Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUSand BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

SUMMARYOver the past few years, Bayesian models for combining output from numerical models and air monitoring data have been applied to environmental data sets to improve spatial prediction. This paper develops a new hierarchical Bayesian model (HBM) for fine particulate matter (PM 2.5 ) that combines U. S. EPA Federal Reference Method (FRM) PM 2.5 monitoring data and Community Multi-scale Air Quality (CMAQ) numerical model output. The model is specified in a Bayesian framework and fitted using Markov Chain Monte Carlo (MCMC) techniques. We find that the statistical model combining monitoring data and CMAQ output provides reliable information about the true underlying PM 2.5 process over time and space. We base these conclusions on results of a validation exercise in which independent monitoring data were compared with predicted values from the HBM and predictions from a standard kriging model based solely on the monitoring data.

The effects of certain chemical additives at maintaining a high level of activity in protein constructs during storage is investigated. We use a semiparametric regression technique to model the effects of the additives on protein activity. The model is extended to handle categorical explanatory variables. On the basis of the available data, the important factors are estimated to be buffer, detergent, protein concentration, and storage temperature. The relationships among protein activity and these factors appear to be moderately nonlinear with strong interaction effects. These features are revealed in a data-adaptive way by the semi parametric model, without explicit modeling of the nonlinearities or interactions. We use cross-validation to assess the fit of our model. The protein activity response appears to be extremely erratic. We recommend several sets of storage conditions and that further design points be chosen in regions around these estimated optima.

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To determine if airborne particulates contribute to excess mortality, researchers have adopted multiple regression techniques to measure the effects of particulates on daily death counts (1,2). Other factors, such as extreme temperatures, can affect mortality, and regression techniques are used to adjust for these other known influences. Though many factors could be involved, research has generally limited attention to meteorological sources such as temperature and humidity. In some cases, other air pollution measures such as sulfur dioxide and ozone are included. The regression coefficient corresponding to a measure of particulate level is then interpreted as the effect of particulate pollution on mortality, accounting for stress from the other influences. If this coefficient is a statistically significant positive number, the conclusion is that mortality increases with increasing levels of particulates. This association is then elevated to a causal interpretation: particulates cause death, and researchers estimate that soot at levels well below the maximum set by federal law "kills up to 60,000 in U.S. each year" (3,4), and similar calculations "put the annual toll in England and Wales at 10,000" (5).Studies vary as to the particulate measures used and the locations analyzed. In the analyses presented here, we used PM1O, which specifies particulate matter with an aerodynamic diameter <10 pm (6). The current U.S. EPA standard is based on this measure. The locations we analyzed, Cook County, Illinois, and Salt Lake County, Utah, both have relatively long records of PM1O monitoring. The monitoring data are discussed in more detail in Methods.The data used in the analyses (meteorological conditions, particulate levels, death counts) are observational; that is, data that are measured and recorded without control or intervention by researchers. Deducing causal relationships from observational data is perilous. A practical approach described by Mosteller and Tukey (P) involves considerations beyond regression analysis. In particular, consideration should be given to whether the association between particulate levels and mortality is consistent across "settings," whether there are plausible common causes for elevated particulate levels and mortality, and whether the derived models reflect reasonable physical relationships.There is a high degree of association of PM1O with meteorology, and a high degree The results for Cook County and Salt Lake County show that the appearance and size of a PMIO effect is quite sensitive to model specification. In particular, the treatment of season affects the estimates of the PMIO effect. In Cook County, we found a significant interaction between the time of year and PM10. Using a standard Poisson regression model, we found that PMIO appears to be significantly associated with mortality in the spring and fall, but not in the winter and summer. Using a semi-parametric model (Appendix A), we found that only the months of May and September exhibit a particulate effect. In Salt Lake Count...