An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately rep...An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.展开更多
E-scooter sharing services have grown exponentially in many cities of the world within the last 10 years, mainly with the goal to serve first/last mile trips. Compared to other shared mobility modes (e.g., autonomous ...E-scooter sharing services have grown exponentially in many cities of the world within the last 10 years, mainly with the goal to serve first/last mile trips. Compared to other shared mobility modes (e.g., autonomous buses and electric taxis), for which Agent-based Models (ABMs) have been applied in many cases, just a few studies attempted to simulate e-scooter trips. This study aims to bridge the gap between ABMs and e-scooter sharing services by reviewing the existing ABMs and conducting a qualitative assessment. Initially, existing ABMs are described based on ten descriptors. To test suitability of each model for simulating e-scooter sharing services, we developed an evaluation checklist based on empirical findings. The ten criteria refer to the capabilities of each model to (a) adjust in new challenges via an open-source code, (b) model shared mobility modes, (c) perform large scale simulation, (d) describe spatiotemporal variation of demand, (e) simulate bicycle, (f) pedestrian, and (g) mixed traffic (h) consider socio-demographic characteristics, (i) integrate new choice models, and (j) model multimodal trips. Our results reveal the advantages and disadvantages of each model in simulating flexible transport modes and services. We end up with a dilemma or a scalability problem: to model e-scooter riding behavior in link level or e-scooter services in network level. It is concluded that the dual behavior of e-scooter users (pedestrian or vehicle) poses new challenges that can be met through the development of new extensions or hybrid simulation models.展开更多
This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionar...This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.展开更多
There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken com...There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken communities have been gaining momentum.As the community resilience analysis aims to guide how to lay out effective hazard mitigation strategies to decrease damage and improve recovery,a comprehensive and accurate approach is necessary.Agent-based modeling,an analysis approach in which different types of agents are created with their properties and behavior clearly defined to simulate the processes of those agents in an external environ-ment,is the most comprehensive and accurate approach so far to conducting community resilience simulations and investigating the decision-making for mitigation and recovery under natural hazards.In this paper,agent-based models(ABMs)are created to simulate the recovery process of a virtual testbed based on the real-world community in Joplin City,MO.The tornado path associated with the real-world tornado event that occurred in May 2011 is adopted in the tornado hazard modeling for the Joplin testbed.In addition,agent-based models are created for another virtual community in the Midwest United States named Centerville using an assumed tornado scenario of the same EF-scale as that in Joplin.The effects of hazard mitigation strategies on the two communities are also explored.A comparison between the analysis results of these two testbeds can indicate the influence of the characteristics of a tornado-prone community on the resilience of the community as well as on the effects of hazard mitigation strategies.It is observed that a community’s level of development significantly impacts the tornado resilience.In addition,the effects of a specific type of hazard mitigation strategy on the recovery process are contingent upon testbed characteristics.展开更多
Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subs...Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models ...BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models are unclear.AIM To establish a translational NERD rat model with anxiety comorbidity via tail clamping and study corticotropin-releasing hormone(CRH)-mediated neuroimmune pathways in visceral hypersensitivity and esophageal injury.METHODS Sprague-Dawley(SD)and Wistar rats were grouped into sham,model,and modified groups(n=10 each).The treatments for the modified groups were as follows:SD rats received ovalbumin/aluminum hydroxide suspension+acid perfusion±tail clamping(40 minutes/day for 7 days),while Wistar rats received fructose water+tail clamping.Esophageal pathology,visceral sensitivity,and behavior were assessed.Serum CRH,calcitonin gene-related peptide(CGRP),5-hydroxytryptamine(5-HT),and mast cell tryptase(MCT)and central amygdala(CeA)CRH mRNA were measured via ELISA and qRT-PCR.RESULTS Tail clamping induced anxiety,worsening visceral hypersensitivity(lower abdominal withdrawal reflex thresholds,P<0.05)and esophageal injury(dilated intercellular spaces and mitochondrial edema).Both models showed raised serum CRH,CGRP,5-HT,and MCT(P<0.01)and CeA CRH mRNA expression(P<0.01).Behavioral tests confirmed anxiety-like phenotypes.NERD-anxiety rats showed clinical-like symptom severity without erosion.CONCLUSION Tail clamping induces anxiety in NERD models,worsening visceral hypersensitivity via CRH neuroimmune dysregulation,offering a translational model and highlighting CRH as a treatment target.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
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
