Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network

Eliminating interactions between non-neighboring qubits in the preparation of cluster states in quantum molecules

We propose a scheme to eliminate the effect of non-nearest-neighbor qubits in preparing cluster state with double-dot molecules. As the interaction Hamiltonians between qubits are Ising-model and mutually commute, we can get positive and negative effective interactions between qubits to cancel the effect of non-nearest-neighbor qubits by properly changing the electron charge states of each quantum dot molecule. The total time for the present multi-step cluster state preparation scheme is only doubled for one-dimensional qubit chain and tripled for two-dimensional qubit array comparing with the time of previous protocol leaving out the non-nearest-neighbor interactions.Comment: 5 pages, 4 figures, 2 table

In silico and microarray-based genomic approaches to identifying potential vaccine candidates against Leptospira interrogans

BACKGROUND: Currently available vaccines against leptospirosis are of low efficacy, have an unacceptable side-effect profile, do not induce long-term protection, and provide no cross-protection against the different serovars of pathogenic leptospira. The current major focus in leptospirosis research is to discover conserved protective antigens that may elicit longer-term protection against a broad range of Leptospira. There is a need to screen vaccine candidate genes in the genome of Leptospira interrogans. RESULTS: Bioinformatics, comparative genomic hybridization (CGH) analysis and transcriptional analysis were used to identify vaccine candidates in the genome of L. interrogans serovar Lai strain #56601. Of a total of 4727 open reading frames (ORFs), 616 genes were predicted to encode surface-exposed proteins by P-CLASSIFIER combined with signal peptide prediction, α-helix transmembrane topology prediction, integral β-barrel outer membrane protein and lipoprotein prediction, as well as by retaining the genes shared by the two sequenced L. interrogans genomes and by subtracting genes with human homologues. A DNA microarray of L. interrogans strain #56601 was constructed for CGH analysis and transcriptome analysis in vitro. Three hundred and seven differential genes were identified in ten pathogenic serovars by CGH; 1427 genes had high transcriptional levels (Cy3 signal ≥ 342 and Cy5 signal ≥ 363.5, respectively). There were 565 genes in the intersection between the set encoding surface-exposed proteins and the set of 307 differential genes. The number of genes in the intersection between this set of 565 and the set of 1427 highly transcriptionally active genes was 226. These 226 genes were thus identified as putative vaccine candidates. The proteins encoded by these genes are not only potentially surface-exposed in the bacterium, but also conserved in two sequenced L. interrogans. Moreover, these genes are conserved among ten epidemic serovars in China and have high transcriptional levels in vitro. CONCLUSION: Of the 4727 ORFs in the genome of L. interrogans, 226 genes were identified as vaccine candidates by bioinformatics, CGH and transcriptional analysis on the basis of the theory of reverse vaccinology. The proteins encoded by these genes might be useful as vaccine candidates as well as for diagnosis of leptospirosis