Simple Model Research Articles

Functional Assessment of a Bioprinted Immuno-Mimetic Peyer's Patch Recapitulating Gut-Associated Lymphoid Tissue.

Gut immune models have attracted much interest in better understanding the microbiome in the human gastrointestinal tract. The gut-associated lymphoid tissue (GALT) has complex structures that interact with microorganisms, including the intestinal monolayer as a physiological barrier and the Peyer's patch (PP) involved in the immune system. Although essential for studying GALT and microbiome interactions, current research often uses simplified models that only recapitulate some components. In this study, GALT is recapitulated to consider the morphology and function of lymphocyte-containing PP beneath the intestinal monolayer and to analyze microbiome interaction. Using the bioprinting technique, a dome-shaped structure array for the PP is fabricated, and epithelial cells are cocultured to form the intestinal monolayer. The developed GALT model shows stable cell differentiation on the hydrogel while exhibiting durability against lipopolysaccharides. It also exhibits increased responsiveness to Escherichia coli, as indicated by elevated nitric oxide levels. In addition, the model underscores the critical role of GALT in maintaining bacterial coexistence and in facilitating immune defense against foreign antigens through the secretion of immunoglobulin A by lymphocyte spheroids. The proposed GALT model is expected to provide significant insights into studying the gut-immune system complexity and microbiome.

Heterogenization of a Sandwich [(PW9O34)2Co4(H2O)2]10− in PCN–222/PCN–222(M): Exploring the Electron Transfer for Electrocatalytic CO2 Reduction

In this study, we designed and prepared polyoxometalate@metal‐organic framework (POM@MOF) composite catalysts through the anchoring of a sandwich POM [(PW9O34)2Co4(H2O)2]10− (shortened as P2W18Co4) to the hexagonal channel of the PCN–222 (metal‐free) or PCN–222(M) (M=Fe, Co) frameworks. The composite materials were applied to the electrocatalytic reduction of CO2 reaction (CO2RR) to analyse the effect of incorporating P2W18Co4 on catalytic activity. The P2W18Co4@PCN–222 composite exhibited enhanced activity across a wide potential range (–0.60~ –0.85 V vs. RHE) and an optimal FECO of 72% at –0.75 V vs. RHE, which was more than double that of PCN–222 (FECO= 33%). The current density surpassed that of the PCN–222 precursors by over sixteen times at the same potential. In contrast, the P2W18Co4@PCN–222(M) composite demonstrated decreased current density, minimal enhancement in CO2RR activity, and a competing HER behaviour. Density functional theory calculations were conducted on simplified models of P2W18Co4@H2–TCPP and P2W18Co4@M–TCPP to elucidate the divergent catalytic performances. The findings revealed that while both configurations exhibited the same rate‐limiting step (formation of the *COOH intermediate), a significantly reduced reaction barrier was only observed in the P2W18Co4@H2–TCPP setup, explaining its substantial activity improvement.

Monte Carlo damage models of different complexity levels predict similar trends in radiation induced DNA damage

Ion therapies have an increased relative biological effectiveness (RBE) compared to X-rays, but this remains poorly quantified across different radiation qualities. Mechanistic models that simulate DNA damage and repair after irradiation could be used to help better quantify RBE. However, there is large variation in model design with the simulation detail and number of parameters required to accurately predict key biological endpoints remaining unclear. This work investigated damage models with varying detail to determine how different model features impact the predicted DNA damage.

Methods: Damage models of reducing detail were designed in TOPAS-nBio and Medras investigating the inclusion of chemistry, realistic nuclear geometries, single strand break damage, and track structure. The nucleus models were irradiated with 1 Gy of protons across a range of linear energy transfers (LETs). Damage parameters in the models with reduced levels of simulation detail were fit to proton double strand break (DSB) yield predicted by the most detailed model. Irradiation of the optimised models with a range of radiation qualities was then simulated, before undergoing repair in the Medras biological response model.

Results: Simplified damage models optimised to proton exposures predicted similar trends in DNA damage across radiation qualities. On average across radiation qualities, the simplified models experienced an 8% variation in double strand break (DSB) yield but a larger 28% variation in chromosome aberrations. Aberration differences became more prominent at higher LETs, with model features having an increasing impact on the distribution and therefore misrepair of DSBs. However, overall trends remained similar with better agreement likely achievable through repair model optimisation. 

Conclusion: Several model simplifications could be made without compromising key damage yield predictions, although changes in damage complexity and distribution were observed. This suggests simpler, more efficient models may be sufficient for initial radiation damage comparisons, if validated against experimental data.&#xD.

Enhancing Heart Disease Prediction through Machine Learning: A Comparative Analysis of Algorithm Generalization across Datasets with Various Distributions

Heart disease, due to its high prevalence and mortality, remains a key area of global research. Although traditional diagnostic methods are effective, they are often invasive and time-consuming, highlighting the need for non-invasive, AI-based approaches. A significant challenge in real-world applications is ensuring model generalization across different datasets, particularly when the datasets are small. In this study, the performance of machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), was evaluated on two distinct heart disease datasets with different distributions and relatively small sizes. Two datasets with varying distributions were used for training and testing, with the primary focus on assessing model generalization in crossdataset applications. It is shown in the results that, while the Decision Tree model performed best after hyperparameter tuning, the improvements in Random Forest and MLP were limited, and SVM exhibited a decline in performance after tuning in the cross-dataset task. It was found that grid search tuning has limitations in cross-dataset scenarios, especially with small datasets, where complex models are prone to overfitting. The study demonstrates that, with smaller datasets, simpler models like Decision Trees often adapt better to different datasets. Furthermore, transfer learning and domain adaptation techniques are suggested as crucial for improving model generalization. Future research should focus on employing these techniques to enhance the robustness and accuracy of heart disease prediction models across diverse datasets.

Hydraulics of Time-Variable Water Surface Slope in Rivers Observed by Satellite Altimetry

The ICESat-2 and SWOT satellite earth observation missions have provided highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSSs using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves, and backwater effects from lakes, reservoirs, or the ocean as the main non-uniform hydraulic situations in natural rivers that cause time changes of WSSs. We illustrate these phenomena at selected river sites around the world, using ICESat-2 data and river discharge estimates. The analysis shows that WSS observations from space can provide new insights into river hydraulics and can enable the estimation of river discharge from combined observations of water surface elevation and WSSs at sites with complex hydraulic characteristics.

Incorporating soil‐structure interaction into simplified numerical models for fragility analysis of RC structures

AbstractSimplified building models are a valuable option for seismic assessment at the regional scale. These models often use calibrated springs to model column behaviour, and recent advances have made them suitable for capturing torsional response in Reinforced‐Concrete Moment‐Resisting‐Frames. Nevertheless, their validation is typically achieved using fixed‐base models, which do not include the influence of soil‐structure interaction (SSI). This study introduces a novel approach to quantify the accuracy of a recently developed simplified model while accounting for dynamic SSI, using a newly implemented, refined 3D Finite Element non‐linear soil model in OpenSees. The accuracy of the simplified structural model is assessed by comparing the results of non‐linear dynamic analyses with those of a refined model in terms of (i) a peak structural demand parameter such as the interstorey‐drift ratio and (ii) fragility curves computed from cloud analysis and accounting for collapse cases. The study presents details of the proposed refined approach for 3D soil modelling in OpenSees, focusing on implementing free‐field boundary conditions and structure‐to‐soil connections. Results show that the accuracy of the simplified model is maintained, even in the presence of SSI, and it successfully captures the overall structural response measured at peak demand. For the proposed case study, the difference between the simplified and refined models’ fragility curves’ medians is 4% and 2% for fixed and SSI models, respectively. The simplified structural model, combined with the refined soil model for SSI effects, presents an innovative and conservative, yet computationally efficient, alternative for seismic risk analysis, even in the presence of structural irregularity.

