The role of perirhinal cortex in visual discrimination learning for visual secondary reinforcement in rats

Perirhinal cortex in monkeys has been thought to be involved in visual associative learning. The authors examined rats' ability to make associations between visual stimuli in a visual secondary reinforcement task. Rats learned 2-choice visual discriminations for secondary visual reinforcement. They showed significant learning of discriminations before any primary reinforcement. Following bilateral perirhinal cortex lesions, rats continued to learn visual discriminations for visual secondary reinforcement at the same rate as before surgery. Thus, this study does not support a critical role of perirhinal cortex in learning for visual secondary reinforcement. Contrasting this result with other positive results, the authors suggest that the role of perirhinal cortex is in "within-object" associations and that it plays a much lesser role in stimulus-stimulus associations between objects.

Meteorology and climate inspire secondary school students

As part of its National Science and Engineering Week activities in 2009 and 2010, the University of Reading organised two open days for 60 local key stage 4 pupils. The theme of both open days was ‘How do we predict weather and climate?’ Making use of the students’ familiarity with weather and climate, several concepts of relevance to secondary science were investigated. The open days also provided an opportunity for more than 30 research staff from the university to interact with the students. Feedback from students and teachers was extremely positive. This article shows how meteorological science can be used to illustrate elements of the secondary science and mathematics curricula.

Secondary uranium mineralization in southern Finland and its relationship to recent glacial events

Uranium series dating has been carried out on secondary uranyl silicate minerals formed during sub-glacial and post-glacial weathering of Proterozoic uraninite ores in south west Finland. The samples were obtained from two sites adjacent to the Salpauselkä III ice marginal formation and cover a range of depths, from the surface to more than 60 m. Measured ages fall into three distinct groups, 70–100 ka, 28–36 ka and < 2500 yr. The youngest set is associated with surface exposures and the crystals display clear evidence of re-working. The most likely trigger for uranium release at depths below the surface weathering zone is intrusion of oxidising glacial melt water. The latter is often characterised by very high discharge rates along channels, which close once the overpressure generated at the ice margin is released. There is excellent correspondence between the two Finnish sites and published data for similar deposits over a large area of southern and central Sweden. None of the seventy samples analysed gave a U–Th age between 40 and 70 ka; a second hiatus is apparent at 20 ka, coinciding with the Last Glacial Maximum. Thus, the process responsible for uranyl silicate formation was halted for significant periods, owing to a change in geochemical conditions or the hydrogeological regime. These data support the presence of interstadial conditions during the Early and Middle Weichselian since in the absence of major climatic perturbations the uranium phases at depth are stable. When viewed in conjunction with proxy data from mammoth remains it would appear that the region was ice-free prior to the Last Glacial Maximum.

Assembling novel protein folds from super-secondary structural fragments

The results of applying a fragment-based protein tertiary structure prediction method to the prediction of 14 CASP5 target domains are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. A number of good predictions for proteins with novel folds were produced, although not always as the first model. For two fold recognition targets, FRAGFOLD produced the most accurate model in both cases, despite the fact that the predictions were not based on a template structure. Although clear progress has been made in improving FRAGFOLD since CASP4, the ranking of final models still seems to be the main problem that needs to be addressed before the next CASP experiment

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.