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247 results for “envelope”
Input Data for "Assembly and Analysis of Cell-Scale Membrane Envelopes"
<p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf protocellmodeling.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*trr" --exclude="*js" --exclude="*[0-9].out" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*state_prev.cpt" --exclude="*.o[0-9]*" cgDracula`, which intentionally excludes large files. The full 4.8TB dataset that includes trajectories is available upon request.</p> <p>The data is split into multiple subdirectories and largely undocumented, however here are the highlights:</p> <ul> <li>The <strong>Analysis</strong> subdirectory is where the analysis in the paper lives. All other directories are related to building or running systems.</li> <li><strong>getsources.py</strong> in the main directory is the script that downloads the initial structure from MemProtMD.</li> <li><strong>transform.py</strong> builds the initial protein models from MemProtMD.</li> <li><strong>vesiclebuilder.py</strong> builds the lipid ball.</li> <li><strong>protpatchplacer.py</strong> sets up the ultra-coarse grained simulation, which is in the <strong>supercg</strong> directory.</li> <li><strong>movepatches.py</strong> takes the results from the ultra-coarse grained simulation, and builds the protein ball.</li> <li><strong>gendx.tcl</strong> generates the density maps from the protein ball.</li> <li>This is used in <strong>lipids/picklipids.py</strong>, which cuts out the pieces of the lipid that need to be removed.</li> <li>The water is added to the system with <strong>addwater/quicksolvate.sh</strong></li> <li>The system is ionized by <strong>ionize.py</strong></li> <li>And a topology is written by <strong>writetop.py</strong></li> </ul>
Simulation input files: Cargo Release from Non-enveloped Viruses and Virus-like nanoparticles: Capsid Rupture or Pore Formation
<p>Simulation input files, and code for modification to simulation software for article:</p> <p>Cargo Release from Non-enveloped Viruses and Virus-like nanoparticles: Capsid Rupture or PoreFormation</p> <p>Lukáš Sukeník,†,‡</p> <p>Liya Mukhamedova,†</p> <p>Michaela Procházková,†</p> <p>Karel Škubník,†</p> <p>Pavel Plevka,†</p> <p>and Robert Vácha∗,†,‡,¶</p> <p>†CEITEC – Central European Institute of Technology, Masaryk University, Kamenice753/5, 625 00 Brno, Czech Republic</p> <p>‡Department of Condensed Matter Physics, Faculty of Science, Masaryk University,Kotl ́aˇrsk ́a 267/2, 611 37 Brno, Czech Republic</p> <p>¶National Centre for Biomolecular Research, Faculty of Science, Masaryk University,Kamenice 5, 625 00 Brno, Czech Republic</p> <p>E-mail: robert.vacha@mail.muni.cz</p>
Data from "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm"
<p>Data associated with Huang et al., 2023, "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm," ApJ, 943, 107</p> <p>The raw data can be obtained by requesting data from program W20BE (PI: J. Huang) from the NOEMA archive. </p> <p><strong>Images</strong></p> <p>images.tar: FITS files of image cubes and moment maps as shown in Figs. 2, 3, 5, 9, and 10 (see also finalimagingscript.py)</p> <p><strong>Spectra</strong></p> <p>spectra.tar: ASCII files of spectra shown in Figs. 1 and 9 (see also finalimagingscript.py) </p> <p><strong>Measurement sets (CASA format)</strong></p> <p>drtau_LINENAME_corrected.ms.tar: Measurement set for a given line, corresponding to drtau_LINENAME_selfcal-contsub.uvt in lines_contsub_uvt.tar </p> <p>drtau_ui_cont_corrected.ms.tar: Continuum measurement set corresponding to the UI baseband, generated from w20be-ui-cont-selfcal.uvt in continuum_uvt.tar </p> <p><strong>UV tables (GILDAS format)</strong></p> <p>LR_uvt.tar: Pipeline-calibrated UV tables of low-resolution data (see w20be-tables-LR.clic)</p> <p>HR_uvt.tar: Pipeline-calibrated UV tables of high-resolution data (see w20be-tables-HR.clic)</p> <p>continuum_uvt.tar: Self-calibrated, spectrally averaged continuum UV tables generated from LR_uvt.tar</p> <p>lines_contsub_uvt.tar: Self-calibrated, continuum-subtracted line UV tables generated from the UV tables in HR_uvt.tar</p> <p><strong>Scripts</strong></p> <p>w20be-tables-LR.clic: CLIC script used to output UV tables of low-resolution data from the pipeline-calibrated data (output contained in LR_uvt.tar)</p> <p>w20be-tables-HR.clic: CLIC script used to output UV tables of high-resolution data from the pipeline-calibrated data (output contained in HR_uvt.tar)</p> <p>uvfitstoms.py: Script to export uvfits files to measurement sets and to correct the metadata in the measurement sets </p> <p>finalimagingscript.py: CASA script used to generate imaging products in images.tar and spectra in spectra.tar </p> <p><strong>Other:</strong></p> <p>calib.tar: Files output by NOEMA pipeline </p> <p> </p>
Dataset related to article: Cell-envelope growth of Gram-negative bacteria proceeds independently of cell-wall synthesis
<p>Single-cell data for the article: </p> <p>Enno R. Oldewurtel, Yuki Kitahara, Baptiste Cordier, Richard Wheeler, Gizem Özbaykal, Elisa Brambilla, Ivo Gomperts Boneca, Lars D. Renner, and Sven van Teeffelen</p> <p>Cell-envelope growth of Gram-negative bacteria proceeds independently of cell-wall synthesis. EMBO J (2023)</p>
Building Envelopes in New York's CBD: Normalized Signed Distance Function Representation
