|
Dissecting carbohydrate-protein interactions for drug designPrinciple Investigator: Richard Brycehttps://research.manchester.ac.uk/en/persons/richard.bryce Project DescriptionCarbohydrates play a key role in biomolecular recognition, for processes including cell-cell signalling and viral adhesion, and invasion. The ability to accurately dissect the key regions of interaction between a carbohydrate and its protein receptor is key to understanding its mechanism of action and in guiding the design of drug molecules that could modulate this process. This project seeks to develop a computational strategy to reliably identify the key parts of a carbohydrate that determine its protein-binding ability. The approach is based on calculating free energies using molecular dynamics simulations and will be initially calibrated using reference carbohydrate-lectin datasets before being applied to probe viral oligosaccharide-protein interactions for drug design.
|
|
Integrating Machine Learning into the Finite Element MethodPrinciple Investigator: Lee Margettshttps://research.manchester.ac.uk/en/persons/lee.margetts Project DescriptionAll products being developed today need to be re-imagined. This is to address increasing demands from the consumer and to conform to tighter regulation, particularly regarding product safety, sustainability, and the environment. Currently, engineers use design software that was originally conceived in the 1960s. The new N8 facility, Bede, will allow researchers at the University of Manchester to develop the next generation of design tools, incorporating artificial intelligence, effectively re-engineering engineering.
|
|
Targetting of the NLRP3 inflammasomePrinciple Investigator: Richard Brycehttps://research.manchester.ac.uk/en/persons/richard.bryce Project DescriptionBesides the beneficial events of inflammation, there are toxicological events arising from prolonged and uncontrolled inflammatory response that leads to tissue damage. Targeting of inflammasomes is a new approach for the treatment of inflammatory diseases. In this project, we have modelled the full-length human NLRP3 (NLR family pyrin domain containing 3) inflammasome. Molecular dynamics (MD) simulation will be used to study the structure and dynamical features of this NLRP3 monomer in water. Moreover, we will design novel inhibitors and MD simulation will be used to assess the interaction of the designed inhibitors with the target protein.
|
|
Particle-in-cell simulation of plasma acceleration for AWAKEPrinciple Investigator: Guoxing Xiahttps://research.manchester.ac.uk/en/persons/guoxing.xia Project DescriptionThe Advanced Wakefield Experiment (AWAKE) at CERN is the world's first ever experiment that accelerates electrons using an electromagnetic wave generated by protons zipping through a plasma. The acceleration obtained over a given distance is already several times higher than that of conventional technologies currently available for particle accelerators. In the upcoming Run 2 of AWAKE, the collaboration team aims to demonstrate the effectiveness of this novel acceleration scheme and achieve high quality beam for future applications. This project will use particle-in-cell (PIC) codes to study the detailed physical processes such as electron seeding instability, electron injection, trapping, and acceleration in the proton-driven plasma wakefield (PD-PWFA). These results will be crucial for a full understanding of this technology and its implementation for many future applications. This project is also well aligned with the recently updated European Strategy for Particle Physics (ESPP-2020), in which the plasma wakefield acceleration has been listed as a key R&D research area.
|
|
Large-scale simulations of viral envelopesPrinciple Investigator: Jim Warwickerhttps://research.manchester.ac.uk/en/persons/j.warwicker Project DescriptionDespite modern medicine advances, viruses remain a major health concern, both from a standpoint of occasional seasonal epidemics or major global pandemics. SARS-CoV-2, a member of the coronavirus family, has recently emerged as the cause of the most debilitating pandemic in the last hundred years. Flaviviruses, on the other hand, are mosquito-borne viruses (including dengue, zika, yellow fever, and West Nile virus) which have been a major health concern in all regions within the Aedes mosquito range.Our group is focused on exploring the dynamics of coronavirus and flavivirus envelopes ,which consist of protein, membrane, and sugar component, and which represent a protective coating for the virus, as well as being functionally important for viral entry into the host cell. We are using large-scale molecular dynamics simulations to investigate motions of coronavirus and flavivirus envelopes, including mapping of cryptic pockets to detect possible drug-binding hotspots."
|
|
Atomistic simulation of liquid crystalsPrinciple Investigator: Andrew John Mastershttps://research.manchester.ac.uk/en/persons/andrew.masters Project DescriptionA system is chiral if it cannot be superimposed upon its mirror image. One example is a left-handed helix, where the mirror image is a right-handed helix. Chiral molecules often form chiral structures, such as helices. It is rare, however, for achiral molecules to form chiral structures. Recent experiments have shown ( https://www.nature.com/articles/s41467-017-02626-6) that a series of achiral molecules can do just this, forming a nematic twist bend phase and a heliconical tilted smectic C (SmCTB) phase. We aim to perform molecular dynamics simulations to understand why these molecules form these phases and the predict the physical properties of these phases.
|
|
Classification of airborne biological particles in cities.Principle Investigator: David Toppinghttps://research.manchester.ac.uk/en/persons/david.topping Project DescriptionPrimary biological aerosols (PBA) consist of bacteria, viruses, fungal and plant spores, fragments of plant or animal matter and pollen. Whilst their role in the environment, human health, and as a potential security threat is known, development of robust detection technologies remains a challenge. In this project,t we use multiple machine learning libraries to cluster images of individual particles into distinct types to better understand their sources, sinks, and potential impacts.
|
|
Spatial-aware decision-making with ring attractors in reinforcement learning systemsPrinciple Investigator: Richard Allmendingerhttps://research.manchester.ac.uk/en/persons/richard.allmendinger Project DescriptionThis project aims to integrate novel neural circuit-inspired models into reinforcement learning algorithms to enhance decision-making and uncertainty estimation in spatial scenarios. We target implementing these neural structures, which encode the spatial distribution of actions, both as standalone networks and as integrated components of deep learning models. The goal is to improve performance in challenging robotics and navigation benchmarks. Experiments will compare our approach to state-of-the-art baselines across various environments and action spaces. We expect this innovative approach to significantly speed up learning and lead to more robust decision-making, especially in partially observable settings.
|
|
Foundation Models for Nanoconfined WaterPrinciple Investigator: James McHughhttps://research.manchester.ac.uk/en/persons/james.mchugh Project DescriptionThis project explores how water behaves when trapped in spaces just a few atoms wide – a state known as nanoconfinement. By using advanced machine learning techniques, we aim to build highly accurate models that predict the behaviour of water and dissolved ions under these extreme conditions. These predictions are based on quantum mechanical calculations, which are usually too expensive to carry out at scale. With the help of GPU-accelerated models, we can rapidly explore thousands of possible combinations of liquids, membranes, and ions. The results could help design better systems for water purification, selective ion extraction from electronic waste, and catalysis for green energy technologies. Ultimately, this work will enable faster, cheaper development of new materials and processes with real-world environmental impact.
|
|
Deep Constrained Clustering and Its ApplicationsPrinciple Investigator: Shaojie Zhanghttps://research.manchester.ac.uk/en/persons/shaojie-zhang Project DescriptionOur project is dedicated to Deep Constrained Clustering (DCC) and Its Applications. There is ample evidence indicating that Constrained Clustering (CC), based on additional constraining priors, can significantly enhance clustering performance. Building upon this, the DCC paradigm, which utilizes Deep Neural Networks (DNNs), further augments the capability to discover patterns in large-scale, complex data. However, the extent to which existing DCC methods are influenced by the distribution of constraint information remains unclear. Our current work is endeavouring to uncover this impact and is attempting to circumvent potential negative effects through appropriate strategies.
|
|
Explainable Deep Reinforcement LearningPrinciple Investigator: Canh An Tien Phamhttps://research.manchester.ac.uk/en/persons/canh-an-tien-pham Project DescriptionThe project aims to develop an explanation of the AI agent for users in decision-making problems (e.g. Atari Games). We are developing a model that has the attention map which can be visualized to demonstrate which locations the agent is focusing on to produce the actions. Furthermore, we also develop the action predictor model based on Generative AI to provide the estimation of agent actions that it could perform in the next few steps
|
|
Modelling opinion shifts of mental health groups via joint deep clustering and dynamic topic modellingPrinciple Investigator: Xian Yanghttps://research.manchester.ac.uk/en/persons/xian.yang Project DescriptionThis research aims to analyze the impact of social events, like COVID-19 and vaccination programs, on the mental health of individuals. It proposes using AI and digital tracing data to understand the behavioural patterns of mental health groups. The study plans to address three key research questions: 1) Identifying consensus opinions among people with diverse mental health issues, 2) Analyzing the impact of events on opinion shifts, using dynamic topic modelling and NLP techniques, and 3) Mitigating bias in digital tracing data from various social media platforms. The research intends to validate its findings through surveys and interviews, offering potential insights for decision support systems in various domains.
|
|
Non-specific binding of adenosine triphosphate (ATP) with proteins and the impact on protein self assemblyPrinciple Investigator: Robin Curtishttps://research.manchester.ac.uk/en/persons/r.curtis Project DescriptionAdenosine tri-phosphate (ATP) is the main energy metabolite consumed by cells to generate energy. Interestingly ATP is only required at micromolar concentrations in order to meet metabolic requirements, while cellular concentrations are one thousand times greater in the mM range. This discrepancy has led researchers to unravel a secondary role of ATP in diseased states of cells through preventing protein mis-assembly. However, the molecular basis for how ATP interacts with proteins and alters their assembly pathways remains poorly understood. We plan to tackle this problem using molecular simulations. Critical to the success of the project is to choose the correct forcefield which will be accomplished by benchmarking the simulations against experimental data derived from NMR spectroscopy. Once developed, the simulations will be extended to elucidate how ATP interacts with protein surfaces and the impact on protein-protein interactions. The project also has important implications for using ATP or ATP-mimic molecules as excipients for stabilizing biological medicines.
|
|
Machine learning for urban temperature estimates in the UK: a modelling testbedPrinciple Investigator: Zhonghua Zhenghttps://research.manchester.ac.uk/en/persons/zhonghua.zheng Project DescriptionUrban areas are particularly vulnerable to the effects of climate change and urbanisation, but they are also the foci for climate-resilient efforts, including adaptation, mitigation, and sustainability. Process-based simulations of urban climates are computationally expensive. Instead, data-driven approaches, such as machine learning (and Deep Learning), offer alternate approaches for estimating urban temperature. In this project, we will develop Deep Learning models for emulating urban climate (e.g. temperature) in the UK.
|
|
Deep Learning-Based Multi-view 3D ReconstructionPrinciple Investigator: Hujun Yinhttps://research.manchester.ac.uk/en/persons/hujun.yin Project Description"The goal of learning-based 3D reconstruction is to infer the 3D geometry and structure of the scene from one or multiple 2D images. It has a wide range of practical applications in areas such as digital twinning, autonomous driving, medical image processing, virtual reality, and cultural heritage protection. The project will investigate and propose an efficient framework for such deep networks and develop effective training algorithms, in both software and hardware implementations, such as embedded GPU systems. Advantages will be demonstrated through case studies and further research along reliability and interpretability of the deep networks will also be conducted."
|
|
Distributed deep reinforcement learning with a group of agentsPrinciple Investigator: Xiao-Jun Zenghttps://research.manchester.ac.uk/en/persons/x.zeng Project DescriptionReinforcement learning has been an essential tool to tackle challenging problems, while it is non-trivial to design dense rewards properly for many challenging tasks. Alternatively, sparse reward settings in which the agent only receives valuable rewards for reaching some specific goals are easier to set up. For various tasks with different goals, they can be unified via formalisation as a goal-conditioned reinforcement learning (GCRL) problem. However, how to solve those GCRL problems efficiently under sparse reward settings remain challenging. In our work, we accelerate the training by decomposing the original task along existing trajectories into multiple sub-tasks that are easier to solve and we can assemble a sub-optimal solution from them for the agent to learn in the beginning. That makes learning faster.
|
|
Personalising renal function monitoring and interventions in people living with heart failure: RENAL-HFPrinciple Investigator: David Jenkinshttps://research.manchester.ac.uk/en/persons/david.jenkins-5 Project Description"Frequency of blood tests to measure kidney function varies widely between GPs. Individuals respond differently to changes in medicines and there is no way to work out how often a kidney blood test is required. If tests are not frequent enough, worsening in kidney function might not be detected early, risking hospitalisation. We aim to improve kidney health in people living with heart failure by developing a tool to predict how often each person with heart failure needs a kidney blood test. We shall use medical records to predict how an individual's kidney function will change over time, allowing us to recommend how often each person should be tested. We shall produce expert advice for GPs and nurses on how to adjust medicine dose and/or type to keep both the heart and kidneys working together at their best."
