Associação entre bebidas adoçadas e consumo calórico em refeições na população brasileira.

Nas últimas décadas, tem sido observado o aumento da oferta de bebidas com elevado conteúdo calórico e com grandes quantidades de açúcar de rápida absorção. Essas bebidas adoçadas, cujo consumo tem aumentado no Brasil assim como em outras partes do mundo, são consideradas fatores de risco para obesidade e diabetes. O consumo de bebidas adoçadas pode levar ao balanço energético positivo e consequentemente ao ganho de peso. Essa associação pode ser explicada pelo mecanismo regulatório de compensação de calorias líquidas. Compensação calórica ocorre quando há redução no consumo de calorias provenientes de alimentos sólidos para compensar as calorias líquidas adicionadas à refeição ou dieta. No entanto, não há consenso em relação a evidências da compensação calórica, dificultando a elaboração de recomendações sobre essas bebidas em saúde pública. Razões para a falta de consenso incluem a diversidade de desenhos de estudos, experimentos realizados em ambientes controlados e não reais em relação ao consumo de alimentos e bebidas, e estudos com amostras pequenas ou de conveniência. Esta dissertação estudou a associação entre bebidas adoçadas e consumo calórico, verificando se calorias de bebidas adoçadas são compensadas em refeições realizadas em um ambiente pragmático. Os dados de consumo calórico de 34.003 indivíduos, com idade igual ou superior a dez anos, foram obtidos pelo Inquérito Nacional de Alimentação 2008-2009, em todo território nacional. Os participantes completaram dois registros alimentares, em dias não consecutivos da mesma semana. Foram selecionadas as refeições dos períodos café da manhã, almoço e jantar de cada indivíduo em cada um dos dias. Para cada refeição, foi calculado o valor calórico de alimentos e de bebidas adoçadas consumidos. Para testar a compensação calórica, um modelo de regressão linear multinível com efeitos mistos foi ajustado para analisar cada período. A variável reposta utilizada foi consumo calórico proveniente de alimentos e a variável explicativa foi consumo calórico de bebida adoçada na refeição. Os efeitos intra-indivíduo da bebida adoçada no consumo calórico foram estimados e interpretados. Esses efeitos são considerados não-enviesados pois são controlados pelas características constantes dos indivíduos, tendo assim o indivíduo atuando como seu próprio controle na análise. Covariadas incluídas no modelo foram variáveis da refeição: local, dia da semana, horário, consumo calórico na refeição anterior e intervalo de tempo desde a última refeição; e do indivíduo: sexo, faixa etária, categoria de Índice de Massa Corpórea e quartos de renda per capita. Efeitos aleatórios dos indivíduos e dos domicílios foram incluídos no modelo para melhor estimar a estrutura de erros de dados correlacionados. A compensação calórica foi de 42% para o café da manhã, não houve compensação no almoço e para o jantar, compensação variou de 0 a 22%, tendo interação com quartos de renda per capita. A conclusão desta dissertação é que as bebidas adoçadas não são completamente compensadas em refeições realizadas em ambiente pragmático. Assim, a redução do consumo de bebidas adoçadas em refeições pode ajudar a diminuir o consumo calórico excessivo e levar a um melhor controle do peso em indivíduos.

Desarrollo de estrategias ambientales que contribuyan a la reducción de los impactos ambientales en la ejecución de obras de ingeniería civil

Los diferentes sectores de la economía ejercen múltiples actividades que impulsan el crecimiento socio-económico de los diferentes países, sin embargo dichas actividades tienen repercusiones importantes a nivel ambiental, dentro de las que se encuentran transformaciones irreversibles del planeta, tanto físicas como químicas. Gracias a esto, en la actualidad, las empresas y organizaciones han empezado a controlar con mayor responsabilidad sus respectivos procesos y operaciones, buscando no solo mitigar los impactos negativos ocasionados al medio ambiente y a la sociedad sino la optimización en el uso de recursos tanto físicos como económicos. El presente estudio tiene como finalidad analizar las operaciones que se realizan en una obra civil, con el fin de identificar cuáles son las principales causantes de contaminación, por otro lado se hará mención de como la normatividad y legislación colombiana aplica y ejerce control en cada uno de los mismos, finalmente se plantearán diferentes soluciones y alternativas para que dicha industria pueda implementarlas en sus quehaceres diarios. Para lograr lo anterior se utilizaron diferentes herramientas que facilitaron la obtención de datos e información para el estudio tales como: entrevistas a los miembros y participantes de la obra civil, visitas de campo, recopilación de información de estudios similares, realización de la matriz de aspectos e impactos y fichas ambientales, entre otras. Los resultados obtenidos permitieron entender que es inevitable que esta industria no genere ciertas contaminaciones e impactos negativos, además de identificar que la normativa del país en cuanto control ambiental se encuentra algo atrasada, factor que fue determinante a la hora de proponer distintas alternativas que buscan tanto facilitar las prácticas que el sector de la construcción tiene en el país como minimizar al máximo los impactos ambientales negativos ocasionados.

Genomic evidence for the Pleistocene and recent population history of Native Americans

How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative “Paleoamerican” relict populations, including the historical Mexican Pericúes and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.

Quality indices for (practical) clustering evaluation

Clustering quality or validation indices allow the evaluation of the quality of clustering in order to support the selection of a specific partition or clustering structure in its natural unsupervised environment, where the real solution is unknown or not available. In this paper, we investigate the use of quality indices mostly based on the concepts of clusters` compactness and separation, for the evaluation of clustering results (partitions in particular). This work intends to offer a general perspective regarding the appropriate use of quality indices for the purpose of clustering evaluation. After presenting some commonly used indices, as well as indices recently proposed in the literature, key issues regarding the practical use of quality indices are addressed. A general methodological approach is presented which considers the identification of appropriate indices thresholds. This general approach is compared with the simple use of quality indices for evaluating a clustering solution.

Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

Comportamento do camarão de água doce macrobrachium rosenbergii (De Man, 1879) em cultivos misto e monossexo

Understanding the behavioral activities of freshwater shrimp in captivity is of paramount importance for the appropriate management of the species. In Brazil, the shrimp Macrobrachium rosenbergii is currently the most widely used species in the freshwater shrimp culture due to its high potential for cultivation and good market acceptance. Thus, the present study aimed to describe and characterize the behavioral activities of M. rosenbergii in monosex and in mixed (male and female) (manuscript 1, 2 and 3) populations and the growth performance of this species in restrictive feeding conditions and in different feeding management (manuscript 4 and 5, respectively) . Juvenile and adult shrimps were collected from ponds of the Aquaculture Station - Unidade Especializada em Ciências Agrárias - Universidade Federal do Rio Grande do Norte (UFRN), Macaíba/RN and then transferred to the Laboratório de Estudos do Comportamento do Camarão LECC (Laboratory for Shrimp Behavioral Studies) of the Universidade Federal do Rio Grande do Norte (UFRN). For each treatment , eight aquaria of 250 L (50 cm x 50 cm x 100 cm) were used in a closed recirculating water system with artificial lighting, constant aeration , continuous filtration through a biochemical and biological filter (canister filter), and fine sand as substrate . The water quality was monitored daily. The lab consisted of two rooms with artificial lighting system , controlled by a timer with dark / light cycle of 12:12 h . In manuscript 1, the behavioral categories of the species were presented through an ethogram, which described 31 behaviors, subdivided into general and agonistic behaviors. Manuscript 2 compared the behavioral profile of shrimps in male and in female monosex and mixed populations over 24 hours in laboratory. In three types (mixed, male monosex and female monosex) of populations during the light and dark phases of the 24 hour cycle, the shrimps showed higher occurrence of cleaning behavior. Manuscript 3 examined the influence of the color of the shelter on the frequency of its use and behavioral activities of shrimp in mixed, in male monosex and in female monosex populations over 24 hours. We observed that the shrimp M. rosenbergii burrow more frequently during the light phase in male monosex and mixed populations; they also tend to choose the black shelters. Female monosex populations tend to use red and orange shelters. In manuscript 4, we evaluated in laboratory the behavioral activities and growth performance of juvenile shrimps under food restriction. We observed that a mild food restriction may be used since there is no loss concerning the growth of the animals; feeding management on alternate days , compared to daily management can be financially productive both reducing labor costs and reducing the amount of feed used . Manuscript 5 evaluated the behavior of shrimps in monosex and in mixed populations, as well as the latency of reach the food according to feed offer (tray or food dispersal) . Our results indicate that animals adjust to both types of feed offer food dispersal as much as tray, but they spend more time to reach the feed when it is offered in trays (feeders). Comparing culture types (mixed, male monosex and female monosex), the latency to reach the food was lower for female monosex population. The data obtained in this study demonstrate the importance of identifying different pressures and environmental stimuli on the behavioral responses of this species. This knowledge would support management improvement to optimize the levels of animals‟ welfare, resulting in a better zootecnical performance

