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Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation 认领 引用
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作者 Xin Luo Fanghui Bi Tiantian He 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1642-1656,共15页
Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent ap... Quality of service(Qo S)data that characterize historical user-service invocations that vary over time are vital to discovering patterns of cloud services and understanding user behaviors.Though effective,prevalent approaches never consider higher-order spatiotemporal connectivity within Qo S data,thus suffering from inferior performance.To address this critical issue,this paper presents spatiotemporal graph convolutional network(GCN)that is equipped with the functionality of latent factorization of tensors(SGLFT).It is achieved by introducing three key innovations:1)Proposing a tensor graph convolution based on the generalized tensor product technique for uniformly modeling the temporal and spatial patterns within dynamic user-service graphs;2)Incorporating the built layer-wise graph convolution into tensor factorization for efficiently capturing the implied spatiotemporal high-order connectivity;and 3)Developing a nodelevel attention pooling mechanism to perceive feature differences among neighbors and across time slots.Theoretical derivations are conducted to demonstrate that the expressivity of the graph neural network proposed in this paper is evidently higher than that of vanilla GCNs.Empirical studies on eight large-scale testing cases arising from two real-world dynamic Qo S datasets show that SGLFT substantially outperforms state-of-the-art Qo S estimators regarding estimation accuracy for missing dynamic QoS data. 展开更多
关键词 Cloud service data science dynamic quality-of-service estimation graph convolutional networks(GCNs) latent factorization of tensors latent feature analysis non-euclidean data representation learning tensor product
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A Spatial-Temporal Traffic Prediction Algorithm Based on Non-Negative Tensor Factorization 认领 引用
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作者 Xiaoxiong Yang Dingde Jiang +1 位作者 Yi Zhang Zhihan Lyu 《Computers, Materials & Continua》 SCIE EI 2026年第8期2432-2445,共14页
The advancement of communication technology has made traffic engineering a critical issue in network systems.The traffic matrix is essential data that supports traffic engineering.The functionality of routing planning... The advancement of communication technology has made traffic engineering a critical issue in network systems.The traffic matrix is essential data that supports traffic engineering.The functionality of routing planning,network monitoring,and other modules within intelligent network management systems relies heavily on the network traffic matrix.However,real-time measurement of the network traffic matrix is costly and often suffers from missing or anomalous values.Consequently,long-term network traffic prediction presents significant challenges.Existing methods often fail to comprehensively address the multidimensional characteristics of traffic and the computational costs of the algorithms.To address these issues,we propose an efficient traffic prediction algorithm based on tensor factorization.First,we introduce a non-negative tensor factorization algorithm that accounts for link errors.This algorithm captures the spatial-temporal characteristics of traffic from different modes,thereby enhancing prediction efficiency.Next,we integrate the tensor factor matrix with a seasonal differential autoregressive moving average model in the temporal mode to identify traffic trends and complete the traffic prediction.Experimental results based on real data demonstrate that our algorithm performs exceptionally well in multi-step predictions and in capturing abnormal fluctuations. 展开更多
关键词 Flow tensor factorization multidimensional features traffic prediction
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Privacy-Preserving Parallel Non-Negative Matrix Factorization with Edge Computing 认领 引用
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作者 Wenxuan Yu Wenjing Gao +2 位作者 Jiuru Wang Rong Hao Jia Yu 《Computers, Materials & Continua》 SCIE EI 2026年第6期498-518,共21页
Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy compu... Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers,effectively solving the problem of local computing difficulties.However,the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation,resulting in low efficiency on the server side.In order to improve the efficiency of outsourcing computation,we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers.We adopt the matrix blocking technique to divide the computation task into multiple subtasks,and design the NMF parallel computation algorithm based on the multiplication updating rule.The proposed scheme implements the parallel outsourcing of non-negative matrix factorization based on multiple edge servers.We use random permutation matrices to encrypt original matrix,thereby protecting data privacy.In addition,we utilize the iterative nature of the NMF algorithm for result verification.Theoretical analysis and experimental results prove the advantages of the proposed scheme. 展开更多
关键词 Secure outsourcing computation non-negative matrix factorization parallel outsourcing edge computing
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Lightweight and Explainable Anomaly Detection in CAN Bus Traffic via Non-Negative Matrix Factorization 认领 引用
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作者 Anandkumar Balasubramaniam Seung Yeob Nam 《Computers, Materials & Continua》 SCIE EI 2026年第6期1263-1306,共44页
The increasing connectivity of modern vehicles exposes the in-vehicle controller area network(CAN)bus to various cyberattacks,including denial-of-service,fuzzy injection,and spoofing attacks.Existing machine learning ... The increasing connectivity of modern vehicles exposes the in-vehicle controller area network(CAN)bus to various cyberattacks,including denial-of-service,fuzzy injection,and spoofing attacks.Existing machine learning and deep learning intrusion detection systems(IDS)often rely on labeled data,struggle with class imbalance,lack interpretability,and fail to generalize well across different datasets.This paper proposes a lightweight and interpretable IDS framework based on non-negative matrix factorization(NMF)to address these limitations.Our contributions include:(i)evaluating NMF as both a standalone unsupervised detector and an interpretable feature extractor(NMF-W)for classical,unsupervised,and deep sequence models;(ii)providing comprehensive benchmarking on the car-hacking dataset(CHD),demonstrating improved robustness in mixed-attack and cross-attack scenarios,with class imbalance addressed through oversampling and class weighting;(iii)offering a component-level interpretability analysis that links NMF factors to meaningful CAN traffic patterns;and(iv)validating cross-dataset transferability on the offset-ratio and time interval-based intrusion detection system(OTIDS)dataset.Additional ablation and efficiency studies confirm the practical feasibility of deploying NMF-based IDS on embedded automotive controllers.Overall,this work presents a balanced IDS solution that combines detection accuracy,computational efficiency,and explainability,thereby advancing the security of in-vehicle networks. 展开更多
关键词 Anomaly detection CAN bus explainable AI intrusion detection system(IDS) non-negative matrix factorization(NMF)
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Multi-Aspect Self-Attending Neural Tucker Factorization for Spatiotemporal Representation Learning 认领 引用
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作者 Yikai Hou Peng Tang Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期986-988,共3页
Dear Editor,This letter introduces a neural network-based tensor factorization approach tailored for learning spatiotemporal representations of highdimensional and incomplete(HDI)tensors,namely the multi-aspect self-a... Dear Editor,This letter introduces a neural network-based tensor factorization approach tailored for learning spatiotemporal representations of highdimensional and incomplete(HDI)tensors,namely the multi-aspect self-attending neural Tucker factorization(MASANT).The model is elaborately designed for modeling intricate nonlinear spatiotemporal feature interaction patterns hidden in real world data with a two-fold idea. 展开更多
关键词 incomplete tensor nonlinear interaction real world data learning spatiotemporal representations high dimensional tensor tensor factorization modeling intricate nonlinear spatiotemporal feature interaction patterns neural network
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Robust azimuth ambiguity detection method in SAR images based on non-negative matrix factorization 认领 引用
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作者 LI Jieshuang TAO Mingliang +2 位作者 CUI Lei LIU Yanyang WANG Ling 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期743-754,共12页
Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and... Azimuth ambiguity significantly degrades the quality of synthetic aperture radar images.Sub-look spectral analysis(SSA)is a common ambiguity-detection method,but its performance is limited by threshold sensitivity and the high correlation of specific ambiguities across sub-looks.To overcome these specific limitations,this paper proposes an improved detection method.It first increases the number of sub-looks and constructs a high-dimensional multi-look matrix to enrich the coherence differences between targets and ambiguities.Non-negative matrix factorization is then employed to decompose this matrix,effectively separating the coherent target components from the variably coherent ambiguity components without relying on predefined thresholds.Experimental results on real data demonstrate that the proposed improvements achieve superior azimuth-ambiguity-detection performance compared with conventional SSA methods. 展开更多
关键词 synthetic aperture radar(SAR) azimuth ambiguity multi-look processing nonnegative matrix factorization(NMF)
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Unconstrained Latent Factorization-Based Improved Relief-F 认领 引用
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作者 Wenting Chen Ming Li 《Journal of Electronic Research and Application》 2026年第4期15-21,共7页
Feature selection is essential for dimensionality reduction on big data,but it faces considerable challenges when applied to high-dimensional and sparse datasets.To address these challenges,this paper proposes Unconst... Feature selection is essential for dimensionality reduction on big data,but it faces considerable challenges when applied to high-dimensional and sparse datasets.To address these challenges,this paper proposes Unconstrained Latent Factorization-based Improved Relief-F(ULF-IR),a novel feature selection method tailored for such complex scenarios.The method integrates two main components:(1)a double factorization(DF)-based unconstrained latent factor model is employed to accurately reconstruct missing data without relying on pre-imputation or strict non-negativity constraints;(2)an improved Relief-F(IRelief-F)algorithm assigns reliable importance weights to features,effectively differentiating among highly similar features even in the presence of noise introduced during imputation.Comprehensive experiments on three real-world datasets show that ULF-IR consistently surpasses state-of-the-art methods in both classification accuracy and robustness,demonstrating its effectiveness as a dependable solution for feature selection on high-dimensional,incomplete data. 展开更多
关键词 Feature selection Incomplete data Latent factor Relief-F
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合作猪NTF3基因克隆、生物信息学分析及组织表达谱构建 认领 引用 被引量：2
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作者 闫尊强 鲁亚伟 +1 位作者 滚双宝 王鹏飞 《西北农业学报》 CAS CSCD 北大核心 2025年第1期1-9,共9页
克隆合作猪神经营养因子3基因(Neurotrophin3,NTF3)的编码区序列(Coding region sequence,CDS),预测分析其结构与功能,并检测基因在不同组织中的表达量,构建组织表达谱。通过PCR扩增、Sanger测序等方法获得合作猪NTF3基因CDS区序列,使... 克隆合作猪神经营养因子3基因(Neurotrophin3,NTF3)的编码区序列(Coding region sequence,CDS),预测分析其结构与功能,并检测基因在不同组织中的表达量,构建组织表达谱。通过PCR扩增、Sanger测序等方法获得合作猪NTF3基因CDS区序列,使用生物信息学方法分析其结构,并使用RT-qPCR技术检测其在不同组织中的表达水平。结果显示,合作猪NTF3基因CDS区长774 bp,编码257个氨基酸;等电点(pI)为9.46,属于碱性蛋白;合作猪与猪、牛亲缘关系较近;二级结构主要为α-螺旋和无规则卷曲,三级结构主要由β-折叠、α-螺旋等组成;整条链中亲水性氨基酸残基数量多于疏水性氨基酸残基数量,是亲水性蛋白;无跨膜区域,是非跨膜蛋白;在37~58位氨基酸残基有一个线圈结构域、64~79位有一个低复杂度结构域和144~249位有一个神经生长因子结构域;存在信号肽,推测是分泌蛋白;NTF3基因在合作猪脾脏的表达量最高,回肠表达量最低。 展开更多
关键词 合作猪 NTF3基因 克隆 生物信息学分析
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Neural Tucker Factorization 认领 引用 被引量：1
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作者 Peng Tang Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期475-477,共3页
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-... Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework. 展开更多
关键词 neu tuc f neural tucker factorization latent factorization model high dimensional tensor tucker decomposition framework neural network incomplete tensor latent factorization
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Predicting CircRNA-Disease Associations via Non-Negative Matrix Factorization Fused with Multiple Similarity Networks 认领 引用 被引量：1
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作者 LU Pengli LI Shiying 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第4期709-719,共11页
CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs a... CircRNAs,widely found throughout the human bodies,play a crucial role in regulating various biological processes and are closely linked to complex human diseases.Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis,treatment,and disease prevention.However,existing models have limitations in accurately capturing similarities,handling the sparse and noise attributes of association networks,and fully leveraging bioinformatical aspects from multiple viewpoints.To address these issues,this study introduces a new non-negative matrix factorization-based framework called NMFMSN.First,we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity,respectively.Given the sparse known associations between circRNAs and diseases,we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions.Finally,we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations.Upon conducting 5-fold cross-validation and leave-one-out cross-validation,the AUC values for NMFMSN reach 0.9712 and 0.9768,respectively,outperforming the currently most advanced models.Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases. 展开更多
关键词 circRNA-disease associations circRNA sequence data disease semantic information non-negative matrix factorization
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Latent-Factorization-of-Tensors-Incorporated Battery Cycle Life Prediction 认领 引用
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作者 Minzhi Chen Li Tao +1 位作者 Jungang Lou Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第3期633-635,共3页
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and... Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T). 展开更多
关键词 health management battery pack bp can latent factorization tensors battery cycle life prediction health management phm battery cycle battery pack battery management system bms which
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Changes in factor profiles deriving from photochemical losses of volatile organic compounds:Insight from daytime and nighttime positive matrix factorization ana 认领 引用 被引量：1
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作者 Baoshuang Liu Tao Yang +9 位作者 Sicong Kang Fuquan Wang Haixu Zhang Man Xu Wei Wang Jinrui Bai Shaojie Song Qili Dai Yinchang Feng Philip K.Hopke 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第5期627-639,共13页
Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data ... Substantial effects of photochemical reaction losses of volatile organic compounds(VOCs)on factor profiles can be investigated by comparing the differences between daytime and nighttime dispersion-normalized VOC data resolved profiles.Hourly speciated VOC data measured in Shijiazhuang,China from May to September 2021 were used to conduct study.The mean VOC concentration in the daytime and at nighttime were 32.8 and 36.0 ppbv,respectively.Alkanes and aromatics concentrations in the daytime(12.9 and 3.08 ppbv)were lower than nighttime(15.5 and 3.63 ppbv),whereas that of alkenes showed the opposite tendency.The concentration differences between daytime and nighttime for alkynes and halogenated hydrocarbonswere uniformly small.The reactivities of the dominant species in factor profiles for gasoline emissions,natural gas and diesel vehicles,and liquefied petroleum gas were relatively low and their profiles were less affected by photochemical losses.Photochemical losses produced a substantial impact on the profiles of solvent use,petrochemical industry emissions,combustion sources,and biogenic emissions where the dominant species in these factor profiles had high reactivities.Although the profile of biogenic emissions was substantially affected by photochemical loss of isoprene,the low emissions at nighttime also had an important impact on its profile.Chemical losses of highly active VOC species substantially reduced their concentrations in apportioned factor profiles.This study results were consistent with the analytical results obtained through initial concentration estimation,suggesting that the initial concentration estimation could be the most effective currently availablemethod for the source analyses of active VOCs although with uncertainty. 展开更多
关键词 Volatile organic compounds Dispersion normalization Photochemical loss Factor profile Positive matrix factorization
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Source apportionment of PM2.5 using dispersion normalized positive matrix factorization(DN-PMF)in Beijing and Baoding,China 认领 引用 被引量：1
