Copper microenvironments in the human body define patterns of copper adaptation in pathogenic bacteria

Copper is an essential micronutrient for most organisms that is required as a cofactor for crucial copper-dependent enzymes encoded by both prokaryotes and eukaryotes. Evidence accumulated over several decades has shown that copper plays important roles in the function of the mammalian immune system. Copper accumulates at sites of infection, including the gastrointestinal and respiratory tracts and in blood and urine, and its antibacterial toxicity is directly leveraged by phagocytic cells to kill pathogens. Copper-deficient animals are more susceptible to infection, whereas those fed copper-rich diets are more resistant. As a result, copper resistance genes are important virulence factors for bacterial pathogens, enabling them to detoxify the copper insult while maintaining copper supply to their essential cuproenzymes. Here, we describe the accumulated evidence for the varied roles of copper in the mammalian response to infections, demonstrating that this metal has numerous direct and indirect effects on immune function. We further illustrate the multifaceted response of pathogenic bacteria to the elevated copper concentrations that they experience when invading the host, describing both conserved and species-specific adaptations to copper toxicity. Together, these observations demonstrate the roles of copper at the host–pathogen interface and illustrate why bacterial copper detoxification systems can be viable targets for the future development of novel antibiotic drug development programs.</jats:p

Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients

The objective of the study is to assess the predictive performance of three different techniques as classifiers for extra-intestinal manifestations in 152 patients with Crohn's disease. Na\uefve Bayes, Bayesian Additive Regression Trees and Bayesian Networks implemented using a Greedy Thick Thinning algorithm for learning dependencies among variables and EM algorithm for learning conditional probabilities associated to each variable are taken into account. Three sets of variables were considered: (i) disease characteristics: presentation, behavior and location (ii) risk factors: age, gender, smoke and familiarity and (iii) genetic polymorphisms of the NOD2, CD14, TNFA, IL12B, and IL1RN genes, whose involvement in Crohn's disease is known or suspected. Extra-intestinal manifestations occurred in 75 patients. Bayesian Networks achieved accuracy of 82% when considering only clinical factors and 89% when considering also genetic information, outperforming the other techniques. CD14 has a small predicting capability. Adding TNFA, IL12B to the 3020insC NOD2 variant improved the accuracy

Behavioral types in programming languages

A recent trend in programming language research is to use behav- ioral type theory to ensure various correctness properties of large- scale, communication-intensive systems. Behavioral types encompass concepts such as interfaces, communication protocols, contracts, and choreography. The successful application of behavioral types requires a solid understanding of several practical aspects, from their represen- tation in a concrete programming language, to their integration with other programming constructs such as methods and functions, to de- sign and monitoring methodologies that take behaviors into account. This survey provides an overview of the state of the art of these aspects, which we summarize as the pragmatics of behavioral types