VictorJuiz/thesis huggingface dataset:
Spatial and contextual long-term and working memory in ageing and disease

Initially thought to be two independent memory systems, working memory (WM) and long-term memory (LTM) both present associative features controlled by the medial temporal lobe (MTL). Despite their similarities, the two memory systems have mostly been explored and compared using distinct methods and measures. Considering their differences and their correspondences, this thesis focuses on spatial associative WM and LTM performance in healthy ageing and in the presence of MTL impairment. The studies presented investigate the mechanisms of the two memory systems, their interplay, and the modulatory relationship between them and attention, using analogous tasks and equivalent behavioural measures. Specifically, Chapter 2 explores if retro-cueing benefits, previously observed in WM, extend to LTM retrieval, and whether this could represent a means of ameliorating age-related LTM impairment. Next, in Chapter 3, I compare the performance of young and older healthy adults in a spatial memory task on two devices – a personal computer and an immersive VR device – to test whether old adults benefit from an increased level of immersion and determine the optimal methods of measuring spatial memory. In the second study, I use a navigation-based memory task measuring WM and LTM, to investigate their patterns of use and interplay that might change with ageing. Lastly, Chapter 4 relies on the same task and aims to analyse patterns of performance modulated by different levels of MTL impairment in old healthy adults and individuals with limbic encephalitis. The results propose that WM and LTM show age-related associative deficits and that their interplay is preserved in ageing, despite old adults’ increased forgetting. Additionally, they suggest that WM and LTM may rely on different retro-cueing mechanisms. Lastly, the most noteworthy outcomes are the tasks developed to measure spatial associative memory of different timespans in equivalent ways, using analogous steps and stimuli, which can become great tools in the research of WM and LTM retrieval processes.

Keywords: spatial memory
associative memory
long-term memory
working memory
limbic encephalitis
retro-cueing
navigation
Advisor: Butler, C
Nobre, AC
Zokaei, N
Stravinsky and his sketches : the composing of Agon and other serial works of the 1950s

This thesis takes as its starting point the unpublished source materials--musical sketches, manuscripts and archival material--that relate to Stravinsky’s stylistic transition in the 1950s, during which serial principles were gradually incorporated into his musical language. Drawn mainly from the Stravinsky Nachlass at the Paul Sacher Stiftung in Basel, and available for research there only since 1986, these previously unexplored sources offer an unusual and flexible approach to Stravinsky’s early serial works.
After a brief consideration of the basic aims of musical sketch studies and the relatively recent appearance of this type of inquiry in Stravinsky research, the rich and complex sketches for the ballet Agon (1953-7) are the principle concern of the thesis. Of all the source materials for Stravinsky’s works of the 1950s, these are perhaps the most extensive in their coverage of his working methods, most intricately developed, and most illustrative of wider issues in his compositional technique. Throughout the thesis, analytical observations are prompted almost exclusively by information contained within the sketches. The procedures and preoccupations reflected in these materials are related to those in other 1950s sketch collections, particularly Canticum Sacrum, allowing Stravinsky’s working methods to be assessed not only in evolutionary terms, but also in terms of recurring compositional premises, or procedural constancies.
Evaluated in conjunction with corroborative evidence in the archival material of the Nachlass, these sources offer an unrivalled opportunity for a re-examination of statements made by Stravinsky on his own compositional method, and of certain interpretations of Agon in secondary sources. Systems of classification of source materials are developed in the thesis, and an Appendix gives detailed listings of the contents and physical characteristics of the Agon and Canticum Sacrum sketchleaves in the Stravinsky Nachlass.

