Quantum correlation measure in arbitrary bipartite systems

A definition of quantum correlation is presented for an arbitrary bipartite quantum state based on the skew information. This definition not only inherits the good properties of skew information such as the contractivity and so on, but also is effective and almost analytically calculated for any bipartite quantum states. We also reveal the relation between our measure and quantum metrology. As applications, we give the exact expressions of quantum correlation for many states, which provides a direct support for our result.Comment: 6 pages, 2 figures. Comments are welcom

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer.

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan-Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment

Using combination of lifting wavelet and multiclass SVM based on global optimization class strategy for fault pattern identification

This paper presents a new method based on lifting wavelet for obtaining a fast multiclass SVM classification based on global optimization class strategy for fault diagnosis of roller bearing. Decision making was performed in two stages: feature extraction by computing the lifting wavelet coefficients and classification using the multiclass SVM classifiers trained on the extracted features. Experiments demonstrate that in comparison to discrete wavelet transform the lifting wavelet feature extraction can speed up the identification phase as well as achieve higher accuracy of multiclass SVM that is based on global optimization class strategy. Experimental results also reveal that the proposed multiclass SVM of global optimization is better than strategy of one against one and DAGSVM

Immunoproteomic Analysis of Human Serological Antibody Responses to Vaccination with Whole-Cell Pertussis Vaccine (WCV)

BACKGROUND: Pertussis (whooping cough) caused by Bordetella pertussis (B.p), continues to be a serious public health threat. Vaccination is the most economical and effective strategy for preventing and controlling pertussis. However, few systematic investigations of actual human immune responses to pertussis vaccines have been performed. Therefore, we utilized a combination of two-dimensional electrophoresis (2-DE), immunoblotting, and mass spectrometry to reveal the entire antigenic proteome of whole-cell pertussis vaccine (WCV) targeted by the human immune system as a first step toward evaluating the repertoire of human humoral immune responses against WCV. METHODOLOGY/PRINCIPAL FINDINGS: Immunoproteomic profiling of total membrane enriched proteins and extracellular proteins of Chinese WCV strain 58003 identified a total of 30 immunoreactive proteins. Seven are known pertussis antigens including Pertactin, Serum resistance protein, chaperonin GroEL and two OMP porins. Sixteen have been documented to be immunogenic in other pathogens but not in B.p, and the immunogenicity of the last seven proteins was found for the first time. Furthermore, by comparison of the human and murine immunoproteomes of B.p, with the exception of four human immunoreactive proteins that were also reactive with mouse immune sera, a unique group of antigens including more than 20 novel immunoreactive proteins that uniquely reacted with human immune serum was confirmed. CONCLUSIONS/SIGNIFICANCE: This study is the first time that the repertoire of human serum antibody responses against WCV was comprehensively investigated, and a small number of previously unidentified antigens of WCV were also found by means of the classic immunoproteomic strategy. Further research on these newly identified predominant antigens of B.p exclusively against humans will not only remarkably accelerate the development of diagnostic biomarkers and subunit vaccines but also provide detailed insight into human immunity mechanisms against WCV. In particular, this work highlights the heterogeneity of the B.p immunoreactivity patterns of the mouse model and the human host

Localized High-Concentration Electrolytes Get More Localized Through Micelle-Like Structures

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media despite having complex chemical composition and atomistic solvation structures, leaving a knowledge gap of microstructural characteristics. Here, we reveal a unique micelle-like structure in a localized high-concentration electrolyte (LHCE), in which the solvent acts as a surfactant between an insoluble salt in diluent. The miscibility of the solvent with the diluent and simultaneous solubility of the salt results in a micelle-like structure with a smeared interface and an increased salt concentration at the centre of the salt-solvent clusters that extends the salt solubility. These intermingling miscibility effects have temperature dependencies, wherein an exemplified LHCE peaks in localized cluster salt concentration near room temperature and is utilized to form a stable solid-electrolyte interphase (SEI) on Li-metal anode. These findings serve as a guide to predicting a stable ternary phase diagram and connecting the electrolyte microstructure with electrolyte formulation and formation protocols to form stable SEI for enhanced battery cyclability

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Genetic Variation in the EGFR Gene and the Risk of Glioma in a Chinese Han Population

Previous studies have shown that regulation of the epidermal growth factor gene (EGFR) pathway plays a role in glioma progression. Certain genotypes of the EGFR gene may be related to increased glioblastoma risk, indicating that germ line EGFR polymorphisms may have implications in carcinogenesis. To examine whether and how variants in the EGFR gene contribute to glioma susceptibility, we evaluated nine tagging single-nucleotide polymorphisms (tSNPs) of the EGFR gene in a case–control study from Xi'an city of China (301 cases, 302 controls). EGFR SNP associations analyses were performed using SPSS 16.0 statistical packages, PLINK software, Haploview software package (version 4.2) and SHEsis software platform. We identified two susceptibility tSNPs in the EGFR gene that were potentially associated with an increased risk of glioma (rs730437, p = 0.016; OR: 1.32; 95%CI: 1.05–1.66 and rs1468727, p = 0.008; OR: 1.31; 95%CI: 1.04–1.65). However, after a strict Bonferroni correction analysis was applied, the significance level of the association between EGFR tSNPs and risk of glioma was attenuated. We observed a protective effect of haplotype “AATT” of the EGFR gene, which was associated with a 29% reduction in the risk of developing glioma, while haplotype “CGTC” increased the risk of developing glioma by 36%. Our results, combined with previous studies, suggested an association between the EGFR gene and glioma development

SIRT1 deacetylates SATB1 to facilitate MARHS2-MARε interaction and promote ε-globin expression

The higher order chromatin structure has recently been revealed as a critical new layer of gene transcriptional control. Changes in higher order chromatin structures were shown to correlate with the availability of transcriptional factors and/or MAR (matrix attachment region) binding proteins, which tether genomic DNA to the nuclear matrix. How posttranslational modification to these protein organizers may affect higher order chromatin structure still pending experimental investigation. The type III histone deacetylase silent mating type information regulator 2, S. cerevisiae, homolog 1 (SIRT1) participates in many physiological processes through targeting both histone and transcriptional factors. We show that MAR binding protein SATB1, which mediates chromatin looping in cytokine, MHC-I and β-globin gene loci, as a new type of SIRT1 substrate. SIRT1 expression increased accompanying erythroid differentiation and the strengthening of β-globin cluster higher order chromatin structure, while knockdown of SIRT1 in erythroid k562 cells weakened the long-range interaction between two SATB1 binding sites in the β-globin locus, MARHS2 and MARε. We also show that SIRT1 activity significantly affects ε-globin gene expression in a SATB1-dependent manner and that knockdown of SIRT1 largely blocks ε-globin gene activation during erythroid differentiation. Our work proposes that SIRT1 orchestrates changes in higher order chromatin structure during erythropoiesis, and reveals the dynamic higher order chromatin structure regulation at posttranslational modification level