Abstract
Purpose: Male Breast Cancer (MBC) remains a poor understood disease. Prognostic factors are not well established and specific prognostic subgroups are warranted. Patients/methods: Retrospectively revision of 111 cases treated in the same Cancer Center. Blinded-central pathological revision with immunohistochemical (IHQ) analysis for estrogen (ER), progesterone (PR) and androgen (AR) receptors, HER2, ki67 and p53 was done. Cox regression model was used for uni/multivariate survival analysis. Two classifications of Female Breast Cancer (FBC) subgroups (based in ER, PR, HER2, 2000 classification, and in ER, PR, HER2, ki67, 2013 classification) were used to achieve their prognostic value in MBC patients. Hierarchical clustering was performed to define subgroups based on the six-IHQ panel. Results: According to FBC classifications, the majority of tumors were luminal: A (89.2%; 60.0%) and B (7.2%; 35.8%). Triple negative phenotype was infrequent (2.7%; 3.2%) and HER2 enriched, non-luminal, was rare (<= 1% in both). In multivariate analysis the poor prognostic factors were: size >2 cm (HR: 1.8; 95% CI: 1.0-3.4years, p = 0.049), absence of ER (HR: 4.9; 95% CI: 1.7-14.3years, p = 0.004) and presence of distant metastasis (HR: 5.3; 95% CI: 2.2-3.1years, p < 0.001). FBC subtypes were independent prognostic factors (p = 0.009, p = 0.046), but when analyzed only luminal groups, prognosis did not differ regardless the classification used (p > 0.20). Clustering defined different subgroups, that have prognostic value in multivariate analysis (p = 0.005), with better survival in ER/PR+, AR-, HER2- and ki67/p53 low group (median: 11.5 years; 95% CI: 6.2-16.8 years) and worst in PR-group (median: 4.5 years; 95% CI: 1.6 -7.8 years). Conclusion: FBC subtypes do not give the same prognostic information in MBC even in luminal groups. Two subgroups with distinct prognosis were identified in a common six-IHQ panel. Future studies must achieve their real prognostic value in these patients.

Abstract
Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.

Abstract
In this paper, the topological and dynamical properties of the heart sounds are assessed. The signal is preprocessed and projected into an embedding subspace, which is more suitable to detect the irregularities and the unstable trajectories registered during the cardiac murmurs than the original heart sound signal. We present a method for heart murmur classification divided into five major steps: a) signal is divided into heart beats; b) entropy gradient envelogram is computed from the pre-processed signal; c) the orbital trajectories are reconstructed using the embedding theory; d) n orbits in the embedding subspace are extracted ( per heart beat); e) the median of the n orbits is used as an input to K-Nearest Neighbors ( KNN) classifier. The experimental results achieved are in agreement with the current state of art for heart murmur classification.

Abstract
Industrial Internet and Industrial Internet of Things are emerging concepts that concern the use of Internet technologies on industrial environments. The main objective of such architectural visions is allowing a tight and seamless integration between all the functional units and layers that compose industrial processes, from the lowest levels (e.g. field level devices such as sensors and actuators) to the higher layers, including management, logistics and maintenance. This kind of architecture promises, among other advantages, improving efficiency and flexibility, reduce installation and maintenance costs and reduce unplanned downtime. However, industrial processes often encompass functionalities like closed-loop control of physical processes that are highly critical and have strict timeliness requirements. These requirements are not satisfied by normal Ethernet-based systems. Standards such as IEEE AVB and TSN are addressing this problem, enhancing the real-time properties of Ethernet. However, considering the information presently available, such standards still present some limitations and inefficiencies. This paper reports the extension of HaRTES, an Ethernet-based real-time architecture originally developed for use at the lower layers of industrial scenarios, with MAC Bridge standard functionalities, to make it capable of being integrated on Industrial Internet of Things frameworks. The paper also presents preliminary results obtained with a prototype realization of the extended HaRTES switch.

Abstract
Many online communities and services continuously generate data that can be used by recommender systems. When explicit ratings are not available, rating prediction algorithms are not directly applicable. Instead, data consists of positive-only user-item interactions, and the task is therefore not to predict ratings, but rather to predict good items to recommend - item prediction. One particular challenge of positive-only data is how to interpret absent user-item interactions. These can either be seen as negative or as unknown preferences. In this paper, we propose a recency-based scheme to perform negative preference imputation in an incremental matrix factorization algorithm designed for streaming data. Our results show that this approach substantially improves the accuracy of the baseline method, outperforming both classic and state-of-the-art algorithms.

Abstract