The Effect of Disturbance on Plant Communities in Tundra Regions of the Soviet Union

An Annotated List of Plants Inhabiting Sites of Natural and Anthropogenic Disturbances of Tundra Cover: Southeasternmost Chukchi Peninsula -- B.A. Yurtsev and A.A. Korobkov; An Annotated List of Plants Inhabiting Sites of Natural and Anthropogenic Disturbances of Tundra Cover in Western Taimyr: The Settlement of Kresty -- N.V. Matveyeva; A Study of Plant Communities of Anthropogenic Habitats in the Area of the Vorkuta Industrial Center -- O.A. Druzhinina and Yu. G. Zharkov

Coupled CP decomposition of simultaneous MEG-EEG signals for differentiating oscillators during photic driving

Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications.Kristina Naskovska, Stephan Lau, Alexey A. Korobkov, Jens Haueisen and Martin Haard

Anion-induced AgI self-assemblies with electron deficient aromatic ligands: Anion-π-system interactions as a driving force for templated coordination networks

Three novel 1D, 2D and 3D coordination polymers were successfully isolated using nitrogen based 3,6-bis(2′-pyrimidyl)-1,2,4,5-tetrazine (BPymTz) and 2,4,6-tris(2-pyrimidyl)-1,3,5-triazine (TPymT) ligands with AgI ions. The formation of these supramolecular assemblies was templated through anion-π-system interactions. © The Royal Society of Chemistry 2015