Computational Model Research Articles

Gain-of-Function and Loss-of-Function Mutations in the RyR2-Expressing Gene Are Responsible for the CPVT1-Related Arrhythmogenic Activities in the Heart

Mutations in the ryanodine receptor (RyR2) gene have been linked to arrhythmia and possibly sudden cardiac death (SCD) during acute emotional stress, physical activities, or catecholamine perfusion. The most prevalent disorder is catecholaminergic polymorphic ventricular tachycardia (CPVT1). Four primary mechanisms have been proposed to describe CPVT1 with a RyR2 mutation: (a) gain-of-function, (b) destabilization of binding proteins, (c) store-overload-induced Ca2+ release (SOICR), and (d) loss of function. The goal of this study was to use computational models to understand these four mechanisms and how they might contribute to arrhythmia. To this end, we have developed a local control stochastic model of a ventricular cardiac myocyte and used it to investigate how the Ca2+ dynamics in the mutant RyR2 are responsible for the development of an arrhythmogenic episode under the condition of β-adrenergic (β-AR) stimulation or pauses afterward. Into the model, we have incorporated 20,000 distinct cardiac dyads consisting of stochastically gated L-type Ca2+ channels (LCCs) and ryanodine receptors (RyR2s) and the intervening dyadic cleft to analyze the alterations in Ca2+ dynamics. Recent experimental findings were incorporated into the model parameters to test these proposed mechanisms and their role in triggering arrhythmias. The model could not find any connection between SOICR and the destabilization of binding proteins as the arrhythmic mechanisms in the mutant myocyte. On the other hand, the model was able to observe loss-of-function and gain-of-function mutations resulting in EADs (Early Afterdepolarizations) and variations in action potential amplitudes and durations as the precursors to generate arrhythmia, respectively. These computational studies demonstrate how GOF and LOF mutations can lead to arrhythmia and cast doubt on the feasibility of SOICR as a mechanism of arrhythmia.

Functional characteristics and computational model of abundant hyperactive loci in the human genome

Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Simulation of time-invariant three-dimensional Freak waves and their scattering identification based on the JONSWAP spectrum and Donelan direction function

Abstract The results of recent maritime accident investigations show that many maritime accidents are related to the attack of freak waves, which can sink ships instantaneously with colossal energy. To identify and forecast freak waves accurately and avoid the danger of freak waves to marine structures and people, we have conducted further research on freak waves. In this paper, we propose an efficient time-invariant three-dimensional (3-D) freak wave computational model to study the scattering characteristics of time-invarian 3-D freak waves and obtain a more explicit identification of their scattering differences. First, this paper adopts linear superposition method to simulate a 3-D random rough sea surface based on the JONSWAP wave spectrum and Donelan directional distribution function. Moreover, we analyze the effect of different angular frequencies on the simulated sea surface. At the same time, the Monte Carlo method is used to further simulate the capillary waves based on the random sea surface, and this method vastly improves the computational accuracy of the random sea surface. Then, based on the two-wave train superposition wave energy focusing mode, a numerical calculation method of time-invariant 3-D freak waves is proposed, and the time-series evolution process of 3-D freak waves is simulated. Finally, the backscattering coefficients of the 3-D freak wave surface are calculated using the two-scale method, and the electromagnetic scattering characteristics of the freak wave surface under different evolution stages, wind speeds, and radar incidence angles are analyzed. By this method, we conclude that the recognition of freak waves is more accurate under medium-high wind speed and significant incidence angle conditions. The experiments show that the research in this paper can identify and predict freak waves more effectively, further improve the forecast accuracy of future freak waves, and also have some application value for freak wave risk warnings.

Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review.

The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient. The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature. We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed. We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining "digital twin," 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization. We propose defining a patient digital twin as "a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions" and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care.

Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models

Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this article, an elegant generalization of the traditional ASM is developed to assess the co-activity of two computer models. This generalization, which we refer to as a Co-Active Subspace (Co-AS) Method, allows for the joint analysis of two or more computer models allowing for thorough exploration of the alignment (or non-alignment) of the respective gradient spaces. We define co-active directions, co-sensitivity indices, and a scalar “concordance” metric (and complementary “discordance” pseudo-metric) and we demonstrate that these are powerful tools for understanding the behavior of a class of computer models, especially when used to supplement traditional AS analysis. Details for efficient estimation of the Co-AS and an accompanying R package (concordance) are provided. Practical application is demonstrated through analyzing a set of simulated rate stick experiments for PBX 9501, a high explosive, offering insights into complex model dynamics.

