Assisted optimal state discrimination without entanglement

A fundamental problem in quantum information is to explore the roles of different quantum correlations in a quantum information procedure. Recent work [Phys. Rev. Lett., 107 (2011) 080401] shows that the protocol for assisted optimal state discrimination (AOSD) may be implemented successfully without entanglement, but with another correlation, quantum dissonance. However, both the original work and the extension to discrimination of $d$ states [Phys. Rev. A, 85 (2012) 022328] have only proved that entanglement can be absent in the case with equal a \emph{priori} probabilities. By improving the protocol in [Sci. Rep., 3 (2013) 2134], we investigate this topic in a simple case to discriminate three nonorthogonal states of a qutrit, with positive real overlaps. In our procedure, the entanglement between the qutrit and an auxiliary qubit is found to be completely unnecessary. This result shows that the quantum dissonance may play as a key role in optimal state discrimination assisted by a qubit for more general cases.Comment: 6 pages, 3 figures. Accepted by EPL. We extended the protocol for assisted optimal state discrimination to the case with positive real overlaps, and presented a proof for the absence of entanglemen

Functional investigation of grass carp reovirus nonstructural protein NS80

<p>Abstract</p> <p>Background</p> <p>Grass Carp Reovirus (GCRV), a highly virulent agent of aquatic animals, has an eleven segmented dsRNA genome encased in a multilayered capsid shell, which encodes twelve proteins including seven structural proteins (VP1-VP7), and five nonstructural proteins (NS80, NS38, NS31, NS26, and NS16). It has been suggested that the protein NS80 plays an important role in the viral replication cycle that is similar to that of its homologous protein μNS in the genus of <it>Orthoreovirus</it>.</p> <p>Results</p> <p>As a step to understanding the basis of the part played by NS80 in GCRV replication and particle assembly, we used the yeast two-hybrid (Y2H) system to identify NS80 interactions with proteins NS38, VP4, and VP6 as well as NS80 and NS38 self-interactions, while no interactions appeared in the four protein pairs NS38-VP4, NS38-VP6, VP4-VP4, and VP4-VP6. Bioinformatic analyses of NS80 with its corresponding proteins were performed with all currently available homologous protein sequences in ARVs (avian reoviruses) and MRVs (mammalian reoviruses) to predict further potential functional domains of NS80 that are related to VFLS (viral factory-like structures) formation and other roles in viral replication. Two conserved regions spanning from aa (amino acid) residues of 388 to 433, and 562 to 580 were discovered in this study. The second conserved region with corresponding conserved residues Tyr565, His569, Cys571, Asn573, and Glu576 located between the two coiled-coils regions (aa ~513-550 and aa ~615-690) in carboxyl-proximal terminus were supposed to be essential to form VFLS, so that aa residues ranging from 513 to 742 of NS80 was inferred to be the smallest region that is necessary for forming VFLS. The function of the first conserved region including Ala395, Gly419, Asp421, Pro422, Leu438, and Leu443 residues is unclear, but one-third of the amino-terminal region might be species specific, dominating interactions with other viral components.</p> <p>Conclusions</p> <p>Our results in this study together with those from previous investigations indicate the protein NS80 might play a central role in VFLS formation and viral components recruitment in GCRV particle assembly, similar to the μNS protein in ARVs and MRVs.</p

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification

Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network. Keywords: Gene expression profiles; Gene selection; Tumor classification; Heuristic breadth-first search; Power-law distributio