A Bayesian model for longitudinal circular data

The analysis of short longitudinal series of circular data may be problematic and to some extent has not been completely developed. In this paper we present a Bayesian analysis of a model for such data. The model is based on a radial projection onto the circle of a particular bivariate normal distribution. Inferences about the parameters of the model are based on samples from the corresponding joint posterior density which are obtained using a Metropolis-within-Gibbs scheme after the introduction of suitable latent variables. The procedure is illustrated both using a simulated data set and a realdata set previously analyzed in the literature.Circular data, Longitudinal data, Gibbs sampler, Latent variables, Mixed-effects linear models, Projected normal distribution

A Bayesian model for longitudinal circular data

The analysis of short longitudinal series of circular data may be problematic and to some extent has not been completely developed. In this paper we present a Bayesian analysis of a model for such data. The model is based on a radial projection onto the circle of a particular bivariate normal distribution. Inferences about the parameters of the model are based on samples from the corresponding joint posterior density which are obtained using a Metropolis-within-Gibbs scheme after the introduction of suitable latent variables. The procedure is illustrated both using a simulated data set and a realdata set previously analyzed in the literature

Estimación de la edad dental usando el método de Demirjian en niños peruanos

Es un estudio retrospectivo y transversal, donde el desarrollo dental de 321 niños y niñas peruanas entre 5,5 a 13,5 años fue evaluado con el método de Demirjian. Una submuestra de 32 radiografías panorámicas fue escogida al azar y vueltas a examinar para evaluar la fiabilidad intraexaminador. El coeficiente de correlación intraclase en las puntuaciones de maduración fue de 0,99. El coeficiente de Cohen’s Kappa fue de 0,82, ambas interpretadas como altamente confiables. Los niños fueron clasificados por sexo y edad. La edad dental y la edad cronológica fueron comparadas usando la prueba t pareada. En la mayoría de los grupos, la edad dental fue sobrestimada y presentaban una diferencia significativa. Nuevos estándares para la población peruana fueron construidos usando una curva logística con la ecuación: y = 1 / ((1/100) + ) como base ya que los estándares propuestos por Demirjian no fueron apropiados para la población peruana.-- In a retrospective cross - sectional study dental development of 321 Peruvian children, aged 5,5 – 13,5 years, were evaluated by Demirjian method. A subset of 32 pantomograms were randomly chosen and re-examined. The intra-class correlation coefficient on maturity scores was 0,99. The Cohen’s Kappa coefficient was 0,82, both interpreted as “substantially reliable”. The children were classified by sex and age. Dental age and chronological age were compared using paired t – test. Dental age was overestimated in most of age groups and there is no statiscal difference. New standards for Peruvian children were built using a logistic curve with the equation: y = 1 / ((1/100) + ) as a basis, because of Demirjian’s standards were not appropriate to Peruvian children.Tesi

Dehydrative coupling of alcohols by Iridium(III) complexes with N-Heterocyclic-Pyridine chelating ligands decorated with Naphthalene-Diimide

A series of dimetallic and monometallic Cp*Ir(III) complexes bearing naphthalene-diimide-decorated N-heterocyclic carbene/pyridine [NDI-(NHC-pyridine)] ligands were prepared and characterized. The complexes show a C,C-chelating coordination of the NHC-pyridine ligand, with the pyridine ring of the ligand bound to the metal via cyclometallation rather than by its nitrogen atom, although the coordination by the nitrogen atom could be achieved by subjecting one of the complexes to reaction with a strong acid. The spectroelectrochemical studies of the new compounds reveal that the complexes are able to undergo two successive reduction events, associated with the sequential reduction of the NDI moiety of the ligand. The new complexes were tested in the dehydrative etherification by cross-coupling of primary alcohols, where they showed good activity and selectivity toward the cross-coupled products. The mechanistic studies allowed us to propose a reaction mechanism which likely involves a redox-neutral acid-catalyzed pathway.Funding for open access charge: CRUE-Universitat Jaume

A supervised visual model for finding regions of interest in basal cell carcinoma images

This paper introduces a supervised learning method for finding diagnostic regions of interest in histopathological images. The method is based on the cognitive process of visual selection of relevant regions that arises during a pathologist's image examination. The proposed strategy emulates the interaction of the visual cortex areas V1, V2 and V4, being the V1 cortex responsible for assigning local levels of relevance to visual inputs while the V2 cortex gathers together these small regions according to some weights modulated by the V4 cortex, which stores some learned rules. This novel strategy can be considered as a complex mix of "bottom-up" and "top-down" mechanisms, integrated by calculating a unique index inside each region. The method was evaluated on a set of 338 images in which an expert pathologist had drawn the Regions of Interest. The proposed method outperforms two state-of-the-art methods devised to determine Regions of Interest (RoIs) in natural images. The quality gain with respect to an adaptated Itti's model which found RoIs was 3.6 dB in average, while with respect to the Achanta's proposal was 4.9 dB

Monograf\'ia de Estad\'istica Bayesiana

Course notes about an introduction to Bayesian Statistics. First, an explanation of the bayesian paradigm is motivated and explained in detail (first three chapters). Then, a brief introduction to the basics about Decision Theory in chapter four, which is self contained, with the purpose of introducing parametrica bayesian inference as a decision problem in chapter five.Comment: 104 pages (in Spanish

Applications of artificial neural networks to neurophysiological studies in focal peripheral neuropathies

Introducción: La interpretación de estudios electrofisiológicos es esencialmente una tarea de clasificación. Las redes neuronales artificiales (ANN) son herramientas adecuadas para la clasificación porque son basado en técnicas de reconocimiento de patrones. Objetivos: Desarrollar un sistema informático para la detección automatizada de neuropatías focales. utilizando ANN. Métodos: El estudio se basó en 300 conjuntos de estudios de conducción nerviosa (NCS) de tres diferentes laboratorios de medicina de electrodiagnóstico. Cada conjunto de datos de entrada estaba formado por 11 parámetros, incluyendo latencias motoras y sensoriales, amplitudes, duraciones y velocidades de un solo nervio. Los conjuntos de entrada se clasificaron en 4 subgrupos de neuropatía focal (distal desmielinización, desmielinización proximal, desmielinización generalizada, pérdida de axones) según sobre el tipo de daño nervioso más 1 adicional para hallazgos normales. Los datos fueron presentados a una ANN de retropropagación con 1 capa oculta. La estructura de la red se modificó para lograr el error cuadrático medio más bajo posible. Los resultados de estas redes de primer nivel se presentaron a una red de segundo nivel para detectar neuropatías generalizadas. Después entrenando a las ANN, la precisión de la clasificación se probó utilizando otro conjunto de datos que se desconocido para las redes. Resultados: Se alcanzó una precisión de clasificación del 99% para la detección de patologías patrones. La precisión para la clasificación de neuropatías focales fue del 95,2%. Conclusiones: las redes neuronales clasifican subgrupos de neuropatía focal con alta precisión (> 95%). Este método puede conducir a la detección automática de neuropatía focal.Instituto Tecnológico de Estudios Superiores de Monterrey ITESMSUMMARY 11 INTRODUCCION 1 1 EL PROBLEMA 3 1.1 DESCRIPCIÓN DEL PROBLEMA 3 1.2 FORMULACIÓN DEL PROBLEMA 5 1.3 OBJETIVO GENERAL 5 1.4 OBJETIVOS ESPECIFICOS 5 1.5 JUSTIFICACIÓN 6 1.6 ALCANCES Y LIMITACIONES 7 2 MARCO DE REFERENCIA 9 2.1 ANTECEDENTES DE LA INVESTIGACIÓN 9 2.2 MARCO TEÓRICO CONCEPTUAL 11 2.2.1 Medicina Electrodiagnóstica 12 2.2.2 Inteligencia Artificial y Medicina 45 2.2.3 Redes Neuronales Artificiales 61 2.2.4 Aplicaciones de redes neuronales a Medicina 94 2.2.5 Aplicaciones de redes neuronales a electrodiagnóstico 104 3 METODOLOGÍA 106 3.1 DATOS 106 3.1.1 Salidas deseadas 106 3.1.2 Selección de los datos de entrada 107 3.1.3 Preprocesamiento de los datos de entrada 109 3.1.4 Datos Faltantes 110 3.1.5 Fuente de los datos 111 3.2 ARQUITECTURA DE LA RED 113 3.2.1 Tipo de red 114 3.2.2 Mejorar la Generalización 115 3.2.3 Arquitectura de la Red 1 116 3.2.4 Arquitectura de la Red 2 121 3.3 SOFTWARE 124 3.4 HARDWARE 125 3.5 ENTRENAMIENTO 125 3.6 VALIDACIÓN DE LA RED 126 4 RESULTADOS 127 4.1 RED 1 (ESTRUCTURA DE RED GENERAL) 127 4.2 RED 2 (RED NERVIO MEDIANO) 128 4.3 RED 3 (RED NERVIO ULNAR) 130 4.4 RED 4 (RED DE GENERALIZACIÓN) 132 4.5 VALIDACIÓN DE RESULTADOS 135 5 DISCUSIÓN 137 CONCLUSIONES 139 RECOMENDACIONES 141 BIBLIOGRAFIA 142 REFERENCIAS ELECTRONICASMaestríaIntroduction: Interpreting electrophysiological studies is essentially a classification task. Artificial neural networks (ANNs) are suitable tools for classification because they are based on pattern recognition techniques. Objectives: To develop a computer system for automated detection of focal neuropathies using ANNs. Methods: The study was based on 300 sets of nerve conduction studies (NCSs) from three different electrodiagnostic medicine laboratories. Each input data set was formed by 11 parameters, including motor and sensory latencies, amplitudes, durations, and velocities of a single nerve. The input sets were classified into 4 focal neuropathy subgroups (distal demyelination, proximal demyelination, generalized demyelination, axon loss) depending on the type of nerve damage plus 1 additional for normal findings. The data were presented to a backpropagation ANN with 1 hidden layer. The network structure was modified to achieve the lowest possible mean square error. The outputs from these first-level networks were presented to a second-level network in order to detect generalized neuropathies. After training the ANNs, the classification accuracy was tested using another data set that was unknown to the networks. Results: A classification accuracy of 99% was reached for the detection of pathologic patterns. The accuracy for focal neuropathies classification was 95.2%.Conclusions: Neural networks classify focal neuropathy subgroups with high accuracy (>95%). This method may lead to automated focal neuropathy detection.Modalidad Presencia