Detecting a set of entanglement measures in an unknown tripartite quantum state by local operations and classical communication

We propose a more general method for detecting a set of entanglement measures, i.e. negativities, in an \emph{arbitrary} tripartite quantum state by local operations and classical communication. To accomplish the detection task using this method, three observers, Alice, Bob and Charlie, do not need to perform the partial transposition maps by the structural physical approximation; instead, they are only required to collectively measure some functions via three local networks supplemented by a classical communication. With these functions, they are able to determine the set of negativities related to the tripartite quantum state.Comment: 16 pages, 2 figures, revte

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network

Lobaplatin arrests cell cycle progression in human hepatocellular carcinoma cells

<p>Abstract</p> <p>Background</p> <p>Hepatocellular carcinoma (HCC) still is a big burden for China. In recent years, the third-generation platinum compounds have been proposed as potential active agents for HCC. However, more experimental and clinical data are warranted to support the proposal. In the present study, the effect of lobaplatin was assessed in five HCC cell lines and the underlying molecular mechanisms in terms of cell cycle kinetics were explored.</p> <p>Methods</p> <p>Cytotoxicity of lobaplatin to human HCC cell lines was examined using MTT cell proliferation assay. Cell cycle distribution was determined by flow cytometry. Expression of cell cycle-regulated genes was examined at both the mRNA (RT-PCR) and protein (Western blot) levels. The phosphorylation status of cyclin-dependent kinases (CDKs) and retinoblastoma (Rb) protein was also examined using Western blot analysis.</p> <p>Results</p> <p>Lobaplatin inhibited proliferation of human HCC cells in a dose-dependent manner. For the most sensitive SMMC-7721 cells, lobaplatin arrested cell cycle progression in G₁and G₂/M phases time-dependently which might be associated with the down-regulation of cyclin B, CDK1, CDC25C, phosphorylated CDK1 (pCDK1), pCDK4, Rb, E2F, and pRb, and the up-regulation of p53, p21, and p27.</p> <p>Conclusion</p> <p>Cytotoxicity of lobaplatin in human HCC cells might be due to its ability to arrest cell cycle progression which would contribute to the potential use of lobaplatin for the management of HCC.</p

Isolation and open reading frame 5 gene analysis of porcine reproductive and respiratory syndrome virus in Yunnan Province, China

Two porcine reproductive and respiratory syndrome virus (PRRSV), respectively named YN-1 and YN-2 strains, were isolated by inoculation into Marc-145 cell. The two isolated strains induce Marc-145 cell stack together, pull net, form plaque and other typical lesions after 4 blind passages. With extracted viral RNA of fourth generation, reverse transcriptase (RT)-PCR based on open reading frame 5 (ORF5) gene showed that there was porcine reproductive and respiratory syndrome virus in Marc-145 cell of fourth generation. TCID50 of isolate measured by Reed-Muench method was 10-3.6/0.1 ml. Genetic evolution of ORF5 indicated that the two isolated strains were in a small branch with high identity of 99.5%. They were in a branch with Shandong strain JN-HS, Hennan-1 and Vietnam 347-T-KSA strain with identity of 99.2 to 99.8%. The two isolated strains were in a different branch with Ch-1a and VR-2332 strains having identity of 94.4 to 94.5%.Key words: Porcine reproductive and respiratory syndrome virus (PRRSV), isolation, ORF5 gene, genetic evolution

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts

Genetic variation and relationships of eighteen Chinese indigenous pig breeds

Chinese indigenous pig breeds are recognized as an invaluable component of the world's pig genetic resources and are divided traditionally into six types. Twenty-six microsatellite markers recommended by the FAO (Food and Agriculture Organization) and ISAG (International Society of Animal Genetics) were employed to analyze the genetic diversity of 18 Chinese indigenous pig breeds with 1001 individuals representing five types, and three commercial breeds with 184 individuals. The observed heterozygosity, unbiased expected heterozygosity and the observed and effective number of alleles were used to estimate the genetic variation of each indigenous breed. The unbiased expected heterozygosity ranged between 0.700 (Mashen) and 0.876 (Guanling), which implies that there is an abundant genetic variation stored in Chinese indigenous pig breeds. Breed differentiation was shown by fixation indices (FIT, FIS, and FST). The FST per locus varied from 0.019 (S0090) to 0.170 (SW951), and the average FST of all loci was 0.077, which means that most of the genetic variation was kept within breeds and only a little of the genetic variation exists between populations. The Neighbor-Joining tree was constructed based on the Nei DA (1978) distances and one large cluster with all local breeds but the Mashen breed, was obtained. Four smaller sub-clusters were also found, which included two to four breeds each. These results, however, did not completely agree with the traditional type of classification. A Neighbor-Joining dendrogram of individuals was established from the distance of – ln(proportions of shared alleles); 92.14% of the individuals were clustered with their own breeds, which implies that this method is useful for breed demarcation. This extensive research on pig genetic diversity in China indicates that these 18 Chinese indigenous breeds may have one common ancestor, helps us to better understand the relative distinctiveness of pig genetic resources, and will assist in developing a national plan for the conservation and utilization of Chinese indigenous pig breeds