The Plant Pathogen Pseudomonas syringae pv. tomato Is Genetically Monomorphic and under Strong Selection to Evade Tomato Immunity

Recently, genome sequencing of many isolates of genetically monomorphic bacterial human pathogens has given new insights into pathogen microevolution and phylogeography. Here, we report a genome-based micro-evolutionary study of a bacterial plant pathogen, Pseudomonas syringae pv. tomato. Only 267 mutations were identified between five sequenced isolates in 3,543,009 nt of analyzed genome sequence, which suggests a recent evolutionary origin of this pathogen. Further analysis with genome-derived markers of 89 world-wide isolates showed that several genotypes exist in North America and in Europe indicating frequent pathogen movement between these world regions. Genome-derived markers and molecular analyses of key pathogen loci important for virulence and motility both suggest ongoing adaptation to the tomato host. A mutational hotspot was found in the type III-secreted effector gene hopM1. These mutations abolish the cell death triggering activity of the full-length protein indicating strong selection for loss of function of this effector, which was previously considered a virulence factor. Two non-synonymous mutations in the flagellin-encoding gene fliC allowed identifying a new microbe associated molecular pattern (MAMP) in a region distinct from the known MAMP flg22. Interestingly, the ancestral allele of this MAMP induces a stronger tomato immune response than the derived alleles. The ancestral allele has largely disappeared from today's Pto populations suggesting that flagellin-triggered immunity limits pathogen fitness even in highly virulent pathogens. An additional non-synonymous mutation was identified in flg22 in South American isolates. Therefore, MAMPs are more variable than expected differing even between otherwise almost identical isolates of the same pathogen strain

Intention Seekers: Conspiracist Ideation and Biased Attributions of Intentionality

Conspiracist beliefs are widespread and potentially hazardous. A growing body of research suggests that cognitive biases may play a role in endorsement of conspiracy theories. The current research examines the novel hypothesis that individuals who are biased towards inferring intentional explanations for ambiguous actions are more likely to endorse conspiracy theories, which portray events as the exclusive product of intentional agency. Study 1 replicated a previously observed relationship between conspiracist ideation and individual differences in anthropomorphisation. Studies 2 and 3 report a relationship between conspiracism and inferences of intentionality for imagined ambiguous events. Additionally, Study 3 again found conspiracist ideation to be predicted by individual differences in anthropomorphism. Contrary to expectations, however, the relationship was not mediated by the intentionality bias. The findings are discussed in terms of a domain-general intentionality bias making conspiracy theories appear particularly plausible. Alternative explanations are suggested for the association between conspiracism and anthropomorphism

Les dix commandements de l'économiste-analyste de politique

The incorporation of economic approaches into policymaking requires special skills on the part of the economist. This article examines the use of economics in government as illustrated by the experience of the natural resources agencies. It presents ten guiding rules for the practicing policy economist : (1 ) be economical about the use of economics ; (2) discount for political demand ; (3) dare to be «quick-and-dirty» ; (4) think like a manager ; (5) analyze equity as well as efficiency ; (6) know your market ; (7) pay your organizational dues ; (8) profit from action-forcing events ; (9) do not oversell economic analysis ; and (10) learn policy economics by doing it.The incorporation of economic approaches into policymaking requires special skills on the part of the economist. This article examines the use of economics in government as illustrated by the experience of the natural resources agencies. It presents ten guiding rules for the practicing policy economist : (1 ) be economical about the use of economics ; (2) discount for political demand ; (3) dare to be «quick-and-dirty» ; (4) think like a manager ; (5) analyze equity as well as efficiency ; (6) know your market ; (7) pay your organizational dues ; (8) profit from action-forcing events ; (9) do not oversell economic analysis ; and (10) learn policy economics by doing it.Leman Christopher K., Nelson Robert H. Les dix commandements de l'économiste-analyste de politique. In: Politiques et management public, vol. 1, n° 1, 1983. pp. 129-160

Ten commandments for policy economists

The incorporation of economic approaches into policymaking requires special skills on the part of the economist. This article examines the use of economics in government as illustrated by the experience of the natural resources agencies. It presents ten guiding rules for the practicing policy economist: (1) be economical about the use of economics; (2) discount for political demand; (3) dare to be “quick-and-dirty”; (4) think like a manager; (5) analyze equity as well as efficiency; (6) know your market; (7) pay your organizational dues; (8) profit from action-forcing events; (9) do not oversell economic analysis; and (10) learn policy economics by doing it.

Modelling the transmission of healthcare associated infections: a systematic review.

BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours