Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Abstract
This paper presents a comparison between different optimization techniques in the context of hybrid small-signal model (SSM) parameter extraction for GaN High Electron Mobility Transistors (HEMTs). The optimization techniques considered in this work are: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The algorithms were tested for their robustness, convergence speed, and efficiency. PSO algorithm was shown to be the most suitable in terms of robustness and efficiency. However, all techniques were able to obtain credible model parameters, which confirms the reliability of the adopted procedure. The quality of extraction was evaluated by means of S-parameter fitting at different bias conditions.

Abstract
Access to information has been made easier in different domains that range from multimedia content, books, music, news, etc. To deal with the huge amount of alternatives, recommendation systems have been often used as a solution to filter the options and provide suggestions of items that might be of interest to an user. The news domain introduces additional challenges due not only to the large amount of new items produced daily but also due to their ephemeral timelife. In this paper, a news recommendation system which combines content-based and georeferenced techniques in a mobility scenario, is proposed. Taking into account the volatility of the information, short-term and long-term user profiles are considered and implicitly built. Besides tracking users' clicks, the system infers different levels of interest an article has by tracking and weighting each action in the system and in social networks. Impact of the different fields that make up a news is also taken into account by following the inverted pyramid model that assumes different levels of importance to each paragraph of the article. The solution was tested with a population of volunteers and results indicate that the quality of the recommendation approach is acknowledged by the users.

Abstract
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring and comparing the ability of these various approaches to accurately discover ADEs. This work is motivated by the observation that text sources such as the Medline/Medinfo library provide a wealth of information on human health. Unfortunately, ADEs often result from unexpected interactions, and the connection between conditions and drugs is not explicit in these sources. Thus, in this work, we address the question of whether we can quantitatively estimate relationships between drugs and conditions from the medical literature. This paper proposes and studies a state-of-the-art NLP-based extraction of ADEs from text.

Abstract
Teachers’ mobility is one of the facets of Higher Education Institutions internationalization, and despite its importance in implementing the program's purposes it is still disregarded by researchers, with most mobility studies focusing on students. This research concentrates on highly active mobility teachers and aims to delve into their experiences, namely by identifying facilitators and goals for this repeated internationalization and by analyzing the outcomes of these initiatives in their personal lives, professional activity, home and host students, and for their Universities as a whole. This study adopts a qualitative exploratory approach. Having as sample universe the teachers of one Portuguese University that in a 7-year period (2009-2016) engaged in mobility experiences under the Erasmus program (N = 107), 8 were identified as having the highest number of initiatives and were invited to participate in this study. From these mobility champions, 5 accepted, resulting in 5 phenomenological interviews. Data was collected in January 2017. The participants shared an integrated view of the Erasmus mobility, being essential for its success the additional opportunities of joint research and the strengthening of international relationships and networks. Prior relations with teachers from the host University and ongoing research projects stood out among the facilitators. The opportunity to observe and get to know other cultural settings was also mentioned as one determinant stimulus. Still, the outcomes in terms of teaching methodologies and best practices as well an effective impact in home students seemed residual. Moreover, the ability to encourage other teachers to join the program was very limited, often confined to close colleagues and research partners. Despite its exploratory nature, this study demonstrates the relevance of further research on mobility champions to assess the success and possible pitfalls of repeated mobility experiences in terms of extended institutional outcomes and well as individual gratification of the teachers involved. Based on the results, we suggest the consideration of a wider set of outcomes in the appraisal of mobility initiatives, as well as the widespread of champions’ insights on the topic in order to motivate inexperienced teachers to embrace internationalization. Hopefully this paper is able to inspire not only research but also teaching mobility initiatives.

Abstract