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non-linear least squares fitting algorithm  (GraphPad Software Inc)


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    GraphPad Software Inc non-linear least squares fitting algorithm
    Non Linear Least Squares Fitting Algorithm, supplied by GraphPad Software 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/nonnegative+linear+least-squares+algorithm/non+linear+least+squares+fitting+algorithm/pm25499554-105-7-13
    Average 90 stars, based on 1 article reviews
    non-linear least squares fitting algorithm - by Bioz Stars, 2026-10
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    Related Articles

    Diffusion-based Assay:

    Article Title: An assessment of the ability of the obstruction-scaling model to estimate solute diffusion coefficients in hydrogels.
    Article Snippet: Contents lists available at ScienceDirect Journal of Controlled Release j ourna l homepage: www.e lsev ie r .com/ locate / jconre l An assessment of the ability of the obstruction-scaling model to estimate solute diffusion coefficients in hydrogels Nicholas A. Hadjiev, Brian G. Amsden ⁎ Department of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada ⁎ Corresponding author.. E-mail address: amsden@queensu.ca (B.G.. Amsden). http://dx.doi.org/10.1016/j.jconrel.2014.12.010 0168-3659/© 2014 Elsevier B.V. All rights reserved. a b s t r a c t a r t i c l e i n f o Article history: Received 9 October 2014 Revised 4 December 2014 Accepted 6 December 2014 Available online 10 December 2014 Keywords: FRAP Diffusion in hydrogels Mathematical model Diffusion in polymer solutions The ability to estimate the diffusion coefficient of a solute within hydrogels has important application in the design and analysis of hydrogels used in drug delivery, tissue engineering, and regenerative medicine.

    Derivative Assay:

    Article Title: An assessment of the ability of the obstruction-scaling model to estimate solute diffusion coefficients in hydrogels.
    Article Snippet: Contents lists available at ScienceDirect Journal of Controlled Release j ourna l homepage: www.e lsev ie r .com/ locate / jconre l An assessment of the ability of the obstruction-scaling model to estimate solute diffusion coefficients in hydrogels Nicholas A. Hadjiev, Brian G. Amsden ⁎ Department of Chemical Engineering, Queen's University, Kingston, ON K7L 3N6, Canada ⁎ Corresponding author.. E-mail address: amsden@queensu.ca (B.G.. Amsden). http://dx.doi.org/10.1016/j.jconrel.2014.12.010 0168-3659/© 2014 Elsevier B.V. All rights reserved. a b s t r a c t a r t i c l e i n f o Article history: Received 9 October 2014 Revised 4 December 2014 Accepted 6 December 2014 Available online 10 December 2014 Keywords: FRAP Diffusion in hydrogels Mathematical model Diffusion in polymer solutions The ability to estimate the diffusion coefficient of a solute within hydrogels has important application in the design and analysis of hydrogels used in drug delivery, tissue engineering, and regenerative medicine.



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    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral <t>unmixing</t> algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.
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    A: FRET spectrum (red solid line) for basal (0 μM) cAMP from Figure 2 showing estimated contributions of CFP (long-dash blue line) and YFP (short-dash green line) calculated using linear <t>unmixing;</t> B: the sum of the estimated CFP and YFP contributions (dashed blue line) very closely matches the FRET spectrum from A (solid red line); C: FRET efficiency calculated using the CFP peak intensity (473 nm) and the YFP peak intensity (525 nm); D: FRET efficiency calculated by linear unmixing, as shown in A, and dividing the CFP abundance by the CFP+YFP abundance (black squares); the linear unmixing FRET has been further corrected by estimating the percent of the acceptor signal that is due to direct excitation and then subtracting this percent from the total acceptor signal before dividing by the donor signal (red triangles), as shown in Eq. (13). Normalized FRET responses are shown in Figure 4. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]
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    A: FRET spectrum (red solid line) for basal (0 μM) cAMP from Figure 2 showing estimated contributions of CFP (long-dash blue line) and YFP (short-dash green line) calculated using linear <t>unmixing;</t> B: the sum of the estimated CFP and YFP contributions (dashed blue line) very closely matches the FRET spectrum from A (solid red line); C: FRET efficiency calculated using the CFP peak intensity (473 nm) and the YFP peak intensity (525 nm); D: FRET efficiency calculated by linear unmixing, as shown in A, and dividing the CFP abundance by the CFP+YFP abundance (black squares); the linear unmixing FRET has been further corrected by estimating the percent of the acceptor signal that is due to direct excitation and then subtracting this percent from the total acceptor signal before dividing by the donor signal (red triangles), as shown in Eq. (13). Normalized FRET responses are shown in Figure 4. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]
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    Image Search Results


    SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques: Imaging

    Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Journal: Journal of Biomedical Optics

    Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

    doi: 10.1117/1.JBO.29.1.016008

    Figure Lengend Snippet: Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

    Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

    Techniques:

