This study establishes that exosomes secreted by cardiomyocytes under ischaemic conditions promote heart angiogenesis, which may pave the way towards the development of add-on therapies to enhance myocardial blood supply.

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Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.

Resumen En este artículo se presentan los primeros resultados del proyecto de investigación cuyo objetivo es detectar patrones de deserción estudiantil a partir de los datos socioeconómicos, académicos, disciplinares e institucionales de los estudiantes de los programas de pregrado de la Universidad de Nariño e Institución Universitaria IUCESMAG, dos instituciones de educación superior de la ciudad de Pasto (Colombia), utilizando técnicas de Minería de Datos. Los resultados obtenidos corresponden a la Universidad de Nariño. Se descubrieron perfiles socioeconómicos y académicos de los estudiantes que desertan utilizando la técnica de clasificación basada en árboles de decisión. El conocimiento generado permitirá soportar la toma de decisiones eficaces de las directivas universitarias enfocadas a formular políticas y estrategias relacionadas con los programas de retención estudiantil que actualmente se encuentran establecidos. Palabras claveExtracción de Perfiles, Deserción Estudiantil, Minería de Datos, Clasificación, Árboles de Decisión   Abstract The first results of the research project that aims to identify patterns of student dropout from socioeconomic, academic, disciplinary and institutional data of students from undergraduate programs at the University of Nariño and IUCESMAG University, two higher education institutions in the city of Pasto (Colombia), using data mining techniques are presented. The results correspond to the University of Nariño. Socioeconomic and academic profiles were discovered of students who drop using classification technique based on decision trees. The knowledge generated will support effective decision-making of university staff focused to develop policies and strategies related to student retention programs that are currently set.KeywordsExtraction of Profiles, Student Dropout, Data Mining, Classification, Decision Trees

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This paper presents one of results obtained in the research project that aimed to apply educational data mining to discover factors associated with the academic performance of Colombian High School students who presented the Saber 11th test between the years 2015 and 2016. Socioeconomic , academic and institutional information of those students was selected from the ICFES databases. CRISP-DM was used as methodology. A data repository for data mining was built, cleaned and transformed. Patterns associated with the good or poor academic performance of students were discovered in the mathematics test, using a classification model based on decision trees. The knowledge discovered will be incorporated into the existing one and it can be integrated into the decision-making processes of the MEN, ICFES and others governmental and educational institutions that ensure the quality of education in Colombia.

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