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    19646 research outputs found

    Exploring ethnic variations in lifestyle and diabetes: using evidence from UK Biobank Data

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    Type 2 diabetes mellitus (T2DM) is an important public health problem, with prevalence rapidly rising in the last decade by 65% in the United Kingdom. Those with type 2 diabetes carry twice the risk of developing cardiovascular disease and premature mortality amongst adults. The UK population is now ageing and the number of multi-ethnic populations in UK is increasing, the burden of T2DM is of prime importance. Improved lifestyle behaviours could significantly prevent the onset and also improve the effect of diabetes disease. However, the underpinning evidences have largely been obtained from studies of populations of white European descent. It is unclear whether these recommendations are appropriate for other ethnic groups. The prevalence of T2DM, it's impacts and controls differ between ethnic populations. T2DM is more common, more severe, develops at an earlier age as well as develops at lower obesity levels in the non-white minority population living within the United Kingdom compared with the majority White population. Therefore, more inclusive epidemiological information is critical for effective planning and designing of interventions to improve population health, particularly amongst non-white minority groups. The aim of this thesis was to assess and analyse epidemiological data on the ethnic differences in sex, adiposity and lifestyle factors on T2DM risk among middle-aged adults in the United Kingdom with focus on European white, South Asians (people originating from India, Pakistan and Bangladesh), Blacks (Black African and Black Caribbean) and Chinese descent populations

    The impact of host community composition on pathogen hazard in a tick-borne disease hotspot

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    Using FLIM-FRET to visualise self-generated gradients in cancer cells

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    Combining T2K with other experiments to better constrain oscillation parameters

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    This thesis presents an analysis of T2K data using a new external reactor constraint from Daya Bay instead of the regular one-dimensional Gaussian provided by the Particle Data Group (PDG). Both the PDG and Daya Bay data sets can be used to update the prior of given parameters in the T2K analyses. Applying Daya Bay’s two-dimensional constraint on the mixing angle θ₁₃ and mass splitting Δm² ₃₂ improves the constraint on the mass splitting parameter by 25% in normal hierarchy and 18% in inverted hierarchy compared to using the PDG external prior. Furthermore, denoted with a Bayes factor value which compares two hypotheses using the posterior results, it was found that there was a small increase in the preference for normal hierarchy over inverted hierarchy, B(NH/IH): PDG = 2.77 and Daya Bay = 2.79. There was a slightly larger increase for the upper octant in the octant degeneracy, B(UO/LO): PDG = 2.27 and Daya Bay = 2.38. The thesis also describes development work towards the first full joint-fit between two long baseline experiments, T2K and NOvA, showcasing the increase in statistical sensitivity for the oscillation parameters and the potential to solve some of the current degeneracies limiting the sensitivity of both experiments. Finally, there is an introductory insight into an alternate parameterisation of neutrino oscillations that could be used to better understand the constraint from the T2K data

    Thermal aging of three-way catalysts: in situ characterisation studies

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    Gender-STEM stereotypes: a cross-cultural, mixed-methods exploration of women’s STEM pathways between the UK and China

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    Women’s underrepresentation in higher education and careers in science, technology, engineering, and mathematics (STEM) fields remains a persistent global problem. Grounded in social psychological theories related to gender stereotypes, this cross-cultural thesis aims to understand the reasons for British and Chinese women’s underrepresentation. A review of existing empirical research highlights gaps in understanding British and Chinese women’s underrepresentation in STEM disciplines and careers identified gaps into why and how women maintain careers in these fields. Therefore, this study aimed to 1) identify British and Chinese women’s explicit and implicit gender-STEM stereotypes and the factors impacting these stereotypes; 2) explore what factors positively influenced women studying to PhD level in STEM fields; 3) investigate and interpret patterns of how Chinese Eearly career researchers (ECRs) achieve in their STEM fields. A sequential explanatory mixed-methods design was conducted. The first phase used a quantitative survey and lab-based Implicit Association Test to compare the explicit and implicit gender-STEM stereotypes and attitudes toward STEM fields of British and Chinese women (n = 113). Using a 2 x 2 ANOVA design, Chinese women in the cohort had higher explicit gender-STEM stereotypes than British women, and women studying in STEM fields had lower explicit attitudes on STEM subjects than women not studying in STEM fields. There were no significant main effects or interactions of nationality and STEM study on the implicit measure. However, a planned independent contrast found that Chinese women studying STEM subjects had lower implicit gender-STEM stereotypes than women not studying STEM. The second phase included qualitative focus groups with women from the UK (n= 5) and China (n= 6) studying STEM in the UK, and interviews with Chinese women working successfully in their STEM fields (n = 4) to more deeply understand why women’s persistence in higher level education and careers in STEM. Analyses uncovered factors influencing women’s attrition in STEM fields and possibilities for how women could maintain and achieve at higher level of education and careers in STEM fields. This mixed-method, cross-cultural, and interdisciplinary thesis makes a significant contribution through uncovering common barriers to STEM fields, women’s cognitive dissonance regarding gender-STEM stereotypes, and cultural differences suggesting “glass ceilings” effects in the UK and the pressures from the “ground floor” from Chinese family and society. Policy and educational recommendations are provided, including the importance of embedding STEM career knowledges early, policies such as flexible working, successful female role models in STEM, and the role of social media in raising women’s career profiles and widening their networks

