Classification of sags according to their origin based on the waveform similarity

A statistical method for classification of sags their origin downstream or upstream from the recording point is proposed in this work. The goal is to obtain a statistical model using the sag waveforms useful to characterise one type of sags and to discriminate them from the other type. This model is built on the basis of multi-way principal component analysis an later used to project the available registers in a new space with lower dimension. Thus, a case base of diagnosed sags is built in the projection space. Finally classification is done by comparing new sags against the existing in the case base. Similarity is defined in the projection space using a combination of distances to recover the nearest neighbours to the new sag. Finally the method assigns the origin of the new sag according to the origin of their neighbours

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms.

BACKGROUND: The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. METHODS: To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. RESULTS: The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men. CONCLUSIONS: Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption.

Age above 60 years is not a negative predictive factor for combined antiviral therapy with pegylated interferon alpha and ribavirin in hepatitis C patients

Background: Age is frequently discussed as negative host factor to achieve a sustained virological response (SVR) to antiviral hepatitis C therapy. However, elderly patients often show relevant fibrosis or cirrhosis which is a known negative predictive factor, making it difficult to interpret age as an independent predictive factor. Methods: From the framework of the Swiss hepatitis C cohort (SCCS), we collected data from 545 antiviral hepatitis C therapies, including data from 67 hepatitis C patients ≥ 60 y who had been treated with PEG-interferon and ribavirin. We analyzed host factors (age, gender, fibrosis, haemoglobin, depression, earlier hepatitis C treatment), viral factors (genotype, viral load) and treatment course (early virological response, end of treatment response, SVR). Generalised estimating equations (GEE) regression modelling was used for the primary end point (SVR), with age ≥ 60 y and < 60 y as independent variable and gender, presence of cirrhosis, genotype, earlier treatment and viral load as confounders. SVR was analysed in young and elderly patients after matching for these confounders. Additionally, classification tree analysis was done in elderly patients using these confounders. Results: SVR analyzed in 545 patients was 55%. In genotype 1/4, SVR was 42.9% in 259 patients < 60 y and 26.1% in 46 patients ≥ 60 y. In genotype 2/3, SVR was 74.4% in 215 patients < 60 y and 84% in 25 patients ≥ 60 y. However, GEE model showed that age had no influence on achieving SVR (Odds ratio 0.91). Confounders influenced SVR as known from previous studies (cirrhosis, genotype 1/4, previous treatment and viral load >600'000 IE/ml as negative predictive factors). When young and elderly patients were matched (analysis in 59 elderly patients), SVR was not different in these patient groups (54.2% and 55.9%, resp.; p=0.795 in binomial test). The classification tree-derived best criterion for SVR in elderly patients was genotype, with no further criteria relevant for predicting SVR in genotype 2/3. In patients with genotype 1/4, further criteria were presence of cirrhosis and low viral load <600'000 IE/ml in non-cirrhotic patients. Conclusions: Age is not a relevant predictive factor for achieving SVR, when confounders were taken into account. In terms of effectiveness of antiviral therapy, age does not play a major role and should not be regarded as relevant negative predictive factor. Since life expectancy in Switzerland at age 60 is more than 22 y, hepatitis C therapy is reasonable in elderly patients with known relevant fibrosis or cirrhosis, because interferon-based hepatitis C therapy improves survival and reduces carcinogenesis.

Gender inequality in health among elderly people in a combined framework of socioeconomic position, family characteristics and social support

This study analyses gender inequalities in health among elderly people in Catalonia (Spain) by adopting a conceptual framework that globally considers three dimensions of health determinants : socio-economic position, family characteristics and social support. Data came from the 2006 Catalonian Health Survey. For the purposes of this study a sub-sample of people aged 65–85 years with no paid job was selected (1,113 men and 1,484 women). The health outcomes analysed were self-perceived health status, poor mental health status and long-standing limiting illness. Multiple logistic regression models separated by sex were fitted and a hierarchical model was fitted in three steps. Health status among elderly women was poorer than among the men for the three outcomes analysed. Whereas living with disabled people was positively related to the three health outcomes and confidant social support was negatively associated with all of them in both sexes, there were gender differences in other social determinants of health. Our results emphasise the importance of using an integrated approach for the analysis of health inequalities among elderly people, simultaneously considering socio-economic position, family characteristics and social support, as well as different health indicators, in order fully to understand the social determinants of the health status of older men and women.

