Modeling Approaches Research Articles

Dosing Strategies and Quantitative Clinical Pharmacology for Bispecific T-Cell Engagers Development in Oncology.

Bispecific T-cell Engagers (TCEs) are promising anti-cancer treatments that bind to both the CD3 receptors on T cells and an antigen on the surface of tumor cells, creating an immune synapse, leading to killing of malignant tumor cells. These novel therapies have unique development challenges, with specific safety risks of cytokine release syndrome. These on-target adverse events fortunately can be mitigated and deconvoluted from efficacy via innovative dosing strategies, making clinical pharmacology key in the development of these therapies. This review assesses dose selection and the role of quantitative clinical pharmacology in the development of the first eight approved TCEs. Model informed drug development (MIDD) strategies can be used at every stage to guide TCE development. Mechanistic modeling approaches allow for (1) efficacious yet safe first-in-human dose selection as compared with in vitro minimum anticipated biological effect level (MABEL) approach; (2) rapid escalation and reducing number of patients with subtherapeutic doses through model-based adaptive design; (3) virtual testing of different step-up dosing regimens that may not be feasible to be evaluated in the clinic; and (4) selection and justification of the optimal clinical step-up and full treatment doses. As the knowledge base around TCEs continues to grow, the relevance and utilization of MIDD strategies for supporting the development and dose optimization of these molecules are expected to advance, optimizing the benefit-risk profile for cancer patients.

Teacher education for mathematical modelling: exploring the experiences of secondary school teachers in two courses

AbstractThis paper focuses on in-service teacher education as a crucial agent in driving educational paradigm shifts for the teaching and learning of mathematical modelling. Our paper presents empirical research based on two case studies, examining training courses for in-service secondary school mathematics teachers. The first course integrates different modelling approaches, while the second one is based on the anthropological theory of the didactic and uses the proposal of the study and research paths for teacher education. Both experiences conclude with teachers working on the design of modelling tasks to be implemented in secondary schools. Both courses are analysed through the lenses of how teachers progress on the collective identification of conditions and constraints for modelling, when teachers deal with the design of modelling tasks and analyse their implementations. First, the results show the kind of modelling tasks teachers design and the level of disciplinary specificity at which they plan modelling to be implemented. Second, we analyse how teachers address the “ecological analysis”, that is, the identification of conditions and constraints that facilitate or hinder the integration of modelling in secondary school classrooms after their experiences.

Stochastic Block Smooth Graphon Model

In this paper, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are generally used for clustering networks into groups of actors with homogeneous connectivity behavior. Smooth graphon models, on the other hand, assume that all nodes can be arranged on a one-dimensional scale such that closeness implies a similar behavior in connectivity. Both frameworks belong to the class of node-specific latent variable models, entailing a natural relationship. While these two modeling concepts have developed independently, this paper proposes their generalization towards stochastic block smooth graphon models. This combined approach enables to exploit the advantages of both worlds. Employing concepts of the EM-type algorithm allows to develop an appropriate estimation routine, where MCMC techniques are used to accomplish the E-step. Simulations and real-world applications support the practicability of our novel method and demonstrate its advantages. The paper is accompanied by supplementary material covering details about computation and implementation.

Stochastic Block Smooth Graphon Model

In this paper, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are generally used for clustering networks into groups of actors with homogeneous connectivity behavior. Smooth graphon models, on the other hand, assume that all nodes can be arranged on a one-dimensional scale such that closeness implies a similar behavior in connectivity. Both frameworks belong to the class of node-specific latent variable models, entailing a natural relationship. While these two modeling concepts have developed independently, this paper proposes their generalization towards stochastic block smooth graphon models. This combined approach enables to exploit the advantages of both worlds. Employing concepts of the EM-type algorithm allows to develop an appropriate estimation routine, where MCMC techniques are used to accomplish the E-step. Simulations and real-world applications support the practicability of our novel method and demonstrate its advantages. The paper is accompanied by supplementary material covering details about computation and implementation.

