Abstract
Objective: Physical exercise has been consistently documented as a complementary therapy in the treatment of depressive disorders. However, despite a higher prevalence among women compared to men, the trials developed in women are scarce. In addition, the optimal dosage of exercise capable of producing benefits that reduce depressive symptoms remains unclear. This clinical trial is designed to measure the effect of a structured physical exercise program as a complement to antidepressant medication in the treatment of women with depression. Methods: From July 2013 to May 2014, we implemented a randomized controlled trial (HAPPY BRAIN study). A total of 26 women (aged 50.16 +/- 12.08) diagnosed with clinical depression were randomized either to a supervised aerobic exercise group (45-50 min/week three times a week for four months) plus pharmacotherapy (intervention group), or only antidepressant medication (control group). Results: The exercise group presented a decrease in BDI-II and DASS-21 total score scales. Relatively to DASS-21, it showed a significant decrease in anxiety and stress. The exercise group when compared to a control group showed improvement in relation to physical functioning parameters between baseline and post-intervention. Moreover, anthropometric parameters presented only significant differences between groups in fat mass percentage. Nonetheless, no differences were found between groups in weight, body mass index, waist circumference, and self-esteem. Conclusion: Our results showed that supervised structured aerobic exercise training could be an effective adjuvant therapy for treating women with depression, reducing depressive symptomatology and improving physical fitness. A key factor of this improvement included strict control of exercise workload parameters and adjustment to each subject's capacity. In our study, due to the sample size there is an increase in the probability of type II errors.

Abstract
Parkinson's disease (PD) is a highly prevalent and disabling condition that requires a constant monitoring of patient's condition. Nevertheless, in Portugal appointments with specialist only occur every 6 months and the patient's capability to recall important past events is not always accurate besides often being a misinterpretation of their symptoms. In this paper we present a user-centred process for the design of a multimedia platform for the self-management of PD.

Abstract

Abstract
Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value<0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Abstract
A high-resolution mtDNA phylogenetic tree allowed us to look backward in time to investigate purifying selection. Purifying selection was very strong in the last 2,500 years, continuously eliminating pathogenic mutations back until the end of the Younger Dryas (approximate to 11,000 years ago), when a large population expansion likely relaxed selection pressure. This was preceded by a phase of stable selection until another relaxation occurred in the out-of-Africa migration. Demography and selection are closely related: expansions led to relaxation of selection and higher pathogenicity mutations significantly decreased the growth of descendants. The only detectible positive selection was the recurrence of highly pathogenic nonsynonymous mutations (m.3394T>C-m.3397A>G-m.3398T>C) at interior branches of the tree, preventing the formation of a dinucleotide STR (TATATA) in the MT-ND1 gene. At the most recent time scale in 124 mother-children transmissions, purifying selection was detectable through the loss of mtDNA variants with high predicted pathogenicity. A few haplogroup-defining sites were also heteroplasmic, agreeing with a significant propensity in 349 positions in the phylogenetic tree to revert back to the ancestral variant. This nonrandom mutation property explains the observation of heteroplasmic mutations at some haplogroup-defining sites in sequencing datasets, which may not indicate poor quality as has been claimed.

Abstract
Numerous stream mining algorithms are equipped with forgetting mechanisms, such as sliding windows or fading factors, to make them adaptive to changes. In recommender systems those techniques have not been investigated thoroughly despite the very volatile nature of users' preferences that they deal with. We developed five new forgetting techniques for incremental matrix factorization in recommender systems. We show on eight datasets that our techniques improve the predictive power of recommender systems. Experiments with both explicit rating feedback and positive-only feedback confirm our findings showing that forgetting information is beneficial despite the extreme data sparsity that recommender systems struggle with. Improvement through forgetting also proves that users' preferences are subject to concept drift.