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curvefit tool (cftool)  (MathWorks Inc)


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    MathWorks Inc curvefit tool (cftool)
    Curvefit Tool (Cftool), 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/curvefitting+tool/pmc10759184-180-7-11
    Average 90 stars, based on 1 article reviews
    curvefit tool (cftool) - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Diffusion-based Assay:

    Article Title: Carrier-Transport Study of Gallium Arsenide Hillock Defects
    Article Snippet: .. We used the curvefitting tool in the Matlab software to fit the two curves in Figure 2d and extracted the effective diffusion lengths. ..

    other:

    Article Title: A medical device-grade T1 and ECV phantom for global T1 mapping quality assurance—the T 1 Mapping and ECV Standardization in cardiovascular magnetic resonance (T1MES) program
    Article Snippet: For defining the model that describes the relation between ingredients and relaxation rates (R 1 |R 2 ), the fitted parameters were found by fitting a surface for both T 1 and T 2 using the MATLAB (The MathWorks Inc., Natick, MA, USA, R2012b) curvefitting tool and the linear least-squares approach.

    Article Title: Actuation dynamic modeling and characterization of an electrohydraulic system
    Article Snippet: The physical model of an electrohydraulic actuation system with pressure-reducing cylinder end cushioning has been obtained.. In order to arrive at the closure of the model, experimental models for actuator friction and the characteristics of the relief, non-return and proportional valves have been constructed.. The variation in discharge through the proportional valve with both pressure and command signal has been modeled by training a neural network with experimental data.

    Article Title: Live-cell super-resolved PAINT imaging of pN cellular traction forces
    Article Snippet: The data were fit to a gaussian via Matlab’s curvefitting tool (blue line).



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    (a) Live-cell sf-tPAINT imaging of platelet tension displayed using different time windows (ranging from 50 sec to 1000 sec). The apparent length or width of cellular tension features depends on the number of frames that are integrated to produce a super-resolved image. To demonstrate this point, we rendered the lamellipodial edge of 3 human platelets (from n = 3 independent experiments, 2 platelets shown) and measured the apparent width of the lamellipodial edge tension ring as tPAINT data is integrated over various time windows. Super-resolved tPAINT images were rendered as greyscale images, and ring width was measured via linescan analysis (black dots). The data were fit to a gaussian via Matlab’s <t>curvefitting</t> tool (blue line). The measured FWHM of the fitted gaussians depends on the number of frames integrated to produce the super-resolved tPAINT image. (b) Plot showing that the localization density generally increased with increasing the number of integrated frames. Each color shows a unique ROI. (c) Plot showing the relation between the FWHM of the tension ring and the number of integrated frames. The data shown are from 3 human platelets from n = 3 independent experiments (2 linescans per platelet). In principle, it is desirable to use the minimum number of frames possible to render an image in order to minimize feature blurring due to cellular dynamics during the imaging window; however, image quality decreases, with localizations becoming more punctate, when fewer frames are integrated. To produce high-quality tPAINT images, these considerations must both be balanced. All scale bars are 2 μm.
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    (a) Live-cell sf-tPAINT imaging of platelet tension displayed using different time windows (ranging from 50 sec to 1000 sec). The apparent length or width of cellular tension features depends on the number of frames that are integrated to produce a super-resolved image. To demonstrate this point, we rendered the lamellipodial edge of 3 human platelets (from n = 3 independent experiments, 2 platelets shown) and measured the apparent width of the lamellipodial edge tension ring as tPAINT data is integrated over various time windows. Super-resolved tPAINT images were rendered as greyscale images, and ring width was measured via linescan analysis (black dots). The data were fit to a gaussian via Matlab’s <t>curvefitting</t> tool (blue line). The measured FWHM of the fitted gaussians depends on the number of frames integrated to produce the super-resolved tPAINT image. (b) Plot showing that the localization density generally increased with increasing the number of integrated frames. Each color shows a unique ROI. (c) Plot showing the relation between the FWHM of the tension ring and the number of integrated frames. The data shown are from 3 human platelets from n = 3 independent experiments (2 linescans per platelet). In principle, it is desirable to use the minimum number of frames possible to render an image in order to minimize feature blurring due to cellular dynamics during the imaging window; however, image quality decreases, with localizations becoming more punctate, when fewer frames are integrated. To produce high-quality tPAINT images, these considerations must both be balanced. All scale bars are 2 μm.
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    (a) Live-cell sf-tPAINT imaging of platelet tension displayed using different time windows (ranging from 50 sec to 1000 sec). The apparent length or width of cellular tension features depends on the number of frames that are integrated to produce a super-resolved image. To demonstrate this point, we rendered the lamellipodial edge of 3 human platelets (from n = 3 independent experiments, 2 platelets shown) and measured the apparent width of the lamellipodial edge tension ring as tPAINT data is integrated over various time windows. Super-resolved tPAINT images were rendered as greyscale images, and ring width was measured via linescan analysis (black dots). The data were fit to a gaussian via Matlab’s <t>curvefitting</t> tool (blue line). The measured FWHM of the fitted gaussians depends on the number of frames integrated to produce the super-resolved tPAINT image. (b) Plot showing that the localization density generally increased with increasing the number of integrated frames. Each color shows a unique ROI. (c) Plot showing the relation between the FWHM of the tension ring and the number of integrated frames. The data shown are from 3 human platelets from n = 3 independent experiments (2 linescans per platelet). In principle, it is desirable to use the minimum number of frames possible to render an image in order to minimize feature blurring due to cellular dynamics during the imaging window; however, image quality decreases, with localizations becoming more punctate, when fewer frames are integrated. To produce high-quality tPAINT images, these considerations must both be balanced. All scale bars are 2 μm.
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    Image Search Results


