Computational Model Research Articles

Biofeed through bioconversion process with the engineered methyl-microbium buryatense strain 5GBC1-RO1

This study presents a novel approach to mitigate methane emissions while simultaneously addressing the growing demand for sustainable animal feed through the development of an engineered methanotrophic strain, Methylomicrobium buryatense 5GB1C-RO1. Utilizing advanced genetic engineering techniques, including CRISPR/Cas9 and horizontal gene transfer, we have optimized the ribulose monophosphate (RuMP) cycle and enhanced oxidase activity in this strain. The bioconversion process is facilitated by innovative bioreactor designs, including Two-Phase Partitioning Bioreactors (TPPBs) and Inverse Membrane Bioreactors (IMBRs), which significantly improve methane solubility and mass transfer. Through metabolic flux analysis and computational modeling, we have achieved high biomass yields and efficient methane utilization. The resulting biofeed demonstrates a superior nutritional profile, with optimized macronutrient content and essential components. This integrated approach not only contributes to greenhouse gas mitigation but also offers a promising solution for sustainable animal nutrition. Our findings suggest that the 5GB1C-RO1 strain and associated bioprocesses have the potential to revolutionize both environmental protection and agricultural sustainability.

Mutations in Mig6 reduce inhibition of the epidermal growth factor receptor.

Mitogen-inducible gene 6 (Mig6) is a cellular inhibitor of epidermal growth factor receptor (EGFR) that binds directly to the EGFR kinase domain and interferes with signaling. Reduced Mig6 expression is correlated with increased EGFR activity in multiple cancer models. Here, we investigated whether disease-associated point mutations could reduce the inhibitory potency of Mig6. We show that several cancer-associated mutations, and a mutation derived from Alzheimer's Disease patients, diminish the ability of Mig6 to bind and inhibit EGFR invitro. In mammalian cells, the mutations decreased the Mig6-induced suppression of basal and EGF-stimulated autophosphorylation, MAP kinase phosphorylation, and cell migration. To probe the mechanisms by which the mutations could lead to reduced Mig6 inhibition, we constructed atomic-level computational models of Mig6 complexed with the EGFR catalytic domain, and performed molecular dynamics simulations for wild-type and mutant complexes.

Human perception of self-motion and orientation during galvanic vestibular stimulation and physical motion

Galvanic vestibular stimulation (GVS) is an emergent tool for stimulating the vestibular system, offering the potential to manipulate or enhance processes relying on vestibular-mediated central pathways. However, the extent of GVS’s influence on the perception of self-orientation pathways is not understood, particularly in the presence of physical motions. Here, we quantify roll tilt perception impacted by GVS during passive whole-body roll tilts in humans (N = 11). We find that GVS systematically amplifies and attenuates perceptions of roll tilt during physical tilt, dependent on the GVS waveform. Subsequently, we develop a novel computational model that predicts 6DoF self-motion and self-orientation perceptions for any GVS waveform and motion by modeling the vestibular afferent neuron dynamics modulated by GVS in conjunction with an observer central processing model. This effort provides a means to systematically alter spatial orientation perceptions using GVS during concurrent physical motion, and we find that irregular afferent dynamics alone best describe resultant perceptions.

The Impact of Storm Sewer Network Simplification and Rainfall Runoff Methods on Urban Flood Analysis

Due to the impact of climate change, the importance of urban flood analysis is increasing. One of the biggest challenges in urban flood simulations is the complexity of storm sewer networks, which significantly affects both computational time and accuracy. This study aimed to analyze and evaluate the impact of sewer network simplification on the accuracy and computational performance of urban flood prediction by comparing different rainfall runoff methods. Using the hyper-connected solution for urban flood (HC-SURF) model, two rainfall runoff methods, the SWMM Runoff method and the Surface Runoff method, were compared. The sewer network simplification was applied based on manhole catchment areas ranging from 10 m2 to 10,000 m2. The analysis showed that the computation time could be reduced by up to 54.5% through simplification, though some accuracy loss may occur depending on the chosen runoff method. Overall, both methods produced excellent results in terms of mass balance, but the SWMM Runoff method minimized the reduction in analytical performance due to simplification. This study provides important insights into balancing computational efficiency and model accuracy in urban flood analysis.

