Mathematical Biosciences and Engineering最新文献

{"title":"Bayesian inference and impact of parameter prior specification in flexible multilevel nonlinear models in the context of infectious disease modeling.","authors":"Olaiya Mathilde Adéoti, Aliou Diop, Romain Glèlè Kakaï","doi":"10.3934/mbe.2025032","DOIUrl":"https://doi.org/10.3934/mbe.2025032","url":null,"abstract":"<p><p>Bayesian flexible multilevel nonlinear models (FMNLMs) are powerful tools to analyze infectious disease data with asymmetric and unbalanced structures, such as varying epidemic stages across countries. However, the robustness of these models can be undermined by poorly designed estimation methods, particularly due to uncertainties in prior distributions and initial values. This study investigates how varying levels of prior informativeness can influence the model convergence, parameter estimation, and computation time in a Bayesian flexible multilevel nonlinear model (FMNLM). A simulation study was conducted to evaluate the impact of modifying prior assumptions on posterior estimates and their subsequent effects on the interpretations. The framework was applied to COVID-19 data from Francophone West Africa. The results indicate that accurate, informative priors enhance the prediction performance with minimal impact on the computation time. Conversely, non-informative or inaccurate priors for nonlinear parameters led to lower convergence rates and a reduced recovery accuracy, although they may remain viable in standard multilevel nonlinear models.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"897-919"},"PeriodicalIF":2.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predator-prey dynamics with refuge, alternate food, and harvesting strategies in a patchy habitat.","authors":"Rajalakshmi Manoharan, Reenu Rani, Ali Moussaoui","doi":"10.3934/mbe.2025029","DOIUrl":"https://doi.org/10.3934/mbe.2025029","url":null,"abstract":"<p><p>A predator-prey dynamic reaction model is investigated in a two-layered water body where only the prey is subjected to harvesting. The surface layer (Layer-1) provides food for both species, while the prey migrates to deeper layer (Layer-2) as a refuge from predation. Although the prey is the preferred food for the predator, the predator can also consume alternative food resources that are abundantly available. The availability of alternative food resources plays a crucial role in species' coexistence by mitigating the risk of extinction. The main objective of the work was to explore the effect of different harvesting strategies (nonlinear and linear harvesting) on a predator-prey model with effort dynamics in a heterogeneous habitat. The analysis incorporates a dual timescale approach: the prey species migrate between the layers on a fast timescale, whereas the growth of resource biomass, prey-predator interactions, and harvesting dynamics evolve on a slow timescale. The complete model involving both slow and fast timescales has been investigated by using aggregated model. The reduced aggregated model is analyzed analytically as well as numerically. Moreover, it is demonstrated that the reduced system exhibits the bifurcations (transcritical and Hopf point) by setting the additional food parameter as a bifurcation parameter. A comparative study using different harvesting strategies found that there is chaos in the system when using linear harvesting in the predator-prey model. However, nonlinear harvesting gives only stable or periodic solutions. This concludes that nonlinear harvesting can control the chaos in the system. Additionally, a one-dimensional parametric bifurcation, phase portraits, and time series plots are also explored in the numerical simulation.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"810-845"},"PeriodicalIF":2.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Persistence and extinction of infection in stochastic population model with horizontal and imperfect vertical disease transmissions.","authors":"Abhijit Majumder, Debadatta Adak, Adeline Samson, Nandadulal Bairagi","doi":"10.3934/mbe.2025030","DOIUrl":"https://doi.org/10.3934/mbe.2025030","url":null,"abstract":"<p><p>Epidemic models are used to understand the dynamics of disease transmission and explore the possible measures for preventing the spread of infection in the population. Disease transmission is intrinsically random and severely affected by environmental factors. We investigated a stochastic population model of the susceptible-infected-susceptible (SIS) type, in which infection spreads via both vertical and horizontal transmission routes. To incorporate stochasticity to the system, white multiplicative noise was taken into account in the horizontal disease transmission term. We proved that noise intensity, disease transmission, and recovery rates are potential routes for eradicating the disease. Furthermore, the parasite population reduces its fitness for some fixed noise if the relative fecundity of infected hosts and the disease transmission are low. However, if either of these is increased, it observes enhanced fitness. A simulation study illustrated the system's analytically dynamic properties and provided different insights. A case study for the imperfect vertical and horizontal infection transmission is also presented, supporting some of our observed theoretical results.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"846-875"},"PeriodicalIF":2.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bistable dynamics of TAN-NK cells in tumor growth and control of radiotherapy-induced neutropenia in lung cancer treatment.","authors":"Donggu Lee, Sunju Oh, Sean Lawler, Yangjin Kim","doi":"10.3934/mbe.2025028","DOIUrl":"https://doi.org/10.3934/mbe.2025028","url":null,"abstract":"<p><p>Neutrophils play a crucial role in the innate immune response as a first line of defense in many diseases, including cancer. Tumor-associated neutrophils (TANs) can either promote or inhibit tumor growth in various steps of cancer progression via mutual interactions with cancer cells in a complex tumor microenvironment (TME). In this study, we developed and analyzed mathematical models to investigate the role of natural killer cells (NK cells) and the dynamic transition between N1 and N2 TAN phenotypes in killing cancer cells through key signaling networks and how adjuvant therapy with radiation can be used in combination to increase anti-tumor efficacy. We examined the complex immune-tumor dynamics among N1/N2 TANs, NK cells, and tumor cells, communicating through key extracellular mediators (Transforming growth factor (TGF-$ beta $), Interferon gamma (IFN-$ gamma $)) and intracellular regulation in the apoptosis signaling network. We developed several tumor prevention strategies to eradicate tumors, including combination (IFN-$ gamma $, exogenous NK, TGF-$ beta $ inhibitor) therapy and optimally-controlled ionizing radiation in a complex TME. Using this model, we investigated the fundamental mechanism of radiation-induced changes in the TME and the impact of internal and external immune composition on the tumor cell fate and their response to different treatment schedules.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"744-809"},"PeriodicalIF":2.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the best mathematical model for implementation of non-pharmaceutical interventions.","authors":"Gabriel McCarthy, Hana M Dobrovolny","doi":"10.3934/mbe.2025026","DOIUrl":"10.3934/mbe.2025026","url":null,"abstract":"<p><p>At the onset of the SARS-CoV-2 pandemic in early 2020, only non-pharmaceutical interventions (NPIs) were available to stem the spread of the infection. Much of the early interventions in the US were applied at a state level, with varying levels of strictness and compliance. While NPIs clearly slowed the rate of transmission, it is not clear how these changes are best incorporated into epidemiological models. In order to characterize the effects of early preventative measures, we use a Susceptible-Exposed-Infected-Recovered (SEIR) model and cumulative case counts from US states to analyze the effect of lockdown measures. We test four transition models to simulate the change in transmission rate: instantaneous, linear, exponential, and logarithmic. We find that of the four models examined here, the exponential transition best represents the change in the transmission rate due to implementation of NPIs in the most states, followed by the logistic transition model. The instantaneous and linear models generally lead to poor fits and are the best transition models for the fewest states.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"700-724"},"PeriodicalIF":2.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictability of viral load dynamics in the early phases of SARS-CoV-2 through a model-based approach.","authors":"Andrea Bondesan, Antonio Piralla, Elena Ballante, Antonino Maria Guglielmo Pitrolo, Silvia Figini, Fausto Baldanti, Mattia Zanella","doi":"10.3934/mbe.2025027","DOIUrl":"https://doi.org/10.3934/mbe.2025027","url":null,"abstract":"<p><p>A pipeline to evaluate the evolution of viral dynamics based on a new model-driven approach has been developed in the present study. The proposed methods exploit real data and the multiscale structure of the infection dynamics to provide robust predictions of the epidemic dynamics. We focused on viral load kinetics whose dynamical features are typically available in the symptomatic stage of the infection. Hence, the epidemiological evolution was obtained by relying on a compartmental approach characterized by a varying infection rate to estimate early-stage viral load dynamics, of which few data are available. We tested the proposed approach with real data of SARS-CoV-2 viral load kinetics collected from patients living in an Italian province. The considered database refers to early-phase infections, whose viral load kinetics have not been affected by the mass vaccination policies in Italy.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 4","pages":"725-743"},"PeriodicalIF":2.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two species competition with a \"non-smooth\" Allee mechanism: applications to soybean aphid population dynamics under climate change.","authors":"Aniket Banerjee, Urvashi Verma, Margaret T Lewis, Rana D Parshad","doi":"10.3934/mbe.2025023","DOIUrl":"10.3934/mbe.2025023","url":null,"abstract":"<p><p>The soybean aphid (<i>Aphis glycines</i>) is an invasive insect pest that continues to cause large-scale damage to soybean crops in the North Central United States. Recent empirical evidence points to differential fitness in the pestiferous aphid biotypes under abiotic stresses such as flooding. As climate change predicts increased flooding in the North Central United States, mathematical models that incorporate such factors are required to better inform pest management strategies. Motivated by these empirical results, we considered the effect of non-smooth Allee type mechanisms, for the two species Lotka-Volterra competition model. We showed that this mechanism can alter classical competitive dynamics in both the ordinary differential equation (ODE) as well as the spatially explicit setting. In particular, an Allee effect present in the weaker competitor could lead to bi-stability dynamics, as well as competitive exclusion reversal. We discuss applications of our results to pest management strategies for soybean aphids in the context of a changing climate.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"604-651"},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal depression detection based on an attention graph convolution and transformer.","authors":"Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He","doi":"10.3934/mbe.2025024","DOIUrl":"10.3934/mbe.2025024","url":null,"abstract":"<p><p>Traditional depression detection methods typically rely on single-modal data, but these approaches are limited by individual differences, noise interference, and emotional fluctuations. To address the low accuracy in single-modal depression detection and the poor fusion of multimodal features from electroencephalogram (EEG) and speech signals, we have proposed a multimodal depression detection model based on EEG and speech signals, named the multi-head attention-GCN_ViT (MHA-GCN_ViT). This approach leverages deep learning techniques, including graph convolutional networks (GCN) and vision transformers (ViT), to effectively extract and fuse the frequency-domain features and spatiotemporal characteristics of EEG signals with the frequency-domain features of speech signals. First, a discrete wavelet transform (DWT) was used to extract wavelet features from 29 channels of EEG signals. These features serve as node attributes for the construction of a feature matrix, calculating the Pearson correlation coefficient between channels, from which an adjacency matrix is constructed to represent the brain network structure. This structure was then fed into a graph convolutional network (GCN) for deep feature learning. A multi-head attention mechanism was introduced to enhance the GCN's capability in representing brain networks. Using a short-time Fourier transform (STFT), we extracted 2D spectral features of EEG signals and mel spectrogram features of speech signals. Both were further processed using a vision transformer (ViT) to obtain deep features. Finally, the multiple features from EEG and speech spectrograms were fused at the decision level for depression classification. A five-fold cross-validation on the MODMA dataset demonstrated the model's accuracy, precision, recall, and F1 score of 89.03%, 90.16%, 89.04%, and 88.83%, respectively, indicating a significant improvement in the performance of multimodal depression detection. Furthermore, MHA-GCN_ViT demonstrated robust performance in depression detection and exhibited broad applicability, with potential for extension to multimodal detection tasks in other psychological and neurological disorders.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"652-676"},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling localized corrosion in biofuel storage tanks.","authors":"Hossein Moradi, Gabriele Grifò, Maria Francesca Milazzo, Edoardo Proverbio, Giancarlo Consolo","doi":"10.3934/mbe.2025025","DOIUrl":"https://doi.org/10.3934/mbe.2025025","url":null,"abstract":"<p><p>This work aims to model the influence of biofuels on localized \"pitting\" corrosion that occurs at the bottom of atmospheric storage tanks. To achieve this purpose, an electro-chemical phase-field model is proposed to include the extra chemical reaction due to the presence of organic acids in an electrolyte solution. The resulting set of nonlinear coupled partial differential equations is numerically integrated by means of finite element methods with a twofold aim: tracking the evolution of the metal/electrolyte interface and predicting the corrosion rates observed when either single or multiple interacting pits are formed in the bottom of a carbon steel tank. The results obtained in the case of single pit, which exhibited a good quantitative agreement with recent experimental data, can be summarized as follows: the presence of organic acids led to higher corrosion rates in comparison with conventional fuels; the corrosion rate is a two-stage process; the dependence of the pit depth as a function of time; and the solid potential, which can be successfully described via a double power law. For multiple interacting pits, the larger corrosivity associated to biofuels was further amplified and the long-time behavior of pit growth gave rise to a \"band\" behavior, with the major role being played by the number of pits rather than the initial spacings among them. Thus, the proposed model can be employed as a sophisticated tool to predict and quantify the real hazards associated with the release of pollutants in the environment, as well as to optimize the maintenance strategies based on an improved risk-based inspection planning.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"677-699"},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAET-SAM: Magneto-Acousto-Electrical Tomography segmentation network based on the segment anything model.","authors":"Shuaiyu Bu, Yuanyuan Li, Guoqiang Liu, Yifan Li","doi":"10.3934/mbe.2025022","DOIUrl":"https://doi.org/10.3934/mbe.2025022","url":null,"abstract":"<p><p>Magneto-Acousto-Electrical Tomography (MAET) is a hybrid imaging method that combines advantages of ultrasound imaging and electrical impedance tomography to image the electrical conductivity of biological tissues. In practical applications, different tissue or disease organization display various conductivity traits. However, the conductivity map consists of overlapping signals measured at multiple locations, the reconstruction results are affected by noise, which results in blurred reconstruction boundaries, low contrast, and irregular artifact distributions. To improve the image resolution and reduce noise of MAET, a dataset of conductivity maps reconstructed from MAET was established, dubbed MAET-IMAGE. Based on this dataset, we proposed a MAET tomography segmentation network based on the Segment Anything Model (SAM), termed as MAET-SAM. Specifically, we froze the encoder weights of SAM to extract rich feature information of image and design, an adaptive decoder with no prompts. In the end, an end-to-end segmentation model for specific MAET images with MAET-IMAGE was proposed. Qualitative and quantitative experiments demonstrated that MAET-SAM outperformed traditional segmentation methods and segmentation models with initial weights in terms of MAET image segmentation performance, bringing new breakthroughs and advancements to the field of medical imaging analysis and clinical diagnosis.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"585-603"},"PeriodicalIF":2.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}