Unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders

Chronic kidney disease is common with up to 5% of the adult population reported to have an estimated glomerular filtration rate of

Structure-Based Mutational Analysis of eIF4E in Relation to sbm1 Resistance to Pea Seed-Borne Mosaic Virus in Pea

Background: Pea encodes eukaryotic translation initiation factor eIF4E (eIF4E(S)), which supports the multiplication of Pea seed-borne mosaic virus (PSbMV). In common with hosts for other potyviruses, some pea lines contain a recessive allele (sbm1) encoding a mutant eIF4E (eIF4E(R)) that fails to interact functionally with the PSbMV avirulence protein, VPg, giving genetic resistance to infection.

Extent and variation of phage-borne bacterial 16S rRNA gene sequences in wastewater environments

Phage metagenomes isolated from wastewater over a 12-month period were analyzed. The results suggested that various strains of Proteobacteria, Bacteroidetes, and other phyla are likely to participate in transduction. The patterns of 16S rRNA sequences found in phage metagenomes did not follow changes in the total bacterial community.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.