A parenting intervenion for childhood behavioral problems: a randomised controlled trial in disadvantaged Community-based settings:A randomized controlled trial in disadvantaged community-based settings

Objective: A community-based randomized controlled trial (RCT) was conducted in urban areas characterized by high levels of disadvantage to test the effectiveness of the Incredible Years BASIC parent training program (IYBP) for children with behavioral problems. Potential moderators of intervention effects on child behavioral outcomes were also explored. Method: Families were included if the child (aged 32-88 months) scored above a clinical cutoff on the Eyberg Child Behavior Inventory (ECBI). Participants (n = 149) were randomly allocated on a 2:1 ratio to an intervention group (n = 103) or a waiting-list control group (n = 46). Child behavior, parenting skills, and parent well-being were assessed at baseline and 6 months later using parent-report and independent observations. An intention-to-treat analysis of covariance was used to examine postintervention differences between groups. Results: Statistically significant differences in child disordered behavior favored the intervention group on the ECBI Intensity (effect size = 0.7, p

Gaussian Approximation for Tracking Occluding and Interacting Targets

In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.

Psychosocial factors in GP work:the effects of taking a GP position or leaving GP work

BACKGROUND: We examined the effects of leaving public sector general practitioner (GP) work and of taking a GP position on changes in work-related psychosocial factors, such as time pressure, patient-related stress, distress and work interference with family. In addition, we examined whether changes in time pressure and patient-related stress mediated the association of employment change with changes of distress and work interference with family. METHODS: Participants were 1705 Finnish physicians (60% women) who responded to surveys in 2006 and 2010. Analyses of covariance were conducted to examine the effect of employment change to outcome changes adjusted for gender, age and response format. Mediational effects were tested following the procedures outlined by Baron and Kenny. RESULTS: Employment change was significantly associated with all the outcomes. Leaving public sector GP work was associated with substantially decreased time pressure, patient-related stress, distress and work interference with family. In contrast, taking a position as a public sector GP was associated with an increase in these factors. Mediation tests suggested that the associations of employment change with distress change and work interference with family change were partially explained by the changes in time pressure and patient-related stress. CONCLUSIONS: Our results showed that leaving public sector GP work is associated with favourable outcomes, whereas taking a GP position in the public sector is associated with adverse effects. Primary health-care organizations should pay more attention to the working conditions of their GPs, in particular, to time pressure and patient-related stress.

Do obesity and sleep problems cluster in the workplace?:A multivariate, multilevel study

OBJECTIVES: The aim of this study was to examine the co-occurrence of obesity and sleep problems among employees and workplaces. METHODS: We obtained data from 39 873 men and women working in 3040 workplaces in 2000-2002 (the Finnish Public Sector Study). Individual- and workplace-level characteristics were considered as correlates of obesity and sleep problems, which were modelled simultaneously using a multivariate, multilevel approach. RESULTS: Of the participants, 11% were obese and 23% reported sleep problems. We found a correlation between obesity and sleep problems at both the individual [correlation coefficient 0.048, covariance 0.047, standard error (SE) 0.005) and workplace (correlation coefficient 0.619, covariance 0.068, SE 0.011) level. The latter, but not the former, correlation remained after adjustment for individual- and workplace-level confounders, such as age, sex, socioeconomic status, shift work, alcohol consumption, job strain, and proportion of temporary employees and manual workers at the workplace. CONCLUSIONS: Obese employees and those with sleep problems tend to cluster in the same workplaces, suggesting that, in addition to targeting individuals at risk, interventions to reduce obesity and sleep problems might benefit from identifying "risky" workplaces.

A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework

This paper proposes a new non-parametric method for estimating model-free, time-varying liquidity betas which builds on realized covariance and volatility theory. Working under a liquidity-adjusted CAPM framework we provide evidence that liquidity risk is a factor priced in the Greek stock market, mainly arising from the covariation of individual liquidity with local market liquidity, however, the level of liquidity seems to be an irrelevant variable in asset pricing. Our findings provide support to the notion that liquidity shocks transmitted across securities can cause market-wide effects and can have important implications for portfolio diversification strategies. ©2012 Elsevier B.V. All rights reserved.

Group Trauma-Focused Cognitive-Behavioural Therapy with Former Child Soldiers and other War-affected Boys in the DR Congo: A Randomised Controlled Trial.

Background: The Democratic Republic of Congo (DRC) has been home to the world’s deadliest con?ict since World War II and is reported to have the largest number of child soldiers in the world. Despite evidence of the debilitating impact of war, no group-based mental health or psychosocial intervention has been evaluated in a randomised controlled trial for psychologically distressed former child soldiers.

Method: A randomised controlled trial involving 50 boys, aged 13–17, including former child soldiers (n = 39) and other war-affected boys (n = 11). They were randomly assigned to an intervention group, or wait-list control group. The intervention group received a 15-session, group-based, culturally adapted Trauma-Focused Cognitive–Behavioural Therapy (TF-CBT) intervention. Assessment interviews were completed at baseline, postintervention and 3-month follow-up (intervention group).

Results: Analysis of Covariance (ANCOVA) demonstrated that, in comparison to the wait-list control group, the TF-CBT intervention group had highly signi?cant reductions in posttraumatic stress symptoms, overall psychosocial distress, depression or anxiety-like symptoms, conduct problems and a signi?cant increase in prosocial behaviour (p < .001 for all). Effect sizes were higher when former child soldier scores were separated for sub-analysis. Three-month follow-up of the intervention group found that treatment gains were maintained.

Conclusions: A culturally modi?ed, group-based TF-CBT intervention was effective in reducing posttraumatic stress and psychosocial distress in former child soldiers and other war-affected boys.

Are power laws that estimate fractal dimension a good descriptor of soil structure and its link to soil biological properties?

The study of interrelationships between soil structure and its functional properties is complicated by the fact that the quantitative description of soil structure is challenging. Soil scientists have tackled this challenge by taking advantage of approaches such as fractal geometry, which describes soil architectural complexity through a scaling exponent (D) relating mass and numbers of particles/aggregates to particle/aggregate size. Typically, soil biologists use empirical indices such as mean weight diameters (MWD) and percent of water stable aggregates (WSA), or the entire size distribution, and they have successfully related these indices to key soil features such as C and N dynamics and biological promoters of soil structure. Here, we focused on D, WSA and MWD and we tested whether: D estimated by the exponent of the power law of number-size distributions is a good and consistent correlate of MWD and WSA; D carries information that differs from MWD and WSA; the fraction of variation in D that is uncorrelated with MWD and WSA is related to soil chemical and biological properties that are thought to establish interdependence with soil structure (e.g., organic C, N, arbuscular mycorrhizal fungi). We analysed observational data from a broad scale field study and results from a greenhouse experiment where arbuscular mycorrhizal fungi (AMF) and collembola altered soil structure. We were able to develop empirical models that account for a highly significant and large portion of the correlation observed between WSA and MWD but we did not uncover the mechanisms that underlie this correlation. We conclude that most of the covariance between D and soil biotic (AMF, plant roots) and abiotic (C. N) properties can be accounted for by WSA and MWD. This result implies that the ecological effects of the fragmentation properties described by D and generally discussed under the framework of fractal models can be interpreted under the intuitive perspective of simpler indices and we suggest that the biotic components mostly impacted the largest size fractions, which dominate MWD, WSA and the scaling exponent ruling number-size distributions. (C) 2010 Elsevier Ltd. All rights reserved.

Moral emotions and the envisioning of mitigating circumstances for wrongdoing

Anger may be more responsive than disgust to mitigating circumstances in judgments of wrongdoing. We tested this hypothesis in two studies where we had participants envision circumstances that could serve to mitigate an otherwise wrongful act. In Study 1, participants provided moral judgments, and ratings of anger and disgust, to a number of transgressions involving either harm or bodily purity. They were then asked to imagine and report whether there might be any circumstances that would make it all right to perform the act. Across transgression type, and controlling for covariance between anger and disgust, levels of anger were found to negatively predict the envisioning of mitigating circumstances for wrongdoing, while disgust was unrelated. Study 2 replicated and extended these findings to less serious transgressions, using a continuous measure of mitigating circumstances, and demonstrated the impact of
anger independent of deontological commitments. These findings highlight the differential relationship that anger and disgust have with the ability to envision mitigating factors.

Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

The prospective effects of workplace violence on physicians' job satisfaction and turnover intentions: the buffering effect of job control

Background: Health care professionals, including physicians, are at high risk of encountering workplace violence. At the same time physician turnover is an increasing problem that threatens the functioning of the health care sector worldwide. The present study examined the prospective associations of work-related physical violence and bullying with physicians’ turnover intentions and job satisfaction. In addition, we tested whether job control would modify these associations.

Methods: The present study was a 4-year longitudinal survey study, with data gathered in 2006 and 2010.The present sample included 1515 (61% women) Finnish physicians aged 25–63 years at baseline. Analyses of covariance (ANCOVA) were conducted while adjusting for gender, age, baseline levels, specialisation status, and employment sector.

Results: The results of covariance analyses showed that physical violence led to increased physician turnover intentions and that both bullying and physical violence led to reduced physician job satisfaction even after adjustments. We also found that opportunities for job control were able to alleviate the increase in turnover intentions resulting from bullying.

Conclusions: Our results suggest that workplace violence is an extensive problem in the health care sector and may lead to increased turnover and job dissatisfaction. Thus, health care organisations should approach this problem through different means, for example, by giving health care employees more opportunities to control their own work.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Validation and application of a reporter gene assay for the determination of estrogenic endocrine disruptor activity in milk

Endocrine disruptors (EDs) are compounds known to interfere with the endocrine system by disturbing the action or pathways of natural hormones which may lead to infertility or cancer.Our diet is considered to be one of the main exposure routes to EDs. Since milk and dairy products are major components of our diet they should be monitored for ED contamination. Most assays developed to date utilise targeted, chromatography based methods which lack information on the biological activity and mixture effects of the monitored compounds.A biological reporter gene assay (RGA) was developed to assess the total estrogen hormonal load in milk. It has been validated according to EU decision 2002/657/EC. Analytes were extracted by liquid-liquid extraction with acetonitrile followed by clean up on a HLB column which yielded good recovery and small matrix effects. The method has been shown to be estrogen specific, repeatable and reproducible, with covariance values below 20%. In conclusion, this method enables the detection of low levels of estrogen hormonal activity in milk with a detection capability of 36pgg EEQ and has been successfully applied in testing a range of milk samples. © 2014 Elsevier Ltd.

Physical activity, sedentary behaviour and fetal macrosomia in uncomplicated pregnancies: A prospective cohort study

Objective: to explore maternal energy balance, incorporating free living physical activity and sedentary behaviour, in uncomplicated pregnancies at risk of macrosomia.

Methods: a parallel-group cross-sectional analysis was conducted in healthy pregnant women predicted to deliver infants weighing Z4000 g (study group) or o4000 g (control group). Women were recruited in a 1:1 ratio from antenatal clinics in Northern Ireland. Women wore a SenseWears Body Media Pro3 physical activity armband and completed a food diary for four consecutive days in the third trimester. Physical activity was measured in Metabolic Equivalent of Tasks (METs) where 1 MET¼1 kcal per kilogram of body weight per hour. Analysis of covariance (ANCOVA) was employed using the General Linear Model to adjust for potential confounders.

Findings: of the 112 women recruited, 100 complete datasets were available for analysis. There was no significant difference in energy balance between the two groups. Intensity of free living physical activity (average METs) of women predicted to deliver macrosomic infants (n¼50) was significantly lower than that of women in the control group (n¼50) (1.3 (0.2) METs (mean, standard deviation) versus 1.2 (0.2) METs; difference in means 0.1 METs (95% confidence interval: 0.19, 0.01); p¼0.021). Women predicted to deliver macrosomic infants also spent significantly more time in sedentary behaviour (r1 MET) than the control group (16.1 (2.8) hours versus 13.8 (4.3) hours; 2.0 hours (0.3, 3.7), p¼0.020).

Key conclusions and implications for practice: although there was no association between predicted fetal macrosomia and energy balance, those women predicted to deliver a macrosomic infant exhibited increased sedentary behaviour and reduced physical activity in the third trimester of pregnancy. Professionals caring for women during pregnancy have an important role in promoting and supporting more active lifestyles amongst women who are predicted to deliver a macrosomic infant given the known associated risks.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.