Serum metabolomics improve risk stratification for incident heart failure

Abstract Background The prediction and early detection of heart failure (HF) is vital to reduce its substantial impact on quality of life, survival and healthcare expenditure. Current incident HF risk stratification strategies achieve only modest performance. Moreover, state-of-the-art risk scores focus on commonly acknowledged clinical risk factors, thus necessitating the integration of heterogenous data sources and prioritizing individuals with obvious risk constellations. Purpose Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. Methods Leveraging data of n = 68,311 individuals and > 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort, we (I) evaluated the association of individual metabolites with incident HF via fitting of per-metabolite COX proportional hazards models and (II) trained and validated elastic net (EN) models to predict incident HF using the serum metabolome. Discriminative performance was benchmarked against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF). External validation was conducted in the independent FINRISK study. Results Several metabolites were independently associated with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈ 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (Harrel’s C: 0.768 vs. 0.755, ΔC = 0.013 (95% confidence interval (CI) 0.004 – 0.022), continuous net reclassification improvement (NRI) = 0.287 (95% CI 0.200 – 0.367), relative integrated discrimination improvement (IDI): 17.47 % (95% CI 9.463 - 27.825)). Simplified models only including age, sex and metabolomics performed almost as well as the PCP-HF model (Harrel’s C: 0.745 vs. 0.755, ΔC = 0.010 (95% CI -0.004 - 0.027), continuous NRI: 0.097 (95% CI -0.025 - 0.217), relative IDI: 13.445 % (95% CI -10.608 - 41.454)). Risk and survival stratification was improved by integrating metabolomics. External validation within FINRISK revealed similar patterns using the original model coefficients. Conclusions Serum metabolomics improve the prediction of incident HF risk. Scores obtained from the combination of age, sex and metabolomics exhibit similar predictive power as clinical risk models. Offering serum metabolomics to a middle-aged cohort might significantly improve HF risk stratification, thus facilitating preventive efforts. Via a single blood draw, serum metabolomics displays a highly cost- and time-effective, standardizable, and scalable alternative to clinical risk scores involving physical measurements and history taking in addition to clinical chemistry.Relative performance (test split)ROC & DCA plots (test split)

Feature importance explanation of prosthetic valve endocarditis through machine learning via SHAP

Abstract Introduction Diagnosing prosthetic valve endocarditis poses a significant challenge in daily clinical practice. Accurately identifying these patients is vital due to its treatment implications. Clinical and imaging assessment of suspected cases yields a substantial number of interrelated variables, which can be analyzed through machine learning techniques in order to improve diagnostic accuracy. Feature ranking facilitates identification and interpretability of relevant clinical and imaging variables in this task. Purpose To utilize machine learning feature ranking and modeling in order to improve interpretable identification of patients with prosthetic valve endocarditis. Methods 89 cases defined by the endocarditis team and 173 controls were analyzed. We implemented the SHapley Additive exPlanations (SHAP) method to evaluate feature importance. 5-fold cross-validated Xgboost models were trained and independently tested with additive clinical, echocardiographic and 18F-FDG PET/CT data. Performance was evaluated through confusion matrices, F1-scores and AUCs. These were compared against conventional logistic regression of the Duke Criteria classification. Results Mean age was 59.6 ± 15.7, with 29.7% women. 14 clinical (demographic, laboratory and cultures), 2 echocardiographic, and 5 PET/CT features were included. The XGBoost model trained with all additive features demonstrated an AUC of 0.91±0.1 and outperformed simpler models as well as the Duke classification model (AUC=0.85). The clinical variables alone achieved an AUC of 0.85±0.11, while integration with echocardiography enhanced its performance to 0.90±0.04 (Fig. 1). Feature ranking demonstrated that antibiotic treatment duration, positive blood cultures, valve type, weight and CRP were the most relevant clinical variables, while the presence of vegetation or abscess, and abnormal uptake and uptake focality on PET/CT represented the most relevant imaging variables (Fig. 2). Conclusion Machine learning-based feature ranking and modeling may significantly enhance identification of patients with prosthetic valve endocarditis beyond standard clinical criteria. Feature selection offers interpretable performance in identifying these cases, model testing in clinical decision support may be warranted.Fig. 1:Comparative AUC Analysis.Fig. 2:SHAP feature importance.

