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Institution

University of Virginia

EducationCharlottesville, Virginia, United States
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.


Papers
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Journal ArticleDOI
TL;DR: Neuraxial anesthesia and analgesia provide several advantages over systemic opioids, including superior analgesia, reduced blood loss and need for transfusion, decreased incidence of graft occlusion, and improved joint mobility following major knee surgery.

926 citations

Journal ArticleDOI
TL;DR: This research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether non parametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.
Abstract: Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown seasonal ARIMA models to deliver results that are statistically superior to basic implementations of nonparametric regression. However, the advantages associated with a data-driven nonparametric forecasting approach motivate further investigation of refined nonparametric forecasting methods. Following this motivation, this research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.

926 citations

Journal ArticleDOI
TL;DR: The results indicate that sex and age have independent and interrelated effects on GH secretion, and an amplifying action of estradiol on the neuroendocrine regulation of pulsatile GH release is suggested.
Abstract: We undertook a study of the separate and combined effects of age and sex on the pulsatile pattern of GH secretion. The 24-h secretory profile of GH was generated by 20-min sampling in 10 young women (aged 18-33 yr), 10 young men (aged 18-33 yr), 8 postmenopausal women (aged greater than 55 yr), and 8 older men (aged greater than 55 yr). A computer-assisted pulse analysis program was used to assess both total GH secretion, as reflected in the 24-h integrated GH concentration (IGHC), and pulsatile secretion, as denoted by pulse frequency, duration, amplitude, and the fraction of GH secreted in pulses during the 24-h period (FGHP). IGHC was significantly greater in women than in men (P less than 0.025) and greater in the young than in the old (P less than 0.003). The mean pulse amplitude, duration, and FGHP were each greater in the young (P less than 0.006, P less than 0.03, and P less than 0.0001, respectively), but not significantly different between the sexes. The mean pulse frequency was not affected by sex or age. The serum concentration of free estradiol, but not free testosterone, correlated with IGHC (r = 0.46; P less than 0.005), pulse amplitude (r = 0.53; P less than 0.001), and FGHP (r = 0.59; P less than 0.0002). After correcting for the effects of estradiol, neither sex nor age influenced IGHC or mean pulse amplitude, while the effect of age on FGHP was reduced from 81% to 29%. Of the indices of GH secretion, FGHP had the strongest correlation (r = 0.43; P less than 0.006) with somatomedin-C. Somatomedin-C declined significantly with age in both sexes. Our results indicate that sex and age have independent and interrelated effects on GH secretion. These effects can be largely accounted for by corresponding variations in endogenous estradiol levels. These observations suggest an amplifying action of estradiol on the neuroendocrine regulation of pulsatile GH release.

926 citations

Journal ArticleDOI
TL;DR: The data suggest that tRFs are not random by-products of tRNA degradation or biogenesis, but an abundant and novel class of short RNAs with precise sequence structure that have specific expression patterns and specific biological roles.
Abstract: New types of small RNAs distinct from microRNAs (miRNAs) are progressively being discovered in various organisms. In order to discover such novel small RNAs, a library of 17- to 26-base-long RNAs was created from prostate cancer cell lines and sequenced by ultra-high-throughput sequencing. A significant number of the sequences are derived from precise processing at the 5' or 3' end of mature or precursor tRNAs to form three series of tRFs (tRNA-derived RNA fragments): the tRF-5, tRF-3, and tRF-1 series. These sequences constitute a class of short RNAs that are second most abundant to miRNAs. Northern hybridization, quantitative RT-PCR, and splinted ligation assays independently measured the levels of at least 17 tRFs. To demonstrate the biological importance of tRFs, we further investigated tRF-1001, derived from the 3' end of a Ser-TGA tRNA precursor transcript that is not retained in the mature tRNA. tRF-1001 is expressed highly in a wide range of cancer cell lines but much less in tissues, and its expression in cell lines was tightly correlated with cell proliferation. siRNA-mediated knockdown of tRF-1001 impaired cell proliferation with the specific accumulation of cells in G2, phenotypes that were reversed specifically by cointroducing a synthetic 2'-O-methyl tRF-1001 oligoribonucleotide resistant to the siRNA. tRF-1001 is generated in the cytoplasm by tRNA 3'-endonuclease ELAC2, a prostate cancer susceptibility gene. Our data suggest that tRFs are not random by-products of tRNA degradation or biogenesis, but an abundant and novel class of short RNAs with precise sequence structure that have specific expression patterns and specific biological roles.

923 citations

Journal ArticleDOI
19 Jun 2014-Nature
TL;DR: The results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions.
Abstract: Bacterial species whose representation defines healthy postnatal assembly of the gut microbiota in Bangladeshi children during their first 2 years are identified, and a model is constructed to compare healthy children to those with severe acute malnutrition (SAM); results show that SAM is associated with microbiota immaturity that is only partially ameliorated by existing nutritional interventions. Childhood malnutrition is a major health problem in many low-income countries, and although mortality can be reduced by therapeutic food interventions, it is difficult to achieve complete restoration of healthy growth in cases of severe acute malnutrition. Here Jeffrey Gordon and colleagues identify a group of 24 bacterial species whose proportional representation in the microbiota defines how a healthy microbiota assembles over the course of the first two postnatal years in a cohort of healthy children in Bangladesh. They define a 'relative microbiota maturity index' and 'microbiota-for-age Z-score' that allow comparison across individuals and use these indices to demonstrate that severe malnutrition is associated with significant relative microbiota immaturity that is only partially ameliorated by two widely used nutritional interventions. This work suggests that more prolonged food-based interventions and/or addition of gut microbes may be needed to achieve durable repair of microbiota immaturity in childhood malnutrition and improved clinical outcomes. Therapeutic food interventions have reduced mortality in children with severe acute malnutrition (SAM), but incomplete restoration of healthy growth remains a major problem1,2. The relationships between the type of nutritional intervention, the gut microbiota, and therapeutic responses are unclear. In the current study, bacterial species whose proportional representation define a healthy gut microbiota as it assembles during the first two postnatal years were identified by applying a machine-learning-based approach to 16S ribosomal RNA data sets generated from monthly faecal samples obtained from birth onwards in a cohort of children living in an urban slum of Dhaka, Bangladesh, who exhibited consistently healthy growth. These age-discriminatory bacterial species were incorporated into a model that computes a ‘relative microbiota maturity index’ and ‘microbiota-for-age Z-score’ that compare postnatal assembly (defined here as maturation) of a child’s faecal microbiota relative to healthy children of similar chronologic age. The model was applied to twins and triplets (to test for associations of these indices with genetic and environmental factors, including diarrhoea), children with SAM enrolled in a randomized trial of two food interventions, and children with moderate acute malnutrition. Our results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions. Immaturity is also evident in less severe forms of malnutrition and correlates with anthropometric measurements. Microbiota maturity indices provide a microbial measure of human postnatal development, a way of classifying malnourished states, and a parameter for judging therapeutic efficacy. More prolonged interventions with existing or new therapeutic foods and/or addition of gut microbes may be needed to achieve enduring repair of gut microbiota immaturity in childhood malnutrition and improve clinical outcomes.

923 citations


Authors

Showing all 53083 results

NameH-indexPapersCitations
Joan Massagué189408149951
Michael Rutter188676151592
Gordon B. Mills1871273186451
Ralph Weissleder1841160142508
Gonçalo R. Abecasis179595230323
Jie Zhang1784857221720
John R. Yates1771036129029
John A. Rogers1771341127390
Bradley Cox1692150156200
Mika Kivimäki1661515141468
Hongfang Liu1662356156290
Carl W. Cotman165809105323
Ralph A. DeFronzo160759132993
Elio Riboli1581136110499
Dan R. Littman157426107164
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023189
2022783
20215,565
20205,600
20195,001
20184,586