Clinical predictors of response to cognitive-behavioural therapy in pediatric anxiety disorders: the Genes for Treatment (GXT) Study

Objective The Genes for Treatment study is an international, multisite collaboration exploring the role of genetic, demographic, and clinical predictors in response to cognitive-behavioral therapy (CBT) in pediatric anxiety disorders. The current article, the first from the study, examined demographic and clinical predictors of response to CBT. We hypothesized that the child’s gender, type of anxiety disorder, initial severity and comorbidity, and parents’ psychopathology would significantly predict outcome. Method A sample of 1,519 children 5 to 18 years of age with a primary anxiety diagnosis received CBT across 11 sites. Outcome was defined as response (change in diagnostic severity) and remission (absence of the primary diagnosis) at each time point (posttreatment, 3-, 6-, and/or 12-month follow-up) and analyzed using linear and logistic mixed models. Separate analyses were conducted using data from posttreatment and follow-up assessments to explore the relative importance of predictors at these time points. Results Individuals with social anxiety disorder (SoAD) had significantly poorer outcomes (poorer response and lower rates of remission) than those with generalized anxiety disorder (GAD). Although individuals with specific phobia (SP) also had poorer outcomes than those with GAD at posttreatment, these differences were not maintained at follow-up. Both comorbid mood and externalizing disorders significantly predicted poorer outcomes at posttreatment and follow-up, whereas self-reported parental psychopathology had little effect on posttreatment outcomes but significantly predicted response (although not remission) at follow-up. Conclusion SoAD, nonanxiety comorbidity, and parental psychopathology were associated with poorer outcomes after CBT. The results highlight the need for enhanced treatments for children at risk for poorer outcomes.

A randomised, double- blind, cross-over study investigating the prebiotic effect of agave fructans in healthy human subjects

This placebo-controlled, randomised, double-blind, cross-over human feeding study aimed to determine the prebiotic effect of agave fructans. A total of thirty-eight volunteers completed this trial. The treatment consisted of 3 weeks' supplementation with 5 g/d of prebiotic agave fructan (Predilife) or equivalent placebo (maltodextrin), followed by a 2-week washout period following which subjects were crossed over to alternate the treatment arm for 3 weeks followed by a 2-week washout. Faecal samples were collected at baseline, on the last day of treatment (days 22 and 58) and washout (days 36 and 72), respectively. Changes in faecal bacterial populations, SCFA and secretory IgA were assessed using fluorescent in situ hybridisation, GC and ELISA, respectively. Bowel movements, stool consistencies, abdominal comfort and mood changes were evaluated by a recorded daily questionnaire. In parallel, the effect of agave fructans on different regions of the colon using a three-stage continuous culture simulator was studied. Predilife significantly increased faecal bifidobacteria (log10 9·6 (sd 0·4)) and lactobacilli (log10 7·7 (sd 0·8)) compared with placebo (log10 9·2 (sd 0·4); P = 0·00) (log10 7·4 (sd 0·7); P = 0·000), respectively. No change was observed for other bacterial groups tested, SCFA, secretory IgA, and PGE2 concentrations between the treatment and placebo. Denaturing gradient gel electrophoresis analysis indicated that bacterial communities were randomly dispersed and no significant differences were observed between Predilife and placebo treatments. The in vitro models showed similar increases in bifidobacterial and lactobacilli populations to that observed with the in vivo trial. To conclude, agave fructans are well tolerated in healthy human subjects and increased bifidobacteria and lactobacilli numbers in vitro and in vivo but did not influence other products of fermentation

Efficient incorporation of channel cross-section geometry uncertainty into regional and global scale flood inundation models

This paper investigates the challenge of representing structural differences in river channel cross-section geometry for regional to global scale river hydraulic models and the effect this can have on simulations of wave dynamics. Classically, channel geometry is defined using data, yet at larger scales the necessary information and model structures do not exist to take this approach. We therefore propose a fundamentally different approach where the structural uncertainty in channel geometry is represented using a simple parameterization, which could then be estimated through calibration or data assimilation. This paper first outlines the development of a computationally efficient numerical scheme to represent generalised channel shapes using a single parameter, which is then validated using a simple straight channel test case and shown to predict wetted perimeter to within 2% for the channels tested. An application to the River Severn, UK is also presented, along with an analysis of model sensitivity to channel shape, depth and friction. The channel shape parameter was shown to improve model simulations of river level, particularly for more physically plausible channel roughness and depth parameter ranges. Calibrating channel Manning’s coefficient in a rectangular channel provided similar water level simulation accuracy in terms of Nash-Sutcliffe efficiency to a model where friction and shape or depth were calibrated. However, the calibrated Manning coefficient in the rectangular channel model was ~2/3 greater than the likely physically realistic value for this reach and this erroneously slowed wave propagation times through the reach by several hours. Therefore, for large scale models applied in data sparse areas, calibrating channel depth and/or shape may be preferable to assuming a rectangular geometry and calibrating friction alone.

Geoadditive models for regional count data: an application to industrial location

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data that allows us to address simultaneously some important methodological issues, such as spatial clustering, nonlinearities, and overdispersion. This model is applied to the study of location determinants of inward greenfield investments that occurred during 2003–2007 in 249 European regions. After presenting the data set and showing the presence of overdispersion and spatial clustering, we review the theoretical framework that motivates the choice of the location determinants included in the empirical model, and we highlight some reasons why the relationship between some of the covariates and the dependent variable might be nonlinear. The subsequent section first describes the solutions proposed by previous literature to tackle spatial clustering, nonlinearities, and overdispersion, and then presents the Geo-NB-GAM. The empirical analysis shows the good performance of Geo-NB-GAM. Notably, the inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity that induces spatial clustering. Allowing for nonlinearities reveals, in keeping with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some threshold value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs never overcome positive agglomeration externalities.

Trends in the CERES dataset, 2000–13: the effects of sea ice and jet shifts and comparison to climate models

We review the effects of dynamical variability on clouds and radiation in observations and models and discuss their implications for cloud feedbacks. Jet shifts produce robust meridional dipoles in upper-level clouds and longwave cloud-radiative effect (CRE), but low-level clouds, which do not simply shift with the jet, dominate the shortwave CRE. Because the effect of jet variability on CRE is relatively small, future poleward jet shifts with global warming are only a second-order contribution to the total CRE changes around the midlatitudes, suggesting a dominant role for thermodynamic effects. This implies that constraining the dynamical response is unlikely to reduce the uncertainty in extratropical cloud feedback. However, we argue that uncertainty in the cloud-radiative response does affect the atmospheric circulation response to global warming, by modulating patterns of diabatic forcing. How cloud feedbacks can affect the dynamical response to global warming is an important topic of future research.

Examining Cognitive Models of Obsessive Compulsive Disorder in Adolescents

Abstract. Three influential theoretical models of OCD focus upon the cognitive factors of inflated responsibility (Salkovskis, 1985), thought-action fusion (Rachman, 1993) and meta-cognitive beliefs (Wells and Matthews, 1994). Little is known about the relevance of these models in adolescents or about the nature of any direct or mediating relationships between these variables and OCD symptoms. This was a cross-sectional correlational design with 223 non-clinical adolescents aged 13 to 16 years. All participants completed questionnaires measuring inflated responsibility, thought-action fusion, meta-cognitive beliefs and obsessive-compulsive symptoms. Inflated responsibility, thought-action fusion and metacognitive beliefs were significantly associated with higher levels of obsessive-compulsive symptoms. These variables accounted for 35% of the variance in obsessive-compulsive symptoms, with inflated responsibility and meta-cognitive beliefs both emerging as significant independent predictors. Inflated responsibility completely mediated the effect of thoughtaction fusion and partially mediated the effect of meta-cognitive beliefs. Support for the downward extension of cognitive models to understanding OCD in a younger population was shown. Findings suggest that inflated responsibility and meta-cognitive beliefs may be particularly important cognitive concepts in OCD. Methodological limitations must be borne in mind and future research is needed to replicate and extend findings in clinical samples. Keywords: Obsessive compulsive disorder, adolescents, cognitive models.

The effect of accountability on loss aversion.

This paper investigates the effect of accountability-the expectation on the side of the decision maker of having to justify his/her decisions to somebody else-on loss aversion. Loss aversion is commonly thought to be the strongest component of risk aversion. Accountability is found to reduce the bias of loss aversion. This effect is explained by the higher cognitive effort induced by accountability, which triggers a rational check on emotional reactions at the base of loss aversion, leading to a reduction of the latter. Connections to dual-processing models are discussed.