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Mathematical modelling of antibiotic release from medical implants to counteract biofilm formation
Get PDFA biofilm is a community of bacteria embedded in a self-produced extracellular matrix (EPS) that adheres to surfaces like medical implants. Biofilms are highly resistant to antibiotics due to the protective EPS barrier and dormant persister cells, leading to chronic infections that are difficult to eradicate and often require surgical intervention. This resistance, along with the increase in antibiotic resistant bacteria, underscores the need for new strategies to manage biofilm-related infections. This thesis aims to address this challenge by investigating the dynamics of biofilm growth under various conditions, including nutrient availability and antibiotic exposure. The goal is to provide insights for developing more effective therapeutic strategies. A simple mathematical model of biofilm growth is introduced, progressively incorporating complexities such as different bacterial phenotypes, nutrient-dependent transition rates between proliferative and persister bacteria, and controlled antibiotic release from porous implant. Before exploring the mathematical models in detail, this thesis introduces a hierarchy of adaptable models tailored to the needs of different studies.
The key findings of this thesis reveal the critical role of nutrient availability and antibiotic distribution in controlling biofilm growth. In nutrient-rich environments, biofilms grew rapidly but were more vulnerable to collapse under antibiotic treatment, while nutrient-poor conditions promoted persister cells, leading to thinner and more resilient biofilms that were harder to eliminate. Controlled antibiotic release from porous implants provided initial biofilm suppression but was insufficient for long-term control without sustained release, as biofilms regrew after antibiotic depletion. It is also clear from the results that higher initial antibiotic concentrations delayed biofilm regrowth but did not ensure complete eradication. Finally, spatially optimised antibiotic loading, which has a higher antibiotic concentration near the implant-biofilm interface, worked better for short-term suppression but resulted in poorer long-term biofilm control. In contrast, distributing the antibiotic farther from the implant-biofilm interface led to more sustained suppression over time. These findings underscore the need for strategies that balance sustained antibiotic presence with nutrient manipulation for effective biofilm control in clinical settings.
This work lays the foundation for several future avenues in optimising antibiotic delivery, including spatially variable implant porosity, pulse dosing, and systemic administration. The final model can also be extended to include environmental factors such as temperature and pH and can be expanded to higher-dimensional biofilm structures
Contactless human activity recognition and vitals sensing for next generation smart homes and healthcare centers
Get PDFAccording to the House of Commons Library United Kingdom Parliament, approximately 7.9 million people live alone in the UK. Out of 7.9 million,over 3.1 million adults aged 65 and above live alone in the UK. The older population in the UK is projected to grow, with people aged 65 and over making up 24% of the population by 2043 (17.4 million people). Given this background, the development of a monitoring system that can recognize an emergency or health condition is desired by healthcare providers and families of individuals living alone. Unusual changes in a lonely living person’s regular daily mobility routine at home can indicate early symptoms of developing health problems.
This thesis paves the way to develop a novel system that exploit Energy, LoRa, WiFi, RF and radar based technologies to monitor human activity, including presence detection, postural transitions such as walking, sitting, standing, lying, and fall detection. Additionally, it introduces a robust framework for contactless vital signs monitoring, enabling accurate measurement of breath rate, pulse, heart rate, and heart sounds. The integration of AI-driven anomaly detection enhances the system’s ability to identify potential health risks in real-time. The research further explores the fusion of human activity recognition with vital signs monitoring to develop a complete, scalable, and privacy preserving solution for both general well-being and clinical healthcare applications. By developing advanced signal processing techniques and machine learning models, the proposed system aims to provide an efficient, non-invasive alternative to conventional health monitoring methods.
Further contributions include a contactless framework for sleep pattern recognition, utilizing micro doppler radar signals to classify sleep postures and detect abnormalities associated with autism spectrum disorder. The study also advances non-invasive health monitoring through radar systems for vital signs detection, achieving high accuracy in respiration and heart rate variability assessment. Moreover, heart sound detection and analysis enhance cardiac monitoring, improving pulse detection, heart rate estimation, and overall reliability of vital signs monitoring. This work contributes to the future of smart living by ensuring continuous, real time health monitoring without compromising user’s comfort and privacy. Future research will focus on improving system adaptability, enhancing multimodal sensing capabilities, and addressing data security challenges to facilitate widespread deployment in next generation smart homes and medical facilities
Exploring CBL as an E3 ubiquitin ligase in proteolysis-targeting chimeras (PROTACs)
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Exploring the boundaries of visual perception and visual imagination
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Hydrogen release in composite complex hydride systems
Get PDFPollution and global warming can result from the rapid usage of fossil fuel resources. In the future, fossil fuels will be run out. As a result, renewable and clean energy sources such as hydrogen technologies may be a good choice to be developed. Many renewable energy sources, have drawbacks, such as high storage and transition costs. One method is to store energy chemically as a clean fuel. Hydrogen have one of the greatest possibilities due to its availability, high energy density, and ability to be produced sustainably.
Currently, the most mature hydrogen storage technologies are compressed gas tanks and liquid hydrogen storage. However, both approaches face certain limitations. For compressed gas tanks, hydrogen’s low density requires a large storage volume; applying high pressure can reduce the volume, but this necessitates tank materials with exceptional compressive strength. Liquid hydrogen storage, on the other hand, requires cryogenic conditions at approximately -253 °C, which poses significant technical and economic challenges. In this context, the development of solid-state hydrogen storage emerges as a promising alternative, as it offers greater stability compared with gaseous or liquid hydrogen and enables higher volumetric storage capacity relative to weight. Nevertheless, the practical application of solid state storage still faces critical challenges, including limited reversibility, high desorption temperatures, and insufficient hydrogen release rates. Therefore, this study investigates five representative solid-state hydrogen storage systems to elucidate their properties and characteristics, thereby providing insights for the future development of solid-state hydrogen storage technologies.
This thesis investigated the dehydrogenation kinetics and reaction mechanisms of five hydride systems: NaH-NaOH, NaAlH4-NaOH, NaBH4-NaOH, Ca4Mg3H14-NaH, and NaAlH4/MgH2-Guanidine(CH5N3). The selection of these systems was based on their potential for practical hydrogen storage. Sodium-based hydrides and hydroxides are inexpensive, lightweight, and exhibit relatively high theoretical hydrogen capacities, making them promising candidates for large-scale applications. The NaH-NaOH, NaAlH4-NaOH, and NaBH4-NaOH systems were studied to evaluate the influence of hydroxide incorporation on the hydrogen release behaviour of simple and complex sodium hydrides. The Ca4Mg3H14 NaH system was chosen as a mixed alkaline composite, where synergistic effects may arise from multicomponent interactions. In addition, the NaAlH4/MgH2-Guanidine(CH5N3) system was introduced as an organic-inorganic hybrid, providing a new strategy to improve dehydrogenation pathways through organic molecular. Through thermal treatment, ball milling, catalyst addition, and variations in molar ratios, the dehydrogenation behaviour of these systems was systematically optimized. Thermal analysis, X-ray diffraction, and Raman spectroscopy were employed to monitor phase change and deduce the corresponding reaction mechanism.
In the NaH-NaOH system, ball milling significantly improved dehydrogenation kinetics, enabling a two-step mechanism. The first step involves the formation of a solid solution, NaH1-x(OH)x, between 170 °C and 210 °C, followed by the decomposition of this intermediate to release hydrogen. Under optimal ball milling conditions (400 rpm, 2 hours) and at a NaH:NaOH molar ratio of 1.15:1, the system exhibited the lowest dehydrogenation peak temperature (346 °C) and the highest hydrogen release (3.08 wt.%). Prior to catalyst optimization, the activation energy of the ball-milled NaH–NaOH system was determined to be 75.85 kJ mol-1. Subsequent addition of catalysts significantly reduced the activation energy, with 5 wt.% Ni and SiC showing the most pronounced effects, lowering the activation energy to 41.24 kJ mol-1 and 46.79 kJ mol-1, respectively. SiC primarily reduces the activation energy via physical mechanisms. As a milling aid, SiC decreases particle size, increases the contact area of reactants, and shortens hydrogen diffusion pathways, thereby facilitating the reaction. In terms of the Arrhenius relationship, this process enhances the pre-exponential factor (A) and increases the probability of effective molecular interactions, which is reflected in an apparent reduction of the activation energy (Ea). Ni nanoparticles may disperse uniformly across the NaOH-NaH interface, providing active sites that facilitate hydrogen atom desorption.
For the NaAlH4-NaOH system, the mechanistic insights gained from the NaH-NaOH system were applied to enhance the kinetics of NaAlH4 decomposition. This led to changes in the third step of NaAlH4 decomposition, which is crucial for improving the overall kinetics. In the hand-mixed NaAlH4-NaOH system, the following reactions were observed:
NaAlH4 + 4NaOH ⇋ 1/3Na3AlH6 + 2/3Al + H2 + 4NaOH
1/3Na3AlH6 + 4NaOH ⇋ NaH + 1/3Al + 1/2H2 + 4NaOH
NaH + Al + 4NaOH ⇋ Na5AlO4 + 5/2H
Notably, the first step of the dehydrogenation reaction transitions from an exothermic to an endothermic process with the addition of NaOH. When even more NaOH is added (e.g., in a 1:4 NaAlH4:NaOH ratio), the second step of the dehydrogenation reaction also shifts from exothermic to endothermic. Under ball-milling conditions, NaOH reacts with NaAlH4 to form Na3AlH6-x(OH)x, As the NaOH content increases, the Na3AlH6-x(OH)x further decomposes to NaH.
