An investigation into the tropospheric chemistry of acidic aerosols and ammonia in the laboratory

This thesis describes a broad range of experiments based on an aerosol flow-tube system to probe the interactions between atmospherically relevant aerosols with trace gases. This apparatus was used to obtain simultaneous optical and size distribution measurements using FTIR and SMPS measurements respectively as a function of relative humidity and aerosol chemical composition. Heterogeneous reactions between various ratios of ammonia gas and acidic aerosols were studied in aerosol form as opposed to bulk solutions. The apparatus is unique, in that it employed two aerosol generation methods to follow the size evolution of the aerosol while allowing detailed spectroscopic investigation of its chemical content. A novel chemiluminescence apparatus was also used to measure [NH4+]. SO2.H2O is an important species as it represents the first intermediate in the overall atmospheric oxidation process of sulfur dioxide to sulfuric acid. This complex was produced within gaseous, aqueous and aerosol SO2 systems. The addition of ammonia, gave mainly hydrogen sulfite tautomers and disulfite ions. These species were prevalent at high humidities enhancing the aqueous nature of sulfur (IV) species. Their weak acidity is evident due to the low [NH4+] produced. An increasing recognition that dicarboxylic acids may contribute significantly to the total acid burden in polluted urban environments is evident in the literature. It was observed that speciation within the oxalic, malonic and succinic systems shifted towards the most ionised form as the relative humidity was increased due to complete protonisation. The addition of ammonia produced ammonium dicarboxylate ions. Less reaction for ammonia with the malonic and succinic species were observed in comparison to the oxalic acid system. This observation coincides with the decrease in acidity of these organic species. The interaction between dicarboxylic acids and ‘sulfurous’/sulfuric acid has not been previously investigated. Therefore the results presented here are original to the field of tropospheric chemistry. SHO3-; S2O52-; HSO4-; SO42- and H1,3,5C2,3,4O4-;C2,3,4O4 2- were the main components found in the complex inorganic-organic systems investigated here. The introduction of ammonia produced ammonium dicarboxylate as well as ammonium disulfite/sulfate ions and increasing the acid concentrations increased the total amount of [NH4+].

Molecular genetic analysis of DNA alterations in Irish colorectal cancer

Colorectal cancer is the most common cause of death due to malignancy in nonsmokers in the western world. In 1995 there were 1,757 cases of colon cancer in Ireland. Most colon cancer is sporadic, however ten percent of cases occur where there is a previous family history of the disease. In an attempt to understand the tumorigenic pathway in Irish colon cancer patients, a number of genes associated with colorectal cancer development were analysed in Irish sporadic and HNPCC colon cancer patients. The hereditary forms of colon cancer include Familial adenomatous polyposis coli (FAP) and Hereditary Non-Polyposis Colon Cancer (HNPCC). Genetic analysis of the gene responsible for FAP, (the APC gene) has been previously performed on Irish families, however the genetic analysis of HNPCC families is limited. In an attempt to determine the mutation spectrum in Irish HNPCC pedigrees, the hMSH2 and hMLHl mismatch repair genes were screened in 18 Irish HNPCC families. Using SSCP analysis followed by DNA sequencing, five mutations were identified, four novel and a previously reported mutation. In families where a mutation was detected, younger asyptomatic members were screened for the presence of the predisposing mutation (where possible). Detection of mutations is particularly important for the identification of at risk individuals as the early diagnosis of cancer can vastly improve the prognosis. The sensitive and efficient detection of multiple different mutations and polymorphisms in DNA is of prime importance for genetic diagnosis and the identification of disease genes. A novel mutation detection technique has recently been developed in our laboratory. In order to assess the efficacy and application of the methodology in the analysis of cancer associated genes, a protocol for the analysis of the K-ras gene was developed and optimised. Matched normal and tumour DNA from twenty sporadic colon cancer patients was analysed for K-ras mutations using the Glycosylase Mediated Polymorphism Detection technique. Five mutations of the K-ras gene were detected using this technology. Sequencing analysis verified the presence of the mutations and SSCP analysis of the same samples did not identify any additional mutations. The GMPD technology proved to be highly sensitive, accurate and efficient in the identification of K-ras gene mutations. In order to investigate the role of the replication error phenomenon in Irish colon cancer, 3 polyA tract repeat loci were analysed. The repeat loci included a 10 bp intragenic repeat of the TGF-β-RII gene. TGF-β-RII is involved in the TGF-β epithelial cell growth pathway and mutation of the gene is thought to play a role in cell proliferation and tumorigenesis. Due to the presence of a repeat sequence within the gene, TGFB-RII defects are associated with tumours that display the replication error phenomenon. Analysis of the TGF-β-RII 10 bp repeat failed to identify mutations in any colon cancer patients. Analysis of the Bat26 and Bat 40 polyA repeat sequences in the sporadic and HNPCC families revealed that instability is associated with HNPCC tumours harbouring mismatch repair defects and with 20 % of sporadic colon cancer tumours. No correlation between K-ras gene mutations and the RER+ phenotype was detected in sporadic colon cancer tumours.

