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1,812 results for “dissection”
FIG. 3 in Les Saxifraga L. de l'herbier Luizet: informatisation et numérisation des types et dissections
FIG. 3. — Étude anatomique comparative de spécimens de Saxifraga × jouffroyi Rouy (P00662724). Figurent dans la colonne de gauche les tailles des pétales, dans la colonne du milieu les pétales séparés ainsi que les corolles entières, et dans la colonne de droite les informations de récolte.
FIG. 5 in Les Saxifraga L. de l'herbier Luizet: informatisation et numérisation des types et dissections
FIG. 5. — Exemple de distorsion entre le binôme figurant sur la fiche imprimée de Guétrot et celui figurant sur les notes manuscrites de Luizet (suite).
FIG. 5 in Les Saxifraga L. de l'herbier Luizet: informatisation et numérisation des types et dissections
FIG. 5. — Exemple de distorsion entre le binôme figurant sur la fiche imprimée de Guétrot et celui figurant sur les notes manuscrites de Luizet.
FIG. 2 in Les Saxifraga L. de l'herbier Luizet: informatisation et numérisation des types et dissections
FIG. 2. — Gros plan sur la dissection n°741 de Luizet figurant sur la part P00659749. Spécimen de Saxifraga maubermeana Luizet & Soulié (Isotype), récolté le 23 août 1912 par Soulié, parmi les éboulis humides au pic de Maubermé, côté Catalan (Espagne).
Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data
<p>Processed Datasets for:</p> <p>EGA Study ID: EGAS00001006141</p> <p>EGA Dataset ID: EGAD00001008662</p> <p> </p> <p>Find processed files and arrow files</p> <p> </p> <p>Abstract:</p> <p>Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8<sup>+</sup> TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.</p>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
Dissecting glial scar formation by spatial point pattern and topological data analysis
<p>These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis". </p> <p>Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline.</p> <p><strong>AUTHORS</strong><br> Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman</p> <p><strong>KEYWORDS</strong><br> Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia, </p> <p><br> <strong>1. STUDY DESCRIPTION </strong> <br> This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals. </p> <p><strong>2. EXPERIMENTAL CONDITIONS</strong><br> Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1 <br> <br> <strong>3. FILE DESCRIPTION</strong></p> <p><strong>- GT5X_Gfap_Iba1_NeuN.rar: </strong>Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters.</p> <p>Objective: Fluar 5x/0.25 M27<br> Scaling per pixel: 1.300 x 1.300 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 3 s<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 4 s<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 1 s<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 50 ms</p> <p>We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim.</p> <p><strong>- GT10X_Gfap_Iba1_NeuN.rar:</strong> Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters.</p> <p>Objective: ECM paln-NeoFluar 10x/0.30 M27<br> Scaling per pixel: 0.45 x 0.45 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 200 ms<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 250 ms<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 100 ms<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 10 ms</p> <p><br> We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.<br> <br> For 5x and 10x images, the following naming strings apply:</p> <p>GT5x: Research project identifier indicating the magnification<br> M01(n): Animal ID<br> 5D(n): Days post-ischemia. 0D refers to healthy (naive) animals. <br> Scene1(n): Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior.</p> <p><strong>- PointPatterns_10x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_10x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- PointPatterns_5x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_5x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- QupathProjects_5x.rar: </strong>QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository. </p> <p><strong>- QupathProjects_10x.rar:</strong> QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders. </p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
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Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
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Data from: Dissecting factors behind temporal trends in the timing of breeding in two songbird species – evolutionary change or phenotypic plasticity?
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Marine demosponge rheology / dissection microscopy
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Data from: Dissecting the genetic architecture of a stepwise infection process
How a host fights infection depends on an ordered sequence of steps, beginning with attempts to prevent a pathogen from establishing an infection, through to steps that mitigate a pathogen's control of host resources, or minimising the damage caused during infection. Yet empirically characterising the genetic basis of these steps remains challenging. Although each step is likely to have a unique genetic and environmental signature, and may, therefore, respond to selection in a specific way, events that occur earlier in the infection process can mask or overwhelm the contributions of subsequent steps. In this study, we dissect the genetic architecture of a stepwise infection process using a quantitative trait loci (QTL) mapping approach. We control for variation at the first line of defence against a bacterial pathogen and expose downstream genetic variability related to the host's ability to mitigate the damage pathogens cause. In our model, the water-flea Daphnia magna, we found a single major effect QTL, explaining 64% variance, that is linked to the host's ability to completely block pathogen entry by preventing their attachment to the host oesophagus; consistent with the detection of this locus in prior studies. In susceptible hosts allowing attachment, however, a further 23 QTL, explaining between 5 to 16% variance, were mapped to traits related to the expression of disease. The general lack of pleiotropy and epistasis for traits related to the different stages of the infection process, together with the wide distribution of QTL across the genome, highlights the modular nature of a host's defence portfolio, and the potential for each different step to evolve independently. We discuss how isolating the genetic basis of individual steps can help resolve discussion over the genetic architecture of host resistance.
Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value
<p>readMe.txt (this file) /Coleus_scans.zip: This directory contains 3 sub-directories:</p> <p>1. raw_scans: Raw scans of coleus leaves. Data collection described below.</p> <p>2. binary_leaves: A zip file of individual binary leaves isolated from the raw scans. Data processing described below.</p> <p>3. colored_leaves: A zip file of individual colored leaves isolated from the raw scans using the binary leaf silhouettes. Data processing described below.</p> <p>Data collection</p> <p>Coleus leaf scans were collected from a starting population of 50,000 seedlings that were originally harvested from 133 open-pollinated mother plants in early January in Gainesville, FL. We organized the seedlings into families based on their maternal parents, grew the plants for five weeks and then selected ~2,000 individuals as potential new cultivars based on their foliar color patterning and branching architecture in mid-February. This data represents the youngest fully expanded leaf from each plant between 5-6 weeks of age. Leaves were imaged on Epson Perfection V550 Scanners with Kodak KOCSGS color separation guides included for color calibration. Analysis App Color analysis can be performed using an open-access software program called ColourQuant (Li et al., 2019); available on github: github.com/maoli0923/ColourQuant).</p> <p>Explanation of isolated binary and colored leaf files</p> <p>To isolate individual leaves from the raw data scans - we adjusted the RGB color balance on each scan by a white balance method so that the white swatch in the Kodak KOCSGS color separation guide is pure white, to ensure that scanners were not biasing the color data. Next, we segmented the leaves from the background by converting the RGB matrix into hue-saturation-value (HSV) format. Since most background pixels become grey in HSV, this was used to set a threshold that separates grey values from true leaf values. We then used the binary leaf silhouettes to extract the individual colored leaves by setting the background to pure white, and the foreground to pure black. We manually adjusted the thresholding for leaves that could not be automatically extracted due to shadows in the scan. The binary and colored leaf folder contain outliers, including leaves that were overlapping on the scanner, very small, or broken. These can be manually removed before analysis. </p> <p>File ID Key</p> <p>Files are named with the following code: Year_Family_Scan#. Files containing selected leaves for cultivar development are prepended with an “S” and files containing maternal leaf scans are prepended with an “M.”</p> <p>For questions regarding released dataset contact: Margaret Frank mhf47@cornell.edu</p> <p><br> Li, M., Frank, M.H., and Migicovsky, Z.. 2019a. ColourQuant: A high throughput technique to extract and quantify colour phenotypes from plant images. arXiv 190301652. http://arxiv.org/abs/1903.01652</p>
Dissecting the genetic basis of variation in Drosophila sleep using a multiparental QTL mapping resource
There is considerable variation in sleep duration, timing and quality in human populations, and sleep dysregulation has been implicated as a risk factor for a range of health problems. Human sleep traits are known to be regulated by genetic factors, but also by an array of environmental and social factors. These uncontrolled, non-genetic effects complicate powerful identification of the loci contributing to sleep directly in humans. The model system, Drosophila melanogaster, exhibits a behavior that shows the hallmarks of mammalian sleep, and here we use a multitiered approach, encompassing high-resolution QTL mapping, expression QTL data, and functional validation with RNAi to investigate the genetic basis of sleep under highly controlled environmental conditions. We measured a battery of sleep phenotypes in >750 genotypes derived from a multiparental mapping panel and identified several, modest-effect QTL contributing to natural variation for sleep. Merging sleep QTL data with a large head transcriptome eQTL mapping dataset from the same population allowed us to refine the list of plausible candidate causative sleep loci. This set includes genes with previously characterized effects on sleep and circadian rhythms, in addition to novel candidates. Finally, we employed adult, nervous system-specific RNAi on the Dopa decarboxylase, dyschronic, and timeless genes, finding significant effects on sleep phenotypes for all three. The genes we resolve are strong candidates to harbor causative, regulatory variation contributing to sleep.
Data from: Dissecting the role of a large chromosomal inversion in life history divergence throughout the Mimulus guttatus species complex
Chromosomal inversions can play an important role in adaptation, but the mechanism of their action in many natural populations remains unclear. An inversion could suppress recombination between locally beneficial alleles, thereby preventing maladaptive reshuffling with less-fit, migrant alleles. The recombination suppression hypothesis has gained much theoretical support but empirical tests are lacking. Here, we evaluated the evolutionary history and phenotypic effects of a chromosomal inversion which differentiates annual and perennial forms of Mimulus guttatus. We found that perennials likely possess the derived orientation of the inversion. In addition, this perennial orientation occurs in a second perennial species, M. decorus, where it is strongly associated with life-history differences between co-occurring M. decorus and annual M. guttatus. One prediction of the recombination suppression hypothesis is that loci contributing to local adaptation will predate the inversion. To test whether the loci influencing perenniality pre-date this inversion, we mapped QTLs for life history traits that differ between annual M. guttatus and a more distantly related, collinear perennial species, M. tilingii. Consistent with the recombination suppression hypothesis we found that this region is associated with life-history in the absence of the inversion, and this association can be broken into at least two QTLs. However, the absolute phenotypic effect of the LG8 inversion region on life-history is weaker in M. tilingii than in perennials which possess the inversion. Thus, while we find support for the recombination suppression hypothesis, the contribution of this inversion to life history divergence in this group is likely complex.
Figure 2. - Nesting architecture of Xylocopanasalis; Dissected nests of Xylocopanasalis revealing the nest structure inside the bamboo culm and its residents. Measurements of the nest parameters are shown in Table 1. The diameters of the nests (excluding the nest thickness) were measured at the nest entrance, followed by the vestibulum (antechamber) length, cell length, and the inner most cell length, respectively (2a). Cells containing larvae with pollen masses and their feces were collected and weighted (2b).
Figure 2. - Nesting architecture of Xylocopanasalis; Dissected nests of Xylocopanasalis revealing the nest structure inside the bamboo culm and its residents. Measurements of the nest parameters are shown in Table 1. The diameters of the nests (excluding the nest thickness) were measured at the nest entrance, followed by the vestibulum (antechamber) length, cell length, and the inner most cell length, respectively (2a). Cells containing larvae with pollen masses and their feces were collected and weighted (2b).
Lifespan Data for: Genetic dissection of nutrition-induced plasticity in insulin/insulin-like growth factor signaling and median lifespan in a Drosophila multiparent population
<p>Daily mortality records. Columns are:</p> <p>setDate: date vial was set up</p> <p>flipDate: date flies moved to new food and mortality recorded</p> <p>Age: age in days of flies (from set up date)</p> <p>RIL: DSPR recombinant inbred line ID</p> <p>rilid: alternate id</p> <p>replicate: replicate id</p> <p>riltreat: unique RIL, treatment identifier</p> <p>Dead: # dead</p> <p>Censored: # escaped or inadvertently killed individuals </p> <p>Carried: # dead flies inadvertently moved to fresh food</p>
Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation (CLIPNET data)
<div>This contains data necessary to reproduce the figures in the CLIPNET paper (preprint <a href="https://www.biorxiv.org/content/10.1101/2024.03.13.583868">here</a>) as well as processed data used to train and evaluate CLIPNET. To preserve subdirectory structure, we've packaged the data into tar archives. Please refer to the README documents in our manuscript GitHub repo for more details on file contents: <a href="https://github.com/Danko-Lab/clipnet_paper/">https://github.com/Danko-Lab/clipnet_paper/</a></div> <div> </div> <div>Pretrained CLIPNET models are archived separately at <a href="../doi/10.5281/zenodo.10408622">DOI 10.5281/zenodo.10408622</a></div> <div> </div> <div>V5: Fixed bug in calculation of profile attribution scores causing them to be off by a factor of exactly 500. Genome-wide DeepSHAP tracks & TF-MoDISco tracks have been accordingly updated. I have not updated the individual examples, as these can be quickly fixed by simply multiplying by 500 when plotting. Additionally, I have uploaded profile and quantity motif calls, which contain genome-wide seqlet annotations. The columns in these files are [chrom, start, end, peak_idx, motif_annotation].</div> <div>V4: Uploaded individual bigWigs. These have been lifted over using CrossMap from the original hg19 (GSE110638) to hg38 and RPM normalized.</div> <div>V3: Final version prior to journal submission. Don't recall exact details of what's changed.</div> <div>V2: evaluation_metrics.tar.gz and evaluation_data.tar.gz have been replaced. Previously, we benchmarked the models by treating each peak in each individual as a separate data point. Here, we instead predicted from the reference genome and compared against the averaged bigWigs.</div>
Dissecting muscle power output: Evidence of multi-scale power amplification in skeletal muscle
<p class="MsoNormal">Many animals use a combination of skeletal muscle and elastic structures to amplify power output for fast motions. Among vertebrates, tendons in series with skeletal muscle are often implicated as the primary power-amplifying spring, but muscles contain elastic structures at all levels of organization, from the muscle tendon to the extracellular matrix to elastic proteins within sarcomeres. The present study used <em>ex vivo</em> muscle preparations in combination with high-speed video to quantify power output, as the product of force and velocity, at several levels of muscle organization to determine where power amplification occurs. Dynamic ramp shortening contractions in isolated frog flexor digitorum superficialis brevis were compared with isotonic power output to identify power amplification within muscle fibers, the muscle belly, free tendon and elements external to the muscle tendon. Energy accounting revealed that artifacts from compliant structures outside of the muscle–tendon unit contributed significant peak instantaneous power. This compliance included deflection of clamped bone that stored and released energy contributing 195.22±33.19 W kg<sup>−1</sup> (mean±s.e.m.) to the peak power output. In addition, we found that power detected from within the muscle fascicles for dynamic shortening ramps was 338.78 ±16.03 W kg<sup>−1</sup>, or nearly twice the maximum isotonic power output of 195.23±8.82 W kg<sup>−1</sup>. Measurements of muscle belly and muscle–tendon unit also demonstrated significant power amplification. These data suggest that intramuscular tissues, as well as bone, have the capacity to store and release energy to amplify whole-muscle power output.</p>
Data from: Dissecting abstract, modality-specific and experience-dependent coding of affect in the human brain
<p>Emotion and perception are tightly intertwined, as affective experiences often arise from the appraisal of sensory information. Nonetheless, whether the brain encodes emotional instances using a sensory-specific code or in a more abstract manner is unclear. Here, we answer this question by measuring the association between emotion ratings collected during a unisensory or multisensory presentation of a full-length movie and brain activity recorded in typically-developed, congenitally blind and congenitally deaf participants. Emotional instances are encoded in a vast network encompassing sensory, prefrontal, and temporal cortices. Within this network, the ventromedial prefrontal cortex stores a categorical representation of emotion independent of modality and previous sensory experience, and the posterior superior temporal cortex maps the valence dimension using an abstract code. Sensory experience more than modality impacts how the brain organizes emotional information outside supramodal regions, suggesting the existence of a scaffold for the representation of emotional states where sensory inputs during development shape its functioning.</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)
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.