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30,948 results for “Profile”
Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores
<p>This repository contains the accompanying inlists and run_star_extras files for Buchele et al. (2024): Asteroseismic Inversions for Internal Sound Speed Profiles of Main-sequence Stars with Radiative Cores.</p> <p> </p> <p>The MESA version used is r22.05.1. We have modified the /star/private/pulse_fgong.f90 file in the MESA source code to output the Gamma1 derivatives that are necessary to compute u-Y kernels by replacing the file with the version from <a href="https://github.com/MESAHub/mesa/pull/431">https://github.com/MESAHub/mesa/pull/431</a>. Additionally, we use the MESA SDK versions 22.6.1 and the GYRE version 6.0 that is included with the r22.05.1 release of MESA. </p> <p> </p> <p>The directory grid_base contains the inlist which set the parameters that do not change across the tracks of our grid</p> <p> </p> <ul> <li> <p>Inlist_all includes parameters that are constant across all tracks and evolutionary stages </p> </li> <li> <p>Inlist_prems includes parameters for the pre-main sequence evolution of all tracks </p> </li> <li> <p>Inlist_ms includes parameters for the main sequence evolution of all tracks </p> </li> <li> <p>run_star_extras.f90 includes the code which stops the evolution of a track when the central hydrogen abundance reaches the target value </p> </li> <li> <p>gyre.in includes parameters for calculating of frequencies using GYRE</p> </li> </ul> <p> </p> <p>The directory reference_models provides the inlists to generate our reference model for each star in our sample. This directory also includes the FGONG structure files of the computed reference models. Each file is labeled with the identifier used in the text, typically the KIC number. </p> <p> </p> <p>The physics_tweaks directory provides the inlists to generate the models with modified input physics. The suffixes refer to the physics that was changed in each model, as follows: </p> <ul> <li> <p>core_kap: Core opacity</p> </li> <li> <p>pp2: change to the He3+He4->Be7 reaction rate</p> </li> <li> <p>cno: change to the N14 + p -> O15. </p> </li> </ul>
From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability
<p>Code: https://github.com/srijitseal/BioMorph_Space<br> <br> Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict <em>in vitro</em> and <em>in vivo</em> drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation.</p> <p> </p> <p>The following datasets are released:<br> </p> <p>Cell_Health_median_357_profiles_70_labels.csv :<br> The Cell Heath dataset for CRISPR perturbations. Contains median consensus signatures for the 357 consensus profiles (119 CRISPR perturbations × 3 cell lines) Ref: Way et al.</p> <p>Cell_Painitng_CRISPR_Perturbations_357_profiles_827_features_scaled.csv:<br> The Cell Painting dataset for CRISPR perturbations. Contains 827 morphology features (and metadata annotation) for 357 consensus profiles (119 CRISPR perturbations × 3 cell lines). Ref: Way et al.</p> <p>Cell_Painting_data_658_compounds_827_Features_scaled.csv<br> The Cell Painting dataset for compound perturbations. Contains 658 structurally unique compounds with 827 Cell Painting features. Ref: Bray et al</p> <p>Endpoints_9_Mitotox_biological_activities_658_compounds.csv<br> The biological assay activity labels for compound perturbations. Contains 658 structurally unique compounds with 9 biological activity consensus hit calls. Ref: ToxCast/MoleculeNet</p> <p>BioMoprh_pvalue_658_compunds_398_BioMorph_terms.csv:<br> The dataset of standardised BioMorph term p-values. Contains 398 BioMorph terms for the 658 compounds in the biological activity dataset. <br> <br> References: <br> Way et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005.<br> Bray et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. <br> MoleculeNet: Wu et al. MoleculeNet: A benchmark for molecular machine learning. Chem Sci. 2018;9(2):513-530. <br> ToxCast: Exploring ToxCast Data | US EPA https://www.epa.gov/chemical-research/exploring-toxcast-data (accessed Jul 9, 2023).</p>
Traditional medicinal use is linked with apparency, not specialized metabolite profiles in the order Caryophyllales
<p>A better understanding of the relationship between plant specialized metabolism and traditional medicine has the potential to aid in bioprospecting and untangling of cross-cultural use patterns. However, given the limited information available for metabolites in most plant species, understanding medicinal use-metabolite relationships can be difficult. The order Caryophyllales has a unique pattern of lineages of tyrosine- or phenylalanine-dominated specialized metabolism, represented by mutually exclusive anthocyanin and betalain pigments, making Caryophyllales a compelling system to explore the relationship between medicine and metabolites by using pigment as a proxy for tyrosine- vs phenylalanine-dominant metabolism. We compiled a list of medicinal species in select tyrosine- or phenylalanine-dominant families of Caryophyllales (Nepenthaceae, Polygonaceae, Simmondsiaceae, Microteaceae, Caryophyllaceae, Amaranthaceae, Limeaceae, Molluginaceae, Portulacaceae, Cactaceae, and Nyctaginaceae) by searching scientific literature until no new medicinal uses were recovered. We then tested for phylogenetic clustering of uses using a "hot nodes" approach. To test potential non-metabolite drivers of medicinal use, like how often humans encounter a species (apparency), we repeated the analysis using only North American species across the entire order and performed phylogenetic generalized least squares regression (PGLS) with occurrence data from the Global Biodiversity Information Facility (GBIF). We hypothesized families with tyrosine-enriched metabolism would show clustering of different types of medicinal use compared to phenylalanine-enriched metabolism. Instead, wide-ranging, apparent clades in Polygonaceae and Amaranthaceae are overrepresented across nearly all types of medicinal use. Our results suggest that apparency is a better predictor of medicinal use than metabolite profile, although metabolism type may still be a contributing factor.</p>
