Computational Model Research Articles

Computational Modeling of Bone Fracture Healing Under Different Initial Conditions and Mechanical Load.

Computational model of bone healing can replace animal experiments to study the parameters affecting the bone healing process, thus reducing the damage to experimental animals and saving a lot of time. We propose a computational model for continuous simulation of four phases of bone healing to study the effects of mechanical environmental and biological factors, including initial conditions at the fracture site, mechanical stimulus loading, and vascular growth rate. A finite element model of mechanobiological fracture healing containing several pre-determined variables was developed for bone healing after fracture in sheep, which included many relevant parameters and biological effects during fracture healing, such as the effects of mechanical environment, blood supply level in the local fracture area, cell migration and diffusion, and resorption effects of fracture healing. The effects of several parameters on indices such as Young's modulus of the callus during bone healing were obtained by simulation. The initial geometry of the healing tissue and mechanical loading had the greatest effect on fracture healing, and different preset values were likely to cause delayed or non-healing fractures. Changed initial tissue properties of the healing tissue showed a nonlinear effect on fracture healing rather than a linear delay or advancement. Parameters related to angiogenesis had a greater effect on fracture healing compared to those related to cell migration. This paper quantified the effect of fracture healing pre-determined variables on fracture healing to better understand the application of mechanobiology in fracture healing simulation models and optimization of treatment strategies. The importance of initial conditions and loads on fracture healing has been shown to help physicians treat bone nonunion or delayed bone healing after a fracture.

Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification

This study introduces an innovative hyperparameter optimization approach for enhancing multilayer perceptrons (MLP) using the Jaya algorithm. Addressing the crucial role of hyperparameter tuning in MLP’s performance, the Jaya algorithm, inspired by social behavior, emerges as a promising optimization technique without algorithm-specific parameters. Systematic application of Jaya dynamically adjusts hyperparameter values, leading to notable improvements in convergence speeds and model generalization. Quantitatively, the Jaya algorithm consistently achieves convergences at first iteration, faster convergence compared to conventional methods, resulting in 7% higher accuracy levels on several datasets. This research contributes to hyperparameter optimization, offering a practical and effective solution for optimizing MLP in diverse applications, with implications for improved computational efficiency and model performance.

Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study

Abstract Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (N = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a pseudo-prospective study (N = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after any resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.

Innovative ICT in Smart Buildings Domain: A Pantentometric Analysis

Background: “Smart Building” constructed using “Smart Materials” for “Smart People” and to create or build “Smart Societies or Cities” is the new trend all over the world. Introduction: To implement smartness holistically, it is essential to surf through databases where innovations from all over the world are visible or can be retrieved. A "One stop solution" to locate innovations from specific domains is patent databases and hence this paper showcases the current scenario of civil constructions using AI-ML or alike technologies. Method: Relecura database is primarily used for performing analysis of all the patents related to these two collaborative domains. A special focus is given to the analysis of data from 2015-2021, to know the latest trends in technology used for constructing smart buildings. Result: Smart home, sensors, and machine learning are highly found keywords. China is the leading country for patents in the smart building domain. The use of computational models and pictorial communication can be explored to increase the patentability of the innovation. Conclusion: This patent analysis will be useful to the end readers, researchers, and students to understand the innovative concepts implemented so far and in turn understand the possible research gaps in these collaborative domains.

Anterior Cruciate Ligament Force and Strain in Males and Females during Anticipated and Unanticipated Side-Stepping

Females have a greater number of Anterior Cruciate Ligament (ACL) injuries per activity hours than males. The cause of this injury rate is speculated to be an increased strain on the ACL in performing a side-stepping manoeuvre. The purpose of this study was to determine the magnitude of ACL force and strain and the knee angle at initial foot contact during anticipated and unanticipated side-stepping. 12 male and 12 female, young, healthy, college-age, athletes were recruited for this study. 3D kinematics and kinetics were collected in each of anticipated and unanticipated conditions. A computational musculo-skeletal model was used to estimate the ACL force and strain. No differences were found between males and females in any of the ACL or knee angle parameters (P>0.05). However, there was a significant difference in ACL force and strain between anticipated and unanticipated side-stepping conditions with unanticipated force and strain greater than in the anticipated condition (P<0.05). These results support previous evidence that athletes may be at greater risk of injury during sidestepping tasks where planning time is reduced due to greater elongation of the ACL. Future research should focus upon investigating tasks to mitigate the high-risk strategies athletes use during unanticipated sports tasks.

Potent Immunomodulators Developed from an Unstable Bacterial Metabolite of Vitamin B2 Biosynthesis

AbstractBacterial synthesis of vitamin B2 generates a by‐product, 5‐(2‐oxopropylideneamino)‐d‐ribityl‐aminouracil (5‐OP‐RU), with potent immunological properties in mammals, but it is rapidly degraded in water. This natural product covalently bonds to the key immunological protein MR1 in the endoplasmic reticulum of antigen presenting cells (APCs), enabling MR1 refolding and trafficking to the cell surface, where it interacts with T cell receptors (TCRs) on mucosal associated invariant T lymphocytes (MAIT cells), activating their immunological and antimicrobial properties. Here, we strategically modify this natural product to understand the molecular basis of its recognition by MR1. This culminated in the discovery of new water‐stable compounds with extremely powerful and distinctive immunological functions. We report their capacity to bind MR1 inside APCs, triggering its expression on the cell surface (EC50 17 nM), and their potent activation (EC50 56 pM) or inhibition (IC50 80 nM) of interacting MAIT cells. We further derivatize compounds with diazirine‐alkyne, biotin, or fluorophore (Cy5 or AF647) labels for detecting, monitoring, and studying cellular MR1. Computer modeling casts new light on the molecular mechanism of activation, revealing that potent activators are first captured in a tyrosine‐ and serine‐lined cleft in MR1 via specific pi‐interactions and H‐bonds, before more tightly attaching via a covalent bond to Lys43 in MR1. This chemical study advances our molecular understanding of how bacterial metabolites are captured by MR1, influence cell surface expression of MR1, interact with T cells to induce immunity, and offers novel clues for developing new vaccine adjuvants, immunotherapeutics, and anticancer drugs.

Potent Immunomodulators Developed from an Unstable Bacterial Metabolite of Vitamin B2 Biosynthesis.

Bacterial synthesis of vitamin B2 generates a by-product, 5-(2-oxopropylideneamino)-d-ribityl-aminouracil (5-OP-RU), with potent immunological properties in mammals, but it is rapidly degraded in water. This natural product covalently bonds to the key immunological protein MR1 in the endoplasmic reticulum of antigen presenting cells (APCs), enabling MR1 refolding and trafficking to the cell surface, where it interacts with T cell receptors (TCRs) on mucosal associated invariant T lymphocytes (MAIT cells), activating their immunological and antimicrobial properties. Here, we strategically modify this natural product to understand the molecular basis of its recognition by MR1. This culminated in the discovery of new water-stable compounds with extremely powerful and distinctive immunological functions. We report their capacity to bind MR1 inside APCs, triggering its expression on the cell surface (EC50 17 nM), and their potent activation (EC50 56 pM) or inhibition (IC50 80 nM) of interacting MAIT cells. We further derivatize compounds with diazirine-alkyne, biotin, or fluorophore (Cy5 or AF647) labels for detecting, monitoring, and studying cellular MR1. Computer modeling casts new light on the molecular mechanism of activation, revealing that potent activators are first captured in a tyrosine- and serine-lined cleft in MR1 via specific pi-interactions and H-bonds, before more tightly attaching via a covalent bond to Lys43 in MR1. This chemical study advances our molecular understanding of how bacterial metabolites are captured by MR1, influence cell surface expression of MR1, interact with T cells to induce immunity, and offers novel clues for developing new vaccine adjuvants, immunotherapeutics, and anticancer drugs.

Jugular Approach for the Transcatheter Pacemaker Implant - Better Access for Smaller Hearts?

The Micra leadless pacemaker was developed to fit inside the right ventricle, thereby reducing overall complications by 48% compared with a historical control group. The current labeling restricts implants to the femoral approach. In this article we used 3-dimensional computer models of human hearts to demonstrate why implants can be difficult in small patients and how using the jugular approach reduces these difficulties.Methods and Results: Cardiac computed tomography scans were made of 45 pacemaker patients, 26 in the US and 19 from a single center in Japan. Dimensional measurements were taken in all 45 hearts, and these dimensions were compared between patient cohorts and between the Micra delivery tool dimension and patient heart dimensions. Hearts were smaller among patients in the Japanese than US cohort. In addition, the tool dimension exceeded heart dimensions in a larger percentage of hearts from Japanese patients. Three dimensions were identified that most likely limit navigating across the tricuspid valve to the right ventricle in smaller hearts and for which the jugular approach improved navigation. Although the femoral procedure today maintains an excellent safety profile and procedure experience for most global implants, this study provides the rationale as to why the jugular approach may improve the ease of the Micra implant in small hearts, namely by reducing the tortuosity of the navigation across the tricuspid valve.

Model Virtues in Computational Cognitive Neuroscience.

There is an abundance of computational models in cognitive neuroscience. A framework for what is desirable in a model, what justifies the introduction of a new one, or what makes one better than another is lacking, however. In this article, we examine key qualities ("virtues") that are desirable in computational models, and how these are interrelated. To keep the scope of the article manageable, we focus on the field of cognitive control, where we identified six "model virtues": empirical accuracy, empirical scope, functional analysis, causal detail, biological plausibility, and psychological plausibility. We first illustrate their use in published work on Stroop modeling and then discuss what expert modelers in the field of cognitive control said about them in a series of qualitative interviews. We found that virtues are interrelated and that their value depends on the modeler's goals, in ways that are not typically acknowledged in the literature. We recommend that researchers make the reasons for their modeling choices more explicit in published work. Our work is meant as a first step. Although our focus here is on cognitive control, we hope that our findings will spark discussion of virtues in other fields as well.

Somatodendritic orientation determines tDCS-induced neuromodulation of Purkinje cell activity in awake mice.

Transcranial direct-current stimulation (tDCS) of the cerebellum is a promising non-invasive neuromodulatory technique being proposed for the treatment of neurological and neuropsychiatric disorders. However, there is a lack of knowledge about how externally applied currents affect neuronal spiking activity in cerebellar circuits in vivo. We investigated how Cb-tDCS affects the firing rate of Purkinje cells (PC) and non-PC in the mouse cerebellar cortex to understand the underlying mechanisms behind the polarity-dependent modulation of neuronal activity induced by tDCS. Mice (n = 9) were prepared for the chronic recording of LFPs to assess the actual electric field gradient imposed by Cb-tDCS in our experimental design. Single-neuron extracellular recording of PCs in awake (n = 24) and anesthetized (n = 27) mice was combined with juxtacellular recordings and subsequent staining of PC with neurobiotin under anesthesia (n = 8) to correlate their neuronal orientation with their response to Cb-tDCS. Finally, a high-density Neuropixels recording system was used to demonstrate the relevance of neuronal orientation during the application of Cb-tDCS in awake mice (n = 6) In this study, we observe that Cb-tDCS induces a heterogeneous polarity-dependent modulation of the firing rate of Purkinje cells (PC) and non-PC in the mouse cerebellar cortex. We demonstrate that the apparently heterogeneous effects of tDCS on PC activity can be explained by taking into account the somatodendritic orientation relative to the electric field. Our findings highlight the need to consider neuronal orientation and morphology to improve tDCS computational models, enhance stimulation protocol reliability, and optimize effects in both basic and clinical applications.

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Abstract This article provides a detailed insight into computational approaches for deciphering Bronze Age Aegean and Cypriot scripts, namely, the Archanes script and the Archanes formula, Phaistos Disk, Cretan hieroglyphic (including the Malia Altar Stone and Arkalochori Axe), Linear A, Linear B, Cypro-Minoan, and Cypriot scripts. The unique contributions of this article are threefold: (1) a thorough review of major Bronze Age Aegean and Cypriot scripts and inscriptions, digital data and corpora associated with them, existing computational decipherment methods developed in order to decipher them, and possible links to other scripts and languages; (2) the definition of 15 major challenges that can be encountered in computational decipherments of ancient scripts; and (3) an outline of a computational model that could possibly be used to simulate traditional decipherment processes of ancient scripts based on palaeography and epigraphy. In the context of this article the term decipherment denotes the process of discovery of the language and/or the set of symbols behind an unknown script, and the meaning behind it.

Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase.

Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

Relationship Between Resting State Functional Connectivity and Reading-Related Behavioural Measures in 69 Adults

Abstract In computational models of reading, written words can be read using print-to-sound and/or print-to-meaning pathways. Neuroimaging data associate dorsal stream regions (left posterior occipitotemporal cortex, intraparietal cortex, dorsal inferior frontal gyrus [dIFG]) with the print-to-sound pathway and ventral stream regions (left anterior fusiform gyrus, middle temporal gyrus) with the print-to-meaning pathway. In 69 typical adults, we investigated whether resting state functional connectivity (RSFC) between the visual word form area (VWFA) and dorsal and ventral regions correlated with phonological (nonword reading, nonword repetition, spoonerisms), lexical-semantic (vocabulary, sensitivity to morpheme units in reading), and general literacy (word reading, spelling) skills. VWFA activity was temporally correlated with activity in both dorsal and ventral reading regions. In pre-registered whole-brain analyses, spoonerisms performance was positively correlated with RSFC between the VWFA and left dorsal regions (dIFG, superior parietal and intraparietal cortex). In exploratory region-of-interest analyses, VWFA-dIFG connectivity was also positively correlated with nonword repetition, spelling, and vocabulary. Connectivity between the VWFA and ventral stream regions was not associated with performance on any behavioural measure, either in whole-brain or region-of-interest analyses. Our results suggest that tasks such as spoonerisms and spellings, which are both complex (i.e., involve multiple subprocesses) and have high between-subject variability, provide greater opportunity for observing resting-state brain-behaviour associations. However, the complexity of these tasks limits the conclusions we can draw about the specific mechanisms that drive these associations. Future research would benefit from constructing latent variables from multiple tasks tapping the same reading subprocess.

Molecular Dynamics Simulation Study of the Self-Assembly of Tau-Derived PHF6 and Its Inhibition by Oleuropein Aglycone from Extra Virgin Olive Oil.

Alzheimer's disease (AD) and other taupathies are neurodegenerative disorders associated with the amyloid deposition of the Tau protein in the brain. This amyloid formation may be inhibited by small molecules, which is recognized as one of the best therapeutic strategies to stop the progression of the disease. This work focuses on the small nucleating segment, hexapeptide-paired helical filament 6 (PHF6), responsible for Tau aggregation. Using computational modeling and classical molecular dynamics simulations, we show that PHF6 monomers collapse in water to form β-sheet rich structures, and the main olive oil polyphenol oleuropein aglycone (OleA) prevents peptide aggregation significantly. We gradually increase the ratio of the PHF6-OleA from 1:1 to 1:3 and find that for the 1:1 ratio, the peptide monomers are prone to form aggregated structures, while for the 1:2 ratio, the formation of the extended β-sheet structure is significantly less. For a 1:3 ratio of protein/OleA, the peptide residues are sufficiently crowded by OleA molecules through hydrogen bonding, hydrophobic interactions, and π-π stacking; hence, the peptide chains prefer to exist in a monomeric random coil conformation.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model.

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

Computational positive psychology: advancing the science of wellbeing in the digital era

ABSTRACT Positive psychology (PP) is in its third wave, evolving towards an interdisciplinary study of wellbeing. This article proposes computational positive psychology (CPP) as an emerging interdisciplinary field that integrates PP with computational science and technology to advance the understanding of wellbeing and develop evidence-based strategies to promote sustainable flourishing. The key features of CPP include addressing novel research questions that have received limited investigation in the existing literature, collecting large-scale and multimodal data, constructing computational models, and utilizing the results to advance theories, research, and the practice of wellbeing. We review CPP research topics such as: (1) advancing theoretical wellbeing models, (2) efficiently measuring wellbeing and positive traits, (3) exploring interdependent dynamics between individual and systemic wellbeing, (4) enhancing positive psychology interventions, and (5) improving collective wellbeing. The transformative potential of CPP for understanding and promoting wellbeing in the digital era is highlighted, and future research directions are discussed.

Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for this modulation are still under investigation. Devising a rule for precisely adjusting the synaptic delays could eventually help in developing more efficient and powerful brain-inspired computational models. In this article, we propose an unsupervised bioplausible learning rule for adjusting the synaptic delays in spiking neural networks. We also provide the mathematical proofs to show the convergence of our rule in learning spatiotemporal patterns. Furthermore, to show the effectiveness of our learning rule, we conducted several experiments on random dot kinematogram and a subset of DVS128 Gesture data sets. The experimental results indicate the efficiency of applying our proposed delay learning rule in extracting spatiotemporal features in an STDP-based spiking neural network.

Passive elasticity properties of Octopus rubescens arm.

In this report, passive elasticity properties of Octopus rubescens arm tissue are investigated using a multidisciplinary approach encompassing biomechanical experiments, computational modeling, and analyses. Tensile tests are conducted to obtain stress-strain relationships of the arm under axial stretch. Rheological tests are also performed to probe the dynamic shear response of the arm tissue. Based on these tests, comparisons against three different viscoelasticity models are reported.