Ganoderma lucidum and Professor Zhi-Bin Lin

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000209177700050&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Pharmacology & PharmacySCI(E)1EDITORIAL MATERIALnull

miRNA-124 in Immune System and Immune Disorders

In recent years, miR-124 has emerged as a critical modulator of immunity and inflammation. Here, we summarize studies on the function and mechanism of miR-124 in the immune system and immunity-related diseases. They indicated that miR-124 exerts a crucial role in the development of immune system, regulation of immune responses, and inflammatory disorders. It is evident that miR-124 may serve as an informative diagnostic biomarker and therapeutic target in the future

In-Situ Radar Observation of Shallow Lunar Regolith at the Chang’E-5 Landing Site : Research Progress and Perspectives

Funding Information: This work is supported by the National Natural Science Foundation of China (Grant No. 42241139 and 42004099), the Opening Fund of the Key Laboratory of Lunar and Deep Space Exploration, Chinese Academy of Sciences (No. LDSE202005), the National Innovation and Entrepreneurship Training Program for College Students (No. 202310590016), the Fund of Shanghai Institute of Aerospace System Engineering (No. PZ_YY_SYF_JY200275), and the Shenzhen Municipal Government Investment Project (No. 2106_440300_04_03_901272).Peer reviewedPublisher PD

Performance analysis of the generalised projection identification for time-varying systems

© The Institution of Engineering and Technology 2016. The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the timevarying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided

Beneficial Effects of Anisodamine in Shock Involved Cholinergic Anti-Inflammatory Pathway

Anisodamine, an antagonist of muscarinic receptor, has been used therapeutically to improve blood flow in circulatory disorders such as septic shock in China since 1965. The main mechanism of anisodamine for anti-shock proposed in Pharmacology for Chinese medical students is to improve blood flow in the microcirculation. Here, we suggest a new mechanism for its anti-shock effect. That is, anisodamine, by blocking muscarinic receptor, results in rerouting of acetylcholine to α7 nicotinic acetylcholine receptor (α7nAChR) bringing about increased acetylcholine-mediated activation of α7nAChR and the cholinergic anti-inflammatory pathway

Uncertainty Quantification of Collaborative Detection for Self-Driving

Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double-M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double-M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4X improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github.io/double-m-quantification.Comment: 6 pages, 3 figure

Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation

Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to improve detection accuracy and reduce uncertainty by leveraging the viewpoints of multiple agents. However, little attention has been paid to how to leverage the uncertainty quantification from COD to enhance MOT performance. In this paper, as the first attempt to address this challenge, we design an uncertainty propagation framework called MOT-CUP. Our framework first quantifies the uncertainty of COD through direct modeling and conformal prediction, and propagates this uncertainty information into the motion prediction and association steps. MOT-CUP is designed to work with different collaborative object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a comprehensive collaborative perception dataset, and demonstrate a 2% improvement in accuracy and a 2.67X reduction in uncertainty compared to the baselines, e.g. SORT and ByteTrack. In scenarios characterized by high occlusion levels, our MOT-CUP demonstrates a noteworthy $4.01\%$ improvement in accuracy. MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation. Our code is public on https://coperception.github.io/MOT-CUP/.Comment: This paper has been accepted by IEEE Robotics and Automation Letter

Intensity measurement bend sensors based on periodically tapered soft glass fibers

We demonstrate a novel technique for tapering periodically an all-solid soft glass fiber, consisting of two types of lead silicate glasses, by the use of a focused CO2 laser beam and investigate the bend sensing applications of the periodically-tapered soft glass fiber. Such a soft glass fiber with periodic microtapers could be used to develop promising bend sensors with a sensitivity of -27.75 µW/m-1 by means of measuring the bend-induced change of light intensity. The proposed bend sensor exhibits a very low measurement error of down to ±1%