What can computational modeling offer for studying the Ca^(2+) dysregulation in Alzheimer's disease:current research and future directions

Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventually leading to memory impairments and cognitive decline.Treatments targetingCa^2＋ signaling pathways are potential therapeutic strategies against AD.The complicated interactions make it challenging and expensive to study the underlying mechanisms as to how Ca^2＋ signaling contributes to the pathogenesis of AD.Computational modeling offers new opportunities to study the signaling pathway and test proposed mechanisms.In this mini-review,we present some computational approaches that have been used to study Ca^2＋ dysregulation of AD by simulating Ca^2＋signaling at various levels.We also pointed out the future directions that computational modeling can be done in studying the Ca^2＋ dysregulation in AD.

An In Vivo Experimental Validation of a Computational Model of Human Foot

Reliable computational foot models offer an alternative means to enhance knowledge on the biomechanics of human foot. Model validation is one of the most critical aspects of the entire foot modeling and analysis process.This paper presents an in vivo experiment combining motion capture system and plantar pressure measure platform to validate a three-dimensional finite element model of human foot.The Magnetic Resonance Imaging(MRI)slices for the foot modeling and the experimental data for validation were both collected from the same volunteer subject.The validated components included the comparison of static model predictions of plantar force,plantar pressure and foot surface deformation during six loading conditions,to equivalent measured data.During the whole experiment,foot surface deformation,plantar force and plantar pressure were recorded simultaneously during six different loaded standing conditions.The predictions of the current FE model were in good agreement with these experimental results.

A review of computational modeling and deep brain stimulation:applications to Parkinson’s disease

Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease(PD).In previous studies,the development of parkinsonian network dynamics modeling has made great progress.Modeling mainly focuses on the cortex-thalamus-basal ganglia(CTBG)circuit and its sub-circuits,which helps to explore the dynamic behavior of the parkinsonian network,such as synchronization.Deep brain stimulation(DBS)is an effective strategy for the treatment of PD.At present,many studies are based on the side effects of the DBS.However,the translation from modeling results to clinical disease mitigation therapy still faces huge challenges.Here,we introduce the progress of DBS improvement.Its specific purpose is to develop novel DBS treatment methods,optimize the treatment effect of DBS for each patient,and focus on the study in closed-loop DBS.Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

Computational modelling of magnesium degradation in simulated body fluid under physiological conditions

Magnesium alloys are highly attractive for the use as temporary implant materials, due to their high biocompatibility and biodegradability.However, the prediction of the degradation rate of the implants is difficult, therefore, a large number of experiments are required. Computational modelling can aid in enabling the predictability, if sufficiently accurate models can be established. This work presents a generalized model of the degradation of pure magnesium in simulated body fluid over the course of 28 days considering uncertainty aspects. The model includes the computation of the metallic material thinning and is calibrated using the mean degradation depth of several experimental datasets simultaneously. Additionally, the formation and precipitation of relevant degradation products on the sample surface is modelled, based on the ionic composition of simulated body fluid. The computed mean degradation depth is in good agreement with the experimental data(NRMSE=0.07). However, the quality of the depth profile curves of the determined elemental weight percentage of the degradation products differs between elements(such as NRMSE=0.40 for phosphorus vs. NRMSE=1.03 for magnesium). This indicates that the implementation of precipitate formation may need further developments. The sensitivity analysis showed that the model parameters are correlated and which is related to the complexity and the high computational costs of the model. Overall, the model provides a correlating fit to the experimental data of pure Mg samples of different geometries degrading in simulated body fluid with reliable error estimation.

Computational model investigating the effect of magnetic field on neural–astrocyte microcircuit

Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that "non-neuronal" cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation(TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its mechanisms.Our model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents(SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.

Computational modelling and simulation to mitigate the risk of daylight exposure in tropical museum buildings

In museum design and operation,daylight is typically discouraged due to high risk of damaging the display objects.However,past studies in high-latitude regions have shown the possibility to apply daylight in museums,so long as it is carefully planned,and the display objects are not from the category of high responsive materials.In the tropical region,the influence of daylighting on light exposure on museum objects is still unknown.This study therefore aims to assess and mitigate the impact of annual daylight exposure on objects with low responsive materials in a tropical daylit museum building.Annual daylight modelling and simulation are performed to achieve the objective,followed with Morris sensitivity analysis and Mahalanobis distance classifier to optimise the outcome.It is found that either WWR or glazing transmissivity gives the greatest influence on the performance indicators.Based on the proposed optimisation algorithm,it is possible to determine the optimum solutions satisfying the performance indicators target,for a certain opening type.Overall,the contribution of this study is the proposed computational modelling and simulation methods to mitigate the exposure risk while optimising daylight as a renewable energy source.

Importance of incorporating systemic cerebroarterial hemodynamics into computational modeling of blood flow in intracranial aneurysm

The importance of properly treating boundary conditions (BCs) in numerical simulation of hemodynamics in intracranial aneurysm (IA) has been increasingly recognized. In this study, we constructed three types of computational model for each IA to investigate how the outcome of numerical simulation is affected by the treatment of BCs. The first type of model (i.e., Type-A model) was obtained by applying 3-D hemodynamic modeling to the entire cerebral arterial network, with its solution being taken as the reference for evaluating the performance of the other two types of model (i.e., Type-B and Type-C models) in which 3-D modeling was confined to the aneurysm region. In addition, patient-specific 1-D models of the cerebral arterial network were developed to provide hemodynamic information for setting the inflow/outflow BCs of the 3-D models. Numerical tests on three IAs revealed that prescribing the outflow BCs of a localized 3-D aneurysm model based on 1-D model-simulated outflow division (i.e., Type-B model) instead of imposing the free outflow BC on all outlets (i.e., Type-C model) helped to improve the fidelity of the simulation of intra-aneurysmal hemodynamics, but could not guarantee a complete reproduction of the reference solution obtained by the Type-A model. Moreover, it was found that the outcome of hemodynamic simulation was more sensitive to the treatment of BCs when an aneurysm was located at arterial bifurcation rather than sidewall. These findings highlight the importance of taking into account systemic cerebroarterial hemodynamics in computational modeling of hemodynamics in IAs, especially those located at bifurcations.

Computational Model for the Underlying Mechanisms Regulating Bone Loss by Mechanical Unloading and Estrogen Deficiency

The objective of this paper is to investigate the different effects of disuse and estrogen deficiency on bone loss and the underlying mechanisms.A mechanical-biological factors coupled computational model was built to simulate different patterns of bone loss induced in female rats by hind limb unloading,ovariectomy,or both in an animal study.A remodeling analysis was performed on a representative cross section of 6 mm2 of cancellous bone in the distal femoral metaphysis of the rats.The BMU activation frequency,the refilling rate,and the principal compressive strain in the state of mechanical unloading and estrogen deficiency were simulated to interpret the underlying mechanisms.Simulated bone loss patterns due to mechanical unloading,estrogen deficiency,or both all corresponded with the experimental observations.The results show that mechanical unloading and estrogen deficiency cause different bone loss patterns;moreover,mechanical unloading induces a greater degree of bone loss than estrogen deficiency,which can lead to improved treatment and prevention strategies for osteoporosis.

Efficient FPGA-based graph processing with hybrid pull-push computational model

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world graphs.Programmability and pipeline parallelism of FPGAs make it potential to process different stages of graph iterations.Nevertheless,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph processing.In this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both worlds.Our approach on FPGAs is unique and novel as follow.First,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the precision.Due to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant computation.Second,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution history.We also present to switch models in advance with few stalls using their state statistics.Our evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

Current progress of computational modeling for guiding clinical atrial fibrillation ablation

Atrial fibrillation(AF)is one of the most common arrhythmias,associated with high morbidity,mortality,and healthcare costs,and it places a significant burden on both individuals and society.Anti-arrhythmic drugs are the most commonly used strategy for treating AF.However,drug therapy faces challenges because of its limited efficacy and potential side effects.Catheter ablation is widely used as an alternative treatment for AF.Nevertheless,because the mechanism of AF is not fully understood,the recurrence rate after ablation remains high.In addition,the outcomes of ablation can vary significantly between medical institutions and patients,especially for persistent AF.Therefore,the issue of which ablation strategy is optimal is still far from settled.Computational modeling has the advantages of repeatable operation,low cost,freedom from risk,and complete control,and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance.This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF,from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation.Finally,we summarize current developments and challenges and provide our perspectives and suggestions for future directions.

Structured Computational Modeling of Human Visual System for No-reference Image Quality Assessment

Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.

Neural Firing Mechanism Underlying Two-Electrode Discrimination by 3D Transcutaneous Electrical Nerve Stimulation Computational Model

Transcutaneous electrical nerve stimulation(TENS) has been widely used for sensory feedback which is a key consideration of improving the performance of prosthetic hands. Two-electrode discriminability is the key to realize high-spatial-resolution TENS, but the neural firing mechanism is not clear yet. The goal of this research is to investigate the neural firing patterns under two-electrode stimulation and to reveal the potential mechanisms. A three-dimensional(3 D) model is established by incorporating Aβ fiber neuron clusters into a layered forearm structure. The diameters of the stimulating electrodes are selected as 5, 7, 9 and 12 mm, and the two-electrode discrimination distance(TEDD) is quantified. It is found that a distant TEDD is obtained for a relatively large electrode size, and 7 mm is suggested to be the optimal diameter of stimulating electrodes. The present study reveals the neural firing patterns under two-electrode stimulation by the 3 D TENS model. In order to discriminate individual electrodes under simultaneous stimulation, no crosstalk of activated Aβ fibers exists between two electrodes. This research can further guide the optimization of the electrode-array floorplan.

A computational model for assessment of speech intelligibility in informational masking

The existing auditory computational mod- els for evaluating speech intelligibility can only account for energetic masking, and the effect of informational masking is rarely described in these models. This study was aimed to make a computational model considering the mechanism of informational masking. Several psy- choacoustic experiments were conducted to test the ef- fect of informational masking on speech intelligibility by manipulating the number of masking talker, speech rate, and the similarity of F0 contour between target and masker. The results showed that the speech recep- tion threshold for the target increased as the F0 contours of the masker became more similar to that of the tar- get, suggesting that the difficulty in segregating the tar- get harmonics from the masker harmonics may underlie the informational masking effect. Based on these stud- ies, a new auditory computational model was made by inducing the auditory function of harmonic extraction to the traditional model of speech intelligibility index （SII）, named as harmonic extraction （HF） model. The predictions of the HF model are highly consistent with the experimental results.

A Computational Model for Pedestrian Level Wind Environment Around Tall Buildings

A computational model has been developed for the simulation of pedestrian level wind environment around tall buildings by coupling the numerical simulation of the full scale site and meteorological station materials. In the first step, the hybrid/mixed finite element method is employed to solve the two dimensional Navier Stokes equation for the flow field around tall buildings, in view of the influence of fluctuating wind, the flow field is revised with the effective wind velocity. The velocity ratio is defined in order to relate numerical wind velocity to oncoming reference wind velocity. In the second step, the frequency occurred discomfort wind velocity as a suitable criterion is calculated by use of the coupling between the numerical wind velocity and the wind velocity at the nearest meteorological station. The prediction accuracy of the wind environment simulation by use of the computation model will be discussed. Using the available wind data at the nearest meteorological station as well as the established criteria of wind discomfort, the frequency of wind discomfort can be predicted. A numerical example is given to illustrate the application of the proposed method.

Review of construction methods for whole-cell computational models

The complex mechanisms of the internal operation of cellular functions have not been fully resolved and these functions are regulated by multiple effects,such as transcription regulation,signal transduction,and enzyme catalysis,forming complex interactive mechanisms.This makes the construction of a whole-cell computational model,containing various complex cellular functions,very challenging.However,biological models have played a significant role in the field of systems biology,such as guiding gene-target mining and studying cell metabolic characteristics.Therefore,there is increasing research interest in the construction of whole-cell computational models.Combining two classical languages of systems biology,this review expounds on the development and challenges of whole-cell computational modeling from the two classical methods of steady-state and dynamic modeling.Finally,we propose a new approach for constructing whole-cell computational models.

Experimental and computational models for tissue-engineered heart valves:a narrative review

Valvular heart disease is currently a common problem which causes high morbidity and mortality worldwide.Prosthetic valve replacements are widely needed to correct narrowing or backflow through the valvular orifice.Compared to mechanical valves and biological valves,tissue-engineered heart valves can be an ideal substitute because they have a low risk of thromboembolism and calcification,and the potential for remodelling,regeneration,and growth.In order to test the performance of these heart valves,various animal models and other models are needed to optimise the structure and function of tissue-engineered heart valves,which may provide a potential mechanism responsible for substantial enhancement in tissue-engineered heart valves.Choosing the appropriate model for evaluating the performance of the tissue-engineered valve is important,as different models have their own advantages and disadvantages.In this review,we summarise the current state-of-the-art animal models,bioreactors,and computational simulation models with the aim of creating more strategies for better development of tissue-engineered heart valves.This review provides an overview of major factors that influence the selection and design of a model for tissue-engineered heart valve.Continued efforts in improving and testing models for valve regeneration remain crucial in basic science and translational researches.Future research should focus on finding the right animal model and developing better in vitro testing systems for tissue-engineered heart valve.

Flow Field and Temperature Field in GaN-MOCVD Reactor Based on Computational Fluid Dynamics Modeling

Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.

NUMERICAL MODELING OF DUCTILE CRACK EXTENSION USING COMPUTATIONAL CELL ELEMENTS FOR WELDED JOINTS

A 3-D computationalframework was suggested to model stable growth of a macroscopic crack under model I condition. The Gurson-Tverpaaof dilatant plasticity model for voided materials describes the damage process. Fixed-sized, computational cell elements (containing voids) defined over a thin layer at the cmck plane simulate the ductile crack extension. Outside of this layer, the material remains undamaged by the void growth. The micro-mechanics parumeters controlling cmck growth are the thickness Of computational cell layen D, and the initial void porosity, fo. These parameters are calculated through analyses of ductile tearing to match R-curve obtained from testing of deep notch bend specimens for welded joints. The R-curve for the double edge notched tension specimens is eNctively predicted using these pammeters.The predicted R-curve gives a good agreement with the expemment results.

Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance materials.The bioinspired structure consists of hard grains and soft material interfaces.While the material interface has a very low volume percentage,its property has the ability to determine the bulk material response.Machine learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired nanocomposite.This model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate predictions.The results of this study demonstrate the efficiency and high accuracy of the machine learning model.The successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.

Potential Maintenance Mechanism in Atrial Fibrillation Patients Subject to Different Treatment Procedures： A Preliminary Study Based on a Human Atrial Model

Two clinical ablation protocols, 2C3L and stepwise, have been routinely used in our group to treat atrial fibrillation （AF）, but with a less than 60% long-term arrhythmia-free outcome achieved in patients. The goal of this study was to examine the underlying mechanism of low success in clinical outcome. MRI images from one patient were used to reconstruct a human atrial anatomical model, and fibrotic tissue was manually added to represent the arrhythmia substrate. AF was induced with standard protocols used in clinical practice. 2C3L and stepwise were then used to test the efficacy of arrhythmia termination in our model. The results showed that re-entries induced in our model could not be terminated by using either 2C3L or the stepwise protocol. Although some of the induced re-entries were terminated, others emerged in new areas. Ablation using only the 2C3L or stepwise method was not sufficient to terminate all re-entries in our model, which may partially explain the poor long-term arrhythmiafree outcomes in clinical practice. Our findings also suggest that computational heart modelling is an important tool to assist in the establishment of optimal ablation strategies.