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non-negative matrix factorization function nnmf  (MathWorks Inc)


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    MathWorks Inc non-negative matrix factorization function nnmf
    Non Negative Matrix Factorization Function Nnmf, 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/pmc06898380-502-3-2
    Average 90 stars, based on 1 article reviews
    non-negative matrix factorization function nnmf - by Bioz Stars, 2026-10
    90/100 stars

    Images

    Related Articles

    Immunohistochemical staining:

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [ , ] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Ion complexation waves emerge at the curved interfaces of layered minerals
    Article Snippet: Non-negative matrix factorization (NNMF) implemented in Matlab was used to establish two absorbance profile factors across all profiles in a given layer.

    Article Title: Generalizability of motor modules across walking-based and in-place tasks – a distribution-based analysis on total knee replacement patients
    Article Snippet: For each trial, the muscle modules were extracted from the processed EMG envelope of that trial using Non-Negative Matrix-Factorization (NNMF) method in MATLAB R2020 ( ).

    Article Title: Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions.
    Article Snippet: The EMG matrix of each subject (contains 16 rows (muscle) in 1200 columns (6 trials × 200 data for each trial) for the 6-trial mode and 16 rows (muscle) in 2400 columns (12 trials × 200 data for each trial) for the 12-trial mode) was entered into the non-negative matrix factorization (nnmf) algorithm in MATLAB R2018a (http://www.mathworks.com) software and the muscle synergy vectors (W) and their activation coefficients (C) were calculated using the alternating least squares (als) algorithm for 1–16 synergy numbers.

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [47, 48] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Effects of orthoses on muscle activity and synergy during gait.
    Article Snippet: The non-negative matrix factorization (NNMF) algorithm extracted muscle synergies using MATLAB R2019b.

    Article Title: Muscle synergy differences between voluntary and reactive backward stepping
    Article Snippet: Muscle synergies were extracted from these EMG data matrices for each trial by non-negative matrix factorization (NNMF) using customized Matlab routines , NNMF was a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy
    Article Snippet: The factor analysis was performed using the non-negative matrix factorization (NNMF) algorithm available in the MATLAB software package.

    Raman Spectroscopy:

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [ , ] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Ion complexation waves emerge at the curved interfaces of layered minerals
    Article Snippet: Non-negative matrix factorization (NNMF) implemented in Matlab was used to establish two absorbance profile factors across all profiles in a given layer.

    Article Title: Generalizability of motor modules across walking-based and in-place tasks – a distribution-based analysis on total knee replacement patients
    Article Snippet: For each trial, the muscle modules were extracted from the processed EMG envelope of that trial using Non-Negative Matrix-Factorization (NNMF) method in MATLAB R2020 ( ).

    Article Title: Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions.
    Article Snippet: The EMG matrix of each subject (contains 16 rows (muscle) in 1200 columns (6 trials × 200 data for each trial) for the 6-trial mode and 16 rows (muscle) in 2400 columns (12 trials × 200 data for each trial) for the 12-trial mode) was entered into the non-negative matrix factorization (nnmf) algorithm in MATLAB R2018a (http://www.mathworks.com) software and the muscle synergy vectors (W) and their activation coefficients (C) were calculated using the alternating least squares (als) algorithm for 1–16 synergy numbers.

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [47, 48] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Effects of orthoses on muscle activity and synergy during gait.
    Article Snippet: The non-negative matrix factorization (NNMF) algorithm extracted muscle synergies using MATLAB R2019b.

    Article Title: Muscle synergy differences between voluntary and reactive backward stepping
    Article Snippet: Muscle synergies were extracted from these EMG data matrices for each trial by non-negative matrix factorization (NNMF) using customized Matlab routines , NNMF was a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy
    Article Snippet: The factor analysis was performed using the non-negative matrix factorization (NNMF) algorithm available in the MATLAB software package.

    Derivative Assay:

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [ , ] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Ion complexation waves emerge at the curved interfaces of layered minerals
    Article Snippet: Non-negative matrix factorization (NNMF) implemented in Matlab was used to establish two absorbance profile factors across all profiles in a given layer.

    Article Title: Generalizability of motor modules across walking-based and in-place tasks – a distribution-based analysis on total knee replacement patients
    Article Snippet: For each trial, the muscle modules were extracted from the processed EMG envelope of that trial using Non-Negative Matrix-Factorization (NNMF) method in MATLAB R2020 ( ).

    Article Title: Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions.
    Article Snippet: The EMG matrix of each subject (contains 16 rows (muscle) in 1200 columns (6 trials × 200 data for each trial) for the 6-trial mode and 16 rows (muscle) in 2400 columns (12 trials × 200 data for each trial) for the 12-trial mode) was entered into the non-negative matrix factorization (nnmf) algorithm in MATLAB R2018a (http://www.mathworks.com) software and the muscle synergy vectors (W) and their activation coefficients (C) were calculated using the alternating least squares (als) algorithm for 1–16 synergy numbers.

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [47, 48] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Effects of orthoses on muscle activity and synergy during gait.
    Article Snippet: The non-negative matrix factorization (NNMF) algorithm extracted muscle synergies using MATLAB R2019b.

    Article Title: Muscle synergy differences between voluntary and reactive backward stepping
    Article Snippet: Muscle synergies were extracted from these EMG data matrices for each trial by non-negative matrix factorization (NNMF) using customized Matlab routines , NNMF was a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy
    Article Snippet: The factor analysis was performed using the non-negative matrix factorization (NNMF) algorithm available in the MATLAB software package.

    Ex Vivo:

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [ , ] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Ion complexation waves emerge at the curved interfaces of layered minerals
    Article Snippet: Non-negative matrix factorization (NNMF) implemented in Matlab was used to establish two absorbance profile factors across all profiles in a given layer.

    Article Title: Generalizability of motor modules across walking-based and in-place tasks – a distribution-based analysis on total knee replacement patients
    Article Snippet: For each trial, the muscle modules were extracted from the processed EMG envelope of that trial using Non-Negative Matrix-Factorization (NNMF) method in MATLAB R2020 ( ).

    Article Title: Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions.
    Article Snippet: The EMG matrix of each subject (contains 16 rows (muscle) in 1200 columns (6 trials × 200 data for each trial) for the 6-trial mode and 16 rows (muscle) in 2400 columns (12 trials × 200 data for each trial) for the 12-trial mode) was entered into the non-negative matrix factorization (nnmf) algorithm in MATLAB R2018a (http://www.mathworks.com) software and the muscle synergy vectors (W) and their activation coefficients (C) were calculated using the alternating least squares (als) algorithm for 1–16 synergy numbers.

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [47, 48] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Effects of orthoses on muscle activity and synergy during gait.
    Article Snippet: The non-negative matrix factorization (NNMF) algorithm extracted muscle synergies using MATLAB R2019b.

    Article Title: Muscle synergy differences between voluntary and reactive backward stepping
    Article Snippet: Muscle synergies were extracted from these EMG data matrices for each trial by non-negative matrix factorization (NNMF) using customized Matlab routines , NNMF was a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy
    Article Snippet: The factor analysis was performed using the non-negative matrix factorization (NNMF) algorithm available in the MATLAB software package.

    In Vivo:

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [ , ] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Ion complexation waves emerge at the curved interfaces of layered minerals
    Article Snippet: Non-negative matrix factorization (NNMF) implemented in Matlab was used to establish two absorbance profile factors across all profiles in a given layer.

    Article Title: Generalizability of motor modules across walking-based and in-place tasks – a distribution-based analysis on total knee replacement patients
    Article Snippet: For each trial, the muscle modules were extracted from the processed EMG envelope of that trial using Non-Negative Matrix-Factorization (NNMF) method in MATLAB R2020 ( ).

    Article Title: Evaluation of the trunk modules in the symmetrical and three-dimensional asymmetrical trunk positions.
    Article Snippet: The EMG matrix of each subject (contains 16 rows (muscle) in 1200 columns (6 trials × 200 data for each trial) for the 6-trial mode and 16 rows (muscle) in 2400 columns (12 trials × 200 data for each trial) for the 12-trial mode) was entered into the non-negative matrix factorization (nnmf) algorithm in MATLAB R2018a (http://www.mathworks.com) software and the muscle synergy vectors (W) and their activation coefficients (C) were calculated using the alternating least squares (als) algorithm for 1–16 synergy numbers.

    Article Title: Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?
    Article Snippet: We used the non-negative matrix factorization (NNMF) [47, 48] function provided by MATLAB (MathWorks, Natick MA, USA).

    Article Title: Effects of orthoses on muscle activity and synergy during gait.
    Article Snippet: The non-negative matrix factorization (NNMF) algorithm extracted muscle synergies using MATLAB R2019b.

    Article Title: Muscle synergy differences between voluntary and reactive backward stepping
    Article Snippet: Muscle synergies were extracted from these EMG data matrices for each trial by non-negative matrix factorization (NNMF) using customized Matlab routines , NNMF was a decomposition algorithm used extensively in muscle synergy analysis , , .

    Article Title: In vivo non-invasive monitoring of tissue development in 3D printed subcutaneous bone scaffolds using fibre-optic Raman spectroscopy
    Article Snippet: The factor analysis was performed using the non-negative matrix factorization (NNMF) algorithm available in the MATLAB software package.



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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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    Image Search Results


    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