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Over the past three decades, Soho has become known, both nationally and internationally, as London's gay district, functioning as a place where gay men could openly interact and express their sexual identities while also developing a sense of community. At the same time, Soho also worked as a bridge between these men and British society, laying the ground for those legal and social changes that contributed to a shift in attitudes towards homosexuality at the start of the millennium. However, many gay-targeted venues in the district have recently gone out of business or are struggling to stay open. Even though this tendency is often described as a direct result of the relentless process of gentrification that is swiftly changing the image of Soho, it does not explain why so many gay men seem to be almost complicit in the district's seeming disappearance. While it is impossible to ignore the part that gentrification is playing in the transformation of the district and the changing face of its gay scene, it is not the only element contributing to the process. Academic research has often concentrated on similar examples of gay districts around the world, but not much attention has been given to Soho. The aim of this work is to reconsider current views on Soho and to try to understand if and how other social, economic, and cultural factors may be playing a part in the dismissal of the area as London's gay district. In particular, through the use of qualitative interviews, the thesis concentrates on the personal experience of gay men in London and their relationship with Soho, bringing new insights to the study of the district and expanding academic literature around gay spaces and communities.

The use of plants to produce recombinant products has advanced tremendously over the past two decades. It is now capable of competing with traditional expression systems for market share, with advantages in terms of low upstream cost, ease of scale-up and safety. The major plant-based platforms for heterologous protein production are cell suspension culture, transient expression and transgenic plants. Transient expression finds application in many fields, for instance, synthetic biology and metabolic engineering, in addition to biopharmaceutical manufacturing, and its outstanding feature is the rapidity with which it is possible to accumulate recombinant protein in the leaf tissue. This is achieved thanks to the infection mechanism of Agrobacterium tumefaciens combined with agroinfiltration in mature biomass that allows the production of recombinant proteins in less than seven days. This study highlights the speed of transient expression, exemplifying its utility in various technological applications. Virus-like particles (VLPs), non-infectious nanoparticles derived from viruses lacking genetic material, have garnered significant interest in biotechnology and nanotechnology. Their unique attributes, including self-assembly, stability, heterogeneity, immunogenicity, and biocompatibility, render them versatile candidates for various applications. To harness the full potential of VLPs, a comprehensive understanding of the mechanisms governing their assembly, disassembly, and intricate interactions with host systems is imperative. The study of VLPs as structural mimics of viruses is a growing trend in structural virology, yet it faces a critical bottleneck in the preparation of high quality samples. A fundamental aspect of this work involves the development of a simple and high-performing protocol for VLP purification. The purification process is essential for obtaining high-quality VLP samples, facilitating in-depth studies of their structural and functional attributes through, for instance, cryogenic electron microscopy. This thesis also explores the flexibility of transient expression in the context of a metabolic engineering endeavour focused on producing polyhydroxybutyrate (PHB), a biopolymer with significant industrial and medical relevance. Breaking new ground, PHB synthesis was achieved in Nicotiana benthamiana through transient expression, marking the first instance of such accomplishment. The investigation involved testing four distinct constructs identifying the most efficient configuration for high PHB accumulation in the cytosol. This achievement lays the groundwork for future studies and opens avenues for developing a plant PHB production platform. Moreover, this work investigates a frequently neglected aspect of binary vectors-their replication system. Building on anecdotal evidence suggesting a potential link between plasmid copy number (PCN) and protein expression levels, experimental evidence is presented to substantiate this link. The research explores the correlation between PCN and protein expression in plants by employing RK2 copy-up mutants as the primary approach and subsequently examining the replication machinery from existing binary vectors as an alternative strategy; the study offers compelling support for the hypothesised relationship. This work provides valuable insights for developing a more efficient transient expression binary vector. Overall, this thesis advances the use and development of transient expression in plants for the benefit of a broad range of bioscience research.

Deep learning (DL) algorithms are prevalent in radiology as workflow assistants and as modality enhancements. Magnetic resonance imaging (MRI), computerized tomography (CT), diagnostic imaging (DI), ultrasound (US), and positron emission tomography (PET) are modalities that benefit from the DL algorithms and shorter exam times or greater image accuracy. Faster scan time is achieved by the signal to noise ratio (SNR). The distinction is that the technology can enhance images beyond the original resolution from the modality or shorten exam time and rebuild the image quality through SNR algorithms back to approximately the original standard of care (SOC) image. While artificial intelligence signal to noise ratio algorithms (AI-SNR) can enhance an image to a greater accuracy, shorter exam times are a measurable component of a return on investment (ROI) in calculating modality utilization. The algorithm-derived images may have visual variations that are not found on normally acquired original images. The research focused on DL-SNR images on a three-tesla magnetic resonance imaging (3.0T MRI) unit, a high resolution MRI deployment in the industry. The primary research question for this research study is: What are radiologists’ perceptions in regard to image quality and increased utilization due to vendor provided DL-SNR on 3.0T MRI? This will be an exploratory qualitative research study using detailed interviews with fellowship-trained radiologists that are using AI-SNR in 3.0T MRI and shortened exam time protocols. Fifteen interviews were conducted. The interview transcriptions were coded using ATLAS.ti to identify common themes and sub-themes in the radiologists’ perceptions of DL-SNR imaging. This paper assumes the reader has an adequate understanding of deep learning and radiology processes. The interviews included discussions on key elements on image quality, workflow, reimbursement, legal concerns, and radiologist workload. Issues were identified and potential solutions for optimizations were rendered.