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Novel dual wavelength and multi-wavelength DFB/DBR lasers for MMW/THz generation
Get PDFMillimeter-wave (MMW) and terahertz (THz) and sources have gained significant attention due to their wide-ranging applications in high-speed wireless communications, high resolution imaging, and advanced sensing technologies. In this thesis, I propose and experimentally demonstrate a series of innovative DFB and DBR semiconductor lasers based on multiple quantum well (MQW) structures operating at 1550 nm for MMW/THz generation.
Three novel findings are reported in the work. Firstly, I demonstrate a dual-wavelength DFB laser array employing a four-phase-shifted sampled Bragg grating (4PS-SBG), which enhances coupling efficiency by approximately 2.83 times compared to conventional designs. The device exhibits stable dual-mode operation with frequency spacings ranging from 320 GHz to 1 THz, achieving an output power of 23.6 mW when integrated with semiconductor optical amplifiers (SOAs). Photomixing in a photoconductive antenna (PCA) result in THz signal generation with a measured power of 12.8 μW. Secondly, I introduce a dual-wavelength DFB laser using a four-phase-shifted sampled Moiré grating (4PS-SMG) with distinct sampling periods on each ridge waveguide side, equivalently introducing two π-phase shifts within the cavity. The device exhibits high stability across a broad injection current range, maintaining a power difference below 2 dB between primary modes. This laser generates a high-purity RF signal at 39.4 GHz with a linewidth of approximately 5.0 MHz. Lastly, I propose mode-locked DBR and DFB lasers employing multiple phase-shift gratings (MPSGs) for high-frequency THz generation. The mode-locked DBR laser achieves stable operation at 150 GHz, 400 GHz, 800 GHz, and 1.2 THz, validated through second harmonic generation measurements. A 200 GHz mode-locked DFB laser, amplified via an erbium-doped fiber amplifier (EDFA) and injected into a PCA, delivers a THz output power of 19.6 μW. Design, fabrication and characterization of these semiconductor lasers are introduced in detail in this thesis
Mental toughness and mental health among health and social care workers in the context of COVID-19
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Understanding the role of genetic testing in parental adjustment to their child’s developmental and epileptic encephalopathy or treatment-resistant epilepsy diagnosis
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Exploring the global majority and racially minoritised women’s experiences of miscarriage and maternity services
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Transdiagnostic mechanisms and their application in therapeutic interventions targeting negative symptoms in schizophrenia: A systematic review and empirical study
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Exploring the contribution of psychology in the self-management of pulmonary hypertension
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A qualitative exploration of the experiences of adults living with congenital heart disease
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MSDeconvolve: A new metabolomics fragmentation spectra resolver using statistics and machine learning
Get PDFIn research fields such as drug discovery and biomarker discovery in diseases, it is often important to identify metabolites present in samples. Metabolites are the end products of metabolic processes in cells, and metabolomics is the study of metabolites. In metabolomics, a widely used approach to identify metabolites in samples is to run liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments. In those experiments, metabolites are fragmented, which means that energy is applied to break the chemical bonds of the metabolite, and produce fragment ions with different masses. The pattern with which a metabolite is fragmented provides valuable information that can lead to its identification. However, a compromise needs to be made between fragmenting a low number of metabolites but having good quality fragmentation patterns (Data Dependent Acquisition, or DDA), or fragmenting all metabolites but obtaining fragmentation patterns from multiple metabolites combined (Data Independent Acquisition, or DIA). In DIA, a deconvolution algorithm is required to determine which metabolite each fragment comes from, and then use the deconvoluted fragmentation patterns to perform identification. The current state-of-the-art deconvolution algorithms do not perform at the required level to produce reliable fragmentation spectra for identification. MSDeconvolve is a framework attempting to improve de-convolution of fragmentation patterns, using statistical methods and models. More specifically, it constructs a design matrix from extracted features from experiment results, where each covariate corresponds to the intensity of the signal of a metabolite. The response variables are constructed from the intensity of the signal of fragment ions. Then, by fitting a model for each response variable against the same design matrix, we obtain coefficient estimates for each fragment ion and each metabolite. By interpreting these coefficients as the proportion of the metabolite that results in a fragment, we can reconstruct the fragmentation patterns of the individual metabolites. Lasso regression was initially used, given its variable selection property that is appropriate in this problem. However, multiple linear regression, Ridge and Elastic Net regression were also evaluated. Further extensions to this modelling approach were also explored. These extensions include applying a penalty such that the coefficients of each metabolite add approximately up to one, to reflect the fact that the signal intensity of fragments for each metabolite should approximately add up to the signal intensity of the original metabolite. Another extension was the combination of data from both a DDA and DIA experiment, in order to improve the deconvolution of metabolites which were not fragmented in the DDA experiment. Although MSDeconvolve performs very well on simulated metabolomics data, it was able to perform similarly, if not marginally better, than the state-of-the-art algorithm on real data. However, future work could potentially further improve its performance by overcoming the limitations of metabolomics data
Epidemiology of Mycoplasma bovis in Scottish dairy herds
Get PDFThe bacterium Mycoplasma bovis (M. bovis) causes major economic losses to dairy herds resulting from increased mortality and morbidity, treatment costs, and reduced growth of young stock. There was limited knowledge on the prevalence of M. bovis in Scotland and no national monitoring scheme.
Two studies were conducted; a longitudinal bulk tank milk (BTM) prevalence study and a cross-sectional seroprevalence study on dairy calves.
In the longitudinal BTM prevalence study, one hundred and eighty-one dairy herds across Scotland participated in the study which required them to submit four BTM samples roughly three months apart that were tested for the presence of active M. bovis infection and for recent exposure. A short questionnaire on general herd management practices were issued to farmers to identify potential risk factors associated with seropositivity. At each of the four sampling points, the proportion of antibody positive herds were 76%, 71%, 83%, and 79%, and overall, 86% of herds tested seropositive in at least one of their four samples. Multivariable logistic regression identified herd history of M. bovis as a potential risk factor for the presence of M. bovis antibodies. The questionnaire results also provide an updated overview of the common structures and practices on Scottish dairy farms.
Herds were then classified based on the antibody results of their four BTM samples using various methods. Sixty-one percent of herds tested consistently positive for all four samples, 15% consistently negative, and 24% transitional. When classified by k-means clustering of the optical density (OD) trend, the majority of herds had a stable trajectory (44%).
A cross-sectional seroprevalence study was then carried out on a subset of herds from the BTM study (n=36) to determine if there was evidence of exposure to M. bovis in youngstock and if there was an association between the BTM and calf seroprevalence. Twenty calves were sampled on each farm (10 animals 4-8 months old and 10 animals 10-14 months old) and a BTM sample collected. There was evidence of youngstock exposure in most herds (58%), and this was associated with the BTM prevalence.
The results of this thesis have demonstrated that M. bovis is likely endemic in Scottish dairy herds and has raised further questions on risk factors and within-herd prevalence estimates of M. bovis
An investigation of computed tomography-derived skeletal muscle measurements in clinical cancer care
Get PDFCancer is a leading cause of death globally, responsible for nearly 10 million deaths annually. In the UK, it is responsible for one in four deaths. Despite the significant mortality rates, cancer survival continues to improve. Such advances are considered multifactorial and attributable to the evolution of new anti-cancer therapies and the identification of novel biomarkers for the optimization of anti-cancer therapy.
Determining which patients will derive benefit from anti-cancer therapy, and when in their cancer journey, remains an area of interest in oncology. At present, such decisions are informed by tumour and host factors. With reference to the tumour, cancer stage and grade are commonly utilised by clinicians for the determination of treatment intent and modality. With reference to the host, age and performance status are routinely considered when determining the appropriateness of anti-cancer therapy. Whilst performance status has historically been considered a robust determinant of likely outcome to anti-cancer therapy, a lack of granularity in the measures of performance status has meant that there is continued interest in the identification of tools that can objectively determine functional status in cancer patients.
Computed tomography (CT)- derived skeletal muscle measurements, skeletal muscle index (SMI) and density (SMD), are considered surrogate markers of muscle quantity and quality, respectively. Readily quantified from the analysis of CT images obtained during routine clinical cancer care, SMI and SMD have been reported to be associated with functional status in patients with cancer. Moreover, are considered to provide a global assessment of the cancer patient, that also inform of nutritional and frailty status. The work presented in this thesis examines how CT-derived measurements of skeletal muscle may be utilised in clinical cancer care.
The prevalence and determinants of CT-derived skeletal muscle measurements, SMI and SMD, are examined in Chapter 4. The results of Chapter 4 reported that a low SMI and SMD had a percentage prevalence of between 30-60% in a substantial cohort of patients with cancer and that this was similar irrespective of threshold values used to define a low SMI/SMD. Moreover, reported that a low SMI and SMD are endemic across a range of cancer subtypes and disease stages, suggesting the poor muscle status is largely constitutional and not the result of the cancer per se.
Given their respective associations with skeletal muscle mass and function, the combination of SMI and SMD, may provide an objective measure by which sarcopenia can be characterized. Chapters 5 and 6 examined the relationships between the CT-derived sarcopenia score (CT SS), a score that combines SMI and SMD, and physical function, malnutrition, systemic inflammation and survival in patients with potentially curative disease. Chapter 5 reported that the CT-SS was significantly associated with malnutrition, systemic inflammation and poorer survival in 1,002 patients with primary operable colorectal cancer. Chapter 6 reported that CT-SS was associated with cardio-pulmonary exercise testing (CPET) performance, an assessment of cardiopulmonary fitness likely to inform on the patient’s baseline functional status, systemic inflammation and survival in 232 oesophagogastric cancer patients with good performance status who underwent neoadjuvant chemotherapy with a view to potentially curative surgical resection. However, that the prognostic value of CT-SS to survival was not maintained when adjusted for systemic inflammation.
