Change in fatty liver status and 5-year risk of incident metabolic syndrome: a retrospective cohort study

INTRODUCTION: Fatty liver is associated with metabolic syndrome (MetS) but it may also occur without MetS. Whether resolution of fatty liver in the general population affects risk of MetS is unknown. Our aim was to determine whether a change in fatty liver status (either the development of new fatty liver or the resolution of existing fatty liver) would modify the risk of de novo MetS.METHODS:Two thousand eighty-nine people without hypertension, diabetes, and MetS were examined at baseline and at 5-year follow-up using a retrospective cohort study design. Fatty liver status was assessed at baseline and at follow-up by ultrasonography. Adjusted hazard ratios (aHR) and 95 % confidence intervals (CIs) for de novo MetS at follow-up were calculated controlling for the potential confounders, compared to the reference group (people who never had fatty liver at baseline and follow-up).RESULTS:During follow-up, fatty liver developed in 251 people and fatty liver resolved in 112 people. After the adjustment for multiple confounders, persisting fatty liver and incident fatty liver development were associated with de novo MetS, with aHR of 2.60 (95 % CIs [1.61,4.20]) and 3.31 (95 % CIs [1.99,5.51]), respectively. Risk of new MetS in resolved fatty liver group was attenuated with insignificant aHR of 1.29 accompanying 95 % CIs of 0.60 and 2.80.DISCUSSION:Development or maintenance of fatty liver is positively associated with occurrence of new MetS. Resolution of fatty liver status has similar risk of de novo MetS with those who never had fatty liver. Therefore, cautious management is needed with those with fatty liver

Radial Glial Dependent and Independent Dynamics of Interneuronal Migration in the Developing Cerebral Cortex

Interneurons originating from the ganglionic eminence migrate tangentially into the developing cerebral wall as they navigate to their distinct positions in the cerebral cortex. Compromised connectivity and differentiation of interneurons are thought to be an underlying cause in the emergence of neurodevelopmental disorders such as schizophrenia. Previously, it was suggested that tangential migration of interneurons occurs in a radial glia independent manner. Here, using simultaneous imaging of genetically defined populations of interneurons and radial glia, we demonstrate that dynamic interactions with radial glia can potentially influence the trajectory of interneuronal migration and thus the positioning of interneurons in cerebral cortex. Furthermore, there is extensive local interneuronal migration in tangential direction opposite to that of pallial orientation (i.e., in a medial to lateral direction from cortex to ganglionic eminence) all across the cerebral wall. This counter migration of interneurons may be essential to locally position interneurons once they invade the developing cerebral wall from the ganglionic eminence. Together, these observations suggest that interactions with radial glial scaffold and localized migration within the expanding cerebral wall may play essential roles in the guidance and placement of interneurons in the developing cerebral cortex

Unconventional MBE Strategies from Computer Simulations for Optimized Growth Conditions

We investigate the influence of step edge diffusion (SED) and desorption on Molecular Beam Epitaxy (MBE) using kinetic Monte-Carlo simulations of the solid-on-solid (SOS) model. Based on these investigations we propose two strategies to optimize MBE growth. The strategies are applicable in different growth regimes: During layer-by-layer growth one can exploit the presence of desorption in order to achieve smooth surfaces. By additional short high flux pulses of particles one can increase the growth rate and assist layer-by-layer growth. If, however, mounds are formed (non-layer-by-layer growth) the SED can be used to control size and shape of the three-dimensional structures. By controlled reduction of the flux with time we achieve a fast coarsening together with smooth step edges.Comment: 19 pages, 7 figures, submitted to Phys. Rev.

Performance of the CREAM calorimeter in accelerator beam test

The CREAM calorimeter, designed to measure the spectra of cosmic-ray nuclei from under 1 TeV to 1000 TeV, is a 20 radiation length (X0) deep sampling calorimeter. The calorimeter is comprised of 20 layers of tungsten interleaved with 20 layers of scintillating fiber ribbons, and is preceded by a pair of graphite interaction targets providing about 0.42 proton interaction lengths (\lambda int). The calorimeter was placed in one of CERN's SPS accelerator beams for calibration and testing. Beams of 150 GeV electrons were used for calibration, and a variety of electron, proton, and nuclear fragment beams were used to test the simulation model of the detector. In this paper we discuss the performance of the calorimeter in the electron beam and compare electron beam data with simulation results.The CREAM calorimeter, designed to measure the spectra of cosmic-ray nuclei from under 1 TeV to 1000 TeV, is a 20 radiation length (X0) deep sampling calorimeter. The calorimeter is comprised of 20 layers of tungsten interleaved with 20 layers of scintillating fiber ribbons, and is preceded by a pair of graphite interaction targets providing about 0.42 proton interaction lengths (\lambda int). The calorimeter was placed in one of CERN's SPS accelerator beams for calibration and testing. Beams of 150 GeV electrons were used for calibration, and a variety of electron, proton, and nuclear fragment beams were used to test the simulation model of the detector. In this paper we discuss the performance of the calorimeter in the electron beam and compare electron beam data with simulation results

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results

Jet size dependence of single jet suppression in lead-lead collisions at sqrt(s(NN)) = 2.76 TeV with the ATLAS detector at the LHC

Measurements of inclusive jet suppression in heavy ion collisions at the LHC provide direct sensitivity to the physics of jet quenching. In a sample of lead-lead collisions at sqrt(s) = 2.76 TeV corresponding to an integrated luminosity of approximately 7 inverse microbarns, ATLAS has measured jets with a calorimeter over the pseudorapidity interval |eta| < 2.1 and over the transverse momentum range 38 < pT < 210 GeV. Jets were reconstructed using the anti-kt algorithm with values for the distance parameter that determines the nominal jet radius of R = 0.2, 0.3, 0.4 and 0.5. The centrality dependence of the jet yield is characterized by the jet "central-to-peripheral ratio," Rcp. Jet production is found to be suppressed by approximately a factor of two in the 10% most central collisions relative to peripheral collisions. Rcp varies smoothly with centrality as characterized by the number of participating nucleons. The observed suppression is only weakly dependent on jet radius and transverse momentum. These results provide the first direct measurement of inclusive jet suppression in heavy ion collisions and complement previous measurements of dijet transverse energy imbalance at the LHC.Comment: 15 pages plus author list (30 pages total), 8 figures, 2 tables, submitted to Physics Letters B. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HION-2011-02

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants