University of 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.

Long-term Effectiveness of Treatment With Terbinafine vs Itraconazole in Onychomycosis: A 5-Year Blinded Prospective Follow-up Study

Abstract: microscopy and culture at the end of follow-up and no requirement of second intervention treatment. Secondary efficacy criteria included clinical cure without second intervention treatment and mycological and clinical relapse rates. Results: Median duration of follow-up was 54 months. At the end of the study, mycological cure without second intervention treatment was found in 34 (46%) of the 74 terbinafine-treated subjects and 10 (13%) of the 77 itraconazole-treated subjects (P.001). Mycological and clinical relapse rates were significantly higher in itraconazolevs terbinafine-treated patients (53% vs 23% and 48% vs 21%, respectively). Of the 72 patients who received subsequent terbinafine treatment, 63 (88%) achieved mycological cure and 55 (76%) achieved clinical cure. Conclusion: In the treatment of onychomycosis, continuous terbinafine provided superior long-term mycological and clinical efficacy and lower rates of mycological and clinical relapse compared with intermittent itraconazole. Arch Dermatol. 2002;138:353-357

Association of JAG1 with Bone Mineral Density and Osteoporotic Fractures: A Genome-wide Association Study and Follow-up Replication Studies

Abstract: Bone mineral density (BMD), a diagnostic parameter for osteoporosis and a clinical predictor of fracture, is a polygenic trait with high heritability. To identify genetic variants that influence BMD in different ethnic groups, we performed a genome-wide association study (GWAS) on 800 unrelated Southern Chinese women with extreme BMD and carried out follow-up replication studies in six independent study populations of European descent and Asian populations including 18,098 subjects. In the meta-analysis, rs2273061 of the Jagged1 (JAG1) gene was associated with high BMD (p = 5.27 × 10−8 for lumbar spine [LS] and p = 4.15 × 10−5 for femoral neck [FN], n = 18,898). This SNP was further found to be associated with the low risk of osteoporotic fracture (p = 0.009, OR = 0.7, 95% CI 0.57–0.93, n = 1881). Region-wide and haplotype analysis showed that the strongest association evidence was from the linkage disequilibrium block 5, which included rs2273061 of the JAG1 gene (p = 8.52 × 10−9 for LS and 3.47 × 10−5 at FN). To assess the function of identified variants, an electrophoretic mobility shift assay demonstrated the binding of c-Myc to the “G” but not “A” allele of rs2273061. A mRNA expression study in both human bone-derived cells and peripheral blood mononuclear cells confirmed association of the high BMD-related allele G of rs2273061 with higher JAG1 expression. Our results identify the JAG1 gene as a candidate for BMD regulation in different ethnic groups, and it is a potential key factor for fracture pathogenesis.

Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields

Abstract: Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependences, which leads to a dramatic decrease in classification accuracies. In this paper, a new automatic framework for the classification of hyperspectral images is proposed. The new method is based on combining hidden Markov random field segmentation with support vector machine (SVM) classifier. In order to preserve edges in the final classification map, a gradient step is taken into account. Experiments confirm that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies compared to the standard SVM method and also outperforms other studied methods.