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19,493 results for “Populism”
Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
<p>This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as concatenated raw results from S-PrediXcan. Files belong to the research paper "Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations" published at HGG Advances (2023, doi: 10.1016/j.xhgg.2023.100216). Please refer to the README file for a more detailed description.</p>
Figs 1–5 in Newly discovered populations of the "terrible hairy fly", Mormotomyia hirsuta Austen (Diptera: Mormotomyiidae) in Kenya, with further observations on natural history
Figs 1–5. (1) Habitus of male Mormotomyia hirsuta Austen, lateral view; (2) typical fissure at Makilu Hill, note pink/purple discoloration of rock face immediately below; (3) map of Kenya, indicating distribution of sites with caves and fissures visited during the 2011–2013 survey; (4) aerial view of Ukasi Hill area (type locality) and nearby hills (A – Ukasi Hill; B – Ngauluka Hill; C – Makilu Hill); (5) aerial view of Mbuinzau Hill (arrow denotes location of fissure inhabited by Mormotomyia) (Figs 4, 5: Google Earth images, data © Digital Globe 2013).
Figs 8–11 in Newly discovered populations of the "terrible hairy fly", Mormotomyia hirsuta Austen (Diptera: Mormotomyiidae) in Kenya, with further observations on natural history
Figs 8–11. (8) Sampling of Mormotomyia hirsuta adults and larvae below the pink/purple discolored rock face at Ukasi Hill (type locality), in November 2010; (9) Mbuinzau Hill, 187 km south of the type locality (arrow indicates area of collecting site); (10) Mbuinzau Hill, detail (arrow indicates collection site below a fissure in rock face); (11) guarding behaviour of male Mormotomyia (above), female (below), enclosed within span of male legs.
Tracing the introduction of the invasive common myna using population genomics
<p>The common myna (<em>Acridotheres tristis</em>) is one of the most invasive bird species in the world, yet its colonisation history is only partly understood. We identified the introduction history and population structure, and quantified the genetic diversity of myna populations from the native range in India and the introduced populations in New Zealand, Australia, Fiji, Hawaii, and South Africa, based on thousands of single nucleotide polymorphism markers in 814 individuals. We were able to identify the source population of mynas in several invasive locations: mynas from Fiji and Melbourne, Australia, were likely founded by individuals from a subpopulation in Maharashtra, India, while mynas in Hawaii and South Africa were likely independently founded by individuals from other localities in India. Our findings suggest that New Zealand mynas were founded by individuals from Melbourne, which, in turn, were founded by individuals from Maharashtra. We identified two genetic clusters among New Zealand mynas, divided by New Zealand's North Island's axial mountain ranges, confirming previous observations that mountains and thick forests may form barriers to myna dispersal. Our study provides a foundation for other population and invasion genomic studies and provides useful information for the management of this invasive species.</p>
Abb. 1 in Eine alpine Population von überwiegend gestreiften Setina roscida ([Denis & Schiffermüller], 1775), ein Fall von Introgression? (Lepidoptera: Arctiinae)
Abb. 1. Setina irrorella. Oben links gepunktete Form (Vals (GR), 1650 m ü. M; 1.8.1992), unten rechts gestreifte Form (S-chanf (GR), 2300 m ü. M; 26.7.2012) sowie Übergangsformen (Tujetsch (GR), 1820 m ü. M; 23.7.2015 und Müstair (GR), 2100 m ü. M; 19.7.2010). (Foto Jürg Schmid)
