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ERA is a digital repository of original research produced at The University of Edinburgh. The archive contains documents written by, or affiliated with, academic authors, or units, based at Edinburgh that have sufficient quality to be collected and preserved by the Library, but which are not controlled by commercial publishers. Holdings include full-text digital doctoral theses, masters dissertations, project reports, briefing papers and out-of-print materials.

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Understanding the role of ubiquitin signalling in barley to improve crop health
(2026-05-25) Brzezinska, Karolina; Orosa Puente, Beatriz; Spoel, Steven; Biotechnology and Biological Sciences Research Council (BBSRC); School of Biological Sciences, University of Edinburgh: School of Biological Sciences Studentship; Gatsby Charitable Foundation; Royal Society; Wellcome Trust
Barley (Hordeum vulgare) is the second most important crop in the UK and ranks fourth among cereal crops globally. Yet, it is highly vulnerable to diseases, including those caused by fungal pathogens, such as Puccinia hordei, which can lead to yield losses of up to 40%, posing a serious threat to food security. Understanding and enhancing plant immune responses against pests is therefore essential to safeguarding crop productivity, and there is a pressing need for innovative, mechanistically informed strategies. Ubiquitination is a complex protein posttranslational modification, which serves various functions from modulating protein activity, localisation, and stability, to promoting protein degradation. Previous studies showed that it plays a pivotal role in regulating plant immune responses. Most of this work, however, is undertaken in model plant species, and little is known about immune-related ubiquitination in crops. This knowledge gap has hindered efforts to utilise the ubiquitin pathway as a strategy for enhancing pest resistance in crops. In this study, I provide a comprehensive profiling of the barley immune ubiquitome, to uncover molecular mechanisms that could be harnessed to enhance resistance to fungal pathogens. This work has been divided into three chapters. Given the strict regulations on genetically modified or edited crops and the urgent need to reduce pesticide use, alternative strategies to enhance crop resilience are essential. In Chapter 3, I explore the use of arbuscular mycorrhizal fungi (AMF) as tools to prime barley immune responses. These results suggest, that AMF colonisation alters the barley transcriptome and modulates expression of ubiquitin-related genes during P. hordei infection. This suggests that AMF-mediated priming involves the ubiquitin system as part of a broader immune regulatory network. Chapter 4 addresses the dynamic nature of ubiquitin signalling in barley during the immune response. Here, I perform an immune ubiquitome profiling by identifying ubiquitin-regulated proteome during (i) hormone-induced immune responses, (ii) pathogen infection in the lab, and (iii) in field conditions. The data reveal that the barley immune ubiquitome is signal-specific and dynamic, with a clear modulation of protein abundance and turnover. Common pathways and novel ubiquitinated targets have been identified, providing a comprehensive view of how ubiquitin signalling contributes to barley immune responses and offering new candidate proteins for the improvement of crop resistance. E3 ubiquitin ligases are the central specificity determinants of the ubiquitination cascade. Identifying immune-related E3 ligases and their substrates is key to understanding the regulatory nodes within the ubiquitin system. In Chapter 5 I employed a substrate-trapping approach to identify substrates of HvRGLG2, a putative immune-related E3 ligase in barley. The captured targets were implicated in core immune processes, and I demonstrated that their ubiquitination directly modulates immune function at the molecular level. Taken together, these findings highlight two complementary routes to strengthen barley immunity: (i) exploiting beneficial associations with arbuscular mycorrhizal fungi (AMF) as sustainable strategies to enhance crop health, and (ii) targeting ubiquitin-mediated immune regulation to uncover novel resistance mechanisms. The immune ubiquitome generated in this study provides a valuable resource for understanding stress-responsive pathways and identifying new targets for resistance breeding. Furthermore, the substrate-trapping method developed here offers a scalable approach for mapping E3 ligase–substrate interactions in crops. Collectively, this research lays the groundwork for innovative tools to improve crop health and resilience.
