Immune responses play a central role in the protection against infections and tumor growth. At the same time can uncontrolled immune responses cause severe tissue damage. Local immune responses, therefore, have to be tightly regulated to prevent immune-mediated pathology. A subset of CD4 T-cells, so called regulatory T-cells, has been shown to constitute an important component of local immuno-regulation. Thus, on the one hand these regulatory T-cells are necessary to ensure a well-balanced immune response. On the other hand has it been shown that pathogens and tumours that co-evolved with the immune system have found ways to use regulatory T-cells to dampen local immune responses and to induce tolerance. Thus, translated into a clinical setting, the targeted interference with regulatory T-cell function could substantially enhance the pathogen-/tumor-specific immune response in patients suffering from infections or of cancer. Unfortunately, little is known about the regulation of regulatory T-cell function at the site of inflammation and, as a consequence, there is a high unmet medical need for treatments that specifically could interfere with regulatory T-cell function in a therapeutic setting. We recently discovered a mechanism by which regulatory T-cell function is controlled; which is via the expression of the growth factor receptor, EGF-R. Inhibitors of this receptor are already in wide clinical use for the treatment of some tumours, and in an experimental setting it was shown that these inhibitors enhance immune responses during viral infections. These findings suggest that these inhibitors may function, at least in part, via the suppression of regulatory T-cell function. Such a suppression of immune regulation could also explain for the severe side-effects, such as skin rashes, stomatitis or diarrhea, that are experienced by cancer patients treated with EGF-R inhibitors. In this proposal, we would like to show that EGF-R inhibitors that are already used in the clinic, enhance anti-viral and antitumor immune responses by suppressing the functionality of regulatory T-cells in vivo. Based on that knowledge, we will further develop inhibitors that will interfere with regulatory T-cells specifically, while keeping other functions of this growth factor receptor untouched. We would expect such novel inhibitors to be more effective than the current generation of inhibitors and to induce less side effects, which would allow their further application also in cancer patients that at this moment would not be considered for treatment with EGF-R inhibitors, or in patients suffering from chronic infections.

We know that geohazards such as earthquakes and volcanic eruptions, give rise to stresses, that generate measurable instantaneous magnetic signals, which can also be recorded by rocks. However, current accepted theories for how rocks respond to stress state that the stresses associated with these geohazards (~100 MPa) are too low to affect the magnetisation. Controlled laboratory experiments also suggest that these 'low' pressures are sufficient to generate magnetic signals. There is a clear mismatch between theory and observation. We have no accurate working model for the effect of stress on the magnetic response of minerals. If we can quantify the link between changes in stress and changes in rock magnetisations, then we can design valuable new tools for easy detection and monitoring of surface stresses. Why have we no working model for the effects of stress on the magnetic signal of minerals? Historically the effects of the induced pressures on the magnetic signal have been thought too small (< 1000 MPa) to alter or reset existing "stable" magnetic recordings (remanent magnetisations) in all but the most extreme impacts where heating also plays a significant role. For example, the impact crater that "killed the dinosaurs" - Chicxulub - is thought to have experienced pressures in excess of 60,000 MPa, i.e., 1 million times higher than a nuclear explosion. However, I show numerically as part of this proposal, that this assumption is incorrect. Using the latest state-of-the-art numerical micromagnetic model, I demonstrate in the case for support clearly that pressures of only ~200 MPa or lower are sufficient to affect "stable" magnetic recordings. Whilst 200 MPa is still a very high pressure, such pressures are very common in seismically active fault zones. It is the aim of this proposal to bridge this gap in understanding of the effect of stress on magnetic minerals, by experimentally verifying the numerical models. I will do this through a combination of three approaches: 1) extending the micromagnetic modelling which are on the nanometric scale, 2) experimental measurements on bulk samples on the centimetre scale, and 3) to the link the first two approaches together using Quantum Diamond Microscopy (QDM) done on the micron scale. With the new understanding, in the future I will apply for funding to quantify the magnetic signature of earthquakes by: (1) Determining the magnitude of stress-induced magnetic fields that might be used in early warning systems. (2) Developing a protocol for magnetically quantifying the palaeo-stress fields of palaeo-earthquakes. It is the QDM imaging which will be done in Utrecht and is key to the success of this research and for which the PI requests travel money as part of this proposal. These visits to Utrecht will be done as part of my sabbatical year. I plan to visit Utrecht University on a monthly basis for a about a week at a time starting in January 2023 for nine months to work on this project.

Antimicrobial resistance (AMR) is a threat to humans, animals, and crops, and requires a coordinated One Health approach. AMR affects many of the goals of the WHO 2030 Agenda for Sustainable Development. Levels of AMR are generally quantified by culturing indicator organisms like E. coli, and determining its resistance. Looking at only one indicator organism in all human, animal, food and environmental samples is like studying ocean life by looking at a single droplet of seawater with a microscope. Interesting differences can be found between droplets but is it really the entire picture? Metagenomic sequencing is a technique that allows identification and quantification of AMR genes and species in a sample without culturing. Currently the use of short read sequencing is more common but limited when AMR quantification is required. One can only quantify genes, not where they are. On a chromosome? Or in a transmittable plasmid or transposon, which can easily spread to pathogens? We propose to use long-read Nanopore sequencing to develop a 'one stop shop' surveillance method. Long reads contain both resistance genes and flanking sequences, thereby identifying the original organism or mobile genetic element, the location, but also genes associated with high transmission risk. We propose integrating into traditional methods to expand surveillance into non-model-organisms. By working with stakeholders in government, industry and academia, this project will support translation of long-read metagenome data into actionable surveillance information for the reduction of risk to human health.

Medical researchers often find that some data which they intended to collect could not be collected: for example, because participants could not be contacted or were unwilling to provide data. These missing data present problems in the analysis of the study, because including only participants who provided data may lead to incorrect results. The commonest way to handle missing data assumes that missing values are similar to observed values within subgroups: for example, for participants whose weight was observed at times 1 and 2 but missing at time 3, the missing weights at time 3 are assumed to have the same average as observed weights at time 3 in participants whose weights were similar at times 1 and 2 and observed at time 3. This approach is called "Missing at Random" and provides a good starting point for analysis but is unlikely to be entirely correct: for example, participants whose weight was unobserved at time 3 may have had a larger weight gain. It is therefore important for researchers to do sensitivity analyses in which different assumptions are made about the missing data. Our research proposes to adapt a popular method for handling missing data called Multiple Imputation by Chained Equations (MICE) to allow for a range of assumptions about the missing data. The idea of this approach is that missing values are filled in iteratively using the relationships between all the variables, and this is then done multiple times in order to express uncertainty about the missing data. However, at present the MICE method is done assuming Missing at Random. We have developed a new way to implement the MICE method which does not assume Missing at Random: instead, the researcher has to specify how big the departures from Missing at Random are, by specifying the likely average differences between missing values and observed values within subgroups. However, we have only explored the new method in idealised settings, and in particular we have not explored its use in randomised trials or in studies where outcomes are measured over time. The work will first extend the statistical theory to handle outcomes that are measured over time and see how well the method performs in randomised trials. It will then extend the methods to tackle a wide range of problems met in practice: for example different types of variables, complex analysis questions, and very large data sets. This work will be supported by writing user-friendly software to implement the new method in two widely used statistics packages. We will implement the method in practice in several data sets, including the Avon Longitudinal Study of Parents and Children where we will explore predictors of self-harm, and randomised trials in smoking cessation and weight loss. Missing self-harm, smoking cessation and weight loss data are all very unlikely to be Missing at Random: we will use our subject matter expertise to specify a range of likely average differences between missing values and observed values within subgroups and hence reach more defensible conclusions. This work is likely to raise unexpected theoretical issues which we will address. Finally, we believe that this method will be widely applicable, so we will disseminate it to researchers via tutorial articles and by running courses.

The West Antarctic Ice Sheet contains over 2.2 million cubic kilometers of ice, which if it all melted would raise global sea level by about 5 metres. Over the last few decades West Antarctica has experienced a significant warming. Air temperatures have increased across the surface of the ice sheet, but in addition warmer ocean currents have been melting the ice sheet where it reaches the ocean. The net result has been that some of the ice flowing down to the coast of West Antarctica has been accelerating and thinning so that the coastal area of West Antarctica is now contributing almost 10% to the current rise in global sea level. The climate of West Antarctica is strongly influenced by the storms over the Southern Ocean between the Antarctic Peninsula and the Ross Sea which force warm, maritime air across the ice sheet. There are a large number of storms in this area of the Southern Ocean which are collectively called the Amundsen Sea Low. This is a highly variable feature and is influenced by the ozone 'hole' and climatic conditions across the tropical Pacific Ocean. It is extremely important to know how the climate of West Antarctica will change over the coming century so that we can produce accurate estimates of sea level rise. However, the only tools we have to predict the future are computer models that simulate the atmosphere, ocean and ice across the Earth. These models run as part of initiatives such as the Intergovernmental Panel on Climate Change have a relatively coarse spatial resolution of about 200 km, which is not sufficient to accurately resolve the complex mountainous terrain of areas such as the coast of West Antarctica. For this project we will run a model with a resolution of 10 km through the 21st century to create the most detailed information yet produced of how temperature, snowfall and wind speed/direction will change as greenhouse gas concentrations increase and the ozone hole recovers. Such data will be of value to those modelling the West Antarctic Ice Sheet and enable the production of better predictions of how the ice sheet will change over the coming century and the contribution it will make to sea level rise.