Debt Financing and Corporate Performance——Taking the case of China’s Listed Companies on Growth Enterprise Market

This article samples 293 listed companies on GEM (Growth Enterprise Market) of Shenzhen Stock Exchange, uses SPSS 17.0 to analyze their annual report data from 2009 to 2011. The results show that corporate performance will increase and then decrease along with the improvement of debt asset ratio, and achieve optimized performance at about 50%-60% of DAR (debt asset ratio); short-term borrowings have significant negative effects on corporate performance, bank debt, commercial credit and long-term borrowings have indistinctive positive effects on corporate performance, and other debt have marked positive effects on corporate performance. This research is helpful for governmental macro-control and SMEs themselves to build reasonable capital structure to improve their corporate performance

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI) with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure. The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency

Lipophilic M(α,α′-OC5H11)8phthalocyanines (M = H2 and Ni(II)): synthesis, electronic structure, and their utility for highly efficient carbonyl reductions

A lipophilic and electron-rich phthalocyanine (α,α′-n-OC5H11)8-H2Pc and its nickel(II) complex (α,α′-n-OC5H11)8-Ni(II)Pc have been synthesized and characterized. Detailed analyses of the electronic structure were carried out by spectroscopy, electrochemistry, spectroelectrochemistry, and TD-DFT calculations. A series of experiments demonstrate that the (α,α′-n-OC5H11)8-Ni(II)Pc complex can be used as a catalyst for highly efficient carbonyl reductions.Original publication is available at http://dx.doi.org/10.1039/C5DT03256

Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem

The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs

Effect of Berberine on PPAR α

Rhizoma coptidis, the root of Coptis chinensis Franch, has been used in China as a folk medicine in the treatment of diabetes for thousands of years. Berberine, one of the active ingredients of Rhizoma coptidis, has been reported to improve symptoms of diabetes and to treat experimental cardiac hypertrophy, respectively. The objective of this study was to evaluate the potential effect of berberine on cardiomyocyte hypertrophy in diabetes and its possible influence on peroxisome proliferator-activated receptor-α (PPARα)/nitric oxide (NO) signaling pathway. The cardiomyocyte hypertrophy induced by high glucose (25.5 mmol/L) and insulin (0.1 μmol/L) (HGI) was characterized in rat primary cardiomyocyte by measuring the cell surface area, protein content, and atrial natriuretic factor mRNA expression level. Protein and mRNA expression were measured by western blot and real-time RT-PCR, respectively. The enzymatic activity of NO synthase (NOS) was measured using a spectrophotometric assay, and NO concentration was measured using the Griess assay. HGI significantly induced cardiomyocyte hypertrophy and decreased the expression of PPARα and endothelial NOS at the mRNA and protein levels, which occurred in parallel with declining NOS activity and NO concentration. The effect of HGI was inhibited by berberine (0.1 to 100 μmol/L), fenofibrate (0.3 μmol/L), or L-arginine (100 μmol/L). MK886 (0.3 μmol/L), a selective PPARα antagonist, could abolish the effects of berberine and fenofibrate. NG-nitro-L-arginine-methyl ester (100 μmol/L), a NOS inhibitor, could block the effects of L-arginine, but only partially blocked the effects of berberine. These results suggest that berberine can blunt HGI-induced cardiomyocyte hypertrophy in vitro, through the activation of the PPARα/NO signaling pathway

Transcriptome profiling in rumen, reticulum, omasum, and abomasum tissues during the developmental transition of pre-ruminant to the ruminant in yaks

The development of the four stomachs of yak is closely related to its health and performance, however the underlying molecular mechanisms are largely unknown. Here, we systematically analyzed mRNAs of four stomachs in five growth time points [0 day, 20 days, 60 days, 15 months and 3 years (adult)] of yaks. Overall, the expression patterns of DEmRNAs were unique at 0 d, similar at 20 d and 60 d, and similar at 15 m and adult in four stomachs. The expression pattern in abomasum was markedly different from that in rumen, reticulum and omasum. Short Time-series Expression Miner (STEM) analysis demonstrated that multi-model spectra are drastically enriched over time in four stomachs. All the identified mRNAs in rumen, reticulum, omasum and abomasum were classified into 6, 4, 7, and 5 cluster profiles, respectively. Modules 9, 38, and 41 were the most significant three colored modules. By weighted gene co-expression network analysis (WGCNA), a total of 5,486 genes were categorized into 10 modules. CCKBR, KCNQ1, FER1L6, and A4GNT were the hub genes of the turquoise module, and PAK6, TRIM29, ADGRF4, TGM1, and TMEM79 were the hub genes of the blue module. Furthermore, functional KEGG enrichment analysis suggested that the turquoise module was involved in gastric acid secretion, sphingolipid metabolism, ether lipid metabolism, etc., and the blue module was enriched in pancreatic secretion, pantothenate and CoA biosynthesis, and starch and sucrose metabolism, etc. Our study aims to lay a molecular basis for the study of the physiological functions of rumen, reticulum, omasum and abomasum in yaks. It can further elucidate the important roles of these mRNAs in regulation of growth, development and metabolism in yaks, and to provide a theoretical basis for age-appropriate weaning and supplementary feeding in yaks

An efficient ECG denoising method by fusing ECA-Net and CycleGAN

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNRimp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD)