Mixed Models as a Tool for Comparing Groups of Time Series in Plant Sciences.

Plants adapt to continual changes in environmental conditions throughout their life spans. High-throughput phenotyping methods have been developed to noninvasively monitor the physiological responses to abiotic/biotic stresses on a scale spanning a long time, covering most of the vegetative and reproductive stages. However, some of the physiological events comprise almost immediate and very fast responses towards the changing environment which might be overlooked in long-term observations. Additionally, there are certain technical difficulties and restrictions in analyzing phenotyping data, especially when dealing with repeated measurements. In this study, a method for comparing means at different time points using generalized linear mixed models combined with classical time series models is presented. As an example, we use multiple chlorophyll time series measurements from different genotypes. The use of additional time series models as random effects is essential as the residuals of the initial mixed model may contain autocorrelations that bias the result. The nature of mixed models offers a viable solution as these can incorporate time series models for residuals as random effects. The results from analyzing chlorophyll content time series show that the autocorrelation is successfully eliminated from the residuals and incorporated into the final model. This allows the use of statistical inference.

Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation.

OBJECTIVE: The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient's parameters such as objective measurements and medical history data. RESULTS: In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important.

Energetic loads and informational entropy during insect metamorphosis: measuring structural variability and self-organization.

In this work an information theory approach is presented for measuring structural variability during insect metamorphosis. Following a self-organizational perspective, the underlying assumption is that an insect pupa is a cybernetic bio-system, which displays a homeostatic control during its metamorphosis. The description of structural variability was based on biochemical data (lipids, glycogen, carbohydrates and proteins) analysed at different time intervals during the metamorphosis of Anarsia lineatella Zeller (Lepidoptera: Gelechiidae). Probabilities of biochemical variables were further treated by considering a finite countable set of progressive metamorphosis states having Markov properties at isothermal conditions (25 °C, 16:8h L:D, 65 ± 5%RH). The probabilities of the biochemical variables, as well as the related informational entropies, are affected when the system moves one step forward for each successive state. In most cases, but protein, there is some observable evidence that histolysis could be related to a decrease in informational entropy H ('disorganization of the system'), followed by a 'stable balance period' during the middle stages of metamorphosis. An initial increase in H is measured at the last stages of metamorphosis, which theoretically correspond to histogenesis ('reorganization of the system'). In this context, the temporal evolution of pupal structural variability was probabilistically quantified according to the classical information theory. The principles of the proposed holistic system are independent of its detailed dynamics and the proposed model can potentially describe part of the observable experimental data during metamorphosis of a holometabolous insect.