Journal: Journal of Biomedical Optics

Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

doi: 10.1117/1.JBO.29.1.016008

Figure Lengend Snippet: SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

Techniques: Imaging

Journal: Journal of Biomedical Optics

Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

doi: 10.1117/1.JBO.29.1.016008

Figure Lengend Snippet: Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

Techniques:

Journal: Bioinformatics

Article Title: Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis

doi: 10.1093/bioinformatics/bty230

Figure Lengend Snippet: Performance measures of different local optimization methods [ lsqnonlin with TR algorithm and fmincon with TR and IP algorithm, using either Hessians (H), FIM, or the BFGS scheme]. The multi-start optimization was carried out multiple times using different starting points for the local optimizations. Mean and standard deviation for ( A ) the ratio of computation time over converged optimization starts and ( B ) the number of converged starts are shown. ( C ) Median and standard deviation of the number of optimization steps over all optimization runs

Article Snippet: Common least-squares algorithms such as the MATLAB function lsqnonlin only use first order derivatives of the residuals, whereas constraint optimization algorithms like the MATLAB function fmincon exploit first and second order derivatives of the objective function.

Techniques: Standard Deviation

Journal: Bioinformatics

Article Title: Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis

doi: 10.1093/bioinformatics/bty230

Figure Lengend Snippet: Profile likelihood computation using either the optimization-based method ( lsqnonlin or fmincon with TR or IP algorithm and Hessian or BFGS approximation), or the hybrid method with either FIM or Hessian. ( A ) Total computation time for all profiles of the considered models. Three methods failed to compute profiles for the RAF/MEK/ERK model. Thus, their computation times are not depicted. ( B ) A profile of the RAF/MEK/ERK model, the three remaining methods in good agreement with each other, with confidence intervals to a confidence level of 95% depicted below. ( C ) A profile of the RAF/MEK/ERK model, for which lsqnonlin failed to compute the profile, with confidence intervals to a confidence level of 95% depicted below

Article Snippet: Common least-squares algorithms such as the MATLAB function lsqnonlin only use first order derivatives of the residuals, whereas constraint optimization algorithms like the MATLAB function fmincon exploit first and second order derivatives of the objective function.

Techniques: