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..c7ba931 --- /dev/null +++ b/001259/environment.yml @@ -0,0 +1,13 @@ +# run: conda env create --file environment.yml +name: schneider_notebook_env +channels: + - conda-forge +dependencies: + - python==3.12 + - ipykernel + - matplotlib + - dandi + - pip + - pip: + - remfile + - schneider-lab-to-nwb @ git+https://github.com/catalystneuro/schneider-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..05a7a4e --- /dev/null +++ b/001259/ephys_example_notebook.ipynb @@ -0,0 +1,813 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ephys Example Session" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "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 OpenEphys recordings in primary auditory cortex (A1)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: Mice performed the auditory guided task while electricophysiological neural activity was recorded in the primary auditory cortex (A1).
identifier: b661cbac-4f29-4f2d-a115-a681eea4b6b2
session_start_time2023-10-29 16:56:01-04:00
timestamps_reference_time2023-10-29 16:56:01-04:00
file_create_date
02024-12-19 10:42:13.233766-08:00
experimenter('Zempolich, Grant W.', 'Schneider, David M.')
acquisition
ElectricalSeries
starting_time: 0.0
rate: 30000.0
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.9499999999999999e-07
offset: 0.0
unit: volts
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electrodes
description: electrode_table_region
table
description: metadata about extracellular electrodes
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video_camera_1
resolution: -1.0
comments: no comments
description: 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.
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description: 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.
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[b'auditory cortex' b'predictive coding' b'optogenetics']
processing
behavior
description: 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.
data_interfaces
behavioral_time_series
time_series
encoder
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
offset: 0.0
unit: a.u.
data
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lick
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comments: no comments
description: 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.
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toneIN
description: Time at which target zone is entered and target entry tone is played.
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description: 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.
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valve
description: Times at which solenoid valve opens to deliver water after a correct trial.
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valued_events_table
description: Metadata about valued events.
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intrinsic_signal_optical_imaging
description: 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.
data_interfaces
images
description: Intrinsic signal optical images.
images
overlaid_image
target_image
electrodes
description: metadata about extracellular electrodes
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id
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electrode_groups
ElectrodeGroup
description: ElectrodeGroup for all channels in the recording probe.
location: Primary Auditory Cortex (A1)
device
description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).
manufacturer: Masmanidis Lab
devices
MasmanidisSiliconMicroprobe128AxN
description: Masmanidis Lab dense 128-channel recording probes (https://masmanidislab.neurobio.ucla.edu/technology.html).
manufacturer: Masmanidis Lab
intrinsic_signal_optical_imaging_laser
description: ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).
manufacturer: ThorLabs
lickometer
description: 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.
manufacturer: Schneider Lab
rotary_encoder
description: H5 BALL BEARING OPTICAL SHAFT ENCODER
manufacturer: US Digital
two_photon_microscope
description: Standard Microscope by Neurolabware.
manufacturer: Neurolabware
intervals
epochs
description: experimental epochs
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start_timestop_timetags
id
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trials
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id
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... and 483 more rows.

subject
age: P12W/
age__reference: birth
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.
genotype: C57BL/6 or VGATChR2-EYFP
sex: U
species: Mus musculus
subject_id: m53
strain: C57BL/6
epochs
description: experimental epochs
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trials
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... and 483 more rows.

units
description: Neural spikes were sorted offline using Kilosort 2.5 and Phy2 software and manually curated to ensure precise spike time acquisition.
waveform_unit: volts
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spike_timesunit_nameampshAmplitudefrContamPctqualityKSLabeloriginal_cluster_iddepthn_spikesch
id
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... and 231 more rows.

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.
session_id: 231029
lab: Schneider
institution: New York University
source_script: Created using NeuroConv v0.6.5
source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x4789700848\n", + "Fields:\n", + " acquisition: {\n", + " ElectricalSeries ,\n", + " video_camera_1 ,\n", + " video_camera_2 \n", + " }\n", + " devices: {\n", + " MasmanidisSiliconMicroprobe128AxN ,\n", + " intrinsic_signal_optical_imaging_laser ,\n", + " lickometer ,\n", + " rotary_encoder ,\n", + " two_photon_microscope \n", + " }\n", + " electrode_groups: {\n", + " ElectrodeGroup \n", + " }\n", + " electrodes: electrodes \n", + " epochs: epochs \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, 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 ,\n", + " trials \n", + " }\n", + " keywords: \n", + " lab: Schneider\n", + " processing: {\n", + " behavior ,\n", + " intrinsic_signal_optical_imaging \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 0x4793637520\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 \n", + " units: units " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Retrieve Ephys and Behavioral Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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_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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+ "text/plain": [ + "
" + ] + }, + "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": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YWP36669asmSJfvzxR7377rvq0qVLtp6raNGimjZtmh577DGVKVNGw4YN05gxYyRJ165dU6lSpTR16lQ9+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+zTa1atXSjh079Pjjj1u37dq1S7Vq1cp0fY0bN9aaNWtUqVIlm9kmZmncuLGOHDmSYRDm6em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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.12.8" + } + }, + "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..2cad949 --- /dev/null +++ b/001259/opto_example_notebook.ipynb @@ -0,0 +1,597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Opto Example Session" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "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, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: Mice performed the auditory guided task while optogenetic stimulation was delivered to the primary auditory cortex (A1).
identifier: 301b47e9-c389-4021-86cf-5dd9856a6922
session_start_time2023-10-13 00:00:00-04:00
timestamps_reference_time2023-10-13 00:00:00-04:00
file_create_date
02024-12-19 10:42:42.007793-08:00
experimenter('Zempolich, Grant W.', 'Schneider, David M.')
acquisition
video_camera_1
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
offset: 0.0
unit: Frames
data
HDF5 dataset
Data typeuint8
Shape(0, 0, 0)
Array size0.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratioundefined

[]
timestamps
HDF5 dataset
Data typefloat64
Shape(17994,)
Array size140.58 KiB
Chunk shape(17994,)
Compressiongzip
Compression opts4
Compression ratio1.4520072624571314
timestamps_unit: seconds
interval: 1
external_file
HDF5 dataset
Data typeobject
Shape(1,)
Array size8.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Compression ratio0.5

[b'sub-m53_ses-231013_behavior+image+ogen/f993b247-51df-483e-939b-2c249f3a3bcd_external_file_0.mp4']
starting_frame
NumPy array
Data typeint64
Shape(1,)
Array size8.00 bytes

[0]
format: external
video_camera_2
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
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unit: Frames
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[b'sub-m53_ses-231013_behavior+image+ogen/761f1e57-c5e6-4669-8e28-bfe4f545da14_external_file_0.mp4']
starting_frame
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format: external
stimulus
optogenetic_series
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
offset: 0.0
unit: watts
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timestamps
HDF5 dataset
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site
device
description: 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.
manufacturer: Opto Engine LLC
description: 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).
excitation_lambda: 473.0
location: Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)
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[b'auditory cortex' b'predictive coding' b'optogenetics']
processing
behavior
description: 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.
data_interfaces
behavioral_time_series
time_series
encoder
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
offset: 0.0
unit: a.u.
data
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timestamps
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interval: 1
lick
resolution: -1.0
comments: no comments
description: 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.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
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timestamps
HDF5 dataset
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timestamps_unit: seconds
interval: 1
targetOUT
description: Time at which the target zone is overshot during a press.
timestamps
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timestamps__unit: seconds
toneIN
description: Time at which target zone is entered and target entry tone is played.
timestamps
HDF5 dataset
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toneOUT
description: 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.
timestamps
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timestamps__unit: seconds
valve
description: Times at which solenoid valve opens to deliver water after a correct trial.
timestamps
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timestamps__unit: seconds
intrinsic_signal_optical_imaging
description: 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.
data_interfaces
images
description: Intrinsic signal optical images.
images
overlaid_image
target_image
devices
intrinsic_signal_optical_imaging_laser
description: ThorLabs 700nm fiber coupled LED (M700F3) driven by their basic LED driver (LEDD1B).
manufacturer: ThorLabs
lickometer
description: 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.
manufacturer: Schneider Lab
optogenetic_stimulation_laser
description: 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.
manufacturer: Opto Engine LLC
rotary_encoder
description: H5 BALL BEARING OPTICAL SHAFT ENCODER
manufacturer: US Digital
two_photon_microscope
description: Standard Microscope by Neurolabware.
manufacturer: Neurolabware
ogen_sites
optogenetic_stimulus_site
device
description: 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.
manufacturer: Opto Engine LLC
description: 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).
excitation_lambda: 473.0
location: Primary Auditory Cortex (-2.8 AP, 4.2 ML relative to bregma; guided by intrinsic optical imaging)
intervals
epochs
description: experimental epochs
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start_timestop_timetags
id
0126.256702786.229029[Active Behavior]
trials
description: experimental trials
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start_timestop_timerewardedtime_reward_sopto_trialopto_timeopto_time_endITI_respectThresholdVectorendZone_ThresholdVector
id
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3128.784895129.004811True129.008747FalseNaNNaNTrue21.027.0

... and 433 more rows.

subject
age: P12W/
age__reference: birth
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.
genotype: C57BL/6 or VGATChR2-EYFP
sex: U
species: Mus musculus
subject_id: m53
strain: C57BL/6
epochs
description: experimental epochs
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start_timestop_timetags
id
0126.256702786.229029[Active Behavior]
trials
description: experimental trials
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start_timestop_timerewardedtime_reward_sopto_trialopto_timeopto_time_endITI_respectThresholdVectorendZone_ThresholdVector
id
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... and 433 more rows.

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.
session_id: 231013
lab: Schneider
institution: New York University
source_script: Created using NeuroConv v0.6.5
source_script_file_name: /opt/anaconda3/envs/schneider_lab_to_nwb_env/lib/python3.12/site-packages/neuroconv/basedatainterface.py
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x4956450992\n", + "Fields:\n", + " acquisition: {\n", + " video_camera_1 ,\n", + " video_camera_2 \n", + " }\n", + " devices: {\n", + " intrinsic_signal_optical_imaging_laser ,\n", + " lickometer ,\n", + " optogenetic_stimulation_laser ,\n", + " rotary_encoder ,\n", + " two_photon_microscope \n", + " }\n", + " epochs: epochs \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, 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 ,\n", + " trials \n", + " }\n", + " keywords: \n", + " lab: Schneider\n", + " ogen_sites: {\n", + " optogenetic_stimulus_site \n", + " }\n", + " processing: {\n", + " behavior ,\n", + " intrinsic_signal_optical_imaging \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 \n", + " }\n", + " subject: subject pynwb.file.Subject at 0x4960712176\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 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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)" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [ + "Plot an example rewarded trial" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "example_trial = example_rewarded_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 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_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)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot an example unrewarded trial" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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/stream_nwbfile.py b/001259/stream_nwbfile.py new file mode 100644 index 0000000..02bff64 --- /dev/null +++ b/001259/stream_nwbfile.py @@ -0,0 +1,35 @@ +from pynwb import NWBHDF5IO +import remfile +import h5py +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=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