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383 results for “growth analysis”
Figure 1 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 1 Mite growth curve of Dermatophagoides pteronyssinus (a) andD. farinae (b) in the semisynthetic culture medium. Data showing the kinetics of the mites' growth in semi-synthetic culture medium are expressed as arithmetic mean ± standard error of triplicates, at each time of growth.
Figure 3 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 3 Kinetics of Der 1 and Der 2 major allergen levels during the growth ofDermatophagoides pteronyssinus (a) andD. farinae (b) in the semi-synthetic culture medium. Data showing the kinetics
Figure 2 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 2 SDS-PAGE IgE-immunoblotting of extracts fromDermatophagoides pteronyssinus (a) and D. farinae (b) mites along their growth. Mw: Molecular weight marker. Numbers on lines indicate
Pseudomonas veronii Tn-seq analysis of soil and liquid growth conditions
<p>Readme file content</p> <p>The files stored here contain the following material as supplementary and source data for the manuscript in submission</p> <p>Fitness-conditional genes for soil adaptation in the bioaugmentation agent Pseudomonas veronii 1YdBTEX2</p> <p>Marian Morales, Vladimir Sentchilo, Nicolas Carraro, Senka Causevic, Dominique Vuarambon & Jan R. van der Meer</p> <p>Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland</p> <p><br> %%%%%%%<br> Folder Tn-seq<br> %%%%%%</p> <p>This folder has the scripts, procedures, raw and intermediate data of the Pseudomonas veronii tn-seq libraries Lib1 and Lib2, at time 0 and incubated in liquid, sand or silt microcosms for up to 51 generations of growth.</p> <p>Subfolders are organized to the steps in the analysis, from the initial experimental design, to the sequence mapping, read cleaning, normalization procedures, essentiality analysis, paired (liq-soil) analysis, gene group plots and the COG-KEGG category analysis.</p> <p>Scripts are text-readable for MATLAB, vs 2021b; or BASH-UNIX slurm command line tools. Data files are either .mat (MATLAB readable) or .xlsx (for the major summary steps).</p> <p>Individual procedures and steps are described in Word documents, which then point to scripts in the script folder, output and the data formats.</p> <p>%%%%%%%<br> Community diversity data<br> %%%%%%</p> <p>This folder has the raw fastq- files of the sample reads of the T1 incubated sand or silt-Tn5 library replicates. Four replicates in total, two sequencing data sets which were combined.</p> <p>Folder also has the meta data as to the sample descriptions, and the OTU-taxa output file from Qiime2 analysis.</p> <p> </p>
Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
<p>The study associated with this dataset proposes a way of performing age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations. Here, you find simulation code in R to produce figures in the manuscript and matrices reporting demographic data upon which code computations are performed.</p>
Dataset: Comparative analysis of 1H NMR and 1H–13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth
<p>NMR datasets used in https://doi.org/10.1021/acs.analchem.8b01196.</p> <p>In the corresponding study, we have performed a comparative chemometric analysis between untargeted <sup>1</sup>H NMR and <sup>1</sup>H-<sup>13</sup>C HSQC NMR analyses of metabolomics samples from <em>Saccharomyces cerevisiae</em> (yeast) extracts. Specifically, yeast was grown in two different liquid media and their metabolism was characterized at 8 different time-points of a 3-day period. The two media used, YPD (Yeast Peptone Dextrose) and YSC (Yeast nitrogen base Synthetic Complete), are broadly used in yeast lab routines, and results from this analysis should be of interest for improving lab methodologies involving yeast.</p>
Red deer growth data and R code for analysis
<p>Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. </p> <p>Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes".</p> <p>This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.</p>
Data and scripts for the analysis of the influence of crop pollinator dependence and growth form on yield decline
