Abstract
Background: Tamoxifen remains the standard hormonotherapy for Male breast cancer patients (MBC). Previous studies, in women, tried to evaluate the impact of CYP2D6 polymorphisms in tamoxifen efficacy with conflicting results. Herein we analyze the relation between CYP2D6*4 polymorphism and survival in MBC patients. Patients and methods: Fifty-three patients, proposed to tamoxifen in adjuvant setting, were enrolled. Clinical information was collected from records and histological revision with additional immunochemistry analysis was done to better characterize the tumors. Comprehensive CYP2D6*4 genotyping from blood or tumor tissue was performed and translated into two predicted metabolic activity groups. Results: Patients included in the two CYP2D6*4 groups did not differ concerning to age, histological characteristics, and primary treatments performed. Median age at diagnosis was 63 years-old and patients were submitted at least to mastectomy and adjuvant hormonotherapy. Recurrence was observed in 7 patients (13.2%) and 13 patients (25.5%) died with a 5-year disease-free survival of 86.2%. The poorer metabolizer group had a high risk for recurrence (p = 0.034) and this outcome effect remains in different subgroups: in tumors larger than 2 cm (p < 0.001), nodal status, N0 vs N+ (p = 0.04) and in advanced stage, stage III (p < 0.001). Poorer metabolizer patients had also a worse overall survival when tumors were larger than 2 cm (p = 0.03). Conclusions: In our series, there was an association between CYP2D6*4 polymorphism and a probability of recurrence, with a consistent effect in risk groups defined by classic prognostic factors. Multicentric studies with larger samples are needed to validate these results.

Abstract
Carpooling is a car sharing practice first adopted in the United States of America during the fuel crisis in the 1970s. Since then, and after some ups and downs, this practice has been growing in recent years, being currently used throughout the world. With the evolution of mobile technologies, carpooling had the opportunity to expand, especially through mobile applications and web pages. With these technologies, it is possible for anyone in any part of the globe to search for others that wish to go to the same place and want to share their car. With this practice, people intend to save money, help preserve the environment, reduce congestions in cities, increase the number of places available to park and meet new people. This paper introduces MoCaS Mobile Carpooling System, a carpool service offered for registered users. In this system, each user can enter his travels and make appointments, assign ratings, register vehicles and add travel preferences. All this is possible via a web interface and also via a mobile application that together give greater support to those seeking such services. MoCaS distinguishes itself from other systems by offering innovative services, namely in the mobile component, that through location services allows for the booking of trips in real-time; in other words, not only trips that have not started, but trips that are already underway and that end up intersecting the user's position. Besides this novelty, this system provides a real-time map, where all trip stops are visible, as well as the location of carpoolers who are currently traveling. Both the web and the mobile applications were successfully developed, achieving good results in the performed tests, and are currently being prepared for deployment.

Abstract
Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess each patient's treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and/or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models.

Abstract
This paper, presents ongoing work that extends MetaProbLog with Most Probable Explanation (MPE) inference method. The MPE inference method is widely used in Hidden Markov Models in order to derive the most likely states of a model. Recently, we started developing an application that uses MetaProbLog to models phonocardiograms. We target to use this application in order to diagnose heart diseases by using phonocardiogram classification. Motivated by the importance of phonocardiogram classification, we started the implementation of the MPE inference method and an improvement of representation for annotated disjunctions.

Abstract
The first step in any non linear time series analysis, is to characterize signals in terms of periodicity, stationarity, linearity and predictability. In this work we aim to find if PCG (phonocardiogram) and ECG (electrocardiogram) time series are generated by a deterministic system and not from a random stochastic process. If PCG and ECG are non-linear deterministic systems and they are not very contaminated with noise, data should be confined to a finite dimensional manifold, which means there are structures hidden under the signal that could be used to increase our knowledge in forecasting future values of the time series. A non-linear process can give rise to very complex dynamic behaviours, even though the underlying process is purely deterministic and probably low-dimensional. To test this hypothesis, we have generated 99 surrogates and then we compared the fitting capability of AR (auto-regressive) models on the original and surrogate data. The results show with a 99\% of confidence level that PCG and ECG were generated by a deterministic process. We compared the fitting capability of an ECG and PCG to AR linear models, using a multi-channel approach. We make an assumption that if a signal is more linearly predictable than another one, it may adjust better to these AR linear models. The results showed that ECG is more linearly predictable (for both channels) than PCG, although a filtering step is needed for the first channel. Finally we show that the false nearest neighbour method is insufficient to identify the correct dimension of the attractor in the reconstructed state space for both PCG and ECG signals.

Abstract
Acoustic heart signals are generated by a turbulence effect created when the heart valves snap shut, and therefore carrying significant information of the underlying functionality of the cardiovascular system. In this paper, we present a method for heart murmur classification divided into three major steps: a) features are extracted from the heart sound; b) features are selected using a Backward Feature Selection algorithm; c) signals are classified using a K-nearest neighbor's classifier. A new set of fractal features are proposed, which are based on the distinct signatures of complexity and self-similarity registered on the normal and pathogenic cases. The experimental results show that fractal features are the most capable of describing the non-linear structure and the underlying dynamics of heart sounds among the all feature families tested. The classification results achieved for the mitral auscultation spot (88% of accuracy) are in agreement with the current state of the art methods for heart murmur classification.