VictorJuiz/thesis huggingface dataset:
Hong Kong preservice teachers' beliefs and attitudes towards teaching proof in school mathematics: a design-based research
Keywords: design-based research
teacher beliefs
preservice teachers
proof and proving

Researchers have suggested that proof is essential for the learning of mathematics and should be placed at the centre of school mathematics. Yet, many studies suggest that teachers have counterproductive beliefs and attitudes towards teaching proof in school mathematics, which are constraints on making proof central to school mathematics. This study developed an intervention of short duration that promotes preservice teachers’ more productive beliefs and attitudes towards teaching proof, which in turn can support preservice teachers’ future teaching of proof.
Design-based research methodology was used to develop the intervention, which involved four research cycles of implementation, evaluation, and revision. The intervention comprised three weekly two-hour workshops. Preservice teachers who enrolled in different initial teacher training programmes of different universities in Hong Kong were recruited to participate in the intervention. Data were collected from multiple sources, including a questionnaire, individual semi-structured interviews, and fieldnotes and recordings of the intervention. In Research Cycles 1‒3, data were used to assess changes in preservice teachers’ beliefs and attitudes towards teaching proof in school mathematics and evaluate early versions of the intervention, which in turn informed refinements of the intervention. In this thesis, I report the findings of Research Cycle 4.
The results indicated that, after the intervention, the preservice teachers developed more productive beliefs and attitudes towards teaching proof in school mathematics, namely, enhanced beliefs about the importance of proof in school mathematics, greater enjoyment and interest in proof, less anxiety about proof, and less negative responses when failing to prove, and became more open and felt more prepared to implement proof in their future teaching. Components of the intervention that facilitated the change in the preservice teachers’ beliefs and attitudes towards teaching proof have also been investigated.
This study can extend our understanding of multifaceted beliefs and attitudes which influence whether and how preservice teachers plan to implement proof in their future teaching, particularly from an East-Asian perspective, and the design principles for facilitating change in beliefs and attitudes towards teaching proof in school mathematics. In practice, the intervention developed in this study can promote preservice teachers’ more productive beliefs and attitudes towards teaching proof in school mathematics in a relatively short period.
Investigating gender bias in Oxford MChem chemistry tutorials
Keywords: Cognitive load theory
Unconscious gender bias
Chemistry education
Tutorial
Self-determination theory

There is a gender attainment gap in Oxford’s MChem degree outcomes, which the university aims to eliminate. One hypothesised explanation for the gap is unconscious gender bias in chemistry tutorials.
This study investigated what makes tutorial experiences positive or negative for students and how female students experience chemistry tutorials.
Qualitative data were collected through interviews with current MChem students which were subsequently transcribed and analysed. These data provided a rich insight into the experiences of students in chemistry tutorials.
Students’ autonomy, competence and relatedness are not always supported in tutorials. According to self-determination theory, this could cause students to develop less autonomous motivation, leading to poorer learning outcomes. When exploring the gendered elements of chemistry tutorials, it was found that female students do a substantial amount of extra work in tutorials compared to their male peers. Female students must: push themselves to speak up; battle imposter syndrome; and deal with negative emotions such as anxiety, frustration and loneliness. This adds to their extraneous cognitive load, restricting the mental space they can devote to understanding chemistry, which could potentially lead to poorer learning outcomes.
Characterising peripheral responses to immune checkpoint inhibitors
Keywords: immune checkpoint blockade
melanoma
monocytes
circulating epigenetic cell-free DNA profiles
single-cell RNA sequencing

The introduction of immune checkpoint blockade (ICB) therapy for metastatic melanoma (MM) has dramatically improved survival in this patient group. However, only some patients experience durable clinical response, and many will develop immune-related adverse events (irAEs). The identification of predictive biomarkers which can be assessed in a biopsy-free manner may facilitate early detection of non-responders and optimal targeting of treatment.
There is emerging evidence to show that the circulating monocyte population has a prognostic and predictive role in the context of ICB, but this has yet to be fully characterised. Circulating-cell free DNA (cfDNA) is derived predominantly from leukocytes, but in patients with cancer may comprise circulating tumour DNA (ctDNA). Assessment of epigenetic cfDNA profiles may provide tumour profiling and reflect immune responses as a ‘liquid biopsy’. Although epigenetic tumour profiles have diagnostic and prognostic utility across multiple cancer types, epigenetic cfDNA profiles in MM and following ICB are not yet characterised.
In this thesis I dissect both circulating myeloid and epigenetic cfDNA profiles in MM patients. Firstly, I explore MM-associated monocyte transcriptomic profiles with bulk RNA sequencing (RNA-seq), characterising on-treatment modulation and association with clinical response. I then use scRNA-seq to explore the transcriptional heterogeneity of the peripheral monocyte compartment, and describe monocyte subset-wise responses to ICB. Lastly, I characterise epigenetic cfDNA profiles in this cohort, revealing distinct 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) cfDNA profiles in MM and following ICB.
In this thesis I present novel findings regarding peripheral myeloid and epigenetic cfDNA responses to ICB and demonstrate that these profiles may have potential diagnostic and predictive clinical utility.
Students' experiences of mental health and studying chemistry at Oxford
Keywords: student mental health
chemistry
Chemistry Education
DASS-21
mental health
psychological well-being
student well-being

This study sought to assess the prevalence of mental illness among undergraduate chemistry students at Oxford, and to build an understanding of the relationship between studying chemistry and student mental health.
The data for this study was collected via an online anonymous survey, distributed to all Oxford undergraduate chemistry students. The survey featured the DASS-21 instrument, and free text questions. Demographic information was also collected pertaining to participants’ year group, gender, and domestic/international status.
Participants scored higher levels of mental illness than similar groups. First-year students, female and gender-minority students showed the highest levels of depression, anxiety, and stress. There were no significant differences in levels of depression, anxiety, and stress between domestic and international students.
Participants’ qualitative experience of the interaction between mental health and studying chemistry can be described as a balance between the expectations students experience (academic and otherwise) and students’ capacity to meet those expectations. Factors affecting this interaction included: workload; the student-tutor relationship; and pre-existing conditions such as disability and illness. Poor mental health was qualitatively associated with a decreased capacity to learn effectively.
Far-field linear optical super-resolution imaging via Hermite-Gaussian microscopy
Keywords: optical interferometry
super-resolution imaging
quantum information
quantum metrology
machine learning

The resolution of optical imaging devices is ultimately limited by the diffraction of light. To circumvent this limit, modern super-resolution microscopy techniques employ active interaction with the object by exploiting its optical nonlinearities, nonclassical properties of the illumination beam, or near-field probing. Thus, they are not applicable in areas where interaction is not possible, for example, in astronomy or non-invasive biological imaging. Far-field and linear-optical super-resolution technique based on passive analysis of light coming from the object would cover these gaps. This thesis presents the first proof-of-principle demonstration of such a technique for general objects. It works by accessing information about spatial correlations of the image optical field and, hence, about the object itself via measuring projections into Hermite-Gaussian transverse spatial modes. In theory, the resolution of the technique scales as the inverse square root of the number of modes in each transverse dimension. With a basis of 21 spatial modes in both dimensions (21^2 = 441 modes), this experimental work demonstrates two-dimensional imaging with twofold resolution enhancement beyond the diffraction limit.
Fragment hotspot mapping to drive the rational elaboration of fragment screening hits
Keywords: structural biology
binding site
protein-ligand interactions
computational methods
fragment-based drug discovery

Fragment-based drug discovery (FBDD) has established itself as a powerful tool for developing probe and drug candidates by rationally elaborating small chemical fragment hits into larger, optimised lead compounds. The use of X-ray crystallography as a medium-throughput screening tool for FBDD results in a wealth of structural data on low molecular weight molecules in complex with a protein target. Interpreting this data and distilling it into prioritised suggestions for the elaboration of fragment hits into leads with increased potency and selectivity for the target protein is currently a significant challenge to using the technique. Thus, computational methods and pipelines designed to streamline and automate the process, providing follow-up hypotheses in an objective and high-throughput way, are in high demand.
Fragment hotspot mapping is a computational method that highlights specific interactions within a protein’s binding site that drive the binding of small molecule fragments. As crystallographic FBDD campaigns result in an ensemble of structures of the same protein, a method to combine fragment hotspot maps information for these structures into an "ensemble map" for the protein target was developed. A workflow for comparing ensemble maps between a target and a related off-target protein was implemented and extended to allow comparisons across a protein family. This workflow was applied to examples from the well-researched human bromodomain and kinase families, and was able to identify selectivity-determining regions that have been exploited in past drug discovery campaigns.
Dynamic undocking, a steered molecular dynamics method for estimating the structural stability of protein-ligand complexes, was then investigated as a way of characterising specific binding site interactions. To facilitate integration into computational workflows, an open-source implementation of the method was benchmarked and shown to perform comparably.
A workflow combining the extended fragment hotspot maps method, dynamic undocking, docking and a chemistry recommendation engine was developed and used to suggest follow-up compounds in three ongoing medicinal chemistry projects. The compounds showed detectable binding affinity, a significant improvement from the starting fragment hits, demonstrating the workflow’s utility in the initial round of compound elaboration.
Chemistry student enthusiasm: exploring student and teaching staff perspectives
Keywords: higher education
workload
self-determination theory
student enthusiasm
chemistry education
student interest
subjective vitality

The first aim of a general chemistry degree course is to ‘instil in students an enthusiasm for chemistry’ (QAA Chemistry Benchmark Statement, 2019), and chemistry enthusiasm is a required student characteristic within the Oxford MChem admissions criteria.
Through qualitative interviews with students and teaching staff, this study explored what student enthusiasm means, why it is important in chemistry education, and how the Oxford MChem course affects student enthusiasm.
Findings suggested the four-phase model interest of development usefully explains definitions of chemistry enthusiasm, how it develops and why it is important in education. Subjective vitality explained how students’ general enthusiasm affected their experience of chemistry enthusiasm, and self-determination theory explained why it developed.
A rich theme across interview responses was workload, or more specifically how workload and chemistry student enthusiasm interacted. Respondents perceived that the Oxford chemistry degree had a distinctively heavy workload; chemistry enthusiasm reduced students’ experience of workload; high workload could make or break student enthusiasm; and excessive workload caused three things: superficial learning, perceived lack of control, and significant enthusiasm loss. This analysis agreed with previous studies which found that interest reduced perceptions of workload, and high workload caused superficial learning and reduced interest.
Motility induced phase separation: numerical and experimental approaches towards the high Péclet regime
Keywords: Active Particles
Hard Interaction
Motility Induced Phase Separation

Active particles, those able to self-propel, have been shown to phase separate if they interact via a repulsive interaction. This is called motility-induced phase separation or MIPS. It was initially observed in systems of bacteria but, as is often the case in the study of active matter, many have gone on to examine the phenomena in numerical simulations and in theory. Although active Brownian dynamics simulations, the numerical approach of choice when it comes to active particles, are decently understood and have been used extensively, there remain issues related to the stiffness of interaction potentials in certain conditions. By nature, experimental systems can often be difficult to create let alone extend into a whole phase diagram. In this work, we tackle issues to do with MIPS both in simulation and experiment. We show how by using an empirically derived interaction, we are able to extend the Péclet/density phase diagram and discover that MIPS is hindered when the persistence length of particle trajectories are large. When no rotational coupling is included in simulation, particles are able to escape clusters with no resistance after they have rotated. The crystallinity of the dense phase locking particles in place couples with an imbalance of particle orientations from the random nature with which particles join a cluster to create the rotation.
Experimentally, we show that induced-charge electrophoretic particles are excellent for tuneable phase separating active systems. We manipulate the conditions of our system such that we control the velocity and interaction of our particles. We show that an experimental phase diagram is possible and similar to simulated ones. Cluster dynamics also play an important role in the phenomena we observe experimentally. We add dipolar interactions in our simulations to show that we recover experimental behaviours when particles align. We foresee that this work could be a much needed bridging point between active matter in simulation and experiment whilst also providing multiple directions to advance the field further in both cases. Our interaction potential can be used to create simulated systems with strong attractions between particles. The experimental conditions we use can be a starting point for creating more complex systems by changing electric field frequency or particle size for example.
Probing Mn-Mn and Mn-Si bonding in silicon cages
Keywords: infrared multiple photon dissociation spectroscopy
endohedral clusters
multiconfigurational self-consistent field theory
density functional theory

This thesis explores the geometry and electronic structure of some manganese doped silicon clusters. Experimental characterisation of these clusters is severely limited. Theoretical investigations with density functional theory (DFT) yield conflicting results due to differences in the choice of functional. Hence, a combination of both theory and experiment has been employed: our experimental collaborators have measured the IR-MPD spectra of these clusters while we have used DFT calculations to explore the potential energy surfaces. For a given cluster, the vibrational spectra for low-lying isomers have been computed and compared with the measured spectrum to identify the experimentally observed isomers. The information content of the spectra has been expanded by assigning vibrational modes to various peaks and by identifying vibrational signatures. Given that there is no consensus about the choice of functional, the results have been computed with a range of functionals. The influence of functional choice on the conclusions has been studied which reveals how certain functionals stabilise structures with low vertex connectivity while others tend to favour deltahedral frameworks. Multiconfigurational self-consistent field theory (MC-SCF) has been used to get an alternative perspective on bonding in these clusters. By making use of generalised and restricted active spaces (GAS and RAS), important contributions to static correlation in these clusters have been probed. An attempt has been made to account for dynamic correlation by applying perturbation theory corrections to energies obtained by the RASSCF method. However, sufficient static correlation has not been captured in the RASSCF wavefunction to allow for a reliable perturbative treatment of the remaining, missing, correlation.
Heterogeneous copper-based catalysts for low-temperature methanol steam reforming
Keywords: XRD
heterogeneous
XAS
AMOST
hydrotalcite
BET
high surface area
LDH
aluminium
copper
catalysis
hydrogen
gallium
dispersion
modelling
zinc

This thesis concerns the development of a new class of methanol steam reforming (MSR) catalyst precursors. The work aims to facilitate advances in on-board, MSR-powered proton exchange membrane fuel cell technology by producing catalysts with high H2 production rates and optimal H2:CO selectivity.
CuZn-based layered double hydroxides (LDHs) were synthesised and exfoliated using a novel, simple and scalable aqueous miscible organic solvent method to yield catalyst precursors with high surface areas and copper dispersions. This is the ﬁrst time such methods were used in the preparation of MSR catalysts; a CuZnAl-LDH precursor achieved a BET surface area and copper dispersion percentage of 252 m2.g−1 and 53.9 %, respectively.
It was found that an optimal content of Ce doped into the catalysts suppressed CO production. Many of the catalysts in this work produced a higher H2:CO mol ratio than the previous best recorded ratio in literature, 3000 molH2:molCO, under conditions that favoured higher reverse water gas shift activity; speciﬁcally, 27 % less water content and a 70 % lower weight hourly space velocity (WHSV). Notably, a CuZnGaCe catalyst from this work produced 4721 molH2:molCO, with an activity of 14.4 µmolH2.s−1.gcat, at 180 °C using a 1.1:1 mol ratio of water/methanol reactants and a 2.03 h−1 WHSV.
Additionally, this work investigated the previously unsolved structure of lanthanide-doped LDHs with a wide variety of techniques including high resolution-EDX, Ce L3 edge XAS and DFT analysis; this allowed the structure to be proposed. XAS was also used to analyse catalyst samples from diﬀerent stages of water gas shift reactions to gather insight on how the catalysts improved selectivity.
The use of whole genome sequencing for the analysis of isolates belonging to the bacterial genus Salmonella
Keywords: rMLST
outbreak
molecular epidemiology
novel subspecies
whole genome sequencing
Salmonella
wgMLST
cgMLST

The global burden of disease from Salmonella infections is substantial and the increase of trade and travel across borders presented very real challenges, such as the investigation of multi-country outbreaks. The primary aim of this thesis was to examine WGS for the analysis of Salmonella, especially in regard to the public health applications of sequencing. A review was conducted in Chapter Two to establish the impact of whole genome sequencing on public health to date.
The analyses undertaken here were primarily conducted by upscaling the MLST- approach. In Chapter Three cgMLST was shown to be highly congruent with SNP-based approaches for outbreak investigations. The use of cgMLST enabled easier communication across borders as it was reference free and readily available online.
In Chapter Four the current core scheme was shown to be problematic for whole genus analyses. A new scheme was therefore developed and used for the identification and analysis of isolates from across the genus. This included the identification of novel subspecies and the reclassification of subspecies IIIa as a separate species – S. arizonae, as demonstrated in Chapter Five. This in turn enabled more accurate reporting of the salmonellae.
WGS can also be used to characterise datasets of related organisms as demonstrated through an analysis of serovar Enteritidis isolates from New South Wales, as shown in Chapter Six. This analysis identified differences between regions and countries in serovar Enteritidis strains and their AMR profiles.
This work provided an overview of the utility of WGS for public health purposes and expanded the current understanding of the molecular epidemiology of Salmonella.
Quantifying spatial heterogeneity in tumours from dynamic contrast-enhanced MRI
Keywords: DCE-MRI
heterogeneity
perfusion
cancer
applied computer vision
tumour
PK analysis
applied machine learning

Cancerous tumours are characterized by an inherent perfusion heterogeneity that can change over time and following therapy. The process of angiogenesis often results in increasingly disorganised and leaky vasculature as tumours progress. This may lead to heterogeneous perfusion, which is known to produce locally varied subregions in tumours. Tracking such subregional differences might be the key to understanding and predicting tumour progression, which could be in turn applied to therapy response assessment. Dynamic contrast-enhanced MRI (DCE-MRI) can monitor such changes in perfusion and vascularity, which can then be quantified into a set of parameter maps. Subsequently, segmentation and classification methods are often applied to the maps to define tumour subregions that can be tracked over time. In this work we propose a texture-based Bag of Visual Words framework for the problem of assessing tumour perfusion heterogeneity from DCE-MRI images. We adapt and evaluate a number of shallow feature descriptors and encodings to estimate their suitability to the problem. Furthermore, we combine lightweight deep representations with shallow encodings and recent advances in encoder-decoder architectures to introduce a novel deep image representation for 3D segmentation and subsequent classification of images with no ground truth and limited training data. We show that even in extremely simple forms they are able to outperform the frequently used methods in tumour heterogeneity assessment. Finally, we demonstrate the suitability of the proposed methods in the more clinically-relevant context of therapy response assessment. We employ a number of strategies to improve confidence and transparency of the proposed methods.