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Institution

University of Alberta

EducationEdmonton, Alberta, Canada
About: University of Alberta is a education organization based out in Edmonton, Alberta, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 65403 authors who have published 154847 publications receiving 5358338 citations. The organization is also known as: Ualberta & UAlberta.


Papers
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Journal ArticleDOI
13 Jun 2018-BMJ
TL;DR: Ana M Valdes and colleagues discuss strategies for modulating the gut microbiota through diet and probiotics and suggest that a Mediterranean diet supplemented with probiotics can be a viable alternative to a probiotic regime.
Abstract: Ana M Valdes and colleagues discuss strategies for modulating the gut microbiota through diet and probiotics

1,019 citations

Journal ArticleDOI
TL;DR: The results suggest the possibility that 16S rRNA genes of culturable and nonculturable Mollsicutes can be amplified for detection and for a phylogenetic study using crude Mollicutes DNA preparations under appropriately controlled thermocycling conditions.

1,019 citations

Journal ArticleDOI
TL;DR: This article presents international consensus criteria for and classification of AbAR developed based on discussions held at the Sixth Banff Conference on Allograft Pathology in 2001, to be revisited as additional data accumulate in this important area of renal transplantation.

1,018 citations

Journal ArticleDOI
TL;DR: Exercise is an effective intervention to improve quality of life, cardiorespiratory fitness, physical functioning and fatigue in breast cancer patients and survivors and larger trials that examine the long-term benefits of exercise are needed for this patient group.
Abstract: Background: Physical exercise has been identified as a potential intervention to improve quality of life in women with breast cancer. We sought to summarize the available evidence concerning the effects of exercise on breast cancer patients and survivors. Methods: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, PsychINFO, CancerLit, PEDro and SportDiscus as well as conference proceedings, clinical practice guidelines and other unpublished literature resources. We included only randomized controlled trials that examined exercise interventions for breast cancer patients or survivors with quality of life, cardiorespiratory fitness or physical functioning as primary outcomes. We also extracted data on symptoms of fatigue, body composition and adverse effects. Results: Of 136 studies identified, 14 met all the inclusion criteria. Despite significant heterogeneity and relatively small samples, the point estimates in terms of the benefits of exercise for all outcomes were positive even when statistical significance was not achieved. Exercise led to statistically significant improvements in quality of life as assessed by the Functional Assessment of Cancer Therapy–General (weighted mean difference [WMD] 4.58, 95% confidence interval [CI] 0.35 to 8.80) and Functional Assessment of Cancer Therapy–Breast (WMD 6.62, 95% CI 1.21 to 12.03). Exercise also led to significant improvements in physical functioning and peak oxygen consumption and in reducing symptoms of fatigue. Interpretation: Exercise is an effective intervention to improve quality of life, cardiorespiratory fitness, physical functioning and fatigue in breast cancer patients and survivors. Larger trials that have a greater focus on study quality and adverse effects and that examine the long-term benefits of exercise are needed for this patient group.

1,017 citations

Journal ArticleDOI
01 Aug 2003-Genetics
TL;DR: In this article, a Markov chain Monte Carlo algorithm is implemented to integrate over uncertain gene trees and branch lengths (or coalescence times) at each locus as well as species divergence times.
Abstract: The effective population sizes of ancestral as well as modern species are important parameters in models of population genetics and human evolution. The commonly used method for estimating ancestral population sizes, based on counting mismatches between the species tree and the inferred gene trees, is highly biased as it ignores uncertainties in gene tree reconstruction. In this article, we develop a Bayes method for simultaneous estimation of the species divergence times and current and ancestral population sizes. The method uses DNA sequence data from multiple loci and extracts information about conflicts among gene tree topologies and coalescent times to estimate ancestral population sizes. The topology of the species tree is assumed known. A Markov chain Monte Carlo algorithm is implemented to integrate over uncertain gene trees and branch lengths (or coalescence times) at each locus as well as species divergence times. The method can handle any species tree and allows different numbers of sequences at different loci. We apply the method to published noncoding DNA sequences from the human and the great apes. There are strong correlations between posterior estimates of speciation times and ancestral population sizes. With the use of an informative prior for the human-chimpanzee divergence date, the population size of the common ancestor of the two species is estimated to be ∼20,000, with a 95% credibility interval (8000, 40,000). Our estimates, however, are affected by model assumptions as well as data quality. We suggest that reliable estimates have yet to await more data and more realistic models.

1,016 citations


Authors

Showing all 66027 results

NameH-indexPapersCitations
Salim Yusuf2311439252912
Yi Chen2174342293080
Robert M. Califf1961561167961
Douglas R. Green182661145944
Russel J. Reiter1691646121010
Jiawei Han1681233143427
Jaakko Kaprio1631532126320
Tobin J. Marks1591621111604
Josef M. Penninger154700107295
Subir Sarkar1491542144614
Gerald M. Edelman14754569091
Rinaldo Bellomo1471714120052
P. Sinervo138151699215
David A. Jackson136109568352
Andreas Warburton135157897496
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023234
20221,084
20219,315
20208,831
20198,177