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11,506 results for “Dynamics”
Code and Data for Salish Exchange Dynamics paper
<p>This is a compressed (zipped) folder containing all the data files, python code, and output files associated with the manuscript "Estuarine Exchange Flow in the Salish Sea" by Parker MacCready and W. Rockwell Geyer, submitted to Journal of Geophysical Research - Oceans.</p>
T helper cells exhibit a dynamic and reversible 3'UTR landscape.
<p>This archive contains large files necessary to reproduce the related paper analysis.</p>
On Dynamical Decomposition of Multiscale Oceanic Motions
<p>The MATLAB script (multiscale_decomp_WLL2022.m) performs the decomposition proposed by Chuanyin Wang, Zhiyu Liu and Hongyang Lin ('On Dynamical Decomposition of Multiscale Oceanic Motions' submitted to JAMES). This is the updated version.<br> </p>
Deep learning low-cost photogrammetry for 4D short-term glacier dynamics monitoring - Supplementary material
<p>This repository contains the supplementary material for the paper "Deep learning low-cost photogrammetry for 4D short-term glacier dynamics monitoring".</p><p>The paper is currently under revision. This repository will be updated once the paper will be published.</p><p>In particular the repository includes:</p><ol><li>The datasets used for performing the multi-temporal stereoscopic analysis of the Belvedere Glacier during the summer season 2022.</li><li>The code of ICEpy4D v1.9.0 used for processing the stereoscopic data. An updated version of ICEpy4D can be found in the Github repository https://github.com/franioli/icepy4d </li></ol><p> </p><p><br> </p>
Exploring the Depths: A Visual Journey through the porous medium and the dynamics of biogeochemical interactions
<p>1. "Supp-1.mpg" shows the 3D structure of the porous material, </p><p>2. "Supp-2.mpg" and "Supp-3.mpg" illustrate the steady-state distribution of water and air in the pore volume, respectively, </p><p>3. Succinate and Oxygen distributions in the domain can be found in "Supp-4.mpg" and "Supp-5.mpg"brespectively.</p>
Molecular dynamics results of the complex 3CLpro active site with compound 5
<p>Molecular dynamics results of the complex 3CLpro active site with compound <strong>5</strong>. The protein structure is shown in gray and the compound <strong>5</strong> is shown in orange.</p>
Molecular dynamics results of the complex PLpro compound 5.
<p>Molecular dynamics results of the complex PLpro with compound 5. The protein structure is shown in gray, the BL2loop is shown in yellow and the compound 5 is shown in blue.</p>
Polarization dynamics of solid-state quantum emitters
<p>A. Kumar et al., Polarization dynamics of solid-state quantum emitters</p> <p>Quantum emitters in solid-state crystals have recently attracted a lot of attention due to their simple applicability in optical quantum technologies. The polarization of single photons generated by quantum emitters is one of the key parameters that play a crucial role in the applications, such as quantum computation that uses the indistinguishability of photons. However, the degree of single photon polarization is typically quantified using time-averaged photoluminescence intensity of single emitters, which provides limited information about the dipole properties in solids. In this work, we use single defects in hexagonal boron nitride and nanodiamond as efficient room-temperature single photon sources to reveal the origin and the temporal evolution of dipole orientation in solid-state quantum emitters. The angle of excitation and emission dipoles relative to the crystal axes are determined experimentally and then calculated using density functional theory, which results in characteristic angles for every specific defect that can be used as an efficient tool for defect identification and understanding their atomic structure. Moreover, the temporal polarization dynamics reveal a strongly modified linear polarization visibility that depends on the excited state decay time of individual excitation. This effect can be traced back potentially to the excitation of excess charges in the local crystal environment. Understanding such hidden time-dependent mechanisms can further be used to improve the performance of polarization-sensitive experiments, in particular that of quantum communication with single photon emitters. We, therefore, provide a promising pathway for the identification of color centers as well as important insight into the dynamics of solid-state quantum emitters in general.</p> <p>(preprint at <a href="https://arxiv.org/abs/2303.04732">arXiv:2303.04732</a>; published at <a href="https://pubs.acs.org/doi/full/10.1021/acsnano.3c08940">https://pubs.acs.org/doi/full/10.1021/acsnano.3c08940</a>)</p> <p>This supplementary file contains the properties of defect candidates, which include ZPL, excitation and emission polarization, polarization misalignment, and linear polarization visibility to support the findings in the paper.</p> <p>Questions regarding this dataset should be sent to the corresponding authors.</p>
Dynamics of bacterial recombination in the human gut microbiome — figure data
<p>Data associated with Figures 1-6, S1-41 in the manuscript "Dynamics of bacterial recombination in the human gut microbiome" (DOI: <a href="https://doi.org/10.1101/2022.08.24.505183">10.1101/2022.08.24.505183)</a></p>
Simulation results: effects of Chiari type 1 malformation on cerebrospinal fluid dynamics during arterial pulsations and coughing
<p>The folder contains the simulation results corresponding to the computational fluid dynamics study: Effects of Chiari type 1 malformation on cerebrospinal fluid dynamics during arterial pulsations and coughing</p> <p>Data is organized in the following way with files in .csv and .xlsx format</p> <ul> <li>Cropped model: <ul> <li>output_*: flow time in seconds, pressure in the fourth ventricle (Pv), outflow in m³/s or kg/s, and pressure at plane in spinal SAS (Psas) for physical herniations and herniations created by porous zones</li> </ul> </li> <li>Full model <ul> <li>arterial_pulsations: data with all boundary conditions (arterial) except from cough</li> <li>arterial_pulsations_and_cough: data with all boundary conditions (arterial) including cough</li> <li>file content: <ul> <li>*_boundary_data: time step number, number of coupling iterations necessary per time step, flow time (s), pressure (P) and flow (Q) at outlets with interstitium (1), spinal (2), lymphatic (3) and arachnoid villi (4), first element of Jacobian (J11), flow residual, value of perturbation (dP) in Pa, converged?: 1 when converged, 0 when not, perturbation parameter, number of times the perturbation value needed to be reduced.</li> <li>*_flow: flow time in seconds, volumetric flow through aqueduct, and spinal SAS</li> <li>*_pressure_data: flow time in seconds, relative pressure compared to interstitium outlet at the fourth ventricle (v4), the spinal SAS (sas) and the lateral ventricle (lv)</li> </ul> </li> </ul> </li> </ul>
SUPPORTING INFORMATION FOR: Stochastic dynamic mass spectrometric 3D structural analysis of caffeine metabolites
<p>Supporting information for the entitled contribution.</p><p>It contains:</p><p>Static quantum chemical and high accuracy molecular dynamics computational data on protomers, tautomers, zwitterions, and isotopomers of caffeine (CAFF), paraxanthine (PARAXAN), theobromine (THEOBR), theophylline (THEOPH), and guanine (GUA), uric acid (UA), and xantine (XAN), as well as their derivatives.</p><p>The content includes data on characteristic parent and product ions of the analytes in ion mobility spectrometric and mass spectrometric experimental conditions. Tautomers, charge transfer processes, and intramolecular rearrangement; if any, are accounted for considering. </p><p>Molecular mechanics/molecular dynamics data are shown as *.txt files. </p><p>High accuracy molecular dynamics includes adiabatic computations using Born-Oppenheimer approach.</p><p>High accuracy static ground state and transition state computations use M062X/SDD level of theory. </p><p>Figures in color, illustrating the entitled contribution shown as *.pdf files.</p><p>The experimental ion mobility spectrometry and mass spectrometry data are according to reference [1].</p><p>[1] H. Sepman, A. Kruve, S. Tshepelevitsh, H. Hupatz, Experimental IMS and MS/MS data of caffeine metabolites (2022). Zenodo, [https://doi.org/10.5281/zenodo.6637393][ https://zenodo.org/record/6637393] (Accessible for 04.04.2022.)</p><p>They have been used and processed via the following software:</p><p>[2] ProteoWizard 3.0.11565.0 (2017) [https://proteowizard.sourceforge.io/download.html];</p><p>[3] mMass 5.0.0 [http://www.mmass.org/download/old.php];</p><p>[4] AMDIS 2.71 (2012) software [https://chemdata.nist.gov/mass-spc/amdis/downloads/AMDIS_Installer-17.zip]; and</p><p>[5] NIST Search Software 2.0 [https://chemdata.nist.gov/dokuwiki/lib/exe/fetch.php?media=chemdata:nist17:nist17demo.zip], respectively.</p><p> </p><p> </p>
MD data for the article "Structure comparison of beta amyloid peptide Aβ 1-42 isoforms. Molecular dynamics modeling" by Anna P. Tolstova, Alexander A. Makarov, Alexei A. Adzhubei.
