Computational Model Research Articles

Finite Element Analysis of Evolut Transcatheter Heart Valves: Effects of Aortic Geometries and Valve Sizes on Post-TAVI Wall Stresses and Deformations

Background/Objectives: For transcatheter aortic valve implantation (TAVI) therapy, a catheter-guided crimped valve is deployed into the aortic root. Valve types such as Edwards balloon-expandable valves and Medtronic self-expandable valves come in different sizes and are chosen based on patient-specific aortic anatomy, including aortic root diameter measurement. Complications may arise due to variations in anatomical characteristics and the implantation procedure, making pre-implantation assessment important for predicting complications. Methods: Computational modeling, particularly finite element analysis (FEA), has become popular for assessing wall stresses and deformations in TAVI. In this study, a finite element model including the aorta, native leaflets, and TAVI device was used to simulate procedures and assess patient-specific wall stresses and deformations. Results: Using the Medtronic Evolut R valve, we simulated TAVI for 14 patients to analyze the effects of geometrical variations on structural stresses. Virtual TAVIs with different valve sizes were also simulated to study the influence of TAV size on stresses. Our results show that variations in aortic wall geometries and TAV sizes significantly influence wall stresses and deformations. Conclusions: Our study is one of the first comprehensive FEA investigations of aortic geometrical variations and valve sizes on post-TAVI stresses, demonstrating the non-linear relationship between aortic dimensions, TAV sizes, and wall stresses.

Advancing Atrial Fibrillation Research: The Role of Animal Models, Emerging Technologies and Translational Challenges

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, presenting a significant global healthcare challenge due to its rising incidence, association with increased morbidity and mortality, and economic burden. This arrhythmia is driven by a complex interplay of electrical, structural, and autonomic remodelling, compounded by genetic predisposition, systemic inflammation, and oxidative stress. Despite advances in understanding its pathophysiology, AF management remains suboptimal, with ongoing debates surrounding rhythm control, rate control, and anticoagulation strategies. Animal models have been instrumental in elucidating AF mechanisms, facilitating preclinical research, and advancing therapeutic development. This review critically evaluates the role of animal models in studying AF, emphasizing their utility in exploring electrical, structural, and autonomic remodelling. It highlights the strengths and limitations of various models, from rodents to large animals, in replicating human AF pathophysiology and advancing translational research. Emerging approaches, including optogenetics, advanced imaging, computational modelling, and tissue engineering, are reshaping AF research, bridging the gap between preclinical and clinical applications. We also briefly discuss ethical considerations, the translational challenges of animal studies and future directions, including integrative multi-species approaches, omics technologies and personalized computational models. By addressing these challenges and addressing emerging methodologies, this review underscores the importance of refining experimental models and integrating innovative technologies to improve AF management and outcomes.

Alternative Role of B/b Knob-Hole Interactions in the Fibrin Assembly.

The self-assembly of fibrin is a vital process in blood clotting, primarily facilitated by the interactions between knobs "A" and "B" in the central E region of one molecule and the corresponding holes "a" and "b" in the peripheral D regions of two other fibrin molecules. However, the precise function of the interactions between knob "B" and hole "b" during fibrin polymerization remains a subject of ongoing debate. The present study focuses on investigating intermolecular interactions between knob "B" and hole "b". We investigated the D-E-D interactions within the fibrin protofibril to accomplish this objective. Our investigation involved studying the formation of supramolecular complexes involving desAB fibrin with fibrin(ogen) fragments, specifically the D-dimer and D fragment. The research utilized analytical size-exclusion chromatography, SDS-PAGE and densitometry of SDS-PAGE images, dynamic light scattering measurements, turbidity studies, electron microscopy, and computer modeling. Our findings indicate that the interference of the D fragment into classical D-E-D interaction occurs through knob "B" of the fibrin molecule. Molecular dynamics simulations elucidate the binding of only one D region, attributed to the shift of the D-dimer toward the fibrin desAB molecule. The formation of such a complex can be considered evidence supporting the potential mechanism of the branching of protofibrils. According to this theoretical mechanism, the inclusion of the D region from an external fibrin molecule into D-E-D interactions is facilitated through "B/b" contacts.

Herb-disease association prediction model based on network consistency projection

A growing number of biological and clinical reports indicate the usefulness of herbs in the treatment of complex human diseases, giving an essential supplement for modern medicine. Similar to drugs, the use of experimental validation to identify related diseases of given herbs is both expensive and time-consuming. Such validation is even more difficult because each herb always contains several components. It is alternative to design computational models to predict herb-disease associations (HDAs). Nevertheless, only a few computational models have been developed for HDA prediction. In this study, we make full use of several properties of herbs and diseases, which are collected in a public database HERB, to design a model named HDAPM-NCP for predicting HDAs. Based on these properties, six herb kernels and five disease kernels are constructed, which are further fused into one unified herb kernel and one disease kernel. These kernels and herb-disease adjacency matrix are fed into network consistency projection to quantify the strength of herb-disease pairs. The cross-validation results show the high performance of HDAPM-NCP. Such performance is higher than that of two previous models. The ablation experiments prove the effects of modules in this model. Finally, we also analyze the weakness and strength of the model, uncovering which herb-disease pairs that HDAPM-NCP can yield reliable or unsatisfied predictions, and a case study is conducted to prove that HDAPM-NCP can discover latent HDAs.

Intelligent computational ensemble model for predicting cerebral aneurysm using the concept of region localization in multi-section CT angiography

Intelligent computational ensemble model for predicting cerebral aneurysm using the concept of region localization in multi-section CT angiography

Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach

A comprehensive understanding of the dynamics of tribological interactions is essential for enhancing efficiency and durability in a multitude of technical domains. Conventional experimental techniques in tribology are frequently costly and time-consuming. In contrast, elastohydrodynamic lubrication (EHL) simulation models present a viable alternative for calculating frictional forces in sealing contacts. These calculations are based on the hydrodynamics within the sealing contact, as defined by the Reynolds equation, the deformation of the seal, and the contact mechanics. However, a significant drawback of these simulations is the time-consuming calculation process. To overcome these experimental and computational limitations, machine learning algorithms offer a promising solution. Physics-informed machine learning (PIML) improves on traditional data-driven models by incorporating physical principles. In particular, physics-informed neural networks (PINNs) are as effective hybrid solvers that combine data-driven and physics-based methods to solve the partial differential equations that drive EHL simulations. By integrating physical laws into the parameter optimization of the neural network (NN), PINNs provide accurate and fast solutions. Thus, unlike traditional NNs, PINNs have the potential to make accurate predictions beyond the limited training domain. The objective of this study is to demonstrate the feasibility of spatial and temporal extrapolation of the PINN and to analyze its reliability, both with and without consideration of cavitation. Two test cases are employed to examine the pressure and cavitation distribution within a sealing contact that extends beyond the spatial and temporal training range. The findings indicate that PINNs can surmount the typical constraints associated with NNs in the extrapolation of solution spaces, which represents a notable advancement in terms of computational efficiency and model flexibility.

TFAP2E is implicated in central nervous system, orofacial and maxillofacial anomalies

BackgroundPrevious studies in mouse, Xenopus and zebrafish embryos show strong tfap2e expression in progenitor cells of neuronal and neural crest tissues suggesting its involvement in neural crest specification. However, the...

Symmetric Instruction Machines and Symmetric Turing Machines

Symmetric instruction machines (SIAs) and symmetric Turing machines (STMs) are models of computation involving concepts derived from those of classical Turing machines such as tape (memory) and head (processor), but with different functional and structural characteristics. The former model (SIAs) introduced in this paper and preferred by Mark Burgin is a result of a reformulation of the latter model (STMs) published in several articles by the second author in the past. The properties of both models are analyzed and compared. The word “symmetric” in both cases represents the feature of the design which is distinct from classical Turing machines where only cells on the tape change under the action of the head. In both models, symmetric computing involves changes of the tape and parallel (“symmetric”) changes of instructions listed in the head. The key difference between SIAs and STMs is in the dynamic of the changes, which in the former model has the form of compound one-way actions and in the latter model, it has the form of uniform mutual interactions, which only in specific realizations can be separated into a pair of actions. Because of the untimely passing of Mark Burgin, the discussion of the two models and cooperation on the paper has never been finished. For this reason, the arguments of both authors are reported even though, in some cases, they are mutually inconsistent or even contradictory.

The Novel Association of a Single Nucleotide Variant in the COL3A1 Gene with Diffuse Coronary Aneurysms

The COL3A1 gene, encoding the pro-alpha chain of type III collagen, has been implicated in a range of collagen-mediated diseases such as Ehlers–Danlos syndrome and aortic aneurysms. In this report, we present evidence for the first time associating a single nucleotide variant p.P517R in exon 22 of COL3A1 with the development of diffuse coronary aneurysms in a human subject without prior atherosclerotic cardiovascular disease, connective tissue disorder, or phenotypic characteristics diagnostic for vascular Ehlers–Danlos syndrome. Computational modeling of this specific variant in AlphaFold and in silico analyses predict deleterious alterations in the structure and function of the COL3A1 gene product, alpha 1 chain of type III collagen. This novel phenotype-to-genotype correlation should prompt further investigation into the mechanistic basis of this association.

In silico modelling of organ-on-a-chip devices: an overview

An organ-on-a-chip (OOAC) is a microscale device designed to mimic the functions and complexity of in vivo human physiology. Different from traditional culture systems, OOACs are capable of replicating the biochemical microenvironment, tissue-tissue interactions, and mechanical dynamics of organs thanks to the precise control offered by microfluidic technology. Diverse OOAC devices specific to different organs have been proposed for experimental research and applications such as disease modelling, personalized medicine and drug screening. Previous studies have demonstrated that the mathematical modelling of OOAC can facilitate the optimization of chips’ microenvironments, serving as an essential tool to design and improve microdevices which allow reproducible growth of cell culture, reducing the time and cost of experimental testing. Here, we review recent modelling approaches for various OOAC devices, categorized according to the type of organs. We discuss the opportunities for integrating multiphysics with multicellular computational models to better characterize and predict cell culture dynamics. Additionally, we explore how developing more detailed OOAC models would support a more rapid and effective development of microdevices, and the design of robust protocols to grow and control cell cultures.

Prioritizing working memory resources depends on prefrontal cortex.

How the prefrontal cortex contributes to working memory remains controversial, as theories differ in their emphasis on its role in storing memories versus controlling their content. To adjudicate between these competing ideas, we tested how perturbations to the human (both sexes) lateral prefrontal cortex impact the storage and control aspects of working memory during a task that requires human subjects to allocate resources to memory items based on their behavioral priority. Our computational model made a strong prediction that disruption of this control process would counterintuitively improve memory for low-priority items. Remarkably, transcranial magnetic stimulation of retinotopically-defined superior precentral sulcus, but not intraparietal sulcus, unbalanced the prioritization of resources, improving memory for low-priority items as predicted by the model. Therefore, these results provide direct causal support for models in which the prefrontal cortex controls the allocation of resources that support working memory, rather than simply storing the features of memoranda.Significance statement Although higher-order cognition depends on working memory, the resources that support our memory are severely limited in capacity. To mitigate this limitation, we allocate memory resources according to the behavioral relevance of items. Nonetheless, the neural basis of these abilities remains unclear. Here, we tested the hypothesis that a region in lateral prefrontal cortex controls prioritization in working memory. Indeed, perturbing this region with transcranial magnetic stimulation disrupted the prioritization of working memory resources. Our results provide causal evidence for the hypothesis that prefrontal cortex primarily controls the allocation of memory resources, rather than storing the contents of working memory.

Operation of spinal sensorimotor circuits controlling phase durations during tied-belt and split-belt locomotion after a lateral thoracic hemisection.

Locomotion is controlled by spinal circuits that interact with supraspinal drives and sensory feedback from the limbs. These sensorimotor interactions are disrupted following spinal cord injury. The thoracic lateral hemisection represents an experimental model of an incomplete spinal cord injury, where connections between the brain and spinal cord are abolished on one side of the cord. To investigate the effects of such an injury on the operation of the spinal locomotor network, we used our computational model of cat locomotion recently published in eLife (Rybak et al., 2024) to investigate and predict changes in cycle and phase durations following a thoracic lateral hemisection during treadmill locomotion in tied-belt (equal left-right speeds) and split-belt (unequal left-right speeds) conditions. In our simulations, the 'hemisection' was always applied to the right side. Based on our model, we hypothesized that following hemisection the contralesional ('intact', left) side of the spinal network is mostly controlled by supraspinal drives, whereas the ipsilesional ('hemisected', right) side is mostly controlled by somatosensory feedback. We then compared the simulated results with those obtained during experiments in adult cats before and after a mid-thoracic lateral hemisection on the right side in the same locomotor conditions. Our experimental results confirmed many effects of hemisection on cat locomotion predicted by our simulations. We show that having the ipsilesional hindlimb step on the slow belt, but not the fast belt, during split-belt locomotion substantially reduces the effects of lateral hemisection. The model provides explanations for changes in temporal characteristics of hindlimb locomotion following hemisection based on altered interactions between spinal circuits, supraspinal drives, and somatosensory feedback.

The trajectory of crime: Integrating mouse-tracking into concealed memory detection.

The autobiographical implicit association test (aIAT) is an approach of memory detection that can be used to identify true autobiographical memories. This study incorporates mouse-tracking (MT) into aIAT, which offers a more robust technique of memory detection. Participants were assigned to mock crime and then performed the aIAT with MT. Results showed that mouse metrics exhibited IAT effects that correlated with the IAT effect of RT and showed differences in autobiographical and irrelevant events while RT did not. Our findings suggest the validity of MT in offering measurement of the IAT effect. We also observed different patterns in mouse trajectories and velocity for autobiographical and irrelevant events. Lastly, utilizing MT metric, we identified that the Past Negative Score was positively correlated with IAT effect. Integrating the Past Negative Score and AUC into computational models improved the simulation results. Our model captured the ubiquitous implicit association between autobiographical events and the attribute True, and offered a mechanistic account for implicit bias. Across the traditional IAT and the MT results, we provide evidence that MT-aIAT can better capture the memory identification and with implications in crime detection.

Operation of spinal sensorimotor circuits controlling phase durations during tied-belt and split-belt locomotion after a lateral thoracic hemisection

Locomotion is controlled by spinal circuits that interact with supraspinal drives and sensory feedback from the limbs. These sensorimotor interactions are disrupted following spinal cord injury. The thoracic lateral hemisection represents an experimental model of an incomplete spinal cord injury, where connections between the brain and spinal cord are abolished on one side of the cord. To investigate the effects of such an injury on the operation of the spinal locomotor network, we used our computational model of cat locomotion recently published in eLife (Rybak et al., 2024) to investigate and predict changes in cycle and phase durations following a thoracic lateral hemisection during treadmill locomotion in tied-belt (equal left-right speeds) and split-belt (unequal left-right speeds) conditions. In our simulations, the ‘hemisection’ was always applied to the right side. Based on our model, we hypothesized that following hemisection the contralesional (‘intact’, left) side of the spinal network is mostly controlled by supraspinal drives, whereas the ipsilesional (‘hemisected’, right) side is mostly controlled by somatosensory feedback. We then compared the simulated results with those obtained during experiments in adult cats before and after a mid-thoracic lateral hemisection on the right side in the same locomotor conditions. Our experimental results confirmed many effects of hemisection on cat locomotion predicted by our simulations. We show that having the ipsilesional hindlimb step on the slow belt, but not the fast belt, during split-belt locomotion substantially reduces the effects of lateral hemisection. The model provides explanations for changes in temporal characteristics of hindlimb locomotion following hemisection based on altered interactions between spinal circuits, supraspinal drives, and somatosensory feedback.

Enhancing seismic risk assessment through probabilistic analysis of liquefaction potential index: a computational intelligence approach

ABSTRACT This study proposes a novel computational intelligence approach for enhancing seismic risk assessment through probabilistic analysis of the liquefaction potential index (LPI). The methodology leverages a hybridised model that combines backpropagation neural networks (BcNN) with an improved firefly algorithm (IFF) to predict LPI values accurately and efficiently. The BcNN-IFF model was trained and validated using a comprehensive dataset comprising the geotechnical and seismic parameters from various earthquake-prone regions worldwide. Feature importance analysis revealed that total stress (σv’) and peak ground acceleration (amax) were the most influential factors in the LPI prediction. The BcNN-IFF model demonstrated superior performance compared to other hybrid models, achieving 91% accuracy in the training phase and 85% accuracy in the testing phase, with low root mean square error and mean absolute error values. The practical implications of the BcNN-IFF model highlights its potential for reliable liquefaction susceptibility assessments, particularly in resource-constrained environments. Future research directions are outlined, emphasising the need for uncertainty quantification, data augmentation, and computational efficiency enhancements to improve the applicability of the model in real-world scenarios. This study advances state-of-the-art computational modelling and provides a foundation for integrating such tools into geotechnical engineering practices for effective seismic risk assessment and infrastructure resilience.

Investigation of Diverse Urban Carbon Emission Reduction Pathways in China: Based on the Technology–Organization–Environment Framework for Promoting Socio-Environmental Sustainability

In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across 81 cities in China. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), this research assesses the roles of technological, organizational, and environmental drivers in urban carbon reduction. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to uncover distinct carbon reduction pathways and causal asymmetries between cities. The findings reveal that technological, organizational, and environmental factors significantly drive carbon reduction, with technological and organizational factors playing the central roles. Environmental factors exert primarily indirect effects, interacting with technological and organizational drivers. This study categorizes cities into three distinct carbon reduction models: cities with high carbon-neutral potential primarily leverage technological innovation and energy efficiency optimization; cities with moderate potential integrate technology and policy, emphasizing green landscape planning to achieve balanced development; and cities with lower carbon reduction potential are mainly policy-driven, constrained by technological and resource limitations. This study underscores the role of computational modeling in providing valuable insights for the development of context-tailored carbon reduction strategies. It highlights the synergetic interactions among technological, organizational, and environmental factors, offering essential guidance for advancing sustainable development planning and facilitating the low-carbon transition of cities and communities.

Investigation of Thermal Deformation Behavior in Boron Nitride-Reinforced Magnesium Alloy Using Constitutive and Machine Learning Models

Accurate flow stress prediction is vital for optimizing the manufacturing of lightweight materials under high-temperature conditions. In this study, a boron nitride (BN)-reinforced AZ80 magnesium composite was subjected to hot compression tests at temperatures of 300–400 °C and strain rates ranging from 0.01 to 10 s−1. A data-driven Support Vector Regression (SVR) model was developed to predict flow stress based on temperature, strain rate, and strain. Trained on experimental data, the SVR model demonstrated high predictive accuracy, as evidenced by a low mean squared error (MSE), a coefficient of determination (R2) close to unity, and a minimal average absolute relative error (AARE). Sensitivity analysis revealed that strain rate and temperature exerted the greatest influence on flow stress. By integrating machine learning with experimental observations, this framework enables efficient optimization of thermal deformation, supporting data-driven decision-making in forming processes. The results underscore the potential of combining advanced computational models with real-time experimental data to enhance manufacturing efficiency and improve process control in next-generation lightweight alloys.

Specifying Precision in Visual-orthographic Prediction Error Representations for a Better Understanding of Efficient Reading.

Efficient visual word recognition presumably relies on orthographic prediction error (oPE) representations. On the basis of a transparent neurocognitive computational model rooted in the principles of the predictive coding framework, we postulated that readers optimize their percept by removing redundant visual signals, allowing them to focus on the informative aspects of the sensory input (i.e., the oPE). Here, we explore alternative oPE implementations, testing whether increased precision by assuming all-or-nothing signaling and more realistic word lexicons results in adequate representations underlying efficient word recognition. We used behavioral and electrophysiological data (i.e., EEG) for model evaluation. More precise oPE representations (i.e., implementing a binary signaling and a frequency-sorted lexicon with the 500 most common five-letter words) explained variance in behavioral responses and electrophysiological data 300 msec after stimulus onset best. The original less-precise oPE representation still best explains early brain activation. This pattern suggests a dynamic adaption of represented visual-orthographic information, where initial graded prediction errors convert into binary representations, allowing accurate retrieval of word meaning. These results offer a neuro-cognitive plausible account of efficient word recognition, emphasizing visual-orthographic information in the form of prediction error representations central to the transition from perceptual processing to the access of word meaning.

Photoelasticity as a Tool for Stress Analysis of Re-Entrant Auxetic Structures

The presented study illustrates the use of photoelasticity as an effective tool for validating the results of finite element method (FEM) simulations of auxetic structures. This research focuses on comparing stress distributions in planar auxetic models under symmetrical and asymmetrical loading conditions. Experimental measurements, conducted using an optically sensitive material (PSM-1), were found to align closely with FEM predictions, with deviations within 5%. This agreement highlights the accuracy of both methods, though discrepancies were noted in areas with lower stress levels due to fringe order reading precision. The experimental process makes it possible to take into account real conditions and inaccuracies in production, while numerical modelling is based on ideal conditions. The findings affirm the value of photoelasticity for stress field analysis in complex geometries, particularly for auxetic structures, and underscore its role in verifying and refining computational models. The study concludes that photoelasticity can be a valuable tool for designers and engineers in verifying FEM simulations, even without the use of digital processing and the evaluation of measured data.

Semantic and Phonological Abilities Inform Efficacy of Transcranial Magnetic Stimulation on Sustained Aphasia Treatment Outcomes

Abstract A growing body of evidence has shown that repetitive transcranial magnetic stimulation (rTMS) can enhance word-retrieval abilities in chronic aphasia. However, there remains significant variability in the efficacy of combined rTMS and language treatments. This study investigated how semantic and phonological characteristics of baseline word-retrieval impairments may influence the efficacy of rTMS on long-term naming improvements following language treatment in individuals with chronic aphasia. Thirty participants with post-stroke aphasia underwent 10 sessions of 1 Hz rTMS to right pars triangularis followed by a modified constraint-induced language treatment (mCILT). Nineteen participants were randomly assigned to active rTMS and 11 participants were assigned to sham rTMS. All participants completed the Philadelphia Naming Test (PNT) at baseline and at 3 and 6 months post-treatment. We coded PNT errors and fit data to the semantic-phonological (or SP) computational model (Foygel & Dell, 2000) to derive semantic and phonological parameter weights. We ran linear regressions for the proportional improvement in naming, with fixed effects for interactions between rTMS, time, and baseline parameter weights. While there was no immediate effect of rTMS post-treatment, rTMS combined with mCILT improved long-term naming more than language therapy alone. Furthermore, greater baseline semantic and phonological characteristics of word-retrieval abilities were each associated with increased rTMS-induced gains in proportional naming improvements. These patterns were maintained at both 3 and 6 months post-treatment. This study is among the first in a larger sample to demonstrate that individual differences in lexical retrieval contribute to variability in sustained rTMS and aphasia treatment outcomes.