Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

Prenatal immunologic predictors of postpartum depressive symptoms : a prospective study for potential diagnostic markers

In postpartum depression (PPD), immunologic changes have been proposed to be involved in the disease pathology. The study evaluates the regulation of the innate and adaptive immune response over the course of late pregnancy and postpartum period and their association with the development of postpartum depressive symptoms. Furthermore, prenatal immunologic markers for a PPD were investigated. Hundred pregnant women were included. At 34th and 38th week of pregnancy as well as 2 days, 7 weeks and 6 months postpartum, immune parameters (neopterin, regulatory T cells, CXCR1, CCR2, MNP1 and CD11a) were measured by flow cytometry/ELISA, and the psychopathology was evaluated. We found that regulatory T cells were significantly increased prenatal (p = 0.011) and postnatal (p = 0.01) in mothers with postnatal depressive symptoms. The decrease in CXCR 1 after delivery was significantly higher in mother with postnatal depressive symptoms (p = 0.032). Mothers with postnatal depressive symptoms showed already prenatal significantly elevated neopterin levels (p = 0.049). Finally, regulatory T cells in pregnancy strongly predict postnatal depressive symptoms (p = 0.004). The present study revealed that prenatal and postnatal immunologic parameters are associated with postpartum depressive symptoms in mothers. In addition, we found immune markers that could eventually be the base for a biomarker set that predicts postnatal depressive symptoms already during pregnancy