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7,742 results for “individuals”
Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography - Supplementary Material
<p>Supplementary Material belonging to the manuscript entitled "Comparative analysis of autofocus criteria in reconstructed images of individual plankton for digital in line holography", by Moreno et al., submitted to the journal OPTIK.</p>
The Impact of Cognitive Distraction on Gustatory Perception in Overweight Individuals Implications for Distracted Eating
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HDX-MS raw data for "Structural elucidation of full-length Pfs48/45 in complex with potent mAbs isolated from a naturally exposed individual"
<p>These are the raw files of the HDX-MS data for the paper "Structural elucidation of full-length Pfs48/45 in complex with potent mAbs isolated from a naturally exposed individual."</p><p>Peptide identification files are provided in MGF/MZID, as well as in CSV format. </p>
Datasets for "Individual and environmental factors influencing preen gland's morphology and physiology in the barn owl (Tyto alba)"
<p><span>Avian preen gland helps birds cope with their environment, although its overall functioning remains unclear. We shed light on the complexity of the preen gland’s functioning by studying how multiple factors associate with gland morphology (size and shape) and physiology (wax secreted) in barn owls (<em>Tyto alba</em>). Individual factors (sex, breeding stage, body condition) were more important predictors of preen gland than environmental factors (temperature, humidity, brood size). Sex, depending on breeding stage in adults, influenced preen gland traits, pointing to the preen gland’s regulation by sex hormones and a greater pressure on females to protect their eggs, offspring and themselves throughout reproduction. Adults and fledglings in better condition had larger glands, pointing to the existence of physiological costs. Temperature and humidity, in interaction with plumage coloration, also influenced but to a lesser extent preen gland traits, suggesting that melanin pigmentation and preen gland act as superseding mechanisms when protecting plumage against microorganisms. Finally, fledglings living in larger broods had larger glands, suggesting a role for the social environment in preen gland’s functioning. Overall, our study supports the idea that preen gland functions in diverse biological contexts within the same species and is thus subject to multiple selective pressures.</span></p>
Carnosine levels in the same individuals as measured by different modalities
<p>Carnosine is an endogenous di-peptide (b-alanine -L- histidine) involved in maintaining tissue homeostasis. It is most abundant in skeletal muscle where its concentration has been determined in biopsy samples using tandem mass spectrometry (MS-MS). Carnosine levels can also be assessed in intact leg muscles by proton magnetic resonance spectroscopy (1H-MRS) or in blood and urine samples using mass spectrometry. Nevertheless, it remains uncertain how carnosine levels from these distinct compartments are correlated with each other when measured in the same individual. Furthermore, it is unclear which measurement modality might be most suitable for large-scale clinical studies. Hence, in 31 healthy volunteers, we assessed carnosine levels in skeletal muscle, via 1H-MRS, and in erythrocytes and urine by MS-MS. While muscle carnosine levels were higher in males, there was no sex-associated difference in urinary or erythrocyte levels. In a linear regression model adjusted for age, sex, race, and diet, there was a positive association between erythrocyte and urinary carnosine. However, no association was observed between 1H-MRS and erythrocytes or urinary measures. In the relationship between muscle versus urinary and erythrocyte measures, females had a positive association, while males did not show any association. We also found that 1H-MRS measures were highly sensitive to location of measurement. Thus, it is uncertain whether 1H-MRS can accurately and reliably predict endogenous carnosine levels. In contrast, urinary and erythrocyte carnosine measures may be stable and in greater synchrony, and given financial and logistical concerns, may be a feasible alternative for large-scale clinical studies.</p>
2D Monolayer Cardiomyocytes of Healthy control individual differentiated from iPSC
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Data and code for: Partitioning variance in a signaling trade-off under sexual selection reveals among-individual covariance in trait allocation
<p>Understanding the evolution of traits subject to trade-offs is challenging because phenotypes can (co)vary at both the among- and within-individual levels. Among-individual covariation indicates consistent, possibly genetic, differences in how individuals resolve the trade-off, while within-individual covariation indicates trait plasticity. There is also the potential for consistent among-individual differences in behavioral plasticity, although this has rarely been investigated. We studied the sources of (co)variance in two characteristics of an acoustic advertisement signal that trade off with one another and are under sexual selection in the gray treefrog, <em>Hyla chrysoscelis</em>: call duration and call rate. We recorded males on multiple nights calling spontaneously and in response to playbacks simulating different competition levels. Call duration, call rate, and their product, call effort, were all repeatable both within and across social contexts. Call duration and call rate covaried negatively, and the largest covariance was at the among-individual level. There was extensive plasticity in calling with changes in social competition, and we found some evidence for among-individual variance in call rate plasticity. The significant negative among-individual covariance in trait values is perpendicular to the primary direction of sexual selection in this species, indicating potential limits on the response to selection.</p>
