21684 research outputs found
Sort by
Once an urban gull, always an urban gull? Migrating lesser black-backed gulls vary in whether or not they niche-track
No full textAbstract not currently available
Precision medicine based treatment strategies in Inflammatory Bowel Disease with a focus on Therapeutic Drug Monitoring
Get PDFBackground: The treatment landscape in Inflammatory Bowel Disease (IBD) has expanded rapidly in the last decade. Despite this, the rates of treatment failure for patients with the condition remains high. Precision medicine has been suggested as a strategy to improve patient care, by personalising treatment strategies to the individual and their disease, aiming to meet an agreed set of treatment targets. Therapeutic drug monitoring (TDM) for adalimumab and infliximab, measuring of serum drug levels and anti-drug antibodies, has emerged as a tool to aid precision medicine, but high quality research to support it is lacking. Traditionally TDM was performed reactively when a patient lost response to their treatment, but evidence for proactive monitoring when a patient is well to keep them in remission has emerged. The role of TDM at specific stages in treatment, for example, induction or maintenance of treatment, is also debated. The aim of this work was to explore precision medicine strategies for IBD, focusing on TDM strategies.
Methods: A structured literature search was conducted reviewing current TDM literature in the field. The Scottish TDM service was used to inform projects looking at uptake of TDM in Scotland and when to perform testing. The SERAFINA study evaluated serum and faecal infliximab TDM during the most severe stages of disease. Finally the EVALTDM was a large population based data study reviewing the impact of proactive TDM, reactive TDM and no TDM on clinical outcomes.
Results: A large quantity of TDM focused research exists evaluating its use in IBD, but the quality of the literature remains variable. In particular, the field lacks large, well powered, prospective studies. Despite this, TDM has been enthusiastically embraced by physicians in Scotland. The SERAFINA study suggested that serum TDM is less helpful in the most severe stages of disease, but that faecal infliximab may prove to be a useful biomarker. It also suggested that further work to review earlier accelerated dosing is required. The EVAL-TDM is one of the largest datasets in the field and demonstrated that proactive TDM is superior to reactive TDM to improve clinical outcomes for IBD patients.
Conclusion: Precision medicine is necessary to improve the outcomes of patients with IBD. At present TDM, and in particular proactive TDM, is the best tool at our disposal to aid precision medicine in IBD
Spatio-temporal areal data modelling: COVID-19 applications and outlier detection for big data
Get PDFThe COVID-19 pandemic has been the greatest challenge to global public health in the 21st century. The novel virus demanded scientific progress in several fields of research, from medical innovations to the development of political strategies that aimed to contain the spreading of the virus and protect the most vulnerable, often assisted by statistical analyses. The work presented in this thesis is a timely analysis of important public health aspects of COVID-19 in the UK, detecting overall trends and patterns in mortality risk after the three national lockdowns in England and identifying differences in COVID-19 vaccine attrition rates for the second and third doses by age group, sex, and council area in Scotland. The presented statistical analyses fit spatio-temporal areal data using generalised linear mixed effects models in a Bayesian hierarchical framework, where the correlated spatial random effects are assigned prior distributions from the class of conditional autoregressive (CAR) models. These models typically induce spatial smoothness in the inferred disease risk or prevalence surface, where strength in the estimation is borrowed from neighbouring observations, according to some neighbourhood structure. The spatial smoothness assumption is often accredited to Waldo R. Tobler, who said, “Everything is related to everything else, but near things are more related than distant things”. However, the presented COVID-19 analyses suggest that the spatial smoothness assumption might not always hold for all areas. Hence, this thesis proposes a novel relative density-based outlier score (RDOS) for identifying potential singleton spatial outliers that violate the spatial smoothness assumption and a novel modified spatial smoothing model to remove the potential outliers’ impact on the estimated disease prevalence surface. The following summarises the key findings from this thesis. The study on the impact of national lockdowns on COVID-19 mortality risk in England shows that the risks increased drastically before the implementation of lockdowns 1 and 3 and decreased to pre-lockdown levels after ten and six weeks, respectively. Further, the study identifies areas with a higher peak risk during these lockdowns, detecting an urban/rural divide for lockdown 1 and an association between higher risk and the early spreading of the Alpha variant during lockdown 3. The study on COVID-19 vaccine attrition rates in Scotland identifies a strong association between age and attrition rates, where the odds in favour of attrition decrease smoothly with increasing age. The odds in favour of attrition tend to be overall higher for males than females and higher in the second transition (from doses 2 to 3) than the first (from doses 1 to 2). Lastly, a simulation study shows that the novel singleton spatial outlier detection method for areal data produces much better detection results than the commonly used local Moran’s I statistic. Similarly, the modified smoothing model is shown to produce overall better prevalence estimates than a conventional smoothing model when the number of outliers is large or at least some outliers have a large magnitude, even when the identified outlier sets are sub-optimal. The proposed methods are combined in a two-stage modelling approach and applied in a motivating study on asthma prevalence at the lower super output area (LSOA) level in England, where potential singleton spatial outliers are identified, and the estimated risk surface obtained from the modified smoothing model is compared to that of a conventional smoothing model. The comparison shows that the prevalence estimates of the identified outliers and their neighbouring inliers differ noticeably between the two models, highlighting the importance of considering such potential singleton spatial outliers in the analysis of areal unit data
Byronising in Greece: travel and the poetic imagination in three Greek journeys, 1840-1924
No full textAbstract not currently available
Imani na Maisha (Faith and Life): Faith Encounters with four individuals from a rural Pentecostal community in Tanzania
Get PDFThis Thesis explores how four individuals in a rural Pentecostal community in Tanzania recounted their journeys to faith, their understanding of God, their own calling in the church, approach to mission and how they perceive the challenges around them, including the Covid-19 pandemic. It does so by drawing upon the accounts of my encounter with them through ‘hanging out’, that is through ‘intensive informal and interpersonal interactions’ (Rodgers 2004, page 48) as we shared faith together and journeyed through the Covid-19 Pandemic together in 2020-21. My ‘hanging out’ approach (set out further in Chapter Two), builds on the Pentecostal practice in Tanzania of believers sharing with others, in day-to-day conversation, their experiences of God. My research aims to enrich our understanding of Pentecostal expression in Tanzania.
Much of the literature on Tanzanian Pentecostalism focusses on what is described as Pentecostal churches and charismatic movements established in the 1980s and 1990s and situated in urban areas such as Dar es Salaam. It tends to explore, in the context of rural to urban migration and social and economic change, the ‘prosperity gospel’, how expressions of urban Pentecostalism resonate with older concerns around witchcraft and how other Christian denominations such as the Evangelical Lutheran Church and Roman Catholic Church have responded to Pentecostal and charismatic renewal movements.
Most Tanzanians, however, live in rural areas and Pentecostal churches there have been active since the 1950s. The research is also in a setting, community, and church (the Free Pentecostal Church of Tanzania) which have been relatively under researched. The research had been planned to take place in person, in Tanzania, over the summer of 2020 but as the Covid-19 pandemic prohibited travel, the research took place over 2020 and 2021 by means of telephone calls, WhatsApp calls and correspondence.
Key features of my encounters included interlocutors articulating a Christian as opposed to uniquely Pentecostal identity; the church as the locus of community; and continuous, persistent prayer as the foundation for their Christian life. God’s presence was felt through their encounter with the Holy Spirit and in their experience of God’s healing. Giving to the church brought blessings but these were not necessarily financial and the routes out of poverty were hard work and entrepreneurship. The interlocutors had ambivalent views about Covid-19 and sickness – it had demonic as well as viral causes. Covid-19 was a sign of the end of the age, but environmental changes were not
Prioritisation algorithms for data acquisition in liquid chromatography mass spectrometry
Get PDFLiquid chomatography mass spectrometry (LC-MS/MS) is a powerful analytical platform frequently used to identify the composition of biological samples. For example, LC-MS/MS is one of the leading measurement technologies within metabolomics, which has applications in discovering disease biomarkers and novel drugs, in ecology and environmental science and in forensics and toxicology, among many others.
The goal of an untargeted LC-MS/MS experiment is to discover as many unique analytes in the sample as possible in order to generate hypotheses relevant to the experiment’s goals. One of the most powerful tools in annotating analytes is the fragmentation spectra produced by tandem mass spectrometry, which are a kind of “molecular fingerprint” which can be matched against databases. However, collection of unambiguous fragmentation spectra requires individually targeting analytes for acquisition. As a consequence, resources (tandem mass spectrometry scans) must be efficiently allocated in order to collect as many fragmentation spectra as possible at the highest possible quality. The goal is to target as many possible “peaks” at the correct acquisition time to maximise their “intensity” (a proxy for acquisition quality).
To address this important resource allocation problem, this thesis presents several new “fragmentation strategies”. Firstly we present TopNEXt, a framework for Data-Dependent Acquisition (DDA) strategies which utilises area and intensity comparisons between LC-MS/MS runs to develop advanced DDA strategies. We show that the strategy using all of these features, Intensity Non-Overlap is highly effective and is able to acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity.
