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468 results for “Spikes”
Spikemoid: Updated Spike-Based Loss Methods for Classification
<p>A Multi-Label Classification Dataset based on the NMNIST (Neuromorphic MNIST) dataset with two digits</p>
Data used in the study "mRNA vaccination boosts spike-specific T cell memory and promotes expansion of CD45RAint TEMRA-like CD8+ T cells in COVID-19 recovered individuals"
<p>Data associated with an original research study examining T cell responses to mRNA vaccination in COVID-19 recovered individuals. 10X Cell Ranger outputs, bulk TCR sequencing data, and T cell functional (ICS) data in this study have been deposited. Authors KMB and HR contributed equally to this effort. Address correspondence to EWN.</p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Research on COVID-19 vaccination in immune-deficient/disordered people (IDP) has primarily focused on cancer and organ transplantation populations. In a prospective cohort of 195 IDP and 35 healthy volunteers, anti-spike IgG was detected in 88% of IDP post-dose 2, increasing to 93% by six months post-dose 3. Despite high seroconversion, median IgG levels for IDP never surpassed 1/3 that of healthy volunteers. IgG binding to Omicron BA.1 was lower than all other variants. Angiotensin-converting enzyme 2 pseudo-neutralization (% inhibition) was only modestly correlated with anti-spike IgG concentration. IgG levels were not significantly altered by participants' use of different mRNA-based vaccines, immunomodulating treatments, and prior SARS-CoV-2 infections. While our data show that three doses of COVID-19 vaccinations induce anti-spike IgG in most IDP, additional doses are needed to achieve the levels of protection in healthy volunteers. Due to the strikingly reduced IgG response to Omicron BA.1, the efficacy of additional vaccinations, including bivalent vaccines, should be studied in this population.</p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
Open the record for dataset details and reuse information.
Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition
Open the record for dataset details and reuse information.
SIRAH-CoV2 initiative: SARS-Cov2 Spike´s RBD / ACE2-B0AT1 complex (PDB id:6M17)
<p>This dataset contains the trajectory of a 3 microseconds-long coarse-grained molecular dynamics simulation of the hexameric complex between SARS-CoV2 Spike´s RBD, ACE2, and B0AT1 (PDB id: 6M17). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6M17_SIRAHcg_rawdata_0-1.tar, 6M17_SIRAHcg_rawdata_1-2.tar, and 6M17_SIRAHcg_rawdata_2-3.tar contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M17_SIRAHcg_3us_prot.tar contains only the protein coordinates, while 6M17_SIRAHcg_3us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M17_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M17_SIRAHcg_prot.prmtop 6M17_SIRAHcg_prot.ncrst 6M17_SIRAHcg_3us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: UPDATED TRAJECTORY of SARS-Cov2 Spike´s RBD / ACE2-B0AT1 complex (PDB id:6M17)
<p>This dataset contains an updated trajectory of a four microseconds-long coarse-grained molecular dynamics simulation of the hexameric complex between SARS-CoV2 Spike´s RBD, ACE2, and B0AT1 (PDB id: 6M17). It substitutes the previous one on the same system, which was performed in the absence of disulfide bridges.</p> <p>Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6M17_SIRAHcg_rawdata_0-1.tar, 6M17_SIRAHcg_rawdata_1-2.tar, 6M17_SIRAHcg_rawdata_2-3.tar, and 6M17_SIRAHcg_rawdata_3-4.tar contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M17_SIRAHcg_4us_prot.tar contains only the protein coordinates, while 6M17_SIRAHcg_4us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M17_SIRAHcg_4us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M17_SIRAHcg_prot.prmtop 6M17_SIRAHcg_prot.ncrst 6M17_SIRAHcg_4usprot_skip.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: S2 Spike core fragment in postfusion state (PDB id:6M1V)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Spike S2 fragment in its postfusion form (PDB id: 6M1V). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M1V_SIRAHcg_rawdata_0-5us.tar, and 6M1V_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD 1.9.3), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M1V_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6LU7_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M1V_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 6M1V_SIRAHcg_prot.prmtop 6M1V_SIRAHcg_prot.ncrst 6M1V_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
Thermal spike during simulated deposition of tetrahedral amorphous carbon films
