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Aims. The needs of young people attending mental healthcare can be complex and often span multiple domains (e.g., social, emotional and physical health factors). These factors often complicate treatment approaches and contribute to poorer outcomes in youth mental health. We aimed to identify how these factors interact over time by modelling the temporal dependencies between these transdiagnostic social, emotional and physical health factors among young people presenting for youth mental healthcare. Methods. Dynamic Bayesian networks were used to examine the relationship between mental health factors across multiple domains (social and occupational function, self-harm and suicidality, alcohol and substance use, physical health and psychiatric syndromes) in a longitudinal cohort of 2663 young people accessing youth mental health services. Two networks were developed: (1) 'initial network', that shows the conditional dependencies between factors at first presentation, and a (2) 'transition network', how factors are dependent longitudinally. Results. The 'initial network' identified that childhood disorders tend to precede adolescent depression which itself was associated with three distinct pathways or illness trajectories; (1) anxiety disorder; (2) bipolar disorder, manic-like experiences, circadian disturbances and psychosis-like experiences; (3) self-harm and suicidality to alcohol and substance use or functioning. The 'transition network' identified that over time social and occupational function had the largest effect on self-harm and suicidality, with direct effects on ideation (relative risk [RR], 1.79; CI, 1.59-1.99) and self-harm (RR, 1.32; CI, 1.22-1.41), and an indirect effect on attempts (RR, 2.10; CI, 1.69-2.50). Suicide ideation had a direct effect on future suicide attempts (RR, 4.37; CI, 3.28-5.43) and self-harm (RR, 2.78; CI, 2.55-3.01). Alcohol and substance use, physical health and psychiatric syndromes (e.g., depression and anxiety, at-risk mental states) were independent domains whereby all direct effects remained within each domain over time. Conclusions. This study identified probable temporal dependencies between domains, which has causal interpretations, and therefore can provide insight into their differential role over the course of illness. This work identified social, emotional and physical health factors that may be important early intervention and prevention targets. Improving social and occupational function may be a critical target due to its impacts longitudinally on self-harm and suicidality. The conditional independence of alcohol and substance use supports the need for specific interventions to target these comorbidities. [ABSTRACT FROM AUTHOR]

We investigate the mean locally measured velocity dispersions of ionized gas (σgas) and stars (σ*) for 1090 galaxies with stellar masses |$\log \, (M_{\!\ast }/M_{\odot }) \ge 9.5$| from the SAMI Galaxy Survey. For star-forming galaxies, σ* tends to be larger than σgas, suggesting that stars are in general dynamically hotter than the ionized gas (asymmetric drift). The difference between σgas and σ* (Δσ) correlates with various galaxy properties. We establish that the strongest correlation of Δσ is with beam smearing, which inflates σgas more than σ*, introducing a dependence of Δσ on both the effective radius relative to the point spread function and velocity gradients. The second strongest correlation is with the contribution of active galactic nuclei (AGN) (or evolved stars) to the ionized gas emission, implying that the gas velocity dispersion is strongly affected by the power source. In contrast, using the velocity dispersion measured from integrated spectra (σap) results in less correlation between the aperture-based Δσ (Δσap) and the power source. This suggests that the AGN (or old stars) dynamically heat the gas without causing significant deviations from dynamical equilibrium. Although the variation of Δσap is much smaller than that of Δσ, a correlation between Δσap and gas velocity gradient is still detected, implying that there is a small bias in dynamical masses derived from stellar and ionized gas velocity dispersions. [ABSTRACT FROM AUTHOR]