<p>There are CMD and REMD trajectories for Aβ isoforms discussed in the paper together with final coordinate files for these trajectories. The resulting dataset of modeled structures includes wild type Aβ42, isoD7, pS8, D7H and H6R-Aβ42, and wild type Aβ16, isoD7, pS8, D7H and H6R-Aβ16.</p>
Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly
<p>Movie 1 of 16 is a supplementary material for the manuscript 'Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly.' It visually demonstrates the effect of various treatments on the formation and dynamics of oncostreams in high-grade glioma cells. The time-lapse videos provide insightful observations into how various pharmacological drugs influence the initial formation and structural dynamics of oncostreams."</p> <p>It aims to enhance the understanding of the foundational stages of oncostream formation in glioma cells and their response to various pharmacological treatments, supporting the findings discussed in the manuscript.</p> <p>Individual Movie Descriptions:</p> <p><strong>Movie 1: Low Density Oncostream Dynamics: </strong>Demonstrates the formation and dynamics of oncostreams in GFP+ NPA glioma cells at low seeding density (1 × 10^5 cells), over 24 hours using time-lapse confocal imaging.</p> <p><strong>Movie 2: High Density Oncostream Dynamics:</strong> Explores the effect of high cell seeding density (2 × 10^5 cells) on the formation and behavior of oncostreams in GFP+ NPA glioma cells, analyzed over 24 hours.</p> <p><strong>Movie 3: Spatiotemporal Progression of Oncostreams:</strong> Showcases the sequential self-formation and developmental stages of oncostreams over a 24-hour period.</p> <p><strong>Movie 4: Collagenase Impact on Oncostreams:</strong> Description: Illustrates the disassembly of aggressive and malignant oncostreams with 15 U/ml collagenase treatment over 15 hours.</p> <p><strong>Movie 5: TC-I-15 Inhibition of Oncostream Formation:</strong> Details the impact of TC-I-15, an integrin antagonist, on adhesion and oncostream formation in glioma cells, recorded over 20 hours.</p> <p><strong>Movie 6: Cytochalasin D Disruption of Oncostreams:</strong> Captures the effect of Cytochalasin D on oncostream formation by inhibiting actin polymerization, observed over a 1-hour period with rapid imaging intervals.</p> <p><strong>Movie 7: Myosin II Inhibition in Oncostreams with p-nitro Blebbistatin: </strong>Presents the influence of p-nitro Blebbistatin on oncostream formation by inhibiting myosin II, documented over 1 hour with dynamic cellular responses.</p> <p><strong>Movie 8: Control - Oncostream Formation without BAPTA-AM:</strong> Demonstrates oncostream formation and dynamics without BAPTA-AM treatment, monitored over 16 hours.</p> <p><strong>Movie 9: BAPTA-AM (Calcium Modulation) Impact on Oncostream Formation:</strong> Reveals the effects of BAPTA-AM treatment on oncostream dynamics, observed over a 16-hour period.</p> <p><strong>Movie 10: Glutamate Influence on Oncostreams:</strong> Shows the effects of glutamate treatment on oncostream formation and dynamics, monitored over 16 hours.</p> <p><strong>Movie 11: Histamine Impact on Oncostream Dynamics:</strong> Demonstrates the influence of histamine on the formation and behavior of oncostreams, captured over a 16-hour period.</p> <p><strong>Movie 12: Oncostream Formation on Non-Laminin Coated Surfaces:</strong> Illustrates the compromised organization of oncostreams on poly-D-lysine coated dishes without laminin, observed over 45 hours.</p> <p><strong>Movie 13: 4-HAP Treatment Effect on Oncostreams:</strong> Shows the impact of 4-HAP treatment on the structure of oncostreams, monitored over 20 hours.