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69 results for “active transport”
Overview of PASTA Questionnaires: Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study
<p>Overview of questionnaires used in the PASTA project (Physical Activity through Sustainable Transport Approaches (PASTA))</p> <p>English version</p>
Expression of mgtA encoding a magnesium transporter is activated in the absence of the main Mg2+ transporter CorA in Salmonella
<p><i>Salmonella</i> imports magnesium via three known transporters, the widely conserved CorA transporter and the MgtA and MgtB P-type ATPases. CorA is expressed under various growth conditions whereas the other two magnesium transporters, MgtA and MgtB, are expressed under conditions of magnesium starvation under the positive control of the PhoP-PhoQ regulatory system. <br>Our recent results suggest that production of MgtA, but not that of MgtB, is activated in the absence of the main Mg2+ transporter CorA (Metaane <i>et al </i>2022, 2023 ) <br>This hypothesis was further validated in this study by using transcriptional and translational <i>lacZ </i>fusions in the <i>mgtA </i>and <i>mgtB</i> genes. In addition, our data suggest that regulation of expression of MgtA in the absence of CorA is post-transcriptional . </p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong> </p><p>Metaane S, Monteil V, Ayrault S, Bordier L, Levi-Meyreuis C, Norel F. The stress sigma factor sigmaS/RpoS counteracts Fur repression of genes involved in iron and manganese metabolism and modulates the ionome of <i>Salmonella enterica </i>serovar Typhimurium. PLoS One 2022;17(3):e0265511.</p><p>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PLoS One 2023;18(9):e0291736.</p><p> </p>
McMurdo Dry Valleys Community Respiration Rates : Quantifying the Activity of the Electron Transport System (ETS)
An important part of the McMurdo Long Term Ecological Research (LTER) is monitoring of spatial and temporal patterns, and processes that control net primary production (carbon dynamics) in perennial ice-covered lakes. One of the primary losses of carbon fixed by phytoplankton is through respiration, directly by the phytoplankton themselves and secondarily through the metabolic contributions of heterotrophic organisms such as bacterioplankton and protozoa. The coupling of low metabolic activity and supersaturated gases in the water column prohibits a direct measurement of respiration. Therefore, we measure the respiratory electron transport system (ETS) activity which drives oxidative phosphorylation, and hence oxygen consumption in all aerobic organisms (Packard 1985). This data set addresses this core area of research and estimates a community-wide respiration rate at specific depths in McMurdo Dry Valley lakes (Hoare, Fryxell and Bonney).
Supplementary videos for "Active flow network generates molecular transport by packets: case of the endoplasmic reticulum"
<p>Videos showing simulated motion on the active flow network for different switching timescales. In particular, compare <span class="math-tex">\(\tau_{\text{switch}} = 3 \text{ s}\)</span> to <span class="math-tex">\(\tau_{\text{switch}} = 30 \text{ ms}\)</span>. The red bubbles are proportional to the number of particles present in a node. Initially, all particles are placed in a central source node.</p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. <br><strong>04_HTMD_Cavity</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. <br><strong>06_HTMD_Tunnels</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs. <br>- <em>Distances/*/dist_s_r*.csv</em> : Contains .csv file for the frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program. Example input: pytraj.rmsd(traj, mask='1-295@CA') Example input: pytraj.rmsf(traj, mask=':1-295', options='byres')<br>- <em>COM_RMSF/.xlsx</em> : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213)<br>- <em>Kinetics/kinetics.txt</em> : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates.<br>- <em>Percentages/.csv</em> : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Monosaccharide transporter OsMST6 is activated by transcription factor OsERF120 to enhance chilling tolerance in rice seedlings
<p>Chilling stress caused by extreme weather is threatening global rice (<em>Oryza sativa</em> L.) production. Identifying components of the signal transduction pathways underlying chilling tolerance in rice would advance molecular breeding. Here, we report that <em>OsMST6</em>, which<em> </em>encodes<em> </em>a monosaccharide transporter, positively regulates the chilling tolerance of rice seedlings. The <em>mst6</em> mutants showed hypersensitivity to chilling, while the <em>OsMST6</em> overexpression lines were tolerant. During chilling stress, OsMST6 transported more glucose into cells to modulate sugar and ABA signal pathways. We showed that the transcription factor OsERF120 could bind to the DRE/CRT element of the <em>OsMST6</em> promoter and activate the expression of <em>OsMST6</em> to positively regulate chilling tolerance. Genetically, OsERF120 was functionally dependent on OsMST6 when promoting chilling tolerance. In summary, OsERF120 and OsMST6 form a new downstream chilling regulatory pathway in rice in response to chilling stress, providing valuable findings for molecular breeding aimed at achieving global food security.</p> <p><span>The stored data is the analysis results of transcriptome data from ZH11 and <em>mst6-1</em> after 0 hours and 4 hours of chilling treatment. These data form the basis for the transcriptome data visualization in the article.</span></p>
