For embryo implantation to be successful and establish a pregnancy, co-ordination of endometrial receptivity and development of a viable embryo is required. In reality, this occurs infrequently as the rate of conception per natural cycle is as low as ~30%. Little is known about the gene regulatory networks (GRN) that govern implantation in humans, but the distinctly different cellular mechanisms for implantation adopted by different species are reasonably well studied. Tracing these differences back in evolution could help us understand how processes and genes which mediate human interstitial implantation have evolved. This will provide insight into the human-specific features of early life development and the environmental pressures that impact healthy ageing. Our current work has identified genes active at the maternal:embryonic interface in which recent evolutionary change has occurred. We now hypothesise that evolutionary history has strongly impacted subpopulations of the trophectoderm (TE), the outermost cell layer of the implanting embryo, which interacts directly with the maternal environment. The components of the resulting interactome are likely to have changed as distinct interfaces have arisen during evolution. The proposed research will address this hypothesis through a systems-level analysis of the effect of TE subpopulations on early maternal:embryonic interactions. This will be achieved by: 1. Generating single cell RNAseq implantation data using organoid and stem cell models, allowing us to characterise the interactome and define the functional associations of distinct cell subpopulations. 2. Performing a multi-level integrated 'omic analysis of embryo implantation combining transcriptomic, chromatin conformation and epigenomic data. 3. Using our integrated model of multi-omic interactions occurring during implantation as a basis to assess the impact of recent human evolution. The identification of the impact of recent evolution on the implantation process will identify pathways with specific functional relevance. This will provide insight into developmental windows of environmental vulnerability in very early life as well as improving understanding of the fundamental biology of implantation.

The project focuses on optimizing high-level business processes that can be decomposed into low-level operational processes. A relevant business is the energy sector, where companies strive to deliver the vast amounts of energy that are consumed every year by the world's population in the form of electricity for heating, air conditioning, refrigeration, ventilation, and operation of office computers and industrial machinery. For instance, in 2022, a total of 4.07 trillion kWh worth of electricity was consumed on U.S. soil alone.

LLMs have demonstrated a remarkable ability to generate text taking as input images, text, audio and video. They are able to achieve higher performance than traditional neural methods and pre-trained language models without the need of supervised training. The project will examine different approaches for multimodal LLM-based NLP to address complex and fine-grained tasks such as reasoning in model-based systems engineering. The PhD will delve into LLM architectures, data augmentation methods, multi-task and domainspecific LLMs, prompting engineering and interpretability.

Genetic alterations leading to dysregulation of the PI3K/AKT pathway are one of the most common genetic drivers across a wide range of tumour types. These changes generally consist of activating mutations in the gene coding for phosphatidylinositol 3 kinase alpha (PI3Ka), or deletion/deactivation of the phosphatase PTEN. The result of either change is a loss of control of the PIP2-PIP3 system, leading to accumulation of PIP3 and subsequent hyper-activation of the AKT pathway. Drugs targeting the PI3K/AKT pathway have proven to be useful in treatment of some patients, but are limited by toxicity due to the ubiquitous role of PI3K/AKT signalling, as well as resistance mediated by PTEN loss of function. We are working on a series of truncated PIP analogues that remain under the control of PI3Ka/PTEN but with reduced membrane affinity due to alterations in the lipid side chain. It is expected that these compounds will accumulate in the PIP3 form in PTEN deficient cells, leading to deactivation of AKT, but be in a less activated state in healthy cells. This approach would therefore directly target both the toxicity and resistance issues seen with conventional PI3K/AKT pathway inhibitors. Aims of this project: 1) Structure-based design and stereo-controlled synthesis of PIP analogues, targeting a small group of compounds with variable membrane localisation through modification of the side chain using the route shown. 2) Biological evaluation of novel compounds prepared to evaluate their cytotoxicity and impact on AKT localisation and signalling pathways. 3) Deconvolute the mechanism of action of current and novel PIP analogues through phosphoproteome assays and development of chemical probes to identify cellular binding partners of active PIP analogues.

This project aims to create scalable and optimal Process Flow Diagrams (PFDs) for new formulations. A unique characteristic associated with formulated chemicals is their short product life, meaning that tactical and aggressive product innovation is crucial. As both trends and consumers' desires change more frequently and seasonally, manufacturers must adapt and respond quickly to the everchanging needs of the customer, whilst being able to manufacture improved formulations. A prevalent challenge within the formulated chemical industries involves the current process flow diagram (PFD) development procedure which undergoes several essential, but time-consuming steps, i.e. starting from lab-scale product formation to pilot-scale R&D process design, and finally transformation to factory manufacturing. Numerous design iterations and experiments are required to be carried out during upscaling due to the production line becoming more complex to analyse and optimise. These challenges result in a long PFD development cycle with substantial labour and energy costs and waste generation. For example, disposing wastes generated through these trials alone accounts for 10% of total energy costs for a personal care product manufacturing plant. To resolve this challenge, in this project we will focus on developing an efficient feedback loop using real-time process data to significantly improve the performance of a proposed PFD. The innovation of this project is investigating how state-of-the-art interpretable machine learning and hybrid modelling techniques can be used to: (1) identify key correlations between formulation properties and PFDs; (2) create scalable and optimal PFDs for new formulations; and (3) develop an efficient feedback loop using real-time process data to further improve the performance of a proposed PFD. It aligns well with EPSRC's priority in Manufacturing the Future and Digital Manufacturing.