Gene Expression Profiling of the Peritumoral Immune Cell Infiltrate of Penile Squamous Cell Carcinomas

Penile carcinomas are rare tumors in Europe and need further investigations due to their inferior prognosis in late tumor stages. The presence of disparate immune cell infiltrates was observed in these tumors, which were subsequently demonstrated to give rise to divergent tumor prognoses. The objective was to further characterize this immune cell infiltrate with the use of immunohistochemistry and RNA expression. A total of twelve well-characterized cases of penile squamous cell carcinomas with known infection status by human papillomavirus (HPV) and p16 status were assessed. The cases were classified according to their morphological characteristics, including those exhibiting a pronounced peritumoral immune cell infiltrate and those with less peritumoral immune cell infiltration. The generation of RNA expression data was conducted using the nCounter® PanCancer Immune Profiling Panel. Computational models were employed to calculate the proportions of immune cells. To corroborate the findings, an immunohistochemical analysis was conducted using antibodies against CD20, CD3, CD4, CD8, MUM1, CD68, and CD117. Our cases were clustered according to the immune cell infiltrate detected via histology in a group with less immune cell infiltrate density and in a group with increased immune cell infiltrate density. Generally, all immune cells showed an increased amount in the group with pronounced immune cell infiltrate density. The clusters were found to relate to cell functions, the complement system, cytotoxicity, pathogen defense, regulation, and T-cell functions. In cases exhibiting a pronounced immune cell infiltrate, the top three genes that exhibited the greatest upregulation were GZMA, MICB, and GNLY. No relationship to HPV infection status was demonstrated. Immunohistochemistry validated the data gained via RNA expression and showed a correlation with EPIC and Cibersort. The clustering of cases based on immune cell infiltrate density revealed significant distinctions between groups with lower and higher immune cell infiltrate density. The group with increased immune cel infiltrate density showed a greater abundance of immune cells, aligning with key functions like cytotoxicity, pathogen defense, and T-cell regulation. Among these cases, the genes GZMA, MICB, and GNLY were significantly upregulated, suggesting their involvement in an increased immune response. The role of HPV infection status in our cases with regard to the peritumoral immune cell infiltrate remains inconclusive.

Flow in filopodia: re-organization and the representation of biological entities as computational objects

AbstractActin is the most abundant protein in eukaryotic cells. They form filamentous polymers that are organized in different ways within the cell to perform various functions. For instance, prominent parallel bundles of F-actins mediate the formation and dynamics of filopodia that are long, finger-like protrusions of cell membrane occurring in certain cells, like growing neurons. Understanding actin organization dynamics and its regulation is a crucial problem for biologists that cannot be solved exclusively by biological methods, requiring the support of mathematical and computational modelling. In this work, grounded on a previous hypothesis of ours about the cytosol flow within filopodia, we address several modelling challenges posed by the growth of filopodia in neurons. We use alternative stochastic models and particle-centered numerical methods for transport and elongations, as well as an innovative object-oriented modelling-strategy to represent chemical transformations, polymerization, and their regulation. These modelling strategies allowed for simulating elongations 20 times longer than the typical ranges attained by commonly used filopodia diffusion models, and show that our hypothesis is feasible, acting as a proof-of-concept about the importance of considering organization as a key element in biological explanations.

A Workflow for Creating Gastric Computational Models from SPARC Scaffolds

In-silico studies are an ideal medium to model and improve our understanding of the mechanisms underlying gastric motility in health and disease. In this study, a workflow to create computational models of the stomach was developed using SPARC scaffolds. Three anatomically based finite element method (FEM) models of the rat stomach incorporating experimental measurements of muscle layer thickness and fiber orientations across the stomach were developed: (i) 2D (surface) FEM model with no thickness, (ii) 3D (volume) FEM model with a fixed thickness across the longitudinal and circular muscle layers, and (iii) 3D (volume) FEM model with varying thickness across the longitudinal and circular muscle layers. The three FEM models were subsequently used in whole-organ slow wave simulations and the impact of anatomical details on the simulation outcomes was investigated. The 3D FEM model with varying thickness was the most computationally expensive, while the 2D FEM model provided the fastest solution (a 200 s simulation took 8 min vs. 38 h to solve). The spatiotemporal profiles of the slow wave activation and propagation in the three FEM models were in good agreement. The largest temporal difference of 1 s in cellular activation was observed between the 2D FEM model and the varying thickness 3D FEM model in the most distal-stomach regions. These FEM models and developed workflow will be used in in-silico studies to improve our understanding of the structure-function relationship in the stomach and identify the optimal parameters of electrical therapies, an alternative treatment for the motility disorders in the stomach. In addition, the developed workflow can be readily used to generate computational models of other organs using SPARC scaffolds.

