To decide which treatments to recommend to patients, we need reliable estimates of how the different treatments compare to each other. However, studies that directly compare all treatments of interest may not be available. Instead, we often have a mixture of studies that compare a selection of different treatments, or in some cases only a single treatment. Furthermore, there may be differences between the patients in the different studies that change how well the treatments work. To address these issues, a statistical method called "multilevel network meta-regression" (ML-NMR) is available. This method combines evidence from multiple studies, where some studies provide individual-level data on every participant and some only provide published summary estimates, and accounts for differences between patient populations - a process known as "population adjustment". Importantly, this method can produce estimates that are specific to a relevant population for decision-making (e.g. the UK patient population). This means that decision makers such as the National Institute for Health and Care Excellence (NICE) can make better decisions that are targeted to the relevant population. However, there are several barriers to the use of ML-NMR in practice which need to be addressed if it is to be used more widely and effectively for decision-making. Firstly, the method requires substantial amounts of data on each treatment, which are not always available. For example, a company making a submission to NICE is likely to have individual-level data from their own trials of their own treatment, but only published summaries from their competitors' trials. Without enough data, we may instead attempt to simplify the statistical model by making assumptions about how different groups of treatments work, but these assumptions may not be appropriate, which can lead to systematic errors in the results and the wrong conclusions being drawn. Secondly, it is common for clinical trials to encounter issues such as missing data, participants not receiving the treatment they were assigned, or participants being allowed to switch treatments (e.g. if their disease progresses). Statistical methods are available to account for these issues, since if they are not handled correctly they can lead to systematic errors in the results. However, currently these methods cannot be used together with methods to account for differences between populations like ML-NMR. This project aims to address these issues to ensure that ML-NMR works well in situations most frequently encountered by decision makers. This will be achieved by: i) developing novel statistical methods for ML-NMR to use additional information available from published trial reports; ii) making recommendations to update guidelines for how clinical trials are reported, to improve the availability of this additional information in published reports; iii) investigating the performance of the statistical methods through real and simulated examples; iv) developing novel statistical methods to combine population adjustment with methods that account for common issues in clinical trials such as missing data or switching treatments; and v) developing accessible software tools and training courses to support the uptake of the methods. This research will have direct impact for decision makers such as NICE and will lead to better informed treatment decisions. The proposed advances in statistical methods and updated recommendations for reporting clinical trials have the potential to transform healthcare decision-making in wider contexts, even when only published summary data are available, such as the development of NICE clinical guidelines. Additionally, there are direct applications in personalised medicine, where recommendations are targeted to individuals or smaller groups.

This grant has been awarded to complete a set scope of work for IRIS. FAST-HEP (github.com/fast-hep), an open source volunteer effort started in May 2017, is a UK-led (University of Bristol & RAL) effort to tackle data analysis challenges posed by the expected large data volumes from upcoming experiments such as HL-LHC, DUNE and SKA by first exploring existing python-based data analysis tools from the wider data science community (e.g. numpy, numba, matplotlib) as well as the HEP community (awkward-array, uproot, mplhep, pyHF), and then creating high-level abstractions and glue to use these tools within particle physics research and related fields for physics analysis. This project will focus on upgrading FAST-HEP's underlying packages and integrate new tools (e.g. boost-histogram, mplhep, pyHF) resulting in significant speed improvements. Together, these changes will make the FAST-HEP tools more useful to a wider range of analyses such as CMS L1 trigger development, DUNE & LZ data analysis, and increase compatibility with astrophysics workflows. As part of this upgrade effort we will strengthen our links to the stakeholders of these analyses by supporting their use of FAST-HEP tools. This will allow us to benchmark the improvements on a wide range of workloads.

