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1,994 results for “Mortality;”
Ash dieback mortality and damage at the Botanic Garden Meise, Belgium, 2016
<p>These data add to the data already published for previous years and are available at Groom, Quentin J. (2015). Ash dieback mortality and damage at the Botanic Garden Meise, Belgium. Zenodo. 10.5281/zenodo.17640.</p> <p>Four 10m × 10m plots were laid out in the naturally regenerating woodland at the Botanic Garden Meise (WGS84: 50° 55ʹ 37ʺ N, 4° 19ʹ 18ʺ E; 50° 55ʹ 37ʺ N, 4° 19ʹ 17ʺ E; 50° 55' 38.6" N 4° 19ʹ 21ʺ E; 50° 55ʹ 39ʺ N, 4° 19ʹ 29ʺ E). They were selected because the areas contained a large number of ash saplings. Within these plots all ash seedlings greater than 40 cm tall were labelled with a small (2 cm × 4 cm) plastic tag attached with stretchable plant tie. Each tag was engraved with a unique number so that the tree could be identified. These plots were not intended to be replicates but just a convenient method of refinding the tagged trees. In the first year either the height or the girth of the tree was measured with a tape measure, depending upon whether the tree was small enough to measure the height. In 2013 and each subsequent year each tree was scored for the apparent damage caused by ash dieback (<em>Hymenoscyphus pseudoalbidus</em>). The same scoring scheme was used as that by Pliūra et al. (2011). This is a 5 point system where 1 is a dead tree; 5 is an undamaged tree and 2–4 are progressively less damaged trees. In 2016 the plots were scored on 4<sup>th</sup> May.</p>
Honey bee Seasonal mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution.The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding seasonal mortality. The dataset comprises 4758 observations from apiaries across Europe.</p>
Honey bee Winter mortality 2012-2014 - Epilobee analysis
<p>EPILOBEE was the first active epidemiological surveillance program implemented in 17 EU Member States, over 2 consecutive years (from autumn 2012 to summer 2014), following a harmonised protocol based on the EU reference laboratory guidelines. EFSA requested a statistical analysis on the EPILOBEE dataset to establish associations between colony mortalities and some factors including disease prevalence, the context of beekeeping and the apiary geographical distribution. The data set published is the result of the data cleaning and categorization performed on the EPILOBEE original dataset regarding winter mortality. The dataset comprises 4758 observations from apiaries across Europe.</p> <p>The present dataset has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. </p> <p>The dataset is in EXCEL format.</p>
Data used in the article "Cryptic disease-induced mortality may cause host extinction in an apparently-stable host-parasite system"
<p>These data files include all the capture-history matrices that were used in the article, including two matrices with the age of captured individuals (adults or juveniles) according to the definition presented in the main text. Infection intensity (zoospore equivalents per swab) is provided in separate files for all <em>Rhinoderma darwini</em>i and<em> Eupsophus contulmoensis</em> individuals that tested positive for <em>Batrachochytrium dendrobatidis</em> infection. Also, the R code used for the fully paramaterized matrix population model 1 (including figures) is provided. Other codes used to analyze our data, especifically capture-recapture models, were obtained from Kéry and Schaub 2012 (<em>Bayesian population analysis using WinBUGS. A hierarchical perspective</em>. Waltham, USA: Academic Press.)</p>
Age-adjusted Covid-19 mortality rates for Brazilian municipalities
<p>This dataset present Covid-19 crude and age-adjusted mortality rates for Brazilian municipalities from 2020 to 2022 per epidemiological week, on 100,000 inhabitants base. </p><p>The mortality data source is the "Sistema de Informações de Mortalidade -- SIM", available at https://opendatasus.saude.gov.br/dataset/sim .</p><p>The population reference is the Brazilian age-structure at 2020.</p><p>Notebook with method and code: https://rfsaldanha.github.io/posts/std_br_covid_rates.html</p>
Russian Short-Term Mortality Fluctuations database
<p><strong>1. Database contents</strong></p><p>The Russian Short-Term Mortality Fluctuations database (RusSTMF) contains a series of standardized and crude death rates for men, women and both sexes for Russia as a whole and its regions for the period from 2000 to 2021.</p><p>All the output indicators presented in the database are calculated based on data of deaths registered by the Vital Registry Office. The weekly death counts are calculated based on depersonalized individual data provided by the Russian Federal State Statistics Service (Rosstat) at the request of the HSE. Time coverage: 03.01.2000 (Week 1) – 31.12.2021 (Week 1148)</p><p><strong>2. A brief description of the input data on deaths</strong></p><p><i>Date of death:</i> date of occurrence</p><p><i>Unit of time</i>: week</p><p><i>First and last days of the week:</i> Monday – Sunday</p><p><i>First and last week of the year:</i> The weeks are organized according to ISO 8601:2004 guidelines. Each week of the year, including the first and last, contains 7 days. In order to get 7-day weeks, the days of previous years are included in this first week (if January 1 fell on Tuesday, Wednesday or Thursday) or in the last calendar week (if December 31 fell on Thursday, Friday or Saturday).</p><p><i>Age groups:</i> the entire population</p><p>Sex: men, women, both sexes (men and women combined)</p><p><i>Restrictions and data changes</i>: data on deaths in the Pskov region were excluded for weeks 9-13 of 2012</p><p><i>Note: </i>Deaths with an unknown date of occurrence (unknown year, month, or day) account for about 0.3% of all deaths and are excluded from the calculation of week-age-specific and standardized death rates.</p><p><strong>3. Description of the week-specific mortality rates data file</strong></p><p>Week-specific standardized death rates for Russia as a whole and its regions are contained in a single data file presented in .csv format. The format of data allows its uploading into any system for statistical analysis. Each record (row) in the data file contains data for one calendar year, one week, one territory, one sex. </p><p>The decimal point is dot (.) </p><p>The first element of the row is the territory code ("PopCode" column), the second element is the year ("Year" column), the third element ("Week" column) is the week of the year, the fourth element ("Sex" column) is sex (F – female, M – male, B – both sexes combined). This is followed by a column "CDR" with the value of the crude death rate and "SDR" with the value of the standardized death rate. If the indicator cannot be calculated for some combination of year, sex, and territory, then the corresponding meaningful data elements in the data file are replaced with ".".</p>
Data from: Physiological mortality rates of planktonic ciliates
<p>Contrasting physiological mortality with predator-induced mortality is of tremendous importance for the population dynamics of many organisms but is difficult to assess. I performed a meta-analysis using planktonic ciliates as model organisms to estimate the maximum physiological mortality rates (δmax) across pelagic ecosystems in relation to environmental and biotic factors. Data were compiled from published numerical response (NR) experiments and experimentally determined rates of decline (ROD). Variables reported are ciliate species and order, ciliate specific growth rates (r<sub>max</sub>), prey species, temperature, habitat (marine vs freshwater), the coefficients of the numerical response experiments, and reported or calculated ciliate mortality rates. The median δ<sub>max</sub> of planktonic ciliates was 0.62 d<sup><span>−</span>1</sup> and did not differ between marine and freshwater species. Maximum ciliate mortality rates were species-specific and affected by their r<sub>max</sub>, cell volume, and ability to encyst. Cyst-forming species had, on average, higher δ<sub>max</sub> than species unable to encyst. Maximum mortality rates of ciliates were positively related to r<sub>max </sub>but appeared unaffected by temperature. I conclude that (i) in the ocean, physiological mortality is more critical for controlling ciliate population size than ciliate losses imposed by microcrustacean predation, but (ii) in many lakes, the opposite holds; (iii) cyst-formation is an effective ciliate trait to cope with the high mortality of motile cells upon starvation. The lack of a temperature effect on δmax deserves further study; if correct, planktonic ciliates may take advantage of rising ocean and lake temperatures, with important implications for the pelagic food web.</p>
Figure 4 in Individual growth and mortality of Rhithropanopeus harrisii (Decapoda: Panopeidae) in the estuarine region of Patos Lagoon, Southern Brazil
Figure 4. Size-converted catch curve. Significant fit (Fcalc.= 239.81> Fcrit.0.05 1,11 = 4.84; R2 = 0.95).
PI3Kg inhibition circumvents inflammation and mortality in SARS-CoV-2 and other infections
<p>Virulent infectious agents such as SARS-CoV-2 and Methicillin Resistant <em>Staphylococcus Aureus</em> (MRSA) induce tissue damage that recruits neutrophils and monocyte/macrophages that promote T cell exhaustion, fibrosis, vascular leak, epithelial cell depletion, and fatal organ damage. Neutrophils and macrophages recruited to pathogen infected lungs, including SARS-CoV-2 infected lungs, express phosphatidylinositol 3-kinase gamma (PI3Kg), a signaling protein that coordinately controls granulocyte and monocyte trafficking to diseased tissues and immune suppressive, pro-fibrotic transcription in myeloid cells. PI3Kg deletion and inhibition with the clinical PI3Kg inhibitor eganelisib promoted survival in models of infectious diseases, including SARS-CoV-2 and MRSA, by suppressing inflammation, vascular leak, organ damage and cytokine storm. These results demonstrate essential roles for PI3Kg in inflammatory lung disease and support the potential use of PI3Kg inhibitors to suppress inflammation in severe infectious diseases.</p>
Figure 4. Alpheus brasileiro Anker, 2012. A in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 4. Alpheus brasileiro Anker, 2012. A, Cohorts identified during sampling describing the growth of each sex. B, Bertalanffy´s equation parameters estimated for males and females. The central line = mean; external lines = prediction intervals (95%).
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50% of females reach functional sexual maturity (CL50).
Figure 3. Alpheus brasileiro Anker, 2012 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 3. Alpheus brasileiro Anker, 2012. Size–frequency distribution of individuals both sexually immature (2.5 to 4.5 mm CL) and sexually mature (5.5 to 9.5 mm CL). Undifferentiated individuals (white bars), males (black bars) and females (dark grey bars). The values of morphological sexual maturity (4.9 and 4.7 mm CL for males and females respectively) are from the study of population structure and relative growth with the same population (Pescinelli et al., 2018a).
Figure 2. A in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 2. A, Lateral view of an ovigerous female of Alpheus brasileiro Anker, 2012; B, sampling area at the intertidal zone of the estuary of Cananéia, São Paulo, south–eastern Brazil.
Figure 1 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 1. Location of the study area, water represented by dark grey in the map of the intertidal estuarine zone of Cananéia, São Paulo, south–eastern Brazil. Adapted from Pescinelli et al. (2017a).
Data for: Competition, prey, and mortalities influence gray wolf group size
<p>Data and R code for "Competition, prey, and mortalities influence gray wolf group size" by Sells et al. (2022, Journal of Wildlife Management). The datasets can be used with the included R code to re-create analyses and figures from Sells et al. (2022). The metadata file describes each column in the datasets.</p>
Diabetes-related excess mortality in Mexico: a comparative analysis of national death registries between 2017-2019 and 2020
<p>Dataset to replicate the article "Diabetes-related excess mortality in Mexico: a comparative analysis of national death registries between 2017-2019 and 2020" available in MedRxiv at: https://www.medrxiv.org/content/10.1101/2022.02.24.22271337v1</p> <p>Code available at: https://github.com/oyaxbell/diabetes_excess</p>
Fig. 3 in Road Mortality Of Carnivores (Mammalia, Carnivora) In Belarus
Fig. 3. Seasonal dynamics of mortality rates of carnivorous mammals on national highways in Belarus, 2007– 2018.
Fig. 6. Amphibian fences with bucket-traps along T1425 in Mortality Of Amphibians On The Roads Of Lviv Region (Ukraine): Trend For The Last Decade
Fig. 6. Amphibian fences with bucket-traps along T1425 road section in Roztochia Nature Reserve (April, 2018).
Fig. 4 in Mortality Of Amphibians On The Roads Of Lviv Region (Ukraine): Trend For The Last Decade
Fig. 4. The example of degradation of amphibian breeding pond (Strilkovychi village; 49.497690 23.141543).
Fig. 3 in Mortality Of Amphibians On The Roads Of Lviv Region (Ukraine): Trend For The Last Decade
Fig. 3. The distribution of amphibian mortality (%) based on the distance between their breeding habitat and the nearest road section.
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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.