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custom-developed algorithms  (MathWorks Inc)


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    MathWorks Inc custom-developed algorithms
    Custom Developed Algorithms, 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/pmc12163178-107-12-13
    Average 90 stars, based on 1 article reviews
    custom-developed algorithms - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    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: Post-processing of the captured data was performed using custom-developed algorithms in the MathWorks MATLAB environment.

    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: Processing of the videos was performed using custom-developed algorithms in MathWorks MATLAB environment.

    Article Title: The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network
    Article Snippet: We obtain the results of the operation of the research object in the unlicensed frequency range by implementing the mathematical models presented in Section in the verified MathWorks MATLAB environment (5G Toolbox), which allows us to draw an inductive conclusion about their adequacy.

    Article Title: Optimal Sizing of Battery Energy Storage System (BESS) for Multiple Applications using Regression Analysis and Deep Sleep Optimizer Algorithm.
    Article Snippet: Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size.

    Article Title: Machine Condition Monitoring System Based on Edge Computing Technology.
    Article Snippet: In the Matlab environment, function trainscg permits the training of any network, provided that its weight, net input, and transfer functions possess derivative functions.

    Article Title: Estimation of elbow flexion torque from anthropometric and NMES MMG variables using random forest regression
    Article Snippet: The MATLAB environment (MATLAB ® 2022b MathWorks, Inc., Natick, MA, USA) installed on a 64-bit operating system on Windows 11, 12th Gen Intel(R) Core (TM) i7-12700 2.10 GHz, was used for the signal processing and the computational aspects of RFR model development.

    Software:

    Article Title: Method for assessing nitrogen nutritional status in plants by visible-to-shortwave infrared reflectance spectroscopy of carbohydrates
    Article Snippet: .. It is another object of the present invention to disclose the method as defined above, wherein the computer-implemented non-transitory software is selected from a group consisting of PLS toolbox in Matlab environment, R, python and any combination thereof. ..

    Article Title: Pore Structure Influence on Properties of Air-Entrained Concrete.
    Article Snippet: .. Unlike the transverse method recommended in the standard, the study utilized the image analysis method supported by a proprietary computer software operating in the MATLAB environment (ver. ..



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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