Magnetic resonance imaging (MRI) has transformed the way we look through the human body by offering exquisite soft-tissue contrast in high-resolution images, noninvasively. This has made MRI the gold-standard imaging technique for diagnosis and monitoring of many diseases. However, conventional MRI scans do not produce "quantitative" measurements, i.e. standardised measures, and therefore it is difficult to compare MRI images acquired at different hospitals, or at different points in time, limiting the potential of this imaging technology for advanced diagnostic and monitoring precision. Quantitative MRI (qMRI) aims to overcome this problem by yielding reproducible measurements that quantify tissue bio-properties, independent of the scanner and scanning times. This could transform the existing scanners from picture-taking machines to scientific measuring instruments, enabling objective comparisons across clinical sites, individuals and different time-points. But unfortunately qMRIs have excessively long acquisition times which currently create a major obstacle for their wide adoption in clinical routines. Therefore, the main goal of this project is to develop new computational methodologies based on compressed sampling and machine learning that will substantially reduce the scan times of qMRI. Compressed sampling techniques enable efficient acquisition of signals and images from tightly constrained sensor/imaging systems. They have been recently applied to address the issue of scan time in qMRI, but these techniques require much better computational methods for removing image compression artefacts at higher acceleration (compression) rates needed for this application. The project aims to address this gap through advanced machine learning-based models and appropriately chosen datasets to train them. The research has two streams of beneficiaries: (i) A large community of UK and international clinical academics that use qMRI techniques for their research on precision imaging and evaluation of diseases such as cancer, cardiac or neurodegenerative disorders, each with significant socioeconomic impact. The outcomes of this project would allow these studies to become more available and more economically feasible. (ii) A large community of UK and international non-clinical academics/professionals who work on compressed sampling inverse problem techniques, motivated by variety of other sensing/imaging applications that could benefit in their studies from methodologies developed by this project. A number of activities have been carefully designed to effectively engage with beneficiaries of this research. These activities include co-production and validation of knowledge with clinical academics and healthcare industry as our project partners, publishing of the results in leading academic journals/conferences, a project website to publicize up-to-date project advances and share open-source software and demonstrators, and a workshop with field specialists and national academic and non-academic stakeholders in medical technologies.

The CP2K software (www.cp2k.org) is a highly efficient and parallelizable open-source atomistic simulation tool able to calculate the energies and forces (as well as other properties) of large collections of atoms at a variety of levels of theory. This makes it a prime candidate for usage in novel machine learning and other data driven areas of research as well as more traditional materials science work. Indeed, CP2K was one of the most intensively used codes on ARCHER and has an extensive and growing user base on ARCHER2. CP2K was one of the acceptance test codes evaluated on ARCHER2, where it underperformed in comparison to the average of the suite of test codes, highlighting the need for this code to be refactored and optimised for the ARCHER2 hardware and emerging systems like Bede. In addition to the clear need to tune CP2K to the ARCHER2 architecture, there are two other main drivers for our bid: 1) to support the UK user base of ~200 researchers through user meetings and workshops 2) develop CP2K so that it can become the favoured density functional theory (DFT) engine for the rapidly expanding community applying machine learning (ML) methods to materials and structure prediction. Previously, we have obtained funding to support the CP2K community and develop the CP2K code. The "CP2K-UK" grant ran from 2013 to 2018 and helped build a large, connected and productive community of CP2K users and developers in the UK. Annual meetings typically attracted around 100 attendees demonstrating a clear demand from CP2K users. Usage of CP2K on national supercomputers grew significantly during this period. Currently, we are experiencing a dramatic shift in the way that the materials modelling community tackles scientific problems as ML and artificial intelligence transform more areas of research. For machine learning there are two application scenarios: (1) machine learning interaction potentials, and (2) machine learning molecular/materials properties. For (1), CP2K can provide energies and forces, and for (2) CP2K can provide a range of properties that can be learned. We will address these scenarios by providing software for: - Rapid and efficient sampling of high-quality ab initio data - Easy and reproducible environments for developing and utilizing ML potentials - Clear documentation of workflows and scientific method - Better integration with materials databases allowing data-mining of results. CP2K is particularly well placed to address these challenges through its intrinsic efficiency in generating data. This also means that less energy is used for during the training process of building ML methods helping the UK's net zero targets. Because CP2K is open source with a sustained and growing development base for over a decade and a clear code development ethos, it is readily amenable for integration with other software and libraries. We will support and expand this extremely successful community and develop a suite of tools for CP2K to enable highly efficient, reproducible, and flexible workflows on the new and next generation of UK hardware and the emerging generation of materials modelers that rely upon ML methods. We will provide community led improvements to CP2K, develop a flexible and robust ML potential work environment encompassing CP2K and partner ML codes. The community will be grown by a series of hands-on workshops that encompass both local and international experts and use both traditional presentation and online learning materials. We will also extend our activities to industrial partners including Johnson Matthey.

Plants are nature's other successful experiment with multicellular life. To coordinate growth and development of their cells, tissues and organs plants have evolved unique plasma membrane receptor kinases (RKs). Several members of this protein family function as pattern recognition receptors, and as hormone receptors shaping the architecture of the plant. There is mounting evidence that different plant RKs are organized in membrane signaling complexes. RKs have a common structural architecture and share downstream signaling components. As such, it is presently unclear how the recognition of specific endogenous or foreign signals at the cell surface is translated into the activation of specific developmental programs or immune responses in the cytosol. We propose to combine physiology, genetics and cell biology with phosphoproteomics, quantitative biochemistry and structural biology to identify the shared and specific mechanisms by which plant developmental and immune receptor complexes are activated. We will dissect, in molecular detail, how activated receptor complexes generate specific signaling output in the cytosol and how the activity of plant RKs are regulated by inhibitor proteins. We envision that our work will provide a molecular framework for understanding how specificity is encoded at the molecular level in RK signaling, setting the stage for engineering these pathways in crops in the future.

The epidermis is the largest, most complex epithelial tissue in the human body and its mechanical integrity is vital in protecting the human body from harm. Keratinizing skin disorders are a large group of highly debilitating, difficult-to-manage hereditary skin conditions that present major treatment challenges in the clinic. Collectively these diseases affect ~1 in 2000 people, but because they are individually quite rare very little progress has been made towards developing effective treatments. The quality-of-life impacts for these patients are life-long and can be devastating, thus, the overall healthcare and societal burden is very great. This proposal aims to begin to tackle the major challenge of developing effective, long-term treatments for these conditions. Most keratinizing disorders arise from single nucleotide mutations in one of several critically important structural molecules of the skin, which leads to the development of weakened, thick skin - epidermal fragility and hyperkeratosis. The great challenge in developing treatments for these disorders lies in selectively repairing or silencing the causative mutation. RNA interference (RNAi) is a remarkable natural cellular process that uses a unique class of molecules, called small interfering RNA (siRNA), to specifically and potently control gene activity. Harnessing the therapeutic potential of the RNAi pathway, several siRNAs that specifically target keratinizing skin disorder mutations have been identified. Unfortunately, the physical properties of siRNA molecules, they are very large and carry a negative charge, make epidermal delivery difficult. One of the major goals of this research proposal is to develop a patient-friendly way to delivery siRNA into the skin. We will use a unique in vivo reporter model, that produces a visually-trackable enzyme and facilitates real-time monitoring, to develop clinically-viable siRNA skin delivery methods. Once we have an efficient and effective mode of delivery, we will use it to test whether disease targeting siRNA treatments alleviate disease symptoms in an in vivo keratinizing skin disorder model displaying symptoms similar to the human disease. Our findings will provide the preclinical evidence required to progress siRNA therapeutics into clinical trials and, ultimately, patient prescribed treatments. Because each condition is individually rare and multiple mutation-specific siRNAs will be required to treat each patient population, we feel that it is important to also explore the possibility to developing one common treatment for all of these disorders. New medicines aimed at a common disease feature, like hyperkeratosis, rather than the specific mutant gene, may allow treatment of several keratinizing disorders regardless of the genetic abnormality. Unfortunately, we do not understand why hyperkeratosis develops or how to stop it. Therefore, the other major goal of this research proposal is to define the molecules and identify the biological pathways that cause a single gene mutation to develop into the phenotypic end product of weak, thickened, blistering, painful skin. We will do this by examining unique in vivo keratinizing skin disorder models, that develop hyperkeratosis, using cutting edge molecular profiling techniques to assemble an in-depth inventory of the individual cellular components that are present in normal but not diseased states, and vise versa. This will enable us to identify the molecular mechanisms that trigger hyperkeratosis and begin to understand how hyperkeratosis is regulated. It is our hope that these findings will seed the development of a generic treatment for most, if not all, keratinizing skin disorders.

Risk is the potential of experiencing a loss when a system does not operate as expected due to uncertainties. Its assessment requires the quantification of both the system failure potential and the multi-faceted failure consequences, which affect further systems. Modern industries (including the engineering and financial sectors) require increasingly large and complex models to quantify risks that are not confined to single disciplines but cross into possibly several other areas. Disasters such as hurricane Katrina, the Fukushima nuclear incident and the global financial crisis show how failures in technical and management systems cause consequences and further failures in technological, environmental, financial, and social systems, which are all inter-related. This requires a comprehensive multi-disciplinary understanding of all aspects of uncertainty and risk and measures for risk management, reduction, control and mitigation as well as skills in applying the necessary mathematical, modelling and computational tools for risk oriented decision-making. This complexity has to be considered in very early planning stages, for example, for the realisation of green energy or nuclear power concepts and systems, where benefits and risks have to be considered from various angles. The involved parties include engineering and energy companies, banks, insurance and re-insurance companies, state and local governments, environmental agencies, the society both locally and globally, construction companies, service and maintenance industries, emergency services, etc. The CDT is focussed on training a new generation of highly-skilled graduates in this particular area of engineering, mathematics and the environmental sciences based at the Liverpool Institute for Risk and Uncertainty. New challenges will be addressed using emerging probabilistic technologies together with generalised uncertainty models, simulation techniques, algorithms and large-scale computing power. Skills required will be centred in the application of mathematics in areas of engineering, economics, financial mathematics, and psychology/social science, to reflect the complexity and inter-relationship of real world systems. The CDT addresses these needs with multi-disciplinary training and skills development on a common mathematical platform with associated computational tools tailored to user requirements. The centre reflects this concept with three major components: (1) Development and enhancement of mathematical and computational skills; (2) Customisation and implementation of models, tools and techniques according to user requirements; and (3) Industrial and overseas university placements to ensure industrial and academic impact of the research. This will develop graduates with solid mathematical skills applied on a systems level, who can translate numerical results into languages of engineering and other disciplines to influence end-users including policy makers. Existing technologies for the quantification and management of uncertainties and risks have yet to achieve their significant potential benefit for industry. Industrial implementation is presently held back because of a lack of multidisciplinary training and application. The Centre addresses this problem directly to realise a significant step forward, producing a culture change in quantification and management of risk and uncertainty technically as well as educationally through the cohort approach to PGR training.