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.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

Elevated atmospheric carbon dioxide impairs the performance of root-feeding vine weevils by modifying root growth and secondary metabolites

Predicting how insect crop pests will respond to global climate change is an important part of increasing crop production for future food security, and will increasingly rely on empirically based evidence. The effects of atmospheric composition, especially elevated carbon dioxide (eCO(2)), on insect herbivores have been well studied, but this research has focussed almost exclusively on aboveground insects. However, responses of root-feeding insects to eCO(2) are unlikely to mirror these trends because of fundamental differences between aboveground and belowground habitats. Moreover, changes in secondary metabolites and defensive responses to insect attack under eCO(2) conditions are largely unexplored for root herbivore interactions. This study investigated how eCO(2) (700 mu mol mol-1) affected a root-feeding herbivore via changes to plant growth and concentrations of carbon (C), nitrogen (N) and phenolics. This study used the root-feeding vine weevil, Otiorhynchus sulcatus and the perennial crop, Ribes nigrum. Weevil populations decreased by 33% and body mass decreased by 23% (from 7.2 to 5.4 mg) in eCO(2). Root biomass decreased by 16% in eCO(2), which was strongly correlated with weevil performance. While root N concentrations fell by 8%, there were no significant effects of eCO(2) on root C and N concentrations. Weevils caused a sink in plants, resulting in 8-12% decreases in leaf C concentration following herbivory. There was an interactive effect of CO(2) and root herbivory on root phenolic concentrations, whereby weevils induced an increase at ambient CO(2), suggestive of defensive response, but caused a decrease under eCO(2). Contrary to predictions, there was a positive relationship between root phenolics and weevil performance. We conclude that impaired root-growth underpinned the negative effects of eCO(2) on vine weevils and speculate that the plant's failure to mount a defensive response at eCO(2) may have intensified these negative effects.