An unusual pulse shape change event in PSR J1713+0747 observed with the Green Bank Telescope and CHIME: Profile data and figure reproduction scripts
<p>This Zenodo entry contains data used in the paper "An unusual pulse shape change event in PSR J1713+0747 observed with the Green Bank Telescope and CHIME", to be published in The Astrophysical Journal. This includes minimally processed profile data in PSRFITS format, as well as pulsar ephemerides (.par files), time-of-arrival estimates (.tim files), and scripts which can be used to reproduce the figures and tables in the paper. For more information, see README.md.</p>
Supporting material for von Fromm et al (2024) Global synthesis of SOC abundance and persistence profile data
<p>This file contains the supporting material for von Fromm et al (2024) Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions published in Global Change Biology (https://doi.org/10.1111/gcb.17320). </p> <p>The data comes originally from the International Soil Radiocarbon Database (ISRaD; <a href="https://soilradiocarbon.org/">https://soilradiocarbon.org/</a>). We filtered the data and applied a mass-preserving spline function. For more details see the corresponding manuscript and the github repository (https://github.com/SophievF/Global_SOC_Abundance_Persistence).</p>
Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile
<p>Supporting data for AFProfile</p> <p>casp15.tar.zst - predicted structures and MSAs for the CASP15 set<br>native_afm_2_6_bench.tar.zst - native cif files for all complexes with ranking confidence <0.75 in the AFM 2-6 chains benchmark ( https://doi.org/10.1093/bioinformatics/btad424)<br>pred_top_models_afm_2_6_bench.tar.zst - predicted top ranked models and scores for all 100 samples.<br>afm_opt_metrics.csv - the best models, confidences and MMscores for the AFProfile run on the AFM 2-6 chains benchmark (n=427 structures)<br>msa_shapes.csv - the shape of the MSA as input to AFM for each structure<br>directed.tar.zst - contains all models for the AFProfile run on the AFM 2-6 chains benchmark (n=42700 samples)</p> <p>The directories are compressed with zstd: https://github.com/facebook/zstd<br>Uncompress:<br>tar --use-compress-program full/path/to/zstd -xvf file.tar.zst</p>
PubMLST allele profiles and sequence alignments of 382 carbapenem-resistant Pseudomonas aeruginosa isolates collected from Japanese hospitals in 2019-2020
<p>This dataset provides PubMLST allele profiles, sequence alignments, and Mash distance data used in the study of "Nationwide genome surveillance of carbapenem-resistant Pseudomonas aeruginosa in Japan". </p>
Single-cell RNA-seq profiles of lung adenocarcinoma patients and tumor-bearing mice
<p>single-cell RNA sequencing (scRNA-seq) profiles from eight patients with lung adenocarcinoma (LUAD) and four samples of tumor tissues from tumor bearing mice were performed. By integrating other scRNA-seq data and clinical information, we identified activated adaptive immune responses in older patients, reflected by enriched dysfunctional T cell signature scores and immune checkpoint molecules. Our study shows increased efficacy of immune checkpoint blockade therapy in older patients, addressing the prominent role of age when considering immunotherapy.</p>
Experimental absorption spectra used in "Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF6: testing the β-correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model"
<p>Experimental absorption spectra used in the article entitled <strong>"Spectral profile of ro-vibrational transitions of HCl broadened by He, Ar and SF<sub>6</sub>: testing the β-</strong><strong>correction to the Hartmann-Tran profile and the speed dependent (complex) hard collision model", </strong>to be published in the Journal of Quantitative Spectroscopy and Radiative Transfer. Accepted 19 March 2024.</p> <div> <div> <div> <div> <h3><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jqsrt.2024.108977" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jqsrt.2024.108977</a></h3> </div> </div> </div> </div> <p> </p> <p>Comma separated ASCII files with 1 header line.</p> <p>First column is wavenumber in cm-1</p> <p>The rest of the columns contain the napierian absorbance at the total pressure given in the header. </p> <p>Mind the units!: pressure of Ar-mixtures is expressed in Torr, pressure of He- and SF6-mixtures is expressed in mbar</p> <p> </p>
RAYUELA - Open Data (small) Preliminary Pilots - Data collected through a serious game created to identify patterns and profiles of young potential victims/perpetrators of cybercrimes.
