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715 results for “folding”
In silico data for: Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling
<p>Directory layout and description for deposited data, scripts, and results associated with</p> <p><strong>Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling</strong></p> <p>Naoto Soya, Haijin Xu, Ariel Roldan, Zhengrong Yang, Haoxin Ye, Fan Jiang, Aiswarya Premchandar, Guido Veit, Susan P.C. Cole, John Kappes, Tamas Hegedus, and Gergely L. Lukacs</p> <p> </p> <p>Two files are provided:</p> <ol> <li><strong>soya_md_trajectories.tar</strong> - This file contains the trajectories merged from the last part (450-500 ns) of the parallel simulations: md_450000_500000.xtc</li> <li><strong>soya_insilico_data.zip</strong> - This file contains all other deposited files including input data, scripts, results files.<br> The content of this file can be found below:</li> </ol> <p><strong>README.md </strong>- the content of this description</p> <p><strong>homo - Homology modeling</strong></p> <ul> <li>run*.py, myloopmodel.py, and mymodel.py files are separated for technical reasons, for running model-building in parallel mode</li> <li><strong>cftr-loop</strong> - Demonstrates the removal of the RI and seeling the break with loopmodeling</li> <li><strong>mrp1</strong> - Scripts and input files for human MRP1 homology modeling; the large unresolved loop in NBD1 was not modeled but sealed for MD; this required renumbering of the ouput</li> <li><strong>mrp6</strong> - Scripts, input, and output files for human MRP1 homology modeling; output: mrp6_human_closed.pdb; the selected CFTR and MRP1 models were the input for MD simulaitons; to see these energy minimized structures, please see the corresponding 'md' directory below.</li> </ul> <p><strong>md - Moldecular dynamics</strong></p> <ul> <li>The <strong>md_system_info.xlsx</strong> file contains the basic properties of simulation boxes</li> <li>MD parameter files: step6*.mdp for minimization and equilibration; step7_production.mdp for production run</li> <li>wordom.dat is the wordom configuration file</li> <li>cmap_mda.mp.py is a script for contact map calculation</li> <li><strong>cftr-*, mrp1-*</strong> <ul> <li>the simulation system generated by CHARMM-GUI: step5_charmm2gmx.pdb</li> <li>the output gro file of parallel simulations (the last state of the sysmtems): md_[1-6].gro</li> <li>! the trajectory merged from the last part (450-500 ns) of the parallel simulations: trajectories md_450000_500000.xtc are in a separte file (soya_md_trajectories.tar) with the same directory structure</li> <li>the merged trajectory contains only the SOLU; the corresponding structure file: prot.pdb</li> <li>index.ndx</li> </ul> </li> </ul> <p><strong>figures</strong></p> <ul> <li><strong>figure-3a</strong> <ul> <li>pdb files are the output of gmx rmsf</li> <li>pse file is saved visualization of the pdb files for PyMOL<br> </li> </ul> </li> <li><strong>figure-3c-s4b</strong> <ul> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> <li>dri in file names and in scripts refers to 6ss<br> </li> </ul> </li> <li><strong>figure-s3a</strong> <ul> <li>tmd1_structures.pse contains the structures for PyMOL</li> <li>Please see the Source Data file for plotting RMSF</li> <li>Contact map data are in the tmd1_wt.npy and tmd1_r170g.npy file<br> </li> </ul> </li> <li><strong>figure-s3e</strong> <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures<br> </li> </ul> </li> <li><strong>figure-s4a</strong> <ul> <li>Contains the calculated betweenness.txt files</li> <li>betweenness_plots.py for plotting</li> <li>wt_prot.pdb: required for plotting with resi thick-labels<br> </li> </ul> </li> <li><strong>figure-s5c</strong> <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures<br> </li> </ul> </li> <li><strong>figure-s8d</strong> <ul> <li>Data for MRP1/ABCC1</li> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> </ul> </li> </ul>
Expiratory Muscle Strength Training and Phonation Resistance Training Exercises For Elderly Patients With Vocal Fold Atrophy
ClinicalTrials.gov study NCT03696576. IPD Sharing: YES. Countries: 1. Publications: 33.
Asymmetric fluctuations and self-folding of active interfaces
Open the record for dataset details and reuse information.
Wbbyyr: FastText language models for Mandarin Chinese, trained on 14m Sina Weibo posts for each year in 2012-2018 (Fold 1 of 10)
<p>Wbbyyr: FastText language models for Mandarin Chinese, trained on 14,440,000 Sina Weibo posts for each year in 2012-2018.</p> <p>The 14,440,000 posts from each year are split into 10 folds. Due to Zenodo size limit, this dataset contains only the first fold from each year.</p> <p>Each model is trained for 20 iterations. Each vector is 300 dimensions long.</p>
Average fold coverage and annotation results of those ORF that were increasing over treatment time
<p>Table listing those ORF that presented an increased frequency across the treatment time</p> <p>This a supplementary material for the doctoral thesis entitle: <em><strong>Understanding microbiome supra-metabolism responses under strong selective pressures as a resource for designing synthetic gene arrangements encoding key co-selected functions for environmental biotechnology applications. </strong></em>Villegas-Plazas M, 2020. Universidad del Valle. Cali, Colombia</p>
Predicted secondary structures (2-D representation of this self-folding) of the terminal untranslated regions (UTR) of FvMV1 and FaMV1-162
<p>Schemes (a) and (c) represent the 5`-UTR and 3`-UTR of FvMV1 with dG = -82.52 and dG = -29.55, respectively. Schemes (b) and (d) represent the 5`-UTR and 3`-UTR of FaMV1-162 with dG = -73.96 and dG = -13.93, respectively. The +ssRNA molecules were folded, and the free energy was calculated with the RNA Folding Form V 2.3 Energies (MFOLD) program. For these calculations, the following conditions were sectioned: 25ºC, 1M NaCl and 0M divalent ions. The rendering of the structures has been defined with natural angles and annotated using colored base characters, based on p-num information. Colors are ranged from red to black representing the probability as well-determined (1) to poorly determined (0), respectively.</p>
Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases
Glycosyltransferases (GTs) are prevalent across the tree of life and regulate nearly all aspects of cellular functions. The evolutionary basis for their complex and diverse modes of catalytic functions remain enigmatic. Here, based on deep mining of over half million GT-A fold sequences, we define a minimal core component shared among functionally diverse enzymes. We find that variations in the common core and emergence of hypervariable loops extending from the core contributed to GT-A diversity. We provide a phylogenetic framework relating diverse GT-A fold families for the first time and show that inverting and retaining mechanisms emerged multiple times independently during evolution. Using evolutionary information encoded in primary sequences, we trained a machine learning classifier to predict donor specificity with nearly 90% accuracy and deployed it for the annotation of understudied GTs. Our studies provide an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.
Supplementary Dataset S8 for publication "Universality in human cortical folding in health and disease"
<p>The data sources and method of extracting the data are described in the publication "Universality in human cortical folding in health and disease". PNAS 2016</p> <p>2019 update: The matlab code for extraction of relevant variables from Freesurfer subjects is now published on <a href="https://doi.org/10.5281/zenodo.3608675">Zenodo</a> and <a href="https://github.com/cnnp-lab/CorticalFoldingAnalysisTools">Github</a>.</p>
Gigantic animal cells suggest organellar scaling mechanisms across a 50-fold range in cell volume
<p>Across the tree of life, cell size varies by orders of magnitude, and organelles scale to maintain cell function. Depending on their shape, organelles can scale by increasing volume, length, or number. Scaling may also reflect demands placed on organelles by increased cell size. The 8,653 species of amphibians exhibit diverse cell sizes, providing a powerful system to investigate organellar scaling. Using transmission electron microscopy and stereology, we analyzed three frog and salamander species whose enterocyte cell volumes range from 228 to 10,593 μm3. We show that the nucleus increases in radius while the mitochondria increase in total network length; the endoplasmic reticulum and Golgi apparatus, with their complex shapes, are intermediate. Notably, all four organelles increase in volume proportionate to cell volume. This pattern suggests that protein concentrations are the same across amphibian species that differ 50-fold in cell size, and that organellar building blocks are incorporated into more or larger organelles following the same "rules" across cell sizes, despite variation in metabolic and transport demands. This conclusion contradicts results from experimental cell size increases, which produce severe proteome dilution. We hypothesize that salamanders have evolved the biosynthetic capacity to maintain a functional proteome despite a huge cell volume. </p>
Five-fold training dataset of fossil pollen images from Burgäschisee used for automated fossil pollen identification (von Allmen et al. study)
<p>This dataset consists of a training dataset for the CNN model containing pollen grain images of nine common pollen taxa, one marker class (Lycopodium clavatum) and four abundant non-pollen debris classes. The dataset was split five-times so that each image is part of the validation dataset in just one of these splits. Additionally, the dataset contains annotated images used to train the object detection model and a dataset of images that were used to evaluate the performance of the CNN model. For further information on the datsets themselves and how they were used the reader may refer to the github repository here attached (<a href="https://github.com/RobinVonAllmen/FOSSILPOLLEN">https://github.com/RobinVonAllmen/FOSSILPOLLEN)</a></p>
135 film folding camera
A Certo Super Dollina II camera of German production, which was used to take most of the pictures during Priest Karol Wojtyła's trips in the 1950s. It belonged to Jacek Fedorowicz, a former student of the Kraków University of Technology who participated in famous trips organised by Priest Wojtyła. ID no.: 192/X "Certo Super Dollina II" Museum: Cardinal Karol Wojtyła Archdiocesan Museum in Kraków https://muzea.malopolska.pl/en/objects-list/542 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Medival folding crossbow "Biting Turtle"
Small folding crossbow attached to the forearm. Inspired by ACs hidden crossbow. Thinking about doing some animation for the actual "folding"part to get more practise in animating. WIP Always up for some advices how to improve my work, feel free to say something. Source: Objaverse 1.0 / Sketchfab
Data from: Pre- and post- treatment fold change plasma proteomics in metastatic NSCLC patients
<p>Blood plasma samples were collected from advanced-stage NSCLC patients as part of a clinical study (PROPHETIC; NCT04056247). All clinical sites received IRB approval for the study protocol. Patient blood samples were drawn at baseline (referred to as T0) and, on average, 4 weeks after the treatment commenced, prior to the second dose of treatment (referred to as T1). Blood samples were drawn into tubes containing EDTA as an anticoagulant, and plasma was separated from the whole blood. The protocol adheres to the Clinical and Laboratory Standards Institute (CLSI) guidelines.</p>
Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment.
<p>Genomics polymorphisms of Staphylococcus aureus strain NCTC 8325 in the lab stock maintained at TUM (WT), after 30 passes in BHI media (D) and after 30 passes detecting 4 -fold MIC increase to isocyanide -code I16- 3 biological replicates (A,B,C), and 3 independent colonies sequenced per replicate at the end of the experiment. Determined from Illumina shotgun genomic sequencing datasets, mapping and analyses vs the reference genome of the strain https://www.ncbi.nlm.nih.gov/nuccore/NC_007795.1/</p>
Folded data for first three observing runs of Advanced LIGO and Advanced Virgo
<p>This release contains folded datasets from the first three observing runs of Advanced LIGO and Advanced Virgo detectors, which were recently used to perform the broadband directional search (https://dcc.ligo.org/LIGO-P2000500/public) and the all-sky-all-frequency directional search (https://dcc.ligo.org/LIGO-P2100292/public) for a persistent anisotropic gravitational wave background. The DataContent_Readme.md file provides information on the content of the files and how to read these datasets.</p>
Molecular dynamics trajectories of protein folding
<p>Molecular dynamics trajectories of protein folding are deposited for educational purposes.</p> <p>Currently, the following trajectories are available:</p> <ul> <li>Chignolin (five independent NVT simulations up to 1.5 micro-sec): <ul> <li>`movie.pse` is a PyMOL session file of MD trajectories. </li> <li>`movie.mp4` is a movie file that shows you how a protein folds during simulation.</li> <li>xtc files (Gromacs compressed format) and corresponding tpr files.</li> <li>gro files for movie making</li> </ul> </li> </ul> <p> </p> <p>Computational setting </p> <ul> <li>Amber ff99SB-ILDN for protein (Lindorff-Larsen, K. <em>et al.</em> Improved side-chain torsion potentials for the Amber ff99SB protein force field. <em>Proteins</em> <strong>78</strong>, 1950–1958 (2010))</li> <li>TIP3P water model</li> <li>0.1 M salt concentration </li> <li>NVT ensemble at 300 K with V-rescale thermostat (Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. <em>J. Chem. Phys.</em> <strong>126</strong>, 014101 (2007))</li> <li>Time step : 2 fs</li> <li>gromacs-2020.6</li> </ul>
