99 research outputs found

    Mental health research priorities for Europe

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    Mental and brain disorders represent the greatest health burden to Europe—not only for directly affected individuals, but also for their caregivers and the wider society. They incur substantial economic costs through direct (and indirect) health-care and welfare spending, and via productivity losses, all of which substantially affect European development. Funding for research to mitigate these effects lags far behind the cost of mental and brain disorders to society. Here, we describe a comprehensive, coordinated mental health research agenda for Europe and worldwide. This agenda was based on systematic reviews of published work and consensus decision making by multidisciplinary scientific experts and affected stakeholders (more than 1000 in total): individuals with mental health problems and their families, health-care workers, policy makers, and funders. We generated six priorities that will, over the next 5–10 years, help to close the biggest gaps in mental health research in Europe, and in turn overcome the substantial challenges caused by mental disorders

    On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

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    A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

    Overview of the JET ITER-like wall divertor

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    Power exhaust by SOL and pedestal radiation at ASDEX Upgrade and JET

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    Multi-machine scaling of the main SOL parallel heat flux width in tokamak limiter plasmas

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    ELM divertor peak energy fluence scaling to ITER with data from JET, MAST and ASDEX upgrade

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    Assessment of erosion, deposition and fuel retention in the JET-ILW divertor from ion beam analysis data

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    Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

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    The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

    Progress in understanding disruptions triggered by massive gas injection via 3D non-linear MHD modelling with JOREK

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    3D non-linear MHD simulations of a D 2 massive gas injection (MGI) triggered disruption in JET with the JOREK code provide results which are qualitatively consistent with experimental observations and shed light on the physics at play. In particular, it is observed that the gas destabilizes a large m/n = 2/1 tearing mode, with the island O-point coinciding with the gas deposition region, by enhancing the plasma resistivity via cooling. When the 2/1 island gets so large that its inner side reaches the q = 3/2 surface, a 3/2 tearing mode grows. Simulations suggest that this is due to a steepening of the current profile right inside q = 3/2. Magnetic field stochastization over a large fraction of the minor radius as well as the growth of higher n modes ensue rapidly, leading to the thermal quench (TQ). The role of the 1/1 internal kink mode is discussed. An I p spike at the TQ is obtained in the simulations but with a smaller amplitude than in the experiment. Possible reasons are discussed
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