Discovery of botanical flavonoids as dual peroxisome proliforator, activated receptor (PPAR) ligands and functional characterization of a natural PPAR polymorphism that enhances interaction with nuclear compressor

Ph.DDOCTOR OF PHILOSOPH

Hitchhiking motility of Staphylococcus aureus involves the interaction between its wall teichoic acids and lipopolysaccharide of Pseudomonas aeruginosa

Staphylococcus aureus, which lacks pili and flagella, is nonmotile. However, it hitchhikes motile bacteria, such as Pseudomonas aeruginosa, to migrate in the environment. This study demonstrated that the hitchhiking motility of S. aureus SA113 was reduced after the tagO, which encodes an enzyme for wall teichoic acids (WTA) synthesis, was deleted. The hitchhiking motility was restored after the mutation was complemented by transforming a plasmid expressing TagO into the mutant. We also showed that adding purified lipopolysaccharide (LPS) to a culture that contains S. aureus SA113 and P. aeruginosa PAO1, reduced the movement of S. aureus, showing that WTA and LPS are involved in the hitchhiking motility of S. aureus. This study also found that P. aeruginosa promoted the movement of S. aureus in the digestive tract of Caenorhabditis elegans and in mice. In conclusion, this study reveals how S. aureus hitchhikes P. aeruginosa for translocation in an ecosystem. The results from this study improve our understanding on how a nonmotile pathogen moves in the environment and spreads in animals

Antidiabetic effect of Tibetan medicine Tang-Kang-Fu-San in db/db mice via activation of PI3K/Akt and AMPK pathways

This study was to investigate the anti-diabetic effects and molecular mechanisms of Tang-Kang-Fu-San (TKFS), a traditional Tibetan medicine, in treating type 2 diabetes mellitus of spontaneous diabetic db/db mice. Firstly HPLC fingerprint analysis was performed to gain the features of the chemical compositions of TKFS. Next different doses of TKFS (0.5 g/kg, 1.0 g/kg, and 2.0 g/kg) were administrated via oral gavage to db/db mice and their controls for 4 weeks. TKFS significantly lowered hyperglycemia and ameliorated insulin resistance (IR) in db/db mice, indicated by results from multiple tests, including fasting blood glucose test, intraperitoneal insulin and glucose tolerance tests, fasting serum insulin levels and homeostasis model assessment of IR analysis as well as histology of pancreas islets. TKFS also decreased concentrations of serum triglyceride, total and low-density lipoprotein cholesterol, even though it did not change the mouse body weights. Results from western blot and immunohistochemistry analysis indicated that TKFS reversed the down-regulation of p-Akt and p-AMPK, and increased the translocation of Glucose transporter type 4 in skeletal muscles of db/db mice. In all, TKFS had promising benefits in maintaining the glucose homeostasis and reducing IR. The underlying molecular mechanisms are related to promote Akt and AMPK activation and Glucose transporter type 4 translocation in skeletal muscles. Our work showed that multicomponent Tibetan medicine TKFS acted synergistically on multiple molecular targets and signaling pathways to treat type 2 diabetes mellitus

Preparation of FeO(OH) Modified with Polyethylene Glycol and Its Catalytic Activity on the Reduction of Nitrobenzene with Hydrazine Hydrate

Iron oxyhydroxide was prepared by dropping ammonia water to Fe(NO3)3.9H2O dispersed in polyethylene glycol (PEG) 1000. The catalyst was characterized by X-ray powder diffraction, Fourier transform infrared spectroscopy and laser particle size analyzer. The results showed the catalyst modified with polyethylene glycol was amorphous. The addition of PEG during the preparation make the particle size of the catalyst was smaller and more uniform. The catalytic performance was tested in the reduction of nitroarenes to corresponding amines with hydrazine hydrate, and the catalyst showed excellent activity and stability.

How to Achieve Efficiency and Accuracy in Discrete Element Simulation of Asphalt Mixture: A DRF-Based Equivalent Model for Asphalt Sand Mortar

The clump-based discrete element model is one of the asphalt mixture simulation methods, which has the potential to not only predict mixture performance but also simulate particle movement during compaction, transporting, and other situations. However, modelling of asphalt sand mortar in this method remains to be a problem due to computing capacity. Larger-sized balls (generally 2.0-2.36 mm) were usually used to model the smaller particles and asphalt binder, but this replacement may result in the mixture\u27s unrealistic volumetric features. More specifically, replacing original elements with equal volume but larger size particles will increase in buck volume and then different particle contacting states. The major objective of this research is to provide a solution to the dilemma situation through an improved equivalent model of the smaller particles and asphalt binders. The key parameter of the equivalent model is the diameter reduction factor (DRF), which was proposed in this research to minimize the effects of asphalt mortar\u27s particle replacement modelling. To determine DRF, the DEM-based analysis was conducted to evaluate several mixture features, including element overlap ratio, ball-wall contact number, and the average wall stress. Through this study, it was observed that when the original glued ball diameters are ranging from 2.00 mm and 2.36 mm, the diameter reduction factor changes from 0.82 to 0.86 for AC mixtures and 0.80 to 0.84 for SMA mixtures. The modelling method presented in this research is suitable not only for asphalt mixtures but also for the other particulate mix with multisize particles

Representation and measurement of the beam health based on one-dimensional model

This paper proposes a method for online structural health evaluation, and analyzes the correlation between online monitoring data and structural health status. On the basis of this analysis, the structural health can be evaluated by using the deviation of the current status from the initially designed status. The health degree index, representation and measurement models are also defined for structural health evaluation in this work. A numerical case study is conducted to validate the related concept and health evaluation model using a beam under pressure loads. The results indicate that the proposed method can effectively represent the structural health status

Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network

Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system. However, due to the irregular traffic time series produced by intelligent intersections, the traffic forecasting task becomes much more intractable and imposes three major new challenges: 1) asynchronous spatial dependency, 2) irregular temporal dependency among traffic data, and 3) variable-length sequence to be predicted, which severely impede the performance of current traffic forecasting methods. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic states of the lanes entering intelligent intersections in a future time window. Specifically, by linking lanes via a traffic diffusion graph, we first propose an Asynchronous Graph Diffusion Network to model the asynchronous spatial dependency between the time-misaligned traffic state measurements of lanes. After that, to capture the temporal dependency within irregular traffic state sequence, a learnable personalized time encoding is devised to embed the continuous time for each lane. Then we propose a Transformable Time-aware Convolution Network that learns meta-filters to derive time-aware convolution filters with transformable filter sizes for efficient temporal convolution on the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network consisting of a state evolution unit and a semiautoregressive predictor is designed to effectively and efficiently predict variable-length traffic state sequences. Extensive experiments on two real-world datasets demonstrate the effectiveness of ASeer in six metrics