Path planning for unmanned systems in complex environments must simultaneously satisfy safety,kinematic feasibility,and real-time performance requirements.Monte Carlo Tree Search(MCTS)offers advantages such as model-f...Path planning for unmanned systems in complex environments must simultaneously satisfy safety,kinematic feasibility,and real-time performance requirements.Monte Carlo Tree Search(MCTS)offers advantages such as model-free operation,strong interpretability,and anytime planning capability,but it suffers from large branching factors,excessive search depths,and poor convergence under sparse reward conditions in high-dimensional state spaces.To address these challenges,this paper proposes a Heuristic Rolling Monte Carlo Tree Search(HRMCTS)framework.First,the path planning problem is formulated as a constrained Markov decision process,where the state consists of position and heading,and actions are discretized heading changes.Second,a heuristic selection strategy incorporating goal-directed guidance and obstacle safety margins is introduced to improve search directionality,while a limited-depth forward simulation with branch pruning is employed during the rollout phase.A multi-objective reward function is designed to integrate distance,goal progress,tangent-based obstacle avoidance,smoothness,and efficiency,thereby jointly optimizing path quality and computational performance.Experiments are conducted in three scenarios:static polygonal environments,dynamic circular obstacle environments,and dynamic polygonal obstacle environments.Simulation results demonstrate that the proposed method offers significant advantages in terms of planning efficiency,environmental adaptability,generalization capability,and interpretability.展开更多
With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.展开更多
Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
Discrete Fracture Network(DFN)modeling is widely employed to simulate fractured rock masses for various geotechnical applications.However,challenges in statistically characterizing three-dimensional fractures arise du...Discrete Fracture Network(DFN)modeling is widely employed to simulate fractured rock masses for various geotechnical applications.However,challenges in statistically characterizing three-dimensional fractures arise due to orientation,size,truncation,and censoring biases when inferring parameters from two-dimensional rock surface observation.This study introduces a fully automated method that leverages Monte Carlo Tree Search(MCTS),a reinforcement learning-based algorithm,to optimize DFN parameters,including fracture density and length.By defininga reward function that compares the simulated rock masses with observed data,the proposed method iteratively minimizes biases and enhances accuracy.Examined on natural outcrop data of a granitic rock body,this method demonstrates significantimprovements in parameter inference,achieving robust convergence and high fidelityto observed fracture characteristics.The approach offers a practical and efficientsolution for DFN modeling in rock engineering and geological applications.展开更多
Lactate(LA)is now recognized as a critical carbon source for tumor metabolism,making its transport blockade a promising anticancer therapeutic strategy.In this study,we incorporatedα-cyano-4-hydroxycinnamate(CHC)into...Lactate(LA)is now recognized as a critical carbon source for tumor metabolism,making its transport blockade a promising anticancer therapeutic strategy.In this study,we incorporatedα-cyano-4-hydroxycinnamate(CHC)into hollow-structured Cu S@PCN nanoparticles to inhibit LA influx by suppressing the expression of the monocarboxylate transporter 1(MCT1)in tumor cells.This intervention shifted tumor cell metabolism from LA-fueled oxidative phosphorylation towards anaerobic glycolysis,consequently elevating intratumoral oxygen(O2)levels.The photosensitizer-based metal-organic framework(PCN)component was then able to efficiently convert this elevated O2into abundant reactive oxygen species(ROS),thereby enhancing photodynamic therapy(PDT)efficacy.Notably,the hollow mesoporous Cu S nanoparticle core functioned dually as a high-capacity CHC carrier and a photothermal agent that enables CHC release under near-infrared(NIR)irradiation.Further surface conjugation with folic acidpolyethylene glycol(FA-PEG)imparted tumor-targeting specificity via folate receptor recognition and prolonged systemic circulation.Both in vitro and in vivo evaluations demonstrated the excellent biocompatibility and significantly improved PDT performance of the synthesized CHC-Cu S@PCN-FA(CHC-CP-FA)nanoplatform.These findings underscore the considerable potential of CHC-CP-FA for future cancer treatment applications.展开更多
教学干预滞后是高职智能制造虚拟仿真实训中尚未解决的难题,这直接拖慢了学生技能内化的进程。为打破这一僵局,文章提出应转化能够实时反映学习者认知状态的连续信号,其关键在于挖掘实训过程中孤立且未被充分利用的操作数据。基于此设想...教学干预滞后是高职智能制造虚拟仿真实训中尚未解决的难题,这直接拖慢了学生技能内化的进程。为打破这一僵局,文章提出应转化能够实时反映学习者认知状态的连续信号,其关键在于挖掘实训过程中孤立且未被充分利用的操作数据。基于此设想,研究构建了一套以“行为流”感知为核心的自适应路径生成框架。该框架能实时捕获学生在调试、编程等环节留下的细粒度操作序列,借此动态描绘其认知与技能发展的微观轨迹。借助领域知识图谱提供的结构化约束,并引入蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)算法进行前瞻性推演,系统能够自主完成学习路径的动态规划与实时调优,形成一个从数据感知到干预执行的快速响应闭环。实验结果表明,使用该系统的学生在技能考核成绩与过程性能力成长趋势上均显著优于传统实训组的学生。此框架为实施数据驱动的精准教学干预提供了可行的技术路径,并为透视技能习得的“黑箱”提供了新视角。展开更多
Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effecti...Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effectively improve pregnancy outcomes.Wen Yang Hua Zhuo(WYHZ)is a clinically employed classical formula known for its effects in warming yang,tonifying the spleen and kidneys,and resolving dampness.However,its underlying mechanisms remain unclear.This study aimed to elucidate how WYHZ modulates the immunometabolic microenvironment at the maternal-fetal interface in CE by targeting the MCT/HIF-1α/LDHA pathway to promote embryo implantation.Methods:In vivo,the model of CE was established by intrauterine injection of lipopolysaccharide(LPS)(1 mg/mL)into female C57/BL mice,followed by WYHZ treatment for 3 weeks to evaluate its effects on embryo implantation.Mechanistic studies were further conducted using the MCT-1 inhibitor AZD3965 and adeno-associated virus-mediated HIF-1αknockdown.In vitro,an in vitro CE model consisting of M1 macrophages and Ishikawa,as well as an in vitro embryo implantation model mediated by JAR cells,were constructed using Transwell,and the therapeutic mechanisms of WYHZ was validated using AZD3965 and lentiviral sh HIF-1αintervention.Metabolic enzyme activity assays,protein antibody microarrays,immunofluorescence,Western blotting,Seahorse analysis,and ELISA were employed.Results:WYHZ improved the immune-inflammatory microenvironment at the maternal-fetal interface by reducing pro-inflammatory cytokines and increasing anti-inflammatory factors.In parallel,WYHZ reprogrammed endometrial metabolism by enhancing glycolysis and suppressing mitochondrial oxidative phosphorylation,thereby improving endometrial receptivity and embryo implantation.Mechanistically,WYHZ activated the MCT/HIF-1α/LDHA pathway in endometrial epithelial cells,alleviating inflammatory stress and restoring receptivity.Both AZD3965 intervention and HIF-1αknockdown impaired endometrial receptivity and implantation,effects that were reversed by WYHZ.Conclusion:WYHZ modulates the immunometabolic microenvironment of the endometrium in the context of CE by targeting the activation of the MCT/HIF-1α/LDHA pathway,which improves endometrial receptivity and promotes embryo implantation.展开更多
Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adep...Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.展开更多
In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere wa...In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.展开更多
基金supported by the Hunan Provincial Natural Science Foundation of China(No.2025JJ60072)the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-01)+2 种基金the Postdoctoral Fellowship Program of CPSF(No.GZB20240989)the China Postdoctoral Science Foundation(No.2024M754304)the Military High-Level Talent Program(No.202401-RCGC-ZZ-004).
摘要Path planning for unmanned systems in complex environments must simultaneously satisfy safety,kinematic feasibility,and real-time performance requirements.Monte Carlo Tree Search(MCTS)offers advantages such as model-free operation,strong interpretability,and anytime planning capability,but it suffers from large branching factors,excessive search depths,and poor convergence under sparse reward conditions in high-dimensional state spaces.To address these challenges,this paper proposes a Heuristic Rolling Monte Carlo Tree Search(HRMCTS)framework.First,the path planning problem is formulated as a constrained Markov decision process,where the state consists of position and heading,and actions are discretized heading changes.Second,a heuristic selection strategy incorporating goal-directed guidance and obstacle safety margins is introduced to improve search directionality,while a limited-depth forward simulation with branch pruning is employed during the rollout phase.A multi-objective reward function is designed to integrate distance,goal progress,tangent-based obstacle avoidance,smoothness,and efficiency,thereby jointly optimizing path quality and computational performance.Experiments are conducted in three scenarios:static polygonal environments,dynamic circular obstacle environments,and dynamic polygonal obstacle environments.Simulation results demonstrate that the proposed method offers significant advantages in terms of planning efficiency,environmental adaptability,generalization capability,and interpretability.
基金Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500)Hubei Province Natural Science Foundation of China(No.2017CFB277)。
摘要With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
摘要Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
基金funding support from the Japan Science and Technology Agency Support for Pioneering Research Initiated by the Next Generation(Grant No.JPMJSP2110).
摘要Discrete Fracture Network(DFN)modeling is widely employed to simulate fractured rock masses for various geotechnical applications.However,challenges in statistically characterizing three-dimensional fractures arise due to orientation,size,truncation,and censoring biases when inferring parameters from two-dimensional rock surface observation.This study introduces a fully automated method that leverages Monte Carlo Tree Search(MCTS),a reinforcement learning-based algorithm,to optimize DFN parameters,including fracture density and length.By defininga reward function that compares the simulated rock masses with observed data,the proposed method iteratively minimizes biases and enhances accuracy.Examined on natural outcrop data of a granitic rock body,this method demonstrates significantimprovements in parameter inference,achieving robust convergence and high fidelityto observed fracture characteristics.The approach offers a practical and efficientsolution for DFN modeling in rock engineering and geological applications.
