Written language is complex. A written text can be considered an attempt to convey a meaningful message which ends up being constrained by language rules, context dependence and highly redundant in its use of resources. Despite all these constraints, unpredictability is an essential element of natural language. Here we present the use of entropic measures to assert the balance between predictability and surprise in written text. In short, it is possible to measure innovation and context preservation in a document. It is shown that this can also be done at the different levels of organization of a text. The type of analysis presented is reasonably general, and can also be used to analyze the same balance in other complex messages such as DNA, where a hierarchy of organizational levels are known to exist.

Background. Cognitive impairment is a feature of Parkinsons Disease (PD) from the early stages but currently, no treatment for cognitive deficits in PD is available. Erythropoietin (EPO) has been studied for its potential neuroprotective properties in neurologic disorders with a beneficial action on cognition. Objective: We want to know if NeuroEPO, a new formulation of EPO with low content of sialic acid has effects on cognitive function in PD in a double-blind randomized placebo and after a post-trial intervention. Methods: The sample was composed of 26 PD patients (HY stages I-II), where 15 received intranasal NeuroEPO for 5 weeks and another age and gender-matched 11 patients were randomly assigned to the placebo. During a post-trial all the sample received 9 months of intensive NeuroEPO treatment. Cognitive functions were assessed using a comprehensive neuropsychological battery before, one week and 6 months after the first intervention and 9months after the post-trial. The effects of NeuroEPO were evaluated using a multivariate linear mixed-effects model using a latent variable for cognition instead of the raw neuropsychological scores. Results: We found a significant and direct effect of the dose of NeuroEPO (p=0.00001) on cognitive performance with a strong positive influence of educational level (p=0.0004) and negative impact of age (p=0.007). Conclusions: This preliminary results showed a positive effect of NeuroEPO on cognition in PD patients.

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity–entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

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