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202 results for “v7”
Svalbard Surge Database 2024 (RGI2000-v7.0-G-07)
<p>We have developed a new database of surge-type glaciers in Svalbard by combining existing compilations and reviewing studies examining their dynamics. Our database is based upon the Global Land and Ice Measurements from Space (GLIMS) database (König et al. 2014), which is now incorporated into RGI 7.0 (RGI 7.0 Consortium 2023) and consists of 1,583 glaciers in Svalbard. Therefore, the first five fields come from the RGI 7.0 database:</p> <ul> <li><strong>rgi_id</strong>: Glacier ID from RGI database.</li> <li><strong>glims_id</strong>: Glacier ID from GLIMS database.</li> <li><strong>cenlon</strong>: Longitude of glacier centre point.</li> <li><strong>cenlat</strong>: Latitude of glacier centre point.</li> <li><strong>glac_name</strong>: Name of glacier.</li> </ul> <p>Our compilation of existing Svalbard-wide glacier surge databases is sourced from several studies: Lefauconnier and Hagen (1991) [LH1991]; Hagen et al. (1993) [H1993]; Sevestre and Benn (2015) [SB2015]; Farnsworth et al. (2016) [F2016]; Kääb et al. (2023) [KA2023]; and Koch et al. (2023) [KO2023]. The compilation of LH1991 only covers eastern Svalbard and is focused on marine-terminating glaciers but is included as it contains several important details on surge characteristics. H1993 is the original database of glaciers across Svalbard and similarly contains details of historical surges. The current RGI 7.0 database defines the “surge status” of each glacier according to Sevestre and Benn (2015): no evidence of surging (0); possible surge (1), probably surge (2), and observed surge (3). Where the SB2015 database does not have corresponding evidence from one of the other compilations, we determine the glaciers surge status to be ‘undefined’ and do not include it in the S_All field. The F2016 compilation was manually translated into the RGI 7.0 database. The glacier names described in F2016 often referred to tributaries which are now combined into single glacier catchments (e.g., Nuddbreen / Strongbreen), hence we manually combined these entries. The recent compilations from KA2023 and KO2023 were manually transcribed from tables in PDF files. The subsequent eight fields document each compilation:</p> <ul> <li><strong>SB2015</strong>: Surge database from Sevestre and Benn (2015). [0-3]</li> <li><strong>F2016</strong>: Surge database from Farnsworth et al. (2016). [0-1]</li> <li><strong>H1993</strong>: Surge database from Hagen et al. (1993). [0-1]</li> <li><strong>LH1991</strong>: Surge database from Lefauconnier and Hagen (1991). [0-1]</li> <li><strong>KA2023</strong>: Surge observations from Kääb et al. (2023). This data set is based on manual surge identification in annual Sentinel-1 interferometric wide-swath (IW) satellite radar backscatter differences between 2017 and 2022 (Kääb et al. 2023). This has been updated in this database (version 3) by mapping more recent surges from winter-to-winter differences 2022-2023, 2023-2024, and 2024-2025 using new IW data. Before 2017, no Sentinel-1 IW data are available over Svalbard, and we use 2015-2016 and 2016-2017 extended wide-swath data (EW) instead, acknowledging that these coarser data (compared to IW) might lead to less detailed surge identification, or overlooking of surges of small glaciers or surges accompanied by only limited backscatter changes. Based on these additional data, we are also able to update some surge information contained in the original KA2023, for instance concerning surge start and end years, and by adding the last year of strongly enhanced backscatter (before backscatter reduction). The new 2015-2025 backscatter-derived surge inventory over Svalbard contains now 40 surging glaciers (the 2017-2022 KA2023 contained 26 surging glaciers). [0-1]</li> <li><strong>KO2023</strong>: Surge observations from Koch et al. (2023). [0-1]</li> <li><strong>Other</strong>: Surge observations from other literature sources. [0-1]</li> <li><strong>S_Direct</strong>: All surges that have been directly observed. [0-1]</li> <li><strong>S_Indirect</strong>: All surges that have been indirectly observed e.g. from palaeo-glaciological analysis. [0-1]</li> <li><strong>S_All</strong>: All surges that have been either directly observed or inferred from the palaeo-glaciological record. [0-1]</li> </ul> <p>Contemporary and palaeo-glaciological evidence of surges is generally limited to the period ~1850–present, which broadly corresponds to the end of the LIA through to the modern-day. Where multiple surges have been recorded, we separate these using “;” in the database, and use “n/a” where the details of the surge have not been recorded. Surges have been classified as a binary 0 (not surge-type) or 1 (surge-type), with the exception of the Sevestre and Benn (2015) database as described above.</p> <p>The subsequent eight fields document (if known) the following characteristics for each glacier in Svalbard:</p> <ul> <li><strong>S_Onset</strong>: Surge Onset (Year)</li> <li><strong>S_Term</strong>: Surge Termination (Year)</li> <li><strong>S_Act_Vel</strong>: Max Active-Phase Velocity (m/d)</li> <li><strong>S_Qui_Vel</strong>: Mean Quiescence Velocity (m/d)</li> <li><strong>S_Term_Ch</strong>: Terminus Change (m)</li> </ul> <p>Here, we use 'n/a' for glaciers with no evidence of surging, whilst 'Not observed' is used where we have not observed any of the above characteristics for a glacier with evidence of surging.</p> <p>The final column contains references to where surges have been reported.</p> <p>Included in this version is also a version of the RGI for Svalvard with the new database included.</p> <div> </div>
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep - 2023), <em>Automated cataloging of American silver perch (Bairdiella chrysoura) calls using machine learning</em>. The software accompanying this paper is known as PerchPicker (<a href="https://github.com/drbohnen/PerchPicker" rel="noopener">GitHub - drbohnen/PerchPicker)</a>, and the classifier model presented in the paper is v7. It consists of more than 6000 labeled perch and 6000 labeled other signals. Labeled scalogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 90 ms long. The center 30 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'perch' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the PerchPicker model or develop alternative classifiers. </p>
ESBMC-v7.3 evaluation
<p>Evaluation for ESBMC-v7.3. Please follow the instructions in README to obtain the dataset, scripts and binaries.</p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
Open the record for dataset details and reuse information.
GTEx v7 prediction models
<p>PrediXcan's prediction models on gene expression from GTEx v7.</p> <p>Also contains LD compilations for S-PrediXcan and S-MultiXcan.</p>
POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for openMM simulation engine v7
<p>POPC lipid membrane, 303K, Charmm36 force field, simulation files and 200 ns trajectory for for openMM simulation engine v7</p> <p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 128 POPC molecules, 5120 tip3p waters, 200ns trajectory (preceded with equilibration).</p> <p>Note that the provided trajectories are in Gromacs XTC format, whereas NAMD DCD format was generated by openMM. This required trajectory conversion using Gromacs package (v5.1.2) with binary topology file from https://doi.org/10.5281/zenodo.153944 </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field through the use of Gromacs input files, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Specifically, Gromacs file format provided by [1] was specifically used for this simulation.</p> <p>Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
Experimental data for the article "High Resolution Rovibrational Spectroscopy of the ν6 and ν3+v7 Bands of H2CCCH+"
<p> data measured with COLTRAP apparatus 6th December - 11th December 2023<br> column 1 (x-axis) is frequency in cm-1, accuracy ~0.001 cm-1, precision ~0.0002 cm-1<br> column 2 (y-axis) are ion counts on mass 39u, integer<br><br> The data given here are original data with 14 concatenated data files.<br> Some spurious data points have been commented out by using "#"<br><br></p>
The gross primary productivity and leaf area index from TRENDY v7 project
<p>The gross primary productivity and leaf area index from TRENDY v7 DGVMs S3, including:</p> <p>CABLE-POP</p> <p>CLASS-CTEM</p> <p>CLM5.0</p> <p>DLEM</p> <p>JSBACH</p> <p>JULES</p> <p>LPX</p> <p>OCN</p> <p>ORCHIDEE</p> <p>ORCHIDEE-CNP</p> <p>SDGVM</p> <p>SURFEX</p> <p>VISIT</p>
Biomarker-Driven Therapy With Nivolumab and Ipilimumab in Treating Patients With Metastatic Hormone-Resistant Prostate Cancer Expressing AR-V7
ClinicalTrials.gov study NCT02601014. IPD Sharing: NO. Countries: 1. Publications: 2.
Trial of Rad-223 Activity in Asymptomatic Patients With mCRPC While on Abiraterone or Enzalutamide Besides AR-V7 Status
ClinicalTrials.gov study NCT03002220. IPD Sharing: NO. Countries: 1. Publications: 1.
