Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Regression models with MoPs Bayesian networks

We present a model of Bayesian network for continuous variables, where densities and conditional densities are estimated with B-spline MoPs. We use a novel approach to directly obtain conditional densities estimation using B-spline properties. In particular we implement naive Bayes and wrapper variables selection. Finally we apply our techniques to the problem of predicting neurons morphological variables from electrophysiological ones.

Desarrollo de metodologías para mejorar la estimación de los hidrogramas de diseño para el cálculo de los órganos de desagüe de las presas

En la presente Tesis se ha llevado a cabo el contraste y desarrollo de metodologías que permitan mejorar el cálculo de las avenidas de proyecto y extrema empleadas en el cálculo de la seguridad hidrológica de las presas. En primer lugar se ha abordado el tema del cálculo de las leyes de frecuencia de caudales máximos y su extrapolación a altos periodos de retorno. Esta cuestión es de gran relevancia, ya que la adopción de estándares de seguridad hidrológica para las presas cada vez más exigentes, implica la utilización de periodos de retorno de diseño muy elevados cuya estimación conlleva una gran incertidumbre. Es importante, en consecuencia incorporar al cálculo de los caudales de diseño todas la técnicas disponibles para reducir dicha incertidumbre. Asimismo, es importante hacer una buena selección del modelo estadístico (función de distribución y procedimiento de ajuste) de tal forma que se garantice tanto su capacidad para describir el comportamiento de la muestra, como para predecir de manera robusta los cuantiles de alto periodo de retorno. De esta forma, se han realizado estudios a escala nacional con el objetivo de determinar el esquema de regionalización que ofrece mejores resultados para las características hidrológicas de las cuencas españolas, respecto a los caudales máximos anuales, teniendo en cuenta el numero de datos disponibles. La metodología utilizada parte de la identificación de regiones homogéneas, cuyos límites se han determinado teniendo en cuenta las características fisiográficas y climáticas de las cuencas, y la variabilidad de sus estadísticos, comprobando posteriormente su homogeneidad. A continuación, se ha seleccionado el modelo estadístico de caudales máximos anuales con un mejor comportamiento en las distintas zonas de la España peninsular, tanto para describir los datos de la muestra como para extrapolar a los periodos de retorno más altos. El proceso de selección se ha basado, entre otras cosas, en la generación sintética de series de datos mediante simulaciones de Monte Carlo, y el análisis estadístico del conjunto de resultados obtenido a partir del ajuste de funciones de distribución a estas series bajo distintas hipótesis. Posteriormente, se ha abordado el tema de la relación caudal-volumen y la definición de los hidrogramas de diseño en base a la misma, cuestión que puede ser de gran importancia en el caso de presas con grandes volúmenes de embalse. Sin embargo, los procedimientos de cálculo hidrológico aplicados habitualmente no tienen en cuenta la dependencia estadística entre ambas variables. En esta Tesis se ha desarrollado un procedimiento para caracterizar dicha dependencia estadística de una manera sencilla y robusta, representando la función de distribución conjunta del caudal punta y el volumen en base a la función de distribución marginal del caudal punta y la función de distribución condicionada del volumen respecto al caudal. Esta última se determina mediante una función de distribución log-normal, aplicando un procedimiento de ajuste regional. Se propone su aplicación práctica a través de un procedimiento de cálculo probabilístico basado en la generación estocástica de un número elevado de hidrogramas. La aplicación a la seguridad hidrológica de las presas de este procedimiento requiere interpretar correctamente el concepto de periodo de retorno aplicado a variables hidrológicas bivariadas. Para ello, se realiza una propuesta de interpretación de dicho concepto. El periodo de retorno se entiende como el inverso de la probabilidad de superar un determinado nivel de embalse. Al relacionar este periodo de retorno con las variables hidrológicas, el hidrograma de diseño de la presa deja de ser un único hidrograma para convertirse en una familia de hidrogramas que generan un mismo nivel máximo en el embalse, representados mediante una curva en el plano caudal volumen. Esta familia de hidrogramas de diseño depende de la propia presa a diseñar, variando las curvas caudal-volumen en función, por ejemplo, del volumen de embalse o la longitud del aliviadero. El procedimiento propuesto se ilustra mediante su aplicación a dos casos de estudio. Finalmente, se ha abordado el tema del cálculo de las avenidas estacionales, cuestión fundamental a la hora de establecer la explotación de la presa, y que puede serlo también para estudiar la seguridad hidrológica de presas existentes. Sin embargo, el cálculo de estas avenidas es complejo y no está del todo claro hoy en día, y los procedimientos de cálculo habitualmente utilizados pueden presentar ciertos problemas. El cálculo en base al método estadístico de series parciales, o de máximos sobre un umbral, puede ser una alternativa válida que permite resolver esos problemas en aquellos casos en que la generación de las avenidas en las distintas estaciones se deba a un mismo tipo de evento. Se ha realizado un estudio con objeto de verificar si es adecuada en España la hipótesis de homogeneidad estadística de los datos de caudal de avenida correspondientes a distintas estaciones del año. Asimismo, se han analizado los periodos estacionales para los que es más apropiado realizar el estudio, cuestión de gran relevancia para garantizar que los resultados sean correctos, y se ha desarrollado un procedimiento sencillo para determinar el umbral de selección de los datos de tal manera que se garantice su independencia, una de las principales dificultades en la aplicación práctica de la técnica de las series parciales. Por otra parte, la aplicación practica de las leyes de frecuencia estacionales requiere interpretar correctamente el concepto de periodo de retorno para el caso estacional. Se propone un criterio para determinar los periodos de retorno estacionales de forma coherente con el periodo de retorno anual y con una distribución adecuada de la probabilidad entre las distintas estaciones. Por último, se expone un procedimiento para el cálculo de los caudales estacionales, ilustrándolo mediante su aplicación a un caso de estudio. The compare and develop of a methodology in order to improve the extreme flow estimation for dam hydrologic security has been developed. First, the work has been focused on the adjustment of maximum peak flows distribution functions from which to extrapolate values for high return periods. This has become a major issue as the adoption of stricter standards on dam hydrologic security involves estimation of high design return periods which entails great uncertainty. Accordingly, it is important to incorporate all available techniques for the estimation of design peak flows in order to reduce this uncertainty. Selection of the statistical model (distribution function and adjustment method) is also important since its ability to describe the sample and to make solid predictions for high return periods quantiles must be guaranteed. In order to provide practical application of previous methodologies, studies have been developed on a national scale with the aim of determining a regionalization scheme which features best results in terms of annual maximum peak flows for hydrologic characteristics of Spanish basins taking into account the length of available data. Applied methodology starts with the delimitation of regions taking into account basin’s physiographic and climatic characteristics and the variability of their statistical properties, and continues with their homogeneity testing. Then, a statistical model for maximum annual peak flows is selected with the best behaviour for the different regions in peninsular Spain in terms of describing sample data and making solid predictions for high return periods. This selection has been based, among others, on synthetic data series generation using Monte Carlo simulations and statistical analysis of results from distribution functions adjustment following different hypothesis. Secondly, the work has been focused on the analysis of the relationship between peak flow and volume and how to define design flood hydrographs based on this relationship which can be highly important for large volume reservoirs. However, commonly used hydrologic procedures do not take statistical dependence between these variables into account. A simple and sound method for statistical dependence characterization has been developed by the representation of a joint distribution function of maximum peak flow and volume which is based on marginal distribution function of peak flow and conditional distribution function of volume for a given peak flow. The last one is determined by a regional adjustment procedure of a log-normal distribution function. Practical application is proposed by a probabilistic estimation procedure based on stochastic generation of a large number of hydrographs. The use of this procedure for dam hydrologic security requires a proper interpretation of the return period concept applied to bivariate hydrologic data. A standard is proposed in which it is understood as the inverse of the probability of exceeding a determined reservoir level. When relating return period and hydrological variables the only design flood hydrograph changes into a family of hydrographs which generate the same maximum reservoir level and that are represented by a curve in the peak flow-volume two-dimensional space. This family of design flood hydrographs depends on the dam characteristics as for example reservoir volume or spillway length. Two study cases illustrate the application of the developed methodology. Finally, the work has been focused on the calculation of seasonal floods which are essential when determining the reservoir operation and which can be also fundamental in terms of analysing the hydrologic security of existing reservoirs. However, seasonal flood calculation is complex and nowadays it is not totally clear. Calculation procedures commonly used may present certain problems. Statistical partial duration series, or peaks over threshold method, can be an alternative approach for their calculation that allow to solve problems encountered when the same type of event is responsible of floods in different seasons. A study has been developed to verify the hypothesis of statistical homogeneity of peak flows for different seasons in Spain. Appropriate seasonal periods have been analyzed which is highly relevant to guarantee correct results. In addition, a simple procedure has been defined to determine data selection threshold on a way that ensures its independency which is one of the main difficulties in practical application of partial series. Moreover, practical application of seasonal frequency laws requires a correct interpretation of the concept of seasonal return period. A standard is proposed in order to determine seasonal return periods coherently with the annual return period and with an adequate seasonal probability distribution. Finally a methodology is proposed to calculate seasonal peak flows. A study case illustrates the application of the proposed methodology.

