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16 results for “Counting process”
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>
Processed RNA expression count data from Groen et al.: The strength and pattern of natural selection on rice gene expression
<p>We assessed transcriptome variation in populations of 216 accessions of rice, <em>Oryza sativa</em>, which represented all major varietal groups including indica and japonica. During the 2016 Philippines dry season the accessions were planted in triplicate (with two accessions planted in triplicate three times as replicated checks) in identical alpha-lattice layouts of 660 plots in two fields: a continuously wet paddy, and a field where plants were exposed to intermittent drought in the vegetative and reproductive stages. We measured transcript levels in leaf blades of 50-day-old plants at 33 days after seedling transplant, and 17 days after withholding water in the dry field, using a liquid automation-based 3’ mRNA-seq quantification approach. Samples were multiplexed in batches of 96 per library. Raw sequencing data are available at the SRA in BioProject accession number PRJNA588478. A key to the raw sequencing data in this BioProject can be found in the metadata of the processed RNA expression count data here.</p>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
Processed read counts from macrophage RNA-seq and ATAC-seq experiments
<p><strong>RNA-seq files:</strong></p> <ol> <li>RNA_count_matrix.txt.gz - raw read counts</li> <li>RNA_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>RNA_gene_metadata.txt.gz - information about the genes</li> <li>RNA_sample_metadata.txt.gz - information about the samples</li> </ol> <p><strong>ATAC-seq files:</strong></p> <ol> <li>ATAC_count_matrix.txt.gz - raw read counts</li> <li>ATAC_cqn_matrix.txt.gz - read counts quantile normalised with the cqn R package</li> <li>ATAC_peak_metadata.txt.gz - peak coordinates and other metadata</li> <li>ATAC_sample_metadata.txt.gz - sample metadata</li> <li>ATAC_consensus_peaks.gff3.gz - GFF3 file containing the GRCh38 coordinates of the peaks</li> </ol>
Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
The evolutionary dynamics of complex ecological traits – including multistate representations of diet, habitat, and behavior – remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. A necessary first step in the analysis of many complex traits is therefore to categorize species into a pre-determined number of univariate ecological states, but this procedure can lead to distortion and loss of information. This approach also confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations for individual species into the statistical inference model. In this study, we develop a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Our approach is expressly designed to model ecological traits that are multidimensional and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.
Processed RNA expression count data and metadata from Gupta et al.:Systems genomics of salinity stress response in rice
<p>We assessed gene expression variation in a population of 130 accessions of rice (Oryza sativa) belonging to the major varietal group indica. The field experiment was conducted in the dry season of 2017 at IRRI in Los Banos, Laguna, Philippines. Seeds from each accession were sown on December 16, 2016, and seedlings were then transplanted into the experimental fields at 17 days after sowing (DAS), on January 5, 2017. The field experiments was conducted across two locations close-by: one non-salinized normal field and the other salinized field. Both field environments used a randomized complete block experimental design with each accession planted in three replicates. Each experimental plot included the accessions NSICRC 222 and NSICRC 182 serving as border rows. The application of salt in Block L5 started on January 19, 2017 when the plants were 31 days old. The salinity level was monitored by recording electrical conductivity (EC), using EC meters installed in each of the parcels at a depth of 30 cm. The salinity levels were then maintained at 6 dS/m (considered mild to moderate salinity stress) until maturity. Management and maintenance of the fields included the application of basal fertilizer, spraying of insecticides against thrips and removal of plants potentially infected with the rice tungro virus disease. Tissue collection for transcriptome. Briefly, leaf collection was done at 38 DAS (8 days after the beginning of the salt treatment) in the non-saline and saline field. Transcript levels were measured using a liquid automation-based 3 prime mRNA-seq quantification approach. Samples were multiplexed in batches of 96 per library. Raw sequencing data are available at the SRA in BioProject accession number PRJNA1010833. A key to the raw sequencing data in this BioProject can be found in the metadata of the processed RNA expression count data here</p>
Data from: Counting chicks before they hatch: Extending the observed lifetime to better characterise evolutionary processes in the wild
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Complex ecological phenotypes on phylogenetic trees: a Markov process model for comparative analysis of multivariate count data
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Genome-wide haplotype counting in diatoms S. robusta and P. tricornutum - processed datasets
<p><strong>Datasets used for genome-wide haplotype counting in <em>S. robusta</em> and <em>P. tricornutum</em> next-generation sequencing data.</strong></p> <p>For <em>S. robusta</em> and <em>P. tricornutum </em>genome-wide haplotype counting, reliable SNPs set was first identified in ILLUMINA short-read sequencing datasets and then used for counting of the number of haplotypes in the PacBio RS II and MinION long reads. The ILLUMINA and PacBio data of <em>S. robusta</em> were downloaded from <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB36614">https://www.ebi.ac.uk/ena/browser/view/PRJEB36614</a> and ILLUMINA and Minion data of <em>P. tricornutum</em> were downloaded from <a href="https://www.ebi.ac.uk/ena/browser/view/PRJNA487263">https://www.ebi.ac.uk/ena/browser/view/PRJNA487263</a>.</p> <p>Reference genome assembly for<em> S. robusta</em>: CAICTM010000001-CAICTM010004752 (European Nucleotide Archive) and</p> <p>Reference genome assembly for<em> P. tricornutum</em>: GCA_000150955.2 (European Nucleotide Archive).</p> <p>Uploaded files: </p> <p>-.bam files containing S. robusta PacBio self-corrected CCS reads aligned to reference and processed and P. tricornutum self-corrected MinION reads aligned to reference and processed: </p> <p><em>S_robusta_aligned_corrected_PacBio_reads.bam</em></p> <p><em>P_tricornutum_aligned_corrected_MinION_reads.bam</em></p> <p>-.table files with selected reliable SNPs used for haplotype counting with CHROM, POSITION, REFERENCE and ALTERNATIVE allele </p> <p><em>S_robusta_SNPs.table</em></p> <p><em>P_tricornutum_SNPs.table</em></p> <p> </p>