In combinatorics, a Stirling number of the second kind S (n,k)? is the number of ways to partition a set of n objects into k nonempty subsets. The empty subsets are also added in the models presented in the article in...In combinatorics, a Stirling number of the second kind S (n,k)? is the number of ways to partition a set of n objects into k nonempty subsets. The empty subsets are also added in the models presented in the article in order to describe properly the absence of the corresponding type i of state in the system, i.e. when its “share” Pi =0?. Accordingly, a new equation for partitions P (N, m)? in a set of entities into both empty and nonempty subsets was derived. The indistinguishableness of particles (N identical atoms or molecules) makes only sense within a cluster (subset) with the size?0≤ni ≥N. The first-order phase transition is indeed the case of transitions, for example in the simplest interpretation, from completely liquid state?typeL = {n1 =N, n2 = 0} to the completely crystalline state??typeC= {n1 =0, n2 = N }. These partitions are well distinguished from the physical point of view, so they are ‘typed’ differently in the model. Finally, the present developments in the physics of complex systems, in particular the structural relaxation of super-cooled liquids and glasses, are discussed by using such stochastic cluster-based models.展开更多
Insect diversity and abundance are in drastic decline worldwide,but quantifying insect populations to better conserve them is a difficult task.Mark-release-recapture(MRR)is widely used as an ecological indicator for i...Insect diversity and abundance are in drastic decline worldwide,but quantifying insect populations to better conserve them is a difficult task.Mark-release-recapture(MRR)is widely used as an ecological indicator for insect populations,but the accuracy of MRR estimates can vary with factors such as spatial scale,sampling effort and models of inference.We conducted a 3-year MRR study of B.thaidina in Yanzigou valley,Mt.Gongga but failed to obtain sufficient data for a robust population estimate.This prompted us to integrate B.thaidina life history information to parameterize agent-based models and evaluate the conditions under which successful MRR studies could be conducted.We evaluated:(1)the performance of MRR models under different landscape types,and(2)the influence of experimental design on the accuracy and variance of MRR-based estimates.Our simulations revealed systematic underestimates of true population parameters by MRR models when sampling effort was insufficient.In a total of 2772 simulations,subjective decisions in sampling protocol(e.g.,frequency,number of sampling locations,use of spatially explicit models,type of estimands)accounted for nearly half of the variation in estimates.We conclude that MRR-based estimates could be improved with the addition of more field-specific parameters.展开更多
Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that t...Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.展开更多
Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems.As a process-oriented modelling approach,agent based model(ABM)plays an important role in reveali...Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems.As a process-oriented modelling approach,agent based model(ABM)plays an important role in revealing the driving forces of land change and understanding the process of land change.This paper starts from three aspects:The theory,application and modeling framework of ABM.First,we summarize the theoretical basis of ABM and introduce some related concepts.Then we expound the application and development of ABM in both urban land systems and agricultural land systems,and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region,China.On the basis of combing the ABM modeling protocol,we propose the land system ABM modeling framework and process from the perspective of agents.In terms of urban land use,ABM research initially focused on the study of urban expansion based on landscape,then expanded to issues like urban residential separation,planning and zoning,ecological functions,etc.In terms of agricultural land use,ABM application presents more diverse and individualized features.Research topics include farmers’behavior,farmers’decision-making,planting systems,agricultural policy,etc.Compared to traditional models,ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data.However,due to its unique bottom-up model structure,ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.展开更多
Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant＇s brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the ter- ritory that they patr...Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant＇s brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the ter- ritory that they patrol in search of energetically rich forage. When members of 2 colonies randomly interact at the territory boundary a decision to fight occurs when： 1） there is a mismatch in nest- mate recognition cues and 2） each ant has a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants will decide to recruit more workers from the nest to the fighting location, and in this way a positive feedback mediates the development of colony wide wars. In ants, the monoamines serotonin （5-HT） and octopamine （OA） modulate many behaviors associated with colony organization and in particular behaviors associated with nestmate recognition and ag- gression. In this article, we develop and explore an agent-based model that conceptualizes how in- dividual changes in brain concentrations of 5-HT and OA, paired with a simple threshold-based de- cision rule, can lead to the development of colony wide warfare. Model simulations do lead to the development of warfare with 91% of ants fighting at the end of 1 h. When conducting a sensitivity analysis, we determined that uncertainty in monoamine concentration signal decay influences the behavior of the model more than uncertainty in the decision-making rule or density. We conclude that pavement ant behavior is consistent with the detection of interaction rate through a single timed interval rather than integration of multiple interactions.展开更多
Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to i...Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathema...Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.展开更多
Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden p...Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.展开更多
文摘An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.
基金This research has been co-financed by the European Union and Greece,National Strategic Reference Framework 2014-2020(NSRF),through the Operational Program Competitiveness,Entrepreneurship and Innovation,under the call RESEARCH–CREATE–INNOVATE(project code:T2EDK-02494).
文摘E-scooter sharing services have grown exponentially in many cities of the world within the last 10 years, mainly with the goal to serve first/last mile trips. Compared to other shared mobility modes (e.g., autonomous buses and electric taxis), for which Agent-based Models (ABMs) have been applied in many cases, just a few studies attempted to simulate e-scooter trips. This study aims to bridge the gap between ABMs and e-scooter sharing services by reviewing the existing ABMs and conducting a qualitative assessment. Initially, existing ABMs are described based on ten descriptors. To test suitability of each model for simulating e-scooter sharing services, we developed an evaluation checklist based on empirical findings. The ten criteria refer to the capabilities of each model to (a) adjust in new challenges via an open-source code, (b) model shared mobility modes, (c) perform large scale simulation, (d) describe spatiotemporal variation of demand, (e) simulate bicycle, (f) pedestrian, and (g) mixed traffic (h) consider socio-demographic characteristics, (i) integrate new choice models, and (j) model multimodal trips. Our results reveal the advantages and disadvantages of each model in simulating flexible transport modes and services. We end up with a dilemma or a scalability problem: to model e-scooter riding behavior in link level or e-scooter services in network level. It is concluded that the dual behavior of e-scooter users (pedestrian or vehicle) poses new challenges that can be met through the development of new extensions or hybrid simulation models.
文摘This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.
基金Financial support for this work was provided by the US Department of Commerce,the National Institute of Standards and Technology(NIST)under the Financial Assistance Award Number#70NANB20H008the US National Science Foundation(NSF)under Award Number 2052930.
文摘There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken communities have been gaining momentum.As the community resilience analysis aims to guide how to lay out effective hazard mitigation strategies to decrease damage and improve recovery,a comprehensive and accurate approach is necessary.Agent-based modeling,an analysis approach in which different types of agents are created with their properties and behavior clearly defined to simulate the processes of those agents in an external environ-ment,is the most comprehensive and accurate approach so far to conducting community resilience simulations and investigating the decision-making for mitigation and recovery under natural hazards.In this paper,agent-based models(ABMs)are created to simulate the recovery process of a virtual testbed based on the real-world community in Joplin City,MO.The tornado path associated with the real-world tornado event that occurred in May 2011 is adopted in the tornado hazard modeling for the Joplin testbed.In addition,agent-based models are created for another virtual community in the Midwest United States named Centerville using an assumed tornado scenario of the same EF-scale as that in Joplin.The effects of hazard mitigation strategies on the two communities are also explored.A comparison between the analysis results of these two testbeds can indicate the influence of the characteristics of a tornado-prone community on the resilience of the community as well as on the effects of hazard mitigation strategies.It is observed that a community’s level of development significantly impacts the tornado resilience.In addition,the effects of a specific type of hazard mitigation strategy on the recovery process are contingent upon testbed characteristics.
基金supported by National Natural Science Foundation of China(72288101,72361137002,and 72101018)the Dutch Research Council(NWO Grant 482.22.01).
文摘Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
基金Supported by the National Key Specialty of Traditional Chinese Medicine(Spleen and Stomach Diseases),No.0500004National Natural Science Foundation of China,No.82205104 and No.82104850+1 种基金Hospital Capability Enhancement Project of Xiyuan Hospital,CACMS,No.XYZX0303-07the Fundamental Research Funds for the Central Public Welfare Research Institutes,Excellent Young Scientists Training Program of China Academy of Chinese Medical Sciences,No.ZZ16-YQ-002.
文摘BACKGROUND Non-erosive reflux disease(NERD),the main gastroesophageal reflux subtype,features reflux symptoms without mucosal damage.Anxiety links to visceral hypersensitivity in NERD,yet mechanisms and animal models are unclear.AIM To establish a translational NERD rat model with anxiety comorbidity via tail clamping and study corticotropin-releasing hormone(CRH)-mediated neuroimmune pathways in visceral hypersensitivity and esophageal injury.METHODS Sprague-Dawley(SD)and Wistar rats were grouped into sham,model,and modified groups(n=10 each).The treatments for the modified groups were as follows:SD rats received ovalbumin/aluminum hydroxide suspension+acid perfusion±tail clamping(40 minutes/day for 7 days),while Wistar rats received fructose water+tail clamping.Esophageal pathology,visceral sensitivity,and behavior were assessed.Serum CRH,calcitonin gene-related peptide(CGRP),5-hydroxytryptamine(5-HT),and mast cell tryptase(MCT)and central amygdala(CeA)CRH mRNA were measured via ELISA and qRT-PCR.RESULTS Tail clamping induced anxiety,worsening visceral hypersensitivity(lower abdominal withdrawal reflex thresholds,P<0.05)and esophageal injury(dilated intercellular spaces and mitochondrial edema).Both models showed raised serum CRH,CGRP,5-HT,and MCT(P<0.01)and CeA CRH mRNA expression(P<0.01).Behavioral tests confirmed anxiety-like phenotypes.NERD-anxiety rats showed clinical-like symptom severity without erosion.CONCLUSION Tail clamping induces anxiety in NERD models,worsening visceral hypersensitivity via CRH neuroimmune dysregulation,offering a translational model and highlighting CRH as a treatment target.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘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.
文摘In combinatorics, a Stirling number of the second kind S (n,k)? is the number of ways to partition a set of n objects into k nonempty subsets. The empty subsets are also added in the models presented in the article in order to describe properly the absence of the corresponding type i of state in the system, i.e. when its “share” Pi =0?. Accordingly, a new equation for partitions P (N, m)? in a set of entities into both empty and nonempty subsets was derived. The indistinguishableness of particles (N identical atoms or molecules) makes only sense within a cluster (subset) with the size?0≤ni ≥N. The first-order phase transition is indeed the case of transitions, for example in the simplest interpretation, from completely liquid state?typeL = {n1 =N, n2 = 0} to the completely crystalline state??typeC= {n1 =0, n2 = N }. These partitions are well distinguished from the physical point of view, so they are ‘typed’ differently in the model. Finally, the present developments in the physics of complex systems, in particular the structural relaxation of super-cooled liquids and glasses, are discussed by using such stochastic cluster-based models.
基金supported by a graduate fellowship from Harvard University Department of Organismic and Evolutionary Biology,as well as a Joan Mosenthal DeWind Award in Lepidoptera conservation from the Xerces Society(2017)for studying the spatial ecology of butterflies in Yanzigou valleyZW and AJ were supported by a Rufford Small Grant(2016)to work on the conservation of B.thaidinaAspects of the modeling approach used in this research were supported by a grant from the Harvard Global Institute(HG1)to YL,ZW and NEP.
文摘Insect diversity and abundance are in drastic decline worldwide,but quantifying insect populations to better conserve them is a difficult task.Mark-release-recapture(MRR)is widely used as an ecological indicator for insect populations,but the accuracy of MRR estimates can vary with factors such as spatial scale,sampling effort and models of inference.We conducted a 3-year MRR study of B.thaidina in Yanzigou valley,Mt.Gongga but failed to obtain sufficient data for a robust population estimate.This prompted us to integrate B.thaidina life history information to parameterize agent-based models and evaluate the conditions under which successful MRR studies could be conducted.We evaluated:(1)the performance of MRR models under different landscape types,and(2)the influence of experimental design on the accuracy and variance of MRR-based estimates.Our simulations revealed systematic underestimates of true population parameters by MRR models when sampling effort was insufficient.In a total of 2772 simulations,subjective decisions in sampling protocol(e.g.,frequency,number of sampling locations,use of spatially explicit models,type of estimands)accounted for nearly half of the variation in estimates.We conclude that MRR-based estimates could be improved with the addition of more field-specific parameters.
文摘Since the rapid growth of urban populations,the study of urban systems has gained considerable attention from researchers,decision makers,governments,and organizations.Urban systems are complex and dynamic such that they produce emergent patterns such as selforganization and nonlinearity.Agent-based modelling presents an approach to simulating and abstracting urban systems to reveal and study emergent patterns from urban-related entities.However,agent-based models are difficult to effectively optimize and validate without high quality real-world data.Geosocial media data provides agent-based models with location-enabled data at high volumes and frequencies.Integrating agent-based models with geosocial media data presents opportunities in advancing and developing studies in urban systems.This paper provides a general overview of concepts,review of recent applications,and discussion of challenges and opportunities in the context of using geosocial media data in agentbased models for urban systems.We argue that ABMs focused on studying urban systems can benefit greatly from geosocial media data,given that research moves towards standard guidelines that enable the comparison and effective use of ABMs,and geosocial media data under appropriate circumstances and applications.
基金National Natural Science Foundation of China,No.41571098,No.41530749National Key R&D Program of China,No.2017YFC1502903,No.2018YFC1508805。
文摘Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems.As a process-oriented modelling approach,agent based model(ABM)plays an important role in revealing the driving forces of land change and understanding the process of land change.This paper starts from three aspects:The theory,application and modeling framework of ABM.First,we summarize the theoretical basis of ABM and introduce some related concepts.Then we expound the application and development of ABM in both urban land systems and agricultural land systems,and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region,China.On the basis of combing the ABM modeling protocol,we propose the land system ABM modeling framework and process from the perspective of agents.In terms of urban land use,ABM research initially focused on the study of urban expansion based on landscape,then expanded to issues like urban residential separation,planning and zoning,ecological functions,etc.In terms of agricultural land use,ABM application presents more diverse and individualized features.Research topics include farmers’behavior,farmers’decision-making,planting systems,agricultural policy,etc.Compared to traditional models,ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data.However,due to its unique bottom-up model structure,ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.
文摘Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant＇s brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the ter- ritory that they patrol in search of energetically rich forage. When members of 2 colonies randomly interact at the territory boundary a decision to fight occurs when： 1） there is a mismatch in nest- mate recognition cues and 2） each ant has a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants will decide to recruit more workers from the nest to the fighting location, and in this way a positive feedback mediates the development of colony wide wars. In ants, the monoamines serotonin （5-HT） and octopamine （OA） modulate many behaviors associated with colony organization and in particular behaviors associated with nestmate recognition and ag- gression. In this article, we develop and explore an agent-based model that conceptualizes how in- dividual changes in brain concentrations of 5-HT and OA, paired with a simple threshold-based de- cision rule, can lead to the development of colony wide warfare. Model simulations do lead to the development of warfare with 91% of ants fighting at the end of 1 h. When conducting a sensitivity analysis, we determined that uncertainty in monoamine concentration signal decay influences the behavior of the model more than uncertainty in the decision-making rule or density. We conclude that pavement ant behavior is consistent with the detection of interaction rate through a single timed interval rather than integration of multiple interactions.
基金supported by the Shanghai Philosophy and Social Science Foundation(2022ECK004)Shanghai Soft Science Research Project(23692123400)。
文摘Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
文摘Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.
基金supported by the National Natural Science Foundation of China (No.21273209)
文摘Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.