<p>Marcelo A. Aizen, Gabriela R. Gleiser, Thomas Kitzberger, Ruben Milla. <strong>Being a tree crop increases the odds of experiencing yield declines irrespective of pollinator dependence </strong>(to be submitted to PCI)</p> <p> </p> <p>Data and R scripts to reproduce the analyses and the figures shown in the paper. All analyses were performed using R 4.0.2.</p> <p> </p> <p><strong>Data</strong></p> <p>1. FAOdata_21-12-2021.csv</p> <p>This file includes yearly data (1961-2020, column 8) on yield and cultivated area (columns 6 and 10) at the country, sub-regional, and regional levels (column 2) for each crop (column 4) drawn from the United Nations Food and Agriculture Organization database (data available at <a href="http://www.fao.org/faostat/en">http://www.fao.org/faostat/en</a>; accessed July 21-12-2021). [Used in Script 1 to generate the synthesis dataset]</p> <p>2. countries.csv</p> <p>This file provides information on the region (column 2) to which each country (column 1) belongs. [Used in Script 1 to generate the synthesis dataset]</p> <p>3. dependence.csv</p> <p>This file provides information on the pollinator dependence category (column 2) of each crop (column 1).</p> <p>4. traits.csv</p> <p>This file provides information on the traits of each crop other than pollinator dependence, including, besides the crop name (column1), the variables type of harvested organ (column 5) and growth form (column 6). [Used in Script 1 to generate the synthesis dataset]</p> <p>5. dataset.csv</p> <p>The synthesis dataset generated by Script 1.</p> <p>6. growth.csv</p> <p>The yield growth dataset generated by Script 1 and used as input by Scripts 2 and 3.</p> <p>7. phylonames.csv</p> <p>This file lists all the crops (column 1) and their equivalent tip names in the crop phylogeny (column 2). [Used in Script 2 for the phylogenetically-controlled analyses]</p> <p>8.phylo137.tre</p> <p>File containing the phylogenetic tree.</p> <p> </p> <p><strong>Scripts</strong></p> <p>1. dataset</p> <p>This R script curates and merges all the individual datasets mentioned above into a single dataset, estimating and adding to this single dataset the growth rate for each crop and country, and the (log) cumulative harvested area per crop and country over the period 1961-2020.</p> <p>2. analyses</p> <p>This R script includes all the analyses described in the article’s main text.</p> <p>3. figures</p> <p>This R script creates all the main and supplementary figures of this article.</p> <p>4. lme4_phylo_setup</p> <p>R function written by Li and Bolker (2019) to carry out phylogenetically-controlled generalized linear mixed-effects models as described in the main text of the article.</p> <p> </p> <p><strong>References</strong></p> <p>Li, M., and B. Bolker. 2019. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4- verse. Zenodo. https://doi.org/10.5281/zenodo.2639887.</p>
The raw microarray data and the differential expression analysis results from "Manipulating the growth environment through co-culture to enhance stress tolerance and viability of probiotic strains in the gastrointestinal tract".
<p>The signal data for each spot were subsequently quantified by using Feature Extraction software (Agilent Technologies).M1.txt to M5.txt: monoculture; C1.txt to C5.txt: co-culture; P1.txt to P5.txt: pH-controlled monoculture. The differential expression analysis results were obtained by using limma.</p>
Comparative analysis of helminth infectivity: growth in intermediate hosts increases establishment rates in the next host
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Supporting information for: Age-specific sensitivity analysis of stable, stochastic and transient growth for stage-classified populations
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Data from: Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis
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Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
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Dung beetles increase plant growth: A meta-analysis
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Can diet composition estimates using stable isotope analysis of feathers predict growth and condition in nestling mountain bluebirds (Sialia currucoides)
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Weights and lengths from retrospective growth analysis of different stem age classes of Betula nana, 1995, Arctic LTER, Toolik Lake, Alaska.
This data file contains the data on weights and lengths from retrospective growth analysis of different stem age classes of Betula nana ramets from the LTER Nutrient and Warming manipulations in tussock tundra at Toolik Lake.
