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interactive spindle photoconversion analysis gui matlab 2020b  (MathWorks Inc)


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    MathWorks Inc interactive spindle photoconversion analysis gui matlab 2020b
    ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the <t>photoconversion</t> data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.
    Interactive Spindle Photoconversion Analysis Gui Matlab 2020b, 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/interactive spindle photoconversion analysis gui matlab 2020b/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    interactive spindle photoconversion analysis gui matlab 2020b - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Self-organization of kinetochore-fibers in human mitotic spindles"

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    Journal: eLife

    doi: 10.7554/eLife.75458

    ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the photoconversion data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.
    Figure Legend Snippet: ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the photoconversion data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.

    Techniques Used: Generated, Tomography, Comparison

    ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Comparison of the predicted spatial dependence tubulin flux speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean. ( C ) Relative probabilities of hybrid version of the two models.
    Figure Legend Snippet: ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Comparison of the predicted spatial dependence tubulin flux speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean. ( C ) Relative probabilities of hybrid version of the two models.

    Techniques Used: Comparison

    ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the capture from spindle model. ( C ) Comparison of the predicted spatial dependence tubulin speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean.
    Figure Legend Snippet: ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the capture from spindle model. ( C ) Comparison of the predicted spatial dependence tubulin speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean.

    Techniques Used: Comparison

    Parameters values and sources.
    Figure Legend Snippet: Parameters values and sources.

    Techniques Used: Electron Microscopy


    Figure Legend Snippet:

    Techniques Used: Transfection, Construct, Labeling, Retroviral, Plasmid Preparation, Selection, Marker, Software, Control, Imaging, Light Microscopy



    Similar Products

    interactive spindle photoconversion analysis gui matlab 2020b  (MathWorks Inc)
    90
    MathWorks Inc interactive spindle photoconversion analysis gui matlab 2020b
    ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the <t>photoconversion</t> data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.
    Interactive Spindle Photoconversion Analysis Gui Matlab 2020b, 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/interactive spindle photoconversion analysis gui matlab 2020b/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    interactive spindle photoconversion analysis gui matlab 2020b - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the photoconversion data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.

    Journal: eLife

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    doi: 10.7554/eLife.75458

    Figure Lengend Snippet: ( A ) Photoactivation experiment showing PA-GFP:alpha-tubulin and SNAP-SIR:centrin immediately preceding photoactivation, 0 s, 30 s, and 60 s after photoactivation with a 750nm femtosecond pulsed laser; 500ms 488nm excitation, 514/30 bandpass emission filter; 300ms 647nm excitation, 647 longpass emission filter; 5s frame rate. ( B ) Line profile generated by averaging the intensity in 15 pixels on either side of the spindle axis in the dotted box shown in A. The intensity is corrected for background from the opposite side of the spindle (see methods). ( C ) Line profiles (shades of green) fit to Gaussian profiles (shades of grey) at 0s, 5s and 25s. Lighter shades are earlier times. The solid line on the fit represents the fit pixels. ( D ) Blue dots: fit position of the line profile peak from the sample cell shown in A, B, and C over time. Black line: linear fit to the central position of the fit peak over time. ( E ) Red dots: fit height of the line profile peak from the sample cell shown in A, B, and C over time. Black line: dual-exponential fit to the fit height of the peak over time. ( F ) Sample ultrastructure from a 3D spindle reconstructed by electron tomography . KMTs are shown in red, non-KMTs yellow. ( G ) Comparison between the mean slow fraction from the photoconversion data (26% ± 2%, n=52 cells, error bars are standard error of the mean) and the fraction of KMTs (25% ± 2%, n=3 cells, error bars are standard error of the mean) from the EM data. The two means are statistically indistinguishable with P =0.86 on a Student’s t-test.

    Article Snippet: Software algorithm , Interactive spindle photoconversion analysis GUI (MATLAB 2020b) , This paper (Dryad) , - , -.

    Techniques: Generated, Tomography, Comparison

    ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Comparison of the predicted spatial dependence tubulin flux speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean. ( C ) Relative probabilities of hybrid version of the two models.

    Journal: eLife

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    doi: 10.7554/eLife.75458

    Figure Lengend Snippet: ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Comparison of the predicted spatial dependence tubulin flux speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean. ( C ) Relative probabilities of hybrid version of the two models.

    Article Snippet: Software algorithm , Interactive spindle photoconversion analysis GUI (MATLAB 2020b) , This paper (Dryad) , - , -.

    Techniques: Comparison

    ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the capture from spindle model. ( C ) Comparison of the predicted spatial dependence tubulin speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean.

    Journal: eLife

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    doi: 10.7554/eLife.75458

    Figure Lengend Snippet: ( A ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the nucleate at kinetochore model. ( B ) Sample simulated images and line profiles from a photoconversion simulation using KMT minus end speeds in the capture from spindle model. ( C ) Comparison of the predicted spatial dependence tubulin speed in the nucleate at kinetochore and capture from spindle models. Error bars are standard error of the mean.

    Article Snippet: Software algorithm , Interactive spindle photoconversion analysis GUI (MATLAB 2020b) , This paper (Dryad) , - , -.

    Techniques: Comparison

    Parameters values and sources.

    Journal: eLife

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    doi: 10.7554/eLife.75458

    Figure Lengend Snippet: Parameters values and sources.

    Article Snippet: Software algorithm , Interactive spindle photoconversion analysis GUI (MATLAB 2020b) , This paper (Dryad) , - , -.

    Techniques: Electron Microscopy

    Journal: eLife

    Article Title: Self-organization of kinetochore-fibers in human mitotic spindles

    doi: 10.7554/eLife.75458

    Figure Lengend Snippet:

    Article Snippet: Software algorithm , Interactive spindle photoconversion analysis GUI (MATLAB 2020b) , This paper (Dryad) , - , -.

    Techniques: Transfection, Construct, Labeling, Retroviral, Plasmid Preparation, Selection, Marker, Software, Control, Imaging, Light Microscopy