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作者 Ilhan Ryoo Taeyeon Kim +6 位作者 Jiwon Ryu Yeonseung Cheong Kwang-joo Moon Kwon-ho Jeon Philip K.Hopke Seung-Muk Yi Jieun Park 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2025年第9期395-408,共14页
Fine particulatematter(PM2.5)samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM2.5 in which the average mass concentration exceeded 75μg/m3 were freque... Fine particulatematter(PM2.5)samples were collected in two neighboring cities,Beijing and Baoding,China.High-concentration events of PM2.5 in which the average mass concentration exceeded 75μg/m3 were frequently observed during the heating season.Dispersion Normalized Positive Matrix Factorization was applied for the source apportionment of PM2.5 as minimize the dilution effects of meteorology and better reflect the source strengths in these two cities.Secondary nitrate had the highest contribution for Beijing(37.3%),and residential heating/biomass burning was the largest for Baoding(27.1%).Secondary nitrate,mobile,biomass burning,district heating,oil combustion,aged sea salt sources showed significant differences between the heating and non-heating seasons in Beijing for same period(2019.01.10–2019.08.22)(Mann-Whitney Rank Sum Test P<0.05).In case of Baoding,soil,residential heating/biomass burning,incinerator,coal combustion,oil combustion sources showed significant differences.The results of Pearson correlation analysis for the common sources between the two cities showed that long-range transported sources and some sources with seasonal patterns such as oil combustion and soil had high correlation coefficients.Conditional Bivariate Probability Function(CBPF)was used to identify the inflow directions for the sources,and joint-PSCF(Potential Source Contribution Function)was performed to determine the common potential source areas for sources affecting both cities.These models facilitated a more precise verification of city-specific influences on PM2.5 sources.The results of this study will aid in prioritizing air pollution mitigation strategies during the heating season and strengthening air quality management to reduce the impact of downwind neighboring cities. 展开更多
关键词 Source apportionment Dispersion normalized positive matrix factorization Adjacent cities Inter-city impact Source location Heating season Air quality management
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NTF2在恶性肿瘤发生和治疗中的研究进展 认领 引用
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作者 罗红玉 王健力 +2 位作者 田彩平 魏政丽 毛爱红 《甘肃医药》 2025年第1期9-12,25,共4页
核转运因子2(NTF2)是一种参与核运输的细胞质因子,通过与Ran-GDP结合参与生物大分子核质运输过程,负责将特定蛋白质从细胞质转运至细胞核。近年来,随着对恶性肿瘤发生发展机制的深入研究,越来越多的证据表明,NTF2在多种恶性肿瘤中异常... 核转运因子2(NTF2)是一种参与核运输的细胞质因子,通过与Ran-GDP结合参与生物大分子核质运输过程,负责将特定蛋白质从细胞质转运至细胞核。近年来,随着对恶性肿瘤发生发展机制的深入研究,越来越多的证据表明,NTF2在多种恶性肿瘤中异常表达。NTF2可通过调控核大小及信号转导通路参与细胞周期、凋亡及免疫应答等细胞过程,在肿瘤发生发展和转移过程中发挥关键的推动作用。本文就NTF2的生物学特性、转运机制、异常表达及其参与的信号通路进行综述,并进一步揭示NTF2潜在的治疗可能性,旨在为恶性肿瘤靶向治疗提供一种新思路。 展开更多
关键词 NTF2 恶性肿瘤 生物标志物 靶向治疗
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Nonlinear Control for Unstable Networked Plants in the Presence of Actuator and Sensor Limitations Using Robust Right Coprime Factorization 认领 引用
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作者 Yuanhong Xu Mingcong Deng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第3期516-527,共12页
In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and l... In this paper,a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed.The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise.With this nonlinear control method,we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant,so that bounded input bounded output(BIBO)stability can be guaranteed.Next,continuous-time generalized predictive control(CGPC)is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics.Meanwhile,a second-Do F(degrees of freedom)switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed.By using the CGPC controller that possesses predictive behavior and the second-Do F switched stabilizer,the overall stability of the plant subjected to actuator limitations is guaranteed.To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation,we implement an identity operator definition in the feedback loop to compensate for these adverse effects.Further,a pre-operator is designed to ensure that the plant output tracks the reference input.Finally,the effectiveness of the proposed design scheme is demonstrated by simulations. 展开更多
关键词 Actuator and sensor limitations identity operator definition network-induced limitations robust right coprime factorization unstable plant
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A Comprehensive Review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization 认领 引用
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作者 Qicong Hu Hao Wu Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2399-2426,共28页
High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a c... High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a commercial recommender system or the user-user interactions in a social network services system. The factorization of such an HDI matrix can embed the involved entities into the low-dimensional feature space for acquiring their principal representation, which is a vital task in various application scenes and is often established through the Latent Factor Analysis(LFA). Nevertheless, an HDI matrix can be huge when the corresponding application explodes to involve millions of users, items, or other interactive nodes. In this case, a parallel optimization algorithm is desired for raising the scalability and time efficiency of an LFA model. This paper provides a comprehensive review of the existing parallel optimization algorithms for the LFA model. Specifically, it performs: 1) discussion and summary of these algorithms based on computing architecture and mode, 2) empirical studies of representative models, and3) summary of the current challenges and future directions in this domain. This survey aims to offer an exhaustive review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization, thereby fostering further research in this field. 展开更多
关键词 Data-related application high-dimensional and incomplete(HDI)data high-performance computing latent feature analysis(LFA) matrix factorization parallel optimization algorithm
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Microglia overexpressing brain-derived neurotrophic factor promote vascular repair and functional recovery in mice after spinal cord injury 认领 引用 被引量：3
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作者 Fanzhuo Zeng Yuxin Li +6 位作者 Xiaoyu Li Xinyang Gu Yue Cao Shuai Cheng He Tian Rongcheng Mei Xifan Mei 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第1期365-376,共12页
Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in s... Spinal cord injury represents a severe form of central nervous system trauma for which effective treatments remain limited.Microglia is the resident immune cells of the central nervous system,play a critical role in spinal cord injury.Previous studies have shown that microglia can promote neuronal survival by phagocytosing dead cells and debris and by releasing neuroprotective and anti-inflammatory factors.However,excessive activation of microglia can lead to persistent inflammation and contribute to the formation of glial scars,which hinder axonal regeneration.Despite this,the precise role and mechanisms of microglia during the acute phase of spinal cord injury remain controversial and poorly understood.To elucidate the role of microglia in spinal cord injury,we employed the colony-stimulating factor 1 receptor inhibitor PLX5622 to deplete microglia.We observed that sustained depletion of microglia resulted in an expansion of the lesion area,downregulation of brain-derived neurotrophic factor,and impaired functional recovery after spinal cord injury.Next,we generated a transgenic mouse line with conditional overexpression of brain-derived neurotrophic factor specifically in microglia.We found that brain-derived neurotrophic factor overexpression in microglia increased angiogenesis and blood flow following spinal cord injury and facilitated the recovery of hindlimb motor function.Additionally,brain-derived neurotrophic factor overexpression in microglia reduced inflammation and neuronal apoptosis during the acute phase of spinal cord injury.Furthermore,through using specific transgenic mouse lines,TMEM119,and the colony-stimulating factor 1 receptor inhibitor PLX73086,we demonstrated that the neuroprotective effects were predominantly due to brain-derived neurotrophic factor overexpression in microglia rather than macrophages.In conclusion,our findings suggest the critical role of microglia in the formation of protective glial scars.Depleting microglia is detrimental to recovery of spinal cord injury,whereas targeting brain-derived neurotrophic factor overexpression in microglia represents a promising and novel therapeutic strategy to enhance motor function recovery in patients with spinal cord injury. 展开更多
关键词 angiogenesis apoptosis brain-derived neurotrophic factor colony stimulating factor 1 receptor inflammation microglia motor function spinal cord injury vascular endothelial growth factor
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Organelle symphony:Nuclear factor erythroid 2-related factor 2 and nuclear factor-kappa B in stroke pathobiology 认领 引用 被引量：1
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作者 Ziliang Hu Mingyue Zhao +4 位作者 Hangyu Shen Liangzhe Wei Jie Sun Xiang Gao Yi Huang 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第4期1483-1496,共14页
Strokes include both ischemic stroke,which is mediated by a blockade or reduction in the blood supply to the brain,and hemorrhagic stroke,which comprises intracerebral hemorrhage and subarachnoid hemorrhage and is cha... Strokes include both ischemic stroke,which is mediated by a blockade or reduction in the blood supply to the brain,and hemorrhagic stroke,which comprises intracerebral hemorrhage and subarachnoid hemorrhage and is characterized by bleeding within the brain.Stroke is a lifethreatening cerebrovascular condition characterized by intricate pathophysiological mechanisms,including oxidative stress,inflammation,mitochondrial dysfunction,and neuronal injury.Critical transcription factors,such as nuclear factor erythroid 2-related factor 2 and nuclear factor kappa B,play central roles in the progression of stroke.Nuclear factor erythroid 2-related factor 2 is sensitive to changes in the cellular redox status and is crucial in protecting cells against oxidative damage,inflammatory responses,and cytotoxic agents.It plays a significant role in post-stroke neuroprotection and repair by influencing mitochondrial function,endoplasmic reticulum stress,and lysosomal activity and regulating metabolic pathways and cytokine expression.Conversely,nuclear factor-kappa B is closely associated with mitochondrial dysfunction,the generation of reactive oxygen species,oxidative stress exacerbation,and inflammation.Nuclear factor-kappa B contributes to neuronal injury,apoptosis,and immune responses following stroke by modulating cell adhesion molecules and inflammatory mediators.The interplay between these pathways,potentially involving crosstalk among various organelles,significantly influences stroke pathophysiology.Advancements in single-cell sequencing and spatial transcriptomics have greatly improved our understanding of stroke pathogenesis and offer new opportunities for the development of targeted,individualized,cell typespecific treatments.In this review,we discuss the mechanisms underlying the involvement of nuclear factor erythroid 2-related factor 2 and nuclear factor-kappa B in both ischemic and hemorrhagic stroke,with an emphasis on their roles in oxidative stress,inflammation,and neuroprotection. 展开更多
关键词 inflammation nuclear factor erythroid 2-related factor 2 nuclear factor-kappa B organelles oxidative stress stroke
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Qiweizhigan granule ameliorates metabolic dysfunction-associated steatohepatitis by modulating the galectin 3/tumor necrosis factor receptor-associated factor 6-mediated ferroptosis 认领 引用 被引量：1
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作者 Yu-Nuo Yang Yun-Hong Sun +9 位作者 Ming-Zhe Zhu Yi-Rong Wang Meng Li Kai Wang Jing Ma Li Zhang Dan Hu Wen-Jun Zhou Guang Ji Yan-Qi Dang 《World Journal of Gastroenterology》 SCIE CAS 2026年第17期116-134,共19页
BACKGROUND Qiweizhigan granule(QWZG)is employed in clinical settings for the treatment of metabolic dysfunctionassociated steatohepatitis(MASH).However,the precise biological mechanisms underlying its therapeutic effe... BACKGROUND Qiweizhigan granule(QWZG)is employed in clinical settings for the treatment of metabolic dysfunctionassociated steatohepatitis(MASH).However,the precise biological mechanisms underlying its therapeutic effects are not yet fully elucidated.AIM To assess the efficacy and the mechanism of QWZG against MASH.METHODS Animal models were established,including normal group,a choline-deficient,L-amino acid-defined high-fat diet(CDAHFD)group,and low/medium/high-dose QWZG groups,as well as a rosiglitazone group.Through comprehensive biochemical,histopathological,RNA sequencing,and bioinformatics analyses,galectin 3(LGALS3)was identified as a critical target of QWZG in the treatment of MASH.The level of LGALS3 was quantitatively assessed and validated using Western blotting,real-time quantitative PCR,and immunofluorescence.The role and function of LGALS3 in inflammation and MASH progression were further investigated through gene knockdown,overexpression,iron assay,and transmission electron microscopy.RESULTS QWZG significantly ameliorated liver pathology by reducing steatosis,inflammation,and fibrosis.RNA sequencing analysis identified 1507 co-expressed differentially expressed genes among the CDAHFD,normal,and QWZG groups.Among these,LGALS3 was identified as one of the most significantly altered differentially expressed genes.Both mRNA and protein levels of LGALS3 were elevated in the CDAHFD group compared to the normal group,whereas treatment with QWZG reduced their levels.Analysis of Human Protein Atlas database indicated that LGALS3 was predominantly expressed in Kupffer cells,and was validated by real-time quantitative PCR and immunofluorescence.Furthermore,the level of LGALS3 was significantly increased in lipopolysaccharide-induced RAW264.7 cells,where its overexpression and recombinant LGALS3 protein both significantly enhanced the expression of interleukin-6,interleukin-1β,and tumor necrosis factor-α.LGALS3 overexpression significantly inhibited glutathione peroxidase 4(GPX4)expression,and exacerbated mitochondrial damage,whereas LGALS3 knockdown markedly increased GPX4 level,and significantly reduced the levels of both total iron and ferrous iron.QWZG treatment significantly reduced the levels of malondialdehyde and ferrous iron,increased the levels of superoxide dismutase and glutathione.In addition,QWZG treatment also significantly enhanced GPX4 expression.Mechanistically,LGALS3 knockdown was associated with reduced expression of tumor necrosis factor receptor-associated factor 6(TRAF6)and NOD-like receptor family pyrin domain containing 3,while its overexpression led to increased levels of these proteins.The TRAF6 inhibitor C25-140 effectively reversed the LGALS3-induced alterations in GPX4 expression and iron accumulation.Furthermore,QWZG treatment significantly decreased the levels of TRAF6 and NOD-like receptor family pyrin domain containing 3.CONCLUSION QWZG ameliorated the progression of MASH by modulating ferroptosis through the LGALS3/TRAF6/GPX4 axis. 展开更多
关键词 Qiweizhigan granule Metabolic dysfunction-associated steatohepatitis Galectin 3 Tumor necrosis factor receptorassociated factor 6 Ferroptosis
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Temperature determines the biomass of forest floor bryophytes:A cross-regional investigation in 413 sites 认领 引用 被引量：1
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作者 Zhe Wang Defeng Feng +4 位作者 Yanqiang Jin Mijun Zou Beibei Gao Xin Liu Weikai Bao 《Forest Ecosystems》 SCIE CAS CSCD 2026年第1期212-219,共8页
Understory bryophytes play unique and disproportionately important roles in water retention,biogeochemical cycling,and biodiversity conservation,and serve as bioindicators of environmental health in forest ecosystems.... Understory bryophytes play unique and disproportionately important roles in water retention,biogeochemical cycling,and biodiversity conservation,and serve as bioindicators of environmental health in forest ecosystems.However,biogeographical research on the biomass of forest bryophytes is inadequately studied and has been limited to elevational gradients.We conducted a systematic cross-regional survey of bryophyte biomass across 413 forest sites in Sichuan Province,China.We analyzed how each environmental variable,including climatic and atmospheric factors,overstory covers,and soil nutrients,relates to bryophyte biomass and quantified their relative contributions.The results indicate that,largely similar to previous local investigations and experiments,at a large scale,bryophytes are abundant in forests with lower temperature,nitrogen deposition,vapor pressure deficit,and tree and herb covers,as well as higher light availability.Moreover,bryophyte biomass is positively associated with soil carbon and nitrogen content.These environmental variables are closely related and jointly influence bryophyte biomass,with mean annual temperature being the most significant factor(accounting for 83%of the relative contribution).The biogeographical patterns of bryophyte biomass contribute to deepening our understanding of their adaptations to multiple environmental variables and enable us to predict their responses to global climate change.These patterns also provide essential evidence for establishing more accurate terrestrial vegetation ecosystem models. 展开更多
关键词 Biogeography Environmental factor Nonvascular photoautotrophs Phytomass Relative contribution
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