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1,670 results for “forcing”
GMX_lipid17.ff: Gromacs Port of the amber LIPID17 force field
<p>This is a Gromacs port of the amber LIPID17 force field. To use this force field, the user can construct the lipid bilayer using Charmm-GUI and convert the atom names to the amber atom names using charmmlipid2amber.py. This port has also retained the modular feature of the LIPID17 force field, where the user can customise the head group or acryl chain and use pdb2gmx to construct the topology.</p> <p>The coordinate files for the amber lipids can also be obtained from the `gro` folder. The force field `lipid17.ff`, itp file `lipid17.itp` and a custom PI head group are all included in the attached compressed file. For the details of the generation and validation protocol, please consult the relevant <a href="https://github.com/xiki-tempula/gmx_lipid17.ff">Github</a> page.</p>
EOSC Task Force on FAIR Metrics and Data Quality: FAIR Evaluation community survey 2023
<p>The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the <a href="https://ec.europa.eu/eusurvey/">EUsurvey tool</a> between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.</p>
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent
<p>This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). <br><br>For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>.</p> <p>The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.<br><br>For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:</p> <p>bioclim01 = Annual Mean Temperature<br>bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))<br>bioclim03 = Isothermality (BIO2/BIO7) (×100)<br>bioclim04 = Temperature Seasonality (standard deviation ×100)<br>bioclim05 = Max Temperature of Warmest Month<br>bioclim06 = Min Temperature of Coldest Month<br>bioclim07 = Temperature Annual Range (BIO5-BIO6)<br>bioclim08 = Mean Temperature of Wettest Quarter<br>bioclim09 = Mean Temperature of Driest Quarter<br>bioclim10 = Mean Temperature of Warmest Quarter<br>bioclim11 = Mean Temperature of Coldest Quarter<br>bioclim12 = Annual Precipitation<br>bioclim13 = Precipitation of Wettest Month<br>bioclim14 = Precipitation of Driest Month<br>bioclim15 = Precipitation Seasonality (Coefficient of Variation)<br>bioclim16 = Precipitation of Wettest Quarter<br>bioclim17 = Precipitation of Driest Quarter<br>bioclim18 = Precipitation of Warmest Quarter<br>bioclim19 = Precipitation of Coldest Quarter<br>huss = Average (arithmetric mean) specific humidity<br>rsds = Average (arithmetric mean) Surface downwelling shortwave radiation<br>sfcwind = Average near-surface wind speed (arithmetric mean)<br><br>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> <p><br>All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse. </p>
Input files for simulation of potassium channels using the AMOEBA polarizable force field
<p>This dataset contains input Tinker xyz and key files for the simulation of KcsA potassium channels in DOPC bilayer, a simple script for converting CHARMM pdb file to Tinker xyz file, and modified Tinker source code to support one-dimensional position restraints.<br> "params.tar.gz" contains a description of the force field modifications.<br> <br> To use "mod2", add the following lines to the key file.</p> <pre><code>#compatible with amoebabio18.prm polarize 5 1.4500 0.3900 3 polarize 11 1.4500 0.3900 9 polarize 3 1.7500 0.3900 1 5 7 50 225 227 polarize 9 1.7500 0.3900 1 7 11 50 225 227</code></pre> <p> </p>
Atomic force microscopy indentation data of zebrafish spinal cord sections
<p>The HDF5 file was created using the Python package nanite. It contains 1132 raw atomic force microscopy (AFM) force-indentation curves of zebrafish spinal cord sections, the preprocessed curves, and the corresponding fits to the approach part. In addition, a manual rating was assigned to each force-indentation curve. The intended use of this dataset is the application of machine-learning approaches to quantify AFM data quality for biological tissues.</p>
Conformations and cryo-force spectroscopy of spray-deposited single-strand DNA on gold: Lifting atomic coordinates
<p>Here we provide the atomic coordinates and the topology file concerning the lifting process of a single stranded DNA molecule previously adsorbed on gold. In order to visualize it you will need a visualization software. Using VMD, you would only need to do in a terminal:</p> <p>vmd -e visualize.vmd </p> <p>and that is it. If you find this useful, please cite the corresponding paper:<br> Nature Communications 10, 685 (2019) [DOI: https://doi.org/10.1038/s41467-019-08531-4 ]</p>
Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years
<p>The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.</p>
Adenylate Kinase Potential of Mean Force
<p>Adenylate kinase (AdK) is a enzyme that undergoes a large hinge-like motion. Because of an abundance of structural and functional data, it has become a standard system to test computational methods for sampling conformational transitions.<sup>1</sup></p> <p>In 2009, we studied the conformational transition between open and closed <em>E. coli</em> AdK without substrate, i.e. “apo AdK”, with a variety of computational methods.<sup>2</sup> As part of the study we also produced a free energy landscape (a <strong>potential of mean force</strong> or <strong>PMF</strong>) as a function of two collective variables, the angles formed by the LID and NMP domains with the CORE domain.<sup>3</sup> We <sup>2</sup> and others<sup>4</sup><sup>,</sup><sup>5</sup> have used this PMF to compare methods that sample transition paths to the underlying free energy landscape.</p> <p><strong>Terms of Use</strong></p> <p>The data are made available under a <strong>Attribution-ShareAlike 4.0 International</strong> licence (include the following when using the data):</p> <p><em>Adenylate Kinase Potential of Mean Force</em> by O Beckstein, EJ Denning, JR Perilla, TB Woolf is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Based on a work at http://becksteinlab.physics.asu.edu/file_download/11/free_bw2_tol1e-05.dat.</p> <p>When you make use of the data (contained in the file free_bw2_tol1e-05.dat) in published work, <strong>cite</strong> the paper<sup>2</sup></p> <p>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open ? closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</p> <p><strong>Data</strong></p> <p>The file free_bw2_tol1e-05.dat contains the PMF data shown in Fig. 4a of the JMB paper<sup>2</sup>.</p> <p>The image shows the data plotted with cubic spline smoothing.</p> <p>File format</p> <p>free_bw2_tol1e-05.dat is the output from WHAM. The important data columns are</p> <ol> <li>NMP-core angle (degrees)</li> <li>LID-core angle (degrees)</li> <li>free energy in kcal/mol</li> </ol> <p>(Other columns are output from wham and can be ignored.)</p> <p><strong>Methods</strong></p> <p>Conformations of <em>E. coli</em> AdK were umbrella-sampled in the space of the NMP-core and LID-core angles.<sup>3</sup> The protein was modelled in implicit solvent with the ACE2 electrostatics model. The resulting umbrella data were unbiased using Alan Grossfield’s wham code with</p> <ul> <li>bin size 2º</li> <li>tolerance of the self consistency procedure 1e-5 <em>kT</em></li> <li>limits 34º < NMP < 80º and 94º < LID < 156º</li> </ul> <p>The first 2000 frames (200ps) of each window were discarded as equilibration and the remaining 3000 frames were used for the PMF. For further details please see the paper.<sup>2</sup></p> <p><strong>References</strong></p> <ol> <li>S. L. Seyler and O. Beckstein, O. <em>Sampling large conformational transitions: adenylate kinase as a testing ground</em>. Mol. Simul., 40(10–11): 855–877, 2014.</li> <li>O. Beckstein, E. J. Denning, J. R. Perilla, and T. B. Woolf. <em>Zipping and unzipping of adenylate kinase: Atomistic insights into the ensemble of open / closed transitions</em>. J. Mol. Biol., 394(1):160–176, 2009.</li> <li>See the 2009 paper<sup>2</sup> for the definitions and the MDAnalysis tutorial’s Exercise 4 for Python code to calculate the angles.</li> <li>M. Gur, J. D. Madura, and I. Bahar. <em>Global transitions of proteins explored by a multiscale hybrid methodology: Application to adenylate kinase</em> Biophysical Journal, 105(7):1643 – 1652, 2013.</li> <li>A. Uyar, N. Kantarci-Carsibasi, T. Haliloglu, and P. Doruker. <em>Features of large hinge-bending conformational transitions. Prediction of closed structure from open state</em>. Biophysical Journal, 106(12):2656 – 2666, 2014 </li> </ol>
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Dataset - Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force.
<p>README<br>====================<br>This repository contains the data and documentation for a research project. It includes the dataset,<br>which is provided in CSV format and the original PDF with the survey answers.</p> <p>Research Information<br>====================<br>Terminology of e-Oral Health: Consensus Report of the IADR’s e-Oral Health Network Terminology<br>Task Force. Authors reported multiple definitions of e-oral health and related terms, and used several definitions<br>interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International<br>Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on<br>terminology related to digital technologies used in oral healthcare.</p> <p>This dataset contains data from a survey about digital oral health. The survey asked participants to provide their definition of various terms related to digital oral health, as well as their agreement with the provided definitions. The dataset also includes three figures that the participants were asked to review.</p> <p>The purpose of this dataset is to collect data on the public's understanding of digital oral health terms and to identify areas where there may be confusion or misinterpretation. The data from this dataset could be used to develop educational materials or to improve the way that digital oral health information is communicated to the public.</p> <p>Additional notes<br>====================<br>The data is not currently cleaned or preprocessed.</p> <p>Dataset<br>====================<br>The dataset file, named "dataset.csv," is in this repository. It contains the raw anonymized data<br>collected from the participants in a structured format. Each row represents a respondent, and the<br>columns correspond to different variables.</p> <p>Codebook<br>====================<br>The codebook file, named "codebook.pdf," is also included in this repository. It provides a<br>comprehensive description of the variables present in the dataset. The codebook outlines each<br>variable's meaning, type, and possible values, allowing users to understand and analyze the data<br>effectively.</p> <p>Metadata<br>====================<br>No metadata is provided</p> <p>Files<br>====================<br>01_readme.txt this readme file<br>02_codebook.pdf The codebook of the dataset<br>03_dataset.csv The dataset in csv format<br>04_e-OHN Delphi (2023-02-03).pdf The output from the survey</p> <p>Usage<br>====================<br>To work with the dataset, you can download the "dataset.csv" file and import it into your preferred<br>software or programming language for analysis. The codebook provides valuable information about<br>the variables, allowing you to understand the data structure and make informed decisions during your<br>analysis.<br>Please note that while every effort has been made to ensure the accuracy and quality of the data, it is<br>important to review the codebook and understand the context of the research before concluding the<br>dataset.</p> <p>License<br>====================<br>The data and documentation in this repository are provided under the CC BY-SA.<br>This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-SA includes the following elements:</p> <p> BY: credit must be given to the creator.<br> SA: Adaptations must be shared under the same terms.<br> <br>Please refer to the license file for further details on how the data can be used and shared.</p> <p>Contact Information<br>====================<br>For any questions, clarifications, or inquiries related to the dataset or research project, please contact<br>Assoc Prof Dr Sergio Uribe, sergio.uribe@rsu.lv</p>
Confocal Microscopy Visualizes Particle-Crack Interactions in Epoxy Composites with Optical Force Probe-Crosslinked Rubber Particles
<p>Data (*.csv and *.lif) corresponding to Figures 2-7 of the manuscript and Figures S1-S2 of the Supporting Information.</p>
Mechanical characterisation of the developing cell wall layers of tension wood fibres by Atomic Force Microscopy
<p>This dataset corresponds to the Arnould et al. (2022) paper (available at https://www.biorxiv.org/content/10.1101/2021.09.23.461481v1.full) on the mechanical characterization of developing cell wall layers of tension wood fibers by Atomic Force Microscopy. It contains all raw AFM files (Bruker format .spm, readable by the free software Gwyddion for example) corresponding to mechanical measurements of poplar reaction wood cells (clone 717-1B4) along 3 radial lines/rows, starting from the cambium. Each cell is identified by its "macroscopic" distance from the cambium (value in µm in the name of each file corresponding to the displacement of the sample in the AFM) which was corrected after using the AFM optical image captures. Some files, with a -z extension after the distance value, correspond to a zoom into the cell wall. The data also contain measurements made for mechanical calibration on epoxy embedded Kevlar fibers, controlled measurements in the embedding resin between each radial line and measurements in normal wood cells. Two csv files containing final data extracted from AFM measurements that give the value of the indentation modulus and the relative thickness to cell diameter ratio (by AFM and by phase contrast optical microscopy) in each cell wall layer as a function of cambium distance are also provided.</p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
Calibration Dataset of Device for Measuring Forces and Torques in Flexible Connection Joints for Parabolic Trough Collector
<p>This dataset corresponds with the calibration tests of device for measuring forces and torques in flexible connection joints for parabolic trough collector. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 823802 (SFERA-III), and it is related with the milestone number MS29 of task 10.1.B - Enhancement of sensor monitoring/calibration and measurement accuracy of laboratory test benches of RI.</p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Kinematic and Electromyographic Recordings during Dynamic, Repetitive, Low-Force Movements
<p>Experimental recordings of kinematic (XSens Awinda, Full-Body) and electromyographic (Delsys Trigno, 8 Right Arm Muscles) data during 4 repetive upper limb exercises with a 1.5Kg dumbell: A) elbow counter-gravity flexion and gravity-assisted extension, with torso tilted forwards; B) shoulder counter-gravity abduction and gravity-assisted adduction; C) shoulder counter-gravity flexion and gravity-assisted extensio; D) composite sequence of elbow/shoulder flexion/extension motions, executed until self-reported fatigue.</p> <p>A total of 17 healthy volunteers (11 Male; 6 Female; 23.82 ± 2.79 years old, 69.06 ± 14.75 Kg) were recruited. Each participant willingly agreed to participate in the study and gave their signed, informed consent, following the standard set by the declaration of Helsinki and the Oviedo Conventions.</p> <p>Additionally, self-reported fatigue after each exercise, according to Borg's Perceived Exertion Scale (Borg, 1998), is provided for all subjects.</p> <p>For a detailed description of the experimental protocol and instrumentation, refer to associated research paper (submission under review).</p>
Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation: Confocal and atomic force microscopy
<p><strong>Summary</strong></p> <p>Dataset of imaging data related to the publication Raudenska, M., Kratochvilova, M., Vicar, T., Gumulec, J., Balvan, J., Polanska, H. Pribyl, J. & Masarik, M.:Cisplatin enhances cell stiffness and decreases invasiveness rate in prostate cancer cells by actin accumulation. <em>Scientific Reports </em><strong>2019, </strong>9, 1660</p> <p>This dataset includes image data of <em>atomic force microcopy</em> (Young modulus) and <em>confocal microscopy</em>(staining of F-actin and β-tubulin) of prostate cell lines PNT1A, 22Rv1, and PC-3. </p> <p><strong>Materials and Methods</strong></p> <p><em>Cells, cell culture conditions</em></p> <p>Cells confluent up to 50–60% were washed with a FBS-free medium and treated with a fresh medium with FBS and required antineoplastic drug concentration (IC50 concentration for the particular cell line). The cells were treated with 93 µM (PC-3), 38 µM (PNT1A), and 24 µM (22Rv1) of cisplatin (Sigma-Aldrich, St. Louis, Missouri), respectively. IC50 concentrations used for treatment with docetaxel (Sigma-Aldrich, St. Louis, Missouri) were 200nM for PC-3, 70nM for PNT1A, and 150nM for 22Rv1. </p> <p><em>Long-term zinc (II) treatment of cell cultures</em></p> <p>Cells were cultivated in the constant presence of zinc(II) ions. Concentrations of zinc(II) sulphate in the medium were increased gradually by small changes of 25 or 50 µM. The cells were cultivated at each concentration no less than one week before harvesting and their viability was checked before adding more zinc. This process was used to select zinc resistant cells naturally and to ensure better accumulation of zinc within the cells (accumulation of zinc is usually poor during the short-term treatment of prostate cancer cells). Total time of the cultivation of cell lines in the zinc(II)-containing media exceeded one year. Resulting concentrations of zinc(II) in the media (IC50 for the particular cell line) were 50 µM for the PC-3 cell line, 150 µM for the PNT1A cell line, and 400 µM for the 22Rv1 cell line. The concentrations of zinc(II) in the media and FBS were taken into account. </p> <p><em>Actin and tubulin staining</em></p> <p>β-tubulin was labeled with anti- β tubulin antibody [EPR1330] (ab108342) at a working dilution of 1/300. The secondary antibody used was Alexa Fluor® 555 donkey anti-rabbit (ab150074) at a dilution of 1/1000. Actin was labeled with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen); 1 unit per slide. For mounting Duolink® In Situ Mounting Medium with DAPI (DUO82040) was used. The cells were fixed in 3.7% paraformaldehyde and permeabilized using 0.1% Triton X-100. </p> <p><em>Confocal microscopy</em></p> <p>The microscopy of samples was performed at the Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic. Leica DM RXA microscope (equipped with DMSTC motorized stage, Piezzo z-movement, MicroMax CCD camera, CSU-10 confocal unit and 488, 562, and 714 nm laser diodes with AOTF) was used for acquiring detailed cell images (100× oil immersion Plan Fluotar lens, NA 1.3). Total 50 Z slices was captured with Z step size 0.3 μm.</p> <p><em>Atomic force microscopy</em></p> <p>We used the bioAFM microscope JPK NanoWizard 3 (JPK, Berlin, Germany) placed on the inverted optical microscope Olympus IX‑81 (Olympus, Tokyo, Japan) equipped with the fluorescence and confocal module, thus allowing a combined experiment (AFM‑optical combined images). The maximal scanning range of the AFM microscope in X‑Y‑Z range was 100‑100‑15 µm. The typical approach/retract settings were identical with a 15 μm extend/retract length, Setpoint value of 1 nN, a pixel rate of 2048 Hz and a speed of 30 µm/s. The system operated under closed-loop control. After reaching the selected contact force, the cantilever was retracted. The retraction length of 15 μm was sufficient to overcome any adhesion between the tip and the sample and to make sure that the cantilever had been completely retracted from the sample surface. Force‑distance (FD) curve was recorded at each point of the cantilever approach/retract movement. AFM measurements were obtained at 37°C (Petri dish heater, JPK) with force measurements recorded at a pulling speed of 30 µm/s (extension time 0.5 sec).</p> <p>The Young's modulus (E) was calculated by fitting the Hertzian‑Sneddon model on the FD curves measured as force maps (64x64 points) of the region containing either a single cell or multiple cells. JPK data evaluation software was used for the batch processing of measured data. The adjustment of the cantilever position above the sample was carried out under the microscope by controlling the position of the AFM‑head by motorized stage equipped with Petri dish heater (JPK) allowing precise positioning of the sample together with a constant elevated temperature of the sample for the whole period of the experiment. Soft uncoated AFM probes HYDRA-2R-100N (Applied NanoStructures, Mountain View, CA, USA), i.e. silicon nitride cantilevers with silicon tips are used for stiffness studies because they are maximally gentle to living cells (not causing mechanical stimulation). Moreover, as compared with coated cantilevers, these probes are very stable under elevated temperatures in liquids – thus allowing long-time measurements without nonspecific changes in the measured signal.</p> <p><em>Image analysis</em></p> <p>Fluorescence microscopy data were analyzed in ImageJ 1.52h and Python 3.7.1 as follows: cells were manually segmented using actin fluorescence channel, two regions were created for analysis: whole cell and cell periphery, lining a 4 μm thick region around cell border and including most of periphery actin cytoskeleton. In these two regions following parameters were measured for both actin and tubulin fluorescence: Integrated intensity, median intensity, and following regions were measured to describe cell morphology: Cell area, Maximum caliper (max feret diameter), roundness, and aspect ratio. Moreover, stress fibers were manually segmented in every cell and following parameters were measured: number of fibers per cell, feret angle of fiber, integrated intensity, fiber length, mean intensity. Next, a standard deviation of feret angles of individual fibers was calculated relatively to mean of feret angle using a circstd function from scipy package for Python.