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matlab runtime software  (MathWorks Inc)


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    MathWorks Inc matlab runtime software
    Matlab Runtime Software, supplied by MathWorks Inc, 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/matlab+runtime+software/pm36010347-96-23-26
    Average 90 stars, based on 1 article reviews
    matlab runtime software - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Harnessing Dynamic Cell Responses to Optimise Pluripotency Protocols
    Article Snippet: Image acquisition and analysis were performed using custom MATLAB software.

    Article Title: Study on the gas outflow pattern and outflow prediction model of the return mining face under complex geological conditions
    Article Snippet: ACO-BP, FA-BP, KPCA-WOA-BP, and BP are the four prediction models that were developed with the MATLAB software platform.

    Article Title: A glyoxal-specific aldehyde signaling axis in Pseudomonas aeruginosa that influences quorum sensing and infection
    Article Snippet: The resulting data was analyzed with custom MATLAB software following the analysis detailed in ref. .

    Article Title: Local distortion as a pitfall of iterative MLEM reconstruction in myocardial perfusion imaging - A phantom study.
    Article Snippet: Image processing and analysis, including MLEM tomographic reconstruction,are implemented and conducted in MATLAB software package using image processing toolbox (The MathWorks Inc., version 2021b under Linux, Natick, Massachusetts, USA).

    Article Title: Local distortion as a pitfall of iterative MLEM reconstruction in myocardial perfusion imaging – A phantom study
    Article Snippet: Image processing and analysis, including MLEM tomographic reconstruction, are implemented and conducted in MATLAB software package using image processing toolbox (The MathWorks Inc., version 2021b under Linux, Natick, Massachusetts, USA).

    Article Title: Heart rate defined sustained attention relates to visual attention in autism and fragile X syndrome
    Article Snippet: Eye-tracking data were processed with custom Matlab software that accommodated standard techniques for processing eye-tracking data including blink detection, data calibration, and Region of Interest (ROI) analysis .

    Selection:

    Article Title: Study on the gas outflow pattern and outflow prediction model of the return mining face under complex geological conditions.
    Article Snippet: .. Predictive model parameter selection ACO-BP, FA-BP, KPCA-WOA-BP, and BP are the four prediction models that were developed with the MATLAB software platform. ..

    Imaging:

    Article Title: Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channels
    Article Snippet: .. Data collection , Droplet impact, spreading, and rebound on surfaces with different surface properties was captured using backlit imaging with a high-speed optical camera. The quantitative data was extracted from the captured footage via image processing with custom-developed algorithms in MathWorks MATLAB software environment.. ..



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    Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from <t>ctFIRE</t> <t>software.</t> (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels
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    matlab compiler runtime software  (MathWorks Inc)
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    Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from <t>ctFIRE</t> <t>software.</t> (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels
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    Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from ctFIRE software. (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels

    Journal: Methods in molecular biology (Clifton, N.J.)

    Article Title: Intravital Imaging of Tumor Cell Motility in the Tumor Microenvironment Context

    doi: 10.1007/978-1-4939-7701-7_14

    Figure Lengend Snippet: Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from ctFIRE software. (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels

    Article Snippet: The ctFIRE software requires MATLAB compiler runtime (MCR 7.17 2012a) installation.

    Techniques: Microscopy, Extraction, Software