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13,587 results for “specification”
ROS-specific Huntingtin Interactions: Inducible huntingtin-specific chromobody expression by nucleofection
<p>Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.</p>
ROS-Specific Huntingtin Interactions: ROS Source Optimization in Mouse Striatal Cells
<p>Optimization step in the lead up to mass spec identification of ROS-specific huntingtin protein-protein interactions. </p>
ROS-specific Huntingtin Interactions: GFP reactivation assay in HD patient fibroblasts
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins. </p>
Data from: Mesostriatal dopamine is sensitive to changes in specific cue-reward contingencies
<p>Learning causal relationships relies on understanding how often one event precedes another. To gain an understanding of how dopamine neuron activity and neurotransmitter release change when a retrospective relationship is degraded for a specific pair of events, we used outcome-selective Pavlovian contingency degradation in rats. Two cues were paired with distinct food rewards, one of which was also delivered in the absence of either cue. Conditioned responding was attenuated for the cue-reward contingency that was degraded. Dopamine neuron activity in the midbrain and dopamine release in the ventral striatum in response to the cue and subsequent reward were attenuated during degraded versus non-degraded trials, and contingency degradation also abolished the trial-by-trial history dependence of dopamine responses at the time of trial outcome. This profile of changes in cue- and reward-evoked responding is not easily explained by a standard reinforcement learning model. An alternative model based on learning causal relationships was better able to capture evoked dopamine responses during contingency degradation, as well as conditioned behavior following optogenetic manipulations of dopamine during noncontingent rewards. Our results suggest that mesostriatal dopamine encodes the contingencies between meaningful events during learning.</p>
Data from: Sex-specific effects of dietary restriction on physiological variables in Japanese quails
<p>Nutritional limitation is a common phenomenon in nature that leads to trade-offs among processes competing for limited resources. These trade-offs are mediated by changes in physiological traits such as growth factors and circulating lipids. However, studies addressing the sex-specific effect of nutritional deficiency on these physiological variables are limited in birds. We used dietary restriction to mimic the depletion of resources to various degrees and investigated sex-specific effects on circulating levels of insulin-like growth factor 1 (IGF-1) and triglycerides in Japanese quails (<em>Coturnix japonica</em>) subjected to <em>ad libitum</em>, 20%, 30% or 40% restriction of their daily requirement, for two weeks. We also explored the association of both physiological variables with body mass and egg production. While dietary restriction showed no effects on circulating IGF-1, this hormone exhibited a marked sexual difference, with females having 64.7% higher IGF-1 levels than males. Dietary restriction significantly reduced plasma triglyceride levels in both sexes. Females showed more than six-fold higher triglyceride levels than males. Triglyceride levels were positively associated with body mass in females while showed not association in males. Overall, our findings revealed sex-specific expression of physiological variables under dietary restriction conditions, which coincide with body size.</p>
Replication package for our paper entitled "Faster and Better Quantum Software Testing through Specification Reduction and Projective Measurements"
<p><br> -Experiment1<br> Contains the source files for experiment 1 and the study subjects.</p> <p> -Experiment2<br> Contains the source files for experiment 2 and a mutation generator.</p> <p> -results<br> Contains the results from experiment 1 and 2 and postprocessing files</p> <p> -RQ1,RQ2,RQ3<br> Contains source files for figures, tables and data for each research question<br> of the paper</p> <p><br>Please inspect the README.txt file for more details.</p>
Data from: Sexually discordant selection is associated with trait specific morphological changes and a complex genomic response
<p>Sexes often have differing fitness optima, potentially generating intra-locus sexual conflict, as each sex bears a genetic 'load' of alleles beneficial to the other sex. One strategy to evaluate conflict in the genome is to artificially select populations discordantly, against established sexual dimorphism, reintroducing attenuated conflict. We investigate a long-term artificial selection experiment reversing sexual size dimorphism in <em>Drosophila melanogaster</em> during ~350 generations of sexually discordant selection. We explore morphological and genomic changes to identify loci under selection between the sexes in discordantly and concordantly size selected treatments. Despite substantial changes to overall size, concordant selection maintained ancestral sexual dimorphism. However, discordant selection altered size dimorphism in a trait-specific manner. We observe multiple, possible soft selective sweeps in the genome, with size related genes showing signs of selection. Patterns of genomic differentiation between the sexes within lineages identified potential sites maintained by sexual conflict. One discordant selected lineage shows a pattern of elevated genomic differentiation between males and females, on chromosome 3L, consistent with the maintenance of sexual conflict. Our results suggest visible signs of conflict and differentially segregating alleles between the sexes due to discordant selection.</p>
Supplemental Figures - Detection of SARS-CoV-2-Specific Secretory IgA and Neutralizing Antibodies in the Nasal Secretions of Exposed Seronegative Individuals
<p>Figure S1: Flow diagram of exposed seronegative cohort.</p> <p>Figure S2: SARS-CoV-2-specific neutralization activity at Days 1 and 8 relative to enrollment in exposed seronegative nasal SIgA positive and infected participants. NPS SARS-CoV-2-specific neutralization activity is shown for exposed seronegative and infected participants with normalized OD490 at Day 1 and Day 8, respectively. Sample sizes (N) are indicated in parentheses. Wilcoxon signed-rank tests were used to determine if the median SARS-CoV-2 nasal SIgA neutralization activity differed significantly. A two-tailed p < 0.05 was considered significant.</p>
Antigen-specific memory NK cell responses against HIV and influenza utilize the NKG2/HLA-E axis
<p>For over a decade, multiple studies have disputed the notion of natural killer (NK) cells as purely innate lymphocytes by demonstrating that they are capable of putative antigen-specific immunological memory against multiple infectious agents including two critical global health priorities – human immunodeficiency virus (HIV) and influenza. However, the mechanisms underlying antigen specificity remain unknown. Herein, we demonstrate that antigen-specific human NK cell memory develops upon exposure to both HIV and influenza, unified by a conserved and epitope-specific targetable mechanism largely dependent on the activating CD94/NKG2C receptor and its ligand HLA-E, and confirm these findings by three rigorous assays. We validated the permanent acquisition of antigen-specificity by individual memory NK cells by single-cell cloning. We identified biomarkers of antigen-specific NK cell memory through complex immunophenotyping by 30-parameter flow cytometry showing elevated expression of KLRG1, a4b7, and NKG2C. Finally, we show individual HLA-E-restricted peptides that may constitute the dominant NK cell response in HIV-1- and influenza-infected persons in vivo. Our findings clarify the mechanisms behind antigen-specific memory NK cell responses, and suggest they could be targeted for future vaccines, cure strategies, or other therapeutic interventions.</p>
Data from: Age-specific induction of mutant p53 drives clonal hematopoiesis and acute myeloid leukemia in adult mice
<p>The investigation of the mechanisms behind p53 mutations in acute myeloid leukemia (AML) has been limited by the lack of suitable mouse models, which historically have resulted in lymphoma rather than leukemia. This study introduces two new AML mouse models. One model induces mutant p53 and <em>Mdm2</em> haploinsufficiency in early development, showing the role of Mdm2 in myeloid-biased hematopoiesis and AML predisposition, independent of p53. The second model mimics clonal hematopoiesis by inducing mutant p53 in adult hematopoietic stem cells, demonstrating that the timing of p53 mutation determines AML versus lymphoma development. In this context, age-related changes in hematopoietic stem cells (HSCs), collaborates with mutant p53 to predispose towards myeloid transformation rather than lymphoma development. Our study unveils new insights into the cooperative impact of HSC age, <em>Trp53</em> mutations and <em>Mdm2</em> haploinsufficiency on clonal hematopoiesis and the development of myeloid malignancies.</p>
Fig. 6 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 6 Possibilities of the B. lobatum life cycle
Data from: Dietary plasticity and broad North Atlantic origins inferred from bulk and amino acid-specific δ15N and δ13C favor killer whale range expansions into Arctic waters
<p>Killer whales (<em>Orcinus orca</em>) occur seasonally in the eastern Canadian Arctic (ECA), where their range expansion associated with declining sea ice have raised questions about the impacts of increasing killer whale predation pressure on Arctic-endemic prey. We assessed diet and distribution of ECA killer whales using bulk and compound specific stable isotope analysis (CSIA) of amino acids (AA) of 54 skin biopsies collected from 2009-2020 around Baffin Island, Canada. Bulk ECA killer whale skin δ15N and δ13C values did not overlap with potential Arctic prey after adjustment for trophic discrimination, and instead reflected foraging history in the North Atlantic prior to their arrival in the ECA. Adjusted killer whale stable isotope (SI) values primarily overlapped with several species of North Atlantic baleen whales or tuna. Amino acid (AA)-specific δ15N values indicated the ECA killer whales fed primarily on marine mammals, having similar glutamic acid δ15N – phenylalanine δ15N (δ15NGlx-Phe) and threonine δ15N (δ15NThr) as mammal-eating killer whales from the eastern North Pacific (ENP) that served as a comparative framework. However, one ECA whale grouped with the fish-eating ENP ecotype based δ15NThr. Distinctive essential AA δ13C of ECA killer whale groups, along with bulk SI similarity to killer whales from different regions of the North Atlantic, indicate different populations converge in Arctic waters from a broad source area. Generalist diet and long-distance dispersal capacity favor range expansions, and integration of these insights will be critical for assessing ecological impacts of increasing killer whale predation pressure on Arctic-endemic species.</p>
Annelid comparative genomics and the evolution of massive lineage-specific genome rearrangement in bilaterians
<p>The organization of genomes into chromosomes is critical for processes such as genetic recombination, environmental adaptation, and speciation. All animals with bilateral symmetry inherited a genome structure from their last common ancestor that has been highly conserved in some taxa but seemingly unconstrained in others. However, the evolutionary forces driving these differences and the processes by which they emerge have remained largely uncharacterized. Here we analyze genome organization across the phylum Annelida using 23 chromosome-level annelid genomes. We find that while most annelids have maintained the conserved bilaterian genome structure, a group containing leeches and earthworms possesses completely scrambled genomes. We develop a rearrangement index to quantify the extent of genome structure evolution and show leeches and earthworms to have the most highly rearranged genomes of any currently sampled bilaterian. We further show that bilaterian genomes can be classified into two distinct categories—high and low rearrangement—largely influenced by the presence or absence, respectively, of chromosome fission events. Our findings demonstrate that animal genome structure can be highly variable within a phylum and reveal that genome rearrangement can occur both in a gradual, stepwise fashion or as rapid, all-encompassing changes over short evolutionary timescales.</p>
Data from: Taxon-specific hydrogen isotope signals in cultures and mesocosms facilitate ecosystem and hydroclimate reconstruction
<p>Phytoplankton play a key role in biogeochemical cycles, impacting atmospheric and aquatic chemistry, food webs, and water quality. However, it remains challenging to reconstruct changes in algal community composition throughout the geologic past, as existing proxies are suitable only for a subset of taxa and/or influenced by degradation. Here, we investigate if compound-specific hydrogen isotope ratios (δ<sup>2</sup>H values) of common algal lipids can serve as (paleo)ecological indicators. First, we grew 20 species of algae – representing cyanobacteria, diatoms, dinoflagellates, green algae, and cryptomonads – in batch cultures under identical conditions and measured δ<sup>2</sup>H values of their lipids. Despite identical source water δ<sup>2</sup>H values, lipid δ<sup>2</sup>H values ranged from -455 ‰ to -52 ‰, and clustered according to taxonomic groups and chemical compound classes. In particular, green algae synthesized fatty acids with higher δ<sup>2</sup>H values than other taxa, cyanobacteria synthesized phytol with relatively low δ<sup>2</sup>H values, and diatoms synthesized sterols with higher δ<sup>2</sup>H values than other eukaryotes. Second, we assessed how changes in algal community composition can affect net δ<sup>2</sup>H values of common algal lipids in 20 experimental outdoor ponds, which were manipulated via nutrient loading, and the addition of macrophytes and mussels. High algal biomass in the ponds, which was mainly caused by cyanobacterial and green algal