arXiv subjects

James P. Hague

Publications and source records attributed to James P. Hague.

12 recordsLinked to original sources

arXiv2026

Quantum Monte Carlo study of low-dimensional Fermi fluids of dipolar atoms

Fermionic cold atoms in optical traps provide viable quantum simulators of correlation effects in electronic systems. For dressed Rydberg atoms in two-dimensional traps with out-of-plane dipole moments, a realistic model of the pairwise interaction is of repulsive dipolar $1/r^3$ form at long range, softened to a constant at short range. This study provides parameterizations of fixed-node diffusion Monte Carlo energy data for ferromagnetic (one-component) and paramagnetic (two-component) two-dimensional homogeneous Fermi fluids of interacting dipolar atoms. We find itinerant ferromagnetism to be unstable within our parameter spaces for dipolar interactions both with and without softening. Our parameterization of the energy as a function of density will enable density functional theory to support experimental studies of inhomogeneous fermionic cold atom systems.

cond-mat.quant-gas↗

arXiv2025

Brain pulsations enhance cerebrospinal fluid flow in perivascular spaces

A novel approach is adopted to model cerebrospinal fluid (CSF) flow in human perivascular spaces (PVSs) surrounding brain-penetrating arteries. It is proposed that the outer PVS boundary oscillates due to brain pulsations and the arterial wall motion is driven by a blood pressure wave. Lubrication theory is employed to derive a mathematical model for the CSF flow, which is then solved numerically. A parametric analysis is undertaken to investigate the effect of the brain pulsations, which shows that pulsations magnify the net axial CSF flows created by the arterial wall motion. The findings suggest that net axial CSF flows are almost entirely positive (deeper into the brain), with arterial wall motion highly dependent on PVS-penetrating artery configurations. Given the glymphatic hypothesis, the findings support the clinical practice of treating dilated PVS as indicators of an increased likelihood of neurodegenerative conditions, such as dementia.

physics.flu-dyn↗

arXiv2025

Designing cultured tissue moulds using evolutionary strategies

There is an unmet need for artificial intelligence techniques that can speed up the design of growth strategies for cultured tissues. Cultured tissue is increasingly important for a range of applications such as cultivated meat, pharmaceutical assays and regenerative medicine. In this paper, we introduce a method based around evolutionary strategies, machine learning and biophysical simulations that can be used to speed up the process of identifying new tissue growth strategies for these diverse applications. We demonstrate the method by designing tethering strategies to grow tissues containing various cell types with desirable properties such as high cellular alignment and uniform density.

physics.bio-ph↗

arXiv2025

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models

We present a machine learning approach for predicting the organisation of corneal, glial and fibroblast cells in 3D cultures used for tissue engineering. Our machine-learning-based method uses a powerful generative adversarial network architecture called pix2pix, which we train using results from biophysical contractile network dipole orientation (CONDOR) simulations. In the following, we refer to the machine learning method as the RAPTOR (RApid Prediction of Tissue ORganisation) approach. A training data set containing a range of CONDOR simulations is created, covering a range of underlying model parameters. Validation of the trained neural network is carried out by comparing predictions with cultured glial, corneal, and fibroblast tissues, with good agreements for both CONDOR and RAPTOR approaches. An approach is developed to determine CONDOR model parameters for specific tissues using a fit to tissue properties. RAPTOR outputs a variety of tissue properties, including cell densities of cell alignments and tension. Since it is fast, it could be valuable for the design of tethered moulds for tissue growth.

physics.bio-ph↗

arXiv2023

Simultaneous self-organization of arterial and venous networks driven by the physics of global power optimization

Understanding of vascular organization is a long-standing problem in quantitative biology and biophysics and is essential for the growth of large cultured tissues. Approaches are needed that (1) make predictions of optimal arteriovenous networks in order to understand the natural vasculatures that originate from evolution (2) can design vasculature for 3D printing of cultured tissues, meats, organoids and organs. I present a method for determining the globally optimal structure of interlocking arterial and venous (arteriovenous) networks. The core physics is comprised of the minimization of total power associated with the whole vascular network, with penalties to stop arterial and venous segments from intersecting. Specifically, the power needed for Poiseuille flow through vessels and the metabolic power cost for blood maintenance are optimized. Simultaneous determination of both arterial and venous vasculatures is essential to avoid intersections between vessels that would bypass the capillary network. As proof-of-concept, I examine the optimal vascular structure for supplying square- and disk-like tissue shapes that would be suitable for bioprinting in multi-well plates. Features in the trees are driven by the bifurcation exponent and metabolic constant which affect whether arteries and veins follow the same or different routes through the tissue. They also affect the level of tortuosity in the vessels. The method could be used to understand the distribution of blood vessels within organs, to form the core of simulations, and combined with 3D printing to generate vasculatures for arbitrary volumes of cultured tissue and cultured meat.

q-bio.TO↗

arXiv2023

High-throughput design of cultured tissue moulds using a biophysical model

The technique presented here identifies tethered mould designs, optimised for growing cultured tissue with very highly-aligned cells. It is based on a microscopic biophysical model for polarised cellular hydrogels. There is an unmet need for tools to assist mould and scaffold designs for the growth of cultured tissues with bespoke cell organisations, that can be used in applications such as regenerative medicine, drug screening and cultured meat. High-throughput biophysical calculations were made for a wide variety of computer-generated moulds, with cell-matrix interactions and tissue-scale forces simulated using a contractile-network dipole-orientation model. Elongated moulds with central broadening and one of the following tethering strategies are found to lead to highly-aligned cells: (1) tethers placed within the bilateral protrusions resulting from an indentation on the short edge, to guide alignment (2) tethers placed within a single vertex to shrink the available space for misalignment. As such, proof-of-concept has been shown for mould and tethered scaffold design based on a recently developed biophysical model. The approach is applicable to a broad range of cell types that align in tissues and is extensible for 3D scaffolds.

physics.bio-ph↗

arXiv2023

Rapid prediction of lab-grown tissue properties using deep learning

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set with $N=6500$ cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation (CONDOR) model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of the \texttt{pix2pix} deep learning model, reserving $740$ cases that were unseen in the training of the neural network for training and validation. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.

q-bio.TO↗

arXiv2022

Three dimensional simulations of embolic stroke: clinical comparisons and an equation for sizing emboli from imaging

There is a need to develop Monte Carlo simulations of stroke to run in-silico trials to replace animal models, explore clinical scenarios to develop hypotheses for clinical studies and for interpreting clinical monitoring. We perform three-dimensional (3D) stroke simulations, carrying out in-silico trials to relate lesion volume to embolus diameter and calculate probabilistic lesion overlap maps, building on our previous Monte Carlo method. Simulated emboli were released into a 3D in silico vasculature, supplying gray and white matter brain volumes, to generate individual lesion estimates and probabilistic lesion overlap maps. Computer generated lesions were assessed by clinicians and compared with real world radiological images. Simulations of large single emboli reproduced similar middle cerebral artery (MCA), posterior cerebral artery (PCA) and anterior cerebral artery (ACA) lesions to those observed clinically. A proof-of-concept in-silico trial led to a conjecture relating estimated infarct volume as a percentage of total brain volume to relative embolus diameter: $\mathrm{relative diameter} = [\% \mathrm{infarct volume} / a]^{1/b}$, where $a= 104.2 \pm 0.98$, $b=3.380 \pm 0.030$. Probabilistic lesion overlap maps were created, confirming the MCA territory as the most probable resting place of emboli in the computational vasculature, followed by the PCA then ACA. The article shows proof of concept for developing a 3D stroke model from an automatically constructed vasculature.

physics.med-ph↗

arXiv2020

The role of vascular complexity on optimal junction exponents

We examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization (SALVO) algorithm, which we have previously used to reproduce features of cardiac and cerebral vasculatures. Fundamental biophysical understanding of complex vascular structure has applications to modelling of cardiovascular diseases, and for improved representations of vasculatures in large artificial tissues. In order to progress in-silico methods for growing arterial networks, we need to understand the stability of computational arterial growth algorithms to complexity, variations in physiological parameters such as tissue demand, and underlying assumptions regarding the value of junction exponents. We determine the globally optimal structure of two-dimensional arterial trees; analysing sensitivity of tree morphology and optimal bifurcation exponent to physiological parameters. We find that, for physiologically relevant simulation parameters, arterial structure is stable, whereas optimal junction exponents vary. We conclude that the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. These results are important for establishing that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while identifying that optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are sensitive to complexity and the boundary conditions dictated by organs.

q-bio.TO↗

arXiv2018

Development of a globally optimised model of the cerebral arteries

The cerebral arteries are difficult to reproduce from first principles, featuring interwoven territories, and intricate layers of grey and white matter with differing metabolic demand. The aim of this study was to identify the ideal configuration of arteries required to sustain an entire brain hemisphere based on minimisation of the energy required to supply the tissue. The 3D distribution of grey and white matter within a healthy human brain was first segmented from Magnetic Resonance Images. A novel simulated annealing algorithm was then applied to determine the optimal configuration of arteries required to supply brain tissue. The model is validated through comparison of this ideal, entirely optimised, brain vasculature with the known structure of real arteries. This establishes that the human cerebral vasculature is highly optimised; closely resembling the most energy efficient arrangement of vessels. In addition to local adherence to fluid dynamics optimisation principles, the optimised vasculature reproduces global brain perfusion territories with well defined boundaries between anterior, middle and posterior regions. This validated brain vascular model and algorithm can be used for patient-specific modelling of stroke and cerebral haemodynamics, identification of sub-optimal conditions associated with vascular disease, and optimising vascular structures for tissue engineering and artificial organ design.

physics.med-ph↗

arXiv2016

Simulated annealing approach to vascular structure with application to the coronary arteries

Does the complex processes of angiogenesis during organism development ultimately lead to a near optimal coronary vasculature in the organs of adult mammals? We examine this hypothesis using a powerful and universal method, built on physical and physiological principles, for the determination of globally energetically optimal arterial trees. The method is based on simulated annealing, and can be used to examine arteries in hollow organs with arbitrary tissue geometries. We demonstrate that the approach can generate in-silico vasculatures which closely match porcine anatomical data for the coronary arteries on all length scales, and that the optimised arterial trees improve systematically as computational time increases. The method presented here is general, and could in principle be used to examine the arteries of other organs. Potential applications include improvement of medical imaging analysis and the design of vascular trees for artificial organs.

physics.bio-ph↗

arXiv2008

Rise of the centrist: from binary to continuous opinion dynamics

We propose a model that extends the binary ``united we stand, divided we fall'' opinion dynamics of Sznajd-Weron to handle continuous and multi-state discrete opinions. Disagreement dynamics are often ignored in continuous extensions of the binary rules, so we make the most symmetric continuum extension of the binary model that can treat the consequences of agreement (debate) and disagreement (confrontation) within a population of agents. We use the continuum extension as an opportunity to develop rules for persistence of opinion (memory). Rules governing the propagation of centrist views are also examined. Monte Carlo simulations are carried out. We find that both memory effects and the type of centrist significantly modify the variance of average opinions in the large timescale limits of the models. Finally, we describe the limit of applicability for Sznajd-Weron's model of binary opinions as the continuum limit is approached. By comparing Monte Carlo results and long time-step limits, we find that the opinion dynamics of binary models are significantly different to those where agents are permitted more than 3 opinions.

physics.soc-ph↗