Is altitude training an efficient treatment for obesity?

Worldwide, about half the adult population is considered overweight as defined by a body mass index (BMI - calculated by body weight divided by height squared) ratio in excess of 25 kg.m-2. Of these individuals, half are clinically obese (with a BMI in excess of 30) and these numbers are still increasing, notably in developing countries such as those of the Middle East region. Obesity is a disorder characterised by increased mass of adipose tissue (excessive fat accumulation) that is the result of a systemic imbalance between food intake and energy expenditure. Although factors such as family history, sedentary lifestyle, urbanisation, income and family diet patterns determine obesity prevalence, the main underlying causes are poor knowledge about food choice and lack of physical activity3. Current obesity treatments include dietary restriction, pharmacological interventions and ultimately, bariatric surgery. The beneficial effects of physical activity on weight loss through increased energy expenditure and appetite modulation are also firmly established. Another viable option to induce a negative energy balance, is to incorporate hypoxia per se or combine it with exercise in an individual's daily schedule. This article will present recent evidence suggesting that combining hypoxic exposure and exercise training might provide a cost-effective strategy for reducing body weight and improving cardio-metabolic health in obese individuals. The efficacy of this approach is further reinforced by epidemiological studies using large-scale databases, which evidence a negative relationship between altitude of habitation and obesity. In the United States, for instance, obesity prevalence is inversely associated with altitude of residence and urbanisation, after adjusting for temperature, diet, physical activity, smoking and demographic factors.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.