Distant Supervised Construction and Evaluation of a Novel Dataset of Emotion-Tagged Social Media Comments in Spanish

Tagged language resources are an essential requirement for developing machine-learning text-based classifiers. However, manual tagging is extremely time consuming and the resulting datasets are rather small, containing only a few thousand samples. Basic emotion datasets are particularly difficult to classify manually because categorization is prone to subjectivity, and thus, redundant classification is required to validate the assigned tag. Even though, in recent years, the amount of emotion-tagged text datasets in Spanish has been growing, it cannot be compared with the number, size, and quality of the datasets in English. Quality is a particularly concerning issue, as not many datasets in Spanish included a validation step in the construction process. In this article, a dataset of social media comments in Spanish is compiled, selected, filtered, and presented. A sample of the dataset is reclassified by a group of psychologists and validated using the Fleiss Kappa interrater agreement measure. Error analysis is performed by using the Sentic Computing tool BabelSenticNet. Results indicate that the agreement between the human raters and the automatically acquired tag is moderate, similar to other manually tagged datasets, with the advantages that the presented dataset contains several hundreds of thousands of tagged comments and it does not require extensive manual tagging. The agreement measured between human raters is very similar to the one between human raters and the original tag. Every measure presented is in the moderate agreement zone and, as such, suitable for training classification algorithms in sentiment analysis field

Determination of contents based on learning styles through artificial intelligence

The study presents the development of a platform for structuring adaptive courses based on active, reflexive, theoretical and pragmatic learning styles using artificial intelligence techniques. To this end, the following phases were followed: search, analysis and classification of information about the process of generating content for courses; analysis and coding of the software component for generating content according to learning styles; and application of tests for validation and acceptance. The main contribution of the paper is the development of a model using neural networks and its integration in an application server to determine the contents that correspond to the active, reflexive, theoretical and pragmatic learning styles

Thoracic aortopathy in Turner syndrome and the influence of bicuspid aortic valves and blood pressure: a CMR study

<p>Abstract</p> <p>Background</p> <p/> <p>To investigate aortic dimensions in women with Turner syndrome (TS) in relation to aortic valve morphology, blood pressure, karyotype, and clinical characteristics.</p> <p>Methods and results</p> <p>A cross sectional study of 102 women with TS (mean age 37.7; 18-62 years) examined by cardiovascular magnetic resonance (CMR- successful in 95), echocardiography, and 24-hour ambulatory blood pressure. Aortic diameters were measured by CMR at 8 positions along the thoracic aorta. Twenty-four healthy females were recruited as controls. In TS, aortic dilatation was present at one or more positions in 22 (23%). Aortic diameter in women with TS and bicuspid aortic valve was significantly larger than in TS with tricuspid valves in both the ascending (32.4 ± 6.7 vs. 26.0 ± 4.4 mm; p < 0.001) and descending (21.4 ± 3.5 vs. 18.8 ± 2.4 mm; p < 0.001) aorta. Aortic diameter correlated to age (R = 0.2 - 0.5; p < 0.01), blood pressure (R = 0.4; p < 0.05), a history of coarctation (R = 0.3; p = 0.01) and bicuspid aortic valve (R = 0.2-0.5; p < 0.05). Body surface area only correlated with descending aortic diameter (R = 0.23; p = 0.024).</p> <p>Conclusions</p> <p/> <p>Aortic dilatation was present in 23% of adult TS women, where aortic valve morphology, age and blood pressure were major determinants of the aortic diameter.</p

Chiari’s Network as a Cause of Fetal and Neonatal Pathology

Chiari’s network is a remnant of the eustachian valve located in the right atrium. Incomplete involution of the fetal sinus venosus valves results in “redundant” Chiari’s network, which may compromise cardiovascular function. This report describes a case with the novel finding of prenatal compromise due to redundant Chiari’s network and an uncommon case with significant postnatal symptoms. In both cases, the symptoms (fetal hydrops and postnatal cyanosis) resolved spontaneously. The variety of cardiovascular pathologies described in the literature is believed to be associated with persistence of a Chiari network. Knowledge about this not always harmless structure is important for perinatologists, pediatricians, and pediatric cardiologists alike. The clinical importance of this rare pathology is that prenatal counseling may anticipate a generally positive outcome and that surgical intervention generally should be avoided

Dilation of the ascending aorta in Turner syndrome - a prospective cardiovascular magnetic resonance study