Keywords: Stravinsky
nineteenth fifties
sketches
1950s
nineteenth-fifties
composing
Agon
Symposion and philosophy

The present work has a double purpose: to redefine the notion of the ‘philosophical symposion’, formulated by Wilamowitz, from a larger perspective on the relation between symposion and philosophy; and to study the origins of this phenomenon in Plato’s conception of the symposion, especially as illustrated in the Laws. Section i is dedicated to the reconstruction of the philosophical symposion: it analyses the role played by the symposion in the main Hellenistic schools at a relatively early stage after Plato - roughly down to the cessation of scholarchs. The most specific features of the notion which I propose to define result from the fact that philosophers started to take a theoretical approach to the symposion; Chapter I considers this issue. Chapter 2 deals with the philosophical symposion in the literary sense: it presents some of the problems raised by the very fragmentary evidence on ‘Symposia’ written after Plato in the Academy, Peripatos, Stoa and Garden. Chapter 3 examines the scanty evidence for an early adoption of sympotic practices by philosophers, at school or otherwise. Section 11 is devoted to Plato’s conception of the symposion. The Athenian’s ideas about reforming this institution are analysed in Chapters 4 and 5: each chapter focuses on a paragraph containing a different project. Chapter 6 considers, in the end, a few cases of earlier Platonic imagery, which is relevant to the formation of the theory in the Laws and allows us to integrate it in a broader perspective on Plato. By adopting this order, the present work attempts to present Plato’s conception as a part of a much wider phenomenon, generated by Plato himself.

Keywords: symposium
Classical Greek
drinking party
philosophy
The phoenix of power: the reconstruction of political authority following revolutionary regime change

Periods following revolutionary regime change are often governed by Transitional Administrations (TAs). Such bodies are seldom elected or even selected in any formal process. As such the political authority they enjoy is a priori deeply puzzling. This thesis seeks to understand the factors which affect the likelihood that a TA will successfully be able to establish its authority. It closely examines two cases of political transition in Libya and Tunisia, as well as the broader universe of TAs. The research concludes that the composition of the TA, in particular its relative inclusivity of the principal socio-political components of the society, greatly impacts the prospects that the TA will establish its authority. However, the research also finds that the inclusivity of the TA is conditioned by other factors, including historical socio-institutional legacies, pre-existing social ties between elites, chance contingencies leading to path-dependent outcomes, and the agency of leaders. As such the thesis proposes a novel socio-institutional explanation of authority- building in political transitions.

Keywords: Political Authority
Arab Spring
Political transitions
Legitimacy
Regime change
Revolutions
Transitional Administrations
Advisor: Caplan, R
DNA nanotubes: An elastic mechanical perspective

DNA nanotubes are a versatile nanoscale structural element that have seen an increasingly wide range of uses, such as appendages to facilitate the propulsion of magnetic beads and as potential drug carriers that can help us achieve targeted drug delivery especially in the case of cancer. To enable their use for such applications, it is vital to understand the elastic properties and mechanical limits of such nanotubes.
Simulations and modelling have the potential to play a particularly important role in this regard, because detailed mechanical characterization of such nanoscale structures is hard to obtain experimentally. To study the elastic properties of rod- like DNA nanostructures (with and without defects) we perform long simulations of these structures using the oxDNA coarse-grained model. By analysing the thermal fluctuations in these trajectories we obtain estimates of the bend and twist persistence lengths, and the underlying bend and twist elastic moduli and couplings between them. That the bending persistence lengths we obtain are generally somewhat higher than in experiment probably reflects both that the simulated origami have no assembly defects and that the oxDNA extensional modulus for double-stranded DNA is too large. We also study some DNA nanotubes under torsional stress providing both an alternative means to obtain twist moduli and the first insights into the nature of torsional buckling in DNA nanostructures. The structures of these plectonemes show significant differences as compared to DNA duplexes because they are in a different mechanical regime with respect to their basic mechanical properties.
Finally, we simulate the properties of a recently developed nanomechanical device to help interpret the experiments on this system. In particular, we show that a proper accounting of the elastic mechanical response of the origami component of this force clamp is essential if the forces that the device can exert are to be correctly estimated.

