A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

In recent years, many researchers have attempted to achieve cross-domain diagnosis of faults through domain adaptation (DA) methods. However, owing to the complex physical environments, applications of DA-based approach are not guaranteed to unknown operating environments. Some existing domain generalization (DG) methods require enough fully labeled source domains to train, which are often unavailable in practical settings. In this study, an adversarial domain generalization network (ADGN) based on class boundary feature detection is proposed. The ADGN can diagnose faults in unknown operating environments, and only one fully labeled domain is used in training. Although ADGN has to access fully unlabeled auxiliary domains, a large number of unlabeled samples exist under actual working conditions. In our method, fuzzy features at a classification boundary are detected by maximizing the classifier differences. Better feature mapping functions and domain-invariant features are obtained by adversarial training. As the training proceeds, the differences in the distribution of features among the source, auxiliary, and unknown domains become smaller so domain-invariant features can be used for fault diagnosis in unknown operating environments. Comprehensive experiments showed that ADGN can achieve higher fault diagnosis accuracies than other methods when only one fully labeled domain is used in an unknown operating environment. The ADGN can even cope comfortably with complex transfer tasks with different speeds and loads

TRIM29 acts as a potential senescence suppressor with epigenetic activation in nasopharyngeal carcinoma.

Epigenetic alterations marked by DNA methylation are frequent events during the early development of nasopharyngeal carcinoma (NPC). We identified that TRIM29 is hypomethylated and overexpressed in NPC cell lines and tissues. TRIM29 silencing not only limited the growth of NPC cells in vitro and in vivo, but also induced cellular senescence, along with reactive oxygen species (ROS) accumulation. Mechanistically, we found that TRIM29 interacted with voltage-dependent anion-selective channel 1 (VDAC1) to activate mitophagy clearing up damaged mitochondria, which are the major source of ROS. In patients with NPC, high levels of TRIM29 expression are associated with an advanced clinical stage. Moreover, we detected hypomethylation of TRIM29 in patient nasopharyngeal swab DNA. Our findings indicate that TRIM29 depends on VDAC1 to induce mitophagy and prevents cellular senescence by decreasing ROS. Detection of aberrantly methylated TRIM29 in the nasopharyngeal swab DNA could be a promising strategy for the early detection of NPC

Methylation profiling of twenty promoter-CpG islands of genes which may contribute to hepatocellular carcinogenesis

BACKGROUND: Hepatocellular carcinoma (HCC) presents one of the major health threats in China today. A better understanding of the molecular genetics underlying malignant transformation of hepatocytes is critical to success in the battle against this disease. The methylation state of C5 of the cytosine in the CpG di-nucleotide that is enriched within or near the promoter region of over 50 % of the polymerase II genes has a drastic effect on transcription of these genes. Changes in the methylation profile of the promoters represent an alternative to genetic lesions as causative factors for the tumor-specific aberrant expression of the genes. METHODS: We have used the methylation specific PCR method in conjunction with DNA sequencing to assess the methylation state of the promoter CpG islands of twenty genes. Aberrant expression of these genes have been attributed to the abnormal methylation profile of the corresponding promoter CpG islands in human tumors. RESULTS: While the following sixteen genes remained the unmethylated in all tumor and normal tissues: CDH1, APAF1, hMLH1, BRCA1, hTERC, VHL, RARβ, TIMP3, DAPK1, SURVIVIN, p14(ARF), RB1, p15(INK4b), APC, RASSF1c and PTEN, varying degrees of tumor specific hypermethylation were associated with the p16(INK4a ), RASSF1a, CASP8 and CDH13 genes. For instance, the p16(INK4a )was highly methylated in HCC (17/29, 58.6%) and less significantly methylated in non-cancerous tissue (4/29. 13.79%). The RASSF1a was fully methylated in all tumor tissues (29/29, 100%), and less frequently methylated in corresponding non-cancerous tissue (24/29, 82.75%). CONCLUSIONS: Furthermore, co-existence of methylated with unmethylated DNA in some cases suggested that both genetic and epigenetic (CpG methylation) mechanisms may act in concert to inactivate the p16(INK4a )and RASSF1a in HCC. Finally, we found a significant association of cirrhosis with hypermethylation of the p16(INK4a )and hypomethylation of the CDH13 genes. For the first time, the survey was carried out on such an extent that it would not only provide new insights into the molecular mechanisms underscoring the aberrant expression of the genes in this study in HCC, but also offer essential information required for a good methylation-based diagnosis of HCC

Global Analysis of DNA Methylation by Methyl-Capture Sequencing Reveals Epigenetic Control of Cisplatin Resistance in Ovarian Cancer Cell

