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684 results for “gating”
Detection of neurofilament light chain with label-free electrolyte-gated organic field-effect transistors
<p>The uploaded data include</p> <p>i) transfer curves registered at every NF-L concentration;</p> <p>ii) transfer curves for control experiments;</p> <p>iii) raw AFM data acquired after electrode incubation in 0 M, 1 pM, 100 pM and 10 nM [NF-L].</p> <p>Electrical measurements were acquired in 50 mM PBS, pH 7.4 under static conditions.</p> <p>Anti-NF-L antibodies were immobilized on the gate electrode through cys-Protein G. The gate surface has been subsequently passivated through 11-mercaptoundecyl-triethylene glycol SAM.</p>
To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)
<p>The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories.</p> <p>The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories:</p> <ol> <li><strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows:</li> </ol> <ul> <li><strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong></li> <li><strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong></li> <li><strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong></li> <li><strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong></li> <li><strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong></li> </ul> <p><strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel.</p> <p>The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). </p> <p>The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV).</p> <p> 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files.</p> <p>The Python code is placed in two files:</p> <ol> <li><strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook.</li> <li><strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.</li> </ol> <p> </p> <p> </p> <p> </p>
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment
<p>The file contains two benchmark sets: Heterodimer-AF2 and Docking benchmark 5.5-AF2 test. Each set includes (1) doecy folder, (2) native folder, and (3) label_info.csv. </p>
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment: MAF2 set
<p>Multimer AF2 set. For more information, please read our paper on biorxiv:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2">https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2</a> </p>
Engineering dynamic gates in binding pocket of penicillin G acylase to selectively degrade bacterial signaling molecules
<p>(01-mutants_design.tar.gz) Mutants design:</p> <ol> <li>Input structures of ecPGA from the PDB database (PDB IDs: 1GK9, 1GM7, and 1GM9), processed to resemble wild-type state, repaired by RepairPDB module of FoldX 4</li> <li>Double-point mutants preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA double-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA double-point mutants</li> </ol> </li> <li>Triple-point mutant preparation, analysis and filtering: <ol> <li>text files including configuration for FoldX 4</li> <li>inputs and outputs of CAVER 3.02 calculations on FoldX 4 PDB files of ecPGA triple-point mutants</li> <li>input configuration file and TransportTools library 0.9.4 calculations outputs generated based on the inputs produced in step 01 and 02 above</li> <li>CSV files containing complete information about FoldX 4 stability prediction and geometrical properties from CAVER 3.02 and TransportTools library version 0.9.4 for ecPGA triple-point mutants</li> </ol> </li> </ol> <p>(02-docking.tar.gz) Preparation of protein-ligand complexes using molecular docking for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>PDB files of ligand, PDBQT files of the receptor and PDB files of the complexes selected from docking experiment:</li> </ol> <p>Full names of presented protein variants:<br>ecPGA_wt, wild-type Escherichia coli penicillin G acylase<br>LAF, Phe138αLeu & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_22<br>LSF, Phe138αLeu & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_98<br>MAF, Phe138αMet & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_23<br>MSF, Phe138αMet & Met142αSer & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_99<br>VAF, Phe138αVal & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_30<br>YAF, Phe138αTyr & Met142αAla & Ile177βPhe ecPGA variant internally referred as 1GK9_Repair_33<br>Full names of presented AHLs:<br>C06, N-hexanoyl-L-homoserine lactone;<br>C06-3O, N-3-oxo-hexanoyl-L-homoserine lactone;<br>C08, N-octanoyl-L-homoserine lactone;<br>C08-3O, N-3-oxo-octanoyl-L-homoserine lactone;<br>C10, N-decanoyl-L-homoserine lactone;<br>C12-3O, N-3-oxo-dodecanoyl-L-homoserine lactone</p> <p>(03-protein_ligand_MDs.tar.gz) Ligand-enzyme complexes molecular dynamics for wild-type ecPGA and 6 