We then present a “pre-scheduled” method which uses a maximum bipartite matching algorithm to plan an acquisition in advance. We extend an existing technique to map the LC-MS/MS acquisition problem to an instance of the maximum bipartite matching problem. Our extensions include extending the technique to plan multiple runs and samples as a set, solving a weighted version of the problem to optimise acquisition times and redundantly assigning unassigned scans to improve the robustness of the method. We show that this schedule can theoretically obtain completely comprehensive coverage of a sample in a low number of injections compared to other methods. However, we also investigate the trade-off between DDA and pre-scheduled methods by testing this pre-scheduled method in a situation significantly different than the one which it has planned for (which may happen frequently in reality). In this scenario we show that it still has performance comparable to the state-of-the-art, but only with the improvements we have made to the technique. Finally, we reflect on the common elements that make our techniques successful: namely, accounting for acquisition time and quality, and judicious use of redundancy to improve their robustness
All that glitters: What was the role of silver in Roman Iron Age Scotland and in the development of early medieval polities?
Get PDFThis thesis will explore how emerging kingdoms in early medieval Scotland utilised Roman silver as a reusable resource to promote their ideological ambitions and power until the Viking Age. This paper will examine the arrival of Roman silver from troop payments, supplying the Roman legions and payments made, firstly, in denarii to local communities and later in silver plate.
Local communities and growing kingdoms transformed Roman silver from coin and hacksilver into valued objects that reflected their own needs and ambitions. Roman silver was first transformed into massive silver chains, indicating the large quantities of silver available to indigenous communities, while resting in the hands of limited elites. The chains had extended use-life, evidenced by the replacement of clasps in at least two cases. There was also a transformation of silver into other esteemed objects, including brooches.
The development of the brooch styles, ornamentation and decoration offers insight into the developing communities of Early Medieval Scotland. Styles merge over time becoming influenced by other communities including the Anglo-Saxon world and the growing impact of Christianity. Prior to the Viking Age there is evidence of the shortage of silver available to local people while they maintained a desire to craft objects that displayed status and position by debasing silver with other alloys
Bayesian hierarchical modelling frameworks for correcting reporting delays in disease surveillance
Get PDFAccurate and timely surveillance of infectious diseases is critical for effective public health responses. Up-to-date quantitative indicators for the prevalence of diseases in a population, e.g. case or death counts, can provide early warning of outbreaks, empowering public health bodies to develop targeted interventions, allocate limited resources, and communicate risks to influence public behaviour. However, data collection for such indicators often suffers from delays, for example due to administrative protocols, testing processes, or resource limitations. These delays mean that available information on outbreaks lags behind reality; delays also vary randomly and systematically in space and time, making it difficult to confidently detect disease outbreaks and provide timely, effective interventions.
From a statistical perspective, correcting delayed reporting is a compositional count data prediction problem. Compositional data, take the form of parts of some whole, in this case a set of non-negative counts reported after each delay that sum to a total count, such as the number of disease cases. In a nowcasting setting, the total count is not yet observed and we aim to predict it given the observed parts of the total for delays that have already elapsed. Applying appropriate statistical methodology for count data with this structure can yield models that learn about the properties of the delay distribution, to provide nowcasting predictions. At the same time, this means that methodological advancements in the field of correcting delayed reporting can potentially lead to innovation in the general field of modelling compositional counts, relevant to a wide range of research fields beyond disease epidemics.
Research carried out prior to this project developed a general multivariate Bayesian hierarchical framework, based on the Generalized-Dirichlet-Multinomial (GDM) family of distributions, that can flexibly account for the different sources of variability in count data suffering from delayed reporting. The framework was developed into a model for a time series of an individual disease in one geographic region. The model demonstrated theoretical and practical potential for the GDM method to provide more accurate and precise predictions, compared to alternative methods.
The work presented here is underpinned by two broad aims: to make the GDM approach more practical for real-time public health applications and to develop novel extensions to the methodology to account for more complex data challenges and features. For the first aim, we developed improvements in computational efficiency and in streamlining applications to real data. Then, we demonstrated the efficacy of the improved GDM model as a solution for nowcasting COVID-19 hospital deaths in different regions of England. Through an unprecedented rolling prediction experiment, we assessed the performance of the GDM against a cohort of competing methods representing the current state-of-the-art, finding that predictions from the GDM were the most accurate and most precise.
For the second aim, our work was informed by a collaboration with experts at Brazil’s leading public health institute, the Oswaldo Cruz Foundation (Fiocruz). This offered unique insights into the specific data challenges affecting Brazil’s current operational disease warning systems, while also supporting our understanding of more general issues in correcting delayed reporting. One component of this work was motivated by the challenge of nowcasting COVID-positive severe acute respiratory illness (SARI) cases, as an indicator of COVID outbreaks in Brazil. Here, we developed a joint modelling framework for nowcasting total SARI and COVID-positive SARI cases. The framework addressed the novel challenge of correcting delayed reporting of disease counts where information on the length of the reporting delay was not recorded. Applied to data spanning the whole of the Brazil, our approach allowed for predictions of COVID-positive cases, which suffer from this data challenge, through leveraging the more timely and complete data for the total SARI cases. A rolling prediction experiment demonstrated improvements in predictive performance from incorporating links between overall SARI incidence and COVID-positive rates, as well as from accounting for patient age distributions.