Cisplatin resistance is one of the major reasons leading to the high death rate of ovarian cancer. Methyl-Capture sequencing (MethylCap-seq), which combines precipitation of methylated DNA by recombinant methyl-CpG binding domain of MBD2 protein with NGS, global and unbiased analysis of global DNA methylation patterns. We applied MethylCap-seq to analyze genome-wide DNA methylation profile of cisplatin sensitive ovarian cancer cell line A2780 and its isogenic derivative resistant line A2780CP. We obtained 21,763,035 raw reads for the drug resistant cell line A2780CP and 18,821,061reads for the sensitive cell line A2780. We identified 1224 hyper-methylated and 1216 hypomethylated DMRs (differentially methylated region) in A2780CP compared to A2780. Our MethylCap-seq data on this ovarian cancer cisplatin resistant model provided a good resource for the research community. We also found that A2780CP, compared to A2780, has lower observed to expected methylated CpG ratios, suggesting a lower global CpG methylation in A2780CP cells. Methylation specific PCR and bisulfite sequencing confirmed hypermethylation of PTK6, PRKCE and BCL2L1 in A2780 compared with A2780CP. Furthermore, treatment with the demethylation reagent 5-aza-dC in A2780 cells demethylated the promoters and restored the expression of PTK6, PRKCE and BCL2L1

The DNA Methylome of Human Peripheral Blood Mononuclear Cells

Analysis across the genome of patterns of DNA methylation reveals a rich landscape of allele-specific epigenetic modification and consequent effects on allele-specific gene expression

An integrated model for vent area design of hydrocarbon-air mixture explosion inside cubic enclosures with obstacles

© 2018 Elsevier Ltd This study aims to develop an integrated model - NFPA-68-BRANN model, which can be used to calculate the vent areas of cubic enclosures with obstacles. Seven experiments regarding vented explosion inside the obstructed enclosure are reviewed and applied to check the accuracy of two existing standards, i.e. the NFPA-68 2018 and the BS EN 14994:2007. Accordingly, the parameters to describe the flame development in the NFPA-68 2018 are amended by adopting the Bauwens model. Bayesian Regularization Artificial Neuron Network (BRANN) model presenting the non-linear relationship between the turbulent flame enhancement factor X and its affecting factors is subsequently developed. Eventually, the NFPA-68-BRANN model is generated by incorporating the BRANN model into the modified NFAP-68 2018. The accuracy of the NFPA-68-BRANN model is validated by using a series of the New Baker Test data

Robust data-driven model to study dispersion of vapor cloud in offshore facility

Data driven models are increasingly used in engineering design and analysis. Bayesian Regularization Artificial Neural Network (BRANN) and Levenberg-Marquardt Artificial Neural Network (LMANN) are two widely used data-driven models. However, their application to study the dispersion in complex geometry is not explored. This study aims to investigate the suitability of BRANN and LMANN in estimating dimension of flammable cloud in congested offshore platform. A large number of numerical simulations are conducted using FLACS. Part of these simulations results are used to training the network. The trained network is subsequently used to predict the vapor cloud dimension and compared against remaining simulation results. The predictive abilities of these network along with Response Surface Method and Frozen Cloud Approach (FCA) are studied. The comparative results indicate BRANN model with 20 hidden neurons is the most robust and precise. The developed BRANN would serve an effective and tool for quick Explosion Risk Analysis ERA

Stochastic analysis of explosion risk for ultra-deep-water semi-submersible offshore platforms

© 2018 Elsevier Ltd The Response Surface Method (RSM)-based non-intrusive method has been widely used to reduce the computational cost for stochastic Explosion Risk Analysis (ERA) in oil and gas industry. However, the RSM, which may cause the overfitting problem, can reduce robustness and efficiency of the ERA procedure. Therefore, a more robust Bayesian Regularization Artificial Neural Network (BRANN) is introduced in this study. The BRANN-based non-intrusive method is developed along with its executive procedure for stochastic ERA. The BRANN-Dispersion-Deterministic (BDD) models and the BRANN-Explosion-Deterministic (BED) models are firstly developed based on representative simulations. Optimal simulation input numbers of the aforementioned deterministic models are then identified. Furthermore, the exceedance frequency curve is generated by combing the deterministic models with Latin Hypercube Sampling (LHS). Sensitivity analysis of simulation input numbers with regard to the exceedance frequency curve is conducted. Eventually, comparison of the exceedance probability curves between the BRANN-based method and the RSM-based method is carried out. The ultra-deep-water semi-submersible offshore platform is used to demonstrate the advantages of the BRANN-based non-intrusive method