Application Of Computational Models Research Articles

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes.

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.

CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

Advancement in computational simulation and validation of congenital heart disease: a review

The improvement in congenital heart disease (CHD) treatment and management has increased the life expectancy in infants. However, the long-term efficacy is difficult to assess and thus, computational modelling has been applied for evaluating this. Here, we provide an overview of the applications of computational modelling in CHD based on three categories; CHD involving large blood vessels only, heart chambers only, and CHD that occurs at multiple heart structures. We highlight the advancement of computational simulation of CHD that uses multiscale and multiphysics modelling to ensure a complete representation of the heart and circulation. We provide a brief future direction of computational modelling of CHD such as to include growth and remodelling, detailed conduction system, and occurrence of myocardial infarction. We also proposed validation technique using advanced three-dimensional (3D) printing and particle image velocimetry (PIV) technologies to improve the model accuracy.

Systems-level computational modeling in ischemic stroke: from cells to patients.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Progress in the analysis of phytocannabinoids by HPLC and UPLC (or UHPLC) during 2020-2023.

Organic molecules that bind to cannabinoid receptors are known as cannabinoids. These molecules possess pharmacological properties similar to those produced by Cannabis sativa L. High-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC, also known as ultra-high-performance liquid chromatography, UHPLC) have become the most widely used analytical tools for detection and quantification of phytocannabinoids in various matrices. HPLC and UPLC (or UHPLC) are usually coupled to an ultraviolet (UV), photodiode array (PDA), or mass spectrometric (MS) detector. To critically appraise the literature on the application of HPLC and UPLC (or UHPLC) methods for the analysis of phytocannabinoids published from January 2020 to December 2023. An extensive literature search was conducted using Web of Science, PubMed, and Google Scholar and published materials including relevant books. In various combinations, using cannabinoid in all combinations, cannabis, hemp, hashish, C. sativa, marijuana, analysis, HPLC, UHPLC, UPLC, and quantitative, qualitative, and quality control were used as the keywords for the literature search. Several HPLC- and UPLC (or UHPLC)-based methods for the analysis of phytocannabinoids were reported. While simple HPLC-UV or HPLC-PDA-based methods were common, the use of HPLC-MS, HPLC-MS/MS, UPLC (or UHPLC)-PDA, UPLC (or UHPLC)-MS, and UPLC (or UHPLC)-MS/MS was also reported. Applications of mathematical and computational models for optimization of protocols were noted. Pre-analyses included various environmentally friendly extraction protocols. During the last 4 years, HPLC and UPLC (or UHPLC) remained the main analytical tools for phytocannabinoid analysis in different matrices.

Form, function, mind: What doesn't compute (and what might)

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing animal (metazoan) tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to the metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, classic forms of dynamicism are similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life. Finally, some connections are drawn between the viewpoint described here and active inference models of cognition, such as the Free Energy Principle.

Exploring the Efficiency of Algerian Kaolinite Clay in the Adsorption of Cr(III) from Aqueous Solutions: Experimental and Computational Insights.

The current study comprehensively investigates the adsorption behavior of chromium (Cr(III)) in wastewater using Algerian kaolinite clay. The structural and textural properties of the kaolinite clay are extensively characterized through a range of analytical methods, including XRD, FTIR, SEM-EDS, XPS, laser granulometry, N2 adsorption isotherm, and TGA-DTA. The point of zero charge and zeta potential are also assessed. Chromium adsorption reached equilibrium within five minutes, achieving a maximum removal rate of 99% at pH 5. Adsorption equilibrium is modeled using the Langmuir, Freundlich, Temkin, Elovich, and Dubinin-Radushkevitch equations, with the Langmuir isotherm accurately describing the adsorption process and yielding a maximum adsorption capacity of 8.422 mg/g for Cr(III). Thermodynamic parameters suggest the spontaneous and endothermic nature of Cr(III) sorption, with an activation energy of 26.665 kJ/mol, indicating the importance of diffusion in the sorption process. Furthermore, advanced DFT computations, including COSMO-RS, molecular orbitals, IGM, RDG, and QTAIM analyses, are conducted to elucidate the nature of adsorption, revealing strong binding interactions between Cr(III) ions and the kaolinite surface. The integration of theoretical and experimental data not only enhances the understanding of Cr(III) removal using kaolinite but also demonstrates the effectiveness of this clay adsorbent for wastewater treatment. Furthermore, this study highlights the synergistic application of empirical research and computational modeling in elucidating complex adsorption processes.

Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research

Background Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated.

Impact of High-Pressure-Turbine Purge Flow on the Evolution of Turbulence in a Turbine Vane Frame

Abstract This paper describes the measurement and postprocessing of turbulence data. The experiments were carried out in a two-stage, two-spool transonic turbine test rig at the Institute for Thermal Turbomachinery and Machine Dynamics at the Graz University of Technology, which includes relevant purge and turbine rotor tip leakage flows. The test setup consists of a high-pressure turbine (HPT) stage, a turbine vane frame (TVF) with turning struts and splitters, and a counter-rotating low-pressure turbine (LPT) to allow engine realistic measurements. Time-resolved area traverse measurements have been performed for three different operating conditions in three measurement planes downstream of the HPT rotor, which enable the measurement of the turbulence quantities at the TVF inlet and outlet as well as the LPT outlet. The turbulence quantities are evaluated using triaxial- and single hot-film probes by means of Constant-Temperature-Anemometry, and their results were validated with five-hole probe (FHP) measurements. Ensemble- and time-averaging, as well as Fourier transforms, were applied for data reduction. It is shown how the turbulence intensity and integral length scale vary over different purge flowrates (PFR). The acquired measurement data illustrate that the interaction of the ejected purge flow with mean flow enhances the turbulent mixing in the secondary flow structures at the TVF inlet and TVF outlet, respectively. Furthermore, the flow downstream of the LPT rotor is affected by TVF and LPT rotor secondary flow structures, which are identified as high turbulence regions. These regions strongly depend on the purge flowrate. These results acquired under engine realistic rig flow conditions enable a deeper understanding of the relationship between loss and turbulence quantities and will help improve the application of computational fluid dynamics turbulence models to turbomachinery modeling.

ASA's computational acoustics Technical Committee

Computational acoustics is in its second full year as a Technical Committee, after being promoted from a Technical Specialty Group in May 2022. The CA TC provides a forum for researchers interested in numerical methods pertinent to acoustic wave propagation and structures, data analytics, validation, optimization, visualization, as well as application of computational models to engineering, noise control, and other practical problems. Some areas of current and emerging interest include artificial intelligence, uncertainty characterization, model reduction techniques, efficient parallelization, and time-domain treatments of dissipation. The CA TC is also interested in building collaborations with other TCs on repositories and benchmarks for codes and data.

NSF DARE—transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies

In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation (“Using computational models to understand complex mechanisms in neurorehabilitation” section), improve rehabilitation care in the context of digital twin frameworks (“Using computational models to improve delivery and implementation of rehabilitation care” section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models (“Using computational models in neurorehabilitation requires an interdisciplinary workforce” section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit—a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art—and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/.

Computational models of bone fracture healing and applications: a review.

Fracture healing is a very complex physiological process involving multiple events at different temporal and spatial scales, such as cell migration and tissue differentiation, in which mechanical stimuli and biochemical factors assume key roles. With the continuous improvement of computer technology in recent years, computer models have provided excellent solutions for studying the complex process of bone healing. These models not only provide profound insights into the mechanisms of fracture healing, but also have important implications for clinical treatment strategies. In this review, we first provide an overview of research in the field of computational models of fracture healing based on CiteSpace software, followed by a summary of recent advances, and a discussion of the limitations of these models and future directions for improvement. Finally, we provide a systematic summary of the application of computational models of fracture healing in three areas: bone tissue engineering, fixator optimization and clinical treatment strategies. The application of computational models of bone healing in clinical treatment is immature, but an inevitable trend, and as these models become more refined, their role in guiding clinical treatment will become more prominent.

Application of computer models and Artificial Intelligence technologies to improve the quality of wastewater treatment

The article provides a brief overview of the use of computer simulation models and artificial intelligence technologies to predict the quality of wastewater treatment from agro-industrial enterprises, which have a complex composition, variable flow rate and require taking into account a large number of factors both at the design and operation stages. This predetermines the need to model complex systems and predict the quality of cleaning using computer models and artificial intelligence technologies. Based on the reviewed foreign and Russian publications, a conclusion was made about the most common types of models currently used to predict the quality of wastewater treatment.

Application of computer modeling in the production of electric-welded pipe of triangular cross-section

Application of computer modeling in the production of electric-welded pipe of triangular cross-section

Computer models as a means of improving the effectiveness of teaching physics

Computer models and programs are one of the main tools in education, therefore, the development of a plan for their development and use in the field of education is one of the main problems. The use of computer models will help to explore a new topic, a demonstration experiment and phenomena without violating safety regulations. Many experts believe that the computer currently makes it possible to make a qualitative breakthrough in the education system. Models of physical phenomena and experiments actively shape students' knowledge. In teaching a number of sections of the physics course, such as atomic and nuclear physics, quantum mechanics, and elementary particle physics, essential questions about conducting experiments are obvious. These sections relate to important areas of physics that deal with the study of phenomena and processes in the microcosm. Therefore, due to some difficulties in equipping physics classrooms, the computer acts as an assistant. In quantum physics and atomic physics, computer models are considered that help explain the phenomena under study, the course of processes, as well as methods of working with computer programs that simulate laboratory installations in this section, since physical laboratory assistants are an integral part of training. The use of computer models and programs for educational purposes gives students a unique opportunity to plunge into the world of quantum and atomic physics. This study analyzes the effectiveness of such models in the educational process, their impact on students' understanding of the basic concepts of quantum and atomic physics, and also compiles questionnaires based on these problems. The survey covers about 40 physics teachers. The work is based on the analysis of existing pedagogical experiments and research in the field of application of computer models for teaching physics. The focus is on assessing the impact of these models on the quality of knowledge and their ability to make complex concepts accessible and understandable to students. The obtained results can serve as a basis for further improvement of methods of teaching quantum and atomic physics using computer programs. The introduction of computer programs can help the student create an effective learning environment for his work at his own pace and in collaboration with the class. It can be said that the use of models of physical processes in preparing students for effective learning opens up great opportunities for creating high-quality and new forms and methods, the use of computer models of physical phenomena plays an important role. Of particular importance in the development of students' abilities to observe, think, generalize based on observed facts, and predict the course of the observed process.

Occurrence of Mycotoxins in Foods: Unraveling the Knowledge Gaps on Their Persistence in Food Production Systems.

In this review, the intricate issue about the occurrence levels of mycotoxins in foods is discussed aiming to underline the main knowledge gaps on the persistence of these toxicants in the food production system. Mycotoxins have been a key challenge to the food industry, economic growth, and consumers' health. Despite a breadth of studies over the past decades, the persistence of mycotoxins in foods remain an overlooked concern that urges exploration. Therefore, we aimed to concisely underline the matter and provide possible biochemical and metabolic details that can be relevant to the food sector and overall public health. We also stress the application of computational modeling, high-throughput omics, and high-resolution imaging approaches, which can provide insights into the structural and physicochemical characteristics and the metabolic activities which occur in a stored cereal grain's embryo and endosperm and their relationship with storage fungi and mycotoxins on a cellular level. In addition, there is a need for extensive collaborative network and funding, which will play a key role in finding effective solutions against the persistence of mycotoxins at the genetic and molecular to metabolic levels in the food system.

Rational Atom Substitution to Obtain Efficient, Lead‐Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations

AbstractInorganic lead‐free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2AgBiBr6. Conspicuously, d10‐configured Zn2+ boosted the photoactivity of Cs2AgBiBr6. Experimental verification validated these model results, revealing a 13‐fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron‐deficient Br, and altering the distance between the B‐site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure–property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations.

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2 AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2 AgBiBr6 . Conspicuously, d10 -configured Zn2+ boosted the photoactivity of Cs2 AgBiBr6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR SOLVING PROBLEMS OF ACOUSTIC NONDESTRUCTIVE TESTING (REVIEW)

Nowadays, artificial neural networks are finding more and more applications in various fields of human activity. This paper reviews the current state of research and development in the field of application of artificial neural networks for solving problems in acoustic non-destructive testing. The areas of application of neural networks include processing of inspection data, flaw detection, determination of flaw parameters, and determination of material properties. Test data processing includes increasing the signal-to-noise ratio of results, data compression, restoration and processing of flaw images. Within flaw parameter determination, flaw sizing and flaw type identification tasks have been solved using artificial neural networks. One of the most challenging problems related to the introduction of artificial neural networks in acoustic nondestructive testing is the small amount of inspection data that complicates the training of artificial networks. The ways to solve this problem are data exchange between organizations and standardization of inspection results, data augmentation and application of computer modeling.

Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes

Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models. MethodsWe calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters. ResultsAcross the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively. ConclusionWe have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment. Statement of SignificanceThis manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.