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Institution

University of Nevada, Reno

EducationReno, Nevada, United States
About: University of Nevada, Reno is a education organization based out in Reno, Nevada, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13561 authors who have published 28217 publications receiving 882002 citations. The organization is also known as: University of Nevada & Nevada State University.


Papers
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Journal ArticleDOI
TL;DR: This paper surveys key advances in mechanical design and control of dynamic effects and nonlinearities, in the context of high-speed nanopositioning, as well as future challenges and research topics.
Abstract: Recent interest in high-speed scanning probe microscopy for high-throughput applications including video-rate atomic force microscopy and probe-based nanofabrication has sparked attention on the development of high-bandwidth flexure-guided nanopositioning systems (nanopositioners). Such nanopositioners are designed to move samples with sub-nanometer resolution with positioning bandwidth in the kilohertz range. State-of-the-art designs incorporate uniquely designed flexure mechanisms driven by compact and stiff piezoelectric actuators. This paper surveys key advances in mechanical design and control of dynamic effects and nonlinearities, in the context of high-speed nanopositioning. Future challenges and research topics are also discussed.

411 citations

Journal ArticleDOI
TL;DR: A central role for airway smooth muscle in the pathogenesis of airway hyperresponsiveness in asthma is explored and an attempt is made to address a fundamental abnormality of asthma, that of exaggerated airway narrowing due to excessive shortening of ASM.
Abstract: Excessive airway obstruction is the cause of symptoms and abnormal lung function in asthma. As airway smooth muscle (ASM) is the effecter controlling airway calibre, it is suspected that dysfunction of ASM contributes to the pathophysiology of asthma. However, the precise role of ASM in the series of events leading to asthmatic symptoms is not clear. It is not certain whether, in asthma, there is a change in the intrinsic properties of ASM, a change in the structure and mechanical properties of the noncontractile components of the airway wall, or a change in the interdependence of the airway wall with the surrounding lung parenchyma. All these potential changes could result from acute or chronic airway inflammation and associated tissue repair and remodelling. Anti-inflammatory therapy, however, does not "cure" asthma, and airway hyperresponsiveness can persist in asthmatics, even in the absence of airway inflammation. This is perhaps because the therapy does not directly address a fundamental abnormality of asthma, that of exaggerated airway narrowing due to excessive shortening of ASM. In the present study, a central role for airway smooth muscle in the pathogenesis of airway hyperresponsiveness in asthma is explored.

409 citations

Journal ArticleDOI
TL;DR: The results suggest that Zat12 is an important component of the oxidative stress response signal transduction network of Arabidopsis required for Zat7, WRKY25, and Apx1 expression during oxidative stress.

409 citations

Journal ArticleDOI
TL;DR: Some suggestions are offered to address and potentially ameliorate the current dilemma posed by burnout during medical education, particularly if burnout continues into residency and beyond.
Abstract: Summary Background Burnout is a state of mental and physical exhaustion related to work or care-giving activities. Distress during medical school can lead to burnout, with significant consequences, particularly if burnout continues into residency and beyond. The authors reviewed literature pertaining to medical student burnout, its prevalence, and its relationship to personal, environmental, demographic and psychiatric factors. We ultimately offer some suggestions to address and potentially ameliorate the current dilemma posed by burnout during medical education. Methods A literature review was conducted using a PubMed/Medline, and PsycInfo search from 1974 to 2011 using the keywords: ‘burnout’, ‘stress’, ‘well-being’, ‘self-care’, ‘psychiatry’ and ‘medical students’. Three authors agreed independently on the studies to be included in this review. Results The literature reveals that burnout is prevalent during medical school, with major US multi-institutional studies estimating that at least half of all medical students may be affected by burnout during their medical education. Studies show that burnout may persist beyond medical school, and is, at times, associated with psychiatric disorders and suicidal ideation. A variety of personal and professional characteristics correlate well with burnout. Potential interventions include school-based and individual-based activities to increase overall student well-being. Discussion Burnout is a prominent force challenging medical students’ well-being, with concerning implications for the continuation of burnout into residency and beyond. To address this highly prevalent condition, educators must first develop greater awareness and understanding of burnout, as well as of the factors that lead to its development. Interventions focusing on generating wellness during medical training are highly recommended.

408 citations

Posted Content
TL;DR: TernGrad as discussed by the authors uses ternary gradients to accelerate distributed deep learning in data parallelism, which can reduce the communication cost of synchronizing gradients and parameters by ternarizing and gradient clipping.
Abstract: High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary gradients to accelerate distributed deep learning in data parallelism. Our approach requires only three numerical levels {-1,0,1}, which can aggressively reduce the communication time. We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients. Guided by the bound, we propose layer-wise ternarizing and gradient clipping to improve its convergence. Our experiments show that applying TernGrad on AlexNet does not incur any accuracy loss and can even improve accuracy. The accuracy loss of GoogLeNet induced by TernGrad is less than 2% on average. Finally, a performance model is proposed to study the scalability of TernGrad. Experiments show significant speed gains for various deep neural networks. Our source code is available.

408 citations


Authors

Showing all 13726 results

NameH-indexPapersCitations
Robert Langer2812324326306
Thomas C. Südhof191653118007
David W. Johnson1602714140778
Menachem Elimelech15754795285
Jeffrey L. Cummings148833116067
Bing Zhang121119456980
Arturo Casadevall12098055001
Mark H. Ellisman11763755289
Thomas G. Ksiazek11339846108
Anthony G. Fane11256540904
Leonardo M. Fabbri10956660838
Gary H. Lyman10869452469
Steven C. Hayes10645051556
Stephen P. Long10338446119
Gary Cutter10373740507
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202368
2022222
20211,756
20201,743
20191,514
20181,397