VictorJuiz/thesis huggingface dataset:
Essays on natural resource economics in developing countries
Keywords: growth
natural resource economics
debt
development economics
oil

This thesis comprises three papers within the field of natural resource economics in developing countries. In broad terms, this thesis investigates the challenges and opportunities surrounding the macroeconomic management of natural resource revenues in developing economies. It is a mix of theoretical and empirical work covering three distinct but related topics.
The first paper, titled “Commodity price shocks, growth and structural transformation in low-income countries”, is an empirical investigation of the macroeconomic response of resource-rich, low-income countries to movements in commodity prices. A panel-VAR approach is used to estimate both the dynamic and structural macroeconomic response to global commodity price shocks. The results point to small, but positive, growth and investment responses to commodity price booms. I also find evidence of significant transformation of the sectoral value-added shares in these economies following price shocks, and present evidence of a Dutch disease mechanism at work.
The second paper, titled “Surfing a wave of economic growth”, investigates whether the geographic determinants of growth extend to natural amenities. It combines data on spatial and temporal variation in the quality of over 5,000 surf breaks globally with data on local economic performance, proxied by night-time lights. The findings document a strong association between natural amenity quality and local economic development. Economic activity grows faster near good surf breaks; following the discovery of new breaks, or the technology needed to ride them; and during El Ni˜no events that generate high-quality waves. The effects are concentrated in nearby towns and emerging economies, and population changes are consistent with tourism.
The third paper, titled “Pricing sovereign debt in resource-rich economies”, investigates the link between commodity price movements and risk premiums in resource-dependent, developing economies. I develop a stochastic general equilibrium model of a resource-rich economy with sovereign debt and endogenous default risk. I show that the bond price is decreasing in the level of indebtedness and negatively correlated with the oil price. The model can explain a large proportion of the business cycle fluctuations in interest rate spreads in resource-dependent, developing economies. Higher risk-aversion, more impatience, larger oil shares and a stronger correlation between domestic output and oil price shocks all help to explain the key macroeconomic co-movements in these settings.
Investigation of the mechanisms of oxygen delivery and cell death in oxygen-enhanced sonodynamic therapy
Keywords: oxygen delivery
targeted therapy
ultrasound
targeted drug delivery
therapeutic ultrasound
sonodynamic therapy
hypoxia

Sonodynamic therapy, which is usually defined as ultrasound-mediated activation of certain drugs, is of substantial interest in the field of oncology. However, currently there is no consensus on the mechanisms of its action, and elucidating them would assist in the transition of sonodynamic therapy into clinical practice. There is a discrepancy between the observed low quantities of reactive oxygen species produced by drugs combined with ultrasound and the substantial in vivo effects seen in a number of studies; in addition, for oxygen-enhanced sonodynamic therapy, there is also a mismatch between the low dose of oxygen transported by the carrier agents and the observed improvements in therapy outcomes. The overall goal of this thesis was to investigate these discrepancies. Hypotheses were proposed to explain both mismatches, and new setups and experiments were designed to test them, resulting in a better understanding of oxygen-enhanced sonodynamic therapy mechanisms.
Physiological investigation of causal genetic variants in metabolic disease
Keywords: diabetes
infra-red thermographydiabetes
genetics
brown adipose tissue
pharmocogenetics
PAM
physiology
PTEN

Type 2 diabetes (T2D) is a leading cause of morbidity and mortality. Understanding the molecular pathways that regulate glucose control and weight regulation may help predict disease course and develop novel interventions to improve health outcomes. In this thesis, I use naturally occurring genetic variation in combination with physiological tests to interrogate molecular pathways implicated in metabolic disease. Specifically, I examine impact of T2D risk alleles in peptidylglycine alpha-amidating monooxygense (PAM) on glucose physiology and the impact of phosphate and tensing homologue (PTEN) haploinsufficiency on brown adipose tissue function (BAT) and insulin sensitivity.
In Chapter 3, I demonstrate that carriers of loss of function alleles (LoF) in PAM have altered serum PAM activity and increased glucagon-like peptide-1 (GLP-1) concentrations in the postprandial period. Based on the similar glycaemic profiles despite significantly increased GLP-1 concentration, I speculate that these results may be due to GLP-1 resistance. In Chapter 4, I perform a prospective study to determine if PAM regulates GLP-1 resistance. I demonstrate that carriers of the LoF allele, S539W, have altered PAM activity, increased postprandial GLP-1 concentration but no difference in the incretin effect, suggesting GLP-1 resistance. I show that carriers of both alleles have a clinically meaningful reduction in response to GLP-1 receptor agonists but no other oral hypoglycaemic agents. I conclude that this may facilitate personalised medication choice in T2D.
In Chapter 5, I explore the role of PTEN in BAT regulation. I recruited individuals with PTEN haploinsufficiency which causes Cowden syndrome (CS). I demonstrate that individuals with CS have significantly reduced skin temperature overlying the supraclavicular BAT deposits, reduced maximal cold induced energy expenditure and reduced cold tolerance. I attribute this to reduced BAT function. I conclude that PTEN positively regulates BAT and that reduction in BAT activity contributes to the obesity phenotype seen in individual with CS.
In Chapter 6, I characterised an individual with mosaic CS to assess the importance of tissue specific regulation of the PTEN/PI3K/AKT pathway. I show that the percentage mosaicism in muscle and fat is in keeping with the impact on whole body insulin sensitivity.
In conclusion, I used a combination of physiological and genetic tools to determine how pathways under genetic regulation alter metabolic traits. The results of these studies have added to the understanding of the regulation of GLP-1 and BAT physiology and provide information that may help personalise treatment of T2D in the future and I validate PTEN as a regulator and potential target for activating BAT.
Integrating genetic control tools for insect pest and insecticide resistance management
Keywords: self-limiting releases
genetic control
population genetics
pest insects
population dynamics
insecticide resistance

