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surface curvature  (MathWorks Inc)


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    MathWorks Inc surface curvature
    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a <t>Gaussian</t> fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.
    Surface Curvature, 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/surface+curvature/pmc10238495-394-0-3
    Average 90 stars, based on 1 article reviews
    surface curvature - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "A deep learning framework for quantitative analysis of actin microridges"

    Article Title: A deep learning framework for quantitative analysis of actin microridges

    Journal: NPJ Systems Biology and Applications

    doi: 10.1038/s41540-023-00276-7

    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a Gaussian fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.
    Figure Legend Snippet: a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a Gaussian fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.

    Techniques Used: Sequencing

    Related Articles

    Modification:

    Article Title: Grayscale gel lithography for programmed buckling of non-Euclidean hydrogel plates.
    Article Snippet: Shape programmable materials capable of morphing from a flat sheet into controlled three dimensional (3D) shapes offer promise in diverse areas including soft robotics, tunable optics, and bio-engineering.. We describe a simple method of ‘grayscale gel lithography’ that relies on a digital micromirror array device (DMD) to control the dose of ultraviolet (UV) light, and therefore the extent of swelling of a photocrosslinkable poly(N-isopropyl acrylamide) (PNIPAm) copolymer film, with micrometer-scale spatial resolution.. This approach allows for effectively smooth profiles of swelling to be prescribed, enabling the preparation of buckled 3D shapes with programmed Gaussian curvature.

    Article Title: A deep learning framework for quantitative analysis of actin microridges
    Article Snippet: Gaussian curvature ( https://www.mathworks.com/matlabcentral/fileexchange/11168-surface-curvature ) was modified to compute the Gauss gradient with σ = 1.2 μm using ( https://www.mathworks.com/matlabcentral/fileexchange/8060-gradient-using-first-order-derivative-of-gaussian ) to extract the first and second derivatives at each point in the image.

    Sequencing:

    Article Title: Grayscale gel lithography for programmed buckling of non-Euclidean hydrogel plates.
    Article Snippet: Shape programmable materials capable of morphing from a flat sheet into controlled three dimensional (3D) shapes offer promise in diverse areas including soft robotics, tunable optics, and bio-engineering.. We describe a simple method of ‘grayscale gel lithography’ that relies on a digital micromirror array device (DMD) to control the dose of ultraviolet (UV) light, and therefore the extent of swelling of a photocrosslinkable poly(N-isopropyl acrylamide) (PNIPAm) copolymer film, with micrometer-scale spatial resolution.. This approach allows for effectively smooth profiles of swelling to be prescribed, enabling the preparation of buckled 3D shapes with programmed Gaussian curvature.

    Article Title: A deep learning framework for quantitative analysis of actin microridges
    Article Snippet: Gaussian curvature ( https://www.mathworks.com/matlabcentral/fileexchange/11168-surface-curvature ) was modified to compute the Gauss gradient with σ = 1.2 μm using ( https://www.mathworks.com/matlabcentral/fileexchange/8060-gradient-using-first-order-derivative-of-gaussian ) to extract the first and second derivatives at each point in the image.



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    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a <t>Gaussian</t> fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.
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    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a <t>Gaussian</t> fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.
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    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a <t>Gaussian</t> fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.
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    Image Search Results


    a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a Gaussian fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.

    Journal: NPJ Systems Biology and Applications

    Article Title: A deep learning framework for quantitative analysis of actin microridges

    doi: 10.1038/s41540-023-00276-7

    Figure Lengend Snippet: a A magenta box demarcates the 2D sub-image of a skeletonized microridge branch for estimation of L p . b Microridge skeleton contours (blue) were smoothened using a Gaussian fit (red curve). The inset shows a microridge skeleton (blue line) with the endpoints of the contour (magenta) used to obtain the boundary trace that returned the discrete x–y coordinates. c A cubic spline interpolation on the Gaussian smoothened microridge trace contours preserved the sequence of points to give several intermediate points. d Tangent angle ( θ k ) along the length ( ℓ ) of the microridge. e Rescaled κ s along the length ( ℓ ) of the microridge contour. f Distribution of κ s of microridges from 1052 cells (293, 1084, and 125 from the flank, yolk, and head, respectively) fitted to a Gaussian distribution (red line trace), whose variance gives an estimate of the effective persistence length ( L p ) as ~6.1 μm.

    Article Snippet: Gaussian curvature ( https://www.mathworks.com/matlabcentral/fileexchange/11168-surface-curvature ) was modified to compute the Gauss gradient with σ = 1.2 μm using ( https://www.mathworks.com/matlabcentral/fileexchange/8060-gradient-using-first-order-derivative-of-gaussian ) to extract the first and second derivatives at each point in the image.

    Techniques: Sequencing