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1,826 results for “burned”
All Differentially Expressed miRNA in burned murine skin tissue compared to normal skin tissue
GEO Series GSE46660. synthetic construct; Mus musculus. 2 samples. Type: Non-coding RNA profiling by array.
A burned-out CD8+ T-cell subset expands in the tumor microenvironment and curbs cancer immunotherapy
GEO Series GSE167235. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Il-1/MyD88-Dependent G-CSF and IL-6 Secretion Mediates Post Burn Anemia
GEO Series GSE214717. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Exclusive heat-not-burn cigarette smoking alters the profile of circulating microRNAs.
GEO Series GSE233237. Homo sapiens. 9 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Non-severe thermal burn injuries induce long lasting gene expression changes in specific cortical neuron and glia cell types
GEO Series GSE229384. Mus musculus. 63 samples. Type: Expression profiling by high throughput sequencing.
Modelling the developmental spliceosomal craniofacial disorder Burn-McKeown syndrome using induced pluripotent stem cells
GEO Series GSE149369. Homo sapiens. 36 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic and behavioural characterisation of a mouse model of burns pain
GEO Series GSE75691. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Modeling Neutrophil Heterogeneity Contribution to Burn Healing in Larval Zebrafish
GEO Series GSE250610. Danio rerio. 6 samples. Type: Expression profiling by high throughput sequencing.
Skeletal Muscle Transcriptome Is Affected By Age In Severely Burned Mice
GEO Series GSE208548. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Role of myeloid cells in heterotopic ossification (HO) in a burn and incision-induced mouse model
GEO Series GSE126060. Mus musculus. 19 samples. Type: Expression profiling by high throughput sequencing.
Combustion products of burn pit constituents induce more changes in murine lungs with pre-existing asthma than naïve lungs
GEO Series GSE281365. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing.
Data set of resilience indices to drought and prescribed burning of Pinus nigra ssp. salzmannii and P. sylvestris L. trees
<p>Data on resilience and resistance indices to drought and burning inferred from tree ring width and carbon 13 isotopes for burned and unburned <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris </em>trees<em>.</em>The dataset contains two files:</p> <p>TreeGrowth.txt: Data on resilience and resistance indices to drought and prescribed burning as well as the ratio of latewood to earlywood. Indices are inferred from total tree-ring, earlywood, and latewood widths. Included variables:</p> <ul> <li> Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li> Idplot (factor): code to identify uniquely burned plots. Four levels: Miravé-Fall (1), Mirave-Spring (2), Lloreda-Fall (3) and Lloreda-Spring (4).</li> <li>Treatment (factor): whether the plot was burned or not. Two levels: control or left unburned (C) or burned (B)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall burn (F) or spring burn (S)</li> <li>Sp (factor): species. Two levels: <em>Pinus sylvestris</em> (ps) or<em> Pinus nigra </em>(pn)</li> <li>TreeCode (numeric): code to identify trees in the field</li> <li>dbh (numeric): diameter at breast height (cm)</li> <li>c12 (numeric): competition index before burning</li> <li>rci15 (numeric): release from tree competition 2 years post-burning calculated as the difference between pre (CI12) and post-burning competition (CI15) indices relative to pre-burning levels </li> <li>bchmin (numeric): minimum bole scorch height (cm)</li> <li>bchmax (numeric): maximum bole scorch height (cm)</li> <li>whiteAshes (numeric): white ashes after burning (%, in 1m radius from tree center)</li> <li>Resistance (numeric): Average basal area increment (BAI) during the stress period (drought 2012 and prescribed burning 2013) divided by the average BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR (numeric): Average total tree ring BAI of the two years after the stress period (2014 and 2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR14 (numeric): Total tree ring BAI of the first year after the stress period (2014) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR15 (numeric): Total tree ring BAI of the second year after the stress period (2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW (numeric): Average earlywood BAI of the two years after the stress period (2014 and 2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW14 (numeric): Earlywood BAI of the first year after the stress period (2014) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW15 (numeric): Earlywood BAI of the second year after the stress period (2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW (numeric): Average latewood BAI of the two years after the stress period (2014 and 2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW14 (numeric): Latewood BAI of the first year after the stress period (2014) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW15 (numeric): Latewood BAI of the second year after the stress period (2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>preLWEW (numeric): mean pre-stress ratio of latewood to earlywood calculated for the period 2006-2011</li> <li>difLWEW (numeric): mean post-stress latewood:earlywood (2014-2015) minus mean pre-stress latewood:earlywood (2006-2011)</li> </ul> <p>d13c.txt: Early and late-wood carbon 13 isotope from 2011 to 2015 for <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris</em> burned in spring and fall in year 2013 at two sites. </p> <ul> <li>Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li>Sp (factor): species. Two levels: Pinus sylvestris (ps) or Pinus nigra (pn)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall (F) or spring (S)</li> <li>TreeCode (factor): code to identify trees in the field."Pool" means that a pool of 5 individuals was used to determine carbon 13 isotope.</li> <li>SeasonalGrowth (factor): seasonal wood growth. Two levels: earlywood (E) and latewood (L).</li> <li>year (numeric): calendar year.</li> <li>d13c (numeric): carbon 13 isotope<br> </li> </ul>
Data from: Effects of nicotine on corneal wound healing following acute alkali burn
Epidemiological studies have indicated that smoking is a pivotal risk factor for the progression of several chronic diseases. Nicotine, the addictive component of cigarettes, has powerful pathophysiological properties in the body. Although the effects of cigarette smoking on corneal re-epithelialization have been studied, the effects of nicotine on corneal wound healing-related neovascularization and fibrosis have not been fully demonstrated. The aim of this study was to evaluate the effects of chronic administration of nicotine on corneal wound healing following acute insult induced by an alkali burn. BALB/C female mice randomly received either vehicle (2% saccharin) or nicotine (100 or 200 μg/ml in 2% saccharin) in drinking water ad libitum. After 1 week, animals were re-randomized and the experimental group was subjected to a corneal alkali burn, and then nicotine was administered until day 14 after the alkali burn. A corneal alkali burn model was generated by placing a piece of 2 mm-diameter filter paper soaked in 1N NaOH on the right eye. Histopathological analysis and the expression level of the pro-angiogenic genes vascular endothelial growth factor (VEGF) and matrix metalloproteinase-9 (MMP9) revealed that chronic nicotine administration enhanced alkali burn-induced corneal neovascularization. Furthermore, the mRNA expression of the pro-fibrogenic factors α-smooth muscle actin (αSMA), transforming growth factor-β (TGF-β), and collagen α1 (Col1) was enhanced in the high-concentration nicotine-treated group compared with the vehicle group after corneal injury. Immunohistochemical analysis also showed that the αSMA-positive area was increased in chronic nicotine-treated mice after corneal alkali burn. An in vitro assay found that expression of the α3, α7, and β1 nicotinic acetylcholine receptor (nAChR) subunits was significantly increased by chemical injury in human corneal fibroblast cells. Moreover, alkali-induced fibrogenic gene expression and proliferation of fibroblast cells were further increased by treatment with nicotine and cotinine. The proliferation of such cells induced by treatment of nicotine and cotinine was reduced by inhibition of the PI3K and PKC pathways using specific inhibitors. In conclusion, chronic administration of nicotine accelerated the angiogenic and fibrogenic healing processes in alkali-burned corneal tissue.
A Global Forest Burn Severity Dataset from Landsat Imagery (2003–2016)
<p>Global Forest Burn Severity database provides information on the amounts of biomass that are consumed by wildfire between 2003 and 2016. Based on the Global Fire Atlas product, when and where forest fires occurred during that period are firstly determined and then overlay the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between pre-fire NBR and post-fire NBR as dNBR and the relative difference as RdNBR. The dNBR and RdNBR for each individual forest fire are saved as GeoTiff file.</p>
BRASA - A merger proposal for burned area in Brazil.
