In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to...In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between th...Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between the bond exchange kinetics and segmental dynamics near the glass transition temperature(T_(g))remain scarce.Herein,we employed molecular dynamics simulations to investigate the dynamic heterogeneity of the segment motion and bond exchange in vitrimers.The simulation results revealed that the bond exchange energy barrier exerts a much stronger influence on the bond exchange kinetics than on the segmental dynamics.At lower temperatures,slower segmental relaxation further constraind the bond exchange rate.Additionally,increasing the bond exchange energy barrier markedly enhanced the dynamic heterogeneity of segment motion.A close correlation was observed between heterogeneity and bond exchange.This study elucidated the coupling mechanism between bond exchange and segmental dynamics at the molecular scale,thereby providing a theoretical basis for designing vitrimer materials with tunable dynamic properties.展开更多
Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to ...Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to climate change.Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022,this paper constructs a framework consisting of spatiotemporal evolution,synergy effect measurement,differences in contributions across regions,and influencing factors analysis to reveal the relationship between agricultural PC and CR.The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85%and 0.79%,respectively.However,the annual decline rates of their emission intensities were 3.14%and 4.32%,respectively.This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results,that the effect of PC was weaker than that of CR and had an obvious“policy node effect.”Simultaneously,the synergy between PC and CR evolved from“basic coordination”to“basic imbalance.”The contribution of inter-regional differences was relatively large,while intra-regional differences were smaller,highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR.The basic conditions,industrial structure,input intensity,and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR,and the influence of these significant factors exhibited clear spatiotemporal heterogeneity.These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.展开更多
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digesti...In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess ...Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess Plateau(CLP)remain scarce,especially at the vertical scale.We conducted laboratory tests on undisturbed loess cores collected from boreholes in CLP to analyze soil physical parameters(SPPs)and SWRP.Measured soil water characteristic curves(SWCCs)were fitted to the Brooks-Corey(BC),Fredlund-Xing(FX),and van Genuchten(vG)models.It was revealed that the FX and vG models outperformed the BC model.The geostatistical analysis identified the Gaussian model as optimal for describing the semivariograms of both SPPs and SWCC fitting parameters(FPs).Strikingly,over 90%of these parameters exhibited strong vertical spatial dependence,with an average autocorrelation length of 213.878 cm for SPPs and 320.678 cm for FPs.Moreover,SWRP was found to be significantly influenced by both SPPs and the vertical position relative to the loess ridge slope surface.Parameters near the ridge slope surface showed significantly degraded spatial dependence.These findings provide valuable insights for parameterizing the spatial heterogeneity of soil water retention properties,which are beneficial for hydrogeological modelling in shallow CLP loess strata.展开更多
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogen...Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.展开更多
Traditional clinical subtype classifications(such as amnestic and non-amnestic mild cognitive impairment)rely on subjective interpretations of overlapping patterns of performance on cognitive tests,which may lead to u...Traditional clinical subtype classifications(such as amnestic and non-amnestic mild cognitive impairment)rely on subjective interpretations of overlapping patterns of performance on cognitive tests,which may lead to unreliable categorization.A more precise and objective classification of mild cognitive impairment subtypes can be achieved through data-driven clustering techniques.However,because previous studies have not restricted their cohorts to patients who have mild cognitive impairment with the pathology of Alzheimer’s disease,the nature of cognitive variability and its impact on disease progression in a strictly defined biomarker-positive preclinical Alzheimer’s disease cohort remains unknown.We examined cognitive heterogeneity among participants with mild cognitive impairment due to Alzheimer’s disease and evaluated its prognostic utility.Neuropsychological test data from 389 patients with mild cognitive impairment in whom the cerebrospinal fluid biomarker confirmed Alzheimer’s disease were obtained from the Alzheimer’s Disease Neuroimaging Initiative cohorts.Principal component analysis and model-based clustering were used to identify cognitive profiles,which were then validated through a 100-time bootstrap analysis.Pairwise comparisons tested for differences between the identified subgroups in participant characteristics,scores on cognitive and clinical outcomes,levels of cerebrospinal fluid biomarkers,and magnetic resonance imaging-derived brain volumes.Longitudinal analyses evaluated differences in rate of change of magnetic resonance imaging volumetric measurements and clinical outcomes over 48 months.Survival analysis assessed risk for conversion to dementia.Alpha-synuclein levels and white matter hyperintensity volumes were considered for sensitivity analysis.Two distinct cognitive profiles were identified:a“typical”group(56.04%of the sample)that demonstrated relatively poorer scores on memory testing than non-memory tests,and an“atypical”group(43.96%of the sample)with smaller differences between memory and non-memory measures,indicating a more uniform pattern of impairment across cognitive domains.While the groups had comparable levels of overall cognitive impairment and cerebrospinal fluid biomarkers of Alzheimer’s disease,the typical group displayed accelerated atrophy rates every 6 months across multiple brain regions(hippocampus:29.02 mm^(3),standard error[SE]=10.13,P=0.005;whole brain:1799.85 mm^(3),SE=781.57,P=0.023;entorhinal cortex:22.26 mm^(3),SE=11.15,P=0.048;fusiform gyrus:66.24 mm^(3),SE=28.53,P=0.021).Survival analysis revealed markedly higher dementia conversion risk(hazard ratio:1.70,95%confidence interval:1.27,2.27,P<0.001)and shorter progression time in the typical group.These findings persisted after controlling for comorbid pathologies.In conclusion,this data-driven approach identified two distinct cognitive subtypes of mild cognitive impairment due to Alzheimer’s disease that differed in their rates of clinical decline and neurodegeneration.These findings could be used to improve prognostic models and inform clinical trial stratification.展开更多
Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More r...Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.展开更多
The presence or absence of adult neural stem cells in the mammalian forebrain ependyma has been debated for two decades.In this study,we performed single-cell RNA sequencing to investigate the cellular composition of ...The presence or absence of adult neural stem cells in the mammalian forebrain ependyma has been debated for two decades.In this study,we performed single-cell RNA sequencing to investigate the cellular composition of the ependymal surface of the adult mouse forebrain using whole mounts of lateral walls of lateral ventricles.We identified 12 different cell subtypes in the ependymal surface.Immunocytochemical analyses revealed that CD133^(+)multi-ciliated cells comprised 67.6%of ependymal cells,while the remaining 32.4%were CD133^(-).CD133^(+)ependymal cells can be further classified into FOXJ1^(+)/SOX2^(+)/ACTA2^(+)cells,FLT1^(+)/CD31^(+)/CLDN5^(+)endothelial-like cells,and PDGFRB^(+)/VTN^(+)/NG2^(+)pericyte-like cells,as well as endothelial-pericyte-like cells and Foxj1^(+)endothelial-like cells.CD133^(-)ependymal cells can be further divided into endothelial-like cells,Foxj1^(+)ependymal cells,Foxj1^(+)endothelial-like cells,pericyte-like cells,endothelial-pericyte-like cells,VIM^(+)cells,and cells negative for all of these markers.This comprehensive profiling confirms the heterogeneity of the ependymal surface in the adult mouse forebrain.Debate regarding whether adult ependymal cells contain neural stem cells has arisen because different researchers have examined different populations of ependymal cells.Our study provides a new perspective for investigation of clinical endogenous neural stem cells,ultimately paving the way for stem cell therapies in neurological diseases.展开更多
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and...The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.展开更多
Objective:Leucine-rich alpha-2 glycoprotein 1(Lrg1)could regulate diverse cells in cerebral ischemiareperfusion.Our study seeks to uncover Lrg1’s impact on endothelial cell heterogeneity via differentiation pathways ...Objective:Leucine-rich alpha-2 glycoprotein 1(Lrg1)could regulate diverse cells in cerebral ischemiareperfusion.Our study seeks to uncover Lrg1’s impact on endothelial cell heterogeneity via differentiation pathways and transcription factors.Method:The CSOmap model measured cell-to-brain-center distances using single-cell RNA sequencing(scRNA-seq)data in middle cerebral artery occlusion reperfusion(MCAO/R).Monocle2 mapped endothelial differentiation paths.Gene set enrichment analysis(GSEA)analyzed endothelial subcluster variations.Database searches revealed a zinc finger MIZ-type containing 1 protein-frizzled 3(Zmiz1-Fzd3)promoter interaction.Endothelial cells were transfected with a Fzd3 promoter-luciferase plasmid.Polymerase chain reaction(PCR)and western blotting assessed MCAO/R or Zmiz1 overexpression effects on Fzd3-related mRNA and proteins.A retroviral vector carrying Zmiz1 was injected into the brains of mice to study its effect on Fzd3.Result:Lrg1−/−mice exhibited elevated cell adhesion proteins and decreased microvascular leakage after MCAO/R.CSOmap showed widened astrocyte spacing in thesemice.RSS revealed Zmiz1 overexpression inMCAO/R+Lrg1−/−mice.MCAO/R and pcDNA3-Zmiz1 transfection both enhanced luciferase activity with Fzd3,indicating Zmiz1 binding to Fzd3.Retroviral Zmiz1 injection or knockdown disrupted ischemic brain tight junctions,highlighting Zmiz1’s key role in blood-brain barrier protection,likely through Fzd3 pathway modulation.Conclusion:The findings indicate Lrg1 knockout induces endothelial differentiation by activating Zmiz1,which is crucial for maintaining blood-brain barrier function,possibly via modulating the Fzd3 pathway.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Increased exposure to campus green spaces can make a positive contribution to the healthy development of students.However,understanding of the current supply of campus green space(CGS)and its drivers at different educ...Increased exposure to campus green spaces can make a positive contribution to the healthy development of students.However,understanding of the current supply of campus green space(CGS)and its drivers at different education stages is still limited.A new framework was established to evaluate the spatial heterogeneity and its influencing factors across all education stages(kindergarten,primary school,middle school,college)in 1100 schools at the urban scale of Xi’an,China.The research results show that:1)CGS is lower in the Baqiao district and higher in the Yanta and Xincheng districts of Xi’an City.‘Green wealthy schools are mainly concentrated in the Weiyang,Chang’an and Yanta districts.2)CGS of these schools in descending order is college(31.40%)>kindergarten(18.32%)>middle school(13.56%)>primary school(10.70%).3)Colleges have the most recreation sites(n(number)=2),the best education levels(11.93 yr),and the lowest housing prices(1.18×10^(4) yuan(RMB)/m^(2));middle schools have the highest public expenditures(3.97×10^(9) yuan/yr);primary schools have the highest CGS accessibility(travel time gap(TTG)=31.33).4)Multiscale Geographically Weighted Regression model and Spearman’s test prove that recreation sites have a significant positive impact on college green spaces(0.28–0.35),and education level has a significant positive impact on kindergarten green spaces(0.16–0.24).This research framework provides important insights for the assessment of school greening initiatives aimed at fostering healthier learning environments for future generations.展开更多
Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design...Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design/methodology/approach:An exploratory survey is administered to researchers at the Sapienza University of Rome,one of Europe’s oldest and largest generalist universities.To measure metric-wiseness,we use attitude statements that are evaluated by a 5-point Likert scale.Moreover,we analyze documents of recent initiatives on assessment reform to shed light on how researchers’heterogeneous attitudes regarding and knowledge of bibliometric indicators are taken into account.Findings:We found great heterogeneity in researchers’metric-wiseness across scientific disciplines.In addition,within each discipline,we observed both supporters and critics of bibliometric indicators.From the document analysis,we found no reference to individual heterogeneity concerning researchers’metric wiseness.Research limitations:We used a self-selected sample of researchers from one Italian university as an exploratory case.Further research is needed to check the generalizability of our findings.Practical implications:To gain sufficient support for research evaluation practices,it is key to consider researchers’diverse attitudes towards indicators.Originality/value:We contribute to the current debate on reforming research assessment by providing a novel empirical measurement of researchers’knowledge and attitudes towards bibliometric indicators and discussing the importance of the obtained results for improving current research evaluation systems.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.12471281in part by the National Statistical Science Research Project under Grant No.2022LD03。
文摘In this article,the authors explore the online updating estimation for general estimating equations(EEs)in heterogeneous streaming data settings.The framework is based on more conservative model assumptions,leading to more robust estimations and preventing misspecification.The authors establish the standard renewable estimation under blockwise heterogeneity assumption,which can correctly specify model in some sense.To mitigate heterogeneity and enhance estimation accuracy,the authors propose two novel online detection and fusion strategies,with corresponding algorithms provided.Theoretical properties of the proposed methods are demonstrated in the context of small block sizes.Extensive numerical experiments validate the theoretical findings.Real data analysis of the Ford Gobike docked bike-sharing dataset verifies the feasibility and robustness of the proposed methods.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
基金financially supported by the National Natural Science Foundation of China(Nos.52173020 and 52573023)。
文摘Vitrimers belong to a class of polymeric materials capable of bond exchange reactions,showing great promise for environmental protection and sustainable development.However,studies on the coupling mechanism between the bond exchange kinetics and segmental dynamics near the glass transition temperature(T_(g))remain scarce.Herein,we employed molecular dynamics simulations to investigate the dynamic heterogeneity of the segment motion and bond exchange in vitrimers.The simulation results revealed that the bond exchange energy barrier exerts a much stronger influence on the bond exchange kinetics than on the segmental dynamics.At lower temperatures,slower segmental relaxation further constraind the bond exchange rate.Additionally,increasing the bond exchange energy barrier markedly enhanced the dynamic heterogeneity of segment motion.A close correlation was observed between heterogeneity and bond exchange.This study elucidated the coupling mechanism between bond exchange and segmental dynamics at the molecular scale,thereby providing a theoretical basis for designing vitrimer materials with tunable dynamic properties.
基金National Social Science Fund of China,No.22BGL182。
文摘Promoting the synergistic governance of pollution control(PC)and carbon reduction(CR)in the agricultural sector was an important way for the Chinese government to implement the“dual carbon”initiative and respond to climate change.Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022,this paper constructs a framework consisting of spatiotemporal evolution,synergy effect measurement,differences in contributions across regions,and influencing factors analysis to reveal the relationship between agricultural PC and CR.The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85%and 0.79%,respectively.However,the annual decline rates of their emission intensities were 3.14%and 4.32%,respectively.This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results,that the effect of PC was weaker than that of CR and had an obvious“policy node effect.”Simultaneously,the synergy between PC and CR evolved from“basic coordination”to“basic imbalance.”The contribution of inter-regional differences was relatively large,while intra-regional differences were smaller,highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR.The basic conditions,industrial structure,input intensity,and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR,and the influence of these significant factors exhibited clear spatiotemporal heterogeneity.These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.
文摘In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金supported by the National Natural Science Foundation of China(Grant No.52379097)the National Natural Science Foundation of China(No.52509138)+2 种基金the Water Conservancy Science and Technology Project of Jiangxi Province(Grant No.202426ZDKT27)Chongqing Natural Science Foundation Doctoral Program(CSTB2025NSCQ-BSX0020)the Research and Innovation Program for Graduate Students of Chongqing Municipality(Grant No.CYB23251).
文摘Quantifying spatial heterogeneity in soil water retention properties(SWRP)is crucial for enhancing the accuracy of hydrogeological simulations.However,studies on the spatial heterogeneity of SWRP in the Chinese Loess Plateau(CLP)remain scarce,especially at the vertical scale.We conducted laboratory tests on undisturbed loess cores collected from boreholes in CLP to analyze soil physical parameters(SPPs)and SWRP.Measured soil water characteristic curves(SWCCs)were fitted to the Brooks-Corey(BC),Fredlund-Xing(FX),and van Genuchten(vG)models.It was revealed that the FX and vG models outperformed the BC model.The geostatistical analysis identified the Gaussian model as optimal for describing the semivariograms of both SPPs and SWCC fitting parameters(FPs).Strikingly,over 90%of these parameters exhibited strong vertical spatial dependence,with an average autocorrelation length of 213.878 cm for SPPs and 320.678 cm for FPs.Moreover,SWRP was found to be significantly influenced by both SPPs and the vertical position relative to the loess ridge slope surface.Parameters near the ridge slope surface showed significantly degraded spatial dependence.These findings provide valuable insights for parameterizing the spatial heterogeneity of soil water retention properties,which are beneficial for hydrogeological modelling in shallow CLP loess strata.
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
基金supported by the National Natural Science Foundation of China (Nos.42422705,42207175,42177117 and 42577170)the Ningbo Youth Leading Talent Project (No.2024QL051)+1 种基金the Chinese Academy of Engineering Science and Technology Strategy Consulting Project (No.2025-XZ-57)the Central Government Funding Program for Guiding Local Science and Technology Development (No.2025ZY01028)。
文摘Rock mass stability is significantly influenced by the heterogeneity of rock joint roughness and shear strength.While modern technology facilitates assessing roughness heterogeneity,evaluating shear strength heterogeneity remains challenging.To address this,this study first captures the morphology of large-scale(1000 mm × 1000 mm) slate and granite joints via 3D laser scanning.Analysis of these surfaces and corresponding push/pull tests on carved specimens revealed a potential correlation between the heterogeneity of roughness and shear strength.A comparative evaluation of five statistical metrics identified information entropy(Hs) as the most robust indicator for quantifying rock joint heterogeneity.Further analysis using Hsreveals that the heterogeneity is anisotropic and,critically,that shear strength heterogeneity is governed not only by roughness heterogeneity but is also significantly influenced by the mean roughness value,normal stress,and intact rock tensile strength.Consequently,a simple comparison of roughness Hsvalues is insufficient for reliably comparing shear strength heterogeneity.To overcome this limitation,a theoretical framework is developed to explicitly map fundamental roughness statistics(mean and heterogeneity) to shear strength heterogeneity.This framework culminates in a practical workflow that allows for the rapid,field-based assessment of shear strength heterogeneity using readily obtainable rock joint roughness data.
基金funded by Shanghai Baiyulan Pujiang Project(No.24PJD087)funded by the National Natural Science Foundation of China(No.12401347)+5 种基金Shanghai“Science and Technology Innovation Action Plan”Computational Biology Key Project(Nos.23JS1400500 and 23JS1400800)Chinese MOE Foundation on Humanities and Social Sciences(No.23YJC910006)the Natural Science Foundation of Shanghai(No.24ZR1420400)MWB was funded by NIH/NIA(Nos.R01AG082073,R01AG079280,and P30AG062429)HHF was funded by NIH/NIA(Nos.U19AG079774-01,R01AG061146,P30AG062429,R01AG076634-01,CIHR 137794)funded by NIH/NIA R01AG064002,P30AG062429,R01AG076634,and the Epstein Family Alzheimer’s Research Collaboration.
文摘Traditional clinical subtype classifications(such as amnestic and non-amnestic mild cognitive impairment)rely on subjective interpretations of overlapping patterns of performance on cognitive tests,which may lead to unreliable categorization.A more precise and objective classification of mild cognitive impairment subtypes can be achieved through data-driven clustering techniques.However,because previous studies have not restricted their cohorts to patients who have mild cognitive impairment with the pathology of Alzheimer’s disease,the nature of cognitive variability and its impact on disease progression in a strictly defined biomarker-positive preclinical Alzheimer’s disease cohort remains unknown.We examined cognitive heterogeneity among participants with mild cognitive impairment due to Alzheimer’s disease and evaluated its prognostic utility.Neuropsychological test data from 389 patients with mild cognitive impairment in whom the cerebrospinal fluid biomarker confirmed Alzheimer’s disease were obtained from the Alzheimer’s Disease Neuroimaging Initiative cohorts.Principal component analysis and model-based clustering were used to identify cognitive profiles,which were then validated through a 100-time bootstrap analysis.Pairwise comparisons tested for differences between the identified subgroups in participant characteristics,scores on cognitive and clinical outcomes,levels of cerebrospinal fluid biomarkers,and magnetic resonance imaging-derived brain volumes.Longitudinal analyses evaluated differences in rate of change of magnetic resonance imaging volumetric measurements and clinical outcomes over 48 months.Survival analysis assessed risk for conversion to dementia.Alpha-synuclein levels and white matter hyperintensity volumes were considered for sensitivity analysis.Two distinct cognitive profiles were identified:a“typical”group(56.04%of the sample)that demonstrated relatively poorer scores on memory testing than non-memory tests,and an“atypical”group(43.96%of the sample)with smaller differences between memory and non-memory measures,indicating a more uniform pattern of impairment across cognitive domains.While the groups had comparable levels of overall cognitive impairment and cerebrospinal fluid biomarkers of Alzheimer’s disease,the typical group displayed accelerated atrophy rates every 6 months across multiple brain regions(hippocampus:29.02 mm^(3),standard error[SE]=10.13,P=0.005;whole brain:1799.85 mm^(3),SE=781.57,P=0.023;entorhinal cortex:22.26 mm^(3),SE=11.15,P=0.048;fusiform gyrus:66.24 mm^(3),SE=28.53,P=0.021).Survival analysis revealed markedly higher dementia conversion risk(hazard ratio:1.70,95%confidence interval:1.27,2.27,P<0.001)and shorter progression time in the typical group.These findings persisted after controlling for comorbid pathologies.In conclusion,this data-driven approach identified two distinct cognitive subtypes of mild cognitive impairment due to Alzheimer’s disease that differed in their rates of clinical decline and neurodegeneration.These findings could be used to improve prognostic models and inform clinical trial stratification.
基金supported by the National Natural Science Foundation of China,Nos.82404892(to QY),82061160374(to ZZ)the Science and Technology Development Fund,Macao Special Administrative Region,China,Nos.0023/2020/AFJ,0035/2020/AGJ+2 种基金the University of Macao Research Grant,Nos.MYRG2022-00248-ICMS,MYRG-CRG2022-00010-ICMS(to MPMH)the Natural Science Foundation of Guangdong Province,No.2024A1515012818(to ZZ)the Fundamental Research Funds for the Central Universities,No.21623114(to ZZ).
文摘Drug development for Alzheimer’s disease is extremely challenging,as demonstrated by the repeated failures of amyloid-β-targeted therapeutics and the controversies surrounding the amyloid-βcascade hypothesis.More recently,advances in the development of Lecanemab,an anti-amyloid-βmonoclonal antibody,have shown positive results in reducing brain A burden and slowing cognitive decline in patients with early-stage Alzheimer’s disease in the Phase Ⅲ clinical trial(Clarity Alzheimer’s disease).Despite these promising results,side effects such as amyloid-related imaging abnormalities(ARIA)may limit its usage.ARIA can manifest as ARIA-E(cerebral edema or effusions)and ARIA-H(microhemorrhages or superficial siderosis)and is thought to be caused by increased vascular permeability due to inflammatory responses,leading to leakages of blood products and protein-rich fluid into brain parenchyma.Endothelial dysfunction is an early pathological feature of Alzheimer’s disease,and the blood-brain barrier becomes increasingly leaky as the disease progresses.In addition,APOE4,the strongest genetic risk factor for Alzheimer’s disease,is associated with higher vascular amyloid burden,increased ARIA incidence,and accelerated blood-brain barrier disruptions.These interconnected vascular abnormalities highlight the importance of vascular contributions to the pathophysiology of Alzheimer’s disease.Here,we will closely examine recent research evaluating the heterogeneity of brain endothelial cells in the microvasculature of different brain regions and their relationships with Alzheimer’s disease progression.
基金supported by the State Key Program of the National Natural Science Foundation of China,No.82030035(to YES)Peak Disciplines(Type IV)of Institutions of Higher Learning in Shanghai(to LZ).
文摘The presence or absence of adult neural stem cells in the mammalian forebrain ependyma has been debated for two decades.In this study,we performed single-cell RNA sequencing to investigate the cellular composition of the ependymal surface of the adult mouse forebrain using whole mounts of lateral walls of lateral ventricles.We identified 12 different cell subtypes in the ependymal surface.Immunocytochemical analyses revealed that CD133^(+)multi-ciliated cells comprised 67.6%of ependymal cells,while the remaining 32.4%were CD133^(-).CD133^(+)ependymal cells can be further classified into FOXJ1^(+)/SOX2^(+)/ACTA2^(+)cells,FLT1^(+)/CD31^(+)/CLDN5^(+)endothelial-like cells,and PDGFRB^(+)/VTN^(+)/NG2^(+)pericyte-like cells,as well as endothelial-pericyte-like cells and Foxj1^(+)endothelial-like cells.CD133^(-)ependymal cells can be further divided into endothelial-like cells,Foxj1^(+)ependymal cells,Foxj1^(+)endothelial-like cells,pericyte-like cells,endothelial-pericyte-like cells,VIM^(+)cells,and cells negative for all of these markers.This comprehensive profiling confirms the heterogeneity of the ependymal surface in the adult mouse forebrain.Debate regarding whether adult ependymal cells contain neural stem cells has arisen because different researchers have examined different populations of ependymal cells.Our study provides a new perspective for investigation of clinical endogenous neural stem cells,ultimately paving the way for stem cell therapies in neurological diseases.
文摘The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety.
基金supported by the Foundation Project:National Natural Science.Foundation of China(Nos.:82460249,82100417,81760094)The Foundation of Jiangxi Provincial Department of Science and Technology Outstanding Youth Fund Project(20212BAB206022,20242BAB23080).
