A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Diseño, desarrollo y evaluación de sistemas de traducción automática para reducir las barreras de comunicación de las personas sordas

La principal aportación de esta tesis doctoral ha sido la propuesta y evaluación de un sistema de traducción automática que permite la comunicación entre personas oyentes y sordas. Este sistema está formado a su vez por dos sistemas: un traductor de habla en español a Lengua de Signos Española (LSE) escrita y que posteriormente se representa mediante un agente animado; y un generador de habla en español a partir de una secuencia de signos escritos mediante glosas. El primero de ellos consta de un reconocedor de habla, un módulo de traducción entre lenguas y un agente animado que representa los signos en LSE. El segundo sistema está formado por una interfaz gráfica donde se puede especificar una secuencia de signos mediante glosas (palabras en mayúscula que representan los signos), un módulo de traducción entre lenguas y un conversor texto-habla. Para el desarrollo del sistema de traducción, en primer lugar se ha generado un corpus paralelo de 7696 frases en español con sus correspondientes traducciones a LSE. Estas frases pertenecen a cuatro dominios de aplicación distintos: la renovación del Documento Nacional de Identidad, la renovación del permiso de conducir, un servicio de información de autobuses urbanos y la recepción de un hotel. Además, se ha generado una base de datos con más de 1000 signos almacenados en cuatro sistemas distintos de signo-escritura. En segundo lugar, se ha desarrollado un módulo de traducción automática que integra dos técnicas de traducción con una estructura jerárquica: la primera basada en memoria y la segunda estadística. Además, se ha implementado un módulo de pre-procesamiento de las frases en español que, mediante su incorporación al módulo de traducción estadística, permite mejorar significativamente la tasa de traducción. En esta tesis también se ha mejorado la versión de la interfaz de traducción de LSE a habla. Por un lado, se han incorporado nuevas características que mejoran su usabilidad y, por otro, se ha integrado un traductor de lenguaje SMS (Short Message Service – Servicio de Mensajes Cortos) a español, que permite especificar la secuencia a traducir en lenguaje SMS, además de mediante una secuencia de glosas. El sistema de traducción propuesto se ha evaluado con usuarios reales en dos dominios de aplicación: un servicio de información de autobuses de la Empresa Municipal de Transportes de Madrid y la recepción del Hotel Intur Palacio San Martín de Madrid. En la evaluación estuvieron implicadas personas sordas y empleados de los dos servicios. Se extrajeron medidas objetivas (obtenidas por el sistema automáticamente) y subjetivas (mediante cuestionarios a los usuarios). Los resultados fueron muy positivos gracias a la opinión de los usuarios de la evaluación, que validaron el funcionamiento del sistema de traducción y dieron información valiosa para futuras líneas de trabajo. Por otro lado, tras la integración de cada uno de los módulos de los dos sistemas de traducción (habla-LSE y LSE-habla), los resultados de la evaluación y la experiencia adquirida en todo el proceso, una aportación importante de esta tesis doctoral es la propuesta de metodología de desarrollo de sistemas de traducción de habla a lengua de signos en los dos sentidos de la comunicación. En esta metodología se detallan los pasos a seguir para desarrollar el sistema de traducción para un nuevo dominio de aplicación. Además, la metodología describe cómo diseñar cada uno de los módulos del sistema para mejorar su flexibilidad, de manera que resulte más sencillo adaptar el sistema desarrollado a un nuevo dominio de aplicación. Finalmente, en esta tesis se analizan algunas técnicas para seleccionar las frases de un corpus paralelo fuera de dominio para entrenar el modelo de traducción cuando se quieren traducir frases de un nuevo dominio de aplicación; así como técnicas para seleccionar qué frases del nuevo dominio resultan más interesantes que traduzcan los expertos en LSE para entrenar el modelo de traducción. El objetivo es conseguir una buena tasa de traducción con la menor cantidad posible de frases. ABSTRACT The main contribution of this thesis has been the proposal and evaluation of an automatic translation system for improving the communication between hearing and deaf people. This system is made up of two systems: a Spanish into Spanish Sign Language (LSE – Lengua de Signos Española) translator and a Spanish generator from LSE sign sequences. The first one consists of a speech recognizer, a language translation module and an avatar that represents the sign sequence. The second one is made up an interface for specifying the sign sequence, a language translation module and a text-to-speech conversor. For the translation system development, firstly, a parallel corpus has been generated with 7,696 Spanish sentences and their LSE translations. These sentences are related to four different application domains: the renewal of the Identity Document, the renewal of the driver license, a bus information service and a hotel reception. Moreover, a sign database has been generated with more than 1,000 signs described in four different signwriting systems. Secondly, it has been developed an automatic translation module that integrates two translation techniques in a hierarchical structure: the first one is a memory-based technique and the second one is statistical. Furthermore, a pre processing module for the Spanish sentences has been implemented. By incorporating this pre processing module into the statistical translation module, the accuracy of the translation module improves significantly. In this thesis, the LSE into speech translation interface has been improved. On the one hand, new characteristics that improve its usability have been incorporated and, on the other hand, a SMS language into Spanish translator has been integrated, that lets specifying in SMS language the sequence to translate, besides by specifying a sign sequence. The proposed translation system has been evaluated in two application domains: a bus information service of the Empresa Municipal de Transportes of Madrid and the Hotel Intur Palacio San Martín reception. This evaluation has involved both deaf people and services employees. Objective measurements (given automatically by the system) and subjective measurements (given by user questionnaires) were extracted during the evaluation. Results have been very positive, thanks to the user opinions during the evaluation that validated the system performance and gave important information for future work. Finally, after the integration of each module of the two translation systems (speech- LSE and LSE-speech), obtaining the evaluation results and considering the experience throughout the process, a methodology for developing speech into sign language (and vice versa) into a new domain has been proposed in this thesis. This methodology includes the steps to follow for developing the translation system in a new application domain. Moreover, this methodology proposes the way to improve the flexibility of each system module, so that the adaptation of the system to a new application domain can be easier. On the other hand, some techniques are analyzed for selecting the out-of-domain parallel corpus sentences in order to train the translation module in a new domain; as well as techniques for selecting which in-domain sentences are more interesting for translating them (by LSE experts) in order to train the translation model.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Análisis, modelado e igualación no lineal de arrays paramétricos acústicos

La presente Tesis analiza y desarrolla metodología específica que permite la caracterización de sistemas de transmisión acústicos basados en el fenómeno del array paramétrico. Este tipo de estructuras es considerado como uno de los sistemas más representativos de la acústica no lineal con amplias posibilidades tecnológicas. Los arrays paramétricos aprovechan la no linealidad del medio aéreo para obtener en recepción señales en el margen sónico a partir de señales ultrasónicas en emisión. Por desgracia, este procedimiento implica que la señal transmitida y la recibida guardan una relación compleja, que incluye una fuerte ecualización así como una distorsión apreciable por el oyente. Este hecho reduce claramente la posibilidad de obtener sistemas acústicos de gran fidelidad. Hasta ahora, los esfuerzos tecnológicos dirigidos al diseño de sistemas comerciales han tratado de paliar esta falta de fidelidad mediante técnicas de preprocesado fuertemente dependientes de los modelos físicos teóricos. Estos están basados en la ecuación de propagación de onda no lineal. En esta Tesis se propone un nuevo enfoque: la obtención de una representación completa del sistema mediante series de Volterra que permita inferir un sistema de compensación computacionalmente ligero y fiable. La dificultad que entraña la correcta extracción de esta representación obliga a desarrollar una metodología completa de identificación adaptada a este tipo de estructuras. Así, a la hora de aplicar métodos de identificación se hace indispensable la determinación de ciertas características iniciales que favorezcan la parametrización del sistema. En esta Tesis se propone una metodología propia que extrae estas condiciones iniciales. Con estos datos, nos encontramos en disposición de plantear un sistema completo de identificación no lineal basado en señales pseudoaleatorias, que aumenta la fiabilidad de la descripción del sistema, posibilitando tanto la inferencia de la estructura basada en bloques subyacente, como el diseño de mecanismos de compensación adecuados. A su vez, en este escenario concreto en el que intervienen procesos de modulación, factores como el punto de trabajo o las características físicas del transductor, hacen inviables los algoritmos de caracterización habituales. Incluyendo el método de identificación propuesto. Con el fin de eliminar esta problemática se propone una serie de nuevos algoritmos de corrección que permiten la aplicación de la caracterización. Las capacidades de estos nuevos algoritmos se pondrán a prueba sobre un prototipo físico, diseñado a tal efecto. Para ello, se propondrán la metodología y los mecanismos de instrumentación necesarios para llevar a cabo el diseño, la identificación del sistema y su posible corrección, todo ello mediante técnicas de procesado digital previas al sistema de transducción. Los algoritmos se evaluarán en términos de error de modelado a partir de la señal de salida del sistema real frente a la salida sintetizada a partir del modelo estimado. Esta estrategia asegura la posibilidad de aplicar técnicas de compensación ya que éstas son sensibles a errores de estima en módulo y fase. La calidad del sistema final se evaluará en términos de fase, coloración y distorsión no lineal mediante un test propuesto a lo largo de este discurso, como paso previo a una futura evaluación subjetiva. ABSTRACT This Thesis presents a specific methodology for the characterization of acoustic transmission systems based on the parametric array phenomenon. These structures are well-known representatives of the nonlinear acoustics field and display large technological opportunities. Parametric arrays exploit the nonlinear behavior of air to obtain sonic signals at the receptors’side, which were generated within the ultrasonic range. The underlying physical process redunds in a complex relationship between the transmitted and received signals. This includes both a strong equalization and an appreciable distortion for a human listener. High fidelity, acoustic equipment based on this phenomenon is therefore difficult to design. Until recently, efforts devoted to this enterprise have focused in fidelity enhancement based on physically-informed, pre-processing schemes. These derive directly from the nonlinear form of the wave equation. However, online limited enhancement has been achieved. In this Thesis we propose a novel approach: the evaluation of a complete representation of the system through its projection onto the Volterra series, which allows the posterior inference of a computationally light and reliable compensation scheme. The main difficulty in the derivation of such representation strives from the need of a complete identification methodology, suitable for this particular type of structures. As an example, whenever identification techniques are involved, we require preliminary estimates on certain parameters that contribute to the correct parameterization of the system. In this Thesis we propose a methodology to derive such initial values from simple measures. Once these information is made available, a complete identification scheme is required for nonlinear systems based on pseudorandom signals. These contribute to the robustness and fidelity of the resulting model, and facilitate both the inference of the underlying structure, which we subdivide into a simple block-oriented construction, and the design of the corresponding compensation structure. In a scenario such as this where frequency modulations occur, one must control exogenous factors such as devices’ operation point and the physical properties of the transducer. These may conflict with the principia behind the standard identification procedures, as it is the case. With this idea in mind, the Thesis includes a series of novel correction algorithms that facilitate the application of the characterization results onto the system compensation. The proposed algorithms are tested on a prototype that was designed and built for this purpose. The methodology and instrumentation required for its design, the identification of the overall acoustic system and its correction are all based on signal processing techniques, focusing on the system front-end, i.e. prior to transduction. Results are evaluated in terms of input-output modelling error, considering a synthetic construction of the system. This criterion ensures that compensation techniques may actually be introduced, since these are highly sensible to estimation errors both on the envelope and the phase of the signals involved. Finally, the quality of the overall system will be evaluated in terms of phase, spectral color and nonlinear distortion; by means of a test protocol specifically devised for this Thesis, as a prior step for a future, subjective quality evaluation.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Sistema inteligente baseado em decomposição por componentes ortogonais e inferência fuzzy para localização de faltas de alta impedância em sistemas de distribuição de energia elétrica com geração distribuída

Os sistemas elétricos de potência modernos apresentam inúmeros desafios em sua operação. Nos sistemas de distribuição de energia elétrica, devido à grande ramificação, presença de extensos ramais monofásicos, à dinâmica das cargas e demais particularidades inerentes, a localização de faltas representa um dos maiores desafios. Das barreiras encontradas, a influência da impedância de falta é uma das maiores, afetando significativamente a aplicação dos métodos tradicionais na localização, visto que a magnitude das correntes de falta é similar à da corrente de carga. Neste sentido, esta tese objetivou desenvolver um sistema inteligente para localização de faltas de alta impedância, o qual foi embasado na aplicação da técnica de decomposição por componentes ortogonais no pré-processamento das variáveis e inferência fuzzy para interpretar as não-linearidades do Sistemas de Distribuição com presença de Geração Distribuída. Os dados para treinamento do sistema inteligente foram obtidos a partir de simulações computacionais de um alimentador real, considerando uma modelagem não-linear da falta de alta impedância. O sistema fuzzy resultante foi capaz de estimar as distâncias de falta com um erro absoluto médio inferior a 500 m e um erro absoluto máximo da ordem de 1,5 km, em um alimentador com cerca de 18 km de extensão. Tais resultados equivalem a um grau de exatidão, para a maior parte das ocorrências, dentro do intervalo de ±10%.

Sistema especialista para análise pós-evento da proteção em subestações distribuidoras.

