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A Random Forest Approach to Understanding CRISPR-Cas Associations in Bacteria
Get PDFCRISPR-Cas systems are a crucial and intriguing defence mechanism found in bacteria and archaea. This defence mechanism is able to adapt and defend against attacks from Mobile Genetic Elements (MGEs). This mechanism also has many uses outside of genomic defence. For example, certain types of CRISPR-Cas proteins allow for modification of eukaryotic genomes in vivo. Currently, there are applications and algorithms capable of finding CRISPR-Cas types and the associated arrays, however, the idea of predicting whether a genome might contain a CRISPR-Cas locus, based purely on the background genome content offers a faster query time. To test whether the presence of CRISPR-Cas systems could be predicted from the background genome, a Random Forest algorithm was employed using a large data set - a bacterial pangenome containing 9,689 genomes. To annotate this pangenome with ’CRISPR’ identifiers, the annotation tool Bakta was used, allowing for the use of custom scripts to find the relevant information needed from the annotated genomes. The algorithm was shown to have an accuracy of 0.89, and an AUC-ROC score of 0.96. These results imply a strong ability to classify the predictions correctly, based on background genome content. The algorithm calculated the ’feature importance’ of all genes that were present in the pangenome; the gene of highest importance was ’pbp4b’ followed closely by ’csy3’ (a positive control variable). The ten genes that had the highest feature importance all had a statistically significant association with CRISPR-Cas systems when evaluated using chi-squared tests. The algorithm was capable of predicting CRISPR-Cas systems in γ-proteobacteria and offers potential for research candidates when investigating CRISPR-Cas associations. This approach could be used to predict CRISPR-Cas more broadly across prokaryotic life, upon data availability
Self-assembling peptide-based materials for 3D-printed tissue engineering constructs
Get PDFThere is a growing unmet global need for the transplant and repair of tissues and organs in humans. In some cases, such as those for traumatic spinal cord injury, current therapies focus on relieving pain and improving quality of life, and the chance of a full recovery of motor function is slim. Beyond personal impact, the inability to repair or replace tissues and organs has a profound economic impact. In this thesis, biological self-assembly is presented as a pathway towards future biomedical innovation in tissue engineering, regenerative medicine, and tissue modelling. The growth and development of biological systems is guided and underpinned by non-covalent interactions, yielding shapes, forms, and functions which combine to facilitate life. By exploiting the fundamentals of biological self-assembly, it is possible to create materials which closely recreate cellular milieus, mimicking the components and properties of the ECM. Here, peptide-based supramolecules are adapted for use in 3D printing systems, yielding precision control from the molecular to macroscopic scale. A heparin-mimetic peptide amphiphile is used to exploit the paracrine effect and is demonstrated to have the potential to improve neurite outgrowth in damaged neural cells. This system is then integrated into additive manufacturing by means of two-photon polymerisation, being combined with a photopolymerisable gelatin. Additive manufacturing is then used to aid the fabrication of peptide-based co-assembled supramolecular tubular membranes with a thickness in the range of native basement membranes. These membranes are demonstrated to support endothelial cell adhesion for up to 7 days post-seeding. By demonstrating the incorporation of additive manufacturing with molecular self-assembly in this manner, it is hoped that this work contributes to the growing body of research for tissue engineering and regenerative medicine which utilise peptide-based supramolecules as their foundation
Evaluating the antiviral role of thapsigargin against respiratory syncytial virus and influenza A virus
Get PDFRespiratory syncytial virus (RSV) and influenza A virus (IAV) represent significant global health threats, exacerbated by their ability to co-infect hosts and evade immune responses. Current antiviral treatments are limited by specificity, resistance development, and variable efficacy in co-infection scenarios. This thesis investigates the potential of thapsigargin (TG) as a broad-spectrum antiviral agent targeting RSV, IAV, and their co-infections. TG demonstrated potent inhibition of viral replication across diverse cell models, including immortalised lines and
primary human bronchial epithelial cells, with reductions in viral titres exceeding 85% at non-cytotoxic concentrations.
Mechanistically, TG disrupts calcium homeostasis by inhibiting the SERCA pump, triggering endoplasmic reticulum stress and activating the unfolded protein response (UPR). This state induces antiviral pathways, limiting viral protein synthesis and enhancing innate immune signalling. Notably, TG established a prolonged antiviral effect,
maintaining efficacy for 48 hours post-treatment. In co-infection models, TG significantly reduced viral replication for both RSV and IAV.
TG’s host-centric approach offers advantages over conventional
antivirals, reducing the risk of resistance while providing broad-spectrum
efficacy. However, challenges remain, including variability in response
across cell types, potential off-target effects, and the need for extensive
in vivo validation. Future directions include optimising TG delivery methods, exploring combination therapies, and expanding its evaluation
to other RNA viruses. This work establishes TG as a promising antiviral
with implications for pandemic preparedness and respiratory disease
management, highlighting its potential to redefine the therapeutic
landscape against emerging viral threats
Improving vaccination programmes and experiences for people with dementia and their carers in the United Kingdom
No full textIntroduction
Vaccine hesitancy poses a significant public health challenge. Despite the critical importance of vaccination for protecting People with Dementia (PwD) and their carers from infectious diseases such as COVID-19 and influenza, factors influencing their vaccination decisions remain understudied.
This thesis aimed to investigate vaccination experiences in PwD and their carers to: (1) identify factors affecting vaccine thoughts and behaviours; (2) compare influenza and COVID-19 vaccination experiences; and (3) develop a framework to inform best practice guidelines for future vaccination programmes to reduce vaccine hesitancy and enhance vaccination rates in the United Kingdom.
Methods
The research employed a comprehensive mixed-methods approach, including: an umbrella review of COVID-19 vaccine hesitancy factors (n=31); a systematic review of UK interventions to improve all kinds of vaccine uptake and reduce vaccine hesitancy (n=50); qualitative interviews with people with young-onset dementia and their carers (n=50); a mixed-method online survey of PwD and their carers (n=551), and a Patient Public Involvement (PPI) consultation workshop (n=7) to evaluate findings and develop a framework.
Results
This thesis identified 79 factors associated with vaccine hesitancy in the general population, revealing a significant gap in understanding of dementia-specific considerations. Multidimensional interventions combining organisational, recipient-oriented, and provider-oriented approaches were found to be the most effective in reducing vaccine hesitancy and increasing vaccine uptake. Dementia-specific factors affecting vaccination included physical and emotional challenges during vaccination, additional carer burden, carers' perception of necessity, and concerns about vaccines worsening dementia symptoms. Despite these barriers, vaccination rates among PwD and carers were higher than in the general UK population.
From these findings, a comprehensive framework was developed with six domains for improving vaccination programmes for PwD and their carers: (1) developing structures and procedures; (2) enhancing communication and engagement with PwD and their carers; (3) improving access; (4) knowledge dissemination; (5) developing competencies and attitudes; and (6) designing public health-oriented vaccination programmes.
Conclusion
This thesis demonstrates that addressing vaccine hesitancy in PwD and their carers requires multidisciplinary and individualised approaches. The developed framework takes the first step towards having evidence-based guidance to improve vaccination experiences for PwD and their carers, ensuring equitable and high-quality care for PwD and their carers while providing a foundation for future targeted interventions in vulnerable populations
The genetic adaptation of indigenous chickens to harsh environments
Get PDFIndigenous chicken populations are recognized for their strong local adaptation, limited productivity, and substantial genetic diversity. Unfortunately, this diversity is under threat, highlighting the urgent need for conservation strategies to safeguard these breeds. These birds are typically resilient to diseases and are well-adapted to the demands of smallholder farmers in harsh and resource-limited environments. As such, they play a crucial role in promoting food security, particularly in low- and medium-income countries.
Understanding the genetics of environmental adaptations is key to improving breeding programs and guiding conservation efforts, both crucial in mitigating the effects of climate change on agriculture and livestock diversity. Indigenous chickens of the Arabian Peninsula have uniquely adapted to desert ecologies and scavenging conditions. However, their genetic adaptation mechanisms remain poorly understood. Here, genome analyses, including signatures of positive selection and copy number variation analyses, were applied to identify genetic responses to key environmental stressors and compare them with populations from different environmental conditions.
Following a brief literature review in Chapter 1 (Setting the Scene), the thesis comprises three results chapters. Chapter 2 explore the diversity and population structure of thirteen indigenous and two commercial chicken populations from diverse climatic zones, representing both cold and warm ecological conditions. About 24.9 million SNPs were characterized in the populations of which 38.26% were novel. Our results clearly demonstrate the clustering of all populations according to their geographic region of origin, with minimal genetic differentiation within the populations. Admixture analysis shows evidence of shared ancestry among all Arabian Peninsula indigenous populations. Our findings suggest that the Arabian Peninsula populations represent a distinct gene pool with a significant genomic diversity compared to other geographic regions.
In Chapter 3, populations were ranked accordingly to their region of origin. Signatures of positive selection for each population and pair population (warm and cold locations) were investigated using ZHp, iHS, ZFST and XP-EHH methods. Strong candidate selected regions identified overlapping genes that have highly relevant functions for adaptation to thermotolerance. These are involved in energy balance and metabolism (SUGCT, HECW1, and MMADHC), cells apoptosis (APP, SRBD1, NTN1, PUF60, SLC26A8, DAP, and SUGCT), angiogenesis (RYR2, and LDB2), skin protection to solar radiation (FZD10, BCO2, WNT5B, COL6A2 and SIRT1), and immunity (CD300LG, KIAA1549L, and IL22RA1) as well as growth (NELL1, LBFABP, and MYOM3).
In Chapter 4, using whole-genome sequences, we examine deletion and duplication variants in twelve chicken populations adapted to harsh environments. After applying a hard filtering threshold for each population, 1,391 unique deletions and duplications were identified. Among these, 442 CNVRs overlapped across at least two populations, comprising 372 deletions, 68 duplications, and 3 mixed deletion-duplication events. Through gene annotation and gene ontology analyses of the identified CNVRs, specific genes and biological processes related to nervous system function (TAFA5, HMX2, LYSMD2, FADS6, NSF, ABHD14A, SEMA3B, and SEMA3F), growth (LBFABP, MYOM3, SNX29, XKR4, CPPED1, LDB2, GK2, HAO2, RHOA, ECM2, SLC25A30, AAMDC, and TCEB3), and immunity (CD300LG, KIAA1549L, and IL22RA1) were identified. Five genes (LDB2, CD300LG, KIAA1549L, IL22RA1, and MYOM3) were detected in both the signature of selection and copy number variation chapters.
In summary, our study provides new insights into the genetic diversity and population structure of indigenous chickens from the Arabian Peninsula. We identified several key candidate genes likely under selection for adaptation, particularly linked to thermotolerance in response to the harsh desert environment. Additionally, numerous critical copy number variation regions (CNVRs) associated with thermotolerance adaptation were uncovered. These results will help guide the development of targeted breeding programs and poultry management strategies aimed at reducing heat stress improving disease resistance and enhancing overall productivity.
To the best of our knowledge, this is the first comprehensive analysis utilizing whole-genome resequencing to identify diverse adaptive candidate genes in indigenous chickens from the Arabian Peninsula. This work represents a significant advancement in promoting breeding programs aimed at the sustainable conservation of the genetic resources of these chicken populations
Epidemiology of chronic shoulder pain in the United Kingdom
Get PDFBackground
Chronic shoulder pain (CSP) is a common musculoskeletal condition that can significantly affect a person’s ability to work, sleep, and perform daily activities. It affects between 5% and 47% of the adult population annually worldwide. In the United Kingdom (UK), about 2.4% of adult people aged between 18 and 60 years old consulted their general practitioners (GPs) for CSP in 2006. However, whether the occurrence of CSP has changed in the past 20 years in the UK, whether it varies between geographical regions, and its associated comorbidities and consequences remain largely unknown.
Objectives
This research aimed to answer five objectives
[1] to systematically review the existing literature on the prevalence and incidence of CSP and its related risk factors, and associated comorbidities.
[2] to determine the current prevalence and incidence of CSP in the UK (2019).
[3] to determine the trends of prevalence and incidence of CSP in the UK over the past twenty years (2000 - 2020).
[4] to examine potential risk factors, and comorbidities that precede the diagnosis of CSP.
[5] to explore the outcomes of CSP including associated comorbidities, all-cause mortality, consultations and hospitalisations.
Methods
[1] a systematic review and meta-analysis were performed to summarise the literature on the prevalence and incidence of CSP and the associated risk factors in people aged 40 years and over.
The nationally representative UK primary care database, the Clinical Practice Research Datalink (CPRD) Aurum was used to determine:
[2&3] the cross-sectional prevalence, incidence, and trend of CSP in the UK
[4] risk factors and comorbidities occurring before the diagnosis of CSP using a case-control study design
[5] outcomes occurring after CSP diagnosis using a cohort study design.
Results
A total of 29 studies from 19 countries were identified in the systematic review. Of which, 20 had a high quality, and nine had moderate quality. The pooled prevalence of CSP in the included studies was 29% and higher in specific populations such as people with physically demanding occupations (36% prevalence), and people with diabetes (35% prevalence). The incidence of CSP was higher in females and in those aged over 40 years. In addition to age and sex, CSP was associated with smoking, lower educational level, manual labour, have been reported to associate with CSP. Also, CSP was found to be associated with a number of comorbidities, including arthritis, diabetes, angina, and other sites of musculoskeletal (MSK) pain.
In the UK, the prevalence of CSP in people aged 18 and above in 2019 was found to be 1.9 % and the incidence was 1.2 per 1000 person-years. The prevalence and incidence were more common in females than males and increased with age, especially after age 40 years. The prevalence was found to increase during the study period from 0.42% in 2000 to 1.83 in 2020, whereas the incidence increased significantly from 0.88 in 2000 to 2.00 per 1000-person year in 2011, then decreased afterwards. The significant decline of the incidence in 2020 resulted from the reduced consultations during the COVID-19 pandemic and lockdown. Smoking, low socioeconomic status, Asian and mixed ethnicity, and a high body mass index increased the risk of CSP, while current alcohol consumers had a significantly lower risk of having CSP.
People with CSP were more likely to have comorbidities prior to and post the diagnosis of CSP compared to the control group. Retrospectively, people with other MSK conditions (aOR 1.71, 95% CI 1.68 to1.75), osteoarthritis (OA) (aOR 1.76, 95% CI 1.70 to1.82), diabetes (aOR 1.48, 95% CI 1.43 to1.53), fibromyalgia (aOR 1.40, 95% CI 1.32 to 1.48), and insomnia (aOR 1.63, 95% CI 1.58 to1.69) were more likely to have CSP.
Prospectively, people with CSP were more likely to develop other long-term conditions compared to the control group. Of the twenty-two comorbidities studied, significant associations were seen with eighteen conditions. The strongest associations found were with sarcopenia (HR 1.74, 95% CI 1.11 to 2.71), fibromyalgia (HR 1.71, 95% CI 1.62,1.81), osteoarthritis (HR 1.60, 95% CI 1.55 to 1.64), other MSK conditions (HR 1.61, 95% CI 1.58 to1.64), and insomnia (HR 1.55, 95% CI 1.48 to1.63). Following their diagnosis, people with CSP had three times higher risk of GP consultations, 44% higher risk of hospitalisations, and 6% higher risk of all-cause mortality than those without CSP.
