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Differential Expression of BMI1 and PARP1 in Reticular, Erosive and Atrophic Oral Lichen Planus
Get PDFBackground: Oral lichen planus (OLP) is an oral potentially malignant disorder (OPMD) with a risk of progression to oral squamous cell carcinoma (OSCC). The erosive and atrophic variants appear to have an increased malignant transformation risk compared to the reticular variant. Cancer stem cell (CSC) marker B cell-specific Moloney murine leukemia virus integration site 1 (BMI1) and DNA repair enzyme Poly (ADP-Ribose) Polymerase 1 (PARP1) have been shown to be upregulated in OSCC.
Objectives: The aims of this study were to identify BMI1 and PARP1 expression patterns in erosive and atrophic OLP compared with reticular OLP. Furthermore we aimed to determine if BMI1 and PARP1 could be used as potential biomarkers for identification of high risk OLP lesions.
Materials and Methods: This was a cross-sectional, observational, pilot study. Patients were prospectively recruited from the Oral Medicine clinic and underwent oral tissue biopsy. 63 paraffin-embedded tissue blocks of histologically confirmed OLP were stained for BMI1 and PARP1 using immunohistochemistry (IHC). BMI1 and PARP1 IHC expression in both the full thickness (FT) and basal half (BH) of the epithelium was analysed using the Immunoreactive Scoring system (IRS).
Results: PARP1 expression was higher than BMI1 expression in both reticular and erosive/atrophic OLP samples. No significant differences were identified between the OLP variants and PARP1 IRS scores for either the FT (p = .858) or the BH of the epithelium (p = .681) or BMI1 scores for either the FT (p = .492) or the BH of the epithelium (p = .649). However there was a small increase in the number of erosive/atrophic OLP samples that had strong PARP1 staining compared to reticular. There was a significant positive correlation seen between BMI1 and PARP1 expression in both the FT (r (61) = .432, p <.001) and the BH of the epithelium (r (61) = .530, p <.001).
Conclusions: Although not significant, PARP1 expression was slightly increased in erosive/atrophic OLP compared to reticular, suggesting a possible association with high risk lesions. Additionally, the findings demonstrated a positive correlation between BMI1 and PARP1 indicating co expression of the proteins in OLP. Further studies with larger sample sizes, negative tissue controls and follow-up data are indicated to determine if BMI1 and PARP1 could be used as potential biomarkers for identification of OLP lesions at high risk for malignant transformation
Enzyme-responsive nanomaterials for the delivery of antimicrobial peptides
Get PDFThe rate of resistance to antibiotics that are commonly used in the clinic is escalating rapidly, surpassing the introduction of new antimicrobial drugs. To address this problem, alternative strategies are being explored, such as the re-evaluation of antibiotics, that have not yet gained widespread clinical application. Antimicrobial peptides (AMPs) represent one of those antibiotics, offering remarkable antimicrobial efficacy against various pathogens. However, in clinical settings, AMPs are typically considered a last-resort option due to their off-target effects and poor stability in-vivo resulting from their cationic and amphiphilic peptide nature. Therefore, most of current strategies addressing these limitations focus primarily on the control and shielding of the cationic charge and the amphiphilic nature of AMPs. These can potentially be achieved through encapsulation of AMPs inside stimuli-responsive polyelectrolyte complexes (PECs) by combining the cationic drug with anionic polyelectrolytes. Stimuli-responsive polymers can be employed as encapsulation materials in PECs to design systems that activate drug release in response to specific changes encountered during microbial infection, such as variations in pH, enzyme activity, or temperature.
The overarching aim of this Thesis was to explore the creation of PECs capable of encapsulating the clinically approved antimicrobial peptide, Polymyxin B and its subsequent enzyme-induced release. In Chapters 2 and 3, the aims were: Firstly, to synthesize anionic and helical polymers incorporating enzyme-degradable peptide side chains (Aim 1.1), followed by evaluation of the degradation properties of these polymers in response to the enzyme released by gram-negative bacterium Pseudomonas aeruginosa (Aim 1.2). In Chapter 4, the first objective (Aim 1.3) was to assemble Polymyxin B and the anionic enzyme-degradable polymers into PECs. The next objective (Aim 1.4 ) involved investigating the P. aeruginosa-induced drug release from these PECs, while Aim 1.5 focused on assessing the antimicrobial activity of the developed PECs against P. aeruginosa strains.
Chapter 1 provides a review of the current developments in the field of the stimuli- responsive delivery of AMPs using polyelectrolyte complexes. Chapters 2 and 3 discuss the synthesis of polymers with poly(methacrylamide) and poly(acetylene) backbones respectively coupled to enzyme-degradable peptide side chains. Of the synthesized materials, two poly(methacrylamide) polymers from Chapter 2 were found to be the most effective in terms of degradation by the enzyme released by P.aeruginosa, while none of the synthesized acetylene-containing peptides from Chapter 3 polymerized. Subsequently, the poly(methacrylamide) polymer with the highest multivalency was used to form PECs with Polymyxin B in Chapter 4. Eight different formulations of PECs were created, with one being the most optimized in terms of encapsulation efficacy and physiological stability. The stability of the particles was further improved by the addition of Tannic Acid, which acted as a protective coating and a cross-linker. The Thesis then evaluated the ability of the PECs to release Polymyxin B under enzymatic degradation. Finally, the preliminary evaluation of the antimicrobial activity of the PECs against various P.aeruginosa strains were presented
Fundamental understanding of silicon alkoxide polymerization for the optimal design of silica capsules
No full textEncapsulation is currently recognized as a formulation tool that enables researchers in both industry and academia to design solutions for the protection and targeted release of consumer goods active ingredients. Most current encapsulation solutions use capsule walls (shells or membranes) which are formed from non-sustainable and non-biodegradable materials and could be classed as microplastics, thus effort is pivoting to develop technologies which address these ecological challenges. A leading candidate in these new material groups is silica due to its ubiquitous nature and chemical inertia towards biological and ecological systems.
Silica can be synthetized from various molecular precursors, the most popular one being the class of silicon alkoxides. The mechanism of their interfacial polymerization is further elucidated in this work via models, both conceptual and computational, which enabled the formulation of a novel concept called interfacial yield. Various synthesis levers such as the nature of the precursor and its reaction kinetics were leveraged to control this interfacial yield, allowing the tuning and improvement of the mechanical and barrier properties of the silica capsules. Furthermore, unique features of the capsules were uncovered. These stem from the fundamental material properties of silica such as its brittleness and hydrophilicity, paving the way for new exciting applications
Graph-based extractive summarisation for long documents
Get PDFThe ability to extract the most important information from a longer document or a collection of documents quickly and accurately has always been essential for effective communication and decision-making. By leveraging text summarisation systems, this process can be facilitated more efficiently. Text summarisation help streamline the process by extracting only the essential information from a document or series of documents. Despite significant progress in methods for text summarisation, challenges remain, particularly for unsupervised methods.
This thesis investigates novel unsupervised methods and models in Natural Language Processing (NLP) to improve the quality of text summarisation. Our research makes three major contributions. The first contribution involves improving the performance of sentence similarity detection by combining Deep Learning/Transformer-based models with cluster-based approaches. Our proposed approach improves upon state-of-the-art performance on the Financial News Summarisation (FNS) dataset, indicating its potential for improving the quality of text summarisation. The second contribution explores improving graph models by incorporating more features when calculating node weights. Our proposed approach achieves significant performance gains on four benchmark datasets, demonstrating the potential of incorporating additional features for improving text summarisation. Finally, we propose a novel ranking algorithm for unsupervised graph-based text summarisation. Our proposed algorithm is based on graph centrality measures and can be used to identify the most important nodes in a graph-based summary. We demonstrate the effectiveness of our algorithm through analysis and experiments on four benchmark datasets
Synthesis and characterisation of new materials for use in solid oxide fuel cells and electrolysers
Get PDFIn this thesis, the work presented focuses on the development of anion doping strategies (Oxyanions and Halides) into a variety of different structures for potential use as cathodes within solid oxide fuel cells and electrolysers.
