Artefactual structure from least squares multidimensional scaling

We consider the problem of illusory or artefactual structure from the visualisation of high-dimensional structureless data. In particular we examine the role of the distance metric in the use of topographic mappings based on the statistical field of multidimensional scaling. We show that the use of a squared Euclidean metric (i.e. the SSTRESs measure) gives rise to an annular structure when the input data is drawn from a high-dimensional isotropic distribution, and we provide a theoretical justification for this observation.

Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

The valuation of travel time savings in least developed countries:theoretical and empirical challenges and results from a field study

In developed countries travel time savings can account for as much as 80% of the overall benefits arising from transport infrastructure and service improvements. In developing countries they are generally ignored in transport project appraisals, notwithstanding their importance. One of the reasons for ignoring these benefits in the developing countries is that there is insufficient empirical evidence to support the conventional models for valuing travel time where work patterns, particularly of the poor, are diverse and it is difficult to distinguish between work and non-work activities. The exclusion of time saving benefits may lead to a bias against investment decisions that benefit the poor and understate the poverty reduction potential of transport investments in Least Developed Countries (LDCs). This is because the poor undertake most travel and transport by walking and headloading on local roads, tracks and paths and improvements of local infrastructure and services bring large time saving benefits for them through modal shifts. The paper reports on an empirical study to develop a methodology for valuing rural travel time savings in the LDCs. Apart from identifying the theoretical and empirical issues in valuing travel time savings in the LDCs, the paper presents and discusses the results of an analysis of data from Bangladesh. Some of the study findings challenge the conventional wisdom concerning the time saving values. The Bangladesh study suggests that the western concept of dividing travel time savings into working and non-working time savings is broadly valid in the developing country context. The study validates the use of preference methods in valuing non-working time saving values. However, stated preference (SP) method is more appropriate than revealed preference (RP) method.

Quantification of hydroxycinnamic acids and lignin in perennial forage and energy grasses by Fourier-transform infrared spectroscopy and partial least squares regression

Levels of lignin and hydroxycinnamic acid wall components in three genera of forage grasses (Lolium,Festuca and Dactylis) have been accurately predicted by Fourier-transform infrared spectroscopy using partial least squares models correlated to analytical measurements. Different models were derived that predicted the concentrations of acid detergent lignin, total hydroxycinnamic acids, total ferulate monomers plus dimers, p-coumarate and ferulate dimers in independent spectral test data from methanol extracted samples of perennial forage grass with accuracies of 92.8%, 86.5%, 86.1%, 59.7% and 84.7% respectively, and analysis of model projection scores showed that the models relied generally on spectral features that are known absorptions of these compounds. Acid detergent lignin was predicted in samples of two species of energy grass, (Phalaris arundinacea and Pancium virgatum) with an accuracy of 84.5%.

Human amniotic epithelial cells induce localized cell-mediated immune privilege in vitro:implications for pancreatic islet transplantation

Chronic systemic immunosuppression in cell replacement therapy restricts its clinical application. This study sought to explore the potential of cell-based immune modulation as an alternative to immunosuppressive drug therapy in the context of pancreatic islet transplantation. Human amniotic epithelial cells (AEC) possess innate anti-inflammatory and immunosuppressive properties that were utilized to create localized immune privilege in an in vitro islet cell culture system. Cellular constructs composed of human islets and AEC (islet/AEC) were bioengineered under defined rotational cell culture conditions. Insulin secretory capacity was validated by glucose challenge and immunomodulatory potential characterized using a peripheral blood lymphocyte (PBL) proliferation assay. Results were compared to control constructs composed of islets or AEC cultured alone. Studies employing AEC-conditioned medium examined the role of soluble factors, and fluorescence immunocytochemistry was used to identify putative mediators of the immunosuppressive response in isolated AEC monocultures. Sustained, physiologically appropriate insulin secretion was observed in both islets and islet/AEC constructs. Activation of resting PBL proliferation occurred on exposure to human islets alone but this response was significantly (p <0.05) attenuated by the presence of AEC and AEC-conditioned medium. Mitogen (phytohaemagglutinin, 5 µg/ml)-induced PBL proliferation was sustained on contact with isolated islets but abrogated by AEC, conditioned medium, and the islet/AEC constructs. Immunocytochemical analysis of AEC monocultures identified a subpopulation of cells that expressed the proapoptosis protein Fas ligand. This study demonstrates that human islet/AEC constructs exhibit localized immunosuppressive properties with no impairment of ß-cell function. The data suggest that transplanted islets may benefit from the immune privilege status conferred on them as a consequence of their close proximity to human AEC. Such an approach may reduce the need for chronic systemic immunosuppression, thus making islet transplantation a more attractive treatment option for the management of insulin-dependent diabetes.

Business privilege and the strategic planning agenda of the Greater London Authority

The establishment of the Greater London Authority (GLA) in 2000 brought a new form of politics to London and new powers to formulate strategic policy. Through an investigation of the access of business interests in the formulation of London's strategic agenda, this article illuminates one aspect of the pressures on city government. It uses the urban regime approach as a framework for analysing the co-operation between the Mayor and business interests in shaping strategic priorities. Although there was a surrounding rhetoric that pointed towards a greater consensus-seeking approach, the business sector was very active in maintaining its privileged access. Strategic priorities were established in the GLA's first year and were then subsequently embodied in the London Plan. Our analysis is based on a detailed examination of this agenda-setting period using material from meetings, written reports and interviews with key actors. © 2005 The Editors of Urban Studies.

The value of time in least developed countries:the African studies : final report

This is a report on a study designed to test the applicability of conventional Stated Preference and Revealed Preference models for valuing the time savings of rural travellers in least developed countries and to develop and demonstrate a robust methodology for estimating values of travel time savings which could be used in developing countries.

How to manual:the valuation of rural travel time savings in least developed countries (LDCs)

The manual is designed to bring out issues that are relevant in the valuation of rural travel time savings in Least Developed Countries (LDCs). It should also be relevant for other developing countries which do not have LDC status but have rural economy features typical of low income developing countries. The manual elaborates step-by-step procedures on how to design and execute studies to estimate the value of time (VoT) savings of rural travellers.

Paths of the least resistance:understanding how motives form in international retail joint venturing

Developing the premise that strategies are forged through an ongoing mutual process of developing motives and responses to multiple degrees of resistance, this paper examines the motives underpinning the adoption of joint venture strategies using empirical details from four British retail firms. The findings point to multiple motives forming from multiple paths of resistance in the foreign market, but also among individuals within the firm as well as across the whole international programme. Moreover, this study reveals a paradoxical tension between management's operational impatience to immediately ground the retail format and an overall wariness or gloomy perceptions associated with adopting an international retail joint venture. The paper therefore concludes that the motives and barriers are manifestations of the struggles involved in internationalising retail operations.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Towards the in silico identification of class II restricted T-cell epitopes:a partial least squares iterative self-consistent algorithm for affinity prediction

Motivation: The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides. Results: An iterative self-consistent partial least squares (PLS)-based additive method was applied to a set of 66 pep- tides no longer than 16 amino acids, binding to DRB1*0401. A regression equation containing the quantitative contributions of the amino acids at each of the nine positions was generated. Its predictability was tested using two external test sets which gave r pred =0.593 and r pred=0.655, respectively. Furthermore, it was benchmarked using 25 known T-cell epitopes restricted by DRB1*0401 and we compared our results with four other online predictive methods. The additive method showed the best result finding 24 of the 25 T-cell epitopes. Availability: Peptides used in the study are available from http://www.jenner.ac.uk/JenPep. The PLS method is available commercially in the SYBYL molecular modelling software package. The final model for affinity prediction of peptides binding to DRB1*0401 molecule is available at http://www.jenner.ac.uk/MHCPred. Models developed for DRB1*0101 and DRB1*0701 also are available in MHC- Pred

A laser induced breakdown spectroscopy quantitative analysis method based on the robust least squares support vector machine regression model

Data fluctuation in multiple measurements of Laser Induced Breakdown Spectroscopy (LIBS) greatly affects the accuracy of quantitative analysis. A new LIBS quantitative analysis method based on the Robust Least Squares Support Vector Machine (RLS-SVM) regression model is proposed. The usual way to enhance the analysis accuracy is to improve the quality and consistency of the emission signal, such as by averaging the spectral signals or spectrum standardization over a number of laser shots. The proposed method focuses more on how to enhance the robustness of the quantitative analysis regression model. The proposed RLS-SVM regression model originates from the Weighted Least Squares Support Vector Machine (WLS-SVM) but has an improved segmented weighting function and residual error calculation according to the statistical distribution of measured spectral data. Through the improved segmented weighting function, the information on the spectral data in the normal distribution will be retained in the regression model while the information on the outliers will be restrained or removed. Copper elemental concentration analysis experiments of 16 certified standard brass samples were carried out. The average value of relative standard deviation obtained from the RLS-SVM model was 3.06% and the root mean square error was 1.537%. The experimental results showed that the proposed method achieved better prediction accuracy and better modeling robustness compared with the quantitative analysis methods based on Partial Least Squares (PLS) regression, standard Support Vector Machine (SVM) and WLS-SVM. It was also demonstrated that the improved weighting function had better comprehensive performance in model robustness and convergence speed, compared with the four known weighting functions.