Conclusion
Chronic shoulder pain (CSP) affects around 2% of adults in the UK. The prevalence of this condition in primary care increased gradually in the past 20 years whereas the incidence has increased until 2011 then decreased afterwards (reasons to be investigated). The findings demonstrated that age, sex, smoking, socioeconomic deprivation, Asian or mixed ethnicity and higher BMI were associated with an increased risk of CSP. Alcohol consumption was associated with a decreased risk of CSP. People with CSP had a higher burden of comorbidities, higher risk of mortality, and increased healthcare utilisations. This study has provided essential background information/evidence of CSP in the UK for policymakers to allocate resources, healthcare providers to optimally manage CSP, and researchers to undertake further research such as the causality between shoulder pain and individual comorbidities
Remote sensing and machine learning for the field-scale prediction of maturity and yield in vining pea (Pisum sativum L.)
No full textVining pea (Pisum sativum L.) is an important legume species that is cultivated in the UK as a staple vegetable for freezing and canning. Vining peas are harvested prior to physiological maturity, where quality attributes are the main determiner of optimal harvest date. A short harvest window means production is carefully managed. Since the mid-20th century, the primary method of maturity forecasting in vining peas has been the use of accumulated heat units (AHU), which are used to space sowings and estimate the order of harvests. Labour-intensive field sampling is employed in the days or weeks approaching harvest to ensure crops are harvested at the optimal time. Peas are highly sensitive to environmental conditions, and crops are prone to incurring waste as a result of bypassing due to accelerated maturation, or when higher than expected yields cannot be processed due to factory capacity constraints. The threats to global agriculture from climate change are well understood, but many crops, including vining peas, are still forecasted using techniques developed under past climatic conditions.
To understand the climatic changes that vining pea agriculture has experienced in the past decades and determine whether the AHU remains a reliable forecasting method, a long term analysis of historic Processors and Growers Research Organisation (PGRO) variety trials data was conducted. Evidence was found for a significant increase in maximum (+1.46 °C) and mean (+0.74 °C) temperatures at trial sites between 1985 and 2022. There was additional evidence for declining growth period lengths of standard early vining pea varieties as a result, with total growing days decreasing by over 3 weeks over the same period. Concurrently, the number of AHU building up between sowing and harvest of these standard varieties was found to have decreased, due to the decrease in the number of growing days, and opportunity for heat units to accumulate. The applicability of using 11.6 AHU as a standard daily estimate, based on an average daily temperature at harvest of 16 °C and a base temperature of 4.4 °C, was tested. Analysis of tenderometer reading (TR) curves of four standard varieties between 2015 and 2022 showed that using 11.6 AHU as a proxy for a day is still a valid assumption, but that basing estimates of harvest date on a fixed target AHU value at harvest was the main driver of a decline in predictive ability.
In a study to improve temperature-based harvest date forecasts, six different supervised machine learning models were tested for predictive ability, and a Cubist model was selected as the best performing algorithm. Using data from over 14000 crops, including agronomic data and meteorological variables from 2001-2022, a Cubist model was developed for estimation of harvest dates. An average mean absolute error (MAE) of 1.10 days was achieved, rising to 2.01 days across four recent individual years 2019-2022. Using a daily time step and forecasted weather data, the model was tested to simulate daily forecasting within-season, and predictions generated 5-15 days before harvest were not significantly different to those produced 2 days before harvest, which represents the current limit of accurate forecasts.
There is currently no standardised method for yield prediction beyond pod, node and plant counts. Six Sentinel-2 multispectral canopy reflectance-derived vegetation indices (VIs) were analysed for association with final yield at different dates. Correlation with yield was highest on the day prior to the date of full-flowering. Using area under the curve (AUC) analysis, it was determined that the window from 23 days prior to full-flowering to 11 days after was optimal for Sentinel-2 data acquisition to correlate most strongly with yield, whilst maximising data availability. Using agronomic and meteorological data for over 4000 crops between 2016 and 2022, and associated remotely-sensed Sentinel-2 data, a Cubist model was trained to predict yield with an average MAE of 0.61 t/ha. When validated on individual years 2021 and 2022, MAE was 1.05 t/ha and 0.81 t/ha, respectively.
The forecasting ability of the yield model was further tested in combination with the harvest date model, providing predicted harvest dates in place of observations. Results indicated only a slight drop in predictive performance, with an overall MAE of 0.63 t/ha, and MAE of 1.06 t/ha and 0.98 t/ha when tested in 2021 and 2022, respectively.
