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Improved immunocompatibility of active targeting liposomes by attenuating nucleophilic attack of cyclic RGD peptides on complement 3

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Biomaterials 2025年 321卷 123350-123350页

作者： Meng, Nana Lu, Jiasheng Zhou, Jianfen Yang, Shengmin Zhang, Chen Jia, Ruiyi Ding, Yuan Bao, Yanning Wang, Jun Ma, Xiaopei Chen, Ruohan Jiang, Zhixuan Xie, Cao Lu, Linwei Lu, Weiyue Department of Pharmaceutics School of Pharmacy Fudan University & National Key Laboratory of Advanced Drug Formulations for Overcoming Delivery Barriers Shanghai201203 China Department of Integrative Medicine Huashan Hospital Fudan University Shanghai200040 China Institutes of Integrative Medicine Fudan University Shanghai200040 China Shanghai Engineering Technology Research Center for Pharmaceutical Intelligent Equipment and Shanghai Frontiers Science Center for Druggability of Cardiovascular Non-coding RNA Institute for Frontier Medical Technology Shanghai University of Engineering Science Shanghai201620 China

One of the challenges for the clinical translation of active targeting nanomedicines is the adverse interactions between targeting ligands and blood components. Herein, a novel regularity, which reveals the interactions between cyclic RGD (Arg-Gly-Asp) peptide-modified liposomes and complement components in blood, is reported. As the nucleophilicity of arginine guanidine group within the cyclic RGD-like peptide increases, targeting liposomes potentiate complement cascade via the amplification loop of complement 3 (C3), ultimately leading to accelerated blood clearance, increased deposition in the reticuloendothelial system (RES) organs, enhanced immune responses, and potential side effects. By appropriately reducing the nucleophilicity of guanidine group, cyclic R2 peptide is designed for modification of liposomes to target integrin αvβ3. Compared to the widely used targeting molecule c(RGDyK), R2 eliminates the negative effects of C3 opsonization and specific antibody production, significantly improves the in vivo immunocompatibility of targeting liposomes, and demonstrates superior anti-tumor efficacy in mouse models of orthotopic breast cancer and glioma. Thus, the proposed regularity of interactions between guanidine nucleophilicity and C3, along with the successful application of the low complement activation capacity targeting ligand R2, provides new insights for addressing challenges related to complement activation in the clinical translation of active targeting nanomedicines. © 2025 Elsevier Ltd

关键词： Ligands

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Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms

arXiv

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arXiv 2025年

作者： Ma, Nachuan Song, Zhengfei Hu, Qiang Liu, Chuang-Wei Han, Yu Zhang, Yanting Fan, Rui Xie, Lihua College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Donghua University Shanghai201620 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. Also, we create a dataset called UDTIRI-Crack, comprising 2, 500 high-quality images from seven public annotated sources, as the first extensive online benchmark in this field. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. In addition, the feasibility of foundation models and large language models (LLMs) for road crack detection is explored. Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The released benchmark UDTIRI-Crack is available at https://***/submission/. © 2025, CC BY.

关键词： Self-supervised learning

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Two-Stage Hand-Eye Calibration based on Variance Minimization Principle

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ding, Tao Tian, Dazhuang Wu, Hao Zhu, Dahu Li, Wenlog Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components School of Automotive Engineering Wuhan430070 China Wuhan University of Technology Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Advanced Technology for Automotive Components Hubei Provincial Engineering Research Center of Robot and Intelligent Manufacturing Wuhan430070 China Huazhong University of Science and Technology State Key Laboratory of Intelligent Manufacturing Equipment and Technology Wuhan430074 China