</p> <p><strong>Identification of files</strong></p> <p><em>Microscopy data</em></p> <p>Files are separated into individual zip files. The dataset of <em>confocal microscopy </em>is separated based on treatments: untreated control, docetaxel-treated cells, cisplatin-treated cells, zinc-treated cells. Filenames actin_tubulin_Zstack_cisplatin.zip, actin_tubulin_Zstack_untreated_control.zip, actin_tubulin_Zstack_zinc.zip, actin_tubulin_Zstack_docetaxel.zip. Files included in these ZIP archives are named as follows: "cellline_treatment_FOV". Files are 3-layer 16bit tiff files with layer sequence as follows: F-Actin (Phalloidin)/b-tubulin/Hoechst 33342. The dataset contains 242 FOVs of three cell line types/three treatments + one control, files are Z-stacks made of 50 slices.</p> <p>The dataset of <em>atomic force microscopy </em>(AFM) is included in one ZIP archive "AFM_YoungModulus_SetpointHeight.zip", which includes data on Young modulus and Setpoint Height of cell lines 22Rv1, PNT1A and PC-3 and treatments zinc, docetaxel, cisplatin (+control), i.e. identical like for confocal microscopy. The file naming is as follows: "AFM_cellline_treatment_FOV_Youngmodulus.tif" for Young modulus and "AFM_cellline_treatment_FOV_setpointheight.tif" for setpoint height. The data are filtered 32-bit tiff images, where the pixel value correspond to cell stiffness (young modulus) in Pa or setpoint height in m.</p> <p><em>Confocal microscopy analysis files</em></p> <p>Following files are csv tables including image analysis of actin/tubulin staining captured by confocal microscope:</p> <p>Cytoskeleton_fluo_analysis_Cell_Cell_periphery_morphology.csv: table includes analyzed data for actin and tubulin staining in following cellular regions: cell, cell periphery. Standard ImageJ parameters regarding intensity and morphology included.</p> <p>Cytoskeleton_fluo_analysis_Fibers.csv: table includes results of manual segmentation and consequent analysis of actin stres fibers in the cells. Apart from standard ImageJ parameters, also number of stress fibers per cell and standard deviation of fiber angle relative to the cell mean angle (for details see methods) are included.</p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Change in total simulated seafloor biomass between the late Scenario period (2081-2100) and late Historical period (1995-2014) under the SSP scenarios 126 to 585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a.</p> <p> </p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Dataset of knee joint contact force peaks and corresponding subject characteristics from 4 open datasets
<p>This dataset contains data from overground walking trials of 166 subjects with several trials per subject (approximately 2900 trials total).</p> <p><strong>DATA ORIGINS & LICENSE INFORMATION</strong></p> <p>The data comes from four existing open datasets collected by others:</p> <p>Schreiber & Moissenet, A multimodal dataset of human gait at different walking speeds established on injury-free adult participants</p> <ul> <li>article: https://www.nature.com/articles/s41597-019-0124-4</li> <li>dataset: https://figshare.com/articles/dataset/A_multimodal_dataset_of_human_gait_at_different_walking_speeds/7734767</li> </ul> <p>Fukuchi et al., A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals</p> <ul> <li>article: https://peerj.com/articles/4640/</li> <li>dataset: https://figshare.com/articles/dataset/A_public_data_set_of_overground_and_treadmill_walking_kinematics_and_kinetics_of_healthy_individuals/5722711</li> </ul> <p>Horst et al., A public dataset of overground walking kinetics and full-body kinematics in healthy adult individuals</p> <ul> <li>article: https://www.nature.com/articles/s41598-019-38748-8</li> <li>dataset: https://data.mendeley.com/datasets/svx74xcrjr/3</li> </ul> <p>Camargo et al., A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions</p> <ul> <li>article: https://www.sciencedirect.com/science/article/pii/S0021929021001007</li> <li>dataset (3 links): https://data.mendeley.com/datasets/fcgm3chfff/1 https://data.mendeley.com/datasets/k9kvm5tn3f/1 https://data.mendeley.com/datasets/jj3r5f9pnf/1</li> </ul> <p>In this dataset, those datasets are referred to as the Schreiber, Fukuchi, Horst, and Camargo datasets, respectively.