Dissertation (MSc)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--University of Chicago, Committee on Immunology, August 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 119-139).

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 73-126).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 140-172).

The research question is formulated as “what are the key success factors making a mobile game become a big success in China? ” to view the key success factors behind new launched mobile games and how company’s business model and marketing strategy that bring them into and help them succeed in the China market.   A qualitative method with the deductive approach has been using in this paper to be able to answer and interpret the studied questions. Four in-depth interviews were conducted to collect the primary data, which have been following as the purpose is to do a cross-case analysis to identify the similarities and difference of each company behave their business model and marketing strategy, to contribute game success in China market.   The main factors contributing to the success of mobile game in China market including internally strategic factors and externally tactic factors. Technical skill and resource, R&D ability and market knowledge and experience as the internal key success factors behind mobile game success in China. The mobile game companies use localization, wide distribution channel collaboration and social integration to suit the market needs and requirements.   From the results of the study have been identified to as to how is the business model for the China mobile game market. Through collaborating with abroad local distribution channel can increase their knowledge capacity of the local market to create a better value proposition. In China mobile game market, social integration and cross promotion can be seen as very important and through collaborating firms can work around these factors and create, capture and deliver better value to the customers.

Differential Item Functioning (DIF) occurs when examinees with the same ability have different probabilities of endorsing an item. Conventional DIF detection methods (e.g., the Mantel-Hansel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable (e.g., Cohen & Bolt, 2005). True source of DIF may be unobserved, including variables such as personality, response patterns, or unmeasured background variables. The Factor Mixture Model (FMM) is designed to detect unobserved sources of heterogeneity in factor structures, and an FMM with binary outcomes has recently been used for assessing DIF (DeMars & Lau, 2011; Jackman, 2010). However, FMMs with binary outcomes for detecting DIF have not been thoroughly explored to investigate both types of between-class latent DIF (LDIF) and class-specific observed DIF (ODIF). The present simulation study was designed to investigate whether models correctly specified in terms of LDIF and/or ODIF influence the performance of model fit indices (AIC, BIC, aBIC, and CAIC) and entropy, as compared to models incorrectly specified in terms of either LDIF or ODIF. In addition, the present study examined the recovery of item difficulty parameters and investigated the proportion of replications in which items were correctly or incorrectly identified as displaying DIF, by manipulating DIF effect size and latent class probability. For each simulation condition, two latent classes of 27 item responses were generated to fit a one parameter logistic model with items’ difficulties generated to exhibit DIF across the classes and/or the observed groups. Results showed that FMMs with binary outcomes performed well in terms of fit indices, entropy, DIF detection, and recovery of large DIF effects. When class probabilities were unequal with small DIF effects, performance decreased for fit indices, power, and the recovery of DIF effects compared to equal class probability conditions. Inflated Type I errors were found for invariant DIF items across simulation conditions. When data were generated to fit a model having ODIF but estimated LDIF, specifying LDIF in the model fully captured ODIF effects when DIF effect sizes were large. / text

Investigations on platelet-derived growth factor (PDGF) and serotonin (5-HT), molecules stored in platelet granules, imply their potential effects in regulating megakaryopoiesis, which also intimates the existence of an autocrine and/or paracrine loop constructed by megakaryocytes/platelets and their granular constituents. In addition, numerous reports indicate that melatonin, a derivative from serotonin effectively enhances platelet counts in patients with thrombocytopenia. However, their exact roles on human megakaryocytes and the underlying mechanisms remain unknown. Present studies showed that PDGF, like thrombopoietin (TPO), significantly promoted platelet recovery and the formation of bone marrow colony-forming unit-megakaryocyte (CFU-MK) in an irradiated-mouse model. An increased number of hematopoietic stem/progenitor cells and a reduction of apoptosis were found in the bone marrow aspirate. In the M-07e apoptotic model, PDGF had a similar anti-apoptotic effect as TPO on megakaryocytes. Our findings demonstrated that PDGF activated the PI3-k/Akt signaling pathway, while addition of imatinib mesylate reduced p-Akt expression. Our findings suggested that the PDGF-initiated radioprotective effect is likely to be mediated via PDGF receptors (PDGFRs) with subsequent activation of the PI3-k/Akt pathway. We also provide a possible explanation that blockade of PDGFR may reduce thrombopoiesis and play a role in imatinib mesylate-induced thrombocytopenia. We explored how serotonin regulated megakaryopoiesis and proplatelet formation. Our results indicated that serotonin (5-HT) significantly promoted CFU-MK formation and reduced apoptosis on megakaryocytes through phosphorylation of Akt. These effects were attenuated by addition of ketanserin, a 5-HT2 receptor inhibitor. In addition, serotonin was able to stimulate the F-actin reorganization in megakaryocytes through activating the p-Erk1/2 expression. Bone marrow mesenchymal stromal cells (MSCs) are important in regulating megakaryopoiesis through stimulating the release of thrombopoietic growth factor, such as TPO. Our studies suggested that when activated by serotonin, bone marrow MSCs were induced to release significant amount of TPO. Furthermore, thousands of membrane-derived microparticles (MPs) arose from MSCs and the TPO RNA/proteins contained within MPs were also considerably increased under serotonin treatment. In summary, our findings demonstrated an important role serotonin played on megakaryopoiesis. This effect was likely mediated via 5HT2 receptors with subsequent activation of Akt and Erk 1/2 phosphorylation, which led to survival of megakaryocytes and proplatelet formation. Serotonin also stimulated TPO released from MSCs in both dissociative and MP-encapsulated form, which indirectly promoted megakaryopoiesis. The effects of melatonin on megakaryopoiesis were also determined in our studies. Our findings showed that melatonin enhanced proliferation and reduced doxorubicin-induced toxicity on MKs. We further demonstrated the mechanism for melatonin-mediated protection on MKs maybe via repair of G2/M phase cell cycle arrest and inhibition of cell apoptosis on MK cells. The effects of melatonin on megakaryopoiesis were also determined in our studies. Our findings showed that melatonin enhanced proliferation and reduced doxorubicin-induced toxicity on MKs. We further demonstrated the mechanism for melatonin-mediated protection on MKs maybe via repair of G2/M phase cell cycle arrest and inhibition of cell apoptosis on MK cells. / published_or_final_version / Paediatrics and Adolescent Medicine / Doctoral / Doctor of Philosophy

This study completed the analysis of mutational frequencies and clinicopathological patterns of six EGFR pathway-related genes (EGFR, HER2, HER4, KRAS, BRAF and MET) in 212 resected lung adenocarcinomas (AD) from 98 male and 114 female Chinese patients without prior chemotherapy or tyrosine kinase inhibitor (TKI) therapy. Genomic DNA and cDNA sequencing, quantitative PCR and fluorescence in-situ hybridization (FISH) were employed to investigate mutation and amplification status of the relevant genes. Overall, more than 75% of tumours were detected to harbour mutations or amplification in one of these six genes. The commonest mutation was found to involve EGFR, comprising 60.38% of cases, followed by KRAS (9.43%), HER2 (2.36%), MET (2.36%), BRAF (1.42%) and HER4 (0.47%). Four somatic mutations in MET exon 14 splicing region were found, leading to alternative splicing and a transcript lacking exon 14. Two of the MET mutant tumours and one MET wild-type tumour showed MET amplification of more than 3.5 fold increase in copy number. Mutations of EGFR were significantly more frequent in female (p = 0.0196), non-smokers (p < 0.001) and well differentiated tumours (p = 0.0209). KRAS mutations showed significant association with male (p = 0.0099) and smoking history (p = 0.0011). A novel HER2 D769Y mutation was found and HER2 mutations were associated with smokers (p = 0.0013) and poorly differentiated tumours (p = 0.0147). BRAF, MET mutations and MET amplification were not associated with clinicopathological factors. Mutations were mutually exclusive except for two cases with KRAS and HER4/BRAF. MET amplification was co-existent with MET mutations in two cases. MET amplification was found to negatively correlate with disease-free and cancer-specific survivals. The results suggested that MET amplification may contribute to disease progression and could be a therapeutic target in primary lung AD in Hong Kong Chinese patients. / published_or_final_version / Pathology / Master / Master of Medical Sciences

Reconstruction of Transcription Regulatory Network (TRN) and Transcription Factor Activity (TFA) from gene expression data is an important problem in systems biology. Currently, there exist various factor analysis methods for TRN reconstruction, but most approaches have specific assumptions not satisfied by real biological data. Network Component Analysis (NCA) can handle such limitations and is considered to be one of the most effective methods. The prerequisite for NCA is knowledge of the structure of TRN. Such structure can be obtained from ChIP-chip or ChIP-seq experiments, which however have quite limited applications. In order to cope with the difficulty, we resort to heuristic optimization algorithm such as Particle Swarm Optimization (PSO), in order to explore the possible structures of TRN and choose the most plausible one. Regarding the structure estimation problem, we extend classical PSO and propose a novel Probabilistic binary PSO. Furthermore, an improved NCA called FastNCA is adopted to compute the objective function accurately and fast, which enables PSO to run efficiently. Since heuristic optimization cannot guarantee global convergence, we run PSO multiple times and integrate the results. Then GCV-LASSO (Generalized Cross Validation - Least Absolute Shrinkage and Selection Operator) is performed to estimate TRN. We apply our approach and other factor analysis methods on the synthetic data. The results indicate that the proposed PSOFastNCA-GCV-LASSO algorithm gives better estimation. In order to incorporate more prior information on TRN structure and gene expression dynamics in the linear factor analysis model for improved estimation of TRN and TFAs, a linear Bayesian framework is adopted. Under the unified Bayesian framework, Bayesian Linear Sparse Factor Analysis Model (BLSFM) and Bayesian Linear State Space Model (BLSSM) are developed for instantaneous and dynamic TRN, respectively. Various approaches to incorporate partial and ambiguous prior network structure information in the Bayesian framework are proposed to improve performance in practical applications. Furthermore, we propose a novel mechanism for estimating the hyper-parameters of the distribution priors in our BLSFM and BLSSM models, which can significantly improve the estimation compared to traditional ways of hyper-parameter setting. With this development, reasonably good estimation of TFAs and TRN can be obtained even without use of any structure prior of TRN. Extensive numerical experiments are performed to investigate our developed methods under various settings, with comparison to some existing alternative approaches. It is demonstrated that our hyper-parameter estimation method improves the estimation of TFA and TRN in most settings and has superior performance, and that structure priors in general leads to improved estimation performance. Regarding application to real biological data, we execute the PSO-FastNCAGCV-LASSO algorithm developed in the thesis using E. Coli microarray data and obtain sensible estimation of TFAs and TRN. We apply BLSFM without structure priors of TRN, BLSSM without structure priors as well as with partial structure priors to Yeast S. cerevisiae microarray data and obtain a reasonable estimation of TFAs and TRN. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy