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739 results for “data loss”
Data from: Habitat connectivity is determined by the scale of habitat loss and dispersal strategy
Understanding factors that ameliorate the impact of habitat loss is a major focus of conservation research. One key factor influencing species persistence and evolution is the ability to disperse across increasingly patchy landscapes. Here we ask whether interpatch distance (a proxy for habitat loss) and dispersal strategy can interact to form thresholds where connectivity breaks down. We assayed dispersal across a range of interpatch distances in fruit flies carrying allelic variants of a gene known to underlie differences in dispersal strategy. Dispersal-limited flies experienced a distinct negative threshold in connectivity at greater interpatch distances, and this was not observed in more dispersive flies. Consequently, this differential response of dispersal-limited and more dispersive flies to decreasing connectivity suggests that habitat loss could have important implications on the evolution and maintenance of genetic variation underlying dispersal strategy.
Data from: Lipid profiles and obesity as potential risk factors of sudden sensorineural hearing loss
Objectives: The objective of our study was to establish whether increased lipid profiles and obesity affect the prevalence and prognosis of sudden sensorineural hearing loss (SSNHL). Methods: This was a case-controlled study with a longitudinal design. According to our criteria, 324 patients with SSNHL were included in this study. To manage potential covariates, 972 subjects with normal hearing from the Korean National Health and Nutrition Examination Survey were matched as control group according to their propensity scores. Age, level of total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and body mass index (BMI) were obtained from the clinical data. Multivariate logistic regression analysis was used to investigate the association between SSNHL and lipid profiles or obesity in the 1296 subjects. Multivariate Cox regression analysis was used to determine whether lipid profiles and obesity are prognostic factors in patients with SSNHL. Results: Mean body weight, BMI, TC, and TG were significantly higher in patients with SSNHL compared with control subjects (p<0.05). However, LDL-C values did not differ significantly between the two groups. Subjects with elevated TC and TG levels had a 2.20- (95% CI 1.50–3.24) and 1.50-fold (95% CI 1.08–2.08) increased odds, respectively, of SSNHL compared with subjects with normal TC and TG levels. Subjects with grade III BMI had a 1.59-fold (95% CI 1.17–2.16) increased odds of SSNHL. Multivariate Cox regression analyses revealed that BMI was an independent risk factor of treatment outcome, as patients with BMI ≥27.5 were less likely to achieve complete recovery than those with BMI <27.5 (p<0.05). Conclusions: The results of this study revealed that elevated TC and TG levels and increased BMI are significantly associated with the prevalence of SSNHL and its prognosis, indicating that vascular compromise may play an important role in the pathogenesis of SSNHL.
Data and script for "rec-1 loss of function increases recombination in the central gene clusters at the expense of autosomal pairing centers."
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Data and code for the paper titled "Tibetan Plateau Warming Amplification Boosts Arctic Sea-ice Loss"
<p>This is the data and code for the paper titled "Tibetan Plateau Warming Amplification Boosts Arctic Sea-ice Loss", which is to be submitted for peer review.</p>
Data from: Canine mammary tumours are affected by frequent copy number aberrations, including amplification of MYC and loss of PTEN
Background: Copy number aberrations frequently occur during the development of many cancers. Such events affect dosage of involved genes and may cause further genomic instability and progression of cancer. In this survey, canine SNP microarrays were used to study 117 canine mammary tumours from 69 dogs. Results: We found a high occurrence of copy number aberrations in canine mammary tumours, losses being more frequent than gains. Increased frequency of aberrations and loss of heterozygosity were positively correlated with increased malignancy in terms of histopathological diagnosis. One of the most highly recurrently amplified regions harbored the MYC gene. PTEN was located to a frequently lost region and also homozygously deleted in five tumours. Thus, deregulation of these genes due to copy number aberrations appears to be an important event in canine mammary tumour development. Other potential contributors to canine mammary tumour pathogenesis are COL9A3, INPP5A, CYP2E1 and RB1. The present study also shows that a more detailed analysis of chromosomal aberrations associated with histopathological parameters may aid in identifying specific genes associated with canine mammary tumour progression. Conclusions: The high frequency of copy number aberrations is a prominent feature of canine mammary tumours as seen in other canine and human cancers. Our findings share several features with corresponding studies in human breast tumours and strengthen the dog as a suitable model organism for this disease.