</p> <p><strong>Movie 14: Rho-Activator I Untreated Control:</strong> Presents the formation and dynamics of oncostreams without Rho-Activator I treatment, recorded over 16 hours.</p> <p><strong>Movie 15: Rho-Activator I Influence on Oncostreams Dynamics:</strong> Details the effects of Rho-Activator I on oncostream formation and dynamics, observed over a 16-hour period.</p> <p><strong>Movie 16: Rho-Inhibitor Effect on Oncostreams Dynamics:</strong> Demonstrates the impact of Rho-Inhibitor on the formation and behavior of oncostreams, monitored over 16 hours.</p>
Molecular dynamics trajectories related to manuscript: SARS-CoV-2 nsp3 and nsp4 are minimal constituents of a pore spanning replication organelle
<p>This dataset contains two folders, each with sequentially numbers coordinates files for all-atom molecular dynamics trajectories related to the manuscript "SARS-CoV-2 nsp3 and nsp4 are minimal constituents of a pore spanning replication organelle" by Zimmermann et al. Each folder contains PDB and PSF files specifying the components of each system depicted in Fig. S10 of that manuscript as well as 200 sequentially named DCD files, each containing 5 nanoseconds of a 1 microsecond trajectory.</p>
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Multiscale drainage dynamics with Haines jumps monitored by stroboscopic 4D X-ray microscopy
<p>This dataset accompanies the article "Multiscale drainage dynamics with Haines jumps monitored by stroboscopic 4D X-ray microscopy" (DOI: 10.1073/pnas.2305890120) and constitutes the segmented volume differences of Haines jump H9 and the associated interface velocities. These datasets are provided as two zip-folders:velocityH9 and jump9.</p> <p>The segmented dataset has the following labels:<br>0: No volume change<br>1: Advancing air phase<br>2: Retracting air phase<br>3: Glass shards</p> <p>The voxel size is 3.66 um.</p> <p>The velocities are provided as .vtk files and can be visualized in open-source software such as Paraview.</p>
A two-frame movie of X-ray induced structural dynamics in single free nanoparticles
Open the record for dataset details and reuse information.
tetramethylammonium chloride molecular dynamics simulations
<p>Simulation data used for the publication: "Hydration of biologically relevant tetramethylammonium cation by neutron scattering and molecular dynamics".</p> <p>Gromacs FFMD simulations:</p> <ul> <li>center-bead</li> <li>center-N</li> <li>charmm</li> <li>lowCHdipole</li> <li>neopentane_plumed</li> <li>surface-H</li> </ul> <p>AIMD simulation:</p> <ul> <li>TMA_64H2O_AIMD_revPBED3_production_trajs</li> </ul>
Interaction Fingerprints for Molecular Dynamics Simulation of MC-LR and MC-LF with PPP1 - Data
<p>This data sets contains all data to reproduce the conclusions of the following manuscript to analyse, aggregate and visualise interaction fingerprints of Molecular Dynamics Simulation data. </p> <p>S. Jaeger-Honz, K. Klein, F. Schreiber: Systematic Analysis, Aggregation and Visualisation of Interaction Fingerprints for Molecular Dynamics Simulation Data, Journal of Cheminformatics 16 (28), 2024, https://doi.org/10.1186/s13321-024-00822-3.</p> <p>The scripts and libraries necessary to rerun the analysis are published under the following DOI: 10.5281/zenodo.10424417</p> <p> </p>
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks: dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p>The associated codes are available in the repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></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.