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We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
Durch die Kombination von Oligonukleotid-Liganden (Aptameren) hoher Bindungsaffinitäten
mit hochselektiv abtrennbaren magnetisierbaren Mikropartikeln
wird eine einstufige Separation von Zielmolekülen aus mikrobiologischen
Produktionsansätzen möglich. Die Aptamere werden hierfür reversibel
auf den Partikeloberflächen gebunden und für die spezifische Isolierung von
Bioprodukten eingesetzt. Die Abtrennung der beladenen Partikel erfolgt
durch einen neuen Rotor-Stator-Separator mit Hochgradient-Magnetfeld.