Abb. 4 in Eine alpine Population von überwiegend gestreiften Setina roscida ([Denis & Schiffermüller], 1775), ein Fall von Introgression? (Lepidoptera: Arctiinae)
Abb. 4. Männliches Genital, Valven (alle Bilder gleicher Massstab). Oben: aus einer gepunkteten Population von Setina roscida (Ofenpass (GR), 2550 m ü. M; 4.8.2009). Mitte: aus einer gestreif- ten Population von Setina aurita (Brigels (GR), 2480 m ü. M; 18.9.1980). Unten: eine gestreifte S. roscida (Avers (GR), 2530 m ü. M; 6.7.2015). (Gen. präp. und Fotos Jürg Schmid)
Abb. 5 in Eine alpine Population von überwiegend gestreiften Setina roscida ([Denis & Schiffermüller], 1775), ein Fall von Introgression? (Lepidoptera: Arctiinae)
Abb. 5. Unterseiten von Setina aurita, gestreifte Form, links (Maienfeld (GR), 1900 m ü. M; 1.8.2009) und Setina roscida, gestreifte Form, rechts (Avers (GR), 2530 m ü. M; 4.7.2015). (Foto Jürg Schmid)
Abb. 3. Setina roscida und sympatrische S in Eine alpine Population von überwiegend gestreiften Setina roscida ([Denis & Schiffermüller], 1775), ein Fall von Introgression? (Lepidoptera: Arctiinae)
Abb. 3. Setina roscida und sympatrische S. aurita von Avers (GR), um 2500 m ü. M.; 16.7.2009. Oben links: S. roscida, gepunktete Form, darunter Übergangsform. Oben und Mitte rechts: S. roscida, gestreifte Form. Unten Mitte: S. aurita. (Foto Jürg Schmid)
Abb. 2 in Eine alpine Population von überwiegend gestreiften Setina roscida ([Denis & Schiffermüller], 1775), ein Fall von Introgression? (Lepidoptera: Arctiinae)
Abb. 2. Setina aurita. Gepunktete Tieflandform (Rothenbrunnen (GR), 660 m ü. M; 10.9.2004) und gestreifte Höhenform (Maienfeld (GR), 1900 m ü. M; 1.8.2009). (Foto Jürg Schmid)
Data from: Population divergence in heat and drought responses of a coastal plant: from metabolic phenotypes to plant morphology and growth
<p>This dataset supports the article "Population divergence in heat and drought responses of a coastal plant: from metabolic phenotypes to plant morphology and growth", which is under minor revision in Journal of Experimental Botany. The study addresses the combined effects of and plant population origin, drought and heat stress on plant growth, plant morphology and the leaf metabolome. The data were assessed in Northern and Southern European individuals of <em>Cakile maritma</em> (See Rocket). An R-script containing all statistical analyses that have been implemented with these data is also provided.</p>
Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre> <p> </p>
Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions
<p>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</p> <p>We use the communes adminitrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds acoording to QA60 band following the example<br> given in GEE dataset info page at:<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: Global Human Settlement Layers, Population Grid 2015</strong></p> <p> labels range from 0 to 31, with the following meaning:<br> label value original value in GEE dataset<br> 0 0<br> 1 1-10<br> 2 11-20<br> 3 21-30<br> ...<br> 31 >=291 </p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a><br> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Figure 5 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 5. Genetic clustering based on allele frequencies of 12 microsatellite loci genotyped for Rattus rattus Complex samples from MSEA and extralimital distribution in Indonesia (IDN). (A) PCoA, (B) STRUCTURE barplot.
Figure 4. Haplotype network for Rattus rattus Complex II in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 4. Haplotype network for Rattus rattus Complex II. The Nusa Tenggara samples are illustrated on the right of the network.
Figure 1 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 1. Cranial measurements taken for each specimen: supraoccipital height (BH), basal length (BLL), basilar length (BRL), length of the bullae (BULL), condylobasal length (CBL), condylobasilar length (CBRL), minimum corpus length (CL), length of the diastema (DA), foramen magnum height (FMH), foramen magnum width (FMW), length of the incisive foramina (FOR), length of the face (GES), length of the braincase (HKL), thickness of the incisor (ID), interorbital breadth (IOB), mandibular diastema length (LAL), thickness lower incisor (LID), mandibular alveoli length (MAL), mandibular toothrow length (crown) (MCL),mandibular depth (MD), maximum mandibular height (MDL), mandibular depth at M1 (MID), mandibular length (ML), nasal length (NAS), nasal breadth (NASB), occipital breadth (OCB), occipital length (OCN), supraoccipital width at the occipital condyles (OCW), length of the upper molar row (alveoli) (OZRA), length of the upper molar row (crown) (OZRK), palatal length (PL), palatine breadth (PRL), rostral breadth (RB), rostral height (RH), breadth of braincase (SKB), height of braincase with bullae (SKH), zygomatic plate (ZP), zygomatic breadth (ZYG).
Figure 3. Haplotype networks for Rattus exulans, R in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 3. Haplotype networks for Rattus exulans, R. argentiventer, and Rattus rattus Complex LIV. Sunda refers to the islands of Borneo, Java, and Sumatra; the Indonesian sample (brown) lacks further collection information.
Figure 6 in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 6. Summary of likely movements of the three commensal rodents through the Nusa Tenggara island chain. Rat illustrations redrawn and adapted from R. Budden, location of RS3 Dong Song drums from Calo (2014).
Genetic differentiation following recent domestication events: A study of farmed Nile tilapia (Oreochromis niloticus) populations
<p>SNP array data from our research article. It contains the SNP markers in common across the different Nile tilapia (Oreochromis niloticus) populations assessed in the study</p>
Distribution, population density and behavior of dwarf galagos (Paragalago sp.) in Taita Hills, Kenya
<p>We studied habitat preferences and behavior of dwarf galagos (<em>Paragalago </em>sp.), recently rediscovered from the Taita Hills, Kenya. Small populations of Taita dwarf galagos survive in the two largest remnants of moist montane forest. Inspection of several smaller forest fragments failed to provide evidence of additional survivors. Acoustic data on the two remaining populations were obtained with AudioMoths, and analyzed in relation to forest structure data obtained by airborne lidar and by ground-level observations. A Zero-inflated negative binomial GLMM was implemented with calls per hour as the response variable and indicator of relative population density. Our results demonstrate that Taita dwarf galagos prefer dense canopy coverage and avoid forest edges. Regarding forest height, they prefer lower 20–30 m tall forest. Forest size also significantly affects Taita dwarf galago population size. Mbololo forest (185 ha) has a relatively viable population, whereas in Ngangao forest (120 ha) dwarf galagos are nearly extinct. The calls of Taita dwarf galagos resemble calls of Kenya coast dwarf galagos (<em>Paragalago cocos</em>). However, some differences exist between the Taita animals and those recently recorded by us at the Kenyan coast, and even between the two remaining populations in the Taita Hills. In addition to other data, we present the first ever photographs of the Taita dwarf galagos from the Mbololo forest and compare them to those from Ngangao forest and the Kenya coast from Diani and Shimba Hills. We conclude that DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills.</p>
Figure 5 in EVIDENCE OF PARTHENOGENETIC POPULATIONS FROM THE PARATANYTARSUS LACCOPHILUS SPECIES GROUP (DIPTERA: CHIRONOMIDAE) IN THE ALASKAN ARCTIC Abstract Introduction
Figure 5. Development of parthenogenetic Paratanytarsus laccophilus species-group progeny in the 15.3 °C treatment in 2016. a) An egg mass freshly laid on 11 July. b) The same egg mass on 15 July, just before hatch. c-g) Daily progression of development. c) Eggs on 12 July 2016 at one day old. d) At two days old. e) At three days old, eye spots (arrow) and head capsules were evident inside the egg. f) At four days old, eye spots and head capsules (arrow) were well-defined within the egg. g) Larvulae (arrow) hatched on 16 July, five days after oviposition. Several vacated eggs are clearly notable. Scale bar = 1 mm for a and b, and 250 μm for c-g.
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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.