Analysis of variables measured in the visual screening of University of Birmingham freshmen and a critical review of visual screening methods and approach

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The identification of pathological subtypes of Alzheimer's disease using cluster analysis

A culster analysis was performed on 78 cases of Alzheimer's disease (AD) to identify possible pathological subtypes of the disease. Data on 47 neuropathological variables, inculding features of the gross brain and the density and distribution of senile plaques (SP) and neurofibrillary tangles (NFT) were used to describe each case. Cluster analysis is a multivariate statistical method which combines together in groups, AD cases with the most similar neuropathological characteristics. The majority of cases (83%) were clustered into five such groups. The analysis suggested that an initial division of the 78 cases could be made into two major groups: (1) a large group (68%) in which the distribution of SP and NFT was restricted to a relatively small number of brain regions, and (2) a smaller group (15%) in which the lesions were more widely disseminated throughout the neocortex. Each of these groups could be subdivided on the degree of capillary amyloid angiopathy (CAA) present. In addition, those cases with a restricted development of SP/NFT and CAA could be divided further into an early and a late onset form. Familial AD cases did not cluster as a separate group but were either distributed between four of the five groups or were cases with unique combinations of pathological features not closely related to any of the groups. It was concluded that multivariate statistical methods may be of value in the classification of AD into subtypes. © 1994 Springer-Verlag.

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

Influence of birth cohort on age of onset cluster analysis in bipolar I disorder

PURPOSE: Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database. METHODS: The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared. RESULTS: There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups. CONCLUSION: These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

Analysis of interactions among variables of renewable energy projects:a case study on renewable energy project in India

This study highlights the variables associated with the implementation of renewable energy (RE) projects for sustainable development in India, by using an interpretive structural modeling (ISM) - based approach to model variables' interactions, which impact RE adoption. These variables have been categorized under enablers that help to enhance implementation of RE projects for sustainable development. A major finding is that public awareness regarding RE for sustainable development is a very significant enabler. For successful implementation of RE projects, it has been observed that top management should focus on improving highdriving power enablers (leadership, strategic planning, public awareness, management commitment, availability of finance, government support, and support from interest groups).

A study of cluster analysis techniques and their applications

This thesis seeks to describe the development of an inexpensive and efficient clustering technique for multivariate data analysis. The technique starts from a multivariate data matrix and ends with graphical representation of the data and pattern recognition discriminant function. The technique also results in distances frequency distribution that might be useful in detecting clustering in the data or for the estimation of parameters useful in the discrimination between the different populations in the data. The technique can also be used in feature selection. The technique is essentially for the discovery of data structure by revealing the component parts of the data. lhe thesis offers three distinct contributions for cluster analysis and pattern recognition techniques. The first contribution is the introduction of transformation function in the technique of nonlinear mapping. The second contribution is the us~ of distances frequency distribution instead of distances time-sequence in nonlinear mapping, The third contribution is the formulation of a new generalised and normalised error function together with its optimal step size formula for gradient method minimisation. The thesis consists of five chapters. The first chapter is the introduction. The second chapter describes multidimensional scaling as an origin of nonlinear mapping technique. The third chapter describes the first developing step in the technique of nonlinear mapping that is the introduction of "transformation function". The fourth chapter describes the second developing step of the nonlinear mapping technique. This is the use of distances frequency distribution instead of distances time-sequence. The chapter also includes the new generalised and normalised error function formulation. Finally, the fifth chapter, the conclusion, evaluates all developments and proposes a new program. for cluster analysis and pattern recognition by integrating all the new features.

Identification and analysis of moderator variables:investigating the customer satisfaction-loyalty link

Purpose – Research on the relationship between customer satisfaction and customer loyalty has advanced to a stage that requires a more thorough examination of moderator variables. Limited research shows how moderators influence the relationship between customer satisfaction and customer loyalty in a service context; this article aims to present empirical evidence of the conditions in which the satisfaction-loyalty relationship becomes stronger or weaker. Design/methodology/approach – Using a sample of more than 700 customers of DIY retailers and multi-group structural equation modelling, the authors examine moderating effects of several firm-related variables, variables that result from firm/employee-customer interactions and individual-level variables (i.e. loyalty cards, critical incidents, customer age, gender, income, expertise). Findings – The empirical results suggest that not all of the moderators considered influence the satisfaction-loyalty link. Specifically, critical incidents and income are important moderators of the relationship between customer satisfaction and customer loyalty. Practical implications – Several of the moderator variables considered in this study are manageable variables. Originality/value – This study should prove valuable to academic researchers as well as service and retailing managers. It systematically analyses the moderating effect of firm-related and individual-level variables on the relationship between customer satisfaction and loyalty. It shows the differential effect of different types of moderator variables on the satisfaction-loyalty link.

Moderation of cognition-intention and cognition-behaviour relations:a meta-analysis of properties of variables from the theory of planned behaviour

Meta-analysis was used to quantify the moderating effects of seven properties of cognitions-accessibility, temporal stability, direct experience, involvement, certainty, ambivalence and affective-cognitive consistency-on cognition-intention and cognition-behaviour relations. Literature searches revealed 44 studies that could be included in the review. Findings showed that all of the properties, except involvement, moderated attitude-behaviour consistency. Similarly, all relevant moderators improved the consistency between intentions and behaviour. Temporal stability moderated PBC-behaviour relations, certainty moderated subjective norm-intention relations, and ambivalence, certainty, and involvement all moderated attitude-intention relations. Overall, temporal stability appeared to be the strongest moderator of cognition-behaviour relations.

Impaired balance in developmental dyslexia? A meta-analysis of the contending evidence

Background: Developmental dyslexia is typically defined by deficits in phonological skills, but it is also associated with anomalous performance on measures of balance. Although balance assessments are included in several screening batteries for dyslexia, the association between impairments in literacy and deficits in postural stability could be due to the high co-occurrence of dyslexia with other developmental disorders in which impairments of motor behaviour are also prevalent. Methods: We identified 17 published studies that compared balance function between dyslexia and control samples and obtained effect-sizes for each. Contrast and association analyses were used to quantify the influence of hypothesised moderator variables on differences in effects across studies. Results: The mean effect-size of the balance deficit in dyslexia was .64 (95% CI = .44-.78) with heterogeneous findings across the population of studies. Probable co-occurrence of other developmental disorders and variability in intelligence scores in the dyslexia samples were the strongest moderator variables of effect-size. Conclusions: Balance deficits are associated with dyslexia, but these effects are apparently more strongly related to third variables other than to reading ability. Deficits of balance may indicate increased risk of developmental disorder, but are unlikely to be uniquely associated with dyslexia.

Identification, mutagenesis, and transcriptional analysis of the methanesulfonate transport operon of methylosulfonomonas methylovora

Recently identified genes located downstream (3') of the msmEF (transport encoding) gene cluster, msmGH, and located 5' of the structural genes for methanesulfonate monooxygenase (MSAMO) are described from Methylosulfonomonas methylovora. Sequence analysis of the derived polypeptide sequences encoded by these genes revealed a high degree of identity to ABC-type transporters. MsmE showed similarity to a putative periplasmic substrate binding protein, MsmF resembled an integral membraneassociated protein, and MsmG was a putative ATP-binding enzyme. MsmH was thought to be the cognate permease component of the sulfonate transport system. The close association of these putative transport genes to the MSAMO structural genes msmABCD suggested a role for these genes in transport of methanesulfonic acid (MSA) into M. methylovora. msmEFGH and msmABCD constituted two operons for the coordinated expression of MSAMO and the MSA transporter systems. Reverse-transcription-PCR analysis of msmABCD and msmEFGH revealed differential expression of these genes during growth on MSA and methanol. The msmEFGH operon was constitutively expressed, whereas MSA induced expression of msmABCD. A mutant defective in msmE had considerably slower growth rates than the wild type, thus supporting the proposed role of MsmE in the transport of MSA into M. methylovora.

Spatial pattern analysis of beta-amyloid (A beta) deposits in Alzheimer disease by linear regression

The spatial patterns of discrete beta-amyloid (Abeta) deposits in brain tissue from patients with Alzheimer disease (AD) were studied using a statistical method based on linear regression, the results being compared with the more conventional variance/mean (V/M) method. Both methods suggested that Abeta deposits occurred in clusters (400 to <12,800 mu m in diameter) in all but 1 of the 42 tissues examined. In many tissues, a regular periodicity of the Abeta deposit clusters parallel to the tissue boundary was observed. In 23 of 42 (55%) tissues, the two methods revealed essentially the same spatial patterns of Abeta deposits; in 15 of 42 (36%), the regression method indicated the presence of clusters at a scale not revealed by the V/M method; and in 4 of 42 (9%), there was no agreement between the two methods. Perceived advantages of the regression method are that there is a greater probability of detecting clustering at multiple scales, the dimension of larger Abeta clusters can be estimated more accurately, and the spacing between the clusters may be estimated. However, both methods may be useful, with the regression method providing greater resolution and the V/M method providing greater simplicity and ease of interpretation. Estimates of the distance between regularly spaced Abeta clusters were in the range 2,200-11,800 mu m, depending on tissue and cluster size. The regular periodicity of Abeta deposit clusters in many tissues would be consistent with their development in relation to clusters of neurons that give rise to specific neuronal projections.