Data from: Island biodiversity in peril: anticipating a loss of mammals' functional diversity with future species extinctions
<p>Islands are biodiversity hotspots that host unique assemblages. However, a substantial proportion of island species are threatened and their long-term survival is uncertain. Identifying and preserving vulnerable species has become a priority, but it is also essential to combine this information with other facets of biodiversity like functional diversity, to understand how future extinctions might affect ecosystem stability and functioning. Focusing on mammals, we (i) assessed how much functional space would be lost if threatened species go extinct, (ii) determined the minimum number of extinctions which would cause a significant functional loss, (iii) identified the characteristics (e.g., biotic, climatic, geographic, or orographic) of the islands most vulnerable to future changes in the functional space, and (iv) quantified how much of that potential functional loss would be offset by introduced species. Using trait information for 1,474 mammal species occurring in 318 islands worldwide, we built trait probability density functions to quantify changes in functional richness and functional redundancy in each island if the mammals categorized by IUCN as threatened disappeared. We found that the extinction of threatened mammals would reduce the functional space in 63% of the assessed islands, although these extinctions in general would cause a reduction of less than 15% of their overall functional space. Also, on most islands, the extinction of just a few species would be sufficient to cause a significant loss of functional diversity. The potential functional loss would be higher on small, isolated and/or species rich islands and, in general, the functional space lost would not be offset by introduced species. Our results show that the preservation of native species and their ecological roles remains crucial for maintaining the current functioning of island ecosystems. Therefore, conservation measures considering functional diversity are imperative to safeguard the unique functional roles of threatened mammal species on islands.</p> <p><strong>Datasets and R scripts provided: </strong></p> <p><strong>1. Mammals_occurrence_islands.xlxs</strong> - Matrix of presence/absence of mammals species (columns) per island (rows) and bibliographic sources from which the information has been extracted. Islands are grouped according to the zoogeographical regions proposed by Holt el al. (2013).</p> <p><strong>2. Traits_matrix.csv</strong> – Matrix of species functional traits included in this study and the bibliographic sources of this information. Note that trait values are scaled and centered and that some of them have been imputed (see Methods section of the original manuscript).</p> <p><strong>3.</strong> <strong>Functional_space_analysis.R</strong> – script to:</p> <p>· Build the functional space of islands</p> <p>· Calculate observed functional richness and functional redundancy</p> <p>· Calculate functional richness and functional redundancy after simulating the extinction of threatened species</p> <p>· Calculate the functional space offset by introduced species </p> <p><strong>4. mixed_models_islands.R</strong> - R script to perform the linear mixed models to explore whether islands with higher values of predicted functional diversity loss due to threatened species extinction share some characteristics.</p> <p><strong>5. data_models_islands.csv </strong>- Dataset used to perform the models. For each island the following information is provided:</p> <ul> <ul> <li>Island ID (ID)</li> <li>Number of species (SppRich)</li> <li>Number of threatened species (Thre_sp)</li> <li>Functional richness (Island_FRic)</li> <li>Functional redundancy (Island_Red)</li> <li>Functional richness standard effect size (SES_FRic)</li> <li>Functional redundancy standard effect size (SES_FRed)</li> <li>Island group (Archipielago)</li> <li>Island past connectivity (Type)</li> <li>Percentage of protected area coverage (protected_percentageI_VI)</li> <li>Distance to the nearest continent (dContinent_km)</li> <li>Mean annual temperature (Anntemp_promedio)</li> <li>Mean annual precipitation (Annprec_promedio)</li> <li>Island area (Area_km2)</li> <li>Maximum elevation (Elev_max)</li> <li>Species richness (SR)</li> <li>Mean human footprint (Human_foot)</li> <li>Distance to the nearest larger landmass (distance_biggerLandmass)</li> </ul> </ul> <p>Island area, distance to the nearest continent and distance to the nearest larger landmass were calculated with ArcMap (ESRI, 2019) and the ‘terra 1.7-71’ R package (Hijmans, 2023), using the shapefile of the world’s islands available in Martin et al. (2022). Distance to the nearest mainland and to the nearest larger landmass were calculated as the shortest distance between coastlines (Weigelt & Kreft, 2013). Maximum elevation of each island was extracted from the Global Bathymetry and Elevation Database (Becker et al., 2009). We also used this database to access the bathymetry around the continents and islands and determine whether an island was connected to the mainland during the Last Glacial Maximum (about 20,000 years ago), assuming a sea level of 122 m below the present level (glacial maximum mainland connection; Weigelt, Jetz and Kreft, 2013). Averaged values of annual temperature and annual precipitation for each island were calculated using the climatic variables available in the CHELSA 2.1 database (Karger et al., 2018) at a resolution of 30 arc seconds. To calculate the percentage of protected area on each island, we gathered the protected surface’s shapefile from The World Database on Protected Areas (UNEP-WCMC & IUCN, 2022). Finally, we used the mean human footprint index from Human Footprint maps (see Venter et al., 2018).