<p>This dataset, developed to substantiate the findings of the study by Zhuang et al. [1], comprises 1,521 building envelopes from the Central Business District (CBD) of New York City (NYC), represented in three formats: the wavefront (obj) format, the Signed Distance Function (SDF) format and 3D binary-volume format. </p> <p>The original mesh data is procured from the NYC Open Data Portal [2]. The process of data manipulation is thoroughly delineated in the referenced research paper [1].</p> <p>[1] Zhuang, X., Ju, Y., Yang, A., & Caldas, L. (2023). Synthesis and Generation for 3D Architecture Volume with Generative Modeling. International Journal of Architecture Computing, AI, Architecture, Accessibility, & Data Justice. DOI: 10.1177/14780771231168233.</p> <p>[2] New York city department of city planning. NYC 3D model by community district, manhattan district, MN05. 2018. Available at: https://www.nyc.gov/site/planning/data-maps/open-data/dwn-nyc-3d-model-download.page. Accessed 15 July 2023.</p> <p> </p>
Computer Code and Data - Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model
<p>Computer code and data related to the research project "Determination of server location in emergency care systems: an index proposal using Data Envelopment Analysis and the Hypercube Queuing Model".</p>
World-wide Randomized Antibiotic Envelope Infection Prevention Trial
ClinicalTrials.gov study NCT02277990. IPD Sharing: Not stated. Countries: 25. Publications: 6.
TYRX™ Envelope for Prevention of Infection Following Replacement With a CRT or ICD
ClinicalTrials.gov study NCT01043705. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Long Term Follow-Up Study 16-20 Years After Hepatitis B Vaccination in Newborns of Mothers Who Were Seropositive for Hepatitis B Envelope Antigen (HBeAg) & Hepatitis B Surface Antigen (HBsAg)
ClinicalTrials.gov study NCT00240539. IPD Sharing: YES. Countries: 1. Publications: 2.
A Post Market Study on the Use of Cormatrix® Cangaroo ECM® (Extracellular Matrix) Envelope
ClinicalTrials.gov study NCT02530970. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data and code from: Life-history traits predict ability of British wild bees to fill their climate envelopes
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The role of cell-envelope synthesis for envelope growth and cytoplasmic density in Bacillus subtilis
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Data from: A pulsar-helium star compact binary system formed by common envelope evolution
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Data from: Cryoelectron tomography of enveloped human cytomegalovirus particles
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Data from: Primary envelopment of Kaposi’s sarcoma-associated herpesvirus at the nucleoplasmic reticulum
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Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
<p>1. Animal-borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer's high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine-scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine-scale behaviour. 2. We address this issue by conceptualising fine-scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a 'seek-and-learn' approach: a novel microevent detection step first locates where shock signals occur ('seek') by searching for peaks in envelopes of acceleration data. Robust machine learning ('learn') employing meaningful features then separates microevents. We showcase the application of our method on tri-axial accelerometer data collected on ten free-living meerkats (Suricata suricatta) for four fine-scale foraging behaviours – searching for digging sites, one-armed digging, two-armed digging, and head jerks during prey ingestion. Annotated videos served as groundtruth, and performance was benchmarked against that of a variety of classical machine-learning approaches. 3. Microevent identification (μEvId) with eight features in a three-node hierarchical classification scheme employing logistic regression at each node achieved a mean overall accuracy of >85% during leave-one-individual-out cross-validation, and exceeded that of the best classical machine learning approach by 9%. μEvId was found to be robust not only to inter-individual variation but also to large changes in model parameters. 4. Our results show that microevents can be modelled as impulse responses of the animal body-and-sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving-window-based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition.</p>
Prospects of gravitational-waves detections from common-envelope evolution with LISA
<p>Input and output files for the <a href="https://cosmic-popsynth.github.io/">COSMIC</a> simulations used in <a href="https://arxiv.org/abs/2102.00078">arXiv:2102.00078</a>.</p>
Data from: Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar
1. Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models have led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. 2. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996-2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest aboveground carbon density. 3. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg.ha-1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R 2 ≤ 26% and RMSE ≥ 63 Mg.ha −1 ). 4. Combining our model with the climatic projections for Madagascar from seven IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7-24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. 5. Synthesis: Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated to anthropogenic deforestation.
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Tunable force transduction through the Escherichia coli cell envelope
<p>Killing Assays, SDS Assays, Microscopy profiles and segmented cells (.res files for MicrobeJ).</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.