VictorJuiz/thesis huggingface dataset:
Essays on the contextual determinants of demographic processes and family dynamics
Keywords: sex ratio at birth
maternal stress
education
child mortality
armed conflict
intimate partner violence

This thesis consists of three self-contained articles studying whether and how different social contexts shape population and family dynamics. In the first article, I retest the Trivers–Willard hypothesis, which argues for a negative correlation between maternal stress and sex ratio at birth (SRB), with 243 years of time series data from Sweden. I find no supportive evidence for the hypothesis because the associations of SRB with most of the covariates used as proxies for maternal stress are not statistically significant, and in many cases the level of maternal stress is indeed positively correlated with SRB. In the second article, I exploit quasi-experimental variations in the duration of exposure to a school stipend project to identify the effect of maternal education on child mortality in Bangladesh. Using birth history data from the Demographic and Health Surveys, I find that an additional year of maternal schooling reduces both under-five and infant mortality by about 20%. I also document a number of mechanisms, including greater wealth and literacy, positive assortative mating, lower fertility, delayed marriage and childbearing, greater health-related knowledge, better health-seeking behaviours, and female empowerment, but not female employment. In the third article, I combine individual-level data from the Multiple Indicator Cluster Survey and province-level data on wartime bombing to assess the long-term impact of the Vietnam War on Vietnamese women's attitudes towards intimate partner violence (IPV). To establish a causal link, I use a province's distance to the arbitrarily drawn border between North Vietnam and South Vietnam as an instrument for bombing intensity in that province. I find that women living in provinces that were heavily bombed during the Vietnam War are more likely to accept IPV, reflecting the normalisation of and desensitisation to violence in the private sphere among those who were exposed to conflict violence.
Early Christian readings of Paul on moral regeneration
Keywords: morality
Paul
Tertullian
Origen
Irenaeus
grace
sin
Clement of Alexandria

Since God judges everyone according to their deeds, Paul regards the cultivation of moral conduct as a crucial task. Responding to the scholarly deadlock on whether believers’ ethical capacities are themselves regenerated or simply overlaid with divine power and otherwise unchanged, we engage with Romans 6:1-14 and its direct citations up to the death of Origen, where direct citations are identified by an attribution signal and literality. We ask whether moral regeneration is present in the early readings of Romans 6:1-14. Irenaeus’ three citations argue for the unity of Christ, the salvageability of the flesh, and a distinction between fleshly deeds and the flesh itself.
Understanding the Spirit as formative of those whom he indwells, Irenaeus cites Romans 6:4 in order to demonstrate believers’ moral regeneration if they continue in the Spirit. Clement of Alexandria’s four citations are proof-texts against the Basilideans and the Valentinians. Clement’s Apostle signals believers’ exoneration for involuntary misdeeds because he says that they are “under grace”. Tertullian adapts two extended citations to his sympathetic audience in order to argue for the salvageability of the flesh and for the exclusion of recidivist baptized adulterers from the Church. Tertullian’s Apostle expects that all wrongdoing comes to an end with baptism. Origen adopts a voluntarist hermeneutic in his Commentary on Romans against opponents who promoted moral determinism. Thus, we find his strong witness to personal responsibility for moral action. His Commentary also contains his deduction from Romans 6:12 that the desires of the Spirit overlay the desires of sin, which believers still have. Origen’s other works contain proof-texts from our passage which display symbolic readings of “sin” and moral degeneration in recidivists; these too mainly make the case for personal responsibility. Thus, Clement, Tertullian, and Origen witness to perspectival renewal, and Irenaeus and Origen also to moral regeneration.
Understanding extracellular vesicle biology for the development of bioinspired nanotechnology platforms
Keywords: biotherapeutic
nanomedicine
heterogeneity
nanoparticles
targeted therapy
nanotechnology
extracellular vesicles
stem cells
purification
characterisation

Extracellular vesicles (EVs) are membranous nanoparticles released by cells that are important mediators of intercellular communication via transfer of their cargoes to recipient cells. For this reason, EVs have been harnessed as a novel targeted therapeutic delivery platform. However, EV research faces numerous challenges that need to be addressed if their potential as therapeutic devices is to be fully realised.
EV heterogeneity is arguably the most crucial challenge to be addressed. EVs differ in size, biogenesis, and composition, and these characteristic differences have been linked to functional differences which may impact EV therapeutic activity. Here, functionally heterogeneous EV populations were identified among size-based and subclonal EV populations. EVs were characterised revealing differences in marker and functional protein expression, including integrins, cell adhesion proteins and EV marker proteins. These differences may explain functional differences between EV subpopulations and could aid selective purification of therapeutically advantageous EVs. Furthermore, the subclonal nature of the analysed EV populations also indicated that EV heterogeneity might be linked to cellular heterogeneity. Separately, changes in EV protein expression were documented among EVs harvested at different time points, highlighting another critical consideration for EV therapeutic development.
Advancement of in vivo EV biodistribution and purification techniques was also sought to improve EV therapeutic development and characterisation. While requiring further experimentation, EV DNA barcoding, a technique that labels EVs with synthetic DNA identifiers, showed potential as a novel EV biodistribution and therapeutic siRNA delivery tool. Additionally, Capto Core binding chromatography showed significant improvement over size exclusion chromatography for purification of high concentrations of EVs from high cell density cultures.
Overall, this research offers insights into EV heterogeneity and novel techniques to aid therapeutics EV production. It also highlights the importance of understanding EV biology and overcoming the challenges associated with EV research to improve EV therapeutic development.
Exploring spectral unmixing algorithms for applications to estimating asteroid surface compositions
Keywords: OSIRIS-REx
spectral deconvolution algorithms
asteroid composition
remote sensing
spectral unmixing algorithms
asteroids
linear spectral mixing model
algorithms
thermal infrared spectroscopy
spectra
Bayesian inference
mineralogy estimates
planetary physics

Understanding asteroid composition is key to better understanding a variety of specific fields within planetary science. Thermal infrared (TIR; 5 − 25μm) spectroscopy is a valuable method for estimating modal mineralogy due to its non-destructive nature, and utility in laboratories on Earth and in spacecraft instrumentation to provide remote sensing observations. The current most widely used forward model (i.e., mathematical expression to describe phenomena), and algorithm (i.e., computational technique to estimate parameters of interest) are linear mixing and a linear least squares, respectively. Whilst this method for spectral interpretation is accurate for whole rock/coarse grained samples, and applications for which we have a wealth of contextual information (e.g., Mars), this combination of model and algorithm becomes unsuitable where fine grains (< 50μm) dominate, and when there is little additional information about the measurement target. The simplicity behind the linear mixing model and linear least squares algorithm raises the question whether a more complex model is required, or can linear mixing still be used effectively within more sophisticated algorithms?
As the linear mixing model has advantages over others, this project has investigated linear mixing within a selection of increasingly sophisticated spectral unmixing algorithms, whilst adopting a Bayesian approach. Issues of degeneracy and the use of implicit a priori information, that are intrinsic to current methods, have also been tackled. Results from this investigation (as presented within this thesis) showed that an alternative model is required; thus, also presented, is preliminary work towards a novel method of parameterising linear mixing via empirical studies of quantitative spectral morphology changes with decreasing grain size. Initial results show promise, but refinement through more in depth future spectral morphology studies is required.
This work has demonstrated the unsuitability of linear mixing with linear least squares for fine-grained mixture composition estimates, and begun laying foundations for improved spectral interpretation tools.
Risk and justice: an account of just risk imposition and fair risk sharing
Keywords: social insurance
risk
risk and responsibility
risk sharing
precautionary principle
risk imposition
Scanlonian contractualism
justice

This thesis examines how a society should deal with risk justly. Many risks commonly faced by members of a society could be regarded as being imposed on them through the collective choice of social institutions. Since subjecting people to risk sets back their basic interest in autonomy, risk imposition of this kind requires justification. In this thesis, I aim to answer questions including under what conditions it is justifiable for a society to impose risk on people and how the burden of social risk materialisation should be fairly allocated among members of a society.
To answer these questions, I develop an account of just social risk imposition and fair risk sharing by employing the Scanlonian contractualist framework. I argue that social risk imposition should be regulated by not only principles concerning potential outcomes of the risk in question but also principles concerning the nature or the characteristics of the risk. Moreover, the burden of compensating the victims of risk materialisation should be shared by all those who benefit from the imposition of the risk and its fair allocation should also take personal responsibility into consideration. This account is then expanded to cover topics such as long-term risk imposition on people from future generations and risk sharing with people who are not fully capable of contributing to social cooperation (such as people with severe disabilities). Finally, the thesis considers the institutional and policy implications of the account. I argue that the account provides a fairness-based justification for social insurance and may help to address policy questions such as what risks should be covered by social insurance and how a social insurance scheme should be funded.
Leveraging BCR data to improve computational design of therapeutic antibodies
Keywords: antibody
drug discovery
computational biology

Antibodies are highly useful therapeutics but their development can be costly and resource intensive. As increasing quantities of high quality B cell receptor (BCR) data become available, the challenge of how to best utilise the data to improve the antibody development process remains. In the results chapters of this thesis, we begin by describing our novel approach to numbering antibody sequences, ANARCII. Our model, trained using deep learning-based sequence embeddings derived from BCR data, can correctly number antibody sequences that existing tools cannot. Next, we describe our development of existing antibody comparison tools Ab-Ligity and paratyping, to create computational pipelines for identification of antibodies likely to bind to a target antigen, with or without a known binding antibody. We compare antibodies based on sequence, paratope and structural similarity to predict similarlybinding antibodies from an antibody model library. We expand on the approach of identifying structurally similar, functionally similar, antibodies in our final results chapter where we describe our novel computational epitope binning method, SPACE. Our method of clustering antibody models based on predicted structural similarity can group sequence-dissimilar antibodies into epitope bins with high accuracy, as demonstrated on coronavirus-binding and malaria-binding antibodies. We conclude with potential future research directions, including how latest developments in antibody structure prediction and protein language models might be incorporated into our work.
A mixture model to decompose the heritable basis of complex traits
Keywords: genetic correlation
functional annotations
heritability
mixture model
CAD
GWAS
biological pathways
ASD

Pharmacological drugs aim at modulating traits by targeting their causal mechanisms. However, knowledge of such causal mechanisms is scarce and constitutes the main bottleneck in drug development today. To address the issue, the consensus is to associate as many genetic variants as possible to traits of interest in order to then investigate their function and assign them to different mechanisms on that basis. With that purpose, there is an ongoing large-scale coordinated effort to systematically sequence and phenotype larger cohorts of individuals and to map functional elements in the genomes of human cells across tissues. Despite these advances, existing methods do not take full advantage of these increasingly available resources to associate genetic variants to traits and isolate the different mechanisms through which they operate. We have developed a mixture model that integrates multi-trait Genome-Wide Association Study (GWAS) z-scores and functional annotations of Single Nucleotide Polymorphisms (SNPs) to simultaneously boost GWAS power and group together SNPs that likely operate through a similar mechanism. The parameters of the model can be quickly inferred, and we show with realistic simulations that we can recover substantially more true associations than linear regression or a multi-trait GWAS meta-analysis method (MTAG), while recovering the simulated interpretable mixture model components. We applied our model to Coronary Artery Disease (CAD) and Autism Spectrum Disorder (ASD), finding three components for CAD (which point to the regulation of LDL, risk for smoking, and systolic blood pressure, respectively), and a single component for ASD that implicates the fetal and adult brain. Overall, our mixture model constitutes a powerful new framework to integrate the increasingly available functional annotations of the genome and multi-trait GWAS z-scores to uncover the mechanisms that drive complex traits. We expect our method to be progressively insightful as more and better data becomes available. This is expected to be specially true for complex neurodevelopmental disorders like ASD, whose driving mechanisms may only be detectable when considering developmental stage and cell-type specific functional annotations and traits.
Preserving known anatomical topology in medical image segmentation using deep learning
Keywords: topology
deep learning
fetal brain
medical imaging
ultrasound
segmentation

Since the rise of deep learning, new medical image segmentation methods have rapidly been proposed with promising results, with each one reporting marginal improvements on the previous state-of-the-art (SOTA) method. However, on visual inspection, errors are often revealed, such as topological mistakes (e.g. holes or folds), that are not detected using traditional evaluation metrics, such as Dice. Moreover, correct topology is often essential in ensuring segmentations are anatomically and pathologically plausible and, ultimately, suitable for downstream image processing tasks. Therefore, there is a need for methods to focus on ensuring that the predicted segmentations are topologically correct, rather than just optimising the pixel-wise accuracy. In this thesis, I propose a method that utilises prior knowledge of anatomy to segment structures, whilst preserving the known topology.
The presented model, Topology Encouraging Deformation Segmentation Network (TEDS-Net), performs segmentation by deforming a prior shape with the same topological features as the anatomy of interest using learnt topology-preserving deformation fields. However, here I show that such fields only guarantee topology preservation in the continuous domain and that their properties begin to break down when applied in discrete spaces. To overcome this effect, I introduced additional modifications in TEDS-Net to more strictly enforce topology preservation, a step often overlooked in previous work.
Across this thesis, TEDS-Net is applied to a range of natural and medical image segmentation tasks. I show how it can be used for multiple topology types, multiple structures and in both two- and three-dimensions. Further, I show how TEDS-Net can be used to segment whole volumes using minimally annotated training data. Across these experiments, TEDS-Net outperforms all SOTA baselines on topology, whilst maintaining competitive pixel-wise accuracy.
Finally, TEDS-Net is integrated into a whole medical imaging pipeline, to illustrate the importance of topologically correct segmentations for downstream tasks. TEDS-Net is used to segment the developing cortical plate from in-utero fetal brain ultrasound scans in 3D, to enable the characterisation of its complex growth and development during gestation. To the best of my knowledge, this task has only been previously attempted from magnetic resonance imaging (MRI), despite ultrasound being the modality of choice in prenatal care. This is likely due to large acoustic shadows obstructing key brain regions in ultrasound. Due to TEDS-Net anatomical constraints, it can anatomically guide the cortical plate segmentation in regions of shadows, producing a complete segmentation that enables accurate downstream analysis.
Ocean heat uptake: drivers, variability and impacts
Keywords: slab ocean
ocean heat uptake
large ensemble modelling
ocean dynamics
climate dynamics
Mixed-layer feedbacks
air-sea interactions
climate modelling
extreme value analysis

Attributions of causes in the fully coupled atmosphere-ocean system are difficult due to a wide range of interactions in the climate system. One way to address this is to break down the fully coupled closed system into two open systems. Such studies often depend on the specification of sea surface temperature to link ocean and atmosphere. Instead, in this thesis we focus on understanding the impacts of perturbations to surface heat fluxes as this gives a better handle on imposing changing processes and improves the realism of air-sea interactions.
We identify (Chapter 2) the local and non-local response of ocean heat content to idealised surface heat flux perturbation patterns, demonstrating the importance of circulation change in communicating the non-local response. The global heat content response pattern is shown to be a linear combination of the responses to flux perturbations in individual ocean basins, primarily the North Atlantic and the Southern Ocean.
We further investigate (Chapter 3) processes and forcings that regulate the ocean heat content change in the South Pacific Gyre, finding an important cooling contribution from a weakening of the ocean circulation as a response to high-latitude Southern Ocean heat flux forcing. This mechanism is not robust in CMIP5 but is identified in the MPI-GE fully-coupled large ensemble.
In order to investigate the impact of changing ocean circulation on atmospheric extreme events, we introduce, develop and validate (Chapter 4) the novel HadSM4 slab ocean-atmosphere model configuration. Using large ensembles of HadSM4, we quantify the impact of the ocean heat convergence anomaly that occurred in the North Atlantic in the 2013-14 winter season. Our results indicate that the 2013-14 anomaly induced a warming of around 1 K in the North Atlantic, 0.5 K in Western Europe, and an increase in winter precipitation that up to halves the return times of extremely wet winters over parts of Europe.
To isolate the impact of air-sea feedbacks on mean state, variability and extreme events, sea surface temperature realisations from this slab ensemble are used to force an atmosphere-only sister ensemble (Chapter 5). Disabling the feedback of heat fluxes onto SST strongly increases the ensemble spread of seasonal mean air-sea heat flux components, as the first-order impact of coupling is a negative feedback. Regionally important changes in extreme events indicate the importance of representing atmosphere-ocean coupling for studies exploring the impact of external forcing on weather extremes.
This work improves our understanding on how ocean and atmosphere force each other’s dynamics and variability through surface energy exchange, by the way of the centennial impact of atmospheric forcing on ocean dynamics, the seasonal impact of ocean forcing on atmospheric dynamics, and the atmospheric impact of mixed-layer coupling.
Free groups and Stallings' folding
Keywords: free non-abelian groups
free factors
equations over groups
Whitehead's algorithm

In the present work we investigate various aspects of free groups and their automorphisms, utilizing Stallings' folding techniques introduced in 1983. These techniques have provided new insights into several classical results and have been widely applied over the years to study various properties of free groups and to generalize results to wider families of groups.
We apply folding techniques to uncover a fine property of the classical Whitehead's algorithm for recognizing primitive elements and free factors in a free group. This property is then used to the study of the complex of free factors, a crucial object on which Out(F_n) acts. We also make use of these techniques to investigate echelon subgroups of F_n, a particular type of subgroup which is relevant to the study of fixed-point subgroups of automorphisms of F_n.
Next we turn our attention to the problem of studying equations in a free group. Given two free groups H≤F and an element g ∈ F, we study the ideal of the equations w(x) in H* ⟨x⟩ with g as a solution. In particular, we focus on the relationship between equations and their degree, providing an algorithm that determines the minimum integer d that appears as the degree of an equation in a given ideal. More generally, we study the subset of the equation in a given ideal of a fixed degree d. This is done using two different techniques, one based on Stallings' folding operations and the other using context-free languages.