<p>These videos show the thermal spike arising from a 100 eV C atom impinging on a growing tetrahedral amorphous carbon film, as simulated with a GAP potential [1,2,3] following the deposition protocol and methodology outlined by Caro <em>et al</em>. [4,5]. The molecular dynamics simulations (MD) were carried out with QUIP's GAP implementation [6] using LAMMPS [7,8] as MD engine. The atomic visualization was carried out with VMD [9,10]. The final videos were composed using, in addition, the following software: gnuplot [11], Inkscape [12] and FFmpeg [13].</p> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. <strong>104</strong>, 136403 (2010).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B <strong>87</strong>, 184115 (2013).</li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B <strong>95</strong>, 094203 (2017).</li> <li>M.A. Caro, V.L. Deringer, J. Koskinen, T. Laurila, and G Csányi. Phys. Rev. Lett. <strong>120</strong>, 166101 (2018).</li> <li>M.A. Caro, G Csányi, T. Laurila, and V.L. Deringer. Phys. Rev. B <strong>102</strong>, 174201 (2020).</li> <li>http://libatoms.github.io</li> <li>https://lammps.sandia.gov</li> <li>S. Plimpton. J. Comp. Phys., <strong>117</strong>, 1 (1995).</li> <li>https://www.ks.uiuc.edu/Research/vmd</li> <li>W. Humphrey, A. Dalke, and K. Schulten. J. Molec. Graphics <strong>14</strong>, 33 (1996).</li> <li>http://www.gnuplot.info</li> <li>https://inkscape.org</li> <li>https://ffmpeg.org</li> </ol>
Production and purification of receptor-binding domain (RBD) of the Spike protein from a transiently transfected mammalian cell
<p> </p> <p>Production and purification of recombinant receptor-binding domain (RBD) of the Spike protein from a transiently transfected EXPI293 mammalian cell .</p>
Production and purification protocols for Ectodomain of SARS-CoV-2 Spike (S) protein
<p>Production and purification protocols for Ectodomain of SARS-CoV-2 Spike (S) protein </p>
Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Precise timing is ubiquitous, consistent and coordinated across a comprehensive, spike-resolved flight motor program
<p>Sequences of action potentials, or spikes, carry information in the number of spikes and their timing. Spike timing codes are critical in many sensory systems, but there is now growing evidence that millisecond-scale changes in timing also carry information in motor brain regions, descending decision-making circuits, and individual motor units. Across all the many signals that control a behavior how ubiquitous, consistent, and coordinated are spike timing codes? Assessing these open questions ideally involves recording across the whole motor program with spike-level resolution. To do this, we took advantage of the relatively few motor units controlling the wings of a hawk moth, <em>Manduca sexta</em>. We simultaneously recorded nearly every action potential from all major wing muscles and the resulting forces in tethered flight. We found that timing encodes more information about turning behavior than spike count in every motor unit, even though there is sufficient variation in count alone. Flight muscles vary broadly in function as well as in the number and timing of spikes. Nonetheless, each muscle with multiple spikes consistently blends spike timing and count information in a 3:1 ratio. Coding strategies are consistent. Finally, we assess the coordination of muscles using pairwise redundancy measured through interaction information. Surprisingly, not only are all muscle pairs coordinated, but all coordination is accomplished almost exclusively through spike timing, not spike count. Spike timing codes are ubiquitous, consistent, and essential for coordination. </p>
Paavo Nurmi's Spike Shoes
Paavo Nurmi used these spike shoes made of leather on his American tour of 1925. The manufacturer is not known. The shoes were in frequent use, as attested by the six worn spikes at the front. Source: Objaverse 1.0 / Sketchfab
Supplemental Movie for "The basis of sharp spike onset in standard biophysical models"
<p>Simulation of extracellular field during action potential.</p>
Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes
<p><strong>Abstract:</strong> Development of vaccines and therapeutics that are broadly effective against known and emergent coronaviruses is an urgent priority. We screened the circulating B cell repertoires of COVID-19 survivors and vaccinees to isolate over 9,000 SARS-CoV-2-specific monoclonal Abs (<strong>mAbs</strong>), providing an expansive view of the SARS-CoV-2-specific Ab repertoire. Among the recovered antibodies was TXG-0078, an NTD-specific neutralizing mAb that recognizes diverse alpha- and beta-coronaviruses. TXG-0078 achieves its exceptional binding breadth while utilizing the same VH1-24 variable gene signature and heavy chain-dominant binding pattern seen in other NTD supersite-specific neutralizing Abs with much narrower specificity. We also report the discovery of CC24.2, a pan-sarbecovirus neutralizing antibody that targets a novel RBD epitope and shows similar neutralization potency against all tested SARS-CoV-2 variants, including BQ.1.1 and XBB.1.5. A cocktail of TXG-0078 and CC24.2 protects <i>in vivo</i>, suggesting potential use in variant-resistant therapeutic Ab cocktails and as templates for pan-coronavirus vaccine design.</p><p><strong>Datasets: </strong>This repository contains the 10x Genomic cellranger outputs (matrix and vdj contig files) as well as complied functional characterization dataset used to generate figures on the publication "Deep repertoire mining uncovers ultra-broad coronavirus neutralizing antibodies targeting multiple spike epitopes". </p><p>Post-vaccination samples for donors CC10, CC25, CC31, CC66 were processed in single 10x Genomic reactions. The timepoints samples consist of multiplexing donors CC10, CC25, CC31, CC66 into one 10x Genomic reaction. Similarly, donors CC26, CC42, CC62, CC67 were multiplexed into a single 10x Genomic reaction.</p><p><strong>Files:</strong></p><p>feature names.csv - csv file with sort bait/antigen barcode key </p><p>feature_reference.csv - csv file with cell hash and antigen barcode reference</p><p>filtered_contig<i>_</i>annotations.csv - High-level annotations of each high-confidence contigs from cell-associated barcodes. This is a subset of all_contig_annotations.csv.</p><p>filtered_contig.fasta - filtered antibody fasta</p><p>filtered_matrix.mtx.gz - 10x Genomic matrix file for filtered cells. Contains counts data for feature and gene expression library.</p><p>raw_matrix.mtx.gz - 10x Genomic matrix file for unfiltered cells. Contains counts data for feature and gene expression library.</p><p><strong>Code: </strong>All code used to generate analysis and figures is available under the MIT license on Github<br> </p>
Guerrero Copper Spike on Bedrock (2019)
Site 2-04-8MO02343 Copper Spike. This spike, approximately 16 centimeters long, sits in a shallow recess in the bedrock. It is one of many copper spikes found across the shipwreck site. This model was made in June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
1900s Railroad Spike
This railroad spike was found near the old Power Plant by CAP. As the college expanded in the late 19th and early 20th centuries, energy demands also increased. Particularly in the winter months, coal was needed to provide heat all over campus for stoves, furnaces, and boilers. To make this process easier, a railroad was built in 1900 by the Pere Marquette Railroad Company to transport coal from right into the heart of campus where the Boiler House was being built. This railroad supplied over 3000 tons of coal to be transportetd in the first year alone, giving the campus heat and power and allowed for further and faster expansion of the institution. Although trains had been in the United States since the 1830s, the construction of a railroad on campus in 1900 was indicative of the expansion that was about to take place. Thus, this small railroad spike represents the dawn of a new era at the college. Photogrammetry model created by Jack A. Biggs using Agisoft Metashape. Source: Objaverse 1.0 / Sketchfab
Spike-timing based coding in neuromimetic tactile system enables dynamic object classification
<p>Coding dynamic tactile information in spike timing is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects and are unfortunately ill-suited for efficient coding of temporal information and rapid feature extraction. Here, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.</p>
Spike-MD Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>A synthetic cryo-EM dataset with heterogeneity from a molecular dynamics simulation and ground truth atomic models, poses, labels, mask, and consensus volume:</p> <ul> <li>Spike-MD: 100k particle images (256x256, 1.5A/pix) of SARS-CoV-2 spike protein with conformations sampled from a molecular dynamics simulation from Wieczór et al. (2023)</li> <li>sampled_pdbs.xtc: atomic models stitched into a trajectory</li> <li>seed_structure.pdb: initial seed structure for .xtc trajectory</li> </ul>
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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)
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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.