Balancing deceit and disguise: How to successfully fool the defender in a 1 vs. 1 situation in rugby

Suddenly changing direction requires a whole body reorientation strategy. In sporting duels such as an attacker vs. a defender in rugby, successful body orientation/reorientation strategies are essential for successful performance. The aim of this study is to examine which biomechanical factors, while taking into account biomechanical constraints, are used by an attacker in a 1 vs. 1 duel in rugby. More specifically we wanted to examine how an attacker tries to deceive the defender yet disguise his intentions by comparing effective deceptive movements (DM+), ineffective deceptive movements (DM-), and non-deceptive movements (NDM). Eight French amateur expert rugby union players were asked to perform DMs and NDMs in a real 1 vs. 1 duel. For each type of movement (DM+, DM-, NDM) different relevant orientation/reorientation parameters, medio-lateral displacement of the center of mass (COM), foot, head, upper trunk, and lower trunk yaw; and upper trunk roll were analyzed and compared. Results showed that COM displacement and lower trunk yaw were minimized during DMs while foot displacement along with head and upper trunk yaw were exaggerated during DMs (DM+ and DM-). This would suggest that the player is using exaggerated body-related information to consciously deceive the defender into thinking he will run in a given direction while minimizing other postural control parameters to disguise a sudden change in posture necessary to modify final running direction. Further analysis of the efficacy of deceptive movements showed how the disguise and deceit strategies needed to be carefully balanced to successfully fool the defender. (C) 2010 Elsevier B.V. All rights reserved.

NADPH oxidase-4 mediates protection against chronic load-induced stress in mouse hearts by enhancing angiogenesis

Cardiac failure occurs when the heart fails to adapt to chronic stresses. Reactive oxygen species (ROS)-dependent signaling is implicated in cardiac stress responses but the role of different ROS sources remains unclear. Here, we report that NADPH oxidase-4 (Nox4) facilitates cardiac adaptation to chronic stress. Unlike other Nox proteins, Nox4 activity is regulated mainly by its expression level which increased in cardiomyocytes during stresses such as pressure overload or hypoxia. To investigate the functional role of Nox4 during the cardiac response to stress, we generated mice with a genetic deletion of Nox4 or a cardiomyocyte-targeted overexpression of Nox4. Basal cardiac function was normal in both models but Nox4-null animals developed exaggerated contractile dysfunction, hypertrophy and cardiac dilatation during exposure to chronic overload whereas Nox4-transgenic mice were protected. Investigation of mechanisms underlying this protective effect revealed a significant Nox4-dependent preservation of myocardial capillary density after pressure overload. Nox4 enhanced stress-induced activation of cardiomyocyte Hif1 and the release of VEGF, resulting in an increased paracrine angiogenic activity. These data indicate that cardiomyocyte Nox4 is a novel inducible regulator of myocardial angiogenesis, a key determinant of cardiac adaptation to overload stress. Our results also have wider relevance to the use of non-specific antioxidant approaches in cardiac disease and may provide an explanation for the failure of such strategies in many settings.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.