl7KRQzupQTqDWrVs3wfJejtjYWMTHx8PV1RWLFi1C06ZNFevp4u3bt4iJicH79+9x69YtxXpz587VsiBzd3cX/pezlPyVlDLs89q+fXujlAb68ufo8pRhhbY8165dg52dHSpVqoScOXPqteThXcZNgZeXl6BkYOGfVX7Cr8vyVxfS62tmZoZ//vlHZ04NnsePH2P//v2oWrUqAN2eMtJjsULk/fv3G9xu6aD92bNnwiSH3Tf77P+q7vI9evTAsWPHREpkubwJPN26dRNNCAGxFbaPj49eIQMPe82kwi5TXk/pc6i2z2JD4vDwfa1c3zFw4EDExMQgLi5OFIZy4sSJJhFGWFhYpKrQeIYwceJElCxZUvbb2LVrV5w7d06Ix66PFStW4MmTJ0aFVVMSpLB5c+7evYvLly/Dzc0NOXPmxL59+1C9enWRYMxU1rCmQE64r9FoEB0djZEjR2L27NkoVaqUXuUaL6DnWbJkiV7P2urVq+Phw4cmyccBKN8fHhcXF3Tr1k1xvVShYmVlJVL6ScesLVu2NLSpWhjigcb2s0kV9keNpwwAODs76+wL69SpI/otfX740KIrVqxAwYIFVeWeatSoESpWrKhVzn9fX79+DR8fH4wfP14IISe9Z0ptfvHiBV6+fGnwuE4uVJ10Hz4+PmjevDlGjx6tKmSYkgHVtWvXEB0djWnTphktHGSvh77clYQYdmwSHx+Pw4cP4/bt28K87q+//hLW6+svdfVDUqTvZFIoZPn2/vz5E9u2bcObN29MfozUSHIpZdh7mD59eq2wyTyGPBdq2bFjh+g3G9GG7QN05fUaMGCAUceWjvd1IefhnRKw32SNRoO5c+cKv1l5DkEQhKlRrZS5fv06bt26hYEDB6a6xKTEr0OtWrUwf/58rF69Wmtdjhw54Ojo+Mt72djY2ODly5daYQxYpQwb3o2d9MpNGuWsrl++fIl27drh5cuXsm2IiYnBhw8fhN+3bt3CwYMHterpCpd17do15MyZE7Vq1dKytpJa7ckpjtiyGjVqaCkl+HM1ZYLqpIKPKQ6oC02ghC6rtejoaMX7MXDgQC3LRl1hxORIjmTL/D3nBSXGhL0BtAUb8fHx6NWrF2rXrq1324IFC4pCEunKKSOFFeh//PhR+F8p9BwvfJLmCZFDSSmTWC+GlMLKygp16tQRtV/Oq4Gf+MqFqOGf97dv3+LixYuqj83eJ2nMclMKLNesWYMCBQoIHo8TJ05E4cKFBctBOU+Nvn37yj4vSp4yPDt27BBCg+hj1apVOtdLlaGfPn3Cs2fPjPJaS2mKFCmCmzdvwsvLS3Z9lSpVUKFCBRQpUkRULneu5ubmQsglV1dXkaWmPqysrEQKM7Z88ODBaNOmjcgQgYX1CFNjuZzSWFhYYN68eYI3nL7Qm9JvkZznhzTvBiv4UxO2k1da8jx79gyHDh3Sux1PQECATotfqaBb+p6ySsGPHz+KLOeNxZC+nzcyKFasWKKPq4TcfTYkxOyLFy9w6NAh2e80+03mv5fx8fFaXi2GWmUHBgYiIiICuXPnFgnGNRoNzMzMBIOWli1bYurUqVrbV6lSBXnz5lX1DZci15dLvz+9evUS2sOOI5Vo1aqVbPmTJ0+wadMmTJo0SctDSC368kkQyrBKGU9PTzRs2BAlS5YUPObq168vPOP6QjEZmp+M93YYNGgQjhw5YtC2ujAzM8PRo0cFRY+lpSXatm1rcq+s1EpyGEjw49+xY8eicePGqFWrlurwk7ly5Uq0vI3tEwFxHk02zK4+o5158+Ylqh1qSColmZ+fH+rUqaPoQfnjxw9UrVoVixYt0jLqYiMC/EqGpARB/HqoHm2nliRcROpHaYLNfnDlJrS8VYVS7PdfAV2DimPHjsmuZye9csIMXQM4JYsmOQ+Yxo0b4927d3rr8fCKs3Pnzmmtk3plyE1OWYsTS0tLWFpaonv37kIZP8AZN24cNmzYoGghmBpYtmyZ8H+XLl2M3o+uUBj6wpcZk9iWJTny9/CebvxfY5M9m1JRwT6bcgJ7VnnJDsjZeyF3Ho6Ojti8eTMmT56sN5wgoKyUYf//Vb0ZdBEbG4vg4GBZjw8+nEzOnDlV5Uvh0eUNY8prWKpUKTx58kTIT5Q7d248ePBAsByUS6KrNHHTp5T5+vWr6hAOLVu2FIXikj5/gwcPFv22srJCvnz5jMqFxbN+/XoACbHl9U2e+RxDauFzkFWpUsWotgHAv//+K/qty3umfPnyePr0KRo1amTQMaTPHX89Fy1ahO3btys+e66urggJCUFMTMwvk0fNEOSeaak1cNWqVUXho1j0hWyNjo6Gm5ubSBjt6uqq+v7xIeZsbW1FYxBdnDx5UvSbz8l27949nSH1DMGQ71zp0qXx9OlTk+WGU4sh/WnevHm18gnysH1CmTJlZOs4OztjwIABBuWiatOmjU4Py4EDB2LmzJlanna8182wYcNUH0uKXF+flJ4NciHzDIENq6XvnSPEsGGd5PIGmZubqzIaAgw3VsqXLx84jsPixYtN6sXi7++PunXrmmx/vxrJ4SnDM2vWLOzfv1/oM+Tm1VJev34t+20tXLiw0e1g+14+ekJcXJwov4scI0eOFP1+8uSJoORev3492rRpoxUeVp/nj9RT1NRKsk+fPuF///sfPD09ceLECVnv1rCwMCxZsgTnz5/H0KFDdXqvJpWHKkEQBGCAUoYg1KLkssrG3pUqH9jB0a88WWBDkUnZu3cvChcurGVFxSpG5GIR61LKKF2r2NhY2QEn76Fz69YtVKxYEWfOnJHdfv369TqtQkqXLi0KJSEnXGAHN/z/bIxZfv8WFhbo3LmzKO5uaiNHjhwIDQ3FlStXFC221cAnKpdDV/gyQGx9JxfKJzXAeoABhrmvs0jDP4wdO9boNhniKcPWZUOqsO3hrcIfPHgAR0dHeHt7w9nZWW87lJQyv6MihmXkyJHInj27Yr4FYyx2jUk8nxTITdKUBK36lDJ8Ulu1x2Wfm2rVqmnti4UPp2ZIDjEpXbp0wefPnzFw4EAACUmElTA09N+iRYswaNAgvR5AusifPz9CQkKwZcsWhIaGwsXFxeh9KSF97gwJZ5EuXbo/alLPJwxm2bZtm2xduT7g1atXwv937tzBixcvcO3aNVUJgcePHw8gQRHAcRzGjRsnrFNSCOiDN2gxpaeKof2Yq6urYo4vU3H9+nUcOvyfB5KZxjTTRH0e4QDQu3dvAAlhCdXy7NkzveMMqbU48J/SNjHWz3L7NTc3x7Nnz2BlZYX58+cbvW851HiUscTExIiE+Oz8gs9NQigTFxcnXD994xSO43Dv3r3kaJbB3LlzB2vXrsWePXuEsnfv3skalfxJJFf4Mjl0hfplkVPqSr2CnZ2dsXbtWpFHLkt8fDwqVaqEpk2bIigoSGv93r17dYYnl6NAgQKYOHEiwsPD0aVLF2zfvh2rV68WhROuWbOmyDN4y5Yton1I5RemNjho27atyEBJeu737t1D2rRphfECIG8Yy/M7hNcnCCL1QkoZwuR8/fpVtpydEEiTI+pTTKRm4uLi8OLFCwC6lTJAgoeQ1OqeDUsld+66BPlK+QZiY2NlE1Dyk7o6derg0qVLWrHgebp166Y3JjwrfJQTNLLreeEDW/aruQKnTZsWZcuWTZQQnRVMSJM6/vz5U6fQlBXYGhrP3hTx79XQsWNH0W9jlTLSd0SaTNsQWEGoIVbqlSpVEv5nLeQvX76M+Ph42fBIvMBaDvYdUXr2TZEcNbWxePFinev15feQm5RKFdUfPnxA+fLlcerUKcMbmAikQvacOXMKE2OpxydvVaiklAkODlatSDA3N9dpVSj1IMidOzcACBbo0uTuasmUKZPQ/+nygjPUQ87FxQWLFy+WzdFgCOnSpUP79u2TLBE6+94+fvz4t/R6MQY5wVOZMmW03m3WU4YVLPHGIaxQk43lznoIKI0vWWbMmIGXL1/KhvpRGzpGirOzs2L4E2NJjaErS5cujWpV/1PymuoZZ5V0SuMCvm/Ul5NDippcLUoYKoxkadWqFaZNmyYqMzc3R/78+REVFYURI0ZobZOY3H5yIYQePHiAjRs3yr6Dbdq0Qa5cuQQv1EmTJgnrNm/enCgl/Z9A8+bNkStXLpw8eVLvvNTBwUErsXnZsmXRp08fk7ZJrZdN/vz5hf+LFSuGbt26CSEQgaTJTfOrkRxKGaX5opp55L///isK9f7XX3+hUKFCWsrewMBAdOvWDXPmzNEK5wskeFL7+/tj//79Wl6Fbm5uisocKVu3btUqk471WCWMvb09ihcvLvyWM9RgkeYiSyxSJY90zDx9+nStbXQpvkePHo3OnTtDo9Hg2LFjpmkkQRDE/0MzSsLk8BZF7GQuPj5eNKiVTgbYdb+aUqZz587Inz8/fH19ZRUh+mAHXXLb8wI1OXQpZeTirfMDDjUWH9LwHVJYgYI+Txl+PRue51dTypiCpUuXwsHBAX369EH9+vVF627evKkzfI9S6Bd9hISEwNfX16htDUVqZSTNg6MWdqDPCoKNgRWCG7IfFxcXDBo0CN7e3lqeLUr7URt+Sfq+rF69GsOHD9fyePgTYMOCsJQqVQrR0dGyebOkcbazZMmCS5cuGa1sMBapUubVq1eCV0qZMmUEy8AiRYrAzc1NdhueOXPmaIWF1HVcXQocR0dH0cSd9+5r3bo17t+/r/o4+togx5AhQxK979QKO27R9V3+02BzZfCeHObm5lqhdlghGKsc4Y0GlBIKsxau+pQyvDAyT548sgrQ1q1bC/8bIvy3sLCQFUoZCp/kHvg1xkCmUsp4eHjg1KlTeP78uaKRBX+/DFVWsffUUNR4XuliwoQJIsMpfd5w+sIEGYq7uzu6dOki8oLg2bt3L4AEYei3b99EyqsPHz4YnNvkT4PPOTZr1iwtL3Apbm5uWl70CxYswIoVK5AjRw6hLDHPKgDZ3KAsfO5IHx8frXWZMmXCzJkzMWvWLNjb2yeqHb8DyRm+TA7WG1/K27dv0aRJE1FEigkTJuDhw4fInTu3MNaVKqTlvC+limOWJ0+eIDAwUHF9w4YNhf/btWuH169f61RcsHO3tGnTaskBEmNgZwhyin9pu+VkTfqiUGzatAkAUK9ePfTr1y/R3w/iz4WXJSgthngMmxpnZ2dFo20p/v7+aNCgATJkyABra2sULVoUPj4+Bnv2rl+/Xqen2p+AwaPtVatW6bWgJ/5s+A8d+3GOiYnRGc/4V4h1/OHDBzRt2lRLoMVP1KdNm2aUQmnt2rWCyy+vlGEnpcYoZWJiYmRDyJhC4bVmzRoA+j1lWOEJPzBjEyv/CgIJU1OyZEm8f/8eK1as0BI89OnTBz9+/FDcll9n6EQiXbp0yRYiS5pzQUnIZsh+vnz5kqg26ROS6ArjsXjxYkyePFn1NW/ZsiV8fHz0Jq2XWjv26NEDPj4+yXKf0qRJg5YtW6Jly5ZJ8g6ePn1aFA7AWA4cOAALCwvZvsXQmOxJhfT6Se9fu3btcPXqVVEScVbIuW/fPlH9EydOqD7ukCFDMGbMGFy6dEm2zuDBg7FixQo8ffpUOKZGo0GRIkVMEv5N7r1yc3NTPZD/FcmbNy/y5s2LokWL6sxr9KcQHByMt2/fwsHBAdHR0di/f78oR5dGoxFZo7Lvh9yE7cbNm7LHYZVhX79+1eklpi83lYWFBUJCQnDs2DGDDB0iIiJUJWrXxdOnT1GoUCHh968QutLWzri8cHLUqFED+fLlQ65cuWTX82MipfUA0LdvX5O1BzCNYJYVXOobb2g0mkQL5oEE7zE2j5Y+b2g5L1xdQuE/CdaAge2/eE6fPi2r9OIpWrQoNBoNKlSoIMqHxHtbs2OYxHhKARB5u0jx9fXFvn37EBwcjKZNm8rWGTduXKLCAScnST1WTWmlzPz58xEWFiYbtpX3MrW0tMSLFy/w4sUL0Zjj2LFj+PTpk9Y3Se7bqCbnpRLSb1TOnDl1KurZsbm9vb3o2beyssKgQYOMboshNG/eXKtMOuZIrEJlxYoVWrnzCEItwcHBwrJw4UKkS5dOVLZo0SKD9pcSBu179+5FtWrVkDNnTpw5cwaPHz/GkCFDMGPGDLRt2zbF+9hfDYOVMj4+PnBzc0P27NnRrl07rFy5UjZpO/HnwncM7MdZqpSRKhN0edGkFsaPH4/9+/eLJmAsERERRneKfMxzfnvWisnV1VX4nw3pASiHgYiNjZW1Gk/sIKRfv36C9l5fKDJWMM1bpM6YMUPnNn8C/MDaUMEoL0AKDQ01eZtMhVRQycbyNwRTCqtYIYncfvPkyaN3H2oTUJqZmWH48OEi6zY5OnTogPz586NXr16q9mtKrK2tsXPnTuzcuTNJBMvVq1fHjBkzUKpUqUTth52UsmzevDlR+zUl7LMll0Bco9HAw8ND1J+z/R4b6sEQNBoNbG1tMXv2bMWQEBYWFujTp4/o+2FKpMLH69ev4/79+0lyrNSCubk5njx5glu3bv0SAvWkxsnJSXhPLSws0LhxY63vOhu+hB1/GGJFxxrqfP36VVGYXLduXVU5V9KlS4c6deoY5AUSERGRqETE5cuXh6ur6y83Sd2+bbvJ97lhwwbZcjZ82adPn2RDXy5cuFC18loNiVW0AeJxj768dQDwv//9D2PGjBGVLVmyxKBjurq6olmzZgZtI0eDBg3Qr1+/VJsPJamJjIyEt/dk4bdcyEN9sGP5tm3baq3fvj3hHRoyZIjIa8YYzMzMBM+sXbt2YeDAgTh79iwWLVqEFi1aQKPRCN66vzpJPVZN6b5Yo9HAzs5Ob34Z3hiExdzcXDa0uTHPry5KlixpUH32m2pnZye6xpaWlno9tORy3hiDXL5cXrb0/v17/PPPPyYJQcbmFVuzZk2qmp8QQHh0eLIuhuDk5CQsDg4OQt/t5OQECwsL9O3bFzlz5oStrS2KFi2qlZPR09MTAwcOxPDhw5E5c2bUrl0bQEJOT1dXV9jY2KB69erYsGEDNBqNKHKJv78/qlatChsbG+TKlQuDBw8WQuR7enoiKCgIw4YN0xkZJDw8HL169UKTJk3wzz//oESJEnB2dkbPnj2xYcMG7Nq1S4jScvbsWa023L59GxqNBi9fvsTZs2fRrVs3hISECMf09vY26Hr+DhicdfTx48d4//49zpw5Az8/P/z999/o378/HB0d4enpKQw+UjvLli3DvHnzEBwcjCJFimDhwoWqQ88QulGjlJEmNGYn3ak1fJk0FquUyMhIITyXtbW1wQmsr169KnjK5M6dW/A0sbOzw4sXLxAeHi4kJ+VRcrWNjY2VFforedaoIU+ePFi2bJnwmxWWnj17VtYyhYePtczGkf9TlTI8hobo4D1lqlevnhTNMQnSiVNquMeWlpZwcnLC9+/f4e7urrVejXDVmMnb+PHjFV3106ZNi4CAgN9asGts7gYprJArf/786NChg0n2awpYxYTavE3sO6GkmG3RooVi354UCeyNQaqUYcMy/c6oEbr+zqRJkwYODg7o3Lmzqvp169YV/mfHRPqUMpaWloiLi0OHDh2wY8cOoXzmzJm4fv267DbSnA6mJCgoSCv01N69e3WOe1j4vj4xip2UwFChnBqUhJDsu5U5c2YMGjQIERERgmX/zp07YWVlhVq1aiE4OFhWqf327VuDBN9yQvTEoM9TBkgItzl79myEh4cLyhhnZ2eTtkMtvBB31apVqdYgLimRzkX15UHMmjUrRo0ahdy5cwseT+x3vEqVKli4cKFI2VeuXDnEx8ebbLwXFBSEiIgIZM6cGS1atACAPzL0bWJJjr5YzT13c3ND8+bNhXCDifEmqVmzpknkcJcvX8axY8cwatQog7ZjFUXOzs6icawaQ0RnZ2eEh4cbnJeQRSmMPH+/a9eubVIDom3btqFmzZro2bMngIRcXn/6ODG1YD8recM0cpNNo+iNiopC6dKlMWbMGKRLlw6HDh1Cp06dkC9fPpQrV06ot2HDBvTr1w8XL14Ex3F4+fIlWrZsiSFDhqBnz564desWRo4cKdr3vXv3ULduXUybNg1r1qzBp0+fMHDgQAwcOBDr1q3Dnj17ULx4cfTu3Vun4ejx48fx5csXrf0DQOPGjVGgQAFs27YNbdq00Xu+FStWxMKFCzFp0iQhJOOfGGLTqGDBTk5OaNeuHXx8fLBo0SJ07twZX758wa5du0zdviRhx44dGDp0KP766y/cunULVapUQf369fHq1auUbtpvgZrwZRcuXACQEM90yZIlojASqTV8mT4helRUlDCYioqKEuqrdRUvV66ccI2yZs0qlFtbWyNv3rxwd3dXbdkZGxsrGwqLV8oUK1ZM1X5YpAMq9ne+fPm06rMCcD6pJDthTQ0C+5REo9Hg/v37qpO78/dTV2JaDw8P+Pv7CyGEkju/g6EJepMDjUaDJ0+e4Pnz53B0dNRar8Zi2xhhReHChYX/J0yYINuu3xlTJZJl+zx98dSTGzUCOCns+VhZWcl6k0kFRSypJWwWTTr/TLJnz46PHz+qDtthYWEhJDyPjIxEkyZNUL16dS3jm8aNG4t+W1tb4+jRoyKFDABFhQxg3Nhx/fr1quqdOXNGK5Rms2bNFBN5Fy1aVPSb/86ntHV2akCp35QbY7OefqxnCG9NytKxY0fBc0sOjuO0hNemCOXI3lNDvgnDhg0T/peOnezt7eHv74+bCiH9pMgpJNl5hD7Ueq5FRkb+Vs+wtM/466/xOhUz3bp1w4gRI+Dh4SGUScPVDhkyRCtnpCnHe7a2trJeEoRhpKZryCq/pd9CQ+C9C1kDSGMoV64cJk2aZPCcrnTp0li+fDlOnz6tFWaY76/1ebPKhRA0BF0hqQGY3KO7ffv2IqPd8+fPm3T/xJ9Hjhw5MHLkSJQoUQL58uXDoEGDULduXezcuVNUz8XFBXPnzkXBggXh5uaGFStWoGDBgpg3bx4KFiyItm3bauWmmTdvHtq3b4+hQ4fC1dUVFStWxOLFi7Fx40ZERUUhY8aMSJMmDdKmTSt47sjx9OlTABCF5GVxc3MT6ujD0tJSy1voT1TKGCxROHLkCPz8/HD27FncuXMHRYoUQdWqVbF79+5fxtNkwYIF6NGjh6DVXrhwIY4dO4bly5dj1qxZWvV//vwpGril5vBBqQHeSsHKygrm5uaIjY3VUsrwQk4++XGXLl2Edfz1NsVkyZToUyJIB93379/H58+fUaFCBa2EfYC8lSV/7djOiJ3kSQdISsLimJgYWaVMZGQkjh8/jrt37+o8FzmkwkC2XXLWhoMHD0bv3r0B/CeIJKWMmCJFiqBSpUqiXDtK/PjxQ289f39/mJubo3z58mjUqJGWy3tSwypiU1Mi7HTp0il6bowbNw4HDx7EwIEDFbfXJSRXgu0PWOFLShMeHi70L2FhYUmWn8WUA6rt27fj8+fPwvcitWBMH8ZuY21tjWbNmmmNO+rXr4+jR48CAB48eID79+8L1kapRRnC9uW/cx4ZIoEJEyZgzpw5+Oeffwx+7vlxy/79+xUnaQcPHgRrG2tlZYX3798bdBw2x4Za5AREHh4euHbtGoAEAxa58RKfJ8rHxwfm5uZYunSpaP3OnTsxceJEuLq64tSpU8L6X81TJilQElDr69ukCg+pRbSab5lU4G6KcaixShl2vCTdLiwsDBUqVFC9r5CQEPj4+AgKUI7jEi3clLJ582Z06tQJQ4YM+W36fLmQziVKlFBMfs7PZVKTQP93JanGqtu2bcOmTZswceJEk+xPF2qVca9fvxb+NzSKAoujoyM4jkNcXJzOvqh169ZCaCFTw+b9Yo/D97WscUPWrFnh6OgoUpTky5cPc+fOxahRo7Br1y4cOnQIK1asUC0T4iOWyJFURr+sEWrNmjURHBz824QR/JUJG6c+b2BqIi4uDrNnz8aOHTvw9u1bQS4q7QPLlCkj+v3kyRORwQAAlC1bVvT7xo0bePbsGbZs2SKUcRyH+Ph4BAYGKipZlFAy0uA47rc3PjU1BnvKNGzYEOvWrYOXlxfev3+P69evY8GCBWjSpAkyZMiQFG00KdHR0bhx4wbq1KkjKq9Tp45WaCieWbNmwcHBQVh0JaEk/vOUsbS0FCZZUqWMdCLExv+cPn06rK2tZWOCpiT6vFSknY+rq6swqapQoYJWHHS5ySAvhGAFmux+eVd1HqUcMbo8ZXSF29A1QdVloS23rlatWgASQjXwsJNuUsokoNZNOzAwEE2aNFFcv2rVKmEQrtFokD9/foNi5puCTJkyCRbBai08U5oKFSrg69evOpPq8e+uIXlSWMvT1KZgTg7kvpO5cuXCo0ePVFvP8LRp0wYDBgwwVdNMhik8ZeTef3t7e3AcB47jULhwYVFyX2OOmRSw7fgTLZr+NKZNm4YvX76gXr16Bm/LK2UMee9//vyJb9++GXScFStWGFQfgMhLu0+fPmjWrJnIe0YuRNaMGTOEEBJ2dnZYsmQJJk+eLKpTsGBB+Pr6YsaMGbh8+bKQu+x38jIwNXLCyJo1a8LOzk5vnjbgv7HU69evMXHiRC2BBZAQx5zFFEIDY5UyrJGVKZR1bBiRxIQqVmLKlCkAYHAC4qQmKCgIu3btMurdkgsz/fLlS8X6NWvWBCD+5uXMmdPg4xIpR9u2bXHo0KFkkVmpHfuzCuZKlSol+rj65tfTpk1L9DHUsH37dvTv3x979uwRylgDhkGDBuH06dNa240ePRpxcXFo1aoV1q9fr2X0YCzS77QSDRo0SNRxWCUbkXLYWdol62IqfHx88Pfff2P06NE4ffo0bt++jbp162p5mEuVNHKKEOl3MT4+Hn369MHt27eF5c6dOwgICED+/PlVt5EP0fno0SPZ9Y8fPxY8nfl5L9sWpTCDfzIGS+wWLFiASpUqCa5Rbdq0wfLlyxVvSmrj8+fPiIuL03Lrzpo1q6JV3rhx4xASEiIs1NnqRo1SRjroZdfxigY2liHHcbJKhuRETsD97t070XpdlhEzZswQWZDIwQ882AE/O6iztrZG06ZNhd+GKmX4JFpK6FIQ6FLKyA088+bNi8DAQAQEBAhl5CmjjVqljDTJmxS50FzJjUajwevXr/HlyxdVCZdTC+nTp9f5XgwaNAhLly7FqVOnVO+T9WL7E5UycgmULSws4ObmlmTJ55Mbtj9T68HCDkqtra1lw7xJn0X2e8Bb8Kc07LknJv438etgbEhCY0LuLVq8WHU8++PHj2PTpk2KocR0wX43V6xYgb1794reNznjAjklpLe3N1atWoUOHTroHKuSUkYZuT7UwcEBnz59wrlz5/Ruz3vD5syZE1OnTkXBggVN3kY5WIWKIeNaVimjK0SqIYISNfszFkMt+JPrWXd2dkarVq2wdetW2fU7d+5E0aJFZeUUSpbzSoqZGjVqaJXJ5SokCED9t499X01leMNazEuVvkmZf41Fo9Fg6dKlImNQNoJDy5YtFa8R2+cbK3eTKnzURrrZsmULwsLCEBcXh48fPyI6Ohocx+HBgweqtif5BpEYzp8/j6ZNm6Jjx44oXrw48uXLJ5KlKeHm5qY1R5SG/C1VqhQePHgAFxcXrYX/xvM5HXVRp04dZMyYET4+Plrr9u/fj4CAALRr1w7Af+Ps4OBgoY7UQEbNMX93DFbKDB06FHv27MGnT59w4sQJVKlSBSdPnkTx4sVlLcpSK3KaRCWhnJWVlRD+RlcYHCIBXsFiYWGhqJQBxB9HuQmE1EMkXbp0ePbsmao2JMVkgFXK8PtnvavMzMwED6w5c+ZobW9tbY1FixahTp06et2mWSGXdMC/a9cuQXmjpJQ5ePAgNm/erFX++fNnxW0A3TlBpAMn9horCSSdnZ1F7wspZbTRdc2PHDmC5cuXA9Dvdi11WU0pbG1tVefJ0cW1a9eQOXNm/PPPPyZoVeLIlCkT+vfvj/Tp06vehh1cpBbvhuRETon9u3lUsOdozOTbyspKNgyKdCySGpUepJQh1CJnCWtKateujY4dOxq1bY0aNTBjxgwcOnRIKGOtDxs1aqS1jdIcoGfPnti8ebPOfo7ClymjNI60sbGRHS9KxxlS70zpnEOKqSzl2bGwIZ437PnqUqLcu3dPdc5Tvi269qfPY+379+94/Pgx7t27h3bt2gn1DZlXHT58GFmzZsWRI0dUb6MP/vhPnjyRFY4q9TOtW7fG/fv30aFDB611cp4yALQiG8jBG9nxHkQEwcMbJfFCSX1MnjwZWbJkwYwZM0zWBl6B6OLiotUvKSllHB0dRd/CpIAdK1tZWSkarbEKUH19uRLS+bXcvEROXpMmTRrY2dnBzMwMjo6OQl+dI0cOVcddtWoVGWAQRuPi4oITJ07A398fjx49Qp8+fVSF8+3Tpw8eP36MMWPG4OnTp/D19RU8v/k+YMyYMbh06RIGDBiA27dvIyAgAPv37xdyYgMJsrtz587h7du3+Pz5s+yx7OzssHLlSvz777/o3bs37t69i5cvX2LNmjXo2rUrWrZsidatWwvnkytXLnh7e+Pp06c4dOiQljLH2dkZYWFhOHXqFD5//pwk3r6pHaNj29y6dQsnT57E8ePHcfr0acTHx/8SLryZM2dGmjRptB7ujx8/GpQUkVBGjacMAFFiNDk3Nr4DefXqFfbu3Qsg4UOnjwkTJsDGxsbkSaHZjzkvIJeGouHPQ8mizNLSEseOHcPUqVN1Hsva2hrBwcEICgrSGjyZm5sLAz5dChY5oqOjdQoFDPWG4VGrYCGljDa6BJoODg56LfBPnz6NZ8+e6Uxw+ytSpkwZfPz4UeQx9yuRFJaqvxJySkQ575lfGXaiqzZ/E/utUwpfJp1AJ3cYQjWw/dKf6AlGqEdpUmcK2LAoxqDRaDB+/HhRuBJWKdO9e3etbRJjZUyCmgSuXLmCfv36iYwuDPXEeP78uei3VGCmz5BFraJDH8beU7afj42NxV9//SX8ZnO02tjYIFeuXDh16pTOELZAQr6G3r17i8IGS3F1dcXatWu1yvm5Qfbs2VGoUCEUK1YM27dvR+3atQHAICvWhg0b4tOnT2jQoAF69uyZ6OTaISEh/8fefYc3Vb5/HP+kLXTSsimjbBHZWwEVENlDREEBB4iogLIRURmigggCfkWmCioqDlBEHCAiiAKyBRkie6OMlqYthTa/P/pLTNqkTdo0aZP367p6NTnnycmd5OTknHOf535UuXJl9evXT9WrV1etWrUyjA+UVcLz9OnTGaZNnDjR6bbmcd3M5syZo8uXL2eo2Q9s3LhRX331ldPjSVatWlXnzp3T888/77YYRo8eraeffloffPBBhnn2jvW7dOmis2fP5rh0V1ask+fBwcFO9TJ3NimT/rgrfa98e71Yn332WZsT0pLj8xPO/kaZe90C2TFu3Dg1aNBA7dq1U8uWLRUdHa1u3bpl+bhKlSrpiy++0PLly1WnTh3NnTvXsl9hPk6rU6eO1q9fr0OHDumOO+5Q/fr1NW7cOJuOFZMmTdKxY8dUpUqVTKuw3H///Vq3bp1OnjypO++8UzfffLNmzJihF154QUuXLrXs4xQoUECffPKJDhw4oLp162rq1Kl65ZVXbJbVrFkzPfXUU3rggQdUokQJvf76666+bfmey5fvdu3aVRs3blRcXJzq1aunli1b6oknntCdd96ZL3qQFCxYUA0bNtSaNWtsulOuWbPGpiwUXJOYmKiPPvpISUlJlh/YrJIyLVu2tNxOv3MtpR2wGI1GSy1uybmrkc1XmnTp0kUbN250S31WyfZHOjExUSEhITYH7+Hh4ZYSU87USmzVqpXDeSEhIZmWQjO/r+lPdvTp08dm8K70sjpRXKJECYcHqtkpQZKe9UFoXhmw2tscJWWeeuop3XbbbVl2Wa1Tp06+KhXmivw8SJy/J2XsXYF65MgRy+37779fX3zxhd3Hmsejyg+2bNmiq1evOn0FnfVvg6MDv/y23jvbgxX+KScDF1srX768zf7J999/r3bt2rll2dask4z2Si3mpBZ2kyZNOFmjtPehSZMmWrFihWWaq/uEhQsXVoMGDSwl5tJXa8gqKeOunpvuSLTVq1fPpuxI+gSAlHbl+F133aUrV64oJSXF0sty0KBBmjNnjiSpfv36OnXqVJbP169fvwwJR0e/R+bvnHXSIzU11eHFAumTN++++64+/fTTbJegNplMll7K1uM9HTt2THXr1rWJKTPpnz8lJUU//vijpIz74L///nuGaW+99ZbNfYPB4FLvafiPEiVKuHxOyd37fcWKFcuwzlqbPn26Vq5cqfXr10uSPv74Y49cLFm1alU9++yzunr1qqKjo5163e+8844WLFiQZVvr44533nlHxYoV04MPPqilS5dKksOLdWfNmmXzXuU0KSOlXTDSvXt3p9vDf/Xt21d9+/a13C9atKi++uqrTB/z888/253etWtXm4s3Xn31VZUrV87mHF7jxo21evVqh8u+7bbbtHv3bqdiv+OOO5zqEdu8eXP98ccfNtPS7zvNnTvXUh3GH7l8+WW1atX0wQcf6NKlS9q2bZumT5+uzp0754uEjNmIESP0zjvv6L333tP+/fs1fPhwnThxIsvxPuBYUlKSBgwYoGeeecbS5SyrpExWBw4BAQGaMmWKzbTz58+7FNfKlStdap8Z6x1+e93qrDcuznT/jYiI0HfffWd3wL2srjx2dPCa1U6g0WjMtD719u3bHc7LrHxZdpCUSWMvKRMVFaW5c+fKYDA4fJ9Gjx6tnTt3+mxCJr/z99qo9k54WV8FuGTJEpsyjoGBgXrkkUf0ww8/aNmyZR6J0R2aNGliGfzXGc6UsssPSRnrpKP1BRZAetYDkOeE9Xo2atSoXEnISGnfv4MHD2rnzp12exzkpKfM8OHDNW3atAwHp/7Kev8mO/uE1o+pU6eOzTx7V1dPmzZNkuMTGtmRk33hc+fO6cCBAypXrpzNicDM3ovChQvb7PcVKFDA0lPamYRMdlnv01if/DSZTDa99u2N5RMfH5/t57V30Z69ZVofo5nHgLCWmJho08bVmPLCuI1ATt1+++2SpJEjR+rnn3/WJ598ouXLl3u0vPDUqVM1Z84cl/Z1Mzs/YWZ90Ua/fv0kpZ1gNrt06ZLdx6VPMDtKygQGBjpdKjw/7MfD98yZM0dbt27VkSNH9OGHH2ratGl69NFHvR0WnOByUiY/JmHSe+CBBzRr1ixNmjRJ9erV04YNG/Ttt9/a9MiAa6xP2Jt3oLNKymQlISFBM2fOtJk2f/58l5axfv16DRo0SEaj0aXH2WN91Z35AMT6wMR6/i233OLUMtu3b68XX3wxw/SseqU4OmDLKpmzdOlSVa5c2anY0nPX1a5m/jjOhj32rjbMarye6dOn6/XXX1e9evVyMzTkQF49UR0YGKiOHTuqY8eOuXpV3EMPPaR+/frp888/19y5c7Vo0SLdd999lvnBwcE2V6H/9NNPev/999W2bdt8vX+Rle7du6tNmzY2ZS1WrFhhs9Ps7m1tbrDuJVqjRg0vRoK8LquSS1Ja2dleDz7ocH67du1seqPl9jaiWrVqqlevXoYTKy1btrSUc8qO4OBgjRo1SrVr185piD7Bev8mO9s967LZ6T8r67JS/fv3l5SWzLt27ZpatGjh8nM5kpOkTKlSpSxJDOt9QVf2j5s1a2Z3bDKzMWPGqEOHDhmmZ9Yb3x7rRLz1hWm9e/dW4cKFtWjRIi1dujRDWTlnHDx4UAcOHLA7z1EPmL1799qUt01JSdFLL72kyZMnKzo62u54UJ988olu3LihLVu2+PUVufmBp/ZV/cWWLVvUu3dvffzxxzbTH3zwQZvKMd7w888/Z5n0TExM1JUrVzRlyhTNnTtXr776qkwmk5YsWSKDwaBHH31UNWvWtLQ3b0+feOIJh8tMPwi6WWbr2+bNm7M9xg2Q2w4dOqR77rlHNWrU0Msvv6yRI0c6LNOJvCVbZ0WNRqPWr1+vEydOZNgwDRkyxC2B5bZBgwZp0KBB3g7DZ1gnEeLi4iTlPCkTHx/v1JgpZ86cUXR0tAICAjKUzNm8ebM2b96sIkWK5HgAPeuky7Fjx1S1alWbada3na0j60h2eso4M/6IuZ0jzz33nF577TXL/XvvvddSasP6JFx2ZZVs8Efpr+6UbN8newfn1uMxIW+qW7euduzY4XRZK08JCQnJ9YE8zc9jr269NeuTUDkpCZSfhIeHZ+g2bu5unpSUpNWrV9vtefPDDz9oxIgR2rBhg6dCzZR1qaC8OOYN8odt27YpIiIi7cS00Sj9f5kRaydOnFBMTIzGjh1rmZbZWGy5ad26dV55Xl+V054yb775plJSUjR48OAM88aNG6eIiAjddtttNttUdye93TVOkPWJQGe2qYcPH9bvv/+uHj16aN68eQ7bvfbaa/rtt98ylBh55ZVX9PjjjzsV25dffmlzPLZ582ZL0sNcGsje+EvWTCaT3avHr1+/rurVq0uSZsyYkeH4Ka3EWEbpq1uYr/o3+/bbbzM8ZtOmTdq+fXuGC/6Q93hqX9VfNGnSJNPy5t7UokULzZo1S3369HHYZvPmzZo7d67Nd7xhw4Z6+OGHJcnu2DlS2phcd9xxh3755Reb6StWrFDDhg3tPiazXi4BAQEKCAjQtm3b1Lp1a4c9+QBvmDlzJr9v+ZTLSZmdO3eqY8eOSkhIkNFoVNGiRfXvv/8qLCxMJUuWzDdJGbiXwWBQSEiIkpKSHPaUyaq+c3qBgYFZHuysXr1a7dq108MPP6wPPvjAYeInO1dupWcdv/mkePqeMoGBgUpJSVGhQoVy9FzZ6Slz7ty5DDsd9hw7dkxS2ueT/v1KfyBoXf88fdmz7ByIZpVs8Ef2alJnlbwaMWJEboYEN6lfv763Q8jTKlWqZLldvnx5L0aSN5gPmO1dpde2bdscD5bsTnfddZduv/32TMthAmbFixfPMAaelJaktFfuyJp5YGDrK/W90ZvM0QkcZJ/1fmB2kjJly5Z1OEZPeHi43Z7o7pbVWCbOst7uO9MzoHLlypae71mVsW3WrJlef/11m96p/fv3V0xMjFNlANOPjdClSxeXjwEOHz6sqlWrZphuXUZsxIgRGZIyWSV7zM6ePZth2pUrVyzHplLaRXyujulUpEgRvfvuuy49BoBrzL/zjjz77LMZyomePHnSqWWnPzcyePDgDD144+LiNHLkSLvjednTsGFDXbp0SatXr9ZNN91kd9sGAM5y+fLG4cOHq0uXLrp06ZJCQ0O1efNmHT9+XA0bNtT06dNzI0bkE+ZEgrt6yjhzUGLu/fLhhx9ansdVc+bMUbNmzXT58uVM21nHb25rnahJSEiw1Fx29YRB+jEUsuopY68cW0hIiN2D2k6dOtl97AsvvJBlW+sas64m1eyxfl3UW02TVQ8ke58pta3hC5o1a6YnnnhCo0ePtjugtr8JDAzMN2U6ChQooF9++cVm4GXAEUc907O7vlsndD0lNDTU48/p63JaviwvyI2eMq5+LzIrX2Y2evRodevWzWZaTsYkdPV1p6Sk6MyZM/ruu+9sHptVGbGclJ8uX768zcVzWSVkGjWyHS+iQ4cOunjxotfLOwG+zjzWjSS99dZbdtukP29gfaGGK+z1JChUqJAWLFjg0hiRAQEBat++vd2LkzjHAcAVLidldu3apZEjR1pOHly7dk0xMTF6/fXXbQbxhf8xH7Cae8oUKFAgx0mZrMb5SX+QnJ2kzODBg7Vp0ybNmDEj03bWSQnzYHHW3fmtb7t6cJn+KrSsesqkv6LEfLWhvd4n48aN08KFCy33r169KimtZrr5gGzVqlXau3evmjVrZvNY652kK1euZPEqsla3bl316dNHY8aMyfGyfEVWgyvaS8qws4fsMhqNCg8PV3h4uFvG2sqp+fPn6/XXX/d2GABy0dixY/Xyyy9r165dNlfCu3Ly2fpEbvoLSHKTeRB16zGx4B457SmTF+SFpEx2kys5iT05OdnpK9Ul6ejRoypbtqw6duyoxYsX69KlS7r//vszXCBmvrjNHczHO8568803be4HBwezv+0leW1fFbnL+ntm/s1NL301D0fnfAYOHGhzP/0YdJ74rTl8+LA6d+5MyVMATnE5KVOgQAHLhrNUqVI6ceKEpLRxKsy34Z/MiQRH5ctcTcpcunQpyxPW6X+gczL4WvrxaNKzTsqYe8pYd7u3ltMr/rLqKWNdz1+SNm7cKMl+UqZgwYI2V8edOnXKMv3vv/+2lCS0HiDPrFSpUpbb6Q9MMhubxhGDwaAlS5bYjFvj77KTlAFyIiEhwWagXgDITSEhIXrxxRdVt27dbJ98Hjp0qMLCwjRgwACPnijdunWrPv30Uz399NMee05/Yb0Pn1/3ddyVlLF+L1xNymS3tGVOYp84caJL34kOHTrYPLZbt24ZqgRIaSWWzXHt27cv2/E5a/bsty230x8HZXWBHHIX+6r+yVEps/QlCh2VQU+/XRs2bJhb4nLFb7/9plWrVumuu+7Srl27PP78APIXlwd1qF+/vrZt26Zq1aqpVatWGj9+vP799199+OGHql27dm7EiHzC3GvFXeXLChQoYNP7xB5nr5pwRlaJkNxOykRFRVkSWlnFkp75wMFRUsZeaYPg4GAVLlxY9erVs5netWtXff3115Jskz/mgTjNxo0bp+3bt+vRRx91KVbYyuqgL/1n+s033+RmOAAA5Brr3zRXTj7HxMTo0qVLHi9zVaZMGfXs2dOjz+kvrPer8+sJ8LzQUya7ZQBzMpZbTi6uCgkJcTgGpnlshi+++EIDBgzI9nM4y7qXUVBQkMqWLavTp09Lcv1YDED2rVixQkePHlXjxo2zbizH43mlnz5u3Di99957lotSPa1+/fo2vxMmk0mrV69W/fr1M4yTA8A/udxTZvLkyZYTtS+//LKKFSumgQMH6sKFC5o/f77bA0T+4e6kzLVr1yyD0pulP2hLn5RxVF/0008/zfL5XEnKmMuXORpnJTtXcZoTMpLrB6fmcUnsjU/i6HU5OrFx991329z/5ptv9Nxzz6l3794204sVK6ZffvlFjz/+uEuxwlZW6136z8mTZVsAAHAn6xPI6ffhskI5Id9y8803S0rb582q13Be5ejEoKtykpR58MEHs/WcpUqV8koyzJkL6O6//36bsT5vvfXWXImlQYMGNvetk8YkZQDP6dq1q4YOHep0+2eeecbu9PTnQoKCgrR161bVqVPH4Xg1OTVkyJBM55u3ZWvWrFH79u3Vvn17denSJVdiAZD/uJyUadSokVq1aiUpbbDpb7/9VnFxcdqxY0eGK+7hX9xdvuzatWsZDnaSkpLUo0cPy33rg/Pk5ORMB32zV1vY+sAg/cnv4cOHy2AwaPfu3Zblm5l/XHNSLi0zrh4IFCpUSJL9UliOki+Opg8YMECdOnXS22+ndenv1KmTpkyZ4vLJEzjHUfdrMw4KAQC+wpmeMn/s3q3u3btry5YtngoLXlCkSBGdPHkyQ1ma/CQv9JS5//77s/28Z86c0WOPPZZhem4ma7JT1WDdunU6ceKEvvjiC7fGUrZsWZv71mX08mvvLcBfRURE2IxbZxYdHa3du3fnWhnSN998U/Hx8SpcuLDd+UWLFlVSUpLatm2r1atXS5J+//13bdy4USNHjtT48eMtFzUDzmjZsqVXSvMhd7jtLOuOHTvUuXNndy0O+ZC5p4x5QL6cJmUcsd4ht04UnDt3LtOkjL2B6q174oSFhVlum0wmzZo1S5IsyUbr+M0/nO5MyliXEXDmQODXX3+13DaXJ7N+DWbm5Iu5JICZ+fNKLyQkRN98840GDRqUddDIMXtX/Vqv19ZJGesxfgAAyG+cGTujSpUqWrZsmZo0aeKpsOAl5cqVc3giKz9wV0+ZnIwpYzAYlJqammXJZ3uKFCmid999V927d7eZfvXq1Vw74ZOdMkKhoaGKiYnRHXfckQsR/efvv/+23M7uWD0APG/48OH6999/HY5Jk9vCw8N1/vx5h/MHDhyYYdodd9yhGTNm6OWXX9aECRNs5l28eFFTp061lFOE7+jSpUuGyjRmmzZtksFg0I4dOzwcFbzJpaTMmjVrNHr0aD3//PM6cuSIJOnAgQPq1q2bGjdunOkJcfi+9Cf5Y2NjcyUpI/13EJSUlGSZduHChUzXQXtXs1mXH7N+rL2ruKynmR+XkzFs0rO+esOZ3hHNmjXT1atXlZiYaDmAszdQqnlZhw4dsjnQ4wqwvGPw4MGqVq2a5b6jhNkjjzziqZAAAMhV2R0LA8gr8kJPGSktMZOT/Xrr446hQ4cqKCgow0nCEiVKZHv5OREZGWm5XaRIEY89708//eSx5wKQfYsXL9arr77q9eoSmY15t3jx4kwfm75n8EMPPaTnnntOHTt2dEdoyEP69++vn376ScePH88w77333lO9evUylNaEb3M6KfP++++rXbt2WrRokV577TXddtttWrJkiZo0aaIiRYpo9+7d+v7773MzVuRx6UtnxcXF2U3KWNf6bNOmjcOaoNb27Nljc9+cDElISLBMS0hIUEpKisNl2EugWLe3TtDYu4rLOqlkTga5M9G0fPlyy21nD6wiIiJs2tpLyljvINx1110uPwdy3+zZs3Xw4EFLT5gOHTpY5lkfjNrrCQW4IiAgQC1atFCLFi0oSQjAq0jKIL8znzDL6Zg4OU3K5JT18UO5cuUkSYULF9aIESMs07dv3+7w8RMmTFCrVq30448/6ty5c27tZWJd1cA6zhIlSujAgQM2bb/55hunl5vZMaMkm9cOz2Jf1b9NnjxZkrRixQqn2j/66KMOL2jML0wmkzp37qyWLVvKZDJZzqv+8ccfDh9z7NgxzZo1y1KlxtrZs2e1atUqXb16VX/99VeuxZ1XJRuTPfrnis6dO6tkyZIZEnUJCQn69NNP1a1bN/Xq1UvlypVTWFiYateurU8++cTh8saOHavbbrstw/Q6derYXFyxaNEi3XLLLQoJCVH16tU1Z84cl+JG7sl8MAMrM2fO1OTJk/Xcc8/ps88+04MPPqiZM2dq586dqlKlSm7GiHzCPK6JWUJCgt2kTEREhOXHIzg42G4iIT3rXgTm5QUHB9v8CCUkJGQ6Pod10sXMeofcOsEyZsyYDG3tJWXc2VPG+uR7ZldaZMbe67delvVJfZIyec+mTZv0xRdf6Mknn7RMK1mypOU2BybIqdDQUP3888/eDgOAn7Iu2UlSBvndY489puLFi+d4IHpvJ2VefvllffDBB5Jsj3esj3PMyRp7Jk6caHO/VKlSOnPmjMqUKZOjuOrVq+ewd0xAQIBuvvlmy/2vv/5anTp1stvWeiwH68dnJrPXi9zFvqp/Gzt2rIYPH+7UuYqhQ4d6ICLnlS1bNlslxzZv3my5XalSJZt5nTp10sMPP6wHH3zQZnrTpk117tw57d+/X/Pnz7eZd/PNN9uMp7x161Y1atTI5bjyqykRUzz6fBNME7Ju9P+CgoL0yCOPaPHixRo/frxlv/jzzz9XcnKyHn/8cX3yyScaM2aMIiMjtWrVKj388MOqXLmy3X2NPn366LXXXtPhw4ct5+X//PNP7dmzxzLsw8KFCzVhwgTNnj1b9evX186dOzVgwACFh4fr0UcfdcM7gJxw+gzf4cOH9cADD0hKG1AwMDBQM2bMICEDC1eSMmbBwcE2Xf/tDc4mZewBYl6WdVLGaDRmWr7MlaRM+oEkL1++bPNc5mXZ6ylTunRphzFkZsmSJZbbUVFR2VqGvaSM9TTrbr0kZfKeSpUqafTo0TYJOmvpv2MAAORXJGWQ3wUGBuree+/NcfIhJ2PKuIP1uJbWt62TMgaDQe3atcvwWEdjP2X3eMiavavfzRcupR/7smbNmg6Xs2DBAq1du1aDBw9W48aN9eyzz9ptZz2+kblUOwDPM5+nyCo5l37MXG8bNWpUjpeRvqzVt99+q169emVod+7cOUlp27f0rBMykvTVV1/lOC64z2OPPaZjx47ZrN/vvfeeunfvrrJly2rUqFGqV6+eKleurGeeeUbt2rXT559/bndZtWrVUp06dfTxxx9bpn300Udq3Lix5cL2l19+WW+88Ya6d++uSpUqqXv37ho+fHiGZB68w+meMkaj0VJ2KiAgQCEhIV4bSAt5U/qu+/369bMkNxyVL0uflJkxY4ZmzJihkJAQS+KjQoUKGQZDt5eUWbJkic0VU+ldu3ZNK1as0LZt2zRp0iQdP37c5kAis1JkM2bMsLmfWfmyLl26OFxOZkqUKKGrV68qNTU10x4/mbE3aLyjq1JJyuQfc+bM0cqVK2160AAAkN9Y75PQ+xNI466eMps3b7ZbxsQZq1ev1vr1621O/qWvCPDBBx9o/vz52r9/v1avXq0RI0Zo9OjRDpcZFRWl2NhYh/OXL1+ue+65RwEBAXaPYeyNETFlyhT16NHDUpL50KFD+ueff1S5cmWHz1OgQAHdddddNmWc7SlTpoyuXLkiiQuhgLygRYsWmc7Pa8fGQ4YMcXiRsTfZ2776srHxY70dQqaqV6+uZs2a6b333lOrVq10+PBh/fLLL1q9erVSUlL02muv6dNPP9Xp06d17do1Xbt2zeYcanp9+vTRe++9p3HjxslkMumTTz7RsGHDJEn//POPTp48qf79+2vAgAGWx9y4cSPbF4LDvVw68/vDDz9YPrjU1FStXbs2Q83Yrl27ui865Cvpr+6/8847LbVArZMyZcqUsdTHDAkJsTtIpnWvFnsbIPNBgnVSJqsrAK5du6Zu3bpJkho3bpyhjqO9njRm1vWMpbSkjMlkssTx1FNPaf/+/SpTpoxeffXVTOPITE5rUqeX/io169dIUib/GDhwoAYOHOjtMOADjEajKlasKCltu5bZDh4AuBvly4CM3JWUufXWW1WwYMFsjXnZpk0btWnTxmZa+qRMyZIlNW7cOKeXOWLECEtN++HDh2vmzJk28++9917L7ejoaMuV32aDBw/OsMwiRYqodevWlvtVq1a1uVp+165duuuuu3Tp0iXLNGcvditfvrz27dsnSRo5cqRTj4H7sa8Ka6+88opefPFFHThwQNWrV7dMv3TpklNl8D0pICBAn332mX788Ue7PVhyi8FgUNGiRfXGG2+ob9++duPyJwXDszcUgCtSUlJ07dq1bI9n1L9/fz399NN6++23tWjRIlWoUEGtW7fWtGnTNHPmTM2aNUu1a9dWeHi4hg0blunveu/evfXcc89px44dSkxM1MmTJy3l7lJTUyWllTBLX/6M/fC8waWkTPp6c+kz0waDIctB8+C7ihYtmmGavfJl1jV6HSVlrJk3JNbs9ZTJinVC4vjx4/ryyy/tLlOS7rjjDv3yyy+W++mv8kpNTdWNGzcsj2nVqpXmzp3rdCy5qXbt2vrrr7+0ePHiDF16//33X8vtvLYTA8AzrLcDAOBJ1scJ/naSAHDEnWPKuPOK6D59+uiDDz5Qx44ds/X4MWPGqEKFCrr77rtVtmxZ7dy502E5ov3792vdunXq3r27Zdp9993n8nPWrVtXFy9e1KRJkywJIWeTMtZXDRcrVszl54b7sK8KsxdeeEEvvPBChumOxpvyth49eqhHjx5uT8qcP39ee/fu1R133KHHH388w/xLly6pX79+JGU85ODBg0pISFCVKlUs6+LJkyd19epVVa9ePcv3vGfPnho6dKg+/vhjvf/++xowYIAMBoN++eUX3XPPPXrooYckpZ13PHTokG655RaHyypXrpzuvPNOffTRR0pMTNTdd9+tUqVKSUob461s2bI6cuSI+vTp46ZXD3dyOilj78Q4YM3ZpEyJEiUs80NCQjIdB0aS3URfcnKypSufs6zb2ntO66RM+mTPX3/9laF9YmKi5QqyvJTg2Llzp5KTk+1m7a3j9LdurAAAwLvM5V8l9kMAM2+PKeNI27ZttWfPnmyPmRMcHGxzUWdmiY7ChQvb9JyR3LeNcPY4jSoCAHJTYGCg5syZk62ya9HR0ZKkjh076ttvv3XYzt65M0/ub50/f17bt29X+/btHSYmYmNjNWvWLD344IOZDj+QV5lMJiUkJEhKSyCbkzLnz5+XlJYgK168eKbLiIiI0AMPPKDnn39esbGxlmRa1apVtWzZMv32228qUqSIZsyYoXPnzmWalJHSLqKYOHGikpOTM/RKnThxooYMGaLIyEh16NBB165d07Zt23T58mWNGDEiO28B3IiUKdzGuvbuunXrJDmXlDGPweJoR71p06YZpl2/fl0XL150KT7rEwHpu+NLtkmZ9IOjpe9OL6UlZcyPKVgw97tIOiswMNBhN8rsjlUDAACQU65cTAP4C+v985wmZbK62M1VtWrVsnvhXXakL3Vtz9NPPy0pbUzRnLCuxODs8c/LL7+s6OhoTZw4MUfPDQBr1qzR3Xffrfr161um1atXT0888USOlptZQkZKO0eVXlxcXI6e0xW1atVSp06d9NFHHzls8/zzz2vixImqWbOmzp07p3fffVcnT57UO++849FYs8tc5tKRrCoBmfXv31+XL1/W3XffrfLly0uSxo0bpwYNGqhdu3Zq2bKloqOjLUMwZKZHjx66ePGiEhISMrR//PHH9c4772jx4sWqXbu2WrRoocWLF6tSpUpOxYncxRlauE2tWrUst8PCwiRlnZS5cuWK7r77bv3666+qVq2a3eW+8cYbGaZ9+OGHeuCBB1yKLz4+3uZ507M+UWBOypQqVUrnz5+3KV8WHh4uo9Eoo9FoSe7kpaRMZkjKAAAAb7G+QAZAGuueHDlNyuTlUuLOHIdMnDhR8fHxuv/++3P0XNlJysTExOjMmTP04gPyqDVr1uiee+7RJ5984u1QsnT33Xfr7rvv1o0bNyzbePN2afny5TalGt3pzTffzDDtjTfe0LRp03J923b8+HFL6cEXXnhBvXr1UmBgoBITEy3nB1esWKE5c+ZISvu9atWqlQ4cOGBZxrp16zJN6OQF1omv2NhYbdu2zWaIBmc1bdo0QwInKipKS5YsUXh4uMPPy14Z0MKFC2e6j927d2/17t3b5RiR++gpA7exrsNrLndnLyljrm8oST/++KMkqVmzZna7+AUEBNidPn36dA0ZMsSl+KwTMVOmTMkw//PPP1e1atX03HPP6fLly5L+64JozfyDUqVKFcuPTl4qX5aZ/BInAADwPSRlgIzcmZQxVxioXLlyjpaTG5xJjhQrVkyLFi1Sp06dcvRc2UnKSJRVBPKyu+++W/Hx8eratau3Q3Ga9fbHvF269957NWrUqFx5vhdffNHudHs9aNzt/ffft9w+efKkRo8erREjRqhw4cL6448/lJqamqEXh3VCRpI+/vhj/fnnn7kea3Y56gVz6tQpy2175xCzkpqaqtTUVB05ckQHDhxwuSoQ8i+SMnAb6wMKc/1I84/QjRs3LEkZ69JaERERmS4zswGyNm3a5FJ85kRLZg4dOqSpU6dmWl7DnJSRpNOnT0vKPz1lSMoAAABvYfBsICPr/fOcjmvy+eef69lnn9XatWtzGpbbWdfEz6o+fk5Znzgj0QL4jvz4fb711lslSa1bt7ZMK1y4sEdjuHTpUq4/x/79+23uz5o1S7NmzdL169c1cuRIu0MI2GNdgSevcaY0WVJSksu9Vvfu3atdu3ZZKvTYGz4BvomkDNzGYDDo8ccf1913361GjRpJst9TpmDBgnrsscckSRMmTMh0mdk9MLE3psr27dvttq1evXqOl51fkh3PP/+8JKlfv35ejgSANwQEBKhRo0Zq1KhRpklvAMgNY8eOVdeuXfXZZ595OxQgz7C+ktrRuJDOKlu2rKZOnaqKFSvmMCr3e/LJJ9W1a1dVrVpVq1atytXncramP/Ie9lXha95//33NmTPHplrL0KFDM/Qaeeihh1xabvHixdWkSROn2uZmSbDffvtNDRs21Pfff++wzYULF1xKDOXVbbizcZkrBzm7zOTkZJceA9/BABNwq4ULF9rcd5SUWbhwoV5++WWVKVPG7nJat26ttWvX2lxN4IpKlSplGIDL0aBh3bt31+TJk7P1PGb5padMvXr1FBcXl2UPJQC+KTQ0VFu3bvV2GAD8VJEiRbRixQpvhwHkKdYnYnKalMnLChYs6LHvf149oYessa8KX3PzzTfr5ptvtpkWERGhL7/80qbnz4cffqglS5Y4vdzg4GBt2bLFqd5Dzz33nG6//XY1b97c+cCd1LJlyyx7wfzxxx/q0aOH08u0HocmL3E2ceKoXUpKigICAiyfGYkYuO3Sg927d+e4Bm5mXn31VTVr1kxhYWEOu/qdOHFCXbp0UXh4uIoXL64hQ4ZYEgFme/bsUYsWLRQaGqqyZctq0qRJ7LTlInvlywoWLKiAgACHCRlJmjlzpl544QUtWrQoy+eoVKlShmk1atTIMM08/kt6mfVySf8D17hxY7vt8ktSRpIKFSqUL7v9AgAAAL7m5ptvVmRkpG666SaFh4d7OxyfwPE9gPwgODg4x489fvy43fnpS8befvvt2X4us/j4eP39998205wtS/brr786/TxGo9GluDwlISHBqXb2ki03btzQzp07tWPHDiUkJOjgwYPasWOHduzYkaFtSkqKyyXQkD+5tT9obu78JCcnq0ePHho4cKDd+SkpKerUqZOMRqM2btyopUuXatmyZRo5cqSlTVxcnNq0aaMyZcpo69ateuuttzR9+nTNmDEj1+L2d456ymSldu3aeuWVV1SkSJEs2952220Zptm7ysxcnzG9zAZ/TP8juXTpUrvreX4pXwYAAAAg7yhSpIhOnDihPXv2UK7JTUjKAMgPChUqZHP/vvvukyQNGjRIpUqV0v/+9z+HjzWfgypfvnyGUmg3btzQP//8k+ExBoPB6cRCekajUYUKFdJNN91kqZDj6MLnnMpsjGdvuXz5sg4dOuRUW3u/QeZklslk0r59+3T16lWHj79+/bp27dqVrTiRvzhdvqx79+6Zzo+Njc3Vq+9feuklSdLixYvtzl+9erX27dunkydPWnpgvPHGG+rbt69effVVRUZG6qOPPlJSUpIWL16s4OBg1apVS3/99ZdmzJihESNG0HsgF2Q3KeMKe8uzl5RxlG2vXLmyw2VPmzZNzzzzjOV+mTJl7G5g81NPGQD+KyEhwdKTcN++fXmyWzgAAP4mKirK2yH4FJIy+Rf7qvAnK1eu1P3336+ZM2dKkj7//HNJacmT2bNna8+ePQ4fe/78ecvtmJgYm3mZVTGqXLlytgaS/+OPPyy3n3/+eQ0YMMBu4scdkpKScmW5OXH27Fmn29rrKRMfH+/S8+X271hKSkquVruCc5y+FGflypVKSkpSVFSU3T9vj1GxadMm1apVy6YkVrt27XTt2jXLAO+bNm1SixYtbHo/tGvXTmfOnNGxY8ccLvvatWuKi4uz+YNzzL1QEhMTLRvW7CYwOnfubHd6QECAfvvtN5tpISEhlrqdPXv2lGR/I9i4ceNMe7mkr3vpqHspPWUA5Acmk0nHjx/X8ePHOWEBAAB8Evs4+Rf7qvAnt912m06dOmU572QwGCwXixsMBtWuXVvDhg3TtGnTMjzW+qJy6/lDhgyx3LY3jpd1Mie7zN9NZ0uXueqRRx7JleVml8lkcqmH0ZUrVySl9Vi6du1atpNM7t4GxsXF6eLFizp79qx27txpiRPe43RPmVtuuUX33Xef+vfvb3f+rl279M0337gtMFedO3dOpUqVsplWpEgRFSxY0JIFPnfunCpWrGjTxvyYc+fO2R2bRJKmTJli6akD15iTFZs3b7ZMy25S5ssvv1SVKlV04sQJm+nJyclq2rSp6tatq927d0tK6ymza9cuXbx4UYsXL9Znn31mN1tdvXr1TGs1WidhduzYIYPBoL/++itDO5IyAAAAAAAAcAeDwWDpRTN69Gibeda9HIKDg+2ewG/evHmuxHXx4kVJ0ty5c3Nl+Zs2bcqV5WaXq8mLc+fOKTo6OsclyI4ePaqAgABFR0crJCTEMj0lJUWxsbGKiopSUlKSrly5oujo6Ex7vphMpgznMv/++281atQoRzEiZ5zuKdOwYUO7AxCZBQcHq3z58i49+cSJEy2ZYEd/27Ztc3p59sqPmUwmm+np25g3XJmVLhs7dqxiY2MtfydPnnQ6Jn9nL1mR3aRMUFCQypUrl2G6ud6k9XJDQ0MVEhKismXLZjp4WlBQkLp27epwflRUlBo3bqyGDRuqTp06DtvZK5cGAAAAAPAselgA8DU7d+7UnDlz1KRJE0nO9SZxdO7NHdvI5557TvPmzXM4L6dcLfeVm7IzDo87xoS5dOmS/v33X+3du9cmMXT8+HEdOXJER44c0f79+3X27NksS9LduHEjx/EYDAZ99dVXOV4O/uN0T5l58+Zl2qPglltu0dGjR1168qeffloPPvhgpm3S92xxJDo6Wlu2bLGZdvnyZV2/ft3SGyY6OjrDinrhwgVJytDLxlpwcHCmJ/bhmDuTMpL92ozm9dJ6I2OdJLHOKKcXFBSksLAwff/992rfvr3NvHr16slgMGjz5s023UiPHz+uChUq2LQlKQMAAAAA3mfvmBEA8rN69eqpXr166tmzp1avXq177703y8c4quhy/fp1p8/LGY1GLViwQCVLlrSZPnXqVIftw8LC1KtXL7355pt67733nHqe9IYMGZLtx7qb+byxO1y6dEnz5s3Tb7/9pkuXLqlQoUKqVq2aBgwYYLkQvHHjxpo2bZpatmxpedzhw4fVsGFDyzKktLHdzRwljo4dO6ZKlSpp7dq1ioyMtNtm4sSJWVaHOnr0qM6ePasiRYo4/VqRNad7ygQHB7t9kLXixYurevXqmf5ldkLdWtOmTbV3716bwZdWr16t4OBgy4rbtGlTbdiwwTLgvLlNmTJlnE7+wDXmMWWs5aTUl72alead7p07d1qmFSpUyHI7s/GOzPHZS7rddNNNktLGrLHuSVW+fHl99NFHlvuhoaEKCHD6qwQAAAAAyCWdOnWSJIWHh3s5EgBwr2LFiqlXr15OnSt1lHhJX47r1KlTevDBB/Xbb79p//79uvnmm/Xhhx9KkiZNmqQRI0booYcecio+83njOnXq6N1331Xr1q1t5hsMBq1bt04PP/xwpstZu3atU8+X2y5dupShg0JMTIzdts6cFxwzZowOHTqkiRMnatmyZZoxY4YaNGiQ5djlJpNJR44c0fHjxx3OT9/2yJEj2rt3ryTZnCtPb9SoUTp79qzlLzo6Wk8++aS+++47nT59WmfPnlVMTIyio6PpsOBm+eZM8okTJ7Rr1y6dOHFCKSkp2rVrl3bt2mXp0ta2bVvVqFFDDz/8sHbu3Km1a9dq1KhRGjBggCUb2Lt3bwUHB6tv377au3evvvzyS02ePFkjRozItHwZss9eAiYn77W9Lnf2roTKSVKma9euevTRRzV79myHj7P+cXN3shIAAAAAkD0tW7bUli1bdOzYMW+HAgBeExAQoGeffTbD9PQlqJ588kl9+umnat68uR5//HH99ddflvJoK1asyFEMS5Ys0SOPPKLffvtNJpNJqampatmypT744AOZTCb973//k5SWrLBWv379bD2f0Wh0+JeUlOR028TERF26dElHjhyRJCUmJioxMVFhYWGKiIiw3LdWtGjRTGO7evWqdu3apaefflqNGjVS6dKlVbNmTfXr10+33367JFmGVxg9erQaN25suX/q1Cn169dPDRo00J133qlHHnnEplpUYmKiKlasqFdeeUV9+vRRnTp1NGTIEHXp0kWS9NBDD6lx48Z68sknM8QVERGh6Ohoy5/BYFBYWJiKFy+ulJQUnTp1ShcvXrQpX3bs2DEZDAZ99tlnuuOOOxQaGqrGjRvrr7/+0tatW9WoUSNFRESoffv2+ueff2yeb9GiRbrlllsUEhKi6tWra86cOZm+b77M6fJl3jZ+/Hi9//77lvvmL+i6devUsmVLBQYGatWqVRo0aJCaN2+u0NBQ9e7dW9OnT7c8JioqSmvWrNHgwYPVqFEjFSlSRCNGjNCIESM8/nr8RU56xdhjr6eMOSNcpUoVHT58WJJsuuVZJ2jSMw+EZZ1kadWqlYYNG5ZpHNavi6QMgPzCYDCoRo0altsAAAC+yDzuAvIX9lUB95o6daqioqL0wgsvWKYNHjxYTz75pCpVqqRly5bp22+/tcw7ePCgzePT389M586dM0yLjo62OZeb3uDBg9WmTRvdfPPNeuuttyxluAoXLuz081rL7KLsjh07atWqVZb7JUuWdFj2q0WLFjbnk7t27Zqhh5Ekbd261XLbXqUga6GhoQoLC9P69etVu3Ztuz2Z3n//fbVt21bjx49X06ZNLecsExIS1Lx5cw0cOFAFCxbUqlWrNHLkSH3xxReWRIokTZs2TSNHjlSPHj0kST169FDfvn319ttvq3Llyi6foz1//ryktI4SZv/++6/27dsnSZowYYJmzZql8uXL67HHHlOvXr0UGRmpN998U2FhYerZs6fGjx+vuXPnSpIWLlyoCRMmaPbs2apfv7527typAQMGKDw8XI8++qhLsfmCfJOUWbx4sRYvXpxpm/Lly+ubb77JtE3t2rW1YcMGN0aGzGS1UXJVZuXLOnfurDfffFNS5j1lbrvtNm3evNkmPuueMs7U1iQpAyA/CgsL059//untMAAAAIAM2FcF3O/555/XyZMnNW/ePEn/VaA5evSoGjRoYNP24sWLltvvvPOOU8vv1KmTunTpkuWY4fYEBASoevXqktISQP369dOPP/6o06dPu7wsbytSpEiGccytBQUFacKECXr11Ve1fPly1a5dW7Vq1VLbtm0twydER0dLSjunWbx4cctjq1WrpmrVqlnuDxw4UOvWrdOGDRvUs2dPS/Lmrrvu0pAhQ/T3339Lks6cOSMprZOC9fKyKykpSceOHbOsQ6NGjVK7du0kSUOHDlWvXr20du1aNW/eXJLUv39/m3P5L7/8st544w11795dklSpUiXt27dP8+fPJykDuJsne8pYJ4Cse8qkT8qcOnXKctteUsY8aFZmrF9XaGholu0BAAAAAAAAT3vttdcsSRlnffLJJ5nOb9mypf7++2+99dZbqlSpUk7CkySVK1dO3bt3148//qgff/xRU6ZM0a5du7Rw4UKHg9SnZx7iwh5z4sLswoULDtsGBATYJIi//vprSVKNGjUUEhKiHTt2ZHhMeHi4qlWrpr/++svhcu+66y41b95cu3bt0sWLF/X999/rww8/1AsvvKAuXbrYjA1jLTExUQsXLtTGjRv1zz//KCUlRdeuXbMkgRITE5WcnGwZU91ZKSkpGd6XzJw9e1ZVq1a13K9Tp47ldqlSpSSldYawnmZ+n//55x+dPHlS/fv314ABAyxtbty4oaioKJfi9hUkZZCr0idlzHUps6t58+YZagObe8pYJ2Wse8qk761jLylj3Tvm/fff14svvphpHNbtnelZAwAAAAAAAHhaVFSUGjZsqO3btzv9mJ9++inT+RMnTlSLFi1yGpoN6/FHnn/+eUnSTTfdpFdeecWpx4eHhzv9XJm1TT92tfli7GLFitncTy998qhGjRqWUl9SWlm2K1euqFOnTqpYsaLGjx+vxx9/XAsWLLCM/2LPm2++qc2bN2vo0KGKiYlRcHCwxowZk+HCdYPB4FLpx4SEhEyHfEgvJSUlw/Olv219HthgMFjeS/P/hQsX6tZbb7VZjiuJIV/iVFLG3K3IGcuXL892MPA96RMimdWSdMacOXNUpUoV3Xfffapbt66k/77YAQEBlnaZJWWsmb/41hvUe+65J8s4rDcymWXiASAvSUhIUOPGjSWl1b+l/CIAAADyCvZVgdzj7uEFcuP7aS/Z4Y2Sho56rLgqLCxMDRs2VEJCgkJDQ5Wamqq4uDibMXNq1KihL774wvK8BQoUyJAU2rVrlzp37qxWrVpJSttWnj17NsPzXbx40VK6TPrv3GX65ZkdPXpU0dHRKlSokFNVgNInfMxlzJxRqlQplS1bVkeOHFGfPn2cfpwvc+ob6a/diJBzISEhltvjx4/P8fIiIyP10ksv2Uwz77TFxcVZppmz11LGH54OHTrou+++s5lXpEgRy/wHHnggyzisl5lZzUgAyEtMJpPlSh137WgCAAAA7sC+KpB7tmzZ4tblWQ8D4C4dOnTQs88+azPtq6++cvvzZMVeEsP6QnBXGAwGhYeH6+LFi+rRo4cee+wx1alTR4UKFdK2bdv0+uuvW3ocmUwmVaxYUb///rvq1KmjggULKjIyUjExMVq3bp3uuOMOGQwGzZs3z+E20np6kSJFFBwcrE2bNqlkyZIKDg62GeIhOTlZJ06ckCQ1atQoy9eS0+3yxIkTNWTIEEVGRqpDhw66du2atm3bpsuXL2vEiBE5WnZ+5FRSZtGiRbkdB3xUZmO75NSePXv09ddfa/jw4ZKkZcuWWeZZlxRL3w3u7bffVuXKlSX9l1wpUKCAGjVqpPPnz9vURHQG5csAAAAAAADgL8yD07tTzZo13b7M7EhOTs4wLafn/iIiInTrrbdq5syZOnz4sK5fv66YmBgNGDBA7dq1s7R74403NGzYMHXp0kUlSpSwnPd8+eWX1b9/fxUuXFiPPPKIjEZjls8ZFBSkUaNG6Z133tH8+fNVr149zZ8/325bk8nkUukz82Nc8fjjjyssLEzTpk3Ts88+q/DwcNWuXVvDhg1zaTm+Ilt9127cuKGff/5Zhw8fVu/evVWoUCGdOXNGkZGRbj/xjvzN2TJi2VGrVi3VqlXLcr9x48ZauXJlhnbpn9c6UWS9wdm8ebNSUlJc3tCSlAEAAAAAAIAvGz16tHbu3Knhw4c7Ve7KVY6SAqdPn1axYsVsqvHkpqSkpAzTrAe4z47g4GBNmTJFU6ZMyTBv27ZtlttdunRRly5ddOHCBUsvlqZNm2rp0qVKTk62JGN69uxps4yvv/7a7vOOGTNG3bp1yzK+q1evKjIy0uFytm7darldpkwZbd261aZKUcuWLTMkafr27au+ffvaTOvdu7d69+6dZTz+wOW+V8ePH1ft2rV1zz33aPDgwZZBmF5//XWNGjXK7QEif7Pu3mc9DktuWLFihQYMGKBffvnFZnr6pIx1F0vrQaoCAwOdTrBYb2hyo8smAAAAAAAA4A7t27fP8TIqVKigNWvWqGPHjm6IyHnlypVzaUD6nErfU6ZRo0YOE0JBQUGqVKlShuk5vYC7aNGiCgoKUtGiRVWiRAlVqVJF0dHRkpwfZqRevXoZ3reaNWvajff69esu93y5ePGiw/FqkDWXkzJDhw5Vo0aNdPnyZZus6L333qu1a9e6NTj4liZNmuTq8g0GgxYsWKDbb7/dZnr68mWOkjLZldvJJgAAAAAAACC7mjdv7vJj+vXrZ3P/+vXr7grHZTdu3NDly5c98lynT5+23DYPf+BIvXr1bHqMVK1aVaGhoTnuWRMUFKS6devaPH/hwoVVs2ZNValSxellpBcaGurw4vJjx465HKc314n8zuWkzMaNG/Xiiy9myPhVqFDBZqUFzPbv369Vq1blelLGkfQbIev72U3KVKtWzXLb1ZqLAAAAAAAAgKe0bt3a4bzx48fbnd6nTx/16tXLcv/GjRtujyu9iRMnOpx3/PjxXH/+9IoWLepSe3PiJCwsLMfPnf58o8FgUGhoqE1VouxIf/G6lFa+7OLFiy4vK6ex+DOX37nU1FS7J7JPnTrl0a5kyD+qV6/u8a6N1ooUKWJz33qjlt2kjPUyScoAyC8MBoMqVKigChUqsO0CAABAnsK+KpB7mjZtqk2bNunOO++0mV6lShV16dLF7mOCg4P18ccfW+57olfEhAkTdO3aNSUkJGSY5+6xqu3xROLJ2+xV/Elfss0sq6QLSZnsc/mda9OmjWbNmmW5bzAYFB8frwkTJnj1xDuQHe4oX8bOIoD8IiwsTMeOHdOxY8fccuUOAAAA4C7sqwK567bbbtOyZcs0fPhwy7SRI0c6/L6lv8jZU+OHFCxY0O4YLiNHjsz1575w4YLldkxMTK4/nzuULl06w7SbbrrJYfugoKAMj4mLi7Pctk60ZPWZc040+1xOMc6cOVOtWrVSjRo1lJSUpN69e+vQoUMqXry4Pvnkk9yIEcjT2AABAAAAAAAgrytevLhmzJihESNGaNOmTerevbsCAwM1YsQIBQQEaPr06Za2t9xyi81jXR0IPifsnWtbvXq1RweWzw9jSAcHB9t9r7JKbJctW1ZlypTRgQMHZDQabea58h7TUyb7XH7nypQpo127dmnUqFF68sknVb9+fb322mvauXOnSpYsmRsxAnkaSRkAAAAAAADkF+XKlVOPHj0s44u88cYbmjZtmmV+REREhhPueeH819dff+32ZZpMJl25ckUnTpzQ+fPnLdPdUV0nt1SvXl2RkZGqWrWq3fnOfFYGg8Fuu5CQEJUqVUqSVKtWLYfLzg9Jq7wsW8X4QkND9dhjj+mxxx5zdzxArqpRo4bNfXf8oOSFHyUAcEZiYqKlhvCGDRsUGhrq5YgAAACANOyrAt4XFBSkGzduqFKlSpZpgwYN0hdffKEnnnjCi5Gluffee5WYmOi25ZlMJu3evTvfjSUTERGhatWqSZIuXrxomV6xYkVJzo+/Y++cZlJSkmrVquWwfFv16tV15swZlStXzsWoYS1bSZm//vpLP//8sy5cuJChS9P48ePdEhiQGzp16mRz3x3d7Nq0aZPjZQCAJ6Smpmrbtm2W2wAAAEBewb4q4H3btm3TpEmT9Morr1imvf3223rrrbd8tlRVfkvIpGc9HkzRokVd+pyuXbvmcN7EiRP11Vdf6Z133rGZHh4enumYNXCOy0mZhQsXauDAgSpevLiio6NtMmoGg4GkDPK0KlWqSJKee+45ffzxxxoyZEi2l7V582b973//y9EyAAAAAAAAgLygbt26WrZsWYbpvpiQiY2N1bFjxxzOd1Seq2jRorp06ZLLw3hcuHBB48aN03fffafz58+rSJEiqlu3rh544AHVqVNHUtq59S+//FLdunVzernBwcFKSEiwPN4ZWbV79NFHNXv2bD3zzDOSpKNHj0qSpXcOcs7lpMwrr7yiV199VWPGjMmNeIBcsXr1av3444/q37+/JGnKlCmaPHlyjkqP3Xrrrfroo4/cFSIAAAAAAACATBw+fFjXr1/P1mMTEhJ0/vx5lSlTRocOHcq0bVRUlN3pFSpUUJEiRRQZGenSc9933326fv263n//fVWuXFnnz5/X2rVrLT1dsnuO0jph5uwyzp49K0natWuX1qxZo/nz5+uLL76wzG/atKkiIiIUEREhSSpSpIjDMWiQPS6nOS9fvqwePXrkRixArmnTpo2mTp1qU1ORDQkAAAAAAACQf3Tq1Ennzp2zJGZMJpMOHjyoEydOZPnYgwcP6uLFizpy5EiWbR2dNwwMDFSRIkUUGBjodMxXrlzRxo0bNXXqVLVq1UoVKlRQkyZNNHbsWN1+++2SpA4dOkhKGzfHYDBYxoeRpLlz56pKlSoqWLCgbr75Zn344YeWeSVKlFDjxo21cuVKdejQQaGhoapUqZI+//xzh/FER0crOjpaxYsXV0REhAwGg4oXL67ixYurRo0aioqK0sSJE1WvXj1JaYmffv36qVu3bpo8ebJKlSqlwoUL66WXXtKNGzc0evRoFS1aVOXKldN7771n81ynT5/WAw88oCJFiqhYsWK65557Mu2h5C9cTsr06NFDq1evzo1YAAAAAAAAAAB+bs+ePVq4cKH++usvy7RixYpZbicnJ0uSrl69qqtXr+r48eMyGo12/5KSknTt2jWlpKRIkv79918lJiba/UtKSrKJw9EyXWHudfLVV185HMfFXI1n0aJFOnv2rLZu3SpJ+vLLLzV06FCNHDlSe/fu1ZNPPql+/fpp3bp1lmVLaWP/3Hfffdq9e7ceeugh9erVS/v37880rltuuSVDnDExMQ7b//TTTzpz5ow2bNigGTNmaOLEiercubOKFCmiLVu26KmnntJTTz2lkydPSkrrmdSqVStFRERow4YN2rhxoyIiItS+fXvL5+evXC5fVrVqVY0bN06bN29W7dq1M9TXY3wNAAAAAAAAAEB21apVS7Vq1ZIkffzxx+rdu7cuXrxoSUKYpaamSpLuvPNOh8vq2LGjXnrpJcv9tm3bZki+mDVo0EDr16+33K9YsaL+/fffDO1MJpPTryUoKEiLFy/WgAEDNG/ePDVo0EAtWrTQgw8+aGlTtGhRSVLhwoUVHR1tmT59+nT17dtXgwYNkiSNGDFCmzdv1vTp09WqVStLux49eujxxx+XJL388stas2aN3nrrLT322GMO4woPD7e5n5KSkmlloaJFi+p///ufAgICdPPNN+v1119XQkKCnn/+eUnS2LFj9dprr+nXX3/Vgw8+qKVLlyogIEDvvPOOZbmLFi1S4cKF9fPPP6tt27ZOvX++yOWkzIIFCxQREaH169fbrKBSWrcukjIAAORdxYsX93YIAAAAgF3sqwKwJyQkJMM0c1LEPCaLu4SHh2dI/LjDfffdp06dOumXX37Rpk2b9P333+v111/XCy+8oC5dujhMhuzfv19PPPGEzbTmzZvrzTfftJnWtGnTDPd37dqVZVzWY+dkNVZPzZo1bcawKVWqlCVxJqWVditWrJguXLggSdq+fbv+/vtvFSpUyGY5SUlJOnz4cJax+TKXkzJHjx7NjTgAAEAuCw8P1z///OPtMAAAAIAM2FcF4EhwcHCGaeYeMuYEwIYNGyzzSpcurdKlS1vuX7x40dJOksOhOUJDQ1WzZk2bae4c/yQkJERt2rRRmzZtNH78eD3++ONasGBBpkkZKeP4NiaTyamxsp1pY+6hI0lhYWGZtk1fMctgMNidZv5sUlNT1bBhQ0tpNmslSpTIMjZf5vKYMt5w7Ngx9e/fX5UqVVJoaKiqVKmiCRMmZKg9d+LECXXp0kXh4eEqXry4hgwZkqHNnj171KJFC4WGhqps2bKaNGmSS93NAAAAAAAAAACeYR4LxtrVq1dtErmhoaGWv7CwMIWHhys8PFyBgYE2CZn0ba3/atasqdDQUJu25uWk/3OHGjVqKDExUdJ/CY70r/WWW27Rxo0bbab99ttvGcaD2bx5c4b71atXdyoOc/KmSJEiLsWflQYNGujQoUMqWbKkqlatavNn3UPHH7ncUyYlJUWLFy/W2rVrdeHCBUvmy+ynn35yW3BmBw4cUGpqqubPn6+qVatq7969GjBggIxGo6ZPn26Jq1OnTipRooQ2btyoixcv6tFHH5XJZNJbb70lKa07W5s2bdSqVStt3bpVf/31l/r27avw8HCNHDnS7XEDAAAAAAAAALLvxo0bGaZdvnxZly9fzvKx165dc+o56tSpo4IFC7ocmzMuXryoHj166LHHHlOdOnVUqFAhbdu2Ta+//ro6dOggSSpfvrwqVqyotWvXqnnz5goODlaRIkU0evRo9ezZUw0aNFDr1q21cuVKLV++XD/++KPNc3z++edq1KiRbr/9dn300Uf6/fff9e6776po0aI6deqUypcv7zC+gIAA3XTTTYqMjHTr6+7Tp4+mTZume+65R5MmTVK5cuV04sQJLV++XKNHj1a5cuXc+nz5ictJmaFDh2rx4sXq1KmTatWq5VQ3qJxq37692rdvb7lfuXJlHTx4UHPnzrUkZVavXq19+/bp5MmTKlOmjCTpjTfeUN++ffXqq68qMjJSH330kZKSkrR48WIFBwerVq1a+uuvvzRjxgyNGDHCI68FAABvSUxMtOzwfffddxmuAAIAAAC8hX1VAI6k7xTgrBs3bujQoUNZtitfvnyuJWQkKSIiQrfeeqtmzpypw4cP6/r164qJidGAAQP0/PPPKzg4WAEBAXrjjTc0YsQILVy4UGXLltWxY8fUrVs3vfnmm5o2bZqGDBmiSpUqadGiRWrZsqXNc7z00ktaunSpBg0apOjoaH300UeqUaOGJKlYsWIZyoyllxs9V8LCwrRhwwaNGTNG3bt319WrV1W2bFm1bt3a7Qmg/MZgcrF2V/HixfXBBx+oY8eOuRWTU1588UV9//332rZtmyRp/PjxWrFihXbv3m1pc/nyZRUtWlQ//fSTWrVqpUceeUSxsbFasWKFpc3OnTvVoEEDHTlyRJUqVbL7XNeuXbPJqsbFxSkmJkaxsbF+vwIBAPIPo9FoGbAwPj7ebV2uASBHjEbJPJhqfLzEtskrjMlGRUz5/9+IsfEKL8jnAN/FZidvYl8VgCO///67br31VklShQoVNG/ePBUvXtxh+7Jly6p06dI6ePCgrl69mumyK1asqKJFi9oMYJ/fGAwGffnll+rWrZu3Q/ELSUlJOnr0qCpVqqSQkBCbeXFxcYqKisoyb+Dy2lawYEFVrVrV9Wjd6PDhw3rrrbf01FNPWaadO3dOpUqVsmlXpEgRFSxYUOfOnXPYxnzf3MaeKVOmKCoqyvIXExPjrpcCAAAA+LfwcMlkSvvjBJzXhBcMl2mCSaYJJhIy8HlsdgAgf2nSpInefvttp9ubqyFllZApXbq0ihcvnq8TMsifXF7jRo4cqTfffFMudrCxa+LEiTIYDJn+mXvCmJ05c0bt27dXjx499Pjjj9vMs1d+zGQy2UxP38b8OjIrXTZ27FjFxsZa/k6ePOnyawUAAAAAAAAAuG7QoEFOtz116pTi4+OzbMdQFvAWl8eU2bhxo9atW6fvvvtONWvWzFCPbvny5U4v6+mnn9aDDz6YaZuKFStabp85c0atWrVS06ZNtWDBApt20dHR2rJli820y5cv6/r165beMNHR0Rl6xFy4cEGSMvSgsRYcHKzg4OAsXw8AAAAAAAAAwP1eeeUVrV271qm2Bw4cyDAtLCxMCQkJlvu+kpRxR+cJeJbLSZnChQvr3nvvdcuTFy9ePNP6f9ZOnz6tVq1aqWHDhlq0aFGGbmVNmzbVq6++qrNnz6p06dKSpNWrVys4OFgNGza0tHn++eeVnJxsGbxp9erVKlOmjE3yBwAAAAAAAACQd7zwwgsaMWKE1q9f7/Jjw8PDVbZsWf3111+Wab6SlEH+43JSZtGiRbkRR6bOnDmjli1bqnz58po+fbr++ecfy7zo6GhJUtu2bVWjRg09/PDDmjZtmi5duqRRo0ZpwIABlkF1evfurZdeekl9+/bV888/r0OHDmny5MkaP348X0IAAAAAAAAAyMMMBoMqVKig4OBgXbp0yenHVaxYUUlJSbkYGeA8l5My3rB69Wr9/fff+vvvv1WuXDmbeebuWYGBgVq1apUGDRqk5s2bKzQ0VL1799b06dMtbaOiorRmzRoNHjxYjRo1UpEiRTRixAiNGDHCo68HAABvCQsL83YIAAAAgF3sqwJwlqsX2IeGhur69es5WgYgSampqTlehsGUjaJzX3zxhT777DOdOHFCycnJNvN27NiR46Dyuri4OEVFRSk2NtbSCwcAAAAAAAAAkHtSUlJ06NAhGQwGXb161enH1apVS9evX9fBgwct00qXLq1ixYrlRpjwQSaTScnJyfrnn3+UkpKim266KcMQK87mDVzuKfO///1PL7zwgh599FGtWLFC/fr10+HDh7V161YNHjzY9VcDAAAAAAAAAEAWAgMDVa5cOZ08eVI3btxQaGiozp8/n2nvhdDQUB09elSpqan6999/LdNNJpPi4uI8ETZ8SFhYmMqXL58hIeMKl3vKVK9eXRMmTFCvXr1UqFAh7d69W5UrV9b48eN16dIlzZ49O9vB5Bf0lAEAAAAAAAAA70hJSbGUI2vWrFmm48vs3btXQUFBMhqNatiwoWX6G2+8oU6dOuV6rPAdgYGBCgoKclj6Ltd6ypw4cULNmjWTlJZlNHcTe/jhh3Xbbbf5RVIGAID8KCkpSffdd58kadmyZQoJCfFyRAAAAEAa9lUBuCIwMFCBgYGSpOPHj2dIyixcuFADBgzQRx99pIiICMv048ePW24HBQWxrYFXuJyUiY6O1sWLF1WhQgVVqFBBmzdvVt26dXX06FFlY3gaAADgISkpKfr2228ttwEAAIC8gn1VANllnZAZNWqUBgwYoGrVqql3794KCwuzzCtQoIDN4xITEz0WI2DN5aTMXXfdpZUrV6pBgwbq37+/hg8fri+++ELbtm1T9+7dcyNGAAAAAAAAAAAyNW3aNMtt64SMJEvPGsDbXE7KLFiwwDJw0lNPPaWiRYtq48aN6tKli5566im3BwgAAAAAAAAAgD3t2rXTDz/84FTbOnXq6I8//pAkqj7Ba1xOygQEBCggIMByv2fPnurZs6dbgwIAAAAAAAAAICuOBl23x7r3TNOmTXMjHCBLLidlJOnKlSv6/fffdeHCBUuvGbNHHnnELYEBAAAAAAAAAOAus2fPVqNGjdSvXz9VrFjR2+HAT7mclFm5cqX69Okjo9GoQoUK2WQiDQYDSRkAAAAAAAAAgEeEhIQ43bZhw4ZKTk5WgQIFcjEiIHMuJ2VGjhypxx57TJMnT84wWJK/MNcbjIuL83IkAAA4z2g0Wm7HxcUpJSXFi9EAAAAA/2FfFUB2DRo0SN9884369evn9PnaxMTEXI4K/si8/mU1XpHB5OKIRuHh4dqzZ48qV66c/ejyuVOnTikmJsbbYQAAAAAAAAAAgDzk5MmTKleunMP5LveUadeunbZt2+bXSZkyZcro5MmTGcq3+bu4uDjFxMTo5MmTioyM9HY4gFuwXsPXsE7DF7FewxexXsPXsE7DF7FewxexXsPXeHKdNplMunr1qsqUKZNpO6eSMl9//bXldqdOnTR69Gjt27dPtWvXzlB/r2vXrtkIN38JCAjINNPl7yIjI9low+ewXsPXsE7DF7FewxexXsPXsE7DF7FewxexXsPXeGqdjoqKyrKNU0mZbt26ZZg2adKkDNMMBgM1PwEAAAAAAAAAAOxwKimTmpqa23EAAAAAAAAAAAD4tABvBwDfERwcrAkTJig4ONjboQBuw3oNX8M6DV/Eeg1fxHoNX8M6DV/Eeg1fxHoNX5MX12mDyWQyOdPwp59+0tNPP63NmzdnqL0WGxurZs2aae7cubrzzjtzJVAAAAAAAAAAAID8zOmeMrNmzdKAAQPsDoYTFRWlJ598UjNnznRrcAAAAAAAAAAAAL7C6aTM7t271b59e4fz27Ztq+3bt7slKAAAAAAAAAAAAF/jdFLm/PnzKlCggMP5QUFB+ueff9wSFAAAAAAAAAAAgK9xOilTtmxZ7dmzx+H8P/74Q6VLl3ZLUAAAAAAAAAAAAL7G6aRMx44dNX78eCUlJWWYl5iYqAkTJqhz585uDQ4AAAAAAAAAAMBXGEwmk8mZhufPn1eDBg0UGBiop59+WjfffLMMBoP279+vt99+WykpKdqxY4dKlSqV2zEDAAAAAAAAAADkO04nZSTp+PHjGjhwoH744QeZH2YwGNSuXTvNmTNHFStWzK04AQAAAAAAAAAA8jWXkjJmly9f1t9//y2TyaSbbrpJRYoUyY3YAAAAAAAAAAAAfEa2kjIAAAAAAAAAAABwTYC3AwAAAAAAAAAAAPAHJGUAAAAAAAAAAAA8gKQMAAAAAAAAAACAB5CUAQAAAAAAAAAA8ACSMgAAAAAAAAAAAB5AUgYAAAAAAAAAAMADSMoAAAAAAAAAAAB4AEkZAAAAAAAAAAAADyApAwAAAAAAAAAA4AEkZQAAAAAAAAAAADyApAwAAAAAAAAAAIAHkJQBAAAAAAAAAADwAJIyAAAAAAAAAAAAHkBSBgAAAAAAAAAAwANIygAAAAAAAAAAAHgASRkAAAAAAAAAAAAPICkDAAAAAAAAAADgAUHeDiA/Sk1N1ZkzZ1SoUCEZDAZvhwMAAAAAAAAAALzIZDLp6tWrKlOmjAICHPeHISmTDWfOnFFMTIy3wwAAAAAAAAAAAHnIyZMnVa5cOYfzScpkQ6FChSSlvbmRkZFejgYAAAAAAAAAAHhTXFycYmJiLPkDR0jKZIO5ZFlkZCRJGQAAAAAAAAAAIElZDnniuLAZAAAAAAAAAAAA3IakDAAAAAAAAAAAgAeQlAEAAAAAAAAAAPAAkjIAAAAAAAAAAAAeQFIGAAAAAAAAAADAA0jKAC4yGo0yGAwyGAwyGo3eDiff8JX3LbuvI7dfv6+8v/BN3lw/Pf3cef27mNfiy2vx5Jb8/DrzQ+yeijE/vBcAAAAA8j6SMgAAAAAAAAAAAB5AUgYAAAAAAAAAAMADDCaTyeTtIPKbuLg4RUVFKTY2VpGRkd4OBwAAAAAAAAAAeJGzeQN6ygAAAAAAAAAAAHgASRkAAAAAAAAAAAAPICkDAAAAAAAAAADgASRlAAAAfJTRaJTBYJDBYJDRaPR2OFnKb/ECAID8hX0NALmJbQycRVIGAAAAAAAAAADAA4K8HQAAwHcYjUZFRERIkuLj4xUeHu7liAD/Fh4eLpPJ5O0wnJbf4gUAAPkL+xoAchPbGDiLnjIAAAAAAAAAAAAeQE8ZAIDbcFUIAAAAAAAA4Bg9ZQAAAAAAAAAAADyApAwAAAAAAAAAAIAH+F1S5uTJkzp16pTl/u+//65hw4ZpwYIFXowKyMhoNMpgMMhgMMhoNHo7HCDf8ZfvUF54nXkhBgDewfcfyIjvBXwN6zSQf/B9zR28r3A3v0vK9O7dW+vWrZMknTt3Tm3atNHvv/+u559/XpMmTfJydAAAAAAAAAAAwFf5XVJm7969atKkiSTps88+U61atfTbb7/p448/1uLFi70bHGDFPGC6yWRSeHi4t8MB8h173yFfvLolL2wr8kIMyJovrv/wPr7/yI9ye3vI9wLZkZd/p/PCOp2X3x8gL8mt76u/fwfzwnbQXfz9s8wr/C4pc/36dQUHB0uSfvzxR3Xt2lWSVL16dZ09e9aboQEAAAAAAAAAAB/md0mZmjVrat68efrll1+0Zs0atW/fXpJ05swZFStWzMvRAf6JLD08xZeubgFcxfoPwBW+vH/G9hB5Eetl5nh/AO/Ka99BX95PyW157bP0V36XlJk6darmz5+vli1bqlevXqpbt64k6euvv7aUNQMAAAAAAAAAAHA3g8lkMnk7CE9LSUlRXFycihQpYpl27NgxhYWFqWTJklk+Pi4uTlFRUYqNjVVkZGRuhor/ZzQaFRERIUmKj48nkwsAAAC3Y58TcA3fGSB/8+R3mO0FkDfx3XQvZ/MGftdTRpJMJpO2b9+u+fPn6+rVq5KkggULKiwszMuRAQAAAAAAAAAAXxXk7QA87fjx42rfvr1OnDiha9euqU2bNipUqJBef/11JSUlad68ed4OEXaY6x0CgCNc3QEAyCn2Of0H+w3uwXcGyN88+R1mewHkTXw3vcPvesoMHTpUjRo10uXLlxUaGmqZfu+992rt2rVejAwAAAAAAAAAAPgyv+sps3HjRv36668qWLCgzfQKFSro9OnTXooKAAAAAAAAAAD4Or/rKZOamqqUlJQM00+dOqVChQp5ISLfYjQaZTAYZDAYZDQavR0OAD9i7nJrMpkoQQIAXsT+IPID9hsAAADgLX6XlGnTpo1mzZpluW8wGBQfH68JEyaoY8eO3gsMAAAAAAAAAAD4NIPJz0byOXPmjFq1aqXAwEAdOnRIjRo10qFDh1S8eHFt2LBBJUuWzHIZcXFxioqKUmxsrCIjIz0Qdf7GIJqewfsMAMht/NYAyA62HQAAAPAHzuYN/G5MmTJlymjXrl1aunSptm/frtTUVPXv3199+vRRaGiot8MDAAAAAAAAAAA+yu/Kly1ZskShoaHq16+fZs+erTlz5ujxxx9XaGioRo8e7e3w8j17NcTdUa/ZldrkWbX1tTrn5tcTERGh+Ph4j9fFdudn4894b3JHXn9fcxIf373clRvvmTc/B3c9tyfHYGC99T3+/Jnmh9eemzGm33bkh/cjK+56De58L3zhfXWH7LwPvHe+y/qzvXDhQq58zqw/gH/JT7/dbJ/yLr9Lyjz99NP65ptvMkwfPny4lixZ4oWIAAAAAAAAAACAP/C7MWW+//57Pfjgg/r666915513SpKeeeYZLV++XGvXrlX16tWzXAZjygAAAADwBsZnAbyD7x4AIC/hdylvcjZv4Hc9Zdq3b6958+apW7du2rZtmwYNGqTly5dr3bp1TiVkAAAAAAAAAAAAsiPI2wF4w4MPPqjLly/r9ttvV4kSJbR+/XpVrVrV22EBeR5ZeAAAkBf48z6JeXwWAJ7Fdw+AO+T2Pow/7yP5G36X8je/SMqMGDHC7vSSJUuqfv36mjNnjmXajBkzPBUWAAAAAAAAAADwI36RlNm5c6fd6VWqVFFcXJxlvsFg8GRYAAAAAAAAAADAj/hFUmbdunXeDgFeRNdN93G2ayTvOXwZ6zdyQ35er7wVu6eeNz9/Nr6Mcg0AACA/yu19GPaRgPwhwNsBAAAAAAAAAAAA+AO/6CnTvXt3LV68WJGRkerevXumbZcvX+6hqOApXCXgebzn8GWs38gN+Xm98lbsnnre/PzZAAAAAADyHr9IykRFRVnGi4mKivJyNAAAAAAAAAAAwB8ZTFz657K4uDhFRUUpNjZWkZGR3g4nz/HF2uu58Zp88X3KDbxPyEtYH5HbWMcAAAAAIHMcNyGvcjZv4Bc9Zey5cOGCDh48KIPBoGrVqqlkyZLeDgkAAAAAAAAAAPgwv0vKxMXFafDgwVq6dKlSUlIkSYGBgXrggQf09ttvU97MDXyx9npuvCZffJ9yA+8T8hLWR+Q21jEAAAAAyBzHTcjvArwdgKc9/vjj2rJli7755htduXJFsbGx+uabb7Rt2zYNGDDA2+EBAAAAAAAAAAAf5XdJmVWrVum9995Tu3btFBkZqUKFCqldu3ZauHChVq1a5e3w4MeMRqMMBoMMBoOMRqO3wwEyYB2FP2A994+qUYgAALCTSURBVB7ee9/HZ5y/8fnlfXxGeQefhXfwvgP+w5+/7/782n2J3yVlihUrZrdEWVRUlIoUKeKFiAAAAAAAAAAAgD/wu6TMiy++qBEjRujs2bOWaefOndPo0aM1btw4L0YGAAAAAAAAAAB8mcHkZ6Mi1a9fX3///beuXbum8uXLS5JOnDih4OBg3XTTTTZtd+zYYXcZcXFxioqKUmxsrCIjI3M9ZgAAAAAAAAAAkHc5mzcI8mBMeUK3bt28HQIAAAAAAAAAAPBDftdTxh3oKZM/GY1GRURESJLi4+MVHh7u5YgAAAAAIGvWxzISxzMAkB9xXgrwffSUcUJSUpI+/fRTGY1GtWnTJkP5MgAAAAAAAAAAAHfxm54yo0ePVnJyst58801JUnJyspo0aaJ9+/YpLCxMN27c0OrVq9WsWbMsl0VPGQAAAAAAAAAAYOZs3iDAgzF51XfffafWrVtb7n/00Uc6ceKEDh06pMuXL6tHjx569dVXvRghAAAAAAAAAADwZX6TlDlx4oRq1Khhub969Wrdf//9qlChggwGg4YOHaqdO3d6MUIAAAAAAAAAAODL/CYpExAQIOtKbZs3b9Ztt91muV+4cGFdvnzZG6EBAAAAAAAAAAA/4DdJmerVq2vlypWSpD///FMnTpxQq1atLPOPHz+uUqVKeSs8AAAAAAAAAADg44K8HYCnjB49Wr169dKqVav0559/qmPHjqpUqZJl/rfffqsmTZp4MUIAAAAAAAAAAODL/KanzH333advv/1WderU0fDhw/Xpp5/azA8LC9OgQYO8FB0AAAAAAAAAAPB1BpP1QCtwSlxcnKKiohQbG6vIyEhvhwMAAAAAAAAAALzI2byB3/SUAQAAAAAAAAAA8CaSMgAAAAAAAAAAAB5AUgYAAAAAAAAAAMADSMoAXmY0GmUwGGQwGGQ0Gr0dDgAAAAAAAAAgl/hdUuauu+7SlStXMkyPi4vTXXfd5fmAAAAAAAAAAACAX/C7pMzPP/+s5OTkDNOTkpL0yy+/eCEi+Lvw8HCZTCaZTCaFh4d7OxwAAAAAHkbveQAAAP8R5O0APOWPP/6w3N63b5/OnTtnuZ+SkqLvv/9eZcuW9UZoAAAAAAAAAADAD/hNUqZevXqWK4/slSkLDQ3VW2+95YXIADjDaDQqIiJCkhQfH0+vIgAAAPgMc+95ICc4ZgIAIH/wm6TM0aNHZTKZVLlyZf3+++8qUaKEZV7BggVVsmRJBQYGejFCAAAAAAAAAADgy/wmKVOhQgVJUmpqqpcjAQAAAAAAAAAA/sgvkjJff/21OnTooAIFCujrr7/OtG3Xrl09FBUAV1DSAQAAAAAc45gpI0q6AchtbGeQHX6RlOnWrZvOnTunkiVLqlu3bg7bGQwGpaSkeC4wAAAAAAAAAADgNwK8HYAnpKamqmTJkpbbjv5IyMDdjEajDAaDDAaDjEajt8MBkAm+r4D/4PsO5C/++p31pdftS68lr/CF99QTr8Hce8hkMnH1OvIUX/gOI01+2s6w3uUdfpGUAQAAAAAAAAAA8DaDyQ8Ljq5du1Zr167VhQsXlJqaajPvvffey/LxcXFxioqKUmxsrCIjI3MrTCBbqGUJf8G6Dk9hXQOQW9i+AADcid8VAPAuZ/MGfjGmjLWXXnpJkyZNUqNGjVS6dGkZDAZvhwQAAAAAAAAAAPyA3yVl5s2bp8WLF+vhhx/2dihArjDXsgR8Hes6PIV1DUBuYfsCAHAnflcAIH/wuzFlkpOT1axZM2+HAQAAAAAAAAAA/IzfJWUef/xxffzxx94OAwAAAAAAAAAA+Bm/K1+WlJSkBQsW6Mcff1SdOnVUoEABm/kzZszwUmQAAAAAAAAAAMCX+V1S5o8//lC9evUkSXv37rWZZzAYvBARAAAAAAAAAADwB36XlFm3bp23Q4CHGI1GRURESJLi4+MVHh7u5YhgxmcDIDexjQH8G9sA+DrWccC9+E4hv2Md9g98zr7F78aUAQAAAAAAAAAA8Aa/6SnTqlUru+XJoqKidPPNN2vw4MGKiYnxQmTILeHh4TKZTN4OA3bw2QDITWxjAP/GNgC+jnUccC++U8jvWIf9A5+zb/GbpIx5HJn0rly5om+//VazZ8/Wxo0bHbYDAAAAAAAAAADICYOJFJskafDgwTp69Ki+/fbbLNvGxcUpKipKsbGxioyM9EB0AAAAAAAAAAAgr3I2b8CYMv/vySef1M6dO70dBgAAAAAAAAAA8FEkZf5faGiokpKSvB0GAAAAcpnRaJTBYJDBYJDRaPR2OAAAIB9jvwIA4CqSMv9v9erVqlatmrfDAAAAAAAAAAAAPirI2wF4ytdff213emxsrLZu3ap3331Xixcv9mxQAAAAAAAAAADAb/hNUqZbt252pxcqVEjVq1fX4sWL1aNHD88G5aeMRqMiIiIkSfHx8QoPD/dyRL4hN95XdyzTHz5vb7zGvP6+5oX48kIMcIzPB+6SnXUpPDxcJpMpt0PLNXx/AADInCd/K53Zr+C3G4CnsL3JH/wmKZOamurtEAAAAAAAAAAAgB9jTBnkKga8c8zd74356hyTyZRnesnkRlx5kTdeY15/X/NCfHkhhvzE09trPh+4iz+uS/74mgH4B384fvSH15gX5JXfSvPnHRERofj4eK/Hg4z4TsLX5Mb5Qb4j7kdSBgAAAAAAAAAAwAMMpvxcUNtL4uLiFBUVpdjYWEVGRno7HAAAAAAAAAAA4EXO5g3oKQMAAAAAAAAAAOABJGUAAAAAAAAAAAA8gKQMAAAAAAAAAACAB/hNUiYgIECBgYGW/wAA9zIajTIYDDIYDDIajV5dfm7HAgAAAAD+yp3HZtk9duOYD0B+FuTtADzl6NGj3g4BAAAAAAAAAAD4Mb9JylSoUMHbIQAAAAAAAAAAAD/mF0mZP/74w+m2derUycVIAMB3hYeHy2Qy5Ynl53YsAAAA3mA0GhURESFJio+PV3h4uJcjAuCP3Hlslt1jN475AORnfpGUqVevngwGg0wmkwwGQ6ZtU1JSPBQVAAAAAAAAAADwJwHeDsATjh49qiNHjujo0aNatmyZKlWqpDlz5mjnzp3auXOn5syZoypVqmjZsmXeDhWAj/H1wQd99fXlldeVV+LIq3h/4A9Yz/M+PiN4kvnKcJPJRC+ZPI5tA5A7+G7lfXxGQNb8oqeM9XgyPXr00P/+9z917NjRMq1OnTqKiYnRuHHj1K1bNy9ECAAAAAAAAAAAfJ1fJGWs7dmzR5UqVcowvVKlStq3b58XIgLgy3y9zq2vvr688rryShx5Fe8P/AHred7HZwTAHrYNQO7gu5X38RkBWfOL8mXWbrnlFr3yyitKSkqyTLt27ZpeeeUV3XLLLV6MDAAAAAAAAAAA+DK/6ykzb948denSRTExMapbt64kaffu3TIYDPrmm2+8HB28wWg0KiIiQpIUHx+fZ2sz55c4AQAAAHdjXxgAAAC+wu+SMk2aNNHRo0e1ZMkSHThwQCaTSQ888IB69+7Njj0AAAAAAAAAAMg1BhNF/lwWFxenqKgoxcbGKjIy0tvhAAAAAAAAAAAAL3I2b+B3PWUk6fDhw5o1a5b2798vg8GgW265RUOHDlWVKlW8HRoAAAAAAAAAAPBRAd4OwNN++OEH1ahRQ7///rvq1KmjWrVqacuWLapZs6bWrFnj7fAAAAAAAAAAAICP8rukzHPPPafhw4dry5YtmjFjhmbOnKktW7Zo2LBhGjNmjLfDQz5jNBplMBhkMBhkNBq9HQ7gFNZb35XTz5Z1w3nueK94v/MHPqe8j88o/+Ezc4z3xjm++j756uvyFt5PpMc6kX/wWbkH72Pe5XdJmf3796t///4Zpj/22GPat2+fFyICAAAAAAAAAAD+wO/GlClRooR27dqlm266yWb6rl27VLJkSS9FhfwqPDxcJpPJ22EALmG99V05/WxZN5znjveK9zt/4HPK+/iM8h8+M8d4b5zjq++Tr74ub+H9RHqsE/kHn1XOGI1GRURESJLi4+MVHh7u5YiQnt8lZQYMGKAnnnhCR44cUbNmzWQwGLRx40ZNnTpVI0eO9HZ4AAAAAAAAAADARxlMfpZ2NJlMmjVrlt544w2dOXNGklSmTBmNHj1aQ4YMkcFgyHIZcXFxioqKUmxsrCIjI3M7ZHgRmWX4Al9fj3399cExPntkhXUEcD++V/Al+WV9zi9xAgDyNn5Pcp+zeQO/6yljMBg0fPhwDR8+XFevXpUkFSpUyMtRAQAAAAAAAAAAX+d3SRmzf/75RwcPHpTBYNDNN9+s4sWLezsk5EHUsIQv8PX12NdfHxzjs0dWWEcA9+N7BV+SX9bn/BInACBv4/ck7wjwdgCeZjQa9dhjj6l06dK68847dccdd6h06dLq37+/EhISvB0eAAAAAAAAAADwUX6XlBkxYoTWr1+vlStX6sqVK7py5YpWrFih9evXa+TIkd4ODwAAAH7GaDTKYDDIYDDIaDR6OxwAAAAAQC4ymPysz1Lx4sX1xRdfqGXLljbT161bp549e+qff/7JchnODtgDAAAAZIUBNwEAAAAg/3M2b+B3PWUSEhJUqlSpDNNLlixJ+TIAAAAAAAAAAJBr/C4p07RpU02YMEFJSUmWaYmJiXrppZfUtGlTL0YGAAAAf2QecNNkMtFLBgAAAAB8XJC3A/C0N998U+3bt1e5cuVUt25dGQwG7dq1SyEhIfrhhx+8HR4AAAAAAAAAAPBRfjemjJTWM2bJkiU6cOCATCaTatSooT59+ig0NNSpxzOmDAAAAAAAAAAAMHM2b+B3PWUkKTQ0VAMGDPB2GAAAAAAAAAAAwI/4TVImNTVVf/75p2rXri1JmjdvnpKTky3zAwMDNXDgQAUE+N0wOwAAAAAAAADgN4xGoyIiIiRJ8fHxjO0Ij/KbpMzSpUs1f/58rV+/XpI0evRoFS5cWEFBaW/Bv//+q5CQEPXv39+bYQIAAAAAAAAAAB/lN91CFi1apKeeespm2vr163X06FEdPXpU06ZN05IlS7wUHQD4B6PRKIPBIIPBIKPR6O1wgHyB7w0AAAAAuFd4eLhMJpNMJpPbeslw7AZn+U1SZv/+/apRo4bD+S1atNDu3bs9GBEAAAAAAAAAAPAnfpOU+ffffy11AiXpyJEjqlixouV+gQIFyGACQC7LjStR4F5c2ZP3eON7k1fXg7wal7/hc4A9rBeA7feA7wMA+B/OecBZfpOUKVWqlA4ePGi5X6JECQUE/Pfy9+/fr+joaG+EBgAAAAAAAAAA/IDfJGVat26tV1991e48k8mkKVOmqHXr1h6OCgAAAAAAAAAA+AuDyWQyeTsITzh8+LAaNGig6tWra9SoUapWrZoMBoMOHDig6dOn6+DBg9q+fbuqVq2a5bLi4uIUFRWl2NhYRUZGeiB6ZJfRaLSUrYuPj6frIOAEvjf5D5+ZZ/A+AwAA5C3snwEA8hJn8wZBHozJq6pUqaI1a9aob9++euCBB2QwGCSl9ZKpXr26Vq9e7VRCBgAAAAAAAAAAIDv8pqeMtZ07d+rQoUOSpJtuukn169d36fH0lAEAAAAAAAAAAGb0lMlE/fr1XU7EAAAAAAAAAAAA5ITfJGUmTZpkc3/8+PFeigQAAAAAAAAAAPgjv0nKHD161HLbPJ4MAAAAAAAAAACAp/hNUmbRokXeDgEAAAAAAAAAAPixAG8HAAAAAAAAAAAA4A9IygAAAAAAgDzNaDTKYDDIYDDIaDR6OxwAAIBsIykDAAAAAAAAAADgASRlAAAAAAAAAAAAPCDI2wEAAAAAAABkJjw8XCaTydthAAAA5Bg9ZQAAAAAAAAAAADyApAwAAAAAAAAAAIAHkJQBAAAAAAAAAADwAJIyAAAAgA8wGo0yGAwyGAwyGo3eDgcAAAAAYAdJGQAAAAAAAAAAAA8I8nYAAAAAAHIuPDxcJpPJ22EAAAAAADJBTxkAAAAAAAAAAAAPICkDAAAAAAAAAADgASRlAAAAAAAAAAAAPICkDAAAAAAAAAAAgAeQlAEAAAAAAAAAAPAAkjIAAAAAAAAAAAAeQFIGAAAAAAAAAADAA4K8HUB+ZDKZJElxcXFejgQAAAAAAAAAAHibOV9gzh84QlImG65evSpJiomJ8XIkAAAAAAAAAAAgr7h69aqioqIczjeYskrbIIPU1FSdOXNGhQoVksFg8HY4eUZcXJxiYmJ08uRJRUZGejscwC1Yr+FrWKfhi1iv4YtYr+FrWKfhi1iv4YtYr+FrPLlOm0wmXb16VWXKlFFAgOORY+gpkw0BAQEqV66ct8PIsyIjI9low+ewXsPXsE7DF7FewxexXsPXsE7DF7FewxexXsPXeGqdzqyHjJnjdA0AAAAAAAAAAADchqQMAAAAAAAAAACAB5CUgdsEBwdrwoQJCg4O9nYogNuwXsPXsE7DF7FewxexXsPXsE7DF7FewxexXsPX5MV12mAymUzeDgIAAAAAAAAAAMDX0VMGAAAAAAAAAADAA0jKAAAAAAAAAAAAeABJGQAAAAAAAAAAAA8gKQMAAAAAAAAAAOABJGUAAAAAAAAAAAA8gKQMAAAAAAAAAACAB5CUAQAAAAAAAAAA8ACSMgAAAAAAAAAAAB5AUgYAAAAAAAAAAMADSMoAAAAAAAAAAAB4AEkZAAAAAAAAAAAADyApAwAAAAAAAAAA4AEkZQAAAAAAAAAAADyApAwAAAAAAAAAAIAHkJQBAAAAAAAAAADwAJIyAAAAAAAAAAAAHkBSBgAAAAAAAAAAwANIygAAAAAAAAAAAHgASRkAAAAAAAAAAAAPICkDAAAAAAAAAADgASRlAAAAAAAAAAAAPICkDAAAAAAAAAAAgAeQlAEAAAAAAAAAAPAAkjIAAAAAAAAAAAAeQFIGAAAAAAAAAADAA4K8HUB+lJqaqjNnzqhQoUIyGAzeDgcAAAAAAAAAAHiRyWTS1atXVaZMGQUEOO4PQ1ImG86cOaOYmBhvhwEAAAAAAAAAAPKQkydPqly5cg7nk5TJhkKFCklKe3MjIyO9HA0AAAAAAAAAAPCmuLg4xcTEWPIHjpCUyQZzybLIyEiSMgAAAAAAAAAAQJKyHPLEcWEzAAAAAAAAAAAAuA1JGQAAAAAAAAAAAA8gKQMAAAAAAAAAAOABJGUAAAAAAAAAAAA8gKQMAAAAAAA+Yvz48QoNDVVwcHCO/2JiYnTo0CFvvyQAAACfYjCZTCZvB5HfxMXFKSoqSrGxsYqMjPR2OAAAAAAA6OjRo6pWrZpu3LjhtmU+8sgjev/99922PAAAAF/lbN4gyIMxAQAAAACAXDJ58mTduHFDd911lxYvXpyjZe3fv1/t2rXTxx9/rEmTJqlChQruCRIAAMDPkZQBAAAAACAPun79uoYMGeJ0CbH169dLkl566SXFxMTk6LljYmLUunVrrV27Vu3bt1fZsmVdenyLFi00bty4HMUAAADgi3y6fNmUKVP0/PPPa+jQoZo1a5YkyWQy6aWXXtKCBQt0+fJl3XrrrXr77bdVs2ZNp5dL+TIAAAAAQG5bunSpevXq5dJj2rZtqx9++MEtz//TTz+pdevW2X78li1b1KRJE7fEAgAAkNfly/Jl165dU3BwsFuWtXXrVi1YsEB16tSxmf76669rxowZWrx4sapVq6ZXXnlFbdq00cGDB1WoUCG3PDcAAAAAADm1cOFCSdJDDz2kjh07Ztk+KChId999t9ue/6677tKGDRt06tQplx730UcfadWqVZo6daqWLVvmtngAAAB8gVd7yvzwww/65JNP9Msvv+jEiRNKTU1VWFiYGjRooLZt26pfv34qU6aMy8uNj49XgwYNNGfOHL3yyiuqV6+eZs2aJZPJpDJlymjYsGEaM2aMpLREUKlSpTR16lQ9+eSTTi2fnjIAAAAAAHc5dOiQPv30U6WkpFimJScna/LkyTIYDDp69Gi+GtNl3759qlmzpgwGg8aOHauCBQs6bHvbbbepXbt2HowOAAAgdzibN/BKUuarr77SmDFjFBsbq44dO6pJkyYqW7asQkNDdenSJe3du1e//PKLNm3apL59++rll19WiRIlnF7+o48+qqJFi2rmzJlq2bKlJSlz5MgRValSRTt27FD9+vUt7e+55x4VLlxY77//vt3lXbt2TdeuXbPcj4uLU0xMDEkZAAAAAECOmEwmNWjQQLt27bI7v127dvr+++89G5QbdO3aVStXrsyyXWBgoA4fPpyvkk4AAAD25OnyZZMnT9b06dPVqVMnBQQEZJjfs2dPSdLp06f15ptv6oMPPtDIkSOdWvbSpUu1Y8cObd26NcO8c+fOSZJKlSplM71UqVI6fvy4w2VOmTJFL730klPPDwAAAACAs3777Tft2rVLISEh6tevn828ggUL6umnn/ZSZDkze/ZsVa1aVUlJSQ7brFu3TgcOHNDcuXP12muveTA6AAAA7/FKUub33393ql3ZsmX1+uuvO73ckydPaujQoVq9erVCQkIctjMYDDb3TSZThmnWxo4dqxEjRljum3vKAAAAAACQlR07dujKlSt2582cOVOS1Lt3b82ZM8eDUeWu8uXLa8aMGZm2WbFihbp166aFCxfq7rvvznDRZrVq1VSuXLncDBMAAMDjvJKUyS3bt2/XhQsX1LBhQ8u0lJQUbdiwQbNnz9bBgwclpfWYKV26tKXNhQsXMvSesRYcHKzg4ODcCxwAAAAA4JOWLl2qXr16Zdlu8ODBHogmb+ncubMqVqyoY8eOqU2bNhnmR0VF6ciRIypatKgXogMAAMgdeTYps2LFCsXGxuqRRx5x+jGtW7fWnj17bKb169dP1atX15gxY1S5cmVFR0drzZo1ljFlkpOTtX79ek2dOtWt8QMAAAAA/FtqaqqlFHaFChVUqFAhu+3atm2rBg0aeDK0PCEwMFBvvvmmJk6cqOvXr9vMO3HihGJjY/Xpp59q4MCBXooQAADA/Qwmk8nk7SDsqV69ug4dOqSUlJQcLadly5aqV6+eZs2aJUmaOnWqpkyZokWLFummm27S5MmT9fPPP+vgwYMOd5DTc3bAHgAAAACAf7hx44bSH16vWLFCPXr0UFRUlE6cOMHxowtmzJihkSNHqkmTJtq4caNlelBQUKblxwEAALzF2bxBgMM5XnbgwIEcJ2TsefbZZzVs2DANGjRIjRo10unTp7V69WqnEzIAAAAAAFgbP368ChYsmOGvR48ekqRnnnmGhIyL+vTpo8DAQP3+++8272mFChV07tw5b4cHAACQbXm2p0xeRk8ZAAAAAIAknT17VhUrVlRycrLd+WXLltWuXbtUvHhxD0eW/w0YMEDvvPNOhunjxo3TpEmTvBARAACAY87mDbyelNmwYUOm8++8804PReI8kjIAAAAAACmtGsO0adPUrFkzrVq1KsP8iIgIBQXl2eFc87y4uDilpqZKklauXKlHHnlEJUqU0MmTJxUcHOzl6AAAAP6Tb5IyAQEZK6hZ14fNjRJmOUVSBgAAAAD8x59//qkhQ4bIaDRmmLd7924lJSVp5cqV6ty5sxei8x/Xr19X5cqVderUKdWsWVMRERGWeTVq1ND8+fNVoEABL0YIAAD8mbN5A69frnP58mWb+9evX9fOnTs1btw4vfrqq16KCgAAAACANC+88IJ++uknh/MbNGigTp06eTAi/1SgQAENGzZMo0aN0p9//mkzb8uWLWrQoIGefvppL0UHAADgHK/3lHFkw4YNGj58uLZv3+7tUDKgpwwAAAAA+Ifjx4+rcuXKSk1N1eLFi1W0aFGb+QaDQU2bNlWxYsW8FKF/SUlJ0YYNGxQfH2+ZtmnTJk2ZMkVFixbVoUOHMnxGAAAAnpBveso4UqJECR08eNDbYQAAAAAAfNzatWv13Xff2Z23a9cupaamqnXr1nr00Uc9HBnSCwwMVKtWrWymdejQQd9884327Nmjnj17ql69ejbza9WqpS5dupA4AwAAeYLXe8r88ccfNvdNJpPOnj2r1157TdevX9evv/7qpcgco6cMAAAAAPiGuLg4lSlTxu54MdaWL1+ue++910NRwVVr167V3Xff7XB+YGCgWrZsqfvuu0/dunVT6dKlPRgdAADwB87mDbyelAkICJDBYFD6MG677Ta99957ql69upcic4ykDAAAAAD4hvnz5+upp55S+fLl9cADD9htU7FiRQ0cOFAGg8HD0cEVS5YsyXDhZ3JystatW2cz3Vxyrnv37urevbsqVark6VABAIAPyjdJmePHj9vcDwgIUIkSJRQSEuKliLJGUgYAAAAA8pf4+Hht2LBB169ft5k+btw47dmzR2+88YZGjBjhpeiQ2/7++299+eWXWrZsmbZs2WIzr379+urevbtq1aolg8GgggULqmXLlgoNDfVStAAAID/KN0mZ/IikDAAAAADkL71799Ynn3xid17BggV1+vRpFS9e3MNRwRtOnTqlr776SsuXL9f69euVmpqaoc0TTzyh+fPneyE6AACQX+X7pMy2bduUkJCgO++809uhZEBSBgAAAADyj3PnzikmJkY3btxQkyZNFBAQYJlnMBjUp08fDR482IsRwlv++ecfff311/r666914cIFXb9+Xdu3b1doaKjOnDmjwoULeztEAACQT+T7pMwtt9yiv/76SykpKd4OJQOSMgAAAACQd5hMJl25ciXDWKVms2fP1oQJE3Tbbbdp06ZNHo4O+YnJZFLt2rX1559/avr06erXr58kKTIyUkFBQV6ODgAA5GX5Pilz5swZXb9+XRUqVPB2KBmQlAEAAACAvKN79+768ssvs2y3aNEi9e3bN/cDQr721ltvaciQITbTKlasqN27d3MOAAAAOORs3iDA4RwvK1OmTJ5MyAAAAAAA8o49e/Y4lZCpVauWevbs6YGIkN898sgjqlatms20Y8eO6dNPP/VSRAAAwJfkqaRMYmKi4uLibP4AAAAAAHBk7ty5kqT77rtPN27ccPj3xx9/KCwszMvRIj+IiorSgQMHLOvO1KlTJUnvvfeelyMDAAC+wOvlyxISEvTss8/qs88+08WLFzPMZ0wZAAAAAPBvixcv1owZM+weH/79999KTk7WTz/9pFatWnkhOvi68+fPq2zZskpJSdEtt9wig8GggIAADRs2TP379/d2eAAAII/IN2PKDB48WOvWrdOkSZP0yCOP6O2339bp06c1f/58vfbaa+rTp483w7OLpAwAAAAAeEZiYqLKlSunS5cuOWxTr1497dixQwaDwYORwZ/06tVLS5cutZkWHh6uI0eOqGTJkl6KCgAA5CX5JilTvnx5ffDBB2rZsqUiIyO1Y8cOVa1aVR9++KE++eQTffvtt94Mzy6SMgAAAADgGe+//7769u2rChUqaPHixRnmGwwG1a1bV4ULF/Z4bPAfiYmJ2r59u27cuCFJGjlypHbs2KERI0bojTfe8HJ0AAAgL3A2bxDkwZjsunTpkipVqiRJioyMtFz9dPvtt2vgwIHeDA0AAAAA4GaxsbGaN2+ejh496lT7NWvWSJKefPJJtWzZMhcjAxwLDQ3V7bffbrn/6quvqkOHDnr77bcVHx8vg8Gg2rVra9CgQfTYAgAAmfJ6UqZy5co6duyYKlSooBo1auizzz5TkyZNtHLlSq50AgAAAAAfkZCQoNmzZ+u1117T5cuXXXpswYIFGbsDeUq7du3UvHlz/frrr1qwYIFler169dS8eXMvRgYAAPI6rydl+vXrp927d6tFixYaO3asOnXqpLfeeks3btzQjBkzvB0eAAAAACAHrl+/rnfeeUcvv/yyzp49K0mqUaOGevbsqYCAAKeWcccddzBuB/IUg8GgTz75RB999JGuX7+uH3/8URs2bNDChQtJygAAgEx5fUyZ9E6cOKFt27apSpUqqlu3rrfDsYsxZQAAAADkZ9evX9eqVatkNBpz9XkuX76smTNn6siRI5KkihUr6qWXXlKfPn0UGBiYq88NeNKmTZvUrFkzSdLChQvVs2dPzhcAAOBnnM0b5LmkTE7MnTtXc+fO1bFjxyRJNWvW1Pjx49WhQwdJkslk0ksvvaQFCxbo8uXLuvXWW/X222+rZs2aLj0PSRkAAAAA+dnEiRP10ksveez5SpUqpRdffFEDBgxQcHCwx54X8BSTyaTatWvrzz//lCQ99NBD+vDDD70cFQAA8KQ8nZRZunSpHnzwQafanjx5UidOnHCq++/KlSsVGBioqlWrSpLef/99TZs2TTt37lTNmjU1depUvfrqq1q8eLGqVaumV155RRs2bNDBgwdVqFAhp+MnKQMAAAAgv0pMTFRMTIwuXryopk2bKiIiIteey2AwqFWrVnrmmWcUHh6ea88D5AXr16/XwIEDtX//fgUHB+vMmTMqWrSot8MCAAAekqeTMi1atND58+fVr18/de3aVbfccovN/NjYWP36669asmSJfvzxR7377rvq0qVLtp6raNGimjZtmh577DGVKVNGw4YN05gxYyRJ165dU6lSpTR16lQ9+eSTTi/T8uaeOWP/zQ0MlEJC/rufWUmAgAApNDR7bRMSJEcfn8EghYVlr21iopSa6jgO64MpV9omJUkpKe5pGxaWFrckXbsm3bjhnrahoWnvsyQlJ0vXr7unbUhI2nrhatvr19PaOxIcLAUFud72xo2098KRggWlAgVcb5uSkvbZOVKgQFp7V9umpqata+5oGxSU9l5Iad+JhAT3tHXle882wn5bthGut2UbkXabbUT22rKNSLvNNsL1tmwj0m7n8W3E9evXde7CBZms2hr+P4avvvpKz40dq/IxMdqzZ4+CgoLYRlhjG5GGbYTrbf//e1+/fn3t2rVL0yZO1AMPPKACBQqoVKlSMpjXFau2ktiPYBuRhm1EGj/YRkjK8/sRkthGZLct24g0frqNiIuLU1SZMll35jB5ycqVK01t27Y1BQQEmAoVKmSqWrWqqVatWqayZcuaAgMDTdHR0aaxY8eazp8/n63l37hxw/TJJ5+YChYsaPrzzz9Nhw8fNkky7dixw6Zd165dTY888kimy0pKSjLFxsZa/k6ePGmSZIpN22Rk/OvY0XYBYWH220kmU4sWtm2LF3fctlEj27YVKjhuW6OGbdsaNRy3rVDBtm2jRo7bFi9u27ZFC8dtw8Js23bs6Lht+lXx/vszbxsf/1/bRx/NvO2FC/+1HTQo87ZHj/7XdtSozNvu3ftf2wkTMm/7++//tX399czbrlv3X9vZszNv+803/7VdtCjztp999l/bzz7LvO2iRf+1/eabzNvOnv1f23XrMm/7+uv/tf3998zbTpjwX9u9ezNvO2rUf22PHs287aBB/7W9cCHzto8++l/b+PjM295/v8lGZm3ZRqT9sY34749tRNof24i0P7YRaX9sI/77YxuR9sc2Iu3PhW3EOskkq78LmS2XbcR/f2wj0v7YRqT9ZWMb8b///c9UPLN2Up7YRrAfYfVnjW1EGrYRafx4P4JthNWfNbYRadhGpEm3jYhV2n53bGysKTNBjtM1uatz587q3LmzLl68qI0bN+rYsWNKTExU8eLFVb9+fdWvX18B5oyeC/bs2aOmTZsqKSlJERER+vLLL1WjRg399ttvktJqGVsrVaqUjh8/nukyp0yZ4tF6ywAAAADgDiHWV8NmdsUfALd5+OGHtWzePGnfPm+HAgAA8iCvlC/LTcnJyTpx4oSuXLmiZcuW6Z133tH69et15coVNW/eXGfOnFHp0qUt7QcMGKCTJ0/q+++/d7jMa9eu6ZpVV6i4uDjFxMRQvszVtnQFdL0tXQHTbtNdOHtt2Uak3WYb4XpbthFpt9lGZK8t24i022wjXG/LNuK/+zncRnTu3Fnrfv5ZI0aO1MvTpzu3XLYR/2EbkYZthOtt7XzvTSaTmjZtqj1792rciy/queeec9jWIfYj/sM2wvW2bCPS5NFthFvaso34D9sI19uyjUjj4fJlPpeUSe/uu+9WlSpVNGbMGFWpUkU7duxQ/fr1LfPvueceFS5cWO+//77Ty3R2wB4AAAAAyC3z5s3T1KlTlZLuxMDJkycVEBCgI0eOqEKFCl6KDoDZ0qVL1atXLxUsWFBly5bVyy+/rD59+ng7LAAA4GbO5g1crw+Wz5hMJl27dk2VKlVSdHS01qxZY5mXnJys9evXq1mzZl6MEAAAAABck5CQoLFjx+rYsWM6efKkzZ8k9ezZk4QMkEfcf//9qlmzppKTk3X06FGNHTs2QzIVAAD4D6+NKZMbnn/+eXXo0EExMTG6evWqli5dqp9//lnff/+9DAaDhg0bpsmTJ+umm27STTfdpMmTJyssLEy9e/f2dugAAAAA4LRPP/1UV65cUcWKFfX555/bzAsMDFStWrW8FBmA9IKCgrR582bt27dP7du318mTJ7V27Vq1bdvW26EBAAAv8KmkzPnz5/Xwww/r7NmzioqKUp06dfT999+rTZs2kqRnn31WiYmJGjRokC5fvqxbb71Vq1evVqFChbwcOQAAAABkdOTIEU2ePFmJ6WrUb9y4UZL05JNPqlGjRt4IDYALIiIi1KRJE/Xu3Vtvv/22hg0bpnvuuUeTJk1SAXO9ewAA4Be8OqbM9evXdfPNN+ubb75RjRo1vBWGyxhTBgAAAIAn3HfffVq+fLndecHBwTp+/LhKlSrl4agAZNfOnTvVoEEDy/05c+Zo4MCBXowIAAC4i7N5A6/2lClQoICuXbsmg8HgzTAAAAAAIM85ffq0VqxYIUmaNGmSIiIibObfeuutJGSAfKZ+/fr69ttv9fnnn2vRokV67bXX1L9/fxUsWNDboQEAAA/xak8ZSXrttdd04MABvfPOOwoKyh/V1OgpAwAA8H/s3Xd4FNXbxvF70wshlBBCCRB674iANKUrxYKgiDRFEEWKiEhHBQRBQREVISAWUIGfCBpFpAgoAoKgQZpAKImRloSSPu8fvFlZU0jdSTbfz3XtZXbm7Myz68lhs/eeMwBy25EjR7Rlyxbr/W3btmnVqlVq3bq1tm/fbmJlAHLbjRs3VLlyZUVERGjEiBGqW7euSpcurV69evHFVQAACqgCMVNGknbv3q3Nmzfru+++U7169eTt7W2zP72p+gAAAADgKOLj49W+fXuFh4en2jds2DATKgKQlzw9PTVu3DiNHTtWixYtsm7/4osv9OCDD5pYGQAAyGumhzLFihXjDQcAAACAQm3dunUKDw9X8eLF1a5dO+v2oKAg9e7d27zCAOSZp59+WqdOndLZs2d1+vRp/frrr3r33Xf5jAQAAAdn+vJlBRHLlwEAAADIjuTkZB0/flwJCQk224cNG6YdO3ZoypQpmj59uknVATDL6dOnFRQUJMMw9NVXX6lz585ydXU1uywAAJAFmc0N8kUok5iYqK1bt+rEiRN69NFH5ePjo/Pnz6to0aKpLmaZHxDKAAAAAMiO8ePHa86cOWnuc3Jy0qlTpxQYGGjnqgDkB127dlVISIj156+//trkigAAQFYUmGvKnD59Wl26dFFYWJji4uLUsWNH+fj4aM6cOYqNjdW7775rdokAAAAAkGNXr17V4sWLJUklS5aUk5OTdZ/FYtGgQYMIZIBCbPLkyTp48KDOnz+vb775RgcOHFDDhg3NLgsAAOQyp9s3yVvPPfecmjZtqsuXL8vT09O6/f7779fmzZtNrAwAAAAAcs+nn36qmJgYVatWTZGRkTa3v//+W7Nnzza7RAAmatmypc6dO6c+ffpIkhYsWGByRQAAIC+YPlNmx44d2rlzp9zc3Gy2V6xYUefOnTOpKgAAAABIX1xcnHr16qXdu3dn+jFXr16VdPP6MbfOkgGAWz333HNavXq1li9fru+//15fffUVM2YAAHAgpocyycnJSkpKSrX97Nmz8vHxMaEiAAAAAMjYCy+8YL32Q1b4+flpwIABeVARAEdx5513qn379tqyZYvOnj2r+fPn68MPPzS7LAAAkEsshmEYZhbQp08f+fr66v3335ePj48OHjyoUqVKqWfPnqpQoYKCg4PNLC9NKRfsOX/+fJoX7HF2dpaHh4f1/rVr19I9lpOTk82ybVlpe/36daX3v89iscjLyytbbW/cuKHk5OR06/D29s5W29jY2DQDuOy09fLyksVikXTzW4qJiYm50tbT09P6rcX4+HglJCTkSlsPDw85OztnuW1CQoLi4+PTbevu7i4XF5cst01MTFRcXFy6bd3c3OTq6prltklJSYqNjU23raurq3VWXFbaJicn68aNG7nS1sXFRe7u7pIkwzB0/fr1XGmbld97xoi02zJGMEYwRmS9LWNE9toyRtzEGJH1tim/9+vXr1fPnj0lSUuXLlWjRo3SbJtyXMMwrMctU6ZMqi+fMUb8izEi620ZI27KT2OElPP3EQkJCfrss8/01FNPydPTU5GRkSpSpIgkxojstGWMYIxwtDHiVryP+BdjRNbbMkbclFtjRHR0tMqWLauoqKg0cwMrw2Tnzp0zqlevbtSqVctwcXEx7rzzTqNkyZJGjRo1jL///tvs8tIUFRVlSEr31q1bN5v2Xl5e6bZt27atTVs/P7902zZt2tSmbcWKFdNtW7t2bZu2tWvXTrdtxYoVbdo2bdo03bZ+fn42bdu2bZtuWy8vL5u23bp1y/B1u9VDDz2UYdurV69a2w4YMCDDtpGRkda2Tz/9dIZtT548aW37/PPPZ9j2999/t7adOnVqhm1/+eUXa9s5c+Zk2HbLli3Wtm+//XaGbTds2GBtGxwcnGHbzz77zNr2s88+y7BtcHCwte2GDRsybPv2229b227ZsiXDtnPmzLG2/eWXXzJsO3XqVGvb33//PcO2zz//vLXtyZMnM2z79NNPW9tGRkZm2HbAgAHWtlevXs2w7UMPPWTThzNqyxhx88YY8e+NMeLmjTHi5o0x4uaNMeLfW0EbIxo2bGj06NHD6NGjh9G8efMM29arV8/atmXLlhm2rV27trVt69atM2xbvXp1a9t27dpl2LZKlSrWth06dMiwbaVKlYwePXoYxYoVy7CdxBiRcmOM+PfGGHHzxvuIm7esjBGSjFatWhm9evW67WvGGHHzxhjx740x4ubNkccI3kfcvDFG/HtjjLh5M3OMiIqKMjJi+vJlZcuW1YEDB/Tpp5/q119/VXJysoYMGaJ+/frZJKwAAAAA8r8DBw7owIEDmWp76NAhHTp0KFNtQ0NDFRoamqm2R48e1dGjRzPV9sSJEzpx4kSm2p46dUqnTp2SJNWvX18HDx7M1OMAIKd27twpSdq0aZPJlQAAgJwyffmygojly5gKmNW2TAW8ienCWW/LGJG9towRNzFGZL0tY8RNjBHZa1tYx4guXbpox44d6tOnj1q3bi1nZ2dr26SkpAxfs1vbJicnZ/jc8kNbJycnubq6ysXFRffee69N3/8vxoi02zJGFL4x4r94H5G1tvHx8dqwYYPi4uIUHBysXbt2qUWLFgoJCbH+v7wVY0TabRkjGCMcdYyQeB9xK8aIrLdljLjJ3suXmRLKrF+/PtNte/TokYeVZE9KKHPbteEAAACAAiwpKUkffvihLl68mOb+2NhYTZ48WRaLRadPn1ZgYKCdKwSAwuPUqVOqV6+erl69qkGDBql27dqqUKGCevfubf1QDwAAmCezuYEpy5f16tXL5r7FYkmViKa8ocgoeQQAAACQd5YsWaLhw4fftl2nTp0IZAAgj1WqVEkLFizQkCFDFBwcbN1+/fp1DRw40LzCAABAlpgSytw6dez777/X+PHjNXPmTLVo0UIWi0W7du3SpEmTNHPmTDPKAwAAAAo9wzC0cOFCSVL79u3TDV3c3d01duxYe5YGAIXWoEGDdOnSJR06dEgRERH67rvvNHLkSLVv314VK1Y0uzwAAJAJpl9Tpm7dunr33Xd111132Wz/8ccfNXToUB0+fNikytLH8mUAAAAo6K5evao//vgj3f2///67nnjiCRUpUkTnzp3jfS8A5DNJSUlq06aNdu3apXbt2mn27NlycXFR/fr1rWvoAwAA+8nXy5fd6sSJE/L19U213dfXV6dOnbJ/QQAAAICDMwxD7dq10759+27bduDAgQQyAJAPOTs768MPP1SDBg20detW3XnnnZKk3r17a/Xq1VxnBgCAfMr0mTJt2rSRq6urPvroI5UpU0aSFBERof79+ys+Pl7btm0zs7w0MVMGAAAABdmOHTvUunVrubi4ZHgtGD8/P61Zs4brxQBAPrZq1SpNnTpVCQkJCgsLU1JSkhYvXqxhw4aZXRoAAIVKZnMD00OZ48eP6/7779eRI0dUoUIFSVJYWJiqV6+u//3vf6pataqZ5aWJUAYAAAAF2eOPP66VK1dqyJAh+uCDD8wuBwCQS+bNm6fnn39eHh4e+uWXX1SvXj2zSwIAoNDIbG7gZMea0lS1alUdPHhQGzZs0MiRI/Xss89q48aNOnToUJYDmVmzZqlZs2by8fGRv7+/evXqpSNHjti0MQxD06ZNU9myZeXp6al27dpluJY2AAAAkF+NHDlSbm5ucnV1zdJt5cqVkqQnn3zS5GcAAMhNo0ePVpcuXRQbG6sGDRrI1dVVlSpV0vHjx80uDQAA/D/TZ8rkpi5duqhv375q1qyZEhMTNXHiRB06dEihoaHy9vaWJL322mt69dVXtXz5clWvXl2vvPKKtm/friNHjsjHxydT52GmDAAAAMx2/vx5BQYGKjk5OVuPb9u2rbZs2cI1BwDAwURGRqpZs2YKCwuzbmvatKl27twpNzc3EysDAMCxFZjlyyRp27Ztev3113X48GFZLBbVqlVL48aNU+vWrXN03H/++Uf+/v7atm2b2rRpI8MwVLZsWY0aNUrjx4+XJMXFxal06dJ67bXX9NRTT2XquIQyAAAAMNvs2bM1YcIEtWjRQp9//nmWH1+mTBk5OZk+cR4AkAfi4uJ04cIFXbx4Ue3bt9elS5f0/PPPa+7cuWaXBgCAw8psbuBix5rS9NFHH2nQoEF64IEHNHLkSBmGoV27dumee+7R8uXL9eijj2b72FFRUZKkEiVKSJJOnjypiIgIderUydrG3d1dbdu21a5du9INZeLi4hQXF2e9Hx0dne2aAAAAkL8ZhqEpU6Zo586dZpeSod9++03SzSXIypUrZ3I1AID8xN3dXeXKlVO5cuW0bNky9erVS6+//rr27NkjJycn1atXT/Pnz5ezs7PZpQIAUOiYPlOmVq1aGjp0qEaPHm2zff78+VqyZIkOHz6creMahqGePXvq8uXL+vHHHyVJu3btUqtWrXTu3DmVLVvW2nbo0KE6ffq0vv322zSPNW3aNE2fPj3VdmbKAAAAOJ69e/eqWbNmZpeRKcWLF1dYWJiKFClidikAgHzsmWee0aJFi2y2vfrqq3rppZdMqggAAMdTYGbK/PXXX+revXuq7T169MjRm4NnnnlGBw8e1I4dO1Lt+++62YZhZLiW9oQJEzRmzBjr/ejoaAUGBma7NgAAAORfy5YtkyR16NBBQ4YMMbmajDVp0oRABgBwW2+++aY6deqk69evKzQ0VC+//LKmTp2qDh066I477jC7PAAAChXTQ5nAwEBt3rxZVatWtdm+efPmbAcfzz77rNavX6/t27erfPny1u0BAQGSpIiICJUpU8a6PTIyUqVLl073eO7u7nJ3d89WLQAAAMiesLAwffjhh0pISLDreT/55BNJ0vjx49WhQwe7nhsAgLzg4uKiHj16SLr5xdSjR49q9erV6tevn/bv30/ADwCAHZkeyowdO1YjR47UgQMH1LJlS1ksFu3YsUPLly/XggULsnQswzD07LPPat26ddq6dauCgoJs9gcFBSkgIECbNm1So0aNJEnx8fHatm2bXnvttVx7TgAAAMi5p556SiEhIaacu1KlSrr77rtNOTcAAHnJYrFo8eLF2rVrl44fP67Ro0dryZIlZpcFAEChYXooM3z4cAUEBGjevHn67LPPJN28zszq1avVs2fPLB1rxIgR+uSTT/Tll1/Kx8dHERERkiRfX195enrKYrFo1KhRmjlzpqpVq6Zq1app5syZ8vLy0qOPPprrzw0AAADZc+bMGev1/p566im5uNjvbauTk5P69esnJycnu50TAAB7Kl68uD788EPdfffd+uCDD9S1a1c98MADZpcFAEChYDEMwzC7iNyS3nVhgoODNXDgQEk3Z9NMnz5d7733ni5fvqzmzZtr0aJFqlu3bqbPk9kL9gAAADi65ORk/fLLL7p69WquHnft2rVavHix2rVrpy1btuTqsQEAwE0TJkzQ7NmzVaJECX344Ydyd3eXp6enWrRowZcTAADIoszmBqaHMnv27FFycrKaN29us3337t1ydnZW06ZNTaosfYQyAAAAN7399tt69tln8+z4H374ofr3759nxwcAoDCLj49Xy5YttW/fPpvtXbt21fr16+06UxUAgIIus7mB6f+6jhgxQi+88EKqUObcuXN67bXXtHv3bpMqAwAAQEYMw9CiRYskSZUrV871iwRXq1ZNvXv3ztVjAgCAf7m5uWnVqlUaPHiwoqKiJElHjhzRN998o9GjR+utt94yuUIAAByP6TNlihQpooMHD6py5co220+ePKn69esrJibGpMrSx0wZAABQ0CUmJiopKSlHx/jll1/Upk0beXl5KTw8nPdFAAA4gLVr1+rBBx+UJC1YsEBPPfWUpJsBTnrLxgMAgMznBqYvEOru7q6///471fbw8HCmyQIAAOSBLVu2qGjRovLw8MjRrU2bNpKk3r17E8gAAOAgHnjgAc2ePVuS9Nxzz1n/3W/btq2uXLlibnEAADgA00OZjh07asKECdZpspJ05coVvfTSS+rYsaOJlQEAADimN954Qzdu3MiVY3l5eem5557LlWMBAID84YUXXtCoUaNsZsb8+OOP6tmzp2JjY02sDACAgs/05cvOnTunNm3a6OLFi2rUqJEk6cCBAypdurQ2bdqkwMBAM8tLE8uXAQCAguqff/5R2bJllZiYqL1796patWo5Op6Hh4fc3NxyqToAAJCfXLt2TUlJSTp69KjuueceRUdH66GHHtLq1avl5GT693wBAMhXMpsbmB7KSDf/kf/444/122+/ydPTU/Xr19cjjzwiV1dXs0tLE6EMAACwt3/++UeDBw9Oc9nXrIiKitLRo0fVpEkT7d27N5eqAwAAjm7Lli3q3LmzEhISVKtWLRUpUkSVKlXSggULVKZMGbPLAwDAdAUqlCloCGUAAIA9GYah+++/X19++WWuHXPx4sUaNmxYrh0PAAA4vlWrVunRRx/VrR8l3XHHHdq2bZs8PDxMrAwAAPMVqFDm6NGj2rp1qyIjI5WcnGyzb8qUKSZVlT5CGQAAYE8rVqzQwIED5erqqmXLlql48eI5Op6Pj4/uuusulh0xSVJSkhISEswuA0AG3NzcGCOBdBw7dkxHjx7VjRs3NHToUF2+fFn9+/fXihUrbK5BAwBAYZPZ3MDFjjWlacmSJRo+fLj8/PwUEBBg8w+4xWLJl6EMAABAZu3atUtr165Vdr8HYxiGli5dKkmaMWOGHnvssdwsD3ZkGIYiIiJ05coVs0sBcBtOTk4KCgrimllAGqpVq2a9Jl2JEiXUqVMnrVy5UvXq1dO4ceNMrg4AgPzP9JkyFStW1NNPP63x48ebWUaWMFMGAABkxo4dO3T33XfnyqyIli1bavv27XJ2ds6FymCG8PBwXblyRf7+/vLy8uLbxEA+lZycrPPnz8vV1VUVKlTgdxW4jUWLFumZZ56Rk5OTtmzZojZt2phdEgAApigwM2UuX76s3r17m10GAABArjp9+rQeeOABJSQkqF27dmrevHm2j+Xu7q5hw4YRyBRgSUlJ1kCmZMmSZpcD4DZKlSql8+fPKzExUa6urmaXA+RrTz/9tPbs2aMVK1bokUce0YEDB1SqVCmzywIAIN8yPZTp3bu3vvvuOy40CwAA8pUzZ85o79692XqsYRiaPn26/vnnHzVs2FAbNmyQt7d3LleIgiRltpSXl5fJlQDIjJRly5KSkghlgNuwWCx6++23tXv3bv3555/q16+fhg8fLovFolatWhHQAADwH6aHMlWrVtXkyZP1888/q169eqne8I4cOdKkygAAQGEVERGhpk2bKjIyMkfHKV26tL788ksCGVixDBJQMPC7CmRNkSJFtHr1ajVv3lybNm3Spk2bJEk1atTQvn37eC8EAMAtTL+mTFBQULr7LBaL/vrrLztWkzlcUwYAAMdlGIa6deumkJAQBQQEqHLlytk6TrFixfTyyy+rcePGuVwhCqLY2FidPHlSQUFB8vDwMLscALfB7yyQPV9//bVef/11xcXF6fDhw7p8+bKefPJJvf/++2aXBgBAnisw15Q5efKk2SUAAIBCxjAMXbx4Mc19H374oUJCQuTh4aHvv/9ederUsXN1AG7n1KlTCgoK0v79+9WwYUOzywEA/L9u3bqpW7dukqQffvhBHTp00JIlS9S5c2c9+OCDJlcHAED+4GR2AQAAAPZkGIY6d+6sUqVKpXkbO3asJGnu3LkEMoCkgQMHymKxpLp16dLF7NIAAPnY3XffrfHjx0uSnnzySZ05c8bkigAAyB9MC2Vq166tS5cuWe8PHTpU//zzj/V+ZGQkF0IFAAC57scff7Suc56ePn36aMSIEXaqCMj/unTpovDwcJvbp59+anZZORIfH292CQDg8GbMmKFmzZrp8uXL6t+/v5KSkswuCQAA05kWyvz5559KTEy03l+1apViYmKs9w3DUGxsrBmlAQAAB/bGG29IuvmNzaSkpDRvq1at4iLPwC3c3d0VEBBgcytevLikm9eB/OCDD3T//ffLy8tL1apV0/r1620e/8cff+jee+9V0aJF5ePjo9atW+vEiROSpOTkZM2YMUPly5eXu7u7GjZsqJCQEJvH//LLL2rUqJE8PDzUtGlT7d+/P1WNoaGh6tatm4oUKaLSpUurf//+unDhgnV/u3bt9Mwzz2jMmDHy8/NTx44dc/tlAgD8h6urqz755BN5e3tr27Zteu2118wuCQAA0+Wb5csMw0i1jQ9DAABAdixatEh16tRRrVq1Ut2+/PJLSdLo0aPl5OSU5g3Ia4Zh6Nq1a6bc0nrfnVPTp0/Xww8/rIMHD6pbt27q16+fdVb8uXPn1KZNG3l4eOiHH37Qvn37NHjwYOsXtBYsWKB58+bp9ddf18GDB9W5c2f16NFDx44dkyRdu3ZN9913n2rUqKF9+/Zp2rRpev75523OHx4errZt26phw4bau3evQkJC9Pfff+vhhx+2abdixQq5uLho586deu+993L9dQAApFa1alUtWrRIkjRlyhTre7KJEyeaXBkAAOZwMbsAAACA3BQZGannn38+wxm3999/v2rVqmXHqgBb169fV5EiRUw599WrV+Xt7Z2lx2zYsCFVvePHj9fkyZMl3bzuzCOPPCJJmjlzpt566y398ssv6tKlixYtWiRfX1+tWrVKrq6ukqTq1atbj/P6669r/Pjx6tu3ryTptdde05YtW/Tmm29q0aJF+vjjj5WUlKRly5bJy8tLderU0dmzZzV8+HDrMRYvXqzGjRtr5syZ1m3Lli1TYGCgjh49aj1f1apVNWfOnCw9dwBAzj3++OP67rvv9Mknn+jPP/+UdPPfizvvvFPdu3c3uToAAOzLtFAm5QKh/90GAACQE2+++aZiY2PVtGlTzZs3L9V+Z2dnNW7c2ITKgIKrffv2Wrx4sc22EiVKWH+uX7++9Wdvb2/5+PgoMjJSknTgwAG1bt3aGsjcKjo6WufPn1erVq1strdq1Uq//fabJOnw4cNq0KCBzfUmW7RoYdN+37592rJlS5pB14kTJ6yhTNOmTTP1fAEAuctisWj58uV65plnlJCQoE8//VTvvvuuhg0bptatW6tYsWJmlwgAgN2YFsoYhqF77rlHLi43S7hx44a6d+8uNzc3SbK53gwAAMiZjRs3WpftcnSrV6+WJE2aNElt2rQxuRogbV5eXrp69app584qb29vVa1aNd39/w1cLBaLkpOTJUmenp63Pf5/v5xlGIZ1W2aWW0tOTlb37t3TvFZBmTJlrD9ndYYQACD3uLq6WkP1Zs2aafPmzTp27JjuvfdetWjRQhMnTrRerwwAAEdmWigzdepUm/s9e/ZM1ebBBx/M8nG3b9+uuXPnat++fQoPD9e6devUq1cv637DMDR9+nS9//77unz5spo3b25ddx4AAEcUEhKi7t2758l1JPKrOnXqsBQG8jWLxVJoAoL69etrxYoVSkhISBXeFC1aVGXLltWOHTtsQtRdu3bpjjvukCTVrl1bK1eu1I0bN6wBz88//2xznMaNG2vNmjWqVKmS9UtfAID8y9PTU8uWLVObNm20a9cu7dq1S05OTiwxCQAoFPJNKJNbrl27pgYNGmjQoEFphjpz5szR/PnztXz5clWvXl2vvPKKOnbsqCNHjsjHxydPagIAwCxhYWHq16+fDMNQjx49rB9yOjJnZ2c9+OCDcnJyMrsUwGHExcUpIiLCZpuLi4v8/Pxu+9hnnnlGb731lvr27asJEybI19dXP//8s+644w7VqFFD48aN09SpU1WlShU1bNhQwcHBOnDggD7++GNJ0qOPPqqJEydqyJAhmjRpkk6dOqXXX3/d5hwjRozQkiVL9Mgjj2jcuHHy8/PT8ePHtWrVKi1ZskTOzs6592IAAHLFXXfdpQ0bNujLL7/U+++/r48++kgzZ84kXAcAODyH+5eua9eu6tq1a5r7DMPQm2++qYkTJ+qBBx6QJK1YsUKlS5fWJ598oqeeesqepQIACqktW7bo7NmzdjnXW2+9pUuXLqlp06b67LPP5O7ubpfzAnAsISEhNsuASVKNGjWsF2vOSMmSJfXDDz9o3Lhxatu2rZydndWwYUPrdWRGjhyp6OhojR07VpGRkapdu7bWr1+vatWqSZKKFCmir776SsOGDVOjRo1Uu3ZtvfbaazZfwCpbtqx27typ8ePHq3PnzoqLi1PFihXVpUsXAloAyMe6deumDh06aM2aNQoPD9f333+vLl26mF0WAAB5ymI48FomFovFZvmyv/76S1WqVNGvv/6qRo0aWdv17NlTxYoV04oVK9I8TlxcnOLi4qz3o6OjFRgYqKioKBUtWjRPnwMAwLH88MMPuueee+x6zmLFiunXX39VUFCQXc8L4F+xsbE6efKkgoKC5OHhYXY5AG6D31nAvkaOHKm33npLvXv31meffWZ2OQAAZEt0dLR8fX1vmxs43EyZjKQsuVC6dGmb7aVLl9bp06fTfdysWbM0ffr0PK0NAFA4vPXWW5KkmjVrqmLFinl+Pk9PT73wwgsEMgAAAMi3hgwZorfeekvr1q1TRESEAgICzC4JAIA8U6hCmRQWi8XmvmEYqbbdasKECRozZoz1fspMGQAAYmNjU11nIT0XLlzQ+vXrJUlr165VrVq18rI0AAAAoEBo0KCBWrRooZ9++kkLFy7U0KFDc3xMZ2dnlS9fPsPPewAAMIPpoUzKlHB7SPmmRUREhM2a2JGRkalmz9zK3d2dNfgBAKlcvXpVNWvW1Llz57L0uHbt2hHIAAAAALcYPny4fvrpJ82aNUuzZs3KlWPWqFFDw4YN04ABA1S8ePFcOSYAADll+lUvq1atqvbt2+ujjz5SbGxsnp4rKChIAQEB2rRpk3VbfHy8tm3bppYtW+bpuQEAjufzzz/XuXPn5OTkJC8vr0zdSpUqpSlTpphdOgAAAJCv9O7dW61atcr0++rb3VxcXHTkyBGNHj1a5cqV0+DBg7Vnzx6znyYAAOaHMr/99psaNWqksWPHKiAgQE899ZR++eWXbB/v6tWrOnDggA4cOCDp5kycAwcOKCwsTBaLRaNGjdLMmTO1bt06/f777xo4cKC8vLz06KOP5tIzAgAUFsHBwZKkV199VdeuXcvULTIyUu3btze5cgAAACB/8fDw0I4dOzL9vvp2t0uXLmnx4sWqX7++bty4oeDgYN1xxx1q2rSpli5dqmvXrpn9lAEAhZTFMAzD7CIkKTExUV999ZWWL1+ub775RtWqVdOQIUPUv39/lSpVKtPH2bp1a5ofdg0YMEDLly+XYRiaPn263nvvPV2+fFnNmzfXokWLVLdu3UyfIzo6Wr6+voqKilLRokUz/TgAgP1FRkaqR48eWV5iLDPOnj0rJycnnTlzRmXLls314wNwHLGxsdZlez08PMwuB8Bt8DsLOA7DMPTTTz9p8eLF+uyzzxQfHy9J8vb2VvHixVWrVi19+eWX8vT0NLlSAEBBl9ncIN+EMini4uL0zjvvaMKECYqPj5erq6v69Omj1157zeY6MGYilAGAgmPChAmaPXt2nh3/oYce0ueff55nxwfgGPiAFyhY+J0FHNOFCxcUHBysd999V3/99Zd1e3BwsAYOHGheYQAAh1DgQpm9e/dq2bJlWrVqlby9vTVgwAANGTJE58+f15QpUxQTE5OjZc1yE6EMABQMV69eVWBgoK5cuaJ33nlHzZs3z9XjOzk5qXbt2nJzc8vV4wJwPHzACxQs/M4Cji05OVmhoaFasWKFXn/9dbVs2VI7d+40uywAQAFXYEKZ+fPnKzg4WEeOHFG3bt30xBNPqFu3bnJy+vdyN8ePH1fNmjWVmJhoYqX/IpQBgNz3999/a/LkyYqJicm1Y54/f17bt29XtWrV9Oeff9r82wIA9sQHvEDBwu8sUDiEh4crMDBQSUlJ6tu3r5599lm1bNnS7LIAAAVUZnMDFzvWlKbFixdr8ODBGjRokAICAtJsU6FCBS1dutTOlQEA7Omll17SsmXL8uTYzz//PIEMAOSBU6dOKSgoSPv371fDhg3tdt6U60hevnxZxYoVy/ZxLBaL1q1bp169eqW536znBwCwjzJlyqhXr15as2aNVq1apb179+rPP/+Us7Oz2aUBAByYqaFMYmKi+vXrp8ceeyzdQEaS3NzcNGDAADtWBgCwp3PnzmnlypWSpClTpqhEiRK5duySJUvq0UcfzbXjAUBhYbFYMtw/YMAATZs2zT7F5FP16tVT8+bN9cEHH6Ta9+mnn+rxxx/X2bNnVbp06WyfY/ny5Ro1apSuXLmSg0oBAOlZvHix7rnnHk2cOFHHjx/X+vXrdf/995tdFgDAgZkayri4uGjevHlcTA0A8qmEhAStXr1a0dHReXqezZs3KyEhQa1bt9b06dPz9FwAgMwJDw+3/rx69WpNmTJFR44csW7z9PTU5cuXs3zcpKQkWSwWh5jBOGTIEE2ZMkULFy6Ul5eXzb5ly5bpvvvuy1Egk5sc6XUHgNxUqlQpDR8+XOfOndOrr76q6dOn6+LFi3rooYdyNBsTAIB0GSbr2bOnERwcbHYZWRIVFWVIMqKioswuBQDy1Ny5cw1Jdrtt2LDB7KcMAHnixo0bRmhoqHHjxg2zS8mW4OBgw9fXN9X2kydPGpKMNWvWGO3atTM8PT2N+vXrG7t27Ur12K+++sqoVauW4ezsbPz1119GXFycMW7cOKNs2bKGl5eXcccddxhbtmyxPu7UqVPGfffdZxQrVszw8vIyateubWzcuNEwDMPYsmWLIcn4/vvvjSZNmhienp5GixYtjD///NOmvnfeeceoXLmy4erqalSvXt348MMPbfZLMtatW2e9v3v3bqNhw4aGu7u70aRJE2Pt2rWGJGP//v1pvi4XLlww3NzcjOXLl9tsP336tOHk5GR89dVXhmEYxvr1643GjRsb7u7uRlBQkDFt2jQjISHB2v7y5cvGk08+afj7+xvu7u5GnTp1jK+++sr6PG+9TZ061TAMw7h06ZLRv39/o1ixYoanp6fRpUsX4+jRo7d93ZE5Bf13FkDWRUREGG5ubtbx9pFHHjG7JABAAZPZ3MD0a8p07dpVEyZM0O+//64mTZrI29vbZn+PHj1