Two-Step Iterative Medical Microwave Tomography

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

A codifiable methodology for estimating pallet sliding displacements on steel racking systems

AbstractTo facilitate the logistic processes within a modern warehouse, the contents of steel racking systems are not mechanically connected to the supporting beams, allowing a content‐sliding mechanism to develop when static friction is exceeded. Given that the mass of contents is dominant, this is beneficial for limiting the apparent inertia. On the other hand, pallet fall‐off may occur during strong seismic excitations, which is a failure mode that is not addressed by current seismic design processes and guidelines for racks. Along these lines, a codifiable methodology is proposed for estimating sliding displacements on steel racking systems, based on the statistical interpretation of a large set of response history analyses, using different rack configurations and ground motions. Firstly, a multi‐parametric analysis is conducted using simplified rack models to (i) select the intensity measure and engineering demand parameter that can best describe the problem of pallet sliding and (ii) identify the salient rack characteristics that dominate sliding behavior. Thereafter, a series of multi‐stripe analyses are performed using 180 rack realizations with different feature combinations to derive a so‐called, Empirical Sliding Prediction Equation (ESPE) by following a three‐step procedure: (a) perform regression on maximum sliding, (b) perform regression on the normalized sliding profile, and (c) combine steps a‐b to derive the denormalized profile at a given confidence level. The proposed empirical relationships are then validated through a comparison between the observed and fitted sliding displacements in three rack case studies.

Constraints on simplified dark matter models involving an s-channel mediator with the ATLAS detector in pp collisions at s=13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13$$\\end{document} TeV

This paper reports a summary of searches for a fermionic dark matter candidate in the context of theoretical models characterised by a mediator particle exchange in the s-channel. The data sample considered consists of pp collisions delivered by the Large Hadron Collider during its Run 2 at a centre-of-mass energy of s=13TeV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} = 13\\,\ extrm{TeV}$$\\end{document} and recorded by the ATLAS detector, corresponding to up to 140 fb-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}. The interpretations of the results are based on simplified models where the new mediator particles can be spin-0, with scalar or pseudo-scalar couplings to fermions, or spin-1, with vector or axial-vector couplings to fermions. Exclusion limits are obtained from various searches characterised by final states with resonant production of Standard Model particles, or production of Standard Model particles in association with large missing transverse momentum.

Validity of the Johns Hopkins Adjusted Clinical Groups system on the utilisation of healthcare services in Norway: a retrospective cross-sectional study

BackgroundThe Adjusted Clinical Groups (ACG) System is a validated electronic risk stratification system. However, there is a lack of studies on the association between different ACG risk scores and the utilisation of different healthcare services using different sources of input data. The aim of this study was therefore to assess the validity of the association between five different ACG risk scores and the utilisation of a range of different healthcare services using input data from either general practitioners (GPs) or hospitals.MethodsRegistry-based study of all adult inhabitants in four Norwegian municipalities that received somatic healthcare in one year (N = 168 285). The ACG risk scores resource utilisation band, unscaled ACG concurrent risk, unscaled concurrent risk, frailty flag and chronic condition count were calculated using age, sex and diagnosis codes from GPs and a hospital, respectively. Healthcare utilisation covered GP, municipal and hospital services. Areas under the receiver operating curve (AUC) were calculated and compared to the AUC of a model using only age and sex.ResultsUtilisation of all healthcare services increased with increasing scores in the “resource utilisation band” (RUB) and all other investigated ACG risk scores. The risk scores overall distinguished well between levels of utilisation of GP visits (AUC up to 0.84), hospitalisation (AUC up to 0.8) and specialist outpatient visits (AUC up to 0.72), but not out-of-hours GP visits (AUC up to 0.62). The score “unscaled ACG concurrent risk” overall performed best. Risk scores based on data from either GPs or hospitals performed better for the classification of healthcare services in their respective domains. The model based on age and sex performed better for distinguishing between levels of utilisation of municipal services (AUC 0.83–0.90 compared to 0.46–0.79).ConclusionsRisk scores from the ACG system is valid for classifying GP visits, hospitalisation and specialist outpatient visits. It does not outperform simpler models in the classification of utilisation of municipal services such as nursing homes and home services and outpatient emergency care in primary healthcare. The ACG system can be applied in Norway using administrative data from either GPs or hospitals.