Large dump critical zone of interest identification and slope failure prediction using realistic 3D numerical modelling

Dump failure is one of the most catastrophic hazards in the opencast mining industry and involves sudden and violent failures. Therefore, susceptible region identification is essential for slope failure mitigation in the large dump. This study proposed a sustainable approach for large dump critical zone of interest (CZI) identification, stability monitoring and slope failure prediction. UAV close-range photogrammetry survey and structure-from-motion (SfM) approach were used for the dump realistic 3D model reconstruction. 3D numerical modelling was employed for dump stability analysis. In this investigation, the dump critical zone, slope factor of safety (FOS), displacement and slope failure prediction were analysed using the 3D numerical modelling. The results indicate that maximum displacement was found in the critical zone I. However, the dump was stable under static conditions. Dump critical slope failure time is predicted using the inverse velocity method. The results of this investigation are reliable and establish the scope of UAV photogrammetry for realistic 3D modelling. In addition, the slope stability state and failure model were consistent with field observations. The proposed approach can be used for the mine, hill, and roadcut slopes. UAV technology and 3D modelling approaches have substantially reduced data collection time and expense.

Associations of activity, sedentary and sleep behaviors with prevalent steatotic liver disease in middle-aged and older adults: the ELSA-Brasil study

BackgroundSteatotic liver disease (SLD) is a prevalent metabolic disease. While single component movement behaviors have been related to its development, comprehensive assessments of their joint associations are scarce.ObjectiveTo investigate the single-component and multi-component associations of moderate and vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep with prevalent SLD in Brazilian adults.MethodsA cross-sectional analysis using data from the third wave of the ELSA-Brasil cohort (2017–2019). Participants wore an ActiGraph wGT3X-BT in the waist for seven days and completed a sleep diary. SLD was defined by a Fatty Liver Index ≥ 60. To investigate single-component and multi-component associations, we used three exposure modeling approaches based on Poisson models: multivariable-adjusted regression, restricted cubic splines, and compositional data analysis.ResultsAmong 8569 participants (55.7% women, mean age 59.2 ± 8.60), 43.9% had SLD. Total activity volume adjusted for covariates was inversely associated with prevalent SLD. Every 1 mg/day increase in total activity volume was associated with a PR of 0.95 in individuals sleeping < 7 h/day (95% CI 0.94–0.97) and 0.95 (95% CI 0.93–0.96) in those sleeping ≥ 7 h/day. Increasing 30 min/day of MVPA was associated with decreasing prevalence of SLD (sleep ≥ 7 h/day [PR 0.83; 95% CI 0.77–0.89]; sleep ≥ 7 h/day [PR 0.78; 95% CI 0.74–0.83]). Sleep, SB, and LPA were not associated with SLD. Associations of total activity volume and MVPA were more pronounced among females. Adjustment with adiposity markers attenuated the associations.ConclusionsIn adults, total activity volume and MVPA were inversely associated with SLD in a dose–response fashion. Substituting lower-intensity behaviors with MVPA was associated with a lower prevalence of SLD independent of sleep duration, sex, and age.

The EQ-5D-5L valuation study for Trinidad and Tobago

PurposeThe 2016 EQ-5D-3L value set for Trinidad and Tobago (T&T) allows for the calculation of EQ-5D-5L values via the crosswalk algorithm. The 2016 value set was based on methods predating the EQ-VT protocol, now considered the gold standard for developing EQ-5D value sets. Furthermore, direct elicitation of EQ-5D-5L is preferred over crosswalked values. This study aimed to produce an EQ-5D-5L value set for T&T.MethodsA representative sample (age, sex, geography) of adults each completed 10 composite Time Trade-Off (cTTO) tasks and 12 Discrete Choice Experiment (DCE) tasks in face-to-face interviews. The cTTO data were analyzed using a Tobit model that corrects for heteroskedasticity. DCE data were analyzed using a mixed logit model. The cTTO and DCE data were combined in hybrid models.ResultsOne thousand and seventy-nine adults completed the valuation interviews. Among the modelling approaches that were explored, the hybrid heteroskedastic Tobit model produced all internally consistent, statistically significant coefficients, and performed best in terms of out-of-sample predictivity for single states. Compared to the existing EQ-5D-5L crosswalk set, the new value set had a higher number of negative values (236 or 7.6% versus 21 or 0.7%). The mean absolute difference was 0.157 and the correlation coefficient between the two sets was 0.879.ConclusionThis study provides a value set for the EQ-5D-5L for T&T using the EQ-VT protocol. We recommend this value set for QALY computations relating to T&T.

Quantitative Analysis of the Relationship Between Cryptocurrency Market and U.S. Stock Market Performance

With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

Particle-based food systems subject to lipid migration – A review of measurement, modelling, and mitigation approaches

Many consumer food products such as confectionary or culinary seasonings are particle-based systems containing varying amounts of lipids such as oils, fats, and greases. Lipid migration is the result of inherent metastability of multiphasic particulate food systems. Lipid instability is further aggravated by climatic conditions, interaction with packaging, porosity, material transitions, and even gravity. Resulting lipid mobility can lead to various quality defects such as fat bloom on chocolate or oil stains on fibrous paper-based wrappers. This review revisits the factors influencing lipid migration and the generally accepted transport mechanisms diffusion and capillary flow. The most common measurement methods and modelling approaches described in the literature are discussed and assessed. Modern mitigation strategies to control lipid mobility are reviewed, with discussion on applicability to different particle-based food types and structures. Current trends towards healthier diets, clean-label recipes and sustainable packaging challenge traditional methods to stabilise lipids in food. As such a fundamental understanding, and measurement, modelling and mitigation strategies of lipid migration are highly relevant for a wide range of lipid-containing particulate foods.

An OpenSim thoracolumbar spine model applying a bottom-up modelling approach is similar to a top-down approach

The kinetic demands of the spine can be assessed using a top-down (TD) or bottom-up (BU) approach, which start calculations from the either the hands or from the feet, respectively. Biomechanists have traditionally favored a BU approach, though existing modeling approaches encourage a TD approach. Regardless of the approach the demands should be similar, provided the external forces and linked segment parameters are equivalently measured and modeled. Demonstrating a level of agreement between the two approaches can help evaluate a model. Further, having both approaches can be advantageous when data is inaccurate or unavailable for one. The purpose of this study was to compare the internal moments and forces at multiple lumbar and thoracic intervertebral joint (IVJ) levels during lifting tasks from an established OpenSim thoracolumbar spine model that applies a TD approach and a similar model modified to adopt a BU approach. Kinematics and external forces were recorded from twelve participants during sagittal and lateral lifts of different lifting speeds and crate masses. For both approaches IVJ kinetics were estimated using a standard OpenSim modeling pipeline. The BU and TD approach IVJ joint moments generally agreed both temporally (R2 = .94 ± 0.17) and in magnitude (RMSE=6.2 ± 3.5 Nm) in the primary planes of movement. There were however some temporal fit exceptions for off axes moments with low magnitudes (i.e., < 10 Nm). Bland-Altman plots also indicated acceptable agreement for IVJ peak forces (BU-TD difference of 12 ± 111 and 8 ± 31 N in compression and resultant shear, respectfully). These results support the application of the BU approach and the assigned linked segment parameters of the model. The new BU model is available on the SimTK site.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data

The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in South Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil. Environmental implicationRadionuclide contaminated soil, likely to pose adverse effects for site remediation, needs to be managed via modeling approaches. Previous studies are limited in that they merely consider parametric Kd models or single machine learning models. This study aimed to investigate the adsorption characteristics of soil samples under various environmental conditions and estimate Kd by presenting multimodal models combining trained sub-networks for diverse experimental data. The results based on the Kd represent a significant advance in the management of radioactive contaminants in soil, promising safer and more sustainable environmental management practices. Overall, this study paves a new approach to estimating Kd.

Dimensionality and scales of preferential flow in soils of Shale Hills hillslope simulated using HYDRUS

AbstractPreferential flow (PF) processes are governed by subsurface soil structures at various scales. Still, model validation and mechanistic understanding of PF are very lacking. We hypothesize that PF at hillslope and larger scales cannot be described and quantified when neglecting small‐scaled spatially variable processes and simplifying the model dimensionality. The objective was to learn from comparing simulation results of multidimensional (1D, 2D, and 3D) and multiscale (pedon, catena, and catchment) modeling approaches with comprehensive datasets, and so as to evaluate PF simulations based on the Richards’ equation (solved by the HYDRUS software). Results showed limited alignment between 1D simulations and soil moisture data, mainly affected by vertical changes in porosity, permeability, and precipitation features. 2D and 3D simulations outperformed 1D models. 3D simulations provided satisfactory description of PF dynamics at the pedon scale, considering accurate representations of soil and bedrock structures for three dimensions (vertical, horizontal, and surrounding area). In 2D simulations at the pedon scale, models incorporating dual‐porosity and anisotropy of soils yielded more accurate predictions of water dynamics than single‐porosity and isotropic models. Furthermore, the application of 2D simulation at the catena scale identify PF pathways owing to the enhanced representation of the hydraulic connectivity between different locations along the slope. The results confirmed the significance of multidimensional and multiscale modeling approaches for PF simulations in hillslope hydrology. Considering the complexity and parameterization of 2D and 3D “bottom‐up” physically based models in representing spatial variability within and between soil profiles and/or underlying bedrock geology, the results contribute to creating a modeling framework applicable to identify the PF processes and thus their implications in managing water resources.

Rapid design and optimization of complex surfactant microemulsions for high-efficiency oil extraction processes integrating experimental and modeling approaches

Microemulsions have been widely used as a means of high-efficiency oil extraction processes in the field of oil recovery, separation, deposit removal, et al. Phase behavior is considered the most reliable evidence for formulating surfactant systems. There, however, few methods exist that can predict the phase behavior of microemulsions formed by complex surfactant compositions.This work developed a modified model based on the Hydrophilic Lipophilic Deviation-Net Average Curvature (HLD-NAC) model that could rapidly and accurately predict the phase behavior and phase evolution of complex surfactant microemulsions. The phase evolution of the microemulsions with equivalent alkane carbon number (EACN), surfactant concentration, and water–oil ratio (WOR) were explored. The results indicated that the new model successfully delineated the salinity boundaries of Winsor Type III microemulsion and predicted the inversions of phase behavior. A linear correlation between the EACN and the optimal salinity was demonstrated, with a precisely defined predictive range of EACN for Winsor Type III microemulsions. A fish diagram that illustrated the evolution path of the microemulsion phase was established through concentration scanning. The WOR test suggested that Winsor Type III microemulsion could be formed through salinity regulation once the surfactant concentration exceeded the critical micelle concentration (CMC).

Investigation of the potential of pomegranate peel as a treatment option for heavy metal contaminated wastewater: Experimental and modeling approaches

Heavy metals can cause serious environmental and human health problems, and their removal from wastewater is critical to protect our planet and communities. This study investigated the ability of crushed pomegranate peel to remove mercury and cadmium ions from contaminated water as a function of different experimental parameters. The experimental results showed that the pH of the solution influenced the adsorptive removal of heavy metals, with the best performance observed at pH 4.8. Optimization studies and process balance modeling were performed to optimize the process for commercial use. The performance of pomegranate peel was compared with that of other materials, and the highest adsorption capacities for both cadmium (Ca (II)) and mercury (Hg (II)) ions were observed to be 89.59 and 42.125 mg/g, respectively. The results were interpreted using the Langmuir model, which provided the best fit to describe the behavior of the process.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.

The study of transporter proteins is key to understanding the mechanism behind multi-drug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Control strategies applied to distribution networks with the integration of distributed energy resources - A review

The continuous integration of distributed energy resources (DERs) in power systems has opened the need to propose control strategies that tend to mitigate the possible impacts that an unlimited integration can produce. Authors have presented studies that present different control strategies that take advantage of the devices already present in the network or of new elements added, such as DERs. Also, different control architectures that can take full advantage of the capabilities that DERs can offer are studied. Therefore, this article focuses on reviewing the recent literature related to control strategies and architectures applied to distribution networks with DERs as protagonists. The control strategies that exploit the characteristics of DERs to perform control actions are described, and the control architectures adapted to the integration of DERs in distribution networks are detailed. Device modeling approaches are also mentioned, considering that some DERs depend on random variables and can introduce uncertainty in the analyses if their behavior is not modeled correctly. Finally, current and future trends in this area are discussed. Keywords. Control architectures, control strategies, deterministic approach, Distributed Energy Resources, stochastic approach.

Evaluating different CFD surrogate modelling approaches for fast and accurate indoor environment simulation

Indoor Environment Quality (IEQ) holds significant importance in building design and operation, and the simulation of indoor environments playing a crucial role in enhancing IEQ. Although Computational Fluid Dynamics (CFD) has been widely employed for simulating building environments, it is computationally demanding, particularly for large spaces. To tackle this challenge, we conducted a systematic evaluation of three surrogate models for accelerating CFD: Proper Orthogonal Decomposition (POD), Artificial Neural Networks (ANN), and a combined POD-ANN approach. Our evaluation criteria focused on assessing the model accuracy, the model size, computational time and extrapolation ability. A validated CFD case model and a real campus building are employed for model evaluation. The findings demonstrate that the top five modes can reconstruct the original data matrix accurately, and the POD-ANN significantly reduces model complexity and computation time by reducing the number of parameters in the neural network, the POD-ANN parameters is only 0.14 % of the ANN, and computation time is reduced by 63 %. In addition, the combination with ANN helps increase the extrapolation ability of POD significantly. In conclusion, this research proves that the POD-ANN can enhance the efficiency of CFD calculations with the advantages of both ANN and POD. By applying the POD-ANN to predict indoor temperature, we achieve faster predictions without compromising model accuracy, and an excellent extrapolation ability is achieved. This approach also reduces model complexity, highlighting its practical value for indoor environment prediction, particularly for large and complicated spaces.

Data-driven evaluation of building materials using Ground Penetrating Radar

Ground Penetrating Radar is a widely used technology in the nondestructive evaluation and monitoring of structures during repair and maintenance phases. Within this domain, a critical focus lies in assessing layered structures such as building envelopes. Evaluating attributes like thickness and material type in layered structures traditionally demands labor-intensive manual efforts involving calculations of dielectric properties and extensive signal processing. This study addresses these challenges by leveraging machine learning data-driven models that harness the entire recorded waveform, presenting a potential breakthrough for expediting diagnostic monitoring within the construction industry. We evaluate both supervised and unsupervised machine learning models for classification and regression tasks predicting the properties of common building materials. Experimental GPR A-scans serve as input for these models, with a thorough evaluation of both instance-based and parametric modeling approaches. A detection accuracy of 100% is achieved for identifying outlier scans, while material classification accuracy is 85%. Layer thickness predictions also had a high accuracy, with a typical error of 5%. Additionally, we explore the impact of feature learning and other preprocessing strategies on model performance. Our findings demonstrate the suitability of standard data-driven models for a spectrum of supervised learning tasks for layered structure diagnostics. However, we emphasize the importance of careful attention to the distribution of training data and its relevance to intended use in the field.

Fisher–KPP-type models of biological invasion: open source computational tools, key concepts and analysis

This review provides open-access computational tools that support a range of mathematical approaches to analyse three related scalar reaction–diffusion models used to study biological invasion. Starting with the classic Fisher–Kolomogorov (Fisher–KPP) model, we illustrate how computational methods can be used to explore time-dependent partial differential equation (PDE) solutions in parallel with phase plane and regular perturbation techniques to explore invading travelling wave solutions moving with dimensionless speed c ≥ 2 . To overcome the lack of a well-defined sharp front in solutions of the Fisher–KPP model, we also review two alternative modelling approaches. The first is the Porous–Fisher model where the linear diffusion term is replaced with a degenerate nonlinear diffusion term. Using phase plane and regular perturbation methods, we explore the distinction between sharp- and smooth-fronted invading travelling waves that move with dimensionless speed c ≥ 1 / 2 . The second alternative approach is to reformulate the Fisher–KPP model as a moving boundary problem on 0 &lt; x &lt; L ( t ) , leading to the Fisher–Stefan model with sharp-fronted travelling wave solutions arising from a PDE model with a linear diffusion term. Time-dependent PDE solutions and phase plane methods show that travelling wave solutions of the Fisher–Stefan model can describe both biological invasion ( c &gt; 0 ) and biological recession ( c &lt; 0 ) . Open source Julia code to replicate all computational results in this review is available on GitHub; we encourage researchers to use this code directly or to adapt the code as required for more complicated models.