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Low-input redoxomics facilitates global identification of metabolic regulators of oxidative stress in the gut

Signal Transduction and Targeted Therapy 2025年第2期10卷 885-902页

作者： Xina Xiao Meng Hu Li Gao Huan Yuan Baochen Chong Yu Liu Rou Zhang Yanqiu Gong Dan Du Yong Zhang Hao Yang Xiaohui Liu Yan Zhang Huiyuan Zhang Heng Xu Yi Zhao Wenbo Meng Dan Xie Peng Lei Shiqian Qi Yong Peng Tao Tan Yang Yu Hongbo Hu Biao Dong Lunzhi Dai National Clinical Research Center for Geriatrics State Key Laboratory of BiotherapyWest China HospitalSichuan UniversityChengduChina Advanced Mass Spectrometry Center Research Core FacilityFrontiers Science Center for Disease-Related Molecular NetworkNHC Key Lab of Transplant Engineering and ImmunologyWest China HospitalSichuan UniversityChengduChina School of Life Sciences Tsinghua UniversityBeijingChina Department of Rheumatology and Immunology West China HospitalSichuan UniversityChengduChina The First School of Clinical Medicine Lanzhou UniversityLanzhouChina State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingChina Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology and Key Laboratory of Assisted Reproduction Ministry of EducationCenter for Reproductive MedicineDepartment of Obstetrics and GynecologyPeking University Third HospitalBeijingChina Frontiers Medical Center Tianfu Jincheng LaboratoryChengduChina

Oxidative stress plays a crucial role in organ aging and related diseases, yet the endogenous regulators involved remain largelyunknown. This work highlights the importance of metabolic homeostasis in protecting against oxidative stress in the large *** developing a low-input and user-friendly pipeline for the simultaneous profiling of five distinct cysteine (Cys) states, includingfree SH, total Cys oxidation (Sto), sulfenic acid (SOH), S-nitrosylation (SNO), and S-glutathionylation (SSG), we shed light on Cysredox modification stoichiometries and signaling with regional resolution in the aging gut of monkeys. Notably, the proteinsmodified by SOH and SSG were associated primarily with cell adhesion. In contrast, SNO-modified proteins were involved inimmunity. Interestingly, we observed that the Sto levels ranged from 0.97% to 99.88%, exhibiting two distinct peaks and increasingwith age. Crosstalk analysis revealed numerous age-related metabolites potentially involved in modulating oxidative stress and Cysmodifications. Notably, we elucidated the role of fumarate in alleviating intestinal oxidative stress in a dextran sulfate sodium (DSS)-induced colitis mouse model. Our findings showed that fumarate treatment promotes the recovery of several cell types, signalingpathways, and genes involved in oxidative stress regulation. Calorie restriction (CR) is a known strategy for alleviating oxidativestress. Two-month CR intervention led to the recovery of many antioxidative metabolites and reshaped the Cys redoxome. Thiswork decodes the complexities of redoxomics during the gut aging of non-human primates and identifies key metabolic regulatorsof oxidative stress and redox signaling.

关键词： metabolic homeostasis total cys oxidation cysteine modifications sulfenic acid gut aging redoxomics oxidative stress

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Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval

arXiv

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arXiv 2022年

作者： Ma, Xinyu Guo, Jiafeng Zhang, Ruqing Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China

Pre-training and fine-tuning have achieved significant advances in the information retrieval (IR). A typical approach is to fine-tune all the parameters of large-scale pre-trained models (PTMs) on downstream tasks. As the model size and the number of tasks increase greatly, such approach becomes less feasible and prohibitively expensive. Recently, a variety of parameter-efficient tuning methods have been proposed in natural language processing (NLP) that only fine-tune a small number of parameters while still attaining strong performance. Yet there has been little effort to explore parameter-efficient tuning for IR. In this work, we first conduct a comprehensive study of existing parameter-efficient tuning methods at both the retrieval and re-ranking stages. Unlike the promising results in NLP, we find that these methods cannot achieve comparable performance to full fine-tuning at both stages when updating less than 1% of the original model parameters. More importantly, we find that the existing methods are just parameter-efficient, but not learning-efficient as they suffer from unstable training and slow convergence. To analyze the underlying reason, we conduct a theoretical analysis and show that the separation of the inserted trainable modules makes the optimization difficult. To alleviate this issue, we propose to inject additional modules alongside the pre-trained models (PTMs) to make the original scattered modules connected. In this way, all the trainable modules can form a pathway to smooth the loss surface and thus help stabilize the training process. Experiments at both retrieval and re-ranking stages show that our method outperforms existing parameter-efficient methods significantly, and achieves comparable or even better performance over full fine-tuning. Copyright © 2022, The Authors. All rights reserved.

关键词： Information retrieval

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Multi-action self-healing coatings with simultaneous recovery of corrosion resistance and adhesion strength

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Journal of Materials science & technology 2022年第6期101卷 18-27页

作者： Chenhao Ren Yao Huang Wenkui Hao Dawei Zhang Xiejing Luo Lingwei Ma Jinke Wang Thee Chowwanonthapunya Chaofang Dong Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Advanced Power Transmission Technology(Global Energy Interconnection Research Institute Co.Ltd.) Beijing 100083China Faculty of International Maritime Studies Kasetsart UniversitySriracha Campus199 TungsuklaSrirachaChonburi 20230Thailand BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528300China

In this study, a new self-healing strategy that can simultaneously recover the corrosion resistance and the adhesion strength of coatings was introduced. The coating was based on a shape memory epoxy resin containing ethylene vinyl acetate(EVA) microspheres loaded with Ce(NO_(3))_(3)inhibitors, and was cured at a relatively high temperature to facilitate the fusion of adjacent microspheres for a strengthened self-healing effect. The electrochemical impedance spectroscopy(EIS) and scanning electrochemical microscopy(SECM) results demonstrated that the shape memory effect of epoxy matrix, the filling of molten EVA microspheres as well as the release of Ce(NO_(3))_(3)inhibitors contributed synergistically to suppress the corrosion reaction at the coating damage. After healing, the low frequency impedance modulus of the coatings containing Ce(NO_(3))_(3)-EVA microspheres was three orders of magnitude higher than that of the blank epoxy coating. The adhesion strength of the coatings containing Ce(NO_(3))_(3)-EVA microspheres on the metal substrate was also largely repaired thanks to the strong bonding effect of the EVA microspheres.

关键词： Self-healing coating Corrosion protection Adhesion strength Shape memory effect Bonding effect

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Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection 34

Ethereum Smart Contract Representation Learning for Robust B...

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34th International Conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Photothermal and pH dual-responsive self-healing coating for smart corrosion protection

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Journal of Materials science & technology 2022年第12期107卷 34-42页

作者： Yao Huang Panjun Wang Weimin Tan Wenkui Hao Lingwei Ma Jinke Wang Tong Liu Fan Zhang Chenhao Ren Wei Liu Dawei Zhang Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China National Engineering Research Center for Coatings CNOOC Changzhou Paint and Coatings Industry Research InstituteChangzhou 213016China State Key Laboratory of Advanced Power Transmission Technology(Global Energy Interconnection Research Institute Co.Ltd. )Beijing 100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528300China

A novel self-healing coating with photothermal and pH dual-responsive properties has been designed to protect carbon steel against corrosion by loading the stimuli-responsive microcapsules into a shape memory epoxy coating. The sandwich-like microcapsules were based on reduced graphene oxide/mesoporous silica(r GO@MS) assembled with a p H-responsive poly(N,N-dimethylaminoethyl methacrylate)(PDMAEMA) layer, and were loaded with benzotriazole(BTA) inhibitors(abbreviated as rGO@MS-P-BTA). Under near-infrared(NIR) light irradiation, the prominent photothermal effect of rGO could not only elevate the coating temperature to activate the shape memory effect and close the coating scratch, but also facilitate the release of corrosion inhibitors to suppress the corrosion activity. Moreover,the PDMAEMA as a p H-driven “gatekeeper” realized the controlled release of BTA from microcapsules at acid conditions. The surface morphology analysis, electrochemical impedance spectroscopy(EIS), and scanning electrochemical microscopy(SECM) were performed to evaluate the self-healing performance of the composite coatings. The results showed that the combination of NIR light and p H-responsive selfhealing effects endowed the coating with short healing time and prominent healing efficiency.

关键词： Self-healing coatings Corrosion inhibitors Photothermal effects pH-responsive Shape memory polymers

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SDWD: Style Diversity Weighted Distance Evaluates the Intra-Class data Diversity of Distributed GANs

SDWD: Style Diversity Weighted Distance Evaluates the Intra-...

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IEEE International Conference on Image Processing

作者： Wei Wang Ziwen Wu Mingwei Zhang Yue Li Trusted AI Laboratory College of Computer Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

Due to the distributed storage of massive data, efficient deployment of Generative Adversarial networks (GANs) in distributed scenarios has become a hot topic. This paper analyzes the characteristics of existing distributed GANs. Moreover, it discusses the evaluation methods of these models. Through experiments, we found that Fréchet Inception Distance (FID) cannot sensitively reflect the diversity of intra-class data, and the evaluation effect of different distributed GANs is greatly affected by different datasets, so we cannot accurately rank the models. To more accurately evaluate the generation capability of distributed GANs, we propose a novel evaluation model, Style Diversity Weighted Distance (SDWD), which introduces a Siamese network to measure data similarity. We conduct experiments on two datasets, and the results prove that SDWD can sensitively reflect the intra-class diversity of data and can reliably evaluate different models and frameworks.

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He’s Homotopy Perturbation Method and Fractional Complex Transform for Analysis Time Fractional Fornberg-Whitham Equation

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Sound & Vibration 2021年第4期55卷 295-303页

作者： Yanni Zhang Jing Pang College of Science Inner Mongolia University of TechnologyHohhot010000China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010000China

In this article,time fractional Fornberg-Whitham equation of He’s fractional derivative is *** transform the fractional model into its equivalent differential equation,the fractional complex transform is used and He’s homotopy perturbation method is implemented to get the approximate analytical solutions of the fractional-order *** graphs are plotted to analysis the fractional-order mathematical modeling.

关键词： Time fractional Fornberg-Whitham equation fractional complex transform He’s homotopy perturbation method

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Differentiable and Scalable Generative Adversarial Models for data Imputation (Extended Abstract) 40

Differentiable and Scalable Generative Adversarial Models fo...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wu, Yangyang Wang, Jun Miao, Xiaoye Wang, Wenjia Yin, Jianwei Software College Zhejiang University Ningbo China Academy of Interdisciplinary Studies The Hong Kong University of Science and Technology Hong Kong Hong Kong Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Guangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350317152

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Moreover, SCIS can also accelerate the autoencoder based imputation models. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 6.23x. Using around 1.27% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in much shorter computation time. © 2024 IEEE.

关键词： Large datasets

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MDFD: Study of Distributed Non-IID Scenarios and Frechet Distance-Based Evaluation

MDFD: Study of Distributed Non-IID Scenarios and Frechet Dis...

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IEEE International Conference on Image Processing

作者： Wei Wang Mingwei Zhang Ziwen Wu Qianxi Chen Yue Li Trusted AI Laboratory College of Computer Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

With the development of distributed machine learning and federated learning, the solution to the data island problem is promoted. People use computer clusters to train machine learning models on data distributed in different regions. In the early stage of research, researchers usually assume that the data sets of each node are independent identically distribution (IID), but this is a strong assumption, which is challenging to meet in practical applications. Therefore, research on non-IID has become a hot spot in recent years. However, there is no uniform standard for designing and evaluating non-IID scenarios. This paper proposes a Frechet distance-independent non-IID distribution dataset metric MDFD. And we conducted experiments on different types of distributed machine-learning methods in different non-IID scenarios to verify the effectiveness of MDFD.

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Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Transformer for Medical Image Segmentation

Diff-SFCT: A Diffusion Model with Spatial-Frequency Cross Tr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Yuxuan Ding, Yi Zhu, Guobin Qin, Zhen Pang, Minghui Cao, Mingsheng University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu China Yibin Great Technology Co. Ltd. Yibin644000 China Sichuan Provincial People's Hospital Department of Geriatric General Surgery Chengdu610054 China

ISBN: (纸本)9798350337488

Most existing semantic segmentation methods primarily employ supervised learning with discriminative models. Although these methods are straightforward, they overlook the modeling of underlying data distributions. In this paper, we propose a novel medical image segmentation framework called Diff-SFCT based on Diffusion Model. We formulate semantic segmentation as a generative problem for segmentation masks, replacing the conventional pixel-wise discriminative learning with a latent prior learning process to produce more accurate segmentation results. Diff-SFCT employs a backbone network combining Convolutional Neural network (CNN) and Transformer, and utilizes the local perception of CNN and the global information modeling capability of Transformer. In Diff-SFCT, we design a Semantic Encoder that effectively extracts fine-grained semantic features from real images. Meanwhile, we propose a novel Spatial-Frequency Cross Transformer (SFCT) framework, which can effectively model and interact the global features of the diffuse noise mask and the real semantic features, reducing the domain gap between the two and enhancing the model's representational capacity. Additionally, to preserve spatial and frequency information in the diffusion model, we design a Spatial-Frequency Attention Module (SFAM) as part of the Convolutional Block. This module improves the model's spatial and frequency perception abilities while incurring negligible computational overhead. Experimental results evince that our DiffSFCT substantially outperforms other segmentation methods, exhibiting remarkable performance across various medical image segmentation datasets. © 2023 IEEE.

关键词： Attention mechanism CNN Diffusion Model Medical Image Segmentation Transformer

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