CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets

On-line optimization of glutamate production based on balanced metabolic control by RQ

In glutamate fermentations by Corynebacterium glutamicum, higher glutamate concentration could be achieved by constantly controlling dissolved oxygen concentration (DO) at a lower level; however, by-product lactate also severely accumulated. The results of analyzing activities changes of the two key enzymes, glutamate and lactate dehydrogenases involved with the fermentation, and the entire metabolic network flux analysis showed that the lactate overproduction was because the metabolic flux in TCA cycle was too low to balance the glucose glycolysis rate. As a result, the respiratory quotient (RQ) adaptive control based “balanced metabolic control” (BMC) strategy was proposed and used to regulate the TCA metabolic flux rate at an appropriate level to achieve the metabolic balance among glycolysis, glutamate synthesis, and TCA metabolic flux. Compared with the best results of various DO constant controls, the BMC strategy increased the maximal glutamate concentration by about 15% and almost completely repressed the lactate accumulation with competitively high glutamate productivity

N-Terminal Arginines Modulate Plasma-Membrane Localization of Kv7.1/KCNE1 Channel Complexes

BACKGROUND AND OBJECTIVE: The slow delayed rectifier current (I(Ks)) is important for cardiac action potential termination. The underlying channel is composed of Kv7.1 α-subunits and KCNE1 β-subunits. While most evidence suggests a role of KCNE1 transmembrane domain and C-terminus for the interaction, the N-terminal KCNE1 polymorphism 38G is associated with reduced I(Ks) and atrial fibrillation (a human arrhythmia). Structure-function relationship of the KCNE1 N-terminus for I(Ks) modulation is poorly understood and was subject of this study. METHODS: We studied N-terminal KCNE1 constructs disrupting structurally important positively charged amino-acids (arginines) at positions 32, 33, 36 as well as KCNE1 constructs that modify position 38 including an N-terminal truncation mutation. Experimental procedures included molecular cloning, patch-clamp recording, protein biochemistry, real-time-PCR and confocal microscopy. RESULTS: All KCNE1 constructs physically interacted with Kv7.1. I(Ks) resulting from co-expression of Kv7.1 with non-atrial fibrillation '38S' was greater than with any other construct. Ionic currents resulting from co-transfection of a KCNE1 mutant with arginine substitutions ('38G-3xA') were comparable to currents evoked from cells transfected with an N-terminally truncated KCNE1-construct ('Δ1-38'). Western-blots from plasma-membrane preparations and confocal images consistently showed a greater amount of Kv7.1 protein at the plasma-membrane in cells co-transfected with the non-atrial fibrillation KCNE1-38S than with any other construct. CONCLUSIONS: The results of our study indicate that N-terminal arginines in positions 32, 33, 36 of KCNE1 are important for reconstitution of I(Ks). Furthermore, our results hint towards a role of these N-terminal amino-acids in membrane representation of the delayed rectifier channel complex

NADPH oxidases in cardiovascular disease: insights from in vivo models and clinical studies

NADPH oxidase family enzymes (or NOXs) are the major sources of reactive oxygen species (ROS) that are implicated in the pathophysiology of many cardiovascular diseases. These enzymes appear to be especially important in the modulation of redox-sensitive signalling pathways that underlie key cellular functions such as growth, differentiation, migration and proliferation. Seven distinct members of the family have been identified of which four (namely NOX1, 2, 4 and 5) may have cardiovascular functions. In this article, we review our current understanding of the roles of NOX enzymes in several common cardiovascular disease states, with a focus on data from genetic studies and clinical data where available

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets

Public speaking anxiety decreases within repeated virtual reality training sessions

Therapy for public speaking phobia using virtual reality exposure (VRE) has focused on distress arousal rather than distress habituation. Understanding habituation will help optimise session duration, making treatment more affordable and accessible. This pilot study utilised within-speech repeated measures to examine distress habituation during three brief public speaking scenarios in a non-clinical sample (n = 19; 18-76 years). VRE elicited significant distress in all three scenarios. Although within-scenario distress habituation was not observed, between-scenario habituation was partially supported. An increase in distress during the second scenario indicated that three consecutive speech performances were critical in achieving habituation. Brief repeated VRE scenarios using an agent audience were effective in eliciting public speaking distress, as well as habituation