VictorJuiz/thesis huggingface dataset:
Cervantes and the rise of the Russian novel
Keywords: Turgenev
Cervantes
Spanish
Dostoevsky
Gogol'
Pushkin
Russian

This thesis examines Cervantes’ contribution to the development of the Russian novel tradition by exploring his reception by four canonical nineteenth-century Russian writers: Pushkin, Gogol’, Turgenev and Dostoevsky. These authors have been chosen for the significance of their contribution to the development of the Russian canon and the clear presence of responses to Cervantes in their oeuvres. In order to paint a comprehensive picture of Cervantes’ reception in Russia (and, by extension, his impact on the Russian novel), this thesis takes into account the authors’ entire oeuvres while concentrating on the texts most pertinent to the study. While focus rests on Cervantes’ magnum opus, Don Quixote, his other works, particularly his Exemplary Tales, are also considered in relation to the Russian authors’ narrative developments. References and allusions to Cervantes and intertextual echoes with his texts are examined in both the Russian writers’ narrative fiction works (narrative poems, short stories and novels) and paraliterary texts (correspondence, notes and essays). This is the first time that references and allusions to Cervantes by these four writers are enumerated, examined and analysed all in one place. This comprehensiveness allows trends to be traced across the authors’ reception of Cervantes and its coincidence with their narrative developments. This enables the reader to understand both the extent to and manner in which Cervantes and his works contributed not only to the development of each writer’s individual oeuvre, but also, by extension, to the rise of the Russian novel tradition.
Beyond the Standard Model Higgs sectors
Keywords: THDM
two higgs doublet model
standard model
BSM

Although the experimentally detected 125 GeV spin-0 resonance has properties very close to the one predicted by the Standard Model, it is still possible that the Higgs sector in nature is not SM-like. In this work we will discuss two projects investigating the experimental validity of THDM (Two Higgs Doublet Model) and THDM+S models.
In the first project we demonstrate that the peak (2.3 σ) at 95 GeV as recently seen by CMS collaboration in the di-photon final state can be explained within the type-I THDM by means of fermiophobic CP-even Higgs boson H. In this context we discuss the other Higgs anomalies such as LEP excess in Higgsstrahlung and observation of enhanced rates in tt¯h at the LHC, showing that parameters capable of explaining the CMS di-photon excess can address these deviations as well while at the same time being compatible with LHC Run-I Higgs signal strength measurements.
In the second project we investigate experimental bounds on the exotic Higgs decays into the pair of light pseudoscalars devoting special attention to the mass regions of [3,4] GeV and [9,11] GeV where the pseudoscalar can mix with the QCD bound states ηc and ηb respectively. We find that mixing effects can modify the total decay width of the pseudoscalar and therefore have an impact on the derived bounds. We present exclusion limits on μhBR(h → aa) and μh BR(h → aZ) at a 95% C.L. in the framework of THDM+S model in order to facilitate the comparison with the recent results by the CMS collaboration.
Origin & evolution of the universe
Keywords: Statistics
Gravity
Cosmology

The aim of this thesis is to question some of the basic assumptions that go into building the ΛCDM model of our universe. The assumptions we focus on are the initial conditions of the universe, the fundamental forces in the universe on large scales and the approximations made in analysing cosmological data. For each of the assumptions we outline the theoretical understanding behind them, the current methods used to study them and how they can be improved and finally we also perform numerical analysis to quantify the novel solutions/methods we propose to extend the previous assumptions.
The work on the initial conditions of the universe focuses on understanding what the most general, gauge invariant, perturbations are present in the beginning of the universe and how they impact observables such as the CMB anisotropies. We show that the most general set of initial conditions allows for a decaying adiabatic solution which can have a non-zero contribution to the perturbations in the early universe. The decaying mode sourced during an inflationary phase would be highly suppressed and should have no observational effect, thus, if these modes are detected they could potentially rule out most models of inflation and would require a new framework to understand the early universe such as a bouncing/cyclic universe.
After studying the initial conditions of the universe, we focus on understanding the nature of gravity on the largest scales. It is assumed that gravity is the only force that acts on large scales in the universe and we propose a novel test of this by cross-correlating two different types of galaxies that should be sensitive to fifth-force's in the universe. By focusing on a general class of scalar-tensor theories that have a property of screening, where the effect of the fifth force depends on the local energy density, we show that future surveys will have the power to constrain screened fifth-forces using the method we propose.
Finally, to test theoretical models with observations a complete understanding of the statistical methods used to compare data with theory is required. The goal of a statistical analysis in cosmology is usually to infer cosmological parameters that describe our theoretical model from observational data. We focus on one particular aspect of cosmological parameter estimation which is the covariance matrix used during an inference procedure. The usual assumption in modelling the covariance matrix is that it can be computed at a fiducial point in parameter space, however, this is not self-consistent. We check this claim explicitly by calculating the effect of including the parameter dependence in the covariance matrix on the constraining power of cosmological parameters.
Quantitative modelling of T cell activation and co-stimulation
Keywords: T lymphocytes
T cell signalling
CD27
tumour necrosis factor receptor superfamily
T cell co-stimulation
4-1BB

Precisely regulated activity of T cells is crucial to effectively control pathogens while limiting immunopathology to a minimum. Even though T cell activation is predominantly governed by antigen recognition through the T cell receptor (TCR), the activity of an array of various accessory receptors can markedly alter this decision-making process, with the potential to serve as attractive therapeutic intervention points. Therefore, many of these co-stimulatory and co-inhibitory receptors and their molecular signalling pathways have been characterised. However, how exactly these receptors integrate with TCR signalling to affect the T cell response quantitatively remains poorly understood.

Here, we used in vitro expanded primary human T cells in a minimal stimulation platform to study co-stimulation through receptors of the TNF receptor superfamily (TNFRSF) in isolation. This allowed us to precisely define the ligand doses and stimulation times in order to capture the quantitative effects of TNFRSF signalling on the T cell cytokine response, with a particular focus on two representative TNFRSF members, 4-1BB and CD27. We found that 4-1BB costimulation was able to prolong the cytokine response, while engagement of CD27 increased the amount of cytokine produced without affecting the duration of the response and the T cells still became unresponsive after 8 hours. However, in combination with computational modelling, detailed analysis of our results showed that the distinct phenotypes of these receptors were mainly caused by their differential expression pattern in time, while the TNFRSF members share a common co-stimulatory mechanism, which we summarised in a minimal mathematical model. Simulations of the model predicted that co-stimulation through either of 4-1BB and CD27 could rescue TCR-dependent cytokine production in T cells which had become unresponsive through previous stimulation, and that CD27 could increase the potency of 4-1BB by enhancing its expression. We were then able to validate the model by confirming the both the rescue of the response and the synergy between TNFRSF members experimentally.
High-fidelity mixed species entanglement of trapped ions
Keywords: Trapped ions
Quantum computing

Harnessing quantum mechanics to solve problems intractable on classical computers is one of the foremost goals of applied physics. Trapped ions make an excellent physical platform for such a quantum computer, as they have demonstrated unparalleled qubit operation fidelities and memory times, but significant challenges remain in scaling up these systems. A promising scalable architecture is a photonically connected network of mixed element trapped ion nodes, each containing a few ions of two species. Poor quality remote entanglement links between nodes can be tolerated, if entanglement between ions in the same node can be achieved with high fidelity. While local gates have been repeatedly demonstrated between ions of the same species, the fidelity of mixed element quantum gates have not previously surpassed the fault tolerant threshold, above which errors can in principle be corrected.
In this thesis we demonstrate an entangling operation between 43Ca+, useful for its long lived qubits, and 88Sr+, well suited for remote entanglement, using a novel mechanism requiring only one laser to manipulate both species. This relatively simple scheme, which is independent of either qubit frequency, allows us to achieve Bell state fidelities of F = 99.8(1)%. We assess the operation of the gate further with state and process tomography, which achieve Bell state fidelity F = 98.1% and average gate fidelity F̅ = 99.05(6)% respectively, though they are hampered by experimental drifts during the long data collection. Finally, we use interleaved randomised benchmarking to get the best estimate of our gate errors, finding an average gate fidelity of F̅ = 99.62(3)% for a sequence of 60 interleaved gates.
Along with a demonstration of entanglement between two 88Sr+ ions, achieved with fidelity F = 95.9(4)%, this work paves the way towards demonstrating an elementary mixed species quantum network in future experiments.
The impact of domestic electric vehicle charging on electricity networks
Keywords: power system
electric vehicles
demand side response
smart charging

