Are you familiar with piping icing onto cakes? Would you be surprised to know that our understanding of many of the flow processes taking place whilst you lay down beads are not actually fully understood? Wherever a fluid is "strained" - slid, squashed, or changed in shape, it responds with a force, or "stress". Studying fluid response to straining is known as "rheology". These forces influence how the rest of the fluid nearby moves, making it vital information to computationally model fluid flow problems: models that inform processing molten plastic into everyday objects, or our understanding of how a spider spins it's silk. Colloquially, rheology describes how "thick" a fluid is, but fluids can have hugely varying behaviours, all dependent on microscopic interactions occurring in the fluid. The flow and straining occurring through a piping nozzle is quite complicated. Near the nozzle walls, icing is mainly undergoing a "shearing" flow, where fluid layers slide over one another - this flow type is well understood and measurable in a lab. Near the centre, the fluid is experiencing "extension", where fluid packets are stretched in the flow direction and squashed in other directions. The nozzle tapering causes this. This extensional flow is less well understood or measureable, but in the last 50 years our understanding has improved, mainly because of the plastics industry. Between the location of the wall and the centre of the flow, simultaneous shear and extension exists - we call this a "kinematically mixed" flow. Not stirred, but mixed as in more than one type of straining present. To date, our only approach to validate models in this region has been to measure fluid velocity (for example) and see if our mathematical model predictions agree - models based on data from pure shear or extensional flows. Until now there hasn't been a way to unambiguously isolate and measure separate stresses within the middle of such flows, something that depends, via microscopic interactions in the fluid, on both shear and extension together. Making the situation even more complex, icing is an example of a "suspension", a class of fluids that display what is called a "yield" stress - it only flows when an applied stress exceeds some threshold. This allows icing to flow when the piping bag is squeezed, but means it resists flow under gravity after being deposited on a cake. The behaviour of suspensions under extension is particularly poorly understood at this time, versus what we know for plastics, let alone their behaviour under kinematically mixed flows. Not just icing cakes is affected. 3D printing cement to build novel houses is conceptually the same process, scaled up, and must handle much more stress without flowing. Depositing solder paste in electronics manufacture has similarities, as does processing graphene fibres into next-gen high performance materials. Plastics processing, a mixed flow, is not perfectly understood, and even lubricant flow in engine bearings is mixed. In fact, few flows are purely shear or extensional, and lacking a method to directly see how fluid stresses are responding under these mixed flows is detrimental to being able to accurately model and predict them. This impacts our ability to design industrial processes around it, and perhaps in the future, to use it to engineer new materials with exacting flow responses for specific applications. This fellowship will develop a new experimental technique that allows us to measure what shearing stress is occurring throughout a kinematically mixed flow by using magnetic resonance imaging - the same technology used in hospitals - and critically, makes whether a fluid is clear or opaque unimportant. With members of the modelling community interested in the project and a "round table" planned, benchmark experiments will be conducted to inform new fluid model development, and thereby facilitate a wide range of next generation materials and manufacturing processes.

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.

Stratified medicine (which is allied to personalised or precision medicine) is an approach to treating patients through categorising them into groups based on their risk of developing a particular disease, or how they are likely to respond a particular drug or therapy. It is key that the correct tests and techniques are available which can put individuals into groups (stratify patients), depending on their exact disease type and likely response to particular treatments. One way in which this might be possible is by application of molecular pathology, a specific type of pathology ( which is the study of disease), focused on the diagnosis and repeated characterisation of disease through the examination of molecules within organs, tissues or bodily fluids, such as blood, urine or synovial fluid (the fluid found in joints). The aim of the Manchester molecular pathology node (Manchester Molecular Pathology Innovation Centre- MMPathIC) is to create an environment which enables new tests, based on molecular pathology techniques, to be developed. These can then be used to stratify patients, to allow more accurate diagnosis or prediction of the best treatments to use. As we already have significant groups of patient samples from people who suffer from inflammatory disease (psoriasis, rheumatoid arthritis and lupus), we will focus on these diseases in the first instance. These diseases are also important as, between them, they affect a large part of our population, treatment can be expensive (and often doesn't work first time, meaning patients have to try a number of expensive drugs before getting any relief from their symptoms), and these diseases often severely affect a person's quality of life due to pain and discomfort. In addition, we are going to build upon the lessons already learned in this area from our established expertise in cancer molecular pathology, which is far further developed in the provision of targeted therapies. We will look at samples from patients with these inflammatory diseases, using a new technique that supports the measurement of many proteins within a minimally invasive sample (such as blood, urine or tissue). This will allow differences between samples from, for example, healthy people and people with a specific disease, to be examined- differences in certain proteins may prove useful as biomarker tests which can be used to diagnose a disease. In addition, by examining the differences in the levels of particular marker proteins from patients who respond to a drug compared to those who don't respond, doctors will be able to identify which drug is the best treatment for specific patients. This will hopefully have economic benefit as drugs will not be used on patients who will receive no benefit from them, but MMPathIC will ensure there is economic benefit through undertaking health economic analysis of potential markers- this will also allow informed decisions to be made by NHS officials who have to make decisions about which tests are viable for introduction into the health service. As we can measure these proteins, we also propose ensuring that this data is linked to genomic data (the blueprint for these building blocks that are proteins) and health records- this integration will be facilitated by MMPathIC's staff, which will include skilled information specialists who can make sense of the data produced and data which already exists (data mining). In collaboration with industry (who have the expertise to commercialise new tests, and navigate the necessary regulatory hurdles), we aim to produce at least 6 new tests which are ready to be commercialised, or ready to be used in hospital pathology laboratories in the first 3 years of the grant

Gastrointestinal cancers, affecting the oesophagus, stomach and colon, are among the top ten cancers worldwide. Minimally invasive surgery uses endoscopes to access the body and offers important advantages compared to traditional open surgery through a large cut, including less trauma and faster recovery. Surgeons who use flexible endoscopy to treat patients need to be very experienced. Endoscopies are complex and can take a long time to do. A large number of endoscopies fail to reach the end of the colon and the small intestine due to the number of tight bends in the gut. Incomplete removal of tumours leads to regrowth and complications. Improving access to flexible endoscopy for diagnosis and treatment is very important to patients and doctors. We also need to make sure that the procedure is safe, accurate and affordable for the NHS. This research aims to transform early diagnosis and treatment of gut cancers using flexible endoscopy. We will combine a soft robotic endoscope with a probe carrying a miniature surgical laser, and a powerful tissue analysis device. This will be easier to use than standard endoscopes and will allow endoscopists with less experience to perform the surgery. The ability to find and treat early tumours will reduce the number of patients requiring further surgery, reduce discomfort and lower the number of tumours that grow back. Automation of key steps of the test - including deployment of the instrument, detection of cancer, and laser surgery - will eventually allow cancer surgeries to be done in outpatient clinics or GP surgeries. In this programme, we are bringing together leading experts in robotics, medical imaging, control, engineering, surgery and cancer. This expertise will help us to design a device which will have a major healthcare impact and benefit the largest possible number of patients.

The aim of this research project is to provide a step change in the measurement and understanding of ionisation biases in mass spectrometry imaging (MSI). MSI is an important emerging technology which enables the mapping of thousands of molecules, including metabolites and drugs, detected as ions in the mass spectrometry instrument. MSI, as a suite of modalities, can be employed to analyse almost any molecule in almost any sample type and so has the potential to revolutionise how we evaluate living systems. Diseases such as cancer involve the disruption of the bodies' natural processes including cellular metabolism. MSI, in mapping thousands of metabolites in every image pixel, can therefore provide powerful insights into how different cancers grow and evolve, helping identify new targets for treatment. Despite this promise, MSI suffers from unknown biases in detected ion signal. These can lead to misleading observations with potentially costly implications. Ionisation biases, or matrix effects, encompass a range of phenomena which can lead to unknown relationships between the number of detected ions and the original number of associated molecules in the sample. These biases may also be non-linear across concentration ranges present within biological samples. Therefore, if the MSI practitioner cannot be certain that, for example, a 5 fold increase in detected ion intensity reflects a 5 fold increase in the original sample metabolite concentration, it is clear that a significant hurdle is present. Furthermore, this phenomenon will be present to varying unknow extents for every ion in every pixel of a dataset. This corresponds to significantly upwards of 1,000,000 ion measurements per image, each with different (unknown) ionisation bias. Therefore, drastically limiting opportunities for quantitation and providing erroneous impression of endogenous metabolite concentrations. Typical approaches for characterising ionisation biases or quantifying endogenous metabolite concentration in MSI will only study a few molecules at most. Currently there are no existing methods allowing generalized study and correction of ionisation biases in MSI. Additionally, no standard samples have been developed to allow assessment of these phenomena and so there is a lack of understanding of these biases across between MSI modalities. This project aims to produce a robust foundation for the study and correction of ionisation biases in MSI. A rigorous empirical approach will be pursued through the development of standard samples suitable for studying ionisation bias behaviours. A suite of molecules will be selected for: their relevance to critical pathologies e.g. cancer metabolism; physico-chemical properties; relevance to the mass spectrometry imaging field. These samples will used to characterize the biases in detection across multiple mass spectrometry imaging modalities including MALDI and DESI MSI. Models describing these ionisation behaviours will be produced and computational approaches for evaluation and transformation of these models will be developed. Multivariate and machine learning approaches will be employed to evaluate the contribution and association of mass spectral and physico-chemical properties of the systems in question.