Advisor: Doye, J
Large scale methods for kernels, causal inference and survival modelling

Machine Learning is widely applied in industrial applications such as product design and recommendations, sales and churn prediction among many others. This thesis proposes scalable methods geared towards industrial application where we address problems related to dimensionality, uncertainty quantification and inference.
First, we inferring features from images of products that are pertinent to high sales. We propose an additional regularization objective inspired by kernel two-sample testing to separate high-sellers from low-sellers in latent space for generative models. Experiments demonstrate that we can achieve linear separation in latent space and consequently infer important features using lasso methods.
Next, we develop a non-parametric test for causal association. We propose the backdoor-Hilbert Schmidt Independence Criterion with importance weights that encode the interventional distribution. Experiments demonstrate that the proposed test is calibrated and has good power.
We then propose Kernel Fried Tensor (KFT), which is a tensor factorization model augmented with kernelized side information, designed to handle high-dimensional forecasting. To improve the utility KFT, we derive a variational inference scheme to provide uncertainty quantification around our estimates. Experiments demonstrate competitive forecasting results and calibrated uncertainty quantification.
Further, we consider right-censored data where existing survival models do not have a tractable likelihood and evaluate on non-proper-scores. We propose survival monotonic neural networks (SuMo-net) to train and evaluate on the right-censored likelihood, a proper score. Experiments demonstrate that SuMo-net achieves improved likelihood scores with lower inference times.
As many of our proposed methods rely on kernel matrix vector multiplication (KMVM), they are bottlenecked by the O(n2) memory and complexity. We propose Faster Fast and Free Memory Method (F3M) and achieve O(n) memory and complexity for tall (n 109) and skinny data (D ≤ 7).
In summary, the methods proposed in this thesis provide a collection of scalable methods for various use cases often encountered in industrial settings and contributes both through novel methodology and statistical characterization of the tasks, which hopefully inspires further research in the machine learning community.

Keywords: Kernel methods
Causal inference
Survival analysis
Interpretability
Advisor: Sejdinovic, D
Evans, R
Compositionality and functorial invariants in machine learning

The objective of this thesis is to show that studying the underlying compositional and functorial structure in machine learning systems allows us to better understand them. In order to do this, we explore category theoretic formulations of many subareas of machine learning, including optimization, probability, unsupervised learning, and supervised learning.
We begin with an investigation of how various optimization algorithms behave when we replace the gradient with a generic category theoretic structure. We prove that the key properties of these algorithms hold under very relaxed assumptions, and demonstrate this result through numerical experiments. We also explore a category theoretic perspective on dynamical systems that enables us to build powerful optimizers from the composition of simple operations.
Next, we take a category theoretic perspective on the relationship between probabilistic modeling and gradient based optimization. We use this perspective to study how maximum likelihood estimation preserves certain key structures in the transformation from a statistical model to a supervised learning algorithm.
Next, we take a functorial perspective on unsupervised learning. We develop taxonomies of unsupervised learning algorithms based on the category theoretic properties of their functorial representations, and demonstrate that these taxonomies are predictive of algorithm behavior. We use this perspective to derive a host of new unsupervised learning algorithms for clustering and manifold learning, and demonstrate that these new algorithms can outperform commonly used alternatives on real world data. We also use these tools to prove new results on the behavior and limitations of popular unsupervised learning algorithms, including refinement bounds and stability in the face of noise.
Finally, we turn to supervised learning and demonstrate that many of the most common problems in data science and machine learning can be expressed as Kan extensions. We use this perspective to derive novel classification and supervised clustering algorithms. We also explore the performance of these algorithms on real data.

Keywords: Category Theory
Machine Learning
Advisor: Gibbons, J
Ionescu, C
Structural and biochemical characterisation of human integral membrane proteins regarded as potential therapeutic targets

Integral membrane proteins are responsible for a wide range of cellular functions and are important targets for the development of novel therapeutics. Therefore, obtaining structural and mechanistic insights into their function is of central importance for rational drug design. In this thesis, I focused on characterising three human integral membrane proteins using X-ray crystallography, cryo-EM, mass spectrometry and other biochemical methods.
MFSD10 is a member of the drug:H+ antiporter family 1 (DHA1) of the Major Facilitator Superfamily of solute carriers and has been proposed to act as an efflux pump for organic anions, including several non-steroidal anti-inflammatory drugs (NSAIDs). Here, I present a crystal structure of human MFSD10 in an outward-facing partially occluded conformation, with a bound citrate ion from the crystallisation solution. The structure provides insights into the recognition of organic anions by MFSD10 and suggests a potential mechanism of proton coupling. An in vitro assay was employed to confirm the NSAID:H+ antiporter function of MFSD10 and will constitute the starting point for additional functional assays to dissect the mechanism of this transporter.
The second focus of this thesis falls on elucidating the mechanisms of two enzyme families responsible for the acyl chain specificity in fatty acid elongation and sphingolipid biosynthesis, respectively. ELOVL elongases catalyse the first rate-limiting condensation step of the fatty acid elongation cycle in the endoplasmic reticulum (ER). The recently solved crystal structure of human ELOVL7 provided the first structural insights into this enzyme family. Here, I followed up on the structural data by characterising the mechanism of this enzyme using mass spectrometry. I demonstrated that catalysis by human ELOVL7, and likely other ELOVLs, proceeds via a ping-pong type mechanism involving an unusual covalent acyl-imidazole intermediate. Lastly, I focused on obtaining structural insights into the function of human ceramide synthases, which catalyse the N-acylation of sphingoid bases in the ER and determine the acyl chain composition of sphingolipids. I conducted a systematic screening of expression and purification conditions for two members of this family, identified suitable truncations for structural studies, obtained and validated nanobody binders, and optimised cryo-EM conditions. Together, these provide an advanced position for future structural studies to obtain valuable three-dimensional information on these important drug targets.

Advisor: Carpenter, E
Siebold, C
Yue, W
Pike, A
Machine learning approaches to extract higher-order features from non-contrast computerised tomography images enables stratification of diseases

Medical Imaging, which allows for the non-invasive assessment of biological tissues, is a rapidly growing health care service that has evolved from a diagnostic tool to a platform for personalized precision medicine. Computerized Tomography (CT), which is a commonly obtained imaging study worldwide, utilizes X-ray radiation to differentiate tissues based on density differences. More complex imaging studies are obtained based on the clinical question to supplement the CT. Image overutilization, which is the acquisition of medical images that have minimal impact on patient care and increasing global inequities due to the cost/availability of medical imaging services are significant problems that need to be addressed.
One possible solution would be to maximize the amount of clinically relevant information extracted from routine CT images using machine learning approaches. Recent developments in machine and deep learning have provided a powerful set of tools for the automated and complex analysis of medical images. Leveraging these techniques, it is possible to develop algorithms capable of learning from human annotations or paired images. These methods can also be applied to large datasets to minimise human input and to enable the rapid scale up of analyses.
In this thesis, I hypothesize that Non-Contrast CT images contain higher-order information to differentiate tissue anatomy or pathology without the need of intravenous contrast agents or radioactive tracers. I further hypothesized that such higher-order information enables stratification of disease progression without the need of additional imaging studies. I focus on two pathologies, abdominal aortic aneurysms (AAA) and Head and Neck Squamous Cell Carcinoma (HNSCC) as demonstration of feasibility. The methods described in this thesis can be applied to other pathologies and will be poised to disrupt clinical pathways in the future.

