On p.p. structural matrix rings

AbstractA ring is called a left p.p. ring if every principal left ideal is projective. The objective here is to completely determine the left p.p. structural matrix rings over a von Neumann regular ring

A sparse approach for high-dimensional data with heavy-tailed noise

High-dimensional data have commonly emerged in diverse fields, such as economics, finance, genetics, medicine, machine learning, and so on. In this paper, we consider the sparse quantile regression problem of high-dimensional data with heavy-tailed noise, especially when the number of regressors is much larger than the sample size. We bring the spirit of Lp-norm support vector regression into quantile regression and propose a robust Lp-norm support vector quantile regression for high-dimensional data with heavy-tailed noise. The proposed method achieves robustness against heavy-tailed noise due to its use of the pinball loss function. Furthermore, Lp-norm support vector quantile regression ensures that the most representative variables are selected automatically by using a sparse parameter. We use a simulation study to test the variable selection performance of Lp-norm support vector quantile regression, where the number of explanatory variables greatly exceeds the sample size. The simulation study confirms that Lp-norm support vector quantile regression is not only robust against heavy-tailed noise but also selects representative variables. We further apply the proposed method to solve the variable selection problem of index construction, which also confirms the robustness and sparseness of Lp-norm support vector quantile regression

Post-pandemic assessment of public knowledge, behavior, and skill on influenza prevention among the general population of Beijing, China

SummaryBackgroundThe aim of this study was to assess the knowledge, behavioral, and skill responses toward influenza in the general population of Beijing after pandemic influenza A (H1N1) 2009.MethodsA cross-sectional study was conducted in Beijing, China, in January 2011. A survey was conducted in which information was collected using a standardized questionnaire. A comprehensive evaluation index system of health literacy related to influenza was built to evaluate the level of health literacy regarding influenza prevention and control among residents in Beijing.ResultsThirteen thousand and fifty-three valid questionnaires were received. The average score for the sum of knowledge, behavior, and skill was 14.12±3.22, and the mean scores for knowledge, behavior, and skill were 4.65±1.20, 7.25±1.94, and 2.21±1.31, respectively. The qualified proportions of these three sections were 23.7%, 11.9%, and 43.4%, respectively, and the total proportion with a qualified level was 6.7%. There were significant differences in health literacy level related to influenza among the different gender, age, educational level, occupational status, and location groups (p<0.05). There was a significant association between knowledge and behavior (r=0.084, p<0.001), and knowledge and skill (r=0.102, p<0.001).ConclusionsThe health literacy level remains low among the general population in Beijing and the extent of relativities in knowledge, behavior, and skill about influenza was found to be weak. Therefore, improvements are needed in terms of certain aspects, particularly for the elderly and the population of rural districts. Educational level, as a significant factor in reducing the spread of influenza, should be considered seriously when intervention strategies are implemented

A Novel Classification of Glioma Subgroup, Which Is Highly Correlated With the Clinical Characteristics and Tumor Tissue Characteristics, Based on the Expression Levels of Gβ and Gγ Genes

PurposeGlioma is a classical type of primary brain tumors that is most common seen in adults, and its high heterogeneity used to be a reference standard for subgroup classification. Glioma has been diagnosed based on histopathology, grade, and molecular markers including IDH mutation, chromosome 1p/19q loss, and H3K27M mutation. This subgroup classification cannot fully meet the current needs of clinicians and researchers. We, therefore, present a new subgroup classification for glioma based on the expression levels of Gβ and Gγ genes to complement studies on glioma and Gβγ subunits, and to support clinicians to assess a patient’s tumor status.MethodsGlioma samples retrieved from the CGGA database and the TCGA database. We clustered the gliomas into different groups by using expression values of Gβ and Gγ genes extracted from RNA sequencing data. The Kaplan–Meier method with a two-sided log-rank test was adopted to compare the OS of the patients between GNB2 group and non-GNB2 group. Univariate Cox regression analysis was referred to in order to investigate the prognostic role of each Gβ and Gγ genes. KEGG and ssGSEA analysis were applied to identify highly activated pathways. The “estimate” package, “GSVA” package, and the online analytical tools CIBERSORTx were employed to evaluate immune cell infiltration in glioma samples.ResultsThree subgroups were identified. Each subgroup had its own specific pathway activation pattern and other biological characteristics. High M2 cell infiltration was observed in the GNB2 subgroup. Different subgroups displayed different sensitivities to chemotherapeutics. GNB2 subgroup predicted poor survival in patients with gliomas, especially in patients with LGG with mutation IDH and non-codeleted 1p19q.ConclusionThe subgroup classification we proposed has great application value. It can be used to select chemotherapeutic drugs and the prognosis of patients with target gliomas. The unique relationships between subgroups and tumor-related pathways are worthy of further investigation to identify therapeutic Gβγ heterodimer targets

Development of an Infectious Cell Culture System for Hepatitis C Virus Genotype 6a Clinical Isolate Using a Novel Strategy and Its Sensitivity to Direct-Acting Antivirals

Hepatitis C virus (HCV) is classified into seven major genotypes, and genotype 6 is commonly prevalent in Asia, thus reverse genetic system representing genotype 6 isolates in prevalence is required. Here, we developed an infectious clone for a Chinese HCV 6a isolate (CH6a) using a novel strategy. We determined CH6a consensus sequence from patient serum and assembled a CH6a full-length (CH6aFL) cDNA using overlapped PCR product-derived clones that shared the highest homology with the consensus. CH6aFL was non-infectious in hepatoma Huh7.5 cells. Next, we constructed recombinants containing Core-NS5A or 5′UTR-NS5A from CH6a and the remaining sequences from JFH1 (genotype 2a), and both were engineered with 7 mutations identified previously. However, they replicated inefficiently without virus spread in Huh7.5 cells. Addition of adaptive mutations from CH6a Core-NS2 recombinant, with JFH1 5′UTR and NS3-3′UTR, enhanced the viability of Core-NS5A recombinant and acquired replication-enhancing mutations. Combination of 22 mutations in CH6a recombinant with JFH1 5′UTR and 3′UTR (CH6aORF) enabled virus replication and recovered additional four mutations. Adding these four mutations, we generated two efficient recombinants containing 26 mutations (26m), CH6aORF_26m and CH6aFL_26m (designated “CH6acc”), releasing HCV of 104.3–104.5 focus-forming units (FFU)/ml in Huh7.5.1-VISI-mCherry and Huh7.5 cells. Seven newly identified mutations were important for HCV replication, assembly, and release. The CH6aORF_26m virus was inhibited in a dose- and genotype-dependent manner by direct-acting-antivirals targeting NS3/4A, NS5A, and NS5B. The CH6acc enriches the toolbox of HCV culture systems, and the strategy and mutations applied here will facilitate the culture development of other HCV isolates and related viruses

A sheep pangenome reveals the spectrum of structural variations and their effects on tail phenotypes

Structural variations (SVs) are a major contributor to genetic diversity and phenotypic variations, but their prevalence and functions in domestic animals are largely unexplored. Here we generated high-quality genome assemblies for 15 individuals from genetically diverse sheep breeds using Pacific Biosciences (PacBio) high-fidelity sequencing, discovering 130.3 Mb nonreference sequences, from which 588 genes were annotated. A total of 149,158 biallelic insertions/deletions, 6531 divergent alleles, and 14,707 multiallelic variations with precise breakpoints were discovered. The SV spectrum is characterized by an excess of derived insertions compared to deletions (94,422 vs. 33,571), suggesting recent active LINE expansions in sheep. Nearly half of the SVs display low to moderate linkage disequilibrium with surrounding single-nucleotide polymorphisms (SNPs) and most SVs cannot be tagged by SNP probes from the widely used ovine 50K SNP chip. We identified 865 population-stratified SVs including 122 SVs possibly derived in the domestication process among 690 individuals from sheep breeds worldwide. A novel 168-bp insertion in the 5' untranslated region (5' UTR) of HOXB13 is found at high frequency in long-tailed sheep. Further genome-wide association study and gene expression analyses suggest that this mutation is causative for the long-tail trait. In summary, we have developed a panel of high-quality de novo assemblies and present a catalog of structural variations in sheep. Our data capture abundant candidate functional variations that were previously unexplored and provide a fundamental resource for understanding trait biology in sheep

A framework for surrogate-based aerodynamic optimization

At DLR, an optimization framework combining different CFD solvers, Design of Experiment methods, optimization algorithms, and various surrogate modeling methodologies with sample refinement strategies for efficient surrogate-based global optimization is under development. Several Kriging predictors are used because of their ability to approximate multi-dimensional, highly-nonlinear functions. In order to find global optima accurately, the surrogate model is adaptively refined based on the Kriging error and the Expected Improvement Function. With this hybrid refinement strategy, only a few initial samples need to be evaluated, which improves the performance of the overall optimization process. Additionally, the strategy of running a local optimizer starting from the “optimum” found on the surrogate model is investigated in order to further improve the efficiency and accuracy of the framework. Two test cases indicate that the developed framework combined with hybrid strategy is more efficient