The last major piece of work of the thesis explored potential effects of the level of a disease in the population on the severity of reporting delays. We investigated this issue in data for different diseases, offering new insights into potential capacity limitations or elasticity within the respective reporting processes. We propose a framework that flexibly models the effect of the prevalence of the disease on the delay distribution. Through a simulation study aiming to imitate real data, we demonstrated the framework’s ability to disentangle the various sources of variability in the data, including the prevalence-delay interaction, and improve overall prediction accuracy. Since the existing statistical and biostatistical literature on correcting delayed reporting does not assume an explicit effect of disease prevalence on reporting delays, this work could represent the first step for a new paradigm of nowcasting frameworks.
Overall, the work in this thesis provides substantial methodological advancements in correcting reporting delays for disease surveillance, taking the initial proof-of-concept of the GDM framework and greatly enhancing its practicality and versatility. All aspects of the work were driven by and demonstrated using real-world data challenges, employing realistic prediction experiments to develop a robust evidence base for the potential of advanced methods based on the GDM framework to enhance public health responses and policy decisions
Selection techniques for optimal meta-analysis of beyond standard model physics
Get PDFThis thesis addresses the development of selection techniques tailored for optimising meta-analyses in Beyond Standard Model (BSM) physics, focusing on three key objectives: (i) identifying a minimally overlapping set of results, (ii) implementing these selections for model exclusion, and (iii) extending them to anomaly detection applications. Central to this study are the hdfs and whdfs algorithms—graph-based methods that systematically address the combinatorial challenge of selecting optimal result combinations.
In the context of model exclusion, the thesis applies the whdfs algorithm within the taco project to optimise combinations of analyses by estimating overlaps in signal regions (SRs). Using simplified model spectra looking at susy-like processes, the project demonstrates a measurable increase in exclusion. The proto-models project, an extension to the taco project and previous work by the SModelS collaboration, focused on anomaly detection, adapting the whdfs algorithm to construct a test statistic for identifying significant deviations from the Standard Model (SM) hypothesis. Through iterative improvements in the algorithms’ weighting mechanisms, the study presents a self-regulating test statistic for the measure of significance.
The findings highlight the dual utility of the hdfs and whdfs algorithms across domains, from collider-based physics applications to machine learning contexts. This work thus contributes a computationally robust framework that enhances reinterpretation capacity in particle physics and supports further integration with reinterpretation tools like SModelS, MadAnalysis 5 Rivet and Contur. The research underscores the increasing importance of efficient, adaptable algorithms for data-intensive BSM analyses. It lays the groundwork for future reinterpretation methodologies necessary for maximising data utility in HL-LHC and related high-energy physics experiments
Timed bigraphs for formal verification of sensor network routing protocols
Get PDFGiven that more end-user applications depend on the Internet of things (IoT) technology, which relies heavily on wireless sensor networks (WSNs), it is essential that the routing protocols underpinning these applications are reliable. Using formal methods for reasoning on protocol specifications is an established technique but, due to their perceived difficulty and mathematical nature, they receive limited use in practice. This thesis proposes an approach based on Milner’s bigraphs – a flexible diagrammatic modelling language – that allows developers to “draw” the protocol updates as a way to increase the use of formal methods in protocol design. Bigraphical reactive systems (BRSs) are a graph-rewriting formalism describing systems evolving in two dimensions: spatially, e.g. a person in a room, and non-spatially, e.g. mobile phones communicating regardless of location. To show bigraphs in action, this thesis models part of the routing protocol for low-power and lossy networks (RPL), popular in wireless sensor networks. The model is implemented using the BigraphER toolkit and verified with the PRISM model checker.
Simulation, on the other hand, is a common approach in the field of protocol analysis and validation. However, it does not extensively verify protocols in the same way as formal methods do. This thesis experimentally compares the two approaches, the results of which show that analysing the bigraph model often finds more valid routes than simulation while providing comparable performance. The bigraphs model is open to extension with less implementation effort than simulation, which is shown by adding more features to the initial model. Bigraphs seem to be a promising approach for protocol design; this is the first step in promoting their use.
Despite the use of bigraphs in domains that include communication protocols, agent programming, biology, and security, there is no support for real-time systems. Therefore, this thesis extends BRSs to support real-time systems by using a modelling approach that employs multiple perspectives to represent digital clocks. It uses Action BRSs, a recent extension of BRSs, where the resulting transition system is a Markov decision process (MDP). This allows a natural representation of the choices in each system state: to either allow time to pass or perform a specific action. The effectiveness of this approach is demonstrated using examples, including extending the RPL initial model with timed aspects using the BigraphER toolkit
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