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The article deals with the issues of mental burnout in sports activities. The relationship between perceived stress and mental burnout among athletes involved in individual and team sports was studied. The study made it possible to establish mental burnout as a consequence of the process of chronic stress. Empirically, it has also been possible to establish a positive relationship between stress and burnout. The study showed a significant expression of perceived stress among athletes involved in individual sports, as opposed to team sports, which is associated with the presence of a social factor in team sports. Likewise, mental burnout, according to its indicators, has received wider development among athletes in individual sports, as opposed to team sports. It has been empirically proven that mental burnout is expressed in a feeling ofphysical tiredness, a decrease in the level of claims, followed in the future by deep disappointment, a drop in interest in sports activities and the termination of a sports career. The work provides some recommendations that could allow an athlete to maintain an interest in training and competitive activities, improve himself and strive to maintain his sports career.

The majority of opinion mining tasks in natural language processing (NLP) have been focused on sentiment analysis of texts about products and services while there is comparatively less research on automatic detection of political opinion. Almost all previous research work has been done for English, while this thesis is focused on the automatic detection of stance (whether he or she is favorable or not towards important political topic) from Twitter posts in Catalan, Spanish and English. The main objective of this work is to build and compare automatic stance detection systems using supervised both classic machine and deep learning techniques. We also study the influence of text normalization and perform experiments with differentt methods for word representations such as TF-IDF measures for unigrams, word embeddings, tweet embeddings, and contextual character-based embeddings. We obtain state-of-the-art results in the stance detection task on the IberEval 2018 dataset. Our research shows that text normalization and feature selection is important for the systems with unigram features, and does not affect the performance when working with word vector representations. Classic methods such as unigrams and SVM classifier still outperform deep learning techniques, but seem to be prone to overfitting. The classifiers trained using word vector representations and the neural network models encoded with contextual character-based vectors show greater robustness.

BACKGROUND: Andexanet alfa is a modified recombinant inactive form of human factor Xa developed for reversal of factor Xa inhibitors. METHODS: We evaluated 352 patients who had acute major bleeding within 18 hours after administration of a factor Xa inhibitor. The patients received a bolus of andexanet, followed by a 2-hour infusion. The coprimary outcomes were the percent change in anti-factor Xa activity after andexanet treatment and the percentage of patients with excellent or good hemostatic efficacy at 12 hours after the end of the infusion, with hemostatic efficacy adjudicated on the basis of prespecified criteria. Efficacy was assessed in the subgroup of patients with confirmed major bleeding and baseline anti-factor Xa activity of at least 75 ng per milliliter (or ≥0.25 IU per milliliter for those receiving enoxaparin). RESULTS: Patients had a mean age of 77 years, and most had substantial cardiovascular disease. Bleeding was predominantly intracranial (in 227 patients [64%]) or gastrointestinal (in 90 patients [26%]). In patients who had received apixaban, the median anti-factor Xa activity decreased from 149.7 ng per milliliter at baseline to 11.1 ng per milliliter after the andexanet bolus (92% reduction; 95% confidence interval [CI], 91 to 93); in patients who had received rivaroxaban, the median value decreased from 211.8 ng per milliliter to 14.2 ng per milliliter (92% reduction; 95% CI, 88 to 94). Excellent or good hemostasis occurred in 204 of 249 patients (82%) who could be evaluated. Within 30 days, death occurred in 49 patients (14%) and a thrombotic event in 34 (10%). Reduction in anti-factor Xa activity was not predictive of hemostatic efficacy overall but was modestly predictive in patients with intracranial hemorrhage. CONCLUSIONS: In patients with acute major bleeding associated with the use of a factor Xa in-hibitor, treatment with andexanet markedly reduced anti-factor Xa activity, and 82% of patients had excellent or good hemostatic efficacy at 12 hours, as adjudicated according to prespecified criteria. (Funded by Portola Pharmaceuticals; ANNEXA-4 .)