Caracterización y simulación de arborizaciones dentríticas con redes bayesianas incluyendo variables angulares

El funcionamiento interno del cerebro es todavía hoy en día un misterio, siendo su comprensión uno de los principales desafíos a los que se enfrenta la ciencia moderna. El córtex cerebral es el área del cerebro donde tienen lugar los procesos cerebrales de más alto nivel, cómo la imaginación, el juicio o el pensamiento abstracto. Las neuronas piramidales, un tipo específico de neurona, suponen cerca del 80% de los cerca de los 10.000 millones de que componen el córtex cerebral, haciendo de ellas un objetivo principal en el estudio del funcionamiento del cerebro. La morfología neuronal, y más específicamente la morfología dendrítica, determina cómo estas procesan la información y los patrones de conexión entre neuronas, siendo los modelos computacionales herramientas imprescindibles para el estudio de su rol en el funcionamiento del cerebro. En este trabajo hemos creado un modelo computacional, con más de 50 variables relativas a la morfología dendrítica, capaz de simular el crecimiento de arborizaciones dendríticas basales completas a partir de reconstrucciones de neuronas piramidales reales, abarcando desde el número de dendritas hasta el crecimiento los los árboles dendríticos. A diferencia de los trabajos anteriores, nuestro modelo basado en redes Bayesianas contempla la arborización dendrítica en su conjunto, teniendo en cuenta las interacciones entre dendritas y detectando de forma automática las relaciones entre las variables morfológicas que caracterizan la arborización. Además, el análisis de las redes Bayesianas puede ayudar a identificar relaciones hasta ahora desconocidas entre variables morfológicas. Motivado por el estudio de la orientación de las dendritas basales, en este trabajo se introduce una regularización L1 generalizada, aplicada al aprendizaje de la distribución von Mises multivariante, una de las principales distribuciones de probabilidad direccional multivariante. También se propone una distancia circular multivariante que puede utilizarse para estimar la divergencia de Kullback-Leibler entre dos muestras de datos circulares. Comparamos los modelos con y sin regularizaci ón en el estudio de la orientación de la dendritas basales en neuronas humanas, comprobando que, en general, el modelo regularizado obtiene mejores resultados. El muestreo, ajuste y representación de la distribución von Mises multivariante se implementa en un nuevo paquete de R denominado mvCircular.---ABSTRACT---The inner workings of the brain are, as of today, a mystery. To understand the brain is one of the main challenges faced by current science. The cerebral cortex is the region of the brain where all superior brain processes, like imagination, judge and abstract reasoning take place. Pyramidal neurons, a specific type of neurons, constitute approximately the 80% of the more than 10.000 million neurons that compound the cerebral cortex. It makes the study of the pyramidal neurons crucial in order to understand how the brain works. Neuron morphology, and specifically the dendritic morphology, determines how the information is processed in the neurons, as well as the connection patterns among neurons. Computational models are one of the main tools for studying dendritic morphology and its role in the brain function. We have built a computational model that contains more than 50 morphological variables of the dendritic arborizations. This model is able to simulate the growth of complete dendritic arborizations from real neuron reconstructions, starting with the number of basal dendrites, and ending modeling the growth of dendritic trees. One of the main diferences between our approach, mainly based on the use of Bayesian networks, and other models in the state of the art is that we model the whole dendritic arborization instead of focusing on individual trees, which makes us able to take into account the interactions between dendrites and to automatically detect relationships between the morphologic variables that characterize the arborization. Moreover, the posterior analysis of the relationships in the model can help to identify new relations between morphological variables. Motivated by the study of the basal dendrites orientation, a generalized L1 regularization applied to the multivariate von Mises distribution, one of the most used distributions in multivariate directional statistics, is also introduced in this work. We also propose a circular multivariate distance that can be used to estimate the Kullback-Leibler divergence between two circular data samples. We compare the regularized and unregularized models on basal dendrites orientation of human neurons and prove that regularized model achieves better results than non regularized von Mises model. Sampling, fitting and plotting functions for the multivariate von Mises are implemented in a new R packaged called mvCircular

La detección de trastornos graves de conducta en el ámbito escolar a través del CIPEC

