4 resultados para Nutrients of accumulation
em Instituto Politécnico do Porto, Portugal
Resumo:
Context: Some chemicals used in consumer products or manufacturing (eg, plastics, pesticides) have estrogenic activities; these xenoestrogens (XEs) may affect immune responses and have recently emerged as a new risk factors for obesity and cardiovascular disease. However, the extent and impact on health of chronic exposure of the general population to XEs are still unknown. Objective: The objective of the study was to investigate the levels of XEs in plasma and adipose tissue (AT) depots in a sample of pre- and postmenopausal obese women undergoing bariatric surgery and their cardiometabolic impact in an obese state. Design and Participants: We evaluated XE levels in plasma and visceral and subcutaneous AT samples of Portuguese obese (body mass index ≥ 35 kg/m2) women undergoing bariatric surgery. Association with metabolic parameters and 10-year cardiovascular disease risk was assessed, according to menopausal status (73 pre- and 48 postmenopausal). Levels of XEs were determined by gas chromatography with electron-capture detection. Anthropometric and biochemical data were collected prior to surgery. Adipocyte size was determined on tissue sections obtained during surgery. Results: Our data show that XEs are pervasive in this obese population. Distribution of individual and concentration of total XEs differed between plasma, visceral AT, and subcutaneous AT, and the pattern of accumulation was different between pre- and postmenopausal women. Significant associations between XE levels and metabolic and inflammatory parameters were found. In premenopausal women, XEs in plasma seem to be a predictor of 10-year cardiovascular disease risk. Conclusions: Our findings point toward a different distribution of XE between plasma and AT in pre- and postmenopausal women, and reveal the association between XEs on the development of metabolic abnormalities in obese premenopausal women
Resumo:
A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.
Resumo:
With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.
Resumo:
Sertraline is widely prescribed worldwide and frequently detected in aquatic systems. There is, however, a remarkable gap of information on its potential impact on estuarine and coastal invertebrates. This study investigated sertraline accumulation and effects in Carcinus maenas. Crabs from a moderately contaminated (Lima) and a low-impacted (Minho) estuary were exposed to environmental and high levels of sertraline (0.05, 5, 500 μg L−1). A battery of biomarkers related to sertraline mode of action was employed to assess neurotransmission, energy metabolism, biotransformation and oxidative stress pathways. After a seven-day exposure, sertraline accumulation in crabs’ soft tissues was found in Lima (5 μg L−1: 15.3 ng L−1 ww; 500 μg L−1: 1010 ng L−1 ww) and Minho (500 μg L−1: 605 ng L−1 ww) animals. Lima crabs were also more sensitive to sertraline than those from Minho, exhibiting decreased acetylcholinesterase activity, indicative of ventilatory and locomotory dysfunction, inhibition of anti-oxidant enzymes and increased oxidative damage at ≥0.05 μg L−1. The Integrated Biomarker Response (IBR) index indicated their low health status. In addition, Minho crabs showed non-monotonic responses of acetylcholinesterase suggestive of hormesis. The results pointed an influence of the exposure history on differential sensitivity to sertraline and the need to perform evaluations with site-specific ecological receptors to increase relevance of risk estimations when extrapolating from laboratory to field conditions.