Cost-effective low-coverage whole-genome sequencing assay for the risk stratification of gastric cancer

BACKGROUND Gastric cancer(GC), a multifactorial disease, is caused by pathogens, such as Helicobacter pylori(H. pylori) and Epstein-Barr virus(EBV), and genetic components.AIM To investigate microbiomes and host genome instability by cost-effective,low-coverage wholegenome sequencing,as biomarkers for GC subtyping.METHODS Samples from 40 GC patients were collected from Taizhou Hospital,Zhejiang Province,affiliated with Wenzhou Medical University.DNA from the samples was subjected to low-coverage wholegenome sequencing with a median genome coverage of 1.86×(range:1.03×to 3.17×) by Illumina×10,followed by copy number analyses using a customized bioinformatics workflow ultrasensitive chromosomal aneuploidy detector.RESULTS Of the 40 GC samples,20 (50%) were found to be enriched with microbiomes.EBV DNA was detected in 5 GC patients (12.5%).H.pylori DNA was found in 15 (37.5%) patients.The other 20(50%) patients were found to have relatively higher genomic instability.Copy number amplifications of the oncogenes,ERBB2 and KRAS,were found in 9 (22.5%) and 7 (17.5%) of the GC samples,respectively.EBV enrichment was found to be associated with tumors in the gastric cardia and fundus.H.pylori enrichment was found to be associated with tumors in the pylorus and antrum.Tumors with elevated genomic instability showed no localization and could be observed in any location.Additionally,H.pylori-enriched GC was found to be associated with the Borrmann type Ⅱ/Ⅲ and gastritis history.EBV-enriched GC was not associated with gastritis.No statistically significant correlation was observed between genomic instability and gastritis.Furthermore,these three different molecular subtypes showed distinct survival outcomes (P=0.019).EBV-positive tumors had the best prognosis,whereas patients with high genomic instability (CIN+) showed the worst survival.Patients with H.pylori infection showed intermediate prognosis compared with the other two subtypes.CONCLUSION Thus,using low-coverage whole-genome sequencing,GC can be classified into three categories based on disease etiology;this classification may prove useful for GC diagnosis and precision medicine.

Anomaly detection of hot components in gas turbine based on frequent pattern extraction

Hot components operate in a high-temperature and high-pressure environment. The occurrence of a fault in hot components leads to high economic losses. In general, exhaust gas temperature(EGT) is used to monitor the performance of hot components.However, during the early stages of a failure, the fault information is weak, and is simultaneously affected by various types of interference, such as the complex working conditions, ambient conditions, gradual performance degradation of the compressors and turbines, and noise. Additionally, inadequate effective information of the gas turbine also restricts the establishment of the detection model. To solve the above problems, this paper proposes an anomaly detection method based on frequent pattern extraction. A frequent pattern model(FPM) is applied to indicate the inherent regularity of change in EGT occurring from different types of interference. In this study, based on a genetic algorithm and support vector machine regression, the relationship model between the EGT and interference was tentatively built. The modeling accuracy was then further improved through the selection of the kernel function and training data. Experiments indicate that the optimal kernel function is linear and that the optimal training data should be balanced in addition to covering the appropriate range of operating conditions and ambient temperature. Furthermore, the thresholds based on the Pauta criterion that is automatically obtained during the modeling process, are used to determine whether hot components are operating abnormally. Moreover, the FPM is compared with the similarity theory, which demonstrates that the FPM can better suppress the effect of the component performance degradation and fuel heat value fluctuation. Finally, the effectiveness of the proposed method is validated on seven months of actual data obtained from a Titan130 gas turbine on an offshore oil platform. The results indicate that the proposed method can sensitively detect malfunctions in hot components during the early stages of a fault, and is robust to various types of interference.