Experimental characterization and 3D simulations of turbulent flames assisted by nanosecond plasma discharges

This paper presents quantitative experimental data generated for the validation of plasma-assisted combustion (PAC) simulations. These data are then used to validate the phenomenological model of Castela et al. They are also useful to test other PAC models. In the experiment presented here, Nanosecond Repetitively Pulsed (NRP) discharges are applied to a lean-premixed turbulent methane-air flame initially near the lean blow-off limit. The discharges significantly enhance the combustion and stabilize the flame after a few pulses. Electrical and optical diagnostics are employed to extensively quantify the transient and steady state of the plasma-stabilization process. The flame shape is characterized by OH* chemiluminescence imaging. In the discharge region, OH density profiles are obtained by 1D laser-induced fluorescence, and the gas temperature is measured by optical emission spectroscopy measurements. The local gas temperature increases by 1250 K, and the OH number density rises sevenfold when NRP discharges are applied. These results evidence the cumulative thermal and chemical effects of NRP discharges, which are especially challenging to replicate numerically. A Large Eddy Simulation (LES) of the experiment is performed. Combustion chemistry is modeled by an analytically reduced mechanism, while the plasma discharge is described by the low-CPU cost phenomenological model of Castela et al., which aims to capture the main thermal and chemical effects induced by the discharges. The model of Castela et al. is validated in the burnt gases by the remarkable agreement between the simulations and the experiments regarding the flame shape, the local gas temperature, and the OH number density. More generally, this work demonstrates the relevance of simplified plasma models in LES solvers to simulate complex plasma-assisted burners.

Contrasting behavior of urea in strengthening and weakening confinement effects on polymer collapse.

Biomolecules inhabit a crowded living cell that is packed with high concentrations of cosolutes and macromolecules that result in restricted, confined volumes for biomolecular dynamics. To understand the impact of crowding on the biomolecular structure, the combined effects of the cosolutes (such as urea) and confinement need to be accounted for. This study involves examining these effects on the collapse equilibria of three model 32-mer polymers, which are simplified models of hydrophobic, charge-neutral, and uncharged hydrophilic polymers, using molecular dynamics simulations. The introduction of confinement promotes the collapse of all three polymers. Interestingly, addition of urea weakens the collapse of the confined hydrophobic polymer, leading to non-additive effects, whereas for the hydrophilic polymers, urea enhances the confinement effects by enhancing polymer collapse (or decreasing the polymer unfolding), thereby exhibiting an additive effect. The unfavorable dehydration energy opposes collapse in the confined hydrophobic and charge-neutral polymers under the influence of urea. However, the collapse is driven mainly by the favorable change in polymer-solvent entropy. The confined hydrophilic polymer, which tends to unfold in bulk water, is seen to have reduced unfolding in the presence of urea due to the stabilizing of the collapsed state by urea via cohesive bridging interactions. Therefore, there is a complex balance of competing factors, such as polymer chemistry and polymer-water and polymer-cosolute interactions, beyond volume exclusion effects, which determine the collapse equilibria under confinement. The results have implications to understand the altering of the free energy landscape of proteins in the confined living cell environment.

Simplified calculation models for evaluating the post-earthquake axial load-carrying capacity of RC bridge piers

This study aims to develop simplified calculation models of the post-earthquake axial load-carrying capacity of reinforced concrete columns to determine the post-earthquake traffic flow capacity of the bridges for the evaluation and design process. To this end, the maximum and residual drift ratios are first determined as the engineering demand parameters for the simplified calculation model for the evaluation and design process, respectively. Then, the optimal models describing the relationships between the maximum/residual drift ratio and the post-earthquake axial load-carrying capacity are selected. The analysis results show the normal cumulative model in the coordinate system of ln(x)-y is the optimal relationship model. On this basis, the effects of high-sensitivity parameters (i.e. the axial compressive ratio and shear span ratio) on the post-earthquake axial load-carrying capacity are investigated. Results indicate that the axial compressive ratio negatively affects, while the shear span ratio positively affects the post-earthquake axial load-carrying capacity. Finally, the simplified calculation models of the post-earthquake axial load-carrying capacity are developed from the optimal model and the effects of the high-sensitivity parameters, and with validated high accuracy. The simplified calculation models provide engineers and policymakers with a tool to predict the post-earthquake traffic flow capacity of bridges.

Development of an intestinal mucosa ex vivo co-culture model to study viral infections.

Studying viral infections necessitates well-designed cell culture models to deepen our understanding of diseases and develop effective treatments. In this study, we present a readily available ex vivo 3D co-culture model replicating the human intestinal mucosa. The model combines fully differentiated human intestinal epithelium (HIE) with human monocyte-derived macrophages (hMDMs) and faithfully mirrors the in vivo structural and organizational properties of intestinal mucosal tissues. Specifically, it mimics the lamina propria, basement membrane, and the air-exposed epithelial layer, enabling the pioneering observation of macrophage migration through the tissue to the site of viral infection. In this study, we applied the HIE-hMDMs model for the first time in viral infection studies, infecting the model with two globally significant viruses: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and human norovirus GII.4. The results demonstrate the model's capability to support the replication of both viruses and show the antiviral role of macrophages, determined by their migration to the infection site and subsequent direct contact with infected epithelial cells. In addition, we evaluated the production of cytokines and chemokines in the intestinal niche, observing an increased interleukin-8 production during infection. A parallel comparison using a classical in vitro cell line model comprising Caco-2 and THP-1 cells for SARS-CoV-2 experiments confirmed the utility of the HIE-hMDMs model in viral infection studies. Our data show that the ex vivo tissue models hold important implications for advances in virology research.IMPORTANCEThe fabrication of intricate ex vivo tissue models holds important implications for advances in virology research. The co-culture model presented here provides distinct spatial and functional attributes not found in simplified models, enabling the evaluation of macrophage dynamics under severe acute respiratory syndrome coronavirus 2 and human norovirus (HuNoV) infections in the intestine. Moreover, these models, comprised solely of primary cells, facilitate the study of difficult-to-replicate viruses such as HuNoV, which cannot be studied in cell line models, and offer the opportunity for personalized treatment evaluations using patient cells. Similar co-cultures have been established for the study of bacterial infections and different characteristics of the intestinal tissue. However, to the best of our knowledge, a similar intestinal model for the study of viral infections has not been published before.

Transverse compressive behavior of plain and polypropylene fiber-reinforced concrete infilled steel tubes as hybrid steel-concrete nodes for discrete reticulated shell structures

This study investigates using concrete infilled steel tubes as hybrid nodes for discrete reticulated shell structures under compressive loading transverse to the tube axis. Three configurations were tested: empty, plain concrete infilled and polypropylene fiber-reinforced concrete infilled steel tubes. Displacement-controlled loading was applied through welded steel plates. Simplified beam theory models and nonlinear finite element analysis were used to evaluate the structural behavior. The concrete infilled steel tube specimens had higher stiffness and load-bearing capacity than the empty ones. The structural behavior of the concrete infilled specimens could be split into pre- and post-split-tensile cracking phases. No significant difference was found between the plain and fiber-reinforced concrete specimens under loads up to 1500 kN, which was the load limit of the testing machine. An additional fiber-reinforced concrete specimen, tested on a higher capacity machine, had a load-bearing capacity of 2400 kN and exhibited ductile failure.

Multifractality in monitored single-particle dynamics

We study multifractal properties in time evolution of a single particle subject to repeated measurements. For quantum systems, we consider circuit models consisting of local unitary gates and local projective measurements. For classical systems, we consider models for estimating the trajectory of a particle evolved under local transition processes by partially measuring particle occupations. In both cases, multifractal behaviors appear in the ensemble of wave functions or probability distributions conditioned on measurement outcomes after a sufficiently long time. While the nature of particle transport (diffusive or ballistic) qualitatively affects the multifractal properties, they are even quantitatively robust to the measurement rate or specific protocols. On the other hand, multifractality is generically lost by generalized measurements allowing erroneous outcomes or by postselection of the outcomes with no particle detection. We demonstrate these properties by numerical simulations and also propose several simplified models, which allow us to analytically obtain multifractal properties in the monitored single-particle systems. Published by the American Physical Society 2024

Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.