Recent epidemiological studies indicate a strong linkbetween intrauterine under-nutrition and propensityof such off spring for developing obesity and meta-bolic syndrome in later life. Garg et al investigated the mech...Recent epidemiological studies indicate a strong linkbetween intrauterine under-nutrition and propensityof such off spring for developing obesity and meta-bolic syndrome in later life. Garg et al investigated the mechanistic basis of this phenomenon and its potential reversibility in rats. The authors found that rats experiencing gestational under-nutrition but fed normally after birth (IUGR) gained body mass with excessive subcutaneous and visceral fat. The IUGR rats were alsome tabolically inflexible since they showed similar rates of energy expenditure and O2consumption in the fedand fasted states. However, if such pups were food-restricted during lactation (PNGR), their metabolic profiles resembled those of control and IPGR (subject tofood restriction during pre- and postnatal periods) rats.Thus, postnatal caloric restriction superimposed on intrauterine under nutrition significantly improved insulinsensitivity and adiposity of rats that would otherwise develop metabolic inflexibility and visceral obesity. The observations of Garg et al , have serious implications interm of the design of the future experimental studiesas well as their clinical translation in humans.展开更多
An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 ...An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.展开更多
文摘Recent epidemiological studies indicate a strong linkbetween intrauterine under-nutrition and propensityof such off spring for developing obesity and meta-bolic syndrome in later life. Garg et al investigated the mechanistic basis of this phenomenon and its potential reversibility in rats. The authors found that rats experiencing gestational under-nutrition but fed normally after birth (IUGR) gained body mass with excessive subcutaneous and visceral fat. The IUGR rats were alsome tabolically inflexible since they showed similar rates of energy expenditure and O2consumption in the fedand fasted states. However, if such pups were food-restricted during lactation (PNGR), their metabolic profiles resembled those of control and IPGR (subject tofood restriction during pre- and postnatal periods) rats.Thus, postnatal caloric restriction superimposed on intrauterine under nutrition significantly improved insulinsensitivity and adiposity of rats that would otherwise develop metabolic inflexibility and visceral obesity. The observations of Garg et al , have serious implications interm of the design of the future experimental studiesas well as their clinical translation in humans.
文摘An artificial neural network （ANN） model is established to predict plastic flow behaviors of the 603 armor steel, based on experiments over wide ranges of strain rates （0. 001 -4 500 s -1 ） and temperatures （288 -873 K）. The descriptive and predictive capabilities of the ANN model are com- pared with several phenomenological and physically based constitutive models. The ANN model has a much better applicability than the other models in characterization of the flow stress. The tempera- ture and the strain rate effects on the flow stress can be described successfully by the ANN model, with an average error of 1.78% for both quasi-static and dynamic loading conditions. Besides its high accuracy in prediction of the strain rate jump tests, the ANN model is more convenient in model es- tablishment and data processing. The ANN model developed in this study may serve as a valid and ef- fective tool to predict plastic behaviors of the 603 steel under complex loading conditions.
