40 research outputs found

    An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes

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    In this paper we compare the simulated Arctic Ocean in 15 global ocean–sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II). Most of these models are the ocean and sea-ice components of the coupled climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments. We mainly focus on the hydrography of the Arctic interior, the state of Atlantic Water layer and heat and volume transports at the gateways of the Davis Strait, the Bering Strait, the Fram Strait and the Barents Sea Opening. We found that there is a large spread in temperature in the Arctic Ocean between the models, and generally large differences compared to the observed temperature at intermediate depths. Warm bias models have a strong temperature anomaly of inflow of the Atlantic Water entering the Arctic Ocean through the Fram Strait. Another process that is not represented accurately in the CORE-II models is the formation of cold and dense water, originating on the eastern shelves. In the cold bias models, excessive cold water forms in the Barents Sea and spreads into the Arctic Ocean through the St. Anna Through. There is a large spread in the simulated mean heat and volume transports through the Fram Strait and the Barents Sea Opening. The models agree more on the decadal variability, to a large degree dictated by the common atmospheric forcing. We conclude that the CORE-II model study helps us to understand the crucial biases in the Arctic Ocean. The current coarse resolution state-of-the-art ocean models need to be improved in accurate representation of the Atlantic Water inflow into the Arctic and density currents coming from the shelves

    North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states

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    Simulation characteristics from eighteen global ocean–sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort

    Two high-risk susceptibility loci at 6p25.3 and 14q32.13 for Waldenström macroglobulinemia

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    Waldenström macroglobulinemia (WM)/lymphoplasmacytic lymphoma (LPL) is a rare, chronic B-cell lymphoma with high heritability. We conduct a two-stage genome-wide association study of WM/LPL in 530 unrelated cases and 4362 controls of European ancestry and identify two high-risk loci associated with WM/LPL at 6p25.3 (rs116446171, near EXOC2 and IRF4; OR = 21.14, 95% CI: 14.40–31.03, P = 1.36 × 10−54) and 14q32.13 (rs117410836, near TCL1; OR = 4.90, 95% CI: 3.45–6.96, P = 8.75 × 10−19). Both risk alleles are observed at a low frequency among controls (~2–3%) and occur in excess in affected cases within families. In silico data suggest that rs116446171 may have functional importance, and in functional studies, we demonstrate increased reporter transcription and proliferation in cells transduced with the 6p25.3 risk allele. Although further studies are needed to fully elucidate underlying biological mechanisms, together these loci explain 4% of the familial risk and provide insights into genetic susceptibility to this malignancy

    Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes

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    Lymphoma risk is elevated for relatives with common non-Hodgkin lymphoma (NHL) subtypes, suggesting shared genetic susceptibility across subtypes. To evaluate the extent of mutual heritability among NHL subtypes and discover novel loci shared among subtypes, we analyzed data from eight genome-wide association studies within the InterLymph Consortium, including 10,629 cases and 9505 controls. We utilized Association analysis based on SubSETs (ASSET) to discover loci for subsets of NHL subtypes and evaluated shared heritability across the genome using Genome-wide Complex Trait Analysis (GCTA) and polygenic risk scores. We discovered 17 genome-wide significant loci (P < 5 × 10−8) for subsets of NHL subtypes, including a novel locus at 10q23.33 (HHEX) (P = 3.27 × 10−9). Most subset associations were driven primarily by only one subtype. Genome-wide genetic correlations between pairs of subtypes varied broadly from 0.20 to 0.86, suggesting substantial heterogeneity in the extent of shared heritability among subtypes. Polygenic risk score analyses of established loci for different lymphoid malignancies identified strong associations with some NHL subtypes (P < 5 × 10−8), but weak or null associations with others. Although our analyses suggest partially shared heritability and biological pathways, they reveal substantial heterogeneity among NHL subtypes with each having its own distinct germline genetic architecture

    Exploiting Execution Locality with a Decoupled Kilo-Instruction Processor

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    Selected single-nucleotide polymorphisms in <em>FOXE1, SERPINA5, FTO, EVPL, TICAM1</em> and <em>SCARB1</em> are associated with papillary and follicular thyroid cancer risk: Replication study in a German population.

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    Several single-nucleotide polymorphisms (SNPs) have been associated with papillary and follicular thyroid cancer (PTC and FTC, respectively) risk, but few have replicated. After analyzing 17525 tag SNPs in 1129 candidate genes, we found associations with PTC risk in SERPINA5, FTO, HEMGN (near FOXE1) and other genes. Here, we report results from a replication effort in a large independent PTC/FTC case-control study conducted in Germany. We evaluated the best tagging SNPs from our previous PTC study and additionally included SNPs in or near FOXE1 and NKX2-1 genes, known susceptibility loci for thyroid cancer. We genotyped 422 PTC and 130 FTC cases and 752 controls recruited from three German clinical centers. We used polytomous logistic regression to simultaneously estimate PTC and FTC associations for 79 SNPs based on log-additive models. We assessed effect modification by body mass index (BMI), gender and age for all SNPs, and selected SNP by SNP interactions. We confirmed associations with PTC and SNPs in FOXE1/HEMGN, SERPINA5 (rs2069974), FTO (rs8047395), EVPL (rs2071194), TICAM1 (rs8120) and SCARB1 (rs11057820) genes. We found associations with SNPs in FOXE1, SERPINA5, FTO, TICAM1 and HSPA6 and FTC. We found two significant interactions between FTO (rs8047395) and BMI (P = 0.0321) and between TICAM1 (rs8120) and FOXE1 (rs10984377) (P = 0.0006). Besides the known associations with FOXE1 SNPs, we confirmed additional PTC SNP associations reported previously. We also found several new associations with FTC risk and noteworthy interactions. We conclude that multiple variants and host factors might interact in complex ways to increase risk of PTC and FTC
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