Analysis of haemochromatosis mutations in the North West population

Background: Hereditary haemochromatosis is a heritable disorder caused by an inborn error in the metabolism of iron. It results in over absorption of iron by the body, which can manifest clinically as fatigue, arthritis, diabetes and cardiovascular problems. The highest prevalence for the genetic mutations that cause hereditary haemochromatosis can be found in the Irish population. Individuals with diabetes may also have haemochromatosis (and vice versa), due to the bi-directional relationship between iron metabolism and glucose metabolism. Objectives: To determine the incidence of the three haemochromatosis mutations C282Y, H63D & S65C, in a population from the North West of Ireland and to investigate whether there is an increased frequency of these three mutations in a diabetic population from the same region. Method: DNA was extracted from 500 whole blood samples (250 diabetic samples and 250 ‘control’ samples) using a Wizard™ kit. PCR was conducted utilising specific primers for each mutation and in accordance with a set protocol. Following amplification, PCR product was subjected to restriction endonuclease digestion, where different restriction enzymes (Rsa I, Nde II & Hinf I) were employed to determine the HFE genotype status of samples. Results: The incidence of C282Y homozygosity (1/83) and C282Y heterozygosity (1/6) in the ‘control’ group was similar to those reported for the general Irish population (1/83 and 1/5, respectively). Incidences of H63D homozygotes and H63D heterozygotes or ‘carriers’ in the diabetic population were greater than that of the ‘control’ population. A significant finding of this study was that of an incidence of 1/32 S65C carriers in the control population. This is, to our knowledge, the highest incidence of the genotype reported to date in the general Irish population. Statistical analysis showed that there was no significant differences between the HFE genotype frequencies in the Diabetic and Control Populations. Conclusion: Results of the study concord with published literature in terms of C282Y homozygosity and C282Y heterozygosity in the general Irish population. An increased frequency of the H63D mutation in diabetic individuals was also found but was not statistically significant. The biochemical effect of the H63D mutation is still unknown. The significance of such a high incidence of S65C carriers in the ‘control’ population warrants further investigation.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.