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RRx-001 ameliorates inflammatory diseases by acting as a potent covalent NLRP3 inhibitor

Cellular & Molecular Immunology 2021年第6期18卷 1425-1436页

作者： Yun Chen Hongbin He Bolong Lin Yun Chen Xianming Deng Wei Jiang Rongbin Zhou Hefei National Laboratory for Physical Sciences at Microscale the CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Department of Geriatrics Gerontology Institute of Anhui Provincethe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina CAS Centre for Excellence in Cell and Molecular Biology University of Science and Technology of ChinaHefeiChina State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamenFujianChina

The NLRP3 inflammasome plays a crucial role in innate immune-mediated inflammation and contributes to the pathogenesis of multiple autoinflammatory,metabolic and neurodegenerative diseases,but medications targeting the NLRP3 inflammasome are not available for clinical ***-001 is a well-tolerated anticancer agent currently being investigated in phase III clinical trials,but its effects on inflammatory diseases are not ***,we show that RRx-001 is a highly selective and potent NLRP3 inhibitor that has strong beneficial effects on NLRP3-driven inflammatory ***-001 inhibits the activation of the canonical,noncanonical,and alternative NLRP3 inflammasomes but not the AIM2,NLRC4 or Pyrin ***,RRx-001 covalently binds to cysteine 409 of NLRP3 via its bromoacetyl group and therefore blocks the NLRP3-NEK7 interaction,which is critical for the assembly and activation of the NLRP3 *** importantly,RRx-001 treatment attenuates the symptoms of lipopolysaccharide(LPS)-induced systemic inflammation,dextran sulfate sodium(DSS)-induced colitis and experimental autoimmune encephalomyelitis(EAE)in ***,our study identifies RRx-001 as a new potential therapeutic agent for NLRP3-driven diseases.

关键词： RRx-001 NLRP3 inflammasome inflammatory diseases

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DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

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arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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Image recognition and detection based on fast area convolutional neural network

Image recognition and detection based on fast area convoluti...

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2021 International Conference on Computer, Communication, Control, Automation and Robotics, CCCAR 2021

作者： Zhang, Hanwen Qin, Zhen Xie, Hua School of Automation Engineering UESTC Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China

Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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LSketch: A Label-Enabled Graph Stream Sketch Toward Time-Sensitive Queries

arXiv

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arXiv 2023年

作者： Zeng, Yiling Song, Chunyao Li, Yuhan Ge, Tingjian Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin300350 China Department of Computer Science University of Massachusetts Lowell United States

Graph streams represent data interactions in real applications. The mining of graph streams plays an important role in network security, social network analysis, and traffic control, among others. However, the sheer volume and high dynamics cause great challenges for efficient storage and subsequent query analysis on them. Current studies apply sketches to summarize graph streams. We propose LSketch that works for heterogeneous graph streams, which effectively preserves the label information carried by the streams in real scenes, thereby enriching the expressive ability of sketches. In addition, as graph streams continue to evolve over time, edges too old may lose their practical significance. Therefore, we introduce the sliding window model into LSketch to eliminate the expired edges automatically. LSketch uses sub-linear storage space and can support structure based queries and time-sensitive queries with high accuracy. We perform extensive experiments over four real datasets, demonstrating the superiority of the proposed method over state-of-the-art methods, in aspects of query accuracy and time efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： network security

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Learning Multimodal Attention Mixed with Frequency Domain Information as Detector for Fake News Detection

Learning Multimodal Attention Mixed with Frequency Domain In...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zihan Ma Huan Liu Zhi Zeng Hao Guo Xiang Zhao Minnan Luo School of Computer Science and Technology Xi’an Jiaotong University Xi'an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi'an China Laboratory for Big Data and Decision Nation University of Defence Technology Changsha China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Detecting fake news on social media has become a crucial task in combating online misinformation and countering malicious propaganda. Existing methods rely on semantic consistency across modalities to fuse features and determine news authenticity. However, cunning fake news publisher manipulate image to ensure a high level of semantic consistency between news post and image, making it more difficult to distinguish fake news. To this end, we propose MHFFD (Mixed High-Frequency Feature Detector), a novel fake news detection framework that utilizes token-level semantic consistency evaluation to identify key elements in news content and provide guidance for discovering image manipulation and learning better news representations. Extensive experiments demonstrate that MHFFD outperforms state-of-the-art methods on two widely used fake news detection datasets. Further research also validates the effectiveness of token-level semantic alignment and manipulation detection.

关键词： Social networking (online) Fuses Frequency-domain analysis Semantics Detectors Feature extraction Fake news

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural network

Drug-Protein Interaction Prediction by Fusion of Attention a...

引用

Chinese Control Conference (CCC)

作者： Tianrui Chen Shouheng Tuo Zengyu Feng School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG.

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Assessment of precipitation type discrimination methods on glacier of Qilian Mountains

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Journal of Mountain science 2023年第12期20卷 3476-3487页

作者： CHEN Ji-zu DU Wen-tao KANG Shi-chang QIN Xiang SUN Wei-jun JIANG You-yan SUN Wen-xuan Qilian Shan Station of Glaciology and Eco-environment State Key Laboratory of Cryospheric ScienceNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China College of Geography and Environment Shandong Normal UniversityJinan 250014China Gansu Provincial Industry Technology Center of Intelligent Equipment and Big Data for Disaster Prevention Northwest Institute of Eco-Environment and ResourcesChinese Academy of Science(CAS)Lanzhou 730000China Lanzhou Regional Climate Center Lanzhou 730020China 5 Zhangye Meteorological BureauZhangye 734000China Zhangye Meteorological Bureau Zhangye 734000China

Solid precipitation is not only the main supply for glacier mass,but also exerts an important influence on surface albedo and intensifies glacier ***,precipitation type observation is very scarce in the high alpine glaciers,which limits the precise simulation of glacier mass *** study assessed three discrimination methods of precipitation types including Ding method,Dai method and Froidurot method based on surface albedo observation data on the Laohugou Glacier No.12(LHG Glacier)in western Qilian *** results showed that Ding method had a best applicability on the LHG Glacier,the other two need to calibrate parameters when they are used in the high elevation glacier *** we fitted the relationship between snowfall probability and fresh snow albedo,and put forward a revised formula to simulate fresh snow albedo based on Ding method,which is expected to reduce the uncertainty in glacier mass and energy balance ***,we found a best air temperature threshold of 4℃for discriminating monthly precipitation *** order to accurately simulate the glacier melt,it is necessary to obtain the threshold temperature appropriately in different glacier region with different elevation and humidity.

关键词： Precipitation type Surface albedo Laohugou Glaicer No.12 Glacier melting

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A New Model Based on Differential Evolutionary Algorithm and Markov Clustering for Identifying Protein Complexes

A New Model Based on Differential Evolutionary Algorithm and...

引用

Chinese Control Conference (CCC)

作者： Zengyu Feng Shouheng Tuo Tianrui Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology, ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database.

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SGSM: A FOUNDATION-MODEL-LIKE SEMI-GENERALIST SENSING MODEL

arXiv

引用

arXiv 2024年

作者： Yang, Tianjian Zhou, Hao Liu, Shuo Guo, Kaiwen Hou, Yiwen Du, Haohua Liu, Zhi Li, Xiang-Yang School of Computer Science and Technology University of Science and Technology China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology China Deqing Alpha Innovation Institute Zhejiang Huzhou China School of Cyber Science and Technology Beihang University China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

The significance of intelligent sensing systems is growing in the realm of smart services. These systems extract relevant signal features and generate informative representations for particular tasks. However, building the feature extraction component for such systems requires extensive domain-specific expertise or data. The exceptionally rapid development of foundation models is likely to usher in newfound abilities in such intelligent sensing. We propose a new scheme for sensing model, which we refer to as semi-generalist sensing model (SGSM). SGSM is able to semiautomatically solve various tasks using relatively less task-specific labeled data compared to traditional systems. Built through the analysis of the common theoretical model, SGSM can depict different modalities, such as the acoustic and Wi-Fi signal. Experimental results on such two heterogeneous sensors illustrate that SGSM functions across a wide range of scenarios, thereby establishing its broad applicability. In some cases, SGSM even achieves better performance than sensor-specific specialized solutions. Wi-Fi evaluations indicate a 20% accuracy improvement when applying SGSM to an existing sensing model. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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