Hand-eye calibration in industrial scenarios greatly determines the efficiency and effectiveness of 3D vision-guided robotic tasks. Most of the existing hand-eye calibration methods, however, rely on collecting large-scale point cloud samples to guarantee high measurement accuracy, which is time-consuming and particularly influenced by measurement defects such as local missing, noise, and uneven density. To overcome this challenging problem, we present a novel two-stage hand-eye calibration method based on the variance minimization principle. In the first stage, a robust function weighted variance minimization (RFWVM) registration algorithm is proposed for fine registration of defective point clouds, aiming at solving the coarse hand-eye calibration matrix with few-shot point clouds. In the second stage, an objective function of the calibration error correction algorithm is established via the point-to-plane distance variance minimization, which is intended to suppress the influence of measurement defects. Through these two stages, the globally optimal hand-eye matrix is solved by iteratively correcting the coarse hand-eye calibration matrix. A series of experiments validate the universality, accuracy, and robustness of the proposed method. © 1963-2012 IEEE.

关键词： Error correction

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Decoupling of nanoindentation residual stress field in single-crystalline 4H-SiC via micro-Raman spectroscopy

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Optics Letters 2025年第11期50卷 3513-3516页

作者： Yang, Zhoudong Tian, Jing Wang, Xinyue Chen, Junwei Zuo, Yuanhui Zhang, Rongjun Tang, Hongyu Fan, Xuejun Zhang, Guoqi Fan, Jiajie Shanghai Engineering Technology Research Center for SiC Power Device School of Intelligent Robotics and Advanced Manufacturing Fudan University Shanghai 200433 China Research Institute of Fudan University in Ningbo Ningbo 315336 China School of Information Science and Technology Fudan University Shanghai 200433 China Department of Mechanical Engineering Lamar University PO Box 10028 Beaumont 77710 TX United States EEMCS Faculty Delft University of Technology Delft 2628 Netherlands

This Letter presents a combined analytical and experimental method to effectively decouple the radial and tangential residual stress fields induced by Berkovich nanoindentation in single-crystalline 4H-SiC using micro-Raman spectroscopy. By integrating the Raman stress characterization model with Yoffe’s expanding cavity model, precise extraction of individual residual stress components around the indentation region is realized. Through the vertical backscattering micro-Raman mapping of the E₂ phonon mode, we systematically investigate the residual stress distribution near the indentation. The results highlight significant anisotropy in nanoindentation-induced stress fields, strongly dependent on the crystal orientation of 4H-SiC, predominantly featuring radial tensile stress gradients. This comprehensive theoretical–experimental approach offers a robust optical framework for residual stress characterization in 4H-SiC and provides foundational insights for extending Raman spectroscopy-based stress characterization to other crystalline materials and related device structures. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Anisotropy Crystal orientation Crystalline materials Curvature Spatial resolution Strain

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Catalytic remodeling of complex alkenes to oxonitriles through C=C double bond deconstruction

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Science (New York, N.Y.) 2025年第6738期387卷 1083-1090页

作者： Cheng, Zengrui Huang, Kaimeng Wang, Chen Chen, Lili Li, Xinyao Hu, Zhibin Shan, Xinyuan Cao, Peng-Fei Sun, Haofeng Chen, Wei Li, Chenhao Zhang, Ziyao Tan, Hui Jiang, Xue Zhang, Guikai Zhang, Zhongying Lin, Min Wang, Liang Zheng, Anmin Xia, Changjiu Wang, Teng Song, Song Shu, Xingtian Jiao, Ning State Key Laboratory of Natural and Biomimetic Drugs Chemical Biology Center School of Pharmaceutical Sciences Peking University Beijing China State Key Laboratory of Catalytic Materials and Reaction Engineering Research Institute of Petroleum Processing SINOPEC Beijing China Department of Chemistry College of Sciences Shanghai Engineering Research Center of Organ Repair Shanghai University Shanghai China College of Chemistry State Key Laboratory of Organic-Inorganic Composites Beijing University of Chemical Technology Beijing China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Hefei China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan China Beijing Synchrotron Radiation Facility Institute of High Energy Physics University of Chinese Academy of Sciences Beijing China Key Lab of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological Engineering Zhejiang University Hangzhou China Interdisciplinary Institute of NMR and Molecular Sciences School of Chemistry and Chemical Engineering Wuhan University of Science and Technology Wuhan China Changping Laboratory Beijing China

Deconstructive transformation of carbon-carbon double bonds (C=C) is a pivotal strategy in synthetic chemistry and drug discovery. Despite the substantial advances in olefin metathesis and ozonolysis for natural product synthesis through C=C double-bond cleavage, the catalytic remodeling of complex molecules through C=C double-bond deconstruction has been underdeveloped. We report a heterogeneous copper-catalyzed C=C double-bond cleavage, which enables the remodeling of complex molecules by converting the carbons on either side of the C=C double bond to carbonyl and cyano groups, respectively. In particular, this method provides an efficient protocol to conveniently transform terpenoids, glycals, steroids, and bioactive molecules to privileged scaffolds with underexplored chemical space.

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Exploring Video-Based Driver Activity Recognition under Noisy Labels

arXiv

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arXiv 2025年

作者： Fan, Linjuan Wen, Di Peng, Kunyu Yang, Kailun Zhang, Jiaming Liu, Ruiping Chen, Yufan Zheng, Junwei Wu, Jiamin Han, Xudong Stiefelhagen, Rainer Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai AI Lab China University of Sussex United Kingdom

As an open research topic in the field of deep learning, learning with noisy labels has attracted much attention and grown rapidly over the past ten years. Learning with label noise is crucial for driver distraction behavior recognition, as real-world video data often contains mislabeled samples, impacting model reliability and performance. However, label noise learning is barely explored in the driver activity recognition field. In this paper, we propose the first label noise learning approach for the driver activity recognition task. Based on the cluster assumption, we initially enable the model to learn clustering-friendly low-dimensional representations from given videos and assign the resultant embeddings into clusters. We subsequently perform co-refinement within each cluster to smooth the classifier outputs. Furthermore, we propose a flexible sample selection strategy that combines two selection criteria without relying on any hyperparameters to filter clean samples from the training dataset. We also incorporate a self-adaptive parameter into the sample selection process to enforce balancing across classes. A comprehensive variety of experiments on the public Drive&Act dataset for all granularity levels demonstrates the superior performance of our method in comparison with other label-denoising methods derived from the image classification field. The source code is available at https://***/ilonafan/DAR-noisy-labels. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Single-shot common-path encoded coherent diffraction imaging with OAM multiplexing

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Photonics research 2025年第6期13卷 1620-1630页

作者： Mingli Sun Yingming Xu Yuanyuan Liu Chiye Li Bijun Xu Xiaogang Wang Junhui Shi Qiwen Zhan School of Science Zhejiang University of Science and Technology Hangzhou 310023 China Research Center for Novel Computational Sensing and Intelligent Processing Zhejiang Lab Hangzhou 311100 China e-mail: yingmingxu@*** School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China e-mail: junhuishi@*** e-mail: qwzhan@***

Single-shot multi-frame phase imaging plays an important role in detecting continuous extreme physical phenomena, particularly suitable for measuring the density of media with non-repeatable changes and uncertainties. However, traditional single-pattern multiplexed imaging faces challenges in retrieving amplitude and phase information of multiple frames without sacrificing spatial resolution and phase accuracy. In this study, we demonstrate single-shot common-path encoded coherent diffraction imaging with orbital angular momentum (OAM) multiplexing. It employs a sequence of vortex illumination fields, combined with encoding wavefront modulation and a vortex multiplexing phase retrieval algorithm, to achieve the retrieval of complex amplitudes from dynamic samples in single shots. Our experimental validation demonstrated the capability to achieve 9-frame high-resolution phase imaging of the dynamic sample in a single diffraction pattern. The spatial resolution and phase accuracy improve to 9.84 μm and 4.7% with this lensless multiplexed imaging system, which is comparable to single-mode imaging. This technology provides a multiplexed dimension with orbital angular momentum and holds potential in the study of transient continuous phenomena.

关键词： Imaging systems Phase imaging Phase modulation Spatial frequency Spatial light modulators Spatial resolution

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engineering stress resistance: advances in glycine betaine production for sustainable agriculture

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Crop Health 2025年第1期3卷 1-14页

作者： Zhao, Fei Luo, Jinyan Ibrahim, Ezzeldin Chen, Lei Shen, Ying Ibrahim, Muhammad Alonazi, Wadi B. Lu, Jianfei Luo, Yuanchan Wu, Hui State Key Laboratory of Bioreactor Engineering Shanghai Collaborative Innovation Center for Biomanufacturing Technology School of Biotechnology East China University of Science and Technology Shanghai China Department of Plant Quarantine Shanghai Extension and Service Center of Agriculture Technology Shanghai China Department of Vegetable Diseases Research Agriculture Research Centre Plant Pathology Research Institute Giza Egypt Station for the Plant Protection & Quarantine and Control of Agrochemicals of Zhejiang Province Hangzhou China Health Administration Department College of Business Administration King Saud University Riyadh Saudi Arabia MOE Key Laboratory of Bio-Intelligent Manufacturing School of Bioengineering Dalian University of Technology Dalian China

With the rapid development of industry and frequent extreme weather events, sustainable agricultural production is increasingly confronted with various adversities. Improving the resistance of plants and eco-friendly microbial fertilizers to abiotic stresses is essential for sustainable agricultural development. Glycine betaine (GB), a trimethyl derivative of glycine, plays a crucial role as a cellular osmotic pressure regulator, enhancing the resistance of agricultural plants and microorganisms to stress. Betaine is typically obtained through natural extraction or chemical synthesis; however, the former faces seasonal and geographical limitations, whereas the latter is associated with environmental pollution. Additionally, the post-extraction processes for both methods can increase production costs. Biosynthesis via plants or microorganisms represents a more sustainable approach to betaine production, aligning with the principles of green agriculture. Genetically engineered plants incorporating the betaine synthesis pathway can directly increase GB production, improving plant stress resistance and eliminating the need for costly post-extraction processes. Similarly, genetically engineered microorganisms that construct or enhance the GB synthesis pathway can effectively increase GB production and associated resistance, with relatively simple post-extraction processes that further reduce costs and can be produced continuously. However, the GB synthesis pathway in microorganisms has been less studied. This review summarizes metabolic engineering studies of the GB synthesis pathway in plants and microorganisms and proposes strategies to further increase GB production in engineered microorganisms. In this paper, we review the methods of GB production, its protective effects, its synthesis pathways in organisms, and advancements in genetic engineering research. We also discuss the potential for developing genetically engineered plants or microorganisms for GB synthesis t

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Cooperative Hierarchical Coverage Control of Multi-Agent Systems with Non-holonomic Constraints in Poriferous Environments

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IEEE Transactions on Control of Network Systems 2025年

作者： Zou, Jiayu Zhang, Hai-Tao Zhai, Chao Xing, Ning Ma, Yong Liu, Xingjian Huazhong University of Science and Technology School of Artificial Intelligence and Automation The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China China University of Geosciences School of Automation China The MOE Engineering Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Academician Workstation of China COSCO Shipping Corporation Ltd. Shanghai200120 China Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572000 China Wuhan University of Technology Chongqing Research Institute Chongqing401120 China

It has long posed a challenging task to optimally deploy multi-agent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this paper proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering non-holonomic constraints, into designated niche positions. Additionally, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the non-convex characteristics inherent in the loss function induced by porous environments. This non-convexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach. © 2014 IEEE.

关键词： Nonlinear control systems

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Modality Unified Attack for Omni-Modality Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Modality unified attack Multi Modality Collaborative Disruption Omni-Modality re-id

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