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Ecosystem service models are indeed being validated:A response to Pereira et al.(2025) 认领 引用 被引量：1
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作者 James M.Bullock Danny A.P.Hooftman +1 位作者 John W.Redhead Simon Willcock 《Geography and Sustainability》 CSCD 2026年第1期247-248,共2页
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ... In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade. 展开更多
关键词 evaluation mapping modeling es model ecosystem services validation
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AI and Deep Learning for Terahertz Ultra-Massive MIMO:From Model-Driven Approaches to Foundation Models 认领 引用 被引量：1
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作者 Wentao Yu Hengtao He +4 位作者 Shenghui Song Jun Zhang Linglong Dai Lizhong Zheng Khaled B.Letaief 《Engineering》 SCIE EI CSCD 2026年第1期14-33,共20页
This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the ch... This study explored the transformative potential of artificial intelligence(AI)in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output(UM-MIMO)systems.It begins by outlining the characteristics of terahertz UM-MIMO systems and identifies three primary challenges for transceiver design:computational complexity,modeling difficulty,and measurement limitations.The study posits that AI provides a promising solution to these challenges.Three systematic research roadmaps are proposed for developing AI algorithms tailored to terahertz UM-MIMO systems.The first roadmap,model-driven deep learning(DL),emphasizes the importance of leveraging available domain knowledge and advocates the adoption of AI only to enhance bottleneck modules within an established signal processing or optimization framework.Four essential steps are discussed:algorithmic frameworks,basis algorithms,loss function design,and neural architecture design.The second roadmap presents channel state information(CSI)foundation models,aimed at unifying the design of different transceiver modules by focusing on their shared foundation,that is,the wireless channel.The training of a single compact foundation model is proposed to estimate the score function of wireless channels,which serve as a versatile prior for designing a wide variety of transceiver modules.Four essential steps are outlined:general frameworks,conditioning,site-specific adaptation,and the joint design of CSI foundation models and model-driven DL.The third roadmap aims to explore potential directions for applying pretrained large language models(LLMs)to terahertz UM-MIMO systems.Several application scenarios are envisioned,including LLM-based estimation,optimization,search,network management,and protocol understanding.Finally,the study highlights open problems and future research directions. 展开更多
关键词 Terahertz communications Ultra-massive multiple-input multiple-output Model-driven deep learning Foundation models Large language models
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A decision framework for rural domestic sewage treatment models and process:Evidence from Inner Mongolia Autonomous Region,China 认领 引用 被引量：1
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作者 Ying Yan Pengyu Li +5 位作者 Zixuan Wang Yubo Tan Tianlong Zheng Jianguo Liu Xiaoxia Yang Junxin Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第1期302-311,共10页
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys... Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making. 展开更多
关键词 Rural domestic sewage Sewage treatment model Decision-making Environmental-economic benefits Inner Mongolia
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Landslide susceptibility assessment integrating deep transfer learning and physical models in the Baihetan reservoir area,China 认领 引用 被引量：1
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作者 Ming Peng Yue Wang +5 位作者 Chenyi Ma Haojie Wang Shaoqiang Meng Zhenming Shi Weijiang Chu Jianrong Xu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4382-4401,共20页
Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effe... Reservoir landslides pose significant risks to hydropower projects,potentially leading to catastrophic disasters that threaten downstream lives and properties.Landslide susceptibility assessments are critical for effective regional disaster prevention and mitigation.However,the complexity,model uninterpretability,and data scarcity related to reservoir landslides,particularly when adapting models across diverse geographic regions,present significant challenges.This study proposes an interpretable Deep Transfer Learning model coupled with multi-source data and Physical methods(DTLP).The model is trained on multi-source data from the Three Gorges Reservoir Area(TGRA)and Lower Jinsha River Basin(LJRB),tested in Baihetan Reservoir Area(BHT),addressing the issues of limited data and cross-regional generalization.The physical method captures the effect of dynamic water level changes on slope stability.SHAP values are used to interpret the model,providing clear insights into its internal mechanisms.Results demonstrate that DTLP outperforms TrAdaBoost in data-scarce regions,achieving higher accuracy(AUC=0.953,Accuracy=0.941)with better feature generalization and susceptibility zone identification.Incorporating dynamic water level changes into the physical model enhances identification of high-susceptibility areas and reduces misclassifications.SHAP analysis indicates that elevation,lithology,and distance to river significantly influence the model decisions.Using TGRA as the source domain further validates the superiority of DTLP framework.However,due to the initial discrepancies between TGRA and the target domain,the transferability is constrained to some extent,resulting in models trained on LJRB data outperforming those trained on TGRA data. 展开更多
关键词 Landslide susceptibility assessment Deep transfer learning Reservoir landslides Infiniteslope model SHAP values
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Human cerebral organoids:Complex,versatile,and human-relevant models of neural development and brain diseases 认领 引用 被引量：3
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作者 Raquel Coronel Rosa González-Sastre +8 位作者 Patricia Mateos-Martínez Laura Maeso Elena Llorente-Beneyto Sabela Martín-Benito Viviana S.Costa Gagosian Leonardo Foti Ma Carmen González-Caballero Victoria López-Alonso Isabel Liste 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第3期837-854,共18页
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb... The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering. 展开更多
关键词 assembloids bioengineering challenges disease modeling drug screening and toxicology human brain organoids human pluripotent stem cells neurodegenerative diseases neurodevelopment vascularization
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Amelioration of behavioral and neural deficits in animal models of neurodegenerative disease by nanoformulations of curcumin and quercetin 认领 引用 被引量：2
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作者 Bridget Martinez Philip V.Peplow 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第8期3311-3322,共12页
Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerativ... Neurodegenerative diseases are increasing in prevalence due largely to aging populations worldwide and improved medical care for the elderly.Currently approved drugs can reduce some of the symptoms of neurodegenerative diseases but cannot cure them.Inflammation is involved in the development and progression of neurodegenerative diseases,and oxidative stress is implicated in neurodegeneration associated with cognitive decline and age-related cognitive impairment.Polyphenols such as curcumin,quercetin,and resveratrol possess potent anti-inflammatory and antioxidant properties.Nanoformulations of curcumin and quercetin can optimize their pharmacological effects in the treatment of neurodegenerative diseases.Nanocarriers play a crucial role in delivering drugs across the blood-brain barrier,thereby lowering the risk of peripheral side effects.Various nanoforms have been developed to induce bioavailability and solubility of curcumin and quercetin,including nanoparticles and nanoemulsions.The studies reviewed included 17 using curcumin nanoformulations and seven with quercetin nanoformulations and were tested in widely used animal models of Alzheimer’s disease,Parkinson’s disease,Huntington’s disease,and multiple sclerosis.Many of the curcumin and quercetin nanoformulations brought about improvements in learning and memory in behavioral tests of Alzheimer’s disease models and were effective in reducing oxidative stress in the brain.Both nanocurcumin and nanoquercetin decreased the levels of inflammatory markers in the brain.Nanocurcumin formulations improved motor behavior,gait,and memory in Parkinson’s disease models and increased dopaminergic neurons in the striatum and substantia nigra.Furthermore,nanocurcumin improved locomotor activity,memory,and learning,and the number of dendrites of medium spiny neurons in Huntington’s disease models.Nanocurcumin formulations decreased oxidative stress and inflammation in a model of demyelination.Several important limitations were identified in the studies reviewed and these need to be considered in future studies.Also,clinical trials could be performed using the currently available nanoforms of curcumin and quercetin. 展开更多
关键词 animal models behavioral deficits curcumin inflammation nanoformulations neural deficits neurodegeneration oxidative stress quercetin
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Machine learning models for predicting carbonation depth in fly ash concrete:performance and interpretability insights 认领 引用 被引量：1
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作者 Arslan Qayyum Khan Syed Ghulam Muhammad +1 位作者 Ali Raza Amorn Pimanmas 《Journal of Road Engineering》 EI CAS 2026年第1期74-90,共17页
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc... This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO2,exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R2)values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R2 values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth. 展开更多
关键词 Fly ash concrete Carbonation depth Machine learning Ensemble models SHAP analysis
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A Bayesian-based framework for quantifying model-inherent uncertainties in soil constitutive models 认领 引用
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作者 Haibin Xiong Shaoheng Dai +3 位作者 Feng Shan Xuzhen He Sheng Zhang Daichao Sheng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4867-4881,共15页