É importante que as redes elétricas tenham altos índices de confiabilidade, de forma a se manter a agilidade e a manutenção ideais para um melhor funcionamento. Por outro lado, o crescimento inesperado da carga, falhas em equipamentos e uma parametrização inadequada das funções de proteção tornam a análise de eventos de proteção mais complexas e demoradas. Além disso, a quantidade de informações que pode ser obtida de relés digitais modernos tem crescido constantemente. Para que seja possível uma rápida tomada de decisão e manutenção, esse projeto de pesquisa teve como objetivo a implementação de um sistema completo de diagnóstico que é ativado automaticamente quando um evento de proteção ocorrer. As informações a serem analisadas são obtidas de uma base de dados e de relés de proteção, via protocolo de comunicação IEC 61850 e arquivos de oscilografia. O trabalho aborda o sistema Smart Grid completo incluindo: a aquisição de dados nos relés, detalhando o sistema de comunicação desenvolvido através de um software com um cliente IEC61850 e um servidor OPC e um software com um cliente OPC, que é ativado por eventos configurados para dispará-lo (por exemplo, atuação da proteção); o sistema de pré-tratamento de dados, onde os dados provenientes dos relés e equipamentos de proteção são filtrados, pré-processados e formatados; e o sistema de diagnóstico. Um banco de dados central mantém atualizados os dados de todas essas etapas. O sistema de diagnóstico utiliza algoritmos convencionais e técnicas de inteligência artificial, em particular, um sistema especialista. O sistema especialista foi desenvolvido para lidar com diferentes conjuntos de dados de entrada e com uma possível falta de dados, sempre garantindo a entrega de diagnósticos. Foram realizados testes e simulações para curtos-circuitos (trifásico, dupla-fase, dupla-fase-terra e fase-terra) em alimentadores, transformadores e barras de uma subestação. Esses testes incluíram diferentes estados do sistema de proteção (funcionamento correto e impróprio). O sistema se mostrou totalmente eficaz tanto no caso de disponibilidade completa quanto parcial de informações, sempre fornecendo um diagnóstico do curto-circuito e analisando o funcionamento das funções de proteção da subestação. Dessa forma, possibilita-se uma manutenção muito mais eficiente pelas concessionárias de energia, principalmente no que diz respeito à prevenção de defeitos em equipamentos, rápida resposta a problemas, e necessidade de reparametrização das funções de proteção. O sistema foi instalado com sucesso em uma subestação de distribuição da Companhia Paulista de Força e Luz.

Resolution limits to object tracking with subpixel accuracy

Subpixel methods increase the accuracy and efficiency of image detectors, processing units, and algorithms and provide very cost-effective systems for object tracking. Published methods achieve resolution increases up to three orders of magnitude. In this Letter, we demonstrate that this limit can be theoretically improved by several orders of magnitude, permitting micropixel and submicropixel accuracies. The necessary condition for movement detection is that one single pixel changes its status. We show that an appropriate target design increases the probability of a pixel change for arbitrarily small shifts, thus increasing the detection accuracy of a tracking system. The proposal does not impose severe restriction on the target nor on the sensor, thus allowing easy experimental implementation.

Use of Costas arrays in subpixel metrology

Subpixel methods increase the accuracy and efficiency of image detectors, processing units, and algorithms and provide very cost-effective systems for object tracking. A recently proposed method permits micropixel and submicropixel accuracies providing certain design constraints on the target are met. In this paper, we explore the use of Costas arrays - permutation matrices with ideal auto-ambiguity properties - for the design of such targets.

Improvements in the data partitioning approach for frequent itemsets mining

Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Estimation of the production cost of fast pyrolysis bio-oil

A number of papers and reports covering the techno-economic analysis of bio-oil production has been published. These have had different scopes, use different feedstocks and reflected national cost structures. This paper reviews and compares their cost estimates and the experimental results that underpin them. A comprehensive cost and performance model was produced based on consensus data from the previous studies or stated scenarios where data is not available that reflected UK costs. The model takes account sales of bio-char that is a co-product of pyrolysis and the electricity consumption of the pyrolysis plant and biomass pre-processing plants. It was concluded that it should be able to produce bio-oil in the UK from energy crops for a similar cost as distillate fuel oil. It was also found that there was little difference in the processing cost for woodchips and baled miscanthus. © 2011 Elsevier Ltd.

Sustainable energy from paper industry wastes

Secondary fibre paper mills are significant users of both heat and electricity which is mainly derived from the combustion of fossil fuels. The cost of producing this energy is increasing year upon year. These mills are also significant producers of fibrous sludge and reject waste material which can contain high amounts of useful energy. Currently the majority of these waste fractions are disposed of by landfill, land-spread or incineration using natural gas. These disposal methods not only present environmental problems but are also very costly. The focus of this work was to utilise the waste fractions produced at secondary fibre paper mills for the on-site production of combined heat and power (CHP) using advanced thermal conversion methods (gasification and pyrolysis), well suited to relatively small scales of throughput. The heat and power can either be used on-site or exported. The first stage of the work was the development of methods to condition selected paper industry wastes to enable thermal conversion. This stage required detailed characterisation of the waste streams in terms of proximate and ultimate analysis and heat content. Suitable methods to dry and condition the wastes in preparation for thermal conversion were also explored. Through trials at pilot scale with both fixed bed downdraft gasification and intermediate pyrolysis systems, the energy recovered from selected wastes and waste blends in the form of product gas and pyrolysis products was quantified. The optimal process routes were selected based on the experimental results, and implementation studies were carried out at the selected candidate mills. The studies consider the pre-processing of the wastes, thermal conversion, and full integration of the energy products. The final stage of work was an economic analysis to quantify economic gain, return on investment and environmental benefits from the proposed processes.

CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.