Crucial cytokine interactions in nitric oxide production induced by Mycoplasma arthritidis superantigen

Mycoplasma arthritidis causes autoimmune arthritis in rodents. It produces a superantigen (MAM) that simultaneously activates antigen presenting cells and T cells inducing nitric oxide and cytokine release. Nitric oxide is a key inducer and regulator of the immune system activation. Here, we investigated nitric oxide and cytokine production and interactions of these molecules in MAM-stimulated co-cultures of macrophages (J774A.1 cell line) with spleen lymphocytes. We found that: a) MAM-induced nitric oxide, interferon-gamma, membrane-associated tumor necrosis factor and interleukin-2 production in co-cultures of macrophages with lymphocytes from BALB/c and C3H/HePas but not from C57B1/6 mice; b) production of nitric oxide was dependent on interferon-gamma whereas that of interferon-gamma was dependent on interleukin-2 and membrane-associated tumor necrosis factor; c) these cytokines up regulated MAM-induced nitric oxide production. Unraveling the mechanisms of cell activation induced by MAM might be helpful to design strategies to prevent immune system activation by superantigens and therefore in seeking amelioration of associated immunopathologies. (C) 2008 Elsevier Masson SAS. All rights reserved.

Infeasibility handling in genetic algorithm using nested domains for production planning

In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers. (C) 2009 Elsevier Ltd. All rights reserved.

Useful Strategies for Algal Volatile Analysis

The production of volatile organic compounds (VOC) by plants is well known. However, few scientific groups have studied VOC produced by green, brown and red algae. Headspace collection of volatiles and solid phase microextraction, as well as the traditional extraction by hydrodistillation combined with analytical chromatographic techniques (i.e., GC-MS), have significantly improved the investigation of VOC from plants and algae. The major volatile compounds found in seaweeds are hydrocarbons, terpenes, phenols, alcohols, aldehydes, ketones, esters, fatty acids and halogen or sulfur-containing compounds. This article presents an overview of VOC isolated from and identified in marine macro-algae. Focus is given to non-halogenated and non-sulfur volatile compounds, as well as strategies to analyze and identify algal VOC by GC-MS.