TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

SOLUTION: Bayesian mixture analysis for metagenomic community profiling

PROBLEM: Aims: The chronic consumption of alcohol is known to result in neurodegeneration and impairment of cognitive function. Pathological and neuroimaging studies have confirmed that brain atrophy in alcoholics is mainly due to widespread white matter (WM) loss with neuronal loss restricted to specific regions, such as the prefrontal cortex. Neuroimaging studies of cigarette smokers also suggest that chronic inhalation of tobacco smoke leads to brain atrophy, although the neurotoxic component is unknown. As a high proportion of chronic alcoholics also smoke cigarettes it has been hypothesized that at least some alcohol-related brain damage is due to tobacco smoke exposure. Methods: 39 Long Evans rats were subjected to 8 weeks exposure to alcohol and/or 5 weeks coexposure to nicotine-specific nitrosamine ketone (NNK), a proxy for tobacco smoke. Their frontal WM was then assayed with transmission electron microscopy. Results: NNK and ethanol co-exposure had a synergistic effect in decreasing myelinated fibre density. Furthermore, NNK treatment led to a greater reduction in myelin sheath thickness than ethanol whereas only the ethanol-treated animals showed a decrease in unmyelinated fibre density. Conclusion: These data suggest that NNK causes WM degeneration, an effect that is exacerbated by alcohol, but unlike alcohol, it has little impact on the neuronal components of the brain.

SOLUTION: The Differential Effects of Alcohol and Nicotine- Specific Nitrosamine Ketone on White Matter Ultrastructure

PROBLEM: Background: Health care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics. Results: The AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources. The AsiaFluCap Simulator is freely available software (www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.

SOLUTION:
Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator