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11,177 results for “identify”
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Zoë Goodwin, <a href="https://orcid.org/0000-0003-2926-1645">https://orcid.org/0000-0003-2926-1645</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Samantha Matlick, <a href="https://orcid.org/0000-0003-1476-1962">https://orcid.org/0000-0003-1476-1962</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Julie Craves, <a href="https://orcid.org/0000-0002-9391-2767">https://orcid.org/0000-0002-9391-2767</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by K. Samanta Orellana, <a href="https://orcid.org/0000-0002-4098-5823">https://orcid.org/0000-0002-4098-5823</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Christopher C. Grinter, <a href="https://orcid.org/0000-0002-3769-1664">https://orcid.org/0000-0002-3769-1664</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Marco O. O. Pellegrini, <a href="https://orcid.org/0000-0002-8783-1362">https://orcid.org/0000-0002-8783-1362</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Dmitry Lyskov, <a href="https://orcid.org/0000-0003-0818-1662">https://orcid.org/0000-0003-0818-1662</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Robert L. Minckley, <a href="https://orcid.org/0000-0002-1217-7693">https://orcid.org/0000-0002-1217-7693</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Bevan Weir, <a href="https://orcid.org/0000-0003-2580-0701">https://orcid.org/0000-0003-2580-0701</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Timothy Andrew Hammer, <a href="https://orcid.org/0000-0003-3816-7933">https://orcid.org/0000-0003-3816-7933</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Shelley A. James, <a href="https://orcid.org/0000-0003-1105-1850">https://orcid.org/0000-0003-1105-1850</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Nigel Pitman, <a href="https://orcid.org/0000-0002-9211-2880">https://orcid.org/0000-0002-9211-2880</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
26,000+ machine-identified images of Modern planktonic foraminifera from 23 Atlantic coretops
<p>This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv).</p> <p>The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol.</p> <p><strong>References</strong></p> <p>Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. <em>Paleoceanography and Paleoclimatology</em>. 34(7):1157-1177. (<a href="https://doi.org/10.1029/2019PA003612">https://doi.org/10.1029/2019PA003612</a>)</p> <p>Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) <em>AutoMorph</em>: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. <em>Methods in Ecology and Evolution. </em>9(3):605-612. (<a href="https://doi.org/10.1111/2041-210X.12915">https://doi.org/10.1111/2041-210X.12915)</a></p> <p>Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.</p>
Data for: Testing for fitness epistasis in a transplant experiment identifies a candidate adaptive locus in Timema stick insects
<p>Identifying the genetic basis of adaptation is a central goal of evolutionary biology. However, identifying genes and mutations affecting fitness remains challenging because a large number of traits and variants can influence fitness. Selected phenotypes can also be difficult to know <em>a priori</em>, complicating top-down genetic approaches for trait mapping that involve crosses or genome-wide association studies. In such cases, experimental genetic approaches, where one maps fitness directly and attempts to infer the traits involved afterward, can be valuable. Here, we re-analyse data from a transplant experiment involving <em>Timema</em> stick insects, where five physically clustered SNPs associated with cryptic body colouration were shown to interact to affect survival. Our analysis covers a larger genomic region than past work and revealed a locus previously not identified as associated with survival. This locus resides near a gene, <em>Punch</em> (<em>Pu</em>), involved in pteridine pigments production, implying that it could be associated with an unmeasured colouration trait. However, by combining previous and newly obtained phenotypic data, we show that this trait is not eye or body colouration. We discuss the implications of our results for the discovery of traits, genes, and mutations associated with fitness in other systems, as well as for supergene evolution.</p>
Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
<p>Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.</p>
A meta-analysis of pre-pregnancy maternal body mass index and placental DNA methylation identifies 27 CpG sites with implications for mother-child health
<p>Higher maternal pre-pregnancy body mass index (ppBMI) is associated with increased neonatal morbidity, as well as with pregnancy complications and metabolic outcomes in offspring later in life. Placenta is a key organ in fetal development and has been proposed to act as a mediator between the mother and different health outcomes in children. The overall aim of the present work is to investigate the association of ppBMI with epigenome-wide placental DNA methylation (DNAm) in 10 studies from the PACE consortium, amounting to 2,631 mother-child pairs. We identify 27 CpG sites at which we observe placental DNAm variations of up to 2.0% per 10 ppBMI-unit. The CpGs that are differentially methylated in placenta do not overlap with CpGs identified in previous studies in cord blood DNAm related to ppBMI. Many of the identified CpGs are located in open sea regions, are often close to obesity-related genes such as GPX1 and LGR4 and altogether, are enriched in cancer and oxidative stress pathways. Our findings suggest that placental DNAm could be one of the mechanisms by which maternal obesity is associated with metabolic health outcomes in newborns and children, although further studies will be needed in order to corroborate these findings.</p> <p>File descriptions:</p> <ul> <li>ppBMI_placenta_unadjustedmodel_NFJ.csv = EWAS summary statistics for the EWAS of ppBMI using the unadjusted model</li> <li>ppBMI_placenta_adjustedmodel_NFJ.csv = EWAS summary statistics for the EWAS of ppBMI using the adjusted model</li> </ul> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a Zenodo DOI generated for you go to ewascatalog.org/upload.</p>
Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding
<p>Data for non-regenerative bivalent analyte binding interaction between CH505 transmitted founder gp120 and CH31 monoclonal antibody, an HIV-1 mAb with CD4-binding site specificity.</p> <p>This data set is submitted as part of the manuscript titled "Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding"</p>
Appendix of Empirical Study of Co-Renamed Identifiers
<p>This data contains four CSVs: <strong>Rename.csv, Rename_Ignore_Inflection.csv, Relation.csv, Relation_Ignore_Inflection.csv.</strong></p> <p> </p> <p><strong>Rename.csv</strong> and <strong>Rename_Ignore_Inflection.csv</strong> contain renamings used in our study.</p> <p>The difference between these CSVs is whether we ignored inflections. </p> <p>Each row represents a renaming.</p> <p>Columns descriptions are below.</p> <ul> <li><strong>repository</strong>: repository name</li> <li><strong>commit</strong>: commit hash</li> <li><strong>filepath</strong>: location of the renamed identifier</li> <li><strong>oldname</strong>: identifier name before the renaming</li> <li><strong>newname</strong>: identifier name after the renaming</li> <li><strong>type</strong>: identifier type (Class, Method, Attribute, Parameter, Variable)</li> <li><strong>operational_chunk</strong>: operational chunks of the renaming</li> <li><strong>meaningful_rename_set_id</strong>: meaningful rename set IDs (independent for each repository)</li> </ul> <p> </p> <p><strong>Relation.csv</strong> and <strong>Relation_Ignore_Inflection.csv</strong> contain relationships detected in our study.</p> <p>The difference between these CSVs is whether we ignored inflections. </p> <p>Each row represents a relationship between renamed identifier 1 and renamed identifier 2.</p> <p>Columns descriptions are below.</p> <ul> <li><strong>repository</strong>: repository name</li> <li><strong>meaningful_rename_set_id</strong>: meaningful rename set IDs (independent for each repository)</li> <li><strong>commit</strong>: commit hash</li> <li><strong>relation:</strong> type of relationship</li> <li><strong>file{1,2}</strong>: location of the identifier</li> <li><strong>oldname{1,2}</strong>: identifier name before the renaming</li> <li><strong>newname{1,2}</strong>: identifier name after the renaming</li> <li><strong>type{1,2}</strong>: identifier type (Class, Method, Attribute, Parameter, Variable)</li> </ul>
Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation
<p>Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7</p> <p><strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong></p> <p>Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup></p> <p><strong>Abstract </strong></p> <p>Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks. </p>
A novel camera trapping method for individually identifying pumas by facial features
<p>Camera traps (CTs), used in conjunction with capture-mark-recapture analyses (CMR; photo-CMR), are a valuable tool for estimating abundances of rare and elusive wildlife. However, a critical requirement of photo-CMR is that individuals are identifiable in CT images (photo-ID). Thus, photo-CMR is generally limited to species with conspicuous pelage patterns (e.g., stripes or spots) using lateral-view images from CTs stationed along travel paths. Pumas (Puma concolor) are an elusive species for which CTs are highly effective at collecting image data, but their suitability to photo-ID is controversial due to their lack of pelage markings. For a wide range of taxa, facial features are useful for photo-ID, but this method has generally been limited to images collected with traditional handheld cameras. Here we evaluate the feasibility of using puma facial features for photo-ID in a CT framework. We consider two issues: 1) the ability to capture puma facial images using CTs, and 2) whether facial images improve human ability to photo-ID pumas. We tested a novel CT accessory that used light and sound to attract the attention of pumas, thereby collecting face images for use in photo-ID. Face captures rates increased at CTs that included the accessory (n = 208, χ2 = 43.23, P ≤ 0.001). To evaluate if puma faces improve photo-ID, we measured the inter-rater agreement of 5 independent assessments of photo-ID for 16 of our puma face capture events. Agreement was moderate to good (Fleiss' kappa = 0.54, 95% CI = 0.48–0.60), and was 92.90% greater than a previously published kappa using conventional CT methods. This study is the first time such a technique has been used for photo-ID, and we believe a promising demonstration of how photo-ID may be feasible for an elusive but unmarked species.</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.