Modeling Studies Research Articles

MiRNA packaging into small extracellular vesicles and implications in pain.

Extracellular vesicles (EVs) are a heterogenous group of lipid bilayer bound particles naturally released by cells. These vesicles are classified based on their biogenesis pathway and diameter. The overlap in size of exosomes generated from the exosomal pathway and macrovesicles that are pinched off from the surface of the plasma membrane makes it challenging to isolate pure populations. Hence, isolated vesicles that are less than 200 nm are called small extracellular vesicles (sEVs). Extracellular vesicles transport a variety of cargo molecules, and multiple mechanisms govern the packaging of cargo into sEVs. Here, we discuss the current understanding of how miRNAs are targeted into sEVs, including the role of RNA binding proteins and EXOmotif sequences present in miRNAs in sEV loading. Several studies in human pain disorders and rodent models of pain have reported alterations in sEV cargo, including miRNAs. The sorting mechanisms and target regulation of miR-939, a miRNA altered in individuals with complex regional pain syndrome, is discussed in the context of inflammation. We also provide a broad overview of the therapeutic strategies being pursued to utilize sEVs in the clinic and the work needed to further our understanding of EVs to successfully deploy sEVs as a pain therapeutic.

Akkermansia muciniphila: A promising probiotic against inflammation and metabolic disorders.

Metabolic disease is a worldwide epidemic that has become a public health problem. Gut microbiota is considered to be one of the important factors that maintain human health by regulating host metabolism. As an abundant bacterium in the host gut, A. muciniphila regulates metabolic and immune functions, and protects gut health. Multiple studies have indicated that alterations in the abundance of A. muciniphila are associated with various diseases, including intestinal inflammatory diseases, obesity, type 2 diabetes mellitus, and even parasitic diseases. Beneficial effects were observed not only in live A. muciniphila, but also in pasteurized A. muciniphila, A. muciniphila-derived extracellular vesicles, outer membrane, and secreted proteins. Although numerous studies have only proven the simple correlation between multiple diseases and A. muciniphila, an increasing number of studies in animal models and preclinical models have demonstrated that the beneficial impacts shifted from correlations to in-depth mechanisms. In this review, we provide a comprehensive view of the beneficial effects of A. muciniphila on different diseases and summarize the potential mechanisms of action of A. muciniphila in the treatment of diseases. We provide a comprehensive understanding of A. muciniphila for improving host health and discuss the perspectives of A. muciniphila in the future studies.

Exploring multi-scale damage mechanisms in 8Cr4Mo4V alloy by molecular dynamics simulations and experiments

8Cr4Mo4V, as an ideal choice for aeroengines bearing materials, involves multi-scale damage mechanisms during the process of material failure. Herein, we propose a multi-scale analysis method to explore the crack initiation and propagation damage mechanisms of 8Cr4Mo4V alloy. We find that different crystal orientations produce distinctly different damage mechanisms. The second phase doping also has different strengthening effects on the matrix conformed to cut-off and Orowan mechanism. Meanwhile, the study of the polycrystalline model verified inverse Hall-Petch relation at the micro-scale, where material strength decreases with grain refinement. Phase transitions caused by dislocations lead to stress concentration at specific grain boundaries, becoming the main inducer for the nucleation of initial voids. To address micro-to-macroscale-crossing issues, we constructed a large-scale mesoscopic molecular dynamics simulation and proposed an analytical formula to predict the relationship between average grain size and yield stress. Finally, we combine macroscopic experiments, crystallographic analyses, and atomic-scale characterization, verifying the combined action of dislocations and stacking faults leads to the failure of the material.

A comparative study of factor models for different periods of the electricity spot price market

Due to major shifts in the European energy supply, a structural change can be observed in Austrian electricity spot price data starting from the second quarter of the year 2021 onward. In this work, we study the performance of two different factor models for the electricity spot price in three different time periods. To this end, we consider three samples of EEX data for the Austrian base load electricity spot price, one from the pre-crisis from 2018 to 2021, the second from the time of the crisis from 2021 to 2023, and the whole data from 2018 to 2023. For each of these samples, we investigate the fit of a classical 3-factor model with a Gaussian base signal and one positive and one negative jump signal and compare it with a 4-factor model to assess the effect of adding a second Gaussian base signal to the model.For the calibration of the models, we develop a tailor-made Markov Chain Monte Carlo method based on Gibbs sampling. To evaluate the model adequacy, we provide simulations of the spot price as well as a posterior predictive check for the 3- and the 4-factor model. We find that the 4-factor model outperforms the 3-factor model in times of non-crises. In times of crisis, the second Gaussian base signal does not lead to a better fit of the model. To the best of our knowledge, this is the first study regarding stochastic electricity spot price models in this new market environment. Hence, it serves as a solid base for future research.

Analysis of tangential stress response of ice-workpiece interface in ice-based machining of thin-walled parts: Modeling and experiment

The contact stress between the fixture and the workpiece surface is a crucial factor in evaluating the constraint performance of the fixture. According to the concept of an ice-based fixturing(IBF) proposed by the author, this paper conducts a modeling and experimental study of the tangential stress response at the ice-workpiece interface during ice-based machining. Initially, the principle of IBF is explained. Subsequently, the Dundurs parameters are used to characterize the mechanical properties of the ice-workpiece interface. According to the interface continuous conditions of stress and displacement, a predictive model for the tangential stress at the ice-workpiece interface is established using the Goursat formula. Afterwards, interface temperature experiments are carried out to analyze the temperature state of the ice-workpiece interface. Finally, milling experiments are conducted to analyze the variation of cutting force and interface tangential stress with the thickness of TWP, and to verify the accuracy of the analytical model. This study can provide valuable references for the evaluation of the constraint state of the fixture.

Solvents and their influence on electronic properties in IEFPCM solvation model, anticancer activity, and docking studies on (E)-2-((4-chlorobenzylidene) amino)phenol

The Schiff base (E)-2-((4-chlorobenzylidene)amino)phenol (4CL2AM) has been synthesized from 4-chlorobenzaldehyde and 2-aminophenol. Spectroscopic techniques like FTIR, Raman, UV, fluorescence, and NMR were used to characterize the synthesized compound. The experimental data well matched with the DFT method (B3LYP/cc-pVDZ basis set). The frontier molecular orbital (FMO), molecular electrostatic potential (MEP), and electronic spectra (UV–visible) analysis were conducted on three different solvents such as gas, DMSO, and water. The frontier molecular orbital (FMO) analysis calculated the HOMO-LUMO energy gap as 4.0109 eV, 4.0406 eV, and 4.0411 eV for the gas phase, DMSO, and water respectively. Molecular electrostatic potential (MEP), localized orbital locator (LOL), and electron localize function (ELF) analysis revealed positions of localized and delocalized orbitals as well as electrophilic and nucleophilic attack sites. The natural bond orbital analysis (NBO) predicted highest stabilization energy was 33.37 kcal/mol. The locations of the covalent and non-covalent interactions were investigated by density overlaps region indicator (DORI), interaction region indicator (IRI), and noncovalent interaction (NCI) analysis. Drug likeness and ADME analysis were investigated. In vitro, anticancer activity studies revealed against MCF-7 breast cancer cell lines. In the molecular docking study, the predicted lowest binding energy is −5.77 kcal/mol for the 6NLV-4CL2AM system.

