Two-photon microscopy is a leading-edge imaging technology and a powerful research tool that combines long wavelength excitation and laser scanning microscopy. Of importance to our work it can enable capture of high resolution three dimensional images of living cells within 3D constructs as well as in-depth penetration of specimens tagged with very specific fluorophores. This technology is now becoming a method of choice for the dynamic imaging of biological and polymeric systems, not otherwise possible by other optical approaches and therefore will underpin a broad number of research programmes in biomaterials and tissue engineering.

To support the development of challenging, difficult to manufacture products, increased reliance is placed on techniques to allow accurate dimensional measurement of parts and components. New measurement systems are needed that provide data quickly with higher levels of accuracy and precision than is currently possible. Currently high accuracy measurements are made using dedicated expensive instrumentation in temperature controlled labs. The wide range of measurement challenges mean there is no single instrument available to suit all needs. In fact, the range of lab based instrument systems required to meet the measurement needs of industry continues to grow. It includes techniques ranging from contact measurements made using a mechanical probe, to non-contact measurements which use light, lasers, or X-ray based measurement methods. The main drawback of these systems is that they are usually slow to set-up, and significant time is required to take measurements. This means that although they are very accurate they are less useful for the control and improvement of challenging manufacturing processes, where data must be collected and analysed quickly. Improved measurement systems are required which provide higher speed measurements, at lower cost, without compromising accuracy. Currently two approaches address this need. One approach uses on machine sensors to provide high-speed measurements, while the other approach is to position instruments closer to the manufacturing environment to reduce the time required to transfer work to the measurement lab. Both approaches have obvious benefits as they provide faster data; however, they are also less accurate as a result of the unwanted disturbances experienced on the factory floor. These limitations result in a trade-off: the user can either have high accuracy, or high speed measurement, but not both at once. The research undertaken within this Fellowship will develop a new way of collecting and effectively processing critical measurement data. Instead of a reliance on high accuracy instruments, this approach will provide a new way of thinking with respect to how measurement systems are designed and implemented. The goal will be to allow different types of lower accuracy data to be combined in a beneficial way. For example, computer simulations of a machine, product, and process will be combined with sensors that monitor environmental conditions. In addition sensors used to take high speed measurements of parts during the manufacturing process itself will be used. Through a collaborative process these data will be combined to provide fast high quality data. To verify and further improve the system a small quantity of accurate feedback data from high accuracy instruments in temperature controlled labs will be used. In effect the approach will be to combine slow accurate data, with fast less reliable data, to produce enhanced accuracy fast measurements. For example, if a batch of high precision components must be produced, the components must also have their geometry verified and corrected if required. On machine sensors may be used to verify geometry, but accuracy is limited due to environmental effects such as temperature and humidity. To compensate for these errors a collaborative measurement system will initially make measurements using both on-machine sensors as well as off-machine lab instruments. It will blend these data sets in addition to data from on-machine environmental monitoring sensors, and computer simulations to correct for errors and therefor enhance the accuracy of the measurements. The system will automatically adapt to changing environmental conditions by triggering the need for more lab-based data which will allow an improved error correction to be made. In this way the system will adapt and optimise the measurement process to suit the current manufacturing conditions.

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.

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 leishmaniasis are parasitic diseases caused by one of several species of single cell parasites (Leishmania) that are transmitted to humans by the bite of infected phlebotamine sand flies. These diseases affect over 150 million people across 98 countries worldwide, including many low and middle income countries (LMICs). Some forms of leishmaniasis are fatal, whereas other are very stigmatising and affect quality of life, particularly in children and women. Few drugs are available for patients leishmaniasis and no vaccines are currently registered for use in preventing or treating these diseases. Importantly, the drugs that we do have are not universally effective and often have significant side effects. Sometimes patients even in the same geographical area will respond quite differently to therapy, and for some drugs effectiveness may vary widely between different countries. In order to make the best use of current and future drugs for the leishmaniasis, we need to understand more about why this is the case, and use that information to select appropriate drugs or drug combination for use in different settings. Using the appropriate treatment would save costs in health care, minimise the patient suffering that results from administering ineffective treatments, and reduce the economic burden of disease on patients, their families and communities. In this proposal, we are aiming to lay the foundation blocks that will drive a new way of managing patients with leishmaniasis and conducting research into these diseases. We will use new molecular approaches to extract as much information as possible from small tissue samples that are collected from patients to diagnose their leishmaniasis, and use this information to start to develop new tests that can help clinicians decide on the best course of treatment. We will use the internet to ensure that the information obtained from these tissue samples is used most effectively for research, clinical decision making and for education and training. We will conduct an analysis of the added value of these changes in approach, in order to provide a case for their adoption by health systems in LMICs and by the funders of research. Ultimately, by adopting these practices we will seek to deliver improvements in health and economic prosperity in LMICs. The research we propose over the next two years will not provide all the answers, but will provide necessary proof of concept data to support applications for future funding that may allow us to realise this longer term ambition.