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non-negative matrix factorization nnmf function  (MathWorks Inc)


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    MathWorks Inc non-negative matrix factorization nnmf function
    Non Negative Matrix Factorization Nnmf Function, 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/non-negative+matrix+factorization+(nnmf)+function/us12171783-2044-5-13
    Average 90 stars, based on 1 article reviews
    non-negative matrix factorization nnmf function - by Bioz Stars, 2026-11
    90/100 stars

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    Article Snippet: The MRI average signal intensity of tumors at each time was calculated by drawing an ROI on the tumor region, using MATLAB software.

    Article Title: Targeting the Urotensin II/UT G Protein-Coupled Receptor to Counteract Angiogenesis and Mesenchymal Hypoxia/Necrosis in Glioblastoma
    Article Snippet: Quantifications of [ 99m Tc]-HYNIC-RGD uptake in tumors were performed using MATLAB software (MathWorks, Meudon, France), expressed in terms of standardized uptake values (SUVs) and tumor to muscle (T/M) ratios.

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    Article Snippet: When explored the issue of tumors in the brain, they removed images from MRI scans utilizing MATLAB software.

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    Article Snippet: For each of the 10 tumors, non-negative matrix factorization (as implemented by the Matlab nnmf function, with the number of factors set to 10) was used to identify variable expression programs.

    Article Title: Methods and compositions for targeting developmental and oncogenic programs in H3K27M gliomas
    Article Snippet: For each of the six tumors, Non-Negative Matrix Factorization (as implemented by the Matlab nnmf function, with number of factors set to 10) was applied to the centered expression data of malignant cells from that tumor, after converting all negative values to zero.

    Article Title: In Vivo Photoacoustic Lifetime Based Oxygen Imaging with Tumor Targeted G2 Polyacrylamide Nanosonophores
    Article Snippet: To examine whether there is statistically significant difference between the tumors and the muscles, a two tailed t-test was performed using the built-in functions of the MATLAB (R2016b, Mathworks, Natick, Massachusetts), which achieved a p value <0.01.

    Two Tailed Test:

    Article Title: Chitosan-Imidazolium Core-Shell Nanoparticles of Gd-Mn-Mo Polyoxometalate as Novel Potential MRI Nano-Agent for Breast Cancer Detection.
    Article Snippet: The MRI average signal intensity of tumors at each time was calculated by drawing an ROI on the tumor region, using MATLAB software.

    Article Title: Targeting the Urotensin II/UT G Protein-Coupled Receptor to Counteract Angiogenesis and Mesenchymal Hypoxia/Necrosis in Glioblastoma
    Article Snippet: Quantifications of [ 99m Tc]-HYNIC-RGD uptake in tumors were performed using MATLAB software (MathWorks, Meudon, France), expressed in terms of standardized uptake values (SUVs) and tumor to muscle (T/M) ratios.

    Article Title: Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer
    Article Snippet: Expression Programs of Intra -tumoral Heterogeneity For each of the 10 tumors, non-negative matrix factorization (as implemented by the Matlab nnmf function, with the number of factors set to 10) was used to identify variable expression programs.

    Article Title: Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features.
    Article Snippet: When explored the issue of tumors in the brain, they removed images from MRI scans utilizing MATLAB software.

    Article Title: Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer
    Article Snippet: For each of the 10 tumors, non-negative matrix factorization (as implemented by the Matlab nnmf function, with the number of factors set to 10) was used to identify variable expression programs.

    Article Title: Methods and compositions for targeting developmental and oncogenic programs in H3K27M gliomas
    Article Snippet: For each of the six tumors, Non-Negative Matrix Factorization (as implemented by the Matlab nnmf function, with number of factors set to 10) was applied to the centered expression data of malignant cells from that tumor, after converting all negative values to zero.

    Article Title: In Vivo Photoacoustic Lifetime Based Oxygen Imaging with Tumor Targeted G2 Polyacrylamide Nanosonophores
    Article Snippet: To examine whether there is statistically significant difference between the tumors and the muscles, a two tailed t-test was performed using the built-in functions of the MATLAB (R2016b, Mathworks, Natick, Massachusetts), which achieved a p value <0.01.



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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy <t>(NNMF</t> Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).
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    Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Journal: Biomaterials and Biosystems

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy

    doi: 10.1016/j.bbiosy.2022.100059

    Figure Lengend Snippet: Histology and immunohistochemical analysis of explanted scaffolds. (a) Representative micrographs of histological sections showing Hematoxylin/Eosin (H&E), human vimentin (hVim) and Pico Sirius Red (PSR) stains after 16 weeks subcutaneous implantation in mice. H&E and hVim images are shown at approximately identical locations on the samples. PSR images represent entire scaffolds by stitched together micrographs. (b) , Quantitative analysis of collagen content from Raman spectroscopy (NNMF Component 3) and PSR images after 16 weeks of implantation. Collagen (Coll.) content from PSR histology is represented by a ratio of total PSR positive area divided by total scaffold cavity perimeter (Area-to-perimeter ratio). Histology derived measures of collagen content correlate well with both ex vivo and in vivo Raman derived collagen estimates. Pearson's correlation coefficient (r). Scales bars: 200 µm (H&E, hVim micrographs) and 1 mm (PSR micrographs). Groups: scaffolds without (No cells ) human mesenchymal stem cells (hMSCs), with hMSCs (MSC 1.5 , MSC 7.5 , MSC 7.5 +GF ) amount indicated by subscript ( e.g. 7.5 = 7.5 × 10 5 cells per scaffold). hMSCs preconditioned with BMP2 growth factor for 24 h prior to implantation (+GF).

    Article Snippet: Following pre-processing, spectral models were developed using the MATLAB statistics toolbox function non-negative matrix factorization (NNMF) ( c) with in vivo spectra ( a), ex vivo , and reference spectra ( b) as input.

    Techniques: Immunohistochemical staining, Raman Spectroscopy, Derivative Assay, Ex Vivo, In Vivo