VictorJuiz/thesis huggingface dataset:
Scrub typhus in Indonesia
Keywords: Orientia tsutsugamushi
Leptotrombidium
Indonesia
scrub typhus

Scrub typhus is a neglected and understudied disease. In 1902, Schüffner first described scrub typhus in Indonesia. However, until today, data are lacking and we do not understand its public health significance yet. In this DPhil project, I aimed to (1) characterise scrub typhus epidemiology in Indonesia and its public health importance, and (2) address knowledge gaps in scrub typhus by optimising available data. I mapped and ranked the available evidence on scrub typhus in Indonesia based on the likelihood of sustaining infectious reservoirs. Despite limited data, it appears that scrub typhus does exist and is widespread within the Indonesian archipelago. North Sumatra has the highest number of data points. South Sumatra and Biak have the best evidence in sustaining infective vectors. Serosurvey of archived sera indicated active transmission of scrub typhus in Jambi, Denpasar, and Tabanan. However, the majority of areas in Indonesia did not have any data. To address this, a seroprevalence study to compare exposure to scrub typhus in rural and urban areas of North Sumatra was planned. Diagnosis seems to be the main challenge hindering scrub typhus research. I reviewed the evidence on the accuracy of scrub typhus point-of-care tests. I also performed a meta-analysis on the limited data. The available immunochromatography tests have good specificity – indicating the potential role for ruling in diagnosis. However, they suffer from relatively low sensitivity and are not widely available. A systematic review on scrub typhus treatment studies demonstrated heterogeneity in methods. The optimal treatment dose and duration for scrub typhus is still uncertain. There is an absence of consensus in treatment and research methodologies. There are still gaps in our knowledge on scrub typhus in Indonesia and globally. One way to improve awareness of scrub typhus is to recognise scrub typhus as a neglected tropical disease. Scrub typhus research is also influenced by economic and political factors. Stakeholder engagement and cooperation are therefore essential to fill these knowledge gaps.
The ethics of human genomic enhancement: a framework for public health policy
Keywords: genome editing
human enhancement
public health ethics
practical ethics

The pursuit of human genomic enhancement raises bioethical questions surrounding not only individual rights and the consequences of enhancement for enhanced individuals, but surrounding the interests of other groups and whole societies that might be affected by human enhancement. A thorough examination of these ‘collective-level’ morally relevant considerations is needed for bioethics that aims to inform policymaking. The question of whether a given type of human genomic enhancement should be implemented by policymakers in a given country can only be answered by undertaking more collective-oriented ethical analysis. The novel collective-oriented framework for ethical analysis that I present here treats heritable human genomic enhancements as potential public health measures. Considering enhancements as public health measures has a distinct advantage in comparison to past approaches, primarily because public health ethics is an area where a collective-oriented approach has been particularly well developed—thus, taking concepts from that area and applying them to enhancement considered as a public health measure contributes to thorough ethical analysis. I call my resulting framework for performing ethical analysis the ‘Collectivist Public Health framework’. The framework first assesses the consequences of enhancement for groups and society based on their identifiable interests, determining whether it benefits the collective, and whether the magnitude of any harms imposed on individuals or groups are acceptable. It then assesses whether the distribution of these harms and benefits across the country’s population is acceptable. Finally, it compares alternative policies for implementing a given enhancement. The framework can be effectively defended against claims that it may lead to eugenics, that it will incur a loss of human dignity, and that it poses problems surrounding public health resource allocation. Applying the framework to case studies, I find some human genomic enhancements to be ethically desirable public health measures.
Halogen bonding fluorescent receptors for sensing hydrosulfide and other anions
Keywords: Hydrogen sulphide
Supramolecular
anion recognition
Catenane
Hydrosulphide
fluorescent sensing
Anion
Hydrosulfide
Macrocycle
Rotaxane
Coumarin
Hydrogen sulfide
Halogen Bonding
Sensing
BODIPY
Fluorescence

This thesis describes the preparation and study of fluorescent iodotriazole containing host molecules exploiting halogen bonding for the selective molecular recognition and sensing of hydrosulfide and other anions.
Chapter One introduces the field of anion supramolecular chemistry, highlighting the areas of relevance to this thesis, including the non-covalent interaction halogen bonding (XB), and reviews artificial molecular receptors for the selective recognition and sensing of anion guest species, in particular hydrosulfide.
Chapter Two describes the development of a series of halogen bonding coumarin based receptors, including acyclic, macrocyclic and mechanically interlocked systems. The synthetic procedures and characterisation of each receptor are described. The capability of each receptor to recognise anions in organic solvent media, coupled with the coumarin fluorophore’s ability to relay and sense the binding event via changes in fluorescence, is explored using 1H NMR and fluorescence emission spectroscopy titration experiments.
Chapter Three builds on the results of Chapter 2, detailing the synthesis and characterisation of two generations of acyclic water-soluble halogen bonding coumarin receptors. The receptors, each employing water solubilising TEG groups, are investigated for their ability to recognise and sense hydrosulfide and other anions in pure and buffered water. Computational Density Functional Theory (DFT) and Molecular Dynamics simulations are used to corroborate the experimental anion binding observations.
Chapter Four investigates the use of BODIPY as a fluorescent reporter group in anion receptor design. A variety of BODIPY-iodotriazole containing receptors are prepared, including acyclic receptors, mechanically interlocked molecules, and porphyrin-BODIPY [2]rotaxanes. The anion recognition and fluorescence emission sensing properties of each receptor are investigated in organic and organic-aqueous solvent mixtures.
Chapter Five provides a summary of the main conclusions of this thesis.
Chapter Six details the experimental procedures employed in this work, including synthetic protocols and characterisation data for novel compounds.
Control of electronic properties of metal halide perovskites through vacuum co-deposition
Keywords: solar cells
perovskite
vacuum-deposition
optoelectronics

As perovskite-based photovoltaic devices near commercialisation, it is imperative to develop thin-film deposition techniques that can be applied on an industrial scale. Vapor deposition is a solvent-free fabrication technique that is widely implemented in industry and can be used to fabricate metal halide perovskite thin-films. Understanding and controlling grain growth and structure of polycrystalline thin films of metal halide perovskite semiconductors is an important step in improving the performance of solar cells based on these materials, as it allows for the development of thin-film passivation techniques compatible with large-scale manufacturing.
I demonstrate that it is possible to accurately control the crystallite size of CH3NH3PbI3 (MAPbI3) thin films by adjusting the substrate temperature during vacuum co-deposition of inorganic (PbI2) and organic (CH3NH3I) (MAI) precursors. Films co-deposited onto a cold substrate temperatures (-2 °C) had large, micrometer-sized crystal grains while films that formed on a room temperature (23 °C) substrate produced grains of only 100 nm in average diameter. I was able to isolate the effects of substrate temperature on crystal growth by developing a new method to control sublimation of the organic precursor. Furthermore, I found substrate temperature directly affects the adsorption rate of MAI, which in turn affects crystal formation and solar cell device performance through changes to the conversion rate of PbI2 to MAPbI3 and stoichiometry.
Next, the passivating effect of using PbCl2 as the inorganic precursor for vapor-deposited CH3NH3PbI3(MAPbI3) is demonstrated, as compared to films fabricated using PbI2 as the inorganic precursor, which is the conventional approach. Furthermore, I find the partial substitution of PbI2 for PbCl2 in [CH(NH2)2]0.83Cs0.17PbI3 films enhances photoluminescence lifetimes from 5.6 ns to over 100 ns, photoluminescence quantum yield by more than an order of magnitude, and charge carrier mobility from 46 cm2/Vs to 56 cm2/Vs. This approach results in significantly improved solar cell power conversion efficiency, from 16.4% to 19.3% for the devices employing perovskite films deposited with 20% substitution of PbI2 with PbCl2. This method presents a scalable, dry and solvent-free route to passivating non-radiative recombination centres and hence improving the performance of vapor-deposited metal halide solar cells.
This thesis combines an overview of the fundamentals of solar cells and the vapour co-evaporation technique with comprehensive studies of the impact of a range of paramters on the optoelectronic properties of the resulting metal-halide perovskite films, providing detailed understanding of the mechanisms that can be used to improve the performance of vacuum-deposited metal-halide perovskite solar cells.
Inference and machine learning across the spatial scales in geophysics
Keywords: Geophysics
Machine learning

The Anthropocene and the concomitant Great Acceleration – defined by super-exponential growth rates in a collection of measures of human activity – are having a tremendous impact on Earth and its ecosystems. To better understand the consequences of our activity, we must increase our knowledge of all the elements in the Geosystem, loosely defined as Earth, the oceans, atmosphere, and the Sun, and in constant evolution as human activity ties in new elements, e.g. the Moon or Mars. In this thesis, I am focusing on improving our knowledge of elements pertaining to geophysics, while also showcasing the diversity of thematics in the Geosystem. I also emphasize the common grounds and cross-cutting themes in our sundry of projects, in particular the importance of a data-centric approach, including the creation of new sensors, and the necessity of both methodological improvements to our numerical tools, and the adoption of novel methods from machine learning to help us exploit the newly available vast amount of data.
I begin with wildlife conservation, where, with collaborators, we prototype an elephant monitoring system based on the classification of animal generated seismic signals with deep learning. We achieve promising results, with high accuracies in classification of elephants and their behaviour, and with further research I believe our method will bring about a seismic shift to wildlife monitoring. Next, I present a novel method for the computation of Frechet sensitivity kernels for full waveform inversion based on the discrete adjoint approach, resulting in a more advantageous frequency scaling of the computational cost than traditional methods. This discount is crucial to enable us to image the interior of the Earth at higher resolutions, which is necessary to unlock the answers to some of the biggest questions in Earth science, e.g. related to the mechanisms of mantle convection and plate tectonics. Finally, I introduce how, with my teammates, we developed a deep learning replacement to a now defunct Sun-monitoring spectrograph onboard a NASA satellite. The best performing model, a combined linear-CNN architecture, achieves 1.6% median error, virtually reviving the spectrograph and restoring a vital data source, for instance for satellite operators to model atmospheric dynamics and avoid catastrophic collisions.
The selective partitioning of mRNA in biomolecular condensates
Keywords: Biomolecular Condensates
Liquid-liquid Phase Separation
Membraneless Organelles
mRNA

Biomolecular condensates (BMCs) are complex assemblies of RNA and protein that form via the process of liquid-liquid phase separation. They have a diverse range of functionalities, often centered around RNA metabolism, and are found throughout the cell. However, how they selectively partition certain molecules whilst excluding others remains a poorly understood, yet pertinent question in biology. Here we addressed this from an mRNA perspective by developing a simplified, in vitro approach, utilising cytoplasmic RNA in combination with a panel of model BMCs. We identified that the length, GC %, and G-quadruplex frequency of an mRNA were critical in defining how it partitioned in these models. Remarkably, very similar rules persist in defining mRNA selection in cellular P-bodies (PBs) and stress granules (SGs). Supporting this notion, we demonstrated that the transcriptome captured by our in vitro model, DDX4N, was highly comparable to that of PBs. These results strongly suggest that the properties of the mRNA are the fundamental driving force behind how it partitions in BMCs. Moreover, we provide evidence for a novel role of 3’ untranslated region alternative cleavage and polyadenylation (3’UTR-APA), in manipulating the length, GC %, and G-quadruplex content of mRNA in order to selectively distribute mRNA isoforms to BMCs, with profound implications for mRNA metabolism. In addition, we demonstrated that SG and PB proteome components were significantly more likely to exhibit selective isoform partitioning within their respective condensate than the rest of the transcriptome. This raises the intriguing prospect that 3’UTR-APA may also be essential in BMC formation and maintenance.
The effect of plant domestication on the seed microbiome
Keywords: Plant-microbe interactions
Domestication

There is a growing body of literature demonstrating the role of microbial communities in plant growth, health, and resilience. It is becoming increasingly clear that advances in our understanding of plant-microbe interactions could make a significant difference in sustainable agricultural production.
In my thesis, we approach this ever-evolving topic to try to advance our understanding of plant-microbe interactions within the agricultural ecosystem. One key question that has attracted recent research efforts is whether plant domestication has influenced hostmicrobiome assembly and composition.
In Chapter 2, we develop an evolutionary framework explaining that domesticated plants could showcase a reduction in positive microbe-to-host effects as a result of plant phenotypic traits selected by the domesticator through artificial selection. In Chapter 3, we provide evidence of the effect of domesticated plant traits on seed microbiome composition and assembly, partially validating the framework developed in Chapter 2. In Chapter 4, we approach the plant and its microbiome from a broader perspective and reflect on the consequences of plant domestication from a community evolution point of view. In Chapter 5, we develop a tool that will allow researchers to study bacterial plant colonization and how bacteria could eventually be transmitted to the seed compartment. Overall, we show that domesticated plant traits have consistent effects on seed microbiome composition and assembly, independently of the domestication event. We also demonstrate that monitoring bacterial localization in plants can be effectively achieved by combining bacterial bioluminescence with fluorescence.
The effect of context on human decision-making during information integration
Keywords: human perception
decision-making
optimality
uncertainty
value construction

Perceiving and interacting with our world is an amazingly complicated computational problem. Our brains need to infer the best cause of action from a long chain of ambiguous and noisy signals reflecting properties from the environment. To solve this problem, our brains make use of multiple pieces of information available in a given context. In these thesis, we explore the influence of context on decision-making. First, we focus on decisions made in the context were three options are available to be chosen. Second, we focus on decisions made in the context of variable prior information about the probability of occurrence of a given event. A particular emphasis is placed on distinguishing mechanisms that optimize the accuracy of decisions from those that comply with axiomatic prescriptions of rational behaviour. We show that even as simple an assumption as having Gaussian noise distorting the representation of value is enough to drive irrational behaviour, calling into question the validity of the axioms of rationality as a yardstick for measuring human behaviour. We also show that uncertainty arising at different stages of the long chain of information processing, can drastically impact the nature of the inference problem that humans have to solve in different contexts. We show two mechanisms present when humans construct their value preferences between three available options. These mechanisms prioritize what information gets further processed in a way that minimizes the effects of noise arising at later stages of processing. Finally, we provide a novel computational explanation for the mismatch typically observed between near-optimal perceptual choices and suboptimal cognitive judgments. Namely, that people are metacognitive blind to noise arising when integrating of multiple pieces of evidence. Together, we show that the brain tends to make excellent use of contextual information to guide it decisions. Perhaps so much so, that we often trust our judgments blindly.
Physics for particle detectors and particle detectors for physics: Timing performance of semiconductor detectors with internal gain and constraints on high-scale interactions of the Higgs boson
Keywords: Single-photon avalanche diode
Higgs
SPAD
Timing
SiPM
Particle physics
Effective field theory
ATLAS
LHC

