SIMAP—structuring the network of protein similarities

Protein sequences are the most important source of evolutionary and functional information for new proteins. In order to facilitate the computationally intensive tasks of sequence analysis, the Similarity Matrix of Proteins (SIMAP) database aims to provide a comprehensive and up-to-date dataset of the pre-calculated sequence similarity matrix and sequence-based features like InterPro domains for all proteins contained in the major public sequence databases. As of September 2007, SIMAP covers ∼17 million proteins and more than 6 million non-redundant sequences and provides a complete annotation based on InterPro 16. Novel features of SIMAP include a new, portlet-based web portal providing multiple, structured views on retrieved proteins and integration of protein clusters and a unique search method for similar domain architectures. Access to SIMAP is freely provided for academic use through the web portal for individuals at http://mips.gsf.de/simap/and through Web Services for programmatic access at http://mips.gsf.de/webservices/services/SimapService2.0?wsdl

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

Drivers of risk perceptions about the invasive non-native plant Japanese knotweed in domestic gardens

This is the final version of the article. Available from Springer Verlag via the DOI in this record.How people perceive risks posed by invasive non-native plants (INNP) can influence attitudes and consequently likely influence behavioural decisions. Although some drivers of risk perception for INNP have been identified, research has not determined those for INNP in domestic gardens. This is concerning as domestic gardens are where people most commonly encounter INNP, and where impacts can be particularly acute. Using a survey approach, this study determined the drivers of perceptions of risk of INNP in domestic gardens and which risks most concern people. Japanese knotweed Fallopia japonica, in Cornwall, UK, where it is a problematic INNP in domestic gardens, was used as a case study. Possible drivers of risk were chosen a priori based on variables previously found to be important for environmental risks. Participants perceived Japanese knotweed to be less frequent on domestic property in Cornwall if their occupation involved the housing market, if they had not had Japanese knotweed in their own garden, if they did not know of Japanese knotweed within 5 km of their home, or if they were educated to degree level. Participants who thought that the consequences of Japanese knotweed being present on domestic property could be more severe had occupations that involved the housing market, knew of Japanese knotweed within 5 km of their home, or were older. Although concern about the damage Japanese knotweed could do to the structure of a property was reported as the second highest motivation to control it by the majority of participants, the perception of threat from this risk was rated as relatively low. The results of this study have implications for policy, risk communication, and garden management decisions. For example, there is a need for policy that provides support and resources for people to manage INNP in their local area. To reduce the impact and spread of INNP we highlight the need for clear and accurate risk communication within discourse about this issue. The drivers identified in this study could be used to target awareness campaigns to limit the development of over- or under-inflated risk perceptions.This project was funded as part of the Wildlife Research Co-Operative between the University of Exeter and the Animal and Plant Health Agency

Influence of amphipathic peptides on the HIV-1 production in persistently infected T lymphoma cells.

The effects of several amphipathic peptides on HIV-1 production in persistently infected cells are described. Melittin, a 26 amino acid α-helical amphipathic peptide, reduces HIV-1 production dose-dependently, whereas other amphipathic peptides do not. Six melittin derivatives which retain the α-helical portion have similar effects as melittin. The reduction of viral infectivity is not due to an effect of melittin on the virus particles but to an intracellular action of the peptide, which is readily taken up into cells, as shown by quantitative ELISA. Western blots of cells from melittin-treated cultures suggest that the processing of the gag/pol precursor is impaired

Bayesian neural networks for uncertainty estimation of imaging biomarkers.

Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference