Society faces major challenges from viral diseases. The recent Zika and Ebola outbreaks are only two examples of the devastating impact of viral illnesses on human health, and viral pathogens infecting agriculturally important livestock and plants simultaneously reduce food production and inflict great annual financial losses worldwide. Viruses, however, also have positive impacts on health and ecology. They balance and stabilise our gut microbiome, preventing serious illnesses such as certain autoimmune diseases, and influence our climate owing to their roles in carbon cycling in the oceans. It is therefore paramount to better understand virus structure and function across the entire virosphere in order to control, and even take advantage of, viruses in medicine and biotechnology. I have demonstrated previously that mathematical approaches developed in tandem with experimentalists are drivers of discovery of functionally crucial structural viral features, revealing their novel functional roles in viral life cycles, and enabling their exploitation in therapy and biotechnology. Previously developed mathematical approaches were geared towards a specific major sub-group of the virosphere. In this research programme, I will both broaden and deepen the development of novel mathematical techniques. Working in close collaboration with leading experimental groups, at a larger scale, I will identify functionally important geometric viral features in a number of major groups of viruses. This will include: geometric strand assortment in multipartite viruses, such as the major agricultural pathogen Bluetongue virus; the assembly of retroviruses like HIV, with applications to the construction of virus-like particles from viral components as vectors for gene editing and therapy; and the structure and evolution of viruses important for the gut microbiome and marine ecology. By linking structural features with their functions, I will address open problems regarding drivers of evolution in one of the simplest yet most important groups of biological entities. This approach will unmask evolutionarily conserved functional features that can be used as novel targets in anti-viral therapy, for the development of novel safer vaccines or repurposed in bionanotechnology.

Over the last five years, research led by this team has made it apparent that the reaction of iodide with ozone at the sea surface plays an important role in controlling the chemical composition of the troposphere. This process directly controls the deposition of O3 to the oceans and is the dominant source of reactive iodine to the atmosphere, which leads to significant loss of tropospheric O3. Ozone concentrations are directly impacted but through changes to the atmospheric oxidants, indirect changes also occur to methane and aerosols leading to potential ramifications on climate, air quality and food security. This is likely a biogeochemical negative feedback for tropospheric O3 and oxidants, which, since it is dependent on both atmospheric O3 and ocean iodide concentrations, will have changed over time. Iodine is also an essential human nutrient. The transport of iodine from the oceans to the atmosphere and subsequent deposition over land is a pathway by which marine iodine may enter the terrestrial food chain, and iodine radioisotopes released to the sea may be dispersed. These iodine ocean-atmosphere processes are now being incorporated into chemical transport models but critical uncertainties remain. The marine iodide distribution is poorly understood, yet it is likely that it will be subject to change as a result of changes in ocean circulation, biological productivity and ocean deoxygenation. This proposal brings together marine and atmospheric scientists in order to address uncertainties in the marine iodine flux and associated ozone sink. Specifically, it aims to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition. This will be achieved through a combination of laboratory experiments, field measurements and ocean and atmospheric modelling.

Understanding of turbulent flow characteristics over porous media is central for unravelling the physics underlying the natural phenomena (e.g., soil evaporation, forest and urban canopies, bird feathers and river beds) as well as man-made technologies including energy storage, flow/noise control, electronics cooling, packed bed nuclear reactors and metal foam heat exchangers. In these natural and engineering applications, a step change in the fundamental understanding of turbulent flow and heat transfer in composite porous-fluid systems, which consists of a fluid-saturated porous medium and a flow passing over it, is crucial for characterisation and diagnostic analysis of such systems. Flow and thermal characteristics of the composite systems depends heavily on the interaction between the external flow, downstream wake, and the fluid flow in the porous media. Despite the clear relevance and wide-ranging impact of this problem in nature and engineering, there is a clear lack of fundamental understanding of the flow and thermal characteristics of turbulent flow in composite porous-fluid systems, and the models that relate the exchange of the flow and thermal properties between the porous region and the external fluid passing over it. In particular, the characterisation of the velocity and thermal boundary layers over the porous media, understanding the mechanisms governing flow passage through porous media, possible flow leakage and its interaction with the wake flow, as well as their relationship with the geometric characteristics of porous media, have remained major scientific challenges. This highlights the clear need for a systematic fundamental study aimed at understanding the flow and thermal characteristics of turbulent flow over realistic porous media and the relationship between the properties of porous substrate, the flow within the porous media and the structure of turbulent flow over and past the porous region. In this ambitious collaborative project, we combine the computational and modelling expertise at the University of Manchester and Southampton with the experimental expertise at the University of Bristol, to gain fundamental understanding of the turbulent boundary layer, flow leakage and downstream wake on the flow and thermal characteristics of fluid-saturated porous media. This will be used to establish evidence-based interface flow and thermal models, representing the exchange of flow properties between two regions through the interface. These models will then be used to develop a design tool based on the volume-averaged approach, which is a popular low-cost engineering approach for studying transport in porous media, for real-scale applications where the pore-scale analysis in computationally prohibitive.

Phenotypic variation is the raw material of evolution and the target of natural and sexual selection. However, evolution can only occur through changes in gene frequencies at the level of DNA. Our proposal capitalizes on a recently discovered adaptive morphological mutation to explicitly link how changes at the genomic level translate into changes at the phenotypic level, which are then subject to rapid and quantifiable evolutionary change in a wild population. We will study a mutation in the Pacific field cricket, Teleogryllus oceanicus, that erases sound-producing structures on male wings. Males ordinarily sing to attract females for mating, but in doing so, they also attract a deadly, acoustically-orienting parasitoid fly. The silencing mutation, flatwing, arose in a wild population in 2003 and rapidly spread to near-fixation over the course of approximately 20 generations because it protects males from attack by the fly. Silent flatwing males appear to act as satellites to the remaining callers in the population by intercepting and mating with responding females. The rapid spread of the flatwing mutation represents one of the fastest rates of evolution ever recorded in the wild. The mutation is a simple Mendelian trait inherited on the sex chromosome. The main goals of our proposal are to (1) identify in what region of the genome the mutation resides, and the underlying genetic changes, and (2) characterize broad-scale differences in gene expression between flatwing and normal-wing male crickets, and between crickets that have experienced different social environments resulting from the presence or absence of silent males. These goals will provide the evolutionary biology community with a better understanding of the type of genomic variation targeted by selection in the wild (e.g. coding genes vs. regulatory genes). Our results will also demonstrate how a major evolutionary event has knock-on effects on the regulation and expression of other genes, thereby exposing new phenotypic traits to selection. The T. oceanicus study system provides an excellent opportunity to demonstrate how evolution works in real time, in the wild, and how change in a single trait initiates a cascade of effects that alter gene expression, phenotypic traits, and selection pressure.

In recent years, machine learning frameworks such as scikit-learn have become essential infrastructure of modern data science. They have become the principal tool for practitioners and central components in scientific, commercial and industrial applications. But despite the ubiquity of time series data, until recently, no such framework exists for machine learning with time series. In 2019, sktime was conceived to fill this gap and it has become an established toolkit and software component for time series analysis used world-wide by academics and industry alike. It is an easy-to-use, flexible and modular framework for a wide range of time series machine learning tasks. Techniques for learning from time series have been developed in a range of disciplines, including: statistics; machine learning; signal processing; econometrics; and finance. sktime aims to link these communities by providing a unified interface for related time series tasks such as forecasting, classification, clustering, regression, annotation, anomaly detection and segmentation. It provides scikit-learn compatible algorithms and gives easy access to implementations of state of the art algorithms not accessible in other packages. This project will allow sktime to continue to sustain and grow its operations by providing dedicated maintenance resource, enhancing the functionality and increasing engagement with scientific and industrial stakeholders. We wish to broaden the functionality of sktime to include new areas of active machine learning research and deepen our user base to reach new communities of researchers. Our aim is to link theory and practice by making it easier and faster for state of the art time series algorithms to be applied to real world problems of genuine scientific interest. To demonstrate this potential we will collaborate with domain experts on two applications. The first relates to predicting the early onset of dementia using electroencephalography (EEG). EEG are time series that record electrical activity in the brain using a series electrodes placed on the scalp. The equipment is relatively cheap and portable. If we could use it to screen for early onset dementia it could make a huge difference to the outcomes for many patients. However, the accuracy needed for clinical use is very hard to achieve. We will collaborate with experts in Cambridge who have clinical data and see if the state of the art predictive models can outperform traditional approaches. The second application involves analysing data generated from intensive care monitoring of children in Great Ormond Street Hospital (GOSH). Intensive care patients are continually monitored for vital body functions (heart rate, blood pressure, breathing rate, etc). Increasingly, this time series data is captured and can be mined to improve clinical practice. We will collaborate with a research team already working with GOSH to explore whether sktime can be used to decrease the time it takes to analyse this data. This research may lead to insights that improve clinical practice by answering questions such as "when is the best time to remove the tube that is helping a patient breathe?". It will also help us reach our broader goal to speed up the discovery and dissemination of best practice. Data sharing between hospitals is, quite sensibly, difficult and time consuming. We wish to develop a new user base of hospital data scientists willing to share their research findings and code rather than their data. So, for example, if we discover something interesting in the GOSH data, we would like to rapidly share this finding and the code that verifies it in our data. This code sharing via sktime will dramatically reduce the time taken to test hypotheses on different observational data sets and give greater confidence in finding verified on independent groups of patients conducted transparently by different researchers.