102 resultados para Stream-gradiente index


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BACKGROUND: Intronic variation in the FTO (fat mass and obesity-associated) gene has been unequivocally associated with increased body mass index (BMI; in kg/m(2)) and the risk of obesity in populations of different ethnicity. OBJECTIVE: We examined whether this robust genetic predisposition to obesity can be attenuated by being more physically active. DESIGN: The FTO variant rs1121980 was genotyped in 20,374 participants (39-79 y of age) from the European Prospective Investigation into Cancer and Nutrition-Norfolk Study, an ethnically homogeneous population-based cohort. Physical activity (PA) was assessed with a validated self-reported questionnaire. The interaction between rs1121980 and PA on BMI and waist circumference (WC) was examined by including the interaction term in mixed-effect models. RESULTS: We confirmed that the risk (T) allele of rs1121980 was significantly associated with BMI (0.31-unit increase per allele; P < 0.001) and WC (0.77-cm increase per allele; P < 0.001). The PA level attenuated the effect of rs1121980 on BMI and WC; ie, whereas in active individuals the risk allele increased BMI by 0.25 per allele, the increase in BMI was significantly (P for interaction = 0.004) more pronounced (76%) in inactive individuals (0.44 per risk allele). We observed similar effects for WC (P for interaction = 0.02): the risk allele increased WC by 1.04 cm per allele in inactive individuals but by only 0.64 cm in active individuals. CONCLUSIONS: Our results showed that PA attenuates the effect of the FTO rs1121980 genotype on BMI and WC. This observation has important public health implications because we showed that a genetic susceptibility to obesity induced by FTO variation can be overcome, at least in part, by adopting a physically active lifestyle.

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Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

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The use of ageostrophic flow to infer the presence of vertical circulations in the entrances and exits of the climatological jet streams is questioned. Problems of interpretation arise because of the use of different definitions of geostrophy in theoretical studies and in analyses of atmospheric data. The nature and role of the ageostrophic flow based on constant and variable Coriolis parameter definitions of geostrophy vary. In the latter the geostrophic divergence cannot be neglected, so the vertical motion is not associated solely with the ageostrophic flow. Evidence is presented suggesting that ageostrophic flow in the climatological jet streams is primarily determined by the kinematic requirements of wave retrogression rather than by a forcing process. These requirements are largely met by the rotational flow, with the divergent circulations present being geostrophically forced, and so playing a secondary, restoring role.

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A one-dimensional, thermodynamic, and radiative model of a melt pond on sea ice is presented that explicitly treats the melt pond as an extra phase. A two-stream radiation model, which allows albedo to be determined from bulk optical properties, and a parameterization of the summertime evolution of optical properties, is used. Heat transport within the sea ice is described using an equation describing heat transport in a mushy layer of a binary alloy (salt water). The model is tested by comparison of numerical simulations with SHEBA data and previous modeling. The presence of melt ponds on the sea ice surface is demonstrated to have a significant effect on the heat and mass balance. Sensitivity tests indicate that the maximum melt pond depth is highly sensitive to optical parameters and drainage. INDEX TERMS: 4207 Oceanography: General: Arctic and Antarctic oceanography; 4255 Oceanography: General: Numerical modeling; 4299 Oceanography: General: General or miscellaneous; KEYWORDS: sea ice, melt pond, albedo, Arctic Ocean, radiation model, thermodynamic

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It is currently estimated that over 370 million individuals have diabetes, making diabetes a major public health issue contributing significantly to global morbidity and mortality. The steep rise in diabetes prevalence over the past decades is attributable, in a large part, to lifestyle changes, with dietary habits and behaviour significant contributors. Despite the relatively wide availability of anti-diabetic medicine, it is lifestyle approaches that still remain the cornerstone of diabetes prevention and treatment. Glycemic index (GI) is a nutritional tool, which represents the glycemic response to carbohydrate ingestion. In light of the major impact of nutrition on diabetes pathophysiology, with the rising need to combat the escalating diabetes epidemic, this review will focus on the role of GI in glycemic control, the primary target of diabetic treatment and beyond. The review will present the evidence relating GI and diabetes treatment and prevention, as well as weight loss, weight maintenance and CVD risk factors.

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This paper examines the lead–lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using 10-min observations from June 1996–1997, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.

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In the absence of market frictions, the cost-of-carry model of stock index futures pricing predicts that returns on the underlying stock index and the associated stock index futures contract will be perfectly contemporaneously correlated. Evidence suggests, however, that this prediction is violated with clear evidence that the stock index futures market leads the stock market. It is argued that traditional tests, which assume that the underlying data generating process is constant, might be prone to overstate the lead-lag relationship. Using a new test for lead-lag relationships based on cross correlations and cross bicorrelations it is found that, contrary to results from using the traditional methodology, periods where the futures market leads the cash market are few and far between and when any lead-lag relationship is detected, it does not last long. Overall, the results are consistent with the prediction of the standard cost-of-carry model and market efficiency.

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This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

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Predicting the future response of the Antarctic Ice Sheet to climate change requires an understanding of the ice streams that dominate its dynamics. Here we use cosmogenic isotope exposure-age dating (26Al, 10Be and 36Cl) of erratic boulders on ice-free land on James Ross Island, north-eastern Antarctic Peninsula, to define the evolution of Last Glacial Maximum (LGM) ice in the adjacent Prince Gustav Channel. These data include ice-sheet extent, thickness and dynamical behaviour. Prior to ∼18 ka, the LGM Antarctic Peninsula Ice Sheet extended to the continental shelf-edge and transported erratic boulders onto high-elevation mesas on James Ross Island. After ∼18 ka there was a period of rapid ice-sheet surface-lowering, coincident with the initiation of the Prince Gustav Ice Stream. This timing coincided with rapid increases in atmospheric temperature and eustatic sea-level rise around the Antarctic Peninsula. Collectively, these data provide evidence for a transition from a thick, cold-based LGM Antarctic Peninsula Ice Sheet to a thinner, partially warm-based ice sheet during deglaciation.

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In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.