Influence of High Magnetic Field on Access to Stationary H-modes and Pedestal Characteristics in Alcator C-Mod

Recent Alcator C-Mod experiments have explored access to and characteristics of H-modes at magnetic fields approaching 8 T, the highest field achieved to date in a diverted tokamak. The H-modes originated from L-mode densities ranging from $1.1 \times 10^{20} m^{-3}$ to $2.8 \times 10^{20} m^{-3}$, allowing insight into the density dependence of the H-mode power threshold at high magnetic field. This dependence is compared to predictions from the ITPA scaling law (\cite{martin2008power}), finding that the law is approximately accurate at 7.8 T. However, the law underpredicted the high density H-mode threshold at lower magnetic field in previous C-Mod experiments (\cite{ma2012scaling}), \hl{suggesting that the overall dependence of the threshold on magnetic field is weaker than predicted by the scaling law.} The threshold data at 7.8 T also indicates that the onset of a low density branch at this magnetic field on C-Mod occurs below $1.4 \times 10^{20} m^{-3}$, which is lower than predicted by an existing model for low density branch onset. The H-modes achieved steady-state densities ranging from $2.3 \times 10^{20} m^{-3}$ to $4.4 \times 10^{20} m^{-3}$, and higher transient densities, and had values of $q_{95}$ from 3.3 to 6.0. This parameter range allowed the achievement of all three types of H-mode routinely observed at lower magnetic field on C-Mod: the stationary, ELM-suppressed enhanced D-alpha (EDA) regime, seen at high densities and high values of $q_{95}$; the nonstationary ELM-free regime, seen at lower densities and values of $q_{95}$; and the ELMy regime, seen at low density, moderate $q_{95}$, and specialized plasma shape. The parameter space in which these regimes occur at 7.8 T is consistent with lower magnetic field experience. Pressure pedestal height at 7.8 T is compared to EPED \cite{snyder2009development, snyder2011first} predictions, and a scaling law for EDA density pedestal height developed between 4.5 and 6.0 T is updated to include fields from 2.7 T to 7.8 T. Overall, this analysis increases confidence in the use of low magnetic field experience to predict some elements of high magnetic field tokamak behavior

Enhancing discovery of genetic variants for posttraumatic stress disorder through integration of quantitative phenotypes and trauma exposure information

BACKGROUND: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). METHODS: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. RESULTS: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. CONCLUSIONS: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods.</p