A Note on Data-Driven Contaminant Simulation

Abstract. In this paper we introduce a numerical procedure for per-forming dynamic data driven simulations (DDDAS). The main ingredi-ent of our simulation is the multiscale interpolation technique that maps the sensor data into the solution space. We test our method on various synthetic examples. In particular we show that frequent updating of the sensor data in the simulations can significantly improve the prediction results and thus important for applications. The frequency of sensor data updating in the simulations is related to streaming capabilities and ad-dressed within DDDAS framework. A further extension of our approach using local inversion is also discussed.

Summertime carbonaceous aerosols collected in the marine boundary layer of the Arctic Ocean

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95484/1/jgrd13027.pd

Environmental Adaptation: Genomic Analysis of the Piezotolerant and Psychrotolerant Deep-Sea Iron Reducing Bacterium Shewanella piezotolerans WP3

Shewanella species are widespread in various environments. Here, the genome sequence of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant iron reducing bacterium from deep-sea sediment was determined with related functional analysis to study its environmental adaptation mechanisms. The genome of WP3 consists of 5,396,476 base pairs (bp) with 4,944 open reading frames (ORFs). It possesses numerous genes or gene clusters which help it to cope with extreme living conditions such as genes for two sets of flagellum systems, structural RNA modification, eicosapentaenoic acid (EPA) biosynthesis and osmolyte transport and synthesis. And WP3 contains 55 open reading frames encoding putative c-type cytochromes which are substantial to its wide environmental adaptation ability. The mtr-omc gene cluster involved in the insoluble metal reduction in the Shewanella genus was identified and compared. The two sets of flagellum systems were found to be differentially regulated under low temperature and high pressure; the lateral flagellum system was found essential for its motility and living at low temperature

Limited Awareness and Low Immediate Uptake of Pre-Exposure Prophylaxis among Men Who Have Sex with Men Using an Internet Social Networking Site

Background: In 2010, the iPrEx trial demonstrated that oral antiretroviral pre-exposure prophylaxis (PrEP) reduced the risk of HIV acquisition among high-risk men who have sex with men (MSM). The impact of iPrEx on PrEP knowledge and actual use among at-risk MSM is unknown. Online surveys were conducted to assess PrEP awareness, interest and experience among at-risk MSM before and after iPrEx, and to determine demographic and behavioral factors associated with these measures. Methods and Findings: Cross-sectional, national, internet-based surveys were administered to U.S. based members of the most popular American MSM social networking site 2 months before (n = 398) and 1 month after (n = 4 558) publication of iPrEx results. Comparisons were made between these samples with regards to PrEP knowledge, interest, and experience. Data were collected on demographics, sexual risk, and experience with post-exposure prophylaxis (PEP). Regression analyses were performed to identify factors associated with PrEP awareness, interest, and experience post-iPrEx. Most participants were white, educated, and indicated high-risk sexual behaviors. Awareness of PrEP was limited pre- and post-iPrEx (13% vs. 19%), whereas interest levels after being provided with a description of PrEP remained high (76% vs. 79%). PrEP use remained uncommon (0.7% vs. 0.9%). PrEP use was associated with PEP awareness (OR 7.46; CI 1.52–36.6) and PEP experience (OR 34.2; CI 13.3–88.4). PrEP interest was associated with older age (OR 1.01; CI 1.00–1.02), unprotected anal intercourse with ≥1 male partner in the prior 3 months (OR 1.40; CI 1.10–1.77), and perceiving oneself at increased risk for HIV acquisition (OR 1.20; CI 1.13–1.27). Conclusions: Among MSM engaged in online networking, awareness of PrEP was limited 1 month after the iPrEx data were released. Utilization was low, although some MSM who reported high-risk behaviors were interested in using PrEP. Studies are needed to understand barriers to PrEP utilization by at-risk MSM

Computational Biology Methods and Their Application to the Comparative Genomics of Endocellular Symbiotic Bacteria of Insects

Comparative genomics has become a real tantalizing challenge in the postgenomic era. This fact has been mostly magnified by the plethora of new genomes becoming available in a daily bases. The overwhelming list of new genomes to compare has pushed the field of bioinformatics and computational biology forward toward the design and development of methods capable of identifying patterns in a sea of swamping data noise. Despite many advances made in such endeavor, the ever-lasting annoying exceptions to the general patterns remain to pose difficulties in generalizing methods for comparative genomics. In this review, we discuss the different tools devised to undertake the challenge of comparative genomics and some of the exceptions that compromise the generality of such methods. We focus on endosymbiotic bacteria of insects because of their genomic dynamics peculiarities when compared to free-living organisms

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.National Institutes of Health (U.S.) (Grant P50-GM068762)National Institutes of Health (U.S.) (Grant R24-DK090963)United States. Army Research Office (Grant W911NF-09-0001)German Research Foundation (Grant GSC 111

Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

<p>Abstract</p> <p>Background</p> <p>Protein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.</p> <p>Results</p> <p>We introduce <monospace>Bio::Homology::InterologWalk</monospace>, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, <it>Drosophila melanogaster</it>. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation.</p> <p>Conclusions</p> <p>Our interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by post-processing, allowing the putative PPI datasets to be easily integrated into existing analysis workflows. The <monospace>Bio::Homology::InterologWalk</monospace> module, sample scripts and full documentation are freely available from the Comprehensive Perl Archive Network (CPAN) under the GNU Public license.</p

Bacterial Communities of Diverse Drosophila Species: Ecological Context of a Host–Microbe Model System

Drosophila melanogaster is emerging as an important model of non-pathogenic host–microbe interactions. The genetic and experimental tractability of Drosophila has led to significant gains in our understanding of animal–microbial symbiosis. However, the full implications of these results cannot be appreciated without the knowledge of the microbial communities associated with natural Drosophila populations. In particular, it is not clear whether laboratory cultures can serve as an accurate model of host–microbe interactions that occur in the wild, or those that have occurred over evolutionary time. To fill this gap, we characterized natural bacterial communities associated with 14 species of Drosophila and related genera collected from distant geographic locations. To represent the ecological diversity of Drosophilids, examined species included fruit-, flower-, mushroom-, and cactus-feeders. In parallel, wild host populations were compared to laboratory strains, and controlled experiments were performed to assess the importance of host species and diet in shaping bacterial microbiome composition. We find that Drosophilid flies have taxonomically restricted bacterial communities, with 85% of the natural bacterial microbiome composed of only four bacterial families. The dominant bacterial taxa are widespread and found in many different host species despite the taxonomic, ecological, and geographic diversity of their hosts. Both natural surveys and laboratory experiments indicate that host diet plays a major role in shaping the Drosophila bacterial microbiome. Despite this, the internal bacterial microbiome represents only a highly reduced subset of the external bacterial communities, suggesting that the host exercises some level of control over the bacteria that inhabit its digestive tract. Finally, we show that laboratory strains provide only a limited model of natural host–microbe interactions. Bacterial taxa used in experimental studies are rare or absent in wild Drosophila populations, while the most abundant associates of natural Drosophila populations are rare in the lab