基金financially supported by Capital Medical University Outstanding Young Talents Project(No.B2407,China)Young Elite Scientist Sponsorship Program by Best(No.BYESS2024059)。
摘要Lactate(LA)is now recognized as a critical carbon source for tumor metabolism,making its transport blockade a promising anticancer therapeutic strategy.In this study,we incorporatedα-cyano-4-hydroxycinnamate(CHC)into hollow-structured Cu S@PCN nanoparticles to inhibit LA influx by suppressing the expression of the monocarboxylate transporter 1(MCT1)in tumor cells.This intervention shifted tumor cell metabolism from LA-fueled oxidative phosphorylation towards anaerobic glycolysis,consequently elevating intratumoral oxygen(O2)levels.The photosensitizer-based metal-organic framework(PCN)component was then able to efficiently convert this elevated O2into abundant reactive oxygen species(ROS),thereby enhancing photodynamic therapy(PDT)efficacy.Notably,the hollow mesoporous Cu S nanoparticle core functioned dually as a high-capacity CHC carrier and a photothermal agent that enables CHC release under near-infrared(NIR)irradiation.Further surface conjugation with folic acidpolyethylene glycol(FA-PEG)imparted tumor-targeting specificity via folate receptor recognition and prolonged systemic circulation.Both in vitro and in vivo evaluations demonstrated the excellent biocompatibility and significantly improved PDT performance of the synthesized CHC-Cu S@PCN-FA(CHC-CP-FA)nanoplatform.These findings underscore the considerable potential of CHC-CP-FA for future cancer treatment applications.
摘要教学干预滞后是高职智能制造虚拟仿真实训中尚未解决的难题,这直接拖慢了学生技能内化的进程。为打破这一僵局,文章提出应转化能够实时反映学习者认知状态的连续信号,其关键在于挖掘实训过程中孤立且未被充分利用的操作数据。基于此设想,研究构建了一套以“行为流”感知为核心的自适应路径生成框架。该框架能实时捕获学生在调试、编程等环节留下的细粒度操作序列,借此动态描绘其认知与技能发展的微观轨迹。借助领域知识图谱提供的结构化约束,并引入蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)算法进行前瞻性推演,系统能够自主完成学习路径的动态规划与实时调优,形成一个从数据感知到干预执行的快速响应闭环。实验结果表明,使用该系统的学生在技能考核成绩与过程性能力成长趋势上均显著优于传统实训组的学生。此框架为实施数据驱动的精准教学干预提供了可行的技术路径,并为透视技能习得的“黑箱”提供了新视角。
基金supported by the National Natural Science Foundation of China(grant number:82205172,82274570).
摘要Background:Chronic endometritis(CE)is an important pathological factor contributing to female infertility and recurrent pregnancy loss.Although antibiotics are the primary clinical treatment for CE,they do not effectively improve pregnancy outcomes.Wen Yang Hua Zhuo(WYHZ)is a clinically employed classical formula known for its effects in warming yang,tonifying the spleen and kidneys,and resolving dampness.However,its underlying mechanisms remain unclear.This study aimed to elucidate how WYHZ modulates the immunometabolic microenvironment at the maternal-fetal interface in CE by targeting the MCT/HIF-1α/LDHA pathway to promote embryo implantation.Methods:In vivo,the model of CE was established by intrauterine injection of lipopolysaccharide(LPS)(1 mg/mL)into female C57/BL mice,followed by WYHZ treatment for 3 weeks to evaluate its effects on embryo implantation.Mechanistic studies were further conducted using the MCT-1 inhibitor AZD3965 and adeno-associated virus-mediated HIF-1αknockdown.In vitro,an in vitro CE model consisting of M1 macrophages and Ishikawa,as well as an in vitro embryo implantation model mediated by JAR cells,were constructed using Transwell,and the therapeutic mechanisms of WYHZ was validated using AZD3965 and lentiviral sh HIF-1αintervention.Metabolic enzyme activity assays,protein antibody microarrays,immunofluorescence,Western blotting,Seahorse analysis,and ELISA were employed.Results:WYHZ improved the immune-inflammatory microenvironment at the maternal-fetal interface by reducing pro-inflammatory cytokines and increasing anti-inflammatory factors.In parallel,WYHZ reprogrammed endometrial metabolism by enhancing glycolysis and suppressing mitochondrial oxidative phosphorylation,thereby improving endometrial receptivity and embryo implantation.Mechanistically,WYHZ activated the MCT/HIF-1α/LDHA pathway in endometrial epithelial cells,alleviating inflammatory stress and restoring receptivity.Both AZD3965 intervention and HIF-1αknockdown impaired endometrial receptivity and implantation,effects that were reversed by WYHZ.Conclusion:WYHZ modulates the immunometabolic microenvironment of the endometrium in the context of CE by targeting the activation of the MCT/HIF-1α/LDHA pathway,which improves endometrial receptivity and promotes embryo implantation.
基金the Innovation Program of Shanghai Industrial Synergy(No.XTCX-KJ-2023-2-12)。
摘要Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.
基金supported by the National Natural Science Foundation of China(Grant No.12372351).
摘要In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.