Higher-resolution structure of the human insulin receptor ectodomain: Multi-modal inclusion of the insert domain

Insulin receptor (IR) signaling is critical to controlling nutrient uptake and metabolism. However, only a low-resolution (3.8 Å) structure currently exists for the IR ectodomain, with some segments ill-defined or unmodeled due to disorder. Here, we revise this structure using new diffraction data to 3.3 Å resolution that allow improved modeling of the N-linked glycans, the first and third fibronectin type III domains, and the insert domain. A novel haptic interactive molecular dynamics strategy was used to aid fitting to low-resolution electron density maps. The resulting model provides a foundation for investigation of structural transitions in IR upon ligand binding.

The Human UDP-Glucuronosyltransferases: Studies on Substrate Binding and Catalytic Mechanism

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Human error risk management methodology for rail crack incidents

The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

Microarrays in the Diagnosis of Human Herpesvirus infections

Currently, there are nine known human herpesviruses and these viruses appear to have been a very common companion of humans throughout the millenia. Of human herpesviruses, herpes simplex viruses 1 and 2 (HSV-1, HSV-2), causative agents of herpes labialis and genital herpes, and varicella-zoster virus (VZV), causative agent of chicken pox, are also common causes of central nervous system (CNS) infections. In addition, human cytomegalovirus (CMV), Epstein-Barr virus (EBV) and human herpesviruses 6A, 6B, and 7 (HHV-6A, HHV-6B, HHV-7), all members of the herpesvirus family, can also be associated with encephalitis and meningitis. Accurate diagnostics and fast treatment are essential for patient recovery in CNS infections and therefore sensitive and effective diagnostic methods are needed. The aim of this thesis was to develop new potential detection methods for diagnosing of human herpesvirus infections, especially in immunocompetent patients, using the microarray technique. Therefore, methods based on microarrays were developed for simultaneous detection of HSV-1, HSV-2, VZV, CMV, EBV, HHV-6A, HHV-6B, and HHV-7 nucleic acids, and for HSV-1, HSV-2, VZV, and CMV antibodies from various clinical samples. The microarray methods developed showed potential for efficiently and accurately detecting human herpesvirus DNAs, especially in CNS infections, and for simultaneous detection of DNAs or antibodies for multiple different human herpesviruses from clinical samples. In fact, the microarray method revealed several previously unrecognized co-infections. The microarray methods developed were sensitive and provided rapid detection of human herpesvirus DNA, and therefore the method could be applied to routine diagnostics. The microarrays might also be considered as an economical tool for diagnosing human herpesvirus infections.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Mapping of assembled epitopic regions of human chorionic gonadotropin reveals proximity of CTP alpha to the determinant loop beta 93-100

Three overlapping assembled epitopes of beta hCG have been mapped using MAb probes and a single step solid phase radioimmunoassay. These epitopes have been shown to be at receptor binding region comprising of the loop region beta Cys93-Cys100. Importance of disulphide bonds in maintaining integrity of these epitopes is assessed. Two MAbs (INN 58 and INN 22) interact with the beta region as well as the alpha C-terminal peptide, while the other MAb INN 24 interacts with only the beta region. Cross-reactivity pattern with beta hCG and hLH as web as the reported crystal structure of hCG substantiates the epitope identification. The results demonstrate utility of MAbs as probes in investigations on three-dimensional structure of gonadatropins.