VictorJuiz/thesis huggingface dataset:
Using light and electron microscopy to understand the replication of aphthoviruses
Keywords: Aphthovirus
Replication
FMDV
Foot-and-mouth-disease virus
Equine rhinitis A virus
ERAV
Microscopy

Aphthovirus is a genus of the family Picornaviridae which includes Foot-and-mouth-disease virus (FMDV) and Equine rhinitis A virus (ERAV). FMDV is a highly contagious pathogen infecting cloven-hoofed animals and is hence economically important. FMDV replication takes place in the cytoplasm and induces massive rearrangement of the host cell membranes to facilitate virus replication. Rearranged membranes form structures providing the site of viral genome replication known as the replication organelle (RO). The understanding of the RO, viral proteins and site of virus assembly is not well established. This project applies various microscopy approaches to investigate details of aphthovirus replication in cells. FMDV 3A protein is known to play a key role in viral replication machinery. We generated recombinant viruses of FMDV with various tags fused to this protein, subsequently allowing 3A to be detected in confocal microscopy. We developed a split-GFP system to study the dynamics of 3A protein in vitro. We showed that 3A signals appeared contiguous to the Golgi membrane signals suggesting that it potentially serve as a main source of membrane associated with viral replication. This approach was taken with the aim of facilitating the development of a correlative light electron microscopy (CLEM) system to unravel the localisation of virus proteins and their link to RO in cells. ERAV was used as a surrogate model to study FMDV replication in a lower containment laboratory using cryo-electron tomography (cryo-ET). Virus particles were observed associated with membrane structures with single membrane vesicles being more predominant than double membrane vesicles in infected cells. By sub-tomogram averaging, we reconstructed 3-dimensional (3D) models of intracellular ERAV full and empty particles which were compared with structures obtained for the virus purified from tissue culture and crystallized. Additional density was identified in the ERAV empty particles potentially corresponding to RNA contact sites inside the capsid.
Hjördis Lorenz - Thesis Submitted in Partial Fulfilment of the Degree of Doctor of Clinical Psychology (DClinPsych)
Keywords: rumination
PTSD
online interventions
trauma
dwelling

Titles of thesis components
• Literature Review: Online Interventions to Decrease Repetitive Negative Thinking: A Systematic Review
• Service Improvement Project: Trauma-informed Care Training: A Theory-Driven Qualitative Evaluation and Recommendations for Improvement
• Theoretically Driven Research Project: Evaluation of a Novel Rumination Intervention for Individuals with PTSD: A Pilot Feasibility Study
Re-defining exacerbations of chronic obstructive pulmonary disease and developing tools to characterise heterogeneity and treatment response
Keywords: Artificial Intelligence
Chronic Obstructive Pulmonary Disease
Cluster Analysis
Statistics
Disease Trajectories
Trajectories
Supervised Learning
AI
Machine learning
Data Science
Crises
Clinical Machine Learning
Asthma
Predictive Modelling
Personalised Medicine
COPD
ML
Phenotyping
Predictive Analytics
Endotyping
Exacerbations
Respiratory Medicine
Mathematical Modelling

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Progressive in nature, COPD is punctuated by acute periods of worsening termed ‘exacerbations’, a cause of morbidity and mortality. Exacerbations are heterogenous with respect to symptoms and inflammatory biology.
In this thesis, mathematical methods were researched for their appropriateness to characterise COPD heterogeneity and predict treatment response. Algorithms were developed to predict treatment outcome for a cohort of hospitalised patients with airways disease exacerbations. In a second patient cohort, cluster analysis was performed on longitudinal measurements at 'stable state’ and ‘exacerbation’ in order to define objective ‘states’ of COPD based on symptoms and inflammatory biology. Objective states included a low overall symptom burden state and crisis states with elevated symptoms with or without particular inflammation. Objective states were compared to conventional ‘stable state’ and ‘exacerbation’ definitions and found to better correspond to distinct patterns of symptoms and inflammation. Algorithms were trained and tested to identify to which objective state any given measurement of symptoms and inflammation from a patient belongs at a given point in time. Different treatment response trajectories or ‘trajectotypes’ of symptoms and inflammation were characterised for crisis states between the time of treatment and follow-up measurements. Trajectotypes included symptom resolution with or without different patterns of inflammatory resolution, as well as a trajectotype of no change between treatment and follow-up. Individualised symptom shape profile trajectories post-treatment were found from daily time series of self-reported symptom scores.
This thesis provides a new definition of COPD states as an alternative to ‘stable state’ and ‘exacerbation’ definitions. Together with the characterisation of distinct treatment response trajectories based on symptom and inflammatory resolution, the findings of this thesis set the stage for future clinical studies evaluating enhanced personalisation of treatment allocation and monitoring for patients with COPD.
Characterising CD8+ T cell transcriptomic and clonal consequences of immune checkpoint blockade for the treatment of cancer
Keywords: Immuno-oncology
T cell receptor
Melanoma
Tumour immunology
T cells
CD8 effector cells

Background: Immune checkpoint blockade (ICB) is arguably the greatest advance in medical oncology this century. Nonetheless, responses are highly variable. Mechanistic insights into this heterogeneity promises to improve patient stratification and identification of therapeutic targets.
Methods: Single-cell RNA sequencing and T cell receptor (TCR) sequencing was performed on peripheral CD8+ T cells taken at multiple time points before, during and after treatment with ICB from patients with metastatic melanoma and renal cell carcinoma (n=30 scRNAseq, n=139 TCRseq), coupled with bulk RNAseq, genotyping and clinical follow-up data across a larger cohort (n=214 patients). Analysis was via custom-written R scripts.
Results: The clonal size of CD8+ T cells is key to their response to ICB, with large clones differentially regulating more genes relevant to anti-tumour immunity. Through analysis of peripheral repertoires, highly public TCRs that strongly associate with clinical outcomes in patients with melanoma can be identified. These TCRs are found intra-tumourally, associate with HLA type, and their importance validates in two external datasets. CD8+ T cell clones which persist long-term post cessation of ICB have a cytotoxic ‘long-lived effector cell’, rather than conventional memory, phenotype – an entirely novel finding in the context of human cancer. Finally, carriers of a single nucleotide polymorphism at rs16906115 have increased odds of toxicity following ICB, with differential CD8+ T cell repertoire features. Risk-allele carriers are also less likely to experience decreases in lymphocyte count following ICB. A stable lymphocyte count post-treatment is a strong predictor of overall survival across multiple tumour types, regardless of genotype, a finding which replicates across multiple cohorts.
Conclusions: This work has high translational potential. Descriptions of key TCR in the anti-cancer response could inform vaccine or cellular therapeutic approaches. Further understanding lymphocyte stability may guide biomarker or drug development and the novel description of cytotoxic persistent clones in cancer is fundamentally relevant to human immunology.
Uncertainty Estimation: single forward pass methods and applications in Active Learning
Keywords: Uncertainty Estimation
Active Learning
Machine Learning
Deep Learning
Bayesian Deep Learning

Machine Learning (ML) models are now powerful enough to be used in complex automated decision-making settings such as autonomous driving and medical diagnosis. Despite being very accurate in general, these models do still make mistakes. A critical factor in being able to depend on such models is that they can quantify the uncertainty of their predictions, and it is paramount that this is taken into account by users of the model. Unfortunately, deep learning models cannot readily express their uncertainty, rendering them unsafe for many real-world applications. Bayesian modelling provides a mathematical framework for learning models that can express their uncertainty. However, exact Bayesian methods are computationally expensive to learn and evaluate, and approximate methods often reduce accuracy or are still prohibitively expensive. Meanwhile, ML models continue to increase in number of parameters, meaning that one has to make a decision between being (more) Bayesian or using a larger model. So far it has always fallen in favour of larger models. Instead of building on Bayesian methods, we deconstruct uncertainty estimation and formulate desiderata that we base our work on throughout the thesis (Chapter 1). In Chapter 3, we introduce a new model (DUQ) that is able to estimate uncertainty in a single forward pass by carefully constructing the model’s parameter and output space based on the desiderata. We then extend this model in Chapter 4 (DUE) by placing it in the framework provided by Deep Kernel Learning. This enables the model to work well for both classification and regression tasks (as opposed to just classification), and estimate uncertainty over a batch of inputs jointly. Both models are competitive with standard softmax models in terms of accuracy and speed, while having significantly improved uncertainty estimation. We additionally consider the problem of Active Learning (AL), where the goal is to maximise label efficiency by selecting only the most informative data points to be labelled. In Section 4.5, we evaluate the DUE model in AL for personalised healthcare. Here, the labelled dataset needs to adhere to specific assumptions made in causal inference, which makes this a challenging problem. In Chapter 5, we look at AL in the batch setting. We show that current methods do not select diverse batches of data, and we introduce a principled method to overcome this issue. Building upon deep kernel learning, this thesis provides a compelling foundation for single forward pass uncertainty and advances the state of the art in active learning. In the conclusions (Section 6, and at the end of each chapter), we discuss how users of ML models could make use of these tools for making sound and confident decisions.
Avian influenza H9N2-specific changes in the adaptive immune receptor repertoire of Gallus gallus domesticus following vaccination and infectious challenge
Keywords: H9N2
Repertoire
T cell
Avian influenza
B cell
Antibodies

Avian influenza viruses cause major losses to the poultry sector each year and also pose a significant risk for cross-species transmission to humans, where the disease manifestations can be very severe. However, within avian hosts, there is still a limited understanding of the adaptive immune system in the contexts of both health and disease, and the immunological mechanisms which underpin vaccine-induced protective responses against infectious challenge with pathogens such as avian influenza. As the ability of the adaptive immune system to recognise specific antigens is dependent on the T and B cell receptors which together comprise the adaptive immune receptor repertoire, understanding the determinants which shape its specificities and diversity is paramount for both improving our knowledge of the avian immune system and improving current prevention and control strategies such as vaccination. In this thesis, I present a comprehensive analysis of the domestic chicken (Gallus gallus domesticus) adaptive immune receptor repertoire upon infection and/or vaccination with H9N2 avian influenza – a pathogen that is widely prevalent across the world and poses significant risk both to the poultry sector and to human health and wellbeing. At the time of writing, no published research has examined the avian adaptive immune repertoire using high throughput sequencing, and no repertoire studies have been performed in birds that were infected with and/or vaccinated against avian influenza. My analyses thus provide valuable information on the avian adaptive immune system and the impacts of H9N2 infection and/or vaccination on the avian adaptive immune receptor repertoires.
The case of case: A morphological investigation of Russian heritage speakers in the UK

Keywords: Case inflection
Language Attrition
Heritage language
ALSLA
First language acquisition
Russian
Bilingualism
Education
ALSLA2023
Language acquisition

With the field of heritage language study only emerging in linguistics in the last few decades, Russian as a heritage language spoken in the UK remains an understudied area. In the present study, we aimed to investigate noun case inflections of 10–13-year-old children and adults residing in the UK from birth or early childhood and speaking Russian with their families at home from birth. Testing was done using a sentence translation task, a type of task not used previously in the field, and compared against age-matched monolingual controls. A background questionnaire was also administered to gather information about the way participants use their dominant and heritage languages in their household, family, friends, and entertainment. Our findings show that the case performance of heritage speakers significantly differs from age-matched controls, and worsens from the younger group to the older, suggesting a deterioration in the case system as the speaker moves away from their family and community upon transitioning into adulthood. These findings, however, do not entirely support earlier data from similar studies completed in the US and present the UK as a new underexplored playing field for future heritage language studies.
Characterisation of novel transcripts in the 3’ region of L-type calcium channel genes in human brain
Keywords: alternative TSS
L-type calcium channel genes
5’RACE
long-read nanopore sequencing

The alpha-1 subunit (CACNA1 family) of L-type voltage-gated calcium channels (LTCCs) play significant roles in brain function and neuropsychiatric disorders. Transcription of LTCCs is complex, with each alpha1 gene producing multiple isoforms. CACNA1S and CACNA1F were reported to have short-length transcripts from their 3’ region in the human brain which were not well characterized. Moreover, whether other CACNA1 genes also produce the 3’ transcripts have not been studied.
This thesis aims to identify and understand the short-length transcripts arising from the 3’ region of LTCC genes in human brain, especially CACNA1S and CACNA1F, using a series of technical approaches and analyses. Firstly, using PCR amplification and long-read nanopore sequencing, multiple 3’ transcripts with exon-skipping possibilities were identified. Secondly, 5’ Rapid Amplification of cDNA Ends (5’ RACE) identified the 3’ transcripts as being complete transcripts, arising from alternative transcription start sites (TSS). These findings were corroborated by exon expression level findings from Genotype-Tissue Expression (GTEx) data. Analyses using PhyloP showed that the 5’-untranslated region (5’-UTR) of the 3’ transcripts are more conserved than the intronic regions. Finally, the translation and localization of the protein encoded by each 3’ transcript was investigated in transfected HEK-293T and SH-SY5Y cell lines. All proteins were detectable by western blot. C-CaV1.3 (encoded by CACNA1D) localized to the nucleus in both cell types; C-CaV1.2 (encoded by CACNA1C) translocated to the nucleus in the excitable (SH-SY5Y) cells but not the non-excitable (HEK-293T) cells. C-CaV1.1 and C-CaV1.4 (encoded by CACNA1S and CACNA1F respectively) were not localized to the nucleus. This thesis shows that LTCC alpha1 subunit genes produce 3’ transcripts using alternative TSS in human brain. Their potential functionality is supported by the evolutionary conservation of their 5’-UTRs and by their translation in vitro. However, further studies are required to identify the significance of these transcripts for brain function.
Legal practice and its continuity
Keywords: legal system
general jurisprudence
legal norms
constitutional crisis
legal theory
legal meaning
legal philosophy

This thesis has a particular and a general aim. The particular aim is to provide a satisfactory account of constitutional crises. Available accounts tend to either exaggerate the disruptive character of such crises or, conversely, to deny it; the challenge is thus to acknowledge the disruption without blowing it out of proportion.
The general aim is to outline a theory of law from which such a satisfactory account would follow. The available accounts oscillate between the two extremes because they are attached to the idea that law is a system of legal norms. I critique this idea—not to reject it, though, but to show that it is only one element in a complete account of law. Instead of viewing legal practice as governed by a system of norms, posited and administered by state institutions, we may view it as a collective practice among the general population, whereby they make sense of each other’s actions in legal terms so as to know how best to navigate their mutual interactions. The legal system does have a place in this latter picture, but this place corresponds to the important yet limited role of the state in wider social practice.
Articulating and substantiating this proposal takes up the better part of the thesis, eventually to yield an understanding of legal continuity which translates into a satisfactory account of constitutional crises. The continuity of legal practice is not a direct function of the continued efficacy of some normative system, but depends on whether participants in the practice can make enough legal sense of their interactions to know how to carry on with them. In a constitutional crisis, no such legal sense can be made of at least certain practices of constitutional actors; but many everyday interactions may still make perfectly good legal sense.
Providing and assessing intelligible explanations in autonomous driving
Keywords: autonomous vehicles
intelligibility
human factors in autonomous driving
explanations
explainable AI
human-machine interaction
autonomous driving
interpretability

Intelligent vehicles with automated driving functionalities provide many benefits, but also instigate serious concerns around human safety and trust. While the automotive industry has devoted enormous resources to realising vehicle autonomy, there exist uncertainties as to whether the technology would be widely adopted by society. Autonomous vehicles (AVs) are complex systems, and in challenging driving scenarios, they are likely to make decisions that could be confusing to end-users. As a way to bridge the gap between this technology and end-users, the provision of explanations is generally being put forward. While explanations are considered to be helpful, this thesis argues that explanations must also be intelligible (as obligated by the GDPR Article 12) to the intended stakeholders, and should make causal attributions in order to foster confidence and trust in end-users. Moreover, the methods for generating these explanations should be transparent for easy audit. To substantiate this argument, the thesis proceeds in four steps: First, we adopted a mixed method approach (in a user study N=101
) to elicit passengers' requirements for effective explainability in diverse autonomous driving scenarios. Second, we explored different representations, data structures and driving data annotation schemes to facilitate intelligible explanation generation and general explainability research in autonomous driving. Third, we developed transparent algorithms for posthoc explanation generation. These algorithms were tested within a collision risk assessment case study and an AV navigation case study, using the Lyft Level5 dataset and our new SAX dataset---a dataset that we have introduced for AV explainability research. Fourth, we deployed these algorithms in an immersive physical simulation environment and assessed (in a lab study N=39
) the impact of the generated explanations on passengers' perceived safety while varying the prediction accuracy of an AV's perception system and the specificity of the explanations. The thesis concludes by providing recommendations needed for the realisation of more effective explainable autonomous driving, and provides a future research agenda.
Regulatory technologies for the study of data and platform power in the app economy
Keywords: iOS
GDPR
privacy
tracking
platform power
data power
smartphones
digital platforms
security

Tracking, the large-scale collection of data about user behaviour, is commonplace in mobile apps. While some see tracking as a necessary evil to making apps available at lower prices by showing users personalised advertising and selling their data to third parties, tracking can also have highly disproportionate effects on the lives of individuals and society as a whole. For example, tracking has significant effects on the rights to privacy and data protection, but also on other fundamental rights, such as the right to non-discrimination (e.g. when data from mobile tracking is used in AI systems, such as targeted ads for job offers) or the right to free and fair elections (e.g. when political microtargeting is used, as in the Brexit vote or the Trump election).
This thesis develops and applies techno-legal methods to study choice over app tracking at four levels: the impact of the GDPR (Chapter 4), consent to tracking in apps (Chapter 5), differences between Android and iOS (Chapters 6), and the impact of Apple’s App Tracking Transparency (ATT) framework (Chapter 7). While many previous studies looked at data protection and privacy in apps, few studies analysed tracking over time, took a compliance angle, or looked at iOS apps at scale. Throughout our analysis of apps, we find compliance problems within apps as regards key aspects of US, EU and UK data protection and privacy law, particularly the need to seek consent before tracking. For instance, while user consent is usually required prior to tracking in the EU and UK (under the ePrivacy Directive), our empirical findings suggest that tracking takes place widely and usually without users’ awareness or explicit agreement.
This thesis contributes 1) a scalable downloading and analysis framework for iOS and Android privacy and compliance analysis (PlatformControl), 2) an improved understanding of the legal requirements and empirical facts regarding app tracking, 3) a comprehensive database of the relations between companies in the app ecosystem (X-Ray 2020), and 4) an Android app to support the easy and independent analysis of apps’ privacy practices (TrackerControl).