MANDRÁGORAS: MITOS E ASPECTOS DA CULTURA DE ISRAEL A PARTIR DE GÊNESIS 25 A 36

A sexualidade de Lea e Raquel, o útero, as mandrágoras e o corpo de Jacó são fatores que definem o alicerce do nosso texto como espaços de diálogo, mediação e estrutura do cenário. O destaque principal está sob o capítulo 30.14-16 que retrata a memória das mandrágoras. Como plantas místicas elas dominam o campo religioso e como plantas medicinais elas são utilizadas para solucionar problemas biológicos. As instituições e sociedades detentoras de uma ideologia e de leis que regulamentam uma existência apresentam na narrativa, duas irmãs, mas também esposas de um mesmo homem que, manipuladas por essa instituição que minimiza e oprime a mulher, principalmente a estéril, confina-as como simples objeto de sexualidade e mantenedoras da descendência por meio da maternidade. A memória das mandrágoras é sinal de que a prática existente circundava uma religião não monoteísta. Ela existia sociologicamente por meio de sincretismos, força e poderes sócio-culturais e religiosos. Era constituída das memórias de mulheres que manipulavam e dominavam o poder sagrado para controle de suas necessidades. O discurso dessas mulheres, em nossa unidade, prova que o discurso dessa narrativa não se encontra somente no plano individual, mas também se estende a nível comunitário, espaço que as define e lhes concede importância por meio do casamento e dádivas da maternidade como continuidade da descendência. São mulheres que dominaram um espaço na história com suas lutas e vitórias, com atos de amor e de sofrimento, de crenças e poderes numa experiência religiosa dominada pelo masculino que vai além do nosso conhecimento atual. As lutas firmadas na fé e na ideologia dessas mulheres definiram e acentuaram seu papel de protagonistas nas narrativas 9 bíblicas que estudamos no Gênesis. A conservação dessas narrativas, e do espaço teológico da época, definiu espaços, vidas, gerações e tribos que determinaram as gerações prometidas e fecharam um ciclo: o da promessa de Iahweh quanto à descendência desde Abraão. Os mitos e as crenças foram extintos para dar espaço a uma fé monoteísta, mas a experiência religiosa

Myostatin dysfunction is associated with reduction in overload induced hypertrophy of soleus muscle in mice

