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155 results for “structure control”
Lithospheric structure controls the sequentially active detachment faulting at the Longqi segment on the Southwest Indian Ridge
<p><strong>supplementary materials for "Lithospheric structure controls the sequentially active detachment faulting at the Longqi segment on the Southwest Indian Ridge"</strong></p>
Data from: Effects of the control of an invasive tree on the structure of a plant-frugivore network
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Data and code for: A supergene controlling social structure in Alpine ants also affects the dispersal ability and fecundity of each sex
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Data from: warming and top-down control of stage-structured prey: linking theory to patterns in natural systems
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Data for paper: Control of fault weakening on the structural styles of underthrusting-dominated non-cohesive accretionary wedges
<p>This repository contains data related to the paper:<br> A. Bauville, M. Furuchi and M. Gerbault, Control of fault weakening on the structural styles of underthrusting-dominated non-cohesive accretionary wedges, Journal of Geophysical Research, 2020.</p> <p>It contains data for all simulations shown in Fig. 7 (last timestep)</p> <p>List of file for each simulations:<br> modelState.json: json file containing various information about the state of the model<br> particles_strain.bin: binary file containing strain data for all particles<br> particles_x.bin: current x position for all particles<br> particles_y.bin: current y position for all particles<br> particles_xIni.bin: initial x position for all particles<br> particles_yIni.bin: initial y position for all particles</p> <p># File format<br> n, u, data</p> <p>n = number of particles bytesize=[1 * int32]<br> u = characteristic unit bytesize=[1 * double]<br> data = data for n particles bytesize=[n * single]</p> <p># example to extract data from bin files in python<br> import numpy as np<br> import os</p> <p>with open(FileName, 'rb') as f:<br> f.seek(12, os.SEEK_SET)<br> data = np.fromfile(f, dtype=np.single, count=-1, sep='')</p>
Dataset related to article "NK cell recruitment in salivary glands provides early viral control but is dispensable for tertiary lymphoid structure formation."
<p>Salivary glands (SGs) represent a permissive site for several sialotropic viruses whose persistence is linked to the development of autoimmunity. Natural Killer (NK) cells play a key role in viral clearance but their involvement in viral infection control and in tertiary lymphoid structures (TLS) development within SGs is unknown. By using an inducible model of TLS in the SGs of wild-type C57BL/6 mice, induced by the local delivery of a replication-defective adenovirus (AdV), we demonstrated that circulating NK cells are rapidly recruited to SGs and highly enrich the early inflammatory infiltrate prior to TLS development. NK cells migrating to SGs in response to AdV infection up-regulate NKp46, undergo proliferation, acquire cytotoxic potential, produce Granzyme-B and IFN-γ, and reduce viral load in the acute phase of the infection. Nonetheless, the selective depletion of both circulating and infiltrating NK cells in AdV-infected mice neither affect the development and frequency of TLS nor the onset of autoimmunity. These data demonstrate that, upon local viral delivery of AdV, peripheral NK cells homing to SGs can exert an early control of the viral infection but are dispensable for the formation of TLS and breach of immunologic tolerance.</p> <p> </p> <p>This research used pzf and ets file form extensions, we attach pdfs information about</p>
Data associated with "Robust Structured Illumination Microscopy with Bayesian Noise Control"
<p>Experimental and synthetic data saved in tiff and/or zarr formats and SIM reconstruction scripts written in Python (Wiener and FISTA-SIM0. These also include estimated SIM patterns which are needed for B-SIM</p><ul><li>Synthetic data consisting of variably spaced line pairs. Found in <a href="https://zenodo.org/uploads/10037823">2023_10_02_synthetic_line_pairs.zip</a></li><li>Experimental data. Fluorescence images of one of the variably spaced line pair patterns on an ArgoSIM calibration slide. Found in <a href="https://zenodo.org/uploads/10037823">2023_08_02_folder=002_argosim_slide.zip</a></li><li>Experimental data. MitoTracker Red labelled mitochondria in live HeLa cells. Found in <a href="2023_08_07_folder=011_mitos_live_hela.zip">2023_08_07_folder=011_mitos_live_hela.zip</a></li><li>Camera calibration maps, including gain, variance, and offset. Found in <a href="camera_calibration.zip">camera_calibration.zip</a></li></ul>
Predicted structures of SLC-protein complexes and controls
