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.

Mga, a dual-specificity transcription factor that interacts with Max and contains a T-domain DNA-binding motif.

Abstract: The basic-helix-loop-helix-leucine zipper (bHLHZip) proteins Myc, Mad and Mnt are part of a transcription activation/repression system involved in the regulation of cell proliferation. The function of these proteins as transcription factors is mediated by heterodimerization with the small bHLHZip protein Max, which is required for their specific DNA binding to E-box sequences. We have identified a novel Max-interacting protein, Mga, which contains a Myc-like bHLHZip motif, but otherwise shows no relationship with Myc or other Max-interacting proteins. Like Myc, Mad and Mnt proteins, Mga requires heterodimerization with Max for binding to the preferred Myc-Max-binding site CACGTG. In addition to the bHLHZip domain, Mga contains a second DNA-binding domain: the T-box or T-domain. The T-domain is a highly conserved DNA-binding motif originally defined in Brachyury and characteristic of the Tbx family of transcription factors. Mga binds the preferred Brachyury-binding sequence and represses transcription of reporter genes containing promoter-proximal Brachyury-binding sites. Surprisingly, Mga is converted to a transcription activator of both Myc-Max and Brachyury site-containing reporters in a Max-dependent manner. Our results suggest that Mga functions as a dual-specificity transcription factor that regulates the expression of both Max-network and T-box family target genes.

The Heritability of Prostate Cancer in the Nordic Twin Study of Cancer

Abstract: Background: Prostate cancer is thought to be the most heritable cancer, although little is known about how this genetic contribution varies across age. Methods: To address this question, we undertook the world's largest prospective study in the Nordic Twin Study of Cancer cohort, including 18,680 monozygotic (MZ) and 30,054 dizygotic (DZ) same-sex male twin pairs. We incorporated time-to-event analyses to estimate the risk concordance and heritability while accounting for censoring and competing risks of death, essential sources of biases that have not been accounted for in previous twin studies modeling cancer risk and liability. Results: The cumulative risk of prostate cancer was similar to that of the background population. The cumulative risk for twins whose co-twin was diagnosed with prostate cancer was greater for MZ than for DZ twins across all ages. Among concordantly affected pairs, the time between diagnoses was significantly shorter for MZ than DZ pairs (median, 3.8 versus 6.5 years, respectively). Genetic differences contributed substantially to variation in both the risk and the liability [heritability = 58% (95% confidence interval, 52%–63%)] of developing prostate cancer. The relative contribution of genetic factors was constant across age through late life with substantial genetic heterogeneity even when diagnosis and screening procedures vary. Conclusions: Results from the population-based twin cohort indicate a greater genetic contribution to the risk of developing prostate cancer when addressing sources of bias. The role of genetic factors is consistently high across age. Impact: Findings affect the search for genetic and epigenetic markers and frame prevention efforts. Cancer Epidemiol Biomarkers Prev; 23(11); 2303–10. ©2014 AACR . This article is featured in Highlights of This Issue, [p. 2199][1] [1]: /lookup/volpage/23/2199?iss=11

Dynamic melting of the Iceland plume

Abstract: Icelandic high-magnesia basalts show striking correlations between major element abundances and incompatible element and radiogenic isotope ratios. The most MgO-rich lavas have the most depleted incompatible element ratios and among the least radiogenic lead isotopes recorded in Atlantic mid-ocean-ridge basalts, highlighting a decoupling of the major and trace element characteristics expected of plume melts. This paradox can be explained by the process that mixes melts segregated from different depths of the melting column. The resulting model provides insight into the processes governing melt compositions at spreading ridges.

On chromatic sums and distributed resource allocation

Abstract: This paper studies an optimization problem that arises in the context of distributed resource allocation: Given a conflict graph that represents the competition of processors over resources, we seek an allocation under which no two jobs with conflicting requirements are executed simultaneously. Our objective is to minimize theaverage response timeof the system. In alternative formulation this is known as theMinimum Color Sum (MCS)problem (E. Kubicka and A. J. Schwenk, 1989. An introduction to chromatic sums,in“Proceedings of the ACM Computer Science Conference,” pp. 39–45.). We show that the algorithm based on finding iteratively a maximum independent set (MaxIS) is a 4-approximation to the MCS. This bound is tight to within a factor of 2. We give improved ratios for the classes of bipartite, bounded-degree, and line graphs. The bound generalizes to a 4ρ-approximation of MCS for classes of graphs for which the maximum independent set problem can be approximated within a factor ofρ. On the other hand, we show that ann1−e-approximation is NP-hard, for somee>0. For some instances of the resource allocation problem, such as theDining Philosophers, an efficient solution requiresedgecoloring of the conflict graph. We introduce theMinimum Edge Color Sum (MECS)problem which is shown to be NP-hard. We show that a 2-approximation to MECS(G) can be obtained distributively usingcompactcoloring withinO(log2 n) communication rounds

Modular, efficient and constant-memory single-cell RNA-seq preprocessing.

Abstract: We describe a workflow for preprocessing of single-cell RNA-sequencing data that balances efficiency and accuracy Our workflow is based on the kallisto and bustools programs, and is near optimal in speed with a constant memory requirement providing scalability for arbitrarily large datasets The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses