Pioglitazone Attenuates Vascular Fibrosis in Spontaneously Hypertensive Rats

Objective. We sought to investigate whether the peroxisome proliferator-activated receptor-γ (PPAR-γ) ligand pioglitazone can attenuate vascular fibrosis in spontaneously hypertensive rats (SHRs) and explore the possible molecular mechanisms. Methods. SHRs (8-week-old males) were randomly divided into 3 groups (n = 8 each) for treatment: pioglitazone (10 mg/kg/day), hydralazine (25 mg/kg/day), or saline. Normal male Wistar Kyoto (WKY) rats (n = 8) served as normal controls. Twelve weeks later, we evaluated the effect of pioglitazone on vascular fibrosis by Masson's trichrome and immunohistochemical staining of collagen III and real-time RT-PCR analysis of collagen I, III and fibronectin mRNA.Vascular expression of PPAR-γ and connective tissue growth factor (CTGF) and transforming growth factor-β (TGF-β) expression were evaluated by immunohistochemical staining, western blot analysis, and real-time RT-PCR. Results. Pioglitazone and hydralazine treatment significantly decreased systolic blood pressure in SHRs. Masson's trichrome staining for collagen III and real-time RT-PCR analysis of collagen I, III and fibronectin mRNA indicated that pioglitazone significantly inhibited extracellular matrix production in the aorta. Compared with Wistar Kyoto rats, SHRs showed significantly increased vascular CTGF expression. Pioglitazone treatment significantly increased PPAR-γ expression and inhibited CTGF expression but had no effect on TGF-β expression. Conclusions. The results indicate that pioglitazone attenuated vascular fibrosis in SHRs by inhibiting CTGF expression in a TGF-β-independent mechanism

Investigation of Electron-Phonon Coupling in Epitaxial Silicene by In-situ Raman Spectroscopy

In this letter, we report that the special coupling between Dirac fermion and lattice vibrations, in other words, electron-phonon coupling (EPC), in silicene layers on Ag(111) surface was probed by an in-situ Raman spectroscopy. We find the EPC is significantly modulated due to tensile strain, which results from the lattice mismatch between silicene and the substrate, and the charge doping from the substrate. The special phonon modes corresponding to two-dimensional electron gas scattering at edge sites in the silicene were identified. Detecting relationship between EPC and Dirac fermion through the Raman scattering will provide a direct route to investigate the exotic property in buckled two-dimensional honeycomb materials.Comment: 15 pages, 4 figure

Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce time consumption and guarantee control performance.Comment: 25 pages, 12 figure

Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure

Light Detection and Ranging (LiDAR) systems are known to capture high density and accuracy data much more efficiently than other surveying methods. Therefore they are used for many applications, e.g. mobile mapping and surveying, 3D modelling, hazard detection, etc. However, while the accuracy of the laser measurements is very high, the accuracy of the resulting 3D point cloud is greatly affected by the geo-referencing accuracy. This is especially problematic for mobile laser scanning systems, where the LiDAR is installed on a moving platform, e.g. a vehicle, and the point cloud is geo-referenced by the data provided by a navigation system. Owing to the complexity of the surrounding environments and external conditions, the accuracy of the navigation system varies and thereby changes the quality of the point cloud. Conventional methods for correcting the point cloud accuracy either rely heavily on manual work or semi-automatic registration methods. While they can provide geo-referencing under different conditions, each has their own problems. This paper presents a semi-automated geo-referencing trajectory correction method by extracting features from the pre-processed point cloud and integrating this information to reprocess the navigation trajectory which is then able to produce better quality point clouds. The method deals with the changing errors within a point cloud dataset, and reducing the trajectory error from metre level to decimetre level, improving the accuracy by at least 56%. The accuracy of the regenerated point cloud then becomes suitable for many accuracy-demanding monitoring and change detection applications

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images

Monitoring rail infrastructure using multisensor navigation on a moving platform and autonomous robots

RailSat aims to use Global Navigation Satellite System (GNSS) to monitor and maintain railway assets and its surrounding environment by railway asset owners and/or other relevant stakeholders. The rail sector is looking for continuous monitoring solutions which have no impact on the train service, both wayside (track bound) and onboard (train bound), which require accurate positioning while travelling at high speeds (>120kmh). This paper focuses on the combination of positioning data from traditional GNSS/INS system with processed LIDAR point cloud and discusses real-life results from the Snake Pass, Peak District, England. Data have been collected using a dedicated multisensory van but the nature of the road allows us to draw conclusions relevant to the rail industry. This paper discusses the proposed deployment of a mobile LiDAR monitoring system consisting of a set of laser scanners and a navigation component. While the LIDAR component is capable of centimetre accuracy, it is limited by the navigation accuracy, predominantly affected by the difficult railway environment, frequent multipath and NLOS interference combined with a loss of signal next to the monitoring structures itself (bridges, cuttings, tunnels, embankments etc.), making precise positioning the biggest challenge. The proposed navigation system combines IMU positioning system with a computer vision system capable of localisation using features in the natural environment. This paper outlines the combination of the proposed navigation system with the LIDAR’s information, which provides two ways of correcting navigation trajectory in post-processing