This thesis investigates the impact that home charging of a large private fleet of electric vehicles would have on the power system.
A large multi-regional travel survey dataset is used to model vehicle use and charging spatially heterogeneously, and a selection of representative network models are used to assess the impact of charging on system operation. A stochastic data-driven model is proposed to model uncontrolled charging of vehicles, and convex optimisation is used to calculate the optimal smart charging strategy.
The power system is commonly broken down into the generation, transmission, and distribution systems. The operation of each of these systems will be impacted by the addition of EV charging to residential networks. A variety of objectives have been proposed for smart charging, each of which would protect the system in a different way. Existing research tends to focus on a single part of the system, and considers only the smart charging objective that most benefits that part of the system. Here, the three systems are modelled simultaneously, and a large range of smart charging objectives are investigated.
The value of explicit loss minimising smart charging is quantified, compared to a simpler and more standard load flattening algorithm. These results are used to propose a novel optimisation formulation which reduces losses without requiring extensive network information. The value of bi-directional smart charging is also quantified compared to uni-directional smart charging, in order to investigate the viability of residential vehicle-to-grid.
It is demonstrated that it is not possible to optimise the transmission level and distribution level systems simultaneously, and the penalty of only optimising for one is quantified. A method for finding a compromising solution between both system levels is proposed, which exploits the sections of the distribution where components are over-specified.
Two specific case studies are investigated. The majority of the analysis in the thesis is based on the GB power system, however the Texas system is also presented as a comparative case study.
Computed tomography phenotyping of perivascular adipose tissue for cardiovascular disease diagnosis  and risk stratification
Keywords: inflammation
perivascular fat
cardiovascular imaging
pericoronary fat
radiomics
computed tomography
adipose tissue

Coronary inflammation is a major driver of cardiovascular disease and has been implicated in all stages of coronary atherosclerosis, from the early formation of coronary lesions to plaque rupture. Far from being an innocent bystander, perivascular adipose tissue (PVAT) is now recognised as a critical regulator of vascular biology. Further to its direct effects on the arterial wall through outside-to-inside paracrine signals mediated by a wide range of adipocytokines, recent evidence suggests that PVAT may also function as a sensor of vascular biology. More specifically, we have shown that coronary inflammation inhibits adipogenesis and triggers lipolysis in the adjacent perivascular adipocytes, resulting in smaller, fat-free adipocytes closer to the diseased artery. These phenotypic changes can now be captured as perivascular attenuation (radiodensity) gradients on coronary computed tomography angiography (CCTA) using a novel metric, the perivascular Fat Attenuation Index (FAI).
My thesis focuses on the technological development of this method of PVAT characterisation (FAI mapping) and its value in improving the cardiac risk stratification of patients undergoing CCTA imaging. First, I demonstrate that perivascular FAI mapping not only detects coronary segments that have a higher likelihood of coronary atherosclerosis progression at five years but can also identify culprit lesions associated with recent acute myocardial infarction. Using longitudinal CCTA imaging, I also show that perivascular FAI exhibits dynamic changes, which reflect the resolution of vascular inflammation and injury around culprit coronary lesions. Next, using two independent cohorts of 3,912 CCTA scans with long-term follow-up, I show that higher perivascular FAI values at baseline are associated with a higher risk of future major adverse cardiac events. Of note, the prognostic value of FAI mapping is incremental to traditional cardiovascular risk factors and the standard interpretation of CCTA scans and remains significant in both patients with and without established coronary artery disease at baseline. Finally, using a radiotranscriptomic, machine learning-guided approach, I develop radiomic signatures that describe adverse fibrotic and microvascular remodelling changes in coronary PVAT, beyond inflammation. As opposed to FAI, these reflect persistent changes in the PVAT structure and offer incremental value for major adverse cardiac event prediction. Taken together, my findings propose that the radiomic phenotyping of coronary PVAT on standard CCTA can maximise the diagnostic and prognostic yield of one of the most commonly used non-invasive imaging modalities in cardiovascular medicine.
Morphology, systematics and palaeoecology of pachycormid fishes
Keywords: suspension-feeding
palaeontology
Bayes
morphology
systematics
CT scanning
pachycormiforms
ecology
phylogenetics

Pachycormiformes are one of the earliest diverging groups of stem teleosts, and are distinguished by substantial ecomorphological disparity: from mid-sized predators, to giant suspension-feeders. Despite this, and their relatively abundant fossil record, they lack detailed study. Here, I use computed tomography scanning to re-examine and redescribe well-preserved suspension-feeding (Martillichthys) and predatory (Pachycormus) pachycormiforms. I report previously unobserved aspects of the gill skeleton (Martillichthys), neurocranium (both) and pectoral girdle (Pachycormus). I also identify features previously unreported in pachycormiforms, including a median vomer (Martillichthys: considered a teleost character but likely convergent), a urohyal (Pachycormus: considered a teleost character), and a metapterygium-like radial (Pachycormus: considered a primitive actinopterygian character). These morphological observations, along with molecular and stratigraphic data, inform phylogenetic analyses using maximum parsimony and Bayesian methods in order to resolve pachycormiform interrelationships, estimate divergence times, and test a hypothesized second origin of suspension-feeders within the group. Results corroborate previous hypotheses of pachycormiform relationships, but the inclusion of stratigraphic data results in an alternative pattern of relationships. Finally, I use quantitative biostratigraphic models, incorporating fossil record heterogeneity, to estimate credible intervals of suspension-feeding pachycormiform extinction and suspension-feeding chondrichthyan emergence. I find some chondrichthyan lineages may have appeared as early as the Early Cretaceous, and cannot reject that pachycormiforms may have persisted into the Eocene. However, the presence of other putative suspension-feeding chondrichthyans in the Cretaceous that are absent in the Cenozoic suggests modern lineages may (as hypothesized here and elsewhere) have emerged following the faunal turnover of the end Cretaceous. This thesis builds a clearer picture of pachycormiform morphology and interrelationships, which can be used as a model to interpret the ecology and evolution of other fossils of pachycormiforms and stem teleosts. It also provides further context for the evolution of modern teleosts and how the extinction of pachycormiforms impacted modern marine diversity.
Computational and neural mechanisms of human aversive learning
Keywords: state learning
fear extinction
reinforcement learning
anxiety
dorsal anterior cingulate cortex
neuroimaging
pupilommetry
aversive learning

