VictorJuiz/thesis huggingface dataset:
Fibroblast Growth Factor 2 (FGF2) in cartilage injury and repair
Keywords: FGF2
Cartilage

Fibroblast Growth Factors (FGFs) are important pleiotropic growth factors with a proven role in joint development and postnatal homeostasis. Previously our group has shown that FGF2 is released upon cartilage injury and Fgf2 knockout (KO) mice develop accelerated osteoarthritis (OA), suggesting it is chondroprotective in vivo. Work from others has shown that the accelerated OA phenotype in the Fgf2 KOs is phenocopied by deletion of Fgfr3 but not Fgfr1, suggesting that the FGFR may determine FGF2’s action in the joint.
In the work presented here, I investigate the role of FGF2 in promoting enhanced cartilage regeneration. I first explore the control and function of different FGF receptors (FGFRs) and ligands; then perform an in vivo model of focal cartilage injury in the Fgf2 KO mouse.
FGFR3 mRNA expression levels were rapidly down-regulated by injury in an FGF2-dependent manner, but recovered after chondrocytes were isolated and cultured in vitro. I did not observe significant differences in intracellular signalling or expression of a panel of FGF-dependent genes using receptor selective mutant ligands. In vivo, Fgf2 KO mice failed to repair focal cartilage defects compared with wild-type (WT) controls. Mesenchymal stem cells (MSCs) were activated by FGF2; leading to enhanced scratch assay closure and keeping cells in a more motile state but not in promoting cell adhesion to damaged tissue. FGF2 suppressed chondrogenesis of MSCs in vitro. Taken together, I identified a primary role for FGF2 in promoting intrinsic cartilage repair, which may be due to local activation of MSCs. These results provide mechanistic insights into how cartilage repairs and may have important clinical implications.
The evolution of doctrinal religions: Using semantic network analysis and computational models to examine the evolutionary dynamics of large religions
Keywords: Pentecostal Christianity
social identity
ritual
divergent modes of religiosity
Charismatic Christianity
fusion
network analysis
Singapore

How do large scale religions achieve social cohesion among their members? The divergent modes of religiosity (DMR) theory suggests that imagistic religions rely on rare and emotionally intense rituals to foster fusion by means of reflection (e.g. tribal initiations in small tribal religions), while doctrinal religions achieve cohesion, in the form of social identification, through the performance of frequent low arousal rituals (e.g. daily or weekly rituals in Judeo-Christian religions). Even though the DMR theory proposes that in doctrinal religions, high-frequency emotionally intense rituals should die out or transform, such rituals have become increasingly common in Pentecostal and Charismatic Christian communities. In order to explain the rise and spread of emotionally intense but frequent rituals in doctrinal traditions, this thesis makes the case for an Information Identity System (IIS), through which emotionally intense experiences in doctrinal religions generate a socially regulated process of reflection, that in turn creates conceptual ties, which are links between central aspects of one’s personal identity and essential beliefs in their group’s social schema. The IIS proposes conceptual ties are the basis for extended fusion. This research uses surveys and semi-structured interviews to test 3 key hypotheses: H1) There is a positive relationship between frequency of in-group ritual attendance and social identification, when controlling for fusion; H2a-b) The relationship between the intensity of emotional arousal associated with an experience and fusion to both a group (H2a) and the concept God (H2b) are mediated positively by a) the perceived frequency of reflection on the experience and b) how central one views that experience to their sense of self; H3) The relationship between frequency of in-group ritual attendance and social consensus is mediated a) positively by social identification and b) negatively by fusion. The results suggest support for hypothesis H1 but also found an interaction between fusion and frequency of in-group ritual attendance and that fusion has a greater effect than expected. Results also found support for H2a and H2b. However, the sample size used to test hypothesis 3 resulted in an underpowered study. In addition, two alternative hypotheses from cultural epidemiology were tested but the study was also underpowered. The findings suggest that the IIS’ proposal of conceptual ties may be a plausible explanation for extended fusion. In future research, larger sample sizes should be used in future studies testing H3. Additionally. future research may wish to investigate how conceptual ties may underpin religious extremism and its relation to sacred values theory. The dissertation ends by discussing how the methods devised here can be used in future studies.
The α-alkylation of ketones using hydrogen borrowing catalysis
Keywords: iridium
catalysis
hydrogen autotransfer
hydrogen borrowing
green chemistry

Introduction – Hydrogen Borrowing Alkylation Reactions Using Alcohols
The introduction reviews the origins of hydrogen borrowing catalysis and how it has been applied to utilise alcohols as alkylating agents. The predominant focus is on highlighting the scope for the use of primary alcohols in the α-alkylation of ketones. Other nucleophiles are discussed with respect to the use of secondary alcohols as alkylating agents. Examples of related methodologies are also briefly discussed.
Results and Discussion – Formation of α-Branched Products Using Primary Alcohols
This chapter explores the alkylation of several ketone substrates and identifies cyclopropyl ketones as key structural motifs that facilitate the formation of α-branched products using primary alcohols. The development of an iridium–catalysed protocol for this reaction is described, including the optimisation of reaction conditions and investigation of the substrate scope with respect to the ketone and alcohol. The derivatisation of the resulting α-branched cyclopropyl ketones is also explored through utilisation of several ring-opening reactions.
Results and Discussion – Formation of β-Branched Products Using Secondary Alcohols
This chapter focuses on the development of an Ir-catalysed procedure for the alkylation of methyl ketones using secondary alcohols to form β-branched products. In developing this procedure, ortho-disubstituted aryl ketones were identified as key structural motifs that facilitated the reaction due to the unique properties they possessed. An overview of the optimisation of reaction conditions is provided, followed by exploration of the substrate scope with respect to the methyl ketone and the secondary alcohol. Derivatisation of the resulting β-branched products through bromine-initiated release of the aryl group, followed by trapping with several nucleophiles led to the formation of carboxylic acid derivatives. The final part of the chapter introduces preliminary studies towards the development of an asymmetric route to β-branched products.
Results and Discussion – Formation of Cyclic Products Using Diols
This chapter explores the development of an unprecedented Ir-catalysed procedure for alkylation of a methyl ketone using diols to form five-, six- and seven-membered carbocyclic rings. The optimisation of the reactions conditions is described followed by a systematic investigation into the diastereoselectivity observed in the formation of the carbocyclic rings. With the formation of cyclohexane rings identified as being most effective under the reaction conditions, these were then explored further in a more general substrate scope. The derivatisation of the cyclic products was also explored through a bromine-mediated release of the aryl group followed by trapping with a variety of nucleophiles.
Experimental Section
Experimental procedures are provided along with full spectroscopic characterisation of the compounds synthesised.
Discovering genetic variation in populations using next generation sequencing and de novo assembly
Keywords: de Bruijn graph
genetics
algorithms

The de Bruijn Graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, sequencing error correction, sequencing read compression and variant discovery. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn Graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn Graphs can produce sub-optimal results given their input.
We present a novel assembly graph data structure: the Linked de Bruijn Graph. Constructed by adding annotations on top of a de Bruijn Graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn Graph. Through simulation we show that links improve performance and reduce the sensitivity to the parameter k. Many algorithms can be built on top of Linked de Bruijn Graphs, which we illustrate by implementing read error correction, variant calling, genotyping and assembly algorithms in a software package called ‘McCortex’.
With assembly simulations we demonstrate that the Linked de Bruijn Graph data structure outperforms both the de Bruijn Graph and the String Graph Assembler (SGA). Using human whole genome sequence, we show that Linked de Bruijn Graphs scale up to mammalian genomes. Finally we apply McCortex to Klebsiella pneumoniae short read data to call SNPs, indels and large events, which we validate using PacBio sequencing data. Although kmer-based methods have reduced sensitivity for calling SNPs, McCortex finds 3x more polymorphic bases due to small events in K. pneumoniae versus mapping-based approaches and 48% more than existing de Bruijn Graph de novo assembly methods. We also find multiple 10kbp events that cannot be resolved with a plain de Bruijn Graph.
In conclusion the Linked de Bruijn Graph is a versatile data structure with utility in multi-sample variant calling of indels and large events, in both de novo and reference-aware scenarios.
Modelling the breakup of droplets in a turbulent jet
Keywords: Droplet breakup
Droplet population models
Turbulent jet
Surfactants
Deep-sea oil spill

In this thesis, we model the breakup of oil droplets in a turbulent jet and the effect of surfactants in enhancing this process. The problem is motivated by deep-sea oil spills where, hundreds of metres below the sea surface, a pipe transporting oil ruptures and a turbulent oil jet is released into the sea. The jet’s turbulent mixing breaks up the oil into smaller droplets. These droplets are then eaten by microbes in the sea. To encourage this natural clean-up process, surfactants are added to the oil jet to reduce the oil-water surface tension and encourage droplet breakup. Our aim is to understand how to best apply the surfactant to maximise droplet breakup. To do so, we first examine and unify the large scale jet and small scale droplets without the presence of surfactants. We then incorporate the effect of surfactant addition to these models.
We begin by modelling an axisymmetric turbulent oil jet. Our key approach is to take advantage of the self-similarity of a jet to find similarity solutions for its velocity field and energy. Scaling laws are then used to understand the breakup of droplets due to turbulent mixing. We combine these large-scale jet models and small-scale drop models, using droplet population models to predict the droplet size distributions at different locations in the jet. We first do so numerically. We then find similarity solutions for the drop size distribution, by taking advantage of the self-similarity of the jet.
Finally we include the effect of surfactants. We use numerical and asymptotic methods to model their effect on the drop size distribution. This allows us to address two important questions: how much surfactant should be used and where should it be injected.
Formal methods for evolutionary inference using ancient DNA
Keywords: time-series data
Methods
Bayesian analyses
Mobility
Chickens
Ancient DNA
Demographic history
Genetic selection
Wolves

Ancient DNA has revolutionised our ability to study past evolutionary processes by enabling direct observation of genetic variation in past populations. However, current formal methods for evolutionary inference are challenged by the sparse and heterogeneous distribution of data in space and in time, typical of ancient DNA datasets, as well as sample age uncertainty. In this thesis, I introduce analytical approaches that explicitly address these problems and show how inference from ancient DNA can greatly benefit from analysis that formally compares different evolutionary and demographic scenarios. I apply these approaches to three different cases that represent a range of demographic histories and evolutionary questions spanning different time- scales, species and types of data. The first approach combines space and time into a single matrix that can be used to estimate levels of past within population mobility. I apply this method to human ancient DNA data spanning the last 35 thousand years to recover changes in mobility over this time period. The second approach combines spatially and temporally explicit simulations with ancient mitochondrial genome data, spanning the Northern hemisphere and the last 50 thousand years to reconstruct large- scale population dynamics in Grey wolves over this time period. The third approach uses likelihood-based analysis to reconstruct episodes of past selection from time series of ancient DNA data. I apply this method to genotype data spanning two thousand years from two loci in domestic chicken to estimate a number of selection parameters including the starting time of the selection. These case studies all illustrate how formal hypothesis testing in spatially and temporally explicit frameworks make it possible to directly link evolutionary histories to the climatic and archaeological records as well as historical sources and show how identifying potential drivers of evolution allow building a more detailed and complete picture of the past.
Simulating brain resting-state activity: what matters?
Keywords: Delay-differential
Differential equations
Bayesian optimisation
Gaussian-Process surrogate
Neuronal mass
Neural network
Mean-field model
Resting-state
Biophysical model
Magnetoencephalography

In the field of computational neuroscience, large-scale biophysical modelling is a bottom-up approach to study the interaction between brain structure and function. In this thesis, we propose two models of varying biophysical plausibility as a mechanistic explanations for spontaneous brain activity, as measured with magnetoencephalography (MEG).
Mathematically, these models take the form of large systems of non-linear coupled delay-differential equations, and we implement software to numerically solve such systems efficiently. After analysing the empirical data and extracting key features of interest (relating to the temporal dynamics of measured signals), we use Bayesian optimisation to fit our models with two different parameterisation of increasing complexity: first assuming a spatially uniform brain in which the pattern of connections between cortical regions is the only source of temporal structure in the simulations; and second allowing smooth variations of intrinsic parameters across the cortical surface. Our results outperform those published in the scientific literature to date.
We contribute an original derivation of a conductance-based model, and an in-depth analysis of the effects of intrinsic model parameters; software to build and simulate large models of delay-networks efficiently; a new approach to the exploration of high-dimensional spaces in the context of Bayesian optimisation (using space-partitioning); an original parameterisation allowing smooth spatial variations of intrinsic parameters across the cortical surface (using spherical harmonics); a novel analysis of structural brain data (from tractography), and several original methods to analyse MEG data (e.g. exploiting the Hilbert phase and extending Riemannian metrics).
Modulating reward learning in healthy adults with transcranial direct current stimulation: Towards a novel treatment for depression
Keywords: Depression
reinforcement learning
transcranial direct current stimulation
computational
tDCS

It has recently been proposed that deficits in reinforcement learning are a core feature of depressive disorders. This thesis aimed to investigate whether transcranial direct current stimulation (tDCS) could be applied to alter these learning processes in healthy adults and individuals with symptoms of depression. A first study assessed the psychometric properties of a computerised reinforcement learning task, which was the main paradigm used in the tDCS studies. Next, a neurostimulation study was designed to examine the effects of tDCS of the dorsolateral prefrontal cortex (DLPFC) on reinforcement learning parameters in healthy adults. It was found that prefrontal stimulation was associated with faster learning from positive but not negative outcomes when applied during task performance. The subsequent study investigated whether this effect of neurostimulation could be harnessed to augment a cognitive training paradigm aimed to enhance learning from either positive or negative events. No evidence was found that tDCS could boost effects of cognitive training on either reinforcement learning or affective biases in information processing in healthy adults. Finally, a pilot study examined whether tDCS of the DLPFC could affect reward learning in a sample of individuals with subclinical depression. While interpretations of the findings were limited by the study’s small sample size, there were indications that active tDCS might enhance learning rates for both positive and negative outcomes during stimulation in this population. Altogether, this work presents preliminary evidence that non-invasive brain stimulation can transiently alter reinforcement learning processes in healthy adults and subclinical depression. Additional research is needed to examine the potential of this technique as a clinical intervention for depression and related psychiatric disorders.
Factors affecting the commercialization of cellular based therapeutics
Keywords: CAR-T
immunotherapy
stem cell
commercialization

Introduction
Cell based therapeutics represent a commercially and clinically important treatment modality that offers a transformative approach to managing untreatable diseases. However, despite promising global demand and mounting investor appetite, the translation of cellular based therapeutics from “bench-to-bedside” remains challenging. This research systematically delineates the commercialization pathway and offers viable solutions to expedite clinical translation.
Methods
A systematic review was conducted to establish principal factors in the commercialization pathway. Two independent reviewers examined the retrieved publications, performed data extraction and desegregated barriers into a proprietary scorecard to assess impact utility and factor prioritization. The findings were used to inform a clinical trial and manufacturing landscape analysis of CAR-T therapeutics, alongside proprietary scorecards to identify critical bottlenecks. A model manufacturing protocol was developed and biomanufacturing modeling software was used to determine key inflection points for cell therapy manufacturing campaigns.
Results
The systematic review identified n=3374 unique publications. N=138 manuscripts qualified for full assessment and n=83 were selected for data extraction and final meta-analysis. Major barriers included cell therapy clinical trials and manufacturing. Clinical trial searches yielded n=2597 records. N=20 published trials were fully analyzed, alongside ClinicalTrials.gov data (n=113 active study protocols involving 8000 patients). Variation was seen with trial design, outcome reporting and patient populations. A mixed-methods analysis of global manufacturing identified n=19 studies with insufficient protocol evidence relating to unit operations and procedural steps. Modelling demonstrated a cut-off vessel volume of 20L as a cost-effective point for scale-up.
Conclusion
Ongoing translational challenges include CAR-T development, manufacturing practicalities and clinical trial approaches. Investigators should focus on accumulating harmonized data sets that can be aggregated to formulate evidence-based manufacturing protocols and treatment strategies. This will enable stakeholders to streamline the pathway to market for high-value candidate therapeutics, proficiently assess viable clinical trial and manufacturing options and contribute to prompter patient access.
Languages and language use in classical Greek historiography and the Alexander tradition
Keywords: Thucydides
Diodorus Siculus
Xenophon
Curtius Rufus
Alexander the Great
language
Plutarch
Herodotus
Arrian
Bilingualism
ethnicity
Multilingualism
Greek historiography
identity

This thesis examines six ancient historians and one biographer who wrote about Greek contact with the Persian Empire. It explores how these writers explicitly discuss the Greek language in its varying dialects (Chapter One), minor bilingual figures like interpreters and envoys (Chapter Two), and major bilingual individuals who adopted foreign tongues in their own interest (Chapter Three). Previous work in this area has largely been historical in focus, seeking the ‘reality’ behind the text whilst underappreciating any literary aims of the historians. Some scholarship has attempted to move beyond this, at least for certain ancient historians (e.g. Herodotus), but no single study yet exists examining this phenomenon in Greek historiography more broadly, incorporating works at the permeable frontiers of and across the genre to arrive at a more integrated description and analysis.
The three thematic chapters are each divided into two sections: one on classical Greek historiography (Herodotus, Thucydides, and Xenophon’s Anabasis); the other on the main surviving historians and biographers of Alexander (Diodorus, Curtius Rufus, Plutarch, Arrian – all from the Roman period). Throughout, I place language in its context by considering it alongside other, cultural, aspects of identity (e.g. nomoi). More generally, I seek to situate details of language and language use in their wider contexts: on a narrative level (why do such details occur where they do?), and on a historical level (how do such details fit into a broader contemporary cultural picture?).
Influenced by recent work on ancient identities as well as the ‘literary turn’ in ancient historiographical scholarship – that is, the appreciation that historians were writers crafting their own cultural products, not just chroniclers of events – this thesis employs the insights of narratology and philology to attempt to evaluate the historians’ approaches to language and identity on their own terms and in their own contemporary cultural, political, and literary contexts.