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1,467 results for “Genetic structure”
Figure 3 in Effects of nanoparticles treatments and salinity stress on the genetic structure and physiological characteristics of Lavandula angustifolia Mill.
Figure 3. Effects of Fe O and ZnO nanoparticles on concentration of the leaves Fe2+ amounts. Whiskers indicate the standard deviation, 2 3 and dissimilar letters showed the significant variation based on Duncan test (P≤ 0.05).
Figure 4 in Effects of nanoparticles treatments and salinity stress on the genetic structure and physiological characteristics of Lavandula angustifolia Mill.
Figure 4. Effects of ZnO and Fe O nanoparticles on intracellular Zn 2+ concentration. Whiskers reveal the standard deviation, and 2 3 dissimilar letters showed the significant variation according to Duncan test (P≤ 0.05).
Bauder Et Al. - Landscape features fail to explain spatial genetic structure
<p> RMarkdown script and data in an Excel sheet to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We used genetic data from 619 individual deer from 24 counties across Ohio tested at 11 microsatellites and haplotypes from a 547-bp fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data.</p> <p>v2 (BauderEtAl_Files_for_archiving2.zip) includes additional and updated files not in v1. </p>
Fig. 4 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 4. Multidimensional Scaling (MDS) plot (stress: 0.0045) performed using average pairwise TN93 (Tamura & Nei, 1993) distances among investigated Lutrogale perspicillata groups created according to the country of origin of samples (modern + museum DNA and GenBank entries).
Fig. 3. A in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 3. A, Lutrogale perspicillata network computed using haplotypes (h) from the 305 bp-long sequence alignment (modern + museum DNA and GenBank entries). A scale to infer the number of sequences for each pie (i.e., haplotype) was provided together with a length bar to compute the number of mutational changes. The colour of each country and the number of each haplotype are indicated. See Table S1 for more details. B, Mismatch Distributions (MD) of the mtDNA pairwise differences (dotted: observed; line: expected) calculated for South East Asia haplogroup (Fig. 3A). Estimates of FS and R2 statistics (with related P values), r (raggedness index) and the outcome of SSD and SSD* test under a model (H0) of sudden demographic and spatial population expansion, respectively, are provided.
Fig. 2 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 2. Photos of MNHN-ZM-MO-2001-350, L. p. perspicillata holotype resident in the mammal collection of the National Museum of Natural History of Paris, France. A, right side, lateral view (bar length = 20 cm); B, left forelimb, lateral view; C, basement, in French "Lutra perspicillata = Lutra leptonix Horsf., loutre de Java par m Diard, mai 1821, la tête est au lab d'anatomie", which can be translated into and interpreted as: "Lutra perspicillata = Lutra leptonix (Horsfield, 1824), Java otter from M. Diard, May 1821, skull is in the lab of anatomy" (see also Material and Methods). Photos courtesy and copyright: © MNHN - RECOLNAT - Laura Flamme - 2014.
Fig. 1 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 1. Lutrogale perspicillata distribution (in yellow; see insets for Iraq and Pakistan) including sampling localities of modern (white circles) and museum (green squares) individuals. As far as the latter are concerned, we reported only sites for which samples were successfully investigated (see Table S1 for the entire sample size of this study; symbol "?" stands for unknown locality). The white stars indicate, in Iraq, the locality (TaqTaq, Kurdistan) where the sample of Omer et al. (2012) was collected, in Cambodia/Thailand and Malaysia, the country/ies of origin of EF472348 and KY117557 GenBank sequence, respectively. In Iraq, Pakistan, and supposedly Java, Indonesia, the green squares indicate localities (when known) of L. p. maxwelli, L. p. sindica, and L. p. perspicillata museum holotypes, respectively. Finally, Naga Hills at the border between Myanmar and India as well as Bahoo-Kalat River Basin between Iran and Pakistan are indicated (see text for more details). The species' geographic range was adapted from IUCN (International Union for Conservation of Nature) 2015. Lutrogale perspicillata. The IUCN Red List of Threatened Species 2019-3 was modified using CorelDraw!12 (2003). Digital images (insets) were obtained from Google Earth 7.1.5.1557 (2015 Google Inc.) and Google Earth map data (Data SIO, NOAA, U.S. Navy, NGA, GEBCO - Image Landsat). Please note that thick dotted lines mark out new borders for L. p. sindica and L. p. perspicillata subspecies as established in this study (see text for more details).
Figure 2. Bayesian posterior probability 50 in Population genetic structure and demographic history of the Chinese endemic Mongoloniscus sinensis (Dollfus, 1901) (Isopoda: Oniscidea)
Figure 2. Bayesian posterior probability 50% majority-rule consensus tree of the M. sinensis haplotypes. Out-group was Ligia occidentalis; the map showed mitochondrial haplotype clades of Porcellio gigliotose and Trachelipus semiproiectus. The numbers above joints are the bootstrap support values of MP value, ML value, and the posterior probabilities of the BI tree, respectively (MP/ML/BI).