文摘Objective:Leucine-rich alpha-2 glycoprotein 1(Lrg1)could regulate diverse cells in cerebral ischemiareperfusion.Our study seeks to uncover Lrg1’s impact on endothelial cell heterogeneity via differentiation pathways and transcription factors.Method:The CSOmap model measured cell-to-brain-center distances using single-cell RNA sequencing(scRNA-seq)data in middle cerebral artery occlusion reperfusion(MCAO/R).Monocle2 mapped endothelial differentiation paths.Gene set enrichment analysis(GSEA)analyzed endothelial subcluster variations.Database searches revealed a zinc finger MIZ-type containing 1 protein-frizzled 3(Zmiz1-Fzd3)promoter interaction.Endothelial cells were transfected with a Fzd3 promoter-luciferase plasmid.Polymerase chain reaction(PCR)and western blotting assessed MCAO/R or Zmiz1 overexpression effects on Fzd3-related mRNA and proteins.A retroviral vector carrying Zmiz1 was injected into the brains of mice to study its effect on Fzd3.Result:Lrg1−/−mice exhibited elevated cell adhesion proteins and decreased microvascular leakage after MCAO/R.CSOmap showed widened astrocyte spacing in thesemice.RSS revealed Zmiz1 overexpression inMCAO/R+Lrg1−/−mice.MCAO/R and pcDNA3-Zmiz1 transfection both enhanced luciferase activity with Fzd3,indicating Zmiz1 binding to Fzd3.Retroviral Zmiz1 injection or knockdown disrupted ischemic brain tight junctions,highlighting Zmiz1’s key role in blood-brain barrier protection,likely through Fzd3 pathway modulation.Conclusion:The findings indicate Lrg1 knockout induces endothelial differentiation by activating Zmiz1,which is crucial for maintaining blood-brain barrier function,possibly via modulating the Fzd3 pathway.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金Under the auspices of Natural Science Basic Research Plan in Shaanxi Province of China(No.2024JC-YBMS-196)。
文摘Increased exposure to campus green spaces can make a positive contribution to the healthy development of students.However,understanding of the current supply of campus green space(CGS)and its drivers at different education stages is still limited.A new framework was established to evaluate the spatial heterogeneity and its influencing factors across all education stages(kindergarten,primary school,middle school,college)in 1100 schools at the urban scale of Xi’an,China.The research results show that:1)CGS is lower in the Baqiao district and higher in the Yanta and Xincheng districts of Xi’an City.‘Green wealthy schools are mainly concentrated in the Weiyang,Chang’an and Yanta districts.2)CGS of these schools in descending order is college(31.40%)>kindergarten(18.32%)>middle school(13.56%)>primary school(10.70%).3)Colleges have the most recreation sites(n(number)=2),the best education levels(11.93 yr),and the lowest housing prices(1.18×10^(4) yuan(RMB)/m^(2));middle schools have the highest public expenditures(3.97×10^(9) yuan/yr);primary schools have the highest CGS accessibility(travel time gap(TTG)=31.33).4)Multiscale Geographically Weighted Regression model and Spearman’s test prove that recreation sites have a significant positive impact on college green spaces(0.28–0.35),and education level has a significant positive impact on kindergarten green spaces(0.16–0.24).This research framework provides important insights for the assessment of school greening initiatives aimed at fostering healthier learning environments for future generations.
基金supported by the Sapienza Universitàdi Roma Sapienza Awards no.6H15XNFS.
文摘Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design/methodology/approach:An exploratory survey is administered to researchers at the Sapienza University of Rome,one of Europe’s oldest and largest generalist universities.To measure metric-wiseness,we use attitude statements that are evaluated by a 5-point Likert scale.Moreover,we analyze documents of recent initiatives on assessment reform to shed light on how researchers’heterogeneous attitudes regarding and knowledge of bibliometric indicators are taken into account.Findings:We found great heterogeneity in researchers’metric-wiseness across scientific disciplines.In addition,within each discipline,we observed both supporters and critics of bibliometric indicators.From the document analysis,we found no reference to individual heterogeneity concerning researchers’metric wiseness.Research limitations:We used a self-selected sample of researchers from one Italian university as an exploratory case.Further research is needed to check the generalizability of our findings.Practical implications:To gain sufficient support for research evaluation practices,it is key to consider researchers’diverse attitudes towards indicators.Originality/value:We contribute to the current debate on reforming research assessment by providing a novel empirical measurement of researchers’knowledge and attitudes towards bibliometric indicators and discussing the importance of the obtained results for improving current research evaluation systems.
