Macronutrients and energy balance in obesity.

Energy balance is the difference between metabolizable energy intake and total energy expenditure. Energy intake is difficult to measure accurately; changes in body weight, for example, are not a good measure of the adequacy of energy intake, because fluctuations in body weight are common even if the overall trend is toward weight loss. It is now customary to assess energy requirements indirectly from total energy expenditure. Total energy expenditure consists of basal metabolism, postprandial thermogenesis, and physical activity. Energy expenditure is related to both body weight and body composition. A reduction in total energy expenditure accompanies weight loss, because basal metabolic rate decreases with the loss of lean tissue mass. Similarly, with weight gain, there is an increase in basal metabolic rate, because lean tissue mass grows to support the increase in fat tissue mass. Excess energy intake over energy expenditure causes weight gain and an accompanying increase in total energy expenditure. Following a period of adaptation, total energy expenditure will match energy intake and body weight will stabilize at a higher level. This same relationship holds for weight loss. Respiratory quotient (measured in steady state) is an indication of the proportion of energy expenditure derived from fat and carbohydrate oxidation. Over long periods of time, fat balance is equivalent to energy balance, as an excess of fat intake over fat oxidation causes fat storage.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.