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175 results for “fatalities”
COVID-19 case fatality derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a><br> doi:<a href="https://doi.org/10.5281/zenodo.3787930">10.5281/zenodo.3787930</a></p> <p>This is the plot of the COVID-19 risk of death adjusted by age based on the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library published on April 3, 2020, (release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 case-fatality-derived age-adjusted risk of death
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><a href="https://zenodo.org/record/3787931"><em>https://zenodo.org/record/3787931</em></a><br> doi:10.5281/zenodo.3787931</p> <p>This is the plot of the COVID-19 risk of death adjusted by age derived from the case fatality data from the Kaggle project Data Science for COVID-19 in South Korea (DS4C) hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset</p> <p>The plot was created with the Python & C library initially published on April 3, 2020, (with the release 2.0 of April 21, 2020) on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality</p>
COVID-19 age-adjusted risk of death derived from case fatality
<p><strong>This is an old version of the plot. For the newer version, please visit </strong><em>https://zenodo.org/record/3787931</em><br> doi:10.5281/zenodo.3787931</p> <p>This is a plot of the risk of death in COVID-19 adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.0 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
COVID-19 case fatality-derived risk of death adjusted by age and gender
<p>This figure plots the risk of death in COVID-19 adjusted by the age and gender, which was modeled with the case fatality data kindly collected by the team of data scientists and data engineers in the Kaggle project <em>Data Science for COVID-19 in South Korea (DS4C)</em>, which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was built with the release 2.2 of the open-sourced Python & C library for macOS and Linux initially published under <strong>GPLv3</strong> license on April 3, 2020, on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit <a href="https://github.com/yuryatin/covid19_age_adjusted_mortality"><strong>https://github.com/yuryatin/covid19_age_adjusted_mortality</strong></a> for more detailed description of the model and for the source code.</p>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
Fatal traffic accidents in Catalonia
<p>This dataset contains 1024 fatal traffic accidents ocurred in Catalonia between June 13, 2014 and October 20, 2021. Each record has data about the date and time of the accident, it's localization and a description. The dataset has been obtained from applying web scrapping techniques on the Catalonia's Government (Generalitat de Catalunya) website: http://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/</p> <p> </p> <p> </p>
Dataset for "Demographic yearbooks as a source of weather-related fatalities: the Czech Republic, 1919–2022"
<p><span>This deposit contains three .xlsx files.</span></p> <p><span>The file „01_fatalities_1919-2022“ contains annual numbers of fatalities (males, females and sum) for individual categories of external death causes attributed to weather and natural extremes, excerpted from demographic yearbooks for the Czech Republic for the period 1919–2022. </span></p> <p><span>The file „02_age_categories_1931-2022“ contains eight sheets with annual numbers of weather-related fatalities in the Czech Republic in the period 1931–2022 for eight age categories and for males and females separately. Sheets represent individual categories of death causes – Cold, Heat, Lightning, Natural hazards, Fall on ice or snow, Air pressure. Heat and Natural hazards are divided into two sheets – one with summarized numbers and one with numbers for individual sub-categories.</span></p> <p><span>The file „03_clima_factors“ contains mean temperature of January–February (Brázdil et al., 2012, extended) and mean annual number of days with a thunderstorm in the Czech Republic for the period 1919–2022 and mean temperature of winter season (DJF) in the Czech Republic for the period 1986/1987–2021/2022 (Brázdil et al., 2012, extended).</span></p>
Supplementary Data -A STUDY ON SOME STRUCTURAL FEATURES RESPONSIBLE FOR SARS-COV-2 INFECTION FATALITY
<p>A correlation between hydrodynamic properties like radius of gyration ( Rg ) vs Molecular weight of spike protein of SARS - COV-2 biopolymers .</p>
Fig. 1 in Fatal systemic toxoplasmosis in Valley quail (Callipepla californica)
Fig. 1. Toxoplasmosis in Valley quail. (A) Heart with whitish areas (arrow). (B) Lungs diffusely red and consolidated (arrow) and splenomegaly (arrowhead). Liver (C) and heart (D) with multifocal to coalescent severe necrosis associated with Toxoplasma gondii tachyzoites (arrows) (H&E stain). Toxoplasma-positive immunohistochemistry in liver (E) and bone marrow (F). Streptavidine-biotine ligated to peroxidase (Bars 100 μm).
Fig. 2 in Apparent fatal winter tick (Dermacentor albipictus) infestation in captive reindeer (Rangifer tarandus)
Fig. 2. Dorsal (A) and ventral view (B) of an adult female Dermacentor albipictus collected from captive reindeer. Inset image shows large goblet cells on the spiracular plate.
Fig. 4 in Transuterine infection by Baylisascaris transfuga: Neurological migration and fatal debilitation in sibling moose calves (Alces alces gigas) from Alaska
Fig. 4. Parsimony analysis of the combined nuclear and mitochondrial genes yielded four equally parsimonious trees (CI 0.93). Strict consensus supported monophyly of B. transfuga and identity of the L3 recovered from moose.
Fig. 3 in Transuterine infection by Baylisascaris transfuga: Neurological migration and fatal debilitation in sibling moose calves (Alces alces gigas) from Alaska
Fig. 3. Molecular phylogenetic analyses establishing identity of Baylisascaris transfuga in moose calves. Branch support indicated by parsimony bootstrap above and Bayesian posterior probability below. Fig 3A. Parsimony analysis of the 12S rDNA sequences showing strict consensus of 2 equally parsimonious trees. Fig 3B. Parsimony analysis showing strict consensus of the cox2 sequences which yielded four equally parsimonious trees (CI 0.86). Fig 3C. Parsimony analysis of the 28S rDNA sequences yielded one most parsimonious tree of length (CI 0.97). Fig. 3D. Parsimony analysis of the ITS rDNA sequences yielded one most parsimonious tree of length (CI 0.97).
Fig. 1 in Transuterine infection by Baylisascaris transfuga: Neurological migration and fatal debilitation in sibling moose calves (Alces alces gigas) from Alaska
Fig. 1. Third stage larvae of Baylisascaris transfuga in histological sections of brain of female moose calf (USNPC 108284/Alaska Department of Fish and Game OMC ID Tag 56 Alaska V-11-201); scale = 50 μm. Fig. 1. Brain tissue with L3's in transverse sections. Note prominent lateral alae, coelomyarian polymyarian musculature and morphology consistent with Baylisascaris; maximum diameter of L3, 85 μm.
Fig. 2 in Transuterine infection by Baylisascaris transfuga: Neurological migration and fatal debilitation in sibling moose calves (Alces alces gigas) from Alaska
Fig. 2. Third stage larvae of Baylisascaris transfuga in histological sections of brain of female moose calf (USNPC 108284/Alaska Department of Fish and Game OMC ID Tag 56 Alaska V-11-201); scale = 50 μm. Fig. 2. Third stage larva in longitudinal section, view of cephalic region in brain tissue.
Fig. 3. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 3. A Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil. Scale bar = 200 μm. B Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil under plane-polarized light. Note birefringent eggs (arrow). Scale bar = 200 μm. C Egg dissected from Rameshwarotrema uterocrescens Rao (1975) (Digenea, Pronocephalidae) from Chelonia mydas Linnaeus 1758 (Testudines, Cheloniidae) from Brazil. Note polar filament (arrow). Scale bar = 50 μm.
Fig. 1. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 1. A Obstructive ulcerous exudative gastroesophagitis, gastroesophageal region, large ulcerated area covered by solid caseous exudate. Scale bar = 3 cm. B Granulomatous necrotic hepatitis, liver, miliary caseous parasitic granulomas. Scale bar = 4 cm.
Fig. 2. A in Fatal Rameshwarotrema uterocrescens infection with ulcerative esophagitis and intravascular dissemination in green turtles
Fig. 2. A Initial lesion caused by R. uterocrescens (red arrow) associated with esophageal gland desquamation (black arrow). Scale bar = 100 μm. B Ulcerative esophagitis, esophagus, extensive loss of esophageal mucosa with eight specimens of R. uterocrescens (arrow) embedded in necrotic amorphous eosinophilic tissue in submucosa with marked heterophilic inflammatory infiltrate. Scale bar = 500 μm. C Marked inflammation with heterophils and macrophages (*) in esophageal submucosa. Scale bar = 50 μm. D. R. uterocrescens in ectatic vessel, note red blood cells (arrow). Scale bar = 100 μm. E Heart with R. uterocrescens under plane-polarized light, with birefringent eggs between myocardiocytes (arrow). Scale bar = 100 μm. F Granulomatous hepatitis, liver, R. uterocrescens (arrow) next to necrotic mass (*) formed by degenerate leukocytes, rare parasite eggs, cell debris peripherally enveloped by multinucleated giant cells. Scale bar = 200 μm. G Granulomatous hepatitis, liver, birefringent R. uterocrescens eggs (black arrow) under plane-polarized light enveloped by multinucleated giant cells (red arrow). Scale bar = 50 μm. H Immersed eggs (black arrow) in thrombotic (*) arteritis (red arrow). Parasite in kidney artery seen under planepolarized light with intensely birefringent eggs. Scale bar = 100 μm. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Fatal infection caused by Cytauxzoon felis in a captive-reared jaguar (Panthera onca)
Fig. 5. Micrograph of the brain on infected jaguar. Schizonts inside macrophages contain numerous round to oval 1–2 μm diameter basophilic organisms (merozoites). HE.
Fig. 4 in Fatal infection caused by Cytauxzoon felis in a captive-reared jaguar (Panthera onca)
Fig. 4. Micrograph of the pancreas on infected jaguar. The macrophages are enlarged up to twice normal size and contained cytoplasmic schizonts. Note eccentric and pyknotic nuclei of macrophages. HE.
Fig. 3 in Fatal infection caused by Cytauxzoon felis in a captive-reared jaguar (Panthera onca)
Fig. 3. Micrograph of the pancreas on infected jaguar. The central blood vessel is partially obstructed by macrophages containing high numbers of C. felis schizonts. HE.
ScienceDex guides
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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.