<p><strong>Contact:</strong> <a href="mailto:henri.leaos@gmail.com">henri.leaos@gmail.com</a> | <a href="mailto:paulo.leao@cemaden.gov.br">paulo.leao@cemaden.gov.br</a></p><p><strong>Purpose:</strong> A burned area product that maps the largest extent of burned coverage in Brazil. This asset is the result from the fusion of three datasets at different scales: Global products that offer a broader and more conservative view with a coarser resolution (MCD64A1 - 500m and Fire_CCI 250m), combined with regional products with a finer resolution (Mapbiomas Collection 2 - 30m).</p><p><strong>Coverage:</strong> Brazil</p><p><strong>Period:</strong> 2001 - 2020</p><p><strong>Spatial resolution:</strong> 30-meters</p><p><strong>Temporal resolution:</strong> Annual</p><p><strong>Coordinate reference system:</strong> Geographic Coordinate System with Datum WGS-84 (EPSG:4326)</p><p><strong>File format: </strong>Each zip file contains nine tiles in compressed TIFF format. The pixel values represent the area burned and the origin of the information. </p><blockquote><p><strong>Legend: </strong></p><ul><li>Mapbiomas2 - 1</li><li>Fire_CCI - 3</li><li>Mapbiomas2 + Fire_CCI = 4</li><li>MCD64A1 - 7</li><li>Mapbiomas2 + MCD64A1 = 8</li><li>Fire_CCI + MCD64A1 = 10</li><li>Mapbiomas2 + Fire_CCI + MCD64A1 = 11</li></ul></blockquote><p><strong>Code:</strong> https://doi.org/10.5281/zenodo.10183061</p><p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please cite the repository and our paper correctly. We also welcome users to invite us for collaboration.</p><p><strong>Acknowledgements:</strong> To the MAP-Fire Maranhão Project: FAPESP (process no. 2020/16457-3, 2022/06158-4. ), FAPEMA (No. CACD 02989/20) and CNPq (process no. 409531/2021-9). </p><p><strong>For this dataset, please use the following references:</strong></p><ul><li><i>Paulo Henrique A. Leão et al. Contribuição de produtos de área queimada na Amazônia Maranhense: proposta de avaliações combinadas. Geoinfo. 2022. Disponível em: <</i><a href="http://mtc-m16c.sid.inpe.br/col/sid.inpe.br/mtc-m16c/2022/12.16.16.32/doc/311-316_Leao_contribuicao.pdf"><i>http://mtc-m16c.sid.inpe.br/col/sid.inpe.br/mtc-m16c/2022/12.16.16.32/doc/311-316_Leao_contribuicao.pdf</i></a><i>></i></li><li><i>Eduarda Vaz Braga et al. COMPARISON BETWEEN PRODUCTS FROM BURNT AREAS AND THE RELATIONSHIP WITH LAND USE AND COVERAGE IN THE STATE OF MARANHÃO.. In: ANAIS DO XX SIMPóSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 2023, Florianópolis. <strong>Anais eletrônicos...</strong> São José dos Campos, INPE, 2023. Disponível em: <</i><a href="https://proceedings.science/sbsr-2023/trabalhos/comparison-between-products-from-burnt-areas-and-the-relationship-with-land-use?lang=en"><i>https://proceedings.science/sbsr-2023/trabalhos/comparison-between-products-from-burnt-areas-and-the-relationship-with-land-use?lang=en</i></a><i>>. </i></li></ul><p> </p>
Quinene Burn Site (RGB) - February 14 2024
<p>This orthomosaic of Quinene (Malesso) was created from 1230 images taken on February 19 2024, at 400 ft AGL, with a M350. The mission was planned using the DJI Pilot application and post-processed using DroneDeploy. </p> <p>This project was made possible through funding by the following grant(s): NASA Guam EPSCoR (80NSSC22M0052), NASA Guam Space Grant (80NSSC20M0039 / UH Subaward: MA1550), and PI-CASC (G19AC00087)."</p>
As Gadao Burn Site - RGB (March 21, 2024)
<p>This orthomosaic of As Gadao was created from 816 images taken on March 21 2024, at 400 ft AGL, with a M350. The mission was planned using the DJI Pilot application and post-processed using DroneDeploy. </p> <p>This project was made possible through funding by the following grant(s): NASA Guam EPSCoR (80NSSC22M0052), NASA Guam Space Grant (80NSSC20M0039 / UH Subaward: MA1550), and PI-CASC (G19AC00087)."</p>
Burned Jasper Bead, SJ13
Tubular bead, jasper, unidrectional drill direction, burned, North Florida, Archaic (6200-5200 ya) Part of the Coontie Island Collection, SJ13_227 Model by Emma Dietrich This model was made using RealityCapture software by Capturing Reality. Internal mesh reconstructed with Geomagic Wrap Source: Objaverse 1.0 / Sketchfab
Figure 2 in Effect of reed burning and precipitation on the breeding success of Great Reed Warbler, Acrocephalus arundinaceus, on a mining pond
Figure 2. Mean (±SE error bars) clutch size and number of fledglings at Bager Pond, 2008–2011.
Figure 1 in Effect of reed burning and precipitation on the breeding success of Great Reed Warbler, Acrocephalus arundinaceus, on a mining pond
Figure 1. Kaplan–Meier survival curves of GRW nests in breeding seasons 2008–2011.
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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.