检索结果-内蒙古大学图书馆

Junctional and somatic hypermutation-induced CX4C motif is critical for the recognition of a highly conserved epitope on HCV E2 by a human broadly neutralizing antibody

引用

Cellular & Molecular Immunology 2021年第3期18卷 675-685页

作者： Chunyan Yi Jing Xia Lan He Zhiyang Ling Xuesong Wang Yu Yan Jiangjun Wang Xinhao Zhao Weiguo Fan Xiaoyu Sun Ronghua Zhang Sheng Ye Rongguang Zhang Yongfen Xu Liyan Ma Yaguang Zhang Honglin Zhou Zhong Huang Junqi Niu Gang Long Junxia Lu Jin Zhong Bing Sun State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of Sciences/University of Chinese Academy of SciencesShanghaiChina CAS Key Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of Sciences/University of Chinese Academy of SciencesShanghaiChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina CAS Key Laboratory of Synthetic Chemistry of Natural Substances Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghaiChina College of Biology Hunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangshaChina National Laboratory of Biophysics Institute of BiophysicsChinese Academy of Sciences/University of Chinese Academy of Sciences BdjingBeijingChina Interdisciplinary Innovation Institute of Medicine&Engineering Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Nanjing Galaxy Biopharma Co. LtdNanjingChina Hepatology Section First HospitalUniversity of JilinChangchunChina

Induction of broadly neutralizing monoclonal antibodies(bNAbs)that bind to the viral envelope glycoproteins is a major goal of hepatitis C virus(HCV)vaccine *** study of bNAbs arising in natural infection is essential in this *** generated a human antibody,8D6,recognizing the E2 protein of HCV isolated from a chronic hepatitis C *** antibody shows broadly neutralizing activity,which covers a pan-genotypic panel of cell culture-derived HCV virions(HCVcc).Functional and epitope analyses demonstrated that 8D6 can block the interaction between E2 and CD81 by targeting a highly conserved epitope on *** describe how the 8D6 lineage evolved via somatic hypermutation to achieve broad *** found that the V(D)J recombination-generated junctional and somatic hypermutation-induced disulfide bridge(C-C)motif in the CDRH3 is critical for the broad neutralization and binding activity of *** motif is conserved among a series of broadly neutralizing HCV antibodies,indicating a common binding ***,the 8D6 inferred germline(iGL)was reconstructed and tested for its binding affinity and neutralization ***,8D6 iGL-mediated relatively strong inhibition of the 1b genotype PR79L9 strain,suggesting that PR79L9 may serve as a potential natural viral strain that provides E2 sequences that induce ***,our detailed epitope mapping and genetic studies of the HCV E2-specific mAb 8D6 have allowed for further refinement of antigenic sites on E2 and reveal a new mechanism to generate a functional CDRH3,while its iGL can serve as a probe to identify potential HCV vaccine strains.

关键词： Hepatitis C virus neutralizing antibody inferred germline CDRH3 disulfide bridge motif

来源：评论

学校读者我要写书评

暂无评论

Network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

引用

5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

An Adversarial Complementary Erasing Network for Salient Object Detection in Optical Remote Sensing Images

SSRN

引用

SSRN 2023年

作者： Liang, Yanhua Qin, Guihe Sun, Minghui Qin, Jun Yan, Jie Zhang, Zhonghan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130012 China

Salient object detection in optical remote sensing images (RSI-SOD) aims to locate and extract visually discriminative objects or regions, which has attracted more and more attention from the community. The key challenge of RSI-SOD lies in the characteristics of optical RSIs, such as scale diversification of salient objects, irregular edges, and cluttered backgrounds. In this paper, we strive to embrace challenges towards effective and efficient RSI-SOD. To this end, we develop a framework, termed Adversarial Complementary Erasing Network (ACENet), which comprehensively mines discriminative features of different layers from foreground, background, local and global perspectives to perform robust and accurate saliency detection. Specifically, ACENet is a typical encoder-decoder structure that embeds Adversarial Erasing Synthesis Module (AESM) in multi-scale encoding features to fully explore more useful and complete representations. In AESM, we introduce Adversarial Discriminant Information Mining (ADIM) component to facilitate adaptive adjustment between salient features and background, further enabling more representative and strong integrity feature generation. Experimental results on three datasets prove that our ACENet significantly outperforms cutting-edge SOD models both qualitatively and quantitatively with a quick reasoning speed of 76 FPS. Code will be available at https://***/Liangyh18/ACENet. © 2023, The Authors. All rights reserved.

