Rats fed a sucrose-rich diet (SRD) develop hypertriglyceridemia and a marked decline in beta cell function. The purpose of this study was to determine whether changes in triglyceride concentration and/or altered pyruvate dehydrogenase complex (PDHc) activity contribute to the beta cell dysfunction, and to analyze the effect of dietary fish oil on the altered patterns of insulin secretion and peripheral insulin resistance. Rats were fed an SRD for 210 d. One-half of the rats continued consuming the SRD until d 270. The other half received an SRD in which fish oil (FO) was partially substituted for corn oil until d 270. A group of rats was fed a control diet (CD) throughout the experiment. The islets of rats fed the SRD had a greater triglyceride concentration and lower PDHc activity than those fed the CD. Insulin secretion patterns under the stimulus of glucose, palmitate or L-arginine were impaired in SRD-fed compared with CD-fed rats. This was accompanied by peripheral insulin resistance, mild hyperglycemia, a sharp increase of plasma triglyceride and free fatty acid levels and greater epididymal and retroperitoneal fat weights. FO normalized and/or improved these variables. Our results indicate that the increased fat storage and decreased PDHc activity in the beta cells play a key role in the abnormal insulin secretion of rats chronically fed an SRD. This is consistent with the reversion of these alterations by dietary FO.
Meat and meat products constitute important source of protein, fat, and several functional compounds. Although beef consumption may implicate possible negative impacts on human health, its consumption can also contribute to human health. Quality traits of beef, as well as its nutritional properties, depend on animal genetics, feeding, livestock practices, and post mortem procedures. Available data show that emerging beef production systems are able to improve both, quality and nutritional traits of beef in a sustainable way. In this context, Argentina's actions are aimed at maximising beef beneficial effects and minimising its negative impact on human health, in a way of contributing to global food security.
It is by now well-known that practical deep supervised learning may roughly be cast as an optimal control problem for a specific discrete-time, nonlinear dynamical system called an artificial neural network. In this work, we consider the continuous-time formulation of the deep supervised learning problem, and study the latter's behavior when the final time horizon increases, a fact that can be interpreted as increasing the number of layers in the neural network setting.When considering the classical regularized empirical risk minimization problem, we show that, in long time, the optimal states converge to zero training error, namely approach the zero training error regime, whilst the optimal control parameters approach, on an appropriate scale, minimal norm parameters with corresponding states precisely in the zero training error regime. This result provides an alternative theoretical underpinning to the notion that neural networks learn best in the overparametrized regime, when seen from the large layer perspective.We also propose a learning problem consisting of minimizing a cost with a state tracking term, and establish the well-known turnpike property, which indicates that the solutions of the learning problem in long time intervals consist of three pieces, the first and the last of which being transient short-time arcs, and the middle piece being a long-time arc staying exponentially close to the optimal solution of an associated static learning problem. This property in fact stipulates a quantitative estimate for the number of layers required to reach the zero training error regime.Both of the aforementioned asymptotic regimes are addressed in the context of continuous-time and continuous space-time neural networks, the latter taking the form of nonlinear, integro-differential equations, hence covering residual neural networks with both fixed and possibly variable depths. Contents 1. Introduction 2 2. A roadmap to continuous-time supervised learning 8 3. Asymptotics without tracking 13 4. Asymptotics with tracking 25 5. The zero training error regime 47 Date: August 7, 2020.
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