<br> The Schreiber, Fukuchi, Horst, and Camargo datasets are licensed under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).</p> <p>We have modified the datasets by analyzing the data with musculoskeletal simulations & analysis software (OpenSim).<br> In this dataset, we publish modified data as well as some of the original data.</p> <p><br> <strong>STRUCTURE OF THE DATASET</strong><br> The dataset contains two kinds of text files: those starting with "predictors_" and those starting with "response_".</p> <p>Predictors comprise 12 text files, each describing the input (predictor) variables we used to train artifical neural networks to predict knee joint loading peaks.<br> Responses similarly comprise 12 text files, each describing the response (outcome) variables that we trained and evaluated the network on.<br> The file names are of the form "predictors_X" for predictors and "response_X" for responses, where X describes which response (outcome) variable is predicted with them.<br> X can be:<br> - loading_response_both: the maximum of the first peak of stance for the sum of the loading of the medial and lateral compartments<br> - loading_response_lateral: the maximum of the first peak of stance for the loading of the lateral compartment<br> - loading_response_medial: the maximum of the first peak of stance for the loading of the medial compartment<br> - terminal_extension_both: the maximum of the second peak of stance for the sum of the loading of the medial and lateral compartments<br> - terminal_extension_lateral: the maximum of the second peak of stance for the loading of the lateral compartment<br> - terminal_extension_medial: the maximum of the second peak of stance for the loading of the medial compartment<br> - max_peak_both: the maximum of the entire stance phase for the sum of the loading of the medial and lateral compartments<br> - max_peak_lateral: the maximum of the entire stance phase for the loading of the lateral compartment<br> - max_peak_medial: the maximum of the entire stance phase for the loading of the medial compartment<br> - MFR_common: the medial force ratio for the entire stance phase<br> - MFR_LR: the medial force ratio for the first peak of stance<br> - MFR_TE: the medial force ratio for the second peak of stance</p> <p>The predictor text files are organized as comma-separated values. Each row corresponds to one walking trial. A single subject typically has several trials.<br> The column labels are DATASET_INDEX,SUBJECT_INDEX,KNEE_ADDUCTION,MASS,HEIGHT,BMI,WALKING_SPEED,HEEL_STRIKE_VELOCITY,AGE,GENDER.</p> <ul> <li>DATASET_INDEX describes which original dataset the trial is from, where {1=Schreiber, 2=Fukuchi, 3=Horst, 4=Camargo}</li> <li>SUBJECT_INDEX is the index of the subject in the original dataset. If you use this column, you will have to rewrite these to avoid duplicates (e.g., several datasets probably have subject "3").</li> <li>KNEE_ADDUCTION is the knee adduction-abduction angle (positive for adduction, negative for abduction) of the subject in static pose, estimated from motion capture markers.</li> <li>MASS is the mass of the subject in kilograms</li> <li>HEIGHT is the height of the subject in millimeters</li> <li>BMI is the body mass index of the subject</li> <li>WALKING_SPEED is the mean walking speed of the subject during the trial</li> <li>HEEL_STRIKE_VELOCITY is the mean of the velocities of the subject's pelvis markers at the instant of heel strike</li> <li>AGE is the age of the subject in years</li> <li>GENDER is an integer/boolean where {1=male, 0=female}</li> </ul> <p>The response text files contain one floating-point value per row, describing the knee joint contact force peak for the trial in newtons (or the medial force ratio). Each row corresponds to one walking trial.<br> The rows in predictor and response text files match each other (e.g., row 7 describes the same trial in both predictors_max_peak_medial.txt and response_max_peak_medial.txt).</p> <p><br> See our journal article "Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks" (https://doi.org/10.1007/s10439-023-03278-y) for more information.</p> <p>Questions & other contacts: jere.lavikainen@uef.fi</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)
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DANDI Archive for NWB datasets
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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
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