关键词： Optical remote sensing

来源：评论

学校读者我要写书评

暂无评论

Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning 29

Parsing Natural Language into Propositional and First-Order ...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Lu, Xuantao Liu, Jingping Gu, Zhouhong Tong, Hanwen Xie, Chenhao Huang, Junyang Xiao, Yanghua Wang, Wenguang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. Beijing China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China DataGrand Inc. Shanghai China

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Small target particleboard defect detection algorithm based on improved YOLO

Small target particleboard defect detection algorithm based ...

引用

Chinese Control Conference (CCC)

作者： Jinghui Cheng Xianzhong Chen Qingwen Hou Jie Zhang Tianyu Liu School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing China

Appearance quality defects can reduce the strength of particleboard and affect the appearance of the product. Therefore, fast and accurate detection of particleboard defects can greatly release the production potential and has a very significant research value. The traditional target detection algorithm has the disadvantages of insufficient detection capability and slow detection speed, which cannot well meet the needs of actual production. In order to solve the detection difficulties of small target and unobvious features in particleboard defect dataset, and considering the limitation of model resources by application scenarios, an improved YOLOv5 defect detection model is proposed in this paper. Firstly, the high-resolution defect images are sliced by sliding window slicing to build a low-resolution sub-image dataset. To avoid color distortion in the fusion process, the dataset is expanded with Poisson fusion and gamma enhancement. Then, a microscale layer was added to YOLOv5 and the original prediction head was replaced with the C3STR module to enhance the modeling capability of local and global features. In addition, a context information-enhanced BiFPN structure (CE-BiFPN) is designed to improve the small target detection capability of BiFPN by cross-domain fusion of different scale feature layers and obtaining context information of different sensory fields. The experimental results show that the improved YOLOv5 model achieves an average accuracy (mAP) of93.6% in the particleboard defect dataset, which is 4.9% better than YOLOv5. The APs of glue spots with obvious small-target characteristics and large shavings were improved by 17.2% and 6.4%, respectively, which proved that the improved model in this paper can effectively detect small-target defects in particleboards.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

Journal of Network Intelligence

引用

Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

来源：评论

学校读者我要写书评

暂无评论

A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning 6th

A Class Imbalance Monitoring Model for Fetal Heart Contracti...

引用

6th International Conference on data Mining and Big data, DMBD 2021

作者： Qin, Chen Liu, Shaopeng Lin, Shengxiang Li, Guangzhe Hong, Jiaming School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Department of Computer Science Guangdong Polytechnic Normal University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9789811675010

Aiming at the imbalance and cost-sensitive problem of sample categories in actual fetal monitoring, as well as actual needs, we proposed a category imbalance fetal contraction monitoring model based on GBDT (Gradient Boosting Decision Tree) combined learning. Subsets with balanced category were generated by random under-sampling and applied to train several GBDT base classifiers using the method of feature selection. We integrated the base classifiers by the simple average method and calculated the final prediction probability. In this study, AUC and cost-sensitive error rate were used as evaluation indicators to compare with the commonly used single learning models such as Decision Tree, Logistic Regression and combined learning models like Random Forest to verify the effectiveness of the model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations

arXiv

引用

arXiv 2024年

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model’s expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP’s robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Event Prediction Model Combining Ordinary Differential Equation and Hypernetworks

SSRN

引用

SSRN 2023年

作者： Zhang, Junhe Ma, Fuyuan Wang, Xin Yang, Bo Wang, Ying College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Artificial Intelligence Jilin University Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Jilin Changchun130012 China

Modeling the temporal evolution patterns of network events is crucial for enhancing user experience and creating commercial value. Existing methods combine graph neural networks and time-series models to capture spatial and temporal features, but they assume non-interaction among nodes when the network structure is stable, implying that node states do not undergo updates. Moreover, utilizing a single model to fit all events in these methods may lead to an excessive emphasis on global evolutionary patterns, potentially neglecting the individual heterogeneity among events. In this paper, we propose an event prediction model combining Ordinary Differential Equations and Hypernetworks, named ODEH. Firstly, to model the continuous evolution process of network structures under stability, we utilize ordinary differential equations based on graph neural networks to capture the continuous information dissemination among nodes and the interaction between node representation channels. Then, we use a message passing mechanism to model the direct impact of network topology changes on network entities. Finally, a hypernetwork is used to capture the individual characteristics of events and fine-tune the prediction model. Experimental results on multiple datasets demonstrate ODEH's superiority over baseline methods, highlighting its effectiveness in event prediction. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Causal Adversarial Graph Neural Network for Multi-Center Autism Spectrum Disorder Identification

SSRN

引用

SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：