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15,277 results for “GENETICS”
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 16. Failed Generations
<p>For some images there is a chance to get stuck where fitness function maxed, but we are not near to the original image like in figure 16, both row and column fitness matched. But image lost a key portion from original image, in these cases we should increase the weight of fitness function Fx(X), which will solve the issue.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 2. Image stored in cloud each year (Perret, 2017)
<p>Currently, 4.7 trillion of photos are saved in the cloud (Perret, 2017). And only a few percentage are called to use again. So less used files can be stores in a compression technique which can save more space than time and make the cloud system faster as memory redundancy time will be reduced.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 1. Photo amount by year (Perret, 2017)
<p>Revolution of a portable camera with computer started to produce an exponential rate of media files, and users are sharing these files with everyone. So, using the cloud to store images is becoming a favorite choice for users. But cloud does not only store huge files which are approximately 1.2 trillion in 2017 (Perret, 2017), it also has to transfer these files to a different network to serve users. To reduce load, the cloud system started to use different compression algorithm. These algorithms have a tradeoff between time and space. Most of these have better time complexity than space. But as the cloud has powerful and distributed computing power, it may be better to focus on saving space. As data transfer takes more time than processing same data in the cloud. A perfect use case is a mobile sending the large image to the cloud takes more data transfer time than the compression and decompression process in the cloud. So, in this age of the distributed computer, it is better to reduce size as computation time is less important than network data transfer time.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 9. Resized image and extracted data for a 100*100 size image
<p>For the extraction, our goal is to divide an image into smaller blocks and keep the row and column data for these blocks. But for our experiment we used a single block, which means taking the full image as a single block. For bigger image we should always divide the image in separate blocks and work on them par rally. As in figure 8, after extracting the data we can add the row and column bits information in the resized image or saved in a separate file. For proof of concept we saved it in a text file. And later that file is used to feed GA to make the fitness function, in figure 9 an extraction has been shown. In upper and side textbox containing the information which later is saved in a text file.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 7. Basic Methodology
<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 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>
Literature Review 13/02/2018: Genetic risk of Parkinson's disease dementia due to APOE4 or MAPT
<p>Literature review assessing genetic risk of dementia due to <em>APOE4</em> or <em>MAPT </em>in Parkinson's disease, performed on the 13<sup>th</sup> February 2018. All studies had to fulfil three<em> a priori </em>inclusion criteria:</p> <p>1) Case control studies using clinically diagnosed or pathologically confirmed PD and PDD.</p> <p>2) Time between motor diagnosis and experimental assessment could be defined or estimated.</p> <p>3) Genotype information supplied, allowing the odds ratio (OR) and confidence intervals (CI) to be calculated that aligned with the genotype categories used in this work.</p> <p>For <em>MAPT</em>, a PubMed search for the term “<em>MAPT Parkinson’s dementia</em>” identified 105 potential matches, of which 9 met the inclusion criteria. For <em>APOE4</em>, a PubMed search for the term “<em>APOE Parkinson’s dementia</em>” identified 188 potential matches, of which 19 met the inclusion criteria. Note, the review includes several publications arising from the CamPaIGN cohort; As we were interested in genetic risk as a function of time from diagnosis, we included each unique study time-point. </p>
A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance
<p>Simulated data associated with the paper "A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance".</p> <p>The main dataset is contained within the mermaids.csv, with columns explained within the associated README file. Epigenetic and social network information are contained within the other two datasets</p>
Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Processed data for the manuscript: Cell specificty of regulatory annotations and their genetics effects on gene expression.</p> <p>The code to generate these data is included in the zip-file: regulatoryAnnotations_comparisons-master.zip</p> <p>and at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Individual dataset information:</p> <p>numberOfSegments.dat.gz: Number of segments in each annotation</p> <p>lengthDistributionOfSegments.dat.gz: Length distribution of segments in each annnotation</p> <p>genomeCoverage.dat.gz: Total genome coverage of regions in each annoation</p> <p>overlapFraction.dat.gz: Basepair level overlap between two pairs of annotations</p> <p>annotations_chromatinStateOverlap.dat.gz: Overlap of annotations with chromatin states</p> <p>information.fourcells_enhancer.dat.gz: Information content of the mean enhancer chromatin posterior probability for annotation segments across four cell types</p> <p>information.fourcells_promoter.dat.gz: Information content of the mean promoter chromatin posterior probability for annotation segments across four cell types</p> <p>gwas_enrichment_stats.txt.gz: Enrichment of GWAS SNPs in annotations</p> <p>GTEx_v7.ESI_medianTPM_min0.15.dat.gz : ESI for genes across 50 tissues</p> <p>gtexv7_lcleqtl.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL in annotations</p> <p>gtexv7_lcleqtl.binned_lclESI.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL binned by lclESI in annotations</p> <p>K562.gtexv7_bloodeqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 blood eQTL effect sizes in K562 annotations </p> <p>GM12878.gtexv7_lcleqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 LCL eQTL effect sizes in GM12878 annotations</p> <p>GM12878.dsqtl.prune0.8.maf0.2.ld0.99.annotations.dat.gz : GM12878 DNase QTL effect sizes in GM12878 annotations</p> <p>GM12878.allelicBiasResults.FracRef.downsampled30.annotations.withMAF.dat.gz: GM12878 ATAC-seq allelic bias effect sizes in GM12878 annotations </p> <p> <br> <br> <br> </p>
An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)
<p>Data files for construction of the haploid genetic map for Norway spruce (<em>Picea abies</em>). Available at <a href="https://doi.org/10.1101/292151">https://doi.org/10.1101/292151</a></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>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (Metadata)
<p>Associated demographic, lifestyle, environmental and biochemical metadata accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </p>
Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies
<p>GWAS summary results from the paper</p> <p>The Lancet Respiratory Medicine: http://dx.doi.org/10.1016/S2213-2600(19)30055-4</p> <p>Preprint: https://doi.org/10.1101/427427</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>
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.