    (a) Live-cell sf-tPAINT imaging of platelet tension displayed using different time windows (ranging from 50 sec to 1000 sec). The apparent length or width of cellular tension features depends on the number of frames that are integrated to produce a super-resolved image. To demonstrate this point, we rendered the lamellipodial edge of 3 human platelets (from n = 3 independent experiments, 2 platelets shown) and measured the apparent width of the lamellipodial edge tension ring as tPAINT data is integrated over various time windows. Super-resolved tPAINT images were rendered as greyscale images, and ring width was measured via linescan analysis (black dots). The data were fit to a gaussian via Matlab’s curvefitting tool (blue line). The measured FWHM of the fitted gaussians depends on the number of frames integrated to produce the super-resolved tPAINT image. (b) Plot showing that the localization density generally increased with increasing the number of integrated frames. Each color shows a unique ROI. (c) Plot showing the relation between the FWHM of the tension ring and the number of integrated frames. The data shown are from 3 human platelets from n = 3 independent experiments (2 linescans per platelet). In principle, it is desirable to use the minimum number of frames possible to render an image in order to minimize feature blurring due to cellular dynamics during the imaging window; however, image quality decreases, with localizations becoming more punctate, when fewer frames are integrated. To produce high-quality tPAINT images, these considerations must both be balanced. All scale bars are 2 μm.

    Journal: Nature methods

    Article Title: Live-cell super-resolved PAINT imaging of pN cellular traction forces

    doi: 10.1038/s41592-020-0929-2

    Figure Lengend Snippet: (a) Live-cell sf-tPAINT imaging of platelet tension displayed using different time windows (ranging from 50 sec to 1000 sec). The apparent length or width of cellular tension features depends on the number of frames that are integrated to produce a super-resolved image. To demonstrate this point, we rendered the lamellipodial edge of 3 human platelets (from n = 3 independent experiments, 2 platelets shown) and measured the apparent width of the lamellipodial edge tension ring as tPAINT data is integrated over various time windows. Super-resolved tPAINT images were rendered as greyscale images, and ring width was measured via linescan analysis (black dots). The data were fit to a gaussian via Matlab’s curvefitting tool (blue line). The measured FWHM of the fitted gaussians depends on the number of frames integrated to produce the super-resolved tPAINT image. (b) Plot showing that the localization density generally increased with increasing the number of integrated frames. Each color shows a unique ROI. (c) Plot showing the relation between the FWHM of the tension ring and the number of integrated frames. The data shown are from 3 human platelets from n = 3 independent experiments (2 linescans per platelet). In principle, it is desirable to use the minimum number of frames possible to render an image in order to minimize feature blurring due to cellular dynamics during the imaging window; however, image quality decreases, with localizations becoming more punctate, when fewer frames are integrated. To produce high-quality tPAINT images, these considerations must both be balanced. All scale bars are 2 μm.

    Article Snippet: The data were fit to a gaussian via Matlab’s curvefitting tool (blue line).

    Techniques: Imaging