Molecular composition of biogenic secondary organic aerosols using ultrahigh resolution mass spectrometry: comparing laboratory and field studies

Numerous laboratory experiments have been performed in an attempt to mimic atmospheric secondary organic aerosol (SOA) formation. However, it is still unclear how close the aerosol particles generated in laboratory experiments resemble atmospheric SOA with respect to their detailed chemical composition. In this study, we generated SOA in a simulation chamber from the ozonolysis of α-pinene and a biogenic volatile organic compound (BVOC) mixture containing α- and β-pinene, Δ3-carene, and isoprene. The detailed molecular composition of laboratory-generated SOA was compared with that of background ambient aerosol collected at a boreal forest site (Hyytiälä, Finland) and an urban location (Cork, Ireland) using direct infusion nanoelectrospray ultrahigh resolution mass spectrometry. Kendrick Mass Defect and Van Krevelen approaches were used to identify and compare compound classes and distributions of the detected species. The laboratory-generated SOA contained a distinguishable group of dimers that was not observed in the ambient samples. The presence of dimers was found to be less pronounced in the SOA from the VOC mixtures when compared to the one component precursor system. The elemental composition of the compounds identified in the monomeric region from the ozonolysis of both α-pinene and VOC mixtures represented the ambient organic composition of particles collected at the boreal forest site reasonably well, with about 70% of common molecular formulae. In contrast, large differences were found between the laboratory-generated BVOC samples and the ambient urban sample. To our knowledge this is the first direct comparison of molecular composition of laboratory-generated SOA from BVOC mixtures and ambient samples.

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.

Biomethane production from grass silage: laboratory assessment to maximise yields

On-farm biogas production is typically associated with forage maize as the biomass source. Digesters are designed and operated with the focus of optimising the conditions for this feedstock. Thus, such systems may not be ideally suited to the digestion of grass. Ireland has ca. 3.85 million ha of grassland. Annual excess grass, surplus to livestock requirements, could potentially fuel an anaerobic digestion industry. Biomethane associated with biomass from 1.1 % of grassland in Ireland, could potentially generate over 10 % renewable energy supply in transport. This study aims to identify and optimise technologies for the production of biomethane from grass silage. Mono-digestion of grass silage and co-digestion with slurry, as would occur on Irish farms, is investigated in laboratory trials. Grass silage was shown to have 7 times greater methane potential than dairy slurry on a fresh weight basis (107 m3 t-1 v 16 m3 t-1). However, comprehensive trace element profiles indicated that cobalt, iron and nickel are deficient in mono-digestion of grass silage at a high organic loading rate (OLR) of 4.0 kg VS m-3 d-1. The addition of a slurry co-substrate was beneficial due to its wealth of essential trace elements. To stimulate hydrolysis of high lignocellulose grass silage, particle size reduction (physical) and rumen fluid addition (biological) were investigated. In a continuous trial, digestion of grass silage of <1 cm particle size achieved a specific methane yield of 371 L CH4 kg-1 VS when coupled with rumen fluid addition. The concept of demand driven biogas was also examined in a two-phase digestion system (leaching with UASB). When demand for electricity is low it is recommended to disconnect the UASB from the system and recirculate rumen fluid to increase volatile fatty acid (VFA) and soluble chemical oxygen demand (SCOD) production whilst minimising volatile solids (VS) destruction. At times of high demand for electricity, connection of the UASB increases the destruction of volatiles and associated biogas production. The above experiments are intended to assess a range of biogas production options from grass silage with a specific focus on maximising methane yields and provide a guideline for feasible design and operation of on-farm digesters in Ireland.

A qualitative analysis of parental coping with an early diagnosis of hearing loss in Ireland

The recent implementation of Universal Neonatal Hearing Screening (UNHS) in all 19 maternity hospitals across Ireland has precipitated early identification of paediatric hearing loss in an Irish context. This qualitative, grounded theory study centres on the issue of parental coping as families receive and respond to (what is typically) an unexpected diagnosis of hearing loss in their newborn baby. Parental wellbeing is of particular concern as the diagnosis occurs in the context of recovery from birth and at a time when the parent-child relationship is being established. As the vast majority of children with a hearing loss are born into hearing families with no prior history of deafness, parents generally have had little exposure to childhood hearing loss and often experience acute emotional vulnerability as they respond to the diagnosis. The researcher conducted in-depth interviews primarily with parents (and to a lesser extent with professionals), as well as a follow-up postal questionnaire for parents. Through a grounded theory analysis of data, the researcher subsequently fashioned a four-stage model depicting the parental journey of receiving and coping with a diagnosis. The four stages (entitled Anticipating, Confirming, Adjusting and Normalising) are differentiated by the chronology of service intervention and defined by the overarching parental experience. Far from representing a homogenous trajectory, this four-stage model is multifaceted and captures a wide diversity of parental experiences ranging from acute distress to resilient hopefulness