Associations Between Adolescent Cannabis Use Trajectories and Anxiety

Understanding the effects of cannabis use is critical for reducing adverse behavioral, social, and academic outcomes, particularly among adolescent users who are most at risk for cannabis related problems. Although support from both the animal and human literatures suggests the relationship between cannabis and anxiety may be associated with levels of use, much is still unknown. Thus, examining relationships between the most common mental health issue in adolescence and one of the most commonly used drugs is of great public health significance and impact. Prior longitudinal studies assessing effects of cannabis use on anxiety have not evaluated different patterns of use, limiting our ability to identify who may be most at risk for poorer anxiety outcomes. The need to assess the impact of cannabis trajectories on prospective anxiety will allow us to answer the question of specificity, and the role different cannabis patterns have on changes in anxiety symptoms and disorders. The current study leveraged data from a large NIDA-funded study (R01 DA031176) to examine associations between adolescent cannabis use patterns and changes in anxiety symptomology, disorder development, and the interactive effects on decision-making, with data collected from 401 teens across two years. We employed advanced latent growth curve modeling techniques within an accelerated cohort framework, to allow for additional examination of the effects patterns of cannabis use have on anxiety during mid to late adolescence. Three cannabis use trajectories emerged, including a minimal, escalating, and chronic use trajectory. Overall, findings suggest cannabis use during adolescence is associated with changes in anxiety over time. Specifically, state anxiety symptoms increased relative to minimal users at the two-year follow up. Surprisingly, less cannabis use was associated with greater likelihood of anxiety disorder development and several hypotheses are provided as to why this may occur. Further, cannabis trajectory did not influence risky decision-making independently, but rather interacted with anxiety to influence risk-taking among minimal users. Overall, the results of the present study were the first to identify how trajectories of cannabis use influence prospective anxiety in a sample of adolescents across a two-year time span. Our findings contribute to prevention and intervention efforts by identifying adolescent users who are most at risk for developing anxiety symptoms and making poor decisions. Future interventions that target reducing anxiety and cannabis cessation concurrently will further improve cognitive functioning among heavy users

Decision-Making as a Latent Construct and its Measurement Invariance in a Large Sample of Adolescent Cannabis Users

OBJECTIVE: Relative to the vast literature that employs measures of decision-making (DM), rigorous examination of their psychometric properties is sparse. This study aimed to determine whether three measures of DM assess the same construct, and to measure invariance of this construct across relevant covariates. METHOD: Participants were 372 adolescents at risk of escalation in cannabis use. DM was assessed via four indices from the Cups Task, Game of Dice Task (GDT), and Iowa Gambling Task (IGT). We used confirmatory factor analysis to assess unidimensionality of the DM construct, and moderated nonlinear factor analysis (MNLFA) to examine its measurement invariance. RESULTS: The unidimensional model of DM demonstrated good fit. MNLFA results revealed that sex influenced mean DM scores, such that boys had lower risk-taking behaviors. There was evidence of differential item functioning (DIF), such that IQ and age moderated the IGT intercept and GDT factor loading, respectively. Significant effects were retained in the final model, which produced participant-specific DM factor scores. These scores showed moderate stability over time. CONCLUSIONS: Indices from three DM tasks loaded significantly onto a single factor, suggesting that these DM tasks assess a single underlying construct. We suggest that this construct represents the ability to make optimal choices that maximize rewards in the presence of risk. Our final DM factor accounts for DIF caused by covariates, making it comparable across adolescents with different characteristics. (JINS, 2019, 25, 661-667)

A quantum informational approach for dissecting chemical reactions

The focal-plane detector system for the KArlsruhe TRItium Neutrino (KATRIN) experiment consists of a multi-pixel silicon p-i-n-diode array, custom readout electronics, two superconducting solenoid magnets, an ultra high-vacuum system, a high-vacuum system, calibration and monitoring devices, a scintillating veto, and a custom data-acquisition system. It is designed to detect the low-energy electrons selected by the KATRIN main spectrometer. We describe the system and summarize its performance after its final installation

Dissecting the bond-formation process of d(10)-metal-ethene complexes with multireference approaches

The bonding mechanism of ethene to a nickel or palladium center is studied by the density matrix renormalization group algorithm, the complete active space self consistent field method, coupled cluster theory, and density functional theory. Specifically, we focus on the interaction between the metal atom and bis-ethene ligands in perpendicular and parallel orientations. The bonding situation in these structural isomers is further scrutinized using energy decomposition analysis and quantum information theory. Our study highlights the fact that when two ethene ligands are oriented perpendicular to each other, the complex is stabilized by the metal-to-ligand double-back-bonding mechanism. Moreover, we demonstrate that nickel-ethene complexes feature a stronger and more covalent interaction between the ligands and the metal center than palladium-ethene compounds with similar coordination spheres

New Strategies in Modeling Electronic Structures and Properties with Applications to Actinides

This chapter discusses contemporary quantum chemical methods and provides general insights into modern electronic structure theory with a focus on heavy-element-containing compounds. We first give a short overview of relativistic Hamiltonians that are frequently applied to account for relativistic effects. Then, we scrutinize various quantum chemistry methods that approximate the $N$-electron wave function. In this respect, we will review the most popular single- and multi-reference approaches that have been developed to model the multi-reference nature of heavy element compounds and their ground- and excited-state electronic structures. Specifically, we introduce various flavors of post-Hartree--Fock methods and optimization schemes like the complete active space self-consistent field method, the configuration interaction approach, the Fock-space coupled cluster model, the pair-coupled cluster doubles ansatz, also known as the antisymmetric product of 1 reference orbital geminal, and the density matrix renormalization group algorithm. Furthermore, we will illustrate how concepts of quantum information theory provide us with a qualitative understanding of complex electronic structures using the picture of interacting orbitals. While modern quantum chemistry facilitates a quantitative description of atoms and molecules as well as their properties, concepts of quantum information theory offer new strategies for a qualitative interpretation that can shed new light onto the chemistry of complex molecular compounds.Comment: 43 pages, 3 figures, Version of Recor