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175 results for “fatalities”
Fig. 1. a in Serious lesions in Green turtles (Chelonia mydas) afflicted by fatal Spirorchiidiasis found stranded in south and southeastern Brazil
Fig. 1. a. Severe and Generalized Granulomatous Interstitial Pneumonia, Lung, C. mydas. Lung tissue extensively affected by large parasitic multifocal to coalescent severe granulomas in interfaveolar septa with diffusely compressed and collapsed faveolus (*). Upper Inset: Parasite of the spirorchiidae family in pulmonary artery lumen. Bottom Inset: Lung. Normal C. mydas lung. Fig. 1 b. Severe and Generalized Granulomatous Interstitial Pneumonia, Lung, C. mydas. Higher magnification of severely enlarged interfaveolar septa with diffusely compressed and collapsed faveolus (*), note egg type 3 (red arrow). Inset: Thrombus formed by eggs, cellular debris, macrophages and multinucleated giant cells in artery. Fig. 1 c. Granulomatous Meningitis, Brain, Chelonia mydas. Parasitic granulomas associated with severe nervous tissue atrophy associated with cerebral cortex loss, note red line with 290 μm (cerebral cortex compression area) and black line (722 μm) without cerebral cortex compression. Inset: Embolism, Spinal Cord. Embolus formed by cluster of eggs (*) in arteriole. Fig. 1 d. Brain, Granulomatous Encephalitis, Chelonia mydas. Parasitic granulomas associated with neural parenchyma compression, note egg type 1 (red arrow), (hematoxylin and eosin staining). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article).
Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population
<p>This record contains all the fully processed single cell RNA sequencing objects included in the paper Nyirenda et al (2024) '<em>Spatially resolved single-cell atlas unveils a distinct cellular signature of fatal lung COVID-19 in a Malawian population</em>'. </p> <h3>Abstract</h3> <p><em>Postmortem single-cell studies have transformed understanding of lower respiratory tract diseases (LRTD) including Covid19, but there is minimal data from African settings where HIV, malaria and other environmental exposures may affect disease pathobiology and treatment targets. We used histology and high-dimensional imaging to characterise fatal lung disease in Malawian adults with (n=9) and without (n=7) Covid19, and generated single-cell transcriptomics data from lung, blood and nasal cells. Data integration with other cohorts showed a conserved Covid19 histopathological signature, driven by contrasting immune and inflammatory mechanisms: in USA, European and Asian cohorts by type I/III interferon responses, particularly in blood-derived monocytes, in the Malawi cohort, by response to interferon-gamma (IFN-γ) in lung-resident macrophages. HIV status had minimal impact on histology or immunopathology. Our study provides a data resource and highlights the importance of studying the cellular mechanisms of disease in underrepresented populations, indicating shared and distinct targets for treatment. </em></p> <h3>Figure 3</h3> <p>COSMIC_Lung_Atlas.h5ad</p> <p>COSMIC_Lung_Stromal_Atlas.h5ad</p> <p>COSMIC_Lung_Immune_Atlas.h5ad</p> <h3>Figure 4</h3> <p>COSMIC_HCLA_Integration.rds</p> <h3>Figure 5</h3> <p>COSMIC_Nasal_Atlas.h5ad</p> <p>COSMIC_Blood_Atlas.h5ad</p> <h3>Total Post-mortem COVID-19 Lung Autopsy Papers Integration</h3> <p>This data object was generated whereby all publicly available fatal COVID-19 lung single cell cohorts were integrated together. We include this analysis to show that the observed interferon responses detailed in the paper are preserved in not only the HLCA, but also existing post-mortem lung cohorts. The codebase for this additional analysis can be found here: <strong><a href="https://github.com/olympiahardy/Malawi_Integration">https://github.com/olympiahardy/Malawi_Integration</a> </strong></p> <p>COSMIC_Delorey_Melms_Integration.rds</p> <p> </p> <p> </p> <h3>Imaging Mass Cytometry Analysis</h3> <p>This Zenodo record can be accessed using the DOI here: <strong><a href="10.5281/zenodo.13899297">10.5281/zenodo.13899297</a> </strong>. The GitHub repository can be accessed here:<strong> <a href="https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID">https://github.com/joaolsf/Spatial_Single_Cell_Lung_Atlas_Malawi_COVID</a></strong></p> <p> </p>
Queensland Road Traffic Crashes Fatalities and Hospitalisations 2011-2021
<p>Data sets that have been prepared from the Open Data Portal of the Queensland Government (2022) available from <a href="https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads">https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads</a>.</p> <p>Provides information from 2011 - 2021 on all police reported fatalities and hospitalisations that have occurred during this time period, showing road conditions and driver demographics.</p>
Evidence of absence regression: a binomial N-mixture model for estimating fatalities at wind power facilities
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Data From: Global huntingtin knockout in adult mice leads to fatal neurodegeneration that spares the pancreas
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Data from: Coevolution promotes the coexistence of Tasmanian devils and a fatal, transmissible cancer
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COVID-19 case-fatality–derived age-adjusted risk of death
<p><strong>This is a version of the plot without split into gender-adjusted risks. For the newer version with gender-adjusted curves, please visit </strong><a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a><br> doi:10.5281/zenodo.3787931</p> <p>This is a plot of the risk of death in COVID-19, which was adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers participating 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 2.1 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>
Longitudinal proteomic profiling of high-risk patients with COVID-19 reveals markers of severity and predictors of fatal disease
<p>End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We performed dense serial blood sampling in hospitalised and non-hospitalised ESKD patients with COVID-19 (n=256 samples from 55 patients) and used Olink immunoassays to measure 436 circulating proteins. Comparison to 51 non-infected ESKD patients revealed 221 proteins differentially expressed in COVID-19, of which 69.7% replicated in an independent cohort of 46 COVID-19 patients. 203 proteins were associated with clinical severity scores, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g proteinase-3) and epithelial injury (e.g. KRT19). Random Forests machine learning identified predictors of current or future severity such as KRT19, PARP1, PADI2, CCL7, and IL1RL1 (ST2). Survival analysis with joint models revealed 69 predictors of death including IL22RA1, CCL28, and the neutrophil-derived chemotaxin AZU1 (Azurocidin). Finally, longitudinal modelling with linear mixed models uncovered 32 proteins that display different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. Our findings point to aberrant innate immune activation and leucocyte-endothelial interactions as central to the pathology of severe COVID-19. The data from this unique cohort of high-risk individuals provide a valuable resource for identifying drug targets in COVID-19.</p>
Data from: Fatal attraction? Intraguild facilitation and suppression among predators
Competition and suppression are recognized as dominant forces that structure predator communities. Facilitation via carrion provisioning, however, is a ubiquitous interaction among predators that could offset the strength of suppression. Understanding the relative importance of these positive and negative interactions is necessary to anticipate community-wide responses to apex predator declines and recoveries worldwide. Using state-sponsored wolf (Canis lupus) control in Alaska as a quasi-experiment, we conducted snow track surveys of apex, meso-, and small predators to test for evidence of carnivore cascades (e.g., mesopredator release). We analyzed survey data using an integrative occupancy and structural equation modeling framework to quantify the strengths of hypothesized interaction pathways, and we evaluated fine-scale spatiotemporal responses of non-apex predators to wolf activity clusters identified from radio-collar data. Contrary to the carnivore cascade hypothesis, both meso- and small predator occupancy patterns indicated guild-wide, negative responses of non-apex predators to wolf abundance variations at the landscape scale. At the local scale, however, we observed a near guild-wide, positive response of non-apex predators to localized wolf activity. Local-scale association with apex predators due to scavenging could lead to landscape patterns of mesopredator suppression, suggesting a key link between occupancy patterns and the structure of predator communities at different spatial scales.
Fatal enhanced respiratory syncytial virus disease in toddlers
<p>In 1967, two toddlers immunized with a formalin-inactivated vaccine against respiratory syncytial virus (FIRSV) in the United States died from enhanced RSV disease (ERD), a severe form of illness resulting from aberrant priming of the antiviral immune response during vaccination. Up to 80% of immunized children subsequently exposed to wild-type virus were hospitalized. These events hampered RSV vaccine development for decades. In this manuscript, we provide a characterization of the clinical, immunopathological, and transcriptional signature of fatal human ERD, outlining novel evidence for safety evaluation of RSV vaccines and a framework for understanding disease enhancement for pathogens in general. These are the scanned histology slides from this study. </p>
Genome-wide DNA methylation profiling identifies epigenetic signatures of β-lactams induced fatal anaphylactic shock
<p>Drug hypersensitivity is one of the most frequent causes of anaphylaxis in adults, of which antibiotics are the most common culprits, particularly β-lactams induced anaphylactic shock deaths. We provided the genome wide DNA methylation profiling study of PBMC from 14 individuals peripheral venous blood samples. Illumina Infinium Human Methylation EPIC BeadChip was used. Among the 14 individuals, 8 patients were died from β-lactams induced anaphylactic shock, 6 healthy individuals were controls. Inclusion criteria for the β-lactams induced anaphylactic shock as follows: ①A clear history of β-lactams transfusion or components of β-lactams detected from blood (or skin of suspicious infusion site); ②Shock symptoms occurred after infusion of β-lactams and died within a short period of time; ③The immunohistochemical results of throat, lung and gastrointestinal tissues showed that tryptase or chymase were mostly positive expression; ④ Excluding other causes (disease, poisoning) of death; ⑤ No decay occurred in the corpse.Inclusion criteria for controls as follows: ① Healthy, no previous common underlying diseases and no history of genetic disease; ② No history of allergies to drugs, food, pollen and so on; ③Skin prick test (SPT) is negative, allergen-specific IgE (sIgE)< 100IU/mL; the skin test results of β-lactam drugs were negative.</p>
Confirmation that eagle fatalities can be reduced by automated curtailment of wind turbines
<p>1. Automated curtailment is potentially a powerful technique to reduce collision mortality of wildlife with wind turbines. Previously, we used a before-after-control-impact framework to demonstrate that eagle fatalities declined after automated curtailment was implemented with the IdentiFlight system at a wind power facility in Wyoming, USA. We received substantial interest and feedback regarding our study and, here, we implement several analytical suggestions and include more recent data that strengthen the inference we draw from our results.</p> <p>2. The five main analytical suggestions we received were to 1) exclude from analysis data that were collected during the period when automated curtailment was only partially implemented; 2) only analyze data from a single make and model of turbine; 3) evaluate changes in the rate of fatality, instead of the yearly numbers of fatalities that result from fluctuations around that rate; 4) calculate the standard measure determining effects of a treatment in a before-after-control-impact study; and, 5) examine yearly fluctuations of the fatality rate during the before period.</p> <p>3. After incorporating these suggestions and including additional data collected since the prior paper was published, our results confirm prior work. We demonstrate that eagle fatalities were reduced by 85% (95% highest density interval = 12%, 100%) after implementation of automated curtailment. Rate of fatalities declined by 2.85 eagles per year (-0.67, 5.70) between before and after periods at the treatment site and increased by 2.26 eagles per year (-7.37, 1.77) at the control site. Overall, the fatality rate declined by 4.91 (-0.27, 11.27) more eagles per year at the treatment site than at the control site. The probability that the fatality rate declined at the treatment site relative to the control site was 0.97.</p> <p>4. Our re-analysis strengthens our inference by using more robust analyses and data to support the conclusions of the prior study suggesting that automated curtailment was effective at reducing eagle fatalities at our treatment site. Because of the site- and species-specific nature of our work, future research should examine the efficacy of automated curtailment at other sites, with other species, and under different curtailment regimes.</p>
Fig. 1 in Apparent fatal winter tick (Dermacentor albipictus) infestation in captive reindeer (Rangifer tarandus)
Fig. 1. Captive reindeer skin densely infested by Dermacentor albipictus.
Fig. 8 in Fatal toxoplasmosis in Little Penguins (Eudyptula minor) from Penguin Island, Western Australia
Fig. 8. Dividing protozoan forming merozoites (x15000, bar = 1 μm).
Fig. 7 in Fatal toxoplasmosis in Little Penguins (Eudyptula minor) from Penguin Island, Western Australia
Fig. 7. Protozoan with apical complex (x15000, bar = 1 μm).
Fig. 2 in Fatal toxoplasmosis in Little Penguins (Eudyptula minor) from Penguin Island, Western Australia
Fig. 2. Enlarged spleen with speckled tan areas of discolouration.
Fig. 1 in Fatal toxoplasmosis in Little Penguins (Eudyptula minor) from Penguin Island, Western Australia
Fig. 1. Enlarged liver with multiple, pinpoint cream to white spots.
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
Dataset about discretionary damages of mental suffering in fatal car accident cases in Taiwan
<p>We use the Taiwan Judicial Yuan Law and Regulations Retrieving System to search and collect the judgments from 2006 to 2020 in Taiwan Taichung District Court, and then we delete the irrelevant judgments such as judgments against the plaintiff (without assessing the mental suffering damages). Furthermore, this dataset only focuses on the judgments with one victim and one defendant, excluding the judgments with more than one victim or defendant.</p> <p>Next, we take one plaintiff as one data unit, and we extract the legal factors from the judgments and take them as features in the dataset. Apart from the legal factors which judges considered while assessing the mental suffering damages, we add some extra features which we think important and relevant to the assessment of mental suffering dam- ages. For example, we add some features that might affect the discretionary damages, including the total number of plaintiffs who are victim’s parents in the same case, the total number of plaintiffs who are victim’s children in the same case, and the total number of plaintiffs in the same case.</p> <p>Besides, we extract the discretionary damages from the judgments and take them as the target values (𝑦 vector) in the dataset.</p> <p>Finally, there are some features in the dataset lacking the unified unit, so we need to determine the unified unit for those feature columns.</p> <p>To sum up, the dataset contains 483 observations and 34 features</p>
How User Language Affects Conflict Fatality Estimates in ChatGPT, Query script and dataset
<p>*both authors contributed equally</p> <p>Automated query script for automated language bias studies in GPT 3-5</p> <p>Dataset of the paper "How User Language Affects Conflict Fatality Estimates in ChatGPT" preprint available on ArXiv</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.