Visualising ribosome profiling and using it for reading frame detection and exploration of eukaryotic translation initiation

Ribosome profiling (ribo-seq) is a recently developed technique that provides genomewide information on protein synthesis (GWIPS) in vivo. The high resolution of ribo-seq is one of the exciting properties of this technique. In Chapter 2, I present a computational method that utilises the sub-codon precision and triplet periodicity of ribosome profiling data to detect transitions in the translated reading frame. Application of this method to ribosome profiling data generated for human HeLa cells allowed us to detect several human genes where the same genomic segment is translated in more than one reading frame. Since the initial publication of the ribosome profiling technique in 2009, there has been a proliferation of studies that have used the technique to explore various questions with respect to translation. A review of the many uses and adaptations of the technique is provided in Chapter 1. Indeed, owing to the increasing popularity of the technique and the growing number of published ribosome profiling datasets, we have developed GWIPS-viz (http://gwips.ucc.ie), a ribo-seq dedicated genome browser. Details on the development of the browser and its usage are provided in Chapter 3. One of the surprising findings of ribosome profiling of initiating ribosomes carried out in 3 independent studies, was the widespread use of non-AUG codons as translation initiation start sites in mammals. Although initiation at non-AUG codons in mammals has been documented for some time, the extent of non-AUG initiation reported by these ribo-seq studies was unexpected. In Chapter 4, I present an approach for estimating the strength of initiating codons based on the leaky scanning model of translation initiation. Application of this approach to ribo-seq data illustrates that initiation at non-AUG codons is inefficient compared to initiation at AUG codons. In addition, our approach provides a probability of initiation score for each start site that allows its strength of initiation to be evaluated.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.

Early detection of delirium in older medical inpatients: prodrome, predictors and motor subtyping

Background Delirium is highly prevalent, especially in older patients. It independently leads to adverse outcomes, but remains under-detected, particularly hypoactive forms. Although early identification and intervention is important, delirium prevention is key to improving outcomes. The delirium prodrome concept has been mooted for decades, but remains poorly characterised. Greater understanding of this prodrome would promote prompt identification of delirium-prone patients, and facilitate improved strategies for delirium prevention and management. Methods Medical inpatients of ≥70 years were screened for prevalent delirium using the Revised Delirium Rating Scale (DRS--‐R98). Those without prevalent delirium were assessed daily for delirium development, prodromal features and motor subtype. Survival analysis models identified which prodromal features predicted the emergence of incident delirium in the cohort in the first week of admission. The Delirium Motor Subtype Scale-4 was used to ascertain motor subtype. Results Of 555 patients approached, 191 patients were included in the prospective study. The median age was 80 (IQR 10) and 101 (52.9%) were male. Sixty-one patients developed incident delirium within a week of admission. Several prodromal features predicted delirium emergence in the cohort. Firstly, using a novel Prodromal Checklist based on the existing literature, and controlling for confounders, seven predictive behavioural features were identified in the prodromal period (for example, increasing confusion; and being easily distractible). Additionally, using serial cognitive tests and the DRS-R98 daily, multiple cognitive and other core delirium features were detected in the prodrome (for example inattention; and sleep-wake cycle disturbance). Examining longitudinal motor subtypes in delirium cases, subtypes were found to be predominantly stable over time, the most prevalent being hypoactive subtype (62.3%). Discussion This thesis explored multiple aspects of delirium in older medical inpatients, with particular focus on the characterisation of the delirium prodrome. These findings should help to inform future delirium educational programmes, and detection and prevention strategies.

Capillary electrophoresis with boron doped diamond electrode for amperometric detection of low molecular weight biological substrate

