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Abstract Background Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it. Results In this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms. Conclusion Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.

The purpose of the present study was to determine if there were differences between high and low hypnotic susceptible subjects based upon fast Fourier power spectral analysis of the EEG recorded both before and during hypnotic tasks from frontal-temporal and occipital-parietal locations. Significant differences were obtained based upon EEG recording electrode location, EEG frequency within six different frequency domains, and hypnotic tasks. However, no main effect differences were obtained based upon hypnotic susceptibility. In contrast to some evoked potential studies in which a few differences have been obtained based on hypnotic susceptibility the lack of any EEG differences in this study even when positive and negative hallucination tasks were employed may have implications for the role of the neocortex in mediating hypnotic phenomena.

The influence of a membrane environment on the conformational energetics of a polypeptide chain has been investigated through studies of model peptides in a variety of membrane-mimetic media. Nuclear magnetic resonance (NMR) and circular dichroism (CD) data have been obtained for the peptides in bulk hydrophobic solvents, normal micelles, and reversed micelles. Several hydrophobic peptides which are sparingly soluble in water have been solubilized in aqueous sodium dodecyl sulfate (SDS) solution. NMR and CD data indicate that the micelle-solubilized peptides experience an environment with the conformational impact of bulk methanol, and have decreased conformational freedom. The site of residence of the peptides interacting with the micelles appears to be near the surfactant head groups, in a region permeated by water, and not in the micelle core. Strongly hydrophilic peptides have been solubilized in nonpolar solvents by reversed micelles. These peptides are located in small water pools in close association with the head groups of the surfactant. NMR and CD data show that there is a conformational impact of this interfacial water region on peptide solubilizates distinct from that of bulk water.