Use Of Computational Models Research Articles

1464-P: Computational Modeling for Design of Next Generation Microphysiological Systems for Evaluation of ß-Cell Health and Function

Dynamic insulin secretion is a key focus of β cell research, with microfluidic devices emerging as a potential means to effectively evaluate this performance parameter in vitro. However, most devices maintain isolated islets in suspension despite evidence that collagen-islet interactions are critical to β cell viability and function. When designing a microphysiological system with encapsulated islets, important considerations include 1) sufficient oxygenation, 2) rapid exchange of glucose and insulin between cells and perfusate, and 3) detectable levels of secreted insulin. Computational models provide a means to simulate different device design parameters prior to fabrication, reducing the time and cost of testing multiple physical devices and microenvironmental conditions. For this study, COMSOL was used to model a microfluidic device containing islets macroencapsulated in a fibrillar collagen scaffold. A range of flow rates, collagen fibril densities, and islet-collagen construct dimensions were tested, with outcome measures including spatiotemporal changes in oxygen, glucose, and insulin. Simulations for all designs tested showed maintenance of islet viability as indicated by less than 1% of the construct area falling below the hypoxia-induced dysfunction threshold. Additionally, decreasing the construct thickness and increasing the medium glucose concentration yielded more rapid delivery of the target glucose stimulus to the islets, since glucose transport is primarily driven by diffusion within the construct. By contrast, transport of secreted insulin was found to be flow-limited and the presence of an encapsulation material altered the shape of the insulin secretion curve. Collectively, these results support the integrated use of computational models, together with experimental validation, as an efficient strategy for the creation of next generation microphysiological systems for evaluation of β cell health and function. Disclosure E.Vanderlaan: None. A.Buganza tepole: None. S.L.Harbin: Other Relationship; GeniPhys, Inc.

Mechanisms and mathematical modeling of ROS production by the mitochondrial electron transport chain.

Reactive oxygen species (ROS) are recognized both as damaging molecules and intracellular signaling entities. In addition to its role in ATP generation, the mitochondrial electron transport chain (ETC) constitutes a relevant source of mitochondrial ROS, in particular during pathological conditions. Mitochondrial ROS homeostasis depends on species- and site-dependent ROS production, their bioreactivity, diffusion, and scavenging. However, our quantitative understanding of mitochondrial ROS homeostasis has thus far been hampered by technical limitations, including a lack of truly site- and/or ROS-specific reporter molecules. In this context, the use of computational models is of great value to complement and interpret empirical data, as well as to predict variables that are difficult to assess experimentally. During the past decades, various mechanistic models of ETC-mediated ROS production have been developed. Although these often-complex models have generated novel insights, their parameterization, analysis, and integration with other computational models are not straightforward. In contrast, phenomenological (sometimes termed "minimal") models use a relatively small set of equations to describe empirical relationship(s) between ROS-related and other parameters and generally aim to explore system behavior and generate hypotheses for experimental validation. In this review, we first discuss ETC-linked ROS homeostasis and introduce various detailed mechanistic models. Next, we present how bioenergetic parameters (e.g., NADH/NAD+ ratio and mitochondrial membrane potential) relate to site-specific ROS production within the ETC and how these relationships can be used to design minimal models of ROS homeostasis. Finally, we illustrate how minimal models have been applied to explore pathophysiological aspects of ROS.

Estimation of 2,4-dichlorophenol photocatalytic removal using different artificial intelligence approaches

Abstract Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R 2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.

