Inflammation of the Hypothalamus Leads to Defective Pancreatic Islet Function

Type 2 diabetes mellitus results from the complex association of insulin resistance and pancreatic beta-cell failure. Obesity is the main risk factor for type 2 diabetes mellitus, and recent studies have shown that, in diet-induced obesity, the hypothalamus becomes inflamed and dysfunctional, resulting in the loss of the perfect coupling between caloric intake and energy expenditure. Because pancreatic beta-cell function is, in part, under the control of the autonomic nervous system, we evaluated the role of hypothalamic inflammation in pancreatic islet function. In diet-induced obesity, the earliest markers of hypothalamic inflammation are present at 8 weeks after the beginning of the high fat diet; similarly, the loss of the first phase of insulin secretion is detected at the same time point and is restored following sympathectomy. Intracerebroventricular injection of a low dose of tumor necrosis factor a leads to a dysfunctional increase in insulin secretion and activates the expression of a number of markers of apoptosis in pancreatic islets. In addition, the injection of stearic acid intracerebroventricularly, which leads to hypothalamic inflammation through the activation of tau-like receptor-4 and endoplasmic reticulum stress, produces an impairment of insulin secretion, accompanied by increased expression of markers of apoptosis. The defective insulin secretion, in this case, is partially dependent on sympathetic signal-induced peroxisome proliferator receptor-gamma coactivator Delta a and uncoupling protein-2 expression and is restored after sympathectomy or following PGC1 alpha expression inhibition by an antisense oligonucleotide. Thus, the autonomic signals generated in concert with hypothalamic inflammation can impair pancreatic islet function, a phenomenon that may explain the early link between obesity and defective insulin secretion.

Three-dimensional, fully adaptive simulations of phase-field fluid models

We present an efficient numerical methodology for the 31) computation of incompressible multi-phase flows described by conservative phase-field models We focus here on the case of density matched fluids with different viscosity (Model H) The numerical method employs adaptive mesh refinements (AMR) in concert with an efficient semi-implicit time discretization strategy and a linear, multi-level multigrid to relax high order stability constraints and to capture the flow`s disparate scales at optimal cost. Only five linear solvers are needed per time-step. Moreover, all the adaptive methodology is constructed from scratch to allow a systematic investigation of the key aspects of AMR in a conservative, phase-field setting. We validate the method and demonstrate its capabilities and efficacy with important examples of drop deformation, Kelvin-Helmholtz instability, and flow-induced drop coalescence (C) 2010 Elsevier Inc. All rights reserved

A Robust, Fully Adaptive Hybrid Level-Set/Front-Tracking Method for Two-Phase Flows with an Accurate Surface Tension Computation

We present a variable time step, fully adaptive in space, hybrid method for the accurate simulation of incompressible two-phase flows in the presence of surface tension in two dimensions. The method is based on the hybrid level set/front-tracking approach proposed in [H. D. Ceniceros and A. M. Roma, J. Comput. Phys., 205, 391400, 2005]. Geometric, interfacial quantities are computed from front-tracking via the immersed-boundary setting while the signed distance (level set) function, which is evaluated fast and to machine precision, is used as a fluid indicator. The surface tension force is obtained by employing the mixed Eulerian/Lagrangian representation introduced in [S. Shin, S. I. Abdel-Khalik, V. Daru and D. Juric, J. Comput. Phys., 203, 493-516, 2005] whose success for greatly reducing parasitic currents has been demonstrated. The use of our accurate fluid indicator together with effective Lagrangian marker control enhance this parasitic current reduction by several orders of magnitude. To resolve accurately and efficiently sharp gradients and salient flow features we employ dynamic, adaptive mesh refinements. This spatial adaption is used in concert with a dynamic control of the distribution of the Lagrangian nodes along the fluid interface and a variable time step, linearly implicit time integration scheme. We present numerical examples designed to test the capabilities and performance of the proposed approach as well as three applications: the long-time evolution of a fluid interface undergoing Rayleigh-Taylor instability, an example of bubble ascending dynamics, and a drop impacting on a free interface whose dynamics we compare with both existing numerical and experimental data.