The research work in this thesis included the sensitive and selective separation of biological substance by capillary electrophoresis with a boron doped diamond electrode for amperometric detection. Chapter 1 introduced the capillary electrophoresis and electrochemical detection. It included the different modes of capillary electrophoresis, polyelectrolyte multilayers coating for open tubular capillary electrochromatography, different modes of electrochemical detection and carbon based electrodes. Chapter 2 showed the synthesized and electropolymerized N-acetyltyramine with a negatively charged sulfobutylether-β-cyclodextrin on a boron doped diamond (BDD) electrode followed by the electropolymerzation of pyrrole to form a stable and permselective film for selective dopamine detection. For comparison, a glassy carbon (GC) electrode with a combined electropolymerized permselective film of polytyramine and polypyrrole-1-propionic acid was used for selective detection of dopamine. The detection limit of dopamine was improved from 100 nM at a GC electrode to 5 nM at a BDD electrode. Chapter 3 showed field-amplified sample stacking using a fused silica capillary coated with gold nanoparticles embedded in poly(diallyldimethylammonium) chloride, which has been investigated for the electrophoretic separation of indoxyl sulphate, homovanillic acid and vanillylmandelic acid. The detection limit of the three analytes obtained by using a boron doped diamond electrode was around 75 nM, which was significantly below their normal physiological levels in biological fluids. This combined separation and detection scheme was applied to the direct analysis of these analytes and other interfereing chemicals including uric and ascorbic acids in urine samples without off-line sample treatment or preconcentration. Chapter 4 showed the selective detection of Pseudomonas Quinolone Signal, PQS for quorum sensing from its precursor HHQ, using a simply boron doped diamond electrode. Furthermore, by combining poly(diallyldimethylammonium) chloride modified fused silica capillary with a BDD electrode for amperometric detection, PQS was separated from HHQ and other analogues. The detection limit of PQS was as low as 65 nM. Different P. aeruginosa mutant strains were studied. Chapter 5 showed the separation of aminothiols by layer-by-layer coating of silica capillary with a boron doped diamond electrode. The capillary was layer-by-layer coated with the polycation poly(diallyldimethylammonium) chloride and negatively charged silica nanoparticles. All the aminothiols was separated and detected using a BDD electrode in an acidic electrolyte. It was a novel scheme for the separation and detection of glutathione reduced and oxidized forms, which is important for estimated overstressed level in the human system.

Phase estimation receiver for full-field detection: a novel receiver structure for electronic dispersion compensation of metropolitan area networks

The development of ultra high speed (~20 Gsamples/s) analogue to digital converters (ADCs), and the delayed deployment of 40 Gbit/s transmission due to the economic downturn, has stimulated the investigation of digital signal processing (DSP) techniques for compensation of optical transmission impairments. In the future, DSP will offer an entire suite of tools to compensate for optical impairments and facilitate the use of advanced modulation formats. Chromatic dispersion is a very significant impairment for high speed optical transmission. This thesis investigates a novel electronic method of dispersion compensation which allows for cost-effective accurate detection of the amplitude and phase of the optical field into the radio frequency domain. The first electronic dispersion compensation (EDC) schemes accessed only the amplitude information using square law detection and achieved an increase in transmission distances. This thesis presents a method by using a frequency sensitive filter to estimate the phase of the received optical field and, in conjunction with the amplitude information, the entire field can be digitised using ADCs. This allows DSP technologies to take the next step in optical communications without requiring complex coherent detection. This is of particular of interest in metropolitan area networks. The full-field receiver investigated requires only an additional asymmetrical Mach-Zehnder interferometer and balanced photodiode to achieve a 50% increase in EDC reach compared to amplitude only detection.

Women's help seeking behaviour and the associated influencing factors on self discovery of a breast symptom

To investigate women’s help seeking behavior (HSB) following self discovery of a breast symptom and determine the associated influencing factors. A descriptive correlation design was used to ascertain the help seeking behavior (HSB) and the associated influencing factors of a sample of women (n = 449) with self discovered breast symptoms. The study was guided by the ‘Help Seeking Behaviour and Influencing Factors” conceptual framework (Facione et al., 2002; Meechan et al., 2003, 2002; Leventhal, Brissette and Leventhal, 2003 and O’Mahony and Hegarty, 2009b). Data was collected using a researcher developed multi-scale questionnaire package to ascertain women’s help seeking behavior on self discovery of a breast symptom and determine the factors most associated with HSB. Factors examined include: socio-demographics, knowledge and beliefs (regarding breast symptom; breast changes associated with breast cancer; use of alternative help seeking behaviours and presence or absence of a family history of breast cancer),emotional responses, social factors, health seeking habits and health service system utilization and help seeking behavior. A convenience sample (n = 449 was obtained by the researcher from amongst women attending the breast clinics of two large urban hospitals within the Republic of Ireland. All participants had self-discovered breast symptoms and no previous history of breast cancer. The study identified that while the majority of women (69.9%; n=314) sought help within one month, 30.1% (n=135) delayed help seeking for more than one month following self discovery of their breast symptom. The factors most significantly associated with HSB were the presenting symptom of ‘nipple indrawn/changes’ (p = 0.005), ‘ignoring the symptom and hoping it would go away’ (p < 0.001), the emotional response of being ‘afraid@ on symptom discovery (p = 0.005) and the perception/belief in longer symptom duration (p = 0.023). It was found that women who presented with an indrawn/changed nipple were more likely to delay (OR = 4.81) as were women who ‘ignored the symptoms and hoped it would go away’ (OR = 10.717). Additionally, the longer women perceived that their symptom would last, they more likely they were to delay (OR = 1.18). Conversely, being afraid following symptom discovery was associated with less delay (OR = 0.37; p=0.005). This study provides further insight into the HSB of women who self discovered breast symptoms. It highlights the complexity of the help seeking process, indicating that is not a linear event but is influenced by multiple factors which can have a significant impact on the outcomes in terms of whether women delay or seek help promptly. The study further demonstrates that delayed HSB persists amongst women with self discovered breast symptoms. This has important implications for continued emphasis on the promotion of breast awareness, prompt help seeking for self discovered breast symptoms and early detection and treatment of breast cancer, amongst women of all ages.

Uniformly convergent finite element and finite difference methods for singularly perturbed ordinary differential equations

This thesis is concerned with uniformly convergent finite element and finite difference methods for numerically solving singularly perturbed two-point boundary value problems. We examine the following four problems: (i) high order problem of reaction-diffusion type; (ii) high order problem of convection-diffusion type; (iii) second order interior turning point problem; (iv) semilinear reaction-diffusion problem. Firstly, we consider high order problems of reaction-diffusion type and convection-diffusion type. Under suitable hypotheses, the coercivity of the associated bilinear forms is proved and representation results for the solutions of such problems are given. It is shown that, on an equidistant mesh, polynomial schemes cannot achieve a high order of convergence which is uniform in the perturbation parameter. Piecewise polynomial Galerkin finite element methods are then constructed on a Shishkin mesh. High order convergence results, which are uniform in the perturbation parameter, are obtained in various norms. Secondly, we investigate linear second order problems with interior turning points. Piecewise linear Galerkin finite element methods are generated on various piecewise equidistant meshes designed for such problems. These methods are shown to be convergent, uniformly in the singular perturbation parameter, in a weighted energy norm and the usual L2 norm. Finally, we deal with a semilinear reaction-diffusion problem. Asymptotic properties of solutions to this problem are discussed and analysed. Two simple finite difference schemes on Shishkin meshes are applied to the problem. They are proved to be uniformly convergent of second order and fourth order respectively. Existence and uniqueness of a solution to both schemes are investigated. Numerical results for the above methods are presented.

A study of the extent to which Irish law, policy and practice allow for Ireland's application of the United Nations Convention on the Rights of the Child, 1989 to pre-natal children

The central research question of this thesis asks the extent to which Irish law, policy and practice allow for the application of the United Nations Convention on the Rights of the Child (CRC) to pre-natal children. First, it is demonstrated that pre-natal children can fall within the definition of ‘child’ under the Convention and so the possibility of applying the Convention to children before birth is opened. Many State Parties to the CRC have interpreted it as applicable to pre-natal children, while others have expressed that it only applies from birth. Ireland has not clarified whether or not it interprets it as being applicable from conception, birth, or some other point. The remainder of the thesis examines the extent to which Ireland interprets the CRC as applicable to the pre-natal child. First, the question of whether Ireland affords to the pre-natal child the right to life under Article 6(1) of the Convention is analysed. Given the importance of the indivisibility of rights under the Convention, the extent to which Ireland applies other CRC rights to pre-natal children is examined. The rights analysed are the right to protection from harm, the right to the provision of health care and the procedural right to representation. It is concluded that Ireland’s laws, policies and practices require urgent clarification on the issue of the extent to which rights such as protection, health care and representation apply to children before birth. In general, there are mixed and ad hoc approaches to these issues in Ireland and there exists a great deal of confusion amongst those working on the frontline with such children, such as health care professionals and social workers. The thesis calls for significant reform in this area in terms of law and policy, which will inform practice.