Lay summary:Type 2 diabetes is a serious disease. It runs in families, but is mainly caused by being overweight and not being physically active. About 20% of British South Asians have diabetes, compared to 4% of the UK population. Weight loss and exercise programmes prevent diabetes internationally but this has not been proven, either in the UK or in UK South Asians. This research will investigate whether a family-based lifestyle change programme (based on diet and physical activity) will prevent diabetes in people living in families where there is diabetes. This will benefit the population by helping us understand how to prevent diabetes. Wider issues: The Universities involved are active in public engagement in science. The PI (Bhopal) will take primary responsibility for ensuring dissemination of the results via the popular media, through press releases, and interviews with radio and television. Bhopal has a track record of such work. Our links with the community and professionals are excellent, and several of the investigators and collaborators will work to ensure policy makers, practitioners, and the public are directly engaged during the dissemination process.

Promoter sequences sit upstream of genes and function to control under what conditions, at what time and how much of the protein a gene codes for are expressed. An important step in the emergence of synthetic biology as a fully-fledged engineering discipline is to provide yeast promoters which result in well characterised and predictable protein expression. Unfortunately current efforts to predict patterns of protein expression from DNA sequences of promoters do not yet yield sufficiently accurate results. We propose to apply machine learning techniques to uncover what features within promoter sequences contribute to particular expression patterns based on a large dataset of activity assays provided by the Edinburgh Genome Foundry. The end goal of these efforts would be to reverse this process allowing the predictable design of yeast promoters fine tuned with the desired parameters for a variety of genetic engineering applications from Biofuels (Azhar et. al. 2017) to manufacturing of biopharmaceuticals (Nielsen et. al. 2012). This work will build on the findings of Cuperus et. al. 2017 who utilised linear regression models and convolutional neural networks to predict the effects on protein expression of any 5' untranslated region in yeast. Similar work, again using convolutional neural networks, was carried out by Umarov et. al. 2017 in humans, mice, Arabidopsis, E.coli and B.subtilis. The use of convolutional neural networks using unsupervised learning in both of these studies provides the benefit of allowing the model to take into account previously unevaluated features. Convolutional neural networks do however have the drawback that spatial relationships between features are of far less importance than their presence. Recent developments in machine learning in the form of capsule networks (Sabour et. al. 2017) may provide a solution to this through capsule structures which learn to recognise a feature over a small range of different conditions and deformations. Cuperus et. al. 2017 indicates the importance of taking into account spatial orientation of features through the improved performance of their position-dependent linear regression models compared to position-independent implementations due to the position dependence of many key features in yeast protein expression. In order to achieve the end goal of predictable design of yeast promoters we will again expand on Cuperus et. al. 2017's work on in silico evolution of 5' untranslated sequences by using a hybrid approach of a genetic algorithm with a similarity measure between the prediction of our promoter parameter model and the desired promoter properties acting as a surrogate for the fitness function. A similar technique has been utilised by Dias et. al. 2014 to improve clinical practice in the placement of beams in Radiotherapy. By utilising machine learning to understand patterns in yeast promoter design we hope to contribute to the advent of faster and less error prone workflows in synthetic biology facilitating a new generation of environmentally friendly, highly efficient manufacturing techniques.

The research focuses on examining the dynamics of alkali metals under extreme temperatures and pressures. This will involve the design of diamond anvil cells (DACs) by extending existing designs and using computer simulations. Following the subsequent manufacture of these designs, materials will be loaded and experiments conducted at high-energy x-ray sources around Europe. We anticipate that our efforts will enable studies of hitherto inaccessible regions on P-T space using the improved DAC design.

"How did the Universe begin and evolve" is one of the three science challenges identified in the STFC Astronomy Programme. We address this question by modelling physical processes from the micro (nuclear, stellar) to the macro scales (galactic, cosmological), studying the ionising and chemical feedback from stars and the wider context of galaxy formation. The BRIDGCE consortium is a multidisciplinary collaboration between nuclear, stellar and extra-galactic astrophysicists, which aims to achieve a comprehensive understanding of the evolution of the Universe from the era of reionisation up to now, using chemical elements as fingerprints of the physical processes that occur in stars and galaxies. Elements heavier than helium are produced in stars and supernovae on different timescales, and the stellar populations and interstellar medium within galaxies keep a record of star formation and chemical enrichment histories of galaxies. Therefore, it is also possible to constrain galaxy formation theory from the observed elemental abundances, and to do this more accurately we need to understand stellar and nuclear Astrophysics. Moreover, the discovery of gravitational waves (GW) has opened a new window to the Universe, allowing us to observe the formation of black holes and neutron stars more directly than ever before. GWs can provide independent new constraints on stellar winds, evolution, and stellar deaths via black hole remnants, and the seeds of super-massive black holes in galaxies. The development of high-performance computing enables us to study the theory of stars and galaxies self-consistently: we simulate how stars lose mass via stellar winds prior to supernovae explosions (Project-1); we simulate the full evolution of stars in one-dimension (1D) and compute 3D scans of their interiors (Project-2). Furthermore, by combining stellar evolution and nucleosynthesis to galactic dynamical evolution, we reproduce the entire chemodynamical history of local dwarf galaxies (Project-3) and of the Milky Way (Project-4). Our research addresses some of the key questions of 21st century Astronomy: How black holes and neutron stars are formed (Projects 1 & 2)?, How many GW events will be detected in future missions?, and How we can trace the evolution of the Universe from GWs (Project-5)? Nuclear data (nuclear reaction rates in particular) are a key input for stellar evolution models since nuclear reactions provide the energy that powers stars. This information determines stellar lifetimes and the composition of their ejecta. Stars provide important feedback into galaxies through the light they radiate, their powerful winds and explosions, and all the chemical elements they produce. The outputs of stellar models are thus key ingredients for galactic chemical evolution models. These models follow successive episodes of star formation and trace the history of the enrichment of the elements. The model predictions can then be compared to observations of stars, stellar populations, and the inter-stellar medium that carries the chemical fingerprints of the cumulative chemical enrichment that preceded their birth. Comparison to observations can thus constrain both the galactic and stellar properties. Finally, most stars are not born on their own, but may instead evolve interacting with a companion. Although this has been known for decades, the impact of binarity on galaxy evolution is poorly known. In the BRIDGCE 2021-2024 grant, our galaxy experts will explore this new scientific problem together with our stellar experts. Our consortium project applies innovative techniques across different disciplines and tackles this challenge through 5 projects corresponding to very different physical scales: stellar envelopes (Project-1), stellar cores (Project-2), local dwarf galaxies (Project-3), the Milky Way (Project-4), and the Universe as a whole (Project-5). These impact many areas of Astrophysics as well as Cosmology & Nuclear Physics.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.