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Epidemic Models of the Onset of Social Activities (EMOSA) describe behaviors that spread through social networks. Two social influence methods are represented, social contagion (one-to-one spread) and general diffusion (spread through cultural channels). Past models explain problem behaviors-smoking, drinking, sexuality, and delinquency. We provide review, and a tutorial (including examples). Following, we present new EMOSA models explaining changes in adolescent and young adult religious participation. We fit the model to 10 years of data from the 1997 U.S. National Longitudinal Survey of Youth. Innovations include a three-stage bi-directional model, Bayesian Markov Chain Monte Carlo (MCMC) estimation, graphical innovations, and empirical validation. General diffusion dominated rapid reduction in church attendance during adolescence; both diffusion and social contagion explained church attendance stability in early adulthood.

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The potential for modern coalfield methanogenesis was assessed using formation water from the Illinois Basin, Powder River Basin and Cook Inlet gas field as inocula for nutrient-replete incubations amended with C1-C5 fatty acids as presumed intermediates formed during anaerobic coal biodegradation. Instead of the expected rapid mineralization of these substrates, methanogenesis was inordinately slow (∼1 µmol day-1), following long lag periods (>100 days), and methane yields typically did not reach stoichiometrically expected levels. However, a gene microarray confirmed the potential for a wide variety of microbiological functions, including methanogenesis, at all sites. The Cook Inlet incubations produced methane at a relatively rapid rate when amended with butyrate (r = 0.98; p = 0.001) or valerate (r = 0.84; p = 0.04), a result that significantly correlated with the number of positive mcr gene sequence probes from the functional gene microarray and was consistent with the in situ detection of C4-C5 alkanoic acids. This finding highlighted the role of syntrophy for the biodegradation of the softer lignite and subbituminous coal in this formation, but methanogenesis from the harder subbituminous and bituminous coals in the other fields was less apparent. We conclude that coal methanogenesis is probably not limited by the inherent lack of metabolic potential, the presence of alternate electron acceptors or the lack of available nutrients, but more likely restricted by the inherent recalcitrance of the coal itself.

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Understanding the impact of clinical findings in discriminating between possible causes of a patient's presentation is essential in clinical judgment. A balance beam is a natural physical analogue that can accurately represent the combination of several pieces of evidence with varying ability to discriminate between disease hypotheses. Calculation of Bayes' theorem using log(posterior odds) as a function of log(prior odds) and the logarithms of the evidence's likelihood ratios maps onto the physical forces affecting objects placed on a balance beam. We describe the rules governing the functioning of tokens representing clinical findings in the comparison of 2 competing diseases. The likelihood ratios corresponding to positive (LR+) or negative (LR-) observations for each symptom determine the lateral position at which the symptom's token is placed on the beam, using a weight if the finding is present and a helium balloon if it is absent. We discuss how a balance beam could represent concepts of dynamic specificity (due to changes in competitor diseases' probabilities) and dynamic sensitivity (due to class-conditional independence). Utility-based thresholds for acting on a diagnosis could be represented by moving the balance beam's fulcrum. It is suggested that a balance beam can be a useful aid for students learning clinical diagnosis, allowing them to build on existing intuitive understanding to develop an appreciation of how evidence combines to influence degree of belief. The balance beam could also facilitate exploration of the potential impact of available questions or investigations.

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We describe a balance beam aid for instruction in diagnosis (BBAID) and demonstrate its potential use in supplementing the training of medical students to diagnose acute chest pain. We suggest the BBAID helps students understand the process of diagnosis because the impact of tokens (weights and helium balloons) attached to a beam at different distances from the fulcrum is analogous to the impact of evidence to the relative support for 2 diseases. The BBAID presents a list of potential findings and allows students to specify whether each is present, absent, or unknown. It displays the likelihood ratios corresponding to a positive (LR+) or negative (LR-) observation for each symptom, for any pair of diseases. For each specified finding, a token is placed on the beam at a location whose distance from the fulcrum is proportional to the finding's log(LR): a downward force (a weight) if the finding is present and a lifting force (a balloon) if it is absent. Combining the physical torques of multiple tokens is mathematically identical to applying Bayes' theorem to multiple independent findings, so the balance beam is a high-fidelity metaphor. Seven first-year medical students and 3 faculty members consulted the BBAID while diagnosing brief patient case vignettes. Student comments indicated the program is usable, helpful for understanding pertinent positive and negative findings' usefulness in particular situations, and welcome as a reference or self-test. All students attended the effect of the tokens on the beam, although some stated they did not use the numerical statistics. Faculty noted the BBAID might be particularly helpful in reminding students of diseases that should not be missed and identifying pertinent findings to ask for.

