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10,356 results for “Severity”
Data from: Long-term severe hypoxia adaptation induces non-canonical EMT and a novel Wilms Tumor 1 (WT1) isoform
<p>The majority of cancer deaths are caused by solid tumors, where the four most prevalent cancers (breast, lung, colorectal and prostate) account for more than 60% of all cases (1). Tumor cell heterogeneity driven by variable cancer microenvironments, such as hypoxia, is a key determinant of therapeutic outcome. We developed a novel culture protocol, termed the Long-Term Hypoxia (LTHY) time course, to recapitulate the gradual development of severe hypoxia seen in vivo to mimic conditions observed in primary tumors. Cells subjected to LTHY underwent a non-canonical epithelial to mesenchymal transition (EMT) based on miRNA and mRNA signatures as well as displayed EMT-like morphological changes. Concomitant to this, we report production of a novel truncated isoform of WT1 transcription factor (tWt1), a non-canonical EMT driver, with expression driven by a yet undescribed intronic promoter through hypoxia-responsive elements (HREs). We further demonstrated that tWt1 initiates translation from an intron-derived start codon, retains proper subcellular localization and DNA binding. A similar tWt1 is also expressed in LTHY-cultured human cancer cell lines as well as primary cancers and predicts long-term patient survival. Our study not only demonstrates the importance of culture conditions that better mimic those observed in primary cancers, especially with regards to hypoxia, but also identifies a novel isoform of WT1 which correlates with poor long-term survival in ovarian cancer.</p>
Fig. 4 in Can artificial retreat sites help frogs recover after severe habitat devastation? Insights on the use of "coqui houses" after Hurricane Maria in Puerto Rico
Fig. 4. Artificial coqui houses built with (A) bamboo shoots and (B) PVC tubes.
Monthly production and open-circuit string voltage measurements after 10-year operation of three photovoltaic plants in Southern Spain affected by severe potential-induced degradation
<p>Data are formated in a spreadsheet file. Data are presented from three photovoltaic plants in Southern Spain (Córdoba province - Northern Andalucía) designed and installed by the same person, with the same photovoltaic module and the same model of inverter, deployed at the same time (end 2009). The plants are severely affected by potential-induced degradation (PID), so that secondary effects produce some by-pass diodes to activate, producing three families of open-circuit voltage (Voc) in the modules (~40V), (~26V) and (~12V) of a total of nominal Voc of 42,6V. </p> <p>Monthly production is shown along 11 years (2010-2021), and the measurements of the open-circuit voltage of the strings after 10 years of operation along with the voltage range of the modules of each string.</p> <p>Sheet 1: configuration of the architecture of the three photovoltaic plants and the features of the photovoltaic module installed.</p> <p>Sheet 2: energy production for 11 years of opetation. In the last years some recovery is shown in plant 1 and 2 because a repowering project.</p> <p>Sheet 3: partial climate data of the towns were the plants are located.</p> <p>Sheet 4: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V) and (<=26V) in plant 1, july-2018.</p> <p>Sheet 5: open-circuit voltages of the strings and number of modules in each string with open-circuit voltage in the ranges (~40V), (~26V) and (~12V) in plant 2, july-2020.</p>
Data from: Severity of topsoil compaction controls the impact of skid trails on soil ecological processes
<p>Skid trails are a major management-induced disturbance in temperate forest ecosystems with considerable impact on soil ecological processes that are so far poorly understood. In German forests, skid trails comprise 10 – 20 % of the forest area that is potentially affected by soil compaction through heavy machinery. We systematically investigated the influence of skid trails on physical, chemical, and microbiological soil parameters at 84 paired plots across four Central European forest types. In low mountain forests with steeper topography, skid trails had more drastic effects than in lowland forests. Skid trails in low mountain areas showed a decrease in the C to N ratio of microbial biomass (MBC/MBN), as well as increased microbial (MBC/SOC) and enzyme activities leading to faster carbon turnover (lower C/N, EOC/EN) and increased CO<sub>2</sub> losses (CO<sub>2</sub>/SOC) from the soil. The overall effects of the skid trails in lowland forests were small. On base-poor soils, we found an increase in the MBC/MBN ratio, while skid trails in base-rich lowland soils showed a reduction in CO<sub>2</sub>/SOC, suggesting a proportional increase in soil carbon storage. Regardless of region-specific effects, the relative increase in the bulk density of the fine soil was identified as a 'golden trait' that determined the effects of skid trails on many soil parameters, as shown by negative correlations with SOC, N, MBC, MBN, MBP, MBC/SOC and CO<sub>2</sub>/SOC and positive ones with the activities of certain hydrolytic enzymes.</p> <p>Synthesis and Applications: Our data clearly showed that carbon conversion processes and soil respiration leading to significant carbon and nutrient losses increased significantly on skid trails in low mountain regions with relatively steep slopes, which was in sharp contrast to lowland sites. The strong context dependence of our findings suggests that the mapping of soil conditions in terms of slope, substrate and moisture with high spatial resolution is mandatory to assess the vulnerability of sites to soil compaction by heavy machinery. Based on such vulnerability analysis, negative impacts can be minimized through the designation of permanently fixed skid trails, the technical adaptation of vehicles (e.g., wide base tyres) as well as careful planning and timing of management operations that should be restricted to dry weather and soil moisture conditions or periods of frost.</p>
Ocean variability drives severe increases in heavy rainfall in the Yellow River Basin-Data availability part
Open the record for dataset details and reuse information.
