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Keywords
Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
Our world is well ordered in measurement and number : or why natural constants are as they are
(2013)
All the important natural constants can be logically explained with and derived from the first four ordinal numbers, 1, 2, 3 and 4, its addition to ten and finally the standard values for obviously maximal feasibility Ω and the optimum in our world, the Golden Section (GS), i.e. the number sequences 273 and 618. They both are the first three numbers of irrational results by an arithmetical transformation of simple geometrical relationships by creating multiplicity out of singularity. Both of them show that the infinite is inherent in finiteness and explain in a simple way the smallest deviations and fluctuations between the physical AS-IS state and the obvious spiritual ideal behind: Wherever we look in this world, and especially in important key-positions, we regularly find these sequences. All of the above mentioned numbers so seem to be key players in our world, what can be demonstrated by the derivation of natural constants.
Optical coherence tomography : a potential tool to predict premature rupture of fetal membranes
(2013)
The chemical imaging sensor is a device to visualize the spatial distribution of chemical species based on the principle of LAPS (light-addressable potentiometric sensor), which is a field-effect chemical sensor based on semiconductor. In this study, the chemical imaging sensor has been applied to investigate the ion profile of laminar flows in a microfluidic channel. The chemical images (pH maps) were collected in a Y-shaped microfluidic channel while injecting HCl and NaCl solutions into two branches. From the chemical images, it was clearly observed that the injected solutions formed laminar flows in the channel. In addition, ion diffusion across the laminar flows was observed, and the diffusion coefficient could be derived by fitting the pH profiles to the Fick's equation.