<p>Solute carrier (SLC) transporters form a major superfamily which transport a wide range of substrates across cellular and organellar membranes. To study their regulation on protein level and position SLCs in the human interactome, we conducted a large-scale interrogation of the protein-protein interactions (PPIs) of SLCs employing affinity purification combined with mass spectrometry (AP-MS). This study resulted in thousands of novel protein interactions of SLCs. For a subset of SLC protein complexes, we performed structural predictions using AlphaFold (v2.2, v2.3 and v3). The dataset attached contains the structures in PDB/CIF format. The structures were further discussed in the associated manuscript. In addition, an annotation table is provided, which summarizes the scores for each modelled complex. </p>
Structure controlled earthquake behaviors induced by shale gas development in southern Sichuan basin, China
<p>This is the dataset available for the article titled "Structure controlled earthquake behaviors induced by shale gas development in southern Sichuan basin, China", including earthquake catalog, waveforms of 53 moderate earthquakes and three dimentional shear wave velocity model.</p>
Structure, function, and control of the musculoskeletal network - Data
<p>Supplementary data for: Structure, function, and control of the musculoskeletal network</p> <p>Table S8: The assigned homunculus categories and data driven community assignments of muscles.</p> <p>Table S9: The hypergraph of muscles and bones from the Hosford muscle tables used in the main text.</p> <p>Table S10: The hypergraph of muscles and bones from Grant's atlas used in the supplementary text.</p> <p> </p> <p>Data for Figures:</p> <p>2e</p> <p>3a, 3b</p> <p>4b, 4c, 4d</p> <p>S4a-h</p> <p>S5</p> <p>S6a, S6b</p> <p>S7a, S7b</p> <p>S8</p> <p>S9</p> <p>S10</p> <p>S11</p> <p>S12</p> <p> </p>
Anisotropic coverage control for surveillance of 3D structures
<p>This demonstration illustrates the execution of a distributed algorithm for the deployment of a team of Aerial Robotic Workers (ARWs) for coverage of a 3D structure. One physical ARW and two simulated ARWs are used in the demonstration.</p> <p>The position and orientation of each ARW is represented as a colored circle and arrow.</p> <p>The structure to cover is abstracted into a finite set of landmarks, and each landmark is represented as a colored arrow. The color of a landmark corresponds to the ARW which is currently responsible for covering that landmark. The direction of the arrow corresponds to the outward normal to the surface at the position of the landmark.</p> <p>The control algorithm prescribes the motion of the ARWs and the distribution of the landmarks among the ARWs. In particular, an ARW is allowed to intermittently yield responsibility of some of its landmarks to a different ARW. The motion and the landmark transfers are programmed in such a way to gradually increment the coverage of the structure. The algorithm is based on a generalization of the concept of Voronoi tessellations.</p> <p>The algorithm is implemented on a ROS architecture, where the controller for each ARW corresponds to a different ROS node. The pose of the physical ARW is measured in real time with a motion capture system.</p> <p>In the demonstration, we can see that three clusters of landmarks emerge, and that the ARWs position themselves in front of the landmarks that they are responsible to cover, at a suitable distance.</p>
Control of groundwater-lake interaction zone structure on spatial variability of lacustrine groundwater discharge
<p>This is the paper "Control of groundwater-lake interaction zone structure on spatial variability of lacustrine groundwater. Data set of discharge ". The data includes various isotopes, water temperature and conductivity data for lake and groundwater, wind speed and air temperature data during the study period, and water depth measurements.</p>
Temperature-controlled nanomosaic of AuCu bimetallic structure towards smart light management
<p>The following dataset contains research data that is the basis of the research article:</p> <p>"Temperature-controlled nanomosaic of AuCu bimetallic structure towards smart light management"</p> <p>Contents of the package are the following:</p> <p>a) Linear voltammetry carried out in 0.1 M NaOH for 10AuCu, 5Au5Cu and 5Cu5Au annealed at 300°C and 600°C</p> <p>b) Experimental results of UV-vis absorbance for 10AuCu, 5Au5Cu and 5Cu5Au annealed at 200°C, 400°C and 600°C</p> <p>c) XPS for 10AuCu annealed at 500°C</p>
Research data supporting "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2019, "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection", ACS Nano. DOI: 10.1021/acsnano.8b08117.</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
Optimized structures for Optical control of ultrafast structural motion in a fluorescent protein
<p>QM-MM Optimized structures of the<strong> </strong>hydrogen bonding configuration in states A1, A2 and Transition State (TS) between them for rsKiiro protein on ground (s0) and first excited (s1) states. Structures were optimized at PBE0-D3/cc-pVDZ//Amber03 level.</p>
The impact of genetically controlled splicing on exon inclusion and protein structure
<p><strong>This repository contains raw and processed files used in Einson et. al 2022. </strong></p> <p>Code used to generate these files can be found here: https://github.com/jeinson/sqtl_manuscript</p> <p><strong><em>Descriptions of files contained within each sub directory</em></strong></p> <p><strong>01_raw_psi</strong></p> <ul> <li><em>{GTEx_tissue_id}_v8.psi.tsv.gz: </em>Unfiltered PSI output from IPSA-nf, per tissue. See methods for details about how files were created. </li> <li><em>gtex_v8_exon_id_map.tsv: </em>Mapping file between exon coordinates and Ensembl gene IDs, with suffix used in GTEx v8 gencode annotation. </li> </ul> <p><strong>02_qtl_results</strong></p> <ul> <li><strong>cross_tissue</strong> <ul> <li><em>top_sQTLs_MAF05.tsv: </em>List of top GTEx v8 sQTLs across tissues, with one exon and top variant per tissue. See methods for details. See matching file for column descriptions. </li> <li><em>top_sQTLs_median_psi.tsv: </em>The median, mean, and standard deviation of PSI of each significant exon from the previous file, taken across all individuals from GTEx with data available.</li> <li><em>top_sQTLs_MAF05_w_anc_allele.tsv: </em>List of top sQTLs across tissues, with additional columns for the top ψQTL ancestral and derived alleles, where available. </li> </ul> </li> <li><strong>per_tissue</strong> <ul> <li><em>{GTEx_tissue_id}_combined_sQTLs.tsv.gz: </em>Raw output of ψQTL calling using QTLtools in grouped permutational mode per tissue, with groups specified by gene. See methods for more details, and https://qtltools.github.io/qtltools/ for column descriptions. </li> </ul> </li> </ul> <p><strong>03_qtl_credible_sets</strong></p> <ul> <li> <em>GTEx_psi_{GTEx_tissue_id}.collapsed.txt.gz: </em>Output of the QTL catalog fine mapping pipeline (https://github.com/eQTL-Catalogue/qtlmap), run on all exons and tissues, and collapsed using the procedure described in Methods. </li> </ul> <p><strong>04_qtl_coloc</strong></p> <ul> <li><em>combined_coloc_results_full.tsv.gz: </em>Combined output of running coloc on ψQTLs from the 18 GTEx tissues against 87 sets of GWAS summary statistics. This file contains all results, including non-significant associations. A nominal QTLtools pass was used as input. We do not include these files in this repository due to size limitations, but contact the authors if you need access to nominal QTL calls. </li> <li><em>top_sQTLs_with_top_coloc_event.tsv: </em>The QTLs in <em>top_sQTLs_MAF05.tsv</em> with additional columns for the GWAS with the highest posterior probability of a colocalization event. Importantly, the tissue and top variant may not match the main <em>top_sQTLs_MAF05.tsv </em>file for every gene. </li> </ul> <p><strong>05_exon_features: </strong>See matching files for description of each column. </p> <ul> <li><em>cross_tissue_constitutive_exons_with_AF.tsv: </em>Detailed features of cross tissue constitutive exons. See methods for definition of constitutive exons. </li> <li><em>cross_tissue_nonsignificant_genes_with_AF.tsv: </em>Detailed features of sufficiently variable exons with no significant variant across tissues. See methods for more details. </li> <li><em>top_sQTLs_MAF05_with_AF.tsv: </em>Detailed features of top sQTLs. </li> <li><em>top_sQTLs_with_top_coloc_with_AF.tsv: </em>Detailed features of sQTLs that colocalize with at least one GWAS trait. Contains columns for Euclidean distances between PAE matrices and RMSD between isoforms, among genes with a significant GWAS colocalization event. </li> </ul> <p><strong>06_predicted_structures: </strong>Each prediction was run 5 times, and we report the best model in the manuscript. </p> <ul> <li><strong>{protein.id}[_mutant].result</strong> <ul> <li><em>{protein.id}[_mutant]{_run.id}_coverage.png.gz: </em>Plot of the number of sequences per position in MSA</li> <li><em>{protein.id}[_mutant]{_run.id}_PAE.png.gz: </em>PAE matrix plots for each model</li> <li><em>{protein.id}[_mutant]{_run.id}_plddt.png.gz: </em>pLDDT plots for each model</li> <li><em>{protein.id}[_mutant]{_run.id}_predicted_aligned_error_v1.json.gz</em>: A PAE matrix for the best model using <a href="https://alphafold.ebi.ac.uk/faq#faq-7">AlphaFold-DB's format</a></li> <li><em>{protein.id}[_mutant]{_run.id}_unrelaxed_rank_{rank.num}_model_{model.num}_scores.json.gz</em>: Per model array (list of lists) with PAE, a list of the average pLDDT and the pTM score. </li> <li><em>{protein.id}[_mutant]{_run.id}_unrelaxed_rank_{rank.num}_model_{model.num}_pdb.gz: </em>Per model predicted structure in pd format</li> <li><em>{protein.id}[_mutant]{_run.id}.a3m.gz</em>: A3M formatted input MSA</li> <li><em>cite.bibtex: </em>BibTex file with citations for all used tools and databases</li> <li><em>config.json</em>: Model input parameters</li> </ul> </li> </ul> <p><strong>07_other_data</strong></p> <ul> <li><em>cross_tissue_constitutive_exons.tsv: </em>List of exons that are constitutively spliced across multiple tissues. See methods for details. </li> <li><em>cross_tissue_nonsignificant_genes.tsv</em>: List variably spliced exons with no significant sVariant in any tissue. See methods for details. </li> <li><em>gtex_v8_exon_id_map.rds: </em>rds representation of a map between exon IDs, as used in the modified version of gencode v26, and exon hg38 coordinates. </li> <li><em>gtex_v8_n_exons_per_gene.tsv: </em>Number of exons per gene, as annotated in the modified version of gencode v26 used in GTEx v8. </li> </ul> <p><strong>08_geuvadis</strong></p> <ul> <li><em>geuvadis_psi.tsv.gz: </em>Unfiltered PSI output from IPSA-nf, run on Geuvadis BAM files. See methods for details. (Raw data was downloaded from ftp://ftp.ebi.ac.uk/pub/databases/microarray/data/experiment/GEUV)</li> <li><em>geuvadis_sQTLs.tsv.gz: </em>Raw output of ψQTL calling using QTLtools in grouped permutational mode for geuvadis data, with groups specified by gene. See methods for more details, and https://qtltools.github.io/qtltools/ for column descriptions. </li> <li><em>remapped_gencode.v26.GRCh37.GTEx_v8.nochr.genes.gtf.gz:</em> Lifted over version of the gencode v26 gtf file, used to define exons for PSI and qtl mapping in the geuvadis analysis. The original version that was used in the GTEx analysis is based on GRCh38, and is available here: https://storage.googleapis.com/gtex_analysis_v8/reference/gencode.v26.GRCh38.genes.gtf</li> </ul>
Data files for 'Tan et al., (2023). Structural heterogeneity-controlled rupture process of the 2021 Mw 7.1 Fukushima, Japan earthquake revealed by joint inversion of seismic and geodetic data'
<p>slip model.dat: rupture model of the 2021 Mw 7.1 Fukushima earthquake</p> <p>In 'slip model.dat', each row contains the moment rate function of each sub-fault. The numbers of the sub-faults are given in the first two columns.</p>
Orientation data for: Pre-thrusting stratigraphic control on the transition from a thin- to thick-skinned structural style: an example from the double-decker Idaho-Montana fold-thrust belt
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Isolation controls reestablishment mechanisms and post-drying community structure in an intermittent stream
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Invertebrate community structure predicts natural pest control resilience to insecticide exposure
<p>Biological pest control has become one of the central principles of ecological intensification in agriculture. However, invertebrate natural enemies within agricultural ecosystems are exposed to a myriad of different pesticides at both lethal and sub-lethal doses, that may limit their capacity to carry out pest control. An important question is how underlying diversity in invertebrate predator species, linked to their unique susceptibility to insecticides, can act to increase the resilience of natural pest control.</p> <p>We explore this issue by assessing the effects of sub-lethal insecticide exposure on the predation rates of 12 generalist predators feeding on the aphid <i>Sitobion avenae</i> (Aphididae). Predation rates within a 24 hr period were assessed (predation assessment) for each species after receiving one of the following treatments: 1) no prior deltamethrin exposure before the predation assessment (control); 2) deltamethrin exposure immediately before the predation assessment (resistance); and 3) deltamethrin exposure five days before the predation assessment (recovery). Extrapolating from these species-specific measures of resistance and recovery, we predicted the resilience of community level predation to insecticide exposure for predator communities associated with 256 arable fields in the UK.</p> <p>There was large variation in sub-lethal effects of the insecticide between even closely related species. This ranged from species showing no change in predation rates following sub-lethal insecticide exposure (high resistance), species showing only immediate depressed feeding rates after 24 hrs (high recovery), or those with depressed feeding rates after five days (low resistance and recovery).</p> <p>The community level analysis showed that resistance and recovery of natural pest control was predicted by both community phylogenetic diversity (positively) and weighted mean body mass (negatively). However, the removal of numerically dominant species from the analysis modified these effects.</p> <p><strong>Synthesis and applications. </strong>Our results highlight the role of community diversity in maintaining the resilience of natural pest control following insecticide use. Importantly, less diverse assemblages dominated by predator species that show low resilience to insecticide exposure, may show a greater depression in pest control than diverse assemblages under insecticide based farmland management. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.