|
|
Generative methods for subgrid turbulence closurePrinciple Investigator: Alex Skillenhttps://research.manchester.ac.uk/en/persons/alex.skillen Project DescriptionDirect simulation of turbulent flows is typically too expensive, even with modern supercomputers. Coarser (and hence cheaper) meshes are employed in "Large Eddy Simulation" (LES). However, turbulent transport at scales finer than the mesh must be modelled in LES. Traditional turbulence models are empirical and tend to be inaccurate, particularly for flows where body forces promote turbulence anisotropy. In this project, we use generative methods in machine learning to generate sub-grid structures that approximately match the distribution of direct simulation. This is similar to upscaling images with “super-resolution” in the image processing field. In doing so, we hope to find an approximate inverse LES filter that can subsequently be used in improved LES modelling of plasma flows and liquid metal flows for the nuclear fusion industry.
|
|
Predicting the genomic risk of coronary artery disease through deep learningPrinciple Investigator: David Talaverahttps://research.manchester.ac.uk/en/persons/david.talavera Project DescriptionCoronary artery disease (CAD) is the most common cause of death in the UK. Whilst the risk of developing CAD depends heavily upon lifestyle factors such as diet, exercise and cigarette smoking, genetics also play an important role. We aim to leverage deep learning approaches to improve upon the current methods used to predict people’s genetic risk of CAD, enabling targeted treatment interventions for those at the greatest risk of the disease.
|
|
Supervised learning of nonlinear superposition principle for hydrodynamic loading on structuresPrinciple Investigator: Ajay Bangalore Harishhttps://research.manchester.ac.uk/en/persons/ajay-harish Project DescriptionStructures like offshore wind turbines, marine renewable energy structures, bridge piers, and floating vessels, are routinely exposed to harsh environmental conditions. This is even more frequent during hazard events. These frequently drive the design. The physics and statistics of wave-structure interaction are complex and still not fully understood for strongly non-linear loads as experienced in the most severe conditions. In this work we aim to demostrate the possibility of a neural-network-based-surrogate model capable of probabilistically predicting the drag coefficients on the structures. In this regard, a building array subjected to wave loading as a demonstration for the more complex city configuration to be considered in future studies.
|
|
Large language models in accounting and finance (A&F-LLMs)Principle Investigator: Eghbal Rahimikiahttps://research.manchester.ac.uk/en/persons/eghbal.rahimikia Project DescriptionFinText (FinText.ai) is a collection of financial natural language processing (NLP) models that we developed in collaboration with the University of Oxford. We plan to develop FinText further with more advanced financial modules. All existing advanced NLP models are developed using only general textual information and, hence, are not precise enough for use in accounting and finance. We focus on incorporating high-quality textual accounting and financial resources to develop more reliable, state-of-the-art, robust, and transparent large language models (LLMs). We expect this new repository and related papers to become the primary foundation for future research in financial and accounting analysis, financial forecasting, ESG (Environmental, Social, and corporate Governance) monitoring, climate risk assessments, and business analytics.
|
|
A Neural Network Based Surrogate to SWAN Nearshore Wave ModellingPrinciple Investigator: Lee Margettshttps://research.manchester.ac.uk/en/persons/lee.margetts Project DescriptionOffshore renewable energy generation relies on accurate and high-resolution wave model simulation for both device longevity and efficient energy production. Downscale wave modelling is an expensive task due to its high computational demand and inaccessibility of software. The consequence is that access to offshore power development is limited to areas with a pre-established wave modelling community and access to high performance computing. A machine learning approach can be adopted to simplify the modelling framework. Neural networks can be written to work in both GPU and CPU format. With added simplicity and computational efficiency, machine learning-driven wave models can be better equipped to handle decades-worth of data within a reasonable time frame. This flexibility renders increased accessibility within industry by vastly reducing the computation time required by traditional wave models This project aims to demonstrate a surrogate neural network framework for the well-established SWAN (Simulating Waves Nearshore) model in the Outer Hebrides, UK.
|
|
Goal-conditioned Reinforcement LearningPrinciple Investigator: Ke Chenhttps://research.manchester.ac.uk/en/persons/ke.chen Project DescriptionReinforcement learning has been an essential tool to tackle challenging problems while it is non-trivial to design dense rewards properly for many challenging tasks. Alternatively, sparse reward settings in which the agent only receives valuable rewards for reaching some specific goals are easier to set up. For various tasks with different goals, they can be unified via formalisation as a goal-conditioned reinforcement learning (GCRL) problem. However, how to solve those GCRL problems efficiently under sparse reward settings still remain challenging. In our work, we accelerate the training by decomposing the original task along existing trajectories into multiple sub-tasks that are easier to solve and we can assemble a sub-optimal solution from them for the agent to learn in the beginning. That makes learning faster.
|