Spatially Localized Visual Perception Estimation by Means of Prosthetic Vision Simulation

Retinal prosthetic devices aim to repair some vision in visually impaired patients by electrically stimulating neural cells in the visual system. Although there have been several notable advancements in the creation of electrically stimulated small dot-like perceptions, a deeper comprehension of the physical properties of phosphenes is still necessary. This study analyzes the influence of two independent electrode array topologies to achieve single-localized stimulation while the retina is electrically stimulated: a two-dimensional (2D) hexagon-shaped array reported in clinical studies and a patented three-dimensional (3D) linear electrode carrier. For both, cell stimulation is verified in COMSOL Multiphysics by developing a lifelike 3D computational model that includes the relevant retinal interface elements and dynamics of the voltage-gated ionic channels. The evoked percepts previously described in clinical studies using the 2D array are strongly associated with our simulation-based findings, allowing for the development of analytical models of the evoked percepts. Moreover, our findings identify differences between visual sensations induced by the arrays. The 2D array showed drawbacks during stimulation; similarly, the state-of-the-art 2D visual prostheses provide only dot-like visual sensations in close proximity to the electrode. The 3D design could offer a technique for improving cell selectivity because it requires low-intensity threshold activation which results in volumes of stimulation similar to the volume surrounded by a solitary RGC. Our research establishes a proof-of-concept technique for determining the utility of the 3D electrode array for selectively activating individual RGCs at the highest density via small-sized electrodes while maintaining electrochemical safety.

Bridging theory and data: A computational workflow for cultural evolution

Cultural evolution applies evolutionary concepts and tools to explain the change of culture over time. Despite advances in both theoretical and empirical methods, the connections between cultural evolutionary theory and evidence are often vague, limiting progress. Theoretical models influence empirical research but rarely guide data collection and analysis in logical and transparent ways. Theoretical models themselves are often too abstract to apply to specific empirical contexts and guide statistical inference. To help bridge this gap, we outline a quality-assurance computational workflow that starts from generative models of empirical phenomena and logically connects statistical estimates to both theory and real-world explanatory goals. We emphasize and demonstrate validation of the workflow using synthetic data. Using the interplay between conformity, migration, and cultural diversity as a case study, we present coded and repeatable examples of directed acyclic graphs, tailored agent-based simulations, a probabilistic transmission model for longitudinal data, and an approximate Bayesian computation model for cross-sectional data. We discuss the assumptions, opportunities, and pitfalls of different approaches to generative modeling and show how each can be used to improve data analysis depending on the structure of available data and the depth of theoretical understanding. Throughout, we highlight the significance of ethnography and of collecting basic cultural and demographic information about study populations and call for more emphasis on logical and theory-driven workflows as part of science reform.

Biomechanics of Parkinson’s Disease with Systems Based on Expert Knowledge and Machine Learning: A Scoping Review

Patients with Parkinson’s disease (PD) can present several biomechanical alterations, such as tremors, rigidity, bradykinesia, postural instability, and gait alterations. The Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) has a good reputation for uniformly evaluating motor and non-motor aspects of PD. However, motor clinical assessment depends on visual observations, which are mostly qualitative, with subtle differences not recognized. Many works have examined evaluations and analyses of these biomechanical alterations. However, there are no reviews on this topic. This paper presents a scoping review of computer models based on expert knowledge and machine learning (ML). The eligibility criteria and sources of evidence are represented by papers in journals indexed in the Journal Citation Report (JCR), and this paper analyzes the data, methods, results, and application opportunities in clinical environments or as support for new research. Finally, we analyze the results’ explainability and the acceptance of such systems as tools to help physicians, both now and in future contributions. Many researchers have addressed PD biomechanics by using explainable artificial intelligence or combining several analysis models to provide explainable and transparent results, considering possible biases and precision and creating trust and security when using the models.

Towards Designer Photocatalysts: Structure‐Property Relationships in 2,6‐Diaryl‐pyryliums

Fully organic photocatalyst systems are highly attractive, not merely because they are transition‐metal free, but more importantly due to their unique and often potent reactivity. A detailed understanding of the various redox states, both ground and excited state, and specifically what structural parameters control them is therefore crucial for harnessing the full potential of these systems in organic synthesis. However, unlike their organometallic counterparts, detailed structure‐property relationships for organic photocatalysts are largely absent from the literature. In this study, we demonstrate linear free‐energy relationships across a range of key photophysical and electrochemical properties of 2,6‐diarylpyryliums. Electronic absorption and emission maxima can be carefully tuned over the ranges of 83 nm and 102 nm respectively. Intramolecular charge transfer (ICT) interactions were revealed in cases of substitution with polarizable heavy‐atoms. A strong linear dependence of ground state reduction potentials on substituent electronics was observed. Notably, the excited state reduction potential, E*red, could be controlled over a range of nearly 1000 mV. Systematic errors in computational modeling of ground and excited state redox potentials were identified and corrected. We believe the quantitative structure‐property relationships identified here provide foundational tools for rational and predictive organic photocatalyst design.

The history of hydrological studies on the Mekong floodplains – from colonial experiments to computational models

ABSTRACT This paper investigates the interwoven history of hydrological studies and water infrastructure development on the Mekong floodplains. On the basis of an extensive literature review, archival work, and key informant interviews, we unravel the making of an expert-led understanding of the Mekong floodplains – from the detailed reports written by colonial engineers during the French protectorate in the late 19th century to the development of the first computational models, developed in the 1960s on punch-cards and in Fortran. We show how these studies not only reflect the objectives of successive powers and their relationships to local communities but also shaped the landscape and hydrology of the Mekong floodplains and continue to do so through a variety of water infrastructure development projects. This serves as a call to conceive of hydrological knowledge as contingent rather than as a neutral depiction of physical processes that exists independently from the conditions of its production.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Computational model of a feeding copepod engaged in three modes of movement

Abstract Objectives: Copepods display distinct feeding modes to capture prey in the surrounding water. However, the mechanism and range of prey detection are not fully understood. Methods: Using the method of regularized Stokeslets, we constructed a mathematical model of the flow field around a copepod engaged in three modes of movement: sinking, swimming and hovering. The model assumes that the copepod is negatively buoyant with a simplified body and a pair of long antennae. We then introduced a rigid, neutrally buoyant sphere in the model to predict where potential prey items become detectable in theory. Key Findings: We find that the flow fields around the sinking and swimming models are generally unidirectional, while the flow around a hovering copepod has a significant cross-stream component. Our model shows that the volume of the surrounding fluid that is predicted to be inspected has distinctive shapes that resemble a thin sheet while sinking, a cylinder while swimming and a funnel while hovering. Conclusion: The results suggest that the most efficient mode of feeding depends on the distribution of prey items in the surrounding water.

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Two dimensional computational model of calcium and IP3 interacting system dynamics in an obese hepatocyte cell

Abstract Calcium ion (Ca$^{2+}$) signaling is crucial in regulating numerous cellular processes vital for preserving structural integrity and functional equilibrium across diverse cell types. Both the calcium stores and mitochondria play significant roles in this signaling pathway. The calcium source may be in the form of a blip or a puff depending on the various conditions of the cellular systems. The one dimensional model of calcium dynamics with IP$_{3}$ gives crucial insight of feedback mechanisms influencing calcium homeostasis. In order to obtain deeper insights of local impacts of various mechanisms and feedbacks in hepatocyte cell, it is necessary to develop the models in higher dimensions. In order to get more deeper insights, two dimensional model is proposed assuming the phenomena to be uniform along z dimension. This research presents a two-dimensional computational model to analyse the interactive system dynamics of inositol 1,4,5-trisphosphate (IP$_{3}$) and Ca$^{2+}$, aiming to assess how these signaling patterns influence hepatocyte functionality which allows to incorporate puff type of calcium source under both obese and normal physiological states. It further examines the implications of calcium signaling on NADH synthesis, ATP production, and degradation rates. Numerical simulations are executed utilising the Crank-Nicolson method for temporal analysis and the Linear Finite Element Method for spatial analysis. Additionally, the study conducts a comparative analysis of calcium signaling between obese and normal hepatocyte. The findings offer enhanced insights into the interactive system dynamics of IP$_{3}$ and Ca$^{2+}$ in hepatocytes, elucidating the effects of various parameter alterations on cellular behaviour in both states.

An Agent-Based Model of Human–Whale Interactions in the North Atlantic

We created an agent-based model (ABM; a computational model) for human–whale interactions in the North Atlantic area. We specifically looked at boats, lobster lines, and rogue fishnet interactions with the North Atlantic right whales (Eubalaena glacialis), and we assessed the conditions under which such interactions are more likely to result in injuries and fatalities for this critically endangered whale, such as the density of ships in the water. ABMs are a methodology particularly useful in data poor problems (where machine learning cannot be used), and are based on rules of interactions between various agents (human and nonhuman) and/or a specific physical environment; they can showcase and assess potential future scenarios. We informed our model with current data from the North Atlantic Right Whale Consortium and the National Oceanic and Atmospheric Administration and present our preliminary findings here.

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

Quasinormal mode expansion in thin elastic plates

Elastic wave manipulation using large arrays of resonators is driving the need for advanced simulation and optimization methods. To address this we introduce and explore a robust framework for wave control: quasinormal modes (QNMs). Specifically, we consider the problem for thin elastic plates, where the Green's function formalism is well known and readily exploited to solve multiple scattering problems. By studying the associated nonlinear eigenvalue problem we derive a dispersive QNM expansion, providing a reduced-order model for efficient forced response computations which reveals physical insight into the resonant mode excitation. Furthermore, we derive eigenvalue sensitivities with respect to resonator parameters and apply a gradient-based optimization to design quasi-bound states in the continuum and position eigenfrequencies precisely in the complex plane. Scattering simulations validate our approach in structures such as graded line arrays and quasicrystals. Drawing on QNM concepts from electromagnetism we demonstrate significant advances in elastic metamaterials, highlighting their potential for tailored wave manipulation. Published by the American Physical Society 2024

Multimodal fusion-powered English speaking robot

IntroductionSpeech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.MethodsTo overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.Results and discussionIn the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.

Prognostic value of morphology and hemodynamics in moyamoya disease for long-term outcomes and disease progression.

To explore the relationship between morphological and hemodynamic parameters, baseline characteristics, and long-term outcomes in patients with moyamoya disease (MMD) using a computational fluid dynamics model. We retrospectively reviewed 129 patients at Beijing Tiantan hospital between July 2020 and December 2021. Perioperative clinical variables and Suzuki stage were recorded. Logistic regression analysis was employed to identify the risk factors for unfavorable long-term outcomes. The association between morphological, CT perfusion parameters, hemodynamic parameters and the Suzuki stage, clinical variables of MMD was also analyzed. Patients with high relative Wall Shear Stress (rWSS) were older and had more cases with higher Suzuki stage and worse follow-up mRS scores (p < 0.05). High rWSS at the terminal ICA and diabetes mellitus were identified as independent predictors of unfavorable long-term outcomes [OR = 3.039(1.191-7.754), p = 0.020; OR = 3.164(1.141-8.723), p = 0.027, respectively]. ROC analysis demonstrated that predictive models incorporating rWSS improved AUC values, with the highest AUC in Model 2 (AUC = 0.889). High rWSS was significantly associated with future TIA and stroke events (p = 0.032). We speculated that high rWSS and diabetes mellitus were independent risk factors for unfavorable long-term outcomes in patients with MMD. rWSS and morphological parameters are crucial for predicting MMD progression and understanding its pathogenesis.

Hydrogel-based Soft Robotics for Surgical Machinery

Hydrogel-based soft robotics represent a transformative approach in surgical machinery, offering unparalleled adaptability, biocompatibility, and precision for minimally invasive procedures. Hydrogels, with their high water content and tunable mechanical properties, mimic soft biological tissues, making them ideal for applications in delicate surgical environments. This research explores the integration of hydrogel materials into soft robotic systems, focusing on their fabrication, actuation mechanisms, and performance in surgical applications. Key advancements include the development of stimuli-responsive hydrogels that enable precise control of movement and force, enhancing the capability to navigate complex anatomical structures. The study also examines computational models for simulating hydrogel behavior and optimizing robotic designs. While the potential benefits of hydrogel-based soft robotics in improving patient outcomes are significant, challenges remain in ensuring durability, scalability, and reliable control systems. This research aims to address these limitations by integrating advanced materials science with robotic engineering. By doing so, it contributes to the evolution of surgical technologies, paving the way for safer and more effective procedures.

Physical reservoir computing: a tutorial

AbstractThis tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.