Habitability of ammoniacal waters on icy moons and Earth
(2026-05-25) Hopton, Cassie M.; Cockell, Charles; Nienow, Peter; Titmuss, Simon; Brown, Aidan; Natural Environment Research Council (NERC); Natural Environment Research Council (NERC) E4 Doctoral Training Partnership
The search for life has expanded to include the icy moons of Jupiter (Europa, Ganymede, and Callisto) and Saturn’s moons Titan and Enceladus. These moons feature surfaces encrusted in ice ranging from several to hundreds of kilometres thick, beneath which substantial subsurface oceans of liquid water are thought to exist. Liquid water is a prerequisite for life, and thus the habitability prospects of these oceans is speculated. Preservation of this liquid water is thought possible by freezing point depressants, such as ammonia. Indeed, the Cassini-Huygens mission revealed not only active cryovolcanism on Enceladus but also the presence of ammonia. On Earth, ammonia facilitates biotic chemistry at low concentrations and is a common pollutant from agricultural and industrial processes. As a proton acceptor, elevated concentrations of ammonia are known to disrupt biological chemistries. The presence of ammonia in extraterrestrial oceans, as well as terrestrial ecosystems, could therefore constrain the habitability prospects of these environments. In this thesis, I explore whether the presence of ammonia could impact the potential for habitability in icy moon oceans, with additional implications for the habitability of Earth environments. I use growth dynamics and cellular viability assays to establish the growth response and cultivation limits of the extremophile Halomonas meridiana Slthf1 in concentrations of aqueous ammonia relevant to the oceans of Enceladus, Titan, and Europa. Through these approaches, I also examine the growth impacts of indirect ammonia exposure occurring by volatilized ammonia gas. The morphological and physiological changes exerted by ammonia on H. meridiana are additionally examined by transmission electron microscopy and metabolomics. Through this research I show that aqueous ammonia exposure, either as dissolved ammonia gas (NH3) or as a salt (NH4)2SO4), can extend lag phase duration and doubling time, slow growth rate, diminish cell density and reduce cell viability, even in cultures indirectly exposed to NH3 by volatilization. I elucidate that exposure to ammonia can disfigure cell morphology and elevate the occurrence of cell lysis events. I present evidence that ammonia toxicity is distinct from external pH toxicity and could be encouraged by internal and potentially destructive NH3-driven reactions. Toxicity of ammonia may also be driven by modulation to essential nitrogen, carbon, and energy metabolism. Possible survival strategies, such as cell wall remodelling, were indicated by metabolomics. The results demonstrate that at specified molar thresholds, ammonia can impose constraints on growth, viability, and the metabolism of H. meridiana. This data cannot suggest whether icy moons oceans are or have been inhabited but can provide a foundation for which to assess the potential for habitability. The molar concentrations at which the outlined effects occur exceed the putative ammonia concentrations in the oceans of Enceladus and Europa. Based on this evidence, it is plausible dissolved or volatilized ammonia in these environments may not pose as a limiting factor for habitability. For Titan, the ammonia content of the interior ocean ranges to as high as 15%. High accumulations of ammonia from agricultural and industrial sources are also possible on Earth. In the case of these higher concentration thresholds, the results of this research indicate ammonia could constrain the habitability potential of both Titan’s ocean and certain Earth environments. These findings advance the current understanding of bacterial life in ammonia and demonstrate the importance of ammonia concentration when assessing conditions that could support life in extra-terrestrial and terrestrial environments.
Data-driven approach to predicting heterogeneous nucleation in phase change materials
(2026-05-25) Wang, Zixuan; Morrison, Carole; Pulham, Colin
There is a current requirement for technologies that store heat for both domestic and industrial applications. Phase-change materials (PCMs) are an important class of substances with strong potential for heat storage. For practical use, storage systems must withstand repeated melt/freeze cycles while maintaining a stable melting-crystallisation point and consistent heat output. Salt hydrates are attractive candidates on account of their high energy densities, but there are issues associated with its strong tendency to subcool well below its normal freezing point. While the nucleation problem can be readily solved by the addition of seed crystals of another material, there are a lot of problems that can be encountered that result in nucleator deactivation. Therefore, the problem of identifying suitable heterogeneous nucleating crystallites (NUCs) for PCMs under variable temperature conditions remains a challenging task. In this regard, in silico screening methods offers a practical solution to both problems. Through a data driven approach, a workflow is generated by learning from existing experimental reports of working PCM/NUC pairs, in the light of searching for other NUC candidates that may offer improved properties over the additives that are currently used. The focus of this research is therefore to demonstrate the feasibility of a data-driven approach to establish a high-throughput NUC prediction model that could be applied to any given liquid/solid PCM. In Chapter 2, a workflow generation process is described. The workflow is based on a data-driven approach, and a high-throughput workflow is created based on geometric matching under five related features that returns a binary decision of working/non-working NUC for a given PCM. In Chapter 3, the trained model is applied with a most extensively studied PCM, ice-Ih. The model is firstly utilised to evaluate the degree of nucleation effectiveness then compared with already existing experimental reports. Bulk water immersion experiments on a set of ten known nucleators sets a delineating temperature to distinguish between good and poor nucleation behaviour. The algorithm is then used to screen 3,500 simple metal oxides and halides taken from the Inorganic Chemistry Structural Database (ICSD), and show that just 7% of the former and 3% of the latter were predicted to nucleate ice on the basis of geometric slab matching. Subsequent experimental testing of 22 compounds suggests a 64% correct