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Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction. However, L2BLDA is only proposed to cope with vector input samples. When facing with two-dimensional (2D) inputs, such as images, it will lose some useful information, since it does not consider intrinsic structure of images. In this paper, we extend L2BLDA to a two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance which is measured by the weighted pairwise distances of class means and meanwhile minimizes the matrix-based within-class distance. The weighting constant between the between-class and within-class terms is determined by the involved data that makes the proposed 2DBLDA adaptive. In addition, the criterion of 2DBLDA is equivalent to optimizing an upper bound of the Bhattacharyya error. The construction of 2DBLDA makes it avoid the small sample size problem while also possess robustness, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of the proposed methods.

To explore the differential expression of messenger RNA (mRNA) in myocardial tissues of rats with sudden coronary death (SCD), and to provide ideas for the forensic identification of SCD.The rat SCD model was established, and the transcriptome sequencing was performed by next-generation sequencing technology. Differentially expressed genes (DEGs) in myocardial tissues of SCD rats were screened by using the R package limma. A protein-protein interaction (PPI) network was constructed by using the STRING database and Cytoscape 3.8.2 on DEG, and hub genes were screened based on cytoHubba plug-in. Finally, the R package clusterProfiler was used to analyze the biological function and signal pathway enrichment of the selected DEG.A total of 177 DEGs were associated with SCD and were mainly involved in the renin-angiotensin system and PI3K-Akt signaling pathway. The genes including angiotensinogen (AGT), complement component 4a (C4a), Fos proto-oncogene (FOS) and others played key roles in the development of SCD.Genes such as

To explore the mRNA differential expressions and the sequential change pattern in acute myocardial infarction (AMI) mice.The AMI mice relevant dataset GSE4648 was downloaded from Gene Expression Omnibus (GEO). In the dataset, 6 left ventricular myocardial tissue samples were selected at 0.25, 1, 4, 12, 24 and 48 h after operation in AMI group and sham control group, and 6 left ventricular myocardial tissue samples were selected in blank control group, a total of 78 samples were analyzed. Differentially expressed genes (DEGs) were analyzed by R/Bioconductor package limma, functional pathway enrichment analysis was performed by clusterProfiler, protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape software, the key genes were identified by Degree topological algorithm, cluster sequential changes on DEGs were analyzed by Mfuzz.A total of 1 320 DEGs were associated with the development of AMI. Functional enrichment results included cellular catabolic process, regulation of inflammatory response, development of muscle system and vasculature system, cell adhesion and signaling pathways mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathway. The key genes of AMI includedThe functional signaling pathway of DEGs and the sequential expression of key genes in AMI may provide a reference for the forensic identification of AMI.

In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

Substrates in excited state differ significantly from the corresponding ground state so that they are treated as different chemical species with diverse physical properties and chemical reactivity....

Recently, Chen and Ma [A generalized shift-splitting preconditioner for complex symmetric linear systems, J. Comput. Appl. Math., 344 (2018), pp. 691–700] have presented the generalized shift-split...

We describe a tert-butyl nitrite-catalyzed oxidative dehydrogenation of hydrazobenzenes for producing azobenzenes. This method proceeds at ambient temperature and under an atmospheric environment by employing eco-friendly EtOH as the medium, representing a mild, general route to the synthesis of various symmetrical and nonsymmetrical azobenzenes in excellent yields with broad functional group tolerance.

Supplemental Digital Content is available in the text., Objective: METTL3 (methyltransferase-like protein 3)-mediated N6-methyladenosine modification is the most abundant RNA modification on eukaryote mRNAs and plays a crucial role in diverse physiological and pathological processes. However, whether N6-methyladenosine modification has function in thrombosis is unknown. This study aims to determine the role of METTL3 in the endothelial cells-mediated thrombosis. Approach and Results: RNA-sequencing and real-time quantitative PCR revealed that the expression of PAI-1 (plasminogen activator inhibitor-1) was downregulated in METTL3 knockdown human umbilical vein endothelial cells. In vitro experiments showed that METTL3 suppressed fibrinolysis. Mechanically, RNA methylation sequencing and meRIP-quantitative real-time PCR showed that METTL3 catalyzed N6-methyladenosine modification on 3′ UTR of JUN mRNA. Western blotting analysis showed that METTL3 promoted JUN protein expression. Chromatin immunoprecipitation analysis demonstrated that JUN bound to the PAI-1 promoter in human umbilical vein endothelial cells. Furthermore, mice challenged with lipopolysaccharide resulted in higher METTL3 expression in vessels. Endothelial-specific knockdown of Mettl3 decreased expression of active PAI-1 in plasma and attenuated fibrin deposition in livers and lungs during endotoxemia. Conclusions: Our study reveals that METTL3-mediated N6-methyladenosine modification plays a crucial role in fibrinolysis and is an underlying target for the therapy of thrombotic disorders.

