All Outputs (1645)

Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport (2023)
Journal Article
Bellows, S., Janes, G., Avitabile, D., King, J. R., Bishopp, A., & Farcot, E. (2023). Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport. PLoS Computational Biology, 19(11), Article e1011646. https://doi.org/10.1371/journal.pcbi.1011646

Auxin is a well-studied plant hormone, the spatial distribution of which remains incompletely understood. Here, we investigate the effects of cell growth and divisions on the dynamics of auxin patterning, using a combination of mathematical modelling... Read More about Fluctuations in auxin levels depend upon synchronicity of cell divisions in a one-dimensional model of auxin transport.

Neural variance reduction for stochastic differential equations (2023)
Journal Article
Hinds, P., & Tretyakov, M. (2023). Neural variance reduction for stochastic differential equations. Journal of Computational Finance, 27(3), 1-41. https://doi.org/10.21314/JCF.2023.010

Variance reduction techniques are of crucial importance for the efficiency of Monte Carlo simulations in finance applications. We propose the use of neural SDEs, with control variates parameterized by neural networks, in order to learn approximately... Read More about Neural variance reduction for stochastic differential equations.

Seasonality as a driver of pH1N12009 influenza vaccination campaign impact (2023)
Journal Article
Bolton, K. J., McCaw, J. M., Dafilis, M. P., McVernon, J., & Heffernan, J. M. (2023). Seasonality as a driver of pH1N12009 influenza vaccination campaign impact. Epidemics, 45, Article 100730. https://doi.org/10.1016/j.epidem.2023.100730

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza... Read More about Seasonality as a driver of pH1N12009 influenza vaccination campaign impact.

Closed form expressions for the Green’s function of a quantum graph – a scattering approach (2023)
Journal Article
Lawrie, T., Gnutzmann, S., & Tanner, G. K. (2023). Closed form expressions for the Green’s function of a quantum graph – a scattering approach. Journal of Physics A: Mathematical and Theoretical, 56(47), Article 475202. https://doi.org/10.1088/1751-8121/ad03a5

In this work we present a three step procedure for generating a closed form expression of the Green's function on both closed and open finite quantum graphs with general self-adjoint matching conditions. We first generalize and simplify the approach... Read More about Closed form expressions for the Green’s function of a quantum graph – a scattering approach.

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics (2023)
Journal Article
Shuttleworth, J. G., Lei, C. L., Whittaker, D. G., Windley, M. J., Hill, A. P., Preston, S. P., & Mirams, G. R. (2024). Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics. Bulletin of Mathematical Biology, 86(1), Article 2. https://doi.org/10.1007/s11538-023-01224-6

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological system... Read More about Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics.

The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk (2023)
Journal Article
Lei, C. L., Whittaker, D. G., & Mirams, G. R. (2024). The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk. British Journal of Pharmacology, 181(7), 987-1004. https://doi.org/10.1111/bph.16250

Background and Purpose Drug-induced reduction of the rapid delayed rectifier potassium current carried by the human Ether-à-go-go-Related Gene (hERG) channel is associated with increased risk of arrhythmias. Recent updates to drug safety regulatory... Read More about The impact of uncertainty in hERG binding mechanism on in silico predictions of drug-induced proarrhythmic risk.

Efficient and Scalable Inverse Kinematics for Continuum Robots (2023)
Journal Article
Wild, S., Zeng, T., Mohammad, A., Billingham, J., Axinte, D., & Dong, X. (2024). Efficient and Scalable Inverse Kinematics for Continuum Robots. IEEE Robotics and Automation Letters, 9(1), 375 - 381. https://doi.org/10.1109/lra.2023.3331291

With their flexible nature, continuum robots offer hyper-redundancy regarding their workspace; their backbone can take many shapes upon a single tip position and orientation. Deciphering which backbone shape to use under certain conditions is crucial... Read More about Efficient and Scalable Inverse Kinematics for Continuum Robots.

The impact of household structure on disease-induced herd immunity (2023)
Journal Article
Ball, F., Critcher, L., Neal, P., & Sirl, D. (2023). The impact of household structure on disease-induced herd immunity. Journal of Mathematical Biology, 87(6), Article 83. https://doi.org/10.1007/s00285-023-02010-7

The disease-induced herd immunity level hD is the fraction of the population that must be infected by an epidemic to ensure that a new epidemic among the remaining susceptible population is not supercritical. For a homogeneously mixing population hD... Read More about The impact of household structure on disease-induced herd immunity.

General upper bounds on fluctuations of trajectory observables (2023)
Journal Article
Bakewell-Smith, G., Girotti, F., Guţǎ, M., & Garrahan, J. P. (2023). General upper bounds on fluctuations of trajectory observables. Physical Review Letters, 131(19), Article 197101. https://doi.org/10.1103/PhysRevLett.131.197101

Thermodynamic uncertainty relations (TURs) are general lower bounds on the size of fluctuations of dynamical observables. They have important consequences, one being that the precision of estimation of a current is limited by the amount of entropy pr... Read More about General upper bounds on fluctuations of trajectory observables.

The Need for a Symbiotic Interface for a Digital Twin (2023)
Conference Proceeding
Palmer, C., Goh, Y. M., Hubbard, E., Grant, R., & Houghton, R. (2023). The Need for a Symbiotic Interface for a Digital Twin. In Leveraging transdisciplinary engineering in a changing and connected world : proceedings of the 30th ISTE international conference on transdisciplinary engineering (873 - 882). https://doi.org/10.3233/ATDE230685

Human interaction with a Digital Twin is an emerging concept for which there are no common definitions. This paper considers the various types of human interaction with Digital Twins. There is very little research considering human cognitive interact... Read More about The Need for a Symbiotic Interface for a Digital Twin.

Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation (2023)
Journal Article
Rutjens, R. J., Band, L. R., Jones, M. D., & Owen, M. R. (2023). Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation. PLoS ONE, 18(10), Article e0293344. https://doi.org/10.1371/journal.pone.0293344

The Elementary Effects method is a global sensitivity analysis approach for identifying (un)important parameters in a model. However, it has almost exclusively been used where inputs are dimensionless and take values on [0, 1]. Here, we consider mode... Read More about Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation.

Analogues of the Bol operator for half-integral weight weakly holomorphic modular forms (2023)
Journal Article
Diamantis, N., Lee, M., & Rolen, L. (2023). Analogues of the Bol operator for half-integral weight weakly holomorphic modular forms. Proceedings of the American Mathematical Society, 152, 37-51. https://doi.org/10.1090/proc/16435

We define an analogue of the Bol operator on spaces of weakly holomorphic modular forms of half-integral weight. We establish its main properties and relation with other objects.

Normal Families and Quasiregular Mappings (2023)
Journal Article
Fletcher, A. N., & Nicks, D. A. (2024). Normal Families and Quasiregular Mappings. Proceedings of the Edinburgh Mathematical Society, 67(1), 79-112. https://doi.org/10.1017/s0013091523000640

Beardon and Minda gave a characterization of normal families of holomorphic and meromorphic functions in terms of a locally uniform Lipschitz condition. Here, we generalize this viewpoint to families of mappings in higher dimensions that are locally... Read More about Normal Families and Quasiregular Mappings.

Modelling how plant cell-cycle progression leads to cell size regulation (2023)
Journal Article
Williamson, D., Tasker-Brown, W., Murray, J., Jones, A. R., & Band, L. R. (2023). Modelling how plant cell-cycle progression leads to cell size regulation. PLoS Computational Biology, 19(10), Article e1011503. https://doi.org/10.1371/journal.pcbi.1011503

Populations of cells typically maintain a consistent size, despite cell division rarely being precisely symmetrical. Therefore, cells must possess a mechanism of “size control”, whereby the cell volume at birth affects cell-cycle progression. While s... Read More about Modelling how plant cell-cycle progression leads to cell size regulation.

A Post-Quantum Associative Memory (2023)
Journal Article
Lami, L., Goldwater, D., & Adesso, G. (2023). A Post-Quantum Associative Memory. Journal of Physics A: Mathematical and Theoretical, 56(45), Article 455304. https://doi.org/10.1088/1751-8121/acfeb7

Associative memories are devices storing information that can be fully retrieved given partial disclosure of it. We examine a toy model of associative memory and the ultimate limitations it is subjected to within the framework of general probabilisti... Read More about A Post-Quantum Associative Memory.

Accelerating Bayesian inference for stochastic epidemic models using incidence data (2023)
Journal Article
Golightly, A., Wadkin, L. E., Whitaker, S. A., Baggaley, A. W., Parker, N. G., & Kypraios, T. (2023). Accelerating Bayesian inference for stochastic epidemic models using incidence data. Statistics and Computing, 33(6), Article 134. https://doi.org/10.1007/s11222-023-10311-6

We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either the number of new infections or removals in time intervals of fixed len... Read More about Accelerating Bayesian inference for stochastic epidemic models using incidence data.

Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing (2023)
Journal Article
Su, H., Tretyakov, M. V., & Newton, D. P. (in press). Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing. Management Science,

Transition probability density functions (TPDFs) are fundamental to computational finance, including option pricing and hedging. Advancing recent work in deep learning, we develop novel neural TPDF generators through solving backward Kolmogorov equat... Read More about Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing.

Tumor radiogenomics in gliomas with Bayesian layered variable selection (2023)
Journal Article
Mohammed, S., Kurtek, S., Bharath, K., Rao, A., & Baladandayuthapani, V. (2023). Tumor radiogenomics in gliomas with Bayesian layered variable selection. Medical Image Analysis, 90, Article 102964. https://doi.org/10.1016/j.media.2023.102964

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor... Read More about Tumor radiogenomics in gliomas with Bayesian layered variable selection.

Probabilistic Learning of Treatment Trees in Cancer (2023)
Journal Article
Yao, T., Wu, Z., Bharath, K., Li, J., & Baladandayuthapani, V. (2023). Probabilistic Learning of Treatment Trees in Cancer. Annals of Applied Statistics, 17(3), 1884-1908. https://doi.org/10.1214/22-AOAS1696

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems, such as patient-derived xeno... Read More about Probabilistic Learning of Treatment Trees in Cancer.

Machine learning detects terminal singularities (2023)
Conference Proceeding
Kasprzyk, A., Coates, T., & Veneziale, S. (in press). Machine learning detects terminal singularities. In Advances in Neural Information Processing Systems (NeurIPS 2023)

Algebraic varieties are the geometric shapes defined by systems of polynomial equations; they are ubiquitous across mathematics and science. Amongst these algebraic varieties are Q-Fano varieties: positively curved shapes which have Q-factorial termi... Read More about Machine learning detects terminal singularities.