Multilocus sequence typing (MLST) of Mycoplasma hyopneumoniae: a diverse pathogen with limited clonality

A multilocus sequence typing (MLST) scheme was established and evaluated for Mycoplasma hyopneumoniae, the etiologic agent of enzootic pneumonia in swine with the aim of defining strains. Putative target genes were selected by genome sequence comparisons. Out of 12 housekeeping genes chosen and experimentally validated, the 7 genes efp, metG, pgiB, recA, adk, rpoB, and tpiA were finally used to establish the MLST scheme. Their usefulness was assessed individually and in combination using a set of well-defined field samples and strains of M. hyopneumoniae. A reduction to the three targets showing highest variation (adk, rpoB, and tpiA) was possible resulting in the same number of sequence types as using the seven targets. The established MLST approach was compared with the recently described typing method using the serine-rich repeat motif-encoding region of the p146 gene. There was coherence between the two methods, but MLST resulted in a slightly higher resolution. Farms recognized to be affected by enzootic pneumonia were always associated with a single M. hyopneumoniae clone, which in most cases differed from farm to farm. However, farms in close geographic or operational contact showed identical clones as defined by MLST typing. Population analysis showed that recombination in M. hyopneumoniae occurs and that strains are very diverse with only limited clonality observed. Elaborate classical MLST schemes using multiple targets for M. hyopneumoniae might therefore be of limited value. In contrast, MLST typing of M. hyopneumoniae using the three genes adk, rpoB, and tpiA seems to be sufficient for epidemiological investigations by direct amplification of target genes from lysate of clinical material without prior cultivation.

Alarm systems and catastrophes from a diverse point of view

Using a chain of urns, we build a Bayesian nonparametric alarm system to predict catastrophic events, such as epidemics, black outs, etc. Differently from other alarm systems in the literature, our model is constantly updated on the basis of the available information, according to the Bayesian paradigm. The papers contains both theoretical and empirical results. In particular, we test our alarm system on a well-known time series of sunspots.

Learning to drive: learners' self-reported cognitive failure level predicts driving instructor's observation rating of driving performance

OBJECTIVES Evidence increases that cognitive failure may be used to screen for drivers at risk. Until now, most studies have relied on driving learners. This exploratory pilot study examines self-report of cognitive failure in driving beginners and error during real driving as observed by driving instructors. METHODS Forty-two driving learners of 14 driving instructors filled out a work-related cognitive failure questionnaire. Driving instructors observed driving errors during the next driving lesson. In multiple linear regression analysis, driving errors were regressed on cognitive failure with the number of driving lessons as an estimator of driving experience controlled. RESULTS Higher cognitive failure predicted more driving errors (p < .01) when age, gender and driving experience were controlled in analysis. CONCLUSIONS Cognitive failure was significantly associated with observed driving errors. Systematic research on cognitive failure in driving beginners is recommended.

Is there a common pattern of future gas-phase air pollution in Europe under diverse climate change scenarios?

Climate change alone influences future levels of tropospheric ozone and their precursors through modifications of gas-phase chemistry, transport, removal, and natural emissions. The goal of this study is to determine at what extent the modes of variability of gas-phase pollutants respond to different climate change scenarios over Europe. The methodology includes the use of the regional modeling system MM5 (regional climate model version)-CHIMERE for a target domain covering Europe. Two full-transient simulations covering from 1991–2050 under the SRES A2 and B2 scenarios driven by ECHO-G global circulation model have been compared. The results indicate that the spatial patterns of variability for tropospheric ozone are similar for both scenarios, but the magnitude of the change signal significantly differs for A2 and B2. The 1991–2050 simulations share common characteristics for their chemical behavior. As observed from the NO2 and α-pinene modes of variability, our simulations suggest that the enhanced ozone chemical activity is driven by a number of parameters, such as the warming-induced increase in biogenic emissions and, to a lesser extent, by the variation in nitrogen dioxide levels. For gas-phase pollutants, the general increasing trend for ozone found under A2 and B2 forcing is due to a multiplicity of climate factors, such as increased temperature, decreased wet removal associated with an overall decrease of precipitation in southern Europe, increased photolysis of primary and secondary pollutants as a consequence of lower cloudiness and increased biogenic emissions fueled by higher temperatures.