interactive spindle photoconversion analysis gui matlab 2020b Search Results


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.
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( 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