Enlarged PLIN5-uncoated lipid droplets in inner regions of skeletal muscle type II fibers associate with type 2 diabetes.

Skeletal muscle physiology remains of paramount importance in understanding insulin resistance. Due to its high lipid turnover rates, regulation of intramyocellular lipid droplets (LDs) is a key factor. Perilipin 5 (PLIN5) is one of the most critical agents in such regulation, being often referred as a protector against lipotoxicity and consequent skeletal muscle insulin resistance. We examined area fraction, size, subcellular localization and PLIN5 association of LDs in two fiber types of type 2 diabetic (T2D), obese (OB) and healthy (HC) individuals by means of fluorescence microscopy and image analysis. We found that T2D type II fibers have a significant sub-population of large and internalized LDs, uncoated by PLIN5. Based on this novel result, additional hypotheses for the pathophysiology of skeletal muscle insulin resistance are formulated, together with future research directions.

Generative Art with Swarm Landscapes.

We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.

Steady state particle swarm.

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak-Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.