University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

The Role of Epidemic Resistance Plasmids and International High-Risk Clones in the Spread of Multidrug-Resistant Enterobacteriaceae

Abstract: SUMMARY Escherichia coli sequence type 131 (ST131) and Klebsiella pneumoniae ST258 emerged in the 2000s as important human pathogens, have spread extensively throughout the world, and are responsible for the rapid increase in antimicrobial resistance among E. coli and K. pneumoniae strains, respectively. E. coli ST131 causes extraintestinal infections and is often fluoroquinolone resistant and associated with extended-spectrum β-lactamase production, especially CTX-M-15. K. pneumoniae ST258 causes urinary and respiratory tract infections and is associated with carbapenemases, most often KPC-2 and KPC-3. The most prevalent lineage within ST131 is named fimH30 because it contains the H30 variant of the type 1 fimbrial adhesin gene, and recent molecular studies have demonstrated that this lineage emerged in the early 2000s and was then followed by the rapid expansion of its sublineages H30-R and H30-Rx. K. pneumoniae ST258 comprises 2 distinct lineages, namely clade I and clade II. Moreover, it seems that ST258 is a hybrid clone that was created by a large recombination event between ST11 and ST442. Epidemic plasmids with blaCTX-M and blaKPC belonging to incompatibility group F have contributed significantly to the success of these clones. E. coli ST131 and K. pneumoniae ST258 are the quintessential examples of international multidrug-resistant high-risk clones.

Automobile Accidents Involving Patients with Obstructive Sleep Apnea

Abstract: Although patients with obstructive sleep apnea often report falling asleep while driving, the frequency of auto accidents involving these patients has not been rigorously studied. Therefore, we compared the driving records of 29 patients with obstructive sleep apnea with those of 35 subjects without sleep apnea. The patients with sleep apnea had a sevenfold greater rate of automobile accidents than did the subjects without apnea (p < 0.01). The percentage of persons with one or more accidents was also greater in the patients with apnea than in the control subjects without apnea (31% versus 6%, p < 0.01). The percentage of persons having one or more accidents in which they were at fault was also greater in the patients with apnea than in the control subjects (24% versus 3%, p < 0.02). The automobile accident rate of the patients with sleep apnea was 2.6 times the accident rate of all licensed drivers in the state of Virginia (p < 0.02). In addition, 24% of patients with sleep apnea reported falling asleep ...

Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations

Abstract: As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search, in modern datacenters is a major contributor to low machine utilization. Being unable to accurately predict performance degradation due to contention for shared resources on multicore systems has led to the heavy handed approach of simply disallowing the co-location of high-priority, latency-sensitive tasks with other tasks. Performing this precise prediction has been a challenging and unsolved problem. In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of “pressure” to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at “sensible” co-locations in Google's production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.