Molecular dynamics simulations of dipolar fluids in orientationally ordered phases.

It has been established that the strongly interacting dipoles form orientationally ordered liquid phases. However, most of the computer simulations adapt the point dipole model. In this paper, we report molecular dynamics simulations of orientationally order phases formed by extended dipoles, where the potential energy consists of the site-site Lennard-Jones potential and electrostatic contribution of partial charges. The calculations were performed for a range of densities along an isotherm and for different temperatures at the same reduced densities. It is found that orientationally ordered phases are present in the wide density regime, the extended dipole tends to form chains at low density, and the isotropic liquid phase is not seen in the density regime studied for a specific temperature.

A new nucleotide-composition based fingerprint of SARS-CoV with visualization analysis.

It has been observed by conducting an extensive analysis of the two-dimensional cellular automata images of known SARS-CoV genome sequences that the V-shaped cross-lines only exist in some special locations, and hence can be used as a fingerprint to identify the SARS sequences. Such a discovery can be used to rapidly and reliably diagnose SARS coronavirus for both basic research in laboratories and practical application in clinics.

A HMM-based method to predict the transmembrane regions of beta-barrel membrane proteins.

A novel method is developed to model and predict the transmembrane regions of beta-barrel membrane proteins. It is based on a Hidden Markov model (HMM) with architecture obeying those proteins' construction principles. The HMM is trained and tested on a non-redundant set of 11 beta-barrel membrane proteins known to date at atomic resolution with a jack-knife procedure. As a result, the method correctly locates 97% of 172 transmembrane beta-strands. Out of the 11 proteins, the barrel size for ten proteins and the overall topology for seven proteins are correctly predicted. Additionally, it successfully assigns the entire topology for two new beta-barrel membrane proteins that have no significant sequence homology to the 11 proteins. Predicted topology for two candidates for beta-barrel structure of the outer mitochondrial membrane is also presented in the paper.

Hippocampal synaptic metaplasticity requires the activation of NR2B-containing NMDA receptors.

The potential to exhibit synaptic plasticity itself is modulated by previous synaptic activity, which has been termed as metaplasticity. In this paper, we demonstrated that the activation of N-methyl-d-aspartate (NMDA) receptor 2B (NR2B) subunit in NNDA receptors was required for hippocampal metaplasticity at Schaffer collateral-commissural fiber-CA1 synapses. Brief 5 Hz priming stimulation did not cause long-term synaptic plasticity; however, it could result in the inhibition of subsequently evoked long-term potentiation (LTP). Meanwhile, the application of selective antagonists for NR2B subunit of NMDA receptors after delivering priming stimulation could block the metaplasticity. In contrast, LTP induction was not affected by NR2B antagonists in slices without pre-treatment of priming stimulation. These results indicated that the activation of NR2B-containing NMDA receptors was required for metaplasticity.

Detecting the determinism of EEG time series using a nonlinear forecasting method.

The determinism of time series is investigated using a nonlinear non-parametric forecasting method. The goodness of prediction was estimated in terms of the prediction error of the predicted time series. A new definition of the prediction effect was made in the present study. Three typical kinds of time series were detected using our new method. In deterministic chaotic time series, good prediction was obtained in the new definition. However, for Gaussian random noise and schizophrenia EEG signal, the predictability could not found. We concluded that EEGs in schizophrenic patients were not deterministic.

Use of ANN and Complexity Measures in Cognitive EEG Discrimination.

The purpose of this paper is to apply BP ANN to the discrimination of three kinds of subjects (clinical diagnosed 62 schizophrenic patients, 48 depressive patients and 26 normal controls) respectively in resting state with eyes closed and three cognitive tasks, with EEG complexity measures used as feature vectors. EEG activity is recorded from 16 scalp electrodes and recordings are digitized for off-line processing. Features vectors based on Lep-Ziv complexity and classification with ANN are implemented in Matlab6.5. The comparison between the results of classifying in four states is illustrated and discussed. The classification accuracies achieved are 60% and over. The results show that ANN is an effective approach for discrimination of these three kinds of objects both in baseline and some cognitive states.