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Meningeal lymphatic vessel crosstalk with central nervous system immune cells in aging and neurodegenerative diseases

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Neural Regeneration Research 2025年第3期20卷 763-778页

作者： Minghuang Gao xinyue wang Shijie Su Weicheng Feng Yaona Lai Kongli Huang Dandan Cao Qi wang Science and Technology Innovation Center Guangzhou University of Chinese MedicineGuangzhouGuangdong ProvinceChina Institute of Clinical Pharmacology Guangzhou University of Chinese MedicineGuangzhouGuangdong ProvinceChina The Sixth Affiliated Hospital Sun Yat-sen UniversityGuangzhouGuangdong ProvinceChina

Meningeal lymphatic vessels form a relationship between the nervous system and periphery, which is relevant in both health and disease. Meningeal lymphatic vessels not only play a key role in the drainage of brain metabolites but also contribute to antigen delivery and immune cell activation. The advent of novel genomic technologies has enabled rapid progress in the characterization of myeloid and lymphoid cells and their interactions with meningeal lymphatic vessels within the central nervous system. In this review, we provide an overview of the multifaceted roles of meningeal lymphatic vessels within the context of the central nervous system immune network, highlighting recent discoveries on the immunological niche provided by meningeal lymphatic vessels. Furthermore, we delve into the mechanisms of crosstalk between meningeal lymphatic vessels and immune cells in the central nervous system under both homeostatic conditions and neurodegenerative diseases, discussing how these interactions shape the pathological outcomes. Regulation of meningeal lymphatic vessel function and structure can influence lymphatic drainage, cerebrospinal fluid-borne immune modulators, and immune cell populations in aging and neurodegenerative disorders, thereby playing a key role in shaping meningeal and brain parenchyma immunity.

关键词： central nervous system meningeal lymphatic vessels immunity myeloid cells lymphatic cells neurodegenerative disease

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Spatial Distribution Pattern and Influencing Factors of Bed-and-breakfasts(B&Bs)from the Perspective of Urban-rural Differences:A Case Study of Jiaodong Peninsula,China

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Chinese Geographical Science 2024年第4期34卷 752-763页

作者： wang xinyue MA Qian Management College Ocean University of ChinaQingdao 266100China

There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial *** identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration *** study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to *** results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to ***,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and *** B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level *** the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban ***,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial *** terms of differences,populati

关键词： urban-rural bed-and-breakfasts(B&Bs) spatiotemporal evolution density-based spatial clustering of applications with noise(DBSCAN)model multi-scale geographically weighted regression(MGWR) Jiaodong Peninsula China

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Damage-induced material softening and its effect on seismic performance of steel structures

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Science China(Technological Sciences) 2016年第10期59卷 1559-1572页

作者： wang xinyue SUN Bin LI ZhaoXia Department of Engineering Mechanics Jiangsu Key Laboratory of Engineering Mechanics Southeast University

A numerical approach for simulating the seismic performance of steel truss structures, considering damage-induced material softening, is developed based on a ductile damage constitutive model by applying the backward Euler explicit algorithm. It is implemented in ABAQUS through a user-defined material subroutine, by which damage evolution could be incorporated into the analysis of seismic performance of steel structures. The case study taken up here is the investigation of a steel connection with a reduced beam section(RBS) and a steel frame with such connections. The material softening effect during the failure process is particularly investigated. The results show that material softening in the vulnerable zone has a significant effect on the distribution of stress and strain fields, as well as on the carrying capacity of the steel connection with RBS. Further, material softening is found to have almost negligible effect on the seismic performance of the steel frame in the early stages of the loading process, but has a large effect when the steel frame is about to fail. These findings offer a practical reference for the assessment of seismic structural failure, and help in understanding the damage mechanism of steel structures under seismic loading.

关键词： constitutive model damage evolution damage-induced material softening seismic response steel structure

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Low and non-uniform illumination color image enhancement using weighted guided image filtering

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Computational Visual Media 2021年第4期7卷 529-546页

作者： Qi Mu xinyue wang Yanyan Wei Zhanli Li Xi’an University of Science and Technology Xi’an 710054China Shanghai Pudong Development Bank Application Development Service Sub-center Xi’an 710054China

In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform *** these methods are applied to each RGB channel independently,imbalanced inter-channel enhancements(color distortion)can often be observed in the resulting *** the other hand,images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring,halos,and *** address these problems,an improved RGB color image enhancement method is proposed for images captured under nonuniform illumination or in poor visibility,based on weighted guided image filtering(WGIF).Unlike the conventional retinex algorithm and its variants,WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component;it avoids local blurring and halo artifacts due to its anisotropy and adaptive local *** limit color distortion,RGB images are first converted to HSI(hue,saturation,intensity)color space,where only the intensity channel is enhanced,before being converted back to RGB space by a linear color restoration *** results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination,with better visual quality and objective evaluation scores than from comparator *** is also efficient due to use of a linear color restoration algorithm.

