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Lee Sum Wai = 應用多目標基因演算法於合併三維破裂物件 / 李芯慧. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Lee Sum Wai = Ying yong duo mu biao ji yin yan suan fa yu he bing san wei po lie wu jian / Li Xinhui. / Contents --- p.VI / List of Figures --- p.IX / List of Tables --- p.XIII / Chapter Chapter 1 --- Introduction --- p.1-1 / Chapter 1.1. --- A review of assembling objects --- p.1-3 / Chapter 1.1.1. --- Two-Dimensional matching --- p.1-3 / Chapter 1.1.2. --- Three-Dimensional matching --- p.1-4 / Chapter 1.1.3. --- 2.5-Dimensional matching --- p.1-5 / Chapter 1.2. --- Objectives of this research work --- p.1-7 / Chapter 1.2.1. --- Local Matching of fragments --- p.1-7 / Chapter 1.2.2. --- Global Matching fragments --- p.1-8 / Chapter 1.3. --- Thesis Outline --- p.1-9 / Chapter Chapter 2 --- Background Information --- p.2-1 / Chapter 2.1. --- Three-Dimensional Objects Representation --- p.2-1 / Chapter 2.2. --- Three-Dimensional Objects Geometric Transformation --- p.2-3 / Chapter 2.1.1. --- Translation --- p.2-4 / Chapter 2.1.2. --- Rotation --- p.2-5 / Chapter 2.3. --- Orientated Bounding Box (OBB) --- p.2-6 / Chapter 2.4. --- Scan-Line Method --- p.2-7 / Chapter 2.5. --- Mesh Simplification --- p.2-10 / Chapter 2.6. --- Review of the Surface Matching Method --- p.2-12 / Chapter 2.6.1. --- G. Papaioannou et al ´بs method --- p.2-13 / Chapter Chapter 3 --- Genetic Algorithm --- p.3-1 / General introduction --- p.3-1 / Chapter 3.1. --- Characteristics of Genetic Algorithms --- p.3-3 / Chapter 3.2. --- Mechanism of Genetic Algorithms --- p.3-4 / Chapter 3.2.1. --- Coding --- p.3-4 / Chapter 3.2.2. --- Reproduction --- p.3-5 / Chapter 3.2.3. --- Selection --- p.3-8 / Chapter 3.2.4. --- Stopping Criteria --- p.3-9 / Chapter 3.3. --- Convergence of Genetic Algorithms --- p.3-10 / Chapter 3.4. --- Comparison with Traditional Optimization Methods --- p.3-13 / Chapter 3.4.1. --- Test Function - Sphere --- p.3-14 / Chapter 3.4.2. --- Test Function - Rosenbrock's Saddle --- p.3-19 / Chapter 3.4.3. --- Test Function 一 Step --- p.3-22 / Chapter 3.4.4. --- Test Function -Quartic --- p.3-25 / Chapter 3.4.5. --- Test Function - Shekel's Foxholes --- p.3-28 / Chapter 3.5. --- Multi-Objective Genetic Algorithms --- p.3-29 / Chapter 3.5.1. --- Non-Pareto Approach --- p.3-31 / Chapter 3.5.2. --- Pareto-Ranking --- p.3-32 / Chapter 3.5.3. --- Comparison --- p.3-35 / Chapter Chapter 4 --- Assembling broken objects (I) --- p.4-1 / Chapter 4.1. --- System Flow of Single Pair Assemblage --- p.4-2 / Chapter 4.2. --- Parameterization --- p.4-3 / Chapter 4.2.1. --- Degree of Freedom --- p.4-3 / Chapter 4.2.2. --- Reference Plane and Sampling Points --- p.4-4 / Chapter 4.3. --- Matching Error --- p.4-5 / Chapter 4.3.1. --- Counterpart Surface Matching Error --- p.4-5 / Chapter 4.3.2. --- Border Matching Error --- p.4-7 / Chapter 4.4. --- Correlation-Based Matching Method --- p.4-14 / Chapter Chapter 5 --- Assembling Broken Objects (II)- Global Matching --- p.5-1 / Chapter 5.1. --- Arrangement Strategy --- p.5-2 / Chapter 5.1.1. --- Introduction to Packing --- p.5-2 / Chapter 5.1.2. --- Proposed Architecture --- p.5-6 / Chapter 5.2. --- Relational Multi-Objective Genetic Algorithm --- p.5-13 / Chapter 5.2.1. --- Existing Problem --- p.5-13 / Chapter 5.2.2. --- A New Operator --- p.5-14 / Chapter 5.2.3. --- Relationship Function --- p.5-16 / Chapter 5.3. --- Conclusion and summary --- p.5-20 / Chapter Chapter 6 --- Optimization Approach by Genetic Algorithm --- p.6-1 / Chapter 6.1. --- Solution Space --- p.6-1 / Chapter 6.2. --- Formulation of Gene and Chromosome --- p.6-3 / Chapter 6.2.1. --- Matching Three or More Fragments --- p.6-4 / Chapter 6.2.2. --- Matching Two Fragments --- p.6-5 / Chapter 6.3. --- Fitness Function --- p.6-5 / Chapter 6.3.1. --- Matching Two Fragments --- p.6-5 / Chapter 6.3.2. --- Matching Three or More Fragments --- p.6-6 / Chapter 6.4. --- Reproduction --- p.6-7 / Chapter 6.4.1. --- Crossover --- p.6-8 / Chapter 6.4.2. --- Mutation --- p.6-9 / Chapter 6.4.3. --- Inheritance --- p.6-9 / Chapter 6.5. --- Selection --- p.6-9 / Chapter Chapter 7 --- Experimental Results --- p.7-1 / Chapter 7.1 --- Data Acquisition --- p.7-1 / Chapter 7.2 --- Experiment for Mesh Simplification --- p.7-4 / Chapter 7.3 --- Experiment for Correlation-Based Matching Method --- p.7-5 / Chapter 7.4 --- Experiment One: Two Fragments --- p.7-6 / Chapter 7.5 --- Experiment Two: Several Fragments --- p.7-10 / Chapter 7.5.1 --- Constraint Direction Matching --- p.7-10 / Chapter 7.5.2 --- Unconstraint Direction Matching --- p.7-14 / Chapter Chapter 8 --- Conclusion --- p.8-1 / Appendix Reference --- p.1

Ng Sai-Cheong. / Thesis submitted in: December 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 172-178). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Problem Specifications and Motivations --- p.3 / Chapter 1.3 --- Contributions of the Thesis --- p.5 / Chapter 1.4 --- Thesis Roadmap --- p.6 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Supervised Classification Techniques --- p.9 / Chapter 2.1.1 --- Classification Rules --- p.9 / Chapter 2.1.2 --- Decision Trees --- p.11 / Chapter 2.2 --- Evolutionary Algorithms --- p.19 / Chapter 2.2.1 --- Genetic Algorithms --- p.19 / Chapter 2.2.2 --- Genetic Programming --- p.24 / Chapter 2.2.3 --- Evolution Strategies --- p.26 / Chapter 2.2.4 --- Evolutionary Programming --- p.32 / Chapter 2.3 --- Applications of Evolutionary Algorithms to Induction of Classification Rules --- p.33 / Chapter 2.3.1 --- SCION --- p.33 / Chapter 2.3.2 --- GABIL --- p.34 / Chapter 2.3.3 --- LOGENPRO --- p.35 / Chapter 2.4 --- Applications of Evolutionary Algorithms to Construction of Decision Trees --- p.35 / Chapter 2.4.1 --- Binary Tree Genetic Algorithm --- p.35 / Chapter 2.4.2 --- OC1-GA --- p.36 / Chapter 2.4.3 --- OC1-ES --- p.38 / Chapter 2.4.4 --- GATree --- p.38 / Chapter 2.4.5 --- Induction of Linear Decision Trees using Strong Typing GP --- p.39 / Chapter 2.5 --- Spatial Data Structures and its Applications --- p.40 / Chapter 2.5.1 --- Spatial Data Structures --- p.40 / Chapter 2.5.2 --- Applications of Spatial Data Structures --- p.42 / Chapter 3 --- Induction of Classification Rules using Genetic Algorithms --- p.45 / Chapter 3.1 --- Introduction --- p.45 / Chapter 3.2 --- Rule Learning using Genetic Algorithms --- p.46 / Chapter 3.2.1 --- Population Initialization --- p.47 / Chapter 3.2.2 --- Fitness Evaluation of Chromosomes --- p.49 / Chapter 3.2.3 --- Token Competition --- p.50 / Chapter 3.2.4 --- Chromosome Elimination --- p.51 / Chapter 3.2.5 --- Rule Migration --- p.52 / Chapter 3.2.6 --- Crossover --- p.53 / Chapter 3.2.7 --- Mutation --- p.55 / Chapter 3.2.8 --- Calculating the Number of Correctly Classified Training Samples in a Rule Set --- p.56 / Chapter 3.3 --- Performance Evaluation --- p.56 / Chapter 3.3.1 --- Performance Comparison of the GA-based CPRLS and Various Supervised Classifi- cation Algorithms --- p.57 / Chapter 3.3.2 --- Performance Comparison of the GA-based CPRLS and RS-based CPRLS --- p.68 / Chapter 3.3.3 --- Effects of Token Competition --- p.69 / Chapter 3.3.4 --- Effects of Rule Migration --- p.70 / Chapter 3.4 --- Chapter Summary --- p.73 / Chapter 4 --- Genetic Algorithm-based Quadratic Decision Trees --- p.74 / Chapter 4.1 --- Introduction --- p.74 / Chapter 4.2 --- Construction of Quadratic Decision Trees --- p.76 / Chapter 4.3 --- Evolving the Optimal Quadratic Hypersurface using Genetic Algorithms --- p.77 / Chapter 4.3.1 --- Population Initialization --- p.80 / Chapter 4.3.2 --- Fitness Evaluation --- p.81 / Chapter 4.3.3 --- Selection --- p.81 / Chapter 4.3.4 --- Crossover --- p.82 / Chapter 4.3.5 --- Mutation --- p.83 / Chapter 4.4 --- Performance Evaluation --- p.84 / Chapter 4.4.1 --- Performance Comparison of the GA-based QDT and Various Supervised Classification Algorithms --- p.85 / Chapter 4.4.2 --- Performance Comparison of the GA-based QDT and RS-based QDT --- p.92 / Chapter 4.4.3 --- Effects of Changing Parameters of the GA-based QDT --- p.93 / Chapter 4.5 --- Chapter Summary --- p.109 / Chapter 5 --- Induction of Linear and Quadratic Decision Trees using Spatial Data Structures --- p.111 / Chapter 5.1 --- Introduction --- p.111 / Chapter 5.2 --- Construction of k-D Trees --- p.113 / Chapter 5.3 --- Construction of Generalized Quadtrees --- p.119 / Chapter 5.4 --- Induction of Oblique Decision Trees using Spatial Data Structures --- p.124 / Chapter 5.5. --- Induction of Quadratic Decision Trees using Spatial Data Structures --- p.130 / Chapter 5.6 --- Performance Evaluation --- p.139 / Chapter 5.6.1 --- Performance Comparison with Various Supervised Classification Algorithms --- p.142 / Chapter 5.6.2 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a k-D Tree --- p.155 / Chapter 5.6.3 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a Generalized Quadtree --- p.157 / Chapter 5.6.4 --- Effects of Changing the Size of Datasets . --- p.158 / Chapter 5.7 --- Chapter Summary --- p.160 / Chapter 6 --- Conclusions --- p.164 / Chapter 6.1 --- Contributions --- p.164 / Chapter 6.2 --- Future Work --- p.167 / Chapter A --- Implementation of Data Mining Algorithms Specified in the Thesis --- p.170 / Bibliography --- p.178

by Yong Liang. / "November 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. 237-266). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

非洲爪蛙(Xenopus laevis)和熱帶爪蛙(Xenopus tropicalis)是經典的模式動物，廣泛應用於胚胎學，發育生物學和人類疾病模型研究領域。然而，由於缺乏有效地同源重組技術和胚胎幹細胞誘導技術，在上述兩種模式動物中很難通過定點突變研究特定基因的功能。這些技術瓶頸制約其在遺傳學研究方面的應用。近來，通過鋅指核酸酶(ZFNs)和類轉錄激活因子效應物核酸酶(TALENs)介導的特異性基因突變技術成功地應用於各種模式動物模型，包括：線蟲、斑馬魚和大鼠。鋅指核酸酶和TALENs核酸酶都是由可編碼設計的DNA結合功能域和非特異性FokI核酸酶的功能域組成的人工構建DNA核酸酶。結合到相鄰DNA位點的鋅指核酸酶或TALENs核酸酶的單體通過FokI功能域結合形成二聚體，從而啟動其核酸酶活性，在預設靶點產生DNA雙鏈斷裂 (double-strand breaks)。通常，非同源末端連接(NHEJ)在修復DNA雙鏈斷裂過程中會導致缺失或者插入突變(indel)。在此研究中，我們在世界上首次利用TALENs核酸酶在爪蛙胚胎中高效誘導產生體細胞突變。我們優化了Golden Gate TALENs核酸酶的拼接方法，使其便於體外RNA轉錄和顯微注射到爪蛙胚胎中。我們還利用基於聚合酶鏈式反應(PCR)的檢測方法，用於檢測基因突變效率。我們設計八對TALENs核酸酶，它們分別靶向識別爪蛙中八個基因。試驗結果表明它們全部都能夠在爪蛙胚胎中誘導基因突變，突變率最高達百分之九十五點七(95.7%)。我們進一步證明，TALENs核酸酶誘導產生的突變可以通過生殖細胞高效地傳遞給F1代的爪蛙。不僅如此，我們還嘗試運用最新的RNA介導的基因組編輯方法(CRISPR)在小鼠誘導多功能幹細胞(iPSCs)模型中研究基因修正。初步結果顯示, 我們設計的TALENs核酸酶和CRISPR可以在小鼠誘導多功能幹細胞中有效地產生基因突變。 / 綜上所述，我們在世界上首次在爪蛙中報導了運用TALENs核酸酶進行反向遺傳學研究。利用TALENs核酸酶誘導突變的方法簡單但高效。我們的實驗結果表明TALENs核酸酶是一種在爪蛙中進行基因編輯或者基因敲除的有效工具。 / Xenopus laevis and Xenopus tropicalis are classical and powerful animal models widely used in the study of embryonic development and human disease modeling. However, due to the lack of methodologies for homologous recombination and embryonic stem cell derivation, it is difficult to perform specific gene targeting inthese two models, which has impeded their use in genetic studies for decades. Recently, site-specific gene targeting by using either zinc finger nucleases (ZFNs) or transcription activator-like effector nucleases (TALENs) has been successfully applied in various animal models including C. elegans, zebrafish, and rat. Both ZFNs and TALENs are engineered DNA nucleases that consist of a custom-designed DNA-binding domain and a nonspecific nuclease domain derived from FokI endonuclease. Binding of adjacent ZFNs or TALENs allows dimerization of the endonuclease domains, leading to double-strand breaks at the predetermined site. These double-strand DNA breaks are frequently repaired through non-omologous end joining (NHEJ), resulting in deletion or insertion (indel) mutations. Here we reported that TALENs can induce somatic mutations in Xenopus embryos with reliably high efficiency and that such mutations are heritable through germline transmission. We modified the Golden Gate method for TALEN assembly to make the product suitable for in vitro RNA transcription and microinjection into Xenopus embryos, and designed a reliable PCR-based assay for the evaluation of gene disruption efficiency. Totally, eight pairs of TALENs were constructed to target eight different Xenopus genes, and all resulted in indel mutations with high efficiencies of up to 95.7% at the targeted loci according to our PCR-based assay. Furthermore, mutations induced by TALENs were highly efficiently passed through the germline to F1 frogs. Moreover, we tried to employ newly published RNA-mediated genome editing tool, clustered regularly interspaced palindromic repeat (CRISPR), to study gene correction in mice induced pluripotent stem cells (iPSCs) model. Our preliminary data showed that TALENs and CRISPR we constructed can efficiently introduce mutations at predetermined sites in mice iPSCs. / So far, our result is the first report to perform specific reverse genetic via TALENs in Xenopus. Together with simple and reliable approaches for detecting TALEN-induced mutations, our results indicate that TALENs are an effective tool for targeted gene editing/knockout in Xenopus. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lei, Yong. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 146-171). / Abstracts also in Chinese. / Abstract --- p.I / Acknowledgements --- p.V / Statement --- p.VI / Abbreviation --- p.VII / Contents --- p.IX / Chapter 1. --- Research Background --- p.1 / Chapter 1.1 --- Introduction of reverse genetics --- p.1 / Chapter 1.2 --- Introduction of Xenopus tropicalis --- p.3 / Chapter 1.3 --- Xenopus tropicalis as an animal model for reverse genetics --- p.6 / Chapter 1.4 --- Zinc finger nucleases (ZFNs) --- p.10 / Chapter 1.5 --- Transcription activator-like effector nucleases (TALENs) --- p.29 / Chapter 1.6 --- Clustered regularly interspaced short palindromic repeats (CRISPR) --- p.40 / Chapter 2. --- Objective --- p.52 / Chapter 