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1,466 results for “genetic structures”
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. 1 in Geographic Distribution and Genetic Structures of the Tideland Snails and in Taiwan and Japan.
Fig. 1. Sampling sites of Pirenella nipponica and P. asiatica. Sampling at Japanese sites was carried out in the previous study (Kojima et al. 2006). Areas shown in the pie graphs reflect the number of collected individuals. Black and white sectors in the pie graphs indicate relative frequencies of P. nipponica and P. asiatica.
Fig. 2. A in Geographic Distribution and Genetic Structures of the Tideland Snails and in Taiwan and Japan.
Fig. 2. A statistical parsimony haplotype network of Pirenella nipponica (a) and P. asiatica (b) based on nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) gene. The areas of the circles are proportional to the frequency of the occurrence of the haplotypes. Numbers of haplotypes are same as those in table 2 and Kojima et al. (2006). Colors of sections denote the relative occurrence frequency of individuals collected in each area. The haplotypes that were not detected in the sample are indicated by small black circles.
Fig. 4 in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 4. Structure clustering results. (A) the posterior probability of each K; (B) the distribution of Delta K values; (C) Bayesian clustering results at K = 9; S1–3 was the groups defined by SOMOVA.
Fig. 3. The genetic distances among populations. A in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 3. The genetic distances among populations. A: based on Kimura's 2-parameter; B: based on Tamura 3-parameter.
Fig. 2 in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 2. Potential distribution areas of D. fissa in different periods. Potential areas for (A) Current day; (B), Last Glacial Maximum; (C), MidHolocene; (D), year 2070 (RCP 4.5).
Fig. 9 in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 9. The dispersed path of D. fissa. A: Central China; B: South China; C: Yunnan-Guizhou Plateau; D: Southeast coastal area; E: Sichuan Basin.
Fig. 7 in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 7. Mismatch distributions (left) and Bayesian skyline plots (right) for lineages I, IV, and V of D. fissa based on mitochondrial data.
Fig. 6 in Phylogeography and Genetic Structure of the Bush Cricket (Orthoptera, Tettigoniidae) in Southern China.
Fig. 6. The mitochondrial haplotype network of concatenated sequences. The dotted box represents the five lineages based on divergence time analysis.
Fig. 3 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 3. Haplotype network of the Kryptolebias marmoratus species group. Maps represent the distribution of each group.
Fig. 2 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 2. Distribution of K. hermaphroditus: Orange star indicates type locality; and Green circles indicate recorded localities for the species (Costa 2011; 2016; Sarmento-Soares et al. 2014; Lira et al. 2015; Berbel-Filho et al. 2016; Guimarães-Costa et al. 2017; Tatarenkov et al. 2017a; This study).
Fig. 1 in Fig. 3 in Genetic Structure of the Mangrove Killifish Costa, 2011 (Cyprinodontiformes: Aplocheiloidei) Supports A Wide Connection among its Populations.
Fig. 1. Kryptolebias hermaphroditus from Tutóia, Maranhão State, Delta do Parnaíba, north eastern Brazil; UFRJ12666: A: Hermaphrodite, 35.5 mm SL; B: Male, 20.3 mm SL; C: Male, 28.9 mm SL.
Fig. 2 in Genetic diversity and population structure of endangered Neofinetia falcata (Orchidaceae) in South Korea based on microsatellite analysis
Fig. 2. Structure analyses for putative genetic clusters of N. falcata. A: Graphs of ΔK values to determine the ideal number of groups present in the accessions of N. falcata. B: Estimated genetic structure of the 3 populations of brinjal based on STRUCTURE analysis K = 2 and K = 3.
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover
<p>Guided crossover operator is based on the two point separation from parents are selected<br> Left and right parts of them are related to each other by the condition to be meaningful With this<br> new child of his parents is that. But a new generation of the random choice to have reached this<br> stage. The crossover rate is fixed for our algorithm.</p>
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network
<p>A neural network (NN), in the case of artificial neurons called artificial neural<br> network (ANN) or simulated neural network (SNN), is an interconnected group of natural<br> or artificial neurons that uses a mathematical or computational model for information<br> processing based on a connectionist approach to computation. In most cases an ANN is an adaptive<br> system that changes its structure based on external or internal information that flows through the<br> network[9].<br> In more practical terms neural networks are nonlinear statistical data modelling or decision<br> making tools. They can be used to model complex relationships between inputs and outputs or<br> to find patterns in data.<br> Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each<br> edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or<br> activate next active node, and edges with negative weight, disable or inhibit the next connected<br> node (if it is active) ones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Dataset for the article: "Weak genetic structure despite strong genomic signal in lesser sandeel in the North Sea"
<p>Dataset used for the article: "Weak genetic structure despite strong genomic signal in lesser sandeel in the North Sea".</p> <p>Dataset consists on a VCF file from 471 individuals of lesser sandeel, <em>Ammodytes marinus</em> (L.). This VCF is the end product of the bioinformatic analysis described in the paper Jimenez-Mena et al. (2019). Data was obtained from double-digest Restriction-site Associated DNA (ddRAD) sequencing. More information can be obtained in Methods of the article. The information of each of the individuals in the VCF is also included as a separate file, as well as the supplementary tables of the article.</p>
ScienceDex guides
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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.