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38 results for “membrane potential”
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
Pure Water Permeance and Zeta Potential Data of Modified Ultrafiltration Membranes
<p>The data presented here belong to the journal article: <a title="DOI URL" href="https://doi.org/10.1021/acsami.3c18805">https://doi.org/10.1021/acsami.3c18805</a> </p> <p>The datasets contain pure water permeance and zeta potential data of electron beam-modified polymer membranes. The data were used as training data for machine learning models. Additionally, measured data for result comparison are presented.</p>
Source data: Negative Membrane potential accelerates sugar uptake by stabilizing the outward-facing conformation of the Na+/glucose symporter vSGLT
<p><strong>Galactose uptake source data.</strong></p><p>Excel file (annotated and color coded).</p><p> </p><p><strong>Double electron-electron resonance (DEER) source data.</strong></p><p>File name indicates Figure number, construct and conditions.<br>First column: Time in microseconds.<br>Second Column: Magnitude dipolar evolution data (phase corrected and normalized).</p><p> </p><p>Information for associated MD simulations under <a href="http://dx.doi.org/10.5281/zenodo.10000256">10.5281/zenodo.10000256.</a></p><p> </p>
Data from: A double-sided microscope to realize whole-ganglion imaging of membrane potential in the medicinal leech
Studies of neuronal network emergence during sensory processing and motor control are greatly promoted by technologies that allow us to simultaneously record the membrane potential dynamics of a large population of neurons in single cell resolution. To achieve whole-brain recording with the ability to detect both small synaptic potentials and action potentials, we developed a voltage-sensitive dye (VSD) imaging technique based on a double-sided microscope that can image two sides of a nervous system simultaneously. We applied this system to the segmental ganglia of the medicinal leech Hirudo verbana. Double-sided VSD imaging enabled simultaneous recording of membrane potential events from almost all of the identifiable neurons. Using data obtained from double-sided VSD imaging we analyzed neuronal dynamics in both sensory processing and generation of behavior and constructed functional maps for identification of neurons contributing to these processes.
Data for "Electrically Controlling and Optically Observing the Membrane Potential of Supported Lipid Bilayers"
<p>Raw data of all EIS, imaging and time-resolved fluorescence measurements presented in "Electrically Controlling and Optically Observing the Membrane Potential of Supported Lipid Bilayers".</p>
Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force
<p>This repository contains input data referenced in supporting information of the paper titled "Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force" by Alessia Centi, Arghya Dutta, Sapun H. Parekh, and Tristan Bereau.</p>
Dataset of research about Potential of Purwoceng Extract (Pimpinella pruatjan Molkenb.) as Anti-Nicotine Therapy against Viability, Spermatozoa Membrane Integrity, and Organogenesis
<p>The dataset included:</p> <p>1. Ethical test certificate</p> <p>2. Description of ethical clearance</p> <p>3. List of figures of tools and goods used for this study</p> <p>4. Result of active ingredient purwoceng test certificates</p> <p>5. Results of Observation of Viability and Integrity of Spermatozoa Membranes appendices</p>
Evaluation of the Effects of Semen Incubation With ANDROSITOL®DGN on Sperm Motility and Mitochondrial Membrane Potential
ClinicalTrials.gov study NCT04291495. IPD Sharing: NO. Countries: 1. Publications: 11.
Osteogenic Potential of Schneiderian Membrane
ClinicalTrials.gov study NCT06766292. IPD Sharing: NO. Countries: 1. Publications: 0.
The Osteogenic Potential of Human Maxillary Sinus Shneiderian Membrane
ClinicalTrials.gov study NCT02676921. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: A double-sided microscope to realize whole-ganglion imaging of membrane potential in the medicinal leech
Open the record for dataset details and reuse information.
Complementary data to "Molecular dynamics trajectories for 630 drug-membrane potentials of mean force"
<p>Missing data from "Molecular dynamics trajectories for 630 drug-membrane potentials of mean force"</p> <p>Hoffmann, Christian; Centi, Alessia; Menichetti, Roberto; Bereau, Tristan (2020): Molecular dynamics trajectories for 630 drug-membrane potentials of mean force. figshare. Collection.</p> <p>https://doi.org/10.6084/m9.figshare.c.4641551.v1</p> <p>Includes:</p> <ul> <li>'bsResult.xvg' of DIM_P2-P3 in DLPC</li> </ul>
Dataset for Transmembrane Potential of Physiologically Relevant Model Membranes: Effects of Membrane Asymmetry
<p>The trajectories for these five membrane systems are associated with the following work:<br> <br> Lin, Xubo; Gorfe, Alemayehu A. (2020): Transmembrane Potential of Physiologically Relevant Model Membranes: Effects of Membrane Asymmetry. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12478898.v1</p> <p>To reduce the data size, the saving frequency for these trajectories is 50ps. If you need the original trajectories (saving frequency: 4ps), please contact Xubo Lin (Email: linxbseu@buaa.edu.cn).</p>
Data from: Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice
Cortical gamma activity (30–80 Hz) is believed to play important functions in neural computation and arises from the interplay of parvalbumin-expressing interneurons (PV) and pyramidal cells (PYRs). However, the subthreshold dynamics underlying its emergence in the cortex of awake animals remain unclear. Here, we characterized the intracellular dynamics of PVs and PYRs during spontaneous and visually evoked gamma activity in layers 2/3 of V1 of awake mice using targeted patch-clamp recordings and synchronous local field potentials (LFPs). Strong gamma activity patterned in short bouts (one to three cycles), occurred when PVs and PYRs were depolarizing and entrained their membrane potential dynamics regardless of the presence of visual stimulation. PV firing phase locked unconditionally to gamma activity. However, PYRs only phase locked to visually evoked gamma bouts. Taken together, our results indicate that gamma activity corresponds to short pulses of correlated background synaptic activity synchronizing the output of cortical neurons depending on external sensory drive.
Data from: Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice
Open the record for dataset details and reuse information.
Mitochondrial Membrane Potential Identifies Cells with Enhanced Stemness for Cellular Therapy
GEO Series GSE74001. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Mitochondrial Membrane Potential Regulates Nuclear Gene Expression in Macrophages Exposed to PGE2
GEO Series GSE119521. Mus musculus. 34 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
OXPHOS deficiency activates global adaptation pathways to maintain mitochondrial membrane potential
GEO Series GSE162197. Saccharomyces cerevisiae W303. 86 samples. Type: Expression profiling by high throughput sequencing; Other.
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OpenNeuro
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