<p>The data of this dataset have been collected in the pilots carried out by the RAYUELA project in different countries of the European Union. The participants are minors and the game sessions have been carried out in schools and summer camps in a supervised way.</p> <p>This is the first version of a larger dataset: https://doi.org/10.5281/zenodo.10604760</p>
Metadata Profile for FAIR Sensor Data based on the SensOr Interfacing Language
<p>Metadata profile to provide FAIR sensor data. The profile is created using SHACL and is based on the SOSA ontology which accurately specifies restrictions on the properties of specific sensors using the QUDT and the SSN ontology. Generic metainformation is modeled using DCTerms. </p>
Data from: Tissue-specific O-GlcNAcylation profiling identifies substrates in translational machinery in the Drosophila mushroom body contributing to olfactory learning
<p><em>O-</em>GlcNAcylation is a dynamic post-translational modification that diversifies the proteome. Its dysregulation is associated with neurological disorders that impair cognitive function, and yet identification of phenotype-relevant candidate substrates in a brain-region-specific manner remains unfeasible. By combining an <em>O-</em>GlcNAc binding activity derived from<em> Clostridium perfringens</em> OGA (<em>Cp</em>OGA) with TurboID proximity labeling in <em>Drosophila</em>, we developed an <em>O-</em>GlcNAcylation profiling tool that translates <em>O-</em>GlcNAc modification into biotin conjugation for tissue-specific candidate substrates enrichment. We mapped the <em>O-</em>GlcNAc interactome in major brain regions of <em>Drosophila</em> and found that components of the translational machinery, particularly ribosomal subunits, were abundantly <em>O-</em>GlcNAcylated in the mushroom body of <em>Drosophila</em> brain. Hypo-<em>O-</em>GlcNAcylation induced by ectopic expression of active <em>Cp</em>OGA in the mushroom body decreased local translational activity, leading to olfactory learning deficits that could be rescued by dMyc overexpression-induced increase of protein synthesis. Our study provides a useful tool for future dissection of tissue-specific functions of <em>O-</em>GlcNAcylation in <em>Drosophila</em> and suggests a possibility that <em>O-</em>GlcNAcylation impacts cognitive function via regulating regional translational activity in the brain.</p>
Seismic profiles, migration velocities and tomographic inversion results of the deep reflection profiles in the central South China
<h1><strong>Overview</strong></h1> <p>The following set of data and scripts are meant to accompany the paper:</p> <p>Jiang, W. B., Wang, Q., Zhang, Y.Q., Dong, S.W., Ruan, Y.Q., Cui, J.J., Kuang, Z.Y., Paleoproterozoic collision to Mesozoic crustal reworking in central South China: evidence from borehole data and seismic crustal structure, Submitted to JGR: Solid Earth</p> <p>The data and scripts are intended to reproduce seismic profiles, migration velocities, and tomographic inversion results shown in the paper.</p> <p>The repository contains five directories:</p> <p><strong>./01_Seismic_Profiles/</strong> -> Four seismic profiles shown in the manuscript. (1) psdm_line01.sgy (Figure 7a); (2) psdm_line02.sgy (Figure 7b); (3) SCB_PSDM.segy (Figure 9); (4) SCB_PSTM.segy (Figure S5a).</p> <p><strong>./02_Tomographic_Data_Velocity/</strong> -> picked_traveltimes.tt, picked traveltime for the normal shots (Figure 6a). The file contains location of shots and receivers, picked traveltimes; Tomo_inv.mdl, P-wave velocity model derived from first-arrival traveltime tomography (Figure 8a); Tomo_Fx1_Fz1_ray.mdl, Ray density distribution calculated using the Vp model (Figure 8b); traveltime_data_FILE_FORMAT.pdf, this pdf file describes the format of *.tt file; mdl_data_FILE_FORMAT.pdf, this pdf file describes the format of *.mdl file.</p> <p><strong>./03_Migration_Velocity_Models/ </strong>-> Migration velocity models to produce prestack time migration profile and prestack depth migration profile. VEL_RMS.mdl, RMS velocity field used in prestack time migration (Figure S4a); VEL_INTERVAL.mdl, Interval velocity field used in prestack depth migration (Figure S4b).</p> <p><strong>./04_Bouguer_Gravity_Anomaly/ </strong>-> gravity_data.grv, the Bouguer gravity anomaly data used in 2-D gravity modelling; gravity_data_FILE_FORMAT.pdf, this pdf file describes the format of *.grv file.</p> <p><strong>./05_Scripts/</strong> -> Matlab scripts to reproduce seismic profiles, migration velocities, and tomography inversion results shown in the paper. </p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
Рис. 3. Раскоп 2: А – северная стенка, квадраты С–У; В – профиль бровки по линии меЖду квадратами 12–13, квадратами П–Р. Fig. 3. Excavation 3: A – the northern wall, squares С–У; В – the profile of the edge along the line between the squares 12–13, squares П–Р. in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 3. Раскоп 2: А – северная стенка, квадраты С–У; В – профиль бровки по линии меЖду квадратами 12–13, квадратами П–Р. Fig. 3. Excavation 3: A – the northern wall, squares С–У; В – the profile of the edge along the line between the squares 12–13, squares П–Р.