MqgwACrfExES99dZbkqS2bdvKz88vT89Xr149devWLU/PAQD5zbVr19Ld5+zsbHOB8YzaOjk5ydPT87Zt//teO7dMnDhRr7/+uqpVq6aJEyfqkUce0fHjx+XicvPPjevXr2vWrFn64IMPVLJkSfn7+2vQoEE6deqUVq1apbJly2rdunXq0qWLDh06pGrVqmnEiBGKj4/X9u3b5e3trdDQUBUpUiTVeefNm6dSpUpp2LBhGjx4sHbu3ClJWrdunZ577jm9+eab6tChgzZs2KBBgwapfPnyat++farncO3aNd133326++679dFHH+nkyZN67rnnMnzeJUuWVM+ePRUcHGyz3HJwcLBKly6trl276ttvv9Vjjz2mhQsXqnXr1jpx4oSGDh0qSZo6daqSk5PVtWtXxcTE6KOPPlKVKlUUGhoqZ2dntWzZUm+++abNLKWU12DgwIE6duyY1q9fr6JFi2r8+PHq1q2bQkND5erqmu7rDgBIW+nSpbV8+XJ9/vnnWrdunVavXq2XX35ZVapUMbs0AICDMT2UGT58uCRp/vz5qfZZLBYlJSXZuyQAME18fLyOHj0qwzDMLkU7d+5UWFiY/Pz8FBISYvPBIAAgd/w3ZLhVt27dtHHjRut9f39/Xb9+Pc22bdu21datW633K1WqpAsXLqRql1f/vjz//PO69957JUnTp09XnTp1dPz4cdWsWVPSzeUw33nnHTVo0ECSdOLECX366ac6e/asypYtaz1GSEiIgoODNXPmTIWFhenBBx9UvXr1JEmVK1dOdd5XX31Vbdu2lSS9+OKLuvfeexUbGysPDw+9/vrrGjhwoJ5++mlJ0pgxY/Tzzz/r9ddfTzOU+fjjj5WUlKRly5bJy8tLderU0dmzZ61/r6Rn8ODB6tatm/766y9VrlxZhmFo+fLlGjhwoJydnfXqq6/qxRdftIY2lStX1ssvv6wXXnhBU6dO1ffff69ffvlFhw8fVvXq1VM9V19fX1ksFptrcKaEMTt37lTLli2t9QcGBup///ufevfunebrDgDI2COPPKJHHnlEXbt2VUhIiObPn69FixaZXRYAwMGYHsokJyebXQIA5Bs9e/ZUSEiI2WXYePLJJwlkAAAZql+/vvXnMmXKSJIiIyOtoYybm5tNm19//VWGYVhDiBRxcXEqWbKkJGnkyJEaPny4vvvuO3Xo0EEPPvigzTEyOm+FChV0+PBh64yUFK1atdKCBQvSfA6HDx9WgwYNbK4N06JFi9s+906dOql8+fIKDg7Wyy+/rB9++EGnTp3SoEGDJEn79u3Tnj179Oqrr1ofk5SUpNjYWF2/fl0HDhxQ+fLlU70WGTl8+LBcXFzUvHlz67aSJUuqRo0aOnz4sHXbf193AEDmvPDCCwoJCdGyZcs0depUZhoCAHKV6aEMAOCmI0eOKCQkRBaLJd+86S9btqxGjhxpdhkA4LCuXr2a7j5nZ2eb+5GRkem2/e/F20+dOpWjurIqZbks6eZsd8n2y1eenp7W7Sn7nJ2dtW/fvlTPM2X20BNPPKHOnTtr48aN+u677zRr1izNmzdPzz77bKbPe+s5pZszhf677dZ92eHk5KSBAwdq+fLlmj59uoKDg9WmTRtVq1bNWs/06dP1wAMPpHqsh4eHzbJzmZVerf99fv993QEAmdOuXTs1a9ZMe/bs0VtvvaWXX37Z7JIAAA4kX4Qy165d07Zt2xQWFqb4+HibfXwYCKCwCA4OlnRzuZoNGzaYXA0AwB6yco2XvGprhkaNGikpKUmRkZFq3bp1uu0CAwM1bNgwDRs2TBMmTNCSJUtsQpmM1KpVSzt27NDjjz9u3bZr1y7VqlUrzfa1a9fWypUrdePGDWtQ8vPPP2fqXIMGDdIrr7yitWvXau3atXr33Xet+xo3bqwjR46oatWqaT62fv36Onv2rI4ePZrmbBk3N7dUSzrXrl1biYmJ2r17t3X5sosXL+ro0aPpPj8AQOZZLBaNHz9eDz30kBYtWqTx48dnuOQoAABZYXoos3//fnXr1k3Xr1/XtWvXVKJECV24cEFeXl7y9/cnlAGQr+zevVsPPfSQYmJicv3YKd+WHjx4cK4fGwCA/KR69erq16+fHn/8cc2bN0+NGjXShQsX9MMPP6hevXrq1q2bRo0apa5du6p69eq6fPmyfvjhhywFDuPGjdPDDz+sxo0b65577tFXX32ltWvX6vvvv0+z/aOPPqqJEydqyJAhmjRpkk6dOqXXX389U+cKCgrS3XffraFDh8rV1VUPPfSQdd+UKVN03333KTAwUL1795aTk5MOHjyoQ4cO6ZVXXlHbtm3Vpk0bPfjgg5o/f76qVq2qP//8UxaLRV26dFGlSpV09epVbd682bq8WrVq1dSzZ089+eSTeu+99+Tj46MXX3xR5cqVU8+ePTP9GgEA0terVy9Vq1ZNx44dU5kyZdS3b18tWbLE7LIAAA7A6fZN8tbo0aPVvXt3Xbp0SZ6envr55591+vRpNWnSJNN/BAGAvbz66qs6e/asoqKicv2WlJSkypUr67777jP7aQIAkOeCg4P1+OOPa+zYsapRo4Z69Oih3bt3KzAwUNLN666MGDFCtWrVUpcuXVSjRg298847mT5+r169tGDBAs2dO1d16tTRe++9p+DgYLVr1y7N9kWKFNFXX32l0NBQNWrUSBMnTtRrr72W6fMNGTJEly9fVt++fW2uS9O5c2dt2LBBmzZtUrNmzXTnnXdq/vz5qlixorXNmjVr1KxZMz3yyCOqXbu2XnjhBevsmJYtW2rYsGHq06ePSpUqpTlz5lhfvyZNmui+++5TixYtZBiGvv76a5sl3QAA2efs7Kxp06ZJuvkFug8++EDbt283tygAgEOwGNldPDmXFCtWTLt371aNGjVUrFgx/fTTT6pVq5Z2796tAQMG6M8//zSzvDRFR0fL19dXUVFRKlq0qNnlALCTs2fPqmLFikpOTtYPP/yg8uXL5/o5AgMD5eHhkevHBQCYKzY2VidPnlRQUBDjPFAA8DsLIEV4eLgmTZqkZcuWqXXr1tq2bRvX6wIApCmzuYHpy5e5urpa/zErXbq0wsLCVKtWLfn6+iosLMzk6gDklT///FNTpkzRjRs3zC4l086dO6fk5GS1adNG7du3N7scAAAAAEAeK1OmjGbMmKGPP/5YP/74ozp37qyqVatqxowZ8vPzM7u8fO2bb77Ru+++q+TkZLNLSZevr6/mzZun0qVLm10KgELE9FCmUaNG2rt3r6pXr6727dtrypQpunDhglauXKl69eqZXR6APDJ27Fh9/fXXZpeRLcOHDze7BAAAAACAnZQrV04jRozQ/PnztWnTJm3atEnHjh1TSEiInJ2dzS4vX0pKStKTTz6pc+fOmV3KbQUEBHAJBQB2ZfryZXv37lVMTIzat2+vf/75RwMGDNCOHTtUtWpVLVu2TA0bNjSzvDSxfBmQM0eOHFHNmjVlsVi0cOFCm3XX8zs/Pz91796d6eoAgCxhKSSgYOF3FsB/xcXFad26dbp06ZLGjRun69ev6+WXX9akSZPMLi1fCgkJUdeuXVWiRAnNnTvX7HLSdOzYMc2ePVulS5fWmTNnuC4bgBwrMMuXNW3a1PpzqVKlCuw35wF7iYmJ0YoVK3T9+nWzS8m2zZs3S5K6d++uZ555xuRqAAAAAADImLu7u/r27StJKlKkiAYMGKCpU6cqLi5OPj4+atSokTp27GhylXkrPj5ey5cv15UrV27b9ssvv5Qk9evXT4MHD87jyrInISFBy5Yt099//63Ro0erQoUKt31MnTp1dO+999qhOgCOzPRQ5u6779batWtVrFgxm+3R0dHq1auXfvjhB3MKA/KpadOmaf78+WaXkSuee+45s0sAAAAAACBLHn/8cW3dulXBwcF65ZVXJElOTk46ePCg6tSpY3J1eeftt9/W2LFjs/SYQYMG5VE1Oefq6qrHHntM8+fP16JFizL9uP379+fLlX0AFBymhzJbt25VfHx8qu2xsbH68ccfTagIyL/i4+P14YcfSpJ69OihEiVKmFxR9tWrV0/t27c3uwwAAAAAALLs7bffVuXKlXXixAn9+uuvOnjwoKZMmaI1a9aYXVqeMAxD77//viSpY8eOKleu3G0f07RpUzVq1CivS8uRl156SYmJiYqOjr5t23379unQoUNatGiRlixZYofqADgq00KZgwcPWn8ODQ1VRESE9X5SUpJCQkIyNcAD9vbPP//o+PHjppx7z549unDhgkqXLq01a9bIxcX0XBUAAAAAgELHy8vLej2Z0NBQ1a1bV2vXrtVnn32mZs2aKSgoyOQKsyc2Nla//fabkpOTbbYfO3ZMR44ckbe3t9asWSMfHx+TKsxdJUuW1IIFCzLV9scff1SbNm308ccfq1+/fnJ3d7fZ7+zsrAYNGqTaDgD/Zdonug0bNpTFYpHFYtHdd9+dar+np6feeuutPDv/O++8o7lz5yo8PFx16tTRm2++qdatW+fZ+eAYYmJiVKdOHf3zzz+m1tG/f38CGQAAAAAA8oHatWvrscce08qVK9WnTx85Oztr9+7datKkidmlZVnfvn2t14NJy8MPP+wwgUxW3XXXXapXr54OHTqU7sofPXv21P/+9z/7FgagwDHtU92TJ0/KMAxVrlxZv/zyi0qVKmXd5+bmJn9/fzk7O+fJuVevXq1Ro0bpnXfeUatWrfTee++pa9euCg0NzdRFvVB4ff755/rnn3/k5eWlMmXKmFKDn5+fRo4cacq5AQAAAABAaq+++qqOHTumY8eO6eLFi5o5c2aBW8osLCxM69evlyRVqVIl1X5fX1+NHz/e3mXlGxaLRXPnztWYMWMUFxeXav+pU6f05Zdf6rvvvlOnTp1MqBBAQWExDMMwuwh7a968uRo3bqzFixdbt9WqVUu9evXSrFmzbvv46Oho+fr6KioqSkWLFs3LUpHPtGnTRj/++KNmzZqlF1980exyAABAAREbG6uTJ08qKChIHh4eZpdjuq1bt6p9+/a6fPmyihUrZnY5eaJdu3Zq2LCh3nzzTbuet1KlSho1apRGjRqV7WMMHDhQV65cyfCbvmY9P3vhdxZAdv3xxx+qW7euLBaLDh8+rBo1aphdUqa98sormjx5stq1a6ctW7aYXU6BM3r0aL355puqU6eODhw4wAonQCGU2dzAyY41pevEiRN69tln1aFDB3Xs2FEjR47UiRMn8uRc8fHx2rdvX6rEulOnTtq1a1eaj4mLi1N0dLTNDelzdnZ22NuPP/4oJycn9e/f3+yXGQAAIM8NHDjQuuTwrbcuXbrk6XmnTZuW5nlTbtOnT8/T82ckvdfk1lthN2/ePPn6+ur69eup9sXGxqpYsWKaP39+js9jsVhYIgZAvlOnTh11795dhmGoZs2acnZ2VtmyZfPsc66smDp1qtzc3NL9zGPy5MmSpEGDBplcacE0ZcoUlShRQn/88UeGr/Ottw4dOthcZxtA4WB6KPPtt9+qdu3a+uWXX1S/fn3VrVtXu3fvVp06dbRp06ZcP9+FCxeUlJSk0qVL22wvXbp0uoPgrFmz5Ovra70FBgbmel2OJDk52WFvkvTII4+oXLlyJr/KAAAA9tGlSxeFh4fb3D799NM8Pefzzz+f6pzh4eEaOHCgihUrpkcffTRPz5+RBQsW2NQkScHBwam2ZUdCQkJulWmqxx9/XDdu3Ehz2Z41a9bo+vXr+epLTo7yugPIP6ZMmWK92HtycrLCw8P19ttvm1pTdHS05s6dq4SEhAw/86hYsaIefPBBU2stqIoXL645c+ZIkgzDyNTnTJs3b9Ydd9yhgwcPmlw9AHsyPZR58cUXNXr0aO3evVvz58/XG2+8od27d2vUqFF5uk7lf7/BZhhGut9qmzBhgqKioqy3M2fO5FldjuD8+fMOe4uIiNDKlSvNfokBAICjuHYt/VtsbObb3riRubbZ4O7uroCAAJtb8eLFrfstFos++OAD3X///fLy8lK1atWs69Gn+Prrr1W9enV5enqqffv2OnXqVIbnLFKkSKpzbt68WStXrtSqVatUrVo1a9vFixerSpUqcnNzU40aNVK9V8tMfaGhoerWrZuKFCmi0qVLq3///rpw4UKatfn6+trUJUnFihVLtU26+UHcCy+8oBIlSiggIEDTpk1LVdu7776rnj17ytvbW6+88ook6auvvlKTJk3k4eGhypUra/r06UpMTLQ+btq0aapQoYLc3d1VtmzZVNcbvH79ugYPHiwfHx9VqFBB77//vs3+Q4cO6e6775anp6dKliypoUOH6urVq+n+/7h27Zoef/xxFSlSRGXKlNG8efPSbStJpUqVUvfu3bVs2bJU+5YtW6YePXqoVKlSOnfunPr06aPixYurZMmS6tmzZ6q+sWzZMtWpU0fu7u4qU6aMnnnmGUk3l2mTpPvvv18Wi8V6X8pcn0jrdQeA3NK0aVNduHBB58+ft36RYeXKlWleh8ReVq9erRs3bqhmzZoZfu5x7NgxeXt7m1ZnQTdkyBBdunQpU58x/frrr6pevbrOnDmjli1b6quvvjK7fAB2YvrihocPH9Znn32WavvgwYPzZH1iPz8/OTs7p5oVExkZmWr2TAp3d3frNxxwe2XKlDG7BAAAgIKhSJH093XrJm3c+O99f38pjeWgJElt20pbt/57v1IlKa1QIY8uJzl9+nTNmTNHc+fO1VtvvaV+/frp9OnTKlGihM6cOaMHHnhAw4YN0/Dhw7V3716NHTs2S8fft2+fnnzySc2ePVudO3e2bl+3bp2ee+45vfnmm+rQoYM2bNigQYMGqXz58mrfvn2m6gsPD1fbtm315JNPav78+bpx44bGjx+vhx9+WD/88EOOXpcVK1ZozJgx2r17t3766ScNHDhQrVq1UseOHa1tpk6dqlmzZumNN96Qs7Ozvv32Wz322GNauHChWrdurRMnTmjo0KHWtl988YXeeOMNrVq1SnXq1FFERIR+++03m/POmzdPL7/8sl566SV98cUXGj58uNq0aaOaNWvq+vXr6tKli+68807t2bNHkZGReuKJJ/TMM89o+fLlaT6PcePGacuWLVq3bp0CAgL00ksvad++fWrYsGG6z33IkCG67777rNdlkW5eAHnLli3auHGjrl+/rvbt26t169bavn27XFxc9Morr6hLly46ePCg3NzctHjxYo0ZM0azZ89W165dFRUVpZ07d0qS9uzZI39/fwUHB6tLly5ydnaWlPk+8d/XHQByW5EiRVSkSBE99NBDev7553Xu3Dl9+eWXevjhh+1Ww9GjRzVmzBhdvXpVhw8flnTz8zY+t8lbt355JSNlypTRzz//rIceekg//PCDevbsqVatWln/XXJ3d9f06dN155135mW5VtHR0XrqqadyNOsXjm3NmjUqWbKk2WU4BsNk5cuXNz777LNU21evXm0EBgbmyTnvuOMOY/jw4TbbatWqZbz44ouZenxUVJQhyYiKisqL8gAAAOBgbty4YYSGhho3btyw3XEzJkn71q2bbVsvr/Tbtm1r29bPL+12WTRgwADD2dnZ8Pb2trnNmDHjlqcgY9KkSdb7V69eNSwWi/HNN98YhmEYEyZMMGrVqmUkJydb24wfP96QZFy+fPm2Nfz9999GYGCg0a9fv1T7WrZsaTz55JM223r37m10u+W1u119kydPNjp16mRzjDNnzhiSjCNHjty2PknGunXrUm1v27atcdddd9lsa9asmTF+/Hibx44aNcqmTevWrY2ZM2fabFu5cqVRpkwZwzAMY968eUb16tWN+Pj4NOupWLGi8dhjj1nvJycnG/7+/sbixYsNwzCM999/3yhevLhx9epVa5uNGzcaTk5ORkREhGEYN/+/9+zZ0zAMw4iJiTHc3NyMVatWWdtfvHjR8PT0NJ577rk0azAMw0hMTDTKlStnTJkyxbptypQpRrly5YzExERj6dKlRo0aNWz6RVxcnOHp6Wl8++23hmEYRtmyZY2JEyeme460XvvM9on/vu7/le7vLABkw6RJkwxJRseOHe163kcffdSQZL15enoa4eHhdq0BtxcfH28MHTrU5v9Vyq1169Z2q2POnDlp1sCNW8rt/PnzduuPBVVmcwPTZ8o8+eSTGjp0qP766y+1bNlSFotFO3bs0GuvvZblb9Bl1pgxY9S/f381bdpULVq00Pvvv6+wsDANGzYsT84HAAAApCmDJaP032/vR0am39bpP6sS32Z5sKxo3769Fi9ebLOtRIkSNvfr169v/dnb21s+Pj6K/P96Dx8+rDvvvNNmqeAWLVpk6twJCQl66KGHVLp0aX3wwQep9h8+fNg6iyRFq1attGDBgkzXt2/fPm3ZskVF0pi1dOLECVWvXj1Ttabl1vNKN78RG/mf/49Nmza1ub9v3z7t2bNHr776qnVbUlKSYmNjdf36dfXu3VtvvvmmKleurC5duqhbt27q3r27XFz+/dPu1vNaLBYFBATY/P9o0KCBzdI0rVq1UnJyso4cOZJq9YATJ04oPj7e5v9ZiRIlVKNGjQyfu7OzswYMGKDly5dr6tSpslgsWrFihQYOHChnZ2ft27dPx48fl4+Pj83jYmNjdeLECUVGRur8+fO65557MjzPf2W2T/z3dQeAvDRo0CC98sor+v7773Xq1CmbJRfzyuXLl63X9lqwYIECAgJUp04dm2U2kT+4urrq3Xff1eOPP65z585Juvnv4aBBg/Tjjz/q2LFjNku35gXDMLR06VJJ0ujRo+02OwcFS7FixcwuwWGYHspMnjxZPj4+mjdvniZMmCBJKlu2rKZNm5ZqbeTc0qdPH128eFEzZsxQeHi46tatq6+//loVK1bMk/MBAAAAacrKmu151fa2h/JW1apVM2zj6upqc99isVgvGGzkYMm0kSNH6ujRo9q7d688PDzSbJOZa0VmVF9ycrK6d++u1157LdWxc7q8S0bnTfHfdfuTk5M1ffp0PfDAA6mO5+HhocDAQB05ckSbNm3S999/r6efflpz587Vtm3brOe73f+P9K6lmdb2nPz/Gzx4sGbNmmVdBi4sLEyDBg2yPs8mTZro448/TvW4UqVKyem/QWMWZKZPcL0EAPZUuXJl3XPPPdq8ebOaNWumJUuWqFevXnlyrsTERK1cuVJff/214uLiVK9ePT377LPpjv3IHywWi1q1amWzbdWqVfrmm2/07LPPqlmzZqke4+7urieeeOK2QVtCQoIWLVqkixcvptvm8uXLOnLkiLy9vTV9+vRUX5oAkLtMD2UsFotGjx6t0aNHKyYmRpKsv/jnzp1TuXLl8uS8Tz/9tJ5++uk8OTYAAACAm2rXrq3//e9/Ntt+/vnn2z7u/fff17Jly7RlyxaVL18+zTa1atXSjh079Pjjj1u37dq1S7Vq1cp0fY0bN9aaNWtUqVIlm9kmZmncuLGOHDmSYRDm6empHj16qEePHhoxYoRq1qypQ4cOqXHjxrc9fu3atbVixQpdu3bNGkzs3LlTTk5Oac4Kqlq1qlxdXfXzzz+rQoUKkm5+cHP06FG1bds2w3NVqVJFbdu2VXBwsAzDULt27VSlShXr81y9erX8/f1VtGjRNB9fqVIlbd682eZaMLdydXVVUlKSzbbc6BMAkBeeeOIJbd68WRcuXND999+vP//887azDrPq999/16BBg7R3717rtsGDBxPIFFCDBw/WN998o2+//Vbffvttmm1+//13rVq1KsPjLFmyRKNHj87UOfv06UMgA9iB+X913CLllz4iIkKvvvqqPvjgA924ccPkqgAAAIDCKy4uThERETbbXFxc5Ofnl6nHDxs2TPPmzdOYMWP01FNPad++feleUD7Fzp079eyzz2rKlCmqXLlyqvN7enrK19dX48aN08MPP6zGjRvrnnvu0VdffaW1a9fq+++/z/TzGzFihJYsWaJHHnlE48aNk5+fn44fP65Vq1ZpyZIldr8I/JQpU3TfffcpMDBQvXv3lpOTkw4ePKhDhw7plVde0fLly5WUlKTmzZvLy8tLK1eulKenZ6Zn/ffr109Tp07VgAEDNG3aNP3zzz969tln1b9//1RLl0k3L1Q9ZMgQjRs3TiVLllTp0qU1ceLETM9kGTJkiJ588klJslmCrl+/fpo7d6569uypGTNmqHz58goLC9PatWs1btw4lS9fXtOmTdOwYcPk7++vrl27KiYmxto3pH9Dm1atWsnd3V3FixfPlT4BAHmhd+/eCgsL0/jx4yVJS5cu1Zw5c3Ll2AkJCZo9e7ZefvllJSQkqFixYurbt68CAgI0fPjwXDkH7O+BBx7Q3Llzdfr06VT7bty4oaVLl2rdunW6dOlSqqVlb5WyLFmXLl0y/NKHl5dXpsMbADljWihz5coVjRgxQt99951cXV314osv6plnntG0adP0+uuvq06dOlq2bJlZ5QEAAACQFBISkmoZrxo1aujPP//M1OMrVKigNWvWaPTo0XrnnXd0xx13aObMmRo8eHC6j/nggw8UHx+vSZMmadKkSan2p1yrpFevXlqwYIHmzp2rkSNHKigoSMHBwWrXrl2mn1/ZsmW1c+dOjR8/Xp07d1ZcXJwqVqyoLl265GgJrezq3LmzNmzYoBkzZmjOnDlydXVVzZo19cQTT0i6uZb37NmzNWbMGCUlJalevXr66quvVLJkyUwd38vLS99++62ee+45NWvWTF5eXnrwwQc1f/78dB8zd+5cXb16VT169JCPj4/Gjh2rqKioTJ3vwQcf1DPPPCNJNkuyeXl5afv27Ro/frweeOABxcTEqFy5crrnnnusM2cGDBig2NhYvfHGG3r++efl5+enhx56yHqMlLBvyZIlKleunE6dOpUrfQIA8oKzs7NeeOEF1ahRQ7169dKKFSv0yiuvyM3NLcvH+vPPPxUWFiZJun79umbMmKH9+/dLkrp37653331XZcuWzdX6YX9OTk56/vnn092/b98+HThwQDNnzlSnTp3SbBMREaFff/1Vrq6uWrlyZaa/VAMgb1mMnCwSnANPP/20vvrqK/Xp00chISE6fPiwOnfurNjYWE2dOvW2U+HNFB0dLV9fX0VFRaU71R4AAABIERsbq5MnTyooKCjda6MAyD/4nQWQVxISElShQgVFRERozZo1aV5DLCOhoaFq0KCBEhMTbbaXKFFCb731lh555BGWKysk3n77bevs0dt58MEH9cUXX+RxRQAymxuYNlNm48aNCg4OVocOHfT000+ratWqql69ut58802zSgIAAAAAAADyjKurqwYOHKjZs2frgw8+yHIo8/777ysxMVH+/v7W2TANGzbU7Nmz01yGEo5rwIAB2rRpk3XWVHq8vb01efJkO1UFIDNMmynj6uqq06dPW/8B8fLy0i+//KK6deuaUU6WMFMGAAAAWcG37oGChd9ZAHnp2LFjql69uiwWi44ePaoqVapkOLslKSlJ8fHxio+PV5UqVXTx4kVt3LhR3bp1s2PVAIDbyWxuYP9Fkv9fcnKyXF1drfednZ3l7e1tVjkAAAAAAABAnqtWrZratWsnwzBUrVo1devWTel9Z/r48ePy9/eXl5eXihUrposXL6pcuXLq3LmznasGAOQW05YvMwxDAwcOlLu7u6Sb30QaNmxYqmBm7dq1ZpQHAAAAAAAA5IkXXnhBO3fuVEJCgkJCQrR37141a9YsVbu3335bly5dst63WCx64YUX5OzsbM9yAQC5yLRQZsCAATb3H3vsMZMqAQAAAOzDpJWDAWQRv6sA8lrXrl0VExOjwYMH65NPPtEHH3yQKpSJjY3Vhx9+KElat26dOnToIBcXF5ZVBIACzrRryhRkXFMGAAAAWZGUlKSjR4/K399fJUuWNLscALcRFRWl8+fPq2rVqjbLbgNAbtu2bZvatWsnd3f3VNdZvnbtmv78809VqFBBf/31F7NjACCfy2xuYNpMGQAAAKCwcHZ2VrFixRQZGSlJ8vLyyvCCvgDMk5ycrH/++UdeXl5yceFPZgB5q02bNqpbt65+//137du3L802w4cPJ5ABAAfCO0wAAADADgICAiTJGswAyL+cnJxUoUIFwlMAec5isWjLli3au3dvmksnenl56a677jKhMgBAXmH5smxg+TIAAABkV1JSkhISEswuA0AG3Nzc5OTkZHYZAAAAKEBYvgwAAADIh5ydnVmCBAAAAAAKKb76AwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHXFMmGwzDkHTzwj0AAAAAAAAAAKBwS8kLUvKD9BDKZENMTIwkKTAw0ORKAAAAAAAAAABAfhETEyNfX99091uM28U2SCU5OVnnz5+Xj4+PLBaL2eXkG9HR0QoMDNSZM2dUtGhRs8sBcgX9Go6GPg1HRL+GI6Jfw9HQp+GI6NdwRPRrOBp79mnDMBQTE6OyZcvKySn9K8cwUyYbnJycVL58ebPLyLeKFi3KoA2HQ7+Go6FPwxHRr+GI6NdwNPRpOCL6NRwR/RqOxl59OqMZMinSj2sAAAAAAAAAAACQawhlAAAAAAAAAAAA7IBQBrnG3d1dU6dOlbu7u9mlALmGfg1HQ5+GI6JfwxHRr+Fo6NNwRPRrOCL6NRxNfuzTFsMwDLOLAAAAAAAAAAAAcHTMlAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADF7MLKIiSk5N1/vx5+fj4yGKxmF0OAAAAAAAAAAAwkWEYiomJUdmyZeXklP58GEKZbDh//rwCAwPNLgMAAAAAAAAAAOQjZ86cUfny5dPdTyiTDT4+PpJuvrhFixY1uRoAAAAAAAAAAGCm6OhoBQYGWvOD9BDKZEPKkmVFixYllAEAAAAAAAAAAJJ020uepL+wGQAAAAAAAAAAAHJNoQtlFi9erPr161tnubRo0ULffPON2WUBAAAAAAAAAAAHV+hCmfLly2v27Nnau3ev9u7dq7vvvls9e/bUH3/8YXZpAAAAAAAAAADAgVkMwzDMLsJsJUqU0Ny5czVkyJBMtY+Ojpavr6+ioqK4pgwAAACAbElOTlZERITZZQBp8vf3l4sLl6EFAADIrMzmBoX6HVZSUpI+//xzXbt2TS1atEi3XVxcnOLi4qz3o6Oj7VEeAAAAAAfWuXNnff/992aXAaSpVq1aOnTokJydnc0uBQAAwKEUylDm0KFDatGihWJjY1WkSBGtW7dOtWvXTrf9rFmzNH36dDtWCAAAAMDR7dixQ5Lk7Owsi8VicjXAvxITE3X48GFdvnxZfn5+ZpcDAADgUArl8mXx8fEKCwvTlStXtGbNGn3wwQfatm1busFMWjNlAgMDWb4MAAAAQLYYhiFnZ2cZhqHw8HAFBASYXRJg5ebmpoSEBJ09e1blypUzuxwAAIACgeXLMuDm5qaqVatKkpo2bao9e/ZowYIFeu+999Js7+7uLnd3d3uWCAAAAMCBJSYmKuX7cfytgfwmJZSJj483uxQAAACH42R2AfmBYRg2M2EAAAAAIC/d+veHm5ubiZUAqaX0SUIZAACA3FfoZsq89NJL6tq1qwIDAxUTE6NVq1Zp69atCgkJMbs0AAAAAIXEraEMM2WQ36T0Sb68CAAAkPsKXSjz999/q3///goPD5evr6/q16+vkJAQdezY0ezSAAAAABQSKR92Ozk5ycWl0P1ZhnyOmTIAAAB5p9C9+1+6dKnZJQAAAAAo5FI+7GaWDPIjQhkAAIC8wzVlAAAAAMDOUmbKEMogP2L5MgAAgLxDKAMAAAAAdkYog/yMmTIAAAB5h1AGAAAAAOyMUAb5GaEMAABA3iGUAQAAAAA7I5RBfpYSyrB8GQAAQO4jlAEAAAAAOyOUQX6W0i+ZKQMAAJD7CGUAAAAAwM4IZZCfsXwZAABA3iGUAQAAAAA7SwllUj78BvITli8DAADIO4QyAAAAAGBnzJRBfsbyZQAAAHmHUAYAAAAA7IxQBvkZy5cBAADkHUIZAAAAALCzlA+7CWWQH7F8GQAAQN4hlAEAAAAAO2OmDPIzli8DAADIOy5mF5AVZ86c0alTp3T9+nWVKlVKderU4Y8YAAAAAAUOoQzyM5YvAwAAyDv5PpQ5ffq03n33XX366ac6c+aMDMOw7nNzc1Pr1q01dOhQPfjgg3JyYuIPAAAAgPyPUAb5GcuXAQAA5J18nWI899xzqlevno4dO6YZM2bojz/+UFRUlOLj4xUREaGvv/5ad911lyZPnqz69etrz549ZpcMAAAAALdFKIP8jOXLAAAA8k6+ninj5uamEydOqFSpUqn2+fv76+6779bdd9+tqVOn6uuvv9bp06fVrFkzEyoFAAAAgMwjlEF+xvJlAAAAeSdfhzJz587NdNtu3brlYSUAAAAAkHsIZZCfsXwZAABA3snXy5cBAAAAgCNK+bA75cNvID9h+TIAAIC8U+BDmZdeekmDBw82uwwAAAAAyDRmyiA/Y/kyAACAvJOvly/LjHPnzunMmTNmlwEAAAAAmUYog/yM5csAAADyToEPZVasWGF2CQAAAACQJSkzEAhlkB+xfBkAAEDeKfDLlwEAAABAQcNMGeRnLF8GAACQdwrMTJkZM2ZkuH/KlCl2qgQAAAAAcoZQBvkZy5cBAADknQITyqxbt87mfkJCgk6ePCkXFxdVqVKFUAYAAABAgUEog/yMmTIAAAB5p8CEMvv370+1LTo6WgMHDtT9999vQkUAAAAAkD2EMsjPuKYMAABA3inQ15QpWrSoZsyYocmTJ5tdCgAAAABkGqEM8jOWLwMAAMg7BTqUkaQrV64oKirK7DIAAAAAINMIZZCfsXwZAABA3ikwy5ctXLjQ5r5hGAoPD9fKlSvVpUsXk6oCAAAAgKxLCWVSPvwG8pOUsJCZMgAAALmvwIQyb7zxhs19JycnlSpVSgMGDNCECRNMqgoAAAAAso6ZMsjPmCkDAACQdwpMKHPy5EmzSwAAAACAXEEog/yMUAYAACDvFPhrygAAAABAQZPyYTehDPIjli8DAADIOwU+lHnnnXc0Y8YMs8sAAAAAgExjpgzys1tnyhiGYXI1AAAAjqXAhzJr1qzR8uXLzS4DAAAAADKNUAb5WUooI0mJiYkmVgIAAOB4Csw1ZdKzefNms0sAAAAAgExLTExUcnKyJEIZ5E+39su4uDi5urqaWA0AAIBjKfAzZQAAAACgILn1Oh2EMsiPbp0pk3L9IwAAAOSOAjdTJjQ0VGFhYaneGPbo0cOkigAAAAAg8whlkN85OzvLYrHIMAxCGQAAgFxWYEKZv/76S/fff78OHTpkfXMoSRaLRZKUlJRkZnkAAAAAkCkpoYyTk5NcXArMn2QoRCwWi9zd3RUbG2sTIgIAACDnCszyZc8995yCgoL0999/y8vLS3/88Ye2b9+upk2bauvWrWaXBwAAAACZkvIh961LRAH5TUr/ZKYMAABA7iowX8v66aef9MMPP6hUqVJycnKSk5OT7rrrLs2aNUsjR47U/v37zS4RAAAAAG4rJZRh6TLkZ4QyAAAAeaPAzJRJSkpSkSJFJEl+fn46f/68JKlixYo6cuSImaUBAAAAQKYRyqAgSOmfLF8GAACQuwrMTJm6devq4MGDqly5spo3b645c+bIzc1N77//vipXrmx2eQAAAACQKSkzDwhlkJ8xUwYAACBvFJiZMpMmTVJycrIk6ZVXXtHp06fVunVrff3111q4cGGmjzNr1iw1a9ZMPj4+8vf3V69evZhpAwAAAMBumCmDgoBQBgAAIG8UmJkynTt3tv5cuXJlhYaG6tKlSypevLgsFkumj7Nt2zaNGDFCzZo1U2JioiZOnKhOnTopNDRU3t7eeVE6AAAAAFgRyqAgSAllWL4MAAAgdxWYUCYtJUqUyPJjQkJCbO4HBwfL399f+/btU5s2bXKrNAAAAACFmGEYCgkJUURERKp9hw4dkkQog/wtpX+GhISoTp06Klu2rMkVAQAAOIZ8HcoMGzZMEydOVGBg4G3brl69WomJierXr1+WzhEVFSUp44AnLi7O5ttB0dHRWToHAAAAgMLl+++/V7du3TJsU6RIETtVA2RdykoS8+fP1759+7R161ZzCwIAAHAQ+TqUKVWqlOrWrauWLVuqR48eatq0qcqWLSsPDw9dvnxZoaGh2rFjh1atWqVy5crp/fffz9LxDcPQmDFjdNddd6lu3brptps1a5amT5+e06cDAAAAoJDYu3evJKlChQpp/q3h7OyskSNH2rssINMmT54swzC0fft2nT171uxyAAAAHIbFMAzD7CIyEhkZqaVLl2rVqlX6/fffbfb5+PioQ4cOGjp0qDp16pTlY48YMUIbN27Ujh07VL58+XTbpTVTJjAwUFFRUSpatGiWzwsAAADAsQ0cOFArVqzQyy+/rEmTJpldDpAtv/76q5o0aaLy5cvrzJkzZpcDAACQr0VHR8vX1/e2uUG+nikjSf7+/powYYImTJigK1eu6PTp07px44b8/PxUpUoVWSyWbB332Wef1fr167V9+/YMAxnp5lq6rPcMAAAAILOOHDkiSapRo4bJlQDZl/J38K1fUgQAAEDO5PtQ5lbFihVTsWLFcnQMwzD07LPPat26ddq6dauCgoJypzgAAAAA0M2/OQhl4AhSQpnY2FiTKwEAAHAcBSqUyQ0jRozQJ598oi+//FI+Pj6KiIiQJPn6+srT09Pk6gAAAAAUdBcuXNDly5clSVWrVjW5GiD7mCkDAACQ+5zMLsDeFi9erKioKLVr105lypSx3lavXm12aQAAAAAcQMosmQoVKsjLy8vkaoDsSwll4uPjlc8vRwsAAFBgFLqZMryRBAAAAJCXWLoMjsLDw8P6c3x8PNdaBQAAyAUFYqaMYRg6ffq0bty4YXYpAAAAAJCho0ePSiKUQcF3awjDEmYAAAC5o8CEMtWqVdPZs2fNLgUAAAAAMsRMGTgKNzc368+xsbEmVgIAAOA4CkQo4+TkpGrVqunixYtmlwIAAAAAGSKUgaOwWCzWYIaZMgAAALmjQIQykjRnzhyNGzdOv//+u9mlAAAAAECaEhMTdeLECUmEMnAMKUuYEcoAAADkDhezC8isxx57TNevX1eDBg3k5uYmT09Pm/2XLl0yqTIAAAAAuOnkyZNKSEiQp6enypcvb3Y5QI65u7srJiaGUAYAACCXFJhQ5s033zS7BAAAAADIUMrSZdWqVZOTU4FZmABIl4eHhyRmygAAAOSWAhPKDBgwwOwSAAAAACBDR48elcTSZXAcLF8GAACQuwpMKCNJSUlJ+t///qfDhw/LYrGodu3a6tGjh5ydnc0uDQAAAACsM2UIZeAoUkKZ2NhYkysBAABwDAUmlDl+/Li6deumc+fOqUaNGjIMQ0ePHlVgYKA2btyoKlWqmF0iAAAAgEKOUAaOhpkyAAAAuavALHI8cuRIValSRWfOnNGvv/6q/fv3KywsTEFBQRo5cqTZ5QEAAAAAoQwcDqEMAABA7iowM2W2bdumn3/+WSVKlLBuK1mypGbPnq1WrVqZWBkAAAAASNHR0YqIiJBEKAPHQSgDAACQuwrMTBl3d3fFxMSk2n716lW5ubmZUBEAAAAA/CtllkxAQICKFi1qcjVA7vDw8JBEKAMAAJBbCkwoc99992no0KHavXu3DMOQYRj6+eefNWzYMPXo0cPs8gAAAAAUcimhTPXq1U2uBMg9zJQBAADIXQUmlFm4cKGqVKmiFi1ayMPDQx4eHmrVqpWqVq2qBQsWmF0eAAAAgELu6NGjkli6DI4lJZSJjY01uRIAAADHUGCuKVOsWDF9+eWXOn78uA4fPizDMFS7dm1VrVrV7NIAAAAAwDpThlAGjoSZMgAAALmrwIQyKapWraqqVasqKSlJhw4d0uXLl1W8eHGzywIAAABQyBHKwBERygAAAOSuArN82ahRo7R06VJJUlJSktq2bavGjRsrMDBQW7duNbc4AAAAAIVacnIyy5fBIRHKAAAA5K4CE8p88cUXatCggSTpq6++0l9//aU///xTo0aN0sSJE02uDgAAAEBhdvbsWd24cUOurq4KCgoyuxwg1xDKAAAA5K4CE8pcuHBBAQEBkqSvv/5aDz/8sKpXr64hQ4bo0KFDJlcHAAAAoDBLWbqsSpUqcnEpcKtEA+ny8PCQJMXGxppcCQAAgGMoMKFM6dKlFRoaqqSkJIWEhKhDhw6SpOvXr8vZ2dnk6gAAAAAUZilLl1WvXt3kSoDcxUwZAACA3FVgvsI1aNAgPfzwwypTpowsFos6duwoSdq9e7dq1qxpcnUAAAAACrOUmTJcTwaOhlAGAAAgdxWYUGbatGmqW7euzpw5o969e1vfGDo7O+vFF180uToAAAAAhRmhDBwVoQwAAEDuKjChjCQ99NBDqbYNGDDAhEoAAAAA4F+EMnBUhDIAAAC5q0CFMps3b9bmzZsVGRmp5ORkm33Lli0zqSoAAAAAhdmNGzcUFhYmiVAGjodQBgAAIHcVmFBm+vTpmjFjhpo2bWq9rgwAAAAAmO3YsWMyDEPFixeXn5+f2eUAucrDw0OSFBsba3IlAAAAjqHAhDLvvvuuli9frv79+5tdCgAAAABYHT16VNLNWTJ8eQyOhpkyAAAAucvJ7AIyKz4+Xi1btjS7DAAAAACwwfVk4MgIZQAAAHJXgQllnnjiCX3yySdmlwEAAAAANlJCmerVq5tcCZD7CGUAAAByV4FZviw2Nlbvv/++vv/+e9WvX1+urq42++fPn29SZQAAAAAKM2bKwJERygAAAOSuAhPKHDx4UA0bNpQk/f777zb7WLcZAAAAgBkMwyCUgUMjlAEAAMhdBSaU2bJli9klAAAAAICNyMhIRUVFyWKxqGrVqmaXA+Q6Dw8PSTdXrwAAAEDOFZhryqQ4fvy4vv32W924cUPSzW+mAQAAAIAZUmbJVKpUyfrhNeBImCkDAACQuwpMKHPx4kXdc889ql69urp166bw8HBJ0hNPPKGxY8eaXB0AAACAwujo0aOSWLoMjotQBgAAIHcVmFBm9OjRcnV1VVhYmLy8vKzb+/Tpo5CQEBMrAwAAAFBYcT0ZODpCGQAAgNxVYK4p89133+nbb79V+fLlbbZXq1ZNp0+fNqkqAAAAAIVZSihTvXp1kysB8kZKKJOYmKikpCQ5OzubXBEAAEDBVmBmyly7ds1mhkyKCxcuWN8kAgAAAIA9MVMGju7Wv7eZLQMAAJBzBSaUadOmjT788EPrfYvFouTkZM2dO1ft27c3sTIAAAAAhVFCQoL++usvSYQycFweHh7WnwllAAAAcq7ALF82d+5ctWvXTnv37lV8fLxeeOEF/fHHH7p06ZJ27txpdnkAAAAACpm//vpLiYmJ8vb2Vrly5cwuB8gTLi4uslgsMgyDUAYAACAXFJiZMrVr19bBgwd1xx13qGPHjrp27ZoeeOAB7d+/X1WqVDG7PAAAAACFzNGjRyXdvJ6MxWIxuRogb1gsFusSZoQyAAAAOVdgZspIUkBAgKZPn252GQAAAADA9WRQaLi7uys2NpZQBgAAIBfk+5kyly5d0tmzZ222/fHHHxo0aJAefvhhffLJJ1k63vbt29W9e3eVLVtWFotF//vf/3KxWgAAAACFBaEMCgtmygAAAOSefB/KjBgxQvPnz7fej4yMVOvWrbVnzx7FxcVp4MCBWrlyZaaPd+3aNTVo0EBvv/12XpQLAAAAoJAglEFhkRLKxMbGmlwJAABAwZfvly/7+eefFRwcbL3/4YcfqkSJEjpw4IBcXFz0+uuva9GiRerfv3+mjte1a1d17do1SzXExcXZfCMoOjo6S48HAAAA4BiuXLmiyZMn68KFC/r1118l3bymDODIPDw8JEkDBw5USEiIAgMDTa4IQEH13XffacWKFUpOTrZue/DBB/XQQw+ZWBUA2Fe+D2UiIiIUFBRkvf/DDz/o/vvvl4vLzdJ79OihWbNm5WkNs2bN4lo2AAAAALR69WqbWfeenp7MlIHDK1OmjI4dO6bQ0FAtXbpU06ZNM7skAAXU888/r0OHDtls27x5M6EMgEIl3y9fVrRoUV25csV6/5dfftGdd95pvW+xWPJ8XdsJEyYoKirKejtz5kyeng8AAABA/nTu3DlJUps2bbRgwQJt3rxZRYoUMbkqIG8tX75c3t7ekqSYmBiTqwFQkKWMIWPGjLEGvIwrAAqbfB/K3HHHHVq4cKGSk5P1xRdfKCYmRnfffbd1/9GjR/N86rS7u7uKFi1qcwMAAABQ+ERGRkqS2rdvr5EjR6pFixYmVwTkvaCgII0ZM0aS8vxLkQAcW3x8vCTpscce09ChQ222AUBhke+XL3v55ZfVoUMHffTRR0pMTNRLL72k4sWLW/evWrVKbdu2NbFCAAAAAIXF33//LUkqXbq0yZUA9uXm5iaJUAZAzqQEMG5ubtZxJTk5WUlJSXJ2djazNACwm3wfyjRs2FCHDx/Wrl27FBAQoObNm9vs79u3r2rXrm1SdQAAAAAKk5RQxt/f3+RKAPtyd3eXRCgDIGduDWVSxhXp5tji5eVlVlkAYFf5PpSRpFKlSqlnz55p7rv33nuzdKyrV6/q+PHj1vsnT57UgQMHVKJECVWoUCFHdQIAAABwbCnLlzFTBoUNoQyA3JASyri7u1tnyqRsJ5QBUFgUiFAmN+3du1ft27e33k9ZF3fAgAFavny5SVUBAAAAKAhYvgyFFaEMgJwyDMM6hri5ucnV1dW6j+vKAChMCl0o065dOxmGYXYZAAAAAAqY69ev6+rVq5JYvgyFD6EMgJxKSkqyfibn5uYmi8UiV1dXJSQkEMoAKFSczC4AAAAAAAqClKXL3N3dVbRoUZOrAeyLUAZATt0avKQsXcbYAqAwcohQ5saNG2aXAAAAAMDBpYQy/v7+slgsJlcD2BcfnALIqbRCmZT/MlMGQGFSYEKZESNGpLn92rVr6tq1q52rAQAAAFDYcD0ZFGaEMgBy6tbgJeV6MoQyAAqjAhPKfPfdd5o0aZLNtmvXrqlLly5KSkoyqSoAAAAAhUVKKMP1ZFAYEcoAyKmU8SPlejIpP0uEMgAKFxezC8is7777TnfddZdKliyp0aNHKyYmRp07d5aLi4u++eYbs8sDAAAA4OBSli9jpgwKo5RQhg9OAWRXyviREsRIBL4ACqcCE8oEBQXp22+/Vbt27eTk5KRVq1bJ3d1dGzdulLe3t9nlAQAAAHBwLF+GwowPTgHkVFqhDDNlABRGBSaUkaS6detqw4YN6tChg5o3b64NGzbI09PT7LIAAAAAFAIpM2VYvgyFEaEMgJwilAGAm/J1KNOoUSPrGpO3cnd31/nz59WqVSvrtl9//dWepQEAAAAoZJgpg8KMUAZATqUELynjiUQoA6BwytehTK9evcwuAQAAAAAkMVMGhRuhDICcShk/uKYMgMIuX4cyU6dONbsEAAAAAJDETBkUbnxwCiCnWL4MAG5yMruAzNqzZ492796davvu3bu1d+9eEyoCAAAAUFgkJibq4sWLkpgpg8IpJZRJSkpSUlKSydUAKIgIZQDgpgITyowYMUJnzpxJtf3cuXMaMWKECRUBAAAAKCwuXLggwzBksVjk5+dndjmA3d16DQhmywDIDkIZALipwIQyoaGhaty4cartjRo1UmhoqAkVAQAAACgsUpYu8/Pzk7Ozs8nVAPZHKAMgp1KCl1vHE5ZGBFAYFZhQxt3d3fqH0K3Cw8Pl4pKvL40DAAAAoICLjIyUxPVkUHi5uLjIYrFI4sNTANmTMnYwUwZAYVdgQpmOHTtqwoQJioqKsm67cuWKXnrpJXXs2NHEygAAAAA4upQviBHKoLCyWCx8ox1AjrB8GQDcVGCmmMybN09t2rRRxYoV1ahRI0nSgQMHVLp0aa1cudLk6gAAAAA4spSZMv7+/iZXApjH3d1dsbGxhDIAsoVQBgBuKjChTLly5XTw4EF9/PHH+u233+Tp6alBgwbpkUcekaurq9nlAQAAAHBgzJQBuPYDgJxJK5RhXAFQGBWYUEaSvL29NXToULPLAAAAAFDIMFMG4MNTADmTEsqkjCUSM2UAFE75OpRZv369unbtKldXV61fvz7Dtj169LBTVQAAAAAKG2bKAIQyAHImZexg+TIAhV2+DmV69eqliIgI+fv7q1evXum2s1gsSkpKsl9hAAAAAAqVlFCGmTIozFI+PCWUAZAdXFMGAG7K16FMcnJymj8DAAAAgD2lLF/GTBkUZsyUAZATXFMGAG5yMruAnDpz5owGDx5sdhkAAAAAHJRhGIQygPjwFEDOMFMGAG4q8KHMpUuXtGLFCrPLAAAAAOCgoqKirB8WsXwZCjNCGQA5kfJvacpYIhHKACicCnwoAwAAAAB5KeV6MkWLFpWHh4fJ1QDmIZQBkBMpYwczZQAUdoQyAAAAAJCBlKXLmCWDwo5QBkBOcE0ZALiJUAYAAAAAMpAyU4bryaCw48NTADnBNWUA4CYXswu4nQceeCDD/VeuXLFPIQAAAAAKJWbKADcRygDICUIZALgp34cyvr6+t93/+OOP26kaAAAAAIUNM2WAmwhlAORERsuXEcoAKEzyfSgTHBxsdgkAAAAACrGUUIaZMijsCGUA5ETK2JEylkj/BjSMKwAKE64pAwAAAAAZSFm+jJkyKOz4RjuAnGD5MgC4iVAGAAAAADLA8mXATcyUAZAThDIAcBOhDAAAAABkIGWmDMuXobAjlAGQE1xTBgBuIpQBAAAAgAwwUwa4iVAGQE5kNFOGcQVAYUIoAwAAAADpuHHjhmJiYiQxUwYglAGQEymhTMpYIrF8GYDCiVAGAAAAANKRsnSZm5ubfH19Ta4GMBehDICcSBk7uKYMgMKOUAYAAAAA0pGydJm/v78sFovJ1QDmIpQBkBMZXVMmLi5OhmGYUhcA2BuhDAAAAACkI2WmDNeTAQhlAORMRteUMQxDSUlJptQFAPZGKAMAAAAA6UiZKUMoAxDKAMiZjEKZW/cDgKMjlAEAAACAdKTMlPH39ze5EsB8hDIAciIldEkZSyRCGQCFU6ENZd555x0FBQXJw8NDTZo00Y8//mh2SQAAAADyGWbKAP8ilAGQEyljx61BjKura6r9AODoCmUos3r1ao0aNUoTJ07U/v371bp1a3Xt2lVhYWFmlwYAAAAgH2GmDPAvQhkA2ZWUlGS9ZsytoYzFYrHeZ6YMgMKiUIYy8+fP15AhQ/TEE0+oVq1aevPNNxUYGKjFixebXRoAAACAfISZMsC/CGUAZFdCQoL151tDmVvvE8oAKCxczC7A3uLj47Vv3z69+OKLNts7deqkXbt2pfmYuLg4mzed0dHReVpjQVeiRAmzSwAAAAByRVRUlCRmygDSv6HMiRMn+LsPQJYYhmH9Ob1QpnHjxnJ2drZrXQAyLzQ0VAEBAWaX4RAKXShz4cIFJSUlpfqmW+nSpRUREZHmY2bNmqXp06fbozyHcPnyZbNLAAAAAHKNt7e36tevb3YZgOmqVKkiX19fRUVF8XcfgGypVauWNeBN0aRJE23atIkvQQP53K3hKnKm0IUyKSwWi819wzBSbUsxYcIEjRkzxno/OjpagYGBeVpfQXb48GGzSwAAAAByTZkyZeTr62t2GYDpihcvrrCwMJ0/f97sUgAUUEFBQak+f/v666914sQJPvAF8rlSpUqZXYLDKHShjJ+fn5ydnVPNiomMjEx3nWh3d/dUKT7SV7NmTbNLAAAAAADkgaJFi6po0aJmlwHAgbi4uKhGjRpmlwEAduNkdgH25ubmZp0WeatNmzapZcuWJlUFAAAAAAAAAAAcXaGbKSNJY8aMUf/+/dW0aVO1aNFC77//vsLCwjRs2DCzSwMAAAAAAAAAAA6qUIYyffr00cWLFzVjxgyFh4erbt26+vrrr1WxYkWzSwMAAAAAAAAAAA7KYnAVrSyLjo6Wr6+voqKiWEsXAAAAAAAAAIBCLrO5QaGcKZNTKTlWdHS0yZUAAAAAAAAAAACzpeQFt5sHQyiTDTExMZKkwMBAkysBAAAAAAAAAAD5RUxMjHx9fdPdz/Jl2ZCcnKzz58/Lx8dHFovF7HLyjejoaAUGBurMmTMs6waHQb+Go6FPwxHRr+GI6NdwNPRpOCL6NRwR/RqOxp592jAMxcTEqGzZsnJyckq3HTNlssHJyUnly5c3u4x8q2jRogzacDj0azga+jQcEf0ajoh+DUdDn4Yjol/DEdGv4Wjs1aczmiGTIv24BgAAAAAAAAAAALmGUAYAAAAAAAAAAMAOCGWQa9zd3TV16lS5u7ubXQqQa+jXcDT0aTgi+jUcEf0ajoY+DUdEv4Yjol/D0eTHPm0xDMMwuwgAAAAAAAAAAABHx0wZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhlk2uXLl9W/f3/5+vrK19dX/fv315UrVzJ8zNq1a9W5c2f5+fnJYrHowIEDqdrExcXp2WeflZ+fn7y9vdWjRw+dPXs2b54E8B/Z6deGYWjatGkqW7asPD091a5dO/3xxx82bdq1ayeLxWJz69u3bx4+ExRm77zzjoKCguTh4aEmTZroxx9/zLD9tm3b1KRJE3l4eKhy5cp69913U7VZs2aNateuLXd3d9WuXVvr1q3Lq/KBVHK7Ty9fvjzVmGyxWBQbG5uXTwOwkZV+HR4erkcffVQ1atSQk5OTRo0alWY7xmqYLbf7NeM1zJaVPr127Vp17NhRpUqVUtGiRdWiRQt9++23qdoxVsNsud2vGauRH2SlX+/YsUOtWrVSyZIl5enpqZo1a+qNN95I1c6e4zWhDDLt0Ucf1YEDBxQSEqKQkBAdOHBA/fv3z/Ax165dU6tWrTR79ux024waNUrr1q3TqlWrtGPHDl29elX33XefkpKScvspAKlkp1/PmTNH8+fP19tvv609e/YoICBAHTt2VExMjE27J598UuHh4dbbe++9l5dPBYXU6tWrNWrUKE2cOFH79+9X69at1bVrV4WFhaXZ/uTJk+rWrZtat26t/fv366WXXtLIkSO1Zs0aa5uffvpJffr0Uf/+/fXbb7+pf//+evjhh7V79257PS0UYnnRpyWpaNGiNmNyeHi4PDw87PGUgCz367i4OJUqVUoTJ05UgwYN0mzDWA2z5UW/lhivYZ6s9unt27erY8eO+vrrr7Vv3z61b99e3bt31/79+61tGKthtrzo1xJjNcyV1X7t7e2tZ555Rtu3b9fhw4c1adIkTZo0Se+//761jd3HawPIhNDQUEOS8fPPP1u3/fTTT4Yk488//7zt40+ePGlIMvbv32+z/cqVK4arq6uxatUq67Zz584ZTk5ORkhISK7VD6QlO/06OTnZCAgIMGbPnm3dFhsba/j6+hrvvvuudVvbtm2N5557Ls9qB1LccccdxrBhw2y21axZ03jxxRfTbP/CCy8YNWvWtNn21FNPGXfeeaf1/sMPP2x06dLFpk3nzp2Nvn375lLVQPryok8HBwcbvr6+uV4rkFlZ7de3Su89BWM1zJYX/ZrxGmbKSZ9OUbt2bWP69OnW+4zVMFte9GvGapgtN/r1/fffbzz22GPW+/Yer5kpg0z56aef5Ovrq+bNm1u33XnnnfL19dWuXbuyfdx9+/YpISFBnTp1sm4rW7as6tatm6PjApmRnX598uRJRURE2PRZd3d3tW3bNtVjPv74Y/n5+alOnTp6/vnnU82kAXIqPj5e+/bts+mPktSpU6d0+/BPP/2Uqn3nzp21d+9eJSQkZNiGcRl5La/6tCRdvXpVFStWVPny5XXfffel+rYfkFey068zg7EaZsqrfi0xXsMcudGnk5OTFRMToxIlSli3MVbDTHnVryXGapgnN/r1/v37tWvXLrVt29a6zd7jNaEMMiUiIkL+/v6ptvv7+ysiIiJHx3Vzc1Px4sVttpcuXTpHxwUyIzv9OmV76dKlbbb/t8/269dPn376qbZu3arJkydrzZo1euCBB3KxekC6cOGCkpKSbtsfbxUREZFm+8TERF24cCHDNozLyGt51adr1qyp5cuXa/369fr000/l4eGhVq1a6dixY3nzRIBbZKdfZwZjNcyUV/2a8RpmyY0+PW/ePF27dk0PP/ywdRtjNcyUV/2asRpmykm/Ll++vNzd3dW0aVONGDFCTzzxhHWfvcdrlzw5KgqMadOmafr06Rm22bNnjyTJYrGk2mcYRprbcyqvjovCwR79+r/7//uYJ5980vpz3bp1Va1aNTVt2lS//vqrGjdufNvnAGTF7fpjZtr/d3tWjwnkptzu03feeafuvPNO6/5WrVqpcePGeuutt7Rw4cLcKhvIUF6Mq4zVMFtu90HGa5gtu336008/1bRp0/Tll1+m+uIfYzXMltv9mrEa+UF2+vWPP/6oq1ev6ueff9aLL76oqlWr6pFHHsnRMbOLUKaQe+aZZ9S3b98M21SqVEkHDx7U33//nWrfP//8kypFzIqAgADFx8fr8uXLNrNlIiMj1bJly2wfF4VbXvbrgIAASTcT9DJlyli3R0ZGZvi70LhxY7m6uurYsWOEMsg1fn5+cnZ2TvXNjYz6Y0BAQJrtXVxcVLJkyQzb5GS8BzIjr/r0fzk5OalZs2Z8mw92kZ1+nRmM1TBTXvXr/2K8hr3kpE+vXr1aQ4YM0eeff64OHTrY7GOshpnyql//F2M17Ckn/TooKEiSVK9ePf3999+aNm2aNZSx93jN8mWFnJ+fn2rWrJnhzcPDQy1atFBUVJR++eUX62N3796tqKioHIUnTZo0kaurqzZt2mTdFh4ert9//51QBtmWl/06KChIAQEBNn02Pj5e27Zty7DP/vHHH0pISLAJcoCccnNzU5MmTWz6oyRt2rQp3f7YokWLVO2/++47NW3aVK6urhm2YVxGXsurPv1fhmHowIEDjMmwi+z068xgrIaZ8qpf/xfjNewlu336008/1cCBA/XJJ5/o3nvvTbWfsRpmyqt+/V+M1bCn3HoPYhiG4uLirPftPl4bQCZ16dLFqF+/vvHTTz8ZP/30k1GvXj3jvvvus2lTo0YNY+3atdb7Fy9eNPbv329s3LjRkGSsWrXK2L9/vxEeHm5tM2zYMKN8+fLG999/b/z666/G3XffbTRo0MBITEy023ND4ZWdfj179mzD19fXWLt2rXHo0CHjkUceMcqUKWNER0cbhmEYx48fN6ZPn27s2bPHOHnypLFx40ajZs2aRqNGjejXyHWrVq0yXF1djaVLlxqhoaHGqFGjDG9vb+PUqVOGYRjGiy++aPTv39/a/q+//jK8vLyM0aNHG6GhocbSpUsNV1dX44svvrC22blzp+Hs7GzMnj3bOHz4sDF79mzDxcXF+Pnnn+3+/FD45EWfnjZtmhESEmKcOHHC2L9/vzFo0CDDxcXF2L17t92fHwqnrPZrwzCM/fv3G/v37zeaNGliPProo8b+/fuNP/74w7qfsRpmy4t+zXgNM2W1T3/yySeGi4uLsWjRIiM8PNx6u3LlirUNYzXMlhf9mrEaZstqv3777beN9evXG0ePHjWOHj1qLFu2zChatKgxceJEaxt7j9eEMsi0ixcvGv369TN8fHwMHx8fo1+/fsbly5dt2kgygoODrfeDg4MNSaluU6dOtba5ceOG8cwzzxglSpQwPD09jfvuu88ICwuzz5NCoZedfp2cnGxMnTrVCAgIMNzd3Y02bdoYhw4dsu4PCwsz2rRpY5QoUcJwc3MzqlSpYowcOdK4ePGinZ4VCptFixYZFStWNNzc3IzGjRsb27Zts+4bMGCA0bZtW5v2W7duNRo1amS4ubkZlSpVMhYvXpzqmJ9//rlRo0YNw9XV1ahZs6axZs2avH4agFVu9+lRo0YZFSpUMNzc3IxSpUoZnTp1Mnbt2mWPpwJYZbVfp/UeumLFijZtGKthttzu14zXMFtW+nTbtm3T7NMDBgywOSZjNcyW2/2asRr5QVb69cKFC406deoYXl5eRtGiRY1GjRoZ77zzjpGUlGRzTHuO1xbD+P8roQIAAAAAAAAAACDPcE0ZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAA2pk2bpoYNG5pdRpryc20ZsVgs+t///pfp9gX1eaa4ePGi/P39derUqdu2jYyMVKlSpXTu3Lm8LwwAAAAwGaEMAAAAUIhYLJYMbwMHDtTzzz+vzZs3m1LfmjVr1Lx5c/n6+srHx0d16tTR2LFjrfvNrE2SBg4ceNvXMC3h4eHq2rVrrtZyu9cqt4OdgQMHqlevXplqO2vWLHXv3l2VKlW6bVt/f3/1799fU6dOzVmBAAAAQAHgYnYBAAAAAOwnPDzc+vPq1as1ZcoUHTlyxLrN09NTRYoUUZEiRexe2/fff6++fftq5syZ6tGjhywWi0JDQ21CGLNqS7FgwQLNnj3ber9MmTIKDg5Wly5d0mwfHx8vNzc3BQQE5GodmXmtcktSUlK6YVNabty4oaVLl+rrr7/O9GMGDRqkO+64Q3PnzlXx4sWzUyYAAABQIDBTBgAAAChEAgICrDdfX19ZLJZU2/47wyJlhsTMmTNVunRpFStWTNOnT1diYqLGjRunEiVKqHz58lq2bJnNuc6dO6c+ffqoePHiKlmypHr27JnhclYbNmzQXXfdpXHjxqlGjRqqXr26evXqpbfeesvaJr3aXn/9dZUpU0YlS5bUiBEjlJCQYG0TFxenF154QYGBgXJ3d1e1atW0dOlS6/7Q0FB169ZNRYoUUenSpdW/f39duHAhzRp9fX1tXi9JKlasmPV+37599cwzz2jMmDHy8/NTx44dJaVevmz8+PGqXr26vLy8VLlyZU2ePNmm5tu53Wu1fPlyTZ8+Xb/99pt1Bs/y5cslSfPnz1e9evXk7e2twMBAPf3007p69ar12MuXL1exYsW0YcMG1a5dW+7u7ho0aJBWrFihL7/80nq8rVu3plnbN998IxcXF7Vo0cK67fLly+rXr59KlSolT09PVatWTcHBwdb99erVU0BAgNatW5fp1wAAAAAoiAhlAAAAANzWDz/8oPPnz2v79u2aP3++pk2bpvvuu0/FixfX7t27NWzYMA0bNkxnzpyRJF2/fl3t27dXkSJFtH37du3YsUNFihRRly5dFB8fn+Y5AgIC9Mcff+j333/PUm1btmzRiRMntGXLFq1YsULLly+3BhCS9Pjjj2vVqlVauHChDh8+rHfffdc62yY8PFxt27ZVw4YNtXfvXoWEhOjvv//Www8/nL0XStKKFSvk4uKinTt36r333kuzjY+Pj5YvX67Q0FAtWLBAS5Ys0RtvvJHpc9zuterTp4/Gjh2rOnXqKDw8XOHh4erTp48kycnJSQsXLtTvv/+uFStW6IcfftALL7xg8/jr169r1qxZ+uCDD/THH39o4cKFevjhh9WlSxfr8Vq2bJnmubdv366mTZvabJs8ebJCQ0P1zTff6PDhw1q8eLH8/Pxs2txxxx368ccfM/0aAAAAAAURy5cBAAAAuK0SJUpo4cKFcnJyUo0aNTRnzhxdv35dL730kiRpwoQJmj17tnbu3Km+fftq1apVcnJy0gcffGBd+io4OFjFihXT1q1b1alTp1TnePbZZ/Xjjz+qXr16qlixou6880516tRJ/fr1k7u7e7q1FS9eXG+//bacnZ1Vs2ZN3Xvvvdq8ebOefPJJHT16VJ999pk2bdqkDh06SJIqV65sfezixYvVuHFjzZw507pt2bJlCgwM1NGjR1W9evUsv1ZVq1bVnDlzMmwzadIk68+VKlXS2LFjtXr16lThSHpu91qlLEPn4uKSaum0UaNGWX8OCgrSyy+/rOHDh+udd96xbk9ISNA777yjBg0aWLd5enoqLi7utkuxnTp1SmXLlrXZFhYWpkaNGlnDmrSuNVOuXDnt378/U88fAAAAKKiYKQMAAADgturUqSMnp3//fChdurTq1atnve/s7KySJUsqMjJSkrRv3z4dP35cPj4+1uvAlChRQrGxsTpx4kSa5/D29tbGjRt1/PhxTZo0SUWKFNHYsWN1xx136Pr16xnW5uzsbL1fpkwZax0HDhyQs7Oz2rZtm+Zj9+3bpy1btlhrLFKkiGrWrClJ6dZ5O/+dJZKWL774QnfddZcCAgJUpEgRTZ48WWFhYZk+R3ZfK+nmzKKOHTuqXLly8vHx0eOPP66LFy/q2rVr1jZubm6qX79+puu51Y0bN+Th4WGz7f/au3eQttcwjuM/aRWi0C6mQ6H6BwsqREmruHmjiEYoQnWoLagEHayCCiqIKG4iaAeL2MFNS2NBWkE6OHkhUoo3RAVvCREtImI3pajxTCec1FvikRTL9wP/Ie/14d3Cw/O+lZWVcjgcslqtamxs1NTU1Jl5JpPpytgBAACA246kDAAAAIArhYeH+/0OCws7t83r9UqSvF6vUlJSND8/7/etrq7q1atXl+4VFxen8vJy9fX1aXZ2VsvLyxocHAwqtn/jMJlMl+7l9Xr1/PnzM3Gura0pIyPj0rkXiYqKurT/27dvevnypWw2m0ZGRjQ3N6fm5uYLr3W7TLBn5fF4lJ+fL4vFoqGhIc3MzKinp0eS/N60MZlMvgqnYEVHR+vnz59+bTabTR6PR7W1tfrx44eePXum+vp6vzH7+/sym83X2hMAAAC4Lbi+DAAAAMCNe/r0qQYHB/XgwQPdu3fv2usYhqHIyEi/Ko5gJCUlyev1anx83Hd92e9xDg0NyTAM3b0bmr9HTqdTsbGxam5u9rV5PJ7/ve7vZxUREaGTkxO/MdPT0zo+PlZXV5ev8unTp08BrX/eeud58uSJBgYGzrSbzWaVlZWprKxM6enpamhoUGdnp69/cXFRWVlZAcUCAAAA3FZUygAAAAC4ca9fv1Z0dLQKCgo0OTkpt9ut8fFx1dTUaGtr69w5bW1tamxs1NjYmNxut+bm5mS323V0dKScnJxrxWEYhkpLS2W32/Xlyxe53W6NjY35EhFVVVXa399XcXGxvn//LpfLpdHRUdnt9oASENfx+PFjbW5uyuFwaGNjQ93d3fr8+XNQawRyVoZhyO12a35+Xnt7e/r165fi4uJ0fHysd+/eyeVyqb+/X+/fvw9oT8MwtLCwoJWVFe3t7flV1vxXbm6ulpaW/KplWltbNTw8rPX1dS0tLWlkZESJiYm+/oODA83MzJz71hAAAADwNyEpAwAAAODGRUZGamJiQjExMXrx4oUSExNlt9t1eHh4YeVMZmamXC6XSkpKlJCQIJvNpp2dHY2Ojio+Pv7asfT29qqoqEhv3rxRQkKCKioqfNUkDx8+lNPp1MnJiXJzc2WxWFRTU6P79+/7vaFzkwoKClRXV6fq6mpZrVZNTU2ppaUlqDUCOavCwkLl5eUpOztbZrNZHz9+lNVq1du3b9XR0SGLxaIPHz6ovb09oD0rKioUHx+v1NRUmc1mOZ3Oc8clJSUpNTXVrwInIiJCTU1NSk5OVkZGhu7cuSOHw+HrHx4eVkxMjNLT04M6BwAAAOC2CTs9PT3900EAAAAAAP4eX79+VX19vRYXFwNKbqWlpam2tvbK94YAAACA2443ZQAAAAAANyo/P19ra2va3t7Wo0ePLh27u7uroqIiFRcXhyg6AAAA4M+hUgYAAAAAAAAAACAEeFMGAAAAAAAAAAAgBEjKAAAAAAAAAAAAhABJGQAAAAAAAAAAgBAgKQMAAAAAAAAAABACJGUAAAAAAAAAAABCgKQMAAAAAAAAAABACJCUAQAAAAAAAAAACAGSMgAAAAAAAAAAACFAUgYAAAAAAAAAACAE/gExuBldQhYfZQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "example_trial = example_unrewarded_trial\n",
     "\n",
@@ -755,6 +780,13 @@
     "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
     "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -773,7 +805,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

From 72fd548596b8ec842324ea1977c5186f9c09103d Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Thu, 16 Jan 2025 11:51:57 -0800
Subject: [PATCH 5/6] updated opto notebook for streaming

---
 001259/opto_example_notebook.ipynb | 199 +++++++++++++++++------------
 1 file changed, 119 insertions(+), 80 deletions(-)

diff --git a/001259/opto_example_notebook.ipynb b/001259/opto_example_notebook.ipynb
index c5b2c43..7197804 100644
--- a/001259/opto_example_notebook.ipynb
+++ b/001259/opto_example_notebook.ipynb
@@ -1,16 +1,30 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Opto Example Session"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pynwb import NWBHDF5IO\n",
+    "from stream_nwbfile import stream_nwbfile\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook showcases one example session from the 001259 dataset containing operant behavior and concurrent optogenetic stimulation."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -55,7 +69,7 @@
        "                });\n",
        "            });\n",
        "        </script>\n",
-       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">b6a37a08-f2ab-45af-b49b-225aa17c8012</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-18 09:07:21.778849-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".stimulus\"><b>stimulus</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series']\"><b>optogenetic_series</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].description\">description: </span><span class=\"field-value\">In optogenetic perturbation trials (~33% of trials), during each lever press, continuous wave stimulation of 473nm light (15-20mW) was delivered bilaterally over primary auditory cortex A1 (or secondary motor cortex, M2, as necessary using similar protocol - see Aim 2) to activate the terminals of ChR2 expressing neurons.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].unit\">unit: </span><span class=\"field-value\">watts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site\"><b>site</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site.device\"><b>device</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".epoch_tags\"><b>epoch_tags</b></summary><span class=\"field-key\">set()</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['optogenetic_stimulation_laser']\"><b>optogenetic_stimulation_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".ogen_sites\"><b>ogen_sites</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site']\"><b>optogenetic_stimulus_site</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
+       "        <div class='container-wrap'><div class='container-header'><div class='xr-obj-type'><h3>root (NWBFile)</h3></div></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_description\">session_description: </span><span class=\"field-value\">Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".identifier\">identifier: </span><span class=\"field-value\">301b47e9-c389-4021-86cf-5dd9856a6922</span></div><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".session_start_time\"><b>session_start_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".timestamps_reference_time\"><b>timestamps_reference_time</b></summary><span class=\"field-key\">2023-10-13 00:00:00-04:00</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".file_create_date\"><b>file_create_date</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".file_create_date[0]\"><b>0</b></summary><span class=\"field-key\">2024-12-19 10:42:42.007793-08:00</span></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".experimenter\"><b>experimenter</b></summary><span class=\"field-key\">('Zempolich, Grant W.', 'Schneider, David M.')</span></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".acquisition\"><b>acquisition</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1']\"><b>video_camera_1</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 1 is a side angle view of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">uint8</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(0, 0, 0)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">0.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">undefined</td></tr></tbody></table><br>[]</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(17994,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">140.58 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(17994,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.4520072624571314</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].external_file\"><b>external_file</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'sub-m53_ses-231013_behavior+image+ogen/f993b247-51df-483e-939b-2c249f3a3bcd_external_file_0.mp4']</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_1'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">NumPy array<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">int64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr></tbody></table><br>[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_1'].format\">format: </span><span class=\"field-value\">external</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2']\"><b>video_camera_2</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].description\">description: </span><span class=\"field-value\">Two IR video cameras (AAK CA20 600TVL 2.8MM) were used to monitor the experiments from different angles of interest, allowing for offline analysis of body movements, pupillometry, and other behavioral data as necessary. Camera 2 is a zoomed-in view of the pupil of the mouse.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].unit\">unit: </span><span class=\"field-value\">Frames</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">uint8</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(0, 0, 0)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">0.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">undefined</td></tr></tbody></table><br>[]</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(17993,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">140.57 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(17993,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.4513702635665167</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].external_file\"><b>external_file</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'sub-m53_ses-231013_behavior+image+ogen/761f1e57-c5e6-4669-8e28-bfe4f545da14_external_file_0.mp4']</div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".acquisition['video_camera_2'].starting_frame\"><b>starting_frame</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">NumPy array<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">int64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">8.00 bytes</td></tr></tbody></table><br>[0]</div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".acquisition['video_camera_2'].format\">format: </span><span class=\"field-value\">external</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".stimulus\"><b>stimulus</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series']\"><b>optogenetic_series</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].description\">description: </span><span class=\"field-value\">In optogenetic perturbation trials (~33% of trials), during each lever press, continuous wave stimulation of 473nm light (15-20mW) was delivered bilaterally over primary auditory cortex A1 (or secondary motor cortex, M2, as necessary using similar protocol - see Aim 2) to activate the terminals of ChR2 expressing neurons.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].unit\">unit: </span><span class=\"field-value\">watts</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].data\"><b>data</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(274,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">2.14 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(274,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">59.24324324324324</td></tr></tbody></table></div></details><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(274,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">2.14 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(274,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.3008902077151336</td></tr></tbody></table></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].interval\">interval: </span><span class=\"field-value\">1</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site\"><b>site</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".stimulus['optogenetic_series'].site.device\"><b>device</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".stimulus['optogenetic_series'].site.location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".keywords\"><b>keywords</b></summary><div style=\"margin-left: 20px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">object</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(3,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">24.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">None</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">0.5</td></tr></tbody></table><br>[b'auditory cortex' b'predictive coding' b'optogenetics']</div></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".processing\"><b>processing</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['behavior']\"><b>behavior</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].description\">description: </span><span class=\"field-value\">C57BL/6 mice were first be water restricted, habituated to head fixation in the behavioral set up for two days and classically conditioned to associate a 16 kHz tone with a small water reward given 150 ms after the tone plays (~12 seconds inter-tone-interval). Mice were then be trained for 15 to 20 sessions on an auditory guided task described as follows. Inspired by human performance on stringed instruments, whereby a target note is achieved via modulation of forelimb and hand movements, we have engineered a novel behavioral paradigm that requires mice to skillfully adjust the size of lever presses in response to a dynamic virtual acoustic environment. Mice were trained to press a small 3-D printed lever forward with their forelimb toward a 2 mm wide target zone. Mice heard a 16 Hz tone when the lever enters the zone and a 10 kHz tone if the press exceeds the bounds of the zone. Presses that peak within the zone produce only the entry tone and are rewarded when the lever returns to the starting position. Presses that undershoot (producing no tones) or overshoot (producing both an entry and an exit tone) are unrewarded. Every 30 trials, which we refer to as a block, the target zone was relocated without warning and the mice must use acoustic feedback to adjust their lever presses to peak at the new location.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series']\"><b>behavioral_time_series</b></summary><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series\"><b>time_series</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder']\"><b>encoder</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].description\">description: </span><span class=\"field-value\">Sampled values for entire duration of experiment for lever pressing behavior read from a rotary encoder (US Digital). Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].data\"><b>data</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1440982,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.99 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">118.07940344982997</td></tr></tbody></table></div></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1440982,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.99 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.7049238128535824</td></tr></tbody></table></div></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['encoder'].interval\">interval: </span><span class=\"field-value\">1</span></div></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick']\"><b>lick</b></summary><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].resolution\">resolution: </span><span class=\"field-value\">-1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].comments\">comments: </span><span class=\"field-value\">no comments</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].description\">description: </span><span class=\"field-value\">Samples values for entire duration of experiment for voltage signal readout from a custom infrared/capacitive lickometer sensor (Schneider Lab).  Digital signals for licking and lever movement were collected by a data acquisition card (National Instruments) connected to a computer and logged by custom Matlab software (Mathworks, PsychToolBox) and sampled at 2kHz.</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].conversion\">conversion: </span><span class=\"field-value\">1.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].offset\">offset: </span><span class=\"field-value\">0.0</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].unit\">unit: </span><span class=\"field-value\">a.u.</span></div><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].data\"><b>data</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1440982,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.99 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">232.93774374103336</td></tr></tbody></table></div></details><details><summary style=\"display: list-item; margin-left: 120px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 140px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(1440982,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">10.99 MiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(1250000,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.7049238128535824</td></tr></tbody></table></div></details><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].timestamps_unit\">timestamps_unit: </span><span class=\"field-value\">seconds</span></div><div style=\"margin-left: 120px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['behavioral_time_series'].time_series['lick'].interval\">interval: </span><span class=\"field-value\">1</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT']\"><b>targetOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].description\">description: </span><span class=\"field-value\">Time at which the target zone is overshot during a press.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(66,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">528.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(66,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.095435684647303</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['targetOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN']\"><b>toneIN</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].description\">description: </span><span class=\"field-value\">Time at which target zone is entered and target entry tone is played.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(253,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">1.98 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(253,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.2673763306199124</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneIN'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT']\"><b>toneOUT</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].description\">description: </span><span class=\"field-value\">Time at which target exit tone is played (this is delayed 50ms relative to targetOUT so that entry and exit tones don't bleed into each other.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(66,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">528.00 bytes</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(66,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.0909090909090908</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['toneOUT'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve']\"><b>valve</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].description\">description: </span><span class=\"field-value\">Times at which solenoid valve opens to deliver water after a correct trial.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps\"><b>timestamps</b></summary><div style=\"margin-left: 100px;\" class=\"container-fields\">HDF5 dataset<br><table class=\"data-info\"><tbody><tr><th style=\"text-align: left\">Data type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(187,)</td></tr><tr><th style=\"text-align: left\">Array size</th><td style=\"text-align: left\">1.46 KiB</td></tr><tr><th style=\"text-align: left\">Chunk shape</th><td style=\"text-align: left\">(187,)</td></tr><tr><th style=\"text-align: left\">Compression</th><td style=\"text-align: left\">gzip</td></tr><tr><th style=\"text-align: left\">Compression opts</th><td style=\"text-align: left\">4</td></tr><tr><th style=\"text-align: left\">Compression ratio</th><td style=\"text-align: left\">1.231275720164609</td></tr></tbody></table></div></details><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['behavior'].data_interfaces['valve'].timestamps__unit\">timestamps__unit: </span><span class=\"field-value\">seconds</span></div></details></details></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging']\"><b>intrinsic_signal_optical_imaging</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].description\">description: </span><span class=\"field-value\">For precise targeting of auditory cortex, intrinsic optical imaging (IOS) was performed using a 2-photon microscope (Neurolabware). The skull was first bilaterally thinned over a region of interest (ROI) and made translucent. On experiment day, 680nm red light (ThorLabs) is used to image the ROI. Data was collected via MATLAB running custom suites for online and offline analyses.</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces\"><b>data_interfaces</b></summary><details><summary style=\"display: list-item; margin-left: 60px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images']\"><b>images</b></summary><div style=\"margin-left: 80px;\" class=\"container-fields\"><span class=\"field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].description\">description: </span><span class=\"field-value\">Intrinsic signal optical images.</span></div><details><summary style=\"display: list-item; margin-left: 80px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images\"><b>images</b></summary><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['overlaid_image']\"><b>overlaid_image</b></summary></details><details><summary style=\"display: list-item; margin-left: 100px;\" class=\"container-fields field-key\" title=\".processing['intrinsic_signal_optical_imaging'].data_interfaces['images'].images['target_image']\"><b>target_image</b></summary></details></details></details></details></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".devices\"><b>devices</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser']\"><b>intrinsic_signal_optical_imaging_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].description\">description: </span><span class=\"field-value\">ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['intrinsic_signal_optical_imaging_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">ThorLabs</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['lickometer']\"><b>lickometer</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].description\">description: </span><span class=\"field-value\">The lickometer comprised a custom-mounted (3D printed using Formlabs Form2) IR-beam emitter and receiver. IR signal was titrated and pre-processed using a custom printed circuit board (designed by Melissa Caras and Dan Sanes) to generate a binary TTL signal with IR sensitivity controlled by a potentiometer.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['lickometer'].manufacturer\">manufacturer: </span><span class=\"field-value\">Schneider Lab</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['optogenetic_stimulation_laser']\"><b>optogenetic_stimulation_laser</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['optogenetic_stimulation_laser'].manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['rotary_encoder']\"><b>rotary_encoder</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].description\">description: </span><span class=\"field-value\">H5 BALL BEARING OPTICAL SHAFT ENCODER</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['rotary_encoder'].manufacturer\">manufacturer: </span><span class=\"field-value\">US Digital</span></div></details><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".devices['two_photon_microscope']\"><b>two_photon_microscope</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].description\">description: </span><span class=\"field-value\">Standard Microscope by Neurolabware.</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".devices['two_photon_microscope'].manufacturer\">manufacturer: </span><span class=\"field-value\">Neurolabware</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".ogen_sites\"><b>ogen_sites</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site']\"><b>optogenetic_stimulus_site</b></summary><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device\"><b>device</b></summary><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.description\">description: </span><span class=\"field-value\">Real time optogenetic stimulation of brain regions of interest was accomplished via TTL control of an all solidstate 473nm blue laser (MBL-III-473/1~100mW, Opto Engine LLC). Bifurcated fiber cables (ThorLabs, Ø200 µm Core Multimode Fiber) were used for light delivery.</span></div><div style=\"margin-left: 60px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].device.manufacturer\">manufacturer: </span><span class=\"field-value\">Opto Engine LLC</span></div></details><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].description\">description: </span><span class=\"field-value\">To identify cortical neurons that project from the auditory cortex to motor regions (Aim 2), stereotaxic injections of AAV-ChR2 were made into the primary auditory cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging) during head-fixation and animals were trained while expression occurs (~2 weeks). In addition, fiber optics were implanted to target cell bodies in the secondary motor cortex (1.0-1.5 AP, 0.5-0.7 ML).</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].excitation_lambda\">excitation_lambda: </span><span class=\"field-value\">473.0</span></div><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".ogen_sites['optogenetic_stimulus_site'].location\">location: </span><span class=\"field-value\">Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)</span></div></details></details><details><summary style=\"display: list-item; margin-left: 0px;\" class=\"container-fields field-key\" title=\".intervals\"><b>intervals</b></summary><details><summary style=\"display: list-item; margin-left: 20px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>epochs</b></summary><div style=\"margin-left: 40px;\" class=\"container-fields\"><span class=\"field-key\" title=\".intervals['epochs']\">description: </span><span class=\"field-value\">experimental epochs</span></div><details><summary style=\"display: list-item; margin-left: 40px;\" class=\"container-fields field-key\" title=\".intervals['epochs']\"><b>table</b></summary><table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