<p>Abstract</p> <p>Background</p> <p>The risk of aortic dissection is 100-fold increased in Turner syndrome (TS). Unfortunately, risk stratification is inadequate due to a lack of insight into the natural course of the syndrome-associated aortopathy. Therefore, this study aimed to prospectively assess aortic dimensions in TS.</p> <p>Methods</p> <p>Eighty adult TS patients were examined twice with a mean follow-up of 2.4 ± 0.4 years, and 67 healthy age and gender-matched controls were examined once. Aortic dimensions were measured at nine predefined positions using 3D, non-contrast and free-breathing cardiovascular magnetic resonance. Transthoracic echocardiography and 24-hour ambulatory blood pressure were also performed.</p> <p>Results</p> <p>At baseline, aortic diameters (body surface area indexed) were larger at all positions in TS. Aortic dilation was more prevalent at all positions excluding the distal transverse aortic arch. Aortic diameter increased in the aortic sinus, at the sinotubular junction and in the mid-ascending aorta with growth rates of 0.1 - 0.4 mm/year. Aortic diameters at all other positions were unchanged. The bicuspid aortic valve conferred higher aortic sinus growth rates (p < 0.05). No other predictors of aortic growth were identified.</p> <p>Conclusion</p> <p>A general aortopathy is present in TS with enlargement of the ascending aorta, which is accelerated in the presence of a bicuspid aortic valve.</p

Minería de datos y big data: aplicaciones en riesgo crediticio, salud y análisis de mercado

Esta línea de investigación se centra en el estudio y desarrollo de Sistemas Inteligentes para la resolución de problemas de Minería de Datos y Big Data utilizando técnicas de Aprendizaje Automático. Los sistemas desarrollados se aplican particularmente al procesamiento de textos y reconocimiento de patrones en imágenes. En el área de la Minería de Datos se está trabajando, por un lado, en la generación de un modelo de fácil interpretación a partir de la extracción de reglas de clasificación que permita justificar la toma de decisiones y, por otro lado, en el desarrollo de nuevas estrategias para tratar grandes volúmenes de datos. Con respecto al área de Big Data se están realizando diversos aportes usando el framework Spark Streaming. En esta dirección, se está investigando en una técnica de clustering dinámico que se ejecuta de manera distribuida. Además se ha implementado en Spark Streaming una aplicación que calcula el índice de Hurtz de manera online, actualizándolo cada pocos segundos con el objetivo de estudiar un cierto mercado de negocios. En el área de la Minería de Textos se han desarrollado estrategias para resumir documentos a través de la extracción utilizando métricas de selección y técnicas de optimización de los párrafos más representativos. Además se han desarrollado métodos capaces de determinar la subjetividad de oraciones escritas en español.Eje temático: Bases de Datos y Minería de Datos

Candidate biomarkers from the integration of methylation and gene expression in discordant autistic sibling pairs

While the genetics of autism spectrum disorders (ASD) has been intensively studied, resulting in the identification of over 100 putative risk genes, the epigenetics of ASD has received less attention, and results have been inconsistent across studies. We aimed to investigate the contribution of DNA methylation (DNAm) to the risk of ASD and identify candidate biomarkers arising from the interaction of epigenetic mechanisms with genotype, gene expression, and cellular proportions. We performed DNAm differential analysis using whole blood samples from 75 discordant sibling pairs of the Italian Autism Network collection and estimated their cellular composition. We studied the correlation between DNAm and gene expression accounting for the potential effects of different genotypes on DNAm. We showed that the proportion of NK cells was significantly reduced in ASD siblings suggesting an imbalance in their immune system. We identified differentially methylated regions (DMRs) involved in neurogenesis and synaptic organization. Among candidate loci for ASD, we detected a DMR mapping to CLEC11A (neighboring SHANK1) where DNAm and gene expression were significantly and negatively correlated, independently from genotype effects. As reported in previous studies, we confirmed the involvement of immune functions in the pathophysiology of ASD. Notwithstanding the complexity of the disorder, suitable biomarkers such as CLEC11A and its neighbor SHANK1 can be discovered using integrative analyses even with peripheral tissues

Minería de datos y big data: aplicaciones en riesgo crediticio, salud y análisis de mercado

Esta línea de investigación se centra en el estudio y desarrollo de Sistemas Inteligentes para la resolución de problemas de Minería de Datos y Big Data utilizando técnicas de Aprendizaje Automático. Los sistemas desarrollados se aplican particularmente al procesamiento de textos y reconocimiento de patrones en imágenes. En el área de la Minería de Datos se está trabajando, por un lado, en la generación de un modelo de fácil interpretación a partir de la extracción de reglas de clasificación que permita justificar la toma de decisiones y, por otro lado, en el desarrollo de nuevas estrategias para tratar grandes volúmenes de datos. Con respecto al área de Big Data se están realizando diversos aportes usando el framework Spark Streaming. En esta dirección, se está investigando en una técnica de clustering dinámico que se ejecuta de manera distribuida. Además se ha implementado en Spark Streaming una aplicación que calcula el índice de Hurtz de manera online, actualizándolo cada pocos segundos con el objetivo de estudiar un cierto mercado de negocios. En el área de la Minería de Textos se han desarrollado estrategias para resumir documentos a través de la extracción utilizando métricas de selección y técnicas de optimización de los párrafos más representativos. Además se han desarrollado métodos capaces de determinar la subjetividad de oraciones escritas en español.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic