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Ultrasound-triggered biomimetic ultrashort peptide nanofiber hydrogels promote bone regeneration by modulating macrophage and the osteogenic immune microenvironment

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Bioactive Materials 2024年第1期31卷 231-246页

作者： Fan Zhang Mingchen Lv Siyuan Wang Mengyao Li Yu Wang Congjiao Hu Wei Hu Xuekui Wang Xiaogang Wang Zhiduo Liu Zhen Fan Jianzhong Du Yao Sun Department of Oral Implantology Stomatological Hospital and Dental School of Tongji UniversityShanghai Engineering Research Center of Tooth Restoration and Regeneration200072China Department of Gynaecology and Obstetrics Shanghai Fourth People’s HospitalSchool of MedicineTongji UniversityShanghai200434China Department of Polymeric Materials School of Materials Science and EngineeringTongji University201804China Department of Immunology and Microbiology The Minister of Education Key Laboratory of Cell Death and DifferentiationShanghai Institute of ImmunologyShanghai Jiao Tong University School of MedicineChina Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityChina

The immune microenvironment plays a vital role in bone defect *** create an immune microenvironment that promotes osteogenesis,researchers are exploring ways to enhance the differentiation of M2-type *** peptides have been discovered to effectively improve this process,but they are limited by low efficiency and rapid degradation in *** overcome these issues,peptide with both M2 regulatory and self-assembly modules was designed as a building block to construct an ultrasound-responsive nanofiber *** nanofibers can be released from hydrogel in a time-dependent manner upon ultrasound stimulation,activating mitochondrial glycolytic metabolism and the tricarboxylic acid cycle,inhibiting reactive oxygen species production and enhancing M2 macrophage *** hydrogel exhibits advanced therapeutic potential for bone regeneration by triggering M2 macrophages to secrete BMP-2 and IGF-I,accelerating the differentiation of bone marrow mesenchymal stem cells(BMSCs)into ***,modularly designed biomimetic ultrashort peptide nanofiber hydrogels provide a novel strategy to rebuild osteogenic immune microenvironments for bone repair.

关键词： Peptide Nanofiber hydrogel Immune microenvironment Bone tissue regeneration

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Commonsense Knowledge Construction with Concept and Pretrained Model 19th

Commonsense Knowledge Construction with Concept and Pretrain...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Cai, Hanjun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Commonsense knowledge (CSK) is the information that people use in daily life but do not often mention. It summarizes the practical knowledge about how the world works. Existing machines have knowledge but lack commonsense because they do not understand and master commonsense knowledge in the same way that humans do. In the latest works, crowdsourcing-based method is costly and has low coverage, knowledge base completion method can highly fit samples, and methods extracted from unstructured data have the defects of low quality. CG &BF is commonsense knowledge construction with a concept-based generator and a BERT-based filter. We utilize semantic search for node matching and entropy encoder for filtering triples with high abstraction. Two algorithms based on concept aggregation and path credibility are proposed to obtain high-quality CSK triples. We subsequently finetuning a BERT to filter incorrect triples. We obtain 500,000 CSK triples based on ConceptNet, which is superior to other construction methods in novelty and quality. In the reading comprehension task, the three-way attention network is selected as the basic model and the knowledge we generate enables the base model to perform better, which proves that the output of CG &BF has higher quality and ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable. © 2023 ACM.

关键词： Graph neural networks

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RGraph: Asynchronous graph processing based on asymmetry of remote direct memory access

RGraph: Asynchronous graph processing based on asymmetry of ...

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作者： Chen, Hanhua Yuan, Jie Jin, Hai Wang, Yonghui Wu, Sijie Jiang, Zhihao National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The scale of real-world graphs is constantly growing. To deal with large-scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power-law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high-performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA-assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA-assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one-to-many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large-scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%. © 2021 John Wiley & Sons Ltd.

关键词： Mirrors

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and Distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and Distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Multiscale Fusion Graph Convolutional Networks for Hyperspectral Image Classification 2

Multiscale Fusion Graph Convolutional Networks for Hyperspec...

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2022 2nd International Conference on Artificial Intelligence, big data and Algorithms, CAIBDA 2022

作者： Wang, Lei Wang, Xili School of Computer Science Shaanxi Normal University Xi’an China School of Mathematics and Computer Application Shangluo University Shangluo China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China

ISBN: (纸本)9783800760268

Graph neural network (GNN) has shown remarkable performance in many fields, such as edge prediction, node classification, etc. However, the traditional graph neural network can only accept the full graph to train the model, which leads to a very high computational complexity. Besides, the lack of multi-scale mechanism limits the actual classification performance. In this paper, we propose a multiscale fusion model based on graph convolutional network (GCN) and convolutional neural network (CNN) for hyperspectral image (HSI) classification tasks. This model can first sample randomly from the full graph to subgraphs of different scales, then trains GCN on sub-graphs of different scales and extract features, and finally integrates the spatial-spectral features extracted by multi-scale CNN classifier and the structural features extracted by multi-scale GCN for end-to-end mini-batch joint training. Experiments on two real HSI data s demonstrate that the proposed method significantly improves the classification performance compared with other comparison methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Graph neural networks

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MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggrega...

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19th World Congress on Medical and Health Informatics, MedInfo 2023

作者： Kitrungrotsakul, Titinunt Xu, Yingying Chen, Qingqing Liu, Jing Li, Yinhao Lin, Lanfen Hu, Hongjie Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab China Department of Radiology Sir Run Run Shaw Hospital China Graduate School of Information Science and Engineering Ritsumeikan Univ. Japan College of Computer Science and Technology Zhejiang Univ. China

ISBN: (纸本)9781643684567

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. © 2024 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Feature extraction

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion 24

RecCoder: Reformulating Sequential Recommendation as Large L...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Lai, Kai-Huang Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master. © 2024 IEEE.

关键词： Semantics

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