    Multi-task learning for effective Open-Retrieval Conversational Question Answering

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    Conversational Question Answering (ConvQA) is a rapidly growing area of research that aims to improve the search experience for users by allowing for more natural interactions between users and search systems. ConvQA systems are designed to gauge and answer questions in the context of a conversation, taking into account the previous questions and answers in the dialogue. One of the challenges of ConvQA is resolving ambiguities in the user’s questions based on the conversation history. This requires the system to not only consider the question being asked but to also take into account the conversation context to provide relevant and accurate answers. Open-Retrieval Conversational Question Answering (ORConvQA) is a more challenging variant of ConvQA, as it requires the system to retrieve relevant passages from a large collection of documents before extracting the required answers. This task requires the system to effectively search and retrieve the most relevant information, adding further complexity. In order to build an ORConvQA system, to address the ambiguities in conversational questions, a number of approaches have been proposed, such as follow-up question identification, conversational question rewriting, and asking clarifying questions. These approaches can help the system better gauge the user’s intent and context, thereby allowing it to generate more precise and relevant responses. Another challenge in ORConvQA is retrieving relevant passages from a large collection of documents and identifying the most relevant ones based on the conversation context. This is important because the extracted answers need to be based on the relevant passages, in order to ensure accuracy. On the other hand, Multi-Task Learning (MTL) has emerged as a promising approach to facilitate the learning of multiple related tasks by sharing the learner structure in a single model. MTL has gained considerable attention in recent years due to its effectiveness in addressing a diverse range of complex problems within a unified model. Therefore, we argue that learning ORConvQA approaches simultaneously can help to improve the system’s performance. In this thesis, we propose a novel ORConvQA framework leveraging Multi-Task Learning (MTL) to improve the performance of multiple related tasks by sharing their learned structure. By applying MTL to ORConvQA, we aim to leverage the benefits of addressing several related tasks to build a more effective and efficient model that addresses two main challenges: (i) ambiguities in conversational questions; and (ii) retrieving relevant passages from a large collection of documents before extracting the answers. To address ORConvQA effectively, we first propose an ORConvQA framework, which leverages a novel hybrid dynamic MTL method combining Abridged Linear for the main answer extraction task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary related tasks, such as follow-up question identification, yes/no prediction, and unanswerable prediction, so as to automatically fine-tune task weighting during learning, ensuring that each of the tasks’ weights is adjusted by the relative importance of the different tasks. We conduct experiments using QuAC, a large-scale ConvQA dataset. Our results demonstrate the effectiveness of our proposed method, which significantly outperforms both the single-task learning and existing static task weighting methods with improvements ranging from +2.72% to +3.20% in F1 scores. Our findings also show that the performance of using MTL in developing the ORConvQA model is sensitive to the correct selection of the auxiliary tasks as well as to an adequate balancing of the loss rates of these tasks during training by using LBTW. To address the ambiguities in conversational questions, we propose the use of a text generation model with Multi-Task Learning for follow-up question identification and conversational question rewriting. Our derived models are based on text generation models –BART and T5–, and are trained to rewrite the conversational question and identify follow-up questions simultaneously. We evaluate our method using three test sets from the recent LIF (Learning to Identify Follow-up questions) dataset and a test set from the OR-QuAC dataset. Our results show that our proposed method significantly outperforms the single-task learning baselines on the LIF dataset, with statistically significant improvements ranging from +3.5% to +10.5% across all test sets, and also significantly outperforms the single-task learning of question rewriting models for passage retrieval on the OR-QuAC test set. Next, we employ an approach for asking clarifying questions to further address the ambiguities in conversational questions by proposing a novel hybrid method combining the generation and selection processes. Our method leverages Multi-Task Learning, combining the tasks of clarification need classification and the generation of the clarifying question to simultaneously determine when the initial user’s query necessitates a clarifying question and to generate a set of clarifying questions based on the user’s initial query and conversation history. A selection model is used to select the relevant questions from a question pool. To rank the candidate clarifying questions obtained from both the selection and generation approaches, the questions are scored using a text generation model for question classification. By using both the generation and selection approaches, our proposed method is able to generate a comprehensive set of questions while still ensuring that the selected question is relevant to the user’s queries. Our results on the TREC CAsT 2022 datasets demonstrate the effectiveness of our proposed method, which significantly outperforms existing strong baselines with improvements at P@1 by up to 20% on the relevance criteria and 30% on the novelty criteria. Finally, to effectively address our second challenge of retrieving relevant passages from a large collection of documents and extracting the answers, we propose monoQA, which uses a text generation model with Multi-Task Learning for both the reranker and reader. Our model, which is based on the T5 text generation model, is fine-tuned simultaneously for both reranking (in order to improve the precision of the top retrieved passages) and extracting the answer. Our results on the OR-QuAC and OR-CoQA datasets demonstrate the effectiveness of our proposed model, which significantly outperforms existing strong baselines with improvements ranging from +12.31% to +19.51% in MAP and from +5.70% to +23.34% in F1 on all used test sets. Overall, this thesis contributes an effective ORConvQA framework leveraging Multi-Task Learning to address the challenges of resolving ambiguities in conversational questions and retrieving relevant passages from a large collection of documents. Our proposed framework significantly outperforms existing strong baselines on a variety of benchmark datasets, demonstrating the effectiveness of MTL in improving the performance of ORConvQA models

    Synthesis of organic materials for optoelectronic applications

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    Organic electronics have seen a rapid development of research in this area in both academia and industry due to being lighter, more flexible and less expensive than conventional inorganic materials. This thesis describes the synthesis and characterisation of novel organic materials with the aim of optoelectronic applications. The first chapter provides an introduction to organic semiconductors, focussing on their working principle from both a chemical and physical perspective. This is followed by a discussion on a few recent technologies developed in this field including some exemplar materials. The second chapter describes the design and synthesis of spiro-OMeTAD based polymers for application in perovskite solar cells. Spiro-OMeTAD is the benchmark hole transporting material for these devices due to its high performance on doping. Preliminary conductivity measurements determine the potential for these polymers to act as additives in perovskite solar cells. One of the polymer materials was incorporated into a device and the key parameters discussed. In chapter three, the development of green chemistry inspired materials for perovskite solar cells were outlined. Initially, the design and synthesis of several imine-based small molecules were discussed. The optical, electronic and thermal properties are extensively studied. The triphenylamine derivatives were further studied in the later section of this chapter to determine their potential as hole transporting materials in perovskite solar cells. Two flavin-fused truxenes are presented in chapter four. First, details of the successful synthesis and characterisation of these molecules are discussed before a variety of applications were attempted for these materials including organic light emitting diodes, organic field-effect transistors and sensors. Finally, the last chapter describes a variety of fluorescent bio-mimetic materials based on either the flavin moiety or green fluorescent protein chromophore. The chapter is separated into two parts to discuss these individually. Flavins are natural redox-active molecules which have good stability and structural versatility. Green fluorescent proteins have been studied due to their good photoluminescence, photostability and sustainable production. Therefore, both have the potential for applications in optoelectronics

    Crop mapping using deep learning and multi-source satellite remote sensing

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    Crop mapping is the prerequisite process for supporting decision-making and providing accurate and timely crop inventories for estimating crop production and monitoring dynamic crop growth at various scales. However, in-situ crop mapping often proves to be expensive and labour-intensive. Satellite remote sensing offers a more cost-effective alternative that delivers time-series data that can repeatedly capture the dynamics of crop growth at large scales and at regularly revisited intervals. While most existing crop-type products are generated using remote sensing data and machine learning approaches, the accuracy of predictions can be low given that misclassifications persist due to phenological similarities between different crops and the complexities of farming systems in real-life scenarios. Deep neural networks demonstrate great potential in capturing seasonal patterns and sequential relationships in time series data in the context of their end-to-end feature learning manner. This thesis presented a comprehensive exploration of advanced deep learning methodologies for large-scale agricultural crop mapping using multi-temporal and multi-source remote sensing data. Focusing on Bei'an County in Northeast China, the research developed and evaluated innovative frameworks to produce accurate crop-specific map products, addressing challenges such as optimal satellite-based input feature selection, imbalanced crop type distribution, model transferability, and model learning visualisation. This research has effectively addressed these challenges in complex agricultural environments by introducing advanced deep learning architectures that utilise multi-stream models and multi-source data fusion. The classification frameworks developed through this thesis have shown improved performance in accurately mapping crops, particularly in terms of evaluating model generalisability for inference of unseen area, model spatial and interannual transferability across different test sites, and model interpretability for unveiling the model decision process that contributes to a deeper understanding of model learning behaviours for temporal growth patterns of crops. The findings highlight the importance of temporal dynamics, the integration of various data sources, and the effectiveness of ensemble learning in enhancing the accuracy and reliability of crop classification. A deep learning framework using radar-based features was developed, achieving F1 scores for maize (87%), soybean (86%), and other crops (85%) on an imbalanced crop dataset. This approach was extended by integrating Sentinel-1 and Sentinel-2 data, resulting in an overall accuracy of 91.7%, with F1 scores of 93.7%, 92.2%, and 90.9% for maize, soybean, and wheat, respectively. Furthermore, the spatiotemporal transferability of pre-trained models was systematically evaluated across two test sites, resulting in overall accuracies of 96.2% and 90.7%, mean F1 scores of 92.7% and 88.6%, and mean IoUs of 86.9% and 79.7% for site A and site B, respectively

    Gut microbial regulation of organismal health through Tachykinin in Drosophila

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    The complex relationship between the gut microbiota and host physiology is a multifaceted area of investigation with profound implications for systemic health and ageing. Despite residing predominantly in the gut, the microbiota holds the potential to systemically impact overall host health, including complex processes like ageing. This prompts the question, by what mechanisms does the gut microbiota systematically influence the host? Host-derived hormones, particularly gut peptides secreted by enteroendocrine cells, emerge as potential mediators for conveying the microbiota's influence on lifespan and metabolism. However, the exact molecular mechanisms through which microbiota regulate host enteroendocrine signalling, and the relevance of this in systemic host health, is unknown. Drosophila melanogaster was used as an in vivo model to study the impact of microbiota on host via enteroendocrine signalling. To address this, I used a unique approach, integrating germ-free and gnotobiotic conditions with targeted genetic manipulations. This strategy provided a platform to unravel the specific roles of enteroendocrine peptides in the context of microbial influence. RNAseq analysis and fluorescence staining showed that the microbiota shapes the expression levels of gut peptides, and the number EE cells present in the gut. In particular, the differentially expressed host derived gut peptide, tachykinin (TK), proved to be a strong candidate to mediate the influence of microbiota on host health. Germ-free and conventional flies were used to determine if TK responds to microbial cues to regulate complex host phenotypes such lipid metabolism, lifespan, starvation resistance, feeding behaviour and fecundity. The focus was specifically on two phenotypes: lifespan and lipid metabolism. In the presence of microbiota, ubiquitous RNAi against TK extended lifespan, but eliminating the microbiota had no additive effect. TK knockdown also increased lipid levels in conventional flies, but this effect was reversed in germ-free flies, demonstrating that the microbiota regulates complex host traits through a TK mediary. To refine which members of the microbiota interact through TK, gnotobiotic flies mono-colonised by either the gut symbiont Acetobacter pomorum, or Lactobacillus brevis were used. A. pomorum was found to strongly modulate lifespan and lipid levels via TK, while L. brevis had a marginal impact. It was further determined that in order to achieve lifespan modulation, A. pomorum regulated TK expression in the gut, which then targets its receptor TKR99D in the brain. In terms of potential mechanisms mediating the impact of the interaction between A. pomorum and TK - feeding and egg laying assays suggest that nutrient restriction and reduced reproduction can be excluded but impacts on 4E-BP and Akt expression suggest roles for the IIS/TOR signalling network. In support of this, ablation of insulin producing cells phenocopies the TK knockdown lifespan phenotype. However, knockdown of TK in null-dFOXO mutants showed that, while dFOXO is required for TK to modulate lifespan, it is not required for microbial lifespan regulation, suggesting that other interacting mechanisms are likely to be involved. In conclusion, this thesis implicates TK as a pivotal mediator of the effect of microbiota on host lifespan, setting the stage for innovative approaches to delay ageing and improve healthspan

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