Experimental particle physics is a science of many scales. A large number of physical processes spanning energies from meV to TeV must be understood for modern collider experiments to be designed, built, and conducted successfully. This thesis contributes to the understanding of phenomena across this entire dynamic range.
Low-energy physics governs the operation of particle detectors, limits their performance, and guides the development of novel instrumentation. To formalise these aspects, classical electrodynamics is used to derive a general description of the formation of electrical signals in detectors, and ideas from quantum mechanics are applied to the study of charge avalanche amplification in semiconductors. These results lead to a comprehensive analytical characterisation of the time resolution and the efficiency of single-photon avalanche diodes, and isolate the most important design variables. They also reveal the applicability of these devices in precision timing detectors for charged particles, which is experimentally verified in a high-energy hadron beam.
Large detector systems at hadron colliders probe fundamental physics at the energy frontier. Data collected with the ATLAS detector during Run 2 of the Large Hadron Collider are used to measure the cross-section for the production of a Higgs boson together with an electroweak boson as a function of the kinematic scale of the process. This measurement provides the finest granularity available to date for this process. It is highly informative of the structure of interactions beyond the direct kinematic reach of the experiment, and new limits are set on the couplings of such interactions within an effective field theory.
Materialisation and data partitioning algorithms for distributed RDF systems
Keywords: datalog
distributed systems
materialisation
automated reasoning
data partitioning

Many RDF systems support reasoning with Datalog rules via materialisation, where all conclusions of RDF data and the rules are precomputed and explicitly stored in a preprocessing step. As the amount of RDF data used in applications keeps increasing, processing large datasets often requires distributing the data in a cluster of shared-nothing servers. Whereas numerous distributed query answering techniques are known, distributed materialisation is less well understood. In this paper, we present several techniques that facilitate scalable materialisation in distributed RDF systems. First, we present a new distributed materialisation algorithm that aims to minimise communication and synchronisation in the cluster. Second, we present two new algorithms for partitioning RDF data, both of which aim to produce tightly connected partitions, but without loading complete datasets into memory. We evaluate our materialisation algorithm against two state-of-the- art distributed Datalog systems and show that our technique offers competitive performance, particularly when the rules are complex. Moreover, we analyse in depth the effects of data partitioning on reasoning performance and show that our techniques offer performance comparable or superior to the state of the art min-cut partitioning, but computing the partitions requires considerably fewer resources.
The biology of ADGRL4/ELTD1 in angiogenesis and tumour development
Keywords: ADGRL4/ELTD1
cancer
angiogenesis

ADGRL4/ELTD1 is an orphan adhesion G protein-coupled receptor that is expressed in both endothelial cells and vascular smooth muscle cells. ELTD1 expression was significantly upregulated in the tumour vasculature and previous studies in our laboratories identified ELTD1 as a novel regulator of tumour angiogenesis. The aims of this project were to further investigate ELTD1’s in vivo role in the normal and tumour vasculature. In this project, both constitutive and inducible endothelial cell-specific Eltd1 knockout mouse models were generated using Tie2-Cre and Pdgfb-Cre lines respectively. Both models exhibited no significant differences in body and organ weights, or other welfare parameters such as behaviour and movement. There were no observed immunohistochemical differences (CD31 and H&E) in the vital organs namely, brain, heart, lung, liver, kidney and spleen. Interestingly, inducible endothelial cell-specific Eltd1 knockout aortic rings showed inhibition in endothelial cell sprouting that was not observed in the constitutive knockout, suggesting a functional compensatory mechanism in the constitutive model. In both E0771 and MC38 syngeneic tumour models, despite no significant differences in tumour growth curves and survival outcomes, there were observed immunohistochemical differences with generally higher necrosis and hypoxia, and reduction in microvessel density and pericyte coverage in the Eltd1 knockout tumours. Both genetic and drug induced endothelial-Eltd1 deletion gave similar results which affirmed the effects of endothelial-Eltd1 on the tumour vasculature in vivo. Single cell sequencing of acute Eltd1-depleted lung endothelial cells revealed heterogeneity in Eltd1-expressing endothelial cells and showed that lung capillary endothelial cells had higher Eltd1 expression compared to lung arterial endothelial cells. Interestingly, their differential gene expression profiles were distinct. But in both endothelial cell subsets, there were differentially regulated genes whose functions have been associated with endothelial biology, angiogenesis, tumour development and immunology. In summary, endothelial-Eltd1 depletion did not cause toxicity but altered the tumour microenvironment. Therefore, ELTD1 could be considered as a potential target for cancer therapy in combination with other therapies targeting hypoxia and angiogenesis.