979 resultados para Sample selection


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OBJECTIVES: To conduct a national survey on adolescent health and lifestyles in Georgia and to thus set up a database on adolescent. METHODS: A two-stage cluster sample of around 8000-10000 in-school 15-18 years adolescents are being reached through a random selection of classes in Georgia. The sample has been stratified by age, region, type of school and language. A self-administered questionnaire of 87 questions has been developed and translated into the four main languages used in Georgia. RESULTS: Up to June 2004, the researchers have reached 511 classes (9306 pupils). In total, 8039 questionnaires have been considered valid. The main concerns encountered for this survey are linked with acceptance of the survey, cross-cultural issues, political and strategic problems as well as inadequate physical environmental support. CONCLUSION: Despite Georgia's unfavourable economical and political situation, it has been possible to run a national survey on the health of adolescents, according to the usual standards used in the field. This survey should allow for 1) the identification of priorities in the field of health care and health promotion 2) the monitoring of adolescent health in the future.

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Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

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We evaluated the accuracy of skinfold thicknesses, BMI and waist circumference for the prediction of percentage body fat (PBF) in a representative sample of 372 Swiss children aged 6-13 years. PBF was measured using dual-energy X-ray absorptiometry. On the basis of a preliminary bootstrap selection of predictors, seven regression models were evaluated. All models included sex, age and pubertal stage plus one of the following predictors: (1) log-transformed triceps skinfold (logTSF); (2) logTSF and waist circumference; (3) log-transformed sum of triceps and subscapular skinfolds (logSF2); (4) log-transformed sum of triceps, biceps, subscapular and supra-iliac skinfolds (logSF4); (5) BMI; (6) waist circumference; (7) BMI and waist circumference. The adjusted determination coefficient (R² adj) and the root mean squared error (RMSE; kg) were calculated for each model. LogSF4 (R² adj 0.85; RMSE 2.35) and logSF2 (R² adj 0.82; RMSE 2.54) were similarly accurate at predicting PBF and superior to logTSF (R² adj 0.75; RMSE 3.02), logTSF combined with waist circumference (R² adj 0.78; RMSE 2.85), BMI (R² adj 0.62; RMSE 3.73), waist circumference (R² adj 0.58; RMSE 3.89), and BMI combined with waist circumference (R² adj 0.63; RMSE 3.66) (P < 0.001 for all values of R² adj). The finding that logSF4 was only modestly superior to logSF2 and that logTSF was better than BMI and waist circumference at predicting PBF has important implications for paediatric epidemiological studies aimed at disentangling the effect of body fat on health outcomes.

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This work proposes novel network analysis techniques for multivariate time series.We define the network of a multivariate time series as a graph where verticesdenote the components of the process and edges denote non zero long run partialcorrelations. We then introduce a two step LASSO procedure, called NETS, toestimate high dimensional sparse Long Run Partial Correlation networks. This approachis based on a VAR approximation of the process and allows to decomposethe long run linkages into the contribution of the dynamic and contemporaneousdependence relations of the system. The large sample properties of the estimatorare analysed and we establish conditions for consistent selection and estimation ofthe non zero long run partial correlations. The methodology is illustrated with anapplication to a panel of U.S. bluechips.

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Cannabis use is highly prevalent among people with schizophrenia, and coupled with impaired cognition, is thought to heighten the risk of illness onset. However, while heavy cannabis use has been associated with cognitive deficits in long-term users, studies among patients with schizophrenia have been contradictory. This article consists of 2 studies. In Study I, a meta-analysis of 10 studies comprising 572 patients with established schizophrenia (with and without comorbid cannabis use) was conducted. Patients with a history of cannabis use were found to have superior neuropsychological functioning. This finding was largely driven by studies that included patients with a lifetime history of cannabis use rather than current or recent use. In Study II, we examined the neuropsychological performance of 85 patients with first-episode psychosis (FEP) and 43 healthy nonusing controls. Relative to controls, FEP patients with a history of cannabis use (FEP + CANN; n = 59) displayed only selective neuropsychological impairments while those without a history (FEP - CANN; n = 26) displayed generalized deficits. When directly compared, FEP + CANN patients performed better on tests of visual memory, working memory, and executive functioning. Patients with early onset cannabis use had less neuropsychological impairment than patients with later onset use. Together, these findings suggest that patients with schizophrenia or FEP with a history of cannabis use have superior neuropsychological functioning compared with nonusing patients. This association between better cognitive performance and cannabis use in schizophrenia may be driven by a subgroup of "neurocognitively less impaired" patients, who only developed psychosis after a relatively early initiation into cannabis use.

