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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.
Bio-feedstocks
(2011)
Lignocellulosic biorefinery: Process integration of hydrolysis and fermentation (SSF process)
(2011)
The aim of the present work is the process integration and the optimization of the enzymatic hydrolysis of wood and the following fermentation of the products to ethanol. The substrate is a fiber fraction obtained by organosolv pre-treatment of beech wood. For the ethanol production, a co-fermentation by two different yeasts (Saccharomyces cerevisiae and Pachysolen tannophilus) was carried out to convert glucose as well as xylose. Two approaches has been followed: 1. A two step process, in which the hydrolysis of the fiber fraction and the fermentation to product are separated from each other. 2. A process, in which the hydrolysis and the fermentation are carried out in one single process step as simultaneous saccharification and fermentation (SSF). Following the first approach, a yield of about 0.15 g ethanol per gram substrate can be reached. Based on the SSF, one process step can be saved, and additionally, the gained yield can be raised up to 0.3 g ethanol per gram substrate.
Grass silage provides a great potential as renewable feedstock. Two fractions of the grass silage, a press juice and the fiber fraction, were evaluated for their possible use for bioethanol production. Direct production of ethanol from press juice is not possible due to high concentrations of organic acids. For the fiber fraction, alkaline peroxide or enzymatic pretreatment was used, which removes the phenolic acids in the cell wall. In this study, we demonstrate the possibility to integrate the enzymatic pretreatment with a simultaneous saccharification and fermentation to achieve ethanol production from grass silage in a one-process step. Achieved yields were about 53 g ethanol per kg silage with the alkaline peroxide pretreatment and 91 g/kg with the enzymatic pretreatment at concentrations of 8.5 and 14.6 g/L, respectively. Furthermore, it was shown that additional supplementation of the fermentation medium with vitamins, trace elements and nutrient salts is not necessary when the press juice is directly used in the fermentation step.