The thesis examines the use of phosphate doping of the mixed perovskite Mn/Fe system, Sr¬2-xCaxMnFeO6-δ, to design a novel low-cost cathode material. The work shows the successful incorporation of phosphate into these systems and show that conductivities are higher than the previously reported silicon doped variant.
The work on Ba1-xSrxFeO3-δ systems demonstrates the successful incorporation of borate and characterises the effect on the structure and conductivity with a view to possible utilise as a cathode material in a ceramic fuel cell (H+ or O2- conducting). The incorporation of low levels of borate was shown to be sufficient to cause a change in cell symmetry to give a cubic perovskite structure.
Lanthanum germanate apatite was studied to try to confirm which oxide ion conduction mechanisms are present within the La10Ge6O27 and La10-xYxGe6O27 (x = 0, 1 and 2) structures with literature proposing two possible mechanisms; through the channel and perpendicular to the channel. Despite blocking the centre channel in the apatite structure with borate, the observation of high oxide ion conductivity provided support for an alternative oxygen interstitial conduction method perpendicular to the channel was presented. The addition of borate into the structure also introduced larger amounts of interstitial oxide ions, therefore increasing the conductivity in some of these systems.
The first use of two types of biopolymer, Iota and Kappa Carrageenan, was demonstrated as both a gelation agent and a precursor material with a novel sol-gel synthesis for the manufacture of SrFe1-xSxO3-δ materials with comparable results to the standard high-temperature solid-state synthesis.
Finally, the successful synthesis of a range of K2NiF4 structured La doped Sr2CoO3F phases was demonstrated. Contrary to undoped Sr2CoO3F which required high pressure synthesis, La doping was shown to allow synthesis under ambient pressure. We also evaluated these materials for their potential applications as SOFC cathode/ FIB electrode materials via conductivity and thermal characterisation, with some showing high conductivities
Towards an holistic approach for highly flexible robotic assembly systems
Get PDFThis thesis presents a general framework for the control of a robotic agent tasked with solving problems in the domain of industrial assembly. A set of motivating example problems are described which shape the development of the framework. The framework fuses the concepts of a closely coupled, multi-layer reasoning architecture with abstract assembly sequence planning. This framework provides a reusable and flexible structure for robotic manipulation via a modular location graph, and a grasp planning mechanism that considers object and task constraints.
One example assembly task inspired by the building of light gauge steel frame panels for the construction sector is used to develop a benchmark object set and challenge assemblies. These objects are published as the open source Robotic Assembly Manipulation and Planning (RAMP) benchmark [1]. The abstract task planning layer finds valid sequences of part addition to complete an assembly task given a task description specified using Action Language ALd [2] sequentially feeding these steps as subgoals to a closely coupled robotic reasoning framework based on the REBA architecture [3]. Experiments demonstrate application to long time horizon assembly problems requiring the addition of many parts with over 200 low level robot actions. The abstract task planning layer reduced coarse-resolution planning time by 93.5% compared to a baseline which must simultaneously consider assembly sequencing and robot actions. The modular location graph structure links positional information between the task space and logical domain. We utilise this structure to parameterise the assembly actions of the RAMP benchmark tasks. Additionally, a pruning heuristic is proposed to speed up searching in this location graph when the robot is planning assembly motions.
