Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself

Daily magnesium fluxes regulate cellular timekeeping and energy balance

Circadian clocks are fundamental to the biology of most eukaryotes, coordinating behaviour and physiology to resonate with the environmental cycle of day and night through complex networks of clock-controlled genes1, 2, 3. A fundamental knowledge gap exists, however, between circadian gene expression cycles and the biochemical mechanisms that ultimately facilitate circadian regulation of cell biology4, 5. Here we report circadian rhythms in the intracellular concentration of magnesium ions, [Mg2+]i, which act as a cell-autonomous timekeeping component to determine key clock properties both in a human cell line and in a unicellular alga that diverged from each other more than 1 billion years ago6. Given the essential role of Mg2+ as a cofactor for ATP, a functional consequence of [Mg2+]i oscillations is dynamic regulation of cellular energy expenditure over the daily cycle. Mechanistically, we find that these rhythms provide bilateral feedback linking rhythmic metabolism to clock-controlled gene expression. The global regulation of nucleotide triphosphate turnover by intracellular Mg2+ availability has potential to impact upon many of the cell’s more than 600 MgATP-dependent enzymes7 and every cellular system where MgNTP hydrolysis becomes rate limiting. Indeed, we find that circadian control of translation by mTOR8 is regulated through [Mg2+]i oscillations. It will now be important to identify which additional biological processes are subject to this form of regulation in tissues of multicellular organisms such as plants and humans, in the context of health and disease

Palaeosymbiosis Revealed by Genomic Fossils of Wolbachia in a Strongyloidean Nematode

Wolbachia are common endosymbionts of terrestrial arthropods, and are also found in nematodes: the animal-parasitic filaria, and the plant-parasite Radopholus similis. Lateral transfer of Wolbachia DNA to the host genome is common. We generated a draft genome sequence for the strongyloidean nematode parasite Dictyocaulus viviparus, the cattle lungworm. In the assembly, we identified nearly 1 Mb of sequence with similarity to Wolbachia. The fragments were unlikely to derive from a live Wolbachia infection: most were short, and the genes were disabled through inactivating mutations. Many fragments were co-assembled with definitively nematode-derived sequence. We found limited evidence of expression of the Wolbachia-derived genes. The D. viviparus Wolbachia genes were most similar to filarial strains and strains from the host promiscuous clade F. We conclude that D. viviparus was infected by Wolbachia in the past, and that clade F-like symbionts may have been the source of filarial Wolbachia infections

SPARSE CODING AND ROUGH SET THEORY-BASED HYBRID APPROACH TO THE CLASSIFICATORY DECOMPOSITION OF CORTICAL EVOKED POTENTIALS

ABSTRACT This paper presents a novel approach to classification of decomp osed cortical evoked potentials (EPs). The decomposition is based on learning of a sparse set of basis functions using an Artificial Neural Network (ANN). The basis functions are generated according to a probabilistic model of the data. In contrast to the traditional signal decomposition techniques (i.e. Principle Component Analysis or Independent Component Analysis), this allows for an overcomplete representation of the data (i.e. number of basis functions that is greater than the dimensionality of the input signals). Obviously, this can be of a great advantage. However, there arises an issue of selecting the most significant components from the whole collection. This is especially important in classification problems based upon the decomposed representation of the data, where only those components that provide a substantial discernibility between EPs of different groups are relevant. To deal with this problem, we propose an approach based on the Rough Set theory's (RS) feature selection mechanisms. We design a sparse coding-and RS-based hybrid system capable of signal decomposition and, based on a reduced component set, signal classification