Redes bayesianas para identificar perfiles de estudiante. Aplicación al estudio del abandono de las titulaciones de Informática en la Universidad de Castilla-La Mancha

El abandono de los estudiantes es un problema que afecta a todas las universidades siendo más acusado en las titulaciones de las ramas de Ingeniería y Arquitectura. Como docentes del Grado de Ingeniería Informática de la Universidad de Castilla-La Mancha nuestro interés se centra en analizar el perfil del estudiante que abandona estos estudios, con el fin de definir acciones orientadas a reducir la actual tasa de abandono. En ediciones anteriores de las JENUI se ha analizado esta problemática desde el punto de vista de la estadística tradicional y de la minería de datos, mediante árboles de decisión y regresión multivariante; en este trabajo lo abordamos mediante algoritmos de aprendizaje de redes bayesianas, ya que éstas tienen una semántica muy rica y son fácilmente interpretables. Los resultados del trabajo no son concluyentes debido a las restricciones de la base de datos utilizada, pero la descripción del estudio realizado pone en valor el interés de la técnica empleada y sienta las bases para mejorar el alcance de la investigación en trabajos futuros relacionados con la extración de datos de futuros estudiantes

Acute toxicity, bioaccumulation and effects of dietary 1 transfer of silver from brine 2 shrimps exposed to PVP/PEI-coated silver nanoparticles to zebrafish

The extensive use and release to the aquatic environment of silver nanoparticles (NPs) could lead to their incorporation into the food web. Brine shrimp larvae of 24 h showed low sensitivity to the exposure to PVP/PEI-coated Ag NPs (5 nm), with EC50 values at 24 h of 19.63 mg Ag L-1, but they significantly accumulated silver after 24 h of exposure to 100 μg L-1 of Ag NPs. Thus, to assess bioaccumulation and effects of silver transferred by the diet in zebrafish, brine shrimp larvae were exposed to 100 ng L-1 of Ag NPs as an environmentally relevant concentration or to 100 μg L-1 as a potentially effective concentration and used to feed zebrafish for 21 days. Autometallography revealed a dose- and time-dependent metal accumulation in the intestine and in the liver of zebrafish. Three-day feeding with brine shrimps exposed to 100 ng L-1 of Ag NPs was enough to impair fish health as reflected by the significant reduction of lysosomal membrane stability and the presence of vacuolization and necrosis in the liver. However, dietary exposure to 100 μg L-1 of Ag NPs for 3 days did not significantly alter gene transcription levels, neither in the liver nor in the intestine. After 21 days, biological processes such as lipid transport and localization, cellular response to chemical stimulus and response to xenobiotic stimulus were significantly altered in the liver. Overall, these results indicate an effective dietary transfer of silver and point out to liver as the main target organ for Ag NP toxicity in zebrafish after dietary exposure.MINECO (NanoSilverOmicsproject- MAT2012-39372) Basque Government (consolidated research groups IT810-13 and IT620-13; Saiotek S-PE13UN142) University of the Basque Country (UFIs 11/37 and 11/52)

Mitoxantrone in metastatic apudomas: a phase II study of the EORTC Gastro-Intestinal Cancer Cooperative Group.

We performed a phase II study with mitoxantrone in patients with carcinoid tumours, islet cell tumours and medullary carcinomas of the thyroid. Thirty-five eligible patients received mitoxantrone 12 mg m-2 i.v. every 3 weeks. Among 18 previously untreated patients, three responded (17%, 95% CI = 4-41%); no responses were achieved in 17 previously treated patients. Of the 21 patients who had carcinoid tumours, 11 were previously untreated and two achieved a response (18%, 95% CI = 2-52%). Overall response rate was 9% (95% CI = 2-23%). At a median follow-up of 43 months, median overall survival was 16 months. The median survival of 21 patients with a normal alkaline phosphatase was 29 months and 9 months for 14 patients with elevated serum levels (P = 0.005). A similar observation was noticed for gamma-glutamyltransferase (P = 0.007). We concluded that mitoxantrone is not active in APUD tumours. Elevated alkaline phosphatase and gamma-glutamyltransferase are associated with a poor prognosis

Impact of Bayesian network model structure on the accuracy of medical diagnostic systems

While Bayesian network models may contain a handful of numerical parameters that are important for their quality, several empirical studies have confirmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important - diagnostic accuracy of several medical diagnostic models changes minimally when we subject their structures to such transformations as arc removal and arc reversal. © 2014 Springer International Publishing

Use of the infra hyoid musculo-cutaneous flap in soft palate reconstruction.

To review a series of 23 consecutive patients with squamous cell carcinomas arising from oropharynx who underwent infra hyoid musculo-cutaneous flap reconstruction including soft palate in alternative to free radial forearm flap or maxillofacial prosthesis. Post operative radiotherapy was performed for all patients.Every reconstruction healed quickly without major wound complications. The functional results evaluated by speech and swallowing capacities, were good for 17 patients, fair for 4 patients and bad for 2.The infra hyoid musculo-cutaneous flap is a versatile, reliable and convenient flap suitable for repairing small and medium sized defects; it can be used in combination with other flaps, and in selected cases obviates the need for a microvascular free radial forearm flap or maxillofacial prosthesis

Comparative evaluation by semiquantitative reverse transcriptase polymerase chain reaction of MDR1, MRP and GSTp gene expression in breast carcinomas.

Identification and quantitative evaluation of drug resistance markers are essential to assess the impact of multidrug resistance (MDR) in clinical oncology. The MDR1 gene confers pleiotropic drug resistance in tumour cells, but other molecular mechanisms are also involved in drug resistance. In particular, the clinical pattern of expression of the other MDR-related genes is unclear and their interrelationships are still unknown. Here, we report standardization of the procedures used to determine a reliable method of semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) using a standard series of drug-sensitive and increasingly resistant cell lines to evaluate the expression of three MDR-related genes, i.e. MDR1 (multidrug resistance gene 1), MRP (multidrug resistance related protein) and GSTp (glutathione-S-transferase p), reported to be endogenous standard genes for normalization of mRNAs. A total of 74 breast cancer surgical biopsies, obtained before any treatment, were evaluated by this method. When compared with classical clinical and laboratory findings, GSTp mRNA level was higher in diploid tumours. However, the main finding of our study suggests a clear relationship between two of these MDR-related gene expressions, namely GSTp and MRP. This finding provides new insight into human breast tumours, which may possibly be linked to the glutathione conjugate carrier function of MRP. Well defined semiquantitative RT-PCR procedures can therefore constitute a powerful tool to investigate MDR phenotype at mRNA levels of different related genes in small and precious tumour biopsy specimens

La elección de intensificaciones del Grado de Informática de la Escuela Superior de Informática de la Universidad de Castilla-La Mancha y el perfil del estudiante de cada una

En este trabajo se analiza la evolución de la elección de cada una de las intensificaciones que se imparten en el Grado de Informática de la Escuela Superior de Informática de Ciudad Real (ESI) de la Universidad de Castilla-La Mancha (UCLM), y se estudia el perfil del estudiante de cada una de ellas. Para ello, se ha recopilado información demográfica y académica durante los últimos cuatro cursos académicos. Además, para intentar caracterizar el perfil del estudiante de cada intensificación, durante el curso actual se han obtenido otros datos del alumnado relacionados con su actitud personal hacia la programación, su propensión a participar y disfrutar de tareas cognitivamente exigentes, su predisposición a adoptar nuevas tecnologías, y su estilo de aprendizaje. Los resultados reflejan diferencias entre los perfiles de cada intensificación, lo que justifica la continuación del trabajo en esta línea durante los próximos cursos, así como la relación de dicho perfil con su desempeño.In this paper we analyze the evolution of the choice of each of the intensifications taught in the Computer Science Degree of the Escuela Superior de Informática de Ciudad Real (ESI) of the University of Castilla-La Mancha (UCLM) and we study the student profile of each of them. For this purpose, demographic and academic information has been collected during the last four academic years. Furthermore, in order to try to characterize the student profile of each intensification, during the current academic year other data have been obtained from the students related to their personal attitude towards programming, their propensity to participate and enjoy cognitively demanding tasks, their predisposition to adopt new technologies, and their learning style. The results reflect differences between the profile of each intensification, which justifies the continuation of the work in this line during the next courses, as well as the relationship of this profile with their performance

Explanations of Black-Box Model Predictions by Contextual Importance and Utility

The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and transparent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods

COLLECE 2.0: Un sistema para el aprendizaje colaborativo de la programación sobre Eclipse, con una metáfora multidimensional para la visualización de programas

La programación de ordenadores es una tarea compleja y un reto para los estudiantes principiantes. Son numerosas las dificultades para entender los conceptos de programación debido al alto nivel de abstracción que requiere su aprendizaje. Con el propósito de contribuir a paliar estas dificultades, hemos desarrollado el sistema COLLECE-2.0, un plug-in para la plataforma Eclipse, que proporciona un entorno colaborativo de programación, distribuido y en tiempo real. Su interfaz ha sido diseñada para potenciar los aspectos relacionados con el soporte al aprendizaje en grupo. Además, nuestra propuesta hace un especial énfasis en la visualización de los programas, incorporando un conjunto de representaciones gráficas multidimensionales basadas en una metáfora. Estas representaciones son aplicables a una variedad de escenarios que soportan diferentes mecanismos de interacción, dependiendo de la dimensionalidad de las representaciones gráficas y de los dispositivos empleados para su visualización. En este artículo se describen los detalles fundamentales del sistema COLLECE-2.0 y cómo pude emplearse en distintos escenarios, para visualizar e interactuar con aspectos estructurales de los programas y algoritmos.Computer programming is a complex task and a challenge for novice programmers. There are a wide range of difficulties in understanding programming concepts due to the high level of abstraction required to learn them. In order to address these difficulties, we have developed the COLLECE-2.0 system, a plug-in for the Eclipse platform, which provides a real-time, distributed, collaborative programming environment. Its interface has been designed to enhance aspects related to support for group learning. In addition, our proposal makes a special emphasis on the program visualization, incorporating a set of multidimensional graphic representations based on a metaphor. These representations are applicable to a variety of scenarios that support different interaction mechanisms, depending on the dimensionality of the graphic representations and the devices used for their visualization. This paper describes the main details of the COLLECE-2.0 system and how it can be used in different scenarios by visualizing and interacting with structural aspects of the programs and algorithms.Este trabajo ha sido financiado por el Ministerio de Economía, Industria y Competitividad y por el Fondo Europeo de Desarrollo Regional, con referencia TIN2015-66731-C2-2-R

A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made