Recent innovations have elevated mass spectrometry (MS) into a ubiquitous and indispensable analytical technique. However, the increasing complexity of the instrument has gradually turned it into a closed system whereby specific methods are suitable for only a narrow range of well-defined applications/analyses. We propose a new and radical approach to how we perform ion spectrometric analyses - new modes of operation for such experiments. The key enabler of this concept relies on two novel modes for MS analysis in which contrasting polarities are accessible in addition to the traditional positive and negative modes - what we have termed "four dimensional (4D) MS". The 4D MS strategy has potential to generate up to 50% more analytical data, and to enable new experiments including isomer differentiation, enhanced mixture classification and high confidence molecular weight assignments on any ordinary standalone mass spectrometer. This project can lead to the formation of new MS capability providing a means for more comprehensive measurement by way of 4D-MS.

When crops are treated with pesticides both pests and beneficial insects are affected. It would be useful to know how different chemicals affect different species but the current ways we use to determine the impact of such treatments have substantial limitations: They often take a long time (e.g. to detect colony-scale outcomes) or only test a small part of an insect's behaviour. Our project uses a pioneering new tracking method to monitor bees during foraging trips. We use state-of-the-art AI techniques to process the recorded 3d flight paths to detect subtle changes in behaviour in response to pesticide exposure. We use this to explore both the mechanisms through which pesticides affect pollinators and the agricultural implications of the behavioural changes. The objectives are to (a) produce a sensitive method for detecting treatment induced behavioural change in bumblebees, (b) look at the effect of treating plants with fungicides on the foraging behaviour of bumblebees. We will perform the experiments in a controlled environment agricultural (CEA) research facility, with a crop of strawberries, using industry-standard techniques. We will use commercial colonies of the common eastern bumble bee, which are commonly used in CEA to ensure the crop is pollinated (to maximise yield). These bees will then be used to test the impact of insecticidal treatments. The colonies will be exposed to either field-realistic quantities of widely used neonicotinoid pesticides, fungicides or control. The colonies will be allowed to forage within the controlled environment on strawberry plants, a portion of which have also been treated with fungicide. This experimental design will allow us to investigate the impact of the two pesticides on behaviour, and also the effect of the fungicidal plant treatment on foraging (e.g. whether bees avoid/prefer the treated plants), and also allow us to look for interactions between the treatments. The project requires the development of three tools: (1) the tracking tool we use works by taking photos with a flash of bees foraging while wearing retroreflective tags. The tags appear as bright spots in the photos, allowing the bees to be tracked. Different bees are impossible to distinguish with this technique so we will be extending the method with coloured tags to allow multiple bees to be uniquely tracked. (2) the 3d path of the bee might provide important information about the impact of the pesticides, so we will combine multiple tracking cameras and then use recently developed mathematical tools (a method for Bayesian inference) to reconstruct the 3d flight path of each bee. (3) To make sense of this huge dataset of 3d flight paths we will consider several approaches to extract relevant features from the raw data, which will then be used to train a classifier to distinguish between the different treatment groups. The research will provide a novel, sensitive tool for detecting behavioural changes in flying insects, and explore the impact of specific pesticides (and their interactions) on key pollinators. We anticipate the new assay will allow manufacturers to produce more targeted pesticides (and thus support growers) and, through its use by other researchers, provide legislators with the evidence base required for regulatory decision making. Medium term impacts are expected for the wider public through improved biodiversity

Conventional Magnetic Resonance Imaging (MRI) is very time consuming (taking over 1hour/scan) and requires patients to remain still and perform multiple breath-holds. This is particularly difficult for children, and my work focusses on developing fast imaging techniques using Machine Learning (ML), to speed up MRI in children and reduce the need for breath-holding. I have shown that it is possible to use ML to 'learn' the best way to speed up the collection of MRI data, allowing each image to be collected up to 50x faster. To achieve these speed-ups, it is necessary to reduce the amount of data that we collect, however this results in significant errors in the images. I have shown that it is possible to reconstruct clinically useful images very quickly using ML; up to 100x faster than current state-of-the-art mathematical methods. In addition, I have developed fully-automated ML tools for analysis of the MRI images; enabling clinical metrics to be calculated whilst the patient is in the scanner, up to 7000x faster than conventional methods. Combined, these tools have allowed MRI scans in the heart and abdomen to be performed quickly (in as little as 10 minutes) and without the need for breath-holding in children, as well as in sick adults. In initial studies, these tools have been tested in small patient groups. This work focusses on large-scale international clinical testing, to make sure that these ML tools work reliably and accurately across all children's diseases and in different hospital settings. This work will build trust in these tools, enabling them to be shared them with different hospitals across the world, to maximise the benefits to all patients and hospitals. I will also continue benefitting from new developments in ML to further improve these technologies and overcome any limitations encountered during clinical testing. Most hospitals only have conventional MRI scanners, with field strengths of 1.5T or 3.0T. However, recently low-field strength (0.55T) MRI scanners have become available. Although these have not yet been clinically established, they offer significant financial benefits, including lower initial cost (~50% of 1.5T), easier/cheaper installation (70% of 1.5T) and lower running/maintenance costs (~45% of 1.5T). This makes these scanners highly desirable, and may enable MRI to become affordable in some countries for the first time. Low-field scanners, also address some of the remaining challenges of MRI in children; i) They have a bigger bore, so children find them less daunting, ii) They are much quieter, which means children may be able to remain asleep, and iii) There are less concerns over heating in the body. However, the measured signal at low-field strength is <25% of that on conventional scanners, resulting in lower quality images. Therefore, the second part of this extension will build on the ML tools that I have developed for imaging children on conventional scanners, to enable good quality images from rapid scans at low-field MRI. This includes the use of ML to 'find' the best way to collect the data, and ML methods to improve image quality. This extension will work towards clinical validation of these fast scans in children on conventional scanners, making MRI less difficult or daunting for children, improving availability and reducing risks. Quicker scans would help reduce waiting lists and costs for the NHS, and improve diagnostic accuracy and outcomes in childhood diseases. It would also mean that MRI scanning would be used far more often, so it could help many more children. Additionally, by investigating the use of rapid imaging tools at low-field MRI, I will continue to push novel research ideas, enabling improved image quality near to the lungs, in the abdomen and in fetus', with lower risk to patients and significant cost benefits for hospitals.

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.

The behaviour of multiphase particulate or granular systems (e.g. in fluidised beds and pneumatic conveyors) presents severe experimental problems because they are opaque, largely preventing the use of optical techniques. Also, inserting physical probes inevitably disturbs the system under investigation. Thus, it has been difficult to develop reliable scale up criteria or validate numerical simulations of these systems. We aim to validate and explore the limitations of measurement using two complementary, non-intrusive experimental techniques: Electrical Capacitance Tomography (ECT) and Magnetic Resonance Imaging (MRI), using the combined expertise of Ohio State University (ECT) and Cambridge (MRI). Particular regard will be paid to the applicability of these techniques in the validation of the predictions of Discrete Element Modelling (DEM) and in the development of scale-up criteria in gas-fluidised beds. This is timely, given recent developments in all three of these areas, particularly in the potential that ECT could have in the design of truly industrial-scale fluidised beds, provided it is properly validated.The experimental techniques considered here (MRI and ECT) are complementary in that their strengths lie in measuring different features of multi-phase granular systems. MR enables the bulk solids motion to be visualised, as well as the particle velocity profiles, in both the dense solids phase and the lean (bubble, jet, void) phase . Furthermore, it is possible to determine voidage profiles. We also propose to extend MR to be able to image gas directly for the first time in a multiphase system. ECT has a major advantage in that it does not have any serious restrictions regarding size, although equipment must be non-metallic. As with MRI, it is possible to determine the velocity of voids, i.e. bubbles and slugs, and voidage profiles. The velocity of the bulk solids cannot be determined, however. Importantly, there is an overlap in the variables which can be measured by either technique, the most important ones being voidage profiles and the rise velocity of voids. These measurements will be used for cross-validation of the two techniques.