Mechanical non-surgical treatment of peri-implantitis: a single-blinded randomized longitudinal clinical study. II. Microbiological results

BACKGROUND: Peri-implantitis is common in patients with dental implants. We performed a single-blinded longitudinal randomized study to assess the effects of mechanical debridement on the peri-implant microbiota in peri-implantitis lesions. MATERIALS AND METHODS: An expanded checkerboard DNA-DNA hybridization assay encompassing 79 different microorganisms was used to study bacterial counts before and during 6 months following mechanical treatment of peri-implantitis in 17 cases treated with curettes and 14 cases treated with an ultrasonic device. Statistics included non-parametric tests and GLM multivariate analysis with p<0001 indicating significance and 80% power. RESULTS: At selected implant test sites, the most prevalent bacteria were: Fusobacterium nucleatum sp., Staphylococci sp., Aggregatibacter actinomycetemcomitans, Helicobacter pylori, and Tannerella forsythia. 30 min. after treatment with curettes, A. actinomycetemcomitans (serotype a), Lactobacillus acidophilus, Streptococcus anginosus, and Veillonella parvula were found at lower counts (p<0.001). No such differences were found for implants treated with the ultrasonic device. Inconsistent changes occurred following the first week. No microbiological differences between baseline and 6-month samples were found for any species or between treatment study methods in peri-implantitis. CONCLUSIONS: Both methods failed to eliminate or reduce bacterial counts in peri-implantitis. No group differences were found in the ability to reduce the microbiota in peri-implantitis.

First trimester markers for pre-eclampsia: placental vs. non-placental protein serum levels

BACKGROUND/AIM: Parallel investigation, in a matched case-control study, of the association of different first-trimester markers with the risk of subsequent pre-eclampsia (PE). METHOD: The levels of different first trimester serum markers and fetal nuchal translucency thickness were compared between 52 cases of PE and 104 control women by non-parametric two-group comparisons and by calculating matched odds ratios. RESULTS: In univariable analysis increased concentrations of inhibin A and activin A were associated with subsequent PE (p < 0.02). Multivariable conditional logistic regression models revealed an association between increased risk of PE and increased inhibin A and translucency thickness and respectively reduced pregnancy-associated plasma protein A (PAPP-A) and placental lactogen . However, these associations varied with the gestational age at sample collection. For blood samples taken in pregnancy weeks 12 and 13 only, increased levels of activin A, inhibin A and nuchal translucency thickness, and lower levels of placenta growth factor and PAPP-A were associated with an increased risk of PE. CONCLUSIONS: Members of the inhibin family and to some extent PAPP-A and placental growth factor are superior to other serum markers, and the predictive value of these depends on the gestational age at blood sampling. The availability of a single, early pregnancy 'miracle' serum marker for PE risk assessment seems unlikely in the near future.

Choosing Smoothness Parameters for Smoothing Splines by Minimizing and Estimate of Risk

Smoothing splines are a popular approach for non-parametric regression problems. We use periodic smoothing splines to fit a periodic signal plus noise model to data for which we assume there are underlying circadian patterns. In the smoothing spline methodology, choosing an appropriate smoothness parameter is an important step in practice. In this paper, we draw a connection between smoothing splines and REACT estimators that provides motivation for the creation of criteria for choosing the smoothness parameter. The new criteria are compared to three existing methods, namely cross-validation, generalized cross-validation, and generalization of maximum likelihood criteria, by a Monte Carlo simulation and by an application to the study of circadian patterns. For most of the situations presented in the simulations, including the practical example, the new criteria out-perform the three existing criteria.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

EVALUATING INFRASTRUCTURE PERFORMANCE TO ASSIST FACILITY MANAGEMENT TOWARD SUSTAINABLE SYSTEMS

Civil infrastructure provides essential services for the development of both society and economy. It is very important to manage systems efficiently to ensure sound performance. However, there are challenges in information extraction from available data, which also necessitates the establishment of methodologies and frameworks to assist stakeholders in the decision making process. This research proposes methodologies to evaluate systems performance by maximizing the use of available information, in an effort to build and maintain sustainable systems. Under the guidance of problem formulation from a holistic view proposed by Mukherjee and Muga, this research specifically investigates problem solving methods that measure and analyze metrics to support decision making. Failures are inevitable in system management. A methodology is developed to describe arrival pattern of failures in order to assist engineers in failure rescues and budget prioritization especially when funding is limited. It reveals that blockage arrivals are not totally random. Smaller meaningful subsets show good random behavior. Additional overtime failure rate is analyzed by applying existing reliability models and non-parametric approaches. A scheme is further proposed to depict rates over the lifetime of a given facility system. Further analysis of sub-data sets is also performed with the discussion of context reduction. Infrastructure condition is another important indicator of systems performance. The challenges in predicting facility condition are the transition probability estimates and model sensitivity analysis. Methods are proposed to estimate transition probabilities by investigating long term behavior of the model and the relationship between transition rates and probabilities. To integrate heterogeneities, model sensitivity is performed for the application of non-homogeneous Markov chains model. Scenarios are investigated by assuming transition probabilities follow a Weibull regressed function and fall within an interval estimate. For each scenario, multiple cases are simulated using a Monte Carlo simulation. Results show that variations on the outputs are sensitive to the probability regression. While for the interval estimate, outputs have similar variations to the inputs. Life cycle cost analysis and life cycle assessment of a sewer system are performed comparing three different pipe types, which are reinforced concrete pipe (RCP) and non-reinforced concrete pipe (NRCP), and vitrified clay pipe (VCP). Life cycle cost analysis is performed for material extraction, construction and rehabilitation phases. In the rehabilitation phase, Markov chains model is applied in the support of rehabilitation strategy. In the life cycle assessment, the Economic Input-Output Life Cycle Assessment (EIO-LCA) tools are used in estimating environmental emissions for all three phases. Emissions are then compared quantitatively among alternatives to support decision making.

Comparison of two non-bronchoscopic methods for evaluating inflammation in patients with acute hypoxaemic respiratory failure

INTRODUCTION: The simple bedside method for sampling undiluted distal pulmonary edema fluid through a normal suction catheter (s-Cath) has been experimentally and clinically validated. However, there are no data comparing non-bronchoscopic bronchoalveolar lavage (mini-BAL) and s-Cath for assessing lung inflammation in acute hypoxaemic respiratory failure. We designed a prospective study in two groups of patients, those with acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) and those with acute cardiogenic lung edema (ACLE), designed to investigate the clinical feasibility of these techniques and to evaluate inflammation in both groups using undiluted sampling obtained by s-Cath. To test the interchangeability of the two methods in the same patient for studying the inflammation response, we further compared mini-BAL and s-Cath for agreement of protein concentration and percentage of polymorphonuclear cells (PMNs). METHODS: Mini-BAL and s-Cath sampling was assessed in 30 mechanically ventilated patients, 21 with ALI/ARDS and 9 with ACLE. To analyse agreement between the two sampling techniques, we considered only simultaneously collected mini-BAL and s-Cath paired samples. The protein concentration and polymorphonuclear cell (PMN) count comparisons were performed using undiluted sampling. Bland-Altman plots were used for assessing the mean bias and the limits of agreement between the two sampling techniques; comparison between groups was performed by using the non-parametric Mann-Whitney-U test; continuous variables were compared by using the Student t-test, Wilcoxon signed rank test, analysis of variance or Student-Newman-Keuls test; and categorical variables were compared by using chi-square analysis or Fisher exact test. RESULTS: Using protein content and PMN percentage as parameters, we identified substantial variations between the two sampling techniques. When the protein concentration in the lung was high, the s-Cath was a more sensitive method; by contrast, as inflammation increased, both methods provided similar estimates of neutrophil percentages in the lung. The patients with ACLE showed an increased PMN count, suggesting that hydrostatic lung edema can be associated with a concomitant inflammatory process. CONCLUSIONS: There are significant differences between the s-Cath and mini-BAL sampling techniques, indicating that these procedures cannot be used interchangeably for studying the lung inflammatory response in patients with acute hypoxaemic lung injury.

Mechanical non-surgical treatment of peri-implantitis: a double-blind randomized longitudinal clinical study. I: clinical results

BACKGROUND: Peri-implantitis is a frequent finding in patients with dental implants. The present study compared two non-surgical mechanical debridement methods of peri-implantitis. MATERIAL AND METHODS: Thirty-seven subjects (mean age 61.5; S.D+/-12.4), with one implant each, demonstrating peri-implantitis were randomized, and those treated either with titanium hand-instruments or with an ultrasonic device were enrolled. Data were obtained before treatment, and at 1, 3, and 6 months. Parametric and non-parametric statistics were used. RESULTS: Thirty-one subjects completed the study. The mean bone loss at implants in both groups was 1.5 mm (SD +/-1.2 mm). No group differences for plaque or gingival indices were found at any time point. Baseline and 6-month mean probing pocket depths (PPD) at implants were 5.1 and 4.9 mm (p=0.30) in both groups. Plaque scores at treated implants decreased from 73% to 53% (p<0.01). Bleeding scores also decreased (p<0.01), with no group differences. No differences in the total bacterial counts were found over time. Higher total bacterial counts were found immediately after treatment (p<0.01) and at 1 week for ultrasonic-treated implants (p<0.05). CONCLUSIONS: No group differences were found in the treatment outcomes. While plaque and bleeding scores improved, no effects on PPD were identified.

A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis. The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology. We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error. We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.

The Open Door Mission: Measuring and predicting outcomes of one community-based substance abuse treatment program

The objectives of this study were to identify and measure the average outcomes of the Open Door Mission's nine-month community-based substance abuse treatment program, identify predictors of successful outcomes, and make recommendations to the Open Door Mission for improving its treatment program.^ The Mission's program is exclusive to adult men who have limited financial resources: most of which were homeless or dependent on parents or other family members for basic living needs. Many, but not all, of these men are either chemically dependent or have a history of substance abuse.^ This study tracked a cohort of the Mission's graduates throughout this one-year study and identified various indicators of success at short-term intervals, which may be predictive of longer-term outcomes. We tracked various levels of 12-step program involvement, as well as other social and spiritual activities, such as church affiliation and recovery support.^ Twenty-four of the 66 subjects, or 36% met the Mission's requirements for success. Specific to this success criteria; Fifty-four, or 82% reported affiliation with a home church; Twenty-six, or 39% reported full-time employment; Sixty-one, or 92% did not report or were not identified as having any post-treatment arrests or incarceration, and; Forty, or 61% reported continuous abstinence from both drugs and alcohol.^ Five research-based hypotheses were developed and tested. The primary analysis tool was the web-based non-parametric dependency modeling tool, B-Course, which revealed some strong associations with certain variables, and helped the researchers generate and test several data-driven hypotheses. Full-time employment is the greatest predictor of abstinence: 95% of those who reported full time employment also reported continuous post-treatment abstinence, while 50% of those working part-time were abstinent and 29% of those with no employment were abstinent. Working with a 12-step sponsor, attending aftercare, and service with others were identified as predictors of abstinence.^ This study demonstrates that associations with abstinence and the ODM success criteria are not simply based on one social or behavioral factor. Rather, these relationships are interdependent, and show that abstinence is achieved and maintained through a combination of several 12-step recovery activities. This study used a simple assessment methodology, which demonstrated strong associations across variables and outcomes, which have practical applicability to the Open Door Mission for improving its treatment program. By leveraging the predictive capability of the various success determination methodologies discussed and developed throughout this study, we can identify accurate outcomes with both validity and reliability. This assessment instrument can also be used as an intervention that, if operationalized to the Mission’s clients during the primary treatment program, may measurably improve the effectiveness and outcomes of the Open Door Mission.^

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

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.