A phosphate transporter in VIPergic neurons of the suprachiasmatic nucleus gates locomotor activity during the light/dark transition in mice
<p>Raw Data for <strong>An atypical phosphoswitch in specific neurons of the suprachiasmatic nucleus gates locomotor activity at the light/dark transition in mice</strong></p> <ol> <li>Multi-electrode array data from PiT2<sup>+/+</sup> and PiT2<sup>-/-</sup> mice</li> <li>Locomotor activity from PiT2<sup>+/+ </sup>and PiT2<sup>-/-</sup> mice in LD 12:12, DD, LD 4:20, LD 20:4 and DD with light pulses</li> <li>Phosphoproteome data</li> </ol>
Dataset: Survey for Identifying User Groups of Active Transport Modes in Urban Centers
<p>This dataset was derived from an online questionnaire conducted between June and July 2022. The survey aimed to identify groups of individuals based on patterns revealed through clustering methods, considering the importance assigned to various indicators associated with the dimensions of comfort, safety and security, and time and distance. <span>CSV file structure details are available at 'csv_structure.xlsx'. </span></p> <p>The questionnaire also included a set of items designed to capture the respondents' sociodemographic context, specifically:</p> <ul> <li>Gender </li> <li>Parental Status</li> <li>Age Group</li> <li>Educational Level</li> <li>Country of Residence </li> <li>Employment Situation </li> </ul>
Data from: Seamounts generate efficient active transport loops to nourish the twilight ecosystem
<p>Seamounts are ecological oases nurturing abundant fisheries resources and epibenthic megafauna in the vast oligotrophic ocean. Despite their significance, the formation mechanisms underlying these seamount ecological oases remain uncertain. To shed light on this phenomenon, this study conducted interdisciplinary <em>in-situ</em> observations focusing on a shallow seamount in the oligotrophic ocean. In this dataset, we report high-resolution hydrographic and biochemical observational data from both sectional observations and time-series stations observations.</p>
Data files for Axonal transport of autophagosomes is regulated by dynein activators JIP3/JIP4 and ARF/RAB GTPases
<p>Supporting data files for <strong>Axonal transport of autophagosomes is regulated by dynein activators JIP3/JIP4 and ARF/RAB GTPases</strong></p>
Effects of Sildenafil on CFTR-dependent Ion Transport Activity
ClinicalTrials.gov study NCT01132482. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Monosaccharide transporter OsMST6 is activated by transcription factor OsERF120 to enhance chilling tolerance in rice seedlings
Open the record for dataset details and reuse information.
Data from: Seamounts generate efficient active transport loops to nourish the twilight ecosystem
Open the record for dataset details and reuse information.
Data for the evaluatino of the influence of active transportation on body mass index
<p>The dataset is centered on the canton of Geneva Switzerland and is divided into four different folders, available in a shapefile format;</p> <p>Cycle lane : This folder contains the cycle lane available in the State of Geneva.</p> <p>Parking : This folder contains the public and private parking of the State of Geneva.</p> <p>Public transport : This folder contains the public transport servicing quality data in the State of Geneva.</p> <p>Road : This folder contains the road used by the car in the State of Geneva.</p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Data From: Active sinking particles: Flow and transport dynamics due to sessile suspension feeders on sinking aggregates
<p>Sinking or sedimentation of biological aggregates plays a critical role in carbon sequestration in the ocean and in vertical material fluxes in waste-water treatment plants. In both these contexts, the sinking aggregates are "active," since they are biological hot-spots and are densely colonized by microorganisms including bacteria and sessile protists, some of which generate feeding currents. However, the effect of these feeding currents on the sinking rates, trajectories, and mass transfer to these "active sinking particles," has not previously been studied. Here we use a novel scale-free vertical-tracking microscope (a.k.a. Gravity Machine, Krishnamurthy et al. "Scale-free vertical tracking microscopy." Nature Methods (2020)) to follow model sinking aggregates (agar spheres) with attached protists (<em>Vorticella convallaria</em>), sinking over long distances while simultaneously measuring local flows. We find that activity due to attached \vortc causes significant changes to the flow around aggregates in a dynamic manner and reshapes mass transport boundary layers. Furthermore, we find that activity-mediated local flows along with sinking modify the encounter and plume cross-sections of the aggregate and induce sustained aggregate rotations. Overall our work shows the important role of biological activity in shaping the near-field flows around aggregates with potentially important effects on aggregate fate and material fluxes.</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.