Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

A GREM1 gene variant associates with diabetic nephropathy

Gremlin, a cell growth and differentiation factor, promotes the development of diabetic nephropathy in animal models, but whether GREM1 gene variants associate with diabetic nephropathy is unknown. We comprehensively screened the 5' upstream region (including the predicted promoter), all exons, intron-exon boundaries, complete untranslated regions, and the 3' region downstream of the GREM1 gene. We identified 31 unique variants, including 24 with a minor allele frequency exceeding 5%, and 9 haplotype-tagging single nucleotide polymorphisms (htSNPs). We selected one additional variant that we predicted to alter transcription factor binding. We genotyped 709 individuals with type 1 diabetes of whom 267 had nephropathy (cases) and 442 had no evidence of kidney disease (controls). Three individual SNPs significantly associated with nephropathy at the 5% level, and two remained significant after adjustment for multiple testing. Subsequently, we genotyped a replicate population comprising 597 cases and 502 controls: this population supported an association with one of the SNPs (rs1129456; P = 0.0003). Combined analysis, adjusted for recruitment center (n = 8), suggested that the T allele conferred greater odds of nephropathy (OR 1.69; 95% CI 1.36 to 2.11). In summary, the GREM1 variant rs1129456 associates with diabetic nephropathy, perhaps explaining some of the genetic susceptibility to this condition.

Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women

Sex differences in schizophrenia are well known, but their genetic basis has not been identified. We performed a genome-wide association scan for schizophrenia in an Ashkenazi Jewish population using DNA pooling. We found a female-specific association with rs7341475, a SNP in the fourth intron of the reelin ( RELN) gene (p = 2.9 x 10(-5) in women), with a significant gene-sex effect (p = 1.8 x 10(-4)). We studied rs7341475 in four additional populations, totaling 2,274 cases and 4,401 controls. A significant effect was observed only in women, replicating the initial result (p = 2.1 x 10(-3) in women; p = 4.2 x 10(-3) for gene-sex interaction). Based on all populations the estimated relative risk of women carrying the common genotype is 1.58 (p = 8.8 x 10(-7); p = 1.6 x 10(-5) for gene-sex interaction). The female-specific association between RELN and schizophrenia is one of the few examples of a replicated sex-specific genetic association in any disease.

Clonal characteirization of sporadic cribriform-morular variant of papillary thyroid carcinoma by laser microdissection based APC mutation analysis

Cribriform-morular variant (C-MV) of papillary thyroid carcinoma (PTC) is a rare and unusual neoplasm composed of multiple histologic components, including cribriform, papillary, solid, tall columnar, and morular patterns. Analyses of gross C-MV of PTC lesions has linked adenomatous polyposis coli (APC) mutations to its pathogenesis; however, the extent of involvement of mutations in the development Of individual components is unclear We report on bidirectional sequencing of the mutation cluster region (codons 1032-1565) of the APC gene in individually laser-microdissected components of a previously unreported C-MV of PTC. A silent Thr1493Thr gene variant was found in all tumoral components, whereas a 5-base-pair frameshift deletion at codon 1309 was identified only in the morules. Neither variant was observed in matched normal thyroid tissue. These results show the histologic components of C-MV of PTC to have some common mutational background, although additional somatic mutations may be involved in the development of morular structures.

Safe Therapeutic Economic Pharmaceutical Selection (STEPS): development, introduction and use in Northern Ireland

A number of medicine selection methods have been used worldwide for formulary purposes. In Northern Ireland, integrated medicines management is being developed, and related projects have been carried out. This paper deals with the description of the STEPS (Safe Therapeutic Economic Pharmaceutical Selection) programme. The paper outlines the development of STEPS and its application as an element of a cost-effective medicines-management process in Northern Ireland.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.