3. --- Methods and Materials --- p.54 / Chapter 3.1 --- Materials --- p.54 / Chapter 3.1.1 --- Reagents --- p.54 / Chapter 3.1.2 --- Vectors --- p.55 / Chapter 3.1.3 --- Primers --- p.57 / Chapter 3.2 --- Molecular Biology --- p.59 / Chapter 3.2.1 --- Preparation of chemical competent E.coli. --- p.59 / Chapter 3.2.2 --- Transformation --- p.60 / Chapter 3.2.3 --- Mini-preparation of plasmid --- p.61 / Chapter 3.2.4 --- Midi-preparation of plasmid --- p.62 / Chapter 3.2.5 --- Tissue RNA extraction and purification --- p.63 / Chapter 3.2.6 --- Reverse-transcription polymerase chain reaction --- p.64 / Chapter 3.2.7 --- Polymerase chain reaction (PCR) --- p.64 / Chapter 3.2.8 --- PCR/Gel extraction --- p.65 / Chapter 3.2.9 --- Synthesis of mRNA for microinjection --- p.65 / Chapter 3.2.10 --- Synthesis of DIG-labeled anti-sense RNA probe --- p.65 / Chapter 3.2.11 --- Subcloning --- p.66 / Chapter 3.2.12 --- TA cloning --- p.67 / Chapter 3.3 --- Xenopus embryo manipulation --- p.67 / Chapter 3.3.1 --- Xenopus maintenance and handling --- p.67 / Chapter 3.3.2 --- Embryos collection and handing --- p.68 / Chapter 3.3.3 --- Microinjection --- p.69 / Chapter 3.3.4 --- lacZ staining --- p.70 / Chapter 3.3.5 --- Whole-mount in situ hybridization (WHISH) --- p.70 / Chapter 3.4 --- Gene disruption via TALENs --- p.76 / Chapter 3.4.1 --- Extraction and normalization of TALEN assembly vectors --- p.76 / Chapter 3.4.2 --- TALENs assembly with Golden Gate method. --- p.78 / Chapter 3.4.3 --- Synthesize TALEN mRNAs in vitro. --- p.84 / Chapter 3.4.4 --- Microinjection of TALENs into Xenopus embryos and evaluation of gene disruption efficiency --- p.84 / Chapter 3.5 --- CRISPR gRNA synthesis --- p.90 / Chapter 3.6 --- T7E1 assay for mutagenesis detection --- p.94 / Chapter 3.7 --- Tissue culture of mouse induced pluripotent stem cells (iPSCs). --- p.94 / Chapter 3.7.1 --- STO feeder cell collection --- p.94 / Chapter 3.7.2 --- iPSCs passage --- p.95 / Chapter 3.7.3 --- List of tissue culture medium --- p.96 / Chapter 4. --- Results --- p.97 / Chapter 4.1 --- TALENs induce targeted gene disruption in Xenopus embryos --- p.97 / Chapter 4.2 --- Phenotypes of somatic mutations induced by TALENs in X. tropicalis --- p.113 / Chapter 4.3 --- Mutations induced by TALENs were heritable in X. tropicalis --- p.117 / Chapter 4.4 --- TALENs and CRISPR-Cas induced gene disruption in mouse induced pluripotent stem cells (iPSCs) --- p.119 / Chapter 4.5 --- The application of TALE-based regulator --- p.124 / Chapter 4.6 --- List of TALENs, TALE-activator, and CRISPR-Cas vectors --- p.125 / Chapter 5. --- Discussion --- p.131 / Chapter 5.1 --- TALENs induce somatic and heritable mutagenesis in Xenopus --- p.131 / Chapter 5.2 --- Engineered endonucleases are valuable tools in the study of reverse genetics --- p.136 / Chapter 5.3 --- Potential applications of ZFPs and TALEs in genetic manipulation --- p.139 / Chapter 5.4 --- Prospects of genome editing approaches in the therapies of human diseases --- p.141 / Chapter 6. --- References --- p.146 / Chapter 7. --- Publications --- p.172

