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20 páginas, 5 figuras, 5 tablas, The increase in life expectancy has also been accompanied by an increase in the use of medication to treat chronic diseases. Polypharmacy is associated with medication-related problems such as the increase in the anticholinergic burden. Older people are more susceptible to anticholinergic effects on the central nervous system and this, in turn, may be related to cognitive impairment. In this paper, we develop an updated anticholinergic burden scale, the CRIDECO Anticholinergic Load Scale (CALS) via a systematic review of the literature and compare it with the currently most used Anticholinergic Burden Scale (ACB). Our new scale includes 217 different drugs with anticholinergic properties, 129 more than the ACB. Given the effect that anticholinergic medications have on cognitive performance, we then used both scales to investigate the relationship between anticholinergic burden and cognitive impairment in adult Spanish subjects with subjective memory complaint. In our population, we observed an association between cognitive impairment and the anticholinergic burden when measured by the new CALS, but not when the ACB was applied. The use of a more comprehensive and upgraded scale will allow better discrimination of the risk associated with the use of anticholinergic medications on cognitive impairment. CALS can help raise awareness among clinicians of the problems associated with the use of medications, or combinations of them, with large anticholinergic effect, and promote a better personalized pharmacological approach for each patient., This work was supported by SANTANDER-CEU FUSP-BS-PPC26/2018

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1424-8220/21/7/2422 Este artículo pertenece la número especial "Wearable Sensors & Gait". Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power.

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1424-8220/20/21/6388/htm Este artículo pertenece la número especial "Wearable Sensors & Gait". Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between di erent biomechanical patterns and injuries to identify which styles best prevent injuries. Results: Fewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground. Conclusions: The triathletes who had su ered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes.

University CEU Cardenal Herrera (CEU-UCH) has constructed a Solar house, known as SMLSystem, to participate in the Solar Decathlon Europe 2012 competition. Such construction becomes a research facility that University employs in order to test innovative solutions around the area of energy efficiency. A lot of technologies have been integrated to help to reduce the overall power consumption of the house. Among them, a predictive system, based on Artificial Neural Networks (ANNs), has been developed using the data acquired in Valencia, where the house is placed. Such system produces short-term forecast of indoor temperature, using as input the data captured by a complex monitoring system. The system expects to reduce the power consumption mainly related to Heating, Ventilation and Air Conditioning (HVAC) systems because of the following assumptions: the high power consumption for which HVAC is responsible (53,9% of the overall consumption); and the energy needed to keep the temperature is less tan the energy required to lower/increase it.This paper studies the viability of the development of such kind of predictive systems but for totally unknown environments, that is, without historical data. To do that it is possible to apply on-line learning approaches, where the model parameters are estimated following Bayesian methods or Gradient Descent (GD) methods, starting from an unbiased a-priori knowledge, or from a totally random model. These forecasting measures could allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Preliminary experimental results show a high forecasting accuracy with simple models and with a short training time of 4-5 days. The final idea is to develop intelligent agents, with the minimum resources, to be implemented in very cheap computer architectures.