Computational modelling of valvular heart disease: haemodynamic insights and clinical implications

An aging population and an increasing incidence of cardiovascular risk factors form the basis for a global rising prevalence of valvular heart disease (VHD). Research to further our understanding of the pathophysiology of VHD is often confined to the clinical setting. However, in recent years, sophisticated computational models of the cardiovascular system have been increasingly used to investigate a variety of VHD states. Computational modelling provides new opportunities to gain insights into pathophysiological processes that may otherwise be difficult, or even impossible, to attain in human or animal studies. Simulations of co-existing cardiac pathologies, such as heart failure, atrial fibrillation, and mixed valvular disease, have unveiled new insights that can inform clinical research and practice. More recently, advancements have been made in using models for making patient-specific diagnostic predictions. This review showcases valuable insights gained from computational studies on VHD and their clinical implications.

Geodynamic hazard assessment for the archaeological park-museum by the town of Nikopol

This study was conducted to establish the engineering geological and hydrogeological conditions of the site and to assess the geodynamic hazard in relation to the design and construction of the archaeological park-museum and feeder road in the town of Nikopol (Bulgaria). The studied terrain occupies a river terrace and the foot of a delluvial slope with an elevation of 23-68 m. The tasks of the study are to identify: the geological structure of the slope, the area and depth range of the landslides, the location of the slip surface, the physical and mechanical properties of the engineering geological strata, the hydrogeological conditions, the current stability of landslides, the change of slope stability under the action of various factors. The ground investigation includes analysis of archival and published information on the area; geodetic survey of the landslide; engineering geological mapping; exploratory boreholes; geophysical surveys using the VES method; testing of soil and water samples; compilation of longitudinal engineering geological profiles and slope stability analysis using the computational models. To the south of the study site the slope is affected by an ancient landslide with a volume of about 30 million m3 developed on the right valley slope of the Danube River in the section between the plateau and the river terrace. Consistent landslides occur in its lower part. Slope stability calculations show that the landslides are triggered by a rise in the groundwater level, an earthquake and undermining of the slope base. Rock-falls has developed on the steep rock slopes, composed of moderately to highly weathered and fractured cherty and glauconitic limestones. The size of the rockfall blocks is in the range 0.3-0.8 m3 and their transport distance reaches from 10 m to 50 m from the base of the slope. Stabilization measures and facilities are proposed to strengthen the landslides and rockfalls in the considered section of the slope. For the landslides it is proposed to implement a counterfort embankment on which to build the feeder road, drainage ribs and drainage ditches. For protection of the buildings from collapsing rock debris, an anchored high-strength net is recommended. The design takes into account the trajectory of the falling blocks and their kinetic energy. For monitoring the condition of the landslides and rockfalls, as well as for evaluating the effectiveness of the implemented strengthening, drainage and coastal protection structures, a control and measurement system for regime observation was built.

Isoreticular Contraction in Dicopper Paddle-Wheel-Based Metal-Organic Frameworks to Enhance C2H2/CO2 Separation.

Achieving a balance between high selectivity and uptake is a formidable challenge for the purification of acetylene from mixtures with carbon dioxide, particularly when seeking to maximize both C2H2 adsorption capacity and C2H2/CO2 separation selectivity in crystalline porous materials. In this study, leveraging the principles of reticular chemistry, we selected two tetracarboxylate-based linkers and combined them with Cu2+ ions to synthesize two isoreticular dicopper paddle-wheel-based metal-organic frameworks (MOFs): Cu-TPTC (terphenyl-3,3',5,5'-tetracarboxylic acid , H4TPTC) and Cu-ABTC (3,3,5,5-azobenzenetetracarboxylic acid, H4ABTC). The structural and sorption analyses revealed that Cu-ABTC, despite having slightly smaller pores due to the strategic replacement of a phenyl ring with an azo group between two tetratopic ligands, maintains high porosity compared to Cu-TPTC. Furthermore, Cu-ABTC outperforms Cu-TPTC in terms of C2H2 adsorption capacity (196 cm3 g-1 at 298 K and 1 bar) and C2H2/CO2 separation selectivity (16.5~5.6). These findings were corroborated by dynamic breakthrough experiments and computational modeling. This research highlights the potential of the isoreticular contraction strategy in enhancing MOFs for sophisticated gas adsorption and separation processes.

Energy policy implications of Ecuador’s NDC

In the third national communication submitted by Ecuador, the total greenhouse gases (GHG) emission was calculated at 80,627 GgCO2-eq, considering the country’s commitment to the Framework on Climate Change. In 2018, Ecuador ratified its nationally determined contribution (NDC) to reduce its GHG emissions by 11.87% from the business-as-usual (BAU) scenario by 2025. The macroeconomic impacts of NDC implementation in the energy sector are discussed. A Computable Equilibrium Model applied to Ecuador (CGE_EC) is used by developing scenarios to analyze partial and entry implementation, as well as an alternative scenario. Shocks in exogenous variables are linked to NDC energy initiatives. So, the NDC’s feasibility depends on guaranteeing the consumption of hydropower supply, either through local exports or domestic demand. In the last case, the government’s Energy Efficiency Program (PEC) and electricity transport have important roles, but the high levels of investment required and poor social conditions would impair its implementation. NDC implementation implies a GDP increase and price index decrease due to electricity cost reductions in the productive sector. These conditions depend on demand-supply guarantees, and the opposite case entails negative impacts on the economy. The alternative scenario considers less dependence on the external market, achieving higher GDP, but with only partial fulfillment of the NDC goals.

Detecting Fake News Using Data Analytics: A Systematic Literature Review And Machine Learning Approach

This study systematically reviews advancements in fake news detection techniques, examining methodologies and frameworks across machine learning, natural language processing, social network analysis, and multimodal approaches. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, resulting in the selection and evaluation of 254 research articles. The findings reveal that supervised learning models, such as Support Vector Machines, Decision Trees, and Naïve Bayes, have shown strong performance in text-based fake news classification, particularly when feature selection is optimized. Deep learning models, including CNNs, RNNs, and transformers, have further advanced detection accuracy by capturing complex linguistic patterns, though challenges with computational demands and model interpretability remain. In scenarios with limited labeled data, unsupervised learning models and semi-supervised approaches offer adaptability, with clustering, anomaly detection, and iterative self-labeling proving effective for evolving misinformation. Additionally, cross-disciplinary approaches, integrating insights from psychology, sociology, and network science, enhance detection models by accounting for user behavior, emotional appeal, and social conformity in the spread of fake news. Case studies from collaborative projects underscore the potential of interdisciplinary efforts to develop robust, adaptable detection frameworks. This review concludes that effective fake news detection requires a multifaceted approach, combining technical advancements with social science insights to address the complexity and adaptive nature of misinformation. The study emphasizes the need for continued research in hybrid models and adaptive, real-time detection solutions to strengthen defenses against fake news in diverse digital environments.

Enhanced corrosion protection of copper in saline environments using bio-nanocomposite coatings based on chitosan and chitosan Schiff base

An in-depth study focuses on developing new environmentally friendly bio-nanocomposites, by incorporating SrTiO3 (STO) ceramic nanoparticles into matrices of chitosan and its derivatives, aiming to use them as protective coatings against corrosion. The various stages of this study include the cross-linking of chitosan, the synthesis of Schiff base chitosan, the cross-linking of Schiff base chitosan, and the preparation of nanocomposite coatings. The coatings' structure and composition were analyzed using different methods, including Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance (FTIR-ATR), X-ray Diffraction (XRD), Transmission Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (TEM-EDX), and Scanning Electron Microscopy (SEM). In addition, Electrochemical Impedance Spectroscopy (EIS) and Potentiodynamic Polarization (PDP) measurements were carried out to assess the inhibitory efficacy of chitosan crosslinked with epichlorohydrin (Cs-Ep), epichlorohydrin-crosslinked chitosan-salicylaldehyde Schiff base (CS-S-Ep) and CS-Ep-STO and CS-S-Ep-STO nanocomposite coatings, as well as the long-term protection durability of CS-S-Ep-STO. These techniques revealed a significant reduction in corrosion current density after chemical modification of chitosan and incorporation of SrTiO3 (STO) nanoparticles into CS-Ep and CS-S-Ep matrices, confirming a notable improvement in the inhibitory efficiency of these coatings against copper corrosion in a saline environment. Computational modeling methods like Density Functional Theory (DFT), Molecular Dynamics (MD), and Monte Carlo (MC) simulations reinforced these results by demonstrating efficient adsorption of CS-S-Ep-STO nanocomposites on metal surfaces through the interaction with heteroatoms present in the functional groups (-C=N-, -C-O-, -OH) and STO nanoparticles. The present study's findings provide key information for developing innovative protective coatings, highlighting the potential of chitosan-based nanocomposites and derivatives, particularly with SrTiO3 incorporation, in mitigating metal surface corrosion in aggressive environments.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization.