Principal components analysis of Alzheimer's disease based on neuropathological data: a study of 79 patients

A principal components analysis was carried out on neuropathological data collected from 79 cases of Alzheimer's disease (AD) diagnosed in a single centre. The purpose of the study was to determine whether on neuropathological criteria there was evidence for clearly defined subtypes of the disease. Two principal components (PC1 and PC2) were extracted from the data. PC1 was considerable more important than PC2 accounting for 72% of the total variance. When plotted in relation to the first two principal components the majority of cases (65/79) were distributed in a single cluster within which subgroupings were not clearly evident. In addition, there were a number of individual, mainly early-onset cases, which were neither related to each other nor to the main cluster. The distribution of each neuropathological feature was examined in relation to PC1 and 2, Disease onset, rhe degree of gross brain atrophy, neuronal loss and the devlopment of senile plaques (SP) and neurofibrillary tangles (NFT) were negatively correlated with PC1. The devlopment of SP and NFT and the degree of brain athersclerosis were positively correlated with PC2. These results suggested: 1) that there were different forms of AD but no clear division of the cases into subclasses could be made based on the neuropathological criteria used; the cases showing a more continuous distribution from one form to another, 2) that disease onset was an important variable and was associated with a greater development of pathological changes, 3) familial cases were not a distinct subclass of AD; the cases being widely distributed in relation to PC1 and PC2 and 4) that there may be two forms of late-onset AD whic grade into each other, one of which was associated with less SP and NFT development but with a greater degree of brain atherosclerosis.

An econometric analysis of international tourism demand in Hong Kong : a cointegration and error correction approach

The purpose of this study is to develop econometric models to better understand the economic factors affecting inbound tourist flows from each of six origin countries that contribute to Hong Kong’s international tourism demand. To this end, we test alternative cointegration and error correction approaches to examine the economic determinants of tourist flows to Hong Kong, and to produce accurate econometric forecasts of inbound tourism demand. Our empirical findings show that permanent income is the most significant determinant of tourism demand in all models. The variables of own price, weighted substitute prices, trade volume, the share price index (as an indicator of changes in wealth in origin countries), and a dummy variable representing the Beijing incident (1989) are also found to be important determinants for some origin countries. The average long-run income and own price elasticity was measured at 2.66 and – 1.02, respectively. It was hypothesised that permanent income is a better explanatory variable of long-haul tourism demand than current income. A novel approach (grid search process) has been used to empirically derive the weights to be attached to the lagged income variable for estimating permanent income. The results indicate that permanent income, estimated with empirically determined relatively small weighting factors, was capable of producing better results than the current income variable in explaining long-haul tourism demand. This finding suggests that the use of current income in previous empirical tourism demand studies may have produced inaccurate results. The share price index, as a measure of wealth, was also found to be significant in two models. Studies of tourism demand rarely include wealth as an explanatory forecasting long-haul tourism demand. However, finding a satisfactory proxy for wealth common to different countries is problematic. This study indicates with the ECM (Error Correction Models) based on the Engle-Granger (1987) approach produce more accurate forecasts than ECM based on Pesaran and Shin (1998) and Johansen (1988, 1991, 1995) approaches for all of the long-haul markets and Japan. Overall, ECM produce better forecasts than the OLS, ARIMA and NAÏVE models, indicating the superiority of the application of a cointegration approach for tourism demand forecasting. The results show that permanent income is the most important explanatory variable for tourism demand from all countries but there are substantial variations between countries with the long-run elasticity ranging between 1.1 for the U.S. and 5.3 for U.K. Price is the next most important variable with the long-run elasticities ranging between -0.8 for Japan and -1.3 for Germany and short-run elasticities ranging between – 0.14 for Germany and -0.7 for Taiwan. The fastest growing market is Mainland China. The findings have implications for policies and strategies on investment, marketing promotion and pricing.

Influencing water consumption at South Staffordshire Water PLC : a disaggregated behavioural analysis of contributory factors

This research identifies factors which influence the consumption of potable water supplied to customers' property. A complete spectrum of the customer base is examined including household, commercial and industrial properties. The research considers information from around the world, particularly demand management and tariff related projects from North America. A device termed the Flow Moderator was developed and proven, with extensive trials, to conserve water at a rate equivalent to 40 litres/property/day whilst maintaining standards-of-service considerably in excess of Regulatory requirements. A detailed appraisal of the Moderator underlines the costs and benefits available to the industry through deliberate application of even mild demand management. More radically the concept of a charging policy utilising the Moderator is developed and appraised. Advantages include the lower costs of conventional fixed-price charging systems coupled with the conservation and equitability aspects associated with metering. Explanatory models were developed linking consumption to a range of variables demonstrated that households served by a communal water service-pipe (known in the UK as a shared supply) are subject to associated restrictions equivalent to -180 litres/property/day. The research confirmed that occupancy levels were a significant predictive element for household, commercial and industrial customers. The occurrence of on-property leakage was also demonstrated to be a significant factor recorded as an event which offers considerable scope for demand management in its own right.