Lee Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 108-115). / Chapter 1 --- Introduction --- p.8 / Chapter 1.1 --- Introduction to Group Technology --- p.8 / Chapter 1.2 --- Cell design --- p.9 / Chapter 1.3 --- Objectives of the research --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter 2 --- Literature review --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Standard models --- p.14 / Chapter 2.2.1 --- Array-based methods --- p.16 / Chapter 2.2.2 --- Cluster identification --- p.16 / Chapter 2.2.3 --- Graph-based methods --- p.17 / Chapter 2.2.4 --- Integer programming --- p.17 / Chapter 2.2.5 --- Seed-based --- p.18 / Chapter 2.2.6 --- Similarity coefficient --- p.18 / Chapter 2.2.7 --- Artificial intelligence methods --- p.19 / Chapter 2.3 --- Generalized models --- p.19 / Chapter 2.3.1 --- Machine assignment models --- p.20 / Chapter 2.3.2 --- Part family models --- p.20 / Chapter 2.3.3 --- Cell formation models --- p.21 / Chapter 3 --- Genetic cell formation algorithm --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- TSP formulation for a permutation of machines --- p.23 / Chapter 3.3 --- Genetic algorithms --- p.26 / Chapter 3.3.1 --- Representation and basic crossover operators --- p.27 / Chapter 3.3.2 --- Fitness function --- p.28 / Chapter 3.3.3 --- Initialization --- p.29 / Chapter 3.3.4 --- Parent selection strategies --- p.30 / Chapter 3.3.5 --- Crossover --- p.31 / Chapter 3.3.6 --- Mutation --- p.37 / Chapter 3.3.7 --- Replacement --- p.38 / Chapter 3.3.8 --- Termination --- p.38 / Chapter 3.4 --- Formation of machine cells and part families --- p.39 / Chapter 3.4.1 --- Objective functions --- p.39 / Chapter 3.4.2 --- Machine assignment --- p.42 / Chapter 3.4.3 --- Part assignment --- p.43 / Chapter 3.5 --- Implementation --- p.43 / Chapter 3.6 --- An illustrative example --- p.45 / Chapter 3.7 --- Comparative Study --- p.49 / Chapter 3.8 --- Conclusions --- p.50 / Chapter 4 --- A multi-chromosome GA for minimizing total intercell and intracell moves --- p.55 / Chapter 4.1 --- Introduction --- p.55 / Chapter 4.2 --- The model --- p.57 / Chapter 4.3 --- Solution techniques to the workload model --- p.61 / Chapter 4.3.1 --- Logendran's original approach --- p.62 / Chapter 4.3.2 --- Standard representation - the GA approach --- p.63 / Chapter 4.3.3 --- Multi-chromosome representation --- p.65 / Chapter 4.4 --- Comparative Study --- p.70 / Chapter 4.4.1 --- Problem 1 --- p.70 / Chapter 4.4.2 --- Problem 2 --- p.71 / Chapter 4.4.3 --- Problem 3 --- p.75 / Chapter 4.4.4 --- Problem 4 --- p.76 / Chapter 4.5 --- Bi-criteria Model --- p.79 / Chapter 4.5.1 --- Experimental results --- p.85 / Chapter 4.6 --- Conclusions --- p.85 / Chapter 5 --- Integrated design of cellular manufacturing systems in the presence of alternative process plans --- p.88 / Chapter 5.1 --- Introduction --- p.88 / Chapter 5.1.1 --- Literature review --- p.90 / Chapter 5.1.2 --- Motivation --- p.92 / Chapter 5.2 --- Mathematical models --- p.93 / Chapter 5.2.1 --- Notation --- p.93 / Chapter 5.2.2 --- Objective functions --- p.95 / Chapter 5.3 --- Our solution --- p.96 / Chapter 5.4 --- Illustrative example and analysis of results --- p.98 / Chapter 5.4.1 --- Solution for objective function 1 --- p.101 / Chapter 5.4.2 --- Solution for objective function 2 --- p.102 / Chapter 5.5 --- Conclusions --- p.103 / Chapter 6 --- Conclusions --- p.104 / Chapter 6.1 --- Summary of achievements --- p.104 / Chapter 6.2 --- Future works --- p.106

Wong, Man Chun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 141-146). / Abstracts in English and Chinese. / Abstract --- p.iii / 摘要 --- p.v / Acknowledgments --- p.vii / Table of contents --- p.viii / List of tables --- p.xi / List of figures --- p.xii / List of appendix --- p.xv / Abbreviations --- p.xvi / Chapter Chapter 1 --- Literature review --- p.1 / Chapter 1.1 --- Background of Lentinula edodes --- p.1 / Chapter 1.2 --- Life cycle and mating system of Lentinula edodes --- p.1 / Chapter 1.3 --- Breeding and strain improvement --- p.5 / Chapter 1.4 --- Application of molecular markers --- p.6 / Chapter 1.5 --- Objectives and long term significance --- p.9 / Chapter Chapter 2 --- Genome survey sequencing and preliminary analysis --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.1.1 --- Genome sequencing of basidiomycetes --- p.11 / Chapter 2.1.2 --- Polymerase chain reaction-single strand conformational polymorphism --- p.12 / Chapter 2.1.3 --- Sequencing chemistry --- p.13 / Chapter 2.2 --- Materials and methods --- p.15 / Chapter 2.2.1 --- Strain and DNA extraction --- p.15 / Chapter 2.2.2 --- PCR-SSCP analysis --- p.15 / Chapter 2.2.3 --- Shotgun sequencing and sequence assembly --- p.17 / Chapter 2.2.4 --- Comparison with 5 basidiomycetes --- p.17 / Chapter 2.3 --- Results --- p.19 / Chapter 2.3.1 --- PCR-SSCP --- p.19 / Chapter 2.3.2 --- Shotgun sequencing and assembly --- p.21 / Chapter 2.3.3 --- Comparison with 5 basidiomycetes --- p.22 / Chapter 2.4 --- Discussion --- p.30 / Chapter Chapter 3 --- Cloning of A mating-type locus of Lentinula edodes --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Materials and methods --- p.35 / Chapter 3.2.1 --- Genome sequencing and assembly --- p.35 / Chapter 3.2.2 --- Genomic screening of A-mating type genes --- p.35 / Chapter 3.2.3 --- Gap filling and sequence confirmation --- p.36 / Chapter 3.2.4 --- Alignment of overlapping sequences to give contiguous sequence --- p.37 / Chapter 3.2.5 --- Open reading frame prediction and protein homolog search --- p.37 / Chapter 3.2.6 --- Conserved domain search --- p.37 / Chapter 3.2.7 --- Testing for polymorphism --- p.38 / Chapter 3.3 --- Results --- p.39 / Chapter 3.3.1 --- Genomic screening of A-mating type genes --- p.39 / Chapter 3.3.2 --- Gap filling and sequence confirmation --- p.45 / Chapter 3.3.3 --- Protein homologs and putative protein domains --- p.48 / Chapter 3.3.4 --- Polymorphism of A mating-type genes --- p.53 / Chapter 3.4 --- Discussion --- p.55 / Chapter 3.4.1 --- Genome mining of the A mating-type locus of L. edodes --- p.55 / Chapter 3.4.2 --- Genomic structure of the A mating-type region in L. edodes --- p.55 / Chapter 3.4.3 --- Functional protein domains in A mating-type genes --- p.56 / Chapter 3.4.4 --- Polymorphism of A mating- type locus --- p.58 / Chapter 3.4.5 --- Conclusion and future perspectives --- p.59 / Chapter Chapter 4 --- Simple sequence repeat (SSR) markers development --- p.60 / Chapter 4.1 --- Introduction --- p.60 / Chapter 4.2 --- Materials and methods --- p.62 / Chapter 4.2.1 --- Strains --- p.62 / Chapter 4.2.2 --- Datasets for SSRs mining --- p.63 / Chapter 4.2.3 --- in silico detection of SSR motifs and primer design --- p.63 / Chapter 4.2.4 --- SSR amplification --- p.64 / Chapter 4.2.5 --- Cloning and sequencing of PCR products --- p.64 / Chapter 4.2.6 --- Testing for polymorphism --- p.65 / Chapter 4.3 --- Results --- p.66 / Chapter 4.3.1 --- in silico detection of SSR motifs and primer design --- p.66 / Chapter 4.3.2 --- SSR amplification --- p.69 / Chapter 4.3.3 --- SSR polymorphism --- p.83 / Chapter 4.4 --- Discussion --- p.86 / Chapter 4.4.1 --- Efficiency of in silico detection of SSR motifs and primer design --- p.86 / Chapter 4.4.2 --- Effectiveness and polymorphism of SSR primer pairs --- p.89 / Chapter 4.4.3 --- Conclusion and future perspectives --- p.90 / Chapter Chapter 5 --- High-throughput sequencing of AP-PCR amplicons for SCAR markers development and phylogenetic analysis --- p.91 / Chapter 5.1 --- Introduction --- p.91 / Chapter 5.2 --- Materials and methods --- p.94 / Chapter 5.2.1 --- Strains --- p.94 / Chapter 5.2.2 --- AP-PCR analysis --- p.94 / Chapter 5.2.3 --- Re-amplification of AP-PCR amplicons --- p.96 / Chapter 5.2.4 --- GS-FLX sequencing --- p.96 / Chapter 5.2.5 --- Strain-specific sequences identification --- p.97 / Chapter 5.2.6 --- SCAR marker analysis --- p.97 / Chapter 5.2.7 --- Phylogenetic analysis --- p.99 / Chapter 5.3 --- Results --- p.100 / Chapter 5.3.1 --- AP-PCR analysis --- p.100 / Chapter 5.3.2 --- Re-amplification of AP-PCR amplicons --- p.100 / Chapter 5.3.3 --- GS-FLX sequencing and strain-specific sequence identification --- p.103 / Chapter 5.3.4 --- SCAR marker analysis --- p.106 / Chapter 5.3.5 --- Phylogenetic analysis --- p.108 / Chapter 5.4 --- Discussion --- p.111 / Chapter 5.4.1 --- Sensitivity of band detection --- p.111 / Chapter 5.4.2 --- SCAR marker development --- p.111 / Chapter 5.4.3 --- Phylogenetic analysis --- p.113 / Chapter 5.4.4 --- Conclusion --- p.114 / Chapter Chapter 6 --- Concluding remarks --- p.115 / Chapter 6.1 --- Project summary --- p.115 / Chapter 6.2 --- Future perspectives --- p.119 / Appendix --- p.121 / References --- p.141

Previous methods for genetic transformation in Azotobacter vinelandii have employed poorly defined genetic markers or crude DNA extracts. An improved transformation technique has been developed for use in Azotobacter. The technique was used to transform several strains of Azotobacter with DNA carrying a defined genetic marker. A method for isolating pure, high molecular weight, biologically active DNA from Azotobacter is also presented. Purity of the extracted DNA was determined by standard chemical assays. The molecular weight was determined by boundary sedimentation techniques to be 18.2 megadaltons. DNA was obtained from several mutant strains of Azotobacter. Biological activity of these samples was demonstrated by using them to accomplish both intra- and interstrain transformation. Thermal denaturation profiles of several DNA samples are presented, from which guanine plus cytosine content was determined. Among the Azotobacter species examined, GC content ranged from 65.1 to 67.8%. The use of the new transformation and DNA isolation methods in taxonomic and mapping studies is discussed.

I develop the Generalized Evolutionary Nash Equilibrium Estimator (GENEE) library. The tool is designed to provide a generic computational library for running genetic algorithms and individual evolutionary learning in economic decision-making environments. Most importantly, I have adapted the library to estimate equilibria bidding functions in auctions. I show it produces highly accurate estimates across a large class of auction environments with known solutions. I then apply GENEE to estimate the equilibria of two additional auctions with no known solutions: first-price sealed-bid common value auctions with multiple signals, and simultaneous first-price auctions with subadditive values