Aversive learning is characterised by rapid learning which is highly resistant to extinction. This has likely developed as an evolutionary mechanism but it often becomes maladaptive. In this thesis I investigate computational mechanisms governing the learning of aversive beliefs, neural structures that are involved in the process and test whether pharmacological intervention can be used to amend learning.
In a series of three experiments in healthy volunteers ranging in trait anxiety I employ a probabilistic aversive learning paradigm in behavioural (study 2 and 3) and neuroimaging setting (study 1). I first characterise the difference between acquisition and extinction, identifying that while acquisition learning is fast and reaches the target level, during extinction aversive expectations are consistently higher than true reinforcement rates. This extinction-specific overprediction increases over time but only in the low anxious group. High trait anxiety was associated with increased dissociability between acquisition and extinction which was driven by the activity in the dorsal anterior cingulate cortex. Computational modelling revealed that high anxiety is associated with the tendency to internally represent the learning environment as distinct states and switch between them. This finding was later supported by behavioural and computational evidence in study 2. The neuroimaging analysis suggests that state learning is processed by the dorsal anterior cingulate cortex, ventro-medial prefrontal cortex and the inferior parietal lobule. This finding highlights increased context-dependence of anxious individuals which bears importance to clinical practice. In study 3, I test the effect of Angiotensin-II receptor antagonist losartan on aversive learning showing that losartan specifically decreases learning rates without influencing perception or updating.
Development of tuberculosis imaging probes based on sugars and proteins
Keywords: protein chemistry
CCL2
biocatalyst
FDT
Azidohomoalanine
Imaging
Tuberculosis
PET
enzymatic sugar synthesis

Tuberculosis (TB) is one of the oldest known human diseases, caused by Mycobacterium tuberculosis. The techniques currently employed for diagnosis are generally insensitive and non-specific. Positron emission tomography (PET) combined with computed tomography (CT) is used extensively in clinics and is among the advanced molecular imaging modalities for disease diagnosis and for monitoring the response to treatment.
In this thesis we described new probes based on sugars and proteins. We used an optimised biocatalytic approach for the synthesis of 2-fluoro-2-deoxy-D-trehalose (FDT). We scaled up synthesis for pre-clinical studies giving strong consideration to the expression host for enzymes. The use of rare biocatalytic methods employed in the synthesis was unique for the development of a radioisotope probe.
We then sought to develop an LC-MS based method for the detection of FDT in vitro and in vivo. Toxicity of the synthesised sugar compound was assessed in two different animal models. FDT administered to naïve marmosets labeled very little tissue in the lung, but when the animals were infected with M. tuberculosis, the tubercular lesions took up the radiolabelled FDT and [18F] FDT monomycolate was isolated from tubercular lesions indicating that the probe reached the site of bacterial incorporation. In addition, FDT labelling of tubercular lesions was reduced with 4-drug standard TB treatment in marmosets. This work has permitted the development of specific PET imaging probe for TB diagnosis towards the clinic.
We demonstrated that a connvergent protein synthesis using a "tag-and-modify" approach allowed the creation of homogenous, modified CCL2 variants bearing dual labels. We incorporated an unnatural amino acid azidohomoalanine (Aha) in the wild type CCL2 to obtain modified variants. Through initial work we have shown the successful modification of various potential PET imaging probes for TB diagnosis.
Rational herds and endogenous fluctuations in consumer sentiment
Keywords: informational cascades
resolution method
rational herds
information frictions in macroeconomics
bayesian learning
nonlinear business cycle models
endogenous business cycles
two sector new Keynesian models
social learning
asymmetric business cycles
consumer sentiment

This thesis models \textit{endogenous} fluctuations in consumer sentiment and sentiment driven endogenous business cycles in the context of a general equilibrium model with rational herds and social learning in consumer durable goods sector about a hidden state of nature. I want to understand the theoretical conditions under which one gets endogenous and asymmetric cycles in this class of models. The learning dynamics are very sensitive to the variances specified for signal distributions and also to how difficult it is to distinguish the two unobservable states. For some values, convergence of public beliefs to the truth is very slow and one gets rational herds on the wrong action and for others, one gets endogenous cycles in public beliefs and investor mass, leading to fast convergence to truth. In addition to endogenous cycles, my theoretical results also show evidence of asymmetric effects of consumer sentiments across business cycle regimes, which I compare with stylized facts about asymmetric causal effects from local projections. I do not aim to achieve precise quantitative goals with this model, but my qualitative impulse responses provide a sentiment, social learning and information based theory of endogenous and asymmetric business cycles.