</p> <p><strong>References</strong></p> <p>Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S.-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., & Weatherall, P. (2009). Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. <em>Marine Geodesy</em>, 32(4), 355–371. <a href="https://doi.org/10.1080/01490410903297766">https://doi.org/10.1080/01490410903297766</a></p> <p>ESRI (2019). <em>ArcGis for Desktop</em>. Retrieved from https://desktop.arcgis.com/en/</p> <p>Hijmans, R. (2023). terra: Spatial Data Analysis_. R package version 1.7-3, <https://CRAN.R-project.org/package=terra>.</p> <p>Holt, B. G., Lessard, J.-P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P.-H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. <em>Science</em>, 339(6115), 74–78. https://doi.org/10.1126/science.1228282</p> <p>Karger D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2018). Data from: Climatologies at high resolution for the earth's land surface areas [Dataset]. <em>Dryad</em>. https://doi.org/10.5061/dryad.kd1d4</p> <p>Martin, M., Sayre, R., VanGraafeiland, K., McDermott Long, O., Weatherdon, L., Will, D., Spatz, D. R., & Holmes, N. D. (2020). Global Islands (M. I. Goldstein & D. A. B. T.-E. of the W. B. DellaSala (eds.); pp. 47–50). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.12475-3</p> <p>UNEP-WCMC & IUCN (2022). <em>Protected Planet: The World Database on Protected Areas (WDPA).</em> Cambridge, UK: UNEP-WCMC and IUCN. Retrieved from <a href="http://www.protectedplanet.net">www.protectedplanet.net</a>. [Accessed 12/2022]</p> <p>Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2018). <em>Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release</em>. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). <a href="https://doi.org/10.7927/H46T0JQ4">https://doi.org/10.7927/H46T0JQ4</a></p> <p>Weigelt, P., & Kreft, H. (2013). Quantifying island isolation – insights from global patterns of insular plant species richness. <em>Ecography,</em> 36(4), 417-429. https://doi.org/10.1111/j.1600-0587.2012.07669.</p> <p>Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world’s islands. <em>Proceedings of the National Academy of Sciences</em>, 110(38), 15307–15312. https://doi.org/10.1073/pnas.1306309110</p> <p> </p>
The Wideband Acoustic Immittance Data in Thai Normal Hearing and Conductive Hearing Loss Adults
ClinicalTrials.gov study NCT06251817. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Normative Data for Sweating Rate, Sweat Sodium Concentration and Sweat Sodium Loss in Female Athlete
ClinicalTrials.gov study NCT06995768. IPD Sharing: NO. Countries: 1. Publications: 0.
Sharing Digital Self-Monitoring Data With Others to Enhance Long-Term Weight Loss
ClinicalTrials.gov study NCT05180448. IPD Sharing: NO. Countries: 1. Publications: 0.
Genome-wide Nutlin-3 Sensitizing Gene (NSG) loss-of-function CRISPR screen data
GEO Series GSE214890. Homo sapiens. 6 samples. Type: Other.
Data from: Habitat connectivity is determined by the scale of habitat loss and dispersal strategy
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Data from: Extensive horizontal gene transfer, duplication, and loss of chlorophyll synthesis genes in the algae
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Data from: Effect of carboxylic group on the compatibility with retarder and the retarding side effect of the fluid loss control additive used in oil well cement
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Data from: Physical calculations of resistance to water loss improve predictions of species range models
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Data from: Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits
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Data from: Greater growth stability of trees in marginal habitats suggests a patchy pattern of population loss and retention in response to increased drought at the rear edge
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Data from: Canine mammary tumours are affected by frequent copy number aberrations, including amplification of MYC and loss of PTEN
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Data from: Loss of biotic resistance and high propagule pressure promote invasive grass-fire cycles
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Data from: Warming and top predator loss drive ecosystem multifunctionality
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Data from: Generic indicators for loss of resilience before a tipping point leading to population collapse
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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)
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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.