Рис. 2. Раскоп 1: А – сектор 5, профиль бровки по линии меЖду квадратами 20–21; В – северная стенка, квадраты 21–22. Fig. 2. Excavation 1: A – sector 5, the profile of the edge along the line between the squares 20–21; B – the northern wall, squares 21–22. in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy
Рис. 2. Раскоп 1: А – сектор 5, профиль бровки по линии меЖду квадратами 20–21; В – северная стенка, квадраты 21–22. Fig. 2. Excavation 1: A – sector 5, the profile of the edge along the line between the squares 20–21; B – the northern wall, squares 21–22.
'Profiles in Music: Gwendolyn Koldofsky'—Radio Interview with William Triplett
<p>Previously unpublished recording of an extended radio interview from spring of 1989, titled 'Profiles in Music: Gwendolyn Koldofsky'. It was likely broadcast at a later date by the South Carolina Radio Educational Network, under the direction of William D. Hay as part of a broadcast series titled Stage One. The initial broadcast was not initially finished or broadcast due to the untimely death of host William Triplett. </p> <p>This recording of that later broadcast was discovered in the personal archive of Koldofsky student Russell Miller, and was provided by his husband Todd Graber after Miller's passing, although how Miller acquired it has not been determined nor have other rights-holders been located. No other record or further information have been found in the archives of any of Koldofsky's institutes, including KUSC, USC or Santa Barbara, and communication with SCETV was likewise not conclusive. </p> <p>Host: William Triplett</p> <p>Featuring: Gwendolyn Koldofsky as well as students Mark Trawka (Piano), Pauline Burke (Mezzo-Soprano), Hector Vásquez (Baritone)</p> <p>This recording was digitized for this publication, created in connection with the monograph 'Accompaniment in America: Contextualizing Collaborative Piano' by VanderHart et al. (Routledge: 2025).</p> <p> </p>
Memory B Cells and Their Transcriptomic Profiles Associated with Belimumab Resistance in Systemic Lupus Erythematosus in the Maintenance Phase
<p>Normalized count of RNA sequences from systemic lupus erythematosus patients treated with belimumab.</p> <p>We collected blood samples from patients with systemic lupus erythematosus (n=44) before and approximately 3 and 6 months after treatment by belimumab, respectively. We also included healthy individuals (n=17). There were no missing samples in the before-treatment specimen for 44 patients; however, five samples were missing at the three-month time, and four were missing at the six-month time after treatment. Whole blood samples were stored in PAXgene tubes (QUIAGEN). RNA was extracted by PAXgene Blood RNA Kit (QUIAGEN). Library preparation was performed using TruSeq stranded Total RNA Library PrepKit with Ribo-Zero Globin Human. Sequencing was conducted by NovaSeq6000 in the 100-bp paired-end mode. Sequencing reads were trimmed using Trimmomatic ver. 0.36 (leading: 20, trailing: 20, slidingwindow: 4:15, minlen: 36) and aligned to hg38 reference genome using STAR (ver. 2.7.3a). Gene counts were generated by RSEM (ver. 1.3.1) using Homo_sapiens.GRCh38.95.gtf from the Ensembl database. Gene counts were normalized by size factor implemented in DESeq2 and converted to count per million (CPM), and log<sub>2</sub>(CPM+1) was calculated. </p> <p>Sample names starting with 'R_' indicate responders, while those ending with '_NR' indicate non-responders. '0M' represents before treatment, '3M' represents three months after treatment, and '6M' represents six months after treatment. The numeric ID, excluding the last two characters, represents individual patients.</p> <p> </p> <p>If you use this data, please cite Iwasaki et al. Front. Immunol., 04 February 2025 <a href="https://doi.org/10.3389/fimmu.2025.1506298">https://doi.org/10.3389/fimmu.2025.1506298</a></p>
Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling
<p>Constraining the Properties of the Thermonuclear Burst Oscillation Source XTE J1814-338 Through Pulse Profile Modelling</p>
AIoTes DS Validation Profile
<p>This dataset contains the demographic information of the AIoTes validation interviews participants</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.