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Institution

University of Vermont

EducationBurlington, Vermont, United States
About: University of Vermont is a education organization based out in Burlington, Vermont, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17592 authors who have published 38251 publications receiving 1609874 citations. The organization is also known as: UVM & University of Vermont and State Agricultural College.


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Journal ArticleDOI
TL;DR: Rituximab, a monoclonal antibody selectively depleting CD20+ B cells, has demonstrated efficacy in reducing disease activity in relapsing‐remitting multiple sclerosis (MS).
Abstract: Objective Rituximab, a monoclonal antibody selectively depleting CD20+ B cells, has demonstrated efficacy in reducing disease activity in relapsing-remitting multiple sclerosis (MS). We evaluated rituximab in adults with primary progressive MS (PPMS) through 96 weeks and safety through 122 weeks. Methods Using 2:1 randomization, 439 PPMS patients received two 1,000mg intravenous rituximab or placebo infusions every 24 weeks, through 96 weeks (4 courses). The primary endpoint was time to confirmed disease progression (CDP), a prespecified increase in Expanded Disability Status Scale sustained for 12 weeks. Secondary endpoints were change from baseline to week 96 in T2 lesion volume and total brain volume on magnetic resonance imaging scans. Results Differences in time to CDP between rituximab and placebo did not reach significance (96-week rates: 38.5% placebo, 30.2% rituximab; p = 0.14). From baseline to week 96, rituximab patients had less (p < 0.001) increase in T2 lesion volume; brain volume change was similar (p = 0.62) to placebo. Subgroup analysis showed time to CDP was delayed in rituximab-treated patients aged <51 years (hazard ratio [HR] = 0.52; p = 0.010), those with gadolinium-enhancing lesions (HR = 0.41; p = 0.007), and those aged <51 years with gadolinium-enhancing lesions (HR = 0.33; p = 0.009) compared with placebo. Adverse events were comparable between groups; 16.1% of rituximab and 13.6% of placebo patients reported serious events. Serious infections occurred in 4.5% of rituximab and <1.0% of placebo patients. Infusion-related events, predominantly mild to moderate, were more common with rituximab during the first course, and decreased to rates comparable to placebo on successive courses. Interpretation Although time to CDP between groups was not significant, overall subgroup analyses suggest selective B-cell depletion may affect disease progression in younger patients, particularly those with inflammatory lesions. Ann Neurol 2009;66:460–471

791 citations

Journal ArticleDOI
TL;DR: A systematic evaluation on the effect of noise in machine learning separates noise into two categories: class noise and attribute noise, and investigates the relationship between attribute noise and classification accuracy, the impact of noise at different attributes, and possible solutions in handling attribute noise.
Abstract: Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. Accordingly, most existing learning algorithms have integrated various approaches to enhance their learning abilities from noisy environments, but the existence of noise can still introduce serious negative impacts. A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed. Unfortunately, rare research has been conducted to systematically explore the impact of noise, especially from the noise handling point of view. This has made various noise processing techniques less significant, specifically when dealing with noise that is introduced in attributes. In this paper, we present a systematic evaluation on the effect of noise in machine learning. Instead of taking any unified theory of noise to evaluate the noise impacts, we differentiate noise into two categories: class noise and attribute noise, and analyze their impacts on the system performance separately. Because class noise has been widely addressed in existing research efforts, we concentrate on attribute noise. We investigate the relationship between attribute noise and classification accuracy, the impact of noise at different attributes, and possible solutions in handling attribute noise. Our conclusions can be used to guide interested readers to enhance data quality by designing various noise handling mechanisms.

786 citations

Journal ArticleDOI
TL;DR: The future challenge, if costs are to be controlled, appears to lie squarely with prevention and optimum management of disability, rather than perpetrating a myth that low back pain is a serious health disorder.

782 citations

Journal ArticleDOI
24 Oct 1985-Nature
TL;DR: The results suggest that excitatory amino acids stimulate inositol phosphate formation directly, rather than indirectly by the evoked release and subsequent actions of adenosine4 or acetylcholine5.
Abstract: The major excitatory amino acids, glutamate (Glu) and aspartate (Asp), are thought to act at three receptor subtypes in the mammalian central nervous system (CNS). These are termed quisqualate (QA), N-methyl-D-aspartate (NMDA) and kainate (KA) receptors according to the specific agonist properties of these compounds revealed by electrophysiological studies. Although Glu has been shown to stimulate cyclic GMP formation in brain slices, direct regulation of second messenger systems (cyclic AMP, Ca2+ or inositol phosphates) subsequent to activation of excitatory amino-acid receptors, has not been extensively studied. Here we demonstrate that in striatal neurones, excitatory amino acids, but not inhibitory or non-neuroactive amino acids, induce a three- to fourfold increase in inositol mono-, di- and triphosphate (IP, IP, IP) formation with the relative potency QA greater than Glu greater than NMDA, KA. The Glu-evoked formation of inositol phosphates appears to result principally from actions at QA as well as NMDA receptors on striatal neurones. Our results suggest that excitatory amino acids stimulate inositol phosphate formation directly, rather than indirectly by the evoked release and subsequent actions of adenosine or acetylcholine.

782 citations

Journal ArticleDOI
31 Mar 2005-Nature
TL;DR: Results show that CKIδ is a central component in the mammalian clock, and suggest that mammalian and fly clocks might have different regulatory mechanisms despite the highly conserved nature of their individual components.
Abstract: Familial advanced sleep phase syndrome (FASPS) is a human behavioural phenotype characterized by early sleep times and early-morning awakening. It was the first human, mendelian circadian rhythm variant to be well-characterized, and was shown to result from a mutation in a phosphorylation site within the casein kinase I (CKI)-binding domain of the human PER2 gene. To gain a deeper understanding of the mechanisms of circadian rhythm regulation in humans, we set out to identify mutations in human subjects leading to FASPS. We report here the identification of a missense mutation (T44A) in the human CKIdelta gene, which results in FASPS. This mutant kinase has decreased enzymatic activity in vitro. Transgenic Drosophila carrying the human CKIdelta-T44A gene showed a phenotype with lengthened circadian period. In contrast, transgenic mice carrying the same mutation have a shorter circadian period, a phenotype mimicking human FASPS. These results show that CKIdelta is a central component in the mammalian clock, and suggest that mammalian and fly clocks might have different regulatory mechanisms despite the highly conserved nature of their individual components.

780 citations


Authors

Showing all 17727 results

NameH-indexPapersCitations
Albert Hofman2672530321405
Ralph B. D'Agostino2261287229636
George Davey Smith2242540248373
Stephen V. Faraone1881427140298
Valentin Fuster1791462185164
Dennis J. Selkoe177607145825
Anders Björklund16576984268
Alfred L. Goldberg15647488296
Christopher P. Cannon1511118108906
Debbie A Lawlor1471114101123
Roger J. Davis147498103478
Andrew S. Levey144600156845
Jonathan G. Seidman13756389782
Yu Huang136149289209
Christine E. Seidman13451967895
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202359
2022177
20211,841
20201,762
20191,653
20181,569