Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort

Enhanced Oxidative Stress Is Responsible for TRPV4-Induced Neurotoxicity

Transient receptor potential vanilloid 4 (TRPV4) has been reported to be responsible for neuronal injury in pathological conditions. Excessive oxidative stress can lead to neuronal damage, and activation of TRPV4 increases the production of reactive oxygen species and nitric oxide (NO) in many types of cells. The present study explored whether TRPV4-induced neuronal injury is mediated through enhancing oxidative stress. We found that intracerebroventricular injection of the TRPV4 agonist GSK1016790A increased the content of methane dicarboxylic aldehyde (MDA) and NO in the hippocampus, which was blocked by administration of the TRPV4 specific antagonist HC-067047. The activities of catalase (CAT) and glutathione peroxidase (GSH-Px) were decreased by GSK1016790A, whereas the activity of superoxide dismutase remained unchanged. Moreover, the protein level and activity of neuronal nitric oxide synthase (nNOS) were increased by GSK1016790A, and the GSK1016790A-induced increase in NO content was blocked by an nNOS specific antagonist ARL-17477. The GSK1016790A-induced modulations of CAT, GSH-Px and nNOS activities and the protein level of nNOS were significantly inhibited by HC-067047. Finally, GSK1016790A-induced neuronal death and apoptosis in the hippocampal CA1 area were markedly attenuated by administration of a reactive oxygen species scavenger Trolox or ARL-17477. We conclude that activation of TRPV4 enhances oxidative stress by inhibiting CAT and GSH-Px and increasing nNOS, which is responsible, at least in part, for TRPV4-induced neurotoxicity

Long Non-Coding RNA PVT1/miR-150/ HIG2 Axis Regulates the Proliferation, Invasion and the Balance of Iron Metabolism of Hepatocellular Carcinoma

Background/Aims: To investigate the biological roles and underlying molecular mechanisms of long non-coding RNA (lncRNA) PVT1 in Hepatocellular carcinoma (HCC). Methods: qRT-PCR was performed to measure the expression of miRNA and mRNA. Western blot was performed to measure the protein expression. CCK-8 assay was performed to determine cell proliferation. Flow cytometry was performed to detect cell apoptosis. Wounding-healing assay and Transwell assay was performed to detect cell migration and invasion. Dual luciferase reporter assay was performed to verify the target relationship. Quantichrom iron assay was performed to check uptake level of cellular iron. Results: PVT1 expression was up-regulated in HCC tissues and cell lines. Function studies revealed that PVT1 knockdown significantly suppressed cell proliferation, migration and invasion, and induced cell apoptosis in vitro. Furthermore, PVT1 could directly bind to microRNA (miR)-150 and down-regulate miR-150 expression. Hypoxia-inducible protein 2 (HIG2) was found to be one target gene of miR-150, and PVT1 knockdown could inhibit the expression of HIG2 through up-regulating miR-150 expression. In addition, the expression of miR-150 was down-regulated, while the expression of HIG2 was up-regulated in HCC tissues and cell lines. Moreover, inhibition of miR-150 could partly reverse the biological effects of PVT1 knockdown on proliferation, motility, apoptosis and iron metabolism in vitro, which might be associated with dysregulation of HIG2. In vivo results showed that PVT1 knockdown suppressed tumorigenesis and iron metabolism disorder by regulating the expression of miR-150 and HIG2. Conclusion: Taken together, the present study demonstrates that PVT1/miR-150/HIG2 axis may lead to a better understanding of HCC pathogenesis and provide potential therapeutic targets for HCC

The Chebyshev spectral element approximation with exact quadratures

A new Chebyshev spectral element method has been developed in this paper, in which exact quadratures are used to overcome a shortfall of the Gauss–Chebyshev quadrature in variational spectral element projections. The method is validated with the Stokes and the Cauchy–Riemann problems. It is shown that an enhancement of the approximation convergence rate is attained, and numerical accuracy is much better than that from the Gauss–Lobatto–Legendre spectral element method

Complete chloroplast genomes of five classical Wuyi tea varieties (Camellia sinensis, Synonym: Thea bohea L.), the most famous Oolong tea in China

Wuyi tea (Camellia. sinensis, Synonym: Thea Bohea L.) is recognized as the most prestigious oolong tea in China. For germplasm identification and protection, the complete chloroplast genomes of five classical Wuyi tea varieties were determined by next-generation sequencing. These chloroplast genomes showed highly conserved structures and are 157,024–157,126 bp in length, consisting of a pair of reverse repeats (IR) regions of 25,944–26,095 bp, one large single-copy (LSC) region of 86,594–86,859 bp, and one small single copy (SSC) region of 18,276–18,291 bp. A total of 137 genes were observed and overall GC contents were all about 37.3%. Phylogenetic analysis revealed Wuyi tea varieties did not cluster together, suggesting that these Wuyi tea varieties might have diverged early in their evolutionary history and the complete chloroplast genome could be used as a super-barcode to identify these varieties. This study will be valuable for future studies of evolution and intraspecific identification in Wuyi tea