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150 results for “Use of Force”
Figure 2 in Effect of using forced and free oviposition methods to obtain eggs and larvae of Mansonia (Diptera: Culicidae) females from Rondonia, Brazil (western Amazon)
Figure 2 Number of wild females of different Mansonia species collected in Vila Teotônio, a rural region of Porto Velho, Rondonia, Brazil that laid eggs using free and forced oviposition methods in the laboratory. *Significant difference (P <0.05) in the expected frequency of females that oviposited.
Figure 3 in Effect of using forced and free oviposition methods to obtain eggs and larvae of Mansonia (Diptera: Culicidae) females from Rondonia, Brazil (western Amazon)
Figure 3 Number of eggs (A) and larvae (B) obtained in the laboratory from wild females of diferente Mansonia species collected in Vila Teotônio, a rural region of Porto Velho, Rondonia, Brazil. Different letters indicate significant differences (P <0.05) between species. Red lines indicate the mean.
Figure 2 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy
Figure 2. - Juvenile Trachipterus arcticus, 129 mm SL, collected at Faial Island, Azores, 14 May 2014, on the surface. A: Portrait with anterior black facet visible; B: Oblique lateral view with first spines erected; note orange bulbous outgrowths on the prolonged spine; C: Lateral view showing proportions, markings and orientation of fins. Scale bars: A = 1 cm; B, C = 5 cm.
Figure 1 in High-Resolution Functional Imaging of Native Proteins using Force Distance Curve Based Atomic Force Microscopy
Figure 1. - Adult Trachipterus arcticus, about 1.8 m long, observed south of Pico Island, Azores, 18 Aug. 2013, 950 m deep.
Laboratory data of measurements conducted on an n-decane saturated limestone sample using the forced-oscillation method
<p>This supporting information provides the numerical results of the laboratory experiments conducted on an n-decane saturated limestone sample with varying dead fluid volume. The supporting information includes: (1) the extensional attenuation, Poisson ratio, elastic moduli and strains in the rock measured at 0.1 Hz with the dead volume varying from 2 ml to 260 ml and also with the open fluid line, and (2) the frequency dependences of the attenuation, elastic moduli, Poisson ratio and strains obtained in the frequency range from 0.1 Hz to 120 Hz.</p>
Climate Forced Hydropower Simulations for the African Continent Using NASA NEX GDDP
<p>This dataset includes the results of simulations of future hydropower usable capacity for power plants across the five African power pools. These include 87 power plants in 27 different countries. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. We include a PDF file "Description of Data.pdf" that describes all the information included in the dataset. </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C, & Nijssen, Bart. "Power pools for the win: Assessing climate resilience of hydropower resources in African power pools".</p>
Model data used for the paper: "Heat extremes driven by amplification of phase-locked circumglobal waves forced by topography in an idealized atmospheric model"
<p>Atmospheric model output to reproduce the results of the study: "<strong>Heat Extremes Driven by Amplification of Phase-Locked Circumglobal Waves Forced by Topography in an Idealized Atmospheric Model" </strong>published in Geophysical Research letters.<br> <br> Authors: B. Jiménez-Esteve. K. Kornhuber and D. I.V. Domeisen <br> <br> DOI: <a href="https://doi.org/10.1029/2021GL096337">https://doi.org/10.1029/2021GL096337</a><br> <br> For more information about the model setup and the design of the experiments please refer to the above publication.</p>
Pure POPC Membrane with 1000mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 115 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 450mM NaCl simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 51 NaCl, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 650mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 76 CaCl2, and 6400 SWM4 water molecules.</p> <p>The simulation have been performed using OpenMM 7.4.1</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>The initial structures have been obtained from CHARMM-GUI.</p> <p> </p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Pure POPC Membrane with 350mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 41 CaCl2, and 6400 SWM4 water molecules.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies
<p>Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below</p> <p>Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at <a href="https://arxiv.org/abs/2301.00496">https://arxiv.org/abs/2301.00496</a>.</p> <p>Code found at https://doi.org/10.5281/zenodo.7796266.</p> <p> </p>
Pure POPC membrane simulations using Amber Lipid 14 Force Field
<p>Pure POPC membrane simulations using the Amber Lipid 14 force field.</p> <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> <p>The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong></p> <p><strong>The trajectories do not contain water molecules.</strong> </p> <p>Simulation Details:</p> <p>Lipids : 72 POPC lipids, 36 per leaflet</p> <p>Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>)</p> <p>Temperature: 303 K</p> <p>Pressure: 1 bar</p> <p>Thermostat: Langevin</p> <p>Barostat: Berendsen</p> <p>Pressure coupling: Semi-isotropic</p> <p>Trajectory Length: 100 ns (after 100 ns pre-equilibration)</p> <p>Saving frequency: 100 ps</p> <p>Further details are available at the 04_Run.in file</p> <p>All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)</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>
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>
Data from: Decipher soil organic carbon dynamics and driving forces across China using machine learning
<p><span><span>The dynamics of soil organic carbon (SOC) play a critical role in modulating global warming. However, the long-term spatiotemporal changes of SOC at large scale and the impacts of driving forces remain unclear. In this study, we investigated the dynamics of SOC in different soil layers across China through the 1980s to 2010s using a machine learning approach and quantified the impacts of the key factors based on factorial simulation experiments. Our results showed that the latest (2000-2014) SOC stock in the first meter soil (SOC<sub>100</sub>) was 80.68 ± 3.49 Pg C, of which 42.6% was stored in the top 20 cm, sequestrating carbon with a rate of 30.80 </span><span>± 12.37</span><span> g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s. Our experiments focusing on the recent two periods (2000s and 2010s) revealed that climate change exerted the largest relative contributions to SOC dynamics in both layers and warming or drying can result in SOC loss. However, the influence of climate change weakened with soil depth, while the opposite for vegetation growth. </span><span>Relationships between SOC and forest canopy height further confirmed this strengthened impact of vegetation with soil depth, and highlighted the carbon sink function of deep soil in mature forest. Moreover, our estimates suggested that SOC dynamics in 71% of topsoil were controlled by climate change and its coupled influence with environmental variation (CE). Meanwhile CE and the combined influence of climate change and vegetation growth dominated the SOC dynamics in 82.05% of the first meter soil. </span><span>Additionally, the national cropland topsoil organic carbon increased with a rate of 23.6 </span><span>± 7.6 </span><span>g C m<sup>-2</sup> yr<sup>-1</sup> since the 1980s, and the widely applied nitrogenous fertilizer was a key stimulus. </span><span>Overall, our study extended the knowledge about the dynamics of SOC and deepened our understanding about the impacts of the primary factors.</span></span></p>
Data that are used to Explain the Forcing Efficacy with Pattern Effect and Feedback Nonlinearity"
<p> This dataset is for the draft "Explaining the Forcing Efficacy with Pattern Effect and Feedback Nonlinearity".</p> <p> The netcdf file "last150yravg_picontrol.nc" represents the time-average fields for the last 150 years of PI-control experiment. Netcdf files beginning with "last20yravg" represent the time-average fields for the last 20 years (Year131-150) of abrupt forcing experiments. Note that "0p5co2" in the file name denotes 0.5xCO2, "4psolar" denotes +4% solar radiation, and "m2psolar" denotes -2% solar radiation.</p> <p> The netcdf files beginning with "fixedsst" represent the time-average fields for fixed-SST experiments, where the file "fixedsst_control.nc" denotes the fixed SST control experiment.</p> <p> The netcdf files beginning with "uni" represent the time-average fields for uniform warming/cooling experiments, where the magnitude of SST change is indicated by the file names (m2K denotes -2K).</p> <p> The text file "GFA_partialR_over_partial_SST" denote the value of partial_R over partial_SST for each grid, and its grid (96x144) is same as other netcdf files in this datasets .</p> <p> </p>
Data for publication: Nanomechanical and Structural Study of Au38 Nanocluster Langmuir-Blodgett Films Using Bimodal Atomic Force Microscopy and X-Ray Reflectivity
<p>Original data of Figures published in:</p> <p><strong>Nanomechanical and Structural Study of Au<sub>38</sub> Nanocluster Langmuir-Blodgett Films Using Bimodal Atomic Force Microscopy and X-Ray Reflectivity</strong></p> <p>Journal of Colloid and Interface Science, 2022, Michal Swierczewski<sup>,</sup> Alexis Chenneviere, Lay-Theng Lee, Plinio Maroni and Thomas Bürgi*<sup>[</sup></p> <p> </p>
Application Of Substantial And Sustained Force To Vertical Surfaces Using A Quadrotor
<p><strong>One of the challenges in the interaction between aerial manipulators (drones with manipulator arms) and the environment is to maintain stability under high interaction forces. The dynamics of the system change, and due to limitations to the drone-platform-- the drone not being to apply sideways forces and having limited yaw-torque-- stability is not naturally guaranteed. </strong></p> <p> </p> <p><strong>In this video we demonstrate a new control approach to overcome these limitations. The video shows a quadrotor drone with manipulator pushing on the environment. More precisely, the stability is achieved using a state-feedback approach to compensate for the roll and yaw errors. This approach uses the linearized contact dynamics under pseudo-static conditions. The key element is that non-zero roll-states are used to compensate for errors in the yaw-state. At a certain point the drone reaches pitch angles of over 45º. Given that the drone has a mass of 1.5 kg, this means that the drone is pushing with 15N(!) of force on the environment. The video demonstrates that the drone is easily able to maintain stability over a longer time period.</strong></p> <p> </p> <p><strong>These high interaction forces can be extremely useful when operating tools on the environment, as tasks such as grinding and brushing require sufficient contact pressure to function.</strong></p>
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Allen Brain Atlas
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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
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.