A multilevel analysis of team climate and interpersonal exchange relationships at work

This study advances research on interpersonal exchange relationships by integrating social exchange, workplace friendship and climate research to develop a multilevel model. Data were collected from 215 manager-employee dyads working within 36 teams. At the individual level, LMX was positively associated with TMX and workplace friendship. Further, workplace friendship was positively related to TMX, and mediated the LMX-TMX relationship. At the team level, HLM results demonstrated that the relationship between LMX and workplace friendship was moderated by affective climate. Findings suggest that high-quality LMX relationships are associated with enhanced employees' perceptions of workplace friendship when affective group climate was strong.

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.

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.

Analysis of organisational climate to improve the adequacy of primary health care in the State of Kuwait

This study covers two areas of contribution to the knowledge, firstly it tried to investigate rigourously the relationships of a number of factors believed that they may affect the climate perception, classified into three types to arrive to prove a hypothesis of the important role that qualification and personal factors play in shaping the climate perception, this is in contrast with situational factors. Secondly, the study tries to recluster the items of a wide-range applied scale for the measurement of climate named HAY in order to overcome the cross-cultural differences between the Kuwaiti and the American society, and to achieve a modified dimensions of climate for a civil service organisation in Kuwait. Furthermore, the study attempts to carry out a diagnostic test for the climate of the Ministry of Public Health in Kuwait, aiming to diagnose the perceived characteristics of the MoPH organisation, and suggests a number of areas to be given attention if an improvement is to be introduced. The study used extensively the statistical and the computer facilities to make the analysis more representing the field data, on the other hand this study is characterised by the very highly responsive rate of the main survey which would affect the findings reliability. Three main field studies are included, the first one was to conduct the main questionnaire where the second was to measure the "should be" climate by the experts of MoPH using the DELPHI technique, and the third was to conduct an extensive meeting with the very top management team in MoPH. Results of the first stage were subject to CLUSTER analysis for the reconstruction of the HAY tool, whereas comparative analysis was carried on between the results of the second and third stages on one side, the first from the other.

A multivariate spatial analysis of vowel formants in American English

This paper presents the results of a multivariate spatial analysis of 38 vowel formant variables in the language of 402 informants from 236 cities from across the contiguous United States, based on the acoustic data from the Atlas of North American English (Labov, Ash & Boberg, 2006). The results of the analysis both confirm and challenge the results of the Atlas. Most notably, while the analysis identifies similar patterns as the Atlas in the West and the Southeast, the analysis finds that the Midwest and the Northeast are distinct dialect regions that are considerably stronger than the traditional Midland and Northern dialect region indentified in the Atlas. The analysis also finds evidence that a western vowel shift is actively shaping the language of the Western United States.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Management of Information on Program Flow Analysis

The article proposes the model of management of information about program flow analysis for conducting computer experiments with program transformations. It considers the architecture and context of the flow analysis subsystem within the framework of Specialized Knowledge Bank on Program Transformations and describes the language for presenting flow analysis methods in the knowledge bank.

Correlating liposomal adjuvant characteristics to in-vivo cell-mediated immunity using a novel Mycobacterium tuberculosis fusion protein:a multivariate analysis study

Objective In this study, we have used a chemometrics-based method to correlate key liposomal adjuvant attributes with in-vivo immune responses based on multivariate analysis. Methods The liposomal adjuvant composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA) and trehalose 6,6-dibehenate (TDB) was modified with 1,2-distearoyl-sn-glycero-3-phosphocholine at a range of mol% ratios, and the main liposomal characteristics (liposome size and zeta potential) was measured along with their immunological performance as an adjuvant for the novel, postexposure fusion tuberculosis vaccine, Ag85B-ESAT-6-Rv2660c (H56 vaccine). Partial least square regression analysis was applied to correlate and cluster liposomal adjuvants particle characteristics with in-vivo derived immunological performances (IgG, IgG1, IgG2b, spleen proliferation, IL-2, IL-5, IL-6, IL-10, IFN-γ). Key findings While a range of factors varied in the formulations, decreasing the 1,2-distearoyl-sn-glycero-3-phosphocholine content (and subsequent zeta potential) together built the strongest variables in the model. Enhanced DDA and TDB content (and subsequent zeta potential) stimulated a response skewed towards a cell mediated immunity, with the model identifying correlations with IFN-γ, IL-2 and IL-6. Conclusion This study demonstrates the application of chemometrics-based correlations and clustering, which can inform liposomal adjuvant design.

Application of Regularized Discriminant Analysis

2000 Mathematics Subject Classification: 62H30, 62P99

Cost-benefit analysis of climate change – A methodological overview of recent studies

Global warming16 has already begun. Climate change has become a self-propelling and self-reinforcing process as a result of the externality associated with greenhouse- gas (GHG) emissions. Although it is an externality related to humankind, according to a number of unique features we should distinguish it from other externalities. Climate change is a global phenomenon in its causes and consequences. The long-term and persistent impacts of climate change will likely continue over centuries without further anthropogenic mechanism. The preindustrial (equilibrium) level of GHG concentration in the atmosphere cannot be restored since it is irreversible, but if we do not stabilise the actual level of atmospheric concentration, the situation will become much worse than it is now. Assessing the impacts of climate change requires careful considerations because of the pervasive uncertainties and risks associated with it.