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Characterising the dynamic tissue and immune response to radiotherapy in rectal cancer
No full textAbstract not currently available
Investigating drug resistance in RAS-driven models of colon cancer
Get PDFColorectal cancer (CRC) is the second leading cause of cancer-related mortality in the world, accounting for more than 900,000 deaths in 2020. A disproportionate number of these deaths are due to KRAS-mutant CRCs, which account for ~40% of all CRC cases and are notoriously resistant to most therapies. Despite showing great promise in preclinical studies, targeted therapies have performed sub-optimally in clinical trials for KRAS mutant cancers. The mechanisms by which RAS pathway inhibitors have failed to reduce tumour progression remains poorly understood and presents a huge clinically unmet need. This research addresses the significant gap in effective treatments for KRAS-mutant CRC by delving into the mechanisms underlying drug resistance, using advanced CRC models. Several studies have reported that drug resistance is an emergent feature of genetically complex tumours.
To capture tumour genome complexity, I used a diverse panel of CRC models reflecting multigenic and heterogeneous nature of tumours. Our patient-specific Drosophila avatars and transgenic mouse models are designed to explore how genome complexity impacts drug response. Our models comprise alterations in at least three primary pathways implicated in CRCs– APC, KRAS and TP53, providing a robust platform for studying the cellular and molecular dynamics driven by oncogenic Ras signalling.
Key findings demonstrate that CRC tumour complexity significantly impacts the efficacy of RAS-pathway inhibitors, which have shown limited success clinically. By characterizing these models, this research has uncovered that different stages of tumour development exhibit varying dependencies on the MAPK pathway, offering insights into the failure of existing therapies. Additionally, the study identifies and validates the upregulation of the glucuronidation detoxification pathway as a novel resistance mechanism, showing that targeted combination therapies can enhance drug efficacy within tumours.
This comprehensive study not only deepens the understanding of CRC pathogenesis and resistance mechanisms but also opens avenues for developing more effective targeted therapies
Postcolonization of university accounting education: case of Sri Lanka
Get PDFThis study addresses a notable research gap by offering an institutional-level analysis of how and why university accounting education in an ex-colony interacts with the global accountancy discourses and practices. Adopting a postcolonial lens, it examines how and why Sri Lanka's first accounting degree programme engages with global accountancy discourses and practices. Both the literature and my pre-understanding suggest that university accounting education in ex-colonies often reproduces global discourses and practices, as adopted by local professional accounting bodies (PABs). To provide a comprehensive understanding, the study explores the interaction between Institute of Chartered Accountants of Sri Lanka (CA Sri Lanka) and the global accountancy discourses and practices. The findings highlight a complex interplay of complicity and resistance (ambivalence) among key constituencies of CA Sri Lanka has led the local PAB to replicate global discourses and practices, including reproducing global curricula, implementing Mutual Recognition arrangements (MRAs), and adopting International Financial Reporting Standards (IFRSs) and International Standards on Auditing (ISA). This has resulted in CA Sri Lanka developing a hybrid identity as a "globalised local institute," aimed at producing accountants aligned with global capital demands, thus enhancing their global mobility. Similarly, the study of B.Sc. Accounting (Hons.) degree programme reveals that ambivalence among its key constituencies has driven the programme to replicate global discourses and practices, including adopting PAB-based curricular, seeking exemptions and accreditations from PABs, and promoting global standards such as IFRSs and ISA. This has created a hybrid identity for the degree programme: a globally recognised, professionally oriented academic qualification. Through this, the degree programme aims to produce graduates who are prepared to serve global capital, thereby enhancing their employability in international markets, which resembles that of CA Sri Lanka. This thesis contributes to postcolonial studies in accounting by offering a nuanced institutional-level analysis of global-local interactions within accounting profession and education in an ex-colony, from the perspective of colonized
Once an urban gull, always an urban gull? Migrating lesser black-backed gulls vary in whether or not they niche-track
No full textAbstract not currently available
Analysing the serological response to feline coronavirus and potential for cross-reactivity with SARS-CoV-2
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Communication-efficient decentralized federated learning
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Enhancing robustness and adaptability in motion intent recognition: a multimodal approach with advanced neural networks and meta-learning
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Magnetoelectric antennas for wireless power and data transfer at microwave frequencies in medical devices
No full textAbstract not currently available
Methodological developments in data fusion for lake water reflectance from satellite sensors
Get PDFFusing satellite-sensed reflectance data from different sources is of interest to monitor lake water quality, and the satellite sensors have possibly different spatial, temporal and spectral supports. The nonparametric statistical downscaling (NSD) model is an existing state-of-the-art fusion model which can account for a change of spatial and temporal support between two remote sensors [Wilkie et al., 2019]. However, the NSD model is computationally demanding for large datasets and does not allow multivariate responses with an additional spectral dimension. Thus, the aim of this thesis is to improve the computational efficiency of the NSD model and then extend this model to provide an approach that is suitable for a multivariate response to enable the fuse of reflectance data with different spectral and temporal supports from two sensors. The NSD model assumes that the discrete data at each location within a lake from each data source are observations of smooth functions over time and that the coefficients of these smooth functions are modelled as spatially correlated via a covariance matrix. In this thesis, a novel approach proposes using a Gaussian predictive process to approximate the spatial varying coefficients in the NSD model, which requires the inversion of a matrix with smaller dimensions in the Gibbs sampling process and hence reduces the computational time for the parameter estimation. The predictive performance and computational efficiency of the proposed nonparametric statistical downscaling model with Gaussian predictive process (NSD-GPP) are compared to the NSD model through simulation and using satellite reflectance data from Lake Garda. It was found that the NSD-GPP model achieves a similar predictive performance as the NSD model using less computational time. To enable data fusion from the two sensors with a multivariate wavelength dimension, a novel method using the two-dimensional B-spline basis functions was developed where the basis functions were used to represent the reflectance over both time and wavelength at each location, and a different precision parameter was used for each wavelength. Lake Garda is used as an example of interest here, and methods are general for any lake of interest in principle. Overall, it is found that the proposed multivariate NSD-GPP model could be used to make predictions for the unobserved wavelengths and time points within the observed range. It may be beneficial to provide reflectance data at higher temporal and wavelength frequencies, and this model could in principle be extended to consider similar challenges in space
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