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Tumor microenvironment reprogramming by nanomedicine to enhance the effect of tumor immunotherapy

Asian Journal of Pharmaceutical Sciences 2024年第2期19卷 1-37页

作者： Yu Huang hui fan huihui Ti School of Chinese Materia Medica Guangdong Pharmaceutical UniversityGuangzhou 510006China Guangdong Province Precise Medicine Big Date of Traditional Chinese Medicine Engineering Technology Research Center Guangdong Pharmaceutical UniversityGuangzhou 510006China School of Pharmacy Guangdong Pharmaceutical UniversityGuangzhou 510006China

With the rapid development of the fields of tumor biology and immunology, tumor immunotherapy has been used in clinical practice and has demonstrated significant therapeutic potential, particularly for treating tumors that do not respond to standard treatment options. Despite its advances, immunotherapy still has limitations, such as poor clinical response rates and differences in individual patient responses, largely because tumor tissues have strong immunosuppressive microenvironments. Many tumors have a tumor microenvironment (TME) that is characterized by hypoxia, low pH, and substantial numbers of immunosuppressive cells, and these are the main factors limiting the efficacy of antitumor immunotherapy. The TME is crucial to the occurrence, growth, and metastasis of tumors. Therefore, numerous studies have been devoted to improving the effects of immunotherapy by remodeling the TME. Effective regulation of the TME and reversal of immunosuppressive conditions are effective strategies for improving tumor immunotherapy. The use of multidrug combinations to improve the TME is an efficient way to enhance antitumor immune efficacy. However, the inability to effectively target drugs decreases therapeutic effects and causes toxic side effects. Nanodrug delivery carriers have the advantageous ability to enhance drug bioavailability and improve drug targeting. Importantly, they can also regulate the TME and deliver large or small therapeutic molecules to decrease the inhibitory effect of the TME on immune cells. Therefore, nanomedicine has great potential for reprogramming immunosuppressive microenvironments and represents a new immunotherapeutic strategy. Therefore, this article reviews strategies for improving the TME and summarizes research on synergistic nanomedicine approaches that enhance the efficacy of tumor immunotherapy.

关键词： Tumor immunotherapy Tumor micr oenvir onment Nanomedicine Tumor repr ogramming

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Multi-area Frequency-constrained Unit Commitment for Power Systems with High Penetration of Renewable Energy Sources and Induction Machine Load

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 754-766页

作者： Leibao Wang hui fan Jifeng Liang Longxun Xu Tiecheng Li Peng Luo Bo Hu Kaigui Xie the State Grid Hebei Electric Power Research Institute State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University

The increasing penetration of renewable energy sources(RESs) brings great challenges to the frequency security of power systems. The traditional frequency-constrained unit commitment(FCUC) analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines(IMs) in load, which may underestimate the risk and increase the operational cost. In this paper, we consider a multiarea frequency response(MAFR) model to capture the frequency dynamics in the unit scheduling problem, in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations. A multi-area FCUC(MFCUC) is formulated as mixed-integer nonlinear programming(MINLP) on the basis of the MAFR model. Then, we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently. The original MINLP is decomposed into a master problem and subproblems. The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution. Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs. Moreover, simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.

关键词： Security Frequency response Load modeling Indexes Costs Sensitivity Power system dynamics

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FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

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Cybersecurity 2024年第1期7卷 110-121页

作者： Kaisheng fan Weizhe Zhang Guangrui Liu hui He School of Cyberspace Science Harbin Institute of TechnologyHarbin 150001China Department of New Networks Peng Cheng LaboratoryShenzhen 518055GuangdongChina

Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.

