Executive Function in Very Preterm Children at Early School Age

We examined whether very preterm (≤30 weeks gestation) children at early school age have impairments in executive function (EF) independent of IQ and processing speed, and whether demographic and neonatal risk factors were associated with EF impairments. A consecutive sample of 50 children (27 boys and 23 girls) born very preterm (mean age = 5.9 years, SD = 0.4, mean gestational age = 28.0 weeks, SD = 1.4) was compared to a sample of 50 age-matched full-term controls (23 girls and 27 boys, mean age = 6.0 years, SD = 0.6) with respect to performance on a comprehensive EF battery, assessing the domains of inhibition, working memory, switching, verbal fluency, and concept generation. The very preterm group demonstrated poor performance compared to the controls on all EF domains, even after partialing out the effects of IQ. Processing speed was marginally related to EF. Analyses with demographic and neonatal risk factors showed maternal education and gestational age to be related to EF. This study adds to the emerging body of literature showing that very preterm birth is associated with EF impairments

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)