The Diet of Finnish Preschoolers

Verkkoversion ISBN 978-951-740-887-

School based working memory training: Preliminary finding of improvement in children’s mathematical performance

Working memory is a complex cognitive system responsible for the concurrent storage and processing of information. Ggiven that a complex cognitive task like mental arithmetic clearly places demands on working memory (e.g., in remembering partial results, monitoring progress through a multi-step calculation), there is surprisingly little research exploring the possibility of increasing young children’s working memory capacity through systematic school-based training. Tthis study reports the preliminary results of a working memory training programme, targeting executive processes such as inhibiting unwanted information, monitoring processes, and the concurrent storage and processing of information. Tthe findings suggest that children who received working memory training made significantly greater gains in the trained working memory task, and in a non-trained visual-spatial working memory task, than a matched control group. Moreover, the training group made significant improvements in their mathematical functioning as measured by the number of errors made in an addition task compared to the control group. Tthese findings, although preliminary, suggest that school-based measures to train working memory could have benefits in terms of improved performance in mathematics

Transmembrane protein topology prediction using support vector machines

Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins