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Semisynthesis of homogeneous spike RBD glycoforms from SARS-CoV-2 for profiling the correlations between glycan composition and function

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National science Review 2024年第2期11卷 145-154页

作者： Farong Ye Cheng Li Feng-Liang Liu Xinliang Liu Peng Xu Rong-Hua Luo Wenping Song Yong-Tang Zheng Tianlei Ying Biao Yu Ping Wang center for Chemical Glycobiology Frontiers Science Center for Transformative Molecules Zhangjiang Institute for Advanced Study School of Chemistry and Chemical Engineering Shanghai Jiao Tong University MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection Shanghai Institute of Infectious Disease and BiosecurityShanghai Engineering Research Center for Synthetic ImmunologySchool of Basic Medical Sciences Shanghai Medical College Fudan University Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of sciences/Key Laboratory of Bioactive Peptides of Yunnan Province Center for Biosafety Mega-Science Kunming Institute of ZoologyChinese Academy of Sciences State Key Laboratory of Bioorganic and Natural Products Chemistry Center for Excellence in Molecular Synthesis Shanghai Institute of Organic Chemistry Chinese Academy of Sciences Shenzhen research Institute of Shanghai Jiao Tong University

Vaccines have been the primary remedy in the global fight against coronavirus disease 2019（COVID-19）.The receptor-binding domain（RBD） of the spike protein, a critical viral immunogen, is affected by the heterogeneity of its glycan structures and relatively low immunogenicity. Here, we describe a scalable synthetic platform that enables the precise synthesis of homogeneously glycosylated RBD, facilitating the elucidation of carbohydrate structure–function relationships. Five homogeneously glycosylated RBDs bearing biantennary glycans were prepared, three of which were conjugated to T-helper epitope(Tpep) from tetanus toxoid to improve their weak immune response. Relative to natural HEK293-derived RBD, synthetic RBDs with biantennary N-glycan elicited a higher level of neutralising antibodies against SARS-CoV-2 in mice. Furthermore, RBDs containing Tpepelicited significant immune responses in transgenic mice expressing human angiotensin-converting enzyme 2. Our collective data suggest that trimming the N-glycans and Tpepconjugation of RBD could potentially serve as an effective strategy for developing subunit vaccines providing efficient protection.

关键词： peptide ligation glycosylation glycoprotein

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology research center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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The stance and factors of international organizations towards countries from a Chinese perspective

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data Intelligence 2024年第1期6卷 158-182页

作者： Qin Zhang Haili Xue Yaotian Wang Yao Wang Ziqin Zhang Qinghua Qin Haoguang Liang National science Library Chinese Academy of Sciences100190China China center for Modernization research 100190China Beijing International Studies University 100024China Beijing Key Laboratory of National Belt and Road data Analysis and Decision Support 100024China University College London WC1E 6BTUK

The degree and scope of constraints imposed by International Organizations(IOs)on States are increasing,and identifying the factors affecting the IOs’stances on States is helpful to enhance the state's discourse power and influence in the international community.First,by coding the records of Regular Press Conferences of the Speaking Office of the Chinese Ministry of Foreign Affairs during the period of 20182022,we obtained a dataset of IOs’stances on China-related events.Second,we constructed political relation,economic relation,and humanistic relation indicators to complement the influence factors,adopted the Bayesian logit model,and applied the Monte Carlo Markov chain algorithm and Gibbs sampling to analyze the probability of IOs’positive stances towards China.The result shows that IOs’category,length of establishment,functional position,and relationship with China are all related to their tendency of making a statement about China.In terms of the heterogeneity of event types,forum-type IOs are significantly inclined to give positive assessment compared to service-type IOs on events focusing on China's own development.Further analysis reveals that the model for analyzing and predicting the attitudes of IOs is more effective when the international situation is in a stable period.

关键词： Bayesian Logit Model IOs' stances Attitude analysis Monte Carlo Markov chain International relation indicator

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Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

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Energy Engineering 2024年第3期121卷 747-767页

作者： Jun Zhao Chaoying Yang Ran Li Jinge Song Grid center State Grid Shanxi Electric Power Company Electric Power Science Research InstituteTaiyuan030000China

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

关键词： Demand response model predictive control multiple time scales operating simulation

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Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

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Advances in Atmospheric sciences 2024年第8期41卷 1511-1526页

作者： Shengping HE Helge DRANGE Tore FUREVIK Huijun WANG Ke FAN Lise Seland GRAFF Yvan J.ORSOLINI Geophysical Institute University of Bergen and Bjerknes Centre for Climate Research Nansen Environmental and Remote Sensing center Nansen-Zhu International research center Institute of Atmospheric Physics Chinese Academy of Sciences Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD) Nanjing University of Information Science & Technology Southern Marine science and Engineering Guangdong Laboratory (Zhuhai) School of Atmospheric science Sun Yat-Sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Norwegian Meteorological Institute Norwegian Institute for Air research

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment; the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

关键词： Arctic sea ice loss atmospheric internal variability large-ensemble simulation NorESM2 PAMIP warm Arctic–cold East Asia

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace research center Harbin150001China Information Network Engineering and research center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital science research center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and data science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Predictors of Adverse Pregnancy Outcomes Following Traumatic Injuries

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Current Medical science 2024年第3期44卷 642-647页

作者： Wan-rong LU Ping WU Gong SONG Mei-qi GU Zhe XU Li HE National Clinical research center for Gynecology and Obstetrics Cancer Biology Research CenterTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Department of Traumatic Surgery Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China

Objective After traumatic injury in pregnant women,providing timely and appropriate management for high-risk patients is crucial for both pregnant women and fetuses.This study aimed to identify risk factors that predict adverse pregnancy outcomes after traumatic injury.Methods A retrospective cohort study including 317 pregnant patients who experienced trauma was conducted.The collected data included general demographics,injury mechanisms and adverse pregnancy outcomes.Patients were divided into two subgroups based on the absence or presence of trauma-related adverse pregnancy outcomes.Univariate and multivariate logistic regressions were conducted to estimate the associations between clinical variables and adverse pregnancy outcomes.Results A total of 41(12.93%)patients experienced adverse pregnancy outcomes within the first 24 h post-trauma.This study revealed that age>35 years(OR=14.995,95%CI:5.024–44.755,P<0.001),third trimester trauma(OR=3.878,95%CI:1.343–11.204,P=0.012),abdominal pain(OR=3.032,95%CI:1.221–7.527,P=0.017),vaginal bleeding(OR=3.226,95%CI:1.093–9.523,P=0.034),positive scan in focused assessment with sonography for trauma(FAST)positive(OR=8.496,95%CI:2.825–25.555,P<0.001),9≤injury severity score(ISS)<16(OR=3.039,95%CI:1.046–8.835,P=0.041)and ISS≥16(OR=5.553,95%CI:1.387–22.225,P=0.015)increased the probability of posttraumatic adverse pregnancy outcomes.Maternal age,gestational age at delivery,vaginal bleeding and positive FAST results were risk factors for abnormal delivery.Conclusion Advanced maternal age,third trimester,and positive FAST results should alert multidisciplinary trauma teams to closely monitor patients to prevent adverse pregnancy outcomes.

关键词： adverse pregnancy outcomes predictive factors abnormal delivery trauma

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Impact of device resistances in the performance of graphene‑based terahertz photodetectors

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Frontiers of Optoelectronics 2024年第2期17卷 87-97页

作者： O.Castelló Sofía M.López Baptista K.Watanabe T.Taniguchi E.Diez J.E.Velázquez-Pérez Y.M.Meziani J.M.Caridad J.A.Delgado-Notario Department of Applied Physics University of Salamanca37008 SalamancaSpain Unidad de Excelencia en Luz y Materia Estructurada(LUMES) University of Salamanca37008 SalamancaSpain research center for Electronic and Optical Materials National Institute for Materials Science1-1 NamikiTsukuba 305-0044Japan research center for Materials Nanoarchitectonics National Institute for Materials Science1-1 NamikiTsukuba 305-0044Japan Nanotechnology Group USAL-NanolabUniversity of Salamanca37008 SalamancaSpain

In recent years,graphene field-effect-transistors(GFETs)have demonstrated an outstanding potential for terahertz(THz)photodetection due to their fast response and high-sensitivity.Such features are essential to enable emerging THz applications,including 6G wireless communications,quantum information,bioimaging and security.However,the overall performance of these photodetectors may be utterly compromised by the impact of internal resistances presented in the device,so-called access or parasitic resistances.In this work,we provide a detailed study of the influence of internal device resistances in the photoresponse of high-mobility dual-gate GFET detectors.Such dual-gate architectures allow us to fine tune(decrease)the internal resistance of the device by an order of magnitude and consequently demonstrate an improved responsivity and noise-equivalent-power values of the photodetector,respectively.Our results can be well understood by a series resistance model,as shown by the excellent agreement found between the experimental data and theoretical calculations.These findings are therefore relevant to understand and improve the overall performance of existing high-mobility graphene photodetectors.

关键词： Graphene THz Photodetector Field-effect transistor Plasmonic

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The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

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Journal of Biomedical research 2024年第2期38卷 175-188,I0016-I0018页

作者： Ziyu Zhao Yi Zhou Jinxing Guan Yan Yan Jing Zhao Zhihang Peng Feng Chen Yang Zhao Fang Shao Department of Biostatistics School of Public HealthNanjing Medical UniversityNanjingJiangsu 211166China Nanjing Hanwei Public Health research Institute Co. LtdNanjingJiangsu 210005China China International Cooperation center for Environment and Human Health Center for Global HealthNanjing Medical UniversityNanjingJiangsu 211166China The center of Biomedical Big data and the Laboratory of Biomedical Big data Nanjing Medical UniversityNanjingJiangsu 211166China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer Personalized MedicineNanjing Medical UniversityNanjingJiangsu 211166China

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

关键词： compartment models agent-based models compartment-agent mixed models comparative study COVID-19

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