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262 results for “Genetic variability”
Fig. 5 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 5 Haplotyp_ n_twork of Paramecium biaurelia construct_d using th_ 123 s_qu_nc_s of ribosomal ITS1- 5.8S-ITS2-5'LSU fragm_nts (a) and 139 of mitochondrial COI fragm_nts (b). Th_ n_twork pr_s_nts a comparison of haplotyp_s obtain_d in th_ Kraków ar_a vs. th_ oth_r localiti_s, wh_r_ mol_cular data for P. biaurelia is availabl_. Black dash_s on particular branch_s r_pr_s_nt nucl_otid_ substitutions b_tw__n particular haplotyp_s. Analys_s w_r_ conduct_d using th_ M_dian Joining m_thod in PopART softwar_ v. 1.7
Fig. 4 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)
Fig. 4 Haplotyp_ n_twork of Paramecium biaurelia construct_d using th_ 123 s_qu_nc_s of ribosomal ITS1- 5.8S-ITS2-5'LSU fragm_nts (a) and 139 of mitochondrial COI fragm_nts (b). Th_ n_twork pr_s_nts r_ciprocal r_lationships b_tw__n, and th_ origin of P. biaurelia haplotyp_s id_ntifi_d in curr_nt study. Black dash_s on particular branch_s r_pr_s_nt nucl_otid_ substitutions b_tw__n particular haplotyp_s. Analys_s w_r_ conduct_d using th_ M_dian Joining m_thod in PopART softwar_ v. 1.7
Figure 8 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 8. TCS network of the studied S. limbata and their individuals (numbers indicated the populations based on Table 1).
Figure 6 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 6. STRUCTURE analysis of the studied populations, which revealed the best number of K=7 (numbers indicated the populations based on Table 1).
Figure 5 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 5. NJ tree of the evaluated populations and their individuals based on ISSR data (numbers indicated the populations based on Table 1).
Figure 4 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 4. UPGMA tree of the studied populations and their members according to ISSR data (numbers indicated the populations based on Table 1).
Figure 2 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 2. PCA plot of the evaluated populations and their individuals (numbers indicated populations according to Table 1).
Figure 7 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 7. Reticulation dendrogram of the studied populations that indicating gene flow among. Abbreviations: Arak (1- 3), Sangak (4-6), Semnan (7-9), Vidar (10-12), Ahovan (13-15), Zarandiyeh (16-18), Ghoochan (19-21) and Lashkarak (22-24).
Figure 1 in Genetic variability and population structure of some Iranian Salvia limbata C. A. Mey. populations
Figure 1. Distribution map of the investigated populations of S. limbata (numbers indicated populations according to Table 1).
Fig. 2 in Soluble proteins in Messor structor (Latreille, 1798) (Hymenoptera: Formicidae) populations from Bulgaria - genetic variability and possible usage as population-genetic markers
Fig. 2. Spectrum of soluble proteins of M. structor workers (7.5% PAGE): a. Elenovo population; b. Tsalapitsa population.
Fig. 3. a in Soluble proteins in Messor structor (Latreille, 1798) (Hymenoptera: Formicidae) populations from Bulgaria - genetic variability and possible usage as population-genetic markers
Fig. 3. a. UPGMA dendrogram (Sneath et al. 1973); b. Neighbour-joining dendrogram (Saitou & Nei 1987).
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 sequences obtained from specimens of C. osculatum sp. D and C. osculatum sp. E analysed in the present study, based on Bayesian Inference (BI) method using MrBayes v3.2.2 (Ronquist et al., 2012). Evolutionary distance was estimated using the TrN + G (G = 0.60) substitution model as implemented in jModeltest (Posada, 2008), with the AIC approach (Posada and Buckley, 2004). Posterior probability values are the result of 1.000000 of runs and are reported at the nodes. The coloured icons correspond to the two species considered in this study (red = C. osculatum sp. D and blue = C. osculatum sp. E).
Fig. 2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 2. Schematic distribution of the fish species examined in the present study for larval of C. osculatum sp. D and C. osculatum sp. E, along the continental shelf of the Ross Sea coastal ecosystem. Arrows indicating preferred preys and the diet preference for each fish species are reported according to the literature (La Mesa et al., 2004). The represented pelagic organisms comprise species of euphausiids and fish juveniles, benthic and epibenthic organisms are polychaetes, amphipods, decapods and gastropods. A pie chart with the relative proportions of C. osculatum sp. D and C. osculatum sp. E is given for each fish species. Squares and circles represent the hypothetical distribution of C. osculatum sp. D and C. osculatum sp. E larvae in their intermediate hosts.
Fig. 3 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 3. Schematic representation of the hypothetic life-cycle of C. osculatum sp. D (a) and C. osculatum sp. E (b) in the Ross Sea.
Taking advantage from phenotype variability in a local animal genetic resource: identification of genomic regions associated with the hairless phenotype in Casertana pigs
<p>Ped and Map files for 96 Casertana breed pigs genotyped with Illumina BeadChip 60K Porcine.<br> The first field of the ped file contains the id of the farm (1az-6az).<br> The hairless phenotype, in the ped phenotype field, is codified as 1, the hairy phenotype is codified as 2.</p>
Data and code relating to Becher, Jackson & Charlesworth. Patterns of genetic variability in genomic regions with low rates of recombination.
<p>Data and code relating to Becher, Jackson & Charlesworth. Patterns of genetic variability in genomic regions with low rates of recombination.</p> <p>Contains genotype data, R code for analysis and visualisation, a SLiM simulation script, README, etc.</p>
Figure 6 in Morphometric and genetic variability among Mediterranean cereal cyst nematode (Heterodera latipons) populations in Turkey
Figure 6. Phylogenetic tree (maximum likelihood) constructed through the ITS sequence alignment from 42 populations of Heterodera latipons. Bootstrap values (more than 60%) are given for the appropriate clades. Populations are designated with the code described in Table 1.
Fig. 4 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 4. The haplotype network for the mt-CO1 gene (815 bp) of E. canadensis (G6/7). The size of the circles is proportional to the frequency of each haplotype. The number of mutations separating haplotypes is indicated by dash marks. The host diversity of haplotypes is shown in different colors. Hap: Haplotype.
Fig. 2 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 2. Phylogenetic tree of Echinococcus granulosus s.l. isolates generated using mt-CO1 gene sequences (815 bp). The phylogenetic tree was constructed using the Maximum Likelihood method and TN93 + G model. Evolutionary analyses were conducted in MEGA X. For each reference sequence, the GenBank accession number and species name are listed below: MN787562 (E. equinus), KY766905 (E. equinus), KP161210 (E. equinus) AB786665 (E. equinus) AF346403 (E. equinus), KX010854 (E. canadensis) MK321260 (E. canadensis) KX010856 (E. canadensis), AB893263 (E. canadensis), AB777923 (E. canadensis), MK165232 (E. ortleppi), MT072979 (E. granulosus s.s.), NC_044548 (E. granulosus s.s.), MG672293 (E. granulosus s.s.), KT001423 (E. multilocularis), AY684274 (T. saginata). ■: E. canadensis (G6/7) isolates; ▴: E. equinus isolates.
Fig. 3 in Co-infection of Echinococcus equinus and Echinococcus canadensis (G6/7) in a gray wolf in Turkey: First report and genetic variability of the isolates
Fig. 3. The haplotype network for the mt-CO1 gene (815 bp) of E. equinus. The size of the circles is proportional to the frequency of each haplotype. The number of mutations separating haplotypes is indicated by dash marks. The host diversity of haplotypes is shown in different colors. Hap: Haplotype.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.