Genetic polymorphisms of the main transcription factors for adiponectin gene promoter in regulation of adiponectin levels: association analysis in three European cohorts.

Adiponectin serum concentrations are an important biomarker in cardiovascular epidemiology with heritability etimates of 30-70%. However, known genetic variants in the adiponectin gene locus (ADIPOQ) account for only 2%-8% of its variance. As transcription factors are thought to play an under-acknowledged role in carrying functional variants, we hypothesized that genetic polymorphisms in genes coding for the main transcription factors for the ADIPOQ promoter influence adiponectin levels. Single nucleotide polymorphisms (SNPs) at these genes were selected based on the haplotype block structure and previously published evidence to be associated with adiponectin levels. We performed association analyses of the 24 selected SNPs at forkhead box O1 (FOXO1), sterol-regulatory-element-binding transcription factor 1 (SREBF1), sirtuin 1 (SIRT1), peroxisome-proliferator-activated receptor gamma (PPARG) and transcription factor activating enhancer binding protein 2 beta (TFAP2B) gene loci with adiponectin levels in three different European cohorts: SAPHIR (n = 1742), KORA F3 (n = 1636) and CoLaus (n = 5355). In each study population, the association of SNPs with adiponectin levels on log-scale was tested using linear regression adjusted for age, sex and body mass index, applying both an additive and a recessive genetic model. A pooled effect size was obtained by meta-analysis assuming a fixed effects model. We applied a significance threshold of 0.0033 accounting for the multiple testing situation. A significant association was only found for variants within SREBF1 applying an additive genetic model (smallest p-value for rs1889018 on log(adiponectin) = 0.002, β on original scale = -0.217 µg/ml), explaining ∼0.4% of variation of adiponectin levels. Recessive genetic models or haplotype analyses of the FOXO1, SREBF1, SIRT1, TFAPB2B genes or sex-stratified analyses did not reveal additional information on the regulation of adiponectin levels. The role of genetic variations at the SREBF1 gene in regulating adiponectin needs further investigation by functional studies.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.

Adapting football to the child: an application of the logistic regression model in observational mewthodology.

Logistic regression is included into the analysis techniques which are valid for observationalmethodology. However, its presence at the heart of thismethodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child"s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.

Exploring the proposed DSM-5 diagnostic threshold and severity scale for the alcohol use disorder

Aims :¦Several studies have questioned the validity of separating the diagnosis of alcohol abuse from that of alcohol dependence, and the DSM-5 task force has proposed combining the criteria from these two diagnoses to assess a single category of alcohol use disorders (AUD). Furthermore, the DSM-5 task force has proposed including a new 2-symptom threshold and a severity scale based on symptom counts for the AUD diagnosis. The current study aimed to examine these modifications in a large population-based sample.¦Method :¦Data stemmed from an adult sample (N=2588 ; mean age 51.3 years (s.d.: 0.2), 44.9% female) of current and lifetime drinkers from the PsyCoLaus study, conducted in the Lausanne area in Switzerland. AUDs and validating variables were assessed using a semi-structured diagnostic interview for the assessment of alcohol¦and other major psychiatric disorders. First, the adequacy of the proposed 2- symptom threshold was tested by comparing threshold models at each possible cutoff and a linear model, in relation to different validating variables. The model with the smallest Akaike Criterion Information (AIC) value was established as the best¦model for each validating variable. Second, models with varying subsets of individual AUD symptoms were created to assess the associations between each symptom and the validating variables. The subset of symptoms with the smallest AIC value was established as the best subset for each validator.¦Results :¦1) For the majority of validating variables, the linear model was found to be the best fitting model. 2) Among the various subsets of symptoms, the symptoms most frequently associated with the validating variables were : a) drinking despite having knowledge of a physical or psychological problem, b) having had a persistent desire or unsuccessful efforts to cut down or control drinking and c) craving. The¦least frequent symptoms were : d) drinking in larger amounts or over a longer period than was intended, e) spending a great deal of time in obtaining, using or recovering from alcohol use and f) failing to fulfill major role obligations.¦Conclusions :¦The proposed DSM-5 2-symptom threshold did not receive support in our data. Instead, a linear AUD diagnosis was supported with individuals receiving an increasingly severe AUD diagnosis. Moreover, certain symptoms were more frequently associated with the validating variables, which suggests that these¦symptoms should be considered as more severe.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

Application of Multi-SNP Approaches Bayesian LASSO and AUC-RF to Detect Main Effects of Inflammatory-Gene Variants Associated with Bladder Cancer Risk

The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.

Multislice CT for assessing in-stent dimensions after left main coronary artery stenting: a comparison with three dimensional intravascular ultrasound

Objective: To evaluate the agreement between multislice CT (MSCT) and intravascular ultrasound (IVUS) to assess the in-stent lumen diameters and lumen areas of left main coronary artery (LMCA) stents. Design: Prospective, observational single centre study. Setting: A single tertiary referral centre. Patients: Consecutive patients with LMCA stenting excluding patients with atrial fibrillation and chronic renal failure. Interventions: MSCT and IVUS imaging at 912 months follow-up were performed for all patients. Main outcome measures: Agreement between MSCT and IVUS minimum luminal area (MLA) and minimum luminal diameter (MLD). A receiver operating characteristic (ROC) curve was plotted to find the MSCT cut-off point to diagnose binary restenosis equivalent to 6 mm2 by IVUS. Results: 52 patients were analysed. PassingBablok regression analysis obtained a β coefficient of 0.786 (0.586 to 1.071) for MLA and 1.250 (0.936 to 1.667) for MLD, ruling out proportional bias. The α coefficient was −3.588 (−8.686 to −0.178) for MLA and −1.713 (−3.583 to −0.257) for MLD, indicating an underestimation trend of MSCT. The ROC curve identified an MLA ≤4.7 mm2 as the best threshold to assess in-stent restenosis by MSCT. Conclusions: Agreement between MSCT and IVUS to assess in-stent MLA and MLD for LMCA stenting is good. An MLA of 4.7 mm2 by MSCT is the best threshold to assess binary restenosis. MSCT imaging can be considered in selected patients to assess LMCA in-stent restenosis

Si3N4 single-crystal nanowires grown from silicon micro- and nanoparticles near the threshold of passive oxidation

A simple and most promising oxide-assisted catalyst-free method is used to prepare silicon nitride nanowires that give rise to high yield in a short time. After a brief analysis of the state of the art, we reveal the crucial role played by the oxygen partial pressure: when oxygen partial pressure is slightly below the threshold of passive oxidation, a high yield inhibiting the formation of any silica layer covering the nanowires occurs and thanks to the synthesis temperature one can control nanowire dimensions

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.