Gender differences in paediatric patients of the swiss inflammatory bowel disease cohort study.

PURPOSE: Gender differences in paediatric patients with inflammatory bowel disease (IBD) are frequently reported as a secondary outcome and the results are divergent. To assess gender differences by analysing data collected within the Swiss IBD cohort study database since 2008, related to children with IBD, using the Montreal classification for a systematic approach. METHODS: Data on gender, age, anthropometrics, disease location at diagnosis, disease behaviour, and therapy of 196 patients, 105 with Crohn's disease (CD) and 91 with ulcerative or indeterminate colitis (UC/IC) were retrieved and analysed. RESULTS: THE CRUDE GENDER RATIO (MALE : female) of patients with CD diagnosed at <10 years of age was 2.57, the adjusted ratio was 2.42, and in patients with UC/IC it was 0.68 and 0.64 respectively. The non-adjusted gender ratio of patients diagnosed at ≥10 years was 1.58 for CD and 0.88 for UC/IC. Boys with UC/IC diagnosed <10 years of age had a longer diagnostic delay, and in girls diagnosed with UC/IC >10 years a more important use of azathioprine was observed. No other gender difference was found after analysis of age, disease location and behaviour at diagnosis, duration of disease, familial occurrence of IBD, prevalence of extra-intestinal manifestations, complications, and requirement for surgery. CONCLUSION: CD in children <10 years affects predominantly boys with a sex ratio of 2.57; the impact of sex-hormones on the development of CD in pre-pubertal male patients should be investigated.

Event-related potential analyses via single-subject topographic classification.

Introduction: Responses to external stimuli are typically investigated by averaging peri-stimulus electroencephalography (EEG) epochs in order to derive event-related potentials (ERPs) across the electrode montage, under the assumption that signals that are related to the external stimulus are fixed in time across trials. We demonstrate the applicability of a single-trial model based on patterns of scalp topographies (De Lucia et al, 2007) that can be used for ERP analysis at the single-subject level. The model is able to classify new trials (or groups of trials) with minimal a priori hypotheses, using information derived from a training dataset. The features used for the classification (the topography of responses and their latency) can be neurophysiologically interpreted, because a difference in scalp topography indicates a different configuration of brain generators. An above chance classification accuracy on test datasets implicitly demonstrates the suitability of this model for EEG data. Methods: The data analyzed in this study were acquired from two separate visual evoked potential (VEP) experiments. The first entailed passive presentation of checkerboard stimuli to each of the four visual quadrants (hereafter, "Checkerboard Experiment") (Plomp et al, submitted). The second entailed active discrimination of novel versus repeated line drawings of common objects (hereafter, "Priming Experiment") (Murray et al, 2004). Four subjects per experiment were analyzed, using approx. 200 trials per experimental condition. These trials were randomly separated in training (90%) and testing (10%) datasets in 10 independent shuffles. In order to perform the ERP analysis we estimated the statistical distribution of voltage topographies by a Mixture of Gaussians (MofGs), which reduces our original dataset to a small number of representative voltage topographies. We then evaluated statistically the degree of presence of these template maps across trials and whether and when this was different across experimental conditions. Based on these differences, single-trials or sets of a few single-trials were classified as belonging to one or the other experimental condition. Classification performance was assessed using the Receiver Operating Characteristic (ROC) curve. Results: For the Checkerboard Experiment contrasts entailed left vs. right visual field presentations for upper and lower quadrants, separately. The average posterior probabilities, indicating the presence of the computed template maps in time and across trials revealed significant differences starting at ~60-70 ms post-stimulus. The average ROC curve area across all four subjects was 0.80 and 0.85 for upper and lower quadrants, respectively and was in all cases significantly higher than chance (unpaired t-test, p<0.0001). In the Priming Experiment, we contrasted initial versus repeated presentations of visual object stimuli. Their posterior probabilities revealed significant differences, which started at 250ms post-stimulus onset. The classification accuracy rates with single-trial test data were at chance level. We therefore considered sub-averages based on five single trials. We found that for three out of four subjects' classification rates were significantly above chance level (unpaired t-test, p<0.0001). Conclusions: The main advantage of the present approach is that it is based on topographic features that are readily interpretable along neurophysiologic lines. As these maps were previously normalized by the overall strength of the field potential on the scalp, a change in their presence across trials and between conditions forcibly reflects a change in the underlying generator configurations. The temporal periods of statistical difference between conditions were estimated for each training dataset for ten shuffles of the data. Across the ten shuffles and in both experiments, we observed a high level of consistency in the temporal periods over which the two conditions differed. With this method we are able to analyze ERPs at the single-subject level providing a novel tool to compare normal electrophysiological responses versus single cases that cannot be considered part of any cohort of subjects. This aspect promises to have a strong impact on both basic and clinical research.

