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Decreased levels of phosphorylated synuclein in plasma are correlated with poststroke cognitive impairment

Neural Regeneration Research 2025年第9期20卷 2598-2610页

作者： Yi Wang Yuning Li Yakun Gu Wei Ma Yuying Guan Mengyuan Guo Qianqian Shao Xunming Ji Jia Liu Department of Clinical Laboratory Beijing Bo’ai HospitalChina Rehabilitation Research CenterCapital Medical UniversityBeijingChina Beijing Institute of Brain Disorders Laboratory of Brain DisordersMinistry of Science and TechnologyCollaborative Innovation Center for Brain DisordersBeijing Advanced Innovation Center for Big Data-based Precision MedicineCapital Medical UniversityBeijingChina Department of Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijingChina

Poststro ke cognitive impairment is a major secondary effect of ischemic stroke in many patients;however,few options are available for the early diagnosis and treatment of this *** aims of this study were to(1)determine the specific relationship between hypoxic andα-synuclein during the occur of poststroke cognitive impairment and(2)assess whether the serum phosphorylatedα-synuclein level can be used as a biomarker for poststro ke cognitive *** found that the phosphorylatedα-synuclein level was significantly increased and showed pathological aggregation around the cerebral infa rct area in a mouse model of ischemic *** addition,neuronalα-synuclein phosphorylation and aggregation were observed in the brain tissue of mice subjected to chronic hypoxia,suggesting that hypoxia is the underlying cause ofα-synuclein-mediated pathology in the brains of mice with ischemic *** phosphorylatedα-synuclein levels in patients with ischemic stroke were significantly lower than those in healt hy subjects,and were positively correlated with cognition levels in patients with ischemic ***,a decrease in serum high-density lipoprotein levels in stroke patie nts was significantly correlated with a decrease in phosphorylatedα-synuclein *** ischemic stroke mice did not show significant cognitive impairment or disrupted lipid metabolism 14 days after injury,some of them exhibited decreased cognitive function and reduced phosphorylatedα-synuclein *** together,our results suggest that serum phosphorylatedα-synuclein is a potential biomarker for poststroke cognitive impairment.

关键词： biomarker high-density lipoprotein ischemic stroke phosphorylatedα-synuclein poststroke cognitive impairment

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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM

arXiv

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arXiv 2025年

作者： Xue, Xin Zhou, Haoyi Chen, Tianyu Zhang, Shuai Long, Yizhou Li, Jianxin Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100190 China

Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neural network design, has been shown effective in tackling the STSF problem. However, the existing NAS methods for STSF focus on generating architectures in a time-consuming data-driven fashion, which heavily limits their ability to use background knowledge and explore the complicated search trajectory. Large language models (LLMs) have shown remarkable ability in decision-making with comprehensive internal world knowledge, but how it could benefit NAS for STSF remains unexplored. In this paper, we propose a novel NAS method for STSF based on LLM. Instead of directly generate architectures with LLM, We inspire the LLM’s capability with a multi-level enhancement mechanism. Specifically, on the step-level, we decompose the generation task into decision steps with powerful prompt engineering and inspire LLM to serve as instructor for architecture search based on its internal knowledge. On the instance-level, we utilize a one-step tuning framework to quickly evaluate the architecture instance and a memory bank to cumulate knowledge to improve LLM’s search ability. On the task-level, we propose a two-stage architecture search, balancing the exploration stage and optimization stage, to reduce the possibility of being trapped in local optima. Extensive experimental results demonstrate that our method can achieve competitive effectiveness with superior efficiency against existing NAS methods for STSF. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Physics-inspired Machine Learning for Quantum Error Mitigation

arXiv

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arXiv 2025年

作者： Xu, Xiao-Yue Xue, Xin Chen, Tianyu Ding, Chen Li, Tian Zhou, Haoyi Huang, He-Liang Bao, Wan-Su Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan450000 China Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures. © 2025, CC BY.

关键词： Quantum computers

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Motion Forecasting for Autonomous Vehicles: A Survey

arXiv

引用

arXiv 2025年

作者： Shi, Jianxin Chen, Jinhao Wang, Yuandong Sun, Li Liu, Chunyang Xiong, Wei Wo, Tianyu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Information Engineering College Capital Normal University Beijing100048 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Didi Chuxing Beijing100094 China

In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motion forecasting for AVs. We propose a formal problem formulation for motion forecasting and summarize the main challenges confronting this area of research. We also detail representative datasets and evaluation metrics pertinent to this field. Furthermore, this study classifies recent research into two main categories: supervised learning and self-supervised learning, reflecting the evolving paradigms in both scenario-based and perception-based motion forecasting. In the context of supervised learning, we thoroughly examine and analyze each key element of the methodology. For self-supervised learning, we summarize commonly adopted techniques. The paper concludes and discusses potential research directions, aiming to propel progress in this vital area of AV technology. © 2025, CC BY.

关键词： Self-supervised learning

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Characteristic Decomposition: From Regular Sets to Normal Sets

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Journal of Systems Science & Complexity 2019年第1期32卷 37-46页

作者： MOU Chenqi WANG Dongming LMIB–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University LMIB–SKLSDE–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

In this paper it is shown how to transform a regular triangular set into a normal triangular set by computing the W-characteristic set of their saturated ideal and an algorithm is proposed for decomposing any polynomial set into ?nitely many strong characteristic pairs, each of which is formed with the reduced lexicographic Gr?bner basis and the normal W-characteristic set of a characterizable ideal.

关键词： Characteristic decomposition characteristic pair normal set regular set W-characteristic set

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Unifying logic rules and machine learning for entity enhancing

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Science China(Information Sciences) 2020年第7期63卷 142-160页

作者： Wenfei FAN Ping LU Chao TIAN School of Informatics University of Edinburgh Shenzhen Institute of Computing Sciences Shenzhen University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Alibaba Group

This paper proposes a notion of entity enhancing, which unifies entity resolution and conflict resolution, to identify tuples that refer to the same real-world entity and at the same time, correct semantic inconsistencies. We propose to unify rule-based and machine learning(ML) methods for entity enhancing, by embedding ML classifiers as predicates in logic rules. We model entity enhancing by extending the chase. We show that the chase warrants correctness justification and the Church-Rosser property. Moreover, we settle fundamental problems associated with entity enhancing, including the enhancing, consistency, satisfiability,and implication problems, ranging from NP-complete and coNP-complete to Π~p-complete. Taken together,these provide a new theoretical framework for unifying entity resolution and conflict resolution.

关键词： logic rules machine learning entity enhancing entity resolution conflict resolution

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network Science and Engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Erratum to "Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle"

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IEEE transactions on biomedical circuits and systems 2025年第1期19卷 238页

作者： Jing Liang Yuanqi Hu Hefei Innovation Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing and School of Integrated Circuits Science and Engineering Beihang University Beijing China

In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows.

关键词： Circuits and systems Cutoff frequency Tunneling

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Ultimate Negative Sampling for Contrastive Learning 48

Ultimate Negative Sampling for Contrastive Learning

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Huijie Shi, Lei Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9781728163277

Unsupervised learning has received more attention due to the superior performance of contrastive learning methods. Most contrastive methods use data augmentation techniques to construct positive and negative pairs. The augmented view of the same sample is regarded as a positive sample, while the rest are negative samples. This negative sampling strategy has strong randomness and ignores samples that are semantically similar to anchors, namely sampling bias. This problem has been addressed by weighting the similarity of negative samples. In this paper, we propose a novel ultimate negative sampling for contrastive learning. Unlike random sampling, we set a more extreme negative sample selection mechanism based on the ideal representation of the sample. Furthermore, we constrain the consistency between samples across the space. Experiment results demonstrate the proposed method's superiority on multiple benchmark datasets. © 2023 IEEE.

关键词： Learning systems

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