prediction rate, and identifies four new ice nucleators. Inspired by the ice-nucleating efficiency of copper oxides, the copper tubing with local tap water is also tested, and subcooling suppression is observed, most likely due to copper oxide build-up. In Chapter 4, the model is further trained and tested with working/non-working nucleators from readily existing reliable experimental reports and then the trained model is applied in a high-throughput application for sodium acetate trihydrate (SAT), where over 14,000 candidate NUC structures are screened, from which a list of 521 compounds is identified as potential NUCs for SAT. The result reinforces the success of the current industry-standard NUC for SAT, disodium hydrogen-phosphate hydrates (DSP), which is shown to geometrically match slabs of SAT regardless of the level of hydration present. Other PCMs are sought after, i.e. Mg(NO3)2∙6H2O, MgCl2∙6H2O, CaCl2∙6H2O, and LiNO3∙3H2O. The distribution of prediction from working to non-working NUCs for the four PCMs demonstrates mostly the same trend as the confidence range. This meant this model could be readily used for nucleator mining for other PCM materials. In Chapter 5, a supervised machine learning workflow is set up with the goal of predicting effective nucleators for any PCM material based on geometric compatibility between their crystallographic slabs. The algorithm is established by learning from highly granular geometric data generated from ice nucleation in Chapter 3 and salt hydrates in Chapter 4, and this approach avoids manually tuning thresholds and instead lets the model discover which geometric criteria (and value ranges) are statistically associated with successful nucleation. The results show prominent prediction power, i.e. success rate on both ice and salt hydrates, and further data analysis showed equal contributions as well as independence of the five features, proving the comprehensiveness of the algorithm. The impact of this research as well as future works are discussed in Chapter 6.
Modelling animal movement for behavioural inference
(The University of Edinburgh, 2026-05-25) Akeresola, Rebecca Ayodeji; Arregui, Victor Elvira; King, Ruth; Butler, Adam; White, Andy; Engineering and Physical Sciences Research Council (EPSRC); Maxwell Institute Graduate School (MAC-MIGS)
Effective animal conservation is more important than ever in the face of biodiversity and climate crises. Understanding animal movement and behaviour can aid spatial planning and inform conservation management. However, it is difficult to directly observe and record behaviours in remote and hostile terrain such as the marine environment. Researchers often attempt to infer animal behaviour from telemetry data using hidden Markov models (HMMs), as animal movement patterns are influenced by their underlying behaviour. However, these inferred behaviours are not typically validated due to difficulty in obtaining ‘ground truth’ behavioural information. In practice, ecological researchers often use the inferred behaviours to suggest practical ways of improving conservation efforts. This includes identifying and designating important foraging areas as Marine Protected Areas (MPAs), spatial planning, and construction of offshore wind farms. Additionally, since some of these animals, particularly those in the marine environment, receive less protection at sea, it is crucial that the inferred behaviors are actually identified in the right areas. Furthermore, large-scale animal telemetry datasets are readily available due to advances in biologging technologies, and studies on animal movement and behaviour have been on the increase as a result. The use of these telemetry datasets for statistical modelling can be computationally costly due to the large volume and high-resolution of the datasets. To easily fit these models, the need to reduce the number of data points prior to analysis often arises. In the process of reducing the number of data points, some of the information embedded in the original dataset that may be useful for analysis can be lost. In the first part of this thesis, we have a dataset obtained from the visual tracking method that provides a ground truth behavioural dataset of terns and the boat GPS track as a proxy for bird tracks. Leveraging on this unique data, we assess whether (i) the visual tracking information from the boat is a good proxy for true bird locations in relation to inferred behaviours of the fitted HMM, and (ii) the inferred behaviours from HMMs fitted to visual tracking data accurately represent true behaviours as identified by behavioural observations taken from the boat. We demonstrate that visual tracking data can be regarded as a good proxy for true movement data in terms of similarity in inferred behaviours. In the second validation, we assess the validity of HMMs-inferred behaviour by fitting HMMs to boat visual tracks, inferring behaviours from fitted models, and assessing inferred behaviour using ground-truth observed behavioural data. Our results suggest that HMMs fitted to tracking data can accurately identify important conservation-relevant behaviours in seabird species, as demonstrated using visual tracking data. In the second part of this thesis, we examine a more efficient way of subsampling animal telemetry data in addition to the standard thinning approach. We adopt the Nyquist-Shannon sampling theorem (NSST) to inform the choice of subsampling frequency. While NSST can be used to inform the temporal resolutions for obtaining animal movement trajectories, it also describes how to sub-sample a discrete-time signal (animal movement data) in a way that there is little or no information loss about the underlying latent behavioural process. We explain how to adopt NSST in animal movement modelling to inform the choice of sampling frequency and subsequently reduce the number of data points while minimizing information loss. We initially show that NSST can be useful for determining whether a chosen sampling interval is sufficient for behavioural analysis. We also showed using two scenarios (i) cases where the standard thinning approach alone produces results similar to the NSST approach with respect to behavioural inference and close representation of original movement location, (ii) cases where the NSST approach provides better results than the thinning approach. Lastly, we examine how the accuracy of inferred behaviours is sensitive to the choice of subsampling frequencies informed by NSST. Results suggest that the NSST approach is useful for subsampling as it provides improved behavioural accuracy compared to the standard thinning approach.
Computational modeling and machine learning approaches to dense suspension rheology
(2026-05-25) Li, Xuan; Ness, Chris; Beckett, Chris
Dense suspensions of solid particles in viscous liquid are ubiquitous in both industry and nature, and there is a clear need for efficient numerical routines to simulate their rheology and microstructure. Particles of micron size present a particular challenge: at low shear rates, colloidal interactions control their dynamics while at high rates, granular-like contacts dominate. While there are established particle-based simulation schemes for large-scale non-Brownian suspensions using only pairwise lubrication and contact forces, common schemes for colloidal suspensions generally are more computationally costly and thus restricted to relatively small system sizes. Here, we present a minimal particle-based numerical model for dense colloidal suspensions that incorporates Brownian forces in pairwise form alongside contact and lubrication forces. We show that this scheme reproduces key features of dense suspension rheology near the colloidal-to-granular transition, including both shear thinning due to entropic forces at low rates and shear thickening at high rates due to contact formation. This scheme is implemented in LAMMPS, a widely used open source code for parallelised particle based simulations, with a runtime that scales linearly with the number of particles, making it amenable for large-scale simulations. Building on this foundation, we study the rheology of dense suspensions comprising mixed colloids (smaller particles) and grains (larger particles). By systematically varying the volume fraction of the two species, we demonstrate a monotonic increase in viscosity when grains are added to colloids, but, conversely, a nonmonotonic response in both the viscosity and shear thickening onset when colloids are added to grains. Both effects are most prominent at intermediate shear rates where diffusion and convection play similar roles in the dynamics. We rationalize these results by measuring the maximum flowable volume fraction as functions of the P´eclet number and composition, showing that in extreme cases increasing the solids content can allow a jammed suspension to flow. These results establish a constitutive description for the rheology of bidisperse suspensions across the colloidal–to-granular transition, with implications for flow prediction and control in multicomponent particulate systems. Finally, we study the rheology of dense suspensions under inhomogeneous flow—that is, flows in which the shear rate, stress, or particle concentration varies spatially across the system, such as in pressure-driven channels, near solid boundaries, or around obstacles. Understanding inhomogeneous flows is critical because most real world suspensions in industrial and geophysical contexts are not subjected to uniform shear. Instead, they exhibit complex local flow phenomena that strongly influence macroscopic behaviour. Here, we focus on dense suspensions of non-Brownian particles, where thermal fluctuations are negligible and particle motion is governed primarily by hydrodynamic interactions and non-frictional contacts. Conventional constitutive laws, such as the μ(J) rheology, describe homogeneous shear flows effectively but break down under inhomogeneous conditions. To overcome these limitations, we employ Machine Learning (ML) to develop a data driven framework that bypasses constitutive formulations, our ML models are trained on constitutive model dimensionless parameters: the viscous number J, the total solid volume fraction ϕ, the regional solid volume fraction ϕloc, the macroscopic friction coefficient μ, and suspension temperature Θ, which together characterize the inhomogeneous, dense, non-Brownian suspensions [1]. In addition, we introduce an alternative descriptor, the relative velocity difference Δv, defined as the normalized difference between the average local particle velocity and the background fluid velocity. Our results show that ML models trained on (J, μ, ϕ, ϕloc,Δv) achieve nearly identical predictive accuracy to those trained on the full constitutive law parameter set (J, μ, ϕ, ϕloc, Θ). This demonstrates that Δv serves as a experimentally accessible, and computationally efficient descriptor of inhomogeneous, non-Brownian suspension flow. Overall, our framework extends the constitutive description of inhomogeneous flows while offering a computationally efficient and experimentally accessible approach to predicting dense suspension rheology.