Metabolically healthy obesity: different prevalences using different criteria.

To estimate the prevalence of metabolically healthy obesity (MHO) according to different definitions. Population-based sample of 2803 women and 2557 men participated in the study. Metabolic abnormalities were defined using six sets of criteria, which included different combinations of the following: waist; blood pressure; total, high-density lipoprotein or low-density lipoprotein-cholesterol; triglycerides; fasting glucose; homeostasis model assessment; high-sensitivity C-reactive protein; personal history of cardiovascular, respiratory or metabolic diseases. For each set, prevalence of MHO was assessed for body mass index (BMI); waist or percent body fat. Among obese (BMI 30 kg/m(2)) participants, prevalence of MHO ranged between 3.3 and 32.1% in men and between 11.4 and 43.3% in women according to the criteria used. Using abdominal obesity, prevalence of MHO ranged between 5.7 and 36.7% (men) and 12.2 and 57.5% (women). Using percent body fat led to a prevalence of MHO ranging between 6.4 and 43.1% (men) and 12.0 and 55.5% (women). MHO participants had a lower odd of presenting a family history of type 2 diabetes. After multivariate adjustment, the odds of presenting with MHO decreased with increasing age, whereas no relationship was found with gender, alcohol consumption or tobacco smoking using most sets of criteria. Physical activity was positively related, whereas increased waist was negatively related with BMI-defined MHO. MHO prevalence varies considerably according to the criteria used, underscoring the need for a standard definition of this metabolic entity. Physical activity increases the likelihood of presenting with MHO, and MHO is associated with a lower prevalence of family history of type 2 diabetes.

Condom use with steady partners among heterosexual people living with HIV in Europe: testing the information-motivation-behavioral skills model.

Guided by a modified information-motivation-behavioral skills model, this study identified predictors of condom use among heterosexual people living with HIV with their steady partners. Consecutive patients at 14 European HIV outpatient clinics received an anonymous, standardized, self-administered questionnaire between March and December 2007. Data were analyzed using descriptive statistics and two-step backward elimination regression analyses stratified by gender. The survey included 651 participants (n = 364, 56% women; n = 287, 44%). Mean age was 39 years for women and 43 years for men. Most had acquired HIV sexually and more than half were in a serodiscordant relationship. Sixty-three percent (n = 229) of women and 59% of men (n = 169) reported at least one sexual encounter with a steady partner 6 months prior to the survey. Fifty-one percent (n = 116) of women and 59% of men (n = 99) used condoms consistently with that partner. In both genders, condom use was positively associated with subjective norm conducive to condom use, and self-efficacy to use condoms. Having a partner whose HIV status was positive or unknown reduced condom use. In men, higher education and knowledge about condom use additionally increased condom use, while the use of erectile-enhancing medication decreased it. For women, HIV disclosure to partners additionally reduced the likelihood of condom use. Positive attitudes to condom use and subjective norm increased self-efficacy in both genders, however, a number of gender-related differences appeared to influence self-efficacy. Service providers should pay attention to the identified predictors of condom use and adopt comprehensive and gender-related approaches for preventive interventions with people living with HIV.

Gender and attachment representations in the preschool years: comparisons between five countries

Bowlby proposed that the individual's social experiences, as early as in infancy, contribute to the construction of Internal Working Models (IWMs) of attachment, which will later guide the individual's expectations and behaviors in close relationships all along his or her life. The qualitative, individual characteristics of these models reflect the specificity of the individual's early experiences with attachment figures. The attachment literature globally shows that the qualities of IWMs are neither gender specific nor cultural specific. Procedures to evaluate IWMs in adulthood have been well established, based on narrative accounts of childhood experiences. Narrative procedures at earlier ages (e.g., in the preschool years) have been proposed, such as Bretherton's Attachment Story Completion Task (ASCT), to evaluate attachment representations. More than 500 ASCT narratives of preschoolers, coming from five different countries, have been collected, in the perspective of examining possible interactions between gender and culture regarding attachment representations. A specific Q-Sort coding procedure (CCH) has been used to evaluate several dimensions of the narratives. Girls' narratives appeared as systematically more secure than those of same-age boys, whatever their culture. The magnitude of gender differences, however, varied between countries. Taylor's model of gender-specific responses to stress and Harwood's and Posada's hypothesis on inter-cultural differences regarding caregiving are evoked to understand the differences across gender and countries.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical {\sc vc} dimension, empirical {\sc vc} entropy, andmargin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Success of a general school-based physical activity intervention on bone mineral content depends on pubertal stage but not on gender

Aims: We performed a randomised controlled trial in children of both gender and different pubertal stages to determine whether a school-based physical activity (PA) program during a full schoolyear influences bone mineral content (BMC) and whether there are differences in response for boys and girls before and during puberty. Methods: Twenty-eight 1st and 5th grade classes were cluster randomised to an intervention (INT, 16 classes, n=297) and control (CON; 12 classes, n=205) group. The intervention consisted of a multi-component PA intervention including daily physical education during a full school year. Each lesson was predetermined, included about ten minutes of jumping or strength training exercises of various intensity and was the same for all children. Measurements included anthropometry (height and weight), tanner stages (by self-assessment), PA (by accelerometry) and BMC for total body, femoral neck, total hip and lumbar spine using dualenergy X-ray absorptiometry (DXA). Bone parameters were normalized for gender and tanner stage (pre- vs. puberty). Analyses were performed by a regression model adjusted for gender, baseline height, baseline weight, baseline PA, post-intervention tanner stage, baseline BMC, and cluster. Researchers were blinded to group allocation. Children in the control group did not know about the intervention arm. Results: 217 (57%) of 380 children who initially agreed to have DXA measurements had also post-intervention DXA and PA data. Mean age of prepubertal and pubertal children at baseline was 9.0±2.1 and 11.2±0.6 years, respectively. 47/114 girls and 68/103 boys were prepubertal at the end of the intervention. Compared to CON, children in INT showed statistically significant increases in BMC of total body (adjusted z-score differences: 0.123; 95%>CI 0.035 to 0.212), femoral neck (0.155; 95%>CI 0.007 to 0.302), and lumbar spine (0.127; 95%>CI 0.026 to 0.228). Importantly, there was no gender*group, but a tanner*group interaction consistently favoring prepubertal children. Conclusions: Our findings show that a general, but stringent school-based PA intervention can improve BMC in elementary school children. Pubertal stage, but not gender seems to determine bone sensitivity to physical activity loading.