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1,899 results for “Running”
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the first out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 1)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the first out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Instructions on how to run PanGenie on HGSVC3 + HPRC data
<h2>Input Data</h2> <h3>Reference genome</h3> <p>The reference genome used (CHM13) can be obtained from: <a href="https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz">https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz</a></p> <h3>Reads</h3> <p>We used the raw 1kg Illumina reads (FASTA/FASTQ) obtained from: <a href="http://ftp.sra.ebi.ac.uk/vol1/fastq/">http://ftp.sra.ebi.ac.uk/vol1/fastq/</a>.<br>We created a single FASTQ file per sample by concatenating individual files, however, PanGenie can also be run with multiple files per sample (see instructions below on how this is done).</p> <h3>Input panel VCF</h3> <p>The input VCF was derived from Minigraph-Cactus VCFs produced from 65 HGSVC3 and 42 HPRC assemblies. It is available here in this repository: <code> MC_hgsvc3-hprc_chm13_filtered_bubbles.vcf.gz </code><br>There is an additional VCF containing decomposed variant alleles, which will be needed to postprocess the PanGenie output (in order to translate bubble genotypes to genotypes for all nested variants). This VCF is also available in this repository and is called: <code>MC_hgsvc3-hprc_chm13_filtered_decomposed.vcf.gz</code></p> <h2>Running PanGenie</h2> <p>We used PanGenie version v3.1.0 and ran it via singularity as described in the README (<a href="https://github.com/eblerjana/pangenie/tree/master">https://github.com/eblerjana/pangenie/tree/master</a>). The singularity image used is available in this repository: <code>eblerjana_eblerjana_pangenie-v3.1.0.sif</code></p> <h3>Commands used for genotyping</h3> <p><strong>(1) Indexing (run only once):</strong></p> <p> <code> PanGenie-index -v MC_hgsvc3-hprc_chm13_filtered_bubbles.vcf -r chm13v2.0.fa -o index -t 24 </code></p> <ul> <li>Note: the input panel VCF (<code>-v</code>) as well as the reference genome (<code>-r</code>) need to be uncompressed.</li> </ul> <p><strong>(2) Genotyping (run for each sample):</strong></p> <p><code> PanGenie -a 108 -f index -i <(zcat <sample>.R1.fq.gz <sample>.R2.fq.gz) -o pangenie_<sample> -j 24 -t 24 -s <sample> </code></p> <ul> <li><code>-j</code> and <code>-t</code> specify the number of threads to be used for internal k-mer counting and genotyping steps (here 24 cores are used for both).</li> <li>The <code>-i</code> parameter is used to provide input reads (in FASTA/FASTQ format). If you have more than one file per sample (e.g. for paired-end reads) use the syntax shown in the command above to avoid having to concatenate the files beforehand.</li> </ul> <p><br><strong>(3) Decompose bubbles (run for each sample)</strong></p> <p><code>cat pangenie_<sample>_genotyping.vcf | python3 convert-to-biallelic.py MC_hgsvc3-hprc_chm13_filtered_decomposed.vcf > pangenie_<sample>_genotyping_biallelic.vcf</code></p> <ul> <li>Note: the <code>convert-to-biallelic.py</code> script is provided in this repository.</li> </ul>
MAgPIE 4.0 paper - model runs
<p>Archive of runs produced for the MAgPIE 4 modeling framework paper.</p>
Inputdata for NorESM2.1.0 hybrid run with restart in 1701-01-01 compset N1850 (fully coupled) at f19_tn14
<p>Compset longname is</p> <p>1850_CAM60%NORESM_CLM50%BGC-CROP_CICE%NORESM-MIP6_MICOM%ECO_MOSART_SGLC_SWAV_BGC%BDRDDMS</p> <p>Copy the content of the 1701-01-01-00000 folder in the run directory, then change RUN_TYPE and RUN_REFCASE</p>
Molecular Dynamics trajectories and portable binary run input (TPR) files for the dual role of anionic lipids in amyloid aggregation
<h1>About this repository</h1> <p>We conducted Coarse-Grained molecular dynamics simulations to study the aggregation of the amyloid-beta fragment, Aβ16-22 (K16LVFFAE22), on mixed lipid bilayers composed of POPC and POPS. Three bilayer compositions were examined: 0% PS-100% PC, 10% PS-90% PC and 30% PS-70% PC. Simulations were performed using GROMACS 2019.4, employing the WEPROM forcefield for peptide modeling and WEPMEM for lipid modeling. For each POPS percentage, four independent replicas were run for 3000 ns. </p> <h1><strong>Contents</strong></h1> <h3>> Abeta_PCPS.zip </h3> <p>This zip file contains three main folders: 0PS, 10PS, and 30PS. These represent different percentages of PS present in the lipid bilayer.</p> <p>Each folder contains:</p> <p>1. Initial structures:<br> - em1.gro: Lipid bilayer without peptides<br> - eq1.gro: Lipid bilayer at 95 Ų area-per-lipid (APL)<br> - em2.gro: Lipid bilayer at 95 Ų APL with 16 peptides<br> - eq2.gro: Final equilibrated structure with peptides (used for production)</p> <p>2. Supporting files:<br> - index.ndx: Index groups for peptides and lipid bilayer<br> - run.pdf: Structure file for VMD visualization</p> <p>3. Four replica folders (replica1 to replica4), each containing:<br> - red.tpr: Binary input for analysis (peptides and lipid bilayer only)<br> - full_trj_pbc.xtc: Trajectory file (peptides and lipid bilayer only, centered in box)</p> <p>Note: The `.tpr` files were created using the `gmx convert-tpr` tool.</p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/meenaljainumd/PCPS_amyloid_aggregation</a></p>
HLS land tiles - dataset needed to run FloodsNet
<p>This is the hls_land_tile.json dataset, needed to run FloodsNet. Please see the Readme for folder structure and more information.</p>
[VISIR-2: input data for benchmark runs] LSE
<p>Graph, wave field, and namelists needed to run the LSE benchmark for the VISIR-2 ship weather routing model.</p>
Climate-economy model scenario runs and sensitivity analysis using DICE-2016
<p>This paper assesses the prospects for climate stabilization from both positive and normative economic perspectives, and with an eye to the conditions necessary for collective action across the three domains: domestic, international, and intergenerational. While it is well-established that international freeriding and transaction costs pose major impediments to successful environmental agreements, this analysis identifies the intergenerational domain as the source of intractability due to long delays between enduring mitigation costs and enjoying their eventual climate benefits. This lag causes the net benefits for median-aged voters' to be negative over their expected remaining lifespans. Drawing on estimates from several Integrated Assessment Models of the benefits and costs of climate stabilization actions, the analysis concludes that a program of domestic and international climate actions will be hopelessly stymied by the failure of the actions to pass individual and collective rationality tests. However, the dire implications of this conclusion leaves the door open to the possibility that some change in circumstances could undercut this conclusion. The assignment of rights, in particular, has that potential. Indeed, these circumstances echo the canonical insights from Ron Coase's observation in The Problem of Social Cost (1960) that the arrangement of rights can have large effects on welfare when transaction costs for an externality are high. Current climate rights amount to a de facto open access right to pollute the atmosphere. Were a right to a stable climate for both for current and future generations recognized, added weight or leverage would apply in support of climate stabilization policies and international agreements. These legal changes could represent a counterweight to offset the inadequacy of support from the current self-interested generation. Indeed, some recent climate litigation argues that many nations' constitutions already encompass an affirmative right to a stable climate, a proposition that could represent an inimitable means to break the climate impasse.</p>
Pekka Päivärinta's Running Cap
Pekka Päivärinta was one of the best Finnish long-distance runners in the early 1970's. He competed at the Olympic Games of 1972 and 1976 and won a total of 14 Finnish championship titles. His greatest achievement was victory at the inaugural World Cross-Country Championships in 1973. Päivärinta was famous for always wearing a red-and-white cap while running, which earned him the nickname "Lätsä-Pekka" (Pekka the Cap). Actually there were two caps, one for training and one for racing. Päivärinta donated the racing cap, sown by his wife Merja, to the Sports Museum in 1978. Source: Objaverse 1.0 / Sketchfab
Fairlawn. Test Run
FairLawn Museum Superior WI. - Test Run. My 3D asset generated with photogrammetry software 3DF Zephyr v5.001 processing 495 images. Source: Objaverse 1.0 / Sketchfab
Surgical Outcomes of Simple Interrupted Versus Running Epidermal Sutures in Full-thickness Skin Graft Placement
ClinicalTrials.gov study NCT06233812. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of 4 Weeks of Shuttle Run Training on Insulin Sensitivity in Sedentary Men
ClinicalTrials.gov study NCT01962857. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Barefoot Running vs. in Shoes on Physiology and Mood
ClinicalTrials.gov study NCT01733381. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact of Facemasks on Running During COVID-19 Pandemic
ClinicalTrials.gov study NCT04958863. IPD Sharing: NO. Countries: 1. Publications: 0.
XCHT for Irinotecan-Induced Gut Toxicities (Run-in Study)
ClinicalTrials.gov study NCT04926545. IPD Sharing: NO. Countries: 1. Publications: 0.
Running Title: Walking to Music and in Silence on a Treadmill
ClinicalTrials.gov study NCT04856345. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Clinical Trial Comparing Gemcitabine and Carboplatin With and Without P276-00 in Subjects With Metastatic Triple Negative Breast Cancer, With a Run-in of Escalating Dose of P276-00 Added to Gemcitab
ClinicalTrials.gov study NCT01333137. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Carbohydrates and Running Performance
ClinicalTrials.gov study NCT06742398. IPD Sharing: NO. Countries: 1. Publications: 0.
A Pharmacist-run PrEP Program for Women
ClinicalTrials.gov study NCT05755204. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.