Peptide Hormones in Medicine: A 100-Year History

This review is devoted to the 100-year history of the investigation of peptide hormones and the creation of drugs on their basis, starting from the insulin discovery and its introduction into a medical practice in 1921. The basic groups of the peptide hormones are discussed: neurohypophyseal hormones, hypothalamic releasing hormones, incretins, insulin, adrenocorticotropic hormone (ACTH), and calcitonin. The first therapeutic agents based on the peptide hormones were created by a traditional approach that involved the isolation of peptides from animal tissues, their purification to individual compounds, determination of their primary structure, their chemical synthesis or their deep purification, and the creation of a pharmaceutical substance. A modern approach to creation of peptide hormone drugs is based on their consideration as ligands of the corresponding cellular receptors and the use of computer modeling, efficient synthesis methods, and high-throughput screening. The combination of these methods enabled the development of analogs which would be more active than the corresponding natural compounds, exhibit other activities in addition to the hormonal regulation, and be resistant to biodegradation. Such therapeutic agents have been designed on the basis of agonistic and antagonistic analogs of somatostatin and luliberin, and have found wide application in hormonal regulation and cancer treatment. Over the past two decades, the glucagon-like peptide (GLP-1) has been intensively investigated as a potential therapeutic agent. In our review, we describe modifications which resulted in the most highly effective long-acting drugs. Now, natural hormones and their analogs are widely present in the pharmaceutical market.

Development of students’ scientific knowledge on computer modeling while teaching inverse problems for differential equations

Problem statement. Currently, the higher school provides students of physical and mathematical training areas with fundamental subject knowledge, forms professional competencies, develops creative abilities and creativity, teaches them to use modern computer technologies to solve applied problems. One of such academic disciplines, in which students are taught to use computer technologies in solving applied mathematical problems, is a training course called Inverse problems for differential equations. Such an academic discipline has been taught in some Russian universities in the form of elective courses since the 70s of the 20th century. The educational material of this training course includes advanced research results on inverse problems belonging to specialists from different countries, such as Germany, Italy, China, Sweden, Netherlands, Russia, Japan and other foreign countries. During the practical classes, much attention is paid to the use of computer simulation for the study of such applied problems. This circumstance implies the development of educational materials taking into account the professional training of university students, in which attention should be paid to the use of computer modeling in solving mathematical models of inverse problems. Methodology. The implementation of training of university students in physical and mathematical areas of training involves taking into account modern scientific achievements of world science in the field of inverse problems using computer simulation and implementing advanced pedagogical technologies in the classroom. Results. Students acquire scientific knowledge of computer modeling and master the wide possibilities of computer modeling in the study of inverse problems. Conclusion. The presence of scientific knowledge in the field of computer simulation and practical experience of its application for solving inverse problems gives students great advantages and opportunities to be successful specialists in the field of applied mathematics and to be in demand on the labor market in various spheres of human activity.

A review of atomic layer deposition modelling and simulation methodologies: Density functional theory and molecular dynamics

AbstractThe use of computational modelling and simulation methodologies has grown in recent years as researchers try to understand the atomic layer deposition (ALD) process and create new microstructures and nanostructures. This review article explains and simplifies two simulation methodologies, molecular dynamics and the density functional theory (DFT), in solving atomic layer deposition problems computationally. We believe that these simulation methodologies are powerful tools that can be utilised in atomic layer deposition. DFT is used to solve problems in surface science and catalysis (predicting surface energy, adsorption energy, charge transfer,etc.), semiconductors (band structure, defect bands, band gap,etc.), superconductors (electron–phonon coupling, critical transition temperature), and molecular electronics (conductance, current–voltage characteristics). Molecular dynamics (MD) is used to predict the kinetic and thermodynamic properties of a material. Of interest in this article is a review where different material problems emanating from atomic layer deposition from these fields have been addressed by DFT and MD. Selected publications are discussed where DFT and MD have been successfully applied in atomic layer deposition (and related processes in some instances). The applications of DFT stretch from binding energy calculations of molecules and the solid band structure in chemistry and physics, respectively, computing the electron density up to determining the properties of a many-electron system. Also highlighted in this review study are the challenges that DFT and MD simulations must overcome.

Nonlinear calculation of beam reinforcement using the finite element method

The article is devoted to the research of solution of the actual problem in construction, namely increase of ultimate loads of different kinds of constructions and possibility of effective application of innovative methods of reconstruction of beam constructions. In this work 2 kinds of different composite materials have been applied. The project considers the use of carbon fiber as an external reinforcement material for bridge girders. This study analyzes the performance of the reinforced structure under external loads. With the use of computer modeling of the finite element method, an analysis of the selection of reinforcement schemes, which allows to significantly increase the carrying capacity of reinforced concrete beam structures, is performed. Also, some proposals for the implementation of these experiments on real girder structures and constructions were considered. The data obtained as a result of the work allow the conclusion about the successful application of composite materials as reinforcement structures. Correctly chosen scheme of strengthening, confirmed by calculations allows you to significantly increase the carrying capacity of the beam reinforced concrete structures.

Investigating the Design and Implementation of an In-Line Near-Infrared Probe Using Computational Fluid Dynamics for Measurement of Non-Newtonian Fluids.

Process analytical technology (PAT) has developed significantly since its introduction in pharma where many in situ analytical probes and measuring devices are now commercially available, replacing the use of off-line quality control measurements that are typically laborious and time intensive. The use of PAT instrumentation should not interfere with the process itself and subsequently should have no effect on the product whilst measuring representative samples. Implementation of these devices is typically arbitrary using empirical means. Therefore, the objective of this study is to highlight the use of computational fluid dynamics modeling to investigate the effect of interfacing parameters and process parameters of an inline near-infrared (NIR) probe used to determine the viscosity of a non-Newtonian micellar liquid. The parameters investigated for the probe were immersion depth, immersion angle, gap size, and fluid velocity. The results conclude that the immersion angle and depth should both be optimized to prevent stagnant fluid accumulating in the measuring gap ensuring that the NIR measurements are representative of the bulk. The gap size determines the optical pathlength and therefore was also investigated against an existing predictive viscosity model showing no changes in model performance with varying gap size. The use of computational modeling to develop a digital twin prior to PAT implementation at the equipment design stage ensures the technology can perform at its best and will also aid in calibration transfer studies.

Cognition Without Neural Representation: Dynamics of a Complex System.

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

HobPre: accurate prediction of human oral bioavailability for small molecules

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.Graphical

МОДЕЛЮВАННЯ ЕНЕРГЕТИЧНИХ ПРОЦЕСІВ В ГІБРИДНІЙ ФОТОЕЛЕКТРИЧНІЙ СИСТЕМІ З АКУМУЛЯТОРОМ ДЛЯ ПОТРЕБ ЛОКАЛЬНОГО ОБ’ЄКТУ

Purpose. Improving the efficiency of a hybrid photoelectric system with a rechargeable battery for the needs of the local object by improving the management of the forecast with simulation of energy processes in the system, development of principles for the implementation of energy management systems.Methodology. Analysis of energy processes in the electrical circuits of the photoelectric system with the formalization of the principles of control reconfiguration and the use of computer modeling based on archival data of photoelectric battery generation to evaluate the efficiency of energy management.Findings. Block structures and the general structure of the model of energy processes in the system for the daily cycle of work with an estimation of the cost of electricity consumed from the grid have been developed. The principles of control modes and power consumption of the system according to the forecast of photoelectric battery generation were formalized.Originality. The principles of setting the battery current according to the forecast of photoelectric energy generation, the state of battery charge and the power limit consumed from the grid have been improved. It will help to make better use of the energy of the photoelectric battery and reduce the consumption of electricity from the grid. The mathematical model of the rechargeable battery, built on the manufacturer's catalog data has been improved. The formalization of energy processes in the system with the use of additional variables, which provide reconfiguration of work with regulation of photoelectric battery generation or battery current and taking into account the power limit consumed from the grid is substantiated.Practical value. The obtained solutions are the basis for designing photoelectric control systems to meet the needs of local objects.

КВАНТТЫҚ ФИЗИКАДАҒЫ ЭКСПЕРИМЕНТТЕРДІ КОМПЬЮТЕРЛІК МОДЕЛЬДЕУ

This article presents the concept of using modern information technology as a simple tool for understanding quantum physics, in particular the laws of the microcosm. It was analyzed that the use of computer modeling combines all the main didactic concepts necessary for the experiment and thereby increases the effectiveness of teaching.

“The Source of Life” in Bishop’s Basilica Of Philippopolis in the Context of Steam

The main task considered in this material is related to the restoration of the mosaic “Source of Life” in the Bishop’s Basilica of Philippopolis. Ideas for creation and use of computer models of the geometric scheme and of separate figures in the considered mosaic are presented. Technologies such as 360-degree photography, 3D printing, specialized software for creating computer models, etc. are shared to provide conditions for research work. STEAM training, especially in the context of STEAM centers and innovative schools, creates good opportunities for the widespread deployment of such resources. Most of the described educational resources are provided with free access in the Virtual Mathematics Laboratory, developed at the Institute of Mathematics and Informatics of the Bulgarian Academy of Sciences.

Computational Simulator Models and Invasive Hemodynamic Monitoring as Tools for Precision Medicine in Pulmonary Arterial Hypertension.

Precision medicine, providing the right therapeutic strategy for the right patient, could revolutionize management and prognosis of patients affected by cardiovascular diseases. Big data and artificial intelligence are pivotal for the realization of this ambitious design. In the setting of pulmonary arterial hypertension (PAH), the use of computational models and data derived from ambulatory implantable hemodynamic monitors could provide useful information for tailored treatment, as requested by precision medicine.

ACCRUE—An Integral Index for Measuring Experimental Relevance in Support of Neutronic Model Validation

A key challenge for the introduction of any design changes, e.g., advanced fuel concepts, first-of-a-kind nuclear reactor designs, etc., is the cost of the associated experiments, which are required by law to validate the use of computer models for the various stages, starting from conceptual design, to deployment, licensing, operation, and safety. To achieve that, a criterion is needed to decide on whether a given experiment, past or planned, is relevant to the application of interest. This allows the analyst to select the best experiments for the given application leading to the highest measures of confidence for the computer model predictions. The state-of-the-art methods rely on the concept of similarity or representativity, which is a linear Gaussian-based inner-product metric measuring the angle—as weighted by a prior model parameters covariance matrix—between two gradients, one representing the application and the other a single validation experiment. This manuscript emphasizes the concept of experimental relevance which extends the basic similarity index to account for the value accrued from past experiments and the associated experimental uncertainties, both currently missing from the extant similarity methods. Accounting for multiple experiments is key to the overall experimental cost reduction by prescreening for redundant information from multiple equally-relevant experiments as measured by the basic similarity index. Accounting for experimental uncertainties is also important as it allows one to select between two different experimental setups, thus providing for a quantitative basis for sensor selection and optimization. The proposed metric is denoted by ACCRUE, short for Accumulative Correlation Coefficient for Relevance of Uncertainties in Experimental validation. Using a number of criticality experiments for highly enriched fast metal systems and low enriched thermal compound systems with accident tolerant fuel concept, the manuscript will compare the performance of the ACCRUE and basic similarity indices for prioritizing the relevance of a group of experiments to the given application.

A3 adenosine receptor agonists containing dopamine moieties for enhanced interspecies affinity

Following our study of 4′-truncated (N)-methanocarba-adenosine derivatives that displayed unusually high mouse (m) A3AR affinity, we incorporated dopamine-related N6 substituents in the full agonist 5′-methylamide series. N6-(2-(4-Hydroxy-3-methoxy-phenyl)ethyl) derivative MRS7618 11 displayed Ki (nM) 0.563 at hA3AR (∼20,000-fold selective) and 1.54 at mA3AR. 2-Alkyl ethers maintained A3 affinity, but with less selectivity than 2-alkynes. Parallel functional assays of G protein-dependent and β-arrestin 2 (βarr2)-dependent pathways indicate these are full agonists but not biased. Through use of computational modeling, we hypothesized that phenyl OH/OMe groups interact with polar residues, particularly Gln261, on the mA3AR extracellular loops as the basis for the affinity enhancement. Although the pharmacokinetics indicated facile clearance of parent O-methyl catechol nucleosides 21 and 31, prolonged mA3AR activation in vivo was observed in a hypothermia model, suggested potential formation of active metabolites through demethylation. Selected analogues induced mouse hypothermia following i.p. injection, indicative of peripheral A3AR agonism in vivo.

Argumentation schemes: From genetics to international relations to environmental science policy to AI ethics

Argumentation schemes have played a key role in our research projects on computational models of natural argument over the last decade. The catalogue of schemes in Walton, Reed and Macagno’s 2008 book, Argumentation Schemes, served as our starting point for analysis of the naturally occurring arguments in written text, i.e., text in different genres having different types of author, audience, and subject domain (genetics, international relations, environmental science policy, AI ethics), for different argument goals, and for different possible future applications. We would often first attempt to analyze the arguments in our corpora in terms of those schemes, then adapt schemes as needed for the goals of the project, and in some cases implement them for use in computational models. Among computational researchers, the main interest in argumentation schemes has been for use in argument mining by applying machine learning methods to existing argument corpora. In contrast, a primary goal of our research has been to learn more about written arguments themselves in various contemporary fields. Our approach has been to manually analyze semantics, discourse structure, argumentation, and rhetoric in texts. Another goal has been to create sharable digital corpora containing the results of our studies. Our approach has been to define argument schemes for use by human corpus annotators or for use in logic programs for argument mining. The third goal is to design useful computer applications based upon our studies, such as argument diagramming systems that provide argument schemes as building blocks. This paper describes each of the various projects: the methods, the argument schemes that were identified, and how they were used. Then a synthesis of the results is given with a discussion of open issues.

Corrosion inhibition of mild steel by aminobenzoic acid isomers in hydrochloric acid solution: Efficiency and adsorption mechanisms

Ortho-, meta- and para-aminobenzoic acid (ABA) in hydrochloric acid solution are used as mild steel corrosion inhibitors. Modeled as gas-solid and acidic aqueous-solid interfaces, we investigate their performance and adsorption mechanisms using first principles methodologies and Monte Carlo simulations. Computations show that both neutral and protonated ABAs exhibit spontaneous adsorption on Fe(110) surface, which is dominated by weak interactions. We observed an inhibition efficiency isomeric effect, as established experimentally. Therefore, our work should motivate the extensive use of computational modelling and chemical reactivity indices for in-depth studies of corrosion inhibition of metals by organic compounds as those employed in industries.

Forecasting the future while investigating the past. The use of computational models in pre-trial detention decisions.

The paper focuses on the traditional purpose of pre-trial detention (and other precautionary measures) to prevent specific risks. While liberty is the rule, before conviction, pre-trial detention is an absolute exception, competing with the opposite principle of the presumption of innocence: providing valuable and accurate justification for balancing the interest to prevent risk with the presumption of innocence is an overarching difficulty for judges, in the whole western world. Which the solutions? The paper reflects and compares the traditional solution of legal presumptions with the newer trend of actuarial assessment tools, based on psycho-criminological theories, based on the Italian and the uS federal systems.

FLUID DYNAMIC ANALYSIS OF A MARINE SOIL IN JETTING EXCAVATION EMPLOYING RHEOLOGICAL MODELS: INFLUENCE OF DRILLING FLUID ON SOIL DEFORMATION

With the exploration of marine oil fields in deep and ultra-deepwater regions, the need for studying different methods of well construction has increased. Nowadays, the technique of laying conductive casing by jetting is the most widely used for the starting phase of a well in such conditions. In this scenario, in early layers, where the marine soil is in contact with seawater, this material can present itself as a fine mud, characterizing a cohesive non-drained soil, with low shear strength, being considered a material with viscoplastic behavior. Thus, as such, using fluid rheology to analyze it may represent a valid option; being possible to classify it as a Herschel-Bulkley fluid. The use of computational modeling and numerical simulation represent an alternative to understand the behavior of soil during jetting. In this context, this work focuses on developing a computational modeling of the jetting of marine soil, based on the soil fluid dynamics approach, using computational fluid dynamics (CFD - Computational Fluid Dynamics) software SIMULIA XFLOW, version 2020. This work aims to investigate the deformation in the seabed in response to an incident vertical jet using different drilling fluids, also modeled as viscoplastic materials. Drilling fluids suitable for jetting and a fluid with a higher specific mass were considered. For the proposed modeling of the soil and drilling fluids considered, the main parameters used were the yield point, consistency index, behavior index, and the boundary viscosity. The latter was necessary to implement the modified Herschel-Bulkley model used by the software. Results show that the excavated cavity presented a similar behavior for the drilling fluids suitable for jetting, indicating that the rheology of the drilling fluid does not interfere with the deformation of the soil. However, a significant influence on the profile of the excavated cavity was observed when implementing the drilling fluid of higher specific mass in the jetting, which deformed the soil at greater depths.