Pest insects that attack crops and vector disease continue to impose a tremendous burden on both global agricultural production and public health. Insecticidal interventions have greatly enhanced the control of pest insect populations, but their continued efficacy is threatened by the evolution of resistance. Developing novel tools and strategies to manage resistance to insecticides and strengthen population suppression will be essential for our continued ability to mitigate the impact of pest insects on human populations. Releases of insects genetically modified to possess a self-limiting, lethal transgene that conditionally terminates female offspring during development are a novel, species-specific tool that could be used alongside insecticidal interventions to manage resistance and enhance population suppression. However, while their potential as a pest suppression tool has been well characterised, more work is required to expand our understanding of their potential for resistance management. Using a spatially-explicit population dynamics and genetics model, I first evaluate the efficacy of landscape-level strategies combining the planting of refuge crops with self-limiting releases for the control of an agricultural pest and its resistance to insecticidal crops. I show that replacing area-wide releases with releases targeted at population hotspots can provide effective landscape level population suppression and resistance management. I also demonstrate that reinforcing insecticidal crops with releases can compensate for violations of existing resistance management practices to maintain population and resistance control. I then expand considerations of these integrated strategies from agriculture to public health, investigating whether self-limiting releases can be deployed alongside insecticidal nets (LLINs) and indoor residual sprays (IRS). I present a population demographic and genetic model capable of simulating the response of a sex-, stage-, and genotype-structured anopheline population with overlapping generations to control strategies integrating pulsed releases of self-limiting insects with bed nets or indoor sprays. Using this model, I then investigate how resistance evolution and behavioural avoidance, recognised as current threats to the continued efficacy of nets and sprays, erode insecticide-only control. Finally, I demonstrate that plausible release strategies could reinforce population and resistance management to delay or reverse the evolution of resistance, and compensate for the reduced exposure to nets and sprays induced by behavioural avoidance.
Ascetic teachings for householder kings in the Mahābhārata

Keywords: mokṣadharma
householder
asceticism
Mahābhārata
Hinduism and Buddhism
liberation
Indian kingship

This thesis traces the development of the idea that householder kings can be liberated or can benefit in some other way from the ascetic teachings that are usually associated with wandering mendicants. This research objective is pursued by examining the narrative contexts in which ascetic teachings are delivered to householder kings as well as by analysing the key debates about kingship, asceticism, and liberation in the Mahābhārata. The first part of this thesis introduces ascetic literature in the Mahābhārata and discusses the role of ascetic ideals in the Mahābhārata narrative with special reference to the character of Yudhiṣṭhira. The second part of the thesis argues that ascetic teachings are commonly offered to kings as a means to overcome a crisis and/or counter grief in the Mahābhārata as well as the Rāmāyaṇa and early Buddhist literature. Usually these are general or stoic ascetic teachings on such topics as impermanence and the inevitability of death and separation. The third part of the thesis analyses those Mahābhārata passages that engage directly with the question of whether a king can be liberated while remaining a householder. It argues that the Mahābhārata offers some models of the liberated householder king that are different from the better-known karmayoga of the Bhagavadgītā. The main textual passages discussed in this thesis are placed in a tentative chronological framework, arguing for a gradual development of the ideal of the liberated householder king.
When bad behaviour pays: North Korea and the global nuclear order
Keywords: nuclear weapons
North Korea
Korean Peninsula
nuclear order

This thesis analyses the state behaviour of the Democratic People’s Republic of Korea (North Korea/DPRK) with respect to international order, focusing upon the global nuclear order. In so doing, this thesis argues that North Korea, under certain circumstances, engages in behaviour that breaks the rules of international order and the nuclear order owing to the benefits that may be gained. Although existing literature in International Relations argues how norm-breaking behaviour within international society is met with opprobrium and the infliction of negative status, such scholarship overlooks the idea that deviant behaviour can, in fact, catalyse positive outcomes. In making this argument, this thesis pioneers the novel framework of strategic delinquency, which, at its core, focuses on three types of delinquent behaviour – provocations; norm-transgression; deception – that can be exercised in pursuit of particular material and social benefits: material benefits comprise deterrence, regime survival, and economic assistance; social benefits relate to three types of recognition, namely recognition as a sovereign, equal, and exceptional actor.
Through engaging in extensive documentary analysis, as well as in-depth elite interviews of senior US and South Korean officials directly involved in negotiations with North Korea, this thesis seeks to illuminate the repertoire of behaviour in which North Korea has engaged, and the benefits received, across three time periods: the ‘first nuclear crisis’ of the 1990s, the ‘second nuclear crisis’ of the 2000s, and the most recent actions of the DPRK under Kim Jong- un, particularly in the context of the Trump administration. The findings of this thesis carry important theoretical and policy implications. With respect to the former, it challenges existing conceptualisations of status in scholarship, which posit how positive outcomes for states are predominantly a result of conformity with dominant systemic norms. With respect to policy, this study has broader consequences on the current stagnant dialogue between Washington and Pyongyang, and between the two Koreas. Of note, for any dialogue to bear fruit, the importance of understanding how North Korea conceptualises international order, and its place with it, cannot be overstated.
Vaccine development and deep immune profiling in preclinical models of emerging outbreak viral pathogens
Keywords: lassa virus
SARS-CoV-2
lassa fever
COVID-19
vaccine development
immune profiling

Emerging outbreak viral pathogens pose a significant threat to public health. Essential studies on the pathogenesis of and preventive tools for infectious threats often rely upon preclinical models to inform or supplant evaluations in humans. This thesis features studies characterising the immune responses after vaccination and infection in animal models, ranging from rodents to non-human primates, with a focus on two viral pathogens of epidemic/pandemic potential: Lassa virus (LASV) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
LASV is endemic to regions in West Africa and circulates in rodent populations. Upon transmission to humans, LASV infection may cause an acute febrile illness called Lassa fever. Within the last several years, outbreaks of Lassa fever - associated with increased disease severity and mortality - have been reported in regions of endemicity. In the first half of this thesis, I present my work on the development and evaluation of a ChAdOx1-vectored vaccine for the prevention of Lassa fever, ChAdOx1-Lassa-GPC. The testing of this vaccine began with immunogenicity studies in mice and progressed to efficacy evaluations in guinea pig and non-human primate challenge models of LASV infection. Collectively, the results from these studies indicate that ChAdOx1-Lassa-GPC elicits strong, polyfunctional Th1 and cytotoxic T-cell responses targeting the viral surface glycoprotein. Although antibody titres are high, they are non-neutralising, and exhibit the capacity to elicit a variety of Fc-driven effector functions. Importantly, immunisation with ChAdOx1-Lassa-GPC protected guinea pigs and non-human primates from direct challenge with a lethal dose of LASV and significantly reduced viral load in tissues.
SARS-CoV-2 emerged in Wuhan, China in late 2019 and has resulted in an on-going pandemic of a (primarily) respiratory disease, known as coronavirus disease-19 (COVID-19). Applying skills and techniques developed during prior studies on LASV, I subsequently shifted the focus of my thesis work to contribute to research efforts for SARS-CoV-2. Consequently, the second half of this thesis describes the immunological assessment of a COVID-19 vaccine, ChAdOx1 nCoV-19/AD1222 in rhesus macaques, first after intramuscular delivery and next after intranasal delivery. Results demonstrated the robust induction of humoral responses after vaccination, via both routes, and significant reductions in lower respiratory tract viral load and inflammation, which could be detected on a cellular level. Finally, a more detailed investigation of SARS-CoV-2 pathogenesis in rhesus macaques was carried out to profile age-related immune dynamics after infection in older versus younger animals. Overall, through the employment of various animal models, the data presented in this thesis have contributed to the advancement of two vaccine candidates to late stages of development and added to current understanding of host responses to infection.
Mechanisms driving glacial deep ocean deoxygenation
Keywords: ocean biogeochemical cycles
deoxygenation
last glacial maximum

Deep ocean deoxygenation inferred from proxies has been used to support the hypothesis that lower atmospheric carbon dioxide during glacial times was due to an increase in the strength of the ocean’s biological pump. This relies on the assumption that surface ocean oxygen (O2) is equilibrated with the atmosphere such that any O2 deficiency observed in deep waters is a result of organic matter respiration consuming O2 and producing dissolved inorganic carbon. However, this assumption has been shown to be imperfect because of disequilibrium. Here I use an Earth System Model tuned to a suite of observations, which reproduces the pattern of glacial-to-Holocene oxygenation change seen in proxy data, to show that disequilibrium plays an important role in glacial deep ocean deoxygenation. Using a novel decomposition method to track O2, I find a whole-ocean loss of 33 Pmol O2 from the preindustrial to the Last Glacial Maximum (LGM) despite a 27 Pmol gain from increased solubility due to cooler temperatures. This loss is driven by biologically-mediated O2 disequilibrium, which increases from contributing 10% of the reduction of the O2 inventory from solubility equilibrium in the preindustrial, to 27% during the LGM. Sea ice and iron fertilization are found to be the largest contributors to LGM deoxygenation, which occurs despite overall reduced production and respiration of organic matter in the glacial ocean. These results challenge the notion that deep ocean glacial deoxygenation was caused by a stronger biological pump or more sluggish circulation, and instead highlights the importance and previously under-appreciated role of O2 disequilibrium.
SerBoCroatian marriage verbs in queer contexts
Keywords: Croatian
marriage
judgement task
verbs
LGBT
sociolinguistics
Serbocroatian
same-sex marriage
likert scale
queer
Bosnian
acceptability
Serbian
variation
BCMS

The present paper examines marriage verbs in SerBoCroatian (BCMS+) in contexts pertaining to same-sex couples. Marriage verbs in BCMS+ have gendered subcategorisations for their arguments, rooted in traditional heteronormative marriage. With the advent of civil partnerships in the Western Balkans, the question arises how frame same-sex unions can be framed using these verbs. How does this relate to speakers' identities and attitudes toward LGBT+ people? To investigate this, this paper used a web-based acceptability task, filled out by 107 native speakers. The results were analysed using categorisation trees. Results showed that attitudes toward the LGBT+ community as well as prescriptivist ideologies are key factors in the acceptability of non-normative marriage verb uses. There is, however, also a clear linguistic effect whereby non-normative subjects are much less acceptable than non-normative objects, perhaps an artefact of processing differences between the two.}
Causal reasoning and meta learning using kernel mean embeddings
Keywords: meta learning
kernel methods
causal inference

Kernel methods have been an essential instrument in machine learning over the years due to their ability to map data into high dimensional spaces efficiently. Nowadays, especially in machine learning, multimodality in the data is a common phenomenon and hence developing tools that allow us to capture these structures is crucial. Being able to reliably represent these datasets without many distributional assumptions is at the core of the main methodology used in this thesis i.e. Kernel Mean Embeddings. This search for good representations of the datasets become even more challenging when we are only presented with little data. There are two particular areas in machine learning that this thesis aims to contribute to using the kernel mean embeddings, which are 1) Meta learning and 2) Causality.
Meta learning has become a widely popular area of research in machine learning due to its ability to leverage statistical dependencies between similar datasets also known as tasks. By taking advantage of task similarities, meta learning is able to learn and adapt quickly to new unseen problems. This can have many applications, where the data collection or labelling is expensive, such as in computational chemistry or health care. In this thesis, we will tackle limitations of current meta learning methods to multimodal data with the help of kernel mean embeddings and proposed efficient algorithms that do not make explicit assumptions on the distribution of the data. In chapter 2 we consider the problem of conditional density estimation and proposed MetaCDE. MetaCDE is able to accurately determine the density even in multimodal setting, whereas standard methods fail. Furthermore, in chapter 3, we also propose MetaCGNN for causal discovery. MetaCGNN is a meta learning algorithm which leverages the power of kernel mean embeddings in the little data setting and allows us to determine the causal direction in the bivariate case, i.e. determining whether X → Y or vice versa. Here again, we show that we are able to significantly improve upon existing methods.
This leads us to the problem of causality. Causality is a fundamental problem in machine learning, as it allows us to reason about the cause and effects of our actions. Having dived into the realm of causality through causal discovery in chapter 3, chapter 4 will be tackling a problem on the causal inference with the help of kernel mean embeddings. Specifically, we introduce BayesIMP, and we demonstrate how to draw causal conclusions, while also accounting for the uncertainty in the data and the model. We achieve this by combining several literature on kernel methods, Gaussian processes and Causal inference.
This thesis proposes several algorithms that aim to tackle some core issues in machine learning with the help of kernel mean embeddings. Given that the problem of small datasets and lack of causal awareness is likely to remain a concern in the area, we believe that this work can be a guide for future research and development.