Millions of people world-wide die from late diagnosis every year. In particular, in emergency care practice, life- critical decisions must be made rapidly, influencing patients' prognosis and the efficacy of treatment. There is a critical window for diagnosis of many acute diseases such as, traumatic brain injury (TBI) - a leading cause of morbidity and mortality worldwide. TBI has become a major challenge of the 21st century and by 2028, the WHO estimates that neuro-trauma will become the leading cause of death worldwide. While life-changing decisions must be made rapidly, it is notoriously hard to diagnose at the point-of-care, resulting in incorrect patient management and the chances of an individual suffering cognitive or physical impairment are massively increased. Current diagnostic technologies are woefully inadequate either requiring large equipment, long- waiting times, invasive or not sensitive and timely enough. There is an urgent need for new technologies to achieve timely intervention through rapid and accurate TBI diagnostics at the point-of-care. This research will open new horizons in medical devices by exploring how cutting-edge Engineering methods can enable a platform technology that will lead to next generation of applications in miniaturised sensors, monitoring and pharmaceuticals. Underpinning the topic is the development of pioneering portable technology for instantaneous detection of TBI-specific molecular changes, via simultaneous Raman spectroscopy and fundus imaging, to safely measure proxies of cerebral injury via the neuroretina and optic nerve, thus uniquely providing a window into brain biochemistry. Such disruptive technology will enable the low-cost manufacture of a handheld device for early diagnosis of acute injury, whether at the pitch side in contact sports, or the roadside following traffic accidents, will not only inform correct clinical pathways but also mitigate against death and disability from this devastating disease.

Fungal communities are known to play influential roles in woodland ecosystems, yet much remains unknown about their diversity, functioning, and likely responses to environmental change. This project aims to address some of these knowledge gaps by investigating mycorrhizal fungal communities at the Birmingham Institute of Forest Research Free Air Carbon Dioxide Enrichment site (BIFoR FACE). The only facility of its kind in the northern hemisphere, BIFoR FACE is a large-scale experiment subjecting patches of mature temperate woodland to elevated CO2 levels (approx. 550 ppm) with the aim of illuminating likely effects of future increases in atmospheric CO2 on this kind of ecosystem. Though elevated CO2 levels are expected initially to enhance tree growth, it is predicted that this growth enhancement may be relatively short-lived as the availability of soil nutrients - in the northern hemisphere, primarily nitrogen - could become limiting in the longer term. A fuller appraisal of this "progressive nitrogen limitation" (PNL) hypothesis demands consideration of fungal communities as ectomycorrhizal fungi in particular can supply significant quantities of otherwise inaccessible nitrogen to their plant hosts, although the extent to which this occurs varies between different species and environmental conditions. One possible scenario with some empirical support is that under elevated CO2 trees will invest more carbon in their mycorrhizal symbionts and receive more nitrogen from these fungi in return, alleviating PNL. Conversely, experimental evidence from boreal forest ecosystems has suggested that greater allocation of carbon from trees to mycorrhizal fungi can lead to increased amounts of nitrogen being locked up in proliferating mycorrhizal biomass, rather than being transferred to tree hosts, thus exacerbating PNL. This project will use molecular techniques firstly to characterize the mycorrhizal fungal communities present in elevated- and ambient-CO2 plots at the BIFoR FACE site, determining whether differences in community composition are apparent between the two conditions. This will then form the basis for investigating possible changes in mycorrhizal nutrient acquisition and transfer under elevated CO2, whether through community compositional shifts or altered functioning under different conditions, thus connecting mycorrhizal fungal responses to elevated CO2 with their wider effects in terms of nutrient cycling and tree growth.

The central requirement of project safety, stability, and resilience of complex underground systems leads to demands for more efficient computational modelling tools to assist design and decision-making during the project life cycle. The concept of Digital Twins (DTs) provides a robust solution to monitor a construction project during its life cycle, predict its behaviour based on integrated holistic computational models, and protect it from hazards by virtually controlling the physical processes with its digital counterpart. Leveraging the power of a computational framework based on CutFEM combined with a BIM platform incorporating CAD-based data, the TwinSSI project will develop a comprehensive DT for underground design and construction. To validate the computational framework, real-scale experiments of tunnel-soil-structure interaction will be performed at the National Buried Infrastructure Facility (NBIF) at UoB. Moreover, the developed DTs will be applied to real case studies co-created with the industrial partners Network Rail and Maidl Tunnel consultants. The TwinSSI project will thus, for the first time, create and validate detailed DTs in the domain of soil-structure interaction modelling. The project outcomes will lead to a new paradigm for project planning and monitoring by geotechnical engineers.

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.

MIBTP students undertake a period of training during their first year. This includes compulsory taught modules in statistics, programming, data analysis, AI and mini research projects.