Optimizing panchromatic image change detection based on change index multiband image analysis

This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

Efectos de una intervención nutricional personalizada a largo plazo sobre los hábitos alimentarios de jugadores profesionales de baloncesto y su influencia en la percepción del esfuerzo y la fatiga

El presente trabajo consistió en el desarrollo de una intervención nutricional a largo plazo llevada a cabo con jugadores profesionales de baloncesto, en función al cumplimiento de las recomendaciones nutricionales, con los siguientes dos objetivos: 1) valorar los cambios que dicha intervención produce sobre las prácticas nutricionales diarias de estos deportistas y 2) conocer la influencia de las modificaciones nutricionales producidas sobre la tasa de percepción del esfuerzo por sesión (RPE-Sesión) y la fatiga, a lo largo de una temporada competitiva, tanto para entrenamientos como partidos oficiales. Los objetivos del estudio se fundamentan en: 1) la numerosa evidencia científica que muestra la inadecuación de los hábitos nutricionales de los jugadores de baloncesto y otros deportistas respecto a las recomendaciones nutricionales; 2) el hecho ampliamente reconocido en la literatura especializada de que una ingesta nutricional óptima permite maximizar el rendimiento deportivo (a nivel físico y cognitivo), promoviendo una rápida recuperación y disminuyendo el riesgo de enfermedades y lesiones deportivas. No obstante, pocos estudios han llevado a cabo una intervención nutricional a largo plazo para mejorar los hábitos alimentarios de los deportistas y ninguno de ellos fue realizado con jugadores de baloncesto; 3) la elevada correlación entre la percepción del esfuerzo (RPE) y variables fisiológicas relacionadas al desarrollo de un ejercicio (por ej.: frecuencia cardíaca, consumo máximo de oxígeno o lactato sanguíneo) y los múltiples estudios que muestran la atenuación de la RPE durante la realización del ejercicio mediante una ingesta puntual de nutrientes, (especialmente de hidratos de carbono) aunque ninguno fue desarrollado en baloncesto; 4) el estudio incipiente de la relación entre la ingesta nutricional y la RPE-Sesión, siendo éste un método validado en baloncesto y otros deportes de equipo como indicador de la carga de trabajo interna, el rendimiento deportivo y la intensidad del ejercicio realizado; 5) el hecho de que la fatiga constituye uno de los principales factores influyentes en la percepción del esfuerzo y puede ser retrasada y/o atenuada mediante la ingesta de carbohidratos, pudiendo disminuir consecuentemente la RPE-Sesión y la carga interna del esfuerzo físico, potenciando el rendimiento deportivo y las adaptaciones inducidas por el entrenamiento; 6) la reducida evidencia acerca del comportamiento de la RPE-Sesión ante la modificación de la ingesta de nutrientes, encontrándose sólo un estudio llevado a cabo en baloncesto y 7) la ausencia de investigaciones acerca de la influencia que puede tener la mejora del patrón nutricional de los jugadores sobre la RPE-Sesión y la fatiga, desconociéndose si la adecuación de los hábitos nutricionales conduce a una disminución de estas variables en el largo plazo para todos los entrenamientos y partidos oficiales a nivel profesional. Por todo esto, este trabajo comienza con una introducción que presenta el marco teórico de la importancia y función de la nutrición en el deporte, así como de las recomendaciones nutricionales actuales a nivel general y para baloncesto. Además, se describen las intervenciones nutricionales llevadas a cabo previamente con otros deportistas y las consecuentes modificaciones sobre el patrón alimentario, coincidiendo este aspecto con el primer objetivo del presente estudio. Posteriormente, se analiza la RPE, la RPE-Sesión y la fatiga, focalizando el estudio en la relación de dichas variables con la carga de trabajo físico, la intensidad del entrenamiento, el rendimiento deportivo y la recuperación post ejercicio. Finalmente, se combinan todos los aspectos mencionados: ingesta nutricional, RPE percepción del esfuerzo y fatiga, con el fin de conocer la situación actual del estudio de la relación entre dichas variables, conformando la base del segundo objetivo de este estudio. Seguidamente, se exponen y fundamentan los objetivos antes mencionados, para dar lugar después a la explicación de la metodología utilizada en el presente estudio. Ésta consistió en un diseño de estudios de caso, aplicándose una intervención nutricional personalizada a tres jugadores de baloncesto profesional (cada jugador = un estudio de caso; n = 1), con el objetivo de adecuar su ingesta nutricional en el largo plazo a las recomendaciones nutricionales. A su vez, se analizó la respuesta individual de cada uno de los casos a dicha intervención para los dos objetivos del estudio. Para ello, cada jugador completó un registro diario de alimentos (7 días; pesada de alimentos) antes, durante y al final de la intervención. Además, los sujetos registraron diariamente a lo largo del estudio la RPE-Sesión y la fatiga en entrenamientos físicos y de balón y en partidos oficiales de liga, controlándose además en forma cuantitativa otras variables influyentes como el estado de ánimo y el sueño. El análisis de los datos consistió en el cálculo de los estadísticos descriptivos para todas las variables, la comparación de la ingesta en los diferentes momentos evaluados con las recomendaciones nutricionales y una comparación de medias no paramétrica entre el período pre intervención y durante la intervención con el test de Wilcoxon (medidas repetidas) para todas las variables. Finalmente, se relacionaron los cambios obtenidos en la ingesta nutricional con la percepción del esfuerzo y la fatiga y la posible influencia del estado de ánimo y el sueño, a través de un estudio correlacional (Tau_b de Kendall). Posteriormente, se presentan los resultados obtenidos y la discusión de los mismos, haciendo referencia a la evidencia científica relacionada que se encuentra publicada hasta el momento, la cual facilitó el análisis de la relación entre RPE-Sesión, fatiga y nutrición a lo largo de una temporada. Los principales hallazgos y su correspondiente análisis, por lo tanto, pueden resumirse en los siguientes: 1) los tres jugadores de baloncesto profesional presentaron inicialmente hábitos nutricionales inadecuados, haciendo evidente la necesidad de un nutricionista deportivo dentro del cuerpo técnico de los equipos profesionales; 2) las principales deficiencias correspondieron a un déficit pronunciado de energía e hidratos de carbono, que fueron reducidas con la intervención nutricional; 3) la ingesta excesiva de grasa total, ácidos grasos saturados, etanol y proteínas que se halló en alguno/s de los casos, también se adecuó a las recomendaciones después de la intervención; 4) la media obtenida durante un período de la temporada para la RPE-Sesión y la fatiga de entrenamientos, podría ser disminuida en un jugador individual mediante el incremento de su ingesta de carbohidratos a largo plazo, siempre que no existan alteraciones psico-emocionales relevantes; 5) el comportamiento de la RPE-Sesión de partidos oficiales no parece estar influido por los factores nutricionales modificados en este estudio, dependiendo más de la variación de elementos externos no controlables, intrínsecos a los partidos de baloncesto profesional. Ante estos resultados, se pudo observar que las diferentes características de los jugadores y las distintas respuestas obtenidas después de la intervención, reforzaron la importancia de utilizar un diseño de estudio de casos para el análisis de los deportistas de élite y, asimismo, de realizar un asesoramiento nutricional personalizado. Del mismo modo, la percepción del esfuerzo y la fatiga de cada jugador evolucionaron de manera diferente después de la intervención nutricional, lo cual podría depender de las diferentes características de los sujetos, a nivel físico, psico-social, emocional y contextual. Por ello, se propone que el control riguroso de las variables cualitativas que parecen influir sobre la RPE y la fatiga a largo plazo, facilitaría la comprensión de los datos y la determinación de factores desconocidos que influyen sobre estas variables. Finalmente, al ser la RPE-Sesión un indicador directo de la carga interna del entrenamiento, es decir, del estrés psico-fisiológico experimentado por el deportista, la posible atenuación de esta variable mediante la adecuación de los hábitos nutricionales, permitiría aplicar las cargas externas de entrenamiento planificadas, con menor estrés interno y mejor recuperación entre sesiones, disminuyendo también la sensación de fatiga, a pesar del avance de la temporada. ABSTRACT This study consisted in a long-term nutritional intervention carried out with professional basketball players according to nutritional recommendations, with the following two main objectives: 1) to evaluate the changes produced by the intervention on daily nutritional practices of these athletes and 2) to determine the influence of long term nutritional intake modifications on the rate of perceived exertion per session (Session-RPE) and fatigue, throughout a competitive season for training as well as competition games. These objectives are based on: 1) much scientific evidence that shows an inadequacy of the nutritional habits of basketball players and other athletes regarding nutritional recommendations; 2) the fact widely recognized in the scientific literature that an optimal nutrition allows to achieve the maximum performance of an athlete (both physically and cognitively), promoting fast recovery and decreasing risks of sports injuries and illnesses. However, only few studies carried out a long term nutritional intervention to improve nutritional practices of athletes and it could not be found any research with basketball players; 3) the high correlation between the rate of perceived exertion (RPE) and physiological variables related to the performance of physical exercise (e.g.: heart rate, maximum consumption of oxygen or blood lactate) and multiple studies showing the attenuation of RPE during exercise due to the intake of certain nutrients (especially carbohydrates), while none of them was developed in basketball; 4) correlation between nutritional intake and Session-RPE has been recently studied for the first time. Session-RPE method has been validated in basketball players and other team sports as an indicator of internal workload, sports performance and exercise intensity; 5) fatigue is considered one of the main influential factor on RPE and sport performance. It has also been observed that carbohydrates intake may delay or mitigate the onset of fatigue and, thus, decrease the perceived exertion and the internal training load, which could improve sports performance and training-induced adaptations; 6) there are few studies evaluating the influence of nutrient intake on Session-RPE and only one of them has been carried out with basketball players. Moreover, it has not been analyzed the possible effects of the adequacy of players’ nutritional habits through a nutritional intervention on Session-RPE and fatigue, variables that could be decreased for all training session and competition games because of an improvement of daily nutritional intake. Therefore, this work begins with an introduction that provides the conceptual framework of this research focused on the key role of nutrition in sport, as well as on the current nutritional recommendations for athletes and specifically for basketball players. In addition, previous nutritional interventions carried out with other athletes are described, as well as consequential modifications on their food pattern, coinciding with the first objective of the present study. Subsequently, RPE, Session-RPE and fatigue are analyzed, with focus on their correlation with physical workload, training intensity, sports performance and recovery. Finally, all the aforementioned aspects (nutritional intake, RPE and fatigue) were combined in order to know the current status of the relation between each other, this being the base for the second objective of this study. Subsequently, the objectives mentioned above are explained, continuing with the explanation of the methodology used in the study. The methodology consisted of a case-study design, carrying out a long term nutritional intervention with three professional basketball players (each player = one case study; n = 1), in order to adapt their nutritional intake to nutritional recommendations. At the same time, the individual response of each player to the intervention was analyzed for the two main objectives of the study. Each player completed a food diary (7 days; weighing food) in three moments: before, during and at the end of the intervention. In addition, the Session-RPE and fatigue were daily recorded throughout the study for all trainings (training with ball and resistance training) and competition games. At the same time, other potentially influential variables such as mood state and sleeping were daily controlled throughout the study. Data analysis consisted in descriptive statistics calculation for all the variables of the study, the comparison between nutritional intake (evaluated at different times) and nutritional recommendations and a non-parametric mean comparison between pre intervention and during intervention periods was made by Wilcoxon test (repeated measurements) for all variables too. Finally, the changes in nutritional intake, mood state and sleeping were correlated with the perceived exertion and fatigue through correctional study (Tau_b de Kendall). After the methodology, the study results and the associated discussion are presented. The discussion is based on the current scientific evidence that contributes to understand the relation between Session-RPE, fatigue and nutrition throughout the competitive season. The main findings and results analysis can be summarized as follows: 1) the three professional basketball players initially had inadequate nutritional habits and this clearly shows the need of a sports nutritionist in the coaching staff of professional teams; (2) the major deficiencies of the three players’ diet corresponded to a pronounced deficit of energy intake and carbohydrates consumption which were reduced with nutritional intervention; (3) the excessive intake of total fat, saturated fatty acids, ethanol and protein found in some cases were also adapted to the recommendations after the intervention; (4) Session-RPE mean and fatigue of a certain period of the competition season, could be decreased in an individual player by increasing his carbohydrates intake in the long term, if there are no relevant psycho-emotional disorders; (5) the behavior of the Session-RPE in competition games does not seem to be influenced by the nutritional factors modified in this study. They seem to depend much more on the variation of external non-controllable factors associated with the professional basketball games. Given these results, the different characteristics of each player and the diverse responses observed after the intervention in each individual for all the variables, reinforced the importance of the use of a case study design for research with elite athletes as well as personalized nutritional counselling. In the same way, the different responses obtained for RPE and fatigue in the long term for each player due to modification of nutritional habits, show that there is a dependence of such variables on the physical, psychosocial, emotional and contextual characteristics of each player. Therefore it is proposed that the rigorous control of the qualitative variables that seem to influence the RPE and fatigue in the long term, may facilitate the understanding of data and the determination of unknown factors that could influence these variables. Finally, because Session-RPE is a direct indicator of the internal load of training (psycho-physiological stress experienced by the athlete), the possible attenuation of Session-RPE through the improvement in nutritional habits, would allow to apply the planned external loads of training with less internal stress and better recovery between sessions, with a decrease in fatigue, despite of the advance of the season.