关键词： color image enhancement non-uniform illumination low illumination weighted guided image filter(WGIF) color restoration

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Influence of Sputtered Nanocrystalline Coating on Oxidation and Hot Corrosion of a Nickel-based Superalloy M951

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Journal of Materials Science & Technology 2014年第9期30卷 867-877页

作者： xinyue wang Li Xin Fuhui wang Shenglong Zhu Hua Wei Xiaolan wang School of Materials and Metallurgy Northeastern University State Key Laboratory for Corrosion and Protection Institute of Metal ResearchChinese Academy of Sciences

The isothermal and cyclic oxidation behaviors in air and hot corrosion behaviors in Na2SO4 ＋ 25 wt% K2SO4 salt of M951 cast superalloy and a sputtered nanocrystalline coating of the same material were studied. Scanning electron microscopy, energy dispersive X-ray spectroscope, X-ray diffraction, and transmission electron microscopy were employed to examine the morphologies and phase composition of the M951 alloy and nanocrystalline coating before and after oxidation and hot corrosion. The as-sputtered nanocrystalline layer has a homogeneous y phase structure of very fine grain size （30-200 nm） with the preferential growth texture of （111） parallel to the interface. Adherent AI203 rich oxide scale formed on the cast M951 alloy and its sputtered coating after isothermal oxidation at 900 and 1000 ℃. However, when being isothermal oxidized at 1100℃ and cyclic oxidized at 1000 ℃, the oxide scale formed on the cast alloy was a mixture of NiO, NiAl2O4, Al2O3 and Nb205 and spalled seriously, while that formed on the sputtered coating mainly consisted of Al2O3 and was very adherent. Nanocrystallization promoted rapid formation of Al2O3 scale during the early stage of oxidation and enhanced the adhesion of the oxide scale, thus improved the oxidation resistance of the substrate alloy. Serious corrosion occurred for the cast alloy. The sputtered nanocrystalline coating apparently improved the hot corrosion resistance of the cast alloy in the mixed sulfate by the formation of a continuous Al2O3 and Cr2O3 mixed oxide layer on the surface of the coating, and the pre- oxidation treatment of the coating led to an even better effect.

关键词： M951 alloy Sputtered nanocrystalline coating Oxidation Corrosion Pre-oxidation

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Important sampling based active learning for imbalance classification

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Science China(Information Sciences) 2020年第8期63卷 196-209页

作者： xinyue wang Bo LIU Siyu CAO Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University College of Information Science and Technology Hebei Agricultural University

Imbalance in data distribution hinders the learning performance of classifiers. To solve this problem, a popular type of methods is based on sampling(including oversampling for minority class and undersampling for majority class) so that the imbalanced data becomes relatively balanced data. However,they usually focus on one sampling technique, oversampling or undersampling. Such strategy makes the existing methods suffer from the large imbalance ratio(the majority instances size over the minority instances size). In this paper, an active learning framework is proposed to deal with imbalanced data by alternative performing important sampling(ALIS), which consists of selecting important majority-class instances and generating informative minority-class instances. In ALIS, two important sampling strategies affect each other so that the selected majority-class instances provide much clearer information in the next oversampling process, meanwhile the generated minority-class instances provide much more sufficient information for the next undersampling procedure. Extensive experiments have been conducted on real world datasets with a large range of imbalance ratio to verify ALIS. The experimental results demonstrate the superiority of ALIS in terms of several well-known evaluation metrics by comparing with the state-of-the-art methods.

关键词： imbalance classification important sampling active learning oversampling undersampling

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Siamese transformer with hierarchical concept embedding for fine-grained image recognition

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Science China(Information Sciences) 2023年第3期66卷 188-203页

作者： Yilin LYU Liping JING Jiaqi wang Mingzhe GUO xinyue wang Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Alibaba Group

Distinguishing the subtle differences among fine-grained images from subordinate concepts of a concept hierarchy is a challenging task. In this paper, we propose a Siamese transformer with hierarchical concept embedding(STr HCE), which contains two transformer subnetworks sharing all configurations,and each subnetwork is equipped with the hierarchical semantic information at different concept levels for fine-grained image embeddings. In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer. The other subnetwork is for finer-scale patches, which are adaptively sampled from the discriminative regions, to capture subtle yet discriminative visual cues and eliminate redundant information. STr HCE connects the two subnetworks through a score margin adjustor to enforce the most discriminative regions generating more confident predictions. Extensive experiments conducted on four commonly-used benchmark datasets, including CUB-200-2011, FGVC-Aircraft, Stanford Dogs, and NABirds, empirically demonstrate the superiority of the proposed STr HCE over state-of-the-art baselines.