Coseismic folding during ramp failure at the front of the Sulaiman fold-and-thrust belt
<p>It contains the datasets related to "Coseismic folding during ramp failure at the front of the Sulaiman fold-and-thrust belt".</p>
Data from: Between the Cape Fold Mountains and the deep blue sea: comparative phylogeography of selected codistributed ectotherms reveals asynchronous cladogenesis. Sampling locations and MaxEnt input files
<p><span>We compare the phylogeographic structure of thirteen codistributed ectotherms including four reptiles (a snake, a legless skink and two tortoise species) and nine invertebrates (six freshwater crabs and three velvet worm species) to test the presence of congruent evolutionary histories. </span><span>Phylogenies were estimated and dated using maximum likelihood and Bayesian methods with combined mitochondrial and nuclear DNA sequence datasets. </span><span>All taxa demonstrated a marked east/west phylogeographic division, separated by the Cape Fold Mountain range.</span><span> <span>Phylogeographic concordance factors were calculated to assess the degree of evolutionary congruence among the study species and </span></span><span>generally supported a shared pattern of diversification along the east/west longitudinal axis</span><span>. Testing simultaneous divergence between the eastern and western phylogeographic regions indicated </span><span>pseudo-congruent evolutionary histories among the study taxa, with at least three separate divergence events throughout the Mio/Plio/Pleistocene epochs.</span><span> <span>Climatic refugia were identified for each species using climatic niche modeling, </span></span><span>demonstrating taxon-specific responses to climatic fluctuations. Climate and the Cape Fold Mountain barrier explained the highest proportion of genetic diversity in all taxa, while climate was the most significant individual abiotic variable. </span><span>This study highlights the complex interactions between the Cape Fold Mountains and past climatic oscillations during the Mio/Plio/Pleistocene. The congruent east/west phylogeographic division observed in all taxa lends support to the conclusion that the longitudinal climatic gradient within the Greater Cape Floristic Region, mediated in part by the barrier to dispersal posed by the Cape Fold Mountains, plays a major role in lineage diversification and population differentiation.</span></p>
Low-fold seismic reflection data acquired across the Northern Hikurangi subduction margin and incoming Hikurangi Plateau, New Zealand
<p>Two-dimensional seismic reflection data were acquired on two surveys of the Northern Hikurangi subduction margin, New Zealand in 2011 and 2015. The survey data were collected to support research of tectonic structure, slow slip processes, stratigraphic architecture, and thermal state of the subduction margin and incoming plate, as well as to support ocean-floor drilling associated with IODP Expeditions 372 and 375. The surveys include (1) R/V <em>Tangaroa</em> NIWA voyage TAN1114 undertaken in 2011 by the National Institute of Water and Atmospheric Research (NIWA) and GNS Science, as part of the <em>OS2020 Northern Hikurangi Margin Geohazards</em> survey; and (2) R/V <em>Rodger Revelle</em> cruise RR1508 undertaken in 2015 by Oregon State University as part of the <em>Subduction Thrust Investigation of New Zealand using Geothermics and Seismics (STINGS)</em> project (see Figure 1). TAN1114 voyage was funded by the New Zealand Government Oceans 2020 Programme, and core research programme funding by NIWA and GNS Science. Cruise RR1508 was funded by NSF grants OCE-1355878 and OCE-1355870.</p> <p> </p> <p><strong>R/V <em>Tangaroa</em> TAN1114 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The seismic system used on R/V <em>Tangaroa</em> during the 2011 National Institute of Water and Atmospheric Research (NIWA) survey TAN1114 included a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed 35 m behind the vessel RV <em>Tangaroa</em> at 5 m water depth. Lines TAN1114-01 to -13, and part of line 14 were acquired with a shot interval of 10.8 seconds (~25 m sailing at 4.5 knots), providing a nominal coverage of 12-fold data. Part of line TAN1114-14 and lines 15-23 were acquired with a shot interval of 21.6 seconds (~50 m sailing at 4.5 knots), providing a nominal 6-fold coverage. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 7.5 m, apart from line TAN1114-01 where it was towed at 5 m depth. Depth control was maintained with a CSMX depth control system including three DigiCourse 5011 compass birds. The record length was 8 s and the sample rate 2 ms. Differential GPS was used for positioning. Table 1 summarises TAN1114 recording parameters and Table 2 lists TAN1114 lines acquired and processed. TAN1114 line coordinates are detailed in Table 3.