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Atrial fibrillation (AF) is the most common cardiac arrhythmia. Patients with AF have up to seven-fold higher risk of suffering from ischemic stroke. Better understanding of etiologies of AF and its thromboembolic complications are required for improved patient care, as current anti-arrhythmic therapies have limited efficacy and off target effects. Accumulating evidence has implicated a potential role of oxidative stress in the pathogenesis of AF. Excessive production of reactive oxygen species (ROS) is likely involved in the structural and electrical remodeling of the heart, contributing to fibrosis and thrombosis. In particular, NADPH oxidase (NOX) has emerged as a potential enzymatic source for ROS production in AF based on growing evidence from clinical and animal studies. Indeed, NOX can be activated by known upstream triggers of AF such as angiotensin II and atrial stretch. In addition, treatments such as Statins, antioxidants, ACEI or AT1RB have been shown to prevent post-operative AF; among which ACEI/AT1RB and Statins can attenuate NOX activity. On the other hand, detailed molecular mechanisms by which specific NOX isoform(s) are involved in the pathogenesis of AF and the extent to which activation of NOX plays a causal role in AF development remains to be determined. The current review discusses causes and consequences of oxidative stress in AF with a special focus on the emerging role of NOX pathways.

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Oxidative stress plays an important role in the formation of abdominal aortic aneurysm (AAA), and we have recently established a causal role of uncoupled eNOS in this severe human disease. We have also shown that activation of NADPH oxidase (NOX) lies upstream of uncoupled eNOS. Therefore, identification of the specific NOX isoforms that are required for eNOS uncoupling and AAA formation would ultimately lead to novel therapies for AAA. In the present study, we used the Ang II infused hph-1 mice to examine the roles of NOX isoforms in the development of AAA. We generated double mutants of hph-1-NOX1, hph-1-NOX2, hph-1-p47phox, and hph-1-NOX4. After two weeks of Ang II infusion, the incidence rate of AAA substantially dropped from 76.5% in Ang II infused hph-1 mice (n=34) to 11.1%, 15.0%, 9.5% and 0% in hph-1-NOX1 (n=27), hph-1-NOX2 (n=40), hph-1-p47phox (n=21), and hph-1-NOX4 (n=33) double mutant mice, respectively. The size of abdominal aortas of the four double mutant mice, determined by ultrasound analyses, was significantly smaller than the hph-1 mice. Aortic nitric oxide and H4B bioavailabilities were markedly improved in the double mutants, while superoxide production and eNOS uncoupling activity were substantially diminished. These effects seemed attributed to an endothelial specific restoration of dihydrofolate reductase expression and activity, deficiency of which has been shown to induce eNOS uncoupling and AAA formation in both Ang II-infused hph-1 and apoE null animals. In addition, over-expression of human NOX4 N129S or T555S mutant newly identified in aneurysm patients increased hydrogen peroxide production, further implicating a relationship between NOX and human aneurysm. Taken together, these data indicate that NOX isoforms 1, 2 or 4 lies upstream of dihydrofolate reductase deficiency and eNOS uncoupling to induce AAA formation. These findings may promote development of novel therapeutics for the treatment of the disease by inhibiting NOX signaling.

Glycosphingolipids (GSL) on the surface of cells are important receptors in antigen/microbial recognition and cell adhesion. However, their functional characterization is often challenging. We have developed a catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay for the identification of specific interactions between water-soluble proteins or protein complexes with GSL incorporated into nanodiscs. The specificity and sensitivity of the assay is demonstrated for interactions involving cholera toxin and Shiga toxin, with their natural GSL receptors, the ganglioside GM1, and the globotriaosylceramide Gb3, respectively. The detection of binding between cholera toxin and GM1 within a mixture of lipids extracted from cell membranes highlights the potential of this assay for the discovery of biologically relevant protein-GSL interactions.

Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician-making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.

Convolutional Neural Networks (CNN) have been successfully applied to autonomous driving tasks, many in an endto-end manner. Previous end-to-end steering control methods take an image or an image sequence as the input and directly predict the steering angle with CNN. Although single task learning on steering angles has reported good performances, the steering angle alone is not sufficient for vehicle control. In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner. Since it is nontrivial to predict accurate speed values with only visual inputs, we first propose a network to predict discrete speed commands and steering angles with image sequences. Moreover, we propose a multi-modal multi-task network to predict speed values and steering angles by taking previous feedback speeds and visual recordings as inputs. Experiments are conducted on the public Udacity dataset and a newly collected SAIC dataset. Results show that the proposed model predicts steering angles and speed values accurately. Furthermore, we improve the failure data synthesis methods to solve the problem of error accumulation in real road tests.

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