Corticosteroid effect upon intestinal and hepatic interleukin profile in a gastroschisis rat model

PURPOSE: To evaluate the effect of corticosteroids on intestinal and liver interleukin profile in an experimental model of gastroschisis in fetal rats. METHODS: Sprague-Dawley rats at 19.5 days of gestation had its fetuses operated for the creation of gastroschisis. Two groups of fetuses were studied with and without maternal administration of dexamethasone. Each group was composed of fetuses who underwent gastroschisis (G), control fetuses without manipulation (C) and sham fetuses (S). A dosage of the following interleukins was carried out in fetal intestinal and liver tissues: IL-1, IL-6, IL-10, tumor necrosis factor-alpha (TNF-&#945;) and interferon-gamma (IFN-&#947;). The differences between the groups and subgroups were tested by ANOVA with Tukey post-test, with significant values of p<0.05. RESULTS: Dexamethasone led to an increase in intestinal and liver IL-6 (p<0.05) and a decrease in intestinal TNF-&#945; (p<0.001) in fetuses with gastroschisis. CONCLUSION: Corticosteroids had an effect on the intestinal interleukin profile and a small effect on the liver interleukin profile due to immunological immaturity of the fetus, and also of fetuses with gastroschisis. The steroid action may not be exclusively anti-inflammatory, but also pro-inflammatory, varying with time of pregnancy

Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species

Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid

This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units. We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust

Multidisciplinary scientific cruise to the Rio Grande Rise

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