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Uma Análise Comparativa entre Sistemas de Combinação de Classificadores com Distribuição Vertical dos Dados

In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this work, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components

Uma análise da aplicação do modelo de Rede Neural RePART em Comitês de classificadores

RePART (Reward/Punishment ART) is a neural model that constitutes a variation of the Fuzzy Artmap model. This network was proposed in order to minimize the inherent problems in the Artmap-based model, such as the proliferation of categories and misclassification. RePART makes use of additional mechanisms, such as an instance counting parameter, a reward/punishment process and a variable vigilance parameter. The instance counting parameter, for instance, aims to minimize the misclassification problem, which is a consequence of the sensitivity to the noises, frequently presents in Artmap-based models. On the other hand, the use of the variable vigilance parameter tries to smoouth out the category proliferation problem, which is inherent of Artmap-based models, decreasing the complexity of the net. RePART was originally proposed in order to minimize the aforementioned problems and it was shown to have better performance (higer accuracy and lower complexity) than Artmap-based models. This work proposes an investigation of the performance of the RePART model in classifier ensembles. Different sizes, learning strategies and structures will be used in this investigation. As a result of this investigation, it is aimed to define the main advantages and drawbacks of this model, when used as a component in classifier ensembles. This can provide a broader foundation for the use of RePART in other pattern recognition applications

Analisando o desempenho do ClassAge: um sistema multiagentes para classificação de padrões

The use of multi-agent systems for classification tasks has been proposed in order to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems. As a result, the NeurAge system was proposed. This system is composed by several neural agents which communicate and negotiate a common result for the testing patterns. In the NeurAge system, a negotiation method is very important to the overall performance of the system since the agents need to reach and agreement about a problem when there is a conflict among the agents. This thesis presents an extensive analysis of the NeurAge System where it is used all kind of classifiers. This systems is now named ClassAge System. It is aimed to analyze the reaction of this system to some modifications in its topology and configuration

Investigando a combinação de técnicas de aprendizado semissupervisionado e classificação hierárquica multirrótulo

Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been identified a series of classification tasks in which the samples can be labeled at more than one class simultaneously (multi-label classification). Additionally, these classes can be hierarchically organized (hierarchical classification and hierarchical multi-label classification). On the other hand, we have also studied a new category of learning, called semi-supervised learning, combining labeled data (supervised learning) and non-labeled data (unsupervised learning) during the training phase, thus reducing the need for a large amount of labeled data when only a small set of labeled samples is available. Thus, since both the techniques of multi-label and hierarchical multi-label classification as semi-supervised learning has shown favorable results with its use, this work is proposed and used to apply semi-supervised learning in hierarchical multi-label classication tasks, so eciently take advantage of the main advantages of the two areas. An experimental analysis of the proposed methods found that the use of semi-supervised learning in hierarchical multi-label methods presented satisfactory results, since the two approaches were statistically similar results

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Recent trends in intelligent data analysis

European Regional Development Fund

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.