Musca domestica Cecropin (Mdc) Alleviates Salmonella typhimurium-Induced Colonic Mucosal Barrier Impairment: Associating With Inflammatory and Oxidative Stress Response, Tight Junction as Well as Intestinal Flora

Salmonella typhimurium, a Gram-negative food-borne pathogen, induces impairment in intestinal mucosal barrier function frequently. The injury is related to many factors such as inflammation, oxidative stress, tight junctions and flora changes in the host intestine. Musca domestica cecropin (Mdc), a novel antimicrobial peptide containing 40 amino acids, has potential antibacterial, anti-inflammatory, and immunological functions. It remains unclear exactly whether and how Mdc reduces colonic mucosal barrier damage caused by S. typhimurium. Twenty four 6-week-old male mice were divided into four groups: normal group, control group (S. typhimurium-challenged), Mdc group, and ceftriaxone sodium group (Cs group). HE staining and transmission electron microscopy (TEM) were performed to observe the morphology of the colon tissues. Bacterial load of S. typhimurium in colon, liver and spleen were determined by bacterial plate counting. Inflammatory factors were detected by enzyme linked immunosorbent assay (ELISA). Oxidative stress levels in the colon tissues were also analyzed. Immunofluorescence analysis, RT-PCR, and Western blot were carried out to examine the levels of tight junction and inflammatory proteins. The intestinal microbiota composition was assessed via 16s rDNA sequencing. We successfully built and evaluated an S. typhimurium-infection model in mice. Morphology and microcosmic change of the colon tissues confirmed the protective qualities of Mdc. Mdc could inhibit colonic inflammation and oxidative stress. Tight junctions were improved significantly after Mdc administration. Interestingly, Mdc ameliorated intestinal flora imbalance, which may be related to the improvement of tight junction. Our results shed a new light on protective effects and mechanism of the antimicrobial peptide Mdc on colonic mucosal barrier damage caused by S. typhimurium infection. Mdc is expected to be an important candidate for S. typhimurium infection treatment

Deflection Estimation of Truss Structures Using Inverse Finite Element Method

It is well recognized that strain and deflection data are important indexes to judge the safety of truss structures. Specifically, the shape sensing technology can estimate the deformation of a structure by exploiting the discrete strain data without considering the material property conditions. To fill the gap in which most of the methods in SHM (structural health monitoring) cannot be directly used to predict the displacement field, this paper proposed a novel inverse finite element method (iFEM) algorithm based on the equivalent stiffness theory. A deflection sensor is fabricated to focus on predicting the distributed deflection variation of the truss structure. The performance of the deflection sensor was evaluated by a calibration test and a stability test. Finally, it was applied to distributed deflection monitoring in the testing of truss structures. Results of all tests verify that the deflection sensor based on the i-FEM algorithm can predict the distributed deflection variation of the truss structure accurately, in real time, and dynamically

Deflection Estimation of Truss Structures Using Inverse Finite Element Method

It is well recognized that strain and deflection data are important indexes to judge the safety of truss structures. Specifically, the shape sensing technology can estimate the deformation of a structure by exploiting the discrete strain data without considering the material property conditions. To fill the gap in which most of the methods in SHM (structural health monitoring) cannot be directly used to predict the displacement field, this paper proposed a novel inverse finite element method (iFEM) algorithm based on the equivalent stiffness theory. A deflection sensor is fabricated to focus on predicting the distributed deflection variation of the truss structure. The performance of the deflection sensor was evaluated by a calibration test and a stability test. Finally, it was applied to distributed deflection monitoring in the testing of truss structures. Results of all tests verify that the deflection sensor based on the i-FEM algorithm can predict the distributed deflection variation of the truss structure accurately, in real time, and dynamically

Design of Smart Cable for Distributed Cable Force Measurement in Cable Dome Structures and Its Application

The stay cable is one of the most critical structural components of a cable dome structure. However, during its service life, it may lose its stiffness due to environmental factors and metal fatigue, thus making the structure a safety hazard. As the most important mechanical physical parameter of the cable, it is necessary to create a health-monitoring method to ensure the safety of the structure. In this study, a smart cable with a fiber optic Bragg grating (FBG) sensor is proposed. The sensor is embedded in the Z-shaped cable of the stay cable to ensure the simultaneous deformation of the sensor and cable. The monitoring of the cable force can be achieved after obtaining the relationship coefficient between the sensor and the cable force. In the rest of the paper, the sensing principle and fabrication procedure are described. A series of tests are conducted to verify the sensing performance of the smart cable. Finally, the dynamic monitoring and long-term monitoring of the cable force in the cable-supported grid system of Dalian Suoyuwan Football Stadium are carried out by using the smart cable, and the stability and safety of the structure are evaluated by the monitoring results

Data-Enabled Tire-Road Friction Estimation Based on Explainable Dynamics Mechanism under Straight Stationary Driving Maneuvers

The tire-road friction coefficient (TRFC) is the critical parameter that significantly improves the control performance of distributed electric vehicles. Nonetheless, achieving precise TRFC estimation during straight stationary driving maneuvers, characterized by constant longitudinal speed (e.g., where the longitudinal acceleration is nearly zero) on a straight road, poses a particularly formidable challenge. In the paper, we propose a new learning strategy that leverages multi-domain fusion feature extraction in both the time domain and time-frequency domain to estimate the TRFC during straight stationary driving maneuvers. Specifically, the frequency response function of the in-wheel-motor-drive system first is inferred from the longitudinal dynamics model and single wheel dynamics model. Then, the input selection of learning strategy is determined through frequency response characteristics analysis and explainable dynamics mechanism. In addition, a parallel spatial-temporal convolutional neural network (PSTCNN) is built to extract features in both the time domain and in the time-frequency domain, respectively. Finally, the TRFC learning strategy is verified by experimental tests on different road surfaces. Our results demonstrate that the proposed methodology is capable of estimating the TRFC with a lower error than the traditional learning-based method and the classical slip-slope method.</p