The relationship between CT-derived skeletal muscle measurements, systemic inflammation and survival in patients with cancer remains unclear. This relationship was further examined in Chapter 7, that reported systemic inflammation, but not the CT-SS, was significantly associated with survival in 307 good performance status patients with advanced cancer. Taken collectively, the results of Chapter 6 and 7 support the hypothesis that systemic inflammation dominates the prognostic value of CT-derived skeletal muscle measurements in patients CT derived skeletal muscle measurements, systemic inflammation and survival in patients with cancer is required to determine if CT-derived skeletal muscle measurements have independent prognostic value to clinical outcomes and are a useful adjunct for the prediction of likely outcome in patients with cancer.
Sarcopenia is considered a cause of frailty in older adults with cancer. However, the relationship between the CT-SS, frailty and clinical outcomes in patients with cancer is unclear. Specifically, if CT-derived skeletal muscle measurements capture the prognostic value of frailty in patients with cancer. Chapter 8 examined the prevalence and prognostic value of frailty screening tools in patients with colorectal cancer, reporting that frailty was prevalent and had prognostic value to both short- and long-term clinical outcomes. Chapter 9 examined the relationship between frailty and malnutrition, CT-derived skeletal muscle measurements, systemic inflammation and short-term clinical outcomes in 1,002 patients undergoing potentially curative surgery for colorectal cancer. The results reported that frailty was associated with CT-derived skeletal muscle measurements. However, remained independently associated with short-term clinical outcomes (post-operative complications) when adjusted for CT-derived skeletal muscle measurements. The results suggest that whilst sarcopenia and frailty are closely associated in patients with cancer, CT-derived muscle measurements do not completely capture the prognostic significance of frailty. Nevertheless, the results suggest that the CT-SS may be a useful adjunct to frailty screening tools/measures in patients with cancer.
Cancer cachexia is a complex metabolic syndrome that is associated with dysregulated glucose metabolism. However, there is currently a paucity of studies examining the relationship between an elevated serum lactate dehydrogenase (LDH), an early biomarker of dysregulated glucose metabolism, and a low skeletal muscle mass, considered the defining feature of cachexia. Chapter 10 reported that an elevated LDH was significantly associated with performance status, systemic inflammation and survival in 436 patients with advanced cancer. However, also reported that there was no significant association between an elevated LDH and a low SMI. Whilst the results of Chapter 10 do not suggest that the loss of skeletal muscle mass is directly related to dysregulated glucose metabolism in patients with cancer, further study is required.
Whilst skeletal muscle mass is considered to reduce with cancer progression, liver mass is thought to be preserved. CT is considered a reliable modality for the quantification of both skeletal muscle and liver mass. However, the quantification of liver mass is significantly more time-consuming and laborious compared with that of skeletal muscle. The current gold standard methodology requires the measurement of the total liver volume, calculated by manual segmentation of sequential axial CT images. As such, there is a paucity of studies examining the relationship between skeletal muscle and liver mass, quantified using CT, in patients with cancer. We hypothesized that the maximal cross-sectional liver area on an axial CT slice, determined using manual segmentation, may be an easily quantified surrogate measure of liver mass, analogous to how skeletal muscle mass is quantified using CT. Chapter 11 reported that the maximal cross-sectional liver area was strongly correlated with the total liver volume in patients undergoing potentially curative surgery for colonic cancer, suggesting that it was a reliable surrogate marker. Chapter 12 reported that CT-derived liver mass, quantified using the novel proposed methodology, was significantly associated with SMI in 385 patients undergoing potentially curative surgery for colonic cancer, suggesting that a higher skeletal muscle mass is associated with a higher liver mass in patients with early stage disease. The results are informative and provide a foundation for future work examining the relationship between skeletal muscle and liver mass in patients with cancer.
In summary, a low SMI and SMD appear to be constitutional and not the result of cancer per se. The combination of SMI and SMD would appear to objectively characterize sarcopenia and is closely associated with malnutrition, physical function, frailty and systemic inflammation in patients with cancer. However, it remains unclear if CT-derived skeletal muscle measurements have independent prognostic value to clinical outcomes and therefore questions their utility as prognostic tools in patients with cancer. Whilst a low skeletal muscle mass was not found to be significantly associated with biomarkers of dysregulated metabolism, it was significantly associated with CT-derived liver mass. The present work reports a reliable methodology for future examination of this relationship in patients with cancer
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