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    MathWorks Inc custom-developed algorithms in
    Custom Developed Algorithms In, 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/custom-developed+algorithm/pm35354817-460-10-12
    Average 90 stars, based on 1 article reviews
    custom-developed algorithms in - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Actin polymerization promotes invagination of flat clathrin-coated lattices in mammalian cells by pushing at lattice edges.
    Article Snippet: In each ROI, a local area around each clathrin cluster was selected by making a polygon of the clathrin cluster and expanding it symmetrically by 1 pixel, which wasequal to 117 nm (Supplementary Fig. 5a, panel 1).Only the actin and clathrin localizations in these local areas were considered for the colocalization analysis that was performed with home-made algorithms in MATLAB (see Supplementary Fig. 5a for workflow), resulting in two numbers per ROI: the percentage of actin localizations that is colocalized with clathrin coated pits (Supplementary Fig. 5a, overlap zone in panel 3), and the percentage of non-colocalized actin localizations (Supplementary Fig. 5a, yellow shape in panel 5).

    Article Title: Hand2 delineates mesothelium progenitors and is reactivated in mesothelioma.
    Article Snippet: All preprocessing steps and map projection were performed using custom-developed algorithms in MATLAB (9.7).

    Article Title: Pancreatic Cancer Presents Distinct Nanomechanical Properties During Progression.
    Article Snippet: Cancer progression is closely related to changes in the structure and mechanical properties of the tumor microenvironment (TME).. In many solid tumors, including pancreatic cancer, the interplay among the different components of the TME leads to a desmoplastic reaction mainly due to collagen overproduction.. Desmoplasia is responsible for the stiffening of the tumor, poses a major barrier to effective drug delivery and has been associated with poor prognosis.

    Article Title: Test-retest reliability of upper limb robotic exoskeleton assessments in children and youths with brain lesions.
    Article Snippet: We wrote customised algorithms in MATLAB (R2014a, and R2017a, The MathWorks, Inc.) for extracting the assessment parameters.

    Article Title: Actin polymerization promotes invagination of flat clathrin-coated lattices in mammalian cells by pushing at lattice edges
    Article Snippet: Only the actin and clathrin localizations in these local areas were considered for the co-localization analysis that was performed with home-made algorithms in MATLAB (see Supplementary Fig. for workflow), resulting in two numbers per ROI: the percentage of actin localizations that is co-localized with clathrin coated pits (Supplementary Fig. , overlap zone in panel 3), and the percentage of non-colocalized actin localizations (Supplementary Fig. , yellow shape in panel 5).

    Article Title: Characterization of geometric variance in the epithelial nerve net of the ctenophore Pleurobrachia pileus.
    Article Snippet: A semiautomated segmentation system was developed to isolate and define the nerve net architecture in the acquired images using customdeveloped algorithms in Matlab (The MathWorks, Natick, MA).

    Article Title: Monomeric agonist peptide/MHCII complexes activate T-cells in an autonomous fashion.
    Article Snippet: More extensive image analysis was carried out with custom-written algorithms in Matlab (MathWorks).

    Article Title: Decoding Natural Astrocyte Rhythms: Dynamic Actin Waves Result from Environmental Sensing by Primary Rodent Astrocytes.
    Article Snippet: To characterize the organization of the actin meshwork in STED images of fixed cells, as shown in Figure 5, several image processing techniques using custom-written algorithms in MATLAB (Mathworks) were performed.



