The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characte...The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characteristics and unknown composition have hindered MH-Bc product development.In this study,we employed a combination of untargeted and targeted mass spectrometry analyses to characterize MH-Bc honey.As a result,4,7,8-trimethoxydibenzo[b,d]furan-3-ol(TDBF)was identified as a robust chemical marker for distinguishing MH-Bc from other types of honey.This specific marker was detected in both MH-Bc and the Bc plant but was absent in other honey varieties.Furthermore,a targeted mass spectrometry quantitative method was developed and validated to accurately determine the content of TDBF in honey samples.Overall,the presence of TDBF serves as a discerning indicator for future commercial MH-Bc products.展开更多
Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific stai...Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.展开更多
融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差...融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差异.这些因素导致联邦子图学习在训练过程中出现收敛困难和泛化能较差等问题.为了解决此问题,文中提出基于嵌入对齐与参数激活的个性化联邦子图学习方法(Personalized Federated Subgraph Learning with Embedding Alignment and Parameter Activation,FSL-EAPA).首先,根据客户端之间的相似性进行个性化模型聚合,降低数据非独立同分布对整体性能的影响.然后,引入参数选择性激活进行模型更新,应对子图结构特征的异质性.最后,利用更新后的客户端为各本地节点嵌入提供正负聚类表示,聚集同类局部节点.因此,FSL-EAPA能充分学习各节点的特征表示,较好地适应不同客户端之间的差异化数据分布.在真实基准图数据集上的实验表明FSL-EAPA的有效性,并且在不同场景下都能获得较高的分类精度.展开更多
The reservoir landslide is typically characterized by high-speed movement of a particle-fluid mixture,and its flow and deposit mechanisms are complex.This paper presents the mechanism of submerged granular column coll...The reservoir landslide is typically characterized by high-speed movement of a particle-fluid mixture,and its flow and deposit mechanisms are complex.This paper presents the mechanism of submerged granular column collapse under different densities ambient fluids based on coupled computational fluid dynamics and discrete element method(CFD-DEM)analysis.Important fluid-particle interaction forces,such as the drag force and the buoyancy,are considered by exchanging interaction forces between the CFD and DEM computations.We focus on the flow and deposit characteristics of submerged granular column collapse,namely the runout distance,the tail end height,the particle velocity,the energy,and deposit morphology,which are analyzed qualitatively and quantitatively.The change in fluid field caused by submerged granular column collapse and the formation of eddies are also discussed.A relatively dense fluid can significantly hinder the motion of granular flow,but can improve the conversion efficiency of kinetic energy from the vertical to the horizontal direction.Moreover,the eddies caused by fluid turbulence erode the surface of the granular pile,which is especially marked in a high-density fluid.The findings can provide vital theoretical support for the flow and deposit characteristics of granular flow under fluid and offer insights for the study of reservoir landslides.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
Natural phytoconstituents exhibit distinct advantages in the management and prevention of inflammatory bowel disease(IBD),attributed to their robust biological activity,multi-target effects,and elevated safety profile...Natural phytoconstituents exhibit distinct advantages in the management and prevention of inflammatory bowel disease(IBD),attributed to their robust biological activity,multi-target effects,and elevated safety profile.Although promising,the clinical application of phytoconstituents have been impeded by poor water solubility,low oral bioavailability,and inadequate colonic targeting.Recent advancements in nanotechnology has offered prospective avenues for the application of phytoconstituents in the treatment of IBD.A common strategy involves encapsulating or conjugating phytoconstituents with nanocarriers to enhance their stability,prolong intestinal retention,and facilitate targeted delivery to colonic inflammatory tissues.Furthermore,drawing inspiration from the self-assembling nanostructures that emerge during the decoction process of Chinese herbs,a variety of natural active compounds-based nanoassemblies have been developed for the treatment of IBD.They exhibit high drug-loading capacities and surmount the challenges posed by poor water solubility and low bioavailability.Notably,phyto-derived nanovesicles,owing to their unique structure and biological functions,can serve as therapeutic agents or novel delivery vehicles for the treatment of IBD.Consequently,this review provides an extensive overview of emerging phytoconstituent-derived nano-medicines/vesicles for the treatment of IBD,intending to offer novel insights for the clinical management of IBD.展开更多
As a typical seasonal frozen soil region,the slopes of canal projects in Heilongjiang Province frequently experience significant landslide damage due to a high water table and freeze-thaw cycles.This study addresses t...As a typical seasonal frozen soil region,the slopes of canal projects in Heilongjiang Province frequently experience significant landslide damage due to a high water table and freeze-thaw cycles.This study addresses the limitations of existing models in analyzing the hydrothermal coupling processes of saturated soil.It is based on the principles of mass conservation,energy conservation,Darcy's law,and heat conduction theory.A hydrothermal coupling model was developed for saturated soil,incorporating temperature and porosity as variables.By comparing the model's predictions with actual engineering monitoring data,the study effectively validates the model's reliability and elucidates the dynamic changes in the temperature field,water field,and ice content of the saturated canal slopes during the freeze-thaw cycle.The findings indicate that the saturated soil is filled with water in the pore spaces,the temperature field changes gradually during freezing,the water field exhibits minimal fluctuations,and the ice content increases steadily.During the thawing process,the soil rapidly becomes re-saturated,the thawing rate accelerates,the water distribution becomes uniform,and the ice content decreases swiftly to a very low level.In spring,the shallow temperature increased to 23℃ but began to drop in the fall.The upper slope temperature fell to-10℃during winter,and the freezing depth grew as temperatures decreased.The warming in spring facilitated a rise in temperature and shallow melting.There were significant fluctuations in temperature,water,and ice content in the shallow layer of the slope(up to 1.5 m deep).At a depth of 0.5 m,the water content was 38%on day 230,dropped to1.5%on day 257,and further decreased to 0.9%on day 303.The ice content at 0.5 m depth fell from 38.9%on day303 to 24.4%on day 350,while at 1 m depth,it decreased from 36.4%on day 303 to 30%on day 350.展开更多
基金supported by the Natural Science Foundation of Beijing Municipality(6252026)the Youth Innovation Program of CAAS(Y2024QC11)the Agricultural Science and Technology Innovation Program under Grant(CAAS-ASTIP-2024-IAR).
文摘The monofloral honey derived from Bauhinia championii(Benth.)Benth.(MH-Bc)possesses significant nutritional and bioactive value,making it highly suitable for commercial exploitation.However,the poorly defined characteristics and unknown composition have hindered MH-Bc product development.In this study,we employed a combination of untargeted and targeted mass spectrometry analyses to characterize MH-Bc honey.As a result,4,7,8-trimethoxydibenzo[b,d]furan-3-ol(TDBF)was identified as a robust chemical marker for distinguishing MH-Bc from other types of honey.This specific marker was detected in both MH-Bc and the Bc plant but was absent in other honey varieties.Furthermore,a targeted mass spectrometry quantitative method was developed and validated to accurately determine the content of TDBF in honey samples.Overall,the presence of TDBF serves as a discerning indicator for future commercial MH-Bc products.
基金supported by the Fundamental Research Funds for the Central Universities(No.20720230037)the National Natural Science Foundation of China(No.52273305)+2 种基金Natural Science Foundation of Fujian Province of China(No.2023J05012)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(Nos.2023XAKJ0103071,2023XAKJ0102061)Natural Science Foundation of Xiamen,China(No.3502Z20227010).
文摘Histopathological analysis of chronic wounds is crucial for clinicians to accurately assess wound healing progress and detect potential malignancy.However,traditional pathological tissue sections require specific staining procedures involving carcinogenic chemicals.This study proposes an interdisciplinary approach merging materials science,medicine,and artificial intelligence(AI)to develop a virtual staining technique and intelligent evaluation model based on deep learning for chronic wound tissue pathology.This innovation aims to enhance clinical diagnosis and treatment by offering personalized AI-driven therapeutic strategies.By establishing a mouse model of chronic wounds and using a series of hydrogel wound dressings,tissue pathology sections were periodically collected for manual staining and healing assessment.We focused on leveraging the pix2pix image translation framework within deep learning networks.Through CNN models implemented in Python using PyTorch,our study involves learning and feature extraction for region segmentation of pathological slides.Comparative analysis between virtual staining and manual staining results,along with healing diagnosis conclusions,aims to optimize AI models.Ultimately,this approach integrates new metrics such as image recognition,quantitative analysis,and digital diagnostics to formulate an intelligent wound assessment model,facilitating smart monitoring and personalized treatment of wounds.In blind evaluation by pathologists,minimal disparities were found between virtual and conventional histologically stained images of murine wound tissue.The evaluation used pathologists’average scores on real stained images as a benchmark.The scores for virtual stained images were 71.1%for cellular features,75.4%for tissue structures,and 77.8%for overall assessment.Metrics such as PSNR(20.265)and SSIM(0.634)demonstrated our algorithms’superior performance over existing networks.Eight pathological features such as epidermis,hair follicles,and granulation tissue can be accurately identified,and the images were found to be more faithful to the actual tissue feature distribution when compared to manually annotated data.
文摘融合子图学习与联邦学习后,联邦子图学习在保护数据隐私的同时可实现多客户端子图信息之间的协同学习.然而,由于不同客户端的数据收集方式存在差异,图数据通常呈现非独立同分布特性.同时,不同客户端局部图数据的结构和特征也存在较大差异.这些因素导致联邦子图学习在训练过程中出现收敛困难和泛化能较差等问题.为了解决此问题,文中提出基于嵌入对齐与参数激活的个性化联邦子图学习方法(Personalized Federated Subgraph Learning with Embedding Alignment and Parameter Activation,FSL-EAPA).首先,根据客户端之间的相似性进行个性化模型聚合,降低数据非独立同分布对整体性能的影响.然后,引入参数选择性激活进行模型更新,应对子图结构特征的异质性.最后,利用更新后的客户端为各本地节点嵌入提供正负聚类表示,聚集同类局部节点.因此,FSL-EAPA能充分学习各节点的特征表示,较好地适应不同客户端之间的差异化数据分布.在真实基准图数据集上的实验表明FSL-EAPA的有效性,并且在不同场景下都能获得较高的分类精度.