The reaction between NaBH4 and NaOH produces Na-B-O-H intermediates. When ball-milled for 10 hours, the 1:4 NaBH4:4NaOH mixture can be completely converted Na-B-O-H intermediates. Prior to heating to 300°C, these intermediates undergo a phase change at 240 °C-250 °C, and the nature of this phase change varies depending on the molar ratio of NaOH added. For instance, ball-milled (2h) 1:3 NaBH4:NaOH yields Na3BO3, Na, NaOH, and H₂ when heated to 400°C, while ball-milled (2h) 1:4 NaBH4:NaOH produces Na3BO3, Na2O, NaOH, and H2 under the same conditions.
For the Ca4Mg3H14-NaH system, ball milling a 1:1 Ca4Mg3H14:NaH mixture at 400 rpm for 2 hours enables NaH to alter the reaction pathway, leading to the formation of Ca4Mg2H14 and NaMgH3. Upon heating to 348 °C, the interaction between NaMgH3 and Ca4Mg3H14 results in a significant reduction of the overall dehydrogenation peak temperature by 102 °C (from 450 °C to 348 °C). This interaction not only facilitates the earlier decomposition of Ca4Mg3H14 but also promotes the premature decomposition of NaMgH3 (from 400 °C to 348 °C).
For the NaAlH4/MgH2-Guanidine (CH5N3) system, the thermal decomposition of CH5N3 alone follows the reaction:
3CH5N3→ C3H6N6 + 3NH3 T = 179 ℃
Subsequently, C3H6N6 primarily evaporates at approximately 300 °C, with a minor fraction undergoing decomposition. In the NaAlH4-CH5N3 system, a reaction occurs at 150 °C, resulting in the release of hydrogen gas and the formation of Al and an amorphous Na-C-N compound. In contrast, the reaction between MgH₂ and CH5N3 is significantly more complex, and at present, only the possible reaction pathways can be proposed based on available data
Novel statistical methods for inferring human impacts on animal movement and migration from large-scale datasets
Get PDFMultiple stressors contribute to the decline of numerous animal species within and outside protected areas worldwide. While our understanding of anthropogenic habitat loss and degradation, climate change, and anthropogenic pressures as potential drivers of these declines is improving, we still lack a mechanistic understanding of how their finescale effects translate into animal movement decisions and how these decisions ultimately influence survival and, in turn, population dynamics at a broad scale. Unravelling these patterns requires associations of spatial covariate fields and fine-scale movement data, typically collected using Global Positioning System (GPS) tags deployed on animals. These tags provide a bivariate time series of coordinates at defined intervals, facilitating insights into how animals move, where and when they forage, and the nature of both intra- and interspecific interactions.
Despite the availability of such movement data alongside the corresponding environmental data, significant analytical challenges persist. Habitat selection models, particularly resource selection functions (RSFs) and step selection functions (SSFs), represent a fundamental tool to identify the characteristics of suitable habitats for animals at both broad and fine scales. The core concepts underlying these methods are based on the ratio between habitat availability and habitat use by the animal. However, while these models enhance our understanding of habitat suitability, they often yield divergent conclusions even when applied to the same datasets, likely due to differences in their spatial and temporal scales of operation. A pressing question, therefore, is how parameters derived from fine-scale movement models can be reconciled to produce the patterns similar to those from broad-scale models and thereby improving our understanding of how animals’ use of space relates to the distribution of resources, risks, and environmental conditions. Addressing this challenge requires a modelling framework that enables parameter scalability, quantifies uncertainty, and remains computationally efficient while capturing the influence of spatial covariate fields, such as human-made infrastructure.
The objective of this thesis is to advance our understanding of how animal space use relates to the distribution of resources, risks, and environmental conditions by integrating and developing state-of-the-art multiscale statistical methods within a Bayesian framework while maintaining computational efficiency. This will enhance our ability to assess how animals respond to changing landscapes and climate conditions, predict future spatial distributions based on current patterns, identify the key drivers that displace or restrict animals from otherwise suitable habitats, and pinpoint critical habitats that should be preserved from human alteration. Throughout this thesis, I will focus on models of animal movement, particularly habitat selection models, and contribute to expanding the array of statistical methods available for analysing movement data. An overarching goal of the thesis is to develop methods that can be applied to the study of the Serengeti wildebeest migration, a vital ecological process in one of the most biodiverse ecosystems on earth. I will begin by reviewing existing and widely used methods in the literature. Subsequently, I introduce a multiscale step selection model that facilitates the estimation of long-term animal space use without requiring simulations from the fitted model, and I will leverage variational inference within a Bayesian framework to estimate selection and avoidance parameters from movement observations and environmental data while demonstrating the importance of formally quantifying uncertainty in these estimates.