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Fig. 4 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 4 — Extent of damages documented off the Nagapattinam coast, Tamil Nadu
Fig. 3 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 3 — Cyclone shelters in the villages showing distance to access
Fig. 6 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 6 — Periodicity and usage frequency of the weather forecast
Fig. 5 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 5 — Percentage variations in respondents using different source of forecast
Fig. 1 in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 1 — Study area – Nagapattinam coast. Source: Central ground water board, Tamil Nadu
Fig. 2 — a in Very severe cyclonic storm "Gaja" and its impact off the Tamil Nadu coastline
Fig. 2 — a) Percentage variability of total fishing population; and b) affected individuals
Datasets used in "A 23-Year Severe Hail Climatology Using GridRad MESH Observations"
<p>These datasets are those used in the Murillo et al. (2021) publication in the AMS journal of Monthly Weather Review. For any questions, please email Dr. Murillo at emurillo@ucar.edu.</p>
SeverusPT - A multi-source burn severity dataset for mainland Portugal
<h3>Abstract (EN)</h3> <p>The SeverusPT project aims to periodically and timely provide relevant and standardized information on burn severity supported by satellite and field observations. Key objectives include developing a spatially explicit framework for assessing, mapping, and predicting burn severity and delivering a co-designed product/service to enhance institutional and operational capacity for fire hazard management and post-fire ecosystem restoration.</p> <p>The project currently provides standardized satellite-based datasets on mainland Portugal’s observed/historical burn severity, leveraging multiple satellite missions (Sentinel-2, Landsat, MODIS), spectral indices (e.g., Normalized Burn Ratio – NBR, Tasseled Cap Transformation – TCT), and burn severity indicators. The datasets are derived from pre-calculated severity products through algorithms that integrate satellite image time series (SITS) in two main approaches: (i) a delta-based pipeline, employing “classical” severity measurements (e.g., delta NBR) and focused primarily on high spatial resolution satellites; and (ii) a trajectory-based pipeline supported by SITS and the analysis of post-fire trajectories for multiple dimensions of ecosystem functioning and primarily focusing on high-temporal/moderate spatial resolution satellites.</p> <p>Field assessments, critical for validating satellite products and obtaining nuanced results regarding post-fire effects, were used to provide information on burn severity across different structural components of vegetation. The project used a purposive stratified approach for field surveys, focusing on the 2022 fire season across mainland Portugal. Selection criteria based on fire size, location, main vegetation type, and other ancillary layers enabled comprehensive coverage and diversity in post-fire conditions. Approximately 111 sites in 28 burned areas were surveyed in north and centre Portugal (the wildfire foci in the country) using the Geometrically Structured Composite Burn Index (GeoCBI) protocol. Two methods were used to validate the delta-based products by comparing in situ GeoCBI and satellite burn severity estimates: (i) non-parametric linear correlation (Spearman method) and nonlinear correlation; and (ii) a nonlinear exponential model adapted from pre-existing studies.</p> <p>Delta-based SeverusPT products agreed well with GeoCBI field measures of burn severity. The best linear correlation results were bounded between 0.64 and 0.71. Sentinel-2 and the NBR spectral index with RBR generally ranked higher when compared to Landsat-8. For nonlinear correlation, results were between 0.65 and 0.76, with the best results for Landsat-8 TCTG, closely followed by Sentinel-2 NBR spectral index with RDT or RBR indicators. The results for the nonlinear model validation were similar, with the best marks attained by the RdNBR, RBR, and dNBR indicators (R2= 0.64, 0.62, and 0.60, respectively).</p> <p>The project’s data portal is a centralized gateway for accessing and downloading project data and metadata. It offers two primary levels of data access: Level 1 includes the products, and Level 2 comprises image data files along with metadata. The trajectory-based pipeline and products are still under active development and will be added to the SeverusPT Data Portal.</p> <p>SeverusPT builds on a comprehensive approach combining satellite data with rigorous field validation, yielding significant insights into wildfire severity. The project’s innovative methodologies and the data portal’s accessibility contribute to the field of wildfire severity assessment, offering valuable data and tools for fire management and prioritizing post-fire mitigation and recovery strategies.