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Caspase-1 and Gasdermin D Afford the Optimal Targets with Distinct Switching Strategies in NLRP1b Inflammasome-Induced Cell Death

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Research 2022年第4期2022卷 463-479页

作者： Xiang Li Peipei Zhang Zhiyong Yin Fei Xu Zhang-Hua Yang Jun Jin Jing Qu Zhilong Liu Hong Qi Chenggui Yao Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamen 361102China Complex Systems Research Center Shanxi UniversityShanxiTaiyuan 030006China College of Data Science Jiaxing UniversityJiaxing 314000China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of ChineseAcademyof SciencesWenzhouZhejiang325001China

Inflammasomes are essential complexes of innate immune system, which form the first line of host defense against pathogens. Mounting evidence accumulates that inflammasome signaling is highly correlated with coronavirus disease 2019 (COVID-19). However, there remains a significant gap in our understanding of the regulatory mechanism of inflammasome signaling. Combining mathematical modeling with experimental analysis of NLRP1b inflammasome signaling, we found that only the expression levels of caspase-1 and GSDMD have the potential to individually switch cell death modes. Reduction of caspase-1 or GSDMD switches cell death from pyroptosis to apoptosis. Caspase-1 and GSDMD present different thresholds and exert distinct pathway choices in switching death modes. Pyroptosis switches to apoptosis with an extremely low threshold level of caspase-1, but with a high threshold of GSDMD. Caspase-1-impaired cells employ ASC-caspase-8-dependent pathway for apoptosis, while GSDMD-impaired cells primarily utilize caspase-1-dependent pathway. Additionally, caspase-1 and GSDMD can severally ignite the cooccurrence of pyroptosis and apoptosis. Landscape topography unravels that the cooccurrence is dramatically different in caspase-1- and GSDMD-impaired cells. Besides pyroptosis state and apoptosis state, a potential new “coexisting” state in single cells is proposed when GSDMD acts as the driving force of the landscape. The “seesaw model” is therefore proposed, which can well describe the death states that are controlled by caspase-1 or GSDMD in single cells. Our study sheds new light on NLRP1b inflammasome signaling and uncovers the switching mechanisms among various death modes, providing potential clues to guide the development of more rational control strategies for diseases.

关键词： impaired utilize rational

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Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction

Contrastive Meta-Learning for Drug-Target Binding Affinity P...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Mei Xu, Sihan Cai, Xiangrui Zhang, Zhong Ji, Hua Tianjin Key Laboratory of Network and Data Security Technology China College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission China Tianjin Normal University Tianjin China

ISBN: (纸本)9781665468190

Effective drug-target binding affinity (DTA) prediction is essential for drug discovery and development. The development of machine learning techniques considerably advances it. However, the cold-start problems in DTA prediction are still under-explored, which significantly degrades prediction performances on novel drugs and novel targets. In this paper, we propose a contrastive meta-learning (CML) framework to address these issues. We define drug-anchored tasks and target-anchored tasks, which enables the employment of meta-learning to accumulate common knowledge from various tasks so as to adapt to new tasks faster and better. Besides, we utilize a task inequality loss to measure task disparities and enhance model sensitivities to new tasks. We also propose a contrastive learning block (CLB) to explore correlations among drug-target pairs across tasks, which facilitates DTA prediction performance improvements. We compare CML with various baselines on two benchmarks and comparison results show that CML outperforms or achieves competitive results to its competitors. © 2022 IEEE.

关键词： Forecasting

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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PresRecCD: A Novel Herbal Prescription Recommendation Framework with Cross-Domain Learning and Neural Collaborative Filtering

PresRecCD: A Novel Herbal Prescription Recommendation Framew...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wansong Dong, Xin Yang, Kuo Zhang, Lei Wang, Xinyu Huang, Rouye Zhang, Runshun Zhou, Xuezhong Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing100044 China China Academy of Chinese Medical Sciences National Data Center of Traditional Chinese Medicine Beijing China Guang'Anmen Hospital China Academy of Chinese Medical Sciences Beijing China

ISBN: (纸本)9798350386226

Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, alleviating the sparsity of data, and neural collaborative filtering is utilized to carry out prescription recommendations. Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment. © 2024 IEEE.

关键词： Medicinal chemistry

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

arXiv

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

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arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

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Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

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SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Parallelized Collision Detection Algorithm for Non-contact Machining Simulation Based on Space Partitioning and Path Point Equalization 2

Parallelized Collision Detection Algorithm for Non-contact M...

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2nd International Conference on Engineering of Optical Systems and Precision Instruments, ICEOP 2024

作者： Zhang, Ying Zhang, Shijin Ren, Zhongwei Wu, Yuqiang Jiang, Fengyang School of Software Northwestern Polytechnical University Xi’an710129 China AVIC United Technology Center for Multi-axis CNC Machining and Simulation AVIC Information Technology Co. Ltd. Beijing100020 China Suzhou Key Lab of Aerospace Industry Software and Data Science Yangtze River Delta Research Institute of NPU Taicang215400 China

High-energy beam CNC machining, such as laser, water jet, and plasma machining, is widely applied in modern manufacturing due to its non-contact processing, precise energy control, and broad adaptability. However, potential collisions between tools and machine tools can cause serious accidents, making pre-processing simulation *** tasks become more complex, the increase in triangular facets of models and tool path points leads to a significant rise in computational overhead for CNC machining simulations. Although multi-core processors provide powerful computing capabilities, traditional serial collision detection methods fail to fully utilize these *** address this issue, we propose a parallelized collision detection algorithm for high-energy beam CNC machining simulations. By dividing the tool paths into sub-tasks and leveraging multi-core processors for concurrent processing, the algorithm enhances efficiency while maintaining accuracy. Experiments in water jet and laser machining scenarios demonstrate that our approach effectively reduces computation time and improves real-time performance and *** study introduces parallel computing strategies to improve collision detection efficiency, combines the parallelization idea with the collision detection algorithm in CNC machining simulation offering an efficient solution for complex high-energy beam machining tasks. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Laser beam machining

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Xian, Peijie He, Ying Yu, F. Richard Du, Jianbo Shenzhen Univ. College of Computer Science and Software Engineering Shenzhen China Xi'an Univ. of Posts & Telecomm. Shaanxi Key Lab. of Information Comm. Network and Security Shaanxi Xi'an China Carleton Univ. School of Information Technology OttawaON Canada

ISBN: (纸本)9798350351255

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation. © 2024 IEEE.

关键词： Active learning

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