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720 results for “Identification data”
Data from: Identification of the notothenioid sister lineage illuminates the biogeographic history of an Antarctic adaptive radiation
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Data from: Joint histology in Alligator mississippiensis challenges the identification of synovial joints in fossil archosaurs and inferences of cranial kinesis
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Data from: Rapid identification of chloroplast haplotypes using High-Resolution Melting analysis
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Data from: Revisiting the ichthyodiversity of Java and Bali through DNA barcodes: taxonomic coverage, identification accuracy, cryptic diversity and identification of exotic species
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Data from: A framework for the identification of long-term social avoidance in longitudinal datasets
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Data from: Taxonomic identification bias does not drive patterns of abundance and diversity in theropod dinosaurs
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Data from: Comparative analysis of DNA repeats and identification of novel Fesreba centromeric element in fescues and ryagrasses
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Data from: Research on potential disruptive technology identification based on technology network
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Data from: Identification of novel, clinically correlated autoantigens in the monogenic autoimmune syndrome APS1 by PhIP-Seq
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Data pertaining to lobster phyllosoma, Panulirus interruptus, collection methods, locations, identification and staging (1951-2008, months of July and August)
The very large collection of plankton hauls taken in connection with the California Cooperative Oceanic Fisheries Investigations (CalCOFI) has afforded a unique opportunity to study the occurrence and distribution of the phyllosoma larvae of the spiny lobster. JOHNSON, MARTIN W. 1960. The Distribution of Larvae of the Spiny Lobster, Panulirus interruptus (Randall) with Records on P. Gracilis Streets. California Cooperative Fisheries Investigation 7: 413-46.
Peptidomimetics-based identification of FDA approved compounds inhibiting IRE1 activity: Simulation Data
<p>Protocols and data sets for docking and MD simulations of Methotrexate, Cefoperazone, Folinic acd and Folic acid interaction with IRE1. All docking data based on Glide (Schrödinger), and all MD simulations are based on the Gromacs software. 2D interaction maps from the initial docking of the four molecules to the four Lysine sites, and from snapshots every 100 ns of the 500 ns MD simulations of the molecules interacting with Lys599 (kinase site) and Lys907 (RNase site). 2D interaction maps produced using Schrödinger. </p>
In silico identification of vaccine targets for 2019-nCoV (Data tables)
<p><strong>Background</strong> The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (<a href="https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200218-sitrep-29-covid-19.pdf?sfvrsn=6262de9e_2">18-February-2020</a>), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.</p> <p><strong>Methods</strong> The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.</p> <p><strong>Results</strong> We report <em>in silico</em> identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a <em>de novo</em> search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.</p> <p><strong>Conclusions</strong> Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.</p>
Dataset used in Paper 1046 of the IFAC World Congress 2020 "Data Quality Assessment for System Identification in the Age of Big Data and Industry 4.0"
<p><br> The data used was obtained from a section of the lead zinc concentrator at the Mount Isa Mines in Queensland, Australia.The data collected for this investigation consists of two months of plant operation, collected at a a frequency of one minute. The historian’s interpolation routine is used to ensure the data is aligned. No special care was used to ensure that the data had any particular characteristics, other than that the plant was running. It was reported that during this period some step tests had been conducted. Forty-three variables were collected: for each of the PID controllers, setpoint, process value and output (SV/PV/MV) were recorded. The three analysers provide measure of iron, lead and zinc percentages.</p>
FIG. 2. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 2. — Navicordulia pascali n. sp., holotype: A, wings in ventral view; B, base of left HW in ventral view; C, S2 secondary genitalia in left lateral view; D, vesica spermalis in left lateral view (removed from ethanol and air dried); E, distal segments of the vesica spermalis in left lateral view (removed from ethanol and air dried); F, distal segments of the Vesica spermalis in right lateral view (removed from ethanol and air dried); G, distal part of the first segment and distal segments of the vesica spermalis in ventral view (removed from ethanol and air dried). Scale bars: A, 10 mm; B, D-G, 0.5 mm; C, 1 mm.
FIG. 5. — A in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 5. — A, distribution map of Navicordulia Machado & Costa, 1995 species of errans group and tropical today extension of grassland, savannah and shrubland in South America following The Nature Conservancy, terrestrial ecoregions. Map background: Natural Earth II; B, distribution map of Navicordulia species of longistyla and uncertain groups and tropical today extension of grassland, savannah and shrubland in South America following The Nature Conservancy, terrestrial ecoregions. Map background: Natural Earth II.
FIG. 4. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 4. — Navicordulia pascali n. sp., paratype: A, top of the head showing the eyes seam; B, vesica spermalis in ventral view (removed from ethanol and air dried): distal part of the first segment and distal segments; arrow indicates the third flagellum; B', detail of same with artificially colored third flagellum; C, vesica spermalis in ventral view with a slight lateral component (in ethanol). Arrow indicates the third flagellum; D, distal part of the vesica spermalis in ventral view (in ethanol). Arrow indicates the third flagellum; E, S9, S10 and anal appendages in right lateral view; F, S7 and S8 pilose complex: ventral view; F' schematic longitudinal cut (lateral view, ventral part above). Scale bars: A, B, E, F, 1 mm.
Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation
<p class="AbstractSummary"><b>Background</b></p> <p class="AbstractSummary">Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.</p> <p class="AbstractSummary"><b>Methods and findings</b></p> <p class="AbstractSummary">We used Raman micro-spectroscopy to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays, TMAs) from 483 patients treated in three Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. Most of the 483 patients were pT2 stage (44–69%), and pT3a (22–49%) was more frequent than pT3b (9–12%). After approval of the construction of the TMAs by local ethics review board, the diagnostic accuracy study was approved by the Centre hospitalier de l'Université de Montréal (CHUM) ethics review board. Briefly, two consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions / TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. <span>Models were trained and cross-validated using data from one institution. Accuracy, sensitivity and specificity were respectively of 87 ± 5%, 86 ± 6% and 89 ± 8% to differentiate PC from benign tissue, and of 95 ± 2%, 96 ± 4% and 94 ± 2% respectively to differentiate IDC-P from PC. The trained models were then tested on data from two independent institutions, reaching accuracies, sensitivities and specificities of 84 and 86%, 84 and 87%, and 81 and 82%, respectively</span><span> to diagnose PC, and of 85 and 91%, 85 and 88%, and 86 and 93% respectively for the identification of IDC-P.</span> IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation which can be mistaken as IDC-P, with accuracies, sensitivities and specificities >95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult to classify lesions from our datasets.</p> <p class="AbstractSummary"><b>Conclusions</b></p> <p>In this study, we developed classification models for the analysis of Raman micro-spectroscopy data to differentiate IDC-P, PC and benign tissue, including HGPIN. Raman micro-spectroscopy could be a next-generation histopathological technique used to <span>reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.</span></p>
Data-driven identification of reliable sensor species to predict regime shifts in ecological networks
<p>Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially-distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.</p>
Data from: Validation of a simple extraction procedure for bisphenol A identification from human plasma
The general population is exposed to bisphenol A (BPA) orally, parenterally, transdermally, and environmentally as a result of the use of BPA in food packaging, plastics, and personal care products. The majority of the population nowadays (91–99%) has detectable levels of BPA inside their body. In this study, we successfully performed an inexpensive, rapid, and simple protein precipitation procedure for extraction of BPA from human plasma, followed by analysis by LC-MS/MS. This method was specifically developed for handling large numbers of samples with minimum cost and volume of sample. The developed method was accurate, precise, and reproducible for quantification of BPA from human plasma samples in the concentration range of 10–2000 ng/mL. The method was performed on samples from 150 healthy volunteers who were enrolled in the study. The mean of observed BPA level was 2.22 ± 9.91 ng/mL. Higher BPA levels were observed for females compare to that of males (p-value = 0.002), the BPA levels were higher in participants 33 years of age and older compared to those less than 33 years of age (p-value = 0.000), then the BPA levels higher in subjects with tap water as source of drinking (p-value = 0.005). This method may be valuable for general risk assessment of BPA for a large and varied population because of its efficiency and economical aspects.
Data from: Identification of candidate risk factor genes for human idelalisib toxicity using a collaborative cross approach
Idelalisib is a phosphatidylinositol 3-kinase inhibitor highly selective for the delta isoform that has shown good efficacy in treating chronic lymphocytic leukemia and follicular lymphoma. In clinical trials, however, idelalisib was associated with rare, but potentially serious liver and lung toxicities. In this study, we used the Collaborative Cross (CC) mouse population to identify genetic factors associated with the drug response that may inform risk management strategies for idelalisib in humans. Eight (8) male mice (4 matched pairs) from 50 CC lines were treated once daily for 14 days by oral gavage with either vehicle or idelalisib at a dose selected to achieve clinically-relevant peak plasma concentrations (150 mg/kg/day). The drug was well tolerated across all CC lines, and there were no observations of overt liver injury. Differences across CC lines were seen in drug concentration in plasma samples collected at the approximate Tmax on study Days 1, 7, and 14. There were also small but statistically significant treatment-induced alterations in plasma total bile acids and microRNA-122, and these may indicate early hepatocellular stress required for immune-mediated hepatotoxicity in humans. Idelalisib treatment further induced significant elevations in the total cell count of terminal bronchoalveolar lavage fluid, which may be analogous to pneumonitis observed in the clinic. Genetic mapping identified loci associated with interim plasma idelalisib concentration and the other three treatment-related endpoints. Thirteen (13) priority candidate quantitative trait genes identified in CC mice may now guide interrogation of risk factors for adverse drug responses associated with idelalisib in humans.
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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.