Externalizing disorders and substance use: empirically derived subtypes in a population-based sample of adults.

PURPOSE: Attention-deficit/hyperactivity disorder (ADHD), conduct disorder (CD), and oppositional defiant disorder (ODD) are common externalizing disorders of childhood. The common effects of these disorders on substance abuse need further investigation. The current study investigated the joint clusters of childhood/adolescence ADHD, CD, and ODD, and their influence on substance abuse/dependence in a population-based sample of adults. METHODS: The data were drawn from the PsyCoLaus study (n = 3,720) conducted in Lausanne, Switzerland. The population-based sample included 238 subjects meeting criteria for ADHD/ODD/CD diagnoses before the age of 15. Latent class analyses (LCA) were performed to derive comorbidity subtypes, which were subsequently characterized with respect to psychosocial correlates and substance use. RESULTS: The best fit in LCAs was achieved with three latent classes: an ADHD subtype (35.7 %); an externalizing multimorbid subtype (33.6 %) involving ODD, ADHD, and CD; and a third subtype with CD (30.7 %). The CD subtype showed the highest association with substance use. Apart from this, the externalizing multimorbid subtype was also significantly linked to substance use. The ADHD subtype had only elevated frequencies for alcohol dependence in comparison with subjects that had no history of ADHD, ODD, and CD during childhood or adolescence. Finally, important interactions between subtypes and sex were observed with regard to substance use. CONCLUSIONS: This study provides evidence showing that subtyping the externalizing disorders, ADHD, ODD and CD, along their comorbidity patterns leads to important differences regarding substance use. This could have implications for the etiology, prevention, and treatment of substance use disorders.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.

Consommation d'alcool à risque dans un collectif de patients de la Policlinique médicale universitaire de Lausanne [High risk alcohol consumption in a sample of patients of the Lausanne University Medical Polyclinic].

OBJECTIVES AND METHODS: This study indicates the prevalence, the characteristics, and the screening methods of patients with at risk alcohol drinking at the University Medical Clinic of Lausanne. RESULTS: The results reported demonstrate that one patient out of six is a drinker at risk without criteria for alcohol-dependance. The questionnaire AUDIT (Alcohol Use Disorders Identification Test) with a cut-off of five points seems to be the best screening test for at risk alcohol consumption. CONCLUSIONS: The high prevalence of at risk drinking in this study, combined with scientific evidence of the efficiency of brief interventions in changing drinking habits, emphasises the importance of alcohol screening for all patients attending outpatient medical settings.

Association between circulating cytokine levels, diabetes and insulin resistance in a population-based sample (CoLaus study).

OBJECTIVE: The associations between inflammation, diabetes and insulin resistance remain controversial. Hence, we assessed the associations between diabetes, insulin resistance (using HOMA-IR) and metabolic syndrome with the inflammatory markers high-sensitive C-reactive protein (hs-CRP), interleukin-1 beta (IL-1β), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α). DESIGN: Cross-sectional study. PARTICIPANTS: Two thousand eight hundred and eighty-four men and 3201 women, aged 35-75, participated in this study. METHODS: C-reactive protein was assessed by immunoassay and cytokines by multiplexed flow cytometric assay. In a subgroup of 532 participants, an oral glucose tolerance test (OGTT) was performed to screen for impaired glucose tolerance (IGT). RESULTS: IL-6, TNF-α and hs-CRP were significantly and positively correlated with fasting plasma glucose (FPG), insulin and HOMA-IR. Participants with diabetes had higher IL-6, TNF-α and hs-CRP levels than participants without diabetes; this difference persisted for hs-CRP after multivariate adjustment. Participants with metabolic syndrome had increased IL-6, TNF-α and hs-CRP levels; these differences persisted after multivariate adjustment. Participants in the highest quartile of HOMA-IR had increased IL-6, TNF-α and hs-CRP levels; these differences persisted for TNF-α and hs-CRP after multivariate adjustment. No association was found between IL-1β levels and all diabetes and insulin resistance markers studied. Finally, participants with IGT had higher hs-CRP levels than participants with a normal OGTT, but this difference disappeared after controlling for body mass index (BMI). CONCLUSION: We found that subjects with diabetes, metabolic syndrome and increased insulin resistance had increased levels of IL6, TNF-α and hs-CRP, while no association was found with IL-1β. The increased inflammatory state of subjects with IGT is partially explained by increased BMI.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Prospective evaluation of risk of vertebral fractures using quantitative ultrasound measurements and bone mineral density in a population-based sample of postmenopausal women: results of the Basel Osteoporosis Study.

OBJECTIVE: Prospective studies have shown that quantitative ultrasound (QUS) techniques predict the risk of fracture of the proximal femur with similar standardised risk ratios to dual-energy x-ray absorptiometry (DXA). Few studies have investigated these devices for the prediction of vertebral fractures. The Basel Osteoporosis Study (BOS) is a population-based prospective study to assess the performance of QUS devices and DXA in predicting incident vertebral fractures. METHODS: 432 women aged 60-80 years were followed-up for 3 years. Incident vertebral fractures were assessed radiologically. Bone measurements using DXA (spine and hip) and QUS measurements (calcaneus and proximal phalanges) were performed. Measurements were assessed for their value in predicting incident vertebral fractures using logistic regression. RESULTS: QUS measurements at the calcaneus and DXA measurements discriminated between women with and without incident vertebral fracture, (20% height reduction). The relative risks (RRs) for vertebral fracture, adjusted for age, were 2.3 for the Stiffness Index (SI) and 2.8 for the Quantitative Ultrasound Index (QUI) at the calcaneus and 2.0 for bone mineral density at the lumbar spine. The predictive value (AUC (95% CI)) of QUS measurements at the calcaneus remained highly significant (0.70 for SI, 0.72 for the QUI, and 0.67 for DXA at the lumbar spine) even after adjustment for other confounding variables. CONCLUSIONS: QUS of the calcaneus and bone mineral density measurements were shown to be significant predictors of incident vertebral fracture. The RRs for QUS measurements at the calcaneus are of similar magnitude as for DXA measurements.

Sample preparation for two-dimensional gel electrophoresis.

The choice of sample preparation protocol is a critical influential factor for isoelectric focusing which in turn affects the two-dimensional gel result in terms of quality and protein species distribution. The optimal protocol varies depending on the nature of the sample for analysis and the properties of the constituent protein species (hydrophobicity, tendency to form aggregates, copy number) intended for resolution. This review explains the standard sample buffer constituents and illustrates a series of protocols for processing diverse samples for two-dimensional gel electrophoresis, including hydrophobic membrane proteins. Current methods for concentrating lower abundance proteins, by removal of high abundance proteins, are also outlined. Finally, since protein staining is becoming increasingly incorporated into the sample preparation procedure, we describe the principles and applications of current (and future) pre-electrophoretic labelling methods.

Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data.

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as 'fold-difference' results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.

Influence of Individual Characteristics on Work Engagement and Job Stress in a Sample of National and Foreign Workers in Switzerland

In most Western postindustrial societies today, the population is aging, businesses are faced with global integration, and important migration flows are taking place. Increasingly work organizations are hiring crossnational and multicultural workteams. In this situation it is important to understand the influence of certain individual and cultural characteristics on the process of professional integration. The present study explores the links between personality traits, demographic characteristics (age, sex, education, income, and nationality), work engagement, and job stress. The sample consisted of 618 participants, including 394 Swiss workers (200 women, 194 men) and 224 foreigners living and working in Switzerland (117 women, 107 men). Each participant completed the NEO-FFI, the UWES, and the GWSS questionnaires. Our results show an interaction between age and nationality with respect to work engagement and general job stress. The levels of work engagement and job stress appear to increase with age among national wotkers, whereas they decrease among foreign workers. In addition, work engagement was negatively associated with Neuroticism and positively with the other four personality dimensions. Finally, job stress was positively associated with Neuroticism and Conscientiousness, and negatively associated with Extraversion. However, the strength of these relationships appeared to vary according to the worker's nationality, age, sex, education, and income.