    A: FRET spectrum (red solid line) for basal (0 μM) cAMP from Figure 2 showing estimated contributions of CFP (long-dash blue line) and YFP (short-dash green line) calculated using linear unmixing; B: the sum of the estimated CFP and YFP contributions (dashed blue line) very closely matches the FRET spectrum from A (solid red line); C: FRET efficiency calculated using the CFP peak intensity (473 nm) and the YFP peak intensity (525 nm); D: FRET efficiency calculated by linear unmixing, as shown in A, and dividing the CFP abundance by the CFP+YFP abundance (black squares); the linear unmixing FRET has been further corrected by estimating the percent of the acceptor signal that is due to direct excitation and then subtracting this percent from the total acceptor signal before dividing by the donor signal (red triangles), as shown in Eq. (13). Normalized FRET responses are shown in Figure 4. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

    Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

    Article Title: Assessing FRET using Spectral Techniques

    doi: 10.1002/cyto.a.22340

    Figure Lengend Snippet: A: FRET spectrum (red solid line) for basal (0 μM) cAMP from Figure 2 showing estimated contributions of CFP (long-dash blue line) and YFP (short-dash green line) calculated using linear unmixing; B: the sum of the estimated CFP and YFP contributions (dashed blue line) very closely matches the FRET spectrum from A (solid red line); C: FRET efficiency calculated using the CFP peak intensity (473 nm) and the YFP peak intensity (525 nm); D: FRET efficiency calculated by linear unmixing, as shown in A, and dividing the CFP abundance by the CFP+YFP abundance (black squares); the linear unmixing FRET has been further corrected by estimating the percent of the acceptor signal that is due to direct excitation and then subtracting this percent from the total acceptor signal before dividing by the donor signal (red triangles), as shown in Eq. (13). Normalized FRET responses are shown in Figure 4. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

    Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

    Techniques:

    1-FRET response normalized to minimum and maximum FRET levels. Error bars indicate the standard error-of-the-mean (n = 3) for each cAMP concentration. A: one-filter set method; B: two-filter set method; C: three-filter set method; D: three-filter set method and corrected for changes in CFP concentration; E: YFP-CFP peak intensity ratio; F: linear unmixing YFP-CFP ratio; G: linear unmixing YFP-CFP ratio, corrected for direct excitation of YFP. Note that panels A and C represent FRET indices, whereas panels B, D, E, F, and G represent FRET efficiencies. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com]

    Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

    Article Title: Assessing FRET using Spectral Techniques

    doi: 10.1002/cyto.a.22340

    Figure Lengend Snippet: 1-FRET response normalized to minimum and maximum FRET levels. Error bars indicate the standard error-of-the-mean (n = 3) for each cAMP concentration. A: one-filter set method; B: two-filter set method; C: three-filter set method; D: three-filter set method and corrected for changes in CFP concentration; E: YFP-CFP peak intensity ratio; F: linear unmixing YFP-CFP ratio; G: linear unmixing YFP-CFP ratio, corrected for direct excitation of YFP. Note that panels A and C represent FRET indices, whereas panels B, D, E, F, and G represent FRET efficiencies. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com]

    Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

    Techniques: Concentration Assay

    Comparison of different FRET measurement methods.

    Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

    Article Title: Assessing FRET using Spectral Techniques

    doi: 10.1002/cyto.a.22340

    Figure Lengend Snippet: Comparison of different FRET measurement methods.

    Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

    Techniques: Comparison

    Hyperspectral confocal microscope images were unmixed to calculate fluorophore intensities and the FRET efficiency. A: Raw hyperspectral confocal microscope image (all wavelength bands summed) of HEK-293 cells expressing the CFP-Epac-YFP probe; B: the spectral library used for linear unmixing; nonnegatively constrained linear unmixing was used to calculate images for C: Hoechst, D: CFP, and E: YFP; F: the unmixed CFP and YFP images were summed to locate expressing (transfected) cells; G: the FRET efficiency was calculated using equation 23 (note that this image was later masked so that only regions with sufficient signal were used for single-cell FRET calculations, as shown in Figure 6); H: the root-mean-square (RMS) percent error associated with linear unmixing was calculated as the RMS residual from unmixing divided by the RMS signal of the original spectral image. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

    Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

    Article Title: Assessing FRET using Spectral Techniques

    doi: 10.1002/cyto.a.22340

    Figure Lengend Snippet: Hyperspectral confocal microscope images were unmixed to calculate fluorophore intensities and the FRET efficiency. A: Raw hyperspectral confocal microscope image (all wavelength bands summed) of HEK-293 cells expressing the CFP-Epac-YFP probe; B: the spectral library used for linear unmixing; nonnegatively constrained linear unmixing was used to calculate images for C: Hoechst, D: CFP, and E: YFP; F: the unmixed CFP and YFP images were summed to locate expressing (transfected) cells; G: the FRET efficiency was calculated using equation 23 (note that this image was later masked so that only regions with sufficient signal were used for single-cell FRET calculations, as shown in Figure 6); H: the root-mean-square (RMS) percent error associated with linear unmixing was calculated as the RMS residual from unmixing divided by the RMS signal of the original spectral image. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

    Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

    Techniques: Microscopy, Expressing, Transfection