158 resultados para Environmental Sensors


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Cutaneous leishmaniases have persisted for centuries as chronically disfiguring parasitic infections affecting millions of people across the subtropics. Symptoms range from the more prevalent single, self-healing cutaneous lesion to a persistent, metastatic disease, where ulcerations and granulomatous nodules can affect multiple secondary sites of the skin and delicate facial mucosa, even sometimes diffusing throughout the cutaneous system as a papular rash. The basis for such diverse pathologies is multifactorial, ranging from parasite phylogeny to host immunocompetence and various environmental factors. Although complex, these pathologies often prey on weaknesses in the innate immune system and its pattern recognition receptors. This review explores the observed and potential associations among the multifactorial perpetrators of infectious metastasis and components of the innate immune system.

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The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

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A procedure was developed for determining Pu-241 activity in environmental samples. This beta emitter isotope of plutonium was measured by ultra low level liquid scintillation, after several separation and purification steps that involved the use of a highly selective extraction chromatographic resin (Eichrom-TEVA). Due to the lack of reference material for Pu-241, the method was nevertheless validated using four IAEA reference sediments with information values for Pu-241. Next, the method was used to determine the Pu-241 activity in alpine soils of Switzerland and France. The Pu-241/Pu-239,Pu-240 and Pu-238/Pu-239,Pu-240 activity ratios confirmed that Pu contamination in the tested alpine soils originated mainly from global fallout from nuclear weapon tests conducted in the fifties and sixties. Estimation of the date of the contamination, using the Pu-241/Am-241 age-dating method, further confirmed this origin. However, the Pu-241/Am-241 dating method was limited to samples where Pu-Am fractionation was insignificant. If any, the contribution of the Chernobyl accident is negligible.

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The ability of pollutants to affect human health is a major concern, justified by the wide demonstration that reproductive functions are altered by endocrine disrupting chemicals. The definition of endocrine disruption is today extended to broader endocrine regulations, and includes activation of metabolic sensors, such as the peroxisome proliferator-activated receptors (PPARs). Toxicology approaches have demonstrated that phthalate plasticizers can directly influence PPAR activity. What is now missing is a detailed molecular understanding of the fundamental basis of endocrine disrupting chemical interference with PPAR signaling. We thus performed structural and functional analyses that demonstrate how monoethyl-hexyl-phthalate (MEHP) directly activates PPARgamma and promotes adipogenesis, albeit to a lower extent than the full agonist rosiglitazone. Importantly, we demonstrate that MEHP induces a selective activation of different PPARgamma target genes. Chromatin immunoprecipitation and fluorescence microscopy in living cells reveal that this selective activity correlates with the recruitment of a specific subset of PPARgamma coregulators that includes Med1 and PGC-1alpha, but not p300 and SRC-1. These results highlight some key mechanisms in metabolic disruption but are also instrumental in the context of selective PPAR modulation, a promising field for new therapeutic development based on PPAR modulation.