VictorJuiz/thesis huggingface dataset:
Fasting-induced torpor in mice: effects on sleep and behaviour
Keywords: 3Rs
food restriction
sleep
torpor
circadian rhythm
fasting-induced torpor

Torpor is a state of profoundly altered physiology, characterised by metabolic suppression and concurrent hypothermia. Relatively little is understood about torpor, and few researchers are aware that laboratory mice enter this state in response to limited food availability. The aim of this thesis was to further the understanding of torpor by investigating its interaction with sleep and the circadian system, and the effect of torpor on behaviour. Further, a secondary aim was to investigate the potential for torpor to be a cofounding factor in studies that use fasting techniques. First, this thesis characterised torpor induction and propensity in response to a common food restriction paradigm. Meal timing was found to significantly alter torpor characteristics, providing further evidence for the circadian control of torpor. As this food restriction protocol is commonly used within behavioural neuroscience, the effect of torpor induction on performance in behavioural tasks was investigated. This revealed that time of testing relative to torpor significantly impacted locomotor activity. Next, the relationship between sleep and torpor was investigated using chronic electroencephalography, and sleep deprivation and auditory stimulation techniques. Brain activity during torpor closely resembled sleep, although sleep-wake architecture was profoundly altered in the presence of torpor. An increase in sleep intensity following torpor was observed, suggesting that torpor may be a sleep depriving state. For the first time, auditory evoked responses were recorded in torpid mice, revealing a marked similarity with responses recorded during sleep, although response dynamics were found to differ between sleep and torpor. Finally, this thesis presents pilot data which suggests that water restriction does not result in torpor induction in mice, indicating that water restriction may be a suitable alternative to food restriction to avoid torpor. Overall, this thesis provides novel insights into the characteristics, neurophysiology, and regulation of fasting-induced torpor in mice. Moreover, the data presented here highlight the potential for torpor to be a potential confound in research using fasting techniques if not adequately controlled for.
International Baccalaureate teacher training in Japanese higher education
Keywords: ideology
internationalism
whiteness
language
imperialism
top-down politics
digital ethnography
teacher training
Japan
nationalism
neoliberalization
identity
Asia-Pacific
colonialism
pedagogy
International Baccalaureate (ib)
globalism
higher education

In 2013 the International Baccalaureate Organization (IBO) and Japanese Ministry of Education, Sports, Science and Technology (MEXT) jointly established a consortium to promote International Baccalaureate (IB) education in Japan. The collaboration reflects the IBO’s ambition to expand and integrate its internationalist programmes into national education systems, while, for the Japanese Government, it marks yet another project seeking to cultivate a nationalist ‘global’ workforce. This thesis explores a range of resulting internationalist and globalist narratives emerging through the prisms of ideology, pedagogy, and language. The rollout of a majority Japanese-mediated Dual-Language Diploma Programme, seeking to increase linguistic accessibility while boosting students’ English proficiency, has contributed to the significant expansion of IB World Schools (i.e., IBO-licensed schools) in Japan, from 16 in 2013 to 106 in April 2023. A resulting lack of teachers familiar with MEXT’s article-1 education guidelines who can also deliver IB counterparts in Japanese, has contributed to the introduction of IB Educator Certificate (IBEC) teacher training programmes across several Japanese universities. This research, conducted digitally during COVID-19, is an ethnography of one such IBEC programme. It also includes comparative ethnographic work with the IBO’s IBEC representatives across the Asia-Pacific region, and other Japanese universities. Gathered through a year of participant observation alongside 46 extensive semi-structured interviews and 93 survey respondents, actors articulate multiple, and often contradictory agendas in the name of the international and global. A diverse range of agents – from transnational organizations, national governments, and universities to individual officials, facilitators, and students – amplify and contest these discourses while inventing new alternatives. I find these processes of advocacy, acceptance, and resistance to be ways of negotiating with more prevalent top-down notions of international and global education.
Multi-scale, multi-compartment coupled flow-oxygen models of ischaemic stroke
Keywords: organ-scale model
in silico trials
ischaemic stroke
continuum model
finite element method
oxygen transport

