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Metal nanoparticles for cancer therapy:Precision targeting of DNA damage

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Acta Pharmaceutica Sinica B 2024年第3期14卷 1132-1149页

作者： Qian Chen Chunyan Fang Fan Xia qiyue Wang Fangyuan Li Daishun Ling Institute of Pharmaceutics Hangzhou Institute of Innovative MedicineCollege of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Frontiers Science Center for Transformative Molecules School of Chemistry and Chemical EngineeringNational Center for Translational MedicineShanghai Jiao Tong UniversityShanghai 200240China World Laureates Association(WLA)Laboratories Shanghai 201203China Key Laboratory of Precision Diagnosis and Treatment for Hepatobiliary and Pancreatic Tumor of Zhejiang Province Hangzhou 310009China

Cancer,a complex and heterogeneous disease,arises from genomic instability.Currently,DNA damage-based cancer treatments,including radiotherapy and chemotherapy,are employed in clinical practice.However,the efficacy and safety of these therapies are constrained by various factors,limiting their ability to meet current clinical demands.Metal nanoparticles present promising avenues for enhancing each critical aspect of DNA damage-based cancer therapy.Their customizable physicochemical properties enable the development of targeted and personalized treatment platforms.In this review,we delve into the design principles and optimization strategies of metal nanoparticles.We shed light on the limitations of DNA damage-based therapy while highlighting the diverse strategies made possible by metal nanoparticles.These encompass targeted drug delivery,inhibition of DNA repair mechanisms,induction of cell death,and the cascading immune response.Moreover,we explore the pivotal role of physicochemical factors such as nanoparticle size,stimuli-responsiveness,and surface modification in shaping metal nanoparticle platforms.Finally,we present insights into the challenges and future directions of metal nanoparticles in advancing DNA damage-based cancer therapy,paving the way for novel treatment paradigms.

关键词： DNA damage Metal nanoparticles Nucleus-targeting DNA repair inhibition Immune response Size optimization Stimuli-responsiveness Surface modification

Revealing the size effect of Fe S2on solid-state battery performances at different operating temperatures

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Chinese Chemical Letters 2024年第6期35卷 189-194页

作者： Chaochao Wei Ru Wang Zhongkai Wu qiyue Luo Ziling Jiang Liang Ming Jie yang Liping Wang Chuang Yu School of Chemistry and Chemical Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 611731China

FeS_(2) shows significant potential as cathode material for all-solid-state lithium batteries(ASSLBs)due to its high theoretical specific capacity,low cost,and environmental friendliness.However,the poor ion/electron conductivity and large volume variation effect of FeS_(2) inhibit its practical applications.Here,the influence of particle size of FeS_(2) on the corresponding sulfide-based solid-state batteries is carefully investigated by tuning FeS_(2) size.Moreover,low operating temperature is chosen to mitigate the large volume changes during cycling in the battery.S-FeS_(2) with smaller particle sizes delivers superior electrochemical performances than that of the larger L-FeS_(2) in Li_(5.5)PS_(4.5)Cl_(1.5)-based ASSLBs under different operating temperatures.S-FeS_(2) shows stable discharge capacities during 50 cycles with a current density of 0.1 m A/cm^(2)under -20℃.When the current density rises to 1.0 m A/cm^(2),it delivers an initial discharge capacity of 146.9 m Ah/g and maintains 63% of the capacity after 100 cycles.This work contributes to constructing ASSLBs enables excellent electrochemical performances under extreme operating temperatures.

关键词： FeS_(2) Size effect Li_(5.5)PS_(4.5)Cl_(1.5)electrolytes Operating temperatures Electrochemical performances

Distributed Deep Reinforcement Learning:A Survey and a Multi-player Multi-agent Learning Toolbox

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Machine Intelligence Research 2024年第3期21卷 411-430页

作者： qiyue Yin Tongtong Yu Shengqi Shen Jun yang Meijing Zhao Wancheng Ni Kaiqi Huang Bin Liang Liang Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Automation Tsinghua UniversityBeijing 100084China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

With the breakthrough of AlphaGo,deep reinforcement learning has become a recognized technique for solving sequential decision-making problems.Despite its reputation,data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas.Many methods have been developed for sample efficient deep reinforcement learning,such as environment modelling,experience transfer,and distributed modifications,among which distributed deep reinforcement learning has shown its potential in various applications,such as human-computer gaming and intelligent transportation.In this paper,we conclude the state of this exciting field,by comparing the classical distributed deep reinforcement learning methods and studying important components to achieve efficient distributed learning,covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning.Furthermore,we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions.By analysing their strengths and weaknesses,a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released,which is further validated on Wargame,a complex environment,showing the usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games.Finally,we try to point out challenges and future trends,hoping that this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning.

关键词： Deep reinforcement learning distributed machine learning self-play population-play toolbox

Construction of LiCl/LiF/LiZn hybrid SEI interface achieving high-performance sulfide-based all-solid-state lithium metal batteries

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Science China Chemistry 2024年第6期67卷 1990-2001页

作者： Chaochao Wei Yujie Xiao Zhongkai Wu Chen Liu qiyue Luo Ziling Jiang Lin Li Liang Ming Jie yang Shijie Cheng Chuang Yu State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan430074China School of Chemistry and Chemical Engineering Huazhong University of Science and TechnologyWuhan430074China

Sulfide-based all-solid-state lithium metal batteries(ASSLMBs)have received extensive attention due to their high energy density and high safety,while the poor interface stability between sulfide electrolyte and lithium metal anode limits their development.Hence,a hybrid SEI(LICl/Li F/Li Zn)was constructed at the interface between Li_(5.5)PS_(4.5)Cl_(1.5)sulfide electrolyte and lithium metal.The Li Cl and Li F interface phases with high interface energy effectively induce the uniform deposition of Li^(+)and reduce the overpotential of Li^(+)deposition,while the Li Zn alloy interface phase accelerates the diffusion of lithium ions.The synergistic effect of the above functional interface phases inhibits the growth of lithium dendrites and stabilizes the interface between the sulfide electrolyte and lithium metal.The hybrid SEI strategy exhibits excellent electrochemical performance on symmetric batteries and all-solid-state batteries.The symmetrical cell exhibits stable cycling performance over long duration over 500 h at 1.0 mA cm^(-2).Moreover,the LiNbO_(3)@NCM712/Li_(5.5)PS_(4.5)Cl_(1.5)/Li-10%Zn F_(2)battery exhibits excellent cycle stability at a high rate of 0.5 C,with a capacity retention rate of 76.4%after 350 cycles.

关键词： argyrodite electrolytes ASSLMBs electrochemical stability Li-ZnF_2 anode electrochemical performance

Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1220-1230页

作者： qiyue Li Yadong Zhu Jinjin Ding Weitao Li Wei Sun Lijian Ding Hefei University of Technology Engineering Technology Research Center of Industrial Automation Hefei 230009China Electric Power Research Institute of State Grid Anhui Electric Power Co. Ltd.Hefei 230000China Hefei University of Technology Hefei 230009China IEEE CSEE

Real-time fault detection is important for operation of smart grid.It has become a trend of future development to design an anomaly detection system based on deep learning by using the powerful computing power of the cloud.However,delay of Internet transmission is large,which may make the delay time of detection and transmission go beyond the limits.However,the edge-based scheme may not be able to undertake all data detection tasks due to limited computing resources of edge devices.Therefore,we propose a cloud-edge collaborative smart grid fault detection system,next to which edge devices are placed,and equipped with a lightweight neural network with different precision for fault detection.In addition,a sub-optimal and realtime communication and computing resource allocation method is proposed based on deep reinforcement learning.This method greatly speeds up solution time,which can meet the requirements of data transmission delay,maximize the system throughput,and improve communication efficiency.Simulation results show the scheme is superior in transmission delay and improves real-time performance of the smart grid detection system.

关键词： Cloud-edge collaboration communication delay deep reinforcement learning fault detection smart grid

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

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Frontiers of Environmental Science & Engineering 2024年第1期18卷 97-109页

作者： qiyue Wu Yun Geng Xinyuan Wang Dongsheng Wang ChangKyoo Yoo Hongbin Liu Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control College of Light Industry and Food EngineeringGuangxi UniversityNanning 530004China Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province Shandong Huatai Paper Co.Ltd.Dongying 257335China Department of Environmental Science and Engineering College of EngineeringKyung Hee UniversityYongin 446701Republic of Korea

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

关键词： Indoor air quality PM_(2.5)concentration Variational auto-encoder Latent variable Soft measurement modeling

Experimental and numerical analyses of the effect of fibre content on the close-in blast performance of a UHPFRC beam

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Defence Technology（防务技术） 2024年第1期31卷 242-261页

作者： Junbo Yan qiyue Zhang Yan Liu Yingliang Xu Zhenqing Shi Fan Bai Fenglei Huang State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China

Limited research has been conducted on the influences of fiber content on close-in blasting characteristics for ultrahigh-performance fiber-reinforced concrete(UHPFRC)beams.This paper aims to address this knowledge gap through experimental and mesoscale numerical methods.Experiments were conducted on ten UHPFRC beams built with varying steel fiber volumetric fractions subjected to close-in explosive conditions.Additionally,this study considered other parameters,such as the longitudinal reinforcement type and ratio.In the case of UHPFRC beams featuring normal-strength longitudinal reinforcement of diametersΦ12,Φ16,andΦ20,a reduction in maximum displacement by magnitudes of19.6%,19.5%,and 17.4%was observed,respectively,as the volumetric fractions of fiber increased from1.0%to 2.5%.In addition,increasing the longitudinal reinforcement ratio and using high-strength steel longitudinal reinforcement both significantly reduced the deformation characteristics and increase the blasting resistances of UHPFRC beams.However,the effects on the local crushing and spalling damage were not significant.A mesoscale finite element model,which considers the impacts of fiber parameters on UHPFRC beam behaviors,was also established and well correlated with the test findings.Nevertheless,parametric analyses were further conducted to examine the impacts of the steel fiber content and length and the hybrid effects of various types of microfibers and steel fibers on the blasting performance of UHPFRC beams.

关键词： Blast performance Close-in blast Fiber content Mesoscale approach UHPFRC beams

Ablative behavior and mechanism of SiC_(f) /SiBCZr composites prepared by PIP process

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Journal of Advanced Ceramics 2024年第1期13卷 124-134页

作者： Mingwei Chen Yuliang Zhao Haipeng Qiu Shijian Liu Weijie Xie Yi Chen qiyue Zhang AVIC Manufacturing Technology Institute Beijing 100130China

SiC_(f)/SiBCZr composites were prepared by polymer precursor impregnation and pyrolysis process with near stoichiometric ratio SiC fiber preform as reinforcement phase and SiBCZr multiphase ceramic precursor as impregnating reagent.The results highlighted that the SiC_(f)/SiBCZr composites exhibited excellent ablative properties after ablative tests at 1200℃/3600 s and 1400℃/3600 s,and the strength retention rates of the composites reached 90%and 85%,respectively.This was mainly due to the liquid sealing effect of the ablative products represented by B2O_(3) and SiO_(2)∙B_(2)O_(3),which inhibited the ablative reaction by reducing the diffusion rate of the oxidation medium,and the solid pinning effect of the substances represented by SiO_(2),ZrO_(2),and ZrSiO_(4),which could play high viscosity and high strength characteristics to improve anti-erosion ability.The above-mentioned SiC_(f)/SiBCZr composites with corrosion resistance,oxidation resistance,and ablative resistance provided a solid material foundation and technical support for the development of reusable spacecraft hot-end components.

关键词： near stoichiometric ratio SiC fiber SiC_(f)/SiBCZr composite liquid sealing effect solid pinning effect

Three-dimensional porous In_(2)O_(3) arrays for self-powered transparent solar-blind photodetectors with high responsivity and excellent spectral selectivity

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Nano Research 2024年第5期17卷 4471-4477页

作者： Nana Zhang Xinyu Gao Haoran Guan Simin Sun Jiaming Liu Zhitao Shao qiyue Gao Yuan Zhang Ruyu Sun Guang yang Feng Gao Wei Feng College of Chemistry Chemical Engineering and Resource UtilizationNortheast Forestry UniversityHarbin 150040China Key Laboratory for Photonic and Electronic Bandgap Materials Ministry of EducationSchool of Physics and Electronic EngineeringHarbin Normal UniversityHarbin 150025China

Transparent solar-blind ultraviolet photodetectors(SBUV PDs)have extensive applications in versatile scenarios,such as optical communication.However,it is still challenging to simultaneously achieve high responsivity,high transparency,and satisfying self-powered capability.Here,we demonstrated high-performance,transparent,and self-powered photoelectrochemical-type(PEC)SBUV PDs based on vertically grown ultrathin In_(2)O_(3) nanosheet arrays(NAs)with a three-dimensional(3D)porous structure.The 3D porous structure simultaneously improves the transmittance in the visible light region,accelerates interfacial reaction kinetics,and promotes photogenerated carrier transport.The performance of In_(2)O_(3) NAs photoanodes exceeds most reported self-powered PEC SBUV PDs,exhibiting a high transmittance of approximately 80%in the visible light region,a high responsivity of 86.15 mA/W for 254 nm light irradiation,a fast response speed of 15/18 ms,and good multicycle stability.The In_(2)O_(3) NAs also show excellent spectral selectivity with an ultrahigh solar-blind rejection ratio of 1319.30,attributed to the quantum confinement effect induced by the ultrathin feature(2-3 nm).Furthermore,In_(2)O_(3) NAs photoanodes show good capability in underwater optical communication.Our work demonstrated that a 3D porous structure is a powerful strategy to synchronously achieve high responsivity and transparency and provides a new perspective for designing high-performance,transparent,and self-powered PEC SBUV PDs.

关键词： In_(2)O_(3) three-dimensional porous transparent photoelectrochemical solar-blind photodetectors