Optimal control analysis of a mathematical model for guava nutrients in an integrated farming with cost-effectiveness

Because of its high nutritional value and easy availability, guava fruit has become more popular as a crop in tropical regions in recent decades. Unfortunately, its cultivation faces multifaceted challenges, with increasing the guava borer due to global warming posing a significant threat to crop yield, while the cost of pesticides adds to the economic burden on farmers. To overcome this difficulty, an integrated cultivation method has been devised to simultaneously cultivate guava and tuberose flowers for the purpose of biological pest management, while also earning money through the sale of the flowers to support the integrated agricultural plan. The present mathematical modeling study has focused on the optimal control problem using the strategy of spraying pesticides and flower harvesting, to achieve the highest possible profit. Characterization of the proposed optimal control was then carried out using Pontryagin’s maximum principle, where the objective of our farming would be higher when optimal management of our strategies would be provided compared to other scenarios. To find the most efficient and least expensive approach, cost-effectiveness analysis has been performed. According to the findings, an optimal strategy for harvesting flowers is the most economical and efficient way to increase the amount of earnings from this integrated farming. However, the results of this study can help the farmers in developing beneficial strategies to gain maximum profit by reducing the cost.

Design, Synthesis, and Evaluation of Antifungal Activity of Pyrazoleacetamide Derivatives.

Fungal infections have posed a big challenge in the management of their treatment. Due to the resistance and toxicity of existing drug molecules in the light of pandemic infections, like COVID-19, there is an urgent need to find newer derivatives of active molecules, which can be effective in fungal infections. In the present study, we aimed to design pyrazole derivatives using molecular modeling studies against target 1EA1 and synthesize 10 molecules of pyrazole derivatives using a multi-step synthesis approach. Designed pyrazole derivatives were synthesized by conventional organic methods. The newly synthesized pyrazole molecules were characterized by using FT-IR, 1HNMR, 13CNMR, and LC-MS techniques. Molecular docking studies were also performed. The antifungal activity of newly synthesized compounds was assessed in vitro against Candida albicans and Aspergillus niger using the well plate method. Two of the compounds, OK-7 and OK-8, have been found to show significant docking interaction with target protein 1EA1. These two compounds have also been found to show significant anti-fungal activity against Candida albicans and Aspergillus nigra when compared to the standard fluconazole. The Minimum Inhibitory Concentration (MIC) value of these two compounds has been found to be 50 μg/ml. Pyrazole derivatives with -CH3, CH3O-, and -CN groups have been found to be active against tested fungi and can be further explored for their potential as promising anti-fungal agents for applications in the field of medicinal chemistry.

Retrofitting agricultural detention systems can economically enhance nitrogen treatment with payment for services approach

Despite their global presence and multiple services including water storage and treatment, limited field studies have focused on quantifying water quality effectiveness of agricultural stormwater detention areas (SDAs; farm ponds). Limited information that does exist stems from unverified modeling studies. Economics is another neglected aspect of SDAs. An SDA in the Everglades basin of Florida was monitored to quantify its baseline nitrogen (N) treatment. Design flaws of underutilized surface storage in variably ponded areas and short-circuiting were rectified by retrofitting the SDA through compartmentalization, increase in outflow structure control elevation, and installing channel plugs. Post-retrofit total N retentions doubled (7500 from 3700 kg), a similar trend was also observed in surface water volume indicating that water retention drives N retention. Targeting enhanced water retention through retrofits can achieve reductions in downstream nutrient flows. The cost of additional N treatment from retrofits was $12/kg, an order of magnitude less than the published costs for other detention systems including newly constructed wetlands ($180/kg) and tailwater recovery systems ($214/kg). A scale-up showed that retrofitting SDAs could reduce the N discharge to an estuary in the Everglades by more than 50 %. A win-win payoff to farmers for SDA retrofits through a payment for environmental services program can achieve nutrient treatment economically and facilitate large-scale adoption.

