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xml files  (Biotechnology Information)

 
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    Biotechnology Information xml files
    Xml Files, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/xml+files/xml+files/pm40628249-50-7-16
    Average 90 stars, based on 1 article reviews
    xml files - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Body-wide expression profiles of commonly assessed cardiac biomarkers in a large cohort of human tissue donors.
    Article Snippet: Associated donor phenotype data were retrieved as .xml files via the from the National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGaP) via accession number phs000424.v9 and read into R using the ‘XML’ package.

    Article Title: Brain Expression Levels of Commonly Measured Blood Biomarkers of Neurological Damage Differ with Respect to Sex, Race, and Age
    Article Snippet: Donor phenotype data and additional specimen metadata, including RNA quality metrics , were retrieved as .xml files via the from the National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGaP) via accession number phs000424. v9 and read into R using the ‘XML’ package.

    Article Title: Brain expression levels of commonly measured blood biomarkers of neurological damage differ with respect to sex, race, and age.
    Article Snippet: Donor phenotype data and additional specimen metadata, including RNA quality metrics (Supplemental Figure 1), were retrieved as .xml files via the from the National Center for Biotechnology Information (NCBI) database of Genotypes and Phenotypes (dbGaP) via accession number phs000424.v9 and read into R using the ‘XML’ package.



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    Schematic workflow and representative examples for MDCK cell nuclei segmentation and tracking, kinematics and ERK signal analysis using FRET imaging videomicroscopy data (A–E) Schematic shows workflow for the analysis of cell motility, single cell ERK activity and ERK waves of MDCK cell monolayers using time-lapse FRET imaging data. The workflow starts with CFP images of MDCK cell nuclei (B, first frame shown of a 16-h recording). Nuclei are segmented with the StarDist Fiji plugin which is integrated in the Trackmate plugin (C, cell nuclei contours shown in magenta). TrackMate then tracks the nuclei generating two outputs: (1) images of single nuclei tracks (D, nuclei contours in magenta with superimposed color-coded by ID tracks) and (2) a .xml file containing all the tracking data. Tracks shown here are filtered for duration ( > 5.8 h), and nuclei migrate toward the center of the field of view (FOV) where infected cells reside (bacterial fluorescence not shown). Segmented nuclei images are processed in Fiji to generate a binary mask (E). The .xml file, binary masks and ERK activity maps (shown in F) are used as input for a MATLAB script which characterizes single-cell ERK activity and cell motility further. The MATLAB script also generates the ARCOS_matrix.csv file for ERK wave analysis in R to performed at later steps. For more details please see Hundsdorfer et al.

    Journal: STAR Protocols

    Article Title: Protocol for characterizing cell motility and extracellular signal-regulated kinase dynamics in epithelial monolayers using FRET imaging data

    doi: 10.1016/j.xpro.2025.103816

    Figure Lengend Snippet: Schematic workflow and representative examples for MDCK cell nuclei segmentation and tracking, kinematics and ERK signal analysis using FRET imaging videomicroscopy data (A–E) Schematic shows workflow for the analysis of cell motility, single cell ERK activity and ERK waves of MDCK cell monolayers using time-lapse FRET imaging data. The workflow starts with CFP images of MDCK cell nuclei (B, first frame shown of a 16-h recording). Nuclei are segmented with the StarDist Fiji plugin which is integrated in the Trackmate plugin (C, cell nuclei contours shown in magenta). TrackMate then tracks the nuclei generating two outputs: (1) images of single nuclei tracks (D, nuclei contours in magenta with superimposed color-coded by ID tracks) and (2) a .xml file containing all the tracking data. Tracks shown here are filtered for duration ( > 5.8 h), and nuclei migrate toward the center of the field of view (FOV) where infected cells reside (bacterial fluorescence not shown). Segmented nuclei images are processed in Fiji to generate a binary mask (E). The .xml file, binary masks and ERK activity maps (shown in F) are used as input for a MATLAB script which characterizes single-cell ERK activity and cell motility further. The MATLAB script also generates the ARCOS_matrix.csv file for ERK wave analysis in R to performed at later steps. For more details please see Hundsdorfer et al.

    Article Snippet: Make sure to use the correct file path names (see ). c. Execute the “%% Track extraction and formatting from TrackMate (xml) files”, “%% ERK intensity and single-cell track matching” and “%% Save results” sections, which read the data from the .xml file, ERK intensity images and segmentation binary mask images and import them in MATLAB: > Matlab ribbon -> Editor -> Run Section 10.

    Techniques: Imaging, Activity Assay, Infection, Fluorescence