A grasp planning approach is detailed utilising a weighted grasp scoring model considering a combination of measurement uncertainty and variation in the extracted surface, the contact angle of gripper fingers to the surface, as well as task constraints. A three-level representation for objects, compatible with our framework, includes object class membership, point cloud data representing the objects surface, and semantic keypoints linked to the object parts. A learned model is used to encode task-specific knowledge from a small number of exemplars of objects, tasks, and relevant grasps; preserving the relationship between keypoints and grasp points for specific tasks despite changes in factors such as the scale and orientation of objects. The learned models are queried at run time to guide the generation of grasps that balance task and stability constraints. Through experimental evaluation on a Franka robot manipulator with a parallel gripper, it is demonstrated that this method is able to generate grasps on previously unseen objects achieving the desired task-specific trade off whilst maintaining a high degree of grasp stability
3D Pose and shape estimation of hands and manipulated objects from images and videos
Get PDF3D shape and pose estimation of hands and manipulated object is an important and long-standing problem in computer vision. This problem can be particularly challenging due to extreme variations in object shape and texture. In addition, heavy occlusions can be introduced by other objects in the scene or humans during interaction. Nevertheless, modeling hand-object manipulations is essential for understanding how humans interact with the physical world. There are many challenges in modeling hand-object interactions. In this thesis, we focus on three outstanding challenges which unifies geometry-driven and data-driven methods: (1) estimating 3D pose and shape from 2D images is an extremely ill-posed problem due to the loss of depth information during 3D projection to 2D, (2) inexpressive and physically implausible 3D hand reconstructions, 3) inability to recognise seen actions on unseen objects. In this thesis, we propose three main contributions to overcome these challenges. First, we present a new collaborative learning strategy in which two branches of deep neural network mutually exchange information for 3D hand-object reconstruction from single RGB image. Second, we present a new Transformer-based method that estimates the absolute root pose and shape of two-hands with extended forearm at high resolution from egocentric RGB images. Third, we present a new method for compositional action recognition by leveraging 3D geometric information from egocentric RGB videos. Specifically, we exploit superquadrics for both template-free object reconstruction and interaction recognition. This thesis pushes the state-the-art for understanding hand and object from RGB images and videos. First, we show that a collaborative learning framework which allows sharing of 3D geometric information across two branches of networks iteratively can tackle the problem of mutual occlusions. Through this novel network architecture design, we achieve state-of-the-art performance on several common public benchmarks. Second, we present the first method that reconstruct high fidelity two-hand meshes with extended forearms from multi-view RGB images. We demonstrate that by leveraging the properties of graph Laplacian from spectral graph theory can effectively aggregate multi-view features as well as producing smooth meshes. Third, we explore superquadrics as an alternative 3D object representation to bounding boxes and demonstrate that it is beneficial to recognising seen actions on unseen objects
A strontium atom interferometer for advanced large momentum transfer
Get PDFOptical atomic clock atoms are finding applications in atomic physics beyond frequency standards. A new generation of atom interferometers based on the alkaline-Earth species strontium promises unrivalled sensitivity for fundamental physics searches. In this thesis, a new strontium atom interferometry experiment is developed and demonstrated. Strontium-88 atoms are cooled using a 2D magneto-optical trap followed by a two-stage cooling process. Samples of 8 × 106 atoms are prepared in this way and used to perform Mach-Zehnder configuration atom interferometry sequences on the intercombination transition. A laser system is presented, capable of generating 30 ns pulses in alternating directions at a repetition rate of 30 MHz. The experiment will be used as a test bed to develop techniques for enhancing contrast for large momentum transfer to achieve state-of-the-art sensitivity. Such techniques will feed into the Atom Interferometry Observatory and Network (AION) collaboration and have wider-reaching implications in the field of quantum technology
The application of pressurised carbon dioxide to recover electrolyte from lithium-ion batteries in electric vehicles
Get PDFElectric vehicles (EVs) have experienced significant growth and market dominance over the past decade, contributing to a reduction in greenhouse gas emissions as they replace traditional internal combustion engine vehicles. However, the lithium-ion battery (LIB) technology powering these EVs degrades over time and eventually requires replacement. This has led to a growing accumulation of end-of-life LIB waste. Most commercial processes currently focus on recovering high-value materials from spent LIBs, often overlooking the recovery of organic materials, particularly the electrolyte component. This research thesis aims to address this gap by investigating the recovery of EV LIB electrolyte using pressurised carbon dioxide.
The initial study focused on the solubility of the primary solvent components of typical LIB electrolytes, namely, dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), and ethylene carbonate (EC). Each was established in binary and quaternary (1:1:1 wt) systems in carbon dioxide under temperatures of 298.2, 313.2, and 328.2 K, and pressures ranging from 0.12-14.1 MPa. Within the constraints of the system parameters explored, the linear carbonates required mild pressure and high temperature conditions to promote their solubility, whereas EC required the elevation of pressure and low temperature conditions to enhance its dissolution in carbon dioxide. Additionally, both linear carbonates were found to act as co-solvents to the cyclic carbonate, promoting its solubility in carbon dioxide.
Findings from the phase equilibria studies were applied to a pressurised fluid extraction process. Pressurised carbon dioxide was used to extract an artificially created LIB electrolyte mixture (DMC, EMC, and EC) in a 1:1:1 mass ratio, weighing 1.5 g. Initial optimisations focused on extraction duration and dynamic flowrate, and the most optimal conditions were identified to occur between 90-210 minutes at 2.4-2.6 L/min. The combined effects of pressure and temperature were investigated, and both conditions were optimised and evaluated using response surface methodology (RSM). The optimal extraction yield for the artificial LIB electrolyte was 70.2%, achieved at conditions of 12.0 MPa and 328.2 K. Key findings concluded that linear carbonates respond more effectively to temperature enhancement, highlighting the importance of vapour pressure, while the cyclic component demonstrated a strong association with fluid density.
Final investigations explored the analysis, processing, and extraction of commercial EV LIB pouch cells using supercritical carbon dioxide and solvent extraction techniques. The research found that more than 60% of the electrolyte was trapped in the electrodes and separator components, and substantial electrolyte loss was experienced due to the volatility of the linear carbonate, posing a challenge for collection. The electrolyte component was characterised before and after extraction using GC-MS, NMR, and ICP-OES techniques. The supercritical carbon dioxide extraction was performed at 12.0 MPa and 328.2 K, at a flowrate of 6 g/min for 60 minutes of dynamic and 45 minutes of static operation. The extraction produced satisfactory results, recovering a predominantly linear carbonate mixture with maximum recovery yields of 47.6% and 44.4% from the anode and separator materials, respectively. For solvent extraction, acetone was used under an HPV temperature of 323.2 K and a pump flowrate of 6.5 L/min for 165 minutes. The process achieved a maximum electrolyte yield of 96.7% from the anode material and proved effective in recovering the lithium conducting salt
Exploring two-dimensional van der Waals supramolecular networks: a scanning tunnelling microscopy approach
Get PDFSupramolecular self-assembly has emerged as a strategic avenue for engineering chemically bespoke surfaces. Over the preceding three decades, research focused on a variety of key parameters, such as temperature, solute concentration, and molecular architecture, has led to the successful fabrication of self-assembled molecular networks (SAMNs). Various types of intermolecular interactions have been explored in the construction of SAMNs. The study of self-assembly on surfaces began with early investigations of hydrogen-bonded assemblies and has since expanded to include other non-covalent interactions, such as coordination bonds and weaker van der Waals (vdW) interactions.
Among the many techniques in characterising molecular assembly on surfaces, scanning tunnelling microscopy (STM) has become the most popular choice due to its ability to provide real space images with atomic resolution. One of the useful properties of the STM is that it can provide information on structures not having long range-order as well as single atom level defects.
This thesis presents a study on the role of vdW interaction in molecular assembly, exploring how this non-directional and relatively weak force influences molecular self-assembly patterns with a large number of molecules. The study examines the chemisorbed alkanethiol molecules on the Au(111) surfaces and the formation of two-dimensional (2D) vdW supramolecular structures via the self-assembled co-crystallisation of alkanethiol and fullerene molecules. The absence of specific functional groups on the molecules used in this study leads to much more complex self-assembled structures and structural diversity. The fullerene/alkanethiol co-assembly exhibits different phases on the Au(111) substrate, as investigated using high-resolution STM at room temperature. The results show how collective interactions among many molecules dictate the stable structure transition
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