Is the generation of neo-antigenic determinants by free radicals central to the development of autoimmune rheumatoid disease?

Biomolecules are susceptible to many different post-translational modifications that have important effects on their function and stability, including glycosylation, glycation, phosphorylation and oxidation chemistries. Specific conversion of aspartic acid to its isoaspartyl derivative or arginine to citrulline leads to autoantibody production in models of rheumatoid disease, and ensuing autoantibodies cross-react with native antigens. Autoimmune conditions associate with increased activation of immune effector cells and production of free radical species via NADPH oxidases and nitric oxide synthases. Generation of neo-antigenic determinants by reactive oxygen and nitrogen species ROS and RNS) may contribute to epitope spreading in autoimmunity. The oxidation of amino acids by peroxynitrite, hypochlorous acid and other reactive oxygen species (ROS) increases the antigenicity of DNA, LDL and IgG, generating ligands for which autoantibodies show higher avidity. This review focuses on the evidence for ROS and RNS in promoting the autoimmune responses observed in diseases rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). It considers the evidence for ROS/RNS-induced antigenicity arising as a consequence of failure to remove or repair ROS/RNS damaged biomolecules and suggests that an associated defect, probably in T cell signal processing or/or antigen presentation, is required for the development of disease.

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series

We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.