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matlab program deeranalysis  (MathWorks Inc)


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    MathWorks Inc matlab program deeranalysis
    Matlab Program Deeranalysis, 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/matlab program deeranalysis/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab program deeranalysis - by Bioz Stars, 2026-04
    90/100 stars

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    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using <t>DeerAnalysis</t> (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.
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    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using <t>DeerAnalysis</t> (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.
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    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using <t>DeerAnalysis</t> (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.
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    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using <t>DeerAnalysis</t> (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.
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    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using <t>DeerAnalysis</t> (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.
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    Image Search Results


    Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using DeerAnalysis (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.

    Journal: Physical Chemistry Chemical Physics

    Article Title: Determining and controlling conformational information from orientationally selective light-induced triplet–triplet electron resonance spectroscopy for a set of bis-porphyrin rulers †

    doi: 10.1039/d3cp03454b

    Figure Lengend Snippet: Orientation-independent analysis of LITTER with [3]. (a) Background-corrected and modulation depth-normalised LITTER traces shown in (colour) and individual orientation-independent fits by Tikhonov regularization using DeerAnalysis (black), α = 10. (b) Spin–spin distance distributions obtained from the analysis shown in panel b. (c) Averaged LITTER trace (red) and corresponding orientation-independent fit by Tikhonov regularization (black). (d) Spin–spin distance distribution obtained from the analysis shown in panel c. 95% confidence bounds (grey) have been estimated using the Comparative Deer Analyzer in DeerAnalysis2022.

    Article Snippet: For the orientation-independent analysis of LITTER and DEER, the dipolar traces acquired at different field positions were averaged weighted by the corresponding spectral intensities to obtain an orientation-independent form factor, which was then analysed via Fourier Transform and Tikhonov regularization using the Matlab® DeerAnalysis routine to extract the corresponding distance distribution.

    Techniques:

    DeerAnalysis parameters.

    Journal: Frontiers in Molecular Biosciences

    Article Title: DEER Data Analysis Software: A Comparative Guide

    doi: 10.3389/fmolb.2022.915167

    Figure Lengend Snippet: DeerAnalysis parameters.

    Article Snippet: DeerAnalysis ( ) is a Matlab-based package that exists in two modes; “automation” (ComparativeDeerAnalyzer) and user-defined.

    Techniques:

    Results from each program for the simulated DEER-with-no-noise data set.

    Journal: Frontiers in Molecular Biosciences

    Article Title: DEER Data Analysis Software: A Comparative Guide

    doi: 10.3389/fmolb.2022.915167

    Figure Lengend Snippet: Results from each program for the simulated DEER-with-no-noise data set.

    Article Snippet: DeerAnalysis ( ) is a Matlab-based package that exists in two modes; “automation” (ComparativeDeerAnalyzer) and user-defined.

    Techniques: Standard Deviation

    Results from each program for the simulated DEER-with-noise data set.

    Journal: Frontiers in Molecular Biosciences

    Article Title: DEER Data Analysis Software: A Comparative Guide

    doi: 10.3389/fmolb.2022.915167

    Figure Lengend Snippet: Results from each program for the simulated DEER-with-noise data set.

    Article Snippet: DeerAnalysis ( ) is a Matlab-based package that exists in two modes; “automation” (ComparativeDeerAnalyzer) and user-defined.

    Techniques: Standard Deviation

    Results from each program for the AGAO data, with both 0 ns cut and 800 ns cut.

    Journal: Frontiers in Molecular Biosciences

    Article Title: DEER Data Analysis Software: A Comparative Guide

    doi: 10.3389/fmolb.2022.915167

    Figure Lengend Snippet: Results from each program for the AGAO data, with both 0 ns cut and 800 ns cut.

    Article Snippet: DeerAnalysis ( ) is a Matlab-based package that exists in two modes; “automation” (ComparativeDeerAnalyzer) and user-defined.

    Techniques: Standard Deviation

    Results from each program for the DNA data set, with both 0 ns cut and 800 ns cut.

    Journal: Frontiers in Molecular Biosciences

    Article Title: DEER Data Analysis Software: A Comparative Guide

    doi: 10.3389/fmolb.2022.915167

    Figure Lengend Snippet: Results from each program for the DNA data set, with both 0 ns cut and 800 ns cut.

    Article Snippet: DeerAnalysis ( ) is a Matlab-based package that exists in two modes; “automation” (ComparativeDeerAnalyzer) and user-defined.

    Techniques: Standard Deviation