Acknowledgements This project was also supported by Marie Curie International Reintegration Grant 249156 (A. Lionikas) and the grants VP1-3.1-SMM-01-V-02-003 (A. Kilikevicius) and MIP-067/2012 (T. Venckunas) from the Research Council of Lithuania as well as the grant from the Ministry of Higher Education of Saudi Arabia (Y. Alhind). We wish also to thank Mrs Indre Libnickiene for her excellent technical assistance provided during the project

Da tradução ou não de pronomes e partículas pronominais do Italiano ao Português: algumas escolhas feitas por dois tradutores

Este estudo é movido pela curiosidade quanto a como se resolvem, nas traduções do italiano para o português, questões de colocação pronominal. Encontramos, normal e frequentemente, na língua italiana, principalmente na língua falada, pronomes cujos equivalentes em português existem em gramáticas normativas da língua portuguesa, mas que, na prática, não são utilizados pelos falantes e escritores brasileiros. Encontramos, também, na língua italiana, um significativo número de verbos pronominais (como esserci, volerci, averne etc.) e um considerável número de verbos pronominais múltiplos (como andarsene, farcela, fregarsene etc.) que, juntamente com esses pronomes, constituem, para os professores brasileiros de italiano língua estrangeira (LE), elementos difíceis de trabalhar na sala de aula. Além disso, tais elementos também podem dificultar o trabalho dos tradutores, que devem fazer determinadas escolhas ao traduzi-los para o português. Como são traduzidos os pronomes combinados do italiano nas versões brasileiras? Será que os portugueses, que possuem, por exemplo, tais pronomes utilizam-nos em todos os casos em que os encontramos nos textos de partida? E as partículas pronominais são simplesmente eliminadas no texto de chegada ou são substituídas? Tais aspectos, se observados e organizados, podem levar a uma melhor compreensão das duas línguas em contato e dar subsídios a estudantes, professores e tradutores. Pensando nessa dificuldade, esta pesquisa buscou e listou alguns autores e obras disponíveis para consulta e analisou um corpus com cento e sessenta e três ocorrências de pronomes no italiano, mais sete acréscimos de pronomes no português brasileiro (PB) e/ou português europeu (PE), partindo do romance Uno, nessuno e centomila de Luigi Pirandello e suas respectivas traduções em PB e PE. Nosso objetivo consiste em encontrar respostas úteis à diminuição do estranhamento, por parte de um italiano, que escuta, de um brasileiro, frases sem pronomes (ainda que o italiano as entenda) e/ou a sensação de inadequação e, até mesmo, de desconforto, por parte de um brasileiro, ao produzir frases com todos os pronomes. No corpus analisado, temos uma amostra das escolhas e respectivas traduções propostas pelos tradutores para casos de pronomes reflexivos, de pronomes pessoais do caso reto, de pronomes pessoais do caso oblíquo, de pronomes combinados e de partículas pronominais ne, ci e vi, com manutenções, omissões, trocas por outros pronomes (possessivos, retos, oblíquos, demonstrativos) e, até mesmo, uma espécie de compensação numérica com a inclusão de palavra inexistente no texto de partida.

Aplicação de métodos geoestatísticos no estudo das distribuições espaciais de condutividade hidráulica em áreas do Sistema Aquífero Guarani (SAG) e do Sistema Aquífero Bauru (SAB) no Estado de São Paulo