The focus then shifts to examining the effects of anthropogenic structures, such as buildings, on the spatial distribution of migratory wildebeest using multiscale inference from the previous chapter. This analysis will provide insight into whether wildebeest select or avoid areas near buildings and how these selection patterns influence their space use at the population level within the ecosystem. These findings will be essential for a later chapter, where I simulate how wildebeest space use is expected to change in response to the introduction of new additional buildings in the ecosystem.
In Chapter 5, I use hierarchical sparse Gaussian processes to estimate the mean migration routes of the Serengeti wildebeest population. These modelled routes form the basis for improving spatial predictions of where wildebeest are likely to spend most of their time during critical life-history stages such as calving, weaning, rutting, or migration. This is achieved by integrating wildebeest space use patterns derived from local environmental features such as anthropogenic structures, as detailed in Chapter 4 with the population mean migration routes inferred here. The latter are used as a proxy for the influence of long-term spatial memory on movement decisions. This integrative modelling framework offers a more ecologically grounded understanding of wildebeest spatial distribution across specific days of the year and during key life-history events.
In Chapter 6, I will develop a novel simulation approach to model the placement of buildings in different scenarios and explore the impact of different allocation strategies on wildebeest space use. This will be achieved by simulating hypothetical building distributions using a nonlinear preferential attachment rule to place buildings at specific locations and incorporating an accept-reject mechanism to increase and decrease building clustering. Then I will estimate the new patterns of wildebeest space use using the methodology introduced in chapter 4 and quantify the shift from observed space use by employing the Kullback-Leibler divergence.
This thesis demonstrates that multiscale animal movement models provide valuable insights into how animal space use is shaped by the distribution of resources and risks in changing landscapes. A key finding is that considerable uncertainty can persist even in large telemetry datasets, underscoring the importance of quantifying uncertainty in resource selection analyses. The study on the spatial distribution of migratory wildebeest reveals that while these animals tend to avoid areas near anthropogenic structures, this behavior does not lead to complete exclusion. Instead, it results in a reduced duration of time spent in the vicinity of such structures. Furthermore, the study incorporating local environmental responses with long-term spatial memory effects reveals that spatial predictions of wildebeest distribution during key life-history stages, such as calving, are improved by reducing uncertainty about where populations are most likely to spend time on specific days or during particular events. Finally, a simulation study indicates that the impact on wildebeest space use is more pronounced when new developments occur in previously undeveloped regions or in isolation from existing infrastructure, highlighting the importance of strategic spatial planning in conservation efforts
Towards non-disruptive visual motion cues that balance motion sickness against distraction for passenger VR
Get PDFUsing Virtual Reality (VR) technology in moving vehicles holds great potential to enhance the passenger experience and support innovative Non-Driving Related Tasks (NDRTs). However, VR usage in vehicles presents significant challenges due to motion sickness. This condition often arises from a sensory mismatch between visually perceived motion and the vestibular system’s input. This problem is especially pronounced when passengers engage with immersive VR environments while in transit as the VR visual cues are often in contradiction to vestibular cues. Consequently, current VR experiences tend to rely on matched motion cues, meaning experiences where the virtual movements mirror the real movements of the vehicle. While matched visual motion cues that align with optic flow can effectively reduce sensory conflict and mitigate motion sickness, they often impose constraints that may distract users from their primary tasks in VR. Moreover, the requirement for matched cues significantly limits design flexibility, constraining the diversity of VR scene designs. To address these issues, this thesis investigates alternative visual cue designs that move beyond the traditional approach of the VR scene matching visual motion. These novel cues reduce motion sickness by minimizing sensory mismatch without replicating optic flow, thereby reducing distraction and enabling non-disruptive visual motion cues. This thesis addresses two primary types of vehicle motion: rotational and linear. In terms of rotational motion, innovative cues were validated through four studies, ranging from implicitly manipulating the movements of existing virtual interfaces to explicitly presenting virtual objects that react to the forces experienced by the vehicle. For linear motion, visual cues that represent vehicle accelerations rather than velocity changes (as used in traditional matchedmotion cues) were tested in two studies in real driving environments. By comparing user motion sickness ratings and distraction levels with those of solutions commonly used in the field, these designs demonstrated their ability to strike a balance between mitigating motion sickness and minimizing distraction from NDRTs. This thesis contributes novel insights into how visual motion perception, beyond simple matched motion cues, can improve passenger VR experience without inducing motion sickness. By strategically designing to reduce the motion sickness associated with VR use and integrating VR devices into vehicles, this research also underscores the importance of minimally distracting cues to enhance the overall user experience, which will become a vital component of future vehicles interactions
Poetic practices and spatial agency: writing into new situations
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