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249 results for “3D Structure”
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>
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
Dataset of "Stochastic multi-observables inversion for 3D thermochemical structure of lithosphere in spherical coordinates"
<p>### Introduction</p> <p>This repository contains source codes and raw data files associated with the manuscript entitled "<strong>Stochastic multi-observables inversion for 3D thermochemical structure of a lithosphere in spherical coordinates</strong>" by Yi Zhang (yizhang-geo@zju.eud.cn) and Xu Yixian at the School of Earth Sciences, Zhejiang University, Hangzhou 310027, China. Please see the copyright file before you use the enclosed files.</p> <p>Please check former versions for more code and data files.</p> <p> </p> <p>### Abbreviations</p> <p>* **GCTL**: Geophysical Computational Tools & Library (<a href="http://sanqian.synology.me:8418/zhangyiss/gctl">http://sanqian.synology.me:8418/zhangyiss/gctl</a>);</p> <p>* **GIST**: Geophysical Inversions under the Spherical coordinates using Tetrahedral meshes (see former versions of this repository).</p> <p> </p> <p>### Files</p> <p>* CNTS_DATA: data files for the field application.</p>
Dataset for Estimating the 3D structure of the Enceladus ice shell from flexural and Crary waves
<p>Data for the publication "Estimating the 3D structure of the Enceladus ice shell from flexural and Crary waves" Includes sac files for 4 different velocity models at two sources. The naming convention is based on the location of the receiver and the component (e.g.) N85100..XDE is North 85 degree, 100 degrees, East component). Each model also has the outputs of the mineos code which provides the surface wave dispersion. </p>
Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T
<p>This dataset includes the k-space data of two 2D sagittal slices from the conventional and seg-CAIPI(8,3) 3D multi-shot EPI datasets, as well as the coil sensitivity maps. The conventional sampling corresponds to seg-CAIPI(2,1). These two 3D multi-shot EPI datasets were acquired at 1.8mm isotropic resolution and acceleration factor R=2x2. Other protocol parameters are: matrix size=116x116x96, 40 volumes, TE/TR=23/55ms. </p>
3D chromatin structures associated with ncRNA roX2 for hyperactivation and co-activation across the entire X chromosome
<p>The SMLM datasets of roX2 and roX2/H3K27me3.</p>
RNA 3D structural models used to train, test and validate lociPARSE
<p>This repository contains all the training, validation and test decoy sets to train and evalaute lociPARSE. It also contains training and benchmarks set-2 decoys from ARES.</p>
Structural 3D domain reconstruction of the RNA genome from viruses from a secondary structure model
<p>Fragments and final models of reconstructed STMV genome from in virio and in vitro secondary structures reported in Larman et al. (2017).</p> <p>Simulation scripts for simulations of genome and fragments.</p> <p>Full code of SPQR package for performing simulations.</p>
Altered 3D chromatin structure permits inversional recombination at the IgH locus
<p>Immunoglobulin heavy chain (<i>IgH</i>) genes are assembled by two sequential DNA rearrangement events that are initiated by recombinase activating gene products (RAG) 1 and 2. Diversity gene segments (D<sub>H</sub>) rearrange first, followed by variable (V<sub>H</sub>) gene rearrangements. Here we provide evidence that each rearrangement step is guided by different rules of engagement between rearranging gene segments. D<sub>H</sub> gene segments, that recombine by deletion of intervening DNA, must be located within a RAG1/2 scanning domain for efficient recombination. In the absence of intergenic control region 1, a regulatory sequence that delineates the RAG scanning domain on WT <i>IgH</i> alleles, V<sub>H</sub> and D<sub>H</sub> gene segments can recombine with each other by both deletion and inversion of intervening DNA. We propose that V<sub>H</sub> gene segments find their targets by diffusion-controlled mechanisms. These distinct mechanisms may underlie differential allelic choice associated with each step of <i>IgH</i> gene assembly.</p>
Figure 1. Hydroschendyla submarina, 3D in The preoral chamber in geophilomorph centipedes: comparative morphology, phylogeny, and the evolution of centipede feeding structures
Figure 1. Hydroschendyla submarina, 3D reconstructions of the head (antennae cut off at their base and forcipules removed) based on histological sections (volume rendering), showing the position of the regions of interest of the present study. A, complete head reconstruction in oblique-frontal view; rectangle indicates the position where heads were sliced into two halves to examine epi- and hypopharynx by SEM; B, anterior half of the head, oblique view from behind on epipharynx; C, posterior half of the head, same view as in A but with the anterior half removed to show the hypopharynx and mandibular gnathal lobes. Abbreviations: an, antenna; br, brain; ep, epipharynx; hy, hypopharynx; md, mandible; mo, mouth; mx I, first maxilla; mx II, second maxilla.
3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping
<p>3D-BUDA data acquired from a 3T Siemens scanner and a 7T Siemens scanner</p>
Fig. 7. Superimposed 3D in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 7. Superimposed 3D structure of HRP-C (in brown) on A) 1AP2 and B) POXalf. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data from: The effects of aging on neuropil structure in mouse somatosensory cortex—A 3D electron microscopy analysis of layer 1
Open the record for dataset details and reuse information.
Altered 3D chromatin structure permits inversional recombination at the IgH locus
Open the record for dataset details and reuse information.
Molding 3D curved structures by selective heating
Open the record for dataset details and reuse information.
Data and R code for What you see is where you go: visibility influences movement decisions of a forest bird navigating a 3D structured matrix
<p>Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal's PR, making information availability a function of an animal's location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we model forest birds' EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.57 times more likely to move to a tree within their EVPR than to an equivalent tree outside it. This demonstrates that behavioural choices can be substantially impacted by the characteristics of an individual's EVPR and highlights that inferences made from movement data may be improved by accounting for the EVPR.</p>
RWQM 3d SHO Structure 3
<p>This is an interactive plot of the 3rd structure for a 3D SHO potential calculated from Real Wave Quantum Mechanics using the matrix method with a radius of 27. It can be downloaded and should run in most browsers.</p> <p>The size of the dots represents the magnitude of the FR at each point. The colour is a cyclic mapping of the phase after removal of the phase alternation which characterizes RWQM fields.</p> <p> </p>
Dataset for: Numerical simulations of sheared granular materials by 3D DEM for analyzing formation of internal structures and relationship between it and frictional behavior
<p>We conducted numerical simulations of sheared granular materials under normal stress by using 3D DEM method in order to analyze internal structures and relationship between their formation and frictional behavior. This dataset includes raw data for plotting figures in our article and run scripts and CAD files for simulations.</p>
Figure 4 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 4 - Time table for the eEcoLiDAR project (assuming a start in March 2017). The work plan covers tasks for the NLeSC engineers, the proposed PhD student, and two associated Postdoc projects.
Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.
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.