</p> <p><strong>SeverusPT Data Products Manual</strong>: <a href="https://doi.org/10.5281/zenodo.10640961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10640961</a></p> <p>___</p> <h3>Resumo (PT)</h3> <p>O projecto SeverusPT tem como objetivo disponibilizar, de forma regular e atempada, informação relevante e pradonizada da severidade da área ardida, baseada em dados de satélite e observações no terreno. Os principais objetivos incluem o desenvolvimento de uma moldura de análise espacialmente explícita para avaliar, mapear e prever a severidade da área ardida, fornecendo um produto/serviço resultante de co-desenho para melhorar a capacidade institucional e operacional para a gestão do risco de incêndio e o restauro dos ecossistemas pós-incêndio.</p> <p>Atualmente este projeto disponibiliza conjuntos de dados derivados de imagens de satélite para Portugal Continental acerca da severidade histórica/observada da área ardida. Estes conjuntos de dados fazem uso de informação proveniente de múltiplas missões espaciais (Sentinel-2, Landsat, MODIS), índices espetrais (p.ex. “Normalized Burn Ratio” – NBR, “Tasseled Cap Transformation” – TCT), e indicadores da severidade da área ardida. Os conjuntos de dados derivam de produtos pré-calculados de severidade através de algoritmos que integram séries temporais de imagens de satélite (SITS) em duas abordagens principais: (i) a cadeia de processamento (“pipeline”) baseado em deltas, que implementa medidas “clássicas” de severidade (p.ex. delta-NBR) e que se foca principalmente em satélites de alta resolução espacial; e (ii) a cadeia de processamento baseado em trajetórias, que se baseia na análise de trajetórias pós-incêndio para múltiplas dimensões do funcionamento dos ecossistemas e que se foca em dados de satélite de alta resolução temporal e resolução espacial moderada.</p> <p>No sentido de fornecer informação acerca da severidade da área ardida em vários componentes estruturais da vegetação, foram utilizados dados recolhidos no terreno, os quais são cruciais para validar produtos derivados de imagens de satélite e obter resultados pormenorizados relativamente a efeitos pós-incêndio. No âmbito do projeto foi usada uma abordagem estratificada para efetuar os levantamentos no terreno, focada na época de incêndios de 2022 em Portugal Continental. Critérios de seleção baseados no tamanho da área ardida, localização, tipo de vegetação principal e outras camadas auxiliares de informação permitiram cobrir uma maior diversidade de condições pós-incêndio. Foram visitados aproximadamente 111 locais pertencentes a um total de 28 áreas ardidas no Norte e Centro de Portugal Continental (as zonas do país mais afetadas por incêndios), usando o protocolo “Geometrically Structured Composite Burn Index” (GeoCBI). Foram utilizados dois métodos para validar os produtos baseados em deltas, através da comparação do GeoCBI in situ com estimativas obtidas por satélite: (i) correlações lineares não paramétricas (método de Spearman) e correlações não-lineares; e (ii) um modelo exponencial não-linear adaptado de estudo pré-existentes.</p> <p>Os produtos SeverusPT baseados em deltas apresentaram elevada concordância com as medidas de severidade da área ardida recolhidas no terreno através do GeoCBI. Os valores resultantes mais elevados para a correlação linear situaram-se entre 0,64 e 0,71. Foram obtidos valores geralmente mais elevados para Sentinel-2 e para o índice espetral NBR e o indicador de severidade RBR, em comparação com os resultados obtidos para Landsat-8. Relativamente às correlações não-lineares, foram obtidos valores entre 0,65 e 0,76, tendo os valores mais elevados sido obtidos para o índice espetral TCTG derivado de Landsat-8, seguido do índice espetral NBR derivado de Sentinel-2 com os indicadores de severidade RDT ou RBR. Foram obtidos resultados semelhantes para a validação através de modelo não-linear, com os valores mais elevados correspondendo aos indicadores de severidade RdNBR, RBR e dNBR (R2 = 0,64, 0,62 e 0,60, respetivamente).</p> <p>O portal de dados do projeto constitui uma via de acesso centralizada para a visualização e descarregamento de dados e metadados do projeto, oferecendo dois níveis primários de acesso: o Nível 1 inclui os produtos e o Nível 2 é constituído por ficheiros de imagens e metadados. Os produtos provenientes da cadeia de processamento baseada em trajetórias estão ainda em desenvolvimento activo e serão adicionados posteriormente no Portal de Dados do SeverusPT.</p> <p>O SeverusPT assenta numa abordagem abrangente que combina dados provenientes de satélites com uma rigorosa validação baseada em dados recolhidos no terreno, oferecendo uma melhor compreensão da severidade dos incêndios. As suas metodologias inovadoras e a acessibilidade do seu portal de dados contribuem para o campo da avaliação da severidade dos incêndios, providenciando dados e ferramentas valiosos para a gestão do fogo e para a priorização de estratégias de mitigação e recuperação pós-incêndio.</p> <p> </p>
Figure 2 in Biomedical role of L-carnitine in several organ systems, cellular tissues, and COVID-19
Figure 2. The role of L-carnitine in fatty acid metabolism in mitochondria (Wang et al., 2021b).
