Mathematical Biosciences and Engineering最新文献

{"title":"Determining the best mathematical model for implementation of non-pharmaceutical interventions.","authors":"Gabriel McCarthy, Hana M Dobrovolny","doi":"10.3934/mbe.2025026","DOIUrl":"https://doi.org/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":"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":"https://doi.org/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":"https://doi.org/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}

{"title":"AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.","authors":"Saleh I Alzahrani, Wael M S Yafooz, Ibrahim A Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh","doi":"10.3934/mbe.2025021","DOIUrl":"https://doi.org/10.3934/mbe.2025021","url":null,"abstract":"<p><p>In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation between COVID-19 and other infectious diseases. We explored the use of AI-powered predictive models and classifiers to enhance healthcare preparedness by forecasting respiratory disease trends using COVID-19 data. We developed mathematical models based on autoregressive (AR), moving average (MA), ARMA, and machine and deep learning algorithms to predict daily confirmed deaths. Statistical models were trained on 80% of the data and tested on the remaining 20%, with predicted results compared to actual values. The ARMA model demonstrated promising performance. Additionally, eight machine learning (ML) classifiers and deep learning (DL) models were utilized to identify COVID-19 severity indicators. Among the ML classifiers, the Decision Tree (DT) achieved the highest accuracy at 74.70%, followed closely by Random Forest (RF) at 74.66%. DL models showed comparable accuracy scores, around 70%. In terms of AUC-ROC, the kernel Support Vector Machine (SVM) outperformed others, achieving 71% accuracy, with precision, recall, F-measure, and area under the curve values of 0.7, 0.75, 0.59, and 0.72, respectively. These findings underscore the potential of AI-driven health analysis to optimize resource allocation and enhance forecasting for respiratory diseases.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"554-584"},"PeriodicalIF":2.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626607","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":"Uncertainty CNNs: A path to enhanced medical image classification performance.","authors":"Vasileios E Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras","doi":"10.3934/mbe.2025020","DOIUrl":"https://doi.org/10.3934/mbe.2025020","url":null,"abstract":"<p><p>The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient modeling techniques and enhanced data storage capabilities. However, methodologies that account for the uncertainty of predictions remain relatively uncommon in medical imaging. Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. In this paper, we introduce a low-complexity uncertainty-based CNN architecture for medical image classification, particularly focused on tumor and heart failure (HF) detection. The model's predictive (aleatoric) uncertainty is quantified through a test-set augmentation technique, which generates multiple surrogates of each test image. This process enables the construction of empirical distributions for each image, which allows for the calculation of mean estimates and credible intervals. Importantly, this methodology not only provides UQ, but also significantly improves the model's classification performance. This paper represents the first effort to demonstrate that test-set augmentation can significantly improve the classification performance of medical images. The proposed DL model was evaluated using three datasets: (a) brain magnetic resonance imaging (MRI), (b) lung computed tomography (CT) scans, and (c) cardiac MRI. The low-complexity design of the model enhances its robustness against overfitting, while it is also easily re-trainable in case out-of-distribution data is encountered, due to the reduced computational resources required by the introduced architecture.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"528-553"},"PeriodicalIF":2.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626613","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":"A within-host model on the interaction dynamics between innate immune cells and Mycobacterium tuberculosis.","authors":"Eduardo Ibargüen-Mondragón, M Victoria Otero-Espinar, Miller Cerón Gómez","doi":"10.3934/mbe.2025019","DOIUrl":"https://doi.org/10.3934/mbe.2025019","url":null,"abstract":"<p><p>Tuberculosis is the leading cause of death worldwide from a single infectious agent; it has also been declared a threat to humanity by the World Health Organization. New insights indicate that the innate immune response plays a crucial role in determining the outcome of the infection. In this study, we assessed the role of macrophages in the innate immune response through a simple mathematical model. Our results confirm that macrophages provide the primary protective response against <i>Mycobacterium tuberculosis</i>. However, they also highlight the importance of other innate cells in the outcome of infection. Specifically, our findings suggest that, in addition to macrophage activity, the involvement of other innate immune cells is essential for eliminating or controlling bacterial progression, ultimately leading to an adaptive immune response.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 3","pages":"511-527"},"PeriodicalIF":2.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626606","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":"Mathematical modelling of the dynamics of typhoid fever and two modes of treatment in a Health District in Cameroon.","authors":"Thierry Jimy Tsafack, Cletus Kwa Kum, Arsène Jaurès Ouemba Tassé, Berge Tsanou","doi":"10.3934/mbe.2025018","DOIUrl":"https://doi.org/10.3934/mbe.2025018","url":null,"abstract":"<p><p>In this paper, we propose a novel mathematical model for indirectly transmitted typhoid fever disease that incorporates the use of modern and traditional medicines as modes of treatment. Theoretically, we provide two Lyapunov functions to prove the global asymptotic stability of the disease-free equilibrium (DFE) and the endemic equilibrium (EE) when the basic reproduction number $ (mathcal{R}_0) $ is less than one and greater than one, respectively. The model is calibrated using the number of cumulative cases reported in the Penka-Michel health district in Cameroon. The parameter estimates thus obtained give a value of $ mathcal{R}_0 $ = 1.2058 > 1, which indicates that the disease is endemic in the region. The forecast of the outbreak up to November 2026 suggests that the number of cases will be 21,270, which calls for urgent attention on this endemic disease. A sensitivity analysis with respect to the basic reproduction number is conducted, and the main parameters that impact the widespread of the disease are determined. The analysis highlights that the environmental transmission rate $ beta $ and the decay rate $ mu_b $ of the bacteria in the environment are the most influential parameters for $ mathcal{R}_0 $. This underscores the urgent need for potable water and adequate sanitation within this area to reduce the spread of the disease. Numerically, we illustrate the usefulness of recourse to any mode of treatment to lessen the number of infected cases and the necessity of switching from modern treatment to the traditional treatment, a useful adjuvant therapy. Conversely, we show that the relapse phenomenon increases the burden of the disease. Hence adopting a synergistic therapy approach will significantly mitigate typhoid disease cases and overcome the cycle of poverty within the afflicted communities.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 2","pages":"477-510"},"PeriodicalIF":2.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626561","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":"Computational modeling of peritoneal dialysis: An overview.","authors":"Sangita Swapnasrita, Joost C de Vries, Carl M Öberg, Aurélie Mf Carlier, Karin Gf Gerritsen","doi":"10.3934/mbe.2025017","DOIUrl":"https://doi.org/10.3934/mbe.2025017","url":null,"abstract":"<p><p>Peritoneal dialysis (PD) is a kidney replacement therapy for patients with end-stage renal disease. It is becoming more popular as a result of a rising interest in home dialysis. Its effectiveness depends on several physiological and technical factors, which have led to the development of various computational models to better understand and predict PD outcomes. In this review, we traced the evolution of computational PD models, discussed the principles underlying these models, including the transport kinetics of solutes, the fluid dynamics within the peritoneal cavity, and the peritoneal membrane properties, and reviewed the various PD models that can be used to optimize and personalize PD treatment. By providing a comprehensive overview, we aim to guide both current clinical practice and future research into novel PD techniques such as the application of continuous flow and sorbent-based dialysate regeneration where mathematical modeling may offer an inexpensive and effective tool to optimize design of these novel techniques at a patient specific level.</p>","PeriodicalId":49870,"journal":{"name":"Mathematical Biosciences and Engineering","volume":"22 2","pages":"431-476"},"PeriodicalIF":2.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626552","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}