The ability to forecast harvest dates and yields of vining peas, and thereby implement a comprehensive within-season forecasting system has significant positive implications for reducing wastage and improving efficiency in the vining pea harvest process. The work in this thesis represents a novel innovation in vining pea forecasting, and has resulted in a first-in-the-world, commercially available online application available to processers and growers, which has the potential to significantly improve industry operations
Boundary line methodology for yield gap analysis of farm systems
Get PDFThe growing global food demand necessitates improvements in agricultural productivity to achieve global food security. This can be attained by closing the current yield gaps in cropping systems. The boundary line methodology has emerged as a popular tool for determination of yield gaps and has been widely used in many agronomic studies on yield analysis. However, there is no standard procedure for fitting boundary line models and there is a lack of exploratory analysis tools to ascertain the suitability of data for fitting a boundary line model. Furthermore, whereas the fitting of other types of statistical model is supported by the availability of functions in statistical software no such tool is available for applying boundary line analysis. In this thesis I advance the application of the boundary line methodology in yield gap analysis. First, I undertook a comprehensive review of the use of the boundary line methodology in yield gap analysis. This review revealed inconsistencies in application, particularly in the boundary line fitting procedures. Heuristic methods, though widely used, rely on subjective decisions such as the choice of bin width and often lack a statistical basis for assessing the suitability of the boundary model, underscoring the need for standardized and reproducible approaches. My research addresses these gaps by proposing an exploratory data analysis method using convex hull peels, which assesses whether datasets exhibit boundary-limited responses. This method improves the reliability of boundary line analysis by preventing misapplications to datasets that do not support boundary constraints. I also present a comparative evaluation of multiple boundary line fitting techniques, including binning, boundary line determination technique, quantile regression, and the censored bivariate normal model. Results showed that the censored bivariate normal model provides more stable estimates of critical values than heuristic methods, enhancing the precision of yield-limiting factor identification. However, the original model lacked the ability to accommodate categorical independent variables. This limitation was addressed by developing a framework that extends the censored model to handle categorical data. In this study, data size was found to significantly influence boundary line model fitting. In many cases, smaller datasets did not provide sufficient statistical evidence to support the fitted boundary models. A significant outcome of my research was the development of the BLA R package, which integrates multiple boundary line methodologies, exploratory tools (including those I developed), and interactive functions for enhanced usability. This open-source tool promotes transparency, reproducibility, and accessibility for researchers, aiding in the robust application of boundary line analysis in yield gap assessments. To test the useability of the BLA R package and to evaluate how users engage with various boundary line methods, I held stakeholder engagement workshops in Nairobi and Harare to elicit their opinions on preferred boundary line fitting methods. Although no specific method was favoured, participants emphasized the need for a more objective approach to fitting boundary line models in agronomic research. This research provides a critical foundation for improving yield gap assessments, supporting sustainable agricultural intensification, and contributing to global food security through data-driven decision-making
Assessing the future climate resilience of UK Building Regulations and EnerPHit Standard: a case study for the residential stock in Nottingham
Get PDFIn the 21st century, climate change is considered as one of the main concerns that humanity faces, due to the rapid climate variable fluctuations, and the increase of the extreme weather events. The climate change and the built environment have an interdependent relationship. Globally, 40% of the total annual energy use comes from the residential sector, resulting in higher emissions of carbon dioxide, which is the main driver of global warming and climate change.
With the rapid urbanisation, and the constantly increasing requirements for human thermal comfort in urban areas, there is an inevitable need for retrofit interventions in the existing residential stock. Since 1990, the greenhouse gases were decreased only
by 20%, from which only 30-40% comes from the application of energy efficient measures. Therefore, improving the energy efficiency of the existing residential building stock remains a significant challenge. Nonetheless, the EU’s current annual refurbishment rate of 1% falls under the essential 3% needed for 30% energy use reduction by 2050, underlining the significance of renovation efforts acceleration.
To tackle the situation for establishing energy efficient building interventions for the adaptation to climate change, Urban Building Energy Modelling under the future climate scenarios is an effective solution. Targetting the most energy inefficient urban areas in the future, and applying appropriate energy efficient measures according to each case, is a key factor for adapting the residential stock to climate change. Nevertheless, most of the past Urban Building Energy Modelling research studies focus on the commercial buildings both due to their higher energy needs and due to the data availability. Thus, the most impactful building and urban parameters in residential energy use need to be investigated.
The aim of this research was to identify the key variables influencing building energy use and apply this knowledge to the residential stock in Nottingham, UK, in order to determine efficient building interventions, according to future climate scenarios. Hence, two key steps were undertaken: investigating the data requirements for urban building energy modelling (UBEM), and then using this to evaluate the future energy demand.
To accomplish this an UBEM bottom-up physics-based approach was implemented, for the estimation of the percentage difference in the urban energy demand. The study selected two residential neighbourhoods, consisting of 580 buildings in total. After defining the data requirements and the most impactful parameters, the developed energy model accounted for the evaluation of the climate change and the impact of the energy efficient measures on the energy demand, both on heating and cooling.
The results showed that there is still room for UBEM dataset improvements, and that choosing the right renovation strategies for the current residential building stock is essential for appropriate adaptation to climate change. The findings suggest that the most influential building parameter is the airtightness, with average energy demand percentage change equal to 0.52%, for 1% change. Moreover, the addition of urban surroundings, namely trees and surrounding buildings, is critical, since some buildings experienced more than 10% energy demand difference.
Finally, regarding the climate change impact, in terms of the heating demand, the EnerPHit standard is more resilient than the UK Building Regulations. However, as regards the cooling demand, the analysis showed that under the EnerPHit standard there is a progressive increase with higher pace than the UK Building regulations. Thus, the key finding is that the policymakers should focus on establishing renovation strategies that will reduce the heating loads presently, but will also prevent the overheating effect in the advent of the rising temperatures due to global warming
Continuous ambulatory blood pressure monitoring using optical fibre sensors
Get PDFAbnormal blood pressure (BP) is a key indicator of cardiovascular dysfunction and a leading risk factor for stroke, heart failure, and mortality. Hypertension affects more than a quarter of adults in England, costing the NHS approximately £2.1 billion annually. Despite effective therapies, rates of uncontrolled BP remain high, underscoring the need for earlier detection and continuous monitoring. Beat-to-beat BP measurement offers distinct advantages by capturing rapid cardiovascular dynamics, yet current methods face limitations: invasive catheters are unsuitable for long-term use, oscillometric cuffs lack temporal resolution, and photoplethysmography (PPG) is prone to artefacts, latency, and skin-tone variability.
This thesis presents a novel, non-invasive cuffless blood pressure (BP) monitoring system that employs fibre Bragg grating (FBG) cantilever sensors to estimate pulse transit time (PTT) and integrates electrocardiography (ECG) to derive pulse arrival time (PAT). The proposed system offers a light-insensitive and motion-resilient solution capable of high-fidelity pulse detection, making it suitable for real-time monitoring across clinical, home, and telemedicine settings.
A validated pipeline -- spanning mechanics, temperature-strain decoupling, phantom haemodynamics, and in-human exercise trials -- demonstrates technical feasibility, with a biocompatible FBG cantilever and compact interrogator achieving tens-of-milliseconds timing resolution. In human trials, PAT showed strong inverse correlations with systolic BP and heart rate, supporting its use in cuffless monitoring. Algorithmically, a GAN-based reconstruction stage restored morphology under artefacts, while Bayesian Gaussian Process Regression (GPR) produced calibrated mmHg estimates (systolic ≤10 mmHg, diastolic ≤4 mmHg) and modelled inter- and intra-subject variability.
By combining high-sensitivity optical sensing, probabilistic modelling, and AI-based signal reconstruction, this work establishes a new pathway toward continuous, personalised BP monitoring. The findings have direct implications for next-generation medical devices that support early diagnosis, community screening, and digital health strategies aimed at reducing the global burden of hypertension
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