Automated supervised classification of variable stars in the CoRoT programme Method and application to the first four exoplanet fields

Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.

Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

A prática de exercícios físicos em pacientes com transtornos alimentares

CONTEXTO: Diferentes estudos discutem a relação da prática excessiva de exercícios físicos com transtornos alimentares como estratégia para perda de peso. OBJETIVO: Revisar a literatura sobre a prática de exercícios físicos em pacientes com transtornos alimentares, discutindo definições, critérios diagnósticos e propostas terapêuticas. MÉTODOS: Levantamento bibliográfico foi realizado por meio de MedLine, LiLacs e Cochrane Library, com os termos "transtornos alimentares", "anorexia", "bulimia", "exercício físico excessivo", "atividade física", "exercício obrigatório", "exercício compulsivo" e "exercício excessivo". RESULTADOS: Dos 80 artigos encontrados, foram selecionados 12 que incluíam a investigação de um padrão de atividade física considerado excessivo em indivíduos acima dos 18 anos e uso de algum instrumento de avaliação para essa finalidade. A prática de exercícios físicos em pacientes com transtornos do comportamento alimentar é revisada. CONCLUSÃO: Não há consenso sobre critérios diagnósticos e instrumentos para considerar o exercício físico como inadequado ou excessivo e seu uso como recurso para perder peso. Por outro lado, a prática de exercícios físicos durante o tratamento de pacientes com transtornos alimentares pode ser benéfica desde que orientada e supervisionada.

Diferenças na adesão ao tratamento da tuberculose em relação ao sexo no distrito de saúde da Freguesia do Ó/Brasilândia - São Paulo

Os motivos para as diferenças epidemiológicas e para a adesão ao tratamento da tuberculose em relação a homens e mulheres são desconhecidos. Este trabalho tem como objetivo verificar diferenças na adesão ao tratamento da tuberculose em relação ao sexo; identificar aspectos facilitadores e dificultadores para a adesão ao tratamento da tuberculose em relação ao sexo; analisar as crenças consideradas importantes para a adesão ao tratamento da tuberculose. Foi utilizado o referencial teórico do Modelo de Crenças em Saúde de Rosenstock e a técnica da Análise de Conteúdos de Bardin. Foram realizadas 28 entrevistas semiestruturadas com homens e mulheres em tratamento supervisionado de tuberculose do Distrito de Saúde da Freguesia do Ó/Brasilândia. Os resultados mostraram que o perfil daqueles que falharam na terapêutica da tuberculose em relação ao sexo foi: mulher - solteira e separada, com atividade remunerada não comprovada, nível de escolaridade entre fundamental I completo e ensino médio completo; homem - casado, com atividade remunerada comprovada, nível de escolaridade entre ensino fundamental II completo e ensino médio completo. Os aspectos facilitadores encontrados para a boa adesão residem no bom atendimento dos profissionais de saúde e na percepção, por parte do paciente, da sua melhora de saúde. As crenças para a boa adesão ao tratamento no sexo masculino e feminino foram: bom atendimento do serviço de saúde e bom tratamento (em relação aos medicamentos).

Gastroenterology training in Latin America

Latin America is characterized by ethnic, geographical, cultural, and economic diversity; therefore, training in gastroenterology in the region must be considered in this context. The continent's medical education is characterized by a lack of standards and the volume of research continues to be relatively small. There is a multiplicity of events in general gastroenterology and in sub-disciplines, both at regional and local levels, which ensure that many colleagues have access to information. Medical education programs must be based on a clinical vision and be considered in close contact with the patients. The programs should be properly supervised, appropriately defined, and evaluated on a regular basis. The disparity between the patients' needs, the scarce resources available, and the pressures exerted by the health systems on doctors are frequent cited by those complaining of poor professionalism. Teaching development can play a critical role in ensuring the quality of teaching and learning in universities. Continuing professional development programs activities must be planned on the basis of the doctors' needs, with clearly defined objectives and using proper learning methodologies designed for adults. They must be evaluated and accredited by a competent body, so that they may become the basis of a professional regulatory system. The specialty has made progress in the last decades, offering doctors various possibilities for professional development. The world gastroenterology organization has contributed to the speciality through three distinctive, but closely inter-related, programs: Training Centers, Train-the-Trainers, and Global Guidelines, in which Latin America is deeply involved. (C) 2011 Baishideng. All rights reserved.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

New concept for lifting in onshore oil wells

This paper presents the development of a prototype of a tubular linear induction motor applied to onshore oil exploitation, named MAT AE OS (which is the Portuguese acronym for Tubular Asynchronous Motor for Onshore Oil Exploitation). The function of this motor is to directly drive the sucker-rod pump installed in the down hole of the oil well. Considering the drawbacks and operational costs of the conventional oil extraction method, which is based on the walking beam and rod, string system, the developed prototype is intended to become a feasible alternative from both technical and economic points of view. At the present time, the MAT AE OS prototype is installed in a test bench at the Applied Electromagnetism Laboratory at the Escola Politecnica da Universidade de Sao Paulo. The complete testing system is controlled and supervised by special software, enabling good flexibility in operation, data acquisition, and performance analysis. The test results indicate that the motor develops a constant lift force along the pumping cycle, as shown by the measured dynamometric charts. Also, the evaluated electromechanical performance seems to be superior to that obtained by the traditional method. The system utilizing the MAT AE OS prototype allows the complete elimination of the rod string sets required by the conventional equipment, indicating that the new system may advantageously replace the surface mechanical components presently utilized.

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcriptional responses of host peripheral blood cells to tuberculosis infection

Host responses following exposure to Mycobacterium tuberculosis (TB) are complex and can significantly affect clinical outcome. These responses, which are largely mediated by complex immune mechanisms involving peripheral blood cells (PBCs) such as T-lymphocytes, NK cells and monocyte-derived macrophages, have not been fully characterized. We hypothesize that different clinical outcome following TB exposure will be uniquely reflected in host gene expression profiles, and expression profiling of PBCs can be used to discriminate between different TB infectious outcomes. In this study, microarray analysis was performed on PBCs from three TB groups (BCG-vaccinated, latent TB infection, and active TB infection) and a control healthy group. Supervised learning algorithms were used to identify signature genomic responses that differentiate among group samples. Gene Set Enrichment Analysis was used to determine sets of genes that were co-regulated. Multivariate permutation analysis (p < 0.01) gave 645 genes differentially expressed among the four groups, with both distinct and common patterns of gene expression observed for each group. A 127-probeset, representing 77 known genes, capable of accurately classifying samples into their respective groups was identified. In addition, 13 insulin-sensitive genes were found to be differentially regulated in all three TB infected groups, underscoring the functional association between insulin signaling pathway and TB infection. Published by Elsevier Ltd.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Aerobic Exercise Improves Reverse Cholesterol Transport in Cholesteryl Ester Transfer Protein Transgenic Mice

We analyzed the effect of a 6-week aerobic exercise training program on the in vivo macrophage reverse cholesterol transport (RCT) in human cholesteryl ester transfer protein (CETP) transgenic (CETP-tg) mice. Male CETP-tg mice were randomly assigned to a sedentary group or a carefully supervised exercise training group (treadmill 15 m/min, 30 min sessions, five sessions per week). The levels of plasma lipids were determined by enzymatic methods, and the lipoprotein profile was determined by fast protein liquid chromatography (FPLC). CETP activity was determined by measuring the transfer rate of (14)C-cholesterol from HDL to apo-B containing lipoproteins, using plasma from CETP-tg mice as a source of CETP. The reverse cholesterol transport was determined in vivo by measuring the [(3)H]-cholesterol recovery in plasma and feces (24 and 48 h) and in the liver (48 h) following a peritoneal injection of [(3)H]-cholesterol labeled J774-macrophages into both sedentary and exercise trained mice. The protein levels of liver receptors were determined by immunoblot, and the mRNA levels for liver enzymes were measured using RT-PCR. Exercise training did not significantly affect the levels of plasma lipids or CETP activity. The HDL fraction assessed by FPLC was higher in exercise-trained compared to sedentary mice. In comparison to the sedentary group, a greater recovery of [(3)H]-cholesterol from the injected macrophages was found in the plasma, liver and feces of exercise-trained animals. The latter occurred even with a reduction in the liver CYP7A1 mRNA level in exercised trained animals. Exercise training increased the liver LDL receptor and ABCA-1 protein levels, although the SR-BI protein content was unchanged. The RCT benefit in CETP-tg mice elicited by exercise training helps to elucidate the role of exercise in the prevention of atherosclerosis in humans.