The ability to monitor and influence the physiological state of a cell (population) is of critical importance not just for reproducible results in academia but especially in the pharmaceutical industry. Here, the cell physiological state is directly correlated with the production of biologicals and consequently profitability and quality. There are multiple ways to measure the cell state but most look at a single parameter. It is much more informative to integrate and correlate different types of data. Correlative Light Electron Microscopy (CLEM) is one of the most powerful imaging technologies combining the advantages of (live) light microscopy (LM) with the nanometer spatial resolution of electron microscopy (EM) into one experiment. Using this technology, key biological questions have been answered [1]. Cell structures and metabolic activity affect the electrical properties of the cell and by employing a broadband approach to impedance spectroscopy multiple cell properties can be investigated. In general terms, high-frequencies are associated with changes in the cell cytoplasm/internal structures (10-60Mz), middle-frequencies with the cell membrane (2-10MHz) and lower-frequencies with cell size (0.1-2 MHz). We have shown that dose-dependent characteristics of the impedance spectra of cell culture when exposed to a toxic challenge are related to the toxin's mode of action [2]. There is a need and drive to extract more than just LM and EM data from a single experiment and to include other types of information, expanding the CLEM field to an approach called Correlative Multimodal Imaging (CMI). In principle the CMI approach would extract any type of information from a single event and correlate and integrate the data. This project enables, for the first time, to understand and correlate morphological and anatomical changes in the cell structure, detected microscopically, to changes in characteristics of an impedance spectrum. Impedance spectroscopy can be employed to monitor cells growing in a bioreactor and by understanding the changes in the impedance spectra related to cell stress will enable the early detection and mitigation of cell stress. We propose to develop a CMI approach that combines live imaging with impedance measurements in control and stressed (e.g. toxins) conditions in order to better understand the different responses to stressors. We will use live calcium and / or ATP imaging in combination with impedance measurements. Subsequently we will also aim to introduce stress receptor-GFP imaging and EM morphology into this correlative approach to increase the power of the technology.

Climate change was declared a climate emergency by the UK Parliament in 2019. The Institute of Structural Engineers (IStructE) followed this declaration with the creation of the Climate Emergency Task Group (CETG) and since then have highlighted the responsibility of the structural engineer in tackling this climate emergency through publications, magazine articles and seminars. For example, a recent IStructE publication, Design for zero, stated that structural engineers need to "make carbon as important as safety in our calculations". Research shows that overdesign is common in structural engineering, from the use of imposed loads that are higher than recommended by the Eurocode, to a utilisation factor of 0.8 being typical. Research suggests that some of this overdesign comes from design uncertainty, concerns about quality assurance onsite, and a potential lack of understanding of structural behaviour. Some overdesign is unavoidable, a utilisation factor of exactly 1 is difficult to achieve - for example, the required area of steel in a reinforced beam will likely have to be rounded up based on available bar dimensions. However, structural engineers need to be aware of the carbon impact of design decisions and avoid unnecessary overdesign. This research aims to understand the causes of overdesign and consider ways to reduce the level of overdesign that takes place. To achieve this aim, individuals who influence structural engineering design and construction decisions will be interviewed to determine what they consider overdesign, why they think overdesign occurs and if the declaration of the climate emergency has changed their approach to design decisions. This qualitative analysis will be carried out to inform the questions for a larger quantitative study. From the analysis of the findings, potential solutions to some of the identified causes will be investigated.

The research will investigate the emotional support family lawyers give their clients. In particular, focusing on the following research questions: a) Do family lawyers consciously accept that emotional support forms a necessary and central part of their role, and are they comfortable addressing emotional issues? b) What emotional support to clients expect and seek from their lawyers, and to what degree to they feel their needs are being met? c) Are there any gender differences in the emotional support which clients seek or accept? It has been established that an individual's emotional readiness is a key aspect in the settlement process (Hitchings et al, 2014). Clients of family law solicitors appear to struggle because the family law system expects them to deal with factual issues in isolation from the emotional reality of their circumstances. Psychological research tells us that emotions play a crucial role in decision making (Damasio, 2006), and that emotional support helps clients to negotiate and resolve family disputes in a positive way (Hunt, 2011). This research will combine knowledge from both psychology and socio-legal research to look at family dispute resolution and settlement in a novel way.