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2,562 results for “SARS CoV 2”
Electron microscopy of SARS-CoV-2 particles - Dataset 07
<p>The dataset contains 134 transmission electron microscopy images of ultrathin (45 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.54 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 07 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.
<p>The spectra were provided as unprocessed raw data in the manufacturers data format (Bruker), as labelled two zip archives with SARS CoV 2 positives and negative, according to the reviewer's recommendation.</p> <p>This information belongs to the publication (in review in <em>Journal of Virological Methods</em>)<br> "A Combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs"<br> All the information belongs to the National Reference Institute, INEI-ANLIS DR CARLOS G MALBRAN, BUENOS AIRES, ARGENTINA.</p>
SARS-CoV-2 transmission via speech-generated respiratory droplets
<p>The physics of generating acoustic waves involves the high-speed passage of air pressurized by the lungs through narrow passages, past the mucosal epithelial layers of the vibrating vocal folds. Sounds are further modulated by the passage of this air through narrow passages between the tongue, lips, and teeth, dislodging oral fluid at all of these locations. Generation of droplets is inevitably linked to the physics of speech generation, and not limited to one person as is highlighted in a short video recording</p>
Supplementary material from "Possible fates of the dispersion of SARS-COV-2 in the Mexican context"
<p>The determination of the adequate time for house confinement and when social distancing restrictions should end are now two of the main challenges that any country has to face in an effective battle against. The possibility of a new outbreak of the pandemic and how to avoid it is, nowadays, one of the primary objectives of epidemiological research. In this work, we go deep in this subject by presenting an innovative compartmental model, that explicitly introduces the number of active cases, and employing it as a conceptual tool to explore the possible fates of the dispersion of SARS-COV-2 in the Mexican context. We incorporated the impact of starting, inattention, and end of restrictive social policies on the time evolution of the pandemics via time-in-run corrections to the infection rates. The magnitude and impact on the epidemic due to post-social restrictive policies are also studied. The scenarios generated by the model can help authorities to determine an adequate time and population load that may be allowed to reassume normal activities.</p>
VTR case studies datasets: myoglobin against hemoglobin, RBDs of SARS-CoV-1 vs. SARS-CoV-2, and glucose-tolerant vs. non-tolerant β-glucosidases
<p>Description of the four files:</p> <ol> <li><strong>contacts.xlsx</strong> <ul> <li>List of detected contacts for the three case studies</li> </ul> </li> <li><strong>pymol_files_case_study_1.zip</strong> <ul> <li>Contains files in PDB format of the analyzed structures, and files in PML format used to display visualizations in the PyMOL tool for the case study 1: comparison between contacts of myoglobin against hemoglobin</li> </ul> </li> <li><strong>pymol_files_case_study_2.zip</strong> <ul> <li>Contains files in PDB format of the analyzed structures, and files in PML format used to display visualizations in the PyMOL tool for the case study 2: comparison between contacts of RBDs of SARS-CoV-1 vs. SARS-CoV-2 both complexed with the cell receptor ACE2</li> </ul> </li> <li><strong>pymol_files_case_study_3.zip</strong> <ul> <li>Contains files in PDB format of the analyzed structures, and files in PML format used to display visualizations in the PyMOL tool for the case study 3: comparison between contacts of glucose-tolerant vs. non-tolerant β-glucosidases </li> </ul> </li> </ol>
Pandemic-related Attitudes, Stressors and Work Outcomes among Medical Assistants during the SARS-CoV-2 ("Coronavirus") Pandemic in Germany: a cross-sectional Study
<p>File type: SPSS file (.sav)</p> <p>Study type: Cross-sectional study</p> <p>Population: Medical assistants in Germany</p> <p>Study period: April 7th-April 14th, 2020</p> <p>Number of participants: 2150</p> <p>Research question: Investigation of pandemic-related attitudes, stressors and work outcomes among medical assistants during the SARS-CoV-2 (“Coronavirus”) pandemic</p> <p>Missing values: None (due to online survey) </p> <p>Original variables: v_982, v_1, v_2, v_3, v_5, v_6, v_7, v_13, v_14, v_21, v_22, v_23, v_24, v_26, v_27, v_28, v_29, v_31, v_32, v_33, v_40, v_41, v_42, v_43, v_46, v_47, v_48 v_49, v_52, v_57, Beruf_MFA</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p>
SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study
<p><strong>Background</strong>: Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions.</p> <p><strong>Methods</strong>: We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss.</p> <p><strong>Findings</strong>: The use of high efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% CrI: 73.1% - 85.7%) and 87% (CrI: 80.0% - 92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation.</p> <p><strong>Conclusions</strong>: Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.</p>
Predicting reservoir hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic
<p><span>The SARS-CoV-2 pandemic has raised the concern for reservoir hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features, to calculate the infection likelihoods and further predict the probable hosts of novel viruses. Overcoming the limitation of sequence similarity-based methods, DeepHoF was applied to the analysis of SARS-CoV-2 in the 2020 pandemic. Using the isolates sequenced in the earliest stage of COVID-19, DeepHoF identified minks, bats, dogs and cats can be highly susceptible to SARS-CoV-2, while minks might be one of the most noteworthy reservoir hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the infection likelihood on human or the host range. With a large-scale genome analysis based on DeepHoF's computation for the later world-wide pandemic, it should not be slighted for the probably bidirectional transmission of SARS-CoV-2 between humans and minks.</span></p>
Targeted Intracellular Degradation of SARS-CoV-2 via Computationally-Optimized Peptide Fusions
<p>The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has elicited a global health crisis of catastrophic proportions. With only a few vaccines approved for early or limited use, there is a critical need for effective antiviral strategies. In this study, we report a unique antiviral platform, through computational design of ACE2-derived peptides which both target the viral spike protein receptor binding domain (RBD) and recruit E3 ubiquitin ligases for subsequent intracellular degradation of SARS-CoV-2 in the proteasome. Our engineered peptide fusions demonstrate robust RBD degradation capabilities in human cells and are capable of inhibiting infection-competent viral production, thus prompting their further experimental characterization and therapeutic development. </p>
SIRAH-CoV2 initiative: SARS-CoV-2 helicase (PDB id:6ZSL)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Helicase protein (PDB id:6ZSL). 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>. Parameters for Zinc ions are those reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6ZSL_SIRAHcg_rawdata_0-4us.tar, 6ZSL_SIRAHcg_rawdata_4-8us.tar, and 6ZSL_SIRAHcg_rawdata_8-10us.tar contain all the raw information required to visualize (using 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 6ZSL_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6ZSL_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 6ZSL_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6ZSL_SIRAHcg_prot.prmtop 6ZSL_SIRAHcg_prot.ncrst 6ZSL_SIRAHcg_prot_10us_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 Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</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>
Electron microscopy of SARS-CoV-2 particles - Dataset 12
<p>The dataset contains transmission electron microscopy image stacks (tomograms) of ultrathin sections through extracellular SARS-CoV-2 particles in Vero cell cultures. The dataset contains 11 image stacks of 1900 x 1900 pixel dimensions, which were recorded at 0.57 nm pixel size (12 bit). Image stacks were size calibrated and stored in 8 bit TIF format. Visualization can be done using ImageJ or Fiji. A PDF document describes the methods used for generation of the image files. The dataset was generated as dataset 12 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 10
<p>The dataset contains 66 transmission electron microscopy images of ultrathin (110 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 10 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 09
<p>The dataset contains 101 transmission electron microscopy images of ultrathin (85 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimensions at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 09 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</p>
