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12,871 results for “aging;”
Figure 7 in Assessing age, growth, and reproduction of Alburnus mossulensis and Acanthobrama marmid (Cyprinidae) populations in Karakaya Dam Lake (Turkey)
Figure 7. Length–weight relationship of A. marmid in Karakaya Dam Lake (N: number of female and male individuals).
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Gaia DR3 asteroid reflectance spectra: L-type families, memberships and ages
<p>The Gaia Data Release 3 (DR3) contains reflectance spectra at visible wavelengths for 60,518 asteroids over the range between 374-1034 nm, representing a large sample that is well suited to studies of asteroid families.</p> <p>We wanted to assess the potential of Gaia spectra in identifying asteroid family members. Here, we focus on two L-type families, namely Tirela/Klumpkea and Watsonia. These families are known for their connection to Barbarian asteroids, which are potentially abundant in calcium-aluminum rich inclusions (CAIs).</p> <p>The method we developed to establish family memberships is based (1) on a color taxonomy specifically built on Gaia data and (2) on the similarity of spectra of candidate members with the template spectrum of a specific family.</p> <p>Our work demonstrates the advantage of combining the classical hierarchical clustering method (HCM) approach to spectral properties obtained by Gaia for the study of asteroid families. Future data releases are expected to further expand the capabilities in this domain.</p> <p>The memberships for the Tirela/Klumpkea and Watsonia families are reported here. The columns report, from left to right: the identifier of the asteroid, the absolute magnitude, the proper elements (semi-major axis, eccentricity and sine of the inclination, taken from AFP, Novaković et al., 2022), NEOWISE albedo (Masiero et al., 2011) and spectral type from our color taxonomy. For the objects that are not directly classified into the S and L classes, their most probable spectral type is also reported. </p>
Overview Map of Cilicia with Main Bronze and Iron Age, Hellenistic and Modern Sites
<p>Overview map (for use on screen please use the jpg with the file extension RGB, for print please use the jpg with the file extension CMYK, the PDF has different layers) and dataset (SRTM DEM with hillshade and shaded relief) of Plain Cilicia with main Bronze and Iron Age, Hellenistic and modern sites</p> <p>Recommended symbology for QGIS: set DEM_srtm_hillshade.tif transparency to 45 %, use for the shaded relief DEM_srtm.tif a layer below the layer-file (DEM_srtm.qml), set here the transparency to 60%.</p> <p>Recommended symbology for Esri ArcGIS: set DEM_srtm_hillshade.tif transparency to 45 %, use for the shaded relief a layer below DEM_srtm.tif the layer-file (DEM_srtm.lyrx), set here the transparency to 5%.</p> <p>The selected sites can be downloaded as geojson file.</p>
Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""
<p>Related to the publication: <a href="https://doi.org/10.1016/j.jpowsour.2021.230459">https://doi.org/10.1016/j.jpowsour.2021.230459</a></p> <p>Datasets for the following Figures:</p> <p>Figure 2.</p> <p>Figure 3.</p> <p>Figure 5.</p> <p>Figure 7.</p>
FIG. 1 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 1. — Map of Anatolia and the Levant with the main sites mentioned in the text (modified data courtesy of National Centers for Environmental Infor- mation – ETOPO1, Natural Earth and Geo Network opensource. https://doi. org/10.7289/V5C8276M).
FIG. 4 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 4. — Arslantepe, tarsometatarsi (left and right) of rooster from level IIIB. Photo credits: R. Ceccacci, ©MAIAO. Scale bar: 3 cm.
Salmonella, Shiga toxin-producing Escherichia coli O157:H7 and Listeria monocytogenes numbers during dry-aging of beef loins
<p>This dataset contains bacterial count data and loin characteristics from an experimental study assessing the survival/growth of <em>Salmonella</em>, <em>Escherichia coli</em> O157:H7 and <em>Listeria monocytogenes</em> during dry-aging of beef loins, after artificial inoculation. </p> <p>Four different csv files are provided with tabular data. A detailed description of the data is provided in the readme file.</p> <p> </p>
Fig. 2 in Age Determination And Some Growth Parameters Of A Rana Ridibunda Population In Turkey
Fig. 2. Cross-section taken at the middle of the diaphysis of Rana ridibunda, a male, 58 mm SVL, 4 RL (scale: 108 µm)
Fig. 1 in Efficiency Against The Two-Spotted Spider Mite Tetranychus Urticae And Prey-Age-Related Choice Of Three Predatory Mites
Fig. 1. Number of adult spider mites surviving in the presence of predatory mites at different predator densities. Light grey boxes represent treatments with low predator densities, dark grey boxes represent treatments with high predator densities. Boxes show the median and the 25–75 percentiles; dashed lines indicate the range
OpenData for the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript
<p>Results from processed structural MRI images from the <a href="https://www.sciencedirect.com/science/article/pii/S1053811920306868">AHEAD</a> and <a href="https://www.nature.com/articles/s41597-021-00870-6">AOMIC</a> dataset included in the Supplementary Information at the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript from de Moraes et al. submitted to PNAS.</p> <p>For this data, we processed the datasets with <a href="https://surfer.nmr.mgh.harvard.edu/">FreeSurfer</a> v6.0.0 standard processing pipeline (<em>recon-all</em>) and the estimation of the <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/LGI">local Gyrification Index</a> from <a href="http://ltswww.epfl.ch/~schaer/Schaer_TMI.pdf">Schaer, M. et al. 2008</a>. We further extracted the morphological measurements from the generated surfaces using the Cortical Folding Analysis Tools from <a href="https://zenodo.org/record/3608675">Wang et al. 2019</a>. Here, we included the raw morphological datasets joined with the demographics and subjects' information.</p>
Fig. 7 in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 7. Percentages of immature (white) and mature-resting females (black) per size classes (total length, mm) for Mycteroperca marginata individuals collected in the study area. n = 111.
Fig. 6 in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 6. Percentage of mature resting females for specimens of the dusky grouper (Mycteroperca marginata) collected in the study area by total length classes, fitted to a logistic function. L50 = 451.3 mm. n = 111.
Fig. 4 in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 4. von Bertalanffy growth curve fitted to age-at-length data for dusky grouper specimens (Mycteroperca marginata) collected in the study area (K = 0.069 e L = 1249 mm). n = 108.
Fig. 2 in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 2. Transverse section of an otolith of a 4+ year-old dusky grouper (Mycteroperca marginata) collected in the study area. The photograph was taken under a stereomicroscope (20x) and reflected light, and shows a clear differentiation between opaque (white) and translucent (dark) growth bands.
Fig. 1. A in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 1. A) Patos Lagoon (10.360 km2) in southern Brazil and B) the 4.5 km long pair of rocky jetties that connect the estuarine zone of Patos Lagoon to the Atlantic Ocean. C) A detail of the west jetty and its large pieces of rock (nearly 10 ton each), located in the municipality of Rio Grande (Rio Grande do Sul state) in southern Brazil.
Fig. 5 in Age, growth, and reproductive aspects of the dusky grouper Mycteroperca marginata (Actinopterygii: Epinephelidae) in a man-made rocky habitat in southern Brazil
Fig. 5. Ovary sections of the dusky grouper (Mycteroperca marginata) collected in the study area. A) Immature gonad showing several primary germ cells (PGC) and oogonia. B) Resting gonad with small previtellogenic oocytes in chromatin nucleolus stage (CN) and in perinucleolar stage (PN).
Meta-GWAS for age-related hearing impairement
<p>The dataset comprises summary statistics from the meta-GWAS of 17 studies on age-related hearing impairement. The dataset accompanies the following paper:</p> <p><strong>Genome-wide association meta-analysis identifies 48 risk variants and highlights the role of the stria vascularis in age-related hearing impairment</strong></p> <p>Please cite the paper if using this dataset.</p> <p>Phenotype of ARHI was established using ICD diagnoses and self-reported hearing loss. The study comprised 148,152 cases and 575,472 controls or European ancestry. Adult male and female participants were included from the following 17 population-based cohort studies: Age, Genes/Environment Susceptibility - Reykjavik (AGES), the Danish Twin Registry (DTR), the Estonian Genome Center at the University of Tartu (EGCUT), FinnGen, Framingham Heart Study (FHS), Health Aging and Body Composition (HABC), Italian Network of Genetic Isolates - Friuli Venezia Giulia (INGI-FVG), the Rotterdam Study (RS, cohorts 1 - 3), the Salus in Apulia study (SA; formerly known as Great Age study), Screening Across the Lifespan Twin (SALT and SALTY - young), Screening Twin Adults: Genes and Environment (STAGE), TwinsUK, UK Biobank (UKBB), and the Women’s Genome Health Study (WGHS). </p> <p>UK Biobank data have been used under project #11516.</p> <p>Individual GWASs have been QC'd and harmonyzed using EasyQC followed by fixed-effects IVW meta-analysis using METAL. The dataset includes the results of meta-analysis for n = 8,244,938 SNV with MAF >0.001 and present in at least 9 cohorts.<br> <br> <strong>Dataset columns:</strong></p> <p>SNP, rsID</p> <p>CHR, chromosome</p> <p>BP, genomic position (hg19)</p> <p>Allele1, effect allele</p> <p>Allele2, other allele</p> <p>Freq1, mean frequency of Allele1</p> <p>FreqSE, standard error of Freq1</p> <p>MinFreq, minimal frequency of Allele1 in the study cohorts</p> <p>MaxFreq, maximal frequency of Allele1 in the study cohorts</p> <p>Effect, effect size from the meta-analysis for Allele1</p> <p>StdErr, standard error of Effect</p> <p>P.value, corresponding p-value for meta-analysis</p> <p>Direction, direction of effects in individual studies</p> <p>HetISq, I<sup>2</sup> statistic for heterogeneity between studies</p> <p>HetChiSq, chi<sup>2</sup> statistic for heterogeneity between studies</p> <p>HetDf, degrees of freedom for the chi<sup>2</sup> statistic</p> <p>HetPval, p-value for heterogeneity between studies</p> <p>N, summary sample size</p> <p><strong>Dataset columns description:</strong></p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Seventeen studies included:</p> <p> </p> <p> </p>
Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study
<p>This dataset refers to the article "Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study" Front. Physiol. 12:639722 2021</p> <p>DOI: 10.3389/fphys.2021.639722</p>
Incorporating effects of age on energy dynamics predicts non-linear maternal allocation patterns in iteroparous animals
<p>Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.</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.