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Comparing Δsx,rms vs N obtained by using four different methods: experimental measurements (solid squares), simulations (circles), numerical integrations (crosses), and analytical calculations (dashed line). Each experimental Δsx,rms data point is the SD from the <t>Gaussian</t> fit to the sx distribution of a single streptavidin-Cy3 monomer. For each data point, its experimental N and background distributions were used for simulation, and its experimental 〈N〉, 〈sx,y〉, σb, and 〈b〉 values were used for the numerical integrations and analytical calculations. The experimental data are on average 57% higher than the analytical calculation data.
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MathWorks Inc recurrence plot of gaussian random numbers generated by matlab's random
Comparing Δsx,rms vs N obtained by using four different methods: experimental measurements (solid squares), simulations (circles), numerical integrations (crosses), and analytical calculations (dashed line). Each experimental Δsx,rms data point is the SD from the <t>Gaussian</t> fit to the sx distribution of a single streptavidin-Cy3 monomer. For each data point, its experimental N and background distributions were used for simulation, and its experimental 〈N〉, 〈sx,y〉, σb, and 〈b〉 values were used for the numerical integrations and analytical calculations. The experimental data are on average 57% higher than the analytical calculation data.
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Comparing Δsx,rms vs N obtained by using four different methods: experimental measurements (solid squares), simulations (circles), numerical integrations (crosses), and analytical calculations (dashed line). Each experimental Δsx,rms data point is the SD from the Gaussian fit to the sx distribution of a single streptavidin-Cy3 monomer. For each data point, its experimental N and background distributions were used for simulation, and its experimental 〈N〉, 〈sx,y〉, σb, and 〈b〉 values were used for the numerical integrations and analytical calculations. The experimental data are on average 57% higher than the analytical calculation data.

Journal: Optics express

Article Title: Precision analysis for standard deviation measurements of immobile single fluorescent molecule images

doi:

Figure Lengend Snippet: Comparing Δsx,rms vs N obtained by using four different methods: experimental measurements (solid squares), simulations (circles), numerical integrations (crosses), and analytical calculations (dashed line). Each experimental Δsx,rms data point is the SD from the Gaussian fit to the sx distribution of a single streptavidin-Cy3 monomer. For each data point, its experimental N and background distributions were used for simulation, and its experimental 〈N〉, 〈sx,y〉, σb, and 〈b〉 values were used for the numerical integrations and analytical calculations. The experimental data are on average 57% higher than the analytical calculation data.

Article Snippet: PSF and background simulations Single-fluorescent-molecule PSFs were generated using the Gaussian random number generator in MATLAB.

Techniques:

(A) Representative images with increasing N of 151, 393, and 1891 photons of single streptavidin-Cy3 molecules. (B) 1D intensity profiles (circles) of the molecules in (A) and their Gaussian fits (lines). The respective 1D SD values are 195.4 nm, 140.5 nm, and 110.9 nm. The scale bar is 500 nm.

Journal: Optics express

Article Title: Precision analysis for standard deviation measurements of immobile single fluorescent molecule images

doi:

Figure Lengend Snippet: (A) Representative images with increasing N of 151, 393, and 1891 photons of single streptavidin-Cy3 molecules. (B) 1D intensity profiles (circles) of the molecules in (A) and their Gaussian fits (lines). The respective 1D SD values are 195.4 nm, 140.5 nm, and 110.9 nm. The scale bar is 500 nm.

Article Snippet: PSF and background simulations Single-fluorescent-molecule PSFs were generated using the Gaussian random number generator in MATLAB.

Techniques: