Institution
University of Iceland
Education•Reykjavik, Suðurnes, Iceland•
About: University of Iceland is a education organization based out in Reykjavik, Suðurnes, Iceland. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 5423 authors who have published 16199 publications receiving 694762 citations. The organization is also known as: Háskóli Íslands.
Papers published on a yearly basis
Papers
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TL;DR: A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed, using opening and closing morphological transforms to isolate bright and dark structures in images, where bright/dark means brighter/darker than the surrounding features in the images.
Abstract: Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed. In this approach, opening and closing morphological transforms are used in order to isolate bright (opening) and dark (closing) structures in images, where bright/dark means brighter/darker than the surrounding features in the images. A morphological profile is constructed based on the repeated use of openings and closings with a structuring element of increasing size, starting with one original image. In order to apply the morphological approach to hyperspectral data, principal components of the hyperspectral imagery are computed. The most significant principal components are used as base images for an extended morphological profile, i.e., a profile based on more than one original image. In experiments, two hyperspectral urban datasets are classified. The proposed method is used as a preprocessing method for a neural network classifier and compared to more conventional classification methods with different types of statistical computations and feature extraction.
1,308 citations
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University of Cambridge1, University of Exeter2, The Chinese University of Hong Kong3, Middlesex University4, Southend University Hospital NHS Foundation Trust5, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico6, University of Milan7, Université libre de Bruxelles8, National and Kapodistrian University of Athens9, Rabin Medical Center10, University of Iceland11, University College London12
TL;DR: Treatment with low‐dose aspirin in women at high risk for preterm preeclampsia resulted in a lower incidence of this diagnosis than placebo, and there were no significant between‐group differences in the incidence of neonatal adverse outcomes or other adverse events.
Abstract: BackgroundPreterm preeclampsia is an important cause of maternal and perinatal death and complications. It is uncertain whether the intake of low-dose aspirin during pregnancy reduces the risk of preterm preeclampsia. MethodsIn this multicenter, double-blind, placebo-controlled trial, we randomly assigned 1776 women with singleton pregnancies who were at high risk for preterm preeclampsia to receive aspirin, at a dose of 150 mg per day, or placebo from 11 to 14 weeks of gestation until 36 weeks of gestation. The primary outcome was delivery with preeclampsia before 37 weeks of gestation. The analysis was performed according to the intention-to-treat principle. ResultsA total of 152 women withdrew consent during the trial, and 4 were lost to follow up, which left 798 participants in the aspirin group and 822 in the placebo group. Preterm preeclampsia occurred in 13 participants (1.6%) in the aspirin group, as compared with 35 (4.3%) in the placebo group (odds ratio in the aspirin group, 0.38; 95% confidenc...
1,299 citations
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University of Edinburgh1, University of Greifswald2, University of London3, Queen Mary University of London4, University of Trieste5, Vita-Salute San Raffaele University6, University of Tartu7, National Institutes of Health8, University of Oxford9, Johns Hopkins University10, Erasmus University Rotterdam11, University of Verona12, University of Iceland13, University of Otago14, Uppsala University15, University of Lübeck16, University of Split17, Harvard University18, University Hospital of Lausanne19, Ludwig Maximilian University of Munich20, University of Auckland21, VU University Amsterdam22, University of Regensburg23, Max Planck Society24, Greifswald University Hospital25, University of Helsinki26, Charles III University of Madrid27, Wellcome Trust Sanger Institute28, University College London29, Leiden University30, Catholic University of the Sacred Heart31, University of Bern32, University of Freiburg33
TL;DR: Interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, and regression-type analyses in a non BMI-stratified overall sample suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum.
Abstract: We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.
1,293 citations
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01 Mar 2013
TL;DR: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper and several techniques are investigated for combining both spatial and spectral information.
Abstract: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
1,225 citations
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TL;DR: In insights into the role of alcohol consumption in the genetic architecture of hypertension, a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions is conducted.
Abstract: Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.
1,218 citations
Authors
Showing all 5561 results
Name | H-index | Papers | Citations |
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Albert Hofman | 267 | 2530 | 321405 |
Kari Stefansson | 206 | 794 | 174819 |
Ronald Klein | 194 | 1305 | 149140 |
Eric Boerwinkle | 183 | 1321 | 170971 |
Unnur Thorsteinsdottir | 167 | 444 | 121009 |
Vilmundur Gudnason | 159 | 837 | 123802 |
Hakon Hakonarson | 152 | 968 | 101604 |
Bernhard O. Palsson | 147 | 831 | 85051 |
Andrew T. Hattersley | 146 | 768 | 106949 |
Fernando Rivadeneira | 146 | 628 | 86582 |
Rattan Lal | 140 | 1383 | 87691 |
Jonathan G. Seidman | 137 | 563 | 89782 |
Christine E. Seidman | 134 | 519 | 67895 |
Augustine Kong | 134 | 237 | 89818 |
Timothy M. Frayling | 133 | 500 | 100344 |