Processed gene expression read counts from the AcLDL and IFNg+Salmonella studies
<p>Raw and normalised read count matrices together with metadata.</p>
Counting and Sequential Information Processing in Mechanical Metamaterials
<p>This dataset contains images and driving protocols used in the paper: "Counting and Sequential Information Processing in Mechanical Metamaterials", published in Physical Review Letters.</p> <p>In this paper we demonstrate "beam counters"; metamaterials that count driving cycles. We demonstrate the counters sensitivity to various driving amplitudes and show how this might be used to infer more information from the applied driving and how to construct a "lock and key" metamaterial with an internal state that can only be reached with one unique input sequence.<br> <br> The data in this replication package is hierarchically organized by making use of a directory per figure in the paper. Contained in this replication package are a number of files used in the figure as described below.</p> <p>Experimental data from the measurement setup is stored in matched subdirectorys with a name, for example "001/" and a file "times_001.csv". Here the subdirectory contains images taken of the sample and the csv file contains the times at which the images were taken, the change in pixel intensity from one image to the next (sampled at a shorter interval than the saved images) and the position of the driving stage and a measured inductive position.<br> </p> <ul> <li>fig1 <ul> <li>001/<br> - directory containing the original unedited figures comparing above and below D*</li> <li>001_cropped_selection/<br> - directory containing a cropped selection of the images used in the figure</li> <li>times_001.csv</li> </ul> </li> <li>fig2 <ul> <li>002/<br> - directory containing images of the ten counter being compressed with marked m-beams</li> <li>times_002.csv</li> <li>Kymograph.tif<br> - A kymograph image calculated from 002 by filtering the colored m-beams and taking stacking a single horizontal slice from all of the images.</li> <li>kymograph.svg<br> - The plotted horizontal position of the beam traces visible in Kymograph.tif</li> </ul> </li> <li>fig3 <ul> <li>000/<br> - directory containing images of the compression of counter with uncut a-beams</li> <li>000_cropped/</li> <li>times_000.csv</li> <li>004/<br> - directory containing images of the same counter with the a-beams slit cut</li> <li>004_cropped/</li> <li>times_004.csv</li> <li>comparison/<br> - directory containing a selection of cropped images at comparable driving used in the figure</li> </ul> </li> <li>fig4 <ul> <li>Alternative starting condition/ <ul> <li>001/<br> - directory containing images of the ten-counter compressed in an alternative starting state</li> <li>001_cropped/<br> - directory with cropped versions of the images in 001/ and further cropped versions in further subdirectories</li> <li>001_selection/</li> <li>times_001.csv</li> </ul> </li> <li>BBAC machine <ul> <li>006/<br> - directory containing images of the four counters making up the BBAC machine under a driving of BAC</li> <li>006_state/<br> - selection of images used in the paper with a subdirectory contining cropped versions</li> <li>times_006.csv</li> </ul> </li> </ul> </li> </ul>
Cell counts (per liter) by size groups of diatoms, autotrophic and heterotrophic plankton, via epifluorescent microscopy (EPI) from CCE LTER process cruises in the California Current region, 2006 - 2016.
Microbial community assemblages of the California Current Ecosystem (CCE) are assessed for abundance of diatoms, autotrophic (dinoflagellate and other eukaryotes) and heterotrophic (dinoflagellate and other eukaryotes) plankton using high-throughput digital epifluorescence microscopy (EPI). Samples to estimate the nano- and microplankton (0.2-2.0-µm and 2.0-20-µm size, respectively) are collected at various depths, preserved, stained, and filtered onto a membrane filter and mounted on a glass microscope slide aboard the process cruises (since 2006, ongoing). Slides are then frozen at -80°C for subsequent imaging and analysis in the laboratory onshore.
Size group (pico, nano, micro) and group total carbon estimates from cell counts via epifluorescent microscopy (EPI) of heterotrophic and autotrophic plankton from CCE LTER process cruises in the California Current region, 2006 - 2016
Microbial community assemblages of the California Current Ecosystem (CCE) are assessed for biomass of heterotrophic (dinoflagellate and other eukaryotes) and autotrophic (dinoflagellate and other eukaryotes) plankton using high-throughput digital epifluorescence microscopy (EPI). Samples to estimate the nano- and microplankton (0.2-2.0-µm and 2.0-20-µm size, respectively) are collected at various depths, preserved, stained, and filtered onto a membrane filter and mounted on a glass microscope slide aboard the process cruises (since 2006, ongoing). Slides are then frozen at -80°C for subsequent imaging and analysis in the laboratory onshore. Carbon biomass is computed from cell biovolumes.
Picophytoplankton and bacteria total carbon estimates from cell counts analyzed with flow cytometry (FCM) from CCE LTER process cruises in the California Current region, 2006 - 2017.
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from various depths. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact. FCM abundance estimates for each are converted to carbon biomass equivalents using mixed-layer estimates.
Home Monitoring of Complete Blood Count Performed by Patients - a Pilot Study on the Implementation Process in South Baltic Countries.
ClinicalTrials.gov study NCT06809101. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
Processing RNASequencing files to a count table for the IL-10 project
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