关键词： fine-grained image recognition transformer hierarchical concept embedding adaptive sampling Siamese network

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Deep Reinforcement Learning Based Spectrum Prediction for Bursty Bands

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China Communications 2023年第7期20卷 241-257页

作者： Tao Peng Chao Yang Peiliang Zuo xinyue wang Rongrong Qian Wenbo wang Key Laboratory of Universal Wireless Communication Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Department of Electronics and Communication Engineering Beijing Institute of Electronic Science and TechnologyBeijing 100070China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum ***,currently utilized prediction methods are not well applied to spectrum with high burstiness,as parameters of prediction models cannot be adjusted *** paper studies the prediction problem of bursty ***,we first collect real Wi Fi transmission data in 2.4GHz Industrial,Scientific,Medical(ISM)band which is considered to have bursty *** analysis of the data indicates that the spectrum occupancy law of the data is time-variant,which suggests that the performance of commonly used single prediction model could be *** that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance,we then propose a deep-reinforcement learning based multilayer perceptron(DRL-MLP)method to address this matching *** state space of the method is composed of feature vectors,and each of the vectors contains multi-dimensional feature ***,the action space consists of several multilayer perceptrons(MLPs)that are trained on the basis of multiple classified data *** finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed *** results demonstrate that the proposed method significantly outperforms the stateof-the-art methods in terms of the prediction accuracy.

关键词： spectrum prediction burstiness ISM band IEEE 802.11 deep-reinforcement learning multilayer perceptron

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A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system

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Science China(Information Sciences) 2016年第4期59卷 160-171页

作者： Zhongwei TAN Chuanchuan YANG Yuliang LI Yan YAN Changhong HE xinyue wang Ziyu wang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Information Science and Technology Xiamen University College of Information and Electrical Engineering China Agricultural University

Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches(86% reducing for a 0.5 min visual tracking). Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme(edge detection), and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.

关键词： visual tracking camera pose estimation fiber-optic gyroscope low-complexity sensor fusion

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Spatio-temporal Evolution and Driving Factors of the High-quality Development of Provincial Tourism in China

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Chinese Geographical Science 2022年第5期32卷 896-914页

作者： wang xinyue wang Mengmeng LU Xuejing GUO Lizhen ZHAO Ruixin JI Ranran Management College Ocean University of ChinaQingdao 266100China

Accelerating the promotion of high-quality development of tourism(HQDT) is of great significance to the sustainable development of tourism. This paper defined the concept of HQDT, and then built an evaluation system for HQDT measurement to analyze the spatio-temporal evolution characteristics of China’s HQDT based on provincial panel data from 2010 to 2019, using Geodetector to explore the similarities and differences between driving factors of HQDT and tourism development scale(TDS). The results show that:1) Taking the development concepts of innovation, coordination, green, openness and sharing as the guidance, and considering the organic unity of quantity and quality, the evaluation index system of the HQDT consists of six dimensions of economic stability, innovation driving, coordination and linkage, green and sustainability, openness and cooperation, and sharing and harmony, which respectively represent the basis, momentum, means, orientation, direction and purpose of the HQDT;2) The level of China’s HQDT shows an upward trend, presenting the characteristics of eastern region > central region > western region > northeastern region in 2019. The regional differences in China’s HQDT show a downward trend, and the intra-regional differences have replaced the inter-regional differences as the main source of regional differences;3) China’s HQDT shows the characteristics of higher in the east and lower in the west along the Hu line, while the improvement speed of HQDT shows the characteristics of faster in the west and slower in the east, making the decline of east-west differentiation of China’s HQDT and the movement of the gravity center towards southwest;4) Both HQDT and TDS are obviously driven by tourism capital investment and regional consumption. In terms of differences, the HQDT is more driven by government guidance, innovation driving force, and opening up, while the main driving factors of TDS are more biased toward capital elements and hardware facilities,

关键词： high-quality development of tourism(HQDT) spatio-temporal evolution Geodetector tourism development scale(TDS)

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