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oligodt-cy3 matlab matrices  (MathWorks Inc)


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    MathWorks Inc oligodt-cy3 matlab matrices
    Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by <t>MATLAB</t> before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.
    Oligodt Cy3 Matlab Matrices, 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/result/oligodt-cy3 matlab matrices/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    oligodt-cy3 matlab matrices - by Bioz Stars, 2026-06
    90/100 stars

    Images

    1) Product Images from "BUHO: A MATLAB Script for the Study of Stress Granules and Processing Bodies by High-Throughput Image Analysis"

    Article Title: BUHO: A MATLAB Script for the Study of Stress Granules and Processing Bodies by High-Throughput Image Analysis

    Journal: PLoS ONE

    doi: 10.1371/journal.pone.0051495

    Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by MATLAB before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.
    Figure Legend Snippet: Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by MATLAB before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.

    Techniques Used: Staining



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    oligodt-cy3 matlab matrices  (MathWorks Inc)
    90
    MathWorks Inc oligodt-cy3 matlab matrices
    Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by <t>MATLAB</t> before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.
    Oligodt Cy3 Matlab Matrices, 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/result/oligodt-cy3 matlab matrices/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    oligodt-cy3 matlab matrices - by Bioz Stars, 2026-06
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by MATLAB before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.

    Journal: PLoS ONE

    Article Title: BUHO: A MATLAB Script for the Study of Stress Granules and Processing Bodies by High-Throughput Image Analysis

    doi: 10.1371/journal.pone.0051495

    Figure Lengend Snippet: Cells were grown, treated and stained in 384 MW plates as indicated in . A, B and C, Three representative cells are shown. A, The oligodT-Cy3 allows the visualization of SGs in the cytoplasm and of accumulated polyadenylated RNA in the nucleus. B, Merged image created by MATLAB before application of the Watershed transform algorithm for the identification of cell border and nucleus. C. Objects were identified as SGs by correlation with the selected prototypes using the MATLAB normxcorr2 function. D, Prototypes used to identify and analyze SGs. These were selected among 50 different examples to include differences in size, shape and intensity pattern. Size bar, 2 µm. E and F, A collection of 6 micrographs containing 125 cells with a total of 282 granules (see ) were used to investigate the effect of varying the similarity threshold (ST) and of increasing numbers of prototypes. E, The proportion of false positives relative to the number of seeds and the proportion of positive hits relative to the manual-counted number of SGs was measured at different ST for the prototype IV, depicted in D. An ST of 0.85 allowed no false positives. SG recognition dropped from 80% to 20% at this ST value, and similar values were observed for the other prototypes (not shown). F, The number of positive hits that resulted by correlation with the indicated prototypes subsets was measured. All the SGs present in the micrographs were identified with the six prototypes depicted in D. Size bar, 5 µm. G, A representative SG from the cell in the first row showing the seeds (red) created by the script.

    Article Snippet: We combined the nuclear mask and the negative counterpart of the oligodT-Cy3 MATLAB matrices to generate a single image where all DAPI-positive pixels were converted to 0 and cells are brightly stained.

    Techniques: Staining