Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences最新文献

Addressing model discrepancy in a clinical model of the oxygen dissociation curve. 解决氧解离曲线临床模型中的模型差异。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0213

Gevik Grigorian, Victoria Volodina, Samiran Ray, Francisco Alejandro DiazDelao, Claire Black

{"title":"Addressing model discrepancy in a clinical model of the oxygen dissociation curve.","authors":"Gevik Grigorian, Victoria Volodina, Samiran Ray, Francisco Alejandro DiazDelao, Claire Black","doi":"10.1098/rsta.2024.0213","DOIUrl":"10.1098/rsta.2024.0213","url":null,"abstract":"Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we consider methods for addressing this issue. In the first approach, a mathematical model is treated as a black box system, and model discrepancy is defined as an independent and additive term that accounts for the difference between the physical phenomena and the model representation. A Gaussian Process (GP) is commonly used to capture the model discrepancy. An alternative approach is to construct a hybrid grey box model by filling in the incomplete parts of the mathematical model with a neural network. The neural network is used to learn the missing processes by comparing the observations with the model output. To enhance interpretability, the outputs of this non-parametric model can then be regressed into a symbolic form to obtain the learned model. We compare and discuss the effectiveness of these approaches in handling model discrepancy using clinical data from the ICU and the Siggaard-Andersen oxygen status algorithm.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240213"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fully personalized modelling of Duchenne Muscular Dystrophy ambulation. 杜氏肌营养不良症的完全个性化建模。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0218

Victor Applebaum, Evan Baker, Thomas Kim, Georgia Stimpson, Peter Challenor, Kyle Carlton Abesser Wedgwood, Matthew Anderson, Ian Bamsey, Giovanni Baranello, Adnan Manzur, Francesco Muntoni, Krasimira Tsaneva-Atanasova

{"title":"Fully personalized modelling of Duchenne Muscular Dystrophy ambulation.","authors":"Victor Applebaum, Evan Baker, Thomas Kim, Georgia Stimpson, Peter Challenor, Kyle Carlton Abesser Wedgwood, Matthew Anderson, Ian Bamsey, Giovanni Baranello, Adnan Manzur, Francesco Muntoni, Krasimira Tsaneva-Atanasova","doi":"10.1098/rsta.2024.0218","DOIUrl":"https://doi.org/10.1098/rsta.2024.0218","url":null,"abstract":"Duchenne Muscular Dystrophy is a progressive neuromuscular disorder characterized by the gradual weakening and deterioration of muscles, leading to loss of ambulation in affected individuals. This decline in mobility can be effectively assessed using the North Star Ambulatory Assessment (NSAA) scores, along with measures such as the 10-m walk time and the time taken to rise from the floor. We propose a dynamic linear model to predict the trajectories of these clinical outcomes, with a primary focus on NSAA scores. Our model aims to assist clinicians in forecasting the progression of the disease, thereby enabling more informed and personalized treatment plans for their patients. We also evaluate the effectiveness of our models in generating synthetic NSAA score datasets. We assess the performance of our modelling approach and compare the results with those of a previous study. We show that the most robust model demonstrates narrower prediction intervals and improved quantile coverage, indicating superior predictive accuracy and reliability.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240218"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models. 集总参数血流动力学模型的平摊推理和可辨识性分析。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0215

Guoxiang Grayson Tong, Carlos A Sing-Long, Daniele E Schiavazzi

{"title":"InVAErt networks for amortized inference and identifiability analysis of lumped-parameter haemodynamic models.","authors":"Guoxiang Grayson Tong, Carlos A Sing-Long, Daniele E Schiavazzi","doi":"10.1098/rsta.2024.0215","DOIUrl":"https://doi.org/10.1098/rsta.2024.0215","url":null,"abstract":"Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to a common output, while practical non-identifiability can result due to limited data, model misspecification or noise corruption. To address the resulting ill-posed inverse problem, optimization-based or Bayesian inference approaches typically use regularization, thereby limiting the possibility of discovering multiple solutions. In this study, we use inVAErt networks, a neural network-based, data-driven framework for enhanced digital twin analysis of stiff dynamical systems. We demonstrate the flexibility and effectiveness of inVAErt networks in the context of physiological inversion of a six-compartment lumped-parameter haemodynamic model from synthetic data to real data with missing components.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240215"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analysis of spatial and temporal uncertainty propagation in agent-based models. 基于智能体模型的时空不确定性传播分析。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0229