Natural- Petroglyph V7 Khatm Al Melaha, Sharjah
Natural and Painter Version. Petroglyph V7 Khatm Al Melaha, Kalba, Sharjah. Nubian Ibex with Anthropomorphs. probably other images no longer visible. Likely Neolithic 5th-4th Millennium BCE [Fossati 2019 Messages from the Past: Rock Art of the Al-Hajar Mountains (Oman)]. Khatm Al Melaha is a spectacular archaeological site on the coast of the Oman Sea near Kalba in Sharjah, UAE. It is one of the largest rock art sites in the UAE. There are also stone houses, a shell midden, stone tombs and other features that date this site has having occupations from at least the early Holocene to the 19th century. Over 175 stones with petroglyphs were documented and close to 400 motifs were identified. Every rock with a glyph was given an ID number and a GPS coordinate. In total 25455 terrestrial photographs, 5244 drone photographs, 44 drone videos, and 182 GPS points (+/- 1cm) were done in a single day. Processed in Reality Capture, modified in Substance Painter. Source: Objaverse 1.0 / Sketchfab
Raw cutadapt miseq output - minibarcode 18S-V7 of macroalgae
<p>Macroalgae are key primary producers in North Atlantic and Arctic coastal ecosystems, and tracing their fate and distribution is vital to improve our understanding of their ecological role and provision of ecosystem services. Recent advances from environmental DNA (eDNA) have added a new capacity to fingerprint and trace macroalgae. However, further development of resources for amplifying and identifying macroalgal eDNA are much needed. Here, we examined the performance in terms of resolution and specificity of two 18S primers (18S-V7 & 18S-V9) recently applied in identifying macroalgae from eDNA. We also built a local barcode database for primer 18S-V7 with 31 widespread Arctic and North Atlantic macroalgal species to complement the existing DNA databases. Furthermore, we applied metabarcoding of eDNA to identify macroalgae in Arctic marine sediments (Disko Bay, W. Greenland) and evaluated the contributions from our local barcode database. We identified macroalgal DNA from 19 families across 11 orders in surface (0-1 cm, with both primers) and sub-surface (5-10 cm, with 18S-V7 primer) sediments. The barcode database developed here with the 18S-V7 primer improved the identification of unique families, from 16 to 19 families, thereby strengthening the taxonomic assignment possible relative to pre-existing barcode reference sequences. Overall, this study demonstrates the feasibility of eDNA to resolve contributions of macroalgae in Arctic marine sediments, and enhances the fingerprinting resolution. We thereby document a novel pathway to answer key questions on the ecological role and fate of macroalgae in the Arctic.</p>
DStretch- Petroglyph V7 Khatm Al Melaha, Sharjah
DStretch and Painter Version. Petroglyph V7 Khatm Al Melaha, Kalba, Sharjah. Nubian Ibex with Anthropomorphs. probably other images no longer visible. Likely Neolithic 5th-4th Millennium BCE [Fossati 2019 Messages from the Past: Rock Art of the Al-Hajar Mountains (Oman)]. Khatm Al Melaha is a spectacular archaeological site on the coast of the Oman Sea near Kalba in Sharjah, UAE. It is one of the largest rock art sites in the UAE. There are also stone houses, a shell midden, stone tombs and other features that date this site has having occupations from at least the early Holocene to the 19th century. Over 175 stones with petroglyphs were documented and close to 400 motifs were identified. Every rock with a glyph was given an ID number and a GPS coordinate. In total 25455 terrestrial photographs, 5244 drone photographs, 44 drone videos, and 182 GPS points (+/- 1cm) were done in a single day. Processed in Reality Capture, modified in Substance Painter. Source: Objaverse 1.0 / Sketchfab
CLL-Irl Study. CTRIAL-IE (ICORG) 07-01, V7
ClinicalTrials.gov study NCT00812669. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CIRCULATING MICRO-RNA (miRNA) AND AR-V7 MUTATIONAL STATUS IN METASTATIC CASTRATION-RESISTANT PROSTATE CANCER (CRPC): PRIMERA+ STUDY (PROSTATE CANCER INNOVATING MARKERS OF EXPECTED RESPONSE TO AGONIST
ClinicalTrials.gov study NCT04188275. IPD Sharing: UNDECIDED. Countries: 1. Publications: 22.
A Study of Galeterone Compared to Enzalutamide In Men Expressing Androgen Receptor Splice Variant-7 mRNA (AR-V7) Metastatic CRPC
ClinicalTrials.gov study NCT02438007. IPD Sharing: Not stated. Countries: 8. Publications: 1.
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.