@@ -126,7 +140,7 @@
        "      <td>126.976659</td>\n",
        "      <td>127.150010</td>\n",
        "      <td>True</td>\n",
-       "      <td>142823.197278</td>\n",
+       "      <td>127.155627</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -152,7 +166,7 @@
        "      <td>128.784895</td>\n",
        "      <td>129.004811</td>\n",
        "      <td>True</td>\n",
-       "      <td>142825.050398</td>\n",
+       "      <td>129.008747</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -232,7 +246,7 @@
        "      <td>126.976659</td>\n",
        "      <td>127.150010</td>\n",
        "      <td>True</td>\n",
-       "      <td>142823.197278</td>\n",
+       "      <td>127.155627</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -258,7 +272,7 @@
        "      <td>128.784895</td>\n",
        "      <td>129.004811</td>\n",
        "      <td>True</td>\n",
-       "      <td>142825.050398</td>\n",
+       "      <td>129.008747</td>\n",
        "      <td>False</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -270,7 +284,7 @@
        "</table><p>... and 433 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231013</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
       ],
       "text/plain": [
-       "root pynwb.file.NWBFile at 0x4858229584\n",
+       "root pynwb.file.NWBFile at 0x5011575040\n",
        "Fields:\n",
        "  acquisition: {\n",
        "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
@@ -286,8 +300,8 @@
        "  epochs: epochs <class 'pynwb.epoch.TimeIntervals'>\n",
        "  experiment_description: Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.\n",
        "  experimenter: ['Zempolich, Grant W.' 'Schneider, David M.']\n",
-       "  file_create_date: [datetime.datetime(2024, 12, 18, 9, 7, 21, 778849, tzinfo=tzoffset(None, -28800))]\n",
-       "  identifier: b6a37a08-f2ab-45af-b49b-225aa17c8012\n",
+       "  file_create_date: [datetime.datetime(2024, 12, 19, 10, 42, 42, 7793, tzinfo=tzoffset(None, -28800))]\n",
+       "  identifier: 301b47e9-c389-4021-86cf-5dd9856a6922\n",
        "  institution: New York University\n",
        "  intervals: {\n",
        "    epochs <class 'pynwb.epoch.TimeIntervals'>,\n",
@@ -310,7 +324,7 @@
        "  stimulus: {\n",
        "    optogenetic_series <class 'pynwb.ogen.OptogeneticSeries'>\n",
        "  }\n",
-       "  subject: subject pynwb.file.Subject at 0x5028608960\n",
+       "  subject: subject pynwb.file.Subject at 0x5011396256\n",
        "Fields:\n",
        "  age: P12W/\n",
        "  age__reference: birth\n",
@@ -330,9 +344,9 @@
     }
    ],
    "source": [
-    "nwbfile_path = '/Volumes/T7/CatalystNeuro/Schneider/conversion_nwb/sub-m53_ses-231013.nwb'\n",
-    "io = NWBHDF5IO(nwbfile_path, 'r')\n",
-    "nwbfile = io.read()\n",
+    "DANDISET_ID = '001259'\n",
+    "file_path = 'sub-m53/sub-m53_ses-231013_behavior+image+ogen.nwb'\n",
+    "nwbfile, io = stream_nwbfile(DANDISET_ID, file_path)\n",
     "display(nwbfile)"
    ]
   },
@@ -375,16 +389,23 @@
     "example_unrewarded_trial = trials[trials[\"rewarded\"] == False].iloc[0]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot an example rewarded trial"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 2000x1000 with 4 Axes>"
+       "<Figure size 2000x1000 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -395,8 +416,8 @@
     "example_trial = example_rewarded_trial\n",
     "\n",
     "# Define plot parameters\n",
-    "lineoffsets = 1\n",
-    "linelengths = 2\n",
+    "lineoffsets = 0\n",
+    "linelengths = 2.5\n",
     "ylim = [-2, 2]\n",
     "grace_time = 0.100\n",
     "y = np.arange(-1, 1, 0.1)\n",
@@ -425,7 +446,7 @@
     "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
     "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
     "\n",
-    "fig, axs = plt.subplots(4, 1, figsize=(20, 10), sharex=True)\n",
+    "fig, axs = plt.subplots(3, 1, figsize=(20, 10), sharex=True)\n",
     "axs[0].set_title(\"Example Rewarded Trial\")\n",
     "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
     "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
@@ -442,88 +463,106 @@
     "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha)\n",
     "\n",
     "axs[0].set_ylim(ylim)\n",
-    "axs[0].set_ylabel(\"Channel 1 Raw Voltage (uV)\")\n",
+    "axs[0].set_yticks([])\n",
     "axs[0].legend()\n",
     "\n",
-    "axs[2].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
-    "axs[2].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
-    "axs[2].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
-    "axs[2].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
-    "axs[2].legend()\n",
+    "axs[1].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[1].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[1].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[1].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[1].legend()\n",
     "\n",
-    "axs[3].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
-    "axs[3].set_ylabel(\"Lick Sensor (a.u.)\")\n",
-    "_ = axs[3].set_xlabel(\"Time Since Trial Start (s)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.0063527])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "normalized_opto_onset_times"
+    "axs[2].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[2].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[2].set_xlabel(\"Time Since Trial Start (s)\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.0845106])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "normalized_opto_offset_times"
+    "Plot an example unrewarded trial"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
-       "start_time                    198.942435\n",
-       "stop_time                     200.023562\n",
-       "rewarded                            True\n",
-       "time_reward_s              142896.074106\n",
-       "opto_trial                          True\n",
-       "opto_time                  142894.990439\n",
-       "opto_time_end              142896.068597\n",
-       "ITI_respect                         True\n",
-       "ThresholdVector                     27.0\n",
-       "endZone_ThresholdVector             33.0\n",
-       "Name: 60, dtype: object"
+       "<Figure size 2000x1000 with 3 Axes>"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "example_trial"
+    "example_trial = example_unrewarded_trial\n",
+    "\n",
+    "# Define plot parameters\n",
+    "lineoffsets = 0\n",
+    "linelengths = 2.5\n",
+    "ylim = [-2, 2]\n",
+    "grace_time = 0.100\n",
+    "y = np.arange(-1, 1, 0.1)\n",
+    "alpha = 0.3\n",
+    "\n",
+    "# Define time windows for plotting\n",
+    "plot_start_time = example_trial[\"start_time\"] - grace_time\n",
+    "plot_end_time = example_trial[\"stop_time\"] + grace_time\n",
+    "opto_onset_mask = (opto_onset_times >= plot_start_time) & (opto_onset_times <= plot_end_time)\n",
+    "opto_offset_mask = (opto_offset_times >= plot_start_time) & (opto_offset_times <= plot_end_time)\n",
+    "tone_in_mask = (tone_in >= plot_start_time) & (tone_in <= plot_end_time)\n",
+    "tone_out_mask = (tone_out >= plot_start_time) & (tone_out <= plot_end_time)\n",
+    "target_out_mask = (target_out >= plot_start_time) & (target_out <= plot_end_time)\n",
+    "valve_mask = (valve >= plot_start_time) & (valve <= plot_end_time)\n",
+    "encoder_mask = (encoder_timestamps >= plot_start_time) & (encoder_timestamps <= plot_end_time)\n",
+    "lick_mask = (lick_timestamps >= plot_start_time) & (lick_timestamps <= plot_end_time)\n",
+    "\n",
+    "# Normalize times to trial start time\n",
+    "trial_start_time = example_trial[\"start_time\"]\n",
+    "normalized_opto_onset_times = opto_onset_times[opto_onset_mask] - trial_start_time\n",
+    "normalized_opto_offset_times = opto_offset_times[opto_offset_mask] - trial_start_time\n",
+    "normalized_tone_in = tone_in[tone_in_mask] - trial_start_time\n",
+    "normalized_tone_out = tone_out[tone_out_mask] - trial_start_time\n",
+    "normalized_target_out = target_out[target_out_mask] - trial_start_time\n",
+    "normalized_valve = valve[valve_mask] - trial_start_time\n",
+    "normalized_encoder_timestamps = encoder_timestamps[encoder_mask] - trial_start_time\n",
+    "normalized_lick_timestamps = lick_timestamps[lick_mask] - trial_start_time\n",
+    "\n",
+    "fig, axs = plt.subplots(3, 1, figsize=(20, 10), sharex=True)\n",
+    "axs[0].set_title(\"Example Unewarded Trial\")\n",
+    "axs[0].eventplot(normalized_tone_in, color=\"red\", label=\"Tone In\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_tone_out, color=\"blue\", label=\"Tone Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_target_out, color=\"green\", label=\"Target Out\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].eventplot(normalized_valve, color=\"purple\", label=\"Valve\", lineoffsets=lineoffsets, linelengths=linelengths)\n",
+    "axs[0].axvline(0, color=\"black\", linestyle=\"--\", label=\"Start Time\")\n",
+    "axs[0].axvline(example_trial[\"stop_time\"] - example_trial[\"start_time\"], color=\"black\", linestyle=\"--\", label=\"Stop Time\")\n",
+    "for i, (onset_time, offset_time) in enumerate(zip(normalized_opto_onset_times, normalized_opto_offset_times)):\n",
+    "    x1 = onset_time * np.ones(len(y))\n",
+    "    x2 = offset_time * np.ones(len(y))\n",
+    "    if i == 0:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha, label=\"Optogenetic Stimulation\")\n",
+    "    else:\n",
+    "        axs[0].fill_betweenx(y, x1, x2, color=\"blue\", alpha=alpha)\n",
+    "\n",
+    "axs[0].set_ylim(ylim)\n",
+    "axs[0].set_yticks([])\n",
+    "axs[0].legend()\n",
+    "\n",
+    "axs[1].plot(normalized_encoder_timestamps, encoder_data[encoder_mask], color=\"k\", label=\"Encoder\")\n",
+    "axs[1].axhline(example_trial[\"ThresholdVector\"], color=\"black\", linestyle=\"--\", label=\"Threshold Vector\")\n",
+    "axs[1].axhline(example_trial[\"endZone_ThresholdVector\"], color=\"red\", linestyle=\"--\", label=\"End Zone Threshold Vector\")\n",
+    "axs[1].set_ylabel(\"Rotary Encoder (a.u.)\")\n",
+    "axs[1].legend()\n",
+    "\n",
+    "axs[2].plot(normalized_lick_timestamps, lick_data[lick_mask], color=\"k\", label=\"Lick\")\n",
+    "axs[2].set_ylabel(\"Lick Sensor (a.u.)\")\n",
+    "_ = axs[2].set_xlabel(\"Time Since Trial Start (s)\")"
    ]
   },
   {
@@ -536,7 +575,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "schneider_lab_to_nwb_env",
+   "display_name": "schneider_notebook_env",
    "language": "python",
    "name": "python3"
   },
@@ -550,7 +589,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,

From 26af4a1ee9c2ccd3a516a6059d5d6a726086b0a5 Mon Sep 17 00:00:00 2001
From: pauladkisson <paul.wesley.adkisson@gmail.com>
Date: Thu, 16 Jan 2025 11:57:23 -0800
Subject: [PATCH 6/6] finalized notebooks

---
 001259/ephys_example_notebook.ipynb | 20 ++++++++++----------
 001259/opto_example_notebook.ipynb  |  8 ++++----
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/001259/ephys_example_notebook.ipynb b/001259/ephys_example_notebook.ipynb
index e799906..05a7a4e 100644
--- a/001259/ephys_example_notebook.ipynb
+++ b/001259/ephys_example_notebook.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,12 +22,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook showcases one example session from the 001259 dataset containing operant behavior and concurrent electrophysiology recording."
+    "This notebook showcases one example session from the 001259 dataset containing operant behavior and concurrent OpenEphys recordings in primary auditory cortex (A1)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Primary Auditory Cortex (A1)</td>\n",
-       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4995524336\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4995525104\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4788585536\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4368008688\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
        "      <td>ElectrodeGroup</td>\n",
        "      <td>CH16</td>\n",
        "      <td>200.0</td>\n",
@@ -152,7 +152,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Primary Auditory Cortex (A1)</td>\n",
-       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4995524336\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4995525104\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
+       "      <td>ElectrodeGroup pynwb.ecephys.ElectrodeGroup at 0x4788585536\\nFields:\\n  description: ElectrodeGroup for all channels in the recording probe.\\n  device: MasmanidisSiliconMicroprobe128AxN pynwb.device.Device at 0x4368008688\\nFields:\\n  description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).\\n  manufacturer: Masmanidis Lab\\n\\n  location: Primary Auditory Cortex (A1)\\n</td>\n",
        "      <td>ElectrodeGroup</td>\n",
        "      <td>CH16</td>\n",
        "      <td>200.0</td>\n",
@@ -487,7 +487,7 @@
        "</table><p>... and 231 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231029</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
       ],
       "text/plain": [
-       "root pynwb.file.NWBFile at 0x4987780608\n",
+       "root pynwb.file.NWBFile at 0x4789700848\n",
        "Fields:\n",
        "  acquisition: {\n",
        "    ElectricalSeries <class 'pynwb.ecephys.ElectricalSeries'>,\n",
@@ -526,7 +526,7 @@
        "  session_start_time: 2023-10-29 16:56:01-04:00\n",
        "  source_script: Created using NeuroConv v0.6.5\n",
        "  source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py\n",
-       "  subject: subject pynwb.file.Subject at 0x4993512896\n",
+       "  subject: subject pynwb.file.Subject at 0x4793637520\n",
        "Fields:\n",
        "  age: P12W/\n",
        "  age__reference: birth\n",
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
diff --git a/001259/opto_example_notebook.ipynb b/001259/opto_example_notebook.ipynb
index 7197804..2cad949 100644
--- a/001259/opto_example_notebook.ipynb
+++ b/001259/opto_example_notebook.ipynb
@@ -284,7 +284,7 @@
        "</table><p>... and 433 more rows.</p></details></details><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".experiment_description\">experiment_description: </span><span class=\"field-value\">Identifying mistakes is important for improving performance during acoustic behaviors like speech and musicianship. Although hearing is instrumental for monitoring and adapting these behaviors, the neural circuits that integrate motor, acoustic, and goal-related signals to detect errors and guide ongoing sensorimotor adaptation in mammals remain unidentified. Here, we develop a novel closed-loop, sound-guided behavior that requires mice to use real-time acoustic feedback to guide skilled ongoing forelimb movements. Large scale electrophysiology recordings reveal that the mouse auditory cortex integrates information about sound and movement, as well as encodes error- and learning-related signals during this sound-generating behavior. Distinct groups of auditory cortex neurons signal different error types, and the activity of these neurons predicts both within-trial and across-trial behavioral adaptations. Brief, behavior-triggered optogenetic suppression of auditory cortex during error signaling hinders behavioral corrections on both rapid and long time scales, indicating that cortical error signals are necessary for skilled acoustic behaviors. Together, these experiments identify a cortical role for detecting errors and learning from mistakes and suggest that the auditory cortex plays a critical role in skilled, sound-generating behavior in mammals.</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".session_id\">session_id: </span><span class=\"field-value\">231013</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".lab\">lab: </span><span class=\"field-value\">Schneider</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".institution\">institution: </span><span class=\"field-value\">New York University</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script\">source_script: </span><span class=\"field-value\">Created using NeuroConv v0.6.5</span></div><div style=\"margin-left: 0px;\" class=\"container-fields\"><span class=\"field-key\" title=\".source_script_file_name\">source_script_file_name: </span><span class=\"field-value\">/opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py</span></div></div>"
       ],
       "text/plain": [
-       "root pynwb.file.NWBFile at 0x5011575040\n",
+       "root pynwb.file.NWBFile at 0x4956450992\n",
        "Fields:\n",
        "  acquisition: {\n",
        "    video_camera_1 <class 'pynwb.image.ImageSeries'>,\n",
@@ -324,7 +324,7 @@
        "  stimulus: {\n",
        "    optogenetic_series <class 'pynwb.ogen.OptogeneticSeries'>\n",
        "  }\n",
-       "  subject: subject pynwb.file.Subject at 0x5011396256\n",
+       "  subject: subject pynwb.file.Subject at 0x4960712176\n",
        "Fields:\n",
        "  age: P12W/\n",
        "  age__reference: birth\n",
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {