Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis

Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiff(max)) for q not equal 1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiff(max) values were capable of distinguish HRV groups (p-values 5.10 x 10(-3); 1.11 x 10(-7), and 5.50 x 10(-7) for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4758815]

Abnormal function of monoamine oxidase-A in comorbid major depressive disorder and cardiovascular disease: Pathophysiological and therapeutic implications (Review)

The association between major depressive disorder (MDD) and cardiovascular disease (CVD) is among the best described medical comorbidities. The presence of MDD increases the risk of cardiac admissions and mortality and increases healthcare costs in patients with CVD, and similarly, CVD affects the course and outcome of MDD. The potential shared biological mechanisms involved in these comorbid conditions are not well known. However, the enzyme monoamine oxidase-A (MAO-A), which has a key role in the degradation of catecholamines, has been associated with the pathophysiology and therapeutics of both MDD and CVD. Increased MAO-A activity results in the dysregulation of downstream targets of this enzyme and thus affects the pathophysiology of the two diseases. These deleterious effects include altered noradrenaline turnover, with a direct elevation in oxidative stress parameters, as well as increased platelet activity and cytokine levels. These effects were shown to be reversed by MAO inhibitors. Here, a model describing a key role for the MAO-A in comorbid MDD and CVD is proposed, with focus on the shared pathophysiological mechanisms and the potential therapeutic relevance of agents targeting this enzyme.

A biophysical model of Sugarcane growth

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in crop-based biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process-based sugarcane model (included as a module within the Agro-IBIS dynamic agro-ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/overestimated the daily sensible/latent heat flux (by -10.5% and 14.8%, H and E, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between -10.1% and 12.5%) when compared against an agro-meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of Sao Paulo, Brazil, over a period of 16 years, with a yield relative bias of -0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (-2.67%), but exhibited a lower interannual variability than the observed yields.

Effects of lithium on oxidative stress parameters in healthy subjects

Increased neuronal oxidative stress (OxS) induces deleterious effects on signal transduction, structural plasticity and cellular resilience, mainly by inducing lipid peroxidation in membranes, proteins and genes. Major markers of OxS levels include the thiobarbituric acid reactive substances (TBARS) and the enzymes superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase. Lithium has been shown to prevent and/or reverse DNA damage, free-radical formation and lipid peroxidation in diverse models. This study evaluates OxS parameters in healthy volunteers prior to and following lithium treatment. Healthy volunteers were treated with lithium in therapeutic doses for 2-4 weeks. Treatment with lithium in healthy volunteers selectively altered SOD levels in all subjects. Furthermore, a significant decrease in the SOD/CAT ratio was observed following lithium treatment, wich was associated with decreased OxS by lowering hydrogen peroxide levels. This reduction in the SOD/CAT ratio may lead to lower OxS, indicated primarily by a decrease in the concentration of cell hydrogen peroxide. Overall, the present findings indicate a potential role for the antioxidant effects of lithium in healthy subjects, supporting its neuroprotective profile in bipolar disorder (BD) and, possibly, in neurodegenerative processes.