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    EC-Confetti mice treated with d-flow and hypercholesterolemia at 4 weeks post-PCL (N=6 male and 15 female) were imaged macroscopically ( A ) and LCAs/RCAs were longitudinally sectioned, stained, imaged by fluorescence microscopy ( B-R ), and quantified ( S-V ). A shows representative gross image of LCA, RCA, and aortic arch. B-R . LCAs and RCAs were immunostained with markers of endothelial inflammation (Vcam1 and Icam1, B-D ); EndMT (Acta2, Snai1, and Cnn1, E-H ); EndIT (Cd68, C1qa, C1qb, and Lyz2, I-M ); and EndFT (Spp1, Lgals3, Trem2, and BODIPY, N-R ). B, E, I, and N show merged images of confetti and FIRE markers at low magnification (10X), while the rest show 40X images. Confetti signals show eGFP (green), YFP (green), and RFP (red). All FIRE markers are shown in white except for green BODIPY ( R ). White arrows indicate confetti + ECs co-expressing the FIRE markers. S-V. Percent confetti + ECs co-expressing each FIRE marker was quantified by a combined <t>Matlab</t> and ImageJ analysis. Confetti + ECs expressing markers of inflammation (Icam1 and Vcam1, S ); EndMT (Acta2, Cnn1, and Snai1, T ); EndIT (C1qa, C1qb, Lyz2, and Cd68, U ); and EndFT (Lgals3, Trem2, and Spp1, V ). Shown are mean ± SEM, each dot represents % of confetti + ECs coexpressing FIRE markers in each longitudinal section used for quantification (N=4 to 10 longitudinal sections for RCA; N=6 to 31 longitudinal sections for LCA). P values were calculated by 2-tailed unpaired Student t test with or without Welch’s correction for normal data and 2-tailed unpaired Mann-Whitney U test for non-normal data.
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    EC-Confetti mice treated with d-flow and hypercholesterolemia at 4 weeks post-PCL (N=6 male and 15 female) were imaged macroscopically ( A ) and LCAs/RCAs were longitudinally sectioned, stained, imaged by fluorescence microscopy ( B-R ), and quantified ( S-V ). A shows representative gross image of LCA, RCA, and aortic arch. B-R . LCAs and RCAs were immunostained with markers of endothelial inflammation (Vcam1 and Icam1, B-D ); EndMT (Acta2, Snai1, and Cnn1, E-H ); EndIT (Cd68, C1qa, C1qb, and Lyz2, I-M ); and EndFT (Spp1, Lgals3, Trem2, and BODIPY, N-R ). B, E, I, and N show merged images of confetti and FIRE markers at low magnification (10X), while the rest show 40X images. Confetti signals show eGFP (green), YFP (green), and RFP (red). All FIRE markers are shown in white except for green BODIPY ( R ). White arrows indicate confetti + ECs co-expressing the FIRE markers. S-V. Percent confetti + ECs co-expressing each FIRE marker was quantified by a combined <t>Matlab</t> and ImageJ analysis. Confetti + ECs expressing markers of inflammation (Icam1 and Vcam1, S ); EndMT (Acta2, Cnn1, and Snai1, T ); EndIT (C1qa, C1qb, Lyz2, and Cd68, U ); and EndFT (Lgals3, Trem2, and Spp1, V ). Shown are mean ± SEM, each dot represents % of confetti + ECs coexpressing FIRE markers in each longitudinal section used for quantification (N=4 to 10 longitudinal sections for RCA; N=6 to 31 longitudinal sections for LCA). P values were calculated by 2-tailed unpaired Student t test with or without Welch’s correction for normal data and 2-tailed unpaired Mann-Whitney U test for non-normal data.
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    EC-Confetti mice treated with d-flow and hypercholesterolemia at 4 weeks post-PCL (N=6 male and 15 female) were imaged macroscopically ( A ) and LCAs/RCAs were longitudinally sectioned, stained, imaged by fluorescence microscopy ( B-R ), and quantified ( S-V ). A shows representative gross image of LCA, RCA, and aortic arch. B-R . LCAs and RCAs were immunostained with markers of endothelial inflammation (Vcam1 and Icam1, B-D ); EndMT (Acta2, Snai1, and Cnn1, E-H ); EndIT (Cd68, C1qa, C1qb, and Lyz2, I-M ); and EndFT (Spp1, Lgals3, Trem2, and BODIPY, N-R ). B, E, I, and N show merged images of confetti and FIRE markers at low magnification (10X), while the rest show 40X images. Confetti signals show eGFP (green), YFP (green), and RFP (red). All FIRE markers are shown in white except for green BODIPY ( R ). White arrows indicate confetti + ECs co-expressing the FIRE markers. S-V. Percent confetti + ECs co-expressing each FIRE marker was quantified by a combined <t>Matlab</t> and ImageJ analysis. Confetti + ECs expressing markers of inflammation (Icam1 and Vcam1, S ); EndMT (Acta2, Cnn1, and Snai1, T ); EndIT (C1qa, C1qb, Lyz2, and Cd68, U ); and EndFT (Lgals3, Trem2, and Spp1, V ). Shown are mean ± SEM, each dot represents % of confetti + ECs coexpressing FIRE markers in each longitudinal section used for quantification (N=4 to 10 longitudinal sections for RCA; N=6 to 31 longitudinal sections for LCA). P values were calculated by 2-tailed unpaired Student t test with or without Welch’s correction for normal data and 2-tailed unpaired Mann-Whitney U test for non-normal data.
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    EC-Confetti mice treated with d-flow and hypercholesterolemia at 4 weeks post-PCL (N=6 male and 15 female) were imaged macroscopically ( A ) and LCAs/RCAs were longitudinally sectioned, stained, imaged by fluorescence microscopy ( B-R ), and quantified ( S-V ). A shows representative gross image of LCA, RCA, and aortic arch. B-R . LCAs and RCAs were immunostained with markers of endothelial inflammation (Vcam1 and Icam1, B-D ); EndMT (Acta2, Snai1, and Cnn1, E-H ); EndIT (Cd68, C1qa, C1qb, and Lyz2, I-M ); and EndFT (Spp1, Lgals3, Trem2, and BODIPY, N-R ). B, E, I, and N show merged images of confetti and FIRE markers at low magnification (10X), while the rest show 40X images. Confetti signals show eGFP (green), YFP (green), and RFP (red). All FIRE markers are shown in white except for green BODIPY ( R ). White arrows indicate confetti + ECs co-expressing the FIRE markers. S-V. Percent confetti + ECs co-expressing each FIRE marker was quantified by a combined Matlab and ImageJ analysis. Confetti + ECs expressing markers of inflammation (Icam1 and Vcam1, S ); EndMT (Acta2, Cnn1, and Snai1, T ); EndIT (C1qa, C1qb, Lyz2, and Cd68, U ); and EndFT (Lgals3, Trem2, and Spp1, V ). Shown are mean ± SEM, each dot represents % of confetti + ECs coexpressing FIRE markers in each longitudinal section used for quantification (N=4 to 10 longitudinal sections for RCA; N=6 to 31 longitudinal sections for LCA). P values were calculated by 2-tailed unpaired Student t test with or without Welch’s correction for normal data and 2-tailed unpaired Mann-Whitney U test for non-normal data.

    Journal: bioRxiv

    Article Title: Disturbed Flow Induces Reprogramming of Endothelial Cells to Immune-like and Foam Cells under Hypercholesterolemia during Atherogenesis

    doi: 10.1101/2025.03.06.641843

    Figure Lengend Snippet: EC-Confetti mice treated with d-flow and hypercholesterolemia at 4 weeks post-PCL (N=6 male and 15 female) were imaged macroscopically ( A ) and LCAs/RCAs were longitudinally sectioned, stained, imaged by fluorescence microscopy ( B-R ), and quantified ( S-V ). A shows representative gross image of LCA, RCA, and aortic arch. B-R . LCAs and RCAs were immunostained with markers of endothelial inflammation (Vcam1 and Icam1, B-D ); EndMT (Acta2, Snai1, and Cnn1, E-H ); EndIT (Cd68, C1qa, C1qb, and Lyz2, I-M ); and EndFT (Spp1, Lgals3, Trem2, and BODIPY, N-R ). B, E, I, and N show merged images of confetti and FIRE markers at low magnification (10X), while the rest show 40X images. Confetti signals show eGFP (green), YFP (green), and RFP (red). All FIRE markers are shown in white except for green BODIPY ( R ). White arrows indicate confetti + ECs co-expressing the FIRE markers. S-V. Percent confetti + ECs co-expressing each FIRE marker was quantified by a combined Matlab and ImageJ analysis. Confetti + ECs expressing markers of inflammation (Icam1 and Vcam1, S ); EndMT (Acta2, Cnn1, and Snai1, T ); EndIT (C1qa, C1qb, Lyz2, and Cd68, U ); and EndFT (Lgals3, Trem2, and Spp1, V ). Shown are mean ± SEM, each dot represents % of confetti + ECs coexpressing FIRE markers in each longitudinal section used for quantification (N=4 to 10 longitudinal sections for RCA; N=6 to 31 longitudinal sections for LCA). P values were calculated by 2-tailed unpaired Student t test with or without Welch’s correction for normal data and 2-tailed unpaired Mann-Whitney U test for non-normal data.

    Article Snippet: To determine the proportion of confetti + ECs undergoing FIRE, multi-level image thresholding was performed via combined use of custom-developed MATLAB algorithms and ImageJ .

    Techniques: Staining, Fluorescence, Microscopy, Expressing, Marker, MANN-WHITNEY