The role of Interleukin-38 in modulating T cells in chronic Colitis: A mouse model study

BackgroundInterleukin (IL)-38 belongs to the IL-36 subfamily within the IL-1 family. Patients with inflammatory bowel diseases (IBD) exhibit higher levels of IL-38 in their intestinal tissue compared to healthy controls, suggesting that IL-38 may play a role in the pathogenesis of IBD. However, IL-38′s impact on T cell-mediated immune responses in gastrointestinal inflammation has not been investigated. Therefore, the objective of this work was to elucidate the role of IL-38 in modulating T cells in a mouse model of dextran sulfate sodium (DSS)-induced chronic colitis. MethodsRecombinant IL-38 (rIL-38) was administered intraperitoneally (i.p.) to mice with chronic colitis induced by DSS. Clinical symptoms, length of colon, and histologic alterations were assessed. Cytokine production was quantified using ELISA, and helper T (Th) cell subsets were evaluated via flow cytometry. ResultsAdministration of recombinant IL-38 (rIL-38) alleviated DSS-induced chronic colitis. In addition, animals with chronic colitis treated with rIL-38 exhibited a significant decrease in the spontaneous production of inflammatory cytokines by neutrophils in the lamina propria. Furthermore, rIL-38 treatment increased the absolute numbers and percentages of regulatory T cells (Tregs) but decreased the absolute numbers and percentages of Th1 and Th17 cells. Moreover, rIL-38 treatment also decreased IL-17A-producing γδT cells substantially. ConclusionThis study’s results show that IL-38 reduces the effects of chronic colitis caused by DSS by boosting Treg reactions and reducing Th1/Th17 reactions and IL-17A-producing γδT cells in LPL. Therefore, we propose that IL-38 has the potential to be utilized as a biological therapy agent for IBD.

Anisotropic Elastoplastic Behavior Formulation and Analysis of the Instability in the Large Deformations – A Numerical Modeling Study in Forming Processes

Anisotropic Elastoplastic Behavior Formulation and Analysis of the Instability in the Large Deformations – A Numerical Modeling Study in Forming Processes

Fabrication of chitosan coated bentonite clay multifunctional nanosorbents from waste biomass for the effective elimination of hazardous pollutants from waterbodies: A fixed bed biosorption, mechanism, and mathematical model study

The anthropogenic activity and hasty fluctuating technologies have been responsible for the generation of massive effluent which is so hazardous due to the loading of several toxicants. While most industries usually discharge it directly into the environment resulting in harsh damage to the ecology/public security. Therefore, it is critical to treat with a sustainable/cost-effective technique. Here, a new route of fabrication of chitosan-coated activated natural bentonite clay (CCANBC) bionanocomposites/bionanosorbents from waste biomass has been developed. Their potential application for the simultaneous removal of Ni2+ and Eosin Y from wastewater were investigated. The effective parameters like concentration (10–30 ppm), flow rate (2–4 mL/min), and bed height (0.5–1.5 cm) were inspected. The bionanosorbents were characterized by FTIR-ATR, XRD, FESEM, TGA, and BET analysis. Additionally, the effluents were explored by AAS and UV–vis-NIR spectroscopy. According to the findings it has been stated that the CCANBC bionanosorbents possessed significant dynamic edges, greater crystallinity (94.27 %), and higher thermal stability. They have exhibited a remarkable 2D honeycomb-like mesoporous microstructure with substantial specific surface area (19.29 m2/g). These outstanding features could be responsible for the dramatic adsorption enactment around 186.42 and 238.37 mg/g for Ni2+ and Eosin Y. The obtained data were evaluated by several mathematical models for better understanding the experimental BTC curve, reaction mechanism, and adsorption isotherm.

Comparative Analysis of Machine Learning Algorithms: KNN, SVM, Decision Tree and Logistic Regression for Efficiency and Performance

This paper presents a comparative study of machine learning models namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression on two datasets: 20Newsgroups and Wine datasets. Based on basic accuracy, and precision, and recall of the models, F1 measures were assessed. When compared with other classifiers such as KNN and Decisions Trees, SVM and Logistic regression gave better results especially in the case of the 20Newsgroups dataset dominated by textual high dimensional data. KNN had poor recall results and Decision Tree was moderate. In the Wine dataset, since the structure of data is comparatively less complex in our context then all the models yielded almost similar results with accuracy and precision factors very close to 1.0 which of course manifested that the choice of the model does not affect much on simple data. These results stress the importance of the choice of the appropriate model for tasks with a certain level of data complexity; detailed models show the greatest efficiency at the accomplishment of complicated tasks, though at the same time, they are not required for simple, structured data.

