Institution
Memorial University of Newfoundland
Education•St. John's, Newfoundland and Labrador, Canada•
About: Memorial University of Newfoundland is a education organization based out in St. John's, Newfoundland and Labrador, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 13818 authors who have published 27785 publications receiving 743594 citations. The organization is also known as: Memorial University & Memorial University of Newfoundland and Labrador.
Topics: Population, Context (language use), Health care, Gadus, Computer science
Papers published on a yearly basis
Papers
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Boston University1, University of Greifswald2, University of Alabama at Birmingham3, Johns Hopkins University4, University of Oxford5, Indiana University6, University of Texas Health Science Center at Houston7, King's College London8, Utrecht University9, University of California, Los Angeles10, University of Gothenburg11, National Institutes of Health12, Wake Forest University13, Harvard University14, University of Exeter15, University of Oulu16, University of Lübeck17, Imperial College London18, California Pacific Medical Center19, French Institute of Health and Medical Research20, University of Massachusetts Amherst21, Erasmus University Rotterdam22, Hannover Medical School23, Lund University24, University of Helsinki25, National Institute for Health and Welfare26, Fred Hutchinson Cancer Research Center27, Uppsala University28, University of Parma29, University of Pittsburgh30, Broad Institute31, Ludwig Maximilian University of Munich32, Cedars-Sinai Medical Center33, University of Washington34, Wellcome Trust Sanger Institute35, Memorial University of Newfoundland36, University of Turku37
TL;DR: Evidence of sex-differentiated genetic influences on sex steroid hormone-binding globulin is found and the importance of considering these features when estimating complex trait variance is highlighted.
Abstract: Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8x10(-106)), PRMT6 (rs17496332, 1p13.3, p=1.4x10(-11)), GCKR (rs780093, 2p23.3, p=2.2x10(-16)), ZBTB10 (rs440837, 8q21.13, p=3.4x10(-09)), JMJD1C (rs7910927, 10q21.3, p=6.1x10(-35)), SLCO1B1 (rs4149056, 12p12.1, p=1.9x10(-08)), NR2F2 (rs8023580, 15q26.2, p=8.3x10(-12)), ZNF652 (rs2411984, 17q21.32, p=3.5x10(-14)), TDGF3 (rs1573036, Xq22.3, p=4.1x10(-14)), LHCGR (rs10454142, 2p16.3, p=1.3x10(-07)), BAIAP2L1 (rs3779195, 7q21.3, p=2.7x10(-08)), and UGT2B15 (rs293428, 4q13.2, p=5.5x10(-06)). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5x10(-08), women p=0.66, heterogeneity p=0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained similar to 15.6% and similar to 8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
231 citations
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TL;DR: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time to enable users to calculate temporal trends in biodiversity within and amongst assemblage using a broad range of metrics.
Abstract: Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)).Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
231 citations
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TL;DR: This correspondence proposes, based on a random sample X_{1, \cdots, X_{n} generated from F, a nonparametric estimate of H(f) given by -(l/n) \sum_{i = 1}^{n} \In \hat{f}(x) , where f is the kernel estimate of f due to Rosenblatt and Parzen.
Abstract: Let F(x) be an absolutely continuous distribution having a density function f(x) with respect to the Lebesgue measure. The Shannon entropy is defined as H(f) = -\int f(x) \ln f(x) dx . In this correspondence we propose, based on a random sample X_{1}, \cdots , X_{n} generated from F , a nonparametric estimate of H(f) given by \hat{H}(f) = -(l/n) \sum_{i = 1}^{n} \In \hat{f}(x) , where \hat{f}(x) is the kernel estimate of f due to Rosenblatt and Parzen. Regularity conditions are obtained under which the first and second mean consistencies of \hat{H}(f) are established. These conditions are mild and easily satisfied. Examples, such as Gamma, Weibull, and normal distributions, are considered.
230 citations
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TL;DR: In this paper, a conceptual model for the deglaciation of Atlantic Canada in which a role is played by ice streams is presented, where a major ice stream in the Laurentian Channel, secondary streams in the Bay of Fundy/Gulf of Maine, Trinity Trough and Notre Dame Channel, and lesser ice streams elsewhere.
230 citations
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TL;DR: The use of millets, as nutraceuticals and specialty foods in disease risk reduction and overall health and wellness is warranted, because they are bioaccessible, possess bioactivities against several pathophysiological conditions and may serve as potential natural sources of antioxidants in food and biological systems.
228 citations
Authors
Showing all 13990 results
Name | H-index | Papers | Citations |
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Daniel Levy | 212 | 933 | 194778 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Peter W.F. Wilson | 181 | 680 | 139852 |
Martin G. Larson | 171 | 620 | 117708 |
Peter B. Jones | 145 | 1857 | 94641 |
Dafna D. Gladman | 129 | 1036 | 75273 |
Guoyao Wu | 122 | 764 | 56270 |
Fereidoon Shahidi | 119 | 951 | 57796 |
David Harvey | 115 | 738 | 94678 |
Robert C. Haddon | 112 | 577 | 52712 |
Se-Kwon Kim | 102 | 763 | 39344 |
John E. Dowling | 94 | 305 | 28116 |
Mark J. Sarnak | 94 | 393 | 42485 |
William T. Greenough | 93 | 200 | 29230 |
Soottawat Benjakul | 92 | 891 | 34336 |