Aged insulin granules display reduced microtubule-dependent mobility and are disposed within actin-positive multigranular bodies.

Insulin secretion is key for glucose homeostasis. Insulin secretory granules (SGs) exist in different functional pools, with young SGs being more mobile and preferentially secreted. However, the principles governing the mobility of age-distinct SGs remain undefined. Using the time-reporter insulin-SNAP to track age-distinct SGs we now show that their dynamics can be classified into three components: highly dynamic, restricted, and nearly immobile. Young SGs display all three components, whereas old SGs are either restricted or nearly immobile. Both glucose stimulation and F-actin depolymerization recruit a fraction of nearly immobile young, but not old, SGs for highly dynamic, microtubule-dependent transport. Moreover, F-actin marks multigranular bodies/lysosomes containing aged SGs. These data demonstrate that SGs lose their responsiveness to glucose stimulation and competence for microtubule-mediated transport over time while changing their relationship with F-actin.

A Bayesian approach to blood rheological uncertainties in aortic hemodynamics.

Computational hemodynamics has received increasing attention recently. Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty quantification. In this contribution, we investigated the impact of blood rheological models on wall shear stress uncertainties in aortic hemodynamics obtained in numerical simulations. Based on shear-rheometric experiments, we compare the non-Newtonian Carreau model to a simple Newtonian model and a Reynolds number-equivalent Newtonian model. Bayesian Probability Theory treats uncertainties consistently and allows to include elusive assumptions such as the comparability of flow regimes. We overcome the prohibitively high computational cost for the simulation with a surrogate model, and account for the uncertainties of the surrogate model itself, too. We have two main findings: (1) The Newtonian models mostly underestimate the uncertainties as compared to the non-Newtonian model. (2) The wall shear stresses of specific persons cannot be distinguished due to largely overlapping uncertainty bands, implying that a more precise determination of person-specific blood rheological properties is necessary for person-specific simulations. While we refrain from a general recommendation for one rheological model, we have quantified the error of the uncertainty quantification associated with these modeling choices.

High-Resolution MR Imaging of Muscular Fat Fraction-Comparison of Three T2-Based Methods and Chemical Shift-Encoded Imaging.

Chemical shift-encoded imaging (CSEI) is the most common magnetic resonance imaging fat-water separation method. However, when high spatial resolution fat fraction (FF) images are desired, CSEI might be challenging owing to the increased interecho spacing. Here, 3 T2-based methods have been assessed as alternative methods for obtaining high-resolution FF images. Images from the calf of 10 healthy volunteers were acquired; FF maps were then estimated using 3 T2-based methods (2- and 3-parameter nonlinear least squares fit and a Bayesian probability method) and CSEI for reference. In addition, simulations were conducted to characterize the performance of various methods. Here, all T2-based methods resulted in qualitatively improved high-resolution FF images compared with high-resolution CSEI. The 2-parameter fit showed best quantitative agreement to low-resolution CSEI, even at low FF. The estimated T2-values of fat and water, and the estimated muscle FF of the calf, agreed well with previously published data. In conclusion, T2-based methods can provide improved high-resolution FF images of the calf compared with the CSEI method.