Sarcopenia from mechanism to diagnosis and treatment in liver disease

Sarcopenia or loss of skeletal muscle mass is the major component of malnutrition and is a frequent complication in cirrhosis that adversely affects clinical outcomes. These include survival, quality of life, development of other complications and post liver transplantation survival. Radiological image analysis is currently utilized to diagnose sarcopenia in cirrhosis. Nutrient supplementation and physical activity are used to counter sarcopenia but have not been consistently effective because the underlying molecular and metabolic abnormalities persist or are not influenced by these treatments. Even though alterations in food intake, hypermetabolism, alterations in amino acid profiles, endotoxemia, accelerated starvation and decreased mobility may all contribute to sarcopenia in cirrhosis, hyperammonemia has recently gained attention as a possible mediator of the liver-muscle axis. Increased muscle ammonia causes: cataplerosis of α-ketoglutarate, increased transport of leucine in exchange for glutamine, impaired signaling by leucine, increased expression of myostatin (a transforming growth factor beta superfamily member) and an increased phosphorylation of eukaryotic initiation factor 2α. In addition, mitochondrial dysfunction, increased reactive oxygen species that decrease protein synthesis and increased autophagy mediated proteolysis, also play a role. These molecular and metabolic alterations may contribute to the anabolic resistance and inadequate response to nutrient supplementation in cirrhosis. Central and skeletal muscle fatigue contributes to impaired exercise capacity and responses. Use of proteins with low ammoniagenic potential, leucine enriched amino acid supplementation, long-term ammonia lowering strategies and a combination of resistance and endurance exercise to increase muscle mass and function may target the molecular abnormalities in the muscle. Strategies targeting endotoxemia and the gut microbiome need further evaluatio

S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification

This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each step, S2 selects the vertex to be labeled based on the structure of the graph and all previously gathered labels. Specifically, S2 queries for the label of the vertex that bisects the *shortest shortest* path between any pair of oppositely labeled vertices. We present a theoretical estimate of the number of queries S2 needs in terms of a novel parametrization of the complexity of binary functions on graphs. We also present experimental results demonstrating the performance of S2 on both real and synthetic data. While other graph-based active learning algorithms have shown promise in practice, our algorithm is the first with both good performance and theoretical guarantees. Finally, we demonstrate the implications of the S2 algorithm to the theory of nonparametric active learning. In particular, we show that S2 achieves near minimax optimal excess risk for an important class of nonparametric classification problems.Comment: A version of this paper appears in the Conference on Learning Theory (COLT) 201

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to traditional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with $\ell_1$-minimization from the phase transition point of view and demonstrate that it outperforms $\ell_1$-minimization in the regions of phase transition plot where $\ell_1$-minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances the overall recovery performance, speeds up training of recovery framework, and leads to having fewer parameters to learn