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization. Compared to previous active learning approaches, our algorithm adds two key features: a modified delta method incorporating force information to enhance efficiency in uncertainty estimation, and a quasi-Newton approach based upon a Hessian matrix calculated from the neural network; the later improving stability of optimization near critical points. We benchmarked our optimizer against commonly used optimization algorithms using systems including bulk metal, metal surface, metal hydride, and an oxide cluster. The results demonstrate that our optimizer effectively reduces the number of DFT force calls by 2-3 times in all test systems.

A context-based model of collaborative inhibition during memory search.

Contrary to common intuition, a group of people recalling information together remembers less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model, which captures how individuals recall information alone. We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but one also listens to someone else's recall and uses it as an external retrieval cue. Attending to this cue updates the listener's context to be more similar to the context of someone else's recall. Over an existing dataset of individual and collaborative recall in small and large groups, we show that our model successfully captures the difference in memory performance between individual recall and collaborative recall across different group sizes from 2 to 16, as well as additional recall patterns such as recency effects and semantic clustering effects. Our model further shows that collaborating individuals reach similar areas in the context space, whereby their contexts converge more than the contexts of individuals recalling alone. This convergence constrains their ability to search memories effectively and is negatively associated with recall performance. We discuss the contributions of our modeling results in relation to previous accounts of the collaborative inhibition effect.

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks.

Abstract 4139141: Predicted efficacy of inclisiran on cardiovascular outcomes in lower extremity peripheral artery disease: results of the In Silico Sirius study

Introduction: Symptomatic lower extremity peripheral artery disease (PAD) patients have the highest risk of cardiovascular (CV) outcomes among patients with established atherosclerotic cardiovascular disease (ASCVD). Inclisiran, an siRNA targeting PCSK9 mRNA, reduces LDL-C levels. In SIRIUS in silico trial (NCT05974345), inclisiran was predicted to lower CV events in ASCVD patients. Research question/Hypothesis: SIRIUS in silico study aims to predict the efficacy of inclisiran on CV outcomes in subgroups of patients with or without PAD. Methods: The SIRIUS in silico trial was conducted using a knowledge-based mechanistic computational model of ASCVD applied to a virtual ASCVD population with LDL-C ≥ 70 mg/dL. Each virtual patient is its own control. This model was previously calibrated and validated before running the trial. SIRIUS compared the efficacy of inclisiran vs placebo on top of High Intensity (HI) statins with or without ezetimibe on 3-Point-MACE defined as a composite of time to first occurrence of CV death, nonfatal myocardial infarction (MI) or nonfatal ischemic stroke (IS) over 5 years. Occurrence of major acute limb events (MALE) was also individually assessed in time-to-first-event analyses. Results: Among 204, 691 virtual SIRIUS ASCVD patients, 28,072 (13.7%) had PAD. At 5 years, the mean predicted percentage reduction in LDL-C with inclisiran as compared to placebo was -49.3% and -49.8% in patients with or without PAD, respectively. The predicted rate of 3P-MACE in the inclisiran arm was consistently lower than in the placebo arm in patients with PAD (17.62% vs 22.88%; Hazard Ratio (HR): 0.75 medium uncertainty) and in patients without PAD (10.33% vs 13.64%; HR: 0.75 low uncertainty). Compared to placebo, inclisiran was also predicted to consistently reduce MALE in patients with or without PAD (2.71% vs 4.11%; HR: 0.65 medium uncertainty and 0.19% vs 0.29%; HR: 0.66 high uncertainty respectively). Conclusion: Pending the results of ORION-4 and VICTORION-2-P, this first In Silico trial in virtual PAD patients predicted a potential effect of inclisiran on 3P-MACE and MALE reductions over 5 years follow-up.