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.

A community-driven global reconstruction of human metabolism

Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

The second catalog of active galactic nuclei detected by the Fermi Large Area Telescope

Abstract: The second catalog of active galactic nuclei (AGNs) detected by the Fermi Large Area Telescope (LAT) in two years of scientific operation is presented. The second LAT AGN catalog (2LAC) includes 1017 γ-ray sources located at high Galactic latitudes (|b| > 10°) that are detected with a test statistic (TS) greater than 25 and associated statistically with AGNs. However, some of these are affected by analysis issues and some are associated with multiple AGNs. Consequently, we define a Clean Sample which includes 886 AGNs, comprising 395 BL Lacertae objects (BL Lac objects), 310 flat-spectrum radio quasars (FSRQs), 157 candidate blazars of unknown type (i.e., with broadband blazar characteristics but with no optical spectral measurement yet), 8 misaligned AGNs, 4 narrow-line Seyfert 1 (NLS1s), 10 AGNs of other types, and 2 starburst galaxies. Where possible, the blazars have been further classified based on their spectral energy distributions (SEDs) as archival radio, optical, and X-ray data permit. While almost all FSRQs have a synchrotron-peak frequency 1015 Hz. The 2LAC represents a significant improvement relative to the first LAT AGN catalog (1LAC), with 52% more associated sources. The full characterization of the newly detected sources will require more broadband data. Various properties, such as γ-ray fluxes and photon power-law spectral indices, redshifts, γ-ray luminosities, variability, and archival radio luminosities and their correlations are presented and discussed for the different blazar classes. The general trends observed in 1LAC are confirmed.

Lunar impact crater identification and age estimation with Chang’E data by deep and transfer learning

Abstract: Impact craters, which can be considered the lunar equivalent of fossils, are the most dominant lunar surface features and record the history of the Solar System. We address the problem of automatic crater detection and age estimation. From initially small numbers of recognized craters and dated craters, i.e., 7895 and 1411, respectively, we progressively identify new craters and estimate their ages with Chang’E data and stratigraphic information by transfer learning using deep neural networks. This results in the identification of 109,956 new craters, which is more than a dozen times greater than the initial number of recognized craters. The formation systems of 18,996 newly detected craters larger than 8 km are estimated. Here, a new lunar crater database for the mid- and low-latitude regions of the Moon is derived and distributed to the planetary community together with the related data analysis. Using Chang’E data, the authors here identify more than 109,000 previously unrecognized lunar craters and date almost 19,000 craters based on transfer learning with deep neural networks. A new lunar crater database is derived and distributed to the planetary community.

A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches

Abstract: Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajokull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events.