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The microbial metabolism of hydrocarbons is increasingly associated with the corrosion of carbon steel in sulfate-rich marine waters. However, how such transformations influence metal biocorrosion in the absence of an electron acceptor is not fully recognized. We grew a marine alkane-utilizing, sulfate-reducing bacterium, Desulfoglaeba alkanexedens, with either sulfate or Methanospirillum hungatei as electron acceptors, and tested the ability of the cultures to catalyze metal corrosion. Axenically, D. alkanexedens had a higher instantaneous corrosion rate and produced more pits in carbon steel coupons than when the same organism was grown in syntrophic co-culture with the methanogen. Since anaerobic hydrocarbon biodegradation pathways converge on fatty acid intermediates, the corrosive ability of a known fatty acid-oxidizing syntrophic bacterium, Syntrophus aciditrophicus was compared when grown in pure culture or in co-culture with a H2-utilizing sulfate-reducing bacterium (Desulfovibrio sp., strain G11) or a methanogen (M. hungatei). The instantaneous corrosion rates in the cultures were not substantially different, but the syntrophic, sulfate-reducing co-culture produced more pits in coupons than other combinations of microorganisms. Lactate-grown cultures of strain G11 had higher instantaneous corrosion rates and coupon pitting compared to the same organism cultured with hydrogen as an electron donor. Thus, if sulfate is available as an electron acceptor, the same microbial assemblages produce sulfide and low molecular weight organic acids that exacerbated biocorrosion. Despite these trends, a surprisingly high degree of variation was encountered with the corrosion assessments. Differences in biomass, initial substrate concentration, rates of microbial activity or the degree of end product formation did not account for the variations. We are forced to ascribe such differences to the metallurgical properties of the coupons.

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Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

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OBJECTIVES: In this trial, we compared Child Protective Services (CPS) recidivism outcomes between the home-based SafeCare (SC) model for child neglect and comparable home-based services, but without SC modules, for parents in the CPS system across 2 quality control strategies: coached (C) and uncoached implementation. SC is a home-based behavioral skills training model designed for neglecting or maltreating parents. The study was conducted in a scaled-up, statewide implementation setting. METHODS: Two thousand one hundred seventy-five maltreating parents, treated by 219 home visitors, were enrolled and treated in a 2 × 2 (SC versus services as usual × C versus uncoached implementation strategy) randomized cluster experiment. Cases were followed for an average of 6 years for CPS recidivism events. Subpopulation analyses were conducted for parents meeting customary SC inclusion criteria. RESULTS: Consistently significant main effects in favor of SC were found across simple and more complex modeling approaches (hazard ratios = 0.74-0.83). Larger effects were found among the subpopulation meeting customary SC inclusion criteria. C implementation yielded smaller and occasionally significant effects in analyses that included more diverse cases falling outside customary SC inclusion criteria. CONCLUSIONS: Findings support the adoption and use of SC within CPS home-based services systems. C implementation may be especially valuable for cases where the client-model fit is less strong.

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The soil microbiome is responsible for mediating key ecological processes; however, little is known about its sensitivity to climate change. Observed increases in global temperatures and alteration to rainfall patterns, due to anthropogenic release of greenhouse gases, will likely have a strong influence on soil microbial communities and ultimately the ecosystem services they provide. Therefore, it is vital to understand how soil microbial communities will respond to future climate change scenarios. To this end, we surveyed the abundance, diversity and structure of microbial communities over a 2-year period from a long-term in situ warming experiment that experienced a moderate natural drought. We found the warming treatment and soil water budgets strongly influence bacterial population size and diversity. In normal precipitation years, the warming treatment significantly increased microbial population size 40-150% but decreased diversity and significantly changed the composition of the community when compared with the unwarmed controls. However during drought conditions, the warming treatment significantly reduced soil moisture thereby creating unfavorable growth conditions that led to a 50-80% reduction in the microbial population size when compared with the control. Warmed plots also saw an increase in species richness, diversity and evenness; however, community composition was unaffected suggesting that few phylotypes may be active under these stressful conditions. Our results indicate that under warmed conditions, ecosystem water budget regulates the abundance and diversity of microbial populations and that rainfall timing is critical at the onset of drought for sustaining microbial populations.

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This article proposes 2 new approaches to test a nonzero population correlation (rho): the hypothesis-imposed univariate sampling bootstrap (HI) and the observed-imposed univariate sampling bootstrap (OI). The authors simulated correlated populations with various combinations of normal and skewed variates. With alpha set=.05, N> or =10, and rho< or =0.4, empirical Type I error rates of the parametric r and the conventional bivariate sampling bootstrap reached .168 and .081, respectively, whereas the largest error rates of the HI and the OI were .079 and .062. On the basis of these results, the authors suggest that the OI is preferable in alpha control to parametric approaches if the researcher believes the population is nonnormal and wishes to test for nonzero rhos of moderate size.

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