Humans and other animals can learn to perform complex and adaptive behaviours based on limited experience. Understanding the neural basis of learning is a key challenge in systems neuroscience and artificial intelligence (AI) that could lead to novel treatments for neurological disorders and enable the development of AI systems that learn with human-like efficiency. Thus, significant effort and funding is currently being invested to understand how neural circuits reorganise during learning to improve performance in various cognitive, perceptual, and motor tasks, both in academic research organisations and private companies such as Google DeepMind. Recent advances in neural recording technologies enable the activity of thousands of neurons to be tracked simultaneously at millisecond precision, and stably over days, so that neural activity can be surveyed over the entire course of learning. Through careful analysis of these recordings, scientists hope to determine how changes in the underlying neural circuit support improvements in task performance. In particular, learning is thought to modify the strength of connections between neurons, which leaves a functional signature that can be detected via the coordinated activity of interconnected groups of neurons. However, during learning, many other changes also take place, including changes in motor behaviour, attention, and sensory input, all of which may influence the activity of the recorded neurons. Thus, a key challenge is to disentangle the learning-related changes in recorded neural activity from those arising from sensory, motor, and internal state variables which covary with learning. The proposed project will develop novel methodologies for analysis of large-scale neural recordings to address this need.

When we learnt classical thermodynamics from undergraduate physics/chemistry, we often assumed a large number of particles ~10^23, equilibrium, and quasi-static process. In this very restrictive limit, thermodynamic quantities such as heat dissipation Q, can be computed using the textbook formula Q= T\Delta S, where S is the configurational entropy. However, in real lives, most physical processes are neither quasi-static nor equilibrium. Furthermore, in many biological systems, the number of degrees of freedom is also much less than 10^23, and in this regime, thermal fluctuations become important. Thus, thermodynamic quantities such as heat, work and entropy need to be redefined properly (Stochastic Thermodynamics). The first aim of this research is to extend the theory of stochastic thermodynamics to include birth and death process, e.g., cellular division and apoptosis in living tissues and growing bacterial colonies. One important application of stochastic thermodynamics is the prospects of biological machines, which are powered by the swimming motility in some bacteria, or even cellular division and apoptosis in our bodies. For instance, it has been well known experimentally and theoretically that if we place an asymmetric cog inside a bath full of swimming bacteria, the cog can somehow rotate persistently in one direction. The bacteria themselves, in the absence of the cog, swim in a completely random direction; and yet the interaction between the bacteria and the asymmetric cog can break time reversal symmetry to create a macroscopic unidirectional current. Although this phenomenon has been well established in motile active matter (such as swimming bacteria), very little is known about non-motile growing active matter (such as cell division and apoptosis in living tissues and bacterial colonies). In this research, I will explore cellular division and apoptosis as a new route to the development of biological machines. This is important because unlike cell motility, cell division and apoptosis are universal properties of living matter. My design principles for such machines will pave the way for future possible applications in healthcare technologies and tissue engineering, such controlling the growth of tissue using non-uniform scaffolding. Finally, I will investigate the thermodynamic properties of these machines. In particular, I will quantify the informatic entropy production of the birth and death process inside biological tissues and bacterial colonies, i.e., particles dividing into two and disappearing elsewhere. To achieve this, I will extend the current theory of stochastic thermodynamics to include birth and death process and stochastic processes that are much faster than quasi-static (i.e., quenching). This information will be crucial in understanding how time reversal symmetry breaking at small scales (i.e., cell cycle) can be translated into large scales (i.e., collective motion in tissues and bacterial colonies). Apart from obvious applications to active/living matter, my research will also help to transform the science of thermodynamics, such as understanding the energy flow in a quenching process and/or processes close to a critical point, where thermal fluctuations are important.

Epidemic models for pathogens transmitted from human to human are, naturally, concerned with the interaction between individuals that leads to transmission. This is clearly a major simplification; there are many processes at work, from the feedack loop of epidemics on behaviour and interventions, to resource constraints limiting the production of prophylaxis and availability of diagnostic tests, to the response of the immune system to the pathogen and pharmaceuticals. Epidemic models do not normally include an account of these highly influential processes. Instead, only the assumed effect of these processes is sometimes included. This strongly limits the scope of epidemic models. By contrast, in molecular biology, it is typical to consider a much larger class of possible interactions. There exist methods as well as mature software for expressing and simulating systems with many interactions. We have successfully shown that these techniques can be fruitfully applied directly to epidemics, including in a multi- scale setting incorporating immune response and, with suitable extensions, to detailed epidemic reconstruction in a complex community setting. We will build on this success in order to consolidate this capability within the infectious disease modelling community. We will improve accessibility of the tools that we used in our pioneering work, facilitating adoption of our epidemic modelling methods more widely. We will foster a community of practice by conducting a series of case studies to establish documented and standardisable approaches to bringing our advanced techniques to bear on pressing current and future questions relevant to reducing the public health burden of infectious disease.