blooms, was associated with higher δ<sup>2</sup>H values for generic fatty acids, relatively stable δ<sup>2</sup>H values for phytol and the dinoflagellate biomarker dinostanol, and lower δ<sup>2</sup>H values for the more cosmopolitan sterol stigmasterol. These results are consistent with expectations from our culture-based analyses, suggesting that measuring δ<sup>2</sup>H values of multiple lipids from sediment and calculating <sup>2</sup>H-offsets between them can resolve changes in algal community composition from changes in source water isotopes. With an appropriate availability of sedimentary lipids, this approach could permit the reconstruction of both taxonomic variability and hydroclimate from diverse sedimentary systems.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
The intracellular C-terminus confers compartment-specific targeting of voltage-gated calcium channels
<p>This table contains all tabulated data for:</p> <p>Chin and Kaeser, 2024. "The intracellular C-terminus confers compartment-specific targeting of voltage-gated calcium channels."</p> <p>Detailed methods are provided in the paper. </p>
Archived - Specific chronic conditions: Medicare beneficiary enrollment and characteristics
<h1>Chronic conditions: Medicare beneficiary enrollment and characteristics</h1> <p>According to the <a href="https://data.cms.gov/medicare-chronic-conditions" rel="nofollow">current page</a>, "CMS has retired the historical chronic conditions datasets ('Multiple Chronic Conditions' and 'Specific Chronic Conditions'). Refer to the Mapping Medicare Disparities tool below which can be used for retrieving similar data."</p> <p>The original page and the ZIP files are <a href="https://web.archive.org/web/20231014192833/https://www.cms.gov/data-research/statistics-trends-and-reports/chronic-conditions">archived on the Wayback Machine</a>. As of June 30, 2024, the ZIP files can also be downloaded from CMS.gov using a direct link to each file (e.g., <a href="https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/chronic-conditions/downloads/beneficiary_enrollment_tables.zip">beneficiary_enrollment_tables.zip</a>).</p>
Epidemiologic data relating to age and gender specific HIV viral suppression in Rakai, Uganda, 2013-2019
<div> <p>This repository contains the data for the analyses presented in the paper "<span><span>Age and gender profiles of HIV infection burden and viraemia: novel metrics for HIV epidemic control in African populations with high antiretroviral therapy coverage</span></span>", soon available as a preprint. </p> <p>We thank all contributors, program staff, and participants to the Rakai Community Cohort Study.</p> <p>We also extend our gratitude to the <a href="https://doi.org/10.14469/hpc/2232">Imperial College Research Computing Service</a> for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the <a href="https://www.niaid.nih.gov/">National Institute for Allergy and Infectious Diseases</a> for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app. </p> <p>All analysis code is available from <a href="https://github.com/MLGlobalHealth/longi_viral_loads">https://github.com/MLGlobalHealth/longi_viral_loads</a>.</p> </div>
Key habitat for male Strix nebulosa (Great Gray Owls) varies across the diurnal cycle and reflects sex-specific role, data archive
<p>We used GPS tracking and remotely-sensed environmental data to evaluate whether breeding-season habitat selection by adult male <em>Strix nebulosa </em>(Great Gray Owls) (n = 19) varied across diurnal periods (dawn, day, dusk, and night). To address knowledge gaps related to nocturnal habitat, we also evaluated finer-scale, microhabitat selection by male owls at night. Here, we include both the remotely-sensed habitat data and on-the-ground microhabitat data associated with owl locations. Generally, <em>S. nebulosa </em>are associated with mature forests for nesting and meadows for foraging. Yet, in our study, owls avoided herbaceous wetlands during the day but strongly selected them at dawn, dusk, and at night, indicating context-dependent habitat selection. Moreover, owls avoided dry meadows at all times of the day, suggesting that wet rather than xeric meadows are important for foraging. Owls also preferred nighttime microhabitats that facilitated foraging, such as those with presence of primary prey and open understories dominated by graminoids and forbs. During the daytime, owls preferred higher canopy cover and areas with increased soil moisture, which likely provided suitable roosting habitat. Understanding of habitat preferences across sexes, activity periods, and other contexts can improve the identification and conservation of critical habitat for wildlife.</p>
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.