Cerebrovascular accident, also known as stroke, constitutes a major cause of death and disability worldwide. Ischaemic stroke, accounting for 85% of all stroke, occurs when a cerebral artery is occluded. The consequence is a significant reduction of blood flow in the affected region leading to an insufficient supply of oxygen which is vital for the survival of cerebral tissue.
The development of novel therapies for ischaemic stroke has been dependent on experiments in animals, mostly in rats and mice, followed by clinical trials. However, the fundamental differences between the human brain and the rodent brain restrict the translation of therapies from animal to human. Computational physiological models provide a promising alternative for therapy development and improvement, as well as quantitative assessment of individual treatment plans and predicting patient outcome.
The in silico clinical trials for treatment of acute ischaemic stroke (INSIST) consortium aims to build a computational framework to simulate acute ischaemic stroke and to evaluate its treatments. In parallel with the INSIST project, this thesis presents a multi-scale, multi-compartment oxygen transport and tissue metabolism model of the human brain, which sets the foundation for estimating treatment outcomes.
The proposed model is coupled to an established circulation model and both take a porous continuum approach. The finite element method is employed for numerical implementation which is verified using the method of manufactured solutions. Effective geometric parameters capturing the micro-scale heterogeneities are obtained using statistically accurate microvascular networks; the maximum consumption rate of oxygen is optimised to uniquely define the oxygen distribution.
Simulations of healthy state and right middle cerebral artery (R-MCA) occlusion are then carried out on a patient-specific brain mesh. The oxygen predicted lesion appears to be more homogenous and has a larger volume than the one estimated by perfusion alone, possibly due to the effect of oxygen diffusion and metabolism in the tissue. The simulated infarct volume is in qualitative agreement with computed tomography images of a patient who suffered from a R-MCA stroke and compares well to quantitative data reported in the literature.
Understanding approximation for Bayesian inference in neural networks
Keywords: neural networks
variational inference
uncertainty in deep learning
Bayesian deep learning
active learning
continual learning
approximate inference

Bayesian inference has theoretical attractions as a principled framework for reasoning about beliefs. However, the motivations of Bayesian inference which claim it to be the only "rational" kind of reasoning do not apply in practice. They create a binary split in which all approximate inference is equally "irrational". Instead, we should ask ourselves how to define a spectrum of more- and less-rational reasoning that explains why we might prefer one Bayesian approximation to another. I explore approximate inference in Bayesian neural networks and consider the unintended interactions between the probabilistic model, approximating distribution, optimization algorithm, and dataset. The complexity of these interactions highlights the difficulty of any strategy for evaluating Bayesian approximations which focuses entirely on the method, outside the context of specific datasets and decision-problems. For given applications, the expected utility of the approximate posterior can measure inference quality. To assess a model's ability to incorporate different parts of the Bayesian framework we can identify desirable characteristic behaviours of Bayesian reasoning and pick decision-problems that make heavy use of those behaviours. Here, we use continual learning (testing the ability to update sequentially) and active learning (testing the ability to represent credence). But existing continual and active learning set-ups pose challenges that have nothing to do with posterior quality which can distort their ability to evaluate Bayesian approximations. These unrelated challenges can be removed or reduced, allowing better evaluation of approximate inference methods.
The role of isoform switching and neoantigen formation in the response to radiation
Keywords: immunopeptidomics
antigen presentation
cancer
radiotherapy
breast cancer
isoform switching
immunotherapy
ionising radiation
SRSF1
neoantigens
alternative splicing
RNA-seq

This thesis investigates the role of isoform switching in response to ionising radiation (IR) and the modulation of this process by SRSF1, a proto-oncogenic splicing factor. Utilising deep RNA-sequencing of B-cell lines from ten healthy individuals, the study reveals extensive IR-induced isoform switching across the transcriptome, leading to potentially shorter transcripts that influence DNA damage response, apoptosis, and cell cycle arrest. Intriguingly, nearly half of the genes exhibiting isoform-level changes showed no differential expression at the gene level, highlighting the importance of isoform-specific analysis in understanding cellular responses to IR.
The RNA-binding protein SRSF1 is identified as a mediator of IR-induced isoform switching. Loss of SRSF1 expression, which is a common response to IR across various cell types, enhances radiosensitivity in cell lines and in cancer patients.
Moreover, the thesis explores the combined effect of SRSF1 knockdown and IR on triple-negative breast cancer cells, revealing an altered antigenic landscape with 86 putative neoantigens, and therefore offering insights into novel targeted immunotherapies. The findings propose SRSF1 as a prognostic marker for radiotherapy efficacy in the short-term, and present a foundation for future therapeutic approaches targeting SRSF1 in cancer treatment.
Critical events and ministerial turnover in Latin American presidential democracies
Keywords: machine learning
instrumental variables estimation
ministerial turnover
cabinets
survival analysis
presidential approval
presidents
executive politics
propensity score matching
competing risks
Latin America

What are the causes and consequences of ministerial turnover in presidential systems? This thesis presents three articles on different theoretical and methodological aspects of this question and integrates elements of the agency theory to explain the causes of ministerial terminations and their effects on presidential approval dynamics.
The first article refers to the creation of a novel dataset on turnover in 124 governments in 12 presidential cabinets in Latin America from the mid-1970s to the early 2020s. Its construction required extensive digitalisation of archives and the use of machine learning models, producing a dataset of unprecedented indicators, such as calls for the resignation of ministers and their reallocation within the cabinet. By contrast, most previously existing data focused on reshuffles or individual terminations. The article also includes measurement validity checks to cross-validate the dataset and information on potential applications.
The second article analyses the effect of resignation calls on reallocations and dismissals in presidential cabinets. A president can limit agency loss and incentivise ministerial performance by combining protection and dismissals. The article’s empirical strategy relies on competing risks models and propensity score matching to estimate the causal effect on ministers who received questioning. The risk increases eightfold from the second call for their resignation, but is mitigated in the case of nonpartisan and more senior ministers, with both conditions denoting strategies to limit agency problems and optimise presidential evaluation.
The third article asks how ministerial terminations affect presidential approval. It proposes that dismissing ministers who have received resignation calls presents an opportunity for the president to send signals to the electorate and, through a blame-shifting dynamic, achieve a corrective effect on popularity. This empirical expectation is tested using instrumental variables (IV) regressions. A corrective effect of about 10 points was found in coalition governments where the greater ease with which responsibility can be attributed to different actors facilitates blame-shifting.
Applications of social data science to environmental communication on social media
Keywords: activism
social science
psychology
content analysis
network science
social media
news media
climate change
data science

Effective environmental communication and activism are critical to the development of public support for the policies, ideological shifts, and behavioural changes needed to systemically address climate change. The last five years have seen a significant increase in global environmental activism on social media, yet there are still many gaps in our understanding of the strategies used, their effectiveness, and the challenges they face in supporting the pro-environmental movement.
This thesis presents a combination of interdisciplinary case studies examining three under-studied contexts of environmental communication and activism on social media. Together, they advance our understanding of important dynamics impacting the effectiveness of online activism and provide motivation for key methodological shifts in the way scholars approach researching this topic.
The thesis begins with general motivation and contextualisation of the work in Chapter 1. Then, I provide an overview of the research which has analysed environmental communication and activism on social media so far and identify key blind spots in Chapter 2. I then present four empirical chapters, each addressing one of these gaps using mixed-method and interdisciplinary computational social science approaches.
The first empirical chapter (Chapter 3) addresses commercial engagement and controversy in vegan activism as a site of targeted environmental activism. The second empirical chapter (Chapter 4) investigates the relationship between psycholinguistic framing in posts of environmental activists and audience engagement on Twitter. The third empirical chapter (Chapter 5) presents an experiment in which the correlational findings of the previous chapter, and a potential cognitive mechanism driving them, are investigated offline. The fourth and final empirical chapter (Chapter 6) compares the topic and sentiment framing used to discuss COP26 conference outcomes in a sample of English-language mainstream (Australia, India, UK, and US) and social media (Facebook and Instagram), paying particular attention to how different stakeholders (major news outlets, politicians, activists, and NGOs) overlap and diverge in their commentary and discussing what implications this has for the development of coherent public dialogue on environmental policy moving forward.
To wrap up the thesis, the Conclusion (Chapter 7) summarises the empirical work and individual contributions of each chapter. It also discusses contributions of the thesis as a whole, bringing the findings of each study into conversation with one another to motivate the importance for future research to 1) take increasingly interdisciplinary approaches to the study of environmental activism and evaluations of its effectiveness, and 2) move away from social media-only studies and connect social media dynamics to micro- and macro-level offline outcomes.
Fast and correct variational inference for probabilistic programming: Differentiability, reparameterisation and smoothing
Keywords: smoothing
reparameterisation gradient
variational inference
gradient estimation
probabilistic programming

Probabilistic programming is an innovative programming paradigm for posing and automatically solving Bayesian inference problems. In this thesis, we study the foundations of fast yet correct inference for probabilistic programming.
Many of the most successful inference techniques (e.g. Hamiltonian Monte Carlo or Stochastic Variational Inference) harness gradients of the so-called density function, which therefore needs to be differentiable at least almost everywhere. We resolve a question posed by Hongseok Yang by demonstrating the following: densities of almost surely terminating programs are differentiable almost everywhere.
Having established this property necessary for the correctness of gradient-based inference algorithms, we investigate variational inference, which frames posterior inference as an optimisation problem, in more detail. The dominant approach for stochastic optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits low variance, resulting in fast convergence in a traditional statistics setting. Unfortunately, although having measure 0, discontinuities can compromise the correctness of this approach.
Therefore, we propose a smoothed interpretation parameterised by an accuracy coefficient and present type systems establishing technical pre-conditions. Thus, we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem. Besides, via a uniform convergence result, we can solve the original problem up to any error tolerance by choosing an accuracy coefficient suitably.
Furthermore, rather than fixing an accuracy coefficient in advance (limiting the quality of the final solution), we propose a novel variant of stochastic gradient descent, Diagonalisation Stochastic Gradient Descent, which progressively enhances the accuracy of the smoothed approximation during optimisation, and we prove convergence to stationary points of the unsmoothed (original) objective.
An empirical evaluation reveals benefits of our approaches over the state of the art: our approaches are simple, fast and attain orders of magnitude reduction in work- normalised variance. Besides, Diagonalisation Stochastic Gradient Descent is more stable than standard stochastic gradient descent for a fixed-accuracy smoothing.
Finally, we show unbiasedness of the reparameterisation gradient estimator for continuous but non-differentiable models, and we propose a method based on higher-order logic to establish continuity in the presence of conditionals. We provide a sound and complete reduction for verifying continuity of models to a satisfiability problem, and we propose novel efficient randomised decision procedures.
Modelling the influence of widespread afforestation on UK hydrology
Keywords: hydrology
land surface model
afforestation

With increasing atmospheric CO2, the hydrological system is moving towards more frequent and intense hydroclimatic extremes. With the greater risk this poses to society, we need solutions that reduce atmospheric CO2 and mitigate water-related hazards. Afforestation, mooted to reduce atmospheric CO2 and mitigate flood risk, is being proposed internationally at greater temporal and spatial scales than ever witnessed before. Previous work has assessed the hydrological consequences of woodland planting at relatively small scales (< 10 km2) or at global scales with low process and spatial resolution. There is a clear need for evidence at countrywide scales on whether afforestation will achieve its intended goals.
The work here seeks to determine the influence of widespread afforestation on UK hydrology. The UK plans to annually plant 30 000 hectares of trees to reach its Net Zero goals. This work uses land surface modelling at a higher complexity than is often undertaken when understanding woodland hydrology. Land surface models include a relevant set of Earth system processes, which is critical when drawing conclusions about woodland hydrology. The final research piece uniquely couples a land surface model and convection-permitting atmospheric model to simulate the hydrometeorological consequences of UK widespread afforestation.
In this thesis, afforestation location has a minimal impact on terrestrial hydrology compared to afforestation extent. However, in a land-atmosphere model configuration, woodland along Great Britain’s west coastline increases surface roughness, producing heavier rainfall. Median streamflow reduces by 2.8% ± 1.0 (1 s.d.) for a ten-percentage point increase in catchment broadleaf woodland but there is no consistent reduction of extreme floods. Afforestation minimally impacts hydrological processes compared to changes in precipitation, temperature, and CO2. More arid catchments show greater streamflow sensitivity to woodland expansion potentially increasing the likelihood of drought formation with afforestation.
Work here provides a critical step forward in our understanding of afforestation impact on hydrology and the utility of land surface models in answering policy-relevant questions.
Development and enactment of an EAL policy: improving provision at an international sixth form
Keywords: MLT
MLT 2021

This study investigates the development and enactment of an English as an additional language (EAL) policy at an international sixth form that was motivated by the lack of local and national guidance on how to provide for EAL pupils. The policy development process included training teachers on second language acquisition (SLA) processes, organising collaborative experiences with peers to share expertise, encouraging teachers to trial new EAL strategies, and having teachers consult on policy texts and strategies in workshops and meetings. Data from five different surveys and eight 45-minute peer observations were collected to investigate teachers’ criteria for effective implementation of an EAL policy, their experiences with the policy development process, and how the EAL strategies advocated in the policy were enacted in classrooms. Emergent themes from the survey data measuring teachers’ criteria revealed that clear communication, formal implementation, and ensuring policy is applicable to the local context are required for effective implementation of EAL policy. Furthermore, teachers’ experiences with the policy development process were predominantly positive, cooperative, educational, and illustrated a small improvement in their underlying understanding of SLA processes. Peer observations demonstrated that the key EAL themes of planning for language input, planning for language output, and treating first language as a fund of knowledge were all actively employed in the classroom. The promotion of reading however was lacking. Recommendations for school leadership include encouraging wider participation with EAL strategies, formal implementation of EAL provision into schemes of work, appraisals, and observations, and to consider reading interventions. Furthermore, the wider educational community should consider integrating the EAL strategies used in this study into their own practice, and to address the paucity of research into teachers’ criteria for effective policy.