Influence of polymorphisms in the vascular endothelial growth factor gene on allograft rejection after kidney transplantation: a meta-analysis
<p><span><b><i>Purpose</i></b><i> </i>Reported associations of allograft rejection in kidney transplant patients with <i>VEGF</i> single nucleotide polymorphisms (SNPs) have been inconsistent between studies, which prompted a meta-analysis to obtain more precise estimates.</span></p> <p><span><b><i>Methods</i></b><i> </i>Using the PICO elements, kidney transplant patients (P) were compared by genotype data between rejectors (I) and non-rejectors (C) in order to determine the risk of allograft rejection (O) attributed to the <i>VEGF</i> SNPs. Literature search of four databases yielded seven articles. To calculate risks for allograft rejection, four SNPs were examined. Meta-analysis treatments included outlier and subgroup analyses, the latter was based on ethnicity (Indians/Caucasians) and rejection type (acute/chronic). Multiple comparisons were corrected with the Bonferroni test. </span></p> <p><span><span><b><i>Results</i></b> Five highly significant outcomes (P<sup>a</sup> < 0.01) survived Bonferroni correction, one of which showed reduced risk for the <i>var</i> allele (OR 0.61, 95% CI 0.45-0.82). The remaining four indicated increased risk for the <i>wt</i> allele where the chronic rejection (OR 2.10, 95% CI 1.36-3.24) and Indian (OR 1.44, 95% CI 1.13-1.84) subgroups were accorded susceptibility status. </span></span></p> <p><span><b><i>Conclusions</i></b> Risk associations for renal allograft rejection were increased and reduced on account of the <i>wt</i> and <i>var</i> alleles, respectively. These findings could render the <i>VEGF</i> polymorphisms useful in the clinical genetics of kidney transplantation. </span></p>
The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings
<p>Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (<a href="http://www.issi-society.org/">http://www.issi-society.org/</a>). </p> <p>We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.</p>
Comparative analysis of larval growth in Lepidoptera reveals instar-level constraints
1. Juvenile growth trajectories evolve via the interplay of selective pressures on age and size at maturity, and developmental constraints. In insects, the moulting cycle is a major constraint on larval growth trajectories. 2. Surface area to volume ratio of a larva decreases during growth, so renewal of certain surfaces by moulting is likely needed for the maintenance of physiological efficiency. A null hypothesis of isometry, implied by Dyar's Rule, would mean that the relative measures of growth remain constant across moults and instars. 3. We studied ontogenetic changes and allometry in instar-specific characteristics of larval growth in 30 lepidopteran species in a phylogenetic comparative framework. 4. Relative instar-specific mass increments (RMI) typically, but not invariably, decreased across instars. Ontogenetic change in RMIs varied among families with little within-family variation. End-of-instar growth deceleration (GD) became stronger with increasing body size across instars. Across-instar change in GD was conserved across taxa. Ontogenetic allometry was generally non-isometric both in RMI and GD. 5. Results indicate that detailed studies on multiple species are needed for generalizations concerning growth trajectory evolution. Developmental and physiological mechanisms affecting growth trajectory evolution show different degrees of evolutionary conservatism, which must be incorporated into models of age and size at maturation.
Population Genomics Analysis of 30 Samples from Each of Multiple Populations Reveals Signatures of Selection from Breeding for Rapid Growth and Apical Dominance in Paulownia fortunei
<p><i>Paulownia fortunei</i> is an ecologically and economically valuable tree cultivated for its rapid growth and high-quality timber. To enhance <i>Paulownia</i> germplasm, we have developed the elite variety QingT with patented advantages in growth rate and apical dominance. To illuminate the genetic basis of QingT's superior traits, here we harness comparative population genomics to analyze genomic variation patterns between QingT and common <i>Paulownia</i>. We performed whole-genome re-sequencing of 30 QingT and 30 common samples, detecting 15.6 million SNPs and 2.6 million indels. Phylogeny and population structure analyses robustly partitioned common and QingT into distinct groups which indicate robust genome stabilization. QingT exhibited reduced heterozygosity and linkage disequilibrium decay compared to common <i>Paulownia</i>, reflecting high recombination, indicating hybridizing effects with common white-flowered string is the source of its patented advantages. Genome selection scans uncovered 25 regions of 169 genes with elevated nucleotide diversity, indicating selection sweeps among groups. Functional analysis of sweep genes revealed upregulation of ribosomal, biosynthesis and growth pathways in QingT, implicating enhanced protein production and developmental processes in its rapid growth phenotype. This study's insights comprehensively chart genomic variation during <i>Paulownia</i> breeding, localizing candidate loci governing agronomic traits, and underpinnings of future molecular breeding efforts to boost productivity. </p>
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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)
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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.