When we hear conversation in unfamiliar languages, it can seem fast, with sounds run together and few distinct words. In fact, sounds flow rapidly and overlap in all spoken languages, but knowledge of our own language allows us to use various unconscious strategies to extract words and to interact in fluently-timed conversations. One key strategy for effective spoken interaction is listeners' generation of timing predictions. Sounds are often lengthened at important points in speech, such as the start of words and the end of conversational turns. To detect lengthened sounds, it seems that listeners use the rate of the speech they have already heard to generate expectations about how long upcoming sounds will be. When sounds are longer than expected, listeners can interpret that as an important point in the speech stream, such as the start of a new word. Although there is good evidence that listeners make timing predictions to interpret speech, theoretical understanding of how this is achieved is very limited. In particular, it is unclear what features of speech support listeners' use of timing prediction. For example, one theory about language processing in the brain implies that very regularly-timed speech is more useful for listeners in making timing prediction, but another theory implies that the irregularly-timed flow of natural speech supports timing predictions. These theories also have different implications for understanding how timing predictions are affected by acquired disorders, such as in people with aphasia (PWA) due to stroke. Aphasia typically arises due to interruption to blood-flow in the brain which causes damage in language-processing areas. PWA usually have difficulties producing spoken language, such as specific words or grammatical phrases. PWA can also have problems in understanding speech, but these may be less immediately apparent, despite having a profound impact. The project will test both people with typical language skills and people with aphasia in order to improve understanding of how they produce timing predictions. As these are so important for natural interactions, our findings will have implications for the design of devices using artificially-generated speech, from satellite navigation interfaces to augmentative communication systems for people with speech difficulties. We will test how timing predictions are generated using a new listening task ("nonword segmentation") in which listeners hear short meaningless sequences of syllables, "nonword targets" (e.g., "libeku") followed by longer sequence of syllables, "carrier utterances" (e.g., "mimasikonebubilibekududi"). When listeners hear a nonword target in a carrier, they have to respond by pressing a computer key as quickly as possible. Over multiple trials, with careful variation in targets and carriers, this task will allow us to build up a picture of timing prediction. Importantly, our pilot studies have shown that longer initial consonants make target detection easier, but only when targets are quite late in carriers. This supports the theory that listeners build up timing predictions based on speech already heard, but to fully test this, we need to explore how timing predictions are affected by a range of factors: - Regular or irregular carrier utterance timing. - Hearing speech in noise and/or with meaningful linguistic content. - Hearing familiar voices and accents. - Seeing as well as hearing speakers. Because PWA have variable speech production and perception difficulties, we will test their ability to make timing predictions compared to age-match listeners without aphasia. One theory implies PWA should have relatively good timing predictions compared to their overall language, but another theory implies poor timing prediction in PWA. Ultimately such work will boost understanding of speech perception and comprehension can be affected in aphasia and how any difficulties may be remediated.

Changes in glycosylation of proteins are hallmarks of disease, but the underlying molecular mechanisms of these processes are not fully understood. This project will use synthetic chemistry to produce a range of glycosylated amino acids that will be combined with peptide mRNA-display to generate a glycopeptide display method to completely cover protein sequence space. This will be used to identify the substrates of disease related enzymes such as the glycosyltransferase GALNT7, which is upregulated and drives tumour growth in prostate cancer.

To be confirmed

Bacteria can be divided into two types. Gram-positive bacteria have a thick cell wall on the cell surface and a single, cytoplasmic membrane. By contrast, Gram-negative bacteria such as E. coli have two membranes: a cytoplasmic (inner) membrane (IM) and an outer membrane (OM). Both membranes are separated by the periplasmic space that contains a thin cell wall. The OM is a unique lipid bilayer that is essential for most Gram-negative bacteria for two reasons. First, the OM is important for the mechanical stability of the cell. Second, the OM creates a physical barrier that prevents entry of harmful small molecules such as antibiotics, and therefore forms a protective layer on the outside of the cell. However, for the bacterium to grow, OM channels and other OM proteins ("OMPs") are needed, for example for the uptake of nutrients. Like all proteins, OMPs are made in the cytoplasmic space in an unfolded form. Therefore, in Gram-negative bacteria, the newly-made, unfolded OMPs have to cross the IM and the periplasmic space before they arrive at their OM destination. There, they are folded into their active form and inserted into the OM. Both processes are carried out by an OMP complex named BAM, which stands for barrel assembly machine due to the fact that many OMPs form barrel-like structures. Due to its important functions, BAM is essential for the viability of all Gram-negative bacteria, and much research has been devoted in the past 20 years to determine how BAM works. However, most of this research has been done in model bacteria such as E. coli, and it is not clear whether BAM from very different bacteria, such as those from the human gut, has the same structure and functions in the same way as BAM from, for example, E. coli. From preliminary experiments that form the basis of this proposal, we have good evidence that the BAM complex from an abundant group of human gut bacteria is in fact very different from E. coli BAM, both in structure but likely also regarding its functions. This proposal will characterise the functions of this novel BAM complex (named BtBAM) via three research aims. In the first aim, we will determine the structure of BtBAM to a level that will allow building of complete atomic models for all the seven proteins that form the complex. This will be done via cryogenic electron microscopy (cryo-EM), in which individual, frozen protein molecules can be visualised and imaged at high magnifications. Knowing the detailed structure of BtBAM is important because it will allow us to hypothesise how all the individual components of this protein machine work together, much like it is only possible to understand how a car engine works if one can see what it looks like. In the second aim, we will do experiments to get more information on the function of the different protein components of BtBAM. We will remove one or more components, followed by experiments to understand how that affects BAM function. For example, we can culture "mutant" bacteria with the modified BAM complex and compare growth with bacteria that still have the original complex. Due to its importance, bacteria with a defective BAM will likely grow slower, or not at all. For making more subtle changes in BtBAM, the structural information from aim 1 is also important, because it suggests where changes should be made. Besides growth experiments, we will also analyse which OMPs are present in the bacteria and their quantities in the OM, something that is likely to be affected if BAM doesn't function properly. The final, third aim will be to study the function(s) of BtBAM in the test tube, rather than in live bacteria, to visualise more directly what BAM does. Together, these experiments will clarify the structure and function of a BAM complex that is likely to be very different from that of model bacteria like E. coli. The project will show that even fundamental processes like OMP biogenesis can differ substantially in bacteria.

TBC