best designed triple-point mutants with 6 various bacterial signaling molecules:</p> <ol> <li>Force field parameters in Amber format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Output files generated during post-processing of production runs trajectories in a form of text files generated by cpptraj</li> </ol> <p>(04-free_enzymes_MDs.tar.gz) Free enzymes molecular dynamics of 3 best triple-point ecPGA (VAF, YAF and MSF) mutants prioritized based on protein-ligand molecular dynamics simulations and experimental assays:</p> <ol> <li>Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber format</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format</li> <li>Post-processing analysis of generated trajectories: <ol> <li>Text files with distances, CSV files containing result of PCA and clustering, PNG files with clustered PCA results</li> <li>Inputs and outputs of MDpocket analysis and visualization of the pocket frequency grid as an isomesh</li> <li>CAVER input configuration files in text format, CAVER output data including parsed CSV and text files for visualization of entrance opening time evolution and cavity profiles inspection</li> <li>cpptraj generated text files including RMSD, distances and chi1 angles measurements</li> </ol> </li> </ol> <p>All plots were generated using matplotlib or seaborn Python libraries. Figures containing structural representations were generated using PyMOL 2.0.1.</p> <p>(05-ecPGA_VAF_YAF_MSF_penG_MDs.tar.gz) PenG-enzyme complexes molecular dynamics for wild-type ecPGA and 3 best designed triple-point mutants (VAF, YAF, MSF):</p> <ol> <li>PenG force field parameters in Amber (GAFF) format</li> <li>Input coordinates *.inpcrd, parameters *.parm7 and *.pdb files for each complex ready for simulation in Amber</li> <li>Amber input files *.in for minimization, equilibration and production runs</li> <li>Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format</li> <li>Simulation output files for each stage of the minimization, equilibration and production runs in Amber *.mdout format and analysis output files generated during post-processing of production runs trajectories in a form of text files</li> <li>Reactive Stabilization Score [RSS] statistics summarized in CSV files</li> </ol>
Fig. 1 in First report of kdr mutations in the voltage-gated sodium channel gene in the arbovirus vector, Aedes aegypti, from Nouakchott, Mauritania
Fig. 1 The combinations of kdr point mutations S989P, V1016G, and F1534C in adult female Aedes aegypti mosquitoes in Nouakchott, Mauritania
Dataset: Marblegate Acquisition Corp. (GATE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data used in "The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators" by Hunter N. Ratliff et al.
<p>This repository contains the raw experimental and PHITS-simulated data used in the JINST article “The Backscatter Gating method for time, energy, and position resolution characterization of long form factor organic scintillators” by Hunter N. Ratliff et al., available at <a href="https://doi.org/10.1088/1748-0221/19/07/P07002">https://doi.org/10.1088/1748-0221/19/07/P07002</a> (the accepted manuscript can also be found at <a href="https://hdl.handle.net/11250/3145287">https://hdl.handle.net/11250/3145287</a> and <a href="https://hratliff.com/publications/">https://hratliff.com/publications/</a>).</p> <p> </p> <p>The data consists of three top-level directories, each with various subdirectories. Within the “PHITS-simulated-data” directory are the PHITS simulations (including input and output files) used in producing Figures 4 and 5 in the manuscript, showing energy spectra in the bar for BSG events from a Cs-137 emission for placement of the source on the bar and on the BSG detector and with spacings between the bar and BSG detector of 20, 80, and 150 mm, along with the energy-dependent spatial distribution of these recoil electrons in the bar.</p> <p> </p> <p>The other two top-level directories contain raw data produced by the CAEN CoMPASS software when acquiring data experimentally. In the analysis for each measurement, the contents of the “RAW” folders were used. The “Energy-calibrations-of-CeBr3” directory contains the energy spectra of the CeBr3 detector (used as the BSG detector) when exposed to a variety of radioactive sources, used together to energy-calibrate the CeBr3 detector. The “Backscatter-gating-measurements” directory contains all of the list-mode data acquired with CoMPASS used for all of the other experimental measurements presented in the manuscript. It contains subdirectories for varied sources, source positions along the bar, distances between the bar and BSG detector, measurements with the source affixed to the BSG detector instead, and a few miscellaneous measurements. The “images” directories within each measurement’s directory (at the same level as the “RAW” folders) contain images produced by the analysis script written for this work used for diagnostics and presenting analyzed data.</p> <p> </p>
Supporting data for "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002)
<p>Supporting data and analysis scripts for FIG. 2 - FIG. 6 of "Benchmarking Gate Fidelities in a Si/SiGe Two-Qubit Device" (arXiv:1811.04002, Physical Review X in press)</p> <p>Data for FIG. 2 and FIG. 3 and analysis script are in the file 'single_two_qubit_RB.zip'.</p> <p>Data for FIG. 4 and analysis script are in the file 'CRB.zip'.</p> <p>Data for FIG. 5 and FIG. 6 and analysis script are in the file 'Append.zip'.</p> <p>To run the scripts, QCoDeS needs to be installed. Please refer to:</p> <p>http://qcodes.github.io/Qcodes/</p> <p>for instructions about installation and Dataset. If there's a problem, try to use an earlier version 0.1.7.</p> <p>For more information, contact the author: Xiao Xue, x.xue-1@tudelft.nl </p>
Raw and sinogram data of simulated GATE PET data
<p>This dataset contains simulated positron emission tomography (PET) data saved in sinogram format as well as in a raw data format for use in MATLAB/Octave. The simulated phantom contains three rods/cylinders of varying size and activity inside a cylindrical water phantom. The simulation was performed by using the GATE software (<a href="http://www.opengatecollaboration.org/">http://www.opengatecollaboration.org/</a>). Prompt, true, scatter, random and delayed coincidences are included.</p> <p>These datafiles have been created with the OMEGA software (<a href="https://github.com/villekf/OMEGA">https://github.com/villekf/OMEGA</a>) and the primary purpose is to serve as a test data for the software. The sinogram data can be used anywhere, but the raw data should be used with OMEGA. The organization of the raw data can be found from the OMEGA GitHub wiki. For the sinogram, the first dimension is the number of angular positions (views), second number of angles (tangential positions) and third the number of sinograms. Machine information can be found from OMEGA documentation and the GATE macros on the GitHub page.</p> <p>An "ideal image" is also included, that contains the counts in their original decay locations.</p>
Simulated GATE PET data
<p>This dataset contains simulated GATE data both in ASCII and ROOT format. The dataset in <a href="https://doi.org/10.5281/zenodo.3522199">https://doi.org/10.5281/zenodo.3522199</a> was constructed from these source files. Three cylinders of different sizes and activities are used as the sources inside a cylindrical water phantom. The macros which were used to simulate this data are available from <a href="https://github.com/villekf/OMEGA/tree/master/exampleGATE">https://github.com/villekf/OMEGA/tree/master/exampleGATE</a></p> <p>Both ROOT and ASCII formats contain the same information, though for ROOT the delayed coincidences are integrated into the same files as the coincidences. The included data are (for both coincidences): event IDs, source position in X-, Y- and Z-directions, time index, volume IDs, number of Compton scatter in phantom, number of Compton scatter in detector, number of Rayleigh scatter in phantom and number of Rayleigh scatter in detector.</p> <p>This dataset is designed as a test data for the OMEGA software: <a href="https://github.com/villekf/OMEGA">https://github.com/villekf/OMEGA</a></p>
Figure 1 in Haplotype diversity of brown trout Salmo trutta (L.) in the broader Iron Gate area
Figure 1. Sampling sites on the streams Brnjica (1), Dobrinjska reka (2), Kožica (3), Mala Boljetinska reka (4), Zlatica (5), Porečka reka (6), Rečka reka (7), Vratna (8), and Zamna (9) in the broader Iron Gate area with the position in the Balkan region given in small figure in lower left.
Figure 2 in Haplotype diversity of brown trout Salmo trutta (L.) in the broader Iron Gate area
Figure 2. Relationships between CR haplotypes of brown trout populations in the broader Iron Gate area constructed using maximum likelihood (A) and maximum parsimony (B) methods (numbers at particular branches represent bootstrap probabilities; bootstrap values under 40% are not represented).
Fig. 2 in Ostracods and facies of the Early and Middle Frasnian at Devils Gate in Nevada: Relationship to the Alamo Event
Fig. 2. Lithology of the lower member of the type Devils Gate Limestone showing positions of ostracod, conodont, and sedimentological samples. Ostracod samples numbered DVG are from Sandberg et al. (1988, 2003). Other columns indicate conodont zones revised herein by Sandberg, paleoenvironmental interpretation, and microfacies evolution. Abbreviation: Pa., Palmatolepis.