Supplementary Materials for the manuscript "Inter-individual speed variation of bacteria dispersing on fungal highways"
<p>This repository includes all datasets used and created for the manuscript "Inter-individual speed variation of bacteria dispersal on fungal highways". </p> <p>----5 videos were used for producing the dispersal speed distribution of the RFP labelled Pseudomonas putida UWC1 cells:</p> <p>100x_rfp_2days***.gif</p> <p>----5 videos were used for producing the dispersal speed distribution of the GFP labelled Pseudomonas putida KT2440 cells:</p> <p>100x_gfp_2days***.gif</p> <p>----Data used for location tracking of the two videos shown in Figure 1:</p> <p>100x_rfp_2days028.nd2</p> <p>TrackMate capture of 100x_rfp_2days028.tif</p> <p>100x_gfp_2days005.nd2</p> <p>TrackMate capture of 100x_gfp_2days005.tif</p> <p>----5 Speed distribution raw data files for the RFP labelled cells:</p> <p>rfp_2days***_11fps_2_2movement.csv</p> <p>----5 Speed distribution raw data files for the GFP labelled cells:</p> <p>gfp_2days***_11fps_2_movement.csv</p> <p>----R script for calculating speed distribution of cells:</p> <p>speed calculation.R</p> <p>----Mathematica Notebook file for data visulization:</p> <p>HistCompare.nb</p> <p>----10 best fits of probability distribution functions and goodness-of-fit criteria analysis of the speed of RFP labelled cells:</p> <p>rfp-FitTable.csv</p> <p>----10 best fits of probability distribution functions and goodness-of-fit criteria analysis of the speed of GFP labelled cells:</p> <p>gfp-FitTable.csv</p>
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) - code and data sets
<p>This repository contains the scripts for the paper in revision to the AAPS J: Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) </p> <p>Authors: Niels Hendrickx, MSc, France Mentré, MD, PhD, Andreas Traschütz, MD, PhD, Cynthia Gagnon, PhD, Rebecca Schüle, MD, ARCA Study Group, EVIDENCE-RND consortium, Matthis Synofzik, MD, Emmanuelle Comets, PhD</p> <p>A simulated dataset (<strong>simulated_arsacs.csv</strong>) has been included in the repository to make the code executable as a standalone. Four main scripts have been provided in addition with the present Readme describing the files. The repository also includes 3 R objects and 2 folders which will be overwritten when the scripts are run, and are included as examples of the expected outputs. The main scripts are:</p> <p>- <strong>Script_imputation_selection.R</strong>: runs the covariate selection method. It uses a simulated dataset provided in the depot. The multiple imputation model is hardcoded as an input to the mice package to generate 10 imputed datasets, saved in current_directory/imputed_data_sets/df_arsacs_mi_i.csv. The script then runs the covariate selection method. The script prints out the list of selected covariates and returns a saemixObject containing the fit of the selected covariate model.<br> After the script executes, a list will be saved with the name of the selected covariates in the current directory (an example is included under the name "cov_matrix_model.RData" in the repository), the output of the selection, containing the whole history of runs will be saved under "final_covariate_model.RData", the list of selected covariate names will be saved under "list_covariates.RData".</p> <p>- <strong>source_mi.R</strong>: contains the functions used by Script_imputation_selection.R</p> <p>- <strong>script_bootstrap_indfit.R</strong>: This script loads "cov_matrix_model.RData" containing the matrix of covariate effects (used by saemix) and "list_covariates.RData", the list of covariates included, fits the model on the imputed data sets and computes its bootstrap distribution for each imputed data set (in the script, using only 20 samples for computation time, saved in current_directory/bootstrap/boot.arsacs.case.mi.i). It then computes the mean parameter and relative standard error of each parameter. It then computes the conditional distribution of each patient in each bootstrap samples and returns a data frame of individual predictions. The script will then plot 4 indivudal predictions. </p> <p>-<strong> source_bootstrap.R</strong>: contains the functions used by script_bootstrap_indfit.R</p> <p>Both scripts need the saemix package to run, which we haven’t included in the repository as it is freely available on the CRAN (https://cran.r-project.org/web/packages/saemix/index.html). Additional libraries we make use of in the code (MICE, tidyverse, ggplot2) also need to be installed prior to execution. <br>The R code provided can be further customised to be adapted to different scenarios.</p> <p>For the code to run, it is preferable to unzip the whole folder and set the working directory to the source file location as the script uses the "bootstrap" and "imputed_data_sets" sub-folders</p> <p>To execute this code, assuming the required libraries are available in the local R installation, please open an R session and run:<br>source("Script_imputation_selection.R") # for the covariate selection method (runtime: 3h on a i7-8565U laptop)<br>source("script_bootstrap_indfit.R") # to obtain individual trajectories (runtime: 1h on a i7-8565U laptop)</p>
Point clouds and QSM for 598 individually scanned tree branches to support "Terrestrial laser scanning to reconstruct branch architecture from harvested branches"
<p>A collection of 598 harvested branches scanned in high resolution using terrestrial LiDAR. Branches were collected from GEM forest plots in Malaysia, Australia and Brazil.</p> <p>Branch nomenclature is <em>PLOT</em>-<em>TREE</em>-B<em>N</em><S or SH> where <em>PLOT </em>codes can be found in the manuscript, <em>N</em> refers to the sample number of branch harvested from a tree, and S and SH refer to sun or shade branch respectivetly.</p> <p>For each branch there are 3 files: </p> <ul> <li>Unfiltered point clouds (raw_pc) are clipped from the original data only and have had no post processing applied</li> <li>Filtered point clouds (filtered_pc) have been filtered according to the steps in the manuscript</li> <li>QSMs were produced using <em>treegraph </em>(<a href="https://doi.org/10.5281/zenodo.5226212">https://doi.org/10.5281/zenodo.5226212</a>)</li> </ul> <p>Polygon File Format (.ply) files can be viewed in software such as CloudCompare.</p> <p>Full details of methods can be found in: Wilkes, P., Shenkin, A., Disney, M., Malhi, Y., Bentley, L. P., & Vicari, M. B. (2021). Terrestrial laser scanning to reconstruct branch architecture from harvested branches. <em>Methods in Ecology and Evolution</em>, 12, 2487–2500. <a href="https://doi.org/10.1111/2041-210X.13709">https://doi.org/10.1111/2041-210X.13709</a></p>
Synchronization of in vivo MacoNPV-A baculovirus infection by analysis of individual Mamestra configurata larval guts
<p><span>Many studies have examined the gene expression of baculoviruses during host infection by infecting cultured insect host cells; however, only a few have attempted to characterize the interaction between baculoviruses and insect larvae, which more accurately models the virus-insect relationship. The greatest challenge in assessing gene expression profiles <em>in vivo</em> in the larval gut is the lack of infection synchronization compared to cultured cells. Working with bertha armyworm, <em>Mamestra configurata</em>, larvae infected with the baculovirus Mamestra configurata nucleopolyhedrovirus-A<em> </em><span> </span>(MacoNPV-A), viral gene expression was measured using droplet-digital PCR showing that the rate of infection in individual insects varies extremely widely. Subsequent RNA-seq of individual guts revealed that gene expression patterns were consistent in individuals with similar levels of viral gene expression. Therefore, the best approach for analyzing baculovirus gene expression <em>in vivo</em> is to use the expression of an early viral gene as a means to select individuals that are closely matched in infection progress to produce the most synchronized infection cohorts for measuring gene expression profiles. In this way, a profile of gene expression was identified very early in infection that would have been masked by the activity of a few very robustly infected individuals present in sample pools made up of multiple individuals.</span></p>
TIFF file for the frame-wise tracking trajectories in Figure 1 of the manuscript "Assessing the speed of individual bacteria dispersing on mycelial networks"
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Memory trajectories in lonely individuals: An analysis of the Survey of Health, Aging, and Retirement in Europe (SHARE)
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Spatial Mapping for Visually Impaired Individuals
<p>By understanding the strengths and limitations of different assistive devices, blind individuals can make informed choices to improve their independence and quality of life.</p>
Social clues in risky pools: Perceived conspecific rivals modify individual mosquito oviposition decisions in response to larval predation and competition
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Data for: A neural correlate of individual odor preference in Drosophila
<p>Data associated with: A neural correlate of individual odor preference in <em>Drosophila</em></p> <p>Abstract: Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of <em>Drosophila</em> odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3,062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.</p>
Raw GC-ToF-MS and processed data from individuals sampled in allopatric and contact zones and MZmine 3.9.0 and Rstudio analysis
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Supplementary material S16: All honeycomb jolting pulses shown as individual spectrograms.
<p>Video showcasing all 28 honeycomb jolting pulse spectrograms. All pulses are showcased in the same way as those seen in S12 and S14 and share the same analysis. The magnitude of acceleration is logarithmic (to the base 10), where the highest magnitude is 6.5x10<sup>-4 </sup>m/s<sup>2</sup>, and the lowest magnitude set to be 1/40 of the maximum.</p>
Supplementary material S12: All petri-dish jolting pulses shown as individual spectrograms.
<p>Video showcasing the full collection of 250 petri-dish <em>Varroa</em> jolting pulses. Every pulse in the collection is aligned to the centre of the window and presented in decreasing order of strength. The full breadth of variation can be seen between the jolting pulses when viewing them in this way. The magnitude of acceleration is logarithmic (to the base 10) where the highest magnitude is 2x10<sup>-3 </sup>m/s<sup>2</sup> and the lowest magnitude forced to be 1/40 of the maximum to reduce the contribution of meaningless noise.</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.