Electron microscopy of SARS-CoV-2 particles - Dataset 08
<p>The dataset contains 85 transmission electron microscopy images of ultrathin (65 nm) plastic sections through extracellular SARS-CoV-2 particles in Vero cell cultures. Images were recorded with 1376 x 1032 pixel dimension at 0.64 nm pixel size (12 bit) and stored in 16 bit TIF format. For visualization of the images, use an image viewer capable of reading 16 bit images (e.g. IrfanView). Image files are size calibrated and can be opened with the correct size calibration using ImageJ or Fiji using the Bioformats importer. The image files are accompanied by a PDF document which describes the methods which were used for generation of the images. The dataset was produced as dataset 08 for a comparative morphometric analysis of SARS-CoV and SARS-CoV-2. Further datasets which were used for the analysis are available in this repository (see dataset description document).</p> <p>Related publication: Laue M, Kauter A, Hoffmann T, Möller L, Michel J, Nitsche A. Morphometry of SARS-CoV and SARS-CoV-2 particles in ultrathin plastic sections of infected Vero cell cultures. Sci Rep. 2021 Feb 10;11(1):3515. doi: 10.1038/s41598-021-82852-7. PMID: 33568700; PMCID: PMC7876034.</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>
Attitudes and Stressors related to the SARS-CoV-2 Pandemic among Emergency Medical Services Workers in Germany: A cross-sectional Study
<p>This dataset stems from a cross-sectional study conducted in April and Mai 2020 among n=1537 emergency medical services workers (EMS) from entire Germany during the first peak of the SARS-CoV-2 pandemic. The study questionnaire was distributed online with help of the German Association of Emergency Medical Service on their social media channels. The collected data provides insights into major stressors among EMS workers at the first peak of the pandemic in Germany and allows for analysis of possible determinants of major stressors via logistic regression analysis. No funding was obtained for this study.</p> <p> </p> <p><strong>Research question:</strong></p> <p>Investigation of pandemic-related attitudes, stressors and work outcomes among emergency medical services workers during the SARS-CoV-2 pandemic</p> <p><strong>Study population: </strong></p> <p>Emergency medical services workers in Germany</p> <p><strong>Study type: </strong></p> <p>Cross-sectional study (two independent cross-sectional waves)</p> <p><strong>File type: </strong></p> <p>SPSS file (.sav)</p> <p><strong>Study periods: </strong></p> <p>First wave: April 9th-16th 2020<br> Second wave: Mai 14th-21st 2020</p> <p><strong>Number of participants: </strong></p> <p>1537</p> <p><strong>Missing values: </strong></p> <p>None (due to online survey) </p> <p><strong>Original variables: </strong></p> <p>v_982, v_1, v_2, v_31, v_4, v_5, v_7, dupl1_v_13, dupl1_v-14, v_57, v_13, v_37, v_38, v_39, v_40, v_41, v_42, v_43, v_44, v_45, v_46, v_47, v_48, v_49, v_50, dupl1_v_40, dupl1_v_41, dupl1_v_42, dupl1_v_43, v_55, Beruf_Rettungsdienst, Welle</p> <p>All other variables were calculated from the original variables either by rescaling or dichotomization. </p> <p>Dichotomization of attitudes, stressors and work outcomes: <br> Answer options "Strongly disagree" and "Disagree" were labelled as "no"<br> Answer options "Agree" and "Strongly Agree" were labelled as "yes"</p> <p>Dichotomization of self-rated health:<br> Answer options "Very bad", "Bad" and "Moderate" were labelled as "Bad".<br> Answer options "Good" and "Very good" were labelled as "Good".</p>
Supplementary Material and Code of Evidence for immunity to SARS-CoV-2 from epidemiological data series
<p>This Supplementary Material file, Code and Data correspond to the manuscript:</p> <p>Evidence for immunity to SARS-CoV-2 from epidemiological data series<br> Pablo Yubero, Alvar A. Lavin, Juan F Poyatos<br> medRxiv 2020.07.22.20160028; doi: https://doi.org/10.1101/2020.07.22.20160028</p>
"A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset
<p>Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats and FORTRAN code.</p>
An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2
<p>An Introduction to a Bayesian Analysis of the Laboratory Origin of SARS-CoV-2</p>
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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)
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.