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.

Information content: assessing meso-scale structures in complex networks

We propose a novel measure to assess the presence of meso-scale structures in complex networks. This measure is based on the identi?cation of regular patterns in the adjacency matrix of the network, and on the calculation of the quantity of information lost when pairs of nodes are iteratively merged. We show how this measure is able to quantify several meso-scale structures, like the presence of modularity, bipartite and core-periphery con?gurations, or motifs. Results corresponding to a large set of real networks are used to validate its ability to detect non-trivial topological patterns.

Enhancement of a body area network to support smart health monitoring at the digital home

The deployment of home-based smart health services requires effective and reliable systems for personal and environmental data management. ooperation between Home Area Networks (HAN) and Body Area Networks (BAN) can provide smart systems with ad hoc reasoning information to support health care. This paper details the implementation of an architecture that integrates BAN, HAN and intelligent agents to manage physiological and environmental data to proactively detect risk situations at the digital home. The system monitors dynamic situations and timely adjusts its behavior to detect user risks concerning to health. Thus, this work provides a reasoning framework to infer appropriate solutions in cases of health risk episodes. Proposed smart health monitoring approach integrates complex reasoning according to home environment, user profile and physiological parameters defined by a scalable ontology. As a result, health care demands can be detected to activate adequate internal mechanisms and report public health services for requested actions.

AFRICA BUILD Portal: developing a social network of African health researchers and educators

One of the main outputs of the project is a collaborative platform which integrates a myriad of research and learning resources. This article presents the first prototype of this platform: the AFRICA BUILD Portal (ABP 1.0). The ABP is a Web 2.0 platform which facilitates the access, in a collaborative manner, to these resources. Through a usable web interface, the ABP has been designed to avoid, as much as possible, the connectivity problems of African institutions. In this paper, we suggest that the access to complex systems does not imply slow response rates, and that their development model guides the project to a natural technological transfer, adaptation and user acceptance. Finally, this platform aims to motivate research attitudes during the learning process and stimulate user?s collaborations.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Evaluating alternative industrial network organizations and information systems

Purpose – The purpose of this paper is to present a simulation‐based evaluation method for the comparison of different organizational forms and software support levels in the field of supply chain management (SCM). Design/methodology/approach – Apart from widely known logistic performance indicators, the discrete event simulation model considers explicitly coordination cost as stemming from iterative administration procedures. Findings - The method is applied to an exemplary supply chain configuration considering various parameter settings. Curiously, additional coordination cost does not always result in improved logistic performance. Influence factor variations lead to different organizational recommendations. The results confirm the high importance of (up to now) disregarded dimensions when evaluating SCM concepts and IT tools. Research limitations/implications – The model is based on simplified product and network structures. Future research shall include more complex, real world configurations. Practical implications – The developed method is designed for the identification of improvement potential when SCM software is employed. Coordination schemes based only on ERP systems are valid alternatives in industrial practice because significant investment IT can be avoided. Therefore, the evaluation of these coordination procedures, in particular the cost due to iterations, is of high managerial interest and the method provides a comprehensive tool for strategic IT decision making. Originality/value – Reviewed literature is mostly focused on the benefits of SCM software implementations. However, ERP system based supply chain coordination is still widespread industrial practice but associated coordination cost has not been addressed by researchers.

Correlation Dimension of Complex Networks

We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers.

An algorithm for controllability in complex networks

In this paper structural controllability of complex networks is anyzed. A new algorithm is proposed which constructs a structural control scheme for a given network by avoiding the absence of dilations and by guaranteeing the accessibility of all nodes. Such accessibility is solved via a wiring procedure; this procedure, based on determining the non-accessible regions of the network, has been improved in this new proposed algorithm. This way, the number of dedicated controllers is reduced with respect to the one provided by previous existing algorithms.

A combined algorithm for analyzing structural controllability and observability of complex networks

In this paper a combined algorithm for analyzing structural controllability and observability of complex networks is presented. The algorithm addresses the two fundamental properties to guarantee structural controllability of a system: the absence of dilations and the accessibility of all nodes. The first problem is reformulated as a Maximum Matching search and it is addressed via the Hopcroft- Karp algorithm; the second problem is solved via a new wiring algorithm. Both algorithms can be combined to efficiently determine the number of required controllers and observers as well as the new required connections in order to guarantee controllability and observability in real complex networks. An application to a Twitter social network with over 100,000 nodes illustrates the proposed algorithms.

Wind tunnel analysis of the flow topology over complex topographies

El principal objetivo de este trabajo es aportar conocimiento para contestar la pregunta: ¿hasta que punto los ensayos en túnel aerodinámico pueden contribuir a determinar las características que afectan la respuesta dinámica de los aerogeneradores operando en terreno complejo?. Esta pregunta no es nueva, de hecho, el debate en la comunidad científica comenzó en el primer tercio del siglo pasado y aún está intensamente vivo. El método generalmente aceptado para enfrentar el mencionado problema consiste en analizar un caso de estudio determinado en el cual se aplican tanto ensayos a escala real como análisis computacionales y ensayos en túnel aerodinámico. Esto no es ni fácil ni barato. Esta es la razón por la cual desde el experimento de Askervein en 1988, los modelizadores del flujo atmosférico tuvieron que esperar hasta 2007 a que el experimento de Bolund fuese puesto en marcha con un despliegue de medios técnicos equivalentes (teniendo en cuenta la evolución de las tecnologías de sensores y computación). El problema contempla tantos aspectos que ambas experiencias fueron restringidas a condiciones de atmósfera neutra con efectos de Coriolis despreciables con objeto de reducir la complejidad. Este es el contexto en el que se ha desarrollado la presente tesis doctoral. La topología del flujo sobre la isla de Bolund ha sido estudiada mediante la reproducción del experimento de Bolund en los túneles aerodinámicos A9 y ACLA16 del IDR. Dos modelos de la isla de Bolund fueron fabricados a dos escalas, 1:230 y 1:115. El flujo de entrada en el túnel aerodinámico simulando la capa límite sin perturbar correspondía a régimen de transición (transitionally rough regime) y fue usado como situación de referencia. El modelo a escala 1:230 fue ensayado en el túnel A9 para determinar la presión sobre su superficie. La distribución del coeficiente de presión sobre la isla proporcionó una visualización y estimación de una región de desprendimiento sobre el pequeño acantilado situado al frente de la misma. Las medidas de presión instantánea con suficiente grado de resolución temporal pusieron de manifiesto la no estacionariedad en la región de desprendimiento. El modelo a escala 1:115 fue ensayado utilizando hilo caliente de tres componentes y un sistema de velocimetría por imágenes de partículas de dos componentes. El flujo fue caracterizado por el ratio de aceleración, el incremento normalizado de energía cinética turbulenta y los ángulos de inclinación y desviación horizontal. Los resultados a lo largo de la dirección 270°y alturas de 2 m y 5 m presentaron una gran similitud con los resultados a escala real del experimento de Bolund. Los perfiles verticales en las localizaciones de las torres meteorológicas mostraron un acuerdo significativo con los resultados a escala real. El análisis de los esfuerzos de Reynolds y el análisis espectral en las localizaciones de los mástiles meteorológicos presentaron niveles de acuerdo variados en ciertas posiciones, mientras que en otras presentaron claras diferencias. El mapeo horizontal del flujo, para una dirección de viento de 270°, permitió caracterizar el comportamiento de la burbuja intermitente de recirculación sobre el pequeño acantilado existente al frente de la isla así como de la región de relajación y de la capa de cortadura en la región corriente abajo de Bolund. Se realizaron medidas de velocidad con alta resolución espacial en planos perpendiculares a la dirección del flujo sin perturbar. Estas medidas permitieron detectar y caracterizar una estructura de flujo similar a un torbellino longitudinal con regiones con altos gradientes de velocidad y alta intensidad de turbulencia. Esta estructura de flujo es, sin duda, un reto para los modelos computacionales y puede considerarse un factor de riesgo para la operación de los aerogeneradores. Se obtuvieron y analizaron distribuciones espaciales de los esfuerzos de Reynolds mediante 3CHW y PIV. Este tipo de parámetros no constituyen parte de los resultados habituales en los ensayos en túnel sobre topografías y son muy útiles para los modelizadores que utilizan simulación de grades torbellinos (LES). Se proporciona una interpretación de los resultados obtenidos en el túnel aerodinámico en términos de utilidad para los diseñadores de parques eólicos. La evolución y variación de los parámetros del flujo a lo largo de líneas, planos y superficies han permitido identificar como estas propiedades del flujo podrían afectar la localización de los aerogeneradores y a la clasificación de emplazamientos. Los resultados presentados sugieren, bajo ciertas condiciones, la robustez de los ensayos en túnel para estudiar la topología sobre terreno complejo y su comparabilidad con otras técnicas de simulación, especialmente considerando el nivel de acuerdo del conjunto de resultados presentados con los resultados a escala real. De forma adicional, algunos de los parámetros del flujo obtenidos de las medidas en túnel son difícilmente determinables en ensayos a escala real o por medios computacionales, considerado el estado del arte. Este trabajo fue realizado como parte de las actividades subvencionadas por la Comisión Europea como dentro del proyecto FP7-PEOPLE-ITN-2008WAUDIT (Wind Resource Assessment Audit and Standardization) dentro de la FP7 Marie-Curie Initial Training Network y por el Ministerio Español de Economía y Competitividad dentro del proyecto ENE2012-36473, TURCO (Determinación en túnel aerodinámico de la distribución espacial de parámetros estadísticos de la turbulencia atmosférica sobre topografías complejas) del Plan Nacional de Investigación (Subprograma de investigación fundamental no orientada 2012). El informe se ha organizado en siete capítulos y un conjunto de anexos. En el primer capítulo se introduce el problema. En el capítulo dos se describen los medios experimentales utilizados. Seguidamente, en el capítulo tres, se analizan en detalle las condiciones de referencia del principal túnel aerodinámico utilizado en esta investigación. En el capítulo tres se presentan resultados de ensayos de presión superficial sobre un modelo de la isla. Los principales resultados del experimento de Bolund se reproducen en el capítulo cinco. En el capítulo seis se identifican diferentes estructuras del flujo sobre la isla y, finalmente, en el capitulo siete, se recogen las conclusiones y una propuesta de lineas de trabajo futuras. ABSTRACT The main objective of this work is to contribute to answer the question: to which extend can the wind tunnel testing contribute to determine the flow characteristics that affect the dynamic response of wind turbines operating in highly complex terrains?. This question is not new, indeed, the debate in the scientific community was opened in the first third of the past century and it is still intensely alive. The accepted approach to face this problem consists in analysing a given case study where full-scale tests, computational modelling and wind tunnel testing are applied to the same topography. This is neither easy nor cheap. This is is the reason why since the Askervein experience in 1988, the atmospheric flow modellers community had to wait till 2007 when the Bolund experiment was setup with a deployment of technical means equivalent (considering the evolution of the sensor and computing techniques). The problem is so manifold that both experiences were restricted to neutral conditions without Coriolis effects in order to reduce the complexity. This is the framework in which this PhD has been carried out. The flow topology over the Bolund Island has been studied by replicating the Bolund experiment in the IDR A9 and ACLA16 wind tunnels. Two mock-ups of the Bolund island were manufactured at two scales of 1:230 and 1:115. The in-flow in the empty wind tunnel simulating the incoming atmospheric boundary layer was in the transitionally rough regime and used as a reference case. The 1:230 model was tested in the A9 wind tunnel to measure surface pressure. The mapping of the pressure coefficient across the island gave a visualisation and estimation of a detachment region on the top of the escarpment in front of the island. Time resolved instantaneous pressure measurements illustrated the non-steadiness in the detachment region. The 1:115 model was tested using 3C hot-wires(HW) and 2C Particle Image Velocimetry(PIV). Measurements at met masts M3, M6, M7 and M8 and along Line 270°were taken to replicate the result of the Bolund experiment. The flow was characterised by the speed-up ratio, normalised increment of the turbulent kinetic energy, inclination angle and turning angle. Results along line 270°at heights of 2 m and 5 m compared very well with the full-scale results of the Bolund experiment. Vertical profiles at the met masts showed a significant agreement with the full-scale results. The analysis of the Reynolds stresses and the spectral analysis at the met mast locations gave a varied level of agreement at some locations while clear mismatch at others. The horizontal mapping of the flow field, for a 270°wind direction, allowed to characterise the behaviour of the intermittent recirculation bubble on top of the front escarpment followed by a relaxation region and the presence of a shear layer in the lee side of the island. Further detailed velocity measurements were taken at cross-flow planes over the island to study the flow structures on the island. A longitudinal vortex-like structure with high mean velocity gradients and high turbulent kinetic energy was characterised on the escarpment and evolving downstream. This flow structure is a challenge to the numerical models while posing a threat to wind farm designers when siting wind turbines. Spatial distribution of Reynold stresses were presented from 3C HW and PIV measurements. These values are not common results from usual wind tunnel measurements and very useful for modellers using large eddy simulation (LES). An interpretation of the wind tunnel results in terms of usefulness to wind farm designers is given. Evolution and variation of the flow parameters along measurement lines, planes and surfaces indicated how the flow field could affect wind turbine siting. Different flow properties were presented so compare the level of agreement to full-scale results and how this affected when characterising the site wind classes. The results presented suggest, under certain conditions, the robustness of the wind tunnel testing for studying flow topology over complex terrain and its capability to compare to other modelling techniques especially from the level of agreement between the different data sets presented. Additionally, some flow parameters obtained from wind tunnel measurements would have been quite difficult to be measured at full-scale or by computational means considering the state of the art. This work was carried out as a part of the activities supported by the EC as part of the FP7- PEOPLE-ITN-2008 WAUDIT project (Wind Resource Assessment Audit and Standardization) within the FP7 Marie-Curie Initial Training Network and by the Spanish Ministerio de Economía y Competitividad, within the framework of the ENE2012-36473, TURCO project (Determination of the Spatial Distribution of Statistic Parameters of Flow Turbulence over Complex Topographies in Wind Tunnel) belonging to the Spanish National Program of Research (Subprograma de investigación fundamental no orientada 2012). The report is organised in seven chapters and a collection of annexes. In chapter one, the problem is introduced. In chapter two the experimental setup is described. Following, in chapter three, the inflow conditions of the main wind tunnel used in this piece of research are analysed in detail. In chapter three, preliminary pressure tests results on a model of the island are presented. The main results from the Bolund experiment are replicated in chapter five. In chapter six, an identification of specific flow strutures over the island is presented and, finally, in chapter seven, conclusions and lines for future works related to the presented one are included.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.