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

Evaluation of Causal Sentences in Automated Summaries

This paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.Instituto de Investigación en Informátic

Cross-sectional analysis of the correlation between daily nutrient intake assessed by 7-day food records and biomarkers of dietary intake among participants of the NU-AGE study

Methods for measuring diet composition and quantifying nutrient intake with sufficient validity are essential to study the association between nutrition and health outcomes and risk of diseases. 7-day food records provides a quantification of food actually and currently consumed and is interesting for its use in intervention studies to monitor diet in a short-term period and to guide participants toward changing their intakes. The objective of this study is to analyze the correlation/association between the daily intake of selected nutrients (collected by a 7-day food records plus a mineral/vitamin supplementation questionnaire) and estimates of energy expenditure as well as blood and urine biomarkers of dietary intakes in 1,140 healthy elderly subjects (65–79 years) at baseline of the NU-AGE intervention study (NCT01754012, clinicaltrials.gov). The results show that: the daily intake of energy correlated significantly with predicted total energy expenditure (pTEE) (ρ = 0.459, p < 0.001, and q < 0.001); protein intake correlated significantly with the ratio of 24 h urinary urea to creatinine excretion (ρ = 0.143 for total protein intake, ρ = 0.296 for animal protein intake, and ρ = 0.359 for protein intake/body weight, p < 0.001 and q < 0.001 for each correlation); vitamin B12 and folate intakes correlated significantly with their serum concentrations (ρ = 0.151 and ρ = 0.363, respectively; p < 0.001 and q < 0.001 for each correlation); sodium and potassium intakes correlated significantly with their 24 h urinary excretion (ρ = 0.298 and ρ = 0.123, respectively; p < 0.001 and q < 0.001 for each correlation); vitamin B12 and folate intakes were negatively associated with plasma homocysteine measure (p = 0.001 and p = 0.004, respectively); stratifying subjects by gender, the correlations between energy intake and pTEE and between potassium intake and its 24 h urinary excretion lost their significance in women. Even if the plasma and urinary levels of these nutrients depend on several factors, the significant correlations between daily reported intake of nutrients (protein, vitamin B12, folate, and sodium) and their blood/urinary markers confirmed that the 7-day food records (plus a supplementation questionnaire) provides reliable data to evaluate short-term current dietary intake in European elderly subjects and it can be exploited to guide and monitor NU-AGE participants through the shift of their diet according NU-AGE recommendations

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 temático: Bases de Datos y Minería de Datos

Minería de datos y big data: aplicaciones en señales y textos

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 señales y textos. Con respecto al procesamiento de Señales el énfasis está puesto en el análisis de videos con el objetivo de identificar acciones humanas que faciliten la interfaz hombre/máquina y en la detección de patrones de movimiento de los objetos presentes. 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 a Minería de Textos se han desarrollado métodos capaces de extraer las palabras clave de documentos independientemente del lenguaje. Además, se han desarrollando estrategias para resumir documentos a través de la extracción de párrafos.Eje: Bases de datos y Minería de datos.Red de Universidades con Carreras en Informátic

DIMBOA levels in hexaploid Brazilian wheat are not associated with antibiosis against the cereal aphids Rhopalosiphum padi and Sitobion avenae.

The objective of this study was to evaluate the natural levels of the plant defence compound DIMBOA in young leaves of eight hexaploid Brazilian wheat genotypes and the impact of the genotypes upon development of cereal aphids, Rhopalosiphum padi and Sitobion avenae. HPLC Analysis revealed that the DIMBOA levels varied from 5.376 (in BRS Guabiju) to 30.651 mmol/kgFW (in BRS Timbaúva) with two genotypes outperforming Solstice, a UK variety used as reference. Bioassays were conducted to evaluate the development and fecundity of both aphids when grown on the wheat genotypes. Although BRS Guabiju and BRS Timbaúva were among the genotypes showing the highest and lowest susceptibility respectively, against both aphids, no correlation could be found between DIMBOA levels and antibiosis effects. The cultivar BRS 327 that was among the genotypes showing lower intrinsic rate of population increase for the two aphid species. Elucidating the role of secondary metabolites in plant resistance to aphids and the characterisation of the genotypes that allowed reduced aphid development are important steps to achieve a better natural resistance in hexaploid Brazilian wheat

Cancer therapy and cardiotoxicity: The need of serial Doppler echocardiography

Cancer therapy has shown terrific progress leading to important reduction of morbidity and mortality of several kinds of cancer. The therapeutic management of oncologic patients includes combinations of drugs, radiation therapy and surgery. Many of these therapies produce adverse cardiovascular complications which may negatively affect both the quality of life and the prognosis. For several years the most common noninvasive method of monitoring cardiotoxicity has been represented by radionuclide ventriculography while other tests as effort EKG and stress myocardial perfusion imaging may detect ischemic complications, and 24-hour Holter monitoring unmask suspected arrhythmias. Also biomarkers such as troponine I and T and B-type natriuretic peptide may be useful for early detection of cardiotoxicity. Today, the widely used non-invasive method of monitoring cardiotoxicity of cancer therapy is, however, represented by Doppler-echocardiography which allows to identify the main forms of cardiac complications of cancer therapy: left ventricular (systolic and diastolic) dysfunction, valve heart disease, pericarditis and pericardial effusion, carotid artery lesions. Advanced ultrasound tools, as Integrated Backscatter and Tissue Doppler, but also simple ultrasound detection of "lung comet" on the anterior and lateral chest can be helpful for early, subclinical diagnosis of cardiac involvement. Serial Doppler echocardiographic evaluation has to be encouraged in the oncologic patients, before, during and even late after therapy completion. This is crucial when using anthracyclines, which have early but, most importantly, late, cumulative cardiac toxicity. The echocardiographic monitoring appears even indispensable after radiation therapy, whose detrimental effects may appear several years after the end of irradiation