Keywords: image processing
abdominal aortic aneurysms
machine learning
computerized tomography
image segmentation
generative adversarial networks
Advisor: Handa, A
Lee, R
Grau Colomer, V
Muon sites and muon states in model fluorides and magnets

The F–μ–F state occurs when a spin polarised muon implanted in a fluoride crystal becomes entangled with its nearest neighbour fluorine nuclei, resulting in a beating pattern observed in the muon decay asymmetry. Ever since these states were discovered in the 1980s, they have never been fully understood, as fitting the data always required a phenomenological relaxation function to account for the observed decay of the oscillations.
This thesis shows how this relaxation can be understood by considering it as a decohering process, achieved by taking into account the effect of all the nuclear spins beyond the nearest-neighbours. Starting with the simple cubic compounds CaF2 and NaF, techniques are developed which allows one to calculate the way in which the quantum information of the muon gets irreversibly lost into the crystal. This thesis then expands on this to discuss the compounds PbF2 and YF3, which have a more complicated crystal structure, and in the former compound it is possible to identify Frenkel defects with μSR for the first time.
This thesis then shows how it is possible to apply these techniques to magnets: starting with KPF6 and KBF4, which contain the [PF6]− and [BF4]− molecular ions which are often found in molecular magnets, it shows how the location of the muon site in the compound considerably changes the observed muon polarisation, determining the muon stopping site by carefully analysing the muon’s environment. Finally, the antiferromagnetic material KAgF3 is analysed with these methods, showing how a combination of DFT+μ, dipole field calculations and the techniques developed here can be used to determine the size of the electronic moments, confirm magnetic structures and narrow down the muon site. This final chapter also shows how the nearest-neighbour fluorine nuclear moment can affect the muon polarisation in magnets in a fascinating way.

Keywords: decoherence
nuclear magnetism
muon
fluoride
magnets
quantum
DFT
inorganic
entanglement
Advisor: Blundell, S
Molecular assembly and interactions of membrane proteins and viral glycoproteins using mass photometry

Integral membrane proteins (IMPs) and their interactions form the basis underlying many processes essential for life of all living organisms. Despite their pivotal importance, they are challenging to study due to the presence of a bilayer-embedded transmembrane domain requiring astute strategies to achieve solubilisation in aqueous environments. Here we present mass photometry as a novel technique to investigate IMPs and their interactions in a variety of membrane mimetic systems, uncovering the interactions underlying the bacterial outer membrane architecture. We then apply mass photometry to viral membrane glycoproteins, the HIV-1 envelope spike and SARS-CoV-2 spike, and their interactions with a soluble inhibiting protein and host cell receptor, respectively. In both cases, we describe how the oligomeric nature of the involved proteins facilitates intermolecular crosslinking, facilitating enhanced inhibition.
We begin by establishing the applicability of mass photometry to measure a variety of membrane mimetic systems, including detergents, by determining the molecular weight of different IMPs in detergents, amphipols, lipid nanodiscs, and native nanodiscs. We further show the heterogeneity stemming from native membrane protein extraction with native nanodiscs, as well as the utility of mass photometry in predicting IMP functionality in different lipid nanodisc assemblies. We apply these findings to characterise the architecture and interactions between the IMP of the Gram-negative bacterial outer membrane, providing important insights into the outer membrane island formation and contributions to antibiotic resistance.
Next, we focus on the interactions of the HIV-1 envelope spike glycoprotein with its natural inhibitor, banana lectin BanLec. Through careful mass photometry measurements, we determine the dynamics of this multivalent interaction, resulting in the formation of highly heterogeneous species through intermolecular crosslinking. This approach is then applied to investigate the mechanism behind SARS-CoV-2 infection. We measure the interactions between the viral spike and its human host cell receptor, ACE2, showing the importance of its dimeric nature for achieving its full neutralisation potential through spike crosslinking.
This work introduced mass photometry as an important tool for membrane protein research with broad applications in protein-protein interactions as well as in characterisation of complex heterogeneous reactions important in viral infections.

Keywords: label-free
membrane protein
in solution
single-molecule imaging
HIV-1
SARS-CoV-2
mass photometry
viral glycoprotein
interferometric scattering
Advisor: Kukura, P
Robinson, C
Struwe, W