Figure 1 in Population genetic structure and demographic history of the Chinese endemic Mongoloniscus sinensis (Dollfus, 1901) (Isopoda: Oniscidea)
Figure 1. Locations of sampled populations and geographical distribution of Mongoloniscus sinensis mtDNA clades. Mountain ranges are in green letters as follows: H—Heng Mountain, TH—Taihang Mountain, WT—Wutai Mountain, LL—Lvliang Mountain, TY–Taiyue Mountain, ZT—Zhongtiao Mountain.
Figure 3 in Population genetic structure and demographic history of the Chinese endemic Mongoloniscus sinensis (Dollfus, 1901) (Isopoda: Oniscidea)
Figure 3. Parsimonious network of M. sinensis haplotypes. Each circle represents a haplotype, with the area of the circle proportional to its frequency. The evolutionary clades C1 - C6 are shown in different colors, and median vector (mvl-mv3) is indicated in red.
Figure 4 in Genetic diversity, population structure and demographic history of Dugesia japonica in Taihang Mountains
Figure 4. Median-joining haplotype network based on mitochondrial gene COI. The four ellipses represent four clades in Figure 3, respectively. Each circle represents a haplotype, the area of the circle is proportional to the frequency of haplotypes, and black dots represent hypothetical unobserved haplotypes. Different populations are shown in different colors.
Figure 3 in Genetic diversity, population structure and demographic history of Dugesia japonica in Taihang Mountains
Figure 3. Maximum likelihood (ML) and Bayesian inference (BI) phylogentic trees based on mitochondrial gene COI. Dugesia ryukyuensis (Genbank accession no. AB618488) serves as the outgroup. The broken lines denote inconsistent branches. Bootstrap percentages (BP,>50 only) of ML analysis and posterior probabilities (PP,>0.50 only) of Bayesian inference are shown above and below the branch, respectively. HG—haplogroup.
Fig. 2 in Microsatellite variation and population genetic structure of a neotropical endangered Bryconinae species Brycon insignis Steindachner, 1877: implications for its conservation and sustainable management
Fig. 2. UPGMA clustering of the Nei's genetic distance (1972) of the Brycon insignis sampling locations based on six microsatellite loci. Bootstrap values above 50% are shown above branches indicating percentage support in 5000 permutations. Power Company Hatchery (PCH), São João River (SJR), Paraíba do Sul River (PSR), Imbé River (IMR), Muriaé River (MUR) and Itabapoana River (ITR).
Fig. 1 in Fish passage ladders from Canoas Complex - Paranapanema River: evaluation of genetic structure maintenance of Salminus brasiliensis (Teleostei: Characiformes)
Fig. 1. Partial view of the Paranapanema River and its principal affluents (Tibagi and Cinzas Rivers). Featured are the two collection sites: HEP Canoas I and HEP Canoas II.
Data for: Amazonian birds in more dynamic habitats have less population genetic structure and higher gene flow
<p>Understanding the factors that govern variation in genetic structure across species is key to the study of speciation and population genetics. Genetic structure has been linked to several aspects of life history, such as foraging strategy, habitat association, migration distance, and dispersal ability, all of which might influence dispersal and gene flow. Comparative studies of population genetic data from species with differing life histories provide opportunities to tease apart the role of dispersal in shaping gene flow and population genetic structure. Here, we examine population genetic data from sets of bird species specialized on a series of Amazonian habitat types hypothesized to filter for species with dramatically different dispersal abilities: stable upland forest, dynamic floodplain forest, and highly dynamic riverine islands. Using genome-wide markers, we show that habitat type has a significant effect on population genetic structure, with species in upland forest, floodplain forest, and riverine islands exhibiting progressively lower levels of structure. Although morphological traits used as proxies for individual-level dispersal ability did not explain this pattern, population genetic measures of gene flow are elevated in species from more dynamic riverine habitats. Our results suggest that the habitat in which a species occurs drives the degree of population genetic structuring via its impact on long-term fluctuations in levels of gene flow, with species in highly dynamic habitats having particularly elevated gene flow. These differences in genetic variation across taxa specialized in distinct habitats may lead to disparate responses to environmental change or habitat-specific diversification dynamics over evolutionary time scales.</p>
Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years
<p>Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. Here, we developed the Temporal Population Structure (TPS), the first DNA-based dating method for ancient genomes ranging from the Late Mesolithic to modern-days, and applied it to 3,591 ancient and 1,307 modern Eurasians. We show that TPS predictions align with their known dates and correctly account for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We demonstrated how TPS improved the ability to study phenotypic traits over time.</p>
Figure 5 Nav kdr 1016 and 1534 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 5 Nav kdr 1016 and 1534 site allele frequencies in UEL. Collection site locations were distributed in the three regions of the campus.
Figure 4 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 4 Allele frequencies of Nav kdr 1016 and 1534 genotyping distributed in the five different regions of Londrina.
Figure 3 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 3 Haplotypic network obtained through specimens collected in UEL. The circles are proportional to the number of specimens observed in each haplotype.
Figure 2 in Genetic study in Aedes (Stegomyia) aegypti (Linnaeus, 1762) from Londrina (Paraná State, Brazil): an approach to population structure and pyrethroid resistance
Figure 2 Haplotype network observed in five regions of Londrina. The circles are proportional to the number of specimens observed in each haplotype. The haplotypes observed are in bold. The numbers represent nucleotide change positions.
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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.