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Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-C]glucose and [N]leucine, confirming the formation of [C,N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.

Building electrification is central to decarbonizing cold-climate regions, yet it concentrates electricity demand in winter hours when solar generation is minimal and marginal grid emissions are high. This dissertation evaluates whether buildings can function as distributed thermal storage resources that shift electrified heating demand away from carbon-intensive hours without compromising indoor comfort, and identifies the physical design conditions under which this is possible.

A multi-scale framework links four stages: fit-for-purpose EnergyPlus calibration of HouseZero®, a high-performance retrofitted building; real-world field validation of a transparent, rule-based control strategy driven by forecast marginal emissions; stock-scale scenario analysis of the Suffolk County, Massachusetts residential and commercial building stock using ResStock and ComStock; and machine-learning analysis of the design determinants of comfort-constrained demand-response capability. Threshold screening identified a 95th-percentile forecast marginal-emissions trigger as the preferred operating point. Field experiments, including an extreme-cold stress test at -7.5 °C, showed that HouseZero® repeatedly reduced heat-pump load by approximately 95% while median zone-air temperature dropped by no more than 0.5 °C, yielding measurable rebound-adjusted emissions reductions.

At the urban scale, the modeled standard heat-pump pathway with electric backup reduced site energy but increased grid-side marginal emissions by roughly 37% and created a winter-dominant electric peak, whereas a high-performance geothermal-and-envelope pathway reduced marginal emissions by about 10% relative to the current baseline. Demand response reduced electricity-related emissions in every pathway, yet comfort-constrained screening revealed that the deployable participant pool is far smaller than the technical potential, expanding substantially with added thermal mass. Machine-learning analysis showed that physical design variables explain approximately half of the weighted variation in response capability, with thermal mass emerging as the most robust design lever, alongside envelope exposure, airtightness, and other envelope characteristics.

The dissertation concludes that buildings can serve as grid-interactive thermal batteries, but their usable grid value depends on integrating electrification, envelope performance, thermal mass, and rebound-aware control into a cohesive system rather than as independent interventions.

Governments across the world are exploring how generative AI might improve their ability to effectively pass legislation and deliver public services. This includes the UK government, which has stated that utilising AI to reduce costs and improve public services is a national priority. Current efforts to deploy AI tools are focused on streamlining internal bureaucracy, such as writing ministerial briefings or analysing public consultations, and supporting the direct delivery of public services. In contrast, there has been limited exploration of the role generative AI could play in directly improving policy development and implementation, in support of a government’s high-level agenda or manifesto commitments.

This project seeks to add practical context to hypothetical considerations of how generative AI might impact policymaking. Published articles and commentary provide insights and recommendations, with varying degrees of specificity, on the opportunities, risks, and limitations of using AI to support national policymaking. However, there does not appear to be any instances of meaningful ‘direct experimentation’ with currently available generative AI tools, to explore the potential role of generative AI in more detail, identifying context-specific areas of strength, weakness, or risk.

This project is designed as an initial first step towards addressing this research gap. Using our direct experience of government policymaking, we begin to assess the performance and relative ‘value-add’ of using publicly available generative AI tools to support typical policymaking functions. This is conducted through ‘policy simulations’ in which we assessed how useful (or problematic) AI tools were in helping us to perform a range of policymaking tasks.

Our simulated ‘performance assessments’ were conducted in two phases. Firstly, we undertook basic assessments of the utility of publicly available generative AI across typical policymaking tasks for three different high-level policy contexts. Secondly, we conducted a more granular and in-depth assessment in an area where we have domain expertise (Regional Growth and Development), to explore the impact of output verification (by experts) and corpus design on the utility of generative AI in a national policymaking context.

From this research, we have identified an indicative set of policymaking functions for which generative AI appears to offer significant value (provided this deployment is specific, deliberate, and targeted). We also found that the use of generative AI does not replace the need for in-depth policy expertise but rather increases the utilisation of this expertise by a wider range of government stakeholders, and significant risks may be introduced in the absence of meaningful human engagement with AI outputs. Significant limitations of using generative AI to support policymaking also emerged from this preliminary research, alongside the well-documented broader risks associated with generative AI (such as unclear provenance, biased outputs, hallucinations, and broader security risks). The full set of our indicative findings are below, which we use to inform a proposed set of basic recommendations for governments seeking to better utilise generative AI to support national policymaking.

Most importantly, we judge that the findings from this preliminary exploratory research were sufficiently informative to indicate that there would be meaningful benefit in pursuing further ‘direct experimentation’ with generative AI tools in a policymaking context. We propose that this would be an effective way to develop more detailed understanding and subsequently inform government guidance and adoption initiatives (including for professional training purposes). We conclude this report with a set of proposals for how this further research could be conducted, including how to address the limitations of our initial exploratory research project.

This research is likely to deliver the greatest benefit if conducted in close collaboration with government stakeholders, incorporating direct domain expertise and adjusting for real-world policymaking requirements and constraints. This includes wider considerations on ensuring AI tools are used in accordance with democratic principles and core national values, including retaining sovereignty over internal government data and maintaining sufficient control over AI enabled decision-making. Further, as set out in the Cabinet Secretary’s Future Civil Service initiative, achieving the widespread utilisation and adoption of AI across the UK government will require a whole-of-institution approach, incorporating changes to workforce design and development, novel procurement models, risk management and governance mechanisms, and fundamentally redesigning government processes. Future research on the role of generative AI in policymaking should account for these broader institutional considerations and align closely with existing government initiatives.

Over the last century, the Clinical Pathological Conferences (CPC) have enabled the Case Records database to provide valuable information regarding the diagnosis and treatment of various diseases. Diseases involving Staphylococcus aureus, Influenza, or Tuberculosis are some of the most prominent infectious diseases that have appeared throughout the database, and which were investigated. Cases with a final diagnosis for each disease were chosen and the amount tracked and compared to case data from the CDC. The average time to diagnosis and time in hospital were analyzed and used to see how our understanding of each disease had evolved over the past 100 years. Over the course of the span, the time for diagnosis and time in hospital decreased overall for all three diseases, but with some outliers present. Additionally, patient survivability also increased. Furthermore, the usage of AI models in medical diagnosis was commented on, and which presents a potentially revolutionary ability to positively affect the CPC/Case Records as it enters its second century.

The recent publication of Manichaean manuscripts preserved by the Mingshi ritual tradition in Qingtian, Zhejiang, has brought to light substantial materials ultimately deriving from texts transmitted in Tang China. This article examines pages 23–48 of the Mingshi Yinglun jing ke 明氏應輪經科 (Ritual Manual of the Gospel of the Mingshi Tradition), a composite ritual manuscript centered on Mani’s Living Gospel. Its most significant feature is a sequence of eleven passages transcribing Middle Persian in Chinese characters. Building on previous identifications of sections (5)–(11), the article proposes new readings of sections (1)–(4) and the concluding words of section (11), and presents all eleven passages in six corresponding layers: Chinese transcription, reconstructed Middle Chinese pronunciation, normalized Middle Persian reading, Middle Persian transliteration, word-for-word English translation, and an English translation of the accompanying Tang Chinese interpretive rendering. The article further compares the Chinese text following these transcriptions with surviving witnesses to the Living Gospel in Middle Persian and Sogdian, quotations preserved in the Greek Cologne Mani Codex, and related materials in the Coptic Synaxeis. These comparisons indicate that a substantial textual core of pages 23–41 derives from a Tang Chinese translation or adaptation of the Living Gospel, although it has been combined with Chinese hymnic renderings and later ritual materials. Pages 41–48 praise the scripture, enumerate the benefits of copying, reciting, expounding, hearing, and preserving it, and conclude with ritual benedictions. The Qingtian manuscript thus preserves both previously unknown evidence for the Chinese transmission of Mani’s Living Gospel and a record of its continuing transformation within a local ritual tradition.