Regional differences in leaf evolution facilitate photosynthesis following severe drought
<p>Characterizing physiological and anatomical changes that underlie rapid evolution following climatic perturbation can broaden our understanding of how climate change is affecting biodiversity. It can also provide evidence of cryptic adaptation despite stasis at higher levels of biological organization.</p> <p>Here we compared evolutionary changes in populations of <em>Mimulus cardinalis </em>from historically different climates in the north and south of the species' range following an exceptional drought. We grew seeds produced from pre-drought ancestral plants alongside peak-drought descendants in a common greenhouse and exposed them to wet and dry conditions.</p> <p>Prior to the drought, northern ancestral populations expressed traits contributing to drought escape, while southern ancestral populations expressed drought avoidance. Following the drought, both regions evolved to reduce water loss and maintain photosynthesis in dry treatments (drought avoidance), but via different anatomical alterations in stomata, trichomes, and palisade mesophyll. Additionally, southern populations lost the ability to take advantage of wet conditions.</p> <p>These results reveal rapid evolution towards drought avoidance at an anatomical level following an exceptional drought, but suggest that differences in the mechanisms between regions incur different trade-offs. This sheds light on the importance of characterizing underlying mechanisms for downstream life-history and macromorphological traits.</p>
Research Data - Microstructural and material property changes in severely deformed Eurofer-97
<p>Research data and associated processing and plotting scripts for the article:</p> <p>Song <em>et al</em>., 'Microstructural and material property changes in severely deformed Eurofer-97', <em>Materials Characterization</em>, 114144, 2024</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.matchar.2024.114144" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.matchar.2024.114144</span></span></a></p>
Fig. 1 in Feeding behavior of Diaphorina citri (Hemiptera: Liviidae) and its acquisition of 'Candidatus Liberibacter asiaticus', on huanglongbing-infected Citrus reticulata leaves of several maturity stages
Fig. 1. Sketches of citrus leaves at several maturity stages.
Data from a field plot experiment with the canola pathogen Leptosphaeria maculans including disease severity at the leaf spot and canker stages of the epidemic, and population composition as isolates infectivity pathotypes.
<p><strong>Data set</strong></p> <p>Data are from an experiment simulating how differences in <em>Brassica napus</em> resistance deployment strategies and landscape connectivity influence epidemic severity and pathogen population composition of the fungus <em>Leptosphaeria maculans</em> on field plots inoculated with combinations of stubble in 2016 at CSIRO Canberra, ACT, Australia. Disease severity was assessed on the 60 field plots [Data_severity.csv] and 1490 isolates were sampled and assessed for infectivity [Data_infectivity.csv]. This dataset is described and analyzed in Bousset et al. (2018).</p> <p>Treatments were factorial combination of Resistance, Genetic Connectivity and Spatial Connectivity, replicated in 4 blocks (B1 to B4). Resistance has 3 categories (Rlm4, Rlm6 LepR1) differing by the resistance genes in oilseed rape varieties. Genetic Connectivity has 2 levels (HighGC, LowGC) differing by the pre-adaptation of the stubble populations to the host variety. Spatial Connectivity has two levels (HighSC, LowSC) differing by the stubble load. Control plots had NoStubble.</p> <p><strong>Data files</strong></p> <p>[Data_severity.csv] Disease severity was assessed on the 60 field plots at leaf spot and canker stages of the epidemic. Leaf spots data are counts. Canker data are numbers of stems in 12 categories defined by the cankered area on cross section (0 = no canker to 100 = fully cankered).</p> <p>[Data_infectivity.csv] Two types of isolates (122 from 3 stubble sources with contrasting preadaptation and 1368 from leaves sampled on 50 field plots) were tested for infectivity response (V = infective; A = non-infective) on the three host varieties (Rlm4, Rlm6 LepR1), at the seedling stage in greenhouse.</p> <p><strong>Associated publication</strong></p> <p>Bousset L, Sprague S, Thrall PH, Barrett LG (2018). Spatio-temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen <em>Leptosphaeria maculans. Evolutionary Applications</em> [ DOI: 10.1111/eva.12630 ].</p> <p><strong>Funding information</strong></p> <p>This work benefited from the financial support of INRA – the French National Institute for Agronomical Research, a CSIRO Sir Frederick McMaster fellowship to L. Bousset (Impact of inoculum carry-over on landscape dynamics of the blackleg canola pathogen) and the Grains Research & Development Corporation (GRDC Grant CSP00192)</p>
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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.