Describe the research in simple terms in a way that could be publicised to a general audience. This will be made publicly available, and Applicants are responsible for ensuring that the content is suitable for publication. No more than, 4000 characters including spaces and returns. Seismic hazards endanger both human lives and critical infrastructure, underscoring the need for a more profound understanding of earthquake dynamics and precise risk assessment, especially in regions prone to infrequent, long-recurrence events. Globally, earthquakes predominantly cluster along tectonic plate boundaries, where heightened seismic risk is acknowledged despite the lack of precise information on location and timing of earthquakes. Conversely, intraplate regions, situated away from these boundaries, also experience significant earthquakes, presenting a distinct challenge due to their rarity. Notably, these seismically active intraplate regions often coincide with large urban centres, amplifying potential risks. Key features that distinguish interplate and intraplate earthquakes include (1) variations in the stress on the fault that drives slip; (2) earthquake magnitude-frequency distributions - the number of small earthquakes in a region relative to large earthquakes; and (3) source parameters that dictate the severity of an earthquake, including the stress drop, duration of the event, and precursory phases that occur immediately preceding earthquakes. Investigating these distinctions and the underlying reasons responsible for them, as well as documenting historical earthquake events, holds promise for both a more comprehensive understanding of earthquakes and a pathway to enhanced regional seismic hazard assessment. Our project is dedicated to exploring the fundamental physics of earthquake rupture and documenting historical earthquakes within the Indian subcontinent including the public and state response to these events. These endeavours are inextricably linked and involve distinguishing the characteristic features between interplate and intraplate regions through a combination of laboratory experiments, borehole stress measurements, and seismic monitoring, and developing better records of historical seismicity and the response to it. By scrutinizing stress conditions, rock properties, earthquake magnitudes, source characteristics, and infrequent historical events, we aim to elevate the precision of risk analysis in key regions within India. The knowledge gained from these activities will be brought together to craft an educational and outreach initiative aimed at both the general population (through schools) and local government through education on the scientific and historical nature of earthquake hazard and development of tools to improve decision making. The program will heighten public awareness regarding earthquake risks, promote straightforward life-saving measures, and develop better planning to prepare and deal with future seismic hazard.

Emissions of carbon dioxide (CO2) from our society are rapidly warming our climate to currently 1.1 degrees C warmer than in preindustrial times. Global governments have pledged to reduce emissions to stabilise our warming climate at 1.5 degrees requiring us to reduce emissions of CO2 to a point where they no longer accumulate in the atmosphere: Net Zero. A crucial consideration in this effort are natural reservoirs of carbon on the Earth's surface such as permafrost and soils that store large amounts of carbon away from the atmosphere, but which are vulnerable to environmental change. The destabilisation of these reservoirs over time, releasing more CO2 into the atmosphere, presents a challenge to stabilising climate upon reaching Net Zero. Therefore, predicting how these natural carbon reservoirs will change in the future is a crucially important task. The Biological Carbon Pump is one of these natural reservoirs of carbon in our Earth System. It stores carbon in the ocean by plankton (microscopic plants) taking up CO2 as they grow in the surface ocean. The sinking remains of these plankton carry the carbon into the deep ocean locking it away for hundreds to thousands of years. This carbon pool is equivalent in size to the anthropogenically-driven increase in atmospheric CO2 over the 20th century. The Biological Carbon Pump is widely expected to be sensitive to environmental change and could therefore release CO2 in the future. However, we have limited knowledge of what those changes might be and why because we don't have the necessary outputs from the state-of-the-art future projections by Earth System Models that underpin the Intergovernmental Panel on Climate Change (IPCC) reports that inform social, economic and political decisions about Climate Change. PREdicting biological Carbon in the Ocean Globally (PRECOG) will build a team of experts at the University of Liverpool to comprehensively explore the future of the Biological Carbon Pump using state-of-the-art Earth System Model projections. PRECOG will strategically align with an international network of researchers and industry partners to build a new knowledge framework that will inform future IPCC reports and mitigation strategies. PRECOG will: 1) Derive new standard quantitative measures of the Biological Carbon Pump in a future changing ocean. 2) Quantify how and why the Biological Carbon Pump changes in state-of-the-art future projections that underpin the IPCC reports. 3) Determine the long-term impact of the Biological Carbon Pump beyond the year 2100 using new Earth System Model simulations. 4) Predict which future projections of the Biological Carbon Pump are most likely and how this might impact schemes to artificially enhance carbon storage by combining future projections with new compilations of observations. PRECOG has a strong focus on connecting scientific outcomes to societally relevant outcomes. The research team will maintain a strong and active link with IPCC activities through its international network with the aim of raising the profile of Biological Carbon Pump research. PRECOG will also work with industry partners interested in techniques that will enhance the carbon storage of the Biological Carbon Pump to help mitigate rising CO2 such as kelp farming and seeding the ocean with iron. PRECOG will provide the state-of-the-art estimates for the best locations to undertake these activities and disseminate these findings through its industrial partners. In summary, the Biological Carbon Pump is a vulnerable natural carbon pool in the ocean that can influence atmospheric CO2 in response to environmental change. The future of this carbon pool is however poorly known. This Future Leaders Fellowship, PRECOG, will establish a team of experts to explore the Biological Carbon Pump in state-of-the-art IPCC projections to find out what the likely future changes are and translate this is into a societally relevant agenda.