BACKGROUND AND PURPOSE We recently discovered a unique enzyme (HTG or hetero-trans-b-glucanase), found only in a group of non-flowering plants, the horsetails. Flowering plants lack HTG even though their cell walls contain the chain-like molecules which, at least in the test-tube, HTG can cut and re-join. We now aim to discover (a) what good HTG does horsetail plants, (b) the full range of 'cutting and re-joining' reactions that HTG can achieve, (c) what happens when HTG from horsetails is artificially transferred to crop plants. We predict that the horsetail enzyme will endow flowering crops, e.g. wheat, with the ability to strengthen their stems in a manner hitherto only available to horsetails. Such crops may acquire improved resistance to lodging (storm damage). OBJECTIVES AND EXPECTED OUTCOMES Remarkably, horsetail HTG is the only known enzyme from any living thing that can 'cut and re-join' molecules of cellulose, the major constituent of plant cell walls. It can graft a cellulose chain onto a chain of a different cell-wall building material called xyloglucan. HTG can also graft chains of a third such material (MLG or mixed-linkage glucan) onto xyloglucan. HTG can thus create cellulose-to-xyloglucan and MLG-to-xyloglucan linkages. The resulting 'hybrid' polymers are thought to strengthen horsetails. We will discover exactly when and where HTG is produced, and such linkages are formed, in horsetails. This will potentially give clues about HTG's natural roles. We will also discover what new reactions HTG can catalyse when mixed in the test-tube with diverse plant cell-wall polysaccharides. This may afford new 'hybrid' polymers, which when scaled up may be commercially valuable new materials. To further our fundamental knowledge of HTG, we will also investigate which of the enzyme's amino acids are important for its ability (in the test-tube) to re-configure cellulose and MLG. A major part of this project involves artificially introducing the horsetail's HTG activities into flowering plants, including both dicotyledons and cereals, and measuring the consequences. Our industrial collaborators (Bayer CropScience) will do this work in the case of wheat. We predict that any crop plants genetically transformed in this way will be able to create cellulose-to-xyloglucan linkages in their cell walls, and that cereals (which, unlike dicots, possess MLG as well as cellulose and xyloglucan) will in addition be able to make MLG-to-xyloglucan linkages. We will test these predictions experimentally. We will also test whether the HTG-endowed flowering plants are stronger, and whether they have an altered shape or size. We will quantify the plants' mechanical strength by measuring the force required to bend or break their stems. Any changes to the molecular architecture a plant's cell walls are likely to affect its growth and strength because of the pivotal roles that cell walls play in dictating these features. BENEFICIARIES OF THE PROJECT Cereal varieties with stronger stems often suffer less lodging, but such strengthening is usually achieved by the plant growing thicker stems at the expense of lower grain yield. Artificially giving cereals HTG may form novel inter-polymer linkages in the cell wall and confer similar strengthening without significant increases in stem biomass and thus without compromising the harvest. Modifying cereals in this way would benefit plant breeders and farmers, as well as the general public, by improving the reliability of grain production in a changing climate as storms and heavy rains become more frequent. In addition, increasing knowledge of HTG's ability to reconfigure biomass materials, especially cellulose (the world's most abundant organic substance), offers biotechnologists novel opportunities to create new materials (e.g. for specialist papers and medical applications) via non-polluting 'green' processes.

This project will combine large-scale electronic patient data, artificial intelligence algorithms, and mechanistic mathematical models, to develop systems that can improve the diagnosis, and hence treatment, of critically ill patients with acute respiratory distress syndrome (ARDS). The key idea is to use mechanistic virtual patient models as "filters" to extract relevant medical information on individual patients, significantly reducing biases introduced by machine learning on heterogeneous datasets, and allowing improved discovery of patient cohorts driven exclusively by medical conditions. I propose to establish a collaboration with Prof Andreas Schuppert at Aachen University and Dr Jörg Lippert at Bayer Healthcare in Germany that will give me access to large-scale patient data and internationally leading expertise in applying machine learning to real clinical problems. As noted recently by leading medical researchers in the journal Intensive Care Medicine, "Artificial Intelligence approaches such as machine learning may assist in identification of patients at risk of or fulfilling diagnostic criteria for ARDS, although this technology is not yet ready for clinical implementation". In ARDS, patient outcomes are poor, while hospital costs are huge - this collaboration will make breakthroughs in the clinical applicability of digital technologies for the earlier identification of ARDS, improving treatment of patients and reducing costs to healthcare providers.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.