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Overall introduction.- Longitudinal studies have been designed to investigate prospectively, from their beginning, the pathway leading from health to frailty and to disability. Knowledge about determinants of healthy ageing and health behaviour (resources) as well as risks of functional decline is required to propose appropriate preventative interventions. The functional status in older people is important considering clinical outcome in general, healthcare need and mortality. Part I.- Results and interventions from lucas (longitudinal urban cohort ageing study). Authors.- J. Anders, U. Dapp, L. Neumann, F. Pröfener, C. Minder, S. Golgert, A. Daubmann, K. Wegscheider,. W. von Renteln-Kruse Methods.- The LUCAS core project is a longitudinal cohort of urban community-dwelling people 60 years and older, recruited in 2000/2001. Further LUCAS projects are cross-sectional comparative and interventional studies (RCT). Results.- The emphasis will be on geriatric medical care in a population-based approach, discussing different forms of access, too. (Dapp et al. BMC Geriatrics 2012, 12:35; http://www.biomedcentral.com/1471-2318/12/35): - longitudinal data from the LUCAS urban cohort (n = 3.326) will be presented covering 10 years of observation, including the prediction of functional decline, need of nursing care, and mortality by using a self-filling screening tool; - interventions to prevent functional decline do focus on first (pre-clinical) signs of pre-frailty before entering the frailty-cascade ("Active Health Promotion in Old Age", "geriatric mobility centre") or disability ("home visits"). Conclusions.- The LUCAS research consortium was established to study particular aspects of functional competence, its changes with ageing, to detect pre-clinical signs of functional decline, and to address questions on how to maintain functional competence and to prevent adverse outcome in different settings. The multidimensional data base allows the exploration of several further questions. Gait performance was exmined by GAITRite®-System. Supported by the Federal Ministry for Education and Research (BMBF Funding No. 01ET1002A). Part II.- Selected results from the lausanne cohort 65+ (Lc65 + ) Study (Switzerland). Authors.- Prof Santos-Eggimann Brigitte, Dr Seematter-Bagnoud Laurence, Prof Büla Christophe, Dr Rochat Stéphane. Methods.- The Lc65+ cohort was launched in 2004 with the random selection of 3054 eligible individuals aged 65 to 70 (birth year 1934-1938) in the non-institutionalized population of Lausanne (Switzerland). Results.- Information is collected about life course social and health-related events, socio-economics, medical and psychosocial dimensions, lifestyle habits, limitations in activities of daily living, mobility impairments, and falls. Gait performance are objectively measured using body-fixed sensors. Frailty is assessed using Fried's frailty phenotype. Follow-up consists in annual self-completed questionnaires, as well as physical examination and physical and mental performance tests every three years. - Lausanne cohort 65+ (Lc65 + ): design and longitudinal outcomes. The baseline data collection was completed among 1422 participants in 2004-2005 through self-completed questionnaires, face-to-face interviews, physical examination and tests of mental and physical performances. Information about institutionalization, self-reported health services utilization, and death is also assessed. An additional random sample (n = 1525) of 65-70 years old subjects was recruited in 2009 (birth year 1939-1943). - lecture no 4: alcohol intake and gait parameters: prevalent and longitudinal association in the Lc65+ study. The association between alcohol intake and gait performance was investigated.

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BACKGROUND/AIMS: Cannabis use is a growing challenge for public health, calling for adequate instruments to identify problematic consumption patterns. The Cannabis Use Disorders Identification Test (CUDIT) is a 10-item questionnaire used for screening cannabis abuse and dependency. The present study evaluated that screening instrument. METHODS: In a representative population sample of 5,025 Swiss adolescents and young adults, 593 current cannabis users replied to the CUDIT. Internal consistency was examined by means of Cronbach's alpha and confirmatory factor analysis. In addition, the CUDIT was compared to accepted concepts of problematic cannabis use (e.g. using cannabis and driving). ROC analyses were used to test the CUDIT's discriminative ability and to determine an appropriate cut-off. RESULTS: Two items ('injuries' and 'hours being stoned') had loadings below 0.5 on the unidimensional construct and correlated lower than 0.4 with the total CUDIT score. All concepts of problematic cannabis use were related to CUDIT scores. An ideal cut-off between six and eight points was found. CONCLUSIONS: Although the CUDIT seems to be a promising instrument to identify problematic cannabis use, there is a need to revise some of its items.

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BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

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How positive selection molds the T cell repertoire has been difficult to examine. In this study, we use TCR-beta-transgenic mice in which MHC shapes TCR-alpha use. Differential AV segment use is directly related to the constraints placed on the composition of the CDR3 loops. Where these constraints are low, efficient selection of alphabeta pairs follows. This mode of selection preferentially uses favored AV-AJ rearrangements and promotes diversity. Increased constraint on the alpha CDR3 loops leads to inefficient selection associated with uncommon recombination events and limited diversity. Further, the two modes of selection favor alternate sets of AJ segments. We discuss the relevance of these findings to the imprint of self-MHC restriction and peripheral T cell activation.

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A new statistical parallax method using the Maximum Likelihood principle is presented, allowing the simultaneous determination of a luminosity calibration, kinematic characteristics and spatial distribution of a given sample. This method has been developed for the exploitation of the Hipparcos data and presents several improvements with respect to the previous ones: the effects of the selection of the sample, the observational errors, the galactic rotation and the interstellar absorption are taken into account as an intrinsic part of the formulation (as opposed to external corrections). Furthermore, the method is able to identify and characterize physically distinct groups in inhomogeneous samples, thus avoiding biases due to unidentified components. Moreover, the implementation used by the authors is based on the extensive use of numerical methods, so avoiding the need for simplification of the equations and thus the bias they could introduce. Several examples of application using simulated samples are presented, to be followed by applications to real samples in forthcoming articles.

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A new statistical parallax method using the Maximum Likelihood principle is presented, allowing the simultaneous determination of a luminosity calibration, kinematic characteristics and spatial distribution of a given sample. This method has been developed for the exploitation of the Hipparcos data and presents several improvements with respect to the previous ones: the effects of the selection of the sample, the observational errors, the galactic rotation and the interstellar absorption are taken into account as an intrinsic part of the formulation (as opposed to external corrections). Furthermore, the method is able to identify and characterize physically distinct groups in inhomogeneous samples, thus avoiding biases due to unidentified components. Moreover, the implementation used by the authors is based on the extensive use of numerical methods, so avoiding the need for simplification of the equations and thus the bias they could introduce. Several examples of application using simulated samples are presented, to be followed by applications to real samples in forthcoming articles.

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Alpha-band activity (8-13 Hz) is not only suppressed by sensory stimulation and movements, but also modulated by attention, working memory and mental tasks, and could be sensitive to higher motor control functions. The aim of the present study was to examine alpha oscillatory activity during the preparation of simple left or right finger movements, contrasting the external and internal mode of action selection. Three preparation conditions were examined using a precueing paradigm with S1 as the preparatory and S2 as the imperative cue: Full, laterality instructed by S1; Free, laterality freely selected and None, laterality instructed by S2. Time-frequency (TF) analysis was performed in the alpha frequency range during the S1-S2 interval, and alpha motor-related amplitude asymmetries (MRAA) were also calculated. The significant MRAA during the Full and Free conditions indicated effective external and internal motor response preparation. In the absence of specific motor preparation (None), a posterior alpha event-related desynchronization (ERD) dominated, reflecting the main engagement of attentional resources. In Full and Free motor preparation, posterior alpha ERD was accompanied by a midparietal alpha event-related synchronization (ERS), suggesting a concomitant inhibition of task-irrelevant visual activity. In both Full and Free motor preparation, analysis of alpha power according to MRAA amplitude revealed two types of functional activation patterns: (1) a motor alpha pattern, with predominantly midparietal alpha ERS and large MRAA corresponding to lateralized motor activation/visual inhibition and (2) an attentional alpha pattern, with dominating right posterior alpha ERD and small MRAA reflecting visuospatial attention. The present results suggest that alpha oscillatory patterns do not resolve the selection mode of action, but rather distinguish separate functional strategies of motor preparation.

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Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.