Preparation for football competition at moderate to high altitude.

Analysis of approximately 100 years of home-and-away South American World Cup matches illustrate that football competition at moderate/high altitude (>2000 m) favors the home team, although this is more than compensated by the likelihood of sea-level teams winning at home against the same opponents who have descended from altitude. Nevertheless, the home team advantage at altitudes above approximately 2000 m may reflect that traditionally, teams from sea level or low altitude have not spent 1-2 weeks acclimatizing at altitude. Despite large differences between individuals, in the first few days at high altitude (e.g. La Paz, 3600 m) some players experience symptoms of acute mountain sickness (AMS) such as headache and disrupted sleep, and their maximum aerobic power (VO2max) is approximately 25% reduced while their ventilation, heart rate and blood lactate during submaximal exercise are elevated. Simulated altitude for a few weeks before competition at altitude can be used to attain partial ventilatory acclimation and ameliorated symptoms of AMS. The variety of simulated altitude exposures usually created with enriched nitrogen mixtures of air include resting or exercising for a few hours per day or sleeping approximately 8 h/night in hypoxia. Preparation for competition at moderate/high altitude by training at altitude is probably superior to simulated exposure; however, the optimal duration at moderate/high altitude is unclear. Preparing for 1-2 weeks at moderate/high altitude is a reasonable compromise between the benefits associated with overcoming AMS and partial restoration of VO2max vs the likelihood of detraining.

Mathematical and computational techniques to deduce complex biochemical reaction mechanisms.

Time series data can now be routinely collected for biochemical reaction pathways, and recently, several methods have been proposed to infer reaction mechanisms for metabolic pathways and networks. In this paper we provide a survey of mathematical techniques for determining reaction mechanisms for time series data on the concentration or abundance of different reacting components, with little prior information about the pathways involved.

Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings.

The electro-encephalogram is a time-varying signal that measures electrical activity in the brain. A conceptually intuitive non-linear technique, multi-dimensional probability evolution (MDPE), is introduced. It is based on the time evolution of the probability density function within a multi-dimensional state space. A synthetic recording is employed to illustrate why MDPE is capable of detecting changes in the underlying dynamics that are invisible to linear statistics. If a non-linear statistic cannot outperform a simple linear statistic such as variance, then there is no reason to advocate its use. Both variance and MDPE were able to detect the seizure in each of the ten scalp EEG recordings investigated. Although MDPE produced fewer false positives, there is no firm evidence to suggest that MDPE, or any other non-linear statistic considered, outperforms variance-based methods at identifying seizures.

Testing the assumptions of linear prediction analysis in normal vowels.

In this paper we develop an improved surrogate data test to show experimental evidence, for all the simple vowels of U.S. English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or non-Gaussianity common to more recent, complex, efforts at dynamical modeling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/non-Gaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/non-Gaussian modeling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance.