Quality management (QM) implementation in the Kuwaiti Oil Industry:An empirical study and a proposed generic framework

In an Arab oil producing country in the Middle East such as Kuwait, Oil industry is considered as the main and most important industry of the country. This industry’s importance emerged from the significant role it plays in both country’s national economy and also global economy. Moreover, Oil industry’s criticality comes from its interconnectivity with national security and power in the Middle East region. Hence, conducting this research in this crucial industry had certainly added values to companies in this industry as it investigated thoroughly the main components of the TQM implementation process and identified which components affects significantly TQM’s implementation and its gained business results. In addition, as the Oil sector is a large sector that is known for its richness of employees with different national cultures and backgrounds. Thus, this culture-heterogeneous industry seems to be the most appropriate environment to address and satisfy a need in the literature to investigate the national culture values’ effects on TQM implementation process. Furthermore, this research has developed a new conceptual model of TQM implementation process in the Kuwaiti Oil industry that applies in general to operations and productions organizations at the Kuwaiti business environment and in specific to organizations in the Oil industry, as well it serves as a good theoretical model for improving operations and production level of the oil industry in other developing and developed countries. Thus, such research findings minimized the literature’s gap found the limited amount of empirical research of TQM implementation in well-developed industries existing in an Arab, developing countries and specifically in Kuwait, where there was no coherent national model for a universal TQM implementation in the Kuwaiti Oil industry in specific and Kuwaiti business environment in general. Finally, this newly developed research framework, which emerged from the literature search, was validated by rigorous quantitative analysis tools including SPSS and Structural Equation Modeling. The quantitative findings of questionnaires collected were supported by the qualitative findings of interviews conducted.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

A novel and rational approach towards designing aluminium chelators

Aluminium (Al) is known to be neurotoxic and has been associated with the aetiology of Alzheimer's Disease. To date, only desferrioxamine (DFO), a trihydroxamic acid siderophore has been used in the clinical environment for the removal of Al from the body. However, this drug is expensive, orally inactive and is associated with many side effects. These studies employed a theoretical approach, with the use of quantum mechanics (QM) via semi-empirical molecular orbital (MO) calculations, and a practical approach using U87-MG glioblastoma cells as a model for evaluating the influence of potential chelators on the passage of aluminium into cells. Preliminary studies involving the Cambridge Structural Database (CSD) identified that Al prefers binding to bidentate ligands in a 3:1 manner, whereby oxygen was the exclusive donating atom. Statistically significant differences in M-O bond lengths when compared to other trivalent metal ions such as Fe3+ were established and used as an acceptance criterion for subsequent MO calculations. Of the semi-empirical methods parameterised for Al, the PM3 Hamiltonian was found to give the most reliable final optimised geometries of simple 3:1 Al complexes. Consequently the PM3 Hamiltonian was used for evaluating the Hf of 3:1 complexes with more complicated ligands. No correlation exists between published stability constants and individual parameters calculated via PM3 optimisations, although investigation of the dicarboxylates reveals a correlation of 0.961 showing promise for affinity prediction of closely related ligands. A simple and inexpensive morin spectrofluorescence assay has been developed and optimised producing results comparable to atomic absorption spectroscopy methods for the quantitative analysis of Al. This assay was used in subsequent in vitro models, initially on E. coli, which indicated that Al inhibits the antimicrobial action of ciprofloxacin, a potent quinolone antibiotic. Ensuing studies using the second model, U87-MG cells, investigated the influence of chelators on the transmembrane transport of Al, identifying 1,2-diethylhydroxypyridin-4-one as a ligand showing greatest potential for chelating Al in the clinical situation. In conclusion, these studies have explored semi-empirical MO Hamiltonians and an in-vitro U87-MG cell line, both as possible methods for predicting effective chelators of Al.

MHC class II binding prediction by molecular docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15% of all overlapping peptides, originating from one protein. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.