Oral microbiome and risk of malignant esophageal lesions in a high-risk area of China: A nested case-control study.

OBJECTIVE: We aimed to prospectively evaluate the association of oral microbiome with malignant esophageal lesions and its predictive potential as a biomarker of risk. METHODS: We conducted a case-control study nested within a population-based cohort with up to 8 visits of oral swab collection for each subject over an 11-year period in a high-risk area for esophageal cancer in China. The oral microbiome was evaluated with 16S ribosomal RNA (rRNA) gene sequencing in 428 pre-diagnostic oral specimens from 84 cases with esophageal lesions of severe squamous dysplasia and above (SDA) and 168 matched healthy controls. DESeq analysis was performed to identify taxa of differential abundance. Differential oral species together with subject characteristics were evaluated for their potential in predicting SDA risk by constructing conditional logistic regression models. RESULTS: A total of 125 taxa including 37 named species showed significantly different abundance between SDA cases and controls (all P0.84. CONCLUSIONS: The oral microbiome may play an etiological and predictive role in esophageal cancer, and it holds promise as a non-invasive early warning biomarker for risk stratification for esophageal cancer screening programs

Insights into Metal Sheet Novelty Detection via Simulated Electromagnetic Ultrasonic Surface Wave

Metal sheets have good performance and have been widely used. Different kinds of defects can be generated during the preparation and service of metal plates, which will cause the structural performance of the metal plates to decline, thus requiring structural health monitoring (SHM). This study proposes an electromagnetic ultrasonic (EMUS) surface wave detection technique for metal sheet defects via simulation. The numerical results show that after the excitation parameters of the EMUS transducer are optimized through orthogonal experimental design, the amplitude of the EMUS signal generated is increased by about 80%. The power spectrum density (PSD) of the EMUS response signal is used to detect defects. Compared with the peak-to-peak detection, the accuracy is higher, and the reliability is better. The accuracy of the proposed “central zero-point” method for measuring the time delay of the EMUS signal wave packet is higher than that of the “peak-to-peak amplitude” method and the “vibration starting point” method and is close to the accuracy of the “cross-correlation” method

Insights into the Effect of WJ-7 Fastener Rubber Pad to Vehicle-Rail-Viaduct Coupled Dynamics

The high-speed railway (HSR) has been a long-term hotspot in both scientific and engineering societies to enhance the long-term high quality HSR service. This study aims to investigate the WJ-7B type small resistance fastener rubber pad applied in HSR, and temperature sweep test is applied to determine the mechanical parameters of the fastener rubber pad, which are hereafter introduced into the vehicle-track-viaduct vertical coupling model via dynamic flexibility method. The track irregularity spectrum is considered as fixed-point excitation to investigate the temperature-dependent effect of fastener rubber pad on the dynamic responses. The results reveal that the rigidity of the fastener rubber pad is low temperature sensitive and high temperature stable, and the temperature variation has little effect on the vertical dynamic responses of the vehicle. The dynamic flexibility of the rail increases in amplitude and the dominant frequency decreases as the temperature of the fastener rubber pad increases. The vertical dynamic responses of the wheel-rail force, the wheelset and the rail-viaduct system gradually decrease as the temperature of the fastener rubber pad increases, and the peak frequency follows the similar rule. While under high temperature circumstances, the temperature dependent stiffness of the fastener rubber pad has little influence on the peak of the dominant frequency in the vertical dynamic response of the track-viaduct system

Ambient Effect Filtering Using NLPCA-SVR in High-Rise Buildings

The modal frequencies of a structure are affected by continuous changes in ambient factors, such as temperature, wind speed etc. This study incorporates nonlinear principal component analysis (NLPCA) with support vector regression (SVR) to build a mathematical model to reflect the correlation between ambient factors and modal frequencies. NLPCA is first used to eliminate the high correlation among different ambient factors and extract the nonlinear principal components. The extracted nonlinear principal components are input into the SVR model for training and predicting. The proposed method is verified by the measured data provided in the Guangzhou New TV Tower (GNTVT) Benchmark. The grid search method (GSM), genetic algorithm (GA) and fruit fly optimization algorithm (FOA) are applied to determine the optimal hyperparameters for the SVR model. The optimized result of FOA is most suitable for the NLPCA-SVR model. As evaluated by the hypothesis test and goodness-of-fit test, the results show that the proposed method has a high generalization performance and the correlation between the ambient factor and modal frequency can be strongly reflected. The proposed method can effectively eliminate the effects of ambient factors on modal frequencies