Development Of Computational Models Research Articles

Regional differences in stomach stretch during organ filling and their implications on the mechanical stress response

As part of the digestive system, the stomach plays a crucial role in the health and well-being of an organism. It produces acids and performs contractions that initiate the digestive process and begin the break-up of ingested food. Therefore, its mechanical properties are of interest. This study includes a detailed investigation of strains in the porcine stomach wall during passive organ filling. In addition, the observed strains were applied to tissue samples subjected to biaxial tensile tests. The results show inhomogeneous strains during filling, which tend to be higher in the circumferential direction (antrum: 13.2%, corpus: 22.0%, fundus: 67.8%), compared to the longitudinal direction (antrum: 4.8%, corpus: 24.7%, fundus: 50.0%) at a maximum filling of 3500 ml. Consequently, the fundus region experienced the greatest strain. In the biaxial tensile experiments, the corpus region appeared to be the stiffest, reaching nominal stress values above 400 kPa in the circumferential direction, whereas the other regions only reached stress levels of below 50 kPa in both directions for the investigated stretch range. Our findings gain new insight into stomach mechanics and provide valuable data for the development and validation of computational stomach models.

An enhanced deep learning approach for vascular wall fracture analysis

AbstractThis work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.

The Virtual Child.

We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity.

Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

Particle separation mechanisms in suspension-feeding fishes: key questions and future directions

Key unresolved questions about particle separation mechanisms in suspension-feeding fishes are identified and discussed, focusing on areas with the potential for substantial future discovery. The published hypotheses that are explored have broad applicability to biological filtration and bioinspired improvements in commercial and industrial crossflow microfiltration processes and microfluidics. As the first synthesis of the primary literature on the particle separation mechanisms of marine, estuarine, and freshwater suspension-feeding fishes, the goals are to enable comparisons with invertebrate suspension-feeding processes, stimulate future theoretical and empirical studies, and further the development of biomimetic physical and computational fluid dynamics models. Of the eight particle separation mechanisms in suspension-feeding fishes, six have been proposed within the past twenty years (inertial lift and shear-induced migration, reduction of effective gap size by vortices, cross-step filtration, vortical flow along outer faces of gill raker plates, ricochet filtration, and lateral displacement). The pace of discovery is anticipated to continue accelerating. Multidisciplinary collaboration and integration among biologists and engineers (including chemical, mechanical, biomedical, and filtration engineering) will result in new perspectives to identify patterns and potential unifying mechanisms across the breadth of suspension-feeding fish taxa, morphology, and function.

What's behind their various activities? Discerning the importance of surface terminations in Ag3PO4 semiconductor from DFT calculations

The surface energy and band gap of inorganic semiconductors are essential properties for a wide variety of applications in materials science, and studying morphologies holds significant importance. However, it is challenging to find a correlation between them at the atomic scale when these materials are multifunctional. This motivates the development of predictive computational models that can optimize these materials based on these quantities. Present findings shed light on the structure and electronic properties of (100), (110), and (111) surfaces of Ag3PO4 by using density functional theory calculations. By introducing two concepts, namely, polyhedron energy and polyhedron band gap, we provide compelling evidence for the effects of surface termination of Ag3PO4 photocatalyst. We compare our predictions to experimental crystal habits, and our results not only allow us to explain their evolution for obtaining a given morphology but also offer a coherent explanation, based on atomic-scale insights, for the discrepancies of morphology-photocatalytic activity relationship of Ag3PO4 in the recent literature. Overall, this work opens a novel avenue for first-principles calculations of morphology and provides a roadmap for the design of more effective inorganic semiconductors with desirable physicochemical properties for potential applications.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Highly logical and non-emotional decisions in both risky and social contexts: understanding decision making in autism spectrum disorder through computational modeling.

In risky contexts, autism spectrum disorder (ASD) individuals exhibit more logical consistency and non-emotional decisions than do typical adults (TAs). This way of deciding could be also prevailing in social contexts, leading to maladaptive decisions. This evidence is scarce and inconsistent, and further research is needed. Recent developments in computational modeling allow analysis of decisional subcomponents that could provide valuable information to understand the decision-making and help address inconsistencies. Twenty-seven individuals with ASD and 25 TAs were submitted to a framing-task and the ultimatum game (UG). The Rescorla-Wagner computational model was used to analyze UG decisions. Results showed that in the UG, the ASD group exhibited a higher utilitarianism, characterized by lower aversion to unfairness and higher acceptance of offers. Moreover, this way of deciding was predicted by the higher economic rationality found in the framing task, where people with ASD did not manifest emotional biases such as framing effect. These results could suggest an atypical decision making, highly logical and non-emotional, as a robust feature of ASD.

An IoT enabled computational model and application development for monitoring cardiovascular risks

Technological advancement in the rise of the Internet of Things (IoT) and computational modelling and application development has revolutionized medical care. Monitoring cardiovascular risks is faster, easier, and more accurate than ever. Using IoT-enabled computational modelling and application development, medical professionals can detect, monitor, and predict certain conditions more efficiently. That paper explores the Internet of Things (IoT) enabled computational model and application development that can be used to monitor cardiovascular risks effectively. Cardiovascular diseases are the leading cause of death globally, and currently, there is a lack of comprehensive and reliable monitoring systems to assess the risk of such diseases. IoT enables the integration of different data sources, such as physical activity, diet, BMI and the environmental context, to form a comprehensive tracking tool that can provide accurate cardiovascular risk assessment. The developed application can offer personalized health coaching, leveraging machine learning algorithms to identify patterns and adapt a user's healthcare journey. Ultimately, this paper assesses the potential of IoT technology for monitoring cardiovascular risks and integrating it into current healthcare systems.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Forecasting the phytosanitary state of agrocenoses: innovation developments

Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops.
 Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine.
 Results. The concept of formation of the phytosanitary state of crops and plantations under the conditions of climate change has been developed. The theory of the dynamics of the number of locusts and the prediction of emergency situations in the agricultural sphere of Ukraine has been developed. The widespread use of pheromone traps for diagnosing the phytosanitary state of agrocenoses of Ukraine is recommended. The cyclicity of outbreaks of mass reproduction of the Anisoplia austriaca beetle and the European corn borer was established, depending on the amount of solar activity. The possibility of applying GIS-technologies for the analysis of long-term data was studied. Predictive computer programs (models) of harvest shortages of rapeseed, sunflower, corn, sugar beet, and winter wheat from a complex of pests have been developed. An algorithm for the application of information technology programs for the analysis of multi-year dynamics of the phytosanitary state of agrocenoses has been developed. The role of the developed computer model of the development of leaf spots of grain crops in determining the moment for the most effective application of fungicides has been established. Methods of forecasting the development of pathogens of grape diseases in the South of Ukraine have been developed. A system of forecasting the phytosanitary state of agrocenoses of Ukraine in the conditions of the year and planning the implementation of protective measures has been developed.
 Conclusions. Innovative developments of the Scientific and Methodological Center «Plant Protection» make it possible to solve the following issues at a modern level: forecasting changes in the agrosphere based on the analysis of a long-term database of hydrothermal conditions and indicators of the phytosanitary state of agrocenoses; prevention of emergency situations in the agricultural sector on the basis of a modern monitoring system using GPS-navigation and the development of regulations for carrying out protective measures; forecasting the development of dangerous pests and diseases of agricultural crops; forecasting crop failures and determining the economic feasibility of chemical plant protection; forecasting the appearance and spread of quarantine pests.

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

Development of quantum computational model for analysis of drug delivery: Loading of human beta-defensin 3 and mitoxantrone onto composite polymers

A molecular dynamics simulation study evaluated the co-loading of mitoxantrone (MTX) and human beta-defensin 3 (HBD3) on PLGA and chitosan chains. Simulations were performed on four different polymeric systems. Electrostatic and vdW interactions between MTX and HBD3 are examined. According to the obtained results, MTX strongly interacts with PLGA. In addition, HBD3 interacts strongly with CHI. Moreover, the radial distribution function (RDF) was estimated for the compounds. The structural changes of HBD3 and the interaction potentials were considered when selecting a suitable polymeric carrier for HBD3. For this purpose, HBD3 in a water medium was considered a reference system. RMSD, RMSF, radius of gyration, and the secondary structure of HBD3 were analyzed. According to the results, HBD3 has a secondary structure similar to water when adsorbed on 10C and 5PC. CHI could stabilize the structure of HBD3. 5PC (5 PLGA and 5 CHI chains) is suitable for co-loading MTX and HBD3. Polymeric carriers can be designed using these molecular results.

Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle

Abstract In recent years, electric vehicles (EVs) have grown in popularity as a viable way to reduce greenhouse gas emissions by replacing conventional vehicles. The need for EV batteries is steadily increasing. An essential and expensive part of electric transportation is the battery. The operating temperature of the lithium-ion (Li-ion) battery significantly impacts the performance of the EV battery pack. Battery packs undergo temperature fluctuations during the charging and discharging procedures due to internal heat generation, necessitating an examination of the temperature distribution of the battery pack. The geometrical spacing between cells is considered larger and identical and is kept open on two sides for free air circulation. A novel battery thermal management system (BTMS) design is required to effectively dissipate heat from the prismatic battery pack module. The electro-thermal behaviour of the prismatic Li-ion battery pack module was investigated based on the high charge/discharge rate. This study presents the development of a three-dimensional free open-source OpenFOAM computational fluid dynamics model for prismatic cell battery packs that simulates heat generation, air flow field, and temperature distribution across the width and depth of the battery pack module. The prismatic battery pack simulation results are compared with the experimental and simulation results of the cylindrical battery pack. It was also revealed that prismatic cells generate more heat on the backside, requiring battery packs to have increased cooling and space between individual cells to ensure sufficient air circulation for cooling and gas removal. The BTMS is improved by designing with increased space among the prismatic battery cells as compared with the conventional prismatic cell battery pack design.

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Multiscale physics-based in silico modelling of nanocarrier-assisted intravascular drug delivery

A rational design of drug nanocarriers supported by in silico modelling tools can improve the efficacy of nanosystem-based intravascular drug delivery (IVDD). Computational model development stems from the vision of replacing conventional (pre)clinical trials with advanced simulations and applies to the development of more efficient nanocarriers for intravascular therapies. To establish a standardized framework for in silico preclinical trials, it is necessary to include in silico tools that can model each experimental stage of a preclinical trial for a respective nanocarrier system and give accurate and verifiable results. This review paper highlights the status of intravascular drug delivery supported by nanocarriers and discusses the modelling stages of a physics-based multiscale modelling framework that should be developed, validated and exploited to address the need for an effective preclinical assessment of nanocarriers for IVDD.

Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging.

Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.

Effects of glucose and lactate on Streptococcus mutans abundance in a novel multispecies oral biofilm model.

The oral microbiome plays an important role in protecting oral health. Here, we established a controlled mixed-species in vitro biofilm model and used it to assess the impact of glucose and lactate on the ability of Streptococcus mutans, an acidogenic and aciduric species, to compete with commensal oral bacteria. A chemically defined medium was developed that supported the growth of S. mutans and four common early colonizers of dental plaque: Streptococcus gordonii, Actinomyces oris, Neisseria subflava, and Veillonella parvula. Biofilms containing the early colonizers were developed in a continuous flow bioreactor, exposed to S. mutans, and incubated for up to 7 days. The abundance of bacteria was estimated by quantitative polymerase chain reaction (qPCR). At high glucose and high lactate, the pH in bulk fluid rapidly decreased to approximately 5.2, and S. mutans outgrew other species in biofilms. In low glucose and high lactate, the pH remained above 5.5, and V. parvula was the most abundant species in biofilms. By contrast, in low glucose and low lactate, the pH remained above 6.0 throughout the experiment, and the microbial community in biofilms was relatively balanced. Fluorescence in situ hybridization confirmed that all species were present in the biofilm and the majority of cells were viable using live/dead staining. These data demonstrate that carbon source concentration is critical for microbial homeostasis in model oral biofilms. Furthermore, we established an experimental system that can support the development of computational models to predict transitions to microbial dysbiosis based on metabolic interactions.IMPORTANCEWe developed a controlled (by removing host factor) dynamic system metabolically representative of early colonization of Streptococcus mutans not measurable in vivo. Hypotheses on factors influencing S. mutans colonization, such as community composition and inoculation sequence and the effect of metabolite concentrations, can be tested and used to predict the effect of interventions such as dietary modifications or the use of toothpaste or mouthwash on S. mutans colonization. The defined in vitro model (species and medium) can be simulated in an in silico model to explore more of the parameter space.

Development and Validation of Computational Fluid Dynamics Model of Ladle Furnace with Electromagnetic Stirring System.

Numerical methods are crucial to supporting the development of new technology in different industries, especially steelmaking, where many phenomena cannot be directly measured or observed under industrial conditions. As a result, further designing and optimizing steelmaking equipment and technology are not easy tasks. At the same time, numerical approaches enable modeling of various phenomena controlling material behavior and, thus, understanding the physics behind the processes occurring in different metallurgical devices. With this, it is possible to design and develop new technological solutions that improve the quality of steel products and minimize the negative impact on the environment. However, the usage of numerical approaches without proper validation can lead to misleading results and conclusions. Therefore, in this paper, the authors focus on the development of the CFD-based (computational fluid dynamics) approach to investigate the liquid steel flow inside one metallurgical device, namely a ladle furnace combined with an EMS (electromagnetic stirring) system. First, a numerical simulation of electromagnetic stirring in a scaled mercury model of a ladle furnace was carried out. The numerical results, such as stirring speed and turbulent kinetic energy, were compared with measurements in the mercury model. It was found that the results of the transient multiphase CFD model achieve good agreement with the measurements, but a free surface should be included in the CFD model to simulate the instability of the flow pattern in the mercury model. Based on the developed model, a full-scale industrial ladle furnace with electromagnetic stirring was also simulated and presented. This research confirms that such a coupled model can be used to design new types of EMS devices that improve molten steel flow in metallurgical equipment.