Emotion Recognition from EEG and Facial Expressions: a Multimodal Approach.

The understanding of a psychological phenomena such as emotion is of paramount importance for psychologists, since it allows to recognize a pathology and to prescribe a due treatment for a patient. While approaching this problem, mathematicians and computational science engineers have proposed different unimodal techniques for emotion recognition from voice, electroencephalography, facial expression, and physiological data. It is also well known that identifying emotions is a multimodal process. The main goal in this work is to train a computer to do so. In this paper we will present our first approach to a multimodal emotion recognition via data fusion of Electroencephalography and facial expressions. The selected strategy was a feature-level fusion of both Electroencephalography and facial microexpressions, and the classification schemes used were a neural network model and a random forest classifier. Experimental set up was out with the balanced multimodal database MAHNOB-HCI. Results are promising compared to results from other authors with a 97% of accuracy. The feature-level fusion approach used in this work improves our unimodal techniques up to 12% per emotion. Therefore, we may conclude that our simple but effective approach improves the overall results of accuracy.

Today´s medical self and the other: Challenges and evolving solutions for enhanced humanization and quality of care.

BACKGROUND: Recent scientific developments, along with growing awareness of cultural and social diversity, have led to a continuously growing range of available treatment options; however, such developments occasionally lead to an undesirable imbalance between science, technology and humanism in clinical practice. This study explores the understanding and practice of values and value clusters in real-life clinical settings, as well as their role in the humanization of medicine and its institutions. The research focuses on the values of clinical practice as a means of finding ways to enhance the pairing of Evidence-Based Medicine (EBM) with Values-based Medicine (VBM) in daily practice. METHODS AND FINDINGS: The views and representations of clinical practice in 15 pre-CME and 15 post-CME interviews were obtained from a random sampling of active healthcare professionals. These views were then identified and qualitatively analyzed using a three-step hermeneutical approach. A clinical values space was identified in which ethical and epistemic values emerge, grow and develop within the biomedical, ethical, and socio-economic dimensions of everyday health care. Three main values-as well as the dynamic clusters and networks that they tend to form-were recognized: healthcare personnel-patient relationships, empathy, and respect. An examination of the interviews suggested that an adequate conceptualization of values leads to the formation of a wider axiological system. The role of clinician-as-consociate emerged as an ideal for achieving medical excellence. CONCLUSIONS: By showing the intricate clusters and networks into which values are interwoven, our analysis suggests methods for fine-tuning educational interventions so they can lead to demonstrable changes in attitudes and practices.

Response to arsenate treatment in Schizosaccharomyces pombe and the role of its arsenate reductase activity.

Arsenic toxicity has been studied for a long time due to its effects in humans. Although epidemiological studies have demonstrated multiple effects in human physiology, there are many open questions about the cellular targets and the mechanisms of response to arsenic. Using the fission yeast Schizosaccharomyces pombe as model system, we have been able to demonstrate a strong activation of the MAPK Spc1/Sty1 in response to arsenate. This activation is dependent on Wis1 activation and Pyp2 phosphatase inactivation. Using arsenic speciation analysis we have also demonstrated the previously unknown capacity of S. pombe cells to reduce As (V) to As (III). Genetic analysis of several fission yeast mutants point towards the cell cycle phosphatase Cdc25 as a possible candidate to carry out this arsenate reductase activity. We propose that arsenate reduction and intracellular accumulation of arsenite are the key mechanisms of arsenate tolerance in fission yeast.