Leveraging Generative Artificial Intelligence (AI) for cybersecurity: Analyzing diffusion models in detecting and mitigating cyber threats

Due to the increased number of cyber dangers in today's digitally connected world, more advanced and flexible security measures have had to be created. Using generative artificial intelligence (AI), especially diffusion models, to find and stop cyber threats is investigated in this study. A new type of generative models called diffusion models have shown great promise in many areas, including picture creation and natural language processing. The purpose of this study is to look at their use in cybersecurity, especially for finding strange patterns, predicting future threats, and stopping attacks as they happen. The study utilized five scientific databases and a systematic search strategy to identify research articles on PubMed, Google Scholar, Scopus, IEEE, and Science Direct relating to the topic. Furthermore, books, dissertations, master's theses, and conference proceedings were utilized in this study. This study encompassed all publications published until 2024. Through a thorough study of the diffusion model's structure and how it can be applied to cybersecurity issues, we look at how these models can improve current systems for finding threats. Additionally, we talk about their ability to add to datasets by creating fake data, which makes anomaly detection more accurate in cyberattack cases that aren't well represented. Due to their stability and ability to predict, diffusion models are seen as a useful tool for finding complex threats like advanced persistent threats (APTs) and zero-day attacks. Some problems still exist, though, such as the need for a lot of computing power, models that are hard to understand, and the fact that online threats are always changing. This article suggests avenues for future study and talks about how diffusion models might change the way cybersecurity is done.

Respiratory system mechanics during noninvasive proportional assist ventilation: A model study

AbstractPurposeTo assess the accuracies of airway resistance (Raw) and compliance (Crs) calculations using the expiratory time constant (RCexp) method as well as the accuracy of Pmus estimation in obstructive lung models.MethodsA Respironics V60 ventilator was connected to an active lung simulator. The driving pressure was maintained at 5–10 cmH2O and positive end-expiration pressure (PEEP) was 5 cmH2O. Maximal Pmus, estimated based on equations of motion and respiratory mechanical properties, was calculated by the RCexp method to derive respiratory system compliance (Crs) and inspiratory (Rinsp) and expiratory (Rexp) resistance.ResultsDuring PAV, the assist proportion was adjusted to 55% and 40% with Pmus of 5 and 10 cmH2O, respectively. Pmus measurement errors were <20% of the preset values in most lung conditions. In the active lung model with PAV, an overestimation of Raw was found in the normal resistance condition, and Rinsp was underestimated in the severe obstructive model (P < 0.01). Crs was overestimated significantly except in the severe obstructive model at a Pmus of 10 cmH2O (all P < 0.01). Using the RCexp method, the target of ≤20% between the calculated and preset values in airway resistance was achieved in most obstructive models at a Pmus of 5 cmH2O.ConclusionsThe RCexp method might provide real-time assessments of respiratory mechanics (elastance and resistance) in the PAV mode. With low inspiratory effort, the estimation error was acceptable (<20%) in most obstructive lung models.

Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence

The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.

Bioinformatics analysis to design a multi-epitope mRNA vaccine againstS. agalactiaeexploiting pathogenic proteins.

Antibiotic resistance in bacterial pathogen infections is a growing global issue that occurs due to their adaptation to changing environmental conditions. Therefore, producing an efficient vaccine as an alternative approach can improve the immune system, eradicate related pathogens, and overcome this growing problem.Streptococcus agalactiaebelongs to group B Streptococcus (GBS). Colonization of GBS during pregnancy is a significant risk factor for infants and young children. S. agalactiae infected population exhibits resistance to beta-lactams, including penicillin and the second-line antibiotics erythromycin and clindamycin.On the other hand, there are currently no commercial vaccines against this pathogen. Vaccination of pregnant women is a highly effective method to protect newborns and infants from S. agalactiaeinfection, and it has been identified as an urgent demand by the World Health Organization. This study employed various immunoinformatic tools to develop an effective vaccine that could trigger both humoral and cell-mediated immunity and prevent disease. For this purpose, three conserved antigenic proteins of the main pathogenic strains ofS. agalactiaewere utilized to predict CTL, HTL, and B-cell epitopes for producing an mRNA vaccine against different strains ofS. agalactiae. The selected epitopes were fused using proper linkers. The Resuscitation promoting factor E (RpfE) sequence was incorporated in the designed vaccine construct as an adjuvant to boost its immune response. Different physicochemical characteristics of the final designed vaccine, modeling of the three-dimensional structure, molecular docking, molecular dynamics simulation, and immunological response simulation were screened following vaccine administration in an in vivo model.Computational immune simulation data identified that IgG1, IgM, INF γ, IL-2, T helper, and B-cell populations increased significantly after vaccination. These findings suggested that the vaccine candidate may provide good protection againstS. agalactiaeinfection. However,experimental and animal model studies are required for additional validation and implementation in human vaccination programs.

A Comparative Study of Swin Transformer and CNN Models for Crowd Counting

Abstract. Crowd counting, a critical component in the management and safety planning of large gatherings and public spaces, is essential for ensuring smooth event operations and preventing potential overcrowding issues. While the standard convolutional neural network (CNN) based model performs well in head counting tasks, it has certain drawbacks when applied to complex scenarios. With the rapid development of artificial intelligence, Transformer models that rely on self-attention mechanisms, as Swin Transformer, have demonstrated exceptional performance in visual tasks, such as image classification, and segmentation in recent times. This study examines the experimental findings of Swin Transformer's head counting tasks and contrasts them with the CNN-based model. Mean Absolute Error (MAE) and Mean Square Error (MSE) evaluation indicators show that the Transformer model outperforms the classic CNN model in terms of generalization abilities when dealing with complicated scenarios. Future research work will increase the diversity of data sets and focus on optimizing model structure and improving training efficiency.

Study of the Spectral Energy Distribution Model of the Protoplanetary Disk Geometric around the Brown Dwarf CFHT-BD-Tau 4

The accretion circumstellar disk of young stars and the Brown dwarf plays an essential role in the formation and evaluation of the planet. Our main work in this paper is to investigate the geometrical shape model for the protoplanetary disk around one of the Brown Dwarfs. The photometric measurements for the brown dwarf CFHT-BD-Tau 4 were extracted from the Vizier archive. We used a numerical simulation to build a model of the spectral energy distribution of our target CFHT-BD-Tau 4. The spectral energy distribution model was fitted with observational data for the brown dwarf CFHT-BD-Tau 4. A transitional disk has been assumed around CFHT-BD-Tau 4. We obtained physical properties of the two disks and the size of the gap between them by fitting the SED. The gap in the protoplanetary disk proves that a planet formation process occurred around the Brown dwarf.

Statistical Inference of Uncertain Autoregressive Model via the Principle of Least Squares

In the study of uncertain autoregressive models, how to estimate the unknown parameters and uncertain disturbance term in the models is always a key problem. In view of this, this paper proposes a statistical inference method based on the principle of least squares to determine the unknown parameters and uncertain disturbance term in an uncertain autoregressive model, and designs a numerical algorithm to calculate the numerical solutions of the corresponding estimators. Then, the uncertain hypothesis test is used to verify the applicability of the estimated uncertain autoregressive model, and point forecast and interval forecast are also made for the time series of future moments. Finally, a case study of the Consumer Price Index for all items in U.S. cities is provided to illustrate the effectiveness of the approach proposed in this paper.