Yahya Gamal, Alison Heppenstall, William Strachan, Ricardo Colasanti, Kashif Zia

{"title":"An analysis of spatial and temporal uncertainty propagation in agent-based models.","authors":"Yahya Gamal, Alison Heppenstall, William Strachan, Ricardo Colasanti, Kashif Zia","doi":"10.1098/rsta.2024.0229","DOIUrl":"https://doi.org/10.1098/rsta.2024.0229","url":null,"abstract":"Spatially explicit simulations of complex systems lead to inherent uncertainties in spatial outcomes. Visualizing the temporal propagation of spatial uncertainties is crucial to communicate the reliability of such models. However, the current Uncertainty Analyses (UAs) either consider spatial uncertainty at the end of model runs, or consider non-spatial uncertainties at different model states. To address this, we propose a Spatio-Temporal UA (ST-UA) approach to generate an uncertainty propagation index and visualize the temporal propagation of different uncertainty measures between two temporal model states. We select the total effects sensitivity measure (a Sobol index) for a sample application within the ST-UA approach. The application is the Tobacco Town ABM, a spatial model simulating smoking behaviours. We showcase the effect of the statistical distributions of wages and smoking rates on the propensity to buy cigarettes, which leads to the propagation of uncertainty in the number of purchased cigarettes by individuals. The findings highlight the usefulness of the ST-UA in (i) communicating the reliability of the spatial outcomes of the model; and (ii) guiding modellers towards the spatial areas with relatively high uncertainties at different temporal steps. This approach can be readily transferred to other application areas that are characterized with spatio-temporal uncertainty.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240229"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessment of uncertainty quantification in universal differential equations. 通用微分方程中不确定度量化的评价。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0444

Nina Schmid, David Fernandes Del Pozo, Willem Waegeman, Jan Hasenauer

{"title":"Assessment of uncertainty quantification in universal differential equations.","authors":"Nina Schmid, David Fernandes Del Pozo, Willem Waegeman, Jan Hasenauer","doi":"10.1098/rsta.2024.0444","DOIUrl":"https://doi.org/10.1098/rsta.2024.0444","url":null,"abstract":"Scientific machine learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques to uncover the governing equations of complex processes. Among the available approaches, universal differential equations (UDEs) combine prior knowledge in the form of mechanistic formulations with universal function approximators, such as neural networks. Integral to the efficacy of UDEs is the joint estimation of parameters for both the mechanistic formulations and the universal function approximators using empirical data. However, the robustness and applicability of these resultant models hinge upon the rigorous quantification of uncertainties associated with their parameters and predictive capabilities. In this work, we provide a formalization of uncertainty quantification (UQ) for UDEs and investigate key frequentist and Bayesian methods. By analyzing three synthetic examples of varying complexity, we evaluate the validity and efficiency of ensembles, variational inference and Markov-chain Monte Carlo sampling as epistemic UQ methods for UDEs.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240444"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical accuracy of closed-loop steady state in a zero-dimensional cardiovascular model. 零维心血管模型闭环稳态的数值精度。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0208

Nick van Osta, Gitte van den Acker, Tim van Loon, Theo Arts, Tammo Delhaas, Joost Lumens

{"title":"Numerical accuracy of closed-loop steady state in a zero-dimensional cardiovascular model.","authors":"Nick van Osta, Gitte van den Acker, Tim van Loon, Theo Arts, Tammo Delhaas, Joost Lumens","doi":"10.1098/rsta.2024.0208","DOIUrl":"10.1098/rsta.2024.0208","url":null,"abstract":"Closed-loop cardiovascular models are becoming vital tools in clinical settings, making their accuracy and reliability paramount. While these models rely heavily on steady-state simulations, accuracy because of steady-state convergence is often assumed negligible. Using a reduced-order cardiovascular model created with the CircAdapt framework as a case study, we investigated steady-state convergence behaviour across various integration methods and simulation protocols. To minimize the effect of numerical errors, we first quantified the numerical errors originating from integration methods and model assumptions. We subsequently investigate this steady-state convergence error under two distinct conditions: first without, and then with homeostatic pressure-flow control (PFC), providing a comprehensive assessment of the CircAdapt framework's numerical stability and accuracy. Our results demonstrated that achieving a clinically accurate steady state required 7-15 heartbeats in simulations without regulatory mechanisms. When homeostatic control mechanisms were included to regulate mean arterial pressure and blood volume, more than twice the number of heartbeats was needed. By simulating a variable number of heartbeats tailored to each simulation's characteristics, an efficient balance between computational cost and steady-state accuracy can be achieved. Understanding this balance is crucial as cardiovascular models progress towards clinical use.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240208"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity analysis for exploring the variability and parameter landscape in virtual patient cohorts of multi-vessel coronary artery disease. 探讨多支冠状动脉疾病虚拟患者队列的变异性和参数景观的敏感性分析。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0230

Pjotr Hilhorst, Bregje van de Wouw, Karol Zajac, Marcel van 't Veer, Pim Tonino, Frans van de Vosse, Wouter Huberts

{"title":"Sensitivity analysis for exploring the variability and parameter landscape in virtual patient cohorts of multi-vessel coronary artery disease.","authors":"Pjotr Hilhorst, Bregje van de Wouw, Karol Zajac, Marcel van 't Veer, Pim Tonino, Frans van de Vosse, Wouter Huberts","doi":"10.1098/rsta.2024.0230","DOIUrl":"10.1098/rsta.2024.0230","url":null,"abstract":"Virtual patient cohorts (VPC) are crucial in in silico clinical trials, offering a promising, cost-effective and ethically advantageous alternative to real clinical randomized controlled trials to evaluate the safety and efficacy of clinical decision support tools and medical devices. This article focuses on the role of sensitivity analysis (SA) in evaluating a VPC created through a virtual cohort generator, which includes a one-dimensional pulse wave propagation model of the coronary circulation. Given the inherent limitations of clinical data, a synthetic VPC was generated that captured the global population variability of the fractional flow reserve distribution observed in the FAME study, a real-world randomized clinical trial. The synthetic VPC was created using random parameter variation and filtering with acceptance criteria, possibly inducing correlations between inputs. An SA methodology was employed that is able to account for correlations caused by acceptance criteria to explore the input-output relationship of the VPC and to explain its variability. The severity of the stenosis was found to be a key driver of the variability of the VPC. In general, the proposed SA approach, capable of handling correlated inputs, demonstrates an effective method for evaluating VPCs, providing a robust framework for in silico clinical trial applications.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240230"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic structural discrepancy assessment for computer models. 计算机模型的系统结构差异评估。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0214

Michael Goldstein, Ian Vernon, Jonathan A Cumming

{"title":"Systematic structural discrepancy assessment for computer models.","authors":"Michael Goldstein, Ian Vernon, Jonathan A Cumming","doi":"10.1098/rsta.2024.0214","DOIUrl":"https://doi.org/10.1098/rsta.2024.0214","url":null,"abstract":"Model or structural discrepancy is an essential component in the analysis of computer simulators, representing the differences between the outputs of the simulator and the real-world system that the simulator seeks to represent. This discrepancy can arise from various sources such as simplifications of the model science in the simulator, choices made in our particular implementation of that science, and epistemic uncertainties such as the absence of features or science that we did not know to include or have yet to discover. In this paper, we define and distinguish two types of discrepancy: internal discrepancy that can be assessed by experiments on the simulator itself; and external discrepancy which lies outside the scope of such experiments. We present a tractable methodology and workflow for the assessment of structural discrepancy on the basis of collections of experiments applied to the computer model and illustrate our approach in the context of a simple biological model.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)' .","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240214"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation. 高级别胶质瘤对放化疗反应模型的可识别性和模型选择框架。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0212

Khushi C Hiremath, Kenan Atakishi, Ernesto A B F Lima, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Caroline Chung, Thomas E Yankeelov, David A Hormuth Ii

{"title":"Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.","authors":"Khushi C Hiremath, Kenan Atakishi, Ernesto A B F Lima, Maguy Farhat, Bikash Panthi, Holly Langshaw, Mihir D Shanker, Wasif Talpur, Sara Thrower, Jodi Goldman, Caroline Chung, Thomas E Yankeelov, David A Hormuth Ii","doi":"10.1098/rsta.2024.0212","DOIUrl":"https://doi.org/10.1098/rsta.2024.0212","url":null,"abstract":"We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated the accuracy of predictions using the selected model at four weeks post-chemoradiation. We observed median parameter errors from 0.04% to 72.96%. Our model selection framework selected the model that was used to generate the data in 82% of the cases. Finally, we predicted the growth of the virtual tumours using the selected model resulting in low error at the voxel-level (concordance correlation coefficient (CCC) ranged from 0.66 to 0.99) and global level (percentage error in total tumour cellularity ranged from -12.35% to 0.07%). These results demonstrate the reliability of our framework to identify the most appropriate model under noisy conditions expected in the clinical setting.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240212"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preface to the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'. 主题问题“医疗保健和生物系统的不确定性量化（第2部分）”的序言。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0231

Louise M Kimpton, L Mihaela Paun, Mitchel J Colebank, Victoria Volodina

引用次数: 0