Quantifying Human Dietary Change over the Last 30,000 Years

Dietary change has been linked to many aspects of human evolution over the last three million years, including tool use, brain size increase, aerobic capacity and gut biology. Furthermore, failure to adapt to dietary changes over the last 10,000 years has been implicated in a number of complex and chronic diseases including obesity, type II diabetes, some cancers and coronary heart disease. Such ‘diseases of modernity’ are more common in agrarian and industrial societies than among hunter-gatherers, and it has been argued that this is due to a mismatch between modern diets and the ancestral diets to which our metabolism should be optimised. The aims of this research have grown out of the qualitative studies that perpetuate narratives around human and hominin diets, particularly around the central theme of dietary mismatch and ‘paleo’-named diets. In this work, I investigate nutrient-level differences between modern post-industrial diets, modern hunter-gatherer diets, prehistoric (Palaeolithic, Neolithic and Bronze Age) diets reconstructed from archaeological data, clinical intervention diets, fad diets including The Paleo Diet, Keto Diet and Atkins Diet, fast food diets and milk. Using these data, I develop a hypothesis on the evolution of dietary choice. Modern diets are enriched for certain nutrients, for some of which we have strong taste avidities (e.g. sodium, sucrose, starch, certain fatty acids). By quantifying differences in inferred nutrient profiles between ancestral and modern diets, I examine the nutrients enriched in modern diets, the trajectories of nutrient composition change through time, what might be driving these changes, and why we have evolved taste preferences for some nutrients that in a modern setting are considered ‘unhealthy’. I also examine how nutrients correlate in ancestral foods and explore if avidities for nutrients enriched in modern diets would lead to healthy nutrient profiles in an ancestral setting

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Mining the microbiome for markers of microbiota-gut brain communication and mental health

There has been a growing acknowledgement of the involvement of the gut microbiome - the collection of microbes that reside in our gut - in regulating our mood and behaviour. This phenomenon is referred to as the microbiota-gut-brain axis. While our techniques to measure the presence and abundance of these microbes has been steadily improving, there are many factors that prevent us from understanding what aspects of the gut microbiome specifically influence the microbiota-gut-brain axis. In this thesis, we set out to identify and investigate aspects of the microbiome that are informative to gut-brain communication. We do this by investigating the state of the gut microbiome in both health and disease, as well as after supplementing or perturbing it. While all of the work presented here is based on real data from real experiments, the thesis has a strong bioinformatics focus, that means that while the physiological background and interpretation are important, my role in these projects has been to bioinformatically and statistically zoom in on the features of the microbiome that are the most informative to our questions. As such, all results will be discussed from a primarily bioinformatics point of view. Two main aspects of the gut microbiome came out as the most promising features to measure, namely functional capacity and volatility. Traditionally, the microbiome is thought of as a collection of microbes and most analysis is done on the taxonomical level. However, we find that by investigating microbial function - as defined by the genes that are found or associated in the detected microbes - rather than taxonomy, we are able to perform more sensitive analysis and that our results are more easily interpretable. Second, microbiome studies are typically conducted using a single sample per subject. We find that the degree of change in the microbial ecosystem, called volatility, is an important feature of the microbiome and that is linked to severity of stress response. While volatility was coined before in the context of the microbiome, this was only in passing. We were the first to investigate volatility as a feature of the microbiome. Our research in this thesis reconfirms the existence of the microbiota-gut-brain axis and demonstrates novel metrics that can be used to interrogate the microbiome. We utilize mathematical frameworks originally from geology and classical ecology to bolster our analysis. We show that considering the microbiome as an ecosystem is a powerful model that can help us better formulate our scientific questions and interpret our findings. We argue for strategies to unify bioinformatics methodology in the microbiome-gut-brain axis field in an effort to move towards mechanistic understanding