Quantifying uncertainty in soil constitutive models is both challenging and essential for improving predictive reliability.While the Bayesian approach offers an effective framework for this purpose,previous studies ha... Quantifying uncertainty in soil constitutive models is both challenging and essential for improving predictive reliability.While the Bayesian approach offers an effective framework for this purpose,previous studies have primarily focused on uncertainties in input parameters of standard“ideal”constitutive models.However,models such as the Mohr-Coulomb,Cam-Clay,and Modified Cam-Clay are based on simplified assumptions that do not fully capture real soil behaviour,making it impossible for them to match real-world data perfectly.This study introduces a novel Bayesian-based framework that transforms deterministic models into probabilistic ones,allowing for the quantification of model-inherent uncertainty.A modified maximum a posteriori(MAP)estimator is employed,incorporating a penalty term into the negative log-likelihood to form a new loss function,which enhances bias estimates and model reliability.Using this method,three assumptions about the model bias term are evaluated against triaxial test data from Karlsruhe sand and White clay.The results demonstrate that non-constant assumptions about the bias term significantly enhance model performance.Among the models tested,the probabilistic Modified Cam-Clay model with a nonlinear bias assumption exhibits the best performance.Conversely,the probabilistic Mohr-Coulomb model reveals certain limitations,including overpredicted peak deviatoric stress and overfitted volumetric strain,underscoring the need for further refinements. 展开更多
关键词 Uncertainty quantification Maximum a posteriori(MAP) Probabilistic models Model bias Constitutive models
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When Federated Learning Meets Large Language Models:Taxonomy,Challenges,and Opportunities 认领 引用
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作者 Shan Jiang Wenxin You +2 位作者 Haoran Zhang Shichang Xuan Jiaxing Shen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1-34,共34页
Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized... Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training.Federated Learning(FL)provides a promising alternative by enabling training LLMs collaboratively without sharing raw data.However,integrating FL and LLMs introduces new challenges,including model size,device heterogeneity,non-IID data,and alignment requirements.This survey offers a structured overview of the federated LLM ecosystem.We present a comprehensive taxonomy encompassing system architectures,advanced data strategies for addressing heterogeneity,and retrieval-augmented generation in federated contexts.Additionally,we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns.We conclude by summarizing emerging applications in healthcare,industry,software engineering,and finance,and by outlining open problems and research opportunities for scalable,secure,and responsible federated LLM deployment. 展开更多
关键词 Large language models federated learning foundation models federated large language models
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Reply to Bullock et al.(2026)on ecosystem service models are indeed being validated 认领 引用
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作者 Paulo Pereira Miguel Inacio +1 位作者 Damia Barcelo Wenwu Zhao 《Geography and Sustainability》 CSCD 2026年第2期294-295,共2页
First and foremost,we are very happy that the topic of model vali-dation raised in Pereira et al.(2025)promoted a scientific discussion on this essential issue.Indeed,the question of model validation was raised by Och... First and foremost,we are very happy that the topic of model vali-dation raised in Pereira et al.(2025)promoted a scientific discussion on this essential issue.Indeed,the question of model validation was raised by Ochoa and Urbina-Cardona(2017)and by other scholars,such as Boerema et al.(2017). 展开更多
关键词 model validation issue model validation ecosystem service models scientific discussion
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Incorporating temperature-adapted growing periods enhances simulation of crop yield sensitivity to temperature in global gridded crop models 认领 引用
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作者 Xiaobo Wang Shaoqiang Wang +6 位作者 Christian Folberth Rastislav Skalsky Juraj Balkovic Florian Kraxner Xia Li Jinyuan Liu Bangyou Zheng 《Geography and Sustainability》 CSCD 2026年第2期205-215,共11页
Global Gridded Crop Models(GGCMs)have been widely used to simulate the impacts of global warming on crop production,but their accuracy in capturing the real-world temperature sensitivity of crop yields remains unclear... Global Gridded Crop Models(GGCMs)have been widely used to simulate the impacts of global warming on crop production,but their accuracy in capturing the real-world temperature sensitivity of crop yields remains unclear.Here,we evaluated the performance of eight GGCM emulators(incorporating versus not incorporating cultivar adaptation of crop growing periods at 0.5°×0.5°resolution)in modelling yield sensitivities to 1 K temperature increase(ST)and optimized their ensembles against statistically-inferred STfor maize,rice,and wheat using a Bayesian Model Averaging approach.Our results suggest that multi-GGCM ensembles assuming a fixed crop growing period(i.e.,a gradually temperature-adapted crop cultivar)show higher goodness-of-fit to statistically inferred STthan those assuming a temperature-sensitive growing period for the crops in major food-producing countries.When setting a temperature-adapted growing period instead of a temperature-sensitive growing period in the GGCM ensembles,the R 2 between GGCM-simulated and statistically-inferred STincreased from 0.63 to 0.81 for maize,0.28 to 0.52 for rice,and 0.40 to 0.85 for wheat,meanwhile the RMSE was reduced for all three crops across their respective top 20 producing countries.The crop models may exaggerate historical responses of crop growing periods to climate warming,resulting in an overestimation of yield STfor maize and an underestimation of yield STfor rice and wheat in major food-producing countries.The study highlights the importance of adopting dynamic phenological parameters in GGCM simulations to reflect crop cycle adaptation under global warming. 展开更多
关键词 Crop model GGCM Climate change Temperature sensitivity Growing period Model ensemble
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Integrating process-based and deep learning models for flood simulation in karst basins 认领 引用
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作者 Bin-quan Li Yi-jie Xia +3 位作者 Si-ji Tao Yun-yao Chen Jian-fei Zhao Zhong-min Liang 《Water Science and Engineering》 EI CAS CSCD 2026年第1期23-34,共12页
Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms,often resulting in low accuracy.This study investigated two typical karst basins(the Maiweng and Li... Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms,often resulting in low accuracy.This study investigated two typical karst basins(the Maiweng and Liudong river basins)in Guizhou Province,China,and developed two hydrological models for flood simulation:the karst-Xin'anjiang(Karst-XAJ)model,a modified Xin'anjiang(XAJ)hydrological model adapted for karst runoff characteristics,and the long short-term memory(LSTM)deep learning model.Their performances were compared,and their results were integrated using Bayesian model averaging(BMA).The Karst-XAJ model accurately simulated flood peak time and runoff depth but showed limited peak flow accuracy.The LSTM model performed well within a 2-h computational window,with accuracy declining for longer computational windows(3-4 h)yet maintaining a Nash-Sutcliffe model efficiency coefficient above 0.7.The BMA approach further enhanced simulation accuracy beyond individual models.Overall,both models effectively captured flood dynamics in karst basins,with the LSTM model achieving superior precision.This study offers a novel framework for simulating flood processes in karst regions with complex runoff processes. 展开更多
关键词 Karst basin Karst-Xin'anjiang model Long short-term memory neural network Bayesian model averaging Flood simulation
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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Evaluating the Shanghai Typhoon Model against State-of-the-Art Machine-Learning Weather Prediction Models:A Case Study for Typhoon Danas(2025) 认领 引用
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作者 Zeyi NIU Wei HUANG +5 位作者 Yuhua YANG Mengqi YANG Lin DENG Haibo WANG Hong LI Xu ZHANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第4期744-750,共7页
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou... This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models. 展开更多
关键词 Shanghai Typhoon Model(SHTM) machine-learning weather prediction machine learning-physics hybrid model
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Research advances in animal models of high-altitude qi-deficiency and blood-stasis pattern 认领 引用
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作者 Zhixing Wang Xin Shen +3 位作者 Baoying Shen Lijun Huang Jie Huang Chengcai Lai 《Journal of Traditional Chinese Medical Sciences》 CAS CSCD 2026年第1期19-26,共8页
This study summarizes the theoretical basis,modeling strategies,pathological mechanisms,and therapeutic advances related to high-altitude qi-deficiency and blood-stasis pattern.Traditional concepts such as“qi drives ... This study summarizes the theoretical basis,modeling strategies,pathological mechanisms,and therapeutic advances related to high-altitude qi-deficiency and blood-stasis pattern.Traditional concepts such as“qi drives blood”and“deficiency leads to stasis”closely align with modern evidence demonstrating that hypoxia disrupts energy metabolism,impairs microcirculation,and amplifies inflammation and oxidative stress.Current animal models commonly use hypobaric hypoxia combined with fatigue loading,dietary restriction,ice-water stimulation,or adrenaline injection to mimic the combined effects of qi deficiency,blood stasis,and hypoxic injury.These composite approaches reproduce systemic abnormalities,including reduced arterial oxygen partial pressure,increased blood viscosity,impaired cardiac and pulmonary function,microcirculatory obstruction,and mitochondrial dysfunction.Enhanced inflammatory signaling,oxidative stress,and disturbances in metabolic and epigenetic networks further characterize the pattern.The findings indicate that its pathogenesis arises from multi-system,multi-target interactions rather than a single pathway.Representative herbal formulas,such as Buyang Huanwu decoction,Xuefu Zhuyu decoction,and prescriptions rich in Astragalus membranaceus(Fisch.)Bunge(A.membranaceus,Huang qi)or Salvia miltiorrhiza Bunge(S.miltiorrhiza,Dan Shen)have demonstrated the ability to improve energy metabolism,attenuate endothelial injury,enhance microcirculation,and suppress inflammation through network-level regulation.Future research should focus on standardizing exposure parameters,developing quantitative syndrome evaluation systems,and integrating multi-omics,systems biology and artificial intelligence to improve model reproducibility and mechanistic precision.These efforts may help establish objective criteria for high-altitude qi-deficiency and blood-stasis pattern and support the development of targeted therapeutic strategies. 展开更多
关键词 High-altitude qi-deficiency and blood-stasis pattern Animal model Hypobaric hypoxia Model establishment Evaluation system
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Advances and perspectives in animal models of human hepatitis A virus 认领 引用
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作者 Jian Li Pei-Yu Jiang +1 位作者 Hui Zhao Cheng-Feng Qin 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第6期1180-1187,共8页
Hepatitis A virus(HAV)is an important pathogen that has continuously posed a threat to global public health for over 5000 years.The development of accessible and reliable small animal models,especially murine models,i... Hepatitis A virus(HAV)is an important pathogen that has continuously posed a threat to global public health for over 5000 years.The development of accessible and reliable small animal models,especially murine models,is essential for elucidating HAV pathogenesis and advancing preventive and therapeutic strategies.The first mouse model for HAV infection was established using human-liver chimeric mice,which supported robust viral replication and high viral loads.Later,an Ifnar1−/−mice model was established with a specific mouse-adapted strain,recapitulating key clinical features of human hepatitis A.Recently,to overcome limitations associated with the difficulty in obtaining and amplifying stocks of HAV for animal models,our laboratory established a novel hepatitis A mouse model using lipid nanoparticle-encapsulated viral genomic RNA(LNP-vRNA).This approach provides a new strategy for modeling infections of hard-to-culture RNA viruses.In this review,we systematically summarize and compare these mouse models,respectively highlighting their advantages and limitations,and offer guidance for their application in future HAV research. 展开更多
关键词 animal models hepatitis A virus LNP-vRNA based mouse model
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Data-driven computing ligament loading mechanisms:integration of the computational ligament mechanics models with deep learning 认领 引用
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作者 Datao Xu Huiyu Zhou +7 位作者 Yi Yuan Zanni Zhang Tianle Jie Zhifeng Zhou Zixiang Gao Liangliang Xiang Meizi Wang Yaodong Gu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期628-672,共45页
Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This st... Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This study combines medical imaging data to construct the subject-specific ankle musculoskeletal model,which considers the subject's individualized characteristics and ligamentous attributes.Furthermore,we developed the structural constitutive model to restore the nonlinear short-term viscoelastic properties of the ligament-dense connective tissue,which can more realistically revert the LLM and reveal the mechanical properties of ankle injury.Based on the computational ligament mechanics(CLM)model,we developed a deep learning-based prediction model to predict LLM by CLM data-driven modeling.The modeling simulation results are highly consistent with the calculation results from the dual fluoroscopic imaging system,which demonstrated that the CLM model has high accuracy.The data-driven modeling performs exceptionally well in predicting ligament loading forces.The findings indicate that the constructed CLM data-driven model has the potential to enhance the accuracy and safety of ankle rehabilitation robots,while also providing personalized,dynamically adjusted rehabilitation training programs.The proposed comprehensive solutions would bring benefits to more patients with sports injuries and the general rehabilitation population,and promote the development and advancement of the research field of CLM and biomechanical variable prediction. 展开更多
关键词 Computational ligament mechanics Subject-specific musculoskeletal model Structural constitutive model Ankle ligament injury mechanisms Biomechanical variable prediction
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A comprehensive analysis and improvement of classical nonconformal contact force models for revolute joints with clearance 认领 引用
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作者 Fushou Liu Wenguang Pan Dongping Jin 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第4期56-68,共13页
Revolute joints are widely used in various engineering fields such as deployable space structures and robotics.Currently,various theoretical models have been applied to evaluate the normal contact force of the revolut... Revolute joints are widely used in various engineering fields such as deployable space structures and robotics.Currently,various theoretical models have been applied to evaluate the normal contact force of the revolute joints;however,the results obtained by these models often exhibit significant discrepancies,and there is a lack of explanation for the reasons behind these discrepancies.This study explores the underlying theories of three classical non-conformal contact force models for revolute joints,namely the ESDU-78035 model,the Radzimovsky model,and the Dubowsky-Freudenstein model.The theoretical differences among these classical models are analyzed,and their deficiencies are discovered,and then an improved non-conformal normal contact force model is presented.To validate the accuracy of the presented model,three-dimensional refined finite element models employing surface-to-surface contact elements are constructed to simulate the normal contact of the revolute joint with different clearance sizes and different contact lengths.A comprehensive comparison is carried out between the semi-contact-width,contact pressure,and load-deformation curves obtained from the theoretical models and the finite element model.The results indicate that the presented contact force model outperforms the three classical contact force models in terms of accuracy.The differences between the ideal contact model and the real revolute joint are also investigated,and a sub-model modeling method is presented for the real revolute joint based on the ideal contact force model. 展开更多
关键词 Revolute joint Clearance Normal contact force model Non-conformal contact Three-dimensional finite element model
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Recent advances in the construction of humanized animal models and applications in translational medicine 认领 引用
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作者 Yanan Lyu Yong-Guang Yang Zheng Hu 《Animal Models and Experimental Medicine》 CAS CSCD 2026年第5期914-931,共18页
Over the past several decades,humanized mouse models have undergone significant refinement and become essential tools in biomedical research.Advances in genetic engineering,particularly the targeted knock-in of human ... Over the past several decades,humanized mouse models have undergone significant refinement and become essential tools in biomedical research.Advances in genetic engineering,particularly the targeted knock-in of human cytokines,have enabled robust development and function of human immune cells in these models.Recent breakthroughs-including the directed differentiation of human pluripotent stem cells into thymic epithelial cells and the efficient expansion of hematopoietic stem cells-have alleviated the constraint of scarce human tissue sources.Furthermore,the reconstruction of lymph node structures and successful engraftment of solid organ tissues have significantly improved the efficiency and physiological relevance of human immune system reconstitution.These models now provide a unique plat-form for in vivo studies in cancer immunotherapy,infectious diseases,regenerative medicine,and autoimmune disorders under near-physiological conditions.Beyond rodents,substantial progress in humanized large animals,such as pigs,offers prom-ising avenues for large-scale immune system reconstruction and mass production of immunotherapeutic cells.This review presents a comprehensive overview of the development,optimization,and expanding applications of humanized animal models,highlighting their transformative role in advancing translational medicine. 展开更多
关键词 cancer immunotherapy disease models humanized animal models regenerative medicine translational medicine
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From cells to patients:Multiscale computational pathology in the era of foundation models and vision-language systems 认领 引用
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作者 Zeyu Gao Jiusong Ge +8 位作者 Junbo Lu Lei Wu Hannah Clayton Di Zhang Jiashuai Liu Inês Prata Machado Yan Chen Mireia Crispín‑Ortuzar Chen Li 《Intelligent Oncology》 CAS 2026年第3期66-75,共10页
Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide im... Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide images for patient-level prognostic profiling.While traditional deep learning approaches have achieved remarkable success in specific tasks,the recent emergence of large-scale foundation models and vision-language models has precipitated a paradigm shift in the field.These data-driven systems,characterized by their robust representation learning and semantic reasoning capabilities,are redefining how we analyze pathological data across diverse spatial scales.In this review,we provide a comprehensive synthesis of this transformation through a multiscale lens.We systematically survey the application of foundation models and vision-language models in deciphering biological complexity,ranging from cell-level segmentation and tissue phenotyping to whole-slide image-level prediction and multimodal integration.Furthermore,we critically analyze the limitations of current approaches,such as interpretability,computational efficiency,and data bias,then outline promising future directions for developing holistic,context-aware systems that bridge the gap between pixel-level features and patient-centric clinical decision-making. 展开更多
关键词 Computational pathology Foundation model Multiscale modeling
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