Hubble space telescope STIS spectroscopy of the peculiar nova-like variables BK Lyn, V751 Cygni, and V380 Oph

We obtained Hubble STIS spectra of three nova-like variables: V751 Cygni, V380 Oph, and—the only confirmed nova-like variable known to be below the period gap—BK Lyn. In all three systems, the spectra were taken during high optical brightness state, and a luminous accretion disk dominates their far-ultraviolet (FUV) light. We assessed a lower limit of the distances by applying the infrared photometric method of Knigge. Within the limitations imposed by the poorly known system parameters (such as the inclination, white dwarf mass, and the applicability of steady state accretion disks) we obtained satisfactory fits to BK Lyn using optically thick accretion disk models with an accretion rate of for a white dwarf mass of Mwd = 1.2M and for Mwd = 0.4M. However, for the VY Scl-type nova-like variable V751 Cygni and for the SW Sex star V380 Oph, we are unable to obtain satisfactory synthetic spectral fits to the high state FUV spectra using optically thick steady state accretion disk models. The lack of FUV spectra information down to the Lyman limit hinders the extraction of information about the accreting white dwarf during the high states of these nova-like systems

The evolution of dynamic and flexible courtship displays that reveal individual quality

Code and Data Availability: The data sets generated during the current study, as well as the simulation code used to generate it, are available in the following repository: DOI 10.5061/dryad.vx0k6djvtThis is the final version. Available on open access from Springer via the DOI in this recordSexual selection is a major force shaping morphological and behavioral diversity. Existing theory focuses on courtship display traits such as morphological ornaments whose costs and benefits are assumed be to fixed across individuals’ lifetimes. In contrast, empirically observed displays are often inherently dynamic, as vividly illustrated by the acrobatic dances, loud vocalizations, and vigorous motor displays involved in courtship behavior across a broad range of taxa. One empirically observed form of display flexibility occurs when signalers adjust their courtship investment based on the number of rival signalers. The predictions of established sexual selection theory cannot readily be extended to such displays because display expression varies between courtship events, such that any given display may not reliably reflect signaler quality. We thus lack an understanding of how dynamic displays coevolve with sexual preferences and how signalers should tactically adjust their display investment across multiple courtship opportunities. To address these questions, we extended an established model of the coevolution of a female sexual preference and a male display trait to allow for flexible, dynamic displays. We find that such a display can coevolve with a sexual preference away from their naturally selected optima, though display intensity is a weaker signal of male quality than for non-flexible displays. Furthermore, we find that males evolve to decrease their display investment when displaying alongside more rivals. This research represents a first step towards generalizing the findings of sexual selection theory to account for the ubiquitous dynamism of animal courtship

Healthy Campus Trial: A multiphase optimization strategy (MOST) fully factorial trial to optimize the smartphone cognitive behavioral therapy (CBT) app for mental health promotion among university students: Study protocol for a randomized controlled trial

This is the final version. Available from the publisher via the DOI in this record.After the publication of the primary findings, the de-identified and completely anonymized individual participant-level dataset will be posted on the UMIN-ICDR website (http://www.umin.ac.jp/icdr/index-j.html) so that it can be accessed by qualified researchers.Background: Youth in general and college life in particular are characterized by new educational, vocational, and interpersonal challenges, opportunities, and substantial stress. It is estimated that 30-50% of university students meet criteria for some mental disorder, especially depression, in any given year. The university has traditionally provided many channels to promote students' mental health, but until now only a minority have sought such help, possibly owing to lack of time and/or to stigma related to mental illness. Smartphone-delivered cognitive behavioral therapy (CBT) shows promise for its accessibility and effectiveness. However, its most effective components and for whom it is more (or less) effective are not known. Methods/design: Based on the multiphase optimization strategy framework, this study is a parallel-group, multicenter, open, fully factorial trial examining five smartphone-delivered CBT components (self-monitoring, cognitive restructuring, behavioral activation, assertion training, and problem solving) among university students with elevated distress, defined as scoring 5 or more on the Patient Health Questionnaire-9 (PHQ-9). The primary outcome is change in PHQ-9 scores from baseline to week 8. We will estimate specific efficacy of the five components and their interactions through the mixed-effects repeated-measures analysis and propose the most effective and efficacious combinations of components. Effect modification by selected baseline characteristics will be examined in exploratory analyses. Discussion: The highly efficient experimental design will allow identification of the most effective components and the most efficient combinations thereof among the five components of smartphone CBT for university students. Pragmatically, the findings will help make the most efficacious CBT package accessible to a large number of distressed university students at reduced cost; theoretically, they will shed light on the underlying mechanisms of CBT and help further advance CBT for depression

Search for microwave emission from ultrahigh energy cosmic rays

We present a search for microwave emission from air showers induced by ultrahigh energy cosmic rays with the microwave detection of air showers experiment. No events were found, ruling out a wide range of power flux and coherence of the putative emission, including those suggested by recent laboratory measurements.Comment: 5 pages, 3 figure

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database [version 2; peer review: 2 approved]

BACKGROUND: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: “disorder severity”. In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of “disorder severity” related factors are needed. AIMS: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) “disorder severity” which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline “disorder severity” and the type of treatment received. METHODS: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline – the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. CONCLUSIONS: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. REGISTRATION: PROSPERO CRD42019129512 (01/04/2019

Does publication bias inflate the apparent efficacy of psychological treatment for major depressive disorder? A systematic review and meta-analysis of US national institutes of health-funded trials

Background The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression. Methods and Findings We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. Thirteen (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started. Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI95% -0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the "true" effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively. Conclusion The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression

Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

Aims: To develop, validate, and compare the performance of nine models predicting post-treatment outcomes for depressed adults based on pre-treatment data. / Methods: Individual patient data from all six eligible RCTs were used to develop (k=3, n=1722) and test (k=3, n=1136) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum-scores were developed using coefficient weights derived from network centrality statistics (Models 1-3) and factor loadings from a confirmatory factor analysis (Model 4). Unweighted sum-score models were tested using Elastic Net Regularized (ENR) and ordinary least squares (OLS) regression (Models 5-6). Individual items were then included in ENR and OLS (Models 7-8). All models were compared to one another and to a null model using the mean post-baseline BDI-II score in the training data (Model 9). Primary outcome: BDI-II scores at 3-4 months. / Results: Models 1-7 all outperformed the null model. Individual-item models (particularly Model 8) explained less variance. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum-scores had little impact. / Conclusions: Any of the modelling techniques (1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomized control trials

Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety co-morbidity, and consider the association of a wide range of symptoms with treatment outcomes. / Method: Individual patient data from six RCTs of depressed patients (total n=2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention timepoints using individual items and sum scores. Symptom networks (Graphical Gaussian Model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators. / Results: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms. / Conclusion: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

BACKGROUND: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. METHOD: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. RESULTS: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). CONCLUSIONS: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments