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Application of CUT&Tag to the mapping and analysis of VEZF1 binding sites in K562 cells
Get PDFVEZF1 is a highly conserved vertebrate transcription factor that has ubiquitous expression in vertebrates. VEZF1 is essential for the barrier activity of the chicken ẞ globin HS4 insulator, where it prevents de novo DNA methylation. Knock-out of VEZF1 results in lethal haemorrhaging and edema in murine embryos, indicating a role for VEZF1 in the maintenance of vascular integrity.
In this study, the CUT&Tag method, previously reported to be an improvement on CUT&RUN and ChIP-seq, was utilised to map VEZF1 binding sites in K562 cells. The binding sites identified by CUT&Tag are similar to those previously identified using ChIP-seq, but the data generated from CUT&Tag shows significant advantages of lower background and higher peaks. Peak analysis indicates that most of the VEZF1 peaks are associated with promoters or enhancers, and there is strong co-localisation of the binding of VEZF1 and GATA2. VEZF1 tends to recognize and bind to GGGNGGGG motifs, but no difference is found between GGGNGGGG motifs discovered at VEZF1-associated promoters and enhancers. Furthermore, co-localisation of GATA2 does not affect the sequence of the GGGGNGGGG motifs enriched at VEZF1 peaks. Sequence analysis also revealed other motifs that are recognized by a variety of transcription factors, which may act alongside VEZF1 to regulate gene expression in K562 cells.
Our study shows that CUT&Tag is an efficient method for mapping and analyzing the binding sites of transcription factors, and provides new opportunities for research on the interactions between VEZF1 and other transcription factors
Unveiling successful leadership: A case study of high-performing primary school principals in urban southwest China
Get PDFThis doctoral study investigates the intricate dynamics of educational leadership within the context of high-performing schools in urban Southwest China. Adopting a bounded realism ontology and a constructivist epistemological stance, the research examines the leadership mindsets, practices, and impacts of successful principals at Churchill Primary School (pseudonym) in the Riverside District of Chengdu. The study utilizes a case study methodology, integrating semi-structured interviews and non-participant observation to collect and analyze data.
The research identifies common leadership traits among the principals, emphasizing a strong sense of mission, moral values, and adherence to democratic principles. Furthermore, the study reveals shared patterns in the leadership model at Churchill Primary School, highlighting the impact of moral vision, systemic thinking, and democratic values on school operations and management. Also, the study evaluates the applicability of existing leadership models and proposes a “Full Spectrum Leadership” model that outlines the characteristics and capabilities of a comprehensive leader. It also emphasizes the need for modifications to established 20th-century leadership models to address the complexities of 21st-century educational environments.
The research acknowledges limitations, including the employment of a single-case study methodology and the potential for researcher subjectivity. The study’s quality was preserved as the researcher actively managed challenges posed by the COVID-19 pandemic and other misfortunes while ensuring valid and reliable data collection and analysis.
Overall, this study provides valuable insights into effective educational leadership within the unique cultural and societal context of urban Southwest China. The findings contribute to the current discourse on educational leadership and offer recommendations for future research and practice in the field
Single-molecule detection of fluorescent biomolecular building blocks via pulse-shaped multiphoton excitation
No full textAbstract not currently available
Bayesian methods for inference in biostatistical longitudinal studies and modelling of missing data
Get PDFLongitudinal studies repeatedly collect data from the same individuals over time to study long-term factors. A commonly used model in longitudinal studies is the linear mixed effects model, which considers the correlation between observations within individuals. There are two ways to fit the model in statistical fields: the Frequentist and Bayesian approaches. The Frequentist approach is widely used, while the Bayesian approach has become more common with computational advancements. The work in this thesis comprises a comparison study between the Frequentist linear mixed effects model and the Bayesian Hierarchical model, using simulated longitudinal data and data from a heart failure study (BIOSTAT-CHF). It was observed that inferences from both approaches were similar. However, the Bayesian approach offers an advantage by providing a probability distribution for the parameter estimates. This shows the probability of values falling within a certain range and incorporates prior information from previous studies into the inference.
In longitudinal studies, missing data is a common problem that can impact the statistical analysis estimates by producing biased estimates. A method that deals with non-ignorable missingness in the response using Correlated Random Effects (CRE) based on latent variables and Gibbs sampling has been proposed in the literature and has performed well in scenarios assuming semi-parametric modelling. However, when applied to linear mixed-effect modelling, the covariance matrix parameters had difficulty converging. To address this issue, the work in this thesis considers a weakly informative prior using the Inverse Wishart distribution. Additionally, this CRE method is unable to accommodate incomplete data in the analysis model explanatory variables. To address this problem, the work in this thesis proposed three methods to deal with missingness in the response and explanatory variables by adapting the CRE method.
Two proposed methods, the Two-Step and the GCRE-MAR methods, were designed to address non-ignorable missingness in the model response and ignorable missingness in the model explanatory variables. The GCRE-MNAR method was designed for non-ignorable missingness in both the model response and explanatory variables. In the Two-Step method, the CRE method was adapted by incorporating an additional step using the MICE algorithm, a common approach for handling MAR data and producing imputed datasets.
The CRE method is then applied to the imputed MICE datasets. The GCRE-MAR and GCRE-MNAR represent generalised versions of the CRE method. The GCRE-MAR method incorporates the incomplete explanatory variable model. The GCRE-MNAR method incorporates the incomplete explanatory variable model and the incomplete explanatory variable missingness process model. It considers correlated random effects between the incomplete explanatory variable model and the missingness process.
The proposed methods were compared with the CRE method and some baseline models using simulated longitudinal data for different numbers of repeated measures and missing proportion factors. The proposed methods perform similarly to the CRE method, given that the proposed methods consider missing data in both the response and explanatory variables. In contrast, the CRE method only has missing data in the response (no missing values are in the explanatory variables). Furthermore, the proposed methods outperform the available data method in out-of-sample predictive performance, and the parameter estimates closely match the parameters that generated the data.
Additionally, the proposed methods were applied to the BIOSTAT-CHF data, and the results were consistent regardless of the applied method. The correlated random effects indicated that the NT-proBNP missingness was MAR, and the eGFR missingness wasMNAR. Finally, the sensitivity analysis for the misspecified missingness mechanism for the proposed methods had a small impact on the overall results, whereas the misspecified response missingness model resulted in biased parameter estimates for some of the analysis model coefficients
Timelike Compton Scattering from a longitudinally polarised target with CLAS12 at Jefferson Lab
Get PDFExplorations into the internal dynamics of hadrons are constantly evolving, and the requirement for experimental results to verify theoretical models of hadron structure is paramount. A key area in this field is the study of Generalised Parton Distributions (GPDs), which are functions used to model the momenta of quarks and gluons within hadrons, and the methods to access GPDs experimentally. One such scattering process that allows access to these is Timelike Compton Scattering. TCS complements existing Deeply Virtual Compton Scattering experiments and allows investigation into the universality of GPDs through access to the real and imaginary parts of the parton helicity independent GPD H q via beam spin asymmetries (BSA), and it provides novel access to the real and imaginary parts of the parton helicity dependent GPD H˜ q through target polarisation asymmetries (TSA). This thesis work presents a comparative study with the first published BSA for TCS at the Thomas Jefferson National Accelerator Facility (JLab), alongside a first time extraction of a Target Spin Asymmetry with the Summer 2022 data taking run.
JLab hosts the Continuous Electron Beam Accelerator Facility (CEBAF) which provides a 12 GeV electron beam to four experimental halls. Hall-B contains the CEBAF Large Acceptance Spectrometer, which took data across three run periods on a longitudinally polarised NH3 and ND3 fixed target from 2022-2023, to extract measurements of electron-proton scattering, from which a TCS signal could be extracted. The thesis discusses work done to understand and eliminate contributions from the non-/low-polarised nuclear background, testing pre-established cuts to eliminate pion background from a dilepton (e +e −) final state and modifying them as needed for the new experimental run, and attempts to hone in on a clean TCS signal from which to extract the two asymmetry observables.
A comparison with existing BSA results was performed; however, the statistical errors are too large to draw a significant conclusion as to whether there is agreement across each bin. More data is needed for a multidimensionally binned extraction. A proof of principle was achieved in the TSA measurements, with two out of four kinematic bins showing preliminary agreement in shape with theoretical values. Again, the errors are significant due to the contributions from the nuclear background. To support these conclusions, a further study was done, which takes into account an estimate of the asymmetries with the full available dataset (this thesis is based only on data taken in the summer set; at the time of writing processing was still being conducted for the final two datasets), as well as an estimate including additional future experiment days that were awarded in July 2024. Additional work was done on a secondary project exploring the feasibility of measuring TCS at the upcoming Electron Ion Collider, supporting the design proposal for the detector for the first interaction region and giving a positive outlook for the future of these types of measurements beyond JLab
Cosmological parameter inference using gravitational waves and machine learning
Get PDFIn 1929, Edwin Hubble’s discovery of the relationship between galaxy distances and their recession velocities unveiled the universe’s expansion, laying the foundation for modern cosmology and the task to measure the Hubble constant, H0. In 1986, Bernard F. Schutz proposed using gravitational waves from compact binary mergers, such as neutron stars and black holes, as a novel method to estimate H0. This innovative approach marked the beginning of a new era in cosmological research, significantly advanced by the advent of gravitational wave detection through the Laser Interferometer Gravitational Wave Observatory (LIGO). The field has since evolved, employing advanced Bayesian techniques and extensive galaxy catalogues to improve the precision of H0 measurements. However, as the sensitivity of detectors increases and the rate of gravitational wave observations grows, computational challenges—particularly in hierarchical Bayesian analysis—pose significant hurdles due to the intensive and time-consuming nature of traditional methods. In response to these challenges, this thesis, under the supervision of Dr. Christopher Messenger and Prof. Martin Hendry, explores the integration of machine learning into cosmological research, specifically focusing on a novel approach called CosmoFlow to extract cosmological information from gravitational waves. CosmoFlow uses Normalising Flows, machine learning models capable of efficiently estimating probability distribution functions of complex datasets, providing a faster and and potentially advantageous approach to hierarchical Bayesian inference of the Hubble constant. Our work demonstrates how CosmoFlow can significantly accelerate the process compared to existing methodologies. Throughout this thesis, we rigorously compare the results of CosmoFlow with those obtained using gwcosmo, a well-established tool in gravitational wave cosmology. By contrasting CosmoFlow with gwcosmo results, we highlight the strengths and limitations of each method, emphasising the potential of machine learning to address existing computational bottlenecks in cosmological analyses. This comparative study aims to contribute to the ongoing efforts to resolve the current 4.4σ tension between different H0 measurement techniques, paving the way for more efficient and accurate future analyses in this rapidly evolving field
Computational imaging with the human brain
Get PDFHuman augmentation, which involves enhancing cognitive and physical abilities as a natural extension of the human body, has been significantly advanced by Brain-Computer Interfaces (BCIs). This thesis explores BCI-based human augmentation, focusing on computational ghost imaging and developing a phenomenological brain model for Steady-State Visual Evoked Potentials (SSVEPs).
Initially, the concept of BCIs as a conduit for computational imaging is introduced, demonstrating the potential to integrate brain function with external silicon-based processing systems. A key example is the ghost imaging of a hidden scene using the human visual system in conjunction with an adaptive computational imaging scheme. This technique, known as projection pattern ‘carving,’ utilizes real-time brain feedback to modify light projector patterns, resulting in more efficient and higher-resolution imaging. This brain-computer connectivity represents a form of augmented human computation, potentially expanding the sensing range of human vision and offering new methodologies for studying the neurophysics of human perception. An illustrative experiment is presented, highlighting how image reconstruction quality can be influenced by simultaneous conscious processing and readout of perceived light intensities.
Subsequently, the thesis delves into the phenomenon of SSVEP, which has attracted attention across various fields including neuroscience and human augmentation. The analysis of SSVEP under multiple frequency stimuli, a complex task due to frequency intermodulation terms, is addressed by proposing a phenomenological model. This model provides a mathematical framework for analysing the essential frequency mixing features in SSVEP when exposed to multifrequency stimuli. The analysis is extended to both narrowband and broadband categories using analytical and statistical methods. Experimental results confirm the model’s accuracy, shedding light on the mathematical model behind SSVEP responses to multiple frequency stimuli and offering insights for practical applications and a deeper understanding of this phenomenon.
Addressing the neuromorphic aspect of SSVEP, the thesis discusses the extensive use of SSVEP in BCIs due to their stability and efficiency in connecting the computer and the brain using simple flickering light. Moving beyond prior research that focused on low-density frequency division multiplexing techniques, this work demonstrates the feasibility of efficiently encoding information in SSVEPs through high-density frequency division multiplexing, involving hundreds of frequencies. The capability to transmit complete images from the computer to the brain/EEG read-out within a short timeframe is also illustrated. High-density frequency multiplexing enables the implementation of a photonic neural network that leverages SSVEPs for performing simple classification tasks, showcasing promising scalability through serial brain connectivity. This research opens innovative pathways in neural interfacing, with implications for assistive technologies and cognitive enhancement, significantly advancing human-machine interaction.
Lastly, the concept of SSVEPs is extended to multi-frequency light modulation, relying on the broadband scenario of the phenomenological brain model. The research demonstrates the brain’s ability to support the SSVEP read-out transmitting image. When the bandwidth spans more than an octave, the higher harmonics and nonlinear mixing between signal pairs overlap with the fundamental harmonics, creating a highly complex EEG signal. By utilizing a DNN trained on synthetic data, it is feasible to retrieve the original input signal, which can be employed to reconstruct images with each pixel encoded at a distinct single frequency. This approach facilitates precise image transmission, with each pixel encoded at a unique frequency. The BCI developed in this thesis enables multi-channel data transmission, and networked interfaces, and has potential applications in diagnostics, assistive technologies, and cognitive enhancement tools
Threat modelling technique for GDPR compliance based on logical reasoning
Get PDFData-driven applications and services are increasingly being deployed across various sectors, where they collect, aggregate, and process vast amounts of personal data from diverse sources on centralized servers. Consequently, safeguarding the privacy and security of this data is crucial. Since May 2018, the EU/UK’s General Data Protection Regulation (GDPR) has necessitated sophisticated compliance models. Current threat modeling techniques, however, do not adequately address GDPR compliance, particularly in complex systems where personal data is collected, processed, manipulated, and shared with third parties. This thesis proposes a comprehensive solution to develop a threat modeling technique that addresses and mitigates non-compliance threats by integrating GDPR requirements with existing security and privacy modeling techniques, namely STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege) and LINDDUN (Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance). The proposed technique in this thesis introduces a new data flow diagram aligned with GDPR principles, develops a knowledge base for non-compliance threats, and employs an inference engine to reason about these threats using the developed knowledge base. Additionally, this thesis presents a practical solution for modeling GDPR compliance using Defeasible Logic Programming (DeLP), enhancing the robustness and reasoning capabilities of compliance models in real-world scenarios. To address the challenges of undecided outputs in logical reasoning, this work incorporates explicit priorities for conflicting rules and suggests related knowledge for queries in an incomplete knowledge base. Furthermore, the technique includes a threat mitigation mechanism that identifies reasons for non-compliance threats and recommends actions to mitigate them. This approach is demonstrated through case studies on Telehealth Services and Fitbit (i.e., health tracking devices), focusing on addressing non-compliance threats and resolving UNDECIDED query results. Finally, the complexity of the defeasible reasoning mechanism is analyzed, and its performance is compared across different query outcomes, namely "YES/NO/UNDECIDED," based on vertical and horizontal complexities. The findings indicate that DeLP offers a flexible and dynamic framework suitable for implementing GDPR in real-world settings, making a significant contribution to the fields of legal reasoning and compliance modeling. Additionally, our findings show that the inference engine efficiently identifies non-compliance threats, handles UNDECIDED query results, and suggests appropriate threat mitigation measures
The role of Piezo1 in transducing matrix viscoelasticity
No full textAbstract not currently available
Biochemical characterization of the Parkinson’s disease-associated deubiquitylase USP30 using its physiological substrates
Get PDFMitochondria are essential for eukaryotic life, existing under tightly regulated control mechanisms. Clearance of damaged mitochondria (mitophagy) is a crucial part of mitochondrial homeostasis and relies on the ubiquitination of proteins on damaged mitochondria, which leads to degradation and removal of the damaged organelle. Crucially, dysregulation of mitophagy is among the leading causes of diverse neurodegenerative disorders, including Parkinson’s Disease (PD).
Several deubiquitinating enzymes (DUBs) have gained attention due to their ability to counteract ubiquitination-dependent mitophagy. USP30 is a DUB that emerged as a potential therapeutic target for PD due to its unique position within a mitophagy signalling cascade, whereby USP30 antagonises the heightened mitophagic flux that is common in hereditary forms of PD. Hence, considerable effort has been invested in developing USP30 inhibitors. However, this has been challenging because USP30 substrate recognition is generally poorly understood and there is a dearth of published USP30-inhibitor complex structures available.
By producing a physiologically relevant USP30 substrate, this project aims to develop an in vitro enzyme assay to understand USP30 substrate recognition, as well as examine the inhibition of USP30 by the new sulphonamide derivative inhibitors: MF-094 and Compound 39. This information can also be used to guide future structural analysis of USP30 in complex with one of its physiologically relevant substrates in the presence or absence of available inhibitors for the development of a crystal system from which to develop and design new inhibitors
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