The recognition of risk factors, such as early onset of seizures, more than 10 seizures per month before treatment, and EEG abnormalities, could lead to the identification of risk groups among patients with MTLE-HS and refractory epilepsy, possibly designating these individuals as candidates for early epilepsy surgery.

SUMMARYObjectives: To validate and translate the English version of the Neurologic Depression Disorders Inventory in Epilepsy (NDDI-E) into Spanish as a screening instrument for major depressive episodes (MDE) for patients with epilepsy from Argentina and Uruguay. Methods: One hundred fifty-five consecutive outpatients with epilepsy participated in this study. The module of MDE of the MINI International Neuropsychiatric Instrument (MINI Plus version) was used as the gold standard against which the translated version of the NDDI-E was validated. Results: Among the 155 patients, 25 (16%) met Diagnostic and Statistical Manual, Fourth Edition (DSM-IV) criteria for MDE according to the MINI. With a total score of >15, The NDDI-E identified MDE with an 80% sensitivity, 90% specificity, 60% positive predictive value, and 95.5% negative predictive value. Significance: These data indicate that the Spanish version of the NDDI-E can reliably identify MDE in patients with epilepsy from Argentina and Uruguay.

In recent years, humanoid-biped type robots are being adopted by academic and research institutions to create an ecosystem that promotes the development of innovation and social appropriation of technology. Robot-taken decision projects on NAO robots in the field of artificial vision are more common than those ones related to auditory events for the same purpose. The main motivation for this work is to demonstrate the convenience of using an acoustic signal stage, for the classification of common wastes produced at the university. This project establishes an ideal scenario which allows taking it as reference for future projects in the acoustics field, by a methodology that enables audio signals acquisition and its real-time processing in NAO robots. This methodology consists in the robot's programming for autonomous identification of sounds, which are sampled and saved in raw data. Then, a mathematical treatment is made in order to establish the ranges of frequency for an appropriate classification of materials. In the end, NAO robot identified and classified main wastes produced on the campus: cardboard, plastic, glass, and metal.

Felicidad, depresión y creencia en la benevolencia humana en adultos mayores institucionalizados y no institucionalizados ARTÍCULOS DE INVESTIGACIÓN

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