</p> <p><strong>Data Processing:</strong> A total of 29 seismic lines were processed providing 1350 km of multichannel seismic reflection data. The lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. With allowance for overlap of line segments the data were grouped into 51167 shot-point locations. Raw data were written to disk as IBM standard SEG-Y files. IBM Claritas Extended SEG-Y data were written to disk after geometry was added, after stack, and after migration. Shots were CDP sorted from disk during the stacking process to avoid creating large and unnecessary separate CDP sorted files. </p> <p>Post-stack migration (finite difference migration) has been applied to the stacked sections to produce a dip-true image, this results in clearer resolution of structural features such as faults and folds, and of detailed sedimentary features such as on-lapping and truncated reflections. Sea-floor multiple reflections disturb structural imaging especially in water depths less than 500 m. All seismic data are written to disk as processed sections in SEG-Y format. Line TAN1114-A is a composite splice including parts of lines TAN1114-4A, -6A and 7A. Details of the TAN1114 processing parameters are given in Table 4 and SEG-Y trace headers in Table 5.</p> <p> </p> <p><strong>R/V <em>Rodger Revelle</em> RR1508 Seismic Data</strong></p> <p><strong>Data Acquisition:</strong> The 2015 R/V <em>Rodger Revelle</em> survey RR1508 used a seismic system operated by Scripps Institute of Oceanography. Of two sub-regions surveyed during this cruise, only data from the northern Hikurangi margin are presented here. The seismic system used was similar to that on <em>Tangaroa</em> TAN1114, including a source comprising two Sodera 45/105 GI guns operated in true GI mode. The guns were deployed at a depth of 3.5 m and the shot spacing was 25 m. Data were recorded on a Geometrics GeoEel 48-channel seismic streamer with 6 X 100 m active sections, and a group interval of 12.5 m. The streamer was deployed at a depth of 3.5 m. During acquisition of the HKS01 lines, only the nearest 40 data channels were recorded. The record length was 8 s and the sample rate 1 ms. Differential GPS was used for positioning. Table 6 summarises RR1508 recording parameters and Table 7 lists RR1508 lines acquired and processed.</p> <p><strong>Data Processing: </strong>A total of 13 HKS01 seismic lines were processed to post-stack time-migrated SEGY sections, using GNS Science GLOBE CLARITAS. Data processing included application of geometry, sorting, trace editing, normal moveout correction, stack, filtering and finite difference migration. All seismic data are written to disk as processed sections in SEG-Y format. Details of the RR1508 processing parameters are given in Table 8 and SEG-Y trace headers in Table 9.</p> <p> </p> <p><strong>List of files</strong></p> <p>Figure 1. TAN1114 and RR1508 seismic line locations on the northern Hikurangi margin.</p> <p>Table 1. Summary of TAN1114 recording parameters.</p> <p>Table 2. Summary of TAN1114 lines acquired and processed.</p> <p>Table 3. Summary of TAN1114 line coordinates.</p> <p>Table 4. Summary of TAN1114 seismic processing sequence.</p> <p>Table 5. Summary of TAN1114 SEG-Y trace headers.</p> <p>Table 6. Summary of RR1508 recording parameters.</p> <p>Table 7. Summary of RR1508 lines acquired and processed.</p> <p>Table 8. Summary of RR1508 seismic processing sequence.</p> <p>Table 9. Summary of RR1508 SEG-Y trace headers.</p> <p> </p> <p>Processed SEGY seismic data</p> <p>TAN1114-01.sgy</p> <p>TAN1114-02.sgy</p> <p>TAN1114-03.sgy</p> <p>TAN1114-04.sgy</p> <p>TAN1114-04A.sgy</p> <p>TAN1114-05.sgy</p> <p>TAN1114-06.sgy</p> <p>TAN1114-06A.sgy</p> <p>TAN1114-07.sgy</p> <p>TAN1114-07A.sgy</p> <p>TAN1114-08.sgy</p> <p>TAN1114-09.sgy</p> <p>TAN1114-10.sgy</p> <p>TAN1114-10B.sgy</p> <p>TAN1114-11.sgy</p> <p>TAN1114-12.sgy</p> <p>TAN1114-12T.sgy</p> <p>TAN1114-13.sgy</p> <p>TAN1114-14.sgy</p> <p>TAN1114-15.sgy</p> <p>TAN1114-16.sgy</p> <p>TAN1114-17.sgy</p> <p>TAN1114-18.sgy</p> <p>TAN1114-19.sgy</p> <p>TAN1114-20.sgy</p> <p>TAN1114-21.sgy</p> <p>TAN1114-22.sgy</p> <p>TAN1114-23.sgy</p> <p>TAN1114-A.sgy</p> <p>RR1508-HKS01_01.sgy</p> <p>RR1508-HKS01_02.sgy</p> <p>RR1508-HKS01_02A.sgy</p> <p>RR1508-HKS01_03.sgy</p> <p>RR1508-HKS01_04.sgy</p> <p>RR1508-HKS01_05.sgy</p> <p>RR1508-HKS01_05A.sgy</p> <p>RR1508-HKS01_06.sgy</p> <p>RR1508-HKS01_07.sgy</p> <p>RR1508-HKS01_08.sgy</p> <p>RR1508-HKS01_09.sgy</p> <p>RR1508-HKS01_09A.sgy</p> <p>RR1508-HKS01_10.sgy</p>
Folded
<p>Folded is a database that stores the results of ColabFold (alphafold) predictions.</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.