基金supported by the National Natural Science Foundation of China(No.51825905).
文摘The reservoir landslide is typically characterized by high-speed movement of a particle-fluid mixture,and its flow and deposit mechanisms are complex.This paper presents the mechanism of submerged granular column collapse under different densities ambient fluids based on coupled computational fluid dynamics and discrete element method(CFD-DEM)analysis.Important fluid-particle interaction forces,such as the drag force and the buoyancy,are considered by exchanging interaction forces between the CFD and DEM computations.We focus on the flow and deposit characteristics of submerged granular column collapse,namely the runout distance,the tail end height,the particle velocity,the energy,and deposit morphology,which are analyzed qualitatively and quantitatively.The change in fluid field caused by submerged granular column collapse and the formation of eddies are also discussed.A relatively dense fluid can significantly hinder the motion of granular flow,but can improve the conversion efficiency of kinetic energy from the vertical to the horizontal direction.Moreover,the eddies caused by fluid turbulence erode the surface of the granular pile,which is especially marked in a high-density fluid.The findings can provide vital theoretical support for the flow and deposit characteristics of granular flow under fluid and offer insights for the study of reservoir landslides.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
基金supported by the National Natural Science Foundation of China(Nos.82273824,31670359 and 82372111)the Liao Ning Revitalization Talents Program(No.XLYC 1905019)。
文摘Natural phytoconstituents exhibit distinct advantages in the management and prevention of inflammatory bowel disease(IBD),attributed to their robust biological activity,multi-target effects,and elevated safety profile.Although promising,the clinical application of phytoconstituents have been impeded by poor water solubility,low oral bioavailability,and inadequate colonic targeting.Recent advancements in nanotechnology has offered prospective avenues for the application of phytoconstituents in the treatment of IBD.A common strategy involves encapsulating or conjugating phytoconstituents with nanocarriers to enhance their stability,prolong intestinal retention,and facilitate targeted delivery to colonic inflammatory tissues.Furthermore,drawing inspiration from the self-assembling nanostructures that emerge during the decoction process of Chinese herbs,a variety of natural active compounds-based nanoassemblies have been developed for the treatment of IBD.They exhibit high drug-loading capacities and surmount the challenges posed by poor water solubility and low bioavailability.Notably,phyto-derived nanovesicles,owing to their unique structure and biological functions,can serve as therapeutic agents or novel delivery vehicles for the treatment of IBD.Consequently,this review provides an extensive overview of emerging phytoconstituent-derived nano-medicines/vesicles for the treatment of IBD,intending to offer novel insights for the clinical management of IBD.
基金financial support from the National Natural Science Foundation of China(42401175)Heilongjiang Provincial Key Research and Development Program Project(JD2023SJ46)+2 种基金Heilongjiang Provincial Research Institutes Scientific Research Business Fund Project(CZKYF2025-1-B008)Open Fund for Key Laboratory of Heilongjiang Province Hydraulic Research Institute(DT2024A01)Heilongjiang Province Postdoctoral Special Grant(LBH-TZ2418)。
文摘As a typical seasonal frozen soil region,the slopes of canal projects in Heilongjiang Province frequently experience significant landslide damage due to a high water table and freeze-thaw cycles.This study addresses the limitations of existing models in analyzing the hydrothermal coupling processes of saturated soil.It is based on the principles of mass conservation,energy conservation,Darcy's law,and heat conduction theory.A hydrothermal coupling model was developed for saturated soil,incorporating temperature and porosity as variables.By comparing the model's predictions with actual engineering monitoring data,the study effectively validates the model's reliability and elucidates the dynamic changes in the temperature field,water field,and ice content of the saturated canal slopes during the freeze-thaw cycle.The findings indicate that the saturated soil is filled with water in the pore spaces,the temperature field changes gradually during freezing,the water field exhibits minimal fluctuations,and the ice content increases steadily.During the thawing process,the soil rapidly becomes re-saturated,the thawing rate accelerates,the water distribution becomes uniform,and the ice content decreases swiftly to a very low level.In spring,the shallow temperature increased to 23℃ but began to drop in the fall.The upper slope temperature fell to-10℃during winter,and the freezing depth grew as temperatures decreased.The warming in spring facilitated a rise in temperature and shallow melting.There were significant fluctuations in temperature,water,and ice content in the shallow layer of the slope(up to 1.5 m deep).At a depth of 0.5 m,the water content was 38%on day 230,dropped to1.5%on day 257,and further decreased to 0.9%on day 303.The ice content at 0.5 m depth fell from 38.9%on day303 to 24.4%on day 350,while at 1 m depth,it decreased from 36.4%on day 303 to 30%on day 350.