关键词： AI security Model stealing attack Network intrusion detection Meta learning

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Hypomethylation of glycine dehydrogenase promoter in peripheral blood mononuclear cells is a new diagnostic marker of hepatitis B virus-associated hepatocellular carcinoma

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Hepatobiliary & Pancreatic Diseases International 2024年第1期23卷 35-42页

作者： Li-Li Miao Jing-Wen Wang hui-hui Liu Shuai Gao Yu-Chen fan Kai Wang Department of Hepatology Qilu Hospital of Shandong UniversityJinan 250012China Experimental Center Shandong University of Traditional Chinese MedicineJinan 250355China Institute of Hepatology Shandong UniversityJinan 250012China

Background: Glycine dehydrogenase(GLDC) plays an important role in the initiation and proliferation of several human cancers. In this study, we aimed to detect the methylation status of GLDC promoter and its diagnostic value for hepatitis B virus-associated hepatocellular carcinoma(HBV-HCC). Methods: We enrolled 197 patients, 111 with HBV-HCC, 51 with chronic hepatitis B(CHB), and 35 healthy controls(HCs). The methylation status of GLDC promoter in peripheral mononuclear cells(PBMCs) was identified by methylation specific polymerase chain reaction(MSP). The mRNA expression was examined using real-time quantitative polymerase chain reaction(q PCR). Results: The methylation frequency of the GLDC promoter was significantly lower in HBV-HCC patients(27.0%) compared to that in CHB patients(68.6%) and HCs(74.3%)( P < 0.001). The methylated group had lower alanine aminotransferase level( P = 0.035) and lower rates of tumor node metastasis(TNM) Ⅲ/Ⅳ( P = 0.043) and T3/T4( P = 0.026). TNM stage was identified to be an independent factor for GLDC promoter methylation. GLDC mRNA levels in CHB patients and HCs were significantly lower than those in HBV-HCC patients( P = 0.022 and P < 0.001, respectively). GLDC mRNA levels were significantly higher in HBV-HCC patients with unmethylated GLDC promoters than those with methylated GLDC promoters( P = 0.003). The diagnostic accuracy of alpha-fetoprotein(AFP) combined with GLDC promoter methylation for HBV-HCC was improved compared with that of AFP alone(AUC: 0.782 vs. 0.630, P < 0.001). In addition, GLDC promoter methylation was an independent predictor for overall survival of HBV-HCC patients( P = 0.038). Conclusions: The methylation frequency of GLDC promoter was lower in PBMCs from HBV-HCC patients than that from patients with CHB and HCs. The combination of AFP and GLDC promoter hypomethylation significantly improved the diagnostic accuracy of HBV-HCC.

关键词： Hepatocellular carcinoma Glycine dehydrogenase DNA methylation Peripheral blood mononuclear cells

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Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 708-722页

作者： Hongmin Liu Qi Zhang Yufan Hu hui Zeng Bin fan the School of Intelligence Science and Technology and the Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China IEEE

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

关键词： Multi-expert learning underwater image enhancement unsupervised learning

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Resolution Enhancement of SOHO/MDI Magnetograms

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Research in Astronomy and Astrophysics 2024年第6期24卷 360-369页

作者： Ying Qin Kai-fan Ji hui Liu Xiao-Guang Yu Yunnan Observatories Chinese Academy of Sciences University of Chinese Academy of Sciences Yunnan Key Laboratory of Solar Physics and Space Science Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of Sciences

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency. The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation. Within the realm of astronomical data processing,super-resolution(SR) reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR) and high-resolution(HR) images, thereby achieving higherresolution astronomical images. This paper is an application study in high-dimensional nonlinear regression. Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms, thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information. For this study, a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set, 1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set. After data preprocessing, we randomly cropped 128 × 128 sub-images as the LR cases from the full-disk MDI magnetograms, and the corresponding 512 × 512 sub-images as HR ones from the HMI full-disk magnetograms for model training. The tests conducted have shown that the study successfully produced reliable 4× SR reconstruction of full-disk MDI magnetograms. The MESR model's results(0.911) were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values. Furthermore, the method achieved the best PSNR, SSIM, MAE and RMSE values, indicating that the MESR model can effectively reconstruct magnetograms.

关键词： magnetic fields Sun: magnetic fields techniques: image processing virtual observatory tools

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Effects of acid-rock reaction on physical properties during CO_(2)-rich industrial waste gas(CO_(2)-rich IWG)injection in shale reservoirs

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Petroleum Science 2024年第1期21卷 272-285页

作者： Yi-fan Wang Jing Wang hui-Qing Liu Xiao-Cong Lv Ze-Min Ji National Key Laboratory of Petroleum Resources and Engineering China University of PetroleumBeijing102249China MOE Key Laboratory of Petroleum Engineering China University of PetroleumBeijing102249China Research Institute of Exploration and Development PetroChinaBeijing100083China

"Carbon peaking and carbon neutrality"is an essential national strategy,and the geological storage and utilization of CO_(2)is a hot issue today.However,due to the scarcity of pure CO_(2)gas sources in China and the high cost of CO_(2)capture,CO_(2)-rich industrial waste gas(CO_(2)-rich IWG)is gradually emerging into the public's gaze.CO_(2)has good adsorption properties on shale surfaces,but acidic gases can react with shale,so the mechanism of the CO_(2)-rich IWG-water-shale reaction and the change in reservoir properties will determine the stability of geological storage.Therefore,based on the mineral composition of the Longmaxi Formation shale,this study constructs a thermodynamic equilibrium model of water-rock reactions and simulates the regularity of reactions between CO_(2)-rich IWG and shale minerals.The results indicate that CO_(2)consumed 12%after reaction,and impurity gases in the CO_(2)-rich IWG can be dissolved entirely,thus demonstrating the feasibility of treating IWG through water-rock reactions.Since IWG inhibits the dissolution of CO_(2),the optimal composition of CO_(2)-rich IWG is 95%CO_(2)and 5%IWG when CO_(2)geological storage is the main goal.In contrast,when the main goal is the geological storage of total CO_(2)-rich IWG or impurity gas,the optimal CO_(2)-rich IWG composition is 50%CO_(2)and 50%IWG.In the CO_(2)-rich IWG-water-shale reaction,temperature has less influence on the water-rock reaction,while pressure is the most important parameter.SO2 has the greatest impact on water-rock reaction in gas.For minerals,clay minerals such as illite and montmorillonite had a significant effect on water-rock reaction.The overall reaction is dominated by precipitation and the volume of the rock skeleton has increased by 0.74 cm3,resulting in a decrease in shale porosity,which enhances the stability of CO_(2)geological storage to some extent.During the reaction between CO_(2)-rich IWG-water-shale at simulated temperatures and pressures,precipitation is the main reaction,and shale porosity decrease

关键词： CO_(2)-rich industrial waste gas Geological storage Acid-rock reaction Shale Geochemical modelling

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Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

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China Communications 2024年第4期21卷 38-52页

作者： Jiang fan Qin Junwei Liu Lei Tian hui School of Communications and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

关键词： associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC)

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Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

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Opto-Electronic Advances 2024年第1期7卷 36-46页

作者： Yuncheng Liu Ke Xu Xuhao fan Xinger Wang Xuan Yu Wei Xiong hui Gao Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Optics Valley Laboratory Wuhan 430074China

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

关键词： interactive display meta-holography bitwise operation ultra-high frame rate

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A survey on model-based reinforcement learning

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Science China(Information Sciences) 2024年第2期67卷 59-84页

作者： fan-Ming LUO Tian XU Hang LAI Xiong-hui CHEN Weinan ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir. ai Department of Computer Science and Engineering Shanghai Jiao Tong University

Reinforcement learning(RL) interacts with the environment to solve sequential decision-making problems via a trial-and-error approach. Errors are always undesirable in real-world applications, even though RL excels at playing complex video games that permit several trial-and-error attempts. To improve sample efficiency and thus reduce errors, model-based reinforcement learning(MBRL) is believed to be a promising direction, as it constructs environment models in which trial-and-errors can occur without incurring actual costs. In this survey, we investigate MBRL with a particular focus on the recent advancements in deep RL. There is a generalization error between the learned model of a non-tabular environment and the actual environment. Consequently, it is crucial to analyze the disparity between policy training in the environment model and that in the actual environment, guiding algorithm design for improved model learning, model utilization, and policy training. In addition, we discuss the recent developments of model-based techniques in other forms of RL, such as offline RL, goal-conditioned RL, multi-agent RL, and meta-RL. Furthermore,we discuss the applicability and benefits of MBRL for real-world tasks. Finally, this survey concludes with a discussion of the promising future development prospects for MBRL. We believe that MBRL has great unrealized potential and benefits in real-world applications, and we hope this survey will encourage additional research on MBRL.

关键词： reinforcement learning model-based reinforcement learning planning model learning model learning with reduced error model usage

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