Quality Of Attachment, Perinatal Risk, And Mother-Infant Interaction In A High-Risk Premature Sample

Thirty-three families, each with a premature infant born less than 33 gestational weeks, were observed in a longitudinal exploratory study. Infants were recruited in a neonatal intensive care unit, and follow-up visits took place at 4 months and 12 months of corrected age. The severity of the perinatal problems was evaluated using the Perinatal Risk Inventory (PERI; A.P. Scheiner & M.E. Sexton, 1991). At 4 months, mother infant play interaction was observed and coded according to the CARE-index (P.M. Crittenden, 2003); at 12 months, the Strange Situation Procedure (SSP; M.D.S. Ainsworth, M.C. Blehar, E. Waters. & S. Wall, 1978) was administered. Results indicate a strong correlation between the severity of perinatal problems and the quality of attachment at 12 months. Based on the PERI, infants with high medical risks more frequently tended to be insecurely attached. There also was a significant correlation between insecure attachment and dyadic play interaction at 4 months (i.e., maternal controlling behavior and infant compulsive compliance). Moreover, specific dyadic interactive patterns could be identified as protective or as risk factors regarding the quality of attachment. Considering that attachment may have long-term influence on child development, these results underline the need for particular attention to risk factors regarding attachment among premature infants.

Obesity markers and blood pressure in a sample of Portuguese children and adolescents.

Little information exists regarding the effect of several obesity markers on blood pressure (BP) levels in youth. Transverse study including 2494 boys and 2589 girls. Height, weight and waist were measured according to the international criteria and body fat (BF) by bioimpedance. BP was measured by an automated device. Hypertension was defined using sex-specific, age-specific and height-specific observation-points. Body mass index (BMI) and waist were positively related with systolic blood pressure (SBP) and diastolic blood pressure (DBP) and heart rate in both sexes, whereas the relationships with BF were less consistent. Stepwise linear regression analysis showed that BMI was positively related with SBP and DBP in both sexes, whereas BF was negatively related with SBP in both sexes and with heart rate in boys only; finally, waist was positively related with SBP in boys and heart rate in girls. Age and heart rate-adjusted values of SBP and DBP increased with BMI: for SBP, 117+/-1, 123+/-1 and 124+/-1 mmHg in normal, overweight and obese boys, respectively; corresponding values for girls were 111+/-1, 114+/-1 and 116+/-2 mmHg (mean+/-SE, P<0.001). Overweight and obese boys had an odds ratio for being hypertensive of 2.26 (95% confidence interval: 1.79-2.86) and 3.36 (2.32-4.87), respectively; corresponding values for girls were 1.58 (confidence interval 1.25-1.99) and 2.31 (1.53-3.50). BMI, not BF or waist, is consistently and independently related to BP levels in children; overweight and obesity considerably increase the risk of hypertension.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Prevalence of social phobia in a clinical sample of drug dependent patients.

INTRODUCTION: Social phobia is among the most frequent psychiatric disorders and can be classified into two subtypes, nongeneralized and generalized. Whereas it significantly worsens the morbidity of comorbid substance abuse disorders, and it often is associated with reduced treatment responses, there is still lacking data on its prevalence in clinical populations of drug abusing patients. METHODS: The study sample consisted of 75 inpatients and 75 outpatients meeting DSM-IV criteria for drug dependence. Symptoms of social phobia were assessed with the French-language version of the Liebowitz Social Anxiety Scale (LSAS). RESULTS: Prevalence rate were 20% for the generalized subtype and 42.6% for the nongeneralized subtype. Gender difference emerged in the severity of fear, women reporting significantly greater fear relating to performance situations than men. CONCLUSIONS: An important proportion of patients with substance dependence present a comorbid generalized or nongeneralized social phobia. Early recognition of social phobia and adequate interventions is warranted for these patients in order to improve their treatment response with regard to quality of life and relapse prevention.