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BackgroundFamily carers of youth recovering from early psychosis experience significant stress; however, access to effective family interventions is poor. Digital interventions provide a promising solution. ObjectiveOur objective was to evaluate across multiple Australian early psychosis services the effectiveness of a novel, web-based early psychosis intervention for carers. MethodsIn this cluster randomized controlled trial conducted across multiple Australian early psychosis services, our digital moderated online social therapy for carers (Altitudes) plus enhanced family treatment as usual (TAU) was compared with TAU alone on the primary outcome of perceived stress and secondary outcomes including mental health symptoms and family variables at the 6-month follow-up. ResultsEighty-six caregivers were randomized and data were available for 74 young people in their care. Our primary hypothesis that carers randomized to Altitudes+TAU would report greater improvements in perceived stress at follow-up compared with carers randomized to TAU alone was not supported, with the TAU alone group showing more improvement. For secondary outcomes, the TAU alone group showed improved mindfulness over time. Regardless of group assignment, we observed improvements in satisfaction with life, quality of life, emotional overinvolvement, and burden of care. In contrast, hair cortisol concentration increased. Post hoc analyses revealed more contact with early psychosis services in the intervention group compared to TAU alone and that improvements in perceived stress and social support were associated with use of the intervention in the Altitudes+TAU group. In this study, 80% (12/15) reported a positive experience with Altitudes and 93% (14/15) would recommend it to others. ConclusionsOur trial did not show a treatment effect for Altitudes in perceived stress. However, our post hoc analysis indicated that the amount of use of Altitudes related to improvements in stress and social support. Additional design work is indicated to continue users’ engagement and to significantly improve outcomes in problem-solving, communication, and self-care. Trial RegistrationAustralian and New Zealand Clinical Trials Registry ACTRN12617000942358; https://trialsearch.who.int/Trial2.aspx?TrialID=ACTRN12617000942358

Facing your fears, or exposure therapy, is an effective psychological intervention for anxiety disorders that is often thought to work through fear extinction learning. Fear extinction learning is a type of associative learning where fear reduces through repeated encounters with a feared situation or stimulus in the absence of aversive outcomes. Laboratory research suggests fear extinction learning is driven by threat prediction errors, defined as when fearful predictions do not eventuate. Threat prediction error and its relationship to exposure therapy outcomes haven’t been studied enough in actual therapy settings. It remains unclear whether prediction error and extinction learning are central mechanisms of exposure therapy. We are conducting a longitudinal and observational study of how threat prediction error during exposure in social anxiety disorder (SAD) treatment relates to session-by-session symptom change and treatment outcome in addition to exposure surprise and learning outcome. We aim to recruit 65 adults with a primary diagnosis of SAD through an outpatient psychology clinic. Participants will receive 12 sessions of individual manualized cognitive behavioral therapy (CBT), adapted from an efficacious group protocol, that includes graded exposure. Exposure processes, including self-report measures of anxiety, threat prediction, threat outcomes, surprise, and learning outcome, will be measured with smartphone-based event-contingent ecological momentary assessments (EMAs) of all behavioral experiments completed during treatment. Clinical outcomes include self-reported social anxiety symptoms and social threat appraisals, at each session, post and 3-months after treatment. Prediction error will be operationalized as the mismatch between the threat prediction and threat outcome. The joint effect of threat prediction and threat outcome on session-by-session symptom change, treatment outcome, exposure surprise, and learning outcome will be explored using multilevel modeling. The present study will help determine whether threat prediction error during exposures in SAD treatment is related to theoretically implied clinical outcomes. This would contribute to the larger research aim of clarifying exposure therapy mechanisms.

Technology plays an increasingly prominent role in emotional lives. Researchers have begun to study how people use devices to cope with and shape emotions: a phenomenon that has been called Digital Emotion Regulation. We report a study of the impact of the COVID-19 pandemic upon young people's digital habits and emotion regulation behaviors. We conducted a two-wave longitudinal survey, collecting data from 154 university students both before and during the COVID-19 pandemic. During the pandemic, participants were subject to increased emotional distress as well as restrictions on movement and social interaction. We present evidence that participants' emotion regulation strategies changed and became more homogeneous during the pandemic, with participants resorting to digital tools when offline strategies were less available, while also becoming more emotionally dependent upon their devices. This study underscores the growing significance of the digital for contemporary emotional experience, and contributes to understanding the potential role for technology in supporting well-being during high-impact events.

Modelling the inelastic scattering of electrons in water is fundamental, given their crucial role in biological damage. In Monte Carlo track-structure (MC-TS) codes used to assess biological damage, the energy loss function (ELF), from which cross sections are extracted, is derived from different semi-empirical optical models. Only recently have first ab initio results for the ELF and cross sections in water become available. For benchmarking purpose, in this work, we present ab initio linear-response time-dependent density functional theory calculations of the ELF of liquid water. We calculated the inelastic scattering cross sections, inelastic mean free paths, and electronic stopping power and compared our results with recent calculations and experimental data showing a good agreement. In addition, we provide an in-depth analysis of the contributions of different molecular orbitals, species and orbital angular momenta to the total ELF. Moreover, we present single-differential cross sections computed for each molecular orbital channel, which should prove useful for MC-TS simulations.

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors (including so-called “Minnesota priors”) while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.

A prerequisite to characterize magnetic materials is the capability to describe systems containing unpaired electrons. In this study, we benchmark the one-shot GW ( G _0 W _0 ) on top of different unrestricted mean-field solutions for open-shell molecules using Dunning’s correlation-consistent basis sets expanded in terms of Gaussian functions. We find that the G _0 W _0 correction to hybrid functionals provides reasonably accurate results for the ionization energies of open-shell systems when compared to those obtained from high-level ab initio methods. Moreover, the quality of the G _0 W _0 exchange–correlation approximation is evaluated by the discrepancy between the ionization energy of the neutral molecules and the electron affinity of the corresponding cations. Furthermore, we assess the capability of the GW to reproduce the correct energy ordering of molecular spin–orbitals. To such an aim, we thoroughly discuss three open-shell molecules CN, NH _2 , and O _2 , for which approximate functionals fail to correctly capture the single-electron spectrum. Particularly, we demonstrate that the overestimation of the exchange energy in the studied spin–orbitals is reduced by the GW dynamic correlation term, restoring the molecular orbital ordering. Interestingly, we find that deviations of the exchange and correlation energies, in comparison with our ab initio reference, can be very different for molecular orbitals with different symmetry, e.g. σ and π -type orbitals.

Ionization potentials (IPs) and electron affinities (EAs) are important quantities input into most models for calculating the open-circuit voltage (Voc) of organic solar cells. We assess the semi-empirical density-functional tight-binding (DFTB) method with the third-order self-consistent charge (SCC) correction and the 3ob parameter set (the third-order DFTB (DFTB3) organic and biochemistry parameter set) against experiments (for smaller molecules) and against first-principles GW (Green’s function, G, times the screened potential, W) calculations (for larger molecules of interest in organic electronics) for the calculation of IPs and EAs. Since GW calculations are relatively new for molecules of this size, we have also taken care to validate these calculations against experiments. As expected, DFTB is found to behave very much like density-functional theory (DFT), but with some loss of accuracy in predicting IPs and EAs. For small molecules, the best results were found with ΔSCF (Δ self-consistent field) SCC-DFTB calculations for first IPs (good to ± 0.649 eV). When considering several IPs of the same molecule, it is convenient to use the negative of the orbital energies (which we refer to as Koopmans’ theorem (KT) IPs) as an indication of trends. Linear regression analysis shows that KT SCC-DFTB IPs are nearly as accurate as ΔSCF SCC-DFTB eigenvalues (± 0.852 eV for first IPs, but ± 0.706 eV for all of the IPs considered here) for small molecules. For larger molecules, SCC-DFTB was also the ideal choice with IP/EA errors of ± 0.489/0.740 eV from ΔSCF calculations and of ± 0.326/0.458 eV from (KT) orbital energies. Interestingly, the linear least squares fit for the KT IPs of the larger molecules also proves to have good predictive value for the lower energy KT IPs of smaller molecules, with significant deviations appearing only for IPs of 15–20 eV or larger. We believe that this quantitative analysis of errors in SCC-DFTB IPs and EAs may be of interest to other researchers interested in DFTB investigation of large and complex problems, such as those encountered in organic electronics.

The aim of the study was to investigate trajectories of social functioning in young people with first-episode psychosis (FEP) with and without cannabis misuse using a secondary analysis of data from the Episode-II trial. Forty-two young people with FEP and comorbid cannabis use disorder were compared with 39 young people with FEP but without a cannabis use disorder. Social functioning was assessed every 6 months during a 30-month follow-up. Multilevel linear growth curve modeling was used to compare the social functioning trajectories over time for those with and without cannabis misuse. Cannabis misuse was not associated with social functioning at baseline assessment. Over a 30-month follow-up, FEP patients without cannabis disorder showed significant improvements in their social functioning, whereas patients with cannabis misuse at baseline displayed no such improvement. Patients with and without cannabis misuse differed significantly in their levels of social functioning after 24 months. Similar results were obtained after adjusting for potential confounders (i.e., age, gender, negative symptoms, premorbid functioning, DSM-IV diagnoses, baseline social functioning and other substance use). In the context of a specialized early intervention service, patients with cannabis misuse at baseline did not attain the improvements in social outcomes observed in their counterparts without cannabis misuse. There is a need to develop effective interventions to reduce cannabis misuse to ultimately improve social outcomes in young people with psychosis.

Previous research has shown that being affectively unstable is an indicator of several forms of psychological maladjustment. However, little is known about the mechanisms underlying affective instability. Our research aims to examine the possibility that being prone to extreme fluctuations in one's feelings is related to maladaptive emotion regulation. We investigated this hypothesis by relating affective instability, assessed in daily life using the experience sampling method, to self-reported emotion regulation strategies and to parasympathetically mediated heart rate variability (HRV), a physiological indicator of emotion regulation capacity. Results showed that HRV was negatively related to instability of positive affect (as measured by mean square successive differences), indicating that individuals with lower parasympathetic tone are emotionally less stable, particularly for positive affect.

Much emphasis has been given to algal biomass growth in dairy farm wastewater. Most of the systems examined require productive land to be converted and/or freshwater use to dilute high concentrations of nutrients found in dairy effluent. A rotating algal biofilm (RABR) provides the capacity to grow algae without sacrificing productive land or freshwater. In theory, this system would overcome some of the economic and environmental challenges that other systems have. A combination of theoretical information, nutrient uptake formulas, and economic formulas were used to calculate the potential of biogas production from algae grown in an RABR with dairy effluents. The average nutrient uptake was 0.8 mgN/m2 per day and 0.1 mgP/m2 per day. The maximum methane production from the anaerobic digestion of algae was 112 m3/RABR·year. The minimum and maximum economic scenarios resulted in gross profits of NZD −2101 and −1922. After evaluating this system for the first time in the New Zealand dairy farming context, it was found that biogas production from an RABR is not a feasible option for New Zealand dairy farmers.

The anaerobic digestion of New Zealand low-input dairy farms has previously been evaluated for energy production, but farming systems have recently become more intensive with increased feed supplementation and feed replacement; therefore, we are studying how these changes affect the overall energy production for water heating. A combination of literature review, surveys, chemical analyses, biomethane potential analysis, and modeling were used for this study. On a case study farm with a solid separator, it was found that 558 MJ/day and 176–861 MJ/day could be produced with the solid and liquid portions of effluent, respectively. There is enough biogas to satisfy the dairy farm’s water heating requirements with a tankless water heater.