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1,670 results for “FORCE”
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field through the use of Gromacs input files, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Specifically, Gromacs file format provided by [1] was specifically used for this simulation.</p> <p>Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
Data presented in "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states"
<p>These are the data presented in our paper "Three-dimensional Doppler, polarization-gradient, and magneto-optical forces for atoms and molecules with dark states" which has been accepted for publication in the New Journal of Physics (as of 07 November 2016).</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for GROMACS simulation engine v5
<p>All runs were performed with GROMACS simulation engine v5 and CHARMM36 additive force field parameters obtained from MacKerell lab website (http://mackerell.umaryland.edu/charmm_ff.shtml, also available at</p> <p>https://doi.org/10.5281/zenodo.209080). Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration). </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p>
Methanol-water mixtures obtained with the OPLS force field and SPCE water model
<p>These are Gromacs trajectories, 20 ps each, with MD information corresponding to pure water (one trajectory), methanol (one trajectory), and 2:1 molar mixtures of the two (10 trajectories). The pure water system contains 800 molecules, pure methanol contains 400 molecules, and each of the mixtures contains 400 water molecules and 200 methanol molecules. The force field employed is OPLS and water was simulated with the rigid SPCE model.</p> <p>These trajectories are made available with the main purpose of being used to run one of the DoSPT tutorials:</p> <p>http://dospt.org/index.php/Tutorial_2:_entropy_of_mixing_of_methanol%2Bwater</p>
Predictive and sensory signals systematically lower peak forces in the exploration of softer objects
<p>An object’s softness is stereotypically judged by pressure movements indenting the surface [1]. In exploration without movement constraints, participants repeat such indentation movements. We investigated how people modulate executed peak forces for different indentations depending on stimulus softness. Participants performed a 2AFC discrimination task for stimulus pairs from one of 4 softness categories. We assumed that movement control at different exploration moments is based on variations in the predictive and sensory signals available. We manipulated availability of predictive signals on softness category, by presenting either stimuli of the same category in a blocked fashion (high predictability) or by randomly mixing stimuli from different categories (low predictability). Effects of sensory signals were examined by contrasting first and last indentation, as sensory signals are hardly available when initiating exploration but gathered during exploration. The results show that participants systematically apply lower forces when sensory or predictive signals indicate softer objects as compared to harder objects. We conclude that softness exploration can be considered as a sensorimotor control loop, in which predictive and sensory signals determine movement control. Further, the results indicate a high importance of predictive processes throughout the entire exploration, as effects of predictive signals maintain in the last indentation.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2015). Predictive and sensory signals systematically lower peak forces in the exploration of softer objects. World Haptics Conference, 2015 IEEE (pp. 69-74). IEEE.</p> <p>The Zip file contains all data relative to the publication. For each participant code and each session exists a *txt and</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M CsCl, Berger force field for lipids, Dang's for Cs+ and ffgmx for Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M CsCl (102 POPC, 26 POPS, 4290 WAT, 106 Cs+, 80 Cl-). Additional Cs+ cations added to neutralize the negative charge of POPS. Berger force field for lipids, Dang's for Cs+ (), ffgmx for Cl- are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here).<br> K+ nonbonding parameters (from Dang's Cs+ from JPC B 1999, 103, 8195):<br> sig=0.383086, eps=0.41840</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M KCl, Berger force field for lipids, Dang's for K+ and ffgmx for Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M KCl (102 POPC, 26 POPS, 4290 WAT, 106 K+, 80 Cl-). Additional K+ cations added to neutralize the negative charge of POPS. Berger force field for lipids, Dang's for K+ (), ffgmx for Cl- are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here).K+ nonbonding parameters (from Dang's JPC B 1999, 103, 8195 based on Vacha et al. Biophys. J 2009, 96, 4493.):<br> sig=0.3048655 eps=0.418400</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M NaCl, Berger force field for lipids and ffgmx for ions
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M NaCl (102 POPC, 26 POPS, 4290 WAT, 106 Na+, 80 Cl-). Additional Na+ cations added to neutralize the negative charge of POPS. Berger force field for lipids and ffgmx for ions are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here.</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p> <p> </p>
CICE tx1 JRA55 Forcing Data 2020.09.19
<p>This file contains the tx1 JRA55 forcing data for CICE. </p><p>The latest information about CICE forcing data and files can be found at the GitHub Resource Index: <a href="https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index">https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index</a> under the "Input Data" link.</p><p>These data are provided by the CICE Consortium (<a href="https://github.com/CICE-Consortium">https://github.com/CICE-Consortium</a>).</p><p>*Note that there was a bug found in the previous version. This version 2023-09-19 has been fixed.</p>
Accompanying data for "The Response of Tropical Rainfall to Idealized Small-Scale Thermal and Mechanical Forcing", by Martin Velez-Pardo and Timothy W. Cronin
<p>Data accompanying the manuscript "The Response of Tropical Rainfall to Idealized Small-Scale Thermal and Mechanical Forcing", by Martin Velez-Pardo and Timothy W. Cronin.</p>
Dataset: Thermal noise calibration of functionalized cantilevers for force microscopy: effects of the colloidal probe position.
<p>Dataset for article "Thermal noise calibration of functionalized cantilevers for force microscopy: effects of the colloidal probe position"</p><p>The raw data is in the 6 zip files (AIO*.zip), which contains thermal noise spectra measured on the raw cantilevers close to the free end (folders AIO#, where #=1, 2 or 3 for samples A, B or C), or on the loaded cantilever at various positions along its length (folders AIO#-ScanHF, where #=1, 2 or 3 for samples A, B or C). The file format is Matlab data file (.mat), it include the vectors f (for frequency axis, in Hz) and p (power spectrum density, in m^2/Hz). Other variables are erreur (some internal check that the calibration of the interferometer is pertinent) and PointW (mean intensity collected by the interferometer, and mean contrast on the 2 quadrature signals). For the loaded cantilever, we also record the laser spot position (XFaisceau and YFaisceau, in µm, origin close to the free end on the cantilever), some timing information to track for drifts, and ellipse (a calibration step of the quadrature phase interferometer).</p><p>The SEM images of the samples are included in the zip file SEMimages.zip</p><p>All analysis scripts (Matlab .m files) are included:</p><ul><li>AnalyseAll.m reads the raw data files and extract all the pertinent information, saving it to file AnalyseAll.mat</li><li>Analysis_cp analyses the pre-processed data with the single contact point model</li><li>Analysis_endload analyses the pre-processed data with the rigid end load model</li><li>figspectrum.m creates Fig. 4 of the article</li><li>plot_cp_endload.m creates Fig. 5 and 6 of the article</li></ul><p>All other scripts (.m) are dependencies that are necessary for the 3 former scripts to run. All scripts are commented and should be self explanatory. Of interest are the scripts mode_shape_cp.m and mode_shape_endload.m, which compute the resonant mode shape of a loaded cantilever for the two models, when given the parameters on the load size and position.</p>
Data for manuscript: "East Antarctic warming forced by ice loss during the Last Interglacial".
<p>This repository contains data accompanying the manuscript:</p> <p>David K. Hutchinson, Laurie Menviel, Katrin J. Meissner, Andrew McC. Hogg, 2024: <strong>East Antarctic warming forced by ice loss during the Last Interglacial</strong>, <em>Nature Communications</em>, 15, 1026. doi: <a href="https://doi.org/10.1038/s41467-024-45501-x">10.1038/s41467-024-45501-x</a></p> <p>The data correspond to Figures 1-5 in the main manuscript and Figures S1-S12 in the Supplementary material. Generally the surface fields are provided in both monthly and annual frequency, while the deep ocean metrics are annual only. Topography and grids used in each simulation are provided in <strong>topog_grids</strong>, and the <strong>runoff</strong> folder contains the freshwater forcing applied as a perturbation in each case.</p> <p>Data are provided in self-describing netcdf format. Experiment details are provided in the manuscript and will be available upon publication.</p>
Residual force depression data
<p>Raw data used to create the results text and figures presented in the manuscript entitled "Residual force depression is not related to positive muscle fascicle work during submaximal voluntary dorsiflexion contractions in humans".</p>
Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers - Part II
<p>Dataset Part II for publication "Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers", in Nature Communications.</p>
Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers - Part I
<p>Dataset Part I for publication "Determining intrinsic potentials and validating optical binding forces between colloidal particles using optical tweezers", in Nature Communications.</p>
GENDER DIVERSITY AND ITS IMPACT ON THE WOMEN PARTICIPATION IN THE WORKFORCE: WITH REFERENCE TO INDIAN CORPORATE SECTOR. (HRM: PARTICIPATION OF WOMEN IN THE WORK FORCE
<p>Gender roles have been two different sized fruits on the same tree branch since olden times. <br>The distinguished rule book for both males and females which had been followed for since <br>long still has its effects widely seen in the Indian corporate sector also. With the recurrent <br>fluctuation experienced in the women participation in the Indian workforce, the main pulling <br>back factor always has been Gender Diversity issues. There are many workplace factors <br>which infuse more power to this aspect called gender diversity; some of which are out of <br>one’s control while many of which are manageable. These factors include salary range, job <br>positions, promotional opportunities, workplace behavior and many more. The list is endless, <br>but the impact of all the gamut of such aspects has to be borne by the female employees. This <br>paper is a descriptive analysis on the effect of various factors on the gender diversity issues in <br>Indian Corporate sector. Secondary data has been used in order to correlate the factors like <br>salary, promotional opportunities, and other factors in the corporate sector with the gender <br>diversity issues. The statistical evidences have been depicted with the help of graphs and pie <br>charts which would contribute in making the whole situation of diversity issues more crystal <br>clear and transparent for the readers. The paper is an attempt towards finding the in-depth <br>causes for the ups and downs in the women participation in the nations’ workforce and also to <br>bring about a change in the current plight of female workers in Indian Corporate sector</p>
Sharp force trauma to a human skull
SFT or hacking on historical specimen. Model extracted from clinical CT data. Shows clear associated fracturing of the inner and outer table of the vault. Source: Objaverse 1.0 / Sketchfab
Polish Air Force in the West officers's cap
ID no.: MLP 755/1 Museum: Polish Aviation Museum http://muzea.malopolska.pl/en/obiekty/-/a/26873/1124080 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
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