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Raw and processed data, gating strategy, and photographs of instrumental setup of NAVETTA
<p>Supplementary Information and Raw Data for Weiss et al., Comp Struct Biotechn J: Nanosci Adv Mat, 2024</p> <p>1. xls sheet of raw and processed data for all figures</p> <p>2.-3. gating strategies for flow cytometry experiments</p> <p>4.-8. photographs of instrumental setups</p>
GATE simulated cylindrical PET with NEMA-like phantom
<p>This is a GATE-simulated data using a cylindrical PET scanner (based on the GATE cylindrical PET example) and a NEMA-like phantom. Included are the sinograms created by OMEGA software, for both TOF and non-TOF cases as mat-files as well as normalization correction coefficients. You can open these mat-files in MATLAB, Octave, Python, Julia or in practically any other language. This data can be also used as a testing data for OMEGA. Included is also the ground truth image (i.e. the source image), attenuation image as created by GATE, as well as the original ROOT files. The ROOT file package also contains the original macros that give details on the scanner and the phantom.</p> <p>The sinogram data contains several different sinograms. raw_SinM is the raw sinogram with no modifications, SinM contains normalization and randoms correction precorrected (not available for TOF data), SinDelayed contains delayed coincidences, SinTrues the trues, SinRandoms the true randoms, SinScatter the true scattered photons, appliedCorrections show the corrections applied to SinM and RandProp and ScatterProp show whether variance reduction or smoothing was applied to the delayed coincidences or (not present) scatter estimation data. The attenuation data is already correctly scaled and is saved as a MetaImage file.</p> <p>Also included is the ground truth, or original source, image. This is saved as the variable C. RA is the randoms image while SC is the scatter image. The latter two are in singles mode, i.e. they show the locations of the photons that either were random (two different events) or scattered along the way.</p> <p>The normalization data works for other measurements with the same scanner as long as the sinogram dimensions remain the same. OMEGA will automatically use the normalization data if it's present in the mat-files folder.</p> <p>This new version includes also the detector coordinates for each measurement. This is mainly for OMEGA-example showcasing the use of custom data. See, for example, <a title="https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m" href="https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m" target="_blank" rel="noopener">https://github.com/villekf/OMEGA/blob/master/main-files/custom_detector_exampleSimple.m</a></p>
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
Figures of merit that characterize silicon gate-all-around nanowire FETs affected by line edge roughness variability
<p>Off-current, threshold voltage, sub-threshold slope and on-current values for two silicon gate-all-around nanowire FETs affected by line edge roughness (LER) variability, a 22 nm gate length device and a 10 nm gate length one. The LER profile that characterizes the roughness deformation is also included in the dataset. Different correlation length (CL) and root mean square (RMS) heights values are characterized.</p>
Monte Carlo simulation of water cylinder phantoms for Data Driven Gating (DDG) in single photon emission tomography
<p><strong>Objective</strong>: Multiple different algorithms have been proposed for Data Driven Gating (DDG) in Single Photon Emission Computed Tomography (SPECT) and have successfully been applied to Myocardial Perfusion Imaging (MPI). Application of DDG to acquisition types other than SPECT MPI has not been demonstrated so far, as the limitations and pitfalls of current methods are unknown.</p> <p><strong>Approach</strong>: We create a comprehensive set of phantoms that allow the characterization of DDG algorithms and perform Monte Carlo simulations, simulating the influence of different motion artifacts, view angles, moving feature diameters, contrasts, and count levels. We derive quantitative metrics from the data and evaluate the Center of Light (COL) and Laplacian Eigenmaps (LE) methods as sample DDG algorithms.</p> <p><strong>Main results</strong>: View angle, feature size, count rate density, and contrast influence the accuracy of both DDG methods. Moreover, the ability to extract the respiratory motion in the phantom was shown to correlate with the contrast of the moving feature to the background, the Signal to Noise ratio, and the noise in the data.</p> <p><strong>Significance</strong>: We showed that reporting the average correlation to an external physical reference signal per acquisition is not sufficient to characterize DDG methods. Assessing DDG methods on a view-by-view basis using the simulations and metrics from this work could enable the identification of pitfalls of current methods, and extend their application to acquisitions beyond SPECT MPI. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.