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5,153 results for “Genetic data”
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 4. Image regeneration using GA
<p>In 2008, Roger Johansson was able to regenerate a Mona Lisa image from random sampling (Roger Johansson, 2017). It uses a genetic algorithm to model a population of individuals, each containing a string of DNA which can be visualized in the form of an image (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p> <p>By starting with a population consisting of a randomly generated gene pool, each individual is compared to the reference image (the one on the left), and the individuals can then be ranked by their likeness to it, known as their "fitness", with the best fit being displayed on the output image (the one on the right) (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017). By breeding the fittest individuals from the population, the DNA which produces the most accurate representation of the reference image is selected over successive generations, effectively demonstrating the power of a natural selection process to produce the best candidate for any given environment (Grow Your Own Picture Genetic Algorithms & Generative Art, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 12. After few generation
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 13. Reached convergence
<p>In figure 11 it is the initial population showed and figure 12 the population started to change and figure 13 we reached a convergence.</p>
Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication
<p>All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" <em>(doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>)</em></p> <p><strong>Abstract</strong></p> <p>Geneticists have long used olfactory conditioning techniques in <em>Drosophila</em> to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.</p>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (16S rRNA gene sequencing data)
<p>Microbiome data accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for alpha- and beta- diversity, as well as for individual taxa both in binary and quantitative phenotypic representation. Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </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>
data for the PCI publication "New insights into the population genetics of partially clonal organisms: when seagrass data meet theoretical expectations"
<p><strong>Data analyzed int he article "New insights into the population genetics of partially clonal organisms: when seagrass data meet theoretical expectations", doi </strong> <a href="https://arxiv.org/abs/1902.10240v5">https://arxiv.org/abs/1902.10240v5</a> <strong> doi of the PCI recommandation: </strong>https://doi.org/10.24072/pci.evolbiol.100083</p>
Figure 6 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya
Figure 6. Maximum likelihood (ML) tree of bufonid frogs based on nucleotide sequences of the mitochondrial 16S rRNA gene with Leptophryne borbonica as an outgroup. The bootstrap support (>50%) is indicated at nodes in the order of ML (500) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%). Specimens examined in this study are indicated by boldface type.
Figure 2 in Genetic divergences of South and Southeast Asian frogs: a case study of several taxa based on 16S ribosomal RNA gene data with notes on the generic name Fejervarya
Figure 2. Maximum likelihood (ML) tree based on nucleotide sequences of the mitochondrial 16S rRNA gene from 88 haplotypes of frogs (Table 1), with Xenopus laevis as an outgroup. Bootstrap support (>50%) is indicated at nodes in the order of ML (1000) replicates. Asterisks represent Bayesian posterior probability (BPP; * ≥95%).
Figs. 7–11 in New morphological and genetic data of Gigantorhynchus echinodiscus (Diesing, 1851) (Acanthocephala: Archiacanthocephala) in the giant anteater Myrmecophaga tridactyla Linnaeus, 1758 (Pilosa: Myrmecophagidae)
Figs. 7–11. Light microscopy of adult Gigantorhynchus echinodiscus from Mymercophaga tridactyla. 7. Proboscis with a crown of large hooks in the apex and small hooks, and a proboscis receptacle (Re); 8. Trunk with pseudo segmentation (arrows) and the end of the lemnisci (Le); 9. Male reproduction organs, testis (Te), cement glands in pair (Cgl), ejaculatory duct (Ed); 10. Detail of the posterior end of adult female showing the uterus (Ut), vagina (Va, arrow) and gonopore subterminal (Gp); 11. Egg ellipsoid showing the outer membrane thick (Om), inner membrane (Im) thin, embryo (Em).
Figs.1–6 in New morphological and genetic data of Gigantorhynchus echinodiscus (Diesing, 1851) (Acanthocephala: Archiacanthocephala) in the giant anteater Myrmecophaga tridactyla Linnaeus, 1758 (Pilosa: Myrmecophagidae)
Figs.1–6. Line drawing of Gigantorhynchus echinodiscus from Mymercophaga tridactyla. 1. Praesoma with the proboscis presenting a crown with robust hooks followed by small hooks, a receptacle proboscis, and papillae in the base of the neck; 2. Three different robust hooks in the crown and a one small type in the proboscis; 3. Unsegmented anterior part of the trunk, and lemnisci filiform reaching the middle region of the trunk; 4. Posterior region of adult male showing reproductive organs; 5. Posterior region of adult female showing the uterus, vagina and gonopore subterminal; 6. Egg.
Fig. 12–17 in New morphological and genetic data of Gigantorhynchus echinodiscus (Diesing, 1851) (Acanthocephala: Archiacanthocephala) in the giant anteater Myrmecophaga tridactyla Linnaeus, 1758 (Pilosa: Myrmecophagidae)
Fig. 12–17. Scanning electron micrographs of adult Gigantorhynchus echinodiscus from Mymercophaga tridactyla. 12 and 13. Cylindrical proboscis armed with hooks (Ho) showing a space (Sp) between the two circles of large hooks and small rootless spines, neck (Ne), trunk (Tr), lateral papillae (Pa, arrowhead); 14. Detail of the crown with two circles of large hooks (arrow – 1st row and asterisk – 2nd row); 15. Detail of the lateral papillae; 16 and 17. Posterior end of adult male showing the region without pseudo-segmentation (cross) and a copulatory bursa protruding from the body (CB).
Figure 2 in Investigation of genetic variation among Turkish populations of Andricus lignicola using mitochondrial cytochrome b gene sequence data
Figure 2. Bayesian analysis tree. Posterior probability values are given on the branches. Outgroup haplotypes: Ac (Andricus caliciformis) and Ak (Andricus kollari).
Fig. 5 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 5. Heatmap of pairwise genetic distances estimated from nucleotide sequences of the cytochrome b gene (479 nucleotides) of Plasmodium spp. using the JukesCanter model.
Fig. 4 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 4. Bayesian phylogeny based on the partial cytochrome b gene (479 base pairs) of Plasmodium lineages. The lineages isolated in this study are given in red bold. MalAvi lineage codes and GenBank accession numbers are given after species names. Node values indicate percentages of posterior probabilities. Plasmodium isolated from this study are clustered into three clades (clade I, II and II). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 6. Haemoproteus spp. infected in Blyth's hawk-eagles (Spizaetus alboniger), KU549 (A-C) and KU589 (D-F). Young gametocytes (A&D), microgametocytes (B&E) and macrogametocytes (C&F). Giemsa staining.
Fig. 2 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 2. Bayesian phylogeny based on partial cytochrome b gene (479 nucleotides) of Haemoproteus lineages. The lineages isolated in this study are given in red bold. MalAvi lineage codes and GenBank accession numbers are given after species names. Node values indicate percentages of posterior probabilities. Vertical bars indicate clades of subgenus Haemoproteus (A) and Parahaemoproteus (B) Haemoproteus isolated from this study are clustered into two clades (clade I and II). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
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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.