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Gene-metabolite network analysis in different nonalcoholic fatty liver disease phenotypes
Xiao-Lin Liu,Ya-Nan Ming,Jing-Yi Zhang,Xiao-Yu Chen,Min-De Zeng,Yi-Min Mao 생화학분자생물학회 2017 Experimental and molecular medicine Vol.49 No.-
We sought to identify common key regulators and build a gene-metabolite network in different nonalcoholic fatty liver disease (NAFLD) phenotypes. We used a high-fat diet (HFD), a methionine-choline-deficient diet (MCDD) and streptozocin (STZ) to establish nonalcoholic fatty liver (NAFL), nonalcoholic steatohepatitis (NASH) and NAFL+type 2 diabetes mellitus (T2DM) in rat models, respectively. Transcriptomics and metabolomics analyses were performed in rat livers and serum. A functional network-based regulation model was constructed using Cytoscape with information derived from transcriptomics and metabolomics. The results revealed that 96 genes, 17 liver metabolites and 4 serum metabolites consistently changed in different NAFLD phenotypes (42-fold, Po0.05). Gene-metabolite network analysis identified ccl2 and jun as hubs with the largest connections to other genes, which were mainly involved in tumor necrosis factor, P53, nuclear factor-kappa B, chemokine, peroxisome proliferator activated receptor and Toll-like receptor signaling pathways. The specifically regulated genes and metabolites in different NAFLD phenotypes constructed their own networks, which were mainly involved in the lipid and fatty acid metabolism in HFD models, the inflammatory and immune response in MCDD models, and the AMPK signaling pathway and response to insulin in HFD+STZ models. Our study identified networks showing the general and specific characteristics in different NAFLD phenotypes, complementing the genetic and metabolic features in NAFLD with hepatic and extra-hepatic manifestations.
( Xiao Mao Xiong ),( Yuan Liang Hu ),( Nan Feng Yan ),( Ying Na Huang ),( Nan Peng ),( Yun Xiang Liang ),( Shu Miao Zhao ) 한국미생물 · 생명공학회 2014 Journal of microbiology and biotechnology Vol.24 No.8
A systematic investigation was performed on the bacterial, Bacillus, fungal, and yeast communities of the three types of Daqu (mechanically prepared, manually prepared, and mixed prepared) used in Baiyunbian Company by reconditioning PCR-denaturing gradient gel electrophoresis (PCR-DGGE). The DGGE results showed that the microbes in the three types of Daqu were mainly thermotolerant and thermophilic microbes, and the most dominant bacterial species were Bacillus and Virgibacillus, followed by Lactobacillus and Trichococcus. Furthermore, the dominant fungi were found to be molds, such as Rasamsonia, Penicillium, Aspergillus, and Monascus, and the dominant yeasts were Saccharomyces cerevisiae, Saccharomycopsis fibuligera, Pichia anomala, and Debaryomyces hansenii. In general, the three types of Daqu showed slight differences in microbial communities, and the Shannon indexes (H`) of the manually prepared and mechanically prepared Daqu were similar. The results suggest that mechanically prepared Daqu can replace manually prepared Daqu in liquor production, and this research provides useful information for liquor production and process improvement.
Gaussian mixture model for automated tracking of modal parameters of long-span bridge
Jian-Xiao Mao,Hao Wang,Billie F. Spencer Jr. 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.2
Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.
Variability analysis on modal parameters of Runyang Bridge during Typhoon Masta
Jian-Xiao Mao,Hao Wang,Zhi-Xiang Xun,Zhong-Qin Zou 국제구조공학회 2017 Smart Structures and Systems, An International Jou Vol.19 No.6
The modal parameters of the deck of Runyang Suspension Bridge (RSB) as well as their relationships with wind and temperature are studied based on the data recorded by its Structural Health Monitoring System (SHMS). Firstly, frequency analysis on the vertical responses at the two sides of the deck is carried out to distinguish the vertical and torsional vibration modes. Then, the vertical, torsional and lateral modal parameters of the deck of RSB are identified using Hilbert-Huang Transform (HHT) and validated by the identified results before RSB was opened to traffic. On the basis of this, the modal frequencies and damping ratios of RSB during the whole process of Typhoon Masta are obtained. And the correlation analysis on the modal parameters and wind environmental factors is then conducted. Results show that the HHT can achieve an accurate modal identification of RSB and the damping ratios show an obvious decay trend as the frequencies increase. Besides, compared to frequencies, the damping ratios are more sensitive to the environmental factors, in particular, the wind speed. Further study on configuring the variation law of modal parameters related with environmental factors should be continued.
Jian-Xiao Mao,Hao Wang,Jian Li 대한토목학회 2020 KSCE JOURNAL OF CIVIL ENGINEERING Vol.24 No.2
Bayesian model updating technique has been widely investigated and utilized in the field of finite element model (FEM) updating for its advantages in system uncertainty quantification. Most existing studies focus on numerical and experimental models. More studies on large-scale civil infrastructures based on field monitoring are still required. A case study on Bayesian FEM updating of the Runyang Suspension Bridge (RSB), a long-span suspension bridge with a main span of 1,490 m, is carried out in this paper. The Bayesian updating method is utilized to update the initial FEM of RSB, aiming to make the numerical modal properties match the field monitoring results. Two stochastic sampling algorithms, i.e., the Metropolis-Hastings (MH) algorithm and the Hybrid Monte Carlo (HMC) algorithm, are respectively investigated to show their advantages and limitations in Bayesian updating. Subsequently, based on the experimentalsamples generated by the Latin hypercube sampling algorithm, a Kriging predictor is established as a surrogate model to reduce the computational burden of model updating. Results show that the HMC algorithm could guarantee much higher acceptance rate of the sampled chain than the MH algorithm especially when the updating step size is large. In addition, combined with the Kriging predictor, Bayesian model updating method could serve as an effective and efficient tool to calibrate the FEM of large-scale civil infrastructures.
Effects of Ohmic Area Etching on Buffer Breakdown Voltage of AlGaN/GaN HEMT
Chong Wang,Xiao-Xiao Wel,Meng-Di Zhao,Yun-Long He,Xue-Feng Zheng,Wei Mao,Xiao-Hua Ma,Jin-Cheng Zhang,Yue Hao 한국전기전자재료학회 2017 Transactions on Electrical and Electronic Material Vol.18 No.3
This study is on how ohmic area etching affects the buffer breakdown voltage of AlGaN/GaN HEMT. The surfacemorphology of the ohmic metal can be improved by whole etching on the ohmic area. The buffer breakdown voltagesof the samples with whole etching on the ohmic area were improved by the suppression of the metal spikes formedunder the ohmic contact regions during high-temperature annealing. The samples with selective etching on theohmic area were investigated for comparison. In addition, the buffer leakage currents were measured on the differentradii of the wafer, and the uniformity of the buffer leakage currents on the wafer were investigated by PL mappingmeasurement.
Effects of Ohmic Area Etching on Buffer Breakdown Voltage of AlGaN/GaN HEMT
Wang, Chong,Wel, Xiao-Xiao,Zhao, Meng-Di,He, Yun-Long,Zheng, Xue-Feng,Mao, Wei,Ma, Xiao-Hua,Zhang, Jin-Cheng,Hao, Yue The Korean Institute of Electrical and Electronic 2017 Transactions on Electrical and Electronic Material Vol.18 No.3
This study is on how ohmic area etching affects the buffer breakdown voltage of AlGaN/GaN HEMT. The surface morphology of the ohmic metal can be improved by whole etching on the ohmic area. The buffer breakdown voltages of the samples with whole etching on the ohmic area were improved by the suppression of the metal spikes formed under the ohmic contact regions during high-temperature annealing. The samples with selective etching on the ohmic area were investigated for comparison. In addition, the buffer leakage currents were measured on the different radii of the wafer, and the uniformity of the buffer leakage currents on the wafer were investigated by PL mapping measurement.