Differentially expressed genes selection via Laplacian regularized low-rank representation method

With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data.In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods.The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.     

Proteomic response and molecular regulatory mechanisms of Bacillus cereus spores under ultrasound treatment

This study was aimed at providing new insights on the proteomic response of bacterial spores to ultrasound. Data-independent-acquisition method was used to quantify the proteome change of Bacillus cereus spores after ultrasound treatment (200 W). This study revealed that 2485 proteins were extracted from Bacillus cereus spores, most of them were related to metabolism. After ultrasound treatment, the expression of 340 proteins were significantly changed (the fold change ≥ 2 and p < 0.05), of which 207 proteins were significantly down-regulated. KEGG pathway analysis showed that differentially expressed proteins mainly distributed in metabolism pathway, cell process pathway and genetic information processing pathway after ultrasound treatment. Furthermore, this study analyzed the differentially expressed proteins in significant enrichment pathways. In particular, the expression of key proteins in the phosphorylation reaction of spores was significantly decreased after ultrasound treatment. Thus, ATP synthesis rate decreased and the phosphorylation reaction inhibited. Also, the decrease of the expression of key proteins related to the tricarboxylic acid cycle led to the decrease of nutrients metabolism of spores. Ultrasound treatment induced the down-regulation of fatty acid synthetase expression and promoted fatty acid metabolism at the same time. The content of fatty acids decreased in spores consequently.     

Unsteady flow and heat transfer adjacent to the sidewall wall of a differentially heated cavity with a conducting and an adiabatic fin

The unsteady natural convection flow adjacent to the finned sidewall of a differentially heated cavity is numerically investigated through comparisons between the cases with a conducting fin and an adiabatic fin. The results show that the flow and temperature structures in the transition to a periodic flow induced by a conducting fin are considerably different from those by an adiabatic fin. Based on the present numerical results, the temporal development and spatial structures of the flow adjacent to the finned sidewall are described, and instabilities are characterized. It is found that the conducting fin improves the transient convective flows in the cavity and enhances heat transfer across the cavity (by up to 52% in comparison with the case without a fin).     

Étude numérique du couplage de la convection naturelle avec le rayonnement de surfaces en cavité carrée remplie d'airNumerical study of natural convection-surface radiation coupling in air-filled square cavities

A numerical tool is developed for coupling natural convection in cavities with surface radiation and computations are performed for an air-filled square cavity whose four walls have the same emissivity. Compared to the adiabatic case without radiation, the top wall is cooled, the bottom wall is heated, air flow along the horizontal walls are reinforced and thermal stratification in cavity core is reduced. Detailed analysis shows that net radiative heat flux is linear with ΔT if $\mathrm{\Delta }T?T_0$, which is the case at low Rayleigh number, and that radiative Nusselt number is a linear function of the cavity height. Surface radiation induces an early transition to time-dependent flows: for $?=0.2$ and a cavity height of 0.335 m the critical Rayleigh number is equal to $9.3\times {10}^6$ and the corresponding Hopf bifurcation is supercritical. Furthermore, multiple periodic solutions are observed between $\mathit{Ra}=1.2\times {10}^7$ and $1.3\times {10}^7$. To cite this article: H. Wang et al., C. R. Mecanique 334 (2006).     

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BackgroundIt is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC.MethodsTwo expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes.Results1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups.ConclusionsIn summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.     

Differentially weighted direct simulation Monte Carlo method for particle collision in gas–solid flows

In gas–solid flows, particle–particle interaction (typical, particle collision) is highly significant, despite the small particles fractional volume. Widely distributed polydisperse particle population is a typical characteristic during dynamic evolution of particles (e.g., agglomeration and fragmentation) in spite of their initial monodisperse particle distribution. The conventional direct simulation Monte Carlo (DSMC) method for particle collision tracks equally weighted simulation particles, which results in high statistical noise for particle fields if there are insufficient simulation particles in less-populated regions. In this study, a new differentially weighted DSMC (DW-DSMC) method for collisions of particles with different number weight is proposed within the framework of the general Eulerian–Lagrangian models for hydrodynamics. Three schemes (mass, momentum and energy conservation) were developed to restore the numbers of simulation particle while keeping total mass, momentum or energy of the whole system unchanged respectively. A limiting case of high-inertia particle flow was numerically simulated to validate the DW-DSMC method in terms of computational precision and efficiency. The momentum conservation scheme which leads to little fluctuation around the mass and energy of the whole system performed best. Improved resolution in particle fields and dynamic behavior could be attained simultaneously using DW-DSMC, compared with the equally weighted DSMC. Meanwhile, computational cost can be largely reduced in contrast with direct numerical simulation.     

双龙方组分诱导大鼠BMSCs分化的差异基因筛选及聚类分析

利用基因芯片筛选双龙方有效组分(总人参皂苷及总丹酚酸)诱导大鼠骨髓间充质干细胞(BMSCs)类心肌细胞分化过程中的差异表达基因, 并对其进行聚类分析, 在基因水平研究了双龙方组分对大鼠BMSCs分化的影响. 对大鼠BMSCs进行分组培养, 分别收集10, 20, 30及40 d的细胞样本, 提取tRNA, 经基因芯片检测, 筛选出BMSCs变化过程中的差异表达基因并进行生物信息学分析, 同时通过差异表达基因对样本进行Hierarchical聚类分析. 在BMSCs的分化过程中, 筛选出179条差异表达基因, 经分析发现它们与能量代谢和信号传导等多类基因密切相关. 对样本进行聚类分析发现其聚为两大类: 10和20 d的样本聚为一类, 30和40 d的样本聚为一类. 说明BMSCs在20~30 d之间可能发生了显著的改变.     

Constructing new differentially 4-uniform permutations from known ones

In this paper we study a new construction of differentially 4-uniform permutations from known ones and the inverse function. We focus on constructing methods of [20]. We split a finite field into its subfield and remainder, and, we choose known differentially 4-uniform permutations over the subfield and the inverse function over the entire field. As a result, we obtain two families of differentially 4-uniform permutations.     

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BackgroundThe underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).Material and methodsWe obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the “limma” package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.ResultsThe preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.ConclusionsWe identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.