Polymorphisms in the interleukin-4 and IL-4 receptor genes modify risk for chronic inflammatory arthropathies in women.

Rheumatoid and juvenile idiopathic arthritis (RA, JIA) are chronic inflammatory arthropathies with polygenic autoimmune background. We analysed the IL-4 +33 C/T and IL-4R Q551R single nucleotide polymorphisms (SNPs) in 294 RA, 72 JIA and 165 controls from Northern Ireland. Analysis of the individual phenotypes (RA or JIA) showed that both the IL-4 +33 TT (P = 0.02; OR: 0.25, 95% CI: 0.07-0.87) and the IL-4R Q551R CC genotypes (P = 0.001; OR: 0.19, 95% CI: 0.06-0.56) were exclusively decreased in female RA patients compared to female controls. Similar non-significant trends were observed in female JIA patients (OR: 0.25, 95% CI: 0.03-2.11 and OR: 0.31, 95% CI: 0.07-1.47, respectively). Analysis of the common phenotype (inflammatory arthropathy; i.e. JIA and RA combined) corroborated the unique association of these polymorphisms with female inflammatory arthropathy (P = 0.013 and 0.002, respectively). This is the first demonstration of sex-specific association of the two foremost genes of the IL-4 signalling cascade with chronic inflammatory arthropathies.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.