A condutividade hidráulica (K) é um dos parâmetros controladores da magnitude da velocidade da água subterrânea, e consequentemente, é um dos mais importantes parâmetros que afetam o fluxo subterrâneo e o transporte de solutos, sendo de suma importância o conhecimento da distribuição de K. Esse trabalho visa estimar valores de condutividade hidráulica em duas áreas distintas, uma no Sistema Aquífero Guarani (SAG) e outra no Sistema Aquífero Bauru (SAB) por meio de três técnicas geoestatísticas: krigagem ordinária, cokrigagem e simulação condicional por bandas rotativas. Para aumentar a base de dados de valores de K, há um tratamento estatístico dos dados conhecidos. O método de interpolação matemática (krigagem ordinária) e o estocástico (simulação condicional por bandas rotativas) são aplicados para estimar os valores de K diretamente, enquanto que os métodos de krigagem ordinária combinada com regressão linear e cokrigagem permitem incorporar valores de capacidade específica (Q/s) como variável secundária. Adicionalmente, a cada método geoestatístico foi aplicada a técnica de desagrupamento por célula para comparar a sua capacidade de melhorar a performance dos métodos, o que pode ser avaliado por meio da validação cruzada. Os resultados dessas abordagens geoestatísticas indicam que os métodos de simulação condicional por bandas rotativas com a técnica de desagrupamento e de krigagem ordinária combinada com regressão linear sem a técnica de desagrupamento são os mais adequados para as áreas do SAG (rho=0.55) e do SAB (rho=0.44), respectivamente. O tratamento estatístico e a técnica de desagrupamento usados nesse trabalho revelaram-se úteis ferramentas auxiliares para os métodos geoestatísticos.

Aplicação de métodos computacionais multidisciplinares de engenharia para otimização de carteiras de investimentos.

Este trabalho apresenta uma nova metodologia para otimizar carteiras de ativos financeiros. A metodologia proposta, baseada em interpoladores universais tais quais as Redes Neurais Artificiais e a Krigagem, permite aproximar a superfície de risco e consequentemente a solução do problema de otimização associado a ela de forma generalizada e aplicável a qualquer medida de risco disponível na literatura. Além disto, a metodologia sugerida permite que sejam relaxadas hipóteses restritivas inerentes às metodologias existentes, simplificando o problema de otimização e permitindo que sejam estimados os erros na aproximação da superfície de risco. Ilustrativamente, aplica-se a metodologia proposta ao problema de composição de carteiras com a Variância (controle), o Valor-em-Risco (VaR) e o Valor-em-Risco Condicional (CVaR) como funções objetivo. Os resultados são comparados àqueles obtidos pelos modelos de Markowitz e Rockafellar, respectivamente.

Invisible work, unseen hazards: the health of women immigrant household service workers in Spain

BACKGROUND: Household service work has been largely absent from occupational health studies. We examine the occupational hazards and health effects identified by immigrant women household service workers. METHODS: Exploratory, descriptive study of 46 documented and undocumented immigrant women in household services in Spain, using a phenomenological approach. Data were collected between September 2006 and May 2007 through focus groups and semi-structured individual interviews. Data were separated for analysis by documentation status and sorted using a mixed-generation process. In a second phase of analysis, data on psychosocial hazards were organized using the Copenhagen Psychosocial Questionnaire as a guide. RESULTS: Informants reported a number of environmental, ergonomic and psychosocial hazards and corresponding health effects. Psychosocial hazards were especially strongly present in data. Data on reported hazards were similar by documentation status and varied by several emerging categories: whether participants were primarily cleaners or carers and whether they lived in or outside of the homes of their employers. Documentation status was relevant in terms of empowerment and bargaining, but did not appear to influence work tasks or exposure to hazards directly. CONCLUSIONS: Female immigrant household service workers are exposed to a variety of health hazards that could be acted upon by improved legislation, enforcement, and preventive workplace measures, which are discussed.

Partial rescue of retinal function in chronically hypoglycemic mice

Purpose. Mice rendered hypoglycemic by a null mutation in the glucagon receptor gene Gcgr display late-onset retinal degeneration and loss of retinal sensitivity. Acute hyperglycemia induced by dextrose ingestion does not restore their retinal function, which is consistent with irreversible loss of vision. The goal of this study was to establish whether long-term administration of high dietary glucose rescues retinal function and circuit connectivity in aged Gcgr−/− mice. Methods. Gcgr−/− mice were administered a carbohydrate-rich diet starting at 12 months of age. After 1 month of treatment, retinal function and structure were evaluated using electroretinographic (ERG) recordings and immunohistochemistry. Results. Treatment with a carbohydrate-rich diet raised blood glucose levels and improved retinal function in Gcgr−/− mice. Blood glucose increased from moderate hypoglycemia to euglycemic levels, whereas ERG b-wave sensitivity improved approximately 10-fold. Because the b-wave reflects the electrical activity of second-order cells, we examined for changes in rod-to-bipolar cell synapses. Gcgr−/− retinas have 20% fewer synaptic pairings than Gcgr+/− retinas. Remarkably, most of the lost synapses were located farthest from the bipolar cell body, near the distal boundary of the outer plexiform layer (OPL), suggesting that apical synapses are most vulnerable to chronic hypoglycemia. Although treatment with the carbohydrate-rich diet restored retinal function, it did not restore these synaptic contacts. Conclusions. Prolonged exposure to diet-induced euglycemia improves retinal function but does not reestablish synaptic contacts lost by chronic hypoglycemia. These results suggest that retinal neurons have a homeostatic mechanism that integrates energetic status over prolonged periods of time and allows them to recover functionality despite synaptic loss.

Unifying the observational diversity of isolated neutron stars via magneto-thermal evolution models

Observations of magnetars and some of the high magnetic field pulsars have shown that their thermal luminosity is systematically higher than that of classical radio-pulsars, thus confirming the idea that magnetic fields are involved in their X-ray emission. Here we present the results of 2D simulations of the fully coupled evolution of temperature and magnetic field in neutron stars, including the state-of-the-art kinetic coefficients and, for the first time, the important effect of the Hall term. After gathering and thoroughly re-analysing in a consistent way all the best available data on isolated, thermally emitting neutron stars, we compare our theoretical models to a data sample of 40 sources. We find that our evolutionary models can explain the phenomenological diversity of magnetars, high-B radio-pulsars, and isolated nearby neutron stars by only varying their initial magnetic field, mass and envelope composition. Nearly all sources appear to follow the expectations of the standard theoretical models. Finally, we discuss the expected outburst rates and the evolutionary links between different classes. Our results constitute a major step towards the grand unification of the isolated neutron star zoo.

The outburst decay of the low magnetic field magnetar SGR 0418+5729

We report on the long-term X-ray monitoring of the outburst decay of the low magnetic field magnetar SGR 0418+5729 using all the available X-ray data obtained with RXTE, Swift, Chandra, and XMM-Newton observations from the discovery of the source in 2009 June up to 2012 August. The timing analysis allowed us to obtain the first measurement of the period derivative of SGR 0418+5729: ˙ P = 4(1) × 10−15 s s−1, significant at a ∼3.5σ confidence level. This leads to a surface dipolar magnetic field of Bdip 6 × 1012 G. This measurement confirms SGR 0418+5729 as the lowest magnetic field magnetar. Following the flux and spectral evolution from the beginning of the outburst up to ∼1200 days, we observe a gradual cooling of the tiny hot spot responsible for the X-ray emission, from a temperature of ∼0.9 to 0.3 keV. Simultaneously, the X-ray flux decreased by about three orders of magnitude: from about 1.4 × 10−11 to 1.2 × 10−14 erg s−1 cm−2. Deep radio, millimeter, optical, and gamma-ray observations did not detect the source counterpart, implying stringent limits on its multi-band emission, as well as constraints on the presence of a fossil disk. By modeling the magneto-thermal secular evolution of SGR 0418+5729, we infer a realistic age of ∼550 kyr, and a dipolar magnetic field at birth of ∼1014 G. The outburst characteristics suggest the presence of a thin twisted bundle with a small heated spot at its base. The bundle untwisted in the first few months following the outburst, while the hot spot decreases in temperature and size. We estimate the outburst rate of low magnetic field magnetars to be about one per year per galaxy, and we briefly discuss the consequences of such a result in several other astrophysical contexts.

A new low magnetic field magnetar: the 2011 outburst of Swift J1822.3–1606

We report on the long-term X-ray monitoring with Swift, RXTE, Suzaku, Chandra, and XMM-Newton of the outburst of the newly discovered magnetar Swift J1822.3–1606 (SGR 1822–1606), from the first observations soon after the detection of the short X-ray bursts which led to its discovery, through the first stages of its outburst decay (covering the time span from 2011 July until the end of 2012 April). We also report on archival ROSAT observations which detected the source during its likely quiescent state, and on upper limits on Swift J1822.3–1606's radio-pulsed and optical emission during outburst, with the Green Bank Telescope and the Gran Telescopio Canarias, respectively. Our X-ray timing analysis finds the source rotating with a period of P = 8.43772016(2) s and a period derivative P = 8.3(2)×10−14 s s−1, which implies an inferred dipolar surface magnetic field of B sime 2.7 × 1013 G at the equator. This measurement makes Swift J1822.3–1606 the second lowest magnetic field magnetar (after SGR 0418+5729). Following the flux and spectral evolution from the beginning of the outburst, we find that the flux decreased by about an order of magnitude, with a subtle softening of the spectrum, both typical of the outburst decay of magnetars. By modeling the secular thermal evolution of Swift J1822.3–1606, we find that the observed timing properties of the source, as well as its quiescent X-ray luminosity, can be reproduced if it was born with a poloidal and crustal toroidal fields of Bp ~ 1.5 × 1014 G and B tor ~ 7 × 1014 G, respectively, and if its current age is ~550 kyr.

Spectral features in isolated neutron stars induced by inhomogeneous surface temperatures

The thermal X-ray spectra of several isolated neutron stars display deviations from a pure blackbody. The accurate physical interpretation of these spectral features bears profound implications for our understanding of the atmospheric composition, magnetic field strength and topology, and equation of state of dense matter. With specific details varying from source to source, common explanations for the features have ranged from atomic transitions in the magnetized atmospheres or condensed surface, to cyclotron lines generated in a hot ionized layer near the surface. Here, we quantitatively evaluate the X-ray spectral distortions induced by inhomogeneous temperature distributions of the neutron star surface. To this aim, we explore several surface temperature distributions, we simulate their corresponding general relativistic X-ray spectra (assuming an isotropic, blackbody emission), and fit the latter with a single blackbody model. We find that, in some cases, the presence of a spurious ‘spectral line’ is required at a high significance level in order to obtain statistically acceptable fits, with central energy and equivalent width similar to the values typically observed. We also perform a fit to a specific object, RX J0806.4−4123, finding several surface temperature distributions able to model the observed spectrum. The explored effect is unlikely to work in all sources with detected lines, but in some cases it can indeed be responsible for the appearance of such lines. Our results enforce the idea that surface temperature anisotropy can be an important factor that should be considered and explored also in combination with more sophisticated emission models like atmospheres.

PipX, the coactivator of NtcA, is a global regulator in cyanobacteria

To modulate the expression of genes involved in nitrogen assimilation, the cyanobacterial PII-interacting protein X (PipX) interacts with the global transcriptional regulator NtcA and the signal transduction protein PII, a protein found in all three domains of life as an integrator of signals of the nitrogen and carbon balance. PipX can form alternate complexes with NtcA and PII, and these interactions are stimulated and inhibited, respectively, by 2-oxoglutarate, providing a mechanistic link between PII signaling and NtcA-regulated gene expression. Here, we demonstrate that PipX is involved in a much wider interaction network. The effect of pipX alleles on transcript levels was studied by RNA sequencing of S. elongatus strains grown in the presence of either nitrate or ammonium, followed by multivariate analyses of relevant mutant/control comparisons. As a result of this process, 222 genes were classified into six coherent groups of differentially regulated genes, two of which, containing either NtcA-activated or NtcA-repressed genes, provided further insights into the function of NtcA–PipX complexes. The remaining four groups suggest the involvement of PipX in at least three NtcA-independent regulatory pathways. Our results pave the way to uncover new regulatory interactions and mechanisms in the control of gene expression in cyanobacteria.