Symbolic Mathematical Laboratory

A large computer program has been developed to aid applied mathematicians in the solution of problems in non-numerical analysis which involve tedious manipulations of mathematical expressions. The mathematician uses typed commands and a light pen to direct the computer in the application of mathematical transformations; the intermediate results are displayed in standard text-book format so that the system user can decide the next step in the problem solution. Three problems selected from the literature have been solved to illustrate the use of the system. A detailed analysis of the problems of input, transformation, and display of mathematical expressions is also presented.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35

Intelligent software for laboratory automation

Whelan, K. E. and King, R. D. (2004) Intelligent software for laboratory automation. Trends in Biotechnology 22 (9): 440-445

Taking advantage of an old concept, "illegitimate transcription", for a proposed novel method of genetic diagnosis of McArdle disease

McArdle disease is a metabolic disorder caused by pathogenic mutations in the PYGM gene. Timely diagnosis can sometimes be difficult with direct genomic analysis, which requires additional studies of cDNA from muscle transcripts. Although the "nonsense-mediated mRNA decay" (NMD) eliminates tissue-specific aberrant transcripts, there is some residual transcription of tissue-specific genes in virtually all cells, such as peripheral blood mononuclear cells (PBMCs).We studied a subset of the main types of PYGM mutations (deletions, missense, nonsense, silent, or splicing mutations) in cDNA from easily accessible cells (PBMCs) in 12 McArdle patients.Analysis of cDNA from PBMCs allowed detection of all mutations. Importantly, the effects of mutations with unknown pathogenicity (silent and splicing mutations) were characterized in PBMCs. Because the NMD mechanism does not seem to operate in nonspecific cells, PBMCs were more suitable than muscle biopsies for detecting the pathogenicity of some PYGM mutations, notably the silent mutation c.645G>A (p.K215=), whose effect in the splicing of intron 6 was unnoticed in previous muscle transcriptomic studies.We propose considering the use of PBMCs for detecting mutations that are thought to cause McArdle disease, particularly for studying their actual pathogenicity.

Perda de dimensão vertical de oclusão e estruturas dentárias remanescentes

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

Revisão bibliográfica

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Medicina Dentária

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.