IMPACT OF RNA VIRUSES ON THE REGULATION OF IL-23 IN MOUSE AND HUMAN MODELS OF INFECTION.

Interluekin-23 (IL-23) is a pro-inflammatory cytokine critical to the regulation of innate and adaptive immune responses. The main role for this cytokine is in the proliferation and differentiation of the IL-17 producing CD4 T helper cell, Th17. Virus infection deregulates IL-23 expression and function, but little is known about the mechanism behind this phenomena. Here, I demonstrate a reduction of Toll like receptor (TLR) ligand-induced IL-23 expression in lymphocytic choriomeningitis virus (LCMV)-infected bone marrow-derived dendritic cells (BMDCs), indicating that a function of these cells is disrupted during virus infection. I propose a mechanism of TLR ligand-induced IL-23 expression inhibition upon LCMV infection via the deactivation of p38, AP-1, and NF-κB. Further analysis revealed a direct relationship between LCMV infection with the IL-10 and SOCS3 expression. To understand IL-23 function, I characterized IL-23-induced JAK/STAT signalling pathway and IL-23 receptor expression on human CD4 T cells. My results demonstrate that IL-23 induces activation of p-JAK2, p-Tyk2, p-STAT1, p-STAT3, and p-STAT4 in CD4 T cells. For the first time I show that IL-23 alone induces the expression of its own receptor components, IL-12Rβ1 and IL-23Rα, in CD4 T cells. Blocking JAK2, STAT1, and STAT3 activation with specific inhibitors detrimentally effected expression of IL-23 receptor demonstrating that activation of JAK/STAT signalling is important for IL-23 receptor expression. I also addressed the effect of viral infection on IL-23 function and receptor expression in CD4 T cells using cells isolated from HIV positive individuals. These studies were based on earlier reports that the expression of IL-23 and the IL-23 receptor are impaired during HIV infection. I demonstrate that the phosphorylation of JAK2, STAT1, and STAT3 induced by IL-23, as well as IL-23 receptor expression are deregulated in CD4 T cells isolated from HIV positive individuals. This study has furthered the understanding of how the expression and function of IL-23 is regulated during viral infections.

An Interactive Model to Teach Motor Skills

There has been a tremendous increase in our knowledge of hum motor performance over the last few decades. Our theoretical understanding of how an individual learns to move is sophisticated and complex. It is difficult however to relate much of this information in practical terms to physical educators, coaches, and therapists concerned with the learning of motor skills (Shumway-Cook & Woolcott, 1995). Much of our knowledge stems from lab testing which often appears to bear little relation to real-life situations. This lack of ecological validity has slowed the flow of information from the theorists and researchers to the practitioners. This paper is concerned with taking some small aspects of motor learning theory, unifying them, and presenting them in a usable fashion. The intention is not to present a recipe for teaching motor skills, but to present a framework from which solutions can be found. If motor performance research has taught us anything, it is that every individual and situation presents unique challenges. By increasing our ability to conceptualize the learning situation we should be able to develop more flexible and adaptive responses to the challege of teaching motor skills. The model presented here allows a teacher, coach, or therapist to use readily available observations and known characteristics about a motor task and to conceptualize them in a manner which allows them to make appropriate teaching/learning decisions.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.