Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatmen...Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput...While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging ...Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.展开更多
The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast ...The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.展开更多
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def...This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.展开更多
Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB ...Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.展开更多
This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in So...This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,a...Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Musculoskeletal disorders,including osteoarthritis,rheumatoid arthritis,osteoporosis,bone fracture,intervertebral disc degeneration,tendinopathy,and myopathy,are prevalent conditions that profoundly impact quality of ...Musculoskeletal disorders,including osteoarthritis,rheumatoid arthritis,osteoporosis,bone fracture,intervertebral disc degeneration,tendinopathy,and myopathy,are prevalent conditions that profoundly impact quality of life and place substantial economic burdens on healthcare systems.Traditional bulk transcriptomics,genomics,proteomics,and metabolomics have played a pivotal role in uncovering disease-associated alterations at the population level.However,these approaches are inherently limited in their ability to resolve cellular heterogeneity or to capture the spatial organization of cells within tissues,thus hindering a comprehensive understanding of the complex cellular and molecular mechanisms underlying these diseases.To address these limitations,advanced single-cell and spatial omics techniques have emerged in recent years,offering unparalleled resolution for investigating cellular diversity,tissue microenvironments,and biomolecular interactions within musculoskeletal tissues.These cutting-edge techniques enable the detailed mapping of the molecular landscapes in diseased tissues,providing transformative insights into pathophysiological processes at both the single-cell and spatial levels.This review presents a comprehensive overview of the latest omics technologies as applied to musculoskeletal research,with a particular focus on their potential to revolutionize our understanding of disease mechanisms.Additionally,we explore the power of multi-omics integration in identifying novel therapeutic targets and highlight key challenges that must be overcome to successfully translate these advancements into clinical applications.展开更多
Pediatric cataract,a leading cause of blindness in children globally,imposing a significant financial burden on both families and society.The extensive phenotypic heterogeneity of this condition means that the underly...Pediatric cataract,a leading cause of blindness in children globally,imposing a significant financial burden on both families and society.The extensive phenotypic heterogeneity of this condition means that the underlying mechanisms remain poorly understood,limiting the development of precise and effective treatments.The advent of omics technologies has provided potent tools for unraveling the pathogenesis of pediatric cataract.By mapping expression profiles across various molecular levels,these omics approaches enhance our understanding of the disease’s etiological mechanisms,aid in the identification of novel biomarkers and key pathways,and offer researchers new insights for the innovative strategies in disease diagnosis and targeted therapies.In this review,we summarize the application of omics approaches in clinical and basic research on pediatric cataract over the past decade,encompassing genomics,transcriptomics,proteomics,and metabolomics.Furthermore,we discuss the current challenges and future prospects of omics analyses in pediatric cataract studies.展开更多
Soybean(Glycine max)is a vital foundation of global food security,providing a primary source of highquality protein and oil for human consumption and animal feed.The rising global population has significantly increase...Soybean(Glycine max)is a vital foundation of global food security,providing a primary source of highquality protein and oil for human consumption and animal feed.The rising global population has significantly increased the demand for soybeans,emphasizing the urgency of developing high-yield,stresstolerant,and nutritionally superior cultivars.The extensive collection of soybean germplasm resources—including wild relatives,landraces,and cultivars—represents a valuable reservoir of genetic diversity critical for breeding advancements.Recent breakthroughs in genomic technologies,particularly highthroughput sequencing and multi-omics approaches,have revolutionized the identification of key genes associated with essential agronomic traits within these resources.These innovations enable precise and strategic utilization of genetic diversity,empowering breeders to integrate traits that improve yield potential,resilience to biotic and abiotic stresses,and nutritional quality.This review highlights the critical role of genetic resources and omics-driven innovations in soybean breeding.It also offers insights into strategies for accelerating the development of elite soybean cultivars to meet the growing demands of global soybean production.展开更多
Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physio...Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.展开更多
OBJECTIVE:To develop a safe and effective green therapy for triple-negative breast cancer,this study combines hydrogen-rich water with acupuncture point injection,and finds that it can prevent tumor growth and minimiz...OBJECTIVE:To develop a safe and effective green therapy for triple-negative breast cancer,this study combines hydrogen-rich water with acupuncture point injection,and finds that it can prevent tumor growth and minimize cancer metastasis.METHODS:After 21 d of hydrogen rich water injection treatment on 4T1(mouse breast cancer cells)xenograft mice,in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli(ST36)group injected with hydrogen rich water at acupoints,with a focus on functional proteins or signaling pathways related to tumor occurrence and development,researchers conducted four-dimensional data independent acquisition(4D-DIA)proteomic analysis on tumor tissues.In order to further investigate the dynamic changes of metabolites after therapeutic intervention,researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum.The results of the joint proteomics–metabolomics analysis were validated using experimental methods such as immunofluorescence,Western blotting,and quantitative reverse transcription polymerase chain reaction detection.RESULTS:Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth(P<0.05).4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses uncovered pathways such as T helper 1 cell(Th1)and T helper 2 cell(Th2)cell differentiation.The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism.Based on the combined proteomics and metabolomics analysis,the key pathways included the Ctype lectin receptor signaling pathway.The major cancerrelated differential proteins detected in Th1 and Th2 cell differentiation[interleukin 6 signal transducer,nuclear factor of activated T cells 4,recombinant mitogen activated protein kinase 10(MAPK10),and MAPK11]were upregulated after the injection of hydrogen-rich water into the Zusanli(ST36)acupoint,whereas Linker for activation of T cells(Lat),signal transducer and activator of transcription 1,and protein kinase C,theta were downregulated.CONCLUSION:The injection of hydrogen-rich water into the Zusanli(ST36)acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis,potentially exerting a therapeutic effect through multiple pathways and targeting various sites.展开更多
In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicin...In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.展开更多
基金College Students Innovation and Entrepreneurship Training Program(X202511049398)College Students Innovation and Entrepreneurship Training Program(X202511049201)+1 种基金College Students Innovation and Entrepreneurship Training Program(X202511258005S)University-Level Research Funding Program of Hainan Science and Technology Vocational University(HKKY2024-87)。
文摘Sandfly fever is a viral infectious disease transmitted by sand flies that is widely prevalent in tropical and subtropical regions.Previous studies on its infection mechanism,immune response and diagnosis and treatment methods were lack of systematic.This study applied spatio-temporal omics technology to comprehensively explain the dynamic changes of immunity in the incubation period,exacerbation period,peak period and recovery period of Sandfl y fever,and integrated with diff erent coping strategies.To provide new research ideas for its overall research.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by the National Natural Science Foundation of China(32171022,32221005,and 32401246).
文摘While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
基金supported by the grants from the Key Research and Development Program of Xinjiang Uygur autonomous region in China(Grant No.2023B02017)the National Key Research and Development Program of China(Grant No.2024YFD2300703)+1 种基金the financial support from the Beijing Rural Revitalization Agricultural Science and Technology Project(Grant No.NY2401080000),BAIC01-2025the 2115 Talent Development Program of China Agricultural University.
文摘Phytomelatonin,an emerging plant hormone,plays vital roles in plant growth,development,and stress adaptation(Arnao et al.,2022;Ullah et al.,2024).It acts both as a direct antioxidant and a signaling molecule,engaging complex networks and interacting with other phytohormones(Liu et al.,2022;Khan et al.,2023).Although phytomelatonin receptors(PMTRs)have been identified in many plants(Wei et al.,2018;Wang et al.,2022;Liu et al.,2025),the downstream signaling mechanisms,particularly receptor-mediated protein modifications and transcriptional regulation,remain poorly characterized.
基金supported by the National Natural Science Foundation of China(Nos.22479092 and 22078190)。
文摘The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00249743).
文摘This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.
基金supported by the Guangdong Provincial Clinical Research Center for Tuberculosis(No.2020B1111170014)。
文摘Objective To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis(TB)in the Guangdong Province between 2010 and 2019.Method Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering.Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive(ST-CAR)model.Results Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000in 2019.Spatial hotspots were found in northeastern Guangdong,particularly in Heyuan,Shanwei,and Shantou,while Shenzhen,Dongguan,and Foshan had the lowest rates in the Pearl River Delta.The STCAR model showed that the TB risk was lower with higher per capita Gross Domestic Product(GDP)[Relative Risk(RR),0.91;95%Confidence Interval(CI):0.86–0.98],more the ratio of licensed physicians and physician(RR,0.94;95%CI:0.90-0.98),and higher per capita public expenditure(RR,0.94;95%CI:0.90–0.97),with a marginal effect of population density(RR,0.86;95%CI:0.86–1.00).Conclusion The incidence of TB in Guangdong varies spatially and temporally.Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection.Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
文摘This study examines the effects of rapid land use changes in India,with a specific focus on Sonipat District in Haryana—a region undergoing significant urban expansion.Over the past two decades,rural landscapes in Sonipat have undergone notable transformation,as open spaces and agricultural lands are increasingly converted into residential colonies,commercial hubs,and industrial zones.While such changes reflect economic development and urban growth,they also raise critical concerns about sustainability,especially in terms of food security,groundwater depletion,and environmental degradation.The study examines land use changes between 2000 and 2024 using remote sensing techniques and spatial analysis.It further incorporates secondary data and insights from community-level interactions to assess the socio-economic and ecological impacts of this transformation.The findings indicate rising land fragmentation,loss of agricultural livelihoods,pressure on civic infrastructure,and increasing pollution—factors that threaten long-term regional sustainability.The study underscores the urgent need to reconcile urban development with environmental and social sustainability.By offering a detailed case study of Sonipat,this research contributes to the broader discourse on India’s urbanisation pathways.It aims to provide policymakers,planners,and researchers with evidence-based recommendations to manage land transitions more responsibly,promoting urban growth models that ensure ecological integrity,equitable development,and long-term resilience.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金supported by the National Natural Science Foundation of China(82302757)Shenzhen Science and Technology Program(JCY20240813145204006,SGDX20201103095600002,JCYJ20220818103417037,KJZD20230923115200002)+1 种基金Shenzhen Key Laboratory of Digital Surgical Printing Project(ZDSYS201707311542415)Shenzhen Development and Reform Program(XMHT20220106001).
文摘Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金supported by two DoD grants(HT94252310534 to R.J.T.and HT94252310519 to C.M.K.)the following NIH/NIAMS grants:R01 grants(AR078035 and AR076900 to C.L.+10 种基金AG069401 and AG067698 to L.Q.AI186118,HD112474,and HD107034 to R.J.T.AR076325 and AR071967 to C.M.K.AR080902 and AR072999 to F.G.AR074441 and AR077678 to S.Y.T.AR082667 and AR077527 to A.E.L.AR083900,AR075860 and AR077616 to J.S.),R21 grants(AR077226 to J.S.AR083217 to A.E.L.AR081517 to S.Y.T.)a T32 grant(HD007434 to D.R.K.)P30 center grants(AR074992 and AR073752).
文摘Musculoskeletal disorders,including osteoarthritis,rheumatoid arthritis,osteoporosis,bone fracture,intervertebral disc degeneration,tendinopathy,and myopathy,are prevalent conditions that profoundly impact quality of life and place substantial economic burdens on healthcare systems.Traditional bulk transcriptomics,genomics,proteomics,and metabolomics have played a pivotal role in uncovering disease-associated alterations at the population level.However,these approaches are inherently limited in their ability to resolve cellular heterogeneity or to capture the spatial organization of cells within tissues,thus hindering a comprehensive understanding of the complex cellular and molecular mechanisms underlying these diseases.To address these limitations,advanced single-cell and spatial omics techniques have emerged in recent years,offering unparalleled resolution for investigating cellular diversity,tissue microenvironments,and biomolecular interactions within musculoskeletal tissues.These cutting-edge techniques enable the detailed mapping of the molecular landscapes in diseased tissues,providing transformative insights into pathophysiological processes at both the single-cell and spatial levels.This review presents a comprehensive overview of the latest omics technologies as applied to musculoskeletal research,with a particular focus on their potential to revolutionize our understanding of disease mechanisms.Additionally,we explore the power of multi-omics integration in identifying novel therapeutic targets and highlight key challenges that must be overcome to successfully translate these advancements into clinical applications.
基金supported by the General Program of Natural Science Foundation of Guangdong Province(2023A1515011102).
文摘Pediatric cataract,a leading cause of blindness in children globally,imposing a significant financial burden on both families and society.The extensive phenotypic heterogeneity of this condition means that the underlying mechanisms remain poorly understood,limiting the development of precise and effective treatments.The advent of omics technologies has provided potent tools for unraveling the pathogenesis of pediatric cataract.By mapping expression profiles across various molecular levels,these omics approaches enhance our understanding of the disease’s etiological mechanisms,aid in the identification of novel biomarkers and key pathways,and offer researchers new insights for the innovative strategies in disease diagnosis and targeted therapies.In this review,we summarize the application of omics approaches in clinical and basic research on pediatric cataract over the past decade,encompassing genomics,transcriptomics,proteomics,and metabolomics.Furthermore,we discuss the current challenges and future prospects of omics analyses in pediatric cataract studies.
基金supported by the National Key Research and Development Program of China(2022YFF1003301,2023YFF1000101,2022YFE0130200)the Taishan Scholars Program。
文摘Soybean(Glycine max)is a vital foundation of global food security,providing a primary source of highquality protein and oil for human consumption and animal feed.The rising global population has significantly increased the demand for soybeans,emphasizing the urgency of developing high-yield,stresstolerant,and nutritionally superior cultivars.The extensive collection of soybean germplasm resources—including wild relatives,landraces,and cultivars—represents a valuable reservoir of genetic diversity critical for breeding advancements.Recent breakthroughs in genomic technologies,particularly highthroughput sequencing and multi-omics approaches,have revolutionized the identification of key genes associated with essential agronomic traits within these resources.These innovations enable precise and strategic utilization of genetic diversity,empowering breeders to integrate traits that improve yield potential,resilience to biotic and abiotic stresses,and nutritional quality.This review highlights the critical role of genetic resources and omics-driven innovations in soybean breeding.It also offers insights into strategies for accelerating the development of elite soybean cultivars to meet the growing demands of global soybean production.
基金supported by the National Natural Science Foundation of China(32160578)the Ningxia Hui Autonomous Region Key Research and Develoment Program(2023BCF01027).
文摘Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.
基金the National Natural Science Foundation of China:Exploring the Advantages of Micro-needle System Therapy for Specific Diseases and the Patterns of Acupoint Selection through Data Mining Techniques(No.81473773)Key Project of Hebei Provincial Education Department:Exploring the Molecular Mechanism behind How Hydrogen Mitigates the Aging of Vascular Endothelial Cells Induced by Chronic Intermittent Hypoxia,Utilizing the Keap1-Nuclear factor erythroid 2-related factor 2 Signaling Pathway(No.ZD2020142)Hebei University of Traditional Chinese Medicine 2024 Graduate Student Innovation Funding Project:Study on the Inhibitory Effect and Mechanism of Hydrogen-rich Water Acupoint Injection on Tumors in Mice with Triple-negative Breast Cancer(No.CXZZBS2024154)。
文摘OBJECTIVE:To develop a safe and effective green therapy for triple-negative breast cancer,this study combines hydrogen-rich water with acupuncture point injection,and finds that it can prevent tumor growth and minimize cancer metastasis.METHODS:After 21 d of hydrogen rich water injection treatment on 4T1(mouse breast cancer cells)xenograft mice,in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli(ST36)group injected with hydrogen rich water at acupoints,with a focus on functional proteins or signaling pathways related to tumor occurrence and development,researchers conducted four-dimensional data independent acquisition(4D-DIA)proteomic analysis on tumor tissues.In order to further investigate the dynamic changes of metabolites after therapeutic intervention,researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum.The results of the joint proteomics–metabolomics analysis were validated using experimental methods such as immunofluorescence,Western blotting,and quantitative reverse transcription polymerase chain reaction detection.RESULTS:Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth(P<0.05).4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses uncovered pathways such as T helper 1 cell(Th1)and T helper 2 cell(Th2)cell differentiation.The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism.Based on the combined proteomics and metabolomics analysis,the key pathways included the Ctype lectin receptor signaling pathway.The major cancerrelated differential proteins detected in Th1 and Th2 cell differentiation[interleukin 6 signal transducer,nuclear factor of activated T cells 4,recombinant mitogen activated protein kinase 10(MAPK10),and MAPK11]were upregulated after the injection of hydrogen-rich water into the Zusanli(ST36)acupoint,whereas Linker for activation of T cells(Lat),signal transducer and activator of transcription 1,and protein kinase C,theta were downregulated.CONCLUSION:The injection of hydrogen-rich water into the Zusanli(ST36)acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis,potentially exerting a therapeutic effect through multiple pathways and targeting various sites.
文摘In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.
