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Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

BackgroundSeveral single-site alcohol treatment clinical trials have demonstrated efficacy for immediate-release (IR) gabapentin in reducing drinking outcomes among individuals with alcohol dependence. The purpose of this study was to conduct a large, multisite clinical trial of gabapentin enacarbil extended-release (GE-XR) (HORIZANT® ), a gabapentin prodrug formulation, to determine its safety and efficacy in treating alcohol use disorder (AUD).MethodsMen and women (n = 346) who met DSM-5 criteria for at least moderate AUD were recruited across 10 U.S. clinical sites. Participants received double-blind GE-XR (600 mg twice a day) or placebo and a computerized behavioral intervention (Take Control) for 6 months. Efficacy analyses were prespecified for the last 4 weeks of the treatment period.ResultsThe GE-XR and placebo groups did not differ significantly on the primary outcome measure, percentage of subjects with no heavy drinking days (28.3 vs. 21.5, respectively, p = 0.157). Similarly, no clinical benefit was found for other drinking measures (percent subjects abstinent, percent days abstinent, percent heavy drinking days, drinks per week, drinks per drinking day), alcohol craving, alcohol-related consequences, sleep problems, smoking, and depression/anxiety symptoms. Common side-effects were fatigue, dizziness, and somnolence. A population pharmacokinetics analysis revealed that patients had lower gabapentin exposure levels compared with those in other studies using a similar dose but for other indications.ConclusionsOverall, GE-XR at 600 mg twice a day did not reduce alcohol consumption or craving in individuals with AUD. It is possible that, unlike the IR formulation of gabapentin, which showed efficacy in smaller Phase 2 trials at a higher dose, GE-XR is not effective in treating AUD, at least not at doses approved by the U.S. Food and Drug Administration for treating other medical conditions.

This project interrogates a workshop leader and whole-meeting talk among a group of scientists gathered at a workshop to discuss cyberinfrastructure and the sharing of both 'light' and 'dark' data in the sciences. This project analyzes discourses working through the workshop talk to interrogate the social relations, interdisciplinary identities, concerns, and commonalities in the sciences and in relation to emerging opportunities for computing and data sharing in the cloud. The findings point to the efficacy of arranging scientists around data collection processes for collaborative work as opposed to groupings around data type, discipline, work sectors, or collection location. This research provides an opportunity to consider the democratization of data, academic boundaries in the sciences, as well as interdisciplinary and collaborative problem-solving processes that happen in groups across academic and applied contexts.

Advancement in quantum networking is becoming increasingly more sophisticated, with some arguing that a working quantum network may be reached by 2030. Just how these networks can and will come to be is still a work in progress, including how communications within those networks will be secured. While debates about the development of quantum networking often focus on technical specifications, less is written about their social impacts and the myriad of ways individuals can engage in conversations about quantum technologies, especially in non-technical ways. Spaces for legal, humanist or behavioral scholars to weigh in on the impacts of this emerging capability do exist, and using the example of criticism of the quantum protocol quantum key distribution (QKD), this paper illustrates five entry points for non-technical experts to help technical, practical, and scholarly communities prepare for the anticipated quantum revolution. Selecting QKD as an area of critique was chosen due to its established position as an application of quantum properties that reaches beyond theoretical applications. [ABSTRACT FROM AUTHOR]

Self-determination theory (SDT) underpins research on learner empowerment, but it is rarely discussed in empowerment-related literature. In addition, a motivational measure stemming from SDT has received little visibility in communication research. To address these concerns, this study focuses on motivational theory and measurement in an attempt to tease out the relationship between motivation and learner empowerment as well as how these constructs are related to students' choice making opportunities in the classroom. In essence, this study aims to offer a strong synthesis of the literature related to these constructs, and also to make methodological and practical advancements in understanding student motivation, learner empowerment, and how freedom in the college classroom shapes students' enthusiasm for learning. [ABSTRACT FROM AUTHOR]

This research examines the contemporary landscape relative to information-driven strategies used for global gain. With Russia functioning as a case of global democratic disruption (Blackwill and Gordon, 2018), this exploratory project studies documented information-based, computational, and media-related political strategies. The findings provide a way to see patterns in tactics suggesting 'hybrid' warfare or information warfare identified in recent literature. Our work has found several previously unknown centers of activity related to online propaganda, in locations near Russian military and security services installations. This aligns with and supports assertions that at least some of the Russian hacking is likely the work of state-sponsored Russian operatives. This work also allows readers to connect events in recent years in order to view them together and conceive an online hybrid war and Russia's potential role in those efforts. These findings provide scholars, practitioners, and citizens interested in democratic processes around the globe the opportunity to consider the many threats to contemporary political processes, and contributes to ongoing academic conversations about digital political disruptions and warfare. [ABSTRACT FROM AUTHOR]