Los trastornos de conducta en el ámbito escolar son motivo de preocupación. Hasta ahora, los criterios que se han tenido en cuenta para la detección temprana de estos trastornos por parte del profesorado se han basado, en general, en valoraciones cualitativas y en apreciaciones globales y subjetivas del profesorado. Con la intención de facilitar la detección de los trastornos graves de conducta de forma más objetiva y con el menor riesgo de error en cuanto a la apreciación de las observaciones del profesorado, Este trabajo explica tanto la fundamentación teórica que lo sustenta, como el procedimiento seguido para su construcción, las características psicométricas, los factores que se derivan Los resultados obtenidos nos desvelan que el CIPEC es un instrumento eficaz para la detección de los trastornos graves de conducta.Behavioral problems in schools are of concern. So far, the criteria to be considered for early detection of these disorders by teachers have been based, in general, qualitative assessments and global evaluations and subjective-as teachers. In order to facilitate detection of severe behavioral disorders more objectively and with less risk of error as to the assessment of teacher observations, we designed the instrument to detect these disorders (CIPEC). This work explains both the theoretical foundation that supports it, as the procedure followed for its construction, the psychometric properties, the factors are derived and the critical items that allow further adjustment of the significance of the findings of the faculty. The results obtained reveal that the CIPEC is an efficient tool for detection of severe behavioral disorders

Analysis of the Degree of Satisfaction with Life Before and During the COVID-19 Pandemic in University Teachers

This paper focuses on analyzing the degree of satisfaction with the life of university teachers before and during the COVID-19 pandemic in the context of social isolation. The present study adopts a quantitative and cross-sectional approach. The sample included 129 university professors, between 18 and 74 years, from the Faculty of Physical Culture Sciences of the Autonomous University of Chihuahua. Satisfaction with Life Scale (SWLS) was obtained to measure the degree of teacher satisfaction (Atienza et al., 2000; Diener et al., 1985; Pons et al., 2002). The results globally showed significant differences between life satisfaction before and during the pandemic according to the means comparison test, using the T-test for related samples, with values ​​of 4.06 before and 3.6 during the pandemic. When categorizing the results according to the escalation, it was shown that 55.7% of the teachers perceived themselves as satisfied before the pandemic, while the opposite happened during the isolation, decreasing, with only 45.5% feeling satisfied. Only 27% felt very satisfied before, and this percentage decreased to only 14.5% during isolation. The  COVID-19 not only wreaked havoc on health, but it also had a negative effects on people's psychological, emotional, and social spheres, thereby modifying healthy lifestyles and leaving possible effects on physical and mental health as a consequence

Degree of Physical Activity in University Teachers Before and During the COVID-19 Pandemic

The objective of this work was to analyze the degree of physical activity of university teachers before and during the Covid-19 pandemic, this in the context of social isolation. The present study adopts a quantitative and cross-sectional approach. The sample was determined randomly, made up of 129 university professors from the Faculty of Physical Culture Sciences of the Autonomous University of Chihuahua, aged between 18 and 74 years. The results show that 50.39% of teachers before the pandemic maintained a high degree of physical activity, while during confinement they presented a lower degree of physical activity, decreasing to only 39.53%. The Covid-19 not only wreaked havoc on health, but also negative effects in the psychological, emotional and social sphere of people, as well as havoc in the practice of physical activity, modifying healthy lifestyles and leaving possible effects for the consequent physical health

Analysis of the Degree of Satisfaction with Life Before and During the COVID-19 Pandemic in University Teachers

This paper focuses on analyzing the degree of satisfaction with the life of university teachers before and during the COVID-19 pandemic in the context of social isolation. The present study adopts a quantitative and cross-sectional approach. The sample included 129 university professors, between 18 and 74 years, from the Faculty of Physical Culture Sciences of the Autonomous University of Chihuahua. Satisfaction with Life Scale (SWLS) was obtained to measure the degree of teacher satisfaction (Atienza et al., 2000; Diener et al., 1985; Pons et al., 2002). The results globally showed significant differences between life satisfaction before and during the pandemic according to the means comparison test, using the T-test for related samples, with values ​​of 4.06 before and 3.6 during the pandemic. When categorizing the results according to the escalation, it was shown that 55.7% of the teachers perceived themselves as satisfied before the pandemic, while the opposite happened during the isolation, decreasing, with only 45.5% feeling satisfied. Only 27% felt very satisfied before, and this percentage decreased to only 14.5% during isolation. The  COVID-19 not only wreaked havoc on health, but it also had a negative effects on people's psychological, emotional, and social spheres, thereby modifying healthy lifestyles and leaving possible effects on physical and mental health as a consequence

Regularized multivariate von Mises distribution

Regularization is necessary to avoid overfitting when the number of data samples is low compared to the number of parameters of the model. In this paper, we introduce a flexible L1 regularization for the multivariate von Mises distribution. We also propose a circular distance that can be used to estimate the Kullback-Leibler divergence between two circular distributions by means of sampling, and also serves as goodness-of-fit measure. We compare the models on synthetic data and real morphological data from human neurons and show that the regularized model achieves better results than non regularized von Mises model

Analysis of the Degree of Satisfaction with Life before and during the COVID-19 Pandemic in University Teachers

The objective of this work was to analyze the degree of satisfaction with the life of university teachers before and during the Covid-19 pandemic, this in the context of social isolation. The present study adopts a quantitative and cross-sectional approach, the sample is extended randomly, made up of 129 university teachers from the Faculty of Physical Culture Sciences of the Autonomous University of Chihuahua, aged between 18 and 74 years. To measure the degree of teacher satisfaction, the Satisfaction with Life Scale (SWLS) was obtained (Atienza et al., 2000; Diener et al., 1985; Pons et al., 2002). The results globally showed significant differences between life satisfaction before and during the pandemic according to the means comparison test, using the T test for related samples, with values ​​of 4.06 before and 3.6 during the pandemic. When carrying out the categorization of the results according to the escalation, it was shown that 55.7% of the teachers perceived themselves satisfied before the pandemic, the opposite happening during the isolation, decreasing, only 45.5% feeling satisfied. Only 27% felt very satisfied before and this percentage decreased to only 14.5% during isolation. The Covid-19 not only wreaked havoc on health, but also negative effects on people's psychological, emotional and social spheres, thus modifying healthy lifestyles and leaving possible effects on physical and mental health as a consequence

Analysis of the Degree of Satisfaction with Life before and during the COVID-19 Pandemic in University Teachers

The objective of this work was to analyze the degree of satisfaction with the life of university teachers before and during the Covid-19 pandemic, this in the context of social isolation. The present study adopts a quantitative and cross-sectional approach, the sample is extended randomly, made up of 129 university teachers from the Faculty of Physical Culture Sciences of the Autonomous University of Chihuahua, aged between 18 and 74 years. To measure the degree of teacher satisfaction, the Satisfaction with Life Scale (SWLS) was obtained (Atienza et al., 2000; Diener et al., 1985; Pons et al., 2002). The results globally showed significant differences between life satisfaction before and during the pandemic according to the means comparison test, using the T test for related samples, with values ​​of 4.06 before and 3.6 during the pandemic. When carrying out the categorization of the results according to the escalation, it was shown that 55.7% of the teachers perceived themselves satisfied before the pandemic, the opposite happening during the isolation, decreasing, only 45.5% feeling satisfied. Only 27% felt very satisfied before and this percentage decreased to only 14.5% during isolation. The Covid-19 not only wreaked havoc on health, but also negative effects on people's psychological, emotional and social spheres, thus modifying healthy lifestyles and leaving possible effects on physical and mental health as a consequence

Feline osteochondromatosis in a 12-year-old feline leukaemia virus-negative cat

Feline osteochondromatosis is a spontaneous osteocartilaginous exostosis associated with feline leukaemia virus (FeLV) infection or due to a frameshift variant in the exostosin glycosyltransferase 1 (EXT1) gene. Osteochondromatosis was diagnosed in an indoor-only, 12-year-old, neutered female, Russian Blue cat. Radiographs revealed bilateral calcified proliferations in the elbow, costochondral and sternochondral joints, which distorted the normal skeletal structure. Grossly, the proliferated joints presented with consistent, rounded masses, causing complete ankylosis. The main histopathological finding was an osteocartilaginous proliferation composed of multiple irregular islands of well-differentiated hyaline cartilage surrounded and delimited by osteoid tissue. Immunohistochemistry of the osteochondromas, bone marrow and mediastinal lymph nodes, using a primary anti-FeLV gp70 antibody, and FeLV proviral DNA real-time polymerase chain reaction on bone marrow were negative. Sequencing of exon 6 of the EXT1 gene was performed and nucleotide BLAST analysis demonstrated the absence of a frameshift variant. This study reports the only case of spontaneous feline osteochondromatosis in an animal more than 10 years old