Spatio-temporal distribution of pyrethroids in soil in Mediterranean paddy fields

Purpose The demand of rice by the increase in population in many countries has intensified the application of pesticides and the use of poor quality water to irrigate fields. The terrestrial environment is one compartment affected by these situations, where soil is working as a reservoir, retaining organic pollutants. Therefore, it is necessary to develop methods to determine insecticides in soil and monitor susceptible areas to be contaminated, applying adequate techniques to remediate them. Materials and methods This study investigates the occurrence of ten pyrethroid insecticides (PYs) and its spatio-temporal variance in soil at two different depths collected in two periods (before plow and during rice production), in a paddy field area located in the Mediterranean coast. Pyrethroids were quantified using gas chromatography?mass spectrometry (GC?MS) after ultrasound-assisted extraction with ethyl acetate. The results obtained were assessed statistically using non-parametric methods, and significant statistical differences (p < 0.05) in pyrethroids content with soil depth and proximity to wastewater treatment plants were evaluated. Moreover, a geographic information system (GIS) was used to monitor the occurrence of PYs in paddy fields and detect risk areas. Results and discussion Pyrethroids were detected at concentrations ?57.0 ng g?1 before plow and ?62.3 ng g?1 during rice production, being resmethrin and cyfluthrin the compounds found at higher concentrations in soil. Pyrethroids were detected mainly at the top soil, and a GIS program was used to depict the obtained results, showing that effluents from wastewater treatment plants (WWTPs) were the main sources of soil contamination. No toxic effects were expected to soil organisms, but it is of concern that PYs may affect aquatic organisms, which represents the worst case scenario. Conclusions A methodology to determine pyrethroids in soil was developed to monitor a paddy field area. The use of water fromWWTPs to irrigate rice fields is one of the main pollution sources of pyrethroids. It is a matter of concern that PYs may present toxic effects on aquatic organisms, as they can be desorbed from soil. Phytoremediation may play an important role in this area, reducing the possible risk associated to PYs levels in soil.