The effects of grain size on the tellurium corrosion behaviors have been investigated systematically in GH3535 alloy. After the exposure to Te vapor, the triple-layer scales, which consist of Ni3Te2, Cr3Te4, MnTe, M6C carbides and Fe-riched γ phase, occur in both the fine-grained and coarse-grained specimens. The thicker outer layer, the more discrete inner layer, the shallower Te permeation and Cr3Te4 existence along grain boundaries can be observed in the fine-grained specimens. By comparing the Gibbs free energies and calculating the effective diffusion coefficients, the tellurization mechanism and the role of grain size were discussed.

Hydrogen sulfide (H2S) is an endogenous gaseous signaling molecule that plays important roles in the cardiovascular system. In our previous studies, we demonstrated that H2S regulates lipid metabolism. In the present study, we aimed to explore the mechanisms through which H2S regulates lipid metabolism in HepG2 cells in vitro. Treatment of the HepG2 cells with H2S inhibited the expression of proprotein convertase subtilisin/kexin type 9 (PCSK9) and increased the level of low‑density lipoprotein receptor (LDLR) in a time‑ and dose‑dependent manner. The knockdown of PCSK9 by siRNA effectively increased the levels of LDLR and 1,1'‑dioctadecyl‑3,3,3',3'‑tetramethyl‑indocarbocyanine perchlorate‑labeled LDL (DiI‑LDL) uptake in the H2S‑treated HepG2 cells. Furthermore, the phosphoinositide 3‑kinase (PI3K)/protein kinase B (Akt)‑sterol regulatory element binding proteins 2 (SREBP‑2) signaling pathway was confirmed to be involved in H2S‑regulated PCSK9 expression. Notably, the HepG2 cells were incubated with 30% serum and DiI‑LDL for 24 h, and the results revealed that H2S increased lipid uptake, but caused no increase in lipid accumulation. On the whole, the findings of this study demonstrate that H2S is involved in the regulation of lipid metabolism in HepG2 cells through the regulation of the expression of PCSK9 via the PI3K/Akt‑SREBP‑2 signaling pathway. To the very best of our knowledge, this study is the first to report that H2S can regulate the expression of PCSK9.

In this paper, a new quadratic kernel-free least square twin support vector machine (QLSTSVM) is proposed for binary classification problems. The advantage of QLSTSVM is that there is no need to select the kernel function and related parameters for nonlinear classification problems. After using consensus technique, we adopt alternating direction method of multipliers to solve the reformulated consensus QLSTSVM directly. To reduce CPU time, the Karush-Kuhn-Tucker (KKT) conditions is also used to solve the QLSTSVM. The performance of QLSTSVM is tested on two artificial datasets and several University of California Irvine (UCI) benchmark datasets. Numerical results indicate that the QLSTSVM may outperform several existing methods for solving twin support vector machine with Gaussian kernel in terms of the classification accuracy and operation time.

A new thermomechanical processing of warm rolling by a low deformation followed by annealing, which was specially developed for grain boundary (GB) engineering of large-size materials, was performed on a large-size 316 stainless steel. To examine usefulness of the thermomechanical process, the GB-networks before and after this process were quantitatively compared, and intergranular attack susceptibility was measured. Results clearly demonstrate that the GB-engineered 316 has higher proportions of coincidence site lattice boundaries, larger grain-clusters, longer twin boundary chains, and lower susceptibility to intergranular attack, indicating a successful process for GB-engineering. Additionally, a new parameter called twin boundary chain was proposed to evaluate the GB-network optimization.

The molecular mechanisms underlying the variation in N-use efficiency (NUE) in pepper (Capsicum annuum L.) genotypes are poorly understood. In this work, two genotypes (750-1, low-N tolerant, ZCFB, low-N sensitive) with contrasting low-N tolerance were selected from 100 pepper cultivars on the basis of their relative leaf areas, shoot dry weights, root dry weights, and plant dry weights at the seedling stage. Subsequently, using RNA-Seq, the transcriptome of these two pepper genotypes under N starvation for 28 days was analyzed. We detected 2621/2470 and 3936/4218 different expressed genes (DEGs) in the leaves/roots of 750-1 and ZCFB, respectively. The changes in the expression of basic N metabolism genes were similar between 750-1 and ZCFB. However, different DEGs not directly involved in N metabolism were identified between the 750-1 and ZCFB cultivars. In 750-1, 110 unique DEGs were detected in the leaves, of which 103 were down-regulated, including genes associated with protein metabolism, photosynthesis, secondary metabolism, cell wall metabolism, stress response, and disease resistance. In ZCFB, 142 unique DEGs were detected in the roots, of which 117 were up-regulated, resulting in enhancement of processes such as protein degradation, secondary metabolites synthesis, lipid metabolism, endocytosis, the tricarboxylic acid cycle (TCA), transcriptional regulation, stress response, and disease resistance. Our results not only facilitate an understanding of the different regulatory process in low-N-tolerant and low-N-sensitive pepper cultivars, but also provide abundant candidate genes for improving the low-N tolerance of pepper cultivars.