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

UWB/GNSS-based cooperative positioning method for V2X applications

Limited availability of GNSS signals in urban canyons is a challenge for the implementation of many positioning-based traffic safety applications, and V2X technology provides an alternative solution to resolve this problem. As a key communication component in V2X technology, Dedicated Short Range Communication (DSRC) not only allows vehicles to exchange their position, but also traffic safety related information such as real-time congestion, up-to-date accident details, speed limits, etc. This position and traffic information could underpin various traffic safety applications - for instance, lane departure warnings, potential collision avoidance, and traffic congestion warnings. By taking advantage of DSRC, a vehicle in a GNSS denied environment is able to calculate its position using the assistance of other vehicles with sufficient GNSS signals to fix their locations. The concept of cooperative positioning, which is also called collaborative positioning, has been proposed to achieve this goal

A novel dynamical filter based on multi-epochs least-squares to integrate the carrier phase and pseudorange observation for GNSS measurement

© 2020 by the authors. The high noise of pseudorange and the ambiguity of carrier phase observation restrain the GNSS (Global Navigation Satellite System) application in military, industrial, and agricultural, to name a few. Thus, it is crucial for GNSS technology to integrate the pseudorange and carrier phase observations. However, the traditional method proposed by Hatch has obtained only a low convergence speed and precision. For higher convergence speed and precision of the smoothed pseudorange, aiming to improve positioning accuracy and expand the application of GNSS, we introduced a new method named MELS (Multi-Epochs Least-Squares) that considered the cross-correlation of the estimating parameters inspired by DELS (Double-Epochs Least-Square). In this study, the ionospheric delay was compensated, and so its impact was limited to the performance of the filters, and then exploited the various filters to integrate carrier phase observation and pseudorange. We compared the various types of Hatch's filter and LS (Least-Square) methods using simulation datasets, which confirmed that the types of LS method provided a smaller residual error and a faster convergence speed than Hatch's method under various precisions of raw pseudorange. The experimental results from the measured GNSS data showed that LS methods provided better performance than Hatch's methods at E and U directions and a lower accuracy at N direction. Nevertheless, the types of LS method and Hatch's methods improved about 12% and 9-10% at the 3D direction, respectively, which illustrated the accumulating improvement at the enhanced directions was more than the decreased direction, proving that the types of LS method resulted to better performance than the Hatch's filters. Additionally, the curve of residual and precision based on various LS methods illustrated that the MELS only provided a millimeter accuracy difference compared with DELS, which was proved by the simulated and measured GNSS datasets

A tightly-coupled GPS/INS/UWB cooperative positioning sensors system supported by V2I communication

This paper investigates a tightly-coupled Global Position System (GPS)/Ultra-Wideband (UWB)/Inertial Navigation System (INS) cooperative positioning scheme using a Robust Kalman Filter (RKF) supported by V2I communication. The scheme proposes a method that uses range measurements of UWB units transmitted among the terminals as augmentation inputs of the observations. The UWB range inputs are used to reform the GPS observation equations that consist of pseudo-range and Doppler measurements and the updated observation equation is processed in a tightly-coupled GPS/UWB/INS integrated positioning equation using an adaptive Robust Kalman Filter. The result of the trial conducted on the roof of the Nottingham Geospatial Institute (NGI) at the University of Nottingham shows that the integrated solution provides better accuracy and improves the availability of the system in GPS denied environments. RKF can eliminate the effects of gross errors. Additionally, the internal and external reliabilities of the system are enhanced when the UWB observables received from the moving terminals are involved in the positioning algorithm

HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet.Comment: Accepted by NeurIPS 202

High-dimensional quantum key distribution based on mutually partially unbiased bases

We propose a practical high-dimensional quantum key distribution protocol based on mutually partially unbiased bases utilizing transverse modes of light. In contrast to conventional protocols using mutually unbiased bases, our protocol uses Laguerre-Gaussian and Hermite-Gaussian modes of the same mode order as two mutually partially unbiased bases for encoding, which leads to a scheme free from mode-dependent diffraction in long-distance channels. Since only linear and passive optical elements are needed, our experimental implementation significantly simplifies qudit generation and state measurement. Since this protocol differs from conventional protocols using mutually unbiased bases, we provide a security analysis of our protocol

Exploring the Intersection of Complex Aesthetics and Generative AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic Era

This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact. Through literature reviews, case studies, surveys, and text analysis, it examines art and technology applications in rural contexts and identifies key challenges. The study finds artworks often fail to resonate locally, while reliance on external artists limits sustainability. Hence, nurturing grassroots "artist villagers" through AI is proposed. Our approach involves training machine learning on subjective aesthetics to generate culturally relevant content. Interactive AI media can also boost tourism while preserving heritage. This pioneering research puts forth original perspectives on the intersection of AI and aesthetics to invigorate rural culture. It advocates holistic integration of technology and emphasizes AI's potential as a creative enabler versus replacement. Ultimately, it lays the groundwork for further exploration of leveraging AI innovations to empower rural communities. This timely study contributes to growing interest in emerging technologies to address critical issues facing rural China.Comment: Accepted by 2023 the 1st International Conference on AI-generated Content (AIGC2023